Repository: KxSystems/kdbai-samples
Branch: main
Commit: ad492f122018
Files: 71
Total size: 143.4 MB

Directory structure:
gitextract_79vuf6ne/

├── .gitignore
├── HuggingFace_search/
│   └── huggingface_inference.ipynb
├── KDB.AI_course/
│   ├── README.md
│   ├── course_specific_content/
│   │   ├── making_queries.ipynb
│   │   ├── managing_tables.ipynb
│   │   └── rag_example.ipynb
│   └── notebook_references.md
├── LICENSE
├── LlamaIndex_advanced_RAG/
│   └── KDBAI_Advanced_RAG_Demo.ipynb
├── LlamaIndex_samples/
│   ├── Hybrid_Search_LlamaIndex_KDBAI.ipynb
│   ├── Multimodal_RAG_LLamaIndex_CLIP_KDBAI.ipynb
│   └── Sub_Question_Query_Engine_LlamaIndex_KDBAI.ipynb
├── LlamaParse_pdf_RAG/
│   └── llamaParse_demo.ipynb
├── README.md
├── TSS_non_transformed/
│   ├── Non_Transformed_TSS_Technical_Analysis.ipynb
│   ├── Temporal_Similarity_Search_KDB+.ipynb
│   ├── Temporal_Similarity_Search_Non-Transformed_Demo.ipynb
│   ├── createHDB.q
│   └── data/
│       └── marketTrades.parquet
├── TSS_transformed/
│   ├── Temporal_Similarity_Search_Transformed_Demo.ipynb
│   ├── Transformed_TSS_pattern_matching.ipynb
│   └── data/
│       └── marketTrades.parquet
├── document_search/
│   └── document_search.ipynb
├── fuzzy_filtering_on_metadata/
│   └── fuzzy_filtering_demo.ipynb
├── hybrid_search/
│   ├── data/
│   │   └── inflation.txt
│   └── hybrid_search_inflation.ipynb
├── image_search/
│   └── image_search.ipynb
├── metadata_filtering/
│   ├── data/
│   │   └── filtered_embedded_movies.pkl
│   └── metadata_filtering_demo.ipynb
├── multi_index_multimodal_search/
│   ├── data/
│   │   ├── bat1.txt
│   │   ├── bat2.txt
│   │   ├── bear1.txt
│   │   ├── bear2.txt
│   │   ├── caterpillar1.txt
│   │   ├── caterpillar2.txt
│   │   ├── deer1.txt
│   │   ├── deer2.txt
│   │   ├── fox1.txt
│   │   ├── fox2.txt
│   │   ├── hedgehog1.txt
│   │   └── hedgehog2.txt
│   └── multi_index_multimodal_search.ipynb
├── multimodal_RAG_VoyageAI/
│   ├── Multimodal_RAG_VoyageAI.ipynb
│   └── data/
│       └── text/
│           ├── bat.txt
│           ├── bear.txt
│           ├── caterpillar.txt
│           ├── deer.txt
│           ├── fox.txt
│           └── hedgehog.txt
├── multimodal_RAG_unified_text/
│   ├── data/
│   │   └── text/
│   │       ├── bat.txt
│   │       ├── bear.txt
│   │       ├── caterpillar.txt
│   │       ├── deer.txt
│   │       ├── fox.txt
│   │       └── hedgehog.txt
│   └── multi_modal_demo.ipynb
├── music_recommendation/
│   ├── data/
│   │   └── song_data.csv
│   └── music_recommendation.ipynb
├── pattern_matching/
│   └── pattern_matching.ipynb
├── qFlat_index_pdf_search/
│   └── pdf_qFlat_Search.ipynb
├── qHnsw_index_pdf_search/
│   └── pdf_qHNSW_Search.ipynb
├── quickstarts/
│   └── python_quickstart.ipynb
├── requirements.txt
├── retrieval_augmented_generation/
│   ├── data/
│   │   └── state_of_the_union.txt
│   ├── retrieval_augmented_generation.ipynb
│   └── retrieval_augmented_generation_evaluation.ipynb
├── sentiment_analysis/
│   ├── data/
│   │   └── disneyland_reviews.csv
│   └── sentiment_analysis.ipynb
├── unstructured_io_RAG/
│   └── Table_RAG_Unstructured_KDBAI_LangChain_RAG.ipynb
└── video_RAG/
    ├── video_RAG_TwelveLabs.ipynb
    └── video_RAG_VoyageAI.ipynb

================================================
FILE CONTENTS
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================================================
FILE: .gitignore
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*.ipynb_checkpoints
.venv/
.DS_Store


================================================
FILE: HuggingFace_search/huggingface_inference.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb2094b8-13a5-4f7c-bd21-d2c709dab914",
   "metadata": {
    "id": "bb2094b8-13a5-4f7c-bd21-d2c709dab914"
   },
   "source": [
    "# Using Hugging Face Inference with KDB.AI to Create a AI Tool Search Engine\n",
    "\n",
    "##### Note: This example requires a KDB.AI endpoint and API key. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "How to get started with using the Huggingface Inference API with KDB.AI.\n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "1. Connect to KDB.AI\n",
    "2. Create a KDB.AI Database & Table\n",
    "3. Load Data\n",
    "4. Use the Sentence Transformers library to embed every description in the dataset\n",
    "5. Insert the data into our KDB.AI table\n",
    "6. Perform Similarity Search using the Huggingface Inference API\n",
    "7. Delete the KDB.AI Database & Table to Conserve Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nZHRcTHI9bZG",
   "metadata": {
    "id": "nZHRcTHI9bZG"
   },
   "source": [
    "# Why Use Hugging Face for Embeddings?\n",
    "\n",
    "When building production applications that utilize embeddings, it's often advantageous to use open-source embedding models for several reasons:\n",
    "\n",
    "1. **Control**: Open-source models give developers more control over the embeddings process, reducing dependence on third-party embedding providers.\n",
    "\n",
    "2. **Local Embedding**: With open-source models, you can create embeddings locally, which is particularly useful for embedding your dataset.\n",
    "\n",
    "A common approach is to use a Python framework like sentence-transformers, developed by Hugging Face, which offers state-of-the-art sentence, text, and image embeddings. Here's a typical workflow:\n",
    "\n",
    "1. **Embed your dataset locally**: Use a library like Sentence Transformers to embed your dataset, which might consist of AI tools and associated metadata.\n",
    "\n",
    "2. **Embed queries at inference time**: When a user submits a query, use an external service like Hugging Face's Inference API to embed the query. This eliminates the need to deploy your own model, allowing you to leverage a fully optimized external service.\n",
    "\n",
    "By following this approach, you can build a system that searches through hundreds of AI tools without the need to deploy any infrastructure (and scale to millions!). Additionally, since you embed the dataset locally, you can use Hugging Face's free plan without requiring a credit card or worrying about hitting rate limits, at least until you are ready for production.\n",
    "\n",
    "In this tutorial, we will walk through the process of embedding a dataset of AI tools using Sentence Transformers, and then using Hugging Face's Inference API to embed queries at inference time, enabling efficient and scalable search capabilities.\n",
    "\n",
    "You will need a Hugging Face api token for this sample. Please create a Hugging Face account by going to [Hugging Face – The AI community building the future](https://huggingface.co/) and create a token by going to https://huggingface.co/settings/tokens\n",
    "\n",
    "You can then enter this token below or set it to HF_TOKEN in your environment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260d0f4b-ef09-4bd2-a197-a9351be24684",
   "metadata": {
    "id": "260d0f4b-ef09-4bd2-a197-a9351be24684"
   },
   "source": [
    "# 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1468bd3",
   "metadata": {
    "id": "d1468bd3"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f4996e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "491cd6d6",
   "metadata": {
    "id": "491cd6d6"
   },
   "outputs": [],
   "source": [
    "!pip install sentence-transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6d17b7",
   "metadata": {
    "id": "cc6d17b7"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "805d97da",
   "metadata": {
    "id": "805d97da"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a55ae34e-472b-4aa7-9add-1fcb2ee24a41",
   "metadata": {
    "id": "a55ae34e-472b-4aa7-9add-1fcb2ee24a41"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c660c7d",
   "metadata": {
    "id": "8c660c7d"
   },
   "source": [
    "# 1. Connect to KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a3aa22",
   "metadata": {
    "id": "d3a3aa22"
   },
   "source": [
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e85c1ff",
   "metadata": {
    "id": "2e85c1ff"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "Dpi_auWw68cy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dpi_auWw68cy",
    "outputId": "fb43c068-7893-426b-b5bf-559e31a401e2"
   },
   "outputs": [],
   "source": [
    "HF_TOKEN = (\n",
    "    os.environ[\"HF_TOKEN\"]\n",
    "    if \"HF_TOKEN\" in os.environ\n",
    "    else getpass(\"Hugging Face token: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8788a6b1",
   "metadata": {
    "id": "8788a6b1"
   },
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7877f51c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7877f51c",
    "outputId": "0e6fca8a-e50b-4b01-a080-b082bf23d889"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI database \"default\"]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "i5NYByShWqeK",
   "metadata": {
    "id": "i5NYByShWqeK"
   },
   "source": [
    "### Create a Database Called \"myDatabase\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "97e5f4a9",
   "metadata": {
    "id": "97e5f4a9"
   },
   "outputs": [],
   "source": [
    "# ensure no database called \"myDatabase\" exists\n",
    "try:\n",
    "    session.database(\"myDatabase\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "Gbvw4SzqWprx",
   "metadata": {
    "id": "Gbvw4SzqWprx"
   },
   "outputs": [],
   "source": [
    "# Create the database\n",
    "db = session.create_database(\"myDatabase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33f03c3",
   "metadata": {
    "id": "e33f03c3"
   },
   "source": [
    "# 2. Create a KDB.AI Table\n",
    "\n",
    "To create a table we can use `create_table`, this function takes two arguments - the name and schema of the table.\n",
    "\n",
    "This schema must meet the following criteria:\n",
    "- It must contain a list of columns.\n",
    "- All columns must have either a `type` or a `qtype`.\n",
    "- One column of vector embeddings, this column is implicitly an array of `float64s`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da55253",
   "metadata": {
    "id": "9da55253"
   },
   "source": [
    "### Define Schema\n",
    "The schema contains all metadata columns, and a 'description_embedding' column which will be used for similarity search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e5e8b782",
   "metadata": {
    "id": "e5e8b782"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "        {\"name\": \"id\", \"type\": \"str\"},\n",
    "        {\"name\": \"name\", \"type\": \"str\"},\n",
    "        {\"name\": \"description\", \"type\": \"str\"},\n",
    "        {\"name\": \"summary\", \"type\": \"str\"},\n",
    "        {\"name\": \"title\", \"type\": \"str\"},\n",
    "        {\"name\": \"visitors\", \"type\": \"int64\"},\n",
    "        {\"name\": \"description_embedding\", \"type\": \"float64s\"},\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "i9ePLlo3adwt",
   "metadata": {
    "id": "i9ePLlo3adwt"
   },
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- type = hnsw : HNSW enhances efficiency while maintaining accuracy. You have the choice of using other indexes like, qHNSW, and IVFPQ, qFlat or a Flat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- name = hnsw_index : this is a custom name you give your index.\n",
    "\n",
    "#### params:\n",
    "- dims = 384 : In the next section, we generate embeddings that are 384-dimensional to match this. The number of dimensions should mirror the output dimensions of your embedding model.\n",
    "- metric = L2 : We chose L2/Euclidean distance. Our dummy dataset is low dimensional which Euclidean distance is suitable for. You have the choice of using other metrics here like IP/Inner Product and CS/Cosine Similarity and the one you chose depends on the specific context and nature of your data.\n",
    "\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1-2uL1JMXP37",
   "metadata": {
    "id": "1-2uL1JMXP37"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'hnsw',\n",
    "        'name': 'hnsw_index',\n",
    "        'column': 'description_embedding',\n",
    "        'params': {'dims': 384, 'metric': \"L2\"},\n",
    "    },\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a5caa0",
   "metadata": {
    "id": "09a5caa0"
   },
   "source": [
    "### Create Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "34067680",
   "metadata": {
    "id": "34067680"
   },
   "outputs": [],
   "source": [
    "table = db.create_table(table=\"ai_tools\", schema=schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20afbea1",
   "metadata": {
    "id": "20afbea1"
   },
   "source": [
    "# 3. Load Data\n",
    "\n",
    "We fetch data from a github gist containing companies, descriptions, and some metadata. We will then add these to pandas dataframe with column names/types matching the target table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "37581e86",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "37581e86",
    "outputId": "aebcdcdb-303e-4eda-8c58-36610243e3ac"
   },
   "outputs": [
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       "1  rec_cfn1112cibvc11jnn2r0         lumalabs   \n",
       "2  rec_cfn1112cibvc11jnn2rg            plask   \n",
       "3  rec_cfn1112cibvc11jnn2s0  AI Room Planner   \n",
       "4  rec_cfn1112cibvc11jnn2sg           AI TWO   \n",
       "\n",
       "                                             summary  \\\n",
       "0  TextureLab is a website that provides 3D textu...   \n",
       "1  Luma Labs is a website that offers an early ex...   \n",
       "2  Plask is an AI-powered mocap animation tool th...   \n",
       "3  AI Room Planner is an online platform that uti...   \n",
       "4  AI TWO is a website that provides a platform f...   \n",
       "\n",
       "                                               title  visitors  \n",
       "0  Instant And Unique 3D Textures For Your Next G...     23913  \n",
       "1                            Imagine 3D V1.2 (Alpha)    456963  \n",
       "2                   Ai-Powered Mocap Animation Tool.     90960  \n",
       "3                              Interior Design By Ai    211540  \n",
       "4  Aitwo.Co - The Ai-Powered All-In-One Design Pl...      7201  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "gist_url = \"https://gist.github.com/mrmps/2f62a2287cb2c1ca63a2762fcaac89bc/raw\"\n",
    "response = requests.get(gist_url)\n",
    "ai_tools_data = response.json()\n",
    "df = pd.DataFrame.from_dict(ai_tools_data)\n",
    "\n",
    "# drop column with unecessary metadata\n",
    "df.drop(columns=[\"xata\"], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bsfi5TO_65G",
   "metadata": {
    "id": "3bsfi5TO_65G"
   },
   "source": [
    "# 4. Use the Sentence Transformers library to embed every description in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PxPJZcUmBajt",
   "metadata": {
    "id": "PxPJZcUmBajt"
   },
   "source": [
    "We set the embedding model to BAAI/bge-small-en-v1.5, which is a fast and small model. This is what we will use during inference time as well.\n",
    "\n",
    "If you want faster inference, you can try the [FastEmbed](https://github.com/qdrant/fastembed) library, a much faster and more lightweight embedding library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f5dc41e8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "f5dc41e8",
    "outputId": "035258f8-a679-4696-c2fc-d7361eea91d4"
   },
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "model = SentenceTransformer(\"BAAI/bge-small-en-v1.5\")\n",
    "\n",
    "descriptions = [tool[\"description\"] for tool in ai_tools_data]\n",
    "embeddings = model.encode(descriptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "yxhFJUkwf8M4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yxhFJUkwf8M4",
    "outputId": "130f1178-ed5c-491b-9bfb-b615b681106b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.06802839,  0.01769779,  0.07132471, ...,  0.04166844,\n",
       "        -0.01963805, -0.036344  ],\n",
       "       [ 0.00284367,  0.0034911 ,  0.0392653 , ..., -0.01490238,\n",
       "         0.0041208 ,  0.02246646],\n",
       "       [-0.08536491, -0.05372242,  0.01503714, ...,  0.01607881,\n",
       "         0.04058064, -0.02476997],\n",
       "       ...,\n",
       "       [ 0.00551532, -0.02548731, -0.00431467, ..., -0.00406338,\n",
       "         0.06047558, -0.03689232],\n",
       "       [-0.08149453, -0.00607409, -0.00040346, ...,  0.02765157,\n",
       "         0.04479544, -0.00464933],\n",
       "       [-0.09128137, -0.05604199,  0.01856982, ...,  0.01355306,\n",
       "         0.05817638, -0.05754769]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zJeeJHvgAeZ1",
   "metadata": {
    "id": "zJeeJHvgAeZ1"
   },
   "source": [
    "# 5. Insert the data into our KDB.AI table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "730c9f08",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "730c9f08",
    "outputId": "61dd248a-8372-421c-c1dc-047f405da5b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 851}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with the AI tools data\n",
    "data = pd.DataFrame(ai_tools_data)[[\"id\", \"name\", \"description\", \"summary\", \"title\", \"visitors\"]]\n",
    "data[\"description_embedding\"] = embeddings.tolist()\n",
    "\n",
    "# Bulk insert the data into KDB.AI\n",
    "table.insert(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "EJtF_k4iZSGe",
   "metadata": {
    "id": "EJtF_k4iZSGe"
   },
   "source": [
    "## Confirm data is loaded correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b4nBmPXrZPUQ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 770
    },
    "id": "b4nBmPXrZPUQ",
    "outputId": "ec15f76e-9c32-4bf1-baf1-55a67ef1d8bb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>summary</th>\n",
       "      <th>title</th>\n",
       "      <th>visitors</th>\n",
       "      <th>description_embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rec_cfn1112cibvc11jnn2qg</td>\n",
       "      <td>TextureLab</td>\n",
       "      <td>Generate 3D textures for your game in seconds ...</td>\n",
       "      <td>TextureLab is a website that provides 3D textu...</td>\n",
       "      <td>Instant And Unique 3D Textures For Your Next G...</td>\n",
       "      <td>23913</td>\n",
       "      <td>[-0.06802839040756226, 0.017697788774967194, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rec_cfn1112cibvc11jnn2r0</td>\n",
       "      <td>lumalabs</td>\n",
       "      <td>Luma Labs enables users to explore 3D modeling...</td>\n",
       "      <td>Luma Labs is a website that offers an early ex...</td>\n",
       "      <td>Imagine 3D V1.2 (Alpha)</td>\n",
       "      <td>456963</td>\n",
       "      <td>[0.0028436651919037104, 0.003491099225357175, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rec_cfn1112cibvc11jnn2rg</td>\n",
       "      <td>plask</td>\n",
       "      <td>Make motion capture from video easier and more...</td>\n",
       "      <td>Plask is an AI-powered mocap animation tool th...</td>\n",
       "      <td>Ai-Powered Mocap Animation Tool.</td>\n",
       "      <td>90960</td>\n",
       "      <td>[-0.08536490797996521, -0.05372241884469986, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rec_cfn1112cibvc11jnn2s0</td>\n",
       "      <td>AI Room Planner</td>\n",
       "      <td>Get hundreds of interior design ideas for your...</td>\n",
       "      <td>AI Room Planner is an online platform that uti...</td>\n",
       "      <td>Interior Design By Ai</td>\n",
       "      <td>211540</td>\n",
       "      <td>[0.020655963569879532, 0.028269633650779724, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rec_cfn1112cibvc11jnn2sg</td>\n",
       "      <td>AI TWO</td>\n",
       "      <td>A platform powered by AI to help you create be...</td>\n",
       "      <td>AI TWO is a website that provides a platform f...</td>\n",
       "      <td>Aitwo.Co - The Ai-Powered All-In-One Design Pl...</td>\n",
       "      <td>7201</td>\n",
       "      <td>[-0.02213478274643421, -0.03189412131905556, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>rec_cod2au57l1i4603r3hvg</td>\n",
       "      <td>Scott Krager</td>\n",
       "      <td>Thumbnails.com uses AI to generate dozens of u...</td>\n",
       "      <td>Unlock the power of eye-catching thumbnails wi...</td>\n",
       "      <td>Thumbnails.com</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.07755479961633682, -0.05978638306260109, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>rec_codntepuqmnhe7ku1ing</td>\n",
       "      <td>Nen Fard</td>\n",
       "      <td>StockTune: AI-powered, public-domain music for...</td>\n",
       "      <td>\\nStockTune is a revolutionary platform offeri...</td>\n",
       "      <td>StockTune</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.03542690351605415, -0.057283081114292145, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>rec_codr709uqmnhe7ku1te0</td>\n",
       "      <td>Nen Fard</td>\n",
       "      <td>StockCake: Free, AI-generated stock photos in...</td>\n",
       "      <td>StockCake is a revolutionary stock photo site ...</td>\n",
       "      <td>StockCake</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.005515319295227528, -0.025487307459115982, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>rec_coidgc9uqmnhe7l0eug0</td>\n",
       "      <td>Jason West</td>\n",
       "      <td>FastBots enables anyone to quickly create a po...</td>\n",
       "      <td>FastBots is a no-code AI chatbot builder for b...</td>\n",
       "      <td>FastBots</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.0814945250749588, -0.006074093747884035, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>rec_coj3l5aa8o7fb0ajha0g</td>\n",
       "      <td>Dubformer</td>\n",
       "      <td>AI-driven translation and dubbing services</td>\n",
       "      <td>Dubformer is an end-to-end innovative service ...</td>\n",
       "      <td>AI dubbing and video translation solution</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.09128136932849884, -0.05604198947548866, 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>851 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id             name  \\\n",
       "0    rec_cfn1112cibvc11jnn2qg       TextureLab   \n",
       "1    rec_cfn1112cibvc11jnn2r0         lumalabs   \n",
       "2    rec_cfn1112cibvc11jnn2rg            plask   \n",
       "3    rec_cfn1112cibvc11jnn2s0  AI Room Planner   \n",
       "4    rec_cfn1112cibvc11jnn2sg           AI TWO   \n",
       "..                        ...              ...   \n",
       "846  rec_cod2au57l1i4603r3hvg     Scott Krager   \n",
       "847  rec_codntepuqmnhe7ku1ing         Nen Fard   \n",
       "848  rec_codr709uqmnhe7ku1te0         Nen Fard   \n",
       "849  rec_coidgc9uqmnhe7l0eug0       Jason West   \n",
       "850  rec_coj3l5aa8o7fb0ajha0g        Dubformer   \n",
       "\n",
       "                                           description  \\\n",
       "0    Generate 3D textures for your game in seconds ...   \n",
       "1    Luma Labs enables users to explore 3D modeling...   \n",
       "2    Make motion capture from video easier and more...   \n",
       "3    Get hundreds of interior design ideas for your...   \n",
       "4    A platform powered by AI to help you create be...   \n",
       "..                                                 ...   \n",
       "846  Thumbnails.com uses AI to generate dozens of u...   \n",
       "847  StockTune: AI-powered, public-domain music for...   \n",
       "848   StockCake: Free, AI-generated stock photos in...   \n",
       "849  FastBots enables anyone to quickly create a po...   \n",
       "850         AI-driven translation and dubbing services   \n",
       "\n",
       "                                               summary  \\\n",
       "0    TextureLab is a website that provides 3D textu...   \n",
       "1    Luma Labs is a website that offers an early ex...   \n",
       "2    Plask is an AI-powered mocap animation tool th...   \n",
       "3    AI Room Planner is an online platform that uti...   \n",
       "4    AI TWO is a website that provides a platform f...   \n",
       "..                                                 ...   \n",
       "846  Unlock the power of eye-catching thumbnails wi...   \n",
       "847  \\nStockTune is a revolutionary platform offeri...   \n",
       "848  StockCake is a revolutionary stock photo site ...   \n",
       "849  FastBots is a no-code AI chatbot builder for b...   \n",
       "850  Dubformer is an end-to-end innovative service ...   \n",
       "\n",
       "                                                 title  visitors  \\\n",
       "0    Instant And Unique 3D Textures For Your Next G...     23913   \n",
       "1                              Imagine 3D V1.2 (Alpha)    456963   \n",
       "2                     Ai-Powered Mocap Animation Tool.     90960   \n",
       "3                                Interior Design By Ai    211540   \n",
       "4    Aitwo.Co - The Ai-Powered All-In-One Design Pl...      7201   \n",
       "..                                                 ...       ...   \n",
       "846                                     Thumbnails.com         0   \n",
       "847                                          StockTune         0   \n",
       "848                                          StockCake         0   \n",
       "849                                           FastBots         0   \n",
       "850          AI dubbing and video translation solution         0   \n",
       "\n",
       "                                 description_embedding  \n",
       "0    [-0.06802839040756226, 0.017697788774967194, 0...  \n",
       "1    [0.0028436651919037104, 0.003491099225357175, ...  \n",
       "2    [-0.08536490797996521, -0.05372241884469986, 0...  \n",
       "3    [0.020655963569879532, 0.028269633650779724, 0...  \n",
       "4    [-0.02213478274643421, -0.03189412131905556, 0...  \n",
       "..                                                 ...  \n",
       "846  [-0.07755479961633682, -0.05978638306260109, -...  \n",
       "847  [-0.03542690351605415, -0.057283081114292145, ...  \n",
       "848  [0.005515319295227528, -0.025487307459115982, ...  \n",
       "849  [-0.0814945250749588, -0.006074093747884035, -...  \n",
       "850  [-0.09128136932849884, -0.05604198947548866, 0...  \n",
       "\n",
       "[851 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0MOZtXLniJLe",
   "metadata": {
    "id": "0MOZtXLniJLe"
   },
   "source": [
    "# 6. Perform Similarity Search Using the Hugging Face Inference API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "V6KjGhJOANyf",
   "metadata": {
    "id": "V6KjGhJOANyf"
   },
   "source": [
    "## Embed the Query with the Hugging Face Inference API\n",
    "Use the Hugging Face Inference API to embed the query so that it can be used to search our index\n",
    "\n",
    "!! Note that you might need to run this cell a few times as it takes a few seconds for the model to be ready."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a31f878",
   "metadata": {
    "id": "4a31f878"
   },
   "outputs": [],
   "source": [
    "# Perform a search using Hugging Face embeddings\n",
    "import requests\n",
    "\n",
    "# make sure your URL looks like this to ensure you get instant results, and not a model loading error\n",
    "embedding_url = \"https://router.huggingface.co/hf-inference/models/BAAI/bge-small-en-v1.5/pipeline/feature-extraction\"\n",
    "\n",
    "def generate_query_embedding(text: str) -> list[float]:\n",
    "    response = requests.post(\n",
    "        embedding_url,\n",
    "        headers={\"Authorization\": f\"Bearer {HF_TOKEN}\", \"x-wait-for-model\": \"true\"},        \n",
    "        json={\"inputs\": text}\n",
    "    )\n",
    "\n",
    "    if response.status_code != 200:\n",
    "        raise ValueError(f\"Request failed with status code {response.status_code}: {response.text}\")\n",
    "    return response.json()\n",
    "\n",
    "query = \"AI tool for creating 3D textures\"\n",
    "query_embedding = generate_query_embedding(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Wrp9GUwEZmvT",
   "metadata": {
    "id": "Wrp9GUwEZmvT"
   },
   "source": [
    "## Run the query with our query embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Xumxvz7gaGGp",
   "metadata": {
    "id": "Xumxvz7gaGGp"
   },
   "source": [
    "We are searching based on the description for the most relevant startups to the query. Remember that \"hnsw_index\" is the index name we created when defining our index before creating the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "FDrpIofxZl4Z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "FDrpIofxZl4Z",
    "outputId": "2d5259a5-762a-4ef4-b37c-e8d6ec9d0f68"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.25221</td>\n",
       "      <td>rec_cfn1112cibvc11jnn2qg</td>\n",
       "      <td>TextureLab</td>\n",
       "      <td>Generate 3D textures for your game in seconds ...</td>\n",
       "      <td>TextureLab is a website that provides 3D textu...</td>\n",
       "      <td>Instant And Unique 3D Textures For Your Next G...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.26723</td>\n",
       "      <td>rec_cfn11a2cibvc11jnndbg</td>\n",
       "      <td>Ponzu.gg</td>\n",
       "      <td>Create realistic 3D images with AI-generated t...</td>\n",
       "      <td>Ponzu is a website that helps 3D artists and d...</td>\n",
       "      <td>Ponzu.</td>\n",
       "      <td>6526</td>\n",
       "      <td>[-0.06463481485843658, -0.014672131277620792, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.34271</td>\n",
       "      <td>rec_cfn119acibvc11jnncf0</td>\n",
       "      <td>Masterpiece Studio</td>\n",
       "      <td>Create 3D models with Generative AI and deploy...</td>\n",
       "      <td>Masterpiece Studio is a company that has devel...</td>\n",
       "      <td>Masterpiece Studio.</td>\n",
       "      <td>38954</td>\n",
       "      <td>[-0.04131263867020607, -0.0035701903980225325,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance                        id                name  \\\n",
       "0        0.25221  rec_cfn1112cibvc11jnn2qg          TextureLab   \n",
       "1        0.26723  rec_cfn11a2cibvc11jnndbg            Ponzu.gg   \n",
       "2        0.34271  rec_cfn119acibvc11jnncf0  Masterpiece Studio   \n",
       "\n",
       "                                         description  \\\n",
       "0  Generate 3D textures for your game in seconds ...   \n",
       "1  Create realistic 3D images with AI-generated t...   \n",
       "2  Create 3D models with Generative AI and deploy...   \n",
       "\n",
       "                                             summary  \\\n",
       "0  TextureLab is a website that provides 3D textu...   \n",
       "1  Ponzu is a website that helps 3D artists and d...   \n",
       "2  Masterpiece Studio is a company that has devel...   \n",
       "\n",
       "                                               title  visitors  \\\n",
       "0  Instant And Unique 3D Textures For Your Next G...     23913   \n",
       "1                                             Ponzu.      6526   \n",
       "2                                Masterpiece Studio.     38954   \n",
       "\n",
       "                               description_embedding  \n",
       "0  [-0.06802839040756226, 0.017697788774967194, 0...  \n",
       "1  [-0.06463481485843658, -0.014672131277620792, ...  \n",
       "2  [-0.04131263867020607, -0.0035701903980225325,...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = table.search(vectors={\"hnsw_index\":[query_embedding]},n=3,)\n",
    "\n",
    "results[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8aed9bc-72b2-4e70-b763-e7ce054557db",
   "metadata": {
    "id": "d8aed9bc-72b2-4e70-b763-e7ce054557db"
   },
   "source": [
    "# 7. Delete the KDB.AI Table & Database to Conserve Resources\n",
    "\n",
    "\n",
    "We can use `table.drop()` to delete a table, and db.drop() to delete the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "548a9d95-aac3-4d63-a87a-99eedfe55f07",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "548a9d95-aac3-4d63-a87a-99eedfe55f07",
    "outputId": "ba89c1d4-997e-46e7-97d6-e33a8e50d51e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.drop()\n",
    "db.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc6d801-1371-48d0-98b4-0baa53bc8446",
   "metadata": {
    "id": "8bc6d801-1371-48d0-98b4-0baa53bc8446"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Warning:</b> Once you drop a table, you cannot use it again.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RU2pzQCAn-Wm",
   "metadata": {
    "id": "RU2pzQCAn-Wm"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/UGvwprmK)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7672241-42b0-4798-90d7-95aa9fefe68c",
   "metadata": {
    "id": "a7672241-42b0-4798-90d7-95aa9fefe68c"
   },
   "source": [
    "## Next Steps\n",
    "\n",
    "Now that you’re successfully making indexes with KDB.AI, you can start inserting your own data or view more examples:\n",
    "- [PDF Document Search](../document_search)\n",
    "- [MRI Image Search](../image_search)\n",
    "- [Music Recommendation System](../music_recommendation)\n",
    "- [Sensor Pattern Matching](../pattern_matching)\n",
    "- [Retrieval Augmented Generation with LangChain](../retrieval_augmented_generation)\n",
    "- [Sentiment Analysis of Reviews](../sentiment_analysis)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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}


================================================
FILE: KDB.AI_course/README.md
================================================
# KDB.AI Course

Welcome to the KDB.AI course! This course combines custom content with existing examples from the KDB.AI samples repository.

## Course Outline

1. Introduction to KDB.AI
   - [Introduction](./course_specific_content/making_queries.ipynb)
   - [Managing Tables](./course_specific_content/managing_tables.ipynb)

2. Advanced Search Techniques
   - [Hybrid Search](../hybrid_search/hybrid_search_inflation.ipynb)
   - [Temporal Similarity Search (Non-Transformed)](../TSS_non_transformed/Temporal_Similarity_Search_Non-Transformed_Demo.ipynb)
   - [Temporal Similarity Search (Transformed)](../TSS_transformed/Temporal_Similarity_Search_Transformed_Demo.ipynb)

3. Retrieval Augmented Generation
   - [RAG Example](./course_specific_content/rag_example.ipynb)

## Note on Referenced Notebooks

Some notebooks in this course are referenced from other parts of the repository. This ensures you're always working with the most up-to-date versions of these examples. For a full list of referenced notebooks and their locations, please see [notebook_references.md](./notebook_references.md).

================================================
FILE: KDB.AI_course/course_specific_content/making_queries.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CpIrSWxiuFxX"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "[Video Walkthrough](https://www.youtube.com/watch?v=0kpseJLbEP4&list=PLypX5sYuDqvrqsXTw876gGHosCKvK_7QS&index=7)\n",
    "\n",
    "In this section of the course, we will focus on querying and searching data in KDB.AI tables. By the end of this notebook, you will have a thorough understanding of the following:\n",
    "- Selecting tables to query\n",
    "- Performing queries and applying filters\n",
    "- Customizing filters\n",
    "- Conducting similarity searches\n",
    "- Processing query results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3nESqJz6uP_7"
   },
   "source": [
    "### Setup\n",
    "\n",
    "Install kdbai_client and import the necessary dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "HBtJhVzlagJt",
    "outputId": "e7e59f12-d603-4104-9ddf-c0022202dc9b"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client fastembed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gitkLFwlag8H"
   },
   "outputs": [],
   "source": [
    "import kdbai_client as kdbai\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from fastembed import TextEmbedding\n",
    "import os\n",
    "import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GoGkIrL9ugk7"
   },
   "source": [
    "##### Connect to KDB.AI Server\n",
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "2ApVTaRvajlt",
    "outputId": "90b29cb5-7629-446d-bef2-c66fe35a256f"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ey1mSxH2an1e"
   },
   "outputs": [],
   "source": [
    "database = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8l8qWymnunY7"
   },
   "source": [
    "### Create Our Table and Insert Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qNQVDDtZapTQ"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    database.table(\"data\").drop() # Drop the table if it already exists\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wRoZgm4LxR0Z"
   },
   "source": [
    "##### Define a Schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "574BY9ZQax4p"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "    {'name': 'id', 'type': 'int32'},\n",
    "    {'name': 'name', 'type': 'str'},\n",
    "    {'name': 'age', 'type': 'int16'},\n",
    "    {'name': 'city', 'type': 'str'},\n",
    "    {'name': 'description', 'type': 'str'},\n",
    "    {'name': 'embeddings', 'type': 'float32s'}\n",
    "]\n",
    "index_name = 'hnws_index'\n",
    "indexes = [{'name': index_name, 'column': 'embeddings', 'type': 'hnsw', 'params': {'dims': 384}}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "stFJG7sCa19_"
   },
   "outputs": [],
   "source": [
    "table = database.create_table(\"data\", schema=schema, indexes=indexes)\n",
    "\n",
    "# Generate real vectors using FastEmbed\n",
    "descriptions = [\n",
    "    \"A passionate environmentalist with 5 years of experience in conservation projects and enjoys hiking and outdoor activities.\",\n",
    "    \"A software engineer with 7 years of experience in full-stack development, living in London, who loves to cook Italian cuisine.\",\n",
    "    \"A guitarist with over 10 years of experience performing at local cafes and enjoys reading science fiction.\",\n",
    "    \"A data scientist in Tokyo with 4 years of experience in machine learning and a keen interest in AI research.\",\n",
    "    \"An avid reader and travel blogger with 3 years of experience visiting and writing about historic sites around the world.\",\n",
    "    \"A graphic designer based in Berlin with 8 years of experience and a talent for creating digital art.\",\n",
    "    \"A high school teacher with 15 years of experience in education who loves cycling and participates in charity rides.\",\n",
    "    \"A professional photographer with 6 years of experience specializing in wildlife photography.\",\n",
    "    \"A fitness trainer with 5 years of experience who enjoys helping people achieve their health goals.\",\n",
    "    \"A chef with 12 years of experience who runs a popular restaurant and enjoys experimenting with new recipes.\",\n",
    "    \"A journalist with 9 years of experience writing about technology and enjoys exploring new gadgets.\",\n",
    "    \"A musician with 20 years of experience who plays multiple instruments and performs in a jazz band.\",\n",
    "    \"A software developer with 6 years of experience in creating mobile apps and enjoys coding challenges.\",\n",
    "    \"An artist with 10 years of experience who paints abstract pieces and has exhibited in several galleries.\",\n",
    "    \"A historian with 7 years of experience who loves researching and writing about ancient civilizations.\",\n",
    "    \"A marketing manager with 8 years of experience in digital marketing and social media strategy.\",\n",
    "    \"A nurse with 12 years of experience in emergency care and patient management.\",\n",
    "    \"A financial analyst with 5 years of experience in investment banking and portfolio management.\",\n",
    "    \"A project manager with 10 years of experience in IT project coordination and execution.\",\n",
    "    \"A UX designer with 6 years of experience in creating user-friendly interfaces for web and mobile applications.\",\n",
    "    \"A sales executive with 8 years of experience in B2B sales and client relationship management.\",\n",
    "    \"A content writer with 5 years of experience in creating engaging articles and blog posts.\",\n",
    "    \"A civil engineer with 10 years of experience in infrastructure development and urban planning.\",\n",
    "    \"A teacher with 15 years of experience in primary education and curriculum development.\",\n",
    "    \"A business analyst with 7 years of experience in business process optimization and data analysis.\",\n",
    "    \"A psychologist with 6 years of experience in clinical practice and mental health counseling.\",\n",
    "    \"A software architect with 9 years of experience in designing scalable software solutions.\",\n",
    "    \"A research scientist with 8 years of experience in biotechnology and genetic engineering.\",\n",
    "    \"An operations manager with 12 years of experience in supply chain management and logistics.\",\n",
    "    \"A public relations specialist with 7 years of experience in media relations and corporate communications.\"\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zk_CkMaPvGwv"
   },
   "source": [
    "##### Define an Embedding Model and Embed People Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "collapsed": true,
    "id": "AsTjc7_ca39U",
    "outputId": "91abba63-224f-4fa7-bdc5-948e7a008a13"
   },
   "outputs": [],
   "source": [
    "embedding_model = TextEmbedding()\n",
    "embeddings = list(embedding_model.embed(descriptions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s2JneJwna7SP"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(42) # for reproducibility\n",
    "\n",
    "names = [\"Alice\", \"Bob\", \"Charlie\", \"Monica\", \"Eve\", \"Frank\", \"Grace\", \"Hannah\", \"Ivy\", \"Jack\", \"Kara\", \"Leo\", \"Mia\", \"Nate\", \"Olivia\", \"Paul\", \"Quinn\", \"Rita\", \"Sam\", \"Tina\", \"Uma\", \"Victor\", \"Wendy\", \"Xander\", \"Yara\", \"Zane\", \"Alice\", \"Cody\", \"Diana\", \"Ethan\"]\n",
    "cities = [\"New York\", \"London\", \"New York\", \"Paris\", \"Berlin\", \"New York\", \"San Francisco\", \"Amsterdam\", \"Rome\", \"Toronto\", \"Chicago\", \"Barcelona\", \"Madrid\", \"New York\", \"Moscow\", \"Dubai\", \"Singapore\", \"New York\", \"Istanbul\", \"Munich\", \"Vienna\", \"Dublin\", \"Zurich\", \"Stockholm\", \"Lisbon\", \"Prague\", \"Budapest\", \"Berlin\", \"Copenhagen\", \"Seoul\"]\n",
    "\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    'id': np.array(list(range(0, 30)), dtype='int32'),\n",
    "    'name': names,\n",
    "    'age': np.array([random.randint(18, 60) for _ in range(30)], dtype='int16'),\n",
    "    'city': cities,\n",
    "    'description': descriptions,\n",
    "    'embeddings': embeddings\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9JCMSVFOxmkg"
   },
   "source": [
    "##### Insert the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZCg1E3hka9SE",
    "outputId": "da6d6a02-5a2d-406c-f4cb-cf68d7349192"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5-92TfnnxyUx"
   },
   "source": [
    "### Query Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "collapsed": true,
    "id": "JY4aJ6Jla_vM",
    "outputId": "37f36ee3-9524-4199-fd7d-1fe6df512fd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All data in the table:\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"table\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          27,\n          15,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"Diana\",\n          \"Quinn\",\n          \"Mia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"int16\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          58,\n          31,\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Chicago\",\n          \"Vienna\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"A research scientist with 8 years of experience in biotechnology and genetic engineering.\",\n          \"A marketing manager with 8 years of experience in digital marketing and social media strategy.\",\n          \"A teacher with 15 years of experience in primary education and curriculum development.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embeddings\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-97fcf1bd-13ad-4779-9029-a721eda12bdc\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>description</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Alice</td>\n",
       "      <td>58</td>\n",
       "      <td>New York</td>\n",
       "      <td>A passionate environmentalist with 5 years of ...</td>\n",
       "      <td>[-0.006158471, 0.063678846, 0.09181005, -0.023...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Bob</td>\n",
       "      <td>25</td>\n",
       "      <td>London</td>\n",
       "      <td>A software engineer with 7 years of experience...</td>\n",
       "      <td>[-0.035581246, 0.07986437, 0.04891828, -0.0604...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>19</td>\n",
       "      <td>New York</td>\n",
       "      <td>A guitarist with over 10 years of experience p...</td>\n",
       "      <td>[0.050266247, 0.05255312, 0.048840936, -0.0032...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Monica</td>\n",
       "      <td>35</td>\n",
       "      <td>Paris</td>\n",
       "      <td>A data scientist in Tokyo with 4 years of expe...</td>\n",
       "      <td>[-0.008097345, 0.030305384, 0.012246384, -0.04...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Eve</td>\n",
       "      <td>33</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>An avid reader and travel blogger with 3 years...</td>\n",
       "      <td>[0.029772803, 0.07571457, 0.042140756, 0.06809...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Frank</td>\n",
       "      <td>32</td>\n",
       "      <td>New York</td>\n",
       "      <td>A graphic designer based in Berlin with 8 year...</td>\n",
       "      <td>[0.013257692, 0.045190323, 0.0074770325, -0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Grace</td>\n",
       "      <td>26</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>A high school teacher with 15 years of experie...</td>\n",
       "      <td>[-0.011028861, 0.051242497, 0.063257486, -0.05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Hannah</td>\n",
       "      <td>24</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>A professional photographer with 6 years of ex...</td>\n",
       "      <td>[0.04469839, 0.07050187, 0.046390466, -0.03404...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Ivy</td>\n",
       "      <td>52</td>\n",
       "      <td>Rome</td>\n",
       "      <td>A fitness trainer with 5 years of experience w...</td>\n",
       "      <td>[0.0002550126, 0.024398372, 0.09861772, 0.0062...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Jack</td>\n",
       "      <td>23</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>A chef with 12 years of experience who runs a ...</td>\n",
       "      <td>[-0.008186043, 0.051337104, 0.02683556, -0.030...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Kara</td>\n",
       "      <td>55</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>A journalist with 9 years of experience writin...</td>\n",
       "      <td>[-0.017909497, 0.08548332, 0.0022086229, -0.04...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Leo</td>\n",
       "      <td>45</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>A musician with 20 years of experience who pla...</td>\n",
       "      <td>[0.008686635, 0.03110498, 0.05405915, -0.07571...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Mia</td>\n",
       "      <td>20</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>A software developer with 6 years of experienc...</td>\n",
       "      <td>[-0.04372146, 0.06704399, 0.022140108, -0.1017...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Nate</td>\n",
       "      <td>19</td>\n",
       "      <td>New York</td>\n",
       "      <td>An artist with 10 years of experience who pain...</td>\n",
       "      <td>[0.01933304, 0.023277232, 0.044062667, 0.01242...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>23</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>A historian with 7 years of experience who lov...</td>\n",
       "      <td>[-0.0051849326, 0.16519417, 0.06066864, 0.0311...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Paul</td>\n",
       "      <td>31</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>A marketing manager with 8 years of experience...</td>\n",
       "      <td>[0.010789718, 0.017695278, 0.018274685, -0.033...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Quinn</td>\n",
       "      <td>32</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>A nurse with 12 years of experience in emergen...</td>\n",
       "      <td>[-0.041632365, 0.034463193, 0.06313535, 0.0160...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Rita</td>\n",
       "      <td>50</td>\n",
       "      <td>New York</td>\n",
       "      <td>A financial analyst with 5 years of experience...</td>\n",
       "      <td>[0.015000028, 0.024906091, 0.0010010687, 0.011...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Sam</td>\n",
       "      <td>56</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>A project manager with 10 years of experience ...</td>\n",
       "      <td>[-0.020330371, 0.079401195, 0.02162953, -0.080...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Tina</td>\n",
       "      <td>19</td>\n",
       "      <td>Munich</td>\n",
       "      <td>A UX designer with 6 years of experience in cr...</td>\n",
       "      <td>[-0.030572662, 0.04520395, 0.04553928, -0.0925...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Uma</td>\n",
       "      <td>53</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>A sales executive with 8 years of experience i...</td>\n",
       "      <td>[-0.014194918, 0.032352123, -0.0070426096, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Victor</td>\n",
       "      <td>30</td>\n",
       "      <td>Dublin</td>\n",
       "      <td>A content writer with 5 years of experience in...</td>\n",
       "      <td>[-0.018195461, 0.032041155, 0.059233848, -0.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Wendy</td>\n",
       "      <td>59</td>\n",
       "      <td>Zurich</td>\n",
       "      <td>A civil engineer with 10 years of experience i...</td>\n",
       "      <td>[-0.00980266, 0.04713828, 0.05187823, -0.03932...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Xander</td>\n",
       "      <td>52</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>A teacher with 15 years of experience in prima...</td>\n",
       "      <td>[-0.013646452, 0.028070105, 0.05104053, -0.064...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Yara</td>\n",
       "      <td>44</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>A business analyst with 7 years of experience ...</td>\n",
       "      <td>[-0.044623584, 0.054378174, 0.0015794634, -0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Zane</td>\n",
       "      <td>32</td>\n",
       "      <td>Prague</td>\n",
       "      <td>A psychologist with 6 years of experience in c...</td>\n",
       "      <td>[0.016778275, 0.09543604, 0.048281595, -0.0022...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Alice</td>\n",
       "      <td>46</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>A software architect with 9 years of experienc...</td>\n",
       "      <td>[-0.06051296, 0.031862404, -0.031203829, -0.07...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Cody</td>\n",
       "      <td>55</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>A research scientist with 8 years of experienc...</td>\n",
       "      <td>[-0.01787689, 0.07915241, -0.004790489, -0.031...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Diana</td>\n",
       "      <td>35</td>\n",
       "      <td>Copenhagen</td>\n",
       "      <td>An operations manager with 12 years of experie...</td>\n",
       "      <td>[0.011406942, 0.02994747, 0.06136875, -0.02639...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>Ethan</td>\n",
       "      <td>18</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>A public relations specialist with 7 years of ...</td>\n",
       "      <td>[-0.001325855, 0.089781284, 0.05144235, -0.036...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97fcf1bd-13ad-4779-9029-a721eda12bdc')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-97fcf1bd-13ad-4779-9029-a721eda12bdc button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-97fcf1bd-13ad-4779-9029-a721eda12bdc');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
       "<div id=\"df-626a5255-223c-4f1e-b16c-eaa273fbb22f\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-626a5255-223c-4f1e-b16c-eaa273fbb22f')\"\n",
       "            title=\"Suggest charts\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
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       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
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       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-626a5255-223c-4f1e-b16c-eaa273fbb22f button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    id     name  age           city  \\\n",
       "0    0    Alice   58       New York   \n",
       "1    1      Bob   25         London   \n",
       "2    2  Charlie   19       New York   \n",
       "3    3   Monica   35          Paris   \n",
       "4    4      Eve   33         Berlin   \n",
       "5    5    Frank   32       New York   \n",
       "6    6    Grace   26  San Francisco   \n",
       "7    7   Hannah   24      Amsterdam   \n",
       "8    8      Ivy   52           Rome   \n",
       "9    9     Jack   23        Toronto   \n",
       "10  10     Kara   55        Chicago   \n",
       "11  11      Leo   45      Barcelona   \n",
       "12  12      Mia   20         Madrid   \n",
       "13  13     Nate   19       New York   \n",
       "14  14   Olivia   23         Moscow   \n",
       "15  15     Paul   31          Dubai   \n",
       "16  16    Quinn   32      Singapore   \n",
       "17  17     Rita   50       New York   \n",
       "18  18      Sam   56       Istanbul   \n",
       "19  19     Tina   19         Munich   \n",
       "20  20      Uma   53         Vienna   \n",
       "21  21   Victor   30         Dublin   \n",
       "22  22    Wendy   59         Zurich   \n",
       "23  23   Xander   52      Stockholm   \n",
       "24  24     Yara   44         Lisbon   \n",
       "25  25     Zane   32         Prague   \n",
       "26  26    Alice   46       Budapest   \n",
       "27  27     Cody   55         Berlin   \n",
       "28  28    Diana   35     Copenhagen   \n",
       "29  29    Ethan   18          Seoul   \n",
       "\n",
       "                                          description  \\\n",
       "0   A passionate environmentalist with 5 years of ...   \n",
       "1   A software engineer with 7 years of experience...   \n",
       "2   A guitarist with over 10 years of experience p...   \n",
       "3   A data scientist in Tokyo with 4 years of expe...   \n",
       "4   An avid reader and travel blogger with 3 years...   \n",
       "5   A graphic designer based in Berlin with 8 year...   \n",
       "6   A high school teacher with 15 years of experie...   \n",
       "7   A professional photographer with 6 years of ex...   \n",
       "8   A fitness trainer with 5 years of experience w...   \n",
       "9   A chef with 12 years of experience who runs a ...   \n",
       "10  A journalist with 9 years of experience writin...   \n",
       "11  A musician with 20 years of experience who pla...   \n",
       "12  A software developer with 6 years of experienc...   \n",
       "13  An artist with 10 years of experience who pain...   \n",
       "14  A historian with 7 years of experience who lov...   \n",
       "15  A marketing manager with 8 years of experience...   \n",
       "16  A nurse with 12 years of experience in emergen...   \n",
       "17  A financial analyst with 5 years of experience...   \n",
       "18  A project manager with 10 years of experience ...   \n",
       "19  A UX designer with 6 years of experience in cr...   \n",
       "20  A sales executive with 8 years of experience i...   \n",
       "21  A content writer with 5 years of experience in...   \n",
       "22  A civil engineer with 10 years of experience i...   \n",
       "23  A teacher with 15 years of experience in prima...   \n",
       "24  A business analyst with 7 years of experience ...   \n",
       "25  A psychologist with 6 years of experience in c...   \n",
       "26  A software architect with 9 years of experienc...   \n",
       "27  A research scientist with 8 years of experienc...   \n",
       "28  An operations manager with 12 years of experie...   \n",
       "29  A public relations specialist with 7 years of ...   \n",
       "\n",
       "                                           embeddings  \n",
       "0   [-0.006158471, 0.063678846, 0.09181005, -0.023...  \n",
       "1   [-0.035581246, 0.07986437, 0.04891828, -0.0604...  \n",
       "2   [0.050266247, 0.05255312, 0.048840936, -0.0032...  \n",
       "3   [-0.008097345, 0.030305384, 0.012246384, -0.04...  \n",
       "4   [0.029772803, 0.07571457, 0.042140756, 0.06809...  \n",
       "5   [0.013257692, 0.045190323, 0.0074770325, -0.00...  \n",
       "6   [-0.011028861, 0.051242497, 0.063257486, -0.05...  \n",
       "7   [0.04469839, 0.07050187, 0.046390466, -0.03404...  \n",
       "8   [0.0002550126, 0.024398372, 0.09861772, 0.0062...  \n",
       "9   [-0.008186043, 0.051337104, 0.02683556, -0.030...  \n",
       "10  [-0.017909497, 0.08548332, 0.0022086229, -0.04...  \n",
       "11  [0.008686635, 0.03110498, 0.05405915, -0.07571...  \n",
       "12  [-0.04372146, 0.06704399, 0.022140108, -0.1017...  \n",
       "13  [0.01933304, 0.023277232, 0.044062667, 0.01242...  \n",
       "14  [-0.0051849326, 0.16519417, 0.06066864, 0.0311...  \n",
       "15  [0.010789718, 0.017695278, 0.018274685, -0.033...  \n",
       "16  [-0.041632365, 0.034463193, 0.06313535, 0.0160...  \n",
       "17  [0.015000028, 0.024906091, 0.0010010687, 0.011...  \n",
       "18  [-0.020330371, 0.079401195, 0.02162953, -0.080...  \n",
       "19  [-0.030572662, 0.04520395, 0.04553928, -0.0925...  \n",
       "20  [-0.014194918, 0.032352123, -0.0070426096, -0....  \n",
       "21  [-0.018195461, 0.032041155, 0.059233848, -0.03...  \n",
       "22  [-0.00980266, 0.04713828, 0.05187823, -0.03932...  \n",
       "23  [-0.013646452, 0.028070105, 0.05104053, -0.064...  \n",
       "24  [-0.044623584, 0.054378174, 0.0015794634, -0.0...  \n",
       "25  [0.016778275, 0.09543604, 0.048281595, -0.0022...  \n",
       "26  [-0.06051296, 0.031862404, -0.031203829, -0.07...  \n",
       "27  [-0.01787689, 0.07915241, -0.004790489, -0.031...  \n",
       "28  [0.011406942, 0.02994747, 0.06136875, -0.02639...  \n",
       "29  [-0.001325855, 0.089781284, 0.05144235, -0.036...  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"All data in the table:\")\n",
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1IqGPc21yGc3"
   },
   "source": [
    "##### Query with Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "collapsed": true,
    "id": "9FdMeiywbBpp",
    "outputId": "c1650759-cef5-4857-8a37-d0f1a1823c47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Querying data where age >= 30 and city is Rome or Paris:\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"table\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Ivy\",\n          \"Monica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"int16\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          52,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Rome\",\n          \"Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"A fitness trainer with 5 years of experience who enjoys helping people achieve their health goals.\",\n          \"A data scientist in Tokyo with 4 years of experience in machine learning and a keen interest in AI research.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embeddings\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
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       "      <th>id</th>\n",
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       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   id    name  age   city                                        description  \\\n",
       "0   3  Monica   35  Paris  A data scientist in Tokyo with 4 years of expe...   \n",
       "1   8     Ivy   52   Rome  A fitness trainer with 5 years of experience w...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-0.008097345, 0.030305384, 0.012246384, -0.04...  \n",
       "1  [0.0002550126, 0.024398372, 0.09861772, 0.0062...  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Querying data where age >= 30 and city is Rome or Paris:\")\n",
    "table.query(filter=[(\">=\", \"age\", 30), (\"in\", \"city\", [\"Rome\", \"Paris\"])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "oDnVcKV5bML1",
    "outputId": "01fa9a05-c554-4270-fd78-b04c3b493d6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregated query for names and cities:\n",
      "       name           city\n",
      "0     Alice       New York\n",
      "1       Bob         London\n",
      "2   Charlie       New York\n",
      "3    Monica          Paris\n",
      "4       Eve         Berlin\n",
      "5     Frank       New York\n",
      "6     Grace  San Francisco\n",
      "7    Hannah      Amsterdam\n",
      "8       Ivy           Rome\n",
      "9      Jack        Toronto\n",
      "10     Kara        Chicago\n",
      "11      Leo      Barcelona\n",
      "12      Mia         Madrid\n",
      "13     Nate       New York\n",
      "14   Olivia         Moscow\n",
      "15     Paul          Dubai\n",
      "16    Quinn      Singapore\n",
      "17     Rita       New York\n",
      "18      Sam       Istanbul\n",
      "19     Tina         Munich\n",
      "20      Uma         Vienna\n",
      "21   Victor         Dublin\n",
      "22    Wendy         Zurich\n",
      "23   Xander      Stockholm\n",
      "24     Yara         Lisbon\n",
      "25     Zane         Prague\n",
      "26    Alice       Budapest\n",
      "27     Cody         Berlin\n",
      "28    Diana     Copenhagen\n",
      "29    Ethan          Seoul\n"
     ]
    }
   ],
   "source": [
    "print(\"Aggregated query for names and cities:\")\n",
    "print(table.query(aggs={\"name\": \"name\", \"city\": \"city\"})) # returns only the names and cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "dyj4HxfUbTW4",
    "outputId": "8ecf8d36-9cf7-4b56-a270-edd1b567325c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregated query for maximum age grouped by city:\n",
      "             city  maxAge\n",
      "0           Seoul      18\n",
      "1          Munich      19\n",
      "2          Madrid      20\n",
      "3          Moscow      23\n",
      "4         Toronto      23\n",
      "5       Amsterdam      24\n",
      "6          London      25\n",
      "7   San Francisco      26\n",
      "8          Dublin      30\n",
      "9           Dubai      31\n",
      "10         Prague      32\n",
      "11      Singapore      32\n",
      "12     Copenhagen      35\n",
      "13          Paris      35\n",
      "14         Lisbon      44\n",
      "15      Barcelona      45\n",
      "16       Budapest      46\n",
      "17           Rome      52\n",
      "18      Stockholm      52\n",
      "19         Vienna      53\n",
      "20         Berlin      55\n",
      "21        Chicago      55\n",
      "22       Istanbul      56\n",
      "23       New York      58\n",
      "24         Zurich      59\n"
     ]
    }
   ],
   "source": [
    "print(\"Aggregated query for maximum age grouped by city:\")\n",
    "print(table.query(aggs={'maxAge': ['max', 'age']}, group_by=['city'], sort_columns=['maxAge']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 851
    },
    "collapsed": true,
    "id": "Ex8dE0cmbUXy",
    "outputId": "8340e11b-c1d4-4734-b7b0-040a93a0ac6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregated query for average age and count of distict people in each city:\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"table\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Dublin\",\n          \"Lisbon\",\n          \"Seoul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avgAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.004219827937904,\n        \"min\": 18.0,\n        \"max\": 59.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          18.0,\n          55.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"countCity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>avgAge</th>\n",
       "      <th>countCity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Munich</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Madrid</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toronto</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>London</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dublin</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dubai</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prague</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Copenhagen</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Paris</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>New York</td>\n",
       "      <td>35.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lisbon</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Barcelona</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Budapest</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rome</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Stockholm</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Istanbul</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Zurich</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "        document.querySelector('#df-07ac5e33-bf99-46a5-b6bd-1a4d6df150ea button.colab-df-convert');\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "        element.innerHTML = '';\n",
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       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
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       "    async function quickchart(key) {\n",
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       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "             city  avgAge  countCity\n",
       "0           Seoul    18.0          1\n",
       "1          Munich    19.0          1\n",
       "2          Madrid    20.0          1\n",
       "3          Moscow    23.0          1\n",
       "4         Toronto    23.0          1\n",
       "5       Amsterdam    24.0          1\n",
       "6          London    25.0          1\n",
       "7   San Francisco    26.0          1\n",
       "8          Dublin    30.0          1\n",
       "9           Dubai    31.0          1\n",
       "10         Prague    32.0          1\n",
       "11      Singapore    32.0          1\n",
       "12     Copenhagen    35.0          1\n",
       "13          Paris    35.0          1\n",
       "14       New York    35.6          5\n",
       "15         Berlin    44.0          2\n",
       "16         Lisbon    44.0          1\n",
       "17      Barcelona    45.0          1\n",
       "18       Budapest    46.0          1\n",
       "19           Rome    52.0          1\n",
       "20      Stockholm    52.0          1\n",
       "21         Vienna    53.0          1\n",
       "22        Chicago    55.0          1\n",
       "23       Istanbul    56.0          1\n",
       "24         Zurich    59.0          1"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Aggregated query for average age and count of distict people in each city:\")\n",
    "table.query(aggs={'avgAge': ['avg', 'age'], 'countCity': ['count', 'id']}, group_by=['city'], sort_columns=['avgAge'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WKkXE7-yvtQf"
   },
   "source": [
    "##### Customizing Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "0TR-GFQ7bW5_",
    "outputId": "ef98b740-aad6-4ebb-9266-36e8d49d6ccd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Querying data where age < 30 and name starts with H:\n",
      "   id    name  age       city  \\\n",
      "0   7  Hannah   24  Amsterdam   \n",
      "\n",
      "                                         description  \\\n",
      "0  A professional photographer with 6 years of ex...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [0.04469839, 0.07050187, 0.046390466, -0.03404...  \n"
     ]
    }
   ],
   "source": [
    "print(\"Querying data where age < 30 and name starts with H:\")\n",
    "print(table.query(filter=[(\"<\", \"age\", 30), (\"like\", \"name\", \"H*\")]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "ySsv3TZxbYbu",
    "outputId": "fbf7919c-9f77-4d7b-e108-6eda3b6d210d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Querying data where age > 30 and city is Rome or New York:\n",
      "   id   name  age      city  \\\n",
      "0   0  Alice   58  New York   \n",
      "1   5  Frank   32  New York   \n",
      "2   8    Ivy   52      Rome   \n",
      "3  17   Rita   50  New York   \n",
      "\n",
      "                                         description  \\\n",
      "0  A passionate environmentalist with 5 years of ...   \n",
      "1  A graphic designer based in Berlin with 8 year...   \n",
      "2  A fitness trainer with 5 years of experience w...   \n",
      "3  A financial analyst with 5 years of experience...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.006158471, 0.063678846, 0.09181005, -0.023...  \n",
      "1  [0.013257692, 0.045190323, 0.0074770325, -0.00...  \n",
      "2  [0.0002550126, 0.024398372, 0.09861772, 0.0062...  \n",
      "3  [0.015000028, 0.024906091, 0.0010010687, 0.011...  \n"
     ]
    }
   ],
   "source": [
    "print(\"Querying data where age > 30 and city is Rome or New York:\")\n",
    "print(table.query(filter=[(\">\", \"age\", 30), (\"in\", \"city\", [\"Rome\", \"New York\"])]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I3sX3ucWvzDZ"
   },
   "source": [
    "#### Vector Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cuxZlgX2wwHp"
   },
   "source": [
    "##### Embedding a Query Vector and Searching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c8KMGQO2i7gT"
   },
   "outputs": [],
   "source": [
    "person_query = \"a software engineer with lots of experience\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dBjWSreqjGOd"
   },
   "outputs": [],
   "source": [
    "person_embedding = list(embedding_model.embed([person_query]))[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "9Ud3vcsbjOJ4",
    "outputId": "8d11a7f1-57f8-425c-d676-c82c4065e167"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(person_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "Zhld2bOLbfn3",
    "outputId": "12fe2934-ed17-4d4e-91dd-04a3cc7859ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for the three closest people to the example vector:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[   id   name  age      city  \\\n",
       " 0  26  Alice   46  Budapest   \n",
       " 1  12    Mia   20    Madrid   \n",
       " 2  19   Tina   19    Munich   \n",
       " \n",
       "                                          description  \\\n",
       " 0  A software architect with 9 years of experienc...   \n",
       " 1  A software developer with 6 years of experienc...   \n",
       " 2  A UX designer with 6 years of experience in cr...   \n",
       " \n",
       "                                           embeddings  __nn_distance  \n",
       " 0  [-0.06051296, 0.031862404, -0.031203829, -0.07...       0.322560  \n",
       " 1  [-0.04372146, 0.06704399, 0.022140108, -0.1017...       0.356629  \n",
       " 2  [-0.030572662, 0.04520395, 0.04553928, -0.0925...       0.457364  ]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Searching for the three closest people to the example vector:\")\n",
    "table.search({index_name: [person_embedding]}, n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ashW6LLjv7zb"
   },
   "source": [
    "##### Batch Search with Multiple Query Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "4GFgKzsCbhRg",
    "outputId": "9be28206-4142-4038-8fc6-6b87ecffcb29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch search with multiple query vectors:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[   id   name  age      city  \\\n",
       " 0  26  Alice   46  Budapest   \n",
       " 1  12    Mia   20    Madrid   \n",
       " 2  19   Tina   19    Munich   \n",
       " \n",
       "                                          description  \\\n",
       " 0  A software architect with 9 years of experienc...   \n",
       " 1  A software developer with 6 years of experienc...   \n",
       " 2  A UX designer with 6 years of experience in cr...   \n",
       " \n",
       "                                           embeddings  __nn_distance  \n",
       " 0  [-0.06051296, 0.031862404, -0.031203829, -0.07...       0.322560  \n",
       " 1  [-0.04372146, 0.06704399, 0.022140108, -0.1017...       0.356629  \n",
       " 2  [-0.030572662, 0.04520395, 0.04553928, -0.0925...       0.457364  ,\n",
       "    id    name  age    city                                        description  \\\n",
       " 0   3  Monica   35   Paris  A data scientist in Tokyo with 4 years of expe...   \n",
       " 1  24    Yara   44  Lisbon  A business analyst with 7 years of experience ...   \n",
       " 2  27    Cody   55  Berlin  A research scientist with 8 years of experienc...   \n",
       " \n",
       "                                           embeddings  __nn_distance  \n",
       " 0  [-0.008097345, 0.030305384, 0.012246384, -0.04...       0.231473  \n",
       " 1  [-0.044623584, 0.054378174, 0.0015794634, -0.0...       0.617759  \n",
       " 2  [-0.01787689, 0.07915241, -0.004790489, -0.031...       0.642228  ]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Batch search with multiple query vectors:\")\n",
    "queries = [\"a software engineer with lots of experience\", \"a data scientist with experience in machine learning and a keen interest in AI research\"]\n",
    "queries_embeddings = list(embedding_model.embed(queries))\n",
    "table.search(vectors={index_name: [q.tolist() for q in queries_embeddings]}, n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nApqLnYdwIAF"
   },
   "source": [
    "##### Combining Aggregations with Vector Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "VtgsqjOWbisW",
    "outputId": "114c1f09-c68b-45b2-b673-132c72b7292e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching with aggregated results for name, city, and description:\n",
      "[    name      city                                        description\n",
      "0  Alice  Budapest  A software architect with 9 years of experienc...\n",
      "1    Mia    Madrid  A software developer with 6 years of experienc...\n",
      "2   Tina    Munich  A UX designer with 6 years of experience in cr...]\n"
     ]
    }
   ],
   "source": [
    "print(\"Searching with aggregated results for name, city, and description:\")\n",
    "print(table.search(vectors={index_name: [person_embedding]}, n=3, aggs={\"name\": \"name\", \"city\": \"city\", \"description\": \"description\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7b4GvZSrweRS"
   },
   "source": [
    "##### Vector Search with Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "cSkZPE3FblJD",
    "outputId": "046c490f-8df8-47cd-8643-0667bcf8bd79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching with filter to find people younger than 30:\n",
      "[    name  age                                        description\n",
      "0    Mia   20  A software developer with 6 years of experienc...\n",
      "1   Tina   19  A UX designer with 6 years of experience in cr...\n",
      "2    Bob   25  A software engineer with 7 years of experience...\n",
      "3  Ethan   18  A public relations specialist with 7 years of ...\n",
      "4   Jack   23  A chef with 12 years of experience who runs a ...]\n"
     ]
    }
   ],
   "source": [
    "print(\"Searching with filter to find people younger than 30:\")\n",
    "print(table.search(vectors={index_name: [person_embedding]}, n=5, filter=[(\"<\", \"age\", 30)], aggs={\"name\": \"name\", \"age\": \"age\", \"description\": \"description\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_uPg6NsgwgEc"
   },
   "source": [
    "### Drop Table To Conserve Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wzZvVkTBbmgg",
    "outputId": "84df09a6-b19b-4a46-8e1d-77b0b822590c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.drop()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: KDB.AI_course/course_specific_content/managing_tables.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb2094b8-13a5-4f7c-bd21-d2c709dab914",
   "metadata": {
    "id": "bb2094b8-13a5-4f7c-bd21-d2c709dab914"
   },
   "source": [
    "# Managing Tables in KDB.AI\n",
    "[Video Walkthough](https://www.youtube.com/watch?v=XH5iNkcFKXc&list=PLypX5sYuDqvrqsXTw876gGHosCKvK_7QS&index=6)\n",
    "\n",
    "##### Note: This example requires a KDB.AI endpoint and API key. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "\n",
    "\n",
    "How to get started with the KDB.AI vector database. Here, you'll get a quick taste of KDB.AI in  ~10 minutes.\n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "1. Connect to KDB.AI\n",
    "1. Create a KDB.AI Table\n",
    "1. Add Data to the KDB.AI Table\n",
    "1. Query the Table\n",
    "1. Perform Similarity Search\n",
    "1. Delete the KDB.AI Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260d0f4b-ef09-4bd2-a197-a9351be24684",
   "metadata": {
    "id": "260d0f4b-ef09-4bd2-a197-a9351be24684"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1468bd3",
   "metadata": {
    "id": "d1468bd3"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "491cd6d6",
   "metadata": {
    "id": "491cd6d6"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6d17b7",
   "metadata": {
    "id": "cc6d17b7"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "805d97da",
   "metadata": {
    "id": "805d97da"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a55ae34e-472b-4aa7-9add-1fcb2ee24a41",
   "metadata": {
    "id": "a55ae34e-472b-4aa7-9add-1fcb2ee24a41"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c660c7d",
   "metadata": {
    "id": "8c660c7d"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Connect to KDB.AI Server\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e85c1ff",
   "metadata": {
    "id": "2e85c1ff"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f330098",
   "metadata": {
    "id": "6f330098"
   },
   "outputs": [],
   "source": [
    "database = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec2c77b",
   "metadata": {
    "id": "1ec2c77b"
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Need help understanding a function?</b><br/>\n",
    "Add ? before or after any function name in KDB.AI to bring up the documentation for that function along with sample code and arguments.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e54917b",
   "metadata": {
    "id": "6e54917b"
   },
   "outputs": [],
   "source": [
    "?kdbai.Session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8788a6b1",
   "metadata": {
    "id": "8788a6b1"
   },
   "source": [
    "### Verify Defined Tables\n",
    "\n",
    "We can check our connection using the `session.list()` function.\n",
    "This will return a list of all the tables we have defined in our vector database thus far.\n",
    "This should return an empty list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97e5f4a9",
   "metadata": {
    "id": "97e5f4a9"
   },
   "outputs": [],
   "source": [
    "# ensure no table called \"data\" exists\n",
    "try:\n",
    "    database.table(\"data\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7877f51c",
   "metadata": {
    "id": "7877f51c",
    "outputId": "a6deb89e-0325-4686-f111-b611f5acb2e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33f03c3",
   "metadata": {
    "id": "e33f03c3"
   },
   "source": [
    "## 2. Create a KDB.AI Table\n",
    "\n",
    "To create a table we can use `create_table`, this function takes two mandatory arguments - the name and schema of the table.\n",
    "\n",
    "This schema must meet the following criteria:\n",
    "- It must contain a list of columns.\n",
    "- All columns must have `type` specified.\n",
    "\n",
    "If you want to create indexes, you must provide them as separate parameter.\n",
    "- It must contain a list of index definitions\n",
    "- Each index must have `name`, `colummn` and `type` attributes. Index-specific parameters can be passed in `params`, it's mandatory for some index types.\n",
    "\n",
    "Run `?database.create_table` for more details and sample code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f40400b",
   "metadata": {
    "id": "6f40400b"
   },
   "outputs": [],
   "source": [
    "?database.create_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da55253",
   "metadata": {
    "id": "9da55253"
   },
   "source": [
    "### Define Schema\n",
    "\n",
    "Our table will have two columns the first `id` with a list of dummy ID's, the second will be the vector embeddings we will use for similarity search later on in this example.\n",
    "\n",
    "We will define our dimensionality, similarity metric and index type in the `indexes` parameter. For this example we chose:\n",
    "- `dims = 8` : In the next section, we generate embeddings that are eight-dimensional to match this. You can chose any value here.\n",
    "- `metric = L2` : We chose [L2/Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance). Our dummy dataset is low dimensional which Euclidean distance is suitable for. You have the choice of using other metrics here like [IP/Inner Product](https://en.wikipedia.org/wiki/Inner_product_space) and [CS/Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) and the one you chose depends on the specific context and nature of your data.\n",
    "- `type = flat` : We use a [Flat index](https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexFlat.html) here as we have a simple data structure so this is more than adequate. You have the choice of using other indexes like [HNSW](https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexHNSW.html) and [IVFPQ](https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexIVFPQ.html) here, as with metrics the one you chose depends your data and your overall performance requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e8b782",
   "metadata": {
    "id": "e5e8b782"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "    {\"name\": \"id\", \"type\": \"str\"},\n",
    "    {\"name\": \"vectors\", \"type\": \"float32s\"},\n",
    "]\n",
    "\n",
    "index_name = \"flat_index\"\n",
    "indexes = [{\"name\": index_name, \"column\": \"vectors\", \"type\": \"flat\", \"params\": {\"dims\": 8, \"metric\": \"L2\"}}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a5caa0",
   "metadata": {
    "id": "09a5caa0"
   },
   "source": [
    "### Create Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34067680",
   "metadata": {
    "id": "34067680"
   },
   "outputs": [],
   "source": [
    "table = database.create_table(\"data\", schema=schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20afbea1",
   "metadata": {
    "id": "20afbea1"
   },
   "source": [
    "## 3. Add Data to the KDB.AI Table\n",
    "\n",
    "First, generate a vector of five 8-dimensional vectors which will be the vector embeddings in this example. We will then add these to pandas dataframe with column names/types matching the target table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37581e86",
   "metadata": {
    "id": "37581e86"
   },
   "outputs": [],
   "source": [
    "# Create a NumPy array of 5 eight-dimensional float32 arrays\n",
    "vectors = np.array(\n",
    "    [\n",
    "        [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],\n",
    "        [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
    "        [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "        [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1],\n",
    "        [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2],\n",
    "    ],\n",
    "    dtype=np.float32,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5dc41e8",
   "metadata": {
    "id": "f5dc41e8"
   },
   "outputs": [],
   "source": [
    "# Example ID values\n",
    "ids = [\"h\", \"e\", \"l\", \"l\", \"o\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "730c9f08",
   "metadata": {
    "id": "730c9f08"
   },
   "outputs": [],
   "source": [
    "# column names/types matching the schema\n",
    "embeddings = pd.DataFrame({\"id\": ids, \"vectors\": list(vectors)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a31f878",
   "metadata": {
    "id": "4a31f878",
    "outputId": "933caa30-7fd4-4d11-c717-ecfff36fa6c9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>h</td>\n",
       "      <td>[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>l</td>\n",
       "      <td>[0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l</td>\n",
       "      <td>[0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>o</td>\n",
       "      <td>[0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id                                   vectors\n",
       "0  h  [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]\n",
       "1  e  [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
       "2  l  [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
       "3  l  [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1]\n",
       "4  o  [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cd2ad8",
   "metadata": {
    "id": "43cd2ad8"
   },
   "source": [
    "We can now add data to our KDB.AI table using `insert`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7e0f8c5",
   "metadata": {
    "id": "b7e0f8c5"
   },
   "outputs": [],
   "source": [
    "table.insert(embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09577e8e",
   "metadata": {
    "id": "09577e8e"
   },
   "source": [
    "## 4. Query the Table\n",
    "\n",
    "We can use `query` to query data from the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4b8b8e5",
   "metadata": {
    "id": "f4b8b8e5",
    "outputId": "f96e7323-a9f0-4154-abef-dd012be6b1b9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>h</td>\n",
       "      <td>[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>l</td>\n",
       "      <td>[0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l</td>\n",
       "      <td>[0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>o</td>\n",
       "      <td>[0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id                                   vectors\n",
       "0  h  [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]\n",
       "1  e  [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
       "2  l  [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
       "3  l  [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1]\n",
       "4  o  [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c267a58",
   "metadata": {
    "id": "9c267a58"
   },
   "source": [
    "## 5. Perform Similarity Search\n",
    "\n",
    "Finally, let's perform similarity search on the table. We do this using the `search` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "595829ff",
   "metadata": {
    "id": "595829ff"
   },
   "outputs": [],
   "source": [
    "?table.search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb341f3",
   "metadata": {
    "id": "9bb341f3"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> The dimension of input query vectors must match the vector embedding dimensions in the table, defined in schema above.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9301c97",
   "metadata": {
    "id": "c9301c97",
    "outputId": "880dfe5d-86e2-4487-d3d6-771c7be40f57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[  id                                   vectors  __nn_distance\n",
       " 0  e  [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]           0.01]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the closest neighbor of a single query vector\n",
    "table.search(vectors={index_name: [[0.1, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]]}, n=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49758e9d",
   "metadata": {
    "id": "49758e9d"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> The output was a list of length one, matching the number of vectors we input to the search. This can be indexed on position [0] to extract the dataframe corresponding to the single input vector.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8aed9bc-72b2-4e70-b763-e7ce054557db",
   "metadata": {
    "id": "d8aed9bc-72b2-4e70-b763-e7ce054557db"
   },
   "source": [
    "## 6. Delete the KDB.AI Table\n",
    "\n",
    "We can use `table.drop()` to delete a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548a9d95-aac3-4d63-a87a-99eedfe55f07",
   "metadata": {
    "id": "548a9d95-aac3-4d63-a87a-99eedfe55f07",
    "outputId": "53a714f1-ad13-410c-dbdb-39e5a22e7a86"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc6d801-1371-48d0-98b4-0baa53bc8446",
   "metadata": {
    "id": "8bc6d801-1371-48d0-98b4-0baa53bc8446"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Warning:</b> Once you drop a table, you cannot use it again.\n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
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  "kernelspec": {
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================================================
FILE: KDB.AI_course/course_specific_content/rag_example.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "on0mJqL80KsJ"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "[Video Walkthrough](https://www.youtube.com/watch?v=Obbn15rZfvQ&list=PLypX5sYuDqvrqsXTw876gGHosCKvK_7QS&index=13)\n",
    "\n",
    "This notebook demonstrates the implementation of a Retrieval-Augmented Generation (RAG) pipeline using KDB.AI and Large Language Models. By the end of this tutorial, you'll understand how to leverage vector databases and LLMs to create an effective RAG system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s3Eb0JnV0lVJ"
   },
   "source": [
    "### Setup and Dependencies\n",
    "Install kdbai_client and import the necessary dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x68BCmLZ15N2"
   },
   "source": [
    "##### Install Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "OnhoXtx5ggta",
    "outputId": "1e69c47d-0034-47fb-fcd9-35ccade1d6d2"
   },
   "outputs": [],
   "source": [
    "# Install required libraries\n",
    "!pip install llama-index fastembed openai kdbai_client onnxruntime==1.19.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LHMN8-Vd2ANx"
   },
   "source": [
    "##### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "RHlEgCWExKo3"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time\n",
    "from llama_index.core import Document, SimpleDirectoryReader\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "import pandas as pd\n",
    "from fastembed import TextEmbedding\n",
    "import openai\n",
    "import textwrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up OpenAI API key\n",
    "OPENAI_API_KEY = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0aC8tMVy0vPv"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Connect to KDB.AI Server\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4rhRF58Wwxhj",
    "outputId": "355c7966-b409-4a52-f86c-b4f62755df97"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TI-33lMv1LYi"
   },
   "source": [
    "##### Initialize Embedding Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "id": "cemGdbEkufnu",
    "outputId": "4de02d2c-338e-4034-e784-c26a6abb8550"
   },
   "outputs": [],
   "source": [
    "fastembed = TextEmbedding()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sd3rjwg-kmWL"
   },
   "source": [
    "### Data Preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "24Sj5uYC2SBf"
   },
   "source": [
    "##### Download Dataset\n",
    "We'll use the Paul Graham Essay Dataset as our corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZgL3bM7GPkUa",
    "outputId": "0d2e7772-b5dd-4d02-a23d-9ce62d39343e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  4.37it/s]\n",
      "Successfully downloaded PaulGrahamEssayDataset to ./data\n"
     ]
    }
   ],
   "source": [
    "!llamaindex-cli download-llamadataset PaulGrahamEssayDataset --download-dir ./data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BXoGTn42lE-T"
   },
   "source": [
    "### Create a KDB.AI session and table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-gOC5u2KW32F"
   },
   "outputs": [],
   "source": [
    "KDBAI_TABLE_NAME = \"paul_graham\"\n",
    "database = session.database(\"default\")\n",
    "\n",
    "# Drop existing table if it exists\n",
    "try:\n",
    "    database.table(KDBAI_TABLE_NAME).drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "SP9oocI1Z2j1"
   },
   "outputs": [],
   "source": [
    "# Define table schema\n",
    "\n",
    "schema = [\n",
    "    dict(name=\"text\", type=\"bytes\"),\n",
    "    dict(name=\"embedding\", type=\"float32s\")\n",
    "]\n",
    "index_name = \"flat_index\"\n",
    "indexes = [dict(name=index_name, column=\"embedding\", type=\"flat\", params=dict(metric=\"L2\", dims=384))]\n",
    "\n",
    "table = database.create_table(KDBAI_TABLE_NAME, schema=schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "THCzKUyS3E2B"
   },
   "source": [
    "#### Load and Parse Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mwFpmgDSzZ_A",
    "outputId": "78987b65-47af-4d4f-b405-50ad264bb041"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_parser = SentenceSplitter(chunk_size=500, chunk_overlap=100)\n",
    "essays = SimpleDirectoryReader(input_dir=\"./data/source_files\").load_data()\n",
    "docs = node_parser.get_nodes_from_documents(essays)\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fpeYGnog3MLs"
   },
   "source": [
    "##### Generate Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "id": "gjhkkyqHaA5k",
    "outputId": "d7e8f17b-c3e1-406a-f8d0-e35bb22165cf"
   },
   "outputs": [],
   "source": [
    "embedding_model = TextEmbedding()\n",
    "documents = [doc.text for doc in docs]\n",
    "embeddings = list(embedding_model.embed(documents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xMHUKw8FcYDZ",
    "outputId": "e05c1849-9647-4c9c-bca3-a7f6628bf7b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.\n",
      "\n",
      "With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\n",
      "\n",
      "The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\n",
      "\n",
      "Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\n",
      "\n",
      "Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\n",
      "\n",
      "I couldn't have put this into words when I was 18.\n"
     ]
    }
   ],
   "source": [
    "print(documents[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCWVBc0c3Sbs"
   },
   "source": [
    "#####  Insert Data into KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "LcWJHw4caCt3"
   },
   "outputs": [],
   "source": [
    "records_to_insert_with_embeddings = pd.DataFrame({\n",
    "    \"text\": [d.encode('utf-8') for d in documents],\n",
    "    \"embedding\": embeddings\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3TXly52oaGvE",
    "outputId": "3116011d-fdd9-4255-9086-73240d31e4f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 46}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(records_to_insert_with_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0QtreXjz3W_t"
   },
   "source": [
    "### RAG Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bQUkhjTS3Ypm"
   },
   "source": [
    "##### Define Query and Generate Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "m-zgeI0BaLOc"
   },
   "outputs": [],
   "source": [
    "query = \"How does Paul Graham decide what to work on?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "3-YRNxNjaNZT"
   },
   "outputs": [],
   "source": [
    "query_embedding = list(embedding_model.embed([query]))[0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zg8SklaJ3biG"
   },
   "source": [
    "##### Perform Vector Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "gWSOhN6uaQzQ"
   },
   "outputs": [],
   "source": [
    "search_results = table.search({index_name: [query_embedding]}, n=10)\n",
    "search_results_df = search_results[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 842
    },
    "id": "BcM2f_KPaS72",
    "outputId": "e7397190-88b2-47a2-cd67-dc1c80a7d09d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Search Results Based on Query: How does Paul Graham decide what to work on?\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>text</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.823007</td>\n",
       "      <td>b'In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you\\'re working on an interpreter written in itself, it\\'s hard to keep track of what\\'s happening at what level, and errors can be practically encrypted by the time you get them.\\n\\nSo I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I\\'d ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I\\'d check Twitter or HN and see someone asking \"Does Paul Graham still code?\"\\n\\nWorking on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\\n\\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\\n\\nIn the fall of 2019, Bel was finally finished. Like McCarthy\\'s original Lisp, it\\'s a spec rather than an implementation, although like McCarthy\\'s Lisp it\\'s a spec expressed as code.\\n\\nNow that I could write essays again, I wrote a bunch about topics I\\'d had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do?'</td>\n",
       "      <td>[-0.05267877, 0.005840427, -0.01187801, -0.028083289, 0.029767925, -0.01268333, -0.009753024, -0.011209541, 0.030792488, -0.07470311, 0.0005716741, 0.034681723, -0.0025648128, -0.007870674, -0.037071493, -0.0026503617, -0.030294443, -0.046712548, -0.026220752, -0.010382689, -0.047210008, 0.0039388337, -0.009324926, 0.04539282, 0.04298206, 0.051068194, 0.029527958, -0.012021941, -0.051774003, -0.20419116, -0.019487105, 0.03856181, 0.054865412, -0.024023462, 0.005628216, 0.059498444, -0.023029648, -0.011461271, 0.0007990732, 0.01532533, 0.013435846, 0.009714834, 0.010104686, -0.014338494, 0.004052569, 0.020879505, 0.0112869395, -0.048422333, 0.025670612, 0.033183247, -0.071020156, -0.032056253, -0.0013147242, 0.045764726, -0.023884403, 0.013609344, 0.021824384, 0.0791942, 0.0021155155, -0.0058458406, 0.022163069, -0.0010415328, -0.1377265, 0.05194325, -0.035091735, 0.020503322, -0.03358411, -0.039575316, -0.018544003, 0.07090187, -0.030203853, 0.0024145627, -0.050365325, 0.1062729, 0.04504893, 0.020158818, -0.0055481945, 0.0020900085, 0.014658697, -0.01600323, 0.018643875, -0.020128626, 0.001960821, 0.014573526, -0.018745624, -0.011082115, -0.026627902, 0.035287272, 0.033186108, 0.004842385, 0.04288919, -0.051519115, 0.021143924, 0.03511711, -0.032461487, -0.053802498, -2.9269107e-05, 0.022274038, -0.019326271, 0.5066904, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.851789</td>\n",
       "      <td>b\"He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.\\n\\nShe died on January 15, 2014. We knew this was coming, but it was still hard when it did.\\n\\nI kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.)\\n\\nWhat should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]\\n\\nI spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway.\\n\\nI realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.\"</td>\n",
       "      <td>[-0.04173409, -0.020306244, 0.026670614, -0.028619805, 0.013841975, -0.004587492, -0.03740281, -0.0023207841, -0.005583664, -0.02458708, 0.032301717, -0.003981511, -0.0022139344, 0.040776156, 0.008303966, 0.065411426, -0.05266241, -0.0147317415, -0.013039435, -0.02108635, -0.08220996, -0.023095597, 0.009018569, -0.06593445, 0.053503707, 0.02561, -0.011278506, -0.029375598, -0.02894449, -0.17977206, 0.015862752, 0.037204675, 0.028550476, -0.008014831, 0.050124772, 0.053289328, -0.037882008, -0.004310019, -0.040979013, 0.031382367, -0.019382592, 0.041386265, -0.06535482, -0.03808074, 0.013384267, 0.010357172, 0.0032444543, -0.052392986, 0.042238504, 0.020043798, -0.028322041, -0.055793695, -0.011091505, 0.020135079, -0.003494716, 0.01618655, 0.08450317, 0.040414557, 0.032989975, 0.011764182, -0.013049825, -0.029259514, -0.102057606, 0.016020596, 0.016062474, 0.010199196, -0.009390674, -0.043287795, 0.034758028, 0.13968067, 0.025622727, 0.016510569, -0.02354023, 0.073845506, 0.009602881, -0.049839057, 0.022470307, 0.043024465, 0.0017405926, -0.028580481, 0.0027170023, 0.010050958, -0.013109462, 0.014532717, -0.04200619, 0.01677191, -0.07769759, 0.0073121856, 0.0189732, 0.08225239, 0.052873313, 0.020460907, 0.017190987, -0.025781311, -0.057865854, -0.015826138, 0.04352462, 0.040577717, -0.045354914, 0.47870147, ...]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance  \\\n",
       "0       0.823007   \n",
       "1       0.851789   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    text  \\\n",
       "0  b'In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you\\'re working on an interpreter written in itself, it\\'s hard to keep track of what\\'s happening at what level, and errors can be practically encrypted by the time you get them.\\n\\nSo I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I\\'d ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I\\'d check Twitter or HN and see someone asking \"Does Paul Graham still code?\"\\n\\nWorking on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\\n\\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\\n\\nIn the fall of 2019, Bel was finally finished. Like McCarthy\\'s original Lisp, it\\'s a spec rather than an implementation, although like McCarthy\\'s Lisp it\\'s a spec expressed as code.\\n\\nNow that I could write essays again, I wrote a bunch about topics I\\'d had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do?'   \n",
       "1                                                                                                                                                                  b\"He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.\\n\\nShe died on January 15, 2014. We knew this was coming, but it was still hard when it did.\\n\\nI kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.)\\n\\nWhat should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]\\n\\nI spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway.\\n\\nI realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.\"   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            embedding  \n",
       "0  [-0.05267877, 0.005840427, -0.01187801, -0.028083289, 0.029767925, -0.01268333, -0.009753024, -0.011209541, 0.030792488, -0.07470311, 0.0005716741, 0.034681723, -0.0025648128, -0.007870674, -0.037071493, -0.0026503617, -0.030294443, -0.046712548, -0.026220752, -0.010382689, -0.047210008, 0.0039388337, -0.009324926, 0.04539282, 0.04298206, 0.051068194, 0.029527958, -0.012021941, -0.051774003, -0.20419116, -0.019487105, 0.03856181, 0.054865412, -0.024023462, 0.005628216, 0.059498444, -0.023029648, -0.011461271, 0.0007990732, 0.01532533, 0.013435846, 0.009714834, 0.010104686, -0.014338494, 0.004052569, 0.020879505, 0.0112869395, -0.048422333, 0.025670612, 0.033183247, -0.071020156, -0.032056253, -0.0013147242, 0.045764726, -0.023884403, 0.013609344, 0.021824384, 0.0791942, 0.0021155155, -0.0058458406, 0.022163069, -0.0010415328, -0.1377265, 0.05194325, -0.035091735, 0.020503322, -0.03358411, -0.039575316, -0.018544003, 0.07090187, -0.030203853, 0.0024145627, -0.050365325, 0.1062729, 0.04504893, 0.020158818, -0.0055481945, 0.0020900085, 0.014658697, -0.01600323, 0.018643875, -0.020128626, 0.001960821, 0.014573526, -0.018745624, -0.011082115, -0.026627902, 0.035287272, 0.033186108, 0.004842385, 0.04288919, -0.051519115, 0.021143924, 0.03511711, -0.032461487, -0.053802498, -2.9269107e-05, 0.022274038, -0.019326271, 0.5066904, ...]  \n",
       "1               [-0.04173409, -0.020306244, 0.026670614, -0.028619805, 0.013841975, -0.004587492, -0.03740281, -0.0023207841, -0.005583664, -0.02458708, 0.032301717, -0.003981511, -0.0022139344, 0.040776156, 0.008303966, 0.065411426, -0.05266241, -0.0147317415, -0.013039435, -0.02108635, -0.08220996, -0.023095597, 0.009018569, -0.06593445, 0.053503707, 0.02561, -0.011278506, -0.029375598, -0.02894449, -0.17977206, 0.015862752, 0.037204675, 0.028550476, -0.008014831, 0.050124772, 0.053289328, -0.037882008, -0.004310019, -0.040979013, 0.031382367, -0.019382592, 0.041386265, -0.06535482, -0.03808074, 0.013384267, 0.010357172, 0.0032444543, -0.052392986, 0.042238504, 0.020043798, -0.028322041, -0.055793695, -0.011091505, 0.020135079, -0.003494716, 0.01618655, 0.08450317, 0.040414557, 0.032989975, 0.011764182, -0.013049825, -0.029259514, -0.102057606, 0.016020596, 0.016062474, 0.010199196, -0.009390674, -0.043287795, 0.034758028, 0.13968067, 0.025622727, 0.016510569, -0.02354023, 0.073845506, 0.009602881, -0.049839057, 0.022470307, 0.043024465, 0.0017405926, -0.028580481, 0.0027170023, 0.010050958, -0.013109462, 0.014532717, -0.04200619, 0.01677191, -0.07769759, 0.0073121856, 0.0189732, 0.08225239, 0.052873313, 0.020460907, 0.017190987, -0.025781311, -0.057865854, -0.015826138, 0.04352462, 0.040577717, -0.045354914, 0.47870147, ...]  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', None)\n",
    "print(\"Top Search Results Based on Query:\", query)\n",
    "df = pd.DataFrame(search_results_df)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WnIeeDVJ3g47"
   },
   "source": [
    "##### RAG Function Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fhGl5YDXaXak"
   },
   "outputs": [],
   "source": [
    "def RAG(retrieved_data,prompt):\n",
    "    messages = \"Answer the following query in three sentences based on the context and only the context: \" + \"\\n\"\n",
    "    messages += prompt + \"\\n\"\n",
    "    if len(retrieved_data) > 0:\n",
    "        messages += \"Context: \" + \"\\n\"\n",
    "        for data in retrieved_data:\n",
    "            messages += data.decode('utf-8') + \"\\n\"\n",
    "    openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
    "    response = openai.chat.completions.create(\n",
    "        model=\"gpt-4o\",\n",
    "        messages=[\n",
    "            {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\"type\": \"text\", \"text\": messages},\n",
    "            ],\n",
    "            },\n",
    "        ],\n",
    "        max_tokens=300,\n",
    "    )\n",
    "    content = response.choices[0].message.content\n",
    "    return content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y4yJL1kp3pOr"
   },
   "source": [
    "##### Execute RAG Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "hqrg036n3yUs"
   },
   "outputs": [],
   "source": [
    "# Utility Function for Text Wrapping\n",
    "\n",
    "def print_wrapped(text, width=80):\n",
    "    wrapper = textwrap.TextWrapper(width=width)\n",
    "    word_list = wrapper.wrap(text=text)\n",
    "    for line in word_list:\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bI2xx6IygIN5",
    "outputId": "9af82ab0-4637-4501-c398-f0042946ef4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: How does Paul Graham decide what to work on?\n",
      "Paul Graham decides what to work on based on a mix of personal interest, the\n",
      "desire to explore untapped potential in various fields, and the influence of\n",
      "pivotal moments and advice from close acquaintances. His transition from working\n",
      "on Y Combinator, painting, and writing essays to developing the Bel programming\n",
      "language and exploring startup ideas, such as the web app for creating web apps,\n",
      "reflects a combination of seeking out deeply engaging projects and responding to\n",
      "unsolicited advice from trusted collaborators that prompts reflection on his\n",
      "trajectory. Graham's choices are driven by the pursuit of projects that not only\n",
      "challenge him but also promise a significant impact or learning opportunity,\n",
      "reflecting a deliberate process of selection influenced by both internal\n",
      "motivations and external inputs.\n"
     ]
    }
   ],
   "source": [
    "print(\"Query:\", query)\n",
    "\n",
    "print_wrapped(RAG(search_results_df[\"text\"],query))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1TorfCfi37m2"
   },
   "source": [
    "### Drop Table To Conserve Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "tVmA5Dei36s3"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: KDB.AI_course/notebook_references.md
================================================
# KDB.AI Course Notebook References

This document provides a comprehensive list of all notebooks used in the KDB.AI course, including both course-specific content and notebooks referenced from other parts of the repository. This ensures a single point of reference for all course materials.

## Course-Specific Content

These notebooks are located in the `KDB.AI_course/course_specific_content/` directory:

| Notebook Name | Description |
|---------------|-------------|
| [making_queries.ipynb](./course_specific_content/making_queries.ipynb) | Introduction to making queries in KDB.AI |
| [managing_tables.ipynb](./course_specific_content/managing_tables.ipynb) | Guide to managing tables in KDB.AI |
| [rag_example.ipynb](./course_specific_content/rag_example.ipynb) | Example of Retrieval Augmented Generation with KDB.AI |

## Referenced Notebooks

These notebooks are referenced from other parts of the repository:

| Course Section | Notebook Name | Location | Description |
|----------------|---------------|----------|-------------|
| Advanced Search Techniques | Hybrid Search | [/hybrid_search/hybrid_search_inflation.ipynb](../hybrid_search/hybrid_search_inflation.ipynb) | Demonstrates hybrid search techniques using inflation data |
| Advanced Search Techniques | Temporal Similarity Search (Non-Transformed) | [/TSS_non_transformed/Temporal_Similarity_Search_Non-Transformed_Demo.ipynb](../TSS_non_transformed/Temporal_Similarity_Search_Non-Transformed_Demo.ipynb) | Covers non-transformed temporal similarity search |
| Advanced Search Techniques | Temporal Similarity Search (Transformed) | [/TSS_transformed/Temporal_Similarity_Search_Transformed_Demo.ipynb](../TSS_transformed/Temporal_Similarity_Search_Transformed_Demo.ipynb) | Explores transformed temporal similarity search |

## Additional Resources

These notebooks, while not directly part of the course, provide valuable supplementary information:

| Notebook Name | Location | Description |
|---------------|----------|-------------|
| Python Quickstart | [/quickstarts/python_quickstart.ipynb](../quickstarts/python_quickstart.ipynb) | Quick introduction to using KDB.AI with Python |
| Document Search | [/document_search/document_search.ipynb](../document_search/document_search.ipynb) | Example of document search implementation |
| Image Search | [/image_search/image_search.ipynb](../image_search/image_search.ipynb) | Demonstration of image search capabilities |

## Maintenance Notes

- This file should be updated whenever notebooks are added, removed, or relocated within the course or the main repository.

================================================
FILE: LICENSE
================================================
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================================================
FILE: LlamaIndex_advanced_RAG/KDBAI_Advanced_RAG_Demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb212650-86b7-4298-8bbd-c20a5227fbf0",
   "metadata": {
    "id": "cb212650-86b7-4298-8bbd-c20a5227fbf0"
   },
   "source": [
    "# Advanced RAG with temporal filters using LlamaIndex and KDB.AI vector store\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "> [KDB.AI](https://kdb.ai/) is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications, using real-time data, by providing advanced search, recommendation and personalization.\n",
    "\n",
    "This example demonstrates how to use KDB.AI to run semantic search, summarization and analysis of financial regulations around some specific moment in time.\n",
    "\n",
    "To set up your development environment, follow the instructions on the KDB.AI pre-requisites page.\n",
    "\n",
    "The following examples demonstrate some of the ways you can interact with KDB.AI through LlamaIndex."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f36ddd-aa4d-4284-a236-be3028758ae2",
   "metadata": {
    "id": "32f36ddd-aa4d-4284-a236-be3028758ae2"
   },
   "source": [
    "## Install dependencies with Pip\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2QITjsy5Jois",
   "metadata": {
    "id": "2QITjsy5Jois"
   },
   "outputs": [],
   "source": [
    "!pip install llama-index llama-index-llms-openai llama-index-embeddings-openai llama-index-readers-file llama-index-vector-stores-kdbai\n",
    "!pip install kdbai_client pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ba14b7-1208-4494-93ec-ce1930b7bf5b",
   "metadata": {
    "id": "68ba14b7-1208-4494-93ec-ce1930b7bf5b"
   },
   "source": [
    "## Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ca21f7-819c-4abb-8479-e7c6f3175f34",
   "metadata": {
    "id": "f4ca21f7-819c-4abb-8479-e7c6f3175f34"
   },
   "outputs": [],
   "source": [
    "from getpass import getpass\n",
    "import re\n",
    "import os\n",
    "import shutil\n",
    "import time\n",
    "import urllib\n",
    "import datetime \n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from llama_index.core import (\n",
    "    Settings,\n",
    "    SimpleDirectoryReader,\n",
    "    StorageContext,\n",
    "    VectorStoreIndex,\n",
    ")\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.retrievers import VectorIndexRetriever\n",
    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "\n",
    "import kdbai_client as kdbai\n",
    "\n",
    "OUTDIR = \"pdf\"\n",
    "RESET = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef2f90f",
   "metadata": {
    "id": "9ef2f90f"
   },
   "source": [
    "#### Set OpenAI API key and choose the LLM and Embedding model to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7WV4ydgSRlnV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7WV4ydgSRlnV",
    "outputId": "131a47ca-0eab-4a4c-c155-34614600c982"
   },
   "outputs": [],
   "source": [
    "#os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API key: \")\n",
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "15d1eaf6-8743-4c5b-a2dd-81e6101a4c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "# Set OpenAI API \n",
    "if \"OPENAI_API_KEY\" in os.environ:\n",
    "    KDBAI_API_KEY = os.environ[\"OPENAI_API_KEY\"]\n",
    "else:\n",
    "    # Prompt the user to enter the API key\n",
    "    OPENAI_API_KEY = getpass(\"OPENAI API KEY: \")\n",
    "    # Save the API key as an environment variable for the current session\n",
    "    os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "17e19f9f",
   "metadata": {
    "id": "17e19f9f"
   },
   "outputs": [],
   "source": [
    "EMBEDDING_MODEL  = \"text-embedding-3-small\"\n",
    "GENERATION_MODEL = 'gpt-4o-mini'\n",
    "\n",
    "llm = OpenAI(model=GENERATION_MODEL)\n",
    "embed_model = OpenAIEmbedding(model=EMBEDDING_MODEL)\n",
    "\n",
    "Settings.llm = llm\n",
    "Settings.embed_model = embed_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca073f5-3d84-4b0e-8684-1396c1311fb8",
   "metadata": {
    "id": "2ca073f5-3d84-4b0e-8684-1396c1311fb8"
   },
   "source": [
    "## Create KDB.AI session and table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "edf7d774",
   "metadata": {
    "id": "edf7d774"
   },
   "outputs": [],
   "source": [
    "# vector DB imports\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816fa95c",
   "metadata": {
    "id": "816fa95c"
   },
   "source": [
    "##### KDB.AI Server\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3991d719",
   "metadata": {
    "id": "3991d719"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d392b5e",
   "metadata": {
    "id": "6d392b5e"
   },
   "source": [
    "### Create the schema for your KDB.AI table\n",
    "\n",
    "***!!! Note:*** The 'dims' parameter in the embedding column must reflect the output dimensions of the embedding model you choose.\n",
    "\n",
    "\n",
    "- OpenAI 'text-embedding-3-small' outputs 1536 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9104c851",
   "metadata": {
    "id": "9104c851"
   },
   "outputs": [],
   "source": [
    "\n",
    "schema = [\n",
    "        {\"name\":\"document_id\", \"type\":\"bytes\"},\n",
    "        {\"name\":\"text\", \"type\":\"bytes\"},\n",
    "        {\"name\":\"embeddings\",\"type\":\"float32s\"},\n",
    "        {\"name\":\"title\", \"type\":\"str\"},\n",
    "        {\"name\":\"publication_date\", \"type\":\"datetime64[ns]\"},\n",
    "        ]\n",
    "\n",
    "\n",
    "indexFlat = {\n",
    "        \"name\": \"flat_index\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {'dims': 1536, 'metric': 'L2'},\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "df598cbd",
   "metadata": {
    "id": "df598cbd"
   },
   "outputs": [],
   "source": [
    "KDBAI_TABLE_NAME = \"reports\"\n",
    "database = session.database(\"default\")\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "for table in database.tables:\n",
    "    if table.name == KDBAI_TABLE_NAME:\n",
    "        table.drop()\n",
    "        break\n",
    "\n",
    "#Create the table\n",
    "table = database.create_table(KDBAI_TABLE_NAME, schema=schema, indexes=[indexFlat])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a208460c-2c87-4a3f-9926-65d6dcc4b45d",
   "metadata": {
    "id": "a208460c-2c87-4a3f-9926-65d6dcc4b45d"
   },
   "source": [
    "## Financial reports urls and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6143a9ec-7d48-4f61-bb86-a1de427a0279",
   "metadata": {
    "id": "6143a9ec-7d48-4f61-bb86-a1de427a0279"
   },
   "outputs": [],
   "source": [
    "INPUT_URLS = [\n",
    "    \"https://www.govinfo.gov/content/pkg/PLAW-106publ102/pdf/PLAW-106publ102.pdf\",\n",
    "    \"https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf\",\n",
    "]\n",
    "\n",
    "METADATA = {\n",
    "    \"pdf/PLAW-106publ102.pdf\": {\n",
    "        \"title\": \"GRAMM–LEACH–BLILEY ACT, 1999\",\n",
    "        \"publication_date\": pd.to_datetime(\"1999-11-12\"),\n",
    "    },\n",
    "    \"pdf/PLAW-111publ203.pdf\": {\n",
    "        \"title\": \"DODD-FRANK WALL STREET REFORM AND CONSUMER PROTECTION ACT, 2010\",\n",
    "        \"publication_date\": pd.to_datetime(\"2010-07-21\"),\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e6c6c5-f151-4c01-a9a1-ab1d540402eb",
   "metadata": {
    "id": "e1e6c6c5-f151-4c01-a9a1-ab1d540402eb"
   },
   "source": [
    "## Download PDF files locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3ee36757-b0b7-4478-a71a-601220048a05",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ee36757-b0b7-4478-a71a-601220048a05",
    "outputId": "d580a55f-a613-4843-8efe-f70950ae8083"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.govinfo.gov/content/pkg/PLAW-106publ102/pdf/PLAW-106publ102.pdf...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf...\n",
      "CPU times: user 52.6 ms, sys: 1.2 ms, total: 53.8 ms\n",
      "Wall time: 7.86 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "CHUNK_SIZE = 512 * 1024\n",
    "\n",
    "\n",
    "def download_file(url):\n",
    "    print(\"Downloading %s...\" % url)\n",
    "    out = os.path.join(OUTDIR, os.path.basename(url))\n",
    "    try:\n",
    "        response = urllib.request.urlopen(url)\n",
    "    except urllib.error.URLError as e:\n",
    "        logging.exception(\"Failed to download %s !\" % url)\n",
    "    else:\n",
    "        with open(out, \"wb\") as f:\n",
    "            while True:\n",
    "                chunk = response.read(CHUNK_SIZE)\n",
    "                if chunk:\n",
    "                    f.write(chunk)\n",
    "                else:\n",
    "                    break\n",
    "    return out\n",
    "\n",
    "\n",
    "if RESET:\n",
    "    if os.path.exists(OUTDIR):\n",
    "        shutil.rmtree(OUTDIR)\n",
    "    os.mkdir(OUTDIR)\n",
    "\n",
    "    local_files = [download_file(x) for x in INPUT_URLS]\n",
    "    local_files[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d52ad8-b9bd-459e-9ce4-c370982a149f",
   "metadata": {
    "id": "10d52ad8-b9bd-459e-9ce4-c370982a149f"
   },
   "source": [
    "## Load local PDF files with LlamaIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9714258b-f58a-4964-9d3f-0298a98b87e0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9714258b-f58a-4964-9d3f-0298a98b87e0",
    "outputId": "26efae7b-2735-4d59-b703-d09029268ef5"
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "def get_metadata(filepath):\n",
    "    return METADATA[filepath]\n",
    "\n",
    "\n",
    "documents = SimpleDirectoryReader(\n",
    "    input_files=local_files,\n",
    "    file_metadata=get_metadata,\n",
    ")\n",
    "\n",
    "docs = documents.load_data()\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3ba953-4034-4421-acf0-dbac33dfed67",
   "metadata": {
    "id": "2f3ba953-4034-4421-acf0-dbac33dfed67"
   },
   "source": [
    "## Setup LlamaIndex RAG pipeline using KDB.AI vector store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4ed27c4b-a979-4d4f-9f17-7c6bd9844d9a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4ed27c4b-a979-4d4f-9f17-7c6bd9844d9a",
    "outputId": "da3cd72d-f981-442d-c4b8-1ff0eeebd3c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.67 s, sys: 31.9 ms, total: 3.7 s\n",
      "Wall time: 22.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "#llm = OpenAI(temperature=0, model=LLM)\n",
    "vector_store = KDBAIVectorStore(table)\n",
    "\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    docs,\n",
    "    storage_context=storage_context,\n",
    "    transformations=[SentenceSplitter(chunk_size=2048, chunk_overlap=0)],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f5-Ilwz2UawR",
   "metadata": {
    "id": "f5-Ilwz2UawR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>document_id</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "      <th>title</th>\n",
       "      <th>publication_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'272d7d24-c232-41b6-823e-27aa6203c100'</td>\n",
       "      <td>b'PUBLIC LAW 106\\xc2\\xb1102\\xc3\\x90NOV. 12, 19...</td>\n",
       "      <td>[0.034452137, 0.03166917, -0.011892043, 0.0184...</td>\n",
       "      <td>GRAMM–LEACH–BLILEY ACT, 1999</td>\n",
       "      <td>1999-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'89e3f2ee-f5a6-4e40-bb81-0632f08341f0'</td>\n",
       "      <td>b\"113 STAT. 1338 PUBLIC LAW 106\\xc2\\xb1102\\xc3...</td>\n",
       "      <td>[0.02164333, 1.0030156e-05, 0.0028665832, 0.02...</td>\n",
       "      <td>GRAMM–LEACH–BLILEY ACT, 1999</td>\n",
       "      <td>1999-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'56fbe82a-5458-4a4a-a5ed-026d9399151d'</td>\n",
       "      <td>b'113 STAT. 1339 PUBLIC LAW 106\\xc2\\xb1102\\xc3...</td>\n",
       "      <td>[0.01380091, 0.026945233, 0.02838467, 0.043132...</td>\n",
       "      <td>GRAMM–LEACH–BLILEY ACT, 1999</td>\n",
       "      <td>1999-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'b6bf9e48-51b6-45d9-9259-b6346f93831f'</td>\n",
       "      <td>b'113 STAT. 1340 PUBLIC LAW 106\\xc2\\xb1102\\xc3...</td>\n",
       "      <td>[0.0070182937, 0.014063503, 0.026525516, 0.040...</td>\n",
       "      <td>GRAMM–LEACH–BLILEY ACT, 1999</td>\n",
       "      <td>1999-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'f398b133-b4f5-4a34-94d1-9a97fdb658e5'</td>\n",
       "      <td>b\"113 STAT. 1341 PUBLIC LAW 106\\xc2\\xb1102\\xc3...</td>\n",
       "      <td>[0.025041763, 0.01968024, 0.030940715, 0.02899...</td>\n",
       "      <td>GRAMM–LEACH–BLILEY ACT, 1999</td>\n",
       "      <td>1999-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>b'8e84d1d5-d87d-4351-b7eb-5d569fdb8d9c'</td>\n",
       "      <td>b'124 STAT. 2219 PUBLIC LAW 111\\xe2\\x80\\x93203...</td>\n",
       "      <td>[0.024505286, 0.015549232, 0.0536601, 0.028532...</td>\n",
       "      <td>DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...</td>\n",
       "      <td>2010-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>b'0c47f590-050c-4374-bf8c-2a4502dc980f'</td>\n",
       "      <td>b'124 STAT. 2220 PUBLIC LAW 111\\xe2\\x80\\x93203...</td>\n",
       "      <td>[0.014071382, -0.0044553108, 0.03662071, 0.035...</td>\n",
       "      <td>DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...</td>\n",
       "      <td>2010-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>b'63a2235f-d368-43b8-a1a9-a5a11d497245'</td>\n",
       "      <td>b'124 STAT. 2221 PUBLIC LAW 111\\xe2\\x80\\x93203...</td>\n",
       "      <td>[0.0005448305, 0.013075933, 0.044821188, 0.031...</td>\n",
       "      <td>DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...</td>\n",
       "      <td>2010-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>b'bac4d75e-4867-4d89-a71e-09a6762bf3c4'</td>\n",
       "      <td>b'124 STAT. 2222 PUBLIC LAW 111\\xe2\\x80\\x93203...</td>\n",
       "      <td>[0.032077603, 0.016817383, 0.04507993, 0.03376...</td>\n",
       "      <td>DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...</td>\n",
       "      <td>2010-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>b'e262e4da-f6e1-4b9d-9232-77fc3f0c81a7'</td>\n",
       "      <td>b'124 STAT. 2223 PUBLIC LAW 111\\xe2\\x80\\x93203...</td>\n",
       "      <td>[0.0387719, -0.025150038, 0.030345473, 0.04303...</td>\n",
       "      <td>DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...</td>\n",
       "      <td>2010-07-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>994 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 document_id  \\\n",
       "0    b'272d7d24-c232-41b6-823e-27aa6203c100'   \n",
       "1    b'89e3f2ee-f5a6-4e40-bb81-0632f08341f0'   \n",
       "2    b'56fbe82a-5458-4a4a-a5ed-026d9399151d'   \n",
       "3    b'b6bf9e48-51b6-45d9-9259-b6346f93831f'   \n",
       "4    b'f398b133-b4f5-4a34-94d1-9a97fdb658e5'   \n",
       "..                                       ...   \n",
       "989  b'8e84d1d5-d87d-4351-b7eb-5d569fdb8d9c'   \n",
       "990  b'0c47f590-050c-4374-bf8c-2a4502dc980f'   \n",
       "991  b'63a2235f-d368-43b8-a1a9-a5a11d497245'   \n",
       "992  b'bac4d75e-4867-4d89-a71e-09a6762bf3c4'   \n",
       "993  b'e262e4da-f6e1-4b9d-9232-77fc3f0c81a7'   \n",
       "\n",
       "                                                  text  \\\n",
       "0    b'PUBLIC LAW 106\\xc2\\xb1102\\xc3\\x90NOV. 12, 19...   \n",
       "1    b\"113 STAT. 1338 PUBLIC LAW 106\\xc2\\xb1102\\xc3...   \n",
       "2    b'113 STAT. 1339 PUBLIC LAW 106\\xc2\\xb1102\\xc3...   \n",
       "3    b'113 STAT. 1340 PUBLIC LAW 106\\xc2\\xb1102\\xc3...   \n",
       "4    b\"113 STAT. 1341 PUBLIC LAW 106\\xc2\\xb1102\\xc3...   \n",
       "..                                                 ...   \n",
       "989  b'124 STAT. 2219 PUBLIC LAW 111\\xe2\\x80\\x93203...   \n",
       "990  b'124 STAT. 2220 PUBLIC LAW 111\\xe2\\x80\\x93203...   \n",
       "991  b'124 STAT. 2221 PUBLIC LAW 111\\xe2\\x80\\x93203...   \n",
       "992  b'124 STAT. 2222 PUBLIC LAW 111\\xe2\\x80\\x93203...   \n",
       "993  b'124 STAT. 2223 PUBLIC LAW 111\\xe2\\x80\\x93203...   \n",
       "\n",
       "                                            embeddings  \\\n",
       "0    [0.034452137, 0.03166917, -0.011892043, 0.0184...   \n",
       "1    [0.02164333, 1.0030156e-05, 0.0028665832, 0.02...   \n",
       "2    [0.01380091, 0.026945233, 0.02838467, 0.043132...   \n",
       "3    [0.0070182937, 0.014063503, 0.026525516, 0.040...   \n",
       "4    [0.025041763, 0.01968024, 0.030940715, 0.02899...   \n",
       "..                                                 ...   \n",
       "989  [0.024505286, 0.015549232, 0.0536601, 0.028532...   \n",
       "990  [0.014071382, -0.0044553108, 0.03662071, 0.035...   \n",
       "991  [0.0005448305, 0.013075933, 0.044821188, 0.031...   \n",
       "992  [0.032077603, 0.016817383, 0.04507993, 0.03376...   \n",
       "993  [0.0387719, -0.025150038, 0.030345473, 0.04303...   \n",
       "\n",
       "                                                 title publication_date  \n",
       "0                         GRAMM–LEACH–BLILEY ACT, 1999       1999-11-12  \n",
       "1                         GRAMM–LEACH–BLILEY ACT, 1999       1999-11-12  \n",
       "2                         GRAMM–LEACH–BLILEY ACT, 1999       1999-11-12  \n",
       "3                         GRAMM–LEACH–BLILEY ACT, 1999       1999-11-12  \n",
       "4                         GRAMM–LEACH–BLILEY ACT, 1999       1999-11-12  \n",
       "..                                                 ...              ...  \n",
       "989  DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...       2010-07-21  \n",
       "990  DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...       2010-07-21  \n",
       "991  DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...       2010-07-21  \n",
       "992  DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...       2010-07-21  \n",
       "993  DODD-FRANK WALL STREET REFORM AND CONSUMER PRO...       2010-07-21  \n",
       "\n",
       "[994 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0ca610-4038-41c2-a1f9-5c8af9d764ce",
   "metadata": {
    "id": "0a0ca610-4038-41c2-a1f9-5c8af9d764ce"
   },
   "source": [
    "## Setup the LlamaIndex Query Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7d29b82-c1b8-4880-869e-dabc8bdedaa4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f7d29b82-c1b8-4880-869e-dabc8bdedaa4",
    "outputId": "ec0bb384-bd26-4321-e68d-a012ebd5133b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 512 μs, sys: 23 μs, total: 535 μs\n",
      "Wall time: 550 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Using gpt-4o-mini, the 128k tokens context size can take 100 pages.\n",
    "K = 15\n",
    "\n",
    "query_engine = index.as_query_engine(\n",
    "                similarity_top_k=K,\n",
    "                vector_store_kwargs={\n",
    "                        \"index\" : \"flat_index\",\n",
    "                        \"filter\" : [[\"<\", \"publication_date\", datetime.date(2008,9,15)]],\n",
    "                        \"sort_columns\" : [\"publication_date\"]\n",
    "                        }\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e442d238-03cf-41d1-9ea4-9d435bb30278",
   "metadata": {
    "id": "e442d238-03cf-41d1-9ea4-9d435bb30278"
   },
   "source": [
    "## Before the 2008 crisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1d791e0e-67b0-4179-a4c4-a1f5fd6d765b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1d791e0e-67b0-4179-a4c4-a1f5fd6d765b",
    "outputId": "a29088b4-8573-4f07-c27f-04ac623d395e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The main financial regulation in the US before the 2008 financial crisis was the Gramm-Leach-Bliley Act, enacted in 1999. This act facilitated the affiliation among banks, securities firms, and insurance companies, effectively repealing parts of the Glass-Steagall Act, which had previously separated these financial services. The Gramm-Leach-Bliley Act aimed to enhance competition in the financial services industry by providing a framework for the integration of various financial institutions.\n",
      "CPU times: user 61.8 ms, sys: 0 ns, total: 61.8 ms\n",
      "Wall time: 4.24 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "result = query_engine.query(\n",
    "    \"\"\"\n",
    "    What was the main financial regulation in the US before the 2008 financial crisis ?\n",
    "    \"\"\"\n",
    ")\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ee5c52ed-fc1a-4f4f-8cc4-efbb4e5a067f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ee5c52ed-fc1a-4f4f-8cc4-efbb4e5a067f",
    "outputId": "b25e64a9-32c8-4b18-d66f-5ec1deb1383f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Gramm-Leach-Bliley Act of 1999 aimed to enhance competition in the financial services industry by allowing affiliations among banks, securities firms, and insurance companies. Its strengths include the repeal of the Glass-Steagall Act, which had previously separated commercial banking from investment banking, thereby enabling financial institutions to diversify their services and potentially increase competition. This diversification could lead to more innovative financial products and services.\n",
      "\n",
      "However, the Act also has notable weaknesses. By allowing greater affiliations and reducing regulatory barriers, it may have contributed to the creation of \"too big to fail\" institutions, which posed systemic risks to the financial system. The lack of stringent oversight and the ability for financial holding companies to engage in a wide range of activities without adequate regulation may have led to excessive risk-taking. Additionally, the Act did not sufficiently address the complexities of modern financial products, such as derivatives, which played a significant role in the 2008 financial crisis.\n",
      "\n",
      "In summary, while the Gramm-Leach-Bliley Act aimed to foster competition and innovation in the financial sector, its regulatory framework may have inadvertently facilitated the conditions that led to the financial crisis, highlighting the need for a more robust regulatory approach to oversee the interconnectedness and risks within the financial system.\n",
      "CPU times: user 45.7 ms, sys: 255 μs, total: 46 ms\n",
      "Wall time: 21.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "result = query_engine.query(\n",
    "    \"\"\"\n",
    "    Is the Gramm-Leach-Bliley Act of 1999 enough to prevent the 2008 crisis. Search the document and explain its strenghts and weaknesses to regulate the US stock market.\n",
    "    \"\"\"\n",
    ")\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6b52ee-2086-4e50-8e88-6ad6920cc8bc",
   "metadata": {
    "id": "5c6b52ee-2086-4e50-8e88-6ad6920cc8bc"
   },
   "source": [
    "## After the 2008 crisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37753e54-959b-43a3-b596-7cef0e94d4ba",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "37753e54-959b-43a3-b596-7cef0e94d4ba",
    "outputId": "f334e045-0f0b-45db-9162-4338ba84465b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 171 μs, sys: 0 ns, total: 171 μs\n",
      "Wall time: 175 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Using gpt-4o-mini, the 128k tokens context size can take 100 pages.\n",
    "K = 15\n",
    "\n",
    "query_engine = index.as_query_engine(\n",
    "                similarity_top_k=K,\n",
    "                vector_store_kwargs={\n",
    "                        \"index\" : \"flat_index\",\n",
    "                        \"filter\" : [[\">=\", \"publication_date\", datetime.date(2008,9,15)]],\n",
    "                        \"sort_columns\" : [\"publication_date\"]\n",
    "                        }\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "445ebab3-4431-4f75-a07d-c998c98b7cfd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "445ebab3-4431-4f75-a07d-c998c98b7cfd",
    "outputId": "2fafb39b-f1b4-414c-b0eb-4935d5b76f22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the 15th of September 2008, Lehman Brothers, a major global financial services firm, filed for bankruptcy. This event marked one of the largest bankruptcies in U.S. history and was a significant moment in the financial crisis of 2007-2008, leading to widespread panic in financial markets and contributing to the global economic downturn.\n",
      "CPU times: user 51.4 ms, sys: 0 ns, total: 51.4 ms\n",
      "Wall time: 3.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "result = query_engine.query(\n",
    "    \"\"\"\n",
    "    What happened on the 15th of September 2008 ?\n",
    "    \"\"\"\n",
    ")\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a05c539e-85c1-4592-808b-07d68e68e032",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a05c539e-85c1-4592-808b-07d68e68e032",
    "outputId": "77786db9-6080-4bc8-b969-d02b1461bfe2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The new US financial regulation enacted after the 2008 crisis to increase market regulation and improve consumer sentiment is the Dodd-Frank Wall Street Reform and Consumer Protection Act, which was signed into law on July 21, 2010. This legislation aimed to promote financial stability, enhance accountability and transparency in the financial system, and protect consumers from abusive financial practices.\n",
      "CPU times: user 43.7 ms, sys: 0 ns, total: 43.7 ms\n",
      "Wall time: 4.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "result = query_engine.query(\n",
    "    \"\"\"\n",
    "    What was the new US financial regulation enacted after the 2008 crisis to increase the market regulation and to improve consumer sentiment ?\n",
    "    \"\"\"\n",
    ")\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06802cf-c241-4131-8a5d-529ea3933e59",
   "metadata": {
    "id": "f06802cf-c241-4131-8a5d-529ea3933e59"
   },
   "source": [
    "## In depth analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67c2240b-7b0d-4bd8-8c19-fcf7e5ba429c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "67c2240b-7b0d-4bd8-8c19-fcf7e5ba429c",
    "outputId": "1cabbe83-4743-4d5d-cc88-29bb4a5f9638"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 227 μs, sys: 10 μs, total: 237 μs\n",
      "Wall time: 243 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Using gpt-4o-mini, the 128k tokens context size can take 100 pages.\n",
    "K = 20\n",
    "\n",
    "query_engine = index.as_query_engine(\n",
    "                similarity_top_k=K,\n",
    "                vector_store_kwargs={\n",
    "                        \"index\" : \"flat_index\",\n",
    "                        \"sort_columns\" : [\"publication_date\"]\n",
    "                        }\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b5fcb92b-7e2f-4945-82c7-08bffd20a052",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b5fcb92b-7e2f-4945-82c7-08bffd20a052",
    "outputId": "beda7f3d-4654-4471-a064-11e56962c911"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The analysis of U.S. financial regulations before and after the 2008 financial crisis reveals significant changes aimed at preventing a recurrence of such a crisis. \n",
      "\n",
      "Before the crisis, the regulatory framework was characterized by a lack of comprehensive oversight, particularly for nonbank financial institutions. The regulatory environment allowed for excessive risk-taking, inadequate capital requirements, and insufficient transparency in financial transactions. This environment contributed to the housing bubble and the subsequent collapse of major financial institutions, leading to widespread economic turmoil.\n",
      "\n",
      "In response to the crisis, the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 was enacted. This legislation introduced several key reforms:\n",
      "\n",
      "1. **Creation of the Financial Stability Oversight Council (FSOC)**: This body was established to monitor systemic risks and coordinate regulatory efforts across different financial sectors. It has the authority to recommend heightened standards and safeguards for financial activities that could pose risks to financial stability.\n",
      "\n",
      "2. **Enhanced Regulatory Oversight**: Dodd-Frank imposed stricter regulations on bank holding companies and nonbank financial companies, particularly those with significant assets. This includes requirements for stress testing, capital planning, and the submission of resolution plans to ensure orderly wind-downs in case of failure.\n",
      "\n",
      "3. **Consumer Protection Measures**: The establishment of the Consumer Financial Protection Bureau (CFPB) aimed to protect consumers from predatory lending practices and ensure transparency in financial products.\n",
      "\n",
      "4. **Volcker Rule**: This provision restricts proprietary trading by banks and limits their investments in hedge funds and private equity funds, thereby reducing conflicts of interest and excessive risk-taking.\n",
      "\n",
      "5. **Increased Transparency and Reporting Requirements**: Financial institutions are now required to disclose more information regarding their risk exposures and financial health, which enhances market discipline and investor confidence.\n",
      "\n",
      "The arguments for these reforms center around the need for a more resilient financial system that can withstand economic shocks. The reforms aim to address the systemic risks that were prevalent before the crisis, ensuring that financial institutions maintain adequate capital buffers and engage in prudent risk management practices.\n",
      "\n",
      "In conclusion, the regulatory landscape has shifted significantly since the 2008 crisis, with a focus on preventing excessive risk-taking, enhancing transparency, and protecting consumers. These measures are designed to create a more stable financial environment and mitigate the likelihood of future crises.\n",
      "CPU times: user 180 ms, sys: 437 μs, total: 180 ms\n",
      "Wall time: 10.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "result = query_engine.query(\n",
    "    \"\"\"\n",
    "    Analyse the US financial regulations before and after the 2008 crisis and produce a report of all related arguments to explain what happened, and to ensure that does not happen again.\n",
    "    Use both the provided context and your own knowledge but do mention explicitely which one you use.\n",
    "    \"\"\"\n",
    ")\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6604ca0",
   "metadata": {
    "id": "e6604ca0"
   },
   "source": [
    "## Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8e1be781",
   "metadata": {
    "id": "8e1be781"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381ca28b",
   "metadata": {
    "id": "381ca28b"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/kWYXv316)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: LlamaIndex_samples/Hybrid_Search_LlamaIndex_KDBAI.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36bda246-f167-4114-a5d1-a053d8bb6faa",
   "metadata": {
    "id": "36bda246-f167-4114-a5d1-a053d8bb6faa"
   },
   "source": [
    "# Hybrid Search with LlamaIndex & KDB.AI\n",
    "\n",
    "Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/offerings/).\n",
    "\n",
    "KDB.AI hybrid search is a method of similarity search to increase the relevancy of results retrieved from the vector database. It combines two search methods: sparse vector search, and dense vector search.\n",
    "\n",
    "Sparse vector search uses the BM25 algorithm to find the most relevant keyword matches, while dense vector search finds the most semantically relevant matches.\n",
    "\n",
    "In KDB.AI, users can run sparse or dense searches independently, or run a hybrid search which combines both sparse and dense vector searches. The results from each search are re-ranked based on user-defined weights for the sparse and dense indexes. The higher the weight for a specific index (sparse or dense), the more influence it will have in determining the final ranking."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db1388d-fb91-4445-bda3-9ce1ba230e2a",
   "metadata": {
    "id": "6db1388d-fb91-4445-bda3-9ce1ba230e2a"
   },
   "source": [
    "## Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc0fded7-4a7b-42bd-8b89-c350e3c616ff",
   "metadata": {
    "id": "cc0fded7-4a7b-42bd-8b89-c350e3c616ff"
   },
   "outputs": [],
   "source": [
    "%pip install llama-index llama-index-embeddings-huggingface llama-index-llms-openai llama-index-readers-file llama-index-vector-stores-kdbai\n",
    "%pip install kdbai_client langchain-text-splitters pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a20b9-8d2a-4986-b72c-38743ffea15e",
   "metadata": {
    "id": "0f5a20b9-8d2a-4986-b72c-38743ffea15e"
   },
   "source": [
    "## Downloading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b149b4c-8be7-451c-943a-60a919f3a1fd",
   "metadata": {
    "id": "4b149b4c-8be7-451c-943a-60a919f3a1fd"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fee394b-9ed8-4f48-96e0-68fed3181a68",
   "metadata": {
    "id": "2fee394b-9ed8-4f48-96e0-68fed3181a68"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdLs4OLftHti",
   "metadata": {
    "id": "bdLs4OLftHti"
   },
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15527fd-ad03-4ebb-9622-1bbd0beec9c7",
   "metadata": {
    "id": "e15527fd-ad03-4ebb-9622-1bbd0beec9c7"
   },
   "source": [
    "**Data directories and paths**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "974ceca4-7c46-4185-b9b9-0ed5da7eca7f",
   "metadata": {
    "id": "974ceca4-7c46-4185-b9b9-0ed5da7eca7f"
   },
   "outputs": [],
   "source": [
    "# Root path\n",
    "root_path = os.path.abspath(os.getcwd())\n",
    "\n",
    "# Data directory and path\n",
    "data_dir = \"data\"\n",
    "data_path = os.path.join(root_path, data_dir)\n",
    "if not os.path.exists(data_path):\n",
    "    os.mkdir(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecef7f4-0633-41e7-8600-2d894705564b",
   "metadata": {
    "id": "aecef7f4-0633-41e7-8600-2d894705564b"
   },
   "source": [
    "**Downloading text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "516e1f6d-d06e-40c5-bdb3-77e2cdce2cff",
   "metadata": {
    "id": "516e1f6d-d06e-40c5-bdb3-77e2cdce2cff"
   },
   "outputs": [],
   "source": [
    "text_url = \"https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/hybrid_search/data/inflation.txt\"\n",
    "with urllib.request.urlopen(text_url) as response:\n",
    "    text_content = response.read().decode(\"utf-8\")\n",
    "\n",
    "text_file_name = text_url.split('/')[-1]\n",
    "text_path = os.path.join(data_path, text_file_name)\n",
    "if not os.path.exists(text_path):\n",
    "    with open(text_path, 'w') as text_file:\n",
    "        text_file.write(text_content)\n",
    "\n",
    "metadata = {\n",
    "    f\"{data_dir}/{text_file_name}\": {\n",
    "        \"title\": text_file_name,\n",
    "        \"file_path\": text_path\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f718c60d-b577-4e4d-a256-42747173568b",
   "metadata": {
    "id": "f718c60d-b577-4e4d-a256-42747173568b"
   },
   "source": [
    "**Show text data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20948a9b-7b95-4ccd-87b4-5fa1c0eecc8b",
   "metadata": {
    "id": "20948a9b-7b95-4ccd-87b4-5fa1c0eecc8b"
   },
   "outputs": [],
   "source": [
    "def show_text(text_path):\n",
    "    with open(text_path, 'r') as text_file:\n",
    "        contents = text_file.read()\n",
    "    print(contents[:500])\n",
    "    print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "deea78d6-acc8-4819-b948-a6d5d00c04e2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "deea78d6-acc8-4819-b948-a6d5d00c04e2",
    "outputId": "0793df34-5f11-4d87-ad34-262a74e2c999"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " At last year's Jackson Hole symposium, I delivered a brief, direct message. My remarks this year will be a bit longer, but the message is the same: It is the Fed's job to bring inflation down to our 2 percent goal, and we will do so. We have tightened policy significantly over the past year. Although inflation has moved down from its peak—a welcome development—it remains too high. We are prepared to raise rates further if appropriate, and intend to hold policy at a restrictive level until we ar\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "show_text(text_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "720a9744-a9d4-4691-9077-229e1c526522",
   "metadata": {
    "id": "720a9744-a9d4-4691-9077-229e1c526522"
   },
   "source": [
    "## KDB.AI Vector Database - session and tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee3143e-69b8-4182-bdd8-4aa4c471517d",
   "metadata": {
    "id": "9ee3143e-69b8-4182-bdd8-4aa4c471517d"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e536e1f9-67a6-409b-85e0-70aa89b9c26e",
   "metadata": {
    "id": "e536e1f9-67a6-409b-85e0-70aa89b9c26e"
   },
   "outputs": [],
   "source": [
    "import kdbai_client as kdbai\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5d76e2-a62b-4b4e-904c-7408bcd0274f",
   "metadata": {
    "id": "ae5d76e2-a62b-4b4e-904c-7408bcd0274f"
   },
   "source": [
    "**KDB.AI session**\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "HPMi-P35jKFd",
   "metadata": {
    "id": "HPMi-P35jKFd"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f843740-08a5-4379-9b9a-9ac2f554cad0",
   "metadata": {
    "id": "9f843740-08a5-4379-9b9a-9ac2f554cad0"
   },
   "source": [
    "**KDB.AI table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7949d844-6991-4a4f-9ebc-09d0c9e4d4f9",
   "metadata": {
    "id": "7949d844-6991-4a4f-9ebc-09d0c9e4d4f9"
   },
   "outputs": [],
   "source": [
    "# Table - name & schema\n",
    "table_name = \"hs_docs\"\n",
    "table_schema = [\n",
    "        {\"name\":\"document_id\", \"type\":\"bytes\"},\n",
    "        {\"name\":\"text\", \"type\":\"bytes\"},\n",
    "        {\"name\":\"embeddings\",\"type\":\"float32s\"},\n",
    "        {\"name\":\"sparseVectors\", \"type\":\"general\"},\n",
    "        {\"name\":\"title\", \"type\":\"str\"},\n",
    "        {\"name\":\"file_path\", \"type\":\"str\"},\n",
    "        ]\n",
    "\n",
    "indexeSparse = {\n",
    "        \"name\": \"sparse_index\",\n",
    "        \"type\": \"bm25\",\n",
    "        \"column\": \"sparseVectors\",\n",
    "        \"params\": {'k': 1.25, 'b': 0.75},\n",
    "    }\n",
    "\n",
    "indexFlat = {\n",
    "        \"name\": \"flat\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {'dims': 768, 'metric': 'L2'},\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88d4e8ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect with kdbai database\n",
    "db = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "89681936-0a1c-4976-b8eb-8fc63df0cd1b",
   "metadata": {
    "id": "89681936-0a1c-4976-b8eb-8fc63df0cd1b"
   },
   "outputs": [],
   "source": [
    "# Drop table if exists\n",
    "try:\n",
    "    db.table(table_name).drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "72fa4f5d-e24e-4a7d-be8f-863d955c5f25",
   "metadata": {
    "id": "72fa4f5d-e24e-4a7d-be8f-863d955c5f25"
   },
   "outputs": [],
   "source": [
    "# texts table\n",
    "table = db.create_table(table_name, table_schema, indexes=[indexFlat, indexeSparse])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b64e18-57e0-43a5-84ee-9076a9695673",
   "metadata": {
    "id": "51b64e18-57e0-43a5-84ee-9076a9695673"
   },
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "WPxPi5gurf-m",
   "metadata": {
    "id": "WPxPi5gurf-m"
   },
   "outputs": [],
   "source": [
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "from llama_index.core import StorageContext\n",
    "from llama_index.core import Settings\n",
    "from llama_index.core.indices import VectorStoreIndex\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.callbacks import CallbackManager\n",
    "from llama_index.core import SimpleDirectoryReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "pPiL0bAKtypG",
   "metadata": {
    "id": "pPiL0bAKtypG"
   },
   "outputs": [],
   "source": [
    "# Helper function - for getting metadata\n",
    "def get_metadata(file_path):\n",
    "    return metadata[file_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "O5T4OFdEt0AC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O5T4OFdEt0AC",
    "outputId": "ca398606-6579-4bce-ecc1-02a4a9aebbe6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.7 ms, sys: 0 ns, total: 12.7 ms\n",
      "Wall time: 12.9 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "local_files = [fpath for fpath in metadata]\n",
    "documents = SimpleDirectoryReader(input_files=local_files, file_metadata=get_metadata)\n",
    "\n",
    "docs = documents.load_data()\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ZLDNbxPRChuw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "ZLDNbxPRChuw",
    "outputId": "66e6d333-cb7b-4a8d-9330-9a3d33a4560d"
   },
   "outputs": [],
   "source": [
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "EMBEDDING = \"sentence-transformers/all-mpnet-base-v2\"\n",
    "embeddings_model = HuggingFaceEmbedding(model_name=EMBEDDING)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rVfjqWmFFPlo",
   "metadata": {
    "id": "rVfjqWmFFPlo"
   },
   "source": [
    "### Create vector store, storage context and the index for retrieval, query purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uAWvF9PKrf9x",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "id": "uAWvF9PKrf9x",
    "outputId": "d2c3dc88-4e87-4778-aea0-976d0aa465e9"
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "# Vector Store\n",
    "text_store = KDBAIVectorStore(table=table, hybrid_search=True)\n",
    "\n",
    "# Storage context\n",
    "storage_context = StorageContext.from_defaults(vector_store=text_store)\n",
    "\n",
    "# Settings\n",
    "#Settings.callback_manager = callback_manager\n",
    "Settings.transformations = [SentenceSplitter(chunk_size=500, chunk_overlap=0)]\n",
    "Settings.embed_model = embeddings_model\n",
    "Settings.llm = None\n",
    "\n",
    "# Vector Store Index\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    docs,\n",
    "    use_async=True,\n",
    "    storage_context=storage_context,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "rJIVN9IZrf8F",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "rJIVN9IZrf8F",
    "outputId": "af51a11e-5a4b-4309-800b-b5a6f173608b"
   },
   "outputs": [
    {
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       "summary": "{\n  \"name\": \"table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"document_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"b'c26bcfc2-951e-40bd-959a-ae2b8edd2467'\",\n          \"b'e4d97506-7118-49ae-87bf-47c41abe670c'\",\n          \"b'0decb50d-966f-448f-a2a4-88dacc50a375'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"b'At last year\\\\'s Jackson Hole symposium, I delivered a brief, direct message. My remarks this year will be a bit longer, but the message is the same: It is the Fed\\\\'s job to bring inflation down to our 2 percent goal, and we will do so. We have tightened policy significantly over the past year. Although inflation has moved down from its peak\\\\xe2\\\\x80\\\\x94a welcome development\\\\xe2\\\\x80\\\\x94it remains too high. We are prepared to raise rates further if appropriate, and intend to hold policy at a restrictive level until we are confident that inflation is moving sustainably down toward our objective.\\\\n\\\\nToday I will review our progress so far and discuss the outlook and the uncertainties we face as we pursue our dual mandate goals. I will conclude with a summary of what this means for policy. Given how far we have come, at upcoming meetings we are in a position to proceed carefully as we assess the incoming data and the evolving outlook and risks.\\\\n\\\\nThe Decline in Inflation So Far\\\\nThe ongoing episode of high inflation initially emerged from a collision between very strong demand and pandemic-constrained supply. By the time the Federal Open Market Committee raised the policy rate in March 2022, it was clear that bringing down inflation would depend on both the unwinding of the unprecedented pandemic-related demand and supply distortions and on our tightening of monetary policy, which would slow the growth of aggregate demand, allowing supply time to catch up. While these two forces are now working together to bring down inflation, the process still has a long way to go, even with the more favorable recent readings.\\\\n\\\\nOn a 12-month basis, U.S. total, or \\\"headline,\\\" PCE (personal consumption expenditures) inflation peaked at 7 percent in June 2022 and declined to 3.3 percent as of July, following a trajectory roughly in line with global trends (figure 1, panel A).1 The effects of Russia\\\\'s war against Ukraine have been a primary driver of the changes in headline inflation around the world since early 2022. Headline inflation is what households and businesses experience most directly, so this decline is very good news. But food and energy prices are influenced by global factors that remain volatile, and can provide a misleading signal of where inflation is headed. In my remaining comments, I will focus on core PCE inflation, which omits the food and energy components.'\",\n          \"b\\\"On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.\\\\n\\\\nTo understand the factors that will likely drive further progress, it is useful to separately examine the three broad components of core PCE inflation\\\\xe2\\\\x80\\\\x94inflation for goods, for housing services, and for all other services, sometimes referred to as nonhousing services (figure 2).\\\\n\\\\nCore goods inflation has fallen sharply, particularly for durable goods, as both tighter monetary policy and the slow unwinding of supply and demand dislocations are bringing it down. The motor vehicle sector provides a good illustration. Earlier in the pandemic, demand for vehicles rose sharply, supported by low interest rates, fiscal transfers, curtailed spending on in-person services, and shifts in preference away from using public transportation and from living in cities. But because of a shortage of semiconductors, vehicle supply actually fell. Vehicle prices spiked, and a large pool of pent-up demand emerged. As the pandemic and its effects have waned, production and inventories have grown, and supply has improved. At the same time, higher interest rates have weighed on demand. Interest rates on auto loans have nearly doubled since early last year, and customers report feeling the effect of higher rates on affordability.2 On net, motor vehicle inflation has declined sharply because of the combined effects of these supply and demand factors.\\\\n\\\\nSimilar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.\\\"\",\n          \"b'Doing too little could allow above-target inflation to become entrenched and ultimately require monetary policy to wring more persistent inflation from the economy at a high cost to employment. Doing too much could also do unnecessary harm to the economy.\\\\n\\\\nConclusion\\\\nAs is often the case, we are navigating by the stars under cloudy skies. In such circumstances, risk-management considerations are critical. At upcoming meetings, we will assess our progress based on the totality of the data and the evolving outlook and risks. Based on this assessment, we will proceed carefully as we decide whether to tighten further or, instead, to hold the policy rate constant and await further data. Restoring price stability is essential to achieving both sides of our dual mandate. We will need price stability to achieve a sustained period of strong labor market conditions that benefit all.\\\\n\\\\nWe will keep at it until the job is done.'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparseVectors\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"inflation.txt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"file_path\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"/content/data/inflation.txt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a080320c-b4bd-4625-b9fa-04a7aa5e59f4 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                               document_id  \\\n",
       "0  b'c26bcfc2-951e-40bd-959a-ae2b8edd2467'   \n",
       "1  b'e4d97506-7118-49ae-87bf-47c41abe670c'   \n",
       "2  b'0014da23-8348-48af-ab56-c64ec48c47cc'   \n",
       "3  b'1c00e107-b816-40d2-8445-a0ae707c2564'   \n",
       "4  b'73ac6d92-a93f-4b5f-a8c6-8bba94068e3f'   \n",
       "5  b'0decb50d-966f-448f-a2a4-88dacc50a375'   \n",
       "\n",
       "                                                text  \\\n",
       "0  b'At last year\\'s Jackson Hole symposium, I de...   \n",
       "1  b\"On a 12-month basis, core PCE inflation peak...   \n",
       "2  b'In the highly interest-sensitive housing sec...   \n",
       "3  b\"Getting inflation sustainably back down to 2...   \n",
       "4  b'While nominal wage growth must ultimately sl...   \n",
       "5  b'Doing too little could allow above-target in...   \n",
       "\n",
       "                                           embedding  \\\n",
       "0  [-0.035284244, 0.0753799, -0.022666411, -0.017...   \n",
       "1  [-0.04378559, 0.046354603, -0.030167095, 0.013...   \n",
       "2  [-0.07940253, 0.008506958, -0.035946056, -0.00...   \n",
       "3  [-0.046816133, 0.052543037, -0.038334284, -0.0...   \n",
       "4  [-0.033225708, 0.037619803, -0.030979052, -0.0...   \n",
       "5  [-0.042863447, 0.02854309, -0.030805789, -0.03...   \n",
       "\n",
       "                                       sparseVectors          title  \\\n",
       "0  {101: 1, 2012: 4, 2197: 1, 2095: 3, 1005: 3, 1...  inflation.txt   \n",
       "1  {101: 1, 2006: 7, 1037: 5, 2260: 2, 1011: 6, 3...  inflation.txt   \n",
       "2  {101: 1, 1999: 7, 1996: 21, 3811: 1, 3037: 2, ...  inflation.txt   \n",
       "3  {101: 1, 2893: 1, 14200: 2, 15770: 1, 8231: 1,...  inflation.txt   \n",
       "4  {101: 1, 2096: 1, 15087: 2, 11897: 4, 3930: 4,...  inflation.txt   \n",
       "5  {101: 1, 2725: 2, 2205: 2, 2210: 1, 2071: 2, 3...  inflation.txt   \n",
       "\n",
       "                     file_path  \n",
       "0  /content/data/inflation.txt  \n",
       "1  /content/data/inflation.txt  \n",
       "2  /content/data/inflation.txt  \n",
       "3  /content/data/inflation.txt  \n",
       "4  /content/data/inflation.txt  \n",
       "5  /content/data/inflation.txt  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe013a42-6b5a-416c-bc87-30b0ebe02e9a",
   "metadata": {
    "id": "fe013a42-6b5a-416c-bc87-30b0ebe02e9a"
   },
   "source": [
    "## Retrieval from query using Hybrid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30441b4-3fd6-45b3-86cd-a50ab4101d0b",
   "metadata": {
    "id": "c30441b4-3fd6-45b3-86cd-a50ab4101d0b"
   },
   "source": [
    "**Query**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d7dd1230-c0d5-4c07-a4e9-23537ccb6373",
   "metadata": {
    "id": "d7dd1230-c0d5-4c07-a4e9-23537ccb6373"
   },
   "outputs": [],
   "source": [
    "query = '12-month basis'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f5eb97-b6dd-4b36-a1c0-67b5e6a4f525",
   "metadata": {
    "id": "38f5eb97-b6dd-4b36-a1c0-67b5e6a4f525"
   },
   "source": [
    "**Helper function: To display search results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ef30aa91-3ea2-44f5-9043-53b99dc701a5",
   "metadata": {
    "id": "ef30aa91-3ea2-44f5-9043-53b99dc701a5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "80d572c8-409e-4869-a04d-f6fa9b510a3f",
   "metadata": {
    "id": "80d572c8-409e-4869-a04d-f6fa9b510a3f"
   },
   "outputs": [],
   "source": [
    "def display_search_results(nodes):\n",
    "    nodes_df = pd.DataFrame(columns=['score', 'text'])\n",
    "    for node in nodes:\n",
    "        nodes_df.loc[len(nodes_df.index)] = (node.score, node.text)\n",
    "    return nodes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87740e80-6285-4228-8c4a-2037c4cc233c",
   "metadata": {
    "id": "87740e80-6285-4228-8c4a-2037c4cc233c"
   },
   "source": [
    "**Hybrid Search: Giving equal priority to both sparse and dense vector search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a28e06f9-f848-4a87-8107-0425b5bd4c4f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a28e06f9-f848-4a87-8107-0425b5bd4c4f",
    "outputId": "326e6e34-67ff-489d-860d-0fbd2a6f7e51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 49 μs, sys: 0 ns, total: 49 μs\n",
      "Wall time: 51 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "retriever = index.as_retriever(\n",
    "                        vector_store_query_mode=\"hybrid\",\n",
    "                        similarity_top_k=5,\n",
    "                        vector_store_kwargs={\n",
    "                                \"index\" : \"flat\",\n",
    "                                \"indexWeight\" : 0.5,\n",
    "                                \"indexSparse\" : \"sparse_index\",\n",
    "                                \"indexSparseWeight\" : 0.5,\n",
    "                                },\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "40ab98b9-31e2-4d8b-94a7-8973c05f8e91",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "40ab98b9-31e2-4d8b-94a7-8973c05f8e91",
    "outputId": "fc1fe691-994f-42da-a4ec-c320253cb1f8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexg/miniforge/envs/llama_v1.4/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>At last year's Jackson Hole symposium, I deliv...</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>In my remaining comments, I will focus on core...</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>0.291667</td>\n",
       "      <td>Core goods prices fell the past two months, bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>Total hours worked has been flat over the past...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>Over time, restrictive monetary policy will he...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "      score                                               text\n",
       "0  0.375000  At last year's Jackson Hole symposium, I deliv...\n",
       "1  0.333333  In my remaining comments, I will focus on core...\n",
       "2  0.291667  Core goods prices fell the past two months, bu...\n",
       "3  0.250000  Total hours worked has been flat over the past...\n",
       "4  0.200000  Over time, restrictive monetary policy will he..."
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equal_priority_nodes = retriever.retrieve(query)\n",
    "display_search_results(equal_priority_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d065aa5-c685-4c4d-a808-e28913c1cfdc",
   "metadata": {
    "id": "7d065aa5-c685-4c4d-a808-e28913c1cfdc"
   },
   "source": [
    "**Hybrid Search: Giving more priority to sparse vector search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0f5b7356-8dae-40f3-b787-cb34d6b58045",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0f5b7356-8dae-40f3-b787-cb34d6b58045",
    "outputId": "ede097c6-b0fc-49b4-b7cd-f6292f9373ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 39 μs, total: 39 μs\n",
      "Wall time: 41 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "retriever = index.as_retriever(\n",
    "                        vector_store_query_mode=\"hybrid\",\n",
    "                        similarity_top_k=5,\n",
    "                        vector_store_kwargs={\n",
    "                                \"index\" : \"flat\",\n",
    "                                \"indexWeight\" : 0.1,\n",
    "                                \"indexSparse\" : \"sparse_index\",\n",
    "                                \"indexSparseWeight\" : 0.9,\n",
    "                                },\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ecfd0feb-3752-461b-b23b-05a81abe49c9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "ecfd0feb-3752-461b-b23b-05a81abe49c9",
    "outputId": "24bcefd5-f714-4345-efba-f089c8372cb7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexg/miniforge/envs/llama_v1.4/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>In my remaining comments, I will focus on core...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.325000</td>\n",
       "      <td>Core goods prices fell the past two months, bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.275000</td>\n",
       "      <td>At last year's Jackson Hole symposium, I deliv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>Over time, restrictive monetary policy will he...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.183333</td>\n",
       "      <td>Total hours worked has been flat over the past...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      score                                               text\n",
       "0  0.466667  In my remaining comments, I will focus on core...\n",
       "1  0.325000  Core goods prices fell the past two months, bu...\n",
       "2  0.275000  At last year's Jackson Hole symposium, I deliv...\n",
       "3  0.200000  Over time, restrictive monetary policy will he...\n",
       "4  0.183333  Total hours worked has been flat over the past..."
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_priority_nodes = retriever.retrieve(query)\n",
    "display_search_results(sparse_priority_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62f9812-6242-47ae-9a1a-b582bc04f54b",
   "metadata": {
    "id": "f62f9812-6242-47ae-9a1a-b582bc04f54b"
   },
   "source": [
    "**Hybrid Search: Giving more priority to dense vector search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "70583f73-a841-45fd-9169-461ca6b4953a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "70583f73-a841-45fd-9169-461ca6b4953a",
    "outputId": "b0eb3a83-9dae-4a9a-cb7d-6f2e4176e9a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30 μs, sys: 6 μs, total: 36 μs\n",
      "Wall time: 37.7 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "retriever = index.as_retriever(\n",
    "                        vector_store_query_mode=\"hybrid\",\n",
    "                        similarity_top_k=5,\n",
    "                        vector_store_kwargs={\n",
    "                                \"index\" : \"flat\",\n",
    "                                \"indexWeight\" : 0.9,\n",
    "                                \"indexSparse\" : \"sparse_index\",\n",
    "                                \"indexSparseWeight\" : 0.1,\n",
    "                                },\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ac810657-ba83-4f53-91e5-3a3407afa31e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "ac810657-ba83-4f53-91e5-3a3407afa31e",
    "outputId": "1675c444-5b76-4466-bad0-0f24a8014922"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexg/miniforge/envs/llama_v1.4/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.475000</td>\n",
       "      <td>At last year's Jackson Hole symposium, I deliv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.316667</td>\n",
       "      <td>Total hours worked has been flat over the past...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.258333</td>\n",
       "      <td>Core goods prices fell the past two months, bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>Over time, restrictive monetary policy will he...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>In my remaining comments, I will focus on core...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      score                                               text\n",
       "0  0.475000  At last year's Jackson Hole symposium, I deliv...\n",
       "1  0.316667  Total hours worked has been flat over the past...\n",
       "2  0.258333  Core goods prices fell the past two months, bu...\n",
       "3  0.200000  Over time, restrictive monetary policy will he...\n",
       "4  0.200000  In my remaining comments, I will focus on core..."
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_priority_nodes = retriever.retrieve(query)\n",
    "display_search_results(dense_priority_nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65513f0-97bc-409d-8140-5be63f2ebe2a",
   "metadata": {
    "id": "f65513f0-97bc-409d-8140-5be63f2ebe2a"
   },
   "source": [
    "**Conclusion**\n",
    "- In the sparse search results, we can see the terms we are interested directly i.e \"12-month basis\" rather than terms having similar meanings.\n",
    "- In the dense search resutls, we can see the most related or similar text to the query."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0n9ozZmwPJTm",
   "metadata": {
    "id": "0n9ozZmwPJTm"
   },
   "source": [
    "## Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "XLng6hT-PPzi",
   "metadata": {
    "id": "XLng6hT-PPzi"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: LlamaIndex_samples/Multimodal_RAG_LLamaIndex_CLIP_KDBAI.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "TTMDGImH5JOM",
   "metadata": {
    "id": "TTMDGImH5JOM"
   },
   "source": [
    "# Multimodal RAG using LlamaIndex, CLIP, & KDB.AI\n",
    "\n",
    "Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/offerings/).\n",
    "\n",
    "This example explores preparing, embedding (with CLIP), and storing both text and image data within a KDB.AI vector database using LlamaIndex."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89b2a80-22b7-4f06-918c-8fe75ced9572",
   "metadata": {
    "id": "e89b2a80-22b7-4f06-918c-8fe75ced9572"
   },
   "source": [
    "## Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f80ee03b-e9f7-405a-bdc9-58b130275815",
   "metadata": {
    "id": "f80ee03b-e9f7-405a-bdc9-58b130275815",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install llama-index llama-index-embeddings-huggingface llama-index-llms-openai llama-index-readers-file llama-index-vector-stores-kdbai llama-index-embeddings-clip\n",
    "!pip install llama-index-embeddings-clip\n",
    "!pip install 'git+https://github.com/openai/CLIP.git'\n",
    "!pip install kdbai_client matplotlib wikipedia tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e890c7-fd56-4c38-a596-e53e3d5a8ab1",
   "metadata": {
    "id": "c2e890c7-fd56-4c38-a596-e53e3d5a8ab1"
   },
   "source": [
    "## Download data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c84d1c1-6741-434c-ba95-91e2ebb9992e",
   "metadata": {
    "id": "5c84d1c1-6741-434c-ba95-91e2ebb9992e"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a84d604-67b0-4299-9665-ebd2c13c7d1c",
   "metadata": {
    "id": "8a84d604-67b0-4299-9665-ebd2c13c7d1c"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm import tqdm\n",
    "import wikipedia\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08e73fd-1d8f-4806-87f5-49949f4d42c8",
   "metadata": {
    "id": "a08e73fd-1d8f-4806-87f5-49949f4d42c8"
   },
   "source": [
    "**Data directories and paths**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efb9b47d-63c4-44ff-9ccd-2b126cce6945",
   "metadata": {
    "id": "efb9b47d-63c4-44ff-9ccd-2b126cce6945"
   },
   "outputs": [],
   "source": [
    "# Root path\n",
    "root_path = os.path.abspath(os.getcwd())\n",
    "\n",
    "# Data directory and path\n",
    "data_dir = \"data\"\n",
    "data_path = os.path.join(root_path, data_dir)\n",
    "if not os.path.exists(data_path):\n",
    "    os.mkdir(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7133fc24-c66c-4b91-b20f-033cdf571142",
   "metadata": {
    "id": "7133fc24-c66c-4b91-b20f-033cdf571142"
   },
   "source": [
    "**Download data - wikipedia images & texts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad75539c-7531-4f11-a5fc-d052a211085c",
   "metadata": {
    "id": "ad75539c-7531-4f11-a5fc-d052a211085c",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def download_data(WIKI_TITLES, MAX_IMAGES_PER_TITLE):\n",
    "    # Text metadata\n",
    "    text_uuid = 0\n",
    "    text_metadata = dict()\n",
    "\n",
    "    # Image metadata\n",
    "    image_uuid = 0\n",
    "    image_metadata = dict()\n",
    "\n",
    "    # Download data - text and images from wiki pages\n",
    "    for title in tqdm(WIKI_TITLES):\n",
    "        images_per_wiki_page = 0\n",
    "        print(title)\n",
    "        try:\n",
    "            wiki_page = wikipedia.page(title)\n",
    "\n",
    "            # Text - data and metadata\n",
    "            text_uuid += 1\n",
    "            page_content = wiki_page.content\n",
    "\n",
    "            content_path = os.path.join(data_path, f\"{title}.txt\")\n",
    "            with open(content_path, 'w') as f:\n",
    "                f.write(page_content)\n",
    "            text_file_name = f\"{title}.txt\"\n",
    "\n",
    "            text_metadata[text_uuid] = {\n",
    "                \"filename\": text_file_name,\n",
    "                \"file_path\": content_path\n",
    "            }\n",
    "\n",
    "            list_img_urls = wiki_page.images\n",
    "            for url in list_img_urls:\n",
    "                if url.endswith(\".jpg\") or url.endswith(\".png\"):\n",
    "                    # Limiting images to downloaded per wikipedia page\n",
    "                    images_per_wiki_page += 1\n",
    "                    if images_per_wiki_page > MAX_IMAGES_PER_TITLE:\n",
    "                        break\n",
    "\n",
    "                    # Image - data and metadata\n",
    "                    image_uuid += 1\n",
    "                    image_path = os.path.join(data_path, f\"{image_uuid}.jpg\")\n",
    "                    image_file_name = f\"{image_uuid}.jpg\"\n",
    "\n",
    "                    urllib.request.urlretrieve(\n",
    "                        url, image_path\n",
    "                    )\n",
    "\n",
    "                    image_metadata[image_uuid] = {\n",
    "                        \"filename\": image_file_name,\n",
    "                        \"file_path\": image_path,\n",
    "                    }\n",
    "        except Exception:\n",
    "            print(str(Exception(\"No images found for Wikipedia page: \")) + title)\n",
    "            continue\n",
    "\n",
    "    return image_metadata, text_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae283b70-dc68-4061-9fa1-2f436d37c4ef",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ae283b70-dc68-4061-9fa1-2f436d37c4ef",
    "outputId": "9245435d-8c8d-4059-e3df-a2447d0588d6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machiavelli\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 1/5 [00:08<00:32,  8.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beethoven\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 2/5 [00:12<00:18,  6.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Burj khalifa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 3/5 [00:16<00:09,  4.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cherry Blossom\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 4/5 [00:18<00:03,  3.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No images found for Wikipedia page: Cherry Blossom\n",
      "Video Game\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:20<00:00,  4.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No images found for Wikipedia page: Video Game\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Wikipedia titles to retrieve data & max images per title to retrieve\n",
    "WIKI_TITLES = [\n",
    "    \"Machiavelli\",\n",
    "    \"Beethoven\",\n",
    "    \"Burj khalifa\",\n",
    "    \"Cherry Blossom\",\n",
    "    \"Video Game\"\n",
    "]\n",
    "MAX_IMAGES_PER_TITLE = 10\n",
    "\n",
    "# Download the data if not downloaded yet\n",
    "image_metadata, text_metadata = download_data(WIKI_TITLES, MAX_IMAGES_PER_TITLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567cef3f-f19e-4f91-a3fe-eaca7c756ea2",
   "metadata": {
    "id": "567cef3f-f19e-4f91-a3fe-eaca7c756ea2"
   },
   "source": [
    "**Resize Images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a335ad0b-2206-4843-9888-a5dd28b2c5a2",
   "metadata": {
    "id": "a335ad0b-2206-4843-9888-a5dd28b2c5a2"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "from PIL import Image\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3fae01a0-9ebc-411a-89b9-2433ac850313",
   "metadata": {
    "id": "3fae01a0-9ebc-411a-89b9-2433ac850313"
   },
   "outputs": [],
   "source": [
    "def resize_images(data_path):\n",
    "    for file in tqdm(os.listdir(data_path)):\n",
    "        if file.endswith('.jpg'):\n",
    "            img_path = os.path.join(data_path, file)\n",
    "            img = Image.open(img_path)\n",
    "            img_width, img_height = img.size\n",
    "            aspect_ratio = img_width/img_height\n",
    "\n",
    "            if img_width > 2048:\n",
    "                new_width = 2048\n",
    "                new_height = round(new_width/aspect_ratio)\n",
    "\n",
    "                img = img.resize((new_width, new_height))\n",
    "                img.save(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e49408d8-77c6-46ff-9f48-59e5655bc6ad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e49408d8-77c6-46ff-9f48-59e5655bc6ad",
    "outputId": "af661e8a-246a-4332-c690-6e761639052f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 37/37 [00:05<00:00,  6.61it/s]\n"
     ]
    }
   ],
   "source": [
    "resize_images(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4234209-3f2e-40c0-9a4d-3c7b5048f0b4",
   "metadata": {
    "id": "a4234209-3f2e-40c0-9a4d-3c7b5048f0b4"
   },
   "source": [
    "**Show images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f6dac3d-6f26-4de7-adb5-87be0f01b005",
   "metadata": {
    "id": "9f6dac3d-6f26-4de7-adb5-87be0f01b005"
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3619c4a-dc69-415d-a727-cbbb57730d93",
   "metadata": {
    "id": "a3619c4a-dc69-415d-a727-cbbb57730d93"
   },
   "outputs": [],
   "source": [
    "def show_images(image_paths):\n",
    "    images_shown = 0\n",
    "    plt.figure(figsize=(16,9))\n",
    "    for img_path in image_paths:\n",
    "        if os.path.isfile(img_path):\n",
    "            image = Image.open(img_path)\n",
    "\n",
    "            plt.subplot(3, 3, images_shown+1)\n",
    "            plt.imshow(image)\n",
    "            plt.xticks([])\n",
    "            plt.yticks([])\n",
    "\n",
    "            images_shown += 1\n",
    "            if images_shown >= 9:\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d64d559-f157-4dee-8668-0678cf22c58e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "3d64d559-f157-4dee-8668-0678cf22c58e",
    "outputId": "8a5a92b9-1f36-4fdf-943a-f7d4fd19c4ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_paths = []\n",
    "for file in os.listdir(data_path):\n",
    "    if file.endswith('.jpg'):\n",
    "        img_path = os.path.join(data_path, file)\n",
    "        image_paths.append(img_path)\n",
    "show_images(image_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa19ce1-c4c4-4ade-9589-76e1abb9c30a",
   "metadata": {
    "id": "9fa19ce1-c4c4-4ade-9589-76e1abb9c30a"
   },
   "source": [
    "**Show texts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e173c761-89f8-4d5b-bdf4-44fc2181ff54",
   "metadata": {
    "id": "e173c761-89f8-4d5b-bdf4-44fc2181ff54"
   },
   "outputs": [],
   "source": [
    "def show_texts(text_paths):\n",
    "    texts_shown = 0\n",
    "    for text_path in text_paths:\n",
    "        if os.path.isfile(text_path):\n",
    "            with open(text_path, 'r') as text_file:\n",
    "                content = text_file.read()\n",
    "            print(content[0:512])\n",
    "            print(\"=\"*80)\n",
    "\n",
    "            texts_shown += 1\n",
    "            if texts_shown >= 3:\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a65cbd86-4375-40c4-a732-229a54101e18",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a65cbd86-4375-40c4-a732-229a54101e18",
    "outputId": "bcaaf48a-24a5-42b0-cf1f-bb058d678393"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Burj Khalifa (known as the Burj Dubai prior to its inauguration) is a skyscraper in Dubai, United Arab Emirates. It is the world's tallest structure. With a total height of 829.8 m (2,722 ft, or just over half a mile) and a roof height (excluding antenna, but including a 242.6 m spire) of 828 m (2,717 ft), the Burj Khalifa has been the tallest structure and building in the world since its topping out in 2009, surpassing Taipei 101, the previous holder of that status.\n",
      "Construction of the Burj Khalifa beg\n",
      "================================================================================\n",
      "A video game, also known as a computer game or just a game, is an electronic game that involves interaction with a user interface or input device (such as a joystick, controller, keyboard, or motion sensing device) to generate visual feedback from a display device, most commonly shown in a video format on a television set, computer monitor, flat-panel display or touchscreen on handheld devices, or a virtual reality headset. Most modern video games are audiovisual, with audio complement delivered through spe\n",
      "================================================================================\n",
      "Ludwig van Beethoven (baptised 17 December 1770 – 26 March 1827) was a German composer and pianist. He is one of the most revered figures in the history of Western music; his works rank among the most performed of the classical music repertoire and span the transition from the Classical period to the Romantic era in classical music. His early period, during which he forged his craft, is typically considered to have lasted until 1802. From 1802 to around 1812, his middle period showed an individual developme\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "text_paths = []\n",
    "for file in os.listdir(data_path):\n",
    "    if file.endswith('.txt'):\n",
    "        text_path = os.path.join(data_path, file)\n",
    "        text_paths.append(text_path)\n",
    "show_texts(text_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ea1fc4-611f-43f4-8d56-e894c22c1b1b",
   "metadata": {
    "id": "53ea1fc4-611f-43f4-8d56-e894c22c1b1b"
   },
   "source": [
    "## KDB.AI session and tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f927b018-4a8c-41d7-9ff4-0f125de0e8ba",
   "metadata": {
    "id": "f927b018-4a8c-41d7-9ff4-0f125de0e8ba"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb740f79-ac84-44ca-88c9-6acc2185f85e",
   "metadata": {
    "id": "cb740f79-ac84-44ca-88c9-6acc2185f85e"
   },
   "outputs": [],
   "source": [
    "import kdbai_client as kdbai\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcf4957-c194-4320-8463-62be7a0d8d1a",
   "metadata": {
    "id": "1fcf4957-c194-4320-8463-62be7a0d8d1a"
   },
   "source": [
    "**KDB.ai session**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ToBBSeoJ3isR",
   "metadata": {
    "id": "ToBBSeoJ3isR"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ZNfk_x1i3iVs",
   "metadata": {
    "id": "ZNfk_x1i3iVs"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a66f72d-221d-4eab-b66b-15d66cce9502",
   "metadata": {
    "id": "4a66f72d-221d-4eab-b66b-15d66cce9502"
   },
   "source": [
    "**Table for storing Text data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dcfd6eeb-1b83-49d9-a242-d041e71432bb",
   "metadata": {
    "id": "dcfd6eeb-1b83-49d9-a242-d041e71432bb"
   },
   "outputs": [],
   "source": [
    "# Texts table name and schema\n",
    "text_table_name = \"wiki_texts\"\n",
    "text_table_schema = [\n",
    "        dict(name=\"document_id\", type=\"bytes\"),\n",
    "        dict(name=\"text\", type=\"bytes\"),\n",
    "        dict(name=\"embeddings\", type=\"float32s\"),\n",
    "        dict(name=\"filename\", type=\"str\"),\n",
    "        dict(name=\"file_path\", type=\"str\"),\n",
    "    ]\n",
    "\n",
    "indexFlat = {\n",
    "        \"name\": \"flat\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {'dims': 1536, 'metric': 'L2'},\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc779e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect with kdbai database\n",
    "db = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5bfe935c-9895-4dac-aff6-c8ba62185a6c",
   "metadata": {
    "id": "5bfe935c-9895-4dac-aff6-c8ba62185a6c"
   },
   "outputs": [],
   "source": [
    "# Drop table if exists\n",
    "try:\n",
    "    db.table(text_table_name).drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "044792bd-391c-4a3e-a5d2-0d7a9728bf9d",
   "metadata": {
    "id": "044792bd-391c-4a3e-a5d2-0d7a9728bf9d"
   },
   "outputs": [],
   "source": [
    "# Texts table\n",
    "texts_table = db.create_table(text_table_name, text_table_schema, indexes=[indexFlat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "082401c5-7786-4e46-b6cc-2fda65886025",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "082401c5-7786-4e46-b6cc-2fda65886025",
    "outputId": "8f6a0ed0-0c88-4377-de23-6667743a0287"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'document_id', 'type': 'bytes'},\n",
       " {'name': 'text', 'type': 'bytes'},\n",
       " {'name': 'embeddings', 'type': 'float32s'},\n",
       " {'name': 'filename', 'type': 'str'},\n",
       " {'name': 'file_path', 'type': 'str'}]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show texts table scheme\n",
    "texts_table.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88128ba1-04c5-4aa6-8c0e-378235cb7c36",
   "metadata": {
    "id": "88128ba1-04c5-4aa6-8c0e-378235cb7c36"
   },
   "source": [
    "**Table for storing Image data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3ffb18e-234a-4e6d-8a01-d285a8685a0f",
   "metadata": {
    "id": "e3ffb18e-234a-4e6d-8a01-d285a8685a0f"
   },
   "outputs": [],
   "source": [
    "# Image table name and schema\n",
    "image_table_name = \"wiki_imgs\"\n",
    "image_table_schema = [\n",
    "        dict(name=\"document_id\", type=\"bytes\"),\n",
    "        dict(name=\"text\", type=\"bytes\"),\n",
    "        dict(name=\"embeddings\", type=\"float32s\"),\n",
    "        dict(name=\"filename\", type=\"bytes\"),\n",
    "        dict(name=\"file_path\", type=\"bytes\")\n",
    "    ]\n",
    "\n",
    "indexFlatImg = {\n",
    "        \"name\": \"flat\", # Note: In this multi-index setup, ensure that both the text and image indexes share the same name.\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {'dims': 512, 'metric': 'L2'},\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06674cca-bfcf-4d12-a13a-7fbff9bd9a35",
   "metadata": {
    "id": "06674cca-bfcf-4d12-a13a-7fbff9bd9a35"
   },
   "outputs": [],
   "source": [
    "# Drop table if exists\n",
    "try:\n",
    "    db.table(image_table_name).drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b14118fe-adf6-4139-a382-8c7e1330ffc2",
   "metadata": {
    "id": "b14118fe-adf6-4139-a382-8c7e1330ffc2"
   },
   "outputs": [],
   "source": [
    "# Images table\n",
    "imgs_table = db.create_table(image_table_name, image_table_schema, indexes=[indexFlatImg])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40eadab5-b64e-4e23-b3c4-462380aa4314",
   "metadata": {
    "id": "40eadab5-b64e-4e23-b3c4-462380aa4314"
   },
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c4e070b-0306-47e8-a1ac-59575dfc61fc",
   "metadata": {
    "id": "5c4e070b-0306-47e8-a1ac-59575dfc61fc"
   },
   "source": [
    "**Consolidating both image and text metadata**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "62362d3d-a864-4f02-bb38-02bf1908e9d6",
   "metadata": {
    "id": "62362d3d-a864-4f02-bb38-02bf1908e9d6",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "metadata_dict = dict()\n",
    "\n",
    "# Adding image metadata to final metadata dictionary\n",
    "for k, v in image_metadata.items():\n",
    "    ondisk_file_name = v[\"file_path\"].split('/')[-1]\n",
    "    metadata_key = f\"{data_dir}/{ondisk_file_name}\"\n",
    "    metadata_value = {\n",
    "        \"filename\": v[\"filename\"].encode(\"utf-8\"),\n",
    "        \"file_path\": f\"{data_dir}/{ondisk_file_name}\".encode(\"utf-8\")\n",
    "    }\n",
    "    metadata_dict[metadata_key] = metadata_value\n",
    "\n",
    "# Adding text metadata to final metadata dictionary\n",
    "for k, v in text_metadata.items():\n",
    "    ondisk_file_name = v[\"file_path\"].split('/')[-1]\n",
    "    metadata_key = f\"{data_dir}/{ondisk_file_name}\"\n",
    "    metadata_value = {\n",
    "        \"filename\": v[\"filename\"].encode(\"utf-8\"),\n",
    "        \"file_path\": f\"{data_dir}/{ondisk_file_name}\".encode(\"utf-8\")\n",
    "    }\n",
    "    metadata_dict[metadata_key] = metadata_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296404d8-a335-4b39-81a4-639feb68b384",
   "metadata": {
    "id": "296404d8-a335-4b39-81a4-639feb68b384"
   },
   "source": [
    "**Consolidating input paths**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91603f21-b751-446c-96d3-de4622183aa8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "91603f21-b751-446c-96d3-de4622183aa8",
    "outputId": "bef949fd-5da5-45e5-d838-bd25f95f275e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    }
   ],
   "source": [
    "local_files = []\n",
    "for k in metadata_dict:\n",
    "    local_files.append(k)\n",
    "print(len(local_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf970c7f-f3a7-4d9d-b65c-6d2e3e96466c",
   "metadata": {
    "id": "cf970c7f-f3a7-4d9d-b65c-6d2e3e96466c"
   },
   "source": [
    "**Loading data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "81d827e5-7744-4602-8c92-72e455914273",
   "metadata": {
    "id": "81d827e5-7744-4602-8c92-72e455914273"
   },
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e13762c3-1af8-4098-a89c-abfe64749861",
   "metadata": {
    "id": "e13762c3-1af8-4098-a89c-abfe64749861"
   },
   "outputs": [],
   "source": [
    "def get_metadata(filepath):\n",
    "    return metadata_dict[filepath]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0132dee-b001-4eae-94c7-d38020ea301b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c0132dee-b001-4eae-94c7-d38020ea301b",
    "outputId": "1be0bd17-966b-4815-fa65-e86b27b471d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29 ms, sys: 9.77 ms, total: 38.8 ms\n",
      "Wall time: 38.2 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "documents = SimpleDirectoryReader(input_files=local_files, file_metadata=get_metadata)\n",
    "\n",
    "docs = documents.load_data()\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a1b387-16ac-4901-898e-f1a84b1378d2",
   "metadata": {
    "id": "d8a1b387-16ac-4901-898e-f1a84b1378d2"
   },
   "source": [
    "## Creating Multi Modal Vector Index for data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8d0f69-50c8-4631-a744-93ce4046c384",
   "metadata": {
    "id": "ce8d0f69-50c8-4631-a744-93ce4046c384"
   },
   "source": [
    "**OpenAI API Key for CLIP Embeddings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba8a3866-38e0-4d17-a35e-86e1fb80b896",
   "metadata": {
    "id": "ba8a3866-38e0-4d17-a35e-86e1fb80b896"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "11e2afc5-be43-4b8f-ae69-a5da30ef0dbe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "11e2afc5-be43-4b8f-ae69-a5da30ef0dbe",
    "outputId": "c04fdee4-f0f4-4f9d-cd30-7d60a702edce"
   },
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API key: \")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e641c1-5143-4386-89d6-d82cffb004f8",
   "metadata": {
    "id": "f1e641c1-5143-4386-89d6-d82cffb004f8"
   },
   "source": [
    "**Create vector store, storage context and the index for retrieval, query purposes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0b512af-b171-4ce2-8149-cc9b93f8be94",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d0b512af-b171-4ce2-8149-cc9b93f8be94",
    "outputId": "3843c05c-ee6a-496f-c5bc-2fba8155c32a",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "from llama_index.core import StorageContext\n",
    "from llama_index.core import Settings\n",
    "from llama_index.core.indices import MultiModalVectorStoreIndex\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "\n",
    "# Store\n",
    "text_store = KDBAIVectorStore(texts_table)\n",
    "image_store = KDBAIVectorStore(imgs_table)\n",
    "\n",
    "# Storage context\n",
    "storage_context = StorageContext.from_defaults(\n",
    "    vector_store=text_store,\n",
    "    image_store=image_store,\n",
    ")\n",
    "\n",
    "# Settings\n",
    "Settings.transformations = [SentenceSplitter(chunk_size=500, chunk_overlap=0)]\n",
    "\n",
    "# Multi Modal Vector Store Index\n",
    "index = MultiModalVectorStoreIndex.from_documents(\n",
    "    docs,\n",
    "    storage_context=storage_context,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uWGLtvxJ6awf",
   "metadata": {
    "id": "uWGLtvxJ6awf"
   },
   "source": [
    "#### Now the data is inserted into the KDB.AI vector database, let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d1gtNm7A6SL8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "d1gtNm7A6SL8",
    "outputId": "f5ec43d8-85d1-4d99-ac2b-730373efd77f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>document_id</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "      <th>filename</th>\n",
       "      <th>file_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'97d9fa84-5ffe-4d1f-b83f-a397a480166c'</td>\n",
       "      <td>b'Niccol\\xc3\\xb2 di Bernardo dei Machiavelli (...</td>\n",
       "      <td>[-0.024216307, -0.013386093, 0.001253736, -0.0...</td>\n",
       "      <td>Machiavelli.txt</td>\n",
       "      <td>data/Machiavelli.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'9f8d942e-d37b-4776-a982-c02ee524e871'</td>\n",
       "      <td>b\"Machiavelli's political realism has continue...</td>\n",
       "      <td>[-0.026074765, -0.008071378, -0.010988744, -0....</td>\n",
       "      <td>Machiavelli.txt</td>\n",
       "      <td>data/Machiavelli.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'f86edd6d-0c9f-43e3-a844-bc5d13048280'</td>\n",
       "      <td>b\"Shortly thereafter, he was also made the sec...</td>\n",
       "      <td>[-0.01654868, -0.003647899, 0.0055484013, -0.0...</td>\n",
       "      <td>Machiavelli.txt</td>\n",
       "      <td>data/Machiavelli.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'd5ed7c93-f6d6-481a-86d3-eb4ef16c9d89'</td>\n",
       "      <td>b'The Florentine city-state and the republic w...</td>\n",
       "      <td>[-0.035484154, -0.0016756041, 0.00013820048, -...</td>\n",
       "      <td>Machiavelli.txt</td>\n",
       "      <td>data/Machiavelli.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'09f6f434-8a1b-445c-9238-66aa75356fa3'</td>\n",
       "      <td>b'In 1789 George Nassau Clavering, and Pietro ...</td>\n",
       "      <td>[-0.02049841, -0.0031482982, 0.0036144697, -0....</td>\n",
       "      <td>Machiavelli.txt</td>\n",
       "      <td>data/Machiavelli.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>b'fd4746c1-3849-4b82-a5a5-26c8465bc0b3'</td>\n",
       "      <td>b'With the capability to render 3D actors and ...</td>\n",
       "      <td>[-0.016764004, -0.016048055, -0.0012655741, -0...</td>\n",
       "      <td>Video Game.txt</td>\n",
       "      <td>data/Video Game.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>b'c29097ed-a8a2-424d-893b-9efd19361789'</td>\n",
       "      <td>b'A 2018 systematic review found evidence that...</td>\n",
       "      <td>[-0.024174018, -0.0026724807, 0.02956007, -0.0...</td>\n",
       "      <td>Video Game.txt</td>\n",
       "      <td>data/Video Game.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>b'283ace6b-d8f8-47a0-9173-8c893c8e9a90'</td>\n",
       "      <td>b'Parents and children\\'s advocates regularly ...</td>\n",
       "      <td>[-0.019061007, -0.01754194, 0.009266318, -0.03...</td>\n",
       "      <td>Video Game.txt</td>\n",
       "      <td>data/Video Game.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>b'72a289bb-6886-4825-8c4b-74d40e81b958'</td>\n",
       "      <td>b'A further issue in the industry is related t...</td>\n",
       "      <td>[-0.010214739, -0.03079727, 0.015305471, -0.03...</td>\n",
       "      <td>Video Game.txt</td>\n",
       "      <td>data/Video Game.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>b'6ee7b25d-f958-4024-87c8-fcaf4d8b2d32'</td>\n",
       "      <td>b'== See also ==\\n\\nLists of video games\\nList...</td>\n",
       "      <td>[-0.014099253, -0.005158676, -0.0129085295, -0...</td>\n",
       "      <td>Video Game.txt</td>\n",
       "      <td>data/Video Game.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 document_id  \\\n",
       "0    b'97d9fa84-5ffe-4d1f-b83f-a397a480166c'   \n",
       "1    b'9f8d942e-d37b-4776-a982-c02ee524e871'   \n",
       "2    b'f86edd6d-0c9f-43e3-a844-bc5d13048280'   \n",
       "3    b'd5ed7c93-f6d6-481a-86d3-eb4ef16c9d89'   \n",
       "4    b'09f6f434-8a1b-445c-9238-66aa75356fa3'   \n",
       "..                                       ...   \n",
       "124  b'fd4746c1-3849-4b82-a5a5-26c8465bc0b3'   \n",
       "125  b'c29097ed-a8a2-424d-893b-9efd19361789'   \n",
       "126  b'283ace6b-d8f8-47a0-9173-8c893c8e9a90'   \n",
       "127  b'72a289bb-6886-4825-8c4b-74d40e81b958'   \n",
       "128  b'6ee7b25d-f958-4024-87c8-fcaf4d8b2d32'   \n",
       "\n",
       "                                                  text  \\\n",
       "0    b'Niccol\\xc3\\xb2 di Bernardo dei Machiavelli (...   \n",
       "1    b\"Machiavelli's political realism has continue...   \n",
       "2    b\"Shortly thereafter, he was also made the sec...   \n",
       "3    b'The Florentine city-state and the republic w...   \n",
       "4    b'In 1789 George Nassau Clavering, and Pietro ...   \n",
       "..                                                 ...   \n",
       "124  b'With the capability to render 3D actors and ...   \n",
       "125  b'A 2018 systematic review found evidence that...   \n",
       "126  b'Parents and children\\'s advocates regularly ...   \n",
       "127  b'A further issue in the industry is related t...   \n",
       "128  b'== See also ==\\n\\nLists of video games\\nList...   \n",
       "\n",
       "                                            embeddings         filename  \\\n",
       "0    [-0.024216307, -0.013386093, 0.001253736, -0.0...  Machiavelli.txt   \n",
       "1    [-0.026074765, -0.008071378, -0.010988744, -0....  Machiavelli.txt   \n",
       "2    [-0.01654868, -0.003647899, 0.0055484013, -0.0...  Machiavelli.txt   \n",
       "3    [-0.035484154, -0.0016756041, 0.00013820048, -...  Machiavelli.txt   \n",
       "4    [-0.02049841, -0.0031482982, 0.0036144697, -0....  Machiavelli.txt   \n",
       "..                                                 ...              ...   \n",
       "124  [-0.016764004, -0.016048055, -0.0012655741, -0...   Video Game.txt   \n",
       "125  [-0.024174018, -0.0026724807, 0.02956007, -0.0...   Video Game.txt   \n",
       "126  [-0.019061007, -0.01754194, 0.009266318, -0.03...   Video Game.txt   \n",
       "127  [-0.010214739, -0.03079727, 0.015305471, -0.03...   Video Game.txt   \n",
       "128  [-0.014099253, -0.005158676, -0.0129085295, -0...   Video Game.txt   \n",
       "\n",
       "                file_path  \n",
       "0    data/Machiavelli.txt  \n",
       "1    data/Machiavelli.txt  \n",
       "2    data/Machiavelli.txt  \n",
       "3    data/Machiavelli.txt  \n",
       "4    data/Machiavelli.txt  \n",
       "..                    ...  \n",
       "124   data/Video Game.txt  \n",
       "125   data/Video Game.txt  \n",
       "126   data/Video Game.txt  \n",
       "127   data/Video Game.txt  \n",
       "128   data/Video Game.txt  \n",
       "\n",
       "[129 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts_table.query()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "AzMNx0-Q6WqV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 927
    },
    "id": "AzMNx0-Q6WqV",
    "outputId": "cc186595-865b-4d2e-d719-2ae33c067210"
   },
   "outputs": [
    {
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       "      <th>document_id</th>\n",
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       "      <td>b'7fc6e810-c343-43d6-b301-af2914b429ca'</td>\n",
       "      <td>b''</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'5bf41855-d981-4446-833f-75f5b1038f31'</td>\n",
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       "      <th>2</th>\n",
       "      <td>b'987f467b-0fee-4127-8058-9d4955242bfd'</td>\n",
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       "      <td>[0.2142334, -0.014846802, -0.21496582, -0.1887...</td>\n",
       "      <td>b'3.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'2bf05e51-d245-4dae-ab68-d59ce7962fc8'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[0.17700195, -0.05218506, -0.05999756, -0.4033...</td>\n",
       "      <td>b'4.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'85406f6c-38c9-46ee-9760-2ea449eb2bca'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.17883301, 0.16442871, -0.34716797, -0.0219...</td>\n",
       "      <td>b'5.jpg'</td>\n",
       "      <td>b'data/5.jpg'</td>\n",
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       "      <th>5</th>\n",
       "      <td>b'9affd42b-2ac2-4dcd-bf72-7b21f1fa8d86'</td>\n",
       "      <td>b''</td>\n",
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       "      <td>b'data/6.jpg'</td>\n",
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       "      <th>6</th>\n",
       "      <td>b'bccdaede-f99c-4094-b1a9-470b0798b0cf'</td>\n",
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       "      <td>b'7.jpg'</td>\n",
       "      <td>b'data/7.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b'ba58047a-ba93-4645-859f-83ef7e54912a'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.026107788, -0.06652832, -0.007106781, -0.3...</td>\n",
       "      <td>b'8.jpg'</td>\n",
       "      <td>b'data/8.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b'4a8a5f65-8f70-4d1c-9f79-75a409c39bd4'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.35302734, -0.06524658, -0.18603516, -0.509...</td>\n",
       "      <td>b'9.jpg'</td>\n",
       "      <td>b'data/9.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b'8ac68011-9285-46ab-9a1d-0abfd2e24a46'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[0.11975098, 0.19494629, -0.15234375, -0.21606...</td>\n",
       "      <td>b'10.jpg'</td>\n",
       "      <td>b'data/10.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b'4d5f59d0-41af-4acb-a5fb-1fba2bcbac3b'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.22644043, -0.17614746, 0.06756592, -0.5668...</td>\n",
       "      <td>b'11.jpg'</td>\n",
       "      <td>b'data/11.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b'4e340214-2673-4464-8c21-c5fd706c5a94'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.4272461, -0.009635925, -0.22509766, -0.047...</td>\n",
       "      <td>b'12.jpg'</td>\n",
       "      <td>b'data/12.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>b'1e3a7605-8e19-47e4-a894-2ea6b78e6dbb'</td>\n",
       "      <td>b''</td>\n",
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       "      <td>b'13.jpg'</td>\n",
       "      <td>b'data/13.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b'8e74924d-7b1e-4af5-953d-08270885229e'</td>\n",
       "      <td>b''</td>\n",
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       "      <td>b'14.jpg'</td>\n",
       "      <td>b'data/14.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>b'aa40b753-4633-4a7c-9b17-241b34ae2012'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.35766602, -0.1550293, -0.3503418, -0.33764...</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>b'3215a096-4740-4c25-8814-44a0def22337'</td>\n",
       "      <td>b''</td>\n",
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       "      <td>b'16.jpg'</td>\n",
       "      <td>b'data/16.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>b'0b605bbb-b481-4d52-b6d9-3dbf5b80be22'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.43652344, 0.3840332, -0.24523926, -0.02165...</td>\n",
       "      <td>b'17.jpg'</td>\n",
       "      <td>b'data/17.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>b'6f94849b-e2e1-4a13-8879-0acce1d12439'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.8803711, 0.013214111, -0.21557617, -0.25, ...</td>\n",
       "      <td>b'18.jpg'</td>\n",
       "      <td>b'data/18.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>b'b2888ad4-bc74-4f01-9a7a-5f04e7787f96'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.15161133, 0.19128418, -0.43139648, -0.4448...</td>\n",
       "      <td>b'19.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>b'2815cb0b-3a95-4aac-a30b-11abe0d3b065'</td>\n",
       "      <td>b''</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>b'8f0e5464-5aa6-4063-be41-02e55ff89fa5'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.28833008, 0.06768799, -0.57177734, 0.16613...</td>\n",
       "      <td>b'21.jpg'</td>\n",
       "      <td>b'data/21.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>b'64d517f9-54f3-45eb-8987-e986534eaee9'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.076171875, -0.021621704, 0.28271484, -0.51...</td>\n",
       "      <td>b'22.jpg'</td>\n",
       "      <td>b'data/22.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>b'82230793-6bed-4ac9-b37e-6b3801ffd10e'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.27490234, -0.026290894, -0.07720947, -0.37...</td>\n",
       "      <td>b'23.jpg'</td>\n",
       "      <td>b'data/23.jpg'</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>b'0fa87b25-b4bb-4b63-b5db-dd68393baab8'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.5776367, 0.091796875, 0.024261475, 0.10638...</td>\n",
       "      <td>b'24.jpg'</td>\n",
       "      <td>b'data/24.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>b'7f05cd1a-6549-4678-8f21-b0b5dd2f1860'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.023208618, -0.07775879, 0.22302246, -0.003...</td>\n",
       "      <td>b'25.jpg'</td>\n",
       "      <td>b'data/25.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>b'3127662f-41ca-4b10-b158-68de606bad7e'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.10839844, 0.38085938, -0.5332031, -0.08142...</td>\n",
       "      <td>b'26.jpg'</td>\n",
       "      <td>b'data/26.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>b'6fde0c80-8600-4e6c-bd4b-572f45c51988'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[0.0021247864, -0.17321777, -0.13647461, -0.12...</td>\n",
       "      <td>b'27.jpg'</td>\n",
       "      <td>b'data/27.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>b'37055b09-151c-4c9f-a313-cdfb84f6f6af'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[0.3269043, 0.42211914, -0.14086914, 0.0228881...</td>\n",
       "      <td>b'29.jpg'</td>\n",
       "      <td>b'data/29.jpg'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>b'eb7b306e-ff44-401f-91a2-137b1a14d4ce'</td>\n",
       "      <td>b''</td>\n",
       "      <td>[-0.12109375, 0.18664551, 0.03665161, -0.22521...</td>\n",
       "      <td>b'30.jpg'</td>\n",
       "      <td>b'data/30.jpg'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "28  b'eb7b306e-ff44-401f-91a2-137b1a14d4ce'  b''   \n",
       "\n",
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       "4   [-0.17883301, 0.16442871, -0.34716797, -0.0219...   b'5.jpg'   \n",
       "5   [-0.66259766, 0.3112793, 0.032714844, 0.219238...   b'6.jpg'   \n",
       "6   [0.09362793, -0.20043945, 0.027572632, -0.0228...   b'7.jpg'   \n",
       "7   [-0.026107788, -0.06652832, -0.007106781, -0.3...   b'8.jpg'   \n",
       "8   [-0.35302734, -0.06524658, -0.18603516, -0.509...   b'9.jpg'   \n",
       "9   [0.11975098, 0.19494629, -0.15234375, -0.21606...  b'10.jpg'   \n",
       "10  [-0.22644043, -0.17614746, 0.06756592, -0.5668...  b'11.jpg'   \n",
       "11  [-0.4272461, -0.009635925, -0.22509766, -0.047...  b'12.jpg'   \n",
       "12  [-0.090148926, -0.030685425, -0.296875, -0.246...  b'13.jpg'   \n",
       "13  [-0.25830078, -0.08703613, -0.2130127, -0.5092...  b'14.jpg'   \n",
       "14  [-0.35766602, -0.1550293, -0.3503418, -0.33764...  b'15.jpg'   \n",
       "15  [-0.39746094, 0.0010585785, 0.18469238, -0.244...  b'16.jpg'   \n",
       "16  [-0.43652344, 0.3840332, -0.24523926, -0.02165...  b'17.jpg'   \n",
       "17  [-0.8803711, 0.013214111, -0.21557617, -0.25, ...  b'18.jpg'   \n",
       "18  [-0.15161133, 0.19128418, -0.43139648, -0.4448...  b'19.jpg'   \n",
       "19  [-0.20056152, 0.12310791, 0.20739746, -0.21630...  b'20.jpg'   \n",
       "20  [-0.28833008, 0.06768799, -0.57177734, 0.16613...  b'21.jpg'   \n",
       "21  [-0.076171875, -0.021621704, 0.28271484, -0.51...  b'22.jpg'   \n",
       "22  [-0.27490234, -0.026290894, -0.07720947, -0.37...  b'23.jpg'   \n",
       "23  [-0.5776367, 0.091796875, 0.024261475, 0.10638...  b'24.jpg'   \n",
       "24  [-0.023208618, -0.07775879, 0.22302246, -0.003...  b'25.jpg'   \n",
       "25  [-0.10839844, 0.38085938, -0.5332031, -0.08142...  b'26.jpg'   \n",
       "26  [0.0021247864, -0.17321777, -0.13647461, -0.12...  b'27.jpg'   \n",
       "27  [0.3269043, 0.42211914, -0.14086914, 0.0228881...  b'29.jpg'   \n",
       "28  [-0.12109375, 0.18664551, 0.03665161, -0.22521...  b'30.jpg'   \n",
       "\n",
       "         file_path  \n",
       "0    b'data/1.jpg'  \n",
       "1    b'data/2.jpg'  \n",
       "2    b'data/3.jpg'  \n",
       "3    b'data/4.jpg'  \n",
       "4    b'data/5.jpg'  \n",
       "5    b'data/6.jpg'  \n",
       "6    b'data/7.jpg'  \n",
       "7    b'data/8.jpg'  \n",
       "8    b'data/9.jpg'  \n",
       "9   b'data/10.jpg'  \n",
       "10  b'data/11.jpg'  \n",
       "11  b'data/12.jpg'  \n",
       "12  b'data/13.jpg'  \n",
       "13  b'data/14.jpg'  \n",
       "14  b'data/15.jpg'  \n",
       "15  b'data/16.jpg'  \n",
       "16  b'data/17.jpg'  \n",
       "17  b'data/18.jpg'  \n",
       "18  b'data/19.jpg'  \n",
       "19  b'data/20.jpg'  \n",
       "20  b'data/21.jpg'  \n",
       "21  b'data/22.jpg'  \n",
       "22  b'data/23.jpg'  \n",
       "23  b'data/24.jpg'  \n",
       "24  b'data/25.jpg'  \n",
       "25  b'data/26.jpg'  \n",
       "26  b'data/27.jpg'  \n",
       "27  b'data/29.jpg'  \n",
       "28  b'data/30.jpg'  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs_table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9cd570f-9ed7-4e20-8a91-e3238bc43760",
   "metadata": {
    "id": "d9cd570f-9ed7-4e20-8a91-e3238bc43760"
   },
   "source": [
    "## Multi Modal Retrieval from user's query"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "762bb4ba-ac4f-4b36-977b-dc80065035b6",
   "metadata": {
    "id": "762bb4ba-ac4f-4b36-977b-dc80065035b6"
   },
   "source": [
    "**Using index as retriever**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6967b815-e4ba-42a0-85ec-0d872c4c4009",
   "metadata": {
    "id": "6967b815-e4ba-42a0-85ec-0d872c4c4009"
   },
   "outputs": [],
   "source": [
    "retriever_engine = index.as_retriever(\n",
    "                            similarity_top_k=1,     \n",
    "                            image_similarity_top_k=4,\n",
    "                            vector_store_kwargs={\n",
    "                                \"index\" : \"flat\",\n",
    "                            },\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6f74db-6056-4e20-996f-a5690944947c",
   "metadata": {
    "id": "2e6f74db-6056-4e20-996f-a5690944947c"
   },
   "source": [
    "**Retrieve most similar text/image embeddings based on query**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c0eea60a-144c-4a15-b68c-dac49ec98493",
   "metadata": {
    "id": "c0eea60a-144c-4a15-b68c-dac49ec98493"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.response.notebook_utils import display_source_node\n",
    "\n",
    "def retrieve(retriever_engine, query_str):\n",
    "    retrieved_results = retriever_engine.retrieve(query_str)\n",
    "\n",
    "    retrieved_image = []\n",
    "    retrieved_text = []\n",
    "    for res_node in retrieved_results:\n",
    "        if res_node.text == '':\n",
    "            retrieved_image.append(res_node.node.metadata[\"file_path\"])\n",
    "        else:\n",
    "            retrieved_text.append(res_node.text)\n",
    "\n",
    "    return retrieved_image, retrieved_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9466661-98f5-48ee-bc6c-5046bff711e3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "c9466661-98f5-48ee-bc6c-5046bff711e3",
    "outputId": "cedadabd-7775-4c33-9eba-0265317287eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Florentine city-state and the republic were dissolved, with Machiavelli then being removed from office and banished from the city for a year. In 1513, the Medici accused him of conspiracy against them and had him imprisoned. Despite being subjected to torture (\"with the rope\", in which the prisoner is hanged from his bound wrists from the back, forcing the arms to bear the body's weight and dislocating the shoulders), he denied involvement and was released after three weeks.\n",
      "Machiavelli then retired to \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_str = \"Tell me about the machiavelli, where he lived and what he did?\"\n",
    "\n",
    "imgs, txts = retrieve(retriever_engine=retriever_engine, query_str=query_str)\n",
    "\n",
    "# Show retrieved texts\n",
    "context_str = \"\".join(txts)\n",
    "print(context_str[:512])\n",
    "\n",
    "# Show retrieved images\n",
    "show_images(imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc7bc78-8675-4622-b609-d61d70cea567",
   "metadata": {
    "id": "2bc7bc78-8675-4622-b609-d61d70cea567"
   },
   "source": [
    "## Multi Modal RAG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e78948d-d2b3-4413-80f1-538264748682",
   "metadata": {
    "id": "8e78948d-d2b3-4413-80f1-538264748682"
   },
   "source": [
    "**Multi Modal Model: GPT-4o**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bb9e8b15-564f-4a0c-9f21-c0f91fbf1cc1",
   "metadata": {
    "id": "bb9e8b15-564f-4a0c-9f21-c0f91fbf1cc1"
   },
   "outputs": [],
   "source": [
    "from llama_index.multi_modal_llms.openai import OpenAIMultiModal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d851ecdc-041d-4c4c-8c0d-bb8073896d89",
   "metadata": {
    "id": "d851ecdc-041d-4c4c-8c0d-bb8073896d89"
   },
   "outputs": [],
   "source": [
    "mm_model_name = \"gpt-4o\"\n",
    "openai_mm_llm = OpenAIMultiModal(\n",
    "    model=mm_model_name, api_key=os.environ[\"OPENAI_API_KEY\"], max_new_tokens=1500\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7855dacc-716c-4278-a954-2d5133a34b0a",
   "metadata": {
    "id": "7855dacc-716c-4278-a954-2d5133a34b0a"
   },
   "source": [
    "**Setting Prompt Template and Query Engine for MM-RAG**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d10c816b-b41a-465c-9cef-b634c98bf2f3",
   "metadata": {
    "id": "d10c816b-b41a-465c-9cef-b634c98bf2f3"
   },
   "outputs": [],
   "source": [
    "from llama_index.core import PromptTemplate\n",
    "from llama_index.core.query_engine import SimpleMultiModalQueryEngine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f649004f-a5de-45ee-bbfb-120ec258617f",
   "metadata": {
    "id": "f649004f-a5de-45ee-bbfb-120ec258617f"
   },
   "outputs": [],
   "source": [
    "qa_tmpl_str = (\n",
    "    \"Context information is below.\\n\"\n",
    "    \"---------------------\\n\"\n",
    "    \"{context_str}\\n\"\n",
    "    \"---------------------\\n\"\n",
    "    \"Given the context information and not prior knowledge, \"\n",
    "    \"answer the query.\\n\"\n",
    "    \"Query: {query_str}\\n\"\n",
    "    \"Answer: \"\n",
    ")\n",
    "qa_tmpl = PromptTemplate(qa_tmpl_str)\n",
    "\n",
    "query_engine = index.as_query_engine(\n",
    "    llm=openai_mm_llm,\n",
    "    text_qa_template=qa_tmpl,\n",
    "    vector_store_kwargs={\n",
    "            \"index\" : \"flat\",\n",
    "            },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745190cc-490b-4823-abb3-9084ca961f56",
   "metadata": {
    "id": "745190cc-490b-4823-abb3-9084ca961f56"
   },
   "source": [
    "**MM-RAG: Question Answering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c4941a5e-ac3c-4c7e-ae0a-2071a1873037",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c4941a5e-ac3c-4c7e-ae0a-2071a1873037",
    "outputId": "c1a7b3d4-8d02-4cd6-d28c-3effacd47914"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Niccolò di Bernardo dei Machiavelli was a Florentine diplomat, author, philosopher, and historian who lived during the Italian Renaissance. He was born on 3 May 1469 and died on 21 June 1527. Machiavelli is best known for his political treatise \"The Prince\" (Il Principe), written around 1513 but published posthumously in 1532. He is often regarded as the father of modern political philosophy and political science.\n",
      "\n",
      "Machiavelli lived in Florence, where he served as a senior official in the Florentine Republic, handling diplomatic and military affairs. He worked as secretary to the second chancery of the Republic of Florence from 1498 to 1512, during the period when the Medici family was out of power. After the Medici returned to power, Machiavelli was removed from office, accused of conspiracy, and imprisoned. Following his release, he retired to his farm estate at Sant'Andrea in Percussina, near San Casciano in Val di Pesa, where he focused on studying and writing political treatises.\n",
      "\n",
      "In addition to \"The Prince,\" Machiavelli wrote comedies, carnival songs, poetry, and other works of political philosophy, such as the \"Discourses on Livy.\" His writings have been influential in the development of modern republicanism and have sparked considerable debate and controversy over their interpretation. Machiavelli's personal correspondence is also valuable to historians and scholars studying Italian history and political thought.\n"
     ]
    }
   ],
   "source": [
    "# Query by the user\n",
    "query_str = \"Tell me more about the machiavelli, where did he lived and what did he do?\"\n",
    "\n",
    "# Response from Multi Modal model\n",
    "response = query_engine.query(query_str)\n",
    "\n",
    "# Printing response\n",
    "print(response.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145a7b15-03bf-4bc0-b62c-6123344610c9",
   "metadata": {
    "id": "145a7b15-03bf-4bc0-b62c-6123344610c9"
   },
   "source": [
    "**Showing the retrieved documents i.e images and text used for answering the query**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf45d30d-c88a-4237-8017-d21323f12528",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "id": "cf45d30d-c88a-4237-8017-d21323f12528",
    "outputId": "316e2bf8-ea36-438b-b15a-9c6304acbb9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Florentine city-state and the republic were dissolved, with Machiavelli then being removed from office and banished from the city for a year. In 1513, the Medici accused him of conspiracy against them and had him imprisoned. Despite being subjected to torture (\"with the rope\", in which the prisoner is hanged from his bound wrists from the back, forcing the arms to bear the body's weight and dislocating the shoulders), he denied involvement and was released after three weeks.\n",
      "Machiavelli then retired to his farm estate at Sant'Andrea in Percussina, near San Casciano in Val di Pesa, where he devoted himself to studying and writing political treatises. During this period, he represented the Florentine Republic on diplomatic visits to France, Germany, and elsewhere in Italy. Despairing of the opportunity to remain directly involved in political matters, after a time he began to participate in intellectual groups in Florence and wrote several plays that (unlike his works on political theory) were both popular and widely known in his lifetime. Politics remained his main passion, and to satisfy this interest, he maintained a well-known correspondence with more politically connected friends, attempting to become involved once again in political life. In a letter to Francesco Vettori, he described his experience:\n",
      "\n",
      "When evening comes, I go back home, and go to my study. On the threshold, I take off my work clothes, covered in mud and filth, and I put on the clothes an ambassador would wear. Decently dressed, I enter the ancient courts of rulers who have long since died. There, I am warmly welcomed, and I feed on the only food I find nourishing and was born to savour. I am not ashamed to talk to them and ask them to explain their actions and they, out of kindness, answer me. Four hours go by without my feeling any anxiety. I forget every worry. I am no longer afraid of poverty or frightened of death. I live entirely through them.\n",
      "Machiavelli died on 21 June 1527 from a stomach ailment at the age of 58 after receiving his last rites. He was buried at the Church of Santa Croce in Florence.\n",
      "Niccolò di Bernardo dei Machiavelli (3 May 1469 – 21 June 1527) was a Florentine diplomat, author, philosopher, and historian who lived during the Italian Renaissance. He is best known for his political treatise The Prince (Il Principe), written around 1513 but not published until 1532, five years after his death. He has often been called the father of modern political philosophy and political science.\n",
      "For many years he served as a senior official in the Florentine Republic with responsibilities in diplomatic and military affairs. He wrote comedies, carnival songs, and poetry. His personal correspondence is also important to historians and scholars of Italian correspondence. He worked as secretary to the second chancery of the Republic of Florence from 1498 to 1512, when the Medici were out of power.\n",
      "After his death Machiavelli's name came to evoke unscrupulous acts of the sort he advised most famously in his work, The Prince. He claimed that his experience and reading of history showed him that politics has always involved deception, treachery, and crime. He advised rulers to do likewise when political necessity requires it, and argued specifically that successful reformers of states should not be blamed for killing other leaders who could block change. Machiavelli's Prince has been surrounded by controversy since it was published. Some consider it to be a straightforward description of political reality. Others view The Prince as a manual, teaching would-be tyrants how they should seize and maintain power. Even into recent times, some scholars, such as Leo Strauss, have restated the traditional opinion that Machiavelli was a \"teacher of evil\".\n",
      "Even though Machiavelli has become most famous for his work on principalities, scholars also give attention to the exhortations in his other works of political philosophy. While less well known than The Prince, the Discourses on Livy (composed c. 1517) has been said to have paved the way for modern republicanism. His works were a major influence on Enlightenment authors who revived interest in classical republicanism, such as Jean-Jacques Rousseau and James Harrington.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Populate retrieved documents - texts and images\n",
    "imgs = []\n",
    "txts = []\n",
    "for text_node in response.metadata[\"text_nodes\"]:\n",
    "    if text_node.text == '':\n",
    "        imgs.append(text_node.metadata['file_path'].decode('utf-8'))\n",
    "    else:\n",
    "        txts.append(text_node.text)\n",
    "\n",
    "# Display retrieved documents - texts and images\n",
    "for txt in txts:\n",
    "    print(txt)\n",
    "show_images(imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ECxF_Wr4f_S",
   "metadata": {
    "id": "6ECxF_Wr4f_S"
   },
   "source": [
    "## Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4957ba34-612c-478c-b6db-e5735a6bd44c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4957ba34-612c-478c-b6db-e5735a6bd44c",
    "outputId": "49baca60-af52-4ee9-ce34-d01dc3649127"
   },
   "outputs": [],
   "source": [
    "texts_table.drop()\n",
    "imgs_table.drop()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: LlamaIndex_samples/Sub_Question_Query_Engine_LlamaIndex_KDBAI.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f134f72f-ba98-4eed-ac63-276c3057fa95",
   "metadata": {
    "id": "f134f72f-ba98-4eed-ac63-276c3057fa95"
   },
   "source": [
    "# Sub Question Query Engine - LlamaIndex + KDB.AI\n",
    "\n",
    "Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/offerings/).\n",
    "\n",
    "In this notebook, we will walk through using KDB.AI and LlamaIndex's Sub Question Query Engine to create a RAG pipeline on a state of the union speech. The goal of the Sub Question Query Engine is to easily handle user queries that contain multiple questions within them by splitting the questions up."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d858ff-3397-44fc-b0c8-7136b7daba88",
   "metadata": {
    "id": "62d858ff-3397-44fc-b0c8-7136b7daba88"
   },
   "source": [
    "## Install requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057f4e5e-72a3-4016-9621-6ac219d69f63",
   "metadata": {
    "id": "057f4e5e-72a3-4016-9621-6ac219d69f63"
   },
   "source": [
    "**Install required packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54070ca2-534a-4563-ace4-f90313c1de1d",
   "metadata": {
    "id": "54070ca2-534a-4563-ace4-f90313c1de1d",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%pip install llama-index llama-index-embeddings-huggingface llama-index-llms-openai llama-index-readers-file llama-index-vector-stores-kdbai\n",
    "%pip install kdbai_client sentence-transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5fcc7c-d2af-48d8-a351-915ce9674ed8",
   "metadata": {
    "id": "cc5fcc7c-d2af-48d8-a351-915ce9674ed8"
   },
   "source": [
    "**Helper Library - To allow nested loop events**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7aa17679-f501-4316-8940-f5811f907663",
   "metadata": {
    "id": "7aa17679-f501-4316-8940-f5811f907663"
   },
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2780cbaf-a716-4dad-851b-de74e2d982bf",
   "metadata": {
    "id": "2780cbaf-a716-4dad-851b-de74e2d982bf"
   },
   "source": [
    "## Downloading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8ae84a-09b5-401c-92f5-0283033cd409",
   "metadata": {
    "id": "9d8ae84a-09b5-401c-92f5-0283033cd409"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a79a17d8-965c-48d0-ac41-bb2e4a109b31",
   "metadata": {
    "id": "a79a17d8-965c-48d0-ac41-bb2e4a109b31"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700eb791-4299-4424-ba34-9c64323cd6a7",
   "metadata": {
    "id": "700eb791-4299-4424-ba34-9c64323cd6a7"
   },
   "source": [
    "**Data directories and paths**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87c2e167-6a24-41f4-8f3b-80da3b004e07",
   "metadata": {
    "id": "87c2e167-6a24-41f4-8f3b-80da3b004e07"
   },
   "outputs": [],
   "source": [
    "# Root path\n",
    "root_path = os.path.abspath(os.getcwd())\n",
    "\n",
    "# Data directory and path\n",
    "data_dir = \"data\"\n",
    "data_path = os.path.join(root_path, data_dir)\n",
    "if not os.path.exists(data_path):\n",
    "    os.mkdir(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6117b372-5b6f-4fa4-a53f-49e9d59281c4",
   "metadata": {
    "id": "6117b372-5b6f-4fa4-a53f-49e9d59281c4"
   },
   "source": [
    "**Downloading text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2aa8ec7-32ef-4847-8183-3bee9a69ed03",
   "metadata": {
    "id": "b2aa8ec7-32ef-4847-8183-3bee9a69ed03"
   },
   "outputs": [],
   "source": [
    "text_url = \"https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/retrieval_augmented_generation/data/state_of_the_union.txt\"\n",
    "with urllib.request.urlopen(text_url) as response:\n",
    "    text_content = response.read().decode(\"utf-8\")\n",
    "\n",
    "text_file_name = text_url.split('/')[-1]\n",
    "text_path = os.path.join(data_path, text_file_name)\n",
    "if not os.path.exists(text_path):\n",
    "    with open(text_path, 'w') as text_file:\n",
    "        text_file.write(text_content)\n",
    "\n",
    "metadata = {\n",
    "    f\"{data_dir}/{text_file_name}\": {\n",
    "        \"title\": text_file_name.split('.')[0],\n",
    "        \"file_path\": text_path\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2b7088-9220-4246-a840-ed4ba518a9e9",
   "metadata": {
    "id": "9b2b7088-9220-4246-a840-ed4ba518a9e9"
   },
   "source": [
    "**Show text data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bbb4b90-5029-42c5-82a0-e8ce20b8fa61",
   "metadata": {
    "id": "5bbb4b90-5029-42c5-82a0-e8ce20b8fa61"
   },
   "outputs": [],
   "source": [
    "def show_text(text_path):\n",
    "    if os.path.isfile(text_path):\n",
    "        with open(text_path, 'r') as text_file:\n",
    "            contents = text_file.read()\n",
    "        print(contents[:500])\n",
    "        print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59cd13c8-8b49-4f07-8915-175c76bb80bf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "59cd13c8-8b49-4f07-8915-175c76bb80bf",
    "outputId": "3826e306-a632-4619-ca82-c315da882e01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
      "\n",
      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
      "\n",
      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
      "\n",
      "With a duty to one another to the American people to the Constitution. \n",
      "\n",
      "And with an unwavering resolve that freedom will always triumph over tyranny. \n",
      "\n",
      "Six day\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "show_text(text_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8782d349-0ecc-4692-9b16-0ee22afc2d3c",
   "metadata": {
    "id": "8782d349-0ecc-4692-9b16-0ee22afc2d3c"
   },
   "source": [
    "## KDB.AI Vector Database - session and tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dcc3ce4-1580-488b-9bca-61076013dbaa",
   "metadata": {
    "id": "4dcc3ce4-1580-488b-9bca-61076013dbaa"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4338610e-6b47-4589-ba5e-151ab974631f",
   "metadata": {
    "id": "4338610e-6b47-4589-ba5e-151ab974631f"
   },
   "outputs": [],
   "source": [
    "import kdbai_client as kdbai\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a5bb078-1e01-49df-8971-718d7e0bc76c",
   "metadata": {
    "id": "4a5bb078-1e01-49df-8971-718d7e0bc76c"
   },
   "source": [
    "**KDB.ai session**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LhY6jyI2bNQk",
   "metadata": {
    "id": "LhY6jyI2bNQk"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UWZ_jBw8bTyW",
   "metadata": {
    "id": "UWZ_jBw8bTyW"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1785cada-7d7e-4f88-a7a7-60287b0b2110",
   "metadata": {
    "id": "1785cada-7d7e-4f88-a7a7-60287b0b2110"
   },
   "source": [
    "**KDB.AI table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ac72f6f-1226-484a-9c04-6bc89002973f",
   "metadata": {
    "id": "5ac72f6f-1226-484a-9c04-6bc89002973f"
   },
   "outputs": [],
   "source": [
    "# Table - name & schema\n",
    "table_name = \"sqqe_docs\"\n",
    "\n",
    "table_schema = [\n",
    "        dict(name=\"document_id\", type=\"bytes\"),\n",
    "        dict(name=\"text\", type=\"bytes\"),\n",
    "        dict(name=\"embeddings\", type=\"float32s\"),\n",
    "        dict(name=\"title\", type=\"str\"),\n",
    "        dict(name=\"file_path\", type=\"str\")\n",
    "    ]\n",
    "\n",
    "indexFlat = {\n",
    "        \"name\": \"flat\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {'dims': 768, 'metric': 'L2'},\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6adf640",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect with kdbai database\n",
    "db = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c2852e9-f668-46b2-8952-89c1d0c95892",
   "metadata": {
    "id": "7c2852e9-f668-46b2-8952-89c1d0c95892"
   },
   "outputs": [],
   "source": [
    "# Drop table if exists\n",
    "try:\n",
    "    db.table(table_name).drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ae4b5c0-6cac-4189-9305-55c3464a6e29",
   "metadata": {
    "id": "8ae4b5c0-6cac-4189-9305-55c3464a6e29"
   },
   "outputs": [],
   "source": [
    "# Create table\n",
    "table = db.create_table(table_name, table_schema, indexes=[indexFlat])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d1b3ef-44d5-4d89-96dd-2f9996a0a7e8",
   "metadata": {
    "id": "46d1b3ef-44d5-4d89-96dd-2f9996a0a7e8"
   },
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5115549-73bb-43b7-8a9d-ec6619191c32",
   "metadata": {
    "id": "b5115549-73bb-43b7-8a9d-ec6619191c32"
   },
   "source": [
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c80df4f-d690-4f0a-85d3-e36622072b6e",
   "metadata": {
    "id": "7c80df4f-d690-4f0a-85d3-e36622072b6e"
   },
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a4abe0-701f-4674-8106-4e040eee2b65",
   "metadata": {
    "id": "09a4abe0-701f-4674-8106-4e040eee2b65"
   },
   "source": [
    "**Loading data with metadata**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54a1db63-5405-4298-9101-3a3952ef816d",
   "metadata": {
    "id": "54a1db63-5405-4298-9101-3a3952ef816d"
   },
   "outputs": [],
   "source": [
    "# Helper function - for getting metadata\n",
    "def get_metadata(file_path):\n",
    "    return metadata[file_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c56d99f8-12cf-45a7-9f8c-52d47d5fe939",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c56d99f8-12cf-45a7-9f8c-52d47d5fe939",
    "outputId": "13a2517a-1422-4dd0-ffcb-801d9e6768c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.3 ms, sys: 0 ns, total: 11.3 ms\n",
      "Wall time: 10.3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "local_files = [fpath for fpath in metadata]\n",
    "documents = SimpleDirectoryReader(input_files=local_files, file_metadata=get_metadata)\n",
    "\n",
    "docs = documents.load_data()\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69f0061-1ce7-4d57-84e5-49cd2acf2652",
   "metadata": {
    "id": "a69f0061-1ce7-4d57-84e5-49cd2acf2652"
   },
   "source": [
    "## Creating Vector Store Index for data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf4617a-efcb-47e0-a3c7-e148c87a52bf",
   "metadata": {
    "id": "0bf4617a-efcb-47e0-a3c7-e148c87a52bf"
   },
   "source": [
    "**OpenAI API Key**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c5ef3a99-e891-4026-b04b-c1a774d051f4",
   "metadata": {
    "id": "c5ef3a99-e891-4026-b04b-c1a774d051f4"
   },
   "outputs": [],
   "source": [
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9186de3-03b5-45cc-8c7c-edc9db43a330",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c9186de3-03b5-45cc-8c7c-edc9db43a330",
    "outputId": "534003dd-3cf9-4973-b23b-3c4b8e7959b2"
   },
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API key: \")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24942ff8-59dc-4f2c-8ad5-4163f65ab2f7",
   "metadata": {
    "id": "24942ff8-59dc-4f2c-8ad5-4163f65ab2f7"
   },
   "source": [
    "**Text embeddings model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d063169d-1c36-404f-a79d-4ab29e1d266f",
   "metadata": {
    "id": "d063169d-1c36-404f-a79d-4ab29e1d266f"
   },
   "outputs": [],
   "source": [
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd01617e-fe4b-46c0-9127-a60dcca54ba2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "fd01617e-fe4b-46c0-9127-a60dcca54ba2",
    "outputId": "2f903c85-b05d-459a-cc69-27c66a7750b1"
   },
   "outputs": [],
   "source": [
    "EMBEDDING = \"sentence-transformers/all-mpnet-base-v2\"\n",
    "embeddings_model = HuggingFaceEmbedding(model_name=EMBEDDING)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14de2b8c-0381-4676-a8ca-545b31ed748d",
   "metadata": {
    "id": "14de2b8c-0381-4676-a8ca-545b31ed748d"
   },
   "source": [
    "**LLM model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "89f26d0a-50f8-4727-aa24-7fdd2f798edf",
   "metadata": {
    "id": "89f26d0a-50f8-4727-aa24-7fdd2f798edf"
   },
   "outputs": [],
   "source": [
    "from llama_index.llms.openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44f5f8ea-7e1b-4d83-b2f1-a4fd4ad804e0",
   "metadata": {
    "id": "44f5f8ea-7e1b-4d83-b2f1-a4fd4ad804e0"
   },
   "outputs": [],
   "source": [
    "LLM = \"gpt-4o-mini\"\n",
    "llm_model = OpenAI(temperature=0, model=LLM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b2019f-14c9-4d57-8113-9b794d0269e6",
   "metadata": {
    "id": "35b2019f-14c9-4d57-8113-9b794d0269e6"
   },
   "source": [
    "**Setting callbacks and debug handler**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c34c26e-8eba-4a2a-b834-918b0f412a0a",
   "metadata": {
    "id": "5c34c26e-8eba-4a2a-b834-918b0f412a0a"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.callbacks import LlamaDebugHandler\n",
    "from llama_index.core.callbacks import CallbackManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a646792f-0b1b-4088-83f9-47f1034f7f58",
   "metadata": {
    "id": "a646792f-0b1b-4088-83f9-47f1034f7f58"
   },
   "outputs": [],
   "source": [
    "# Using the LlamaDebugHandler to print the trace of the sub questions captured by the SUB_QUESTION callback event type\n",
    "llama_debug = LlamaDebugHandler(print_trace_on_end=True)\n",
    "callback_manager = CallbackManager([llama_debug])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43af046c-cbd8-4d92-8369-69a053a84200",
   "metadata": {
    "id": "43af046c-cbd8-4d92-8369-69a053a84200"
   },
   "source": [
    "**Create vector store, storage context and the index for retrieval, query purposes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ba5a78bd-ec07-4799-80ad-24f976bb53d9",
   "metadata": {
    "id": "ba5a78bd-ec07-4799-80ad-24f976bb53d9"
   },
   "outputs": [],
   "source": [
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "from llama_index.core import StorageContext\n",
    "from llama_index.core import Settings\n",
    "from llama_index.core.indices import VectorStoreIndex\n",
    "from llama_index.core.node_parser import SentenceSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "247968f8-88f4-4cbf-a62c-a33ef21049ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "247968f8-88f4-4cbf-a62c-a33ef21049ed",
    "outputId": "209da4f5-a775-4354-d2d1-5ec3f692a654",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********\n",
      "Trace: index_construction\n",
      "    |_embedding -> 9.827609 seconds\n",
      "    |_embedding -> 9.82735 seconds\n",
      "**********\n",
      "CPU times: user 7.24 s, sys: 4.45 s, total: 11.7 s\n",
      "Wall time: 10.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Vector Store\n",
    "text_store = KDBAIVectorStore(table=table)\n",
    "\n",
    "# Storage context\n",
    "storage_context = StorageContext.from_defaults(vector_store=text_store)\n",
    "\n",
    "# Settings\n",
    "Settings.callback_manager = callback_manager\n",
    "Settings.transformations = [SentenceSplitter(chunk_size=500, chunk_overlap=0)]\n",
    "Settings.embed_model = embeddings_model\n",
    "Settings.llm = llm_model\n",
    "\n",
    "# Vector Store Index\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    docs,\n",
    "    use_async=True,\n",
    "    storage_context=storage_context,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd5af0a-932e-47e2-b06f-612cc578865b",
   "metadata": {
    "id": "dcd5af0a-932e-47e2-b06f-612cc578865b"
   },
   "source": [
    "## Setup sub question query engine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb2c86a-53c6-463f-ab03-37c04407945e",
   "metadata": {
    "id": "3fb2c86a-53c6-463f-ab03-37c04407945e"
   },
   "source": [
    "**Index as Vector Query Engine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66204547-10ed-4e34-81d3-b49fd25a949d",
   "metadata": {
    "id": "66204547-10ed-4e34-81d3-b49fd25a949d"
   },
   "outputs": [],
   "source": [
    "# Vector query engine\n",
    "vector_query_engine = index.as_query_engine(\n",
    "                                vector_store_kwargs={\n",
    "                                    \"index\" : \"flat\",\n",
    "                                },\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b37b91-edfe-43c2-b603-2e74d058c90d",
   "metadata": {
    "id": "17b37b91-edfe-43c2-b603-2e74d058c90d"
   },
   "source": [
    "**Setting up Sub Question Query Engine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df7779b4-6ea8-48b0-951e-99f9e82b3871",
   "metadata": {
    "id": "df7779b4-6ea8-48b0-951e-99f9e82b3871"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
    "from llama_index.core.query_engine import SubQuestionQueryEngine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eb9d7e79-999e-4a17-9089-824e74b7f26e",
   "metadata": {
    "id": "eb9d7e79-999e-4a17-9089-824e74b7f26e"
   },
   "outputs": [],
   "source": [
    "# setup base query engine as tool\n",
    "query_engine_tools = [\n",
    "    QueryEngineTool(\n",
    "        query_engine=vector_query_engine,\n",
    "        metadata=ToolMetadata(\n",
    "            name=\"state_of_union\",\n",
    "            description=\"State of Union Speech\",\n",
    "        ),\n",
    "    ),\n",
    "]\n",
    "\n",
    "query_engine = SubQuestionQueryEngine.from_defaults(\n",
    "    query_engine_tools=query_engine_tools,\n",
    "    use_async=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12137187-cfe5-40ff-a93e-6716b5a571c3",
   "metadata": {
    "id": "12137187-cfe5-40ff-a93e-6716b5a571c3"
   },
   "source": [
    "**Querying the Sub Question Query Engine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "519608b8-5be3-4e4b-8ca0-e77b9631c09e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "519608b8-5be3-4e4b-8ca0-e77b9631c09e",
    "outputId": "63c8f314-84b6-41fd-9b17-2a116e26d0b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 3 sub questions.\n",
      "\u001b[1;3;38;2;237;90;200m[state_of_union] Q: What did the president say about Ukraine in the State of the Union speech?\n",
      "\u001b[0m\u001b[1;3;38;2;90;149;237m[state_of_union] Q: What are the four common sense steps mentioned by the president in the State of the Union speech?\n",
      "\u001b[0m\u001b[1;3;38;2;11;159;203m[state_of_union] Q: How does the president plan to fight inflation according to the State of the Union speech?\n",
      "\u001b[0m\u001b[1;3;38;2;90;149;237m[state_of_union] A: The four common sense steps mentioned by the president in the State of the Union speech are to stay protected with vaccines and treatments, ensure vaccination and boosting for the highest degree of protection, continue efforts to vaccinate more Americans, and maintain vigilance against the virus as it mutates and spreads.\n",
      "\u001b[0m\u001b[1;3;38;2;237;90;200m[state_of_union] A: The president expressed strong support for Ukraine, highlighting the courage and determination of the Ukrainian people in their defense against Russian aggression. He emphasized that the United States stands with Ukraine and is providing over $1 billion in direct assistance to help them. While clarifying that U.S. forces would not engage in conflict with Russian forces in Ukraine, he stated that American military resources are mobilized to defend NATO allies. The president also acknowledged the challenges Ukraine would face in the coming days and weeks but affirmed that the Ukrainian people would not tolerate attempts to undermine their independence. He mentioned the coordinated international response to support Ukraine and the economic measures being taken against Russia.\n",
      "\u001b[0m\u001b[1;3;38;2;11;159;203m[state_of_union] A: The president plans to fight inflation by lowering costs rather than wages, increasing the production of goods like cars and semiconductors in America, and enhancing infrastructure and innovation. Key components of the plan include capping the cost of prescription drugs, specifically insulin, at $35 a month, closing tax loopholes for the wealthy, and promoting competition to prevent price exploitation. Additionally, the plan aims to reduce the deficit and ensure that the economy grows while providing families with a fair shot. The president also emphasizes the importance of training and hiring based on skills rather than degrees to support workers.\n",
      "\u001b[0m**********\n",
      "Trace: query\n",
      "    |_query -> 12.019963 seconds\n",
      "      |_llm -> 2.538993 seconds\n",
      "      |_sub_question -> 3.944205 seconds\n",
      "        |_query -> 3.943248 seconds\n",
      "          |_retrieve -> 0.16153 seconds\n",
      "            |_embedding -> 0.154742 seconds\n",
      "          |_synthesize -> 3.781092 seconds\n",
      "            |_templating -> 1.8e-05 seconds\n",
      "            |_llm -> 3.775643 seconds\n",
      "      |_sub_question -> 2.60479 seconds\n",
      "        |_query -> 2.60426 seconds\n",
      "          |_retrieve -> 0.058268 seconds\n",
      "            |_embedding -> 0.050883 seconds\n",
      "          |_synthesize -> 2.545506 seconds\n",
      "            |_templating -> 1.9e-05 seconds\n",
      "            |_llm -> 2.539947 seconds\n",
      "      |_sub_question -> 4.181837 seconds\n",
      "        |_query -> 4.181234 seconds\n",
      "          |_retrieve -> 0.05158 seconds\n",
      "            |_embedding -> 0.04598 seconds\n",
      "          |_synthesize -> 4.129155 seconds\n",
      "            |_templating -> 2.7e-05 seconds\n",
      "            |_llm -> 4.124066 seconds\n",
      "      |_synthesize -> 5.034915 seconds\n",
      "        |_templating -> 1.7e-05 seconds\n",
      "        |_llm -> 5.031635 seconds\n",
      "**********\n"
     ]
    }
   ],
   "source": [
    "response = query_engine.query(\n",
    "    \"what did the president say about ukraine, what are the four common sense steps and how he planned to fight inflation?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e7a50222-9ff0-4ed4-8c93-365a6c042f7e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e7a50222-9ff0-4ed4-8c93-365a6c042f7e",
    "outputId": "e399ffc1-5224-468c-b8e8-6c1b64e54109"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The president expressed strong support for Ukraine, highlighting the courage and determination of the Ukrainian people in their defense against Russian aggression. He affirmed that the United States stands with Ukraine, providing over $1 billion in direct assistance, while clarifying that U.S. forces would not engage in conflict with Russian forces in Ukraine. He acknowledged the challenges Ukraine would face and emphasized the coordinated international response to support them.\n",
      "\n",
      "The four common sense steps mentioned are to stay protected with vaccines and treatments, ensure vaccination and boosting for the highest degree of protection, continue efforts to vaccinate more Americans, and maintain vigilance against the virus as it mutates and spreads.\n",
      "\n",
      "To fight inflation, the president plans to lower costs rather than wages, increase the production of goods in America, and enhance infrastructure and innovation. Key components include capping the cost of prescription drugs, closing tax loopholes for the wealthy, promoting competition to prevent price exploitation, reducing the deficit, and ensuring economic growth while providing families with a fair shot. He also emphasizes training and hiring based on skills rather than degrees to support workers.\n"
     ]
    }
   ],
   "source": [
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5867c4f-e601-41b8-9e60-bd6ad9530720",
   "metadata": {
    "id": "b5867c4f-e601-41b8-9e60-bd6ad9530720"
   },
   "source": [
    "**Iterate through all subquestions captured in SUB_QUESTION event**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0053423a-6f0c-47dc-b2bf-11773c08473d",
   "metadata": {
    "id": "0053423a-6f0c-47dc-b2bf-11773c08473d"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.callbacks import CBEventType, EventPayload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04107827-4f16-4319-9e30-6c6a51737b53",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "04107827-4f16-4319-9e30-6c6a51737b53",
    "outputId": "0c0bfb04-9b05-4cde-9aac-f178b44f0283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sub Question 0: What did the president say about Ukraine in the State of the Union speech?\n",
      "Answer: The president expressed strong support for Ukraine, highlighting the courage and determination of the Ukrainian people in their defense against Russian aggression. He emphasized that the United States stands with Ukraine and is providing over $1 billion in direct assistance to help them. While clarifying that U.S. forces would not engage in conflict with Russian forces in Ukraine, he stated that American military resources are mobilized to defend NATO allies. The president also acknowledged the challenges Ukraine would face in the coming days and weeks but affirmed that the Ukrainian people would not tolerate attempts to undermine their independence. He mentioned the coordinated international response to support Ukraine and the economic measures being taken against Russia.\n",
      "================================================================================\n",
      "Sub Question 1: What are the four common sense steps mentioned by the president in the State of the Union speech?\n",
      "Answer: The four common sense steps mentioned by the president in the State of the Union speech are to stay protected with vaccines and treatments, ensure vaccination and boosting for the highest degree of protection, continue efforts to vaccinate more Americans, and maintain vigilance against the virus as it mutates and spreads.\n",
      "================================================================================\n",
      "Sub Question 2: How does the president plan to fight inflation according to the State of the Union speech?\n",
      "Answer: The president plans to fight inflation by lowering costs rather than wages, increasing the production of goods like cars and semiconductors in America, and enhancing infrastructure and innovation. Key components of the plan include capping the cost of prescription drugs, specifically insulin, at $35 a month, closing tax loopholes for the wealthy, and promoting competition to prevent price exploitation. Additionally, the plan aims to reduce the deficit and ensure that the economy grows while providing families with a fair shot. The president also emphasizes the importance of training and hiring based on skills rather than degrees to support workers.\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "for i, (start_event, end_event) in enumerate(\n",
    "    llama_debug.get_event_pairs(CBEventType.SUB_QUESTION)\n",
    "):\n",
    "    end_event_exception = end_event.payload.get(EventPayload.EXCEPTION)\n",
    "    if end_event_exception is None:\n",
    "        qa_pair = end_event.payload[EventPayload.SUB_QUESTION]\n",
    "        print(\"Sub Question \" + str(i) + \": \" + qa_pair.sub_q.sub_question.strip())\n",
    "        print(\"Answer: \" + qa_pair.answer.strip())\n",
    "        print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VkyiADnChSnE",
   "metadata": {
    "id": "VkyiADnChSnE"
   },
   "source": [
    "## Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5c4118ee-8632-48af-b389-0e8da13480fc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5c4118ee-8632-48af-b389-0e8da13480fc",
    "outputId": "0aa8bfd3-91fe-4a14-ca19-5821b5a65270"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: LlamaParse_pdf_RAG/llamaParse_demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NBD-4xlhnyxl"
   },
   "source": [
    "## Parsing Complex PDFs with LlamaParse\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "> [KDB.AI](https://kdb.ai/) is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications, using real-time data, by providing advanced search, recommendation and personalization.\n",
    "\n",
    "PDFs and other complex document types are notoriously difficult to work with, yet are the common file formats used for publishing important business related information. Since these file types are so common, it is key to have the capability to parse and ingest these documents swiftly, with accuracy, while cleanly extracting embedded entities such as images, tables, and graphs. If extracted correctly, all of the data held in a complex document like a PDF can be ingested into a RAG workflow to generate accurate and contextual responses for users and the business.\n",
    "\n",
    "This sample will illustrate how to use LlamaParse, an generative AI enabled parsing platform created by LlamaIndex to parse and represent complex files in a way that enables effective retrieval. We will use LlamaIndex to orchestrate a RAG pipeline where LlamaParse is used to parse a complex academic article and extract text and tables from it, and KDB.AI is used as our retrieval mechanism to pass relevant information about the article to an LLM.\n",
    "\n",
    "LlamaParse transforms complex documents like PDFs into markdown or text formats, which are easily ingestible. This parsing also extracts embedded entities like tables and images.\n",
    "\n",
    "Agenda:\n",
    "1. Dependencies, Imports & Setup\n",
    "2. Set API Keys for LlamaCloud, OpenAI, Cohere\n",
    "3. Define KDB.AI Session\n",
    "4. Create Schema and KDB.AI Table\n",
    "5. Download ARXIV Article: '[LLM In-Context Recall is Prompt Dependent](https://arxiv.org/pdf/2404.08865)' by Daniel Machlab and Rick Battle\n",
    "6. LlamaParse & LlamaIndex Setup\n",
    "7. Parse the Document with LlamaParse into Markdown Format\n",
    "8. Extract Text and Table nodes from Markdown Document\n",
    "9. Create the RAG Pipeline with LlamaIndex and KDB.AI\n",
    "10. Query the RAG Pipeline!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "STPHhwfenyxm"
   },
   "source": [
    "## 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xzuVAO2LfiJt"
   },
   "outputs": [],
   "source": [
    "!pip install llama-index\n",
    "!pip install llama-index-core\n",
    "!pip install llama-index-embeddings-openai\n",
    "!pip install llama-parse\n",
    "!pip install llama-index-vector-stores-kdbai\n",
    "!pip install pandas\n",
    "!pip install llama-index-postprocessor-cohere-rerank\n",
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "I5D6E_dxfzgp"
   },
   "outputs": [],
   "source": [
    "from llama_parse import LlamaParse\n",
    "from llama_index.core import Settings\n",
    "from llama_index.core import StorageContext\n",
    "from llama_index.core import VectorStoreIndex\n",
    "from llama_index.core.node_parser import MarkdownElementNodeParser\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "from llama_index.postprocessor.cohere_rerank import CohereRerank\n",
    "from getpass import getpass\n",
    "import os\n",
    "import kdbai_client as kdbai\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VugwLbH4nyxo"
   },
   "source": [
    "## 2. Set API Keys for LlamaCloud, OpenAI, Cohere\n",
    "Get API keys here:\n",
    "- [LlamaCloud](https://cloud.llamaindex.ai/)\n",
    "- [OpenAI](https://platform.openai.com/api-keys)\n",
    "- [Cohere](https://dashboard.cohere.com/welcome/register)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "wfSJbAq7ggm2"
   },
   "outputs": [],
   "source": [
    "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ijjfv6s2JQQb",
    "outputId": "3397470a-0223-43e5-98e4-fd2617167889"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLAMA CLOUD API key: ··········\n"
     ]
    }
   ],
   "source": [
    "# API access to llama-cloud\n",
    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = (\n",
    "    os.environ[\"LLAMA_CLOUD_API_KEY\"]\n",
    "    if \"LLAMA_CLOUD_API_KEY\" in os.environ\n",
    "    else getpass(\"LLAMA CLOUD API key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZNENFqpoJQQb",
    "outputId": "12086fc3-12d6-47bb-a8e1-236ac3bfcc65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI API Key: ··········\n"
     ]
    }
   ],
   "source": [
    "# Using OpenAI API for embeddings/llms\n",
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q69Itjjjnyxo"
   },
   "source": [
    "## 3. Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kwgxg_83iohC"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUOUBxQBnyxo"
   },
   "source": [
    "## 4. Create Schema and KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "lUpAh6CdyWow"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "        dict(name=\"document_id\", type=\"str\"),\n",
    "        dict(name=\"text\", type=\"str\"),\n",
    "        dict(name=\"embeddings\", type=\"float32s\"),\n",
    "    ]\n",
    "\n",
    "indexFlat = {\n",
    "        \"name\": \"flat\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {'dims': 1536, 'metric': 'L2'},\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Cukzzh7fJQQc"
   },
   "outputs": [],
   "source": [
    "# Connect with kdbai database\n",
    "db = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "RiYRfJbxyZe8"
   },
   "outputs": [],
   "source": [
    "KDBAI_TABLE_NAME = \"LlamaParse_Table\"\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(KDBAI_TABLE_NAME).drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass\n",
    "\n",
    "#Create the table\n",
    "table = db.create_table(KDBAI_TABLE_NAME, schema, indexes=[indexFlat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hF0toP5Inyxp"
   },
   "source": [
    "## 5. Download ARXIV Article\n",
    "This is an article from VMware NLP Lab called '[LLM In-Context Recall is Prompt Dependent](https://arxiv.org/pdf/2404.08865)' by Daniel Machlab and Rick Battle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-DPfDHeOnyxp",
    "outputId": "1108cac1-e113-4b32-d031-30d7e30ca62d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-12-13 21:07:45--  https://arxiv.org/pdf/2404.08865\n",
      "Resolving arxiv.org (arxiv.org)... 151.101.131.42, 151.101.3.42, 151.101.67.42, ...\n",
      "Connecting to arxiv.org (arxiv.org)|151.101.131.42|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4601949 (4.4M) [application/pdf]\n",
      "Saving to: ‘./LLM_recall.pdf’\n",
      "\n",
      "./LLM_recall.pdf    100%[===================>]   4.39M  1.70MB/s    in 2.6s    \n",
      "\n",
      "2024-12-13 21:07:48 (1.70 MB/s) - ‘./LLM_recall.pdf’ saved [4601949/4601949]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://arxiv.org/pdf/2404.08865' -O './LLM_recall.pdf'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LrQOG4yonyxp"
   },
   "source": [
    "## 6. LlamaParse & LlamaIndex Setup\n",
    "We define which LLM and embedding model should be used, define the file path of the complex document, and create parsing instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "CJWBLC_dhiww"
   },
   "outputs": [],
   "source": [
    "EMBEDDING_MODEL  = \"text-embedding-3-small\"\n",
    "GENERATION_MODEL = \"gpt-4o\"\n",
    "\n",
    "llm = OpenAI(model=GENERATION_MODEL)\n",
    "embed_model = OpenAIEmbedding(model=EMBEDDING_MODEL)\n",
    "\n",
    "Settings.llm = llm\n",
    "Settings.embed_model = embed_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "bk40jlZMhKKT"
   },
   "outputs": [],
   "source": [
    "pdf_file_name = './LLM_recall.pdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "as8sskUxnQfM"
   },
   "outputs": [],
   "source": [
    "parsing_instructions = '''The document titled \"LLM In-Context Recall is Prompt Dependent\" is an academic preprint from April 2024, authored by Daniel Machlab and Rick Battle from the VMware NLP Lab. It explores the in-context recall capabilities of Large Language Models (LLMs) using a method called \"needle-in-a-haystack,\" where a specific factoid is embedded in a block of unrelated text. The study investigates how the recall performance of various LLMs is influenced by the content of prompts and the biases in their training data. The research involves testing multiple LLMs with varying context window sizes to assess their ability to recall information accurately when prompted differently. The paper includes detailed methodologies, results from numerous tests, discussions on the impact of prompt variations and training data, and conclusions on improving LLM utility in practical applications. It contains many tables. Answer questions using the information in this article and be precise.'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UEJFaS_9nyxp"
   },
   "source": [
    "## 7. Parse the document with LlamaParse into markdown format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vPkDh9JqhP5V",
    "outputId": "12c1c28d-88b1-46e3-975c-f4c372f1ffe7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started parsing the file under job_id eb3b9f3c-5f40-46fb-ac64-9e3dd103fb7f\n"
     ]
    }
   ],
   "source": [
    "documents = LlamaParse(result_type=\"markdown\", parsing_instructions=parsing_instructions).load_data(pdf_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U277GNZus1gm",
    "outputId": "f0dfeeb8-0efc-470c-d3b9-954ba8c4c11d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# LLM In-Context Recall is Prompt Dependent\n",
      "\n",
      "Daniel Machlab\n",
      "\n",
      "VMware NLP Lab\n",
      "\n",
      "daniel.machlab@broadcom.com\n",
      "\n",
      "# Abstract\n",
      "\n",
      "The proliferation of Large Language Models (LLMs) highlights the critical importance of conducting thorough evaluations to discern their comparative advantages, limitations, and optimal use cases. Particularly important is assessing their capacity to accurately retrieve information included in a given prompt. A model’s ability to do this significantly influences how effectively it can utilize contextual details, thus impacting its practical efficacy and dependability in real-world applications.\n",
      "\n",
      "Our research analyzes the in-context recall performance of various LLMs using the needle-in-a-haystack method. In this approach, a factoid (the “needle”) is embedded within a block of filler text (the “haystack”), which the model is asked to retrieve. We assess the recall performance of each model across various haystack lengths and with varying needle placements to identify per\n"
     ]
    }
   ],
   "source": [
    "print(documents[0].text[:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgkhHi4onyxp"
   },
   "source": [
    "## 8. Extract Text and Table nodes from Markdown Document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "8PKK7IC1jdX7"
   },
   "outputs": [],
   "source": [
    "# Parse the documents using MarkdownElementNodeParser\n",
    "node_parser = MarkdownElementNodeParser(llm=llm, num_workers=8).from_defaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ixrSC6rHdr4d",
    "outputId": "7e54d0dc-925e-4959-8d60-bcb8402777cf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:00, 500.27it/s]\n",
      "1it [00:00, 5577.53it/s]\n",
      "4it [00:00, 31655.12it/s]\n",
      "0it [00:00, ?it/s]\n",
      "1it [00:00, 7294.44it/s]\n",
      "0it [00:00, ?it/s]\n",
      "0it [00:00, ?it/s]\n",
      "0it [00:00, ?it/s]\n",
      "1it [00:00, 8004.40it/s]\n",
      "1it [00:00, 6721.64it/s]\n",
      "1it [00:00, 9383.23it/s]\n",
      "1it [00:00, 538.77it/s]\n",
      "0it [00:00, ?it/s]\n",
      "2it [00:00, 14563.56it/s]\n",
      "2it [00:00, 14538.32it/s]\n",
      "5it [00:00, 36345.79it/s]\n",
      "2it [00:00, 3002.37it/s]\n"
     ]
    }
   ],
   "source": [
    "# Retrieve nodes (text) and objects (table)\n",
    "nodes = node_parser.get_nodes_from_documents(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vUk3FIG0nyxp"
   },
   "source": [
    "#### Split nodes into base_nodes (text nodes), and object (table nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "1n4U1ITPdvvl"
   },
   "outputs": [],
   "source": [
    "base_nodes, objects = node_parser.get_nodes_and_objects(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wZbDBC_Unyxq"
   },
   "source": [
    "#### Explore these extracted nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "Nv_1OvuFjq5P"
   },
   "outputs": [],
   "source": [
    "# insert the table markdown into the text of each table object\n",
    "for i in range(len(objects)):\n",
    "  objects[i].text = objects[i].obj.text[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G0JloJk9gtcl",
    "outputId": "946eea4c-0cbb-448e-f40e-a9f414faa3e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node ID: 854942f9-1167-4573-a000-2bd9fe13c6a5\n",
      "Text: The table lists various AI models along with their respective\n",
      "context window sizes, indicating the maximum number of tokens each\n",
      "model can process at once., with the following columns: - Model Name:\n",
      "None - Context Window Size: None  |Model Name|Context Window Size|\n",
      "|---|---| |Llama 2 13B Chat|4,096 Tokens| |Llama 2 70B Chat|4,096\n",
      "Tokens| |Wizard...\n"
     ]
    }
   ],
   "source": [
    "print(objects[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4HTNkJgnyxq"
   },
   "source": [
    "## 9. Create the RAG Pipeline with LlamaIndex and KDB.AI\n",
    "\n",
    "Use KDB.AI as the vector store, insert base_nodes and objects into KDB.AI, create query_engine using Cohere for reranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "J_iNgbTetM2h"
   },
   "outputs": [],
   "source": [
    "vector_store = KDBAIVectorStore(table)\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "-JNmVlkhynG5"
   },
   "outputs": [],
   "source": [
    "#Create the index, inserts base_nodes and objects into KDB.AI\n",
    "recursive_index = VectorStoreIndex(\n",
    "    nodes= base_nodes + objects, storage_context=storage_context\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "CUUN9cQy413y",
    "outputId": "2b511b1a-911e-494d-9dcd-9bf50fb27707"
   },
   "outputs": [
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       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-4d330f2c-4733-4a68-a157-77e055648a5b button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                             document_id  \\\n",
       "0   60ab97dd-699e-4d4a-88ad-8af45252f889   \n",
       "1   eaee75ac-8d87-4aef-9229-2e23d5b4e4a4   \n",
       "2   fde41f83-0476-4003-9ad5-bcb794029a2a   \n",
       "3   170d4b26-1284-4bd6-9c92-eca6a795ad4e   \n",
       "4   f13d3190-1361-49f2-a919-ace65770124d   \n",
       "..                                   ...   \n",
       "56  8344d6bc-b03f-4df2-baf8-70dc5a063b59   \n",
       "57  b22eccd8-e134-4b48-af62-a54a062f9283   \n",
       "58  5e581faf-751f-47d6-b1c0-49b19d72f1e9   \n",
       "59  27bc255e-bbc5-4e58-91bd-93332e5bebcf   \n",
       "60  d4830a76-4ae2-4d49-854b-1abc4fe64cfd   \n",
       "\n",
       "                                                 text  \\\n",
       "0   LLM In-Context Recall is Prompt Dependent\\n\\nD...   \n",
       "1   Table 1. LLMs evaluated with needle-in-a-hayst...   \n",
       "2   Arxiv, April 2024, Preprint\\n\\n Machlab & Batt...   \n",
       "3   Question\\n\\n- What did PistachioAI receive bef...   \n",
       "4   LLM In-Context Recall is Prompt Dependent\\n\\nA...   \n",
       "..                                                ...   \n",
       "56  The table appears to be related to a test cond...   \n",
       "57  The table appears to represent a series of mea...   \n",
       "58  The table appears to represent a series of num...   \n",
       "59  The table appears to contain numerical data wi...   \n",
       "60  The table appears to contain numerical data wi...   \n",
       "\n",
       "                                           embeddings  \n",
       "0   [-0.011131451, 0.027312648, 0.04182894, 0.0080...  \n",
       "1   [-0.0037533694, 0.008636131, 0.04999082, -0.04...  \n",
       "2   [0.0044781016, 0.002235319, 0.044452623, -0.04...  \n",
       "3   [-0.021702243, 0.0040557785, 0.028268011, -0.0...  \n",
       "4   [-0.011283955, -0.0078016864, 0.02792849, 0.03...  \n",
       "..                                                ...  \n",
       "56  [-0.046381496, -0.030222965, 0.082688674, -0.0...  \n",
       "57  [-0.014041103, -0.028811967, 0.073300235, -0.0...  \n",
       "58  [-0.038328543, -0.039194115, 0.06691942, -0.02...  \n",
       "59  [-0.020661851, -0.009247683, 0.07606771, -0.02...  \n",
       "60  [-0.025765242, -0.019740395, 0.06763376, -0.01...  \n",
       "\n",
       "[61 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query KDB.AI to ensure the nodes were inserted\n",
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions to Complete the RAG Pipeline\n",
    "\n",
    "1. embed_query : This function takes in a user query and embeds it using OpenAI's 'text-embedding-3-small'\n",
    "\n",
    "2. retrieve_data : This function takes the query, calls the embed_query function to get the query embedding, executes retrieval on the KDB.AI vector database to  retrieve the most relevant nodes\n",
    "\n",
    "    -During the search, we filter out a specific node, the reason is that node contains what can be perceived by the LLM as strong instructions like ***'you must rate the response on a scale of 1 to 5 by strictly\n",
    "following this format: \"[[rating]]\", for example: \"Rating: [[5]]\".*** This can throw off the LLM into answering with a rating, rather than answering the user's query.\n",
    "\n",
    "3. RAG : This function takes in the query, calls the retrieve_data function, and then passes that retrieved data to OpenAI's GPT-4o LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "HzyYgrQK7iGI"
   },
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "client = OpenAI()\n",
    "\n",
    "def embed_query(query):\n",
    "    query_embedding = client.embeddings.create(\n",
    "            input=query,\n",
    "            model=\"text-embedding-3-small\"\n",
    "        )\n",
    "    return query_embedding.data[0].embedding\n",
    "\n",
    "def retrieve_data(query):\n",
    "    query_embedding = embed_query(query)\n",
    "    results = table.search(vectors={'flat':[query_embedding]},n=5,filter=[('<>','document_id','4a9551df-5dec-4410-90bb-43d17d722918')])\n",
    "    retrieved_data_for_RAG = []\n",
    "    for index, row in results[0].iterrows():\n",
    "      retrieved_data_for_RAG.append(row['text'])\n",
    "    return retrieved_data_for_RAG\n",
    "\n",
    "def RAG(query):\n",
    "  question = \"You will answer this question based on the provided reference material: \" + query\n",
    "  messages = \"Here is the provided context: \" + \"\\n\"\n",
    "  results = retrieve_data(query)\n",
    "  if results:\n",
    "    for data in results:\n",
    "      messages += data + \"\\n\"\n",
    "  response = client.chat.completions.create(\n",
    "      model=\"gpt-4o\",\n",
    "      messages=[\n",
    "          {\"role\": \"system\", \"content\": question},\n",
    "          {\n",
    "          \"role\": \"user\",\n",
    "          \"content\": [\n",
    "              {\"type\": \"text\", \"text\": messages},\n",
    "          ],\n",
    "          }\n",
    "      ],\n",
    "      max_tokens=300,\n",
    "  )\n",
    "  content = response.choices[0].message.content\n",
    "  return content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Query the RAG Pipeline!\n",
    "All the work is complete! Now we can ask questions about the article whether the information is contained in text, or in tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dRUmZGPQ8y85",
    "outputId": "57a90d29-414e-49a3-9b3b-673c572f2c9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The needle-in-a-haystack method is an evaluation approach used to assess the recall performance of Large Language Models (LLMs). This method involves inserting a specific piece of information, called a \"needle,\" into a large amount of filler text, called a \"haystack.\" The purpose is to test how effectively a model can locate and retrieve this \"needle\" when requested.\n",
      "\n",
      "In this process:\n",
      "\n",
      "1. **Haystack Construction:** The haystack's length and the needle's placement within it are varied to evaluate the model's recall abilities across different sections of its context window. This variation helps analyze how much information can be stored in the context window before recall performance starts degrading.\n",
      "\n",
      "2. **Insertion Variability:** The needle's insertion point is varied using a sigmoid distribution, allowing researchers to understand recall performance nuances at different positions within the prompt, particularly near the start and end.\n",
      "\n",
      "3. **Evaluation:** Open-ended responses from LLMs are graded based on recall performance, with scores plotted on heatmaps. Various parameters like haystack size and needle depth are tested, revealing patterns in the model's ability to recall information depending on its position and quantity.\n",
      "\n",
      "4. **Prompt Structure:** The prompt in these tests consists of three parts: a system message (\"You are a helpful AI assistant that answers a question using only the provided information.\"), the haystack with the embedded needle, and a question prompting the model to recall the needle.\n",
      "\n",
      "By examining LLMs’ performance across different haystack sizes and needle\n"
     ]
    }
   ],
   "source": [
    "print(RAG(\"describe the needle in a haystack method only using the provided information\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zacmnPP9_Na9",
    "outputId": "fda9f99f-2d8c-49b8-8b1d-259206bc093d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best thing to do in San Francisco, according to the provided information, is to eat a sandwich and sit in Dolores Park on a sunny day.\n"
     ]
    }
   ],
   "source": [
    "print(RAG(\"what is the best thing to do in San Francisco?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nOOQC8FWLp9R"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0xSrHS6c804u",
    "outputId": "7d0ffa84-cfcb-49df-d560-2741df428e3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AI models evaluated with needle-in-a-haystack testing are:\n",
      "\n",
      "1. Llama 2 13B Chat\n",
      "2. Llama 2 70B Chat\n",
      "3. WizardLM-70B-V1.0\n",
      "4. GPT-3.5 Turbo 1106\n",
      "5. GPT-3.5 Turbo 0125\n",
      "6. Mistral 7B Instruct v0.1\n",
      "7. Mistral 7B Instruct v0.2\n",
      "8. Mixtral 8x7B Instruct v0.1\n",
      "9. GPT-4 Turbo 0125\n"
     ]
    }
   ],
   "source": [
    "print(RAG(\"list the AI models that are evaluated with needle-in-a-haystack testing?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7IX-mtl6LYIL"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l21mYTYjIC7g",
    "outputId": "d35258f2-4bc0-40e2-fa70-44dc6a72afdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the provided scoring system details, let us describe each evaluation score independently:\n",
      "\n",
      "1. **Score 1:**\n",
      "   - Description: The answer is completely unrelated to the reference.\n",
      "   - This score implies that the response does not connect or correspond in any meaningful way to the given reference material. There is a total lack of relevance, making such an answer inapplicable or extraneous to the context.\n",
      "\n",
      "2. **Score 2:**\n",
      "   - Description: The answer has minor relevance but does not align with the reference.\n",
      "   - This indicates that while there might be some trace elements or aspects that somewhat recall the reference, the answer overall fails to properly align with or adhere to the specifics of the reference material.\n",
      "\n",
      "3. **Score 3:**\n",
      "   - Description: The answer has moderate relevance but contains inaccuracies.\n",
      "   - A score of 3 suggests that the answer captures some parts of the concept or material but includes errors or inaccuracies, which detracts from its overall credibility or correctness relative to the reference.\n",
      "\n",
      "4. **Score 4:**\n",
      "   - Description: The answer aligns with the reference but has minor omissions.\n",
      "   - This score reflects a mostly accurate answer that corresponds well with the reference, albeit lacking in some small details or points that were not captured in the response.\n",
      "\n",
      "5. **Score 5:**\n",
      "   - Description: The answer is completely accurate and aligns perfectly with the reference.\n",
      "   - A perfect score is given when the response is fully correct, comprehensive,\n"
     ]
    }
   ],
   "source": [
    "print(RAG(\"describe each of the evaluation scores independently\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZbnuPzOjLhQ_"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4e8NqXmkOyUW",
    "outputId": "c5418f1a-9afb-46ca-d773-6e8cc09ff613"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The provided context outlines a detailed training strategy and methodology employed in evaluating different large language models (LLMs) for their recall abilities using the \"needle-in-a-haystack\" test. This evaluation focuses on how well these models can retrieve specific pieces of information (needles) from a large corpus (haystack) based on variations in size and placement, which ultimately aims to assess the consistency and robustness of information retrieval.\n",
      "\n",
      "Here's a breakdown of the strategy and methodology applied:\n",
      "\n",
      "1. **Needle-in-a-Haystack Test:** The test involves embedding a single factoid (needle) into a large body of text (haystack) and evaluating the model's ability to recall that specific piece of information. The training strategy utilizes different configurations to simulate a “needle in a haystack” scenario, thereby challenging the model's recall accuracy and consistency.\n",
      "\n",
      "2. **Testing Variety:** The evaluation uses three different needle-in-a-haystack tests: PistachioAI, San Francisco, and Thornfield Hollow, to examine recall consistency across different prompts. This approach highlights how conflicting data in training can impact recall performance, particularly in the San Francisco test where training data might conflict with the needle.\n",
      "\n",
      "3. **Model Architecture and Training Adjustments:** For Mistral v0.2, significant improvements were attributed to architectural changes such as a 32k token context window instead of an 8k one, alterations in the rope-theta hyperparameter, and removal of sliding window attention. These modifications enhance its\n"
     ]
    }
   ],
   "source": [
    "print(RAG(\"describe the training strategy in depth\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n2UHo-bzJQQg"
   },
   "source": [
    "## Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0Su6yxSpJQQh"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ARMuYLkPJQQh"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/hLVmfCRV)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: README.md
================================================
![KDB.AI Logo](https://kdb.ai/files/2024/01/kdbai-logo.svg)

The example [KDB.AI](https://kdb.ai) samples provided aim to demonstrate examples of the use of the KDB.AI vector database in a number of scenarios ranging from getting started guides to industry specific use-cases.

## Getting Started with KDB.AI

KDB.AI Server enables you to build and deploy large-scale generative AI applications with full control over your infrastructure. Deploy on-premises or on your preferred cloud provider.

Sign up for a free trial at [https://trykdb.kx.com/kdbaiserver/signup/](https://trykdb.kx.com/kdbaiserver/signup/) to receive your license file and setup instructions.

See the notebooks for detailed connection examples and best practices.

## What is KDB.AI?

KDB.AI is a vector database with time-series capabilities that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. KDB.AI is a key component of full-stack Generative AI applications that use Retrieval Augmented Generation (RAG).

Built by KX, the creators of kdb+, KDB.AI provides users with the ability to combine unstructured vector embedding data with structured time-series datasets to allow for hybrid use-cases which benefit from the rigor of conventional time-series data analytics and the usage patterns provided by vector databases within the Generative AI space.


## What does KDB.AI support?

KDB.AI supports the following feature set:

- Multiple index types: Flat, qFlat, IVF, IVFPQ, HNSW and qHnsw.
- Multiple distance metrics: Euclidean, Inner-Product, Cosine.
- Top-N and metadata filtered retrieval
- Python and REST Interfaces


## Sample Breakdown

At this time, the repository contains the following samples:

### Getting Started

- [Python Quickstart](quickstarts/python_quickstart.ipynb): A quick introduction to the KDB.AI APIs in Python.

### Use-Cases

- [TSS_non_transformed](TSS_non_transformed): Temporal Similarity Search (Non Transformed) time series search.
- [TSS_transformed](TSS_transformed): Temporal Similarity Search (Transformed) for time series search. 
- [LlamaIndex Advanced RAG](LlamaIndex_advanced_RAG): Demonstration on how to use LlamaIndex with KDB.AI for RAG.
- [LlamaIndex Samples](LlamaIndex_samples): Hybrid Search, Multimodal RAG, and Multi Query Retriever LlamaIndex Samples.
- [LlamaParse PDF RAG](LlamaParse_pdf_RAG): Use LlamaParse to extract embedded elements from a PDF and build a RAG pipeline.
- [Document Search](document_search): Semantic Search on PDF Documents.
- [Hybrid Search](hybrid_search): Combine dense and sparse search to improve accuracy.
- [Image Search](image_search): Image Search on Brain MRI Scans.
- [Metadata Filtering](metadata_filtering): Metadata Filtering to increase search speed and accuracy.
- [Fuzzy Filtering](fuzzy_filtering_on_metadata): Fuzzy Filtering to handle typos, international spelling difference, etc upon metadata columns. 
- [Multi-Index Search](multi_index_multimodal_search): Use KDB.AI's multiple index search capability for multimodal retrieval.
- [Multimodal RAG Unified Text](multimodal_RAG_unified_text): Multimodal RAG with images descriptions and text.
- [Multimodal RAG Voyage AI](multimodal_RAG_VoyageAI): Multimodal RAG with text and images using Voyage AI multimodal embeddings.
- [Recommendation System](music_recommendation): Music Recommendation on Spotify Data.
- [Pattern Matching](pattern_matching): Pattern Matching on Sensor Data.
- [qFlat Index](qFlat_index_pdf_search): Document search using KDB.AI's qFlat on-disk index.
- [qHnsw Index](qHnsw_index_pdf_search): Document search using KDB.AI's qHnsw on-disk index.
- [Retreival Augmented Generation with LangChain](retrieval_augmented_generation): Retrieval Augmented Generation (RAG) with LangChain.
- [Retreival Augmented Generation Evaluation with LangChain](retrieval_augmented_generation_evaluation): Retrieval Augmented Generation (RAG) Evaluation with LangChain.
- [Sentiment Analysis](sentiment_analysis): Sentiment Analysis on Disneyland Resort Reviews.
- [Video RAG VoyageAI](video_RAG_VoyageAI): Multimodal RAG on Video using Voyage AI multimodal embeddings.
- [Video RAG TwelveLabs](video_RAG_TwelveLabs): Multimodal RAG on Video using TwelveLabs video embeddings and video chat API.


## What Platforms Does KDB.AI Integrate With?

- [ChatGPT Retrieval Plugin](https://github.com/KxSystems/chatgpt-retrieval-plugin/blob/KDB.AI/examples/providers/kdbai/ChatGPT_QA_Demo.ipynb): Example showing a question and answer session using a ChatGPT retrieval plugin using KDB.AI Vector Database.
- [Langchain](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb): Example showing a question and answer session using a Langchain integration with the KDB.AI Vector Database.
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/api_reference/storage/vector_store/kdbai/): KDB.AI integrates with the LlamaIndex framework for working with LLMs.


## Setup

This section details the setup steps required to run these samples locally on your machine.

### Prerequisites

This setup guide assumes the following:
  1. You are using a Unix terminal or similar
  2. You have `python` >= 3.8 installed
  3. You have `pip` installed

### Install Python Packages

> [!TIP]
> <b>Running out of disk space?</b>
> By default, pytorch installs both GPU and CPU related packages.
> This repo does not require a GPU and hence, will only make use of the CPU related packages.
> By running the below install command, you will only install the CPU related packages and save approximately 1.5GB of disk space.
> This command is optional and if run, should be run at the beginning of the notebook.
> ```bash
> pip install torch --index-url https://download.pytorch.org/whl/cpu
> ```

1. The necessary pip installs are at the beginning of each notebook.

    (optional) To see a comprehensive list of requirements, see the `requirements.txt` file in the repository.

    ```bash
    pip install -r requirements.txt
    ```


### View & Execute The Samples

1. Run a jupter notebook session:

    ```bash
    jupyter notebook --no-browser
    ```

    This will load up the jupyter session in the background and display a URL on screen for you.

1. Paste this URL into your browser

    This will bring up the samples for you to interact with.


## Dataset Disclaimer

In this repository, we may make available to you certain datasets for use with the Software.
You are not obliged to use such datasets (with the Software or otherwise), but any such use is at your own risk.
Any datasets that we may make available to you are provided “as is” and without any warranty, including as to their accuracy or completeness.
We accept no liability for any use you may make of such datasets.


================================================
FILE: TSS_non_transformed/Non_Transformed_TSS_Technical_Analysis.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iSMlD8gdmdpz"
   },
   "source": [
    "# Non-Transformed Temporal Similarity Search For Technical Analysis\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This notebook demonstrates how to use KDB.AI's Non-Transformed Temporal Similarity Search (TSS) for pattern matching and technical analysis in synthetic market data. We'll generate synthetic time series data, insert it into a KDB.AI table, and then perform similarity searches to identify specific patterns.\n",
    "\n",
    "Agenda:\n",
    "\n",
    "1. Dependencies, Imports & Setup\n",
    "2. Define KDB.AI Session\n",
    "3. Load Synthetic Market Time Series Data\n",
    "4. Create KDB.AI Schema & Table\n",
    "5. Pattern Matching and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ECOtMQGemm8C"
   },
   "source": [
    "### 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WTL6N-sN_Ipa",
    "outputId": "1b516a3f-ef48-401f-fdf2-94f872cc7a60"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "Q38JA_Walr60"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import kdbai_client as kdbai\n",
    "import time\n",
    "import os\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GZAXhlx_nwzN"
   },
   "source": [
    "## 2. Load Synthetic Market Time Series Data\n",
    "Let's generate 10 million data points for a hypothetical stock using a random walk. We wil have the following data:\n",
    "\n",
    "- timestamp: The timestamp for the data\n",
    "- close: The price of the stock being traded at a certain timestamp\n",
    "\n",
    "To make the data easier to work with, we will normalize it before inserting into our table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "id": "muMzDDdnbw_8"
   },
   "outputs": [],
   "source": [
    "# Step 1: Generate 10 million points of random walk data\n",
    "n_points = 10_000_000\n",
    "np.random.seed(42)  # Set seed for reproducibility\n",
    "\n",
    "# Generate random steps: +1 or -1\n",
    "steps = np.random.choice([-1, 1], size=n_points)\n",
    "# Perform cumulative sum to create a random walk\n",
    "random_walk = np.cumsum(steps)\n",
    "\n",
    "# Normalize the random walk data\n",
    "min_val = np.min(random_walk)\n",
    "max_val = np.max(random_walk)\n",
    "normalized_walk = (random_walk - min_val) / (max_val - min_val)\n",
    "\n",
    "# Create a timestamp series\n",
    "start_time = pd.Timestamp('2024-01-01 09:30:00')\n",
    "time_series = pd.date_range(start=start_time, periods=n_points, freq='S')\n",
    "\n",
    "# Create the DataFrame\n",
    "synthetic_data = pd.DataFrame({\n",
    "    'timestamp': time_series,\n",
    "    'close': normalized_walk\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                  timestamp     close\n",
       " 0       2024-01-01 09:30:00  0.473890\n",
       " 1       2024-01-01 09:30:01  0.474245\n",
       " 2       2024-01-01 09:30:02  0.473890\n",
       " 3       2024-01-01 09:30:03  0.473535\n",
       " 4       2024-01-01 09:30:04  0.473179\n",
       " ...                     ...       ...\n",
       " 9999995 2024-04-26 03:16:35  0.784014\n",
       " 9999996 2024-04-26 03:16:36  0.784369\n",
       " 9999997 2024-04-26 03:16:37  0.784014\n",
       " 9999998 2024-04-26 03:16:38  0.784369\n",
       " 9999999 2024-04-26 03:16:39  0.784014\n",
       " \n",
       " [10000000 rows x 2 columns]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[synthetic_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X-uLY_IJoMg6"
   },
   "source": [
    "Now we can plot the data, to get some idea of how it looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "TQRvSQ4WeQq1",
    "outputId": "254c4764-d10f-4373-ae88-8ddf543a45bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2: Plot the normalized random walk data\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(synthetic_data['timestamp'], synthetic_data['close'], linewidth=0.5)\n",
    "plt.title('Normalized Random Walk (10 Million Data Points)')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Normalized Close Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eT1NPaRcmrcq"
   },
   "source": [
    "## 3. Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o9CFRr7nlvAr"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-T2Nzw8yolGn"
   },
   "source": [
    "### 3. Create KDB.AI Schema & Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "bXUqqLecmCMj"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"synthetic_data\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "uyfnBM2xmF40"
   },
   "outputs": [],
   "source": [
    "# Set up the schema and indexes for KDB.AI table, specifying embeddings column with 384 dimensions, Euclidean Distance, and flat index\n",
    "schema = [\n",
    "    {\"name\": \"timestamp\", \"type\": \"datetime64[ns]\"},\n",
    "    {\"name\": \"close\", \"type\": \"float64\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Zf-Mn7cmmG6X"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"synthetic_data\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass\n",
    "\n",
    "# Create the table\n",
    "table = database.create_table(\"synthetic_data\", schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01 09:30:00</td>\n",
       "      <td>0.473890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-01 09:30:01</td>\n",
       "      <td>0.474245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-01 09:30:02</td>\n",
       "      <td>0.473890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-01 09:30:03</td>\n",
       "      <td>0.473535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-01 09:30:04</td>\n",
       "      <td>0.473179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999995</th>\n",
       "      <td>2024-04-26 03:16:35</td>\n",
       "      <td>0.784014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999996</th>\n",
       "      <td>2024-04-26 03:16:36</td>\n",
       "      <td>0.784369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999997</th>\n",
       "      <td>2024-04-26 03:16:37</td>\n",
       "      <td>0.784014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999998</th>\n",
       "      <td>2024-04-26 03:16:38</td>\n",
       "      <td>0.784369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999999</th>\n",
       "      <td>2024-04-26 03:16:39</td>\n",
       "      <td>0.784014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp     close\n",
       "0       2024-01-01 09:30:00  0.473890\n",
       "1       2024-01-01 09:30:01  0.474245\n",
       "2       2024-01-01 09:30:02  0.473890\n",
       "3       2024-01-01 09:30:03  0.473535\n",
       "4       2024-01-01 09:30:04  0.473179\n",
       "...                     ...       ...\n",
       "9999995 2024-04-26 03:16:35  0.784014\n",
       "9999996 2024-04-26 03:16:36  0.784369\n",
       "9999997 2024-04-26 03:16:37  0.784014\n",
       "9999998 2024-04-26 03:16:38  0.784369\n",
       "9999999 2024-04-26 03:16:39  0.784014\n",
       "\n",
       "[10000000 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "QFSkSHBP_Ru9",
    "outputId": "7ad9fc7e-e37f-410e-cb2f-233b5acdf992"
   },
   "outputs": [],
   "source": [
    "# Insert data into the table in batches\n",
    "for i in range(0, len(synthetic_data), 600000):\n",
    "    table.insert(synthetic_data.iloc[i:i+600000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "IX0QOWBNmLJV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  timestamp     close\n",
      "0       2024-01-01 09:30:00  0.473890\n",
      "1       2024-01-01 09:30:01  0.474245\n",
      "2       2024-01-01 09:30:02  0.473890\n",
      "3       2024-01-01 09:30:03  0.473535\n",
      "4       2024-01-01 09:30:04  0.473179\n",
      "...                     ...       ...\n",
      "9999995 2024-04-26 03:16:35  0.784014\n",
      "9999996 2024-04-26 03:16:36  0.784369\n",
      "9999997 2024-04-26 03:16:37  0.784014\n",
      "9999998 2024-04-26 03:16:38  0.784369\n",
      "9999999 2024-04-26 03:16:39  0.784014\n",
      "\n",
      "[10000000 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Verify data insertion\n",
    "print(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e4WsgTcNoyK8"
   },
   "source": [
    "### 4. Pattern Matching and Visualization\n",
    "\n",
    "Now we will perform pattern matching using the predefined stock price patterns ('uptrend', 'downtrend', 'double bottom', 'cup and handle', 'head and shoulders'). Here's what we do:\n",
    "\n",
    "1. **Pattern Normalization**: Each pattern is normalized to a range between 0 and 1. This standardization is crucial for accurate comparison across different scales in the synthetic stock data.\n",
    "\n",
    "2. **Similarity Search**: We conduct a similarity search for each normalized pattern against the synthetic market time series data stored in KDB.AI. This process involves querying the most similar sequence within the database that matches our synthetic patterns.\n",
    "\n",
    "3. **Visualization**: For each pattern, we visualize both the normalized synthetic pattern and the most similar pattern found in the synthetic data. This visual representation helps in understanding how well our similarity search is performing.\n",
    "\n",
    "4. **Result Analysis**: The results from the similarity search are printed, including the most similar timestamp and pattern in the synthetic data. We also attempt to match these results back to the original synthetic data to validate and inspect the similarity.\n",
    "\n",
    "We can use this strategy to utilize synthetic price movements to find interesting price movements in real data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Sk4j0sUCCMus",
    "outputId": "f9f9bb97-6b57-431a-d350-af595938bd7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Most Similar Synthetic Pattern (at 2024-01-02 03:16:06):\n",
      "timestamp    2024-01-02 03:16:06\n",
      "close                   0.472469\n",
      "nnIdx                      63966\n",
      "nnDist                  3.807829\n",
      "Name: 0, dtype: object\n",
      "[0.4724689165186501, 0.47211367673179394, 0.47175843694493785, 0.4714031971580817, 0.47175843694493785, 0.4714031971580817, 0.47175843694493785, 0.47211367673179394, 0.47175843694493785, 0.4714031971580817, 0.47104795737122557, 0.4714031971580817, 0.47104795737122557, 0.4714031971580817, 0.47104795737122557, 0.4714031971580817, 0.47104795737122557, 0.4714031971580817, 0.47175843694493785, 0.47211367673179394, 0.47175843694493785, 0.47211367673179394, 0.4724689165186501, 0.4728241563055062, 0.4724689165186501, 0.4728241563055062, 0.47317939609236237, 0.4728241563055062, 0.47317939609236237, 0.4728241563055062, 0.4724689165186501, 0.47211367673179394, 0.47175843694493785, 0.47211367673179394, 0.47175843694493785, 0.47211367673179394, 0.47175843694493785, 0.47211367673179394, 0.4724689165186501, 0.4728241563055062, 0.47317939609236237, 0.47353463587921846, 0.47317939609236237, 0.47353463587921846, 0.4738898756660746, 0.47424511545293074, 0.4738898756660746, 0.47353463587921846, 0.47317939609236237, 0.4728241563055062, 0.4724689165186501, 0.47211367673179394, 0.4724689165186501, 0.47211367673179394, 0.4724689165186501, 0.4728241563055062, 0.47317939609236237, 0.47353463587921846, 0.4738898756660746, 0.47424511545293074, 0.4738898756660746, 0.47424511545293074, 0.4738898756660746, 0.47424511545293074, 0.47460035523978683, 0.474955595026643, 0.4753108348134991, 0.474955595026643, 0.47460035523978683, 0.47424511545293074, 0.4738898756660746, 0.47424511545293074, 0.47460035523978683, 0.47424511545293074, 0.47460035523978683, 0.474955595026643, 0.47460035523978683, 0.47424511545293074, 0.47460035523978683, 0.47424511545293074, 0.47460035523978683, 0.474955595026643, 0.4753108348134991, 0.47566607460035526, 0.47602131438721135, 0.4763765541740675, 0.47673179396092363, 0.4763765541740675, 0.47602131438721135, 0.47566607460035526, 0.4753108348134991, 0.474955595026643, 0.4753108348134991, 0.47566607460035526, 0.4753108348134991, 0.47566607460035526, 0.4753108348134991, 0.47566607460035526, 0.47602131438721135, 0.4763765541740675]\n",
      "\n",
      "Matching data from original synthetic data:\n",
      "                timestamp     close\n",
      "63966 2024-01-02 03:16:06  0.472469\n",
      "63967 2024-01-02 03:16:07  0.472114\n",
      "63968 2024-01-02 03:16:08  0.471758\n",
      "63969 2024-01-02 03:16:09  0.471403\n",
      "63970 2024-01-02 03:16:10  0.471758\n",
      "...                   ...       ...\n",
      "64061 2024-01-02 03:17:41  0.475666\n",
      "64062 2024-01-02 03:17:42  0.475311\n",
      "64063 2024-01-02 03:17:43  0.475666\n",
      "64064 2024-01-02 03:17:44  0.476021\n",
      "64065 2024-01-02 03:17:45  0.476377\n",
      "\n",
      "[100 rows x 2 columns]\n",
      "\n",
      "Most Similar Synthetic Pattern (at 2024-02-09 09:37:11):\n",
      "timestamp    2024-02-09 09:37:11\n",
      "close                   0.411012\n",
      "nnIdx                    3370031\n",
      "nnDist                  3.533016\n",
      "Name: 0, dtype: object\n",
      "[0.41101243339253996, 0.4106571936056838, 0.4103019538188277, 0.4106571936056838, 0.41101243339253996, 0.4106571936056838, 0.41101243339253996, 0.4106571936056838, 0.4103019538188277, 0.4099467140319716, 0.40959147424511544, 0.40923623445825935, 0.4088809946714032, 0.40852575488454707, 0.4088809946714032, 0.40923623445825935, 0.4088809946714032, 0.40852575488454707, 0.4088809946714032, 0.40923623445825935, 0.4088809946714032, 0.40923623445825935, 0.40959147424511544, 0.40923623445825935, 0.4088809946714032, 0.40923623445825935, 0.40959147424511544, 0.40923623445825935, 0.4088809946714032, 0.40852575488454707, 0.4081705150976909, 0.40781527531083483, 0.4074600355239787, 0.40781527531083483, 0.4074600355239787, 0.40781527531083483, 0.4074600355239787, 0.40781527531083483, 0.4074600355239787, 0.40710479573712255, 0.4074600355239787, 0.40781527531083483, 0.4081705150976909, 0.40852575488454707, 0.4088809946714032, 0.40852575488454707, 0.4081705150976909, 0.40781527531083483, 0.4081705150976909, 0.40781527531083483, 0.4074600355239787, 0.40710479573712255, 0.4067495559502664, 0.4063943161634103, 0.4067495559502664, 0.4063943161634103, 0.4060390763765542, 0.4063943161634103, 0.4067495559502664, 0.40710479573712255, 0.4067495559502664, 0.40710479573712255, 0.4067495559502664, 0.4063943161634103, 0.4060390763765542, 0.4063943161634103, 0.4067495559502664, 0.4063943161634103, 0.4060390763765542, 0.4063943161634103, 0.4060390763765542, 0.40568383658969803, 0.40532859680284195, 0.4049733570159858, 0.40461811722912966, 0.4042628774422735, 0.40390763765541743, 0.4042628774422735, 0.40461811722912966, 0.4042628774422735, 0.40461811722912966, 0.4049733570159858, 0.40532859680284195, 0.4049733570159858, 0.40532859680284195, 0.4049733570159858, 0.40461811722912966, 0.4042628774422735, 0.40461811722912966, 0.4042628774422735, 0.40390763765541743, 0.4035523978685613, 0.40390763765541743, 0.4042628774422735, 0.40390763765541743, 0.4035523978685613, 0.40390763765541743, 0.4042628774422735, 0.40461811722912966, 0.4042628774422735]\n",
      "\n",
      "Matching data from original synthetic data:\n",
      "                  timestamp     close\n",
      "3370031 2024-02-09 09:37:11  0.411012\n",
      "3370032 2024-02-09 09:37:12  0.410657\n",
      "3370033 2024-02-09 09:37:13  0.410302\n",
      "3370034 2024-02-09 09:37:14  0.410657\n",
      "3370035 2024-02-09 09:37:15  0.411012\n",
      "...                     ...       ...\n",
      "3370126 2024-02-09 09:38:46  0.403552\n",
      "3370127 2024-02-09 09:38:47  0.403908\n",
      "3370128 2024-02-09 09:38:48  0.404263\n",
      "3370129 2024-02-09 09:38:49  0.404618\n",
      "3370130 2024-02-09 09:38:50  0.404263\n",
      "\n",
      "[100 rows x 2 columns]\n",
      "\n",
      "Most Similar Synthetic Pattern (at 2024-02-27 08:24:24):\n",
      "timestamp    2024-02-27 08:24:24\n",
      "close                   0.398579\n",
      "nnIdx                    4920864\n",
      "nnDist                  3.997789\n",
      "Name: 0, dtype: object\n",
      "[0.3985790408525755, 0.39822380106571936, 0.3978685612788632, 0.39751332149200713, 0.397158081705151, 0.39751332149200713, 0.397158081705151, 0.39680284191829485, 0.3964476021314387, 0.3960923623445826, 0.3957371225577265, 0.39538188277087033, 0.3957371225577265, 0.39538188277087033, 0.3950266429840142, 0.3946714031971581, 0.39431616341030196, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39431616341030196, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39431616341030196, 0.3946714031971581, 0.39431616341030196, 0.3946714031971581, 0.39431616341030196, 0.3946714031971581, 0.39431616341030196, 0.3946714031971581, 0.3950266429840142, 0.3946714031971581, 0.3950266429840142, 0.3946714031971581, 0.3950266429840142, 0.39538188277087033, 0.3957371225577265, 0.3960923623445826, 0.3964476021314387, 0.39680284191829485, 0.3964476021314387, 0.39680284191829485, 0.3964476021314387, 0.39680284191829485, 0.3964476021314387, 0.39680284191829485, 0.3964476021314387, 0.39680284191829485, 0.3964476021314387, 0.3960923623445826, 0.3964476021314387, 0.3960923623445826, 0.3957371225577265, 0.3960923623445826, 0.3957371225577265, 0.39538188277087033, 0.3950266429840142, 0.3946714031971581, 0.3950266429840142, 0.3946714031971581, 0.39431616341030196, 0.3939609236234458, 0.39431616341030196, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39431616341030196, 0.3946714031971581, 0.3950266429840142, 0.3946714031971581, 0.39431616341030196, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39360568383658967, 0.3939609236234458, 0.39431616341030196, 0.3946714031971581, 0.3950266429840142, 0.3946714031971581, 0.3950266429840142, 0.3946714031971581, 0.3950266429840142, 0.39538188277087033, 0.3957371225577265, 0.3960923623445826, 0.3957371225577265, 0.39538188277087033, 0.3957371225577265, 0.3960923623445826, 0.3964476021314387, 0.39680284191829485, 0.397158081705151, 0.39680284191829485]\n",
      "\n",
      "Matching data from original synthetic data:\n",
      "                  timestamp     close\n",
      "4920864 2024-02-27 08:24:24  0.398579\n",
      "4920865 2024-02-27 08:24:25  0.398224\n",
      "4920866 2024-02-27 08:24:26  0.397869\n",
      "4920867 2024-02-27 08:24:27  0.397513\n",
      "4920868 2024-02-27 08:24:28  0.397158\n",
      "...                     ...       ...\n",
      "4920959 2024-02-27 08:25:59  0.396092\n",
      "4920960 2024-02-27 08:26:00  0.396448\n",
      "4920961 2024-02-27 08:26:01  0.396803\n",
      "4920962 2024-02-27 08:26:02  0.397158\n",
      "4920963 2024-02-27 08:26:03  0.396803\n",
      "\n",
      "[100 rows x 2 columns]\n",
      "\n",
      "Most Similar Synthetic Pattern (at 2024-03-19 19:21:58):\n",
      "timestamp    2024-03-19 19:21:58\n",
      "close                   0.618117\n",
      "nnIdx                    6774718\n",
      "nnDist                  1.855804\n",
      "Name: 0, dtype: object\n",
      "[0.6181172291296625, 0.6184724689165186, 0.6181172291296625, 0.6184724689165186, 0.6188277087033748, 0.6191829484902309, 0.6195381882770871, 0.6198934280639432, 0.6195381882770871, 0.6198934280639432, 0.6202486678507992, 0.6198934280639432, 0.6202486678507992, 0.6206039076376554, 0.6209591474245115, 0.6206039076376554, 0.6209591474245115, 0.6206039076376554, 0.6209591474245115, 0.6213143872113677, 0.6216696269982238, 0.62202486678508, 0.6223801065719361, 0.6227353463587921, 0.6230905861456483, 0.6234458259325044, 0.6230905861456483, 0.6227353463587921, 0.6230905861456483, 0.6227353463587921, 0.6223801065719361, 0.6227353463587921, 0.6230905861456483, 0.6234458259325044, 0.6238010657193606, 0.6234458259325044, 0.6238010657193606, 0.6234458259325044, 0.6238010657193606, 0.6234458259325044, 0.6230905861456483, 0.6234458259325044, 0.6238010657193606, 0.6241563055062167, 0.6238010657193606, 0.6234458259325044, 0.6230905861456483, 0.6227353463587921, 0.6230905861456483, 0.6234458259325044, 0.6230905861456483, 0.6227353463587921, 0.6230905861456483, 0.6234458259325044, 0.6230905861456483, 0.6227353463587921, 0.6223801065719361, 0.62202486678508, 0.6216696269982238, 0.6213143872113677, 0.6216696269982238, 0.6213143872113677, 0.6216696269982238, 0.6213143872113677, 0.6216696269982238, 0.6213143872113677, 0.6209591474245115, 0.6206039076376554, 0.6209591474245115, 0.6206039076376554, 0.6202486678507992, 0.6198934280639432, 0.6195381882770871, 0.6191829484902309, 0.6188277087033748, 0.6184724689165186, 0.6181172291296625, 0.6177619893428064, 0.6181172291296625, 0.6184724689165186, 0.6181172291296625, 0.6177619893428064, 0.6181172291296625, 0.6177619893428064, 0.6174067495559503, 0.6170515097690942, 0.616696269982238, 0.6170515097690942, 0.616696269982238, 0.6170515097690942, 0.6174067495559503, 0.6170515097690942, 0.6174067495559503, 0.6170515097690942, 0.616696269982238, 0.6170515097690942, 0.6174067495559503, 0.6170515097690942, 0.6174067495559503, 0.6170515097690942]\n",
      "\n",
      "Matching data from original synthetic data:\n",
      "                  timestamp     close\n",
      "6774718 2024-03-19 19:21:58  0.618117\n",
      "6774719 2024-03-19 19:21:59  0.618472\n",
      "6774720 2024-03-19 19:22:00  0.618117\n",
      "6774721 2024-03-19 19:22:01  0.618472\n",
      "6774722 2024-03-19 19:22:02  0.618828\n",
      "...                     ...       ...\n",
      "6774813 2024-03-19 19:23:33  0.617052\n",
      "6774814 2024-03-19 19:23:34  0.617407\n",
      "6774815 2024-03-19 19:23:35  0.617052\n",
      "6774816 2024-03-19 19:23:36  0.617407\n",
      "6774817 2024-03-19 19:23:37  0.617052\n",
      "\n",
      "[100 rows x 2 columns]\n",
      "\n",
      "Most Similar Synthetic Pattern (at 2024-03-08 21:52:15):\n",
      "timestamp    2024-03-08 21:52:15\n",
      "close                   0.442274\n",
      "nnIdx                    5833335\n",
      "nnDist                  3.272216\n",
      "Name: 0, dtype: object\n",
      "[0.4422735346358792, 0.44191829484902306, 0.4422735346358792, 0.44191829484902306, 0.4422735346358792, 0.44262877442273535, 0.4429840142095915, 0.4433392539964476, 0.4429840142095915, 0.4433392539964476, 0.4429840142095915, 0.4433392539964476, 0.4429840142095915, 0.4433392539964476, 0.4429840142095915, 0.4433392539964476, 0.4436944937833037, 0.4433392539964476, 0.4429840142095915, 0.44262877442273535, 0.4429840142095915, 0.44262877442273535, 0.4429840142095915, 0.44262877442273535, 0.4422735346358792, 0.44262877442273535, 0.4429840142095915, 0.44262877442273535, 0.4422735346358792, 0.44262877442273535, 0.4429840142095915, 0.4433392539964476, 0.4436944937833037, 0.44404973357015987, 0.444404973357016, 0.4447602131438721, 0.44511545293072824, 0.4447602131438721, 0.44511545293072824, 0.4447602131438721, 0.44511545293072824, 0.4454706927175844, 0.44582593250444047, 0.4461811722912966, 0.44653641207815276, 0.4468916518650089, 0.447246891651865, 0.4468916518650089, 0.44653641207815276, 0.4461811722912966, 0.44653641207815276, 0.4461811722912966, 0.44653641207815276, 0.4461811722912966, 0.44582593250444047, 0.4454706927175844, 0.44582593250444047, 0.4454706927175844, 0.44582593250444047, 0.4454706927175844, 0.44582593250444047, 0.4454706927175844, 0.44511545293072824, 0.4447602131438721, 0.444404973357016, 0.44404973357015987, 0.444404973357016, 0.44404973357015987, 0.4436944937833037, 0.4433392539964476, 0.4436944937833037, 0.4433392539964476, 0.4436944937833037, 0.4433392539964476, 0.4436944937833037, 0.4433392539964476, 0.4429840142095915, 0.44262877442273535, 0.4422735346358792, 0.44262877442273535, 0.4429840142095915, 0.44262877442273535, 0.4429840142095915, 0.44262877442273535, 0.4429840142095915, 0.4433392539964476, 0.4429840142095915, 0.44262877442273535, 0.4429840142095915, 0.4433392539964476, 0.4429840142095915, 0.44262877442273535, 0.4422735346358792, 0.44262877442273535, 0.4422735346358792, 0.44191829484902306, 0.441563055062167, 0.44120781527531083, 0.4408525754884547, 0.4404973357015986]\n",
      "\n",
      "Matching data from original synthetic data:\n",
      "                  timestamp     close\n",
      "5833335 2024-03-08 21:52:15  0.442274\n",
      "5833336 2024-03-08 21:52:16  0.441918\n",
      "5833337 2024-03-08 21:52:17  0.442274\n",
      "5833338 2024-03-08 21:52:18  0.441918\n",
      "5833339 2024-03-08 21:52:19  0.442274\n",
      "...                     ...       ...\n",
      "5833430 2024-03-08 21:53:50  0.441918\n",
      "5833431 2024-03-08 21:53:51  0.441563\n",
      "5833432 2024-03-08 21:53:52  0.441208\n",
      "5833433 2024-03-08 21:53:53  0.440853\n",
      "5833434 2024-03-08 21:53:54  0.440497\n",
      "\n",
      "[100 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x2500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def create_pattern(pattern_type, length=100):\n",
    "    if pattern_type == 'head_and_shoulders':\n",
    "        # Define the x positions of the key points\n",
    "        points_x = np.array([0, 0.1, 0.25, 0.5, 0.75, 0.9, 1]) * length\n",
    "        points_y = np.array([0.8, 1, 0.9, 1.2, 0.9, 1, 0.8])\n",
    "\n",
    "        # Generate the full x array\n",
    "        full_x = np.linspace(0, length, length)\n",
    "\n",
    "        # Interpolate to get the full y values\n",
    "        full_y = np.interp(full_x, points_x, points_y)\n",
    "\n",
    "        return full_y\n",
    "\n",
    "    elif pattern_type == 'cup_and_handle':\n",
    "        # Cup\n",
    "        x_cup = np.linspace(0, np.pi, int(0.8 * length))\n",
    "        cup = np.sin(x_cup) * 0.5 + 0.5\n",
    "\n",
    "        # Handle\n",
    "        x_handle = np.linspace(0, 1, int(0.2 * length))\n",
    "        handle = np.linspace(cup[-1], cup[-1] - 0.2, int(0.1 * length))\n",
    "        handle = np.concatenate([handle, np.linspace(handle[-1], handle[0], int(0.1 * length))])\n",
    "\n",
    "        return np.concatenate([cup, handle])\n",
    "\n",
    "    if pattern_type == 'uptrend':\n",
    "        x = np.linspace(0, 1, length)\n",
    "        return 0.8 * x + 0.2 * np.sin(10 * np.pi * x) + 0.5\n",
    "\n",
    "    elif pattern_type == 'downtrend':\n",
    "        x = np.linspace(0, 1, length)\n",
    "        return -0.8 * x + 0.2 * np.sin(10 * np.pi * x) + 1\n",
    "\n",
    "    elif pattern_type == 'double_bottom':\n",
    "        x = np.linspace(0, 1, length)\n",
    "        return 0.5 - 0.3 * np.abs(np.sin(2 * np.pi * x)) + 0.2\n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"Invalid pattern type\")\n",
    "\n",
    "\n",
    "# Define the patterns to search for\n",
    "patterns = {\n",
    "    'uptrend': create_pattern('uptrend'),\n",
    "    'downtrend': create_pattern('downtrend'),\n",
    "    'double_bottom': create_pattern('double_bottom'),\n",
    "    'cup_and_handle': create_pattern('cup_and_handle'),\n",
    "    'head_and_shoulders': create_pattern('head_and_shoulders'),\n",
    "}\n",
    "\n",
    "# Perform similarity search for each pattern and plot results\n",
    "fig, axs = plt.subplots(len(patterns), 2, figsize=(15, 5*len(patterns)))\n",
    "\n",
    "for i, (pattern_name, pattern_data) in enumerate(patterns.items()):\n",
    "    # Normalize the pattern data\n",
    "    pattern_data = (pattern_data - np.min(pattern_data)) / (np.max(pattern_data) - np.min(pattern_data))\n",
    "\n",
    "    # Search for similar vectors in synthetic data\n",
    "    nn_result = table.search(vectors={'close': [pattern_data.tolist()]}, n=1, type=\"tss\")[0]\n",
    "\n",
    "    # Plot synthetic pattern\n",
    "    axs[i, 0].plot(pattern_data)\n",
    "    axs[i, 0].set_title(f'Synthetic {pattern_name.replace(\"_\", \" \").title()} Pattern')\n",
    "\n",
    "    # Plot most similar synthetic pattern\n",
    "    similar_pattern = nn_result.iloc[0]\n",
    "    print(f\"\\nMost Similar Synthetic Pattern (at {similar_pattern['timestamp']}):\")\n",
    "    print(similar_pattern)\n",
    "\n",
    "    # Try to find the matching pattern in the original synthetic data\n",
    "    matching_index = synthetic_data[synthetic_data['timestamp'] == similar_pattern['timestamp']].index\n",
    "\n",
    "    if not matching_index.empty:\n",
    "        start_index = matching_index[0]\n",
    "        end_index = start_index + 100\n",
    "\n",
    "        if end_index > len(synthetic_data):\n",
    "            end_index = len(synthetic_data)\n",
    "\n",
    "        matching_data = synthetic_data.iloc[start_index:end_index]\n",
    "        close_values_vector = matching_data['close'].tolist()\n",
    "\n",
    "        print(close_values_vector)\n",
    "\n",
    "        print(\"\\nMatching data from original synthetic data:\")\n",
    "        print(matching_data)\n",
    "\n",
    "        if not matching_data.empty:\n",
    "            synthetic_pattern = matching_data['close'].values  # Ensure this is a NumPy array\n",
    "\n",
    "            if len(synthetic_pattern) > 0:\n",
    "                synthetic_pattern = (synthetic_pattern - np.min(synthetic_pattern)) / (np.max(synthetic_pattern) - np.min(synthetic_pattern))\n",
    "                axs[i, 1].plot(synthetic_pattern)\n",
    "                axs[i, 1].set_title(f'Most Similar Synthetic Pattern (at {similar_pattern[\"timestamp\"]})')\n",
    "            else:\n",
    "                print(\"Error: Empty synthetic pattern\")\n",
    "        else:\n",
    "            print(\"Error: No matching data found\")\n",
    "    else:\n",
    "        print(\"No matching timestamp found.\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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================================================
FILE: TSS_non_transformed/Temporal_Similarity_Search_KDB+.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Similarity Search on KDB+ HDB for Pattern Matching and Outlier Search\n",
    "\n",
    ">*Note: This example requires an instance of KDB.AI Server to be installed locally to where you run this notebook - see section 2 for details.*\n",
    "\n",
    "This notebook demonstrates how to perform Temporal Similarity Search (TSS) for advanced pattern matching and outlier search on stock price data stored in a [kdb+ Historical Database (HDB)](https://code.kx.com/kdbai/latest/integrations/kdb.html). With this approach, you can search for recurring temporal patterns, detect anomalies, and perform outlier searches—all without the need to move data or create embeddings.\n",
    "\n",
    "The key advantage of this workflow is the seamless integration with your existing kdb+ environment. By keeping the data in kdb+, we leverage the system's high-performance time-series capabilities for efficient TSS, allowing you to run searches and analyses directly on your historical stock data.\n",
    "\n",
    "Agenda:\n",
    "1. Dependencies, Imports & Setup\n",
    "1. Start KDB.AI Session \n",
    "1. Perform temporal similarity searches to identify patterns in stock price movements\n",
    "1. Execute outlier detection to flag unusual patterns or price movements\n",
    "\n",
    "Let's get started with leveraging kdb+ and KDB.AI to optimize temporal pattern recognition and stock price analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kdbai_client as kdbai\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import time\n",
    "import zipfile\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset Overview\n",
    "A kdb+ HDB dataset of 10 million data points across 10 different stocks (symbols) using a random walk  has been created for you. It contains the following data:\n",
    "\n",
    "- timestamp: The timestamp for the data\n",
    "- sym: The ticket symbol of the stock being traded\n",
    "- price: The price of the stock being traded at a certain timestamp\n",
    "- volume: The traded volume of the stock being traded.\n",
    "\n",
    "Let's unzip this dataset (demo_hdb) so its ready to use in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the path to your ZIP file\n",
    "zip_file_path = 'demo_hdb.zip'  \n",
    "\n",
    "# Get the current working directory\n",
    "current_directory = os.getcwd()\n",
    "\n",
    "# Open the ZIP file and extract its contents\n",
    "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
    "    zip_ref.extractall(current_directory)\n",
    "\n",
    "print(f\"Extracted all files to: {current_directory}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're interested in how this synthetic dataset was created you can see the code in createHDB.q. Also if you'd prefer to run this sample using qAPI instead of Python API the q version of the notebook linked [here](https://code.kx.com/kdbai/latest/integrations/kdb.html#drop-table)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Start KDB.AI Server and Define KDB.AI Session\n",
    "\n",
    ">*Note: The kdb+ integration demonstrated in this notebook is only available with KDB.AI Server.*\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container. To do this, you will first need to sign up for free [here](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance. Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "To start a KDB.AI session using on disk kdb+ data you should start your local KDB.AI Server with a volume mount to the location of your data (demo_hdb). \n",
    "\n",
    "```\n",
    "docker run -it \\\n",
    "  -e KDB_LICENSE_B64=\"$KDB_LICENSE_B64\" \\  # Set the KDB license environment variable\n",
    "  -v <PATH_TO_HDB>/demo_hdb:/db \\  # Bind mount demo HDB directory, replace PATH_TO_HDB\n",
    "  -p 8082:8082 \\  # Map port 8082 for web access\n",
    "  portal.dl.kx.com/kdbai-db:1.4.0  # Specify the Docker image to run\n",
    "\n",
    "```\n",
    "\n",
    "Then connect using `kdbai.Session()` and mount the trade table in our demo_hdb database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = kdbai.Session()\n",
    "db = s.database(\"default\")\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"trade\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass\n",
    "\n",
    "table = db.create_table(table=\"trade\",external_data_references=[{\"path\":b'/db', \"provider\" :\"kx\"}],partition_column=\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>sym</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-08-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-08-19 09:30:00.001</td>\n",
       "      <td>218.00</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-08-19 09:30:01.029</td>\n",
       "      <td>217.95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-08-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-08-19 09:30:01.061</td>\n",
       "      <td>217.90</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-08-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-08-19 09:30:01.154</td>\n",
       "      <td>217.92</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-08-19</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-08-19 09:30:01.265</td>\n",
       "      <td>217.83</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999995</th>\n",
       "      <td>2024-08-30</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>2024-08-30 15:59:59.093</td>\n",
       "      <td>233.51</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999996</th>\n",
       "      <td>2024-08-30</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>2024-08-30 15:59:59.249</td>\n",
       "      <td>233.56</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999997</th>\n",
       "      <td>2024-08-30</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>2024-08-30 15:59:59.770</td>\n",
       "      <td>233.43</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999998</th>\n",
       "      <td>2024-08-30</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>2024-08-30 15:59:59.824</td>\n",
       "      <td>233.47</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999999</th>\n",
       "      <td>2024-08-30</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>2024-08-30 15:59:59.993</td>\n",
       "      <td>233.52</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date   sym                    time   price  size\n",
       "0       2024-08-19  AAPL 2024-08-19 09:30:00.001  218.00    46\n",
       "1       2024-08-19  AAPL 2024-08-19 09:30:01.029  217.95    93\n",
       "2       2024-08-19  AAPL 2024-08-19 09:30:01.061  217.90    80\n",
       "3       2024-08-19  AAPL 2024-08-19 09:30:01.154  217.92    86\n",
       "4       2024-08-19  AAPL 2024-08-19 09:30:01.265  217.83    67\n",
       "...            ...   ...                     ...     ...   ...\n",
       "9999995 2024-08-30  TSLA 2024-08-30 15:59:59.093  233.51    38\n",
       "9999996 2024-08-30  TSLA 2024-08-30 15:59:59.249  233.56    24\n",
       "9999997 2024-08-30  TSLA 2024-08-30 15:59:59.770  233.43    68\n",
       "9999998 2024-08-30  TSLA 2024-08-30 15:59:59.824  233.47    50\n",
       "9999999 2024-08-30  TSLA 2024-08-30 15:59:59.993  233.52    68\n",
       "\n",
       "[10000000 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Pattern Matching\n",
    "\n",
    "In this section, we will perform pattern matching using predefined stock price patterns.\n",
    "\n",
    "1. **Define Patterns**: Start by defining each pattern in Q.\n",
    "\n",
    "2. **Similarity Search**: Conduct a similarity search for each pattern of varying lengths against the synthetic market time series data stored in the HDB. This process involves querying the database to find the sequence most similar to our predefined patterns.\n",
    "\n",
    "3. **Visualisation**: For each pattern, visualise both the normalised synthetic pattern and the most similar pattern found in the synthetic data. This visual representation aids in evaluating the performance of the similarity search.\n",
    "\n",
    "4. **Result Analysis**: The results of the similarity search are printed, showing the most similar timestamp and pattern from the synthetic data. We then attempt to map these findings back to the original synthetic data to verify and assess the similarity.\n",
    "\n",
    "This approach can be used to leverage synthetic price movements in identifying significant price trends within real data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Patterns\n",
    "\n",
    "In this section, we define a set of patterns. The primary goal of these functions, `patternA`, `patternB`, `patternC`, and `patternD`.\n",
    "\n",
    "The goal is to use these for similarity searches, allowing us to compare different patterns and identify relationships between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "patternA = np.array([0, 2, 4, 6, 8, 7, 6, 5, 4, 6, 8, 10, 12, 10, 8, 6, 4, 5, 6, 7, 8, 6, 4, 2, 0])\n",
    "patternB = np.array([0, 2, 4, 6, 8, 10, 12, 14, 16, 15, 14, 13, 12, 13, 14, 15, 16, 14, 12, 10, 8, 6, 4, 2, 0])\n",
    "patternC = np.array([0, 2, 4, 6, 8, 6.5, 5.0, 3.5, 2.0, 3.5, 5.0, 6.5, 8.0, 6.5, 5.0, 3.5, 2.0, 3.5, 5.0, 6.5, 8.0, 6, 4, 2, 0])\n",
    "patternD = np.array([0, 1, 2, 3, 4, 3.5, 3.0, 2.5, 2.0, 4.0, 6.0, 8.0, 10.0, 8.0, 6.0, 4.0, 2.0, 2.5, 3.0, 3.5, 4.0, 3, 2, 1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(\n",
    "    rows=2, cols=2, \n",
    "    subplot_titles=(\"Pattern A\", \"Pattern B\", \"Pattern C\", \"Pattern D\"), \n",
    "    vertical_spacing=0.1\n",
    ")\n",
    "\n",
    "fig.add_trace(go.Scatter(mode='lines', y=patternA, name='Pattern A'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(mode='lines', y=patternB, name='Pattern B'), row=1, col=2)\n",
    "fig.add_trace(go.Scatter(mode='lines', y=patternC, name='Pattern C'), row=2, col=1)\n",
    "fig.add_trace(go.Scatter(mode='lines', y=patternD, name='Pattern D'), row=2, col=2)\n",
    "\n",
    "fig.update_layout(\n",
    "    height=800, \n",
    "    margin=dict(l=0, t=40, r=0, b=0)\n",
    ")\n",
    "\n",
    "fig.show(\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal Similarity Search \n",
    "\n",
    "In this section, we conduct a similarity search for each of the four patterns on a single symbol (AAPL) over a specified date range, returning the five closest matches for each pattern.\n",
    "\n",
    "Searching just for patternA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[        date   sym                    time   price  size  nnIdx    nnDist  \\\n",
       " 0 2024-08-23  AAPL 2024-08-23 14:38:41.571  238.66    82  79172  1.785228   \n",
       " 1 2024-08-27  AAPL 2024-08-27 13:55:08.851  273.48    25  68064  2.179401   \n",
       " 2 2024-08-28  AAPL 2024-08-28 13:32:39.735  265.46    20  62436  2.191350   \n",
       " 3 2024-08-22  AAPL 2024-08-22 15:59:12.574  246.93    43  99800  2.238873   \n",
       " 4 2024-08-29  AAPL 2024-08-29 14:11:50.317  291.01    82  72220  2.298780   \n",
       " \n",
       "                                              nnMatch  \n",
       " 0  [238.66, 238.62, 238.75, 238.89000000000001, 2...  \n",
       " 1  [273.48, 273.58, 273.63, 273.75, 273.82, 273.7...  \n",
       " 2  [265.46, 265.45, 265.59000000000003, 265.62, 2...  \n",
       " 3  [246.93, 247.05, 247.07, 247.23000000000002, 2...  \n",
       " 4  [291.01, 291.11, 291.22, 291.31, 291.31, 291.2...  ]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res=table.search(\n",
    "    {\"price\": patternA.reshape(1, -1)},\n",
    "    type=\"tss\",\n",
    "    n=5,\n",
    "    filter=[(\"=\", \"sym\", \"AAPL\")],\n",
    "    options=dict(force=True, returnMatches=True))\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Searching for all 4 patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape patterns to ensure they are 2D\n",
    "patternA = patternA.reshape(1, -1) \n",
    "patternB = patternB.reshape(1, -1) \n",
    "patternC = patternB.reshape(1, -1) \n",
    "patternD = patternB.reshape(1, -1) \n",
    "\n",
    "# Create a combined input with both patterns\n",
    "combined_patterns = np.vstack((patternA, patternB,patternC,patternD))  # Stack vertically\n",
    "\n",
    "res=table.search(\n",
    "    {\"price\": combined_patterns.tolist()},\n",
    "    type=\"tss\",\n",
    "    n=5,\n",
    "    filter=[(\"=\", \"sym\", \"AAPL\")],\n",
    "    options=dict(force=True, returnMatches=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the python functions to plot dataframes and graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_pattern(pattern_res, pattern_p):\n",
    "    pattern_df = res[pattern_res]\n",
    "\n",
    "    # Combine 'sym' and 'time' into one column\n",
    "    sym_time = pattern_df['sym'] + ' ' + pattern_df['time'].astype(str)\n",
    "\n",
    "    # Convert 'nnMatch' to DataFrame and adjust columns\n",
    "    nnMatch_df = pd.DataFrame(np.stack(pattern_df['nnMatch'].values).T)\n",
    "    nnMatch_df.columns = sym_time\n",
    "\n",
    "    # Normalise the pattern\n",
    "    q = combined_patterns[pattern_p]\n",
    "    q = (q - q.mean()) / q.std()\n",
    "\n",
    "    # Normalize the nnMatch_df\n",
    "    for c in nnMatch_df.columns:\n",
    "        nnMatch_df[c] = (nnMatch_df[c] - nnMatch_df[c].mean()) / nnMatch_df[c].std()\n",
    "\n",
    "    # Return both the original and nnMatch_df\n",
    "    return pattern_df, nnMatch_df, q\n",
    "\n",
    "\n",
    "# Function to plot the pattern and its corresponding nearest neighbour matches\n",
    "def plot_pattern(nnMatch_df, q, pattern_name):\n",
    "    fig = px.line(nnMatch_df, height=600, render_mode='webgl')\n",
    "    \n",
    "    # Define a colour sequence for the traces\n",
    "    color_sequence = ['red', 'green', 'blue', 'purple', 'cyan', 'magenta', 'yellow', 'black', 'gray', 'brown']\n",
    "    \n",
    "    # Update the traces' line colours and opacity\n",
    "    for i, trace in enumerate(fig.data):\n",
    "        trace.update(line=dict(color=color_sequence[i % len(color_sequence)]), opacity=0.3)\n",
    "    \n",
    "    # Add the pattern 'q' as an additional trace\n",
    "    fig.add_trace(go.Scattergl(\n",
    "        mode='lines',\n",
    "        y=q,\n",
    "        line=dict(width=3, color='orange'),\n",
    "        name=pattern_name\n",
    "    ))\n",
    "    \n",
    "    # Update layout for better visualization\n",
    "    fig.update_layout(\n",
    "        hovermode='closest',\n",
    "        title=f'{pattern_name} Plot',\n",
    "        xaxis_title='Index',\n",
    "        yaxis_title='Value'\n",
    "    )\n",
    "    \n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size  nnIdx    nnDist  \\\n",
      "0 2024-08-23  AAPL 2024-08-23 14:38:41.571  238.66    82  79172  1.785228   \n",
      "1 2024-08-27  AAPL 2024-08-27 13:55:08.851  273.48    25  68064  2.179401   \n",
      "2 2024-08-28  AAPL 2024-08-28 13:32:39.735  265.46    20  62436  2.191350   \n",
      "3 2024-08-22  AAPL 2024-08-22 15:59:12.574  246.93    43  99800  2.238873   \n",
      "4 2024-08-29  AAPL 2024-08-29 14:11:50.317  291.01    82  72220  2.298780   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [238.66, 238.62, 238.75, 238.89000000000001, 2...  \n",
      "1  [273.48, 273.58, 273.63, 273.75, 273.82, 273.7...  \n",
      "2  [265.46, 265.45, 265.59000000000003, 265.62, 2...  \n",
      "3  [246.93, 247.05, 247.07, 247.23000000000002, 2...  \n",
      "4  [291.01, 291.11, 291.22, 291.31, 291.31, 291.2...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size  nnIdx    nnDist  \\\n",
      "0 2024-08-21  AAPL 2024-08-21 11:50:07.309  218.24    46  35815  1.052655   \n",
      "1 2024-08-22  AAPL 2024-08-22 11:59:24.140  268.93    40  38175  1.316181   \n",
      "2 2024-08-21  AAPL 2024-08-21 10:04:34.018  230.41    28   9064  1.326923   \n",
      "3 2024-08-19  AAPL 2024-08-19 15:10:19.251  230.77    76  87237  1.333702   \n",
      "4 2024-08-27  AAPL 2024-08-27 14:24:44.317  270.15    60  75693  1.347826   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [218.24, 218.24, 218.35, 218.46, 218.43, 218.5...  \n",
      "1  [268.93, 268.95, 269.03000000000003, 269.16, 2...  \n",
      "2  [230.41, 230.48000000000002, 230.5, 230.47, 23...  \n",
      "3  [230.77, 230.86, 230.89000000000001, 230.94, 2...  \n",
      "4  [270.15, 270.23, 270.3, 270.28000000000003, 27...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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3Fz4+PtpNCfBqSyYVRAGLAgK8blwMArz64gnw6mkmwKunlwCvnl4CvI71Sk5Oxr333ouvv/4a4eHhlsIMKfjuu+8wZcoUBa4TJ07E4sWLkZqaqk75u/766/GPf/wDFStWVHV+/vlnrFq1Sr0+Y8YMHD9+HLGxsbAOaXDWjgG87777rhpTSkqKGteTTz6Jpk2bqr5shTSsXr0aP/30E5KSktRJl927d8eYMWPshkXoX0VSQxQo/QoI8LqxRgK8+uIJ8OppJsCrp5cAr55eArzO9XrkkUfQrl07BbCGPfXUU2jUqBEef/xx9dKvv/6KVq1aoVatWjhy5Ag+//xztG3bFo8++qgFePl70a9fP9x///2oUKECAgMDiwCvs3YM4I2IiMDDDz+s4JUgu3nzZvU3AdsaeDdu3IhXX30VDz30EKKjoxVsf/bZZ2jdurXLscPOVZISokDpV0CA1401EuDVF0+AV08zAV49vQR49fQS4HWuFwHyt99+U15cgiqB9vbbb8cnn3yiINeWrVu3Du+8846CTxo9vITZqVOnWry+fN3ZpjXrdgzgff3115W3mHb27FmMHj0aY8eOxcCBA4sA7zPPPIOWLVvivvvuswyVgPz8889j1qxZ8PLyci6ClBAFyoECArxuLKIAr754Arx6mgnw6uklwKunlwCvc734nXXrrbfigw8+UF5bgiuzINCjalh8fDwmTZqEAwcOgJ9Zw2bOnKkAl8C7bNkyfPXVV4U6tAZeZ+0YwEtwrlq1qqUtQm3jxo2VF9fawzt06NBCYzIPgGOuWbOmcxGkhChQDhQQ4HVjEQV49cUT4NXTTIBXTy8BXj29BHhd04ve0Nq1a6uUYAxJ6Ny5M+6++25VmXG+9K4y9KFr164IDg7Gtm3bwLAHZmGoXLmyJYaXoQRmMwOvK+3YA96nn34aTZo0sQm8gwcPVt7dYcOGuTZZKSUKlFMFBHjdWFgBXn3xBHj1NBPg1dNLgFdPLwFe1/RasGABvvjiC7z33nsKbH/88UfUq1dPVV64cKHKeUvPr2EEXZbXAV5X2ilOSAPB29fXV41dTBS4mhUQ4HVj9QV49cUT4NXTTIBXTy8BXj29BHhd0ys7OxujRo1SkEt45KY0wxITE9XmtU8//RTNmjVTmRBefvllFeurA7yutGPetEbw5qa1CRMmICEhAfw9srdp7bnnngM9vYzx9ff3V2NkHC/bEBMFrhYFBHjdWGkBXn3xBHj1NBPg1dNLgFdPLwFe1/XiJjSmHiMkWocHcFPbtGnTVGPM1MBsDOPHj9cCXtZ11o4BvBzL//3f/+HQoUOWtGSEbZqttGTM1MCY4927d6tNavXr11eHXYwYMcJ1AaSkKFDGFRDgdWMBBXj1xRPg1dNMgFdPLwFePb0EePX1khqigChQNhUQ4HVj3QR49cUT4NXTTIBXTy8BXj29BHj19ZIaooAoUDYVEOB1Y90EePXFE+DV00yAV08vAV49vQR49fWSGqKAKFA2FRDgdWPdBHj1xRPg1dNMgFdPLwFePb0EePX1khqigChQNhUQ4HVj3QR49cUT4NXTTIBXTy+3gPf8eSAuDmjZEmjUSK/jMlz69OnTauc+sw+IiQKigChQXhUQ4HVjZQV49cUT4NXTTIBXTy+3gDcxEeCfKlWArl31Oi7DpQV4y/DiydBFAVHAZQUEeF2WqmhBAV598QR49TQT4NXTyy3gjY8H0tMBejr79dPruAyXFuAtw4snQxcFRAGXFRDgdVkqAV43pLJUFeDVU1GAV08vt4B31ixczMuDV4UKQK9eQECAXudltLQAbxldOBm2KCAKaCkgwKslV+HC4uHVF0+AV08zAV49vYoNvOnpyFqxBItObUYT/zpo0XU4ULu2XudltLQAbxldOBm2KCAKaCkgwKsllwCvG3KpqgK8egoK8OrpVWzgTUzEtoSF2HsuFcHegejeYQRQcHKV3gjKXmkB3rK3ZjJiUUAU0FdAgFdfM0sN8fDqiyfAq6eZAK+eXsUG3vh4xCUtwjGfqgg4dwRD2o4AoqL0Oi+jpQV4y+jCFQybRwbzt+izzz5DixYtbE7m6aefxt9//42pU6cWysbxj3/8A/v371d1goOD0aFDBzz66KPq32zv/PnzYF1HxuONv/vuO2zevBnnzp1TY3j44YfRyJTpZMWKFeoo5PT0dLRt2xb//Oc/ERISgpycHPX6mjVrcPz4cdSrVw9jxozB9ddfX6TLJUuW4O2338Z9992HW2+91e6QDhw4gP/85z/Ys2ePau+JJ55A69atVXl+n3766aeqP35XREdHq/erVatms73p06dj7ty5OHjwIKpUqYIbb7wRt912m6Usj3GeP38+kpKSMGDAADz55JN2x3Xs2DF8/PHH2LVrF06ePInJkyejevXqxWqLlQYPHoyzZ89a6lOz119/3fJ/Xhcc/4ULF9CrVy91JLa3t7fN8VGXjz76CKtWrULlypVx++23q/ZpGzZswHPPPVek3r/+9S907ty5yOu8buLj49UcucZDhw7FyJEjLeUcrc+ZM2fwxRdfqPq0QYMG4Z577kEFhpmVgAnwuiGiAK++eAK8epoJ8OrpVVzgzZ7xO6Yf3oUd57qgdnYC7u3QGAG9B+h1XkZLC/CW0YUDkJeXh7vuugsEhR49euDxxx8vMpnU1FRVplKlSgrIunfvbilD4L3lllvQs2dPsNybb76J5s2bY9y4cS4D79atW7Ft2zbExMSoPiZMmICNGzcqCKexXUIqoal9+/aqXf4OvP/++wpAv//+e/Tt2xe1atXCX3/9pYDnm2++UbBqGMsRxJk674YbbrALvPz8sy/CH8F0wYIFMG4IOLYvv/wSW7ZsUfP08/PDe++9p8b8wgsv2LwICPKE4oiICBDUCJQPPfQQ+vTpo8ovX74cPj4+IIwHBAQ4BF7C/sqVK9W8qIU18Oq0ZQAvtapdEHrl5eWlxkKLi4vDV199ZZnfiy++qHQjyNoywi5vXF5++WUkJyeD5d955x20adNG3Rjk5uZaqnG9CbtTpkxBxYoVizRHfWvWrKl0TUlJwauvvqrm27FjR9WWo/XhjcqRI0fw0ksvITs7W/1NYB4yZEiJfEgFeN2QUYBXXzwBXj3NBHj19CoW8KalIXnFXPyxPw/HAiMRmLIFt7fzRtiIMXqdl9HSArxldOEA5VV9/vnn8dRTTylQJERZ51Pm79S6deuU55WeyrfeeqsQ8NJb2rt3b/Xar7/+CnpjCaWuenit1SPYsU16k6tWrYpJkyYpLyFhhkagIXjxdYKRtdHDS6+eGcw5NwLx+vXrERUVZRd4Cd6Eq99++03llqYR9vmHc3zttdfQpEkT3HnnnRYwJLgRDl2x8ePHKy+p9Y0FtaIn1ZGH12g/IyNDeTytgdd4315bLM+5G158emB5YxAaGlpk6NSAXm1jngsXLlTgzz80wjW967zRIcwOHz5cec/pfad9+OGH6m9b3n1jHZ955hmnktHLSw/6TTfdpKDV2fqwHG8+CMe0P//8E/PmzVM3KiVhVyXw6jyG4If266+/LqQ1H8E0btxY3cEad9glsRhXQxsCvHqrLMCrp1exgHf7dqzdEovY5MaoULUhcvfvxK1RJ9G872g+59UbQBksLcDr4qKdPg2cO+di4RIsFhQE+PnZbJDwQU8YIeHmm29W4NulS5dCZQl7BCyC0mOPPaag1niETw+vAbxs55VXXlEQSmAqLvASpliXgMZH0e+++64CX3pGDRsxYoTyIhpgY7zOsAZ6Zvmb26BBA/UyQzHoDSb00gPpCHjnzJmDGTNmqDAJw+iVrV+/vvIsEvwnTpyo5ml4eOm9vffee50uGH/rH3jgAfWo33jcb1TyBPDyJmDUqFEYOHCg6pZj4DpyXPTKc3516tRR73FNCeVGaAhDLu6//37Mnj1bzZuhFcZ1w5sg3mQw/IFeWRr/vWjRIhX+YTaGUPA6402TAce2hKNnnKEgmZmZqFu3ruqP14Cz9bG+Lgi8hF22VRJhDVcl8Oo8OiDw8gPHLxLDeAdN8QV4nX5HFCkgwKunmQCvnl7FAt7ly/Hb7iVIyOyGhnVaYP+uNRjcfA86XTMMCA/XG0AZLC3A6+KibdgAHDrkYuESLNa+PVC3bpEGDfggnDKWkt5Hfr++8cYblrJ8/EwvHb2YjEElNDEukp40GoGXsaX8TWNYRKtWrZTHuEaNGsUC3rS0NAVajOE1PLR8pE2vKsHbMHoex44di27dulleo8eR4N6wYUMF5jTCHD2EhDmCLt93BLz07DJswPBQsg3eFNDbyzbpcWQYA8GX1rJlS/V/hiM4M0Ic6xECrb3ongBe6/ExbKFp06bKU8ubix07duDbb79Vc2UYAK8DakUzvOrTpk1T14HZGOvMmxHGIhtQyVAQtsn2zGaEiNBT7AhA+bvF7xV6dBmzzBsKgraz9eGNDZ8Q8MaGQM6bIsaYE5RL4iTIqxJ4jQV05SIl8PLu6Nlnny3yeRDgdfYVUfR9AV49zQR49fTSBt7cXKTPmooZBw7iUGAftKjRAgk7FqND6AYMvXYY0KqV3gDKYGkBXhcXbfdu4NgxFwuXYDFmCwkJKdIggefzzz9XMEsYYHgDf6fowaU3jcbYTAIEH1fT+JvFOFnjET6Bl55Cgic3K5mhQtfDy37oGBo2bJh6RG6YKx5ePlYnoLF/xm0yHpVG0Nm0aZMCH5oZeBlTSxA2jDDmzIPI+gQvjpN9EegYZ0rQmjlzpsWjSe+lGZqpKYGQehramhfEFZYwyhc3pMHRFcVwCt7EUEOO3ZmH19yWjoeXmw3ZvhEq4cpV/sknn6gbKIaxOFufU6dOKY8ubywYH8zNdrNmzVLhMSVhArxO4m4oNC92fji447B///6WxxkCvPqXoACvnmYCvHp6aQNvSgq2xc/A/P2V4V+nBxrXaIBV25chwm8VRl/THT6drtUbQBksLcBbBhcNUGEHCQkJCGLIQ4HRg0nvKoGTGRP4+JmfCWNzEb2B9OQaYXnmkAZrFXSAl6EIhEj+PlpnUGCsLqGVUEk7evSoClswYngJa3xETuhljK2x8Ypl+TrhxwBxXqt8nxvkDAg2j5seRXqoGbZo1OHj+jvuuEPF8HJsjLO97rrrVDXjUT8fmZv7NbdJryjDJOhBJwPYsisNvFxjhq0wVKNdu3bq2iCYGpvUeHPEzYRGDK95DtSdNyn//ve/LdksCPb0rptjeOklJuiyHVtxw/Y+RQxnYB+M+XW2PtZtMPxk586dheLO3fm0CvA6AV664/nFwQt99+7d6g6Qd5VMT0Lg5XuOUqS4szjlsS7T3PAOW8w1BfhjwEdHhsfDtVpXbyl+SfPL1dXHX15btmDprnlYdqAjmjW5Do3rV8KSrdtR8/QiDI6og6r9L3mqyquq1IvX19V2jdGjWVaNYQiERu6kZwyqYQQ9buyil4wxmAQxenPNMGdkYnjwwQdVSIN505pZD9Zl2ARDCgzjd5H1Z4uQTTBivKjZ82eE/h0+fFiFL9Bzy4wH9EoTegnABDXOgW0w1tZom+Pl9Ug452+sYSwbGRmp4M4M+sb7bI+PzxlOMXr0aHCzFrNA8Lea8alG2i6Ol33Qw8uMEgxXsGWMZaXDi2ER3DRHM2dD4Pcz/1Bv/s1MEtzUZi/9F3//GNfKsRHmGINr/B46a8u8aW3fvn0qfIV7iajPL7/8okI5ONfAwEA1b87tgw8+UP+nZ5uaGABs3rTGOdGbTaBlKAE93rxp4M0GszQYxvHyxsXYtGa8ztcYdsAwCjpoYmNjLRk7+B5DRngzxA1yztaHXntqx7R43OhIWCaIM0a5JEyA18WdlYbYfHzEHH5cdH6IeJHaS/VREgtU3trgF5gRGF/e5nY55sMvM34B2PM+XI4+y3KbBF7GfvFL3hXLXjgPM5K2YOfZGPRq1xW1auVh0foUIHU2BjTyQ8PBdwIFqX5caa8sliHUEDTs/UiXxZYfdzsAACAASURBVDm5MmZXb4pcacvTZQhhS5cuLbJ7nWEFBGF6cLnxi/GwBFuzsR6hk21wE5Yj4KVn02yEvp9//rnQawQcawhiAcOLzH8bYRTWeXiZsszW43HGlHIDk7U5i+FlecIXx8O9N9wwRY+ukYeXnmjOnSBG+GJsMfPThtuJ1edvO0HQbOZ8tz/88IPyVJuNad54I2FtZAV6wK3N2EjmrC3zpjV6PemFZTgCgZkbEnlTYb75oSeWm75s5eE1b1rjeMx5ePn7TI+49ca8u+++W7EOU8iZjdcR8/eyTX6X8KaCjkL+m5vo2A49yIY5Wh9CO52KvAHipkXOmd78kjIBXk3g5R007474eENCGvQvQwlp0NNMQhr09NIKacjORvLcyZix+xTOh/bHjTFNQU6OXZGGjH1z0C8iG5Hdbir3RwxLSIPeNSalRQFRoGwqcFUCr6NHB7yb++OPP9TdEh9dLF68GM2aNVOB6omJico9z+BwpgcR4NW/6AV49TQT4NXTSwt4U1KwdsVUzE+si3rNe2Fwrxrw9QXmxWVh7965iKm7G92vu6XcZ2oQ4NW7xqS0KCAKlE0FrkrgdfTogKk9mFaFyY75iI87DPk4hj8KzE/Yr18/FX9DGBbg1b/oBXj1NBPg1dNLC3gTEvDb+hlISOuE6FY9MWKoH3ig0Lx5wPodc9G+TjwGt7+13GdqEODVu8aktCggCpRNBa5K4C2ppRLg1VdSgFdPMwFePb10gDc9djpm7NqDQ97d0O/aa1BwuA9iY4F1f69BZKU5uKHBdahyQ9G4O71Rle7SAryle31kdKKAKFAyCgjwuqGjAK++eAK8epoJ8Orp5TLwZmdj26wfsPBvL/iGDcSQ7g1Qv35+X8uXAxv37kHt85NxfUi9cn/EsACv3jUmpUUBUaBsKiDA68a6CfDqiyfAq6eZAK+eXi4Db0oK4pZNwF97WqJZ6z4YNiAIxmFLPIRpfeJB+GVMwzU1fBE56B5Y3tQbTpkoLcBbJpZJBikKiAJuKiDA64aAArz64pVV4D19/jQq+3k+b6cAr9415irwZm1Yg+lrFmFnRnv06NQXPXte6icxEVidcAJnjv2ODtUz0anzKFjcv3rDKROlBXjLxDLJIEUBUcBNBQR43RBQgFdfvLIIvAmpCUjOTEbnBp1RrWI1/Um7UUOAV088V4E3Zd4UzN6aiqzgnujXpXWhE4STkoDNWy4g5chkNA7Yg/6dbgV4vGs5NQHecrqwMi1RQBQopIAArxsXhACvvnhlDXizc7IRlxSnJhpRLQKtarbSn7QbNQR49cRzCXizs7Fp5reYu60y6kQOVunIzCeGpqUBa9cCe1NnIrziWgxofmO5PmJYgFfvGpPSooAoUDYVEOB1Y90EePXFK2vAm5ieCP6hhQSEICas5E59cUU9AV5XVLpUxiXgTUrCb4snIiElGtFR/VQ6MrNlZwNxcUDS8Q1o5DUdnWpGIbTfTXoDKUOlBXjL0GLJUEUBUaDYCgjwFls6SB7eYmhX1oB3UdIiZOVkWWY6qNmgYsy6+FUEePW0cwV401ctxow163Eo91r069bVko7M3NOsWcCBjAMIy/sWrQMaInzo3eX2iGEBXr1rrLSV/umnn9Rv0WeffaaOmLVlTz/9tDpqd+rUqeoYacN4BC6PeqUFBwejQ4cOePTRR9W/2d758+fBuo7s0KFD+O6777B582bwKHSO4eGHH0ajRo0s1VasWKGOGrY+WjgnJ0e9vmbNGvDY33r16mHMmDHg8b3WtmTJErz99tu477771HHI9uzAgQPqaOE9e/ao9p544gnL0cL8PuXRteyP3xXR0dHq/WrVbIeqTZ8+HXPnzlVH+FapUgU33nijOrqZpjN2lj927Jg6fpfH7vLo3MmTJ6N69eqWafAU1/nz5yMpKQkDBgxQRyI7Mh7Zy+N7DTMfeXzmzBl88cUXiI+PV28PGjRIHdNboUKFIk3qzMOVNXB1Hvba4vVM3W0diezuZ0+A1w0FxcOrL15ZAt6UzBRsSt2EwLMX4JNxCpm1qiAm7HqEBIboT7yYNQR49YRzBXh3/fk9YredhXfdfhjYvTHCw4v2wVy8KSfTEFLhRzRCAKL7jyER6A2mjJQW4C0jC2VjmHl5ebjrrrtAwOnRo4c6NMnaUlNTVZlKlSopiOrevbulCIH3lltuQc+ePcFyb775Jpo3b45x48a5DLxbt27Ftm3bEBMTo/qYMGECNm7cqCCcxnYJqc899xzat2+v2uXvwPvvvw9+v33//ffo27cvatWqpQ55Iqh98803ClYNYzmCOGH9hhtusAu8/PyzL8IfwXTBggUwbgg4ti+//BJbtmxR8/Tz81Mnp/L1F154weZFQJAnFEdERIAg/frrr+Ohhx5Cnz59XB670TBhf+XKlWpe1MIaeJcvXw4fHx8QBAMCAlwCXmpVu3Zt1QUPw2J9GoGfp8a+9NJLyM7OVn8PHToUQ4YMKTLPkl4DV+Zhbz3j4uLw1VdfWdblxRdfVOt9++23l8iHVIDXDRkFePXFK0vAa3h3ow9eQMaxg0iqF4jIlt0RXs0GIelL4VINAV6XZLIUcgq8mZmIm/UZ4nc2ROPooYXSkZl7Yi7ew8eykJv7M+rlppfrI4YFePWusdJUml7V559/Hk899ZQCRUKU2YPLsfJ3at26dcrzSk/lW2+9VQh46S3t3bu3eu3XX38FvbGEUlc9vNZ6EOzYJr3JVatWxaRJk7BhwwYFYTSCGAGGr/P0Umujh5feSDOYc24E4vXr1yMqKsou8BK8CZO//fYb/P39VdOEff7hHF977TU0adIEd955p3qPgDVlyhQFWa7Y+PHj1Qmstm4sWN/W2K3bzcjIwMiRI4sAr1GOutO7ae3h5dpy7oYXnx5e3hiEhoYWGfpNN92kIL5jwWk6f/75pzo9lsBPI5TSs8sbHVvmzho4mwfft7eeXLvWrVtb1mfhwoXqhoV/SsIEeN1QUYBXX7yyArzGZrVA30D0TMxB8pnDSAjOQmira9Cxbkf9iRezhgCvnnDOgDdrz07MXPwbth1uix5dBhdKR2buKSEBSE4GjmZNRI3zieX6iGEBXteuMaYmPJd7zrXCJVgqyD8Ift6F48yN5gmR9OARbm6++WYFvl26dCnUO2GPgEVQeuyxxxTUGo/w6eE1gJftvPLKKwpCCR7FBV7CFOsS0PgI/d1331XgS8+oYSNGjAC9dwaQGa8zrIGe2a+//hoNGjRQLzMUg95gQtLLL7/sEHjnzJmDGTNmqDAJw+iVrV+/vvL8EvwnTpyo5ml4eOm9vffee52uGL3pDzzwAAia/GNttsZuq9HiAi9vAkaNGoWBAweqZjkGriPHRa8851enTh31nrW+BF7CLsMzuCYMrTCuG1fmobMGzoDXUVu8FnkzYYS0MLzj/vvvx+zZs9V6uWsCvG4oKMCrL15ZAV4jFVkYqiAqMQM5eRcwz2sv/Jq3Qr8m/fQnXswaArx6wjkD3uSV8zBnZSKy/DujZ7cOiIqy3T5z8fLP0bOzUePsanRv1B3BXXrpDaaMlBbgdW2hNhzegEOnDrlWuARLta/THnWD6hZpkfGbhFzCaefOnUHvI79f33jjDUtZhhswBpdeTMagEpoYz0kPII3Ay9hSeoUZFtGqVSvlMa5Ro0axgDctLU0BC2N4DQ/tq6++qryqBG/D6GEdO3YsunXrZnmNHkeCe8OGDRWY0whzjLElzNG7yfcdeXjp2WXYwIcffmhplzcF9PayTcbOMoyB4Etr2bKl+j9DCJwZwxtYjzHA1l50W2O3115xgde6PXqnmzZtqjy1vLnYsWMHvv32WzVX3iDQ084bBIItby4Yq80bAuuxm9stiTVwBLzO1pNhF7x+ucY042nAtGnT1PXrrgnwuqGgAK++eGUBeM2pyHr6NEPg9vwsDfNOJyC3fRS6NeyGYH/PxHMK8OpdY86Ad9OUz7Bge0XUaDoIA3vXQUH4W5FOUlKATZuATJ/NCDo2DZFBjRHBjWvl0AR4XVvU3em7cSzrmGuFS7BUs5BmNvcNEHg+//xzBbOEGIY3PPvss8qDS48q7aOPPlLgw81eNP5mMU7WeIRP4KWnkOBZuXLlQjCk6+FlP/QwDxs2DMOHD7co4IqHNzc3V4EO58F4U8aj0ghomzZtUsBGMwMvY2oJwoYxXteZh5f16SnkONkXATElJUUB4syZMxXM0tq2bVsImqkpN5RRT0Nbo197Y7d3CZQU8JrbZwgEb2KoIcd+6tQp5dEloFesWBG9evXCrFmzVJiJPSvOGji6zG2FZjhaT7YlHt4S/OIo6aYEePUVLQvAa6QiCwsOQ1Qa8p9tA9iUtRcpbRoiMrStx+J4BXj1rjGHwJuejumzPsOGna0Qfd2wIunI2NNxAPQjnEwHuMH5xMV9qJr1EyK8ayBy+P3lMlODAK/eNVZaStOzm5CQgKCgIMuQ6MGkd5XAyYwJ9ADzM0HoodGDR08uH/k3btxYeXjNMbzmuekALx/nEyL79+9fJL6WsbqEVkIl7ejRoypswYjhJawxrpjAxRhbY+MVy/J1QpvhleS1yve5Qc6AYPOYGcNLDzUzBRh1GI96xx13qBhezpWxsdddd52qZjwy56N+c7/mNuldZJgEPegh5oTdgIq1tTd2TwIv15hhKwzVaNeuXZGuGcaxc+fOQvHb1sBcUmtgtGsLeJ2tJ69pAruxSY03ddwEKTG8peBbR4BXfxHKAvAam9Vi6scgZFUCkJUF+Poi6XQKtjUKRFj9VoiqbedZuL4kDmsI8OoJ6gh407evx4yFi3HoVFv07NkXMVYplXlvsxYAtyRGFOTiPZV7HJV9J6DGqQuIueFO2HUJ6w2zVJUW4C1Vy+HSYBiGQGh85513VAYBwwh63NhF796iRYtUWAK9uWaYMzIxPPjgg06Bl2ETDCkwjPGf1o/ECdkMm2DcpbEZjOVZjuUPHz6swhfouWXGA3qlCb0EYH5eOQe2wVhbo22Ol15ewjnB3TCWjYyMVHBnBn3jfbbHeFyGU4wePRrc9MQsEPytZjYG9kHjeNkHPbzMKMFwBVvG9Fj07jIsgpvmaEY2BGdjt9Ue07xlZmaqsRFCGYNrxKYSnvmHa8e/mZWCG+T4h2betLZv3z4VvsKbFurzyy+/qFAOzjUwMFBllGA9ppfjhkHG7P773/9Wsb4086Y1Z/Nwtga8mWG4BMMRaI7m4awtrhfX5IMPPlDzoEeeaylZGlz6Wri8hQR49fUt7cBrSUXGzWqhMfknENAiIpC5eyuWVT8F3zr1PRbHK8Crd405At5di6chNv4IfKp1w4D+rYukI2PGynQAgQC4d5m5eC9cvADvwK/gk3as3B4xLMCrd42VhtKEsKVLl1p23RtjYlgBQZgeXG78YjwswdZsrEfoZBvchOXIw0vPptkIfT///HOh12JjYy0ZGMxvGF5kvmaEUVjn4WXKMjMkG/W5wY0br6zNWQwvyxO+CKjcHFW3bl3l0eXOfxo90Zw7IY3fFYwtfuSRRxBuKzchoECLcaRmM/Ld6o6dIEgPuLUZG7J++OEH5fU2G1PG0QtPM29ao7eW4RXMukFg5oZE3lQYNz+EX4Zn8EaCm/9Yl15xw8yb1nTnYb0GvI5WrVqloJrmbB7m+dlaT3p0uclO8vCWhm8a0xgEePUXpLQDb3xyPNKz0xEdGo362b75Z8zyMRZTv2zbhnkVDyK3QT30b9IfPl75OQ8vpwnw6qnrCHjjJr2NVVvrITxqCIYPqQ7zPpVspigydcWfpWWL8p37uRV/gnfqHvSPHArfdh30BlQGSgvwloFFkiGKAqKA2wrIpjU3JBTg1RevNANvoVRk4T2B7duBvXuBZs3ygXfZMqzNO4i05vXygTi4vr4AmjUEePUEswe82UcPY87Mb5Gwuzl69Lm5SDqyBIZqm7qiLyRpHZPmA6i6FHn74tCpbkeE9iyauF1vhKWvtABv6VsTGZEoIAqUvAICvG5oKsCrL15pBl5LKjJuVguNYqATkJEBFezJlCjz5mHX2YPY3bYeIqpFoFXNVvoCaNYQ4NUTzB7wJm9didgFq3HqwrXo2ef6QunIzN5dpnAn40YCyC6430HNbcjbPRnNKtZD85EP6A2oDJQW4C0DiyRDFAVEAbcVEOB1Q0IBXn3xSivwmlOR9QrvhQDfgPwgTtqgQfl/x8YiPfs44hv7oUqVWujaoKu+AJo1BHj1BLMHvJviJmHB8jOo0aAP+vRthPom5zyTzvFPGAAmm9vGkG0APgW5eANDU3AmaQLC8oLK5RHDArx615iUFgVEgbKpgACvG+smwKsvXmkFXmOzmkpFRu9uekFeKnp2uxaALZOVp6ZiZs0TqFCtmkfieE9mZcHX2xuVCo7J1Ff86qphD3hnTnwVazc1R9T1IzB0SAAKjpxX4iwCkAWAYQy+jN0tSE0WXpCLt0qtTJw8/iMCj59Cr253oxAtlwN5BXjLwSLKFEQBUcCpAgK8TiWyX0CAV1+80gq8hVKRBYbkH7PFP0z506ogdKHgtfgqp5BeOwgqbRnLXibLZaaAc+dQycsLfXyJYmLOFLAFvOlp+zBn2hQcSGuJngMGF0pHlsL8yqbMDGy/wK+PbpkqbFtFs5zM/Qo4eLBcHjEswOvsqpL3RQFRoDwoIMDrxioK8OqLVxqBt1AqMm5Wo/HUAXp5o6OBigeBtIVApZHA1t3YXvEU9jYIAk9A4p/LZYSx1efOqbyPg319cflzQlyumXiuXVvAu2vzYsTO2gGfwGvRe2AHtQfRMMO7Gw2g/oUsYOd4rK3dE2k1YhCTA8THqhTMCAidgYwt69C9aW8EX3vpOFTPzezy9STAe/m0lZZFAVGg9CggwOvGWgjw6otXGoG3UCoyI/OCEb97bWNgYVsgLxeoHAl4/RupXllY1yQQIQEhiAmzOr1AXxK7NZg5YE8B8Hby9cXlzwlRgoO/Qk3ZAt64mZ9iVXwwwpv3xaDhdRFccCq0sVnNyLuLue2AE5uQV8EH8286imZ+VbGtwN0b2nI1DsfPLpdHDAvwXqGLVboVBUQBjyogwOuG3AK8+uKVNuAtkoqMU8oseJYdGAiEJwPxYy5N1P825PjdjNjm3vD18r2sB1CQtXiKDj28zX19cflzQuivZ2mrYQ28WdmnMPeXj5GwPQI9Bt5eKB2ZkYqMm9WitrwObPmXZTrbOoxHheZPIr0gUUdYmyQciP9fuTxiWIC3tF3FMh5RQBS4HAoI8LqhqgCvvnilDXiLpCLjlJKS1CETCAsDjj0JpC0pPNHKn2JRowbIquiNbg27Idi/wGWoL4fdGjzxiyd/XTx3Dhe8vFDD1xeXPydECU7gCjVlDbzJ+7cg9veFOHUmCj0H97SkIzOnIut1Zh8C/uSBwpcsvXYPbO+1GAEFuXjDWqbiQMKPCD4HdO87Nv8wknJiArzlZCFlGqKAKOBQAQFeNy4QAV598UoT8NpMRcYpJSQAyclAw0rAloKYXvNUvSOwrdEnSKoGRNaMRHi1wrCkr0rRGhbvY3Y29vr4qDPmefqXxPE6VtcaeDetnoHF8w+javVu6DO0pSXBQiHvLmH3zL4iDceOPIGwPVXV2SMRTXKxN+Xr/COGr78TRc4lLolFv0JtCPBeIeFLqNuffvoJ/C367LPP1BGztuzpp59WR+1OnTpVfZcYxmNreRQvLTg4GB06dMCjjz6q/s32zp8/D9Z1ZIcOHcJ3332HzZs3qydSHMPDDz+MRo0aWaqtWLFCHXdsfbRwTk6Oen3NmjXq2N969ephzJgx4PG91rZkyRK8/fbbuO+++9RxyPbswIED6mjhPXv2qPaeeOIJy9HCTPPII3fZH78roqOj1fvVqlWz2dz06dMxd+5cdYRvlSpVcOONN6qjmw37/fffMX/+fCQlJWHAgAHqGGN7duzYMXX87q5du9SRv5MnT0b16tUtxTk2HhfMY3orV66sjjUePHhwseZpVOLxvDymOSUlBXPmzLHbFtc6Pj5ejSskJARDhw7FyJEji7UGzubBteG1cvjwYbz44ou44YYbCmmgsz66HyEBXl3FTOUFePXFK03AWyQVmTGdRQVnytbaAOx5Lf/V2j2A45uAnJPqv8k1hiCh0ViEVg5Fx7od9YVwUsPYTNUxKwu7fHxwys8PnTiMEu+pfDVoDbwzf/831q4MQ1S7ARh6S3VLOjJLKrIN4xCyM/8MeGXVolUcL41hDbl+TyJ5W36yjgPHJyBn39/oHT0cAa3blRvhBHjL7lLm5eXhrrvuwpkzZ9CjRw88/vjjRSaTmpqqylSqVEkBWffu3S1lCLy33HILevbsCZZ788030bx5c4wbN85l4N26dSu2bduGmJgY1ceECROwceNGBeE0tktIfe6559C+fXvVLn8H3n//fRCOvv/+e/Tt2xe1atXCX3/9hS+++ALffPONglXDWI4gTlgnINkDXn7+2ReBmWC6YMECGDcEHNuXX36JLVu2qHn6+fnhvffeU2N+4YUXbF4EBHlCcUREBAjSr7/+ugLIPn36qPLLly+Hj48PCOMBAQEOgZewv3LlSjUvamENvIRd3jy8/PLLSE5OVjD4zjvvoE2bNkXG5myeRgXe4LBPQrYj4KUmNWvWVFoQjl999VU1xo4dL/22uboGzubBm4jw8HCMHz8ed999dyHg1V0f3U+uAK+uYqbyArz64pUm4C2SiozTyc4G4uLyt+ZffN4CP4j5ETh/Elh/6Q5+eeSnyKrUrMTjeDMLcsFyM9V1WVlI9PFBip+fOgxB4ngdX3Nm4E0/dQSxP3+PpL8j0G3IzZZ0ykYqsionNqErN6oZZrXGDGtIjFyM9HX5J0vnVV6E1HVLyt0RwwK8+t9jpaUGPWXPP/88nnrqKQWKhCizB5fj5O/UunXrlOeVnsq33nqrEPASHnv37q1e+/XXX0FvLKHUVQ+vtRYEO7ZJ2KpatSomTZqEDRs2KK8r7ciRI8p7ydcJWdZGD+8999xTCMw5NwLx+vXrERUVZRd4Cd4Etd9++w3+BbnLCfv8wzm+9tpraNKkCe68807VbVxcHKZMmYKvvvrKpSUlpHl7exe5saBW9KY68vAaHWRkZCjvqRl4c3NzMXz4cOXBbtu2rSr64Ycfqr8NDzvLc+5cR2fzZL2jR4/in//8Jx577DG88sorhYCXoE7vOm90rI1eXnq9b7rpJgwZcukodVfWwJV5GP3xZovXgdnD6+76OFtEAV5nCjl4X4BXX7zSArw2U5FxOikFpw34nwKOXnp0hZEnAL+qQNwNlpjebP/aWNbqE3RrOgQBPgH6YtipYT75q2lWFk56e2ODvz+qABLH60RlM/Am7lqJBdPXo8KFaPQe2dWSjszw7vaaEY6A0wWhDPWHAd3+yL+pmXbp8WbCjSeQvLwquH+xfusd2DX/l3J3xLAAr2sf3dOnuYnUtbIlWSooCPDzs90iITI7O1t5KG+++WYFvl26dClUmLBHwCIoEX4ItcYjfEKHAbxsh2BECCU0Fhd4CVOsS0CrUKEC3n33XQW+9IwaNmLECOXBNHsQ+R7DGuiZ/frrr9GgQQNVnKEY9AYTuOj9dAS89GLOmDFDhUkYRq9s/fr1leeX4D9x4kQ1T8PDS+/tvffe63TJ6E1/4IEHVJiBdaiBu8DLGxGCPr2f9LLS+O9FixapEAwabwJGjRqFgQMHKnh1NE+W57y7du2KOnXqKGg2e3gZWmFcN8bE6c1m+EZmZibq1q2rwi+4bjpr4Mo8HAGvO+vjdAEBCPC6opKdMgK8+uKVFuC1mYqM09m+HSpo89wvwLlJaoIZvsNxpPJ7aNC5Afy9DkOlryoIbdhbawgqdPi4RON4zSd/BWRlIc/bG4sLvBUSx+v4mjMDb1zct1gV54PwsC7od3MTtc/M2AzYav2TiNj1SX5jvlWBARuBygUxh6abmuT245GwJ9+rH90tBRvnfI/QCkHoNORBIKDkbnL0P0klV0OA1zUtN2wADh1yrWxJlmrfHqhbt2iLZ8+eVZBLOO3cubN6RMzv1zfeeMNSmOEGhB16MRmDSmgaNGiQ8t7RCLyMLaVXmGERrVq1Uh7jGjVqFAt409LSlPeTMbxG6AQfj9OrSvA2jB7WsWPHolu3Szmt6XEkuDds2FCBOY2QSW8jYZWgy/cdAS89u3yEb3hH2QZvCujtZZv0XjKMgWBFa9mypfo/wxGcGYGQ9Qig1l50d4GX8ca8IWA8MG8SaAzH4E3Dt99+W2RozubJGGWuOee+Y8eOIsBra64MWeB3Ab3HDIHgTQBvCnTWQGcetjy87qyPs/Xj+wK8rqhkp4wAr754pQF4baYiM6ayvCAP1el/ABfT1KupAe8i03cEakbWRLXwakD850BS/hcyLe26X1E74hZ9MWzUMLIHcFtJPx55m5WlHqGt8fdHRsHxt+UnP0CJSFaoEQN4/QL9MHvyp9i8qT46XzcE/UYGqXLcrHY+ZTo6LRt+qR5DGcLvvvR/gnBB6ArDGtblLkZODtCtVxaWLvoKgRlZ6NXrH0Dt8hFRLcDr2nW4ezdw7JhrZUuyFA9KsZUUhI/jP//8cwU2BDCGNzz77LPKg2t45hhPyRADPiqn8TeLcbLGI3xCB72VBE9ulDKDnK6Hl/3Qwzxs2DD1eN4wVzy8fBROUGf/L730kkrFSKNXctOmTcobTDMDL2NqCcKGERCdeT5ZnxDHcbIvwiRjVulBnjlzpsWbyrACMzRTU8Io9TS0Na+xu8Cr4xk1dLHn4eXNBD3RDA/gxkFXgdc8n08++UTd9DDkwNEaWF/nOvOwBbyO1qckPlMCvG6oKMCrL15pAF6bqciYAiz3Ik589ztOpx9Ew0aXYnXTIlORkZyBgJAAhMWE5XuBt94H5K5SAuQE1IPvjVvzQx7ctCRulgKgcsOagPdvf3/sBcBDwi7f2W5uP8qJnQAAIABJREFUDr4UVDeAN/P8MSz85Q+cPNgUPW8bqtKR8WZi2fmT6Dav3aVQhogxwHU/FB45wxqYuaHAi7+88kZkeEWDzqil6/OPGO7faTR8m7csBTN2fwgCvO5reCVaoGc3ISEBQYx5KDB6yOhdJXAyYwI9wPxMVKxYMf+7KidHeXL5yL9x48bKw2uO4bWGOFeyNLAOQxEIkf379y8SX8tYXUIroZLG2FKGLRgxvIx9ZVwxoZeQxk1ghvF1elUNEOe1yve5Qc6AYPOY6Z2kh5rZE4w6DBW44447VAwv58o42+uuu05VY3aF+++/Xz3KN/drbnPatGkqfIAedGYwsGXuAi/nzhuFf//735aMEoRreldtZclwNM9mzZop7zkzbdCo76lTpxSocxNc06ZNnV6uDGfgmJ555hm1Nq6ugc48bAFvcdbH6WRMBQR4ddSyKivAqy/elQZeW6nIcrJzcDLpJDK3p+DC5m0IqfEHQmr8nj+5iDG42PE77Jm3R/23Sf8m8EreD2xdA5yid+FMfjkjBlRfkkI1mHuXj93VUbcm4D3p74+1APh1e/nOdnNz8KWgugG8+5LXYemMXQj26ojed1yj0pHRu1s9fgzCkibkj7RSI6DX4kuhDObx0wOcMl29kuj/LyT6v6ZOmd6b9jsydmwqV0cMC/CWggtXcwgMQyA0EmAYg2oYQY8bu7jbnfGfBDF6c80wZ2RiePDBB50CL8MmGFJgGB+3Wz/OJ2QTypgZwdgMxvIsx/JMP0UAo+eWGQ/olSb0EoD5eeUc2AZjTo22OV56eQnnBHfDWDYyMlLFJJtB33if7fFRPMMpRo8ejYULF6osEPytZmws+6BxvOyDHl5mlGC4gi1jHC29uwwN4KY5Gsdl6EmY5B/qzb+ZSYJP5PjHlvEGgjGyHBtjiRlLTY8zjR5lbuhjnDK9zgR3wqaRpcG8ac3RPBmeQT0N2717t5o3+yMEc+zmTWt8ihgbG2vJssGbE4Z58AaGm9qcrQHLM7UdU5m5Mg/edBHkGcJBwOVaGeutuz6aHxsJadAVzFxegFdfvSsNvOZUZE19myI9MR3Z6fT9QXnuAk+loXbNx+DrW/DssuBxd3J8sipXr1M9VAq4ACxbBuSsArLzHxUq6zYdqJ//oS+O5QKYV1CRsbo52cBff51FpUpeuOZ6P8t7g4rT+FVSxwDe5at+wpoF1RHVpAsG3t1QhdsmJk1AM/OpedahDGaNkqcDy/Mfy2Z7NUJc5SS16S3Hby32/jUTkdVbIGKgaVNjGdZXgLfsLR4hbOnSpQq0zMawAoIwPbjc+MV4WIKt2ViP0Mk2+OjbkYeXnk2zEfp+/vnnQq8RlowMDOY3DC8yXzPCKKzz8DJlmRmSjfqEIW5sszZnMbwsT/jieLjZjZuv6NFt3bq1aoqeaM6dkMbvCsYWP/LIIypNli3jI31CqNkI9gaY/fDDD8pTbTameaP30toIxPSAW9vs2bMV9Jrz1xLO6ZU2b44zb1pzNk9zH7ZCGsyb1nhTw/kwbpf/5iY39kuPsy2zXgNeR8wdzDZpzubB9aCH2my8kenUqZP2+tgcoIMXxcOrq5ipvACvvnhXGniZiizjUAaanG8C/zP+lglUi6iGoJTtqHj2b+D0pbgw3JanypxIOoGj246C5Wq2qgnM4sG/zNb+ncUTqDY/DU0qdmiDkS7L8OIynPjIkfyjhQcP9sUyHyBL4ngdXnT8Edt/ZD/i505H0vZ66NrrRnTrXwnpp/chcNENjkMZrFueWs0S1hAfuBiB4T0Q3CAJW2f/gAj/UESOelj/A1AKawjwlsJFkSGJAqJAiSsgwOuGpAK8+uJdKeBlfG7ijkSsX7Mefnl+iAqNgpevl9qERoj18vHKh1hTdgaGMxjxnecyz2H/sv2qTpN+TYCCwyky2zWC76reCDiXv8HNndAG4/QvhjOkFhxpy8d5BN7oaF9kh0PF8UYCKPmz3fTXsjTWIPBu3LMa62asRe7J5uhzVz/lmc1cNhzBBSEKKpSBWRmcxVxvGAcUHEqR5P8kUuuOR7OodKyc903+EcM3PszjqUqjDFpjEuDVkksKiwKiQBlVQIDXjYUT4NUX70oBb9KiJGw5sAWZ5zLRsmFLRDSPyM+4YFhmZn6Ygik7gzpswrR7f0/sHlzMuYiG3RrCP2mnOn44p00k1h37FTG78ncRK3P0qNyBZAU+Y4QlAsmJ+WdfRERkY+tWH4SG+iIsBuAZYBLHa19EAu+c5T9h68ILaFC1E/qNaYuQzAmAOZTB1dCTtCX5eZcZXoIqWB56Et165GLu/C/Udv3Bne8pF0cMC/Dqf49JDVFAFCh7CgjwurFmArz64l0J4OWmtF3zd2Hzyc0IaROCfu2Z8MvKkpKALbOA0wVHczI8YdSJQoWObj+KE3tP5KcnyzkKJCaC7sPlFdNQL/E/iDhSEO9mndfVBZnoH+amtOxUICA/RSRiYpjqNQuLFjFJmS969QPifPmv/JRlYkUVOHPuDKb/+TV2ramFa1r2xqBbsgvlTUaLJ4H2412XziqsIWZYD8xb+j1ykveVmyOGBXhdvxykpCggCpRdBQR43Vg7AV598a4E8DL+ds2KNcgIzkDza5qrcIYilpAAJD4N5MTlv2UjXdWZtDM4uPZgfnqyCD9g7Vp15uz2hoFIPrYF3bY/cSm0oXaP/AwALhrDGXZlASdWA/XOAJGR+c5DbgBITPRBSoqfei2R2bK4P47hwy62fTUVO3DyAOb8NAXZB5uiy5D+6HTh1ksx1gxlYIy1jlmFNYQOHI+texcgdeNyRId1Qlj3wTqtlcqyArylcllkUKKAKFDCCgjwuiGoAK++eFcCeJlhYenmpQhsGYgBMQMQ4GvjVB3G5B65zXLYhIJVQqvJGAdsSU/WrS68li1RcQcpMZHYlLoJoTmp6Jgw9lKNDh8DzS+l9HGk1txzwIadQMtDQIuGQKtW+aUJvCdPemPDBn+VfL5KjMTxOtJxQ9IqLJqwCiEVOqB35/UIOzLuUnHG7VZjhLTrlntkCXwWXgprON7rJDIqFBwxHNQIzYc6P5LU9d6uTEkB3iuju/QqCogCnlVAgNcNvQV49cW7EsC7cspK7D2xFxH9InB9o+uLDjo7G4j9GMgqiMN14Ak00pPVj6mPwHXLmckdOX16IXZ/HHy9fNEvexWw5V/5fbgY2nDiAvDFVtIt0M8X6Njx0hCNk9aWLPFXp32FdQOSg4FQAKZi+gtRTmvMXf4jVv5xETFh9TCwtikFUJt/AW1e0541U8XlTa0K3xyecwdkdlqMzNpNLh0xfNNjgClRvnYHpaCCAG8pWAQZgiggClx2BQR43ZBYgFdfPE8Db1Z6FhbOXIiswCx06d8F9YN5nIOVpaTkb2pyEM5g1GDeXv5R6cnSdwIZGeARXLFH4pFzMQfdGnZD8KLuwAluL0O+R5GeRQf25w5g/SmgQQVgTLvC/GQA799/+2PvXiCsGZDcTOJ4bcnJDYlzZvwPO1dUxVNRbyDYPzG/WHFCGUwdJK9/EmE8bpjW4klkt30HcXP+C59TWejf+4Eyf8SwAK/+95jUEAVEgbKngACvG2smwKsvnqeBN2VzCpbEL0HFRhUxst9I2wPmUcGbezoMZzAqEqBT4lPgG+iL8NBsKAqNjERCYCaSM5IRWTMS4cjI3yhlmAPvIrv+g87DQODelkCd/EN3LGYA7+nT/oiPB6pUAbK65sfx9gJgIzhDf1HKSY3dqXsw8+uFiAqIRa96+aek0dKHJCGkcqNizzIpbQnCC7I1KK/9qBOYtfAL5KWmYsD1d8Enokmx2y4NFQV4S8MqyBhEAVHgcisgwOuGwgK8+uJ5Gnj/mv4X9qXtQ4vuLdCxhZ0ggMVfAIcfyZ+MC95AIz1Z42Ze8E7cydxhSKoTgG1HtyGsShiiakcBW16/FNrAdm3EjzIxxMadwKYQoF1rYHhgUT0N4PX390dsrIqgQGA3ICtY8vGa1aKTfvqs9Th/YDmean0pbje5zWsI4w2HG0Y/cbgprIHx3cv2H0VG4hZc36IPQjp2daP1K19VgPfKr4GMQBQQBS6/AgK8bmgswKsvnieBl+nIpv88HecrnEf/0f0REsgMtjZsam+XwhmMmmkJachIzkDNUC9US92p3K6Z10Rh2f5l+XG8TQqShtHLaye0IT0dymObFgh4twXa1QBs5I5Qm9Z4LjuBl95gOpR9ecRtMyACQMHeNv2FKCc1qCN1YWTJ+vWz8HiDe1DNP/9Y6Mxq0cgcsBE2gli0Zs+UcVkbxiG84BAKhjVsq35b/hHDtdsgos8orfZKW2EB3tK2Inrj+emnn8Dfos8++wwtWrSwWfnpp59WR+1OnToVvkzwXWA8ApdH8dKCg4PRoUMHPProo+rfbO/8+fNgXUd26NAhfPfdd9i8eTN4UA7H8PDDD6NRo0tPVVasWKGOO7Y+WjgnJ0e9vmbNGnWsbL169TBmzBjw+F5rW7JkCd5++23cd9996jhke3bgwAF1tPCePXtUe0888YTlaGF+n3766aeqP+bsjo6OVu9Xq2bKyW5qeMOGDZg4cSJ2796NGjVqgEcJmy05OVm1t3PnTlStWlUdKdy9e3ebQzt27Jg6fpdH+J48eRKTJ09G9erVLWVffvllrF692vJ/Hi88ffqlJ1XWjXKO1Pzw4cN48cUXccMN+ZtrDeN1wfo80rhXr17qCGX+ltiy33//HfPnz0dSUhIGDBigjmM2G8f17bff4uDBg4iIiMBTTz2l/rY2V9aTxxbzCGPDzEc1O9Pb4YXowpsCvC6IZK+IAK++eJ4E3pS/U7Bk4RJUCauCwQPtpI8iMS2IdCmcwZitccxw5RB/1E3fkn9CRL9+iN0Tq+J4+zfpDx8vH+D0Pps5YLlHjmdc0Ft7qjUQ1Ahg7gBbYGYGXgOSz/kD/n2u7jje3FyAx7EnJ+evygXvU6ifNAwdqi6yXJSLhiYhplIjt8M+uHFtbdoSxJjCGpJuWIut834qF0cMC/Dqf4+Vlhp5eXm46667cObMGfTo0QOPP16QR9w0wNTUVFWGAEWQMQMZAe2WW25Bz549wXJvvvkmmjdvjnHjxrkMvFu3bsW2bdsQExOj+pgwYQI2btyoIJzGdgmpzz33HNq3b6/a5e/A+++/r27ov//+e/Tt2xe1atXCX3/9hS+++ALffPONglXDWI4gTlgn2NkDXkIs+yJE3XbbbViwYAGMGwKO7csvv8SWLVvUPP38/PDee++pMb/wwgs2l3THjh0KKAnjs2fPLgS8BEnq17lzZ9xxxx1ITEzESy+9pAA4nDklrYywv3LlSjUvamELeDnu3r17q5oVKlQodHNi3R5hlv2MHz8ed999dyHgjYuLw1dffWWZnwHEt99+u815Ll++HD4+PuBNRUBAQCHgJeTef//9Cqo7duyImTNngoD8448/Kg3N5sp6Eni5xrVr11ZVeZIo+6Y50rskPnMCvG6oKMCrL54ngXflkpXYu2svImMi0a6tKabWPOxtPwEJd+e/4kI4A4sZ6cnUMcP4O59ce/XCuhPbkHo6FdGh0Zc2x3Gz0/pLd8vZPTYifmc0srKAKqFARkGUxSA7UpqBl0WMsAbG8QZWAZiHIP+r4uoxnvfBcBDKTuPRwYE5v6N+wk0WEbZ1GI/c5k/a9JoXR6lYAD3N2Rq6/Imlq7cgONcb3Yc9wRNCitNsqagjwFsqlqFYg6CH7/nnn1ceN0IEIcrswWWj/J1at26d8rwSXt566y1LXwQ2wqMBWb/++ivojSWUuurhtR44wY5t0ptMr+ekSZNAzx09krQjR46A4MXXa9asWWTe9PDec889hcCccyMQr1+/HlFRUXaBl+BNmPztt9/UUzEaYZ9/OMfXXnsNTZo0wZ133qneIxhOmTJFwaEjW7ZsmYJds4d33759CgQJgEZfb7zxhhrngw8+aLe5jIwMjBw50ibwdu3aFf362T5WiGvLuVt78bmG1NPs4aUGrVu3tsxz4cKFCvz5h0bApTeWNzpm45oT5M0e3j///FPpRJA3jOOn5583Oc7Mej0JvLyhCQ1lriHbZktvZ/248r4ArxOVHLnYBXhducQKl/Ek8E7+YTJyzudg2G3DUDmosu3BLroFSJ2S/57GKVwHlh/A2YyzCPNNRUBOJtCpE5L8slQcb0S1CLSqaQo2oGeQx9TyJDWvRlhWaSMCq1ZFeFfnRwVbA68R1nCqORDU1L5nWH9lSn8Nc/gCR8vvSx7GoVjzz3DgzD41iYN1BmHjDTNLdFMfD7+rHD8OLZI+Vn3kNH0E8440Bk6cwOAe9wP13Q2cuHL6C/C6pv350+eRe47+fs+af5A/vP1sP4omRGZnZysP5c0336zAt0uXLoUGSNgjoBCUHnvsMRBqjUf4ZuBlO6+88oqCUAJTcYGXMMW6BDR6Kd99910Fvg899JBlXCNGjLB4DM2DpSeVntmvv/4aDRo0UG8xFIPeYEIvH/s7At45c+ZgxowZKkzCsNdffx3169dXnl+CP0MUOE/Dw8tH8/fe6zifti0A4+P/Bx54ALNmzbJ4Ogm8/DxxvPbMEfDuZcwan/bVr690YMiFYbwJGDVqFAYOHFioaVvAyxsOevuN0BCOlXBOLzXnzdAK47oxN2YLeOlJXrRoUSHgvemmm8A1tOcxNtq0tZ4EXl5/fDrBpwlclzp16hSakwCvZ79jLL05crEL8OoviqeAN2l/ElbMW6FAl8Br1yYRhM/kv61xMIFxzHC1C8dQ0/u4cjOm1w9BfEo8qlSsgq4NTBuZrEIbkis9gRp9P0ZiAMAn8vbCGTgka+A1whqOBQE1ul8dcbwMAWH4Qmpq/jIFBgJRUVAHcSgznYaW41sF82+IR/UaLeHc9+D69cuNa3t2LsHADQVxcr5VEVfnU2Tt243uHUYguKXegRau93z5Swrwuqbx4Q2HcerQKdcKl2CpOu3rIKhuUJEWGQdJyCWc8rE6H23z+5XQZRjDDeiJoxezSpUqynM6aNAgEFhohCXGltIrzLCIVq1aKY8x41WLA7xpaWkKtBjDa4ROvPrqq8qrSvA2jB7WsWPHols3nhmZb/Q4EtwbNmyowJxGKGKMLaGIoMv3HQEvPbsMG/jwww8t7fKmgB5YtsnYWYYxEHxpLVu2VP/nY3xHZgvAcnNzFSjTs8qQBsbmUrvGjRvjk08K0hjaaNQe8DJOljG9HCvnQG/sf//7X5vhEeZmbQHv0KFD1XVArWiGV33atGnqOrBntoCXccoEe3rHGeNNjzZvPkaPHu3wRsHWerJfeoubNm2q1ps3RWQsxgcbXnKWEeAtwS+Q4jRlawEEePWV9BTwrl61GrsTdquYsVad7GztSpqQn3+X5mI4gzFjIz2Zf9YJNAw8CoSFKQqblThLFbHE8RZUSF83ASGJBX3xtV6LMavgJDdH6cWsgZdVGdZwvAJAoqsdBBR+KKW/JrZq5F3Mw4m9JxBULwi+AZc2uZRM6663Yg5fYKg0Q+MYwmCxlD+BZZduaDZe+z0SQ29G50qV3N6sZh4lN67NSgNuX1wNFS+eVG+tq/kcDqf5ITrieoRdbzrkwvXplYqSAryuLUP67nRkHctyrXAJlgppFoLAkKIpXAgOn3/+uYJZAivDG5599lnlwaVHlfbRRx+pjWLc7EXjbxbjZI1H+IQletwInpUrVy4UDqELvOyHHuZhw4Zh+PDhFgVc8fASHglonAfjYBnXSaPHdtOmTcobTDMDLzenEYQNY7yuMw8v69PDyXGyL4JWSkqK8sgS5IzH9m3bti0EzfYAjGENhFJ6ocPCwpRXmp5TeqLtmT3gtS5PHeiVN8Iv7LVXHA+vvbZsAS/LMsyFsdlHjx7FddddB25UZIgIrx1bZm89rcsyfII3X1x7am6YAG8JfoEUpykB3uKoVrSOJ4A3Oycb06dMx4XTFzBo+CBUrZX/5V/EVt0D7P0x/+VinMSl0pNlnEYT/2R4BVUCevZEfHI80rPTEVM/xpIVIi0NWLsW6Jg1DKG5f6rucio1xKIBmxDoVxWOklrZAl4jrOFAW4BP/S5HHG9qQioykzPVWIPDgsEfXU+CLzWjV5exzjSGL/DpXqFDzc6fzN8UWBDKgPrDMLfLb8jMzsaQSpXc3qxmvl74IPuXE0CT+HGIOZkf1pBUtT+2Hu+EiMphiBxmOlK6ZD4qHmtFgNdjUpdoR/TsJiQkICjokveXHkx6VwmczJhADzA3clWsWDH/eycnR3ly+cifnkjrGF7zAHWAl4+uCZH9+/cvEl/LWF1Cq/GYn9DEx/VGDC+hh3HFhCR6EY0NTBwLX6c31ohL5rXK9xk7akCwecyM4aWXlZuqjDqMIaUHloDGR/2MTyW0qc9wwaP+uXPnFurXeqFcBbB//vOfuOaaa1TogT1zFXipBTelcfyOzF4MLwHSCDngzRGB1YjhtdeePeA1l+dvEteP69mskPchv5Sj9bTul9cmw20YYtKu3aV9Nq7qrfuBkhheFxWzB7xM20LXvphrCvBL2PzowrVaeqUOHD2A+JnxqBFYA71upf/UtgVOr2R5I7vPduRVaqjV0cHVB3H2+FnUPrIVVWr54cKAAdhxbAf2ZexDk2pN0LR6U2RmAmvWeKsNVq2anEDLxJZATr6H8FDdwTh67WQ0zcuz2y9/BBgHZ04nw7AGtrmnZgU0uTYP7S9cQP5+15Kz/Uv2Ize7cLxixeoVEVw/WHl9L5cxfGHzZi8cVy5spkrKQ4sWFy+FL5g69tvwAHwOTFSvZKEqjnabhbU1Oqgv3MEV8uuXpM04541TG5ZjbFof1WyOdyXMzRqLEJ9QtBv6UJk9YpgQxOvL8KqVpGalua1AxsaUUWMYAqHjnXfeKZQeiqDHjV3MRsC4SwIMvblmiDQyMXBjlTPgZdgEQwoMs5U5gJDNsAnGi5q9kQROlmeWA4Yv0GPJmFR6pQm9BCYCD+fANhhra0Aqx8vrkXDO3wzDWDYyMlJBkhn0jffZHsMMGE7B32Vu1mIWCHq2mY2BfdA4XvZBDy8zSjCtmi1je/wOplecsMjYYrMGfBzPTWo0QjO9xARL4wbDuk3yQmZmphobY4kZy0qPM3VmGANDEKgB++PaMUzF2KRmvWmNn1uGfDA2miDPORu6cd6c2wcffABe5/Rs830DgK03rfE7k3943fBvZsTgd4Lxu0ONGJbCsXPDGcvwOqLxZoap7RhG4Ww96RGn04s3W1zXX375Rc2ba8RxOtPb3Y+rAK+LCtoDXn4Y+MUj5poCvDu83D8081fPx/Gtx9G6eWu07tLa5sC89v8PvuvyvXJ5gQ1xfsAu1yZgKnVy30kc234M1dP3oEbIRVzs0gVp3tlYf3g9qlesjqiQ67B6tZfyUoaH56Flyzx4HZoJ3/hLd/9ZMVPgXXeI3b75BWlO22IUXLDAC/v9KyCoQx7aVs5DSwfQrDuxzJRMHNl8RJ0mV/fauji++zjOpJ3BxZyLqim+Xq1JNQW/JWVMM5aUVAG7d+eDKsMXGjXKQ9Omtm8GzOvH8hsCb0ONfl9jnbcvgs6fRzdTrtGSGuOKixUQt8kbjx3qiNBz+UdHr74wEkcvtkSfgY+RzkuqK4+2wx8e/lDay9Hp0cF4sLPLfeN9OafCsIWlS5cqQDEbwwr4e0QPLuGM8bDWGQNYj9DJNhibac7SYG6LwMUNYGYj3P3888+FXouNjbVkYDC/YXiR+ZoRRmGdh5cpy2w9sifEcVOUtTmL4WV5whfjdhlmULduXeXRZcYCGj3RnDshjXBFiGN+WltpxNT3yoYNKkbabARQakNjai5u6uL3NPsgKBqb7azHTkikB9zauJGM79FjTY8zmYKb1uiVNm9AtN60xnnRo2023hB06tRJvUTwZoYFW3l4rTetMfsEPe5mY7o63hDRGCrDvgjjHBPXhzcQNF5Hq1atUhvhnK0n8xUzzIbZQgj61JI3Q0ZOX2d6u/uZEuB1UUEJaXBRKCfFLndIA8MZZs6diYtpFzFwwED7UOZmOAOnSQ/o3ri98D6QhMYNclTKgNyGYZi3Zx688vwQdKSvOhCBm6sKZW9ZNhxIKUgozqNqhyYBfrbDLmyFNLBvhjUkpAAnWgBtGsBhWITuyhnhDDUja6Ja+KWE7ATh9MR05GTl5wMj+AaEBLgd7sDwhU2bLqUZYzg0sy8UCl8wT4KbAJn5oiCUITkvCklNX0LYNaOw5eJF1D5zBteaHvPqzt9e+e0Apm0AeqT9C91O5HuKjuU1RHzeXWX6iGEJaSipK0TaEQVEgdKsgACvk9Vx5GKXTWv6l/blBt7tR7dj05xNCPEOQc+betqPO51keuQ9JAmofOlUIJ1ZqTjelMMIC0xHQFQzoFUrLEpahIQNvqjn1QE1qgSCG5HN8JZ0/iRC57VDAMGNVn8Y0O0Pm93aA16GNfy1CkioA7RvX3JxvDydLikuSY0lvFe4Tf2swZdlixPny3APOig4Fxo3D7dqZcq+YG8hTDcMDGVYenEsGvQYjQr122HPxYtodOYM2l4G4E0BMHErUPn4STyaculGYO7Ff6Jdm6EIjeqsc+mUmrICvKVmKWQgooAocBkVEOB1Iq4jF7sAr/6VebmBd8GWBUhbmYY2DdogaoCtw3q5U6H42RmsZ6zSk63fi1rnU1A1qiHQsSPmrd6LlZuOoUWdBhjeJ7TImQQ8CyzAfHIXG435EQgvOADD1Ik94GURZmvYWBlo2BboGwTYOThZa5EIs6mbUpXnNiwmzGFdwjE9vsbmNgN8uaPcUbgDwxeYbpIZGGiMPqBH16VUtua1Y/wYbkNyxTa4pt892FupNo5evIg2p08j/DKEF2QD+GYvcPIo8Mj+GxCSm59beVtePwTUuq/MHjEswKv1EZHCooAoUEYVEOB1Y+EEePXFu5zAm56VjsXLFyMvOQ9dO3VFaJSdk1zMIQXFyM6VkzO0AAAgAElEQVRgnjVjWw+uOoDA3Qmo36Yakpr1w6I1adh/cj963+CHbs0K52clNMUR8gD0M+WPBUMbmAfYytPsCHgZ1rDkCFQy3h4NADvJ17QWyThQIzQ61OUYXVvgy3CHWpG1UKn2pY2BHEhKSr5X1zgljcexc6Ov3fAF8+itj2r2G4KZZ6NQoWFDDOp6H5gQjk9kOp86hRAHuSa1BLEq/P1BYN9B4NYjH6NV5jj1LsMaUgKfQ/TwS4n13enD03UFeD2tuPQnCogCV0IBAV43VBfg1RfvcgJvQmoCdizb8f/sfQl4lNW9/ps9mexMQkJWEkICCZCwBI0IAlFBpYJrF2ultXbzX5e219vbTW2tXW5btb3dvLZVb2u1WgVFJGIQiRKWAAmQBAJkyEKYEMIkQ/bJ8n9+55sz882XWb4vM2SBc54nD5Cc9T3fkPf7nfe8PyQMJmBp8dJRZIvNlqys3rAfR8MLOQN1x9MM4+BBxM+Oxie6G9E/MoiOqE+QkmbBmizHNJEkFqBrBhQ7zVfaapEvb/GHDqC6I7wkBdh6ADDEATcsYra8XhW5nCF7ndzsVn23crkDpV5OX5HOZBHKLGmka6aorqZArPxFJXwm2keexu6uWsQVFSNv1nXYBSBgeBjXXLzo1lxd/WpG19zUChxsANZEdmDZIftztNvvcVyz4T+nZIphQXi9eSJEW4GAQGCqICAIrxc7JQivdvAuJeGlpA8dH3agILEA89Y7d2dwkDPEFkhRVS9LU3kTOnZWwnAhGn4L5iPnqlgY/EtgGbagOLMYYYH2LD7lAEiyasuuZqqUvGR5WfwskGO3AXJHeKnJ1veBPTGUMQj4rJduYTx7HOlxXUbHVWLF+woMD8HFaeloopRyVvkC+ekmaPVRO/4ccOAR++jz38Hx42dQ529CznW3I1qfjf0AYoeHMe8SEt5PTEDJCWBxJLC+xZ4ymmQNede/NIaFqQT0ElYThPcSgiu6FggIBCYNAoLwerEVgvBqB+9SEd5mczP2H9mPgBMBWJiz0LX+lG73t0raS+T/Acjz/hj6/MkOfPg/1Rjp70fGLbkoXJeIipYKGLuMyIvPQ0ZsBhuOyxno7+vk0LmRNngivCRreNtEl8aAT6d5p+M17DAwB4aUohSnmZ207DZFvne/1oTmU/0IiItFWFo8ky6QhEGVfEE+GEkZ3pYwZGXOI0DARnxU8x4upsSjaPEGtOv0IElw1vAwZlxCwnuqH3ipCkjzA74cZSfhJGsIXrRpSqYYFoRXy5Mt6goEBAJTFQFBeL3YOUF4tYN3qQgvZThrOtSElL4U5BTkMKusUUVJnFZWAUn2dIbaVyO1+HBbH85sPYzwrlbc9p1ZzGrAYDKguq0ambGZyI2X1LV0y5/cW2lmo+QHFOWlaC8VWeTZE+ElqcBrR4DzUcC9iwAZLdS0HJ4qmbS3GavH2os0JF1K2/1/J2Hq06GrrReRuiEs3ZCExKwITXOyVZZjY00BbXlnM7Z1HoBfQQHWzbsdFQCMABYPD0N3CQkvvbT8bB8QMgR8f7GjPKZp1iakXrV+bGucwFaC8E4g+GJogYBAYNwQEITXC6gF4dUO3qUgvOS9W2oohbncjIWxC5G5MhMhUSGjJye/4R84H7j94BjCjY7d8jS/fRVHMWfgMHJWJyPo+utg7jdjV8Mu6IJ0WJ2xmjWqAkAn+zY5g7wrpbTBepnOE+GlLt7cJxHpa+dKbg1jKdx7l14UnL4saOi0vaYVu19tgC5kCKnL0oCubsj1vBq6Ao48CRx5wt6ENNcdgTDu/xD7R84grvA6FKUWgZwvKAvxtcPD8LuEhJcm8vQBoG8I+I/5QORu+4lBb8zXEHazYyIATWudoMqC8E4Q8GJYgYBAYFwREITXC7gF4dUO3qUgvHXtdTh25hhCDocgOynbdYRSLmdI+BFQLCUP8FRIc0tmCJRAUh43Jlst+iJbrdkxbRh8twTT08MQc/8drMuSk5KOd23WWgT6BzIXASqU7Niu6pWNriR3Nx1CT0g2y4DlLitU7QngnyYgNRF4IM3Tapz/vG6L5BHmyntXS6/1JXWo/qQDmYk9yFsagRYkocvYhdDoUKQt1zBBZUSeO2pUVaG67hPUxwUgZ8EqZOuzbdjePDyMi5eY8D5fCzRfBDbmAJlGmbaYXqLuPqwFqklRVxDeSbENYhICAYHAJUZAEF4vABaEVzt4l4LwUqKHjqYOJBoTkZmT6fzC1SgdaAmw6EZVC+DRQ6qsA0D+BUGtwH66JQWAMjkG95jQ9soHLKqZ8uB6dlufZBbtve0oTC4EwhPYpapoeMiKppA29Fz3iUfCSwkcnq2VvM4eWQRoTXCrxXtXDWDVf9iJeqMOOen9yE7oxHDhVWio7mL64NjMWMTnxqvpBticYcumJpd5YMsWlJqr0LtgLooyrwN0etBlQMJ2mReElyzmOps6MWQZcju/HeeDUG0JxHUxg1gQ0IbUM3ZZTHtONfSLfWEQpw4iX9QShNcXKE5cHy+//DLodxGlu6VUrc7Kt7/9bZZq9/XXX2fpYXmh1LGUipdKVFQUFi9ezNLj0t+pP0qZS23dlZaWFvzlL3/B4cOHQWmqaQ7f+MY3MHOmPZnPJ598wtIdK1MLWywW9v19+/axtL/JycnYuHEjrrnmmlFD7ty5Ez/96U9x//33s3TIrkpjYyNLLXzy5EnW38MPP2xLLUwnZr/97W/ZeGRhWFBQwH4eGytz7pF1TGmD33vvPZYKNzo6GrfccgtL3UzFVarg2267ja1fWc6fP8/S7x4/fhwdHR147bXXMG3atFH1KD0vrXHBggX42c9+5nSZajDnc6Q0wM3Nzdi6davHh9TT2Gr2gJ6b8vJytka9Xo/169fjzjvvtI1Ne0PPytmzZ1k65VWrVtl+5ixdMqUd/vOf/+xx7moqCMKrBiUXdQTh1Q6erwkvlw4M1g4iNyAXLv1jlXKGa7aqynQg982lXxN0bN7TA5yoApK7gWvSJB9ZZk/2u/cAkwnZX1nJ+qbIM32RjtcSn8vkDESW3Rp+KbxmLXO+j+G8H7qN8NIu/P0gUDcI3DEHyNfIeMllore91zV2Wra5uRnlrzagHXoUXuWPhM46lkKtP38pGnZJv1iTC5OdW8bJx3EmZSCP4uZm9B7chw/6a+GXn4912esgt3qbr5Hw0r4RyaUEGsOWYVUrrewJwj5LCPJ0g1gW1IfUnnsRNriPtW0L/R4CF37HISWzqk4nsJIgvBMIvpdDj4yM4Atf+AK6u7uxcuVKPPTQQ04JFNUJDw8HEYrrrrvOVocI76c//WmsXr0aRHZ+8pOfICcnB48++qhqwnv06FFUV1ejqKiIjfHSSy/h0KFDjIRT4STqP//zP7Fo0SLWL/0e+OUvfwkioH/9619x4403Yvr06fj444/xhz/8Af/7v//LyCovVI+IOJF1IkiuCC+RWCKLRJiJmG7fvh38hYDm9sc//hFHjhxh6wwODsYvfvELNuf/+q//croTROSJFBPpIiL95JNPggjkDTfcwOrTCwEvRPZpzKeffhrz588f1R+R/d27d7N1ERauCO8PfvADtp+hoaEuCa8nzPng9IJDYxLJVkN43Y2tdg8I3/j4eIYrEe0f/ehHbL1Llixh06KXiIyMDDzzzDO47777HAgvvQDRM80L7cvChQvx+c9/3stPitRcEF4vYBSEVzt4via85L3bZG5C0L4gZMRkuD6Sl8sZwh4B1j6tyjO1BkA9980FUNcDvH0MMA8CiYnAHCK8VqnD+Tf2oPfwSaSuX4SwxbmgRBjlzeXQh+nRm1rEyLJLOYMcSoUF10DxXgQnLHULdrkReK8RKEgEbtegGvCF967DxKqqsG1zHwbTZmH5p6IRXVUmvSEUFMBkCUdbdZtnPa87q7aqKjSdOoTK2H7EZS1g+l2+R7QP5NKgRtJARNdUb4LJYLIRXbqwF5MR41z/LVvkqTY/vNYagKSYEWxMGUJQ4+8RVPsYq9EbuBRNuv9j/sNOdeTaPzKXvIUgvJcc4ks2AEXKvvvd7+Jb3/oWI4pEouQRXBqYfk9VVFSwyCtFKp966inbfIjwEnm8/vrr2fdeffVVUDSWSKnaCK9ycUTsqE8iWzExMXjllVdAGUspskfl3LlzuOeee9j3iRgpC0V4v/jFLzoQc1obEeIDBw4gPz/fJeEl4k3k6t///rctSEBkn75ojY8//jiysrJw7733smFLS0vxr3/9S3UEkUgaScycvVjIybW7De/s7GQRT2eEl7Dftm0b8vLyUFVV5UB4qT6t3VkUX4k5jd/W1ob/+I//wDe/+U388Ic/dCC8ZWVlIHJJLzq8uBub6qjdA/naKcpLEfQ77rgDt956qwMs9OzRcyCP8Mor0HNC+0QvLAmafSyd74AgvF78VyQIr3bwfE14Sc5gbjMj62wWoqZFOdeIKuUM8a8AN3xW1eS5nIFcFaIHgfJyoLMTGJgOxCyVIr686CoMiHtvL2bkJSP+9uUYHB7EtpPbcDEgGJGzbmRyCPt/Lx6GlxH0EV06/G6uBIJjXDa6OAj890EgNAD43mJVS2OVKLJJX77w3mUdbtmCd/ZNl9wTbg8GWq3aDzpGLS5GS+U5z3peuZQhZQOw4i37gkpKUNlZh+bcZGTPmMf0u9zbmPaIfHjdEV5yo+gwdLA58EJplOmiHqVEVlNIQvKrY4BOB3xnHhCoSGZyMnI/AsL1jPT6B/qr6XJC6wjCqw5+emL61VX1aS26hxrsokcikb29vSxCeffddzPie+211zrUJrJHBIuIEpEfIrX8CF9OeKkfIkZEQok0jpXwEpmitkTQ/Pz8GGkj4kuRUV5uv/12dpzNo378+yRroCjp888/j7Q06c2dpBgUDSbCRRFId4SXophvv/02k0nwQlHZlJQUFvkl4v/3v/+drZNHeCl6+6UvfcnjnlHk8atf/So+9alPsS9lIXJJMgROpl116Irw9vX14Wtf+xrD66OPPhpFeOkl4K677sLNN988qmsl5lSB1r18+XLMmDGDyVLkEV6SVvDnhup6GlvLHlB/FBknKYjZbEZSUhKTctAzIC+eCC/tU2Vlpe1FyeMGqaggCK8KkFxVEYRXO3i+JLzkvVtprMSwYRhzLHMYaXHqMCCPmAZeDWT+DrAer7hbAV1WIzLFiWpFBR3PSURnxQrJ4IHqkN0YyRX6L3Tj/Hv7keYPrPzsSnYxrayxDEeGhxCbtBj5wZHq0/8q0+iS9+yiZ9wCTjreCxeBr80GkpxL0ka196X3LskNzLuPYlfjTERfNQfLl1uHo7cE8k/LzsZwZhaTNrjU87pLt2wlz6X9x9A7fw6KUoqg1+ltF9bWAvB3QXiJ6BKxJ+kGLxGJESyiq5bo8nZku/arvcBAGPDgIutFRtkLCskaTMH3ITo1Ggn5WjNsaP9MedtCEF51CB4E0KKuqk9rLQKQ5KRHIilEcomcLlu2jB0R0/+vP/7xj2216eibyA5FMUmDSqRp3bp1LOJGhUgHaUspKkzH6Lm5uSxiHBcXNybC29rayqKfpGHl0gk60qaoKhFvXogUPvDAA1hB/5FaC0Ucibinp6czYk6FSCZFCImsEtGln7sjvBTZpSP8X//617Z+6aWALv1SnxRxJBkDEV8qc+fOZf8OC3N6jdgBdSJx1I40wMooOo9GkpwjkY7+3BRXhJdkHCRjIGzopUQZ4XXVpTPMSaNMe05rr62tHUV4lX25G1vrHlDfJH+g/1co4k5yCnqhoBcMefFEeEnuQFIGLh/xxYdKEF4vUBSEVzt4viS8XM4QUxOD6YHTXSdMkEcMSc5Q8BCQ4dlrltuIMc2tzJGB/o9W/v9INIp8Dva9+hGGh0aQdPe1SAsKRN/5WnzQ14HE2ExsCE9wcHnwiJ5cd0yVKe0wpR92UbYYgX2NwMp4QI2Vri+9d9mUqqpgPNyKiqFFSJwXZ3+noJDoLkr8C/am0I8Q53re5s3Arg321RW9CGTcZ/93VRV6G07hg8g2+CUnM/2uXGNNSZxJw8cjvFyfSxFdIthUyB6NiCi9GHkTff3DTsCoA7641Op9LHupskReg4bgl5hUwqWm3OPmj18FQXjVYX0CwHl1VX1ai0umlJ3Scfz//M//MGJDBIzkDY899hgjSzya9pvf/IZdFKPLXlTodxbpZPklICIdFK0k4hkREeFA5LRGeGkcijBv2LABdHGLFzUR3sHBQUbUaR3f//734e8vnYxQVJKifBQNpiInvKSpJSLMC0kKPEV4qT0RL5onjfXCCy8wnSlFkN955x1GZqlQpFZOmgnT999/H4SnMlJJ9bVEI50RXro4SHILimzT/NQSXmeYk66YItHUH10c9ER4PY3tbg/UPOjPPfcce4Ei+YK8uCO8pAOmPSdZDL0E+KoIwusFkoLwagfPV4SXe+8O9w0j05DJPhRZa7JGT0gpZ4h8FbjuJrqS7HHyJQCIJmU3AXXEfilhRBGgd5LTgndW//J2HOkaQN+6IoSnTcP5nvOoN9VjWnAkHomf63FMZYWhD29FwNl3pG9T0gVKhexC2nByCHj5ADBjEPj66EvOo8b2pfcu63zLFhw/E44T8cuQPS+YXeazlaoqsPzCBF5REdPOOuh5A7qlFMvdp6UmSikDfa+kBE3dZ1GVHYXEaWlYkrQErQBzv+DJPIjwdpo6Mdw+7KDPJaIbmxHLXCK8Ibp8Pa+UA8cCgNvygYVk+ayQNZSkvIpIYyqiw6ORtTxrUut5BeHV/LGcFA0osktRwMhIu/k2RTApukqEky5RUQSYPhOcNFAUlSK5dOQ/a9YsFuGVa3jlC9NCeEmKQCRy7dq1o/S1pNUl0kqkkgppS0m2wDW85HZAumIivUTSAmWpGOn7FFXlEVV6VunndEGOk2D5nCmiSBHqN99809aGNMEUKSQNL62VLu5dffXVrJnBYMBXvvIVdvwuH1fe5xtvvMFkEhRBJ9cBZ4WikUTo6PKdp+KM8BLZppcQHmmm6D3hQS4O//jHP5x26QpzehGg6Dk5bVAhfCkIQESdLtTNnj3boT9PY2vdA+VkSc5Aa/nOd77j8CN3hJci0xRZJpmIL4sgvF6gKQivdvB8RXi5nCHUFIq0tjTXGlT5bf+gYoAivOscEvs6XQTPikYiXd0e6d5VXp7nwLBpx0G07axBwNJchKxbhJrhQXx89iDiB/vw9dRrmB+vltLT0QLd9jzA0uGaCFo7NAN4/hjQ0wk8mOWemFMTX3rvkpwBlZWoaE2FMSF/NFakAygtBSwWycctIQEtFS12PW/Q40D9i9JKiNhTNJtcGXixyhkqB88w/S5P2UxRdfoibp1u7kdHYwdaqlvYDWEqpM+dljnNsyuElk0BsKsS+GAAWDIbuJXLR2SyhurUB1B94XoMGAeQMiMFmcsykabXcJtQ43y8qS4IrzfoTUxbkiFwRwDSoPJCRI8udpEbwY4dO5gsgYiUnMxxJwbSi3oivES8SFLAC2lylcf5RLJJNkHOCHL9KtWj+mQ/RQSMIrfkeEBRaSK9RICJjBMJoz5Ic8r7pvlSlJfIORF3XqguXegiTbKc6POfU390fE5yis9+9rP44IMPmAsE/a6m/xNoDCo0XxqDIrzkKEFyBWeFHAUo2koEjC7NUaF5yfHUEo2k6CvpWmluFBUmLTVFdGmNtFZeNm/ezOQAhBnXW8svrbnDnIgi/ZyXEydOsHXTeESCae7yS2uexva0B/QyQ1Fish8jKUNJSYnNsYN+RpIRehniF+S4EwNpuukFhPaK7zfNmUt1iGhTpN2XRRBeL9AUhFc7eL4ivKSN7ezrRIIxAdHmaNdHx0o5Q+LdUpjWQ+FyBpbCrNkWmPTUDMMn63Hy5d0ISE3CrAekK2pbjYdguXgWVyctRkK4Nk0n/QcS1LoVQeV32cdesQlIcZ7C9iUjcKoRuDMCWODGDtbX3rskZ6AI7q6LBTBHpjiPhPNMHSSCXr2aWbmRnjes8zUk9spsgZRSBlq5tf/SyPPonRGHFekrEBUSxVIKN14cQPKx84ht7WK/QOkyRlxanKaLaB43VlGBMuy92gWkpwP38y2VyRp6IrJxfNGrOPbRMQx1DSEkNQT6uXqkRKUgNSoVYUGeNYNa5zTW+oLwjhW5iWtHJIwuNhGxlRc64iYiTBFcOh4nPSwRW3mhdkQ6qQ86+nYX4aXIprwQ6VNGHIngcAcGeV0eRabvcRmF0oeXLMucXfIiMkQX25TFk4aX6hP5ovnQRSu6MEUR3Xnz5rGuKCpKayciRv9XkLb4wQcfZDZZzgpFbUmfKy9E7Dlxpu+T9IH68hSNdOXb++67747StzqTNMgvranBnM/ZmaRBeWlNvj5PcgrlHlD9PXv2sItpRFYJG9Lt0t/pwhxJZkjmwoszr116kSmkQAjAXlJIC03uDPTC5MsiCK8XaArCqx08XxBeLmeg0ecY5jCtpNMMYUo5Q9Q7kmmuw1n76DVwXSizWCwFgoeYwcAo3a7T1be3w/CnElhCIpDyxevZhSiDyYDqtmrmx5sbry0pgS218N7PAM2bpCGDYoD1BqfShr1DwLsHgFl9wH32+yCjpupT713qfYuUR27LwI1AcDDWrnWRtbmsTLK5sO5Df8txBOwqRuDwGWmOmRuBq/82GtodO9Db1YHSTCAoNBxrstYwycIHFwdwoW8QhRd6EeUHRCZHwi/aD9NTpWjMpSrE3d9sA6JSgc+nWZN9MFlDCgBrpOamQzCPZKLmoxo0tTchcG4gguIl03+yqkuNTmUEeKKLILwTvQNifIGAQGA8EBCE1wuUBeHVDp4vCC9P6KC36KE36EH+qRnObmk5kzN4EuFaXRcosHv+OBB3AkhNBfLzVa51cBBtz78F0/khxN5ZzLKKcT/eIP8gRtS0FBvh9ev1rHElTRqAf5NllhG4Yy5TDowqcu/drLVZ3mtarXIGiy4KJT0rWKrlNa6WSW4N5NpgtSlDxQM2KcOgfzJGbjqEoGiFN6e1TbPfRRzKiED4+XCk9aaxF50Pp4ez+d8eEcz0ufCHKh9eLXvgrC4t+e2TQH8ysGG29eIaVSxdB7S+KzWxpkLmeuWLQxcxPH8YrYOkPJaKLkjHyC/Zq01U1FcQXm+fBtFeICAQmAoICMLrxS4JwqsdPF8QXvLe7bH0ILsnm8kNXKarVcoZSMOrQr9LVmSnLwL9e4G4Pg3RXSscPa9vQfORDuhuvBYp10o61JKTJbAMW2xH8WqRsxHekBCgdSdAOlFenEgbyCZtUytgrAfWBDl3X/O5965VbtCemIdyYwa/l+Z6idzfLYjsyp621WsJ+z0G49aN9lKuqkL/8dOoGOjG6f4RpEWkMWlId2wo6rL0SEoIB3dAk7s0qMV4LPXIeOL1CqBtOnDrPNjt5g6/Bhy1pj2lSPxdJta9XK+ctCwJxi4jy8JHz7Gc/JJUQ6vOeyzzl7cRhNdbBEV7gYBAYCogIAivF7skCK928LwlvDxaSpGx2cbZzFc1pShltJeqMzlDdDTs5rDO587lDMTh8ptUKSBGdTS8dz9OvnscmD0bWZ8tZBHImrYa5tbAL1upRc6B8FIjdz611k43DwEHDgBXdwA3rh4tLeDeu6pS/KqZqFXOYMgsRnV9GOgOTa475UZvL7D9n0AXpUGVjv+HZ30TDd2PjvLnZf65r25Hb+tFVCYAA4FAUV4RZsydgVa9DtWyLHisH5WZ1tQsy10duoP35gfAsenAqkWwEW6WaKN0ll3WYLWS43plpf8wyXOI+Lb3tjPyW5hcqFnn7e1aBOH1FkHRXiAgEJgKCAjC68UuCcKrHTxvCS/33k2PSEfIQfKDArLXyf2vrHNyIIZWdwYV+l268b/7PHCxBsgbZHertJeaGjRvPYyeyATE31jA7LC4qwQdX1M6XLVlFOElnajcvot8eYlUyQpFqD85BiQ0AjfmO8oafO69a5UzIDoaNfrlqK9X+ZIgP/onV4b1BvC50VIoat/X2Yfe0+eA2lr0BwDHCkMQkxqDG+ZLeezlKYX5EzBehJfGLykBPokFFi8G1gdYN4CY8OY7gf7N0jdkCUP6zf3O/YdpLdYXIpI20Nd4FkF4xxNtMZZAQCAwUQgIwusF8oLwagfPW8K7pU66HLUkcAnMNWZmO5ValDp6Ig5yhu8DQVd7NtEFQKmEy6uAtFZgVS6QMpY7Rc3N6CjZi3Pd4Yi9fjHT8fI0wzTRtVlrVR9bjyK81IGpUiK9vCx+FsixWwcRaS9rBfprJY9YuSkF9951KQPRuqXcXzdPkjOQ3NbqOua6J3nmO6q19AMgq5jV53IL3ti/0YDYgIvomB+B2rhB5nCQnygJquUphbk75ngSXrp/94k/kLkQWKuzXlyjib3/HHD+EWkJMlkD2zql/3CYdImNX2yki2z5CWoF41o3y3l9QXh9g6PoRSAgEJjcCAjC68X+CMKrHTxvCC+PkpKcIdecC3OTGfF58SyC6lDkhJCihwG/k37sQb9LcoaXzwDnG4Gi3jFGd2kcsxn92z5EQ/0Q/BcusCXEKG8qZ0fXWo6tnRJeGsONtIF0vB8PAbV7gHkmODgm+NR7l+ZhlTOQjUXJrjBms0uZ6Fzm9VBKTYJvBSK/IQmlrYbzxkNGDHQNIDIlEtF1FfC39KNibjSMQ50oSCywORvwxCCUUpi7G48n4SUp8u5uICoPuC5OdnGNPMsql46SNfBn1MF/eLnkzWvuN2NXwy5Eh0ZjeRpXJGv/fI2lhSC8Y0FNtBEICASmGgKC8HqxY4LwagfPG8LLCSORHkuFhek9ndqR7fmiPYmBjvyxHlRlpLuvH9hSBcT3AZ/L85y4we3qt2zBqYOdGFpUiPQV6SzT1ljsyVwSXhqcorxE7qnEFkhZ2AAMAtgG4NgxYPYp4OolkqyBe++6dLXQup3WZBAkZyBtNOe+bt8r5HOml5FpLzvYlEEFEOkAACAASURBVDlMgTs66HQoSbOwS3/FGcXMzYCvkeKjckOI8SS8ZE1W1gwM5gDLk2UX1wwG6YXEiayB1udMzys/AaCUyeNZBOEdT7TFWAIBgcBEISAIrxfIC8KrHbyxEl659+7yhOVoLWt1bUcmlzPM+D3QnaZKWPpCPdB4HlgXBiydr31tDi3KytB2xAhT3GzEF6azKPRY7MncEl430gY67j/UCMQeAxbPlJbvc+9dLmfIzoY5MRu7doFzX+fgKaUMtxqA/kjJpoyK0uzY2r85PRG7QozMwmt1hiSqpig2teIphfmA40l4idfuOQGY5gDz02QX1+hSXsmzQM/3pGkpZA30LWd6Xu7kUZxZjLDA8UtMIQivl5910VwgIBCYEggIwuvFNgnCqx28sRJeLmdIjEjErP5ZaKtuc55OWG7dJY8gevDfbewHXqgCQoaAbxWoTDLhbvlVVeg6XI+WoQSE5aTadMZa7cncEl4aX+41TP+mKG9sAbvQVW7V8S6LBXKzLTCUkksvnEfFtW+ldGuLNAzFxWg1h2H/fiAx0bkVGpRSBqtHLRuW25QpG+/YwXI6GxZlorqr3kG/y1MKU1JVuSHEeBJeCnDvqQDqs5khBxzisoRN+x0uZQ20bKWed0/bHpY9UIvkZSzbpmwjCK8vUBR9CAQEApMdAUF4vdghQXi1gzdWwsvdGZhBf10YsyNLLEhEVEqU4yTkcobUe4BOqyeqB/3uK9XAsW7g6mnAzVna1zWqRV0dhmuP46QxAkhOBk/woNWezCPhpYGdSBsotcFHPUDjIWBRP7Aww+T6JWEsy+VyBmuaYJ412KURhjzqLpNfsKHJ2aC0VCLP/MVEJmeomBPFfGvl+l2e+rkAgPxe4XgSXgrk0rRrZwBzFwOU2M72NBKJb3jKpayBQy7X83bN6RqTdd1Ytk/eRhBebxGcmPa/+93vwFP/hoWF2dLkzppFtniuy9atW7F792489dRTtkqUXvjHP/4xsj1kofTVSuVzDwgIQHJyMjZu3Ijly8dXv+6r9Yh+pgYCgvB6sU+C8GoHb6yEl5JNnCa9ZuJCdJe2sYGdZgmTE6t5rwKN4R71u+Ss9XwLQLkdHpwDxEhuZ94VKyFsMgaiNzET3PNWqz2ZKsJL0VMivZYOac7zn8Dg/MeZjnffPmDpOSBPZ2CaZ6cvCWNZqUzOQHoJHqTNywNGpaVXRqFJyhAhJeSwFc6YuVeyrP8toHgubPpd+nsZgE4oSOY4+vDyeZNuuT4SyLwOyKPoOf8B6R2qXnUra6Cqcj1v57ROtMa1jikF9Vi2kLcRhNcb9CauLZHG/v5+PProo6A9/POf/4yamhq8+OKLbifla8I7MjIC+vL391cNhnzuFosFZWVl+M1vfoNXXnkFsbGKS8iqexUVBQLuERCE14snRBBe7eCNhfDShZ636j/AkbBpWBo6H5l7mhE5LXS0HZlSzjD7Xagxhv3XbuBoIFCYCXwqTvuanLagqOW2bWhvHUR7Qi7TG+uz9QiMCcSOFjI/U2dPporwUmdKfexNh1AWW4BPjgIhzf1YZW5AdJyLFMxjWbJMzoCwMJBFV2enE+c3DxZqDkNbJQwoKACIAPf0oL2oAOXtlQ76XWojmdMpZAQTQHgJhuYgIO46YHaATF7BI9RmOmGQkmswv2TyTVYUruc19ZpgnGXEtLhp4+rUIAjvWD4AE9+GSOPAwAC+/e1vs8nU1tbioYcewpYtW/DPf/4T27dvh9lsZtHTb3zjG1iwYAGam5tZHSLKMTExiIyMRG5uLt59913278DAQBZpveGGG1BXV4c//OEPOH36NKZPn46vf/3rWLhQskOk6DARU4PBAKPRiJ/85Cd47LHHcMcddzDySs9UXl4emxtFcJVFOXf6+S233IJf/vKXrJ0oAoFLgYAgvF6gKgivdvDGQnhbu1ux6VwNLkbOwIyeGQht6MDKhAhGIB2KXM6QuRHouZeRJtsxuZPpUnT31QagPwK4L9/xeFz76hQtSkrQ39mHlui57LSel1OBp2BJsuCqWVd5zKqlmvBS55R2mEg/ldgCtN90CG82AqeqOjAbZtySFozE/ESvl8WyiZFg1ypnoA65Q8NaMsXgHmH0A3nEffpK4HrHJBkOk+H98m/qdKjLT2GZyOT+u2YAuwBEQ3ZRzNpmPCUNNCQR/WbysysCZkayP+yFQOl/waOsgRq0VrXiQsMF1MbUIjwpHGuy5N4T3m+Zux4E4VWJ75EfA61unl+V3WiuNv9HQIIspbi1AzlppOf+hRdewM6dO1mUdMeOHYycRkdHY9u2bfjb3/6Gv//97wgJCYGaCG9HRwe+9KUv4eGHH2Yyg6NHj+KJJ57AX/7yF0Z0ifASwf7tb38LvV7PIrx333035s2bh+9///tsho888ghuv/12rHaSvUc+d4rwfvzxx3j22Wfxj3/8AxEREZohEg0EAmoQEIRXDUou6gjCqx28sRBeIjwlXUYEx85CgCEAQ/1DWJkbj5yIYMcJvB5rP9ZfuRs4SHf5XfvvkgZz516gXAeWDvfTcYBP78aT+wBF+oqK0D0YiovGi8w7mAh8Q0cDZuXMQv78/NFpkWWr0kR4ldKGOY+gOv4Z/N/HRkREDWHZ9YlYHhJg86zVvnvWFgo5gzWYjaAgYM0aYHhkGB+d/ggLW15GTIP1eJWcCuhCnVLKoJwEx4y+n5mJ8shO5l0s1+82AyAzNqLuSxTtx5vwEhSnmwHTVcCMOEeLNOY+QS8gbtwa+PR5wo2a0BoEpgVqSk4y5n20NhSEVyWCH38GaHxNZWUfVlv2KpD+6VEdEmkkMqvT6dDb24u4uDh861vfYpFcZbnnnnuYRpf0vWoI75tvvokDBw7gpz/9qa2rJ598EldffTXWrFnDCO+MGTNw//33235+11134Yc//KFt/L/+9a8gMvvVr37V6dy5/ph+SHIIikKvX7/eh8CJrgQCjggIwuvFEyEIr3bwxkJ4yX/3IwwjMSoHSQe70BAdgrRFM9glIRtBbdoElN0mTYjcGRaXAZWVbmwDACIrh9oAUzqwejbg8/xWlICAcu3KhK2WXgsMRwzYd2gfy7a2aMYim9xh1AU8UIC6hx0JUmRGVVFIG5oSN2P7J7k4MzccC++cwaKiRBK9IvZcemC1EeOn93q9FEwn+7WaE69gefVD9ikXvQhk3Od5CWYzmL8ZlRUrsMUo/V2enY47NFACXmUS3vEmvFx6fH4BEJcGUL44G7Z8/1XIGrpbu3Fm/xkcHzkOvxw/FKUUQa9TnGB4Rm9MNQThVQnbJCS8tHdEKIn0hoaG2hZCcoZNmzbhwoULjEy2t7fjZz/7GYv6qiG8f/zjH/H+++8zmQMvfX19LGJLxJYIL0VzN2zY4EB4SZKQYRXxU7S2ra2NRXqVRRmdJmkERYZJbnHNNdeo3BBRTSCgDQFBeLXh5VBbEF7t4I2F8FI64X3h0zE3aC4W7zuLU7NiMTQ33vFIWy5nIMur4Q1AU5MD2ZTPlt+wr9JLN+yXBvtYzkCD0cWl6mopfJwrN88Cth7diu7GbswLnIeAAUnjxnW+cuKrmfBSR7tuA5o3sT77hpOx+eQmDGfORsIXotEDQCedwI+N9MrcE2A9qlQus/n8Ueg/uh5h/eQVARhjrobfdZs8yjds+3P2LMtW154ah/Lm8lHZx3hK4UIACYpHcLwJL0li6L3KnCFlXHNwjeASDRWyhsHeQdSX1uN072kM5A8gLz4PGbG2K3DaP2gaWgjCqxKszhqg75zKyj6sFp0LhE53SxrlPzxz5gwefPBBPPPMMzbyee+997LLbYsWLWJRYZIQyF0aPve5zzHJAndp+Pe//80uwFHE1lnxJeHl/VM0OTw83ClB9iGaoqsrGAFBeL3YfEF4tYOnlfBStPD9swdQH56A+ReSMKfeBH1+IqpSoxh5s/mwvuJnnwy5AOyrl/S7LvLcUnT3VAtwZg6QnaE4ita+LOcteLSSOw/IasntyfTdetCRNrko8BKVGsU0ypYRi7YIL3Uw0CFpZ62uDcdM96FuxgtYtjYQNTrJ3WDMpFchZ6DheCDTZkkme/noDUlAec7T6AmeDrKUoy+1haQs9JUZm4ncePsLA08p7BBNtXY63oSX839TPBB7lex5pPlwrcfgEVWyBkr7fMZ8Bt1LusfVqUEQXrVP5OSq5+ziF82QLpv96Ec/wssvv4zg4GCUl5ezf//iF79ghHfPnj1Mi/unP/3JdqGM5AQUuV21StIKU0T4K1/5Cr75zW/i2muvZRpd0uwmJiayC2y+ILzcYYI+sxTh/cEPfgAi3vKo8eRCXMxmqiMgCK8XOygIr3bwtBJeIjxl5mb0xGZgVqUfsroGWOKE3rAgdnGJypL6F5FIJIsKyRlurJEMUrmoVDFNTlLOhAPxy4BZwZdAziAnPE7m4cyejFL/djZ1Mo9hXoLjgxEeF65e0mBt6G98BxF1n7f1sz/sLcwo3IDEFClDGZFeSstLkV6Fk7H7TVXIGagyl90WFgIJls3ALvsxJ4peRF1UESOuVChxCEUvKT2wp8JTScsTMfCUwtTWWQLe8Sa8/KTgog6IXD0685vNvqL7YWCoXlqyC7cGww4DzrWfQ2tOK5LikrAkSalQ9oTY2H4uCO/YcJvoVq4IL82LLMr27t2LhIQE5s9LJJdcFojwkq6WCPCxY8fYBTH6PUYR39///vdMC0xE9+abb8aJEydYP6dOnWKyiJycHCY5INLrC8LLNbx+fn7s4huR7S9/+cua7M0meg/E+FMLAUF4vdgvQXi1g6eV8Fa0VKDc0oOQ0CxkVfcjKygAacvT2MCUN6waQH75RqQaXpImQ3KG2Pvd6nc5ZzMtBGKTJdJ3ydSSTggi48LDg9h2kpxyR9uTkc6XIr50wY2iIPTLJohIs8aS1PMgIgY/YK0siEbz4tPIyJE0eVwWQL0SrVK1fidyBuqLO5StWHQaUftXAd3kmEzh943A1X9jf6VIfVVrFXosPcxijMhcVIh7qk1SFiU+rlIKc2jGm/DSuGTGMOQHBNwivURQ5NlmVMHD30OvA90vS9NM2QCseGvUblISivbmdpyMPwldgm7cnBoE4dX4wRLVBQICgSmJgCC8XmybILzawdNKeCkV76GQCCT5z8W8IxeQmhaN+Nx428BE3IqUcoZTnS71u1xzORAJBF8nHe2v1r4M9S14RgYW/nRUnPIIpqsLSkR8z9achd+wHzua1Fr8R8yIb7jRRkAtCesRVCxpe6nwbGX0d2d62FHjOZEzcMJHf66L+yJQL7kykJQh7FPHgGD7pZdeSy8qzlaw9LlU3OlUiSA70++6Sik8kYSXv9PoioGeMCcJKEjHPS0UOH2DNE1yrFhvcMCGvs2dGg4HHUZoRui4OTUIwqv1kyXqCwQEAlMRAUF4vdg1QXi1g6eF8Jr7zdjVsAtVUamYOTAThYdbEZ8Xj9gMeyYeS/2LCJLLGYhIuIiq0mz5j5glQ6pCc6l9OZ5b8Gv8qalAvqMPhCuNqrzTMV1ak3fQrJAYrNgEpNitf2oAWA/aHS9cOVuZE1y5TDnF8iIKeq2yEtIR5v0S2fn/4RQfrl+mHyr1ubwBx0ap++Uk3SGrmWyUiYjw8neazEKgPkHhDyz3bAv8ud0nefGzQM7DDviQpMVYaUStXy0CZgdgRfoKj1Fwzw+g5xqC8HrGSNQQCAgEpj4CgvB6sYeC8GoHTwvhJZ1rWVsNjDEZSG2JxoKGDqQUpTj61souSFUvfgapaV9FFOl3ZUkRbCSqTkrgRT+isC5denN28Un7qty04CFlXoWIL93uCgtjx/wUxQzyD3J5fO014aVxDz4KHHtWmoGT6CKPmtKPnVl9sXYu5AxkRFC99zSKelYhbFiSMjTpi9E6/+duNahcw0z1o0OjsWTGEgddr6voN08p7EqGMhGEV+4+V0d3Ba3aaJtMhGs+ko8Atd+T9oEyrpGWV1Z4xrUGSwP68/odvId9+kwqOhOE91KiK/oWCAgEJgsCgvB6sROC8GoHTwvhrTJWoaK3HZieh+mVA8gx9SJrbRb8A2U522Vyhh3rDQjsCkNRRQWCUlIcIqr8chEjdauBOt04yBk4PER6iTCSTZqc+Or1KOmrhmXYguLMYoQFjr7I5RPCS64N7y20a2udaEg9kl4XcgZ6gYiq3IDEwc1sZSRl2JX7HDKmL/ToyEASByL8pOsl0l8wo8BmXcb1u+uyHa+muUopzGGdCMLLg/j0HhOULenKiezasq7xEPDsZODAIvszcKfJQdYwPDiMk9tO2pwatLpaaP80Si0E4R0rcqKdQEAgMJUQEITXi90ShFc7eFoI7w7DDlT7ByI+YRGm725DmmUIWWuy7IPSRbXyjezflth87LmpEp0GAzIaGpCXlQUQ6bUWzjkowEpyBqKeDp6p2peivQWxbmJHMuJbPdwKQ1wA8uZe59R31SeEl1LXHt6JhKOy9KROjtR5BjNamM3uja/ShUzEWP4iEg12KUPVwhfRFKBX7SNLl/cqjZUwdhnZSETyKOK7/8x+6MP0KEq1J+vlKYXd6a4ngvDyID49W3n5QCm7JCg7PZAbFZ990K2s4WTJSZzrPIf2ee2YETNjXJwaBOHV/lEWLQQCAoGph4AgvF7smSC82sFTS3gp+ldqKMXRsFhkxxQipbwJMyKDkVpEjNVaZAkWyP7KnHEfPjp4kPmfFs6fj8SwMLAj92rJkpfKitXALmJM4yFncAUPEV+K+NbVwWAy4GhvA/SRCbhm5rU2uQNv6ivCS5xr5MAjyBx4TuraRapfShWx3zo4Ic1eGUwmYP9+gDI5ka+xtQR1n0bU1oU2v9/OnIdRnngPTH0mkJVYRmgspJQangvX7LKp+QexqLcywsnn5iylMB9hIgiv0m6Z66JtLw28AmlpSNZwwJp5yomsgZwazjWdg2G6AdFJ0VidcUmvVDLYBOH1/HyKGgIBgcDUR0AQXi/2UBBe7eCpJbxc43lsWhZSh1KxcP8ZxGbG2h0auk4Db8syUVGyieFpMFRU4Khej5E5+YiuAi5aEyNR7gdKdtarByqVR87al+GzFpbG0yipfAMjZjPWRi9GkF8AQKFCik7r9dpTC7uYGedcK7oXImqIEHCuI6Vvk/VXhTVKyeoZDBhpawOSk+GXnGwbYcV7CxFlkvrqjZiJ0lsN2HdmH/v34hmLMcM/AEs1ICW3LqNmSvcKdymFJ5Lwyu+lrVkD8Ei0g0UZ1/GuvhrYZHcZgULW0FbTBlO9CUd0RxCSEgKlpEMDnKqrCsKrGipRUSAgEJjCCAjC68XmCcKrHTy1hJdu8h/tbIRp+nwktukwt7bN0aFBJmewXQAyGGA5XIPN4dk4HDUbURZgfpcUNLWmd7f5z467nMENVOyCVudZFHXFQn++215Tp0Nvair84+M1J56wdUL+vYGSKyz5xUYPV2J510L7GE6kDfRDIm2kRWWF9CD9/QBZq1nt0fTHn0M2j1SSxdl6Ay6GpWLPmT0I8AtAbsrVoy9vqXhcKLJ/4OwBDAwNjIpuEgkn4YM7C7WJiPBybOnPtWsluLnPsc1NQp6d4+hnXMoaTAYT2qrbUBtUi4CM8XFqEIRXxYMpqggEBAJTHgFBeL3YQkF4tYOnlvCWNZahbsgCXXIhYqu7kXnG7OjQULrKThqKXgQy7oNx+2FUlveiP30WqubEIXY6sCYFyLbmbKD8ZaSvpOIsS5f21fimhYM9WUSGTe5AOgxvEk/YZmd1hiirCENnJ7A85glENz5pn/xNh4BYegVwUpy5Myii6+1JT0C/8nGb6wRpb/WpRaCIrFUy7ROgdsCzs8ZEEd6yMjBseSZrniCDHr01tHp+sy0zEwjY7lLW0NPeg+byZtQP1GNw3uC4ODUIwuuTx1N0IhAQCExyBATh9WKDBOHVDp5awku39BuDI5A2cyV0OwxI6bEgex2ZZpHo0FHOYF5pQHXDTJzfLul344oLoC8IRl20VJ1Up5TTix+J+5KEaUdgdAuX9mTNzeirrUXA8PCYMq2xkbh4mSK2rXoYAmYj77o4ZBxbCFjlCIzsEul1Vpy5M2zOsDk+9PrPhGGBgclFSI9c3VaN1KhU5CXmQ8ojB6yVZx4bI2CeUgrzbieK8DrLL8IJOrNQ4y8Oej2weC7wht1LWi5r4E4NLb0t6MrvGqVjHiN8bpsJwnspUBV9CgQEApMNAUF4vdgRQXi1g6eG8HIC2BiVjORpCzBj52lMGxmxOzTI5AzmsJXYFfQhI3aBx45g3pwhpNx9DZsYvzxEd9SI9O6yRghVZRXTvjSvWlBGOWf2ZF5fWuPOEO3tJMXF0YYIZGSMYF5uA3Dq0/Y5U0rm+Y+PXoPSneHIk8CRJ2z1dkQYkDJnJpONKJNFcAmCS29fDYh5Sik80YSXe/ESDvRFhae+ZhZlXOhLP1i3DpCfUChkJeTUcLbjLDrmdyBNn4b8BMeEJRpgU1VVEF5VMIlKAgGBwBRHQBBeLzZQEF7t4KkhvJw4NSYsQPxwPPI/aUKYPszu0CAjC5Vhf0Nz0EbMDqxHZn8tgmY6+u/yRAVEOog0XfJUwtohkch5Ww3qTfWj7Ly8Jryy+bRXG7F7UyuiArpw3TwTMPA20Pe/9hpKaYNSzqCIrFeHPgND8CNM2ktZkytaKpi9GDk0JIQnMLxJy2o71h8jNnLyOMouTdHnREV45c5jFO2mQlFpblHGThm47qGoCDj/d5eyhsayRlw4fwEN6Q2YFjcNy9OWe4Gc56aC8HrGSNQQCAgEpj4CVyThJRLxm9/8Bnv27EFERATuuecefOpTn3K6m6+//jqef/55h5/96U9/wqxZsyAIr/YPgBrCS5e4mvo70Z98FSLa/JF3uBX6bD376m07jbDtdneGipkmZM+LQVR1uaR9LShw8N8l3S5FdskXlcpkkzNwBLkcQOk960vCawsyXryIdbn1kh9wz/eAwSPSNJTSBqWcQSZloLol/odgsdh1q6S77uzrdEiJO+rylvZHRnohsKZAdpVSmHc7UYSX7O/IuS0xEViyxL5IB4syeUq25FiXsgZyajh/8jxqImpUOzXQVcfwMWIrCO8YgRPNBAICgSmFwBVJeInstrS04Ac/+AGamprwve99D08//TTmz58/avOI8J46dQrf+ta3bD8LCgqCn5+fILxjeNTVEF7S754PDEVU+gpEHe9EWr0JiQWJMLRHYaT+RRT0WhMdyDOGkQUBFX5NXjY3frRM3+J63jFM/ZI2oQQM205Kqte1WWsR6C85K/iS8FJ/3B2ruBgIQy9QWwYcux2A1R0i5jPAgv9mlmggZwHSANNNrJOPO6YnvukQtuycyeZIJ/RUnGVHczjW9wJBTpxdpRSeaMLLM/kpM1pz7TFFuVc3NSGIXiI4K3Yha+BODUcDjiJ4VrDLLHx8zVybToFlioBrLYLwakVM1BcICASmIgJXHOEdHBzEbbfdhp/+9KdYsGAB27Nf//rX7M9vf/vbo/aQCK/BYMBjjz026mciwqv9kfdEeM39Zuxq2IVWnR4JKUXQ7W1GSlsPOmPS0doRAgcfWas7A4vsEjkjs93lzo9/KdJGUd5Lq4bUjoe8BbMn62138J/1NeHll6scAuHHn7Mfr9OEdE8DgdaXP2JwC6dJqYl5oSQf+vuwa5cdcr5vlDRiTRbzJbCVEiv2nsiqO/R4SmFPF+AmKsLLCL91kvwFgK+Hk/XswUFkb9sGkFUcGfbKcZcloehu7caZ/Wdw2v80BrIGbBIRZ/hwz1/6mbuEHO6wFYTXu8+taC0QEAhMDQSuOMJ75swZbNy4EZs2bUJ4uHQISH/fsWMHfvvb3zolvK+++iq7Ja/X67F27Vqb/EEQXu0PuSfCy4/2z+tnI06fg+nvn0K3YQjdydnQDZ/G6i5ZsonPjUgTkFs+cQGl9qlNeAsHe7J4SQjqa8LrEip51jr/BCD8OcAvHEiZBjTcY3NlgDWqrjzCb+1udZoOmG2P9WushIzbyanRX08k4ZVLdClAzouDllkeYg/odyprGOwdRH1pPRq7G9G3sM+lUwNFj/lFTBprrFppQXgn/KMvJiAQEAiMAwJXHOE9efIkvv71r+P9999nsgQq27dvx2uvvYYXXnhhFOTHjx9nXqhEdk+cOMFI8f33349bbrmFSRosFgs+97nPjcNWXR5D9PX1IZRS1LoolHSAyFN7ahGiEY3pb55GY2MQIuan4yrdT5B84SnWcij9XlgWS9pq/717mX53ePFi6fbUFC3kTrG3ZS9LrXtD5g1sFfR8+fv7IyBAbZJe94snonrggD8SEkaweLH1hYGaDHQgtGSuLU3wUPL9GNQ/gsALP0NA8yus0xFdOgaWl2AkPB0nTvixr9mzR9jXiQsn2FdGTAbmxs11mAQRs/f9/dn3bhwehiTWUF8opfABf38kjIxg8Yhszk66GBkZwcDAwNgTdaif1qiatbV+MBjsmMgrfOLvj04AS6qqkNjUhJHcXIzMnIngsjXwbyPaClgW/DeGsv4f+/vJrSdxruccOgo6nGJKdQ77+aHZzw/kvkeZs+kEY9XwMMI0roHwCgwMZM/ZlVTc/T90JeEg1ioQuFIQuOIIr9YIr/JB+Ne//oV9+/bhV7/6lY3wfvazn71Snhev19nb24uwMNe/knc27ET7sAWBGasxeGYQg39tQWBMOBasTcD8+jnw62lgcxhY/DyG0j7P/u6/dSv7c/jGG21Zxbye6AR1sL1+O7MnWzVzFcICwxjhpRczIiS+KHRx7f33/dmp+g03DDt0GXB2C4L33G37Hr1UBDT8n+3fcswPHvSH0Ujpmkcwc+YIas/XwtBhQG5cLmbGSNpeeTno78+ypM0eGWFfWsoJPz/Ql5q2RHjpBXUiyAx/maDo7lVXOWJ72s8PNX5+iGtuxtWVlRjJyMDI3LkIaHkHwXsle7jh+BXoMIqk0AAAIABJREFUv1bScTd/0gxjqxHGWUbE6mOxLGWZA2Rn/PxQ5efHorrXDg+j1opv/sgIkjXiS4SXXqh89VKlZW8nsq67/4cmcl5ibIGAQODSIHDFEV7S8G7YsAE///nPMW/ePIYqXWKjX5TONLxK2N98802UlZXhmWeeEZfWxvBMupM0UFrZUkMpTMHhCE9ahYZtJsRXtGHOMj2WLLsIvO1EzqBCvzuGaU5YE6U9ma8lDbQwfqru5H4fIJc2yFHI3Ahc/Tfbd5TH9870x/Lm8mP9Yo2JKNSkFOZjTaSkgbtgcImufP02i7KeHhR9/DH09AKzejWLrIPcLywdUvU7TUBwDFqrWnGh4QJqY2oRnhTuoIuWO4/wFNn8cqAn2zZnD7aQNEzYx10MLBAQCIwjAlcc4SVs6ZLauXPnmEtDc3Mzvvvd7+Kpp55iLg30/bfeegsPPPAAO+L78MMPkZ2djZiYGNTV1eEXv/gF7rjjDtx1112C8I7hQXVHeJvNzag0VsIUnorG8/nQVRgxy2RG8b3JCD/3K3vCAzn54qJUueP/GOY1WZoo7ckuBeGl+330nsD9cx3WriRg9MPwmUDxh0CEPXKrNMXgiTPkDhNKTMdqUcYzlqlx2JhIwkvr5Xk6eIphOQbcoiyjrAx5lIeYv3EcfNTugGFNANJe1w76qgmtQWBaoINzB/eWllvs8RcKkjdode0VhHeyfPrFPAQCAoFLicAVSXjlPrx0ce3zn/+87SJabW0tHnroIWzbto0d8T333HP4+OOPQb8U4uPjsWbNGpCEgciwuLSm/dF0R3irjFVoMjehsjMfAyOpmFXRiKXJfchclY6QD+fYL05xdwYanrM3MvOX3xTSPrVJ0UJpTzbQJx03h4SE+Gx+zrKCOXTevBnYtcH+rRWbgJT1tn87i2Q6syRTTrgZQCXANKdaSBl3aLC6n7nFYaIJr9xqN0N2IEGTtqVHPnYMa06dQhAZ9pLmvHWnlHmNitUL2dxshrHSiOMjx+GX42dzauAXAHn2QC50UZt62Rl4gvD67KMlOhIICAQmMQJXJOH11X4IwqsdSXeEd4dhB47V+sMQcxUiQ8Nwa8MJ6IJGkL0y2C5nCIoB7jLZB3blBaV9apOmhVweEIYwnxNenhUsNRXId+XTVvNz4PDjQO53gQVPOmDDVST0fkHvGTwVdHRotMesYFotyrRGLiea8HJslQkoOIAU5T5/5gyy6+qQQ2SXu4q8HmuXNRR/iP6wIjTsakBTXxN6F/SyDHz62AzmykDFWbSbR37VRMLlGyoI76T56IuJCAQEApcQAUF4vQBXEF7t4LkivBTZfGnnLpwyRCFw/hLkpw9g/v7TCI0ORVrM35zLGbg3Fmde2qczKVtwHW9mbCZmhs/0OeE1m8E8dJVJEtSCoUyjyy3JEiMSsSRJlmbMSYc8QkmuXeTL66lwbaraDHkTTXjd6XhprYzAX7yIwJoarB0elt4YqMhlDXMeARY9g7otdcyxpHNhJxJjM2COz2NuDNnWLyV2arPRKdsJwuvpKRQ/FwgIBC4HBATh9WIXBeHVDp4rwlvX3I6/v3sKw1HRyFidg4zBi0g5dBYRiRFIOrPMLmdY/haQaj1u93g2r31+k6EFj5iSPdnypOU+J7y0RjeJ6TxCoISd+wdn67OZZ6y7Ij9695REgvrhJM4VyVOONdGEl+bDLwU60/HSz8uHhnD+wAEUdnQgkZxFqMhlDdZTDMMOA861n0NrTisuJGYibvo8t3IQrS8HHDtBeD0+8qKCQEAgcBkgIAivF5soCK928JwRXkrL+s8tZ3D6whkkFyYieVEa9HXt0ld8E/SnrpcGostT6+nXurW4cvrXPq1J14JfAitKLEJESIRPNby0WA6dK1LmDhDlpbeKlgoYu4woSCxASlSKRyw5iVXjKKA2pTAfdDIQXnc6XponPcFHjx5FXGcniubMsWvPFbKGlqZstDe345OUFpxLjsdVMxYxKYMrUz+edU1rAgpBeD0+sqKCQEAgcBkgIAivF5soCK928JSEl8guEaiDjccQENmGzLU5CAifjhl7mhF5vgdJg99HRM8b0kBydwZ+dkzfV+Zy1T6tSdeiqrUKTZ1NyIzIxCz9LJ8TXk+kzB0gSieCssYydPZ1OqREdtdei0WZ2pTCk4nwqtFIlzQ2YsBoRFFSEuJSrC8JCllDe8QPcOaUCf9MbEXQ9CB8acYizPR378fMNdJqouccM0F4J93HX0xIICAQuAQICMLrBaiC8GoHT0l4eaTxaOcnmLuwAwE5NwIBQcguNQC9FmR2rUbg8BlpILLGSlgp/f0y1e9yRLk9WbhfOK5Ju8bnhFepw9Wyk8p7gmocGpT9q7Eo4ymFtUQsJ0OEl17iSkvBknusWeMc2eMmE+pOnMCMmBgsIUs99kzL3BqCYmBaUo+3jd2oDzqLuPhe3JdYgKiQKLdbxXEtBKA256AgvFqeflFXICAQmKoICMLrxc4JwqsdPDnh5VHGAf8OIKUc4dHhsKSvYNmjMrbUIWxwL1J7viANopQzXKb6XY4otyejrGHkbRseFq4dbDctxpqvg194i44Gli8H+DxJb7wmywW7czIPShe834NFGa+j9oIbDTMZCC/Ng+t4i4sBZ4kFLYOD2HbwIMsMeNOiRfZ0yzJZQ3XR+/i4LRedA2eRk9CKBXG5yIhVeJ0psOWXAtVqnqm5ILw+/WiJzgQCAoFJioAgvF5sjCC82sHjhJfni6AeEvPqYLTUQRebgZ74PIS19yC1vBkJvd9FtOUtaRBFpi+bw/9l4r/rDEmyJ2vpaGFSgaTYJO1gu2kxVkUID6xz2y1+wU4fpkdRqhrfBfukPFmUjYW8TRbCW1EBlno5Lw9Q+vFyBPbv2QOjvz9y8vORzX2WZbKGU1kP4QM8hqTmU/CbZwa5duTG57p9DsbykiAIr08/WqIzgYBAYJIiIAivFxsjCK928IjwDgzEYj+F9yBl+2oaki496RIL0BOVgniDCbHVba7lDPzMmDq4DPW7HFWyJ6s11mLWtFkoSKYksr4tnqKQzkZTJrbj0gs1ZEzZnyeLMp5SmKfPVbP6yUJ41UhG2mtqsLurC0FpaVhLbxBUZLIGS1A0Pko8jEjjWZzLO4cZMTM82r5xFwwtMhBBeNU8WaKOQEAgMNUREITXix0UhFc7eA0NJhw5Essa8mzA3JFAl1GMnqAwpFe3IebENtdyhuZmoLJSut3OfUy1T2XSt6Do6c5TOxESGILirGKEBbm6nz+2pfAoZEEBwO9NeeqpqgpoarJHLrlnsBpLMmXfRM5KAVh8mEhhshBeLv1wp+OFwYDylha0p6YiLy0NXKxgeT0GQZZOBldL9D/QbpmPk/EnEZ0UjdUZqz1tEXjkXG0CCkF4PUIqKggEBAKXAQKC8HqxiYLwagOPArPvv38RAQGRyMyUkkz1WnpRaiiFLkgHS8ZqRn6ySc7Q/JBdzmA14reNxlkXZ8zapjGlam+v244uSxfiIuOYtCHQwy19LYvj0Vq+F2raKjM586xwhcmFSAhXe03KPpI7izItKYV5j5OF8NJ8eAR97Vom1R1dzGYYKipwVK9HXH4+S8TBTPcOPoqMY8+y+h0h9+FcyPdwRHcEISkhWJftOcFyFYAmAHmkhVexqYLwqgBJVBEICASmPAKC8HqxhYLwagOP7pkdPtyF5OQIduGJSrO5GZXGSuijUtCeWMAurM0qOYmZppV2d4abDgGxsiN9pS+WtmlMqdrmLjPKW8phgQVqMplpWZxSj6umrTJhBaWD7rH0YEX6Co8OAs76l3vHFgO2y1v8+9EArI+KmulNmktrNFlVEfSSEmyLicHgkiUoCAhAJQB9604Ula5i6x1CJE5FVaA2qBYBGQGqcOYJKNT4HNMYgvCqerREJYGAQGCKIyAIrxcbKAivNvAoOtjQ0IVVqyKQYA0GVhmr0GRugj4+F+2xmYgdHEbGO39CUu+DUudKdwY1nk/apjWpa/f09GBgeAB7jXthGbaourikdkGe0uAq+3FWfyyWZMp+nVmUNQOM/KlNKcz7nEwRXjU6XmLFxy9eRF1BAfxiJakPI6oyt4Ym3cuo9ovH4NxBVck9uM+x2pcFQXjVfmJEPYGAQGAqIyAIrxe7JwivNvAoMHvuXBduvjkCUVY7UR4hTExfAWNIFFLae5Cx4/N2OcP8J4D5j9sH4vpdbhOgbQpTrjYR3oCAAPSjH7sadrH558XnebSnUrtQj8fuso64lRmXTpv7zWxO0aHRWJ6mJQ7rODt5IgpubDYWhwbqdTIRXlXWbwYDLDU1KJk/H0hLgw4AU+nK3BpI1lA5vBFd+V0sdbOn9M3UXEvCDkF41X5aRD2BgEBgKiMgCK8XuycIrzbw6Dicfrl+5jMRrCHX79LfE7PXwUhyhnoT5u6ZZu/4VgMQMdP+b+WtKW1TmHK1OeENCQmxyT9oEWPVzCoBUKYJdgeQMmLZ2t2K/Wf2+0RqobQo05pSmM97MhFempPHFworKz6SkIDmwkJQWhV2NVHm1kCyhr2B29A+rx1p+jTkJ+R7fI7LANC1N9IFk4+xuyIIr0c4RQWBgEDgMkBAEF4vNlEQXvXg8VvrQ0MXsX59JGvICRN5uJpTi9iFtWsr/4yYmq9JHSvlDPQ9rt915eivfkpToqac8NKE69rr2BcleiDfW0+ZtzwtUkv+DqUlGZ+L2qijWzINoNpKzoikjSVFLvU/2QivqhcKZeo6DtR7CwETCTuA40F/wrFZiYiMjVTl1MAvA6q5uCYIr6dPifi5QEAgcDkgIAivF7soCK968PgFqeDgi7jxRonwckurTH026vXZ7MLa6h33IMj4itSxUs7A9bs6HbDasz2T+tlN3ppKwkszrWqtQlNnE3O2IOcGb+zKeNQ2NRXI9xA4VJI3vn++kFjILcqI8FKEV4uXLN/ByUZ4VTlh8PzayiQqR54EjjzBltYVeD1K4r6m2qmBa6DJ3XeJh8dbEN7J+/kXMxMICAR8h4AgvF5gKQivevD4L/6EhE4UFtJ1GqCssQydfZ3ITilCnU7Pjl6LXvGzd6qUM3D9rhp2pn5qk7qmM8Irx85b/SyPvKt5h1CaY3BLMiLdep2ng3PPMPOoJD0ddByvJaXwZCW8qnS8rsLsAx3AG9JFNpI1bIn5HQKy9CjOLEZYoHtP5l6rx7GalwZBeD0/m6KGQEAgMPUREITXiz0UhFc9eFx6m5LSiYICifDyG/7Z2etAl5SyDS8hu3yj1KkzOQPvREumBPVTnJQ1XRHeweFBdmGMLMG8tStTWo25AkJ58s4ThqzNWusTf2BuRcbHV2urJZ/vZIvwsufceoPMpR+vO384sicjPS+A/REPonXOWtX6bbWyEEF4J+VHX0xKICAQ8DECgvB6AaggvOrB48fhc+d2YNasGFAWsfLmcpB+Nyi1iF1Yyy/fiFTDS1KnSjkDfW8suXDVT3FS1nRFeGmydOmPSK+3dmX8RH3FCtjcM5Rg8EhwdDSYhzIR7m0nt7FqapIhqAWXX1aj+lpSCvP+JyPhVSbrGIWFO3+4488BBx5hTdqCcrE3/xejnBpGRoD6eiA93THBBU/NXAjAXUoQQXjVPp2inkBAIDCVERCE14vdE4RXPXg8ynXVVSbEx8faLl9lxmbCGJ+LHiJO7uQMWs7e1U9r0td0R3hp8vzFgf4+Vi0tP1HPywMyXKTmUgYh5S8sdHnOV4VblFF/alPjyseejISX4+s2ox3Xiyh1vDJZA3vnW/gqEqfNcXBqcKUT5tZuniLlgvD66ukV/QgEBAKTGQFBeL3YHUF41YHHA1hUe9kyE2JjY8H1nwVJS1AZkYiU+hdRsOeLUoeUVY2yq8kLD5NdQfpdWr4nwkt1eLY6+vtY9LRqEiQoHRoMJgOq26qRGpWK/ETPNlnqnhSp1scAugFwT14tbScj4eUvC9y/2Ol63LFimayhOvUBXEi/18H3mHNlHn3n/beSDEKFFloQXi1PmKgrEBAITFUEBOH1YucE4VUHnjxhwZw5EuHl+t3CrLXY7x+IJbs2ILF5s9Rh0YtAxn32zvllNbpZRefugYHqBr4MaqkhvLRMuV0ZpfnV4tyg5mKV0v7Yl5Zkym0aAUBf/mPYv8lIeOUvfOvWuViUvJJS7CuTNbRHzkfF3P/GmizpdYB/NHiv8qbkfCGJTgBXw9LPBOEdw4MmmggEBAJTDgFBeL3YMkF41YEnt75KSzMhQBfAtKdkq5WSsRrN3aexerPsLF3pzsBDWFfQZTWOrFrCS/UrWipg7DIyXIn0BvqrezFQQ8iUOlQ+lq8SYKh7kjzXmoyEl2btynnMYUX8rSI7G6AvXpzIGopz7mD7yz8aQUGAxQIUFsKWtpua76BTAg/yEEF4PT9XooZAQCAw9REQhNeLPRSEVx148uPw+HgTOtBhOw63JOYjUCZnsEReg6BPfWLvmLNlNb5Z6qYzpWppIbyMWFmt3rTalXm6D6h0GuDjELH2NvmFLzdkshJeVQk+uE6d2CslVpGfZChkDXGLfoIo/wSUlgL00aBM23RxTakTrgLQRPpuAC7k2SLC68sHUPQlEBAITFoEBOH1YmsE4VUHnjxhQXCwCY39jWgyN6EgsQB1USnIL10FvdV6qSvjfxBR9KDUMU80QX9XXuZRN/SUr6WV8I7VrqyiAjAaAWdBdGcmAlyS4kuHBl9s1mQlvKp0vAQA/7Aoo7wKWYP52s0wN2agqQkgWTsR3v37AaVO2GDNYOfu4pqI8PriyRN9CAQEApMdAUF4vdghQXjVgSdPWDA0ZMKhjkPMP3ZFxmpUDJxzkDOYF1Uhas4CqWN+xHuFXVSTo6qV8LL3BJldmdq0v+4ygsk12PTeQf2XGkpZemOuJVX3JFz6WpOV8LpzHnNAhYNNUd41smt7CllD1dV70VSzlDWlYDBV32YV7Mp1wtzbWEdZDF3ALwjvpX8uxQgCAYHAxCMgCK8XeyAIrzrw5AkLWs614GDHQUaWlmStQZNMztAbuBSWa7YjKiXKMbpLv9HD3GeWUjeTqVdrLISXVim3K1Pj3OAu94HSxeFSWZL5YncmK+GltSkz1blcL4/yKnzi+t+6BiG95FQM7Nf9J1oDf86iuzwltCudsDXvBdYCcKbqFoTXF0+e6EMgIBCY7AgIwuvFDgnC6xk8pX3u0cajON13mmUHi0paAr1MzmAM+xliix9FSFSInR0oj3Y9D3lZ1Rgr4SUQatpqUG+qB3kd58bnusXFXQRSaUnGHRrU9DvemzGZCa8av2OGF3/7UOjWe3f/DGGnv8eqNI0sRVX0XgcJiiudME/m4SoBhSC84/2UivEEAgKBiUBAEF4vUBeE1zN4Su1iWV0ZOtHJskUhJBjZb9uv0hgidiDj1lV2r6Ur9KKaHFVvCC+PxKqVHvCLa0pXLLkGOyHBTqTHmujC81Mz9hqTmfByCzHS2y5Z4mGNPBwss11oP3oK+sNZtoZlCSYsL46x/duVTrgGQD2l7rZ+KUcWhHfsz5toKRAQCEwdBATh9WKvBOH1DJ4yOri1eiuGg4ZZgoTOxv9D5sFHWSddgdfjwoy/IK0oCdi1izIuOL9B5XnIy6qGN4SXgCg5WcJSD6txU1ASWw6k8iieJw1RI5UY782YzIRXtY6XQOM6EtktNJPBhND9axE2uI/BalnwDILmSWmHqbjqvxlAJYBEAM54tiC84/2UivEEAgKBiUBAEF4vUBeE1zN48oQFqemDeKPyDURERIBu95vfW4goE/0qBkjOMJz6BSTpOiR/JWXaKM9DXZY1vCW8XNagJhrr6khcrsGWk+i1WWtVe/2O1+ZMZsLLsCuR/HIpf0pUlAdUeGWrQ0m/uR/mD36M+L6npYYJK4HiDx06cZahmCegCHKRvU4Q3vF6OsU4AgGBwEQiIAivF+gLwusZPHnCgsHQVnx4/EOkx6ejYNoMhMmSTdRFHYc+VQd9k0SA2dXzK/SimhxVbwmvlgtmPKgoP3LnGmz5+8dktSQj3CY74VWt46XF8OMRa5R3eHAYO/98CKtjZXHaO01AsF3W4Kr/EooI08cKgPL6pyC8nv8fEzUEAgKBqY+AILxe7KEgvJ7BkycsqO+sw8GGg1iUvgjZLf8EjjzBOugMug2tYT9HUlAbIiwmyViUXz33PMRlXcNbwqslIqu8YEhtle4N5n4zy5KnNbHFeG3SZCe88qyDHh9x0ihQZglZSPgfPz6F65M+hwSdJGvA4meBnIdt8Dp7aaEfVtApCoACACmKzRCEd7yeTjGOQEAgMJEICMLrBfqC8LoHT5mylrSfDW0NuGHODdBvXwJ0n2YdkJzB3LUK6SFGhESHSue9IrrLsPEF4dUia1BmVFNqsFu7W7H/zH7msrEkydPNKy8+XGNsOtkJL8+lorTZdblcvgGpqWjW56Ps7w3IDn0ei6c7lzW40vHWUcAYgLMEFILwjvFhE80EAgKBKYWAILxebJcgvO7BUyYsoKNw+uV65+wlCHxntq0xyRkoyUR2fhig8B71Ynsui6a+ILycpOrD9ChKLXKLC/dy5RpTuQY7IwPglmRqE1qM9yZMdsJLeHhK4+yAmeytsTR4LS6cNCE1rBGLglcg2K9HqnrTISCWYrdScaYTbqckbgCiASxXbIogvOP9lIrxBAICgYlAQBBeL1AXhNc9ePLj25TZ7ShvLoe/xR83D++3yRl6o+9GU9vDCDE2IH1pArDaVT4oLzZqCjf1BeGldMPbTkppuDxdNFNqQOUabJKSVrRUwNhlZGmhU6KUh+MTD/RUILzKlwiPqFVVoelQG6p6sjESGYmc2DboL34Z+pEyqen8J4D5j9u6cdY/v7hGldYJwusRclFBICAQuPwQEITXiz0VhNc9ePLjcOjrWHRQ769HUfXnbHKGzvhfonXfTERHjSDhtmuAlMlHorx4RLxu6gvCS5PgVmKe3BqUWdWUEofJbElG65wKhFeJsceHxGxG5UuVaDJFYPaaWfBva0VPz1YUDEqWfgiKAe4y2bpxpeMletwJgGL8etmgIsLrcQdEBYGAQOAyQEAQXi82URBe9+DJfV2bhqTI4CwEYG7FzbaG7TF70L7nBPRXz4b+5qu82I3Ls6mvCK/BZEB1WzU8yRq4DIVcGcgNa9s2QK435Q4NniLFE7UbU4Hw8suBanW8TPf7+2MYMZtx3booGM/5o6W3BcuG1yFoqFuCmuzJyKYM9qzcyv55Aoo8APZ0L2Ayo5CQEARRA1EEAgIBgcBlioAgvF5srCC87sGTJywoPyclQFh29g3EnnlJaph4N5oP3oYe8yCSvnwzItKmebEbl2dTXxFetbIG+UVDIrz00sJzH/A+1GZum4gdmQqEl3BxldXOGWZMolBzESnnD6Eguxd1fWk4329CYvTPkdn+jtRkziPAomdszZ3phA0AqgGkAsiXDSQI70Q8qWJMgYBAYLwREITXC8QF4XUPHj8OL17Ti1JDKXRBOqys/CL8exulhlFP4NTBLAzFxiHjy6sRFCYiTEpEfUV4qV+1sgZOlsgdrqkJyMwEcnMBLZ6+XnysvGo6VQhvRQVgNKpLJshfHFeH74Gu+zwaLk6DKVSHrmlbcO2ZH0p4KWQNvH/5HVAzgF1UVZGAQhBerx450VggIBCYIggIwuvFRgnC6xo8uadr9pJmVBorkdp3CvlHpVSoFv8IBIS9gpNH+4C5c5F9+zwvduLybepLwqtW1sDJkk4nZXjOzpa+ePvM2EzkxudOStCnCuFVq+NtbgYqKwHai9X57SzkbmwcgDltHo7ojuDWtvucyhpc9c8TUKwFEGjdQUF4J+WjLCYlEBAI+BgBQXi9AFQQXtfg8YQFdByuy6hCk7kJ+YZnkdpeyhp1hq1BcN/X0NQ/HWE5qUgtooNWUZQI+JLwqpU18MuGfC6FhUBCwuS3JKP5ThXC6yyDnbOnn0d3Cwqs9znLytBe04p2XSqOJ53DPPwFGa2bpaYyWYMrnTBZk5FFWSFlJhaEV/yHIxAQCFxBCAjC68VmC8LrGjy5Q0Nz0A70WHpQfOTLCOtvZY2a9L9BxLkstMXmIDYzFvG58V7sxOXb1JeEl1BSI2vgLyscVe7Jy9sWJhciIZzTpcmF/VQhvISaJx0vv0DIorvcrc9ggPmjQzCeD8Tp3FBEJvx/9t4FLqrr3Pv/cRmQAQZxREbl4iBOFFRQQSUGjJIEkpjE5nKSpmlj2qZve9KT5vS8by+e9py2b9O3Sdomaf45aXva5nJ60txOTFLTSBIwARUjqBAFdMAZ5eYMMOAMMCPMAP/P2nv2MAwDzHIYGOTZnw8fBdbae+3vWsz8Zu3f8zzHkXd2rzgJHrYGb+eXAtc0ANgXO2iHN7jWMI2GCBCBwBAgwesHVxK8E8OTcoGmaWzQoRTK3lOuN2arbClaFv83FoUvRY85FEqNUviiYzyB6Ra8ki1hskpp7oFrbES7nIlbK5orYL5sRkFqARSRiqCcrrkkeN2zmLAddM9D+hvyrLRtf+8D6KtNuLgyDr15ISg6eZ9XW4M3nzD7uFnlTEsmlSAhwRuUS5kGRQSIwDQTIMHrB1ASvBPDk97MkzNb0WKvGWNnaIm9FfKVT8JqjoXNZENSXhLkSrkfM3H1dp1uwetrpgVpd5ClJ8t3luaSUpLt0niWLgge/nNJ8EpPQaSgQHeKUgli9rPCQo9K21ottH85BoNsAJZ7E5HZ8kevtgZvPl6pAIV74BoJ3uBZvzQSIkAEAkeABK8fbEnwTgxPytCQvLEOLX36MXaGI8tfwLWbH0RTeRuG7cNIL05HaHioHzNx9XadbsHLSPlSPEL6wKJSATk5gGXAgvIL5YhbEIf8FM/itMHDfy4JXvecx9KHComkJIY9d3eF3zsc0P9mH8wWCy4Uy7Ek6rxXW4NXSwSzUrCgUQAFLFEKWRqCZ/HSSIgAEQgoARI5wIIUAAAgAElEQVS8fuAlwesdnvsj8bj1FQjvPOR6Q+4PV+HolqPYsTwZTQeaECoLRXpRuh+zcHV3DYTg9SXbglRiWMrQYOw3oqqtasrCFbM9G3NJ8DJWnpXsJH5SsBrLhcwCPz2P9nerYK5uQIPGhIgtq7Cr9suA/ZLYzK0Ihbfz1zIPPQCpAAXt8M72qqXrEwEiMBMESPD6QPmVV17BO++8g6GhIRQWFuKRRx5BWFgYSPB6hyftLLE3apNy/xg7w3l5MboK30DGYBhaK1sRpYyiDA2TrMFACF5fbA29vcC5c8DKlUBs7NzI0MAwzjXBK+2kuwvbManIpGA1jzViauiA6a8foi7uHGRF67CzrwRy3R/EVm7ZGrz5hKUCFGkAWHI5Erw+vAlQEyJABOY8ARK8U0xhaWkpfv/73+OJJ55AdHQ09u7dix07duBLX/oSCd4J2EneQbnSBKuyckxQzScJz2LZjY9CqTXBpDVRhoYp1l8gBC+7pBSAlpeUB6V86oDB+s566Hp0yEzIhDrevTBtcL0GzjXBK+2ku/t4x6Ui84LY0mqB4b1jON97AoObYpCbpkTi0fvElm7ZGrz5hFlaMpaeLA4AM6eQ4A2uNUyjIQJEIDAESPBOwfX73/8+1q5diy9/+ctCy48//hhsx5d90Q6vd3iuPK5KLVRhryCn6XGhIcvOUK5+Dzfk5KCnvhM9uh4kZCYgXh0fmNV9FZw1UIJXErC+FpHwxfcbDLjnmuB1fxrCdnmlYDWZDCgqmpjogGUAFw40oLXhIPpX9mBt8VegPpg7ztYwkU94v/PUrADF5b4+REZGQsYuSgcRIAJE4ColQIJ3iom977778Oijj+Laa68VWur1enzjG9/A+++/j9dffx0jIyP4yle+cpUujyu7Lekx6tDSo9jY9aPRYhPyO4HlP0Bcbi5aKlsoQ4MPeAMleKUywbJQGYrSJ1FWzjGWNJXAPmxHcXoxwkOlGl0+3MAMN5lrgtfd787Sv02UiswT47BjWPDAG05Vwpx8DurM67B25APgzDNiU6etwf38xcVAuHPqKljxFwAsNVkkCd4ZXqV0OSJABGaDAAneKajfcccd+NnPfoasrCyhZUdHh2BneOutt/Dee++hqakJ27Ztm425C9prnjq1EFarDB2ao3hu6B9d46y78F00L12HsGXL0PNZj/DzhTkLERIWErT3MtsDGxwcRGhoKMIlpTKNA6ruqcYQhrAmZg0Ussnz6n7W85lw5S3xW6ZxBNN/KvYB9PLly4iKipr+kwfojNLfS1qaBTqdOA/Z2SZERg5PesWe6h5YLSaYZZWQL4jCxrVpyOn/odDHERKDMuX/CP+vr49Db28E1qy5BIWC5WcALkRHwyCXI7WvD/GXLgnri8UlzKfjpptumk+3S/dKBOY9ARK8UyyBqXZ4Dx06hJUssocOF4GGhpXoWRyBa3P+iu8N/VX4eb8tBgcO3Y2G1FTIZFEYbhoGwoAwzfx6k+VdJg6HAyEhIQERI8ZhI7pHurEoZBESQyeunNY/3I/mkWbIIUdqWCrvLcxoeyZ47XY7IiIiZvS6/lzMaFSiu3shZDI77HYZ4uIsWLasc8pTDrcOY8gyhEuyY4gMtyEDK3HP1j9DJhsU+v614xtoGVwJ6fyLFl1CYiJz8ALdcXEwLl6M2P5+JLa0COuLfbCaT8f3vve9+XS7dK9EYN4TIME7xRJgHt7169cLu7rsYEFsL7/8Mnl4J+BmsQB/PQ5oo7rxQP9XsMH4vtgy8n5gyTfRs3YtIgYj0FbVRhkafHj5CZSlgV3aV1tDq6UVNYYaTFadzYdbmZEmc83SwKB4lnLOzgaSkqbG1en0wdcPHUVYbxOK4zZBNvgiMPCu2DlpN1Cwz3V+ljWF+YTZYWOvZSy+DcA2sjRMDZtaEAEiMOcJkOCdYgpZkNof//hHPPXUU5DL5fjhD3+I7du3U5aGCbgxp8L7jcClkWb89pzbbmDMn4DUTehJScFw57CQoYFKCk/9+hFIwcuuLnlzJ8vWoDVpwb40So3wFczHXBS87j5buRzYOUEqMk/uPfoedNZ1ojGyESOpI8hDMpQXqoDOh51No4GMD2FPy0ZJuVjJUCoTLcy9swDFdX19iKagtWBe1jQ2IkAEpoEACV4fILId3XfffZfy8E7BiiW0P9oGtLUBaw2/wD/0/avYgz0uj/kjMyaiJzoaA80DsLRYoMpWQZE0uXfUh+m5qpsEWvD6kq2hur0ahj4DcpfnIjF6YutDMEzEXBS8jFtFBWA2C38iPu3usj79xn7hSUnbUBv61/SPpoz7YAPQUyNOR9RjgKwQ5c2psMiXIu+2xa5CFtUADABW9fcjLSKCsjQEwwKmMRABIhAwAkEleFlhh7Nnz6K9vR033HCDcNO9vb1Cypxg9ORRWrLRdSlVb2o7D0TWDyCr8y5skLnZGSK/CBQWoufyZfSe7sVl82Uk5SVBrhR3nujwTiDQgtcXW4OUs7cgtQCKyOD+gDJXBS+zNbAPihs2ACE+xnBKmRqMl40wrzeDZdzIXpqNRN3vgFM/ERdU+DpA/gvUNcdAZ5DjmlUj0OxYLqhqLSB8qaxWZMlkJHjpRYgIEIGrmkDQCN7Ozk786Ec/QktLixB08tFHHwngn3zySaHgA6tuFmwHCV7A4Uxiz1IcMT9gyFGgvbkLexwJo9PF7AwxK4RntT09Peg8LAbkaHYF9+PxYFhvgRa87B4lW8NEgna/VszaukuzKxiQTDqGuSp4rxRsU0kThu3D6MvqQ7utXfy7il0CzUG3bBq3tsOgB6re78DiyD7krRYzpBhSU1F9zTWIcjhQQIL3SqeA+hEBIjBHCASN4P3pT38qpMZhkbO33HKLS/BWV1fjhRdewJ/+9KegQzrfBa+72GX7tDlM/JYA8p7nkD/8qDhfkp1BpQJycmC4YIDllAUyuQzqncFbsStYFttMCN7Jqqj5UoY4WFixccw3wdtc0ex6WmIMNaKus06Yjryze6HsPSVOzaZn4Fj5HRw4AIT3dKA4/RxgMsEeEoIDCQkYVChwe0wMZL5EygXTZNNYiAARIAIcBIJG8N555514+umnkZqaihtvvNEleJm94eGHHxYKPQTbMZ8FL4vyZuVJrQCY2GXB3zIHhDfVDPNNSAsRd+ix4GEg4nYgMxNQq9F2tg39jf2IUcVgWc6yYJvSoBvPTAheydagjFIiL9kZxu8kMdnvgg7WPBS8UqYGqWKhZcAC5rlObH0NmS3/KU5R4vVA4UFIJYsLCgCFzAZotSi1WnFpaAg7+vuxaLLSbsE42TQmIkAEiAAHgaARvLfddhuef/55pKSkjBG8p06dwr/9279h3759HLc1M03nq+B1F7txzp1dluaflTGtPXweO/vcdm6ZnSF0CSC8yyqgq9LBYXRQhgYfl+hMCF42lIkqqel79MKuoa8liH28rYA1m287vCzbCfuKT4tHQoZoI7LZbahrO4ycIzeOcr67B/VNC6HTuT57Cr+rGRjAuZMnkdXbi/TrrgPmUMGOgC0iOjERIAJXJYGgEbzMv8vELivbK+3w9vf348c//jHi4+OFf4PtmI+C1wKARXeznV0mdtl+oFRoVq8HzDUvIdv2kDhVMZlA6C/F/zvzIZ0pO4NQayiW5y5HdGJ0sE1p0I1npgRvrbEWLeaW0Uh/JwnJ7jAXUpKxIc83wStlavD6xKR0B2D8RJhJ05ofwxT7Y2jPyOB0Fwk/1wM40diIVc3NWJ+WJjyFoYMIEAEicDUSCBrB29zcjO9+97tYvnw56uvrcd111+Hzzz8Xqv88++yzWLYs+B5/z0fBWwGABagpAeS6iV32x6HVAsqaHVA6xDdZqH8JmDIh5EFyZryv+3sdZMMypBakIlIReTX+TU3rPc2U4DX2G1HVVgVPW0NlSyVMNhMmy9M7rTfs58nmm+B12BzQleoQKgtFelH6WHpnnwWOPyYK3th1qL7mafQ35CN6QQQk9wL7APthayuimptxU2goZFu3+jkD1J0IEAEiEJwEgkbwMjwsgn///v1obGwUdmpWrVqF22+/XdjhDcZjPgpeMV4f8BavX3/sPDKa3HaIVpcA7YOARiN+sd2k104gJiaGMjT4uKBnSvBKwWlsWMXpxQgPFfftJatDoboQUTJmXAnuY74JXjYb2v0suRiQXpyO0HC38sCDl4C3Rl87Sza8huqjK5ESsxJ3FMUzh5FwlPX1off0aSyz25HLPpiGS89sgnuuaXREgAgQAR4CQSV4eQYeDG3nm+Blu0HlzvRjRV4mQFf6EtKMTjsDC5SR/0I09rI3UaUSA5YBsB3excsXIyU/JRimMOjHMFOCl4GQdnMzEzKhjhc/uMyllGRsvPNR8EqZGrw+NXGzNejSHsV+453oMkRhe+5iFG5ME+a4s68Px86fx7DVirXp6VAvWhT0fxc0QCJABIgAL4GgEbyshO9kh1SIgvcGA9l+vgleI4Aqp51hbCy/SNn0hpudIe8loI4ZH0b9u5ZWC5oONUGVrqIMDT4uzJkUvFKAmmRrYBH/5RfKEbcgDvkp+T6OeHabzUfBa6w1wtxi9l650M3WwLI1HE74b5RUtmKxyoYNG0KRszQHQwNDuGQy4aTRiBCVCgUpKQju8iKzu8bo6kSACMxNAkEjeO+5554xBNkbl8ViETy8CoUCb775ZtARnm+CV6rMxPaFMjxmw3HpPML/7mZn2FYNnLoIxMUB+fmw2+y4WH0RXW1dSN2YKmRpoGNqAjMpeD1tDcy7y3y9qhgVcpaxLMvBf8xHwestU4NrpjxsDbZbe/C3sgjozGex9to2yGVyrFasRgIioT11CjqFAtEbN6LAw58f/DNPIyQCRIAITE4gaASvt2Eywfvcc89h8+bNQuaGYDvmm+CtB6ADkMni0TwnQ/8yULlH/CmzMyT/XoxiS0uDXb0KrZWtsFvtuDx0GWtuWgNZFKvLRsdUBGZS8LKxuNsa7MN2aE1azJUMDWz881HwWk1W4e+LBa6lFaaN9fEyKG62BlaEouT8d9B/eRDxGSfQP9IFm82GnKQcJB9vxKexsbBlZSFNLh/3oXaqtUq/JwJEgAgEM4GgFrwM3OXLl/HNb34TL730UtBxnG+ClxWaMDlTkY3bn/1gA9BTI84RszOw7AwGAwbS1qClZUQof7ogbgGiV0dDmUC7u74u5pkWvO62Brb712IZn6rM17HPRrv5KHgZ55bKFthMNu/5rT1sDdVxB9mfplALxq7Qora1FvJIOW4dVsPc3IyKDRuAxEQhC0vibEwiXZMIEAEiEAACQS947XY77r33Xrz99tsBuH3/TjnfBG8JADuL4vd83Nl3HnjPbc/3dj1w+Cz6OqwwKFZhOCxCqKymylbB3GsO2qwb/q2GwPSeacHrbmtg3l3zZfOcSUk2X3d42X277/KOS0/mYWvQb+pBXeNC9vAFGRnAO6feAcKAWxfmQHaiBvrERNTl5oI9g2HWhuDPzRGYvz06KxEgAlcXgaARvFr2+NvjMJvNQpoy9sjtySefDDry80nwOgAccM7AuJRk7jtISbuB3Fdhef0DGNqGgKwsxCXHITFL3CtiqeeCNc1c0C0wJmSsVoSFhSEycuZyFku2BomHe5qyYGTkPqb5usPLGEjZGtgHS0WSR9iZm63BnP40Kjoeg0wGIR9veVM5uge7sSkxC8mVdQLOqqIiGGUyId+2twDVYF8HND4iQASIgCeBoBG83jy6MpkMGzduxKOPPoolS5YE3ezNJ8HLrAzM0sCqq42L139XDfSfF+cn7yWYLm6F6UAVkJAAZVHOmAA1Erx8y3g2BK9ka2AjlYXKUJTuLQkd333MVOv5LHh79D3orOtElDIKyXnJY5F72BpKHAdhtwPFxcCp9pO40HcBmgQNMprMQipBe04OylQq4YkOy6AtZtGmgwgQASIwdwkEjeDt6+sbQzEkJATR0cFdenY+CV5WgpTt/agAjInX97Az6Nacg+NQI9DZiSVFG7BwGwtxGz1I8PK9WMyG4HW3NXhWXuMb/cy3ns+Cl9FuKmkS/PJJeUmQK+WjE+Bha6he0QND90JkZwODETp83vU5EmMTkedQAXV1QrCpMSNDSEPIDmZtoFRlM7+e6YpEgAhMH4GgEbzTd0szd6b5JHillGTjdntO/RQ49RMBepOtEMOJ/wHU1kK1PAyKO2+Aq5yTc1pI8PKtz9kQvGyEkq0hLT4NGQmeSej47mEmW893wSulKPO6y+tehCL631Ef9hPBx7ss6RI+af0E4eHh2KUqAMrLIfkdpMwsTDpTqrKZXMl0LSJABKabwKwK3nL2wurjUVDAXm6D65hPgrcagAEYH7ntZmeo7v0R4pMfQFLfGUTKw4Bd4wsQk+DlW8OzJXibzc343Pi5IHaZ6J0rx3wXvMOOYTQdaBKmS12oHpv+zy11oCUsG+XRJ4U02Rs29OHIxSMYHBlEQWoBFJ9UQvA7OCskVgAwe3u6M1cWBY2TCBABIgBgVgXv7t27fZ6Ed955x+e2M9VwPgle6U1vzKNNDzvDu71VKN6RgEjtKaGUsPCG6XGQ4OVbnbMleNkou23dWBQ1t8rMznfBy+ats74TPboexKfFIyEjYXTBMVsD+4BqvyT8rCLmJMyh2bjuuj6c6z+Li/0XIZSVvmgDdDpAoxG+pJLirA+lKuP7+6XWRIAIBA+BWRW8wYPhykYynwTvfieiMXu2bnaGmks3QX7Ta9AMXhzzZkmC98rWltRrNgWvfyOfnd4keIEBywAulF/wXoii/AtAq7h5oI98DHWRTyMjox+OmDZoL2mF3fyM0ESgsnLMh1bJw0+pymZnXdNViQAR8J8ACV4/GM4XwSvt8LA3Oylen72phn+4CmGDLQLBt20/xg33/AyKukohylt6HEqC148FNktpyfwb8ez2JsEr8p+wEIXxE7HympBTOw4liktQqaxQr7ahsr1SKDW8M7kAOOBMQsjSOISHC+0lW5PXTC2zO+10dSJABIjAlASCSvB2dHTgyJEjYP86HCzz6+jxj//4j1PezEw3mC+C18jycgKunJz9xn50fHYM6r6dLuS/GmzA/96zGtjv3Av24t9ljcnSwLdKaYeXjxcJXpHXpIUo3ox32Roq5QfRK9+CHTvCUaJnpWUAIe/yZ1XiB1dWjk0tFpVhr8gs6sJKqcr4FiW1JgJEICgIBI3gPXHiBH784x9j5cqVaGhoQHZ2Ns6fPw+LxYKcnBw8/vjjQQHMfRDzRfBKGRpY6NJSZ67PJQO/wMKBlwUcJ+234nTc7/Hl/AXio1AWCZM/Lluv0JYEL98yJsHLx4sE7ygvqRBFQmYC4tXxo7848c/AmWeE75mtoXr4F9i1KxzVnUdHK+sZLWJ6suRkoXiMdEj5uNn3zKFPRcL51ie1JgJEYPYIBI3gfeSRR7Bt2zbcf//9YEUoPvroI7Cywn/4wx+EfLx79uyZPUoTXHm+CF4pNZGQUbdMD7vVjrS+nQgfbhPI/NX2JOLUX8ctSZ0Aq5gn1Sz1wo0EL98yJsHLx4sE7yivCQtReNga3pFdRH5+OLrDGqHr0UGj1EATlybaGqRybG7TQKnK+NYktSYCRCA4CASN4L3tttvwu9/9DsuXL0dRURHee+89oZzq0NAQHnroIbzyyivBQcxtFPNF8LIKa2xnZ4tjGD0HmhDl+AzJ1q8IJC6PxOAXfaW4+9qNWN/nfAzKstknJXmdLxK8fMuYBC8fLxK8Y3lNWIjCzdbwaXgJFmfuQFyyETWGGqhiVMhZlgOUlbHa1l79+JSqjG9dUmsiQARmn0DQCN677roLv/71r7FixQp88YtfFCwMaWlpgpeXff/mm2/OPi2PEcwXwcucfazEaIHJBkNlCxJtP0CcfZ9Ag9kZ/m5/HPcXZkGtLRHzdxYWAlFRJHinYcWS4OWDSIJ3LK8JC1G42Roaw74Nw7KnkZNnR6mudLScdH29mHHFyxMbm9PPy14X2JMf0eVLBxEgAkQgeAkEjeDdu3cv8vPzcfPNN+M3v/mN4N8tLi7G8ePHBR/vU089FXQU54PgZYEqznhtbHP6d93tDC9aX0DfgiLcv00JZV05IJcDO0eD2TwnjXZ4+ZYxCV4+XiR4x/JihSh0pTqh3HBqQSoiFZFiAw9bw/vyLuzcGY7KjhLYh+1i4FqPeVJPvnuqskIAYi4HOogAESACwUlg1gVvf3+/4NHV6/Ww2WzIyMhAb28vnn/+edTX1yMlJQXf/va3oVKpgo7gfBC8UpAKC05R1xpg13/ksjPYIhPx1KV3kRK7El/KtkCmrR8X5EKC179lS4KXjx8J3vG8JixE4WZrOBT5MRatKYR1YTUMfQZkq7KRpEgCSpxPbdzSk7lfwWtBGr4po9ZEgAgQgRkhMOuC99ZbbwUrG8x2dtevXz8jNz1dF5kPglfaxUkGEF/RjDjDYy47w/mFN+DPLY8Lb453rmgAWlrGpDHyxpl2ePlWHwlePl4keMfzstvs0Jeyv2QgvTgdoeGhYiM3W4Mu4jtojH0Gydla6MzOAhQJGUB1NWAwTPh3LeXmzQbg3bXPN3/UmggQASIQKAKzLnj/9re/4YMPPkBjY6MQsMZsDDfddBMWLQr+kqbzQfBKKck0bAXu147JzvBh4q9Qpb8BBddkId/uDHApKAAUignXKwlevj9lErx8vEjweufltRCFlyIUylQDTJHVUEYpkZecB+j1Ynoy9oQtJ2fcyaWMDez1QXiNoIMIEAEiEKQEZl3wSlzOnTuHAwcOoLS0FMzmsHXrVmHXd/PmzQgNde5IBBnE+SB4pR2cdeYBhB58Y4yd4dX4V2FpSUZ+WhJyzKVeUxh5ThkJXr5FTIKXjxcJXu+8JixE4VGEwhK9DfZUsQDFLs0ugBUAmiA9GWsjPQFiOboz+KaKWhMBIkAEZpRA0Ahe6a5Z7t3Dhw8Lu74nT54UdnrZju9Xv/rVGQXjy8Xmg+CVPHrZrRbEVu5x2RlalIX4O/4V4ZcykJ9gh8ZaAyiVYgqjSQ4SvL6srNE2JHj5eJHgnZiXtMs7phCFRxGKusinMbjsU0TE9KIgtQCKSAVQUQGYzV7Tk0lVGFmExfj9X765o9ZEgAgQgUASCDrB636zR48exRNPPIG+vj6hEEWwHfNB8DoLBSNPa8LCajXC0CtMQ0Xmb1HdlI3loZtQEKeHyqoHNBrxiwTvtC1VErx8KEnwTszL0mqBocaABXELkJKfIjb0Ymvowlks1jQiMyET6ng1MEl6MoszPZkcwMS5WfjmkFoTASJABAJBIOgEL7MzlJWVCfYGrVYrZGlg1oa77747EPfv1zmvdsHr/ma2ofxFxLeKu+z2qOUoyfwdTlfFY23cNuSjHHGweN0B8gRMO7x8S44ELx8vEryT8/JaiMLD1lA/sAaL11VjVYIaGSxwzWQS05NNkHJQ+lC8i2+qqDURIAJEYEYJBIXgHRkZQW1trSByKyoqBM/u9u3bccsttwhpyoL1mLOCt60NiIkB4uImRSs9rmQpyda9cw9irG8J7Q1J96Fa9SWcOZKO1YtXYxecb3m7pn7LI8HLt5pJ8PLxIsE7OS9vhSjsn30bsnPPCx31kY+hKvRxdEUew7q1YchPyRdPOEl6MqkwTTHl4uVbrNSaCBCBGSUw64L3L3/5C0pKSmAwGLB69WphN3fHjh2ImqBS14zSmeJic1Lw2mxAqTPAjGVUmISzlKFBbbMjc1+Ei0b9te/jdHcoes6sw4rwGBTJK3zy77ITkODlW8EkePl4keCdnJe3QhS28x8g6sgtQkc74lCiuIQTxs+QldeFW1cXITw0fNL0ZJLPn7n32YdjOogAESACwUhg1gUvKyl8ww03CEKXlRWeS8ecFLytrUBNjYiZ7fCyILNw7zWSpJRDGbqXkHb0IbFP9ApUbnwVTc29CDfmYcWACXmxp72WH/U2lyR4+VY4CV4+XiR4p+blWYiCxUjEfJAM2C8JnSvlB/Fp92IkrOjA7q1ZUMqVo+nJvASm1gJoAUC5eKdmTy2IABGYPQKzLnhZVgaZTDZ7BPy48pwUvFIAinTfyclAVpZXCpUAWKW1vKMPQal7SWyz7ifYH7kJbfpoLLfvgLrnODLjLwK5uUBi4pQ0SfBOiWhMAxK8fLxI8E7Ni+3yNh1oEhqyQhTWy1ZENT2JsPr/K/zMFLUbr1ufhVXWgrtuXCYGrknpyVgDD+uSe65uysU7NX9qQQSIwOwQmHXBOzu3PT1XnZOClwWfsCCU7GwxobzdPmEVJcmbt+vVEBcw043VqOy5iK4LS7B4YDMyjWVQJ1qBwsJJ7RHSCUjw8q09Erx8vEjw+sarvbodfYY+KDVKRC6LxALbGYR/lCt2li3Ef49U4WxPN66/LhI7M50fiKXXDvZUiO30Og/3aozePzr7NiZqRQSIABEIJAESvH7QnZOCd78zwKy4WBS+VVUiAY8KaQ4AB1i5UN1LyHazM2ivK4HWpEVP42rE9y9D3tAhKBPDgZ2+JSUiwcu34Ejw8vEiwesbL/dCFEu2LEFUdBRk5TeJacoAnIp6Am92XI8licP49u6t4kknSE/mHtw6eRZu38ZGrYgAESACgSBAgtcPqnNO8FosQHn52PRC0psYSznERK/Tz8usDMzSkFW5B8n6lwVK9sx/RYViJ6x2K3rrr0WsyY6ixdWQrUia0BbhiZcEL9+CI8HLx4sEr++8pEIU8hVyJF6TCJnuP4Djj4l/64u24/+1/AaOYQf27tmIBZHho+nJmAWtqMh1IRuAUgCUi9d39tSSCBCBmSdAgtcP5nNO8Or1oo3B07crVVJSqYAcsV4Se0ypHbyEorfiXYTK1v8J1oglCB2JwOUz1yOiuQ271HWiPSIpySeSJHh9wuRqRIKXjxcJXt959Rv70VbVhpGIEaTtSINspB9w+3t/NfYznGkHbt26CluynK8DUo+xKLcAACAASURBVHoyDwsT5eL1nTu1JAJEYHYIkOD1g/ucE7y1tUBLy3jPLktVxnZ+3fy8LBDF6mZnsMjTUJ7xLOIWxEEdkYOa6igoGqtRsMowzg4xGVISvHwLjgQvHy8SvHy8WCGKfks/VuSvQJwqDijd4bI1VMd9B++13o/keBUe/gdnZTbpiVBmJqBWuy4m+f0LAUTxDYFaEwEiQARmhAAJXj8wzznBK+3kegSdCAikakrs/7m5qE5MRFL5bqha3xV+XZf8MByaR4Ryo4b2cNQcG0Tihc+Qm2Ed83hzKpwkeKciNPb3JHj5eJHg5ePVo+9Bc3UzFCoF1Plq4OyzLluDKXYdXrS9AFiV+PLNq8UkLNJTIo/0ZJSLl487tSYCRGDmCZDg9YP5nBO8UsDaRBXRpDczmQzVOanIKVnjotN6bQWSVlwnfK/VAtojXVg1cArXbIp12SB8QUmC1xdKo21I8PLxIsHLycsxjLr9dQhDGFLzUhEdbx9ja3h58bu4cCEJN67dKKTsHpOejAW+Oj3/Ui7eTACj+758Y6HWRIAIEIFAEiDB6wfdOSV4pR1cVmwi31ku1Nu9V1fD3KbDpwvKcbtJLDdqC9+MyDsrERoeKnxfXQ0YjjUjJ/YMVFtWABrfs2+S4OVbcCR4+XiR4OXjxVq3nm6FpcmCKEUU1DvVY2wNtUsfxru6r2H1onUoLJCL2cik9GRuubcpFy8/d+pBBIjAzBIgwesH7zkleIVtWe2UFdF0HVpUHXkPmx1/hnqwQaBjUv4ayqLvukgxZ8Slw6exPa0VisLcMTk5p8JJgncqQmN/T4KXjxcJXj5erDWrtNZR1QFHv0PIy6sc+csYW8PrYb9FZO8a5K1LREaGm60hLQ3iD8Qg1zoAySyzC/8QqAcRIAJEIOAE5qXgZSLiN7/5DY4ePYqYmBh86Utfwm233eYV9ptvvok//OEPY373u9/9DitXrsScErzCtqxhwowKLP1QjaEGhj4DRnrbcNvZb7ruuTP9GBI2O5PSA2DOiJFjx3Db5o5xVZemWrEkeKciRIKXj9DY1iR4+ekxweu45EBHTQdCZaFIy1+E0HdGC0v8fd3rOP/5VqxcnCLUlwk3m8RdXrf0ZFIu3jgAkzw/4h8c9SACRIAITBOBeSl4mdhtb2/Hj370I7S0tGDv3r34xS9+gXXr1o3DygTvuXPn8N3vju5wslLIISEhc0vwlpUBVqvXjAqWAQuq26uF/LqyUBmusdVixanvCyxsg2thzf47lNls7wZgCR1K3+lFeGM9im8chmjs8/0gwes7K9aSdnj5eJHg5ePFWjPBGxkZCUO1ATaTTdzlbbnbla2BBax+2vUtpMg2wJWcQUpP5ixYI+XiZUXiRzP08o+FehABIkAEAkVg3gleh8OBL3zhC3j88cexfv16geuvf/1r4d9/+Zd/8Sp49Xo9vve974373ZzZ4XU4gAOsbhrG7cga+42ouVgD+7BdSDmWszQHUeW3uN7sDBcfRsyKuxBzB9vaCReSORx5qw2Le88j7/YELv8uuzwJXr4/ZRK8fLxI8PLxche8w7ZhXCi/IJxAvfxdyBrE1zyWreHDxF9BbijEgsgwseaEl/RklIuXnz31IAJEYOYIzDvB29bWhj179uCdd95BdHS0QJr9v6ysDL/97W+9Ct7XXnsNbFdXqVSiuLjYZX+YM4LXaBRLCHukEqrvrIeuRyfcc3JcspByLNzaCrw3Gmetv/h7JKtVCE9ZJmRjEKzA752BWm5A5l1rIOYq8v0gwes7K9aSBC8fLxK8fLzcBS97jTPUGmBpsSBmkQPLzrOcC+JRsuE1mLXFkIfFCQ91lI7xrymUi5efPfUgAkRg5ghcVYL38uXLGBkZ8UovLCwMERERaGpqwre+9S18+OGHgi2BHR999BFef/11/PGPfxzX9+zZsxgYGBDEbmNjoyCKv/a1r+HWW28VLA1DQ0OCBzioD60WIY2NGFm1StiRtdltOG44DvNlszDsjMUZUMeLIjf0wn8h7NjXhP/3hhWiJeRX0ES3CUUpRjIyUG9T4/ybx5GZ0ovUL1/nSkvk6/2zx6fMN02HbwTY2gsNDRU+cNExNQH2988+JEgfZqfuQS3Y6yZbX+w1ctgxDH2ZHsP2YawY2AP5UJUA6NTyr+Fi1PdhbVcLn5u35tgRwmwNAEbYlm94OA6FhIC9ouSMjIDvY/DszAH9Tc0Od7oqEZgtAleV4GVCtrW11SvLbdu24Qc/+AF4d3g9T/bGG2/g2LFj+NWvfiUIXiZI7r333tmaP5+uG3bsGEK6uzG0cSNGlizBx/qP4RhxICo8ChsSN0ARqXCdJ7r8OoSaPxe+bw77KWyL78XyFeEIO3FC+FmlIxc9ta3YkmVF3C4+/y7rb7fbSbz5NGtiI/aBin0wY6KXjqkJMMHLmIU788NO3YNaMJsXW1/SGutp6gH7Shj+bywbekoA1Bmdic81z8NyJhcORwi2b3cg5pTzdWXdOowsX45ToaFoCw3F6qEhrJhg4yGYaMfGxgbTcGgsRIAIBJjAVSV4fWHFXtx3796NX/7yl1i7dq3QhQWxsTdKbx5ez3O+/fbbqKiowNNPPz13gtakAJPCQphGrKhsrYRcJkdBagHCQ8NHb7Hv/Bg7g1ZxFvFp8UjISHB59g6cSIDdEYLiOyIh28SfgIgsDb6s0tE2ZGng40WWBj5erLUUtCbteLJdXl2pDhi8hPTe0ewsZZv+B6qhO6HTObMbRumBujpXqkPKxcvPnnoQASIwcwTmneBlaFmQWkdHh5Clge0Is53fn//850KWBvbzffv24eGHHxZ2PA4ePAiNRoOFCxdCq9XiiSeewF133YV77rlnbgheIa1CqSuFkNakBftKi09DRoKYQ9N1nPopcOonwrdm2RdgjPolEjITEK+OF35m+/gwPi6XQR45hMIHk4GkJO6VSoKXDxkJXj5eJHj5eHkTvOxnllYLDDUGJFu/jCjHMeGkLFuDau3zqDwsEzKS7cwxQ1ZZ4XptYc/WaigXL/8EUA8iQARmhMC8FLzueXiZ1++BBx5wBaI1NDTg0UcfxYEDBwRP27PPPotDhw4JuyAJCQkoKirCF7/4RUEMz4mgNWbxqKkBVCoh6KzWUIsWSwuyVdlIUngI1nfVQP95YeGdj34Cg2G7kVqQikhFpPCz1qbLOPlqPVSx/cj9Zg4QFcW9SEnw8iEjwcvHiwQvH6+JBC/7eXNFMyK6XoXK9kPhpCxbg2PnR9B9nihkaxFSlGlLBH8/CgpgUihQCYBy8fLPAfUgAkQg8ATmpeCdLqxzQvBK6YNY+V+NBhXNFUKwGrMzuHt34WFnOLy4CQmDQ9DsGi0bzE517swgNMv7cc0WcdeX9yDBy0eMBC8fLxK8fLwmE7xWkxXtR+qh7t2JMPQKJ9YXlEERtUOoOyFUKVfWQ/A4ZGbCplajFADl4uWfA+pBBIhA4AmQ4PWD8ZwQvFLdeyGXkBL7tWK2zF2aXWPv3M3OcG7xF2Ee/hkWR0cgJT/F1c7jVFdEjgQvHzYSvHy8SPDy8ZpM8LLftVS2IL7lq4hxfCyc2JT+T1Bu/i2ksIA8lR5KQ50r5SHl4uXnTz2IABGYGQIkeP3gPCcEL6sDzI7iYpgGzULAGiswkZ/iUQDUzc5waMVzWNJ9ExTJCqiyVC5C0ql2eWhlHoQkeHloUR5ePloACV5eYuOD1tzPMGAZQEfZG0i2fkX4sTVGA/ntZ8V83FpAGedAnvmAy8dbxtoAKATAb3jiHzv1IAJEgAj4SoAEr6+kvLQLesFrsQDl5YBcDuzcCX2PHnWddeMD1jzsDO9u6caahk6xxKhGKe7smDD6GNNDK/MgJMHLQ4sELx8tEry8vFh7zywNnudgxSgS6jQuWwMKD8KhvN5VvLFIVgqZ3SaUGa9UKmECwHI7zIVcvFfCi/oQASIwNwmQ4PVj3oJe8OqdaYOSk4GsLFfAGquoJhWaEG7fzc7Qon4Qx6OfQEazGUl5SZAr5UITj1NdMTUSvHzoyNLAx4t2ePl4+SJ4WZqyvve/DkX/i+LJVz8GbHzaVV1YM9QATdg5wcdbr1aD1W5kNdpG6zXyj4l6EAEiQASmmwAJXj+IBr3gra0FWlqc4dRqV8BaXlIelHJx51Y43OwM1QX7YGrMgrrfjvTidISGiwUPPE51xdRI8PKhI8HLx4sELx8vXwQva2Ot/zvkNbeKJ5ctBO7pcT31kfV2oyj2iODj1eblgeXjZaGuo+Gu/GOiHkSACBCB6SZAgtcPokEveCsqALNZeNQ4YcBaTw3wwQaBgi1mBT4sbkLsoWak2IeQXpTuolNWxh6vC9mHoBgtzMZNjwQvHzISvHy8SPDy8fJV8LJd3tB9SsB+SbxA4UEg8XrB5mTqGEKetRTK2EG07tpFuXj5p4B6EAEiMAMESPD6ATnoBa9blJnJavIesHbin4EzzwgUmJ3h6OoXkFrVjqWxEUjOSxZ+7nDA5dfzJ2CNnYsEL9+CI8HLx4sELx8vXwWvcFa31wpPW4O66xgyF3fAkpeHcqWScvHyTwP1IAJEIMAESPD6ATioBa9HlJkUsJasSEaWyq0ksJudobLwIPSOjciuMSJx1aJxAWtKpbhZ7M9BgpePHglePl4kePl4cQle4ydA6Q7xAh62BkXXORQsboBdrUZJZibl4uWfBupBBIhAgAmQ4PUDcFALXilvUFoakJHhPWDN7Q3MHp2KkjvOw3y2C7mN3VieuxzRidECHY9T+UGMdnh54ZHg5SNGgpePF5fgZY3fjB9na2A5eQc7elCsOALZkkXY7/xU7Ef2Qv6boB5EgAgQgSkIkOD1Y4kEteCtrgYMBiA7G0hK8h6wdvQhQPeSQMCUtgeVW1/EQHU71hn6xpQU9jiVH8RI8PLCI8HLR4wELx8vbsHrZmsYXvlPCN3yWwivD21DyOz6FOpEK0qLimCTyVAAwA+7P/+NUA8iQASIwCQESPD6sTyCWvB6RJl5rbDmZmfQFx7EqcUFCK9oRnrf4JiSwlJVpcJCIMrPbPJkaeBbcCR4+XiR4OXjxS143Z4KDSEWjl2daDdFoq4OSDTUIFfViuq8PBiUSsrFyz8V1IMIEIEAEiDB6wfcoBW8HlFmXgPW3P140SugvUOP072DiK5uQ8aCcFfAms0GlJa6Cin5QUvsSoKXDyEJXj5eJHj5eHELXtbBzdZgSn4LcXl3CUGt4W3nUbz8NOpWroR+zRrKxcs/FdSDCBCBABIgwesH3KAVvEYjUFXlqm/vNWDNzc6AtD2o3voitG0WpNZ1QpOkQEJGgkDG41R+0CLBeyXwSPDyUSPBy8frSgSvpfJrUOj/LFzoUuSDiLj+P3C8Xg5zay/yUImutDg0btmCNAAZ/MOhHkSACBCBgBAgwesH1qAVvFKUmUYDaDSo76yHrkeHMRXW3OwMuPkkKuKzcUF/CRlnu6C+Rol4dbxAxuNUftAiwXsl8Ejw8lEjwcvH60oEr+n8/0B55G7hQszW0JZ8Gn3KFOgah6Buq4BC40Dtjh1QhYUhh3841IMIEAEiEBACJHj9wBq0glfIBm8CcnOBxMTxAWst7wAVXxDvPHoFcIce+wF0nO7AdecvjSkp7HEqP2iR4L0SeCR4+aiR4OXjdSWC19hvxKL9KyEb6hcu1iJ/BWFZ/4ATdZFQNJ9CdlobPt2yBQvj45HPPxzqQQSIABEICAESvH5gDVrB6xFlNi5gzd3OsO4nsKz7d5QDMB9uQW6PbUzAmlS7orgYCA/3A5azK3l4+RiS4OXjRYKXj9eVCF7HsAMtB++G2viucDFma2DZGirrFBg834ZCVS3KNqyFLCUFRfzDoR5EgAgQgYAQIMHrB9agFLweUWaWAQvKL5QjbkEc8lOc+y3uuTRv18MYswJHrHYMH7+IjfYhqHeqBSoWC1BeDsjlwM6dfoBy60qCl48jCV4+XiR4+XhdieBlfapP/go5Df9HuBizNVzK0UNvUcLQ2ItMaxVOb1mCkLVrUQxgGj4n898U9SACRIAIeBAgwevHkghKwdvaCtTUACoVkJODcQFr+peByj3iXTvtDFoANV1WxJ0yYl1sJJblLBN+7XEqP0iNdiXBy4eRBC8fLxK8fLyuVPDWGmuhrrwTCqtOuGBP2j5cStwtpCdLaj4M04ZQ2DZvRkFYGOXi5Z8S6kEEiEAACJDg9QNqUAre+npApxOC1bwGrHnYGbDu31EP4GSzGUubupGVpHCVFPY4lR+kSPBeKTwSvHzkSPDy8bpSwcs+SNtrfwRN+6vCBS3RD0F+65/F9GSN9VDmmGBcswa5ixcjkX9I1IMIEAEiMO0ESPD6gTQoBa8UZcbKeyqVqGyphMlmQl5SHpRyJfBqyOgd364HYlagEsDZM13IaDYjM0vlKilcUQGYzYDzVH6QIsF7pfBI8PKRI8HLx+tKBS/L7V194WMUnbxPuCCzNYTd3YyyQwth1RsRm9CE3uxkaFJSoOEfEvUgAkSACEw7ARK8fiANSsHrEWU2JmDNi52B3X4JgOYTF7HtYh9WF61EaHioQEU61a5dfkDy6EqWBj6WJHj5eJHg5eN1pYJXeH3Q7kfe2b1Q9p4SL5q/D/W9u6Gr7UV4SD3sGhlWbtxIuXj5p4R6EAEiEAACJHj9gBp0gtcjykwKWJPL5Nip3gm42xk2PQNc8x04ALw/OISukwZsv2RDelG6QIRlNWObxXFxQP405hYiwcu34Ejw8vEiwcvHyx/BW9FcgUUX/guZLf8pXjTxepiyDwqvG5fbTiJygx1LN21CTlgY/6CoBxEgAkRgmgmQ4PUDaNAJXr0eQtRIcjKQlYVWSytqDDVIViQjS5Xl1c5gAlDWY8NIfRfyQuEqKexxKj8oje1KgpcPJQlePl4kePl4+SN4WUGblq5TLlsDO5djdw8OlC1Ef7MOkRtNiF2RjEIWQEsHESACRGCWCZDg9WMCgk7w1tYCLS1AZiagVrsqrGmUGmgslaPZGeKzwaqrsUMPoLLNgoWN3diyLNYVsCadKjsbSEryA5JHVxK8fCxJ8PLxIsHLx8sfwStlgBlja9j0DCq7vwNjgxE9KReRoFmEXSkp/IOiHkSACBCBaSZAgtcPoEEneD2izMYErFV/HWh9R7zbvJcA9YPCf1lKsspGE1StFuStUkKRpBB+XlYGWK1AQQGgEH80LQcJXj6MJHj5eJHg5ePlj+C1OWwo1ZVCbXxvjK1Bn3YQdVVWXIg+h5Sldty8cSPl4uWfFupBBIjANBMgwesH0KATvB5RZlLAWnHqVoT/T8LonTqzM7AfVLMcvLUGrDH2I2drEiIVkXA4IKQXYsd0Bqyx85Hg5VtwJHj5eJHg5ePlj+BlfUuaSgD7pTG2BstNPSg/uhANPVqsvuYStmdlQREZyT8w6kEEiAARmEYCJHj9gBlUgtcjymxMwBpaRu0MidcDhQddd10BoP5YG3K7bdhYPDZgTakUU5JN50GCl48mCV4+XiR4+Xj5K3ilp0hb636AxbY68eKbnkHJ+e+g3nwRy5a249r0ZUheupR/YNSDCBABIjCNBEjw+gEzqASvVguwr7Q0ICPDFbCmilGJJUCNn4h36mZnYN++3TuIzoZO3DwwhJR80WsnncpZu8IPQuO7kuDlw0mCl48XCV4+Xv4KXsnHm6rbh3XdfxYvnng96pcexCfaS4iQX0BBWjQ06eKHaTqIABEgArNFgASvH+SDSvBWVwMGA+CMMmMR1LoeHTSREdAcLhq9Szc7gwXAB8Z+OJq6URgjgypLjKaWalfk5gKJ01wmiQQv34IjwcvHiwQvHy9/Ba9j2IEDTQcw0taD2y5+xXVxY34P9tdHo7u/Ebck9iBz2zb+gVEPIkAEiMA0EiDB6wfMoBK8HlFmroA1uxbK2n9x7by42xmMAD5uNmPBBTN2JsUiXh0vtCthtjw7UFgIREX5AchLVxK8fDxJ8PLxIsHLx8tfwSt8QG6phKHZgGvP7UVi+BlhAPb1T+O19sfQ2K3DLUvbsXXD+umNfuW/TepBBIjAPCdAgtePBRA0gtdLlJkUsFZ0/peQdR0W79LDzsAyNBw504WlF3uRvz4RcqUcNhtQWgrIZECR28awH5jGdCXBy0eSBC8fLxK8fLymQ/AyW8Ppi6ex7LM3sCnyL64P16WKg/i0oxPrIy7g7nSlkCqRDiJABIjAbBEgwesH+aARvEYjUFUFOKPMXAFrgx3Y+fnXRu/Qzc7AflgPoOJYG1b1DeL6glShpHBrK1BTA7Bc8Tk5fsCZoCsJXj6mJHj5eJHg5eM1HYJXsjXYDl3EPQu+4RpAU24PXtEDifYWPJI4EJgXFP7bpR5EgAjMUwIkeP2Y+NkUvM0AkgGEsPF7RJlJFdaSukqRff4Z8Q6TdgMF+8bcbbnVjobTHcgdcGBjfqrwu0AGrLHzk+DlW3AkePl4keDl4zUdgpedg9kazpefx86hn7lsDeb0p/GM458Q0nwOexedR/hNN/EPjnoQASJABKaJAAleP0DOluBtB3ACgJy5FABEeUSZaU1asK+C+u9AYdWJd+hhZ2A/2tdlRYeuBzeHhSJlk5g2SDoVS0fGNoyn+yDBy0eUBC8fLxK8fLymS/AyW0N1ZTXSjO+NsTX8OvYgzDod/im6CQnbt5KPl396qAcRIALTRIAErx8gZ0vwVgIwOcfNRO/Wjz6CfGDAFWXGdlts5oaxdob7R8bcqQPAG20W2Fp7sTt+gauksFS7orgYCA/3A84EXUnw8jElwcvHiwQvH6/pErzM1vDuJ+/C0diFe2O/6RrE25t7UHPWjtusZ5Gbs5x8vPzTQz2IABGYJgIkeP0AORuClwlVZxE0xAEwDw4i6vhx5FqtULC0CgBYwNqYcp9pe4CtL465UyaY/3amC3KTDbemL0J0YjQsFqC8HJDLgZ07/QAzSVcSvHxcSfDy8SLBy8drugQvO0/F6QpcOHwBN0Y97rI1HE55GiWXv4r1LY24e/Xg9Fey4b9d6kEEiMA8JUCC14+Jnw3B28pKAQNgboNcAFVdXejS6SCLj8e1q1ZBZrehVF+KwlNfR9QASzzm3c6gZynJTlzEcssAbtyyHLIoGfR6oK4OSE4GsrL8AEOCd9rgkeDlQ0mCl4/XdApeXZcOR/7nCDSRR7A54gVhIEbldjwfV4bEqjo8omkPTOoX/lumHkSACMxDAiR4/Zj02RC8tQArFIxsFofGxl5fj5OdnWhNT0fI8uVYammFufnDSe0MrNuZoWF8evwiVvQPomiHmC6othZoaQEyMwP35JF2ePkWHAlePl4kePl4TafgZbaG1198HeGOPtwd+38Qhl5hMM9k9mC4ugMPys9DedOmwAQH8N829SACRGCeESDB68eEz4bg3e8cLzMvCDUhnFFmdddeC/2iRWiztCH78x9hS/NLYksvdgb24yO9gzjV0InNYaHY4AxYq6gAzGbxqWMgAtbYdUnw8i04Erx8vEjw8vGaTsHLzvXB/g9gajNhp/w5LAs7JgymPOHfUdH/Lexub0DmDcsAVrOcDiJABIjADBMgwesH8JkWvMygUAUI3t18adxuUWba8HC813UGXy/fiYWXL4otvGRnYD9+v60XrW0W3Bi3AGnXiOkYpFPt2uUHlCm6kuDlY0uCl48XCV4+XtMteKuPVOPMqTPIWN6BjRaxwqMhMhv/segQth/5HIUsNoB9oqaDCBABIjDDBEjw+gF8pgWvZGdItgEhjUDaYgtiToyNMnuz8S3cU3XP6F15ZGeQfvFyowmXey7jHxLkQklhk0ncLI6LA/JdatoPOBN0JcHLx5QELx8vErx8vKZb8F44cwEVn1Ygenk0vmD7KmC/JAzoz6qPsahcgVs3dEB2x638g6QeRIAIEAE/CZDg9QPgTAveMgC9Q0DYJwBsgKK3FQWxNa4oM5vdhpbDX4em/VXhrlrUD6I37yVkeNyjBcBbpzsQaR7A3euWIFIR6So4kZYGZHh28IORZ1cSvHwwSfDy8SLBy8drugXvgGUA+17fhxH5CG5K/QDK5v8SBlS58DHoz34deQoj1LuzAueZ4r996kEEiMA8IUCC14+JnknBy0RqOYDmM0BKk3PQej2yYs8heftKIcrM2G+E4kCWKztDdcE+GJJ2CxXZ3JMuMGvEO8fasHhgCHflpwgnq64GDAYgOxtIEqLhAnOQ4OXjSoKXjxcJXj5e0y14hx3DKHu9DIY+AzZusyDj80eEAV0OXYjXrLXINF9AbpEysJ+q+RFQDyJABOYBARK8fkzyTApeLUsj1gwMnQcyHWIWhdOvn4ZssB+F31wF2ZJFMLaWILG8WLwj2UKY7ulBNQC7WxozVkvidO8gDjd0YnV4KLZvFCuslZUBVitQUBDYYkgkePkWHAlePl4kePl4TbfgZec78d4J1F+sh3KbEjc33O+yNZSMvI2Rc8tQfOMw+Xj5p4l6EAEi4CcBErx+AJxJwfuqATjTDKy5BNzqrNBZ+avD6LLIcM0Dm4XAZ2vFvZC3vCHekTM7A9sZZpXZmOhlwW4sXOSEoQ8nm83YtHABcjRKOBzAAWc1i0AGrLFhkeDlW3AkePl4keDl4xUIwdte3Y7yk+WIuCYC20PfcdkamsK/jYbTX8a1ay5Bec/OwJRy5L996kEEiMA8IUCC14+JninB29wJ/FEPhI8A/2sFkJjIagubYPrwOI5cWIaQtWvBiqyFH0iCzNYm3lHhQSDxeuG/NqfotQJgpYj7L5jRbuzDDUuisXLFQhiNQFWVaKsLdAA1CV6+BUeCl48XCV4+XoEQvCatCSerTuLS4ktCtgbJ1mBHHEraj0ItNyDzrjXOFzL+8VIPIkAEiMCVECDBeyXUnH1mQvDabMAbVcC5BcDW5cAty50X12rBIs2qe6+BIXYVkiM+QVbXDvGX0SuAO1gttdHDXfR2nekCCy65P30R/cZg/QAAIABJREFUYhdFuQLW2C5xoFNkkuDlW3AkePl4keDl4xUIwWtptaDxWCOaRpqwLHcZ8j+73WVrONL1MuzWldh+ezz5ePmninoQASLgBwESvH7AC7TgZWKXpQo7EQnIk4HbU5zV1diYnVFm9swslGmTkWHZg2T7y+LdTFBsgole5umtP9Ym7BY/tGkpQsNDpdoVyM0N/KYLCV6+BUeCl48XCV4+XoEQvOzD9IXyCzjZcxLRW6JRYNoHhf7PwsB0A19Fnf5/oXjHAGQ7Apj/kB8D9SACROAqJ0CCd4oJPnHiBP7yl7+gsbERixcvxosvvujqEWjByyqfdVuAhlRg7VpgTD0ItygzrUGB5BNqRA2fF8fmZmfwvD2r1Y6PqtuhDA/Fddey/A1ASQlgt0OwRUQJ5dsCd5Dg5WNLgpePFwlePl6BELzsnNr9WjSbm3E59zIyRzqhPv5VYWDM1nDgzIdYm9oH9YMF5OPlny7qQQSIwBUSIME7BbiGhgZcvHgR3d3deP/992dM8NbWAi0tQG8cELkFWBohBpwJh2eUmfEToHRiO4P7LRpqDbC0WKBIVkCVpQLbRS4tBWQyoKjoClcRRzcSvBywwDJnWBEWFobIyEi+jvO0NQle/onv6+sT1peMvQhM06Ev06P7UjdaV7RiScIS5B29ZdTW0PwCZJGZyL1fE/hHStN0P3QaIkAE5j4BErw+zmF5ebkgdmdih9dpzxVEqCIfMMmBbLjZGTyjzI4+BOheEu7EIH8Qqt3i/z0Pu80Ofano7VUXqiGLkqG1FaipAVQqICfHRxh+NCPBywePBC8fLxK8fLxY60AIXpapoc/Qh4a4BoQlhmGn+QPIG/9DGJyu5z5oTY+h+O5Y8vHyTxf1IAJE4AoJkOD1EdxEgpe9wT7wwAM+nmXqZkzLVleHCA1zckZQrRL/v3NkBC63gVaLkMZGjKxaJUSZhe1PB/pFO8On8uNYck2W1+Aztrvb29qL2KRYYXeXHfX1gF4fglWrRgIesMauZ7FYoFAopgZBLQQCly9fRmhoKCIiIoiIDwRGRkbQ39+PmJgYH1pTE0aAfahi6ys8nGXpnp6DZWrobuxGW0wb+pf1QzXSic0nvy6c3D4Ugw+0HyNvw2UsuuO66bngFZyFPTmhgwgQgflDYN4K3qGhIQwODk440+wRHxMa0jGR4GWC5N57752WFWOxAEeOiG86q1cPQa4GToSFQTEygmuHhlzXCDt2DCHd3RjauBFhQ8cgP/4l4XeDshS8M9Ig7Axv3+6A5/uX7oBOaJe8PVnY3WXHsWNh6O4OwebNQ1i0aGRa7mOykzDu9EbjO2b2gSokJET4omNqAkzwMma0xqZmJbUIxBrrbetF56lO9MX0oX1ZO2wxKtx/8nbI7Gbhsp+e+y1ioldh1Z7Ns+bjpQ/evq8RakkErgYC81bwVlRU4Mknn5xwDp9//nmkpIhld9kRaEuDlJGBVTtLSxOf9NUCaAGgcX65BuMeZVb7jy47A1Y/hurhp4USwcnJQJZbPWGWKshQY0CUMgrJeWKwGjv27xf/LS6emfcdsjTwvWyQpYGPF1ka+Hix1oGwNDhsDuhKdRgKHcK59HPoCYvA1p6/YZX2/xMGyGwNRtu3kXdfamBrmfPjoB5EgAhcpQTmreDlnc9ACl4Wg1ZeLpb2dS/+UMYeN7KkC8ConcEzyuzV0Z0/S3EtZFHrhSA0driXCWZBJHarHapsFRRJoqWA7Siz68rlwM6dvESurD0JXj5uJHj5eJHg5eMVKMHLzssyNbCjM7MTBocZYWG92H30PuFnzNZwoLEUxXfHQLY+g3/Q7Lz1nYhRxSBqUYBTy1zR6KgTESACwUaABO8UM8LeQB0OBw4dOoRXXnkFf/jDH4THyyyiebrSkjlT6grCk4lUZkVgJYHLnZXRxmhR9ygzZR1QuUe4A1tkInC7HlGy0UISkniWdndlchnUO9WuO9brgbq68bvBgVykJHj56JLg5eNFgpePVyAFb3NFMy6bL2N43TCaBppwZlE6vnNwq8vWcKT5BaiWZiCNpSfjPKTXtFBZKNKL0jl7U3MiQATmIwESvFPMOsvD+/3vf39Mq9WrV+O5556bFsHLgsZ0OnGXlZX1lfLgsr0R9pUGYMz+h9SBlUTr/n8uO4M++UGo88XsDGzHmO3ysty67JyW2vG7u6ydlPosMxNQj+rggP4dkODlw0uCl48XCV4+XoEUvMZaI8wtZixcvRDHho+hVq7EF0x/R0bdz4VBGnoLYBr6KTK/cR23n0rKAsHOk5SXBLmSFU2ngwgQASIwMQESvH6sDn93eKUdVjYEd/sB+16yM7Dcu0r3MbLSayaTqGRLFrt+U7vlHWStvMP1vZTaLHLYitTQVnju7rKGrLCF2Syeiu0Gz8RBgpePMglePl4kePl4BVLwskwN7Cs+LR5NsU04jGGkhvTinvKbhUEyW0P5hX0o/NJK7k/ckl2CncczLoGfAPUgAkRgPhAgwevHLPsjeJlmZdqVHZ47rKwEMLPhsjwK42pBSFFmGgNQ/bDQn9kZWq4vh0bJwttGD1aMravOiKRYM1ZtjkdCRsKY30un2jWmhJsfQHzoSoLXB0huTUjw8vEiwcvHK5CCt9/Yj7aqNkGQOlY78DdLMwajE/FQ7begZMVyANR1/DNUW78F5dZVPg9csjMsiFuAQesghu3DtMvrMz1qSATmLwESvH7MvT+CV9qoZc4E9uV+sNIQdSx9GAC3RAtjo8zk/+WyM2iX3Q/5xieQpEgac57W83Z88me94Am+/TE15IrRSkqS4I6LA/JnsKQ9CV6+BUeCl48XCV4+XoEUvFKmBuazXXHjCvzpQgXaIqJxn6UCGSf/tzDQLutGGGNfQua963weuFQtMiEzQRC7bBeZBa8ty1nm8zmoIREgAvOPAAleP+bcH8E7WTowtvFrgkd1NTZOyQORGAs0Xu8aedn6PyHvmi8KAWvuB3tjOPaRBZcj45BdlDhGWEuWBykFmh8YuLqS4OXCRaWF+XAJOXh7e3sRxz7J0eETgUCkJZMu3FTSJIjS9OJ0vN9Vi6ohO3bGJuD6/Sw6QTw+bj6IG34w+no21aAlOwOrFhkmC0PTgSahC7tGaPho7vSpzkO/JwJEYH4RIMHrx3xfqeCdbHfVAeCAc0zjnAZSlFlUFWD8mdCK2RlK1/0RuzRjW0tlhFmqs+bwNIRGhKOwcDQoTsoMkZ09s2kwSfDyLTja4eXjRYKXjxdrHUjBK2VqWJ67HOfkl/CWpQ2amEQ8UL0HcLc13P8UlEumrnwm2STcYxKkHV/mFfa0bfHToB5EgAhcrQRI8Poxs1cqeCfbXW0FUAOAFf7N8RybFGVmfxKwVQi/rUt+GJYVDyEvmYW3jR6Sz02RrIARKrS0ACoVK1cstmH+XiaGPYPl/MDhU1cSvD5hcjUiwcvHiwQvH69AC16WK7dH1wNmPwhPjcVTF09gQUgY9vYeAo4/JgxWsDWs+RiZ2+KnHLwkbpUaJdgXO6wmK1orW8GsE2mFabTLOyVFakAE5icBErx+zPuVCl7Jv+ttd1WqrpbN0u14jo35IIY7gL6vuX7D7AxJS68bF7DmXmhCrlKMSVPGUqBJxSlmMmCNDZoEL9+CI8HLx4sELx+vQAveHn0POus6hUwNbPf1V11nYBmw4DsxSijfH82fWzqkReGXpw5ck17XmJ1BKo/O7qGlsgU2k00Q1vHqqYUzPyXqQQSIwFwnQILXjxm8UsEr+XfdLQbSMJyVfsdWV2O/lHwQ9lLA9ozQ3BS7DpXX/ALZquwxAWveCk1Iu8os/Rjz7VZVja3q5gcGrq4keLlwkYeXDxd5eDl5BVrwShYEllEhJT8Fr/YZcMbcjC8sUGBDzbdctob64ceRePPeSdMjSju53lIsuu/yUiGKK1gE1IUIzAMCJHj9mOQrEbyTlfM1AqgCwMJtxiVOkBSrdS/gOCWMumbFY2hdXIiC1AIoIsVywezwVkbYvRgFi+dh+Xe9ZYjwA4dPXUnw+oTJ1Yh2ePl40Q4vH69AC95hx7AQVCZVRCu3W/Fxx2lk2Ptx38DnLluDCdfBmF2BjEmqDHuzM7jfrbtfODoxmh8E9SACROCqJkCC14/pvRLBO1k5X8nOwLKUeWQqA1iUWfvn4+wM1oglYwLWJtsFMRrFnV3pyM0FEhP9AHAFXUnw8kEjwcvHiwQvH69AC152fvdMDWfCQ/Fex2kk9Hdg96LUMbaGskU92Fm8cMIbkD7Ipxakoi+8DyabCQnyBMRHiRYGyT5BhSj41wD1IALzgQAJXj9m+UoE72TZEaTqaoWsepDnuFiUmflv4+wMyijlmIA1aRdEla2CIml011c6nRT3xr73ZqnwA4dPXUnw+oTJ1YgELx8vErx8vGZC8Er+WpapoS0xGh+bmxHWfQ75MYnI+PyR0WwNC56GavtjXm0NA5YB6D7RwTRsQtimMEHsssPz9U8S11RumH8dUA8icLUTIMHrxwxfieAtKQHs9vFik718s/y7rCL8Ts8xMT/CgQOAm51Bq9kLrSIPafFpyEgQnwNKqcjY/z2DOqRTSlbgmBjget9TX/pBaWxXErx8KEnw8vEiwcvHayYEr5SpgWVVsGmUqBjsRefFE7jGfhlFQ2dGbQ3h18O49uA4W4PJakLT501orG1ExNIIyFfLIQuVwT5sF262OL0Y4aHhwv+lcsa0y8u/DqgHEbjaCZDg9WOGeQXvZP5dLQD2xdKxj7OxMS/CZ++PsTNU574Jw8iCMQFr0u4uS0WmymKJzbwfFy+K+XgXTvz00A8qk3clwcuHlgQvHy8SvHy8ZkLwSlaDuOQ4RGUlohzAmbbPsLq/E3mJa7zaGhzDDuh6dND36AVha6m0YPjyMFK3pUKj1iAxOhGVLZXCTm9eUh6UcjFFmeQZZv+nQhT8a4F6EIGrmQAJXj9ml1fwTubflewMLJuu+NLtdrCAtboXXHYGJF6PsrQfw2q3ugLWfNnd9eNWp60rCV4+lCR4+XiR4OXjNROCl9kRLpRfAMvUsCw/RSis03bpPJZ3nBafULnZGqrCHodpzY1wLGAhvOIRMhgC+edyJCUkYdWNo6nL6jvrBVHs/pSLtZd2lKkQBf9aoB5E4GomQILXj9nlFbxSobTMTECtHr2wDUApABmAIm/jYYl7mx8AhnTCb+2b/xMlw+IOrlRhzb3QxGS7u37c7rR0JcHLh5EELx8vErx8vGZC8Lrvump2acBSL/YO9iL2/KeCNcHd1qB3bMQHi3+NlPQ+wZ+rUWqAVtGq4PnkilkdKlsrEbcgDvkpo3ltJIFNhSj41wL1IAJXMwESvH7MLq/gnai6mZ5VTAOQDCDL23g++C+g5yuu35hubUKlsWFMwIYUwRzswRokePkWHAlePl4kePl4zYTgZdeQgslYJbTyqHBYAQyc+wiRQwOQDfWj6OR9roH/d0Q97r1zlcuXO1m6sf1aMXO5u4+XfS8FyrlXZOMnQz2IABG4mgiQ4PVjNnkEr80mVjeTyYAij21cFqzGgta8VldjHf/+dWDgVXGkaXugXfVDaE1a16M8b4Um/LitgHYlwcuHlwQvHy8SvHy8Zkrwtle3o8/QB5apoSExGgYWoNtZB2uPXtjlLWjYi6jeE8Lg69yyNbhbtdjusOch+Xhzl+cKvl7poEIU/OuAehCBq50ACV4/ZphH8La2AjU1gEoF5OSMXlSyM7Cf7PI2FtbxUA4w7PS05b2EMiQL/l0pWMNboQk/biugXUnw8uElwcvHiwQvH6+ZErzumRqMGiWYOWvNyDAie9uhilEhvPYpoGGvMHiTW7aGqaxa7IO/+4d/97uXdoYnStHIT4p6EAEiMJcJkOD1Y/Z4BO9E/t1WVjENAHPkuung0VGdOgicGk1UZrvHilJ9qeh9Sy+CVLrTW7lNP24tYF1J8PKhJcHLx4sELx+vmRK87pkazFmJ4zPS6E4DR9e5Bl+58CTybsl2WRMmEq0T+XjZiagQBf9aoB5E4GomQILXj9nlEbwT+Xel6mpe7QxsbJ88CrQ/J47Szc6QrEhGlioLUxWa8OP2AtKVBC8fVhK8fLxI8PLxminBKwWSsQ/msp3q8R/yWerFT290lU3XRv4Eii17YalhEQ6ANzuDdKclTSVC6jJPHy/7PRWi4F8P1IMIXK0ESPD6MbO+Cl7Jv8sutcvDtyCGXABeq6uxX7ymGmNnqAhLg/myWci/myhLhL5UfEOYKzknSfDyLTgSvHy8SPDy8Zopwcuuo93PMo0Dql0aIRdvHABXbgVWXOdvPwFsjwttLGHZqI//BIsGjYhRxWBZzrIJb0zy8bLXxCRF0ph2VIiCfz1QDyJwtRIgwevHzPoqeCX/rlIJ5LFEu86DuXKrnHl33X482uBSE/D30byTkp1BEM6aXa7d3akKTfhxi9PelQQvH1ISvHy8SPDy8ZpJwSvFGizLT8UncZHj0zAeeBvoZtlo+oWb+KTrr1iWthFTeXBZcYq6zrpx+XjZOVhKNF2pDsP2YaQWpCJSEckPiHoQASJwVRAgwevHNPoqeOvrAZ0O0GjEL+mQ7AzsR+PjjwGc+ilw6idi87Q9aM34v6gx1IDZGTLiMly7uxOVEfbj1gLWlQQvH1oSvHy8SPDy8ZpJwStlamACtjxJIQy0GIBYFBhAdTVw4efAwLvCt2d6HkT/or1Yv1sNWRTLUu79sAxYUH6hHHKZHDvV4wqzUyEK/iVBPYjAVUmABK8f0+qr4K2oAP7/9s4FrKoy7ft/DlsElYOEoKAGnkoscQRLTU0sodQp0042WTPp9Do11dQ3b82h76uu6q3J0urrMDUdpxorK3Ws0AKPiZOHwMQQTFRQQUUEEURO7/WszdpuYG/YN6uEtfZ/XRdXhOteez2/+3H749n3up/ycvvqrlrl1Q99dzW35QzLY4FT++ynj30bmf5Dta001Ud3gcWBLpuxGxjOOQml8MowU3hlvCi8Ml7nUnj18gLVGzd7aLjWi3ciALv6AlArA3nLgSp7t4Yz9b2wpdsujL+xeZmCqxG2VcfLjSjkc4IRJGBFAhReA1n1RHhVaVqa2kuzRf2u6rur+u8GAWi9JqEeMc4CvhxlD7SFovraQ1p3Bu06Q6dD/3jQTKu76t4pvLIJR+GV8aLwynidS+HVW4ypmtxDif20XrxJABzdc/W91yvUJhT2sob/2NJwyfUu959sNtCth7aiuLIY8RHxiA1z2say6SxuRCGfF4wgAasRoPAayKgnwqsePt6yxb6y61y/qx7fUF9xAIa7uofNvwb2vm3/E6dyBtWzMrYyVlvdNUsrMufhUXhlE47CK+NF4ZXxOpfC69ypoTo5VuvF26ycq6ICWL8eqPmHo6yhIOA+BF++qNknY65G2FYdrzqfG1HI5wUjSMBqBCi8BjLqifC6q9/dAKBcVSo0PbTW6jacyxkmfIZMRGrlDBcGXYj6rHrt9K6+jbArtBRe2YSj8Mp4UXhlvM6l8KrX0js1NKQOxh5/39a/8K9ciZqKLATgYW0gtQhBfsIJDHe5KnB2rHodr96f3BUFfZU3Ij4CYbFhclCMIAESMDUBCq+B9HkivK7qd9vdXa1kLZA+2X5nPc5H3Yx8pO1JQ8PpBgw6NAi2OhvMukc8hVc24Si8Ml4UXhmvcy28eilWwISB+D4koPWGOxkZKMk5hojQX8PXT1X5AltD1iBx2uXtDqytOl4VrJdUdA/pjgETBrR7PZ5AAiRgLQIUXgP59ER4VzY12nXuv9vu7mpuyhn88v0wqHEQzPyGTeGVTTgKr4wXhVfG61wLb0l2CcoLyxEQ3wffx4a2foYhOxs/Lt+J3n0+QFjwF9pgPC1raK+OV12LG1HI5wcjSMAqBCi8BjLZnvCWlgKZmUBICDDB0WEd0NuRxQNo/XgFAOdyhilrkN0Yhj279qDvsb7oe15frZShrTY9Bob0s4dSeGWIKbwyXhReGa9zLbx6p4aesWHYHh+h3azzXjzV23ahcPl29OxbhH4hf9L+3NOyBr2Ot39If4yMHOkSBDeikM8PRpCAVQhQeA1ksj3hzcsD1FdcHJrVoLXZjsy5nME3EripGMu3L8fJLSe1dmSxl8aiR2QPA3fduaEUXhl/Cq+MF4VXxutcC++pklM4uOWgtnva94n9UNuiF2/x2h9QkbEN4aPPR/iZGUDtCW1AnpQ1VNdVI31vOtqq43XeiKKt7YrlFBlBAiTQ1QlQeA1kqD3hVau7apU3KQmIbOq9o9fvum1H5lzOEHUDDox8BhtXbkTPxp4YP268Vrtr5oPCK8sehVfGi8Ir43Wuhbeuuk7b+czX5ovDKYO1B3ede/EWfJmL2sytiE3sDVvMV0DuYm1AnpY16HW8EwdORHCAo8NvMyhHdx1F2d4yUHjlc4URJGBmAhReA9lrT3j1+t3UVMC/aTuhAgA5APoDcPmhm3M5Q8JSbDwYiX1792HogKEYc9UYA3fbNUIpvLI8UHhlvCi8Ml7nWnjV6+mdGspSBuGozQ8JquOMU+sw/53fIW5ED2BUd2DdldqAPC1r2HV0F/aW7XXbj1ddS63y7knbQ+GVTxVGkICpCVB4DaSvLeF1V7+7FdAarutv8s1evnAZsGGm/Ue+kSgbuAardmaj3rce19xwDXr26mngbrtGKIVXlgcKr4wXhVfGqzOE98CGAzhdfhoVY/ujODzQ0ZpMX3kNO5aPiPMa7Y3LMwaLyhr0Ol7VrzyxX6JbGBUHKxAc7XoFWE6QESRAAmYgQOE1kKW2hNdd/W5T0wa43E7YqZyhrnEytuFR5FfuR8QvIpCS1P5uQwaGcs5CKbwy1BReGS8Kr4xXZwiv3qnBNz4CubFhUEVaqh+53rKsf0gFAsuLgfh4oOwFUVlDXUOd1sKxrTpeOSFGkAAJWIEAhddAFtsSXr1+NyEBiGnaCl7fTjgEgFPThrN38HGYYzVjf+FTyI1OQEXvCoy8ZCSGhqs9icx/UHhlOaTwynhReGW8OkN4ywrKcDTnKJw7NSRX1aIoo8C+e6RqXZOTY3/aN/yIoye5p2UNntTxyikxggRIwOwEKLwGMtiW8Or1u1OmAIGB9hdpczvhgneAzNu182prz0PB8bexs18Nuo3qhimxUxBoa7qIgfvtCqEUXlkWKLwyXhReGa/OEF69U4PqJ35wwgCohYC4vFL455UiuH8womJszfs5Oi0EeNKtwZM6XjklRpAACZidAIXXQAbdCa++JXxQEJCcfPYF2txO2KmcofTYddjnfxsOXgatbjc51ukiBu63K4RSeGVZoPDKeFF4Zbw6Q3j1h8ZUpwa/lMHaQ7y2zCLEllbZt0sP6QakpdkHonbs2f4HUVlDUUURsoqz0F4dr5wUI0iABMxMgMJrIHvuhLegwP6JXP/+wMimVgx1AJrewps1Wne8/Ac+jm8LflyEwvFDUBHTqJUyWKWcQQ2QwiubcBReGS8Kr4xXZwivek19x7Ow1MHY1NCIY98VY9KJagxOGWwfwKpV6qMuQH1EVvGfZmUNu4bsw8ikULcDZR2vfA4wggS8gQCF10CW3Qnv1q1AcTHgXL+rbyesP6DR7GVblDOcOPUydlwRjGrUoq1+kgZuvdNCKbwy9BReGS8Kr4xXZwmv3qkhOikaaY2NOHagHNMC/BEzsqlhuf4mqjcx/3IUUJalDS47+DPEp17raPXoasQZBRmoqq3C2JixCA8yd+9yeUYZQQIk4IoAhdfAvHAnvM6LE3r9rr6dsHr0rNXjZ07lDBUnZwMDf4X1cX4IsgVZqpyBK7zyyUbhlTGj8Mp4dZbw6i3IIuIjsLm6Dgcqz2D8+aEY0adpF0m9zc3QoYD6+v5R4PtHtMGV+l+OuklrHJv5uBox63jl84ARJGB1AhReAxl2Jbzu6nf17YSddxVyvLRTOQN6voGcXj1REB2E/sH9MTLK9Z7wBm67U0O5wivDT+GV8aLwynh1lvA6OjVE9URuRQ3ye3bD6DHRcHTOLSoCsrKAqCggMRGo3AesUO0b7Ef2kLI2yxpKTpVgy8EtCA8Mx9j+qukZDxIgAW8nQOE1MANcCa+r+l19O2EbgFbddJ3KGdRmE+j5D6SHlqG6T5glP46j8MomHIVXxovCK+PVWcJbVVqFokxV6AXU+fhg+/AIRMSGnn1/dLVykD4ZKFmrxeT1Woy4q+51W9ag1/Gqc6cPnS6HwggSIAHLEaDwGkipK+HNzgYKC+0907V+kgD0+l1X2wnXrLwaARVf2k8MmINq2yykR9cgqHek5coZ1BApvLIJR+GV8aLwynh1lvDqnRr0u81NioZvZA9tAwpHxa3e21F1alDH7ueBbfdp36qyhoqkNY73WFej3nBgA8pPl1ty4UCeZUaQAAlQeA3MAVfCm5EBVFUBEycCwU07V7rbTvhoVj4idjlV9PZ8A7vP1CL/4mhLljNQeOWTjcIrY0bhlfHqLOFVr6t3alDf16QMxn6br/Z8g+MdccMGoLz87JvpmRPA0jDHALeeX4bEce67Neh1vFbrdCPPMCNIgAQUAQqvgXnQUnirq4H0dMBmA1KcahdWqc0kgGbbCVcUVaBqy8uIqv6T/Q784oAezyO9Lh/Vw4dYdlWCK7yyCUfhlfGi8Mp4dabwHtp6CJXFlQgMD0S3sf2xpWl111Fx6+rjMqeyhpzuizDsl/e5LWtgHa98LjCCBKxMgMJrILsthbflcxbq0vp2wkEAnLePKMwsxHmFsxFY9639DsL/jOqa0UgPKobt/EFIGdyq2tfAnXadUAqvLBcUXhkvCq+MV2cKr96poe/ovgjs28vRpzwVgL+6Mf2BCLXF8PDh9oEJyhqc63hTB6fC31e7Kg8SIAEvJUDhNZD4lsLrakFC3064Zf1uwYo1iK10UuB+H6OgtBw5ff3QP26U5boz6JgpvLIJR+GV8aLwynh1pvA2NjYWuL7RAAAgAElEQVSi+ng1gsLVcgCQ2bRAkARA68ZbUgJs2QKEhwNjm9Z9W5Q1tNetIbMwE6XVpZb9xEyebUaQgPcS8Erh/fTTT7F69WoUFBTgqquuwn332R+EcHV8/PHHeO2115r90auvvopBgwahpfC6qt9t9SYOQD2hXLH+hbPlDJGXAz4PY13Jtzh5YRwS4sYhJjjGkrOSwitLK4VXxovCK+PVmcLb8k71xYE4ANp6bl2dfYvhljViLbo1DJ1xr9tB63W8cWFxGB7RtEosR8QIEiABCxDwSuHdsGED/P39sXbtWgQGBrYrvD/++CPuv/9+R7ptNht8fHyaCa9ev6tO0h8qVt+vbIpyboxTmleKoKyrz5YzJL6O6tze+LoiCz5jxli6jQ6FV/auQeGV8aLwynh1JeF1Wf6l7+KTmgpHsW6LsgZb6hrHA8ItR19aVYrMokz245VPC0aQgOUIeKXw6ll88cUXUV9f367wqpXg//7v/26VfOcVXr1+1/nTtxKg9YMYAIq/2Yyo/U7N0C/ZjMLsH5Dtfwz9x1xp2XIGBZDCK3sPofDKeFF4Zby6kvCqe9Ef8HXU8WZmAqWlgL7FsDqpRVlD0aVliIlz3a2Bdbzy+cAIErAqAQqvB8K7ZMkSqFXd8PBwpKamYsaMGdp80IX31ltvxa5dwN699l0w1Zc6dgHY29Rmx3k74ZJlTzcrZ2iMeRWZ21fgWGQvjBo9zbLlDIrJiRMnEBrqvpWQVf+idXRc1dXV8PX1RUBAQEcv4VVxSngrKysRrPcE9KrRd2ywp06dQrdu3bT3uM4+9BaO8QC0Nub6G6tzY3PVXigj2bEJxYlBzyFkjPuyNL2ONyk6CZE9tOpg7VCf0vEgARLwHgKWEt7Tp09DPQjh6vDz89Pe1J0PT1Z4d+/ejZqaGk128/Pz8cILL+COO+7AtGnTNOFVQnLDDTcgM9MfFRU+SEqqQ+/e9ntY7++Pah8fJNXVoXfTfamHNHr9ZwZ6Nm7Tzqka+TJOFQ3A2sJ1aBg0CCmjbrT07FP54T80nqeYvDxnpZ9JZjJmXYnXfl9f5Pr5IbqhASPq6+G7fz/8cnPREB2N+hEjHAML2PcqAnfZWzqWdZsAnytWuB30nrI9+PHEjxgYPBAXhF/gOI+/eMvmCc8mAbMTsJTwLliwAEWqtsDFMX78eDz00ENi4W15qY8++gjffvstFi5c6FjhnTNnrvZshTr0+l19O2HtZ04XqcjdgeDtI8/+5JcFKFzzDbLKdyNqfAqSYsebfU61ef8saZCllyUNMl4saZDxUmerFXH1CUJXWOFttQ27vsVwSAgwYcLZwbUoa8DsMqCb60+O9DrekO4hmDDA6RpyVIwgARIwMQFLCa80D56s8La8purwoB56W7RokUN4U1Lmtuqeo28nHAUg0ekileueQM+Df7X/JOZaIOkDbFnxKoobT2LU1b+xdDmDGjKFVzZLKbwyXhReGa+uJrzqfvQ63okAtM0qW24xrA/RqVsDRi8Ghrnv1rAyz/74MPvxyucHI0jAKgS8UnjVg2rq65VXXtH+e/fdd0OVPKivI0eO4LPPPsP8+fO12sk1a9Zg6NChWt1pXl4enn76acyaNQvXX3+9Q3gTE+e2qt/NBlAIIEF5rdNsqVsSA/+Gg/afjH0bsE3Bv9e+BoSF4Yqp/4VAW6BV5pbLcVB4Zeml8Mp4UXhlvLqi8OrPPjjqeF31e1Q37tStAaq145Q1bgfvro5XTosRJEACZiXglcL71ltv4YMPPmiWsxtvvBHz5s3DDz/8gHvuuQdpaWmaAD///PPYuHGj9rFfREQEUlJScPPNN2syrD+0Fhs7V9vyXfVGV10a1JGh6nNbbCdcezQftq+cHl+bXYbCnGxk5aQjfPDFGHfpbLPOI4/vm8LrMSrtRAqvjBeFV8arKwqv/umYeivVetls3QoUFwMJCUCM0/KBoKyhoKwAOUdzwH688vnBCBKwCgGvFN6fKnm68PbuPVe7pF6/W6EeWAPQcjthfP8o8P0j9pePux249C1kpX+AwsO5SLj8JvSPsX5jdAqvbPZReGW8KLwyXl1ReOsAxzbD2vMPeXn2L+cWOPowPSxr0Ot4g2xBSI513uRdzosRJEAC5iRA4TWQNyW8J092w4ABN8L5mQp32wljeSxwap/9FVU5Q+xt+Pd7/1f73ymz/oigwF4G7sYcoRReWZ4ovDJeFF4Zr64ovOqeNgAob1rhDde3GI6KAhKdn4hoUdYQlgBc9Z1bAKv2rEJtQy3reOVThBEkYAkCFF4DaVTCe/hwLwwffi3i4oDhTQu0+nbCzep3K/cBK7TOkvZjTiOKivPw3dfvIbxXJMZdc5eBOzFPKIVXlisKr4wXhVfGq6sKr17Hq20zrG9j2XKLYXXzqqxBLSTUnrAPXAmvEl8Xx9ZDW1FcWYyEqATLPxwsnwWMIAHrE6DwGsixEt78/PMwZsxVzTYC0rcTduwWpF7DVTlD1ioU7vwG8UPGI+6SFAN3Yp5QCq8sVxReGS8Kr4xXVxVefZvhEABaIzG9U4PzFsP6UNfPBIqW2f/vokeAi/6fSwis45XPDUaQgJUIUHgNZFMJ73ffRSM5ORn6+3CrN2r9+i7KGdJWv4LaI4cx5dI5CBp8tiG6gVvq8qEUXlmKKLwyXhReGa+uKrya4zYNRVs42LAB2pPBEycCLXfRK1kLqFpeddhCgevLXEKoqKnA+v3rYfO1IWWwdywwyGcDI0jAugQovAZyqwvvzJnJjp7oev2u6sXg6MfgopxBPUSxKe11BJ2ux5SU/zrb3sHA/ZghlMIryxKFV8aLwivj1ZWFt1lpmJsthh2j/TjsbFmDak+m2pS5OFjHK58fjCABqxCg8BrIpC688+YlO+p3mz1soV97+x+A3MX2/4u7HXVjXseWg1twbN2XGNo9GsNm32ngLswVSuGV5YvCK+NF4ZXx6srCWwAgR71lqjreggIgJwfNHpZwHqrze+wF9wG/WOQShF7HGx8Rj9gwp2cq5NgYQQIkYDICFF4DCdOF9/77k7X2kO62E3buzlBxyTvY6huDquMl8M/Nx6TocQi8/AoDd2GuUAqvLF8UXhkvCq+MV1cWXr29ow1ASmkpkJmJZu1wnIfqYVkD63jl84MRJGAVAhReA5lUwpv9bR889sgEBAX5oMjHB1k+PghvbMTYxkb7lY9/A6y7Uvu21q8HMkZ8oLXGCT52EkkngxGoanf19g4G7sUsoRReWaYovDJeFF4Zr64svOre9G2Gk2trEbRqFeCqU0PTkGs/CIVNa2am+jy6LmtgHa98fjCCBKxCgMJrIJNKeBsyinDbrIu0q2QFB6Owe3eMqKxEbJXaZw1A9WKgNl37trBxJLIaf4mYbudhVJD6oE7tPdxi9yAD92OGUAqvLEsUXhkvCq+MV1cX3mbbDCvhra0FpkwBAltvwV6x9g8IPtRUOtZGWYNexzt9qLatBQ8SIAEvIUDhNZBoJbyROTmYOnWqdpX00FBU+flhUnk5guvUfkEAjswBGkq0bzfZ5iGqx2TEdY+y/1lAAHDppYCfn4G7MFcohVeWLwqvjBeFV8arqwuvXser3jET9S2Gk5KAyMhWAy3ZsRaRO9vv1pBdko3C8kJQeOVzhREkYGYCFF4D2dO3Fp47d66jftd5O+G64q/hn2EvZ0CP81GRmo3ggGADr2j+UAqvLIcUXhkvCq+MV1cXXn2bYa2OV+/U4GqLYQBqQ7be6e2XNeh1vBRe+VxhBAmYmQCF10D2nIVXX4noD2AkAPWm6v/tfPQvtZczqO4MuPQtA69mjVAKryyPFF4ZLwqvjFdXF151f47ON0VFCM/KAvr3B0aqd9nmh/pQrXD5HxBb03ZZQ11DHdL2pHGFVz5VGEECpiZA4TWQPmfh3QqgGEB8Qy1Ki7O1LSynfD8PgTX2coaGSenwjU428GrWCKXwyvJI4ZXxovDKeJlBePU63tjycsSrDSiCgoBk1++lu9asxfDD7Zc1bC7ajEtjLpXDYgQJkIBpCVB4DaTOWXjVrkBVtVUIKswE6qoRfvJ7jN39Z+3qdb7R8J19AL7+vgZezRqhFF5ZHim8Ml4UXhkvMwivvntlOICx+hbD010/cKaqHoZktV/WIKfECBIgAbMToPAayKAuvNPmzsUX1WU4eDwfI04dQUj3EIze//8RVPiRdvWa825GwNQPDLySdUIpvLJcUnhlvCi8Ml5mEF69jlfda0p6OmzV1a63GG6q463a2H5ZA4qKoDVP50ECJOA1BCi8BlKtC2+fGZdhU9UxRNVWYXxgGEZGjgSctrqsuGgdgi+aaOCVrBNK4ZXlksIr40XhlfEyg/Cqe9S3GU7MykKUktX4eCC29U5pqo53y8q1GFvVRlmD/vCbm1ViOUFGkAAJmIEAhddAlpTw5lbnIuyqq1Hha8M1vfphdHAMULgM2DBTu7IqZ6iZvBs9InsYeCXrhFJ4Zbmk8Mp4UXhlvMwivPpDwbH5+Yjfvdv9FsMAMjKACcUuyhqUDauH3orV0xYAKLzyycIIEjAxAQqvgeQp4d1WnY2IX96KIeFDcINNNSUDsPnXwN63tW9PBNyG4GveZP1uE2cKr2zCUXhlvCi8Ml5mEV69jtd2/DhSNm0CoqKAxESXg1ULuIE/tChruPBJQPXxLS+379Y2diwQ7N0tIuUzhREkYG4CFF4D+VPCm+V3DBNvugd9fP0wVr/WBz6Oqx7q9w36XT7OwKtYK5TCK8snhVfGi8Ir42UW4VX3qW0zXF+PlLQ02Pz9gZQUl4MtKACKs1uUNYR/AqjdL1WHByW7LnZqk5NjBAmQgJkIUHgNZEsJ7/7QUCTMmIGhgPaFgneAzNu1q6pyhrKLv0PE8AgDr2KtUAqvLJ8UXhkvCq+Ml5mEV2/9OHzdOsSdPAmkpgJKfFscqnIhLQ1IqXAqawh6Egi/zC67LmLk1BhBAiRgNgIUXgMZU8KbExmJy6ZO1VZ3Vdsc53KG0oDfo/tl/8P6XSfGFF7ZhKPwynhReGW8zCS8eh1v5LZtSDp8GHCzxbC2GrwKiD3yIIb6/c0OpPdUIFWtEfMgARLwVgIUXgOZV8L7XXQ0UpKT4fhwzamcoaBnBgZePYn1uxTeDs8yCq8MHYVXxstMwqu3J/Pftw+pO3e67dSgxpS1shAVO9Zh4vm32oHYQoFrCoBuoXJAjCABErAEAQqvgTTqwjsnORna4xMtyhkORW/CgAkDDLyC9UK5wivLKYVXxovCK+NlJuFV96rV8ZaUYGx2NsJVH93hw1sPODsbBVtLsXN/T4wbch/C/VRTMwCjFwPD7pUDYgQJkIAlCFB4DaRRF977k5OhtTB36s6gyhkaLnyY9bst+FJ4ZROOwivjReGV8TKb8GrbDFdVIX7zZsSqbgsTJpwdsCrezczUOjFUNQQg49SliA14A/Gn/2A/J/JyYMoaOSBGkAAJWIIAhddAGnXhfSI5GYFnTgBLwxxXU+UM4b8YjeAYtr5xRkzhlU04Cq+MF4VXxstswlsCYEt9PcI3b8bYsrKzvXTV7mtKdvVODImJWJUZDJw5gZSTZ9+XMbuMZQ3yKcIIErAEAQqvgTQq4d0XFoaHVQNzp3KGGr8Lsb/HMgxOHcz6Xa7wGphh6t/vKvj5+SEgIMDQdbwlmMIrz3RlZaU2v2xqxbSLH3odb2N2NlKLimBLTgZqa+2yq/4bEmLvzxsYqLXdVXtMqF3XwuvW2kfGsoYunmHeHgn8fAQovAbY6lsLz507F1g/Eyhapl3taPc/ozJ0HmKTW299aeDlLBHKFV5ZGim8Ml4UXhkvs63wqvtVFbnH8vMxrqAA4b17n905TW1GkZDgaDum+vHm5ACxZxazrEE+LRhBApYjQOE1kFKH8F43EVhxVm5VOUPPwSNYv+uCLYVXNuEovDJeFF4ZLzMKbx6A3QcPIk5tM1xZaR9wXFyrB9gqKoD16wFbI8sa5LOCESRgPQIUXgM5dQjvhEbHZhPV/mNQGPRPRCVEsX6XwmtgdtlDKbwyhBReGS8zCq+2zfCxY+i1ezcmqTre+Hgg1vWnaaofr6p0SPG/HLbj6+xwWNYgnySMIAELEKDwGkiiQ3ij3wJK7DVixYH/gwrbdYidEgtbYNeviTMw/A6FcoVXho3CK+NF4ZXxMqPwqntOa2xEbWEhUrt1g02VMrg59Drekd0Xo/8RdmuQzw5GkIB1CFB4DeRSCW9QwxHMsv0fx1VUOQN6ns/6XTdcKbyyCUfhlfGi8Mp4mVV49W2G4wG09aSEXscbF3MCw3exW4N8djCCBKxDgMJrIJdKeON8NmC87+vaVfRyhuD+wYga6X7VwcBLmj6UwitLIYVXxovCK+NlVuHVtxkOAjAUsPdBd3GUltobOKjmDRPOTHZ8EseyBvk8YQQJmJ0AhddABpXwXun7JKJ8crWr6OUMrN91D5XCK5twFF4ZLwqvjJdZhVe1J1uvatybhtuW+K5caT8p5fzFsO1gWYN8hjCCBKxBgMJrII9KeG/1m+u4gipnqPWNZv1uG0wpvLIJR+GV8aLwyniZVXj1URYBUF0bnMVXlTioz9cCm05SK7xqpTdp5AlEbnAqa5jTKIfFCBIgAdMSoPAaSJ2z8NYEjsV+29uwBdlYv0vhNTCrmodSeGUoKbwyXmYXXnfiq37ev6ncoTAPyMtr6lx22KmsgcIrnyyMIAETE6DwGkies/CWhj+L0trpYP1u20C5wiubcBReGS8Kr4yXVYRXH7VqWaZWfQudMPQ4DpT9B4joCUzo8zyw7T77n1J45ZOFESRgYgIUXgPJcxbeQ+fnoPK4P/vvtsOTwiubcBReGS8Kr4yX1YRXH311U6mDEt/6emDbNiDiNHBj0gn0+ayprIHCK58sjCABExOg8BpInkN4427HnvK/oKG2gfW7FF4DM6p1KIVXhpPCK+NlVeFtKb5f5QJq57ULLwSmfjsZ4apvOoVXPlkYQQImJkDhNZA8XXhrRq/A/vxhrN/1gCVXeD2A5HQKhVfGi8Ir42V14dVpfJcLbD9kb9p7UbeVGLNuBoVXPlUYQQKmJkDhNZA+JbxhjXsxfsI9OJpzlPW7HrCk8HoAicIrg+R0NoVXjq6yshIBAQGw2ay7M2RJCbBlCxAeDvQfC5Qc34bRvUfLYTGCBEjAtAQovAZSp28tfMXwK1BZXMn6XQ9YUng9gEThlUGi8HaYl7es8NbVAWlpdkzTpxvCxWASIAGTEqDwGkicLrzjIsexftdDjhReD0E1ncaSBhkvrvDKeHmL8KpxbtgAlJcDY8faV3p5kAAJeBcBCq+BfCvhra+qx7jocazf9ZAjhddDUBReGaimsym8cmzeUNKgqOzaBezdCwwdav/iQQIk4F0EKLwG8q2E98yRM5gwbALrdz3kSOH1EBSFVwaKwtshXt60wutcx6tWeXmQAAl4FwEKr4F8K+E9vfc0Jo2exPpdDzlSeD0EReGVgaLwdoiXNwmvXserns1LSekwLgaSAAmYlACF10DilPCe2nEKyZOS2X/XQ44UXg9BUXhloCi8HeLlTcKrxpqRAVRVsY63w5OFgSRgYgIUXgPJ04T3u1NImZ6C2ORYA1fynlAKryzXfGhNxos1vDJe3ia8eh1vfDwQy7ds+WRhBAmYmACFt53kLVu2DF9++SUOHjyIkJAQTJs2DXPmzNGidOG99rZrETUyysTT4NzdOoVXxprCK+NF4ZXx8jbhLSgAcnKAqCggMVHOihEkQALmJUDhbSd3b7zxBhISEhAXF4cDBw7g0UcfxYIFC3DllVc6hHfO/XMQHBNs3llwDu+cwiuDTeGV8aLwynh5m/Cyjlc+PxhBAlYhQOEVZnLRokXw8/PDPffc4xDeO564A7ZA6+5SJETU5ukUXhlNCq+MF4VXxsvbhFeNd9UqoLaWG1DIZwojSMDcBCi8gvw1NjbizjvvxIwZM7QvrUvDj6cx/5H5gqt496kUXln+KbwyXhReGS9vFN7sbKCwkMIrnymMIAFzE/Ba4a2vr8eZM2fcZk/tLe/r69vsz1V5w9atW/HCCy9o+84r4a2ursb1119v7llwDu9e/dLg4+NzDl+RL0UCJEACZwns3++L3Fw/3HRTT2IhARLwIgJeK7wbNmzA3/72N7epfumllzBgwADHny9ZsgSrV6/Gc889h9DQUO3n+tbCc+fO9aIpY2yoJ06ccPAzdiXviOYKryzPaoVX7RwWHMyaek/JectOazqP6mogPZ0rvJ7OD55HAlYh4LXCK0ng0qVLsWLFCqj63XCnTdgpvBKK9nNZ0iBjRuGV8WJJg4yXOtvbhFeNecMGYMIEOStGkAAJmJcAhbed3Km2ZGp1d+HChejTp492tip18Pf35wpvB+Y9hVcGjcIr40XhlfHyVuGVU2IECZCA2QlQeNvJ4C233IIjR440O2vcuHFaezKu8MqnP4VXxozCK+NF4ZXxovDKeTGCBEjAnAQovAbyRuGVw6PwyphReGW8KLwyXhReOS9GkAAJmJMAhddA3ii8cngUXhkzCq+MF4VXxovCK+fFCBIgAXMSoPAayBuFVw6PwitjRuGV8aLwynhReOW8GEECJGBOAhReA3mj8MrhUXhlzCi8Ml4UXhkvCq+cFyNIgATMSYDCayBvFF45PAqvjBmFV8aLwivjReGV82IECZCAOQlQeA3kjcIrh0fhlTGj8Mp4UXhlvCi8cl6MIAESMCcBCq+BvFF45fAovDJmFF4ZLwqvjBeFV86LESRAAuYkQOE1kDcKrxwehVfGjMIr40XhlfGi8Mp5MYIESMCcBCi8BvJG4ZXDo/DKmFF4ZbwovDJeFF45L0aQAAmYkwCF10DeKLxyeBReGTMKr4wXhVfGi8Ir58UIEiABcxKg8BrIG4VXDo/CK2NG4ZXxovDKeFF45bwYQQIkYE4CFF4DeaPwyuFReGXMKLwyXhReGS8Kr5wXI0iABMxJgMJrIG8UXjk8Cq+MGYVXxovCK+NF4ZXzYgQJkIA5CVB4DeSNwiuHR+GVMaPwynhReGW8KLxyXowgARIwJwEKr4G8UXjl8Ci8MmYUXhkvCq+MF4VXzosRJEAC5iRA4TWQNwqvHB6FV8aMwivjReGV8aLwynkxggRIwJwEKLwG8kbhlcOj8MqYUXhlvCi8Ml4UXjkvRpAACZiTAIXXQN4ovHJ4FF4ZMwqvjBeFV8aLwivnxQgSIAFzEqDwGsgbhVcOj8IrY0bhlfGi8Mp4UXjlvBhBAiRgTgIUXgN5o/DK4VF4ZcwovDJeFF4ZLwqvnBcjSIAEzEmAwmvOvPGuSYAESIAESIAESIAEPCRA4fUQFE8jARIgARIgARIgARIwJwEKrznzxrsmARIgARIgARIgARLwkACF10NQPI0ESIAESIAESIAESMCcBCi8Hczbu+++i2XLlqG+vh5TpkzBXXfdBT8/vw5ezfphM2bMwOnTpx0DHTduHB599FHrD9zDEW7fvh3vvfce8vPzcd555+Gtt95qFqkeXnvuueewefNm9OzZE7fccgsUU28+Fi5ciB07duDw4cP485//jMmTJztw5OXlaX8nnY8FCxbguuuu81pk6v3qyy+/xMGDBxESEoJp06Zhzpw5Dh4HDhyAYrpnzx5ER0fj3nvvxYgRI7yWFwdOAiRgLQIU3g7kMz09HX//+9/x9NNPo0ePHo5/bJWE8HBNQMnZyy+/jMjISO0EX19f+Pv7E1cTgR9++EETt+PHj+Pzzz9vJbxKdg8dOoS//vWvKCws1Obck08+iYsuushrGSqBi42NxaJFi3Dbbbe1El71C5XzLw5qvql5563HG2+8gYSEBMTFxUHJreKjfgm48sorobpb3HHHHVC/iCoJ/uqrr6B+qVedaNR7HA8SIAESMDsBCm8HMvjggw9qKx+33nqrFv31119r/zioLx7uhff1119HVFQUEbVBYP369ZqkOYtaXV0dZs6ciSeeeAIXX3yxFv3ss89q/33ggQe8nue8efO0Fe+WK7xK6N5//32v5+MOgPpFQX0qdc899yAnJwfqfe2TTz5BQECAFjJ37lzt64orriBDEiABEjA9AQpvB1J40003af9IqNUQdRQUFOC3v/2ttjLXrVu3DlzR+iFqhTcsLAyNjY0YNmyYtprUt29f6w9cOEJXwqs+gr799tu1Ehp9tU19n5GRgRdeeEH4CtY73Z3w3nfffQgPD9cELikpSZO3wMBA6wHowIjU38M777xTK4tRX1988QVWrFiBV1991XE19QtDTEyM9neVBwmQAAmYnQCFtwMZvOaaa/DYY49h5MiRWvSRI0e0FaalS5dqtXE8WhNQZSBDhgxBbW0tPvzwQ6iP8P/xj384VpPIzE7AlfCqmkr10fPq1avh4+Ojnac+clYcFUNvP1wJb2lpKXbv3o2BAwfi2LFjeOWVVzBgwACtFIQHoMobtm7dqv3CZLPZtJXdTZs2OT45UIxUPa/6ZeH3v/89kZEACZCA6QlQeDuQQq7wdgCaU4h60G/WrFnaLw36R/TGrmidaK7wynPpSnhbXkXJr3oIa+XKlV5fO75kyRLtlydVFx4aGqqh4gqvfN4xggRIwFwEKLwdyJeqdVOipj+kplYv33nnHdbweshSPSAze/ZsPPzwwxg1apSHUd5xmrsa3muvvRZPPfWU46l5JSvqY2nW8AKeCO+PP/6I3/3ud5rwqhVNbz3Up1CqdEHV76pyD/1QNbwPPfQQPv30UwcfVUbzq1/9ijW83jpZOG4SsBgBCm8HEqoeUlMfJT/zzDMICgrCn/70J0yaNMkhwB24pKVD9u3bh7KyMgwaNAg1NTX417/+pX18+uabb2r8eEB7Sl49nLZx40btF6fXXntNK1/Q5Uw9pKZKZ1SXhqKiIk1OHn/8ca/u0qDKY5T0q3IP9amL+juod2JQbd6Cg4O1OnHF7cUXX9TmmjIBELEAAAh3SURBVGLmrYeq+1aru6pUoU+fPhoGvVuKmn+/+c1vNIY333yz9iCu+vvJLg3eOls4bhKwHgEKbwdzqlZ0ly9fzj68HvDLzc3VPj5VD1+ph/ouuOACzJ8/X2uPxMNOQAma+uTA+VCclKipw7kPr3pwTa28eXsfXvVQmlqZdD5Uqzb1gJr6iF79YqXqd5X4qp+pOefNNfbqEykl/86Hcz/s/fv3azKsVsP79esHxZd9ePkORQIkYBUCFF6rZJLjIAESIAESIAESIAEScEmAwsuJQQIkQAIkQAIkQAIkYGkCFF5Lp5eDIwESIAESIAESIAESoPByDpAACZAACZAACZAACViaAIXX0unl4EiABEiABEiABEiABCi8nAMkQAIkQAIkQAIkQAKWJkDhtXR6OTgSIAESIAESIAESIAEKL+cACZAACZAACZAACZCApQlQeC2dXg6OBEiABEiABEiABEiAwss5QAIkQAIkQAIkQAIkYGkCFF5Lp5eDIwESIAESIAESIAESoPByDpAACZAACZAACZAACViaAIXX0unl4EiABEiABEiABEiABCi8nAMkQAIkQAIkQAIkQAKWJkDhtXR6OTgSIAESIAESIAESIAEKL+cACZAACZAACZAACZCApQlQeC2dXg6OBEiABEiABEiABEiAwss5QAIkQAIkQAIkQAIkYGkCFF5Lp5eDIwESIAESIAESIAESoPByDpAACZAACZAACZAACViaAIXX0unl4EiABEiABEiABEiABCi8nAMkQAIkQAIkQAIkQAKWJkDhtXR6OTgSIAESIAESIAESIAEKL+cACZAACZAACZAACZCApQlQeC2dXg6OBEiABEiABEiABEiAwss5QAIkQAIkQAIkQAIkYGkCFF5Lp5eDIwESIAESIAESIAESoPByDpAACZAACZAACZAACViaAIXX0unl4Eig4wS++OILLFmyBO+++27HL8JIEiABEiABEugCBCi8XSAJvAUS+CkJrFq1CgsXLsRXX31l6LIUXkP4GEwCJEACJNCFCFB4u1AyeCsk8FMQoPD+FBR5DRIgARIgASsRoPBaKZscCwkAaCm877//PjZv3ozJkyfjk08+walTpzBq1Cjcf//96NWrl4PZp59+ig8//FD78zFjxuCCCy7AypUrHSUNjY2NWLp0Kf7973/j2LFj6Nu3L66//nqkpqZq13jvvfewfPlyvP766wgNDdV+9sQTT6CwsBAvvvgibDYb80MCJEACJEACnUKAwtsp2PmiJPDzEXAlvEpGp0+fjjvuuAO1tbV46KGHcOGFF+Luu+/WbmTTpk147LHHcNddd2H06NH45ptvNIENCQlxCO8777yD9PR07ZyBAwciPz8fzz77rCbOEydORENDAx544AH06NEDjz/+uFZSsXjxYrzyyisYMGDAzzdgXpkESIAESIAE2iFA4eUUIQGLEXAlvGpl96OPPoK/v782WrVK+9lnn+HNN9/U/l9Jq1qx/eMf/+ig8fDDD2P//v2a8NbU1GDWrFl45JFHkJiY6Djnn//8J3JycvDUU09pPyspKcGdd96Jq6++Wlsdnj9/PmbMmGExwhwOCZAACZCA2QhQeM2WMd4vCbRDwJXwqhXcl156yRG5fv16LFq0SJNedSiZVau/SlT1Q5U3fP7555rw7tmzBwsWLHD5ykqUnTs5rF69Gs888wwuueQSbaWXBwmQAAmQAAl0NgEKb2dngK9PAj8xAXc1vKqOVj+U8D733HNYtmyZQ3jVyuzUqVMd56iaXvXnSmbz8vK0UobXXnsNsbGxbd6x3iFClT0oyWbt7k+cYF6OBEiABEhATIDCK0bGABLo2gQ6IryqpGHYsGFaOYJ+KHHdsWOHJrynT5/WVoFvueUWzJkzxy2AjRs34sknn9RKHJ5++mlcdtllbleGuzZF3h0JkAAJkICVCFB4rZRNjoUE2ujS0NYKr3pITZUhqK8hQ4ZoovuXv/wFYWFhjnIFJb6qDnjevHlaHa+q6921axfq6uowc+ZMlJaWajW7Sohnz56NnTt3ag+xKQFWD8LxIAESIAESIIHOIkDh7SzyfF0S+JkIdGSFV93Kxx9/rH35+vpqXRhUF4eMjIxm9bkrVqzQWo8dOnQIQUFBGDRoEG644QZNaB988EFtRGpl18fHR/v+7bffRlpamlYKERwc/DONmJclARIgARIggbYJUHg5Q0iABEiABEiABEiABCxNgMJr6fRycCRAAiRAAiRAAiRAAhRezgESIAESIAESIAESIAFLE6DwWjq9HBwJkAAJkAAJkAAJkACFl3OABEiABEiABEiABEjA0gQovJZOLwdHAiRAAiRAAiRAAiRA4eUcIAESIAESIAESIAESsDQBCq+l08vBkQAJkAAJkAAJkAAJUHg5B0iABEiABEiABEiABCxNgMJr6fRycCRAAiRAAiRAAiRAAhRezgESIAESIAESIAESIAFLE6DwWjq9HBwJkAAJkAAJkAAJkACFl3OABEiABEiABEiABEjA0gQovJZOLwdHAiRAAiRAAiRAAiRA4eUcIAESIAESIAESIAESsDQBCq+l08vBkQAJkAAJkAAJkAAJUHg5B0iABEiABEiABEiABCxNgMJr6fRycCRAAiRAAiRAAiRAAhRezgESIAESIAESIAESIAFLE6DwWjq9HBwJkAAJkAAJkAAJkACFl3OABEiABEiABEiABEjA0gQovJZOLwdHAiRAAiRAAiRAAiRA4eUcIAESIAESIAESIAESsDQBCq+l08vBkQAJkAAJkAAJkAAJUHg5B0iABEiABEiABEiABCxNgMJr6fRycCRAAiRAAiRAAiRAAhRezgESIAESIAESIAESIAFLE6DwWjq9HBwJkAAJkAAJkAAJkMD/Ahbt/uATgsrcAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size  nnIdx    nnDist  \\\n",
      "0 2024-08-21  AAPL 2024-08-21 11:50:07.309  218.24    46  35815  1.052655   \n",
      "1 2024-08-22  AAPL 2024-08-22 11:59:24.140  268.93    40  38175  1.316181   \n",
      "2 2024-08-21  AAPL 2024-08-21 10:04:34.018  230.41    28   9064  1.326923   \n",
      "3 2024-08-19  AAPL 2024-08-19 15:10:19.251  230.77    76  87237  1.333702   \n",
      "4 2024-08-27  AAPL 2024-08-27 14:24:44.317  270.15    60  75693  1.347826   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [218.24, 218.24, 218.35, 218.46, 218.43, 218.5...  \n",
      "1  [268.93, 268.95, 269.03000000000003, 269.16, 2...  \n",
      "2  [230.41, 230.48000000000002, 230.5, 230.47, 23...  \n",
      "3  [230.77, 230.86, 230.89000000000001, 230.94, 2...  \n",
      "4  [270.15, 270.23, 270.3, 270.28000000000003, 27...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size  nnIdx    nnDist  \\\n",
      "0 2024-08-21  AAPL 2024-08-21 11:50:07.309  218.24    46  35815  1.052655   \n",
      "1 2024-08-22  AAPL 2024-08-22 11:59:24.140  268.93    40  38175  1.316181   \n",
      "2 2024-08-21  AAPL 2024-08-21 10:04:34.018  230.41    28   9064  1.326923   \n",
      "3 2024-08-19  AAPL 2024-08-19 15:10:19.251  230.77    76  87237  1.333702   \n",
      "4 2024-08-27  AAPL 2024-08-27 14:24:44.317  270.15    60  75693  1.347826   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [218.24, 218.24, 218.35, 218.46, 218.43, 218.5...  \n",
      "1  [268.93, 268.95, 269.03000000000003, 269.16, 2...  \n",
      "2  [230.41, 230.48000000000002, 230.5, 230.47, 23...  \n",
      "3  [230.77, 230.86, 230.89000000000001, 230.94, 2...  \n",
      "4  [270.15, 270.23, 270.3, 270.28000000000003, 27...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of pattern result names and pattern names\n",
    "pattern_results = [0,1,2,3]\n",
    "pattern_names = ['Pattern A', 'Pattern B', 'Pattern C', 'Pattern D']\n",
    "pattern_p = [0,1,2,3]\n",
    "\n",
    "# Process and plot each pattern in a loop\n",
    "for i in range(4):\n",
    "    pattern_df, nnMatch_df, q = process_pattern(pattern_results[i], pattern_p[i])\n",
    "    \n",
    "    # Plot using the normalized nnMatch_df\n",
    "    print(pattern_df.head())\n",
    "    fig = plot_pattern(nnMatch_df, q, pattern_names[i])\n",
    "    fig.show(\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualizations demonstrate how the predefined patterns, align with the synthetic stock data over time. This analysis reveals several key insights:\n",
    "\n",
    "- Pattern Recognition: The matching scores indicate periods where the synthetic stock prices exhibit behaviors similar to the identified patterns. High matching scores suggest that specific market conditions or trends are being accurately captured, which can be crucial for understanding price movements.\n",
    "\n",
    "- Market Dynamics: Observing the temporal alignment allows us to identify how certain patterns correlate with market events or price fluctuations. Recognizing these correlations can help traders anticipate potential market movements based on historical trends.\n",
    "- Risk Management: By leveraging pattern matching, traders can develop more robust risk management strategies. For instance, if a specific pattern frequently precedes a downturn, this information can inform decisions about when to exit positions or adjust portfolios.\n",
    "\n",
    "- Algorithmic Trading: The insights gained from these visualizations can be integrated into algorithmic trading systems. By programming algorithms to recognize and respond to these patterns in real-time, traders can automate buy and sell decisions, potentially capitalizing on market opportunities more effectively.\n",
    "\n",
    "- Strategy Development: This analysis serves as a foundation for developing trading strategies. By understanding which patterns yield successful outcomes, traders can refine their strategies to enhance performance and profitability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Search for Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Outlier Search**: Perform an outlier search for each normalised pattern against the synthetic market time series data in the HDB. This step queries the database for the sequence that is least similar to the patterns.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another key feature of Non-Transformed Temporal Similarity Search is outlier detection, which identifies the most dissimilar vectors.\n",
    "\n",
    "To perform this search, set 𝑛 to the negative value of the number of dissimilar results you want to retrieve (e.g. 𝑛 = −5). Additionally, modifying the pattern length will adjust the sliding window range used during the search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = table.search(\n",
    "    {\"price\": combined_patterns.tolist()},\n",
    "    type=\"tss\",\n",
    "    n=-5,\n",
    "    filter=[(\"=\", \"sym\", \"NVDA\")],\n",
    "    options=dict(force=True, returnMatches=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size   nnIdx    nnDist  \\\n",
      "0 2024-08-29  NVDA 2024-08-29 10:49:45.487  393.77    65  720346  9.799948   \n",
      "1 2024-08-27  NVDA 2024-08-27 15:20:36.757  418.57    72  789862  9.765645   \n",
      "2 2024-08-28  NVDA 2024-08-28 15:55:28.179  428.42    46  798789  9.745112   \n",
      "3 2024-08-21  NVDA 2024-08-21 14:33:53.295  409.10    63  777845  9.740116   \n",
      "4 2024-08-22  NVDA 2024-08-22 10:52:49.310  417.84    96  721097  9.735033   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [393.77, 393.7, 393.46000000000004, 393.340000...  \n",
      "1  [418.57, 418.34000000000003, 418.24, 418.35, 4...  \n",
      "2  [428.42, 428.33, 428.17, 428.09000000000003, 4...  \n",
      "3  [409.1, 408.94, 408.79, 408.74, 408.6500000000...  \n",
      "4  [417.84000000000003, 417.59000000000003, 417.5...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size   nnIdx    nnDist  \\\n",
      "0 2024-08-29  NVDA 2024-08-29 11:01:31.230  384.39    11  723398  9.920381   \n",
      "1 2024-08-22  NVDA 2024-08-22 15:35:46.897  397.67    67  793651  9.919833   \n",
      "2 2024-08-29  NVDA 2024-08-29 13:27:00.608  396.07    16  760825  9.917246   \n",
      "3 2024-08-20  NVDA 2024-08-20 15:29:26.416  452.49    73  792179  9.916484   \n",
      "4 2024-08-28  NVDA 2024-08-28 13:06:39.815  416.63    44  755384  9.910316   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [384.39, 384.29, 384.18, 384.06, 384.07, 383.9...  \n",
      "1  [397.67, 397.53000000000003, 397.63, 397.49, 3...  \n",
      "2  [396.07, 395.92, 395.74, 395.73, 395.62, 395.4...  \n",
      "3  [452.49, 452.2, 452.24, 452.21000000000004, 45...  \n",
      "4  [416.63, 416.64, 416.56, 416.41, 416.340000000...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size   nnIdx    nnDist  \\\n",
      "0 2024-08-29  NVDA 2024-08-29 11:01:31.230  384.39    11  723398  9.920381   \n",
      "1 2024-08-22  NVDA 2024-08-22 15:35:46.897  397.67    67  793651  9.919833   \n",
      "2 2024-08-29  NVDA 2024-08-29 13:27:00.608  396.07    16  760825  9.917246   \n",
      "3 2024-08-20  NVDA 2024-08-20 15:29:26.416  452.49    73  792179  9.916484   \n",
      "4 2024-08-28  NVDA 2024-08-28 13:06:39.815  416.63    44  755384  9.910316   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [384.39, 384.29, 384.18, 384.06, 384.07, 383.9...  \n",
      "1  [397.67, 397.53000000000003, 397.63, 397.49, 3...  \n",
      "2  [396.07, 395.92, 395.74, 395.73, 395.62, 395.4...  \n",
      "3  [452.49, 452.2, 452.24, 452.21000000000004, 45...  \n",
      "4  [416.63, 416.64, 416.56, 416.41, 416.340000000...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        date   sym                    time   price  size   nnIdx    nnDist  \\\n",
      "0 2024-08-29  NVDA 2024-08-29 11:01:31.230  384.39    11  723398  9.920381   \n",
      "1 2024-08-22  NVDA 2024-08-22 15:35:46.897  397.67    67  793651  9.919833   \n",
      "2 2024-08-29  NVDA 2024-08-29 13:27:00.608  396.07    16  760825  9.917246   \n",
      "3 2024-08-20  NVDA 2024-08-20 15:29:26.416  452.49    73  792179  9.916484   \n",
      "4 2024-08-28  NVDA 2024-08-28 13:06:39.815  416.63    44  755384  9.910316   \n",
      "\n",
      "                                             nnMatch  \n",
      "0  [384.39, 384.29, 384.18, 384.06, 384.07, 383.9...  \n",
      "1  [397.67, 397.53000000000003, 397.63, 397.49, 3...  \n",
      "2  [396.07, 395.92, 395.74, 395.73, 395.62, 395.4...  \n",
      "3  [452.49, 452.2, 452.24, 452.21000000000004, 45...  \n",
      "4  [416.63, 416.64, 416.56, 416.41, 416.340000000...  \n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of pattern result names and pattern names\n",
    "pattern_results = [0,1,2,3]\n",
    "pattern_names = ['Pattern A', 'Pattern B', 'Pattern C', 'Pattern D']\n",
    "pattern_p = [0,1,2,3]\n",
    "\n",
    "# Process and plot each pattern in a loop\n",
    "for i in range(4):\n",
    "    pattern_df, nnMatch_df, q = process_pattern(pattern_results[i], pattern_p[i])\n",
    "    \n",
    "    # Plot using the normalized nnMatch_df\n",
    "    print(pattern_df.head())\n",
    "    fig = plot_pattern(nnMatch_df, q, pattern_names[i])\n",
    "    fig.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results from the outlier search to identify the least similar patterns within the synthetic stock data could be crucial for uncovering unexpected behaviors that deviate from established trends in real life. Here are the key findings from the outlier search:\n",
    "\n",
    "- Identification of Anomalies: The outlier search revealed patterns that exhibit significant deviations from the expected behavior represented by patternA and patternB. Identifying these anomalies is essential for understanding underlying market dynamics that may not conform to typical trends.\n",
    "\n",
    "- Market Volatility: The patterns classified as outliers often coincide with periods of increased volatility or unusual market events. Recognizing these outliers can provide insights into potential market disruptions, helping traders stay informed about risks that may not be apparent through standard analyses.\n",
    "\n",
    "- Refining Trading Strategies: By examining the characteristics of these outlier patterns, traders can gain a deeper understanding of market anomalies. This knowledge can be instrumental in refining trading strategies, allowing for more adaptive responses to unexpected market conditions.\n",
    "\n",
    "- Enhanced Decision-Making: Outlier detection adds a layer of depth to market analysis. Understanding what constitutes a significant deviation helps traders differentiate between normal fluctuations and potential signals for action, such as reassessing positions or employing protective strategies.\n",
    "\n",
    "- Future Research: The findings from the outlier search can inform future research endeavors. Analyzing the causes and implications of these outlier patterns may lead to the development of new models or strategies that account for market anomalies more effectively.\n",
    "\n",
    "In summary, the outlier search complements our temporal pattern matching analysis by highlighting significant deviations in market behavior. By integrating these insights, traders and analysts can enhance their understanding of market dynamics and improve their decision-making processes in real-world trading scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Drop the Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.table(\"trade\").drop()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: TSS_non_transformed/Temporal_Similarity_Search_Non-Transformed_Demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58ae5b26-0b58-4416-a69d-f5662fce320d",
   "metadata": {},
   "source": [
    "# Non-Transformed Temporal Similarity Search\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "Non-Transformed TSS enables near real-time analysis directly upon time series windows without the overhead of extracting and embedding vectors or building a search index. \n",
    "\n",
    "This feature leads to optimized processing of fast flowing time series datasets without any model dependency. It offers dynamic search capability allowing the identification of patterns and outliers within temporal windows using query vectors of different sized sequences - including accepting multiple query vectors of differing sizes at once. \n",
    "\n",
    "Agenda:\n",
    "\n",
    "1. Dependencies, Imports & Setup\n",
    "2. Define KDB.AI Session\n",
    "3. Generate Synthetic Market Time Series Data\n",
    "4. Create KDB.AI Schema & Table\n",
    "5. Insert Data into KDB.AI Table\n",
    "6. Run Non-Transformed Temporal Similarity Search\n",
    "   - Similarity Search\n",
    "   - Outlier Search\n",
    "   - Dynamic Search\n",
    "   - Multiple Search\n",
    "7. Search Evaluation \n",
    "8. Drop the Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d242bc0",
   "metadata": {},
   "source": [
    "### 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5ebbb78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.org/simple, https://kx-user-read:****@ext-nexus.kxi-dev.kx.com/repository/kxi/simple\n",
      "Requirement already satisfied: kdbai_client in /home/gflood/.local/lib/python3.10/site-packages (1.4.0.dev10)\n",
      "Requirement already satisfied: packaging in /home/gflood/.local/lib/python3.10/site-packages (from kdbai_client) (23.2)\n",
      "Requirement already satisfied: pandas>=1.5.0 in /home/gflood/.local/lib/python3.10/site-packages (from kdbai_client) (2.1.4)\n",
      "Requirement already satisfied: pykx<3.0.0,>=2.1.1 in /home/gflood/.local/lib/python3.10/site-packages (from kdbai_client) (2.5.1)\n",
      "Requirement already satisfied: requests in /home/gflood/.local/lib/python3.10/site-packages (from kdbai_client) (2.32.3)\n",
      "Requirement already satisfied: numpy<2,>=1.22.4 in /home/gflood/.local/lib/python3.10/site-packages (from pandas>=1.5.0->kdbai_client) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/gflood/.local/lib/python3.10/site-packages (from pandas>=1.5.0->kdbai_client) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/gflood/.local/lib/python3.10/site-packages (from pandas>=1.5.0->kdbai_client) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/gflood/.local/lib/python3.10/site-packages (from pandas>=1.5.0->kdbai_client) (2024.1)\n",
      "Requirement already satisfied: toml~=0.10.2 in /home/gflood/.local/lib/python3.10/site-packages (from pykx<3.0.0,>=2.1.1->kdbai_client) (0.10.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/gflood/.local/lib/python3.10/site-packages (from requests->kdbai_client) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/gflood/.local/lib/python3.10/site-packages (from requests->kdbai_client) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/gflood/.local/lib/python3.10/site-packages (from requests->kdbai_client) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/gflood/.local/lib/python3.10/site-packages (from requests->kdbai_client) (2024.7.4)\n",
      "Requirement already satisfied: six>=1.5 in /home/gflood/.local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->kdbai_client) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eee37861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘./data’: File exists\n",
      "--2024-09-25 14:54:53--  https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/TSS_non_transformed/data/marketTrades.parquet\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1277201 (1.2M) [application/octet-stream]\n",
      "Saving to: ‘./data/marketTrades.parquet.11’\n",
      "\n",
      "marketTrades.parque 100%[===================>]   1.22M  --.-KB/s    in 0.1s    \n",
      "\n",
      "2024-09-25 14:54:53 (10.1 MB/s) - ‘./data/marketTrades.parquet.11’ saved [1277201/1277201]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads sample market data\n",
    "!mkdir ./data \n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/TSS_non_transformed/data/marketTrades.parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0bd8b5ce-096a-4de1-9f32-0eb3a513ca7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# KX Dependencies\n",
    "import kdbai_client as kdbai\n",
    "# Other Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.lib.stride_tricks import sliding_window_view\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9da1ed6b-e589-49ba-a87a-25a9aeed9633",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dd295f42-7c47-42a1-80ee-fbadf0b0324c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies for metric gathering\n",
    "import psutil\n",
    "import datetime\n",
    "\n",
    "# Report memory usage of Python + KDB.AI\n",
    "def get_memory_usage():\n",
    "    virtual_memory = psutil.virtual_memory()\n",
    "    return virtual_memory.used / (1024 ** 2)  # Memory usage in megabytes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d69cad5",
   "metadata": {},
   "source": [
    "## 2. Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "975421d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8117034d-d12e-40bf-84b7-e30b26ceb352",
   "metadata": {},
   "source": [
    "## 3. Load Synthetic Market Time Series Data\n",
    "We have pre-generated 50,000 data points for example stocks 'AAA' and 'BBB' in a parquet file called `marketTrades.parquet`. Let's load these to a dataframe where:\n",
    "\n",
    "- time: When the trade took place\n",
    "- sym: The symbol of the stock being traded\n",
    "- qty: The quantity of stock traded\n",
    "- price: The price of the stock being traded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "af8e7d64-c1da-457f-b1d1-c1e1cd423948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>time</th>\n",
       "      <th>sym</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2024-02-19 00:00:23.408442735</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>25.198061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-19 00:00:50.002746284</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>25.589870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2024-02-19 00:01:13.951318860</td>\n",
       "      <td>AAA</td>\n",
       "      <td>4000</td>\n",
       "      <td>25.435139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2024-02-19 00:01:21.386703997</td>\n",
       "      <td>AAA</td>\n",
       "      <td>1000</td>\n",
       "      <td>25.378082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2024-02-19 00:01:48.257409185</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>25.830731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>49995</td>\n",
       "      <td>2024-03-04 23:56:43.126595914</td>\n",
       "      <td>BBB</td>\n",
       "      <td>3000</td>\n",
       "      <td>17.611573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>49996</td>\n",
       "      <td>2024-03-04 23:56:49.295240789</td>\n",
       "      <td>BBB</td>\n",
       "      <td>3000</td>\n",
       "      <td>17.652760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>49997</td>\n",
       "      <td>2024-03-04 23:57:06.397743076</td>\n",
       "      <td>BBB</td>\n",
       "      <td>1000</td>\n",
       "      <td>17.215983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>49998</td>\n",
       "      <td>2024-03-04 23:59:19.743730723</td>\n",
       "      <td>BBB</td>\n",
       "      <td>10000</td>\n",
       "      <td>17.096576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>49999</td>\n",
       "      <td>2024-03-04 23:59:33.235208541</td>\n",
       "      <td>BBB</td>\n",
       "      <td>5000</td>\n",
       "      <td>17.468638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                          time  sym    qty      price\n",
       "0          0 2024-02-19 00:00:23.408442735  AAA   8000  25.198061\n",
       "1          1 2024-02-19 00:00:50.002746284  AAA   2000  25.589870\n",
       "2          2 2024-02-19 00:01:13.951318860  AAA   4000  25.435139\n",
       "3          3 2024-02-19 00:01:21.386703997  AAA   1000  25.378082\n",
       "4          4 2024-02-19 00:01:48.257409185  AAA   8000  25.830731\n",
       "...      ...                           ...  ...    ...        ...\n",
       "49995  49995 2024-03-04 23:56:43.126595914  BBB   3000  17.611573\n",
       "49996  49996 2024-03-04 23:56:49.295240789  BBB   3000  17.652760\n",
       "49997  49997 2024-03-04 23:57:06.397743076  BBB   1000  17.215983\n",
       "49998  49998 2024-03-04 23:59:19.743730723  BBB  10000  17.096576\n",
       "49999  49999 2024-03-04 23:59:33.235208541  BBB   5000  17.468638\n",
       "\n",
       "[50000 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet('data/marketTrades.parquet')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8fb8e50-94c1-4c30-8fb4-ed8ae62eac5c",
   "metadata": {},
   "source": [
    "## 4. Create KDB.AI Schema & Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fd028215-d0ce-467c-9be8-d37c0bcb3b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"trade_tss\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "447a9927-85bb-41b9-8a9c-6a2f60cc5b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the schema with similar columns to the dataframe. The price column is where the time series vectors will be inserted\n",
    "# The vectorIndex type in the price column uses type 'tss', which represents Non-Transformed Temporal Similarity Search\n",
    "# Set up the schema and indexes for KDB.AI table, specifying embeddings column with 384 dimensions, Euclidean Distance, and flat index\n",
    "schema = [\n",
    "    {\"name\": \"index\", \"type\": \"int64\"},\n",
    "    {\"name\": \"time\", \"type\": \"datetime64[ns]\"},\n",
    "    {\"name\": \"sym\", \"type\": \"str\"},\n",
    "    {\"name\": \"qty\", \"type\": \"int64\"},\n",
    "    {\"name\": \"price\", \"type\": \"float64\"}\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4c06af9e-b371-4982-8e2f-56c8a67d8c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the table called \"trade_tss\"\n",
    "table = database.create_table(\"trade_tss\",\n",
    "                              schema = schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633944dd-0330-4bbb-bc40-670e1a02eea0",
   "metadata": {},
   "source": [
    "## 5. Insert Data into KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "712b0e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>time</th>\n",
       "      <th>sym</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2024-02-19 00:00:23.408442735</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>25.198061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-19 00:00:50.002746284</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>25.589870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2024-02-19 00:01:13.951318860</td>\n",
       "      <td>AAA</td>\n",
       "      <td>4000</td>\n",
       "      <td>25.435139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2024-02-19 00:01:21.386703997</td>\n",
       "      <td>AAA</td>\n",
       "      <td>1000</td>\n",
       "      <td>25.378082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2024-02-19 00:01:48.257409185</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>25.830731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>49995</td>\n",
       "      <td>2024-03-04 23:56:43.126595914</td>\n",
       "      <td>BBB</td>\n",
       "      <td>3000</td>\n",
       "      <td>17.611573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>49996</td>\n",
       "      <td>2024-03-04 23:56:49.295240789</td>\n",
       "      <td>BBB</td>\n",
       "      <td>3000</td>\n",
       "      <td>17.652760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>49997</td>\n",
       "      <td>2024-03-04 23:57:06.397743076</td>\n",
       "      <td>BBB</td>\n",
       "      <td>1000</td>\n",
       "      <td>17.215983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>49998</td>\n",
       "      <td>2024-03-04 23:59:19.743730723</td>\n",
       "      <td>BBB</td>\n",
       "      <td>10000</td>\n",
       "      <td>17.096576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>49999</td>\n",
       "      <td>2024-03-04 23:59:33.235208541</td>\n",
       "      <td>BBB</td>\n",
       "      <td>5000</td>\n",
       "      <td>17.468638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                          time  sym    qty      price\n",
       "0          0 2024-02-19 00:00:23.408442735  AAA   8000  25.198061\n",
       "1          1 2024-02-19 00:00:50.002746284  AAA   2000  25.589870\n",
       "2          2 2024-02-19 00:01:13.951318860  AAA   4000  25.435139\n",
       "3          3 2024-02-19 00:01:21.386703997  AAA   1000  25.378082\n",
       "4          4 2024-02-19 00:01:48.257409185  AAA   8000  25.830731\n",
       "...      ...                           ...  ...    ...        ...\n",
       "49995  49995 2024-03-04 23:56:43.126595914  BBB   3000  17.611573\n",
       "49996  49996 2024-03-04 23:56:49.295240789  BBB   3000  17.652760\n",
       "49997  49997 2024-03-04 23:57:06.397743076  BBB   1000  17.215983\n",
       "49998  49998 2024-03-04 23:59:19.743730723  BBB  10000  17.096576\n",
       "49999  49999 2024-03-04 23:59:33.235208541  BBB   5000  17.468638\n",
       "\n",
       "[50000 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7591d64e-04e1-4d86-a526-ddc4bcb919ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 50000}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3c6c56-6ce2-40ac-a6c2-4a0b9645abe6",
   "metadata": {},
   "source": [
    "## 6. Run Non-Transformed Temporal Similarity Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "250d7d3d-1e27-4b81-bf9b-c4e42cdd475b",
   "metadata": {},
   "source": [
    "#### Create Raw Windows for Query String\n",
    "Now, we create a new DataFrame where each row represents a sliding window of size D = 1000 data points over the original dataframe, grouped by symbol.\n",
    "We take the hundredth vector and use that as our search vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "47a9b401-f435-47d1-9401-5c2909145160",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create raw windows\n",
    "D = 1000 # Sliding Window Size\n",
    "vecdf = df.groupby(['sym']).apply(\n",
    "    lambda x: pd.DataFrame({\n",
    "        'time': sliding_window_view(x['time'], D)[:, 0],  # Adjusted to keep the last time in the window\n",
    "        'sym': x['sym'].iloc[0],\n",
    "        'price': list(sliding_window_view(x['price'], D))\n",
    "    })\n",
    ").reset_index(drop=True).reset_index()\n",
    "memory_vecdf_created=get_memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "839acee3-0568-4ea9-8e13-8961c85a107a",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = vecdf['price'][100].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9506b2ec-b128-4088-96aa-cb3afacde94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>time</th>\n",
       "      <th>sym</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2024-02-19 00:00:23.408442735</td>\n",
       "      <td>AAA</td>\n",
       "      <td>[25.1980605549179, 25.58986971201375, 25.43513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-19 00:00:50.002746284</td>\n",
       "      <td>AAA</td>\n",
       "      <td>[25.58986971201375, 25.43513912591152, 25.3780...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2024-02-19 00:01:13.951318860</td>\n",
       "      <td>AAA</td>\n",
       "      <td>[25.43513912591152, 25.37808242137544, 25.8307...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2024-02-19 00:01:21.386703997</td>\n",
       "      <td>AAA</td>\n",
       "      <td>[25.37808242137544, 25.830730823799968, 25.607...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2024-02-19 00:01:48.257409185</td>\n",
       "      <td>AAA</td>\n",
       "      <td>[25.830730823799968, 25.607446282170713, 26.07...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   index                          time  sym  \\\n",
       "0      0 2024-02-19 00:00:23.408442735  AAA   \n",
       "1      1 2024-02-19 00:00:50.002746284  AAA   \n",
       "2      2 2024-02-19 00:01:13.951318860  AAA   \n",
       "3      3 2024-02-19 00:01:21.386703997  AAA   \n",
       "4      4 2024-02-19 00:01:48.257409185  AAA   \n",
       "\n",
       "                                               price  \n",
       "0  [25.1980605549179, 25.58986971201375, 25.43513...  \n",
       "1  [25.58986971201375, 25.43513912591152, 25.3780...  \n",
       "2  [25.43513912591152, 25.37808242137544, 25.8307...  \n",
       "3  [25.37808242137544, 25.830730823799968, 25.607...  \n",
       "4  [25.830730823799968, 25.607446282170713, 26.07...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b15409e6-81bf-4d2d-ab3b-0355b242eb21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Query Vector')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(q) # See what the search vector looks like\n",
    "plt.grid(True)\n",
    "plt.title('Query Vector')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bbacf2c-b2c0-4a48-a27a-70583a25b338",
   "metadata": {},
   "source": [
    "### Similarity Search\n",
    "Run similarity search using our query vector q, and returning the top 10 most similar matches. We also collect some stats on the search for later analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a183ca10-f801-4803-8245-5d45d5c420a9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>2024-02-19 01:22:33.092023730</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>28.180955</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>1</th>\n",
       "      <td>99</td>\n",
       "      <td>2024-02-19 01:20:12.802501022</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>27.996566</td>\n",
       "      <td>99</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98</td>\n",
       "      <td>2024-02-19 01:19:35.593288242</td>\n",
       "      <td>AAA</td>\n",
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       "      <td>98</td>\n",
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       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>2024-02-19 01:24:08.510938882</td>\n",
       "      <td>AAA</td>\n",
       "      <td>7000</td>\n",
       "      <td>27.933083</td>\n",
       "      <td>102</td>\n",
       "      <td>6.612245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>97</td>\n",
       "      <td>2024-02-19 01:19:32.584855556</td>\n",
       "      <td>AAA</td>\n",
       "      <td>6000</td>\n",
       "      <td>28.734376</td>\n",
       "      <td>97</td>\n",
       "      <td>7.984468</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>103</td>\n",
       "      <td>2024-02-19 01:24:39.233463853</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>28.403252</td>\n",
       "      <td>103</td>\n",
       "      <td>8.011267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>96</td>\n",
       "      <td>2024-02-19 01:19:27.989527434</td>\n",
       "      <td>AAA</td>\n",
       "      <td>1000</td>\n",
       "      <td>28.989396</td>\n",
       "      <td>96</td>\n",
       "      <td>9.142472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>104</td>\n",
       "      <td>2024-02-19 01:26:20.481255054</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>28.880188</td>\n",
       "      <td>104</td>\n",
       "      <td>9.172721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>95</td>\n",
       "      <td>2024-02-19 01:19:09.146237969</td>\n",
       "      <td>AAA</td>\n",
       "      <td>7000</td>\n",
       "      <td>29.201223</td>\n",
       "      <td>95</td>\n",
       "      <td>10.124061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                          time  sym   qty      price  nnIdx     nnDist\n",
       "0    100 2024-02-19 01:22:33.092023730  AAA  8000  28.180955    100   0.000000\n",
       "1     99 2024-02-19 01:20:12.802501022  AAA  2000  27.996566     99   4.527070\n",
       "2    101 2024-02-19 01:22:40.782989859  AAA  2000  27.793489    101   4.532988\n",
       "3     98 2024-02-19 01:19:35.593288242  AAA  9000  28.493217     98   6.594388\n",
       "4    102 2024-02-19 01:24:08.510938882  AAA  7000  27.933083    102   6.612245\n",
       "5     97 2024-02-19 01:19:32.584855556  AAA  6000  28.734376     97   7.984468\n",
       "6    103 2024-02-19 01:24:39.233463853  AAA  9000  28.403252    103   8.011267\n",
       "7     96 2024-02-19 01:19:27.989527434  AAA  1000  28.989396     96   9.142472\n",
       "8    104 2024-02-19 01:26:20.481255054  AAA  9000  28.880188    104   9.172721\n",
       "9     95 2024-02-19 01:19:09.146237969  AAA  7000  29.201223     95  10.124061"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_start=datetime.datetime.now()\n",
    "res = table.search(vectors={'price': [q]}, n=10, type=\"tss\")[0]\n",
    "\n",
    "sim_stop=datetime.datetime.now()\n",
    "memory_sim_post_search=get_memory_usage()\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3bb65b",
   "metadata": {},
   "source": [
    "As expected, our 100th vector, which is identical to the query vector 'q', was returned as the top result. We also see that the vectors surrounding the 100th vector are also returned, which is expected as they will also contain large segments similar to the query vector."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da784748-f674-4d43-b91d-9479a356e2a7",
   "metadata": {},
   "source": [
    "### Outlier Search\n",
    "Another feature of Non-Transformed Temporal Similarity Search is the ability to do outlier search. This will return the most dissimilar vectors.\n",
    "\n",
    "To do this, we run a search with n equal to the negative of the number of most dissimilar results we want returned, for example n = -10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fc97c827-5750-4ea6-b2f6-82f2539c5629",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2024-02-26 20:34:19.050180763</td>\n",
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       "      <td>4000</td>\n",
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       "      <td>13074</td>\n",
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       "      <th>2</th>\n",
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       "      <td>13076</td>\n",
       "      <td>59.111672</td>\n",
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       "      <th>3</th>\n",
       "      <td>13073</td>\n",
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       "      <td>AAA</td>\n",
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       "      <td>59.138623</td>\n",
       "      <td>13073</td>\n",
       "      <td>59.107935</td>\n",
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       "      <th>4</th>\n",
       "      <td>13077</td>\n",
       "      <td>2024-02-26 20:35:33.751646429</td>\n",
       "      <td>AAA</td>\n",
       "      <td>10000</td>\n",
       "      <td>60.131198</td>\n",
       "      <td>13077</td>\n",
       "      <td>59.097348</td>\n",
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       "    <tr>\n",
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       "      <td>13072</td>\n",
       "      <td>2024-02-26 20:32:59.920857399</td>\n",
       "      <td>AAA</td>\n",
       "      <td>4000</td>\n",
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       "      <td>13072</td>\n",
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       "      <td>13078</td>\n",
       "      <td>2024-02-26 20:39:03.432464450</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>59.749958</td>\n",
       "      <td>13078</td>\n",
       "      <td>59.074251</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13071</td>\n",
       "      <td>2024-02-26 20:32:00.419369638</td>\n",
       "      <td>AAA</td>\n",
       "      <td>4000</td>\n",
       "      <td>59.093172</td>\n",
       "      <td>13071</td>\n",
       "      <td>59.071500</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13070</td>\n",
       "      <td>2024-02-26 20:30:26.397550106</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>58.770047</td>\n",
       "      <td>13070</td>\n",
       "      <td>59.057446</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13079</td>\n",
       "      <td>2024-02-26 20:39:26.932638734</td>\n",
       "      <td>AAA</td>\n",
       "      <td>1000</td>\n",
       "      <td>60.111276</td>\n",
       "      <td>13079</td>\n",
       "      <td>59.056613</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   index                          time  sym    qty      price  nnIdx  \\\n",
       "0  13075 2024-02-26 20:34:19.050180763  AAA   4000  59.715426  13075   \n",
       "1  13074 2024-02-26 20:33:45.411256402  AAA   2000  59.351530  13074   \n",
       "2  13076 2024-02-26 20:34:52.536420375  AAA   5000  59.826193  13076   \n",
       "3  13073 2024-02-26 20:33:17.896016389  AAA   5000  59.138623  13073   \n",
       "4  13077 2024-02-26 20:35:33.751646429  AAA  10000  60.131198  13077   \n",
       "5  13072 2024-02-26 20:32:59.920857399  AAA   4000  58.847480  13072   \n",
       "6  13078 2024-02-26 20:39:03.432464450  AAA   9000  59.749958  13078   \n",
       "7  13071 2024-02-26 20:32:00.419369638  AAA   4000  59.093172  13071   \n",
       "8  13070 2024-02-26 20:30:26.397550106  AAA   9000  58.770047  13070   \n",
       "9  13079 2024-02-26 20:39:26.932638734  AAA   1000  60.111276  13079   \n",
       "\n",
       "      nnDist  \n",
       "0  59.125275  \n",
       "1  59.114667  \n",
       "2  59.111672  \n",
       "3  59.107935  \n",
       "4  59.097348  \n",
       "5  59.091311  \n",
       "6  59.074251  \n",
       "7  59.071500  \n",
       "8  59.057446  \n",
       "9  59.056613  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_start=datetime.datetime.now()\n",
    "res = table.search(vectors={'price': [q]}, n=-10, type=\"tss\")[0]\n",
    "\n",
    "out_stop=datetime.datetime.now()\n",
    "memory_out_windows_post_search=get_memory_usage()\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33688949-8314-4a94-8f4d-8ce8986a16aa",
   "metadata": {},
   "source": [
    "### Dynamic Search\n",
    "A strengh of Non-Transformed Temporal Similarity Search is the ability to change the size (number of data points) of the query vector between searches.\n",
    "This means we do not need to create new embeddings to search over different window sizes.\n",
    "\n",
    "See the [documentation](https://code.kx.com/kdbai/reference/non-transformed-tss.html) for more details.\n",
    "\n",
    "In this example, our query vector is reduced to 50 data points from the original 1000 data point query vector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0a09b97f-ed65-4d12-94b4-49ecf158719b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query elements:  50\n"
     ]
    },
    {
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       "      <th>index</th>\n",
       "      <th>time</th>\n",
       "      <th>sym</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>nnIdx</th>\n",
       "      <th>nnDist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>2024-02-19 01:22:33.092023730</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>28.180955</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99</td>\n",
       "      <td>2024-02-19 01:20:12.802501022</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>27.996566</td>\n",
       "      <td>99</td>\n",
       "      <td>2.060435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>2024-02-19 01:22:40.782989859</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>27.793489</td>\n",
       "      <td>101</td>\n",
       "      <td>2.140956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14907</td>\n",
       "      <td>2024-02-27 23:18:12.271115630</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>58.904305</td>\n",
       "      <td>14907</td>\n",
       "      <td>2.369705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27249</td>\n",
       "      <td>2024-02-20 07:46:35.792305469</td>\n",
       "      <td>BBB</td>\n",
       "      <td>6000</td>\n",
       "      <td>15.817259</td>\n",
       "      <td>27249</td>\n",
       "      <td>2.375721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14906</td>\n",
       "      <td>2024-02-27 23:17:01.033421158</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>59.331964</td>\n",
       "      <td>14906</td>\n",
       "      <td>2.385418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42517</td>\n",
       "      <td>2024-02-29 10:50:59.415277093</td>\n",
       "      <td>BBB</td>\n",
       "      <td>6000</td>\n",
       "      <td>10.757307</td>\n",
       "      <td>42517</td>\n",
       "      <td>2.388048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27250</td>\n",
       "      <td>2024-02-20 07:48:04.330449253</td>\n",
       "      <td>BBB</td>\n",
       "      <td>9000</td>\n",
       "      <td>15.494382</td>\n",
       "      <td>27250</td>\n",
       "      <td>2.431020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14905</td>\n",
       "      <td>2024-02-27 23:14:51.579989343</td>\n",
       "      <td>AAA</td>\n",
       "      <td>4000</td>\n",
       "      <td>59.699730</td>\n",
       "      <td>14905</td>\n",
       "      <td>2.434130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19547</td>\n",
       "      <td>2024-03-01 16:56:43.881322592</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>77.814046</td>\n",
       "      <td>19547</td>\n",
       "      <td>2.459691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                          time  sym   qty      price  nnIdx    nnDist\n",
       "0    100 2024-02-19 01:22:33.092023730  AAA  8000  28.180955    100  0.000004\n",
       "1     99 2024-02-19 01:20:12.802501022  AAA  2000  27.996566     99  2.060435\n",
       "2    101 2024-02-19 01:22:40.782989859  AAA  2000  27.793489    101  2.140956\n",
       "3  14907 2024-02-27 23:18:12.271115630  AAA  9000  58.904305  14907  2.369705\n",
       "4  27249 2024-02-20 07:46:35.792305469  BBB  6000  15.817259  27249  2.375721\n",
       "5  14906 2024-02-27 23:17:01.033421158  AAA  9000  59.331964  14906  2.385418\n",
       "6  42517 2024-02-29 10:50:59.415277093  BBB  6000  10.757307  42517  2.388048\n",
       "7  27250 2024-02-20 07:48:04.330449253  BBB  9000  15.494382  27250  2.431020\n",
       "8  14905 2024-02-27 23:14:51.579989343  AAA  4000  59.699730  14905  2.434130\n",
       "9  19547 2024-03-01 16:56:43.881322592  AAA  8000  77.814046  19547  2.459691"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smaller_query_50=q[:-950]\n",
    "print(\"Query elements: \", len(smaller_query_50))\n",
    "table.search(vectors={'price': [smaller_query_50]}, n=10, type=\"tss\")[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7811862a",
   "metadata": {},
   "source": [
    "Let's try another search with a 500 element query vector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1566def3-2bd3-42f6-a541-5a63a8ae0c10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query elements:  500\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>time</th>\n",
       "      <th>sym</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>nnIdx</th>\n",
       "      <th>nnDist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>2024-02-19 01:22:33.092023730</td>\n",
       "      <td>AAA</td>\n",
       "      <td>8000</td>\n",
       "      <td>28.180955</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>2024-02-19 01:22:40.782989859</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>27.793489</td>\n",
       "      <td>101</td>\n",
       "      <td>2.973744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99</td>\n",
       "      <td>2024-02-19 01:20:12.802501022</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2000</td>\n",
       "      <td>27.996566</td>\n",
       "      <td>99</td>\n",
       "      <td>2.989074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>2024-02-19 01:24:08.510938882</td>\n",
       "      <td>AAA</td>\n",
       "      <td>7000</td>\n",
       "      <td>27.933083</td>\n",
       "      <td>102</td>\n",
       "      <td>4.320273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "      <td>2024-02-19 01:19:35.593288242</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>28.493217</td>\n",
       "      <td>98</td>\n",
       "      <td>4.364905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103</td>\n",
       "      <td>2024-02-19 01:24:39.233463853</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>28.403252</td>\n",
       "      <td>103</td>\n",
       "      <td>5.199802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>97</td>\n",
       "      <td>2024-02-19 01:19:32.584855556</td>\n",
       "      <td>AAA</td>\n",
       "      <td>6000</td>\n",
       "      <td>28.734376</td>\n",
       "      <td>97</td>\n",
       "      <td>5.293356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104</td>\n",
       "      <td>2024-02-19 01:26:20.481255054</td>\n",
       "      <td>AAA</td>\n",
       "      <td>9000</td>\n",
       "      <td>28.880188</td>\n",
       "      <td>104</td>\n",
       "      <td>5.912937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96</td>\n",
       "      <td>2024-02-19 01:19:27.989527434</td>\n",
       "      <td>AAA</td>\n",
       "      <td>1000</td>\n",
       "      <td>28.989396</td>\n",
       "      <td>96</td>\n",
       "      <td>6.068448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>105</td>\n",
       "      <td>2024-02-19 01:27:28.738277703</td>\n",
       "      <td>AAA</td>\n",
       "      <td>6000</td>\n",
       "      <td>29.004960</td>\n",
       "      <td>105</td>\n",
       "      <td>6.461498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                          time  sym   qty      price  nnIdx    nnDist\n",
       "0    100 2024-02-19 01:22:33.092023730  AAA  8000  28.180955    100  0.000006\n",
       "1    101 2024-02-19 01:22:40.782989859  AAA  2000  27.793489    101  2.973744\n",
       "2     99 2024-02-19 01:20:12.802501022  AAA  2000  27.996566     99  2.989074\n",
       "3    102 2024-02-19 01:24:08.510938882  AAA  7000  27.933083    102  4.320273\n",
       "4     98 2024-02-19 01:19:35.593288242  AAA  9000  28.493217     98  4.364905\n",
       "5    103 2024-02-19 01:24:39.233463853  AAA  9000  28.403252    103  5.199802\n",
       "6     97 2024-02-19 01:19:32.584855556  AAA  6000  28.734376     97  5.293356\n",
       "7    104 2024-02-19 01:26:20.481255054  AAA  9000  28.880188    104  5.912937\n",
       "8     96 2024-02-19 01:19:27.989527434  AAA  1000  28.989396     96  6.068448\n",
       "9    105 2024-02-19 01:27:28.738277703  AAA  6000  29.004960    105  6.461498"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smaller_query_500=q[:-500]\n",
    "print(\"Query elements: \", len(smaller_query_500))\n",
    "table.search(vectors={'price': [smaller_query_500]}, n=10, type=\"tss\")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa029519-8025-4d65-b773-7eb7b3ac964d",
   "metadata": {},
   "source": [
    "### Multi Search\n",
    "The power of Non-Transformed Temporal Similarity Search is shown with multi search, where we search over multiple window sizes simultaneously.\n",
    "\n",
    "Here we search with each of the 1000, 500, and 50 data point query vectors:\n",
    "\n",
    "As you will see, the time for running multiple searches is significantly less than expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f312b2eb-c17e-47de-bde9-5d393c96d684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index                          time  sym   qty      price  nnIdx     nnDist\n",
      "0    100 2024-02-19 01:22:33.092023730  AAA  8000  28.180955    100   0.000000\n",
      "1     99 2024-02-19 01:20:12.802501022  AAA  2000  27.996566     99   4.527070\n",
      "2    101 2024-02-19 01:22:40.782989859  AAA  2000  27.793489    101   4.532988\n",
      "3     98 2024-02-19 01:19:35.593288242  AAA  9000  28.493217     98   6.594388\n",
      "4    102 2024-02-19 01:24:08.510938882  AAA  7000  27.933083    102   6.612245\n",
      "5     97 2024-02-19 01:19:32.584855556  AAA  6000  28.734376     97   7.984468\n",
      "6    103 2024-02-19 01:24:39.233463853  AAA  9000  28.403252    103   8.011267\n",
      "7     96 2024-02-19 01:19:27.989527434  AAA  1000  28.989396     96   9.142472\n",
      "8    104 2024-02-19 01:26:20.481255054  AAA  9000  28.880188    104   9.172721\n",
      "9     95 2024-02-19 01:19:09.146237969  AAA  7000  29.201223     95  10.124061\n",
      "\n",
      "   index                          time  sym   qty      price  nnIdx    nnDist\n",
      "0    100 2024-02-19 01:22:33.092023730  AAA  8000  28.180955    100  0.000004\n",
      "1     99 2024-02-19 01:20:12.802501022  AAA  2000  27.996566     99  2.060435\n",
      "2    101 2024-02-19 01:22:40.782989859  AAA  2000  27.793489    101  2.140956\n",
      "3  14907 2024-02-27 23:18:12.271115630  AAA  9000  58.904305  14907  2.369705\n",
      "4  27249 2024-02-20 07:46:35.792305469  BBB  6000  15.817259  27249  2.375721\n",
      "5  14906 2024-02-27 23:17:01.033421158  AAA  9000  59.331964  14906  2.385418\n",
      "6  42517 2024-02-29 10:50:59.415277093  BBB  6000  10.757307  42517  2.388048\n",
      "7  27250 2024-02-20 07:48:04.330449253  BBB  9000  15.494382  27250  2.431020\n",
      "8  14905 2024-02-27 23:14:51.579989343  AAA  4000  59.699730  14905  2.434130\n",
      "9  19547 2024-03-01 16:56:43.881322592  AAA  8000  77.814046  19547  2.459691\n",
      "\n",
      "   index                          time  sym   qty      price  nnIdx    nnDist\n",
      "0    100 2024-02-19 01:22:33.092023730  AAA  8000  28.180955    100  0.000006\n",
      "1    101 2024-02-19 01:22:40.782989859  AAA  2000  27.793489    101  2.973744\n",
      "2     99 2024-02-19 01:20:12.802501022  AAA  2000  27.996566     99  2.989074\n",
      "3    102 2024-02-19 01:24:08.510938882  AAA  7000  27.933083    102  4.320273\n",
      "4     98 2024-02-19 01:19:35.593288242  AAA  9000  28.493217     98  4.364905\n",
      "5    103 2024-02-19 01:24:39.233463853  AAA  9000  28.403252    103  5.199802\n",
      "6     97 2024-02-19 01:19:32.584855556  AAA  6000  28.734376     97  5.293356\n",
      "7    104 2024-02-19 01:26:20.481255054  AAA  9000  28.880188    104  5.912937\n",
      "8     96 2024-02-19 01:19:27.989527434  AAA  1000  28.989396     96  6.068448\n",
      "9    105 2024-02-19 01:27:28.738277703  AAA  6000  29.004960    105  6.461498\n",
      "\n"
     ]
    }
   ],
   "source": [
    "multi_start=datetime.datetime.now()\n",
    "results = table.search(vectors={'price': [q, smaller_query_50, smaller_query_500]}, n=10, type=\"tss\")\n",
    "multi_stop=datetime.datetime.now()\n",
    "for result in results:\n",
    "    print(result)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3f5dcf9",
   "metadata": {},
   "source": [
    "## 7. Search Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58afee4-ebcc-4ac3-82bd-9d4263989a37",
   "metadata": {},
   "source": [
    "### Memory Usage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b4a24f6b-16da-459d-897f-cdcab40f6370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sim  (MB): -0.25\n"
     ]
    }
   ],
   "source": [
    "sim_used=memory_sim_post_search-memory_vecdf_created\n",
    "print(f\"Sim  (MB): {sim_used:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f061588d-13ea-4ac7-8093-cd11f907c78b",
   "metadata": {},
   "source": [
    "### Search Timings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "85d2ead0",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Function to calculate how long a similarity search takes\n",
    "def datetime_difference(datetime1, datetime2):\n",
    "    # Calculate the difference between the two datetime objects\n",
    "    difference = datetime2 - datetime1\n",
    "\n",
    "    # Calculate total seconds and milliseconds from the difference\n",
    "    total_seconds = difference.total_seconds()\n",
    "    hours = int(total_seconds // 3600)\n",
    "    minutes = int((total_seconds % 3600) // 60)\n",
    "    seconds = int(total_seconds % 60)\n",
    "    milliseconds = int((total_seconds - int(total_seconds)) * 1000)\n",
    "\n",
    "    # Print the difference in hours, minutes, seconds, and milliseconds\n",
    "    return f\"Hours: {hours}, Minutes: {minutes}, Seconds: {seconds}, Milliseconds: {milliseconds}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eede9dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sim: Hours: 0, Minutes: 0, Seconds: 0, Milliseconds: 15\n",
      "Multi: Hours: 0, Minutes: 0, Seconds: 0, Milliseconds: 36\n"
     ]
    }
   ],
   "source": [
    "print('Sim: ' + datetime_difference(sim_start, sim_stop))\n",
    "print('Multi: ' + datetime_difference(multi_start,multi_stop))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5b593b",
   "metadata": {},
   "source": [
    "## 8. Drop the Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "892a4e75-43f8-468a-8148-82b795f1e764",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498a63f8",
   "metadata": {},
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/84DkpDWQ)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e62011",
   "metadata": {},
   "source": []
  }
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================================================
FILE: TSS_non_transformed/createHDB.q
================================================
dst:`:demo_hdb    / database root

numpart:10;
ed:2024.08.31; / end date the last date partition
dts:{[ed;n] reverse n#d where 1<mod[d:ed-til 2*n]7}[ed;numpart];
bgn:first dts;
end:last dts;
bgntm: 09:30:00.0;  / exchange open time
endtm: 16:00:00.0;  / exchange close time

nt:100000;            / trades per stock per day
qpt:1;

\S 104831           / random seed

/ utils
pi:acos -1
accum:{prds 1.0,-1 _ x}
int01:{(til x)%x-1}
limit:{(neg x)|x & y}
minmax:{(min x;max x)}
normalrand:{(cos 2 * pi * x ? 1f) * sqrt neg 2 * log x ? 1f}
rnd:{0.01*floor 0.5+x*100}
xrnd:{exp x * limit[2] normalrand y}
randomize:{value "\\S ",string "i"$0.8*.z.p%1000000000}
shiv:{(last x)&(first x)|asc x+-2+(count x)?5}
vol:{10+`int$x?90}
vol2:{x?100*1 1 1 1 2 2 3 4 5 8 10 15 20}

/ =========================================================
choleski:{
 n:count A:x+0.0;
 if[1>=n;:sqrt A];
 p:ceiling n%2;
 X:p#'p#A;
 Y:p _'p#A;
 Z:p _'p _A;
 T:(flip Y) mmu inv X;
 L0:n #' (choleski X) ,\: (n-1)#0.0;
 L1:choleski Z-T mmu Y;
 L0,(T mmu p#'L0),'L1}

/ =========================================================
/ paired correlation, matrix of variates, min 0.1 coeff
choleskicor:{
 x:"f"$x;y:"f"$y;
 n:count y;
 c:0.1|(n,n)#1.0,x,((n-2)#0.0),x;
 (choleski c) mmu y}

/ =========================================================
/ volume profile - random times, weighted toward ends
/ x=count
volprof:{
 p:1.75;
 c:floor x%3;
 b:(c?1.0) xexp p;
 e:2-(c?1.0) xexp p;
 m:(x-2*c)?1.0;
 {(neg count x)?x} m,0.5*b,e}

/ =========================================================
write:{
 t:.Q.en[dst] update sym:`p#sym from `sym xasc y;
  (` sv dst,`$x) set t}

/ symbol data for tick demo
sn:2 cut (
  `AAPL;218;
  `TSLA;210;
  `GOOG;135;
  `AMZN;145;
  `MSFT;332;
  `NVDA;458;
  `META;517;
  `NFLX;412;
  `ADBE;555;
  `PYPL;65
  )

s:first each sn
p:last each sn

/ gen
vex:1.0005         / average volume growth per day
ccf:0.5            / correlation coefficient

/ =========================================================
/ qx index, qb/qbb/qa/qba margins, qp price, qn position
batch:{[x;len]
  p0:prices[;x];
  p1:prices[;x+1];
  d:xrnd[0.0003] len;
  qx::0N?raze {(floor len%cnt)#x} each til cnt;
  qb::rnd len?1.0;
  qa::rnd len?1.0;
  qbb::qb & -0.02 + rnd len?1.0;
  qba::qa & -0.02 + rnd len?1.0;
  n:where each qx=/:til cnt;
  s:p0*accum each d n;
  s:s + (p1-last each s)*{int01 count x} each s;
  qp::len#0.0;
  (qp n):rnd s;
  qn::0
 }

/ =========================================================
/ constrained random walk
/ x max movement per step
/ y max movement at any time (above/below)
/ z number of steps
cgen:{
  m:reciprocal y;
  while[any (m>p) or y<p:prds 1.0+x*normalrand z];
  p}

/ =========================================================
getdates:{
 b:x 0;
 e:x 1;
 d:b + til 1 + e-b;
 d where 5> d-`week$d
 }

/ =========================================================
makeprices:{
 r:cgen[0.0375;3] each cnt#nd;
 r:choleskicor[ccf;1,'r];
 (p % first each r) * r *\: 1.1 xexp int01 nd+1}

/ =========================================================
/ day volumes
makevolumes:{x#1}

/ main
cnt:count s
dates:getdates bgn,end
nd:count dates

prices:makeprices nd + 1
volumes:floor (cnt*nt) * makevolumes nd

calcvols:{[vols;x]
  len::vols x;
  batch[x;len];
 }

day:{

  sa:string dx:dates x;
  show"Creating Partition for ",sa;
  / trade table
  calcvols[volumes;x];
  maxtime:max floor (endtm-bgntm)*volprof count qx;
  tvol:$[(count qx)>maxtime;(count qx);(neg count qx)];
  r:asc bgntm+tvol?maxtime;
  cx:0N?raze {(floor len%qpt)#x} each til qpt;
  cn:count n:where cx<qpt;
  t:([]sym:s qx n;time:dx+shiv r n;price:qp n;size:vol cn);
  write[sa,"/trade/";t]
 }

day each til nd;

1"\n\nCreated Sample HDB.\n\n";

================================================
FILE: TSS_transformed/Temporal_Similarity_Search_Transformed_Demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58ae5b26-0b58-4416-a69d-f5662fce320d",
   "metadata": {
    "id": "58ae5b26-0b58-4416-a69d-f5662fce320d"
   },
   "source": [
    "# Transformed Temporal Similarity Search\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "Transformed Temporal Similarity Search is our patent-pending compression model designed to dimensionally reduce time series windows by more than 99%. With Transformed Temporal Similarity Search, KDB.AI can compress times series windows containing thousands of data points, into significantly smaller dimensions, all while maintaining the integrity of the original data’s shape. These compressed windows are then stored within KDB.AI as vector embeddings, setting up the ability to execute vector searches and enabling the efficient analysis of time series datasets.\n",
    "\n",
    "This notebook uses sample market data, creates an index using the Transformed Temporal Similiarity Search feature exposed by KDB.AI, and searches for a pattern of interest in the data.\n",
    "\n",
    "Agenda:\n",
    "1. Dependencies, Imports & Setup\n",
    "2. Define KDB.AI Session\n",
    "3. Generate Synthetic Market Time Series Data\n",
    "4. Method 1: Only Windowing\n",
    "5. Method 2: Transformed TSS\n",
    "6. Search Evaluation\n",
    "7. Drop the Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3db7ad",
   "metadata": {
    "id": "1c3db7ad"
   },
   "source": [
    "## 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9144c71",
   "metadata": {
    "id": "b9144c71"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "965197a7-91a2-4938-a4d6-3d86ba99c645",
   "metadata": {
    "id": "965197a7-91a2-4938-a4d6-3d86ba99c645"
   },
   "outputs": [],
   "source": [
    "# KX Dependencies\n",
    "import kdbai_client as kdbai\n",
    "# Other Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.lib.stride_tricks import sliding_window_view\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb14487",
   "metadata": {
    "id": "abb14487"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads sample market data\n",
    "if os.path.exists(\"./data/marketTrades.parquet\") == False:\n",
    "  !mkdir ./data\n",
    "  !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/TSS_transformed/data/marketTrades.parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9da1ed6b-e589-49ba-a87a-25a9aeed9633",
   "metadata": {
    "id": "9da1ed6b-e589-49ba-a87a-25a9aeed9633"
   },
   "outputs": [],
   "source": [
    "# Ignore Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd295f42-7c47-42a1-80ee-fbadf0b0324c",
   "metadata": {
    "id": "dd295f42-7c47-42a1-80ee-fbadf0b0324c"
   },
   "outputs": [],
   "source": [
    "# Dependencies for metric gathering\n",
    "import psutil\n",
    "import datetime\n",
    "\n",
    "# Report memory usage of Python + KDB.AI\n",
    "def get_memory_usage():\n",
    "    virtual_memory = psutil.virtual_memory()\n",
    "    return virtual_memory.used / (1024 ** 2)  # Memory usage in megabytes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb75cab3",
   "metadata": {
    "id": "bb75cab3"
   },
   "source": [
    "## 2. Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c00e5e",
   "metadata": {
    "id": "31c00e5e"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5jSNxCzhO8FX",
   "metadata": {
    "id": "5jSNxCzhO8FX"
   },
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "C3ltOGrQO_Pd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C3ltOGrQO_Pd",
    "outputId": "772e91a5-1ade-4da2-b01c-a9529850f695"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI database \"default\"]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ShLAt1CsPDNc",
   "metadata": {
    "id": "ShLAt1CsPDNc"
   },
   "source": [
    "### Create a Database called \"myDatabase\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "X_bUloY3PEzl",
   "metadata": {
    "id": "X_bUloY3PEzl"
   },
   "outputs": [],
   "source": [
    "# ensure no database called \"myDatabase\" exists\n",
    "try:\n",
    "    session.database(\"myDatabase\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7_E_tOVcPGfp",
   "metadata": {
    "id": "7_E_tOVcPGfp"
   },
   "outputs": [],
   "source": [
    "# Create the database\n",
    "db = session.create_database(\"myDatabase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8117034d-d12e-40bf-84b7-e30b26ceb352",
   "metadata": {
    "id": "8117034d-d12e-40bf-84b7-e30b26ceb352"
   },
   "source": [
    "## 3. Load Synthetic Market Time Series Data\n",
    "We have pre-generated 50,000 data points for example stocks 'AAA' and 'BBB' in a parquet file called `marketTrades.parquet`. Let's load these to a dataframe where:\n",
    "\n",
    "- time: When the trade took place\n",
    "- sym: The symbol of the stock being traded\n",
    "- qty: The quantity of stock traded\n",
    "- price: The price of the stock being traded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af8e7d64-c1da-457f-b1d1-c1e1cd423948",
   "metadata": {
    "id": "af8e7d64-c1da-457f-b1d1-c1e1cd423948"
   },
   "outputs": [],
   "source": [
    "df = pd.read_parquet('data/marketTrades.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "250d7d3d-1e27-4b81-bf9b-c4e42cdd475b",
   "metadata": {
    "id": "250d7d3d-1e27-4b81-bf9b-c4e42cdd475b"
   },
   "source": [
    "## Method 1: Only Windowing\n",
    "This method is shown as a point of comparison for the Transformed TSS method\n",
    "\n",
    "### Vector Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47a9b401-f435-47d1-9401-5c2909145160",
   "metadata": {
    "id": "47a9b401-f435-47d1-9401-5c2909145160"
   },
   "outputs": [],
   "source": [
    "# Create the vector column\n",
    "D = 1000 # Sliding Window Size\n",
    "vecdf = df.groupby(['sym']).apply(\n",
    "    lambda x: pd.DataFrame({\n",
    "        'sym': x['sym'].iloc[0],\n",
    "        'time': sliding_window_view(x['time'], D)[:, 0],  # Adjusted to keep the last time in the window\n",
    "        'price': list(sliding_window_view(x['price'], D))\n",
    "    })\n",
    ").reset_index(drop=True).reset_index()\n",
    "memory_vecdf_created=get_memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9506b2ec-b128-4088-96aa-cb3afacde94f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "9506b2ec-b128-4088-96aa-cb3afacde94f",
    "outputId": "203b0468-3afe-496e-d1b1-743fdbf1aa70"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"vecdf\",\n  \"rows\": 48002,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13857,\n        \"min\": 0,\n        \"max\": 48001,\n        \"num_unique_values\": 48002,\n        \"samples\": [\n          42994,\n          37561,\n          28969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sym\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"BBB\",\n          \"AAA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-02-19 00:00:23.408442735\",\n        \"max\": \"2024-03-04 09:14:38.011431545\",\n        \"num_unique_values\": 48002,\n        \"samples\": [\n          \"2024-03-01 07:39:42.601527124\",\n          \"2024-02-27 01:57:50.629260689\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "vecdf"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-4a71ecc8-a482-4513-a6ee-2fffbc190213\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sym</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 00:00:23.408442735</td>\n",
       "      <td>[25.1980605549179, 25.58986971201375, 25.43513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 00:00:50.002746284</td>\n",
       "      <td>[25.58986971201375, 25.43513912591152, 25.3780...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 00:01:13.951318860</td>\n",
       "      <td>[25.43513912591152, 25.37808242137544, 25.8307...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 00:01:21.386703997</td>\n",
       "      <td>[25.37808242137544, 25.830730823799968, 25.607...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 00:01:48.257409185</td>\n",
       "      <td>[25.830730823799968, 25.607446282170713, 26.07...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "  <div class=\"colab-df-container\">\n",
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       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-4a71ecc8-a482-4513-a6ee-2fffbc190213 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-4a71ecc8-a482-4513-a6ee-2fffbc190213');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-b8d86280-cbd0-49be-9eeb-6813d6e9672d\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8d86280-cbd0-49be-9eeb-6813d6e9672d')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-b8d86280-cbd0-49be-9eeb-6813d6e9672d button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   index  sym                          time  \\\n",
       "0      0  AAA 2024-02-19 00:00:23.408442735   \n",
       "1      1  AAA 2024-02-19 00:00:50.002746284   \n",
       "2      2  AAA 2024-02-19 00:01:13.951318860   \n",
       "3      3  AAA 2024-02-19 00:01:21.386703997   \n",
       "4      4  AAA 2024-02-19 00:01:48.257409185   \n",
       "\n",
       "                                               price  \n",
       "0  [25.1980605549179, 25.58986971201375, 25.43513...  \n",
       "1  [25.58986971201375, 25.43513912591152, 25.3780...  \n",
       "2  [25.43513912591152, 25.37808242137544, 25.8307...  \n",
       "3  [25.37808242137544, 25.830730823799968, 25.607...  \n",
       "4  [25.830730823799968, 25.607446282170713, 26.07...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vecdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "K4znBsh6PaKb",
   "metadata": {
    "id": "K4znBsh6PaKb"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8fb8e50-94c1-4c30-8fb4-ed8ae62eac5c",
   "metadata": {
    "id": "b8fb8e50-94c1-4c30-8fb4-ed8ae62eac5c"
   },
   "source": [
    "### Index Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "seHQGvRZPOK7",
   "metadata": {
    "id": "seHQGvRZPOK7"
   },
   "outputs": [],
   "source": [
    "# Define the schema with similar columns to the dataframe. The price column is where the time series vectors will be inserted\n",
    "# We are doing similarity search on the raw time series vectors created above\n",
    "schema_raw = [\n",
    "        {\n",
    "            \"name\":'index',\n",
    "            \"type\":'int64'\n",
    "        },\n",
    "        {\n",
    "            \"name\":'sym',\n",
    "            \"type\":'str'\n",
    "        },\n",
    "        {\n",
    "            \"name\":'time',\n",
    "            \"type\":'datetime64[ns]'\n",
    "        },\n",
    "        {\n",
    "            \"name\":'price',\n",
    "            \"type\":'float64s',\n",
    "        }\n",
    "      ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PC-efPWeQJ-N",
   "metadata": {
    "id": "PC-efPWeQJ-N"
   },
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- type = flat : The flat index is the most accurate option. You have the choice of using other indexes like, HNSW, qHNSW, IVFPQ, or a qFlat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- name = flat_index : this is a custom name you give your index.\n",
    "- column = price : this is the column where the embeddings are stored.\n",
    "#### params:\n",
    "- dims = 1000 : The length of the incoming time series windows = 1000-dimensions\n",
    "- metric = L2 : We chose Euclidean Distance. You have the choice of using other metrics here like IP/Inner Product and CS/Cosine Similarity and the one you chose depends on the specific context and nature of your data.\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "zqdLka78QBRP",
   "metadata": {
    "id": "zqdLka78QBRP"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes_raw = [\n",
    "    {\n",
    "        'type': 'flat',\n",
    "        'name': 'flat_index',\n",
    "        'column': 'price',\n",
    "        'params': {'dims': 1000, 'metric': \"L2\"},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "196f4f6a-3b1f-47cb-9601-5b89b618b593",
   "metadata": {
    "id": "196f4f6a-3b1f-47cb-9601-5b89b618b593"
   },
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"trade_raw\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4c06af9e-b371-4982-8e2f-56c8a67d8c4f",
   "metadata": {
    "id": "4c06af9e-b371-4982-8e2f-56c8a67d8c4f"
   },
   "outputs": [],
   "source": [
    "table_raw = db.create_table(table='trade_raw', schema=schema_raw, indexes=indexes_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633944dd-0330-4bbb-bc40-670e1a02eea0",
   "metadata": {
    "id": "633944dd-0330-4bbb-bc40-670e1a02eea0"
   },
   "source": [
    "### Index Population\n",
    "Insert the data into KDB.AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7591d64e-04e1-4d86-a526-ddc4bcb919ac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7591d64e-04e1-4d86-a526-ddc4bcb919ac",
    "outputId": "5e76a8d2-34df-40a8-ca81-cb471e74c511"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [01:08<00:00,  1.39s/it]\n"
     ]
    }
   ],
   "source": [
    "n = 1000  # number of rows per batch\n",
    "\n",
    "for i in tqdm(range(0, vecdf.shape[0], n)):\n",
    "    table_raw.insert(vecdf[i:i+n].reset_index(drop=True))\n",
    "\n",
    "memory_raw_windows_in_table=get_memory_usage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3c6c56-6ce2-40ac-a6c2-4a0b9645abe6",
   "metadata": {
    "id": "cc3c6c56-6ce2-40ac-a6c2-4a0b9645abe6"
   },
   "source": [
    "### Search\n",
    "\n",
    "We take the hundredth vector and use that as our search vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "839acee3-0568-4ea9-8e13-8961c85a107a",
   "metadata": {
    "id": "839acee3-0568-4ea9-8e13-8961c85a107a"
   },
   "outputs": [],
   "source": [
    "q = vecdf['price'][100].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b15409e6-81bf-4d2d-ab3b-0355b242eb21",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "id": "b15409e6-81bf-4d2d-ab3b-0355b242eb21",
    "outputId": "9572ae1f-fa79-42b8-e89c-94463a688bb1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Query Vector')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(q) # See what the search vector looks like\n",
    "plt.grid(True)\n",
    "plt.title('Query Vector')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a183ca10-f801-4803-8245-5d45d5c420a9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "a183ca10-f801-4803-8245-5d45d5c420a9",
    "outputId": "c9d40372-6db6-4d8f-d14c-d3380504fe24"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"res\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"__nn_distance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          342.36419677734375,\n          83.7723388671875,\n          260.9378662109375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 96,\n        \"max\": 105,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          104,\n          99,\n          97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sym\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"AAA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-02-19 01:19:27.989527434\",\n        \"max\": \"2024-02-19 01:27:28.738277703\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2024-02-19 01:26:20.481255054\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "res"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-b1f01bb2-dc2b-4a0c-b981-704c5f634a31\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>index</th>\n",
       "      <th>sym</th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:22:33.092023730</td>\n",
       "      <td>[28.18095506168902, 27.793488922994584, 27.933...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.772339</td>\n",
       "      <td>99</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:20:12.802501022</td>\n",
       "      <td>[27.996565851615742, 28.18095506168902, 27.793...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83.809570</td>\n",
       "      <td>101</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:22:40.782989859</td>\n",
       "      <td>[27.793488922994584, 27.9330833053682, 28.4032...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>177.901764</td>\n",
       "      <td>98</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:19:35.593288242</td>\n",
       "      <td>[28.49321669829078, 27.996565851615742, 28.180...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178.144348</td>\n",
       "      <td>102</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:24:08.510938882</td>\n",
       "      <td>[27.9330833053682, 28.403252121293917, 28.8801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>260.937866</td>\n",
       "      <td>97</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:19:32.584855556</td>\n",
       "      <td>[28.73437575995922, 28.49321669829078, 27.9965...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>261.279205</td>\n",
       "      <td>103</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:24:39.233463853</td>\n",
       "      <td>[28.403252121293917, 28.880188381765038, 29.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>342.180145</td>\n",
       "      <td>96</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:19:27.989527434</td>\n",
       "      <td>[28.989396206568927, 28.73437575995922, 28.493...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>342.364197</td>\n",
       "      <td>104</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:26:20.481255054</td>\n",
       "      <td>[28.880188381765038, 29.00496045150794, 29.219...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>418.405457</td>\n",
       "      <td>105</td>\n",
       "      <td>AAA</td>\n",
       "      <td>2024-02-19 01:27:28.738277703</td>\n",
       "      <td>[29.00496045150794, 29.21952441590838, 29.4126...</td>\n",
       "    </tr>\n",
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       "\n",
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       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('res')\"\n",
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       "\n",
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      ],
      "text/plain": [
       "   __nn_distance  index  sym                          time  \\\n",
       "0       0.000000    100  AAA 2024-02-19 01:22:33.092023730   \n",
       "1      83.772339     99  AAA 2024-02-19 01:20:12.802501022   \n",
       "2      83.809570    101  AAA 2024-02-19 01:22:40.782989859   \n",
       "3     177.901764     98  AAA 2024-02-19 01:19:35.593288242   \n",
       "4     178.144348    102  AAA 2024-02-19 01:24:08.510938882   \n",
       "5     260.937866     97  AAA 2024-02-19 01:19:32.584855556   \n",
       "6     261.279205    103  AAA 2024-02-19 01:24:39.233463853   \n",
       "7     342.180145     96  AAA 2024-02-19 01:19:27.989527434   \n",
       "8     342.364197    104  AAA 2024-02-19 01:26:20.481255054   \n",
       "9     418.405457    105  AAA 2024-02-19 01:27:28.738277703   \n",
       "\n",
       "                                               price  \n",
       "0  [28.18095506168902, 27.793488922994584, 27.933...  \n",
       "1  [27.996565851615742, 28.18095506168902, 27.793...  \n",
       "2  [27.793488922994584, 27.9330833053682, 28.4032...  \n",
       "3  [28.49321669829078, 27.996565851615742, 28.180...  \n",
       "4  [27.9330833053682, 28.403252121293917, 28.8801...  \n",
       "5  [28.73437575995922, 28.49321669829078, 27.9965...  \n",
       "6  [28.403252121293917, 28.880188381765038, 29.00...  \n",
       "7  [28.989396206568927, 28.73437575995922, 28.493...  \n",
       "8  [28.880188381765038, 29.00496045150794, 29.219...  \n",
       "9  [29.00496045150794, 29.21952441590838, 29.4126...  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_start=datetime.datetime.now()\n",
    "res = table_raw.search(vectors={\"flat_index\":[q]}, n=10)[0]\n",
    "raw_stop=datetime.datetime.now()\n",
    "memory_raw_windows_post_search=get_memory_usage()\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e7c7fbf",
   "metadata": {
    "id": "4e7c7fbf"
   },
   "source": [
    "As expected, our 100th vector, which is identical to the query vector 'q', was returned as the top result. We also see that the vectors surrounding the 100th vector are also returned, which is expected as they will also contain large segments similar to the query vector."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a96972-2ea1-48e6-90fe-a46458146c24",
   "metadata": {
    "id": "29a96972-2ea1-48e6-90fe-a46458146c24"
   },
   "source": [
    "## Method 2: Transformed Temporal Similarity Search\n",
    "Now, we will create a second table in myDatabase to enable the embedding and compression of the original raw time series windows. This will reduce the size of the windows from 1000 data points to only 8. (>99% reduction), while maintaining the integrity of the original data's shape.\n",
    "\n",
    "The original windows can have various numbers of data points within them as long as they all have a similar time frame, for example each representing one minute of time. This makes searching over data that has different sample rates straightforward.\n",
    "\n",
    "Ensure that the windows being searched for, and the windows being searched over are for the same time frame. This means that if the windows compressed and stored using Transformed Temporal Similarity Search are 1 minute windows, the query vector must also have a time frame of 1 minute.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b53f5a",
   "metadata": {
    "id": "e8b53f5a"
   },
   "source": [
    "### Index Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "VdKTuoEbRmH1",
   "metadata": {
    "id": "VdKTuoEbRmH1"
   },
   "outputs": [],
   "source": [
    "# Define the schema with similar columns to the dataframe. The price column is where the time series vectors will be inserted\n",
    "schema_tsc = [\n",
    "        {\n",
    "            \"name\":'index',\n",
    "            \"type\":'int64'\n",
    "        },\n",
    "        {\n",
    "            \"name\":'sym',\n",
    "            \"type\":'str'\n",
    "        },\n",
    "        {\n",
    "            \"name\":'time',\n",
    "            \"type\":'datetime64[ns]'\n",
    "        },\n",
    "        {\n",
    "            \"name\":'price',\n",
    "            \"type\":'float64s',\n",
    "        }\n",
    "      ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "PKozH4VNR3n0",
   "metadata": {
    "id": "PKozH4VNR3n0"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes_tsc = [\n",
    "    {\n",
    "        'type': 'flat',\n",
    "        'name': 'flat_index',\n",
    "        'column': 'price',\n",
    "        'params': {'metric': \"L2\"},\n",
    "    },\n",
    "]\n",
    "\n",
    "# Define an embedding configuration, specifying to use 'TSC': transformed TSS\n",
    "# Inserted high dimensional data will be compressed with 'tsc' to 8 dimensions and stored in KDB.AI\n",
    "embedding_conf = {'price':{\"dims\":8, \"type\":\"tsc\", \"on_insert_error\":\"reject_all\"}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "mPaJto2_UQAf",
   "metadata": {
    "id": "mPaJto2_UQAf"
   },
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"trade_tsc\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "lrGVe4D2UayZ",
   "metadata": {
    "id": "lrGVe4D2UayZ"
   },
   "outputs": [],
   "source": [
    "# Create the table with the defined schema, index, and embedding configuration from above\n",
    "table_tsc = db.create_table(table='trade_tsc', schema=schema_tsc, indexes=indexes_tsc, embedding_configurations=embedding_conf)\n",
    "memory_tsc_pre_populate=get_memory_usage()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f0907d-1309-430d-9065-0e7d644a4da7",
   "metadata": {
    "id": "53f0907d-1309-430d-9065-0e7d644a4da7"
   },
   "source": [
    "### Index Population\n",
    "Insert the data into KDB.AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7d55b79c-b663-4825-ad40-bd8e987d4671",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7d55b79c-b663-4825-ad40-bd8e987d4671",
    "outputId": "a9f8f0d2-d10c-4d29-880d-e6dcf94302be"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:45<00:00,  1.08it/s]\n"
     ]
    }
   ],
   "source": [
    "n = 1000  # number of rows per batch\n",
    "\n",
    "for i in tqdm(range(0, vecdf.shape[0], n)):\n",
    "    table_tsc.insert(vecdf[i:i+n].reset_index(drop=True))\n",
    "\n",
    "memory_tsc_post_populate=get_memory_usage()"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "78dc5c55-af3f-4428-bc29-26585ea64554"
   },
   "source": [
    "### Search\n",
    "\n",
    "Run similarity search using our query vector q (the 100th vector in our dataset), and returning the top 10 most similar matches.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3b325aa2-218d-4d7e-95f5-c67e209cef66",
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    "outputId": "be44999f-b7e9-47b0-b677-2f3673ffb417"
   },
   "outputs": [
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       "      <td>[28.880188381765038, 29.00496045150794, 29.219...</td>\n",
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      "text/plain": [
       "   __nn_distance  index  sym                          time  \\\n",
       "0   7.105427e-14    100  AAA 2024-02-19 01:22:33.092023730   \n",
       "1   2.851702e-04    101  AAA 2024-02-19 01:22:40.782989859   \n",
       "2   2.914894e-04     99  AAA 2024-02-19 01:20:12.802501022   \n",
       "3   1.118651e-03     98  AAA 2024-02-19 01:19:35.593288242   \n",
       "4   1.178602e-03    102  AAA 2024-02-19 01:24:08.510938882   \n",
       "5   2.448621e-03     97  AAA 2024-02-19 01:19:32.584855556   \n",
       "6   2.667162e-03    103  AAA 2024-02-19 01:24:39.233463853   \n",
       "7   4.248296e-03     96  AAA 2024-02-19 01:19:27.989527434   \n",
       "8   4.676504e-03    104  AAA 2024-02-19 01:26:20.481255054   \n",
       "9   6.496350e-03     95  AAA 2024-02-19 01:19:09.146237969   \n",
       "\n",
       "                                               price  \n",
       "0  [28.18095506168902, 27.793488922994584, 27.933...  \n",
       "1  [27.793488922994584, 27.9330833053682, 28.4032...  \n",
       "2  [27.996565851615742, 28.18095506168902, 27.793...  \n",
       "3  [28.49321669829078, 27.996565851615742, 28.180...  \n",
       "4  [27.9330833053682, 28.403252121293917, 28.8801...  \n",
       "5  [28.73437575995922, 28.49321669829078, 27.9965...  \n",
       "6  [28.403252121293917, 28.880188381765038, 29.00...  \n",
       "7  [28.989396206568927, 28.73437575995922, 28.493...  \n",
       "8  [28.880188381765038, 29.00496045150794, 29.219...  \n",
       "9  [29.20122275315225, 28.989396206568927, 28.734...  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsc_start=datetime.datetime.now()\n",
    "res = table_tsc.search(vectors={'flat_index':[q]}, n=10)[0]\n",
    "tsc_stop=datetime.datetime.now()\n",
    "memory_tsc_windows_post_search=get_memory_usage()\n",
    "res.merge(vecdf, on=['index','sym','time'], how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5603d8c7-4599-4de1-af54-5328e826f7f5",
   "metadata": {
    "id": "5603d8c7-4599-4de1-af54-5328e826f7f5"
   },
   "source": [
    "**N.B.** We observe the returned indexes lay either side of the index of our query vector. This passes our sanity check as the vectors offset by a few indexes is simply the query pattern shifted slightly left and right. If we want to have more meaningful results we can either increase the number of nearest neighbors returned, or in our sliding window creation we can have windows that aren't largely overlapping."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0367fab7-5171-475f-9dca-7b607618dbba",
   "metadata": {
    "id": "0367fab7-5171-475f-9dca-7b607618dbba"
   },
   "source": [
    "We can clearly see the high correlation between the query vector and the non-trivial nearest neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a51f5e9-9560-4d85-878a-3c590a1edf8a",
   "metadata": {
    "id": "1a51f5e9-9560-4d85-878a-3c590a1edf8a"
   },
   "source": [
    "## 6. Search Evaluation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f061588d-13ea-4ac7-8093-cd11f907c78b",
   "metadata": {
    "id": "f061588d-13ea-4ac7-8093-cd11f907c78b"
   },
   "source": [
    "### Search Timings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ca643dd1",
   "metadata": {
    "id": "ca643dd1"
   },
   "outputs": [],
   "source": [
    "### Function to calculate how long a similarity search takes\n",
    "def datetime_difference(datetime1, datetime2):\n",
    "    # Calculate the difference between the two datetime objects\n",
    "    difference = datetime2 - datetime1\n",
    "\n",
    "    # Calculate total seconds and milliseconds from the difference\n",
    "    total_seconds = difference.total_seconds()\n",
    "    hours = int(total_seconds // 3600)\n",
    "    minutes = int((total_seconds % 3600) // 60)\n",
    "    seconds = int(total_seconds % 60)\n",
    "    milliseconds = int((total_seconds - int(total_seconds)) * 1000)\n",
    "\n",
    "    # Print the difference in hours, minutes, seconds, and milliseconds\n",
    "    return f\"Hours: {hours}, Minutes: {minutes}, Seconds: {seconds}, Milliseconds: {milliseconds}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c3937f07",
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    },
    "id": "c3937f07",
    "outputId": "b7d39658-6b21-4db4-e538-71aa93cfcf4f"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw: Hours: 0, Minutes: 0, Seconds: 0, Milliseconds: 669\n",
      "Transformed TSS: Hours: 0, Minutes: 0, Seconds: 0, Milliseconds: 90\n"
     ]
    }
   ],
   "source": [
    "print('Raw: ' + datetime_difference(raw_start, raw_stop))\n",
    "print('Transformed TSS: ' + datetime_difference(tsc_start,tsc_stop))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1abaac",
   "metadata": {
    "id": "4d1abaac"
   },
   "source": [
    "### 7. Delete the KDB.AI Database & Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd3c4827-64c0-4f14-b407-91b4713e181e",
   "metadata": {
    "id": "cd3c4827-64c0-4f14-b407-91b4713e181e"
   },
   "outputs": [],
   "source": [
    "table_raw.drop()\n",
    "table_tsc.drop()\n",
    "db.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83b09da",
   "metadata": {
    "id": "a83b09da"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/2ENY3DoD)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
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  "kernelspec": {
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   "pygments_lexer": "ipython3",
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================================================
FILE: TSS_transformed/Transformed_TSS_pattern_matching.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b52fbdd6-10c5-4f52-b015-ce0f812c7d94",
   "metadata": {
    "id": "b52fbdd6-10c5-4f52-b015-ce0f812c7d94"
   },
   "source": [
    "# Pattern Matching on Sensor Data\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> We have new features for highly optimized time series analytics.\n",
    "\n",
    "See documentation and notebooks on ***Temporal Similarity Search (TSS):***\n",
    "\n",
    "- Transformed TSS: [Documentation](https://code.kx.com/kdbai/reference/transformed-tss.html)\n",
    "\n",
    "- Non-Transformed TSS: [Documentation](https://code.kx.com/kdbai/use/non-transformed-tss.html) \n",
    "\n",
    "</div>\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example explores the process of conducting pattern matching on time series manufacturing data using *Transformed Temporal Similarity Search* in KDB.AI.\n",
    "\n",
    "Our goal is to identify and retrieve historical time series that exhibit specific patterns. This matching capability is instrumental in a wide array of manufacturing scenarios, including quality control, process optimization, and predictive maintenance. For instance, imagine a scenario where we have time series data representing machinery performance, and we need to pinpoint instances of unusual behaviour, such as spikes, drops, or recurring trends.\n",
    "\n",
    "We will guide you through a straightforward approach that leverages the raw time series data directly, without the need for complex modeling or domain-specific expertise. This approach is particularly attractive because it doesn't require additional resources for model creation. The sample will demonstrate that this simplistic method can yield satisfactory results.\n",
    "\n",
    "### Aim\n",
    "\n",
    "This tutorial will walk through the process of storing time series data in a vector database, generating time series vector embeddings. We will use KDB.AI's vector database to find patterns that match an input query pattern. We will cover the following topics:\n",
    "\n",
    "1. Load Sensor Data\n",
    "2. Create Sensor Vector Embeddings\n",
    "3. Store Embeddings in KDB.AI\n",
    "4. Search For Similar Sequences To A Target Sensor Sequence\n",
    "5. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f232d6de-c110-460a-a69f-e8cf852355b6",
   "metadata": {
    "id": "f232d6de-c110-460a-a69f-e8cf852355b6"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07954b1a",
   "metadata": {
    "id": "07954b1a"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e77efaa7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e77efaa7",
    "outputId": "ac35dc82-2065-4f41-d0f6-d9dffac08749"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e18f8ce",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e18f8ce",
    "outputId": "bcecd79f-3aad-413f-df16-0c2d1981aa35"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads sensor data\n",
    "!mkdir ./data\n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/pattern_matching/data/archive.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e158a3",
   "metadata": {
    "id": "38e158a3"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea686a15",
   "metadata": {
    "id": "ea686a15"
   },
   "outputs": [],
   "source": [
    "# read data\n",
    "from zipfile import ZipFile\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07814a98",
   "metadata": {
    "id": "07814a98"
   },
   "outputs": [],
   "source": [
    "# plotting\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f76e651",
   "metadata": {
    "id": "9f76e651"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7b4076",
   "metadata": {
    "id": "3e7b4076"
   },
   "source": [
    "### Ignore Warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33c637f0",
   "metadata": {
    "id": "33c637f0"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.simplefilter(\"ignore\", UserWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d03b85",
   "metadata": {
    "id": "e6d03b85"
   },
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75ff66c8",
   "metadata": {
    "id": "75ff66c8"
   },
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff790798",
   "metadata": {
    "id": "ff790798"
   },
   "source": [
    "## 1. Load Sensor Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7911ab4",
   "metadata": {
    "id": "b7911ab4"
   },
   "source": [
    "### Dataset Overview\n",
    "\n",
    "The dataset that will be used for this example is the [Water Pump Sensor Dataset](https://www.kaggle.com/datasets/nphantawee/pump-sensor-data) available on Kaggle. The datatset consist of a `sensor.csv` file which has raw values from 52 sensors from a town water pump.\n",
    "\n",
    "As the `sensors.csv` file is >100mb, we cannot host this file on GitHub and must instead zip this file up and extract it locally.\n",
    "\n",
    "### Extract the Data From a ZipFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5cdbf217",
   "metadata": {
    "id": "5cdbf217"
   },
   "outputs": [],
   "source": [
    "def extract_zip(file_name):\n",
    "    with ZipFile(file_name, \"r\") as zipf:\n",
    "        zipf.extractall(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b6142d3-667a-4f65-abe2-b33f95fdd46f",
   "metadata": {
    "id": "2b6142d3-667a-4f65-abe2-b33f95fdd46f"
   },
   "outputs": [],
   "source": [
    "extract_zip(\"data/archive.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5ff469",
   "metadata": {
    "id": "1e5ff469"
   },
   "source": [
    "You should now have a sensor.csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7476f1",
   "metadata": {
    "id": "2c7476f1"
   },
   "source": [
    "### Read In The Sensor Data From The CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c876844",
   "metadata": {
    "id": "3c876844"
   },
   "outputs": [],
   "source": [
    "raw_sensors_df = pd.read_csv(\"data/sensor.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12d3c00a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 463
    },
    "id": "12d3c00a",
    "outputId": "4f2229c2-e66f-4b2b-ee27-3d4c46ed343b"
   },
   "outputs": [],
   "source": [
    "show_df(raw_sensors_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e584484f",
   "metadata": {
    "id": "e584484f"
   },
   "source": [
    "### Pre-process The Data\n",
    "\n",
    "Let's do some preparation on the dataset to clean it up. We will remove duplicates, drop  irrelevant columns and handle missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04f68700",
   "metadata": {
    "id": "04f68700"
   },
   "outputs": [],
   "source": [
    "# Drop duplicates\n",
    "sensors_df = raw_sensors_df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6382c72b",
   "metadata": {
    "id": "6382c72b"
   },
   "outputs": [],
   "source": [
    "# Remove columns that are unnecessary/bad data\n",
    "sensors_df = sensors_df.drop([\"Unnamed: 0\", \"sensor_15\", \"sensor_50\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e338bae",
   "metadata": {
    "id": "3e338bae"
   },
   "outputs": [],
   "source": [
    "# convert timestamp to datetime format\n",
    "sensors_df[\"timestamp\"] = pd.to_datetime(sensors_df[\"timestamp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19fc8b10",
   "metadata": {
    "id": "19fc8b10"
   },
   "outputs": [],
   "source": [
    "# Removes rows with any NaN values\n",
    "sensors_df = sensors_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f35a91a",
   "metadata": {
    "id": "2f35a91a"
   },
   "outputs": [],
   "source": [
    "# Reset the index\n",
    "sensors_df = sensors_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad0f0794-38db-44b2-bcae-63b399b89729",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "ad0f0794-38db-44b2-bcae-63b399b89729",
    "outputId": "27798e03-95e2-47ae-f557-85d133704c42"
   },
   "outputs": [],
   "source": [
    "show_df(sensors_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65d5494",
   "metadata": {
    "id": "f65d5494"
   },
   "source": [
    "This dataset has 52 sensor columns - for the purposes of this example we will only select the first one `sensor_00` for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57757b69",
   "metadata": {
    "id": "57757b69"
   },
   "source": [
    "### Explore The Data For One Sensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53f2b312",
   "metadata": {
    "id": "53f2b312"
   },
   "outputs": [],
   "source": [
    "# Extract the readings from the BROKEN state of the pump\n",
    "broken_sensors_df = sensors_df[sensors_df[\"machine_status\"] == \"BROKEN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cafa18ba-2ad5-4285-a773-9648fc97188f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 154
    },
    "id": "cafa18ba-2ad5-4285-a773-9648fc97188f",
    "outputId": "b64c6066-480a-42ec-a111-3c57b87a7e55"
   },
   "outputs": [],
   "source": [
    "# Plot time series for each sensor with BROKEN state marked with X in red color\n",
    "plt.figure(figsize=(18, 3))\n",
    "plt.plot(\n",
    "    broken_sensors_df[\"timestamp\"],\n",
    "    broken_sensors_df[\"sensor_00\"],\n",
    "    linestyle=\"none\",\n",
    "    marker=\"X\",\n",
    "    color=\"red\",\n",
    "    markersize=12,\n",
    ")\n",
    "plt.plot(sensors_df[\"timestamp\"], sensors_df[\"sensor_00\"], color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69439514",
   "metadata": {
    "id": "69439514"
   },
   "source": [
    "We can see above that over time the sensor values stay generally around 2.5 with a few noisy dropoff spikes. We have plotted the column `machine_status=BROKEN` in red here which corresponds with a lot of these spikes indicating the reason for the dropoffs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c998fbc",
   "metadata": {
    "id": "1c998fbc"
   },
   "source": [
    "## 2. Create Sensor Vector Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee5954a",
   "metadata": {
    "id": "aee5954a"
   },
   "source": [
    "Next, let's create embeddings for these values. We have chosen a simple approach that leverages the raw time series data directly, without the need for complex modelling or domain-specific expertise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755dc49f",
   "metadata": {
    "id": "755dc49f"
   },
   "source": [
    "### Extract One Sensors Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "663fd80b",
   "metadata": {
    "id": "663fd80b"
   },
   "outputs": [],
   "source": [
    "sensor0_df = sensors_df[[\"timestamp\", \"sensor_00\"]]\n",
    "sensor0_df = sensor0_df.reset_index(drop=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b6Ave7lCNVok",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "b6Ave7lCNVok",
    "outputId": "f509aaff-9a7f-4383-9cb9-9c5599ed785e"
   },
   "outputs": [],
   "source": [
    "# This is our sensor data to be ingested into KDB.AI\n",
    "sensor0_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ninBxQZpTk4J",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ninBxQZpTk4J",
    "outputId": "ce930f45-bf8b-49a7-8a52-e051a122ae19"
   },
   "outputs": [],
   "source": [
    "sensor0_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd231f29-6435-4e9d-8732-af8d2d065c2b",
   "metadata": {
    "id": "cd231f29-6435-4e9d-8732-af8d2d065c2b"
   },
   "source": [
    "### Group The Sensor0 Values into Time Windows\n",
    "\n",
    "The code below divides the original time series data into overlapping windows, with each window containing a specified number of rows and a step size determining how they are shifted along the timeline. It also extracts a timestamp from each window as we will want to store this as metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fe8670dc",
   "metadata": {
    "id": "fe8670dc"
   },
   "outputs": [],
   "source": [
    "# Set the window size (number of rows in each window)\n",
    "window_size = 100\n",
    "step_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ed2d459",
   "metadata": {
    "id": "2ed2d459"
   },
   "outputs": [],
   "source": [
    "# define windows\n",
    "windows = [\n",
    "    sensor0_df.iloc[i : i + window_size]\n",
    "    for i in range(0, len(sensor0_df) - window_size + 1, step_size)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bf7e6d88",
   "metadata": {
    "id": "bf7e6d88"
   },
   "outputs": [],
   "source": [
    "# Iterate through the windows & extract column values\n",
    "start_times = [w[\"timestamp\"].iloc[0] for w in windows]\n",
    "end_times = [w[\"timestamp\"].iloc[-1] for w in windows]\n",
    "sensor0_values = [w[\"sensor_00\"].tolist() for w in windows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "wPOuC-sFP-Sm",
   "metadata": {
    "id": "wPOuC-sFP-Sm"
   },
   "outputs": [],
   "source": [
    "# Create a new DataFrame from the collected data\n",
    "embedding_df = pd.DataFrame(\n",
    "    {\"timestamp\": start_times, \"sensor_00\": sensor0_values}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "o9iKgg0vSlrp",
   "metadata": {
    "id": "o9iKgg0vSlrp"
   },
   "outputs": [],
   "source": [
    "embedding_df = embedding_df.reset_index(drop=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "acf95914",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "acf95914",
    "outputId": "257194b2-1ca3-4de0-a7eb-56f7a38f80d9"
   },
   "outputs": [],
   "source": [
    "# Show the resulting DataFrame\n",
    "show_df(embedding_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "-R9FbU1SivBR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-R9FbU1SivBR",
    "outputId": "cdc8fb43-a58f-4da2-d6fc-2e2e28d2f498"
   },
   "outputs": [],
   "source": [
    "# When is the first time a sensor is 'broken'?\n",
    "broken_sensors_df[\"timestamp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7daaa8",
   "metadata": {
    "id": "5f7daaa8"
   },
   "source": [
    "## 3. Store Embeddings in KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6edb73-e4c9-40c7-a295-e9063dc83e42",
   "metadata": {
    "id": "cd6edb73-e4c9-40c7-a295-e9063dc83e42"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a372cd",
   "metadata": {
    "id": "c4a372cd"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c0b1d1",
   "metadata": {
    "id": "f1c0b1d1"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will have three colums: index, timestamp, and sensor_00. Sensor_00 is where the time series embeddings will be stored and searched using Transformed Temporal Similarity Search.\n",
    "\n",
    "The key is that the 100 dimension windows will be compressed to 8 dimensions with Transformed TSS, making search much faster and significantly reducing the memory footprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "uoF9ZEW_WCTT",
   "metadata": {
    "id": "uoF9ZEW_WCTT"
   },
   "outputs": [],
   "source": [
    "# Set up the schema and indexes for KDB.AI table, specifying embeddings column with 384 dimensions, Euclidean Distance, and flat index\n",
    "sensor_schema = [\n",
    "    {\"name\": \"index\", \"type\": \"int64\"},\n",
    "    {\"name\": \"timestamp\", \"type\": \"datetime64[ns]\"},\n",
    "    {\"name\": \"sensor_00\", \"type\": \"float64s\"}\n",
    "]\n",
    "\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\": \"flat_index\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"sensor_00\",\n",
    "        \"params\": {\"dims\": 8, \"metric\": \"L2\"},\n",
    "    }\n",
    "]\n",
    "\n",
    "embedding_conf = {'sensor_00': {\"dims\": 8, \"type\": \"tsc\", \"on_insert_error\": \"reject_all\" }}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e345361-a3b3-4d59-bca1-0c0c6f63f910",
   "metadata": {
    "id": "8e345361-a3b3-4d59-bca1-0c0c6f63f910"
   },
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2a518fd3",
   "metadata": {
    "id": "2a518fd3"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"sensors\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "151df13c",
   "metadata": {
    "id": "151df13c"
   },
   "outputs": [],
   "source": [
    "# Create the table called \"sensors\"\n",
    "table = database.create_table(\"sensors\",\n",
    "                              schema = sensor_schema,\n",
    "                              indexes = indexes,\n",
    "                              embedding_configurations = embedding_conf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "BP-kQ5OAw1gM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "BP-kQ5OAw1gM",
    "outputId": "68fa4725-b67f-46da-a0ea-ff4c17372d27"
   },
   "outputs": [],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f489316",
   "metadata": {
    "id": "9f489316"
   },
   "source": [
    "### Add Embedded Data to KDB.AI Table\n",
    "\n",
    "When adding larger amounts of data, you may need insert data into an index in chunks. It is a good idea to first get an idea of how large the dataset to insert is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "slWtKGGjXE4Y",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "slWtKGGjXE4Y",
    "outputId": "13d95ea1-7f63-42a0-bbe6-badb18d612da"
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "n = 1000  # number of rows per batch\n",
    "\n",
    "for i in tqdm(range(0, embedding_df.shape[0], n)):\n",
    "    table.insert(embedding_df[i:i+n].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f498123",
   "metadata": {
    "id": "3f498123"
   },
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added.\n",
    "\n",
    "Note that while we only see the three columns including our 100 dimension vector/time series window, the 100 dimension time series window has been compressed to 8 dimensions, and that compressed time series windows will be used for similarity search in the backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6b185eca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "6b185eca",
    "outputId": "5b76adca-fba6-4961-8bc0-807e4da1aec7"
   },
   "outputs": [],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b643fd4",
   "metadata": {
    "id": "8b643fd4"
   },
   "source": [
    "## 4. Search For Similar Sequences To A Target Sensor Sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2358732",
   "metadata": {
    "id": "d2358732"
   },
   "source": [
    "Now our data is loaded successfully, we can perform pattern matching on our historical sensor data using KDB.AI `search`.\n",
    "\n",
    "### Define an Example Pattern to Query\n",
    "\n",
    "The first step is to select a pattern that will be used to query.\n",
    "\n",
    "We chose this by selecting a start time, filtering to get the vector's values for that record, and then storing this in a variable called `q`. Any pattern could be selected here.\n",
    "\n",
    "The resulting query pattern is also displayed as a line plot for visual inspection and analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "xS9ZqcDgw7oo",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xS9ZqcDgw7oo",
    "outputId": "7f4a008c-9cf6-463d-dd0c-ff89d37b1523"
   },
   "outputs": [],
   "source": [
    "broken_sensors_df[\"timestamp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "j6V-1VrTQyLR",
   "metadata": {
    "id": "j6V-1VrTQyLR"
   },
   "outputs": [],
   "source": [
    "## This is our query vector, using the 17100th sliding window as an example (this is just before the first instance when the sensor is in a failed state)\n",
    "q = embedding_df['sensor_00'][17100]\n",
    "#q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "oyThHxZHxNvI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "id": "oyThHxZHxNvI",
    "outputId": "362be697-7db2-49ac-ff58-5f565357c1df"
   },
   "outputs": [],
   "source": [
    "# Visualise the query pattern\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(embedding_df['sensor_00'][17100], marker=\"o\", linestyle=\"-\")\n",
    "plt.xlabel(\"Timestamp\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(\"Query & Similar Patterns\")\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d9a6640",
   "metadata": {},
   "source": [
    "#### Let's return the top 100 matches to this query vector to see if we can identify the other instances of a failed state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "hAqCTOiqT7Sx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "hAqCTOiqT7Sx",
    "outputId": "73178590-613d-4528-d484-8e9f38b7620d"
   },
   "outputs": [],
   "source": [
    "nn1_result = table.search(vectors={'flat_index': [q]}, n=100, filter=[(\">\",\"index\", 18000)])\n",
    "nn1_result[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6769cc80",
   "metadata": {},
   "source": [
    "#### Since every timestamp/row has a 100 dimension window, we will have matches that are close to one another and are matching upon the same 'anomaly' pattern. To ensure we are returning only unique pattern matches we will remove any matches within a range of 200 points from our next closest match. This will ensure we are only capturing each potential failed state one time within our final results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "nGZ7tqyvm9MY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nGZ7tqyvm9MY",
    "outputId": "4c144c37-9702-430c-eb1a-531869a00af3"
   },
   "outputs": [],
   "source": [
    "def filter_results(df, range_size=200):\n",
    "\n",
    "    final_results = []\n",
    "    removed_indices = set()\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        current_index = row['index']\n",
    "\n",
    "        # If this index hasn't been removed\n",
    "        if current_index not in removed_indices:\n",
    "            final_results.append(row)\n",
    "\n",
    "            # Mark indices within range for removal\n",
    "            lower_bound = max(0, current_index - range_size // 2)\n",
    "            upper_bound = current_index + range_size // 2\n",
    "            removed_indices.update(range(lower_bound, upper_bound + 1))\n",
    "\n",
    "    # Create a new dataframe from the final results\n",
    "    final_df = pd.DataFrame(final_results)\n",
    "\n",
    "    return final_df\n",
    "\n",
    "filtered_df = filter_results(nn1_result[0])\n",
    "\n",
    "# Display the filtered results\n",
    "print(filtered_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ed5417",
   "metadata": {},
   "source": [
    "#### For our reference, these are all of the times when the sensor returns a failed state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "oWyeUYjUjhzG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oWyeUYjUjhzG",
    "outputId": "5a205e5a-14a2-4ec3-e599-8205ccab625b"
   },
   "outputs": [],
   "source": [
    "broken_sensors_df[\"timestamp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2b5081",
   "metadata": {},
   "source": [
    "### Results:\n",
    "\n",
    "We see that our final results closely capture each of the timestamps of the failed states within a few indexes. There is only one captured pattern that is not within the failed states: 110667. If you go back near the beginning of this notebook you will see within the pattern a large drop in the signal near this index - this could show a time that needs to be investigated as a potential missed failed state. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85686375",
   "metadata": {},
   "source": [
    "## Visualize the matching patterns of other 'failed' states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "yUeWeCsZyBvi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "yUeWeCsZyBvi",
    "outputId": "82527ed4-eff2-4e4d-f375-7f48513fd861"
   },
   "outputs": [],
   "source": [
    "for i in filtered_df['index']:\n",
    "    plt.plot(embedding_df['sensor_00'][i], marker=\"o\", linestyle=\"-\")\n",
    "plt.xlabel(\"Timestamp\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(\"Query & Similar Patterns\")\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bdd5d5",
   "metadata": {
    "id": "c7bdd5d5"
   },
   "source": [
    "## 5. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84213681",
   "metadata": {
    "id": "84213681",
    "outputId": "745a9807-9557-4238-82bb-1102f95c6f00"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6078c94e",
   "metadata": {
    "id": "6078c94e"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/go0ElNsJ)"
   ]
  }
 ],
 "metadata": {
  "colab": {
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  "kernelspec": {
   "display_name": "Python 3",
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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================================================
FILE: document_search/document_search.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3280b01a-d3b7-4ef6-9494-789d15bc48ec",
   "metadata": {
    "id": "3280b01a-d3b7-4ef6-9494-789d15bc48ec"
   },
   "source": [
    "# Semantic Search on PDF Documents\n",
    "\n",
    "This example shows how to use KDB.AI to run semantic search on unstructured text documents.\n",
    "\n",
    "Semantic search allows users to perform searches based on the meaning or similarity of the data rather than exact matches. It works by converting the query into a vector representation and then finding similar vectors in the database. This way, even if the query and the data in the database are not identical, the system can identify and retrieve the most relevant results based on their semantic meaning.\n",
    "\n",
    "### Aim\n",
    "In this tutorial, we'll walk you through the process of performing semantic search on documents, taking PDFs as example, using KDB.AI as the vector store. We will cover the following topics:\n",
    "\n",
    "0. Load PDF Data\n",
    "1. Create Sentence Vector Embeddings\n",
    "2. Store Embeddings in KDB.AI\n",
    "3. Search For Similar Sentences To A Target Sentence\n",
    "4. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75362bc9",
   "metadata": {
    "id": "75362bc9"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda3d787",
   "metadata": {
    "id": "dda3d787"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19c5107-001e-40c2-bfe7-6c9c99e4846d",
   "metadata": {
    "id": "b19c5107-001e-40c2-bfe7-6c9c99e4846d"
   },
   "source": [
    "### Set Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be0c1ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bad9d73",
   "metadata": {
    "id": "8bad9d73"
   },
   "outputs": [],
   "source": [
    "!pip install pypdf sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f23f6513",
   "metadata": {
    "id": "f23f6513"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads research paper pdf into your environment\n",
    "!mkdir -pv ./data\n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/document_search/data/research_paper.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b1c0d58-c704-46e9-8705-93918444abd2",
   "metadata": {
    "id": "2b1c0d58-c704-46e9-8705-93918444abd2"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2784416-e45f-4c51-ad12-3ce84382ff38",
   "metadata": {
    "id": "e2784416-e45f-4c51-ad12-3ce84382ff38"
   },
   "outputs": [],
   "source": [
    "### ignore tensorflow warnings\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21979389",
   "metadata": {
    "id": "21979389"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6858852",
   "metadata": {
    "id": "e6858852"
   },
   "outputs": [],
   "source": [
    "# load data\n",
    "import pypdf\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "nltk.download('punkt')\n",
    "nltk.download('punkt_tab')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b5a731a",
   "metadata": {
    "id": "9b5a731a"
   },
   "outputs": [],
   "source": [
    "# embeddings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2030c51f",
   "metadata": {
    "id": "2030c51f"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8594c911",
   "metadata": {
    "id": "8594c911"
   },
   "source": [
    "### Configure Console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b870117b",
   "metadata": {
    "id": "b870117b"
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a425b33",
   "metadata": {
    "id": "8a425b33"
   },
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a135635",
   "metadata": {
    "id": "9a135635"
   },
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48826990",
   "metadata": {
    "id": "48826990"
   },
   "source": [
    "## 1. Load PDF Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2992812-4705-489d-974f-b7b44132343a",
   "metadata": {
    "id": "b2992812-4705-489d-974f-b7b44132343a"
   },
   "source": [
    "### Read Text From PDF Document\n",
    "\n",
    "We leverage the power of PyPDF2 for PDF processing and `nltk` for advanced natural language processing. The code below extracts content from each page of the PDF and processes it to identify sentences.\n",
    "\n",
    "The PDF we are using is [this research paper](https://arxiv.org/pdf/2308.05801.pdf) presenting information on the formation of Interstellar Objects in the Milky Way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0b573ca",
   "metadata": {
    "id": "e0b573ca"
   },
   "outputs": [],
   "source": [
    "# Read PDF file\n",
    "with open(\"data/research_paper.pdf\", \"rb\") as pdf_file:\n",
    "    pdf_pages = pypdf.PdfReader(pdf_file).pages\n",
    "    page_list = [page.extract_text() for page in pdf_pages]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f36f018",
   "metadata": {
    "id": "7f36f018"
   },
   "outputs": [],
   "source": [
    "# Concatenate text from each page\n",
    "full_pdf_text = \"\".join(page_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ec052a",
   "metadata": {
    "id": "65ec052a"
   },
   "source": [
    "### Split The Text Into Sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f129e3b",
   "metadata": {
    "id": "4f129e3b"
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   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <b>Note: </b>\n",
    "    Before running the following line of code, please ensure that you have installed the English sentence tokenizer as stated in the `README.md` file in this repository.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cbf9ede-6ed3-4171-bc77-2da673dcff6b",
   "metadata": {
    "colab": {
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    },
    "id": "5cbf9ede-6ed3-4171-bc77-2da673dcff6b",
    "outputId": "f921eeeb-614d-4289-dcf3-e31c5cea3d2a"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "591"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the PDF into sentences\n",
    "pdf_sentences = sent_tokenize(full_pdf_text)\n",
    "len(pdf_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2558747e-4559-44b2-8ecb-88a583cc0ceb",
   "metadata": {
    "colab": {
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    },
    "id": "2558747e-4559-44b2-8ecb-88a583cc0ceb",
    "outputId": "e5d48153-3f0f-4286-bf46-6eb95ce8ea84"
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   "outputs": [
    {
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       "type": "string"
      },
      "text/plain": [
       "'Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf_sentences[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ea4179-8b26-4c40-89aa-ef6da11aafdf",
   "metadata": {
    "id": "d9ea4179-8b26-4c40-89aa-ef6da11aafdf"
   },
   "source": [
    "## 2. Create Sentence Vector Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae1706a-6046-4dae-9b54-a59d4805f286",
   "metadata": {
    "id": "3ae1706a-6046-4dae-9b54-a59d4805f286"
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   "source": [
    "Next, we use the Sentence Transformers library to create embeddings for our collection of sentences.\n",
    "\n",
    "### Selecting a Sentence Transformer Model\n",
    "\n",
    "There are 100+ of different types of Sentence Transformers models available - see [HuggingFace](https://huggingface.co/sentence-transformers) for the full list. The diversity among these primarily stems from variations in their training data. Selecting the ideal model for your needs involves matching the domain and task closely, while also considering the benefits of incorporating larger datasets to enhance scale.\n",
    "\n",
    "This tutorial will use the `all-MiniLM-L6-v2` pre-trained model. This embedding model can create sentence and document embeddings that can be used for a wide variety of tasks including semantic search which makes it a good choice for our needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "665d805f",
   "metadata": {
    "id": "665d805f"
   },
   "outputs": [],
   "source": [
    "model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa4aaf7-69c5-4c56-b058-32ced66a62ee",
   "metadata": {
    "id": "7fa4aaf7-69c5-4c56-b058-32ced66a62ee"
   },
   "source": [
    "### Generate Sentence Embeddings Using This Model\n",
    "\n",
    "We prepare embeddings by applying the sentence transformer model to our sentences to encode them. The we do some transformation to get this into DataFrame which is the format accepted by KDB.AI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bddc4003",
   "metadata": {
    "id": "bddc4003"
   },
   "outputs": [],
   "source": [
    "# Create embeddings\n",
    "embeddings_list = model.encode(np.array(pdf_sentences)).tolist()\n",
    "embeddings_df = pd.DataFrame({\"vectors\": embeddings_list, \"sentences\": pdf_sentences})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d05b99a-121c-429e-864a-ee4bc5739224",
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    "outputId": "cfc5c36c-7414-4c81-8456-7e92d5bf7f49"
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "(591, 2)\n"
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       "      <td>Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J.</td>\n",
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       "      <td>Ted Mackereth\\n ,3, 4, 5, ∗and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just ...</td>\n",
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       "      <td>We define a novel framework: firstly to predict\\nthe properties of this Galactic ISO population by combining models of processes across planetary\\nand galactic scales, and secondly to make inferences about the processes modelled, by comparing the\\npredicted population to what is observed.</td>\n",
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       "      <td>Selecting ISO water mass\\nfraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\\nand averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\\ninferred water mass fraction of 2I/Borisov.</td>\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                       vectors  \\\n",
       "0  [-0.059804268181324005, -0.09221810102462769, 0.058069996535778046, 0.06884294003248215, -0.0030452304054051638, 0.007304240483790636, -0.028959110379219055, 0.0595787838101387, 0.017943743616342545, 0.043501030653715134, 0.005012953653931618, -0.07875007390975952, -0.02570340223610401, -0.04147...   \n",
       "1  [-0.08154530823230743, -0.11342314630746841, 0.08425560593605042, 0.08849422633647919, 0.024723634123802185, -0.08773960173130035, -0.06128991022706032, 0.0106121264398098, 0.06387822329998016, 0.021902449429035187, -0.033225465565919876, -0.09321605414152145, -0.04425090178847313, -0.0568475760...   \n",
       "2  [-0.07799109816551208, -0.08398666232824326, 0.02723710611462593, 0.09858055412769318, 0.010515090078115463, -0.01670389622449875, -0.055797528475522995, 0.027984360232949257, 0.03926650434732437, 0.03248238563537598, 0.03837994113564491, -0.12464361637830734, -0.012645130045711994, -0.018288377...   \n",
       "3  [-0.05338575318455696, -0.037147365510463715, 0.07784582674503326, 0.05165949463844299, -0.07674478739500046, -0.007766904775053263, 0.008464669808745384, 0.04289115220308304, 0.04356798157095909, 0.06012833118438721, 0.04407043382525444, -0.10177762806415558, 0.030359704047441483, -0.0340371690...   \n",
       "4  [-0.06326982378959656, -0.01720457151532173, 0.047737207263708115, 0.08325360715389252, 0.03296627104282379, -0.0660020187497139, 0.0015915816184133291, 0.003246008651331067, 0.03456269949674606, -0.020524047315120697, 0.041225481778383255, -0.15274153649806976, -0.011771060526371002, 0.07314326...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                     sentences  \n",
       "0                                                                  Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J.  \n",
       "1  Ted Mackereth\\n ,3, 4, 5, ∗and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just ...  \n",
       "2            We define a novel framework: firstly to predict\\nthe properties of this Galactic ISO population by combining models of processes across planetary\\nand galactic scales, and secondly to make inferences about the processes modelled, by comparing the\\npredicted population to what is observed.  \n",
       "3                                                                      We predict the spatial and compositional distribution of the\\nGalaxy’s population of ISOs by modelling the Galactic stellar population with data from the APOGEE\\nsurvey and combining this with a protoplanetary disk chemistry model.  \n",
       "4                      Selecting ISO water mass\\nfraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\\nand averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\\ninferred water mass fraction of 2I/Borisov.  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(embeddings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd119e3",
   "metadata": {
    "id": "edd119e3"
   },
   "source": [
    "It is important to note the dimension of our embeddings is 384. This will need to match the dimensions we set in the KDB.AI index in the next step. We can easily check this using `len` to count elements in our vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce60a36c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ce60a36c",
    "outputId": "3fd41491-9763-4dcb-a6de-0a4f03b8933d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(embeddings_df[\"vectors\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1f910b",
   "metadata": {
    "id": "8c1f910b"
   },
   "source": [
    "## 3. Store Embeddings in KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257f049c",
   "metadata": {},
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Connect to KDB.AI Server\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b3a4932",
   "metadata": {
    "id": "3b3a4932"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "DgIi_1Z9wsKY",
   "metadata": {
    "id": "DgIi_1Z9wsKY"
   },
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "-oYYPRLUwvY7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-oYYPRLUwvY7",
    "outputId": "8458322c-2450-4b64-9089-949753cbc71b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI database \"default\"]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1g_FbKknwyQg",
   "metadata": {
    "id": "1g_FbKknwyQg"
   },
   "source": [
    "### Create a Database called \"myDatabase\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "W5AN3mlyw22o",
   "metadata": {
    "id": "W5AN3mlyw22o"
   },
   "outputs": [],
   "source": [
    "# ensure no database called \"myDatabase\" exists\n",
    "try:\n",
    "    session.database(\"myDatabase\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "uPK81d3lw4pf",
   "metadata": {
    "id": "uPK81d3lw4pf"
   },
   "outputs": [],
   "source": [
    "# Create the database\n",
    "db = session.create_database(\"myDatabase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c6525c",
   "metadata": {
    "id": "48c6525c"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will have two columns: sentences and vectors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "af604df0",
   "metadata": {
    "id": "af604df0"
   },
   "outputs": [],
   "source": [
    "pdf_schema = [\n",
    "        {\"name\": \"sentences\", \"type\": \"str\"},\n",
    "        {\n",
    "            \"name\": \"vectors\",\n",
    "            \"type\": \"float64s\",\n",
    "        },\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bSgwEmDuxTe_",
   "metadata": {
    "id": "bSgwEmDuxTe_"
   },
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- type = hnsw : HNSW enhances efficiency while maintaining accuracy. You have the choice of using other indexes like, qHNSW, and IVFPQ, qFlat or a Flat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- name = hnsw_index : this is a custom name you give your index.\n",
    "- column = vectors : this is the column where the embeddings are stored.\n",
    "#### params:\n",
    "- dims = 384 : In the next section, we generate embeddings that are 384-dimensional to match this. The number of dimensions should mirror the output dimensions of your embedding model.\n",
    "- metric = L2 : We chose Euclidean Distance. You have the choice of using other metrics here like IP/Inner Product and CS/Cosine Similarity and the one you chose depends on the specific context and nature of your data.\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "jeB_WKGTxrM6",
   "metadata": {
    "id": "jeB_WKGTxrM6"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'hnsw',\n",
    "        'name': 'hnsw_index',\n",
    "        'column': 'vectors',\n",
    "        'params': {'dims': 384, 'metric': \"L2\"},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518cfe1e",
   "metadata": {
    "id": "518cfe1e"
   },
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6e670f9e",
   "metadata": {
    "id": "6e670f9e"
   },
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"pdf\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0",
   "metadata": {
    "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0"
   },
   "outputs": [],
   "source": [
    "table = db.create_table(table=\"pdf\", schema=pdf_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466068bc",
   "metadata": {
    "id": "466068bc"
   },
   "source": [
    "We can use `query` to see our table exists but is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "74e7332e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "74e7332e",
    "outputId": "c53597c1-8f16-4cbb-d724-2cf1c86a901c"
   },
   "outputs": [
    {
     "data": {
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       "repr_error": "Out of range float values are not JSON compliant: nan",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-54910f6f-6c5c-4901-8417-75986a94dca0\" class=\"colab-df-container\">\n",
       "    <div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentences</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
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       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
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       "        document.querySelector('#df-54910f6f-6c5c-4901-8417-75986a94dca0 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-54910f6f-6c5c-4901-8417-75986a94dca0');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sentences, vectors]\n",
       "Index: []"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0a9d0e-80b8-4e09-9101-d22667da551f",
   "metadata": {
    "id": "9a0a9d0e-80b8-4e09-9101-d22667da551f"
   },
   "source": [
    "### Add Embedded Data to KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "83cc156c-8071-4784-8c3e-a049b61e8668",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "83cc156c-8071-4784-8c3e-a049b61e8668",
    "outputId": "504e261e-70bc-4f61-c64f-9eccd1edf6a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 591}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(embeddings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31a4ecd",
   "metadata": {
    "id": "e31a4ecd"
   },
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ee6ecb8d",
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    "id": "ee6ecb8d",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(591, 2)\n"
     ]
    },
    {
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       "summary": "{\n  \"name\": \"show_df(table\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"vectors\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentences\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Ted Mackereth\\n ,3, 4, 5, \\u2217and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences\\u2014Te Kura Mat\\u00af u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just Group plc, Enterprise House, Bancroft road, Reigate, Surrey RH2 7RP, UK\\n4Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON, M5S 3H8, Canada\\n5Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada\\nABSTRACT\\nThe Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering\\napproximately 1015pc\\u22123around the Sun, which are formed and shaped by a diverse set of processes\\nranging from planet formation to galactic dynamics.\",\n          \"Selecting ISO water mass\\nfraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\\nand averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\\ninferred water mass fraction of 2I/Borisov.\",\n          \"We define a novel framework: firstly to predict\\nthe properties of this Galactic ISO population by combining models of processes across planetary\\nand galactic scales, and secondly to make inferences about the processes modelled, by comparing the\\npredicted population to what is observed.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "      <th>sentences</th>\n",
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       "      <th>0</th>\n",
       "      <td>[-0.059804268181324005, -0.09221810102462769, 0.058069996535778046, 0.06884294003248215, -0.0030452304054051638, 0.007304240483790636, -0.028959110379219055, 0.0595787838101387, 0.017943743616342545, 0.043501030653715134, 0.005012953653931618, -0.07875007390975952, -0.02570340223610401, -0.04147...</td>\n",
       "      <td>Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-0.08154530823230743, -0.11342314630746841, 0.08425560593605042, 0.08849422633647919, 0.024723634123802185, -0.08773960173130035, -0.06128991022706032, 0.0106121264398098, 0.06387822329998016, 0.021902449429035187, -0.033225465565919876, -0.09321605414152145, -0.04425090178847313, -0.0568475760...</td>\n",
       "      <td>Ted Mackereth\\n ,3, 4, 5, ∗and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.07799109816551208, -0.08398666232824326, 0.02723710611462593, 0.09858055412769318, 0.010515090078115463, -0.01670389622449875, -0.055797528475522995, 0.027984360232949257, 0.03926650434732437, 0.03248238563537598, 0.03837994113564491, -0.12464361637830734, -0.012645130045711994, -0.018288377...</td>\n",
       "      <td>We define a novel framework: firstly to predict\\nthe properties of this Galactic ISO population by combining models of processes across planetary\\nand galactic scales, and secondly to make inferences about the processes modelled, by comparing the\\npredicted population to what is observed.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-0.05338575318455696, -0.037147365510463715, 0.07784582674503326, 0.05165949463844299, -0.07674478739500046, -0.007766904775053263, 0.008464669808745384, 0.04289115220308304, 0.04356798157095909, 0.06012833118438721, 0.04407043382525444, -0.10177762806415558, 0.030359704047441483, -0.0340371690...</td>\n",
       "      <td>We predict the spatial and compositional distribution of the\\nGalaxy’s population of ISOs by modelling the Galactic stellar population with data from the APOGEE\\nsurvey and combining this with a protoplanetary disk chemistry model.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.06326982378959656, -0.01720457151532173, 0.047737207263708115, 0.08325360715389252, 0.03296627104282379, -0.0660020187497139, 0.0015915816184133291, 0.003246008651331067, 0.03456269949674606, -0.020524047315120697, 0.041225481778383255, -0.15274153649806976, -0.011771060526371002, 0.07314326...</td>\n",
       "      <td>Selecting ISO water mass\\nfraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\\nand averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\\ninferred water mass fraction of 2I/Borisov.</td>\n",
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       "                                                                                                                                                                                                                                                                                                       vectors  \\\n",
       "0  [-0.059804268181324005, -0.09221810102462769, 0.058069996535778046, 0.06884294003248215, -0.0030452304054051638, 0.007304240483790636, -0.028959110379219055, 0.0595787838101387, 0.017943743616342545, 0.043501030653715134, 0.005012953653931618, -0.07875007390975952, -0.02570340223610401, -0.04147...   \n",
       "1  [-0.08154530823230743, -0.11342314630746841, 0.08425560593605042, 0.08849422633647919, 0.024723634123802185, -0.08773960173130035, -0.06128991022706032, 0.0106121264398098, 0.06387822329998016, 0.021902449429035187, -0.033225465565919876, -0.09321605414152145, -0.04425090178847313, -0.0568475760...   \n",
       "2  [-0.07799109816551208, -0.08398666232824326, 0.02723710611462593, 0.09858055412769318, 0.010515090078115463, -0.01670389622449875, -0.055797528475522995, 0.027984360232949257, 0.03926650434732437, 0.03248238563537598, 0.03837994113564491, -0.12464361637830734, -0.012645130045711994, -0.018288377...   \n",
       "3  [-0.05338575318455696, -0.037147365510463715, 0.07784582674503326, 0.05165949463844299, -0.07674478739500046, -0.007766904775053263, 0.008464669808745384, 0.04289115220308304, 0.04356798157095909, 0.06012833118438721, 0.04407043382525444, -0.10177762806415558, 0.030359704047441483, -0.0340371690...   \n",
       "4  [-0.06326982378959656, -0.01720457151532173, 0.047737207263708115, 0.08325360715389252, 0.03296627104282379, -0.0660020187497139, 0.0015915816184133291, 0.003246008651331067, 0.03456269949674606, -0.020524047315120697, 0.041225481778383255, -0.15274153649806976, -0.011771060526371002, 0.07314326...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                     sentences  \n",
       "0                                                                  Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J.  \n",
       "1  Ted Mackereth\\n ,3, 4, 5, ∗and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just ...  \n",
       "2            We define a novel framework: firstly to predict\\nthe properties of this Galactic ISO population by combining models of processes across planetary\\nand galactic scales, and secondly to make inferences about the processes modelled, by comparing the\\npredicted population to what is observed.  \n",
       "3                                                                      We predict the spatial and compositional distribution of the\\nGalaxy’s population of ISOs by modelling the Galactic stellar population with data from the APOGEE\\nsurvey and combining this with a protoplanetary disk chemistry model.  \n",
       "4                      Selecting ISO water mass\\nfraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\\nand averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\\ninferred water mass fraction of 2I/Borisov.  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf8650d",
   "metadata": {
    "id": "8bf8650d"
   },
   "source": [
    "## 4. Search For Similar Sentences To A Target Sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ddb725-e0e7-4c00-a22b-3234eacf6bd1",
   "metadata": {
    "id": "31ddb725-e0e7-4c00-a22b-3234eacf6bd1"
   },
   "source": [
    "Now that the embeddings are stored in KDB.AI, we can perform semantic search using `search`.\n",
    "\n",
    "### Search 1\n",
    "\n",
    "First, we embed our search term using the Sentence Transformer model as before. Then we search our index to return the three most similar vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b24c1179",
   "metadata": {
    "id": "b24c1179"
   },
   "outputs": [],
   "source": [
    "search_term1 = \"number of interstellar objects in the milky way\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d5f8fa5b",
   "metadata": {
    "id": "d5f8fa5b"
   },
   "outputs": [],
   "source": [
    "encoded_search_term1 = model.encode(search_term1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8666561e-e9b2-4c9f-95f4-add789be416d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "8666561e-e9b2-4c9f-95f4-add789be416d",
    "outputId": "363d2d9d-3e75-4ac9-8873-d6815d69aac8"
   },
   "outputs": [
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       "summary": "{\n  \"name\": \"results1[0]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"__nn_distance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6786811947822571,\n          0.6655914783477783,\n          0.6293923854827881\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vectors\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentences\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ted Mackereth\\n ,3, 4, 5, \\u2217and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences\\u2014Te Kura Mat\\u00af u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just Group plc, Enterprise House, Bancroft road, Reigate, Surrey RH2 7RP, UK\\n4Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON, M5S 3H8, Canada\\n5Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada\\nABSTRACT\\nThe Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering\\napproximately 1015pc\\u22123around the Sun, which are formed and shaped by a diverse set of processes\\nranging from planet formation to galactic dynamics.\",\n          \"In this work, we develop\\nthis method and apply it to the stellar population of the Milky Way, estimated with data from the APOGEE survey, to\\npredict a broader set of properties of our own Galaxy\\u2019s population of interstellar objects.\",\n          \"Keywords: Interstellar objects (52), Small Solar System bodies(1469), Galaxy Evolution (594)\\n1.INTRODUCTION\\n1I/\\u2018Oumuamua (Meech et al.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "      <td>0.678681</td>\n",
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       "      <td>Ted Mackereth\\n ,3, 4, 5, ∗and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.665591</td>\n",
       "      <td>[-0.0868825614452362, -0.023635495454072952, 0.06744848191738129, 0.08018778264522552, -0.05974714457988739, -0.03222731873393059, -0.008770040236413479, 0.04886192828416824, -0.05181043595075607, 0.017594708129763603, 0.024309009313583374, -0.09147438406944275, 0.038199249655008316, -0.06181899...</td>\n",
       "      <td>In this work, we develop\\nthis method and apply it to the stellar population of the Milky Way, estimated with data from the APOGEE survey, to\\npredict a broader set of properties of our own Galaxy’s population of interstellar objects.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.629392</td>\n",
       "      <td>[-0.07292614132165909, 0.021450141444802284, 0.01660231687128544, 0.05125197023153305, -0.017564907670021057, -0.08743879944086075, -0.020641718059778214, 0.037198327481746674, -0.024873124435544014, 0.039150986820459366, 0.043217096477746964, -0.1320355087518692, -0.033658064901828766, -0.06451...</td>\n",
       "      <td>Keywords: Interstellar objects (52), Small Solar System bodies(1469), Galaxy Evolution (594)\\n1.INTRODUCTION\\n1I/‘Oumuamua (Meech et al.</td>\n",
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      "text/plain": [
       "   __nn_distance  \\\n",
       "0       0.678681   \n",
       "1       0.665591   \n",
       "2       0.629392   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                       vectors  \\\n",
       "0  [-0.08154530823230743, -0.11342314630746841, 0.08425560593605042, 0.08849422633647919, 0.024723634123802185, -0.08773960173130035, -0.06128991022706032, 0.0106121264398098, 0.06387822329998016, 0.021902449429035187, -0.033225465565919876, -0.09321605414152145, -0.04425090178847313, -0.0568475760...   \n",
       "1  [-0.0868825614452362, -0.023635495454072952, 0.06744848191738129, 0.08018778264522552, -0.05974714457988739, -0.03222731873393059, -0.008770040236413479, 0.04886192828416824, -0.05181043595075607, 0.017594708129763603, 0.024309009313583374, -0.09147438406944275, 0.038199249655008316, -0.06181899...   \n",
       "2  [-0.07292614132165909, 0.021450141444802284, 0.01660231687128544, 0.05125197023153305, -0.017564907670021057, -0.08743879944086075, -0.020641718059778214, 0.037198327481746674, -0.024873124435544014, 0.039150986820459366, 0.043217096477746964, -0.1320355087518692, -0.033658064901828766, -0.06451...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                     sentences  \n",
       "0  Ted Mackereth\\n ,3, 4, 5, ∗and\\nJohn C. Forbes\\n2\\n1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\\n2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\\n3Just ...  \n",
       "1                                                                   In this work, we develop\\nthis method and apply it to the stellar population of the Milky Way, estimated with data from the APOGEE survey, to\\npredict a broader set of properties of our own Galaxy’s population of interstellar objects.  \n",
       "2                                                                                                                                                                     Keywords: Interstellar objects (52), Small Solar System bodies(1469), Galaxy Evolution (594)\\n1.INTRODUCTION\\n1I/‘Oumuamua (Meech et al.  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results1 = table.search(vectors={\"hnsw_index\":[encoded_search_term1]}, n=3)\n",
    "results1[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91762de7",
   "metadata": {
    "id": "91762de7"
   },
   "source": [
    "The results returned from `table.search` show the closest matches along with value of nearest neighbor distances `nn_distance`.\n",
    "We can see these sentences do reference our search term 'number of interstellar objects in the milky way' in some way.\n",
    "\n",
    "### Search 2\n",
    "\n",
    "Let's try another search term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e7486a9c",
   "metadata": {
    "id": "e7486a9c"
   },
   "outputs": [],
   "source": [
    "search_term2 = \"how does planet formation occur\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6765075c",
   "metadata": {
    "id": "6765075c"
   },
   "outputs": [],
   "source": [
    "encoded_search_term2 = model.encode(search_term2).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2d3ce969",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "2d3ce969",
    "outputId": "e40add3e-2045-473a-aaec-968f0d3af7de"
   },
   "outputs": [
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       "summary": "{\n  \"name\": \"results2[0]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"__nn_distance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5463818311691284,\n          0.533591628074646,\n          0.5229740142822266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vectors\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentences\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"The pop-\\nulation\\u2019s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the source systems.\",\n          \"A protoplanetary disk has to first order the same composition as the star it forms around,\\nsince they both form from the same molecular cloud core.\",\n          \"While in reality, stars will each produce a distribution of ISOs that\\nformed at different positions in their protoplanetary disk and thus have a range of compositions, this simplification\\nof only modelling planetesimals which form exterior to the water ice line is justified by the proportionally greater\\nreservoir of snowline-exterior planetesimals, and the higher efficiencies of formation mechanisms dynamically stripping\\nthem into the interstellar population (Fitzsimmons et al.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "      <td>The pop-\\nulation’s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the source systems.</td>\n",
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       "            'suggestCharts', [key], {});\n",
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       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   __nn_distance  \\\n",
       "0       0.546382   \n",
       "1       0.533592   \n",
       "2       0.522974   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                       vectors  \\\n",
       "0  [-0.07665147632360458, -0.06582025438547134, 0.034305740147829056, 0.026705816388130188, 0.07752171903848648, -0.05098922178149223, 0.007996230386197567, 0.023463979363441467, 0.09635236114263535, 0.05890350416302681, -0.009348639287054539, -0.04947573319077492, 0.04072212800383568, -0.086648009...   \n",
       "1  [-0.011731946840882301, -0.06267537921667099, 0.08392807841300964, -0.036336500197649, -0.0021244140807539225, -0.05082397535443306, -0.00048589325160719454, -0.02759421430528164, 0.13681785762310028, 0.06662701815366745, -0.02651246450841427, -0.019386690109968185, 0.0160849429666996, -0.083458...   \n",
       "2  [-0.0878974199295044, -0.053996648639440536, 0.08170221745967865, 0.05057608336210251, -0.001267113140784204, -0.051809072494506836, -0.03642294555902481, 0.014396563172340393, 0.10822276026010513, 0.043686095625162125, -0.07996439933776855, -0.07062527537345886, 0.05373870208859444, -0.06110462...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                     sentences  \n",
       "0                                                                                                                                         The pop-\\nulation’s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the source systems.  \n",
       "1                                                                                                                                                         A protoplanetary disk has to first order the same composition as the star it forms around,\\nsince they both form from the same molecular cloud core.  \n",
       "2  While in reality, stars will each produce a distribution of ISOs that\\nformed at different positions in their protoplanetary disk and thus have a range of compositions, this simplification\\nof only modelling planetesimals which form exterior to the water ice line is justified by the proportional...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2 = table.search(vectors={\"hnsw_index\":[encoded_search_term2]}, n=3)\n",
    "results2[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ce337f",
   "metadata": {
    "id": "25ce337f"
   },
   "source": [
    "Again, we can see these sentences do reference our search term 'how does planet formation occur' in some way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e93878",
   "metadata": {
    "id": "f6e93878"
   },
   "source": [
    "## 5. Delete the KDB.AI Database & Table\n",
    "\n",
    "Once finished with the database & table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d74b6f20",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d74b6f20",
    "outputId": "9fbfd16b-ae0b-4e54-d6b5-3e0be2e0af26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.drop()\n",
    "db.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346d56db",
   "metadata": {
    "id": "346d56db"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/ezUwZ88a)"
   ]
  }
 ],
 "metadata": {
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================================================
FILE: fuzzy_filtering_on_metadata/fuzzy_filtering_demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WgVm-xwOXyhY"
   },
   "source": [
    "# Fuzzy Filtering on Metadata with KDB.AI Vector Database\n",
    "\n",
    "##### Note: This example requires a KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "#### In this example, we will show how to use metadata filtering along with fuzzy filtering in a KDB.AI vector database to increase the speed and accuracy of vector similarity searches.\n",
    "\n",
    "#### NOTE! KDB.AI Also has 'fuzzy filter' capabilities on metadata columns.\n",
    "Data often contains errors such as typos, misspellings, or international spelling variations, which can hinder the accuracy of search results. Fuzzy filters address this issue by enabling the retrieval of documents that contain terms and metadata entries similar to the specified query term and filters, even if there are slight variations.\n",
    "\n",
    "There are many distance metrics you can use for fuzzy filtering, it defaults to Levenshtein distance, but you have the ability to choose the distance metric from a variety of options including: Levenshtein, Damerau-Levenshtein, Hamming, Indel, Jaro, JaroWinkler, Longest Common Subsequence, Optimal String Alignment (OSA), Prefix, or Postfix.\n",
    "\n",
    "#### Agenda:\n",
    "1. Set Up\n",
    "2. Data Import and Understanding\n",
    "3. Set Up KDB.AI Vector Database\n",
    "4. Insert Movie Data into the KDB.AI table\n",
    "5. Run Filtered & Fuzzy Similarity Searches on our KDB.AI vector database\n",
    "\n",
    "Movie Dataset Source: https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D6Py6iDPXyhb"
   },
   "source": [
    "## 1. Set Up\n",
    "#### Installs, imports, and API Key setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z9Fia3FQXyhb"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client\n",
    "!pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "reDIVkL5Xyhc"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "sLp8fEsWXyhc"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads movie data\n",
    "if os.path.exists(\"./data/filtered_embedded_movies.pkl\") == False:\n",
    "  !mkdir ./data\n",
    "  !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/metadata_filtering/data/filtered_embedded_movies.pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CEhGKVP4Xyhd"
   },
   "source": [
    "## 2. Data Import and Understanding\n",
    "### Import movies dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "CyHi02nGXyhd",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Read in the Movies dataframe\n",
    "df = pd.read_pickle(\"./data/filtered_embedded_movies.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RtktEmpwXyhd"
   },
   "source": [
    "### Initial data exploration: Let's understand the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aucHA297Xyhd",
    "outputId": "11869edd-c29e-4e33-83c7-7eaf03dd49cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19161\n"
     ]
    }
   ],
   "source": [
    "# How many rows do we have?\n",
    "print(df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aOVfpVChXyhe",
    "outputId": "c061e2ac-9d13-4b54-aa50-17f511ed95ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReleaseYear\n",
      "Title\n",
      "Origin\n",
      "Director\n",
      "Cast\n",
      "Genre\n",
      "Plot\n",
      "embeddings\n"
     ]
    }
   ],
   "source": [
    "# What columns do we have?\n",
    "for column in df.columns:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "JMCgWg3OXyhe",
    "outputId": "e5498cfa-a4e1-4a31-bde9-264b60d27232"
   },
   "outputs": [
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       "      <td>The Candy Tangerine Man</td>\n",
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       "   ReleaseYear                                   Title    Origin  \\\n",
       "0         1975                 The Candy Tangerine Man  American   \n",
       "1         1975                                  Capone  American   \n",
       "2         1975  Cleopatra Jones and the Casino of Gold  American   \n",
       "3         1975                      Conduct Unbecoming  American   \n",
       "4         1975                             Cooley High  American   \n",
       "\n",
       "           Director                                               Cast  \\\n",
       "0       Matt Cimber               John Daniels Eli Haines Tom Hankason   \n",
       "1      Steve Carver  Ben Gazzara Susan Blakely John Cassavetes Sylv...   \n",
       "2      Charles Bail                       Tamara Dobson Stella Stevens   \n",
       "3  Michael Anderson  Stacy Keach Richard Attenborough Christopher P...   \n",
       "4   Michael Schultz  Lawrence Hilton-Jacobs Glynn Turman Garrett Mo...   \n",
       "\n",
       "         Genre                                               Plot  \\\n",
       "0       action  A successful Los Angeles-based businessperson ...   \n",
       "1  crime drama  The story is of the rise and fall of the Chica...   \n",
       "2       action  The story begins with two government agents Ma...   \n",
       "3        drama  Around 1880 two young British officers arrive ...   \n",
       "4       comedy  Set in 1964 Chicago Preach an aspiring playwri...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-0.06835174, -0.013138616, -0.12417501, 0.002...  \n",
       "1  [-0.01411798, 0.040705115, -0.0014280609, 0.00...  \n",
       "2  [-0.0925895, 0.01188509, -0.08999529, -0.01541...  \n",
       "3  [-0.07435084, -0.06386179, 0.017042944, 0.0288...  \n",
       "4  [-0.041632336, 0.037923656, -0.072276264, -0.0...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us inspect the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D3RntWE6Xyhe"
   },
   "source": [
    "## 3. Set up KDB.AI Vector Database\n",
    "Now that we understand our dataset, we can set up our vector db\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "h0YJMpNeXyhe"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to KDB.AI Server\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R1-2e8l-Xyhf"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CkpOqe6FXyhf"
   },
   "source": [
    "### Set up the table schema\n",
    "Have a table column for each column in the dataframe, as well as an 'embeddings' column for the movie description embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "DwGylgw9Xyhf"
   },
   "outputs": [],
   "source": [
    "# Set up the schema for KDB.AI table, specifying column information\n",
    "table_schema = [\n",
    "        {\"name\": \"ReleaseYear\", \"type\": \"int64\"},\n",
    "        {\"name\": \"Title\", \"type\": \"str\"},\n",
    "        {\"name\": \"Origin\", \"type\": \"str\"},\n",
    "        {\"name\": \"Director\", \"type\": \"str\"},\n",
    "        {\"name\": \"Cast\", \"type\": \"str\"},\n",
    "        {\"name\": \"Genre\", \"type\": \"str\"},\n",
    "        {\"name\": \"Plot\", \"type\": \"str\"},\n",
    "        {\"name\": \"embeddings\", \"type\": \"float64s\"},\n",
    "    ]\n",
    "\n",
    "# Set up the index with 384 dimensions, Euclidean Distance, and flat index\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\": \"flat_index\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {\"dims\": 384, \"metric\": \"L2\"},\n",
    "    }\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RVcZ7PbNXyhf"
   },
   "source": [
    "### Create a table called \"metadata_demo\"\n",
    "First check if the table already exists, then create a new table with the table schema from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "dH23tks9Xyhf"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"metadata_demo\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "mQCdzNeZXyhf"
   },
   "outputs": [],
   "source": [
    "# Create the table called \"metadata_demo\"\n",
    "table = database.create_table(\"metadata_demo\", table_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ubHHWAxfXyhf"
   },
   "source": [
    "## 4. Insert Movie Data into the KDB.AI table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LN6N0IRsXyhf",
    "outputId": "dd2cb866-7e9b-4fa5-8b70-17b47e0ab067"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  5.29it/s]\n"
     ]
    }
   ],
   "source": [
    "# Insert the data into the table, split into 2000 row batches\n",
    "from tqdm import tqdm\n",
    "n = 2000  # chunk row size\n",
    "\n",
    "for i in tqdm(range(0, df.shape[0], n)):\n",
    "    table.insert(df[i:i+n].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "JL4_xRa0Xyhg"
   },
   "outputs": [],
   "source": [
    "# function to view the dataframe within the table\n",
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "-FpZO-9cXyhg",
    "outputId": "926fec9e-5d5a-452f-e7c7-5e6add439e4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19161, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ReleaseYear</th>\n",
       "      <th>Title</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Plot</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>The Candy Tangerine Man</td>\n",
       "      <td>American</td>\n",
       "      <td>Matt Cimber</td>\n",
       "      <td>John Daniels Eli Haines Tom Hankason</td>\n",
       "      <td>action</td>\n",
       "      <td>A successful Los Angeles-based businessperson ...</td>\n",
       "      <td>[-0.06835173815488815, -0.01313861645758152, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>Capone</td>\n",
       "      <td>American</td>\n",
       "      <td>Steve Carver</td>\n",
       "      <td>Ben Gazzara Susan Blakely John Cassavetes Sylv...</td>\n",
       "      <td>crime drama</td>\n",
       "      <td>The story is of the rise and fall of the Chica...</td>\n",
       "      <td>[-0.014117980375885963, 0.0407051146030426, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975</td>\n",
       "      <td>Cleopatra Jones and the Casino of Gold</td>\n",
       "      <td>American</td>\n",
       "      <td>Charles Bail</td>\n",
       "      <td>Tamara Dobson Stella Stevens</td>\n",
       "      <td>action</td>\n",
       "      <td>The story begins with two government agents Ma...</td>\n",
       "      <td>[-0.09258949756622314, 0.011885089799761772, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975</td>\n",
       "      <td>Conduct Unbecoming</td>\n",
       "      <td>American</td>\n",
       "      <td>Michael Anderson</td>\n",
       "      <td>Stacy Keach Richard Attenborough Christopher P...</td>\n",
       "      <td>drama</td>\n",
       "      <td>Around 1880 two young British officers arrive ...</td>\n",
       "      <td>[-0.07435084134340286, -0.06386178731918335, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>Cooley High</td>\n",
       "      <td>American</td>\n",
       "      <td>Michael Schultz</td>\n",
       "      <td>Lawrence Hilton-Jacobs Glynn Turman Garrett Mo...</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Set in 1964 Chicago Preach an aspiring playwri...</td>\n",
       "      <td>[-0.041632335633039474, 0.0379236564040184, -0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ReleaseYear                                   Title    Origin  \\\n",
       "0         1975                 The Candy Tangerine Man  American   \n",
       "1         1975                                  Capone  American   \n",
       "2         1975  Cleopatra Jones and the Casino of Gold  American   \n",
       "3         1975                      Conduct Unbecoming  American   \n",
       "4         1975                             Cooley High  American   \n",
       "\n",
       "           Director                                               Cast  \\\n",
       "0       Matt Cimber               John Daniels Eli Haines Tom Hankason   \n",
       "1      Steve Carver  Ben Gazzara Susan Blakely John Cassavetes Sylv...   \n",
       "2      Charles Bail                       Tamara Dobson Stella Stevens   \n",
       "3  Michael Anderson  Stacy Keach Richard Attenborough Christopher P...   \n",
       "4   Michael Schultz  Lawrence Hilton-Jacobs Glynn Turman Garrett Mo...   \n",
       "\n",
       "         Genre                                               Plot  \\\n",
       "0       action  A successful Los Angeles-based businessperson ...   \n",
       "1  crime drama  The story is of the rise and fall of the Chica...   \n",
       "2       action  The story begins with two government agents Ma...   \n",
       "3        drama  Around 1880 two young British officers arrive ...   \n",
       "4       comedy  Set in 1964 Chicago Preach an aspiring playwri...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-0.06835173815488815, -0.01313861645758152, -...  \n",
       "1  [-0.014117980375885963, 0.0407051146030426, -0...  \n",
       "2  [-0.09258949756622314, 0.011885089799761772, -...  \n",
       "3  [-0.07435084134340286, -0.06386178731918335, 0...  \n",
       "4  [-0.041632335633039474, 0.0379236564040184, -0...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View contents of the table\n",
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eWD7Q4ntXyhg"
   },
   "source": [
    "## 5. Run Filtered Similarity Searches on our KDB.AI Vector Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUn-stWwXyhg"
   },
   "source": [
    "#### Set up embedding model to embed our natural language queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U78VR-7UXyhg"
   },
   "outputs": [],
   "source": [
    "# embedding model to be used to embed user input query\n",
    "from sentence_transformers import SentenceTransformer\n",
    "embedding_model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VDRi-wDwXyhg"
   },
   "source": [
    "#### Create a query vector by using the embedding model to embed a natural language query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "e3NbLcaOXyhg"
   },
   "outputs": [],
   "source": [
    "# Embed a query\n",
    "query_vector = {'flat_index' : [embedding_model.encode('star wars Luke Skywalker').tolist()]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k3nWaUvyXyhg"
   },
   "source": [
    "#### Run vector similarity search, return the top-3 similar movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HEYbnoBUXyhg",
    "outputId": "2e91caa3-385b-42e3-d962-8fac2ccc7b0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       0.748475         1983   \n",
      "1       0.910225         1977   \n",
      "2       0.942763         1980   \n",
      "\n",
      "                                              Title    Origin  \\\n",
      "0                                Return of the Jedi  American   \n",
      "1  Star Wars Episode IV: A New Hope (aka Star Wars)  American   \n",
      "2                           The Empire Strikes Back  American   \n",
      "\n",
      "           Director                                               Cast  \\\n",
      "0  Richard Marquand  Mark Hamill Harrison Ford Carrie Fisher Billy ...   \n",
      "1      George Lucas  Mark Hamill Harrison Ford Carrie Fisher Alec G...   \n",
      "2    Irvin Kershner  Carrie Fisher Harrison Ford Mark Hamill Billy ...   \n",
      "\n",
      "             Genre                                               Plot  \\\n",
      "0  science fiction  Luke Skywalker initiates a plan to rescue Han ...   \n",
      "1  science fiction  The galaxy is in the midst of a civil war. Spi...   \n",
      "2  science fiction  Three years after the destruction of the Death...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.047360002994537354, -0.08337291330099106, ...  \n",
      "1  [-0.10030582547187805, 0.008335104212164879, 0...  \n",
      "2  [-0.050230544060468674, -0.023651080206036568,...  ]\n"
     ]
    }
   ],
   "source": [
    "# Search vector db to find most relevant movies\n",
    "print(table.search(vectors=query_vector, n=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0hdIcL2GXyhg"
   },
   "source": [
    "#### Repeat the search with metadata filters to narrow the search space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1vaqdT0CXyhh",
    "outputId": "893b008a-699d-4ef7-f36a-fd34a830c096"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       0.910225         1977   \n",
      "\n",
      "                                              Title    Origin      Director  \\\n",
      "0  Star Wars Episode IV: A New Hope (aka Star Wars)  American  George Lucas   \n",
      "\n",
      "                                                Cast            Genre  \\\n",
      "0  Mark Hamill Harrison Ford Carrie Fisher Alec G...  science fiction   \n",
      "\n",
      "                                                Plot  \\\n",
      "0  The galaxy is in the midst of a civil war. Spi...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.10030582547187805, 0.008335104212164879, 0...  ]\n"
     ]
    }
   ],
   "source": [
    "print(table.search(vectors=query_vector, n=3, filter=[(\"like\", \"Director\", \"George Lucas\"),(\"=\", \"ReleaseYear\", 1977)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AsnPmebLZbT6"
   },
   "source": [
    "### Fuzzy Filtering\n",
    "What if there are some spelling mistakes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XtAynAljZa6t",
    "outputId": "9a5e5685-c432-41ab-8057-e86757555b7a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       0.910225         1977   \n",
      "1       1.166750         2005   \n",
      "2       1.210034         2002   \n",
      "\n",
      "                                              Title    Origin      Director  \\\n",
      "0  Star Wars Episode IV: A New Hope (aka Star Wars)  American  George Lucas   \n",
      "1      Star Wars: Episode III   Revenge of the Sith  American  George Lucas   \n",
      "2      Star Wars: Episode II   Attack of the Clones  American  George Lucas   \n",
      "\n",
      "                                                Cast                   Genre  \\\n",
      "0  Mark Hamill Harrison Ford Carrie Fisher Alec G...         science fiction   \n",
      "1  Ewan McGregor Hayden Christensen Natalie Portm...  science fiction action   \n",
      "2   Ewan McGregor Natalie Portman Hayden Christensen         science fiction   \n",
      "\n",
      "                                                Plot  \\\n",
      "0  The galaxy is in the midst of a civil war. Spi...   \n",
      "1  Three years after the Battle of Geonosis the g...   \n",
      "2  Ten years after the Trade Federations invasion...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.10030582547187805, 0.008335104212164879, 0...  \n",
      "1  [0.008934415876865387, -0.05330725386738777, 0...  \n",
      "2  [-0.11154422909021378, -0.043663837015628815, ...  ]\n"
     ]
    }
   ],
   "source": [
    "# Fuzzy filter with a misspelled name. This defaults to 'Levenshtein' distance metric\n",
    "print(table.search(vectors=query_vector, n=3, filter=[['fuzzy','Director',[[\"Goerge Lucas\",2]]]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q7-bIyiQZpsj",
    "outputId": "4f90d256-689c-436a-b4a5-9883c6ebc956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       0.748475         1983   \n",
      "1       0.910225         1977   \n",
      "2       0.942763         1980   \n",
      "\n",
      "                                              Title    Origin  \\\n",
      "0                                Return of the Jedi  American   \n",
      "1  Star Wars Episode IV: A New Hope (aka Star Wars)  American   \n",
      "2                           The Empire Strikes Back  American   \n",
      "\n",
      "           Director                                               Cast  \\\n",
      "0  Richard Marquand  Mark Hamill Harrison Ford Carrie Fisher Billy ...   \n",
      "1      George Lucas  Mark Hamill Harrison Ford Carrie Fisher Alec G...   \n",
      "2    Irvin Kershner  Carrie Fisher Harrison Ford Mark Hamill Billy ...   \n",
      "\n",
      "             Genre                                               Plot  \\\n",
      "0  science fiction  Luke Skywalker initiates a plan to rescue Han ...   \n",
      "1  science fiction  The galaxy is in the midst of a civil war. Spi...   \n",
      "2  science fiction  Three years after the destruction of the Death...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.047360002994537354, -0.08337291330099106, ...  \n",
      "1  [-0.10030582547187805, 0.008335104212164879, 0...  \n",
      "2  [-0.050230544060468674, -0.023651080206036568,...  ]\n"
     ]
    }
   ],
   "source": [
    "# Fuzzy filter with a misspelled genre, choosing the distance metric algorithm to use, in this case 'osa', Optimal String Alignment.\n",
    "print(table.search(vectors=query_vector, n=3, filter=[['fuzzy','Genre',[[\"sceince fictoin\",2,'osa']]]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_3kKzl1Xyhh"
   },
   "source": [
    "#### More Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "q0g8NZONXyhh"
   },
   "outputs": [],
   "source": [
    "# Another query\n",
    "query_vector = {'flat_index' :[embedding_model.encode('conspiracy theories involving art').tolist()]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k5PvOBvDXyhh",
    "outputId": "6eaf221f-1101-4131-a6e5-b43a0a803b44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear               Title    Origin          Director  \\\n",
      "0       1.276896         2006   The Da Vinci Code  American        Ron Howard   \n",
      "1       1.395944         2017          The Circle  American    James Ponsoldt   \n",
      "2       1.607655         2017            The Post  American  Steven Spielberg   \n",
      "\n",
      "                                                Cast  \\\n",
      "0  Tom Hanks Audrey Tautou Ian McKellen Alfred Mo...   \n",
      "1  James Ponsoldt (director/screenplay); Tom Hank...   \n",
      "2  Steven Spielberg (director); Liz Hannah Josh S...   \n",
      "\n",
      "                              Genre  \\\n",
      "0                          thriller   \n",
      "1             sci-fi drama thriller   \n",
      "2  biography drama history thriller   \n",
      "\n",
      "                                                Plot  \\\n",
      "0  Jacques Sauni¨re the Louvres curator is pursue...   \n",
      "1  When her car breaks down Mae Holland contacts ...   \n",
      "2  In 1966 Vietnam State Department military anal...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.11887314915657043, -0.04977063462138176, -...  \n",
      "1  [-0.07589969784021378, -0.052303414791822433, ...  \n",
      "2  [-0.06242850422859192, -0.0349779948592186, -0...  ]\n"
     ]
    }
   ],
   "source": [
    "# Another filtered search example\n",
    "print(table.search(vectors=query_vector, n=3, filter=[(\"like\", \"Genre\", \"*thriller*\"),(\"like\",\"Cast\",\"*Tom Hanks*\")]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "HhbmuhVXXyhl"
   },
   "outputs": [],
   "source": [
    "# Another query\n",
    "query_vector = {'flat_index' :[embedding_model.encode('middle earth fantasy adventure in the Shire').tolist()]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-I708ZtyXyhl",
    "outputId": "4a278528-1f71-4805-aa18-bed16c8a25e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       1.014505         2001   \n",
      "1       1.099138         2002   \n",
      "2       1.153412         2003   \n",
      "\n",
      "                                               Title    Origin       Director  \\\n",
      "0  The Lord of the Rings: The Fellowship of the Ring  American  Peter Jackson   \n",
      "1              The Lord of the Rings: The Two Towers  American  Peter Jackson   \n",
      "2      The Lord of the Rings: The Return of the King  American  Peter Jackson   \n",
      "\n",
      "                                                Cast              Genre  \\\n",
      "0  Elijah Wood Ian McKellen Liv Tyler Sean Astin ...            fantasy   \n",
      "1  Elijah Wood Ian McKellen Liv Tyler Viggo Morte...  adventure fantasy   \n",
      "2  Elijah Wood Ian McKellen Liv Tyler Sean Astin ...  adventure fantasy   \n",
      "\n",
      "                                                Plot  \\\n",
      "0  In the Second Age of Middle-earth the lords of...   \n",
      "1  After awakening from a dream of Gandalf the Gr...   \n",
      "2  Many years ago two Hobbits Smeagol and Dagol a...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.04706393554806709, 0.0369022861123085, -0....  \n",
      "1  [-0.05915825068950653, 0.033268801867961884, -...  \n",
      "2  [-0.07969464361667633, -0.006237572990357876, ...  ]\n"
     ]
    }
   ],
   "source": [
    "# Another filtered search example\n",
    "print(table.search(vectors=query_vector, n=3, filter=[(\"within\",\"ReleaseYear\",[2000,2010])]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WDyBpe5u_RjS"
   },
   "source": [
    "#### Another Fuzzy Filtering Example on the Genre metadata column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HIkGoJF4cXZ0",
    "outputId": "1815d586-8cd2-4f0f-db82-fef3ed8574a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       1.014505         2001   \n",
      "1       1.099138         2002   \n",
      "2       1.153412         2003   \n",
      "\n",
      "                                               Title    Origin       Director  \\\n",
      "0  The Lord of the Rings: The Fellowship of the Ring  American  Peter Jackson   \n",
      "1              The Lord of the Rings: The Two Towers  American  Peter Jackson   \n",
      "2      The Lord of the Rings: The Return of the King  American  Peter Jackson   \n",
      "\n",
      "                                                Cast              Genre  \\\n",
      "0  Elijah Wood Ian McKellen Liv Tyler Sean Astin ...            fantasy   \n",
      "1  Elijah Wood Ian McKellen Liv Tyler Viggo Morte...  adventure fantasy   \n",
      "2  Elijah Wood Ian McKellen Liv Tyler Sean Astin ...  adventure fantasy   \n",
      "\n",
      "                                                Plot  \\\n",
      "0  In the Second Age of Middle-earth the lords of...   \n",
      "1  After awakening from a dream of Gandalf the Gr...   \n",
      "2  Many years ago two Hobbits Smeagol and Dagol a...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.04706393554806709, 0.0369022861123085, -0....  \n",
      "1  [-0.05915825068950653, 0.033268801867961884, -...  \n",
      "2  [-0.07969464361667633, -0.006237572990357876, ...  ]\n"
     ]
    }
   ],
   "source": [
    "# Another filtered search example, with fuzzy filtering the Director column reconciling for a typo in what should be 'Peter Jackson'\n",
    "print(table.search(vectors=query_vector, n=3, filter=[(\"within\",\"ReleaseYear\",[2000,2010]),['fuzzy','Director',[[\"Peter Jacksen\",1,\"damerau_levenshtein\"]]]]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "unLQEoaPXyhl"
   },
   "source": [
    "## Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WS99DOEFXyhl",
    "outputId": "c9071a16-8079-4a91-c4ff-2daa3020eab0"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ii2Hd3chXyhl"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/Jvlbdqvt)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: hybrid_search/data/inflation.txt
================================================
 At last year's Jackson Hole symposium, I delivered a brief, direct message. My remarks this year will be a bit longer, but the message is the same: It is the Fed's job to bring inflation down to our 2 percent goal, and we will do so. We have tightened policy significantly over the past year. Although inflation has moved down from its peak—a welcome development—it remains too high. We are prepared to raise rates further if appropriate, and intend to hold policy at a restrictive level until we are confident that inflation is moving sustainably down toward our objective.

Today I will review our progress so far and discuss the outlook and the uncertainties we face as we pursue our dual mandate goals. I will conclude with a summary of what this means for policy. Given how far we have come, at upcoming meetings we are in a position to proceed carefully as we assess the incoming data and the evolving outlook and risks.

The Decline in Inflation So Far
The ongoing episode of high inflation initially emerged from a collision between very strong demand and pandemic-constrained supply. By the time the Federal Open Market Committee raised the policy rate in March 2022, it was clear that bringing down inflation would depend on both the unwinding of the unprecedented pandemic-related demand and supply distortions and on our tightening of monetary policy, which would slow the growth of aggregate demand, allowing supply time to catch up. While these two forces are now working together to bring down inflation, the process still has a long way to go, even with the more favorable recent readings.

On a 12-month basis, U.S. total, or "headline," PCE (personal consumption expenditures) inflation peaked at 7 percent in June 2022 and declined to 3.3 percent as of July, following a trajectory roughly in line with global trends (figure 1, panel A).1 The effects of Russia's war against Ukraine have been a primary driver of the changes in headline inflation around the world since early 2022. Headline inflation is what households and businesses experience most directly, so this decline is very good news. But food and energy prices are influenced by global factors that remain volatile, and can provide a misleading signal of where inflation is headed. In my remaining comments, I will focus on core PCE inflation, which omits the food and energy components.

On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.

To understand the factors that will likely drive further progress, it is useful to separately examine the three broad components of core PCE inflation—inflation for goods, for housing services, and for all other services, sometimes referred to as nonhousing services (figure 2).

Core goods inflation has fallen sharply, particularly for durable goods, as both tighter monetary policy and the slow unwinding of supply and demand dislocations are bringing it down. The motor vehicle sector provides a good illustration. Earlier in the pandemic, demand for vehicles rose sharply, supported by low interest rates, fiscal transfers, curtailed spending on in-person services, and shifts in preference away from using public transportation and from living in cities. But because of a shortage of semiconductors, vehicle supply actually fell. Vehicle prices spiked, and a large pool of pent-up demand emerged. As the pandemic and its effects have waned, production and inventories have grown, and supply has improved. At the same time, higher interest rates have weighed on demand. Interest rates on auto loans have nearly doubled since early last year, and customers report feeling the effect of higher rates on affordability.2 On net, motor vehicle inflation has declined sharply because of the combined effects of these supply and demand factors.

Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.

In the highly interest-sensitive housing sector, the effects of monetary policy became apparent soon after liftoff. Mortgage rates doubled over the course of 2022, causing housing starts and sales to fall and house price growth to plummet. Growth in market rents soon peaked and then steadily declined (figure 3).3

Measured housing services inflation lagged these changes, as is typical, but has recently begun to fall. This inflation metric reflects rents paid by all tenants, as well as estimates of the equivalent rents that could be earned from homes that are owner occupied.4 Because leases turn over slowly, it takes time for a decline in market rent growth to work its way into the overall inflation measure. The market rent slowdown has only recently begun to show through to that measure. The slowing growth in rents for new leases over roughly the past year can be thought of as "in the pipeline" and will affect measured housing services inflation over the coming year. Going forward, if market rent growth settles near pre-pandemic levels, housing services inflation should decline toward its pre-pandemic level as well. We will continue to watch the market rent data closely for a signal of the upside and downside risks to housing services inflation.

The final category, nonhousing services, accounts for over half of the core PCE index and includes a broad range of services, such as health care, food services, transportation, and accommodations. Twelve-month inflation in this sector has moved sideways since liftoff. Inflation measured over the past three and six months has declined, however, which is encouraging. Part of the reason for the modest decline of nonhousing services inflation so far is that many of these services were less affected by global supply chain bottlenecks and are generally thought to be less interest sensitive than other sectors such as housing or durable goods. Production of these services is also relatively labor intensive, and the labor market remains tight. Given the size of this sector, some further progress here will be essential to restoring price stability. Over time, restrictive monetary policy will help bring aggregate supply and demand back into better balance, reducing inflationary pressures in this key sector.

The Outlook
Turning to the outlook, although further unwinding of pandemic-related distortions should continue to put some downward pressure on inflation, restrictive monetary policy will likely play an increasingly important role. Getting inflation sustainably back down to 2 percent is expected to require a period of below-trend economic growth as well as some softening in labor market conditions.

Economic growth
Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic activity, and there is evidence of that in this cycle as well. For example, growth in industrial production has slowed, and the amount spent on residential investment has declined in each of the past five quarters (figure 4).

But we are attentive to signs that the economy may not be cooling as expected. So far this year, GDP (gross domestic product) growth has come in above expectations and above its longer-run trend, and recent readings on consumer spending have been especially robust. In addition, after decelerating sharply over the past 18 months, the housing sector is showing signs of picking back up. Additional evidence of persistently above-trend growth could put further progress on inflation at risk and could warrant further tightening of monetary policy.

The labor market
The rebalancing of the labor market has continued over the past year but remains incomplete. Labor supply has improved, driven by stronger participation among workers aged 25 to 54 and by an increase in immigration back toward pre-pandemic levels. Indeed, the labor force participation rate of women in their prime working years reached an all-time high in June. Demand for labor has moderated as well. Job openings remain high but are trending lower. Payroll job growth has slowed significantly. Total hours worked has been flat over the past six months, and the average workweek has declined to the lower end of its pre-pandemic range, reflecting a gradual normalization in labor market conditions (figure 5).

This rebalancing has eased wage pressures. Wage growth across a range of measures continues to slow, albeit gradually (figure 6). While nominal wage growth must ultimately slow to a rate that is consistent with 2 percent inflation, what matters for households is real wage growth. Even as nominal wage growth has slowed, real wage growth has been increasing as inflation has fallen.

We expect this labor market rebalancing to continue. Evidence that the tightness in the labor market is no longer easing could also call for a monetary policy response.

Uncertainty and Risk Management along the Path Forward
Two percent is and will remain our inflation target. We are committed to achieving and sustaining a stance of monetary policy that is sufficiently restrictive to bring inflation down to that level over time. It is challenging, of course, to know in real time when such a stance has been achieved. There are some challenges that are common to all tightening cycles. For example, real interest rates are now positive and well above mainstream estimates of the neutral policy rate. We see the current stance of policy as restrictive, putting downward pressure on economic activity, hiring, and inflation. But we cannot identify with certainty the neutral rate of interest, and thus there is always uncertainty about the precise level of monetary policy restraint.

That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.

Beyond these traditional sources of policy uncertainty, the supply and demand dislocations unique to this cycle raise further complications through their effects on inflation and labor market dynamics. For example, so far, job openings have declined substantially without increasing unemployment—a highly welcome but historically unusual result that appears to reflect large excess demand for labor. In addition, there is evidence that inflation has become more responsive to labor market tightness than was the case in recent decades.8 These changing dynamics may or may not persist, and this uncertainty underscores the need for agile policymaking.

These uncertainties, both old and new, complicate our task of balancing the risk of tightening monetary policy too much against the risk of tightening too little. Doing too little could allow above-target inflation to become entrenched and ultimately require monetary policy to wring more persistent inflation from the economy at a high cost to employment. Doing too much could also do unnecessary harm to the economy.

Conclusion
As is often the case, we are navigating by the stars under cloudy skies. In such circumstances, risk-management considerations are critical. At upcoming meetings, we will assess our progress based on the totality of the data and the evolving outlook and risks. Based on this assessment, we will proceed carefully as we decide whether to tighten further or, instead, to hold the policy rate constant and await further data. Restoring price stability is essential to achieving both sides of our dual mandate. We will need price stability to achieve a sustained period of strong labor market conditions that benefit all.

We will keep at it until the job is done.

================================================
FILE: hybrid_search/hybrid_search_inflation.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yxwE82TZNkco"
   },
   "source": [
    "## Hybrid Search\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "KDB.AI hybrid search is a method of similarity search to increase the relevancy of results retrieved from the vector database. It combines two search methods: sparse vector search, and dense vector search.\n",
    "\n",
    "Sparse vector search uses the BM25 algorithm to find the most relevant keyword matches, while dense vector search finds the most semantically relevant matches.\n",
    "\n",
    "In KDB.AI, users can run sparse or dense search independently, or run hybrid search which runs both sparse and dense vector searches and then re-ranks to combine the results of each search based on a user defined \"weight\" value.\n",
    "\n",
    "In this sample we will use hybrid search over a Federal Reserve speech to extract chunks of the speech that are similar to a user's prompt. In this notebook we will chunk up the document into smaller subsections, create sparse and dense vectors of the chunks, store the vectors in the KDB.AI vector database, and then run dense search, sparse search, and hybrid search to retrieve the most relevant chunks to a user's query.\n",
    "\n",
    "Agenda:\n",
    "1. Dependencies, Imports & Setup\n",
    "2. Ingest & Chunk Data\n",
    "3. Generate Sparse & Dense Vectors for Each Chunk\n",
    "4. Define KDB.AI Session and Create Database\n",
    "5. Create KDB.AI Schema & Table\n",
    "6. Insert Data into KDB.AI Table\n",
    "7. Create Sparse and Dense Query Vectors\n",
    "8. Run Sparse, Dense, and Hybrid Searches\n",
    "\n",
    "[Inflation: Progress and the Path Ahead](https://www.federalreserve.gov/newsevents/speech/powell20230825a.htm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mDRvL7jiNkcp"
   },
   "source": [
    "## 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9YNwxlHZPKu3"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KTt0VFTYNkcp"
   },
   "outputs": [],
   "source": [
    "!pip install sentence-transformers 'langchain<1.0.0' langchain-community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "FfoOD9FxNkcp"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time\n",
    "from transformers import BertTokenizerFast\n",
    "from collections import Counter\n",
    "\n",
    "# Ignore Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "IpEWr_EvNkcp"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads Federal Reserve Inflation speech data\n",
    "if os.path.exists(\"./data/inflation.txt\") == False:\n",
    "  !mkdir ./data\n",
    "  !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/hybrid_search/data/inflation.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lrw6Iu0bNkcq"
   },
   "source": [
    "## 2. Ingest & Chunk Data\n",
    "Data is from Federal Reserve Chain Jerome H. Powell:\n",
    "\n",
    "[Inflation: Progress and the Path Ahead](https://www.federalreserve.gov/newsevents/speech/powell20230825a.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "nN0GQDIANkcq"
   },
   "outputs": [],
   "source": [
    "### Load the documents we want to prompt an LLM about\n",
    "doc = TextLoader(\"data/inflation.txt\").load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "G6DDD6ddNkcq"
   },
   "outputs": [],
   "source": [
    "### Chunk the documents into 500 character chunks using langchain's text splitter \"RucursiveCharacterTextSplitter\"\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "2Rjkeq_oNkcq"
   },
   "outputs": [],
   "source": [
    "### split_documents produces a list of all the chunks created\n",
    "pages = [p.page_content for p in text_splitter.split_documents(doc)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "J3FwRgq6Nkcq"
   },
   "outputs": [],
   "source": [
    "### Create a blank dataframe to store chunks and vectors in before insertion\n",
    "data = {\n",
    "    'ID':[],\n",
    "    'chunk': [],\n",
    "    'dense': [],\n",
    "    'sparse': []\n",
    "}\n",
    "\n",
    "# Create the DataFrame\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8ghx6kYtNkcq"
   },
   "source": [
    "## 3. Generate Sparse & Dense Vectors for Each Chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AhMYd5guNkcq"
   },
   "outputs": [],
   "source": [
    "### Tokenizer to create sparse vectors\n",
    "token = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
    "\n",
    "### Embedding model to be used to embed user input query\n",
    "from sentence_transformers import SentenceTransformer\n",
    "embedding_model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "IvjNAhrZNkcq",
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      "text/plain": [
       "  ID                                              chunk  \\\n",
       "0  0  At last year's Jackson Hole symposium, I deliv...   \n",
       "1  1  are confident that inflation is moving sustain...   \n",
       "2  2  Today I will review our progress so far and di...   \n",
       "3  3                    The Decline in Inflation So Far   \n",
       "4  4  The ongoing episode of high inflation initiall...   \n",
       "\n",
       "                                               dense  \\\n",
       "0  [-0.022856269031763077, -0.02936530113220215, ...   \n",
       "1  [0.011283153668045998, -0.030178586021065712, ...   \n",
       "2  [-0.03170400112867355, 0.01769343577325344, 0....   \n",
       "3  [0.003466668538749218, 0.007666163146495819, -...   \n",
       "4  [-0.02072943188250065, -0.055148035287857056, ...   \n",
       "\n",
       "                                              sparse  \n",
       "0  {2012: 2, 2197: 1, 2095: 3, 1005: 2, 1055: 2, ...  \n",
       "1  {2024: 1, 9657: 1, 2008: 1, 14200: 1, 2003: 1,...  \n",
       "2  {2651: 1, 1045: 2, 2097: 2, 3319: 1, 2256: 2, ...  \n",
       "3  {1996: 1, 6689: 1, 1999: 1, 14200: 1, 2061: 1,...  \n",
       "4  {1996: 7, 7552: 1, 2792: 1, 1997: 4, 2152: 1, ...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create sparse and dense vectors of each chunk, append to the dataframe\n",
    "\n",
    "id = 0\n",
    "for chunk in pages:\n",
    "    ### Create the dense query vector\n",
    "    dense_chunk = [embedding_model.encode(chunk).tolist()]\n",
    "\n",
    "    ### Create the sparse query vector\n",
    "    sparse_chunk = [dict(Counter(y)) for y in token([chunk], padding=True,max_length=None)['input_ids']]\n",
    "    sparse_chunk[0].pop(101);sparse_chunk[0].pop(102);\n",
    "\n",
    "    new_row_df = pd.DataFrame([{\"ID\": str(id), \"chunk\": chunk, \"dense\": dense_chunk[0], \"sparse\": sparse_chunk[0]}])\n",
    "    df = pd.concat([df, new_row_df], ignore_index=True)\n",
    "    id += int(1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z3i844C3Nkcq"
   },
   "source": [
    "## 4. Define KDB.AI Session & Database\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eVOZCLI3Nkcr"
   },
   "outputs": [],
   "source": [
    "\n",
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "695PM-c1RRCC"
   },
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ewaBpPAiRU4y"
   },
   "outputs": [],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J7UlCvo4RXMr"
   },
   "source": [
    "### Create a Database called \"myDatabase\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "id": "REbU5B15Rajk"
   },
   "outputs": [],
   "source": [
    "# ensure no database called \"myDatabase\" exists\n",
    "try:\n",
    "    session.database(\"myDatabase\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "id": "CsCfxNp-Rcbm"
   },
   "outputs": [],
   "source": [
    "# Create the database\n",
    "db = session.create_database(\"myDatabase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RCcAYgY9Nkcr"
   },
   "source": [
    "## 5. Create Schema, Indexes and KDB.AI Table\n",
    "\n",
    "Now, let us define the schema that will be used to create the KDB.AI table.\n",
    "\n",
    "\"ID\" and \"chunk\" columns will hold the unique identifier and raw text chunk.\n",
    "\n",
    "sparse and dense columns will hold the respective sparse and dense vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "id": "jvSLKsYwSB0V"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "  {\"name\": \"ID\", \"type\": \"str\"},\n",
    "  {\"name\": \"chunk\", \"type\": \"str\"},\n",
    "  {\n",
    "      \"name\":\"sparse\",\n",
    "      \"type\":\"general\",\n",
    "  },\n",
    "  {\n",
    "      \"name\":\"dense\",\n",
    "      \"type\":\"float64s\",\n",
    "  },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_AB9ASDXuOVa"
   },
   "source": [
    "### Define the indexes\n",
    "In this example, we have two indexes, one for dense search and one for sparse search (bm25).\n",
    "\n",
    "- dense_index: uses a flat index type, with 384 dims and Euclidean Distance search metric\n",
    "\n",
    "- sparse_index: uses bm25 search type. We also define the \"b\" and \"k\" parameters. These parameters can be adjusted at runtime, enabling the hyperparameter tuning for term saturation and document length impact on relevance. This will be discussed further during a later example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "id": "sF4HOazLSsFf"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'flat',\n",
    "        'name': 'dense_index',\n",
    "        'column': 'dense',\n",
    "        'params': {'dims': 384, 'metric': \"L2\"},\n",
    "    },\n",
    "    {\n",
    "        'type': 'bm25',\n",
    "        'name': 'sparse_index',\n",
    "        'column': 'sparse',\n",
    "        'params': {'k': 1.25, 'b': 0.75},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "34Yz9TxxUd1m"
   },
   "outputs": [],
   "source": [
    "# List all of the tables in the db\n",
    "db.tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "id": "FyFJdaXRNkcr"
   },
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"inflation\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "id": "A7_D0AWHNkcr"
   },
   "outputs": [],
   "source": [
    "# Create the table with the defined schema and indexes from above\n",
    "table = db.create_table(table=\"inflation\", schema=schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zdFqjFLFWaJk",
    "outputId": "26927cbe-caa6-48c4-f36f-62355601fae6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI table \"inflation\"]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BgZCCewrXhrq",
    "outputId": "ac44f3f5-f512-4801-c0b8-cd8ae1d16e4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'dense_index',\n",
       "  'type': 'flat',\n",
       "  'column': 'dense',\n",
       "  'params': {'metric': 'L2', 'dims': 384}},\n",
       " {'name': 'sparse_index',\n",
       "  'type': 'bm25',\n",
       "  'column': 'sparse',\n",
       "  'params': {'sparse': True, 'k': 1.25, 'b': 0.75}}]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CJaNUVunNkcr"
   },
   "source": [
    "## 6. Insert data into the KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qKx534zGNkcr",
    "outputId": "03653123-bfa0-4d6b-f94c-55313cbb8e2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 43}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Insert the dataframe into the KDB.AI table\n",
    "table.insert(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aHgSC02eWfsS"
   },
   "outputs": [],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-_P_8uF5Nkcr"
   },
   "source": [
    "## 7. Create Sparse and Dense Query Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "id": "c_cMHq9HNkcr"
   },
   "outputs": [],
   "source": [
    "query = '12-month basis'\n",
    "\n",
    "### Create the dense query vector\n",
    "dense_query = [embedding_model.encode(query).tolist()]\n",
    "\n",
    "### Create the sparse query vector\n",
    "sparse_query = [dict(Counter(y)) for y in token([query], padding=True,max_length=None)['input_ids']]\n",
    "sparse_query[0].pop(101);sparse_query[0].pop(102);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Ojk34UtNkcr"
   },
   "source": [
    "## 8. Run Sparse, Dense, and Hybrid Searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "id": "zGRi55vBNkcr"
   },
   "outputs": [],
   "source": [
    "### Adjust display settings so we can see full output\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "id": "YPQ1yHG7Wtz4",
    "outputId": "753c8b50-1007-4829-cfe4-8e5f14577a0f"
   },
   "outputs": [
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       "      <td>That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.</td>\n",
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      ],
      "text/plain": [
       "   ID  \\\n",
       "0   9   \n",
       "1  35   \n",
       "2  29   \n",
       "3  23   \n",
       "4  24   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                chunk  \n",
       "0                                                                                                                                                                                                                                                                                                                                                                    coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.  \n",
       "1  That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.  \n",
       "2                                                                                                                                                                                                                                                                                              Total hours worked has been flat over the past six months, and the average workweek has declined to the lower end of its pre-pandemic range, reflecting a gradual normalization in labor market conditions (figure 5).  \n",
       "3        Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic  \n",
       "4                                                                                                                                                                                                                                                                                   activity, and there is evidence of that in this cycle as well. For example, growth in industrial production has slowed, and the amount spent on residential investment has declined in each of the past five quarters (figure 4).  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Type 1 - dense search\n",
    "table.search(vectors={\"dense_index\":dense_query}, n=5)[0][['ID','chunk']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
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       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>On a 12-month basis, U.S. total, or \"headline,\" PCE (personal consumption expenditures) inflation peaked at 7 percent in June 2022 and declined to 3.3 percent as of July, following a trajectory roughly in line with global trends (figure 1, panel A).1 The effects of Russia's war against Ukraine have been a primary driver of the changes in headline inflation around the world since early 2022. Headline inflation is what households and businesses experience most directly, so this decline is very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic</td>\n",
       "    </tr>\n",
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       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-48507617-38e8-4451-b7d1-1f592afdfea0');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   ID  \\\n",
       "0  14   \n",
       "1   8   \n",
       "2   6   \n",
       "3   9   \n",
       "4  23   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              chunk  \n",
       "0                                                                                                             Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.  \n",
       "1     On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over  \n",
       "2  On a 12-month basis, U.S. total, or \"headline,\" PCE (personal consumption expenditures) inflation peaked at 7 percent in June 2022 and declined to 3.3 percent as of July, following a trajectory roughly in line with global trends (figure 1, panel A).1 The effects of Russia's war against Ukraine have been a primary driver of the changes in headline inflation around the world since early 2022. Headline inflation is what households and businesses experience most directly, so this decline is very  \n",
       "3                                                                                                                                                                                                                                                                                                                                                                  coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.  \n",
       "4      Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Type 2 - sparse search\n",
    "table.search(vectors={\"sparse_index\":sparse_query}, n=5)[0][['ID','chunk']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jpmHB_EONkcr"
   },
   "source": [
    "**After comparing the sparse search and dense search results based on the query of \"12-month basis\", we see that while both return relevant results, the sparse search is returns several chunks that contain specific references to \"12-month basis\".**\n",
    "\n",
    "**This search example shows the advantage of having a sparse search when interested in specific terms.**\n",
    "\n",
    "Let's run a hybrid search to combine the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "id": "TuVbDuTUphyi",
    "outputId": "f04071a8-8d06-45a2-d747-904884ee44d5"
   },
   "outputs": [
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       "      <td>coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.</td>\n",
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       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
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       "\n",
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       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
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       "\n",
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       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
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       "\n",
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       "\n",
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       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    30% {\n",
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       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
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       "    40% {\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "    90% {\n",
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       "    }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "        document.querySelector('#df-3a4a0e18-ffbb-45cf-be67-910038a8d437 button');\n",
       "      quickchartButtonEl.style.display =\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "   ID  \\\n",
       "0   9   \n",
       "1  14   \n",
       "2  23   \n",
       "3   8   \n",
       "4  35   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                chunk  \n",
       "0                                                                                                                                                                                                                                                                                                                                                                    coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.  \n",
       "1                                                                                                               Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.  \n",
       "2        Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic  \n",
       "3       On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over  \n",
       "4  That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.search(\n",
    "    vectors={\"sparse_index\": sparse_query,\"dense_index\": dense_query},\n",
    "    index_params={\"sparse_index\":{'weight':0.5} ,\"dense_index\":{'weight':0.5}},\n",
    "    n=5\n",
    ")[0][['ID','chunk']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7o-kqB4tNkcs"
   },
   "source": [
    "##### Hybrid Search with Sparse Bias, Sparse 'weight = 0.9'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "id": "voHcv_X7qEwe",
    "outputId": "38f5f733-576e-49b2-b3b7-06965a912559"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>On a 12-month basis, U.S. total, or \"headline,\" PCE (personal consumption expenditures) inflation peaked at 7 percent in June 2022 and declined to 3.3 percent as of July, following a trajectory roughly in line with global trends (figure 1, panel A).1 The effects of Russia's war against Ukraine have been a primary driver of the changes in headline inflation around the world since early 2022. Headline inflation is what households and businesses experience most directly, so this decline is very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic</td>\n",
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       "\n",
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      ],
      "text/plain": [
       "   ID  \\\n",
       "0  14   \n",
       "1   8   \n",
       "2   9   \n",
       "3   6   \n",
       "4  23   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              chunk  \n",
       "0                                                                                                             Similar dynamics are playing out for core goods inflation overall. As they do, the effects of monetary restraint should show through more fully over time. Core goods prices fell the past two months, but on a 12-month basis, core goods inflation remains well above its pre-pandemic level. Sustained progress is needed, and restrictive monetary policy is called for to achieve that progress.  \n",
       "1     On a 12-month basis, core PCE inflation peaked at 5.4 percent in February 2022 and declined gradually to 4.3 percent in July (figure 1, panel B). The lower monthly readings for core inflation in June and July were welcome, but two months of good data are only the beginning of what it will take to build confidence that inflation is moving down sustainably toward our goal. We can't yet know the extent to which these lower readings will continue or where underlying inflation will settle over  \n",
       "2                                                                                                                                                                                                                                                                                                                                                                  coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.  \n",
       "3  On a 12-month basis, U.S. total, or \"headline,\" PCE (personal consumption expenditures) inflation peaked at 7 percent in June 2022 and declined to 3.3 percent as of July, following a trajectory roughly in line with global trends (figure 1, panel A).1 The effects of Russia's war against Ukraine have been a primary driver of the changes in headline inflation around the world since early 2022. Headline inflation is what households and businesses experience most directly, so this decline is very  \n",
       "4      Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.search(\n",
    "    vectors={\"sparse_index\": sparse_query,\"dense_index\": dense_query},\n",
    "    index_params={\"sparse_index\":{'weight':0.9} ,\"dense_index\":{'weight':0.1}},\n",
    "    n=5\n",
    ")[0][['ID','chunk']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ej2-k4N5Nkcs"
   },
   "source": [
    "##### Hybrid Search with Dense Bias: Dense 'weight = 0.9'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "id": "SJRN8D__qQTd",
    "outputId": "d40ffb69-c03f-4a20-a12e-4746ac6d0ca0"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>Total hours worked has been flat over the past six months, and the average workweek has declined to the lower end of its pre-pandemic range, reflecting a gradual normalization in labor market conditions (figure 5).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>activity, and there is evidence of that in this cycle as well. For example, growth in industrial production has slowed, and the amount spent on residential investment has declined in each of the past five quarters (figure 4).</td>\n",
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       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
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       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        element.appendChild(docLink);\n",
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       "\n",
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       "\n",
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       "    </g>\n",
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
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       "    fill: var(--fill-color);\n",
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       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a06d8c2e-aee4-40bc-a865-8ec74e8f89f1 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   ID  \\\n",
       "0   9   \n",
       "1  35   \n",
       "2  29   \n",
       "3  23   \n",
       "4  24   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                chunk  \n",
       "0                                                                                                                                                                                                                                                                                                                                                                    coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.  \n",
       "1  That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.  \n",
       "2                                                                                                                                                                                                                                                                                              Total hours worked has been flat over the past six months, and the average workweek has declined to the lower end of its pre-pandemic range, reflecting a gradual normalization in labor market conditions (figure 5).  \n",
       "3        Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic  \n",
       "4                                                                                                                                                                                                                                                                                   activity, and there is evidence of that in this cycle as well. For example, growth in industrial production has slowed, and the amount spent on residential investment has declined in each of the past five quarters (figure 4).  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.search(\n",
    "    vectors={\"sparse_index\": sparse_query,\"dense_index\": dense_query},\n",
    "    index_params={\"sparse_index\":{'weight':0.1} ,\"dense_index\":{'weight':0.9}},\n",
    "    n=5\n",
    ")[0][['ID','chunk']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9e8REO8GNkcs"
   },
   "source": [
    "### Sparse Search Hyperparameter Optimization\n",
    "#### Dynamic Testing /  Override of b and k within index_params\n",
    "\n",
    "Depending on the use-case, it could be beneficial to tune the underlying parameters of sparse search in order to increase relevancy of retrieved data. KDB.AI offers developers the ability to customize the 'b' and 'k' parameters during runtime, ensuring flexibility in sparse search implementation.\n",
    "\n",
    "**b: (values 0 to 1, defaults to 0.75)**\n",
    "<br>Document length impact on relevance\n",
    "<br>In general the more specific a document is the less likely length will detrimentally impact relevance so b should be low. For general documents that cover multiple topics at a high level consider using a higher value of b.\n",
    "<br>\n",
    "<br>**k: (values 0 to 3, defaults to 1.2)**\n",
    "<br>Term saturation\n",
    "<br>How much more relevant do additional instances of  a term make a document. The lower k, the faster term saturation occurs, (i.e. additional terms do not count as much)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
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     "height": 345
    },
    "id": "D7opgiVhrykw",
    "outputId": "d9371e1b-b616-49eb-94f7-1065bffd3bf0"
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       "      <td>9</td>\n",
       "      <td>coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>Total hours worked has been flat over the past six months, and the average workweek has declined to the lower end of its pre-pandemic range, reflecting a gradual normalization in labor market conditions (figure 5).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>activity, and there is evidence of that in this cycle as well. For example, growth in industrial production has slowed, and the amount spent on residential investment has declined in each of the past five quarters (figure 4).</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcb271d4-80a7-4a7d-bcf3-7f58347fc19c')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-dcb271d4-80a7-4a7d-bcf3-7f58347fc19c button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-dcb271d4-80a7-4a7d-bcf3-7f58347fc19c');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-3fd0d1cc-db87-4aa6-9e19-64aa090b14d8\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3fd0d1cc-db87-4aa6-9e19-64aa090b14d8')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-3fd0d1cc-db87-4aa6-9e19-64aa090b14d8 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   ID  \\\n",
       "0   9   \n",
       "1  35   \n",
       "2  29   \n",
       "3  23   \n",
       "4  24   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                chunk  \n",
       "0                                                                                                                                                                                                                                                                                                                                                                    coming quarters. Twelve-month core inflation is still elevated, and there is substantial further ground to cover to get back to price stability.  \n",
       "1  That assessment is further complicated by uncertainty about the duration of the lags with which monetary tightening affects economic activity and especially inflation. Since the symposium a year ago, the Committee has raised the policy rate by 300 basis points, including 100 basis points over the past seven months. And we have substantially reduced the size of our securities holdings. The wide range of estimates of these lags suggests that there may be significant further drag in the pipeline.  \n",
       "2                                                                                                                                                                                                                                                                                              Total hours worked has been flat over the past six months, and the average workweek has declined to the lower end of its pre-pandemic range, reflecting a gradual normalization in labor market conditions (figure 5).  \n",
       "3        Restrictive monetary policy has tightened financial conditions, supporting the expectation of below-trend growth.5 Since last year's symposium, the two-year real yield is up about 250 basis points, and longer-term real yields are higher as well—by nearly 150 basis points.6 Beyond changes in interest rates, bank lending standards have tightened, and loan growth has slowed sharply.7 Such a tightening of broad financial conditions typically contributes to a slowing in the growth of economic  \n",
       "4                                                                                                                                                                                                                                                                                   activity, and there is evidence of that in this cycle as well. For example, growth in industrial production has slowed, and the amount spent on residential investment has declined in each of the past five quarters (figure 4).  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.search(\n",
    "    vectors={\"sparse_index\": sparse_query,\"dense_index\": dense_query},\n",
    "    index_params={\"sparse_index\":{'weight':0.1,'b':0.1, 'k':3} ,\"dense_index\":{'weight':0.9}},\n",
    "    n=5\n",
    ")[0][['ID','chunk']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3nbR-YyzNkcw"
   },
   "source": [
    "**Additionally, upon the insertion of new data into the KDB.AI table, all underlying BM25 statistics are updated. This means that when new data is added, the BM25 scoring is updated and aligns with the all sparse data when a sparse seach is run.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tS88mmhFNkcx"
   },
   "source": [
    "### Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6bs9bLcFNkcx"
   },
   "outputs": [],
   "source": [
    "table.drop()\n",
    "db.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NBt1BOb-Nkcx"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/U2RoT32R)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
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  "kernelspec": {
   "display_name": "Python 3",
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   "pygments_lexer": "ipython3",
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}


================================================
FILE: image_search/image_search.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "25588ca9-dc13-4136-962b-42e5d090fb31",
   "metadata": {
    "id": "25588ca9-dc13-4136-962b-42e5d090fb31"
   },
   "source": [
    "# Image Search on Brain MRI Scans\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example demonstrates how to store [brain scan images](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri) as vector embeddings in KDB.AI, then how to search this dataset to instantly retrieve similar images.\n",
    "\n",
    "Brain tumors are intricate due to variations in size and location, therefore understanding their nature is challenging. Professional neurosurgeons are essential for accurate MRI analysis, but in developing nations, the scarcity of skilled doctors and tumor knowledge results in time-consuming report generation from MRI scans.\n",
    "\n",
    "A potential solution lies in automated systems like vector database search, which could help alleviate this issue. Using semantic search, KDB.AI enables users to effectively retrieve the most similar scan images quickly even when the query and database content aren't an exact match, leveraging semantic context.\n",
    "\n",
    "### Aim\n",
    "In this tutorial, we'll walk through the process of storing images in a vector database, using a pre-trained neural network to generate data structures known as vector embeddings. We will use KDB.AI's vector database offering to find images with similar vector embeddings to an input query image. We will cover the following topics:\n",
    "\n",
    "0. Load Image Data\n",
    "1. Create Image Vector Embeddings\n",
    "2. Store Embeddings in KDB.AI\n",
    "3. Query the KDB.AI Table\n",
    "4. Search For Similar Images To A Target Image\n",
    "5. Delete the KDB.AI Database & Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85732f3-4549-4d41-888f-5c9ff40f7f81",
   "metadata": {
    "id": "e85732f3-4549-4d41-888f-5c9ff40f7f81"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cce6739",
   "metadata": {
    "id": "1cce6739"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6e1de4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70cc969e",
   "metadata": {
    "id": "70cc969e"
   },
   "outputs": [],
   "source": [
    "!pip install matplotlib umap-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a4010ad",
   "metadata": {
    "id": "1a4010ad"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads image data\n",
    "\n",
    "import requests\n",
    "import os\n",
    "from PIL import Image\n",
    "import io\n",
    "\n",
    "!mkdir -p ./data/meningioma_tumor\n",
    "!mkdir -p ./data/glioma_tumor\n",
    "!mkdir -p ./data/no_tumor\n",
    "!mkdir -p ./data/pituitary_tumor\n",
    "\n",
    "\n",
    "def get_github_repo_contents(repo_owner, repo_name, branch, folder_path):\n",
    "    # Construct the API URL\n",
    "    api_url = f\"https://api.github.com/repos/{repo_owner}/{repo_name}/contents/{folder_path}?ref={branch}\"\n",
    "\n",
    "    # Send the request and process the response\n",
    "    contents = requests.get(api_url).json()\n",
    "\n",
    "    # Create the local directory if it doesn't exist\n",
    "    fPath = f\"./data/{folder_path.split('/')[-1]}\"\n",
    "\n",
    "    for item in contents:\n",
    "        # Recursively list contents of subfolder\n",
    "        if item['type'] == 'dir':\n",
    "            get_github_repo_contents(repo_owner, repo_name, branch, f\"{folder_path}/{item['name']}\")\n",
    "        # Download and save file\n",
    "        elif item['type'] == 'file':\n",
    "            file_url = f\"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{branch}{folder_path}/{item['name']}\"\n",
    "            print(file_url)\n",
    "            r = requests.get(file_url, timeout=4.0)\n",
    "            r.raise_for_status()  # Raises an exception for HTTP errors\n",
    "            with Image.open(io.BytesIO(r.content)) as im:\n",
    "                im.save(f\"{fPath}/{item['name']}\")\n",
    "\n",
    "# Get data\n",
    "get_github_repo_contents(\n",
    "    repo_owner='KxSystems',\n",
    "    repo_name='kdbai-samples',\n",
    "    branch='main',\n",
    "    folder_path='/image_search/data'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55159da-1067-445c-9700-55e57fed053f",
   "metadata": {
    "id": "c55159da-1067-445c-9700-55e57fed053f"
   },
   "source": [
    "### Set Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28615b81-33ab-4354-9165-c26a10db683e",
   "metadata": {
    "id": "28615b81-33ab-4354-9165-c26a10db683e"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3db1ac54-cc02-49a6-b36e-6f34245da1a1",
   "metadata": {
    "id": "3db1ac54-cc02-49a6-b36e-6f34245da1a1"
   },
   "outputs": [],
   "source": [
    "### ignore tensorflow warnings\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e08c25c-1852-4037-ac83-aeae3350cf5c",
   "metadata": {
    "id": "1e08c25c-1852-4037-ac83-aeae3350cf5c"
   },
   "outputs": [],
   "source": [
    "# force tensorflow to use CPU only\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce39c7a3",
   "metadata": {
    "id": "ce39c7a3"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5317082",
   "metadata": {
    "id": "b5317082"
   },
   "outputs": [],
   "source": [
    "# download data\n",
    "from zipfile import ZipFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b3a6975-0a39-45df-b28f-5e72a5d93a42",
   "metadata": {
    "id": "9b3a6975-0a39-45df-b28f-5e72a5d93a42"
   },
   "outputs": [],
   "source": [
    "# embeddings\n",
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "from huggingface_hub import from_pretrained_keras\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2003e892-871b-445e-b213-dedbdbc4eb55",
   "metadata": {
    "id": "2003e892-871b-445e-b213-dedbdbc4eb55"
   },
   "outputs": [],
   "source": [
    "# timing\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "905ddff3",
   "metadata": {
    "id": "905ddff3"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e1afce8",
   "metadata": {
    "id": "1e1afce8"
   },
   "outputs": [],
   "source": [
    "# plotting\n",
    "import umap.umap_ as umap\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdfc18a-4bab-4efc-bf61-030bf6d4fb3e",
   "metadata": {
    "id": "ecdfc18a-4bab-4efc-bf61-030bf6d4fb3e"
   },
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62628025",
   "metadata": {
    "id": "62628025"
   },
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea6b2395-dc59-4c8a-895f-2b82bfca8044",
   "metadata": {
    "id": "ea6b2395-dc59-4c8a-895f-2b82bfca8044"
   },
   "outputs": [],
   "source": [
    "def plot_image(axis, source: str, label=None) -> None:\n",
    "    axis.imshow(plt.imread(source))\n",
    "    axis.axis(\"off\")\n",
    "    title = (f\"{label}: \" if label else \"\") + source.split(\"/\")[-1]\n",
    "    axis.set_title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65782e2-8a87-4892-965f-dd30cc01486f",
   "metadata": {
    "id": "d65782e2-8a87-4892-965f-dd30cc01486f"
   },
   "source": [
    "## 1. Load Image Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4001b8e-b97b-4aba-805e-9027e8457b80",
   "metadata": {
    "id": "f4001b8e-b97b-4aba-805e-9027e8457b80"
   },
   "source": [
    "### Dataset Overview\n",
    "\n",
    "The dataset that will be used in this sample is the [Brain Tumor Classification images taken from Kaggle](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri).\n",
    "The dataset consists of MRI brain scan images organized into four classes based on the presence of a tumor in the image: glioma, melignioma, pituitary, and no tumor.\n",
    "\n",
    "The original Kaggle dataset contains two folders a `Training` folder and a `Testing` folder, both of which contain images organized by these tumor class.\n",
    "As a pre-processing step, we have resized the original images to be of dimensions (224, 224, 3) with the height & width being 224 and there being 3 dimensions for the Red, Green, and Blue pixel intensity.\n",
    "We have also renamed each image corresponding to its class and given each one a unique ID within its directory.\n",
    "\n",
    "Post processing, the `Training` folder in this original dataset was used to train the ResNet model which we use to create the embeddings in this notebook.\n",
    "The post processed `Testing` folder has been renamed to `data` and will be what we use in this notebook.\n",
    "This data has not been seen by our ResNet model and will help to avoid any overfitting when it comes to creating the vector embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe56785-0e92-41ca-86f5-ed51ee970431",
   "metadata": {
    "id": "cbe56785-0e92-41ca-86f5-ed51ee970431"
   },
   "source": [
    "### Define List Of Paths To The Extracted Image Files\n",
    "\n",
    "Next, let's extract image file paths from different subfolders within the 'Testing' directory. These are needed to pass to our function in the next section to create embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bd53489",
   "metadata": {
    "id": "9bd53489"
   },
   "outputs": [],
   "source": [
    "def extract_file_paths_from_folder(parent_dir: str) -> dict:\n",
    "    image_paths = {}\n",
    "    for sub_folder in os.listdir(parent_dir):\n",
    "        sub_dir = os.path.join(parent_dir, sub_folder)\n",
    "        image_paths[sub_folder] = [\n",
    "            os.path.join(sub_dir, file) for file in os.listdir(sub_dir)\n",
    "        ]\n",
    "    return image_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d9b2a13-db4b-401c-8b96-92bc91620948",
   "metadata": {
    "id": "2d9b2a13-db4b-401c-8b96-92bc91620948"
   },
   "outputs": [],
   "source": [
    "image_paths_map = extract_file_paths_from_folder(\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0dd8f3-28e1-406f-acaa-7075ea654575",
   "metadata": {
    "id": "de0dd8f3-28e1-406f-acaa-7075ea654575"
   },
   "source": [
    "Lets take a look at some example images from each category. The images are numbered, so we can choose a number between 0 and 73 and fetch that image from each folder:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5bb1f7",
   "metadata": {
    "id": "5a5bb1f7"
   },
   "source": [
    "### Visualize Some Of The Images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1ed8b32",
   "metadata": {
    "id": "c1ed8b32"
   },
   "source": [
    "We can then plot each of our demo images using the `plot_image()` helper function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c05483bb",
   "metadata": {
    "id": "c05483bb"
   },
   "outputs": [],
   "source": [
    "image_index = 20  # feel free to change this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3a093fd-6c6c-4db0-b865-cbca4c3cbe3b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 675
    },
    "id": "e3a093fd-6c6c-4db0-b865-cbca4c3cbe3b",
    "outputId": "8ebda834-dc03-4f84-e26c-d27d79e75da9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create subplots\n",
    "_, ax = plt.subplots(nrows=len(image_paths_map) // 2, ncols=2, figsize=(10, 8))\n",
    "axes = ax.reshape(-1)\n",
    "\n",
    "# get image at specified index\n",
    "for i, (_, image_paths) in enumerate(image_paths_map.items()):\n",
    "    for path in image_paths:\n",
    "        if path.endswith(f\"{image_index}.png\"):\n",
    "            break\n",
    "\n",
    "    # plot each image in subplots\n",
    "    plot_image(axes[i], path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cde8888-73e0-4ee3-b87f-15b342db7c97",
   "metadata": {
    "id": "9cde8888-73e0-4ee3-b87f-15b342db7c97"
   },
   "source": [
    "### Load data using `image_dataset_from_directory()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9769553-537c-4631-a25d-8be96e34e14f",
   "metadata": {
    "id": "e9769553-537c-4631-a25d-8be96e34e14f"
   },
   "source": [
    "The `image_dataset_from_directory()` function saves each image with a class label corresponding to the image's directory. This is a quick and easy way to get our data and its labels in the right format for embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c289a915-261e-43fb-92a0-9e7f800e696f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c289a915-261e-43fb-92a0-9e7f800e696f",
    "outputId": "d41ff16d-680a-4ff2-866c-5487f7cf7522"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 394 files belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "dataset = image_dataset_from_directory(\n",
    "    \"data\",\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    shuffle=False,\n",
    "    seed=1,\n",
    "    image_size=(224, 224),\n",
    "    batch_size=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccea27cc-f52a-4874-a6c7-bc0bf08e8df2",
   "metadata": {
    "id": "ccea27cc-f52a-4874-a6c7-bc0bf08e8df2"
   },
   "source": [
    "## 2. Create Image Vector Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422c58e1-c22e-461a-887b-8270cc04178c",
   "metadata": {
    "id": "422c58e1-c22e-461a-887b-8270cc04178c"
   },
   "source": [
    "To create our image embeddings, we will use a neural network that has been pre-trained on the brain tumor classification problem. In this example, we will use a network containing a ResNet-50 backbone. ResNet-50 is a popular neural network architecture for general image classification tasks.\n",
    "\n",
    "ResNet-50 was originally trained on the ImageNet dataset - although this dataset contains millions of images, including of MRI scans, it does not contain examples of different brain tumor images. Therefore, our custom model was created by taking ResNet-50, pre-trained on ImageNet, and re-training it to classify MRI brain scan images.\n",
    "\n",
    "This is an example of Transfer Learning - we are taking a model that has been pre-trained for a task (ResNet-50 for ImageNet classification) and using it as a starting point to solve a more specific problem (classify MRI brain scan images).s."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8b3736-62f4-4fd3-bb45-ed7be3066c16",
   "metadata": {
    "id": "2b8b3736-62f4-4fd3-bb45-ed7be3066c16"
   },
   "source": [
    "### Load Pre-Trained Classification Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d085495-97ba-4bcf-9f83-3f469f155ab8",
   "metadata": {
    "id": "8d085495-97ba-4bcf-9f83-3f469f155ab8"
   },
   "outputs": [],
   "source": [
    "model = from_pretrained_keras(\"KxSystems/mri_resnet_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1db87c99-fe3d-47bd-bf23-9fc9036ea3cb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1db87c99-fe3d-47bd-bf23-9fc9036ea3cb",
    "outputId": "f5278959-136a-4ac8-a922-8a9fe257bd23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " resnet50 (Functional)       (None, 2048)              23587712  \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 8)                 16392     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 4)                 36        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23604140 (90.04 MB)\n",
      "Trainable params: 23551020 (89.84 MB)\n",
      "Non-trainable params: 53120 (207.50 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000fe49f-013e-471c-b38c-74ed2a0f3ad8",
   "metadata": {
    "id": "000fe49f-013e-471c-b38c-74ed2a0f3ad8"
   },
   "source": [
    "We can see that the model has four layers: ResNet-50, Flatten, and two Dense layers. The ResNet-50 \"layer\" is really many layers, abstracted under one name. This is why it contains millions of parameters. The Flatten layer does not contain any parameters - its sole purpose is to \"flatten\" the outputs of ResNet-50 into a 2048-dimensional vector, known as a feature vector. The final two Dense layers transform the ResNet-50 feature vector into a 4-dimensional classification of the input image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a28163-c822-4d86-b60a-bb77a2fde7b3",
   "metadata": {
    "id": "75a28163-c822-4d86-b60a-bb77a2fde7b3"
   },
   "source": [
    "### Transform Classification Network Into Embedding Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6c0ca5-1827-4b65-b1c4-d1ea37087bcd",
   "metadata": {
    "id": "5e6c0ca5-1827-4b65-b1c4-d1ea37087bcd"
   },
   "source": [
    "For a better understanding of the `model.summary()`, here is a diagram representation of the network:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb742e4-53f9-4c65-977d-64626c6205c7",
   "metadata": {
    "id": "fbb742e4-53f9-4c65-977d-64626c6205c7"
   },
   "source": [
    "![mri-network-diagram](https://github.com/KxSystems/kdbai-samples/blob/main/image_search/images/mri_network_diagram.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37969fab-1f86-42a7-bf2a-fbb31ac4aa32",
   "metadata": {
    "id": "37969fab-1f86-42a7-bf2a-fbb31ac4aa32"
   },
   "source": [
    "Although the Dense layers were essential for training ResNet-50 to classify our four brain tumor classes, we will no longer be needing them. In this example, we are interested in the embedding, not the classification output. Therefore, we will remove the last two layers of the pre-trained model by calling `pop()`. This means that the new output of the model is the 2048-dimensional feature vector - the ResNet-50 embedding of the input image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ae53b2ab-339c-446f-a028-8a8d7b1f31a5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ae53b2ab-339c-446f-a028-8a8d7b1f31a5",
    "outputId": "12947266-53d0-42f7-eb44-772db5d1031f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " resnet50 (Functional)       (None, 2048)              23587712  \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23587712 (89.98 MB)\n",
      "Trainable params: 23534592 (89.78 MB)\n",
      "Non-trainable params: 53120 (207.50 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.pop()\n",
    "model.pop()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf833b3-04e6-47f8-b968-e02950af9761",
   "metadata": {
    "id": "1bf833b3-04e6-47f8-b968-e02950af9761"
   },
   "source": [
    "### Use Embedding Network To Create Image Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d30d51-aca3-415f-9b2b-c9a99791649c",
   "metadata": {
    "id": "b9d30d51-aca3-415f-9b2b-c9a99791649c"
   },
   "source": [
    "##### Generate Embeddings\n",
    "\n",
    "To generate the image embeddings, we will iterate through the dataset, get the 2048-dimension embedding by calling `model.predict()` on the image, and save the corresponding class vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e09e0643",
   "metadata": {
    "id": "e09e0643"
   },
   "outputs": [],
   "source": [
    "# create empty arrays to store the embeddings and labels\n",
    "embeddings = np.empty([len(dataset), 2048])\n",
    "labels = np.empty([len(dataset), 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45307dcb-c238-442c-87d5-a651c8941bb5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "id": "45307dcb-c238-442c-87d5-a651c8941bb5",
    "outputId": "b9df227b-f6c6-41ff-f36c-2699bfb196b2"
   },
   "outputs": [],
   "source": [
    "# for each image in dataset, get its embedding and class label\n",
    "for i, image in tqdm(enumerate(dataset), total=len(dataset)):\n",
    "    embeddings[i, :] = model.predict(image[0], verbose=0)\n",
    "    labels[i, :] = image[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682180e6-e023-40d9-ab2f-f41ec100fc26",
   "metadata": {
    "id": "682180e6-e023-40d9-ab2f-f41ec100fc26"
   },
   "source": [
    "##### Define Class Labels\n",
    "\n",
    "Now we have the class labels, we can get the tumor types by checking which index in the vector is equal to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed164e3f",
   "metadata": {
    "id": "ed164e3f"
   },
   "outputs": [],
   "source": [
    "# list the tumor types in sorted order\n",
    "tumor_types = sorted(image_paths_map.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c23b8886-9e35-4200-9a07-30d9b3dc1d84",
   "metadata": {
    "id": "c23b8886-9e35-4200-9a07-30d9b3dc1d84"
   },
   "outputs": [],
   "source": [
    "# for each vector, save the tumor type given by the high index\n",
    "class_labels = [tumor_types[label.argmax()] for label in labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8f3ac1-6049-4cab-8e7e-35299463e817",
   "metadata": {
    "id": "ce8f3ac1-6049-4cab-8e7e-35299463e817"
   },
   "source": [
    "##### Define Image File Paths\n",
    "\n",
    "It is often useful to save the entire filepath of the image, rather than just its name. In the cell below, we iterate through the files and save their filepaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0821326c",
   "metadata": {
    "id": "0821326c"
   },
   "outputs": [],
   "source": [
    "# get a single list of all paths\n",
    "all_paths = []\n",
    "for _, image_paths in image_paths_map.items():\n",
    "    all_paths += image_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "81172cfb",
   "metadata": {
    "id": "81172cfb"
   },
   "outputs": [],
   "source": [
    "# sort the source_files in alphanumeric order\n",
    "sorted_all_paths = sorted(all_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0dae9b-33ff-487e-adba-2c7dce543dd0",
   "metadata": {
    "id": "0f0dae9b-33ff-487e-adba-2c7dce543dd0"
   },
   "source": [
    "##### Define Embedding DataFrame\n",
    "\n",
    "Now we have all of our components: the image filepath, the image class, and the vector embedding. The next step is to put it all in a DataFrame for insertion to KDBAI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d3fd636a",
   "metadata": {
    "id": "d3fd636a"
   },
   "outputs": [],
   "source": [
    "embedded_df = pd.DataFrame(\n",
    "    {\n",
    "        \"source\": sorted_all_paths,\n",
    "        \"class\": class_labels,\n",
    "        \"embedding\": embeddings.tolist(),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c843aa48-a1c0-4b19-b45b-81af2c8e476d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "c843aa48-a1c0-4b19-b45b-81af2c8e476d",
    "outputId": "a16208fb-281d-4549-eee2-9c4047489493"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(394, 3)\n"
     ]
    },
    {
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       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                  source         class  \\\n",
       "0   data/glioma_tumor/glioma_tumor_0.png  glioma_tumor   \n",
       "1   data/glioma_tumor/glioma_tumor_1.png  glioma_tumor   \n",
       "2  data/glioma_tumor/glioma_tumor_10.png  glioma_tumor   \n",
       "3  data/glioma_tumor/glioma_tumor_11.png  glioma_tumor   \n",
       "4  data/glioma_tumor/glioma_tumor_12.png  glioma_tumor   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.0, 1.3172649145126343, 0.20154666900634766,...  \n",
       "1  [0.10450763255357742, 0.559810221195221, 0.870...  \n",
       "2  [0.055571720004081726, 1.653620958328247, 1.16...  \n",
       "3  [0.7401718497276306, 0.6310665607452393, 0.324...  \n",
       "4  [0.21819375455379486, 0.19898559153079987, 0.0...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(embedded_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500e9c9e-c897-4a0a-9492-3e7058666b04",
   "metadata": {
    "id": "500e9c9e-c897-4a0a-9492-3e7058666b04"
   },
   "source": [
    "### Visualising The Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9630a6d2-297b-402b-ba0c-13221231dda2",
   "metadata": {
    "id": "9630a6d2-297b-402b-ba0c-13221231dda2"
   },
   "source": [
    "It is often challenging to comprehend how feature embeddings are able to organize and cluster data because of their high dimensionality.\n",
    "One trick is to use UMAP: a technique which reduces the number of dimensions to allow us to visualize the clustering in 2D.\n",
    "This will give us a better idea of the success of the classification network, as well as some insight into where possible mis-classifications may occur.\n",
    "\n",
    "##### Use UMAP To Reduce The Embeddings To Two Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "80390bbe",
   "metadata": {
    "id": "80390bbe"
   },
   "outputs": [],
   "source": [
    "_umap = umap.UMAP(n_neighbors=15, min_dist=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "24c5101c",
   "metadata": {
    "id": "24c5101c"
   },
   "outputs": [],
   "source": [
    "umap_df = pd.DataFrame(_umap.fit_transform(embeddings), columns=[\"u0\", \"u1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9f6093d9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "9f6093d9",
    "outputId": "48506b30-050f-4ace-fa1e-489bec768651"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(394, 2)\n"
     ]
    },
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      ],
      "text/plain": [
       "          u0         u1\n",
       "0  11.201051   8.133024\n",
       "1  16.847599  13.015171\n",
       "2  11.072457   8.326119\n",
       "3  11.330195   5.185107\n",
       "4  11.302164   8.262789"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(umap_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b268341f-7a63-4e98-8da6-4e2e98675cac",
   "metadata": {
    "id": "b268341f-7a63-4e98-8da6-4e2e98675cac"
   },
   "source": [
    "##### Visualise UMAP Dimensions\n",
    "\n",
    "Now we will plot the Embeddings in 2D, with each class shown in a different color:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f65af907",
   "metadata": {
    "id": "f65af907"
   },
   "outputs": [],
   "source": [
    "# define color for each class label\n",
    "class_colors = [\"blue\", \"red\", \"green\", \"purple\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b0622784-47e1-4573-be86-1099ffe26b0a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 699
    },
    "id": "b0622784-47e1-4573-be86-1099ffe26b0a",
    "outputId": "b6a42b78-c074-4a18-d6b8-02852f628c10"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure for plotting\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Scatter plot with 'u0' and 'u1' columns as x and y, color mapped by class_labels\n",
    "for tumor_type, color in zip(tumor_types, class_colors):\n",
    "    indices_to_plot = [i for i, label in enumerate(class_labels) if label == tumor_type]\n",
    "    subset = umap_df.iloc[indices_to_plot]\n",
    "    plt.scatter(subset[\"u0\"], subset[\"u1\"], label=tumor_type, color=color, alpha=0.5)\n",
    "\n",
    "# beutify plot\n",
    "plt.title(\"Embeddings Map for MRI Brain Scan Images\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030d64e4-9dae-4a2e-9607-70951225981e",
   "metadata": {
    "id": "030d64e4-9dae-4a2e-9607-70951225981e"
   },
   "source": [
    "As shown above, the network is able to separate most of the data between classes, but there is still some overlap, especially with the glioma class (shown in blue). However, for the vast majority of points on the graph, their \"nearest neighbors\" belong to the same class as them. Therefore, when conducting vector similarity search using the embeddings, the majority of our results should be from the same class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf406695",
   "metadata": {
    "id": "cf406695"
   },
   "source": [
    "## 3. Store Embeddings in KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f04ec9-088a-48c4-b51b-272bbb8cea20",
   "metadata": {
    "id": "85f04ec9-088a-48c4-b51b-272bbb8cea20"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78307533",
   "metadata": {
    "id": "78307533"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LJjKHMDhpnDA",
   "metadata": {
    "id": "LJjKHMDhpnDA"
   },
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "HC1EVhLjppS0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HC1EVhLjppS0",
    "outputId": "09285d00-7d82-413b-8241-4edce55d1138"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI database \"default\"]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tXbYP5gDprCw",
   "metadata": {
    "id": "tXbYP5gDprCw"
   },
   "source": [
    "### Create a Database Called \"myDatabase\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "AmkdisJmpv6J",
   "metadata": {
    "id": "AmkdisJmpv6J"
   },
   "outputs": [],
   "source": [
    "# ensure no database called \"myDatabase\" exists\n",
    "try:\n",
    "    session.database(\"myDatabase\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "i9RLN85cpxQx",
   "metadata": {
    "id": "i9RLN85cpxQx"
   },
   "outputs": [],
   "source": [
    "# Create the database\n",
    "db = session.create_database(\"myDatabase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4167e1d3",
   "metadata": {
    "id": "4167e1d3"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will contain three columns, identical to those in our embeddings DataFrame:\n",
    "1. `source`: filepath to the raw image file\n",
    "2. `class`: tumor class label\n",
    "3. `embedding`: will hold 2048-dimension feature vectors for similarity search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27f6c5e8",
   "metadata": {
    "id": "27f6c5e8"
   },
   "outputs": [],
   "source": [
    "image_schema = [\n",
    "        {\"name\": \"source\", \"type\": \"str\"},\n",
    "        {\"name\": \"class\", \"type\": \"str\"},\n",
    "        {\n",
    "            \"name\": \"embedding\",\n",
    "            \"type\":\"float64s\",\n",
    "        },\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Xb0y1XnRp8n4",
   "metadata": {
    "id": "Xb0y1XnRp8n4"
   },
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- type = hnsw : HNSW enhances efficiency while maintaining accuracy. You have the choice of using other indexes like, qHNSW, and IVFPQ, qFlat or a Flat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- name = hnsw_index : this is a custom name you give your index.\n",
    "\n",
    "####params:\n",
    "- dims = 2048 : In the next section, we generate embeddings that are 2048-dimensional to match this. The number of dimensions should mirror the output dimensions of your embedding model.\n",
    "- metric = L2 : We chose Euclidean Distance. You have the choice of using other metrics here like IP/Inner Product and CS/Cosine Similarity and the one you chose depends on the specific context and nature of your data.\n",
    "\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "tYCIvtdcqLgy",
   "metadata": {
    "id": "tYCIvtdcqLgy"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'hnsw',\n",
    "        'name': 'hnsw_index',\n",
    "        'column': 'embedding',\n",
    "        'params': {'dims': 2048, 'metric': \"L2\"},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a00661",
   "metadata": {
    "id": "94a00661"
   },
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "We then use the KDB.AI `create_table()` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d045223a",
   "metadata": {
    "id": "d045223a"
   },
   "outputs": [],
   "source": [
    "# ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"mri\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cc6ca44a-88d1-4d8a-b27f-b7b2193496d1",
   "metadata": {
    "id": "cc6ca44a-88d1-4d8a-b27f-b7b2193496d1"
   },
   "outputs": [],
   "source": [
    "table = db.create_table(table=\"mri\", schema=image_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b22c7a4-d480-419b-b3d7-66f2a7f461da",
   "metadata": {
    "id": "0b22c7a4-d480-419b-b3d7-66f2a7f461da"
   },
   "source": [
    "### Add Embedded Data to KDB.AI Table\n",
    "\n",
    "It is a good idea to check the memory usage of our data before inserting in to KDB.AI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "078ff102",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "078ff102",
    "outputId": "a77bd789-5494-4b03-d4bf-14994365b8a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.242397308349609"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert bytes to MB\n",
    "embedded_df.memory_usage(deep=True).sum() / (1024**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9b5534",
   "metadata": {
    "id": "aa9b5534"
   },
   "source": [
    "This dataset contains only 6MB, so we can insert all the data at once. For bigger image datasets, we should split the data into batches and insert <10MB at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5e318d65",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5e318d65",
    "outputId": "87630bca-1510-4069-9f36-5925dd8a8eba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 394}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(embedded_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c23edc1-fe26-4aa9-9eb2-94ee80eb34cc",
   "metadata": {
    "id": "2c23edc1-fe26-4aa9-9eb2-94ee80eb34cc"
   },
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be565b0f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "be565b0f",
    "outputId": "9451c849-8b4a-45e7-e2a8-96cc3d899495"
   },
   "outputs": [
    {
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       "      <td>[0.10450763255357742, 0.559810221195221, 0.870...</td>\n",
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       "      <td>[0.055571720004081726, 1.653620958328247, 1.16...</td>\n",
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       "      <td>[0.7401718497276306, 0.6310665607452393, 0.324...</td>\n",
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       "      <td>data/pituitary_tumor/pituitary_tumor_8.png</td>\n",
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       "      <td>pituitary_tumor</td>\n",
       "      <td>[0.4310145080089569, 0.5233449935913086, 0.163...</td>\n",
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       "\n",
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       "    async function quickchart(key) {\n",
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       "        document.querySelector('#' + key + ' button');\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "                                          source            class  \\\n",
       "0           data/glioma_tumor/glioma_tumor_0.png     glioma_tumor   \n",
       "1           data/glioma_tumor/glioma_tumor_1.png     glioma_tumor   \n",
       "2          data/glioma_tumor/glioma_tumor_10.png     glioma_tumor   \n",
       "3          data/glioma_tumor/glioma_tumor_11.png     glioma_tumor   \n",
       "4          data/glioma_tumor/glioma_tumor_12.png     glioma_tumor   \n",
       "..                                           ...              ...   \n",
       "389  data/pituitary_tumor/pituitary_tumor_71.png  pituitary_tumor   \n",
       "390  data/pituitary_tumor/pituitary_tumor_72.png  pituitary_tumor   \n",
       "391  data/pituitary_tumor/pituitary_tumor_73.png  pituitary_tumor   \n",
       "392   data/pituitary_tumor/pituitary_tumor_8.png  pituitary_tumor   \n",
       "393   data/pituitary_tumor/pituitary_tumor_9.png  pituitary_tumor   \n",
       "\n",
       "                                             embedding  \n",
       "0    [0.0, 1.3172649145126343, 0.20154666900634766,...  \n",
       "1    [0.10450763255357742, 0.559810221195221, 0.870...  \n",
       "2    [0.055571720004081726, 1.653620958328247, 1.16...  \n",
       "3    [0.7401718497276306, 0.6310665607452393, 0.324...  \n",
       "4    [0.21819375455379486, 0.19898559153079987, 0.0...  \n",
       "..                                                 ...  \n",
       "389  [0.1535000056028366, 1.7610539197921753, 1.467...  \n",
       "390  [0.1535000056028366, 1.7610539197921753, 1.467...  \n",
       "391  [0.0, 1.1767886877059937, 1.2333405017852783, ...  \n",
       "392  [0.367981880903244, 0.07278978824615479, 0.017...  \n",
       "393  [0.4310145080089569, 0.5233449935913086, 0.163...  \n",
       "\n",
       "[394 rows x 3 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b89bd92",
   "metadata": {
    "id": "0b89bd92"
   },
   "source": [
    "## 4. Query KDB.AI Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89fcbe99-6d9b-4982-b94d-eeb07a73ec98",
   "metadata": {
    "id": "89fcbe99-6d9b-4982-b94d-eeb07a73ec98"
   },
   "source": [
    "Now that all the embeddings of the images are in KDB.AI's database, it's time to demonstrate KDB.AI's fast query capabilities.\n",
    "\n",
    "The `query` function accepts a wide range of arguments to make it easy to filter, aggregate, and sort.\n",
    "Run `?table.query` to see them all.\n",
    "\n",
    "Let's demonstrate this by filtering for images that contain \"glioma\" in the filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "04994291",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "04994291",
    "outputId": "1757e68c-4f65-4b26-aadc-064a6873aac2"
   },
   "outputs": [
    {
     "data": {
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       "summary": "{\n  \"name\": \"table\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"data/glioma_tumor/glioma_tumor_84.png\",\n          \"data/glioma_tumor/glioma_tumor_57.png\",\n          \"data/glioma_tumor/glioma_tumor_72.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"glioma_tumor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>class</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_0.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.0, 1.3172649145126343, 0.20154666900634766,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_1.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.10450763255357742, 0.559810221195221, 0.870...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_10.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.055571720004081726, 1.653620958328247, 1.16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_11.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.7401718497276306, 0.6310665607452393, 0.324...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_12.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.21819375455379486, 0.19898559153079987, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_95.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.6331982016563416, 0.5156578421592712, 0.037...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_96.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[1.1890076398849487, 0.10027773678302765, 0.25...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_97.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.528931736946106, 0.37649887800216675, 0.011...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_98.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[1.0254501104354858, 0.8127456903457642, 0.083...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>data/glioma_tumor/glioma_tumor_99.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.055571720004081726, 1.653620958328247, 1.16...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
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       "\n",
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       "        const charts = await google.colab.kernel.invokeFunction(\n",
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       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                   source         class  \\\n",
       "0    data/glioma_tumor/glioma_tumor_0.png  glioma_tumor   \n",
       "1    data/glioma_tumor/glioma_tumor_1.png  glioma_tumor   \n",
       "2   data/glioma_tumor/glioma_tumor_10.png  glioma_tumor   \n",
       "3   data/glioma_tumor/glioma_tumor_11.png  glioma_tumor   \n",
       "4   data/glioma_tumor/glioma_tumor_12.png  glioma_tumor   \n",
       "..                                    ...           ...   \n",
       "95  data/glioma_tumor/glioma_tumor_95.png  glioma_tumor   \n",
       "96  data/glioma_tumor/glioma_tumor_96.png  glioma_tumor   \n",
       "97  data/glioma_tumor/glioma_tumor_97.png  glioma_tumor   \n",
       "98  data/glioma_tumor/glioma_tumor_98.png  glioma_tumor   \n",
       "99  data/glioma_tumor/glioma_tumor_99.png  glioma_tumor   \n",
       "\n",
       "                                            embedding  \n",
       "0   [0.0, 1.3172649145126343, 0.20154666900634766,...  \n",
       "1   [0.10450763255357742, 0.559810221195221, 0.870...  \n",
       "2   [0.055571720004081726, 1.653620958328247, 1.16...  \n",
       "3   [0.7401718497276306, 0.6310665607452393, 0.324...  \n",
       "4   [0.21819375455379486, 0.19898559153079987, 0.0...  \n",
       "..                                                ...  \n",
       "95  [0.6331982016563416, 0.5156578421592712, 0.037...  \n",
       "96  [1.1890076398849487, 0.10027773678302765, 0.25...  \n",
       "97  [0.528931736946106, 0.37649887800216675, 0.011...  \n",
       "98  [1.0254501104354858, 0.8127456903457642, 0.083...  \n",
       "99  [0.055571720004081726, 1.653620958328247, 1.16...  \n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query(filter=[(\"like\", \"class\", \"*glioma_tumor*\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d382b1",
   "metadata": {
    "id": "69d382b1"
   },
   "source": [
    "## 5. Search For Similar Images To A Target Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d5f441",
   "metadata": {
    "id": "83d5f441"
   },
   "source": [
    "Finally, let's perform similarity search on the images. We do this using the `table.search()` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e574a61c-e590-405a-b009-b84419917a16",
   "metadata": {
    "id": "e574a61c-e590-405a-b009-b84419917a16"
   },
   "source": [
    "### Choose Example Image\n",
    "\n",
    "First let's select a random row from our test dataset and plot the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5085988f",
   "metadata": {
    "id": "5085988f"
   },
   "outputs": [],
   "source": [
    "# Get a sample row\n",
    "random_row_index_1 = 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "17473bf8",
   "metadata": {
    "id": "17473bf8"
   },
   "outputs": [],
   "source": [
    "# Select the random row and the desired column's value\n",
    "random_row_1 = embedded_df.iloc[random_row_index_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dc5dfb8c-fa46-41b4-8a1c-cf7e68999ab6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "dc5dfb8c-fa46-41b4-8a1c-cf7e68999ab6",
    "outputId": "437527a5-828d-4b80-a9e1-d8f9e6a09725"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fz6OPPopTp07hN3/zN/FVX/VVAIBms7kvhDgIAnzf930fAODzP//z3eeT1tp73/veMdj5137t13DlypV9/rJZ6PM///Nx6dIl/PZv/7b7rN/v46d/+qcP3Na9RPetpXDu3Dm8973vxWte8xo873nP25fRXKvV8Eu/9EtjDt0v/dIvxbd927fhC7/wC/HGN74R3W4XP/mTP4lnPetZY47As2fP4vu+7/vw5je/GefPn8crX/lKFItFPP300/jN3/xNfM3XfA2+9Vu/daZ+Li0t4aUvfSl+9Vd/FZVK5ZYxoYNSqVTCP/7H/xg/9EM/hO3tbRw/fhx/8Ad/gKeffnrftW95y1vwB3/wB/hH/+gf4eu+7uuws7ODd7zjHXjsscfwt3/7t+66g8zTX/3VX+GzP/uz8d3f/d0Tnc2pVApvectb8I3f+I34nM/5HLz61a/G+fPn8bM/+7Ne/HYaveAFL8DrX/96vPvd70a9XsdLXvIS/NVf/RV+7ud+Dq985SvHrJHD0EHmdxpls1k897nPxS//8i/jWc96FqrVKh577DE89thj+Mqv/Eq87W1vw+d//ufjq77qq3D9+nX81E/9FB599FE0m81bMhbgxrp985vfjLe+9a146Utfipe//OX46Ec/ine+85148YtffEuUm8XFRbzyla/c9zkFqX73gQ98AK95zWvwmte8BufOnXO+ur/4i7/A13zN1+CTPumT3LWT1lq1WsVnfMZn4A1veAOuXbuGH/uxH8O5c+duyjn8tV/7tXjHO96B17zmNXjTm96EY8eO4Rd+4ReQyWQAzA5Z3nN0Z4Odbj196EMfCv7Fv/gXwerqahCPxwMAQSaTCf7+7//ee/0f/MEfBI899liQSqWCZz/72cH73vc+b2x7EATBr//6rwef8RmfEeTz+SCfzwfPec5zgq//+q8PPvrRj7prwsLnlBj29jVf8zUzjyssJPVlL3vZvmsBBF//9V8/9hlDEn/4h3/YfXbx4sXgC7/wC4NKpRKUy+XgVa96VXD58mVv+OMf/dEfBZ/4iZ8YpFKp4OzZs8F//I//MfiWb/mWIJPJ7Hv+LPN0kDyFIAiCt7/97cFDDz0UpNPp4PHHHw/+4i/+InjRi14UvPSlL903xml5Ctvb28Fb3/rW4OGHHw6SyWRw8uTJ4M1vfrMLvyTdyfmdRu9///uDF73oRS4mX+9/3/veF5w5cyZIpVLBC1/4wuD3f//3Q0NStX9BsPcefvVXf3Xs85/5mZ8JAAR//dd/Pfb5O97xjuA5z3lOkEwmg5WVleDrvu7rxvJPgmC2PXAQ8rX31FNPBa961auC06dPB5lMJsjlcsGLXvSi4Kd+6qf25YD41ho/+8Vf/MXgzW9+c7C8vBxks9ngZS972Vio8aTx+ELan3rqqeBlL3tZkM1mg6WlpeBbvuVbgl//9V8PAAR/+Zd/ebiJuEt03wsFSz/3cz8XxGKx4Mu//Mvvdlcc/dZv/daBY9XvRXrFK14RnDt37q48e2dnJ6hWq/vyDSKKaBYKE4a3g370R380ABBcvHjxtj/rdtB961MIo9e97nX4gR/4Afz8z//8gVPtbxf99E//NM6cOeN1iN+r1Ov1xv5/4okn8Lu/+7t3pEBbv9/fh82/973vxdbW1pEqEBfRvU92n/T7fbzrXe/CI488guPHj9+lXh2O7lufwiT6tm/7Nnzbt33b3e4GfumXfgkf/OAH8Tu/8zv48R//8fsKYzxz5oyr4/PMM8/gJ3/yJ5FKpfBv/+2/ve3P/su//Et80zd9E171qldhYWEBH/jAB/Ce97wHjz32GF71qlfd9uffLtrZ2RlzEPuoUCjcU6G8N0vtdntqfaKlpaXQ0M/7hb7oi74Ip06dwgtf+EI0Gg28733vw0c+8hH8wi/8wt3u2s3T3TZVHmQCEBQKheCrvuqrxkoK3A/0FV/xFQ7TL5VKwed//ucHf/M3f3NHnv30008HX/AFXxCsrKw4LPsNb3hDcO3atTvy/NtFxPkn/RzU93CvEn07k36efvrpO9af2wUf/eiP/mjw6KOPBvl8PshkMsEnfdInBb/0S790S59xpykWBEcgGyOiiO4B6vf7+PM///OJ15w5c2YsL+B+paeeesqbT6H0GZ/xGS5SJ6J7hyKhEFFEEUUUkaMHztEcUUQRRRTRzdPMjmatlBhRRBFFFNH9R7PU4Io4fUQRRRRRRI4ioRBRRBFFFJGjSChEFFFEEUXkKBIKEUUUUUQROYqEQkQRRRRRRI4ioRBRRBFFFJGjSChEFFFEEUXkKBIKEUUUUUQROYqEQkQRRRRRRI4ioRBRRBFFFJGjSChEFFFEEUXkKBIKEUUUUUQROYqEQkQRRRRRRI4eyOM4HwSa9ZiLmzni83YfoXE/HTt6s+Sbw6Mw7ogefIoshYgiiiiiiBxFlkJEt4Vuh9Z8GO38Th0w6OvPvWD1HaZfN9P2/UxH/TDKyFKI6IGgO82YYrHYA8cMI4oIiIRCREeEbgUD9wkC/h8JiIgeFIrgo4geGJrEmIMguGWCIazdSDBE9CBQJBTuUbqdDOYoMq9YLHZorFiZP9u6Fe1GFNG9RJFQuIdpmuZ7u9q+3+lOjM0KhogielAoEgpHlGYRKncqguiwfZi1n2TkNytQw55zUOEQ5ThEdC9TJBQOSD5MOaJbQ3YufU5dvWZSBBA/J+Yfi8WQSCQwGo2ws7Pj2rkZIRWP78VnhN3P63Z3d6M1EtF9RZFQOABZh+Ktcl4epi8+ehCYEBm5zjU/n+U+MmS9V62Eubk5xONxzM3NYW5uDolEAolEwgkeMvPt7W3s7OxMFSY+Z7Ovz7f63Rxm/UXWSUQ+ioRCRPccxePxMY2czJ3fKXPn73g87u5Tq4DMHABGo5H7rlAoIJfLYWFhAeVyGblcDqVSyQmFfr+P4XCIjY0NdDodNBoNdDodDIdDDAaDfRYLACQSCde/3d1d7OzshFozD4LgjujBpEgoRHTPkNWofcyfGnwQBF6hwXZ2d3eRSCSQzWaRz+eRzWZRrVaxuLiISqWCtbU1FItFLCwsIJ1OI5lMIpVKuWdRoLRaLfR6PXQ6HWxtbaHdbuPixYtoNpuo1+uo1+sYDAYYDAbOkvBZlDoufhdp6hHdixQJhQeQ7mVmcxDYyzJ76yS2GrpaCvF4HNlsFplMBsvLyyiXyzh16hROnz6NlZUVPPzwwygWi5ifn/f6CCh8aDH0+31sbGygXq/jIx/5CK5du4bLly/jwoULaLVa2NnZcfco9OUTbPo9x2DHGlFEd4tiwYx2rG6co0qTNMCIDk/xeByJRMIx0tFotG+O6QOYm5tDoVBAJpPB/Pw8MpnMmAA4ceIEFhcXMT8/j9XVVWSzWRQKBec32NraQqvVwsbGBobDIXZ3dzE3N4dkMolMJuOY/Gg0QiKRQDKZRLlcRjqdRjabRa/XQ7vdxoc//GFcu3YN/+f//B9sbGxgY2MDtVoNg8EA3W7XO85IKNzb9CDv61nWWWQpPKB0GGvB50y91dbHNItBfQWJRAKpVApzc3MoFovIZrMODsrlclhZWUE6nXZCoVgsut+5XA65XA6xWAydTsfBQhsbG2g2m1hfX3fP5bPm5uZcH+gXiMfj6HQ6yOVyqFarDr5aXl5GJpPB7u4u1tfXsbGxgatXr6LT6WBjYwPb29vY3t7G7u6u+7FzPClM9l62+iJ6MCmyFA5A95OlMAszCYueuh1CwRdOGnadRvlkMhnk83lUq1WUSiWcPXsW1WoVCwsLOH78uLMKaD2USiUkEgns7u6i3W6j0+ng+vXrqNVq+PjHP47BYIDt7W0Mh0P3u1gsIp1OYzQaYXt7G/1+H3Nzc8430e/30el0kM/nkclksLCwgFwuh3w+j9OnT6NQKCCZTKLRaKBWq+HixYvY2NjAhz/8YdRqNTQaDfR6PYxGI/T7fRfRtL297RUIOjeRULjzdC/v68PSLJZCJBQOSXcr6/ggbU+qzeNzgurfN8OUfPkFZNSMzPHle1BLz2QySKfTWFlZwfz8PBYXF7G8vIxCoYDl5WWkUimkUink83mk02nk83mnkcfjcYxGI3Q6HXS7XQwGA/T7ffR6PWxtbWE0GrmNwWctLS0hm82i2Wyi1+uh1Wq5kFUAGA6H6PV6TlBQeGxvb+PcuXOYn5/H8ePHsb29jcFg4K5pNBrY2NjA1tYWOp0O+v2+i2Lq9XpoNpvOX0FBQYHIiClgf/QVfx8mEe920rQkv7D/D9Le7Sz7Peu83o8CO4KPIpqZwoTDzZIvecxGF+lnsVjM4fnz8/MoFAo4d+4clpaWsLq6iqWlJeRyORSLxTGsPwgCtFotdLtddLtdFzJaq9Wck5iCotfrOcFE30IymXTQU7fbdZ+r43pubs5FJu3s7KDVaqHdbqPZbCKdTqPb7SKbzbpxLSwsIJVKYW1tbUwo9Ho9bG5uotFooN1uY3NzE/1+H+12G71ez1kvFDpkTrqRNerqXqNJFs69KLwi8lNkKRySHhRLIUwo3KylQOaqbVILViskl8s5bX1hYQFra2s4ffo0KpUKFhcXHSxEzZhMfzgcAoDTyPv9PgaDAXZ3d7G9vY1Wq+WEheYuWCc2mSzXN53KCh9pJBFwA9IqFAool8tj1gQZeDqdRi6Xw8mTJ/dFIlGodLtdl/fQ6/WcBVGv19Hv953g4d+ab8F+3muMdppQiCyFu0+RpRDRLSXrFwi7RkNDVego402lUi55rFgs4tixYyiXy85fkMvlnBDo9/sAbizoTqfjICFmHDcaDedA5nXb29sA4JzG/Jw/w+FwDK5hvxiBlEwm9wkKJr8FQYBMJoNqtep8EI1GY8wKabfbAOAc5Ol02jmyASCZTGJ+ft4xoGaziU6ng4WFBQwGA3Q6HdRqNbTbbWxsbDgIjP2+VwSCCnjf32HX3+t0O7PQ73WKhEJE+4ib2qf5TRMMKhRIynQTiYTLDzh58iTOnj2LxcVFHD9+fKw0xWg0wvr6Ovr9Pvr9PuLxOHZ2dtBsNh2DZPmJVqvlGG4ymXTPoZOaAoMJZlq2QstXJJNJx+SVmTMPgmMfjUYu0qndbiMIAmxubu6zhC5cuOD8I6VSCel02jml5+bmsLKy4qwO+j/o8xiNRrh69Sq2trbw1FNPodlsYmtrC1tbW+h2u+j1emPv5W6TrhdfAMO96v+YhWZRhh4kioRCRF7yZdzOGtFEmINaczKZRD6fd47jU6dOoVqtYm1tDZVKBZlMxkXm0CG7vb2NdruNwWAw5gsgZTIZDAYDzM3NOYdzKpVyUT5ksNvb286hy+92dnacpcG25ubmnBUxGo2cL4L5CxQSiUQCw+EQ165dcw7v4XCIXC7nYC1CZ+qUbrfbrrwG7+v1esjlci6Bjgl3FELpdBrLy8tYWFhAq9XC1tYWLl26hHq9jkuXLrlMazLcSTDhrSZfQt4kxumDKXnfvUY+heYoCYZIKBwBIsO42UXt27iTzGs+j4yNMNHJkyexsrKCRx55BOVyGUtLSw7eIURCvH17e3vMcUzohsyZTDwej48JBU06U0tChcvOzg4GgwGAPSgLwJhAIBRFJk8neDKZdHDP+vo6SqUSALhnj0YjJJNJNz+0Unq9HuLxOLa3t5FOp5FOp7G7u4tcLofd3V2XfZ3L5RCPx5FMJlEqlZzPpdPpoFwuu6S94XCIRqPhQlt9ORCT3tGtZsbWOvCttfuRqU6yfh5UihzNh6T7wdF8mPZ8FgMtAAD7NHgKg2q1inK5jGc/+9lYXFzEww8/7BLKSqWSY5QsOMe6QvQP0MFL5y01aeL+tBBSqRQKhYJzLF+4cAHtdtvVJCIcRKapm5zMnto7AIfnUzAweY3aeyqVwvLyskuKIxyUyWTcHNAxHovFXDTR1tYWdnZ2HLTFEF0KzlKphHw+j0ql4u7PZrNIpVKYn593z282m+h2uy4X4sqVK/j4xz+ORqOBzc1N9zy+Ny3QN+tamIVuJnzZt8Zm6ceddjT76H4UaD6KHM0RjdGkqKJpzkHrX/BhxKlUCul0GtVqFceOHcPi4qKzCpaXl5FOp7Gzs+NCMVutFur1Onq9ngsppUZNZqnatwqEubkbS3c4HGJ9fR31eh2NRgPXr193uQbMJdBII41EAm4wcIZ/0gFN60QL3JGhMyKJwmZ7e9vlO5DZp9Np10fez6xnnTP6NyisGJLKMNnhcIh0Oo0gCFzYLNs+ceKEE7DZbNZBSgyVZSSW7/3eCo13kgYdtpbuJ01bx3fUKLIUDkn3k6Uwre1pG5maJwAHr1ATjcViqFQqqFar+IRP+AScPXsWx44dw6lTp5BMJgHARdNcvXoV3W4XjUbD1R2KxWKuXhAzhyuVivMFEPfn70QigVarhU6ngwsXLrjM4Xq97oQBma4dDwUOmS/DT2kd9Pt9Z61wvJqvsLa25oRUr9dDEATuOzqTGV1lz2nQvnHsHD8tEVoOhKsIkVUqFSwtLbn54dxcvXoV9XodTz75JC5fvoyLFy/i+vXrTjBQIFqhcDsZ3q1s+25YCnaOHhThEGU03wE6akKB/5PZJhIJzM/Po1Kp4NFHH8Xi4qLLM8jn8wDgkskYi99sNp2mTS2eWjXj/FOpFEqlksP5M5mME0CMPrp69SqazSYuXrzo8hQYiqoUBnHR8uDaVovBV6GV5bWPHTvm4JzNzU0Mh0MnNJLJJHK53JhwSKVSzh+QSCScsOLz2D6frdaGWh/5fN4Ji0ql4qwH+knq9Tq2trawsbHhBOX58+fRbDbRarXGMrk1TPd2UCQU7k2K4KOIbgn5wlEJweRyOSwuLmJ1ddXVJVpeXnaaNBnS+vo6Wq3WvnBSLTdtT0Lj52RivKfb7aLVaqFWq6HZbKJWq40ld9nkOd/GJ2yjfgZaP/YeZdhkwOwbQ2Yp2BKJBLa3t5FKpZwwo2M5lUohk8mMCSUAY8d20nqggONYCGv1+31Xp6lQKCAW20sAzGQyKBaLqFaryGQy2NzcxGAwcM5xOtrtuwybI449oqNFkVCIyJEveiQIgrG8AzLIUqmEarWK5z//+Th37hxOnTqFhYUFx3zW19fHEq80dFIjidgusXmWuNYQ0SAIcO3atX01gmhl2LwIjoWf+Zyt/D8MXvLNC62Wfr/vPiPkRPiJYahzc3MYjUbOUiAzZ/ltjp0/TG5LJpMYDAZOs+f4KBQ114P5HqwLtbKygoWFBZw4cQIPP/wwer0eXvjCF+LixYu4cOEC/u7v/g61Wg0bGxszCYSIjiZFQiEiL4UlH/E8gbNnz2J5eRnPec5zsLS05MIkB4OBO4ym3W6j3W67aqRk8ixcR7iFWDrxdABjeQqDwQBbW1tjpSRGo5GrGTQt/NFnMk+CJGzcv95DRzPbVUtH29GsajJ2Cg9aD6lUyjnV6b9gW6lUyoWaqpXCaxhSy/DabDaLTqeDSqXioKVEIoGFhQVnTTD09/z582g0Gs4Zrw74SfMY0dGgSChENDEqRSONGFe/srKCxx9/HGtrazh79qz7bn19HY1GAx//+MdRr9fHDpmhpksmxoxj4voUCplMxgmR9fV1NJtNV0BuZ2fH5QNsb2+7ZDTtK3/7fAJh/hH9Xp2yFn7iPRwLmbavTTqVKcAYAjsYDBzcw0N/CCVRUMZiMWSzWSdIOEa2wXwH1ni6cuWK8+0sLy9jbW0Nq6urruQ4y4svLS1hfX0dhUIBH/vYx1wbYTH4Ol7ferHzHtGDQZGj+ZD0IDiafUJBGSRj5k+dOoVz587hzJkzeM5znuNOKKvVaqjX67h48SK63a5j4Fr2gZj/7u4uMpkMstksyuXyWPw/qV6vo9PpuMNqWq2WsxLI8O15BD4oaNq4laxDUeeEP1r6gpnK7Bc1eN5LGIoMX0NWaSmUy2UXAZVKpcYc31p7icl8LALIfqmlBWBs/EtLSyiVSnjooYeQz+ddUtzOzg7a7TaefvppPPPMM/jABz6AWq2Gra2tsfHy7zCn+7Q5jRzN9yZFjuZbQIfRgm6nBnUntDMyCFYEPXXqFE6dOoWTJ0+6TNytrS2sr6+738w+Js5PQaBnGFMjZgw+BZDCQqwS2u120e/3x4rAaWE736Y9TDy+L1xTBQOwl1+gfVILQ/tJYUGnOpPhCMVp5JXtLwUEYSc+2/ZVw4TpjGbxQBbeK5fLDlZiPai5uTmsr687nwdzOnxzclCmeLvXZ2Sd3D6KLIUp9CAvvkkaH5lWLpfDp3zKp+DEiRN4wQte4HDwixcvYmtrC0888YRj3IymoVbNqBkyStWGM5kMyuWyg2JYN2gwGODq1atot9uo1WpjZwwAcM8AxrUejcf3jWfSZ765YNvUxLXyayKRcLAOBQP3hwoq3kdrwUZY8YzpUqmEhYUF5PP5sQgnZmxTY9cqrSzbQcafSqWcoz8IAndOdL/fR6VSwfz8PJ797GejWCwin887gVOv13Ht2jX8z//5P3H+/HlcvXp1zJehTnz72YOmRStFlkJER44UL7ZQUSKRcAXs1tbW8OxnPxvlchntdtsx7osXLzpHMJ2n6hSlRsswTVYGjcfjrgQ0I2wYgslCdr1ebyyHQR280yAiC4HdzHwo49PidsCeE3mShTLNeuC4qNl3u13nYKcQoIVgfRacQ8JtjHYajUao1WpjeQ0sAUJfxsWLF5HNZjE/P+/8GslkEtVqFS94wQtcOOv58+fR6/XGKrHqOrHjjejBokgozEAPagy3hl9qzH4ymcTS0hIeeeQRPO95z8NDDz0EAHjyySextbWFWq2Gq1evYmdnZyySptPpjMFFAJDNZl0iF6EUnkLGyKEgCNw5AXQgUygA41aq5iLob/u3/Wzau+JccB7U8cv7yaA1T8F+F8Y09bAe9YnwO4bgzs/PO0tCC91pWXAKBUJwrPO0sbHhIrgWFxdd7gLhuGeeeQbJZBLLy8sol8solUpYXl7G/Pw8VlZWUK1WsbKy4iqy9nq9sXm2YzsMTBfRvUuRUJhAYWF6DwJZLJ+QDqNUHn/8cVek7emnn0az2cTTTz/tmH6lUnFzw2QyAGMMa25uDsVi0TFbJq81m013nWrnFAiM/ffBQQofHUQoT8PFCWP5xqD+C4WGgPGw1LB21fKwkUx2jVEoZ7NZ16dWq+UEp0Yi0apjX7PZrHOAX7p0CUEQuIJ/hUIBi4uL2NnZwdbWFhqNBpLJJHZ2dlAqlbC4uIiVlRVUKhUkEglcvnwZH/rQh7C5uYl2uz3mbL+XDvmJ6NZTJBRmpAcxhpuCgXBRqVRyQmF5eRm7u7vodrsuNJRHWyqDY1gjs3jJqOzBNDzUfjAYeCN7eK0yWNXEbXSPHYe9J8yCmPTOtC/sf5iPwjL1aQJn0vP0h3NHC2Vubs45jnX8msBHK4RZ4Hp4EEt/sxYTx8Dvm80mALiKrPl8HsePH0cikUCtVnNCj6ffWdhx2ngfVKXqQabI0TyBrGZn6XbVjbmdZKNo4vE4lpaW8PDDD+O5z30uzp49i2w2i2aziY9//ON4+umnXQZvuVx29+pZwoQsFhYWxurqkFnTkUwNk+GR1Pp5DYvkNRqNsVPI9NAe/qjmbp27Olb7t0I9+h2tAzpt6dweDAZjjmK2wXlkUp5q8EoUduqj4DPn5+fd0Z88lnRxcdH1Qwv1qS+CkUWsI8X3WCwWXUkNCmoWHRyNRlhcXEShUMDa2trYOQ9zc3NYXFzE0tISKpUKstmsO5zoAx/4AD72sY/hQx/6EEajkSv5bbPDlR4EofCg+k8iR/MtIJ+D7SD3HoZux4Yi0wyCAJVKBcViEY8++ijW1tbw0EMPubyDy5cvo9lsOggIwFgkECtw0llJKIJMy2rAdJAGQTBWNZSkSWwsEaELWOEk9X9Y+EWjZaxQYJw+LRIyac0NAODGSGarQsFG3XBMOq+W2G9rZfEzYv/MZaBlxfGrMNBAAOYpUGgQ6mJtqZ2dHeeHIPzUaDSwvb09VpOJ/gsK+mq16g75OXfuHAqFgjvzYnNzcyb/jI79sHQ7rfRJe+xm9/39TpFQmEAqEO43jce3oMmUyKSq1SpWV1fxwhe+ENVqFfPz8zh//jy2trZw8eJFhyFns1ns7OxgfX3dnXlARqqlrBXOUEctGS6ZnOLx7BcFh1YHJQNnhI0t+aAF5dRSsLg9P9fzEMjwKdAI27C2EX0n7Au1drangsnCTDbiiJ+xHwqrcQ7JpPm5ZjKrc12T4VhyRI8Z1bMgAIwVGGRS4PXr17G4uIhqtYpSqeTGTSstHo+jVCphfn7eHY507do1JJPJqUJB5/5+tKSVjqJAACL4aCpNgo+mTd3dshQszGGjd8rlMlZXV/Fpn/ZpOH78OKrVKlqtFjY3N3H58mV39jHbYFIaC7LF4/GxDGMyHmXuCsUA4wlnhCDUYUnGT0iKReHInJXx8XplOuqT4JhtWCnvozOXzJ4Zwyp4mFOh9zJngX3XMxGsY9xCXOoUZp8WFxdRLpextrbmhCHvBTAWXsqx6NnTal1pTaRer4d6ve4sO7V2OJeElFZXV1EoFFCtVt01uVzOZZzPz88jk8m4mknvf//78cwzz6DRaIxZaxw/51grvx6W7oaloHvoVj7vblMEH91Cuh8XhbVw4vG4K3V96tQpLC8vo1Qqod/vuyMsNXFJSznYIx41C1dDJRk6SRiIkIyeMqYMXPupbWtmrQo3tS58IZE+yEiFIhkty06zbdX89YQ3tQ4U9tL+cm59FoKPNIGP80S/ig96s0lwhIK0SJ4eAQrA1ZciZLa7u+uEGrPRGe0F7J2aR39KPB5Ht9tFLpdzWdH9fh8PP/wwut2uiybTd3A/7pGI9lMkFB5A0s2pkE0qlcLzn/98nDlzBs973vOc5vn3f//3TlMul8sAgCtXrqBer6Ner6NSqSCXywHYK6VAZqFwD7Vrrf7JPqgmDuydZ+CDRrToG60JZcjWwWyZtkbx8IdE5nfs2DF3MA3bpXWTzWbHYCJ1EFvGZ6OsbAkOtVzYN57tXKlUXP0o+gEoYHkGAoUF/85ms65f9O3QwlAHNdtlCHCz2XSCYX5+HqVSCcPhELVazZXTXlxcdKHGquV3u10cO3YMJ0+exMrKCvL5PJ544gn87d/+rVMYVGCxzxHdnxQJhSl0EKfavUaqbS4vL2NxcRGPPfYYKpUKdnZ20Gg0nNZHocHIH2LHLLVApkiGT1+CwizUVlVjJdPWEE9G1FBIKBOl1ptMJt2Rk0zyUubLa62zmONW7VojgIjfa/4BT0rj6W4KfagmrM5fO0YtT0GBx+usb4L9pLNez6BWnwLnfDgcun7pfJL50qqyDl76hFg2m4cBsex2MplEPp934cKMOOJaUJ8Gz4HI5/M4c+YM0uk0NjY2XDE97de06KSI7m2KhMJ9SrM4+5RpLi8v4+TJkzh37hxisRi63a5zPCrmff36dbRaLTSbTeRyORSLRQc7kMloVU+1CnzavOLj2jdlTnqtQkgsGGejmniPZvfybz7DB8GwTT3ak1nZiUQCpVLJRf5QAOh4lPnqHAMYc17HYjGnKdNZbgUmx0+HcqFQQDKZdO3yPmBPKFAQqH+AQoc+BvaN884xA3CW12AwcLBVLpdz42Jf6MfQQItut+ve94kTJ5DL5fDEE08AABqNxr4aUD4ne0T3B0VC4QEk1ZTpyHzxi1+M48ePO4GwtbWFWCzmjodsNpvY2tpyZa+XlpbG6hYxRJF/K2yjES6A/1QzxfU1Oob9ZQQMLQNq8GSwZJJkfGRchErsc7R/QXAjs7dYLDrYixVYe72ei++fn5/HaDRCq9VyjtNMJuOEB4/HtM7HMJhIrRmFo9iOJvvxO/Xn0OeglhjnVc+g4Bym02nXhuaFAHDnRG9vb6Pb7br5z+fziMfjmJ+fdwcj1et1zM3NOYf0YDBwFgUAB0G96EUvcnN27do1dLvd0KiriO4fioTCfUrTHHuxWMxFkBw7dgzz8/MoFApjmcV66I2eNaz+AWqa9B9o3LuNklGhoFo6+2P7rNCRRvUoPKRwDhkpiaeL8TP1PagQIpaezWbHEsKoIfO51hcQBHtlrdW5bqOMVAjqmJTs9ZwTC0PpvEy6VudGv7fzoP1VCFD7RUuG50SQuWvyXqfTcU5qroulpSW0220sLi66HAdfxFEYxDqLwLD33ikhc5SFWSQU7kNSRmsjcJSZLC8v4/Tp0y4PIZFIYGNjw5W65mHw6+vrrghdLpdzWn8mk3EQki8aB9hjQLQo2Ad+p7/5N2EjdUDrITx2jGR2DJektWKhCnUw01lMZzqT8CgQe72eOxeaxfjojCW+DsDlDmgBO/ZVhQCFDD/X92MjdNhPOoYVyrLCTOeDeRWaMKewEg8disfjrgAhACfweX0ul0O/3x9TDjg/uVwO9XrdvZNut4vhcOiikiiIc7kcTp06hUQigW6369aVHgQ0yzqeBQaN6M5SJBSm0GEW5u1e1AqRKAPiOQiPPPIIjh8/jvn5eezu3qhjRIbHQmsUBkxY0th4FlJTxmidtxo1RKGhlgL7qVEpmkSm9/Na9k/zEijsiJ3zM96j4ZcUPDb0NRaLOYiKz6Z2TG0f2LN8GL2jgiaVSrk55POsENCYfe2fOtzVMrKfsy0KZ51nvYbzpvfofGvkFoUkrR8WytNKtbQW6GSm8ByNRuh0OkgkEuh0OkilUk74ZLNZnDlzBh//+McRBAE+/vGPu77d7uS1Oyk0rCL0IFMkFG4z3a6Fax2zSqlUCsViESdPnsTy8jIKhQIajYbDiMl0u90uut2uw68LhcIYs8xkMg5ztglaysytUAAwJjQUXvFFIqmTWudMoQhqzT5BAeyFhmrYqw2LBeCEDZkaM60VxtK+0ymrzlftozJrjtNaPUoKA+mc2KgmX0SVCgc7/7yX/WNyIa0ADXGNxWJjtZWYj0I/RaFQcGGy7COz2Hn2A9cIw3vX1tbQ6/Vw6dKlsVyLaWt4lv3xoDPhe40ioXAb6U5oMmRIxMdTqRROnz6NU6dO4fTp0+6cg2az6c4x4P+NRgOj0QgLCwvI5XLI5/Ou30xAo+ZrcXJCFsoUea/CTGrBUPtmBFMul3MMXp+VTCYxGAzQ6XQAYCyyhgKKyVkaraMWgTq6KQj0+Es+D8BYgp1aC3w2GT2jlLLZrDtUiNq4Jtv1ej0MBgN3zoGeJU3BEZbtaxPngHE/hL53m4OhDJ7joDOaFVBpbdEKYfG8drsNYO8IUfaDQotZ7f1+H6dOncLOzo47qKdQKOATP/ETcezYMWxtbbkaSerX8NE0+Ciiu0ORULjNdKsFg9VUlWEwwmR1dRXHjh0bC0Ukdt/v99Hr9VyNfNbPIWP0xdPzeT4T2gcnTTK1+T19ENRuqcmq9m6tB00U07Z8P2phqAbvmz/+rWch83lqHfmcxSTtFx252WzWOag1xJQavM3R0Pm0Tm2f89k+W/0M1qHPxDidfz6Hn7Ntvovd3d2xCCfCSI1Gw4XwAnCRXTs7Ozhx4gRisZiLYrPzrTTNUogExt2hSCjcZ+SLxqC5XiqV8JznPAePPvooTp48iWQy6ZyMFAz1et2FnxJaKhaLzunLCCMKCTIakloLCmFQY1UGpUxBNVoy/mw26/B9FQoUZr1ez1kGvJ+wh4bA6nO1ZIQemkMoyMfcFSriuDTBzUYIEX6zh82oUEqlUqhUKmMOW/6t2chsg2MmoyaurzkaCnHpOChcbQFBe65CuVwey/8A4IS/lkUnxDgYDFxmNM91YFmMVquFdDrt7uOBTI8//jg+/OEPo9FooFarOd9LRPcPHXmhYDe10jQt/1bhoWFa1CzX87rFxUWsra3hWc96Fubn511UCKNM+v0+ut0ums0mRqMRisUiVlZWXHIaGU82m3VapGboKgRExmWZk8IFVgNWjZdjUMc0ITDF45VBMqpFcXX2wZaa0LmzODv74kuyUivH9p2MVX0mzDNQCMtCUBTYWoCPfdTyEFreg21Zn4Q65nXs7KOFpKxPp91uj0FDtBJUyNKKyGQyKBaLyGazaLfbrlAiD1ra2dnB9evX3ZwQijpx4gT6/T4ajQb+/u//3hXOs/OrcztpfU+iW2FJHHTvHQU68kIhjKzTc9p1SpMW/LR7D0KK6VcqFSwuLmJ1ddUlfQ0GAxd2SsHAk7jy+TyKxaKDAEi2fDOAfc5bzc71CVWFOXQufCGWVrBY57Fi26rVqzNWfRgqULQPZOxK6hzm/+ybD2YivERGTAiM16jvgO3xO7U6VPBRQHGsPqGk71v7bgWhD1pSoaCnp7H0trZnIUN9z6zUur297dphme1sNotCoYC5uTl3zvOJEyfwzDPPuIg3nUsdz6xO6YjuHEVC4T6gSdgrGfsnfuInYnV1FUtLSxgOhy5rtd/vo16vY2NjA71eD8vLy2ORRu12G4VCAalUymXQAnCRSgxJBDCW0Kb90Sga/m+1Qhs5RKHA73k/oSUVTDzfgHAIGTPj/JPJpLMyyJjVKT2tOBv7YZ22/G1/rEbPMatPJAgCB1epMOTc0QLSg26sD0dDby2EpMKVgkCLDOrfeo8mK5LRsw88MIl5GwxBTiQSWFhYcAUECUXS+UzYMRa7cTrfiRMnUCgUUK/Xkcvl8OSTT+4TWL7/I8Fwb1AkFO4DCtssiUQCxWIRS0tL7ihFQhZkpoSMyFiZd6ClKhibzmqblpmwD9bJq79V41YcXjVVC0XFYrF9GrEyQ+Y0+CwKLTtBDJ1YP+dG2yTDVotEv9cfO++THNoWDuHf1l+hban/hX2x9xNasn30PUPJZ6WqpcT3SuFKX4e+OxUKZPY8Y0GT/dQ3olYpndCrq6suTJV1nsLWciQQ7h2KhMJ9SKqRrqys4MyZMzh16tRYWeXhcOgcyteuXXORJDxMRevYsFwytVs6Gbn5fZg3sN+3oZteNT8yJGXaPriIjI9a8+7urvtbrRPi+8xKpqWgyXVhDmAVXvq9wjF2fFYIaMSVPkuZr1ooPB9CYTG1kobDoQtx1R8t+Kdhv/osq2Frn/U7FcoAxiAstQ7S6bRz/rPvrHvFxLV2u+0cyISlGOrcarUcnHT27FmkUik8+eSTrgyGwm2RILg3KRIK9xmREaRSKedDOHbsmHNgaiZqrVZzRc6IH5MJKFwA7DlJGXlCoaEOSNUmyaRtZIllRGRGyoQsc7bChe0qxq7XKbNMp9Muemp398aB9vocy0jZziThZgWbzzekWjrbU2Fg/QoaCaWO/CDYq8pK4cb3oW3b0F+SCji18HxRS4SzGK0Uj8fHBD8/14xyWpXFYhHdbhflctnrPOZnjCgbjUYolUo4duwYHn74YVy4cAHdbnfsPUR0b1IkFO4DssyJ2nSpVEKlUkG5XHYaK8s4DAYDVwmUWbmq1WqBODIRPbBeYRirZVNAWDhJ+6cwj4WNgPEzjn1jVbgqLEKI/g1WSVXGx4Q31cgtZGT/9s2zD+vW6yxMY6OufHCbjRpSqI7WVtiYfZCVCmsL/fmgOxUKvFffPYVUr9dzfgfmKzA6je1yLvr9PmKxmEs4pK+iVCpheXkZjUbDCb1JcxrR3adIKNwHZJlAJpPB/Pw8zp0753INyFz6/T5qtRo2Nzdx5coV9Pt9jEYjZLNZFItFd91wOHQ1j3jQThAEY5E1fCadt8B4RI0muVmIRRmRjRxS5mVDWzXWnp+p5qsRRxZm0rMDGCqpDmG1Qmz72j+dc30HarnovfyfY9Vn0AGuQoLZ4vycTJwhszz8Rv086qz3rQufcLFQFq0/1oWipUUIi1FFzEp+6qmnsLCwgHK5jHPnzmF7exuFQgGZTAadTgftdhuJRALlctlZHOvr627uy+UycrkcnvWsZ2E4HDook8/3rfGI7j5FQuE2Uhj0wO+m3aukmhQxWx7nqBAFzwhgqQU6LFntlI5oHv8Yi8VcnRzCUvoZMB79YsMnFaYJY5rWZ2AtBmAvcka/81VOVe2ffcxms05gaI6A9kOfPwtxTtW68AkO9Sf4tHj22fbLWhT6XA2N1d8qTNVJzXbUGuPfOq8cjxYL5Bjog2q322g0Gmi3287yZBkM/qa/g++t3+874dXpdJz1xoq7xWIR1WoVy8vLrgKrtYRuBVkYMKKbo0gozEgHWbg+aOJmnmexY27scrmM+fl5LCwsjCWb7ezcOGKz1Wqh0+k4KIXlL+hk7vf7aLVa+55DwRKLxVz1TKttk8FpbaAg2Ku8qYxSGRafYfMH1JpQLZiaszqnqWUDNzY9K3eyJhFzAOhUt4xXBYVvri0jZ3v6Hil8fAKO36kznVAf/+dc0oJQokNdkwAtVKXF/HZ3d12dI36nxM8UjqPiQMuBocl0FtMfRacz+16v19Hr9bC5uTl2fOhoNEKz2UShUAAANJtNAHDCgJbE6urqWBKc7wjRg5LPqrNCWf+eVVBM69ODLnAioXCPEjexMh1q6Kurq1hdXUWxWEQ+n0c6nUaj0UCj0XBx5KrFK1MC9s4xUOcniUxE4Refhq1F1zTSxsJLWjuIzyFzIuRA4UCtVOP8KXTIhPg8CjvVwslAiXlzHtXSCZtrZaDW6pmVcSl85vMJsB2OmXAOhbFaYjbj2ratzmCfUOCcW4tPz0jo9XquT6yHRT8CCxby3TJ0lRnQQRCMQXXtdttFsg2HQ5ejMBqNsLi4iPn5eaytrWFhYQHb29vY2tqaaU4juvMUCYV7mJShUQul1l8ul10hO2r/PBtBzwfQsEmNbCFDUWatjFMTl9gXZfjqMLTPmqSR63O0FpANmaSlQMGm5bZJFJLTHMgW7rFWgTJNC3/52g17V77rwiwkjtn3nf5YstCaCl21UPgMtSRisZhzIvNe+jE0QoyRSJlMxtXN0nfBZ1D46hkVnEce+8kotUwm44Ii2u02arXaPmgvonuDIqFwj5JCK9zUhUIB8/PzOHv2LNbW1nDs2DFX8O769evodDpO22YiEbU6rXtDRkJNXR3DZP6MVlJGTWiD0AXDF9kuI340cYzXE1ZhUp3CFIp37+7eOAyI5RSCIHDwEDVkMi0+p9/vu+xmYH/UkP2t3ym0oL6LSYw5jCY9h3NmI4SsFUGhPhqNkMvl3DtQvwLnQd8ZgLGAAL63wWDgnkk4jJFCjBDiPBIuYnXXfr+PdrvtypRzTbbbbYxGI1dgj+XZud6YHd9sNjEcDrG4uIhMJoOTJ0/i3LlzSCaTuHr16ljSXET3DkVC4R4lq/0GQYB8Po9qtYpKpYJcLueYAy0EMgDF55WZKGxBy4IWgWqaZGCaTexzIKsjlhaH4uB8vjItCi3rbFRmqaW+ybzUYlI/B59tGaT21af1ax/5mS+KStuaRZsNs1R8wkUtK16jzmLNMNf+qBWllo/WXiLD1et0jikICBMqFMV5VMWB5U/UIlS4SiEvWnZck81mE5VKBYVCAceOHcNwOEQul3PrNrIS7i2KhMI9SgoZcdMUi0Wsrq6iWq2607GozbFWkTJV255qj91u12l8WiUV2MO4qfVTg/QJBv4fi8VcpJPmMOg9mhinCV783goF9oH98cE5Cv3wufyfbVqYQhmpavJ8lgrCm/En+ASD73qtOKuMndYBtXMVHhwTgwhIChUpFKfwkX7P8VGR6Pf7Y74htql91M/od9rd3XXrg1Ypx8Y+bG5uukOcTp8+jXj8Rqlurl99j5GAuPsUCYV7nBTOWVhYwNraGvL5PBKJBFqtlgsjZGkKDUWlJt/pdMZOAaOlQEbAMFQyXwDudC6NKFKIh4xB8xTohMxkMu56zay1OQtMnKPAUlhFBY4NSQUwls+g0VFqYVmBwDbCyJ4YF/bDfvl+6zPsZ1Zg6m+OU5UACkYt9aEMnsTryKR1HtW/pHkQ7B8hR9XYVSCGRQnZIAiujc3NTQeBsd/NZhOtVgvdbhfz8/PY2dnBqVOnsLu766KVIrp3KBIK9ygpA4rH466yablcdptai5CREfDgFt5HBsLyxfyepQgITVAbJbPRUM6wKBllcupHGI1G+8IqbXiqMnOLK09yQCqEok5w3udrw7bt+835ssx/EpSkz5jmNPXBaSoYtF2da/1MBaYKPIXl+J0KAxUUmgeh96o/yc4jABfRpvPE9vRdsCBeMplEoVBwa4oKC63c5eVlbG5ujpX28L3HiO48RULhHiVNDksmkzhx4gSOHz/u6hzxDGO1CugEZCZsKpVyG5IhhTTXlYmrs5gChpuZm5aaKhkC/6Z1YEsfWPhFGZEKBBUS/CET04Nk1IkKhGv8Poai16oA8rVnBYAybQuf2XamQSC0dDhGbVuvsRaJ+hu0FAXniYJehYb6JngPLb98Pu+CEdgOHffq/1CLje2pc5ihqwCc9cpAAWbJ8xzndruNS5cuoVQqoVgs4pM/+ZNdaOrGxsa+8h73gmA4qkIqEgr3OMViMVf8rlQqIZ1OO62LSUjE/XkYPRmpPWuXkSbqsLW4M0kdz8Ce1aGapmr6mtRGJ6nNF7DWA59jLQW1JDQUVX0Gvnlie/YzHZMyHv2b45mGaU/73ufQts/3+SrUorJOdE1As9aCJv/p59anAmDfXNJK1Aq4NliAf/ssL1qcABwEOTc35xSRVqvliurxXI9arYZsNov5+XksLi5iaWkJjUbDWQv3CgP2+YXulb7dboqEwj1KXICEjlZWVpxQ6Pf76HQ6LqOXOD7Pz6VWr0c/ZjIZh/XTKUnMmFq4rTukjEbPAFAHKL+jY1GFjmX4WpRP/Qw+Z7AKBGrW2r8wmmRBhME6OteWGUyDhWYhHzNRS4lz6Zs3viNrpdj58wkKKxSU6SucRH+QwkcqONTq0L6r34KZ9el02gmFRqOB4XCIarXqIpE2NjbcKYHLy8s4duwYnnnmGeffutWM96gw8ltJkVC4R4laHGvGnDp1CqVSCUEQoNlsuhLRZJzU9vUkL+Yk7OzsoNlsotvtOmarDmG1Fqjx0Y+gGr11GlPLJLNh2wr/WIakMJNNXqOmqY5Q61C+WcZMBh9mUXAspNvJTKyT1sJuqqnbPvI7tRB80JFGaxHiI+PnOcvWsa7zy/fBgnnaHvuqyZC6HoEb9bkY2bS1tYV8Po98Po+trS13RnihUMCpU6fwoQ99yAVIcKy3iqy1FtF0ioTCPUZ2ARcKBZRKJZRKJQcP0UkM7G1QRhbZozLJcIgf65nGhHOo5StzUIejMgplXNxwFASa72CreSqObjVRC0+o8LBa8ySa9L32N+w61d597+IwZCGrMPw8DKrxteebP9sm36dWo1Xrjtfwt/UB2bXEvlNokFTIU+DQ19Hr9ZBMJpHL5VxZdyowlUoF2WwWqVTKVe31QWsR3TmKhMI9SjTpT548idOnT2NlZWXsrAQ6h2mWX7t2zUX9UItTc7zT6YzFlG9vbyOTyYzBTFpiwuLaim1rHD+ZDBlKMpkca0fPHlYLw0Ie/D4IgrF7CEWoQDrsvPK3Mjnr/NaQ2FtByqR9jFaZrfbPd4/2USOU9Dr+poXAAoHAHozHfIhJxDIqKnD4vgk/Ucmg05sZzWqlzs3NIZ/Pj4UeEw5dWVlxPohZ+xXR7aNIKNwFsozGampkpul0GvPz86hUKk7z6vV6juHH43H0ej20Wi2XmarHOtoIFsuMNCSVGr/VFAHs09RthIoyU1oJCkeoj0K1ZKshayilzocP4phmNagvwzJKCyFZzd33riyTtt/73qlaJeqct9eGCQKFbPTsCgpHG85qnx+LxVzRQF0DCvGRwqwktUb0OnVQa3kRha94LZWZXq+HbDbrfGIMfiiVSi58VUOdfe/DZ+3ZdRRGB3EYh11zFKyWSCjcI2QZnQoF1pgZjUauNAU3dK/XQ7PZdOGjjE6iphWGGavmTxhBMXW1BHwbkZvWXkOhwAxYe70VVuwjmQktILVItOidDRH1/e2DX8LuYd94XxizsUIo7FlWEPMzZao+yCcMSlLBoD4AZaD2Pj6HIcP6jqxfR+fEN29cR3q/KhEUViT1a1DJYD4N/QY857lUKiGfz7swVZvUFyaEJwl33zuapkBYOgqMfxJFQuEeIsXQ8/k8jh07hhMnTmBxcRG9Xg+NRgObm5vOvN7a2sL6+roL89NSBBrVA+xlsVLbtFo8sF8z9zE7H3PUTRwE40dhajilhWl8QgbYq8DK8ZCxKUzl64sdg86rJZs4Z2GwMFx/VghrEiNS68E+g/2YJjSsJs354WcsEEgYh7kmYfMwidQPYfNL6K/SPAeOj05qwp3NZhPZbBaxWAwbGxsOUjp58iR2dnbwxBNPoN/vj5X7nnVeD8LIw6zCiG5QJBTuIVJsOZPJoFQqOWZPRzG1cJ6CxdLGNL9VsFiTHxg/c1k3mA+qYZ98G9HCKhZSYh+U0fqeq+Pm37xf6y8pzMXn6m8dh/18Fv+AD87y0TTo6iBaqQpJ7aN9f74+WoFgn5tMJl3UkcJEvrGFCVEraPRafed6jf2e1gqAsQq+ZP47OzsoFouuyCOj4OzatBQx9dtHkVC4CxTGODQss1KpYG1tzWlbjUbDRQ/1+300m01cunQJw+EQ8fiN4zaZ+WurkCrswAgmn58AGBcgqtUrM9L7eZ2PsWscvP7WTa8x8GopqGNZD9eZdmIXx6lnOuiZAyTf3+yXdaz6oKdp79LXLx/EwzY13Ff75ROW7Kt1vitkmMlknLWg86b9sdFHYX3VebDJizbPRPtCiCmfz2MwGGAwGLgII54O2Ov1sLS0hJ2dHSwvL7sDe9TqjejOUiQU7gL5NDV+xuMLFxYWsLy8jFgs5jYKNat6ve78CMxkZrkBJgEpBq9CQbOMSRbOsVaCT3NUQeCDoVR4qHNUx2/hEZ/zMwwisr99sJLtj+8z+3y91+LXYZaUr2+W2LYdnz5D8wt0XvkctSZs8T99X+pvsJBd2Bz62rHt2cOTtIKt7SMAFzWWzWbd9yy1wjMY2u02VlZWUK1Wcfz4cTQaDWxsbHjnMKI7Q5FQuEdIoZ5SqYRKpYJqtYpYLOZOsdIQP5YQKBQKKBaLSKfTYwfdK/PTzGCrBfOaabBI2LWToCHLbK2Q0WssbKLt2b6G/fYx6zCYwQfNWJ+Fb8xhAsj2z7YdJpjUt6FCkVq2Dwaz+Qn6HJsJrv2fBBvZObZCSftihYL2T/vPEhhMuqT1SIuh2+2i0+m4CKTV1VVcuHBhrBzKpDUZQUi3hyKhcJfIQhP8O51O4/jx41hdXcXCwgJ6vZ4LQ6W5XavVMBgMXPw5IQJqmTYkkMwiDKcNSxiy15NR8cdqkmQKWrabfgEyMa2/47McaFnEYrExB7MPYw9jGNRIp12n11urZtJ9vnm8GQZlNXmf4NA+qiDQOH4VqvF43OUkaD0hzmOYUNX3bQUE2wTGz+NWoWCPdo3F9spzBEHgMuhbrZYTDIxcGgwGyGaz+ORP/mTU63Vsbm7i2rVrY2s4ojtHkVD4fzQJprjdxA3J2jGVSgX5fB7JZBKtVss534bDoTuqkoloWnXTao+WeYUJBR/NOvYw5qWhlHoNczAmaa/AuKCy1sSs2uMk5m7hLv5PTddG0thnTLIQ1JqYJMx8zFeZqp0jnQPf/74yFWHJbb4+h41L++Ebiy+RUdtXXxmv0+q9zHgmbLq4uOgqp07KWbBz6CPfeHwWa0R7FAkF+BfOnRAIFjtmxNHJkydRqVSQSCScqc36MrVazYV8svQFAFdC2+eYO6jAs7CC3UiKafNvPfxGz1zQ32yXpb0VyiJzUefnJAEQxqx9Gz2MmVnGoNAVa0f5yOek17nRc6ltBVrbb+tjYGABMF4+3foUeK/2hdnHwF6xOi1forWoJq0Ja0WyPe2DCgu+Rz6TCg5pNBo5i1brcXW7XdRqNayvrwMATp8+jZMnT6Jer+Ppp592OSt8vp4T4uv7rJBoJBAm05EXCtO059shHHSx6obP5XLOn5BMJseOr9QjNOlYTqfT+zJ8Z9FMZxlT2Kbhs1QIKENQRqTObquthuHimghltWLbB984ff4H/W6SJsznaL+nzQfbUNhJHb2E2ViJNszCsLkeYeP23avvhH3hGAjBhEUahbUdNm69LszBzM98lgkhI16/vb2NWq2GVCqF0WiEarWKs2fPolqturpJlmzIatgYZh3TtHYOev/9TkdeKNwt0g3NTcXT1QqFgtMymSFMi6Hf76NcLiOdTrvzEpTZqFCwZvI0AXjQftv4enWW2uQwvUdDRLX/mh1rMXRtY5IGGEY6R7450TZtNvisvgwL/eiYZ51vvW6aQLDzoMEEWn2WsI0K55thbCrQ7fuzUFwY2Szo0WiEdrvtiuUVi0Wsra2hUqm46CRfm9NgRF5zM98ddYqEwl0iC8/E43Gsra3hxIkTqFQq7mQ1OgxrtRp6vR6CIEA+n3f5BjTF9VQuJetAnIU5hTEmYPzcY0JB1IoVYlBogb9tm7a8M+voKCylkA6AfZYRaVIdIDsmOz82h4LjJEPVuQvTRq1TN8wyCeuXOt1taK7vHhu1pN9Zy5FCmI77gygEav3oM6wg8L0XLevOazKZDJLJJDqdDoLgRoRSvV5HPB7HxYsXsbS0hFOnTuGTPumTUCwWUavVxvwiEd1+ioTCXSJlXNScyuUyyuWyi9Zh9ifrxnBjU9uycIwPegjTdA+6wZQRUAjFYrGxshk6NisU9HnKeHUOrHNXmavV9MMsIN//2iff//xNqGcSLBLWvv1OE8t8ZT0mkQ/SmnSNxdjt9er3CfOTTBvPpO/sb/3bZ0FyXtQKZg0v1kFaXFxErVZDLpdDr9dzSZo6v9P6F1kDN0eRULgHiIecr62tYXV1FbHYjdyEXq+HTqeDVquFRqPhasUodqwJRTbWnZ9bZniQzaIabywWcxUvGRmifVGN22qWllj8jt/b2jl02E5ijLOEj/r6YIUm+6whsMC44znMQtG+kBRSIywWJmBs39RyUOvBjt+G9fI664y2/Tkoo/StHZ/Q91ks7I9asmpZMsuZhz9dv34d+Xwe2WwWJ06cwPb2NhYXF7G+vj4mFKxgiOjWUiQU7jIFQYB0Oo1yuYxKpYJCoeCcy8PhELVazWUv08FM52E6nR7bIGHx/Mo8lHnzO6Uw6Eg3dxDsncHM+jo2U9rCVrbtMItAx2FhEoV6rLBSi8nXf2td+L7T86W1P2EZ3z7rRy0b+wwVYj6LzvbHjsl3rzJm9cNMWg8++CdM2FnLxwcNKowJjBcbJGTEewmzJZNJt8719DgGVZTLZSwuLuLYsWPodDpoNpsT+2cpshJunqLMkHuAUqkUisUi8vm8O+OWsBHPYuYGo3NZN6dlCD4HIOB3eloGFPaZ1RL1sHd7/aSN6mPGyqR8Gqe1QHxwkR1bGPO3pJ9p1JAds+//SWO094TNTdi70r753oPerxYjMJ7x7Mtd8fXD9w5970fJwjm2DpKW4rD9Z58VsqMVOhwOkc1mUSqVsLCwgGw2GxqGGtGtp8hSuEvEDTY3N4dqtYozZ864Sqebm5uo1+uo1Wqo1+vo9/uuxhFPNtvd3UWv13PF7/SITh8T4CZkrHqY6e3LgvZtSK3Ro3kJ+sywcWuCGElLbLMNJQ2p9DE0zZQOY4TabhicpPNALd1n8Sij1898Grz2wTJN2yftl/0eGD+vgM8K8yepxaU/NpN8EnP1XWMtEx/zB/YKG9JaKBQKzqLo9/uYm5tzPgNaDLQ+jx07hpWVFTzvec/D1tYW6vW6y4a2Fl8kHG4tRZbCXSBljNwsPF0tCAJXWpgOtp2dHYfFKuNSrBbYD8MoHm7r4VgK0wiVmczNzbmjFukDmAQ/TaNJlsUsWvmkNm+GfALD50vw9SnMCgm7z34XZrH52pvFKgu7zgqHWQSmMnwbJaWa/6S8Dp7RnMvlHAQKjCfo8UQ2PUmwWq1ifn4epVJpTBhEdPsoshTuEnFxl0olLC4uYnV11YVj0vnWarUwGAwQi8WcCa2aEjFaPf6SGaO7u7tuw/FIxlmYpY9xKXTFbGQATrsDwkNFfRTG9Ozzw+6bpX0fw531Pr3WOr99/bUwi32WzxKx/bIWR1hbytB9fgnL8CdZLjajXPtBsr4cPstaOoSCGP6qFgoAV75lNBqh2+3i+vXrYzkLPFWw0Wggl8uh2Wwil8thdXUVq6uruHbtGq5evTo2D9NoFgsoov0UCYW7SHNzc1haWsLCwgIqlQp2d3edldDv913RsHj8RkEyWgX2BCwfXKJwkV6vsMMkoiXA8xt2dnZcnXuGodIhy35YmOVW052ECvgsHeOsc0dSJm39JMD+kuU+oeH7zPo8rIauPxTgHItdN3a804SaT0hpexogkE6n3fxRgeExs9lsdswZzh/m52xtbWF3dxeVSgVLS0s4efIk/uEf/sG1cxiLMKLJFAmFO0zKuOfm5tw5tTw2kU5mRmaQ+TKxSusM2Q2pWp9CRgfJqGW7KlB4L+v47OzsIJPJOOvBFwY7qe1Jn8167zQoIYyhHlRg6dzZxMBZn0uaBB8pU57WntXyfVo8/1aBMcs6CPveCn0rIFQQcWyaFxEEgRMK/M6Oi9f0+320222k02ksLCygWCyiWq0im826Gl8R3T6KhMJdolQqhUKhgFOnTqFarSIev3EaFXMSeNQmncvA3oE5jKfnZ7FYbKzwGhm6FnXTLGEgfPNTiNCHofCTOhZpsWgJhYNq0pP64aNpQsAHG3FM+llYOz7tk3Nvcw/C7rffK6zk+86Oz1pek3w2YQJWx2GVBYWX+JxZ5tX6FnS8vN9GQbHeUyaTcRV+d3Z2XFQdiRYFEzbb7bYrkletVlGpVPDwww9jdXUVALz1kCK6dXTkHc1Ws7L47804O5V8jCYIbuQm5HI5FItFV/yOzuV+vz8Wz80NE+bg9EEC6vjTOjgWXrDaqyakaZSLOhMnQVdhc6sMaZIg8H036R34NGQdvzIuX18mzSfbte2RrKAIE0phfQ1j+NqOT8D4xhA2/3qt7337fvS7SeGu/LGO6CC4ESxhs7ppHXNNA3DWAz9jZBIP4On1ekgkEigUCqhWqygWi6HjnvSeI7hpdooshf9HYVraYRaSdfbpomUc9vz8PObm5tDr9dBut9Fut9HpdFy9GJ6ZoBq+9kuZOIB9QoGamWK7unl1vLRE7JnIYc5GLV3Az9hH/V+Zpn5u/w6bM73Wh2+rcFIGwOu1bIWG2ep9JEIbeg+vJ4TnGxvvDWM+CveEzYvv/bLPltmFzYm2YcdFsk5g+0ydf5/Foffzf4UqmYBGIUCLMplMIpvNIplMIpPJoN1uY3t7G+VyGQBcjsJwOESz2UQqlUKz2XSJnSsrK2i32/vWm+1zRIejSCjcRppUN4c1XhijHYvFnC+h2+1id3f/ITrqmNR4dWDPBKeFQVPcp+nyRxmiaoWWgYUxJJJlbso8fWMP02p9zNF3/ySywsdqs7ZtC61YYTEYDBxD49zYKq+c7zBrRXMeVNjMMi72yQo61dhnZYi+92iFhZ2PME1b16O1oqzgAG6cHc6zQKjs8Botu845YlDD1atXEY/H3Tkj29vb+NjHPuYi82aFBiOanSKhcBspDO7hpmD2MiEaJqDxEB31Hej9JIV2tNyEwkX6TIVGfO36tGyfxjmJofm0zYPOmY9hhVGYoPH1K0wb9vVdtWD11ehntt/622c9hAnUMIvUB+XYeyYJayscff31ZW/73q+1TvjbPjfsWXQgaxgqEzFtW9wLdDj3+33kcjlXMLJQKDiY1b7/SDAcniKhcIeJETuFQgG5XA6DwcAxmnq97pxxmUwG+Xx+TOvS0gBWqyuVSgDgTrTi+Qux2A1HsYUUlOkB++EBH+OzdBDm7bsvTLjYzw/S/iQLxbatpDAT71GoyUbShGn92p6PyU7qWxjpO7NCe9r90/qgeSY+57NdM9bK5PywHZ0XvZb5NbReGQiRTqeddZvJZNy6297edr6FZrOJubk5FAoFLC4u4sSJE+6sBe4Fa5lEguHmKRIKt5F8WksikUAmk3HZnfl83mGwvV7PVYzkz3A4HNt81lxWE16v4WbU39b8ZxskFUAqGKxWaWEe+9msc6NkNW0d0zSmbmGLMGEzKwMOs7ImjUUtER2LFVL22mltsw3CVvpuLdQTdv+07+z908ZK0hIgJJZdYf8oECgEGF7NpEtaxgDcGeWE8JjQ1m63kUwmUa1WkU6nsbS0hIsXL7o6YL6+HXQ9RrRHkVC4TRTG9Obm5pDNZpHP51EsFlEqldDr9TAajVzxO24c4q3UrKixKpNWpkymTT+Ens/rixyy0IMyMR9z9I2Lnx1kE1ooR5mpD35RQRiWK8B7bVisavyzEJmRj+Hxe21bf+vYgP3nL/N7OyZ7nyXVhNXHZAWXr6/WSa3f2bnR62yEmfZbrUsLtbEsi/UR0GLlmeKdTmdfPk4ul3NKU6PRcKGrzWYT8Xgc5XIZmUwGa2treOqpp7C+vu4s7ci3cOsoEgq3gXRzKXNJJBLIZrOoVqsoFApIpVJot9uupAW/LxaLjjmxBoyex8wNzQ0G7B3Uzk1KpyhD/Pg/D3fn37FYbCzrVfMZLERxK7Qv34b1fWb/12ipsHb1twoXZaSTxkCLjU5ORsmwf3qynBVePsjNOppvljQk1MJ70+ZkmiWhkWtavprP0Wx4tSq0bV9klQoVRiJxDrkuufYoSPr9PrLZLFKplMtjGA6HaLVa2N3dRTabRSaTwYkTJ7C0tIT19XVcu3bNKU3T5iOi2SgSChNoFrzWd63VenVj0lLg4icjUo1nbm5uzAz3wSiqbdIxB+ydHqYhqar9qwCwcJMPR9ZrZ5mLMBPeBwHZz7QPNzP32s9JwowCgkTGyKgiZo1zHq0mrT82p8HCNT7Lx/d5mF/CtmXH7ROwvvbDyFpkqvXb+3UdW7jSRsfpPYSIGB2n1q4KVfoWWHiRTJ6KUavVQiwWQ6VScVY2hUJEt44iofD/yDKww2rFujm5WXZ2dpBMJlEul7GwsIBCoYBLly6h0+mgXq9jMBgAALrdrjOrtT9qyicSCaRSKbfh2u220+y0EB6fS+HAwnq+WG9gPDkpLAfCzpEyL82T8JWzngR5qNWifdH51Pb4nVaAJcNRhmmryHJO+BnPsej1ei6BSjPC+cM5nCUrnP1nX3O53Ng7VAEcZkmocqFCiHNLRmoFlBUWk9Yz1xjLWGcyGad1E77RoAU6h9Ui4PUqTPl+OPeJRALD4RC5XA5BELjQVJ1HJrzFYjEsLCw464HH0W5vb2NlZQXHjx/HqVOnAADnz593CpVv7BEdnI68UAjTZid9dpC2VcvncZoLCwuOYWvxO3XykrEpkwf2tH3VptSkB+AYhWZFA3sbmExUi71xU4dptjdDB9Vq7fOsxknIi1qpz7/gw8+tpm+JY2f7hIyoAfM5KhSsALLvGvA75snM2fZBFRBrFVnrSr/3WS6+9rhWWPhQT/TT8zf4HK4TO16ucZ8As33VSC7O42AwcG2XSiU337SYKZz6/T7y+TwWFxddhB79EpFQODwdeaFwO8kyiHQ67Uplk6Ez3prwD4UCmYxmGOuGIuZNTU8ZEKEnVpNk7SL1KcRie34KYro2JNOnyStNgiistu4TNtO0WV+71EpVgPrI+hR8fhL+raXHCe9piCV/q+NUIRAV4HqP+kH03VGokDTEeBZSgRCWKOibC9sG+0jGqyVWmCejPgX6uDiPfL6SCgW1sigY2Bd9j7yHNbV2d3dRKpWwu7vrhAAtF0bp5fN5xGIxFItFl9NglaNIONwcRULhNpIyn3g8jsXFRaysrGBtbQ3dbhftdtudv5xMJscYtvoTuKH0dxAE7kCSXq83dpwhNT46s9PpNIA9waQQipr3fG6YhjnLZtN7fOGYdn7CvrOfUVtl/0hkvBZe8gkkrS7L9ghRELojUyGjJqTEeVNtlIxMn813o32i4FHrzDe3s8yrYvk+QTtpjmf5HNgLK9UABZ6jEY/H0e/3XQFGFYBUOrgO1ariM7vdLpLJJPL5vFNuKGhisZhzLLdaLTdfrCScSCScsMjn80gmk1haWsJoNHJnOPOd6LyEzefNCo0H3RqJhMJtJt24PIc5m826NH1Gevi0Kd6nfyteTb+DMnJmSO/u7rqsaZYU4HNs+J6FAML6z77NwsAUB7fPYruWufvasf0I2/AK40wSXnY+VUBwfqi5AntQnDJ0Gw00DaIBxovn+fo1y3z6oCMLH+n1vnma9gz2RS0jrYZLy9We+GfHwrY0n4IMW+dPSfMTWK+LnwM3ktuYiEkItVwuo9lszsyodW0cBLaz43uQKRIKd4jm5uZw7Ngx509gGCo1JC3RTEZkM2i5aahZMb8B2NvUGsJKmGpubg6dTmdMy6ZPgtYBo6A0tNXHuNmXWUg1aYs/T/MvWAGoloIN81RnNAWrhkmqr8QKKTIeClR+z/s5L91uF9Vq1TEmhm7qXCjj1/fG3yqA7BxNIxUMYQJcPzsIhKLCxb5zdeImk0nnZI7H4+h0OmOWK+Ee9YkBcILAKgT2/bBcO9uh34jzTMs3lUohl8thbm4OZ8+exc7ODp566qmxEwdnHfvNUGQpRHRgsmY8YRsm3xAfpRnOa8gElYFqW2TYZN4UCPF43JW2GAwGKJVKyOVyY4xNYQDCHMB+xq1Yr402UueoMk/f+MkULKO01yjp/5aJ++ATfq+MTMehQiOMgWofOSeE89LpNM6ePevOAqB1VigU3PMpSDTCTN+X7aNG3fA7YvjTIDS7HnzXaLu++QybA51Tn6WhUUr8XMOlNWqMFqwtA6JCgZYAo+g0fHo0GrlzRIIgwObmpstqDoLAhaOm02ksLy9ja2vLWQxUiqYx7Zu1EnQ8DypFQmEKHQZ3JFErt0KB8BEwHsJJB589nYoaGBmUasa9Xs+1l0qlnCOOuC43YDabHWOGmvXMza2hhIoZ+7RUCzv4YI2webT3Tpt7HS+/5486agl3+KJ/bHs6JsbRkyml02mcPn0avV4PW1tbuH79uou31/mjUFC4xWcRAHBRTtonDSzwWVM+wWDH4ZvXg6xdmxyogoLCEIDzT+kzYrGYY+yatzAajcYCGNTSHA6HTknhczTggVFg8Xgc6+vr6HQ67t13u10XHLCwsIDFxUVXGYABGzp/PpomFB50xj+JIqHw/yjMLL+ZxWHhg0KhgEKhgHK57CqjMsWfm0ohBlY8ZRVJtkcNLJVKuQ2pxe8SiQSWl5dRKpWQzWbR6/VcDXtusGaz6RgfmZutqmrDCDkn1qlrYRqdKwo1hax8IZgW49X/1YKxsJAKAbVuCDVwjnx4N59rBYoKYgoUOkz5PoIgcE5pu1bI1K3loc+bFmWklpfmi/isxjBcXD+z/g+2y//VilKGrpARgDFrk59RichkMk5zj8fj6PV6aLVa2NzcRK/XcyelcZ1yTXJs/CkUCmg0Guj3+9jc3ESxWMTi4iIqlYqzKObm5nD9+nW3j0qlEqrVKo4dO4Z6vY52ux06t0qTfArT9vyDLjAioXBImqRxkOkkk0lnDtNMpqmskBGJ8IeezUzGTXxWHc3D4RDpdBqpVArFYnGsqBg3ODV/+iFGo5Hrj8JFFpbh3wBCGZoVomFavy+nwtfWpE1n8WjeM80q8PXLWhyEdigsKQCo6dpqs5wzZdx8vxQy9lk+Iepj+nqPz3rwjSfsO/t+rIWmQtBnLdi54jh5YA4z9NUvxsgtVToAOA1fS8brPHAuer0e4vEb9ZAolJnbs729jU6ng2QyiUqlgkwm404wDJsbH00SDEeZIqFwm0idhzx6k7irYq4ax60JbJpEtb297QqE9ft9FAoF7OzsYGtry1kbZ86cQSaTQSaTcQKHmm2n03H+i36/7zYDhY5uIo1OUuYHhFtQ3My0ClR4qLBTZmnhkkmCQp8Zxkh0TDbhD9hf0lnDezUrPJPJYH5+3lkMGxsb7hmVSgUAUKvV3D2EK/RMa636qaRCw/bFOs917jT/wRb8s0KdbYbBfvb9KaylPgLeq+9fYZ9EIuEYcqFQcFART0ZLp9NYWFhw4dH0gel1DPVV/w/XfavVcsXwaDmXSiUEQYB+v4+trS30+30cP34c2WwWx44dwz/8wz+MHTTl83dFNJ0ioXAbyGqwrIjKWHeWw1btWTVdbkJumsFggK2tLRexROao5RhoWgNwERvNZnPsOmUOquX6mAzHoUzewkPUOLVQHMegPgqdEx/OzntmnVvVzq3FoBVieb3NbWAbtMh4balUQqlUwrFjx9zhLjwWMp/Puyqe9N2o9aYaNMdp/TW+cWqfrZWk90yDnFQoq+bP8VohoP2yVoOF2HRcGvnW7Xad/4briYqO+meoCFBJGY1GLl+BzJ7ClD4ZheL6/T6AGyGpzOehZcdieywymclk0O12H3iI53ZSJBRuE3Ez0bnLcDoy6UkwjN7LEEBuRCb18DpiusoMqcXyrOft7W1nXs/CBLQvwB5spOGv3LTsm4WG+KPCwzLmmzHdbRu2LY0C4jN9uQ2K+9MqYITY8vKyg+darRZyuRyy2ayD6KjdkmHpPOpY6VQO09b5mdVoLeOeRjaSjP3wwU76bP2tcFjYdYQxqdSolcL29UxmzjPXKbP4B4OBE7YUuOp/oiXCNUdB1G63kc1mUalUnMChUKhUKigUCs6XFtHNUyQUbhPpxlZLgVFCwN6BO7yOTDSXy6FSqaBaraLb7broFzIgG3YXBAGuXbvmzPt0Ou1Od7ORRKwRQ/wV2CsYx7Ysw+CzrMVAhqaMgYJMneXAXtQNrRh9nmru00x+hTQU1tDwRo2X9zm3eT8ZS7vdduf9snQI+7m+vo5UKoV+v49UKoVsNoutrS3k83lUKhUEQYBer4dr164557Yt4aDCWIscWqiO8+QTdiSNbFLrTIVzEARjpU1ICjPpu9T51PmKx+OuQJ5mHavSoqHR7B9Jx8L5zuVyrmT8zs6Oi6RLJpPuJEINZ1WlYn19fcxBnUwm0ev1kMvlsLS0hLW1NbRaLTQajTHrMPIbHIyOvFDwmcpK00z3SUT4JJ1OO20ewNiCtWURNIdgZ2cHnU7HbRxWsmS/yUi1YiZj7BW7181vceuwsYWN22rB2oYyK5JlXhyjatXaro/CrCpfHxWf1zm1GrNaOiTNPWC/GX3UarXQ7XbdmMgMrVasVgnfDd+HLTiofVLmzO9947eCOmxu1a9i/Q38jn22gkKFgm+OFerUubbvzPomqJDQKuO7Yt/4GQ+cUpgwkUg4yI4CXIUsnc3lcnmsamvY/E2jMMtq1usP8qx7jY68UCBZJqUv9GYFAy2BcrmMSqUyBjlwwWpeADdYIpFwhckuX76MbrfrBMLc3Bw2Njac5qYF3OLxuNt4vryCWGyvnLbPT8B5CINo9Hv2V/Fffq818zV8U0MaVTu1MJbvvfjIviO1gGgVWeGljIYMif4WnnHBuSdjYU5JtVp14x4Oh9ja2kI2mx0rjcHQTasQ6ME1lpmSsYUxL7smfcKEzJ7fMwJNYRa1puiDogC05cUpxDh+65fRPaHWojp51YHMxErOT6VSGbM0gyBwewQAnnrqqbHifAygAG6Ulo/FYm5cAFAoFLCysoJ2u+0UJ/oiblapC6MwSO5BoUgozEizaNdKZDzEUrVQGBe7hVLoRGORNgAucoiQEbAX76+1jRg66dNabay6hQk0G1W1RDX9dZMrHs/8h1jsRhgh27cVV1WA+DQ4jVcnk9Pr1K+hGqgKQAohCgc+y9bbVzgMuMFQeM4FBSvfx9raGhqNBmq1GjY3N7G9vY3V1VX0+33UarWxTF8VNPH4XhE5OlG1GqvVwhXLtwJ90npTYa3QGhknI6Q4L+wX16Racta/4HNc28g0K9CtcqDvnzW49P6dnR3UajX0ej1kMhksLy9jfn7ewXobGxvOL6ZWIOes2+1iOBw62JX5EvQ18PkH1fx99KALA1IkFDzkc/DdzEIgw+UPhYBWmGSIHxkZNyoZ62AwGMv01M3J63Wz6IbhRrc1lPjbVl21fde5sBAB7yN2TQZEXNsX3qhM08dMVCiohWD7bS0gHTPLJ6ivhv4FYM9aURw8n88jn8+jXC67qC1CMLQONjY2XGLU2tqagzjq9TpisRiWl5fdu7B4OIAxq0Xfk1U2fJ9PW2OcExXI1KA1UZLX60loKiT5TC03we/pF7GCzGfdKKylvhP6Juz7ZCYyy8pnMhlXI6zT6YxljaswDYJgrOx8JpNxPgtabnYvW8v3foV4bidFQuE2EQUCF2ksFkOz2XSav2qGZK6Eh/Q8hFwu57I3B4MBOp0OstmsY3zxeHxMEwYwBkfoeQxkIMqMuVktrqw5B3oPr+e9AMYS7Rj1xM2rjMfi18pUNCzT4uX6W0kZD78jpMAMbx/DI3MuFApYXV115UfY96tXrzpLI5VKoVAoYHFxEe12G8PhEJcvX3Z1d9rtNjqdDp555hksLS1heXnZQSO0BgGMHZeqjEqtJ+3nLJaCtmGFox4tynY1g51jYUAA4SbF+PkOfYLafu97p1QYuK5ZmJHRR7yWDuePfvSjaLfbWF9fx9raGubm5rC0tIRkMol2u41arbZPGdrc3HRVhylM5ufnMRwO0Wg0xpQaX6BERPvpyAsFi9PeqoVCBppOp12ZCWptxGi1JgyZt9X4dEMy+YzYt5bM4DOpqVofgG/c1unM68PmKMyxrJaP1VqVkSiDD2PyPu1On+lzlFuYSdvSOVUiY2E8fbfbdXkgTPTT8TLUcWdnB81mE4VCYSz8UqO6GAFFx7QPjtN55bgs050VrrDfWyFDpUChRZ/g1bZUQPvmT9+FtSZVsOh41Z+kwRFsfzQaodVqOWWKUCoj2uz6oxXInAlGS/F4VR2H/v2gwz+HpSMvFJRutWCYm5tDoVDA/Pw8KpUKLl++7HINyGCAPUvBanrESoEbmia10uXlZXeebr/fd9mhbMeWENANSkZhT9Xic+182I2o2bhkJupM1ra4+ckotLwH27cYP2EnwF+kTZPhwpzldixkXmw7Hr+RUMjihBsbGxgMBqjVau5kLxWsrNvf7/fR7XZx/fp1Z50xYmYwGCCXyznLcDAYoN1uu0ifbDYLAGMOZ2WqPmGnv31kYRq2oRAWML6+9D1aS0Pft/1f+2MFgRV61jJj3gAtEg2k2N7edhYvo7x6vR52d2+ca10sFt38aZ4PrW/up1qt5g7jqVaraDQa+zLKdR1FFE6RUDA0yTl10HZ4/GaxWHSaJjcDNUpg/PAQYtWM1qDWubu7F8tNzJSQTSwWw2AwGNvcqiGrc5a+BMW7LUTkG7vVuGyxOivQtN4NGbGWuVDHNRk271fGYwXDNAvIZm6r0FHtdGtry/WN97AuFK/RyCTCdcTA6cilwCNj4/GRZFzsA9+B+ozIzIl/Wz+LhTrCmJnvOhWwXCsqeFVIKHPXOVf/jYUc9dnW4uFvtsGoo06n49Y5BRX3gs4TT1/TyKO5uRvnm9MypqWt1irXIvNkwiCjyFqYTJFQgL/0wKymuyVdfDx2kNgqnWI7OzvOHCZT4CZi5BE3kvaD2j1/K6n2bvvvG4MmQVk4x2rndm4UHmD/dRNub287bVtr6ujc2MghPlc1fIWtfILB9stqy5bJcn6Y/8FcAyYWWkajjn0AY4J8e3vblXkgDBUEewcgqZXE96P5Acp09T1pf30WlW/MYWtVmTitBxXk7JcVoLZPPgvGviP92yoWGsXEeeCe4LoB9kqX07qIxWJj5y5QeKjCwXnSYoZafFLXuFo+vrHYcel6PUp05IWCXfBhC8BGPSjMoRo326GDcnl5GTs7O6jX67h69aorXc2qkmSkNJlZ54jMlJt3e3vblVtgVme9Xnels/lcakzsq4UByOw0+9WXVBU2D7yXMAj7VS6XcerUKacJXrx4Eevr66jVavscl2SgDLsFxjV8y8itxeDzE1hhxbHSX6BjHY1GLmqI1pe1JNhfMvnBYOA0VkaEMXKMTIzXaT6GwigWerE+JF1DOg+qffNzCiwVfvpu9T49spV9YRQc+6Tl2NXXpX3VuVbBToFDhcbCgHowj46B659rn20xfJsBA1wbGiDAsRJS3dzcxNLSElKpFObn51Eul5HNZl3mtG9t27Bpkk+RUsHJMahwmiQ47zc68kLBh5Hyc71GSReW1YCVAVJrIQPWA8/ZBheWmsFq0mskETUsQkKDwcAxME0is0xdmQ6vV61RGZCdDyD8mEk+l/3hWQ/lchm7u7soFou4cOGCg8JUc1XfQth8q5C2sArb4Zgspq0Qg9bUIUNU/4IKOdVy9b1oG4VCAf1+371vuz60OCEd0cqAlKw2qpqsBhmoU1bnxoa96udsg0za1tzywSg+5WAabOWDvKwlRPJZpOwfn8v5YuIchS/nn9FyXLuMXuLapr9Nk+cmWf6zXKPf278nXXc/0pEVCrr4yXBV21TmqAvdblrdWBY3p2ONmhrrHulmUc2KzFU3O5kpN4uGp+pGoNaljB7YswzYPss2MDKKpjmZjmqZam34oA1qw9SUNzc3AQCnTp3CysoKEokE8vk8arUa1tfXxxL2OAfWuW3/1/elwonzS0HK/9kmz6RWrd9aApxT7Q8/V/hILYxUKoVqtYper4e5uTnU63WXHOabO9ZLAm4IL1tAj+NRBsaxqpWogQfsE5UE1cR5vyo5FJC0KNVPooLGkl1L+i70fVjrzqdQ8B1wninIAYwpCJrzQj8E54FWqNbT4hpg/ard3V2Uy2VXZM9CY1YoTqLDfn+/0pEVCkrxeNxhnGoKk5FabYtMW/FKMh0uYJY30DMOcrmcY/TKcHkfIQgufgAurJFZt7FYDO12e6ycgmplmp2sjFI1L4UIVBASvuIGZ9irWgP8bn5+3mnaZEybm5vY3NzEU089hWPHjqFarbp6NA8//LDrQ6PRQKfTwdbWlouXB/YENTVezQC2QlyjWui7yWazjgGqE57OfWaFz83NYXl52flCaHGphcb7+Uxgr2w054UZunQ8U7iwBPfKyorrKyvW0sLgO9E1aMuMq+Wna49zxPdJn4ZCQGpBUvjr9/xR2I5Cm+uZVq61Guy82EgnCiwrPDg2Kyg0s1ohJF6vEW9cc3x+sVh0c9lqtZyTmdnNrVbLQXpWGOsYppEVLg8yHXmhoJgpmSiw39mqyVz8zLfAuJHplGS5ZTITxSFVowP2zGgbdklGpgyBmhY/U3hD+2MjjXyan9VQ9beFKEg865n91cgbFjPb2dlBpVJxZxGQuReLRbRaLTdH6oAnU1Lt0Wrx+h4Y+ZPL5VAoFJwD2Ybc0g9A5pvP5x2DsWGiOk/6jlRY0ZlJ/w4tMD1gp1AoOFxcc1SUsanlZSEYkp1/vUdzNMhIydDj8Runo/E6ChJl/hYSUShHYU5lxLpmfXvAWpjaZ98YFQbS/ajX+voai8VcRBiF/mAwcAKb51+E9XeShXSQzx80OvJCgeSLkCGz0wND7Pm8DPdTjRuAK5fN2OnRaIT5+XlnKZA5sJgdmQk/Iw6vm5BaE/ukTI/RGsDeKWBkGNxsyWQSxWLRPYcbRhkSzXFldDaE1MIAnU7HaazpdNodeLKzs+MOB8rlclhZWXEOaeCGcKvX6+h0Orh27RoajQY2NzfRbDbdHCkD5JzrWdXZbBZra2soFArI5XJot9tOULHsOAsIAnDF0sgYdY5Va6ZgpnWhWDYFWjqdRqFQQD6fR6lUcpbStWvXMBgMsLCwMOYQpUVBbbZUKrln8CxjtQ6sj4jMUtcN320Q3DgljieXPetZz0K5XMb8/DxqtRpqtRoajYazVji/KsTYPmHJ4XDoLGjNjLdCzecv8cE2ForkNVpFlu9WoUCFVykg2J90Ou0c3Lx/fn4e8/PzWFtbw9WrV11im88pPCsdFYEAREIBwLiGrxqR4vzUwpkpaRmp1SqZxcwFHwSBqxefyWQc/kmcW+/l4mZ0jsJSugHJmLk5SKptc0yqbXJjkTGqRqdwDTVfzTrVDa2bOplM4tixY8hkMmPZpBROPEKx2+26IxuDYK92TSwWcweukPHTeqATm0xcYQbCQxoWSiZM5qdhvByXnr1stUcyPUIb6k/S7xXiImRBC240GmFzc3NszlXBIIMihMXxhQkDfecAxqrdcv2wflO1WsX8/DySySRqtRqazaaDHG0VU5+FYp/tY4jK1HWMs+43HQ8ZtiZd5vN516ZaOdwvbEfhXp7mtrS05KxBDfeeZiHY/s06ngeNjrxQUK1MtSXCCmRK1EwymQyCIBhbwKrp8H5aCEw+29nZQbFYdA5n1cLJJNSxyJILxIM1RlshIzIQHnBuDykhY6MjmpuI1ym2rE5MjYqikFHtkow8nU5jdXUVpVLJaaeFQsFZAMTzO50OGo2GG6fCdcqEqfGTSRB+YVutVmsMFguCALVabWzT93o9XL582c0DE/6APRhJ55zCkVaYJlNRE2UcPAUA34FaIMVi0Z0vPBqNcOnSJTf3muPAsdBvUywWnbXFd2ZhQBXiulboV4jFbhTlO3HiBE6fPu2cr08//TS63S76/f4YdEiISaE6/lglycJXPqta+6rM1DJW/V4FEq1wvjOeP6L+Ec1FYFvsx3A4RL1ex2g0wsmTJx18p1azwlXsWxjjV8Ewq5Vgx36/0pEXCopPa+ihhvCpM5kLk8yDG0Y3Vjwed4XsaNY3m018/OMfdzABFyv9Daq18UAX1jmiBkxnJu9VLYkMhaTYeBDslaLQTc9nEkLRWHS2r1qc5hXQOllZWcHa2hqWlpawtbWFa9euYXNzc8wpy2fZJD2tVMr+x2IxV1eIDIHMgn3gvDNJrtFooNFoOD/BaDRCo9EYq07LzUr/AyE6wk2xWMxpllr5lWPX967+Hb4zhf8074Flqmk9qf+C85TP51EoFADAxdUzUokKAt9PsVhELpfDwsLCvmRGWqL/8A//gI997GNoNpvY2toac4pzXatgJnENU3hNirTT/aMOcBUgun90vvieydw1tJptMvuZ7zQWi40pIhpFR8tckzqt1WN9IvrufJFnOk4roK1/RL/zfX6/USQUgvEICZs3oC9YnXN2Q1nSSo5kPqy/v7u76+KogXFnN7FcaoVkKtR2eFgKr/dpeNqm3QAsHWDhMrUIeL/CHRR83Ggs9FcoFFx2NqOKrly54uaBzj5aWQrxaDRTp9MZ6zcZszJ0C+2wHWrBZMTK+PQ9czwa2ULhFwSBw/q1DpW14uy8qjDn3Cj2TsGkDk86qXu9nlsfFP4W4qPQ1zmkVcIINy0W1+v1UKvVcP36dRd5A8AJUSoQisnrfNoxKmSoc6b+ASWFvjgGn1Wh19Fq0neh2fy8j3Ohfgz+VgXNQl5hUJiFTZWsQLGWg3V8P0gUCQXBmGOxvRo1YQtGnbBkRsTBlbkT311eXsalS5cwHA5x6dIlAHslhRmxozgvcENbJGR14sQJ5HI5h71r9icZjEbQ2MXMH9+GYugri+i12223OfU8AhaJo/adSqWwtrbmsqvX19dx4cIF5yjNZrMOOmM+Ra/XGwsXTKfTKBaLY1ANi5sRVmGlTApGRhXxvemh7WS+OiYydP6vOLX6Sli3SC0FCkh9vs3W5TxRi+UPNWe2Q0uDlmE+n0cymcT58+fd9ayPpU79QqGAWCyGhx56CLlczvkJkskkqtWqm3/O76VLl5wgeOihh9zaZgVYZfAMf6aGTY0cgNPCVVmilUehS0uZn5GU8eopeFQCOD7OGwAXXs1Iop2dHVy5csX5gxiObUONuQZoLVHgZ7NZB7uyPT3tz4ZjqzDS77TIpFo/RBQiofCAki5iq5GqU9depxoPGY9CBsQzWd202Ww6jU+1KDIoMnyeJEVNnGWAuYG1DXWUqnPY/uYm4sazY+KGo3ViGSSZmIYoarKcFjkD9rRnMjwATutXPwWT8ZQB8h5Gk3DDq/Dg3HPz2yJzqmnOzc05xqe1dagl0zpjH8lYqA36fjSnRROr+C6sMqHMWH1RhULBrRlacVoTSPtFhYXtcJ50zQA38lm49oC96rrtdttVIK3Vas4apYZt4RvfvqBQ82nOei9/eB2tQ657rh9erz4a3UNcB7SoCQXqPGkEnobbUjHTE/h0X3C9UiiohcwxqvBRa1DH5rOW7nc6skJBMUQr8SkULCygm0EZJBcUsFceuFQqIZVKodfroV6vO2coNwnbAOCw8lqt5moZFYtFVKtVt7CHw6GL9efCp+bKTWSFgRI3Ef0cWgtHE7ioebFNJt/t7t5IyqKGreGbFF7sFxmOHiHKMfAZ1N5oLTAJiUyMlgu1Zh7UosEA6XTaCUdWoKUwoWClFkwNU5khhQKdvBQIWrFWfTYUcLxOEwUV+lHIi+9Z4UlSpVIZW1/6LtkHrjO+bx07+8VjUFmDan5+3vkoGLDQarWwtbWFVqsFAM7y5DrX8F8NPQbGS2ooVGWTDlUgUnjyPvXXUShwXrR4He+l34DW5+7ujVLa+m5pHfLZ+p7m5ubcHuR71/7ROmAbGg3I+ee71Gx15Q9WOPIdh0FS9wsdWaFgSTeAagH8jFqujcrggtUFnkwmUSgUEI/HUa/XnUlfKpWc9s2NRw2dgkHj/dPpNK5evYogCNxRkNovdYiyj4rF+jRVKyx0s2oGs2L2jAQhLq0hlRqlpSU8uPk1ooYbkCZ8vV53oYOMhCED1FLIiofr/FEAsOQ1f2KxG5muPDOBPh0yTwBjB9vzvQJwzFfrJmk+gDp2OT6Fkyi8NIzSMhS+28XFRezs7Li8DN5DJs33oSUbuFY2Njbc+6FA39rawsbGBnK5nDvWkmXZFxYWsLS0hCAI8Nhjj7lCcteuXUOr1cK1a9fGQpQ5F3x3HLMqSlw/HJ/VqNUxrGPjdbRymGOjCYWM8tPjNgn/UbBSSVCBGYvFXBUAKisMKlDlTt+3fUfqy6GgUWGiCqJFFB4EqyESCvBLdH25qs3xeo1IsE44LSnQ6/XcZrCmN7BXsVJPZFONmAyPx0uqhqJOZTsWn2mrm1n7TQZPM54bjRiwMk2fsOS1vqxqnRs7b2RoukEpCMiEFKJh37Q8hTIEZV7MEaHQol+E7WvtHWtdUVCqsAfGiw7a8fO9qxavcJZdI8S+Ga7L52owA9+1MmsNGODztYyHZpRns1kUi0UnjOnMrlQqyGQyKBaLCIIbDnYyaLalWLw6cK1GbK0Eq4BQyOscc36ZgawF79SaVsUJwJjgVyVM3xv9g+pj0nMtrFDQ90ti//Qd2j3kY/4PgkAAIqEAYH89Fkp+/UwjkZSJcZGxDTpI6ejc2toac5BR89Awu3a77TJ5Y7GYKybHTWMZuWbZkpFzI6mTVPvPa1SrY3tshxruzs5eHRllSNR8tV1llBpZpNgtmSgZGa0RQkrD4RDNZtNZSMvLy2Pzrlo6o51SqRQ2NzedVl0sFlEsFp2FxlDORqMx5pzne6N2qnOiQQb8jPdQENEy5FgUWiD0VC6X3fthGC/XB52qfE+ESLToHYlROVeuXHFCp1gsuuglXs/ABGWWFy9eHPP30E9UKBRw+vRpVKtVVCoVLC4uotfrYWVlBU899RQuXbqEj33sY25c5XLZrWd1GKsCxPdu4Uu+N7WsqQQ1Go0xRcj6Nej81xMHAbgcFvokCD/SQtje3kar1XJjzeVyY050VUzUGtBaV3omBvc7x6FrWXnCg0RHXihwsXLzWG2Mf1tnlAoSOgp3d3dd1BE3PBO2rI+CC001PJr61O6CIHC/Sfq3LtQwPFvvsxqRjkU1NV7PTUJmabVea6GocLTt6fVWm1ToSyuBcuMDez4cFTaM/V9eXnaCQh3VjPpRBm/HqHOjwlYd5DpPVuhxDXFsFEZ8FuExjmE0GrnoKx0LrQytAGqhSgodKgxcNxplpc5RtRQJxezs7ODpp5/G5cuXHTTFZ1erVSQSCWxubrqqo5xX9QeotedbT7o29XNaeWqJ0b9Ai1Atr0Qi4aAfjjUWizlIkO+aDnBG4FEYMcKNR3qq5aLrkO/G97euE9+aiYTCA0gK99iFCeyZl2Fat7UmVCj0+33U63XnwFIsnia5Zu0uLCy4OjrUyNQpzR8VBGoKc0NoP5UBc5NOih5hW3oiVhhmauE0bV+Zvm1bmSuv0eqsFu7QfAWdQw1xZbsbGxvu3GoVlJZJ2Y2uc+uLPLPRXQqPkKlTwAdBMAZz2aQ9Rk3xmWSAZJBBEDiLgnCURkUFQeCyolkp1r4DVWC4RrjWNLmQJTHOnTvnzhJ/5plnkEgk0G63naKiTnbCOpa5WoVB1wH7wDlQ/5OGaKuPJx6/Ed7M8tnMuVChwEgklpSnhcLrSqUSyuXymCNb961dIz4KUyLsOnpQ6MgLBdVaFK/XuGXGw3PzAnuhc6qZATecW6VSCdls1jlLuXlppjJGm2Y0IYbFxUWXsUpT2DJX65NgX60l4tPSLWPk53qdjarQ73WO1BxX6MW27RM+qmkqDKZWg42E4rXtdhuFQsFpgPl8HvPz82g2m2g0Gi7scnt7e6wekmr42jcqBbFYzL0jzU4mw/dZOb5xKXxH5g7AKR7lctnlJNBSrFarri0+X+EtWkisAMvr4vG4iybzvXerJFDB4BkYhNEGgwHq9TqOHTuG+fl5PPLII2i1Wrh69erYPCmj1/as9aTWhM4b15A67nV+tTgfif6h0WiEZrPpoEa+ewochk4zm50WRaFQwOLiohM4FuKye8n3277nMOHxoNCRFwpcHMQz7QsPY6w+GIILmJogFyJNfd0EhJcYVkoGRvMa2H82sY+xW2tg2hj0Pn6ncIM+R++d1A9rjeh3Ydq5738riNT/QSajGjRhGQpE4vKqwaqJ72PkSlwHdH5TMZhlHApP+aJxALgKptls1hXPo8atzF4dp2zfRu9wPhTWtH2ya0OZoHX4dzodtNttB+VQ+2a/NJdCx6Pj982xhZvsWlUlRC01q4hoCQvCjBpRRHiNwp17i5Vs6YOh4JlEOnckC8nZ7x8kOvJCgSY847fJDJQpWAjEMhQ9+YvOu2KxiHq9jmaziWaziX6/j3K57BjD5uYmWq0W2u22O80rCALnFKVFQaEBYJ/WDOxl55Ksdmk/8wkVEpkNN2YYdEaog/dY0u9sVIk+z0IxVgPV5/IzPdCGsBEAbGxsYHNzE0EQjCU5MRJFk5wsdKQRVIPBAJ1Ox4UWq/ZrmZ+OS+dG3xewB79xfdCS3NzcdGdydzqdsXLpWrOJwkMjnHQdWkvX9o//E7Ihkydmz75zPTIvhc9mP6ils3+Li4tuXrheVJDqmRZqtfBv9aPQalKrl8w/Hr8RoloulzEcDtHpdFw/mL+jyYmtVsv5dkqlEuLxOCqVCnZ3d10Ems/iVvIJfrsGHlQ68kLBase64VQbs5tMYSRuklwuh0qlgvn5eWcJUOtkbD3bUqcqzVxuBj0hTLUxtVJIPuY0acH7tHzfdT7yCUSfReDTpib1h21rf+xvtZ7IMMiseMAOI3nI5HkPr7PCiu+Y71KjazQ5b9q8WMbNcVF4kikxOmpn58bpc1evXkWv1xsra03mru9a2+F4+AwKCp9FZK1ahXss6f0siaFRbHQ8d7vdscQ6H+RqI890PdjPbGAEx0SBQpiMIcSJRMKVRGfuCRM8OS+agU/ro1gsusKCdg7C3u9B9saDREdeKADjTjqNydbNyQUWBtEkEgmXUVoul8dgI7U8uBk0M5hRIMAehEGHI2Os+Z2FCvQzn2Vws1qNTyOa1KYVDhaS8t3jg5uUqeg9qn0TSiGUwcJ8DAFmQhn7ofOj78/HzDTIYJpQUCasjkoKf/VJ0ELI5XJoNBpoNpu4fv36WO0rfab9XyEW9pWato3oUcGi71LDLm0Elc65JhzyM2Z9M1/Gvh+rJKkvw86ljtEnQGKxPeezFbacX57fTKtRhbjuJQqdYrHofA0+WIh/63yFvW/794NGR14o6KawjEwjQwB/lU4Sy0iXy2Wk02lcvnwZtVrNwUPcwNx0ZGjMuORBK8AeVqu1aVQb1CgL9kmdoyR+Z5kOx6YU5hi0G1LnTZkX/7eb3N5DLd3OP/9WRqUORzIZWgo7OzsuB6RSqQDYq/uj/WCbZM6amKXPoNOWWqpG2bANHZs6xWm56FwyC5sRPcw9WV9fx5UrV7C5uekyb/UgH4WE9B1YpyjfIYMR+H+YtaCQn75XrmP9TstcsE2F0vQ0Nn1v9h3qsbHaL0YVaeixCj8LOZLo6yiVSmg0Gi6SqlQquWRAttPtdlGr1dzJa8yBYW4M9/asjP5BFgKWjrxQUPJtKJ8JacPRqNkw0YfZtkwAslaChlVqxijbJNO0GrMPqtF+hJFCCLzeNy6fRnwQs1nbn3TfrP319UO15uFw6DY/wzbpHNUDW8gAeL+1Svi3apuKz1OIqPVhLRqO2WL6hAfj8bhLCux0Og7mmsUKsXPi6/+kubbCe9I8a/t6DQUABcwkwW5/23Xhg+70fp/lS1LhzYgsrQnFeSZkq1a6+uR8c3RYuOhBgZgiofD/yLcwqGHrASvKNIC9TVIqlfDQQw85JzMLtDGyiIlWjICghsfsXo1AUcaiSUP8jAxK72FfeE0YM5jGREja1s0IBm3DQgW+zc7/FZrQ61Rw0urSU9mYoctwzyAIUK/XXSSKwjnK0DSUluGdvJ9zTbgqDEoi06EPiMyeQooBAzwRrtPpOMuCGjX7NQm28FlmYf9bCvvOWhf6fF8Ir73HB7nYMWhkny/HwUJNtl1gz5IlTMtExY2NDSwtLTlLKhaLuZpaLC/ebrexvr7uhIIvqi+iPYqEwgSyGo7CIrpZaB2k02kMBgM0m03HkMiMFDpiHD3NcoUj7POV6fO3CgWNJrEapNWOfVaHkt2IYZ+F3c/Pfdqo4sWTtNNJQsVq9iy90Ol0nIZ4+vRpx9wV8qGA1bYpbFRY+QSStS60jwo/qbCholCpVFAul52jttVqjb0bAPuidibNqf496fppn7GdMAe/jjVMkZgmhDRQg+1p/xWO4z0+wat+EL4zlpFnFeIrV66gUqkgmUzi2rVrLlN7ZWUFy8vLqNfr6Pf77kyTgyo7R4kioTCFfOawNa+1BAEdYK1Wa8zJzHsIbVBDpVluozBUAPigH8vwLGyhbcyqUYYxft/Gte3a9u11s2pmYdf5mBZjzwkfUDgyE5oVNjVSSa0oGy2jIbh8ji0GFzZP6mhl+6y/wzOYGSZpSz5bQWPb9lmx9rubIbuO7disMPA9a9Lz1br2CRgqPSqgLel60vfGPAqWBb9+/bqDlur1OoIgQKVSQaVScf6+a9eujWWFR+SnSCiEkE9j8QmIeDyOYrHooo5isdi+g+F5LR2DPBdYSzhYvFqfactpkNQBbpmWMicdB81s3wbUsevzwxjSLPdqvLtPM9T7OFYL8yjz0MNslJlQU2dm6+LiogsTZmRSEATO10AnL7CXA8EMdAsvEeLR8tHss9X4WZhO8wBoKdDhqVFJ1oKxZOffMslp72MSWctLP9d2FTbV/33XW9JxaqiwtSJsv7R/fKZaDMz3icfjaDab+MhHPoJCoYB8Po9Tp04hCG6cdf7MM8+4isUsneJ7ZkR7FAmFGcknEPg3cWOarJrwo1g2Y+AZkWEXp9XMw5x5vNbGswP7i3n5xnEzDOSglgL/puZtyyDY+33apNUqff0B9k5Mo5C1kTb6XDJlPSdBx+WDiqzD3zJJ2y/2W8/apiWpkUYkjfLxzbu1+GYV0LPQLFrzzbavwtxCcrPcq/Ok61xhJAppnkkRBDci+rRcOn02eorgLJnNR5UioTAj+WAZ4MaGZhG8xcVF7O7uuthpOpn11C9qqtRy7MJn+zY0zxJD+TQSZ1aTeJpmdxiyWi0Zr+YO2OutwOGY7eaPxWJjWjhwYx54mD3LGcRiMZeZ3Gg0HJxA4c1Dg/r9PprN5pjmrkKMWrwKXDvvOqc24a1Wq6HRaGB+ft6VVM/n8+74TE1KmwQh+chq9oeBkHyWiD7DfjeLhaJzx0J29K3peHVN+ISs9Y3R2ub7YvY1w78HgwEWFxddlVxWrGVxvGPHjmFjY2MszyWicYqEQgiFmdNK3NA8TzmTybhQQy0NzB9NLvK1peUdpm1yq0mp70Krj7JtXqsWihVGap1Yrc43Hwpl8DrdtNYSYNvcjGTUthSClp0go2UYotXi4/G4y09gjgILvG1ubjqBwPm30Ug8M4IQjiaBWQjOPl/7wXdAIUblYHNzE+l0GtVqFclkEsvLy1hfX9+3PiyTDHs3VhD5FBUlfS++9xEmXHyCZhqsyP9nFXR2PEr2XrXy1PLLZDLOKmg2m6jVai4aie+BazKVSmFpaclVHGg0GvuS+HyCSfsUNh8PEkVCYQr5Fr3+TxybtXJsmQVdZGEhfmFMJ+zZvmuUOSts4+uzTztUSMRHkzaKbcuOexKToJOe0UPad938GsEFjJ+RTe0/k8k4q4THoBLHZ98YDkxsmWcF6JkRaiko9u1jyj5fEBkXGRWLssViMedf4Olovnfse/d8rm+NhCkRkz7T37qG7Bqb5d3rvZMEySyf+4QUf/QkNl7HSLNer4der+cqyQLYJxTo/8vn82P1k/jeffNzFC2JSCgckHSxaGx6LBZzMfOsZUP4g5uf0BEAVy1T6+7YCCK7QX1Ytg+ntxaDT8u0AmCaxjmJwqwIhc94EDs3NM9BYKQQfTH9ft8VNNNTw9h/zemgIGZI8O7uLi5cuOCKzLFcg0acaDZ4InHjJDdaDyyexz5xfjlfHJM6whXaIGQSj8fdmLvdLi5duoQguIF1F4tFZzVsbGw4n4MKH/aP32m2OpP0bLw9LRzOvR6tat+NMvDbyfQsg/XRNCiTc8nfFn7id8wLarfbDuLLZrO4evWqKzaoc8o5BsZ9Oj6heNQoEgpTyC4Oq8FRMHBhUevkwtNkLC14pscY+kihCz7LCgRgPLva9732VZmBtRZ0bLNsCJ+1Msm6ATA2buZ1qJXF3I1utzsWheSDLSw+TYFDxyKZP4WjbUtDIVOplIMh2Gdfopt+5lsXap1x/FwTrVZrDA7LZDIuodHi6hwTf2tWrh4GpZYK15X9f9L7sO8zDBK6GTrIfbZPYVCOPUuClh4VLK4D9WEwJJn3EWpSH1dYwMdB+/2gUCQUQihsA+mC4eak9kZNVM/t5cbc3d11zmAyRV8kyrQ+2YgkvV+ZgIUcyMisn4PthG2QSeMn2axYfb4yPEJFWpKCTI51aeLxuGOgPuZExsVoHt34PJyGjJT3sFJtOp12c8h3QWiHvgUydr5bn8Vgw1BVW7cMnp9vbGy4A3hKpRLy+Tx6vZ4r+sZ2rDXIuaFAsCfiUdvlc1UhUQGqfQrDxu+UBUHyrX37v/q4KOh52hqPOiVMFIvFXIIgD2hiSZFCoeDWB0uEa5kR3Q+zCoU7NU93miKhMIF8i0M3jZq3TKYhM+ImpbDQTa+1W4A9ZqLlgfV7H/Pm/7afVjO3Y6CA0M2mTC3sPp0T3Qw+K8FnrdCkp1DQkgMMEWUVTp/fQNsls+Y861nMVtjpfGgBQY6BjJlQEhmtnSc7B76/fddqH3iYTRDcOC9ZYS9Gy2i7aplwzJw7rgV12muorV0bCk9q3zTXRT8/jKav457ENH2QpX4WZqVr3gMhM/qTOp0OLl++7E7mo+JRLpeRzWbd2SVU3rj3Jq35o0aRUBDyLUTfZrGwjmqVVqPV5DVgD0LhZ/pDpuzLotU+6O8w30DYdxZGmST4wmAo/rb9D4OflKnxR5kuIR6eMeFzkFvYSy0wCgSfUND++KCz4XDoLBdq8loPadI8+ebICgydFzqfgRsaPat6smSDBiLY98u5UutF6wfp+tKaWJYxh61hO4ZZIaRpTN/H8Gcl3m/fp757CkWGGO/s7KBWqyEIAueT4rtlG4VCAe12G7VazVkfOs6DWO8PIkVCQeigmgIZvCYmccFSIGQymTH8ktoeNR0AY0wD2C8UDrq5dPOQbBisZViWps0FLQyNElJGxufR0awVK1n/no7DWOxGqeNut4tWqzUmvKzQ1DmmpkcLi7WQVKNkX3zCVTVNvgs9ElUtPJ1XZVTKoBR6skKd/Wi322i1Wu67XC6HhYUFdz2jZyyjYlQVx8R5oIM+CALXbw2J1lLhKnBspdODvv87RVosT6Eyvhf6C3Z3d13k36VLl5xfiuOs1+sul2VlZQWxWAzr6+tuHVjl6aDW0oNEkVCYQpMYsGpxvFZDUanZ2XZ88e6+Zx52UYZBPD5NfNLzfBr3JIHA37Nq0mHP0HnUH2XoPobvozAYyMJoVhhPmhffHOnf9jOdtyAI0G63AQDFYtFFyxA+s3kSwPjxnhSc085JUBhSs7dt3/W9WdjOjsU3j1ajtxaZXjdpDsOu0feh86NhzITOer2em0/r9yN8y+RRa1lOetdHRUhEQmECTdsIFndm5BGjWGjqq7AAMKal+WAgZu2GMdCwxWk3sbUOAH8Sk4+BT9sg1D59Ya/6t25YDd/UsEKOi45oWlbU9FKplHMqc360/Lhq6z6LwAeX6fvjdcT07Vh888t79LdPyPBzZcjsKw8JKpfLztJst9tuvWidIDIzHT8jb8Lep1qtOj49G8EKBB9sZNeCb1/4mLnPh2F/2+f51rgKbKuQMLBAc0s4h7oHWVKdcJ0eyKPzZJ/t2wMPOrQUCYUQmgWisQuUZjydqnQIEiu3zJPkY1iz9sd37TSNZhqzCxMS/I64tcJEAMYEmdX4FaqxwoHt2JLgvJbwEx2pwLhTXAXMLOPX8fBvy6DUSpmFpglU5g1obSxW+Lx06RIqlQpyuRxyudxYWRQ9myGXy7n56fV6bvxklhaOtBAcndoqRCetuYNQ2Pr0CWn+9n03K8PlPNAnYKO++v0+Go2GWz+xWMwVrOR53plMxilyDzqjPwhFQmEKTVrsKhBsCKDdrKop2hBB+9vHjH1QT5hmM4sZPE0bstqjtSh8WqSFC7TfitGrtqwaoGLgqiFrRJY6UG+GkYXN5zSaBn2EQTJ2TWh9JCoMzWYTmUzGRWcBGIsiUgezFvKLxWJjTnGNOuL60zwZDVXlfN9s1M0scJnvb/4/6Z5ZhLuFkDQBkPPC86Tpj+Ha0qM5NTjE9u2oUiQUboK4aBTGoIORiTQs18voGK23Q5zXMjbLrPnZrMxoVq1vFithUns+AaFCglBRWN8pGDhnhDMGgwFqtZr7rFAoeM9KJrOz5xjPSmHzqWU0VLBPuseSMnzVxMmw9ExlngfRbrcxNzeHwWCAhYUFdy0FIUOYGX/PdUfhqQ51JbaTTqeRy+XcEbH1et0JKkJSN0u3SyDM8twgCMbyg/Q0Q55bUiqV3PzwTGfmq5TLZVfK/qDv+UGmSCgcgizUwSgkbmIbZhgW9w7MBhlZBjgJA52kSYcJn7Br9bm+/ydZHmopaIQPrQIyOY220XwCPcrUWhW+Pk0ai6VpDOtmrAltN2weKGzUKqDSQIxc7yfzt5naaqnaMVvL1X4H7J1LcNgY/WmCIeyaafdMIn3vCiOpVQvAlbSnD4F5KUEQIJPJOIWEkBrbPsoUCYVDkEY8AHARD7lcbp9QoFajG3RWRnYzlsLNLOxZLAzbF19tHXsKmcI9QRC4TallP7Q91sVnmGkulxvD18kIFJZRp6yPZsG8rXN41igc+32YMOA8sM+0JgE44aihu2zH1kAC9uAhClvrGNfwU1pWCnX6Qm6n0WGvm3T/QQSCfWeEh/g5AxSSySQKhQLm5+ddAUQWSNzd3UU2m3VVBbhuNAT2qFIkFLB/I1tt2y5YhQR8EAPNcmVePtzdMkvNYFVN1Yfh325txvYNCM9+VozajlVxXtVQKVB5lCY3bS6Xc31ge9zEOmfUci1sEsbEdTy+Mdp2dCyToAW7PiZpvpwPRhoR1mC9JpbjoCBk25rlrYl/1pel+RHK3JT5x2I3nNZM+GOuhwoVn8ZshYxvjq3lqnNv27btWEtCP9cftYw0IVLXIddMPp9Hv99356XTimfEltbesnDuUYaRIqEwgbiIw6AdbnJ+T2anm2yS1uRjGmEMfxJkFNb3w9CsuK/d6GH9UEGo2iodpcR5ySyV4dHRqrBJ2LN8mzpMaFnBNw3KOCyj4LPVec4fWkLD4XDfutEgBR9DVgYbpnSogFZ/jL4LX1Scbx5tH6ZZufb7sD01iXzvhgKOCaT0KXCsDG9WoZhIJLC9vT1WZFCPxJ20Z48KRULhEMTFRybWbrfRaDTc4R1qzqrAuJnnTPr/TpLV3MK+B/YzMdXI1arifRQKhJ8IqQBALpdztW1slVAfbKb9sJomryWpFWK/C2NG+lmYELfat4ZR8homVpFJ633KxK2FZvsGhCen0R9By4SZ33ZNWsFyWPIJaJ+A4OcqoMM0dn7OudGTDTc3Nx3DZzvMHs/n8w5W6na7YwciJZNJ9Pt9F/hw1CkSClMozErgb0IZ9sdCLT6hEAY9+LTWWS2Eg/Q/jA6rKdnNbRmnxbEV7rBCk8xUGSRhAwpeLUHu+63zaZ2RGglm53mScJhkzdm/+b8vSYpKBZ3OWkqcY9ayG5MsRoV4LKypMCbLi9h2ZxUIdvzT1phl8Pq//dt+5hPW9h5CRYPBAKlUCpVKBeVyGSsrK7h+/Tq63a7L6+D1rIVka5ZFFAmFQxEZlpYrto48AGMlGRQS8UWN6AYmWQardCsXs217EvQ1S1s+waCmPLDHfK1QsBq2xt3roTw+JjJpPIpJq7NWhZGv/ME0CmOmahH5GLkyqVjsBozGHwoRQksW/lIrywpDbV/HR1iF8+d7F/z7MMqBj8HPMp+Tnsv7bTgyz2rmPjt+/DiOHTuGs2fP4sMf/jCuXbuGra0tdy4zI5FoldqT/446RULhEEQGxVIMpVLJhcZx0wF7jJ6aDDCuOZLCGL4VCHdCq5mVEU66zicQgHFLwZYaB+DgDWp46+vrOHHihKtlY3MXLATBZ9vnWY3YjoXvxJbm5vubJCR9sKBlsFZz11BmKglaYJF9tmcT23VgGbuvf2F9p6DRCCb2y+dn8I1xEhP3vZtZBY7tt1paNrlvd3cvKa1UKjlIrtvtot1uOyc+gxNisZgrkFcqldBoNPaFRh9VioTCTZJdNKqBEc5QZhgEgYv44P2WGQLTI1x8cMytpmkbdprGNyssYNuyGjCxcPoSqN3ZfA9l9hZyCJtPvd5CRhpVRoZpGZnvHVirQH98obL6LJKW9aCGr5qsKhpWAE6DcXyfUyioYJkmQH3jCHvGJKjIjmNSP+07su+b74j+l+FwiEajgW6368qV25PrWFNL62rdDQXsXqNIKByC6DRUZs8IEpbxVY2WZzRTu5l1YzwoZBk2aRrGPBgM0Gw2EQQBFhcXxzLDqaXrwUQ+hqmWie2PCgEb5mgjnia9J1oZPBVMGRA1W4WCMpkMUqkU8vn8mHCggsF7GFnDQ4g0e9dq0hr1pt9p7gPnTMuvaBFGGwhws2tzVovAd53tvxVOejgQhUI8fqM21pUrV3D58mUEQYB6vY7BYIBcLodSqYT5+XnkcjlkMhnkcjn0ej3k83kneBkNF5bvchQoEgq3gDTxhee/chFbJsTYfD2ljWQ1cB8G7buOn92MBXEYTYjP9OVVKNkNb7UyvYefMdeDCYHE2Kkpa0y+PscHx01iTFaT5zvTCBaF+uyc62/GwZfLZVdaggKGuQgUCkEQuOQp5mjY5/BalsBQIaXCz2r5PtiK9ygTVce0Bkcw/Fchs0njnmaNTPtslns5Th2j9anoKXSE4VgiezQaOUFAYUzrgv6cWS2jB50ioQA/E5tlYaimCeyVKxgMBvtgCF24g8HAWRCa/Wsx3bD+6fP5/UEEQtjC923EafdOKg/gg1v0fqvlaiRMPB5HJpMZw/e5ucm4eA/bslZC2Bjsc5XJaey6vjdf3onVXPP5PPL5PFZWVpDL5VAulx2k2Gw2sb297UIfASCbzTqLQpWInZ0d9Pt9F5KrkKQWwiPRUrWQkO2jMla1EniPWlOEzazVpWMPswTCLCrbzkEFhFprChXxfz1FjcKW8Fu320U6nUY+n3cWHK0ChY9UOTiqFAmFW0RkJmo12CgkMpdkMum0yF6vNxaxZBkU75umfd9qupXtH1Rj1I0O7IWsck6VaQPjEUuzUNh11CILhYJr356BHIuN+zX43nlvNpt1h8lr9nEmkxkbGxk0o9ess5g+BQBOcORyubFr6X9gaCnrJk1TEPQ5Clv5ILJZ/AWz0EHXk8X1wywHHS+FayaTQaVSwerqKlqtFnq9Hi5fvoxMJuMUDQBOKNCxr0d2ToMKH2SKhMIhyJqcyiRIPq1fN7MyB9WifVjuNHNdiRslTNs/SBsHoTBrw9cf399h1+g8hcFOByVqm3xnLJxWLBaddWITwghrUTCpUCBjYZE1WxdL1wbhIY5L4Rzf3CisRWiE/gtaEOqvmDTHVij4fCb23VttfdY5n/a+ZyUrJKxVaIMDaJVzzRDa7Xa7TjDYvajW4VGmSCgcgnSTx2Ixd/4ymYbVAnUB04QF9pLbLLwyCdY66Ia0n4c50rSfhzWhfXCB7b9P8Cnj59xoLgiLmHFTH6SImTJMYs4UBoR9SqXSGIMh46BmSSuPCWe0/jiWbreLfr+Pra0t9Pt9d6AL7yEDI+yjY2N7TM7TCqDxeNxBToSlWCaaTmgKiLD5pRCikNF3zLm2EMqktmalmxEIPt8I71clgVnhtBTq9Tr6/T42Njbc/NZqNXQ6HdRqNZw6dQqFQgHFYtG9X/qvOp3Ogcf2oFEkFA5BXJjD4XBs41Lz0FLHwJ72z+sBjFWvVCakjkE+y6fF3CzjniQwJn0/yzMnaaf2Ot+4rIZK5shrKGx5LzXzSSa/PouMPZvNolKpoFQqIZfLYX5+3mn7vtIP6s9gW8T29fCcwWCAfr+PdruNTqeDXq/nNFEepqPZxFYrZygu15dqwVojimuFz7F+EN+8s03Af261tSJ873FWC9KnFPieM4lUIOhzOR+cV84Hw5dVGSO0RmhJ9yvbINRnLcOj6FuIhEIIhcEu9hpuYGp9ulCVkQB7QoFHAGoxLgtF6TOsYDhI328G971VWtI0geD725IyTisUFNf3+W/s89Qfkc/nUSwWsbS05I7CzOfzbr6UafAzvjcKbABOO9fidv1+H71eD51OB61WC51Ox12Tz+edVqvWiJZVV+c5MB5pxQOcKpXKmNbPsalQsJCLnW8yfsvwVXj67jsITRIIs1CYUqQWnJbsoABgFCDvHY1Grhoq3yOTBdUfGOakv9nx348UCYVDEKONGCmiVRq5UZlar7AFP1emNi1z9F6kMMGpG1cF2SwWhtX2LZylIZvWQerrC7VHznGxWEQ+n8fa2hqKxaKzDsjMWcKawlwPoanX687CYwnmVqvlCq3Rkbm9vY1er4darYZms4lWq4UgCJwjk7WNbCgq+6pF8yxT5X3lctlZqZlMxmnCAJyAmDTPlumrIJqFgc+qoEwiC5VOCxiwCpINsaVypklqvI5CWaE3ChEbQWeF4VGjSChMIKtxh2m8Gj1kk6rs2cxhAsAyzrDvfd8dZAy+zw9Kk6yoScxiGhQW1rZtUx28hOMmCQb+ZuG0UqmEUqnk8gSoPVIo9Pt99940XLTb7WI4HGJ3d9cdbdlut51QoALAkNJer+d8Clwf/X7fCRxquRamIQPjPb65CdPkJwlHbUOtV/5WAeELzZxFc/YpBPY6fSe+9g8KLfEetbr4mUJEmUwGxWIRxWLRBQZw/tW69/XFN+YHVXAceaEwSSux//sYtcbXA3CaCB1c6jvQQz4YPcJTxuxzLaZ7v9AswsxaBIpzWwbmu5bOYbXUNLRSn6fYcz6fx9LSEhYXF7G0tOTgvVqthl6vh16v59rkvXQmA0Cj0cBwOMRwOHTvmSHFjEDK5/MIghtZ2I1Gw1mRXA+JRALZbBaZTAYLCwuOMdHRTAGjkBgPwlGnO6t+6jrTWkCcR2XulmErIyVR66Zg8r3LScxwkqVoyacc6Tqw91qhxb5a/0I+n3fJgtyb2WwWS0tLOHv2LFZWVpyDnm2WSiV0u92JGdwRfHREyJqks5BdyMxUVQGgCW00a2OxmBMOwB42ro6/MC1qWl9m0apmpVkFpe/6aXPIcc1iwVjmws+o9SuWrwLa90wezVgul1Eul51AJsTDsiT2vANaIXRgkvkSZuL3dHSS2Wo4qmr9dEDPzc05ayWbzY5ptTpPTNgjVNTtdrG7uzsGPdFqIXxpNWUVlj7t3Kehh2nxYf/b73wCZxKFWTm+63yfUThQKDD3oNvtYmdnB6lUCuVyGcvLy05wX79+3WU5MxqJAvoo05EXCrOSj3FpohqdVwoR6XdasMvmJ2h7vufMyvCnbcKDCr+w+6fRNOExrb+TfBVknFYbngRFcN7z+TwKhQIKhYIL42w0Gq6KZiy25weyviFq55pJDGDf/2RuGjGlDmQy9rm5ORf+qNaInReFYAhxBcGNIzppbVIp0RIgk8JStX2fNXoQZh5GkwQKyWcJTromjPT7WCzmLHFCf3QyFwoFVCoV5z+q1+uuoiqFg1poR5UioXAIUl/C9vY22u22O1uXUSLqWyCs0O12x+riqGbGtm7FxlSym/RumMI+5qSYroaa+oj9brfbY2GF9hxsYLxcAePYGWU0NzeHjY0NbG5u4sKFC47xMmO42+2OhRiz71rMkIxInZ0ahUahY98/GXo8Hkej0cDu7u5Y0hvHrsXpVLFgG1oUr16vO4e3Cskw4apC9LCKwiSaZsWGaf1hwkw/0xwfEueejvhyuYytrS30ej2XtDY3N4dut4tms4lr1665Cqra5lGnSCjgcDH5AMY2LpmGHiauDF/LH9s+zGpC+/qmG3CSY4/PmjSuwzKIWSAkqyVabS+MIdiwXmB/5VO2oc/TiJRYLIZ+v49utzt2vCcFDf0GNopH3y+fxbnU6B17oJLCg4w8YgSMZkYro9YfPoPlLqhsMMqJDu0wKNTOia/9Se/JR/qswygvPiHg67v93vdMa3WrI5l+HFplKqh1bqmc2IOujhJFQuEQxEWjjATYO3yHzIMaoJZPJqPSU9lUcwuDVSZBMD7B4GvvdloKs7RrM7wV6rBjsIKSeLlCM+l02jFh5hHoGLXeFH0RzCFotVpIp9MuY5mMVgUA++Rz5KrQ90GCWq8pmUyiWCyiUCggn8+jXC4jn88jl8u5ufPh/2Ra8XjcVWAdDodot9vuVLFerzcGWfpIISO7blTjPoiVOu26SYqOL68kTAjY733wjmXktMxHoxGq1SoqlQry+Tza7faYIGAVgp2dHeRyOWetH1WKhMKMFKYZUWPThDRivcQzleHRilDNRTVTAGMwgtJBNJdp104y6Q8jNGZhEvqb99jCc8B4uKS9j0LVZ3VoP4IgGINi6PfRaB0yc3U2a3/5XmwsvwoFvYfPVkFP5sOkOTo2WRbc1iwC9iqZ6jj59/b2NrrdLhqNhhNiGgqt82DfiRUI9lqduzCrU+dnFp+Bfqa/LfmUF9sfO0Zeq2cta6kRAHj44YexsLCAlZUVJBIJ51diiDIL5/kiom6XAnWvUiQUhG7GXLSYscY8A3tam25CXkNHWBAEXm151sU4SYDMsvns/9PgpWl0EGhK+6latoU4fH1TWG4SBKW5IxoUwDkn89Z8ApIvqUv9CHqdtSA0vFLr+zPiKJfLOStFx6drSkMuAYwd/kOrRo+AtUzYF6brex9hCo/vmrDrLFkN3/4dtk58z/G9Z/u+NXel3++7SC9aicVi0SX9ZTIZBMHeuQvb29tj/j3fHPE5DzqsFAmFW0TUXJnVqpud2h3hIhbm6vV6Dh8mrGEhkFtF0zQ++/nt0o64eSkYubEVUvMdoKPatwqNsDFZS4HYMusSNZtN57CmMLcWgm8ewuaNFqKGG7NqKbV9MieNgkqlUkgmk84noP4IWqDAXvFF3rO9ve3qIPne2c28vzDI8laSDwqaRmox8v1zDfksNCYgNptNF3nEeS8Wi4jH4yiVSqhWqy7w49KlS670uLZl6UEXCEAkFBz5GMwkDUhJC9jxO1tky2o5TMf3ZbMeRFO3184K/1jt57CwkdIkOIHfa4SMwiIazmt9D5OsHovvK6PU2jaM2qGGrc+x/ZxVINh7FLvnbw175GE8vuJ4CqPxx9bG8hWqm0SzrCWfpWC/P4iiMku/Zp1fuzZ9a1f7qFYWhTFwo07V5uamSxjNZrNOAWMGujqfZx3Hg0aRUEA4lDKNSXLT0uxk9EoqlXLWApOkbMapzZxVBqn9mAQN+fo+K1mYJWzT3WqiNWBhM5rx6XQaAFyil2L8tl/st1ocWkuI12i0Dy0EYsjAHsRnS0kfhHwWngqjYrGIUqmEhYUFzM/Pu/818igejzsIS2EkHsWpkJSGQ99uxjXL/rgVjHSS0J8mqPl89Rft7u46Aby7u4uNjQ33ztPpNM6cOeMESKPRQL1eHxMKR1EgAJFQ8GpIsy5uLkIyeE1u4jmwupltRBId0mSOZE42s/mgNKuW5hMIt9M8tho9P+P49dhNxYdtprK+H1oZANwc82/ey1r5dCY3m82xkFMrjA8zNmAvo51+I0YdpdNpV0phNBq5YAM99Ytlu3lsKyPbKLCazSa63S5qtRra7faY3yKMDmJ5+sal4/PBelYgHIShzrLHJiktalFx3ai/ZXFxEfl8HvF43PkZqtWq23uj0cjVrwqD4o4aHXmhAEw3ncOuJ9F5qRFE3Owa565WgRbGsxrfQU31g5LV7iZpaDdDs9xnNzgFZxiDnvSZJqpZjZqWBEtNsFYSsXtb1uAwzIDPpw+DoY6MPCJj0sg0zWFgKDO/45qiorGzs4Nut4tYLIZWqzV23vO0fh2GfApTGPM8yLOmCeMwgRAm5Cx8xErEhI8YDs7DiejHY22pSeM6ShQJhVtAXIQ083O5HFZXV7GxsYFsNusyV9VRpnixRrjw951yaIX5JG4HsV3OgZ7yxc05HA7HLCWLr/vwdNX2eWKZQnNkyN1u1+UmMGLHJr4dZuysxFkoFMYqoBLSIBSktYzUeuRYgBv4d6fTcQyLQo1F+TY3N12xPRv1dCvJWgr62e0ma83afqnvSXMUhsOhCyhgguJwOEQ6nUaxWMTDDz+MxcVFFIvFsZBeLbF+lCkSCpi8+GYhdWxRw6NjkeWZyQy0Lo3VehRKAbCPOYb1W++dRNOutWO/FRZEWL+tAGSfVHO2USdh/bBCw+dbAMZx+IP0137ug1R8FmAQ7JXGUH8FrQIATpng+qHQ6nQ6TigAcMKO5b21DIedB+2bluTQvvvGFsb0J62LaX6AMGhylr1mLQnfODi3PPdid3fXlTiPxWJjkUiFQmEsoxzY27uAv2rrUaQjLxTCsPVZ7yUTZzYstbdUKuVio7PZrItu0Ggkra2ji1xN2WkbMEygTVvclgHYDRv2vFn9Dz5owDcW3yYnqa9AmbllfDYskUxC58b+qCAKgzEmjY/PUeZrM4XpFxgOh66EAmELhpNSKPR6PQyHQ1fHaHNzE61Wy91LQaK1lThHFlrh5+yjMr5ZyWch2PczaZ58AmFWJcYHKaogt2VGEokEisWim+tGo+EUMAqEa9euYXd3153LTIHBHw0KOOp05IXCYYgCwbe4iWVms1kUi0W3+bkYdYP4mNXNaiy23YO2w7FM0qYPq01Nw5F5jWX2Cr8BGMs/UL+MT7DGYjGH72uSkjKYWfB5JQp19kcL4LFPevSjVu5U3wfXBuEtwh2ENPSsBVoVB406uhXv7E7BjLOQWtsK2VEQAuOh4oxkU6iRUYJ0RAfBXkjwUaZIKByCfFquamsUDISSNNLIas1WE5rFtA5ztvn+nnUs9u9bTWHWDilMK7VjIZPVbGXVLH3as++ks1kpzGpTIaqFDtV/wu/IrHzCiJYmw5rD1ogKiVmUhzAl46DveBqkdLvIWhg+KyIWi7kMZZb7ADDmr1FYkvfHYnv1j6y/axJs+6BTJBQOQWRIWoiMzq1MJgMArqTBYDBw5r9GO/gij2Yp4XurNqVPuExjmndqo/jCLZUZaFivnU9eS0ZBJyLvUUE8q4WggoG/FY+mwKdFaA//oXZKTJsl0vX5ZPjxeNxZNRwftVgVCpPWivV3+KCy+4Hp+SBLFbQsi855ogXJZEFWGVC/AwWsKnAKAR5lioTCLSDdbNT26GBmZU5djLoobcjqJIxf/78dEE7Y8+8k+XwkVjtk/1QgK/Mjo1WhwPtoufnqBIWRDxv39TdsLPo3hZIerKPavC802Saq+daGzxII851MCv29l2gSDMr3rZninU4HQbAXZKCWJGGiUqmEQqGATCbjcovsfUedIq/KIYmbjD+MgyeGXS6XHQOgxmphAAD7tBSfVkcKW7g3u9HtPcqk7M9hnzepTTtm/V7hE25gQkFkGpodDOxFlmgNIfp5NIfkZqA6+95982IZNhmVJqsB41qqntjG8dMvoXj3rAxMhedhmZ6O63YKFauchMGkPO9aaxrRyqLw5ZhzudxYRnmhUMBgMEC73Uaj0XBrKqy6wFGiyFI4BKnDkBFIjUYDiUQCq6urKBQKOHXqFC5cuDAWksiMS63MqA5LIFwD9PVBrw+7h5/ZEEV9HjfGLP6Kw2yYSZaOfqcwFkMwCQ/oATW81r4PMm6eYcxQ4VKp5EpMUEsM8/Vw3JPGOwmSYbsciwoF+pg0I77T6bjMW/bfnjCn5Ps/TJGYZCHM4p+4l4jvW+tI8fRDWoZ0yicSCeRyOZctznOuGa3E6K9+v++KGB5lioTCIYgbSbV/3dCpVArz8/OuBpLCG9QIrcasmzaM0c/KCOz9B9n02gd7r+95Prw9jKYxWd/19n+r1fFz3xjViUsoIZvNuvcVFkHmm/+DWG7aN81Y1mNYacloWCqdzbyXa4sYehjEZ7VrH+w1yYqxc+gb86xz4psH33v3wYRhbdj/+S5pEdBKV4UA2CtISMiQxShtZJdGimm/wpSh+0mIHoQioXAI4oLikYjU9qiJ5PN5LCwsoFQqufr58XjcVWlkBA0Xl6/wm25eXzavb7NYHFmvt/AKycILGvKn10x6/kFM7jDBokyPWh6riAJweDw1bU0GJHRAhz6JMMHGxobLZF1dXXXly7vd7pjDmDVxLPP0MQQfJOObAx4az3Oi6Wju9Xq4cuUKrl27hkajgU6nsy/klPWcaDFYRYJ/02ryCUlrcdggCTsuH8PzMfSwa7U9FQiTLFu7fiYJJ+6HcrmMXC6H7e1t1Go1rK+vY3t720G2POGO8853SyGsUWGEfyNLIaKbJptmT5/B9vY2ms0mEokE8vm8wz11I1qGQzyZcEMYpqqf+xi23WA+jJ6hssr4rbUSRva5k6CgaYwlTDCQyAApEGhtaSFBzQlQZqljYdmLTqfjCtPl83lsb28jn887zVyTCaeNbxZYj+96bm7OnbTGeHoqE5q5rOGUtCJUqOvaoDJhx2v7MItVNgtTPwyFWZF8f/xeo8YUxlTBxjnQ+lIA0G63HfxDwU5BOTc3h1wu5364V5vN5tg5Fmol+ObnqPgYIqFwCFIoSE/zGo1GqNfrSCaTqFarLrPZll5QhqzJTNNi0H0JV6olWgvBChoyKou/3+pF72Oo1rIIg3/YH43/Z7/5w34rg9QyF2yHgrrT6aDX67lolZ2dHRQKBceUrYZo5/IgY+YP/Qf0YxQKBRc6yRLe7XbbwUUUGL5wWWWizHIOy4j3fX47yb7rSc/Td2uFgYUtLdHisUKh0Wig2+06P52GA9vDjUajEfr9vrMUCSkpZKfr56hRJBRuATE2vdVqOYfW5uamC31bXl5Gu90eY/zEPnlEI5NvgPGD1bUMMzcC2xgOh97++DBjbccKIHuO9K2iWdvyWR8qMKgZMnY/LEIkTABRKPR6PXfIyurqKpLJJE6ePIlkMunq6ZM58H5NOJwkqNlvMiIWx+O5CSsrKyiVSkin086pSZjj2LFjaDab6HQ6uHbtmmNaJI53bm7OMS6Fu1S75XvV930vMDarpEwjO9dWQKdSKWSzWczNzWEwGOD69evodDoYjUZjlhgL4mlhwUajgVarhY2NDZc9TqfzQbPaH0SKhMIhSBf6aDRyGCU3NRcZS10wGYm4ZSx248QnajPA5FyBWRyAvs98bWh4p26EO+E8s/iykg/Hn6b5TtrIvE4DAWKxGLrdritzwEikWCzmIB3CSSo8pz2D/VXYKJvNuqM3mdCoZZ1ZVpsRUuyn5mOob0GdoyoQ9EctzTslEMLWTdh69PkZtA2f/4rXUFjTQhyNRk4gKPTEedYCgirkeV6FhohHFAmFQ5FqaP1+H/V6HYuLi05AUBvJZDJYXFzEysoK4vG9Msqj0QitVsvBBtlsdsxPYbOdKUh8kJBqjsB4tAmwP87fMjpquOp0ux0MJcwnQt+BMlgyQd24ZKZkDITvOEZfKXIdf7fbdVphuVzG8ePHcezYsX3F0+r1ugseULw5jNS6SSQSTpOtVCpYWVnB4uIi4vG4Cznd2dlBpVJxDIxQBhUL61thm+wD5wTAPsvpdkCBhyFrJdh3rBVjSXSuW8WF0BkrBVABazQaLkNchSeDP1gunYoAj2Ple+U7jigSCmNkIZRZNxY1tm636ywEYtgbGxsu1b5arToIQ59JZsfoJJukBGBMAJCsE05/h43NOrG1LTsHyujsZ/a+WefLd41lGjaiR522FBy+yBltQ4UcNWdgj4ESS47FYiiXy+5QJJ6Fkcvl3AlntB702XxWmIOXQQZ0LrMfPOCHikAsFnNWAp2eYfPP/mvEFaE163AOszYPSlaxsOM8iPBRy2BWi9bOAyFUBhsQIiLcpgXvgiBwjmZgL7qI8621pmwYcNh7vZeE7e2iSCh4aNIi9X1uhQIXXLfbxcbGBk6cOIF0Oo1qtYp6vb7vjGI6z9ieakV6nd30s4ZD6ucajuhb5D5Gx898QsH33LA+TWMEVovU9nxRW5PaUH+A9p/tEGemNVAsFrG4uOgOYsnn82OCIyyfwQpN9eEQNioUCk7Yk/HHYjF3RCiT11QohJENHbZlP8L6o3OqAmQWP4lvvsMYvD7H/m8ho7C5DOsT3ynLj1Mo9Ho9d+6EFQo2J4TzrOdSUCBQWPvmTPv3oFMkFA5ByiRphjYaDRdtongmsyppNVAYUEPs9/suM5OkZrN9piWLwVrmetDFrBtT21MnOD9TXHsak+H1k55L05/OUjIBYDyXg7i69tMHQSnERkdkLpdzfdnY2MDW1hauX7/uyjAzrv3s2bNoNBooFotjp52xfV+WcC6XQ6VSwfHjx1GpVJBKpVCr1ZwjmYEFbIPzRgbGKDZ9D1xjfAcs6qc/Khh0Pg7DyCYpAdYnMAtZi1ShPus7skKIApFwE89X5rkTFBoaCUjLLpPJOFiJQQV8LuFe+puOOkVC4ZBkmSO1T2oeWqFThQKdzdakVex4FqhmFnN2kmCYZRNMspKsX2Jae5P6a4WQhdAsE1K4yUJQ+hz7/c7OjoMUgD1hocKdTJfYdaVSceGOOg71+xDWYPgp4UAensOcBDqYrQatAtc3b/oeVSO282+FpCoMYVbErGTn/2aZqO4ba0H6LA17DS0sCmm1mKySwoqpdExTEWMb3KvTHM1WoD2oFAmFQ5APTmm324jFYjh27Biy2aw7GjCZTGJ1dRW9Xg8bGxvY3Nx01VTr9TqazSaazSaCIEC5XHZmLZmVZvrerJY2C1nmqkxEtU5+z8/0nIJpTuowa8daIPYz1SrDGKdaC1aoAHuRPzwfmdnRvF8Pu0mn01hbW0M2m8Xa2przMzBsVJ2TyWTS+RDW1tYcbNRqtXD9+nU8/fTTTllIJpMoFovumYQGtUx22Hv1QYbWOlIhaCGvScJnlvV0pzTpMEiK86WnrNFy0+81nyObzWJpaQmVSgWZTAbdbhfNZtO9awYVMIjhQWf60ygSCocgyziZlcxDUphFOxwOkcvlsLS0hFarhWq1iq2tLYxGIxeqmMvlMBwOXQKOL6HMJjLpb9uvw45LyWpIyngsI5n27EnCjMJFi9yRgSlzm2S58Ee1PmWAFo4JgsCFCmu0Ey2GjY0NFAoFZz3oKXq09uLxuBMI/EkkEtjc3MTm5iZqtRpqtZqzUDTZEcC+An9s04e5+35UYfCRL8rMJxTuRfzct764t4A9oUelxVaYZblsQrZMciNc1G63Ua/X95U1OcoUCYVDkA/a4Yan85ghkEEQoFqtotFooFKpuEXMJKd8Po9ut4tYLOYWfBhT8GnUt3o8dlz83sdcJ91v25rEbLQMCLFjav7UhqdpuBYqsn0jU6RjnwmEsdhe/f0gCMYqZrI+DosbAnBnbgM3mHq5XHawEXDjaM7NzU1cv34dGxsb6Ha7iMfjKBaLY3HxNjHRFwRgFQNlfgqR+N6DCg8VmtPgo7slEML6ZSExPfo0TCgwAqxUKjlIttlsOsf0YDBAp9NBs9mMwlGFIqFwCLKMiIJAoxoYMjccDrG6uuogiieffNKFOVLLvHTpEkajEWq1mjtPWDcJGYIW0bvTpMyVjNSWKzgsse1sNuv+rtVqLg497J6w7F5tk6TXkiGoEFB/j5a2ZmFDnqCm9Zi2t7extbWFer3uIs+oldLfoPfpfFIIqlCw8CTHSL+TTziqk573+KDAe8EKmEZhVrBdb3ZctMjm5uZcflAqlUKj0cCVK1ecD6Lb7aLdbqPZbHrrgB1VqyESCjPSLOY0F5VmUrIiIzOXc7kcFhYWXKVM+hXS6bQTLsS7dWFyofoyPW/WYXgz11uNUzFui91bc9wyozBLiJo5BSA3uM3e1TYsLKKMwpL2XTVvX6KUZkKzginHyvtjsZiLcQ+CvUS0drvtYCZaGNYKAuCcwSxBoozcav12rsMEx80y/ptdFyQfJBXG3O07smsjrC++NaDj5vvj+cuJRMIdpqNQke7Rg0JHPit02lzcLxQJhUOQbkQVCAyV48lQ9XrdlTLI5XJ46KGHsLy8jK2tLVy7dg2JRMI5HpngxLouGoZo4/Qn0SS8WPse9n3YeH0hn/yx2diE0RTe8AkFq9kzo5eQCqvMzs3NodPpuPsJv/A6zhcZ7CTfAp/Hd0fmoNo4x0I4icKq2WyOxb/zfgoWxsDzHvaTFgJLWygUwrDJVCo1VpbB+gw4V5q3Yn985zbfrL9A58y39sK0axvxZP0g/NwmICoMO2mdWxgNwNgaYCVcwkYf+chH0Gg0sLGx4dYFYaQw6ChMoFlBzHl4UCgSCjdBVnvn35p93Ol03MYng2g0Gi7EcXV1FfV6HVeuXHHfsw1bUoHM4yAbedLfsy7gsOdZjJv9pabG3AIyOFuuY9I4rHOZ9zFElDi+ChIrrHxj0Pej88ANTgGnGjsANzYyILUMNLOZ2qZepw5lxb7VmuTfFBY8L0ItI2sxzvrOZlEgJlluk66/HUxQ++LL0Ndnco51bWnpFkaDFQoF5PN5F9hBaIlrSc/dYB+mjfFBEgA+ioRCCPk0absxw4TC7u6uy5QlwxgMBmNnLFSrVSwuLgKA+57t+OrsKNO91XjnJKgl7Hqr3fO3FoNjf4nJz9KunUf+cCOH+Q18cMu0sRJq4Hi0hIIPCmNf6Nvg/WTsOjcqrGzklFqUOm8MkdVIKB9TnATD+SzCsDm5m/CGtVbCrBC7DxRysz/aBi3MXC6HdDrtFSC05HwWThgkdBQoEgozki5iNZetJs4FR8iIUSrJZBIf+9jHXPG1tbU17O7u4q//+q/d2bLAXlirrdpI6MIHDRyGZhUIFrfVz/lbtWMef0iIhRaFTRybRJr8x+QjFjajyc9nUusO8ztYge4TIj4oQ3/sedqWceiRq9Y64rkKLP1NC5JKA3/CLAIrzMI02vsJ0vAxXvvOwmAan0DQ9cWy5VpzivPNWlbNZtNlQgP7I+qOqqWwv6JYRGMUxlzsQrULhZmSWuGz3W67EFWWbK5UKkin02NQCdukFuvTqGbRisPIMhWf5eO7xwpD3/WqBR/0nAaOSZkd547M2OeIVeagbWmb2p6dQ/uZCjcfLOW7V5+ppI5l/qi1wB9Gq+k6sO847J3dr2Tfg++dAeFwKK+1/zNjnFYChTF9Oqx/RD+Qr91pfXyQKbIUZiRr7gJ7kA6/12t8Rc5arZY7TCWbzaJareKhhx7ChQsX0Gg0HGOjJqlatdb8sabuYeggzMV3jQ+f11r1PkEyiVQQsi3ivupI5DOB8MqvYUKcwsPHjCgQlGwbfIa9Vv0+eq1CQwwnVs2W8fJa3E1r99jktAdBIJDCBC5pkpISJiwoEKrVKsrlMorFIubn5x2se/36dTQaDe+hOrc6vPp+pEgoHICU8QF7IYuquRJCIMNg4hoAl17PA+QTiQRWVlZQr9fHsHIWfyMMY6GCgzidw8incc3iq5hFY1IYRRl4LOaPjLH90oJnnMNer+et4cO2yZx9NYF4vTJT9kfnUvNNJlk41o9kBY76GjgefT6hNPabgsBaCr5++8bPOTgo+RSdu0HTLC1rPXJ+SVZAJJNJZLNZl8nM09lYeJICWNdyGKx4L8zPnaZIKByQfAs0bFEz3JCLTA/8qFQqSCQSqFaryOfzY5ExeqIUadJz7AKeZbP7mKav/TD4ypc4pNfbOkhqKYQxMJ81QcfuYDAYmyPfJva1OwmbJ9Tgm5tZ2gr73DIVX0KaRihpvPy0KCod/2EgDV27k9ZK2FoLG7ttn3+H9dGuuTBhOAuT5jVzc3MuJJV7aTgcuh895W6apXIU4CJLkVC4RRSLxcbi1ikQmFafzWaxu7uLVquF9fV1HD9+HIVCAWfPnkW9XseFCxccpJDP592Zwrlcbiz7VaOQ+NlBhAGvs//bzajtaLE7DcuctGF8EMCszFYPkOGYWX6cmbxqEajJz/a10uy0mHftswo0wF/czzc2y8RoDTCSKZPJOOd7EOw5mVkKRf0vwHgUDMNTrXY8K8wxC+OeNCc327bP+rQKRtgzdD36vtfQX17H/8vlMhYWFrC8vIx4PO4i/3jqnU1YUwEbUSQUptJhzEfNhGUN/dFo5GAklvQtlUpYXFzE1tbWGEZNIeHDT1XLuxnBoM8I+9ynSU6CMHz3T6IwDVs3KJm6WiDWJ2CzvK2GOQkO0HnzkfUZTXpG2DPV0QyMnz+hY1HGH1YQL8wSm0Z3EgaZxtD1u2l9nyQ4+LnOIQUww1ETiYTzSVFQTzta9WboQYKZouij20QsutZsNrG5uYmtrS3EYjeSnhqNBi5fvoyrV68ilUphcXERZ86ccfHUNkmKB8MQm1bMHrj9C3LWjat0s33Sjcu2NezQJpbRGe+rFEqmo4lO06ArHQ+hPHUQT+q3ZfZ6kLyeRaxj1Iqo2obWQfIJNuYzhFVIvVNa76zWYhjd7Drh++J+IUwbj8dRKBRQLBZRKpWc9c6ADT3nJCI/RZbCIWiSRsTFPhwO0W63kUqlcOLE/9/edza3kWZXH2Sg0UhMYJAoUaKk0VjjnWB7t8r/wD/aH+wvLld512PX7si7ExSoIYmcM4H3g95zdfHM040GgxL7VLFIAo0OD7pvODfdEaHWaDQwmUyQz+cRjUZx584dbG1tyRQotkUYj8eSp8/XTHfcZpWbrvsqusfvs15UgHmttn2sA9Mq1FY6H34K0VQqtVRtTLDNBYW47rS66pxsXoOpeG3roNdH/w+8y5ji+XCWMPBOOejZ3joGZUt3Nc+RdS1e1/I+4Kdog8DLy/Wj7bhGWrlS+GezWWQyGUSjUYnjcZwqW5NTKXxOFv51IVQKV4RN6GorhoUzw+FwiRpguwYOcymVSsjn8+h2u+j3+yJMBoOBCAjSEOYsWZtCWIVVPLONblnlygfZ/yrYBLK2lM0+Qlwbvebm52lNr5qs5XU+l6XjeE46JsP2GDruQGWgi/L0vmzfRVDa6Kbhdw7r0IdeCmGVYtA1KvSe9JQ1XZNgZgpe9rw/hnW/SYRK4RpgUwz6xuE0tkajgXw+j0KhINsymLyxsYEHDx4gFouhVqvJzc2xgb1eT0Z5BhVS5nldRlBrgbyOt2HuY51j68phClUKwfF4LF1lGczv9XpCtQDvxqJq/t1Gw5jnaHuNfLRXTALA0rG1BZ9KpaR4StNYVFLMhOn3+0vtunmOZvW42UaFr63rAX6MCHJv2bxx3h+RSASpVAqO4yCfzyOVSgl922q1pAU693MVwX7Z5+BTQagUrgGrKAlynr1eD/F4XFxb3Xo5kUigVCqh1+uJpZNIJIQmGY1GUrNA6Ewk7serk6qfN+EV0NT/X6Y2wu8YfvAKrANYqgImT8yZDvQitEIJ6kVpL8NcY37Otj/CFpAm50+Kh69zn2Yaqk2Ja29Q17KsEyg1PUpzbVd9dh0P0YSfpW/u39zORpOa72vFwO6zNJw4y0KP6/TzFr08ZK9z/1wRKgUPrCsATYGhby4K9UajgWj07ehGk0eNx+PY29vDxcUFXNeV4KTjOBiNRqjX63AcR/avUx5XPeQ2XnydG/yqlpW5Lz9oq1uDDyzbh8RiMZlJwS6zpGZIs5mdTv0UpXmNZjzF3N78X6cNa36bNBfPjV4PAKGMTAFHL0Efw/R2tFK4TNLBKqHHc7EJUdt2Nqx7j/G3vj4qQ/6tKSPTCOAEQz4n7XYbvV5PhhwB8G29YqNkg3qVnxNCpXAN8HswCE0B6SZctVoNyWQS2WwWhUIBi8UCDx8+RLfbRa/XQy6XQzQaRaVSkUHjelaAV3O5y7i4XvTCKq/jumEKZ/2A6gEpjLvk83lkMhnpNkvPTMcSTEGz6riXsaa1NU/rFVgu4NPCfFU2lL5+nj8Vhuu6onQ4/Oc6hNX7opj0udr6D9m8JgaJGTxmmwoqhmKxiHK5jP39fRlaReOA9SGsHA+zj7wRKoUbhn649Q1JSqHf74ubWywWkc1msbOzg/l8jn6/v5S5QqHHgijTmvHjy4MoCS9L+GPiok3FQO5exx0YuAWWaaF14iKr6CLb9jyejmUA79JVzX15pcl6KWb9/Wp6kZk16+K6vlO/9fQ6Br8Hmydm82i1F6UrwLVnyHnMzOjT7dp18aXZkjzEMkKlcA0wb2qTDuDD3+l04DgO+v0+CoUCotGovMaOjrFYDMfHx5hMJqhUKkJF5PN5jEYjDAYDyb+mINBWD9PuzADkxyTYg8CLjtOeEcddLhaLpWIlpndSAGhhE9QbWgdUVBRQeh6DTn/k/zyWHkC0CnpcKAUeY1HXSe+ti6vcV2ZNDvCOCuO1TiYTMYrYB6vT6Yjn4LquKMe9vT3cuXMHGxsbGA6HQh/1+33xtEIPYTVCpXAFmC7uqu1o6fd6PRkvyTYH3W4Xg8FA+sAXCgUUi0W0220AENqJfYAWi4W40xcXFyIctPXpF1/4UPA7vlamgD14qwU821/otF0Ki0wmI+u0Kth5HdDHMcdn6uOZBW62FFobtPdBr5H71x10/T7/KWOxWEhDOx1L4zxzx3GQy+Xgui5GoxF6vZ7MSzD7TAWpWbnNCJXCNcCkJbwsURYqtdttFItF4Tn7/T4ajQY2NjYQjUaRy+WwubmJcrmMTqcDAHBdV27yfr+P+XwOx3GWhtBQQOi8fa9g2od+IIIGOm3v0yLntbOAaTKZSJDedV0AsFqHN2lVUykxtmDSQ6ZS8Ipj2PZLmoRKQU/sW3VONwnbeprUph+N5KVATVqUniGNoEgkgn6/vxSTy+Vy6Ha7aLVaqNfrQq3xPuDnQ6XgjVApXBNsN5mZzUDLpl6vY3NzU3ofcfJaq9XCYrFAOp1GsVjEw4cPUa1WRQjkcjnMZjPJuy4UChJoo+ALQhtd5oG4zodoFc9M2Pr7aB5ar60ebcn0RA5b0RTeTXHJuvWIznyy1UfYvJega6tTmT9mBLlfvOJiOqDOqv7hcCgGkuM4S15kJpMR2iiTyaBer6PT6aDX64kn3e12pTD0MunVtwlh7yMsC5p1fmz70fvTrwHvahboBrPAitwph6xMp1OkUilsbGxIrAEAUqkUXNcVCkm3dOAxbfn5fsJonddX7ScI1nkYg6QH6gCknmCmK1i5JrbvxAav920CzDwPfc4U4CYNZgugmvuwfX/XhSBemvleUI/G9plVHpD+bX6OCQT6O9Vp2Kz7KRaLyGQyUrOiiwHZVcAc/BT0/G8bQk/hmuEnUEghzWYznJ+fYzweo1QqiUBrNBqYTqdwXRcbGxsolUrY29vDYrFAtVpFLpfD1tYWWq0WxuMxzs/PsbW1Bdd1hT/XDeM07+5HGTHfW3sZZrzkfWUicd/kgPWxTIFp5ufrNWbqrq2xnLk/wuT3zX0TOlisz8mssWDAmVlmQbh/cy1MJWIOmvdaGy9PwozX8HxMao77WKUgbbApPC+Y56m9BB10ZmwgkUggnU5LFfjOzg729/dx9+5d5HI5KWBLpVJIJBLS96her6PdbstwnatgVQzxU0eoFD4Q+v0+IpEIms0mMpkMMpmMFNn0+324rotIJILd3V3M53O8ePFCBFuxWBR3mtYR5wdQaADLN69ZQa3hZZVeVfh7xQRWfcYWownyIGrFRaViHtu0TNc5L9u1aG/Eazsv7zKIYA3ynil89fkEhUnHraO4vM7L6zhBt+ea6c6xWlECQC6XQy6Xk0FV2qvQA4x0XYPX+ft5pEHe/1wQKoUPhF6vh+l0imq1imKxuGQZ9Xo9FItFzOdzHBwcIBKJLAWSNzc30W63Ua1W4bruEq00Go2sFJLmvG2cfNDAoMa6XsOqbU2KRwfMvT7vdb5mbQK3MYWY/ll1HLOyVn/OdgzdkmJdQRLEM/O6Lk2Z8Tz8YhDvS8iZXqhJs3qtO8+fMyn09bCXGBMM2D2AVBEr4LVSCOLBfO6C3w+hUvhAoEVTq9UQiUTgOA6SySRmsxm63S7Oz88xnU6xs7ODYrGIR48e4dWrV6hUKjg4OECxWEQk8ra/y9nZGe7cuSOzGeg9sIhrNBoJrUSqSKdN0qrWaY5BcFlPIiinq6tVzffM43N7VhFTcPN6zc9qrHpfW6y2z2jhrK1Z3dbZ3O9lFOo6VI4tLfYmab+rIsj9wBoVUkDJZBK7u7vY3t6G67oSj2O/I9JOHHQ1mUys99O6+NwVRhho/kCgkCHnydQ5Fuiw3Ta9hL29PTiOs+RKs9uq/nwymYTjOHBdF47jIJPJyMNkNl4j3rewWCXgTEvSds5egUCT29dpobZ9aS7etLC9zkFvqz0wk9fXzfuusuamRxDE0tXbf0j4fX/r7kP3sorFYkilUsjn88hms9JGncaNTkMlheSXqr3q3L2u43NE6Cl8IFD4DQYDtFotucmj0aj0PGKLi1wuh3/4h39Ap9PB2dkZXrx4gWQyif39fTQaDcxmM1SrVUynU+RyOaTTabiui3Q6Lal4tKJY8ZxIJORc6JLrsZfv4/r93vNSYBq2OAGFtG57wW3NgKqXkNXUk/4st9MBbL6vezHxu9VV1auu+SqweQGaYvlQXoLtuEEFq1bqerjSYvG2XUyxWMT29rakogKQrDy2kqHR1e/3xXPQ8Rav1iC3Rfh7IfQUPjAoPHq9ntzIkci7jAu6wrFYDPv7+/jyyy+lLxLnPLuuK6muLI5rNpvodDriavMmN2MLutXAx0AxeHkKfsrK5KW1ctD71Smi3E4fQ/fRsXH08XhcfkwPwSZEtEC7rrWx/eg10LQZrebL1DRc133gZ2mvUvj0mm3txV3XxdbWFgqFgjRD1AN16BnQ49Y03m0X+qsQegrvCTarlq9TKWgXlzGHwWCAaDSKTCaDvb09xONx/Md//Af6/T7a7bZUQNdqNRnYQkqJysVsQ2zmzdsyMoI8NF70zVVg48G9LE6T5rFx++Z669+mMtHrYktPZRtsxi30sfU5679psdtiEJdZF30883/T29HKLghnb67NVQWn7TuybWO+pw0WXYGs32fVfz6fl1jceDxe8hRoKFEp2I5lQ9D7/3NFqBTeM2zcMm9i0ki5XA6tVktSVAeDAabTKUqlEsrlMn73u9/hl19+wQ8//ICNjQ1Jx5vNZmg2m0sPN3lVCgkdX7AJTX1etr9t12Db5jLQVn4kElk671W0Fi1LNoqjEOcakDbjPvT8g2QyKU3VFou3AWI2UeN+0um0VEnTemX9BwUoBQ/bl+jzMr2P6/DKVgkuU5D6fVbTavF4PHDzuFWxHr9zMGtRtLfG758BYp5PPB6H67ool8syf4TeAZvfMU7HPmO9Xm/pHEzvyobbrBhCpYD3E2j1EwIUHNqqAd5ynsPhEPF4HMlkEvl8HrFYDAcHB+j3+3j+/LlYSOTQaSlxXq3m1U0r3Lz2IIrhOh4Ur31oBUUaZxXdYF6HzljifswYgWk5kxJiNaz+rigkOVYTeDsch5asWfBHQWcKQ7+19cNV1l0LtlUKVZ+nSa35nbOXR2EzflYpQpMCMyuTGc9hbYLruvKssBXGcDhcos5IJenrC4pVdOXnilApvCfYhIK+SefzOdrtNiKRCPb29qQXfL/fB/Cu6ng8HuObb75BoVDAjz/+iHq9Ls30mM7KcYSO4/zGUr1Mzvx1YhWPvFgsluIBOl1WW5IU9lqQUEDr19lJ1qR29GeoEEhDUIgQiUQCjuOIkuV3ovdlZjvxbwqymzY8vGiaVbEYvVZcCy2Urws2KpBeoRkviEQi0q5EF6KxG+rh4SF2d3exsbGBfr8v9TnVahWdTmfJyGK9gkkXfu6C/SoIA803DFugzcv6GgwG4gKzpJ/pphR4FxcXyOVy2NnZwZMnT6QpHgWfbsetLdb39SB4Xa+Nbzd/2JJDFyvpkZZevLzpTWjBbIuj6PeYicUCQI745PHS6TQymYy0Oqdi1amu+rdOQ7VVOwdVDut8VyYl4uUJ2mDSc/r6/O4ZG03k95q5TmZfKn0twG+H4UQiESSTSelzxEw9fiempzCdToV6ojehvZ8g9+ltRegp3CDWvblYZDYYDJBMJhGPx5cKz7jPTCaDzc1NPHz4EK9fvxb+lUJO009mEdP7QJDjeFEOmv7RwlSPtlxnvzaBqd+j4mH75VgsJrEcdrHlAB8dsOR+NDWlFY4WtJfBOvSSqSBta2DC9JrMjqVB7xWbB2bbv9+5m9cQiUSWlALXMpFIIJPJSHCZ399i8TZZg4HmeDwuLeZ18obfGoV4h1ApfCDojBTesLPZDMPhEGdnZ4hGozKAnJy34zgyx9lxHHz99deoVqsYj8f429/+Jq/T2huNRkgmk0in01aeG7j+HPbLKgSeE4O0pAwYW2CMhPMDdO4/LUBtEepAstlqWgsFdplNp9NCwTmOg3Q6LQ0LqRhoeZpZYtpTu6wyCPo92L5DTUGaXoLte9ZUjoZOT6anFlSA+ikEfT7m3zr2ow0BUkc6TXtzcxN7e3t48OABXNfFYrEQD6Hf70urdD5HzWZTqEDb+YWwI1QKHxD6odEP9mAwkIwKegEsPtOcd7FYxM7ODnZ3d/HTTz8tCVLgXeUnsCw41qUw1s3E8KMu/KgNLeCBZcFvdn41ha8fzaH3bZ4DAFE8zEIipz0ejyW4T+5bpzbqfdmEsT7eqgZ1QYKal6GUTMVhE9R6/5o6Wvc+WeUp2PZpnodJHzE7KR6PI5/Po1gsIpfLSfGlrlqm4qdCGY/Hv0kyCLEaoVL4QDDpBwBi0bbbbRQKBQwGA8RiMYzHY5yengqNxPYV29vbePjwIS4uLvD999+j0+lgOp2KYGObDKas6uNRuPLvVed6k4FSLRBM4WUGidfZn74+Khbdvpr7JIWUTqcl5TGVSonXoPlppqMSetSjjl8EoezW8Q5sHoL5t34tiLdiKqtVjfOCwosatJ2bqZx5HlTK4/FYZpjfu3cPd+/excbGhhhIXHsGlFnzw9TU6wyW3xaESgFXsyKuKixtDzs7PXKWMydHsf1FKpUSC6lUKuH+/fu4d+8ezs7OUKvVlnjvxWIhM4s1pWIKTL7mRSvZBJifdWvSA/p/MzsHeFc3YNv/YrEQoWt+V+b6+QljHaw3A8U6D14PayEtRQHFlF/Tq9Hnq6/ZVLxewt3rmvi/pqb8rtX2vnmeq87hKvEEm8fE17gW9MwIesTpdFqaCNLrjUQiKBQKKJfLODo6QrlclpgBtxuPxxgMBqIkOO/c1nMq6HXdZoRK4QPDSynwRqfAYmaFnjnLTKRIJIL9/X3pusqHgVYxYxKmVWhrBW2e2yrF4Hdd5rYUyrbaAVPAmVSPH/Wgt9fXo2EKSTODSMckmOUCvM0Ii0QiogwojDSlpY/v9dvcbl3BG2T7VccOclwb7RP087b3NN3ndS28F+Px+NLcbVKhbGmxt7cnbeZ5f9M7YOUzU4ZHo9FvqtJvu7APilApfETQN22v10MkEkG5XF6aNlWpVLC1tSUPPMd1Pnv2DIlEAicnJ5KfHYvFMJlM0Gq15EGi56ELetYdyHIVkFohRUAhEaQZn59S0t4JYE9ZtSkVKmCuWSaTkUykaDQqrzebTfR6PVHK5tQ3M8vLS+l9ylg3rmROqNMZZOPxeKnmhKnH7PXF+QeJRAI7Ozu4f/8+SqWS9Dlii+xKpSK0KT1sTljjcXk+oWIIhlApfGB4WbusNRgOh5JDzzGTtFrH47HMb97e3kaj0UChUBChrwPOfAhpAZtzB4B3nO6q4OB1XbPObuHPKgW1Dg9PeFnnFN668pUUBIU+mxLq3lT0umyUF/e7yuJ+X7ju72/d2I6Xd2dSTACWLH8q3mw2i0KhIPND2MqEVB47oMZiMVHgumDNy9P90N/Lx4xQKXxE0Dctb/BWqyVtstkFst1uA4AI+UQigcPDQ0ynU9y/f1+op0QiIQ9Rt9vFeDyG67pSvWvmgZupm8D1Wrgm35xIJOQ1ppryvavQVLZtuF8NKgTmtg8GA2QymaUsrk6ng2aziUqlIt4VZ1ToalxaxWZ66ofGh/RQzEFOtswtKlngt8Wb6XQau7u7ODg4wP7+vijvwWCATqeDbreLVquF+XyOZDIpc5h7vZ51vjePGcIfoVL4gPAL+FEwttttyYQhB64fhNlshlQqhY2NDdy7dw+///3vhZahC02LnE33stkscrkcHMcRisQ2DOamPAR6BFpY6yZyfvALduv9B+HVKbhpneqJXd1uF7PZDG/evEG9Xl/K6losFtKGRO+H+/WqptbH/lRgC1CvGw8x01w13RaJRCR+wO+AXm42m8XDhw9x584dlMtlycQbj8eiEOi98d5uNBq/mV9h3tOf2nfwvhEqhY8UvKEHg4FUZvJm7vf74i7zNVY5Hx8f46effkK9XsdoNAKwTNOMx2PJrGHVNJWBTrX0Cvxq6PcuEzjVwptKwiZ4guzfzxK0BZ31cRjYJO1wcXEhtSKMJbCYKxqNyja2Sln+1gpBX1vQQK95XavWwC+WEgR+63ZZxUDlqNOAqQh0TMaco8xKfMdxpMeR67oSPOa2TMSgwcSOqDbvMVQEwREqhY8AXhZ5JBKRQTr1el1GD3a7XSwWC5RKJanInUwmSKfT+PLLL/H69Wtpjjcej7FYLIQHp6ve7XYlvbVQKKDX60mLYmBZkVAgefH95kPoFwzWWT+an+d5RSIREbp6f6QezDkJJrgfL+GlA928PgYoqYCZ497r9VCpVDAYDGRfek10kJ4KgutkCmcz6Gpeg14TWtK2vkA8vjlF7DLwE/Cm12N7z/Ya11ZXl7M/USQSEUGeTCblWjqdjtB1TEnd2NjA/v4+jo+Pkc1mpQ0254j0ej2ZNTIej3F+fo56vY5ut/ub2FioENZDqBRW4CoUyjqf9RKqLMxpt9vIZDLy4LC3SzqdlgcDeJuNtLOzg3v37uHly5fodDrSAoBCRlvGnOmcTCaRTCZFifjRNDasop28rGSb10DBr3l6rwfcXLcglrK2vrUAYSyGWUacY0EF5Rdv8frbyzPygubgNdViu+5VmVhBEEQheHkpft4JW05wnCzPt9FoSJNHKgC2IieFGIlEsLGxIZPVGBuj0uaUQuBdEgUzkIJeWwhvhErhI4BNoPBBpKt8fn6OQqEgvOpoNEK9XkehUEA8HpcHJR6P4+7du3BdF6enpzg7O8PJyclSQRv32e12EYvFUCqVMJ/Pl2YKX2eaqs1a19dKwU/FxRYG7DOkrU4/+CkFHkPTVcByMLTf7yMej6NarYoy1grJDCCb6Y62cwzSZsHcp/YEtFLQSslUFH6eWlB6j2ti9j/ieZjnYF6j/jsej6NUKsF1Xdy5c0cMkpcvX6LRaOCnn36STDp6i9rTunv3Lg4PD7GzsyPZRq1WC51OR+aS81nodrsS97FRdR8y2P4pIlQKHxFsVi/nAVSrVeFWXddFJBJBq9USD8J1XRFg29vbODg4wGQywV//+leMRiOcn59jOBzKrIV4PC5tMHK5HBaLtxPIUqmUBF4196sF7mWtLdt+zAIj9s03KR7beawLbX1r+kdTSv1+H7/++qvQG+l0GgBWVlRrms1LyZuf0581BTzz9pn6asYwmCm2Klss6FrpNdFUGAv1bIpP01laabBXFFuQdzodKaDkmjOgDwDZbBbA24y7RCKBRCIhozZ1H6N2u41OpyMFhbPZDJVKZanxXVAF6Ldd0H18rgiVwgfEKuHBB2ixWGAwGKDb7aLdbsN1XQCQPO1erydTwYC3FbmlUgmPHz/GZDLBX/7yFzSbTRkxSWucw0l6vZ50IaULPx6PPS3Oqzw0NuViCkhtmXrx8H60kQ1audj2B7wrqprNZkLV6R+9L69jeJ2Hn1Iw90tFoOMKtuv1UgDXZRlTQZjZVPoYJq1nuybdmE53lY1EIqIEAEhNTi6XQ6FQgOM4Qi+RzmOmHD1eKgmzC67fNXEbP/rtNiNUCh8BvB5mTR1Mp1OcnZ1hNBqhWCxKEdrZ2Rna7Ta+/vprAG+L3k5OTtDpdHB0dCQeAekjjvfMZrPo9XrodruYTCbibeRyOeGCbS2htXW/DndtXp+pEDRFo+kiCo4gVEmQczEpJXMQkU5vjEQi0mfKy0vy4tXNc9GxC/OazdeptKkUvDqzmv2rgq6Bhla+OpWTTQJ1A0B9DK/j8b4ZDodotVoYjUZSgc+EiWaziUQigWw2C9d10e12JeX34cOHOD4+xqNHj+A4DiaTCarVKhqNhqRiZzIZSRU+PT0V75KprbddqF8VoVL4wLC55IQpCMmftttteTjYVrvb7WI+n8NxHPT7fbF6s9ksnj59KsN4nj9/LrQArTamtkYiEaGXWJhFxQDgN3EGPwHk954Xzx5EoPkpUI1VQUYzmAq84+nNls3cv80atikrLwXhZV2bn6cRwDiLGWi30W5+axNESOpj6IptM+3Wtu56bUh1TSYTaTfB9hXxeBz1el0SJJLJpAyFisViODw8xPHxMZ48eYJ8Pi81OaSNAMi+O52O1Cl4ea+XvT+DrtnnilAprMCqm+O6XHXb8bQVyqAaPYbZbIZCoYDRaIThcChFO+l0WrpEttttFItF/P73v0ev10M+n8fPP/+MwWAgQWUGmJkJQg+EnLqe38DirXWvY9XrNsvzMgpDf85mjZvbUbhSqKRSKXmdGTGpVEqCrTbr3rZf2zX4URU6ZgJAFLVJH5lWvaZz1k0MMK9D00RUCvy+SSnq9TKvTSsU9i5qtVpLrUDm8zl+/fVXzOdzHB4eLk1Ny+Vy+N3vfodvv/0WX3zxBdrtNhqNBk5PT1Gv19Hr9WRGdqPRQLVaRbVatQ7Rue5n8rYhVAofELaHS8Nm/SwWC5yfnwMA7t69K7GEWq0mNBCHzL98+VIepkePHqFUKuGXX37Bq1ev8PPPP8vDTouNLYl5bM5uYNsBk+/3u66gsD3AmrIyewz50UixWEwmclHx6W6wOqtKXwtbVrB9hW5d4eUhmDCD2Pp8KUw1R689MBsFpesf9PVrSo2wjVy1KVXb+WsKSrdWJ2WlW6Ho6zEpMv5ND4cxKgp+xsRSqZSkQTOL7uDgAOVyGY8ePUIymUS1WsWPP/6IVquF09NTAG+NFLYcefHihXgJtpiLXvvL4jZ7EqFS+Ihgs7604uBrzNcejUbS+0jPdyYFxJRTtrYgZzudTnF6ero06pAPF91xVjuT2/azui8DL0rC5NzXUT6RSESUpDk20xSW/M0gMpXiYrGQ4Cc/pz9vC1SuWhd9HX4xBS/hZl6j/m37O4gSM983PQ1NMXqtnXlemtail0P6i3MP2NVXb7+9vY1yuYyNjQ0AQKfTQa1WQ6fTwXA4RDKZRDQaRa/XE9pI19PchGfgt8/PWSEAoVL46GEqikgkgn6/j3q9jp9++gn37t3D9vY2ptMput0uTk5OZKbwaDTC2dkZzs/P8ezZM2xtbeFf/uVfsL+/j/F4jB9++AGtVmspSAe8FRCTyUQC2pw+xrYZNw0tjINOMQPeNQjUCo7VyLSudWon0yaBd1kxbK9AL4FC7qp1GzbFYCobL74+yL71GgVV4No7MIW6zXsxlaQ+phmboeAHIGmt6XQajuNIwJnB5u+++w67u7vI5/N48+YNzs/PpRbBcRwMBgMMBgP8+OOPUljI49jqJvT5h1gfoVL4SGG65+Z74/EY1WoV+XxegnbRaFQGlgNv+yFRsLXbbUQib0dzbm1t4dGjRzg/P5cqXhOMVXBEJVtpmJ7LdV6vBgWitjb9QGu00+kgEoksDcPR+9GCWbfa0LUAWjkGKT6znbd5XX4xEltMxfzbD6ay0X+vUixayOt4wTqfN/fFH02TRaNRidnw/1KphJ2dHZRKJSQSCZyfn6NWq6HRaCzNI6/VahJsZuV+EG8oqGK87Gc/V4RK4SOG141PpXB6eop8Po9UKoXDw0MsFgt0u11UKhUMh0McHx8L31yv1zEcDvHkyRNsbW3h2bNn+Omnn9Dr9ST9UlNFLCRKpVIolUqS2krBaXY5vSpsVrPpKXh9Dnhn6TcajaX/SQ1xf/ybaZb0DuLxuLS0IIVmppH6Xas+Z5t1bVNq5vaXUQw2asdUDn5raCoBM5DsNTPC9BJsRXTM4tJjTmnls9ndkydPsLGxgdlshpcvX0r7a34fzWYTJycnqFar6Pf7ck7mmprXt8pbWlfh3iaESuEjh2mJLRYLSZOcTqd48+YNBoOBDDfPZDJot9vScrtUKqFcLsv25+fnSCaTOD4+xh/+8Adsb2/jX//1X6XZGLn0i4sL9Pt9eaDT6TRyuZzMebjJh4UPrMlL+x3TFOCz2Uwa7WnLX0/6MgU5BY1uxsfz8QrU2s7bBhsNeJn4gd++bfEKP9rLpkD8zlt/xvxbv697a+kYTSKRwGw2k/vv/v372NvbkzTVdrst9xuVw5s3b4QyYhIAPcCbxm1UCECoFD5JaAFG6qfVagEANjY2lgqw4vE4Njc3hX/t9/uIRCJwXRd7e3uYTqfY2dnBYrH4zXCSi4sLoZGi0ahQSUxfJbyCsfp9fe6rrot/+wlOL07eJnzN89AeiFdRmRlQ9lMKQYS7F30UVDGY5x8U5vXo/ej3gxzX3N4Wv+B9qWNUOqU3kUjAcRxsbm7CdV3E43H0+33p6MsJeDRs2OhOezy2dbisAPdShLdVIQChUvjoYQoUWmLEcDjEaDTC8+fPsbe3h1KphFwuh/l8jtevX6PT6WA+n+Pu3bvI5XJoNpvSofLevXvY3d3FZDLB999/j263K225i8WitBjgbAbXdRGLxZBOpzEYDCR11GaNBg0Oe12zjYbhWtiUjKkcmIW0WCyWgql8jVQZg88c38j2EqREKFDNeQ/6+zAViO38gnga5hqss63J/9u8ANv/tuOYLT389sUfPfp1NBpJc0HGo3gv7e3tYWtrCw8fPkQ0GkWr1cLZ2ZmMnm00GpJ2ykp8fi/8PvxauAeF17WHCJXCjeK6rA3TItLWPAVWp9NBIpHA69evUS6X4bquNBqrVqtIp9OYTqfI5XIA3jUfi0ajuH//vvRA+uGHH6T1BeMMrJpuNpvSAoOC1mzBYFIy5oAV83q81ktzxgxaruLK/R50U8norCKuoQ40a6G56ny1QvKyur28DZvVq4W8l9LRMR39eXM9/IS6+dlV8LPS+cOiSOAdXccZyul0Gvv7+9jZ2UE+n5c2K91uV7yFer2ORqMh/ZJsSs7rubrN1v11IlQKK/AhrQnbsfVDQaEdjUalzUUymUQ+nxePYTweo1KpSAO8YrEI4J1SSCaTODo6EuurXq9jNpuh3+9L11Q2JGs0Gsjn88jn81alYCoq/Zof9WEqA/5elW6oBfaq7CQT2gvQ7RYYHCVIfXj1P1pFZfB1CvGgNJSNgmPg3KYITaWgaTF9TasEp6no9PmYyk//pjJl1hfwbu1GoxGy2Swcx8Hdu3dRLpeRz+elZUun00Gv15NKZT1Sk+tmZkb5rXlQrDIiVq3R54pQKXzC0IKT8YJffvlF2gl88cUXSCQSKBQKaLVa6PV6yOVycF0X+Xwe1Wp1iUp59uwZFosFTk5O8O///u8yID2bzUrx0Gw2w2AwkOEnrGFgoZOGSfesEzD124ZKx2yxrT0LE+a5kVaioGWsxMyssik8m+LyytK5bmgPhueh7wEqCK7Nqn35HYP7NL08U+lzAA7bYcfjcbiuu5SptrW1hQcPHmB7exvJZBIvXrzA+fk5KpUKzs/P0el0UKlUxEPgepozLHjMEDeHUCl84tBCgt0pG40GFosFDg4O4DgO0um0PLDNZhOLxUK6fwJvs5JSqRQymQwODw+RTCbx17/+VUZRaguNQb90Oi37AX4rLG2W5qqA7TrQwWJ9XL/9217XlvcqmsdGmZj7vgmBtcpCttEqfp1dbfv3+t88ro0GZI0I4zJ6W+DtvIRCoYCtrS3xyFqtltBGegSqVvo270Afd9X3HeJyCJXCJwZTIPDB0a0oqtUqWq0WUqkU9vb2pKfMxcUF/va3v2FzcxPT6RRHR0dwXVdm3QLA48ePpebhv/7rv/Cf//mfkn2Uz+cl1bBWq8moRXoaZv74ugJyVcwBwG/SRU34UQJa0LA2IZVKLTWV0710bILfpuzeB2yCmz+0pnU9hm5RYc6DWOc45uvm/cd4E1tsJ5NJ6dA7m82QTqfx+PFjPHjwAA8fPkSz2USn08Hr168lntBut6WzL2NWND50AaKJ0GO4GYRK4ROGn+V6cXGB8/NzXFxcIJFIoFgsSkrpeDzG2dkZXNfFbDaTSW7AWxokm83im2++kcyRP/3pT9J5NZVKIZvNSq0EPQkAws3buqmu6yms2oYctl/w1gvay9B575oa0XSTV42EFrQ3JaBM69/Wo4g/WhlrDzLI/m3Une3z+jj0HMfjsVBwPM/5fI6NjQ1sbGzg6OgIGxsbiEQiMgN7OByi1+uh3W5L40J+nnUOttkIXgo7xPUhVAor8KnceCZnPp/PpXMq8NYDYIfKyWSC8/Nz5PN5LBYLlEolAO+s8HQ6jWfPniGVSiGVSuHly5fSfyadTiObzS7RAMwvd10XqVRqaQC7hpdiMDNMvKDpAlqROqCqf+v9mp+PRN5VNuteSFqZ6eNoj4zv+XXnvAnYhLf5o70oM/hso7ps+7YpBbOWg/caU3knk4kUGuoYwNbWFvb393F4eLg0XY3NHNngjl4Bi9N0JfRN0nJe+FSe+ZtCqBQ+I5g388XFBTqdDkajEaLRKMrlMp4+fSpC+9WrV6hUKri4uJDZz6xqXiwWyOfz+Md//EdUKhX89NNP+OMf/4jxeIw3b95gc3NT6ClywxyXOJlMkM/nZTYDz8XGy5vWtt8DaX5WD8PRbS38hDWFHJUK4ym0Us311Ly2Vmp6m1WZKl7XFUTQeXlCWvjqNhPa27Gtg5fy8FIaNs+Jxx0Oh7+ZZ8BOqMViEY8fP8b+/j4ymYxkF1UqFbRaLdRqNUk/NdOCGZ8wvTUzpqGPG8YWrg+hUlCwPaSfwo3mxwPTDWd1M+sZotGoWGjValWa3bEtxGAwkL73R0dH0oK7UqlIMdtisUAmk5GOmDyWth5ZRLbK5bc98EGvW+83qGWplYqen2B6BLZgrU0x+B3TKw7hp7jMz9uOqX+8jnmZdbXty1QatOR15hOP4zgOtre3sbW1hUKhgIuLC5kOyCI1GhAcw6mLA/XxbOvshXW2DeGNyCLg6l21dfCngI9NKVz2QTYFwmKxECro3r172N/fx/HxsTzcnU5HeiSVy2U4joN8Pi8zdB3HkarpP/7xj/jTn/6Ev/zlL7i4uMDOzg62t7dRLBbRaDTQ6/VQq9UkHrG9vb3U5oCZKrQMOeVLZ7L4rYdJNdl+gHf3qymAeSxSE/P5XGZSkLowe+tQedgyldb5vrRw9tvWphRMLt+P2jLP0Ww/bnoKtn2a0M0FmX46HA5lH4PBAPP5HK7r4vHjx/juu+9wfHyMdDqNN2/eoFKpyBS1druNFy9eiGJgh1/TsFhHCdjWOgg+tmf+phGknif0FD5jaEFBYXx+fi4ewe7uLtLptLTFrlarAIBcLgfHcQBAKpoBYHt7G19//TXK5TLS6TQqlQqq1arMX2CxG2dHT6dT9Ho9JBIJpNNpOS8trPR5rnoYbRSHKWT9rHYt8HhN+hxIWTDoSUXm17pbC/rLCLGgn+W2ptWvaRObMtHbacW86pimF8b4C+dqMDhszqF4+vQpHj58iHv37iEajWIwGKDZbKLX62EymUjG0WAwEMGv4zr8O8SHQ6gUVuB9BriuCyZVo5UCaxii0SgKhQJSqZSkADL2MJ1OUS6X5UElTZDP55HL5XD//n3UajX8+OOP0sVyNpuhXC5Lu20KENJQTFnVCkGn0QalfWztkm1erI2H13/z2nStg5ntchmr/jpgE/yr3veiTmxru86cCJ3Cy1RTJhYA7zKFHMeB67qiEMrlMlqtFobDITqdjiiBfr8vXU819aSPZ3o+l8FVvpcgHt/njFApfIYwg4fmTdxut9Hr9TAYDFAqlfDw4UPJKmIR0XA4xJ07d3B4eCjDUTjWM5vN4p//+Z/x9OlTHBwc4MWLFzg9PZW5DJx1PJvN0G63hWrg3F5WSJvZKkGyebQA0bSIHnI/n8/FkgWwRFFxXRjr0I3WTCrFrJR+X/DyKLSy5/lSYOvz1t+7Fv7MAOLrOsV1VXyDzenYDjsWi2E0GiESedsm5OnTp7h79y6+++47pFIpaa+is9OYrdZqtSRBwLxPbUr5Y8PnHtQOlYIH3vcNeRM3mU0x0EWfzWZoNpuYzWYoFAooFApiuV9cXKDb7UpsoFQqiQfBISnpdBqlUgkPHjwQq/vs7Ezy1nksnWa4WCykwEm/F5RrtwVstbWvvQ/dqsLMnyd9pvdrUwpBz0ufz3XDz0uwBZBtHpLpFZjxAz9PA3hXE2Krkk6lUsjlciiXy9jd3UU2m8V8PpeW66ykn0wm8j+9DH19tu/Waw1s29twlZjDdcQrPlWESuEzhH7QaTnbaIRGo4F2u43BYID9/X0pMorH48L7npyc4OHDh9Jkj8NQtre3kU6n8fXXX2N7exv379/Hv/3bv6FSqeDk5ESykorFosQWRqPRUi2EVlq2gjcbNAWih/DwJ5lMSqUy6SoAQmWxYRsrb9nVk7y49hAikYgcg+t63bAJ9lXX70UveXUVNT0e/q2tdfNzhC4ko1fATLVEIoGNjQ0cHx/j6dOn2N3dlZGwjUZD6hE4sKlarQqN5OcVfqwewm3BrVcKWlDagpd+N68tU8Pcnymg+R4Flu4CqXP5/WiLdYSTjZvV5xOJRNDtdnF6eorpdIq9vT3JOuJ5kBoaDodSz6AH7uTzecTjcXz77bc4OztDIpFAu92WaVnAWyXAuAbbcrM+wItGsgk42/rr/HZSWFRIuVxOhgi1222cn59LFTa/By30ACxRTfq1Vd7DVZSGlzVsKgDT8zPpFk2hae9ssXjbryqdTqNYLMr9xfcWi8VSLyJNN5ES5Boz1fjw8BAHBwd4/PgxdnZ2kEwmhR4idcSW67VaDbVaTZSv2QrFpuy86M+g621TuOvEify+38/ZW7j1SoGwudGrCqFWpU+a+9X8LYfFM+ini3Ns+9YPSFBO01QIpgAhFz0cDiWIGIlEUCgUkE6n5fzYYZWfyefzSymE6XQamUwGx8fHcF0X7XZbprZR2DKeQMszFouJgDGvV6+TSXXo93WgkgKr1+tJD510Oi1KjNvVajU5J/Y/Mr0DL2PAfM2PclkH+rg2QWh+536GiN6PNjgo/Gndc5vBYCCT9JgwQIoQgCQIULlPJhPpjvvgwQPcuXMHDx48EMXM1hXMOBoOh2i1Wmi32zLAKegsCK+1uszaesH2nt8zf5lz+NQQ1in8f2hBFNRTsOWHmw8mg3lmH3wKXW4bi8WExiCloYupzN+r4MfDUkHxmnXeOmcsbG5uolgsYnNzU9pkA4Drusjlcjg4OBArXNM4k8kE7XYbf/7zn/HixQt8//33Epyk8pvNZkgkEshms5KZxM/rgLGmvvSUN877nUwmGI/HaDQayGQyEufgWlLxOo4j3PfZ2ZlYsIlEQqq4p9Ppb4bDaMvbtubrWLVenzU/59VPyU9h8LfeRnsJzBjKZDKSPQa8vX8bjQZGoxG63S56vR7G47F4cOl0Wu7DZrMp9NE333yDe/fu4euvv5Ztm80m+v0+KpWKDMz59ddf0W638csvv8iEQK3I+X3bYFJftuv1W2tthOm19FOuels/L0FXkH9qCOsUAmKVEvCzWkw6QQtcTQ/xfb5n5sGbN7BpPWkaI2g6oRf0A2e+zlz9Vqu11IeGxW+8LvLDi8W7wjgqjnQ6jXK5DAD49ddfxVI0H3Dd34aWqG29TG9BK0h6JKQ0GE9gBgz7PCUSCYzHY6nE1p/X16/Xx3zdDzb6IwhMIWV6d0H3p70q83XeXwwAs336xcUF2u22tJWgN8dEAADyHvD2e93Z2cHe3p6kHy8WC6lW7na76HQ6kr3G/+mNmN6Bn8F1Hda41zO0av82D/tz9w40br1SsNEF6948ej8UmrS4M5mMZGDQ0uI0MxZ40TrW1JGZM29y2n5eQxBBYub76+tn6mCn08HZ2Rm2t7fhui729/flGl++fIloNIqTkxNsbGyIN8HJb3fu3MGdO3fQ7Xbx8uVL/M///M/S0BUqoMXiXUqlDuqavD55aM6X1p7HeDyW1uAAZC4Eh7j89a9/xWKxwMbGBnK5nBTScX+2kaH6u1hFJ6xC0O+D17RKYJr7NpWK/qE1nkwmZUDSL7/8IsJ+NpshHo+jWCxiY2MDjuOId8GW1sPhEI7joFwu47vvvsPjx4+xubmJ0WiE8XiMXq+HSqWCbrcrLVPYibfVaqHf7y89GzxvP/YhKEXq93lz/0GNPf2+n3f2ueLWKwUbd2vy+0H3Q+tLB06j0agIIU2RsMJzPB4vFXyZhTzmOZrwEhy2OIJ+z2YF2V4ndcC6Bra1KBQKACAN96rVqtA0+Xwem5ubyGazePr0KUqlEiKRCE5OTmRfXA+2mAAg2T863qKVQzweR6FQEDqEKY7xeBz5fB47OztwXRexWAz1el1iIeTFs9nsUq6+Di4D9gworYhXCeog73kJJl67zUjx+h71vk1PygQVINeXa09DpVgsysCkVqslKaX5fB67u7v4u7/7O5TLZRwfH4sCpjdQr9dRq9XQ6/VkzVutFhqNhszi8KJzrhI7WKU0dTbWZZ5n/XeoFG4ZtFBclxvW4E2o87ppCeuHglzvZDKR1tbRaFQsYJMKMM/FdG9t17PqJtZ0h5/XsFgsZAiPpmk4U4HzmxeLhbTOnkwmItjZ+4g8/mQyQb/fl+CmWS+gj80gsD43Wrz0EJjJlEgkkMlkhPpg4zXd61+nwervSnsjev3MdbyKt+D1eR7bRvusgu17Nb0N4J3nydYe9Oj0/ItMJiP3Za/XEy+uUChge3sbT58+xebmJnZ3d9HtdjEajTAajdDv99HpdNDpdKRaud1uo16vy/esR4OaQvqySuGy63OVbT53ZUDceqWgv3B9o63rvpvWGQOqw+FQLDOdacQ0SKZpkk4B3lmN3I8ppMwYRpDrM8/NtKK4rW7/QJA7Ho1GOD09lSrlUqmEg4MDiTuwfXa/38dkMkGr1cKDBw+QTCbx7NkzlEol1Go1/Pd//zfq9Tp+/fVX9Pt9xGIxFAoFWUvSOdyvHgbf7/cBvAvIsqEf02PZhE0rN/ZeyuVy0sxNN72j0mZTPNJ6TJ+kd6fXybZ2q+4T8zsx7zfb30H2rbfT2/Le4neSyWSQyWRQLpel4SGpolarhWq1KpRRoVDAo0eP8O233+Lo6Ah7e3sSmCYldH5+Ll4B74tGoyGeHJ8JemRmY74g9M1VYIvRBNm/l7K9Lbj1SsEL6zyINmhhrjts6liAGTC1BX5tVqvNm7jMOZqK0GtbfRwqulqtJims3IZWObNWSD+wNcbW1hYymQz6/b4M+Ol2u+Jt0MrXConrRyE9mUyWAvOklWazmVAZrKFgSiStYd2t1RzzqJUyvbtYLCZFcDyGX3DahFeMyuZ9mN6qaenbPBfbPrl+PE8WmjHW4zgOMpmMZGjRo2IiADO4isUi9vf3USqV4DiOfJ/1en0pmMwJagw0sx2GjolpA8d2vbbrsf1tW0u/z66C37a3SQmYuPVKwc/SvsqNQcERhGc2U9xsYwjNfV31pl113fqhpQClsBmPx3j9+jXi8bgUq5F6YZonU0RJIR0dHWF3dxeJRAJbW1t48+YNXNfFDz/8gGq1il6vh2QyKZw3KQda7gzWU8Hq+AODo+ypQ3qLrZlZbKdrInSrBe6T9RPMYOK10NNjRbSZYqvX1PyOTCGovUqdXKDX28uDM40GbquVBhUmB+AMh0Mkk0nkcjns7e1JX6rpdIpOHK1+QwAAEVJJREFUp4NqtYp+v496vY7NzU3s7Ozg6dOnKBQK2NzclBYn3W4XrVYLL1++lH1Pp1N0u100Gg00Gg10Oh1ZG3MNbAotiJfr5VV5Uap++wqK26wQgFAphFgBL3qNwrrf74uQZkCdzegmkwlevnwpdMP+/j6KxSLy+Tzu3buHQqGAUqmE09NT/N///Z/QDpFIRP6mkM7lcr/xIJg9w75MjFOYM37pIWhhTAVH6i4SiSCXy6FYLOLOnTtyLF3QpRu7EUG5ba6jzs33Wlu+rj0S7Wl6CVd6VTzfxeLt9Dz2tnIcB4vFAs1mU6qXucb379/HV199hePjY/z93/+9dM5tNBrigTHZgC1LWL18fn4uje/M67Jdn3mdfuu2aj2D7CfEegiVwgfEx3oz+7n2+n1N58RiMaEltBDudrtLVc2LxQK5XE5aaYxGI2QyGRm+0mq1pJBsPp8LfcMaBB181ump9GRsNJOZ8quvTY/0pPLZ29uTdhzdbldiDDoOBKxX0OkVhzDjRNqy9vqcmW2kj8Hr4ee4zq7rYrFYyDUxMJxKpeA4Dg4ODnB0dIRHjx7h6OgIACRG0O/30Ww2JXDPed3sncWWJvS4zPvE/Ft/B0EUg97Oz1te1xMJYUeoFEJ4wsvC05jP55JSe3h4KNkqtKp7vR4uLi4kqJzL5dDpdLC1tYW7d+/iiy++kIZ7r1+/xvPnz1GpVKQClpXJpEby+TzS6TRSqZQ0XGs2m5hMJkJpUDjoVt3MlKL1zboHejzxeBx37tzB0dERnj17hr/85S9oNBp49eqV7Etf81UEjl+An//r70ArIVOh6AphUmzz+VxalWxubmI2mwlVxJoCDkP6p3/6J9y5cwdfffWVpOQ+f/5cYkcvXrxAtVpFo9GQ2A97YVUqFfHo9Pnqa/PDqvvLphgv63WECI5QKdwwrnKzvm9Lx4sLXvUZWtmu66JUKmF/f18ELlNR2ahuOBzi/PxcUnGLxSLi8Tg2NzdF0Lmui06ng3q9jkgkIn37+Rkdr9DpsePxWDKPtIegs7+oYKgU2LETeEt7cVKY7qgKLNM+OrPpMtlHZv2JuR/zR9fNUDFQ+NPL4TUxFsOJeqznGAwG6HQ6mM1mKJVKUpX85ZdfYmtrS7rZMtYwGo3Q6XSE+mPQvtPpCKWkC/9sCQvXIai97kOTojL/Dr2EyyNUCiF+g1VctxlQpXCml3B0dCS0D4vIXr9+jXa7jeFwiFevXqFaraJWq+Hw8BClUkkE08HBAV6+fIlarYY///nPaLfbEsBkwDOTyUiFLY/L1F+dLURhQktWV04zK4fXslgs0Ol0UKlU4DiOKDNdz6AL6cx+VjrYyzUibEJfv6fTXUn9mLUu3I6fIU2kkwCSyaTUIKTTaSwWC1k/0kXpdBqPHz/GN998gy+//BIPHjyQkavtdlvGsnItWAvS6XTQbrdxdnYm36NONTURtDdQEMXhdR/6fTaIlxvCjrAh3keMm8qM8tq3zeryOgedycMuqc+ePcPBwYHMcS4UCpjP5+j1ejg/P5eAZKPREI7ecRyk02mpkt7c3JSK2VevXqFWq+Hs7Az1eh3D4RD9fl8EDseJOo4jVJFWCmycxylrDErzb+baky/nto7jiFLRdJRuS22uo01QmU0QvdZag8L+4uIC2WwWmUwGOzs7QsXVajWpEub1sVAwk8lIdhQ9HdaBJBIJfPXVV9jf38d3330nc7gZg7m4uJAYwcnJicQNWq0Wut0ufvzxR/EYSMPpWI55XddN5wSljLwMl3XwOVNRYUO8a8DHam3cxHn58bZ++eWsI+h0OkL9FAoF5HI5oXdKpZJY7FpoMQuG3H46nZZc+r29PaRSKQkys/UyeWzu4+LiAo7jSD498K4gjcISgHRnZTCclbsUjLSK+/0+HMcRi9tMB9UtqYOuq7luZrBYZxxRSFPwZ7PZpWZ+8/lcvAK2qtC0EiuNSX1x/b/44gvcvXsXjx49ku+MhYZML2UNAl8j7cTWF/Sw/O6X90XjeHmx17lfGz5npQGEnsJKfKxK4SbhlSnC9yKRiFjfmuJgTOGLL77AvXv3pE8SOX0KGmarUAhRiFEIsuJ2b29PBsNwEtzp6SkajQbq9boMzOn3+0sNCCksqVxc15WqZtd14TgOisUiJpMJGo0GXr9+LVayrkNg9g6hC+A0n8+YCpWHDgybmU/k/qkUdFos/6aCZC8px3GkQR3wtv3H/v6+7JtVyJVKRVJnc7kc8vk8nj59iidPnuDx48c4Pj5GIpEQOqnb7UrMoVKpyH5Y51CpVPDzzz+jUqlI62zOxPAKjJv/v8/n531lH33KSiH0FEJcCkEsJbOZHPDWA+h2uzg5OcHFxYW0zE6n08hms0LzFAoFuK4rFbb9fl+qbyncut0u5vM5XNeVDqyu60oMYmdnB8ViUWZJM9uJ1r6ukmYBl+M4GAwGSKVS6PV6WCwW0qU2m80KXcRKXV1EaNY5UPjrmRjmcCDbWmlPhu/rtifm9D2uSyQSkSaEkUhECvrI91ORua4rmV1bW1v48ssvsbu7i+3tbVG+pITYt2g0GomiZmV4p9ORBobD4VC+YzN2cpn756ZwGw24m0CoFEJcCmabCABC5XDQTi6XQ6VSQS6Xw/b2Nu7evYtSqSRVsrFYTFJU2UhtMBig2+0iFothMBigWCxie3sb+/v7yOVy2N/fF4G4u7uLTqeDly9fSuC63W5LOwrdHTWVSiGbzS61r6BnEY1GxYOgMuHwGVMpUIjztx4SoxWDDhYD7/pZae9C929iphP3yZoO0jyZTEZiNOwzpdeMsZ3NzU0cHBzgm2++we7uLp48eSKe2snJyZIiYHoplQvP5ezsDLVaDT///LPUjOhz1ZTXVbj7EB8nQvpoBcIbfhkmZcCsHDPYyABoLpdDJpORzKLNzU08fPgQ29vbuH//vsQInj9/jtPTU7x48QLn5+di5TP4TKWwvb0t++axGDTu9/vSwpm9/Jl1A0AC0GZPI3ZXTafTch3k2jUlRJpH/5BSSqVSS4qA66NnRHC9qJjoHbB9ug6UmxlJLKBjQ79GoyGU2IMHD7C1tYUnT55ge3sb5XJZKsB1jyK2su50OkLlsR6k2WzKyEwGmjkHgeus02ODpuJ+jviUrzmkj0LcCLwEgubJKXTYx386nSIajWI8HqNQKCCTyWA+nyOTySCVSmFra0uC1c1mE6PRSOZCswCNhXIsXmMuPhvuTadTZLNZ9Ho9iSuk02nJdtIjKgmeXzKZlD5HjA+Y6aKmt6DpI1uqqWlFm8FQvW8GjlOplLymW4ST+6dHEY1Gkc1mJQW4XC7jwYMH4okBkEB+r9dDr9cTD6Hb7UqAnXMTms2m9C/qdDpL7T947l6ZPZ+ykAzxW4RKIcRaMAUfg6iaFtF8uc7o6XQ6cBwHo9EI9Xodi8UC9+7dk0ltrusim80KbfP8+XPJj7+4uEAul0Or1RLhycZ77L6aTqdxeHiIxWKBR48eiRVMDp2T2JhnPxgMUKvVRFloSolCmha3rqo2R0syNqHnNjDuoPsx0UrjuhWLRbkWpuhSSOtMID3wnlPu/vCHP+Do6AhPnz7Fzs4OEomECPvT01NMJhMMh0OpJNdxEl3Qdn5+jm63i9PTU/FCWL2tlSBxWxmD24RQKazAKivofdcSfMz79sob5w9TUpvN5lIn00KhgGKxKO0m2KuHVjwzcWgpMyOJrRim06kIdNIv/M3eP9PpFDs7O0InseahWq3KfplyyXbSVE66wI0KgNfKegY28mMRGVNEbQFkrn2n05E1ovKgRzOZTCSjikqvUChgd3cXhUIBR0dHMlqUQXlmT9EDGI/HqNVqcj29Xk/GZ+o6BCoielCmwrOloK66R67Tewhao/Cx7ftTRagUQqyNVZWktrx1LUybzaZY0Y1GA/l8Hvfv3xdh7zgOXNcFgKX+R9lsVmoSSBexfoE8PPdBi5qehG5rQWXS6/WkepeFbLVaDa9evcJoNPqNlczeQMwG0vUKzP6hx8HzoYegvQSuyWAwkFoBndVDb2trawu5XA7lchmbm5sy97pQKGBjY0Oyver1utQTUBlw35xxQIqIsQUqRl4nQQ/H77s1EcbdPi+EgeYr4lOz5t/Hvs39m5QTg6qkTlKpFLa3t1EsFrG1tSUC+3//93/FeqegZPzAdd2l4jhd2UtPgd5DoVCQQHI2m5UpbBTqtOY5f4GtoZlFNRqNMBgMpDisXq9LPQNpJVIv2mPQc7dphevrZ0wkk8lIem6pVFpK4SWNpWdILBaLJZqJ3gUVHqufx+Mxms2m1CTQkxiNRvK96IQB230RJDB509Z26ClcH8JAc4iPAibtwCAnLdpYLCZWL/sNMb2VFjQb1zF+QbollUrJb7Z9YAvt0WiEeDwugeRMJiPZUMwc4vasg3AcB/l8Xo5B67rX6yEajUr7CaZ0kndnW2lSZCzw0xXK9AJ43FwuJ6mypIKo6NiGXE9RYwU3s5CoGPRYV93ym1lFTF9lx1oGqjUtRnzOAjFEMISewhXxqVrz79NTMPdrpjTyR4/NBCAWLbBsydIS18KVljW9gmw2u9T6gZ/V7SF05TOFM4vcuB8qqNFoJLSQbkZH5aUD7Tp9k9dI5XZxcSHZU6VSSa6XQp71EbrPkPYKuG66lTc9BCoAegXj8VioKZ1Kuk46aZBtQk/h00Egzy9UClfDpyq4P7RSMLfRQVgqBU25UKhS0GtLl4I+kUgI5cLWFqSpSClpiondXXVDPdZcsCke6aXpdCrtum3nzf2xfgLAEo1FxcHqYF2DwOA2g8CkpjgZjSm9k8nkN0FseidM5+Vnx+OxeFO21GF9/jdxL4VK4eNESB+F+KigrWj+bbaG8OK2+Zq2dEnJsBqXNQ16TjQ9gFQqBeBdp1Rdh8C226RpGMDmufF1KhUqFq1gmMbKOdO6lbVu400lwHjFcDiUuAUFP8+BikJXVlNZUcEw/mEGrM11A7xboocIoREqhRAfBDrA6WWtaSFGaAFnDqsB3jXoY3CangQDu4w9UIiz3TQ9De6DWU+0tLUgNa0tKiM9R5kKh4qB3g89Bp1uqyuneUzGWHQLCk1PsYiN1BgVjt7OXCd9vra1DRECCJWCVeCEeL8wBdOqlNdV/+sf4K0gjsViEtyOx+OYTqcymYweQDqdBgAJ2lJAA/4VysC76mQtcL1oJR0Y1kFzXexG+khPf9OeFltYa2/LrIPwW0MzwBwiBHHrYwpXVQqfKu//IWIKtn1fVRHbvj8vgafvYR2/YJYPZxOY+2c6qA5Omy2xbUrDBp2JxL+pAEgD6fRSGx1kwjymTXHZ1v1T4eXDmML1IQw0B4CXUgi9hk8XfjUS+nUtyJkqqmEKfB1L0HUDZuDbD6SJqBToHeguqTpbiefh9ZjagvYmPmch9yHwKa9nGGgOcSthe2htGTfmdrpRnmlhmwaDVgL0OFYpBZPaMpWDeSwdhL/MNYcIcRmESiHErYCZcWMTtCY3z7/Nz+ipa3pu87rpnX7nE3L+IT4UQqUQ4rOBnxDVWUHXGTsKQvFcBqu8Hb/tbAjp0BBBESqFELcCNoVgy1zye58wZy6vqwxWpYMGVQghQtwEQqXggc/ddf9Yso+Cfi7IZ4OAdRF6X/ybwn5VUPe6c/zNugKdevqhEeQcwuyjzwu3XimEFtjtwar8/A8hDLziCrddMIX4cLj1SiFEiCC4Kcs9iNcRGi4h3idCpRDi1uOyQjfo5y6rTMK6mRAfAp9nRVqIECFChLgUQk8hRAiEqZ1euG3XGyJUCiFChFiBsIXG7ULg3kchQoQIEeLzRxhTCBEiRIgQglAphAgRIkQIQagUQoQIESKEIFQKIUKECBFCECqFECFChAghCJVCiBAhQoQQhEohRIgQIUIIQqUQIkSIECEEoVIIESJEiBCC/wdEoSLdSN76wQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image(plt.subplots()[-1], random_row_1[\"source\"], label=\"Query Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fc9ec4-00d1-425f-867f-29d94c2803bb",
   "metadata": {
    "id": "90fc9ec4-00d1-425f-867f-29d94c2803bb"
   },
   "source": [
    "We will save the embedding for this image in the `sample embedding` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e8d2bd8a",
   "metadata": {
    "id": "e8d2bd8a"
   },
   "outputs": [],
   "source": [
    "sample_embedding_1 = random_row_1[\"embedding\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f95b4c4-95ec-4eac-b4e4-be82a168f98c",
   "metadata": {
    "id": "0f95b4c4-95ec-4eac-b4e4-be82a168f98c"
   },
   "source": [
    "### Search Based On The Chosen Image\n",
    "\n",
    "Using the the embeddings we extracted above in `sample_embedding` for this random image, we can call `table.search()` to find the `8` nearest neighbors to our query image. Remember that `hnsw_index` is the index name we created when defining our index before creating the table.\n",
    "\n",
    "In a real life scenario, the sample images used to search would be ones selected by a doctor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c935afda-10f4-4781-ac9a-a540dc81dd0e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "c935afda-10f4-4781-ac9a-a540dc81dd0e",
    "outputId": "4c371941-f005-4cca-913e-cede11f8a1eb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"results_1[0]\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"__nn_distance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          751.1834106445312,\n          655.0029907226562,\n          716.41748046875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"data/glioma_tumor/glioma_tumor_12.png\",\n          \"data/glioma_tumor/glioma_tumor_71.png\",\n          \"data/glioma_tumor/glioma_tumor_41.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"glioma_tumor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-48b1e798-97ca-4705-99ac-76ee7056801c\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>source</th>\n",
       "      <th>class</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_45.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.6581199169158936, 0.7380955219268799, 0.374...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>655.002991</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_71.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.5400809645652771, 0.6335983872413635, 0.327...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>695.868774</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_61.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[1.1261733770370483, 1.0511531829833984, 0.607...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>709.470703</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_95.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.6331982016563416, 0.5156578421592712, 0.037...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>714.166199</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_68.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.5686506032943726, 1.2953143119812012, 0.092...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>716.417480</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_41.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.14557020366191864, 0.7579463124275208, 1.33...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>734.459229</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_46.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[1.7890287637710571, 0.47240081429481506, 0.27...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>751.183411</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_12.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.21819375455379486, 0.19898559153079987, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>761.760010</td>\n",
       "      <td>data/glioma_tumor/glioma_tumor_15.png</td>\n",
       "      <td>glioma_tumor</td>\n",
       "      <td>[0.7461263537406921, 1.31121826171875, 0.50695...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   __nn_distance                                 source         class  \\\n",
       "0       0.000000  data/glioma_tumor/glioma_tumor_45.png  glioma_tumor   \n",
       "1     655.002991  data/glioma_tumor/glioma_tumor_71.png  glioma_tumor   \n",
       "2     695.868774  data/glioma_tumor/glioma_tumor_61.png  glioma_tumor   \n",
       "3     709.470703  data/glioma_tumor/glioma_tumor_95.png  glioma_tumor   \n",
       "4     714.166199  data/glioma_tumor/glioma_tumor_68.png  glioma_tumor   \n",
       "5     716.417480  data/glioma_tumor/glioma_tumor_41.png  glioma_tumor   \n",
       "6     734.459229  data/glioma_tumor/glioma_tumor_46.png  glioma_tumor   \n",
       "7     751.183411  data/glioma_tumor/glioma_tumor_12.png  glioma_tumor   \n",
       "8     761.760010  data/glioma_tumor/glioma_tumor_15.png  glioma_tumor   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.6581199169158936, 0.7380955219268799, 0.374...  \n",
       "1  [0.5400809645652771, 0.6335983872413635, 0.327...  \n",
       "2  [1.1261733770370483, 1.0511531829833984, 0.607...  \n",
       "3  [0.6331982016563416, 0.5156578421592712, 0.037...  \n",
       "4  [0.5686506032943726, 1.2953143119812012, 0.092...  \n",
       "5  [0.14557020366191864, 0.7579463124275208, 1.33...  \n",
       "6  [1.7890287637710571, 0.47240081429481506, 0.27...  \n",
       "7  [0.21819375455379486, 0.19898559153079987, 0.0...  \n",
       "8  [0.7461263537406921, 1.31121826171875, 0.50695...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_1 = table.search(vectors={\"hnsw_index\":[sample_embedding_1]}, n=9)\n",
    "results_1[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d171125-3182-47ca-85b9-cefe11d34edc",
   "metadata": {
    "id": "6d171125-3182-47ca-85b9-cefe11d34edc"
   },
   "source": [
    "The results returned from `table.search()` show the closest matches along with value of nearest neighbor distances `__nn_distance`.\n",
    "Since our query vector is taken from this dataset, the \"nearest neighbour\" will be the query image itself, with a `__nn_distance` of zero.\n",
    "As a result, to get the 8 nearest neighbours, we had to select the 9 most similar vectors to our vector."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc46a521-3382-4768-8cee-f7efbd439e8c",
   "metadata": {
    "id": "cc46a521-3382-4768-8cee-f7efbd439e8c"
   },
   "source": [
    "### Plot Most Similar Images\n",
    "\n",
    "Let's visualize these images.\n",
    "The function `plot_test_result_with_8NN()` will plot our query image alongside its 8 nearest neighbours.\n",
    "Before calling this function, will exclude the point itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "32fb5ae8",
   "metadata": {
    "id": "32fb5ae8"
   },
   "outputs": [],
   "source": [
    "def plot_test_result_with_8NN(test_file: str, neighbors: pd.Series) -> None:\n",
    "    # create figure\n",
    "    _, ax = plt.subplots(nrows=3, ncols=3, figsize=(12, 7))\n",
    "    axes = ax.reshape(-1)\n",
    "\n",
    "    # plot query image\n",
    "    plot_image(axes[0], test_file, \"Test\")\n",
    "\n",
    "    # plot nearest neighbors\n",
    "    for i, (_, value) in enumerate(neighbors.items(), start=1):\n",
    "        plot_image(axes[i], value, f\"{i}-NN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "359e63c1",
   "metadata": {
    "id": "359e63c1"
   },
   "outputs": [],
   "source": [
    "nn1_filenames = results_1[0][1:][\"source\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fb1c3aed-d7d1-4a1e-9b07-b9554e290803",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "id": "fb1c3aed-d7d1-4a1e-9b07-b9554e290803",
    "outputId": "1ee327ff-5cc9-4401-fa61-9ca050e939bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_test_result_with_8NN(random_row_1[\"source\"], nn1_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3954e7ec-59d0-4c1f-bf4d-b58dba6615f9",
   "metadata": {
    "id": "3954e7ec-59d0-4c1f-bf4d-b58dba6615f9"
   },
   "source": [
    "We can see that the images returned are similar in nature to the test image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae98276-8d73-49a8-9463-1f751d28cef7",
   "metadata": {
    "id": "4ae98276-8d73-49a8-9463-1f751d28cef7"
   },
   "source": [
    "### Automate This Search Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "682c8222-89ad-4f8d-9b06-dfaa8654d835",
   "metadata": {
    "id": "682c8222-89ad-4f8d-9b06-dfaa8654d835"
   },
   "outputs": [],
   "source": [
    "def mri_image_nn_search(table, df: pd.DataFrame, row_index: int) -> None:\n",
    "    # Select the random row and the desired column's value\n",
    "    row = df.iloc[row_index]\n",
    "\n",
    "    # get the embedding from this row\n",
    "    row_embedding = row[\"embedding\"]\n",
    "\n",
    "    # search for 8 nearest neighbors (exclude self)\n",
    "    nn_results = table.search(vectors={\"hnsw_index\":[row_embedding]}, n=9)[0][1:]\n",
    "\n",
    "    # plot the neighbors\n",
    "    plot_test_result_with_8NN(row[\"source\"], nn_results[\"source\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c42f87d-3dd9-4fe8-8a6a-5f5142d21ffc",
   "metadata": {
    "id": "7c42f87d-3dd9-4fe8-8a6a-5f5142d21ffc"
   },
   "source": [
    "Let's try another sample image and search for similar images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "45914c7f",
   "metadata": {
    "id": "45914c7f"
   },
   "outputs": [],
   "source": [
    "# Get another row\n",
    "random_row_index_2 = 210"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b7a56acd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "id": "b7a56acd",
    "outputId": "582bad2e-c20d-467c-9d9c-57e3c05368c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mri_image_nn_search(table, embedded_df, random_row_index_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c007c27",
   "metadata": {
    "id": "7c007c27"
   },
   "source": [
    "We can see with this second test image that we get a set of similar brain scans back that match the second test image closely and are all in the same category. This kind of result can help a doctor confirm their own assumptions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c52dee",
   "metadata": {
    "id": "35c52dee"
   },
   "source": [
    "## 6. Delete the KDB.AI Database & Table\n",
    "\n",
    "Once finished with the database & table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1f05db86",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1f05db86",
    "outputId": "6a048fb3-b66a-42e7-82a8-79649cfb0e97"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.drop()\n",
    "db.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec247cae",
   "metadata": {
    "id": "ec247cae"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/pmv533GL)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: metadata_filtering/data/filtered_embedded_movies.pkl
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FILE: metadata_filtering/metadata_filtering_demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metadata Filtering with KDB.AI Vector Database\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "#### In this example, we will show how to use metadata filtering in a KDB.AI vector database to increase the speed and accuracy of vector similarity searches.\n",
    "\n",
    "#### Agenda:\n",
    "1. Set Up\n",
    "2. Data Import and Understanding \n",
    "3. Set Up KDB.AI Vector Database\n",
    "4. Insert Movie Data into the KDB.AI table\n",
    "5. Run Filtered Similarity Searches on our KDB.AI vector database\n",
    "\n",
    "Movie Dataset Source: https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Set Up\n",
    "#### Installs and imports\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client\n",
    "!pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads movie data\n",
    "!mkdir ./data \n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/metadata_filtering/data/filtered_embedded_movies.pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Import and Understanding\n",
    "### Import movies dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Read in the Movies dataframe\n",
    "df = pd.read_pickle(\"./data/filtered_embedded_movies.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial data exploration: Let's understand the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19161\n"
     ]
    }
   ],
   "source": [
    "#How many rows do we have?\n",
    "print(df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReleaseYear\n",
      "Title\n",
      "Origin\n",
      "Director\n",
      "Cast\n",
      "Genre\n",
      "Plot\n",
      "embeddings\n"
     ]
    }
   ],
   "source": [
    "#What columns do we have?\n",
    "for column in df.columns:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ReleaseYear</th>\n",
       "      <th>Title</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Plot</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>The Candy Tangerine Man</td>\n",
       "      <td>American</td>\n",
       "      <td>Matt Cimber</td>\n",
       "      <td>John Daniels Eli Haines Tom Hankason</td>\n",
       "      <td>action</td>\n",
       "      <td>A successful Los Angeles-based businessperson ...</td>\n",
       "      <td>[-0.06835174, -0.013138616, -0.12417501, 0.002...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>Capone</td>\n",
       "      <td>American</td>\n",
       "      <td>Steve Carver</td>\n",
       "      <td>Ben Gazzara Susan Blakely John Cassavetes Sylv...</td>\n",
       "      <td>crime drama</td>\n",
       "      <td>The story is of the rise and fall of the Chica...</td>\n",
       "      <td>[-0.01411798, 0.040705115, -0.0014280609, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975</td>\n",
       "      <td>Cleopatra Jones and the Casino of Gold</td>\n",
       "      <td>American</td>\n",
       "      <td>Charles Bail</td>\n",
       "      <td>Tamara Dobson Stella Stevens</td>\n",
       "      <td>action</td>\n",
       "      <td>The story begins with two government agents Ma...</td>\n",
       "      <td>[-0.0925895, 0.01188509, -0.08999529, -0.01541...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975</td>\n",
       "      <td>Conduct Unbecoming</td>\n",
       "      <td>American</td>\n",
       "      <td>Michael Anderson</td>\n",
       "      <td>Stacy Keach Richard Attenborough Christopher P...</td>\n",
       "      <td>drama</td>\n",
       "      <td>Around 1880 two young British officers arrive ...</td>\n",
       "      <td>[-0.07435084, -0.06386179, 0.017042944, 0.0288...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>Cooley High</td>\n",
       "      <td>American</td>\n",
       "      <td>Michael Schultz</td>\n",
       "      <td>Lawrence Hilton-Jacobs Glynn Turman Garrett Mo...</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Set in 1964 Chicago Preach an aspiring playwri...</td>\n",
       "      <td>[-0.041632336, 0.037923656, -0.072276264, -0.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ReleaseYear                                   Title    Origin  \\\n",
       "0         1975                 The Candy Tangerine Man  American   \n",
       "1         1975                                  Capone  American   \n",
       "2         1975  Cleopatra Jones and the Casino of Gold  American   \n",
       "3         1975                      Conduct Unbecoming  American   \n",
       "4         1975                             Cooley High  American   \n",
       "\n",
       "           Director                                               Cast  \\\n",
       "0       Matt Cimber               John Daniels Eli Haines Tom Hankason   \n",
       "1      Steve Carver  Ben Gazzara Susan Blakely John Cassavetes Sylv...   \n",
       "2      Charles Bail                       Tamara Dobson Stella Stevens   \n",
       "3  Michael Anderson  Stacy Keach Richard Attenborough Christopher P...   \n",
       "4   Michael Schultz  Lawrence Hilton-Jacobs Glynn Turman Garrett Mo...   \n",
       "\n",
       "         Genre                                               Plot  \\\n",
       "0       action  A successful Los Angeles-based businessperson ...   \n",
       "1  crime drama  The story is of the rise and fall of the Chica...   \n",
       "2       action  The story begins with two government agents Ma...   \n",
       "3        drama  Around 1880 two young British officers arrive ...   \n",
       "4       comedy  Set in 1964 Chicago Preach an aspiring playwri...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-0.06835174, -0.013138616, -0.12417501, 0.002...  \n",
       "1  [-0.01411798, 0.040705115, -0.0014280609, 0.00...  \n",
       "2  [-0.0925895, 0.01188509, -0.08999529, -0.01541...  \n",
       "3  [-0.07435084, -0.06386179, 0.017042944, 0.0288...  \n",
       "4  [-0.041632336, 0.037923656, -0.072276264, -0.0...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us inspect the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Set up KDB.AI Vector Database\n",
    "Now that we understand our dataset, we can set up our vector db\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up the table schema\n",
    "Have a table column for each column in the dataframe, as well as an 'embeddings' column for the movie description embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up the schema and indexes for KDB.AI table, specifying embeddings column with 384 dimensions, Euclidean Distance, and flat index\n",
    "table_schema = [\n",
    "    {\"name\": \"ReleaseYear\", \"type\": \"int64\"},\n",
    "    {\"name\": \"Title\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"Origin\", \"type\": \"str\"},\n",
    "    {\"name\": \"Director\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"Cast\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"Genre\", \"type\": \"str\"},\n",
    "    {\"name\": \"Plot\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"embeddings\", \"type\": \"float64s\"}\n",
    "]\n",
    "\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\": \"flat_index\",\n",
    "        \"type\": \"flat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {\"dims\": 384, \"metric\": \"L2\"},\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a table called \"metadata_demo\"\n",
    "First check if the table already exists, then create a new table with the table schema from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"metadata_demo\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the table called \"metadata_demo\"\n",
    "table = database.create_table(\"metadata_demo\", schema = table_schema, indexes = indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Insert Movie Data into the KDB.AI table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  9.12it/s]\n"
     ]
    }
   ],
   "source": [
    "#Insert the data into the table, split into 2000 row batches\n",
    "from tqdm import tqdm \n",
    "n = 2000  # chunk row size\n",
    "\n",
    "# convert empty cast values to string form for backend. Here we are using value None tofor empty Cast value.\n",
    "for index, row in df.iterrows():\n",
    "    cast = row['Cast']\n",
    "    if 1 == len(cast):\n",
    "        df.loc[index, 'Cast'] = 'None'\n",
    "    \n",
    "for i in tqdm(range(0, df.shape[0], n)):\n",
    "    data = df[i:i+n].reset_index(drop=True)\n",
    "    # change data types as per table schema\n",
    "    data['Title'] = data['Title'].str.encode('utf-8')\n",
    "    data['Director'] = data['Director'].str.encode('utf-8')\n",
    "    data['Cast'] = data['Cast'].str.encode('utf-8')\n",
    "    data['Plot'] = data['Plot'].str.encode('utf-8')\n",
    "    table.insert(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to view the dataframe within the table\n",
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19161, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ReleaseYear</th>\n",
       "      <th>Title</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Plot</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>b'The Candy Tangerine Man'</td>\n",
       "      <td>American</td>\n",
       "      <td>b'Matt Cimber'</td>\n",
       "      <td>b'John Daniels Eli Haines Tom Hankason'</td>\n",
       "      <td>action</td>\n",
       "      <td>b'A successful Los Angeles-based businessperso...</td>\n",
       "      <td>[-0.06835173815488815, -0.01313861645758152, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>b'Capone'</td>\n",
       "      <td>American</td>\n",
       "      <td>b'Steve Carver'</td>\n",
       "      <td>b'Ben Gazzara Susan Blakely John Cassavetes Sy...</td>\n",
       "      <td>crime drama</td>\n",
       "      <td>b'The story is of the rise and fall of the Chi...</td>\n",
       "      <td>[-0.014117980375885963, 0.0407051146030426, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975</td>\n",
       "      <td>b'Cleopatra Jones and the Casino of Gold'</td>\n",
       "      <td>American</td>\n",
       "      <td>b'Charles Bail'</td>\n",
       "      <td>b'Tamara Dobson Stella Stevens'</td>\n",
       "      <td>action</td>\n",
       "      <td>b'The story begins with two government agents ...</td>\n",
       "      <td>[-0.09258949756622314, 0.011885089799761772, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975</td>\n",
       "      <td>b'Conduct Unbecoming'</td>\n",
       "      <td>American</td>\n",
       "      <td>b'Michael Anderson'</td>\n",
       "      <td>b'Stacy Keach Richard Attenborough Christopher...</td>\n",
       "      <td>drama</td>\n",
       "      <td>b'Around 1880 two young British officers arriv...</td>\n",
       "      <td>[-0.07435084134340286, -0.06386178731918335, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>b'Cooley High'</td>\n",
       "      <td>American</td>\n",
       "      <td>b'Michael Schultz'</td>\n",
       "      <td>b'Lawrence Hilton-Jacobs Glynn Turman Garrett ...</td>\n",
       "      <td>comedy</td>\n",
       "      <td>b'Set in 1964 Chicago Preach an aspiring playw...</td>\n",
       "      <td>[-0.041632335633039474, 0.0379236564040184, -0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ReleaseYear                                      Title    Origin  \\\n",
       "0         1975                 b'The Candy Tangerine Man'  American   \n",
       "1         1975                                  b'Capone'  American   \n",
       "2         1975  b'Cleopatra Jones and the Casino of Gold'  American   \n",
       "3         1975                      b'Conduct Unbecoming'  American   \n",
       "4         1975                             b'Cooley High'  American   \n",
       "\n",
       "              Director                                               Cast  \\\n",
       "0       b'Matt Cimber'            b'John Daniels Eli Haines Tom Hankason'   \n",
       "1      b'Steve Carver'  b'Ben Gazzara Susan Blakely John Cassavetes Sy...   \n",
       "2      b'Charles Bail'                    b'Tamara Dobson Stella Stevens'   \n",
       "3  b'Michael Anderson'  b'Stacy Keach Richard Attenborough Christopher...   \n",
       "4   b'Michael Schultz'  b'Lawrence Hilton-Jacobs Glynn Turman Garrett ...   \n",
       "\n",
       "         Genre                                               Plot  \\\n",
       "0       action  b'A successful Los Angeles-based businessperso...   \n",
       "1  crime drama  b'The story is of the rise and fall of the Chi...   \n",
       "2       action  b'The story begins with two government agents ...   \n",
       "3        drama  b'Around 1880 two young British officers arriv...   \n",
       "4       comedy  b'Set in 1964 Chicago Preach an aspiring playw...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-0.06835173815488815, -0.01313861645758152, -...  \n",
       "1  [-0.014117980375885963, 0.0407051146030426, -0...  \n",
       "2  [-0.09258949756622314, 0.011885089799761772, -...  \n",
       "3  [-0.07435084134340286, -0.06386178731918335, 0...  \n",
       "4  [-0.041632335633039474, 0.0379236564040184, -0...  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View contents of the table\n",
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Run Filtered Similarity Searches on our KDB.AI Vector Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up embedding model to embed our natural language queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# embedding model to be used to embed user input query\n",
    "from sentence_transformers import SentenceTransformer\n",
    "embedding_model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a query vector by using the embedding model to embed a natural language query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Embed a query\n",
    "query_vector = {'flat_index' : [embedding_model.encode('star wars Luke Skywalker').tolist()]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run vector similarity search, return the top-3 similar movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       0.748475         1983   \n",
      "1       0.910225         1977   \n",
      "2       0.942763         1980   \n",
      "\n",
      "                                               Title    Origin  \\\n",
      "0                              b'Return of the Jedi'  American   \n",
      "1  b'Star Wars Episode IV: A New Hope (aka Star W...  American   \n",
      "2                         b'The Empire Strikes Back'  American   \n",
      "\n",
      "              Director                                               Cast  \\\n",
      "0  b'Richard Marquand'  b'Mark Hamill Harrison Ford Carrie Fisher Bill...   \n",
      "1      b'George Lucas'  b'Mark Hamill Harrison Ford Carrie Fisher Alec...   \n",
      "2    b'Irvin Kershner'  b'Carrie Fisher Harrison Ford Mark Hamill Bill...   \n",
      "\n",
      "             Genre                                               Plot  \\\n",
      "0  science fiction  b'Luke Skywalker initiates a plan to rescue Ha...   \n",
      "1  science fiction  b'The galaxy is in the midst of a civil war. S...   \n",
      "2  science fiction  b'Three years after the destruction of the Dea...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.047360002994537354, -0.08337291330099106, ...  \n",
      "1  [-0.10030582547187805, 0.008335104212164879, 0...  \n",
      "2  [-0.050230544060468674, -0.023651080206036568,...  ]\n"
     ]
    }
   ],
   "source": [
    "#Search vector db to find most relevant movies\n",
    "print(table.search(vectors=query_vector, n=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Repeat the search with metadata filters to narrow the search space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       0.910225         1977   \n",
      "\n",
      "                                               Title    Origin  \\\n",
      "0  b'Star Wars Episode IV: A New Hope (aka Star W...  American   \n",
      "\n",
      "          Director                                               Cast  \\\n",
      "0  b'George Lucas'  b'Mark Hamill Harrison Ford Carrie Fisher Alec...   \n",
      "\n",
      "             Genre                                               Plot  \\\n",
      "0  science fiction  b'The galaxy is in the midst of a civil war. S...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.10030582547187805, 0.008335104212164879, 0...  ]\n"
     ]
    }
   ],
   "source": [
    "print(table.search(vectors=query_vector, n=3, filter=[(\"like\", \"Director\", \"George Lucas\"),(\"=\", \"ReleaseYear\", 1977)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another query\n",
    "query_vector = {'flat_index' : [embedding_model.encode('conspiracy theories involving art').tolist()]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear                  Title    Origin  \\\n",
      "0       1.276896         2006  b' The Da Vinci Code'  American   \n",
      "1       1.395944         2017          b'The Circle'  American   \n",
      "2       1.607655         2017            b'The Post'  American   \n",
      "\n",
      "              Director                                               Cast  \\\n",
      "0        b'Ron Howard'  b'Tom Hanks Audrey Tautou Ian McKellen Alfred ...   \n",
      "1    b'James Ponsoldt'  b'James Ponsoldt (director/screenplay); Tom Ha...   \n",
      "2  b'Steven Spielberg'  b'Steven Spielberg (director); Liz Hannah Josh...   \n",
      "\n",
      "                              Genre  \\\n",
      "0                          thriller   \n",
      "1             sci-fi drama thriller   \n",
      "2  biography drama history thriller   \n",
      "\n",
      "                                                Plot  \\\n",
      "0  b'Jacques Sauni\\xc2\\xa8re the Louvres curator ...   \n",
      "1  b'When her car breaks down Mae Holland contact...   \n",
      "2  b'In 1966 Vietnam State Department military an...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.11887314915657043, -0.04977063462138176, -...  \n",
      "1  [-0.07589969784021378, -0.052303414791822433, ...  \n",
      "2  [-0.06242850422859192, -0.0349779948592186, -0...  ]\n"
     ]
    }
   ],
   "source": [
    "# Another filtered search example\n",
    "print(table.search(vectors=query_vector, n=3, filter=[(\"like\", \"Genre\", \"*thriller*\"),(\"like\",\"Cast\",\"*Tom Hanks*\")]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another query\n",
    "query_vector = {'flat_index' : [embedding_model.encode('middle earth fantasy adventure in the Shire').tolist()]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance  ReleaseYear  \\\n",
      "0       1.014505         2001   \n",
      "1       1.099138         2002   \n",
      "2       1.153412         2003   \n",
      "\n",
      "                                               Title    Origin  \\\n",
      "0  b'The Lord of the Rings: The Fellowship of the...  American   \n",
      "1           b'The Lord of the Rings: The Two Towers'  American   \n",
      "2   b'The Lord of the Rings: The Return of the King'  American   \n",
      "\n",
      "           Director                                               Cast  \\\n",
      "0  b'Peter Jackson'  b'Elijah Wood Ian McKellen Liv Tyler Sean Asti...   \n",
      "1  b'Peter Jackson'  b'Elijah Wood Ian McKellen Liv Tyler Viggo Mor...   \n",
      "2  b'Peter Jackson'  b'Elijah Wood Ian McKellen Liv Tyler Sean Asti...   \n",
      "\n",
      "               Genre                                               Plot  \\\n",
      "0            fantasy  b'In the Second Age of Middle-earth the lords ...   \n",
      "1  adventure fantasy  b'After awakening from a dream of Gandalf the ...   \n",
      "2  adventure fantasy  b'Many years ago two Hobbits Smeagol and Dagol...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [-0.04706393554806709, 0.0369022861123085, -0....  \n",
      "1  [-0.05915825068950653, 0.033268801867961884, -...  \n",
      "2  [-0.07969464361667633, -0.006237572990357876, ...  ]\n"
     ]
    }
   ],
   "source": [
    "# Another filtered search example\n",
    "print(table.search(vectors=query_vector, n=3, filter=[(\"within\",\"ReleaseYear\",[2000,2010])]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/wtS7T4Lg)\n"
   ]
  }
 ],
 "metadata": {
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   "pygments_lexer": "ipython3",
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================================================
FILE: multi_index_multimodal_search/data/bat1.txt
================================================
Bat with outstretched wings hovering over an apple on leafy branch

================================================
FILE: multi_index_multimodal_search/data/bat2.txt
================================================
a bat hanging upside down from a metal bar in an urban setting

================================================
FILE: multi_index_multimodal_search/data/bear1.txt
================================================
Brown bear walking through a forest with fallen leaves

================================================
FILE: multi_index_multimodal_search/data/bear2.txt
================================================
Close-up portrait of a grizzly bear's face and upper body

================================================
FILE: multi_index_multimodal_search/data/caterpillar1.txt
================================================
Bright green caterpillar on a wooden surface

================================================
FILE: multi_index_multimodal_search/data/caterpillar2.txt
================================================
Hairy caterpillar with spikes and spots on a green leaf

================================================
FILE: multi_index_multimodal_search/data/deer1.txt
================================================
Buck deer with antlers standing in misty grassland

================================================
FILE: multi_index_multimodal_search/data/deer2.txt
================================================
Side view of buck deer with large antlers in forest setting

================================================
FILE: multi_index_multimodal_search/data/fox1.txt
================================================
Fluffy fox curled up in snow with eyes closed

================================================
FILE: multi_index_multimodal_search/data/fox2.txt
================================================
Red fox with alert expression against rocky background

================================================
FILE: multi_index_multimodal_search/data/hedgehog1.txt
================================================
Small hedgehog curled up in someone's palm, surrounded by autumn colors

================================================
FILE: multi_index_multimodal_search/data/hedgehog2.txt
================================================
Hedgehog in a field of red flowers, some petals on its spines

================================================
FILE: multi_index_multimodal_search/multi_index_multimodal_search.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D0wSB_3mKl5D"
   },
   "source": [
    "## Multi-Index Search\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "KDB.AI enables multiple indexes to be defined within a table. The indexes can be queried and searched independently, or simultaneously. When doing multi-index search, the user defines a 'weight' parameter to determine the weight of each index for that search.\n",
    "\n",
    "In this example we will highlight multi-index search with a multimodal retrieval use-case. We will take images of animals with text descriptions, embed both of them with the CLIP multimodal model, store the image embeddings in one index, and the text embeddings in another index, and perform multi-index search.\n",
    "\n",
    "### Agenda:\n",
    "1. Setup\n",
    "2. Generate Image and Text Embeddings Using [open-clip](https://github.com/mlfoundations/open_clip)\n",
    "3. Define KDB.AI Session\n",
    "4. Define Vector DB Table Schema\n",
    "5. Create Vector DB Table\n",
    "6. Insert DataFrame into the KDB.AI Table\n",
    "7. Execute Multi-Index Search\n",
    "8. Delete the KDB.AI Table\n",
    "\n",
    "Animal Images Source: https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tyyzk6n4Trr-"
   },
   "source": [
    "## 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Xhvz6H2Rk48a"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rqkGNhHDgKk8"
   },
   "outputs": [],
   "source": [
    "# For CPU-only\n",
    "!pip install open_clip_torch torch torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QuGvCEg-gUaS"
   },
   "outputs": [],
   "source": [
    "import open_clip\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ds6aIIaXZrY-"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "import io\n",
    "from PIL import Image\n",
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads image and text data\n",
    "\n",
    "!mkdir -p ./data\n",
    "\n",
    "\n",
    "def get_github_repo_contents(repo_owner, repo_name, branch, folder_path):\n",
    "    # Construct the API URL\n",
    "    api_url = f\"https://api.github.com/repos/{repo_owner}/{repo_name}/contents/{folder_path}?ref={branch}\"\n",
    "\n",
    "    # Send the request and process the response\n",
    "    contents = requests.get(api_url).json()\n",
    "\n",
    "    # Create the local directory if it doesn't exist\n",
    "    fPath = f\"./{folder_path.split('/')[-1]}\"\n",
    "\n",
    "    for item in contents:\n",
    "        # Recursively list contents of subfolder\n",
    "        if item['type'] == 'dir':\n",
    "            get_github_repo_contents(repo_owner, repo_name, branch, f\"{folder_path}/{item['name']}\")\n",
    "        # Download and save file\n",
    "        elif item['type'] == 'file':\n",
    "            file_url = f\"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{branch}/{folder_path}/{item['name']}\"\n",
    "            print(file_url)\n",
    "            r = requests.get(file_url, timeout=4.0)\n",
    "            r.raise_for_status()  # Raises an exception for HTTP errors\n",
    "            file_path = f\"{fPath}/{item['name']}\"\n",
    "\n",
    "            if item['name'].lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):\n",
    "                # Save image file\n",
    "                with Image.open(io.BytesIO(r.content)) as im:\n",
    "                    im.save(file_path)\n",
    "            else:\n",
    "                # Save text file\n",
    "                with open(file_path, 'wb') as f:\n",
    "                    f.write(r.content)\n",
    "\n",
    "# Get images and texts\n",
    "get_github_repo_contents(\n",
    "    repo_owner='KxSystems',\n",
    "    repo_name='kdbai-samples',\n",
    "    branch='main',\n",
    "    folder_path='multi_index_multimodal_search/data'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nU0T3ppiT0i_"
   },
   "source": [
    "## 2. Generate Image and Text Embeddings Using CLIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CqAkL9Q8dHnw",
    "outputId": "f7370cf2-b4f6-4706-97f9-c9b119249448"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "import open_clip\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def load_clip_model():\n",
    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "    model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k', device=device)\n",
    "    return model, preprocess, device\n",
    "\n",
    "def generate_image_embedding(model, preprocess, image_path, device):\n",
    "    image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)\n",
    "    with torch.no_grad():\n",
    "        image_features = model.encode_image(image)\n",
    "    return image_features.cpu().numpy()\n",
    "\n",
    "def generate_text_embedding(model, text, device):\n",
    "    tokenizer = open_clip.get_tokenizer('ViT-B-32')\n",
    "    text_tokens = tokenizer([text]).to(device)\n",
    "    with torch.no_grad():\n",
    "        text_features = model.encode_text(text_tokens)\n",
    "    return text_features.cpu().numpy()\n",
    "\n",
    "def read_text_file(file_path):\n",
    "    with open(file_path, 'r', encoding='utf-8') as file:\n",
    "        return file.read().strip()\n",
    "\n",
    "def process_images_and_descriptions(data_dir):\n",
    "    model, preprocess, device = load_clip_model()\n",
    "\n",
    "    data = []\n",
    "\n",
    "    # First, collect all image files\n",
    "    image_files = [f for f in os.listdir(data_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.gif'))]\n",
    "\n",
    "    for image_file in image_files:\n",
    "        image_path = os.path.join(data_dir, image_file)\n",
    "        text_file = os.path.splitext(image_file)[0] + '.txt'\n",
    "        text_path = os.path.join(data_dir, text_file)\n",
    "\n",
    "        image_embedding = generate_image_embedding(model, preprocess, image_path, device)\n",
    "\n",
    "        if os.path.exists(text_path):\n",
    "            text = read_text_file(text_path)\n",
    "            text_embedding = generate_text_embedding(model, text, device)\n",
    "        else:\n",
    "            text = None\n",
    "            text_embedding = None\n",
    "\n",
    "        data.append({\n",
    "            'image_path': image_path,\n",
    "            'text_path': text_path if os.path.exists(text_path) else None,\n",
    "            'text': text,\n",
    "            'image_embedding': image_embedding.flatten(),\n",
    "            'text_embedding': text_embedding.flatten() if text_embedding is not None else None\n",
    "        })\n",
    "\n",
    "    # Create DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "\n",
    "    return df\n",
    "\n",
    "# Example usage\n",
    "data_dir = \"./data\"\n",
    "result_df = process_images_and_descriptions(data_dir)\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "print(result_df.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDp8m5UheQag"
   },
   "source": [
    "## 3. Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8TQMEp8Be01C"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT, mode='qipc')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xjAmJUJUfWBw"
   },
   "source": [
    "## 4. Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will have two columns.\n",
    "\n",
    "At this point you will select the index and metric you want to use for searching.\n",
    "\n",
    "In this case, we will use the qFlat index, Euclidean Distance (L2) for the search metric, and we specify the number of dimensions of our embeddings (512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G2zoNe5CfnR5"
   },
   "outputs": [],
   "source": [
    "#Set up the schema and indexes for KDB.AI table\n",
    "schema = [\n",
    "    {\"name\": \"image_path\", \"type\": \"str\"},\n",
    "    {\"name\": \"text_path\", \"type\": \"str\"},\n",
    "    {\"name\": \"text\", \"type\": \"str\"},\n",
    "    {\"name\": \"image_embedding\", \"type\": \"float32s\"},\n",
    "    {\"name\": \"text_embedding\", \"type\": \"float32s\"}\n",
    "]\n",
    "\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\": \"image_index_qFlat\",\n",
    "        \"type\": \"qFlat\",\n",
    "        \"column\": \"image_embedding\",\n",
    "        \"params\": {\"dims\": 512, \"metric\": \"L2\"},\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"text_index_qFlat\",\n",
    "        \"type\": \"qFlat\",\n",
    "        \"column\": \"text_embedding\",\n",
    "        \"params\": {\"dims\": 512, \"metric\": \"L2\"},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jFUygXrmfI4i"
   },
   "source": [
    "## 5. Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "C4g64bFtfMjy"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"multi_index_search\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass\n",
    "\n",
    "table = database.create_table(\"multi_index_search\", schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZqvAc96PgqHM"
   },
   "source": [
    "We can use `query` to see our table exists but is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "BSm5wReYgsF9",
    "outputId": "e60c466c-c6a0-4623-9850-81c4d242a033"
   },
   "outputs": [],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ejKrOm_Qg597"
   },
   "source": [
    "## 6. Insert DataFrame into the KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DUmWADuJhAHC",
    "outputId": "fe76600c-a65d-4dd3-b586-a9dab5c8190c"
   },
   "outputs": [],
   "source": [
    "table.insert(result_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "liXcvrpnhFb0"
   },
   "source": [
    "Let's check if the data was ingested into the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "jUGp6eXOhEGO",
    "outputId": "5bb934e1-1965-418a-c55e-f53eddf87399"
   },
   "outputs": [],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RWg_Wi6dhfMG"
   },
   "source": [
    "## 7. Execute Multi-Index Search\n",
    "Now, our table is all set up to search against. We will perform searches for images, text, as well as multimodal search across both, demonstrating multi-index search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VIJgNb2iiDgT"
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "def embed_query(text):\n",
    "    model, preprocess, device = load_clip_model()\n",
    "    # Tokenize the text\n",
    "    tokenizer = open_clip.get_tokenizer('ViT-B-32')\n",
    "    text_tokens = tokenizer([text]).to(device)\n",
    "\n",
    "    # Generate the embedding\n",
    "    with torch.no_grad():\n",
    "        text_features = model.encode_text(text_tokens)\n",
    "\n",
    "    # Convert to numpy array and return\n",
    "    return text_features.cpu().numpy()\n",
    "\n",
    "\n",
    "def view_results(results):\n",
    "  for index, row in results.iterrows():\n",
    "      display(Image.open(row.iloc[1]))\n",
    "      print(row.iloc[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6yVs0_sQht_d"
   },
   "outputs": [],
   "source": [
    "query = 'what are the purpose of antlers?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "04Yc0lj2igAS"
   },
   "outputs": [],
   "source": [
    "query_vector = embed_query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "id": "67X8TCncix6Y",
    "outputId": "0fb95192-64df-436e-8972-5ed6672431c4"
   },
   "outputs": [],
   "source": [
    "# Search across the texts index\n",
    "results = table.search(vectors={\"text_index_qFlat\":query_vector},n=2)[0]\n",
    "view_results(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "id": "42jw3YiSjktG",
    "outputId": "d4bad351-68d1-4319-ffae-05af6dc8c28c"
   },
   "outputs": [],
   "source": [
    "# Search across the images index\n",
    "view_results(table.search(vectors={\"image_index_qFlat\":query_vector},n=2)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "id": "6hNQJLQbj3Ti",
    "outputId": "4be3f185-2c32-431d-f590-86a61be3c640"
   },
   "outputs": [],
   "source": [
    "# Multi-Index Search for both texts and images\n",
    "results = table.search(\n",
    "    vectors={\"text_index_qFlat\":query_vector, \"image_index_qFlat\":query_vector},\n",
    "    index_params={\"text_index_qFlat\":{'weight':0.5} ,\"image_index_qFlat\":{'weight':0.5}},\n",
    "    n=2\n",
    "    )[0]\n",
    "\n",
    "view_results(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RM8bo0uCboOZ"
   },
   "source": [
    "Let's try another example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CfbiUTmxbPMC"
   },
   "outputs": [],
   "source": [
    "query = 'flying animal that eats bugs and fruit and hangs upsidedown'\n",
    "query_vector = embed_query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "ZNxzq72Qbmuz",
    "outputId": "61afa320-6275-44df-f1c5-bafec450bd93"
   },
   "outputs": [],
   "source": [
    "# Multi-Index Search for both texts and images\n",
    "results = table.search(\n",
    "    vectors={\"text_index_qFlat\":query_vector, \"image_index_qFlat\":query_vector},\n",
    "    index_params={\"text_index_qFlat\":{'weight':0.5} ,\"image_index_qFlat\":{'weight':0.5}},\n",
    "    n=2\n",
    "    )[0]\n",
    "\n",
    "view_results(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "595jY_m6hHSA"
   },
   "source": [
    "You could then pass these retrieved results to a multimodal LLM to perform multimodal RAG!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ZkzaXSfWMzY"
   },
   "source": [
    "## 8. Delete the KDB.AI Table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EB1thT08WS8M"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r1SbF84dWUxc"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/U2RoT32R)\n"
   ]
  }
 ],
 "metadata": {
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  "kernelspec": {
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   "codemirror_mode": {
    "name": "ipython",
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   "pygments_lexer": "ipython3",
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================================================
FILE: multimodal_RAG_VoyageAI/Multimodal_RAG_VoyageAI.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J5EZ09A9XKSj"
   },
   "source": [
    "## Multimodal Retrieval Augmented Generation (RAG) with Voyage AI\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example explores preparing, embedding, and storing both text and image data within a KDB.AI vector database. Our goal is to store both images and text within the same vector space so we can search over both modalities at the same time. KDB.AI can then be used as a retrieval tool within a RAG pipeline for both text and images, making it a multimodal retriever.\n",
    "\n",
    "The dataset we are working with contains images of several different animals, and text descriptions of those animals. After implementation, a user should be able to write a query (or use an image) regarding any of these animals and get returned both images and text related to the animal of interest.\n",
    "\n",
    "While this is a simple use-case, we hope it highlights how you can implement a similar flow with your real-world use-cases.\n",
    "\n",
    "The method in this notebook uses a multimodal embedding model from Voyage AI called \"voyage-multimodal-3\" to embed both text and images within the same vector space.\n",
    "\n",
    "### Agenda:\n",
    "0. Setup\n",
    "1. Helper Functions\n",
    "2. Prepare Data\n",
    "3. Embed Texts and Images\n",
    "4. Set Up KDB.AI Vector Database table\n",
    "5. Insert data into KDB.AI Vector Database\n",
    "6. Query Vector Database to Retrieve Most Relevant Data\n",
    "7. Retrieval Augmented Generation\n",
    "8. Drop the table\n",
    "\n",
    "Voyage AI: https://www.voyageai.com/\n",
    "\n",
    "Animal Images Source: https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bwIKh7wTXsxA"
   },
   "source": [
    "## 0. Setup\n",
    " Install requirements and import necessary packages\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9b4mOIllz40a"
   },
   "outputs": [],
   "source": [
    "!pip install -q kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EnEW5Wbu1Hza"
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade --no-deps voyageai>=0.3.0\n",
    "!pip install -q pandas PyMuPDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_AY2l0Kg453P",
    "outputId": "36e9c718-2162-4263-ed22-2f7fbc25ad23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: voyageai\n",
      "Version: 0.3.2\n",
      "Summary: \n",
      "Home-page: \n",
      "Author: Yujie Qian\n",
      "Author-email: yujieq@voyageai.com\n",
      "License: \n",
      "Location: /usr/local/lib/python3.11/dist-packages\n",
      "Requires: aiohttp, aiolimiter, numpy, pillow, pydantic, requests, tenacity, tokenizers\n",
      "Required-by: kdbai-client\n"
     ]
    }
   ],
   "source": [
    "!pip show voyageai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "BoQnfsw71TRa"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import os\n",
    "from PIL import Image\n",
    "import io\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "E6JPH1Qm1K7h"
   },
   "outputs": [],
   "source": [
    "MODEL_NAME = \"voyage-multimodal-3\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SHJYrF4jdANr"
   },
   "source": [
    "[Sign-up for Voyage AI](voyageai.com) to get an API key, it comes with 200,000 free tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OI7dEaSuYGmV"
   },
   "outputs": [],
   "source": [
    "os.environ[\"VOYAGEAI_API_KEY\"] = (\n",
    "    os.environ[\"VOYAGEAI_API_KEY\"]\n",
    "    if \"VOYAGEAI_API_KEY\" in os.environ\n",
    "    else getpass(\"Voyage AI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "gFzlv0o9YQA4"
   },
   "outputs": [],
   "source": [
    "from voyageai import Client\n",
    "vo = Client(api_key=os.environ[\"VOYAGEAI_API_KEY\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3CYXHwSU1WAu"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads image and text data\n",
    "\n",
    "!mkdir -p ./data/images\n",
    "!mkdir -p ./data/text\n",
    "\n",
    "def get_github_repo_contents(repo_owner, repo_name, branch, folder_path):\n",
    "    # Construct the API URL\n",
    "    api_url = f\"https://api.github.com/repos/{repo_owner}/{repo_name}/contents/{folder_path}?ref={branch}\"\n",
    "\n",
    "    # Send the request and process the response\n",
    "    contents = requests.get(api_url).json()\n",
    "\n",
    "    # Create the local directory if it doesn't exist\n",
    "    fPath = f\"./data/{folder_path.split('/')[-1]}\"\n",
    "\n",
    "    for item in contents:\n",
    "        # Recursively list contents of subfolder\n",
    "        if item['type'] == 'dir':\n",
    "            get_github_repo_contents(repo_owner, repo_name, branch, f\"{folder_path}/{item['name']}\")\n",
    "        # Download and save file\n",
    "        elif item['type'] == 'file':\n",
    "            file_url = f\"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{branch}/{folder_path}/{item['name']}\"\n",
    "            print(file_url)\n",
    "            r = requests.get(file_url, timeout=4.0)\n",
    "            r.raise_for_status()  # Raises an exception for HTTP errors\n",
    "            file_path = f\"{fPath}/{item['name']}\"\n",
    "\n",
    "            if item['name'].lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):\n",
    "                # Save image file\n",
    "                with Image.open(io.BytesIO(r.content)) as im:\n",
    "                    im.save(file_path)\n",
    "            else:\n",
    "                # Save text file\n",
    "                with open(file_path, 'wb') as f:\n",
    "                    f.write(r.content)\n",
    "\n",
    "# Get the data\n",
    "get_github_repo_contents(\n",
    "    repo_owner='KxSystems',\n",
    "    repo_name='kdbai-samples',\n",
    "    branch='main',\n",
    "    folder_path='multimodal_RAG_VoyageAI/data'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9FNK9GWZZjd"
   },
   "source": [
    "## 1. Helper Functions\n",
    "**read_text_from_file**: Takes in text file and returns the text within that file\n",
    "\n",
    "\n",
    "**dataToEmbedding**: Takes in the image or text file path and the data type and returns an embedded vector using the Voyage AI multimodal embedding model\n",
    "\n",
    "**queryToEmbedding**: Takes a users query and returns an embedded query vector using the Voyage AI multimodal embedding model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "jteWLljx26sd"
   },
   "outputs": [],
   "source": [
    "# Helper function to read the text from a file\n",
    "def read_text_from_file(filename):\n",
    "    try:\n",
    "        # Open the file in read mode ('r')\n",
    "        with open(filename, 'r') as file:\n",
    "            # Read the contents of the file into a string\n",
    "            text = file.read()\n",
    "            file.close()\n",
    "        return text\n",
    "    except IOError as e:\n",
    "        # Handle any I/O errors\n",
    "        print(f\"An error occurred: {e}\")\n",
    "        return None\n",
    "\n",
    "# Helper function to create embeddings for text and image data\n",
    "def dataToEmbedding(dataIn):\n",
    "  result = vo.multimodal_embed(\n",
    "      inputs=dataIn,\n",
    "      model=MODEL_NAME,\n",
    "      input_type=\"document\"\n",
    "  )\n",
    "  return result.embeddings\n",
    "\n",
    "# Helper function to create a query vector from a natural language query\n",
    "def queryToEmbedding(text):\n",
    "  result = vo.multimodal_embed(\n",
    "      inputs=[text],\n",
    "      model=MODEL_NAME,\n",
    "  )\n",
    "  return result.embeddings[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fwXCMhKPZ2Wg"
   },
   "source": [
    "## 2. Prepare Data\n",
    "Define an empty dataframe to store embeddings and get a list of paths for images and text files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "RtLGsDUJ2VWa"
   },
   "outputs": [],
   "source": [
    "#Define a dataframe to put our embeddings and metadata into - this will later be used to load our vector database\n",
    "df = pd.DataFrame(columns=['path', 'media_type', 'embeddings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Ow9bxnNQ2YUy"
   },
   "outputs": [],
   "source": [
    "#Get a list of paths for images, text\n",
    "images = os.listdir(\"./data/images\")\n",
    "texts = os.listdir(\"./data/text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "QOo1KC4GJAx1"
   },
   "outputs": [],
   "source": [
    "#Generate multimodal embeddings\n",
    "import urllib.request\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "image_data = []\n",
    "text_data = []\n",
    "\n",
    "for image in images:\n",
    "  path = \"./data/images/\" + image\n",
    "  with open(path, \"rb\") as file:\n",
    "        img_bytes = BytesIO(file.read())\n",
    "        extracted_image = Image.open(img_bytes).resize((256, 256))\n",
    "        image_data.append([extracted_image])\n",
    "        new_row = {'path': path,\n",
    "                   'media_type': \"image\",\n",
    "                   'embeddings': \"\"}\n",
    "        df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "\n",
    "for text in texts:\n",
    "  path = \"./data/text/\" + text\n",
    "  extracted_text = read_text_from_file(path)\n",
    "  text_data.append([extracted_text])\n",
    "  new_row = {'path': path,\n",
    "             'media_type': \"text\",\n",
    "             'embeddings': \"\"}\n",
    "  df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "\n",
    "# Combine data for embedding\n",
    "data = image_data + text_data\n",
    "\n",
    "# Get the embeddings\n",
    "embeddings = dataToEmbedding(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iR-3JJcoLHtQ",
    "outputId": "969d4c5e-6271-4245-a79d-463ada5e6624"
   },
   "outputs": [],
   "source": [
    "print(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "B8qKrVZvXURn"
   },
   "outputs": [],
   "source": [
    "# Populate the embeddings column in the dataframe with our newly generated embeddings\n",
    "df[\"embeddings\"] = embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uSFhSR0Jayp6",
    "outputId": "be5c08d6-d20a-487c-cd6c-c523e1dca673"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     path media_type  \\\n",
      "0          /content/data/images/deer1.jpg      image   \n",
      "1      /content/data/images/hedgehog1.jpg      image   \n",
      "2           /content/data/images/fox2.jpg      image   \n",
      "3          /content/data/images/deer2.jpg      image   \n",
      "4          /content/data/images/bear2.jpg      image   \n",
      "5   /content/data/images/caterpillar1.jpg      image   \n",
      "6   /content/data/images/caterpillar2.jpg      image   \n",
      "7      /content/data/images/hedgehog2.jpg      image   \n",
      "8          /content/data/images/bear1.jpg      image   \n",
      "9           /content/data/images/fox1.jpg      image   \n",
      "10          /content/data/images/bat1.jpg      image   \n",
      "11          /content/data/images/bat2.jpg      image   \n",
      "12     /content/data/text/caterpillar.txt       text   \n",
      "13             /content/data/text/fox.txt       text   \n",
      "14             /content/data/text/bat.txt       text   \n",
      "15            /content/data/text/deer.txt       text   \n",
      "16        /content/data/text/hedgehog.txt       text   \n",
      "17            /content/data/text/bear.txt       text   \n",
      "\n",
      "                                           embeddings  \n",
      "0   [0.0361328125, -0.005157470703125, 0.050537109...  \n",
      "1   [-0.004119873046875, 0.004913330078125, 0.0090...  \n",
      "2   [0.0042724609375, 0.00543212890625, 0.03808593...  \n",
      "3   [0.03564453125, -0.0017547607421875, 0.0576171...  \n",
      "4   [0.03369140625, 0.00051116943359375, 0.0405273...  \n",
      "5   [0.039794921875, -0.049072265625, 0.0065307617...  \n",
      "6   [0.053466796875, 0.00191497802734375, 0.044677...  \n",
      "7   [0.032470703125, 0.01446533203125, 0.003692626...  \n",
      "8   [0.036865234375, 0.0137939453125, 0.0480957031...  \n",
      "9   [-0.009765625, 0.019287109375, 0.03076171875, ...  \n",
      "10  [0.005859375, -0.033203125, -0.00726318359375,...  \n",
      "11  [0.03564453125, -0.0263671875, -0.016723632812...  \n",
      "12  [0.002532958984375, 0.01177978515625, 0.001083...  \n",
      "13  [-0.0166015625, 0.0294189453125, 0.04028320312...  \n",
      "14  [-0.04443359375, -0.011474609375, 0.0231933593...  \n",
      "15  [0.005706787109375, 0.007659912109375, 0.04101...  \n",
      "16  [0.01007080078125, -0.006072998046875, 0.01733...  \n",
      "17  [0.025634765625, 0.023193359375, 0.05297851562...  \n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fC4mrWwHaFQ2"
   },
   "source": [
    "## 4. Set Up KDB.AI Vector Database table\n",
    "Time to get KDB.AI vector database set up. If you do not have an account, go to [KDB.AI](https://kdb.ai) to sign-up free!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "TKeeqLxbqbp-"
   },
   "outputs": [],
   "source": [
    "# vector DB imports\n",
    "import os\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9HaOjnwsac3b"
   },
   "source": [
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CBSgyGYxqWcg"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FtEIBrwGamHK"
   },
   "source": [
    "### Connect to a Database\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created. We will connect to this default database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "1chhyHFXq3XA"
   },
   "outputs": [],
   "source": [
    "db = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gASwsDrVazmt"
   },
   "source": [
    "### Define schema and indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "AylMf2B7rJ7m"
   },
   "outputs": [],
   "source": [
    "#Set up a table with three columns, path, media_type, and embeddings\n",
    "table_schema = [\n",
    "        {\"name\": \"path\", \"type\": \"str\"},\n",
    "        {\"name\": \"media_type\", \"type\": \"str\"},\n",
    "        {\n",
    "            \"name\": \"embeddings\",\n",
    "            \"type\": \"float32s\",\n",
    "        },\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p8sXvJjzbAGT"
   },
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- **type** = qFlat : The most accurate index type. qFlat is an on-disk version of a flat index, perfect for memory constrained environments. You have the choice of using other indexes like, HNSW, qHNSW, IVFPQ, or Flat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- **name** = qflat_index : this is a custom name you give your index.\n",
    "- **column** = embeddings : this is the column where the embeddings are stored.\n",
    "#### params:\n",
    "- **dims** = 1024 : In the next section, we generate embeddings that are 1024-dimensional to match this. The number of dimensions should mirror the output dimensions of your embedding model.\n",
    "- **metric** = CS : We chose Cosine Similarity. You have the choice of using other metrics here like IP/Inner Product and L2/Euclidean Distance and the one you chose depends on the specific context and nature of your data.\n",
    "\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "qBOxKb4Aa8yZ"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'qFlat',\n",
    "        'name': 'qflat_index',\n",
    "        'column': 'embeddings',\n",
    "        'params': {'dims': 1024, 'metric': \"CS\"},\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "p3nLPuKQrQhC"
   },
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"multi_modal_Voyage\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "Ham1-KW_rVre"
   },
   "outputs": [],
   "source": [
    "#Create the table called \"multi_modal_Voyage\"\n",
    "table = db.create_table(table=\"multi_modal_Voyage\", schema=table_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5186blwZrXvw"
   },
   "outputs": [],
   "source": [
    "db.tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "x1zVdI-yrlxK",
    "outputId": "d63c7910-bad7-4f4a-87d5-ee49a4837f72"
   },
   "outputs": [
    {
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       "repr_error": "Out of range float values are not JSON compliant: nan",
       "type": "dataframe"
      },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
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       "      <th>embeddings</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      display:flex;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    .colab-df-buttons div {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
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       "        document.querySelector('#df-bce421d8-9923-4ae2-b721-9f3778616d53 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-bce421d8-9923-4ae2-b721-9f3778616d53');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
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       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [path, media_type, embeddings]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BlokhypdbsTz"
   },
   "source": [
    "## 5. Insert data into KDB.AI Vector Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Uuu-GqJHr9Nh",
    "outputId": "063a33f7-6805-48f4-d42f-9471d27700e1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  3.57it/s]\n"
     ]
    }
   ],
   "source": [
    "#Insert the data into the table, split into 2000 row batches\n",
    "from tqdm import tqdm\n",
    "n = 2000  # chunk row size\n",
    "\n",
    "for i in tqdm(range(0, df.shape[0], n)):\n",
    "    table.insert(df[i:i+n].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 613
    },
    "id": "g8hzFrYesEW6",
    "outputId": "d06932d4-9349-484e-debe-96f85125a2da"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"table\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"/content/data/images/deer1.jpg\",\n          \"/content/data/images/hedgehog1.jpg\",\n          \"/content/data/images/bear1.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"media_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"text\",\n          \"image\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embeddings\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
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       "      <th>0</th>\n",
       "      <td>/content/data/images/deer1.jpg</td>\n",
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       "      <td>[0.03564453, -0.0017547607, 0.057617188, 0.041...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>image</td>\n",
       "      <td>[0.033691406, 0.00051116943, 0.040527344, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>/content/data/images/caterpillar1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>[0.039794922, -0.049072266, 0.0065307617, 0.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>/content/data/images/caterpillar2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>[0.053466797, 0.001914978, 0.044677734, 0.0069...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>/content/data/images/hedgehog2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>[0.032470703, 0.014465332, 0.003692627, 0.0412...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>/content/data/images/bear1.jpg</td>\n",
       "      <td>image</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>/content/data/images/fox1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>[-0.009765625, 0.01928711, 0.030761719, 0.0274...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>/content/data/images/bat1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>[0.005859375, -0.033203125, -0.0072631836, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>/content/data/images/bat2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>[0.03564453, -0.026367188, -0.016723633, 0.021...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>/content/data/text/caterpillar.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>[0.002532959, 0.011779785, 0.001083374, -0.027...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>/content/data/text/fox.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>[-0.016601562, 0.029418945, 0.040283203, 0.042...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>/content/data/text/bat.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>[-0.044433594, -0.011474609, 0.02319336, 0.026...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>/content/data/text/deer.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>[0.005706787, 0.007659912, 0.041015625, 0.0610...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>/content/data/text/hedgehog.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>[0.010070801, -0.006072998, 0.017333984, 0.048...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>/content/data/text/bear.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>[0.025634766, 0.02319336, 0.052978516, 0.02856...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "            style=\"display:none;\">\n",
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       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-068dbbff-e389-40d8-a57c-a0a705016dfb button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-068dbbff-e389-40d8-a57c-a0a705016dfb');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        element.appendChild(docLink);\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-78871224-8831-4cc2-aa46-f2f6c68bf945 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                     path media_type  \\\n",
       "0          /content/data/images/deer1.jpg      image   \n",
       "1      /content/data/images/hedgehog1.jpg      image   \n",
       "2           /content/data/images/fox2.jpg      image   \n",
       "3          /content/data/images/deer2.jpg      image   \n",
       "4          /content/data/images/bear2.jpg      image   \n",
       "5   /content/data/images/caterpillar1.jpg      image   \n",
       "6   /content/data/images/caterpillar2.jpg      image   \n",
       "7      /content/data/images/hedgehog2.jpg      image   \n",
       "8          /content/data/images/bear1.jpg      image   \n",
       "9           /content/data/images/fox1.jpg      image   \n",
       "10          /content/data/images/bat1.jpg      image   \n",
       "11          /content/data/images/bat2.jpg      image   \n",
       "12     /content/data/text/caterpillar.txt       text   \n",
       "13             /content/data/text/fox.txt       text   \n",
       "14             /content/data/text/bat.txt       text   \n",
       "15            /content/data/text/deer.txt       text   \n",
       "16        /content/data/text/hedgehog.txt       text   \n",
       "17            /content/data/text/bear.txt       text   \n",
       "\n",
       "                                           embeddings  \n",
       "0   [0.036132812, -0.0051574707, 0.05053711, 0.042...  \n",
       "1   [-0.004119873, 0.00491333, 0.009094238, 0.0177...  \n",
       "2   [0.004272461, 0.005432129, 0.038085938, 0.0217...  \n",
       "3   [0.03564453, -0.0017547607, 0.057617188, 0.041...  \n",
       "4   [0.033691406, 0.00051116943, 0.040527344, 0.00...  \n",
       "5   [0.039794922, -0.049072266, 0.0065307617, 0.03...  \n",
       "6   [0.053466797, 0.001914978, 0.044677734, 0.0069...  \n",
       "7   [0.032470703, 0.014465332, 0.003692627, 0.0412...  \n",
       "8   [0.036865234, 0.013793945, 0.048095703, 0.0216...  \n",
       "9   [-0.009765625, 0.01928711, 0.030761719, 0.0274...  \n",
       "10  [0.005859375, -0.033203125, -0.0072631836, 0.0...  \n",
       "11  [0.03564453, -0.026367188, -0.016723633, 0.021...  \n",
       "12  [0.002532959, 0.011779785, 0.001083374, -0.027...  \n",
       "13  [-0.016601562, 0.029418945, 0.040283203, 0.042...  \n",
       "14  [-0.044433594, -0.011474609, 0.02319336, 0.026...  \n",
       "15  [0.005706787, 0.007659912, 0.041015625, 0.0610...  \n",
       "16  [0.010070801, -0.006072998, 0.017333984, 0.048...  \n",
       "17  [0.025634766, 0.02319336, 0.052978516, 0.02856...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Explore what is in the table/vector database\n",
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jJ4F0gjjbvjp"
   },
   "source": [
    "## 6. Query Vector Database to Retrieve Most Relevant Data\n",
    "Our vector database now contains embeddings representing both text files and images! We can query the vector database to find the most relevant animal based on our query.\n",
    "\n",
    "Retrieved results can then be sent to an LLM for RAG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "JiOYyW0NvhHR"
   },
   "outputs": [],
   "source": [
    "# Helper function to view the results of our similarity search\n",
    "def viewResults(results):\n",
    "    for index, row in results[0].iterrows():\n",
    "        if row.iloc[2] == 'image':\n",
    "            image = Image.open(row.iloc[1])\n",
    "            display(image)\n",
    "        elif row.iloc[2] == 'text':\n",
    "            text = read_text_from_file(row.iloc[1])\n",
    "            print(text)\n",
    "\n",
    "# Multimodal search function, identifies most relevant images and text within the vector database\n",
    "def mm_search(query):\n",
    "    results = table.search(vectors={\"qflat_index\":query}, n=3)\n",
    "    viewResults(results)\n",
    "    return(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "Ie6aPDWvtZRt"
   },
   "outputs": [],
   "source": [
    "#Create a query vector for similarity search\n",
    "query_vector = queryToEmbedding([\"brown animal with antlers\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f53K9q0kt_8l",
    "outputId": "825cafa7-2648-4325-ef23-ad16f1097d8f"
   },
   "outputs": [],
   "source": [
    "print(query_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "RqHSDX23t_7x",
    "outputId": "6bcaefe1-8ea3-4497-f4b3-b95f65398c40"
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=259x194>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=290x174>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deer are hoofed mammals known for their graceful bodies and long legs. Most male deer have antlers, which they shed and regrow annually. They are herbivores, generally feeding on a mix of grasses, plants, and leaves. Deer are found in various habitats across the world, including forests, grasslands, and wetlands.\n"
     ]
    }
   ],
   "source": [
    "#Execute a similarity search\n",
    "results = mm_search([query_vector])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "y2RfekrHzl_m"
   },
   "outputs": [],
   "source": [
    "#Try another query\n",
    "query_vector = queryToEmbedding([\"green and yellow insect that cacoons\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 412
    },
    "id": "rKFTzoZQz8B2",
    "outputId": "163ce84a-ae69-4ec9-9262-87ff2a3c4ab4"
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=310x163>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=259x194>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caterpillars are the larval stage of butterflies and moths. They have a segmented body with a distinct head and typically several pairs of true legs, as well as additional false legs or prolegs. Caterpillars are primarily known for their voracious appetite, often eating leaves voraciously before pupating into their adult form.\n"
     ]
    }
   ],
   "source": [
    "#Execute a similarity search\n",
    "results = mm_search([query_vector])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OoEbRq5-b2k8"
   },
   "source": [
    "Now we have retrieved the most relevant text and images from KDB.AI vector database, the retrieval step of RAG is complete!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-OG1SsyV2Hdk"
   },
   "source": [
    "## 7. RAG Time!\n",
    "We will use Google's Gemini Vision model to handle input text and images. We can now take our retrieved data and pass it to Gemini for the second phase of RAG: Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CvG_V-NU2GJa"
   },
   "outputs": [],
   "source": [
    "# Pip install necessary packages (!!! Note: On Colab, some Google packages may already be installed: do NOT restart the runtime if prompted to)\n",
    "!pip install google-generativeai langchain-google-genai streamlit pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fGT7fftW2LWr",
    "outputId": "cb150902-4ec7-4fea-ccc2-7aeb4a0018d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google API Key: ··········\n"
     ]
    }
   ],
   "source": [
    "# Setup Google API Key, get it here: https://makersuite.google.com/\n",
    "import os\n",
    "from getpass import getpass\n",
    "os.environ[\"GOOGLE_API_KEY\"] = (\n",
    "    os.environ[\"GOOGLE_API_KEY\"]\n",
    "    if \"GOOGLE_API_KEY\" in os.environ\n",
    "    else getpass(\"Google API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "w__JmEF32OL4"
   },
   "outputs": [],
   "source": [
    "import google.generativeai as genai\n",
    "genai.configure(api_key = os.environ['GOOGLE_API_KEY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yfXyKl9T2QZm"
   },
   "outputs": [],
   "source": [
    "# Use Gemini Pro Vision model to handle multimodal inputs\n",
    "vision_model = genai.GenerativeModel('gemini-2.5-flash')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "NK91v4ki2T4N"
   },
   "outputs": [],
   "source": [
    "# Helper function to get retrieved data ready to send to Gemini\n",
    "def RAG_Setup(results, retrieved_data_for_RAG):\n",
    "    for index, row in results[0].iterrows():\n",
    "        if row.iloc[2] == 'image':\n",
    "            image = Image.open(row.iloc[1])\n",
    "            retrieved_data_for_RAG.append(image)\n",
    "            display(image)  # Show the image\n",
    "        elif row.iloc[2] == 'text':\n",
    "            text = read_text_from_file(row.iloc[1])\n",
    "            print(text) # Show the text\n",
    "            retrieved_data_for_RAG.append(text)\n",
    "    return retrieved_data_for_RAG\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KIdwxCoZb6dW"
   },
   "source": [
    "Construct the query to send to Gemini:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "Dti3TDzC2YYX",
    "outputId": "cd2d68c9-7e74-4542-ab39-2d4c661e9c6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieved Data: \n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=310x163>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=259x194>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caterpillars are the larval stage of butterflies and moths. They have a segmented body with a distinct head and typically several pairs of true legs, as well as additional false legs or prolegs. Caterpillars are primarily known for their voracious appetite, often eating leaves voraciously before pupating into their adult form.\n"
     ]
    }
   ],
   "source": [
    "prompt = \"what can you tell me about caterpillars in the images? What species are they?\"\n",
    "\n",
    "#This will serve as the prompt being sent to Gemini:\n",
    "query = \"You will answer the given prompt using attached content: \" + prompt\n",
    "\n",
    "#Gemini accepts a list of inputs, which include texts and images retrieved from the vector database\n",
    "#This input list will begin with the query defined above\n",
    "RAG_list = [query]\n",
    "\n",
    "#The input list and retrieved results are now passed into the RAG_Setup helper function\n",
    "#retieved_data_for_RAG will now contain a list including the prompt and retrieved text/image data\n",
    "print(\"Retrieved Data: \")\n",
    "retrieved_data_for_RAG = RAG_Setup(results, RAG_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GQKh3IYvb-YI"
   },
   "source": [
    "Execute generation step of RAG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162
    },
    "id": "Nw_qD7mG2awY",
    "outputId": "e5fb0659-441a-43b4-f7c2-a3281e4c59b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the provided images and description:\n",
      "\n",
      "The first image shows a hairy caterpillar with dark segments marked by white spots and long, yellowish hairs.  While I cannot definitively identify the species from the image alone, its appearance suggests it is likely a type of **tussock moth caterpillar**. Many tussock moth caterpillars have similar hairy bodies with contrasting colors.  More specific identification would require additional information such as location and host plant.\n",
      "\n",
      "\n",
      "The second image shows a smooth, green caterpillar with diagonal stripes.  This is consistent with the appearance of various **sphinx moth (hawk moth) caterpillars**. Again, without further information (location, host plant, etc.), precise species identification isn't possible.  Many sphinx moth caterpillars share this general body shape and color.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Gemini generates a response based on the retrieved_data_for_RAG input list, completing the RAG iteration\n",
    "response = vision_model.generate_content(retrieved_data_for_RAG)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZCnMmSyLcCbY"
   },
   "source": [
    "## 8. Delete the KDB.AI Table\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "Ok89Ef3e2dkG"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FW2qJA0UcGPB"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/dfAHRG9R)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: multimodal_RAG_VoyageAI/data/text/bat.txt
================================================
Bats are the only mammals capable of sustained flight, distinct from flying squirrels which can only glide. They have wings that are more similar to a human hand, with elongated fingers connected by a thin membrane. Bats are often found in dark places like caves and are known for using echolocation to navigate and find food in the dark.

================================================
FILE: multimodal_RAG_VoyageAI/data/text/bear.txt
================================================
Bears are large mammals with a stocky body, powerful limbs, and a short tail. They have a large brain and are considered among the most intelligent of animals. While many bears are omnivorous, their diets can range from primarily plant-based to entirely carnivorous, depending on the species. They are found in various habitats across the Americas, Europe, and Asia.

================================================
FILE: multimodal_RAG_VoyageAI/data/text/caterpillar.txt
================================================
Caterpillars are the larval stage of butterflies and moths. They have a segmented body with a distinct head and typically several pairs of true legs, as well as additional false legs or prolegs. Caterpillars are primarily known for their voracious appetite, often eating leaves voraciously before pupating into their adult form.

================================================
FILE: multimodal_RAG_VoyageAI/data/text/deer.txt
================================================
Deer are hoofed mammals known for their graceful bodies and long legs. Most male deer have antlers, which they shed and regrow annually. They are herbivores, generally feeding on a mix of grasses, plants, and leaves. Deer are found in various habitats across the world, including forests, grasslands, and wetlands.

================================================
FILE: multimodal_RAG_VoyageAI/data/text/fox.txt
================================================
Foxes are small to medium-sized, omnivorous mammals belonging to the Canidae family, which also includes wolves, dogs, and other similar animals. They are known for their pointed ears, bushy tails, and cunning behavior. Foxes are adaptable creatures and can be found in diverse habitats, including forests, grasslands, mountains, and deserts.

================================================
FILE: multimodal_RAG_VoyageAI/data/text/hedgehog.txt
================================================
Hedgehogs are small, nocturnal mammals known for their distinctive spines, which are modified hairs. These spines provide protection against predators. Hedgehogs have a rounded body, a short tail, and a pointed snout. They are primarily insectivores, eating insects, snails, and other small creatures. Hedgehogs are found in Europe, Asia, and Africa, and have also been introduced to New Zealand.

================================================
FILE: multimodal_RAG_unified_text/data/text/bat.txt
================================================
Bats are the only mammals capable of sustained flight, distinct from flying squirrels which can only glide. They have wings that are more similar to a human hand, with elongated fingers connected by a thin membrane. Bats are often found in dark places like caves and are known for using echolocation to navigate and find food in the dark.

================================================
FILE: multimodal_RAG_unified_text/data/text/bear.txt
================================================
Bears are large mammals with a stocky body, powerful limbs, and a short tail. They have a large brain and are considered among the most intelligent of animals. While many bears are omnivorous, their diets can range from primarily plant-based to entirely carnivorous, depending on the species. They are found in various habitats across the Americas, Europe, and Asia.

================================================
FILE: multimodal_RAG_unified_text/data/text/caterpillar.txt
================================================
Caterpillars are the larval stage of butterflies and moths. They have a segmented body with a distinct head and typically several pairs of true legs, as well as additional false legs or prolegs. Caterpillars are primarily known for their voracious appetite, often eating leaves voraciously before pupating into their adult form.

================================================
FILE: multimodal_RAG_unified_text/data/text/deer.txt
================================================
Deer are hoofed mammals known for their graceful bodies and long legs. Most male deer have antlers, which they shed and regrow annually. They are herbivores, generally feeding on a mix of grasses, plants, and leaves. Deer are found in various habitats across the world, including forests, grasslands, and wetlands.

================================================
FILE: multimodal_RAG_unified_text/data/text/fox.txt
================================================
Foxes are small to medium-sized, omnivorous mammals belonging to the Canidae family, which also includes wolves, dogs, and other similar animals. They are known for their pointed ears, bushy tails, and cunning behavior. Foxes are adaptable creatures and can be found in diverse habitats, including forests, grasslands, mountains, and deserts.

================================================
FILE: multimodal_RAG_unified_text/data/text/hedgehog.txt
================================================
Hedgehogs are small, nocturnal mammals known for their distinctive spines, which are modified hairs. These spines provide protection against predators. Hedgehogs have a rounded body, a short tail, and a pointed snout. They are primarily insectivores, eating insects, snails, and other small creatures. Hedgehogs are found in Europe, Asia, and Africa, and have also been introduced to New Zealand.

================================================
FILE: multimodal_RAG_unified_text/multi_modal_demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multimodal Retrieval Augmented Generation (RAG)\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example explores preparing, embedding, and storing both text and image data within a KDB.AI vector database. Our goal is to store both images descriptions and text within the same vector space so we can search over both modalities at the same time. KDB.AI can then be used as a retrieval tool within a RAG pipeline for both text and images data, making it a multimodal retriever. \n",
    "\n",
    "The dataset we are working with contains images of several different animals, and text descriptions of those animals. After implementation, a user should be able to write a query regarding any of these animals and get returned both images and text related to the animal of interest.\n",
    "\n",
    "The method in this notebook uses a multimodal LLM to describe images within our dataset. Since the images will be described in text, the descriptions can be treated similary to our raw text data. Therefore all data can be embedded by the same model, and stored in the same vector space!\n",
    "\n",
    "### Agenda:\n",
    "0. Setup\n",
    "1. Helper Functions\n",
    "2. Data Import\n",
    "3. Embed Texts and Images\n",
    "4. Set Up KDB.AI Vector Database table\n",
    "5. Insert data into KDB.AI Vector Database\n",
    "6. Query vector database to retrieve most relevant data\n",
    "7. Retrieval Augmented Generation\n",
    "8. Drop the table\n",
    "\n",
    "Animal Images Source: https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Setup\n",
    "Dependencies, Imports, and API Keys\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client langchain openai langchain-community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import io\n",
    "from getpass import getpass\n",
    "import langchain\n",
    "import openai\n",
    "from openai import OpenAI\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.schema.messages import HumanMessage, SystemMessage\n",
    "from IPython.display import display\n",
    "import time\n",
    "import warnings\n",
    "import requests\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/bat1.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/bat2.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/bear1.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/bear2.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/caterpillar1.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/caterpillar2.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/deer1.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/deer2.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/fox1.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/fox2.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/hedgehog1.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/images/hedgehog2.jpg\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/text/bat.txt\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/text/bear.txt\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/text/caterpillar.txt\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/text/deer.txt\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/text/fox.txt\n",
      "https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/multimodal_RAG_unified_text/data/text/hedgehog.txt\n"
     ]
    }
   ],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads image and text data\n",
    "\n",
    "!mkdir -p ./data/images\n",
    "!mkdir -p ./data/text\n",
    "\n",
    "def get_github_repo_contents(repo_owner, repo_name, branch, folder_path):\n",
    "    # Construct the API URL\n",
    "    api_url = f\"https://api.github.com/repos/{repo_owner}/{repo_name}/contents/{folder_path}?ref={branch}\"\n",
    "    \n",
    "    # Send the request and process the response\n",
    "    contents = requests.get(api_url).json()\n",
    "\n",
    "    # Create the local directory if it doesn't exist\n",
    "    fPath = f\"./data/{folder_path.split('/')[-1]}\"\n",
    "\n",
    "    for item in contents:\n",
    "        # Recursively list contents of subfolder\n",
    "        if item['type'] == 'dir':\n",
    "            get_github_repo_contents(repo_owner, repo_name, branch, f\"{folder_path}/{item['name']}\")\n",
    "        # Download and save file\n",
    "        elif item['type'] == 'file':\n",
    "            file_url = f\"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{branch}/{folder_path}/{item['name']}\"\n",
    "            print(file_url)\n",
    "            r = requests.get(file_url, timeout=4.0)\n",
    "            r.raise_for_status()  # Raises an exception for HTTP errors\n",
    "            file_path = f\"{fPath}/{item['name']}\"\n",
    "\n",
    "            if item['name'].lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):\n",
    "                # Save image file\n",
    "                with Image.open(io.BytesIO(r.content)) as im:\n",
    "                    im.save(file_path)\n",
    "            else:\n",
    "                # Save text file\n",
    "                with open(file_path, 'wb') as f:\n",
    "                    f.write(r.content)\n",
    "\n",
    "# Get images and texts\n",
    "get_github_repo_contents(\n",
    "    repo_owner='KxSystems',\n",
    "    repo_name='kdbai-samples',\n",
    "    branch='main',\n",
    "    folder_path='multimodal_RAG_unified_text/data'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
    "client = OpenAI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Helper functions\n",
    "1. encode_image: this function takes in an image file's path, and converts the image to base64 encoding which can be used by OpenAI's LLM \"gpt-4-vision-preview\"\n",
    "2. image_summarize: takes in a base64 encoded image and a prompt, returns a text description of the image\n",
    "3. read_text_from_file: takes in a file path, returns the text within that file\n",
    "4. text_to_embedding: takes in text, outputs a vector embedding of that text that can be stored in a vector database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "\n",
    "# Helper function to convert a file to base64 representation\n",
    "def encode_image(image_path):\n",
    "    with open(image_path, \"rb\") as image_file:\n",
    "        return base64.b64encode(image_file.read()).decode('utf-8')\n",
    "\n",
    "# Takes in a base64 encoded image and a prompt, returns a text description of the image\n",
    "def image_summarize(img_base64,prompt):\n",
    "    ''' Image summary '''\n",
    "    for _ in range(3):\n",
    "        response = openai.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "            messages=[\n",
    "                {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": [\n",
    "                    {\"type\": \"text\", \"text\": prompt},\n",
    "                    {\n",
    "                    \"type\": \"image_url\",\n",
    "                    \"image_url\": {\n",
    "                        \"url\": f\"data:image/jpeg;base64,{img_base64}\",\n",
    "                    },\n",
    "                    },\n",
    "                ],\n",
    "                }\n",
    "            ],\n",
    "            max_tokens=150,\n",
    "        )\n",
    "        content = response.choices[0].message.content\n",
    "\n",
    "        if \"I'm sorry\" not in content:\n",
    "            return content\n",
    "        time.sleep(3)\n",
    "    return content\n",
    "\n",
    "#Return the text from a file\n",
    "def read_text_from_file(filename):\n",
    "    try:\n",
    "        # Open the file in read mode ('r')\n",
    "        with open(filename, 'r') as file:\n",
    "            # Read the contents of the file into a string\n",
    "            text = file.read()\n",
    "        return text\n",
    "    except IOError as e:\n",
    "        # Handle any I/O errors\n",
    "        print(f\"An error occurred: {e}\")\n",
    "        return None\n",
    "\n",
    "#model=\"text-embedding-3-small\"\n",
    "#Takes in text and returns a vector embedding using text-embedding-3-small\n",
    "def text_to_embedding(text):\n",
    "    text = text.replace(\"\\n\", \" \")\n",
    "    return client.embeddings.create(input = [text], model=\"text-embedding-3-small\").data[0].embedding\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Import\n",
    "Get the filepaths for all text and image files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = os.listdir(\"./data/images\")\n",
    "texts = os.listdir(\"./data/text\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare a dataframe to store file path, media_type, text, and embeddings in\n",
    "columns = ['path','media_type','text','embeddings']\n",
    "df = pd.DataFrame(columns=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Embed Texts and Images\n",
    "Loop through all text files and embed them, store relevant data in dataframe.\n",
    "Loop through all image files, get image summaries, embed the summaries, store relevant data in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop through all text files and embed them, store relevant data in dataframe\n",
    "for text in texts:\n",
    "    path = \"./data/text/\" + text\n",
    "    media_type = \"text\"\n",
    "    text1 = read_text_from_file(path)\n",
    "    embedding = text_to_embedding(text1)\n",
    "    new_row = {'path': path,\n",
    "               'media_type':'text',\n",
    "               'text' : text1,\n",
    "               'embeddings': embedding}\n",
    "    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>media_type</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./data/text/bat.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Bats are the only mammals capable of sustained...</td>\n",
       "      <td>[0.043051157146692276, 0.05017940700054169, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./data/text/bear.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Bears are large mammals with a stocky body, po...</td>\n",
       "      <td>[0.052125297486782074, 0.0072755659930408, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./data/text/deer.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Deer are hoofed mammals known for their gracef...</td>\n",
       "      <td>[0.07531881332397461, 0.03122134692966938, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./data/text/caterpillar.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Caterpillars are the larval stage of butterfli...</td>\n",
       "      <td>[0.05927688628435135, -0.017723916098475456, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./data/text/hedgehog.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Hedgehogs are small, nocturnal mammals known f...</td>\n",
       "      <td>[0.0144752012565732, 0.0004009466210845858, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          path media_type  \\\n",
       "0          ./data/text/bat.txt       text   \n",
       "1         ./data/text/bear.txt       text   \n",
       "2         ./data/text/deer.txt       text   \n",
       "3  ./data/text/caterpillar.txt       text   \n",
       "4     ./data/text/hedgehog.txt       text   \n",
       "\n",
       "                                                text  \\\n",
       "0  Bats are the only mammals capable of sustained...   \n",
       "1  Bears are large mammals with a stocky body, po...   \n",
       "2  Deer are hoofed mammals known for their gracef...   \n",
       "3  Caterpillars are the larval stage of butterfli...   \n",
       "4  Hedgehogs are small, nocturnal mammals known f...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [0.043051157146692276, 0.05017940700054169, 0....  \n",
       "1  [0.052125297486782074, 0.0072755659930408, 0.0...  \n",
       "2  [0.07531881332397461, 0.03122134692966938, 0.0...  \n",
       "3  [0.05927688628435135, -0.017723916098475456, 0...  \n",
       "4  [0.0144752012565732, 0.0004009466210845858, 0....  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the dataframe so far\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop through all image files, get image summaries, embed the summaries, store relevant data in dataframe. Takes a couple of minutes\n",
    "for image in images:\n",
    "    path = \"./data/images/\" + image\n",
    "    media_type = \"images\"\n",
    "    base64_image = encode_image(path)\n",
    "    prompt = \"Describe the image in detail.\"\n",
    "    summarization = image_summarize(base64_image,prompt)\n",
    "    embedding = text_to_embedding(summarization)\n",
    "    new_row = {'path': path,\n",
    "               'media_type':'image',\n",
    "               'text' : summarization,\n",
    "               'embeddings': embedding}\n",
    "    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>media_type</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./data/text/bat.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Bats are the only mammals capable of sustained...</td>\n",
       "      <td>[0.043051157146692276, 0.05017940700054169, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./data/text/bear.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Bears are large mammals with a stocky body, po...</td>\n",
       "      <td>[0.052125297486782074, 0.0072755659930408, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./data/text/deer.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Deer are hoofed mammals known for their gracef...</td>\n",
       "      <td>[0.07531881332397461, 0.03122134692966938, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./data/text/caterpillar.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Caterpillars are the larval stage of butterfli...</td>\n",
       "      <td>[0.05927688628435135, -0.017723916098475456, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./data/text/hedgehog.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Hedgehogs are small, nocturnal mammals known f...</td>\n",
       "      <td>[0.0144752012565732, 0.0004009466210845858, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./data/text/fox.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Foxes are small to medium-sized, omnivorous ma...</td>\n",
       "      <td>[0.017004722729325294, 0.017473476007580757, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./data/images/fox2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image features a red fox standing in front...</td>\n",
       "      <td>[-0.0029516557697206736, -0.010554404929280281...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./data/images/bat2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image shows a large bat hanging upside dow...</td>\n",
       "      <td>[0.059701841324567795, -0.008926010690629482, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./data/images/bat1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image depicts a bat in flight at night. Th...</td>\n",
       "      <td>[0.015258435159921646, 0.015619066543877125, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./data/images/deer2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image depicts a mature male deer, known as...</td>\n",
       "      <td>[0.0621773786842823, -0.013964351266622543, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./data/images/hedgehog1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image features a close-up of a small hedge...</td>\n",
       "      <td>[0.03395913168787956, -0.045510806143283844, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./data/images/hedgehog2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image shows a hedgehog surrounded by vibra...</td>\n",
       "      <td>[0.03718235343694687, -0.05030420050024986, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./data/images/bear2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image is a close-up shot of a bear, specif...</td>\n",
       "      <td>[0.009648554027080536, -0.023458998650312424, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./data/images/bear1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image is of a large bear walking toward th...</td>\n",
       "      <td>[0.03627762198448181, -0.0058790878392755985, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./data/images/caterpillar2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image displays a caterpillar. This caterpi...</td>\n",
       "      <td>[0.05489984154701233, -0.015030968934297562, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./data/images/deer1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image shows a male deer, commonly referred...</td>\n",
       "      <td>[0.06323786079883575, -0.020763417705893517, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>./data/images/fox1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image features a red fox resting on a patc...</td>\n",
       "      <td>[0.008236058056354523, -0.015511339530348778, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./data/images/caterpillar1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image showcases a bright green caterpillar...</td>\n",
       "      <td>[0.07789730280637741, -0.03089781105518341, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              path media_type  \\\n",
       "0              ./data/text/bat.txt       text   \n",
       "1             ./data/text/bear.txt       text   \n",
       "2             ./data/text/deer.txt       text   \n",
       "3      ./data/text/caterpillar.txt       text   \n",
       "4         ./data/text/hedgehog.txt       text   \n",
       "5              ./data/text/fox.txt       text   \n",
       "6           ./data/images/fox2.jpg      image   \n",
       "7           ./data/images/bat2.jpg      image   \n",
       "8           ./data/images/bat1.jpg      image   \n",
       "9          ./data/images/deer2.jpg      image   \n",
       "10     ./data/images/hedgehog1.jpg      image   \n",
       "11     ./data/images/hedgehog2.jpg      image   \n",
       "12         ./data/images/bear2.jpg      image   \n",
       "13         ./data/images/bear1.jpg      image   \n",
       "14  ./data/images/caterpillar2.jpg      image   \n",
       "15         ./data/images/deer1.jpg      image   \n",
       "16          ./data/images/fox1.jpg      image   \n",
       "17  ./data/images/caterpillar1.jpg      image   \n",
       "\n",
       "                                                 text  \\\n",
       "0   Bats are the only mammals capable of sustained...   \n",
       "1   Bears are large mammals with a stocky body, po...   \n",
       "2   Deer are hoofed mammals known for their gracef...   \n",
       "3   Caterpillars are the larval stage of butterfli...   \n",
       "4   Hedgehogs are small, nocturnal mammals known f...   \n",
       "5   Foxes are small to medium-sized, omnivorous ma...   \n",
       "6   The image features a red fox standing in front...   \n",
       "7   The image shows a large bat hanging upside dow...   \n",
       "8   The image depicts a bat in flight at night. Th...   \n",
       "9   The image depicts a mature male deer, known as...   \n",
       "10  The image features a close-up of a small hedge...   \n",
       "11  The image shows a hedgehog surrounded by vibra...   \n",
       "12  The image is a close-up shot of a bear, specif...   \n",
       "13  The image is of a large bear walking toward th...   \n",
       "14  The image displays a caterpillar. This caterpi...   \n",
       "15  The image shows a male deer, commonly referred...   \n",
       "16  The image features a red fox resting on a patc...   \n",
       "17  The image showcases a bright green caterpillar...   \n",
       "\n",
       "                                           embeddings  \n",
       "0   [0.043051157146692276, 0.05017940700054169, 0....  \n",
       "1   [0.052125297486782074, 0.0072755659930408, 0.0...  \n",
       "2   [0.07531881332397461, 0.03122134692966938, 0.0...  \n",
       "3   [0.05927688628435135, -0.017723916098475456, 0...  \n",
       "4   [0.0144752012565732, 0.0004009466210845858, 0....  \n",
       "5   [0.017004722729325294, 0.017473476007580757, 0...  \n",
       "6   [-0.0029516557697206736, -0.010554404929280281...  \n",
       "7   [0.059701841324567795, -0.008926010690629482, ...  \n",
       "8   [0.015258435159921646, 0.015619066543877125, 0...  \n",
       "9   [0.0621773786842823, -0.013964351266622543, -0...  \n",
       "10  [0.03395913168787956, -0.045510806143283844, -...  \n",
       "11  [0.03718235343694687, -0.05030420050024986, -0...  \n",
       "12  [0.009648554027080536, -0.023458998650312424, ...  \n",
       "13  [0.03627762198448181, -0.0058790878392755985, ...  \n",
       "14  [0.05489984154701233, -0.015030968934297562, 0...  \n",
       "15  [0.06323786079883575, -0.020763417705893517, -...  \n",
       "16  [0.008236058056354523, -0.015511339530348778, ...  \n",
       "17  [0.07789730280637741, -0.03089781105518341, 0....  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the entire dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Set Up KDB.AI Vector Database table\n",
    "Time to get KDB.AI vector database set up. If you do not have an account, go to KDB.AI to sign-up free!\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector DB imports\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define table schema for our table with columns for path, media_type, text, and embeddings\n",
    "table_schema = [\n",
    "    {\"name\": \"path\", \"type\": \"str\"},\n",
    "    {\"name\": \"media_type\", \"type\": \"str\"},\n",
    "    {\"name\": \"text\", \"type\": \"str\"},\n",
    "    {\"name\": \"embeddings\", \"type\": \"float64s\"}\n",
    "]\n",
    "index_name = \"flat_index\"\n",
    "indexes = [{\"name\": index_name, \"column\": \"embeddings\", \"type\": \"flat\", \"params\": {\"dims\": 1536, \"metric\": \"CS\"}}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = session.database(\"default\")\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"multi_modal_demo\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table called \"multi_modal_demo\"\n",
    "table = database.create_table(\"multi_modal_demo\", schema=table_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Insert data into KDB.AI Vector Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 15.52it/s]\n"
     ]
    }
   ],
   "source": [
    "#Insert the data into the table, split into 2000 row batches\n",
    "from tqdm import tqdm \n",
    "n = 2000  # chunk row size\n",
    "\n",
    "for i in tqdm(range(0, df.shape[0], n)):\n",
    "    table.insert(df[i:i+n].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>media_type</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./data/text/bat.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Bats are the only mammals capable of sustained...</td>\n",
       "      <td>[0.043051157146692276, 0.05017940700054169, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./data/text/bear.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Bears are large mammals with a stocky body, po...</td>\n",
       "      <td>[0.052125297486782074, 0.0072755659930408, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./data/text/deer.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Deer are hoofed mammals known for their gracef...</td>\n",
       "      <td>[0.07531881332397461, 0.03122134692966938, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./data/text/caterpillar.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Caterpillars are the larval stage of butterfli...</td>\n",
       "      <td>[0.05927688628435135, -0.017723916098475456, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./data/text/hedgehog.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Hedgehogs are small, nocturnal mammals known f...</td>\n",
       "      <td>[0.0144752012565732, 0.0004009466210845858, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./data/text/fox.txt</td>\n",
       "      <td>text</td>\n",
       "      <td>Foxes are small to medium-sized, omnivorous ma...</td>\n",
       "      <td>[0.017004722729325294, 0.017473476007580757, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./data/images/fox2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image features a red fox standing in front...</td>\n",
       "      <td>[-0.0029516557697206736, -0.010554404929280281...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./data/images/bat2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image shows a large bat hanging upside dow...</td>\n",
       "      <td>[0.059701841324567795, -0.008926010690629482, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./data/images/bat1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image depicts a bat in flight at night. Th...</td>\n",
       "      <td>[0.015258435159921646, 0.015619066543877125, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./data/images/deer2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image depicts a mature male deer, known as...</td>\n",
       "      <td>[0.0621773786842823, -0.013964351266622543, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./data/images/hedgehog1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image features a close-up of a small hedge...</td>\n",
       "      <td>[0.03395913168787956, -0.045510806143283844, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./data/images/hedgehog2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image shows a hedgehog surrounded by vibra...</td>\n",
       "      <td>[0.03718235343694687, -0.05030420050024986, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./data/images/bear2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image is a close-up shot of a bear, specif...</td>\n",
       "      <td>[0.009648554027080536, -0.023458998650312424, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./data/images/bear1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image is of a large bear walking toward th...</td>\n",
       "      <td>[0.03627762198448181, -0.0058790878392755985, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./data/images/caterpillar2.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image displays a caterpillar. This caterpi...</td>\n",
       "      <td>[0.05489984154701233, -0.015030968934297562, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./data/images/deer1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image shows a male deer, commonly referred...</td>\n",
       "      <td>[0.06323786079883575, -0.020763417705893517, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>./data/images/fox1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image features a red fox resting on a patc...</td>\n",
       "      <td>[0.008236058056354523, -0.015511339530348778, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./data/images/caterpillar1.jpg</td>\n",
       "      <td>image</td>\n",
       "      <td>The image showcases a bright green caterpillar...</td>\n",
       "      <td>[0.07789730280637741, -0.03089781105518341, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              path media_type  \\\n",
       "0              ./data/text/bat.txt       text   \n",
       "1             ./data/text/bear.txt       text   \n",
       "2             ./data/text/deer.txt       text   \n",
       "3      ./data/text/caterpillar.txt       text   \n",
       "4         ./data/text/hedgehog.txt       text   \n",
       "5              ./data/text/fox.txt       text   \n",
       "6           ./data/images/fox2.jpg      image   \n",
       "7           ./data/images/bat2.jpg      image   \n",
       "8           ./data/images/bat1.jpg      image   \n",
       "9          ./data/images/deer2.jpg      image   \n",
       "10     ./data/images/hedgehog1.jpg      image   \n",
       "11     ./data/images/hedgehog2.jpg      image   \n",
       "12         ./data/images/bear2.jpg      image   \n",
       "13         ./data/images/bear1.jpg      image   \n",
       "14  ./data/images/caterpillar2.jpg      image   \n",
       "15         ./data/images/deer1.jpg      image   \n",
       "16          ./data/images/fox1.jpg      image   \n",
       "17  ./data/images/caterpillar1.jpg      image   \n",
       "\n",
       "                                                 text  \\\n",
       "0   Bats are the only mammals capable of sustained...   \n",
       "1   Bears are large mammals with a stocky body, po...   \n",
       "2   Deer are hoofed mammals known for their gracef...   \n",
       "3   Caterpillars are the larval stage of butterfli...   \n",
       "4   Hedgehogs are small, nocturnal mammals known f...   \n",
       "5   Foxes are small to medium-sized, omnivorous ma...   \n",
       "6   The image features a red fox standing in front...   \n",
       "7   The image shows a large bat hanging upside dow...   \n",
       "8   The image depicts a bat in flight at night. Th...   \n",
       "9   The image depicts a mature male deer, known as...   \n",
       "10  The image features a close-up of a small hedge...   \n",
       "11  The image shows a hedgehog surrounded by vibra...   \n",
       "12  The image is a close-up shot of a bear, specif...   \n",
       "13  The image is of a large bear walking toward th...   \n",
       "14  The image displays a caterpillar. This caterpi...   \n",
       "15  The image shows a male deer, commonly referred...   \n",
       "16  The image features a red fox resting on a patc...   \n",
       "17  The image showcases a bright green caterpillar...   \n",
       "\n",
       "                                           embeddings  \n",
       "0   [0.043051157146692276, 0.05017940700054169, 0....  \n",
       "1   [0.052125297486782074, 0.0072755659930408, 0.0...  \n",
       "2   [0.07531881332397461, 0.03122134692966938, 0.0...  \n",
       "3   [0.05927688628435135, -0.017723916098475456, 0...  \n",
       "4   [0.0144752012565732, 0.0004009466210845858, 0....  \n",
       "5   [0.017004722729325294, 0.017473476007580757, 0...  \n",
       "6   [-0.0029516557697206736, -0.010554404929280281...  \n",
       "7   [0.059701841324567795, -0.008926010690629482, ...  \n",
       "8   [0.015258435159921646, 0.015619066543877125, 0...  \n",
       "9   [0.0621773786842823, -0.013964351266622543, -0...  \n",
       "10  [0.03395913168787956, -0.045510806143283844, -...  \n",
       "11  [0.03718235343694687, -0.05030420050024986, -0...  \n",
       "12  [0.009648554027080536, -0.023458998650312424, ...  \n",
       "13  [0.03627762198448181, -0.0058790878392755985, ...  \n",
       "14  [0.05489984154701233, -0.015030968934297562, 0...  \n",
       "15  [0.06323786079883575, -0.020763417705893517, -...  \n",
       "16  [0.008236058056354523, -0.015511339530348778, ...  \n",
       "17  [0.07789730280637741, -0.03089781105518341, 0....  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See what is in our table - confirm the data was inserted\n",
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Query vector database to retrieve most relevant data \n",
    "Our vector database now contains embeddings representing both text files and images! We can query the vector database to find the most relevant animal based on our query. \n",
    "\n",
    "Retrieved results can then be sent to an LLM for RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_vector = [text_to_embedding(\"what is the purpose of having antlers?\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   __nn_distance                     path media_type  \\\n",
      "0       0.438880     ./data/text/deer.txt       text   \n",
      "1       0.417827  ./data/images/deer1.jpg      image   \n",
      "2       0.410783  ./data/images/deer2.jpg      image   \n",
      "\n",
      "                                                text  \\\n",
      "0  Deer are hoofed mammals known for their gracef...   \n",
      "1  The image shows a male deer, commonly referred...   \n",
      "2  The image depicts a mature male deer, known as...   \n",
      "\n",
      "                                          embeddings  \n",
      "0  [0.07531881332397461, 0.03122134692966938, 0.0...  \n",
      "1  [0.06323786079883575, -0.020763417705893517, -...  \n",
      "2  [0.0621773786842823, -0.013964351266622543, -0...  ]\n"
     ]
    }
   ],
   "source": [
    "results = table.search({index_name: query_vector}, n=3)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### View the data that was retrieved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deer are hoofed mammals known for their graceful bodies and long legs. Most male deer have antlers, which they shed and regrow annually. They are herbivores, generally feeding on a mix of grasses, plants, and leaves. Deer are found in various habitats across the world, including forests, grasslands, and wetlands.\n"
     ]
    },
    {
     "data": {
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",
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=290x174>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=259x194>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "retrieved_data_for_RAG = []\n",
    "for index, row in results[0].iterrows():\n",
    "    if row[2] == 'image':\n",
    "        retrieved_data_for_RAG.append(row[3])\n",
    "        image = Image.open(row[1])\n",
    "        display(image)\n",
    "    elif row[2] == 'text':\n",
    "        retrieved_data_for_RAG.append(row[3])\n",
    "        print(row[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. RAG Time!\n",
    "Finally, we can pass our retrieved data and prompt into an LLM -  this is the generation step in RAG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to execute RAG, we pass in a list of retrieved data, and the prompt\n",
    "# The function returns the LLM's response\n",
    "\n",
    "def RAG(retrieved_data,prompt):\n",
    "    messages = \"\"\n",
    "    messages += prompt + \"\\n\"\n",
    "    if retrieved_data:\n",
    "        for data in retrieved_data:\n",
    "            messages += data + \"\\n\"\n",
    "    response = openai.chat.completions.create(\n",
    "        model=\"gpt-4o\",\n",
    "        messages=[           \n",
    "            {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\"type\": \"text\", \"text\": messages},\n",
    "            ],\n",
    "            }\n",
    "        ],\n",
    "        max_tokens=300, \n",
    "    )\n",
    "    content = response.choices[0].message.content\n",
    "    return content\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The purpose of having antlers in deer, particularly for male deer or bucks, primarily revolves around attracting mates and establishing dominance. Antlers are used in a variety of ways including:\n",
      "\n",
      "1. **Mate Attraction**: Antlers play a significant role during the mating season, also known as the rut. Large, well-developed antlers are often considered a sign of a healthy and genetically superior individual, making bucks with impressive antlers more attractive to females.\n",
      "\n",
      "2. **Fighting and Dominance**: Bucks use their antlers to fight with other males to establish dominance and gain access to females. These fights can involve pushing and locking antlers to display strength and resolve. The multiple points on the antlers can be an indicator of the deer's maturity and experience.\n",
      "\n",
      "3. **Defense**: Although not as common, antlers can also be used as a means of defense against predators.\n",
      "\n",
      "The image of the mature male deer, or buck, with its large, multi-pointed antlers standing alert in an open field, highlights the typical features and context where antlers serve these purposes. The buck's antlers are prominent and well-developed, suggesting maturity and readiness for these crucial activities, whether attracting a mate or asserting dominance.\n"
     ]
    }
   ],
   "source": [
    "prompt = \"what is the purpose of having antlers?\"\n",
    "query = \"You will answer the given prompt using attached content: \"+prompt\n",
    "response = RAG(retrieved_data_for_RAG, query)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/dfAHRG9R)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: music_recommendation/data/song_data.csv
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================================================
FILE: music_recommendation/music_recommendation.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c9731fb",
   "metadata": {},
   "source": [
    "# Music Recommendation on Spotify Data\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example demonstrates how you can use KDB.AI to perform similarity recommendations using vector embeddings created from both categorical and numeric music data.\n",
    "\n",
    "Applications like Spotify and YouTube Music perform hundreds of millions of song recommendations for users every single day. They do this by extracting a vast array of features about every given song and artist and comparing their characteristics.\n",
    "By leveraging this sort of data, KDB.AI can be used to productionize a music recommendation system and help to quickly and efficiently find music similar to given input songs.\n",
    "\n",
    "### Aim\n",
    "\n",
    "In this tutorial, we'll break down how you might perform similarity search on music, taking some Spotify data as an example and using KDB.AI as the vector database to store and query this data.\n",
    "This breaks down as follows:\n",
    "\n",
    "1. Load Song Data\n",
    "1. Create Song Vector Embeddings\n",
    "1. Store Embeddings In KDB.AI\n",
    "1. Search For Similar Songs To A Target Song\n",
    "1. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3605c7",
   "metadata": {},
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d625ff",
   "metadata": {},
   "source": [
    "### Install dependencies \n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00e5abb",
   "metadata": {},
   "source": [
    "### Upgrading NumPy\n",
    "Upgrades NumPy to a specific version (1.26.4) to ensure compatibility with other dependencies, particularly relevant after installing multiple packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34f7379b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install numpy==1.26.4 --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b478d96",
   "metadata": {},
   "source": [
    "After this step you currently need to restart your runtime!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bcb2fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client\n",
    "!pip install gensim nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15c41564",
   "metadata": {},
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads song data\n",
    "!mkdir ./data \n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/music_recommendation/data/song_data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f9672c",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115f7122",
   "metadata": {},
   "source": [
    "We will start by importing all of the Python packages needed to run this music recommendation system example.\n",
    "This includes packages for reading in the data, embedding it as vectors, and interacting with the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d851ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b91bc28a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt_tab to /home/gflood/nltk_data...\n",
      "[nltk_data]   Package punkt_tab is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# embedding categorical data\n",
    "import nltk\n",
    "nltk.download('punkt_tab')\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from gensim.models import Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1bf575c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# timing\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afa4c3cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95be3259",
   "metadata": {},
   "source": [
    "### Configure Console"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec282385",
   "metadata": {},
   "source": [
    "In order to fully view our embeddings when it comes to displaying the results, we must increase the maximum allowed column width in Pandas DataFrames from the default value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4f29527",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_colwidth\", 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edaa685",
   "metadata": {},
   "source": [
    "This removes a warning that appears when performing in-place column assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d28588d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc36e465",
   "metadata": {},
   "source": [
    "### Define Helper Functions\n",
    "\n",
    "Defining these two helper functions will allow us to easily show the shape and head of any Pandas DataFrames or embedding arrays passed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf07de0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "082ccef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_embeddings(embeddings: np.array) -> list[int]:\n",
    "    print(\"Num Embeddings:\", len(embeddings))\n",
    "    print(\"Embedding Size:\", len(embeddings[0]))\n",
    "    return list(embeddings[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f889dd7",
   "metadata": {},
   "source": [
    "## 1. Load Song Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24da7ed7",
   "metadata": {},
   "source": [
    "The song data we will read in will be taken from an [open-source Spotify dataset](https://www.kaggle.com/datasets/vatsalmavani/spotify-dataset) on Kaggle. There are 5 files on Kaggle, however, only one file is relevant to this analysis.\n",
    "This dataset contains a list of metadata on 170,000 songs from 1921 to 2020. This metadata includes:\n",
    "- Song Name\n",
    "- Artist Name\n",
    "- Song Year\n",
    "- Various features about the song's music, including:\n",
    "    * acousticness\n",
    "    * danceability\n",
    "    * duration_ms\n",
    "    * energy\n",
    "    * explicit\n",
    "    * instrumentalness\n",
    "    * key\n",
    "    * liveness\n",
    "    * loudness\n",
    "    * mode\n",
    "    * popularity\n",
    "    * release_date\n",
    "    * speechiness\n",
    "    * tempo\n",
    "    * valence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c148de",
   "metadata": {},
   "source": [
    "### Read In The Spotify Data From The CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc57e87",
   "metadata": {},
   "source": [
    "We can read this song data from a CSV into a Pandas DataFrame and show the resulting table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e138b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_song_df = pd.read_csv(\"data/song_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70881392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170653, 19)\n"
     ]
    },
    {
     "data": {
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       "      <td>Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1o6I8BglA6ylDMrIELygv1</td>\n",
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       "      <td>1921</td>\n",
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       "      <td>0.0394</td>\n",
       "      <td>1921</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3ftBPsC5vPBKxYSee08FDH</td>\n",
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       "      <td>4d6HGyGT8e121BsdKmw9v6</td>\n",
       "      <td>When Irish Eyes Are Smiling</td>\n",
       "      <td>['Phil Regan']</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.418</td>\n",
       "      <td>166693</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1921</td>\n",
       "      <td>0.0380</td>\n",
       "      <td>101.665</td>\n",
       "      <td>0.2530</td>\n",
       "      <td>1921</td>\n",
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      "text/plain": [
       "                       id  \\\n",
       "0  4BJqT0PrAfrxzMOxytFOIz   \n",
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       "2  1o6I8BglA6ylDMrIELygv1   \n",
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       "4  4d6HGyGT8e121BsdKmw9v6   \n",
       "\n",
       "                                                               name  \\\n",
       "0  Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve   \n",
       "1                                           Clancy Lowered the Boom   \n",
       "2                                                         Gati Bali   \n",
       "3                                                         Danny Boy   \n",
       "4                                       When Irish Eyes Are Smiling   \n",
       "\n",
       "                                                              artists  \\\n",
       "0  ['Sergei Rachmaninoff', 'James Levine', 'Berliner Philharmoniker']   \n",
       "1                                                      ['Dennis Day']   \n",
       "2             ['KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat']   \n",
       "3                                                    ['Frank Parker']   \n",
       "4                                                      ['Phil Regan']   \n",
       "\n",
       "   acousticness  danceability  duration_ms  energy  explicit  \\\n",
       "0         0.982         0.279       831667   0.211         0   \n",
       "1         0.732         0.819       180533   0.341         0   \n",
       "2         0.961         0.328       500062   0.166         0   \n",
       "3         0.967         0.275       210000   0.309         0   \n",
       "4         0.957         0.418       166693   0.193         0   \n",
       "\n",
       "   instrumentalness  key  liveness  loudness  mode  popularity release_date  \\\n",
       "0          0.878000   10     0.665   -20.096     1           4         1921   \n",
       "1          0.000000    7     0.160   -12.441     1           5         1921   \n",
       "2          0.913000    3     0.101   -14.850     1           5         1921   \n",
       "3          0.000028    5     0.381    -9.316     1           3         1921   \n",
       "4          0.000002    3     0.229   -10.096     1           2         1921   \n",
       "\n",
       "   speechiness    tempo  valence  year  \n",
       "0       0.0366   80.954   0.0594  1921  \n",
       "1       0.4150   60.936   0.9630  1921  \n",
       "2       0.0339  110.339   0.0394  1921  \n",
       "3       0.0354  100.109   0.1650  1921  \n",
       "4       0.0380  101.665   0.2530  1921  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(raw_song_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a918f77d",
   "metadata": {},
   "source": [
    "### Pre-process The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b71c3a9",
   "metadata": {},
   "source": [
    "Here we will perform a few operations on this Pandas DataFrame to get it into the correct format for creating the vector embeddings for our vector database.\n",
    "This will include:\n",
    "- Adding a column prefix\n",
    "- Removing excess columns\n",
    "- Fixing column values\n",
    "- Combining columns into one\n",
    "- Removing duplicate rows\n",
    "\n",
    "Once these pre-processing steps have been carried out, our data will be clean and in the correct format to start creating embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44b59370",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add \"song_\" prefix to col names\n",
    "song_df = raw_song_df.add_prefix(\"song_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "781e33be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unused cols\n",
    "song_df = song_df.drop(columns=[\"song_id\", \"song_release_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d01ceb35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix artists list names - remove quotes\n",
    "def fix_artists(str_list):\n",
    "    return \", \".join([v for v in str_list.rstrip(\"']\").lstrip(\"['\").split(\"', '\")])\n",
    "\n",
    "\n",
    "song_df[\"song_artists\"] = song_df[\"song_artists\"].apply(fix_artists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6049043d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine song_name & song_artists into song_description\n",
    "song_df.insert(\n",
    "    0, \"song_description\", song_df[\"song_name\"] + \" - \" + song_df[\"song_artists\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b9867c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove duplicate rows\n",
    "song_data = song_df[\n",
    "    ~song_df.duplicated(subset=[\"song_description\"], keep=\"first\")\n",
    "].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f3b35c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170653, 18)\n"
     ]
    },
    {
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       "      <th>0</th>\n",
       "      <td>Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve - Sergei Rachmaninoff, James Levine, Berliner Philharmoniker</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>5</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-9.316</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0354</td>\n",
       "      <td>100.109</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>When Irish Eyes Are Smiling - Phil Regan</td>\n",
       "      <td>When Irish Eyes Are Smiling</td>\n",
       "      <td>Phil Regan</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.418</td>\n",
       "      <td>166693</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3</td>\n",
       "      <td>0.229</td>\n",
       "      <td>-10.096</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0380</td>\n",
       "      <td>101.665</td>\n",
       "      <td>0.2530</td>\n",
       "      <td>1921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                song_description  \\\n",
       "0  Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve - Sergei Rachmaninoff, James Levine, Berliner Philharmoniker   \n",
       "1                                                                                           Clancy Lowered the Boom - Dennis Day   \n",
       "2                                                                Gati Bali - KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat   \n",
       "3                                                                                                       Danny Boy - Frank Parker   \n",
       "4                                                                                       When Irish Eyes Are Smiling - Phil Regan   \n",
       "\n",
       "                                                          song_name  \\\n",
       "0  Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve   \n",
       "1                                           Clancy Lowered the Boom   \n",
       "2                                                         Gati Bali   \n",
       "3                                                         Danny Boy   \n",
       "4                                       When Irish Eyes Are Smiling   \n",
       "\n",
       "                                                 song_artists  \\\n",
       "0  Sergei Rachmaninoff, James Levine, Berliner Philharmoniker   \n",
       "1                                                  Dennis Day   \n",
       "2         KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat   \n",
       "3                                                Frank Parker   \n",
       "4                                                  Phil Regan   \n",
       "\n",
       "   song_acousticness  song_danceability  song_duration_ms  song_energy  \\\n",
       "0              0.982              0.279            831667        0.211   \n",
       "1              0.732              0.819            180533        0.341   \n",
       "2              0.961              0.328            500062        0.166   \n",
       "3              0.967              0.275            210000        0.309   \n",
       "4              0.957              0.418            166693        0.193   \n",
       "\n",
       "   song_explicit  song_instrumentalness  song_key  song_liveness  \\\n",
       "0              0               0.878000        10          0.665   \n",
       "1              0               0.000000         7          0.160   \n",
       "2              0               0.913000         3          0.101   \n",
       "3              0               0.000028         5          0.381   \n",
       "4              0               0.000002         3          0.229   \n",
       "\n",
       "   song_loudness  song_mode  song_popularity  song_speechiness  song_tempo  \\\n",
       "0        -20.096          1                4            0.0366      80.954   \n",
       "1        -12.441          1                5            0.4150      60.936   \n",
       "2        -14.850          1                5            0.0339     110.339   \n",
       "3         -9.316          1                3            0.0354     100.109   \n",
       "4        -10.096          1                2            0.0380     101.665   \n",
       "\n",
       "   song_valence  song_year  \n",
       "0        0.0594       1921  \n",
       "1        0.9630       1921  \n",
       "2        0.0394       1921  \n",
       "3        0.1650       1921  \n",
       "4        0.2530       1921  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(song_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d0819e",
   "metadata": {},
   "source": [
    "## 2. Create Song Vector Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76775144",
   "metadata": {},
   "source": [
    "We will create vector embeddings from this data in three steps:\n",
    "\n",
    "- A. Encoding the categorical `song_description` column as numeric values\n",
    "- B. Scaling the numeric column values\n",
    "- C. Joining these two sets of encodings together into one vector embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7edae729",
   "metadata": {},
   "source": [
    "### A. Embed Categorical Song Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28410994",
   "metadata": {},
   "source": [
    "To embed the `song_description` column as numeric vectors, we must perform natural language processing on them.\n",
    "This involves tokenising the descriptions to break them up into their individual sub-parts and then using a `Word2Vec` model to turn these tokenised song descriptions into vectors.\n",
    "\n",
    "The length of the vectors we turn these desciptions into is configurable, however, in this case we chose to set this to `15` as there are also 15 numeric columns which describe the song. We do not want to bias the final embedding vectors in favour of either the categorical columns or the numeric columns, so it made sense to keep the number of values representing each the same for both.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c640b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenize the descriptions\n",
    "tokenised_song_descs = [word_tokenize(v.lower()) for v in song_data[\"song_description\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df2bc20b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create embedding model\n",
    "embedding_dim = 15\n",
    "\n",
    "word2Vec_model = Word2Vec(\n",
    "    sentences=tokenised_song_descs,\n",
    "    vector_size=embedding_dim,\n",
    "    window=5,\n",
    "    min_count=1,\n",
    "    sg=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bedcbe34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create embedding vector from tokens\n",
    "def get_embedding(song_desc_tokens, model, embedding_dim):\n",
    "    vectors = [model.wv[token] for token in song_desc_tokens if token in model.wv]\n",
    "\n",
    "    # Average of word vectors OR zeros if no valid tokens found\n",
    "    return sum(vectors) / len(vectors) if vectors else [0] * embedding_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e20683ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# embed song descriptions as vectors\n",
    "categorical_embeddings = [\n",
    "    get_embedding(song_desc_tokens, word2Vec_model, embedding_dim)\n",
    "    for song_desc_tokens in tokenised_song_descs\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "980bb2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Embeddings: 157685\n",
      "Embedding Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-1.3666918,\n",
       " 1.3530807,\n",
       " 1.2024066,\n",
       " -0.22466607,\n",
       " 0.41878286,\n",
       " -0.46274975,\n",
       " -1.2627567,\n",
       " -0.48880932,\n",
       " -0.63431007,\n",
       " 1.3277227,\n",
       " 0.72425747,\n",
       " 0.22040178,\n",
       " 0.43592826,\n",
       " -0.46053427,\n",
       " -1.2532883]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_embeddings(categorical_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b222f5a",
   "metadata": {},
   "source": [
    "### B. Embed Numeric Song Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03623844",
   "metadata": {},
   "source": [
    "There are 15 numeric columns in our data which we will use to make up the other half of our final embedding vectors.\n",
    "First, however, we will scale these values to make them more uniform.\n",
    "\n",
    "The standard scaled score of a sample `x` is calculated as:\n",
    "\n",
    "$$\n",
    "    z = \\frac{(x - u)}{s}\n",
    "$$\n",
    "\n",
    "where `u` is the mean of the training samples and `s` is the standard deviation of the training samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e8614aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['song_acousticness',\n",
       " 'song_danceability',\n",
       " 'song_duration_ms',\n",
       " 'song_energy',\n",
       " 'song_explicit',\n",
       " 'song_instrumentalness',\n",
       " 'song_key',\n",
       " 'song_liveness',\n",
       " 'song_loudness',\n",
       " 'song_mode',\n",
       " 'song_popularity',\n",
       " 'song_speechiness',\n",
       " 'song_tempo',\n",
       " 'song_valence',\n",
       " 'song_year']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract numeric columns\n",
    "numeric_cols = list(\n",
    "    song_data.drop(columns=[\"song_name\", \"song_artists\", \"song_description\"]).columns\n",
    ")\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "918e76d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale these columns\n",
    "scaled_numeric_cols = [\n",
    "    (song_data[col] - song_data[col].mean()) / np.std(song_data[col])\n",
    "    for col in numeric_cols\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2bb9a127",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transpose the array to get row embeddings\n",
    "numeric_embeddings = list(map(list, zip(*scaled_numeric_cols)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ab28edb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Embeddings: 157685\n",
      "Embedding Size: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.2703070294949106,\n",
       " -1.461259048884883,\n",
       " 4.752569009266444,\n",
       " -1.007676175100162,\n",
       " -0.3092011481361043,\n",
       " 2.262496351074803,\n",
       " 1.3649563314116429,\n",
       " 2.6110012104955738,\n",
       " -1.5078176079821606,\n",
       " 0.6453499264358126,\n",
       " -1.2499471942272533,\n",
       " -0.38364744367670833,\n",
       " -1.1655450558051375,\n",
       " -1.7786347004763523,\n",
       " -2.142666230649]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_embeddings(numeric_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccebdaf",
   "metadata": {},
   "source": [
    "### C. Merge Categorical & Numeric Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec7954d",
   "metadata": {},
   "source": [
    "This leaves us with two sets of vectors: one representing the categorical column and one representing the numeric columns.\n",
    "Both sets have 15 values each, so when we join these together, the resulting vector will have 30 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c5849e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_embeddings = [\n",
    "    np.concatenate([cat_row, num_row])\n",
    "    for cat_row, num_row in zip(categorical_embeddings, numeric_embeddings)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5150a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Embeddings: 157685\n",
      "Embedding Size: 30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[-1.3666918277740479,\n",
       " 1.3530807495117188,\n",
       " 1.202406644821167,\n",
       " -0.2246660739183426,\n",
       " 0.4187828600406647,\n",
       " -0.46274974942207336,\n",
       " -1.2627567052841187,\n",
       " -0.4888093173503876,\n",
       " -0.6343100666999817,\n",
       " 1.3277226686477661,\n",
       " 0.7242574691772461,\n",
       " 0.22040177881717682,\n",
       " 0.43592825531959534,\n",
       " -0.4605342745780945,\n",
       " -1.2532882690429688,\n",
       " 1.2703070294949106,\n",
       " -1.461259048884883,\n",
       " 4.752569009266444,\n",
       " -1.007676175100162,\n",
       " -0.3092011481361043,\n",
       " 2.262496351074803,\n",
       " 1.3649563314116429,\n",
       " 2.6110012104955738,\n",
       " -1.5078176079821606,\n",
       " 0.6453499264358126,\n",
       " -1.2499471942272533,\n",
       " -0.38364744367670833,\n",
       " -1.1655450558051375,\n",
       " -1.7786347004763523,\n",
       " -2.142666230649]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_embeddings(row_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d97c52",
   "metadata": {},
   "source": [
    "### Create DataFrame With Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9205b8",
   "metadata": {},
   "source": [
    "We can take these defined embeddings and create a Pandas DataFrame containing them.\n",
    "This will be the table we insert into our vector database.\n",
    "\n",
    "To enable proper filtering of the data once inserted into the KDB.AI vector database, we will pair these embedding vectors with three song description columns: `song_name`, `song_artists`, and `song_year`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "40e00e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded_song_df = song_data[[\"song_name\", \"song_artists\", \"song_year\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5af8b92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded_song_df[\"song_embeddings\"] = row_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5430d33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(157685, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_artists</th>\n",
       "      <th>song_year</th>\n",
       "      <th>song_embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve</td>\n",
       "      <td>Sergei Rachmaninoff, James Levine, Berliner Philharmoniker</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-1.3666918277740479, 1.3530807495117188, 1.202406644821167, -0.2246660739183426, 0.4187828600406647, -0.46274974942207336, -1.2627567052841187, -0.4888093173503876, -0.6343100666999817, 1.3277226686477661, 0.7242574691772461, 0.22040177881717682, 0.43592825531959534, -0.4605342745780945, -1.2532882690429688, 1.2703070294949106, -1.461259048884883, 4.752569009266444, -1.007676175100162, -0.3092011481361043, 2.262496351074803, 1.3649563314116429, 2.6110012104955738, -1.5078176079821606, 0.6453499264358126, -1.2499471942272533, -0.38364744367670833, -1.1655450558051375, -1.7786347004763523, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clancy Lowered the Boom</td>\n",
       "      <td>Dennis Day</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.4902784526348114, 0.8817081451416016, 1.11865234375, 0.08565742522478104, 0.6903175711631775, 0.5949982404708862, -0.7584375143051147, -0.0548345223069191, -0.2068416178226471, 0.09101372212171555, 0.9204968214035034, 0.6219479441642761, 0.5529213547706604, -0.3401077389717102, -0.10770940780639648, 0.6055353575765545, 1.600008527700499, -0.3958314054754836, -0.5219481676317469, -0.3092011481361043, -0.5349944602375606, 0.5117210944216953, -0.26640400859153673, -0.1646257835500665, 0.6453499264358126, -1.2044048830284118, 1.872793548131166, -1.8164526201212032, 1.6541851866630062, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gati Bali</td>\n",
       "      <td>KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.2844405770301819, 0.631794273853302, -0.17285339534282684, 0.5646762251853943, 0.9662110805511475, 0.2495516836643219, 0.417460560798645, -0.22620971500873566, -0.6103554964065552, 0.26909953355789185, 0.5071016550064087, -0.1065995916724205, 0.22436323761940002, 0.33269351720809937, -1.0331687927246094, 1.2144662090537686, -1.183477361379913, 2.1306274127125904, -1.1758127930699978, -0.3092011481361043, 2.374013638541697, -0.6259258882315683, -0.6025761034947833, -0.5873232499193564, 0.6453499264358126, -1.2044048830284118, -0.3997478418740689, -0.21005905433508043, -1.854615663007104, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Danny Boy</td>\n",
       "      <td>Frank Parker</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.7487789988517761, 1.3591301441192627, 1.1782379150390625, -0.2647874355316162, 0.49281495809555054, 0.7352277636528015, -1.1125710010528564, 0.05070333927869797, -0.19258345663547516, 0.5183664560317993, 1.150445580482483, 0.5057250261306763, 0.8323601484298706, -0.2318739891052246, 0.04909057915210724, 1.2304207291798093, -1.4839351050077376, -0.16284109162119192, -0.6415119848547415, -0.3092011481361043, -0.5349062022700511, -0.05710239690493648, 0.9928168892663868, 0.3837053008849818, 0.6453499264358126, -1.295489505426095, -0.39080317620886856, -0.5426988976237884, -1.3774552183139837, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>When Irish Eyes Are Smiling</td>\n",
       "      <td>Phil Regan</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.3061518669128418, 0.8175497651100159, 0.9532014727592468, -0.05882035940885544, 1.183526635169983, 0.30472809076309204, -0.9971756935119629, 0.1633039265871048, -0.2687903046607971, 0.04581066593527794, 1.3318020105361938, 0.1084437295794487, 0.9008270502090454, -0.5091925263404846, -0.18293435871601105, 1.203829862303075, -0.6732660986156829, -0.5052618172494476, -1.0749308222880962, -0.3092011481361043, -0.5349891074077622, -0.6259258882315683, 0.12674640748175162, 0.24684186220999385, 0.6453499264358126, -1.3410318166249366, -0.3752990890558546, -0.4921038246856425, -1.0431389831786766, -2.142666230649]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          song_name  \\\n",
       "0  Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve   \n",
       "1                                           Clancy Lowered the Boom   \n",
       "2                                                         Gati Bali   \n",
       "3                                                         Danny Boy   \n",
       "4                                       When Irish Eyes Are Smiling   \n",
       "\n",
       "                                                 song_artists  song_year  \\\n",
       "0  Sergei Rachmaninoff, James Levine, Berliner Philharmoniker       1921   \n",
       "1                                                  Dennis Day       1921   \n",
       "2         KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat       1921   \n",
       "3                                                Frank Parker       1921   \n",
       "4                                                  Phil Regan       1921   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             song_embeddings  \n",
       "0       [-1.3666918277740479, 1.3530807495117188, 1.202406644821167, -0.2246660739183426, 0.4187828600406647, -0.46274974942207336, -1.2627567052841187, -0.4888093173503876, -0.6343100666999817, 1.3277226686477661, 0.7242574691772461, 0.22040177881717682, 0.43592825531959534, -0.4605342745780945, -1.2532882690429688, 1.2703070294949106, -1.461259048884883, 4.752569009266444, -1.007676175100162, -0.3092011481361043, 2.262496351074803, 1.3649563314116429, 2.6110012104955738, -1.5078176079821606, 0.6453499264358126, -1.2499471942272533, -0.38364744367670833, -1.1655450558051375, -1.7786347004763523, -2.142666230649]  \n",
       "1           [-0.4902784526348114, 0.8817081451416016, 1.11865234375, 0.08565742522478104, 0.6903175711631775, 0.5949982404708862, -0.7584375143051147, -0.0548345223069191, -0.2068416178226471, 0.09101372212171555, 0.9204968214035034, 0.6219479441642761, 0.5529213547706604, -0.3401077389717102, -0.10770940780639648, 0.6055353575765545, 1.600008527700499, -0.3958314054754836, -0.5219481676317469, -0.3092011481361043, -0.5349944602375606, 0.5117210944216953, -0.26640400859153673, -0.1646257835500665, 0.6453499264358126, -1.2044048830284118, 1.872793548131166, -1.8164526201212032, 1.6541851866630062, -2.142666230649]  \n",
       "2     [-0.2844405770301819, 0.631794273853302, -0.17285339534282684, 0.5646762251853943, 0.9662110805511475, 0.2495516836643219, 0.417460560798645, -0.22620971500873566, -0.6103554964065552, 0.26909953355789185, 0.5071016550064087, -0.1065995916724205, 0.22436323761940002, 0.33269351720809937, -1.0331687927246094, 1.2144662090537686, -1.183477361379913, 2.1306274127125904, -1.1758127930699978, -0.3092011481361043, 2.374013638541697, -0.6259258882315683, -0.6025761034947833, -0.5873232499193564, 0.6453499264358126, -1.2044048830284118, -0.3997478418740689, -0.21005905433508043, -1.854615663007104, -2.142666230649]  \n",
       "3  [-0.7487789988517761, 1.3591301441192627, 1.1782379150390625, -0.2647874355316162, 0.49281495809555054, 0.7352277636528015, -1.1125710010528564, 0.05070333927869797, -0.19258345663547516, 0.5183664560317993, 1.150445580482483, 0.5057250261306763, 0.8323601484298706, -0.2318739891052246, 0.04909057915210724, 1.2304207291798093, -1.4839351050077376, -0.16284109162119192, -0.6415119848547415, -0.3092011481361043, -0.5349062022700511, -0.05710239690493648, 0.9928168892663868, 0.3837053008849818, 0.6453499264358126, -1.295489505426095, -0.39080317620886856, -0.5426988976237884, -1.3774552183139837, -2.142666230649]  \n",
       "4  [-0.3061518669128418, 0.8175497651100159, 0.9532014727592468, -0.05882035940885544, 1.183526635169983, 0.30472809076309204, -0.9971756935119629, 0.1633039265871048, -0.2687903046607971, 0.04581066593527794, 1.3318020105361938, 0.1084437295794487, 0.9008270502090454, -0.5091925263404846, -0.18293435871601105, 1.203829862303075, -0.6732660986156829, -0.5052618172494476, -1.0749308222880962, -0.3092011481361043, -0.5349891074077622, -0.6259258882315683, 0.12674640748175162, 0.24684186220999385, 0.6453499264358126, -1.3410318166249366, -0.3752990890558546, -0.4921038246856425, -1.0431389831786766, -2.142666230649]  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(embedded_song_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59c8f54",
   "metadata": {},
   "source": [
    "## 3. Store Embeddings In KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2724a9",
   "metadata": {},
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53c01082",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3e5fcc",
   "metadata": {},
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define the schema for the table in KDB.AI which will store our embeddings.\n",
    "\n",
    "As mentioned above, our table will have four columns:\n",
    "- Song Name\n",
    "- Song Artists\n",
    "- Song Year\n",
    "- Song Embeddings\n",
    "\n",
    "When defining the schema, we must supply the types of these columns. We can use the `.dtypes()` function on the defined Pandas DataFrame to help with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d535b113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "song_name          object\n",
       "song_artists       object\n",
       "song_year           int64\n",
       "song_embeddings    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedded_song_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c1c94ebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "schema =  [\n",
    "    {\n",
    "        \"name\": \"song_name\",\n",
    "        \"type\": \"str\",\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"song_artists\",\n",
    "        \"type\": \"bytes\",\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"song_year\",\n",
    "        \"type\": \"int64\",\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"song_embeddings\",\n",
    "        \"type\": \"float64s\"\n",
    "    }\n",
    "]\n",
    "\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\" : \"flat_index\",\n",
    "        \"column\" : \"song_embeddings\",\n",
    "        \"type\" : \"flat\",\n",
    "        \"params\":{\n",
    "            \"dims\": len(numeric_cols) + embedding_dim,\n",
    "            \"metric\": \"L2\"\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70075f1",
   "metadata": {},
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "13bd4adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get database connection. Default database is 'default'.\n",
    "database = session.database(\"default\")\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"songs\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e1179907",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "table = database.create_table(\"songs\", schema=schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cef68a",
   "metadata": {},
   "source": [
    "### Add Embedded Data to KDB.AI Table\n",
    "\n",
    "When adding larger amounts of data, you should insert data into an index in chunks.\n",
    "\n",
    "It is a good idea to first get an idea of how large your dataset to insert is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a7c74728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.63715362548828"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedded_song_df.memory_usage(deep=True).sum() / (1024**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3202579",
   "metadata": {},
   "source": [
    "This dataset is 80MB which exceeds the insert limit of <10MB at a time. As such, we'll insert this data in chunks, inserting 10,000 rows at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c2179418",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_size = 10_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6a2fd78",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Convert empty string values to None as empty string will create issue in search filter.\n",
    "for index, row in embedded_song_df.iterrows():\n",
    "    cast = row['song_artists']\n",
    "    if 1 == len(cast):\n",
    "        embedded_song_df.loc[index, 'song_artists'] = 'None'\n",
    "\n",
    "for i in tqdm(range((len(embedded_song_df) // chunk_size) + 1)):\n",
    "    index = i * chunk_size\n",
    "    data = embedded_song_df.iloc[index : index + chunk_size].reset_index(drop=True)\n",
    "    # change data types as per table schema\n",
    "    data['song_artists'] = data['song_artists'].str.encode('utf-8')\n",
    "    table.insert(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a556b3",
   "metadata": {},
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f9a04081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(157685, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_artists</th>\n",
       "      <th>song_year</th>\n",
       "      <th>song_embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve</td>\n",
       "      <td>b'Sergei Rachmaninoff, James Levine, Berliner Philharmoniker'</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-1.3666918277740479, 1.3530807495117188, 1.202406644821167, -0.2246660739183426, 0.4187828600406647, -0.46274974942207336, -1.2627567052841187, -0.4888093173503876, -0.6343100666999817, 1.3277226686477661, 0.7242574691772461, 0.22040177881717682, 0.43592825531959534, -0.4605342745780945, -1.2532882690429688, 1.2703070294949106, -1.461259048884883, 4.752569009266444, -1.007676175100162, -0.3092011481361043, 2.262496351074803, 1.3649563314116429, 2.6110012104955738, -1.5078176079821606, 0.6453499264358126, -1.2499471942272533, -0.38364744367670833, -1.1655450558051375, -1.7786347004763523, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clancy Lowered the Boom</td>\n",
       "      <td>b'Dennis Day'</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.4902784526348114, 0.8817081451416016, 1.11865234375, 0.08565742522478104, 0.6903175711631775, 0.5949982404708862, -0.7584375143051147, -0.0548345223069191, -0.2068416178226471, 0.09101372212171555, 0.9204968214035034, 0.6219479441642761, 0.5529213547706604, -0.3401077389717102, -0.10770940780639648, 0.6055353575765545, 1.600008527700499, -0.3958314054754836, -0.5219481676317469, -0.3092011481361043, -0.5349944602375606, 0.5117210944216953, -0.26640400859153673, -0.1646257835500665, 0.6453499264358126, -1.2044048830284118, 1.872793548131166, -1.8164526201212032, 1.6541851866630062, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gati Bali</td>\n",
       "      <td>b'KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat'</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.2844405770301819, 0.631794273853302, -0.17285339534282684, 0.5646762251853943, 0.9662110805511475, 0.2495516836643219, 0.417460560798645, -0.22620971500873566, -0.6103554964065552, 0.26909953355789185, 0.5071016550064087, -0.1065995916724205, 0.22436323761940002, 0.33269351720809937, -1.0331687927246094, 1.2144662090537686, -1.183477361379913, 2.1306274127125904, -1.1758127930699978, -0.3092011481361043, 2.374013638541697, -0.6259258882315683, -0.6025761034947833, -0.5873232499193564, 0.6453499264358126, -1.2044048830284118, -0.3997478418740689, -0.21005905433508043, -1.854615663007104, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Danny Boy</td>\n",
       "      <td>b'Frank Parker'</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.7487789988517761, 1.3591301441192627, 1.1782379150390625, -0.2647874355316162, 0.49281495809555054, 0.7352277636528015, -1.1125710010528564, 0.05070333927869797, -0.19258345663547516, 0.5183664560317993, 1.150445580482483, 0.5057250261306763, 0.8323601484298706, -0.2318739891052246, 0.04909057915210724, 1.2304207291798093, -1.4839351050077376, -0.16284109162119192, -0.6415119848547415, -0.3092011481361043, -0.5349062022700511, -0.05710239690493648, 0.9928168892663868, 0.3837053008849818, 0.6453499264358126, -1.295489505426095, -0.39080317620886856, -0.5426988976237884, -1.3774552183139837, -2.142666230649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>When Irish Eyes Are Smiling</td>\n",
       "      <td>b'Phil Regan'</td>\n",
       "      <td>1921</td>\n",
       "      <td>[-0.3061518669128418, 0.8175497651100159, 0.9532014727592468, -0.05882035940885544, 1.183526635169983, 0.30472809076309204, -0.9971756935119629, 0.1633039265871048, -0.2687903046607971, 0.04581066593527794, 1.3318020105361938, 0.1084437295794487, 0.9008270502090454, -0.5091925263404846, -0.18293435871601105, 1.203829862303075, -0.6732660986156829, -0.5052618172494476, -1.0749308222880962, -0.3092011481361043, -0.5349891074077622, -0.6259258882315683, 0.12674640748175162, 0.24684186220999385, 0.6453499264358126, -1.3410318166249366, -0.3752990890558546, -0.4921038246856425, -1.0431389831786766, -2.142666230649]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          song_name  \\\n",
       "0  Piano Concerto No. 3 in D Minor, Op. 30: III. Finale. Alla breve   \n",
       "1                                           Clancy Lowered the Boom   \n",
       "2                                                         Gati Bali   \n",
       "3                                                         Danny Boy   \n",
       "4                                       When Irish Eyes Are Smiling   \n",
       "\n",
       "                                                    song_artists  song_year  \\\n",
       "0  b'Sergei Rachmaninoff, James Levine, Berliner Philharmoniker'       1921   \n",
       "1                                                  b'Dennis Day'       1921   \n",
       "2         b'KHP Kridhamardawa Karaton Ngayogyakarta Hadiningrat'       1921   \n",
       "3                                                b'Frank Parker'       1921   \n",
       "4                                                  b'Phil Regan'       1921   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             song_embeddings  \n",
       "0       [-1.3666918277740479, 1.3530807495117188, 1.202406644821167, -0.2246660739183426, 0.4187828600406647, -0.46274974942207336, -1.2627567052841187, -0.4888093173503876, -0.6343100666999817, 1.3277226686477661, 0.7242574691772461, 0.22040177881717682, 0.43592825531959534, -0.4605342745780945, -1.2532882690429688, 1.2703070294949106, -1.461259048884883, 4.752569009266444, -1.007676175100162, -0.3092011481361043, 2.262496351074803, 1.3649563314116429, 2.6110012104955738, -1.5078176079821606, 0.6453499264358126, -1.2499471942272533, -0.38364744367670833, -1.1655450558051375, -1.7786347004763523, -2.142666230649]  \n",
       "1           [-0.4902784526348114, 0.8817081451416016, 1.11865234375, 0.08565742522478104, 0.6903175711631775, 0.5949982404708862, -0.7584375143051147, -0.0548345223069191, -0.2068416178226471, 0.09101372212171555, 0.9204968214035034, 0.6219479441642761, 0.5529213547706604, -0.3401077389717102, -0.10770940780639648, 0.6055353575765545, 1.600008527700499, -0.3958314054754836, -0.5219481676317469, -0.3092011481361043, -0.5349944602375606, 0.5117210944216953, -0.26640400859153673, -0.1646257835500665, 0.6453499264358126, -1.2044048830284118, 1.872793548131166, -1.8164526201212032, 1.6541851866630062, -2.142666230649]  \n",
       "2     [-0.2844405770301819, 0.631794273853302, -0.17285339534282684, 0.5646762251853943, 0.9662110805511475, 0.2495516836643219, 0.417460560798645, -0.22620971500873566, -0.6103554964065552, 0.26909953355789185, 0.5071016550064087, -0.1065995916724205, 0.22436323761940002, 0.33269351720809937, -1.0331687927246094, 1.2144662090537686, -1.183477361379913, 2.1306274127125904, -1.1758127930699978, -0.3092011481361043, 2.374013638541697, -0.6259258882315683, -0.6025761034947833, -0.5873232499193564, 0.6453499264358126, -1.2044048830284118, -0.3997478418740689, -0.21005905433508043, -1.854615663007104, -2.142666230649]  \n",
       "3  [-0.7487789988517761, 1.3591301441192627, 1.1782379150390625, -0.2647874355316162, 0.49281495809555054, 0.7352277636528015, -1.1125710010528564, 0.05070333927869797, -0.19258345663547516, 0.5183664560317993, 1.150445580482483, 0.5057250261306763, 0.8323601484298706, -0.2318739891052246, 0.04909057915210724, 1.2304207291798093, -1.4839351050077376, -0.16284109162119192, -0.6415119848547415, -0.3092011481361043, -0.5349062022700511, -0.05710239690493648, 0.9928168892663868, 0.3837053008849818, 0.6453499264358126, -1.295489505426095, -0.39080317620886856, -0.5426988976237884, -1.3774552183139837, -2.142666230649]  \n",
       "4  [-0.3061518669128418, 0.8175497651100159, 0.9532014727592468, -0.05882035940885544, 1.183526635169983, 0.30472809076309204, -0.9971756935119629, 0.1633039265871048, -0.2687903046607971, 0.04581066593527794, 1.3318020105361938, 0.1084437295794487, 0.9008270502090454, -0.5091925263404846, -0.18293435871601105, 1.203829862303075, -0.6732660986156829, -0.5052618172494476, -1.0749308222880962, -0.3092011481361043, -0.5349891074077622, -0.6259258882315683, 0.12674640748175162, 0.24684186220999385, 0.6453499264358126, -1.3410318166249366, -0.3752990890558546, -0.4921038246856425, -1.0431389831786766, -2.142666230649]  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89aca5e4",
   "metadata": {},
   "source": [
    "## 4. Search For Similar Songs To A Target Song"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d9a23b4",
   "metadata": {},
   "source": [
    "Now that the data has been inserted into the database, we can perform some queries on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6465d403",
   "metadata": {},
   "source": [
    "### Find Songs By A Certain Artist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa6798d",
   "metadata": {},
   "source": [
    "We can query the database to find songs by particular artists using KDB.AI's `.query()` function.\n",
    "\n",
    "Here, we want to return all songs in the dataset by the DJ `Calvin Harris`, sorted by the year they were produced. This returns 32 songs to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d30ac168",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_artists</th>\n",
       "      <th>song_year</th>\n",
       "      <th>song_embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flashback</td>\n",
       "      <td>b'Calvin Harris'</td>\n",
       "      <td>2009</td>\n",
       "      <td>[-0.6639690399169922, 1.2091901302337646, 0.6080996990203857, -0.12048277258872986, 0.9051563739776611, 0.2514127492904663, -0.6373221278190613, -0.29814955592155457, 0.22219587862491608, 0.4700770080089569, 0.8792182207107544, 0.5950366258621216, 0.8773530721664429, -0.03305034339427948, 0.20640605688095093, -1.3351724705550174, -1.5406252453148743, -0.011030115749796763, 1.7460279903169285, -0.3092011481361043, -0.5313940449564866, 1.0805445857483271, -0.7039975151774578, 1.0250333372402145, -1.549546934208044, 0.7539144985217788, -0.307319630000332, 0.36420802995835755, -1.2520866301382436, 1.247808968405853]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>You Used To Hold Me</td>\n",
       "      <td>b'Calvin Harris'</td>\n",
       "      <td>2009</td>\n",
       "      <td>[-0.18039822578430176, 1.2320046424865723, 1.0078709125518799, -0.3332071900367737, 1.0227277278900146, 0.3912988007068634, -1.1201367378234863, 0.06693186610937119, 0.01194898784160614, 0.4125801920890808, 1.7104941606521606, 0.0907072052359581, 1.1263823509216309, 0.005882999859750271, -0.15810611844062805, -1.279491195315136, 0.46053670752705134, 0.004253770518922344, 1.8244917453695186, -0.3092011481361043, -0.5349904137531296, 1.6493680770749588, 0.6224578016611152, 0.958707209266951, -1.549546934208044, 0.5717452537264123, -0.35562082459241384, 0.3955535635781341, -1.2824790151505443, 1.247808968405853]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I'm Not Alone - Radio Edit</td>\n",
       "      <td>b'Calvin Harris'</td>\n",
       "      <td>2009</td>\n",
       "      <td>[-0.4578489363193512, 1.1988781690597534, 0.9186619520187378, -0.3090927302837372, 0.8355614542961121, 0.3251281678676605, -0.956695556640625, 0.2001478672027588, -0.004034769721329212, 0.5499118566513062, 1.427262544631958, 0.3321062922477722, 0.9700223803520203, -0.3015604019165039, -0.10042643547058105, -1.3273547556932577, 0.3074733286977821, -0.1491385696844665, 0.7932538218211915, -0.3092011481361043, -0.14309084999676247, 0.5117210944216953, 0.4914076629700189, 0.8665875870818629, 0.6453499264358126, 0.3895760089310457, -0.411077751716656, 0.46191895166730823, -0.36310936852844955, 1.247808968405853]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>We Found Love</td>\n",
       "      <td>b'Rihanna, Calvin Harris'</td>\n",
       "      <td>2011</td>\n",
       "      <td>[-0.32985055446624756, 1.3834985494613647, 0.926184356212616, -0.28489983081817627, 0.8302570581436157, 0.22301919758319855, -1.0492554903030396, -0.0061478391289711, 0.029022544622421265, 0.4409855902194977, 1.2905532121658325, 0.4727650284767151, 1.1560120582580566, -0.15044355392456055, -0.18808501958847046, -1.2744389306085564, 1.1181423350898372, -0.12151213480453658, 1.0660087798611477, -0.3092011481361043, -0.5305974929031516, -1.1947493795582, -0.5626912786757541, 1.2313812909348116, 0.6453499264358126, 1.9835569008905032, -0.37351015592281456, 0.36375280436636925, 0.2751307167298641, 1.3248652229298272]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dance Wiv Me - Radio Edit</td>\n",
       "      <td>b'Dizzee Rascal, Calvin Harris, Chrome'</td>\n",
       "      <td>2011</td>\n",
       "      <td>[-0.5854893922805786, 1.0818036794662476, 0.7288601994514465, -0.25424012541770935, 0.6128330826759338, 0.41814619302749634, -0.6447377800941467, 0.027640312910079956, -0.1611126810312271, 0.39173322916030884, 0.8761189579963684, 0.3748489320278168, 0.6108167767524719, 0.0187530517578125, -0.023305920884013176, -1.2143435714671371, 1.9344803555126058, -0.20954668716662583, 0.9912813940967762, -0.3092011481361043, -0.5349944602375606, 1.6493680770749588, -0.30059100129356187, 1.2671763441267319, 0.6453499264358126, 1.5281337889020867, -0.3329610049072396, -0.1561798539118754, 1.00454795702508, 1.3248652229298272]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Feel So Close - Radio Edit</td>\n",
       "      <td>b'Calvin Harris'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.5671982169151306, 1.21516752243042, 0.8834764957427979, -0.34776613116264343, 0.8313254117965698, 0.4305516481399536, -0.8739187717437744, 0.12808793783187866, -0.13710293173789978, 0.49484601616859436, 1.3459782600402832, 0.40094777941703796, 1.118976354598999, -0.10406777262687683, 0.11825662106275558, -1.3383314655399736, 0.9650789562605678, -0.19120286091549893, 1.6563551273996828, -0.3092011481361043, -0.5125954164977816, 0.5117210944216953, -0.015699395443352974, 1.5196718418873827, 0.6453499264358126, 2.2112684568847114, -0.4170408621601229, 0.36215951479440944, 1.4870270690953529, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sweet Nothing (feat. Florence Welch)</td>\n",
       "      <td>b'Calvin Harris, Florence Welch'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.5403595566749573, 1.3501120805740356, 0.8737241625785828, -0.30949994921684265, 0.5602115988731384, 0.044263433665037155, -0.8665704727172852, -0.07287808507680893, 0.004971159156411886, 0.4264087975025177, 1.185262680053711, 0.6865300536155701, 0.7812601327896118, -0.16709819436073303, -0.27374014258384705, -0.8170760203287275, 0.20543107614493594, -0.14259962817167257, 1.6750369738407758, -0.3092011481361043, -0.5346376049176665, 0.7961328400850112, -0.8549900662780685, 1.3266593001662457, -1.549546934208044, 1.8469299672939783, 0.048081752430295104, 0.36206196645326905, 0.20674785045218758, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I Need Your Love (feat. Ellie Goulding)</td>\n",
       "      <td>b'Calvin Harris, Ellie Goulding'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.3471231460571289, 1.4163035154342651, 0.9777242541313171, -0.3454452455043793, 0.5281813144683838, 0.0064859092235565186, -0.8150930404663086, -0.003013826906681061, -0.05287719517946243, 0.6403617858886719, 1.3331170082092285, 0.4529487192630768, 0.9392791390419006, -0.1485135555267334, -0.12306036055088043, -0.2506905558542882, 0.8970507878920038, 0.030931386799656055, 1.4508548165476611, -0.3092011481361043, -0.5349944602375606, 0.7961328400850112, 0.17232906441778495, 1.1294355757166477, 0.6453499264358126, 1.8013876560951365, -0.31387905148814554, 0.2663020115671261, 0.1991497541991124, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Let's Go (feat. Ne-Yo)</td>\n",
       "      <td>b'Calvin Harris, Ne-Yo'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.4517887234687805, 1.4095631837844849, 1.0143579244613647, -0.42695263028144836, 0.3167036771774292, 0.22329269349575043, -0.8331954479217529, 0.06706950813531876, -0.11937189847230911, 0.6374547481536865, 1.3507567644119263, 0.6769067049026489, 1.002878189086914, -0.12010153383016586, -0.21650470793247223, -1.3202549942371695, 0.9820859983527089, 0.01743444222608983, 1.4994276172945025, -0.3092011481361043, -0.5104287949127105, -0.34151414256825235, 0.4971054950870231, 1.5038799066556534, -1.549546934208044, 1.5736761001009283, -0.24709221452131636, 0.36472828777777266, 1.3198689515276991, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thinking About You (feat. Ayah Marar)</td>\n",
       "      <td>b'Calvin Harris, Ayah Marar'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.4033048748970032, 1.201682209968567, 0.7232522368431091, -0.21271994709968567, 0.4565010666847229, 0.08975932747125626, -0.7079424262046814, 0.05442984402179718, -0.04738606885075569, 0.5769458413124084, 1.0412596464157104, 0.4582187235355377, 0.8240703344345093, -0.08101780712604523, -0.14304494857788086, -1.3339492906786878, 1.067121208813414, 0.1370883744063931, 1.4695366629887539, -0.3092011481361043, -0.5336817424536646, -1.479161125221516, -0.6322048305032051, 1.3664900701396077, -1.549546934208044, 1.61921841129977, -0.36575811234630756, 0.3637202882526556, 0.8373898394574263, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Spectrum (Say My Name) - Calvin Harris Remix</td>\n",
       "      <td>b'Florence + The Machine, Calvin Harris'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.5985769033432007, 1.3303905725479126, 1.0559614896774292, -0.25539952516555786, 0.8112835884094238, 0.17980347573757172, -0.812416672706604, -0.08991655707359314, 0.104682557284832, 0.2630487084388733, 1.328857660293579, 0.6589300632476807, 0.8192211389541626, -0.11460264772176743, -0.08975117653608322, -1.334933152753127, 0.23377614629850432, -0.09808422222604993, 1.7385552517404912, -0.3092011481361043, -0.5218672823986005, 1.6493680770749588, -0.6276465648096017, 1.3428021672920136, -1.549546934208044, 0.9360837433171454, -0.31447536253249225, 0.30216728499307355, 0.2295421392114131, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bounce (feat. Kelis) - Radio Edit</td>\n",
       "      <td>b'Calvin Harris, Kelis'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.6689270734786987, 1.3969231843948364, 0.8880228400230408, -0.326450377702713, 0.34622687101364136, 0.18351785838603973, -0.7323996424674988, -0.0037096429150551558, -0.1328837126493454, 0.6257920861244202, 1.0089510679244995, 0.6650357842445374, 0.8406809568405151, 0.010336706414818764, 0.018450651317834854, -1.2521026024320998, 1.3732479664719526, -0.06648065605115584, 1.802073529640207, -0.3092011481361043, 1.0358061889389716, -0.9103376338948841, 2.6053033783785695, 1.6454809259001602, -1.549546934208044, 1.4370491665044034, -0.36396917921326755, 0.3622895792492634, 0.8791793688493397, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>We'll Be Coming Back (feat. Example)</td>\n",
       "      <td>b'Calvin Harris, Example'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.29318350553512573, 1.2344664335250854, 0.91950523853302, -0.2705417275428772, 0.5557699203491211, 0.24373485147953033, -0.9255906343460083, 0.11558005213737488, -0.09228789061307907, 0.5509446263313293, 1.300772786140442, 0.46837666630744934, 1.019039273262024, -0.2337048500776291, -0.30271509289741516, -1.3374326942395398, 0.3358183988513505, 0.029769084015640684, 1.7609734674698028, -0.3092011481361043, -0.5349944602375606, 0.5117210944216953, 2.2292464586562937, 1.252612670524137, 0.6453499264358126, 1.5281337889020867, -0.08131774419293655, 0.36241964370411694, 0.16495832106027414, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Drinking from the Bottle (feat. Tinie Tempah)</td>\n",
       "      <td>b'Calvin Harris, Tinie Tempah'</td>\n",
       "      <td>2012</td>\n",
       "      <td>[-0.49500662088394165, 1.250335454940796, 0.836037278175354, -0.21671639382839203, 0.4340763986110687, 0.14523451030254364, -0.6550229787826538, -0.0313369482755661, -0.14998087286949158, 0.44496625661849976, 0.9944913387298584, 0.5686814188957214, 0.6103565096855164, -0.23264175653457642, -0.1452845335006714, -1.2162049321485084, 0.7269803669705941, 0.07710722529387208, 1.5143730944473768, -0.3092011481361043, -0.5347956408450482, 1.0805445857483271, -0.878920961169486, 1.2857757345107685, -1.549546934208044, 1.4370491665044034, -0.2953934091133982, 0.3662240290085925, 0.009197347872233573, 1.363393350191814]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Summer</td>\n",
       "      <td>b'Calvin Harris'</td>\n",
       "      <td>2014</td>\n",
       "      <td>[-0.8264520764350891, 1.3505786657333374, 1.0463076829910278, -0.22568410634994507, 0.913348913192749, 0.2567248046398163, -0.8605219125747681, -0.35306626558303833, 0.03924936056137085, 0.37715211510658264, 1.1773360967636108, 0.5755943655967712, 1.1420295238494873, -0.08487856388092041, 0.3010081648826599, -1.284809368690483, 0.3358183988513505, -0.06374489575680674, 1.4022820158008196, -0.3092011481361043, -0.4782799540401117, -0.34151414256825235, -0.37466281881461627, 1.394389155715663, -1.549546934208044, 2.2112684568847114, -0.3955736645636421, 0.362549708158971, 0.8183945988247383, 1.440449604715788]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Outside (feat. Ellie Goulding)</td>\n",
       "      <td>b'Calvin Harris, Ellie Goulding'</td>\n",
       "      <td>2014</td>\n",
       "      <td>[-0.45815980434417725, 1.4566391706466675, 0.8504025340080261, -0.373926043510437, 0.44716230034828186, 0.05849791318178177, -0.6436365842819214, -0.1507851779460907, -0.06338223814964294, 0.6237001419067383, 1.1598680019378662, 0.6893650889396667, 0.8899211883544922, -0.07148391753435135, -0.03473608195781708, -0.7745306333259528, 0.6192691003870343, -0.02631400201851587, 1.2789818292896065, -0.3092011481361043, -0.5349944602375606, -0.9103376338948841, 0.6566447943631403, 1.2948999637557677, -1.549546934208044, 2.2112684568847114, -0.366950734435001, 0.36534609393832856, -0.41629604229997574, 1.440449604715788]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Blame (feat. John Newman)</td>\n",
       "      <td>b'Calvin Harris, John Newman'</td>\n",
       "      <td>2014</td>\n",
       "      <td>[-0.6763609647750854, 1.6109453439712524, 0.9073587656021118, -0.1523026078939438, 0.6198676228523254, 0.2695436179637909, -1.1315089464187622, -0.25966623425483704, 0.04239802807569504, 0.6183485388755798, 1.0361096858978271, 0.6836062073707581, 0.8744892477989197, -0.3744211196899414, 0.004306115675717592, -1.2646003098641647, -0.6959421547385375, -0.13943689950768517, 1.406018385089038, -0.3092011481361043, -0.51670562509299, -1.479161125221516, 0.7762992688202281, 1.3027959313716324, -1.549546934208044, 2.0290992120893447, -0.12007796207547143, 0.36498841668748067, -0.6822294111576065, 1.440449604715788]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Under Control (feat. Hurts)</td>\n",
       "      <td>b'Calvin Harris, Alesso, Hurts'</td>\n",
       "      <td>2014</td>\n",
       "      <td>[-0.5020278692245483, 1.4283548593521118, 0.8164223432540894, -0.28505995869636536, 0.4534760117530823, 0.2287231981754303, -0.774612307548523, 0.002535061212256551, 0.015209964476525784, 0.5660685896873474, 1.197741150856018, 0.6164646148681641, 0.8045300841331482, -0.029482128098607063, -0.11208241432905197, -0.9978939150905204, 0.04102966925423999, -0.36620454471558167, 1.6227278038057156, -0.3092011481361043, -0.532082265930568, 0.7961328400850112, -0.5228064538567248, 1.4208845137155646, 0.6453499264358126, 1.8924722784928198, -0.10039969761203067, 0.30223231722050037, -0.06678361465851812, 1.440449604715788]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pray to God (feat. HAIM)</td>\n",
       "      <td>b'Calvin Harris, HAIM'</td>\n",
       "      <td>2014</td>\n",
       "      <td>[-0.4818477928638458, 1.3358467817306519, 0.7798342108726501, -0.2659180760383606, 0.4285810887813568, 0.08754778653383255, -0.7763833999633789, -0.006817118264734745, -0.03107377141714096, 0.46428442001342773, 1.156795620918274, 0.6081282496452332, 0.8255685567855835, -0.1547342836856842, -0.21263934671878815, -1.3010297974852907, 0.39250853915848727, 0.01290383341492788, 1.7497643596051469, -0.3092011481361043, -0.5349065208908724, 0.5117210944216953, -0.5569934465587499, 1.5359901749601697, -1.549546934208044, 1.4370491665044034, -0.3550245135480672, 0.10508711977578558, -0.3479131760222992, 1.440449604715788]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Open Wide (feat. Big Sean)</td>\n",
       "      <td>b'Calvin Harris, Big Sean'</td>\n",
       "      <td>2014</td>\n",
       "      <td>[-0.49002212285995483, 1.6617223024368286, 1.0235131978988647, -0.33206960558891296, 0.4414531886577606, 0.11732295900583267, -0.8655310869216919, -0.186820387840271, -0.027344567701220512, 0.6834815144538879, 1.3333178758621216, 0.829616129398346, 0.8085821866989136, -0.11922396719455719, 0.046914491802453995, -1.1553118470007873, 1.2655366998883928, -0.3403729583524646, 1.7273461438758355, 3.2341406428407566, -0.5349944602375606, -1.1947493795582, 1.8133047141149887, 1.5440616085230539, 0.6453499264358126, 1.254879921709037, -0.38543637680974835, 0.36342764322923443, 0.6018488556120964, 1.440449604715788]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>How Deep Is Your Love</td>\n",
       "      <td>b'Calvin Harris, Disciples'</td>\n",
       "      <td>2015</td>\n",
       "      <td>[-0.41091299057006836, 1.258643388748169, 0.8983922004699707, -0.262657105922699, 0.8501120805740356, 0.3192669153213501, -0.9955586194992065, -0.04502531513571739, -0.04089539870619774, 0.2862780690193176, 1.3955638408660889, 0.2914259135723114, 0.8563987612724304, -0.27214497327804565, -0.13632315397262573, -1.2374776256498958, 1.140818391212692, -0.1419670824388751, 1.4471184472594425, -0.3092011481361043, -0.5295460441927495, 1.6493680770749588, 1.032701714085416, 1.251033477000964, -1.549546934208044, 2.0746415232881863, -0.16599391249016654, 0.16920889601873085, -0.7278179886760573, 1.478977731977775]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>This Is What You Came For (feat. Rihanna)</td>\n",
       "      <td>b'Calvin Harris, Rihanna'</td>\n",
       "      <td>2016</td>\n",
       "      <td>[-0.3814137876033783, 1.4554016590118408, 1.0522133111953735, -0.3466358184814453, 0.49159470200538635, 0.13975763320922852, -1.058195948600769, 0.060457807034254074, -0.1191626563668251, 0.4785536825656891, 1.4339147806167603, 0.35597559809684753, 1.0968818664550781, -0.28769201040267944, -0.22834518551826477, -0.8117578469533806, 0.5285648758956155, -0.06669414023597499, 1.6713006045525571, -0.3092011481361043, -0.13990464178342266, 1.0805445857483271, -0.33477799399558694, 1.5293224689734395, -1.549546934208044, 2.16572614568587, -0.4045183302288425, 0.23294047889711456, -0.23774078035270924, 1.517505859239762]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The Weekend - Funk Wav Remix</td>\n",
       "      <td>b'SZA, Calvin Harris, Funk Wav'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.7186599373817444, 1.1158796548843384, 0.731167197227478, -0.19770754873752594, 0.5679613947868347, 0.3211177885532379, -0.6246710419654846, -0.09280967712402344, -0.07223807275295258, 0.3498360812664032, 0.9513226747512817, 0.6551465392112732, 0.7420637011528015, -0.03915492817759514, -0.06295780837535858, -0.024668187402047063, 1.3505719103490978, -0.46483423810202873, 0.3448895072349624, -0.3092011481361043, -0.5349944602375606, 1.6493680770749588, -0.46013030056967885, 1.1527725466702032, 0.6453499264358126, 2.0290992120893447, -0.2530553249647832, -0.48364963512014286, 0.5296669412078823, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Slide (feat. Frank Ocean &amp; Migos)</td>\n",
       "      <td>b'Calvin Harris, Frank Ocean, Migos'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.8330506086349487, 1.4721840620040894, 0.9542821049690247, -0.4979126751422882, 0.4859296679496765, 0.20288336277008057, -0.7739049792289734, -0.3222369849681854, 0.03495460003614426, 0.4573882222175598, 1.1311147212982178, 0.6848472952842712, 1.0187188386917114, -0.017646288499236107, -0.1254880428314209, -0.016690927339026784, 1.1294803631512644, 0.0017235875877324247, 1.1743634892194865, 3.2341406428407566, -0.5349906049256224, -1.1947493795582, 0.2691922104068561, 1.4394839040996015, -1.549546934208044, 1.8924722784928198, -0.2769077667386508, -0.41403263565962234, -0.06298456653198053, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Feels (feat. Pharrell Williams, Katy Perry &amp; Big Sean)</td>\n",
       "      <td>b'Calvin Harris, Pharrell Williams, Katy Perry, Big Sean, Funk Wav'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.4316557049751282, 1.6159189939498901, 1.0156694650650024, -0.3224739134311676, 0.3746965825557709, 0.37119245529174805, -0.8607751131057739, -0.24534280598163605, -0.11486667394638062, 0.7656596302986145, 1.0190465450286865, 0.7592442631721497, 0.878508985042572, -0.08537297695875168, 0.03988835588097572, -1.1702027324517583, 2.019515565973311, -0.05678689269603446, 0.9875450248085577, 3.2341406428407566, -0.5349944602375606, 1.6493680770749588, -0.6407515786787114, 1.473524297821329, -1.549546934208044, 1.9835569008905032, -0.26140367958563687, -0.513141750258252, 1.3084718071480863, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Rollin (feat. Future &amp; Khalid)</td>\n",
       "      <td>b'Calvin Harris, Future, Khalid'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.45464855432510376, 1.5903383493423462, 0.9394875764846802, -0.3995354473590851, 0.38158369064331055, 0.07916951924562454, -0.6712697744369507, -0.25031328201293945, -0.08396566659212112, 0.6828315854072571, 1.174385666847229, 0.8878921270370483, 0.8390617370605469, -0.02976704202592373, -0.09085194766521454, -0.1443270883473511, 1.2315226157041106, 0.3326557013440539, 1.0510633027082734, 3.2341406428407566, -0.5347819401497308, 0.2273093487583794, 0.09825724689673071, 1.2312058249877926, -1.549546934208044, 1.5736761001009283, -0.09562920925725711, -0.8063070314988182, 0.5524612299671078, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Slide (feat. Frank Ocean &amp; Migos)</td>\n",
       "      <td>b'Calvin Harris, Frank Ocean, Migos, Funk Wav'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.8306244015693665, 1.3914999961853027, 0.8867557644844055, -0.4634178578853607, 0.46206897497177124, 0.2473207265138626, -0.7462942004203796, -0.27696043252944946, 0.0020592056680470705, 0.4404282569885254, 1.0507673025131226, 0.6723338961601257, 0.9572036862373352, -0.017701195552945137, -0.15432587265968323, -0.016690927339026784, 1.1294803631512644, 0.0017235875877324247, 1.1743634892194865, 3.2341406428407566, -0.5349906049256224, -1.1947493795582, 0.2691922104068561, 1.4394839040996015, -1.549546934208044, 1.61921841129977, -0.2769077667386508, -0.41403263565962234, -0.06298456653198053, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Faking It (feat. Kehlani &amp; Lil Yachty)</td>\n",
       "      <td>b'Calvin Harris, Kehlani, Lil Yachty, Funk Wav'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.3710099756717682, 1.4853330850601196, 0.8649624586105347, -0.42896997928619385, 0.30232155323028564, 0.11700998991727829, -0.5737792253494263, -0.23985503613948822, -0.13998503983020782, 0.7621097564697266, 1.1860604286193848, 0.8189935088157654, 0.8819252252578735, -0.015730898827314377, -0.18540021777153015, -0.4926674444325697, 1.3562409243798115, 0.07509889259224008, 0.40467141584645966, 3.2341406428407566, -0.5349944602375606, 1.0805445857483271, -0.7706621509464066, 1.2212042660076972, -1.549546934208044, 1.3915068553055618, 0.08386041509109653, 0.10518466811692596, 0.5334659893344199, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Rollin (feat. Future &amp; Khalid)</td>\n",
       "      <td>b'Calvin Harris, Future, Khalid, Funk Wav'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.5150197744369507, 1.480997085571289, 0.8669241070747375, -0.37760406732559204, 0.3724628686904907, 0.14916333556175232, -0.6576970219612122, -0.2119932770729065, -0.10069605708122253, 0.6264132857322693, 1.0778988599777222, 0.8401476740837097, 0.8006543517112732, -0.027807924896478653, -0.12866665422916412, -0.15496343509804483, 1.225853601673397, 0.33254500584081437, 1.0435905641318364, 3.2341406428407566, -0.534894731920483, 0.2273093487583794, -0.14105170201744485, 1.2255909146831776, -1.549546934208044, 1.3915068553055618, -0.0896660988137902, -0.8079003210707776, 0.4916764599425064, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>One Kiss (with Dua Lipa)</td>\n",
       "      <td>b'Calvin Harris, Dua Lipa'</td>\n",
       "      <td>2018</td>\n",
       "      <td>[-0.3802143335342407, 1.540840983390808, 0.8170551657676697, -0.15833815932273865, 0.4922180473804474, 0.14575688540935516, -0.7095619440078735, 0.06333868205547333, 0.08945013582706451, 0.5746606588363647, 1.1774417161941528, 0.595933198928833, 0.7621734142303467, 0.0023274512495845556, -0.030562711879611015, -1.2425298903564754, 1.4412761348405165, -0.12451672703532461, 1.424700231530131, -0.3092011481361043, -0.5349246822776885, 1.0805445857483271, -0.7142536129880652, 1.449836394973735, -1.549546934208044, 2.3023530792823945, 0.054044862873762006, 0.23394847842223204, 0.24473833171756343, 1.5945621137637358]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Promises (with Sam Smith)</td>\n",
       "      <td>b'Calvin Harris, Sam Smith, Jessie Reyez'</td>\n",
       "      <td>2018</td>\n",
       "      <td>[-0.4551751911640167, 1.2134348154067993, 0.7615858316421509, -0.199832022190094, 0.5633880496025085, 0.6762082576751709, -1.0863019227981567, -0.2817540466785431, -0.037644486874341965, 0.6317135095596313, 1.1003193855285645, 0.8381323218345642, 0.7380902171134949, -0.047631580382585526, -0.15066808462142944, -1.3092729662170783, 1.3845859945333798, -0.13667741874835615, 1.073481518437585, -0.3092011481361043, -0.5349788159552331, 1.6493680770749588, 0.6737382907141528, 0.9671295747238734, 0.6453499264358126, 2.256810768083553, -0.366950734435001, 0.2039035893509937, -0.15796076969542017, 1.5945621137637358]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Giant (with Rag'n'Bone Man)</td>\n",
       "      <td>b'Calvin Harris\\', \"Rag\\'n\\'Bone Man\"'</td>\n",
       "      <td>2019</td>\n",
       "      <td>[-0.3992515802383423, 1.2729873657226562, 0.7182956337928772, -0.26333242654800415, 0.3592974543571472, 0.4867834448814392, -0.6996715664863586, 0.08461927622556686, -0.16724444925785065, 0.6392613649368286, 0.9774713516235352, 0.48773056268692017, 0.5017319321632385, -0.6514177918434143, -0.211467906832695, -1.2983707107976172, 1.5319803593319354, -0.011156624896356259, 1.5181094637355954, -0.3092011481361043, -0.5333917975062507, -1.1947493795582, -0.7159629626231665, 1.2619123657161553, -1.549546934208044, 2.16572614568587, -0.38662899889844177, 0.16959908938329238, 0.2979250054890896, 1.6330902410257229]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Over Now (with The Weeknd)</td>\n",
       "      <td>b'Calvin Harris, The Weeknd'</td>\n",
       "      <td>2020</td>\n",
       "      <td>[-0.5559015274047852, 1.355072021484375, 1.1667373180389404, -0.22373133897781372, 0.5634499788284302, 0.28147223591804504, -0.9545113444328308, -0.043781451880931854, 0.010776842944324017, 0.37074705958366394, 1.331946611404419, 0.7073851227760315, 0.8230187296867371, -0.3093962073326111, -0.0394388772547245, -1.1390914182059793, 0.4265226233427693, -0.15655516840151695, 1.5069003558709397, 3.2341406428407566, -0.530788665395952, -0.34151414256825235, 0.2293073855878268, 1.29665462322596, -1.549546934208044, 2.256810768083553, -0.33117207177419955, 1.9914119085210957, 0.5752555187263333, 1.67161836828771]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 song_name  \\\n",
       "0                                                Flashback   \n",
       "1                                      You Used To Hold Me   \n",
       "2                               I'm Not Alone - Radio Edit   \n",
       "3                                            We Found Love   \n",
       "4                                Dance Wiv Me - Radio Edit   \n",
       "5                               Feel So Close - Radio Edit   \n",
       "6                     Sweet Nothing (feat. Florence Welch)   \n",
       "7                  I Need Your Love (feat. Ellie Goulding)   \n",
       "8                                   Let's Go (feat. Ne-Yo)   \n",
       "9                    Thinking About You (feat. Ayah Marar)   \n",
       "10            Spectrum (Say My Name) - Calvin Harris Remix   \n",
       "11                       Bounce (feat. Kelis) - Radio Edit   \n",
       "12                    We'll Be Coming Back (feat. Example)   \n",
       "13           Drinking from the Bottle (feat. Tinie Tempah)   \n",
       "14                                                  Summer   \n",
       "15                          Outside (feat. Ellie Goulding)   \n",
       "16                               Blame (feat. John Newman)   \n",
       "17                             Under Control (feat. Hurts)   \n",
       "18                                Pray to God (feat. HAIM)   \n",
       "19                              Open Wide (feat. Big Sean)   \n",
       "20                                   How Deep Is Your Love   \n",
       "21               This Is What You Came For (feat. Rihanna)   \n",
       "22                            The Weekend - Funk Wav Remix   \n",
       "23                       Slide (feat. Frank Ocean & Migos)   \n",
       "24  Feels (feat. Pharrell Williams, Katy Perry & Big Sean)   \n",
       "25                          Rollin (feat. Future & Khalid)   \n",
       "26                       Slide (feat. Frank Ocean & Migos)   \n",
       "27                  Faking It (feat. Kehlani & Lil Yachty)   \n",
       "28                          Rollin (feat. Future & Khalid)   \n",
       "29                                One Kiss (with Dua Lipa)   \n",
       "30                               Promises (with Sam Smith)   \n",
       "31                             Giant (with Rag'n'Bone Man)   \n",
       "32                              Over Now (with The Weeknd)   \n",
       "\n",
       "                                                           song_artists  \\\n",
       "0                                                      b'Calvin Harris'   \n",
       "1                                                      b'Calvin Harris'   \n",
       "2                                                      b'Calvin Harris'   \n",
       "3                                             b'Rihanna, Calvin Harris'   \n",
       "4                               b'Dizzee Rascal, Calvin Harris, Chrome'   \n",
       "5                                                      b'Calvin Harris'   \n",
       "6                                      b'Calvin Harris, Florence Welch'   \n",
       "7                                      b'Calvin Harris, Ellie Goulding'   \n",
       "8                                               b'Calvin Harris, Ne-Yo'   \n",
       "9                                          b'Calvin Harris, Ayah Marar'   \n",
       "10                             b'Florence + The Machine, Calvin Harris'   \n",
       "11                                              b'Calvin Harris, Kelis'   \n",
       "12                                            b'Calvin Harris, Example'   \n",
       "13                                       b'Calvin Harris, Tinie Tempah'   \n",
       "14                                                     b'Calvin Harris'   \n",
       "15                                     b'Calvin Harris, Ellie Goulding'   \n",
       "16                                        b'Calvin Harris, John Newman'   \n",
       "17                                      b'Calvin Harris, Alesso, Hurts'   \n",
       "18                                               b'Calvin Harris, HAIM'   \n",
       "19                                           b'Calvin Harris, Big Sean'   \n",
       "20                                          b'Calvin Harris, Disciples'   \n",
       "21                                            b'Calvin Harris, Rihanna'   \n",
       "22                                      b'SZA, Calvin Harris, Funk Wav'   \n",
       "23                                 b'Calvin Harris, Frank Ocean, Migos'   \n",
       "24  b'Calvin Harris, Pharrell Williams, Katy Perry, Big Sean, Funk Wav'   \n",
       "25                                     b'Calvin Harris, Future, Khalid'   \n",
       "26                       b'Calvin Harris, Frank Ocean, Migos, Funk Wav'   \n",
       "27                      b'Calvin Harris, Kehlani, Lil Yachty, Funk Wav'   \n",
       "28                           b'Calvin Harris, Future, Khalid, Funk Wav'   \n",
       "29                                           b'Calvin Harris, Dua Lipa'   \n",
       "30                            b'Calvin Harris, Sam Smith, Jessie Reyez'   \n",
       "31                               b'Calvin Harris\\', \"Rag\\'n\\'Bone Man\"'   \n",
       "32                                         b'Calvin Harris, The Weeknd'   \n",
       "\n",
       "    song_year  \\\n",
       "0        2009   \n",
       "1        2009   \n",
       "2        2009   \n",
       "3        2011   \n",
       "4        2011   \n",
       "5        2012   \n",
       "6        2012   \n",
       "7        2012   \n",
       "8        2012   \n",
       "9        2012   \n",
       "10       2012   \n",
       "11       2012   \n",
       "12       2012   \n",
       "13       2012   \n",
       "14       2014   \n",
       "15       2014   \n",
       "16       2014   \n",
       "17       2014   \n",
       "18       2014   \n",
       "19       2014   \n",
       "20       2015   \n",
       "21       2016   \n",
       "22       2017   \n",
       "23       2017   \n",
       "24       2017   \n",
       "25       2017   \n",
       "26       2017   \n",
       "27       2017   \n",
       "28       2017   \n",
       "29       2018   \n",
       "30       2018   \n",
       "31       2019   \n",
       "32       2020   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    song_embeddings  \n",
       "0       [-0.6639690399169922, 1.2091901302337646, 0.6080996990203857, -0.12048277258872986, 0.9051563739776611, 0.2514127492904663, -0.6373221278190613, -0.29814955592155457, 0.22219587862491608, 0.4700770080089569, 0.8792182207107544, 0.5950366258621216, 0.8773530721664429, -0.03305034339427948, 0.20640605688095093, -1.3351724705550174, -1.5406252453148743, -0.011030115749796763, 1.7460279903169285, -0.3092011481361043, -0.5313940449564866, 1.0805445857483271, -0.7039975151774578, 1.0250333372402145, -1.549546934208044, 0.7539144985217788, -0.307319630000332, 0.36420802995835755, -1.2520866301382436, 1.247808968405853]  \n",
       "1          [-0.18039822578430176, 1.2320046424865723, 1.0078709125518799, -0.3332071900367737, 1.0227277278900146, 0.3912988007068634, -1.1201367378234863, 0.06693186610937119, 0.01194898784160614, 0.4125801920890808, 1.7104941606521606, 0.0907072052359581, 1.1263823509216309, 0.005882999859750271, -0.15810611844062805, -1.279491195315136, 0.46053670752705134, 0.004253770518922344, 1.8244917453695186, -0.3092011481361043, -0.5349904137531296, 1.6493680770749588, 0.6224578016611152, 0.958707209266951, -1.549546934208044, 0.5717452537264123, -0.35562082459241384, 0.3955535635781341, -1.2824790151505443, 1.247808968405853]  \n",
       "2            [-0.4578489363193512, 1.1988781690597534, 0.9186619520187378, -0.3090927302837372, 0.8355614542961121, 0.3251281678676605, -0.956695556640625, 0.2001478672027588, -0.004034769721329212, 0.5499118566513062, 1.427262544631958, 0.3321062922477722, 0.9700223803520203, -0.3015604019165039, -0.10042643547058105, -1.3273547556932577, 0.3074733286977821, -0.1491385696844665, 0.7932538218211915, -0.3092011481361043, -0.14309084999676247, 0.5117210944216953, 0.4914076629700189, 0.8665875870818629, 0.6453499264358126, 0.3895760089310457, -0.411077751716656, 0.46191895166730823, -0.36310936852844955, 1.247808968405853]  \n",
       "3       [-0.32985055446624756, 1.3834985494613647, 0.926184356212616, -0.28489983081817627, 0.8302570581436157, 0.22301919758319855, -1.0492554903030396, -0.0061478391289711, 0.029022544622421265, 0.4409855902194977, 1.2905532121658325, 0.4727650284767151, 1.1560120582580566, -0.15044355392456055, -0.18808501958847046, -1.2744389306085564, 1.1181423350898372, -0.12151213480453658, 1.0660087798611477, -0.3092011481361043, -0.5305974929031516, -1.1947493795582, -0.5626912786757541, 1.2313812909348116, 0.6453499264358126, 1.9835569008905032, -0.37351015592281456, 0.36375280436636925, 0.2751307167298641, 1.3248652229298272]  \n",
       "4       [-0.5854893922805786, 1.0818036794662476, 0.7288601994514465, -0.25424012541770935, 0.6128330826759338, 0.41814619302749634, -0.6447377800941467, 0.027640312910079956, -0.1611126810312271, 0.39173322916030884, 0.8761189579963684, 0.3748489320278168, 0.6108167767524719, 0.0187530517578125, -0.023305920884013176, -1.2143435714671371, 1.9344803555126058, -0.20954668716662583, 0.9912813940967762, -0.3092011481361043, -0.5349944602375606, 1.6493680770749588, -0.30059100129356187, 1.2671763441267319, 0.6453499264358126, 1.5281337889020867, -0.3329610049072396, -0.1561798539118754, 1.00454795702508, 1.3248652229298272]  \n",
       "5        [-0.5671982169151306, 1.21516752243042, 0.8834764957427979, -0.34776613116264343, 0.8313254117965698, 0.4305516481399536, -0.8739187717437744, 0.12808793783187866, -0.13710293173789978, 0.49484601616859436, 1.3459782600402832, 0.40094777941703796, 1.118976354598999, -0.10406777262687683, 0.11825662106275558, -1.3383314655399736, 0.9650789562605678, -0.19120286091549893, 1.6563551273996828, -0.3092011481361043, -0.5125954164977816, 0.5117210944216953, -0.015699395443352974, 1.5196718418873827, 0.6453499264358126, 2.2112684568847114, -0.4170408621601229, 0.36215951479440944, 1.4870270690953529, 1.363393350191814]  \n",
       "6   [-0.5403595566749573, 1.3501120805740356, 0.8737241625785828, -0.30949994921684265, 0.5602115988731384, 0.044263433665037155, -0.8665704727172852, -0.07287808507680893, 0.004971159156411886, 0.4264087975025177, 1.185262680053711, 0.6865300536155701, 0.7812601327896118, -0.16709819436073303, -0.27374014258384705, -0.8170760203287275, 0.20543107614493594, -0.14259962817167257, 1.6750369738407758, -0.3092011481361043, -0.5346376049176665, 0.7961328400850112, -0.8549900662780685, 1.3266593001662457, -1.549546934208044, 1.8469299672939783, 0.048081752430295104, 0.36206196645326905, 0.20674785045218758, 1.363393350191814]  \n",
       "7     [-0.3471231460571289, 1.4163035154342651, 0.9777242541313171, -0.3454452455043793, 0.5281813144683838, 0.0064859092235565186, -0.8150930404663086, -0.003013826906681061, -0.05287719517946243, 0.6403617858886719, 1.3331170082092285, 0.4529487192630768, 0.9392791390419006, -0.1485135555267334, -0.12306036055088043, -0.2506905558542882, 0.8970507878920038, 0.030931386799656055, 1.4508548165476611, -0.3092011481361043, -0.5349944602375606, 0.7961328400850112, 0.17232906441778495, 1.1294355757166477, 0.6453499264358126, 1.8013876560951365, -0.31387905148814554, 0.2663020115671261, 0.1991497541991124, 1.363393350191814]  \n",
       "8       [-0.4517887234687805, 1.4095631837844849, 1.0143579244613647, -0.42695263028144836, 0.3167036771774292, 0.22329269349575043, -0.8331954479217529, 0.06706950813531876, -0.11937189847230911, 0.6374547481536865, 1.3507567644119263, 0.6769067049026489, 1.002878189086914, -0.12010153383016586, -0.21650470793247223, -1.3202549942371695, 0.9820859983527089, 0.01743444222608983, 1.4994276172945025, -0.3092011481361043, -0.5104287949127105, -0.34151414256825235, 0.4971054950870231, 1.5038799066556534, -1.549546934208044, 1.5736761001009283, -0.24709221452131636, 0.36472828777777266, 1.3198689515276991, 1.363393350191814]  \n",
       "9             [-0.4033048748970032, 1.201682209968567, 0.7232522368431091, -0.21271994709968567, 0.4565010666847229, 0.08975932747125626, -0.7079424262046814, 0.05442984402179718, -0.04738606885075569, 0.5769458413124084, 1.0412596464157104, 0.4582187235355377, 0.8240703344345093, -0.08101780712604523, -0.14304494857788086, -1.3339492906786878, 1.067121208813414, 0.1370883744063931, 1.4695366629887539, -0.3092011481361043, -0.5336817424536646, -1.479161125221516, -0.6322048305032051, 1.3664900701396077, -1.549546934208044, 1.61921841129977, -0.36575811234630756, 0.3637202882526556, 0.8373898394574263, 1.363393350191814]  \n",
       "10         [-0.5985769033432007, 1.3303905725479126, 1.0559614896774292, -0.25539952516555786, 0.8112835884094238, 0.17980347573757172, -0.812416672706604, -0.08991655707359314, 0.104682557284832, 0.2630487084388733, 1.328857660293579, 0.6589300632476807, 0.8192211389541626, -0.11460264772176743, -0.08975117653608322, -1.334933152753127, 0.23377614629850432, -0.09808422222604993, 1.7385552517404912, -0.3092011481361043, -0.5218672823986005, 1.6493680770749588, -0.6276465648096017, 1.3428021672920136, -1.549546934208044, 0.9360837433171454, -0.31447536253249225, 0.30216728499307355, 0.2295421392114131, 1.363393350191814]  \n",
       "11       [-0.6689270734786987, 1.3969231843948364, 0.8880228400230408, -0.326450377702713, 0.34622687101364136, 0.18351785838603973, -0.7323996424674988, -0.0037096429150551558, -0.1328837126493454, 0.6257920861244202, 1.0089510679244995, 0.6650357842445374, 0.8406809568405151, 0.010336706414818764, 0.018450651317834854, -1.2521026024320998, 1.3732479664719526, -0.06648065605115584, 1.802073529640207, -0.3092011481361043, 1.0358061889389716, -0.9103376338948841, 2.6053033783785695, 1.6454809259001602, -1.549546934208044, 1.4370491665044034, -0.36396917921326755, 0.3622895792492634, 0.8791793688493397, 1.363393350191814]  \n",
       "12          [-0.29318350553512573, 1.2344664335250854, 0.91950523853302, -0.2705417275428772, 0.5557699203491211, 0.24373485147953033, -0.9255906343460083, 0.11558005213737488, -0.09228789061307907, 0.5509446263313293, 1.300772786140442, 0.46837666630744934, 1.019039273262024, -0.2337048500776291, -0.30271509289741516, -1.3374326942395398, 0.3358183988513505, 0.029769084015640684, 1.7609734674698028, -0.3092011481361043, -0.5349944602375606, 0.5117210944216953, 2.2292464586562937, 1.252612670524137, 0.6453499264358126, 1.5281337889020867, -0.08131774419293655, 0.36241964370411694, 0.16495832106027414, 1.363393350191814]  \n",
       "13        [-0.49500662088394165, 1.250335454940796, 0.836037278175354, -0.21671639382839203, 0.4340763986110687, 0.14523451030254364, -0.6550229787826538, -0.0313369482755661, -0.14998087286949158, 0.44496625661849976, 0.9944913387298584, 0.5686814188957214, 0.6103565096855164, -0.23264175653457642, -0.1452845335006714, -1.2162049321485084, 0.7269803669705941, 0.07710722529387208, 1.5143730944473768, -0.3092011481361043, -0.5347956408450482, 1.0805445857483271, -0.878920961169486, 1.2857757345107685, -1.549546934208044, 1.4370491665044034, -0.2953934091133982, 0.3662240290085925, 0.009197347872233573, 1.363393350191814]  \n",
       "14          [-0.8264520764350891, 1.3505786657333374, 1.0463076829910278, -0.22568410634994507, 0.913348913192749, 0.2567248046398163, -0.8605219125747681, -0.35306626558303833, 0.03924936056137085, 0.37715211510658264, 1.1773360967636108, 0.5755943655967712, 1.1420295238494873, -0.08487856388092041, 0.3010081648826599, -1.284809368690483, 0.3358183988513505, -0.06374489575680674, 1.4022820158008196, -0.3092011481361043, -0.4782799540401117, -0.34151414256825235, -0.37466281881461627, 1.394389155715663, -1.549546934208044, 2.2112684568847114, -0.3955736645636421, 0.362549708158971, 0.8183945988247383, 1.440449604715788]  \n",
       "15     [-0.45815980434417725, 1.4566391706466675, 0.8504025340080261, -0.373926043510437, 0.44716230034828186, 0.05849791318178177, -0.6436365842819214, -0.1507851779460907, -0.06338223814964294, 0.6237001419067383, 1.1598680019378662, 0.6893650889396667, 0.8899211883544922, -0.07148391753435135, -0.03473608195781708, -0.7745306333259528, 0.6192691003870343, -0.02631400201851587, 1.2789818292896065, -0.3092011481361043, -0.5349944602375606, -0.9103376338948841, 0.6566447943631403, 1.2948999637557677, -1.549546934208044, 2.2112684568847114, -0.366950734435001, 0.36534609393832856, -0.41629604229997574, 1.440449604715788]  \n",
       "16          [-0.6763609647750854, 1.6109453439712524, 0.9073587656021118, -0.1523026078939438, 0.6198676228523254, 0.2695436179637909, -1.1315089464187622, -0.25966623425483704, 0.04239802807569504, 0.6183485388755798, 1.0361096858978271, 0.6836062073707581, 0.8744892477989197, -0.3744211196899414, 0.004306115675717592, -1.2646003098641647, -0.6959421547385375, -0.13943689950768517, 1.406018385089038, -0.3092011481361043, -0.51670562509299, -1.479161125221516, 0.7762992688202281, 1.3027959313716324, -1.549546934208044, 2.0290992120893447, -0.12007796207547143, 0.36498841668748067, -0.6822294111576065, 1.440449604715788]  \n",
       "17    [-0.5020278692245483, 1.4283548593521118, 0.8164223432540894, -0.28505995869636536, 0.4534760117530823, 0.2287231981754303, -0.774612307548523, 0.002535061212256551, 0.015209964476525784, 0.5660685896873474, 1.197741150856018, 0.6164646148681641, 0.8045300841331482, -0.029482128098607063, -0.11208241432905197, -0.9978939150905204, 0.04102966925423999, -0.36620454471558167, 1.6227278038057156, -0.3092011481361043, -0.532082265930568, 0.7961328400850112, -0.5228064538567248, 1.4208845137155646, 0.6453499264358126, 1.8924722784928198, -0.10039969761203067, 0.30223231722050037, -0.06678361465851812, 1.440449604715788]  \n",
       "18     [-0.4818477928638458, 1.3358467817306519, 0.7798342108726501, -0.2659180760383606, 0.4285810887813568, 0.08754778653383255, -0.7763833999633789, -0.006817118264734745, -0.03107377141714096, 0.46428442001342773, 1.156795620918274, 0.6081282496452332, 0.8255685567855835, -0.1547342836856842, -0.21263934671878815, -1.3010297974852907, 0.39250853915848727, 0.01290383341492788, 1.7497643596051469, -0.3092011481361043, -0.5349065208908724, 0.5117210944216953, -0.5569934465587499, 1.5359901749601697, -1.549546934208044, 1.4370491665044034, -0.3550245135480672, 0.10508711977578558, -0.3479131760222992, 1.440449604715788]  \n",
       "19           [-0.49002212285995483, 1.6617223024368286, 1.0235131978988647, -0.33206960558891296, 0.4414531886577606, 0.11732295900583267, -0.8655310869216919, -0.186820387840271, -0.027344567701220512, 0.6834815144538879, 1.3333178758621216, 0.829616129398346, 0.8085821866989136, -0.11922396719455719, 0.046914491802453995, -1.1553118470007873, 1.2655366998883928, -0.3403729583524646, 1.7273461438758355, 3.2341406428407566, -0.5349944602375606, -1.1947493795582, 1.8133047141149887, 1.5440616085230539, 0.6453499264358126, 1.254879921709037, -0.38543637680974835, 0.36342764322923443, 0.6018488556120964, 1.440449604715788]  \n",
       "20           [-0.41091299057006836, 1.258643388748169, 0.8983922004699707, -0.262657105922699, 0.8501120805740356, 0.3192669153213501, -0.9955586194992065, -0.04502531513571739, -0.04089539870619774, 0.2862780690193176, 1.3955638408660889, 0.2914259135723114, 0.8563987612724304, -0.27214497327804565, -0.13632315397262573, -1.2374776256498958, 1.140818391212692, -0.1419670824388751, 1.4471184472594425, -0.3092011481361043, -0.5295460441927495, 1.6493680770749588, 1.032701714085416, 1.251033477000964, -1.549546934208044, 2.0746415232881863, -0.16599391249016654, 0.16920889601873085, -0.7278179886760573, 1.478977731977775]  \n",
       "21    [-0.3814137876033783, 1.4554016590118408, 1.0522133111953735, -0.3466358184814453, 0.49159470200538635, 0.13975763320922852, -1.058195948600769, 0.060457807034254074, -0.1191626563668251, 0.4785536825656891, 1.4339147806167603, 0.35597559809684753, 1.0968818664550781, -0.28769201040267944, -0.22834518551826477, -0.8117578469533806, 0.5285648758956155, -0.06669414023597499, 1.6713006045525571, -0.3092011481361043, -0.13990464178342266, 1.0805445857483271, -0.33477799399558694, 1.5293224689734395, -1.549546934208044, 2.16572614568587, -0.4045183302288425, 0.23294047889711456, -0.23774078035270924, 1.517505859239762]  \n",
       "22   [-0.7186599373817444, 1.1158796548843384, 0.731167197227478, -0.19770754873752594, 0.5679613947868347, 0.3211177885532379, -0.6246710419654846, -0.09280967712402344, -0.07223807275295258, 0.3498360812664032, 0.9513226747512817, 0.6551465392112732, 0.7420637011528015, -0.03915492817759514, -0.06295780837535858, -0.024668187402047063, 1.3505719103490978, -0.46483423810202873, 0.3448895072349624, -0.3092011481361043, -0.5349944602375606, 1.6493680770749588, -0.46013030056967885, 1.1527725466702032, 0.6453499264358126, 2.0290992120893447, -0.2530553249647832, -0.48364963512014286, 0.5296669412078823, 1.556033986501749]  \n",
       "23      [-0.8330506086349487, 1.4721840620040894, 0.9542821049690247, -0.4979126751422882, 0.4859296679496765, 0.20288336277008057, -0.7739049792289734, -0.3222369849681854, 0.03495460003614426, 0.4573882222175598, 1.1311147212982178, 0.6848472952842712, 1.0187188386917114, -0.017646288499236107, -0.1254880428314209, -0.016690927339026784, 1.1294803631512644, 0.0017235875877324247, 1.1743634892194865, 3.2341406428407566, -0.5349906049256224, -1.1947493795582, 0.2691922104068561, 1.4394839040996015, -1.549546934208044, 1.8924722784928198, -0.2769077667386508, -0.41403263565962234, -0.06298456653198053, 1.556033986501749]  \n",
       "24           [-0.4316557049751282, 1.6159189939498901, 1.0156694650650024, -0.3224739134311676, 0.3746965825557709, 0.37119245529174805, -0.8607751131057739, -0.24534280598163605, -0.11486667394638062, 0.7656596302986145, 1.0190465450286865, 0.7592442631721497, 0.878508985042572, -0.08537297695875168, 0.03988835588097572, -1.1702027324517583, 2.019515565973311, -0.05678689269603446, 0.9875450248085577, 3.2341406428407566, -0.5349944602375606, 1.6493680770749588, -0.6407515786787114, 1.473524297821329, -1.549546934208044, 1.9835569008905032, -0.26140367958563687, -0.513141750258252, 1.3084718071480863, 1.556033986501749]  \n",
       "25       [-0.45464855432510376, 1.5903383493423462, 0.9394875764846802, -0.3995354473590851, 0.38158369064331055, 0.07916951924562454, -0.6712697744369507, -0.25031328201293945, -0.08396566659212112, 0.6828315854072571, 1.174385666847229, 0.8878921270370483, 0.8390617370605469, -0.02976704202592373, -0.09085194766521454, -0.1443270883473511, 1.2315226157041106, 0.3326557013440539, 1.0510633027082734, 3.2341406428407566, -0.5347819401497308, 0.2273093487583794, 0.09825724689673071, 1.2312058249877926, -1.549546934208044, 1.5736761001009283, -0.09562920925725711, -0.8063070314988182, 0.5524612299671078, 1.556033986501749]  \n",
       "26    [-0.8306244015693665, 1.3914999961853027, 0.8867557644844055, -0.4634178578853607, 0.46206897497177124, 0.2473207265138626, -0.7462942004203796, -0.27696043252944946, 0.0020592056680470705, 0.4404282569885254, 1.0507673025131226, 0.6723338961601257, 0.9572036862373352, -0.017701195552945137, -0.15432587265968323, -0.016690927339026784, 1.1294803631512644, 0.0017235875877324247, 1.1743634892194865, 3.2341406428407566, -0.5349906049256224, -1.1947493795582, 0.2691922104068561, 1.4394839040996015, -1.549546934208044, 1.61921841129977, -0.2769077667386508, -0.41403263565962234, -0.06298456653198053, 1.556033986501749]  \n",
       "27    [-0.3710099756717682, 1.4853330850601196, 0.8649624586105347, -0.42896997928619385, 0.30232155323028564, 0.11700998991727829, -0.5737792253494263, -0.23985503613948822, -0.13998503983020782, 0.7621097564697266, 1.1860604286193848, 0.8189935088157654, 0.8819252252578735, -0.015730898827314377, -0.18540021777153015, -0.4926674444325697, 1.3562409243798115, 0.07509889259224008, 0.40467141584645966, 3.2341406428407566, -0.5349944602375606, 1.0805445857483271, -0.7706621509464066, 1.2212042660076972, -1.549546934208044, 1.3915068553055618, 0.08386041509109653, 0.10518466811692596, 0.5334659893344199, 1.556033986501749]  \n",
       "28        [-0.5150197744369507, 1.480997085571289, 0.8669241070747375, -0.37760406732559204, 0.3724628686904907, 0.14916333556175232, -0.6576970219612122, -0.2119932770729065, -0.10069605708122253, 0.6264132857322693, 1.0778988599777222, 0.8401476740837097, 0.8006543517112732, -0.027807924896478653, -0.12866665422916412, -0.15496343509804483, 1.225853601673397, 0.33254500584081437, 1.0435905641318364, 3.2341406428407566, -0.534894731920483, 0.2273093487583794, -0.14105170201744485, 1.2255909146831776, -1.549546934208044, 1.3915068553055618, -0.0896660988137902, -0.8079003210707776, 0.4916764599425064, 1.556033986501749]  \n",
       "29      [-0.3802143335342407, 1.540840983390808, 0.8170551657676697, -0.15833815932273865, 0.4922180473804474, 0.14575688540935516, -0.7095619440078735, 0.06333868205547333, 0.08945013582706451, 0.5746606588363647, 1.1774417161941528, 0.595933198928833, 0.7621734142303467, 0.0023274512495845556, -0.030562711879611015, -1.2425298903564754, 1.4412761348405165, -0.12451672703532461, 1.424700231530131, -0.3092011481361043, -0.5349246822776885, 1.0805445857483271, -0.7142536129880652, 1.449836394973735, -1.549546934208044, 2.3023530792823945, 0.054044862873762006, 0.23394847842223204, 0.24473833171756343, 1.5945621137637358]  \n",
       "30         [-0.4551751911640167, 1.2134348154067993, 0.7615858316421509, -0.199832022190094, 0.5633880496025085, 0.6762082576751709, -1.0863019227981567, -0.2817540466785431, -0.037644486874341965, 0.6317135095596313, 1.1003193855285645, 0.8381323218345642, 0.7380902171134949, -0.047631580382585526, -0.15066808462142944, -1.3092729662170783, 1.3845859945333798, -0.13667741874835615, 1.073481518437585, -0.3092011481361043, -0.5349788159552331, 1.6493680770749588, 0.6737382907141528, 0.9671295747238734, 0.6453499264358126, 2.256810768083553, -0.366950734435001, 0.2039035893509937, -0.15796076969542017, 1.5945621137637358]  \n",
       "31          [-0.3992515802383423, 1.2729873657226562, 0.7182956337928772, -0.26333242654800415, 0.3592974543571472, 0.4867834448814392, -0.6996715664863586, 0.08461927622556686, -0.16724444925785065, 0.6392613649368286, 0.9774713516235352, 0.48773056268692017, 0.5017319321632385, -0.6514177918434143, -0.211467906832695, -1.2983707107976172, 1.5319803593319354, -0.011156624896356259, 1.5181094637355954, -0.3092011481361043, -0.5333917975062507, -1.1947493795582, -0.7159629626231665, 1.2619123657161553, -1.549546934208044, 2.16572614568587, -0.38662899889844177, 0.16959908938329238, 0.2979250054890896, 1.6330902410257229]  \n",
       "32            [-0.5559015274047852, 1.355072021484375, 1.1667373180389404, -0.22373133897781372, 0.5634499788284302, 0.28147223591804504, -0.9545113444328308, -0.043781451880931854, 0.010776842944324017, 0.37074705958366394, 1.331946611404419, 0.7073851227760315, 0.8230187296867371, -0.3093962073326111, -0.0394388772547245, -1.1390914182059793, 0.4265226233427693, -0.15655516840151695, 1.5069003558709397, 3.2341406428407566, -0.530788665395952, -0.34151414256825235, 0.2293073855878268, 1.29665462322596, -1.549546934208044, 2.256810768083553, -0.33117207177419955, 1.9914119085210957, 0.5752555187263333, 1.67161836828771]  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query(filter=[(\"like\", \"song_artists\", \"*Calvin Harris*\")], sort_columns=[\"song_year\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4f8da1",
   "metadata": {},
   "source": [
    "### Find A Specific Song"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8df467",
   "metadata": {},
   "source": [
    "We can filter this query further by looking for the song `We Found Love` by `Calvin Harris` in our dataset.\n",
    "\n",
    "This will only return one song as he only produced one song with this name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4e70a62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_artists</th>\n",
       "      <th>song_year</th>\n",
       "      <th>song_embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>We Found Love</td>\n",
       "      <td>b'Rihanna, Calvin Harris'</td>\n",
       "      <td>2011</td>\n",
       "      <td>[-0.32985055446624756, 1.3834985494613647, 0.926184356212616, -0.28489983081817627, 0.8302570581436157, 0.22301919758319855, -1.0492554903030396, -0.0061478391289711, 0.029022544622421265, 0.4409855902194977, 1.2905532121658325, 0.4727650284767151, 1.1560120582580566, -0.15044355392456055, -0.18808501958847046, -1.2744389306085564, 1.1181423350898372, -0.12151213480453658, 1.0660087798611477, -0.3092011481361043, -0.5305974929031516, -1.1947493795582, -0.5626912786757541, 1.2313812909348116, 0.6453499264358126, 1.9835569008905032, -0.37351015592281456, 0.36375280436636925, 0.2751307167298641, 1.3248652229298272]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       song_name               song_artists  song_year  \\\n",
       "0  We Found Love  b'Rihanna, Calvin Harris'       2011   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               song_embeddings  \n",
       "0  [-0.32985055446624756, 1.3834985494613647, 0.926184356212616, -0.28489983081817627, 0.8302570581436157, 0.22301919758319855, -1.0492554903030396, -0.0061478391289711, 0.029022544622421265, 0.4409855902194977, 1.2905532121658325, 0.4727650284767151, 1.1560120582580566, -0.15044355392456055, -0.18808501958847046, -1.2744389306085564, 1.1181423350898372, -0.12151213480453658, 1.0660087798611477, -0.3092011481361043, -0.5305974929031516, -1.1947493795582, -0.5626912786757541, 1.2313812909348116, 0.6453499264358126, 1.9835569008905032, -0.37351015592281456, 0.36375280436636925, 0.2751307167298641, 1.3248652229298272]  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query(\n",
    "    filter=[\n",
    "        (\"like\", \"song_artists\", \"*Calvin Harris*\"),\n",
    "        (\"like\", \"song_name\", \"*We Found Love*\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70fbb78",
   "metadata": {},
   "source": [
    "### Find Similar Songs To This Song"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d079845c",
   "metadata": {},
   "source": [
    "We can then copy and paste the vector associated with this song below and save it as the variable `my_vec`.\n",
    "\n",
    "We will then use KDB.AI's `.search()` function to find similar songs in the dataset to this song using this vector.\n",
    "We will pull out the 5 songs most similar to this song from the dataset.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <b>Note: </b>\n",
    "    The most similar song will be \"We Found Love\" by \"Calvin Harris\" as this is the vector we are using for the search.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7782270e",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_vec = [[-0.22046250104904175, 1.4409897327423096, 1.0603516101837158, -0.15190696716308594, 0.7062567472457886, 0.4542839527130127, -1.1391438245773315, 0.2879817485809326, 0.00519922748208046, 0.17919489741325378, 1.0258654356002808, 0.5709000825881958, 1.2584115266799927, -0.15058273077011108, 0.18762657046318054, -1.2744389306085728, 1.1181423350898205, -0.12151213480453628, 1.0660087798611322, -0.30920114813582733, -0.5305974929034635, -1.1947493795585982, -0.562691278675705, 1.2313812909347996, 0.6453499264345925, 1.9835569008902227, -0.3735101559228201, 0.36375280436636764, 0.2751307167298588, 1.324865222929866]]\n",
    "vector = {'flat_index' : my_vec}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fbcc1335",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>song_name</th>\n",
       "      <th>song_artists</th>\n",
       "      <th>song_year</th>\n",
       "      <th>song_embeddings</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.514847</td>\n",
       "      <td>We Found Love</td>\n",
       "      <td>b'Rihanna, Calvin Harris'</td>\n",
       "      <td>2011</td>\n",
       "      <td>[-0.32985055446624756, 1.3834985494613647, 0.926184356212616, -0.28489983081817627, 0.8302570581436157, 0.22301919758319855, -1.0492554903030396, -0.0061478391289711, 0.029022544622421265, 0.4409855902194977, 1.2905532121658325, 0.4727650284767151, 1.1560120582580566, -0.15044355392456055, -0.18808501958847046, -1.2744389306085564, 1.1181423350898372, -0.12151213480453658, 1.0660087798611477, -0.3092011481361043, -0.5305974929031516, -1.1947493795582, -0.5626912786757541, 1.2313812909348116, 0.6453499264358126, 1.9835569008905032, -0.37351015592281456, 0.36375280436636925, 0.2751307167298641, 1.3248652229298272]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.471889</td>\n",
       "      <td>Bad At Love</td>\n",
       "      <td>b'Halsey'</td>\n",
       "      <td>2017</td>\n",
       "      <td>[-0.33408525586128235, 1.2016308307647705, 1.164484977722168, 0.007420691661536694, 0.6395940780639648, 0.3916250765323639, -1.0832836627960205, 0.2853870987892151, 0.03573070093989372, 0.391010582447052, 1.6295465230941772, 0.3820643723011017, 0.6865345239639282, -0.18368127942085266, -0.13861115276813507, -1.1803072618649173, 0.7836705072777309, -0.3899329165171471, 1.0099632405378691, -0.3092011481361043, -0.5349944602375606, -1.479161125221516, -0.6692407392637322, 1.3973720768149898, 0.6453499264358126, 1.9380145896916616, -0.4253892167809766, 0.051533080489714826, 0.32071929424831513, 1.556033986501749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.572724</td>\n",
       "      <td>Sweet but Psycho</td>\n",
       "      <td>b'Ava Max'</td>\n",
       "      <td>2020</td>\n",
       "      <td>[-0.34748899936676025, 1.084930658340454, 0.8265242576599121, -0.12685643136501312, 0.7913858294487, 0.2851320207118988, -1.0733402967453003, 0.2622759938240051, -0.07236860692501068, 0.6118565201759338, 0.9629176259040833, 0.5539129376411438, 0.650129497051239, -0.33480215072631836, -0.23625598847866058, -1.15903456836353, 1.0387761386598457, -0.3412506155567211, 0.841826622568033, -0.3092011481361043, -0.5349944602375606, -1.1947493795582, -0.23221701588951166, 1.1903222593323153, 0.6453499264358126, 2.393437701680078, -0.31984216193161247, 0.5268536307530907, 0.3511116792606158, 1.67161836828771]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.677343</td>\n",
       "      <td>Stay Gold</td>\n",
       "      <td>b'BTS'</td>\n",
       "      <td>2020</td>\n",
       "      <td>[-0.39910316467285156, 0.949821412563324, 0.8702532052993774, -0.15616460144519806, 0.7773756980895996, 0.3858698010444641, -0.999448299407959, 0.16055533289909363, -0.40568050742149353, 0.41508427262306213, 1.3218148946762085, 0.34586480259895325, 0.6678043603897095, -0.3104383945465088, 0.05108780786395073, -1.1037255652599227, 1.0557831807519866, 0.1001951445409801, 0.5728080338162957, -0.3092011481361043, -0.5349944602375606, -1.1947493795582, -0.7296377597039766, 1.0681979602069414, 0.6453499264358126, 2.2112684568847114, -0.2786966998716909, 0.39727691760494743, 0.20674785045218758, 1.67161836828771]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.864022</td>\n",
       "      <td>Baby</td>\n",
       "      <td>b'Madison Beer'</td>\n",
       "      <td>2020</td>\n",
       "      <td>[-0.264282763004303, 1.3024324178695679, 0.9755517840385437, -0.4190329313278198, 0.732369065284729, 0.6265831589698792, -1.0190303325653076, 0.062001943588256836, -0.19904416799545288, 0.2775894105434418, 1.141913652420044, 0.5574018955230713, 0.4761144518852234, 0.11363387852907181, -0.10809878259897232, -1.1284550714552857, 0.9537409281991405, -0.18075794950268054, 0.8455629918562516, -0.3092011481361043, -0.5349944602375606, -1.479161125221516, -0.3290801618785828, 1.1104852534385723, 0.6453499264358126, 2.16572614568587, -0.2834671882264644, 0.038201473867195856, -0.28712840599769784, 1.67161836828771]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance         song_name               song_artists  song_year  \\\n",
       "0       0.514847     We Found Love  b'Rihanna, Calvin Harris'       2011   \n",
       "1       1.471889       Bad At Love                  b'Halsey'       2017   \n",
       "2       1.572724  Sweet but Psycho                 b'Ava Max'       2020   \n",
       "3       1.677343         Stay Gold                     b'BTS'       2020   \n",
       "4       1.864022              Baby            b'Madison Beer'       2020   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               song_embeddings  \n",
       "0  [-0.32985055446624756, 1.3834985494613647, 0.926184356212616, -0.28489983081817627, 0.8302570581436157, 0.22301919758319855, -1.0492554903030396, -0.0061478391289711, 0.029022544622421265, 0.4409855902194977, 1.2905532121658325, 0.4727650284767151, 1.1560120582580566, -0.15044355392456055, -0.18808501958847046, -1.2744389306085564, 1.1181423350898372, -0.12151213480453658, 1.0660087798611477, -0.3092011481361043, -0.5305974929031516, -1.1947493795582, -0.5626912786757541, 1.2313812909348116, 0.6453499264358126, 1.9835569008905032, -0.37351015592281456, 0.36375280436636925, 0.2751307167298641, 1.3248652229298272]  \n",
       "1     [-0.33408525586128235, 1.2016308307647705, 1.164484977722168, 0.007420691661536694, 0.6395940780639648, 0.3916250765323639, -1.0832836627960205, 0.2853870987892151, 0.03573070093989372, 0.391010582447052, 1.6295465230941772, 0.3820643723011017, 0.6865345239639282, -0.18368127942085266, -0.13861115276813507, -1.1803072618649173, 0.7836705072777309, -0.3899329165171471, 1.0099632405378691, -0.3092011481361043, -0.5349944602375606, -1.479161125221516, -0.6692407392637322, 1.3973720768149898, 0.6453499264358126, 1.9380145896916616, -0.4253892167809766, 0.051533080489714826, 0.32071929424831513, 1.556033986501749]  \n",
       "2               [-0.34748899936676025, 1.084930658340454, 0.8265242576599121, -0.12685643136501312, 0.7913858294487, 0.2851320207118988, -1.0733402967453003, 0.2622759938240051, -0.07236860692501068, 0.6118565201759338, 0.9629176259040833, 0.5539129376411438, 0.650129497051239, -0.33480215072631836, -0.23625598847866058, -1.15903456836353, 1.0387761386598457, -0.3412506155567211, 0.841826622568033, -0.3092011481361043, -0.5349944602375606, -1.1947493795582, -0.23221701588951166, 1.1903222593323153, 0.6453499264358126, 2.393437701680078, -0.31984216193161247, 0.5268536307530907, 0.3511116792606158, 1.67161836828771]  \n",
       "3        [-0.39910316467285156, 0.949821412563324, 0.8702532052993774, -0.15616460144519806, 0.7773756980895996, 0.3858698010444641, -0.999448299407959, 0.16055533289909363, -0.40568050742149353, 0.41508427262306213, 1.3218148946762085, 0.34586480259895325, 0.6678043603897095, -0.3104383945465088, 0.05108780786395073, -1.1037255652599227, 1.0557831807519866, 0.1001951445409801, 0.5728080338162957, -0.3092011481361043, -0.5349944602375606, -1.1947493795582, -0.7296377597039766, 1.0681979602069414, 0.6453499264358126, 2.2112684568847114, -0.2786966998716909, 0.39727691760494743, 0.20674785045218758, 1.67161836828771]  \n",
       "4       [-0.264282763004303, 1.3024324178695679, 0.9755517840385437, -0.4190329313278198, 0.732369065284729, 0.6265831589698792, -1.0190303325653076, 0.062001943588256836, -0.19904416799545288, 0.2775894105434418, 1.141913652420044, 0.5574018955230713, 0.4761144518852234, 0.11363387852907181, -0.10809878259897232, -1.1284550714552857, 0.9537409281991405, -0.18075794950268054, 0.8455629918562516, -0.3092011481361043, -0.5349944602375606, -1.479161125221516, -0.3290801618785828, 1.1104852534385723, 0.6453499264358126, 2.16572614568587, -0.2834671882264644, 0.038201473867195856, -0.28712840599769784, 1.67161836828771]  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.search(vectors=vector, n=5)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8826521d",
   "metadata": {},
   "source": [
    "### Automate This Song Similarity Search Process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d21923",
   "metadata": {},
   "source": [
    "We can define a function to automate this process and find songs which are the most similar to any input song.\n",
    "This will allow us to use the KDB.AI vector database in a more production-like setting to perform similarity search and music recommendation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "36709ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_songs(\n",
    "    vectorDB_song_tab,\n",
    "    song_name: str,\n",
    "    song_artists: list[str] = None,\n",
    "    song_year: int = None,\n",
    "    n_similar: int = 5,\n",
    "    exact: bool = False,\n",
    ") -> None:\n",
    "    # create filter list\n",
    "    filter_list = [(\"like\", \"song_name\", f\"{song_name}\" if exact else f\"*{song_name}*\")]\n",
    "    if song_artists:\n",
    "        if type(song_artists) == str:\n",
    "            song_artists = list(song_artists)\n",
    "        for artist in song_artists:\n",
    "            filter_list.append((\"like\", \"song_artists\", f\"*{artist}*\"))\n",
    "    if song_year:\n",
    "        filter_list.append((\"like\", \"song_year\", f\"{song_year}\"))\n",
    "\n",
    "    # find songs liks this in vector DB\n",
    "    resulting_song = vectorDB_song_tab.query(filter=filter_list, sort_columns=[\"song_year\"])\n",
    "\n",
    "    # quality check\n",
    "    if resulting_song.empty:\n",
    "        print(\n",
    "            \"Song Not Found! Please double check the values entered or try another song\"\n",
    "        )\n",
    "        return\n",
    "\n",
    "    # find vectors associated with these songs\n",
    "    resulting_vectors = {'flat_index': [v.tolist() for v in resulting_song[\"song_embeddings\"]]}\n",
    "\n",
    "    # search for similar songs to selected songs\n",
    "    similar_songs = vectorDB_song_tab.search(vectors=resulting_vectors, n=n_similar + 1)\n",
    "\n",
    "    # process similar song table\n",
    "    for i, similar_df in enumerate(similar_songs):\n",
    "        name = resulting_song.loc[i, \"song_name\"]\n",
    "        artists = resulting_song.loc[i, \"song_artists\"]\n",
    "        year = resulting_song.loc[i, \"song_year\"]\n",
    "        print(f\"Songs Similar To '{name}' By '{artists}' ({year})\")\n",
    "        for j, song in similar_df[1:].iterrows():\n",
    "            print(\n",
    "                f\"   {j}. {song['song_name']} - {song['song_artists']} ({song['song_year']})\"\n",
    "            )\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3a16f9",
   "metadata": {},
   "source": [
    "##### Songs by multiple artists\n",
    "\n",
    "Here, we will query this function to search the KDB.AI vector database to look for the song `Let's Go` by two artists - `Calvin Harris` and `Ne-Yo`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9562fdb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Songs Similar To 'Let's Go (feat. Ne-Yo)' By 'b'Calvin Harris, Ne-Yo'' (2012)\n",
      "   1. I Cry - b'Flo Rida' (2012)\n",
      "   2. Mmm Yeah (feat. Pitbull) - b'Austin Mahone, Pitbull' (2014)\n",
      "   3. Too Much (feat. Usher) - b'Marshmello, Imanbek, Usher' (2020)\n",
      "   4. All Around The World - b'Justin Bieber, Ludacris' (2012)\n",
      "   5. No Money - b'Galantis' (2016)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "find_similar_songs(table, song_name=\"Let's Go\", song_artists=[\"Calvin Harris\", \"Ne-Yo\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69cbc15c",
   "metadata": {},
   "source": [
    "If you search for these songs on [YouTube](https://www.youtube.com/), you will see that the results returned have quite a similar vibe to the song we searched for showing the similarity search power of KDB.AI.\n",
    "\n",
    "##### Specify different number of similar songs\n",
    "\n",
    "We can adjust the number of results returned to us by specifying the `n_similar` parameter.\n",
    "Here, we will search for the `8` most similar songs to the song `Californiacation` by `Red Hot Chili Peppers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "056f42b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Songs Similar To 'Californication' By 'b'Red Hot Chili Peppers'' (1999)\n",
      "   1. Police Station - b'Red Hot Chili Peppers' (2011)\n",
      "   2. Charlie - b'Red Hot Chili Peppers' (2006)\n",
      "   3. Dark Necessities - b'Red Hot Chili Peppers' (2016)\n",
      "   4. Especially in Michigan - b'Red Hot Chili Peppers' (2006)\n",
      "   5. Don't Forget Me - b'Red Hot Chili Peppers' (2002)\n",
      "   6. Cabron - b'Red Hot Chili Peppers' (2002)\n",
      "   7. Midnight City - b'M83' (2011)\n",
      "   8. Face Down - b'The Red Jumpsuit Apparatus' (2006)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "find_similar_songs(\n",
    "    table,\n",
    "    song_name=\"Californication\",\n",
    "    song_artists=\"Red Hot Chili Peppers\",\n",
    "    n_similar=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edd10a7",
   "metadata": {},
   "source": [
    "When we return these similar songs, you will notice that a lot of the similar songs are by the same artist as the original song we searched for - `Red Hot Chili Peppers`.\n",
    "This makes sense as their music usually has pretty unique features. \n",
    "\n",
    "#### All songs with a given name by any artist\n",
    "\n",
    "The final thing we will perform a similarity search on all songs stored in the KDB.AI vector database with a given song name - `Love Me`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b5474afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Songs Similar To 'Love Me' By 'b'Elvis Presley'' (1956)\n",
      "   1. Without Him - b'Elvis Presley' (1967)\n",
      "   2. Don't - b'Elvis Presley' (1959)\n",
      "   3. Harbor Lights - b'Elvis Presley' (1959)\n",
      "   4. His Hand in Mine - b'Elvis Presley' (1960)\n",
      "   5. Fine And Mellow - b'Billie Holiday' (1957)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Buddy Holly'' (1958)\n",
      "   1. Midnight Shift - b'Buddy Holly' (1958)\n",
      "   2. A Love That's Worth Having - b'Willie Hutch' (1969)\n",
      "   3. Lonely Weekends - b'Wanda Jackson' (1961)\n",
      "   4. Johnny be good - Radio Version - b'Jonny Bombastic' (1955)\n",
      "   5. Rock & Roll Guitar - b'Johnny Knight' (1959)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Sarah Vaughan'' (1958)\n",
      "   1. Summer Is Gone - b'Carmen McRae' (1956)\n",
      "   2. Make the World Go Away - b'Ray Price' (1956)\n",
      "   3. Last Night When We Were Young - b'Carmen McRae' (1955)\n",
      "   4. I'm Just A Lucky So And So - b'Ella Fitzgerald' (1957)\n",
      "   5. I'll Come Back For More - b'Dinah Washington' (1961)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Bo Diddley'' (1960)\n",
      "   1. Keepin' Out Of Mischief Now - b'Barbra Streisand' (1963)\n",
      "   2. It's a Bird, It's a Plane, It's Superman: You've Got Possibilities - b'Charles Strouse, Linda Lavin, Harold Hastings' (1966)\n",
      "   3. Time To Go - b'Lesley Gore' (1963)\n",
      "   4. Come Back Silly Girl - b'The Lettermen' (1962)\n",
      "   5. Baby, What You Want Me To Do - b'Jimmy Reed' (1960)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Bee Gees'' (1976)\n",
      "   1. Keep It Alive - b'Lee Ritenour' (1982)\n",
      "   2. Something's Gotten Hold Of My Heart - b'Gene Pitney' (1975)\n",
      "   3. It Tears Me Up - b'Percy Sledge' (1966)\n",
      "   4. Don't You Ever Get Tired (Of Hurting Me) - b'Ronnie Milsap' (1989)\n",
      "   5. Where There Is Love - b'Patrice Rushen' (1982)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'The Cramps'' (1983)\n",
      "   1. Football Fight - Remastered 2011 - b'Queen' (1980)\n",
      "   2. Let's Get This Party Started - b'Korn' (1999)\n",
      "   3. Coming Home - Live - b'Scorpions' (1985)\n",
      "   4. Rock On The Moon - Remastered - b'The Cramps' (1980)\n",
      "   5. World's On Heroin - b'All' (1998)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Tesla'' (1986)\n",
      "   1. Walks Like a Woman - b'Baton Rouge' (1990)\n",
      "   2. She Sheila - b'The Producers' (1982)\n",
      "   3. Play with Me - b'Jeff Beck' (1976)\n",
      "   4. Kindergarten - b'Faith No More' (1992)\n",
      "   5. Away - b'Toadies' (1994)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Love and Rockets'' (1986)\n",
      "   1. No One Like You - Live - b'Scorpions' (1985)\n",
      "   2. Loving You Sunday Morning - b'Scorpions' (1979)\n",
      "   3. Promises - b'Fugazi' (1989)\n",
      "   4. Strap Me In - b'The Cars' (1987)\n",
      "   5. Season In Hell (Fire Suite) - b'John Cafferty & the Beaver Brown Band' (1983)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Yiruma'' (2001)\n",
      "   1. Think Of Me - b'Stan Whitmire' (1996)\n",
      "   2. I Feel My Savior's Love - b'Paul Cardall' (2000)\n",
      "   3. O, I Love You - b'Essie Jain' (2011)\n",
      "   4. Just the Way You Are - b'The Piano Guys' (2013)\n",
      "   5. Baby Mine - b'Fred Mollin' (2000)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Obie Trice, 50 Cent, Eminem'' (2002)\n",
      "   1. Just a Moment (feat. Quan) - b'Nas, Quan' (2004)\n",
      "   2. Spend Some Time - b'Eminem, Obie Trice, Stat Quo, 50 Cent' (2004)\n",
      "   3. Jealous Got Me Strapped - b'Spice 1, 2Pac' (1994)\n",
      "   4. Girls - b'D12' (2001)\n",
      "   5. A Week Ago - b'JAY-Z, Too $hort' (1998)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'JJ Heller'' (2006)\n",
      "   1. Snake Eyes - b'Ryan Bingham' (2009)\n",
      "   2. Oh, It Is Love - b'Hellogoodbye' (2008)\n",
      "   3. One More Dollar - b'Gillian Welch' (1996)\n",
      "   4. Hard Times - b'Gillian Welch' (2011)\n",
      "   5. Dream About You - Pop A.C. Mix - b'Stevie B' (1998)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Justin Bieber'' (2009)\n",
      "   1. Somebody To Love - b'Justin Bieber' (2010)\n",
      "   2. Somebody To Love Remix - b'Justin Bieber, Usher' (2011)\n",
      "   3. Cut To The Feeling - b'Carly Rae Jepsen' (2017)\n",
      "   4. I Really Like You - b'Carly Rae Jepsen' (2015)\n",
      "   5. 4 Minutes (feat. Justin Timberlake & Timbaland) - b'Madonna, Justin Timberlake, Timbaland' (2009)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Lil Wayne, Drake, Future'' (2013)\n",
      "   1. That Way - Bonus Track - b'Lil Uzi Vert' (2020)\n",
      "   2. Studio - b'ScHoolboy Q, BJ The Chicago Kid' (2014)\n",
      "   3. That Way - b'Lil Uzi Vert' (2020)\n",
      "   4. Lies (feat. Lil Skies) - b'Lil Xan, Lil Skies' (2018)\n",
      "   5. Tokyo Drifting (with Denzel Curry) - b'Glass Animals, Denzel Curry' (2019)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'The 1975'' (2016)\n",
      "   1. Casual Sex - b'My Darkest Days' (2012)\n",
      "   2. Lotus Eater - b'Foster The People' (2017)\n",
      "   3. Monster - b'Lady Gaga' (2009)\n",
      "   4. Moneygrabber - b'Fitz and The Tantrums' (2010)\n",
      "   5. She Lives in My Lap (feat. Rosario Dawson) - b'OutKast, Rosario Dawson' (2003)\n",
      "\n",
      "Songs Similar To 'Love Me' By 'b'Lil Tecca'' (2019)\n",
      "   1. Me and My Guitar - b'A Boogie Wit da Hoodie' (2020)\n",
      "   2. Red Nose - b'Sage The Gemini' (2014)\n",
      "   3. Run Me Dry - b'Bryson Tiller' (2017)\n",
      "   4. Stuntin' On You (feat. DaBaby) - b'Tyla Yaweh, DaBaby' (2020)\n",
      "   5. Best Friend - b'50 Cent' (2005)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "find_similar_songs(table, song_name=\"Love Me\", exact=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf91f429",
   "metadata": {},
   "source": [
    "There are 15 songs in our vector database with `Love Me` as their title and we have returned the most similar songs to each of these.\n",
    "With that, we have built a recommendation system which is able to recommend music based both on user numerical and categorical song data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4839ed",
   "metadata": {},
   "source": [
    "## 5. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "81c95d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a12b1f",
   "metadata": {},
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/gvqeAOuO)"
   ]
  }
 ],
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================================================
FILE: pattern_matching/pattern_matching.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b52fbdd6-10c5-4f52-b015-ce0f812c7d94",
   "metadata": {},
   "source": [
    "# Pattern Matching on Sensor Data\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> We have new features for highly optimized time series analytics.\n",
    "\n",
    "See documentation and notebooks on ***Temporal Similarity Search (TSS):***\n",
    "\n",
    "- Transformed TSS: [Documentation](https://code.kx.com/kdbai/reference/transformed-tss.html) | [Notebook](https://github.com/KxSystems/kdbai-samples/blob/main/TSS_transformed/Temporal_Similarity_Search_Transformed_Demo.ipynb)\n",
    "\n",
    "- Non-Transformed TSS: [Documentation](https://code.kx.com/kdbai/use/non-transformed-tss.html) | [Notebook](https://github.com/KxSystems/kdbai-samples/blob/main/TSS_non_transformed/Temporal_Similarity_Search_Non-Transformed_Demo.ipynb)\n",
    "\n",
    "***Note that this example does not use the new features for highly optimized time series analytics.***\n",
    "</div>\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example explores the process of conducting pattern matching on time series manufacturing data using a similarity search in KDB.AI. \n",
    "\n",
    "Our goal is to identify and retrieve historical time series that exhibit specific patterns. This matching capability is instrumental in a wide array of manufacturing scenarios, including quality control, process optimization, and predictive maintenance. For instance, imagine a scenario where we have time series data representing machinery performance, and we need to pinpoint instances of unusual behaviour, such as spikes, drops, or recurring trends.\n",
    "\n",
    "We will guide you through a straightforward approach that leverages the raw time series data directly, without the need for complex modelling or domain-specific expertise. This approach is particularly attractive because it doesn't require additional resources for model creation. The sample will demonstrate that this simplistic method can yield satisfactory results. \n",
    "\n",
    "### Aim\n",
    "\n",
    "This tutorial will walk through the process of storing time series data in a vector database, using windowing and normalization to generate simple time series vector embeddings. We will use KDB.AI's vector database to find historical patterns that match an input query pattern. We will cover the following topics:\n",
    "\n",
    "1. Load Sensor Data\n",
    "1. Create Sensor Vector Embeddings\n",
    "1. Store Embeddings in KDB.AI\n",
    "1. Search For Similar Sequences To A Target Sensor Sequence\n",
    "1. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f232d6de-c110-460a-a69f-e8cf852355b6",
   "metadata": {},
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07954b1a",
   "metadata": {},
   "source": [
    "### Install dependencies \n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77efaa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client \n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e18f8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads sensor data\n",
    "!mkdir ./data \n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/pattern_matching/data/archive.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e158a3",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea686a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "from zipfile import ZipFile\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07814a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f76e651",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7b4076",
   "metadata": {},
   "source": [
    "### Ignore Warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33c637f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.simplefilter(\"ignore\", UserWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d03b85",
   "metadata": {},
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75ff66c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff790798",
   "metadata": {},
   "source": [
    "## 1. Load Sensor Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7911ab4",
   "metadata": {},
   "source": [
    "### Dataset Overview\n",
    "\n",
    "The dataset that will be used for this example is the [Water Pump Sensor Dataset](https://www.kaggle.com/datasets/nphantawee/pump-sensor-data) available on Kaggle. The datatset consist of a `sensor.csv` file which has raw values from 52 sensors from a town water pump.\n",
    "\n",
    "As the `sensors.csv` file is >100mb, we cannot host this file on GitHub and must instead zip this file up and extract it locally.\n",
    "\n",
    "### Extract the Data From a ZipFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5cdbf217",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_zip(file_name):\n",
    "    with ZipFile(file_name, \"r\") as zipf:\n",
    "        zipf.extractall(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b6142d3-667a-4f65-abe2-b33f95fdd46f",
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_zip(\"data/archive.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5ff469",
   "metadata": {},
   "source": [
    "You should now have a sensor.csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7476f1",
   "metadata": {},
   "source": [
    "### Read In The Sensor Data From The CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c876844",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_sensors_df = pd.read_csv(\"data/sensor.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12d3c00a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(220320, 55)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sensor_00</th>\n",
       "      <th>sensor_01</th>\n",
       "      <th>sensor_02</th>\n",
       "      <th>sensor_03</th>\n",
       "      <th>sensor_04</th>\n",
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       "      <th>sensor_06</th>\n",
       "      <th>sensor_07</th>\n",
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       "      <th>sensor_44</th>\n",
       "      <th>sensor_45</th>\n",
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       "      <th>sensor_47</th>\n",
       "      <th>sensor_48</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2018-04-01 00:00:00</td>\n",
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       "      <td>53.2118</td>\n",
       "      <td>46.310760</td>\n",
       "      <td>634.3750</td>\n",
       "      <td>76.45975</td>\n",
       "      <td>13.41146</td>\n",
       "      <td>16.13136</td>\n",
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       "      <td>39.641200</td>\n",
       "      <td>65.68287</td>\n",
       "      <td>50.92593</td>\n",
       "      <td>38.194440</td>\n",
       "      <td>157.9861</td>\n",
       "      <td>67.70834</td>\n",
       "      <td>243.0556</td>\n",
       "      <td>201.3889</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-04-01 00:01:00</td>\n",
       "      <td>2.465394</td>\n",
       "      <td>47.09201</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.310760</td>\n",
       "      <td>634.3750</td>\n",
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       "      <td>41.92708</td>\n",
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       "      <td>243.0556</td>\n",
       "      <td>201.3889</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-04-01 00:02:00</td>\n",
       "      <td>2.444734</td>\n",
       "      <td>47.35243</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.397570</td>\n",
       "      <td>638.8889</td>\n",
       "      <td>73.54598</td>\n",
       "      <td>13.32465</td>\n",
       "      <td>16.03733</td>\n",
       "      <td>...</td>\n",
       "      <td>41.66666</td>\n",
       "      <td>39.351852</td>\n",
       "      <td>65.39352</td>\n",
       "      <td>51.21528</td>\n",
       "      <td>38.194443</td>\n",
       "      <td>155.9606</td>\n",
       "      <td>67.12963</td>\n",
       "      <td>241.3194</td>\n",
       "      <td>203.7037</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-01 00:03:00</td>\n",
       "      <td>2.460474</td>\n",
       "      <td>47.09201</td>\n",
       "      <td>53.1684</td>\n",
       "      <td>46.397568</td>\n",
       "      <td>628.1250</td>\n",
       "      <td>76.98898</td>\n",
       "      <td>13.31742</td>\n",
       "      <td>16.24711</td>\n",
       "      <td>...</td>\n",
       "      <td>40.88541</td>\n",
       "      <td>39.062500</td>\n",
       "      <td>64.81481</td>\n",
       "      <td>51.21528</td>\n",
       "      <td>38.194440</td>\n",
       "      <td>155.9606</td>\n",
       "      <td>66.84028</td>\n",
       "      <td>240.4514</td>\n",
       "      <td>203.1250</td>\n",
       "      <td>NORMAL</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2018-04-01 00:04:00</td>\n",
       "      <td>2.445718</td>\n",
       "      <td>47.13541</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.397568</td>\n",
       "      <td>636.4583</td>\n",
       "      <td>76.58897</td>\n",
       "      <td>13.35359</td>\n",
       "      <td>16.21094</td>\n",
       "      <td>...</td>\n",
       "      <td>41.40625</td>\n",
       "      <td>38.773150</td>\n",
       "      <td>65.10416</td>\n",
       "      <td>51.79398</td>\n",
       "      <td>38.773150</td>\n",
       "      <td>158.2755</td>\n",
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       "      <td>242.1875</td>\n",
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       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            timestamp  sensor_00  sensor_01  sensor_02  \\\n",
       "0           0  2018-04-01 00:00:00   2.465394   47.09201    53.2118   \n",
       "1           1  2018-04-01 00:01:00   2.465394   47.09201    53.2118   \n",
       "2           2  2018-04-01 00:02:00   2.444734   47.35243    53.2118   \n",
       "3           3  2018-04-01 00:03:00   2.460474   47.09201    53.1684   \n",
       "4           4  2018-04-01 00:04:00   2.445718   47.13541    53.2118   \n",
       "\n",
       "   sensor_03  sensor_04  sensor_05  sensor_06  sensor_07  ...  sensor_43  \\\n",
       "0  46.310760   634.3750   76.45975   13.41146   16.13136  ...   41.92708   \n",
       "1  46.310760   634.3750   76.45975   13.41146   16.13136  ...   41.92708   \n",
       "2  46.397570   638.8889   73.54598   13.32465   16.03733  ...   41.66666   \n",
       "3  46.397568   628.1250   76.98898   13.31742   16.24711  ...   40.88541   \n",
       "4  46.397568   636.4583   76.58897   13.35359   16.21094  ...   41.40625   \n",
       "\n",
       "   sensor_44  sensor_45  sensor_46  sensor_47  sensor_48  sensor_49  \\\n",
       "0  39.641200   65.68287   50.92593  38.194440   157.9861   67.70834   \n",
       "1  39.641200   65.68287   50.92593  38.194440   157.9861   67.70834   \n",
       "2  39.351852   65.39352   51.21528  38.194443   155.9606   67.12963   \n",
       "3  39.062500   64.81481   51.21528  38.194440   155.9606   66.84028   \n",
       "4  38.773150   65.10416   51.79398  38.773150   158.2755   66.55093   \n",
       "\n",
       "   sensor_50  sensor_51  machine_status  \n",
       "0   243.0556   201.3889          NORMAL  \n",
       "1   243.0556   201.3889          NORMAL  \n",
       "2   241.3194   203.7037          NORMAL  \n",
       "3   240.4514   203.1250          NORMAL  \n",
       "4   242.1875   201.3889          NORMAL  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(raw_sensors_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e584484f",
   "metadata": {},
   "source": [
    "### Pre-process The Data\n",
    "\n",
    "Let's do some preparation on the dataset to clean it up. We will remove duplicates, drop  irrelevant columns and handle missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04f68700",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicates\n",
    "sensors_df = raw_sensors_df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6382c72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns that are unnecessary/bad data\n",
    "sensors_df = sensors_df.drop([\"Unnamed: 0\", \"sensor_15\", \"sensor_50\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e338bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert timestamp to datetime format\n",
    "sensors_df[\"timestamp\"] = pd.to_datetime(sensors_df[\"timestamp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19fc8b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removes rows with any NaN values\n",
    "sensors_df = sensors_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f35a91a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the index\n",
    "sensors_df = sensors_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad0f0794-38db-44b2-bcae-63b399b89729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195815, 52)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sensor_00</th>\n",
       "      <th>sensor_01</th>\n",
       "      <th>sensor_02</th>\n",
       "      <th>sensor_03</th>\n",
       "      <th>sensor_04</th>\n",
       "      <th>sensor_05</th>\n",
       "      <th>sensor_06</th>\n",
       "      <th>sensor_07</th>\n",
       "      <th>sensor_08</th>\n",
       "      <th>...</th>\n",
       "      <th>sensor_42</th>\n",
       "      <th>sensor_43</th>\n",
       "      <th>sensor_44</th>\n",
       "      <th>sensor_45</th>\n",
       "      <th>sensor_46</th>\n",
       "      <th>sensor_47</th>\n",
       "      <th>sensor_48</th>\n",
       "      <th>sensor_49</th>\n",
       "      <th>sensor_51</th>\n",
       "      <th>machine_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-01 00:00:00</td>\n",
       "      <td>2.465394</td>\n",
       "      <td>47.09201</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.310760</td>\n",
       "      <td>634.3750</td>\n",
       "      <td>76.45975</td>\n",
       "      <td>13.41146</td>\n",
       "      <td>16.13136</td>\n",
       "      <td>15.56713</td>\n",
       "      <td>...</td>\n",
       "      <td>31.770832</td>\n",
       "      <td>41.92708</td>\n",
       "      <td>39.641200</td>\n",
       "      <td>65.68287</td>\n",
       "      <td>50.92593</td>\n",
       "      <td>38.194440</td>\n",
       "      <td>157.9861</td>\n",
       "      <td>67.70834</td>\n",
       "      <td>201.3889</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-01 00:01:00</td>\n",
       "      <td>2.465394</td>\n",
       "      <td>47.09201</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.310760</td>\n",
       "      <td>634.3750</td>\n",
       "      <td>76.45975</td>\n",
       "      <td>13.41146</td>\n",
       "      <td>16.13136</td>\n",
       "      <td>15.56713</td>\n",
       "      <td>...</td>\n",
       "      <td>31.770832</td>\n",
       "      <td>41.92708</td>\n",
       "      <td>39.641200</td>\n",
       "      <td>65.68287</td>\n",
       "      <td>50.92593</td>\n",
       "      <td>38.194440</td>\n",
       "      <td>157.9861</td>\n",
       "      <td>67.70834</td>\n",
       "      <td>201.3889</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-01 00:02:00</td>\n",
       "      <td>2.444734</td>\n",
       "      <td>47.35243</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.397570</td>\n",
       "      <td>638.8889</td>\n",
       "      <td>73.54598</td>\n",
       "      <td>13.32465</td>\n",
       "      <td>16.03733</td>\n",
       "      <td>15.61777</td>\n",
       "      <td>...</td>\n",
       "      <td>31.770830</td>\n",
       "      <td>41.66666</td>\n",
       "      <td>39.351852</td>\n",
       "      <td>65.39352</td>\n",
       "      <td>51.21528</td>\n",
       "      <td>38.194443</td>\n",
       "      <td>155.9606</td>\n",
       "      <td>67.12963</td>\n",
       "      <td>203.7037</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-01 00:03:00</td>\n",
       "      <td>2.460474</td>\n",
       "      <td>47.09201</td>\n",
       "      <td>53.1684</td>\n",
       "      <td>46.397568</td>\n",
       "      <td>628.1250</td>\n",
       "      <td>76.98898</td>\n",
       "      <td>13.31742</td>\n",
       "      <td>16.24711</td>\n",
       "      <td>15.69734</td>\n",
       "      <td>...</td>\n",
       "      <td>31.510420</td>\n",
       "      <td>40.88541</td>\n",
       "      <td>39.062500</td>\n",
       "      <td>64.81481</td>\n",
       "      <td>51.21528</td>\n",
       "      <td>38.194440</td>\n",
       "      <td>155.9606</td>\n",
       "      <td>66.84028</td>\n",
       "      <td>203.1250</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-01 00:04:00</td>\n",
       "      <td>2.445718</td>\n",
       "      <td>47.13541</td>\n",
       "      <td>53.2118</td>\n",
       "      <td>46.397568</td>\n",
       "      <td>636.4583</td>\n",
       "      <td>76.58897</td>\n",
       "      <td>13.35359</td>\n",
       "      <td>16.21094</td>\n",
       "      <td>15.69734</td>\n",
       "      <td>...</td>\n",
       "      <td>31.510420</td>\n",
       "      <td>41.40625</td>\n",
       "      <td>38.773150</td>\n",
       "      <td>65.10416</td>\n",
       "      <td>51.79398</td>\n",
       "      <td>38.773150</td>\n",
       "      <td>158.2755</td>\n",
       "      <td>66.55093</td>\n",
       "      <td>201.3889</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  sensor_00  sensor_01  sensor_02  sensor_03  sensor_04  \\\n",
       "0 2018-04-01 00:00:00   2.465394   47.09201    53.2118  46.310760   634.3750   \n",
       "1 2018-04-01 00:01:00   2.465394   47.09201    53.2118  46.310760   634.3750   \n",
       "2 2018-04-01 00:02:00   2.444734   47.35243    53.2118  46.397570   638.8889   \n",
       "3 2018-04-01 00:03:00   2.460474   47.09201    53.1684  46.397568   628.1250   \n",
       "4 2018-04-01 00:04:00   2.445718   47.13541    53.2118  46.397568   636.4583   \n",
       "\n",
       "   sensor_05  sensor_06  sensor_07  sensor_08  ...  sensor_42  sensor_43  \\\n",
       "0   76.45975   13.41146   16.13136   15.56713  ...  31.770832   41.92708   \n",
       "1   76.45975   13.41146   16.13136   15.56713  ...  31.770832   41.92708   \n",
       "2   73.54598   13.32465   16.03733   15.61777  ...  31.770830   41.66666   \n",
       "3   76.98898   13.31742   16.24711   15.69734  ...  31.510420   40.88541   \n",
       "4   76.58897   13.35359   16.21094   15.69734  ...  31.510420   41.40625   \n",
       "\n",
       "   sensor_44  sensor_45  sensor_46  sensor_47  sensor_48  sensor_49  \\\n",
       "0  39.641200   65.68287   50.92593  38.194440   157.9861   67.70834   \n",
       "1  39.641200   65.68287   50.92593  38.194440   157.9861   67.70834   \n",
       "2  39.351852   65.39352   51.21528  38.194443   155.9606   67.12963   \n",
       "3  39.062500   64.81481   51.21528  38.194440   155.9606   66.84028   \n",
       "4  38.773150   65.10416   51.79398  38.773150   158.2755   66.55093   \n",
       "\n",
       "   sensor_51  machine_status  \n",
       "0   201.3889          NORMAL  \n",
       "1   201.3889          NORMAL  \n",
       "2   203.7037          NORMAL  \n",
       "3   203.1250          NORMAL  \n",
       "4   201.3889          NORMAL  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(sensors_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65d5494",
   "metadata": {},
   "source": [
    "This dataset has 52 sensor columns - for the purposes of this example we will only select the first one `sensor_00` for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57757b69",
   "metadata": {},
   "source": [
    "### Explore The Data For One Sensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53f2b312",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the readings from the BROKEN state of the pump\n",
    "broken_sensors_df = sensors_df[sensors_df[\"machine_status\"] == \"BROKEN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cafa18ba-2ad5-4285-a773-9648fc97188f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time series for each sensor with BROKEN state marked with X in red color\n",
    "plt.figure(figsize=(18, 3))\n",
    "plt.plot(\n",
    "    broken_sensors_df[\"timestamp\"],\n",
    "    broken_sensors_df[\"sensor_00\"],\n",
    "    linestyle=\"none\",\n",
    "    marker=\"X\",\n",
    "    color=\"red\",\n",
    "    markersize=12,\n",
    ")\n",
    "plt.plot(sensors_df[\"timestamp\"], sensors_df[\"sensor_00\"], color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69439514",
   "metadata": {},
   "source": [
    "We can see above that over time the sensor values stay generally around 2.5 with a few noisy dropoff spikes. We have plotted the column `machine_status=BROKEN` in red here which corresponds with a lot of these spikes indicating the reason for the dropoffs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c998fbc",
   "metadata": {},
   "source": [
    "## 2. Create Sensor Vector Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee5954a",
   "metadata": {},
   "source": [
    "Next, let's create embeddings for these values. To do this we use a combination of windowing and normalizing the data. We have chosen a simple approach that leverages the raw time series data directly, without the need for complex modelling or domain-specific expertise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755dc49f",
   "metadata": {},
   "source": [
    "### Extract One Sensors Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "663fd80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor0_df = sensors_df[[\"timestamp\", \"sensor_00\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd231f29-6435-4e9d-8732-af8d2d065c2b",
   "metadata": {},
   "source": [
    "### Group The Sensor0 Values into Time Windows\n",
    "\n",
    "The code below divides the original time series data into overlapping windows, with each window containing a specified number of rows and a step size determining how they are shifted along the timeline. It also extracts a timestamp from each window as we will want to store this as metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe8670dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the window size (number of rows in each window)\n",
    "window_size = 24\n",
    "step_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ed2d459",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define windows\n",
    "windows = [\n",
    "    sensor0_df.iloc[i : i + window_size]\n",
    "    for i in range(0, len(sensor0_df) - window_size + 1, step_size)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf7e6d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the windows & extract column values\n",
    "start_times = [w[\"timestamp\"].iloc[0] for w in windows]\n",
    "end_times = [w[\"timestamp\"].iloc[-1] for w in windows]\n",
    "sensor0_values = [w[\"sensor_00\"].tolist() for w in windows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "758a7a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new DataFrame from the collected data\n",
    "embedding_df = pd.DataFrame(\n",
    "    {\"start_time\": start_times, \"end_time\": end_times, \"vectors\": sensor0_values}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "acf95914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19580, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-01 00:00:00</td>\n",
       "      <td>2018-04-01 00:23:00</td>\n",
       "      <td>[2.465394, 2.465394, 2.444734, 2.460474, 2.445...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-01 00:10:00</td>\n",
       "      <td>2018-04-01 00:33:00</td>\n",
       "      <td>[2.46441, 2.444734, 2.460474, 2.448669, 2.4535...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-01 00:20:00</td>\n",
       "      <td>2018-04-01 00:43:00</td>\n",
       "      <td>[2.445718, 2.460474, 2.448669, 2.453588, 2.453...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-01 00:30:00</td>\n",
       "      <td>2018-04-01 00:53:00</td>\n",
       "      <td>[2.463426, 2.448669, 2.453588, 2.455556, 2.449...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-01 00:40:00</td>\n",
       "      <td>2018-04-01 01:03:00</td>\n",
       "      <td>[2.449653, 2.453588, 2.453588, 2.448669, 2.460...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  \\\n",
       "0 2018-04-01 00:00:00 2018-04-01 00:23:00   \n",
       "1 2018-04-01 00:10:00 2018-04-01 00:33:00   \n",
       "2 2018-04-01 00:20:00 2018-04-01 00:43:00   \n",
       "3 2018-04-01 00:30:00 2018-04-01 00:53:00   \n",
       "4 2018-04-01 00:40:00 2018-04-01 01:03:00   \n",
       "\n",
       "                                             vectors  \n",
       "0  [2.465394, 2.465394, 2.444734, 2.460474, 2.445...  \n",
       "1  [2.46441, 2.444734, 2.460474, 2.448669, 2.4535...  \n",
       "2  [2.445718, 2.460474, 2.448669, 2.453588, 2.453...  \n",
       "3  [2.463426, 2.448669, 2.453588, 2.455556, 2.449...  \n",
       "4  [2.449653, 2.453588, 2.453588, 2.448669, 2.460...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the resulting DataFrame\n",
    "show_df(embedding_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f7f5425",
   "metadata": {},
   "source": [
    "### Normalize These Sensor Value Windows\n",
    "\n",
    "Next, we perform manual normalization of sensor values within each time-based window. This ensures that the sensor values are scaled uniformly between 0 and 1 which is better for further analysis. Additionally, the DataFrame's index is reset to maintain a clean and continuous row numbering.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d88dffc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to normalize the sensor column\n",
    "def normalize_vector(vectors: list) -> list:\n",
    "    min_val = min(vectors)\n",
    "    max_val = max(vectors)\n",
    "    return (\n",
    "        [0.0] * len(vectors)\n",
    "        if max_val == min_val\n",
    "        else [(v - min_val) / (max_val - min_val) for v in vectors]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b47578cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_df[\"vectors\"] = embedding_df[\"vectors\"].apply(normalize_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb4209d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19580, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-01 00:00:00</td>\n",
       "      <td>2018-04-01 00:23:00</td>\n",
       "      <td>[1.0, 1.0, 0.0, 0.7618586640851966, 0.04762826...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-01 00:10:00</td>\n",
       "      <td>2018-04-01 00:33:00</td>\n",
       "      <td>[1.0, 0.0, 0.7999593413295418, 0.1999898353323...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-01 00:20:00</td>\n",
       "      <td>2018-04-01 00:43:00</td>\n",
       "      <td>[0.05001016466760889, 0.7999593413295418, 0.19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-01 00:30:00</td>\n",
       "      <td>2018-04-01 00:53:00</td>\n",
       "      <td>[0.8571566871581443, 0.14284331284187718, 0.38...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-01 00:40:00</td>\n",
       "      <td>2018-04-01 01:03:00</td>\n",
       "      <td>[0.19047388547365338, 0.3809477709472852, 0.38...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  \\\n",
       "0 2018-04-01 00:00:00 2018-04-01 00:23:00   \n",
       "1 2018-04-01 00:10:00 2018-04-01 00:33:00   \n",
       "2 2018-04-01 00:20:00 2018-04-01 00:43:00   \n",
       "3 2018-04-01 00:30:00 2018-04-01 00:53:00   \n",
       "4 2018-04-01 00:40:00 2018-04-01 01:03:00   \n",
       "\n",
       "                                             vectors  \n",
       "0  [1.0, 1.0, 0.0, 0.7618586640851966, 0.04762826...  \n",
       "1  [1.0, 0.0, 0.7999593413295418, 0.1999898353323...  \n",
       "2  [0.05001016466760889, 0.7999593413295418, 0.19...  \n",
       "3  [0.8571566871581443, 0.14284331284187718, 0.38...  \n",
       "4  [0.19047388547365338, 0.3809477709472852, 0.38...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the resulting DataFrame\n",
    "show_df(embedding_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7daaa8",
   "metadata": {},
   "source": [
    "## 3. Store Embeddings in KDB.AI "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6edb73-e4c9-40c7-a295-e9063dc83e42",
   "metadata": {},
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a372cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c0b1d1",
   "metadata": {},
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will have three colums: startTime, endTime, vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b4438ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the schema\n",
    "sensor_schema = [\n",
    "        {\n",
    "            \"name\": \"start_time\",\n",
    "            \"type\": \"datetime64[ns]\",\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"end_time\",\n",
    "            \"type\": \"datetime64[ns]\",\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"vectors\",\n",
    "            \"type\": \"float64s\",\n",
    "        },\n",
    "    ]\n",
    "\n",
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'hnsw',\n",
    "        'name': 'hnsw_index',\n",
    "        'column': 'vectors',\n",
    "        'params': {'dims': window_size, 'metric': \"L2\"},\n",
    "    },\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26bf9a9d",
   "metadata": {},
   "source": [
    "### Create a reference to our default kdb database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "887bdfec",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = session.database(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e345361-a3b3-4d59-bca1-0c0c6f63f910",
   "metadata": {},
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2a518fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"sensors\").drop()\n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "151df13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = db.create_table(\"sensors\", sensor_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f489316",
   "metadata": {},
   "source": [
    "### Add Embedded Data to KDB.AI Table\n",
    "\n",
    "When adding larger amounts of data, you may need insert data into an index in chunks. It is a good idea to first get an idea of how large the dataset to insert is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "37f7e5d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.07916259765625"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_df.memory_usage(deep=True).sum() / (1024**2)  # Convert bytes to MB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce03a4fe",
   "metadata": {},
   "source": [
    "This is fairly small <10MB due so we are good to add all in one go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "10ff4a53-dd22-4d3c-8c63-4aab10862487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 19580}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(embedding_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f498123",
   "metadata": {},
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b185eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19580, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-01 00:00:00</td>\n",
       "      <td>2018-04-01 00:23:00</td>\n",
       "      <td>[1.0, 1.0, 0.0, 0.7618586640851966, 0.04762826...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-01 00:10:00</td>\n",
       "      <td>2018-04-01 00:33:00</td>\n",
       "      <td>[1.0, 0.0, 0.7999593413295418, 0.1999898353323...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-01 00:20:00</td>\n",
       "      <td>2018-04-01 00:43:00</td>\n",
       "      <td>[0.05001016466760889, 0.7999593413295418, 0.19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-01 00:30:00</td>\n",
       "      <td>2018-04-01 00:53:00</td>\n",
       "      <td>[0.8571566871581443, 0.14284331284187718, 0.38...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-01 00:40:00</td>\n",
       "      <td>2018-04-01 01:03:00</td>\n",
       "      <td>[0.19047388547365338, 0.3809477709472852, 0.38...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  \\\n",
       "0 2018-04-01 00:00:00 2018-04-01 00:23:00   \n",
       "1 2018-04-01 00:10:00 2018-04-01 00:33:00   \n",
       "2 2018-04-01 00:20:00 2018-04-01 00:43:00   \n",
       "3 2018-04-01 00:30:00 2018-04-01 00:53:00   \n",
       "4 2018-04-01 00:40:00 2018-04-01 01:03:00   \n",
       "\n",
       "                                             vectors  \n",
       "0  [1.0, 1.0, 0.0, 0.7618586640851966, 0.04762826...  \n",
       "1  [1.0, 0.0, 0.7999593413295418, 0.1999898353323...  \n",
       "2  [0.05001016466760889, 0.7999593413295418, 0.19...  \n",
       "3  [0.8571566871581443, 0.14284331284187718, 0.38...  \n",
       "4  [0.19047388547365338, 0.3809477709472852, 0.38...  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b643fd4",
   "metadata": {},
   "source": [
    "## 4. Search For Similar Sequences To A Target Sensor Sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2358732",
   "metadata": {},
   "source": [
    "Now our data is loaded successfully, we can perform pattern matching on our historical sensor data using KDB.AI `search`. \n",
    "\n",
    "### Define an Example Pattern to Query \n",
    "\n",
    "The first step is to select a pattern that will be used to query.\n",
    "\n",
    "We chose this by selecting a start time, filtering to get the vector's values for that record, and then storing this in a variable called `query_vector`. Any pattern could be selected here.\n",
    "\n",
    "The resulting query pattern is also displayed as a line plot for visual inspection and analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0196d447",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select historical pattern\n",
    "example_window1 = embedding_df[\n",
    "    embedding_df[\"start_time\"] == pd.to_datetime(\"2018-04-01 13:00:00\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d4c08671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select historical pattern\n",
    "query_df1 = sensors_df[\n",
    "    (sensors_df[\"timestamp\"] >= example_window1.iloc[0][\"start_time\"])\n",
    "    & (sensors_df[\"timestamp\"] <= example_window1.iloc[0][\"end_time\"])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "47082e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the example pattern\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(query_df1[\"timestamp\"], query_df1[\"sensor_00\"], marker=\"o\", linestyle=\"-\")\n",
    "plt.xlabel(\"Timestamp\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(\"Query Pattern\")\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019572d9",
   "metadata": {},
   "source": [
    "### Search KDB.AI Based on this Example Pattern\n",
    "\n",
    "We can extract the vectors from the example window selected above into a `query_vector` and search the vector database to get the five nearest other windows to this pattern.\n",
    "\n",
    "In a real life scenario, the example pattern could be some real time data as selected by a machine operator or engineer to pinpoint instances of unusual behavior such as spikes, drops, or recurring trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c4c2d383",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the sensor values in this window\n",
    "query_vector1 = example_window1[\"vectors\"].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "38b9e61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018-04-01 13:00:00</td>\n",
       "      <td>2018-04-01 13:23:00</td>\n",
       "      <td>[0.1818139814258758, 0.1818139814258758, 0.409...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.177821</td>\n",
       "      <td>2018-04-12 16:00:00</td>\n",
       "      <td>2018-04-12 16:23:00</td>\n",
       "      <td>[0.2499788225328221, 0.2499788225328221, 0.416...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.295486</td>\n",
       "      <td>2018-04-02 06:20:00</td>\n",
       "      <td>2018-04-02 06:43:00</td>\n",
       "      <td>[0.6818370835836001, 0.1818139814258758, 0.500...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.314223</td>\n",
       "      <td>2018-04-05 08:30:00</td>\n",
       "      <td>2018-04-05 08:53:00</td>\n",
       "      <td>[0.4999682358172739, 0.0, 0.43751985261418974,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.321309</td>\n",
       "      <td>2018-06-01 11:33:00</td>\n",
       "      <td>2018-06-01 11:56:00</td>\n",
       "      <td>[0.3332956855658574, 0.44443189518861076, 0.44...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance          start_time            end_time  \\\n",
       "0       0.000000 2018-04-01 13:00:00 2018-04-01 13:23:00   \n",
       "1       0.177821 2018-04-12 16:00:00 2018-04-12 16:23:00   \n",
       "2       0.295486 2018-04-02 06:20:00 2018-04-02 06:43:00   \n",
       "3       0.314223 2018-04-05 08:30:00 2018-04-05 08:53:00   \n",
       "4       0.321309 2018-06-01 11:33:00 2018-06-01 11:56:00   \n",
       "\n",
       "                                             vectors  \n",
       "0  [0.1818139814258758, 0.1818139814258758, 0.409...  \n",
       "1  [0.2499788225328221, 0.2499788225328221, 0.416...  \n",
       "2  [0.6818370835836001, 0.1818139814258758, 0.500...  \n",
       "3  [0.4999682358172739, 0.0, 0.43751985261418974,...  \n",
       "4  [0.3332956855658574, 0.44443189518861076, 0.44...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn1_result = table.search(vectors={\"hnsw_index\": query_vector1}, n=5)\n",
    "nn1_result[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d043f38",
   "metadata": {},
   "source": [
    "The results returned from `table.search` show the closest matches along with value of nearest neighbor distances `nn_distance`. This is helpful to keep in for visulization purposes so we can compare how similar it is to the matches found.\n",
    "\n",
    "The first result is an exact match as the example pattern we chose was from the existing dataset. \n",
    "\n",
    "### Visualize These Nearest Neighbour Results\n",
    "\n",
    "Let's plot these neighbors overlayed with each other on a chart. This is useful for visualizing and analyzing time series data patterns and their relationships.\n",
    "\n",
    "The code snippet below performs a time-based pattern matching and visualization process, calculating time differences, assiging labels to patterns, and then plotting these patterns with legends to distinguish and display them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e0176dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the rows of df1 to filter df and calculate time differences\n",
    "full_nn1_df = pd.DataFrame()\n",
    "for i, row in nn1_result[0].iterrows():\n",
    "    filtered_df = sensors_df[\n",
    "        (sensors_df[\"timestamp\"] >= row[\"start_time\"])\n",
    "        & (sensors_df[\"timestamp\"] <= row[\"end_time\"])\n",
    "    ].copy()\n",
    "    filtered_df[\"time_difference\"] = filtered_df[\"timestamp\"] - row[\"start_time\"]\n",
    "    filtered_df[\"pattern\"] = i + 1\n",
    "    full_nn1_df = pd.concat([full_nn1_df, filtered_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "90d83a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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37lxuv/32Mbfftm0bmqbxyiuvDNtm/fr1BAKBd985QRAEQRAEQRCKiPiaIi688EI0TWPt2rUl2x9++OG9Wiv2l7/8hX/7t38b7+4JgiAIgiAIgjDOiPiaQlwuF+vWraO3t3efz1FfX4/b7R7HXk0ck1FwWhAEQRAEQRDKFRFfU8iKFStoampizZo1w7Z59tlnWbJkCVVVVbS0tHDllVcSj8eL+wemHf7zn//kpJNOwuVysXDhQp588kk0TePhhx8uOe+WLVt4//vfj9vt5sgjj+T5558f9N4PP/wwCxYswOVysXLlSnbu3Fmy/84772T+/Pk4HA4OPvhg7rvvvpL9mqZx5513ctZZZ1FdXc1NN91Eb28v5557btGGf8GCBdx9991782sThAknk8ux/qUnueGp+1n/0pNkcrmp7pIgCBVKXjd4fnM3v3plN89v7iavG1PdJUEQphCp8zWFWK1Wbr75ZlatWsWVV17JrFmzSvZv3ryZ008/nRtvvJGf/OQndHZ2cvnll3P55ZcPKVjy+Twf/vCHmT17Ni+88ALRaJR///d/H/K9v/KVr3DrrbeyYMECvvKVr/DJT36STZs2YbOpP4lEIsFNN93Evffei8Ph4NJLL+UTn/gEf/rTnwB46KGH+PznP893v/tdVqxYwWOPPcZFF13ErFmzeP/73198n2984xusXbuW7373u9hsNr72ta/xxhtv8Nvf/pa6ujo2bdpEMpkcr1+pILxrvvXMz7jvnTswrKHittteDXDegiu5Zsk5U9cxQRAqjg2vt3L9o2/QGk4VtzX7Xaw+cyGnH9Y8hT0TBGGqEPE1xZx99tksXryY1atX8+Mf/7hk35o1azj33HO56qqrAFiwYAF33HEHJ598MnfeeeeggsNPPPEEmzdvZuPGjTQ1NQFw0003ceqppw5636uvvpoPfvCDAFx//fUsWrSITZs2ccghhwAqRfD73/8+xx9/PAD33HMPhx56KC+++CLHHXcct956KxdeeCGXXnopAF/84hf585//zK233loivlatWsVFF11UfL1jxw6OOuoo3vOe9wAqcicI5cK3nvkZ92y+ASzQf+Wlbgmp7SACTBCEMbHh9VY+d//LDIxztYVTfO7+l7nzU0eLABOE/RBJOywD1q1bxz333MObb75Zsv3VV19l/fr1eDye4r+VK1ei6zpbt24ddJ63336blpaWovACOO6444Z8zyOOOKL4c3Ozuvl3dHQUt9lsNo499tji60MOOYRAIFDs45tvvsmJJ55Ycs4TTzxx0DWYIsvkc5/7HA8++CCLFy/mP/7jP3juueeG7J8gTDaZXI773rkDgIGeN+br+96+Q1IQBUEYlbxucP2jbwwSXkBx2/WPviEpiIKwHyLiqwxYunQpK1eu5LrrrivZHovF+OxnP8srr7xS/Pfqq6/yzjvvMH/+/Hf1nna7vfiz6a6o6/q7OudQVFdXl7z+wAc+wPbt2/nCF77Anj17OOWUU7j66qvH/X0FYW954NWNGNbQIOFlomlg2EI88OrGSe2XIAiVx4tbe0pSDQdiAK3hFC9u7Zm8TgmCUBaI+CoT1q5dy6OPPlpifHH00UfzxhtvcOCBBw7653A4Bp3joIMOYufOnbS3txe3/eUvf9mn/uRyOf76178WX7/11luEQiEOPfRQAA499NDi+i+TP/3pTyxcuHDUc9fX13PBBRdw//33893vfpf/+q//2qc+CsJ4siPSNq7tBEHYf+mIDi+89qWdIAjTB1nzVSYcfvjhnHvuudxxxx3Fbddeey3vfe97ufzyy/n0pz9NdXU1b7zxBk888QTf//73B53j1FNPZf78+VxwwQXccsstRKNRvvrVrwLsVe0wUJGxK664gjvuuAObzcbll1/Oe9/73mIa4zXXXMPHPvYxjjrqKFasWMGjjz7KL3/5S5588skRz/v1r3+dY445hkWLFpFOp3nssceKgk4QppLZvqbRG+1FO0EQ9l8avK7RG+1FO0EQpg8S+SojbrjhhpLUvyOOOIKnn36at99+myVLlnDUUUfx9a9/nRkzZgx5vNVq5eGHHyYWi3Hsscfy6U9/mq985SsAg8w5RsPtdnPttdeyatUqTjzxRDweDz/96U+L+z/84Q9z++23c+utt7Jo0SL+8z//k7vvvptly5aNeF6Hw8F1113HEUccwdKlS7FarTz44IN71TdBmAhWHbkMLR/AGGYJhmGAlguw6shlk9ovQRAqj+Pm1dLsdzHctKeGcj08bl7tZHZLEIQyQCJfU8T69esHbZs7dy7pdLpk27HHHsvjjz8+7Hm2bduGrutEIhFAGWM8++yzxf1mauCBBx5YfA9jwOgyEAiUbLvwwgu58MILAfjIRz4y7Ht/7nOf43Of+9yw+we+D8BXv/rVYjROEMoJh83GeQuu5J7NNyih1W/UZP4pn3fQlThsctsUBGFkrBaN1Wcu5HP3vzxon3lrWX3mQqyWvctKEQSh8pFRxDTjoYcewuPxsGDBAjZt2sTnP/95TjzxxHdt0CEI+wOmjfzAOl+WfIDzDpI6X4IgjJ3TD2vmzk8dzXW/fI3eRLa4vUnqfAnCfo2Ir2lGNBrl2muvZceOHdTV1bFixQq+/e1vT3W3BKFiuGbJOVxw1Gmc8ouT1Ia8m79e8JREvARB2GtOP6yZWCrH1T//OwArDm3gP897j0S8BGE/RkYT04zzzz+f888/f6q7IQgVTVust/izoaWxWWR5rCAI+0Yo2Rf1ctqsIrwEYT9HRhSCIAgD2BXuLv6sWfJ0J2JT2BtBECqZUL+Uw95EZgp7IghCOSDiSxAEYQDtsdLCp7si3cO0FARBGJmefoKrvxATBGH/RMSXIAjCANrjA8RXuGuKeiIIQqXTG+8vviTyJQj7OyK+BEEQBtCV6C153TYgEiYIgjBW+qca9krkSxD2e0R8CYIgDKA3FS553ZkQ8SUIwr7RG+8TXMlsnlQ2P4W9EQRhqhHxJQiCMIBwJlTyuisRGrKdIAjCaAw02QgnJfolCPszYjVfxuR1gxe39tARTdHgdXHcvFqxqBWESSCaKY189aZ6h2kpCIIwPIZhDBJfvYkMjT7XFPVIEISpRiJfZcqG11s5ad0f+OSP/sznH3yFT/7oz5y07g9seL11wt7zwgsvRNM01q5dW7L94YcfRtP6RN/GjRvRNI1FixaRz5emTwQCAdavXz/se/zjH//gox/9KHPnzkXTNL773e+O5yUIwriQyEfVD3k1QIpkIlPYG0EQKpV4Jk82bwAww6/uJ+J4KAj7NyK+ypANr7fyuftfpjWcKtneFk7xuftfnlAB5nK5WLduHb29o8/0b9myhXvvvXevzp9IJDjggANYu3YtTU1N+9pNQZhQ0gXx5aQRgGg2PFJzQRCEITGdDl12C01F8SWOh4KwPyPiaxIwDINEJjemf9FUltWP/ANjqPMU/v/GI28QTWVLjktm8kOezzCGOtPwrFixgqamJtasWTNq2yuuuILVq1eTTqfHfP5jjz2Wb33rW3ziE5/A6XTuVd8EYbLIGKqoco19JgCJnES+BEHYe8yUw1q3g4DbUdgmkS9B2J+RNV+TQDKbZ+HXfzcu5zKAtkiKw7/x+Jjav3HDStyOsX/MVquVm2++mVWrVnHllVcya9asYdteddVV3H///Xzve9/j6quvHvN7CEK5k9fiADRXt9AWhbQem+IeCYJQifQUIl8Bt4OA2w5I2qEg7O9I5EsYxNlnn83ixYtZvXr1iO3cbjerV69mzZo1hMOSliVMD3Rdx7AkADiwZi4AWUPElyAIe08x8lXtoKYQ+ZK0Q0HYv5HI1yRQZbfyxg0rx9T2xa09XHj3X0Ztt/6iYzluXi2gBovRSBSvz4vFUqqnq+zWve8wsG7dOpYvXz5qROuSSy7h29/+NuvWrePmm2/ep/cShHKiLRZC03QADmuYz892QF4T8SUIwt5j1viqqXYQqFKRr4Huh4Ig7F9I5GsS0DQNt8M2pn9LFtTT7HcxnKG8BjT7XSxZUF9yXJXDOuT5+rsU7g1Lly5l5cqVXHfddSO2s9ls3HTTTdx+++3s2bNnn95LEMqJ3RFVUNnQ7Rxa36J+tiTJ5aUwqiAIe4cptGrcdgLVZuRL0g4FYX9GxFeZYbVorD5zIcAgAWa+Xn3mwkmp97V27VoeffRRnn/++RHbnXPOOSxatIjrr79+wvskCBPN7kgXABbDzSx/EABNM2iNhqawV4IgVCJ94stBjaz5EgQBEV9lyemHNXPnp44u2tKaNPld3Pmpozn9sOZJ6cfhhx/Oueeeyx133DFq27Vr1/KTn/yEeDw+YrtMJsMrr7zCK6+8QiaTYffu3bzyyits2rRpvLotCO+K1piKfFkND15nFYauZqt3hjunsluCIFQgxbRDt51Alel2KGmHgrA/M6Xia82aNRx77LF4vV4aGhr48Ic/zFtvvTXm4x988EE0TePDH/7woH1vvvkmZ511Fn6/n+rqao499lh27NhR3L9s2TI0TSv59//+3/8bj8saF04/rJlnr13O//7be7n9E4v53397L89eu3zShJfJDTfcgK7ro7Zbvnw5y5cvJ5fLjdhuz549HHXUURx11FG0trZy6623ctRRR/HpT396vLosCO+KzrgSX06LBwCLXg30iTJBEISxUox8VfdzO0xK5EsQ9mem1HDj6aef5rLLLuPYY48ll8vx5S9/mdNOO4033niD6urqEY/dtm0bV199NUuWLBm0b/PmzZx00klccsklXH/99fh8Pv7xj3/gcpVGkv7t3/6NG264ofja7XaPz4WNE1aLxvvmByft/davXz9o29y5cwfV8Vq2bNmQ9cN+97vR7fTnzp2717XHBGEy6U6GAHBZvADYqCZLL23R7inslSAIlYhpNV/jdlBT3ed2aBjGPq/JFgShsplS8bVhw4aS1+vXr6ehoYGXXnqJpUuXDntcPp/n3HPP5frrr+eZZ54hFAqV7P/KV77CGWecwS233FLcNn/+/EHncbvdNDU1jamv6XS6RIREIqroajabJZstncXKZrMYhoGu62OKGr1bTDFjvud0Q9d1DMMgm81ite6be+NUYP5dDPz7EKaW0T6XnmQvAG6bl2w2i8PiIQu0R7vls5xA5PtSnsjn8u7oLYgvn9NCdWHElc0bhOIpPM53NwSTz6Y8kc+lPJmMz2Ws5y4rq3mzVlRtbe2I7W644QYaGhq45JJLeOaZZ0r26brOr3/9a/7jP/6DlStX8re//Y158+Zx3XXXDUpP/J//+R/uv/9+mpqaOPPMM/na1742bPRrzZo1QxpKPP7444OOsdlsNDU1EYvFyGQmL7c7Go1O2ntNJplMhmQyyR//+MdR0xrLkSeeeGKquyAMwXCfy+6uneCCfFznN7/5DVrGDi54Y9s/+c1vfjPJvdz/kO9LeSKfy95jGNAdswIaf3vhWbY5wKpZyRsaD//mcWqd4/M+8tmUJ/K5lCcT+bkkEokxtSsb8aXrOldddRUnnngihx122LDtnn32WX784x/zyiuvDLm/o6ODWCzG2rVrufHGG1m3bh0bNmzgIx/5CE899RQnn3wyAKtWrWLOnDnMmDGDv//971x77bW89dZb/PKXvxzyvNdddx1f/OIXi68jkQgtLS2cdtpp+Hy+krapVIqdO3fi8XgGpTpOBIZhEI1G8Xq90zKNIZVKUVVVxdKlSyfl9zleZLNZnnjiCU499VTsdvtUd0coMNrn8r3//TUYMKdxDmeccQY/+r+niOXAVVPFGWecMQU93j+Q70t5Ip/LvhNP58j9+Q8AfOSDp+F22Lj5H0/TEU1z1PEnsWiGb5QzjIx8NuWJfC7lyWR8LmZW3GiUjfi67LLLeP3113n22WeHbRONRjnvvPP40Y9+RF1d3ZBtzLS7D33oQ3zhC18AYPHixTz33HPcddddRfH1mc98pnjM4YcfTnNzM6eccgqbN28eMkXR6XTidA6eprLb7YM+xHw+j6ZpWCyWQUWPJwLzms33nG5YLBY0TRvyd10JVGq/pzvDfS5JPQoa1Llrsdvt+JwByEE0G5HPcRKQ70t5Ip/L3hOLqRQkp82Cz+1C0zQCbjsd0TTRjD5uv0/5bMoT+VzKk4n8XMZ63rIQX5dffjmPPfYYf/zjH5k1a9aw7TZv3sy2bds488wzi9tM4WGz2XjrrbdoaWnBZrOxcOHCkmMPPfTQEYXd8ccfD8CmTZuGFF+CIOwfpPUYWKHerdKfA04/xCGWG9uMliAIAvS3mXcUs1ICbim0LAj7O1MqvgzD4IorruChhx5i48aNzJs3b8T2hxxyCK+99lrJtq9+9atEo1Fuv/12WlpacDgcHHvssYMs699++23mzJkz7LnNNMbm5sm1chcEobzIGjEAGjw1ANRWBQBI5qbnmkpBECaG/jbzJn2FlqXWlyDsr0yp+Lrssst44IEH+NWvfoXX66WtrQ0Av99PVVUVAOeffz4zZ85kzZo1uFyuQevBAoEAQMn2a665ho9//OMsXbqU97///WzYsIFHH32UjRs3AiqC9sADD3DGGWcQDAb5+9//zhe+8AWWLl3KEUccMfEXLghC2aJrqlD4DK8q89BQiIClDRFfgiCMnaL4cvelIvUVWpbIlyDsr0yp+LrzzjsBVTeqP3fffTcXXnghADt27NjrdUxnn302d911F2vWrOHKK6/k4IMP5he/+AUnnXQSAA6HgyeffJLvfve7xONxWlpa+OhHP8pXv/rVd31NgiBULplcDqxJAGb51LrSRo8SXzkjPmX9EgSh8jBt5vtHvgLVZuRLxJcg7K9MedrhaJjRquEYqjAwwMUXX8zFF1885L6WlhaefvrpUd9bEAQlSB54dSM7Im3M9jWx6shlOGxlsVx03NkV6SukPNOvRFezV/2vW0R8CcK7Ja8bvLi1h45oigavi+Pm1WK1TD+XXoCegsCqcWq8/Nt7ibbuwBN3YzHmS9qhIIwDlTo+Kf8e7s/oedj+HMTawdMIc04AS+UUGRYqn2898zPue+cODGuouO22VwOct+BKrllyztR1bILYFe5SP+gu3HblbjrLX3BWtaRIZNPF7YIg7B0bXm/l+kffoDWcKm5r9rtYfeZCTj9s+q237o1nWNL7az6w9mmqojpVwApgscfCBu10+PjiKe6hIFQulTw+mX6+5NOFNx6B7x4G9/wL/OIS9f93D1PbJ4gLL7wQTdNYu3ZtyfaHH364pH7Yxo0b0TSNRYsWkc/nS9oGAoFho5EAP/rRj1iyZAk1NTXU1NSwYsUKXnzxxXG9DmF8+NYzP+OezTegW0Il23VLiHs238C3nvnZ1HRsAmmN9gBg0auL22b5gsWfd4d7Jr1PgjAd2PB6K5+7/+US4QXQFk7xuftfZsPrrVPUs4nD+td7uO7pp6iJ6iXba2M65z78G579n1unqGeCUNlU+vhExFc58sYj8H/nQ2RP6fZIq9o+gQLM5XKxbt06ent7R227ZcsW7r333r06/8aNG/nkJz/JU089xfPPP18sVL179+597bIwAWRyOe575w4ABtbtNl/f9/Ydao3UNKI9psSVTesTXw6bDfLKAGhXpGtK+iUIlUxeN7j+0TcYaqGBue36R98gr4++FKFSyGUzrPjdbwAYmFRpQV235Y715LKSfigIe8N0GJ+I+JoMDAMy8bH9S0Xgt/8BIz2mNlyr2vU/LpsY+nxjWFfXnxUrVtDU1MSaNWtGbXvFFVewevVq0un0mM//P//zP1x66aUsXryYQw45hP/+7/9G13V+//vf71U/hYnlgVc3YlhDg25sJpoGhi3EA69unNR+TTQdCSW+nJq3ZLvFUGJsT7R70DGCIIzMi1t7BkW8+mMAreEUL26dPpHlvz/5IMGoPkh4mViAmnCevz/54GR2SxAqnukwPpE1X5NBNgE3zxinkxkqIra2pbjFAgSGa/7lPeCoHm7vIKxWKzfffDOrVq3iyiuvHLHo9VVXXcX999/P9773Pa6++uoxv0d/EokE2WyW2trafTpemBh2RNrGtV2l0JMMAVBlKxVfds1Dmi46YqNHhAVBKKUjOrzw2pd2lUC0dQdVY2wnCMLYmQ7jE4l8CYM4++yzWbx4MatXrx6xndvtZvXq1axZs4ZwOLxP73XttdcyY8YMVqxYsU/HCxPDbF/TuLarFEJp9XfssflKtjstHgA6E9NnZl4QJosGr2tc21UC3ubZ49pOEATFdBifSORrMrC7VQRqLGx/Dv7nX0dvd+7PlfshoOs6kWgUn9c7uCaa3b2XnVWsW7eO5cuXjxrRuuSSS/j2t7/NunXruPnmm/fqPdauXcuDDz7Ixo0bcbmmz0N3OrDqyGXc9moA3TJ0aN8wwJIPsOrIZZPet4kkkgkB4HP4S7a7rT4iOvSkQpPfKUGocI6bV0uz30VbODVkQr0GNPmV7fx0YcHSc3jFs47amD7kLLcOhHxWjl/xicnumiBUNNNhfCKRr8lA01Tq31j+zV8OvhkMXqJbPBn4Zqp2/Y+zu4c+33BJsaOwdOlSVq5cyXXXXTdiO5vNxk033cTtt9/Onj1jFJjArbfeytq1a3n88cc54ogj9qmPwsThsNk4b8GVwOBlg+br8w66siLqaewNiVwUgIArULK92q4iYeHUvkV4BWF/xmrRWH3mwiH3mU+o1WcunFb1viJZ+K9jTkZj8ApuHXXdbRd/ApvdMfhgQRCGZTqMT0R8lRsWK5y+rvBi4IOo8Pr0tZNS72vt2rU8+uijPP/88yO2O+ecc1i0aBHXX3/9mM57yy238M1vfpMNGzbwnve8Zzy6KkwA1yw5hwvmfx1NL42eWvIBLpj/9bKvo7EvJPNKfAWrAiXbzUhYJCviSxD2hdMPa+bOTx2N01Y67Gjyu7jzU0dPuzpfvfEMz9R8kNtXrCDhKn2W93gtrDn5/dQu+8wU9U4QKptrlpzD+Qd8bdD2ShmflK8s3J9ZeBZ87F7latjfbt43QwmvhWdNSjcOP/xwzj33XO64445R265du5aVK1eO2m7dunV8/etf54EHHmDu3Lm0takFkR6PB4/H8677LIwv1yw5h8292/lT7z0AuPMH8cwFPy3rGaV3Q1qPggXq3TUl22tcAYhCPBuZmo4JwjTg9MOaObhxE3/frb5Hn116AP9x+iHTKuJl0ptQFvLbDzyHaF2O6gc3AtDeUs2Pzvkuf9oS5uyE2MwLwr6y6sgV3Lf1mwB8aOYXObCmhVVHLquI8Un593B/ZeFZcMgH1RqwWDt4GtUar0mIePXnhhtu4Kc//emo7ZYvX87y5ct5/PHHR2x35513kslk+Nd/LV3Xtnr1ar7xjW+8m64KE0RXqs9eXdO0irix7Ss54gA0eYMl281IWEqPTnaXBGFa0RbpK01SU+2YlsILoDeRBSDgtmNs7jPqsWch4HEDYUKFNoIg7D1vd+1SP+SruHHFRVPbmb1k+o6ipgMWK8xbMmlvt379+kHb5s6dO6iO17JlyzCGqB/2u9/9btT32LZt2752T5gietN9hYXT01x86JY4GjBzgPhqrFZGAGk9NgW9EoTpQTav0xnre550x8ZeI7LS6I2rqFZttQO6+kpUVIVT1LjtAIQk8iUI+8zW3lYAbIZ/lJblh6z5EgRhRGK5vlnbnDZ9xVc0nUSzqMHQTF9dyb5GjxJfeU3ElyDsK53RdMkC+e7Y9BUfZtphjduBrbfvvlmVzFNrNQptJPIlCPvKjrBatuKy1IzSsvwQ8SUIwoik9VDxZ8MSR9f1qevMBLIrrNIrDUOj2Rso2WeKMV1LTHa3BGHa0BYpLaLcHZ/G4iveJ75cvaX3jfqsEmOhpIgvQdhX9sQ6APDaKq9EhYgvQRBGJGfpc/jTNJ090dDUdWYC2RVW6ZWa7sZmLV1b2eJXaYiaJUsoGZ/0vgnCdKA9PFB8TeO0w0JUq6bKijuqhFhKZRtSk1RpiJJ2KAj7TleyE4AaV90oLcsPEV+CIAxLeyyMZlEDJMNQgmR7qH0quzRh7ImqyJfVqB60r6Haj2Go2+WOcOek9ksQpgutBfE1q6YK2D/SDuvySay6qu21o17tc0bbStoIgrD3hDJqwrS+qn6Ke7L3iPgSBGFY3u7cDYChO7Hk1aJWM0I03eiIq9louza45IHFYinWO9sT6Rm0XxCE0WkvpB0umqGKlnfHM0OaN00Hegpph4FkCIBINXT71ZDLHlYTWOJ2KAj7TrywHn2mt2mKe7L3iPgSBGFYNveoOnM23Y9D8wKwJzo9xVdXQokvl8U75H6roURZa7R7yP2CIIyMGfla2KwmcjI5nVg6N5VdmjBMYeWMKEe2Hg9ka9U9xBbqKGkjCMLekzZCAMwNVJ74Eqt5QRCGZUekDU03OHKXndp4jj0ena7G6Sm+elIhAKpsXox8nsRfXyLX2Ymtvh73e47BoVWTBNrjEvkShH2hLZLCYuQIbNvAab2v0WGroSN8Al5X5bmVjYYZ+bKF2jDQ6Kk7mLx3Dr2BLVh6uyEAsXSOTE7HYZt+8+B5Pc/LHS/Tmeik3l3P0Q1HY53kOqXC9EXXdfKWMBbdYMGObsI7f118VmvW8v87E/ElCMKwuJ9/gR88lKcuur24Lfrru4hY5uA77bQp7Nn4E86EADj+nTibTllBrq2tuM/W1MR7T6jmqYOhu5BGJAjC3lH/xv9yz59+T92vdI4rbGv7y2O0fv4iTjr36int23iSyuZJZvMA9OzK88p7v0naVQMp+NtisOejHJS18LZdJ5zMUu91Tm2Hx5kntz/J2hfX0p7oWx/c6G7kS8d9iRVzVkxhz4Tpwu5IL8e/k+HCJ3Sqo19hT2G7ramJxi9fV/bjk+k33SIIwrgQefxxzrj3SYIDSnt5wgl2f/4qIo8/PjUdmyCimQjHvaXzyZ/+pUR4AeTa2/l/v9zMcW/p9Ij4EoS95pn7b+Wy3z1BMFZaqiIQ0an95o959n9unaKejT+mkcYhOSvvdB1B2hko2Z+1eDgr7mBBxjLtHA+f3P4kX9z4xRLhBdCR6OCLG7/Ik9ufnKKeCdOJnY/8kn//pT5ofJJrb6+I8YmIrzImr+f5S9tf+M2W3/CXtr+Q1/NT3SVhP8HI52m/eQ0A2oB95uv2m9dg5KfP32QiE+bCJ4apYVYwBbjwCZ1IqncSeyUIlU8um8F6x93A4PuJBTAAyx3ryWWnhxDpjWfRDFietKuL0wZctaYBGsuTdnqmkeNjXs+z9sW1GAw2UTG3rXtxnYxlhHeFkc/jvusnwOD7ifmsLvfxiYivMuXJ7U+y8hcrufh3F3PtM9dy8e8uZuUvVk7orNGFF16IpmmsXbu2ZPvDDz+M1u/hsXHjRjRNY9GiReQH/HEHAgHWr18/7Hv88pe/5D3veQ+BQIDq6moWL17MfffdN67XIbx7En99iVxb2+Abm4lhkGtrI/HXlyazWxPK3B3t1EWHuJkX0IC6KDRs2jGZ3RKEiufvTz5ITUQf9rtlAWrCef7+5IOT2a0JozeRYVbOQnVeGyy8CmiAz7DQuik0qX2bSF7ueHlQxKs/BgZtiTZe7nh5EnslTDcSf30JZ0+ooscnIr7KkKkM27tcLtatW0dv7+iz+1u2bOHee+/dq/PX1tbyla98heeff56///3vXHTRRVx00UX87ne/29cuCxNArnNstazG2q4SqI7GxtTOGQlNbEcEYZoRbR3bhMVY25U7PfEM1cawQ8MSIr3Tp9B0Z2Jsz4OxthOEoZgO4xMRX5OAYRgksokx/Yumo6x5cc2wYXsDg7UvriWajpYcl8wlhzzf3tZQWbFiBU1NTaxZs2bUtldccQWrV68mnR77w2PZsmWcffbZHHroocyfP5/Pf/7zHHHEETz77LN71U9hYrHVj61o4VjbVQLdnrGl/3S5p6c1tiBMFN7m2ePartwJJTLEtbE9e+OW6VPnrN49tufBWNsJwlBMh/GJuB1OAslckuMfOH7czteeaOeEB08YU9sXVr2A2+4e87mtVis333wzq1at4sorr2TWrFnDtr3qqqu4//77+d73vsfVV++9U5VhGPzhD3/grbfeYt26dXt9vDBxuN9zDFpDA/mOjqFnaDQNW2Mj7vccM9ldmxB0XefNlgxdXggOk3poAN1eeG3mMOvCBEEYkiNWfIIXvOsIRPUh7yc6EPZbOW7FJya7axNCTzzLLptO3gHWtDFM6qFBRDOweKfPHPjRDUfT6G6kI9Ex5ASyhkaju5GjG46egt4J0wX3e46h1+vAH81U7Phk+nzrhXHj7LPPZvHixaxevXrEdm63m9WrV7NmzRrC4fCYzx8Oh/F4PDgcDj74wQ/yve99j1NPPfXddlsYRzSrldBnzkeDQY9QU3o0fvm6iqinMRa6EzEMm876Uy2FtfBDLZCH9adayFnjk99BQahgbHYHGz/wL8PeTzRAv/JCbHbH5HduAuhNZDA0sM8qZIUMzEApvP5DVZZQcvoUWrZarHzpuC8BSmj1x3x97XHXSr0v4V2hWa3cd0rdkPcT81ld7uMTiXxNAlW2Kl5Y9cKY2r7U/hKX/v7SUdv98JQfckyjUvW6rhONRvF6vVgspXq6yla19x0G1q1bx/Lly0eNaF1yySV8+9vfZt26ddx8881jOrfX6+WVV14hFovx+9//ni9+8YsccMABLFu2bJ/6KkwM/zx0Ds98xMKljxm4M323uB4v1Fzz1bKvo7E37Air3PAXFtiZ8d11tN94E/l++eK2xkZSl36aF5NrMYwEuq4P+q4JgjA8b8/9KGtOjnL1s0/h6OfT1OO1oF01vep8mVbzDdVh6v7xMG8cci66rbq435nupda7g3ccBzM/Pn3EF8CKOSu4bdltrHlxDR2JjuL2Rncj1x53rdT5EsaF5xbkyX/Iwhd+VZqJYmtslDpfgkLTNNx295j+nTDjBBrdjYNmjYrnQqPJ3cQJM04oOa7KVjXk+bRhnJZGY+nSpaxcuZLrrrtuxHY2m42bbrqJ22+/nT179ozY1sRisXDggQeyePFi/v3f/51//dd/HdMaM2Fy2RFp48WDLby8wFvctidg4bJLrWw/8qAp7Nn4syfSA4BmVONfuZJZ37ujZP+8X/ycljM/rNpoOh3xsUd6BUGAtnCKZ2o+SFetvbjt9wc1csmKNZy46t+nsGfjT29CCSp/IkRD16vYo32GUhm2cMKfv05d/B8AhJLTx2reZMWcFdx9+t3F136Hnw0f3SDCSxgXdF0nbwmzubkwvrXbmXHrt5h9zz0c+Psny154gYivsqOcwvZr167l0Ucf5fnnnx+x3TnnnMOiRYu4/vrr9+l9dF3fK9MOYXLYE1Num02Rvm3VGTAsGnsi3VPUq4mhLabEl83wAINdknJd3QSqqjF0NXDcGZ5e1y8IE01bJAVAINIX6clpDjKGlUhyepnY9MaVoHJG1T00VdU3gYXNjoaBo3APCSWmV+TLJJLue3DEs3Esmgw3hfFhd6QXzZKjLqIychwzZuD/l3+h+vjjyjrVsD/ybShDzLB9g7uhZHuju5Hblt02abNHhx9+OOeeey533HHHqG3Xrl3LT37yE+LxkdfDrFmzhieeeIItW7bw5ptv8u1vf5v77ruPT33qU+PVbWGcMO2A68N9M7P+hI4tZ9AaK18L132hI64GQg6LSg3KtXeU7M+1twFgMZR5ze5I1yT2ThAqm2QmTziZxZ0P4e43z1afVM+Lrvj0mnwz0w7tIXUfyTlrivss+ABwhHpL2k43elI9xZ9zRo5IJjJCa0EYO2917QIgGFZrRG3NzVPZnX1C1nyVKSvmrOD9Le/n5Y6X6Ux0Uu+u5+iGoyd9oeoNN9zAT3/601HbLV++nOXLl/P444+P2C4ej3PppZeya9cuqqqqOOSQQ7j//vv5+Mc/Pl5dFsaJUKYbe9bAX5ixRtPAMKiJQVdi9DpwlUR3Ul2Py6JmqE2xZZJtVzPYVsNDjjDtsR4EQRgbZtSrRS9NTQ8mkoCqizW/fF2h9xoz8mXtLUzS2GugENyz5j0YgL1XpS5P18hXd7I0O6An1YPf6Z+i3gjTiS296j7SEHECKewivoTxxGqxcmzTsZP2fuvXrx+0be7cuYNSApctWzZk/bCxFEq+8cYbufHGG/e5j8LkEct101BY2mTxeLAGAmR37aI2VjqrOR3oTakLrbapWWlTbJmYkTCnxUMOaI9Pr+sXhImkLazE11yUGMlYwZGH2oJI6Y5Nn8hXOpcnnlGOItaewlpSS5/o0AwrOVsV9kQSZy5DGgfJTJ4qR2WkS42V3nTpBF1vqpd5/nlT1BthOrEzrJ7PjVH1nbE3N01ld/YJSTsUBGFIUnqIhpAS2faWFmyNjQDURgxC6ekV+QqnlfjyOJT4yrWpm7tj7lz1uhAJMyNj3cnQ5HZQECqYtoiKcM3IKvG1bYYaNHmTeRz5LF2x6ZN6Z0ayrBYNayG6ZdE9JW2ibnWfqc9EgemZetiTLJ2gmm4TdsLU0RpXz+f6mHptaxLxJQjCNMB0E2oMqdeOWbOwm+IrBvHc9HL7i2XV9QScAQByhchX1ZFHAH2RsGq7GjSFUtPr+gVhImkLq8hWMKEG4PGWIOlC3k0wFaYnPn3Eh3kttS4r9t4YumbBknMB4KpWhj09PnUfmU0CmJ6phwMjXyK+hPGiK6kmceqiKsJsb54xld3ZJ0R8CYIwiI54GM2SobF3iMhX1CCRn16LpxN5NQNd6wpgGAbZDpVm6DpCiS8z7dBnV+lD4Uxo8jspCBVKu+l0GFOTFlUzZ9NdMAAMJnumVdqhGcVqsaTRDIO0Q12oZtGonaEMfcJeJb5m5ZXhSGgaRr66U6aJkTJFEPEljBfhjBJfgbCKqEvaoSAI04K3u9SC1saQukU4WmZha1Tum8EoZPTolPVtIkgVridYVYMeiWAk1U29yhRfbSrt0O9S4iuenV7iUxAmktbCIMkfVt+zwOwD6fWqe0t9poPuaRT56i0UTW7RVVTLjHK5vXaqA04AYm51H2nKqryp3mkY+TLTDg8IHKBei/gSxolYrhdX2sCZUvcNW1PlGW6I+BIEYRCbe0zxpWrL2VtmYy/kVddGDXLa9BJfWUMNgpo8tcUUQ4vfj2P2bADy4TB6KkWtKwD0RcoEQRidtoiKbPkKxhu+lgOI1yghEkx30z2N1nyZka/mXGE9l08JLbffidunokAppxJkdYVaWNOx0LKZdjg/MF+9Tk2vdcLC1JE2egkWHsEWnw+rp3pqO7QPiPgSBGEQO8JtYBg0hFROtaNlFrYGM+0QDEuCXD4/lV0cV3RNpf80e4PF9V72xkYsPh9aVRWg1oHVu2sBSE+zyJ8gTCRt4SQYOoGw8luvn3sI6VplQlGf6qF7GtX5Mm3m69PqHhGtLkS+fI6i+Mo6lCCrSRbE1zSLfBmGUYx8HRg4EJDIlzA+6LqObolQFy4siahAsw0Q8SUIwhC0xjvwx8GZM8Biwd7cjL2pT3xZyLMnOj0eprl8HsOi0qJm+vrEl62xEU3TsDeodMtsezsNHlUs1YyUCYIwMrm8Tmc0TSDXgSMPOtA0ZxH5oBIg085woxD5qi2Ir3hVQXz5HVT7lfjSrWodmC8RAvoE23Qhno2T0dU1zferyJeIL2E82BXpRrPkqCvMf9oqcL0XiPgSBGEIOhOdNIXUz/amJjSHA1t9PWgaNh28Cdje2zmlfRwv9kR70DQ1izbbX19MOzTFpmljm2vvYIY3CPRFygRBGJmuWAbdgMacSmWOeiw4qtxoDeq7FEzE6IlnyOuDa0dWImYUy1eoBZhx9o98qVRLTVPCsyqqTClCyekV+TJTDKtsVczwKCc6EV/CePBW524AgiFll1qJBZZBxJcgCEPQm+6iwXQ6LKx70ux2rMHCgCkKuyLTQ3ztCqsBkKE7qXY6izW+zDRL02gk197GLF+damtJkcnlpqC3glBZmGYbcw3lUBarVbbr9kY1qVGXSKIb08fxz4ziuSLqenO2wpovnxN3IfJlN5Qgc4WVSJku125iOh3WumqpdalU7VA6hG7oU9ktYRqwNdQKQH1ElW2wV6DZBoj4KmuMfJ74Cy8SfuzXxF94EWMarbERypt4vqevxlfLrOJ2ez+7+dbCrG2lsyeirsOiuwHIdphph0p0mdecbe9gpl8NJDTNYFdkely/IEwkps38jIz6vqSDKuWuqlndV2oSaSyGPm1SD00h5TTvK7YAULrmy56vRtcs2FMpnLn0tHM7NCNfQVeQQMGkSDf0YjF7QdhXdoSV83BDrGAGJmmHwngSefxxNp2ygh0XXMCeq69mxwUXsOmUFUQef3zC3vPCCy9E0zTWrl1bsv3hhx9G07Ti640bN6JpGosWLSI/QBAGAgHWr18/pvd78MEH0TSND3/4w++268I4k9JDNIYKka9ZLcXtfbW+oD0+PcRHWyE9yKYpAwCzppe5kNfWaKYdtuO2O0FXM/e7w9Pj+gVhImktOBzWJdWA3CikG3qbWshrYDXAn47RNU0cD801X/Zedb02AoBa8+WqtqNZNDQ0om4lQmtT0aJD4nTBTDGscdVgt9jxOXwl2wVhX2mNqedzsFBg2SZph8J4EXn8cXZ//qpibSGTXHs7uz9/1YQKMJfLxbp16+jtHd0WdsuWLdx777379D7btm3j6quvZsmSJft0vDBx6LpO3tInvkoiX019ka/uafIg7Uqov3WnKb4K3ztTaJoRsGy72m7Rla3tnmkS+ROEiaStEPmqiSlnP3ONRrC6npD6ylGfDE2fyFc8i8XQsYejGIAtp+4Xbp8DzaLh9qp0qW6/EiTBVJjwNIt8mSLLTDk0/xfxJbxbulNdYBjUFMpXyJovYVgMw0BPJMb0Lx+N0n7jTWAMsfjYMACD9ptuJh+Nlh6bTA55PmOo84zAihUraGpqYs2aNaO2veKKK1i9ejXp9N7ZBOfzec4991yuv/56DjjggL06Vph4OuJhNEuWhpB6bW/pF/nqZzc/Xeq2mOKryupFT6fJh0IA2BpK0w7NiJhNU4Op9pgMJARhNNoLka9AWJnUuGeqNaTBqiDdKvhDMBmZFnbzmZxONJ3Dn46hGQZZmxOLrsSWmXLo9ivTjZDXFF8RQsnsXj+ry5n+kS8Q8SWMH6F0F94k2HNq/aA5SVpp2Ka6A/sDRjLJW0cfM04nUxGwt489btCu9iGaH/zyS2hu95hPb7Vaufnmm1m1ahVXXnkls2bNGrbtVVddxf3338/3vvc9rr766jG/xw033EBDQwOXXHIJzzzzzJiPEyaHf3buxpE1qC24qTtaBqcdBqMQzYSmoHfjTygdAqDa7ivazGtOJ9ZAAOiXdtjZiZHP49S8ZIDOxPQQn4IwkZhph/7CTHWgRVmPB6uC9Ho1wKAu1Tst0g7NYsn1KRXlM6NbdqcVh0sNt0zTjZi7UOsrFSWvG0RSOfxV9snu8oQgkS9hoojne2hWXy+s9XVYHI6p7dA+IpEvYRBnn302ixcvZvXq1SO2c7vdrF69mjVr1hAOj20h7bPPPsuPf/xjfvSjH41HV4UJYEvPHuoLH6fF68Xi9xf39U87jOcjU9G9cSeaUdfhc/gH1fgCsNUFwWqFfJ5cVzdVNjVd350U8SUIo9EeSWHRMwSiaqa6Yd5CAALOAD0e9R2rS3fTMw0iX71xlT7YYqgoX4+vz2bexPw5Uaj/1Vi4/0yn1MPhxNd0yZYQpo60EaIuYhZYrsyUQ5DI16SgVVVx8Msvjalt4q9/ZednPjtqu5b/+k/c73kPoNboRKJRfF4vFkupntaqqva+w8C6detYvnz5qBGtSy65hG9/+9usW7eOm2++ecS20WiU8847jx/96EfU1dXtU7+EiWdnpJ1G02a+ZVaJ2UrRcCMGKX16iK9YTl2H3+Ena5pt9Etl0KxWbPX15NrayHW047H56MhASJy7BGFEDMOgLZKiLteKxYCcBepmLQDAZrGRrK0C4gRTPfxjGkS+zHVrM/NKfEU8BZt5/2DxlSmYUDRmVYpBbyLD7ODYs1TKGVNkmaLLTD+UyJfwbtB1Hd0SJlgYepimWJWIiK9JQNO0Maf+VZ94IramJjUDP1QOuKZha2yk+sQT0axWtU3XseRyWNzuQeJrX1m6dCkrV67kuuuu48ILLxy2nc1m46abbuLCCy/k8ssvH/GcmzdvZtu2bZx55pnFbbquF8/z1ltvMX/+/HHpv7Dv7Im199nM93M6hL41X+40WKeJ+EjmImCBYFUNue2lZhsmtsYGcm1tZNva8Dn8kIFodnpcvyBMFOFkllRW58CMKrAc9luxWvuGHdmgD4hTlwrRPQ0MN0ybeVNQxat8WBgY+VJrvvI2Jb7q0lGAaeV4KGmHwkSwI9SFZskTNCNfMyo38iVph2WGZrXS+OXrCi+0ATvV68YvX9cnvCaQtWvX8uijj/L888+P2O6cc85h0aJFXH/99SO2O+SQQ3jttdd45ZVXiv/OOuss3v/+9/PKK6/Q0tIy4vHC5NCZ6OxzOpxd+plYPdXohYmEQDw26X2bCNKGuo6G6hqy7aU1vkzsDX2mG2bdmnh2ekT+BGGiMJ0OZ+VVQfZ47YBJyLpC0fZklO5Y5acdmjbzdWl1b0i5+gosmxSFmEWJr5qkmsQJJ6dH2qFhGMXIV9Fwo0rEl/DueadbTeLUhdX41yZph8J44jvtNLj9u7TfvKbEbt7W2Ejjl69T+yeBww8/nHPPPZc77rhj1LZr165l5cqVI7ZxuVwcdthhJdsCBVODgduFqSOU6S5GvuyzBgtiW0MD+rZt1MUyhJJxAlXVk9vBcSZbFF+1fTW+GkvTGWxNZq2vNoIz6gFI5qOT2EtBqDxMs42ZGTUYz9b5S/Zbm9QkR10iMS3EV6iwbiuQVOIra/dCfkDaYeFnq6HEly8eAqB3GkT+ACKZCDkjB/SLfDlFfAnvni29BfEVUXGjSrWZBxFfZYvvtNPwnnIKib++RK6zE1t9Pe73HDMpEa/+3HDDDfz0pz8dtd3y5ctZvnw5j09gDTJhcojlemgI9a35GohrxgwS27ZRG4VtoU4WV7j40i1xNGCGNzioxpeJvVjrq51690FAX8RMEIShMW3m65MhALTG+pL9rqYZ6v9cnmw4Si6vY7NWbkKOuebLGy8UlLb4lPjql3ZYXRBfzrwSX45MClcuTe80MdwwBZbX7sVhVdcqaYfCeLAjrJ7PddHC+KRZ1nwJE4BmtVJ9/GBL+Yli/fr1g7bNnTt3UB2vZcuWDVmT5He/+924vKcwtWT0nr41X0Okgtr7mW7sDHWyuHnu5HVunElk02gW9ffd4q8j21GIfDUNXPPVl3bY5FWpUjkRX4IwIn0FltV3xdE8o2R/jb+RqAu8KVVsuCeRocHrmvR+jhfmuq2qiBIZFs1MO+wTX1Ve9bNddxJzOfGk0tSmItMm7XBgymH/n8PpMDk9h80iQ09h72mLd2LRDWpiKrJayWmHlTvFJAjCuKPrOt5EGEcODItlyLC+uR6qNmKwJ9o52V0cV3aGugEwDI3maj+5gvgabLhREF9tbTR71CyubolPYk8FofJoMwssRxIAeGbNKdkfdAXpKRRarkuG6a5wx8PeeAaLnscZVWmHTkOJr2p/35ovh8uG3akyWLr8fYWWp4vhxkCzDVBlBTTUmnWzrqIg7C1dyQ5qYmAxALsdW33lumaL+BIEoUhbLERjRM3A2pqb0OyDi36a9q61MWiPV3Yaye5IFwCa7sISDkM+DxYLtgGlEMxoX7ajg1k+tU+zZIimk5PbYUGoIMzIVyCihEXNnINK9gergvR4C7W+UuFi2l6l0pPIEkjH0AyDvKbhyKqU7P6Rr/6vewviqzYVmXZph/3Fl9ViFbt54V0TznT32cw3NKCNk7v3VFC5PRcEYdx5q2t30enQOXv2kG1Mu/lg1KAj3j1pfZsIWmOF9CDDUzTbsNXVodlK02LMyJeRTNKIFcNQA8Zd4cq+fkGYSNrCKRx6Al9C3VMaCwWWTfpHvoLJMF0VbroRSmQIptTosMvvQcMCGri8pZNYpulGtFpFxmpTEcLTLPLVP+0QoMYp4kt4d8TzvcUCy7bCei9dN9j9Vi9v/6WN3W/1outDlGgqQyTxVhCEIlt7Wvts5odwOoS+9VC1UehN905a3yaC9qgaCNi1anLtQ5ttAFhcLqx+P/lwGKOzE02vAmuCXeEuDm0YbEoiCIKKfDVkdwKQsoM/OLNkv4p8qZ/rUqHKj3zFMywsiK/uQlSrymPHOsBExIx8xav60g5fmsaRL1B285vDm+lJivgS9o20EeqLfDXPYPPfOnjmp+8QD/VN2lQHnCz5+ALmH9UwzFnKA4l8CYJQZEekjcaCnrIPU3fNFCe+OMQSlf0g7Uqoi3VavMPW+DLpb7phNTwA7IlK5EsQhiKVzRNKZGlItwIQrnFgGZAmVOuqpbuQdhhM9Vb0mq9sXieaylFbEF8Rz+AaXybuwhqwtKN/2mHlXnt/TMONgeLLjHxV+oSdMDXouo5uCRcjX+2eg9nwn6+XCC+AeCjNhv98nc1/65iKbo4ZEV+CIBTZE2sv2swPLLBsYq2pIW+1YAGsvV2T2Lvxp6ew+Ntt9ZFrU+JrYI0vE1uTKb7asGtKfHXEZSAhCENhmm00Z5QpT6p2cEkKh9VBskYVXq5PheiOV27aoVnjK1gosByrVsKqf40vEzPylbP3ia9oKkcur09GVyeUYSNfhdfdSZmwEvaeHaEuNEueuggYaLzSPfSyCJNn/++dsk5BFPElCEKRrkTXiAWWATSLhVSgkFITruzIVygVAqDa7iXXYUa+BqcdQj/TjfZ2nBYlvszImSAIpZhmGzOyIQBy9YEh2xl1KiISTEYqOvIVKkSumrPKVj/hKogv3/Diy7D0pR0C08Juvii+qganHfbfLwh7w1vduwEIRjRCgQNJZkaueRvrTdP6TmgSerZviPgSBKFIPN5OTcFB3TFEgWWTfFANmDzR6GR0a8KIZsMA+B0Bsu1D1/gysRUiYrn2Dtw2NWjqKYg3QRBKaY+YBZaVsLA0DZ3Oa2lQhZf96RThcOWWbzDXq9Vn1D0xax9dfFnpi3wB08LxsGi44Sw13Kh1KvFlpiUKwt6wpVelL9dF+tJ1RyMeKd9IuogvQRCKuLuUAMm4lcHEcFgLUaCaaGJS+jVRxLNqoFRTFSDXVjDcaBhOfKnBY7a9Da9d/W7CmdDEd1IQKpDWQtphbaHAsmvG0JM51cEGMgXrr3xnea/TGAlTOJlRLN02vPgy63459EKbXJqqbIpwsnIjfwC6oRfreAWrgiX7JPIlvBt2hduwZw18SR1nJjKmY6qHWG9ZLoj4KmMq1UJTqFxqQmpWMjvMLLWJu1m5ltXEM+Ty+Qnv10SR1NVNvNbpI1sssDz0tdv7GW74CjNv0TE+BARhf8Nc81VTiIB5Z80dsl2wqo4elcWL1lW5RdtNwwx/QkXTNW30NV+urIeEQxmO1Kaj9MYrO/IVTofRDbVuze8snbwz13yJ4YawL7TFOwgWEm1qMnuoDowsrDw1TpoXBCa+Y/uIiK8yZfPfOrj3y8/x8Hf+xhM/foOHv/M37v3ycxPq4HLhhReiaRpr164t2f7www+jaVrx9caNG9E0jUWLFpEfMPAOBAKsX79+2PdYv349mqaV/HO5XON6HcK+oes6dYW0n+Fs5k1qWuYCqtZXJde6yuhqVn6GxY2RUFE8+zBrvopph21txRo28ZyIL0EYirZwCgydmkLR9uDcQ4ZsF6wK0l3IInJHekjnKnMypyeewaLncSfUPcFmqIsaava9ymsHDSxY6fIrI5Lp4HhoRrX8Tj92S2lts2KRZbGaF/aBrmRn0enQ0dTIko8vGLH9SR9bgMWijdhmKhHxVYZs/lvHlFloulwu1q1bR2/v6LNTW7Zs4d57793r9/D5fLS2thb/bd++fV+6Kowze6IhmsI5AAIHHDhi2+pZKvJVGzXYFqrcVKG8psTmzJS6SVt8Pixu95Bt7YWIWD4UosGupurTemWveROEiaItksKT76WqoCca5y4csl2wKkiPR33/6pLhio3+hBIZatIxNMMgr4ErN3zky2K1UOVR4qTH36/QcoUbbgzndAiqoDZANBslk69skSlMPuFMd1+Nr6Ym5h/VwPLzB0/oeGqcnP7Zw8q+zpcUWZ4EDMMglxmbhayuGzzz07dHbPPMT99h1iG1RVWv6zq5TJ5sOo/FUpqaaHNYSqJWo7FixQo2bdrEmjVruOWWW0Zse8UVV7B69WpWrVqF0zn23FpN02hqGtrOW5g63uneXXQ69M47YMS2piNgbQx2RyvXbl7X4mhAY0qJzuGiXgAWvx/N6cRIp5mRVvNWZuRMEIRS2iMpGrLKoSxWpVHtHTwgBzVQ31yIfAVTYbpiaZr8lZcN0RPPEkyplMMerx1HvgoYes2X2u4kGc0S8fiA3QST4WkT+RpotgHgdXixalbyRp7eVC+N1cPfawVhILF8D3UF8WWb0QxATbOKGruq7Sz5xAKqfSrVsJwjXiYiviaBXEbnvz7/9LidLx5K899f+OOY2n7m9pOxO0e25OyP1Wrl5ptvZtWqVVx55ZXMmjW8491VV13F/fffz/e+9z2uvvrqMb9HLBZjzpw56LrO0Ucfzc0338yiRYvGfLwwMWzp2cNss8bXCE6H0GdKURuFv4Qrc51GbyKOZlGiKxhPk2B4m3lQkwa2pkay23fQnFSTKTlNxJcgDCSvG3RE07wnrUxsIrXDT84FXWbky6AuGaY7XpkCJJTIUJNSkfBuvxcAq82Co2roYZbb76B7N8SrCpGvdLTi3Q5N8TXQbAPAolmocdXQleyiJ9Uj4kvYKzJGqJh2aG9S4ivSlQSgptnNQcdW1oS+pB0Kgzj77LNZvHgxq1evHrGd2+1m9erVrFmzhnA4PKZzH3zwwfzkJz/hV7/6Fffffz+6rnPCCSewa9eu8ei68C7Y0dtKQ0j9bJ89cgFDe8Ee2p6HSOfuCe7ZxLArotaqGYaFqlBhncYwZhsm9oLobEgq0WZoCXS98gujCsJ40hVLk9cNmtJqYiYd9AzbNlgVpMdb+DkVpqdCCy33JDLFyFfIFwBU1Gu4zBMzIpZ09dnNh6eJ+Boq7bD/dnE8FPYGXdfRrZG+tMNmJbQincrMx19fNVVd22ck8jUJ2BwWPnP7yWNqu+edEI99/9VR2/3L5Ucyo+Dkous60WgEr9eHxVKqp22OfdPX69atY/ny5aNGtC655BK+/e1vs27dOm6++eZRz/u+972P973vfcXXJ5xwAoceeij/+Z//yTe/+c196qswPkTatuHIQ15TOdUjoTkcRKvteONZcm2VKb52R5X40nQ3+Q41SLQ3jnzdtsLvJRBLghs0S57uRIx6z9jqjgjC/oBpM9+cUWJErx96MA4q8tXtVQIlmAyzqUILLYcSWQ4r2MxH3V6sDL3ey8QUX7lCPbDpkHZo1vAyzTUGUjTdEPEl7AXbQp1oWp5gtBD5alaRr3Ah8uWrqzzxJZGvSUDTNOxO65j+tSysHZOFZsvC2pLjbI6hz7c36736s3TpUlauXMl11103YjubzcZNN93E7bffzp49e/b6fex2O0cddRSbNm3ap34K48ienQCEA9VottHnZaI+lW+tdVdm2mFbTIkvq+Hpq/E1Qtoh9JluOHt6MXSVzrujQtMuBWGiMG3m65MqDc/WPPykhtvuJlGj1ngFUxG6o6mJ7+AE0BPvSztMFFIJh1vvBX21vnRrIfI1jdIOJfIljCdvd+0Gw6CukGBlM9MOO0V8CeOExaKVjYXm2rVrefTRR3n++edHbHfOOeewaNEirr/++r1+j3w+z2uvvUZzYSZDmDpcne0AxOsCY2qfDKhBg723Mh+knfEQAA7NM2qNLxNzrVuuoxPNUOJzT6Qyr18QJor2Qm2vYFS5iVbNGLl0hS1Yh66BzdBJtFfeZEYurxNO9hluZBym0+HwE6mmMNPon3ZY2ZGvouHGMJEv0/HQjJAJwljYGmrDnYaqwtxEMe2wEPmqxLRDEV9lyPyjGjj9s4cNioBNtoXm4Ycfzrnnnssdd9wxatu1a9fyk5/8hHg8PmK7G264gccff5wtW7bw8ssv86lPfYrt27fz6U9/ery6Lewjvu4QALkRZqn7kwuqB2x1uDJrXXUVBgBOq4dcuxKeo6Vb2poK4qutDVtBfLXFRHwJQn/MtMOaqFq/5W+ZP2L7gCdISH2dyBai0JWEaREfLKQd5mxqEdtIkS9zn90otM2lSYYru3RF0XDDNdhwAyTtUNg3doZbi+u9rIEAlqoq8lmdWKEcUyVGvmTNV5ky/6gG5h1ZT+s7IeKR9JRZaN5www389Kc/HbXd8uXLWb58OY8//viI7Xp7e/m3f/s32traqKmp4ZhjjuG5555j4cKha8AIk0cwpISzc9bIZhsm1nolRHzRkQV3udKbCgHgMzzke9RgYPS0Q7U/29GBwxIgB3TEK7fItCBMBO2RFJqRozaiCibXzzt0xPZBlzLdqI2B0VV5kS9zrVYwXRBPltHTDs31YFVZH2mnBWdaxx0LkcrmcdnH7lBcTkjaoTARtMU7ik6HtkKWVLQnBQbYnFZVtLzCEPFVxlgsGjMPHjp8PxGsX79+0La5c+eSTpe6Ty1btgzDMAa1/d3vfjfqe3znO9/hO9/5zj73UZgYdF2nIaw+58ABB43pGGfzDNU+WpnuZJGMmkqbkS6k/zgcWAOBEY8xxVmus5MqZpEAupNjc/oUhP2FtnCKmlw7Nh10DRpmDy6G2p9glTLdOLDVwNpTeXUDexNZrHoeX0qVnrAZhbTDMUS+nHk3vf4qmjri1CbDhJPZihRfOT1HOK3uhaMZbkjaobA3dCe7mN+vwDJAuLDey1/n2mdvg6lExJcgCOyJhmgs1PiavfDwkn16Lkfrn/5EvCtMdZ2f5hNPxGKzUTN7HgC10dyk93c8iGXV3bw5oQY6tsbG4k1c140ho862ujqwWCCXoyHtortKBhKCMJC2SIqGjDJgCnst2B19RZOH+m6ZkS+Aqt7KiyT3xDPUpKNYMMhr4MgXUglNt0M9D9ufg1g7eBphzgk4qmxYbKDnoMvnoakjTjAVoTeRodFXeUWmQ+kQoOp5+R1+8rrBi1t76IimaPC6OG5ebTEdsTtVeZ+xMHWEMl2DnA4jFex0CCK+BEEA3tm5iaaE+rl2QV/ka/Mjv+aZx1PEczWAB8hT/bNfseQ0F03zDiQG1MYMehMxatzD1/IpRxK5CGjQVDBXM802Nv+tg2d++g7xUF9ErzrgZMnHFzD/qAZsdXXkOjqYkbLxZhXF2V5BEMAwDNrCKY5NFwx8avsGR8N9t7wnzeAdryq07Iv3kszkqXJUTvQnlMhQW1jv1esBd1ZFvqr9TnjjEdhwLUT6uQH7ZqCdvg6Xt4ZEb5aearXgrTYVoTdemY6H3UklqALOAE+80cH1j75RXPsH0Ox3cdmpKh1TJqyEvSGe7+1zOiysSa9km3kQww1BEIA9b/8DgKjLgtWrZm03P/JrNvzGRTwXKGkbz/nZ8BsXybe3A1Cdhm2tOya1v+NBSlcpQg1xtS7F3tjE5r91sOE/Xy8ZHALEQ2k2/OfrbP5bR7HWV3NC3T5juco0HBGEiSCSzJHM5qlPqfTBTFAJkZG+W7HH/GRdRwJQlwzTXWGFlnvi2aL46va5sRpqXtu963H4v/NLhRdApBX+73w8DjWAjLrVPbc2FSGcrEzHw960ElR2vHzu/pdLhBeoVNSv/VI9MxK5BMlcctL7KFQmGaO3L/I1wGa+Ep0OQcSXIAhAfJuqs9ZVcNjUczmeedx8eA7Mp7YABs8/nSfuUPtat7w9OR0dR7KGEl+1haKu1oZGnvnpOyMe8+z/vYOtQUXIGhLqYZAQ8SUIRdoKNvNNZkS4IYiuG6N8tzQaMh/FQCOYCtMTrywBEkpkik6HvT4V3XG6bVifvBYYvD7a3FYdU5NeSVeh0HIqUrG1vnqSykSjK+wY/op1JxgqoinRL2Es5PJ5dGu06HZonzE90g5FfE0gQ5lSCHuPrutT3YVpT37PLgB6a1XqYOuf/lRINRxuIauFWK6WPY0qRTG8e/sk9HJ8yWvKpdEXVTfxXvfsQbPyA4n1pgkFVB2+YFytdUvplW0PLQjjiSm+GpJqcsM+Y4Za4zXKd8ul1xAKHKgiXxVm4tMT70s7jFYXzDaqcoMjXiUYuPVWALKOvlpfvRVa68uMfKXSww+GDTT0nHrGiPgSxsL2UCcWctT1M9wwDINwl7rP+Ooqb30kyJqvCcFut6NpGp2dndTX10+4E4uu62QyGVKpFBbL9NHThmGQyWTo7OzEYrHgcAzvHCW8O5yFOlfxoLICjneFUWu8RibuUe5V6YJ4qxR03cCwJNAAd0jd1TNVY3MWzXjrsQE1MXXzzxmVabUvCBNBW1hNZgSjahFp9cw5xCNjE1Nph4+afIatnb1w6MhlH8qJ3kSGRQXxFXf78QFuVxbyIx/ntioBkreqaFltKsKWCo18mWu+jFz1iO2MfDXYw2K6IYyJt7p244uDTQc0DVtDA8lollw6Dxr4gpUZ+RLxNQFYrVZmzZrFrl272LZt24S/n2EYJJNJqqqqKtJyczTcbjezZ8+eVsKy3PD2qBShXGE9U3Wdn1FHDkDapQSI3tkxYX2bCML5NJqmIqq27l7ygKc5AMRGPdbT4EMHPGEluvLa6McIwv5CW6FkRW1UiYjA7PlU+5xjOlZHCZjE7lZgZHv6cqI3kS2mHaacPnzZgpX8KMEdtyUEgGbpn3ZYmZEvs3aXkR950s4oRL6k1pcwFraFWotRL1tDA5rdTmSnGq94Ak6s9socF4r4miA8Hg8LFiwgm534WaxsNssf//hHli5dit1eecXmRsJqtWKz2aalqCwnanvVLLWjZQ4AzSeeSPXPfkU852fo7GQdjy1ErLpQ16ansmYxe7IFa8e8jXyXMgaYtXgW1c9sHTE9ylPjZMYh1ewCXIWImWFJksvnsVkrx51NECaKtkgSm57CH1OTGw3zFhFsDlAdcI743Uo4w2QNtfY01dY2KX0dL3r7pR3mbF7IgnvGTMjPUOYaQ66C0nB7LBABm64MN6ryGWK9lbmG1Ewj9NkDdDPcFYPL4iOHpB0KY2NXuL1YYHlgja9KXe8FIr4mFKvVinUSBmRWq5VcLofL5Zp24kuYeAxdpz6sZltr5qk1XBabjSWnudjwG2X/XLr2Swc0TjqtikeeVal6VaHKGjCE8urmHYi5IdcDmoajoZ4lH7ex4T9fH/a4kz62AGeNEm5aZxcYBpoGrdEQLYHgpPRdEMqZtnCKutweLEDGCsHmA7BYNJZ8fMGI362th73ArLdhTifk2ysrkt7bz2reKKQQuv1OWLROuR0OQt1Pq086D/4PXDkfaZcdZyqL0dk5Wd0eV8xI1sePPpQf/lpdYX8BZj5BTpg7hz92/FUiX8KYaI2302BGvgbW+KpQp0MQww1B2O/JtLZiz0POArMPOqy4ff5ZH+T0M1LYKE2D8dhCnH5GivlnfRCtXjn/ecKJSe3zu8UUX/UxlQ5lrQui2e3MP6qB0z97GNX+0vWF1QEnp3/2MFXnq1GtRTGSSapS6vid4cocMAnCeNMWSdOQVkYTEb+tmC5ufrcs1tIsBk+N+m4xL0K3yr5D664c8ZXXDaLxFDVplQVgQUWxqn0OWHgWfOxecAxIxfPNgI/di3vxCkDVBYv61D1H6+mavM6PI6bhxvKDDuDOTx1Nvbc01bTJ7+LOTx3N0bNaAEk7FMZGd2r4Asv+CjXbABFfgrDf0/rWmwB0+uGgxpaSffPP+iBeR196SI11B+fddibzz/ogAI5GdTMMREtrupQ7kZwSiw0xFfy3NzYV980/qoEzLjuypP0Zlx7B/KOU0LRUVWHxq9nturAaYOyJVFbapSBMFG3hJPVpJZ4SQXfJvgMW16P1G3UcdHwj5910AvOPaiBYFaSnoFEcFSRAIsksgZRyPM1bwJEvuB2a69wWngVzTuo7oLoBrnoNFp6F26sEl9WwEfapi7f2VqYoMa3ma121nH5YMz++4NjiPosGT1/zfk4/rJlalzJ1EvEljIVQprvP6bBQYDliOh1K5GvfWLNmDcceeyxer5eGhgY+/OEP89Zbb435+AcffBBN0/jwhz88aN+bb77JWWedhd/vp7q6mmOPPZYdO/oKwaZSKS677DKCwSAej4ePfvSjtBcc3wRhf2LPW6rWTLvfit9VOljKJ5OEM3XF11nDhSXeWnztmzVX/R/PY0zC+sbxIq4r8dUYV2nBZjTLJBkpjfbFekrFpb1Q66s+pgZP7XEZSAhCKpunN5GloeBkl60LlOyPhzLks33JaBaLhsWiImFBV5Aer/q5Klw536eefimHEY8Fd7YgvvpHz3u39P2c6AJDrYez2i1YC5P3Ib86zhXprrgyNZl8hmhWCVBTXHX1K5StGxSNRER8CXtDIt9DsLDmy9Zkiq/KX/M1peLr6aef5rLLLuPPf/4zTzzxBNlsltNOO414fHTr5m3btnH11VezZMmSQfs2b97MSSedxCGHHMLGjRv5+9//zte+9jVcrr4Q5Re+8AUeffRRfvazn/H000+zZ88ePvKRj4zr9QlCJRDdthmALv/gEH7vW/9Ex4ZWcD5M6LUYXZuL++tnziVnUTeSXFflzFbHDSW+6uPqFmgfIL5ivaViy1zga2KKNTNy1pmQxeOC0FGwlG9IKjGiNdaV7A93lKYnx3r7BujBqiA9KmMPT6y3YgRIbzxDsHC9XR4LVQU3P3chjRA9Dz1b+w4wdAj1TQQ7vGoCKORWFu2BRIR4ZnSn2XLCNM+waTZ8hZplHZHSe2hrWL2ucdWUHCMII5ExQv0iXzPIZfPECsY9/goWX1NquLFhw4aS1+vXr6ehoYGXXnqJpUuXDntcPp/n3HPP5frrr+eZZ54hFAqV7P/KV77CGWecwS233FLcNn/+/OLP4XCYH//4xzzwwAMsX74cgLvvvptDDz2UP//5z7z3ve8d9J7pdJp0uu9BEYmov4ZsNjspjoYjYb7/VPdDKKVSPpf8LlWjq6fGO6ivHW9tBQI0eNroiDWjYyO2YxOuuer7OdNbR7cXGsIQ3b4db13dwNOXHdlslpRRqEUUU4Mcrb6u5NojPaViK9QRL9lvbagH+sRbZ7y77D/ncqdSvi/7G3vzuezsUdGPhqSaQLU3zyg5rrtV7be7rGRTeaLdqeJ+v91fjHwFEyHC8RTVzvL3BOuKJKlNq/FAj1epR82iYXUUfme927DrWQyrEwKz0brfIde1GcM3G4Aqr51kZ56wQw0ka1MROsMJnDWjDyzL5TvTEVNppgFXgFxOFZ9vDZXeQ3f3xFjUVI3PpsRZT6qHTCYzLZ2My+VzqXRy+TwWwgTMai51dfS2xcAAu9OK1bl3v+PJ+FzGeu6yurOFw8q7v7a2dsR2N9xwAw0NDVxyySU888wzJft0XefXv/41//Ef/8HKlSv529/+xrx587juuuuK6YkvvfQS2WyWFStWFI875JBDmD17Ns8///yQ4mvNmjVcf/31g7Y//vjjuN3uQdungieeeGKquyAMQbl/Lq49Ko2wy1PNb37zm5J9tte3AwGcjh4ctgDpXDXv/O1v7IypyE8mn8fmUeJr428fw1ohqbsp1MDA06MGif9obSXa79p7X3MCDqwunXzKwuY3dxL5zdvF/cFwhCDg71WpNFtatw363Qn7Rrl/X/ZXxvK5vNSlAVaCUfX92hNPl3wvQv8sfK+8abIpG+HuBL/+9W/QNNic3VyMfAUycX75q98Q9JTVEGVI/tyhUVuIfIW9PmyAxZ7ntxt+C0B95O+cAETtdcSzHpqBfzz7a7b9U/2O4kk74CJuV+OIYCrCr594ipbRa9wXmervzDvZdwCwpW3Fz/svWyz0T676w/Mvk9tmkDHUPTOdT/Pwbx7GqY2tBlwlMtWfS6XTlolRG9OxALrVyuN/fp5klx1wgyPDb3/7230670R+LonE2MzHyubOpus6V111FSeeeCKHHXbYsO2effZZfvzjH/PKK68Mub+jo4NYLMbatWu58cYbWbduHRs2bOAjH/kITz31FCeffDJtbW04HA4CgUDJsY2NjbQNU1/kuuuu44tf/GLxdSQSoaWlhdNOOw2fz7fX1zueZLNZnnjiCU499VSxmi8jKuVzeeX6rwNgaZnLGWecUbLvt8/fD8Ccg5tIvmOlswv8tmoO79fuvntWAzmaPW7eM+D4ciSbzfLN++4FoC6t1l4cc9ppuI8/vtjmt9teJ04v8w5rZNNfO3FZvJxxxvuL+8OJBJ2//z1N6ULaosc66Hcn7B2V8n3Z39ibz2X3s1vhnXcIRlX04/hTzuDAo08p7v/drjeI0c3ikxbw54e2gq5xysmn4vLY2RTaxN2//gkZKzjycPgBC1j8nkMn9NrGg93PbiWT+gUAMbePAFDT4OOMM04GwPKXPbAZPLOPpNrfAn/5G4fN9LDwFHW/eD61mdda95AvRIRqUxHqjz6ekw4cvXRFuXxnfr311/A8zGmYwxnL1XU9+j9/g/ZOXHYLqaxOsGU+Z5ymSpnc8tNbSOVTHHvysczyzJqyfk8U5fK5VDob3vkbwYINhHPGDM74l3/h9af38NxLm5k5r4HTzli4V+ebjM/FzIobjbIRX5dddhmvv/46zz777LBtotEo5513Hj/60Y+oGya9SdfVYOpDH/oQX/jCFwBYvHgxzz33HHfddRcnn3zyPvXP6XTidA6eobHb7WXz5Sqnvgh9lPPnko/F8RQWQttnzh3Uz56ocvVrOGgOu7qSdHYZJHoTJe16PE4gR6p9T9le50Bympqd8oRUPoNrxsySvicKhhuzDq5l0187iXansFptRXMA14wZANTEVLt4Plox117ulPP3ZX9mLJ9LZyyLKx/Bk1LrtWYuWFxyTKSwdrK+xUeVz0EykiEVzeOtcdPobQRNo8cLTSFI7mnHbj9iwq5nvAin8swspB2mXD7IqNIUxesOqfVelroDwTcTAGt4O9bCfo+ZXmjpE1+70vm9+g5M9XcmnFVZS8GqYLEfnXGVfrVohp+XtvfSGcsW99W6atkT30MkF5nW3/Wp/lwqnR2RdurCfTbzdrudeI965gYa3Pv8u53Iz2Ws5y0Lq/nLL7+cxx57jKeeeopZs4afBdm8eTPbtm3jzDPPxGazYbPZuPfee3nkkUew2Wxs3ryZuro6bDYbCxeWKuJDDz206HbY1NREJpMZtFasvb2dpqYmBGF/IbtbrfeKVEFdQ6nNfLK9nXg+AEDtwkPw1CshFotZIN+X1xz2qsFDvqOCavNYElSlDWzpgvBsbCjZbxoBNM7zYbFq6HmDeKhvzadpuOGNqMFkMhedjG4LQlnTHknRmN0NQMIJvtq+56muG4QLLmWBBjfeGjWZGS04iQacAayatZh6mNjTSiUQimeLaYdZu2kz38/psKdgUBQ8EGrmqp97txV3V/tMu3l1bFU+Q7QnNJFdHndM8wzTyRD6DDeOmKWeG63hvjVgZjsx3RBGYmekjWDh0WrazIengdMhTLH4MgyDyy+/nIceeog//OEPzJs3b8T2hxxyCK+99hqvvPJK8d9ZZ53F+9//fl555RVaWlpwOBwce+yxgyzr3377bebMmQPAMcccg91u5/e//31x/1tvvcWOHTt43/veN/4XKghlSqYwIdERgLmB5pJ93W+q+l8+ezcOf4DqBvXAjOdroHd7sV28YJFs7aqcWleGJUFt4aZu8XqxVFcX9+UyedIJlTblrXXhDSoXyEg/x0NTfLliCWw5g7Qh4ksQWsMp6jNKNEUDpYXKYz0p9JyBxabhqXXhqVXfK3Oiw6JZqHHVFE03Mm2VsX60J5EhmFKRH92qhEaJ+Oo2xdd8qC2McXq3Q8HN0awH5sr5SDnVccnWypnIgj7beFNU6bpBZ1R9rkfOCgDQHumbvDIdD8VuXhiJ9ngndabN/IACy5Vc4wumOO3wsssu44EHHuBXv/oVXq+3uN7K7/dTVaV+seeffz4zZ85kzZo1uFyuQevBzHVb/bdfc801fPzjH2fp0qW8//3vZ8OGDTz66KNs3LixeP5LLrmEL37xi9TW1uLz+bjiiit43/veN6TZRjmT1/P8tf2vvJp5lYb2Bo6bcRxWi3WquyVUCJkdOwFoD2gsrp1Rsq97SxvQQNCnhIWnRg2W4notdG+CugMBSAXUg9QVGluu81STymbQrClqo4Wb+sCoVyHCZbODY9Ov8FcHCaNm3GYerK7VGgigORwYmQw1MejyxJgOZHI5Hnh1Izsibcz2NbHqyGU4bGWTnS6UOe3hFItTnQAkg6WOEaGCzby/rgrL9mfx5KOAi1h33wL1WlctPV4luozOyhAgkWiCQEYZ92iaCttV+wtLFHIZCBUmqmrnQ1VA/ZyOQLIX3LXFemDurI+4rwpXZ4ZMBWURwGDx1ZPIYOTynNzbTs3TeU7u6uAl6wwMw0DTNKn1JYyJrlRnn818UzOGYRTFVyXbzMMUi68777wTgGXLlpVsv/vuu7nwwgsB2LFjBxbL3gXozj77bO666y7WrFnDlVdeycEHH8wvfvELTjqpr8r8d77zHSwWCx/96EdJp9OsXLmSH/7wh+/qeiabJ7c/ydoX19KeUA+rn/3+ZzS6G/nScV9ixZwVoxwtCNC7RblUtdfAgroB4muPGhQFG9RtojqgBhSxfLAvlQbIB5XtenU4UXy4ljO7o4UUGTOdobE01Tj+ylNAFdX6HrRfXoYv/BngA0Te/DucqH5HmqZha2wku3MntVHo9I3N4aic+dYzP+O+d+7AsIaK2257NcB5C67kmiXnTF3HhIogrxu0R9PUJ9WAOl9fU7I/3FFIOYw+D/d8A0/8LOAiYi88DAsPgYVnEXQF6fZqgIG1uzLqBpr1DfMWDUd+QNphaLuq62WvBm8TaBp4myHaqmp/9RNfVTkPEZ+bYGcYo6tzSq5lXzHTB82I1ut3P8G1nVmyzgN4/Z9wnM3DiXtCvPyjxznmMytFfAljIpzpLhZYtjc3kYhkyGV00ChmpFQqUyq+xlJE0YxWDcf69euH3H7xxRdz8cUXD3ucy+XiBz/4AT/4wQ9G7UM58uT2J/nixi9iUPo77Eh08MWNX+S2ZbeJABNGJbZtKy6g3efE7yotmdDdoxaOBueoB6XHFF96EKPrFUyJZatX6QD2vE4+FMJWUzroKjf2RNQDPxi2AZliCiEAbzxC7Pf/DVyFx6rSKH02NbkRef0FeCMDC88CVGHm7M6dBKMGWFIksmnc9sq0Tf7WMz/jns03gAX6S2fdElLbQQSYMCLdsTR53aC+sP7J2lRauDz0z9cBB35DGVB4rUq0xFLV8H/nw8fuJVgVpLOw5svRWxniy9Kj7hMxn52q7ADxVUw5PEAJL1DrvqKt0LsVZh2Dy20HiwG6RiTgA1qxVIjwNOlOqd9BrauW1+9+gn+8XgWO0shE1uHnzy+B8+4nqD1WxJcwOol8L8FC5MvW3Exvl1pH6KlxYrWVhWXFPlPZvd9Pyet51r64dpDwAorb1r24jryen+yuCRWGvnsPAN3+0nIJei5HT1JZHQcPPgDoi3zljCoyHTuKbWu89YQLui1XAXW+WmOFgUJUpecW0w71PGy4lnheDQyqLaqd36rSocP5RtjwJdWOvnVfNYWHw85Q5ax5608ml+O+d+4A+saHJubr+96+g0yheKogDEVbwWChvlDnxjWzn3mWnif0toqyB2zqnuOxKIERzRecizd8iaCrtrjmqzpa/mYMum5gDykB0eu14M4q5WhGs+jepP6vnd93UI257msboAoyW9zKpTnqUcfbKuxeYoqoGqufPz9bSMEe5mby52dj1NgCgBhuCCOjZXrxqtsK9ubmaZNyCCK+KpKXO14uphoOhYFBW6KNlztensReCZWGkc/j7FQP+fCAwuaRzZvIGS6sZPAvULVZ7E4rzkKkP94ZKrZtrK4tOpRlh6mTV060x9RAoS6mBgPFtMPtz0FkDzG9EOkzI1/WQuQr1wiR3aodYCvM7NdFlIjbE62sAZPJA69uxLCGBo2VTDQNDFuIB17dOKn9EiqL1rAaJdXF1JpJ36wD+nZuf45wWkXEA9aC+CpEvuJ6LbqhQWQ3wWS0eC/xxXoxCqVjypVIKktNUplt9Hjs2HU1QVXlLYiv/k6HJkM4Hto96ssXdal1cq5I5YiSZC5JMlcoGL1xO2m7f7DwMtE00nY/7j8rgSaRL2E4cvk8NXE1s2m4q7B6vdPGbANEfFUknYmx5YOPtZ2wf5Jrb8ea18lZIFdTuu6p+y01aKh1d2HpZ7hgRr/i4Sxk1Y1whq++OFuday//heJdyRAAwdgAw42YElnxvIr4mZEvU3ylDD8ZvarYzl6IfNXGKlt87YiMTTCPtZ2wf9IeSYGhE4yoCGndnIOL+/LhdiJ59X3x25QbotsSwkIOAysJXQmzWl0nVA06YDN0ct3l/Z3qTWQJptQAsbNahf9tTgsOV+Ge2d/p0GQI8eXyqhTvuEPNblVHQxPV5XHHjF45LA4yXclRWiusYfW86EmK+BKGZmtvB3VRlWXiKNTVNB2HK91mHkR8VST17vpxbSfsn2R2qhpfHX6oqSr9W+nergY9wZpMyXZPrbJkj+Vr1YJxYJa3vjhbXQlphz2pEAA1UTVItJu1/TxqcBjTlfjyWNXAwGFJ4tLU7HY431hsZ2tQ/wcLWTbt0cocSMz2ja224VjbCfsnreEU3nw3zkJ2auO8RcV90XwDBlZsWopqi/qeWDS9+LOZehj0ziJv1QhVq6FJaOeeSbyCvacnnqE2pZx7om6Vul10OoR+4mvkyJfHr0RX0qr+9ydC5PXR18SXA/3NNjyN/jEdU9OsPu+edM+Y1v4L+x9vd+8uOh06mpX4CkvaoTCVHN1wNI3uRjSGDu1raDS5mzi64ehJ7plQSWR3KZv5joBGQ3Wp3Xp3uxpBBZtLb3LFyJdeW0ypmVvbUHAog9junRPa5/EgnA5jyxn4EqpQdNFwY84J4JvRF/my9s26+20q6hNxLFTtAHsh7TBYmJ3rSlZOqlB/Vh25DC0fYLgxkGGAlguw6shlk9ovobJoD6dozBSKtldruNze4r6Q7RAA/NbWkow0M/Uwlq8H30yCc5YAFCPpoe27JqPr+0wokaG2UOMr5RpgtpFNQqTQ/9ohIl/hXcqKHvDXqEmttKbEV20qQiRZOvFVrvQ325j3weNwZsOMdDNxZsMcfJZyns7pOaJZqZEoDGZrb2uxxpc5QSqRL2FKsVqsfOm4LwEMEmDm62uPu1bqfQkj0lfjC2Z6BoiviBoMBOeXFl6urjHt5uuKs7r1bi/dhZnq2O7dE9nlcSGaiVBjrgm327Ga7owWK/pp60joAQA8lj7xVVz3Nf98sJhGHUp8BWI5NMMoRtQqDYfNxnkLrgQGj5nM1+cddKXU+xJGpC2Soj6tvifRmlIb6HCXWgcWsLaWbPdYVWp8TK+D09cSLGRr9HrVWq/47vJOde2JZ4pph2mHKb4Kka9CZgAuP7j7ran1NIDdDRgQVvfgmlolVDXU/658lt5+62rLmWKNr6parHYb7z2pUN9tmJvJe0/y4HZVU21XzxhJPRSGYlekreh0aJ/RTC6TJx4uTFbImi9hqlgxZwW3LbuNBnfpoLnR3Sg288KYyO4siK8ajTmBPpGViYQJZwpOh4ceUnKMp3/kq+DkZbFYCHnUzTBfAWmH8VyE2oL4sjU2ltQlS8xciYEVjTxVlnBxu69KHRCx9JkI2OrqwGLBqhv4EhCqUPEFykb+gvlfZ+AjwZIPcMH8r4vNvDAqbeEUDWkVycrUeUv2hdoLBZbnlNYS9Baiy9EDPgkLzyrWieouHJ5qLW/x1ZvIUFsQX3lbQXwNdDoMHlhqQKFp/VIPC7b7fnX/dOV8JBzqHhveVd4plyZm2mGtUwnMQy9YwVuZTdiypYXnbdkY2/QtHHbRqQDUONVn3ZuuzIwBYWJpi3cU0w5tTc1EupWhj8NlxVld+ROBIr4qmBVzVvC7j/6Oy464DIAWTwsbPrpBhJcwJjI7+yJfBwb7BkU9b74JWKiyhnE3la7z6Su0XAs9W4rbwwWLZEt3+c9iJvUotRHTbKO0FlE8pGboq915LFqf05rv6OVAX845qKiZLahEam0EotnIhPZ7orlmyTloel+tt2bLSfz1gqdEeAmjYhiGinwl1EBabwiW7A91KPEVcBXuD64A0K92IOr+Y7fYCTgD/Qx8ynsyJxRJEMjEAdC0AWmHptNh/5RDE1N8FaJj5jHujI9wtYocJfaU97WbFCNfZuHkeIZHGmexLb2ppN2m3G5+FpxBKqvStGurCu0l8iUMQVeys6TAcjHlsL6qZMK0UhHxVeFYLVZOmX0KAF2pLiyafKTC2EjtVLW6OgIaB9X1E1+b1DqFOk940DGeGjPy1Zd2CBD3BwCwJ5LohTo/5UpGjxUjX/YB4isWUrNr1fbSdQh+TQlV8wFgYis6HhrEK1x8xdNpDEu8+FonL6mGwpiIpnMkMvligWV7c+mkTbhDfW8CoWfUhuM/C4BH3w5ArDddbFvrqi1Gvugqb/fUdLtKm8xbLdj14QosjyC+CqYbZrTMnfUS9aoJkGQFlO2AfjW+ClHL9kK9t+YBaYfNulayvyjW0iK+hMFEMt0EC49he3NzceJzOqz3AhFf04KZ1TPR0EjmknQmxV5eGJ18LAYhJa7avW68zr4bWtcudcerrR+8aNqMfCV1P/loF6RVW81dQ9JcZ17ms9U5YtRGR458eSyF71Hj4QD4Mm8CEO1OofdzITNrfQUjkNQrW3z9s2sXmtZ3baFsZaQ9CVNPW6HGV31cDZCqZrYU9+WyeaK9ar8/+Tew2OCYiwDwZFTh5VhhP0CwKlh0T7X3dE14398NuU4lDmNeJ+7sALfDoZwOTQYUWjbXidl1JzG/inxlK6BsBwyOfHVE1Wfp09Xw0qare6q3YCZi/q0UxZdEvoQhsMS7cClPLGxNTdOqwDKI+JoW2K12ApYAANsj26e2M0JFYK73Crsh56gp2dfTrW4LdS2BQce5qu1YbWp/PF9TTD30OmroKayzLvdaX7olXpxRK9b4KlBMO8ypqCCHnqleR1/FYtPQdaNkoGhv6It8ZfQ4lcw73cosxTDUDHWadvQyL3IrlAdtxQLLhQXxs/qiPeHOJBjgcOhqHeWs48DXDN4ZxTVfyWiWXCEdLegKFtMOXaHyHpgbXUocRr02qrJKMQ5OOzxg8IHFyJd6XtudVnS7cpiN+5Rdu9Fd3sLTxBRfwSqVatoRUfdQO+r3MKNW/U0YdvWcaRsY+ZJCy8IQeAuFxnN+Hxank0hXQdRPA7MNEPE1baizqLoZIr6EsdB/vVeVpU98GbpOV0y9Di6YPeg4TdOoDqiHaqxf6mHpOo3yTZcJpxJolmwx8mUfsKbNTH+qNlpBs8DBHwDA0vUWvlo1c2s+BKBf2mEU8lrpAvNKY2uvcqJz6i1KgFlTbO0tbyEtlAdt4RQWI0dtVIn1hnkLi/uKKYfOHuU7ccAytaP+IJxaFJtNHWN+9/pHvhyZpIrSlym2XiUeQz5rn/jyOyAVKRZjHzXtsJCep1Up8Rk318+WedTPpFjny2mmHaax6nnyVuVmeMCJSnxmHT4aUolBkS/zeEEwyeXz1MbUZKa18IwNF23mXcMeV0mI+JomBC1q1mlHZMcU90SoBLJmgeWAhtfeZ4Mc372TtO5BI0/NIYcOeWyx1le+tii+al21xQFTOafL7AoXatKYLkoNw6QdWrshMAcaDlVpUtk4voASl/3XfZm1vmqjoGuVHfnaGVFphn5bM5Z8AIC/7H57CnskVAptkRS12VasBuQsUD/roOK+otOhXjBgmP9+9X/dwWgaeFzq+xTrUYPyoCtI2qERd6iSDuVsuuEIq6hNj8eNpTCcqvLY+8yIquuV1fxAArMBDTJRSKh7ktWjRFiySq35sveWf0TIMIwSq3mA9miKxkQPqYL7YcPiuVRllcBaGAsXI1/mGjGJfAkD2dzTTn1hIqd61iwMwyimHcqaL6GsCFqV+JLIlzAWMrv6Il9BV11xe/ebag1GwNmFze0e6lA8hRo+MT1YTK1pqO4TX+U8WNoV6UIzjH6GG6VphzEz7dDaA/UHg9VedCvzu5W46u942Bf5MtAsOULJyhVgHUklmmucdVRbVETwtfZNIx0iCIASXw1ZlbYa8Vqx2R3FfWHT6ZBt4PTBjKPVjnol0Dw2NTA3I19mRKTbo8xeyvV+YhgG7qjqe0+Vuic6PBYsVsvITocAdhf4CiZHhXVfTo8Sm2l7wXY+Wv4RoUQuQTqvPjcz8tURSdMS6ybrUA8EX9CF36HumXPSmUGGG2aRZkEwebtrd9Hp0DlzFolIhnxWR9PAWyuRL6GMkLRDYW/I7uir8VXfr1Zc91Y10An6k0MeB/0jX8Fi5Kupum+dRraM0w7boj14E2DTAU3DVl9f3GcYBvFe03CjG+oKs/eFQaLPpsRJZAjxZa4h2xGuXMObnpTqe6O7kTrXTAC2hrdNYY+ESqEtnKIhpb4f8drSmelQIe3Qb2uFuUvAWnDQrDsYAK+hovDmWkpz7VCPR91PMmVa6yuSylFTcHcMFwyLxuR0aDKM42HGov73xHoxBhYqLjPMqFWVrQq3XU3WdURTzClMQtnI4nTbqQ2qz7wub6FV0g6FUdge7ldguanPZt5T6yquOa90psdVCMW0w53RneiGLJIXRqYv8qUxy9uXetfdWlh30eQY8jgYutDyLH990R66nA03OuI9RaFkra1Fc/RdZzqRI5ctpDqYkS8oDhJ9xjZgQNphQXxVZaAqbRTTGiuRWE71fZaviRaPWu/Xmtg1lV0SKoS2cIr6VKHAcn1pml2xxpe1tW+9FxS/X56cmjCM9hTuPa6C+PIp4RHb3Tph/X439Mb7Ciynncrp0BsoCM+9El+lhZbzRiGKlsuiR6NDHVk2DHQ6BGUl35hRNnXVTmUiUjdP7XdaPbQPEF+hdEjGLEIJO8Nt1PWv8TXNUg5BxNe0wW/xY7PYyOgZ2uLlOVMolAdGPk92t1rf0xGAOYHm4r7ukHrwB+fWD3Uo0L/QchCSPZDspcVfXxGRr85E76g2805LDJuWKYouc5DoSyu7+f5phxa3G4tXqc7aKLTHKnf9QtpQM9DzAjM4JKgWyYdz5TnwFcqLtkiK+mQIAK1fgeVMKkciXHBAtO3pW+8FhfVQATxWFXEdFPnyqYF7Yk95/g32JjIEC+IrZyvYzBcs40dNO4R+hZa3ARCoKRhtZJ1EC6mHuc7yjqQPNNvI6wZdsQw1eXWP9fpUxKvhyDlqv6uOrnCSvG70HWPkiaQru0yHML60xTv6HImbm4tmG/6hzDb0PGx9Bl77ufpfz09iT/cdEV/TBKtmZZZnFgDbItumtjNCWZNra4NcjqwVejwwv1aJr3wqRW9Kpa8GDx2iNk2BYqFlo5Cu2L2FOYG6YuQr39WNkctN3AW8C3pTYWrNm3rD0Ou9ijW+CumGZvqhL/YXANLxHOlEtnhcn+mGQXu8MsWXruvkLSEADqqbxVHNCwDIaGI3L4xMOpenJ56hPqFSzRwzZhb3mYMmlxbGFQiU1rzSNKg/GK+1YNfe0+d2CH2Rr3SZph32huP4M4U1npZCgWW/mXZYWCu5F2mHdcEAAK5MNT0udTNNtpbnejeTgWYb3fE0ed2gGiW6vI3K8bDuyPlY9Cy61cG8aIjuWBq71Y63sC5MTDeE/vQkOorPaXtz8/A28288At89DO75F/jFJer/7x6mtpc5Ir6mEbO9KlVIHA+FkTBt5jv9YFg0DqpTC79Db/8THRsOLYF3zrxhj+9b8xVQluTdm3DZHURcVeQsgK6T6ypPm+RwOlSMfFmHq/Fl6QFPU59LWZ0SIo7UHqoKJgAldvNmra8odCUqc/3C1t4ONIuaMTykbhbHzJqPYVjQLFne6JDUQ2F4zLpO9TEltKpnzinuM50OA7ZWOOD9SnD1p/5gPAXxZUa+nFYnHrunWDdQ7yjPNOboHtWvnMWCzTBrfDkhobIBgKFrfJkMKLRcU1s4R8ZHb8HxMLq7vAudDyqwHEljz2fRbEp0BWaryTyr3YY3r34nB8VjRcdDM8VUTDeEEkJt2HTQLWpd9pBph288Av93PkQGfEcirWp7mQswEV/TCFN8iemGMBKm+GoLaJCvxltYLN79tvq7CVb3oFmGvzW4/Q7QQDesJHVfMcXGgo+QWWi5rTxnq6PZfpGvAWmHptuax9rTF/UCcFSDX323fD4lUIYy3aiNqshaJfLProLAynuodjpx251Y82pA9VexmxdGwDRQCMZUNLhmdl90q1jjy7qndL2XSd3BeCxKfGVTedJJFTGvddXS7VNCzVKmxYaThYhc1FOFO1uIfPkcfTbz3hnq3jEcZuQrshtyaar9alKrKusl7FHHxfdURuTLtI3viKZojneTLogx36y+tWB+j/psZ2Z1Md0QRqQqpMR4OuBHs1qLqf5+M/Kl52HDtcBQhjSFbRu+VNYpiCK+phEivoSx0FfjC2xG3+L47h3qARgMjpwyaLVaiq5ecb3P8dCheYuph+Va6yuei/blkjcME/mydvWt9zIxHQ+rlEd9eIhaX8GoQTgTmoBeTzybu5VNuIO+gtseq0pHfaNry5T0SagM2iIp7HqCQEINehrnHVbcF9qtBud+WysccPLgg+sPxm5J47SqCFmx1le/Qsv2SC9GJjOBV7BvZDuUMIr6nLgz/cTXWFIOAarrwF4NGBDaQZXHjoGBBQvxgLovp9sqQ3yZEaz2SJqZsa5ijS9/v0hFsFn97MdRtJuXWl/CUAQiahLTaKgnm8kX140WI1/bnxsc8SrBUJMa25+b4J7uOyK+phHFtMOopB0Kw5M1nQ5rNKqsfTOT3Z1q8BSc6Rn1HJ7+phuFwYbL6iuabpRrbZ5UPjqs4UYx8mXp53RoUhBjfqu6rpLIV7+0w2imMiNfOyLK1MBt7TNLqBe7eWEMtIdT1BdqfKVtEGhoKe4L71brJ/1BG3gaBh9cWE/psajJmmi/QsvRKsgWIvDlaDyR7yysVfPZqcr2W/M1FqdDUCmY/dZ9WawWck51D0p7lfjKlmnKpUlPsjTtsD2SYla8m4xT9d8b7DNIaDhY1Q602v0S+RKGJZPLURtVz9eqGTOLz1pHlQ2nu1CmIjbG8cVY200BIr6mEab42h3dTVbPjtJa2F/JFGp8dQTAZ+snvqJqABGcP3Oow0qo7m8337MFDAOPzV+crS5Xx8OsES0WWDZFk0lf5Kt7sPgyHQ91FQUqEV/9DDcS+fK2hh6O1oJDqt/eJ77m+NTanfakrPkShqc1nKIho8R7JGDH0i9lOdStzFoCB8wd+mB/C9jdeAviy5wACVYFQdPorlb3mWwZTuZohXTIsNeOM1+o8+V3js3p0KS2dN2XUaVm+DPVhXTFMk25NOlNF9wOi2mHaWak1L3RZsn3DZaBxmPU2tmsK0hPa+lxsuZLMNnS20ZdtHDfmDO3uL7aX1+FZq4Z9TQOd3gpY203BYj4mkY0uBtwWV3kjBx7YuW9UFeYOrL91nzVFtJFUt2dxHLqQVi78NBRz1ES+UpHIN6JzxHoF/kqzxlbWyaGO134eZDhhrrJqwLLQ4svf/ofAIT7GW7Y+635Sleo+OpOqshCg7vvYbWwTpkFRMVuXhiB9kiK+rT6vieCfWucUrEMqay6T/gPO3bogy0WCB6Ix6oG37GeUiOGnmo1eC/HSLq9sC4l7FGZAprNwOGyjj3tEAY5Hlqq1aAzWzDcsPSUtygZGPnqiKSoy6l1Np5qrW+wDHhnN+DIqvujbfPOkuMk7VAweaerlbpC5QHXzFnFupq+/jbzc04A3wxAG3wCUNt9M1W7MkXE1zTCollo8amUD1n3JQxFPhIhH1apcR0BaKhWg+2ef/wTAK+tG2dN7XCHF6k27eatKtpK92ZqXbVFh7JyNNzQdZ2agh121unEUt03UMxl8qTiaq1btTs3OEXKtJtPvQZArDuFnlcDJTN9MZCAfK4yxVckqwZ5zdV94uvoGeqas5ZOMmVaOkCYelrDSRoS6u8nVxcobg9vUiLEbenBseB9w5+gvs90w4x8mYPybq8aopRj5MsVUdGbSFWhKLLHqoaC3YU1ksHhy3UUGSC+7B41mMzb1f3VEe7BMIYyFZh6DMOgJz0w7TCNz1CfmbfWOegYt6ZG1e72ENAnsiXtUDDZFtpDcLQCyxYrnL6OoQ03CoLs9LWqXZki4muaMcerUoXEbl4YiuwulUIWdltJOzRmFsLyXZtVpDToi43pPMVCy1qhQHPPZoJVNUWHsnJcq9CZiFIXVbOyOZ+vZF88rAZ9NtI4G2YNtsR210J1PdWWHqxW0HWjOFC01tRg2NQMvT9emeIrqavB89xAX8rpkU1zMXQrmiXP39u2TVHPhHKnPZKmPqkG1ZamvuLsoTdfBSBQHR/Z9a/uYLzDFVoupDHnytB4ojoWAiBiV+ZDVT47xDshEwXN0iesRqJYaHkrAC6vHQDDou6v1mwGPVqe95RoNkpOV5MyZvpguCeM1aqidr4ZgUHH+P3qvupJ5DEMQww3hEHsjLRT16/GV3go8QWw8Cw48pODT+CbAR+7V+0vY0R8TTPMdRpSaFkYikzB6bC98BCcE1CLoLv3qIhQsGFstwRPsdZXwR2vexNNnmBf5Ku9vexmbHeFu4o287rfX7Kv/3ovreGggYcq6g5G0wx8XjXgMB8KmqZBvapnUxNPVmRR4pwWAuDA4IziNofNhk1Xg+mXW9+Zim4JZY6uG7RHUtQVIsrOGbOK+0LblAlHoME15LFF6g8q1vqK9gwQX4XSDpkyi3wZhkEgEQIgaVWDQq+/qi/l0D8LbIMjP4PoX+vLMIp289acnahdnTdXhhNZ0Jdy6LF7cFqd5HUDR9tuUoVolr/ZP+iYxjlqm0tzE03nJO1QGERXuI1AoXa5rbm5mHboH1hgGSBdeKAfcxF89MdwwWNw1WtlL7xAxNe0wxRfEvkShiK7U/1dtNcogbAgqCId3T0qchOcM3rKIfSLfKXVLCfdm2n21tFbmKk20mnyodA49Xp82BPpLppt5AaIr74aX0Os9zIx7eZdapY/0s9u3tmkIoDBqE5bYUa8UuiMRcCqBr2H1s8q2ee1KXEudvPCUHTF0+R0g/qY+vvxzpqrduRzhDvVd8o/p2WYowv0q/UVC6UxdKNvzZdfGVCk9pTXusNoNIEvo+zxdauKovtq3P2cDseQcggQaAE0yMYh3oU/oCKEjrSDHpc6b7mKr4FmG92xNM2xTlIFQdXf6dCk+Ug1PjGcQfb0JvoiZulwMYom7N/kO9QEcdZmxeLzE+lW95ZBkS/DgJ0vqp+P/AQc/q8wb0lZpxr2R8TXNGO2T+zmheEpRr5qVFTqoOAMjFye7oSK3AQPmjem85jiK5u1ktGroGcLs/31ZG0akcI9stwGDa2xbmoLueQ5f2naYcyMfA1lM29SEGU+a8HZrZ/phqtZia/aGOyOVNYs7pudavG7oTtp8taU7GusUmJse1jWkAqDaS+k69ZFC8WRZxeixq2vEM6oe0rgwFGMJ2oPoNoWBnT0nEEyli1GvnoLka9yM9zo2anWtGYtVmyGmnGq3lunQ1DRMV8h1bd3G7W16r7kyFTRbYqvMrTZhyHMNqKFGl8jiK+G9xwERp683U3rP3YQcAbQ0DAwCKVDk9Z3oXyx9ajverzGSzKaJZ/V0SwanoFrCHu3QrwDrA5oXjz5HX2XiPiaZpiRrz2xPaTz6SnujVBumE6H7QEN8tVUO51Etm4iZ7iwkiGwYBjhMQCHy4ajSkXLYoVCy3P8arBVXKdRZgOmznjvsJGveI+KYlVbu4vmGoMwI185FQXqX2jZVnQ8NNgdKW976IFsKhRYtumBQfvm+ecC0JkSu3lhMK3hJO58CHehBnLTAarAsrHpKUJ5lcLqbxphvReAzYE1OIdqi4qkRHtSuG1uXFZXsWi70dWFUUbpvJFdao1syO3Gbdb42psCy/0pmm5spb5OTX64Mp5i5Ksc189Cnz28Gb1qj6SYEesh7SjU+KodLL4c1S6cBZOO7le3YLPYCDgDgJhuCApPIWMmXxcsPmO9tU6s1gFyZccL6v8ZR4F9lNTmMkTE1zQj6ApSba/GwGBXVAZMQimZguFGR0DDZgQA6H5LiYmaqm4sDvuYz9VX66sOckmC+SiGbqO7YDefLTPHw+5kb7HA8iDDjc4QAB57BAKzhz6BWWg5o+zm+9f6shdqfQWj0BqrrMjX1pAaSFZZBqecFu3m8+X1WQrlQXskRWNGTejEXRqewgRM8u0XyBjVgDH0Wo2B1B9cXPcV602haRq1rlpCHtABLZcl31s+g/NYa6HYurcKd6a/+NoLp0OTfo6HDXUq4ufMV9FTpURruWUQmJhiyUwRbY+kacymQbNgtRpUeYd+llg1taAntTMEIKYbQgmBgsGMral5aKdDk50F8dVy3GR1bVwR8TXN0DStWGxZ7Ob/P3vnHWbHWZ7938yc3sv2Iq26LFly7zYu2I5twBhMjIFACL1jJySULyEQAphQAoRAAqFjOhiMMbYM2Ea25apiq1h9tb2d3svMfH+8M2d3pZW05ZzdI3vv69K1q7NzZt45Z+ad93me+7nvRUyEXi5TGhAL7eEAuBSDq98tFj0NwZlVSj2G3HzaIYISOXYYWfdMqHzV16Ihmo8TNvpzj+n5ioiSmNtvPz5n3NcGNi8+2aQdTl35Gs2cWouIgbRYSPqsDcf87Zx2YYyqKhGypcVK+iImYyiZp7FgBCJBofpHIU3iiLhHvAELFus0ejAaxkU30tFxo2VVkUg4DOGJOqqkFwZFMiLtteM0K19eqzCcBwgtn/7OQl3iZ6wbl8tOWS4BkPSI/RbqbB41YQZL47TDPEHBEsXrt07y+JoIi2EfUE6qk96/GHwtolguE0qJ+9+3pGtc6XCqBE4l+LpwvoZXVcw5+Eomk/zmN79hz5491RjPIqqALl8XsBh8LWIySkNDUC5TtsjEvOC1GF46w+JhH2qdWem+UvmyGn1ikQNY8FaMlkvD9VUtyWTi+AwVpWNoh0nD46spcPwdSBI0rMKniEVgIVsmnxGfnaVp3Gg5kotXddy1xlhOLO7CjmODr3WNneiaFUnS2DawKLqxiMkYTORpKoiepHzYyLr0bCFeEj55/hbf8d46GY1rK6Ibqdhko+Uxt5iX6snry+zDSvosuErivF1yDMo5kC0QWDr9nU1QPJQkiaJdCHnk3UI6tjBUn8GXWfkyK1ex4UhFZt7bdHyqqaNDzL2qJrZdDL4WYeJQbIgGoy+7cdmKSoLTf3TlKxeHESPm6LxgHkdYPcw4+Lrlllv42te+BkAul+Pcc8/llltuYePGjfzqV7+q+gAXMXOYohuLwdciJsLs94oGnOiSRMghZMQjCfEQbFjeMqP9VeTmJUOePHIQh+Sr28qXHB9FBlRFRnW5Kq9rmk4mJ/rXPIZwxnHRuAarXMDpMLLTxsPBpB2GUhDL1g89ajqIFcVCssV97LlbFAWrJhbSWwf2zeu4FlH/GE7maTSEF7QmQ6zl4IPEVXEtBZpdx3vrZDSuxjtF5Qsg6hb0tXqqfOlj4p5JeV0oupg7XEXjeRvsAsUy/Z0d5fWlOsT564YJfHm0vuZRE0dXvspHjlTENnxNnuO+L7BBBJsFW5hyNr9IO1xEBfvHBmgwWgOc7R0VReFjaId9TwO6qDB7GjkVMePg6y9/+QuXXXYZAHfddRe6rhOPx/nqV7/Kv//7v1d9gIuYOSpy84uKh4uYgKIptuEXC4MWdyOldIq4oUoWXrd2RvuryM2rRq9Q9BAui69uBTcccfFwz/o9II9PfblkEV2XkVBxdZ6ELmSIcfjtcWBc8dDS2IgugUUDNV6f6mTHQ0YVn0unb+rg228RC+nnI4fnbUyLODUwlMjTmBXWC4qRgODQQyTKIiETaJpm8DWRdmhQgM1FfcQjaIv1VPlSYuKeybjF+UkODSUxQ6VDE2bwlRqAUh5cogovOY0FZ2Ss7jwTYVxww/yelIG+iseX92hluglo29CFUs6hywoj2w6O2wosBl8vehyODRIW0wnWtlYSY6bM/FGsnN7Hxc9TlHIIswi+EokEoZC42e677z5uvvlmXC4XL3vZy9i/f9GIsx5gBl+L8tCLmIiSITM/aDDuWj3NxPbsAWScchJXa9vx3zwFKpWvokExiRzAaw2MC27U0WIJwJ0UDV+lYGDS62mD5uSSY8jNJ1F7bBQBqk8WvXNm5UuyWsl6xGLJFj211A6LiEXP8uDU33+rS/g09S4mcxZxFIYSeRqygibnbO+E1DCM7KpUvvxN0xDbALC58fhEkJWOiHuqUvkykzlD9TOf2OIi8Mg4xKLQ6pHH+71monQI4AqDzTjJeA8WYzqVbaKHTioW0ZLJOY+52jBph2bw5RgZGK98hY//vbcFXcgF8fkN7Ogepy0uqh2+6DE82oPbbC0ONpJLChnVY0R7eozga8mpSTmEWQRfnZ2dbNmyhUwmw3333ce1114LQCwWw+E49eQeX4gwg6+R3AjZUnaBR7OIeoFZ+Rr0i8zqskArY/vFa2FvfMb7c5uCGxmjoT7WTdDmryyWtEQCLZc7zrvnH/6UaPjSDUUxExmjed6txE6etTY8wHxlISmdmCC6kQ+I/hZnIl6N4c4LsqUCuiwqDWsbpzbDXWb0ryzKzS9iIlL5EpmiSkNaLJB8Hcvh8MPoOiQ04V017coX4GkWC/dMWkNVtfGKiE9IzNdTJd2VEoFC1giQnF7LBIPlGQZfkjRJ8dBmCFLYdQspqyE2UmeKh5quVUyWQ44QqqYTjA6RMytfU3h8mQi4rOR0sS4ZPhRb7PlaRAW5AVEwyDospHPGfeCyYHdNUM5Uy9D/jPj9FO33glkEX7fddhtveMMb6OjooLW1lSuuuAIQdMQNGzZUe3yLmAX8dn/FO6M31buwg1lE3cDs+RoKidTSyoZ2In0ioxo+VmvhpDArX7m0iio7QS3SapHJ2qFgNRSt6mjBFEyL87Y2T6bXpfuFz5XHkQeL7cQ7CSwFxYZfFu9JTvD6KofFIsKTTFdryDXHvrFBJElH1xVWhJqn3GZ9k1hMZrT6+S4XsfAYSuRB1winhGpdw9I1cOghMlqIsmZDkiW8R9OFTgBXWycyJUAiEy+MV778or+yNFIf159WLOLKi0ROATFfePzOcY+vmdIOAYKGQEesW0jWA46ypW6NlhOFBJouguKAI0AkXaA1PUbRbnh8nSD4kiSJjEOwIxJxvRJ8LVa+FsGoUElNBTzHl5kffg5KWXD4K/YvpyJmHHy95z3vYcuWLXznO9/h0UcfRTZ6J5YvX77Y81VHMEU3upPdCzuQRdQNKh5fQdE/sCrUSmRMPATDHdNUJZsAh8eKbBHvz3jPAKCDIkgS0UqfRn1kbMuqSjAtFnGe9o5Jf8uMiIyr2zeN6VCxQHglPkVUyybKzctNhsJb6tSpNu8dFQG5rPqxKFNLgp/fLh5wmhIjkT91zm0RtcVQMk+gPIJNFV5cLUvXCbENo9/LF3Yca4x6AkiNq/Eogo6WjhYqla+YX9CC64V2WB4RgVBJVtB1sTD0B5wQ6xYbzLTyBZOMlr0BsU9HyUbUIYKZejNaNgMln82HVbYynMjTUCqjSwqKAi7viZNY6cYAABnNM97bZ/SQLeLFC0dUXAOFULBisHxM8GWaK3deMKl3+1TDrEZ+7rnn8rKXvYz+/n7KZUFhetnLXsYll1xS1cEtYvZY6jVEN5KLfRqLADWRQEskABjxA6oHl9VKJC349uFVU1POTgRJksb7vpzrAOjUxOLc7Psq10m2eiAVJWyoKIU6uyb9LRMVWWxPcJr9KQ2r8RvBVypaQFVFBthhKCWaFbZTAabBsl0KHnebFaFm0BxIks5TfYt9vYsQGErkaSoZFWCvjC3dD6kBErpI/E1b6dBE45qK3Hw6lq9UvmJ+cT9p6TRaJlOl0c8eZgUu4vDgKongyO8ogVYCxQ6+jhO9fWqExuXmAwGhFGgvOog6BIfbDPjqBUeLbYz2DiFbxbg9IQeSPLXHl4niCkFLLdr8OI2CV6qYoqSWajTiRZwK8BprFKmpqSJmdUy/1ylurmxixsFXNpvlrW99Ky6Xi/Xr19PTIxb373//+7njjjuqPsBFzA4V0Y1FuflFAEVDbKPg91CwSVgJkB3sJ695kVAJnXbarPZbUTy0icVDe0lUkaIeEZCUhurD66s3MUbIYAO6OyYvjtJGUOZuCh/9tqnRuAaXHEeRVXRNr0hj+zvEPRdOqxSNpFS9oy8pvh+P4fk2FWRZxqaLqt6OoQPzMq5F1D+GEnmaCuL6yQQdcOhBAOLOs4AZiG2YaFiDVxFBRno0hc/mwyJZyNslMhYxz9RDJT0zKIKvmNNV8fhyS0bVJrR8dtn4CT1fDWGRCLEXXUSMyle90Q6PFttIHTxM3m6IbRxdqZgC4bYwVqNnLPPsEIokqu5mH9kiXpwIGKwRR3vnBNrh0UqHp7a5sokZzxIf/ehH2bFjBw899NAkgY2rr76an/3sZ1Ud3CJmj8XgaxETUeoT9LJEyBCFkANE9gjfJr8tgsV9fF+WE6FS+ZJFJrMtY2SF/aIPpF68vgYTEUJC7BCLQQ80kckZfRvt01R7bFiNJOn4bGKhYD4kmroE3SiU0ulLnhoUmuGs+L5C9qYTbhewis9mX3RRbn4RAkPJPI1GBaTQ4INDDwEQV1YBMxPbAMAdxuMQi6/U4AiSJI1T0lyG51UdGLen+kRfSsztwFUU86lLExXkWVEOYZLRcmM4AICiK8SdIhCrN8GNoz2+Ct3d4zLzJ1A6NNHsc6AW4wCMPT9c6VFfFN148aJYLtOQFJXPiQbLvomVr3gvJPtBUqD9nIUYZtUw4+DrN7/5DV/72te49NJLkaTx0vL69es5ePBgVQe3iNnD7Pla9PpaBIwrHY4GRMLEZw0TOSQWMuHA7Pt4xr2+xIM3nDiCrktEPQbtsE4ENyJDvVhFPDgp+NIzEdLlAADuJSfx+DJhKh5KoppoctNd7SIADaWhL3FqyM1HCyKj3uQ6cfDVVpGbX0zmLEJgKJGnKScSEHpjCA5vBiCRFwHDjIMvwBMQqmbpMUMIyKAejrnEAqwe7CtyhjpqymfDaVS+XIVu8cfZBl/+TkCCUpYGNPIWQa/MOI3KV50GX6ZMPH09FZl5b+jkIistfgdJBGMgMpgh5DQUD3OLwdeLFQejQ5XWgNblq8dphxMrqWbVq3Uj2GY+v9QTZhx8jY6O0tR07IM6k8lMCsYWsbAwK1/RfJRksf48QhYxv6h4fBkyxiFHA5Eh8fALN1uP+76TwX2U15cl3oukOYka+h31sFgCyA4YlT+XBck6fr7F/n2UdbFYcDcfn3o3CeGVgITfCL7MDJ2lSagFugowPDJQpZHXFqmSqFx0eKc2WDax0sjMR4qnxnktovYYSuZpyIpysi3ggGIKzREiERcLqBnTDgFPowhm0nGRAa8syt2iOl0PohtFo/8q47PjLAvGgCv7vPjjbJQOQais+gUd2pkcImcTn2veKRaY9UY7PLryZRvqJ2cGXydQOjTR4ncwYCjixtMKIYOyGC0sBl8vVuwb7asYLGuBVtSyhixLeIITDLtfIJRDmEXwde655/L73/++8n8z4Pq///s/LrroouqNbBFzgtvqpsEp9MMXRTcWYdIOe72i/NPsaiQSE5NauGsWOvMGPEHxoM2kZbC6QFex6466q3yVR0S2OuG1T3o9fUQYo9oteay2qdX+joHVCcGl+BRxbmbwpXjcZG1iSk30dVdh1LVHQReLna7jGCybOL1ZLCpzen18n4tYeAwn8zRkjKqvQyRy0q3Xo5V1ZIuEZxoVkKPhbRVJgFTaAjDu9WWop9aDgI9mBEJZn0g46ZKOI7lb/DG8cvY7Nvq+pPgRynZD4dEu5qvyyAi6rs9+31XG0cGXd2xwAu1wGsGXz8F+g0qakgKErKKCtlj5evFioPdgRTk1hyHeEnYgT1RMfQGYK5uwzPQNn/nMZ7j++uvZvXs35XKZr3zlK+zevZvHHnuMhx9+uBZjXMQsscS7hLHcGEeSRzi94fSFHs4iFhDFHhF89RmeOe2uBqK5AAANa2e/YKjQDuMF6FgBw8/h060Vo+Xy6Ch6uYxkmfFUU1XIEaOR3z+ZqiAMlltxO2eostWwBt+gKTefr7wc91pxRQrkhvrnNN75gKZpqHICCVgdPrFC2/nta+Bp0JUEo+kkjZ6ZWxMs4oWDQlllLF2kMSUMlv2auBfi/svE/xtdyCdRvJsKniVLjf07KBXVCu3QVE+tB8ENKSIoxXm3E/Igu1SkhEHHnS3tEETw1b0ZYt1ozgDEAJuo0uvFIloigRIIzGXoVcNEwQ1d1wnFR9lrFwGUbxrBV6PXzn6PHyleQlXsdIyI+WRRcOPFi0SfaFtKua3IcSFY5Z8otlFIwfBO8fspbK5sYsaVr0svvZTt27dTLpfZsGEDmzZtoqmpiS1btnDOOad2A9wLDV3+LmCx8vVih14qURoUTeL9ftFLsCwro2HFKuXwLp1mr9MUMCkBmUQBPST2EwLibtBkCTSNcmThxSfsUfFQzwUmBw3pEfG6xz/NqpeJxtWVyldiNFfJSid8gmql10GG/mTojo8iyeIht7ax/YTbdgbCoIpM9ZP9+2o+tkXUN0aSBWStSDAjrvumjLgmElZhORGYBeUQwNaxBqskelDTY+nxypdPHKdcB+qplriozhQNwTGLqwy6BjYPeKY2Kp8WJnh9yW6hFuuWZZLW+qMeTqx8FQaHwOJElxUkRcLlt5/k3WBVZIJ+F5a8CGRDPc5J+13Eiw+lQaOH2u+c2mC5/xlxn/mXgG+a4lh1jFn5fK1YsYJvfetbPPnkk+zevZsf/ehHbNiwodpjW8QcscS7aLS8CEPuXVWR7HaiPqG3HoqIjHXYFUGyzDDwmACX3wYSaGWdnEfI1TfpZXRZIu0VE2c9UA9dCdFDUQxN7uvKxEXVyh2aodpjwxp8FnFexVyZQlYEMRm/CFCUOgg4T4bnR8XDDtWD137yxbIDsbB8dmhRWOnFjuFknobyILIOZRkarAUIdhFPG6bDsxDbAJD8HXgsYgGePnKkQmuL+sT9VVrgpIZWKGAz+tyKVtGH5rAbvn6h5TCXvvcJcvMWMY3gRSHiFAmjejJanhh8je09OE45DNqnXfFs8TsoqOJ5ZBsRAdsi7fDFC8uYuL6zQf/UBsumufILgHIIs6Admr5ex8OSJUtmPZhFVBem6MZi5evFjZKhdKi3toAs6HCWESP4Cs/Nj0pRZJxeG7lkkYxtOS6gVRUBTcxrw5fIUhoawrlx45yOM1d4k4aiY0Pj+IulLOmMmAI9LTPse2tcg1Uq4lLiZNUAybEcDreVfCAA9OOIx6sx7JriYFQEX1Y9MK3tg7Y2BtVD7I8dquGoFnEqYDCRp7ko5pKET0KRgOVXEO8Wi6bZVr6QJLzOPLESpPr7CS81Kl9+MaeoYxH0UmmSaM58wqw+FWULuibmDrc1A2XmRjmESUbLzlZxfm5NIWb3sYyhujFaLmtlEgVhhht0BBnY+8i40uE0ZOZNtPgcRCWVBkBNGpWvRcGNFy2csTgAakNDpfI1yWC51+j3egFQDmEWwVdXV9cJVQ1VVZ3TgBZRPVS8vlJH0HV9UY3yRQqz3yvbEAT6QfVgKqGH29xz3r8nYCeXLJKW22kEWkoJcFqIehSWUh9eX4GUyE7bW1rHX4wcIKOJRYO7MTizHTasBsAvD5JVAyRGczQt9aGGxWLRnUjPfdA1Ro9hsOxWpmcu3eFZwmAC+tO9tRzWIk4BDCfzNBZEFSpj9Hey/EoST4okx2xk5k14vBIkIT0cIewQVOa0N0tZVrBoKuXRUaxtC0M7MgOgiMOHtSiqNT7Z6FOardKhCdPrKzWIa7WCBjjLNiIOUfmqF9phvBBHR0dCImAPcPBwN7kZiG2YaPE7OGK30gDkykJSf7Hy9eKFLymemZbWNqJH0w41FfqeFr+/QIKvGdMOt23bxtatWyv/nnjiCf7nf/6H1atX84tf/KIWY1zELNHpFd48qWJqsZH1RQxT6TASFIGWlQBjSbFiCq88ca/PdFCRm9dEVaklHwdgzIjr6sEYNZgWFT5P+9LKa9LYvoo/mXkO04YzAJ4WfIopuiEeFnKjoOb5U/njvbNuMJQWi2e/bXpVv5UGLSq6KDf/osdgIk+jsVAuucqAhLrkMpIRw5tnLsGXUT1JR/MVwQ0sOcaMIGQh7StMv62ow4PDsNcIGWIjc1I6BHAGwS7O0e8QAkDOkr1CO6wXry+TchiwB1BkBbW3h7whFT8dsQ0TzT4Hu73i3HLWIK68ZXGd8iJGKCnmDk/bcnIpcf1XDJZH9kAhKfoqm9cv1BCrihlXvs4444xjXjv33HNpa2vj85//PK9+9aurMrBFzB0Oi4MWdwtDmSF6kj0V/vwiXlwoGh5fQ17RoxBUw6TL4loInbZ2zvv3mMFX1gJ2P0FNVJlGPaJpfKEVyrRMBndBNOyHO5dVXpfG9pFRBR3SlMyfERpX4xs05OYNjrq9VWTkA8nCXIY8LxjLi++l0Xlig2UTG5pW8pNuyLPwwfQiFhZDyTzLcnEAJJcKbWeSyjrRNR2LTcYdsM16355mEXClkzpBexAJCSSdiNNDSza2oD2kleDL5cJVFIFDIH9Y/HGutENJguBSGHqOkJImBtjLDmL2+jJanqh0CGAZ6CXfJMTWZlL5avU7GHJ4sKRSlK1e1gy2ss3RS76cx2GZxXy8iFMW+VKRcEqw5vxNqwCwuy3YnUaIYlIOO84FefY96vWEWQluTIU1a9bw1FNPVWt3i6gSKtTD5JEFHskiFgpmz1ePRwQgy/Jise2xRHGEZ+/xZcIdnCA3H15O0KAej3lF9mqhBTeSfSL4zNmgo228J1UdOUheFwsbz0wrXwANa/BbDP8wQ27e39kFgC+roReLcxh17ZEwDJZbp6nQdmHnGvGLkqEvsUgPejFjOJGnMStoQnaXKvq9RgTl0N/kmhPF3dshbA/SOQcKEkGHoASPuQzVvwWcT0qjZvBlx1US7AFX4YD441xph1ChHob1OKok5tGUUf2rl+CrIrbhDKGrKs7RofGerxl4u7X4xLZySQRzXVHBwjCDu0W8eHBwZICQwdT3+MWa1T9RbKP3SfHzBWCubGLGwVcymZz0L5FI8Pzzz/PP//zPrFq1qhZjXMQcsNS7GHy9mKHrOkUj+Op2i2CoIysCjrAvVZVjVCpf8QKEVxLURMVrzCeqP6UFph0OHRaLo4gX2nzj1d+ssYhTLDp29yx8yBrXHGO03NK2lLIsJtZ66dE4HnKqWEQt9beeZEuBRo8PVJHtf7Jvb83GtYj6x2AiT0PGoAk5y7D8SuLDZr/XLMU2DHiWdAGQUsPoid5xxUO3mGcWspJeGBLHjrksOEviXnDJcXAEwFUFZolB7Q1nI+SsYn7OOgJA/cwnZvAVtAcpDQ4hq+qMDJZNtPjFtmlNXEfNWRF0L4puvPhw+MCuinJqvmz0Uk4U23gBmSubmHHwFQgECAaDlX+hUIh169axZcsWvvGNb9RijIuYA5b4RKZ/Mfh6cUJLJNBS4iF+2C0ecuG0EXw1VqfwPV75KkJoBUFVBF8xn/hZHh6p+GAtBGI9Qp0v6lGwGWbPkl4mExGpNrffNrtMfcO411c6mkdVNTqCzRWD6URffQtTlCWRYV51EoPliXBJokr23PCBmoxpEfUPTdMZSeVpMPoyAm6g8wISI6bS4ez7vQA8YaPCpTso9O4fN1r2iHt3Ib2+8kPifk95HVg1Qa10yXFBOayGoJURfAWSw2StSQBKVtFbVh5Z2HnUxESZ+WJ3N0WbD122gDQzBoEZfA3K4pwcZZEEWhTdePFhrHs/AFGvhZTBIqmIbaSGIH4EJBnaz12oIVYdM073Pvjgg5P+L8syjY2NrFy5EotlFtnjRcwJuqqSfeopvNu3k21sxHfBBUjKOCe2y9cFQE9qUW6+GiiVNR7c3EtkLEu4wcWVl3VitVSNvVt1mP1elsZGErKQB3Zk/BSB8JIZKvwdB5XKVywP4RU4dR2bBlHDOkvP59GSSRS/vyrHmykyA30EgJhnvA/FXRghUw4A4AnOcrHYuAaXHEOhgKrbSUXytIT9POWVaEroDB8+SMMF9UmTiGRToIjF8trG8eBLLRQ4fOcvSA/F8bQEWPaGv0axjy+owrZ2suX9HIh1z/eQX5DQNJ3B/XEyyQJun53WVYFp+yQtFCKZInIxhU9cPrQs3whWxyTa4VxgsSk4rTlyJSfp3m7CXjP4Ep/LQnp9lUdHkYCSzw0Z0JQiVrlQHcohVIIvf7yHnC0pjmET85ZeKqHG41iC1Zm3Z4uJtMPizu4K5dDusyErE56FmgpHHoP0sDCfXnrxpH4dl82C12HhoMspVHGVFtD0RaPlWaJYLvPjHQ/Rkxxiia+F159xRSXZWO/I9oviQNznQDZl5s3gq9fw92paD4bozgsBM/5mLr/88lqMYxGzQHLTJoY/81nKQ0O0AgM/+SkjLS00f+yj+K69Fphc+VqUm58bfvnbvRza1IdbFZ9hFNj+ywMsv7aD17xyzcIO7jgwlQ6tnZ0U6QEd1Jzo+Qqv6arKMUylwGJepehZjg0IahrDVpmy14MllaY0NLxgwZepjhb3jlNivPkBMpqhdDgbsQ0ATzOSw4/PMkysvITkWI5Ak8vI0JdI9tZvtXn3sOH9ptlo8QQA2PmFb/D4riYK9jagDXrAvvkeLlw/wukfejcAHd4l9MZgMFvfVb1TAQe3jbD5Z/sFXdeAO2DnsteuYsVZ0xNBWQgMJ/M0loXHV84GvnXXAFSCr7nSDgE8HpVcDFIDI4TPFPdpzCcqJOWhhQu+9DERfKkuJ2QAm9GoMlelQxNG8KXEjlB2iOvCJeuk7W48hQzl0dEFD74qghv2EIXDB8g5plA63H033PdhSE5QRvW1wXWfg3U3Vl5q9TvYnQ5wVUqjbPXQkvAt9nzNAp/f/At+uP+r6Eq88tqXdgR446oP8I+X/fXCDWya0IcHAUgHvGBWvkza4QvMXNnEtIKvu+++e9o7vPHGG0++0SLmjOSmTfR/8DY4ioZQHh4Wr3/ly/iuvZYOTweyJJMr5xjNjdLkqt+Hej3jl7/dy9Af+jg6p+tSYegPffwS6jIAq1S+OtrR5J34CiFU3YlMmcDq6ozX5rBgcygU8yoZuQMbEFLLDFts5AIevKk05ZFhWLO6KsebKZQxISyR9o17mnnyA0RVsWiYldgGCJpR42r8Q0NG8CUeGnGPDSiRG+qf07hriQNRw2xbCyDLMju/8A0e3r8ajhKpK9gCPLw/AF/4Bqd/6N2sCi5jSwxipUW5+bng4LYR7vvfnce8nokXuO9/d3LdO0+v2wBsKJGntSDmlaRHR155FeWiSjomgoVA89wqXwCegI3RGKTHkoQdopc86hUCFCb9br4TiVo+j5wWFG7NKQINi0VQA+esdGgisETQq8p5cAnBHreuEXX4RPA1MgqrF2YeNTGx8pU+eLhS+Qo2Gt/77rvh528CjqJIJgfF67f8oBKANfsc7Bu24ixGydkbWD3Uulj5miE+v/kXfP/gv4EME+8ITY6L16HuAzBbVHzn+VCYYsT0+DKC+ReYubKJaQVfN91007R2JknSosnyPEBXVYY/89ljAi/xRx0kieHPfBbvS1+KVbHS7mmnN9XLkeSRxeBrFiiVNQ5tEoGXxOQHvoSEjs6hTX2UXraq7iiIpV5BN800hJAkjVBGSKEHHaMotlkGHVPAHbBTHMqSztsIusKEjHkg6XXiBUoL2KdhjcUByEyovHnzA/SolwCz8PiaiIYJohuG3HzC6wIyaCP10SA/FY4kRKbRIYdQCwUe39UkAq+jF7SSBLrO47saOa1Q4KzWVfzgEBQZRtM0ZLm+rvdTAZqms/ln+0+4zSM/38+yMxrrkoI4mMyzpiD6KHNeCZo3kBjMgg42pwWHxzrnY3gafXA4TzperghuxP0iGNGLxQWh35XHhDN9QbagGPQ5h2S41YeWV+cgihX8HRDvQbGLYNatS4zavSxhsC4UDycJbvQcIe++FDAWy5oqKl5HB15gvCbBfR+BtS8DWaHV6PuyyllyQHu8fTH4mgGK5TI/3P9VEXhNPXXzw31f5YMXvaquKYjuuEhi6OEutKiOLEvC/qWUg8EdYqMXWPA1rSenpmnT+rcYeM0Psk8/c+KmY12nPDRE9ulngEXRjbniwc29uFXpmMDLhISEW5V4cHP9UbEqHl+GrG9LRvT3NASr60PlCU6teBg1+qzKC6hQ5k4IalAxOK5GJmiHRuUrOIfgq3FcdCNhcNVTPtHsZonU7yKiLyWCL5+1gcN3/oKCPXh8wQBJomAPcfjOX3Bexyp0XQIlz6HYwi8ET0UM7o9PohpOhXSswOD++PwMaIYYTuRZkhfXjxp0gixPENtwVqUi5WkVScJUwU1YEsGcas+Qcgo1m4UQ3Rg3WPZhL4kxuTASLNWqfEGFemi3i8/UrSlEjF6XelA8rARfVh8MDowrHYYcoscreaKquA7JfrEd43LzJbt4XgTyi5WvmeDHOx5CV+InmrrRLXF+vOOheR3XTBFIiGvdZlz73rBDJJ76t4JWBm+rqAq/gLCYtjwFMd0J2NzOlJvvSS6KbswGkbFsVbebT5geX71ukaltzgpFqVBrdU0szepROiaazwOm15dbTDEL5c2jl0q4U4IOqDcYVV9dw1MYJG3QDuda+fIfJTefDwQAsMcTs99vjTGaFQvJsL2R9FB8Wu9JD8XxO1zIagCApxbl5meF6GBmWttl6tSoeyiZJ2R4fMkt4p6qltiGCW+jCLLSagPhnMiKS0qaqCsAjPdxziemCr58UgzcjeCoYj+rsQB1WATF0a1ZiJrB1wJXvkpqiVRRjCswVkDSNHJG8OULO4S4xnRgbNdsVL4ibpGks7JY+ZoJepLTS0JMd7uFQthQTnV6RYDlN/u9KpTD86ujJlpHmFUdMpPJ8PDDD9PT00PxKCPRD3zgA1UZ2CKOD0tj44y2WzRanhvCDS6m8zgIN1Rn4VEt6KUSpUGRoT7oKEMWQtkWABqWVZd+6p7o9dW+glC3yGQOuwT9ZKG8vspjY8JzSwZbWJw7yQEUtUi2WpUvw2g5OZpD13VKQWFc7Ulk61bkJl4UdKlmTzOeFj9MIy/jaQkA4JZbSBNj58hBYFGAabrQNJ2dD/fz2K+nJ9Pv9lWPFlxNJGIR3JkyIONYfhpQXbENQFCOgLTaSNhYqEuWNMO2FqGMtwCiG2WDRhxx+LAVxfiCUqx6SocmDKNlnyyEJ1yqncE6Cb5iBTEmRVKwDoyhw7jBctgB6ekZtmMYu5u0w+fdHs5OQcnWTDIVr/awX7BY4mup6nYLgWwqiS8n1gkuZwuQGpeZfwGaK5uYcfC1bds2brjhBrLZLJlMhlAoxNjYGC6Xi6ampsXgax7gOvccLC0topowVd+XJGFpbsZ17jnAYvA1V1x5WSfbf3kAl3pszxeAjk5WEdvVE0oDA6BpSA4Hh5UMimrFXjCUDtdVVxyksliKFWDjCgKaqHwNu40m+QWiHZq9ZjEPNLjFIkEa20dW86NhQZLA5bOdaBcnRmApPlscEGqPhUwZyaiwWVQNNRbDEqqC+WqVkVEjoMASbyvLrnoZ9s33ULAFps4u6jr2YoxlbxBN2w2OdtLFPRyKd8/rmE9ljPakeOjO5xk5IqoGsiKhqcf3bPIEhex8PaI19hSutLhOvMs3AFRoh9WqfFVozFqIYHQrAJKkMeowPMAWQG6+PGpUvpwuXEVRmWuUohBeV90DmXLz6jBpwKpZiDuCxhgWlnZYoRw6gpR7eihZPeiyFR3jGRC+WKgaJgeZuu9LEn9fejEgBDcAdsguzlPzqIqD4BGpbpNW9YbXn3EFX9zuR1cSx5u6kdUArz/jinkf23Rx6PmdKEDeCkrRQSX40rRxmfkXmNIhzIJ2ePvtt/OKV7yCWCyG0+nk8ccf58iRI5xzzjl84QtfqMUYF3EUJEWh+WMfNf4zRZcl0Pyxj1b8vsyer95UL5quzds4XyiwWmTkTldFXGMizP8vv7aj7sQ2zH4vW2cHkUKEYK4ZCRmHnMLV2l7VY02qfIVWEDKMlofcgjq1UMaoZtAX8UKTWyxgpMi+isy882hvmplCVrA0LsMtC0XFxFgOvydM3GUef+FksU+EInEAlofaUOx2Llx/nODYSO5cuH604vfV6RHzyVCu/noc6w3FfJlHfrGfX3z2KUaOpLA5FF5y62quecv6E77v0ltW1aXYBsDqzNP4UmJs4aVCea9S+aqC0iGYxuc6GhbKgwN4bSLYibhFRnxBaYcuO66SqET55BiEqyS2YcIIvkL5YYqKoExnzeBrgStfpgFy0BGk0N1d6fdS3BYUiyx8vK773Il3ct0dFb8vs+drLFvCq8cB6BhuJFfO1eYEXmDYN5yhMHL9lH8z8/JvXP2BuhbbGDj4PAARr0I6KtYLvkYHRPZDLgYWJ7RsXMgh1gQzXnVs376df/iHf0CWZRRFoVAo0NnZyX/8x3/wsY99rBZjXMQU8F17Le1f+TKW5sllfktTE+2GzLyJNncbFtlCUSsylKlv7m89Ipcvo/aJh0HxqPVQWYKW6+vT56vi8dXRSaIYIZwVSodhTwypyip1nknB13ICRvA15hGfm5pIoOXzVT3mdGBmyGNeiWaz8jW6l7QqFg2zlpmfiAmiG8nRHGFngKjhBbmQKo/HQ75URJNFH41psHz6h97NSvWJY7a1ljNcvmpfxecL4LQGsdhMlOvv3OoJh7aP8pNPPsGOP/Wi67DynCZe/8kL2XBFByvPaeK6d55+TL+hJ2iva5n5dKHMhvJzOESLBi3LTqeYL5NNiPaDatEOZUXG7RVzVGo4SthhGi2LReSC0A6NqlPMbcVREqI6LrkWtMMu8SMbJWsV92nRNi64oU/FdpknRAuGzLwjROnIkUq/l90/gT2w7kYhJ2856lqwuSfJzAOE3DZsRvLL4xIsiabUoujGdBBJF3jnD58B66jIueuTn+mS5uJvV3y87mXm40eEcmrEZyMxasrMO8erXu3nCBXQFxhmvAKzWq0VeeGmpiZ6ekSzgN/vp7d3MRM6n/Bdey0r//RH2r7zbcpOMdG1/cfnJgVeAIqs0OkVlLjuZPd8D/OUx50/2YWnDFlZ502fuZgVtyzjaXsZAK/Pxs03LqzvyvFQ7DUNljvIqlFCZvDVUP2Ht7mIzKaKqBYXPrvI1KYcaSSHyG4uRNa2OCgChKgX2n1ioUBkf0XpcE5iGyYa1uBXxHESYzma3SGiHhGlL6TK4/GwLzKAJOnouszKUGvldRlxTTfndtMwKuR929f4JwVeAGe2CN+loiTk5hcxGalonnu/8Sx/+J/nSMcK+BocvPz9Z/BXbz8dt3/8eltxVhNv+szFXP0WQVuTZHjdv15Qt4EXwFjfQexF0feTckm4PIEK5dDptWJ3VW+R5AmJKlo6pRO2BwCImgI+C0A7LBnzV8btREZGR8MpJ6tnsGzCFQKHn6CqVoIv1SKCPb1UQo3Hq3u8GcCsfIUcIYrdRyb3e03EuhuFQh3AUmHpQWjFpMALhD1Rs3FP2MLip6e0KLpxMpRUjff9eBsDqVEc4UcAuOMld/APp/8nbk0kgtd5rqn7wAugOCgYOnF/iHxaZHX8Dc4XrLmyiRkHX2eddRZPPfUUAJdffjkf//jHufPOO7nttts4/fTTqz7ARZwYkqLgOu888stFk25+z54pt1tUPJwd4skC8aeEOEHovEYagk6uu2oZ8ZUuiujkEyVGe1ILPMqpUTJphx2dFEmMV746fFU/ltNjRVYk0CGbKOL3i+tNs5RQmsViciGqQLlBIXsc8Up0BoQAjTS2b7zyFayC6mPjanyWccXDFm+YqGBJLZjQyImwb1QYLMuqD4tBTUZTiRTE57PsNC9rUpsA6O0uUipOthA5p2MFui4jySV2j/TN38DrHJqqsf2PPfz4k09weMcYsixx9nVLufXjF7B0fXjK98iyxOrzmnH5bOgajPWl53nUM0Nx/4PE8iLAShl9WRWlw8bqCg55G4QpelptqMjNR8yK8gIkNcrDovKVNwyWVUsKWdKq5/E1EcEuQqpG1iaCL4ekkHcZMvsL6B9oBkUNsp/S4GAl+Aof3euXT0BMVDS43qAhDu+CwrHXd6vPoJIagj6S0kYsH6v+4F9A+My9e9hyKIK76UF0ucD68HpuWHYdbz7nai5tvRqA3syJvQTrBbLhn5cLCBaGw2PF5rS8YM2VTUw7+DI9vD7zmc/Q2ioyGp/+9KcJBoO8+93vZnR0lG9+85u1GeUiTop8u+jhye/cNeXfF0U3Zocffv85nJpEygJ/84bxxur1nX4OW0XW/9C2hfdemQpm5UvpaEeTk+OVr1UdVT+WJEuT+r6CDWuQDXpMOWT0KyzAgik7IAKNiEei0eWFzBhSLkrGCL7cgTmIbZg4ymi5zRsm4hWVr9IC0KNOhkMx8ZnYpXEhEG1wJ3G76OVqvegMmpeAIx9B1RR6d0/OQrusdhTj83u6f988jbq+Mdyd5Bd3PM2jvzxAuaDSusLPLf/vPC66aQVWm3LC90qSRMsKIVU+dLB+7QkAHL1/IZ0T1L9CWAQDiSorHZowEyMptYGwQWOO+sRPLZlEy86ftYeWz6OlRCBUdhrVPUsCvG1gq4HKbbCLgKaRsxpy87pG2hMQx1/Avi9T7bAtIRx8006RsGlqdk/e0DTG9S+Blg3g7wRdhf5njtmnKTc/1iZaKEq2ENH+E3mFvbjxq2f6+O6j3UjWCJagqA7dds5tFYGSS5ecCUBK7z4lmAnOWBwA3SgQ+BqckBmDiKEK23HeAo2stph28NXe3s5HPvIRfD4fV155JSBoh/fddx/JZJJnnnmGM844o2YDXcSJkW8XC+r8rqmDr0Wj5ZmjfyhNebdYDHVd0YbdNt60urE9wH6rSEgc2l5/wZeu6xWPr0GvE1fJjavkBTRCp1VZncuAZ4LXl7VhJX5j4s/4BWVmIahCqtkk73EJuvSo8KZK6kJ6tyo9X+EV+CziOImRNEsCDZXKl1l5qyf0pkQ1zmMZD74imx9GtTiR1SLNF6zDde55NI5uB6ZOLngU8fntGjtY+wHXMYq5Mn/56T5++bmnGetNY3dZuOINa3jVP5xNuN0z7f20GsHXYD0HX7pGw8gWilkRTGpN4vqJm0qHVRLbMOEJGfOJ2kC4aHjouXKU7PMvumH2exVkCw6rCL4UJVFdc+WJCHbh1HWKNlEp8ugqcZd/0lgWAibtsHFMUJQzRvDlbzgq8B7YLn62nSl+dp4vfvYe21faYlgq9EtWbAalNfPsYPUG/QLCs31xPnrXcwBsPP1xVL3MRa0XcWHruBT7S1ecga4roGTZOnBooYY6bfjiwvdQcYnksL/BMS4x37hW0HBfgJh28PXe976XX/7yl5x22mlcdtllfO973yM7j5mnRZwYhQ5R+Sp2d6Omjy3tm5WvntQi7XC6+Mn3d2LTJRIO+OubJvd1nd7u56BVRUUnNpSdtnHqfEGNx9GM6+CQXSdkmCv7bRGsHm9Njnm04mHQyFbHPSKzOd9VIF3XUSJisZB0G5nZMRF8pTXhxeWuBu3QYsdn9Cuk42V8FicRj1igFgbrbxExnBHfQ8A27hfYv1XQB33lYRSHDddVN9E4JrLXh3eMopYnZ1AbHWK+6U68OJM5uq5z4JkRfvyJx3nuoT7QYfX5zbz+Exey/rJ2pBkqFU6sfC2koMIJMbIHVymKlhFJKEuLqFSMV76qHHyZ9hVaA6GsCEplJUPaa1bS5zH4qhgs+/EaDj02JV7T4AtArlS+IGpfeK8vU3AjMJpFB8pGL94xPV8D28TPSvBlBAdTBV9+EbgNJfPYJaEaqx6uT4PxhcRoSghsFMsaF63Nczgver1uO+e2Sdt57U7smghk/njo2EpjPUHXdYKGmbzNLuYTX6NzsrnyCxTTDr7+5V/+hQMHDvCnP/2J5cuX8773vY/W1lbe/va388QTx95Qi5hfqB4PlpYW0HXyu3cf83cz+OpP9VPSSvM9vFMO+w7FsB4WAdUZN3QJGd0JWNXsAavMEUt9Ug9LfWIxbWlq4lB2XOmwwV+7INEdNCtfeQivrHh9jblFpni+ZdfVeBy5JDK0aa/RLDK6D12HrBoAqlT5AlwtbVgkoeaYiRWJecRCVB8dq8r+q4loQVyrze5xpdSREfEdhdyiwmBdfTbh8iFsxSTFvEr/vsk9GF3+LgCGcy++nq/kWI7ff/1Z7v/WTjKJIv5GJzd+8Eyuecv6WXvGNXZ6Uawy+UyJ+HB9JjXlww8BoGREYsHVLp4p8WHT46u6tENvaALtMDVutBx3B4B5Dr6MalPE6cOliuCrJkqHJgyjZYsigk63LjFiMxkECxh8GZUv91CSssUNspg3zCplBYPbxc/WM8VPUzSh9ynh3zQBptz8cCKP1SuuJS1anwbjC4WSqvHeH29lMJFneaMbT9sD6Ohc13Ud66bwmWtxCBGY7cM753uoM4KWSuEsiWSTDZGA8k0U23gBmiubmLHgxhVXXMH3v/99hoaG+OIXv8iePXu46KKLWL9+PV/60pdqMcZFTBP2deImzO86NvhqcjXhUByU9TID6fqjQtUbfv2DXViQiHtlrr+665i/WxWZda0+9tUp9bBoqJBaOzvpTQxV+r1CzbWTbJ0kNx/sqlS+xuwiszXf4hPm4izhAotdTOyM7aWou1A18TlURe0QkCbKzY/lSLpFdVHJZtEy9VUVTZVFdrnNYwRfqSGiqqhkNa0YV9pzLwvRYFS/jk4unBYWi8Nkuf4qe7WCqmps3XSEn/zbExx5LoKsSJx7Qxe3fvx8Ok+bGzVGscg0LRXXTL1SD6XDfwHAY+gL+ZYsJ58pkc8YCmWN1e75EvdmTgsSjIuAQ1JSjDnEvTyfohuVypfdi7MsAmyvHKu+0qEJo/LlQCRv3JqFAYshuLGQtENDcMPaP0bOENtQ7TIW64S+xlwcogbdre0s8bNpPVjdUEjA6GRRsBaj52swkcfRJuZlvRCs3Umcgvj3e3bz5OEoHruFD9wg8/jgo1gkC+8/6/1Tbr8uLHwEe9L13ZObMdYpSSdIWZHU8Act45XTJYvB1zHweDy87W1v45FHHuF3v/sdQ0ND/OM//mM1x7aIGcK+3gy+ju37kiWZTp+Qm1/s+zoxnt4xhGdIBAyXv2ZVxVrhaGxo93PAqqIDoz0pkmP1YwxZUTrs7GQ4OzJe+Vo6tepaNWAGMul4AawO3Lr4f8Sgzsy34IYZfEW94LaMV75MmXmb04LVfmIxhGmjcVx0IzGaQ3X6yBpFkIVQZjsR8ppYQC0PioBL695C3C6qGK3njdNrnWduGO/72j6Kpo3T4c5uE9uV5VGK5fJ8DHtBMXQowS8+8xRbfn2QclGjbVWA1/7z+Vxw4/LJC885oHVFoHKseoOslZB6HkPVIZgW10Hj0rUVmXm334bNUV0jV4fHimIVc6+rIAIPyZJmyDBcnk/j9om0Q3tJzGsBKVo72qG/AySlYt7uVK0MWxe28pUv58mWjaps37jSoew+6ns3xTYCS8b7dRQLdJwrfj+KemgGXyOpPN51ggpdtDShlSerrL5Y8fOne/n+FrFm+9ItZ/CLQ/8LwM2rb6708h+NSzvPBCClH65r0Y2e/SIQH/XJ5OPiOeLTDoFaAFdDbZRE6wSzDr6y2Szf+973uPzyy7nxxhsJh8N8+tOfrubYFjFDVCpfO6cuNXf5uoBFufmTYdNP9iIhkWiwcOkF7cfdbkO7n5wMCY+4jeqp+lXsG/f4imZHCWaFQEJ4bY0WCxxV+QIcishQR+1iMVkeHUVX5++BavaYRb0SHqtPyBwn+6qrdGhigtdXciyHQ/ZURDcWQmjkeNA0DVWOA7AqJER6xh57clxs47xxs3DnpdcQjO/HUs6SS5UmKfGd0dKFrilIssqzQ93zeQrzinymxEN3Ps+vPv8Mkf4MDreVq950Gjf9/VmEWt0n38EMUM+Kh8HMAaRyjv3FABYNNAmalqwdl5mvcr8XCBVIs/plKYp7VpLL9FmNHtJ5vK/GaYdenEXxvTfI8UqFqupQrODvwCfF0dCQkUg7RDWoNLowwZcp/+4pW9BGx8ibBstHz6NHUw5NmFWMnsnBV5PXjiRBSdVxrF2KpJVQLU6iexbXKdt74/zzXWI9d9vVq1A8u3h27FmcFifvOuNdx33fVSs2GqIbOZ6pY9GNkcNCDn8s0Iim6sgWCXfMENvovACkmfXOnkqYcfD12GOP8ba3vY3W1lbe+9730tXVxYMPPsi+ffv4yEc+UosxLmKacBjB1/FEN5Z4RZbkVDdaLpeKbP3DD3j4O//O1j/8gHKpWLV9/3lzD/64iobOy99w2rHHLuTYeue/8vDn/47Qtq9j0YvslATtpp76viZWvpREGotuBSmPb1mNaDJMrnzpuo7LJh7OUXsSFAVUlfJYpGbHPxoTK19+ux/GBAUjZemaNN6qoGEVPjP4Gk7itviIVuTm68frqy8ZQZJFhnFtk0gsDOwSlTCfOopiH6elOi66AUUp0zD2LDD5+rZZLFg0kaV+ZqC+qS0ng1Yu0//ww+z71d30P/wwWrmMruvse2qIH3/yCXZtHgAd1l7Uwus/eQGnXdxakXWeKwq5LL//+of5xcdfx3MPfRmA2FC2YjZaL2hMCTbFEznBnkh4Faw2RyX4ClRZ6dCEKbpRVsM4JVFhGTGk3svzKOBjGizHnA6cJZFVafLYwFLD3qTQMkKaSt4qnuUFm6l2OLYgoiym2MbqtDj/lFsk9MzevAoqSodnTX69onj4+KSXrYpMg0d8jmXJj7Uo5svhbaemkqpWLjO4eTP2fYcZ3LwZbZbMgJFUnnf98BmKqsY165p5zxXL+Mq2rwDwN6f9DQ3OhsnH1XT698bY99QQye48DlUk1/5Ux6Ib2T6DdmgYcvvCTuT+F7a5solp8wT+4z/+g+9+97vs27ePc889l89//vO87nWvw+utjXLaImYOJRTC0tZKeWCQ/O7duM+frBRTUTw8hStfj9z5BeSvfJdgUsPsMHjS9zm0D/4dl77hQ3Pat6ZpbLnrIAEg2+Fk42mNk/7+yFfehfyjhwmmqBz7B57fsOmcc0H6OwYPJcgkCrj9C98sbMrMWzs6cT8vFis25wiSpUo0uylgBjNaWSefLuFytUHhCHkpjaWhgfLwMOWRYazNTSfZU3Vg9phFPRIhR7ASfGVsovrn9lex8uXw4fOWIQWJ4RTeJv945auOaIe7R8R1gerG73BBKcdIzA9uCHknLxIkuwNnu4vG0R0MtVzIwe0jXPLXKyuBh9fSQpwh9kQOz/dpVA0H7/49mzflyZSDgAdQcf38dziDHiJj4l4JNLu44vVraF9T3T6UX336zbTe9QTLK3my7YxcsI6Cs4XBQwmWbWw40dvnFU0pkX3vzfk5nwgZY8Ft0g6rLbZhwmsIOaTUBsIM0EeZqKEkOr9qhyLxEHfbsaviXH1NNf5+gl0ERp9iwJrEVfJRNgQ3KJVQ43EswfntizLFNpanjO/eEOw5xmD5aKVDEx3nARLEuiE1DN5xwZ8Wn4PRVIFszkmJAaCT4QMjrK/+adQUk+eTjRw8CO5f/5bLrnWw4saXTXs/xbLGe360laFknhWNbr50yxncc/h3HE4cJmAP8Hen/93k424bYfPP9ldYJwC32t7NX7p+zDbXc8Brq3SG1YU2Ip7ReU8bbsAXdozTUl+g5sompl35+vznP891113Hjh07eOKJJ3jHO96xGHjVIZzrxXQ1ldnyqS43/8idXyD0qW8TSE7mMPuTGqFPfZtH7vzCnPb/23sPEsjqlNC55W9Pn3zsr7yL0DceJpCa/B5/Gm55+Gks1lHQ4fCOhVe304vFSrXFtqQTT048pL2+2qqoKRYZp1cEeul4AZ9P8LVzUgFLs2GgOZ99GkbQE/FBgzNQ8fhKyyIjWFXaIeBvEoujZKyM3zYx+Kof2uHBqDBYtmIs3Pq3EkGIZzStbDxme9e6FYRie1D0IulogdGe8RugxWn0kJ6icvMH7/49993rIFMOTHo9q/qIjCnIss75r1jGrf98fk0Cr9N++ASBowgKwbjI9j/8wzurerw5IRcnkBUBds4wWC42iCqMqcxYbZl5ExW5ebWRsFFBMCvK5bEx9NL8VAhN2mHOJYJBVSpia+ys7UGDXYRUjazRM+vQLaheo/q1AH1fpthGR1wEvzm76OdqaZ3gZ5eLQcxIxhxNO3T4odkIp47q+2o2FA8jaZ2ky2AsjFaP0TIfON58kin7ue9eBwfv/v209/Vv9+zi6SMxvHYL33zTuVgtKv+9/b8BeNuGt+G1ja+9D24b4b7/3Tkp8AJwFB1cu+8t6D31VUWfCJthBaM7jMqXtwiZUVBsx14/LzBMu/I1MDCA1Vo7pbRFVAeO9etJPfDHKUU3zObMgfQABbWAXVn4Cs10US4Vkb/yXQCOJvzIgA7YvvAd/lIqIikzb/zWNJ3Dj6/Bjg/Jc5i+h57BFNHW1RLO7z583GNrQOPhRxnsuIlD20Y4/SXH7xObD5QGBkDTkJxOVL8fb14EPk0ttVkgTYQn6CCXKpGJFwg3roVRyMgq1uYm8sxvFahCO/TAOe4QGPS4jG54fFWTdgh4O1phu0apJNMgh4h4JUCfVzPYk+FIQqgTumSxcNK6t5CwC3pt69nLjtneecFlKH/YSUN8J8PBszm0bZSmpUK8pMu/lOdzMJI/9eTmtXKZzZvygINj72rxvTmkBOd0bkPet6Oqxy6UirTe9UTlSBMRSBxmqPUSGHBSyGWxO2t/z54M0pFHkNCJu5biyxrKnU1hdF2vmceXCbPnK62FCRey4LKTdJfRLRakcpny2BjW1taaHNuElsuhJZMAlB1WyEHJmkBqqB2FGxCVL1UjZxPHdusSxUAIZyohKnFr1pxkB9WF2fPVHBF9u7rViwS0tU0IvipiG0unNsftPB+Gd4rga92NlZdb/eNy84nGJN5hSBYW/tqfLk48n4gVykP3qWgtg0jKidknjx4Y48kn+lgjybz7kmXoPVl++OiDeHpb6bCfxgXZqznwjHiO6prOwz/dO+V+JCR0dM4+cgHlsoqlhqyX2cITE9knxdoEefDJhnpu21lgrYIHZx1j2qvUxcDr1IBjvajYTBV8hR1h3FY3mVKGvlQfKwK1E1+oNp79408JJo+v2iMB3pyO944fzmr/g80XYD/tPCylDJfd93Ws90xfuVAGlvZvZ7DjJvr3xslnSjjcC3e/FM1+r44OuuOjFZn5ZWuX1vzY7oCd0Z4U6ViBluXrYTfEFRklIJrU57MKVKr0fEm0+sIw+jwAmaJYLFSbHmppXolHjpLWGmhQwzxbh5WvwbQYi98mAtDRp55DtZwtxDbOXX3M9s6rXg2f+Drhwe0MB8/m4LZRLnjlciRJYl3Dcu4bgrRaPz1t08Xgo48a1KDjQSKrBhj8yT/Tbj92Lp0LdsXcBNP+Kf/mT4jKV861hAe+/W+8/H13VPXYs4F0WCSe9rnPpSEnbEysra3kUiWKeRUk8DXWZqFk9hOJylcRsIM1TckfwhYZoTw8XPPgy6x6FRQrsuH3qFsStVM6NBFcRlBTyViN4EuTyHqDODm8oJWv4GiBksWJbHh8+SZaDFT6vc6ceiedF8LT34GeyX1fpuLhUDJPdplGxzDkrCGKyQw2X3WFbWqB6cwnec3Dpu/sOcE243gl4tnU//s++ukDGrmWtwDwp+emDramPqqEtxjgwS27ueayDdN+33xA1zT8SeGNaTIx/EXDJukFTjmEGQRfizg14DhdlPWL3d2oqRTKBGqoJEks9S1ld2Q3R5JHTqngKzXYw3S6CoYbrRR8M1sIaFg40vIKADyphxhcagHGPzd7Ikvz2IlV+ly5Uey2GIVikO5nx1h7UW0XBCdCqaJ02Mmunr34CkL4onV97Rn07gmKh2vCIugrSxIFu6A+zJfXl5bNVrLVES+0u3wQFXSYdEYBtKrTDoXc/DbSWgOBko+ox6BH1VHwFcmLRVuDowk0jcH9RZDBp41NEtswoTR1YA8rNER2IUsa8eEsscEsoTY357WvgZ2gKhGypQIu66lTSc+MJRA9XifZzr0O2gNVPfZgpIflTD2fuHIjWEtpSlYPyf76MFs2zZW3KWfSkt4KgLtjaUVswxt0VE1u/2iYtMOU1kTY8A2ULGmyRvBVGh6Z1nNhLqgoHTp8eBBCF4qSqL0MtkE7zNkE7dCt66Q9AcJAeQEUDyN5IZbkHU6RNyiHRQWstgnfval0eLTYhglTRGFwB5RyYBXfnmm0PJTIE1zqwVJKUbZ6GX56P51XnVntU6k6pjufBAJlXI1T9woWyxo7BxKUyhpBt41VTR4kSaI/3cdgeginxcm68LpJYj/ZVJH40Mnnie2HDtZd8KVGIlg0HU0CS9Hoo0y8OPq9YDH4esHBEgxOEN3Yg/uCo0Q3vOPB16kEb+vUfhZHw/exD3H29W+a0b7/55vbULfGyCg6f/vdz+JxT16Ub73zX+FTPz/5GMMxCoNBDm4bXdDgq9gjgi9bZydDe/cCq8lbYzgaay904ZmgeBhwurFpUJQhJaewMH+0Q7PqlbdCzg5LtAToKqo1SD4jFnHVph3SsAaf5Q8MlNbjztmIGtZiZm+KVAfsgURpDCRo97RAZD8j2U7wQDhw/Pc4V7VTeLyXJr2bIZZzaPsIobZlrGvqQNesSHKJZ/oOctmydfN2HnOFu8EPxwmAJm132Rvh8surckxd17mv+z5+3v8xPnKcY0uAP3GIsYaN4FhblePOCbEjSLHDaMg8qq7lXWnRhxNYsnKccthcu/DHYwhuFDUnwZK4fyQlTcoTJACU5yGZM+7x5SNgeN1ZlYSg1tUSzgA+m4+cRVgPeHSduNPPUsYFQOYTsXwMd07HmsoRD4uEXtl5lGyAKbZxvH6dwFLwtEB6SGy79GJgcuVruSuEVB4A6xpGdvadEsHXdOeTK66303752ce8XiirvO6bj7PVmWdVk4e73nsJHruFkewIL/v1O8mreb5y5Ve4ask5k97XvzfGb/5z20mPu79wYNrnMl8oDQqK4ZjPAQVxHfmSWwSV6EUQfM1Iar5cLvODH/yA4TrK5C7iWDhN6uEUfl9m39epFnxtvPpWYj6Z4xEPNSDmV9h49a0z2u9YLEdmu6BTNF3UfEzgBbDx5g8T83DCY0e9IF0oHiS9u6MU8wtnPDux8pUZFA/unHN+7ln3UV5fTk0smJKy6BeYrypQRWzDC7puw2cofGZ8RkZW1rEfbQ46V7gb8NlFtc0WL5B0QVkGdJ3y2MILsQBkVXGtdwZaofcJIpLo82rsChz3Pa5zxAO/aVBkJQ8akvOyLGPVREC/fWh/rYZcE7RecgluSww4nmS3hscSpfWSS6pyvN5kL+/647v4p7/8E4f8RdQTKNX7EsKXx994cVWOPSccegiAuHs53fES/oz4vJq61hGvKB3WrjfH5rBgd4n71FsIAKLyFXWJ3+ejn9IMviIOH35dVHmcjqIwDq4x5GAXihF8uXUYsxsG0wtQ+Yrmo7SIaZxUUIgWyRPp9bmYUDIEaD1j6p1I0rjk/ATqYfOEylfIESJnGQAg0pus2vhribnOJ5+4ezdbe+J4HUJgw2MX19b/7Pgf8mqeMxvP5MrOK4897qrACZOIOjopW4ydlqdmeko1R9pYpwyHRSXQ6dSxyXkIrQDPseJPLzTMKPiyWCy8613vIp/P12o8i6gCHKbi4RR9X6eq4qHFakP74N8ZrfCToSEyxtoH3ozFOjMq2Z3f24lDk0ha4fW3TJ1ptjz1DbTz0+IYR/1NN459/9lr2V/S8TU6UcsaPYZ30kKg0vPV2UHZUKYquWLzcuyjjZZtuliYJRQRfJSGh+fFo8Y0No56JWTdXZGZT7vFvaHY9ap5NVUgSfhC4qGpxUrokkTMVIeuE6+vsiSug5XBNrTDj5Owi/mg9ZxjxTZMOK94OQC+w1uRJBjrTZMcEwvvgFVUeJ8/xeTmZYuFy649Hj1ZzCiXXutEtsxtgV1SS/zfc//Hq+5+FY8NPIZTt/JPvwmi6GLuOHo+0QB/UgRfI93pBfFzmgQj+BrxnI4WPYyMoJqFW5eTqLHSoQlTdMNVFFQ32ZJmxC7KyvPh9WXSDqMOH66yeL5456sNKdiFVYkD4NYURgyVu9IC9HzF8jFao+J6THqFqJR9ol2HKbYR7JpabMOEabbc+2TlJbPylS6UcVsDjLpF8BVLLPD1P01Maz65vDzlfHLnE0f4yZM9SBJ89XVnsaxBXFzdiW5+vf/XANx2zm1TPq9kWeKy16467rgkJB7r+jUpDqNpx++ZXwgMHxTP5LhXBFo+p6Gk+yKoesEsTJbPP/98tm/fXoOhLKJamE7wdSrKQ1/6hg8R+ee3oB81ByX8CtF/eeuMfb56+pPoe0VmbdXV7dhsUyy09j8Af/oUlzYlib5+GYmj3BV0YOctZ/OD4Nt4tj/JijPFRHJo28J4O+m6Pu7x1dmJlA0AoHnnR27WbaqTxUTwZZWEsEDSIoIvPZdDS6WmfnMVURoaN1i24KnIzGesok9DcdTmQeRvFhdIOqGja3YideT1Fc9lQBFB02lNSxjdfhDV4hBiG+ccK7Zhwrr+IhSnjq2YpTkkEm+HtosFqSk335M69eaTlpdczVSZao8lznU35GfkyzMVtg5v5ZZ7buErW79CQS1wQesF/NOD57CyP0LW6mDPDaeTOKpNJOGF8tm7kSWVbLJIcmwBE52aBobYRo9rPWFD1TIRsCDL8oTKV227rsy+L2tRNOVLSppBq1EBmofKV2kC7dBWFoFmIDBPSnyhZbgM1oBdVxhUzMrX/NIOdV0nmo9Wgq+0YfDrDU8IOE5GOTTRaQZfT4CRXPDYLXiNao+kejgSFpS0pHrq2Bktvf46ZOlYxotHjnJd4D9YEf2G6HObgKe7o3zibrFO+9C1a7hyzXhrwH9t+y9UXeUlHS/hnObJdMOJWHFWE9e98/RjKmAWm8yVb1nLoeAuUHI82Vdf7IT4EZGwyxhm3T5J2KC80M2VTcw4rfee97yHv//7v6e3t5dzzjkHt3tyCmjjxo1VG9wiZoeK6MaRI8eIbpjB10huhGwpi8t66si5Aiw99woy+ndQJRj54F8TWLqS86++dcYVL4Cffn8XbiTiTol3v2KK7FHkIPzyrYAO5/wdl77iy5Q/nOPZX97Bz577Ha+6P4c/B+cHOmEEdg0kWXb1aWx7oIfu5yKUS/OfaVJjMbRMBiQJS2sr1pyY2KzN85OqNStfxVyZUkHFam0AvYc0SWRfC1oySWloCMXnq+k4KjLzXrBJHhgzPb5ExlZx1Caj6usQlaB01oal7CPmzQJ6pRK3kDANlnXNSptc5tl+D9jAp0WnFNswIckyrq4gqT1x2nI7GOICDm0b5cyrl7AssJSdWRjL98/XaVQNex4bAGRaLLu58KxRMoFzcDf4ab3kpjlVvBKFBP/5zH/yq/2/AiDkCPGhcz+E/dcDdD7xZTQkcv/4cW5+06so5LJs+r+P0/HN3+MoQezdb+RlQ19nZPAAw6U1DB2M42+staTEcTD8HGQj6DY3By0raCyIeygXcqFrOonReap8GYqHUln0GUlKgSOGTcp8VIDM/qqIw0dTWUTL4eOIJlQdwS58ZClLRSy6jVFdHL88OoY+j5WMXDlHXs3TahAochYfChCaGHifTOnQRMsGsDggF4Wx/dAoEj8tfgepkTRa2c3elkEuHNAoWj0kuwfxdS1cD/V00btzBE234pbGuPKvyuzZP8K6czbSseEc5O98GAajcM/tcNM3QJIYSuR514+2UlJ1btjQwnuuGBdA2zW2i01HNiEh8cGzP3jSY684q4llZzQyuD9O3/NRnv7DESw2mdPOa8W+s4MiR/jz4a1cuGR+7QlOhKLR81W0N2PTwV96HqyMB+cvcMy48nXrrbdy+PBhPvCBD3DJJZdw5plnctZZZ1V+LmLhYQkGsbYJpbn8rt2T/ua3+wnYA8CpRz0EOLj5XgCGOl1c9a5/4+zr3zSrwGvX3giOHrF4OO/G5cjyUbdCIQU/fT0UEmIyuP4/ALDYnZz9hk9if8XL2XSOUYK7734cVpl0oUzWo+AO2CkVVPr3zg/VbyLMqpeluZns8CCK5kKVVPwdx6eVVRM2pwWrQ1AdM/ECdrsI/pIyWBsFFWU+qkATaYdO2QNjouE4o4kx1Cr4cnauxCLlAIlgsbVS+SrNAz3qZNg3JioXFi2I3P80I0XxsA8HT/5ZODcKMY3w4T8DMHgoQSZRYEOT8DrKaAt/fjOBpunsekRQm053b6L92pex+uYbab/88lkHXrquc8+he7jxNzdWAq+bV93M3TfdTceBAC3f/S8ADr/89Vz6plcBYHe6eMX7v8Du5eK67N+6A179TVqtQpJ68Mmn53Sec8LBBwHQl1xMtGSl0VC7KzUGyCQKlIsakizhbaitH49JOywoS7EalZIeQ2CvPDRUc2rmOO3Qg6MkbujG9q6aHrOCYBdBTSVreH3FNaO6USqhxuPzMwbGZebbYuKZp0si4G5unVCZOpnSoQmLDdqNSs4Es2WTelgsuMg5ytiK4nMfeqr+xCKmwqHHRHJiuXcHbTfcTGH1Mlovuwy5cQX89fdAUmDHT+CJ/yVfUnnnj55hLF1gbYuXz7/mjEm0wv/c+p8AvHz5y1kdPD4rYSJkWaJ9TZBzb1iGxSaTT5eJ9GdodYg5evvIsRoACwnFuK8kRVT7fPQJI+6G6Z3vqY4ZB1+HDx8+5t+hQ4cqP2eCz372s5x33nl4vV6ampq46aab2Lt3+h4GP/3pT5EkiZtuumnS629+85uRJGnSv+uuu27SNl1dXcdsc8cdC++pUi04Tj++39epKroBkNn2DAD5dXMLJu7+0W4UJOJ+hWuvPEq1StPgrncJXyhvK9zyA/HAmICzms7iz2fIaJJOrifPzVZx7e8cTLLcoB4e3hGZ0xhng4keX5E9gmYQdw7T1dQ5b2OoKB7G8gQMSeKoImMJiCzpfFSBzGAn4oWQbIFyDhQbmZx4wCv22mSNpcbV+BVx7HCxhai3fuTmTYNlhxyE3seJSIKC2XQCsQ0TrkuuEb8c7qW5yws6HN4+yrnt4kGpKTES+fqQRp8OendHSUcL2KUUK3w7oe1YBbKZ4EjyCO944B18dPNHieajrPCv4PvXfZ9PXPwJcv1Jch/5EFZN5cCac7n+jo8e8/7sRiFQ0Lj3AKy9gZazRLA7tG8M+p+Z09hmDaPfS192BfECNGaF8IPU3FihHPoaHCjKjJcRM4LXpDJL7YRVoSg3YhTy9WKx5kGIKbiRddpQdBGYt3TV3rYDEEbLmkbWKqjaVk1BDgr65XxSD6P5KOg6rVGdsmLHIonPYUmHwZvNRk8utjERZl9P77GiG+YcreoiOTK6b+HnzpNBVTUO7xVU+xWrNJCOuieWXw7X/jsA+v0f43t3/pAdvXH8Tiv/+8ZzcNvHEz6PDTzGE4NPYJWtvPes9854LIpVpm1VAIC+56OsD4trtSe9bxZnVjs4Y2I+sUhijeC3DInr4uhE+AsUMz7LpUuXnvDfTPDwww/z3ve+l8cff5wHHniAUqnEtddeSyaTOel7u7u7+dCHPsRll1025d+vu+46BgcHK/9+8pOfHLPNv/3bv03a5v3vf/+Mxl/PGO/7OjbbsdRriG4kT73Kl2uPGHPg3Nnzgrc8PYhvVPRAvfS1U2RZNn8Rnr8HFBu89kfgbT5mkzObziTik9ixQtxCN+/5DQDP9SVYfpYIvo48G0GfZ+bhRKXDkUNGAOIaYKXhuTUfmKh42OAUE2tMUbB6DPGPeRCfKE8wWG6SDAng8ErScSGVXavKF/5OfFaxKGosBIiala958jc7EfrTYgxeSwNa9+Mk7CIJ03J210nf67jsFUiKjpqXWNosFoIHt42yItQMmgNJ0nmqznoKToRdmwVNcq3zQSzLL5y1cl1RLfK/O/6XV//21Tw++Dh2xc4HzvoAv3jFLzi7+WwKuTzb3/oegrkkg8FWLv/u11Asx3pibXzZXwOwsj/LyMgQra94MwCRcieFO98O6XnuGSzloWcLANqyy0kUJRqN57K9tX1cZr7GlEMYpx2my8FK8KXacxAwgpAaUg+1CT2qHpdYXBeUDLZw7Q3rAfB1ENKoVL7cugQN4vkyn0bL0XwUXxacBZ28Q9A/c5JOU8j4/ieKbThPZDZswAy+esYrX61G5SuRFonOpE0EX9Gh+hd4G9gXp1C04pQTtJ51HMuNC98NG1+LpKv89eF/pkMa5b9edxZLw+MtAZqu8eVnvgzAa9e8lnZP+6zG03maeO727oly6ZIzAcjoR+pGdEMvFnGn8miSjK0szt+nDL9oxDZgFsEXwMGDB3n/+9/P1VdfzdVXX80HPvABDh48OOP93Hfffbz5zW9m/fr1nHHGGXzve9+jp6eHZ545caZPVVXe8IY38MlPfpLly6c2OrTb7bS0tFT+BYPHTgher3fSNkf3r53KMIOv3AlEN7qT3fM5pDkjERmkeVA8AFdd9vJZ7+fPPxcZoGSTjQvPPopLvvc+ePDT4veXfQk6zp1yH12+LgL2APefKSob7t29eNQMz/UnaFvpx+G2UsiWKcRqYz56PBQrYhsdDAyJzyrqHBCL5HnCRK+vJrd4CMRlGYtdBD61ph3q5XJF2j3qhVbNeHg3rK6oMNZKcANZxucRgX244CVSqXwtvODGSNYwWLaHGN0zZohtFE4otmFCcrpxtIrFUcuIEGHo3xenmFOx6eLa2jF0atCD0rEC3c+JqvQ61wOw/IpZ7efpoad5ze9ew9e2f42iVuTitou568a7ePvGt2NVRA/dfe/8R5YOHiBjdbD06/+NL+Sfcl/nX3AFw34ZiwZ/uusHuAIOfA12QGY45oWf/y2Ui7Ma56zQ+ziU88KTqWENiSI0ZMR95O1cRtxQOqy12AaMC26kc07CZXHfypY0akj0XZVrmMwxq0tFq42QVQSfJVty/rLzioWAPUDOqHy5NYlywKBvz2PwFcvHxvu9WgRdOWsVVDdg+pRDE6bcfGQ/ZMS9aFa+RpIqXquXQb8IvuLZ+rejPfS0SOYssz+JvPKKqTeSJJ7a8Al2assISynuCn+Dl3RNXnNu6t7Enuge3FY3b9/49lmPp2OtuEYG9se5fMnp6JqlrkQ3SiMjyEDaGUDSZWSphFuOvqiCrxlf1ffffz833ngjZ555JpcYngWPPvoo69ev53e/+x3XXHPNrAeTSIgyZCh0AplSRMWqqamJt771rWzevHnKbR566CGampoIBoNcddVV/Pu//zthwxjQxB133MGnPvUplixZwutf/3puv/12LMfh+xcKBQqFQuX/yaTIRJVKJUql+VGSOx7M408ch2WNWFCVjvSQj0QmCRy0u0U25UjiyIKPfSbY/fBvCQCRoIWVbStnNfYHHuohkFRR0Xn561ZP3kdkP5Zfvw0JHfWct6BtuBVOcIyNDRvZvOJhih6wpSXeMXQP/+t6PaVymaUbQux9fJjckGVeP+PCEVEZVNraiO4VC6SkYwwFad7G4fAZ3l6RHM1NIukRVWQURdwzxcHBmo6lPDQEmkZZhoQLWkriuOXgStKV4Euv2Rh8QQuMgi/vJtpijGl4mGKxWH15+xkgVhALyWWSykB8GdjAr8fQZNCm8Vk41naR69uL8txmQqtvIDqQ4eC2YQKWVkb0I+wZOzinz3SqeawW2PVIH7qm02rbQ8jSR2nJpSe8z49GvBDny9u+zN2H7gYg7AjzobM/xLVLr0WSxu+zP37+W6x+8o9CYOOfPs4Zp594zjq0vJXmbf2ktzxM6S2307zMT3JshEHtDJb0fB/1Dx9Bu+5zczv5aUI+8GcUQFv2EkrlMvEiNKTE2H3tyzn4lAhEvGF7zb8vu1sGCVRVoqHoBLdQPMwHwrjZT35gAHuNxpAfEAFA0uknJIm5Q7Nl5nVOD7ibyVrHK195XxAXUBgawjZP98xoZnRc6TAoKuZlh1w5rtK3FRlQmzdMay7B6sXSsBppbB/l7sfQV19Hg+G7OJjIEuwIcrBxkJUpSCtB8unsCUWBFhK6pnNo2zAgs6zhCCV3y5Rz2WAiz7t+ugt78Xbud3+cxvTzaHe/H/VGIcBR0kp8detXAXjj2jfiVbyz/l59jTacXiu5VInYkTR2vZ0iR3jgwFOc0zp1wWI+kTeSxKNGAsUnD4OiUGreOKO5eKaYj2fMdPc94+DrIx/5CLfffvsx/VEf+chH+PCHPzzr4EvTNG677TYuueQSTjf6labCI488wre//e0Tyt1fd911vPrVr2bZsmUcPHiQj33sY1x//fVs2bIFRRGViA984AOcffbZhEIhHnvsMT760Y8yODjIl770pSn3+dnPfpZPfvKTx7y+adMmXK76UAx84IEHJv1/WTCINRbjL9/7HrmVKyuvD5TFA+VA5AD33nvvvI5xLkje/zvOBQY6fLMat6bBc392EUZhNFDm0L4tHDJo0BY1x0v2fgJvIUXEvZpHy5ein+QYjrwDXZZ4+gwXFz+a5crurXyp/bV8/64/4CspgIvcsIVNmx5gvtbcy/bvxwo8ebibXEFUP6P29Lx+z+k+K+Dg0L4exiQxycYVBbXYDyhEDxxgew3H4+jpYQkQdyvoskQoJrKSTx0uo2s6oKPY9WPul2ohXE6LcWTdFZ8vvVjk/l/+Em0Bq+uJwjDYITxykJHiSrCBRYpP+9poDzbiZi+Z/QOUz4wCdh7ftAtbhwvssG94T1Wus1p9LyCUrYcecgMy6533kbOG2PTEPpBOnhHWdZ3tpe38IfcHsroh1mM7j2tt16LuVvnD7j9Uto3tPsLZP/wmAE9ceC1hn3zSz2ZsyXLY1s+SAz3ce++9pLPiPtrL5ZzPD1Ce+TbPjkJP+PJZn/90cfnzItG1PRGg94EHyGUzeIzc485Dg0QPSYDC3u7nOJLeXvPxyDY3WkEmlA8AceyWOL0lnbXA8488SqRGz2DPjh20AWN2Dy5D2ENVMvM6n7rTlnHaoSZxOFtgPXDo6acZ6RBmx7W8ZwC25rayxAi+hi0BAHJSsfI5XH1oC27g8Z4CY7HpfTZn6q0sZR+HH/4Juw9o9GUALPSMJlkSgu7GCPL+Appi5/5v/xy6pkFnXAAUYjLZrBublKHkLU26NszvpaTBV3YqRDISba4wTy99N5cf+hzyzl+yK2bjUNN1PFF4gt5cL27JTWNPI/f2zvEa8zggZeXh3z+Nx9dMVDnCXw48xsbUwsv3e7dtoxWIBgSF1m8ZIu5Ywl8eeGhejl/L+yWbnV7v84yDrz179vDzn//8mNff8pa38OUvf3mmu6vgve99Lzt37uSRRx457japVIo3vvGNfOtb36Kh4fhSr7feemvl9w0bNrBx40ZWrFjBQw89xEtf+lIA/v7v/76yzcaNG7HZbLzzne/ks5/9LHb7sY7hH/3oRye9J5lM0tnZybXXXouvxrLZJ0OpVOKBBx7gmmuuwWodzw4N/vGPZB74I2d6vQRvuKHyeqaU4eu/+DoZPcNlV1+G17bwN+N08KdvfxaAwEUXcOWE85kufnX3fsKlIUqSzlvecz4drebKWEP55d8iFwbRva343vIbrvc0nXhnQMtIC5v+uIl7zndw8WMZLKMqF6efI7Ty9bx8XTM/2Pk4pQKcufpC2leduJpbDejFIgc/Ipr5Tz9jIwf3Ql7JoLvd3DCLz2u26H4uwqbdu/Hag1xxzUZ+dO9XSMsydmsMaMCVz9d0POkHHmAIiHpkQKdLE3yZrrNeztatKZxeG5LMMfdLtZAIwY5DoJSClBWJpEvBl1W5cuNG7GsWTur3X374eQAucuY5IgnBmuUbOznjhutO9LYK1LPWcvgXj1BOSLz0iiXcdWCYUtTG+RefQ9/wnylYk3P6Xo83j1UTPTuj9Od3YbeWWOHYgnzaa7jhZSf38+pOdvOZpz7D0wmhPrjSv5L/d/7/44zGY8UFBg/20v+vn8Gqq+xfex5v+N/PHaumOgVOW7eK0m83s2SsjLpiCY1nLuOXu7aSy4UpX/NhrI/cwZn9P2TDVbegm2pxtUA2imWbEGPacNP7Weto4L8e/m/xJzvc+KrX8Z1HHgV0rnnZ5ZO9nmqEu3ZtY7QnjUdrA+J4LKPInefCc0+wzO/jghrNJ/FIhDEg4ghg18Q16fTL8zqfjmzeQXaHUC126+Bavga2/Jl2p4szrrmm5vcMwGOPPUaLQTtUvS1QgmBLgBtuOBtyMazbRFX9/BvfDs7AtPYp7YjDPQ+zwh6h64YbiKQLfP7Zh0mXJZa1LKenvweXOkpa6WCJo4nTb5g9q6qWePyuQ4zSz1L70yy76m/oWnPDpLnMYrHw4bt20ZsZIOC08qN3XUBn8K/Qn/LCpo9x+sDP6Lr0FXz5WdFj+d6z38ur1rxqzuPaGxri4Tv341QbuHDp+dw7/CQp6+i8XrvHw0hvL0kg7TIqX8owvvXXcMO1tR3bfDxjTFbcyTDj4KuxsZHt27ezatVkX6Tt27fT1HTyBetUeN/73sc999zDX/7yFzqMTM5UOHjwIN3d3bziFa+ovGY2EFosFvbu3cuKFSuOed/y5ctpaGjgwIEDleDraFxwwQWUy2W6u7tZM8UCyW63TxmUWa3Wmk56M8HRY3Ft2EjmgT9Sev75Sa8HrAEanA2M5cYYyA1wuvv4lcZ6QamYp6lb0FKXXPpXM/7Mi8Uy3Q8N4QWkNT6WLZmQRXvoDtj3B1DsSLfeiTU4vSbXM5rPwCJbOOBIYlnlpbwvzVuO3MuWoRt5zblLWHJ6iIPPjNK7K07Xutr3XBX6+kDXkVwuUqNxIEDUPYDf3jCv16g/LLLQmUSRpaEmdF1GkjSyHnGvarEYiqYhT3E/VQP6mOghiPg0QCJUSAESeaUN2FuRrq7VvRtcuQboA82Oo+wh6sniy6oQiSzYXJEvFdHkFBKwdHQvzzreA0DHuSumPSbrkjXYghLFmI5nz334Gy8mMZpjVV4EclmGqnJ+tZxTn98ihFjWBp7CIpVg5UuRT3Csolrk2899m2899y1KWgmH4uDdZ76bN657I1b52Pflszn2vOv9LMknGQi1ceV3vzblc2MqrF61jt8321g+XOSp3/2EN3/4c9icFoq5MvGV76FxbCfS8/dg+dWb4R0PTykEVBX0PQbo0LgWa2gJmXSOYM4wvQ3YKaRUtLKOYpEJNHnG+35qCF/YyWhPGpe8FNiNzZIg6hJzuDo6VrPrRYuIuWTE5kHRRH+b02+f1/s43LSOrE0IU7h1ibh53pHx8671OiRejFdoh3nJjQT4GpzimD2GqFdwGVZf4/R32iXaVuTB7ciSTpPfjU2RKaoaLqO6ptiToEOsJ1E366yJ0HWd7u1iTlnheALLyr+HCeO0Wq3c+WQfd20bQJbga68/m+VNRt/nRe+B4Z1IO37MT/50G2NeOx2eDm497dZKz+hcsHR9I7Cf0SMpLrn+bO4dhozejaIo00oG1RKxnm4UoGBvwIYIvpSum1Hm6Tuu5f0y3f3O+Bt4+9vfzjve8Q4+97nPsXnzZjZv3swdd9zBO9/5Tt7+9pk1COq6zvve9z7uuusu/vznP7Ns2Ynlw9euXctzzz3H9u3bK/9uvPFGrrzySrZv305n59Ry2n19fUQiEVpbj2/Ut337dmRZnnUAWY9wrBeqO7mdU8jNe08tufkDz/wJZ1FkXleedeWM33/nT/fgLUFO1vmbN08INp+/Fx4SFTVe/p/j/iPTgMPiYF1YfMY9150HQMeRYfZ3C1rnsjNEj+HhHZGae9HAuMeXraODaJ8IVCOuAUL2eTIENWA2yGeTRSQkJE0EY3GnhGQTE1Mtm8XHPb5EsBdQNQguJZMS34HLX5ugz4TStAKPInxxfPlwXXh9HYgMIUka6BLSgBNVEWIbjefOzFPFuUI0seWffLSi6ukeMKiUSpLR9PSyfguBdCzPkeeEEMt6/cfixeWXo2oqTw09xb2H7uWpoadQNaGo9+Tgk9x89818fcfXKWklLm2/lLteeRdvOf0tUwZemqZx/zs+xJKhQ6StTpZ/4+t4gzNjRQys7gJA3vYUkizRsly8f/BwEl71P9C4FlKD8PM31U6Aw5CYZ7mYZ4eTBRqNfsF8g7situFrdM5L4AXjc4pVEs94XckwbBc3Vk0FNwyD5bjDg6QKpoQ7PLVoSq3gCK9CU+Li2JpExDzvkfmTmo/louOVL1Vc+yFT6XK65spHI7wCXGEh7DK4A1mWaPKJudmii3Ms+cV8Eo2ocxl+zTDWmyYZLWOhwJJlEjj8qJrOE4ejPDMm8d3Huvm3e0TV8mM3nMalqyY8iyUJXv6fxNvO4Ltu8Zm+b8M7Zh146apK5oknSdzzezJPPInHbyXQ7ELXYVWp3RDdyPN478JLzmf7hSWOZhHPEKF0+OIwVzYx48rXv/zLv+D1evniF7/IRz8qKE5tbW184hOf4AMf+MCM9vXe976XH//4x/z2t7/F6/UyZEyifr8fp1Nkmd70pjfR3t7OZz/7WRwOxzH9YIFAAKDyejqd5pOf/CQ333wzLS0tHDx4kH/6p39i5cqV/NVf/RUAW7Zs4YknnuDKK6/E6/WyZcsWbr/9dv7mb/5mSlXEUxWOdSIwKPX0oCaTk0Q3uvxdbB3ZesoEX72P/ZFOYHRZcMamyql0kbHHR3Ah4T07TNjwm2J0H/z6HeL3898JZ71hxuM6s/FMnh19lsfOaOJWD5TTMhc/9X007aV0rguBrJMayxPpT9PQUVt6Z0XpcEknEeO5HHUNstRTQ4rSFHB6rMiKhKbqZBNFLLqHMmniikxDwElppER5eBjbcZIlc4UZ5ER9YmHo0zRoWEM6JppW3AEbNQ0RFCs+e4J0tgF/voGIV9xjC+n1tTciHnZO1c5waklFbEOxzuwR4Dr7bBJP/57snkOs+Psmtm3qYXRvCmWDH9Wa4Mn+fbxszdQKoQuN3Y8OouvQ1lYiqPVD8+n8MfIsd/zhDoaz499No7ORpb6lPD0sKIYNzgY+cv5HKoIax8OmO/6H1U//GRWJ8v/7FMvOmDnF1H/xS2HzPlYeGkZVVVpX+OnZFWXwYIKNV3bCrT+Gb14p1Ajv+7BIGFUbhrmyqQI5lMzTmBMrb7UxVPH4CsyD0qEJT0gsyiVNBP8FS5E+iwiGSrVM5JhGsE4dqRwAINA4v8ksgl24ZDFjKUgMyd7K2PR5kg4vj47iKEHZaseiipx9c4uRdJmp0qEJSRLqdnvvFddz53m0+Bz0xXJgBLrRpgSOOCRL9dFXfzQObRfXxxL7NqyrLuW+nYN88ne7GUzkAQX2i0Dn/K4gb710iuKC1cG31l1J+uCvWVMocv2O38Gqm5hpk3hy0yaGP/PZSYkIS0sLzTf8P+LIjOxL4dA7KNDNg4e3cvHStbM95apAN56FFkSC2h+QwHf84sgLETOufEmSxO23305fXx+JRIJEIkFfXx8f/OAHZ6zk9Y1vfINEIsEVV1xBa2tr5d/PfvazyjY9PT0MDg5Oe5+KovDss89y4403snr1at761rdyzjnnsHnz5gr9w26389Of/pTLL7+c9evX8+lPf5rbb7+db37zmzMaf73DEgxiNWic+d27J/3tVKt8Fbc/B4C2Yebu5z/44U5cmkTaAm98g2GOmU/AT18HxRQsvRT+6tOzGtdZTeKBsz3yHP4rzwTgkkPbODSWwWpXcDSUAeGJVGuUKgbLnURSAUBUvjq9LTU/9kRIsoTLLwLkTLyATRZB/2Svr9oFIhWPLw/YVVlkmBrHZeZNH7JawucTmVpvIUzMqMAtpNfXoaioxjZqkhDbAEKBmVdjnZcLTn5+ME9jk4wnaKdUUFmWEAHXjqH6kDI+Gpqms+dR8Rmsb9wOwB/bVvP3D/39pMALYDQ3Wgm8Xrvmtdx90938VddfnfD59sw9D9L+Q9EX1f2qv+WiW0/eRzYVrnnV31C0QCit8+jmTbSsCAAwdFBUsgmvgJv/D5Dg6e/AM9+b1XGOi+hhiB8B2VKhhQ0nCxWDZaWlaV49vkyYla9ySSRHs4pOtySCPy2RQMvlanJcs0LvchSwlMQ8FprnyhcOPz5ZJW8RCpMR1Zi/ymW0GhtMg2AoOQdEJb+0RCza8+i0NRnB18A28bP1zJnvvGK2LGiVLYbXV8kItg51CNpnzhogOxKbxehri4PbxPWx3PE4T0gbePePthqB12Q81R3j/l3Hzv+D6UF+cvh3AHwwnkLe9St47L9mNIbkpk30f/C2YyrA0y8LEgABAABJREFU5eFhbHd/C4De56O0OsW8v33kWO/X+YYtGqNkcWLRxffsW35i1tsLETMOvnK5XEXNw+v1Eo1G+fKXv8ymTZtmfHBd16f89+Y3v7myzUMPPcT3vve94+7je9/7Hr/5zW8q/3c6ndx///2MjIxQLBbp7u7mm9/8Js3N4/z4s88+m8cff5x4PE4ul2P37t189KMfnTY3/1RCxWx55+QbzvT6OlWMloP7xMTSeMFLZvS+kbEshWfFpN1+aQsup1XIHv76HRA5AL4O+OvvwSxL/Wc2nQnA/th+bG/9IEg6llGVvgd/D4CzRQRfh+Yh+CoaBstqKExWFQuEqGuQpYH5Db4APAHDmydWwGkGX7KMxSE+j1pWgUoTDJZ9ZmK4YU1FZt7tn1nldDbwN4hj+PINRIyC80J6ffUmRQJriZojYohtNC0LzHg/trOuQLHr6KpE4bF7WHamoI2sSmwA4ECsuxrDrTp6dkZIxwo43FZWZH+CCtyR3Y/O8QPQkCPER8//6EkFifr3dVP85w9j0TX2r7uAGz79j7MeZzAQYn+7yPo/f9+vaO7yIckS6ViBVNRY1K2+Fq76Z/H77z8EvU/O+njH4JBR9eo4Dwx623AyT4PxzHe2dxIfmT+PLxNm5SuftaEYFO7RchzJUDms1XxiBl8BRw6bUY1pDM8/OyYoOypy8/msjmLY5piVuVoiXUrTaND+Sm2i1z8p68KXKxuFuLGGaD1WfOakmGi2rOu0GF5f+by4tvptY9hLIvAffrq+EjuxoQyxwSwyZZZ6dvOhLbYTzCbwyd/tRtUmb2FSms9tPpdLL/+EePGP/woH/zytMeiqyvBnPitkXI/5o04wvh9J10iM5Njo3AhAb2ZhaYdaJoMjWyDvEBVkpxzD2lWfbIlaYsbB1ytf+Up+8IMfABCPxzn//PP54he/yCtf+Uq+8Y1vVH2Ai5gbjme2bAZfR5JH5qUfaS4YOPQcoYSKJsHaS2dmrvyj7z2HXZdI2uC1rzFK7Q/fAfvuA4sDbv0ReGbQJHwUGpwNdHg60NHZ5VVhqcgGBu4SVVRnUxlJlogOZCq9ErWCWfnKaKK6lLCPUlaKrG6YnoBINWFWlzLxAh6rCARjioLVMAutVRVI1/XKQizihZBmeG40rplQ+ap98OVrEeqWwXyYqCGquZC0Q7O6szSXJukQVe+2c2bu9yLJMs6l4vvMPvJHVhjBV3usA0mX6U/3VmnE1cWuzcJuYO1ZTpTUYba6PAwXEyd8TzQfZevI1hNuk01n2fn29xDIpxgItXPVd74652b26HoxT/l27sRqV2joEBdQpfoFcNk/wGk3glaCn70RktNnh5wQR/V7AQwlCzSkxb3j61g+gXY4f5Uvr1H5yiTKhFRRgfSoB7EYSdVSDRIbWjaLlha2EUGnOH9VKtMSnP3zYrYI2nwVuflypoSlUYxhPoyWo/korTGxRsgGBFU8pegEXdZxymFo+bRVDieh7SxQbJAZgdjhSuUrlXVUju03Kn6jewbmdB7Vhkk57LA9S65lPb3J4/el6QifrycPRyuvHYwf5O6DwivwtnNuQzr/7XDm34CuwS/fIqrQJ0H26WdO2PNoKefwJrsB2KiKeSWjH6GsLlwPXckYb9wrgi+/MvyiMlc2MeOnxNatW7nssssA+OUvf0lLSwtHjhzhBz/4AV/96lerPsBFzA3O083K1+Tgq9NrTKKlFLFC/ZXzJ+LA5nsAGGpz4A1MXxDl4JE4ygHx8Fx33RKsFhn2/A4eNoxKX/GVmfPUp4BJPdw2so3UNa8EwLm7Hy2TQLZC+2qxWDUpCrWAruuVnq9kTpR7Iu4BdF1iRWgBKl/B8eArYBeZ4pgiY1HEArJWVSAtkUA3zNBjXmhQDVGChtXjla/5oB0uEQGvLx8mYvSelRYw+IrmjYXCaDOq4kBRCzSes+ok75oaztNFL1Puud20rvTj8FixlKy0JVcSLfZXbczVQiqa58hOQV9a1yzmwdGm6dGXR7PHryxomsYD7/gHlgwfJm1zseJ/vo4nMHfbkaVXiQTTqp4EmWya1hVi/hg8NCH4kiS46RvQeBqkh+Dnb4RyYW4H1lQ4/Bfxu9HvBTCUyNKYEou1YMdqUhFRgQs0z1/w5fLZkBUJXYfWsli0NSu90GAEITVI5phVJdVmx2MV55yzpvA55t9aJugMkTUSV1pOxdIkzlsdG6v5sWP5GK1GzJA0xJvKDkXQcOdCOQSwOsbf2/tkJfhKpMQcHS/ECQTFMnWsPz27Y9QIJptlueNx+oLTCx5GUuOUxK9s/QqarvHSJS8VlhWSBC/7ohD9ysXgp2+AYuaE+5tO5TMUex4Ab8RdEd14om/hql+lQXGvRv2GzLw1As3rF2w8C4UZB1/ZbBavV9ARNm3axKtf/WpkWebCCy/kyJFTo3/oxYSK6EZvL2pi/OHtsDhocYtFeb1TD5PPPAVA7rSlM3rfL3+wCwsScbfEK65bDiPPw13vEn+84N1wxq0n3sE0YVIPt49ux//a9yK5dLSiRPr7XwCg6wwxydSSeqhGo+jZLEgScfGMJuoaRNK82Cwz1tWZM8wAJx0vEHSI4GtMsWFxikVcrapAZoCTczsoWST8mgaeFop4KOXVSWOrJfwrRGDjLAWJu8TnryWTaNM0YKw2kmWxSPOMdQHgm4XYhgnXxVcBkD0cRZJgmXF9L4tsJM/CBZjHw55HB9B1aF8dIBgV5pqNrWdP672NruNXOTZ95r9ZvfUhVCTUf/4UXRtn3o86Fa76q1eTdEq4ivCn3/2MFiP4mlT5ArB74NY7weGHvqfg3tnTHQEY3CEWfTbvJNXXxGgvNsEWxuVbga7pWOxKpa9zPiDJUuW+bdLaAAhahyiHxLVXi8SGWVXK+oLYZVH5KdqzyNL8y3QH3S0V2qGlqKE0NhljrD3tMJKPVGTmk5KowkpG7+640uEckpid54ufPY9XaIejSbF/HR1vp6AgxpMLK48+EalonpEjKUBjmf1J1K4rpvW+Jq84v+0j23mw90FkSeYDZ00QqrM64LU/AncTjOyC3753akohoJfL5LY+c9JjmsHX4L4EDk1oADx4eNu0xlsLlIdElT7lNoKvkAVkZcHGs1CY8dW8cuVKfvOb39Db28v999/PtddeC8DIyMiCmw0v4lgogcBxRTdM6mG3UZauVzh3dwPgO+f8ab9n+65RXP0iy3TJq1ciF5Lw09dDMQ1dl8G1n6ra+Mzg69nRZ+lq8bF/ufhcE7/9AwBdG8MgwciR1HjfRpVhysxbWlqIxsWiKOIawEagJsc7GTwTaIeNLkHBG1acWF1G70CtejSM/Sa84vh+VYPG8aqXzWnBaq/9RO8I+bHK4ru2EqJkSOwvVPUrbxhNa4YnVzg4e6qx4/JXgqyj5iRKux5nxVliIbgsuhHkLL3xyNwHXCVoqsbuR8XDfv2lrZXKztnrXkuzqxmJqUU0JCRaXC2c3TR1kPb0b/9E+53/A8CR1/wdF95SPXNQq9XK/i5xzwz95f5K5WusL00xX568cXgF3PwdQIKt3xciHLOFSTlcdhko44G5NnYQgIRbIpcQn1egyTljga25wqymhzRRVXZZImT94nOqRSXdrCpk3H5UXSyaVcccq4uzRNDXSc6gHbo1CTVgnPfoPNAOM2M0x8Xv2ZJ4ttjMwHu2MvMTscSQGO99olL5GkmUCdgD4lirxLoySaDi67rQMBOpbdY9uPwONpx1Ia1+x3FmE5CAVr+D85eF0HWd/3xGqJTetPImlgeOon/72uCWHwjRm113waNfOWZ/ued2cviWW4j9+CcnHqgkEXLmsNhk8ukSa1SRVNmxgKIbpQExHxeMni9/2zyrh9YJZhx8ffzjH+dDH/oQXV1dXHDBBVx00UWAqIKdddbcKVyLqD4chgx/7mjRDW/9i26kExGa+0WPwYrLpr/AuffHe5CRiActXHFRG/z67RA9CP7OOQlsTIWVgZV4rV5y5RwHE/v507lvBEmnMFAkeOQ5XD5bZQFl8sSrjaLR72Xt6CCSFdn6qGsAlxKqyfFOBrexUErH8rR4RHN4RFHGK1+jo+g14J2bfPKYR3y/AUNmfj6VDkGowvqcogTpKzSS8pvCAPMvuqFpGqocByCrdwGzE9swIXuDOJvF4iv38D10rAlicyi4S36a00t5sn/vHEdcPRzZFSUTL+DwWFne1C9UTu1+lPZz+Mj5H5lScMMMyD58/odRpsjI9u09TPnjHxECG6dfzPX/9g9VH3f+DPEsbX7+AJ6gA0/Ijq7pjHRPYZKw6mq4+l/F7/f+E/Q8PruDmmIbEyiHJVXDmRSJnVTQMS620Tj/0t/ekFiYe3URfFksSdIeUVWvBe3QlLDPO6wkNXEcyb0wi/9AcHmFdujWJQpG0KmO1p52mO3vwaqCapEpGaKS3pAhtpGYg9iGCbPfZ2QPTVYxTxdVDZ8tIH5f7UDSyqgWB/E99bFWmahyyPIrUBSZf33Fuim3NQOyf33FOhRZYnP/ZraObMWu2Hn3Ge+e+gBLL4Lr/0P8/sdPwIE/AqCm0wz9+6fpfu1rKezeg+zzEbj1tYKyeJxkSOvHPkz7GnH9ri+KtWBPZuHm6Fy/oKZrFrEu8K1YsWBjWUjMOPh6zWteQ09PD08//TT33Xdf5fWXvvSl/Od/1sBzZBFzhmm2nN81deWrnuXm9z56D4oOMZ9C+8ozp/Wev2zpwx8po6Fz/evXwoOfgf2bhMDGa38E7upmWmRJZmOTUBLaPrKdwLqzyLYKqkTzo+IeWW4IE9SKeljsFQ+lQkM7KjZ0qUDSMYbPujDB13jlq0ib0VibkMHi0MTTqFymHKl+hcQMbsY8Ymrzayo0jnt8eeZBbMOE35Ba9ObDxL3iuKYB9HyiPxlDkkvIKqQMsY3Wc+f2wHOu7QIg+8zTKFaZpRvGqYc7hw/Oad/VREVo46JWlB4juFh2GcgKVy+9mtetfd0x72l2NfOlK77E1UuvPuZv2VSGPW97N/5Cmr6GTl767a/MWWBjKpz5ckGJXjGQY3Cwj9blRt/X0dRDE5fcButfNUGAY4biBMXseNA2QWxjJFWgMSfu02KDd0E8vkyYcvNOVdDlVSVP3CU+l1oIbpiUvrJVI2sEX4a12LwjFFxRoR36NY2Mzwg656HyVT4ini2ZljBSQcxp4SbXeL9XaIWgvs4WniYh2IGObfAZGjxirnQpAQDiegqPKir3Q9sOzf44VUI2Wazch2bwBXDd6a3cfs2x1OMWv4Nv/M3ZXHd6K6qm8uWtXwbg9WtfX2n9mBLnvgXOfhOgo//iLSR/9UMO3fAyYj/6EWgavle8ghX3/p7WT3yC9q98uSI+U4Es0/7lL+O79lo614p1QEtKsBSy9CyY6EaqrwdNkpElMSbfmo0LMo6FxqyeGi0tLZx11lmTHjrnn38+a9curHHbIqaG06h85U+geFivGHn8YQBiq5tPsqWApmls/tUBADKtDs5WtsBm0XvFjf81N3rECXBmo9jv9pHtbOzwc0/XxeIPe0bRUjGWnyWCr8EDcbLJYtWPbyodZu3iIVh0DKNLOmHH/CtzAbj9IvhSyxqtNjHJ5uQyyGBxi2pCTahCRnAzZiyS/KoGDRM8vowF3HzA1yCO5S80EPUIGlct/c2Oh92jYvF05kAjmmIXYhtnrZzTPp3nCapQbp9Y5K8wru9l0TM4EO2e076rhVQ0T48htLH+0jY4JOYSVowHF8miWNC+bNnL+Nxln+M7f/Ud7rv5vikDL03TeODtt9MxeoSU3c3q//06bn9tVuPnnnsJgwEFRYc/3/XDY/2+joYkwSv/G5rWC+W4n/0NlGZAce7ZAmoRvG3QMC7EMpTI05CLA6A1hsY9vuZRbMOESTu0FgMAxC2Qk8R9fSLFt9nC7PmSbUUKmphXHd75758FCDjDFbVDty6RMoJOdR56vqRe8dlm2roAKKLTEHaOB1/VeKZW/L4er1AP7ZKgG0bzUfwuoVo7drD2lb6T4fCOUdChyXIArzI2qVLsdwrGxVmdft60SuVHbzmXRz58FdedLgyE7z18L/tj+/HavLx1w1tPfCBJghu+QMl7Fn0PKPT/v89QHhnBunQJnd/+P9o//x9YGoy+qWuvZeWf/siS73+f1s9+Bux20DSsbaI/suM0EawXBzTksgPkhRPdKA0MULAHkVBQpBLupvCCjGOhMePgK5PJ8C//8i9cfPHFrFy5kuXLl0/6t4j6wyTRjQmmjEt8Igvek+qpX7n5naI8bj3j9Glt/vsHDhNIa5TRee0r7HCXUda/6H2w8ZZajXJc8XB0Gxva/fyg8VpDeEMm88Mv4gs7aVziRdeh+9nqP0DMnq8UIiMdc4mFQ4t7+uqQ1YRilXF6xYMorIuHqC7pJGUJi9Pw+qpBFcgMbsbc4hgBTZskM++ZJ9ohgL9NPFS8+TBjbkEJWQi5+YNRUf1ZNyx6P+citmHCddWrAChEyqij/SxZH0aTNfyFBjJjU1DjFgC7TaGNNQECQf2Yyo6u6zw+IF67efXN3LD8Bs5rOW9KqiHA/Z/6Kqu3b0ZFQv+Xf2fp+rkFsCdD9wqxcMo98ZcKbXnoUAJdO85cbXMbAhwB6H8G7v2H4zbrHwOz32vFlZPoS8PJPE1ZQ+ihpWWCx9fC0Q7JGD2tsoKuCxm+8tgYerl8vLfOCmbPl8OWRi0HAHD7FsYLNOQIVSpfVt1CxCbm1HIkInwrawj7oFH5bBb9oglZp8XvHJeZn63S4URMMFs2RTdkXSQ2ovko4SbxuUdHFqbnbiLGVQ63QMMa0aNl4PkhQQ29cFmIcxp0LlgWQpHF/VRUi3xt29cAeMvpb8FvP3G1UC+ViHz/Tg7+MEF6wAGyTvjyVpb/5jd4LrnkmO0lRcF9wfkEXvUqvFeJOS5l+O+GWt24/DbUksaS1HkA/OnQycU6qg1d17FEouScRtDoziPJ89s7Wi+YcfD1tre9jW9/+9tcdtllvO997+ODH/zgpH+LqD8ofj/WTiEtP1F0o8PTgSIp5Mo5RnO1z6DNFKpapvGQYZB88UtPvn1Z47l7RRVP77KybPPboJSBZZfD1Z+s6Vg3NGxAkRSGMkO4XGnsdjt7lnUBkLzboB4a1YFaSM4X+0TlK1ESi6Jhl1jkd3inVzGsBSr9VVkJXTOkg2UFq11k5Eu1yFZXPL5EddErO8HTPK8y8yZ8S0Vyw5cPM+Y2et0WgHbYkxANzuG0GM9cxDZMWLpOw+oDkMj9+ddY7Qp6u1j8hkbm34T2aGiqxp5HRFVu/WXtcGSLoOT5lxgUJ9gf308kH8FpcQqp5xPgqbseoPOn3wKg55a3csFrrqvtCQDWc0X1fOmBPsLtbix2hWJeJTp4Avnp0DL46++CJMO2H8FT/ze9g03R7wXCm6ghI6iG9talpKPiPloQ2qFhtFxOisVaRFGwlQfBYgFNqzqNedxgOYFumNb7gu6qHmO68Nq8lC15VEncY2OqUwTJ5TJKjRVU3UMi6NOCYg0hDJbtMLBDbFAFu5aK6EbfM7T5DHXYsviso/kojatFEjGRX5jg10QhW6LvebEmWeF4fFIVHeB547Na03KsMfvP9/6cgcwATc4m3nDaG054nNyOHRz+61sY+fzn0fN5nKevZvn1MZpan0F++usnHafvmmsAEXzpuo4kSRXq4erMmQA8O7rreG+vGdR4HKVYImeIbfjCC/t9LiRmHHz94Q9/4Be/+AWf+9znuO222xaDr1MEjtOPNVu2KlbaPCJrU4/Uw4PbHsKd1ylYYfV51550+5/ftRd/AYqSzlvCX4fYYbHYes13J6l31QIuq4vVQcH3fm5sB+vbfHy985Ug6eT6ixSeuL9Czep7PkYhW6rasbVCoRJ0xAtikdDvEtWOZcHWqh1nppioeKhoIos56gxOkJuvAe3Q+BzGfEaly7cUJGlBKl++drFg8BXCRN3i+AtBO4zEBQ1XUQXNuGl5dYIj1wpxfrnHNwPQfrp4uC+NdS24Kln3cxEyiSJOr1X0W1aCi8srlR2z6nV289nYlOP3AvbsPoj2iY+i6Br7N1zCdZ+4vebjB7j85r9FAzoiZXbv3k7LMlHtOG7fl4kVV8HVnxC/3/cROPLYibfPjMHQc+L3o4Kv4WSehrRIZNgCotJnd1lweKonWDRdmD1fpayGolqJKTKB/OFxw+EqJ3PM4KvVEUEpiXk1FFoYRWdZkvFb7OQM0Y1kqowSEvebJVnbSnNwVCTLVIfoT0rKOi1KZoLYRhV6dhrWiL6xUob1smBxlAoikRjLx2g+W1x7GUuQYjo39+PNEt3PRdA0nZB9kIBlYNL9omk6e43K15rmyXTkdDHNN5/9JgDvOvNdOC1TJy/UVIqhf/s3um99HYXnn0fx+2n99L+z9Od3YX/dHWKjP30K9m064TjdL7kcyWqleOQIxQNi/jephx1pEUT3ZuafdlgeFIlA02DZ17YwbRH1gBkHX8FgkFBoYZr4FzF7ONdPbbZsUg/rMfg68piYYIa7fFhtJ+7VyeXL9Dwsbuzlrc/j6/8DWJyChuOeH07xRLPl09v97HcsoWx4lMT+7ysEW9wEW1xoqk73c9XL0pb6+0HX0bxBkoYB6YhHfJ+rGtpO9NaaYqLXl9Xwhxl0NWFxioV5tSl4Wj5f8bIb84tjBMKrK2OYOKb5gDfsAHSsmp2k2xDcWADaYT5zAFmFvF3QDlvPqQ493HmmqBZld4uH+0suW4sqqYRyLTy7d2FVyXYbVa+1F7aiWOTJtDoDWwa3AHBR60XH3U82lWHvO9+Dr5ARAhv/9+WaCGxMhSVLlnG4RVyvT/z2zorf1+DB+MnffPEH4PSbQSvDz98Eib7jb2t+Nk3rhfjBBAxG04TS4l6yGWIt/sb5l5kHEfRZDJsITzGAJkm4ykewGkID1bRx0DIZtIyoMAacJRRd3L+NoYWr6oZs3oriYSZZwNIkvqtaBl9qsUA4JpJlOcO2JG0Bf9xYR8xVbMOELEOHsJJZXRTsnGxePDuj+Si+Fa1Yy1l0SWHk6YUzCD641VA5tGwGSYGuSyt/64vlyBZVbBaZrvBkWu73d3+fWCFGl6+LV6181TH71XWd5B/+wMEbbhDy8bqO/6abWP6HewncfDOSLMO5fwfn/B2gw6/eBpHjCxspHjdug5qYfEB4G5qVL1vMjr3kIsOReRfdMNkuKY8hM9+xGHxNG5/61Kf4+Mc/TnaBzEIXMTs4zODrKNGNLl8XUJ9y84VtgtZQPn3VSbaEH/14F54yqHKRG8oGxfCVX6tOVm6aMIOv7aPb2dAuHkh/WiEmwMQThyYJb1RTct7s98q3rwHAocTJ29LousTy4AnUlGoM94TKl0MWGeMBW7BmXl9mYKM77GTtoOg6web1qGWNnCFyYjbtzwcUi4zDLubJvN3o0ahBb8rJUCwPsXqw2RDbyM9ZbMOE8yXXA5Drz6IX8jSGfAx4DwOw9fHDVTnGbJCM5DiySyQ31l3WBukRGDZsNpZdDkBJLfHMsOh5uLD1win3o2kaf3zrbXSM9pC0u1nzzW/UTGDjeBhYI/psrNufrigeHld0YyIkCW78GjRvgMyoUEA8ngDHFIGpieTgfhQdyjLoqgg8FqLfC4R9g9e4f5vKoqLvlIawNBuGw1WsKpv9XrrdTkERn3tBydHoWzhxgIBjXHSjkCphaRLPEiWZqtkxY4f3ouiQt0IuY7BH3BYks9+rGpRDE0tE31dH+lkAkmmjzysfRZZlfJK47kd2niCRUEOUCio9u0WP4XLHFug4D+zj9MI9BuVwVZMHizK+tB7LjfH9Xd8H4P1nvR+LPJmFU+zro/cd76T/9r9HHR3D1tXFku99j7Y7Povl6ELH9f8h+uMKCeFbWjj+d+81qYcPCJl6d8BOsFVQOdvia5HkAlt65ldyvmRUvvIm7XABLCvqBTMOvr74xS9y//3309zczIYNGzj77LMn/VtEfaIiutHXN1l0wyuymfVotOzfK7LXDedfesLt4skCiaeFiMXl3h9ilQtw8fthw2tqPsaJMM2W90b3sqpF0HL+J3A1Vi9oRYnkd+6oGNL27IxQKlYn62R6fOUCYkFid4sHhKz5sFkWRp0LxgOdTKyAy2Jk7S3uCbTD6gZfJqWvHAqCJOHTNOSmtWQSouolW6R5p0v5jAocchgsiuhNGZtfxa4sSVYPC6qJn/icxTZM2M+7Btmqo/9/9v47OrL7Pu/HX7dM78CgLrAAtjeSu2y7bCIlsYmSKKpYdixZViJLcmwfx5aVuCT52rFPfkm++Tpxi21ZjmMrtixRtmSrUl3sXJIil9sbdrHobQbT222/Pz73zgBYAAtgUQZcPOfs2d2ZuXfuzNz7ue/yvJ9Hlyi9KAzFhxoF3TV9fnXMxBeDM8+PgAUde2JEm/01lcPWm6s2E8cmjlHUizR4G9gZm7u489Tv/E92Hn8OQ5KRfue/sHXf2vvRNNwjVBd3XJ4g3hUECTKTpeo5vSDcfvipvwVfDIZfg6//6tUCHJZVS75mUQ6hZrCcCslkk6KAsR5Khw6cNaXZEudzUdGQIyKgXMl5Ssfji7CPlC0zX3RliHnXr/MVC7RWRTfIFat0y9XsfE1dFF2o8QaFYlr8/t6Ia2WVDh3YohvRydcASGbFWp0siftZ1G6wTfYvoviwCug/lcDQTMLeLHG176rr5eyISIT2tM6kpv7F8b+gqBc50HiAh7oeqj5uaRqTn/0sl971bvLPPovkchH/pV+i55//icCRw3MfhOoWBsyhNpg4C1/5+XkFV4JveysoCuUzZ6jYBdpOm3rYnRax+g/7Xlvq13BdcGiHhstOvuJrpz5cb1hy8vXEE0/wa7/2a3z605/mAx/4AO95z3tm/NlEfUKJRHBtFYnW9LkvR26+3jpf44PniSd1TGD3fe9e8LWf++sT+EyJgDLOAe+3hJrZ239nTY5zOloDrbQGWjEsg5x1Gb9boWS5cN0r6Fmpf36KeGeQUKMXXTMZOJVckfd1Ol85r6jKVkKCLuOyaSLrhem0Q8cwcxjXjM7XSqpsOsFXISqqkRHDhKZd5G2Pr0DEs+Z0qYZm0SkJleNYdhVzTamH5SwpxaDZ5vk3RFfu+5ZUFd9W8fmKzwmKcKG1hIWJe8pDNrn2CZhpmJx+XhRt9t1rU27nSC5eGhHzXkfajiBLV98GX/7Hb7P1S38FQP9P/hx3vvehq16zFnjovR+irEIsb3H0pe/Q2C6+70V1vwBi3cJUXpLhjc/Dy38x8/nkJUgPgOyCrrtnPGVZFu6UWFvSITeZifXz+HIQtBUPGwxBNUwoCqbHWU9Wboa02vnyKUxatlWGO0fQtU5GX0DM31RNvjyFLC6HdphdveQrf+kCAMnmIEZOdOwDMS+M2GIbK6F06GDLbSApuPIjtDNJriDOs0wlg2ZqNHaIdX0qtT7KzL2OyqH7BTE2OqtTfG5M/A57poltDOYG+dL5LwHwK7f9SvX+U3jtdS6/7/1M/P7/wCqV8B8+TM8//zNNv/SLyJ5rsDNCrfDB/wuKG85+HZ79/TlfpsZi+O8QyobZ78ykHnZmBfX8jfGTi/34KwJtZARN9SFJooATjq/fWrLeWHLy9du//dsL/tlE/WIus2Un+RrIDmCY62O6NxcuPPN1AMZbPUTjW+Z93dBoDuOMCETuCX4OJdYBH/irVRfYmA+HmgQN4/jkG+xrE4vwybd/XAhvDFcov/TtFVc9dKpaWUlUtZIhEST5lfVVnQtGRaCUT5WJecSiP45UnfmyCgXMXG7F3s9JavIBsayFTQuiXdV5r7WkHDqIbLErfOU45aioiK6l6Ebm0nOkFAWfIZKvlu0re07494uZuuIbQrChtbGNkZAwQl0tQ/GF0HciQWG60IZlzank54htzEU5vHLqIvzuv0exTM4fvI9H/5/1E5KKhKKc7xTryIXvfKU293VpCdX/bQ/AQ78n/v3Ub0Lfc7XnnO+m87CQqp+GqYJGQ0HQNwtRX9Vgeb1oh1AT3QhrYj1JKAqmLIpNKym44Rgsy16DpJ18Gd7yusy6OYh5YxTdorviq1SmzXytHu1Qsw2Wc83iHqxhsTVcFAk7QNvCKqFLgjtQHRO4x9OLZfiQ7RA1VUrRsl8cQ8ZY+wTY0EyunBCMhe3qD8EdEsniNFQ7X2215OvP3vgzdFPn7va7Odx2GCOdZuT/+W2u/PRPU75wASUape2//he2/vX/wbOtZ/EH1HkHvNNOun74n+HcU3O+LPSwQz0UyVf7riiyLOEvBQiVGhksXFj8e64AylcuVZUOPUEFl3tuW48bAcuaHE6lUvzlX/4lv/mbv0kyKar3r732GkNDQyt6cJtYWcxlttwWaMMlu6iYFUYLS7t5mabF0Lkpzr8yytC5Kcz5/GeWgdQrYhg+v6fzque0isZz//gtnvrM5/nHP/gBbkuiSe1le+gY/NTnwb9+gjC3NIub0esTr3OgXQTbR/VmQna7P/W//5DtB0Xy1Xd8EkO/flU4bWAAC0hZYr8DQduLx7W+5oUBO9kpF3TiLvH5E6aOrFrIrpUX3XCSmqxXKEl6LQ/ISs1geQ3FNhxEtogKfbjUSNE2Wl7LztfAxR+hGBKGW1xHKyW24cB3RMxQFS6JIH1PYw+XG8XMxkrONS4Wp54V96C9d9tCG4mLkBkCxVPt7GQqGU4mRMX3rva70DWdV77yXX7wv/4vL/z91zj/8X9NqJxnsKmLhz77P9dMYGM+pPbvBSBy6lTN72uxnS8Hd/0i3PRBsAx48mch2QeXnxVy9AA9b7lqk5F0kSbbYLkcaaSYEdfVuna+7DXFVxJra0KRkRAxiLaCtENH6dDtLpK27IKFf21nNWcj5o1VO19ujTWhHVoDootcaazJzO+T+8STjTvAu8Lqjzb18G73RUAm4BLne7KUpOX2XWCZVFwhsldW3qZkIQyem6JSMvD7NFpc54XQhlKjsBcrBpcTogiwI+7l2Lf/lsGX/54rz3wDybT4N4d+mfTXv0HvY+8k9eSTAETe/z4hqPHEE8tL6m/9CNz+McCCL38cxs+Ka/rEP4i/TYPQ2wVtuXjsGNrYOG6vSss28Zt1pHetuehGeXiQok+ct7GW9bFtqBcsuT1w/PhxHnzwQSKRCH19fXz84x+noaGBL3/5y/T39/O5z31uNY5zEyuAqujGyVqrWZEVOkIdXE5f5krmCluC83eZpqP39XGe/eKFamALIri97yd3VueargeuM2JgPzBrjvB7f/Uk/a/KFM0GoBWnbtLleRX5iT+B1sWZMa8WHNGN4+PHefsOUaE7NZwh+i8+RPY//i/SRy+zo1HHH3ZTyFQYPDdF1/7lJ0mWZVEZHKTsiVIhgITBZU8KTGj0rq+SkNuroHoU9LJBsySOpWjlINSOy69RTstoo2N4dqyMAIST1GS9gu7mthUWc+sgM+8gbHcJwqVG8j6dGKCNrV3gMDHyY3ZNtGAqblts49riNUuB763vA+l/oucltPOvcbB1J59v+H3u6XsfwxdTFDIV/OH5ZdxXEpnJYnUgfu89NuWw1+7sbD0MLpE0vDL6CqZl0h3u5tzfP03vn/4BDYUUTj09BuRVL3v/8s/wh9Y/QNj24OPw9ZfZ1Z8hbM+STvRn0SsG6mIrx5IE7/5DMScyehz+5Hbhe+bglc9C817Y93j1obFMiaaC6ExXQp1QAl/Ihce/9jLzDhyjZZdNSUsoCi5TzJHoY+NVT6PrhUM7DLjS5O2ZL3X9GIcAxDyxquCGaqpYDaKDoKxi8uUasumXoQ6kcWGw3F2x1QZXknLooPMwHP1zDiGEIHxyhCxTJEoJdrfvxq+nKLgaGP3xRUJdaycm5bBUtkVOI2FdNe91YTyLZcHD2W9x+ZF/Ryxj8jbgbUAqANqT/4bhy6Iw5N62jbb/9DtVSuB14dH/CuOnof9F+PN7hLqpg3A7rkf/G76DBykeO0b2+9+j4ad/mo49DYxcTNOR2suZlhd5of8sb+nZf/3Hcg1YhoE1laPYKeKdSPzGFduAZXS+PvWpT/HRj36UCxcu4PXWhuUee+wxnnnmmRU9uE2sLKqiG0ND6FNT1ceXOvfV+/o4T33m5IzECwS97KnPnLxuOl2xkKF1QFSRtt37WPXx7/3Vk5x7uZGiOZs6ZfFq/oN87+X1rUwC7Irtwqf6yGpZwhERCJ4eyeB94pO4wmBqEtm/+X/psbtf10vNMhIJrGKRnJ00xzyTJA3xvi2B9U2+JEmqJjxxRAVTs3LQuH1VRDecynfWJzp/blV0QNe182Vz2gNalKxHnJ+r4W82J0yDTO4iO8eExHyEFLK6sjQPOdaEt0kE48UffpXbOraTdacZD/SDBZffWLvu1+nnh2cKbcC0ea9pEvPDoqt+37lGmv6/3yFWSM3YjwX49RK9z768+ge9CNz/9sdJ+yW8Grz43D/gD7sxDYvxK0ukm7n9Qq4aZiZeILy+nvwInP5q9aHRdJl4ThQyLL8t2LLO6mRV6nDOBRYkFYWQKWw1rFIJM70yYgxO58vnKVIyogC4g+vbAY16o1WpeUwfuVAUADWXw1oFXz2zXMaTEO8n+2oeX215WyFvJZUOHdidr63aJfyUcGHPeZVEvBJxi/Nx4tzasQdMw+TyG4JyuE37mnhwtrnySJb7pr7Br3z/+0QzM3+LSB7cl4ewFJmmf/PL9PzTV1Ym8QIhwHHQNmw2Z8U/mRF48iOEbhaFKId62LlX3Be3pHchWRI/6nt9ZY7lGtAnJ5EsagbLN7DYBiwj+XrllVf45Cc/edXjW7ZsYXSFTQ43sbJQwmFcXUJ0o3R62txXSCRfi/H6Mk2LZ7+4ME/4mS+cZ2osT3qisKw/x779FJorznhjM+HGA6QnCkwOZeh71WnUzq5sSoBF/6sSWmXlzIuXA1VWuTkueOsJ/Twe2aKomVxKlog+KBbc1D8/VaUeXn5j4rrompV+wb0vxrsBaIwWKZoi+eoMtyx7vysFJ+GJmuImasg5aNhWTb5WsgvkSE2n7OTL5RWfvyq4sQ7Jlyegoivi/dPuNfb6Gj9NQtJpyYlrvjG6Om/j2yUC88IrL+N3eVCMRi43ioH8taIeGoYpVA6B/ffZ3XtDhz5hAD29Un105CiSaXH3lwQDYO7VBJQ//QN0bf0LOi6Xiws9ImCZfO67Verhovy+psM04Jn/Ps+T9hr01G+I1wGj6SJNOXs9VUXwHW1Z3wF5R3DD0iTcho+EIhMwEijRKLByohtO8qX6DCq2xL4/svbrx3Q0eBso2rRDCzdTcgAkCck0MZJT19h66dD6+5EsyHtA1aOA6HxFUjZzZiWVDh1EtkCkExmTg/JFMEW70VE8jMVFoScxsnZGyyMX05RyGh4vtKvHhNJgfNeM15wZSvKJHwtV1fnWk4wPoj/3MWT3CjIBTAN+9P+b50lxTYcq3wSg8PIr6FNTNHeHcHkVvIaPxvyWNRPd0C6dASATdGTmb1yxDVgG7dDj8ZCZo819/vx5mppuXMO0jQLf/v1oV/opnTxF0DbhW4rR8siF1FUdr9kopCt8/rePXsdRxuGI8Oo6OWM/0QW2kSmajRz92ve49/3vuI73vn4cbD7I0dGjHJ98g47AvfRm4cRgmvd8/DeY+Mr7KI5odCVexOMPUcxqjPamaN+5PCEEbVAkX/mwqG41trqpkAKgO7p+BssOnEp1QBMVc0kuk4tsxeV3Zr5WJliydL0q4Z4KlgA33oA4r2uCG2tfaZMkCc2bQ817KLjEd7DS/mbzYuAo44pCg9FJBWjeHl2Vt/HfcSdTz12meF6ci0GllUsNxznc/24Gz05RLmirTlXrOz5JISOENnpuETd3hl+DckZIrdvCACO5EfoyfRwYkGi0TXTnggw0FlK8/vUfcsc6KR1OR+XmW+HUt2k910vr+yP0vj6x9LmvKy9AZniBF1hiPu7KC9BzH6OTk0QKTmFIVMvXU2wDwOVW8AZclPIawXKUcY/4DdWGEEYqhT42Crt3XWMv14ZDO1R9BlZFJLvhyPp+9qgniq5oVJQibsPH5GSRtoYGjEQCY2Ic2laWhlfu6wNgpAFk23PLlAu4cvY51LpKHpqdhyE9wO3Seb6lifkkp/PVtK0BJiCdXztBrV67gNTTMopSNkQXfRa1deq1fyaem7/7KAGRnMnx732BW9/xkZU7uEVc026G8Wy7k/KlQXI//BHR972XLbti9B2fpCO9h/P+tTGt1k89D0DJZxss38BKh7CMztfjjz/O7/7u76JpoiImSRL9/f38+q//Ou9///tX/AA3sbKYy2y5arScvTbtMJ9ZhL8MoLgkXF5lWX8kq4SiF5EkrfaYvDhaRW5y5SuAS4Uz9/XG5Bt0BEXwcmIojdqzj9A+EcRk/vqP6blZLEK910E9dJQOcx4xZxfuasCSxZzGznjbsve7UnC6TUpRxrLEcjPoja847VBPJMAwQJEZD4pzJRTZgWVaVU+kQHRtZo9mQwuI7klRsY2WV1hif170H2VccqO7Be2w7bbV8anyvVVYjJQmNMypcZq9HaR94+QDOUzDou9EYlXedzpOPysCkL13twuhDajNe/W8BWRBt3Qk5rdpi7s2ssP1wea49fF/AcD2kRJqRFCvRi6ll3Ye5RZ5rdmvy46IoKzkArMsAqXoOidfAMEGsaYEKzFSiowFqPZc4UoUNoxcHtNOzGUvqLrovsQaQgtttupo8Ip7hyO6kR8Zrhot6xMr7x2oXRHF2JGYhJ4R10+7yzY4bty58mIbDrYKBdLb5POU7PPO6Xy1HOwGIKvEMNagK22ZVnU0YJvyA/HgrHkvy7IoTyzO+Dk7ssKWPou8pkO3i4JE9jvCEsTx+xKiG/1rIrqhXTyOKckY9jjAZudrifj93/99PvCBD9Dc3EyxWOT+++9ndHSUu+66i//8n//zahzjJlYQ3v224uE00Q2n8zWYHUQzNVzy/FXqQHhx1It3/9JBtuxeejfHNE1evf0AoYKF9ue/x80PPALAc//4Ld747rXfOxhfX3l1gJubbkZCYjA3yIFAFohwYkhUqWP/4sNk/8Mfk365j+6PeTj7kpj7uvcndi5rUFwbGMSUFLKKSL5y7Y1IExaWJbMttnYDyfPBmfkqpCtI7gAoWQbkEB3+laUdOkmcGguTUsWNOh6KU8xpmLoFkvD5Wg9YYReMQ0kWybZVLmOm01Wq1Kph4CWUqS1CbEMv0XRoZYRNZsO16xBqAPS8RPGHX6Er0sXFceiLXmR//iCXXp9g9+HVOxczk0X6z4jfvOrtBQvOe8W3HAAGrrnvUPv6X0MAhw4d5vsxhfYpg1dfeBLFdS/lvE5qrECsdZGiIMFF0pDt1xnjtsFyxIVREAntetMOQXSwJwdyBMsxdAkyslT1DlyJTro+IfYhuWUqShgJGROTeCx63fu+HrgVNwFXgIIrS7TUQmViDCXeBJytHvNKwul8jTbINNqjZgf8lwSbbTUohw467wTgkHyBfP7t4IFESRRwGm7ahmz0YipuJo/10nLH7tU7DmD8SpZ8qozLI9OZ/2fRwpqVfE1ky4zIi4s7Qm1bV/YAF3lNh9/2Fiaf/AH555/HyOWrc1+tmW2olslzV87wwLbVFSvT+i9R8jSAJGPJ5poJMdUrltz5ikQifPe73+XrX/86f/RHf8Qv/dIv8c1vfpOnn36aQGD9laE2sTAcry9teLgqutHsb8areDEsg+HcQi1saO4JIysLJwnBmIe2ndFlHd/lk88TKlhUFNh9+NHq44ff/SA+OQnM1wEz8ckJDr/7wWW970oi5A6xIyYCXcnXB8Dp4Qy6YeJ/4hNV4Y3IC59B9SjkpspLH563URkcoOBvwZJU3FKBPlUEIbIZwq2uj9fZdFSNlqdKqJaoHPeZrqrX10p58zgVbzXiJWV3OVpDjVWKrC/krnVE1hjemH2T0ZtQwmKNXHXqYWYEUv2Ek8I7xm9OrrjYxnT4twkFq8JLT7O3UbznuchrAPSfSqCVV6+yeuo5IbTRuTdGxKmmlnMwaAtm2MGSaZkcHRU05nvf9VMk/VHm6xuZQMIf5dC73jrPK9YefTvELFvl1edo6RZdh5GlUA+77oZwO1dPpTiQILylKsnvnhL3gmxDI6Ymrp31FtwACNlU5qgmuj4JRUFVba+vFSjmVCmHfiiYUQCKriyN/vW17gBBPXQUD42pVLXzZYyv/GxlpU90viaiUbBAx+JmVcztrIrSoYPm/ZiuAGGpSLgofleHdqioCiFL/Hv8eN/qHYONS8dEUtu1tYQqVaB5H4RmJjxnR7McD95NIigvuJ5MRRRufvCnVvYAr3lNA+EtuO97P+7ubixNI//M00Rb/ASiHlTLRVt2G0+vtuiGXkafSFQph1KYdfXMqwcsOxq55557+IVf+AX+3b/7dzz44PoHvJtYHJRQqCa6YZsty5K86Lmv5//hIqaxMNXl3g/uRJaXd2Fdef7bAIxtDeLx1bR9XVqauxs+j1hkZidgJiCx9XYLl3v9ZJCnwzFbTsv9BNwKRc2gdyKPpKpEHxKVvew3nqrKzC9X9VDrH6gqHTYGE1zJiJuFa8H5uLWDM/OVT5Vxy+L3HClkUFtFR8GYSmFWKtf9Po7Yhuq3SCtiWdsSjleTr/WQmXcQtiV1XZUm1IiYO1t10Y2BoxhAOC+u60hkdWklvltuAqB46jy3tQuKy1jwJKFGL7pm0n96daiHhmFy5oVZQhsAV54X6l+xbmgQyeD5qfMkS0l8qo+DbYfQf+FX5tynWE3A+IVfQXWtfwHDgefOewHovjhYNVte0tyXrMCj/83+z1yyAAjpalmhUNGJ5ASVrdTUDYA/4sblWX9TVEd0I6aLbn9SUXDJ4vxaiaKGY7CseipVmfmiK0vMu/6sCiG6IQp1Vq6AYs/Z65Mrn3yV+4TdSy4gAuaMbLHLuiieXA2lQweKitQhxKn2GOIcdGiHANGAWMsmL6/uiIFlWfS+ZlMOg8fEg7O6XgBnRzOYkspA9+6quMZ0OOuJ+csfRXWtcLdnwWvaRscdSIpK6CExu5r57neRJGka9XA3xydOzb3tSmH4GFpeomgnX/6G9RWvqQcsOvl68cUX+frXvz7jsc997nP09PTQ3NzMJz7xCcrlxc0DbWJ94ZuDeujIzS+UfJ1+bphTzwyBBLc+2nWVelww5uHRTx64Lp+vwmuiWq4dmDafYujwpY+yR/0+9zf+GT45NfPzyFPsvjPBg//qg8t+35XGweaDAAwY/eyzzZYd6mH0k78FskVpRKPTLzjgva+PL3kOyCyV0MfHyQUE1SreaDCYEZVfv7L+gQLUOl+FTIWAHAVgopBEad+OJIvP6yiLXQ90W2bechfQ7YpaZ6SxKraxHkqHDlpaRaDsLcdRbHKAttrKsANHSSoyPkMoEXbsXF0xJN+9DwNQ7M9xU1MHlqkgKQaBnaIYcr2WCvOh741JipkKvrCbbkdoA6ZRDh+oPvTSsJj3uqP1DlyKi3t/6p3oytXJ1ZQ/ysSnf4f7f+4nV+WYl4sH3v+zmBK0TxkUEefPkjpfIHy8Pvg5CM+aeQu3i8dtn6/RdInmoq2sF7UT+HU0V54OZ+YrrAnqVEKWUSxxfjlFmOuBsx65PBWmbKXDgjtTnblaT0zvfFlFvWq0vNKdLyOXx5wUCa0cEEWNoqzRoE8AErStktiGDWmrkJy/1RLeWE7nC6CxXRSzkpOrO/OVHM6TniiiqDJduX8UD267uhN+djRLoFJk/yV7/nqWrlM6opD8jx/j3g99enUOdL5r2mfHAKf/Cc58jdDDIvnKPf0MZrlMx56a5PxAfpVFNwaOouWVqsx8c+sqzQtuICy6rPe7v/u7PPDAA7zrXe8C4MSJE3zsYx/jox/9KHv37uW///f/Tnt7O7/zO7+zWse6iRWCd/9+Mt/85gzRja2hhTtfo5fSPP0F4e9x+N093P5YD4cf3ybUDzNlAmFBNVxux8tB6KwYXI3dflftwe/+RyEZ7Q7yre0f5s9PKvxS8zB7gzLBeIzD736ibjpeDpzka9gY5vE2L6/0wcmhNB+4rQN1625CexvJnkoS/sGfIfs/RXq8SHIkT2P74p08tSFxY8qHhKBCw5YgY/mzAERc60+RAfCH3MiyhGlaNFotTEjiRirFd6L6TqPlVfSxMdwdHdf1PpoddFXkFOBBMhVi3gAXUyIoWc/OV1dHM32kUS03hkecp6vu9TVwlFFqYhudh69fAW4heO96B5L6m5iahPXjH6CaTRjyKBONk4CHvuOTGLq54tTPU8+Ka2Dv3W0oyrR9O2Ib05KvF0fEvNeRNjHQP/XVr+EydPqDTaQ/8avEtRyh9laOvOutddXxctDRvpXX2rzsHC5x6dg/Ae8gNVagmKvgCy6hor7vcdjzTqGUlhsTcyNdd1dFSQBGMyXieSHcY/k7IE+N0rnOcFRL/SURwA0oflw+kYSuREe5pnRocsUSiUfZXcDvWn/KZcwbY9wlEm+rolSTL+eYVwpav4gD0n4Iy6K451ZS4sn4TvCssviI7fd1r3mJP8ZHTstRNsp4FA9Nu1vhcom0trrnoyOE1bnDgzt1HmRXlZI7HWdHsvzEhR/iKuQodTbx8Z9K8I5sD7sTzRw4fB93PvKhle94zcZ81/S3/z0c/TP4ys/j/dj3UFtb0UdHyT//Ah23ic/SlO/E1BPohoGqrE5n27z8IkZZqXa+tmypj+LwemLRd8Jjx47x9re/vfr/L3zhCxw+fJjPfvazfOpTn+KP/uiPePLJJ1flIDexsvAesDtf05KvhYyW86ky3/rMCUzdYtuhJm57tBsAWZbYsjvGrjta2bI7dt2JV3L0Cs0TQkVz91tEBZY3vgAv/SkA+nv+jL+95MeQZO567zt49JM/zb3vf0fdJV4AHcEO4t44BgbRqAgIjg+mqs/HPvQzABReuUDnDpFwLbU7UFU6DNqdrx2dJEqCptHorQ/bB0mW8EfEjSduioU3VZ6Cxu3VIfmV6AI5QVfZlp6WLD+yLJObEspw69n56mpsIucRldusfRN2OnWrgkoBRt5gONNti20Uid+yOkqHDiSPF1+HCE6Lzz5FWBVV2LPKJfxhN5WSweC5laUJpSeKDJyZAgn2TxfayI7CxBlAgp77ASgbZV4bE131u9ruwrIsRv727wH40a57ed/H3sPbfvFnuOO9D9Vl4uVgdNc2ADwnjhJrFd/36KWrrV+uCVmBnvvgpg+Iv+WZQddoukQ8L7rGpi3mUz/Jl7iW1aIPLIleOVJdS4x0GrNUuq79T/f4Grfs+R7/+vpHOoh5YlW1Q133ojSL32alk69KVekQohVxL4nK9jq9mvNeDjruwERiLxPIiHPT6X613r4TgKIrRnFyGef+IlFVOWy2hXk67wTPzOKoZpgkrwzxnkvCT/C19+7FVGWa7nkrDbe/h4OPfHj1Ey8Hc13TD/8edN8HlRzSFz9E6K1vAYTqYSDiIWZ3ETuyXTx35czqHJdloZ8V87cFnygKR+tgdnS9sejka2pqipaW2qDh008/zTveUfNTuuOOOxgYuLZ61CbWH959e4GZohvz0Q4NzeSpvzhBIV0h1hbg7T+7F+k6k6z5cO7ZrwIw3uSisa0Hhl+Hr/0b8eRb/i0vue8mXdRoDLi5o3v9KSALQZIkbmkS3kIV9RIAp0eE6AaA//GfwxWRMDWJtuRzwNIl57X+ATQ1QNkVBaBh314ymqCKtAaWT/1caTjBUoMpFt68noaG7dPk5leAdmgnX3n73ijb4h7Vma/Y+iVfEa+fjEfMLGTcgoKorQA9al4MvwamzlRWXOeKNrKqYhsO/HuFyEzx2Bu0+ETHrS/TxzbbUHylqYennxOCEFv3NhCe7hnjUA7bbgG/WCeOjR+jZJRo8jWxPbqd0okTqJcuUJFV5EfeiaqsjxjLUhG/T9A7d16epLlHnOOjSzVbXgRGMyXiWUHr0nXxPvVCOwxEPUgSSKaMTwsyqPiQXRaSW5zj19v9qiVfJhlTnD9yYA2sIRaBmDdGwS1mvkpmGDUkuoBGIoG1gnLhlarSoYS/FAWgQxUzYKuqdOjAG2bCtx0JCCDWbmfuK9jRhEcTSdfYK+dW5e1T4wUSQzkkWaLHFHPoc8179U3m+YnT38ZraHgPHeKZbmH+vCOyOsqyS4bigp/4a4h0QrKXkOsVALI//CGWprF1r7gnd6R386O+11bnGJKX0BIZLKDoE/eCG11mHpaQfLW0tHD5srj4KpUKr732GkeOHKk+n81mcbnqrwOxiauhhEK4u0SyVTopul+O4MZIfoSyUZvde+bJ84xeyuD2qTz28zfh9q5eVTj58gsAZHdvgdwEfOHDoJdg16PwwG/x1CkxWP/QvhaUVUoAVxK3xEXy1V84TdCjUtJMeifszoyqEntQCG8En38SSZZIDOZITxQWvf/K4EC16xV2TeKORCmYIpnujNSHRDbUuk5RUyQeBSMDjdtRq0bL19f5siyrOmifttkwjrhHLlWZcQzrhaxHBAtp249tVQU3BoSiX6kkuiSaK7nQq1cMviP3AVDoHac7ItaXidIQ2w6JG+7lNyYwzZUJYg3d5MwLIvnad98sM3En+dpem89w/L2OtB1BkiSmvvBFAJ7Zcgtvu7NOAqVF4OH3fIiSCyIFi3RJ3I+XPPe1CIyODuKvCPGAUtZROlx7k/K5oCgy/kjN62tccSNJ4AqJe9P1im5MN1jWDUFtdAfrIzmPeWudr5IZRpLSWJIEhoGRXLnr3FE6HGmQ8BTFWrrLUTpcTbGNaUg0iPcJ2znldNGNsCoosRNnFlZnXi4u2cbKW3ZF8A5+Tzw4x7zXxddO88gV0dVp/tSvciElBEl2xnauynEtC4E4/OTfgurFX3oeJejBTKcpvPJKVXK+I7Wb42OrJLoxcBStoKCrAUxFJF3hxvpYS9YTi15RHnvsMX7jN36DZ599lt/8zd/E7/dz3333VZ8/fvw427evLrVlEyuH2dTDRm8jQVcQC4vBrJi7OvnMkDAvleDhj+0n2rK6rWL1tPCV8R28Bb70s5AZhMYd8L6/wETi26fETfWRA/WTWCwEp/N1InGcve0iK5hOPYzYwhvmUIrWJkFrufT64s0ytYFBcvYwdGNY3Iw0xP676jD5CulCbaJiZiHahcsvAnFtsO+69m9mMlg21Shhd758st35smmH69n5Ash5xO+TVUUisqpS8/0i+TIsIbZRjq8NZcr3tvcBFloGbrNsiwFjlPZdUTx+lWJWY+RiakXe6/IbkxSzGv6wm+6bpwltWNbc8162v9eR9iMY2Sypb3wTgKd33sM9O6ZtX+cIBoOc3yoSgslzoiI/3pfF0BZnQr9YZAfF7GgqFEYrm4BFKF4/1Wrneg6WY6Ttpq7qszt119lJr3a+vAaSKdasevEkinlilFw5LEwsFMrjQxhBseitJPWwSjuMSsg58V33qL1YSNC6umIbDkptQvEwrok1fHryFYuJ0HVyMLcq7+106bf3lKCUAk9kzqTT/defQbFMBnffSmZvB1ktiyqpdIe6V+W4lo32g/D4HyPJEGq2WRjf/S7tO6NYkkWo0kB6apXmkPtfQi8oFG3KYcldQnWvv2rqemPRydfv/d7voaoq999/P5/97Gf57Gc/i9tdW5D+6q/+iocffnhVDnITKw/v/v0AlE4JxUNJkqrdr75MHyMXUzz7RaGAc+Q92+g6sLoCDpVigVbb66rLOyKkot0h+KnPgzfC6wNTTGTLhDwqd2+vDzGJa2FPbA8qKqlyip5W0dE6OVSrUqtbdxHeJz5L8+APgJqvyGKgDQ5UlQ4bm1VKWgVLFjejXfEtC226pghGRZXLW7Fl1qUsKCpqk/js+vDgde3fSWQUn0LKI5a0gCtMpaRTKYmy6Xp3vope0U3OS6LzZa7AbMqcME0hM2/IVNxi7srauTbDzUrTFjxx0X04dEHQgQwlQdnU6LGVCFeKelgV2rhnltDGxDnIjYLqhU7BzEiX05xOCFuNw62HSX/ta0jlEldCLbTdexiva2MFAul9wqsxeuYo3qALQzeZGFieT+B8MMdFV20yLtYXxWehuuqj+wMQsuXmg5UoBUUkXSvh9WXk8pgFsVarPhO3IRLOULQ+Es+YN4YlWZTszk92eAQ9JApN2gqoxjpwaIdTkSiSJWFiEpCnbLGNxYtCXQ+ULiG61a6Lzzpd8TDeFQUglV15BkxuqsTY5YwYGfUJmh4998EsZdTiiRO0v/EiJhKpD/0c56dEvNQT7cGl1CEL7OYPwpFfJNQp7jvZbz+F6pLwbxHHGs8EqOiroCA58DJaoaZ0qAfqY35yvbHo1TQej/PMM88wNTXF1NQU733ve2c8/6UvfYnf/u3fXvED3MTqwEm+itNFN0KCKtQ3PMRTf3ES07DYfmsTtz7SterHc+7ot3DrkPPBtoF/Eg++7zPQJBzsnzopbqhv39uMZw3mV1YCLsXFFkUkQd6gEDI5PjSTIhT96Y8AED4mqvWjlzLVOaWFYFkWlYFB8o7HV1eMi4lRJMnCsmS2N7RcYw9rh4BtMqyWxM3LkgtCWaldzAVp44vv9s0Fh8Kn+g3SsljSgq5w9Xt0eZVVpcsuBpo9sF8xmpFcImBYFeph4gKUUozldlfFNmI3rR2tzrdTBOuRUyewTBeSZPLjwV622fYTl45NLNlSYTZS4wUGzwqhjX33zKYc2l2vrXeBSwToL4++jIXF9sh2mv3NpL4ohKG+2X2ERw/MkmfeANj5yPsA2D2QJb5VsBFWmnroSgmKdy4mrlGHIlwvmN75MtUCFcmLyyeuMe06Ol/6hNhWVk0qig+3KdauaGxtEo5rwfEac+Tmc+MJ9LDohK6EZQcI0RIjlRLv47Nnq+U8smQirRHlEKChfTtjVpRGQyQEiVLNK7D5JlEozlgRTHNlz81Lx8T9qLUnQmD4u+LBOea9xv/H/wDgB5230nXHLVxIXQBgV2x1lWWvCw/9LoHDdyK7TIxkiuLR59h3UMQQHZntPN+/wqIbxSmYOCOSL1vpUA7X/8jIWmDJpaxIJIIyhxxlQ0PDjE7YJuob3v2ieqoPj6DbXPGuSBeyqZD9ephCpkLjlgBv+8jeNXEiH3pJdH4mWi1kCbj/N4R0KiLReOqUSL4e3SCUQwddqkhcM4iF+cw00Q0A/xMfxx2RcOUzxL3id3D45gtBn5jALJXJBUTw2Lh7OxcSgv8um6FVk4xdDpzOl5kTQbckmQxnk7i2iqRAn8piXccNtJp8uUuk7M8d9USrHl/rKTPvwAqIpdYyYsh+ezZlNUQ3+sVs07Ah5gllbYBdLZ0r/z7zwH/b7QAUz/XjtpXijo1eoHNvDNWjkJsqM37l+ro0Z563hTb2zRLagDnnvaZTDkvHj1M+d46yrPJszx08sLs+VEGXgre89TGmAhIeHVK2P8+SzJavAd0wCWVEAKqFRWCmBuot+bI7X+UokqzR6+6oCfhch3pq1WDZZ9JnifVJkys0hOtDGjvqiQKQt5Ov4lSulnytEO3QoRymwwpeQyRfAcVOfNZC6dBGc8TLq+YuGu375fTOV8uhHUimga76SJ+/PubEbDjsk203R6rzs7PnvXLPP0/hxZfQZIX/u+cRdreEOJ8U1+LOaB3Ne82GoiL95N8Q7BL3yexnfpPuvY7f106evvT6yr7fgOgc6mVfNfkKbhosA8tIvjbx5oASDOLu7gZqc1+dwa3cd/kncE2G8fhV3rHKAhvTYRw7AYDUVIHdj8H9v1597tRwhoFkEa9L5i27NlawtFUVFbpLmVNV0Y2LEzWeuiTLRB8WnibxK8+I1y4i+dIGByn64piKB4UKkZ276LOr1W6iK/wprg9Vo+V0BQwRNPVNjaN27wMsMK3rGhZ3khiXzyApiwJQozdW7XytN+UQwBfwUVYEnalSrVSvQvJlBwsjRZH0F9QB9jWtXfLle+DdAJTGKjTZ1gJnJi+huhS6bery9VAPhdCGOM/33zeLWmto0CeUQ2eYK9tiG3e13cWU3fV6dsst3La/i4CnfmXl54OiKFzsEetgqU9IXI9cSl93R9HBRK5MU8E28vWKYle9JV8O7TBUEYHjSaW12p3TruO6mi4z32f0AFBwZWjw1kfyFXaHUSWVgksUMMpZDcOmHeorZLTsUA5HGiRCZfH9Nit2grMWSoc2PKrCGXUfMVvFcfrMlxrwEjBEMjb6Wu+KvWcxV2H4fAqA7fE+MCoQ7oDGmp6BZZpM/L7oen2j+27U9nYifleVdljXnS+AQCOhn/lVALInxmm6+AdoiobH8HP50gorltv3Iy0vTzNYjq7se2xQbCZfNzBqc18i+XKfa2bv+F1YmDz8sf1E1siLwayUaLwgbhytW8Pw3s+AXDs1v213ve7f1YTfvbGCpa2KSL4uZy6zu118puODM6vUkU/+FpJsEb0sqk5D51OUcgvzorWB2rxXg28SWVUZzIrvya/Ulwx/ICoSIkMz8WkiAB/KTCI170L12gHTdXSBptMOxxWRaDX6o+Sm1l9m3kHEEyXjFdXjUkD8PqtCO7RvdlO6CM6HQ8PE/GtHmXLtO4zis8CUuNv27BvICcqto3rY+/r4shOFS8cmhNBGxE3XTbNmPwdfhUoO/I3QcpN4KDvIQHYAVVK51b+bzDeF0MY3u+/acF306dAOig5j+/mXkVWJYqZCZrK4IvseSZeI50WhQJfEd1x3tEO7eu4kX6eI4VoB64rpSof9mkjui+4MDb76WFMlSSLqjVYVDyslBT1sC26sEO3QUTocjBq15EsdWFOxDQdDoZtosFkRyWJixnNRn1Cynbx4fbT16bj8xiSWBfHOIOHkj8SD2x+Aaeyf7FNPUTp9GsPr4wu7386etjAVo0Jfpg/YAMkXEHz8w0huF1peRfvmn6A0iu9WXWnNjYGjGJqEWTYo2Z2v7d31MxKxnthMvm5gTFc8HL6Q4tI3xMDy0a1fI75r7QaMBz7/80TyoMuw+xP/G7zhGc87814bMVjyy366w90ANDeJz3Fy1tyX2rGT0P44/tIkEXMMy7S4fHzhKmZlYJC8o3TYIG5C43mxTdhVH4GCA9Wl4A2Kod6IJhLG4eykLTdvB0wjy5cMdirdqs9kyjaLbQk21FXnq8EbJeMRQULZL25CK047zE9C4iK6oZBziRvcpebVMyGdC5Is4+8R59+hAVGpniwJcYyuA43IqkR6vEhyOL+s/TveXvvuaZ8ptAG1ea+e+6vFG6frdXPTzWhPfR+rVKIv1MKFeDcP7q0fL7yl4s73/DQA20byRFpEcWOl5r7G0iXi+QoWElpFFODqrfPl0A69lSCyqXDWDNTWkomJZXte1ZQOTSZNcQ0VXFkavfUj8hT1RCnaXl9FM4zss2dIV5h2OBKTCJXF5w4rE0z5u9dMbMNBPrafgC6u5anCzM/X0Gz7f41de0Z6sXBYJ9sPNUHvj8SD0yiHlqYx/od/CMCJe99N2hNkd2uIS+lLGJZB2B2m2V//64rs8xG8/wEAMgM+9ulPAdCSblo50Q1Dg8FX0QsKpqRQ8oju8Y6tm8kXbCZfNzScua+ps/089RcnsEy40nSCY+0/oD/bvzYH8dr/5dKLYt5rdIsH/9bbZjx9cTzHhfEcqizxtj0b86I92HQQANnXB8CJoauDpOiHfxaAxgHhGXItapY20F/1+GpsE4lyoiS2ifvqj5pZ9frSxLGN55MQ7kC1m6v6leWbZeoO7dBvkJNFR6U91FgzWK6D5Ksp0FDtfBU84jtY8c6X3fUaM+/Bkl0oepHR1rVX3fLdLNaVrVdEspk3RdHB7VXZas8XLIZaOxvThTb23jOHUMZC816th6tCG9/qvou7dsSJ+jfujPJNN93GYKOKDOSLwltopea+hpM54lmDsieCaUhIsoTqqw+TYQe+oAtFlZGQCFQi9EseVI8JkgWGgT6ZuPZO5sB02mHatMUtXJmq0EU9oMHbUO18FYwYbq8ovq1c56sPgJEGiFREoSgkj5ONHViR/S8F8WiQUV2IviSnzXwBNO0U62iqtDLXcaWoM3BGFIy27VJhTIxC0HN/9TWpf/gHtCv9KI2NfHWneHxPa2gG5XAtZuRXAqGHHwIgO97IQVnEHa25bp65sEJ+X6PHQS+iaWFK3gaQZDS5QiC66fEFm8nXDQ3vvn0YssrrTe+hmNVo3BJk5I7XQIIrmSurfwCDr8I3PkV+UgTH5QNXq7I5lMO7d8SJ+OpQvnURuDkuqBqTmligTw/PFN0A8L/7Y7ijEvHxNwDoP5OkUpq/AlWZ7vG1XXQEM5oIOFoD9Vd5c6h/UUNUUieLSZBlXLaKmNZ/Ydn7dpIY2WdQlsV31h5uqNIOA7H1X+xbArFq8pV3rZLXl518jZaFv5ys9RPyrH0i7r/nQQC8gwWwLEwlRbokaGyO6mHvMua+Tj/rCG00Em6c1ZkvZcR6AtV5L9MyOToqvpO7p+KUz59HU1x8v/M2Htm/8bros9G/XQSlDImh9hXrfA334jIg7xO/VbjRi1RnkYIkSwTsNSVQiZJUJFDVKo15ufOUjtqh6jOoWKIypHtLeJT1L+A4iHqiVbXDghnF67El9icnl93xc2BZ1rTOl4y/HAEgpIyjt95yXfteDtrCXno1ERcULY2CVqg+13JImMjnlRha/vptO/pOTmLqFtEWP7GCLbTRchMExRpqFgpM/OmfAhD/1z/PiaSgVe9tC3NhagMoHc5C8P77weWiMlkhILsouxIolsrRH59emTewC8maZ1t13ivnyWyY5HS1UWdL6ibWEnIgwMVbP0423I3HDY/965vY2iBu6P2ZVe58Zcfgix8Go4J/wpbzve3wVS9zkq93bEDKoQOn89WbOUPQA2Xd5ML4THNIIbxxF4H8CIHyGKZuceXk/NXb4uBIVT0ovm8vAAUjBUBnuP6+q2rnSxedj1RZVDHVZtv7Y3h5g75muVyVRS4FLLDX9c5wU111vtpCjVXaYV4WQe2Kd75sc+WxgugK5dUB4t61T8S9974bSbGwyhIdE24kyeKVQRGc9NwcR5IlEoM50hOFa+ypBkMzOfOiI7TRfvUL+p4Dy4CGbRAVc5ZnkmdIl9MEXAEav/MaAD9qv4WCx8fD+zZmF306vIfvA6Cr98cAJEfylAvX76GTHRJFomRMfEeR5vrwuJqNkJ18hcoxLKVAOdxdpR5qy1Q81EdtxVi/gmyJ+5JcZ5TLmDdW63yZUXxqRtBsTfO6hIsAjGQSM5fDkiSygRCKqSJhEFQSuDpvXYnDXxJaIl7eMPbisee+psq17ldkVweqXsSSFcZfW37xzoHDNtl2qAnpsmPUXut6JT/3OYyJSVydneQeeje5so5LkeiJB6qdr52xOlY6nAUlHCZwRHgh5iIfRAoImXnzwgoV3m3lXY3maqxS8K7MXOqbAZvJ1w2MEz8aYih4ACyTIx0DhOM+toZqRsurBr0CT34EsiOkQztpGRcL6677H5/xsqFUkeODaSQJHtrAwVJXqIuoJ0rZKLOtIwXMTT2MfOK3kGWL+OgxYH7qoVkskim6QZLxyWl8LSLZ0iSx7+5Y/XkXOQlQyBDKXNlKCgCX4/U1trx5BSeBkRSTVFAEiZbpwau6KGQFHaceZr46I/Fq5ytLExaSqFSvFL9eL8OwEGxJlgVFaiQ8QFtw7a8byRfA1ya6jYcGxQze6yMiOPIGXWzZFQXg0uuLH5S/9MYEpZxGIOKme7bQBtQoh9PmM14aFjf/e8IHyT31bQC+2XOE27bGaA6vfzf0evG2930EQ4LOyRSeMGDB6OXrn/HTx/sAyNhFnHBTfX5XQVvxMFCJIal5MsFt1y26oU3Yc5mNLQRMER65gvVVqZ+efFWsAN7yFEqDKGpdr9GyQzmsNIXx2SyFgJwALCLda598tUW8vG7uIuaIbkxdrj4nyzJhSdxHx09cX7FYrxjVYuf2g/HavJdNYdanpkj85f8GoOmXf5lzCdFp29EcwqXIG0fpcBZCDwmWQvbF48gdotPrSbfBiX+4vh1bVpWJoZd8lKoGy6tg4rxBsZl83aAYOj/F818SAdGO3n8i0i+oK11hIVG9qp2vp34dBl4CT4Rzbe9DBiYbVJo7d898mS20cUd3A/Hg+gfQy4UkSdXuVyQmxAdODF6dfKkd2wkdaKJp8hgAfScT6NrVNBJtaIicba4cD4n9FLUKliy6absat1y1zXrDSYAClQAAeUMED2qnoI7oyeUFjVWlQ5/JREQkcrIZELL2FsiKhC+4/nTVLeEG8u4pTAwMPFQ8ITGbkljebMpVGHkDjDK6p4WsIoKmc60DbI2sTyLu2yt+14ODohNzYVrQtO2goPE4fjqLwalnxXWz95525NlCG1AT25gmMf/iiJj3euSsF6tUYizewdlY14YU7pkLbW0d9LaLgoNWEev1Ssx9eaZEh7FkewhGmuqz81UzWo4iqVkmvbXOlz629M6XkctjlUTBJh3tJGAKZV1feP3Xj+mIeWJoShldEteWXCijNtlzpNedfImuR7YlRNAu4oSVCXrZQjgSva59LwetYS9pggQMUcSZGjw64/loSMwiTvZf33nffzqJXjEJNnhoCoxDZhAUN2y9G4DEZ/4CM5fDs3cv4Xc+xtkRcb/a2xoiWUoyWRRJ+47o2hnarwRCb387yDKlkyc5sE8k14FSJ5kv/xaMnlj+jtMDkB0BWUXLaBR94p4khzdTDgeb38QNiGyyxLc/exLTtNi2XaVz8PuUToohy2rytVqCGz/+a3j1rwAJ3v+XTJwUre70rqsDom87KodvgvmMg80HAaiol4C5O18A0Q9/lFC2H095Cr1sMHD6ahpJpb8mM9/YJG4+FxMjSJKFZcn0xOpw5stOvjxl8XfJtJOvHmF3oGcqy9qvNk1sY9QnujwuKVBTOox4kOT1r1x7XW5My03OI2gzlYi4Ga0Y9dCmeIxxP6bsQtELXGqeZEfD+iTivsP3ANAzJH6HwWnriZN8jV7KVOfyFkJqrMDQuRSSBPvunYNymB6CyfMgydAjqHglvcTrY6+DZbH1B2cB+HL77SBJb4p5Lwdju4X/kGdcdD1HelPXtT/LsghmREHAcIvfqV5ph1Wj5UoMWckx4tpa7XwtZ57SSVxk1WTMvxW/IZKuYKS+On8xbwwkKLpsxdCSgdJsJ1/XqXjozHtNxb1VpcOQMk6vumNdZnVa7O/e0kVXJjF+fMbz8U4xMzyVvD5BmKrK4cFmpMs/Eg92Hga3H214mKm/+zsAmj/1q0iyzNkxoTa5uzVUnffqDHXid62NPc9KQW1sxH+bEDm76coVJvyiyHWsvBe+8NNQWCaN1abA03oz2vg4RVsErB7mr+sFm8nXDQa9YvCtPz9BMasR7wzy1o8dRJIk9NFR9ESCrWFBO0yWkmQqKyxTPfAyfOPT4t9v+/ew62GUkzYd6eDBGS+dyJZ55Yq48B95E1SqDzUfAmCkdBawODOSQTOuniXwv+tf4olKNE0cA+amHmqDAzWxjU4xEH0hYc8qmGFURVmFT3B9cDpfclEcm2aJm5drl1j4TQ2M1NIXer0qM28w7BaVWo8UqiuPLweyFahSD8shmya0UsmXY66cE5VXWRsAWWJPvGNl9r9E+N72PgCCKQgVLBKVoepzgaiH1m3CTuLyG9cOFk/Z8vJbDzRWzXVnwKEcth8CnzgHXht/jYpZ4XAihnXpCqbbw/c7bmN/e5jOho0VIC2ElvsfBWBbn6hSj13OYM6xriwWqYJGPJcVtFjbrL1eky/nXBCdrxx9UgeqzxbcWAbtcLrS4YDZiWKHR9FoaIWOeGXgKC/mbaPliu5FjYn7wPUaLTu0w7FGmZDd+QopEwz7dy+w1eoh5FEJuBVK9qxwMnlxxvPN+8R9MGMElv0ehmHSd1x0rrYdappGYX4AgIk//hMsTcN/550E7r0XoNr52rNBxTamI/SQUD0sfP/7jIXF/eiCeRhS/fClj4KxDKrggCgGWh13oo2MUrStGppb60c1dL2xmXzdQLAsix99/hwT/Vm8ARfv+ORNeBvCuHt6AOH3FXAFiNvDkStKPcyMwBd/RkTZex+H+z6NVinRcll0gDrveWjGy797egzLgps7ImyJ1ufNfynY17gPVVaZKicIBTJCdGMsd9XrJFkm+sjd1eTr8vFJjFnBVLl/gLwjM79TdCr7bKqQ2w6Y6g1OEmSVQTFcmDZFUm7uRnaJqqV+8diS9+skLy6fwbAiqqBeJVRXHl8OVClY8/oK2J2vlfD6msavH0+LICWv9mOZKluj8evf/zKgtvfgjolK+a4hi4I183MuVvXQ0EzOOkIbc3W9YO55L9vf64mTItE6uftO8m7fhhbumQsPvvsnKbihOTmK7DLRKyaTg1evK4vFaKZEPF8U0tDIKKpcF4I1c6FKO6zEkJQSZ43Gab6BQwttOidqBssm/WVxrhXVHA2B+goYY7ZfUt4t7p0FM4oSFBTJ66YdXnEMls2qwXJIGScZ2X9d+10uJEmiJeJlSheshqnciJhvtdF8m0h4yq4wucHlJZ5D56YoF3R8IRet3QG4/Kx4YvtbKV+4QPqf/1m81699CkmSKGkGlydF13G6zPxGEtuYDmfuq/jj11BCYpYtXbgZSw3A5afhe7+99J3a9yMjeoCK5cJQfViY9HS+udbf68Fm8nUD4fgPBjn30iiSLPHIx/cTjoukxrtfLKzFkyeBGvVwxeTm9TI8+TOQG4WmvfDEn4EkceHH38OrQcED2w8+MGOTp05tXGPlueBVvexrFP5HW9pEIDrbbNlB5JP/nlj2Iq5KlnJBZ/h8asbzub4hNFcQyTKI7RFKh4NZsU+/Ul8Gyw7cPhXVLZabQCWCJFdIFfMgSbjCInDQeo8vtIs54SQvqt9kBJsm5AqTmxI3kXpKvjxyqNr5cqR3lyuJPQPJS5CfAMVNsigS0JHwIIoZRZbXb4n37RAzQ7sHLVAyjOdqnXSHejh8IUUxNz/l9NIxW2gj6qHrwBxCG5Z1VaUahNhGoGjR82PRNfu/cTHP8GZZTxwE/EEubI0gYWEZ4rNej+T8aLpEPFehYMvMR5p9dUHbnQuO4IZXD6AabgaLeVzNIkjXxsaxrKVR0RylQ9VnMGxT7oqubF15fEGt81V0pwDh9eXy2x2/66AdWqZJpV8UXC+HS9XkKyBPUG5cn+QLxNxXShfnY1JCzLfa8MXD+DRB5R59ZXmKhw67pOdgE/LoMSinwRuFtoOM/8EfgmkSeughfLcIqf2L4zlMC2J+F80hz4YV23DgamvDe9NNYFnckU1hSDqeip/UA38uXvDin8DxLy1+h+UsjIkxFk3ZWhXbyLvT7Gmtv3n09cJm8nWDYPDcFM//o2jZ3/2+7XTsqQXpjtly6ZTwd1jx5Oub/xYGXwFvBH7q78AjAsTBF74PwPi2GKqrZpSYLmq8cFF0CN4M814ODjUJ6qEvJGTV55v7Utt7CN/URNOkuMnMph4mxkWwGlYmUf2isj9eEBXPiLs+ky9JkqozGv6KOMa+lF1ptr2+9CtLv3k6EvVqACbsBmHYHanJzNcR7dCv1JKvvFsEE9rIyPXv2FGVarqVrJ18n2sdwCuvb9DoPyTO932DIgg+OlAz0o40+Yh3BrFMq0r5mQuO0Ma+e9rmFtoYPw35cXD5ofNOAKZKU5xNnuUtJy3kik556zZOhjvY3hRgR3N9UchWApkDwvzWPymKZ9cjujGUSBHLWRT9dvJVp2IbAB6fitsraMzBSpTJYgK1S3QfrHIFM7M02rw+0AuAGlBIFsVaVXBnaPDW15oa9UQBKFQ7XzFUVdg2XE/nSx8fxyoWQVXp9WYI2snXlCzREFu/taQ14sU0xD0iqcjV9c5BxC3kyyfOL11kxTQtLr0h1p/tB6dRDnveQuHYcXLf/z7IMk2/+ivVbc44lMPWMKZl0psS581GTb6gRj285cIgIyExl95XvgXu/ZR4wVd/aUbSuyAGXwHLhOhWtEyFgs2kyniSdITnKKDdoNhMvm4AZBJFvv3Zk1imxa7DLdzy9s4Zz/vsm3fplKhWOHLzK5J8vfpX8NrfIAQ2/goat1ef0o6JTod100w++Q/OjqGbFrtagmxrCl7/MdQJHNGNLCIJPj5P8gUQ+/C/rCVfr41imSKAtUyTdEUkXI3hGr0oURI3kLh37U11F4tAVCTYoZII7AZS9oxF3KbgDS+d5qqNiuTF1dJKVhffR8QTIVeHtMOAGqp5fSn2gPwyPvNVcMyVtTtmiG2E1PWhHDrwPfBOALaNWCiGxYnxmfMaVdXDeaiHU6N5hs4LoY2998xDOey1VQ677gZV/NZHR49iWSaPnRAd1Rf23QeS9KbrejnY84iYr+sZFMIio5eWn3yN9Z1BBnJ28hVtru/5uGB17itGupxEbtuL4hZVmKXOU+oj4lpU4w1YRUFfLLjqL/lyK26CriBFe+arYEZRFfGbX0/y5Sgdura0U6nIuEw3YHJRjtIcWr91tDXsxdKd5Eupigs5aGgU13liePG+gQ5GL6UpZip4/CpbdseqqqlWz/2M/4/fByD6/vfh2batus3Z0ZrYxkB2gJJRwqt46Qiuz3ztSsChHnreOMW4345PXu+Ht/0H2PEQ6CX4wochvwh1Xttcmc7D6COj1c5X1pNdVyZGvWHzm3iTQ7MFNko5jaatId76oT1XqRZ59+4FR3RjcpLucDewAjNf/S/BN/+d+Pfb/x/Y+eCMp6Pnxc2x6c77Zjz+rRNvHpXD6XCSr/FSH8jFeUU3AHzv/FlaOI+iFynkjGpApU9MkvWKQDTeUbshZjSxKLYF6tcPzUmEQhWxGA9nRSLiahdUBG2JQ/KWYaAnxfeibt1O0Zavj/tidWWw7CDsjlY7XyUpiiG70ZdpBjsDtrLUyJT4Hl3aKMgSDZ71TcTdB+9H8VqohsS2UbiQ7Jvx/LZD4vj6zySpFK8e6j5tC210zSe0AfP6e+0ehJaxCpLXx//2iuLOo/vrz/9uJXDPWx4hGZRoTF0BLHJTZbLJ0rL2lRkS3edMyE6+Wuo8+arKzcfI6Cms+K5pcvNLW08c5VGlpQ2lLNbleky+YJbRshHFhShg6IkElnG1Pcli4Mx7WR1t0yiHSU5aXbSsoy9eW8SLZQtqVDtf0yil8R5xrKnc0oWmnMJP901xFKNQTRxyY0GKr/4YyeMh/ou/OGObc3bytbetNu+1I7oDRa4/oavFwtPTg2fnDtB14ilxT8r0lTEtoUpNwzZI98OXfvbaAhxOctx5GG1kpGqwnPdsGixPx2by9SaGZVn86G/PMjmQwxt08Y6fvwnVffUCIQcCM0Q3HMXDK5krS+bNV5EZrgls7HsC7v3VGU8PXzpBQ9rAkGDvfTVz5UJF5+nzYkF8M6gcTkfcF6cz1ImFRSgyTEU3OW9L1s6GJMs0PnKEeEKomPW+LgIJbbAmthHfVku0iobgvW8J15/MvINgVNzAw5rodI3mRCKidopuqJ5YIk0okQDTAslC7d5PxRKdryZ/A/lU/RgsO4h5o1TUIhVVJIZFXyPa5NTyrzGA4hRMCLuGiYQIlAsucfNs9q/vuSDJMr4uocK2e9BiKD+zmNPQFiDa4sfUrarBqQNdMzj7ovgc+++bZ05AL8OV58W/7Xkvy7J4cfhFHjwmgufs3Q+QwM2WqI8DW8Ir9MnqC4qicLGnGcWsIFlinViu5Lw50QdAadrMVz2j2vmqRDGlDMXYTtSq0fLSChuaXcixWrfit+zOlztbFbioJ8Q8MQpup/MVQy0PgiyDaS7bO9BROtS2xKvJV1iZ4ITZQ0t4/dbRlmmdrylFwcpPwDTfwOZbxJhEVo5haItX5rMsq5p8bTvUBFdeAFPDCncy8dm/ByD24Q/hap0Zh5wdrdEOL6RspcOGjUs5dBB66GEA7ugbpqTkkXWZ8StZ8EXhpz4P7iD0PQvf/Y/z78Q0YPBV8e+tR9BGR6rzzZp/eUWBNys2k683Md74/gDnXx5DkiUe/fiB+avHgPeALbpx6hSdIUFLzGpZpspTS39jrQRf/LCYxWjeD+/5XzCr23bxma8DMNbuJRip0aOePjdBWTfpbPCxr+3NFyw5ZstNcUGXm090AyDyiX9Ps2243PuiSIRLly6T94ubQePemqGjJqUA6InNQ8+qA1Q7X1oUgAnbQ0TtFqIherYClcVTR6oGy14TqWUvmiUUqOJKFEM3Z7xnPaDRFwUg67GNsb1NWBV9ybMpMzAgzNFp2E4yLz7rcEicW1vD69/p8R3YA8DuIYupyvCM5yRJqlIPZ6seXnp9glJeIxjzsHX/PJ2HwVdAK0CgCZrF3OpAdoD05BB3nREJ7fe2C5PURw+0rotP0VrBuPUOAIJTYq5utHd555Q7OYYpKeiq+M7rnXYYmtb5ktQcCW9XzetraGDR+7EsCz0tKvOl1u0E7IKI5dNwKfVlsgyzOl9mBDJDqI02fXuZcvNO5yvfGiFUsjtfygSnrS6aQ+vX+Wqd1vmqSBJ5Sar5SAHxW7Yjmxqm4iZ54vJ8u7kKE/1ZsskSqlumc19DtYueSe+hfP48cjhM/OMfn7lNtsxkroIkwa6WEOeTttJhdGMqHU5H6GEx97W7d4LRkEgqB87Y9i/NtlAawEt/Cm98Ye6djJ+GShbcIWjehz4yWu18ScGN2xlcDWwmX29SDJxN8oItsHHPB3YIPvMC8NmKh6WTp/CqXtoCInBbMvXQsuCbvwZDPxaKQdMENqYj82PR3i/u7ZrxuKNy+I4DbW/KYMmhHsq+PmB+0Q0Apa2bri0TyEaFXEFhciBH8nQvlqyimkVCXaJbWdDKoIiuz+54/aoJORShgCZED5Ilkdi7ukQSqRWUGRXNa6GafPkMaNqFKYvkqxHRbfGFXChq/SxxTbZkddotbmjloO31dT1y847YRusRMrLY3+lmEUR110Ei7r/7bYDofJWsqzsR228VydeVUwn0Sq0yeupZkajtvad9bqENmKlyaM8SvDTyEvefsHAb4N6zh7/PiLXnzTrv5eCu93wYgM4RETQtt/MVzCSFJ48ko3oU/BH3tTdaR0w3WpaUHON6ANU25tX7Ly606QyY+TyWJhKuVPMeApa497iC9XkPinqi1ZkvExcV04faaHt9TSxv7stJvtLNvqrHly4XMVU/YZ+6Ake9PLRGvGC5sUxxLiYVpeojBaC4VEI282PsWN+i9+t0vbr2N+JyK3DpR5gGTPxAiPw0/tzPoUSjM7ZxKIfdjQF8bmXDKx1Oh2f3blydnaiajlwR86NXTk/rou57HN7yb8W/v/ZvYPj1q3fiUA47bgdZoTw6TtkWiPFtGizPQP1EJptYMWQmi3zns6ewLNh9pJWb33rtQVBHbr4qumFTD/syfUt781f+El7/W5Bk+MBfQUPP3O93Riz04dvurD5W1g1+cEbcOB55k817OXDMlqeMi4DBicGFh+ObfuYjNCbFb3Lx+UtMDolqZ1QaQ7IDzosJ0emwLJmuaD0Lbojky28LhmQqKQBUm9ZhlBWs0XNzbjsXtAGhyuTyG+TDXUiyoPNFzcCM96sXtAZFZTrtETf9sv3/pdKjZsAR28jvFUm5XqS3RRRM9jSt/wC49/4nQLaI5qElm2cgNZMS1bQ1RDDmQS8bDJ5LAZAaLTB8QQht7Ltnge6dI7YxTWL+xaEXqpTDxAOPkSkbxIMebt1af9SxlcTefbfQH3cRzYhrIjGYo1JamjlqsWLQmM1WlQ6jzb66L4A5tMOAbbSczFdQW2y5+eHFd770IXE/kl0m48FtBEyRbPhC9df1AmjwNmDKBkV7zcsbMVR7Lms5nS/LMNBsmfmJRjchW2o/ISu0hL3reh7EAx5UWZomuiHP6HwBRALiXJ+4nFz0fi8dm0Y5zI7B+ClSFwNoEynU5mYafubDV21ToxyGKGgFBnODwMb1+JoOSZKqqofbBkRSOXY5M3MdeeC3YOcjNQGO3KxzzVGi3HoES9fJZQ2QZDS5TFNsU+lwOjaTrzcZtIrBN//8BKW8RnNXiAd+eveiFs6q6MbYGPrEBF0h0ZFaUufrygvw1G+Ifz/4O7Dj7XO+LJeepHVIUDx2vOVd1cdf6E2QLes0hzwc6owu/n03ELZHtxNyhaiYJWTvKGdGs/OKbgD43vEztBWEKuTFFy6TyopgoCFYo+ddSIgugWyEUZX6be074heuihvJkslpKQCUWAxJEeeodunEoven94lETY36GSiK78OyJNwl2++rzipt7SHRmUr7RLLl0DH0/uX502BoVX79yJhILkIkkRQLy5LY1bj+nS85GMHXIirWuwYtXh6amVxLklQV3ug7JgRYzrwgvp+um+Lz/4bFFAy/Jv5tJ1+GaZB8+Xk6EmD5PHyj5WYAHt7fglKnXlUriYEdnXjLKSQzjWXBWN/SqIejmRKN+VLN46upvimHMMtoWc6RyJVxdYh711KSEN1ed1Q/jFZ8+Ozkq147f1FvFICCS6x7BTOKGhBr/3IUD7WRESxNQ3K7GQlqBO3O14DkW1elQwBZlsTcV1V0QxFzrsVU9TWN7WI2cWpSW9Q+kyN5pkYLyIpE101xuPw0hiYxeTYKQPwXfxHZd/W843SlQ2feq8nXVHdecMuFo3p4+7kJocxrCi/GKmQZ3vcX0LgDMoPwpY+K+5ADJ/nqvBN9fJyiRyRcGe8kW6PrT4OvJ2wmXxscpq4z8uyzeM5fZvjZZ/nB35wiMZjDF3Lx6CfnFtiYC3IggNuWUy2eOrU4ry/TEG7wJ/4BTn7ZFtjQ4cD74e5fnnezs89/HcWCZEShffvN1ce/fVIEXY/sb0V+kwZLsiRzc7P4zIHwwIKiGyBEC3YeDiGZOhktzKBX2AJg6BhlUfXsmxKdL7dU3zcAX9iNJEtIloS/EqJoiuBQkiTUmLix6v3nF70/p1qtNjcxmBYdFcn0UcqISl29db46IiLJyPpEcFT02l5fl88ub4ejJ0AvgjfKhN08cwWEyp1khvC66iNw9O3pBmDPkMWJsaupYNvt5OvyG5PkBlTOviDO5/33LZA89j0rvGQad0JEdPhOJ05zzyuCehp+7J18w557erOpps6HwJH7AQjbQeFS/b6EwbJO0ecoHda32AbUki+X6cYrGSTyFdQuMWeoJ+dfV2dD7xOiNWrEy1RCXEOGpBMN16cvnCMCUlAF3bxgxlC9Yt1bjtFyVWZ+ayfJ0lRVcOO01LCuSocOWsIeLF3cI6ZCttDU4CvV55t3i2s8XVncsTqUw869DXh8KvT+kOTZIEbRwt3dTfT975tzu+liG28myqED3y23oDY14a+YlCRRKBs8M2vuvyrAEYIrz8F3/oOIBU/9E6T6AQnabxVKh147+fIk6IndGOvwYrGZfG1g9H71G3zuV/6Zrz0Jvb038/Un4eKPJ5Eki0c/sbDAxlyomS0vIvk6/VX4gwPwN++Cf/wY/MO/hMIkRDrh8T++SmBjOsZfegaA1M6aWp9umHzntJh9ebPPZzhmy5Go4JZfk3r4yU8TyIvuVtkjbornSzfzf37+65z8//6MoawI5v1KfSdfsiwRsCvJgUqEslkLjqpeX0sYknd8fFztnYzmBN1EtgLTZObrI/lw0B6KYlnyNK+vOBZSNYlcMqpVxsMks6JSn28UKo8e6udc8N15FyDmvnqn+q56vpDRQAKtbJI66UMrm0gS6JX5O8LVea/tNYn5V8//kMNnxdzO6H2PMZmrEPaqHNl2Y9Bd3v6+j6DL0DIpTF9Hlph8DYyNEC5aFPwiGa53sQ0A1aXgCYpzP6R7mcgWce08CIBRNDDtAtW1oA/1if01RMjY60fBlSXmq5/raDqcTktN8TCK6hKJ2HI6X47Sobu7m8xUErcpYodjUhvN66h06GCG0XLM9imd5vfVcpug/RXUKOXktTu+MyiHloV+8kckzonkrulXfgVJvXrGTTdMzo+J73hvW4gLU6LI8WagHDqQZLna/WqaFEXBgbNzUDmbdsP7PiP+ffTP4f/dJmToAbDgz+5Ce/UbFH1iLcl4E+yOrz8Nvp6wmXxtUPR+9Rs89U0veT161XOWBcUzLyx5nzWz5dPVma/+bP/VUtinvwpPfkTIyc9GehAufn/B95FOiovadfCm6mOv9E2RzFeI+l3c2VN/viorCUd0Q3OJ+YyFRDcAzvzdt8gFO696vOyO8vSFXWz9vrgJRN31H2TW5r6iGFLNJNrVJroc2hJ8rxxperVrF2N28uWSgtMMlte/YjsdsiwjmT5ynhTIYEoqZU8EbbkzX3bwoTffTkYR18yVNlG19yv1cy743/ZeADomIJOYSbHsfX2cb3/2JMxaYiwLvv3Zk1WLhaswx7xX4WvfxG1AYVsr36xEAXhwbwvuOhJdWU00N7fS2+4jYs99jV1KY5qLtzEYvXQagLzfkZmv/+QLIGwXGUOVKKP5SeTug0iKSNz14aFF7UO3zdrVphYKadsKok49vqCWfBXdKUB4famS6FAsK/myxTbcXV0UJkX3WFYy5CXPuiodOmgN+6qdr6Q9K1stPgGhrc24tSxIMmOvLsyeyEwWmejPIknQc3McJs8z+XIOS5fxHthP6JGH59yuL1Ggopv43QqdMf+bsvMFEHpYfP5D585hYZIczpNPz1HE2PNO2CfWdkqpmc9lRtB/8Jlq5yvtnmJLuD4LGeuFG+Ou9CaDqes8+x3HRHOuDpPFc98pYupLG7iuim6cPElHsANFUijqRSaK02gMpgFP/TpXRUvT8dRviNfNAcPQabaVuDrurpkuf9tWOXxwbwuu+dTN3iS4KX4TiqRQMBNIampBuXmjXOalU/P4NdndxUDibmQD4r76Fdtw4Mx9BSsRLDmPbhuCqp1CmEVPF6F07cqlZVloGdHlcW2/uSpb75WDdWmw7ECxAliSiRQU10/RG0dPLK1DAYjsZMAxV+6yxTYKnA2nAIi54wtsvLZQu/aghcXNpu1Kb/Vx07R49osLz7s99+SFqxOIVD8ke0FSoPteAPKVPHueFfOp4Z/4AN++QbroszG+Z6fokptlKiWD5HB+0dvmhi5iyC4qdnd9I9AOAUIN4jiDlSiThQRSZAuqnTfqF+ZQZJsD+qSgLbu2dFLKihmWgjtDo7d+ihjTUaUdusVxF8wYqinO+WXRDq/0ASL50jN218fupK2nx5eD1ogHy+l8ue0fd+jHM+aNIrbi79jphRNup+vVvjOKL+Sm8tI/M9UrErvmT3963hl5h3K4qyWEJFHtfL3Zki//7bcjRyI0ZPPkXEJQZPDMHN0v04DBo1c/DoCFVlAoOQbL7hKy/OaO65aK9dMP3cSyMfL88+T1haoIMjm9gZHfeZAt/sUP83s1gJConP12D+09YQZUhSt/egfNFTuRM3WhdDMvLMgMCfGNnvuuevbi6z/EX7YoueDA7SL5Mk2Lp046EvNv/mDJ7/Kzu2E3pxOnUXxXODPSQEU356zQX/67L1H2LDD7IklUPA0c7t1GvKN+DZYdOJ2vQCWKJFn0pyfZ1tCCa4votGoFBZKXoP3ggvsxJ0ex7FNS3X0nU89/HgC/EiY35XS+1j9omA2XFMJgHN2voWTclHxx9KllCG6kByA7ArLK6IAQGInKaRKaoDQ2rbPB8mzI3TE4PkXXcAbTNJFlmZELqWqiPB9yU2VGLqRmWmU4lMMtt4FXyGuf/N4XaU9YlN0S2t1PMPR/juFzKbxlV/0XJFYSbQ+8A/l7xwlnLpGJ7mW0N0W842qrj7lgjF+pisB4/CreQH0q/c1GcJrXV28pAZKEK+JFy1bQLp8E3n/NfWhTOUBG7dyBfkEsLAVXtm6FFKqdL4/oduXNGKqcBnzoiQSWrs9JnZsPzsyXu7sbekVyU1JFYaw+Zr68tc6XpYvrvpQWc69bbgUgGpWYyAilz4Uww1gZmPjcV8CUCOzvIHDkyLzbnR0RFM89rSHGCmNkKhkUSaEnMrei80aFpKqE3v520l/+Mr7cWfBsZeDsFLuPzBLMuPLC3OwnG5WCTLFFrCcl79IaATcCNlPRDYj85OIq5XktAJXcov/IVg53WFwkpbEKWyuis3BF0mqvWzDxmobc3N5FA89/B4Cx7ggut1jUjw+lGc2UCLgV7tlRPxX71YRjtuwLDVAx5hfdyI2mFrW/WCFCR6Tl2i9cZwQcry97oLs/JW6Ejjy0XpQhcW1/Hv28UPmT3RZyYzupcgqAkByjUhTnsBOU1RO8sgiEC16h9ln0xjHKEubUEqmHjtRy682MDwjFs8ZGmawmKuFbgvVVxIgfEgHSjmGT3qRYG/KZxc3jXPW6Oea9sl/6MgADR7r5dp+4lt66pwmvq37VP1cDD77zgxTc0JASfnkjlxbfVfUkx6cpHda/zLyDqtdXOUa6Yhu3x6MA6NM6rfOilEbPC5qi2rMfqyiSjoK7fmmHQVcQVVZrM19WHNVjgiyBaaInFi+5bmka2pDoFhntzXjttXnMEjOz6612CNAW8dU6X+UkdB4WTwy8XH1NvEsUYlILnPL5dLl6TWw72ETp1EkyJ0QC2/zLv7DgMThKh3taQ1XKYU+kB7dSX7PFKwFn7mvXFXvu60zy6vGTeWI8B/lKFEP1YmFiBTb7PLOx+Y1sQATiEWBuWt+M1z3wc3D4j5e0b1/q96k89QNKu36Z7v15nr/8dfoP/xzs+6h4weCr8OWPL7gPAIJzJwKlY28AYN5UG1J1ul5v3dN8wwRLh5oP8fmzn8cX7ic7DCeH0hzYErnqdcHWKCxC7X/Kn+atdWCqey3UaIfiBj+YsZOvZif5sjtf14DWK+T3XWFx48tWxA01bonk3eVRcK+jMeh88KthUgZkPFlCRCj6baGR86/gPvzuxe9omp9K8qgKLmjZ0UDRTIAM3dH6OheiD7yLqb/5PjuGLV7uO8HOeBuBRdKZZrzONGeaKwP61BQtL4tzxv+BJ3jqRE019UaDz+fnfFeUTlt0YymKh4F0kqLvFgCiLRtj3gsg2OCsKVEK+kUsy8LV0gqMo40OXnN7a/KiKPoAakcPcvk8IFOo45kvSZKIeWIUXIIKVzCjSDKo0SB6Mos+Po6rZXHd78rgIBgGkt9PRk4QtJOvi6ZIdprroPPVOq3zNVWags53wIXvCLPlIz8PQMtNnXBikowVqXbXZ+PyG5NgQUtPmGDMS/8v/y4gEe7R8d73ngWPoap02Bbm1JtQbGM6AnffjeZx0znay4VdGoU0TI0UaGgP1F40T4znIGuI50tqinCdXkfric3O1wZE2z33EFCngPnUwEyCapK2h94NDduW9Md7q6golS4Ns7VJCGL0VVK11xx4P4TbmXvWDPF4eAt03T3ns5FzYrC58Y57ADG789RJ8diNNJ/hiG6UpEGQyhyfZ+6r50M/gac8JWZ85oJl4aokObr9EjvrwNfpWqjRDkWiOZK1Zy1abWPUooI1sYjO1xVReVQbxX4KuqhKxk2xyNdj1wsg6AoDkHKJynQpIAIk/eLxpe1oQIhtaE23kbXFNlrv2IEupwDY0bBlBY525eC540FKHguvBqmj3wKgbWf0mtTQYMxD285o7YGxk1BIgCsAW24HYPjJv0M1LHpbIbznEXon8rgVmbftqS/q5Vohd9PNRDJ9YJlkJktzD8vPgm6YNGTzVaXDjSK2AVRVfYOVKJaSI1PUUTuFbYo+kVhoUwDMwTNYhgiF5MY4bl0UNouuLFFPdHUOegUQ89aSr7Lhx7BUVLtLtZS5r6rSYVcXU8OvErI9vhKyitclE/aufxGrOTxt5quUxOqwO1/9R6v3xubbdiFZBprqJ9M7Nx3uki3gs+1gE/mXXyb/ygmQLJoev1V4WM2DbEljcEqwFaZ3vt5s814OZI8H8/AdKKaOZYjC1sDsua+uu+eNBU1dIq+K9TftSWyIefS1xmbytQEhqyr3PexFnPSzEzATkLj3YR/yEjjfDryO4uHJk1W5+RlGy7ICj/43+z+zLzr7/4/+V/G6WRgfPE88qWMCe94iqkznxrL0JQq4VZkHdt84wVJroJXWQCsWJopvYF7RDcXj4ch+W71qdgJm//9cw5cxZIWuaP0vcE5SFNBCYMF4QQRHalOTEBAxJYyha3t9acNCkl61q7tFW7Y+YohkrB7nvQAiHnF8CZcIjope0anTrpybd5urUM7C2CkARkfjVbGN8tYIkiyowvtarlbHXE9IqspEm5gh8pwR3W9ZlrjvJxeuHN/7wZ0zPf8u2SqH3feC6sayLKaefBKAk/e089IF8fnv2dFIyLsxZpZWGvve8X5Uo1S1pxi5eO3u12SuQjxfouATRZBo88YQ24Bpa0o5iiznSOTLqD17AdCnCqJbugD0PnEtyV6VvOQiYL9cDlgoc9zH6gUxT4yyWsCwY4CCGakJjSxB8XC60mFy7I2qx1dGtmgJe+uCfup1KURtkRHDMsg07xKCO9lhobAMuAJeArqgEI79+OoCXimvMXQuBUDPwTjjv//7AMS2F3Df+Y4F398ZC2gNe4n63W/65Aug6wnhddY+JlRQB2dLzi8QC2oFpTo/OuWfpD1448R2i8Vm8rVBsf3xd/LoYyUC6swba1BN8ehjJbY//s5l7de7Zw/IMvrEBJ0VUWkayA5gTFcv3Pc4fPBzEJ41gBluF4/ve3zOfZ9/5msAjLd6iDSKbR3K4Vt2xgl61r/CtpZw/L4U/xXOjmSp6HMHCQc+/a+5f+d5PJXUjMc9lSk6W4/ynZtOIBthVKV+AwUHgYgIlFRTxW34SBbFzVJyuVCioiu0GN8rfUwkL64tokBQMcWQdciujtZr8hXzRgGYVAVfvqIE0RXPkvzNGHxVGAxHtzJ6Xlz/UTnN+YRd7TV8NPrrzxw2u1XcjBsGJquPbT/UzKOfPHDV7xWMeXj0kwfYfmjWTXsW5bDw8iu4hyYousH76IM8dcoR7pm1Nt1AOHL325kMyUTTomI9uoi5r+GpPPGsUfXl2Ui0Q3/EA5KFjEIAi0S+gmubYG1oBSC9MG9b7xeBuhoLksiXCZhiHfWG6ns9jXljIEFesX3JzBiqVxQflpV8dXcxOd6PxxC/fUa26mLey0FrOIhliONJGkVou1k8MU1yPuIV38XEhas7f33HJzFNi8YtAZTjL1B64ziSYhHfn51hWTEXzjhiG20hNEOjL90HvLmTr8j996MpEl3DojA4dD6FYcyKUeaJBXWaq4XFrDfB1siNw2paLG6saPdNhu2Pv5Oex3QGn3mG0z8+zr7bbqbjLU8sq+PlQPb78WzfRvnCRSKXJ3HJLipmhdHCKFuC06hM+x4XPg9XXhCDl8EW0YZeoFKYeuUlGoH8nlpV3km+Hr0Bg6WDzQf5Vt+38AQGyE4K0Y255r5AJGB7y2Uu/92XyI2mCLZG6fnQT/AHr3wdesEtRdf24JcJ1a3gDbgo5TUClajg79twtbVjTKXRpvJ4C0nwz8MT1yvoqTzgQe3eA4AhCUltf8VHDq0uZeYB4j7xmbJSEk9ApZzXKXkb0caXILhRNVc+wvizWSBEvFHmYlIMzatWdGUPeoVgHTgIzzxF25A24/Hth5rpuaWJgbOTvPD0y9x9/5107onP7HgBaCWx3kBVbGPqi18E4Ln9Ej1Nhzk1nEGW4MF99S8+s1pQFIXebS10jVxiaMtbFmW23D94me2mh4rdmd1ItENZlnCFJLQMhEyZRK6M2i4o2HpRwRo9ixTrnnd7fcTuoscbGZsqodo1aX+kPtcQBw4lsqCWCBs+CkaMoCIo/MulHSZ7BbVOcxXRpPqY93LQFvHSrwWRlDLJUpKeziMw/LrwO7zpAwA0NHsYGoHE2NVU215b5bDn5kYm/ufvitfvzqG2d8EC5wfU5r12t4a4lL6EbumEXCFa/G/edUYOBLi4s4U9ZwexrCJa2cfY5QztO6IzXzhHLKj9eIzSsGC1ZLwJemI3Xnx3LWx2vjY4ZFWl7b77KO/qoe2++64r8XLg3Sf8viqnztAREq7kVzJzdCNkRcjJ3/QB8fc1KBqe00KBK3jbbQD0TeY5O5pFkSUe3HvjtaWduS/ZdwUwr2m2rHg87PhXH+bgb/0SO/7Vh1E8HoayImgPKPUpiTwXnC5HsBwhq9U+s9oqFmi9qEBiAZWy5CW0gli6XF27ME0T006+XGV1xnvUG5oDUQA0K0skLqhdRV8cfSoPWnFxO7HNlem8k2RWfN7mHY30pUTny1dHBsvT0frWD2BIEM1B8fQrM56TZYn2nVH87TrtO6NXJ14gkk69BMFWaNqDnkyS/Y5QT/3RITfDY+L8OdzTSEPgzadAthRYtx0mkhHX0GR/Fq2ysEDT6KXTFG2lQ1/IhacOxWoWgj8mKKYh3U0iX0FtbBRMKEtCv3xs/g0tq5qoqK3tTE4I5dCyUiAWmLsQVi9wxEAKLrH2FczlGS1XO1/xANmy6JiX7A5aSx0YLDsQcvOC2TBVmoKtjuJhrfPVZKslp4szKceVks7AaUGba06eoHLpEkrATePe3DW7XgDnbKXDva3hKuVwZ2xnXVAyVxPZO+9AwiKUqakezolZsaA2NlbtfGU8CXbFO9bqkDcMNpOvTVyFqtnyqVNzz30tA4V8mtYBcZPoufcxoGasfNe2RqL+Gy9Y2hXbhU/1YUpFZM84xweXbrY7mheBQ7SOTHWvheleX3mj9pmrohsFRZjozofJczV1srY2pkp5JNn2EcmLm2G9Cm60hmx1QylPuMlOvrxxO+G8ttCIMLYUMvta/Naq2EbbnTsZsaV/I676TL5u3XM7fXah+MpTf7/0HUynHEoS6a/8E+g6F1uh4Zbb+f5pEXjeSMI98+Gu93wIbymJu5zCNC0mrixsXJ4d6qXgUA43UNfLQdg2Wg7pPiazJSRVRY2Iz6FfPj3/hoUkumPW3rGNVFJYqdSzx5eDqE1hLrhsuXmaUH1iHdQmFpd8maUS+oi4D7s9KYq6OAcKHrGf5jowWHbQFvFiGrbXV2ma3PzYSTEHC7QcFEIrOSWGXqzZ4vSfSmLoJpG4F+2v/xCAxkMSisuaYVkxFyzLqsnMt4W4kHpzKx1Ox/Z3vR9Dgo5Re+7rzNQ1thAoDY9RtrvoaXea9lB0tQ5xw2Iz+drEVaiKbpw6RVdIJF9zdr6WgHMvfAPVhHRQpnO3UClz5jMeuUGDJVVWublJ8NYVX9+8ohsLYaos5mcafRsn+aoOyFeilM1aUDhDbn6Bzpc5fBqjrNjbNDOQFvQGy1IoZ0XQUK+dry1h8TtZcoFQo6gqF31xkXBOLEJ0Y/w0VLLgCTN6GSxZxaUXaNjfxURRBFyN3voUXvG7PFxpFx2V9OuvLn0HjtjGtgewLIuULbTxvUMyBxpu48f9IjB4eP+blwq0WOzZfYD+JhcRe+7rWtRDa3yAgm3MHdlAYhsOGuJiXjRYiVQVVNW4KEzoAwtYVyR70Yr2WtLaRmZKBOxFd4ZGb30WMRw4yWHBJX7bgtqJ6hUzOYulHVb6+8GykMNhlOw5dE2sTzlFrKMtdZR8tU7rfCVKCTFjHtkq5l/tglR071YUvYQlq4y/VruHOCqHbeooxtgYamszsbbLgATd9y34vsPpEtmSjipLbIsHbwixDQdH9t/Oqa0KsSnR+Rrry1R9NBdCdjQLkoxBEU1W5pT9v9Gx+Y1s4ip499ZEN7ZpUeD6k6+xl34EQHJHE7IsM5ou8Xp/CkmCR27g+QzHbFnxXeHsaIayfm3/tunI2Ka6rYGNQ9ucLjevWTVz6arRcmFho2X9slAnk1QFJRplKCMSUEUPUrCr2PWafHVGREAnSQZyWHTpit44eknGGj977R04lMOO2xl5Q6h8ReQ0siyTrojvoa3ODJanY3BLFAClb3LhF85GIQnDx8S/tz1A4ehRKleuUHTD8/skKrkdWBYc7IzSFtl4ycNqYGBnF9HM4pIvV3K8SjvcSPNeDsIN4piDlRhjBXFuuey5L21kZH6rjsRF9JLdRW9urq4fBVe2bj2+HMRs9b+iJwVAQWrB5RP3D2MygaVfO0iernQojbyBpYvkKyWJ76uuaIcRL5YxzesLplEPhdmyLMuESQEwfkJ8Nl0z6Dsh7pOhpz8PQNN77xJTEu2H5p8ttnF2RBQIdzQHcasyF2yPrxsh+VIVhdd3N+ErJXFrSSzTYuj8tbtfmZQ494pqAo9c3x3k9cJm8rWJqyD7fHi2i/Z914hYzPuz10c7NI+fAUC5RVAaHcrhrVtjdTXUu9Y41CwUD12BfjTD4vxobknbFwyxEHZGNs5Aa63zFcGUa593utfXQrTDqjpZPIokSYzale5QpRksMT/kD9UnjTXmDWCZovtT8Ioqe8kXF7MptuT1grCDDDoPM9Fve5s1imU8bwo+/tZw/SZfk9t3A+CfNDGnFj+XwuVnAAua9kC4rdr1ena/hCcY4Y1eURHfpBzWELrr/mrna/RSGsucJwEBQunUhqYdhhyj5XKMyYK4DtQO2+srU4b8PJ2gRK/otCOSr3JOiMEUXJm6px1WO18ekWwWzBiKxwRZErNsiWt7nFXFNrq7YOQYakUUh8bsGmC90Q4do+VkyZ49cqiHtu8hQDQsun+TV0TBYfDsFFrZwKdWCI6cxrNzB5Et9neziHmvKuWwNUSqlGK8INatG4F2CDB6y0FMoGlc3J8Gzi6cfFmWRVZMmJDxJgiq9V3EWC9sJl+bmBPe/YJ62HBFXGiD2UE0U1tok3lhmibxXrHYtd0l+NVVlcP9N3awdHPTzUhI4EogKdlrim7Mhi6J1/dEN873OH3mS5I1pgoiAat2vooKJC7NXa02TbQRoernahWfeTyfAiCsi+39UTfSXIINdQBZlpEtEUCkVXFTL3obsZCqSeWCcIKMzsMks2KovHmn7RWGuFa31ZnB8nQEug4xEQbJkij+8MuL37A67/VW9ESCzHe/B8B3D8kcarqDly7Z8143+HoyHQ++7yN4igPIRoVyXmdqrDDn6yzLIpbNV2mH0ZaN1zkMxmpGy+mKnXy11RQP56P0WomLtfnRpib0gqjYF9wbqfMlkq+8FkCSQQ2I4o4+fm3qYbXz1dqAlZ/AZydf45r4TuqpMNoS9laNlhPF2cnXK2IeFohvsUU5kuL/jsph4+DLSFg0/cqvIPU9Lba7xrwX1JKv3a3h6rzXluAWAq7A9X+oDYAd2+/gfAfEpkQBfXA+0Q0bZiZDURE04ERgkoi7vum764XN5GsTc8IR3VDO9eFTfRiWwXBubtf4a+HyyecJFSwqKuw5/BjJfIWjl0UydqNXqkPuEDtiOwDh97WU5CtfLoMiEpdd8foNuGcjOI12CNCXstXG7JkvU5Mx8jnIzdEZSQ+g58RNVbU9vibtG3HUpszUq8y8A8VOvsaZQpYlLFml7ImgjwyDsQBVKDMCqX6QZLSGA2Rthcv2O3eSLRer58LepvoyWJ6OvY09nOsQiXHhxacXv+G0ea/0V74Cmsbw1gBXWiSC1j4M02JPa4ju+I0REC0G8cZmLrV7CWf7ABidh3qYLmpECiq6SwStkaaN1/kK2p0vvxYmV04BteKMVlBgcu7kyxy5gGXUki+KYm0puNJ1n3w5ghtFtz3zVVSxLFD9omilL0J0Q+uzk6+gTsry4bU7S2k8+FwKoTry3gx7VdwINcbxvN25atkP7qCYgx0XohBNe8W9MK0HMA2Ty2+I+0vT6Kv4br2V4P5WIYmu+qDjzmu+r0M73NMWuqHmvRy8tecQR3fLxFLnwbKYGi2QmyrN+3ptdHSawXKCJn99ziCvNzaTr03Miari4elTdIZEMLfcua8rz38bgLGtIdw+P987PYZpwf72MJ0NG+9Gv9Komi37+jgxlFr0do6prmUpdEc3zgLndL58ehDFcNGfEkGCEgwgB0UAqM9HPZw8j15wBuRFcDVVSgEQ1Rvs/ddPtXYuuGXxGSeLU9NEN5rQciZM9c2/oSOp3LKf0dcGbbGNPLF9Wzk3IbqBlqnW9blwW/suzm2xk69TFxa3UfKy+F5kFWvrXUw9+SUAvn5AePkMDgsZ4xu9kDMXJvbuJpIWFh8j85gtDyUz+EwRLHmDMi5PfZsLzwVvwIWl2EWZcgXTtGYK+Eycv3ojy0IfEnR6ORhA9vlQynbytQE6Xy7ZRdAVpGirHRqGRMXyo7pFYLwYufnylT4A3O4kQ4jrp6wWqOCmOeypKyl1SZKI+0QXZapsd19kBTqEgJezPrbeKRKjsitC7wtXKOd1XFqOSLqX5l/7FNJlu+jTdRe4Fr5XlHWDS5OCQ7enNVSd97pRKIcAhzt28fIOLy69SCgrYsCBBVQPteHhmsy8N0F78Mad6V8Im8nXJuaEI7phTEyy1xQXz3KTr8JrrwGg7RccfEflcJMiJOD4fSn+K5wbzS5adKPXTr5kI7yh1IQ8fhXVJY43oIUZytboMTNFN+ZIvibOVdXJXPZr02URVIb1KFD/nS+fLKq3ieLU1XLz81TogRnmyiPHRNAYlTPIssy5hBDfUMxIXZ8LN7d2c26LOL7CQA5Lq1x7I4dy2HEHhWOn0fr7Mf1ent1r0R7YwssXRIC4mXxdjY63PkYkLa6j4QtzB0yXes9S9grKYaw1uGbHtpKQJAkpKNbNoAWpooarRXwmrShjTcwhZpMbE/Ng1Lrubl10jUruPGFPeA2O/PoQ88TQFY2yZCeNZhTVLT7TtWiHRi6PMSEoi27jMuOm+L4KXtHpqSexDQfNAVuNUctg2DRDOo+Iv/vF+uiLR/BoKQCe/aJYTxsn3yB8/1vw33bbtC76tSmHF8dzGKZFxOeiNey9ITtfqqJQiPZwqQUabNXDwbPzUw+1kVrnK+tJ0BneTL7mQv3epTexrhCiG9sB2DchgtnlJl+hsyIwjN1xN9mSxnMXxIK/GSwJVJMv7zCaWVm06Mbl1AgAbim6Ske2OpAkacbc13i+tpA7CZU2n+/VxNla56tFnD85TQQLAU0EjoE69fhy4FdF8pUspaYZLTcuOJsC1JQOtx5hYkBUuxvjYgnvmxKJuEeq72q9W1UZbmyh6AY0ifLL3732RtP8vaa+KIQ2rtzVTdktscV7MxXdpLvRz+6W0Kod90bFg+/4CdSy6HxlJkoUs1cnu2OXT1O0xTY2avIF4I6IJDxsKCRy5Wohx9JlzOE5Ol+Ji+glZy1pxtANvKbYhxoAWar/8CjqiQKQV2yhEM8OVFvx8Fq0Q61f3M+VhgaU5AmSdpG16BWdnnoS23DQERadLwuTdMXu5G6dKbpx8v98F00WjJqiLoSXEo23kNj1VtAr0Pe8eP1ixDZGamIbFhYXU+KedCMlXwCdgV28vFuuJl8DZ6ew5lEQzQ1OYioeLMsk60myraF9LQ91w6D+V5dNrBsc6mHnkLhhL8doOTl6heYJcWPYfd+7+cHZcSqGybamADuaN+6NfiXREewg7ouDZKB4Bzm+SOrhkG2qG1TqO+CeC1XFw3K0xt9neudrftqhZg/IO5Xtgi6SL3/Flpuu885XyC1m3TLlDGE7+Sp5ba+vyTmCRIBKAUaPi3933kkyK2YxWmyxjaGsOBdCdWqwPB0BdzsX2m2Z/WeeWvjFpgE2TUiP3Ur2e0Jo42sHBLUqnxLd9EcOtNYVRape4PF4uNzhwZ8XhZrROaiH+cFLFGyZ+Y2odOjAHxUCNCHDTSJfQfb5kMMiIdfHJ6A067NPUzp0NTczkSghIWFiEgxvDNGRquKhYptDe7ej+oTan3YN2mFV6bCjDQqTZO3OV9EjOmfNddj5aosGsXRxjlbl5rfcDpIMqX5OfuYrPP2SjCm7ZmynuQI8f66Rk3/yedDy4I9Dy4Frvt/ZUXveqzXEYHaQol7Eo3jYGtq6sh+sznFL8wGO7paIpC8jGxWKmQrJ4fycr03bAiWGNIUpG+zeQPPoa4nN5GsT88IxW471ic7EcjpfZ5/9KgDjTS4a23qqEvOP7t8MlhxIklSVnFf8VxZttjxuyydvRDWh6V5fqXKNDqXaCVVV8XA6LAtr/FxNGtqe+SqZolPoLrtn7Lte4VSrc1qacFNt5mvBztfwa2DqEGqnosTJKuI3b79TzB6MFUTy1eCp33kvBy2+Ds7Z9+PCG8cXfvHocShOgTtE6qXLoOuoB/bxnH8QCYmTF8XnfceBjWO1sNbI33TLgmbL5vgQxQ1ssOwg3CCOPWR4SeREwdDVKs4LUdiYNWOY7K0WctSmJsbHRTBZdGVp9NW3zLwDR/Ew7xJKlgW1c1rna2HaYVXp0CkAIWYnC25RLK0ng2UHrWEvpjFLbt4bhub9GIbMS0ftbszs2ML+/0vHQxiGDNvuh0XQs6sy823hKuVwe3Q7irzx5iKvB2/ruZWhuMRwzCCaFt2/gXlUDzNJkbyXlASW6aI1GF2rw9xQ2Ey+NjEvvPv3AeC60A+WxUh+hLJRXtI+pl5+AYDs7i2UNIMfnhU3hM1gaSZuaboFEGbLi1U8TJbEdxn31X/APRvTvb7SlVT18apCWVEWnS/TrG2Un8BIZcCSQJZR47bEupUV/l52IBWsc9phzCs6XwWj1vkqehvRCjLWxIW5JfarlMPDjB49iyUruPQ80b2iAjtVFudCs7/+zbZ7It1VxcNi7zVEAXrFfIbVfS+pf/hHAIYeFB35Dv9O8iUvbREvN2+JrN4Bb3Dc9NgHq2bL/WevDsjdycmax1fLxu18NcTFjFawEmQyJzpBNfsK+erCxiyPr8RkEYCCO1P3YhsOnEJOQRUFqIJcM1q+1sxXxVE6DIhkS9PF2pFTxfYtdSQz76B1mtFyojTNx2zrYS5P3EzZHb068XIgSZTdMS5P3LyoeS+Y6fFVFduI3jhiGw6OdO4Cw8vLe6AhaVMP5xHdyObF95/xJup+Bnk9sfmtbGJeePcI0Q0zkaSjFMDCYjA7uKR9qKcFdcx/6608c36ComawJerjwJb6H2ZeSzidL9l3hXOjmUWJbmR0UXlqDdR/wD0b02e+clot2awplKmglyA7zd5gmtiG2tiIpArqnSHl8eh+MMSiH4jUd/LV5BeBXcnMVWe+NHcIDT9mPgeZOSwdppkrj74xU2wDIKuLQKQzXP9FjX3xbVxolzCx0LKgXV7AXNqe98oXt6ENDCAHg/xgpy2SUBFzF4/sb0WuU1+3esAdh9+Cboi5r8RAHkMzZzwfyGoYqg+wqufjRkRLi7iugpUIo9kUUOukzyk3n+iteXw1NzOVFAlb0ZWlwbcxki+Hdlh0iyShYDZUaYdGIoGlz29dUaUduu0kpiwKGEnE9s2h+ltHW8NeLN328SpNC/47D5PTFveb5bSGRc17JXJlJrJirdnVcmPKzDuQZZmA1M3RXTIxe+5r+MIUhj5zLbFMk7wukvaEfxKPtDE6yOuBzeRrE/NC9vnw7BAeVHekxEXUl+lb9PaVYoHWK+Km0HXvo1WVw0c2KYdXYW/DXjyKB1ktYCjjnLMrbguhaIibT0dk4wmXTKcdFo1M9XFXqyO4YXP2pyseTp67SmZeNwwsuUCwEgXAF3KhuOp7WWsJiiBBs3K4fSreoPisRW/j3IqHpjlN6fAw4wOiyu2IbQCULXEu9ETrf7j59i27KHolBprs7tcPvjL3C7ViteOXekXQKsOPP87ziVcBuDIkuIuPbKqmLghFURjsUHBVsmDJVbEWB4GymItS3JW6v3YWQq3zFWUkJ7o+LluU5yq5edOE5KVa56upiaztXVRwZap0vnpHtfPlTom/tQCKxwQJIaWfSMy7bZV2qF9Cs9wo9szsmCY67/VksOygbVrna7Iw7bN1HiboWtj810EwKkP02l6Izj24q9FPwKPWkq+GGy/5Atga2MXlVtDUJK5KBr1iXjVDqk9OUvSI+9tYZJKgujGuo/XAxl1pN7EmcEQ39k6IeZqliG6cPfpN3DrkfBIde+/ie6dFALWpcng1XIqL/Y3iu5YXST3UpRQA22L13+2YjaDtxRWsRKlYtWDQoQkZRbBMZioeTtTENpyK9mgujSRZBOzkq97nvQBa7eTLlMSMSVV0w2eLbsz2JJo8D6UUuPzQehPJrEjWHLEN3TAwFXG+7Ip3rMEnuD7sa+7AMl2cdaiHL7809wv7XwSjjK60k31OdP5yj93NeHEcl+wmPdVBQ8DNHd2bN/hrQb7tCBGbejhysba2lDQDryW+v2DDxp5jcejGHsNPIiMC8RkzpNOLGplBLL1cUztsbq4qQRbcG6jz5XE6X+Lz5osuJAlUr0M9nJvWa6RSGKkUAG5Xgpxpe3wpRVKauNfX48xXY9CDZIjO13B2svZEdCs928bxlJNz07YBLAtPOUnP/V2Leq8z0yiHBa3AQHYAuDFphwC3NO8HSeLlHW4apsS1NHh2JvVQHx2lZHuxpX1Jou74mh/nRsFm8rWJBeEkXx3D4sa0FNGN4ZfEvMbEjkZe7psiU9KJB93c1rUZLM2F6aIbJwYXTr7y5TIoInjf3bjx1IScJMlfCWNSk9ZXYjFwieRCGC1PE92YPDdNnUwkaYNpUeH2lx2D5foLGGajI2LLJctFdMMgEndEN+KCBjW78+V0vbbcRqWok7PVLbccERXY3uQYkmRgWRK7m+q/8yXLMm6rpTr3VTg3T0HHphymJreBruO75RZeDojueVTeBZaLh/a2oCqbt7Fr4b73faQqunH+jdr33T86iSSLea+mjuh6HNqKwe1Vqag2dTArAufqDGlBEUbdmnieRC+mJmHZVGW1qYlyzpZrd23AmS+PWAcLeRNc/proxjzJl9P1UhsjyKpFJnQbAFlPEksP4HMpBD3qKh/90qHIEgE1CsBYflryJUko3XdyRPlr8f/ZCZj9/yPKX6PsfNui3uvsiGBk7G4N05vqxcKi0dtIo2/jCVytBN6+TZwjz+8uVqmHA6dmdlaLAyOU7YJAxjNJk3/jzaOvFTbvWptYEL4DIvmKXk6AZS0p+TLeELMc8s17+dZJETQ9tK8VZXM+Y05Uk69FdL7O2wbLlqmwNbrxqkv+iBtJBhkFr6FSsWcTJFnG1VwzR51BO5w4fxXtcCgrFv9QWSzy9S4zD9AZFscqSRbD2eQMo+U5O1/TKIejL52pim1EdgvqzPlJMYcpmUH8rvr//AARtbWafJXGKpjZOYa3e3+IZUHqdfFc9Cd/kheHXwRgarIb2OyiLxbbt+2mIIvK/eTlbNWjp/f8CQq2SEtL98b/LjWfSK7MghDPqApulFTRSnfsK5I1sQ05HEb2ejEKImEpbqDkqzrz5RNJVjFbwYxuu6biYZVy2CDWi4xXKBvnPEksI0hL2FO3owHOZ54szqJUdh7mQMez3B/+Jzx6ZsZTHj3D/cb/5EDn89B976Le59yYSOD3toa4kBJiGzfivJeDOzt2guHjfKdBWB8CYLw/S7mgVV+T6rfPN7NEWS3QFtx48+hrhc3kaxMLwrNnDygKaipHQ3bxtEPTNGm8IC7Epjvv5zunBOXwHZvB0ryoKh56Jjg/MUpJm19040JCLH7yBlUTkmUJX1jQW4JahIF0rYo5w+vLoR2WMpAdrgpuOB5fY7mkvY+N0/kKeDxYpjjOgfRkTfHQF5975muaufJcYhsXk+JccLNxOspt/k4mIlAIWGBJlH70TzNfkE/A6HHyox608SnkUAjfI2/nldFXAEhP9RDyqNy948asQi8Hk50ykqmB6SE9IZKT8b4zVY+vWGtgPQ9vZRAURRxXSQgBqHYhxyhJmAY1xcMZYht2db4o1tv8BlQ7LLtTWFhgQTGwuyq6MW/nq6p0KBgtCUWopmY9SSwjUJfzXg6a/DatrZya+YRttnyg8av8yz99J4886uWeQxUeedTLv/z5LAc6noX2W8EXveZ7GKZVnfmaLjO/M3ZjUg7BEd3owpIlhnbF8BfGsCwYOpeqviYzbHt8WQmQYGt4M96bDxsvatvEmkL2equiG9tGLcaL4xS0wjW3Gzj/KpGciS5DqfMeJnNlwl6VI9s2g6X5EPVG6Yn0AGB6+hYU3biSEqapHim6Foe2KnDmvgKVCP3pWoW2JrphU4UMverRo5dEwqbag/QTedEVCW6gmS8A2RSB7kgmWVWYqwpu5CegYA+P5yZq1fqO2xnvFxTNeFNt6R7IiK5yQNk411ZPpBskicvtgtpUeOGHM19w+UcApAbF7xx5z3s4lbtIQS/glkKYpTbetrcZj7qx55TWEtG730I4K5L34QviuskNXKZoy8xvZI8vB56I6NZ4NRndMFGiUSSPWBNEYcPuKs8yWAZQKiJhKbqy1e5KvSOgBpBRsCSLoiK6mQXvjurM13xGy1WlQ5coeiXt9bPozYOl1qXSoYO2oDhfc3pq5hOtN4Pqg1IKJXOZHU/czcFPPsqOJ+5GuSKM2tm+OIn5K4k8Zd3E65LZ2uC/oZUOp2NrUHz+Z3pcNerhmVoHsubxJc6rbQ31T4NfL2wmX5u4Jpy5r30TYkHuz167+3Xp2W8BMNoZ4AeXRLL24N4W3OrmKbcQFks9HMqJm2pA2RhBwlyoen2Vo9XZLZgmN19yg6lBeqDaDdJnCW5MFu3kSwvN2Ge9wyWJ5Gs0X6MdlryNVMp2AOxU6AdtifmmveCLkcyJebjmnTWq6WhedJU30nDzgebtABzvEAFw8eTZmS+49CO0okz2suhkRD/4E7w0IjqAVnEHIPPopsrhkvDQe3+GYFZIzr/xwmkArPE0puIGyyDcWL/djsUi2CCKMyHDRbJQQZKkmZ1057pK9qI5YhtNTVRKOqqtml32FAi5Qmt+7MuBJEn4ESqFOcmZWeusdb6uRTv0ZEBWyebF91b0CtpmPXp8OdgaEclXxcqhmTXKG4oLtoi5pCpbAISypT0/uhiJeaj5e+1uCSFLVD2+bvTk62CzoKc+3zpFY0nEgf3HRqvPOx5fWa8473bFN5Ov+bAZCW/imnDMlveMi8BvMXNf+deEHHR5XzdP2fNej2xSDq+Jg00HgWuLbozbAXfEvXG6HbMxXW5+JDsH7dCwveASvTBxDkOTMCuiuuuyX5Mqie/IVwnM2Ge9wy0Jxa6JfJJA1IMsgyWr5Az793Soh9PMlSu5Yk1s43AtCEiURCLe5GtZm4NfAdzZsRuA4532nE1/BsuwPYksC3p/RPqSH0wL36FDeHftqiZf2akePKrM/bs3h7mXgliskbxLUFSTl21BiowIliQpi/wmEC6JNop1IKT7SOYFpW7GDOnkedFJn+qb4fHlKB1qchm/11+3805zISCLz5xXRNehIC1stGxZVi35CunQvJdiyr4O3SKZqUelQwddsTiWJX6fdHnWPdKmHlbnZAHGT0FhUqjFdty5qPeoiW2EmChOkCqnUCSFbdFt1338Gxlv67kVgLw6RNvNrWCZZDImmYSgMecrIolP+BNYpovWYHS9DrXusa6r7X/5L/+FO+64g1AoRHNzM0888QTnzp279oY2vvCFLyBJEk888cSMxz/60Y8iSdKMP48++uiM1ySTST70oQ8RDoeJRqN87GMfI5fLsYmr4Tsgqh1bhsqLFt3wnxXD3fqeWxlKFfG5FN6yczNYuhYONh8EQPEOcnxofo+WREkkK3Hfxh1oDU5LvsYLNY+WKu3QphiSFMmXI7Yhh8PIflHtzVRSqIYLt27Pj8Xqt2I7HT5FJJaJYgpZlgjZg+95YvZsik2PqoptHGH0xdO22EauKrYBkNHEudAe3DjJV0+sGQwvfa2AYmGUJbRjz4gnpy5jpfpJXRJBZfQnP0iukuP4xHEA9PwO7t/VhN9df2ps9Y7cVvs7M6OU8hqeiui0Kp7SOh7VyqHR8frSAiRyIqGqegIWFUFfnroMpo5eEsVEtamZfNpOXFxZYr6NxSYIyGItLNhKj8JoeX61QyORwMzlQAJXUEdvuQ0jJ5KZvCq2aw7V7zraHglgGeIzJ64S3Tgi/p7e+XK6Xl33gOpe1HucrcrM1+a9usJdeJT6TUrXAo7ohiQb9N/URdj2fR04k8SqVCgoomM8FkmgmNENOY++VljXb+bpp5/mF3/xF3nppZf47ne/i6ZpPPzww+Tz+Wtu29fXx6c//Wnuu+++OZ9/9NFHGRkZqf75+7//+xnPf+hDH+LUqVN897vf5etf/zrPPPMMn/jEJ1bkc73Z4Nm9GxQFX7ZMY/bana9UYpiWEXEzOx8RNIAHdjfhc2/OZ1wL3eFuwu4okqxzMX12XtGNrCaSldbAxk1onS5VsBIlUawlX9XOl7MMJC7C5LmrxDYAcnqm6vGlehTc3o1xjgVdIkhM2UPjkWaRaBS900Q39DIMvy426LyTEUdsQ8nMuKkVTUG97I5uHL83WZZx04KhSBRaRRBcevYp8dzlHwmhjbyCHA4TfvRRXh17FcMyUIwmLK1hU+Vwmeh68GF8BRGQ958dQzWjAHg2uMeXg5YW0RkOVsJM5sQ9qOr1VXKDUYbeH4j/ayKAV5ubSCZsiXp3hvgGkxIP2BTmvCoWzLzmr9IOjWQSS9NmvN7pernCLmQFciHRzdDkMllTnAfNddz5ao14sXTBHEgWZxkrd9wu/k72giNF32vPky5y3gumJV9toSrl8EYW23AgyzJBqRuA7zWaNGTFPPKVo31UxsYoegX1fTQyuaHn0dcC65p8PfXUU3z0ox9l//793HLLLfz1X/81/f39/PjHP15wO8Mw+NCHPsR/+k//iW3b5m4DezweWltbq39isVo168yZMzz11FP85V/+JYcPH+bee+/lj//4j/nCF77A8PDwin7GNwNmi25cS/Hw3LNfRQYmG1S+PSQW8c1gaXGQJIlb7e4X3ivVm8BsOAH31sjG/V6nd75S05SrHEqhni4Je5bxM4ImVHDmvWqfuWjkCFQi1f1tFLpQyC2Sr0xF0GaqcvO+pv9/e/cdH1d15o//c9v0ppE0KrZcJHcbG5tiDDgYsMExMSX7pYTgQCCbsjYJEEhIwzEkAdJ+SXYJu7CUNDAlkE3AgMHEYBvTXMCOjbssF3Vpep97fn+cmZFGzZLQlDt63q8XLzx37ozO1dXcuc85z3lOcm7KfuDkTiARBczlgLMWrcf4zVVZWeaNclzkfwuTSwt/geXuShQ+H6Chhp+/8M6dAADhyNtwH+I3xvYrroBoMKRLzIe8tZBFARdP184oXyG58JIrYQrw9b7eXf8BBJEHGuU12qjudypVrnIwqJCZgqZWNwBASV4vYgn+d4Z96wAgY4HljrZk2pTiTVfT04pU8BXU8e+KoE+FVFYJCAxgDPH2zNGhrkqHPOD06acm/9+BcJhfhwp5zleFzQCW4Md8ovtCywBgcgLl0/i/j73HO7COvsMfD3K+lz8SR0MHn6fefeRrtM/3Sqmx8CD0Y99+jJnI/15OHA7Ad+hkcv6oCr++Exa5OK4p2VJQeRseD78RcToHPmn33nsvXC4XbrnlFmzatKnPfTZu3AiXy4WSkhJcdNFF+MlPfoLSUn5R3bp1KxwOB84888z0/osXL4Yoinjvvfdw1VVX9Xq/SCSCSCSSfuz18pzgWCyGWI+epVxL/fxstkM3YwYi+/ahtpHhNW/9gD+r9b3NsAFoq6vAodYAFEnAwjpn3n9PuTbc8zK7bDY2Ht8IyViPnQ0dmNlHCei44AYAjLe5NPt71Vv5zY856oA30pk+DpbsKGHxBBIREVLDVghMRSzKb8jF8vL0vhHVi8oo79k22ZVB/S5y8Xk5FbvCbwT9MS9isRgsJckJ78ZSPsLnaUDiwBuQAKhjz0YiHufFNhSgrK403fYmnxuCyK9Lk0oqNfW3UGkcg+YAsH2sBdPQjtDBZoCpiO/ZDN8J/qVu+fxViMVi6eArEZiMBbVOmOT8nj+tEkURQR2fL+o/7IVRzz8746eN6/f3WQifl8EyyUYEFR/MMTuamlsQi02BUMa/91NpzKx+M8CAuD85x7CkBB27eMdGSPHCpdg1cawAPyemZNphSOH3TwFPGMw5DrLhCOIhCeGTJ4HSroAydJgH3zpzGExU4I67ANTz4CvC36vEIBXs70ACoMAKBuBIR2OvdkpjzoLY+gkS9e+ASUbI8RCY2YV4yWRgEMe054QbAOCy6mHVCdjXwafC1FprB/070dJnZqjmlM3E3oa/ozF8AGMu+iqkdRFEocfBnW0AjJATHqiiCoeutOCOPxfnZbDvXTDBl6qquO2223DeeedhVnKOUV82b96Mxx57DDuTvaR9Wbp0KT7/+c9j4sSJOHToEL7//e/js5/9LLZu3QpJktDU1ASXK3OujCzLcDqdaGpq6vM977//fqxZs6bX9vXr18OUnH+Sb6+//nrW3tvOVFQAqG3iqVJ/femvMIp9lyYWdu4FAHxi47/jydYENr25PmttK3RDPS/BOO91k0xH8cp7u1HStivj+XAiDkh8n+O7DmDdvhMj09AcYwkAsEJR9Qj5fVi3bl36uVqLBbLfj3hIhGzgN0mBsAlAAvVeD7Yl942qPpijvCfO7W/LeI9Tyebn5VQCbV5ABryhdqxbtw6hZhmAESFDGcJhI+wIIfze4zAD2OOz4uAL/wd/sifxSLwFR5LHuSfAU8hYwoAP3tqcp6MZHtkjADKwqRy4HkDMw1DR+AG8n8QBJiA0fjw27N8Pzycf4Ij3CMAExAO1qFZbhnSeSaaO0hiMcQCoQMjIR5P3NezDodZPBnxdPj8vQxHU8eDreP0xrFu3DoajDRgHIOzmc8AENY5ETACL8dS8N7Ztw5GDVuigIKjzoeWoF+uatfP31TXyxUfA2xrdOK5XIBsTiIckvPfqqwgcO5bev+q992AFoLPE4dFXY8eH+wDo02t86USGtzesRyEnEcgJE2IAtu3fhXWtmeeqpsOAeQDcu15D+5GjmALguG4Str/yyqDe+51mAYAEpxjCP17+Bw57eLB6fMdxrPt4aH8XWvnMDIUxyDv7IsIJbAz5Yfd0oMM5E3v+FQIkIxLJrBzVpxbsdTqb5yUYPPVSTEABBV8rV67E7t27sXlz/zcQPp8PK1aswKOPPoqysv7LKl933XXpf5922mmYPXs26urqsHHjRlx88cXDat/3vvc93HHHHenHXq8XNTU1uOSSS2Cz2Yb1niMlFovh9ddfx5IlS6AoSlZ+RnjsWBz/2/9hUrMAMIZpC6ZhZunM3m2JhrHvh3cDAPaWnw2EgRsWzcKyM7SVEjUShnteIokInnj2SUD2o12OY9my5RnP72g8AvwTYKqEFZd/XtOTWv9n49sQogIMTMGyZcvS2489+QdE9u5FLGqCAXyUWYwbAfgx7bzzYU/u+8M//SQ952vKzFqcvWzCKX9mLj4vp3LkfRXbDz4PVYlg2bJlaD8RwF+3b0fYWAYWLgPQAXOUp9RMu/gGmPbrcEIIQIn7sfTGq9PnvHH768AngMwcGb8/LYjuLcW2Hc+h3eqD4gBibmDM+r+iI1loY/zXvorTli3DPw7/A3gXSITHQGAm3Hb1BSizFO6clEK3zWXDzv8NIKEkOw3VOBYvXQSTue/OtEL4vAzFex8+AQQAk6zHsmXLEG9qQv3vfw8E42AMEASk1/gSbTZ89oor8OS+bYgiiKDixefmXIJlddr4LMViMex+eTcAIGTg6YWCqmDMrPNw0si3nz5uXPp6CQANjz2OKHilQ9PUhahoGwsfWuHXd4DFa1FdYsZll52f82MZiof+/AEaASgOXe/rXsdU4OFH4QzVwynxdNKq867DsjmDO6cfvLQXOHwM582aiOnnSEisS8Asm3H9564fdFq71j4zQ6GqKp74y8MQpBCU2bUo29iIDgChZCVesAjEBDB74iwsu6CwPke5OC+prLhTKYjga9WqVemiF2PH9n+TfujQIdTX12P58q6bUVVNrmQvy9i3bx/q6up6va62thZlZWU4ePAgLr74YlRWVqKlRxWgeDyOjo4OVFb2PYdGr9dDr+/9ha8oSsF8uLLZFmnmTECWYQ3EUeqVcDx4HKdXnt5rvwMfvgZDDAjqgU2hiRBF4NJZ1QXzO8qHoZ4XRVEw2TENn3TuxrHgHiRwBQxK1zyfI26eNiSq9j7/JrVEsYqItzMYolLG70iprERk717E/QxITu+Jt3cA0EEfb4CiKAjHooAUTs/5spUah/x7ztffZbWNF0qJswAURYGzkk8gjykWhI6HgG5zu+W/fRUt7lUApqNE8mac8+M+/rdgEp2a+4wtGD8D2AGcfdCNuI8BEODfm+pIYJBbd0NRPo/3W/haZ/HAZJw13omqEkve2lwM9rzxL6jipK4Nooxn7ngd8sRG3PyjVf2+rpC+6wbCTMl0wmAciqJArqoCRAFIqEiERchGNV1mXrHza0YswF8T1PlQbi7XxHGmmIRktUM9LysfCyeg2uvSRTfU9vb08TBVRayBz9nWWeMQx56BwEE+IujTd4BFToPLZij44y8zOtEYAToinb3b2r4PEEQIagzwHgcAyBt/CphKgBmXn/K997fwFNSZYxw47PsIADDFOQU63eAqJXanlc/MUFmECfBjL945sQufs9uBblPTmX46Vm7+MaxTOqEsLsxjz+Z5Gez75rXLnDGGVatW4cUXX8Sbb76JiRMnDrj/tGnTsGvXLuzcuTP93+WXX44LL7wQO3fuRE1NTZ+vO378ONrb21FVxauBLViwAG63O6Owx5tvvglVVTF//vyRO8AiMtiiG8ffeQMAn0TPBBlnT3SilHqph+zsKl6BCob6XkU3jrp5amwxVBNKVTw0xfTpjhQAkHW8xzLebQQ/VWpe2fYLYM/fcczDe3pTwZdW1vgCgDE2PgdDFfkXvc4gQy/zXHFvvMeovq8RrUf4KFhpWWZ/2Uk//1uwKdpZYDllrN2JBXslfPsFlZfX7+Hk//cUvI/9FO+e5GWjE4FJtFbgp/T4ff+F0PHpYGLmDUJMcSB0fDoev++/8tSykZOsdg1dJLmG2f51kHU8uEpVTE39X442AHv+DjXInw8pXjgN2ioUkFrnK664oSbv6ILKuK61vrottBxvaQELhwGBQTEngKrT4W1LFd7oBItbCrrYRorLzK93vqg784k9fweevRFgauZ2fwvw7Jf48wNgjKXX+JpWaeuqdOigSofdjbPw4iPmDUewy1vL12bsJqY40HFkInY/UXxplyMlr8HXypUr8ec//xlPPfUUrFYrmpqa0NTUhFAolN7nS1/6Er73ve8BAAwGA2bNmpXxn8PhgNVqxaxZs6DT6eD3+3HXXXfh3XffRX19PTZs2IArrrgCkyZNwqWXXgoAmD59OpYuXYp///d/x/vvv48tW7Zg1apVuO6661BdTSty9ye12HJtU/9rfcV28vlJByr473HpTLpZGo55Lh58Scaj2HXcnfHcCT8f7TBL2rpJ6IuzjI9imOMWdIaTteXVBJR2fsMdSwZcagJIRJI3TKYE8OrdOOHmNxWWCC/QYSnRUPBl5cGXIEYQiEQANQEb4/MyfOi5dhtDpzQBAFAxObMSW2uIj+CXGrW35ACLRXHzG3z+gICe6Tz88YmH/4yOYBuYqiARGo9LZ1KVw+EKhcKIH0kuR9AzfSr5OH6kCqGQttf8MpXwwNIQlfmF49Xv8msGkB7xSqUdysYE2CvfAyL8Zj2oeFFi0Ng6X8k5X4IcRFTh5zEoVEI28GOKN3fNY09XOrQkIMgKEqXTEUyuccbnfJlQYS386+gYKw++goluiywnzzXA+nhFcturd6PPnp6kRk8Y3nAckiigzmWmSof9ON11GsQEUNWWLFrXz/Xk3c1+JGLxHLdOG/IafD388MPweDxYtGgRqqqq0v8988wz6X0aGhrQ2Ng46PeUJAkff/wxLr/8ckyZMgW33HILzjjjDGzatCkjXecvf/kLpk2bhosvvhjLli3D+eefj0ceeWREj6/YpBZbrmvsf60vx34eGLxr4KNk1FM9PHNccwAAor4Z249nLn/Qkiyy4NBrb7Sjp9JSfuNgiThwpIP/7eDoO5CT5dN73iwJIoOkUwHvCXgaNkNgIowx3tWtpZGvapsTjPEvqOPeduDoO7ALPEXGL7oyOhKjMR38Bt6ZUT0+88bYnZwXVmnWXlASXPdH2P3oFXZ1EQA/MP0YQyI4EbPHlGJsSWEUN9KiFx9bi5iupPeNUoogIKYrwYuPrc1tw0ZYaTm/pphjBsQObwG8J9OLDqc6c+Lh5LIVxgTCHi+E5OctpPhQatBWqXmjwOfqCQJDSOLHGYwaIVt5EBpv7Pr+iNbXA+Aph6iYAb+PD1rEhShiigpAKug1vlLGOfj1Lsq6ZYUcfQfwDrRUEAO8J7pKz/dhXzLLpK7cDL0s4YCbj3xNcVLw1d3iujMw/1DtKa8nEcWOIy+/n9vGaURe53wx1lcPRaaNGzcO+PyTTz6Z8dhoNOK111475fs6nU489dRTp9yPdDHM5AU2apsYGjxHwRjLmIB68vAuOD0JJARgj2kuTq9xoMre9yRuMrAyYxlK9dVoj5zEzpaPAZybfq4jkkxBM2g/+LKU8BQXc9SB4942nIlJgL8ZiqnHzVK3nurUn1yn9zhMUStEiBBEAUbr0HPy80UnyxBUIyAFccLbhqnBZth1TUAcCBvK03NTAKDRPQlMkKCLemF3ZpaxDSbaAQkYb9PeiH385MDrBaaU+IG4YRIuPYs6cj6NQIvn1DsNYb9CVVnhwHEA5rgJvtbjcALp60nqOtJ1PVERTPBgKyT7IUoyjLK2vrMkQYJJtiIY98EvRlECA4K+WHI9RB/ibV1rYaUWWNZZ4kDV6fC18ywjn74TEuOdWFpIO6wr5cEXE0OIJqLQSTogmRFySgPst7eJpxxOrbTBE/GgKcBHDSc5JvX7mtHojOpaPO8fXCeFv3lwBShGG+2WSSM5p586FZBl2EKArt2HzkhnxvMH3v4HAOCYS4ewZKOFlT+lucnFlhvDnyAc60qV8MU6AABVlp7padrTFXzZccKbnJtgqUgHHl03S8mealPX76GJielKh2a7DqJYwLWR+yAy3kN/0tcOWCpgl/lNQdBYlp6TAgCNAT7fwBGth2jL/ExF4QYATCzRXvAlV48b1H6dFj7fi64nn47ZZR/R/QpVZXkpEkIcIkQ0hXlqdvp60kdnTlDlaYZBxQur4tDMQu3d2XX8nHkFPjIe9EYhjxkPAEi4fWDJtYfSwZc1DlTPha8jNd+rA2qCp4C7rIUffE1yusBYsuKrPxlcWgY5+j/AfqmRr2mV1vR8r2pzNaw66/AbW4REUUTQPLjCEpaK/FYDL1QUfJFBE/V66CfzG8G6PuZ9+bZ9AAD4VzkfkbmU5nt9KgvGnAEAEIz12NvY1XsUSq6jMc6u/d9vKlXQHHWgyc8LaGD8uZAreGCpxkSoMSE9AsYnkQuAbQwaJIcmi22kKAK/2WnxdwLjz4XNxnPjw8ay9E0iALTGeQXXMuU4ML5rBDQQiQASv1mY4eq72FAhMy37EpiZQe3neQaGNiuwt8qCWvsk1JVTlcNP46pbroMS7ew1OT6NMSjRTlx1y3V9P68RZaYyBHRuAMBRNgWwVUNJBl+xnp05RhVBPS/0FdL5UKLX5jzaEj0PID2CHwAQ9EQgVU9CKp8yNfqVkXZYfTq87Tz48us7EIvwET8tpB3aTQqQ4J1Xh9qTc9rGnwvYqtF/IjP/3uh+De3pk0Z+PZ1eZU2nHE4uoWIbfWmbXX7K64k+5sHEy87ObcM0goIvMiQDFd0w7OWPd5fUYVqlFRPLzDlvXzGZ65oLAJCMx/Dx8Y709rjgBgBMdFTlo1kjypIMmoxxCzr8br5RlCBd/iBEOXXDJHb1VJuSt+pLH0BnxJse+dJSsY0Ug8iDibZgJyBKsC/5KgAgrHciEurKCO+U+M2h67RJgNgVlH3SxueIMVXCxBLtjYIKig6Or14LAegjAONf6E8uERENTcZnZ2lvZK/QGI0GyBOT86d73jAlH8sTG2E0Fv7Ix0CcBif8yeCrrTUALH0wo+AGY91GvgwqAnXXAuAjX06jtoptpJQm2x1QeNGioDcKoaw2Pdct3toKlkggdixZZt4uAK4Z8CeDL5+hA9EY/77WQtqhIAiQwUejDncm0whFCVj6YGqPnq/g/1v6QMY1tLtoXMWhVh68Tq20UbGNU5hTfRr2Of/KH/RzPTnnfAskpSBWtCo4FHyRIUkV3ahtREa5eb+nDZUneP74v6yn06jXCKhz1EERTBDEKLYc41UkfZEQIPH661PLx+SzeSNCb5aREPkNQsAb6HpixuWQK3h6SDwkdY18lViAa/4IzLgcvphX0yNfJomnY7SH+Uim+azPQUQCTJTgC/PqhZGYDn4DD7LHLP9cxusPtJ8AwNd70+pC29VfW4PfXWFAR4+sHtks4PnrXHh/qoh4YDJdT0bIzT9aBePYvVBi7oztSswN49i9A67zpRV2vR1+vRsA0NHu4deSf7sfAE87VKMCmMpvxuUbHoFX4SPLQZ0XLpM259GWGvmIXUjhGRIBTxQomdBV8bClBbHGRrBYHILIIE+cBsj69MiXT98BFjfDrJNg0WvjZtkg8mv/cU+3NVtnXM6/H2w9OiZt1envjf4cavUjrjJYDTKq7QYKvk5hce0ZWH/aLhyyPA59PHNely7mxgXnqJj15SV5al3h08anjBSM7kU3PvAcSW//ZPM/YGZAm1VEq248zc8YAaIgotY6E/u8H2BPx8cArsS+Vn7DzVQZY23aqsrVF0EQENPHIIUkxPyZxSSU8ZMQPdGK+Bl3IX5yA4AGyNf8GpjxWQBAIO6BOcpHRLQYfJkVGxABPBFe4EAQBZj1CfgiEjwVy4B/m4emj2JgmyXoYj7Y6jJHfw538r8Fg6DNVKmUHdNq8c60fbit43zMbfKjbsFCsEv/Dc8/fzEAoEKZiZnVNG9gpNz8o1UIhcJ48bG1CLR4YHbZcdUt12l+xCtFFESE9bwjMOjhHVXKuV8A8EuocRGRM+4BXnwUkt0O8fR/g3srX0g3qHgxzTwhT63+dFJph8Fk0Bn0JoOvVJXH5hYIen5+ddY4hDE8q8LXPfgK1MClgVGvFIvsQEBFV7p6yozLgWmX8aqG/mY+x2v8uf2OeKV8kiy2Mb3SBgaGg50HAVDaYX/mVdcCCRNen/0xPnP6Ssw6Ajy58Rm0mOsx9sJ/w6zF/5bvJhY0Cr7IkOinTAGTRNhCKjwNh9LbW97bhIkA9lbYMaHUhGmVNEF1JJxTPQ/7vB+gPb4P4VgChzp4KV1Jw6MdPTGTCoQAIZSZuiC7+MhXLOFA3MfnQ8mVXUF9KOGDJTnypcW0Q7veDkQAX6yrupzVJsLXCngjZuC0/4fGV14EADhkX6/zfdzL5zpYZG0H4aW6ahyL78eb46tQOul0zFq2DG83vg0GFWqkDMumz9BkEYRCZjQacP2qm/LdjKyJGXlHTiLZoSOaTBBtNqheL8JBPqosu/j//e4oACCo+DS3wHKKQ+8AAAR1PD095I2C2WrSc93iJ4+mU8GUZKVDNaHC7+62xpfHDJeGOrEc+hI0h4DWYHvvJ0UJmLhwSO+Xmu81tdKKE/4TCMaDUEQF423jR6K5RUcURViFCfBhDzY3fowrr/w6Xmr/ERJyC+4o0f6UiGwrjrs3kjOiXg9xEp+DojtwLL1cgLDrEwDAntJxuHRWJd0sjZCF484CAIjGo9jT6MVRN7/h1gmOPLZqZCkWfhlSwpmXo1TaYaypEbFWXglRqeiqVBVlga45Xxq6aUhxGhwAgECsK2XDVsp7nv1B/rtoPc5TMcvKe/eTNQf5XIcSja/3VmPlNzcng8fS27Yc3woAiAcm0yg6GTLBwoMOKdzVoaMki/iEdvMUbrmcPw77ksGXzqvZ4Cs18hXW8UBEVRnCURGyg88rjZ+oR/RoPQBAn6x06HdHwFQGVYgjqPjAEmZNzPdKKU+mWnqinafYc3A+SVU6rLKmUw4nOSZBFmmMoj/jLVMBAHs79gAAEiL/Lqtz0hzdU6HgiwyZ9TS+AHDNiQhagi2Ix2NwHeYXwH/ZZ2Ipzc8YMaeVnQYwEaLiwTv1B3Hcx2+4LbI2J4b3xWjngZM+mrlOl1LJA63I3k+AWAwQBMjl5enn48wPcyQ150s7Nw0pqcn9IbVroVB7Mr0uENeDMYaOAP+dVEztXVCjM7neW4VJewssdzfFyTtzPLHG9La3jm0BAJjV6ZhbUzx/6yQ3dDZ+w6yLdN0483WvgPCu3fyxi3+mogE+OhZSvCgxaPNvLTXyxRQ/oOe3dUFPFHI5HxWPN51E9MBeAIBiBy+2kSwzHzB4AIGBxS1wWbXTiVVp5d8Fvh7zF4crlXY4rdKWLjNPKYcDm1vBawA0hQ+i1e8FRP43NbV8bD6bpQkUfJEhM806DQAwsQlo8DXg0M6NMEWAsAL4XWdizlhHfhtYREyKCaW6CQCArSe2oSXIR4DsOm2PdnTnLOO9s6aoKWN7auQrvIf3qkllpRCUrrVFFFWFzHhwYnZoZ4HlFJeZ3+hFVX96W8kEfl5DSgnCJ1vgl3nvbvU5vSd9++O8l3usTdudHXMq+QKmEbEFqsrQGGhES/g4GBOwpPZcza3fRvLP5uTXEkNch1iUz3tKLV8RPcLnKssuFxIxFSzCR8mCig+lBm2m8KaCRkH2Q00FX94o5EpelCne1oFoPT9u3bgaQNali214dLwTR2sjX2NtPPgKq59+UfDOQBTNXp6CObXSSsU2Bumi2nkAgIh4EjsbDwMAmKpHhUXbawXmAgVfZMgMM5MVD5sYjnrqcWzLegDAvkoTLjmthm6WRtiMktkAgEPe3enRjjJj8QRflRUOAIA5ZkE4Fk1vT835YlG+TXF1jfC4QwFYkqWRdSYJsjLwZOpCVGXhN3oJoavKo2OMAwAQMpbh+KtbAUHss9gGAIQZn99RW6LtqpdnjZ0MxgQIYhjNsQDebXwPAKCGarB8Vl2eW0e0qMLhRDTZCx/o5DfV3VOWAUAuL0cwmXKYEOKIyEHNph2mRr4EKYiYwr9/g54I5DETAACxNi9iLTw7RTftdACZxTbABLCESRNrfKVMLElWw4UfCbWftaYGKZVyWOM0wqKXaeRrkFJFNwQhgRc/+ScAQFKpONJgUPBFhkw/dQpUSYQtBLQc/hfCO3YCAD4pq8SlND9jxKXmfflwEO4oD76qzNpONetuXDUPJM1RO455uiZPp9IOU7oX2zjuaU/P97I6tdNb212Vld/oqd2CL1sZX+g0rphx9EM+B6qkj2IbqqpCFXmP7+RSbQdfdoMJYoL33B8Jt+GVQ5sAAFJ0KubXavNmmOTXWEd5V7n5Vl7xMJV2mCK7XAh4eGAWVLyAAM2mHabmfAliDEGJj+QFvFHIE6YDANRwHFAZRFmFPJUvets9+BKYBYAIl1U719JU8CXIfrT5I5/qvbqnHIbiITT4+DI6NPI1MF50g6eNb2vbDAAwiNr8DOUaBV9kyESdDuHxyRSOPXth3c+LQBx2zcDZE+hmaaQtGs+DL0HfCH+C/67H2YunmpAzuRi3KWZHfWrBTACS0wl0TzOs6Jr3dNzbll7jy6LB+V4AUOPgaTOCGEdnkKceKnoJesbLZJ/w8y+x0j6KbRzpbIGQXB9tWpn28+vNIr+ROh5rx8cd2wAAZ1XMhyLRVxQZujFWFwI6PtLT1MQ7N+SKzHmTsqscQU9XsQ1FMMIga/NaYpSNkAV+rfQLyYDSG4U0bjogdis6YklAqOZl5ruv8aXGeZpmhYZGvspNPHNAEKM42uH+VO+VqnQ4rdKKw+7DUJkKp8Gp2TTUXBpv4QFqQOCl+S0y3QMOBn2zkWGRpvMPnOXD/XB1xKECqDrzs5DpZmnEVVmqoGNOCIIKMXlDMbGkeEYYTTYdVKiQmITjzV0jX4IoQulWYKN7z3WTryM98qXFNb4AoNxkBWM8XfKopy293azwmye/macaVkzrXWxjT0uyMmDCCrNem8ffXbmBB5BH2b8QZV4wVYdrZp2X51YRrSozlsGv59fK9pbkWl+VPUa+yl18PSzw+V4WRbvzVARBgEVxAADcyZH0oCcKobQWsiGR3k9nUwHXDACAr6Mr+EokU7i1tM6XWTFDYLxj6lBH8yn2Htgnzangy5ae7zXZMZmqNg/CvEpeA0AQ+IirQ0cB62DQnbLGJVSG9450YFubgPeOdHzq3OfBss3mvWdnf8Qv4C02EYvnUH50towxTs94vLv5CKLxeJ5aM7JESURY4TdIra3ujOckV1fgkfD7wRL8RqIl0KHpNb4AnrIhqLzH+aS3K+g0GzM/wxVn9J73dKSTr/emgyN7DcyhMRYefLllfuPDQhOxaGrxjO6S3HIanPDr3AAATzsfSZbKMufJSqXOdPAVUryw67SdLmXTOQAAboGPoge9UcBUCsnYtY9gMIIxXore3y34YnELzDoJFr12yqoLggBF4POLjrqHH3ypKsP+pq41vtLBF833GpRU0Y2UaCJSNPcm2UTBl4a9ursR5z/4Jm54/EP88YCEGx7/EOc/+CZe3d146hd/Stv3/QsAoPDODlR6VRhuXoo//XZN1n/2aCQiM8B47OCPcOYfL8QvNj2XpxaNrJAueSPgDqa3edevR2Tv3vTjjkcfxcGLF8O7fj1aQ52aH/kCAJnxHucmHw++dj/xOk66jRn7vPjL7dj9xOsZ2456efBlkrTfy/iLTc9hc+uzAIBUR7NgOIr/fPeFPLaKaFmJoSQdfPk7A/CuX4/6//f/MvY5/Nll8PzrEAAgqPOhRKPFNlKcyflqHXAD4MGX94n7EXV3FSPy7ldx8Jw5aH7kV1ATDExgCOo8mqt0mGKSeAfcSV/bKfbsX0NHEKFYAnpZxIRSU7rYBs33GpwNh7aBsa4RwvrYG0V1b5ItFHxp1Ku7G/GNP29Hoyecsb3JE8Y3/rw9qwHYn367BjNeeAM9x9icfhVnPLyWArAR9otNz+Fg6M1e21XRjT8curcoLnIxA++BDifL/XrXr8eJb90GFsmcSB1vbsaJb92G0g8/0vQCyymKYAUANAc6sfuJ1/HWuyISYubxRGQb3npXzAjAmgK8p9eh8SUHfrHpOfzh0L1gYiDzCTFcNH/bJPdkUUbIwL8b1VYvTnzrNsSbMkdH4s3N6PiAL7gcVLzpRXu1qiy5bqBHcgMAAi0enPj5H8ESmfvFAwz1j/PvE9UcBhMYWMKiqUqHKbbkaGWLf/jBV6rYxpQKKyRRoDLzQ/CLTc/hj4fvA3rcDRbTvUm2UPClQQmVYc0/9vQKfoCuj8Caf+zJSgpiJBLBhD8ne6l7PCcmf/6EPz+LSOTTVR8iXDQex58O/K7P51KjBH/a/zvND/OrRj6EygIMLJFA88/uB1gff7/JbRf837vpghtaHvkySHyNsw5/O97dnFzvq+c8g+Tjdzf7kYjx89we4uu9lRt7zwfTiu5/2/0cclH8bZP8SCSvKUJYBOvnWhLV8bS1kOxBhUXbHRkuMx8FDybnBUcTMlRBRu9vagHhZCGJiJFfc1hcmyNfqSqPbeGOYb/H3saulMP2cDs6I50QBRG1jtoRaWOxouv3p6OdBF+S9v6Rjl4jXt0xAI2eMK57ZCuc5pFdfFa3/2X8h0/t93kRQJlPxUvPPo5/W/GNEf3Zo9FTH20Ek9y9vj5TBAFgshtPfbQRN52xOKdtG0mSmafGiCERwQ+3Id7U1P/OjMHqj8MQ5yl7Wg6+zLINnXHAsb0dEWVa/zsKAiKKHUdefh+TrjwX3lgbIALVFu0WXhktf9skP1LXFFXSIy4bocRDvfZJBV+uTi8qzdoOvlJrlEUVNwSBgTEBUZ0Nhkhnr31TwRcL8/RllrDAZdXeddRlLgXcgDfqHvZ77GvqqnS4v4OPeo2zjoNRNg70slGPrt+fDgVfGtTi6z/w6u6D+t4X3U/rovZjg9rP29gw4j97NGrwDhCEDGO/QmWw804CXURBvLX1lPtHkouKqpIKvUm7lzGrYgfigOiNDWp/fzNPkQmpHYAIjLf3XnxZK0bL3zbJD6vRjrjgg8ysiOhLegVfDF3BlzHs1ewaXymp9gtKAHoxjHDCOEDwxQO1RJxfa1ncosmRr2orD5gDcTcYY8OqTphKO5xeZcMBN19jkIptnBpdvz8d7d61jGKDXQjxlvMnoLbcMqI/u2HzbmDTllPuZ6saN6I/d7QaZxvcyMZg9ytUdicfxTJGjZC7lZfvT0THUw6ZSdV0OWCb3gaEgA69Hzbfqfe3VPCbxbjoBgBMKtVu8DVa/rZJfpQaSxGS3bDGrAjrS2AJnMx4Pi4boYp8baw2q0/zazqlgy8pAEmvAkEgkgwuewolj7XNwOdKqQmzpsrMp4yz87RrVfTBG47DblRO8YpMwWgcRzt4kaeplVas20nzvQaLrt+fDgVfGnT2RCeq7AY0ecJ9zvsSAFTaDfj+shmQxJG9MY2cfhveX/tHOH1qnxMGVQAdVhGfu+bmEf25o9X1cxbh1x85oIruXnnVAJ8CJSYcuH7Oopy3bSS5yh1oQRjmqAXGM+ZBrqxEvLm573lfgoA2O++5VSzanraaqlD2fm0rpjZ6EJFtvRPoAYAx6ONeTLzsM2j1ewGRj37PcGm3k2O0/G2T/Kgwl6Hd7IbVXZMe6ekuNeolxoP417g4nBovuOFIZgMIUhCx0jIgGEJUZ+1z30jy97G7sgUAn/OlxbTDSktyoXopgGZveMjB1/5mPxgDyix6lFn0VGxjCOj6/elo+85llJJEAauX84USe0+l5VYvH/nACwD0ej3qb7gGAnig1Z2a/Pn1N1wDfREs/FoIdLKMFZO/CaB3HJJ6vGLKN6GTtd2PMr6af4kqqh6RiIqK73+PP9Ff8YkZPN3EaNf231m5iQdfQfhwzvnJUep+TvQ551sgKTL2tvLUX6bqUWHR7sKwo+Vvm+THGGs5fAaechcxlPS6lkSTo+cBnRdMFNLFG7QqNedLlAKI6fh8N36MmR8uhq60Q5+hE4yJgGrUZNph6pwJsn/AefD9+aQxlXJoRVyN45CbLz1AaYenRtfvT4eCL41aOqsKD98wD5X2zAtmpd2Ah2+Yh6WzsrdA6Ypvrca2b1yHDmvmn0+HVcS2b1yHFd9anbWfPRrdtfBq3Fh3D0TVkbFdTDhwY909uGvh1flp2Aiqq6hEROLpH8cbO2C75BKM+e1vIFdUZOwnV1Sg+jf/H9odvEfXUWrKeVtHUir4ijEfZn15CS44R4U+7s3YRx/34oJzVMz68hIAwIH24wAAWdX2zSIwOv62SX7U2CsQSFb+w4LFva4l8fIaAECHhef7OjW+zldq5AtSCCGJd40q88+CbM4MOlW7jadbCkBA5waLmwEImhz5So1WClIATe7eBVVO5ZPU4soVVhz1HkVMjcEkmzDGMmZE21ms6Po9fBSSatjSWVVYMqMSWw+2YP2m93DJwvlYMMmVlRGvnlZ8azUiX78bLz37OLyNDbBVjcPnrrkZC2nEKyvuWng1vrXgKjz10UY0eJswzlaJ6+csKppepVKTFX6dB/qQCUdPtmLypErYLrkE1osv5tUPW1shl5fDdOYZaA56YXlrDwCg0uXIb8M/pSobn3uREHjgOevLSzD9hjiOvPw+/M1eWCpsmHjZZyApXee53s3X8DOK2g++gK6/7T/v2IBNu97HwtPOxg1zLy6av22SH2OsLvj1bgCAT3Fg0oY3Mq4lEW8F8NdDCCpeKIIJijS0lLVCY9fbAQgQBAavEIYBQKxqJia9+xGC6/6I+MkGyNXj4J1+JfDrj6HYAFVUwaIWWPQyzHrtfd7SI19iHA1uN4ChpWGnim1Mq7LhQOduAHzUSxRoXGKwiv3eJFvot6Nxkihg/kQn2vcyzJ/ozEnglaLX66mcfA7pZLmoS7YGFT9KQ0Bjc1d1LkGSYJ5/dsZ+xz3t6TW+XK6+J5RrxVgbT59kYhCqqkIURUiKjElXntvva076+WKxNkXbpbG708kyVpx+EUpPhrHs9Iug0Bc3+ZScBif8yZGvgDvS61oSfOEgACCk88EsO/LRxBElizIMogVh1YdOBOCCEUFvFIKig/mKr6T3O/kBv36IVr76MoubNbnAMgCYFBNkQY84i+CYt2VIr2WMpUe+plVa8WYzn+9FKYdDV+z3JtlA4T0hpCCEdDxn35OsPtWfE972olhgGQDG2vnIlyCoOOlzD+o1rSF+81Rq0O4Cy4Rkm9PoTI98RbxRMDVzYkrQG+X/V7ywKo4cty47LAq/LrYzD4CuY+zO18Gvswkz/z9LWFAxyArKhSgVODf524b0umZvBO5gDJIoYJLLQsU2SE5R8EUIKQhhffJmyBMZcL9GbztMUT7iZSnRdvBlN5jAVJ7udMLTPqjXuKP8JqPSXHGKPQkZvfSSHmElChUqoAJBX2Yg0j340nqxjRS7zgEA6IAbAD9G1qMagredB10Rkx+Atke+gK5jbgkM7vqZkko5nFhmhkGRcKDzAABgsoNGvkj2UfBFCCkICQOfJJ7w96yjmam90wsRElSoMNp0uWhaVokqX+PshG9wPbeBBL/JGGfPXlEdQoqBTrIiqOOjQP6OzE6doCcZfOm8KNV4mfmU1FpfbeDXiERMRTQUz9jHlwy+/HqekskS2lxgOaXMyLMH3JGOIb0uXWyj0gpf1IeTyXXgKO2Q5AIFX4SQwmDi8xWF0MCXJW9nAAAQ1oUg5nCOY7bIAi8x3+QfXM9tFPymqa5EuwssE5ILZrkEfp0bAODvzCxFHvTyYCyo+OAyaXuB5ZSyZBDpZx7ojHzeZM/UQ187rwro1rUCAFhCm2t8pVRa+NzXYMKDcCwx6NftSwZf0yut6VGvSnNlsnAJIdlFwRchpCDokzcAuvDAVcciyR7riH7g9ESt0CWDr7ag+5T7BmMRMJGnC00pG5vNZhGieTbFkR7hSc11AgA1oSLkjwHgI19V1vK8tG+kVZh5EKmKPhit/DqaGuEDeJGJ1MhXm8yrpvK0Q+2OfKWOWZD8aPEO/jthb3KNr2mVNko5JDlHwRchpCCY7UYAgD5mQCLWf+phIsB7NxOGeL/7aIlR4muWtQU7T7EnsK/1JASBgTEJdU6a80XIQJyG0nTFQ39n1415yBcDGKBCRUQOoMpSHJVDXelAJADZnAy+uo18hf0xxGMqIABNAl+sXU1YUKHhka/SZNqhIAfQ5B3cQsuxhIpDrbwTa2qllYptkJyj4IsQUhDKS0oQF3hvdGCAohtiKJlqaM5Fq7LPLPPiIZ1h9yn33d/GF1gWE3bIkpTNZhGieWWm0nTFw+5ph6mAJCQHwASWXqxX61JzvgQpCNHIrw/dg69UsQ2zTYe2aDLtMK7tOV9dxxxAo2dwCy0fbg0glmCw6GWMLTFS8EVyjoIvQkhBqLKVIZCaHO/uP/hSInwug2IpjrWgbDoefPli3lPue8TNJ4XrheKozkZINlWby7ulHXZdU1KdO0Ed/8w5DUUWfMl+MEMq+Oo67lTKoblUj1CcByosoe1qh6lzJ8h+NA9y5Cu9uHIlzzo44E6mHVKxDZIjFHwRQgrCGFsZAsnJ8YEBgi9DhPfSmuzavWHozpEsc+2PeU657zEvn6dhlYujQAAh2TTW7uqWdth75CuYvN4UTfCl7xr5iut4hkCg25yvVPCls/PnmCrDqphh0mm3IysdfEkBNJ1imZKUvY1dlQ5PBk4iEAtAFmVMsE/IVjMJyUDBFyGkIIx3uNIjX51tgX73M0ZNAABHaXHkHZYYeHWtYMJ3yn1bgi38NfriKBBASDaNs1ek0w6D3igScTX9b4BXOgQAh96Rj+aNuO4peOFkVnL3tMNUpUPByufL8lEv7aYcAt1HvgJo9AQH9Zp9qZGvKhv2d/CUwzp7HRRx4GJPhIwUCr4IIQVhrK00HXw1tbj73IcxBnOMp4pUuooj9a7czG8eouqpg6+OCJ+nUWGiYhuEnEqVpRxh2c/nkrKuEfVUBcCQzgtFsEAWtTvy010qiBTEOLzIPFagq+KjauH/Z3ELXFZtB1/pgFNI4KT31EWLgK41vqZXWinlkOQFBV+EkIKgk2UEFN5z2dnWdw+m3xeGovKFlcdVF8foT6WFB18x5j/lvr4YXwtsjJWCL0JOpdRYCghAoEfRja41vrwwisWzrpNJMUECvz62gwcY3ed8pQpuhI38OZYwo0LD870AQC/pYZR4FkSz/9QL1XuCMTR6+O9hClU6JHlCwRchpGCElGQ6UD+5+/XH+chPWA5gfGlxBV+q0H+qZUqEdQAAJtACy4SckkkxQWC69LyvVNGNrjlfPlgUR76alxVGiRfwaWVuAEDIH4OaUPkaX8mRL7+B/z5Y3KzpSocpqdGvjkgHVJUNuG+q2MYYhxE2g9K1xheNfJEcouCLEFIwojo+JyPu73udr2ONfOQnoHhh1mu7xzalxp5cY0gKIxyL9rufqqpIiDwtc2opLbBMyGDoBHu64mF65MuTmvPlhUNXHOnLKRaFj+S1JjogCAAYD8AiwThiYb5GoltOlplPWFCu4TW+UsqMqcWl/WgLDFx0I51yWGVFJBFBvbceAI18kdyi4IsQUjASRt5rKQRFsD56MFvbeK9lQHfqFD2tGGPrqrR2wtvR73717lYIIp8oP728JuvtIqQYGEU7/Mmqhv6eI1+KNz1qUixsOgcAwBtzw2jjKYhBTzRd6dBo06Ezwa8zWl/jK8Vp7Co00uQZuNx8auRraqUVh9yHoDIVDr0D5cbiyKQg2kDBFyGkYMgmPRhUCExAyB/r9by3k88FC+kGt5imFhgUHZDgN0DHPP3PWdjbeoz/I2EpmlE/QrLNoji61vrqDCMajiMW4SNAQZ0X5abiWrahJL10hRumZPAV8ETSwZet1ID2MM8gUBPFkXZYauDnUJD9gwi++MjXtEpbRsqhIAjZbSQh3VDwRQgpGDaDLV3+ua+1vsIeHpBFdP2n52mRyPiE8SZ//yNfhztOAAAUFFdPPSHZ5NA5u9b66oikR71iYgxxKYpKc1k+mzfiyox8JD2Y8MKULKYR9EbhTZaZtzoN6Awn53wlLHAVQdph97W+BlpoWVUZ9qWDLyq2QfKHgi9CSMEoMTjT5ea7L4qakggk54QZEjltV7YpggXAwMFXg7cJAGAWi2NBWEJyodRYml7ry98ZTgdfAZkXuKm2Fle6WYWFjwJFmRcmG1+3KuiNpottWEsNaA+l0g7NcGm82iHQfX0zf7qSYV+OdQYRjCagk0RMLDNT8EXyhoIvQkjBKDc5EUjOz+hr5EsM8ksWM+ayVdmnE/naZa2B/oOvRj8Pvuy64uqpJySbKsxl6ZGvSDAOTwsfAUqNsI+xFVfwVZkMviAFIZm6BV/JtEOr04COML/OmGU7TDrtr3HWfaHlpgFGvlIph5MrLJAlsSvt0EGVDkluUfBFCCkYFZaydPDl7+wdfClhPodBtmj/hqE7k8SDr86wp9992sO8Qlm50ZWTNhFSDKqtLsTkCCISv560HOUFF0LJ60ypsbhGklNz2EQpAGbgt3hBTzS9xpfOISCm8tG/8iI59q60w4HnfH3SyIOvqZVWtIXa0B5uhwABdY66nLSTkBQKvgghBWOstQwBPQ9Aeo58xWMJ6OI8+DLYimvoK1Ue2h3p7HcfT3KB5SoLLbBMyGCNs/POCr/Cryst9angi3/WUjfuxaIrBS+AhD4ZfHkj8CfTDhNmPvLHVAWVtuJYYHqwI1/7mvm5n96t2MY42ziYFFP2G0lINxR8EUIKxlh7edecrx7BV8CdmigfRYnNmvO2ZZNV4QujeqPefvcJJXjwNcFBCywTMljj7Lyzwq/nqXZtx/kyFUGdF2AC7PriCEBSUtUOBTmAsMS3uZuDiAT5MhUhIx/9KZYy80BmwY0mTxCM9b3Qcmrka1qVlVIOSV5R8EUIKRgTHOXpNXk8HcGM5wLu5AKpOg/KLcXWW+0AAATi/QdfccENAJjkHJODFhFSHCotfI6k35Aqr85vzIOKD4pghSgU121Q18hXCP7kuoAhH68Sa7Ao8KpuAMVT6RAAHMnrpyAwhBJ++CLxXvuEogkcaedFVqZSpUOSZ8V11SGEaFqJyZKuQpYa6UpJjYQFdG5Umosr+Coz8RumUMLX5/PtQR8g8XShGS5aYJmQwbLpbACTEdBlpvQGdV7oBVueWpU9Np0NAF+zqi2R2ZnTvdgGr3RYHCNfiqgkjztZbr6PeV8HWnxgDCg161Bu0aeDr8klNPJFco+CL0JIQQnKvIy8GmWIhrp6MFMFOPw6D6psxRZ8OQAAUebv8/k9zXyBZabqUWmldb4IGSxBECAza7rcfEpQ8cIsO/LSpmySRAm65NIVzZFOyLqu2zxbqQGdkdQaX2ZUFEGZ+ZSueV/+Pud9dU85TLAEDnsOA6CRL5IfFHwRQgqKIOsRkXjKYfd5X+52vi2gc6PGXlzl1lPloeP9BF8Hkwssy2pxzU8hJBf0oj1dbj4lpPhgUxz5aVCWGUU+CtQW6oDJpktv52t8JdMv4xa4rMUx8gVkVjzsa62vvU18FHBqhQ0NvgZEEhEYZSPGWsfmtJ2EABR8EUIKjEG0pYtudK942NbGey4DihfV1uIa+Rpj5cGkKgb6fL7efRIAYBRLc9YmQoqFWbbDr+8KvhgYQooPjiKrdJhiTlZP7Yx0wtRtdMta2i3tsGhHvvpOO9zX1LvYxiTHpKKb80e0gf7qCCEFxSTZ+1zry5cslRxQgpAlKR9Ny5oxdh5UCWIMnnCw1/MnkgssWxUKvggZKpviREDpWkMvJsbABFZ0a3yl2HQOAIAn0gmTTUlvj4YTaA0k0w6LeeSrR9ohYwx7G/nI1zQqtkEKQHGtVEoI0TybztHnyFfEx+d/BZVon6/TsmqrA4yJEAQVxz3tsBsy151pDbYAAEr15floHiGaNrFzMs751wXpxzpVhy9uXw3F0LsqXjFIlZt3tOpx7ERHevt7/3cYM/SXoW18AEckG4y64unESld57GPkq9UXQWcwBlEAJruseHQ/Fdsg+UUjX4SQguLQO9Ll5lPBl6oyqAFeIjqqT+SraVkjiiIElQdcx71tvZ7vjPJttMAyIUNzaEcLanfMgDnqyNhujjqg31iGQzta8tOwLCo1OjGxfTbOOzIbsaia8ZwhYsEl+2/GadHiWrIiY62vHiNfnyRTDieUmWHUSem0Qxr5IvlCwRchpKCUGp0I6DMXWg75ohCYABUqmL44L1sS48FXo7e913PB5ALLY61VOW0TIVqmqgybnuE32kKy/HoKfyxg87MHoKp9L8qrVS6jE+fVf77P51K/hzNaSorquLunHTZ5egZfXSmH/qgfJ/y8gBEtsEzypTjvYgghmlVucvZKO0z9P6R4YZSteWtbNikCP67WYGev5yLg2+pogWVCBq3xgBsBd6RX4NWdvzOCxgPu3DUqB8p8FbBES/o9bgECDDGhqI67e8GN9kAUkXhXhkS6zHylDQfdBwEALpMrvTgzIblGwRchpKBU28q6FdwIJ/+fXONL74ZFKc5y6wap7+ArHIuCifzmYWo5BV+EDFbAGzn1TkPYTyvsicEtHl1Mx50KvkSZL9fR0u3YUmmH3Ytt0Hwvkk8UfBFCCkqNrTwdfIV8MSTiatcImM4Dm35wNxZaY0oGXx3hzOBrf/tJCAIDYyImOSntkJDBMg+ylPpg99OK0tLBXSOL6bjTBTekIIBEet5XLKHiYAsPyKZV2qjSISkIFHwRQgrKOHs5wnIACYFXIgt4Ium5XwGdG84iTRWxKPyGyRvxZmz/pPU4AEBM2IuuxD4h2VQ12QGzY+AAw1KiR9VkR24alCMTp1bAr+sEQ99zuhgYJItcVMft0DvSaZaCFEzP+6pvCyCaUGHWSRhbYkwX26D5XiSfKPgihBSUsfZSMAjp0a9AZwSBzq6Rr1JjSR5blz0OvQMA4It5MrbXd/IFlvVCcR43IdkiigIWXstvsnsGIqnH518zGaLY/5wwLSoxlWDLhBf6fC513BOXjC2q45ZEKaPcfCr42ptMOZxaaYUggCodkoJAwRchpKDIkgRBNaeLbvjd3Ue+PCg3F2cQ4jDwuWyBeObIV4O3EQBgkYtzQVhCsqlurgtLvzYLQZ0vY3tIF8DSr81C3VxXnlqWPUbZiKPOT7B+yuOAKTPA8uvcWDfmbUw/szJPrcue1PpmguRPpx1+klxceWqlDU2BJvhiPsiCjFp7bd7aSQgtskwIKTgys2Ss9RXolnZYZSnNY8uyp8zIg6twwp+xvSW5wHKJrvhuEgnJhbq5Lrz08TMwesIwBMchbGqArrwSd829PN9NyxqdYMOR0o/RPjOMWyacg4A3guePPYe13kcRbl8EVxHN90pxGp045DnER76Swde+5MjX9CorDrj5qNcE+wQokpK3dhJCwRchpODoRGuPkS/+RRrQeVBtK87gqyI5ohdjmT30HZFWAIDLTMEXIcNlVuw4bt8NZj0CQUxgjmF6vpuUVQbJhnCiDW3hDoyZyq8txzoPgfkY9IIVBqX45o9mjHwl0w67Kh3a8DEV2yAFgtIOCSEFxyjaEUwGX52NAcQjKgA+8jXWXpbPpmVNhZWPfMWFQMZ2X7wNAC2wTMin4dAn14ES+fpPqdLkxcoi8zTm7tVTW4MdAAC7rjhTt7uv9dXkCcMTiuGEOwQAmFphxf4OCr5IYaDgixBScCyKHX69GwDQfITn7IelAGKiinJTcS6yPMbGg0omBqGqanp7WOU3TxMcFHwRMlylhswR81Sab7Gy6Xjw5Ym409s6I/xa4izSY08dlyD50ewNp+d7VdsNsJuUdNohrfFF8o2CL0JIwbHrHelqh5FgsuS8zgNBNUEUi/OyNT45oicICbQGeaqMqqpIiG4AwGTn2Hw1jRDNqzBnjphXWsrz1JLccCRT8Pwxd3qbL8qDrwpTcaZuO/VdCy3HVYYth9oB8EqH0UQURzxHANDIF8m/4ryLIYRoWom+JD3nKyWgd0Nm5jy1KPvsBhOYyqfhHnPzeV4N7jYIIg8+p7nG5K1thGhddY9ga4y1uIOv0uQoUDDBR39UpiKkJkeCrMU5fzQ18qXT8VTDt/bxYkXTqmw47DmMBEvAprOhwlSRtzYSAlDwRQgpQC5zKYKKJ2NtnoDOA0UozpRDABBFEWIyuGz08bkZn7TxBZaRMMNuMOWraYRo3lh7ZsBRYy/u4Mtl5oFIJBlweSIeIHk9rXEU57zZVMENUeHzZj8+wTvwplVauxZXLpkMQSie9c2INlHwRQgpOBVmJ1RRRUjuKj4R0HlgkIo3+AIAKRV8+Xm6zMGOEwAAhRXnBHlCcmViSea6VuMdxTn6k1KVHOmLgS9d0RHmHTosYUSVzZK3dmVTauSLiTxtmyX77qZV2rCfKh2SAkLBFyGk4FQnU4L83VIPAzo3zLItX03KCZ3Ib4paAnxuRoOHL7BskopzgjwhuZIRbKkybPriDEBS0ktySH6EY4l08KXGLagowjW+gK6iKgmEAPB0bUUSUFtuTgdfVGyDFAIKvgghBWd8CQ++ArqO9DZ9zARrkQdfRpGP7HWEePDV6G8GANh1xZkmREiuCBCBRDLoYHrEEon8NijLxliTBXykADqDUXSEUiNfZlTYDPlsWtZYdVZIAl+/TEhmTVTZjRAFIZ12SCNfpBBQ8EUIKTjjHS5MbJ+NMd6uL8pzjl2OBVsX4tCOljy2LLvMCg8uO8JuAEBbmB9rubG4U6QIyaZfbHoOZ/7xQkCK8A1SAGf+8UL8YtNz+W1YFqXXvJLCaPOHcMLHi/iwuBnl1uIc+RIFESaJl9gXJB58NXQEcd4v/o7WED/+yQ4a+SL5R8EXIaTgtO3145L9N0NRdRnblbCCV/9nd9EGYFYltTYPT7f0xvgCy9Xmyn5fQwjp3y82PYc/HLoXanLJhhRVdOMPh+4t2gDMprcBjBeWOO5txQkvDz4U2GBQpHw2LWte3d0It59/ZwiyP729LVIPAHDqqmBSqHARyT8KvgghBUVVGTY9w1NEBGRWpUo93vzsAagq6/VarbPrefAViPEKZcEETxWqoQWWCRmyaDyOPx34HQCgZ4G71OM/7f8dovF4jluWfaIgQgKf13bS146mAO/IsSQ7eIpNQmVY8489YAl+zKmRLwAQ9E0AAI+nDIki/N4g2kPBFyGkoDQecCPgjvQKvLrzd0bQeMCdu0blSKmBVzVMrc0TF/jcr8lOWuOLkKF66qONYJK7V+CVIggAk9146qONOW1XruiSS3M0+9vRFuQVVO264ize8/6RDjR6wmBxXjG2+8iXaODBV9DvwvtHOvp8PSG5RMEXIaSgBLyREd1PS8pMPPgKq350Bv2AxBcLnVZek89mEaJJDd6mEd1Pawwin0PaGuyAO+IG0DUXrNi0+MIAeEERIHPkS0qOfKmRyvR+hOQTBV+EkIJiHmQZ5MHupyUuCw++YsyPPa3HAABM1aHa6shjqwjRpnG2wc2VHOx+WmOWeYphZ7gDvhgfRa+wlOazSVnjsvIKjiyeTDtMj3ypEPW8amwiUpnej5B8ouCLEFJQqiY7YHbowdB/br6lRI+qyY7cNSpHqq38xkgVAjjQxhdYllUHRJEu1YQM1fVzFkFIONKL7fbEGCDEHbh+zqKctitXbLpkAZ+oGyGVpzJXJxdfLjZnT3Siym5Iz/kSkyNfgq4dghgDUxVUGMfg7InFOfJHtIW+0QkhBUUUBSy8lpcD7i8AO/+ayRDF/ueEadUYG1+bh4khHOo8DgAwiCX5bBIhmqWTZayY/E0A6BWApR6vmPJN6GQ5xy3LDYeeBxreWAfi4CNB4x3FGXxJooDVy2f0mvPVlXJYgR8vnwWpCL83iPZQ8EUIKTh1c11oO7sZAZ07Y7ulRI+lX5uFurnFue7VWBsf+RIEhn3JRUFtMi2wTMhw3bXwatxYdw9E1ZGxXUw4cGPdPbhr4dX5aVgOlBp5x40vcRIAwJiAic7iDL4AYOmsKty5eB6ArjlfYjL4OmfsTCydRVVjSWEozu4eQojm2aYY8KT4Y1R562CK2RAR9XjpW/9TlCNeKWa9HkzVQxAjOBE4DAiA01C8N0uE5MJdC6/GtxZchac+2ogGbxPG2Spx/ZxFRTvileIy8ZGvsJAMvhImVNrM+WxS1i2bMQUP7QOMxhB+dt3peP7YS9jZAVxYOyffTSMkrbivPIQQzaq2lYEJDCftBwEAFnV6UQdeKaJqBhMj8KnHAAmotBRnMQBCckkny7jpjMX5bkZOVVlTacw8BY8lzCi3Fl+hou5Kkst1RBIhXDqrFP9z+CgAYErJlHw2i5AMlHZICClIY6yZ6XYGyZKnluSWIvCeaSZ5AAA1Vgq+CCFDV93jGioxKwyKlKfW5IZFsUARFQDACf8JHPPxqrGTSybns1mEZKDgixBSkCaUVGQ8Nsu2PLUkt/TJhVFTJpZU56klhBAtG+fInBtrEIr/GioIQnr06/2m9wEAZcayol3fjGgTBV+EkIJUZXWAsa5LlE3nyF9jcsjYI8icTgssE0KGobLHml4WxZGfhuRYqYEf93uN7wGglENSePIafN1///0466yzYLVa4XK5cOWVV2Lfvn2Dfv3atWshCAKuvPLKfvf5+te/DkEQ8Jvf/CZj+4QJEyAIQsZ/DzzwwDCPhBAy0mRJgqB2TQ536O15bE3umOWukS/GRNQ5Ke2QEDJ0ekkPqF1zvOy60TH6kxrlSo18UfBFCk1eg6+33noLK1euxLvvvovXX38dsVgMl1xyCQKBwClfW19fjzvvvBMLFy7sd58XX3wR7777Lqqr+07buffee9HY2Jj+79Zbbx32sRBCRp7MuuZ5OY2O/DUkh1ILowKAqNqKviIbISR7pG7X0FTp+WKXSjv0RX0AaL4XKTx5/VZ/9dVXMx4/+eSTcLlc2LZtGz7zmc/0+7pEIoEvfvGLWLNmDTZt2gS3291rnxMnTuDWW2/Fa6+9hssuu6zP97FaraispF5lQgqVTrQijkYAXWWTi51D70ByPVToMDpulggh2aETrAihHQBQYR4dawb2nN9FI1+k0BRUl6rHw6t7OZ0D32Tde++9cLlcuOWWW7Bp06Zez6uqihUrVuCuu+7CzJkz+32fBx54APfddx/GjRuH66+/HrfffjvkfnqZI5EIIpFI+rHX6wUAxGIxxGKxUx5bNqV+fr7bQTLRefn0jKINweS/S422EfldFvp5KdF3zfkyS86CbedIK/TzMlrReSlcgzk3etGKUPLflebSUXEeHd3mB0uChBpTTU6Pmz4zhSkX52Ww710wwZeqqrjttttw3nnnYdasWf3ut3nzZjz22GPYuXNnv/s8+OCDkGUZ3/zmN/vd55vf/CbmzZsHp9OJd955B9/73vfQ2NiIX//6133uf//992PNmjW9tq9fvx4mk6n/A8uh119/Pd9NIH2g8zJ8algEDPzfr217C4Z6N2RxZLKlC/W8tHW0pBPCw6Ew/v7SSyN2zFpQqOdltKPzUrgGOjcsLKWvoVv3vosxraGiv540hBvS/7bCivWvroco5P6Y6TNTmLJ5XoLB4Kl3AiAwxljWWjEE3/jGN/DKK69g8+bNGDt2bJ/7+Hw+zJ49G7///e/x2c9+FgBw0003we12429/+xsAYNu2bbjsssuwffv29FyvCRMm4LbbbsNtt93W789//PHH8bWvfQ1+vx96fe9FCPsa+aqpqUFbWxtstvyWb43FYnj99dexZMkSKIqS17aQLnRePp1fbXkBfz7yCwhi1+dOiNtxfd2t+PZ5nx/2+xbyefnVlhfwl8O/ASR/ettIHLMWFPJ5Gc3ovBSuU50bfg39OQQxmt5W7NeTDcc24Kfv/RTuqDu9zWVy4a4z7sLFNRfnpA30mSlMuTgvXq8XZWVl8Hg8A8YGBTHytWrVKrz00kt4++23+w28AODQoUOor6/H8uXL09tUVQUAyLKMffv2YdOmTWhpacG4cePS+yQSCXz729/Gb37zG9TX1/f53vPnz0c8Hkd9fT2mTp3a63m9Xt9nUKYoSsF8uAqpLaQLnZeh+8Wm5/Dn+p8AQuZ2VfLgz/U/gSSJuGvh1Z/qZxTaeUkfs5h52CN5zFpQaOeFcHReCldf5yYX19BC88bRN/CdTd8BQ+aYQmuwFd/Z9B38etGvsXj84py1hz4zhSmb52Ww75vXsWfGGFatWoUXX3wRb775JiZOnDjg/tOmTcOuXbuwc+fO9H+XX345LrzwQuzcuRM1NTVYsWIFPv7444x9qqurcdddd+G1117r97137twJURThcrn63YcQkn3ReBx/OvA7AIDQ48Yh9fhP+3+HaDye45Zlz2g8ZkJIdozG60lCTeCB9x/oFXgBSG978P0HkVATuW4aIb3kdeRr5cqVeOqpp/B///d/sFqtaGpqAgDY7XYYjUYAwJe+9CWMGTMG999/PwwGQ6/5YA6HAwDS20tLS1FamrmwoKIoqKysTI9obd26Fe+99x4uvPBCWK1WbN26FbfffjtuuOEGlJRQdTFC8umpjzaCSe6eHbZpggAw2Y2nPtqIm87IXS9mNo3GYyaEZMdovJ5sb9mO5mBzv88zMDQFm7C9ZTvOqjwrhy0jpLe8Bl8PP/wwAGDRokUZ25944gncdNNNAICGhgaIIzw5VK/XY+3atfjxj3+MSCSCiRMn4vbbb8cdd9wxoj+HEDJ0Dd6mEd1PC0bjMRNCsmM0Xk9ag60juh8h2ZTX4GswtT42btw44PNPPvnkKd+j5zyvefPm4d133z3l6wghuTfONri19wa7nxaMxmMmhGTHaLyelJvKR3Q/QrKpuOuNEkI05/o5iyAkHOivb4YxQIg7cP2cRTltVzaNxmMmhGTHaLyezHPNQ4WpAkI/yZYCBFSaKjHPNS/HLSOkNwq+CCEFRSfLWDGZr9HX8+Yh9XjFlG9C18+C6Fo0Go+ZEJIdo/F6IokS7j77bgDoFYClHn/37O9CEqWct42Qnij4IoQUnLsWXo0b6+6BqDoytosJB26su6foSiQDo/OYCSHZMRqvJ4vHL8avF/0aLlNm1eoKU0XOy8wTMpDi6fYghBSVuxZejW8tuApPfbQRDd4mjLNV4vo5i4qqt7an0XjMhJDsGI3Xk8XjF+PCmguxvWU7WoOtKDeVY55rHo14kYJSvJ9AQojm6WS5aEohD9ZoPGZCSHaMxuuJJEpUTp4UNEo7JIQQQgghhJAcoOCLEEIIIYQQQnKAgi9CCCGEEEIIyQEKvgghhBBCCCEkByj4IoQQQgghhJAcoOCLEEIIIYQQQnKAgi9CCCGEEEIIyQEKvgghhBBCCCEkByj4IoQQQgghhJAcoOCLEEIIIYQQQnKAgi9CCCGEEEIIyQEKvgghhBBCCCEkByj4IoQQQgghhJAckPPdAK1ijAEAvF5vnlsCxGIxBINBeL1eKIqS7+aQJDovhYnOS2Gi81KY6LwULjo3hYnOS2HKxXlJxQSpGKE/FHwNk8/nAwDU1NTkuSWEEEIIIYSQQuDz+WC32/t9XmCnCs9In1RVxcmTJ2G1WiEIQl7b4vV6UVNTg2PHjsFms+W1LaQLnZfCROelMNF5KUx0XgoXnZvCROelMOXivDDG4PP5UF1dDVHsf2YXjXwNkyiKGDt2bL6bkcFms9EHvQDReSlMdF4KE52XwkTnpXDRuSlMdF4KU7bPy0AjXilUcIMQQgghhBBCcoCCL0IIIYQQQgjJAQq+ioBer8fq1auh1+vz3RTSDZ2XwkTnpTDReSlMdF4KF52bwkTnpTAV0nmhghuEEEIIIYQQkgM08kUIIYQQQgghOUDBFyGEEEIIIYTkAAVfhBBCCCGEEJIDFHwRQgghhBBCSA5Q8KURDz30ECZMmACDwYD58+fj/fffH3D/5557DtOmTYPBYMBpp52GdevW5ailo8tQzsujjz6KhQsXoqSkBCUlJVi8ePEpzyMZnqF+XlLWrl0LQRBw5ZVXZreBo9RQz4vb7cbKlStRVVUFvV6PKVOm0LUsC4Z6Xn7zm99g6tSpMBqNqKmpwe23345wOJyj1o4Ob7/9NpYvX47q6moIgoC//e1vp3zNxo0bMW/ePOj1ekyaNAlPPvlk1ts52gz1vLzwwgtYsmQJysvLYbPZsGDBArz22mu5aewoMpzPS8qWLVsgyzJOP/30rLWvJwq+NOCZZ57BHXfcgdWrV2P79u2YM2cOLr30UrS0tPS5/zvvvIMvfOELuOWWW7Bjxw5ceeWVuPLKK7F79+4ct7y4DfW8bNy4EV/4whfwz3/+E1u3bkVNTQ0uueQSnDhxIsctL25DPS8p9fX1uPPOO7Fw4cIctXR0Gep5iUajWLJkCerr6/H8889j3759ePTRRzFmzJgct7y4DfW8PPXUU7j77ruxevVq7N27F4899hieeeYZfP/7389xy4tbIBDAnDlz8NBDDw1q/yNHjuCyyy7DhRdeiJ07d+K2227DV77yFbrRH2FDPS9vv/02lixZgnXr1mHbtm248MILsXz5cuzYsSPLLR1dhnpeUtxuN770pS/h4osvzlLL+sFIwTv77LPZypUr048TiQSrrq5m999/f5/7X3PNNeyyyy7L2DZ//nz2ta99LavtHG2Gel56ihDqrn4AAA9lSURBVMfjzGq1sj/84Q/ZauKoNJzzEo/H2bnnnsv+93//l914443siiuuyEFLR5ehnpeHH36Y1dbWsmg0mqsmjkpDPS8rV65kF110Uca2O+64g5133nlZbedoBoC9+OKLA+7zne98h82cOTNj27XXXssuvfTSLLZsdBvMeenLjBkz2Jo1a0a+QYQxNrTzcu2117If/vCHbPXq1WzOnDlZbVd3NPJV4KLRKLZt24bFixent4miiMWLF2Pr1q19vmbr1q0Z+wPApZde2u/+ZOiGc156CgaDiMVicDqd2WrmqDPc83LvvffC5XLhlltuyUUzR53hnJe///3vWLBgAVauXImKigrMmjULP/vZz5BIJHLV7KI3nPNy7rnnYtu2benUxMOHD2PdunVYtmxZTtpM+kbf+9qgqip8Ph997xeAJ554AocPH8bq1atz/rPlnP9EMiRtbW1IJBKoqKjI2F5RUYFPPvmkz9c0NTX1uX9TU1PW2jnaDOe89PTd734X1dXVvb4wyfAN57xs3rwZjz32GHbu3JmDFo5Owzkvhw8fxptvvokvfvGLWLduHQ4ePIj/+I//QCwWy8uXZTEaznm5/vrr0dbWhvPPPx+MMcTjcXz961+ntMM86+973+v1IhQKwWg05qllpLtf/vKX8Pv9uOaaa/LdlFHtwIEDuPvuu7Fp0ybIcu5DIRr5IiQPHnjgAaxduxYvvvgiDAZDvpszavl8PqxYsQKPPvooysrK8t0c0o2qqnC5XHjkkUdwxhln4Nprr8UPfvAD/Pd//3e+mzaqbdy4ET/72c/w+9//Htu3b8cLL7yAl19+Gffdd1++m0ZIQXvqqaewZs0aPPvss3C5XPluzqiVSCRw/fXXY82aNZgyZUpe2kAjXwWurKwMkiShubk5Y3tzczMqKyv7fE1lZeWQ9idDN5zzkvLLX/4SDzzwAN544w3Mnj07m80cdYZ6Xg4dOoT6+nosX748vU1VVQCALMvYt28f6urqstvoUWA4n5eqqiooigJJktLbpk+fjqamJkSjUeh0uqy2eTQYznn50Y9+hBUrVuArX/kKAOC0005DIBDAV7/6VfzgBz+AKFKfbj70971vs9lo1KsArF27Fl/5ylfw3HPPUbZLnvl8Pnz44YfYsWMHVq1aBYB/7zPGIMsy1q9fj4suuiirbaCrZIHT6XQ444wzsGHDhvQ2VVWxYcMGLFiwoM/XLFiwIGN/AHj99df73Z8M3XDOCwD8/Oc/x3333YdXX30VZ555Zi6aOqoM9bxMmzYNu3btws6dO9P/XX755emKYTU1NblsftEazuflvPPOw8GDB9PBMADs378fVVVVFHiNkOGcl2Aw2CvASgXIjLHsNZYMiL73C9fTTz+NL3/5y3j66adx2WWX5bs5o57NZuv1vf/1r38dU6dOxc6dOzF//vzsNyJnpT3IsK1du5bp9Xr25JNPsj179rCvfvWrzOFwsKamJsYYYytWrGB33313ev8tW7YwWZbZL3/5S7Z37162evVqpigK27VrV74OoSgN9bw88MADTKfTseeff541Njam//P5fPk6hKI01PPSE1U7zI6hnpeGhgZmtVrZqlWr2L59+9hLL73EXC4X+8lPfpKvQyhKQz0vq1evZlarlT399NPs8OHDbP369ayuro5dc801+TqEouTz+diOHTvYjh07GAD261//mu3YsYMdPXqUMcbY3XffzVasWJHe//Dhw8xkMrG77rqL7d27lz300ENMkiT26quv5usQitJQz8tf/vIXJssye+ihhzK+991ud74OoSgN9bz0lOtqhxR8acR//ud/snHjxjGdTsfOPvts9u6776afu+CCC9iNN96Ysf+zzz7LpkyZwnQ6HZs5cyZ7+eWXc9zi0WEo52X8+PEMQK//Vq9enfuGF7mhfl66o+Are4Z6Xt555x02f/58ptfrWW1tLfvpT3/K4vF4jltd/IZyXmKxGPvxj3/M6urqmMFgYDU1New//uM/WGdnZ+4bXsT++c9/9vl9kToXN954I7vgggt6veb0009nOp2O1dbWsieeeCLn7S52Qz0vF1xwwYD7k5ExnM9Ld7kOvgTGKE+AEEIIIYQQQrKN5nwRQgghhBBCSA5Q8EUIIYQQQgghOUDBFyGEEEIIIYTkAAVfhBBCCCGEEJIDFHwRQgghhBBCSA5Q8EUIIYQQQgghOUDBFyGEEEIIIYTkAAVfhBBCCCGEEM16++23sXz5clRXV0MQBPztb38b0uvD4TBuuukmnHbaaZBlGVdeeWWvfV544QUsWbIE5eXlsNlsWLBgAV577bUht5WCL0IIIQXhpptu6vMLr5D0bCNjDF/96lfhdDohCAJ27tzZ5zZCCCHZEwgEMGfOHDz00EPDen0ikYDRaMQ3v/lNLF68uM993n77bSxZsgTr1q3Dtm3bcOGFF2L58uXYsWPHkH6WwBhjw2olIYQQMkiCIAz4/OrVq3H77beDMQaHw5GbRiVt3LgRF154IQDeTqvVitraWixZsgS33347qqqq0vt6PJ6MNr7yyiu44oorsHHjRtTW1qKsrAyvv/56r22yLOf0mAghZLQSBAEvvvhiRkdZJBLBD37wAzz99NNwu92YNWsWHnzwQSxatKjX62+66Sa43e5BjZ7NnDkT1157Le65555Bt4++DQghhGRdY2Nj+t/PPPMM7rnnHuzbty+9zWKxwGKx5KNpafv27YPNZoPX68X27dvx85//HI899hg2btyI0047DQBgt9szXnPo0CFUVVXh3HPPHXDbUDHGkEgkKGgjhJARsGrVKuzZswdr165FdXU1XnzxRSxduhS7du3C5MmTh/WeqqrC5/PB6XQO6XWUdkgIISTrKisr0//Z7XYIgpCxzWKx9ErpW7RoEW699VbcdtttKCkpQUVFBR599FEEAgF8+ctfhtVqxaRJk/DKK69k/Kzdu3fjs5/9LCwWCyoqKrBixQq0tbWdso0ulwuVlZWYMmUKrrvuOmzZsgXl5eX4xje+kd6nextvuukm3HrrrWhoaIAgCJgwYUKf2wD+JX3//fdj4sSJMBqNmDNnDp5//vn0+27cuBGCIOCVV17BGWecAb1ej82bNw/6dRs2bMCZZ54Jk8mEc889NyOwBYB//OMfOOuss2AwGFBWVoarrroq/VwkEsGdd96JMWPGwGw2Y/78+di4ceMpf1+EEKIFDQ0NeOKJJ/Dcc89h4cKFqKurw5133onzzz8fTzzxxLDf95e//CX8fj+uueaaIb2Ogi9CCCEF6w9/+APKysrw/vvv49Zbb8U3vvENXH311Tj33HOxfft2XHLJJVixYgWCwSAAwO1246KLLsLcuXPx4Ycf4tVXX0Vzc/OQvxwBwGg04utf/zq2bNmClpaWXs//9re/xb333ouxY8eisbERH3zwQZ/bAOD+++/HH//4R/z3f/83/vWvf+H222/HDTfcgLfeeivjPe+++2488MAD2Lt3L2bPnj3o1/3gBz/Ar371K3z44YeQZRk333xz+rmXX34ZV111FZYtW4YdO3Zgw4YNOPvss9PPr1q1Clu3bsXatWvx8ccf4+qrr8bSpUtx4MCBIf/OCCGk0OzatQuJRAJTpkxJZ1lYLBa89dZbOHTo0LDe86mnnsKaNWvw7LPPwuVyDem1lM9ACCGkYM2ZMwc//OEPAQDf+9738MADD6CsrAz//u//DgC455578PDDD+Pjjz/GOeecg//6r//C3Llz8bOf/Sz9Ho8//jhqamqwf/9+TJkyZUg/f9q0aQCA+vr6Xl+wdrsdVqsVkiShsrIyvb3ntkgkgp/97Gd44403sGDBAgBAbW0tNm/ejP/5n//BBRdckH7tvffeiyVLlgz5dT/96U/Tj++++25cdtllCIfDMBgM+OlPf4rrrrsOa9asyfi9Al09wg0NDaiurgYA3HnnnXj11VfxxBNPZPweCSFEi/x+PyRJwrZt2yBJUsZzw0l3X7t2Lb7yla/gueee67c4x0Ao+CKEEFKwZs+enf63JEkoLS1Nz78CgIqKCgBIj0x99NFH+Oc//9nnF+qhQ4eGHHylalKdqmDIQA4ePIhgMJgOqlKi0Sjmzp2bse3MM88c1uu6/55SBUJaWlowbtw47Ny5Mx2s9tS9R7i7SCSC0tLSQR4hIYQUrrlz5yKRSKClpQULFy78VO/19NNP4+abb8batWtx2WWXDes9KPgihBBSsBRFyXgsCELGtlRQpKoqAN7DuXz5cjz44IO93qt71cLB2rt3LwCk524Nh9/vB8DT/8aMGZPxnF6vz3hsNpuH9bqBfidGo3HAto1kjzAhhOSD3+/HwYMH04+PHDmCnTt3wul0YsqUKfjiF7+IL33pS/jVr36FuXPnorW1FRs2bMDs2bPTQdSePXsQjUbR0dEBn8+XXibk9NNPB8BTDW+88Ub89re/xfz589HU1ASAX2N7FmMaCAVfhBBCisa8efPw17/+FRMmTPjUlQJDoRAeeeQRfOYzn0F5efmw32fGjBnQ6/VoaGjISBXM1ut6mj17NjZs2IAvf/nLvZ4byR5hQgjJlw8//DC9ZAgA3HHHHQCAG2+8EU8++SSeeOIJ/OQnP8G3v/1tnDhxAmVlZTjnnHPwuc99Lv2aZcuW4ejRo+nHqQyDVAbEI488gng8jpUrV2LlypXp/VI/Y7Ao+CKEEFI0Vq5ciUcffRRf+MIX8J3vfAdOpxMHDx7E2rVr8b//+7+9Rne6a2lpQTgchs/nw7Zt2/Dzn/8cbW1teOGFFz5Vm6xWK+68807cfvvtUFUV559/PjweD7Zs2QKbzYYbb7xxRF/X0+rVq3HxxRejrq4O1113HeLxONatW4fvfve7g+4RJoSQQrZo0SIMtHSxoihYs2ZNxtzXnurr6wf8GSNVBZaCL0IIIUWjuroaW7ZswXe/+11ccskliEQiGD9+PJYuXQpRHLjA79SpUyEIAiwWC2pra3HJJZfgjjvuyCimMVz33XcfysvLcf/99+Pw4cNwOByYN28evv/972fldd0tWrQIzz33HO677z488MADsNls+MxnPpN+fjA9woQQQkaGwAYKEwkhhBBCCCGEjAha54sQQgghhBBCcoCCL0IIIYQQQgjJAQq+CCGEEEIIISQHKPgihBBCCCGEkByg4IsQQgghhBBCcoCCL0IIIYQQQgjJAQq+CCGEEEIIISQHKPgihBBCCCGEkByg4IsQQgghhBBCcoCCL0IIIYQQQgjJAQq+CCGEEEIIISQH/n/5ICY5P+uizAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by neighbour and plot each group separately with a legend\n",
    "nn1_groups = full_nn1_df.groupby(\"pattern\")\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for name, group in nn1_groups:\n",
    "    ax.plot(\n",
    "        group[\"time_difference\"], group[\"sensor_00\"], marker=\"o\", label=f\"NN {name}\"\n",
    "    )\n",
    "\n",
    "ax.set_xlabel(\"Time Difference\")\n",
    "ax.set_ylabel(\"Sensor Values\")\n",
    "ax.legend(title=\"Neighbors\")\n",
    "plt.title(\"Pattern Matches\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29db0b58",
   "metadata": {},
   "source": [
    "Remember the blue line `NN 1` is our query vector, we can see the overall pattern matches closely with the other matches found. \n",
    "\n",
    "Let's try another input query and see what result we get."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7f9be3",
   "metadata": {},
   "source": [
    "### Automate This Pattern Matching Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8812d998",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_neighbors(\n",
    "    table, df: pd.DataFrame, embedding_df: pd.DataFrame, pattern_time, n_neighbors: int\n",
    ") -> None:\n",
    "    # Select historical pattern\n",
    "    example_window1 = embedding_df[embedding_df[\"start_time\"] == pattern_time]\n",
    "\n",
    "    # Extract the sensor values in this window\n",
    "    query_vector1 = example_window1[\"vectors\"].values.tolist()\n",
    "\n",
    "    # search the KDB.AI table\n",
    "    nn_result = table.search(vectors={\"hnsw_index\": query_vector1}, n=5)\n",
    "\n",
    "    # Iterate through the rows of df1 to filter df and calculate time differences\n",
    "    full_nn_df = pd.DataFrame()\n",
    "    for i, row in nn_result[0].iterrows():\n",
    "        filtered_df = sensors_df[\n",
    "            (sensors_df[\"timestamp\"] >= row[\"start_time\"])\n",
    "            & (sensors_df[\"timestamp\"] <= row[\"end_time\"])\n",
    "        ].copy()\n",
    "        filtered_df[\"time_difference\"] = filtered_df[\"timestamp\"] - row[\"start_time\"]\n",
    "        filtered_df[\"pattern\"] = i + 1\n",
    "        full_nn_df = pd.concat([full_nn_df, filtered_df])\n",
    "\n",
    "    # Group by neighbour and plot each group separately with a legend\n",
    "    nn1_groups = full_nn1_df.groupby(\"pattern\")\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "    for name, group in nn1_groups:\n",
    "        ax.plot(\n",
    "            group[\"time_difference\"], group[\"sensor_00\"], marker=\"o\", label=f\"NN {name}\"\n",
    "        )\n",
    "\n",
    "    ax.set_xlabel(\"Time Difference\")\n",
    "    ax.set_ylabel(\"Sensor Values\")\n",
    "    ax.legend(title=\"Neighbors\")\n",
    "    plt.title(\"Pattern Matches\")\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8df1e54f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_neighbors(\n",
    "    table, sensors_df, embedding_df, pd.to_datetime(\"2018-04-12 21:10:00\"), 5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444ebafc",
   "metadata": {},
   "source": [
    "This is also a pretty close match! \n",
    "\n",
    "We can see even with a simplistic method for embedding our time series data we can yield satisfactory results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bdd5d5",
   "metadata": {},
   "source": [
    "## 5. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "84213681",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6078c94e",
   "metadata": {},
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/go0ElNsJ)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: qFlat_index_pdf_search/pdf_qFlat_Search.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3280b01a-d3b7-4ef6-9494-789d15bc48ec",
   "metadata": {
    "id": "3280b01a-d3b7-4ef6-9494-789d15bc48ec"
   },
   "source": [
    "# Semantic Search on PDF Documents with qFlat Index\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example demonstrates how to use KDB.AI to run semantic search on unstructured text documents.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> This sample uses ‘qFlat’ , a new vector index choice in KDB.AI. It will support the same API options as the existing ‘Flat’ index but with the significant difference that the index is stored on-disk and memory-mapped as required. This means data inserts will have negligible memory and cpu footprints. The vector index can grow and be searched as long as there is disk space available and works great for datasets with up to 1,000,000 vectors. Among other cases, this stands out as a great index for memory contrained situations such as edge devices.\n",
    "</div>\n",
    "\n",
    "Semantic search allows users to perform searches based on the meaning or similarity of the data rather than exact matches. It works by converting the query into a vector representation and then finding similar vectors in the database. This way, even if the query and the data in the database are not identical, the system can identify and retrieve the most relevant results based on their semantic meaning.\n",
    "\n",
    "### Aim\n",
    "In this tutorial, we'll walk you through the process of performing semantic search on documents, taking PDFs as example, using KDB.AI as the vector store. We will cover the following topics:\n",
    "\n",
    "0. Setup\n",
    "1. Load PDF Data\n",
    "2. KDB.AI Table Creation\n",
    "3. LlamaIndex index & query_engine setup\n",
    "4. Retrieve Similar Sentences & RAG\n",
    "5. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75362bc9",
   "metadata": {
    "id": "75362bc9"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda3d787",
   "metadata": {
    "id": "dda3d787"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19c5107-001e-40c2-bfe7-6c9c99e4846d",
   "metadata": {
    "id": "b19c5107-001e-40c2-bfe7-6c9c99e4846d"
   },
   "source": [
    "### Set Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bad9d73",
   "metadata": {
    "id": "8bad9d73"
   },
   "outputs": [],
   "source": [
    "!pip install llama-index llama-index-llms-openai llama-index-embeddings-openai llama-index-readers-file llama-index-vector-stores-kdbai\n",
    "!pip install kdbai_client\n",
    "!pip install pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21979389",
   "metadata": {
    "id": "21979389"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "rjfp-08NPdvC",
   "metadata": {
    "id": "rjfp-08NPdvC"
   },
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "from getpass import getpass\n",
    "import re\n",
    "import os\n",
    "import shutil\n",
    "import time\n",
    "import urllib\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from llama_index.core import (\n",
    "    Settings,\n",
    "    SimpleDirectoryReader,\n",
    "    StorageContext,\n",
    "    VectorStoreIndex,\n",
    ")\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.retrievers import VectorIndexRetriever\n",
    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "\n",
    "import kdbai_client as kdbai\n",
    "\n",
    "OUTDIR = \"pdf\"\n",
    "RESET = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f23f6513",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f23f6513",
    "outputId": "f7ab1ea9-14aa-45b2-fd7f-3cb6bde9bad3"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads research paper pdf into your environment\n",
    "if os.path.exists(\"./data/research_paper.pdf\") == False:\n",
    "  !mkdir ./data\n",
    "  !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/document_search/data/research_paper.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "yCCdm-QDPtDZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yCCdm-QDPtDZ",
    "outputId": "eb8d2a16-cfe6-41be-bded-729c43bd09ee"
   },
   "outputs": [],
   "source": [
    "# OpenAI API Key: https://platform.openai.com/api\n",
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "HCI37LxrPywl",
   "metadata": {
    "id": "HCI37LxrPywl"
   },
   "outputs": [],
   "source": [
    "# Set up LlamaIndex Parameters\n",
    "\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()\n",
    "\n",
    "EMBEDDING_MODEL  = \"text-embedding-3-small\"\n",
    "GENERATION_MODEL = 'gpt-4o-mini'\n",
    "\n",
    "llm = OpenAI(model=GENERATION_MODEL)\n",
    "embed_model = OpenAIEmbedding(model=EMBEDDING_MODEL)\n",
    "\n",
    "Settings.llm = llm\n",
    "Settings.embed_model = embed_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8594c911",
   "metadata": {
    "id": "8594c911"
   },
   "source": [
    "### Configure Console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b870117b",
   "metadata": {
    "id": "b870117b"
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a425b33",
   "metadata": {
    "id": "8a425b33"
   },
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a135635",
   "metadata": {
    "id": "9a135635"
   },
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48826990",
   "metadata": {
    "id": "48826990"
   },
   "source": [
    "## 1. Load PDF Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2992812-4705-489d-974f-b7b44132343a",
   "metadata": {
    "id": "b2992812-4705-489d-974f-b7b44132343a"
   },
   "source": [
    "### Read Text From PDF Document\n",
    "\n",
    "We LlamaIndex SimpleDirectorReader to read in our PDF file.\n",
    "\n",
    "The PDF we are using is [this research paper](https://arxiv.org/pdf/2308.05801.pdf) presenting information on the formation of Interstellar Objects in the Milky Way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "NWa3S2iwQd_K",
   "metadata": {
    "id": "NWa3S2iwQd_K"
   },
   "outputs": [],
   "source": [
    "reader = SimpleDirectoryReader(\n",
    "    input_dir=\"data\",\n",
    ")\n",
    "documents = reader.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70505eae-4138-4ba8-80e9-fc13c37d0b32",
   "metadata": {
    "id": "70505eae-4138-4ba8-80e9-fc13c37d0b32"
   },
   "source": [
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "401f8162",
   "metadata": {
    "id": "401f8162"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c6525c",
   "metadata": {
    "id": "48c6525c"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will have two columns.\n",
    "\n",
    "At this point you will select the index and metric you want to use for searching.\n",
    "\n",
    "In this case, we will use the qFlat index, Euclidean Distance (L2) for the search metric, and we specify the number of dimensions of our embeddings (1536)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eArvp20fSDc6",
   "metadata": {
    "id": "eArvp20fSDc6"
   },
   "outputs": [],
   "source": [
    "#Set up the schema and indexes for KDB.AI table, specifying embeddings column with 384 dimensions, Euclidean Distance, and flat index\n",
    "pdf_schema = [\n",
    "    {\"name\": \"document_id\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"text\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"embeddings\", \"type\": \"float32s\"}\n",
    "]\n",
    "\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\": \"qflat_index\",\n",
    "        \"type\": \"qFlat\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {\"dims\": 1536, \"metric\": \"L2\"},\n",
    "    }\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518cfe1e",
   "metadata": {
    "id": "518cfe1e"
   },
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e670f9e",
   "metadata": {
    "id": "6e670f9e"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"pdf\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0",
   "metadata": {
    "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0"
   },
   "outputs": [],
   "source": [
    "table = database.create_table(\"pdf\", pdf_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466068bc",
   "metadata": {
    "id": "466068bc"
   },
   "source": [
    "We can use `query` to see our table exists but is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "74e7332e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "74e7332e",
    "outputId": "53fc8178-89bb-4fb4-c862-03b9f5bf6301"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>document_id</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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       "Empty DataFrame\n",
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     "metadata": {},
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    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YvcNh1v_Wixe",
   "metadata": {
    "id": "YvcNh1v_Wixe"
   },
   "source": [
    "## 3. LlamaIndex index & query_engine setup\n",
    "Define the index: using KDB.AI as the vector store, chunk, embed, and load the document into KDB.AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "He42izi1Soee",
   "metadata": {
    "id": "He42izi1Soee"
   },
   "outputs": [],
   "source": [
    "vector_store = KDBAIVectorStore(table)\n",
    "\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    documents,\n",
    "    storage_context=storage_context,\n",
    "    transformations=[SentenceSplitter(chunk_size=2048, chunk_overlap=0)],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31a4ecd",
   "metadata": {
    "id": "e31a4ecd"
   },
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ee6ecb8d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 674
    },
    "id": "ee6ecb8d",
    "outputId": "56edf391-d474-4f9f-c085-0f4601763abe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22, 3)\n"
     ]
    },
    {
     "data": {
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       "      <td>b'9eb9cc3b-286e-4597-8c8a-f7ca682f2432'</td>\n",
       "      <td>b'Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J. Ted Mackereth\\n ,3, 4, 5, \\xe2\\x88\\x97and\\nJohn C. Forbes\\...</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>b'f576d93a-564d-4ac5-ba33-7e1f13631898'</td>\n",
       "      <td>b'2 Hopkins et al.\\nInitially it was expected that interstellar objects would display cometary characteristics (e.g. Jewitt 2003). The pop-\\nulation\\xe2\\x80\\x99s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the sourc...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'2744c059-b537-4ce9-b602-920da3e53bca'</td>\n",
       "      <td>b'The Galactic ISO Population 3\\nprocesses modelled, and demonstrate this method by constraining the metallicity dependence of the ISO production\\nrate.\\n2.APOGEE AND STELLAR DENSITY MODELLING\\nTo predict the distribution of ISOs in the Milky Way, we first obtain the distribution of all stars th...</td>\n",
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       "      <th>3</th>\n",
       "      <td>b'a554ad80-98d2-4a54-8e0c-ccde627dd2bd'</td>\n",
       "      <td>b'4 Hopkins et al.\\nfollows that the probability of finding a point (i.e. an observed star) with observables in the infinitesimal volume \\xce\\xb4O\\nis given by \\xce\\xbb(O)\\xce\\xb4O, and the total number of points (i.e. stars observed) is a Poisson random variable with mean and\\nvariance \\xce\\x9b...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'3ff2673c-f60f-4b73-bccd-05d81d38d9a4'</td>\n",
       "      <td>b'The Galactic ISO Population 5\\nThis particular form for the density profile has the advantage that the Poisson point process likelihood takes the\\ntractable form\\nlnL(logA , aR, az, \\xcf\\x840, \\xcf\\x89) = const + N\\x10\\nlogA\\xe2\\x88\\x92aR\\xe2\\x9f\\xa8R\\xe2\\x88\\x92R0\\xe2\\x9f\\xa9 \\xe2\\x88\\x92az\\x...</td>\n",
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       "                               document_id  \\\n",
       "0  b'9eb9cc3b-286e-4597-8c8a-f7ca682f2432'   \n",
       "1  b'f576d93a-564d-4ac5-ba33-7e1f13631898'   \n",
       "2  b'2744c059-b537-4ce9-b602-920da3e53bca'   \n",
       "3  b'a554ad80-98d2-4a54-8e0c-ccde627dd2bd'   \n",
       "4  b'3ff2673c-f60f-4b73-bccd-05d81d38d9a4'   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                          text  \\\n",
       "0  b'Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J. Ted Mackereth\\n ,3, 4, 5, \\xe2\\x88\\x97and\\nJohn C. Forbes\\...   \n",
       "1  b'2 Hopkins et al.\\nInitially it was expected that interstellar objects would display cometary characteristics (e.g. Jewitt 2003). The pop-\\nulation\\xe2\\x80\\x99s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the sourc...   \n",
       "2  b'The Galactic ISO Population 3\\nprocesses modelled, and demonstrate this method by constraining the metallicity dependence of the ISO production\\nrate.\\n2.APOGEE AND STELLAR DENSITY MODELLING\\nTo predict the distribution of ISOs in the Milky Way, we first obtain the distribution of all stars th...   \n",
       "3  b'4 Hopkins et al.\\nfollows that the probability of finding a point (i.e. an observed star) with observables in the infinitesimal volume \\xce\\xb4O\\nis given by \\xce\\xbb(O)\\xce\\xb4O, and the total number of points (i.e. stars observed) is a Poisson random variable with mean and\\nvariance \\xce\\x9b...   \n",
       "4  b'The Galactic ISO Population 5\\nThis particular form for the density profile has the advantage that the Poisson point process likelihood takes the\\ntractable form\\nlnL(logA , aR, az, \\xcf\\x840, \\xcf\\x89) = const + N\\x10\\nlogA\\xe2\\x88\\x92aR\\xe2\\x9f\\xa8R\\xe2\\x88\\x92R0\\xe2\\x9f\\xa9 \\xe2\\x88\\x92az\\x...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                    embeddings  \n",
       "0  [-0.004561606794595718, 0.04634055867791176, 0.013912900350987911, 0.0031797082629054785, 0.0200442373752594, -0.020312566310167313, 0.012255963869392872, -0.014811805449426174, -0.011712595820426941, 0.0018984334310516715, -0.020097902044653893, 0.0038371162954717875, -0.00469912588596344, -0.0...  \n",
       "1  [0.0033084347378462553, 0.010728420689702034, -0.004158694297075272, 0.005771661177277565, 0.011960876174271107, -0.02330215647816658, 0.027854831889271736, -0.012802716344594955, 0.015557220205664635, 0.024352774024009705, -0.008337592706084251, 0.009091882035136223, -0.011509649455547333, -0.0...  \n",
       "2  [0.034129347652196884, -0.011306616477668285, 0.06444961577653885, 0.006231018342077732, 0.011325662024319172, -0.01886763796210289, 0.007827657274901867, -0.014207865111529827, -0.02854269929230213, -0.0022870346438139677, -0.023108413442969322, 0.01556643657386303, -0.03113287314772606, 0.0062...  \n",
       "3  [0.020134035497903824, 0.023560522124171257, 0.0745055228471756, -0.01058784406632185, 0.01670754887163639, -0.002545879688113928, 0.01768067106604576, -0.0037965471856296062, -0.011999555863440037, 0.00578048313036561, -0.009292631410062313, 0.0019273987272754312, -0.05098612233996391, 0.008847...  \n",
       "4  [0.027388010174036026, 0.04201922193169594, 0.04892353340983391, 0.009756388142704964, 0.02934333309531212, -0.014199691824615002, 0.02049718052148819, 0.00019879821047652513, -0.0037724252324551344, -0.0010425581131130457, -0.01610107533633709, 0.012965815141797066, -0.03557339683175087, 0.0098...  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tiZ5HWsEThcT",
   "metadata": {
    "id": "tiZ5HWsEThcT"
   },
   "source": [
    "#### Set up the LlamaIndex Query Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "u_IlkIcuTn3e",
   "metadata": {
    "id": "u_IlkIcuTn3e"
   },
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine(\n",
    "    similarity_top_k=5,\n",
    "    vector_store_kwargs={\n",
    "                    \"index\" : \"qflat_index\",\n",
    "                },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf8650d",
   "metadata": {
    "id": "8bf8650d"
   },
   "source": [
    "## 4. Retrieve Similar Sentences & RAG\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ddb725-e0e7-4c00-a22b-3234eacf6bd1",
   "metadata": {
    "id": "31ddb725-e0e7-4c00-a22b-3234eacf6bd1"
   },
   "source": [
    "Now that the embeddings are stored in KDB.AI, we can perform semantic search using through the LlamaIndex query engine.\n",
    "\n",
    "### Search 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b24c1179",
   "metadata": {
    "id": "b24c1179"
   },
   "outputs": [],
   "source": [
    "search_term1 = \"number of interstellar objects in the milky way\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "QbyVfwB8VTcg",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QbyVfwB8VTcg",
    "outputId": "87417a96-d924-424e-ef5b-2dec6ff8d68a"
   },
   "outputs": [],
   "source": [
    "retrieved_chunks = query_engine.retrieve(search_term1)\n",
    "print(retrieved_chunks)\n",
    "for i in retrieved_chunks:\n",
    "    print(i.node.get_text())\n",
    "    print(\"____________________\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9R4vgFZMVvv9",
   "metadata": {
    "id": "9R4vgFZMVvv9"
   },
   "source": [
    "We can see these sentences do reference our search term 'number of interstellar objects in the milky way' in some way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n0n7OglvVvvA",
   "metadata": {
    "id": "n0n7OglvVvvA"
   },
   "source": [
    "### Now we can perform RAG, passing the retrieved chunks from above to the LLM for a generate response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ttZpktxET3tq",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ttZpktxET3tq",
    "outputId": "85f355ee-8355-4eb2-c7ce-56a86b8de138"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximately 10^15 interstellar objects are estimated to be present around the Sun in the Milky Way.\n"
     ]
    }
   ],
   "source": [
    "result = query_engine.query(search_term1)\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91762de7",
   "metadata": {
    "id": "91762de7"
   },
   "source": [
    "### Search 2\n",
    "\n",
    "Let's try another search term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e7486a9c",
   "metadata": {
    "id": "e7486a9c"
   },
   "outputs": [],
   "source": [
    "search_term2 = \"how does planet formation occur\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6765075c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6765075c",
    "outputId": "bae77687-8399-4264-9007-c134f36a849f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Draft version August 14, 2023\n",
      "Typeset using L ATEX default style in AASTeX631\n",
      "The Galactic Interstellar Object Population: A Framework for Prediction and Inference\n",
      "Matthew J. Hopkins\n",
      " ,1Chris Lintott\n",
      " ,1Michele T. Bannister\n",
      " ,2J. Ted Mackereth\n",
      " ,3, 4, 5, ∗and\n",
      "John C. Forbes\n",
      "2\n",
      "1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\n",
      "2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\n",
      "3Just Group plc, Enterprise House, Bancroft road, Reigate, Surrey RH2 7RP, UK\n",
      "4Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON, M5S 3H8, Canada\n",
      "5Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada\n",
      "ABSTRACT\n",
      "The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering\n",
      "approximately 1015pc−3around the Sun, which are formed and shaped by a diverse set of processes\n",
      "ranging from planet formation to galactic dynamics. We define a novel framework: firstly to predict\n",
      "the properties of this Galactic ISO population by combining models of processes across planetary\n",
      "and galactic scales, and secondly to make inferences about the processes modelled, by comparing the\n",
      "predicted population to what is observed. We predict the spatial and compositional distribution of the\n",
      "Galaxy’s population of ISOs by modelling the Galactic stellar population with data from the APOGEE\n",
      "survey and combining this with a protoplanetary disk chemistry model. Selecting ISO water mass\n",
      "fraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\n",
      "and averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\n",
      "inferred water mass fraction of 2I/Borisov. We show that the well-studied Galactic stellar metallicity\n",
      "gradient has a corresponding ISO compositional gradient. We also demonstrate the inference part of\n",
      "the framework by using the current observed ISO composition distribution to constrain the parent star\n",
      "metallicity dependence of the ISO production rate. This constraint, and other inferences made with\n",
      "this framework, will improve dramatically as the Vera C. Rubin Observatory Legacy Survey of Space\n",
      "and Time (LSST) progresses and more ISOs are observed. Finally, we explore generalizations of this\n",
      "framework to other Galactic populations, such as that of exoplanets.\n",
      "Keywords: Interstellar objects (52), Small Solar System bodies(1469), Galaxy Evolution (594)\n",
      "1.INTRODUCTION\n",
      "1I/‘Oumuamua (Meech et al. 2017) and 2I/Borisov1are the first two observed samples from a highly numerous\n",
      "population: interstellar objects (ISOs). Estimated to number ∼1015pc−3around the Sun (Engelhardt et al. 2017;\n",
      "Do et al. 2018), they are implied to have a spatial distribution spanning the entire Galaxy. This population has been\n",
      "predicted to exist for decades (McGlynn & Chapman 1989), based on models of the accretion and migration of the\n",
      "giant planets, which predict that 75–85% of cometary bodies initially in the Solar System must have been scattered\n",
      "into interstellar space (Fernandez & Ip 1984; Brasser et al. 2006). Modern exoplanet surveys consistently find that\n",
      "giant planets are common across the Galaxy around stars with a range of spectral types (Fulton et al. 2021; Sabotta\n",
      "et al. 2021). This makes planetesimal scattering common across the Galaxy. A significant number of planetesimals can\n",
      "also be ejected by close stellar flybys early in a planetary system’s life (e.g. Pfalzner et al. 2021). The protoplanetary\n",
      "disks of other stars are therefore expected to be a source of ISOs (Stern 1990; Moro-Mart´ ın 2022).\n",
      "Corresponding author: Matthew Hopkins\n",
      "matthew.hopkins@physics.ox.ac.uk\n",
      "∗Banting Fellow\n",
      "1https://minorplanetcenter.net/mpec/K19/K19RA6.html and https://minorplanetcenter.net/mpec/K19/K19S72.htmlarXiv:2308.05801v1  [astro-ph.EP]  10 Aug 2023\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "retrieved_chunks = query_engine.retrieve(search_term2)\n",
    "for i in retrieved_chunks:\n",
    "    print(i.node.get_text())\n",
    "    print(\"____________________\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ce337f",
   "metadata": {
    "id": "25ce337f"
   },
   "source": [
    "Again, we can see these sentences do reference our search term 'how does planet formation occur' in some way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2d3ce969",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2d3ce969",
    "outputId": "f6043cd7-0f4c-475c-cbf4-961945db77b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet formation occurs through a series of processes that involve the accumulation and interaction of dust and gas in protoplanetary disks surrounding young stars. Initially, small particles collide and stick together, forming larger bodies called planetesimals. These planetesimals can further collide and merge, leading to the formation of protoplanets. Over time, these protoplanets can clear their orbits of debris and grow into full-fledged planets. The dynamics of this process can be influenced by factors such as the gravitational interactions with nearby stars and the overall composition of the protoplanetary disk.\n"
     ]
    }
   ],
   "source": [
    "result = query_engine.query(search_term2)\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e93878",
   "metadata": {
    "id": "f6e93878"
   },
   "source": [
    "## 5. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d74b6f20",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d74b6f20",
    "outputId": "13103bc6-9740-4dd3-95af-6b1396174818"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346d56db",
   "metadata": {
    "id": "346d56db"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/ejgOzTpo)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: qHnsw_index_pdf_search/pdf_qHNSW_Search.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3280b01a-d3b7-4ef6-9494-789d15bc48ec",
   "metadata": {
    "id": "3280b01a-d3b7-4ef6-9494-789d15bc48ec"
   },
   "source": [
    "# Semantic Search on PDF Documents with qHNSW Index\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example demonstrates how to use KDB.AI to run semantic search on unstructured text documents.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> This sample uses ‘qHNSW’ , a new vector index choice in KDB.AI. It will support the same API options as the existing ‘HNSW’ index but with the significant difference that the index is stored on-disk and memory-mapped as required. This means data inserts will have negligible memory and cpu footprints. The vector index can grow and be searched as long as there is disk space available. Among other cases, this stands out as a great index for memory contrained situations such as edge devices.\n",
    "</div>\n",
    "\n",
    "Semantic search allows users to perform searches based on the meaning or similarity of the data rather than exact matches. It works by converting the query into a vector representation and then finding similar vectors in the database. This way, even if the query and the data in the database are not identical, the system can identify and retrieve the most relevant results based on their semantic meaning.\n",
    "\n",
    "### Aim\n",
    "In this tutorial, we'll walk you through the process of performing semantic search on documents, taking PDFs as example, using KDB.AI as the vector store. We will cover the following topics:\n",
    "\n",
    "0. Setup\n",
    "1. Load PDF Data\n",
    "2. KDB.AI Table Creation (qHNSW index)\n",
    "3. LlamaIndex index & query_engine setup\n",
    "4. Retrieve Similar Sentences & RAG\n",
    "5. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75362bc9",
   "metadata": {
    "id": "75362bc9"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda3d787",
   "metadata": {
    "id": "dda3d787"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19c5107-001e-40c2-bfe7-6c9c99e4846d",
   "metadata": {
    "id": "b19c5107-001e-40c2-bfe7-6c9c99e4846d"
   },
   "source": [
    "### Set Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bad9d73",
   "metadata": {
    "id": "8bad9d73"
   },
   "outputs": [],
   "source": [
    "!pip install llama-index llama-index-llms-openai llama-index-embeddings-openai llama-index-readers-file llama-index-vector-stores-kdbai\n",
    "!pip install pandas\n",
    "!pip install kdbai_client\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21979389",
   "metadata": {
    "id": "21979389"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "rjfp-08NPdvC",
   "metadata": {
    "id": "rjfp-08NPdvC"
   },
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "from getpass import getpass\n",
    "import re\n",
    "import os\n",
    "import shutil\n",
    "import time\n",
    "import urllib\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from llama_index.core import (\n",
    "    Settings,\n",
    "    SimpleDirectoryReader,\n",
    "    StorageContext,\n",
    "    VectorStoreIndex,\n",
    ")\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.retrievers import VectorIndexRetriever\n",
    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.vector_stores.kdbai import KDBAIVectorStore\n",
    "\n",
    "import kdbai_client as kdbai\n",
    "\n",
    "OUTDIR = \"pdf\"\n",
    "RESET = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f23f6513",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f23f6513",
    "outputId": "d2832d7e-e91f-4ba4-e996-6057d2d03632"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads research paper pdf into your environment\n",
    "if os.path.exists(\"./data/research_paper.pdf\") == False:\n",
    "  !mkdir ./data\n",
    "  !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/document_search/data/research_paper.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "yCCdm-QDPtDZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yCCdm-QDPtDZ",
    "outputId": "02495a02-0f49-4181-9f38-7a0f8273ecbe"
   },
   "outputs": [],
   "source": [
    "# OpenAI API Key: https://platform.openai.com/api\n",
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "HCI37LxrPywl",
   "metadata": {
    "id": "HCI37LxrPywl"
   },
   "outputs": [],
   "source": [
    "# Set up LlamaIndex Parameters\n",
    "\n",
    "EMBEDDING_MODEL  = \"text-embedding-3-small\"\n",
    "GENERATION_MODEL = 'gpt-4o-mini'\n",
    "\n",
    "llm = OpenAI(model=GENERATION_MODEL)\n",
    "embed_model = OpenAIEmbedding(model=EMBEDDING_MODEL)\n",
    "\n",
    "Settings.llm = llm\n",
    "Settings.embed_model = embed_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8594c911",
   "metadata": {
    "id": "8594c911"
   },
   "source": [
    "### Configure Console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b870117b",
   "metadata": {
    "id": "b870117b"
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a425b33",
   "metadata": {
    "id": "8a425b33"
   },
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9a135635",
   "metadata": {
    "id": "9a135635"
   },
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48826990",
   "metadata": {
    "id": "48826990"
   },
   "source": [
    "## 1. Load PDF Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2992812-4705-489d-974f-b7b44132343a",
   "metadata": {
    "id": "b2992812-4705-489d-974f-b7b44132343a"
   },
   "source": [
    "### Read Text From PDF Document\n",
    "\n",
    "We LlamaIndex SimpleDirectorReader to read in our PDF file.\n",
    "\n",
    "The PDF we are using is [this research paper](https://arxiv.org/pdf/2308.05801.pdf) presenting information on the formation of Interstellar Objects in the Milky Way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "NWa3S2iwQd_K",
   "metadata": {
    "id": "NWa3S2iwQd_K"
   },
   "outputs": [],
   "source": [
    "reader = SimpleDirectoryReader(\n",
    "    input_dir=\"data\",\n",
    ")\n",
    "documents = reader.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70505eae-4138-4ba8-80e9-fc13c37d0b32",
   "metadata": {
    "id": "70505eae-4138-4ba8-80e9-fc13c37d0b32"
   },
   "source": [
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "401f8162",
   "metadata": {
    "id": "401f8162"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c6525c",
   "metadata": {
    "id": "48c6525c"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings. Our table will have two columns.\n",
    "\n",
    "At this point you will select the index and metric you want to use for searching.\n",
    "\n",
    "In this case, we will use the qHNSW index, Euclidean Distance (L2) for the search metric, and we specify the number of dimensions of our embeddings (1536)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "eArvp20fSDc6",
   "metadata": {
    "id": "eArvp20fSDc6"
   },
   "outputs": [],
   "source": [
    "#Set up the schema and indexes for KDB.AI table, specifying embeddings column with 384 dimensions, Euclidean Distance, and flat index\n",
    "pdf_schema = [\n",
    "    {\"name\": \"document_id\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"text\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"embeddings\", \"type\": \"float32s\"}\n",
    "]\n",
    "\n",
    "indexes = [\n",
    "    {\n",
    "        \"name\": \"qhnsw_index\",\n",
    "        \"type\": \"qHnsw\",\n",
    "        \"column\": \"embeddings\",\n",
    "        \"params\": {\"dims\": 1536, \"metric\": \"L2\"},\n",
    "    }\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518cfe1e",
   "metadata": {
    "id": "518cfe1e"
   },
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6e670f9e",
   "metadata": {
    "id": "6e670f9e"
   },
   "outputs": [],
   "source": [
    "# get the database connection. Default database name is 'default'\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"pdf\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0",
   "metadata": {
    "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0"
   },
   "outputs": [],
   "source": [
    "table = database.create_table(\"pdf\", schema = pdf_schema, indexes = indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466068bc",
   "metadata": {
    "id": "466068bc"
   },
   "source": [
    "We can use `query` to see our table exists but is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "74e7332e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "74e7332e",
    "outputId": "e07a811b-44a7-428a-9252-3a10ac15e035"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>document_id</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [document_id, text, embeddings]\n",
       "Index: []"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YvcNh1v_Wixe",
   "metadata": {
    "id": "YvcNh1v_Wixe"
   },
   "source": [
    "## 3. LlamaIndex index & query_engine setup\n",
    "Define the index: using KDB.AI as the vector store, chunk, embed, and load the document into KDB.AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "He42izi1Soee",
   "metadata": {
    "id": "He42izi1Soee"
   },
   "outputs": [],
   "source": [
    "vector_store = KDBAIVectorStore(table)\n",
    "\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    documents,\n",
    "    storage_context=storage_context,\n",
    "    transformations=[SentenceSplitter(chunk_size=2048, chunk_overlap=0)],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31a4ecd",
   "metadata": {
    "id": "e31a4ecd"
   },
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ee6ecb8d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ee6ecb8d",
    "outputId": "07564fc5-6d97-4e58-cfbd-0dbb6a9f8ba7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>document_id</th>\n",
       "      <th>text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'cb35e257-647f-4953-a447-919fd99f8957'</td>\n",
       "      <td>b'Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J. Ted Mackereth\\n ,3, 4, 5, \\xe2\\x88\\x97and\\nJohn C. Forbes\\...</td>\n",
       "      <td>[-0.001896298, 0.04461563, 0.016039172, 0.004387382, 0.020142527, -0.02016926, 0.014515451, -0.014675843, -0.010986833, 0.001757626, -0.019247007, 0.0028486238, -0.006916893, -0.027132934, 0.027560644, 0.03560696, -0.026772052, -0.0022705453, -0.025194867, 0.018832661, 0.0025261696, -0.014903064...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'f8b82c1f-d988-4f4f-acb5-0fe77d12baa2'</td>\n",
       "      <td>b'2 Hopkins et al.\\nInitially it was expected that interstellar objects would display cometary characteristics (e.g. Jewitt 2003). The pop-\\nulation\\xe2\\x80\\x99s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the sourc...</td>\n",
       "      <td>[0.004427607, 0.00960885, -0.0025132392, 0.006977855, 0.011768823, -0.024210533, 0.029257199, -0.01263012, 0.016055124, 0.024721928, -0.0077987793, 0.008895588, -0.012596475, -0.040830884, -0.01087388, 0.019419566, -0.03523245, -0.0022962324, -0.031814177, -0.00019114243, 0.015880171, -0.0127579...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'35284c91-4732-4831-b78d-c2284ec603b5'</td>\n",
       "      <td>b'The Galactic ISO Population 3\\nprocesses modelled, and demonstrate this method by constraining the metallicity dependence of the ISO production\\nrate.\\n2.APOGEE AND STELLAR DENSITY MODELLING\\nTo predict the distribution of ISOs in the Milky Way, we first obtain the distribution of all stars th...</td>\n",
       "      <td>[0.039092664, -0.01292948, 0.067334704, 0.008587963, 0.011623856, -0.018899858, 0.009500632, -0.013347787, -0.028723726, -0.0010053621, -0.021295615, 0.014830874, -0.03392087, 0.0021153009, 0.018215355, 0.02143505, -0.011446392, -0.007104876, -0.03916872, 0.032222293, 0.009564012, 0.010742877, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'6854d718-c425-4db7-a4dc-2737db1538aa'</td>\n",
       "      <td>b'4 Hopkins et al.\\nfollows that the probability of finding a point (i.e. an observed star) with observables in the infinitesimal volume \\xce\\xb4O\\nis given by \\xce\\xbb(O)\\xce\\xb4O, and the total number of points (i.e. stars observed) is a Poisson random variable with mean and\\nvariance \\xce\\x9b...</td>\n",
       "      <td>[0.023402294, 0.0226368, 0.07654956, -0.010600748, 0.017483374, -0.0025510825, 0.019055374, -0.004674991, -0.0119472, 0.0058847475, -0.0091654435, 0.0012020674, -0.053557355, 0.005966765, 0.03362713, 0.014708452, 0.008085548, -0.014038643, -0.050686747, 0.036224347, -0.010135982, 0.005768556, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'0033757f-3651-4bdd-a52a-4e20aaa2da24'</td>\n",
       "      <td>b'The Galactic ISO Population 5\\nThis particular form for the density profile has the advantage that the Poisson point process likelihood takes the\\ntractable form\\nlnL(logA , aR, az, \\xcf\\x840, \\xcf\\x89) = const + N\\x10\\nlogA\\xe2\\x88\\x92aR\\xe2\\x9f\\xa8R\\xe2\\x88\\x92R0\\xe2\\x9f\\xa9 \\xe2\\x88\\x92az\\x...</td>\n",
       "      <td>[0.0287323, 0.04074224, 0.051109564, 0.010259612, 0.029594, -0.013921836, 0.021421317, 0.0003107252, -0.0040055574, -0.00013253682, -0.015672164, 0.012669679, -0.037403155, 0.008536213, 0.019495957, 0.032125242, -0.033660144, -0.012521574, -0.04103845, -0.014177653, 0.03632603, 0.0049749697, 0.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               document_id  \\\n",
       "0  b'cb35e257-647f-4953-a447-919fd99f8957'   \n",
       "1  b'f8b82c1f-d988-4f4f-acb5-0fe77d12baa2'   \n",
       "2  b'35284c91-4732-4831-b78d-c2284ec603b5'   \n",
       "3  b'6854d718-c425-4db7-a4dc-2737db1538aa'   \n",
       "4  b'0033757f-3651-4bdd-a52a-4e20aaa2da24'   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                          text  \\\n",
       "0  b'Draft version August 14, 2023\\nTypeset using L ATEX default style in AASTeX631\\nThe Galactic Interstellar Object Population: A Framework for Prediction and Inference\\nMatthew J. Hopkins\\n ,1Chris Lintott\\n ,1Michele T. Bannister\\n ,2J. Ted Mackereth\\n ,3, 4, 5, \\xe2\\x88\\x97and\\nJohn C. Forbes\\...   \n",
       "1  b'2 Hopkins et al.\\nInitially it was expected that interstellar objects would display cometary characteristics (e.g. Jewitt 2003). The pop-\\nulation\\xe2\\x80\\x99s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\\nfrom the disks of the sourc...   \n",
       "2  b'The Galactic ISO Population 3\\nprocesses modelled, and demonstrate this method by constraining the metallicity dependence of the ISO production\\nrate.\\n2.APOGEE AND STELLAR DENSITY MODELLING\\nTo predict the distribution of ISOs in the Milky Way, we first obtain the distribution of all stars th...   \n",
       "3  b'4 Hopkins et al.\\nfollows that the probability of finding a point (i.e. an observed star) with observables in the infinitesimal volume \\xce\\xb4O\\nis given by \\xce\\xbb(O)\\xce\\xb4O, and the total number of points (i.e. stars observed) is a Poisson random variable with mean and\\nvariance \\xce\\x9b...   \n",
       "4  b'The Galactic ISO Population 5\\nThis particular form for the density profile has the advantage that the Poisson point process likelihood takes the\\ntractable form\\nlnL(logA , aR, az, \\xcf\\x840, \\xcf\\x89) = const + N\\x10\\nlogA\\xe2\\x88\\x92aR\\xe2\\x9f\\xa8R\\xe2\\x88\\x92R0\\xe2\\x9f\\xa9 \\xe2\\x88\\x92az\\x...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                    embeddings  \n",
       "0  [-0.001896298, 0.04461563, 0.016039172, 0.004387382, 0.020142527, -0.02016926, 0.014515451, -0.014675843, -0.010986833, 0.001757626, -0.019247007, 0.0028486238, -0.006916893, -0.027132934, 0.027560644, 0.03560696, -0.026772052, -0.0022705453, -0.025194867, 0.018832661, 0.0025261696, -0.014903064...  \n",
       "1  [0.004427607, 0.00960885, -0.0025132392, 0.006977855, 0.011768823, -0.024210533, 0.029257199, -0.01263012, 0.016055124, 0.024721928, -0.0077987793, 0.008895588, -0.012596475, -0.040830884, -0.01087388, 0.019419566, -0.03523245, -0.0022962324, -0.031814177, -0.00019114243, 0.015880171, -0.0127579...  \n",
       "2  [0.039092664, -0.01292948, 0.067334704, 0.008587963, 0.011623856, -0.018899858, 0.009500632, -0.013347787, -0.028723726, -0.0010053621, -0.021295615, 0.014830874, -0.03392087, 0.0021153009, 0.018215355, 0.02143505, -0.011446392, -0.007104876, -0.03916872, 0.032222293, 0.009564012, 0.010742877, 0...  \n",
       "3  [0.023402294, 0.0226368, 0.07654956, -0.010600748, 0.017483374, -0.0025510825, 0.019055374, -0.004674991, -0.0119472, 0.0058847475, -0.0091654435, 0.0012020674, -0.053557355, 0.005966765, 0.03362713, 0.014708452, 0.008085548, -0.014038643, -0.050686747, 0.036224347, -0.010135982, 0.005768556, 0....  \n",
       "4  [0.0287323, 0.04074224, 0.051109564, 0.010259612, 0.029594, -0.013921836, 0.021421317, 0.0003107252, -0.0040055574, -0.00013253682, -0.015672164, 0.012669679, -0.037403155, 0.008536213, 0.019495957, 0.032125242, -0.033660144, -0.012521574, -0.04103845, -0.014177653, 0.03632603, 0.0049749697, 0.0...  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tiZ5HWsEThcT",
   "metadata": {
    "id": "tiZ5HWsEThcT"
   },
   "source": [
    "#### Set up the LlamaIndex Query Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "u_IlkIcuTn3e",
   "metadata": {
    "id": "u_IlkIcuTn3e"
   },
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine(\n",
    "    similarity_top_k=5,\n",
    "    vector_store_kwargs={\n",
    "                    \"index\" : \"qhnsw_index\",\n",
    "                },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf8650d",
   "metadata": {
    "id": "8bf8650d"
   },
   "source": [
    "## 4. Retrieve Similar Sentences & RAG\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ddb725-e0e7-4c00-a22b-3234eacf6bd1",
   "metadata": {
    "id": "31ddb725-e0e7-4c00-a22b-3234eacf6bd1"
   },
   "source": [
    "Now that the embeddings are stored in KDB.AI, we can perform semantic search using through the LlamaIndex query engine.\n",
    "\n",
    "### Search 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b24c1179",
   "metadata": {
    "id": "b24c1179"
   },
   "outputs": [],
   "source": [
    "#search_term1 = \"The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering\"\n",
    "search_term1 = \"what is the estimated population of interstellar objects (ISOs) in the milky way\""
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     "text": [
      "Draft version August 14, 2023\n",
      "Typeset using L ATEX default style in AASTeX631\n",
      "The Galactic Interstellar Object Population: A Framework for Prediction and Inference\n",
      "Matthew J. Hopkins\n",
      " ,1Chris Lintott\n",
      " ,1Michele T. Bannister\n",
      " ,2J. Ted Mackereth\n",
      " ,3, 4, 5, ∗and\n",
      "John C. Forbes\n",
      "2\n",
      "1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\n",
      "2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\n",
      "3Just Group plc, Enterprise House, Bancroft road, Reigate, Surrey RH2 7RP, UK\n",
      "4Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON, M5S 3H8, Canada\n",
      "5Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada\n",
      "ABSTRACT\n",
      "The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering\n",
      "approximately 1015pc−3around the Sun, which are formed and shaped by a diverse set of processes\n",
      "ranging from planet formation to galactic dynamics. We define a novel framework: firstly to predict\n",
      "the properties of this Galactic ISO population by combining models of processes across planetary\n",
      "and galactic scales, and secondly to make inferences about the processes modelled, by comparing the\n",
      "predicted population to what is observed. We predict the spatial and compositional distribution of the\n",
      "Galaxy’s population of ISOs by modelling the Galactic stellar population with data from the APOGEE\n",
      "survey and combining this with a protoplanetary disk chemistry model. Selecting ISO water mass\n",
      "fraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\n",
      "and averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\n",
      "inferred water mass fraction of 2I/Borisov. We show that the well-studied Galactic stellar metallicity\n",
      "gradient has a corresponding ISO compositional gradient. We also demonstrate the inference part of\n",
      "the framework by using the current observed ISO composition distribution to constrain the parent star\n",
      "metallicity dependence of the ISO production rate. This constraint, and other inferences made with\n",
      "this framework, will improve dramatically as the Vera C. Rubin Observatory Legacy Survey of Space\n",
      "and Time (LSST) progresses and more ISOs are observed. Finally, we explore generalizations of this\n",
      "framework to other Galactic populations, such as that of exoplanets.\n",
      "Keywords: Interstellar objects (52), Small Solar System bodies(1469), Galaxy Evolution (594)\n",
      "1.INTRODUCTION\n",
      "1I/‘Oumuamua (Meech et al. 2017) and 2I/Borisov1are the first two observed samples from a highly numerous\n",
      "population: interstellar objects (ISOs). Estimated to number ∼1015pc−3around the Sun (Engelhardt et al. 2017;\n",
      "Do et al. 2018), they are implied to have a spatial distribution spanning the entire Galaxy. This population has been\n",
      "predicted to exist for decades (McGlynn & Chapman 1989), based on models of the accretion and migration of the\n",
      "giant planets, which predict that 75–85% of cometary bodies initially in the Solar System must have been scattered\n",
      "into interstellar space (Fernandez & Ip 1984; Brasser et al. 2006). Modern exoplanet surveys consistently find that\n",
      "giant planets are common across the Galaxy around stars with a range of spectral types (Fulton et al. 2021; Sabotta\n",
      "et al. 2021). This makes planetesimal scattering common across the Galaxy. A significant number of planetesimals can\n",
      "also be ejected by close stellar flybys early in a planetary system’s life (e.g. Pfalzner et al. 2021). The protoplanetary\n",
      "disks of other stars are therefore expected to be a source of ISOs (Stern 1990; Moro-Mart´ ın 2022).\n",
      "Corresponding author: Matthew Hopkins\n",
      "matthew.hopkins@physics.ox.ac.uk\n",
      "∗Banting Fellow\n",
      "1https://minorplanetcenter.net/mpec/K19/K19RA6.html and https://minorplanetcenter.net/mpec/K19/K19S72.htmlarXiv:2308.05801v1  [astro-ph.EP]  10 Aug 2023\n",
      "____________________\n",
      "14 Hopkins et al.\n",
      "because the probability that two randomly selected ISOs have the same parent star is approximately equal to one over\n",
      "the total number of stars which could contribute ISOs to the population around the Sun. The ISO sample likelihood\n",
      "can then be combined with priors to form a posterior distribution on the model parameters:\n",
      "p(θ|O1, . . . ,ON)∝p(θ)·Y\n",
      "ip(Oi|θ) (10)\n",
      "This posterior can be used to calculate estimates and confidence intervals for the values of the parameters θ. An\n",
      "example of this calculation is given in section 6.3.\n",
      "5.2. Additional Properties of ISOs\n",
      "As with all minor planets, there are many observable quantities for ISOs that could be used in this framework in\n",
      "the set of observable properties O. If the distribution of ISOs in these properties can be predicted, the inference\n",
      "method of §5.1 can be used to compare the predicted distribution to the observed distribution in these properties, to\n",
      "make inferences about the models used. If multiple properties are included in O, then the joint distribution of ISOs\n",
      "in these properties can be predicted and used in inference. Including different ISO properties in the framework will\n",
      "allow inferences to be made about different processes affecting the ISO population. We briefly consider several such\n",
      "properties here.\n",
      "The Milky Way stellar population is broadly divided between different chemo-dynamical populations: the thin disk\n",
      "(high [Fe /H], low velocity dispersion), thick disk (low [Fe /H], high velocity dispersion), and the halo (very low [Fe /H],\n",
      "radial orbits) (Recio-Blanco et al. 2014; Horta et al. 2023). Since the composition of an ISO depends on the metallicity\n",
      "of the star it formed around, each chemo-dynamical stellar population will contribute ISOs to the Galactic population\n",
      "with a distinct joint distribution in both composition and velocity. Therefore, including the velocities of ISOs in this\n",
      "framework could be used to tie ISOs to the chemo-dynamical stellar populations their parent star belongs to (Eubanks\n",
      "et al. 2021).\n",
      "As an alternative measurement of composition to water mass fraction, the carbon-to-oxygen ratio of a cometary ISO\n",
      "can be estimated from its coma. This contains information about an ISO’s formation location in the protoplanetary\n",
      "disk relative to the H 2O, CO 2and CO ice lines; modelling suggests it would be a useful measure for future ISOs\n",
      "(Seligman et al. 2022).\n",
      "Further additions to our framework could include predictions of the size distribution and aspect ratio distribution\n",
      "of ISOs, which would be especially interesting due to 1I/‘Oumuamua’s extreme shape. The prediction of these distri-\n",
      "butions would depend on the stellar population via models of planetesimal formation which could link the size and\n",
      "shape distribution of planetesimals to the properties of their natal star. Finally, including the binarity rate of ISOs\n",
      "would test models of the applicability of formation mechanism of planetesimals to other protoplanetary disks, as the\n",
      "compact binarity of trans-Neptunian objects does for the Solar System protoplanetary disk (e.g. Nimmo et al. 2018).\n",
      "This could also constrain the ejection mechanisms of ISOs: loosely-bound wide planetesimal binaries would not survive\n",
      "scattering by a giant planet, but would survive a more gentle gravitational interaction with a stellar flyby.\n",
      "5.3. Generalisation to Other Galactic Populations\n",
      "Our method could easily be generalised to any Galaxy-wide population with a dependence on the properties of stars,\n",
      "simply by replacing the “ISO recipe” of section 3.\n",
      "For example, the distribution of planets through the Galaxy could be predicted by substituting a model of the\n",
      "occurrence rate of planets as a function of their host star’s metallicity. This is based on the planet-metallicity correlation\n",
      "(Fischer & Valenti 2005; Osborn & Bayliss 2020): as noted earlier that dust is necessary for planets, stars with higher\n",
      "metallicity are more likely to host planets. Here, the Milky Way metallicity gradient means that planets are more\n",
      "common towards the Galactic centre. This is a testable prediction with microlensing surveys such as OGLE (Udalski\n",
      "et al. 2015), which continue to find exoplanets between the Solar System and the Galactic centre (Tsapras 2018) with\n",
      "distances estimated from followup observations (Vandorou et al. 2023). If ISOs seed planet formation as hypothesised\n",
      "by Pfalzner & Bannister (2019), the ISO gradient we predict in §4.1 could also produce a signature in the planetary\n",
      "population.\n",
      "6.DISCUSSION\n",
      "6.1. Comparison to Previous Work\n",
      "____________________\n",
      "The Galactic ISO Population 15\n",
      "As described in section 3.1, Cabral et al. (2023) updates the protoplanetary disk chemical model of Bitsch & Battistini\n",
      "(2020) that we use, and note that the trend of planetesimal water mass fraction decreasing with stellar metallicity is\n",
      "robust. They do however find that for the APOGEE survey, there was a smaller variation in fH2Oover the same range\n",
      "in [Fe /H] compared to the GalaH data used in Bitsch & Battistini (2020). If the true variation in fH2Ois smaller than\n",
      "in the model of this work, our predictions may overestimate the width of the ISO water mass fraction distribution.\n",
      "Lintott et al. (2022) made a prediction of the ISO population of a simulated Milky Way Galaxy from the EAGLE\n",
      "hydrodynamical cosmological simulation (Schaye et al. 2015), using a model equivalent to that of this work with β= 0.\n",
      "Whereas we predict a single-peaked fH2Odistribution, they predicted an ISO distribution with a significant number of\n",
      "ISOs with water mass fraction both below and above the range of the protoplanetary disk model, which they interpret\n",
      "as a bimodal distribution in ISO composition.\n",
      "There are expected reasons for the difference between the prediction here, based on the observed Milky Way stellar\n",
      "population, and the prediction by Lintott et al. (2022) based on the simulated EAGLE Galaxy. The EAGLE Galaxy\n",
      "has a much wider [Fe /H] distribution than the Milky Way, with many more stars outside of the [Fe /H] range of the\n",
      "protoplanetary disk model. As a smoothed particle hydrodynamics simulation, EAGLE is susceptible to producing\n",
      "Galaxies with [Fe /H] distributions wider than those observed in nature, due to underestimating metal mixing between\n",
      "particles (Wiersma et al. 2009). Therefore the results of this work, based on the observed stellar population of the\n",
      "Milky Way, should give a much more accurate prediction for the Milky Way’s population of ISOs.\n",
      "6.2. Distinguishing Local and Galactic Populations of ISOs\n",
      "The results of §4.1 show that the well-studied metallicity gradient of the Milky Way has a corresponding ISO\n",
      "composition gradient, with ISOs generally having a higher water mass fraction at larger Galactocentric radii. Although\n",
      "we can only observe the compositions of ISOs which pass through the inner Solar System, it is still instructive to\n",
      "model how the distribution of ISOs varies across a wider portion of the Galactic disk. This is because we have made\n",
      "assumptions about the Galactic dynamics of ISOs, under which the distribution of ISOs at a point in the Galaxy\n",
      "corresponds to the distribution of stars at that same point. If these assumptions break down, then the particular way\n",
      "in which they break down will affect the population of ISOs detectable in the Solar System in a related, calculable\n",
      "way.\n",
      "For example, radial migration, caused by the non-axisymmetric potential of spiral arms, flattens the the Milky Way\n",
      "metallicity gradient by blurring the metallicity distribution in the radial direction (Vickers et al. 2021). This widens\n",
      "the stellar metallicity distribution around the Sun as stars migrate in from the metal-poor outer disk and out from\n",
      "metal-rich inner disk. However, ISOs may undergo less radial migration than stars, due to the random motion given\n",
      "to them by their ejection from their home planetary system (Daniel & Wyse 2015). The stars currently in the Solar\n",
      "neighbourhood may thus have a wider range of Galactocentric radii of origin than the ISOs. This would make the\n",
      "distribution of observable ISO compositions narrower than would be predicted from the distribution of stars.\n",
      "Additionally, the low velocity of 1I/‘Oumuamua relative to the local standard of rest implies that it — and therefore\n",
      "a large fraction of observable ISOs — could come from local star-forming regions (e.g. Hallatt & Wiegert 2020). This is\n",
      "also testable: their compositions will match those predicted from the metallicities of the nearest star forming regions.\n",
      "In addition, if the velocity distribution of ISOs is included in future work as described in 5.2, it may be possible to\n",
      "trace individual ISOs back to the star forming regions they came from by matching them up with both composition\n",
      "and velocity. This would be hugely advantageous for studying planetesimal formation, as it would allow us to pair the\n",
      "properties of detected ISOs directly with observations of their parent planetary systems.\n",
      "6.3. An Estimation of the ISO Production Metallicity Dependence\n",
      "The two different predictions made in §4 demonstrate that the Galactic population of ISOs is sensitive to small\n",
      "changes to the processes that affect their formation and evolution. This means that if models of these processes can\n",
      "be combined to make accurate predictions of the ISO population, then the framework described in §5 can be used to\n",
      "make inferences about those processes.\n",
      "In particular, the predictions of sections 4.1 and 4.2 show that the ISO distribution around the Sun is sensitive to\n",
      "the metallicity dependence of the number of ISOs produced by each star, β. As outlined in section 5.1, these two\n",
      "predictions can then be compared to a sample of ISOs. In our example physical property, this is done by calculating\n",
      "the likelihood of each of these predictions producing the observed distribution of water mass fractions, such as for the\n",
      "ISOs expected to be found by the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory. For\n",
      "____________________\n",
      "The Galactic ISO Population 17\n",
      "ISOfH2Orange Fraction of ISOs around Sun Fraction of ISOs in Milky Way Disk\n",
      "fH2O<0.07 0.022 0.031\n",
      "0.07≤fH2O≤0.51 0.960 0.930\n",
      "0.51< fH2O 0.018 0.040\n",
      "Table 3. Predicted fraction of ISOs in each fH2Orange, evaluated at the position of the Sun and integrated over the Milky\n",
      "Way dist, with maximum a posteriori estimate of β= 1.33.\n",
      "6.4. Selection Effects\n",
      "It should be noted that the predictions in this work ignore some specific effects which will influence the population of\n",
      "ISOs we expect to observe from the Earth. The predictions in §4 are the distribution of ISOs in a smooth Galaxy-wide\n",
      "distribution, evaluated at the location of the Solar System. Gravitational focussing by the Sun will increase the density\n",
      "of ISOs in the inner solar system in a velocity-dependent manner (e.g. Engelhardt et al. 2017; Forbes & Loeb 2019;\n",
      "Dehnen & Hands 2022), and therefore also in a composition-dependent manner. This is due to the fact that we expect\n",
      "compositionally and dynamically distinct populations of ISOs to come from the chemo-dynamically distinct stellar\n",
      "populations in the Milky Way thin and thick disks (c.f. Eubanks et al. 2021). This could handily be modelled when\n",
      "incorporating ISO velocities into the predictions of this framework.\n",
      "Additionally, the Pan-STARRS near-Earth object survey (Chambers et al. 2016) that detected 1I/‘Oumuamua, the\n",
      "observations of amateur astronomers such as Gennadiy Borisov who discovered 2I/Borisov, and the LSST which will\n",
      "discover tens more ISOs: all have highly non-trivial selection functions. Since in order to be discovered an ISO needs\n",
      "to be detected in multiple observations which can be linked as the same object (e.g. Meech et al. 2017; Schwamb\n",
      "et al. 2023), these selection functions are dependent on ISO size, approach velocity, perihelion, and composition.\n",
      "Composition has a direct link to the detectability of ISOs, as 2I-sized and cometary ISOs will be more likely to be\n",
      "detected, as they will form a coma as they approach the Sun. These selection effects will need to be accurately\n",
      "accounted for, in order for the Bayesian framework to produce accurate inferences about the processes affecting the\n",
      "Galactic ISO population.\n",
      "7.CONCLUSION\n",
      "In advance of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), we lay out a framework to\n",
      "predict the Galactic distribution of ISOs, using the stellar population of the Milky Way. Using the method of Bovy\n",
      "et al. (2016a), we fit simple density models to a sample of red giants in APOGEE binned in [Fe /H] and [ α/Fe], and use\n",
      "these to evaluate the “sine morte” metallicity distribution of stars throughout the Galaxy’s integrated history, across\n",
      "the Galactic disk. Under the assumption that the spatial distribution of a population of ISOs will be the same as the\n",
      "sine morte distribution of stars which formed them, we use the protoplanetary disk model of Bitsch & Battistini (2020)\n",
      "to map the metallicity distribution of stars to the distribution of ISO water mass fractions. Localising our model to\n",
      "the Solar neighbourhood, we predict that 95% of ISOs around the Sun have water mass fraction fH2Obetween 0.07\n",
      "and 0.51, with a peak around 0.35.\n",
      "By considering the distribution of ISOs over the Galactic disk, we show that the well-studied Milky Way metallicity\n",
      "gradient has an equivalent gradient in ISO composition, with the median ISO water mass fraction increasing with\n",
      "distance from the Galactic Centre as the median stellar metallicity decreases. This causes the ISO water mass fraction\n",
      "distribution averaged over the Milky Way disk to be wider that the distribution around the Sun. Since we also predict\n",
      "higher-metallicity stars produce more ISOs than lower-metallicity stars, the Milky Way metallicity gradient implies\n",
      "that the radial ISO density profile is steeper than the exponential radial stellar density profile, making ISOs much\n",
      "more common in the inner Galactic disk than in the outer disk.\n",
      "We also set out a Bayesian inference framework, which can compare predictions of the ISO distribution to the\n",
      "sample observed by the LSST, in order to make inferences about the many astrophysical processes which influence the\n",
      "ISO population. To demonstrate its use, we use the composition measurement of 2I/Borisov to calculate a maximum\n",
      "a posteriori estimate for the power law slope of the ISO production metallicity dependence, β, to be 1.33, with a\n",
      "symmetric 90% confidence interval of ( −1.3,7.2).\n",
      "Encoded in the population of ISOs we observe is a wealth of information about processes through Galactic history,\n",
      "from the evolution of the Milky Way to planet formation. The framework set out in this work is a novel approach that\n",
      "will allow us to appreciate how these treasures can further our understanding on both planetary and Galactic scales.\n",
      "____________________\n",
      "2 Hopkins et al.\n",
      "Initially it was expected that interstellar objects would display cometary characteristics (e.g. Jewitt 2003). The pop-\n",
      "ulation’s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\n",
      "from the disks of the source systems. More of the cometary ISO population passing through the Solar System could\n",
      "be detected than rocky ISOs, as comae make cometary ISOs brighter down to smaller diameters (Engelhardt et al.\n",
      "2017). 2I/Borisov appeared relatively similar in size and composition to Solar System comets (Jewitt & Seligman\n",
      "2022). Its distinctive features were an exceptionally high CO and NH 2content, implying it formed on the edge of its\n",
      "home system’s protoplanetary disk, beyond the hypervolatile CO ice line (Bodewits et al. 2020; Cordiner et al. 2020).\n",
      "However, 1I/‘Oumuamua had a mix of observed characteristics. A 160-m scale object, it lacked a coma in deep\n",
      "imaging (Jewitt et al. 2017) or detectable outgassing in CO, CO 2(Trilling et al. 2018) or CN (Ye et al. 2017). Despite\n",
      "this, it still underwent non-gravitational acceleration similar to that experienced by Solar System comets (Micheli\n",
      "et al. 2018). The large amplitude of its light curve implied a high-aspect-ratio shape (Mashchenko 2019). At the time,\n",
      "this combination of factors seemed unusual, although the surface reflectance properties were consistent with outer\n",
      "Solar System bodies (e.g. Bannister et al. 2017). Hypotheses for the composition and formation of 1I/‘Oumuamua\n",
      "that match the limited data remain varied. It may be a planetesimal (’Oumuamua ISSI Team et al. 2019); a fragment\n",
      "of a comet devolatized by passages close to its parent star before ejection (Raymond et al. 2018a); an icy fractal\n",
      "aggregate (Moro-Mart´ ın 2019); a hydrogen iceberg formed in a molecular cloud (Seligman & Laughlin 2020); or a\n",
      "nitrogen ice fragment from the surface of a Pluto-like dwarf planet (Jackson & Desch 2021). Recently, observation\n",
      "of similarly-small near-Earth asteroids have identified objects with the lightcurve amplitudes seen in 1I. Additionally,\n",
      "Farnocchia et al. (2023) and Seligman et al. (2023) report six asteroids with significant non-gravitational acceleration\n",
      "and no coma. While from the two ISOs found so far, the compositions of interstellar objects are clearly varied, 1I may\n",
      "be less extreme than it was first considered.\n",
      "ISOs formed in a protoplanetary disk carry information about their home systems in their composition: we have\n",
      "samples of other planetary systems coming to us for study. The composition of a protoplanetary disk correlates with the\n",
      "elemental abundances of its central star, due to their formation from the same gas and dust in a molecular cloud core\n",
      "(¨Oberg & Bergin 2021). We can thus expect stars of different metallicities to produce ISOs of different compositions.\n",
      "The composition of this gas and dust varies in both space and time as the Galaxy chemically evolves due to stellar\n",
      "nucleosynthesis, making the current Galactic ISO population sensitive to the entire history and evolution of the Milky\n",
      "Way over cosmic time (Tinsley & Cameron 1974; Lintott et al. 2022). Since the occurrence of planetesimal-scattering\n",
      "giant planets also has a metallicity dependence (Fischer & Valenti 2005), the relative occurrence of ISOs of different\n",
      "compositions will therefore carry information about the Galaxy’s distribution of planetary architectures. Finally, we\n",
      "expect many ISOs to outlive their parent stars, as they occupy a similar environment to Gyr-old Oort cloud comets.\n",
      "At minor-planet sizes, ISOs are not subject to any known destructive forces in the ISM (Guilbert-Lepoutre et al. 2015),\n",
      "other than minor erosion by dust, in their frequent passages through molecular clouds (Pfalzner et al. 2020). They\n",
      "could also be disrupted or devolatilised in rare close encounters with stars (Raymond et al. 2018a; Forbes & Loeb\n",
      "2019). ISOs thus present the possibility of studying long-lost planetary systems.\n",
      "The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) (Ivezi´ c et al. 2019) is predicted to provide\n",
      "a sample of tens of 1I/‘Oumuamua-like ISOs (e.g. Levine et al. 2021), as well as any more 2I/Borisov-like cometary\n",
      "ISOs that enter our Solar System. This is in addition to the continuing contributions of the NEO surveys and other\n",
      "observatories that found 1I and 2I in the first place (Meech et al. 2017). These will provide a large and varied sample\n",
      "of interstellar objects, for study and comparison to predictions.\n",
      "The many dependencies over planetary and Galactic scales make the observed ISO population a fascinating tool:\n",
      "it has the potential to test models in both Galaxy and planetary physics, with an entirely different set of biases\n",
      "to traditional methods. Lintott et al. (2022) introduced the concept of predicting the composition of a Galaxy’s\n",
      "population of ISOs from the Galaxy’s stellar distribution. Using simulated Galaxies from EAGLE (Schaye et al. 2015),\n",
      "they showed that the water content of ISOs was sensitive to Galactic star formation history. In this work, we develop\n",
      "this method and apply it to the stellar population of the Milky Way, estimated with data from the APOGEE survey, to\n",
      "predict a broader set of properties of our own Galaxy’s population of interstellar objects. We predict the distribution\n",
      "of ISOs in both their spatial position in the Galaxy and their water mass fraction. By evaluating this distribution at\n",
      "the current position of the Solar System, we predict the properties of the population of ISOs from which the observed\n",
      "sample of the 2020s will be drawn, and we compare this to the whole-Galaxy distribution. We then detail a Bayesian\n",
      "method of comparing the predicted and observed distributions to make inferences about the planetary and Galactic\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "retrieved_chunks = query_engine.retrieve(search_term1)\n",
    "for i in retrieved_chunks:\n",
    "    print(i.node.get_text())\n",
    "    print(\"____________________\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9R4vgFZMVvv9",
   "metadata": {
    "id": "9R4vgFZMVvv9"
   },
   "source": [
    "We can see these sentences do reference our search term 'number of interstellar objects in the milky way' in some way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n0n7OglvVvvA",
   "metadata": {
    "id": "n0n7OglvVvvA"
   },
   "source": [
    "### Now we can perform RAG, passing the retrieved chunks from above to the LLM for a generate response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ttZpktxET3tq",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ttZpktxET3tq",
    "outputId": "8395b5b0-6645-46ba-f56a-97069c10afb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The estimated population of interstellar objects (ISOs) in the Milky Way is approximately 10^15 per cubic parsec around the Sun.\n"
     ]
    }
   ],
   "source": [
    "result = query_engine.query(search_term1)\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91762de7",
   "metadata": {
    "id": "91762de7"
   },
   "source": [
    "### Search 2\n",
    "\n",
    "Let's try another search term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e7486a9c",
   "metadata": {
    "id": "e7486a9c"
   },
   "outputs": [],
   "source": [
    "search_term2 = \"how does planet formation occur\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
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     "text": [
      "14 Hopkins et al.\n",
      "because the probability that two randomly selected ISOs have the same parent star is approximately equal to one over\n",
      "the total number of stars which could contribute ISOs to the population around the Sun. The ISO sample likelihood\n",
      "can then be combined with priors to form a posterior distribution on the model parameters:\n",
      "p(θ|O1, . . . ,ON)∝p(θ)·Y\n",
      "ip(Oi|θ) (10)\n",
      "This posterior can be used to calculate estimates and confidence intervals for the values of the parameters θ. An\n",
      "example of this calculation is given in section 6.3.\n",
      "5.2. Additional Properties of ISOs\n",
      "As with all minor planets, there are many observable quantities for ISOs that could be used in this framework in\n",
      "the set of observable properties O. If the distribution of ISOs in these properties can be predicted, the inference\n",
      "method of §5.1 can be used to compare the predicted distribution to the observed distribution in these properties, to\n",
      "make inferences about the models used. If multiple properties are included in O, then the joint distribution of ISOs\n",
      "in these properties can be predicted and used in inference. Including different ISO properties in the framework will\n",
      "allow inferences to be made about different processes affecting the ISO population. We briefly consider several such\n",
      "properties here.\n",
      "The Milky Way stellar population is broadly divided between different chemo-dynamical populations: the thin disk\n",
      "(high [Fe /H], low velocity dispersion), thick disk (low [Fe /H], high velocity dispersion), and the halo (very low [Fe /H],\n",
      "radial orbits) (Recio-Blanco et al. 2014; Horta et al. 2023). Since the composition of an ISO depends on the metallicity\n",
      "of the star it formed around, each chemo-dynamical stellar population will contribute ISOs to the Galactic population\n",
      "with a distinct joint distribution in both composition and velocity. Therefore, including the velocities of ISOs in this\n",
      "framework could be used to tie ISOs to the chemo-dynamical stellar populations their parent star belongs to (Eubanks\n",
      "et al. 2021).\n",
      "As an alternative measurement of composition to water mass fraction, the carbon-to-oxygen ratio of a cometary ISO\n",
      "can be estimated from its coma. This contains information about an ISO’s formation location in the protoplanetary\n",
      "disk relative to the H 2O, CO 2and CO ice lines; modelling suggests it would be a useful measure for future ISOs\n",
      "(Seligman et al. 2022).\n",
      "Further additions to our framework could include predictions of the size distribution and aspect ratio distribution\n",
      "of ISOs, which would be especially interesting due to 1I/‘Oumuamua’s extreme shape. The prediction of these distri-\n",
      "butions would depend on the stellar population via models of planetesimal formation which could link the size and\n",
      "shape distribution of planetesimals to the properties of their natal star. Finally, including the binarity rate of ISOs\n",
      "would test models of the applicability of formation mechanism of planetesimals to other protoplanetary disks, as the\n",
      "compact binarity of trans-Neptunian objects does for the Solar System protoplanetary disk (e.g. Nimmo et al. 2018).\n",
      "This could also constrain the ejection mechanisms of ISOs: loosely-bound wide planetesimal binaries would not survive\n",
      "scattering by a giant planet, but would survive a more gentle gravitational interaction with a stellar flyby.\n",
      "5.3. Generalisation to Other Galactic Populations\n",
      "Our method could easily be generalised to any Galaxy-wide population with a dependence on the properties of stars,\n",
      "simply by replacing the “ISO recipe” of section 3.\n",
      "For example, the distribution of planets through the Galaxy could be predicted by substituting a model of the\n",
      "occurrence rate of planets as a function of their host star’s metallicity. This is based on the planet-metallicity correlation\n",
      "(Fischer & Valenti 2005; Osborn & Bayliss 2020): as noted earlier that dust is necessary for planets, stars with higher\n",
      "metallicity are more likely to host planets. Here, the Milky Way metallicity gradient means that planets are more\n",
      "common towards the Galactic centre. This is a testable prediction with microlensing surveys such as OGLE (Udalski\n",
      "et al. 2015), which continue to find exoplanets between the Solar System and the Galactic centre (Tsapras 2018) with\n",
      "distances estimated from followup observations (Vandorou et al. 2023). If ISOs seed planet formation as hypothesised\n",
      "by Pfalzner & Bannister (2019), the ISO gradient we predict in §4.1 could also produce a signature in the planetary\n",
      "population.\n",
      "6.DISCUSSION\n",
      "6.1. Comparison to Previous Work\n",
      "____________________\n",
      "The Galactic ISO Population 9\n",
      "During the writing of this paper, Cabral et al. (2023) was published, containing an updated version of the Bitsch\n",
      "& Battistini (2020) chemical model which included data from GalaH DR3 (Buder et al. 2021) and APOGEE DR17\n",
      "(Abdurro’uf et al. 2022), and allowed for variation in [ α/Fe] as well as [Fe /H]. They found that the water mass fraction\n",
      "of planetesimals, consistent with the Bitsch & Battistini (2020) assumptions we use, is most dependent on [Fe /H].\n",
      "They also confirmed the trend between [Fe /H] and fH2Ofound in Bitsch & Battistini (2020). However, they also\n",
      "found that for the APOGEE survey there was a smaller variation in fH2Oover the same range in [Fe /H] compared to\n",
      "the GalaH data used in their work. Cabral et al. (2023) notes that this means that the trends seen are robust. The\n",
      "findings of Cabral et al. (2023) do mean that the predictions of the ISO distributions in our work may overestimate\n",
      "the width of the distribution in fH2O.\n",
      "We assume that every star produces ISOs, and the number of ISOs produced by each star depends on its mass and\n",
      "metallicity. Lu et al. (2020) argue that the mass of planet-forming material in a protoplanetary disk is proportional\n",
      "to both the mass of the host star mass M∗and its metal mass fraction Z— well approximated by Z⊙10[Fe/H]for\n",
      "small values of Z ( Z⊙= 0.0153, Caffau et al. (2011)). In the absence of confirmed and comprehensive knowledge\n",
      "of ISO formation mechanisms, we use this as a reasonable proxy for the number of ISOs produced by each star.\n",
      "However, the number of ISOs produced by a star may not be simply proportional to the mass of planet-forming\n",
      "material, because ISO production also requires the ejection of planetesimals — which is dependent on system dynamical\n",
      "architecture. One major ISO ejection pathway is scattering by giant planets, the occurrence of which has its own\n",
      "metallicity dependence (Osborn & Bayliss 2020). Additionally, scattering by giant planets may form ISOs with extra\n",
      "fragmentation, reweighting the number distribution towards lower masses and increasing the number of ISOs produced\n",
      "from the same mass of planet-forming material (Raymond et al. 2018b).\n",
      "We therefore assume the number of ISOs produced by each star is proportional to its mass, while incorporating into\n",
      "the model a power law dependence on metallicity mass fraction: thus the number of ISOs produced is proportional to\n",
      "M∗·10β[Fe/H]. Here β= 1 corresponds to the simple assumption that the number of ISOs produced is proportional to\n",
      "the mass of planet-forming material. β= 0 corresponds to no metallicity dependence at all. However, we expect β >0:\n",
      "planetesimals require some fraction of dust and ice in order to exist, i.e. at the elemental level, C/N/O, Al/Si, &c.\n",
      "must be present, so a minimum metallicity constraint must exist (Johnson & Li 2012). We do not model the constant\n",
      "of proportionality here, as this depends on the size distribution of ISOs, which remains observationally unconstrained\n",
      "with only two ISOs (’Oumuamua ISSI Team et al. 2019; Jewitt & Seligman 2022). We predict only the normalised\n",
      "distribution of ISO water mass fractions.\n",
      "3.2. Predicting the ISO Population From the Stellar Population\n",
      "Since the majority of ISOs are expected to be ejected by dynamical processes within several hundred Myr of a\n",
      "star’s formation (Pfalzner & Bannister 2019; Fitzsimmons et al. 2023), we assume that the birth of a star and release\n",
      "of its ISOs are contemporaneous, and omit accounting for any delay. As Oort cloud comets, which also occupy an\n",
      "interstellar environment (Kaib & Volk 2022), do not exhibit substantive erosion after 4.5 Gyr (Stern & Shull 1988),\n",
      "we continue with the expectation that ISO erosional processes are broadly similar to those of Solar System comets\n",
      "(e.g. Guilbert-Lepoutre et al. 2015). This implies ISOs from stars prior to the Sun will outlive their parent stars. We\n",
      "thus use the sine morte stellar distribution established in §2.3. For assessing the water mass fraction of the resulting\n",
      "population, we assume processing in the interstellar medium has a negligible effect5, and that ISOs broadly represent\n",
      "the water mass fraction of their source planetesimal populations.\n",
      "To link the spatial distribution of stars throughout the Galaxy to the ISOs they produced, we need to model their\n",
      "combined Galactic dynamics. ISOs are not expected to remain near to their parent stars on Gyr timescales, and stars\n",
      "do die. However, it is helpful to consider what the behaviour of the ISO population is in the case that neither of these\n",
      "are true. If ISOs did remain near their parent stars and both ISOs and stars existed for an infinite length of time,\n",
      "the number density distribution of ISOs with a given water mass fraction fH2Oand position in the Galaxy xin the\n",
      "present day, denoted nISO(x, fH2O), would be equal to the number of ISOs produced by stars currently at the same\n",
      "position and of the corresponding metallicity [Fe /H]. Under the model of §3.1, the number of ISOs produced by a\n",
      "star of mass M∗and metallicity [Fe /H] is proportional to M∗·10β[Fe/H], thus\n",
      "nISO(x, fH2O|β)∝10β[Fe/H]·d[Fe/H]\n",
      "dfH2O·ρ(x,[Fe/H]) (7)\n",
      "5While this is valid for water, it may not hold for hypervolatiles; e.g. Seligman et al. (2022) suggest that processing in the interstellar\n",
      "medium will remove CO and CO 2relative to H 2O. However, this is countered by the CO-rich nature of 2I.\n",
      "____________________\n",
      "10 Hopkins et al.\n",
      "where ρ(x,[Fe/H]) is the mass density distribution of stars at position xwith the metallicity [Fe /H] corresponding to\n",
      "the ISO water mass fraction fH2O, and d[Fe /H]/dfH2Ois the gradient of the relationship between [Fe /H] and fH2O\n",
      "described in §3.1.\n",
      "The fact that stars do in fact die is then corrected for by replacing ρ(x,[Fe/H]) in this equation with ρsm(x,[Fe/H]),\n",
      "the sine morte stellar mass density introduced in §2.3. Correcting for the fact that ISOs disperse from near their\n",
      "parent stars is more complex, so we proceed with some simplifying assumptions. Once ejected, unless its resultant\n",
      "velocity exceeds the Galactic escape velocity, an ISO will orbit the Galactic centre, as stars do. We assume ISOs\n",
      "are ejected relatively slowly from their parent planetary systems compared to the stellar velocity dispersion. This\n",
      "is justified assuming ejection velocities <10 km s−1, the maximum ejection velocity from a planetary system under\n",
      "an expected suite of scattering mechanisms (Pfalzner & Bannister 2019; Fitzsimmons et al. 2023), and the velocity\n",
      "dispersions ≳20 km s−1measured in the Solar Neighbourhood (Anguiano et al. 2020). Stars form on near-circular\n",
      "orbits around the Galactic centre (Frankel et al. 2020), so a cloud of recently ejected ISOs will all have similar orbits\n",
      "to their parent star: nearly circular with similar ranges of oscillation in Galactocentric Randz. However, the slight\n",
      "differences in their orbits will give the ISOs different orbital periods around the Galactic centre, meaning they will\n",
      "disperse along their similar near-circular orbital paths. Therefore, though ISOs do not stay near their parent star, we\n",
      "assume here that they only disperse in the azimuthal direction, and remain in the same Randzrange as their parent\n",
      "star. Under this assumption equation 7 still holds if the stellar density model is axisymmetric, depending only on R\n",
      "andz, as does ours:\n",
      "nISO(R, z, f H2O|β)∝10β[Fe/H]·d[Fe/H]\n",
      "dfH2O·ρsm(R, z, [Fe/H]) (8)\n",
      "Orbits around the Galactic centre can evolve with time, due to the influence of perturbing potentials such as spiral\n",
      "arms, the bar and molecular clouds. These effects can cause both dynamical ‘heating’, an increase in the size of radial\n",
      "and vertical excursions from a circular orbit, and ‘migration’, changes to the radius of an orbit while it remains nearly\n",
      "circular (Sellwood & Binney 2002). We introduced Eq. 8 with the assumption that an ISO will stay in the same range\n",
      "ofRandzas its parent star. Due to their azimuthal separation, the star and ISOs will experience slightly different\n",
      "perturbing potentials, causing their orbits to evolve adjacently but independently. However, if the Galactic stellar and\n",
      "ISO distributions are sufficiently axisymmetric, perturbations will consistently change together both the orbits of stars\n",
      "and the orbits of a corresponding number of ISOs. Thus, Eq. 8 holds for our model.\n",
      "In this work we predict the distribution of ISOs in fH2Oboth at particular values of Randzand integrated over\n",
      "the whole Milky Way. Since we do not model the total number of ISOs, we remove the need for the constant of\n",
      "proportionality in Eq. 8 by normalising each nISOdistribution we calculate with\n",
      "p(fH2O|β) =nISO(fH2O|β)R\n",
      "nISO(fH2O|β) dfH2O. (9)\n",
      "This gives us the distribution of ISOs within the bounds of the protoplanetary disk chemical model: 0 .07≤fH2O≤\n",
      "0.51. Outside of this range, we can calculate the fraction of ISOs with fH2O≤0.07 and fH2O≥0.51, by assuming\n",
      "that the relation between [Fe /H] and fH2Oremains monotonic. Thus, all stars with [Fe /H]≤ −0.4 contribute ISOs\n",
      "with fH2O≥0.51, and all stars with [Fe /H]≥0.4 contribute ISOs with fH2O≤0.07.\n",
      "4.RESULTS\n",
      "In this section we demonstrate the prediction framework of section 3 by making two different predictions. We\n",
      "demonstrate two different example values for a stellar metallicity dependence for ISO production, β, with β= 1 for\n",
      "our principal prediction and β= 0 as an alternate prediction.\n",
      "4.1. Principal Prediction: β= 1\n",
      "First, we predict the distribution of ISOs assuming that the number produced by each star is proportional to the\n",
      "star’s metal mass fraction Z, by setting β= 1 in equation 8. As described in section 3.1, in the absence of concrete\n",
      "knowledge of ISO formation mechanisms this is a reasonable value to assume, and thus we consider this our principal\n",
      "prediction.\n",
      "Table 1 lists the the fraction of ISOs within and outside either end of the Bitsch & Battistini (2020) protoplanetary\n",
      "disk chemical model fH2Orange, 0 .07≤fH2O≤0.51. We assess both the distribution of ISOs at the position of the\n",
      "Sun, at R= 8.1 kpc, z= 0.021 kpc (GRAVITY Collaboration et al. 2018; Bennett & Bovy 2019), and the distribution\n",
      "____________________\n",
      "Draft version August 14, 2023\n",
      "Typeset using L ATEX default style in AASTeX631\n",
      "The Galactic Interstellar Object Population: A Framework for Prediction and Inference\n",
      "Matthew J. Hopkins\n",
      " ,1Chris Lintott\n",
      " ,1Michele T. Bannister\n",
      " ,2J. Ted Mackereth\n",
      " ,3, 4, 5, ∗and\n",
      "John C. Forbes\n",
      "2\n",
      "1Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK\n",
      "2School of Physical and Chemical Sciences—Te Kura Mat¯ u, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand\n",
      "3Just Group plc, Enterprise House, Bancroft road, Reigate, Surrey RH2 7RP, UK\n",
      "4Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON, M5S 3H8, Canada\n",
      "5Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada\n",
      "ABSTRACT\n",
      "The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering\n",
      "approximately 1015pc−3around the Sun, which are formed and shaped by a diverse set of processes\n",
      "ranging from planet formation to galactic dynamics. We define a novel framework: firstly to predict\n",
      "the properties of this Galactic ISO population by combining models of processes across planetary\n",
      "and galactic scales, and secondly to make inferences about the processes modelled, by comparing the\n",
      "predicted population to what is observed. We predict the spatial and compositional distribution of the\n",
      "Galaxy’s population of ISOs by modelling the Galactic stellar population with data from the APOGEE\n",
      "survey and combining this with a protoplanetary disk chemistry model. Selecting ISO water mass\n",
      "fraction as an example observable quantity, we evaluate its distribution both at the position of the Sun\n",
      "and averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the\n",
      "inferred water mass fraction of 2I/Borisov. We show that the well-studied Galactic stellar metallicity\n",
      "gradient has a corresponding ISO compositional gradient. We also demonstrate the inference part of\n",
      "the framework by using the current observed ISO composition distribution to constrain the parent star\n",
      "metallicity dependence of the ISO production rate. This constraint, and other inferences made with\n",
      "this framework, will improve dramatically as the Vera C. Rubin Observatory Legacy Survey of Space\n",
      "and Time (LSST) progresses and more ISOs are observed. Finally, we explore generalizations of this\n",
      "framework to other Galactic populations, such as that of exoplanets.\n",
      "Keywords: Interstellar objects (52), Small Solar System bodies(1469), Galaxy Evolution (594)\n",
      "1.INTRODUCTION\n",
      "1I/‘Oumuamua (Meech et al. 2017) and 2I/Borisov1are the first two observed samples from a highly numerous\n",
      "population: interstellar objects (ISOs). Estimated to number ∼1015pc−3around the Sun (Engelhardt et al. 2017;\n",
      "Do et al. 2018), they are implied to have a spatial distribution spanning the entire Galaxy. This population has been\n",
      "predicted to exist for decades (McGlynn & Chapman 1989), based on models of the accretion and migration of the\n",
      "giant planets, which predict that 75–85% of cometary bodies initially in the Solar System must have been scattered\n",
      "into interstellar space (Fernandez & Ip 1984; Brasser et al. 2006). Modern exoplanet surveys consistently find that\n",
      "giant planets are common across the Galaxy around stars with a range of spectral types (Fulton et al. 2021; Sabotta\n",
      "et al. 2021). This makes planetesimal scattering common across the Galaxy. A significant number of planetesimals can\n",
      "also be ejected by close stellar flybys early in a planetary system’s life (e.g. Pfalzner et al. 2021). The protoplanetary\n",
      "disks of other stars are therefore expected to be a source of ISOs (Stern 1990; Moro-Mart´ ın 2022).\n",
      "Corresponding author: Matthew Hopkins\n",
      "matthew.hopkins@physics.ox.ac.uk\n",
      "∗Banting Fellow\n",
      "1https://minorplanetcenter.net/mpec/K19/K19RA6.html and https://minorplanetcenter.net/mpec/K19/K19S72.htmlarXiv:2308.05801v1  [astro-ph.EP]  10 Aug 2023\n",
      "____________________\n",
      "2 Hopkins et al.\n",
      "Initially it was expected that interstellar objects would display cometary characteristics (e.g. Jewitt 2003). The pop-\n",
      "ulation’s dominant dynamical formation mechanisms would preferentially harvest more distant, ice-rich planetesimals\n",
      "from the disks of the source systems. More of the cometary ISO population passing through the Solar System could\n",
      "be detected than rocky ISOs, as comae make cometary ISOs brighter down to smaller diameters (Engelhardt et al.\n",
      "2017). 2I/Borisov appeared relatively similar in size and composition to Solar System comets (Jewitt & Seligman\n",
      "2022). Its distinctive features were an exceptionally high CO and NH 2content, implying it formed on the edge of its\n",
      "home system’s protoplanetary disk, beyond the hypervolatile CO ice line (Bodewits et al. 2020; Cordiner et al. 2020).\n",
      "However, 1I/‘Oumuamua had a mix of observed characteristics. A 160-m scale object, it lacked a coma in deep\n",
      "imaging (Jewitt et al. 2017) or detectable outgassing in CO, CO 2(Trilling et al. 2018) or CN (Ye et al. 2017). Despite\n",
      "this, it still underwent non-gravitational acceleration similar to that experienced by Solar System comets (Micheli\n",
      "et al. 2018). The large amplitude of its light curve implied a high-aspect-ratio shape (Mashchenko 2019). At the time,\n",
      "this combination of factors seemed unusual, although the surface reflectance properties were consistent with outer\n",
      "Solar System bodies (e.g. Bannister et al. 2017). Hypotheses for the composition and formation of 1I/‘Oumuamua\n",
      "that match the limited data remain varied. It may be a planetesimal (’Oumuamua ISSI Team et al. 2019); a fragment\n",
      "of a comet devolatized by passages close to its parent star before ejection (Raymond et al. 2018a); an icy fractal\n",
      "aggregate (Moro-Mart´ ın 2019); a hydrogen iceberg formed in a molecular cloud (Seligman & Laughlin 2020); or a\n",
      "nitrogen ice fragment from the surface of a Pluto-like dwarf planet (Jackson & Desch 2021). Recently, observation\n",
      "of similarly-small near-Earth asteroids have identified objects with the lightcurve amplitudes seen in 1I. Additionally,\n",
      "Farnocchia et al. (2023) and Seligman et al. (2023) report six asteroids with significant non-gravitational acceleration\n",
      "and no coma. While from the two ISOs found so far, the compositions of interstellar objects are clearly varied, 1I may\n",
      "be less extreme than it was first considered.\n",
      "ISOs formed in a protoplanetary disk carry information about their home systems in their composition: we have\n",
      "samples of other planetary systems coming to us for study. The composition of a protoplanetary disk correlates with the\n",
      "elemental abundances of its central star, due to their formation from the same gas and dust in a molecular cloud core\n",
      "(¨Oberg & Bergin 2021). We can thus expect stars of different metallicities to produce ISOs of different compositions.\n",
      "The composition of this gas and dust varies in both space and time as the Galaxy chemically evolves due to stellar\n",
      "nucleosynthesis, making the current Galactic ISO population sensitive to the entire history and evolution of the Milky\n",
      "Way over cosmic time (Tinsley & Cameron 1974; Lintott et al. 2022). Since the occurrence of planetesimal-scattering\n",
      "giant planets also has a metallicity dependence (Fischer & Valenti 2005), the relative occurrence of ISOs of different\n",
      "compositions will therefore carry information about the Galaxy’s distribution of planetary architectures. Finally, we\n",
      "expect many ISOs to outlive their parent stars, as they occupy a similar environment to Gyr-old Oort cloud comets.\n",
      "At minor-planet sizes, ISOs are not subject to any known destructive forces in the ISM (Guilbert-Lepoutre et al. 2015),\n",
      "other than minor erosion by dust, in their frequent passages through molecular clouds (Pfalzner et al. 2020). They\n",
      "could also be disrupted or devolatilised in rare close encounters with stars (Raymond et al. 2018a; Forbes & Loeb\n",
      "2019). ISOs thus present the possibility of studying long-lost planetary systems.\n",
      "The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) (Ivezi´ c et al. 2019) is predicted to provide\n",
      "a sample of tens of 1I/‘Oumuamua-like ISOs (e.g. Levine et al. 2021), as well as any more 2I/Borisov-like cometary\n",
      "ISOs that enter our Solar System. This is in addition to the continuing contributions of the NEO surveys and other\n",
      "observatories that found 1I and 2I in the first place (Meech et al. 2017). These will provide a large and varied sample\n",
      "of interstellar objects, for study and comparison to predictions.\n",
      "The many dependencies over planetary and Galactic scales make the observed ISO population a fascinating tool:\n",
      "it has the potential to test models in both Galaxy and planetary physics, with an entirely different set of biases\n",
      "to traditional methods. Lintott et al. (2022) introduced the concept of predicting the composition of a Galaxy’s\n",
      "population of ISOs from the Galaxy’s stellar distribution. Using simulated Galaxies from EAGLE (Schaye et al. 2015),\n",
      "they showed that the water content of ISOs was sensitive to Galactic star formation history. In this work, we develop\n",
      "this method and apply it to the stellar population of the Milky Way, estimated with data from the APOGEE survey, to\n",
      "predict a broader set of properties of our own Galaxy’s population of interstellar objects. We predict the distribution\n",
      "of ISOs in both their spatial position in the Galaxy and their water mass fraction. By evaluating this distribution at\n",
      "the current position of the Solar System, we predict the properties of the population of ISOs from which the observed\n",
      "sample of the 2020s will be drawn, and we compare this to the whole-Galaxy distribution. We then detail a Bayesian\n",
      "method of comparing the predicted and observed distributions to make inferences about the planetary and Galactic\n",
      "____________________\n"
     ]
    }
   ],
   "source": [
    "retrieved_chunks = query_engine.retrieve(search_term2)\n",
    "for i in retrieved_chunks:\n",
    "    print(i.node.get_text())\n",
    "    print(\"____________________\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ce337f",
   "metadata": {
    "id": "25ce337f"
   },
   "source": [
    "Again, we can see these sentences do reference our search term 'how does planet formation occur' in some way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2d3ce969",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2d3ce969",
    "outputId": "c7e3988b-7e6e-4abd-e6d9-8b0d0805df1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet formation occurs through processes in protoplanetary disks, where dust and gas surrounding a young star coalesce to form solid bodies. Initially, small particles collide and stick together, forming larger aggregates known as planetesimals. These planetesimals can further collide and merge, eventually leading to the formation of protoplanets. The dynamics of this process can be influenced by factors such as the metallicity of the star, which affects the availability of solid material, and the gravitational interactions with giant planets, which can scatter and eject planetesimals into interstellar space. The composition of the resulting bodies is linked to the chemical properties of the protoplanetary disk, which varies with the star's metallicity and the conditions present during formation.\n"
     ]
    }
   ],
   "source": [
    "result = query_engine.query(search_term2)\n",
    "print(result.response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e93878",
   "metadata": {
    "id": "f6e93878"
   },
   "source": [
    "## 5. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d74b6f20",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d74b6f20",
    "outputId": "2f4b3592-0290-48b4-c280-7bd3f8e4d46a"
   },
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346d56db",
   "metadata": {
    "id": "346d56db"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/vm88uwcB)"
   ]
  }
 ],
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================================================
FILE: quickstarts/python_quickstart.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb2094b8-13a5-4f7c-bd21-d2c709dab914",
   "metadata": {
    "id": "bb2094b8-13a5-4f7c-bd21-d2c709dab914"
   },
   "source": [
    "# Quickstart: Hello, KDB.AI\n",
    "\n",
    "##### Note: This example requires a KDB.AI endpoint and API key. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "How to get started with the KDB.AI vector database. Here, you'll get a quick taste of KDB.AI in  ~10 minutes.\n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "1. Connect to KDB.AI\n",
    "1. Create a KDB.AI Table\n",
    "1. Add Data to the KDB.AI Table\n",
    "1. Query the Table\n",
    "1. Perform Similarity Search\n",
    "1. Delete the KDB.AI Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260d0f4b-ef09-4bd2-a197-a9351be24684",
   "metadata": {
    "id": "260d0f4b-ef09-4bd2-a197-a9351be24684"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1468bd3",
   "metadata": {
    "id": "d1468bd3"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells.\n",
    "\n",
    "#### Embedding Library\n",
    "\n",
    "To generate embeddings, we will be using FastEmbed, a fast, lightweight alternative to Sentence Transformers.\n",
    "\n",
    "It supports a variety of popular text models and is built for efficiency and accuracy. In this notebook, we will use FastEmbed to generate embeddings for company descriptions, which we will then store in a KDB.AI table and use for similarity search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "491cd6d6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "collapsed": true,
    "id": "491cd6d6",
    "outputId": "c126432d-bdcb-4b9e-e964-6f6d8bfd803a"
   },
   "outputs": [],
   "source": [
    "!pip install kdbai_client fastembed onnxruntime 'tf_keras>=2.18.0'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6d17b7",
   "metadata": {
    "id": "cc6d17b7"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "805d97da",
   "metadata": {
    "id": "805d97da"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import os\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "from fastembed import TextEmbedding\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a55ae34e-472b-4aa7-9add-1fcb2ee24a41",
   "metadata": {
    "id": "a55ae34e-472b-4aa7-9add-1fcb2ee24a41"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c660c7d",
   "metadata": {
    "id": "8c660c7d"
   },
   "source": [
    "## 1. Connect to KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a3aa22",
   "metadata": {
    "id": "d3a3aa22"
   },
   "source": [
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e85c1ff",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "2e85c1ff",
    "outputId": "67920c0b-0832-439f-f887-793b0ad00617"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec2c77b",
   "metadata": {
    "id": "1ec2c77b"
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Need help understanding a function?</b><br/>\n",
    "Add ? before or after any function name in KDB.AI to bring up the documentation for that function along with sample code and arguments.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e54917b",
   "metadata": {
    "id": "6e54917b"
   },
   "outputs": [],
   "source": [
    "?kdbai.Session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b41f7c",
   "metadata": {},
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dee8fd32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI database \"default\"]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8788a6b1",
   "metadata": {
    "id": "8788a6b1"
   },
   "source": [
    "### Verify Defined Tables\n",
    "\n",
    "We can check our connection using the `session.list()` function.\n",
    "This will return a list of all the tables we have defined in our vector database thus far.\n",
    "This should return an empty list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97e5f4a9",
   "metadata": {
    "id": "97e5f4a9"
   },
   "outputs": [],
   "source": [
    "# ensure no table called \"company_data\" exists\n",
    "try:\n",
    "    for t in session.database('default').tables:\n",
    "            if t.name == 'company_data':\n",
    "                t.drop() \n",
    "    time.sleep(5)\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "884f1b49-7f03-419b-8f10-2086fcc4d0fb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "884f1b49-7f03-419b-8f10-2086fcc4d0fb",
    "outputId": "3ad10654-45bb-4f50-e397-6e3a2f81f442"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.database('default').tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33f03c3",
   "metadata": {
    "id": "e33f03c3"
   },
   "source": [
    "## 2. Create a KDB.AI Table\n",
    "\n",
    "To create a table we can use `create_table`, this function takes two arguments - the name and schema of the table.\n",
    "\n",
    "This schema must meet the following criteria:\n",
    "- It must contain a list of columns.\n",
    "- All columns must have either a `type` or a `qtype` specified, except the column of vectors.\n",
    "- One column of vector embeddings may also have a `vectorIndex` attribute with the configuration of the index for similarity search - this column is implicitly an array of `float64`.\n",
    "\n",
    "Run `?session.database('default').create_table` for more details and sample code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fnOlGAnN6uqa",
   "metadata": {
    "id": "fnOlGAnN6uqa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mdatabase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtable\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'str'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mschema\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[List[Dict[str, Any]]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mindexes\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'List[Dict[str, Any]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mpartition_column\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0membedding_configurations\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Dict[str, Any]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mexternal_data_references\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[List[Dict[str, Any]]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdefault_result_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'str'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'pd'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'Table'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Create a table with a schema\n",
      "\n",
      "Args:\n",
      "    table: Name of the table to create.\n",
      "    schema: list of dictionaries containing column name and type\n",
      "    indexes: list of dictionaries containing index definitions (multiple indexes can be created for each column)\n",
      "    partition_column: column name if table is partitioned\n",
      "    embedding_configurations: dictionary containing TSC configurations\n",
      "    external_data_references: reference info of existing table\n",
      "    default_result_type: default result type for search and query methods (pd|py|q)\n",
      "\n",
      "Returns:\n",
      "    A newly created `Table` object based on parameters.\n",
      "\n",
      "Raises:\n",
      "    KDBAIException: Raised when a error happens during the creation of the table.\n",
      "\n",
      "Example Flat/qFlat Index:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'}\n",
      "    ]\n",
      "    index = {'name': 'flat_index', 'column': 'embeddings', 'type': 'flat',\n",
      "             'params': {'dims': 25, 'metric': 'L2'}}\n",
      "    table = session.create_table('documents', schema=schema, indexes=[index])\n",
      "    ```\n",
      "\n",
      "Example IVF Index:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'}\n",
      "    ]\n",
      "    index = {'name': 'ivf_index', 'column': 'embeddings', 'type': 'ivf',\n",
      "             'params': {'metric': 'L2', 'nclusters': 8}}\n",
      "    table = session.create_table('documents', schema=schema, indexes=[index])\n",
      "    ```\n",
      "\n",
      "Example IVFPQ Index:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'}\n",
      "    ]\n",
      "    index = {{'name': 'ivf_index', 'column': 'embeddings', 'type': 'ivfpq',\n",
      "              'params': {'metric': 'L2', 'nclusters': 8, 'nbits': 8, 'nsplits': 5}}\n",
      "    table = session.create_table('documents', schema=schema, indexes=[index])\n",
      "    ```\n",
      "\n",
      "Example HNSW Index:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'}\n",
      "    ]\n",
      "    index = {'name': 'hnsw_index', 'column': 'embeddings', 'type': 'hnsw',\n",
      "             'params': {'dims': 25, 'metric': 'L2', 'M': 8, 'efConstruction': 8}}\n",
      "    table = session.create_table('documents', schema=schema, indexes=[index])\n",
      "    ```\n",
      "\n",
      "Example Sparse Index:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'}\n",
      "    ]\n",
      "    index = {'name': 'sparse_index', 'column': 'sparse_col', 'type': 'bm25',\n",
      "             'params': {'k': 1.25, 'b': 0.75}}\n",
      "    table = session.create_table('documents', schema=schema, indexes=[index])\n",
      "    ```\n",
      "\n",
      "Example Flat with Sparse Index:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'},\n",
      "        {'name': 'sparse_col', 'type': 'general'}\n",
      "    ]\n",
      "    index_flat = {'name': 'flat_index', 'column': 'embeddings', 'type': 'flat',\n",
      "                  'params': {'dims': 25, 'metric': 'L2'}}\n",
      "    index_sparse = {'name': 'sparse_index', 'column': 'sparse_col', 'type': 'bm25',\n",
      "                    'params': {'k': 1.25, 'b': 0.75}}\n",
      "    table = session.create_table('documents', schema=schema, indexes=[index_flat, index_sparse])\n",
      "    ```\n",
      "\n",
      "Examle Flat with TSC:\n",
      "    ```python\n",
      "    schema = [\n",
      "        {'name': 'id', 'type': 'int32'},\n",
      "        {'name': 'embeddings', 'type': 'float32s'}\n",
      "    ]\n",
      "    index_flat = {'name': 'flat_index', 'column': 'embeddings', 'type': 'flat',\n",
      "                  'params': {'dims': 25, 'metric': 'L2'}}\n",
      "    embedding_conf = {'embeddings': {\"dims\": 4, \"type\": \"tsc\", \"on_insert_error\": \"reject_all\" }}\n",
      "    table = session.create_table('documents',\n",
      "                                 schema=schema,\n",
      "                                 indexes=[index_flat],\n",
      "                                 embedding_configurations=embedding_conf)\n",
      "    ```\n",
      "\u001b[0;31mFile:\u001b[0m      ~/kx/similarity-search/kdbai-python-client/src/kdbai_client/api.py\n",
      "\u001b[0;31mType:\u001b[0m      method"
     ]
    }
   ],
   "source": [
    "database = session.database('default')\n",
    "?database.create_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da55253",
   "metadata": {
    "id": "9da55253"
   },
   "source": [
    "### Define Schema\n",
    "\n",
    "Our table will have three columns: the first two, `company_name` `company_description` contain company names and descriptions, the third will be the vector embeddings we will use for similarity search later on in this example.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5e8b782",
   "metadata": {
    "id": "e5e8b782"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "        {\"name\": \"company_name\", \"type\": \"str\"},\n",
    "        {\"name\": \"company_description\", \"type\": \"str\"},\n",
    "        {\"name\": \"vectors\", \"type\": \"float64s\"}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b0354d",
   "metadata": {},
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- type = flat : You have the choice of using other indexes like, hnsw, qHNSW, and IVFPQ, or a qFlat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- name = vectorIndex : this is a custom name you give your index.\n",
    "- column = vectors : this is the column where the embeddings are stored.\n",
    "#### params:\n",
    "- dims = 384 : In the next section, we generate embeddings that are 384-dimensional to match this. The number of dimensions should mirror the output dimensions of your embedding model.\n",
    "- metric = CS : We chose Cosine Similarity. You have the choice of using other metrics here like IP/Inner Product and CS/Cosine Similarity and the one you chose depends on the specific context and nature of your data.\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a17fc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes = [\n",
    "            {\n",
    "                \"name\": \"vectorIndex\", \"type\": \"flat\", \n",
    "                \"params\": {\"dims\": 384, \"metric\": \"CS\"},\n",
    "                \"column\": \"vectors\"\n",
    "            }\n",
    "        ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a5caa0",
   "metadata": {
    "id": "09a5caa0"
   },
   "source": [
    "### Create Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dXfzhaTCGoH_",
   "metadata": {
    "id": "dXfzhaTCGoH_"
   },
   "outputs": [],
   "source": [
    "database = session.database('default')\n",
    "table = database.create_table(\"company_data\", schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20afbea1",
   "metadata": {
    "id": "20afbea1"
   },
   "source": [
    "## 3. Add Data to the KDB.AI Table\n",
    "\n",
    "First, let's define a list of companies and their descriptions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37581e86",
   "metadata": {
    "id": "37581e86"
   },
   "outputs": [],
   "source": [
    "company_data = [\n",
    "    (\"Apple\", \"A technology company known for its iPhones, MacBooks, and innovative designs\"),\n",
    "    (\"Google\", \"A search engine giant that also specializes in advertising, cloud computing, and AI\"),\n",
    "    (\"Brave\", \"A privacy-focused search engine and browser.\"),\n",
    "    (\"Perplexity\", \"An answer engine that searches the internet and uses a large language model to summarize web data.\"),\n",
    "    (\"Amazon\", \"An e-commerce leader that offers a wide range of products and services, including AWS\"),\n",
    "    (\"Microsoft\", \"A technology company known for its software products like Windows and Office\"),\n",
    "    (\"Facebook\", \"A social media platform that connects people worldwide and owns Instagram and WhatsApp\"),\n",
    "    (\"Tesla\", \"An electric vehicle manufacturer known for its innovative and sustainable energy solutions\"),\n",
    "    (\"Rivian\", \"An electric vehicle company focusing on adventure-oriented trucks and SUVs\"),\n",
    "    (\"Lucid Motors\", \"A company specializing in high-performance electric luxury vehicles\"),\n",
    "    (\"Netflix\", \"A streaming service that offers a wide variety of TV shows, movies, and original content\"),\n",
    "    (\"Hulu\", \"A streaming platform providing a wide range of TV shows, movies, and original content\"),\n",
    "    (\"Disney+\", \"A streaming service offering movies, TV shows, and original content from Disney\"),\n",
    "    (\"Uber\", \"A ride-sharing company that also offers food delivery and freight services\"),\n",
    "    (\"Lyft\", \"A ride-sharing platform connecting passengers with drivers\"),\n",
    "    (\"Didi\", \"A Chinese ride-sharing company offering various transportation services\"),\n",
    "    (\"Airbnb\", \"A platform that allows people to rent out their homes or find lodging worldwide\"),\n",
    "    (\"Vrbo\", \"A vacation rental online marketplace where homeowners list their properties for short-term rentals\"),\n",
    "    (\"Booking.com\", \"An online travel agency offering lodging reservations and other travel products\"),\n",
    "    (\"Spotify\", \"A music streaming service offering a wide range of songs, albums, and podcasts\"),\n",
    "    (\"Apple Music\", \"A music and video streaming service developed by Apple Inc.\"),\n",
    "    (\"YouTube Music\", \"A music streaming service developed by YouTube\"),\n",
    "    (\"Twitter\", \"A social media platform for sharing short messages and real-time updates\"),\n",
    "    (\"Instagram\", \"A photo and video sharing social networking service\"),\n",
    "    (\"Snapchat\", \"A multimedia messaging app known for its disappearing messages\"),\n",
    "    (\"LinkedIn\", \"A professional networking platform for job seekers and employers\"),\n",
    "    (\"Slack\", \"A collaboration platform for team communication and project management\"),\n",
    "    (\"Microsoft Teams\", \"A collaboration platform for team communication and project management\"),\n",
    "    (\"Zoom\", \"A video conferencing platform used for virtual meetings and webinars\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97ace30-d178-4911-a147-0e2a733ac9c3",
   "metadata": {
    "id": "e97ace30-d178-4911-a147-0e2a733ac9c3"
   },
   "source": [
    "Now let's define an embedding model. Here we are using the default embedding model in FastEmbed, BAAI/bge-small-en-v1.5, which has 384 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c75ed61e-7b6b-4614-90be-20bb8e6355e0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "collapsed": true,
    "id": "c75ed61e-7b6b-4614-90be-20bb8e6355e0",
    "outputId": "e0fb5a6f-71de-48b4-af06-b1a1dbab28ce"
   },
   "outputs": [],
   "source": [
    "embedding_model = TextEmbedding()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7831d5a4-99bd-4859-8686-516c8e0a3110",
   "metadata": {
    "id": "7831d5a4-99bd-4859-8686-516c8e0a3110"
   },
   "source": [
    "Let's generate an embedding for each company. Because embedding_model.embed returns a generator, we turn it into a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5dc41e8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f5dc41e8",
    "outputId": "1cf0b9b4-e31a-46d3-b8a8-ca458822bd2c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example ID values\n",
    "embeddings = list(embedding_model.embed([desc for _, desc in company_data]))\n",
    "len(embeddings[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8366f2-172d-48e6-a24e-20a303b1295b",
   "metadata": {
    "id": "ba8366f2-172d-48e6-a24e-20a303b1295b"
   },
   "source": [
    "##### Create a dataframe from our company data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a31f878",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "4a31f878",
    "outputId": "6ae1c292-79dd-40ce-bab0-760a6db5858d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>company_description</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>A technology company known for its iPhones, Ma...</td>\n",
       "      <td>[-0.034876905, 0.032589626, -0.002934602, -0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Google</td>\n",
       "      <td>A search engine giant that also specializes in...</td>\n",
       "      <td>[-0.017866805, -0.057211027, -0.028582964, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brave</td>\n",
       "      <td>A privacy-focused search engine and browser.</td>\n",
       "      <td>[-0.017717587, -0.020544883, -0.024149919, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Perplexity</td>\n",
       "      <td>An answer engine that searches the internet an...</td>\n",
       "      <td>[-0.043428417, 0.0026718834, -0.014964736, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amazon</td>\n",
       "      <td>An e-commerce leader that offers a wide range ...</td>\n",
       "      <td>[-0.04374583, -0.05412757, 0.03689078, -0.0363...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company_name                                company_description  \\\n",
       "0        Apple  A technology company known for its iPhones, Ma...   \n",
       "1       Google  A search engine giant that also specializes in...   \n",
       "2        Brave       A privacy-focused search engine and browser.   \n",
       "3   Perplexity  An answer engine that searches the internet an...   \n",
       "4       Amazon  An e-commerce leader that offers a wide range ...   \n",
       "\n",
       "                                             vectors  \n",
       "0  [-0.034876905, 0.032589626, -0.002934602, -0.0...  \n",
       "1  [-0.017866805, -0.057211027, -0.028582964, 0.0...  \n",
       "2  [-0.017717587, -0.020544883, -0.024149919, -0....  \n",
       "3  [-0.043428417, 0.0026718834, -0.014964736, 0.0...  \n",
       "4  [-0.04374583, -0.05412757, 0.03689078, -0.0363...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [company for company, _ in company_data]\n",
    "descriptions = [description for _, description in company_data]\n",
    "\n",
    "# column names/types matching the schema\n",
    "embeddings_df = pd.DataFrame({\"company_name\": names, \"company_description\": descriptions, \"vectors\": list(embeddings)})\n",
    "embeddings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cd2ad8",
   "metadata": {
    "id": "43cd2ad8"
   },
   "source": [
    "We can now add data to our KDB.AI table using `insert`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7e0f8c5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "b7e0f8c5",
    "outputId": "49944585-b4ac-4242-9f29-1332e36ad24e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 29}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(embeddings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09577e8e",
   "metadata": {
    "id": "09577e8e"
   },
   "source": [
    "## 4. Query the Table\n",
    "\n",
    "We can use `query` to query data from the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4b8b8e5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 959
    },
    "id": "f4b8b8e5",
    "outputId": "5a906745-c9b9-439e-c02b-2f251b07b1e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>company_description</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>A technology company known for its iPhones, Ma...</td>\n",
       "      <td>[-0.034876905381679535, 0.0325896255671978, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Google</td>\n",
       "      <td>A search engine giant that also specializes in...</td>\n",
       "      <td>[-0.01786680519580841, -0.057211026549339294, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brave</td>\n",
       "      <td>A privacy-focused search engine and browser.</td>\n",
       "      <td>[-0.01771758683025837, -0.020544882863759995, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Perplexity</td>\n",
       "      <td>An answer engine that searches the internet an...</td>\n",
       "      <td>[-0.043428417295217514, 0.0026718834415078163,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amazon</td>\n",
       "      <td>An e-commerce leader that offers a wide range ...</td>\n",
       "      <td>[-0.04374583065509796, -0.05412757024168968, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Microsoft</td>\n",
       "      <td>A technology company known for its software pr...</td>\n",
       "      <td>[-0.03227386251091957, 0.018396837636828423, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Facebook</td>\n",
       "      <td>A social media platform that connects people w...</td>\n",
       "      <td>[0.001751603209413588, -0.07014184445142746, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>An electric vehicle manufacturer known for its...</td>\n",
       "      <td>[-0.0014115246012806892, 0.07673310488462448, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rivian</td>\n",
       "      <td>An electric vehicle company focusing on advent...</td>\n",
       "      <td>[-0.004529878031462431, 0.05161484703421593, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lucid Motors</td>\n",
       "      <td>A company specializing in high-performance ele...</td>\n",
       "      <td>[0.003517858451232314, 0.07283230125904083, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Netflix</td>\n",
       "      <td>A streaming service that offers a wide variety...</td>\n",
       "      <td>[-0.06678618490695953, -0.04583403840661049, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hulu</td>\n",
       "      <td>A streaming platform providing a wide range of...</td>\n",
       "      <td>[-0.05717369541525841, -0.06787450611591339, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Disney+</td>\n",
       "      <td>A streaming service offering movies, TV shows,...</td>\n",
       "      <td>[-0.08020051568746567, -0.06656964868307114, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Uber</td>\n",
       "      <td>A ride-sharing company that also offers food d...</td>\n",
       "      <td>[-0.0012249506544321775, -0.06182726100087166,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>A ride-sharing platform connecting passengers ...</td>\n",
       "      <td>[-0.01856295019388199, -0.02381841652095318, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Didi</td>\n",
       "      <td>A Chinese ride-sharing company offering variou...</td>\n",
       "      <td>[-0.025807108730077744, -0.03256842494010925, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>A platform that allows people to rent out thei...</td>\n",
       "      <td>[-0.0020053349435329437, -0.0806913897395134, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Vrbo</td>\n",
       "      <td>A vacation rental online marketplace where hom...</td>\n",
       "      <td>[-0.023459283635020256, -0.017960241064429283,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Booking.com</td>\n",
       "      <td>An online travel agency offering lodging reser...</td>\n",
       "      <td>[-0.011170933023095131, -0.01784929819405079, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Spotify</td>\n",
       "      <td>A music streaming service offering a wide rang...</td>\n",
       "      <td>[-0.04392420873045921, -0.08940696716308594, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Apple Music</td>\n",
       "      <td>A music and video streaming service developed ...</td>\n",
       "      <td>[-0.03607852756977081, -0.07780104875564575, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>YouTube Music</td>\n",
       "      <td>A music streaming service developed by YouTube</td>\n",
       "      <td>[-0.044059764593839645, -0.06850195676088333, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Twitter</td>\n",
       "      <td>A social media platform for sharing short mess...</td>\n",
       "      <td>[-0.07155980169773102, 0.019955234602093697, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Instagram</td>\n",
       "      <td>A photo and video sharing social networking se...</td>\n",
       "      <td>[-0.03469916060566902, -0.016102692112326622, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Snapchat</td>\n",
       "      <td>A multimedia messaging app known for its disap...</td>\n",
       "      <td>[-0.0604371502995491, 0.03670385479927063, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LinkedIn</td>\n",
       "      <td>A professional networking platform for job see...</td>\n",
       "      <td>[-0.08664378523826599, 0.012671931646764278, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Slack</td>\n",
       "      <td>A collaboration platform for team communicatio...</td>\n",
       "      <td>[-0.04067962244153023, 0.02870851941406727, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Microsoft Teams</td>\n",
       "      <td>A collaboration platform for team communicatio...</td>\n",
       "      <td>[-0.04067962244153023, 0.02870851941406727, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>A video conferencing platform used for virtual...</td>\n",
       "      <td>[-0.047964468598365784, -0.001985185779631138,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company_name                                company_description  \\\n",
       "0             Apple  A technology company known for its iPhones, Ma...   \n",
       "1            Google  A search engine giant that also specializes in...   \n",
       "2             Brave       A privacy-focused search engine and browser.   \n",
       "3        Perplexity  An answer engine that searches the internet an...   \n",
       "4            Amazon  An e-commerce leader that offers a wide range ...   \n",
       "5         Microsoft  A technology company known for its software pr...   \n",
       "6          Facebook  A social media platform that connects people w...   \n",
       "7             Tesla  An electric vehicle manufacturer known for its...   \n",
       "8            Rivian  An electric vehicle company focusing on advent...   \n",
       "9      Lucid Motors  A company specializing in high-performance ele...   \n",
       "10          Netflix  A streaming service that offers a wide variety...   \n",
       "11             Hulu  A streaming platform providing a wide range of...   \n",
       "12          Disney+  A streaming service offering movies, TV shows,...   \n",
       "13             Uber  A ride-sharing company that also offers food d...   \n",
       "14             Lyft  A ride-sharing platform connecting passengers ...   \n",
       "15             Didi  A Chinese ride-sharing company offering variou...   \n",
       "16           Airbnb  A platform that allows people to rent out thei...   \n",
       "17             Vrbo  A vacation rental online marketplace where hom...   \n",
       "18      Booking.com  An online travel agency offering lodging reser...   \n",
       "19          Spotify  A music streaming service offering a wide rang...   \n",
       "20      Apple Music  A music and video streaming service developed ...   \n",
       "21    YouTube Music     A music streaming service developed by YouTube   \n",
       "22          Twitter  A social media platform for sharing short mess...   \n",
       "23        Instagram  A photo and video sharing social networking se...   \n",
       "24         Snapchat  A multimedia messaging app known for its disap...   \n",
       "25         LinkedIn  A professional networking platform for job see...   \n",
       "26            Slack  A collaboration platform for team communicatio...   \n",
       "27  Microsoft Teams  A collaboration platform for team communicatio...   \n",
       "28             Zoom  A video conferencing platform used for virtual...   \n",
       "\n",
       "                                              vectors  \n",
       "0   [-0.034876905381679535, 0.0325896255671978, -0...  \n",
       "1   [-0.01786680519580841, -0.057211026549339294, ...  \n",
       "2   [-0.01771758683025837, -0.020544882863759995, ...  \n",
       "3   [-0.043428417295217514, 0.0026718834415078163,...  \n",
       "4   [-0.04374583065509796, -0.05412757024168968, 0...  \n",
       "5   [-0.03227386251091957, 0.018396837636828423, 0...  \n",
       "6   [0.001751603209413588, -0.07014184445142746, 0...  \n",
       "7   [-0.0014115246012806892, 0.07673310488462448, ...  \n",
       "8   [-0.004529878031462431, 0.05161484703421593, 0...  \n",
       "9   [0.003517858451232314, 0.07283230125904083, 0....  \n",
       "10  [-0.06678618490695953, -0.04583403840661049, 0...  \n",
       "11  [-0.05717369541525841, -0.06787450611591339, 0...  \n",
       "12  [-0.08020051568746567, -0.06656964868307114, 0...  \n",
       "13  [-0.0012249506544321775, -0.06182726100087166,...  \n",
       "14  [-0.01856295019388199, -0.02381841652095318, 0...  \n",
       "15  [-0.025807108730077744, -0.03256842494010925, ...  \n",
       "16  [-0.0020053349435329437, -0.0806913897395134, ...  \n",
       "17  [-0.023459283635020256, -0.017960241064429283,...  \n",
       "18  [-0.011170933023095131, -0.01784929819405079, ...  \n",
       "19  [-0.04392420873045921, -0.08940696716308594, 0...  \n",
       "20  [-0.03607852756977081, -0.07780104875564575, -...  \n",
       "21  [-0.044059764593839645, -0.06850195676088333, ...  \n",
       "22  [-0.07155980169773102, 0.019955234602093697, -...  \n",
       "23  [-0.03469916060566902, -0.016102692112326622, ...  \n",
       "24  [-0.0604371502995491, 0.03670385479927063, -0....  \n",
       "25  [-0.08664378523826599, 0.012671931646764278, -...  \n",
       "26  [-0.04067962244153023, 0.02870851941406727, -0...  \n",
       "27  [-0.04067962244153023, 0.02870851941406727, -0...  \n",
       "28  [-0.047964468598365784, -0.001985185779631138,...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e69a5b",
   "metadata": {
    "id": "55e69a5b"
   },
   "source": [
    "The `query` function accepts a wide range of arguments to make it easy to filter, aggregate, and sort.\n",
    "Run `?table.query` to see them all.\n",
    "\n",
    "Let's filter for companies starting with the letter 'A' using the 'like' operator. Four rows are returned as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "41c6f156",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "41c6f156",
    "outputId": "7491f14e-5005-4b8c-dc36-97f2a31528d4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_name</th>\n",
       "      <th>company_description</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>A technology company known for its iPhones, Ma...</td>\n",
       "      <td>[-0.034876905381679535, 0.0325896255671978, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amazon</td>\n",
       "      <td>An e-commerce leader that offers a wide range ...</td>\n",
       "      <td>[-0.04374583065509796, -0.05412757024168968, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>A platform that allows people to rent out thei...</td>\n",
       "      <td>[-0.0020053349435329437, -0.0806913897395134, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple Music</td>\n",
       "      <td>A music and video streaming service developed ...</td>\n",
       "      <td>[-0.03607852756977081, -0.07780104875564575, -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company_name                                company_description  \\\n",
       "0        Apple  A technology company known for its iPhones, Ma...   \n",
       "1       Amazon  An e-commerce leader that offers a wide range ...   \n",
       "2       Airbnb  A platform that allows people to rent out thei...   \n",
       "3  Apple Music  A music and video streaming service developed ...   \n",
       "\n",
       "                                             vectors  \n",
       "0  [-0.034876905381679535, 0.0325896255671978, -0...  \n",
       "1  [-0.04374583065509796, -0.05412757024168968, 0...  \n",
       "2  [-0.0020053349435329437, -0.0806913897395134, ...  \n",
       "3  [-0.03607852756977081, -0.07780104875564575, -...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.query(filter=[(\"like\", \"company_name\", \"A*\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c267a58",
   "metadata": {
    "id": "9c267a58"
   },
   "source": [
    "## 5. Perform Similarity Search\n",
    "\n",
    "Finally, let's perform similarity search on the table. We do this using the `search` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "F35xiMwy69HI",
   "metadata": {
    "id": "F35xiMwy69HI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mvectors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Dict[str, Any]'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'int'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mindex_params\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Dict[str, Any]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0moptions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Dict[str, Any]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mfilter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[List[List[Any]]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msort_columns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[List[str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mgroup_by\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[List[str]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0maggs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[Dict[str, Any]]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mresult_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[str]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Perform similarity search on the table, supports dense or sparse queries.\n",
      "\n",
      "Args:\n",
      "    vectors: Query vectors for the search. Dictionary keys must be index names or column name(tss) to execute\n",
      "        the search on. Values must be list of vectors containing the search vectors\n",
      "    n: Number of neighbours to return.\n",
      "    type: Override basic similarity search type.\n",
      "    index_params: Index specific options for similarity search.\n",
      "    options: Additional options for search, e.g: renaming distance column.\n",
      "    filter: A list of filter conditions as triplets in the following format:\n",
      "        `[['function', 'column name', 'parameter'], ... ]`\n",
      "        See all filter operators [here](https://code.kx.com/kdbai/use/filter.html#supported-filter-functions)\n",
      "    sort_columns: List of column names to sort on.\n",
      "    group_by: A list of column names to use for group by.\n",
      "    aggs: Dictionary specifying aggregation/projection on table.\n",
      "        If we want to return subset of columns, specify column name as key and values.\n",
      "        If we want to aggregate a column, key must be target column name and value mmust be agg function\n",
      "        and source column\n",
      "        See all aggregation functions [here](https://code.kx.com/kdbai/use/query.html#supported-aggregations)\n",
      "    result_type: data type to convert result into (pd|py|q)\n",
      "\n",
      "Returns:\n",
      "    List tables with one table of matching neighbors for each query vector.\n",
      "\n",
      "Examples:\n",
      "    ```python\n",
      "    #Find the closest neighbour of a single (dense) query vector\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0]]}, n=1)\n",
      "\n",
      "    #Find the closest neighbour of a single (sparse) query vector\n",
      "    table.search(vectors={'my_sparse_index':[{101:1,4578:1,102:1}]}, n=1)\n",
      "\n",
      "    #Find the 3 closest neighbours of 2 query vectors\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]]}, n=3)\n",
      "\n",
      "    # With aggregation and sorting\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]]},\n",
      "    n=3,\n",
      "    aggs={'sumSize': ['sum','size']},\n",
      "    group_by=['sym'],\n",
      "    sort_by=['sumSize'])\n",
      "\n",
      "    # Returns a subset of columns for each match\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]]}, n=3,\n",
      "                 aggs={'size': 'size'})\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]]}, n=3,\n",
      "                 aggs={'size': 'size', 'price': 'price'})\n",
      "\n",
      "    # Filter\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]]},\n",
      "    n=3,\n",
      "    filter=[['within','size',(5,999)],['like','sym','AAP*']])\n",
      "\n",
      "    # Customized distance name\n",
      "    table.search(vectors={'my_index': [[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]]},\n",
      "    n=3,\n",
      "    options={\"distanceColumn\" 'myDist'})\n",
      "\n",
      "    # Index options\n",
      "    table.search(vectors=[[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]], n=3, index_params=dict(efSearch=512))\n",
      "    table.search(vectors=[[0,0,0,0,0,0,0,0,0,0], [1,1,1,1,1,1,1,1,1,1]], n=3, index_params=dict(clusters=16))\n",
      "    ```\n",
      "\n",
      "Raises:\n",
      "    KDBAIException: Raised when an error occurs during search.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/kx/similarity-search/kdbai-python-client/src/kdbai_client/api.py\n",
      "\u001b[0;31mType:\u001b[0m      method"
     ]
    }
   ],
   "source": [
    "?table.search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bJgKFuFF1DqX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "bJgKFuFF1DqX",
    "outputId": "ef1cf4eb-30e7-4dd4-aef2-7deaf4f4a6f8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>company_name</th>\n",
       "      <th>company_description</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.730767</td>\n",
       "      <td>Zoom</td>\n",
       "      <td>A video conferencing platform used for virtual...</td>\n",
       "      <td>[-0.047964468598365784, -0.001985185779631138,...</td>\n",
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      "text/plain": [
       "   __nn_distance company_name  \\\n",
       "0       0.730767         Zoom   \n",
       "\n",
       "                                 company_description  \\\n",
       "0  A video conferencing platform used for virtual...   \n",
       "\n",
       "                                             vectors  \n",
       "0  [-0.047964468598365784, -0.001985185779631138,...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
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   ],
   "source": [
    "query = \"A company that helps facilitate meetings\"\n",
    "query_vector = list(embedding_model.embed([query]))[0].tolist()\n",
    "table.search(vectors={'vectorIndex': [query_vector]})[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb341f3",
   "metadata": {
    "id": "9bb341f3"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> The dimension of input query vectors must match the vector embedding dimensions in the table, defined in schema above.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49758e9d",
   "metadata": {
    "id": "49758e9d"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> The output was a list of length one, matching the number of vectors we input to the search. This can be indexed on position [0] to extract the dataframe corresponding to the single input vector.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0bc3820",
   "metadata": {
    "id": "f0bc3820"
   },
   "source": [
    "The closest matching neighbor for the query vector passed in is returned alongside the calculation of L2 ([Euclidean Distance](#https://en.wikipedia.org/wiki/Euclidean_distance)) similarity.\n",
    "\n",
    "We can also rerun the same query for more neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c0cdda8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "0c0cdda8",
    "outputId": "2bdb3554-562b-43a8-e461-d7e6f1266f3f"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>company_name</th>\n",
       "      <th>company_description</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.730767</td>\n",
       "      <td>Zoom</td>\n",
       "      <td>A video conferencing platform used for virtual...</td>\n",
       "      <td>[-0.047964468598365784, -0.001985185779631138,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.714641</td>\n",
       "      <td>Booking.com</td>\n",
       "      <td>An online travel agency offering lodging reser...</td>\n",
       "      <td>[-0.011170933023095131, -0.01784929819405079, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.714121</td>\n",
       "      <td>Microsoft Teams</td>\n",
       "      <td>A collaboration platform for team communicatio...</td>\n",
       "      <td>[-0.04067962244153023, 0.02870851941406727, -0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance     company_name  \\\n",
       "0       0.730767             Zoom   \n",
       "1       0.714641      Booking.com   \n",
       "2       0.714121  Microsoft Teams   \n",
       "\n",
       "                                 company_description  \\\n",
       "0  A video conferencing platform used for virtual...   \n",
       "1  An online travel agency offering lodging reser...   \n",
       "2  A collaboration platform for team communicatio...   \n",
       "\n",
       "                                             vectors  \n",
       "0  [-0.047964468598365784, -0.001985185779631138,...  \n",
       "1  [-0.011170933023095131, -0.01784929819405079, ...  \n",
       "2  [-0.04067962244153023, 0.02870851941406727, -0...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find 3 closest neighbours of a single query vector\n",
    "table.search(vectors={'vectorIndex': [query_vector]}, n=3)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9b0e8a",
   "metadata": {
    "id": "0c9b0e8a"
   },
   "source": [
    "And we can apply a filter to the search results. Here we use the '<>' filter, which keeps data that is not equal to a value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1914948",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "f1914948",
    "outputId": "67995869-3114-4692-b34d-da8b6ad40bec"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>company_name</th>\n",
       "      <th>company_description</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.730767</td>\n",
       "      <td>Zoom</td>\n",
       "      <td>A video conferencing platform used for virtual...</td>\n",
       "      <td>[-0.047964468598365784, -0.001985185779631138,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.714121</td>\n",
       "      <td>Microsoft Teams</td>\n",
       "      <td>A collaboration platform for team communicatio...</td>\n",
       "      <td>[-0.04067962244153023, 0.02870851941406727, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.714121</td>\n",
       "      <td>Slack</td>\n",
       "      <td>A collaboration platform for team communicatio...</td>\n",
       "      <td>[-0.04067962244153023, 0.02870851941406727, -0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance     company_name  \\\n",
       "0       0.730767             Zoom   \n",
       "1       0.714121  Microsoft Teams   \n",
       "2       0.714121            Slack   \n",
       "\n",
       "                                 company_description  \\\n",
       "0  A video conferencing platform used for virtual...   \n",
       "1  A collaboration platform for team communicatio...   \n",
       "2  A collaboration platform for team communicatio...   \n",
       "\n",
       "                                             vectors  \n",
       "0  [-0.047964468598365784, -0.001985185779631138,...  \n",
       "1  [-0.04067962244153023, 0.02870851941406727, -0...  \n",
       "2  [-0.04067962244153023, 0.02870851941406727, -0...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find 3 closest neighbours of a single query vector\n",
    "table.search(\n",
    "    vectors={'vectorIndex': [query_vector]},\n",
    "    n=3,\n",
    "    filter=[(\"<>\", \"company_name\", \"Booking.com\")],\n",
    ")[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c1b38d",
   "metadata": {
    "id": "02c1b38d"
   },
   "source": [
    "And also we can search passing more than one query vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6964035",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c6964035",
    "outputId": "4824cfc4-cdcd-40c2-d5d8-0a33f45100d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[    Company Name\n",
       " 0        Spotify\n",
       " 1    Apple Music\n",
       " 2  YouTube Music,\n",
       "   Company Name\n",
       " 0     Facebook\n",
       " 1      Twitter\n",
       " 2    Instagram]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query1 = \"A company with a music-related product\"\n",
    "query2 = \"A social media company\"\n",
    "\n",
    "query1_vector = list(embedding_model.embed([query1]))[0].tolist()\n",
    "query2_vector = list(embedding_model.embed([query2]))[0].tolist()\n",
    "\n",
    "table.search(\n",
    "    vectors={'vectorIndex': [\n",
    "        query1_vector,\n",
    "        query2_vector,\n",
    "    ]},\n",
    "    n=3,\n",
    "    aggs={'Company Name': 'company_name'}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8aed9bc-72b2-4e70-b763-e7ce054557db",
   "metadata": {
    "id": "d8aed9bc-72b2-4e70-b763-e7ce054557db"
   },
   "source": [
    "## 6. Delete the KDB.AI Table\n",
    "\n",
    "We can use `table.drop()` to delete a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "548a9d95-aac3-4d63-a87a-99eedfe55f07",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "548a9d95-aac3-4d63-a87a-99eedfe55f07",
    "outputId": "e6b08842-86ef-4d63-ebf8-826142ee9538"
   },
   "outputs": [],
   "source": [
    "for t in session.database('default').tables:\n",
    "    if t.name == 'company_data':\n",
    "        t.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc6d801-1371-48d0-98b4-0baa53bc8446",
   "metadata": {
    "id": "8bc6d801-1371-48d0-98b4-0baa53bc8446"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Warning:</b> Once you drop a table, you cannot use it again.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7672241-42b0-4798-90d7-95aa9fefe68c",
   "metadata": {
    "id": "a7672241-42b0-4798-90d7-95aa9fefe68c"
   },
   "source": [
    "## Next Steps\n",
    "\n",
    "Now that you’re successfully making indexes with KDB.AI, you can start inserting your own data or view more examples:\n",
    "- [PDF Document Search](../document_search)\n",
    "- [MRI Image Search](../image_search)\n",
    "- [Music Recommendation System](../music_recommendation)\n",
    "- [Sensor Pattern Matching](../pattern_matching)\n",
    "- [Retrieval Augmented Generation with LangChain](../retrieval_augmented_generation)\n",
    "- [Sentiment Analysis of Reviews](../sentiment_analysis)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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================================================
FILE: requirements.txt
================================================
gensim >= 4.3
jupyter >= 1.0
kdbai_client >= 0.1.2
matplotlib >= 3.7
openai >= 0.28
pypdf >= 3.0
sentence-transformers >= 2.2
tensorflow >= 2.10
tiktoken >= 0.5
umap-learn >= 0.5
langchain
langchain_openai


================================================
FILE: retrieval_augmented_generation/data/state_of_the_union.txt
================================================
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  

Last year COVID-19 kept us apart. This year we are finally together again. 

Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. 

With a duty to one another to the American people to the Constitution. 

And with an unwavering resolve that freedom will always triumph over tyranny. 

Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. 

He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. 

He met the Ukrainian people. 

From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. 

Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. 

In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. 

Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. 

Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. 

Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos.   

They keep moving.   

And the costs and the threats to America and the world keep rising.   

That’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. 

The United States is a member along with 29 other nations. 

It matters. American diplomacy matters. American resolve matters. 

Putin’s latest attack on Ukraine was premeditated and unprovoked. 

He rejected repeated efforts at diplomacy. 

He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready.  Here is what we did.   

We prepared extensively and carefully. 

We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. 

I spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression.  

We countered Russia’s lies with truth.   

And now that he has acted the free world is holding him accountable. 

Along with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland. 

We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. 

Together with our allies –we are right now enforcing powerful economic sanctions. 

We are cutting off Russia’s largest banks from the international financial system.  

Preventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   

We are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.  

Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. 

The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  

We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains. 

And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value. 

The Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame. 

Together with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. 

We are giving more than $1 Billion in direct assistance to Ukraine. 

And we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  

Let me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.  

Our forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.  

For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to protect NATO countries including Poland, Romania, Latvia, Lithuania, and Estonia. 

As I have made crystal clear the United States and our Allies will defend every inch of territory of NATO countries with the full force of our collective power.  

And we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them.  

Putin has unleashed violence and chaos.  But while he may make gains on the battlefield – he will pay a continuing high price over the long run. 

And a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.  

To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. 

And I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. 

Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.  

America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  

These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. 

But I want you to know that we are going to be okay. 

When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger. 

While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly. 

We see the unity among leaders of nations and a more unified Europe a more unified West. And we see unity among the people who are gathering in cities in large crowds around the world even in Russia to demonstrate their support for Ukraine.  

In the battle between democracy and autocracy, democracies are rising to the moment, and the world is clearly choosing the side of peace and security. 

This is a real test. It’s going to take time. So let us continue to draw inspiration from the iron will of the Ukrainian people. 

To our fellow Ukrainian Americans who forge a deep bond that connects our two nations we stand with you. 

Putin may circle Kyiv with tanks, but he will never gain the hearts and souls of the Ukrainian people. 

He will never extinguish their love of freedom. He will never weaken the resolve of the free world. 

We meet tonight in an America that has lived through two of the hardest years this nation has ever faced. 

The pandemic has been punishing. 

And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more. 

I understand. 

I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it. 

That’s why one of the first things I did as President was fight to pass the American Rescue Plan.  

Because people were hurting. We needed to act, and we did. 

Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis. 

It fueled our efforts to vaccinate the nation and combat COVID-19. It delivered immediate economic relief for tens of millions of Americans.  

Helped put food on their table, keep a roof over their heads, and cut the cost of health insurance. 

And as my Dad used to say, it gave people a little breathing room. 

And unlike the $2 Trillion tax cut passed in the previous administration that benefitted the top 1% of Americans, the American Rescue Plan helped working people—and left no one behind. 

And it worked. It created jobs. Lots of jobs. 

In fact—our economy created over 6.5 Million new jobs just last year, more jobs created in one year  
than ever before in the history of America. 

Our economy grew at a rate of 5.7% last year, the strongest growth in nearly 40 years, the first step in bringing fundamental change to an economy that hasn’t worked for the working people of this nation for too long.  

For the past 40 years we were told that if we gave tax breaks to those at the very top, the benefits would trickle down to everyone else. 

But that trickle-down theory led to weaker economic growth, lower wages, bigger deficits, and the widest gap between those at the top and everyone else in nearly a century. 

Vice President Harris and I ran for office with a new economic vision for America. 

Invest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up  
and the middle out, not from the top down.  

Because we know that when the middle class grows, the poor have a ladder up and the wealthy do very well. 

America used to have the best roads, bridges, and airports on Earth. 

Now our infrastructure is ranked 13th in the world. 

We won’t be able to compete for the jobs of the 21st Century if we don’t fix that. 

That’s why it was so important to pass the Bipartisan Infrastructure Law—the most sweeping investment to rebuild America in history. 

This was a bipartisan effort, and I want to thank the members of both parties who worked to make it happen. 

We’re done talking about infrastructure weeks. 

We’re going to have an infrastructure decade. 

It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China.  

As I’ve told Xi Jinping, it is never a good bet to bet against the American people. 

We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. 

And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice. 

We’ll build a national network of 500,000 electric vehicle charging stations, begin to replace poisonous lead pipes—so every child—and every American—has clean water to drink at home and at school, provide affordable high-speed internet for every American—urban, suburban, rural, and tribal communities. 

4,000 projects have already been announced. 

And tonight, I’m announcing that this year we will start fixing over 65,000 miles of highway and 1,500 bridges in disrepair. 

When we use taxpayer dollars to rebuild America – we are going to Buy American: buy American products to support American jobs. 

The federal government spends about $600 Billion a year to keep the country safe and secure. 

There’s been a law on the books for almost a century 
to make sure taxpayers’ dollars support American jobs and businesses. 

Every Administration says they’ll do it, but we are actually doing it. 

We will buy American to make sure everything from the deck of an aircraft carrier to the steel on highway guardrails are made in America. 

But to compete for the best jobs of the future, we also need to level the playing field with China and other competitors. 

That’s why it is so important to pass the Bipartisan Innovation Act sitting in Congress that will make record investments in emerging technologies and American manufacturing. 

Let me give you one example of why it’s so important to pass it. 

If you travel 20 miles east of Columbus, Ohio, you’ll find 1,000 empty acres of land. 

It won’t look like much, but if you stop and look closely, you’ll see a “Field of dreams,” the ground on which America’s future will be built. 

This is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor “mega site”. 

Up to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. 

Some of the most sophisticated manufacturing in the world to make computer chips the size of a fingertip that power the world and our everyday lives. 

Smartphones. The Internet. Technology we have yet to invent. 

But that’s just the beginning. 

Intel’s CEO, Pat Gelsinger, who is here tonight, told me they are ready to increase their investment from  
$20 billion to $100 billion. 

That would be one of the biggest investments in manufacturing in American history. 

And all they’re waiting for is for you to pass this bill. 

So let’s not wait any longer. Send it to my desk. I’ll sign it.  

And we will really take off. 

And Intel is not alone. 

There’s something happening in America. 

Just look around and you’ll see an amazing story. 

The rebirth of the pride that comes from stamping products “Made In America.” The revitalization of American manufacturing.   

Companies are choosing to build new factories here, when just a few years ago, they would have built them overseas. 

That’s what is happening. Ford is investing $11 billion to build electric vehicles, creating 11,000 jobs across the country. 

GM is making the largest investment in its history—$7 billion to build electric vehicles, creating 4,000 jobs in Michigan. 

All told, we created 369,000 new manufacturing jobs in America just last year. 

Powered by people I’ve met like JoJo Burgess, from generations of union steelworkers from Pittsburgh, who’s here with us tonight. 

As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” 

It’s time. 

But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.  

Inflation is robbing them of the gains they might otherwise feel. 

I get it. That’s why my top priority is getting prices under control. 

Look, our economy roared back faster than most predicted, but the pandemic meant that businesses had a hard time hiring enough workers to keep up production in their factories. 

The pandemic also disrupted global supply chains. 

When factories close, it takes longer to make goods and get them from the warehouse to the store, and prices go up. 

Look at cars. 

Last year, there weren’t enough semiconductors to make all the cars that people wanted to buy. 

And guess what, prices of automobiles went up. 

So—we have a choice. 

One way to fight inflation is to drive down wages and make Americans poorer.  

I have a better plan to fight inflation. 

Lower your costs, not your wages. 

Make more cars and semiconductors in America. 

More infrastructure and innovation in America. 

More goods moving faster and cheaper in America. 

More jobs where you can earn a good living in America. 

And instead of relying on foreign supply chains, let’s make it in America. 

Economists call it “increasing the productive capacity of our economy.” 

I call it building a better America. 

My plan to fight inflation will lower your costs and lower the deficit. 

17 Nobel laureates in economics say my plan will ease long-term inflationary pressures. Top business leaders and most Americans support my plan. And here’s the plan: 

First – cut the cost of prescription drugs. Just look at insulin. One in ten Americans has diabetes. In Virginia, I met a 13-year-old boy named Joshua Davis.  

He and his Dad both have Type 1 diabetes, which means they need insulin every day. Insulin costs about $10 a vial to make.  

But drug companies charge families like Joshua and his Dad up to 30 times more. I spoke with Joshua’s mom. 

Imagine what it’s like to look at your child who needs insulin and have no idea how you’re going to pay for it.  

What it does to your dignity, your ability to look your child in the eye, to be the parent you expect to be. 

Joshua is here with us tonight. Yesterday was his birthday. Happy birthday, buddy.  

For Joshua, and for the 200,000 other young people with Type 1 diabetes, let’s cap the cost of insulin at $35 a month so everyone can afford it.  

Drug companies will still do very well. And while we’re at it let Medicare negotiate lower prices for prescription drugs, like the VA already does. 

Look, the American Rescue Plan is helping millions of families on Affordable Care Act plans save $2,400 a year on their health care premiums. Let’s close the coverage gap and make those savings permanent. 

Second – cut energy costs for families an average of $500 a year by combatting climate change.  

Let’s provide investments and tax credits to weatherize your homes and businesses to be energy efficient and you get a tax credit; double America’s clean energy production in solar, wind, and so much more;  lower the price of electric vehicles, saving you another $80 a month because you’ll never have to pay at the gas pump again. 

Third – cut the cost of child care. Many families pay up to $14,000 a year for child care per child.  

Middle-class and working families shouldn’t have to pay more than 7% of their income for care of young children.  

My plan will cut the cost in half for most families and help parents, including millions of women, who left the workforce during the pandemic because they couldn’t afford child care, to be able to get back to work. 

My plan doesn’t stop there. It also includes home and long-term care. More affordable housing. And Pre-K for every 3- and 4-year-old.  

All of these will lower costs. 

And under my plan, nobody earning less than $400,000 a year will pay an additional penny in new taxes. Nobody.  

The one thing all Americans agree on is that the tax system is not fair. We have to fix it.  

I’m not looking to punish anyone. But let’s make sure corporations and the wealthiest Americans start paying their fair share. 

Just last year, 55 Fortune 500 corporations earned $40 billion in profits and paid zero dollars in federal income tax.  

That’s simply not fair. That’s why I’ve proposed a 15% minimum tax rate for corporations. 

We got more than 130 countries to agree on a global minimum tax rate so companies can’t get out of paying their taxes at home by shipping jobs and factories overseas. 

That’s why I’ve proposed closing loopholes so the very wealthy don’t pay a lower tax rate than a teacher or a firefighter.  

So that’s my plan. It will grow the economy and lower costs for families. 

So what are we waiting for? Let’s get this done. And while you’re at it, confirm my nominees to the Federal Reserve, which plays a critical role in fighting inflation.  

My plan will not only lower costs to give families a fair shot, it will lower the deficit. 

The previous Administration not only ballooned the deficit with tax cuts for the very wealthy and corporations, it undermined the watchdogs whose job was to keep pandemic relief funds from being wasted. 

But in my administration, the watchdogs have been welcomed back. 

We’re going after the criminals who stole billions in relief money meant for small businesses and millions of Americans.  

And tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud. 

By the end of this year, the deficit will be down to less than half what it was before I took office.  

The only president ever to cut the deficit by more than one trillion dollars in a single year. 

Lowering your costs also means demanding more competition. 

I’m a capitalist, but capitalism without competition isn’t capitalism. 

It’s exploitation—and it drives up prices. 

When corporations don’t have to compete, their profits go up, your prices go up, and small businesses and family farmers and ranchers go under. 

We see it happening with ocean carriers moving goods in and out of America. 

During the pandemic, these foreign-owned companies raised prices by as much as 1,000% and made record profits. 

Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. 

And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  

That ends on my watch. 

Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. 

We’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. 

Let’s pass the Paycheck Fairness Act and paid leave.  

Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. 

Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges. 

And let’s pass the PRO Act when a majority of workers want to form a union—they shouldn’t be stopped.  

When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long time: build a better America. 

For more than two years, COVID-19 has impacted every decision in our lives and the life of the nation. 

And I know you’re tired, frustrated, and exhausted. 

But I also know this. 

Because of the progress we’ve made, because of your resilience and the tools we have, tonight I can say  
we are moving forward safely, back to more normal routines.  

We’ve reached a new moment in the fight against COVID-19, with severe cases down to a level not seen since last July.  

Just a few days ago, the Centers for Disease Control and Prevention—the CDC—issued new mask guidelines. 

Under these new guidelines, most Americans in most of the country can now be mask free.   

And based on the projections, more of the country will reach that point across the next couple of weeks. 

Thanks to the progress we have made this past year, COVID-19 need no longer control our lives.  

I know some are talking about “living with COVID-19”. Tonight – I say that we will never just accept living with COVID-19. 

We will continue to combat the virus as we do other diseases. And because this is a virus that mutates and spreads, we will stay on guard. 

Here are four common sense steps as we move forward safely.  

First, stay protected with vaccines and treatments. We know how incredibly effective vaccines are. If you’re vaccinated and boosted you have the highest degree of protection. 

We will never give up on vaccinating more Americans. Now, I know parents with kids under 5 are eager to see a vaccine authorized for their children. 

The scientists are working hard to get that done and we’ll be ready with plenty of vaccines when they do. 

We’re also ready with anti-viral treatments. If you get COVID-19, the Pfizer pill reduces your chances of ending up in the hospital by 90%.  

We’ve ordered more of these pills than anyone in the world. And Pfizer is working overtime to get us 1 Million pills this month and more than double that next month.  

And we’re launching the “Test to Treat” initiative so people can get tested at a pharmacy, and if they’re positive, receive antiviral pills on the spot at no cost.  

If you’re immunocompromised or have some other vulnerability, we have treatments and free high-quality masks. 

We’re leaving no one behind or ignoring anyone’s needs as we move forward. 

And on testing, we have made hundreds of millions of tests available for you to order for free.   

Even if you already ordered free tests tonight, I am announcing that you can order more from covidtests.gov starting next week. 

Second – we must prepare for new variants. Over the past year, we’ve gotten much better at detecting new variants. 

If necessary, we’ll be able to deploy new vaccines within 100 days instead of many more months or years.  

And, if Congress provides the funds we need, we’ll have new stockpiles of tests, masks, and pills ready if needed. 

I cannot promise a new variant won’t come. But I can promise you we’ll do everything within our power to be ready if it does.  

Third – we can end the shutdown of schools and businesses. We have the tools we need. 

It’s time for Americans to get back to work and fill our great downtowns again.  People working from home can feel safe to begin to return to the office.   

We’re doing that here in the federal government. The vast majority of federal workers will once again work in person. 

Our schools are open. Let’s keep it that way. Our kids need to be in school. 

And with 75% of adult Americans fully vaccinated and hospitalizations down by 77%, most Americans can remove their masks, return to work, stay in the classroom, and move forward safely. 

We achieved this because we provided free vaccines, treatments, tests, and masks. 

Of course, continuing this costs money. 

I will soon send Congress a request. 

The vast majority of Americans have used these tools and may want to again, so I expect Congress to pass it quickly.   

Fourth, we will continue vaccinating the world.     

We’ve sent 475 Million vaccine doses to 112 countries, more than any other nation. 

And we won’t stop. 

We have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life. 

Let’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease.  

Let’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans.  

We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. 

I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. 

They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. 

Officer Mora was 27 years old. 

Officer Rivera was 22. 

Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. 

I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. 

I’ve worked on these issues a long time. 

I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. 

So let’s not abandon our streets. Or choose between safety and equal justice. 

Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. 

That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers. 

That’s why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption—trusted messengers breaking the cycle of violence and trauma and giving young people hope.  

We should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities. 

I ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.  

And I will keep doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced. 

And I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon? 

Ban assault weapons and high-capacity magazines. 

Repeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued. 

These laws don’t infringe on the Second Amendment. They save lives. 

The most fundamental right in America is the right to vote – and to have it counted. And it’s under assault. 

In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. 

We cannot let this happen. 

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. 

A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. 

And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. 

We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  

We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  

We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. 

We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders. 

We can do all this while keeping lit the torch of liberty that has led generations of immigrants to this land—my forefathers and so many of yours. 

Provide a pathway to citizenship for Dreamers, those on temporary status, farm workers, and essential workers. 

Revise our laws so businesses have the workers they need and families don’t wait decades to reunite. 

It’s not only the right thing to do—it’s the economically smart thing to do. 

That’s why immigration reform is supported by everyone from labor unions to religious leaders to the U.S. Chamber of Commerce. 

Let’s get it done once and for all. 

Advancing liberty and justice also requires protecting the rights of women. 

The constitutional right affirmed in Roe v. Wade—standing precedent for half a century—is under attack as never before. 

If we want to go forward—not backward—we must protect access to health care. Preserve a woman’s right to choose. And let’s continue to advance maternal health care in America. 

And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. 

As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. 

While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. 

And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. 

So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  

First, beat the opioid epidemic. 

There is so much we can do. Increase funding for prevention, treatment, harm reduction, and recovery.  

Get rid of outdated rules that stop doctors from prescribing treatments. And stop the flow of illicit drugs by working with state and local law enforcement to go after traffickers. 

If you’re suffering from addiction, know you are not alone. I believe in recovery, and I celebrate the 23 million Americans in recovery. 

Second, let’s take on mental health. Especially among our children, whose lives and education have been turned upside down.  

The American Rescue Plan gave schools money to hire teachers and help students make up for lost learning.  

I urge every parent to make sure your school does just that. And we can all play a part—sign up to be a tutor or a mentor. 

Children were also struggling before the pandemic. Bullying, violence, trauma, and the harms of social media. 

As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. 

It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. 

And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. 

Third, support our veterans. 

Veterans are the best of us. 

I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. 

My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  

Our troops in Iraq and Afghanistan faced many dangers. 

One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. 

When they came home, many of the world’s fittest and best trained warriors were never the same. 

Headaches. Numbness. Dizziness. 

A cancer that would put them in a flag-draped coffin. 

I know. 

One of those soldiers was my son Major Beau Biden. 

We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. 

But I’m committed to finding out everything we can. 

Committed to military families like Danielle Robinson from Ohio. 

The widow of Sergeant First Class Heath Robinson.  

He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. 

Stationed near Baghdad, just yards from burn pits the size of football fields. 

Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter. 

But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. 

Danielle says Heath was a fighter to the very end. 

He didn’t know how to stop fighting, and neither did she. 

Through her pain she found purpose to demand we do better. 

Tonight, Danielle—we are. 

The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits. 

And tonight, I’m announcing we’re expanding eligibility to veterans suffering from nine respiratory cancers. 

I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve. 

And fourth, let’s end cancer as we know it. 

This is personal to me and Jill, to Kamala, and to so many of you. 

Cancer is the #2 cause of death in America–second only to heart disease. 

Last month, I announced our plan to supercharge  
the Cancer Moonshot that President Obama asked me to lead six years ago. 

Our goal is to cut the cancer death rate by at least 50% over the next 25 years, turn more cancers from death sentences into treatable diseases.  

More support for patients and families. 

To get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health. 

It’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more.  

ARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. 

A unity agenda for the nation. 

We can do this. 

My fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. 

In this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. 

We have fought for freedom, expanded liberty, defeated totalitarianism and terror. 

And built the strongest, freest, and most prosperous nation the world has ever known. 

Now is the hour. 

Our moment of responsibility. 

Our test of resolve and conscience, of history itself. 

It is in this moment that our character is formed. Our purpose is found. Our future is forged. 

Well I know this nation.  

We will meet the test. 

To protect freedom and liberty, to expand fairness and opportunity. 

We will save democracy. 

As hard as these times have been, I am more optimistic about America today than I have been my whole life. 

Because I see the future that is within our grasp. 

Because I know there is simply nothing beyond our capacity. 

We are the only nation on Earth that has always turned every crisis we have faced into an opportunity. 

The only nation that can be defined by a single word: possibilities. 

So on this night, in our 245th year as a nation, I have come to report on the State of the Union. 

And my report is this: the State of the Union is strong—because you, the American people, are strong. 

We are stronger today than we were a year ago. 

And we will be stronger a year from now than we are today. 

Now is our moment to meet and overcome the challenges of our time. 

And we will, as one people. 

One America. 

The United States of America. 

May God bless you all. May God protect our troops.

================================================
FILE: retrieval_augmented_generation/retrieval_augmented_generation.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48eeba82",
   "metadata": {},
   "source": [
    "# Retrieval Augmented Generation (RAG) with LangChain\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This example will demonstrate how to use an advanced prompt engineering technique called Retrieval Augmented Generation (RAG), with hands-on examples using Langchain, KDB.AI and various LLMs.\n",
    "\n",
    "### What is RAG and Why Do We Need it?\n",
    "\n",
    "Large Language Models have remarkable capabilities in generating human-like text. These models are found in applications ranging from chatbots to content generation and translation. However, they face a significant challenge in staying up-to-date with recent world events, as they are essentially frozen in time, operating within the static knowledge snapshot captured during their training.\n",
    "\n",
    "To bridge this gap and address the need for specialized, real-time information, the concept of \"Retrieval Augmented Generation\" (RAG) has emerged as a powerful solution. RAG enables these language models to access relevant data from external knowledge bases, enriching their responses with current and contextually accurate information. For more content on RAG you can check out our videos on [Youtube](https://www.youtube.com/@KxSystems/streams) where we discuss the best practices for RAG, chunking strategies, the variety of approaches as well as how to evaluate your RAG application.\n",
    "\n",
    "### Aim\n",
    "\n",
    "In this tutorial, we'll cover:\n",
    "\n",
    "1. Load Text Data\n",
    "1. Define OpenAI Text Emedding Model\n",
    "1. Store Embeddings In KDB.AI\n",
    "1. Search For Similar Documents To A Given Query\n",
    "1. Perform Retrieval Augmented Generation\n",
    "1. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5ba816",
   "metadata": {},
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274d42eb",
   "metadata": {},
   "source": [
    "### Install dependencies \n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb407992",
   "metadata": {},
   "source": [
    "### Import Packages\n",
    "\n",
    "Load the various libraries that will be needed in this tutorial, including all the langchain libraries we will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fac8d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client langchain langchain_openai langchain-huggingface #langchain-community \n",
    "\n",
    "import os\n",
    "!git clone -b KDBAI_v1.4 https://github.com/KxSystems/langchain.git\n",
    "os.chdir('langchain/libs/community')\n",
    "!pip install ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8e9b27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads State of the Union speech data\n",
    "import os\n",
    "\n",
    "if os.path.exists(\"./data/state_of_the_union.txt\") == False:\n",
    "    !mkdir ./data\n",
    "    !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/retrieval_augmented_generation/data/state_of_the_union.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "36ecd7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector DB\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "49f72689",
   "metadata": {},
   "outputs": [],
   "source": [
    "# langchain packages\n",
    "from langchain.chains import RetrievalQA\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_community.vectorstores import KDBAI\n",
    "from langchain import HuggingFaceHub\n",
    "from langchain_openai import OpenAI\n",
    "from langchain.chains.question_answering import load_qa_chain\n",
    "from langchain_huggingface import HuggingFaceEndpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f273bab",
   "metadata": {},
   "source": [
    "### Set API Keys\n",
    "\n",
    "To follow this example you will need to request both an [OpenAI API Key](https://platform.openai.com/apps) and a [Hugging Face API Token](https://huggingface.co/docs/hub/security-tokens). \n",
    "\n",
    "You can create both for free by registering using the links provided. Once you have the credentials you can add them below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4470f552",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "159357d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = (\n",
    "    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"]\n",
    "    if \"HUGGINGFACEHUB_API_TOKEN\" in os.environ\n",
    "    else getpass(\"Hugging Face API Token: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708acb6e",
   "metadata": {},
   "source": [
    "## 1. Load Text Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f921b0",
   "metadata": {},
   "source": [
    "### Read In Text Document\n",
    "\n",
    "The document we will use for this examples is a State of the Union message from the President of the United States to the United States Congress.\n",
    "\n",
    "In the below code snippet, we read the text file in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b70f894d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the documents we want to prompt an LLM about\n",
    "doc = TextLoader(\"data/state_of_the_union.txt\").load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87d9450",
   "metadata": {},
   "source": [
    "### Split The Document Into Chunks\n",
    "\n",
    "We then split this document into chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "451745c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chunk the documents into 500 character chunks using langchain's text splitter \"RucursiveCharacterTextSplitter\"\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "50dc02a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split_documents produces a list of all the chunks created, printing out first chunk for example\n",
    "pages = [p.page_content for p in text_splitter.split_documents(doc)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b685aa9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny.'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pages[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415d8c79",
   "metadata": {},
   "source": [
    "## 2. Define OpenAI Text Embedding Model\n",
    " \n",
    "We will use OpenAIEmbeddings to embed our document into a format suitable for the vector database. We select `text-embedding-3-small` for use in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f680c912",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6659a7a2",
   "metadata": {},
   "source": [
    "## 3. Store Embeddings In KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f6d6e2",
   "metadata": {},
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8e7b22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec574f8",
   "metadata": {},
   "source": [
    "### Define Vector DB Table Schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6977019a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rag_schema = [\n",
    "    {\"name\": \"id\", \"type\": \"str\"},\n",
    "    {\"name\": \"text\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"embeddings\", \"type\": \"float32s\"},\n",
    "]\n",
    "\n",
    "indexes = [{'name': 'flat_index', 'column': 'embeddings', 'type': 'flat', 'params': {\"dims\": 1536, \"metric\": \"L2\"}}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab5c4c1",
   "metadata": {},
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a844f38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "database = session.database(\"default\")\n",
    "\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"rag_langchain\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "596eaaa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = database.create_table(\"rag_langchain\", schema=rag_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b465157",
   "metadata": {},
   "source": [
    "### Add Embedded Data to KDB.AI Table\n",
    "\n",
    "We can now store our data in KDB.AI by passing a few parameters to `KDBAI.add_texts`:\n",
    "\n",
    "- `session` our handle to talk to KDB.AI\n",
    "- `table_name` our KDB.AI table name\n",
    "- `texts` the chunked document \n",
    "- `embeddings` the embeddings model we have chosen "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "47a92e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'f1a65b30-fa03-4c30-81e5-58c20f421612',\n",
       " '23b9b1af-ca12-41c4-b62c-eac095fe2b6e',\n",
       " 'ff1cc9e4-7ee7-41f1-a674-cb0f1171e267']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use KDBAI as vector store\n",
    "vecdb_kdbai = KDBAI(table, embeddings)\n",
    "vecdb_kdbai.add_texts(texts=pages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a28271",
   "metadata": {},
   "source": [
    "Now we have the vector embeddings stored in KDB.AI we are ready to query."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb116f8",
   "metadata": {},
   "source": [
    "## 4. Search For Similar Documents To A Given Query \n",
    "\n",
    "Before we implement RAG, let's see an example of using similarity search directly on KDB.AI vector store. The search uses Euclidean similarity search which measures distance between two points in vector space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a17c127a",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"what are the nations strengths?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "63aee736",
   "metadata": {},
   "outputs": [],
   "source": [
    "# query_sim holds results of the similarity search, the closest related chunks to the query.\n",
    "query_sim = vecdb_kdbai.similarity_search(query, index='flat_index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e8c9b456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='We are the only nation on Earth that has always turned every crisis we have faced into an opportunity. \\n\\nThe only nation that can be defined by a single word: possibilities. \\n\\nSo on this night, in our 245th year as a nation, I have come to report on the State of the Union. \\n\\nAnd my report is this: the State of the Union is strong—because you, the American people, are strong. \\n\\nWe are stronger today than we were a year ago. \\n\\nAnd we will be stronger a year from now than we are today.', metadata={'id': '23b9b1af-ca12-41c4-b62c-eac095fe2b6e', 'embeddings': array([ 0.03127478,  0.0130375 ,  0.04139533, ..., -0.01439452,\n",
       "        -0.01379844, -0.0240585 ], dtype=float32)})]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8ecfec",
   "metadata": {},
   "source": [
    "This result returns the most similar chunks of text to our query, which is an okay start but it is hard to read. It would be a lot better if we could summarize these findings and return a response that is more human readable - this is where RAG comes in!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b6a865",
   "metadata": {},
   "source": [
    "## 5. Perform Retrieval Augmented Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7aa29b8",
   "metadata": {},
   "source": [
    "There are four different ways to do [question answering (QA) in LangChain](https://python.langchain.com/docs/use_cases/question_answering/#go-deeper-4):\n",
    "- `load_qa_chain` will do QA over all documents passed every time it is called. It is simple and comprehensive, but can be slower and less efficient than `RetrievalQA` as it may not focus on the most relevant parts of the tests for the question. In one example below, we will perform similarity search with KDB.AI before using `load_qa_chain` to act upon \"all documents\" being passed.\n",
    "- `RetrievalQA` retrieves the most relevant chunk of text and does QA on that subset. It uses `load_qa_chain` under the hood on each chunk and is faster and more efficient then the vanilla `load_qa_chain`. These performance gains come at the risk of losing some information or context from the documents as it may not always find the best text chunks for the question. In one example below, we will use KDB.AI as the retriever of `RetrievalQA`.\n",
    "- `VectorstoreIndexCreator` is a higher level wrapper for `RetrievalQA` to make it easier to run in fewer lines of code\n",
    "- `ConversationalRetrievalChain` builds on RetrievalQAChain to provide a chat history component\n",
    "\n",
    "In this tutorial we will implement the first two.\n",
    "\n",
    "### 'load_qa_chain' with OpenAI and HuggingFace LLMs\n",
    "\n",
    "We set up two question-answering chains for different models, OpenAI and HuggingFaceHub, using LangChain's `load_qa_chain` function. To do this we first perform the same similarity search run earlier and then run both chains on the query and the related chunks from the documentation, printing the responses from both models. We compare the responses of OpenAI and HuggingFaceHub models to the query about vector database strengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3f29a8de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
      "Token is valid (permission: write).\n",
      "Your token has been saved to /home/gflood/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "# select two llm models (OpenAI gpt-4o, HuggingFaceHub mistralai/Mistral-7B-Instruct-v0.2)\n",
    "llm_openai = ChatOpenAI(model=\"gpt-4o\", max_tokens=512)\n",
    "llm_mistral = HuggingFaceEndpoint(\n",
    "    repo_id=\"mistralai/Mistral-7B-Instruct-v0.2\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22959c2",
   "metadata": {},
   "source": [
    "\n",
    "We chose the `chain_type =\"stuff\"` which is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "32a49086",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the chain for each model using langchain load_qa_chain\n",
    "chain_openAI = load_qa_chain(llm_openai, chain_type=\"stuff\")\n",
    "chain_HuggingFaceHub = load_qa_chain(llm_mistral, chain_type=\"stuff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b63d6afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='We are the only nation on Earth that has always turned every crisis we have faced into an opportunity. \\n\\nThe only nation that can be defined by a single word: possibilities. \\n\\nSo on this night, in our 245th year as a nation, I have come to report on the State of the Union. \\n\\nAnd my report is this: the State of the Union is strong—because you, the American people, are strong. \\n\\nWe are stronger today than we were a year ago. \\n\\nAnd we will be stronger a year from now than we are today.', metadata={'id': '23b9b1af-ca12-41c4-b62c-eac095fe2b6e', 'embeddings': array([ 0.03127478,  0.0130375 ,  0.04139533, ..., -0.01439452,\n",
       "        -0.01379844, -0.0240585 ], dtype=float32)})]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the most related chunks to the query\n",
    "query_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5e281391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"The nations' strengths as highlighted in the context include the ability to turn every crisis into an opportunity, being defined by possibilities, and the strength and resilience of the American people. The overall message is that the nation is strong and continues to grow stronger over time.\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OpenAI - run the chain on the query and the related chunks from the documentation\n",
    "chain_openAI.invoke({'input_documents':query_sim, 'question':query})['output_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9f590804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' The nation is strong because the American people are strong and full of possibilities. The country has turned every crisis into an opportunity and continues to do so. It is defined by its ability to see and create possibilities. The nation is stronger today than it was a year ago and will be stronger a year from now.'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HugginFace - run the chain on the query and the related chunks from the documentation\n",
    "chain_HuggingFaceHub.invoke({'input_documents':query_sim, 'question':query})['output_text']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b79d56",
   "metadata": {},
   "source": [
    "### RetrievalQA with GPT-4o\n",
    "\n",
    "Let's try the second method using `RetrievalQA`. This time lets use GPT-4o as our LLM of choice.\n",
    "\n",
    "The code below defines a question-answering bot that combines OpenAI's GPT-4o for generating responses and a retriever that accesses the KDB.AI vector database to find relevant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c1a6b18e",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ddaa8a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "qabot = RetrievalQA.from_chain_type(\n",
    "    chain_type=\"stuff\",\n",
    "    llm=ChatOpenAI(model=\"gpt-4o\", temperature=0.0),\n",
    "    retriever=vecdb_kdbai.as_retriever(search_kwargs=dict(k=K, index=\"flat_index\")),\n",
    "    return_source_documents=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37b59a1",
   "metadata": {},
   "source": [
    "`as_retriever` is a method that converts a vectorstore into a retriever. A retriever is an interface that returns documents given an unstructured query. By using <code>as_retriever</code>, we can create a retriever from a vectorstore and use it to retrieve relevant documents for a query. This allows us to perform question answering over the documents indexed by the vectorstore `vecdb_kdbai`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2d1ba3cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what are the nations strengths?\n",
      "-----\n",
      "The strengths of the United States, as highlighted in the context, include:\n",
      "\n",
      "1. **Resilience and Adaptability**: The nation has a history of turning crises into opportunities.\n",
      "2. **Possibilities**: The country is defined by the concept of possibilities, indicating a forward-looking and optimistic outlook.\n",
      "3. **Strength of the People**: The American people are described as strong, contributing to the overall strength of the nation.\n",
      "4. **Diplomacy and Resolve**: American diplomacy and resolve are emphasized as important factors in international relations.\n",
      "5. **Military Preparedness**: The U.S. has mobilized ground forces, air squadrons, and ship deployments to protect NATO allies.\n",
      "6. **Economic Sanctions and Coalition Building**: The U.S. has built a coalition of nations to impose economic sanctions on Russia and support Ukraine.\n",
      "7. **Historical Achievements**: The nation has a legacy of fighting for freedom, expanding liberty, and defeating totalitarianism and terror.\n",
      "8. **Infrastructure Investment**: The passage of the Bipartisan Infrastructure Law is seen as a significant investment in rebuilding and improving the nation's infrastructure.\n",
      "9. **Unity and Collective Action**: The emphasis on unity and collective action as one people, one America, is a key strength.\n",
      "\n",
      "These strengths contribute to the nation's ability to meet and overcome current and future challenges.\n"
     ]
    }
   ],
   "source": [
    "print(query)\n",
    "print(\"-----\")\n",
    "print(qabot.invoke(dict(query=query))[\"result\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20a3d7a",
   "metadata": {},
   "source": [
    "Trying another query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9ed67c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_qabot(qabot, query: str):\n",
    "    print(new_query)\n",
    "    print(\"---\")\n",
    "    return qabot.invoke(dict(query=new_query))[\"result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a1b517b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what are the things this country needs to protect?\n",
      "---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"This country needs to protect several key areas:\\n\\n1. **American Jobs and Businesses**: By ensuring taxpayer dollars support American jobs and businesses through initiatives like Buy American policies.\\n2. **Safety and Security**: By investing in crime prevention, community policing, and measures to reduce gun violence, such as universal background checks and banning assault weapons and high-capacity magazines.\\n3. **Immigration and Border Security**: By providing pathways to citizenship for certain groups, revising laws to meet labor needs, and securing borders with new technology and joint patrols.\\n4. **Voting Rights**: By protecting the fundamental right to vote and ensuring that votes are counted, combating laws that suppress or subvert elections.\\n5. **Liberty and Justice**: By advancing immigration reform, protecting women's rights, and holding law enforcement accountable.\\n6. **National and International Security**: By maintaining strong American diplomacy and resolve, particularly in response to international conflicts like Russia's attack on Ukraine.\\n\\nThese areas are crucial for maintaining the country's integrity, safety, and prosperity.\""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_query = \"what are the things this country needs to protect?\"\n",
    "query_qabot(qabot, new_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7574cc",
   "metadata": {},
   "source": [
    "Clearly, Retrieval Augmented Generation stands out as a valuable technique that synergizes the capabilities of language models such as GPT-3 with the potency of information retrieval.\n",
    "By enhancing the input with contextually specific data, RAG empowers language models to produce responses that are not only more precize but also well-suited to the context. \n",
    "Particularly in enterprize scenarios where extensive fine-tuning may not be feasible, RAG presents an efficient and economically viable approach to deliver personalized and informed interactions with users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f0568a",
   "metadata": {},
   "source": [
    "## 6. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bf0e3026",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8a102d",
   "metadata": {},
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/dgCLUkdx)"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
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================================================
FILE: retrieval_augmented_generation/retrieval_augmented_generation_evaluation.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48eeba82",
   "metadata": {},
   "source": [
    "# Retrieval Augmented Generation Evaluation with LangChain and KDB.AI\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "This notebook serves as a guide to utilizing LangChain tooling for evaluating a basic Retrieval Augmented Generation (RAG) system. \n",
    "\n",
    "The evaluation process involves employing [LangChain's String Evaluators](https://python.langchain.com/docs/guides/evaluation/string/) to assess both conciseness and correctness. KDB.AI serves as the primary knowledge base, enabling the retrieval of semantically relevant content for the evaluation.\n",
    "\n",
    "### Aim\n",
    "\n",
    "In this tutorial, we build upon the retrieval augmented generation pipeline seen in our [retrieval_augmented_generation.ipynb](retrieval_augmented_generation.ipynb) notebook.\n",
    "If you have not seen it, please read and understand that notebook as it will cover the setup steps of RAG in greater detail than we do here.\n",
    "\n",
    "This notebook focuses on the evaluation of your retrieval augmented generation using KDB.AI as the vector store.\n",
    "We will cover the following topics:\n",
    "\n",
    "1. Load Text Data\n",
    "1. Define OpenAI Text Emedding Model\n",
    "1. Store Embeddings In KDB.AI\n",
    "1. Perform Retrieval Augmented Generation\n",
    "1. Evaluate Retrieval Augmented Generation\n",
    "1. Delete the KDB.AI Table\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88331c4",
   "metadata": {},
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d6c97e",
   "metadata": {},
   "source": [
    "### Install dependencies \n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c93b2276",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client langchain langchain_openai #langchain-community\n",
    "\n",
    "import os\n",
    "!git clone -b KDBAI_v1.4 https://github.com/KxSystems/langchain.git\n",
    "os.chdir('langchain/libs/community')\n",
    "!pip install ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c95778f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads State of the Union Speech data\n",
    "import os\n",
    "\n",
    "if os.path.exists(\"./data/state_of_the_union.txt\") == False:\n",
    "    !mkdir ./data\n",
    "    !wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/retrieval_augmented_generation/data/state_of_the_union.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679126f7",
   "metadata": {},
   "source": [
    "### Import Packages\n",
    "\n",
    "Load the various libraries that will be needed in this tutorial, including all the langchain libraries we will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "894980f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector DB\n",
    "from getpass import getpass\n",
    "import kdbai_client as kdbai\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9549fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# langchain packages\n",
    "from langchain.chains import RetrievalQA\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_community.vectorstores import KDBAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ab423cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluation packages\n",
    "from langchain.evaluation import load_evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc263a6e",
   "metadata": {},
   "source": [
    "### Set API Keys\n",
    "\n",
    "To follow this example you will need to request an [OpenAI API Key](https://platform.openai.com/apps). \n",
    "\n",
    "You can create this for free by registering using the links provided.\n",
    "Once you have the credentials you can add them below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed70fbe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"] = (\n",
    "    os.environ[\"OPENAI_API_KEY\"]\n",
    "    if \"OPENAI_API_KEY\" in os.environ\n",
    "    else getpass(\"OpenAI API Key: \")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56faa93",
   "metadata": {},
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b03039cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_dict(d: dict) -> None:\n",
    "    for k, v in d.items():\n",
    "        print(f\"\\n{k.capitalize()}\\n---\\n{v}\".replace('\\n\\n', '\\n'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164f0b99",
   "metadata": {},
   "source": [
    "## 1. Load Text Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04aa63a",
   "metadata": {},
   "source": [
    "### Read In Text Document\n",
    "\n",
    "The document we will use for this examples is a State of the Union message from the President of the United States to the United States Congress.\n",
    "\n",
    "In the below code snippet, we read the text file in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69dfbffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the documents we want to prompt an LLM about\n",
    "doc = TextLoader(\"data/state_of_the_union.txt\").load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed001b92",
   "metadata": {},
   "source": [
    "### Split The Document Into Chunks\n",
    "\n",
    "We then split this document into chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84bfd8a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chunk the documents into 500 character chunks using langchain's text splitter \"RucursiveCharacterTextSplitter\"\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9c70879",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split_documents produces a list of all the chunks created, printing out first chunk for example\n",
    "pages = [p.page_content for p in text_splitter.split_documents(doc)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1cf6a4",
   "metadata": {},
   "source": [
    "## 2. Define OpenAI Text Embedding Model\n",
    " \n",
    "We will use OpenAIEmbeddings to embed our document into a format suitable for the vector database. We select `text-embedding-ada-002` for use in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ffa379e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7287e75d",
   "metadata": {},
   "source": [
    "## 3. Store Embeddings In KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a9f5b2f",
   "metadata": {},
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e62f00a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d72b5b",
   "metadata": {},
   "source": [
    "### Define Vector DB Table Schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "299902d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rag_eval_schema = [\n",
    "    {\"name\": \"id\", \"type\": \"str\"},\n",
    "    {\"name\": \"text\", \"type\": \"bytes\"},\n",
    "    {\"name\": \"embeddings\", \"type\": \"float32s\"}\n",
    "]\n",
    "indexes = [{\"name\": \"flat_index\", \"type\": \"flat\", \"column\": \"embeddings\", \"params\": {\"dims\": 1536, \"metric\": \"L2\"}}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640fceb2",
   "metadata": {},
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0bbc9942",
   "metadata": {},
   "outputs": [],
   "source": [
    "database = session.database(\"default\")\n",
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    database.table(\"rag_eval\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37840395",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = database.create_table(\"rag_eval\", schema=rag_eval_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934da954",
   "metadata": {},
   "source": [
    "### Add Embedded Data to KDB.AI Table\n",
    "\n",
    "We can now store our data in KDB.AI by passing a few parameters to `KDBAI.from_texts`:\n",
    "\n",
    "- `session` our handle to talk to KDB.AI\n",
    "- `table_name` our KDB.AI table name\n",
    "- `texts` the chunked document \n",
    "- `embeddings` the embeddings model we have chosen "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7680e758",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'bb797699-18fa-4df1-889a-e1ef41c312fe',\n",
       " '37f14b6c-26d4-4f45-b69e-747998ce4ce9',\n",
       " '2345f851-e017-4604-b54c-cca25f58598f',\n",
       " '1320c41a-cab0-478e-84e8-ee28e880e38f',\n",
       " '11108ede-09bb-47c3-8739-0be3ee1ace5a',\n",
       " 'bac8a8f7-7d49-4faa-b4cc-a1267392eea4',\n",
       " '58f3355d-1abe-484b-bcbc-30d405c0734d',\n",
       " '1a516946-6ed9-46eb-82c4-5d38c32423d9',\n",
       " '699a3389-3fe9-493c-9e64-979b72dafcb7',\n",
       " 'a6a244eb-26fe-4538-8fb7-4d9a94df7ddb',\n",
       " '67fa79e6-3feb-447c-a173-847070ca9e81',\n",
       " 'bb9b7307-6c5f-4c49-ac16-8d306dc7d368',\n",
       " '4b413721-89b4-4681-8e0e-b1406d4ced37',\n",
       " 'fc2b623a-eb81-4272-880b-b3864dd2ec8c',\n",
       " '443ff2b1-7710-42cc-b805-6fd776468503',\n",
       " '7cbec07a-5336-4025-8a08-eaaf889ee75e',\n",
       " '131a74eb-534f-4b60-87f8-d1f2e782f4d7',\n",
       " '42552610-46ca-4a3b-800f-ad575a94d5db',\n",
       " '64568872-a133-4777-a63c-1324b99fc845']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use KDBAI as vector store\n",
    "vecdb_kdbai = KDBAI(table, embeddings)\n",
    "vecdb_kdbai.add_texts(texts=pages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece8d806",
   "metadata": {},
   "source": [
    "Now we have the vector embeddings stored in KDB.AI we are ready to query."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38569892",
   "metadata": {},
   "source": [
    "## 4. Perform Retrieval Augmented Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34a0636",
   "metadata": {},
   "source": [
    "We will perform [question answering (QA) in LangChain](https://python.langchain.com/docs/use_cases/question_answering/#go-deeper-4) using `RetrievalQA`.\n",
    "\n",
    "`RetrievalQA` retrieves the most relevant chunk of text and does QA on that subset.\n",
    "We will use KDB.AI as the retriever of `RetrievalQA`.\n",
    "\n",
    "### Define QA Bot\n",
    "\n",
    "The code below defines a question-answering bot that combines OpenAI's GPT-4o-mini for generating responses and a retriever that accesses the KDB.AI vector database to find relevant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9011f654",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ca7342e",
   "metadata": {},
   "outputs": [],
   "source": [
    "qabot = RetrievalQA.from_chain_type(\n",
    "    chain_type=\"stuff\",\n",
    "    llm=ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0),\n",
    "    retriever=vecdb_kdbai.as_retriever(search_kwargs=dict(k=K, index=\"flat_index\")),\n",
    "    return_source_documents=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8ee6a4",
   "metadata": {},
   "source": [
    "`as_retriever` is a method that converts a vectorstore into a retriever. A retriever is an interface that returns documents given an unstructured query. By using <code>as_retriever</code>, we can create a retriever from a vectorstore and use it to retrieve relevant documents for a query. This allows us to perform question answering over the documents indexed by the vectorstore `vecdb_kdbai`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057670d5",
   "metadata": {},
   "source": [
    "### Query The QA Bot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de98d6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_qabot(qabot, query: str) -> str:\n",
    "    query_res = qabot.invoke(dict(query=query))[\"result\"]\n",
    "    print(f\"{query}\\n---\\n{query_res}\")\n",
    "    return query_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3ca2ca",
   "metadata": {},
   "source": [
    "##### Query 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "85ca7b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "query1 = \"What improvements could be made in infrastructure?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd88bf8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What improvements could be made in infrastructure?\n",
      "---\n",
      "Improvements that could be made in infrastructure include:\n",
      "\n",
      "1. Rebuilding and modernizing roads, highways, and bridges to ensure safety and efficiency.\n",
      "2. Expanding and upgrading public transportation systems to provide better access and reduce congestion.\n",
      "3. Developing a national network of electric vehicle charging stations to support the transition to electric vehicles.\n",
      "4. Replacing lead pipes to ensure clean drinking water for all Americans.\n",
      "5. Providing affordable high-speed internet access to urban, suburban, rural, and tribal communities.\n",
      "6. Upgrading airports, ports, and waterways to enhance transportation and trade capabilities.\n",
      "7. Implementing sustainable practices to withstand the effects of climate change and promote environmental justice. \n",
      "\n",
      "These improvements aim to enhance the overall infrastructure and support economic growth and competitiveness.\n"
     ]
    }
   ],
   "source": [
    "res1 = query_qabot(qabot, query1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f329b3",
   "metadata": {},
   "source": [
    "##### Query 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0eece5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "query2 = \"How many jobs were created in the country due the electric vehicle manufacturing industry?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "41997198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many jobs were created in the country due the electric vehicle manufacturing industry?\n",
      "---\n",
      "Ford is creating 11,000 jobs and GM is creating 4,000 jobs in the electric vehicle manufacturing industry, which totals 15,000 jobs.\n"
     ]
    }
   ],
   "source": [
    "res2 = query_qabot(qabot, query2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d38581",
   "metadata": {},
   "source": [
    "## 5. Evaluate Retrieval Augmented Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3933ae8c",
   "metadata": {},
   "source": [
    "Here we will carry out two evaluation techniques against the results of our retrieval augmented generation pipeline.\n",
    "We will measure the *Conciseness* and the *Correctness* of the answers.\n",
    "\n",
    "### Evaluate Conciseness\n",
    "\n",
    "We will evaluate the conciseness of the answers the QA bot returns using LangChain's `load_evaluator` function with the `criteria` set to `\"conciseness\"`.\n",
    "\n",
    "In this example, we use GPT-4o as the LLM that performs the evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9c14a699",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_llm = ChatOpenAI(model=\"gpt-4o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d41ed25a",
   "metadata": {},
   "outputs": [],
   "source": [
    "concise_evaluator = load_evaluator(\n",
    "    \"criteria\", criteria=\"conciseness\", llm=evaluation_llm\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a63b03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "concise_eval_res = concise_evaluator.evaluate_strings(prediction=res1, input=query1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a7866960-9256-4df2-8087-49dcf43c3124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reasoning\n",
      "---\n",
      "To determine if the submission meets the criterion of conciseness, we need to assess whether it is brief and to the point. Here is a step-by-step reasoning process:\n",
      "1. **Identify Key Points**: The submission lists seven specific improvements that could be made in infrastructure:\n",
      "   - Rebuilding and modernizing roads, highways, and bridges.\n",
      "   - Expanding and upgrading public transportation systems.\n",
      "   - Developing a national network of electric vehicle charging stations.\n",
      "   - Replacing lead pipes.\n",
      "   - Providing affordable high-speed internet access.\n",
      "   - Upgrading airports, ports, and waterways.\n",
      "   - Implementing sustainable practices.\n",
      "2. **Examine Each Point for Brevity**:\n",
      "   - Each point is presented in a single sentence.\n",
      "   - The points are specific and avoid unnecessary elaboration.\n",
      "3. **Overall Length and Focus**:\n",
      "   - The list format helps in making the submission concise.\n",
      "   - The concluding sentence summarizes the purpose of the improvements succinctly: \"These improvements aim to enhance the overall infrastructure and support economic growth and competitiveness.\"\n",
      "4. **Relevance**:\n",
      "   - Each item on the list is directly relevant to the question about improvements in infrastructure.\n",
      "   - There is no extraneous information or digression from the main topic.\n",
      "5. **Conclusion**:\n",
      "   - The submission effectively communicates the necessary information in a clear and concise manner without unnecessary verbosity.\n",
      "Based on this detailed analysis, the submission meets the criterion of conciseness.\n",
      "Y\n",
      "\n",
      "Value\n",
      "---\n",
      "Y\n",
      "\n",
      "Score\n",
      "---\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print_dict(concise_eval_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8e4e74-876c-4dae-9e0d-42f20698ea43",
   "metadata": {},
   "source": [
    "### Evaluate Correctness\n",
    "\n",
    "We can use the same `load_evaluator` function to calculate correctness by simply changing the `criteria` to `\"correctness\"`.\n",
    "\n",
    "When using this option, we can pass a reference for the evaluator to check the correctness against.\n",
    "Let's pass a reference that matches the information returned as well as one that doesn't.\n",
    "\n",
    "For this evaluation, we will use the result of the second query we ran through our RAG pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d5742f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_evaluator = load_evaluator(\n",
    "    \"labeled_criteria\",\n",
    "    criteria=\"correctness\",\n",
    "    llm=evaluation_llm,\n",
    "    requires_reference=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0fe16e",
   "metadata": {},
   "source": [
    "##### Matching Reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "86e41652",
   "metadata": {},
   "outputs": [],
   "source": [
    "matching_ref = \"15000 jobs were created due to manufacturing of electric vehicles.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a2bd3f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_eval_res1 = correct_evaluator.evaluate_strings(\n",
    "    prediction=res2, input=query2, reference=matching_ref\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "708d3386-f28d-4a6a-bb7c-10a15e8574af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reasoning\n",
      "---\n",
      "Step-by-step reasoning:\n",
      "1. **Correctness**: \n",
      "   - The submission states that Ford is creating 11,000 jobs and GM is creating 4,000 jobs, which totals 15,000 jobs.\n",
      "   - The reference states that 15,000 jobs were created due to the manufacturing of electric vehicles.\n",
      "2. **Accuracy**:\n",
      "   - The total number of jobs mentioned in the submission (15,000) matches the reference number (15,000). \n",
      "   - The specific companies mentioned (Ford and GM) and their respective job creation numbers (11,000 and 4,000) add up correctly to 15,000 jobs.\n",
      "3. **Factuality**:\n",
      "   - There is no conflicting information between the submission and the reference.\n",
      "   - The details provided about the companies (Ford and GM) are not disputed by the reference, and since the total number aligns, it can be considered factual.\n",
      "Conclusion:\n",
      "- Since the submission correctly totals 15,000 jobs, which matches the reference, and there are no inaccuracies or factual errors, the submission meets the criteria.\n",
      "Y\n",
      "\n",
      "Value\n",
      "---\n",
      "Y\n",
      "\n",
      "Score\n",
      "---\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print_dict(correct_eval_res1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1a317f",
   "metadata": {},
   "source": [
    "##### Contradictory Reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a2b8c14e",
   "metadata": {},
   "outputs": [],
   "source": [
    "contractic_ref = \"12000 jobs were created due to manufacturing of electric vehicles.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea0cb2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_eval_res2 = correct_evaluator.evaluate_strings(\n",
    "    prediction=res2, input=query2, reference=contractic_ref\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2172b83f-61ca-4963-8225-0378580a67a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reasoning\n",
      "---\n",
      "First, I will assess the submission based on the criterion of correctness, which includes accuracy and factuality. Here is the step-by-step reasoning:\n",
      "1. **Correctness**:\n",
      "   - The submission states that Ford is creating 11,000 jobs and GM is creating 4,000 jobs in the electric vehicle manufacturing industry, totaling 15,000 jobs.\n",
      "   - The reference data indicates that 12,000 jobs were created due to the manufacturing of electric vehicles.\n",
      "   - There is a discrepancy between the submission and the reference data. The submission claims a total of 15,000 jobs, whereas the reference data states 12,000 jobs.\n",
      "   - Since the submission's total (15,000 jobs) does not match the reference data (12,000 jobs), it is not factually correct.\n",
      "Given this analysis, the submission does not meet the criterion of correctness as it provides an inaccurate total number of jobs created.\n",
      "Therefore, the answer is:\n",
      "N\n",
      "\n",
      "Value\n",
      "---\n",
      "N\n",
      "\n",
      "Score\n",
      "---\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print_dict(correct_eval_res2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7195efbb",
   "metadata": {},
   "source": [
    "## 6. Delete the KDB.AI Table\n",
    "\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1d83ed49",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ed75e9",
   "metadata": {},
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/dgCLUkdx)"
   ]
  }
 ],
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================================================
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================================================
FILE: sentiment_analysis/sentiment_analysis.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48b6c907",
   "metadata": {
    "id": "48b6c907"
   },
   "source": [
    "# Sentiment Analysis on Disneyland Resort Reviews\n",
    "\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "In this example, we will extract valuable sentiments from Disneyland Resort reviews, gaining a deeper understanding of customer experiences.\n",
    "\n",
    "We will leverage the power of Natural Language Processing (NLP) and sentiment analysis techniques to assess the sentiment expressed in these reviews. But that's not all, our approach doesn't stop at sentiment analysis; it extends to the realm of powerful vector databases.\n",
    "\n",
    "Using KDB.AI we can store not only the reviews themselves but also the sentiment labels as metadata. With KDB.AI, we can easily search for any topic, keyword, or sentiment and retrieve relevant customer reviews instantly. Whether you're interested in finding the happiest moments in the park, uncovering areas for improvement, or simply exploring the multitude of experiences Disneyland Resort has to offer, KDB.AI makes it all possible with just a few clicks.\n",
    "\n",
    "### Aim\n",
    "\n",
    "In the sections that follow, we'll walk you through the entire process:\n",
    "\n",
    "1. Load Review Data\n",
    "1. Perform Sentiment Analysis On The Reviews\n",
    "1. Create Review Vector Embeddings\n",
    "1. Store Embeddings in KDB.AI\n",
    "1. Get The Sentiment Of Similar Reviews To A Target Query\n",
    "1. Delete the KDB.AI Database & Table\n",
    "\n",
    "By the end of this tutorial, you'll not only have a deeper understanding of sentiment analysis but also the tools and knowledge to harness the insights hidden within vast datasets of customer reviews. Let's embark on this journey to uncover the magic and meaning behind Disneyland Resort reviews with sentiment analysis and KDB.AI.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a10030c",
   "metadata": {
    "id": "6a10030c"
   },
   "source": [
    "## 0. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91255bcd",
   "metadata": {
    "id": "91255bcd"
   },
   "source": [
    "### Install dependencies\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f936c0ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install kdbai_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dea527a1",
   "metadata": {
    "id": "dea527a1"
   },
   "outputs": [],
   "source": [
    "!pip install sentence_transformers matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acb9eaf5",
   "metadata": {
    "id": "acb9eaf5"
   },
   "outputs": [],
   "source": [
    "### !!! Only run this cell if you need to download the data into your environment, for example in Colab\n",
    "### This downloads customer review data\n",
    "!mkdir ./data\n",
    "!wget -P ./data https://raw.githubusercontent.com/KxSystems/kdbai-samples/main/sentiment_analysis/data/disneyland_reviews.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd6cfb9-6b68-472f-be47-5b7d03e4564d",
   "metadata": {
    "id": "0bd6cfb9-6b68-472f-be47-5b7d03e4564d"
   },
   "source": [
    "### Set Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08f05e63-9ef8-49ca-b198-aab6d0017579",
   "metadata": {
    "id": "08f05e63-9ef8-49ca-b198-aab6d0017579"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5be006c3-2bd1-4944-abef-78664e288fcd",
   "metadata": {
    "id": "5be006c3-2bd1-4944-abef-78664e288fcd"
   },
   "outputs": [],
   "source": [
    "# ignore tensorflow warnings\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f53e98",
   "metadata": {
    "id": "36f53e98"
   },
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c8be1e0",
   "metadata": {
    "id": "4c8be1e0"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51e1c7ef",
   "metadata": {
    "id": "51e1c7ef"
   },
   "outputs": [],
   "source": [
    "# tokenisation\n",
    "from transformers import pipeline\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForSequenceClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "972ed652",
   "metadata": {
    "id": "972ed652"
   },
   "outputs": [],
   "source": [
    "# timing\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5c28352b",
   "metadata": {
    "id": "5c28352b"
   },
   "outputs": [],
   "source": [
    "# plotting\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1d68085",
   "metadata": {
    "id": "b1d68085"
   },
   "outputs": [],
   "source": [
    "# embedding\n",
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "070348ba",
   "metadata": {
    "id": "070348ba"
   },
   "outputs": [],
   "source": [
    "# vector DB\n",
    "import kdbai_client as kdbai\n",
    "from getpass import getpass\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20421bf",
   "metadata": {
    "id": "b20421bf"
   },
   "source": [
    "### Configure Console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7ec66ab",
   "metadata": {
    "id": "a7ec66ab"
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b843f5",
   "metadata": {
    "id": "c8b843f5"
   },
   "source": [
    "### Define Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db32355f",
   "metadata": {
    "id": "db32355f"
   },
   "outputs": [],
   "source": [
    "def show_df(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    print(df.shape)\n",
    "    return df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f3838f",
   "metadata": {
    "id": "13f3838f"
   },
   "source": [
    "## 1. Load Review Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa866120",
   "metadata": {
    "id": "fa866120"
   },
   "source": [
    "### Dataset Overview\n",
    "\n",
    "The dataset that will be used for this example is these [Disneyland Reviews](https://www.kaggle.com/datasets/arushchillar/disneyland-reviews) available on Kaggle. The dataset includes 42,000 reviews of 3 Disneyland branches - Paris, California and Hong Kong, posted by visitors on Trip Advisor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8989a4a6",
   "metadata": {
    "id": "8989a4a6"
   },
   "source": [
    "### Read In Review Data From CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cf7619be",
   "metadata": {
    "id": "cf7619be"
   },
   "outputs": [],
   "source": [
    "raw_reviews_df = pd.read_csv(\"data/disneyland_reviews.csv\", encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3a14cc9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "id": "d3a14cc9",
    "outputId": "07404714-f1da-4d3c-cc78-c3fc55ff1814"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42656, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_ID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Year_Month</th>\n",
       "      <th>Reviewer_Location</th>\n",
       "      <th>Review_Text</th>\n",
       "      <th>Branch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>670772142</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>670682799</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-5</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>670623270</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670607911</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>670607296</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
       "0  670772142       4     2019-4             Australia   \n",
       "1  670682799       4     2019-5           Philippines   \n",
       "2  670623270       4     2019-4  United Arab Emirates   \n",
       "3  670607911       4     2019-4             Australia   \n",
       "4  670607296       4     2019-4        United Kingdom   \n",
       "\n",
       "                                                                                                                                                                                               Review_Text  \\\n",
       "0  If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...   \n",
       "1  Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...   \n",
       "2  Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...   \n",
       "3  HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...   \n",
       "4                                      the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong   \n",
       "\n",
       "                Branch  \n",
       "0  Disneyland_HongKong  \n",
       "1  Disneyland_HongKong  \n",
       "2  Disneyland_HongKong  \n",
       "3  Disneyland_HongKong  \n",
       "4  Disneyland_HongKong  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(raw_reviews_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1b091e",
   "metadata": {
    "id": "4a1b091e"
   },
   "source": [
    "### Subset The Data\n",
    "\n",
    "This is a pretty big dataset and we do not need to use all of this in our sample. As such, we will extract a subset of these reviews.\n",
    "\n",
    "Let's first take a look at the number of reviews per branch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "087498ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 209
    },
    "id": "087498ed",
    "outputId": "e5226a6d-9ad3-47b9-8e4a-dbfd51b60e6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Branch\n",
       "Disneyland_California    19406\n",
       "Disneyland_HongKong       9620\n",
       "Disneyland_Paris         13630\n",
       "Name: Branch, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_reviews_df.groupby(\"Branch\")[\"Branch\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62084d2c",
   "metadata": {
    "id": "62084d2c"
   },
   "source": [
    "We have a good number per branch so let's take the first 50 per branch only to help with performance.\n",
    "\n",
    "The speed of your hardware, particularly your CPU and memory, will influence processing time when running sentiment analysis. If you are running this example on a CPU it is a good idea to restrict the number of rows first to help with performance. If you have access to a GPU you may wish to use that and the full dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1307fe0a",
   "metadata": {
    "id": "1307fe0a"
   },
   "outputs": [],
   "source": [
    "reviews_df = raw_reviews_df.groupby(\"Branch\").head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "736bb9d0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 209
    },
    "id": "736bb9d0",
    "outputId": "b599e971-36a9-44ff-fcdf-b968b6cae1f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Branch\n",
       "Disneyland_California    50\n",
       "Disneyland_HongKong      50\n",
       "Disneyland_Paris         50\n",
       "Name: Branch, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_df.groupby(\"Branch\")[\"Branch\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9e5e03",
   "metadata": {
    "id": "ad9e5e03"
   },
   "source": [
    "## 2. Perform Sentiment Analysis On The Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0651452",
   "metadata": {
    "id": "b0651452"
   },
   "source": [
    "Sentiment analysis, the process of determining the emotional tone or sentiment expressed in text, has seen significant advancements with the use of transformer-based models. In this section, we explore sentiment analysis using the powerful [Hugging Face Transformers](https://huggingface.co/docs/transformers/model_doc/roberta) library.\n",
    "\n",
    "### Import Sentence Tokenizer And Model\n",
    "\n",
    "We begin by importing essential components, including the sentiment analysis model and tokenizer. In this example, we use the RoBERTa model, which is fine-tuned for sentiment classification tasks. The model is capable of classifying text into three sentiment labels: positive, negative, and neutral.\n",
    "\n",
    "Key Components:\n",
    "\n",
    "- **AutoModelForSequenceClassification**: This component is a pre-trained transformer model fine-tuned for sequence classification tasks like sentiment analysis. It's designed to handle various NLP tasks, making it highly versatile.\n",
    "- **AutoTokenizer**: We use the tokenizer to preprocess and encode text data before feeding it to the model.\n",
    "- **MODEL**: This variable specifies the pre-trained sentiment analysis model we're using in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fbc35400",
   "metadata": {
    "id": "fbc35400"
   },
   "outputs": [],
   "source": [
    "MODEL = f\"cardiffnlp/twitter-roberta-base-sentiment\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5bad95",
   "metadata": {
    "id": "3c5bad95"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4423d909",
   "metadata": {
    "id": "4423d909"
   },
   "outputs": [],
   "source": [
    "sentiment_model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    MODEL, num_labels=3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdda7527",
   "metadata": {
    "id": "fdda7527"
   },
   "source": [
    "### Configure Sentiment Analysis Model Pipeline\n",
    "\n",
    "In this section, we configure the sentiment analysis pipeline using the Hugging Face Transformers library.\n",
    "\n",
    "This configuration enables us to easily perform sentiment analysis on text data by utilizing the pre-trained model within the pipeline. The 'sentiment' function streamlines the process of obtaining sentiment labels and scores, making it convenient for analyzing reviews or textual data and interpreting the sentiment expressed within them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aa389a8c",
   "metadata": {
    "id": "aa389a8c"
   },
   "outputs": [],
   "source": [
    "sentiment_pipeline = pipeline(\n",
    "    \"sentiment-analysis\",\n",
    "    model=sentiment_model,\n",
    "    tokenizer=tokenizer,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5fe239da",
   "metadata": {
    "id": "5fe239da"
   },
   "outputs": [],
   "source": [
    "def sentiment(reviews):\n",
    "    # pass the reviews through sentiment analysis pipeline\n",
    "    sentiments = sentiment_pipeline(reviews)\n",
    "    # extract only the label and score from the result\n",
    "    l = [labels[x[\"label\"]] for x in sentiments][0]\n",
    "    s = [x[\"score\"] for x in sentiments][0]\n",
    "    return l, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "20f55ae5",
   "metadata": {
    "id": "20f55ae5"
   },
   "outputs": [],
   "source": [
    "labels = {\"LABEL_0\": \"negative\", \"LABEL_1\": \"neutral\", \"LABEL_2\": \"positive\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ed3694",
   "metadata": {
    "id": "c7ed3694"
   },
   "source": [
    "### Get Sentiment For One Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c05c6529",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 59
    },
    "id": "c05c6529",
    "outputId": "10b9cb5a-d5a1-4e25-d59a-2702f620479a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Disneyland never cease to amaze me! I've been to Disneyland florida and I thought I have exhausted the kid in me but nope! I still had so much fun in disneyland hong kong. 2 DL off my bucketlist and more to come!     \""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = reviews_df[\"Review_Text\"][10]\n",
    "example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9dc613e9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9dc613e9",
    "outputId": "fdce2dcf-c0a2-42d9-a357-625728e0dbdd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('positive', 0.9825872778892517)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d901741f",
   "metadata": {
    "id": "d901741f"
   },
   "source": [
    "We can see that the Roberta Model has identified this as a very positive score which makes sense when reading the text.\n",
    "\n",
    "Great - now let's run our pipeline on the entire dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb09123",
   "metadata": {
    "id": "9fb09123"
   },
   "source": [
    "### Get Sentiment For Entire DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bfe0ee",
   "metadata": {
    "id": "b9bfe0ee"
   },
   "source": [
    "In this code snippet, we perform sentiment analysis on the entire DataFrame containing reviews.\n",
    "\n",
    "The process involves iterating through each row of the DataFrame, extracting text data, and using a sentiment analysis function `sentiment` to analyze the sentiment of each review. Additionally, it includes error handling to gracefully handle any issues that may arize during the sentiment analysis process.\n",
    "\n",
    "This code will take a few minutes to run - you can always reduce or increase the size of the input data depending on your machines performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c00808a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "id": "c00808a7",
    "outputId": "5de17e46-e4c5-4789-826e-b21173435fe6"
   },
   "outputs": [],
   "source": [
    "res = {}\n",
    "for _, row in tqdm(reviews_df.iterrows(), total=len(reviews_df)):\n",
    "    myid = row[\"Review_ID\"]\n",
    "    try:\n",
    "        res[myid] = sentiment(row[\"Review_Text\"])\n",
    "    except RuntimeError:\n",
    "        print(f\"Broke for id {myid}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89edf54b",
   "metadata": {
    "id": "89edf54b"
   },
   "source": [
    "We can see it broke for some IDs which is okay - these may have been too long for the RoBERTa model and have been ignored.\n",
    "\n",
    "Let's merge the sentiment analysis results obtained from the sentiment analysis process with the original DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "59d67f97",
   "metadata": {
    "id": "59d67f97"
   },
   "outputs": [],
   "source": [
    "sentiments_df = (\n",
    "    pd.DataFrame(res)\n",
    "    .T.reset_index()\n",
    "    .rename(columns={\"index\": \"Review_ID\", 0: \"Label\", 1: \"Score\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "655a93fb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "655a93fb",
    "outputId": "88a299d4-957c-4ef9-8da6-4016e7d4a6f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 3)\n"
     ]
    },
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>670682799</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.858987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>670623270</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.898818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670607911</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.863198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>670607296</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.56407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Review_ID     Label     Score\n",
       "0  670772142  positive  0.984786\n",
       "1  670682799  positive  0.858987\n",
       "2  670623270  positive  0.898818\n",
       "3  670607911  positive  0.863198\n",
       "4  670607296  positive   0.56407"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(sentiments_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b37219",
   "metadata": {
    "id": "07b37219"
   },
   "source": [
    "### Add These Sentiment Values To The Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0cd56395",
   "metadata": {
    "id": "0cd56395"
   },
   "outputs": [],
   "source": [
    "review_sentiments_df = sentiments_df.merge(reviews_df, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "527ca6a6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "527ca6a6",
    "outputId": "459018f4-122f-493a-d0ee-a6fc693dec5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Year_Month</th>\n",
       "      <th>Reviewer_Location</th>\n",
       "      <th>Review_Text</th>\n",
       "      <th>Branch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>670772142</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.984786</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>670682799</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.858987</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-5</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>670623270</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.898818</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>670607911</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.863198</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>670607296</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.56407</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Review_ID     Label     Score  Rating Year_Month     Reviewer_Location  \\\n",
       "0  670772142  positive  0.984786       4     2019-4             Australia   \n",
       "1  670682799  positive  0.858987       4     2019-5           Philippines   \n",
       "2  670623270  positive  0.898818       4     2019-4  United Arab Emirates   \n",
       "3  670607911  positive  0.863198       4     2019-4             Australia   \n",
       "4  670607296  positive   0.56407       4     2019-4        United Kingdom   \n",
       "\n",
       "                                                                                                                                                                                               Review_Text  \\\n",
       "0  If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...   \n",
       "1  Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...   \n",
       "2  Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...   \n",
       "3  HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...   \n",
       "4                                      the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong   \n",
       "\n",
       "                Branch  \n",
       "0  Disneyland_HongKong  \n",
       "1  Disneyland_HongKong  \n",
       "2  Disneyland_HongKong  \n",
       "3  Disneyland_HongKong  \n",
       "4  Disneyland_HongKong  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(review_sentiments_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142b87af",
   "metadata": {
    "id": "142b87af"
   },
   "source": [
    "### Plot The Sentiment For Each Branch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f21b19a",
   "metadata": {
    "id": "7f21b19a"
   },
   "source": [
    "Visualizing this, we can see the variety in sentiment across the 3 branches and compare the the rating the customer gave in their review. For the positive reviews we see all 4* or 5* reviews which makes sense. The negative reviews have lower ratings with the California branch getting the lowest ratings off all branches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f273ecf3",
   "metadata": {
    "id": "f273ecf3"
   },
   "outputs": [],
   "source": [
    "def plot_grouped_bar_chart(df: pd.DataFrame, y_label: str) -> None:\n",
    "    # Get list of unique branches and labels\n",
    "    branches = df.index.unique()\n",
    "    labels = df.columns.unique()\n",
    "\n",
    "    # Plot a bar for each branch-label pair\n",
    "    bar_width = 0.2\n",
    "    x_positions = range(len(labels))\n",
    "    for i, branch in enumerate(branches):\n",
    "        metric_ratings = df.loc[branch]\n",
    "        plt.bar(\n",
    "            x=[pos + (i * bar_width) for pos in x_positions],\n",
    "            height=metric_ratings,\n",
    "            width=bar_width,\n",
    "            label=branch,\n",
    "        )\n",
    "\n",
    "    # Beutify the plot\n",
    "    plt.xticks([pos + bar_width for pos in x_positions], labels)\n",
    "    plt.xlabel(\"Label\")\n",
    "    plt.ylabel(y_label)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4ef67d97",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "id": "4ef67d97",
    "outputId": "c672cf20-5c6f-4e0c-bdb0-1a817ea2887a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Label</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Branch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Disneyland_California</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.822222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disneyland_HongKong</th>\n",
       "      <td>3.166667</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>4.351351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disneyland_Paris</th>\n",
       "      <td>2.416667</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.628571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Label                  negative   neutral  positive\n",
       "Branch                                             \n",
       "Disneyland_California  1.500000       NaN  4.822222\n",
       "Disneyland_HongKong    3.166667  3.833333  4.351351\n",
       "Disneyland_Paris       2.416667  2.500000  4.628571"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the DataFrame by 'Branch' and 'Label' and calculate the mean rating for each group\n",
    "grouped_review_sentiments_df = (\n",
    "    review_sentiments_df.groupby([\"Branch\", \"Label\"])[\"Rating\"].mean().unstack()\n",
    ")\n",
    "grouped_review_sentiments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a206e7da",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "a206e7da",
    "outputId": "75370af2-a929-4325-8be0-be4bf70a6b1b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grouped_bar_chart(grouped_review_sentiments_df, y_label=\"Mean Rating\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba67d2d9",
   "metadata": {
    "id": "ba67d2d9"
   },
   "source": [
    " This consolidated dataset can be further analyzed, visualized, and in this example we will add it to our KDB.AI vector database to gain insights into the sentiments expressed in the reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12d4fe9",
   "metadata": {
    "id": "b12d4fe9"
   },
   "source": [
    "## 3. Create Review Vector Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5793e6d7",
   "metadata": {
    "id": "5793e6d7"
   },
   "source": [
    "Before we can add this data to KDB.AI, we must utilize the [Sentence Transformers](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) library to perform text embedding. Text embedding is the process of converting textual data into numerical vectors that capture semantic meaning.\n",
    "\n",
    "The `embedded_review_df` DataFrame provides a combined view of the original data we want to store as metadata in KDB.AI and the associated embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1993e9e1",
   "metadata": {
    "id": "1993e9e1"
   },
   "outputs": [],
   "source": [
    "embedding_model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b717e590",
   "metadata": {
    "id": "b717e590"
   },
   "outputs": [],
   "source": [
    "def encode_text(text):\n",
    "    return embedding_model.encode(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8cf68a26",
   "metadata": {
    "id": "8cf68a26"
   },
   "outputs": [],
   "source": [
    "review_sentiments_df[\"embeddings\"] = review_sentiments_df[\"Review_Text\"].apply(\n",
    "    encode_text\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "60e16dea",
   "metadata": {
    "id": "60e16dea"
   },
   "outputs": [],
   "source": [
    "embedded_review_df = review_sentiments_df[\n",
    "    [\"Branch\", \"Label\", \"Score\", \"Rating\", \"Review_Text\", \"embeddings\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ab15e75",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 536
    },
    "id": "7ab15e75",
    "outputId": "eaa1fefe-364a-4a19-9ea8-43e4d5b89c40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Branch</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review_Text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.984786</td>\n",
       "      <td>4</td>\n",
       "      <td>If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...</td>\n",
       "      <td>[0.12519372, -0.047441825, 0.0871841, 0.010215119, -0.04802456, 0.003773756, 0.0026167803, -0.05580014, -0.01470384, -0.020438066, 0.003935949, -0.02602119, -0.0065161493, -0.01756644, 0.06614383,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.858987</td>\n",
       "      <td>4</td>\n",
       "      <td>Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...</td>\n",
       "      <td>[0.047256097, -0.022195395, 0.12704651, -0.01767993, 0.012125689, 0.023794448, 0.0635631, -0.058635417, -0.07302157, -0.020539569, -0.019598728, -0.07271388, -0.04178504, -0.0031552485, 0.07268948...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.898818</td>\n",
       "      <td>4</td>\n",
       "      <td>Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...</td>\n",
       "      <td>[0.10299566, 0.018655304, 0.12572218, 0.08505048, 0.0050070393, -0.034780897, -0.017856728, -0.027097572, -0.06612961, -0.04508932, -0.05486106, -0.0064851535, -0.026050558, 0.017010149, 0.0760149...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.863198</td>\n",
       "      <td>4</td>\n",
       "      <td>HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...</td>\n",
       "      <td>[0.14166501, -0.01674562, 0.0866899, 0.00852721, -0.037891164, 0.052252676, 0.010435957, -0.04532679, -0.07340584, 0.0044011963, 0.012121033, -0.022934418, -0.039868694, -0.03126051, 0.12090388, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.56407</td>\n",
       "      <td>4</td>\n",
       "      <td>the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong</td>\n",
       "      <td>[0.11346179, 0.0044915066, 0.065638125, 0.055530254, 0.027315829, -0.006237473, -0.07888978, -0.0714263, -0.046673268, 0.011053106, 0.08186102, -0.10437099, -0.03234412, 0.0073166923, 0.082220174,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Branch     Label     Score  Rating  \\\n",
       "0  Disneyland_HongKong  positive  0.984786       4   \n",
       "1  Disneyland_HongKong  positive  0.858987       4   \n",
       "2  Disneyland_HongKong  positive  0.898818       4   \n",
       "3  Disneyland_HongKong  positive  0.863198       4   \n",
       "4  Disneyland_HongKong  positive   0.56407       4   \n",
       "\n",
       "                                                                                                                                                                                               Review_Text  \\\n",
       "0  If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...   \n",
       "1  Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...   \n",
       "2  Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...   \n",
       "3  HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...   \n",
       "4                                      the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong   \n",
       "\n",
       "                                                                                                                                                                                                embeddings  \n",
       "0  [0.12519372, -0.047441825, 0.0871841, 0.010215119, -0.04802456, 0.003773756, 0.0026167803, -0.05580014, -0.01470384, -0.020438066, 0.003935949, -0.02602119, -0.0065161493, -0.01756644, 0.06614383,...  \n",
       "1  [0.047256097, -0.022195395, 0.12704651, -0.01767993, 0.012125689, 0.023794448, 0.0635631, -0.058635417, -0.07302157, -0.020539569, -0.019598728, -0.07271388, -0.04178504, -0.0031552485, 0.07268948...  \n",
       "2  [0.10299566, 0.018655304, 0.12572218, 0.08505048, 0.0050070393, -0.034780897, -0.017856728, -0.027097572, -0.06612961, -0.04508932, -0.05486106, -0.0064851535, -0.026050558, 0.017010149, 0.0760149...  \n",
       "3  [0.14166501, -0.01674562, 0.0866899, 0.00852721, -0.037891164, 0.052252676, 0.010435957, -0.04532679, -0.07340584, 0.0044011963, 0.012121033, -0.022934418, -0.039868694, -0.03126051, 0.12090388, -...  \n",
       "4  [0.11346179, 0.0044915066, 0.065638125, 0.055530254, 0.027315829, -0.006237473, -0.07888978, -0.0714263, -0.046673268, 0.011053106, 0.08186102, -0.10437099, -0.03234412, 0.0073166923, 0.082220174,...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(embedded_review_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26daf9db",
   "metadata": {
    "id": "26daf9db"
   },
   "source": [
    "## 4. Store Embeddings in KDB.AI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70505eae-4138-4ba8-80e9-fc13c37d0b32",
   "metadata": {
    "id": "70505eae-4138-4ba8-80e9-fc13c37d0b32"
   },
   "source": [
    "With the embeddings created, we need to store them in a vector database to enable efficient searching.\n",
    "\n",
    "### Define KDB.AI Session\n",
    "\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b895a01",
   "metadata": {
    "id": "9b895a01"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Fk1GP_9ZjA5R",
   "metadata": {
    "id": "Fk1GP_9ZjA5R"
   },
   "source": [
    "### Verify Defined Databases\n",
    "\n",
    "We can check our connection using the `session.databases()` function.\n",
    "This will return a list of all the databases we have defined in our vector database thus far.\n",
    "This should return a \"default\" database along with any other databases you have already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ARii3dRCjBu1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ARii3dRCjBu1",
    "outputId": "4945150f-92b6-45db-cb65-e24320360ab7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KDBAI database \"default\", KDBAI database \"myDatabase\"]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.databases()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GaF1hrOdjJu9",
   "metadata": {
    "id": "GaF1hrOdjJu9"
   },
   "source": [
    "### Create a Database Called \"myDatabase\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "TPPK7iFFjKTg",
   "metadata": {
    "id": "TPPK7iFFjKTg"
   },
   "outputs": [],
   "source": [
    "# ensure no database called \"myDatabase\" exists\n",
    "try:\n",
    "    session.database(\"myDatabase\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "qiKjROi7jOJd",
   "metadata": {
    "id": "qiKjROi7jOJd"
   },
   "outputs": [],
   "source": [
    "# Create the database\n",
    "db = session.create_database(\"myDatabase\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c6525c",
   "metadata": {
    "id": "48c6525c"
   },
   "source": [
    "### Define Vector DB Table Schema\n",
    "\n",
    "The next step is to define a schema for our KDB.AI table where we will store our embeddings.\n",
    "\n",
    "At this point you will select the index and metric you want to use for searching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "af604df0",
   "metadata": {
    "id": "af604df0"
   },
   "outputs": [],
   "source": [
    "review_schema = [\n",
    "        {\"name\": \"Branch\", \"type\": \"str\"},\n",
    "        {\"name\": \"Label\", \"type\": \"str\"},\n",
    "        {\"name\": \"Score\", \"type\": \"float64\"},\n",
    "        {\"name\": \"Rating\", \"type\": \"int64\"},\n",
    "        {\"name\": \"Review_Text\", \"type\": \"str\"},\n",
    "        {\n",
    "            \"name\": \"embeddings\",\n",
    "            \"type\":\"float64s\",\n",
    "        },\n",
    "    ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "EGUAiZhJlOJg",
   "metadata": {
    "id": "EGUAiZhJlOJg"
   },
   "source": [
    "### Define the indexes\n",
    "We will define our dimensionality, similarity metric and index type with the vectorIndex attribute. For this example we chose:\n",
    "\n",
    "- type = hnsw : HNSW enhances efficiency while maintaining accuracy. You have the choice of using other indexes like, qHNSW, and IVFPQ, qFlat or a Flat index here, as with metrics the one you chose depends your data and your overall performance requirements.\n",
    "- name = hnsw_index : this is a custom name you give your index.\n",
    "\n",
    "####params:\n",
    "- dims = 384 : In the next section, we generate embeddings that are 384-dimensional to match this. The number of dimensions should mirror the output dimensions of your embedding model.\n",
    "- metric = CS : We chose cosine similarity. You have the choice of using other metrics here like IP/Inner Product and L2/Euclidean distance and the one you chose depends on the specific context and nature of your data.\n",
    "\n",
    "!Note, it is possible to define multiple indexes within a table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "PplL9rzsiVaG",
   "metadata": {
    "id": "PplL9rzsiVaG"
   },
   "outputs": [],
   "source": [
    "# Define the index\n",
    "indexes = [\n",
    "    {\n",
    "        'type': 'hnsw',\n",
    "        'name': 'hnsw_index',\n",
    "        'column': 'embeddings',\n",
    "        'params': {'dims': 384, 'metric': \"CS\"},\n",
    "    },\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518cfe1e",
   "metadata": {
    "id": "518cfe1e"
   },
   "source": [
    "### Create Vector DB Table\n",
    "\n",
    "Use the KDB.AI `create_table` function to create a table that matches the defined schema in the vector database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f583df69",
   "metadata": {
    "id": "f583df69"
   },
   "outputs": [],
   "source": [
    "# First ensure the table does not already exist\n",
    "try:\n",
    "    db.table(\"review\").drop()\n",
    "except kdbai.KDBAIException:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0",
   "metadata": {
    "id": "e1d190db-7c19-418e-9140-3dff04c9d4c0"
   },
   "outputs": [],
   "source": [
    "table = db.create_table(table=\"review\", schema=review_schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0a9d0e-80b8-4e09-9101-d22667da551f",
   "metadata": {
    "id": "9a0a9d0e-80b8-4e09-9101-d22667da551f"
   },
   "source": [
    "### Add Embedded Data to KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "83cc156c-8071-4784-8c3e-a049b61e8668",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "83cc156c-8071-4784-8c3e-a049b61e8668",
    "outputId": "1f0f184b-b9d4-4379-f5b5-f37071d3045f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 147}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.insert(embedded_review_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31a4ecd",
   "metadata": {
    "id": "e31a4ecd"
   },
   "source": [
    "### Verify Data Has Been Inserted\n",
    "\n",
    "Running `table.query()` should show us that data has been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ee6ecb8d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570
    },
    "id": "ee6ecb8d",
    "outputId": "74911c26-0fab-41ff-a7dc-b4f3ce539716"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Branch</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review_Text</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.984786</td>\n",
       "      <td>4</td>\n",
       "      <td>If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...</td>\n",
       "      <td>[0.12519371509552002, -0.04744182527065277, 0.08718410134315491, 0.01021511945873499, -0.04802456125617027, 0.00377375609241426, 0.0026167803443968296, -0.05580013990402222, -0.014703840017318726,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.858987</td>\n",
       "      <td>4</td>\n",
       "      <td>Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...</td>\n",
       "      <td>[0.047256097197532654, -0.022195395082235336, 0.12704651057720184, -0.017679929733276367, 0.01212568860501051, 0.023794448003172874, 0.06356310099363327, -0.058635417371988297, -0.0730215683579444...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.898818</td>\n",
       "      <td>4</td>\n",
       "      <td>Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...</td>\n",
       "      <td>[0.10299565643072128, 0.018655303865671158, 0.12572218477725983, 0.08505047857761383, 0.005007039289921522, -0.034780897200107574, -0.01785672828555107, -0.02709757164120674, -0.06612960994243622,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.863198</td>\n",
       "      <td>4</td>\n",
       "      <td>HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...</td>\n",
       "      <td>[0.1416650116443634, -0.016745619475841522, 0.08668989688158035, 0.008527209982275963, -0.03789116442203522, 0.05225267633795738, 0.010435957461595535, -0.04532679170370102, -0.07340583950281143, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.564070</td>\n",
       "      <td>4</td>\n",
       "      <td>the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong</td>\n",
       "      <td>[0.11346179246902466, 0.004491506610065699, 0.06563812494277954, 0.055530253797769547, 0.027315828949213028, -0.006237472873181105, -0.07888977974653244, -0.07142630219459534, -0.04667326807975769...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Branch     Label     Score  Rating  \\\n",
       "0  Disneyland_HongKong  positive  0.984786       4   \n",
       "1  Disneyland_HongKong  positive  0.858987       4   \n",
       "2  Disneyland_HongKong  positive  0.898818       4   \n",
       "3  Disneyland_HongKong  positive  0.863198       4   \n",
       "4  Disneyland_HongKong  positive  0.564070       4   \n",
       "\n",
       "                                                                                                                                                                                               Review_Text  \\\n",
       "0  If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small Wo...   \n",
       "1  Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironm...   \n",
       "2  Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunat...   \n",
       "3  HK Disneyland is a great compact park. Unfortunately there is quite a bit of maintenance work going on at present so a number of areas are closed off (including the famous castle) If you go midwee...   \n",
       "4                                      the location is not in the city, took around 1 hour from Kowlon, my kids like disneyland so much, everything is fine.   but its really crowded and hot in Hong Kong   \n",
       "\n",
       "                                                                                                                                                                                                embeddings  \n",
       "0  [0.12519371509552002, -0.04744182527065277, 0.08718410134315491, 0.01021511945873499, -0.04802456125617027, 0.00377375609241426, 0.0026167803443968296, -0.05580013990402222, -0.014703840017318726,...  \n",
       "1  [0.047256097197532654, -0.022195395082235336, 0.12704651057720184, -0.017679929733276367, 0.01212568860501051, 0.023794448003172874, 0.06356310099363327, -0.058635417371988297, -0.0730215683579444...  \n",
       "2  [0.10299565643072128, 0.018655303865671158, 0.12572218477725983, 0.08505047857761383, 0.005007039289921522, -0.034780897200107574, -0.01785672828555107, -0.02709757164120674, -0.06612960994243622,...  \n",
       "3  [0.1416650116443634, -0.016745619475841522, 0.08668989688158035, 0.008527209982275963, -0.03789116442203522, 0.05225267633795738, 0.010435957461595535, -0.04532679170370102, -0.07340583950281143, ...  \n",
       "4  [0.11346179246902466, 0.004491506610065699, 0.06563812494277954, 0.055530253797769547, 0.027315828949213028, -0.006237472873181105, -0.07888977974653244, -0.07142630219459534, -0.04667326807975769...  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(table.query())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dece108",
   "metadata": {
    "id": "9dece108"
   },
   "source": [
    "## 5. Get The Sentiment Of Similar Reviews To A Target Query"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393ac113",
   "metadata": {
    "id": "393ac113"
   },
   "source": [
    "### Retrieve Reviews Based On A Query"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f448f100",
   "metadata": {
    "id": "f448f100"
   },
   "source": [
    "Next, let's create a function to let us perform a search, retrieve relevant results, and return them in Pandas DataFrame format, allowing for further analysis or examination of the retrieved data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5991de18",
   "metadata": {
    "id": "5991de18"
   },
   "outputs": [],
   "source": [
    "def search_and_extract_results(\n",
    "    table, embedding_model, search_term: str, n_results: int\n",
    ") -> pd.DataFrame:\n",
    "    # Encode the search term\n",
    "    embedded_search_term = [embedding_model.encode(search_term).tolist()]\n",
    "\n",
    "    # Perform the search\n",
    "    search_results = table.search(vectors={\"hnsw_index\":embedded_search_term}, n=n_results)\n",
    "\n",
    "    # Extract the results to a DataFrame - Return None if no results are found\n",
    "    return search_results[0].drop(\"embeddings\", axis=1) if search_results else None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1903f487",
   "metadata": {
    "id": "1903f487"
   },
   "source": [
    "Let's try with a query, calling with the search term \"are customers satisfied with the food at the park?\" and requesting 25 results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "046ba059",
   "metadata": {
    "id": "046ba059"
   },
   "outputs": [],
   "source": [
    "query1 = \"are customers satisfied with the food at the park?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "660a342f",
   "metadata": {
    "id": "660a342f"
   },
   "outputs": [],
   "source": [
    "query1_results = search_and_extract_results(\n",
    "    table, embedding_model, query1, n_results=25\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cc6bd3df",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "id": "cc6bd3df",
    "outputId": "e634fd90-5b29-4be8-9799-a5990592745a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__nn_distance</th>\n",
       "      <th>Branch</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.600825</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.914860</td>\n",
       "      <td>2</td>\n",
       "      <td>Visited the Park today   20 4   and can conclude that they simply let too many people in. Already at opening, queues at the attractions where 1 hour plus. At midday   the so called fast track was ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.568034</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.763060</td>\n",
       "      <td>5</td>\n",
       "      <td>I wish they had better food restaurant choices, but the attractions make up for this deficit so it's all good.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.492384</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.962652</td>\n",
       "      <td>5</td>\n",
       "      <td>We found this park to provide family friendly fun with a variety of shows and rides. We pre paid our tickets to save time which was a benefit and were glad we paid extra for the park hopper ticket...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.483248</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.450606</td>\n",
       "      <td>3</td>\n",
       "      <td>Don't get my wrong, my family have been to Disneyland Paris twice in as many years, my kids love it and the atmosphere is unique. I think its going a bit far to call it magical though. I wont comm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.481315</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.925374</td>\n",
       "      <td>5</td>\n",
       "      <td>Not the same as Disney in the states, none of the usual foods but none the less still fun.  We had a great time.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   __nn_distance                 Branch     Label     Score  Rating  \\\n",
       "0       0.600825       Disneyland_Paris  negative  0.914860       2   \n",
       "1       0.568034  Disneyland_California  positive  0.763060       5   \n",
       "2       0.492384  Disneyland_California  positive  0.962652       5   \n",
       "3       0.483248       Disneyland_Paris  negative  0.450606       3   \n",
       "4       0.481315       Disneyland_Paris  positive  0.925374       5   \n",
       "\n",
       "                                                                                                                                                                                               Review_Text  \n",
       "0  Visited the Park today   20 4   and can conclude that they simply let too many people in. Already at opening, queues at the attractions where 1 hour plus. At midday   the so called fast track was ...  \n",
       "1                                                                                           I wish they had better food restaurant choices, but the attractions make up for this deficit so it's all good.  \n",
       "2  We found this park to provide family friendly fun with a variety of shows and rides. We pre paid our tickets to save time which was a benefit and were glad we paid extra for the park hopper ticket...  \n",
       "3  Don't get my wrong, my family have been to Disneyland Paris twice in as many years, my kids love it and the atmosphere is unique. I think its going a bit far to call it magical though. I wont comm...  \n",
       "4                                                                                         Not the same as Disney in the states, none of the usual foods but none the less still fun.  We had a great time.  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df(query1_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87116d87",
   "metadata": {
    "id": "87116d87"
   },
   "source": [
    "### Group These Reviews By Branch\n",
    "\n",
    "This functions provides a convenient way to perform sentiment analysis on reviews or data associated with different branches or contexts, aggregating sentiment counts for each branch based on a given search term.\n",
    "\n",
    "It enables insights into the sentiments expressed in reviews within specific contexts, which can be valuable for analyzing customer feedback and making data-driven decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6f4a2dbd",
   "metadata": {
    "id": "6f4a2dbd"
   },
   "outputs": [],
   "source": [
    "def sentiment_counts_by_branch(search_results: pd.DataFrame) -> pd.DataFrame:\n",
    "    def sentiment_counts(df: pd.DataFrame) -> dict:\n",
    "        # Store count of sentiment labels\n",
    "        sentiments = {\n",
    "            \"negative\": 0,\n",
    "            \"neutral\": 0,\n",
    "            \"positive\": 0,\n",
    "        }\n",
    "        # Iterate through search results\n",
    "        for _, row in df.iterrows():\n",
    "            # Extract the sentiment label and increase its count\n",
    "            label = row[\"Label\"]\n",
    "            sentiments[label] += 1\n",
    "        return sentiments\n",
    "\n",
    "    grouped_df = search_results.groupby(\"Branch\").apply(sentiment_counts).reset_index()\n",
    "    grouped_df.columns = [\"Branch\", \"Sentiments\"]\n",
    "    return grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d9521679",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "d9521679",
    "outputId": "a473dd42-9332-4722-99e5-d63d2e00c812"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Branch</th>\n",
       "      <th>Sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>{'negative': 2, 'neutral': 0, 'positive': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>{'negative': 1, 'neutral': 0, 'positive': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>{'negative': 4, 'neutral': 0, 'positive': 5}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Branch                                    Sentiments\n",
       "0  Disneyland_California  {'negative': 2, 'neutral': 0, 'positive': 7}\n",
       "1    Disneyland_HongKong  {'negative': 1, 'neutral': 0, 'positive': 6}\n",
       "2       Disneyland_Paris  {'negative': 4, 'neutral': 0, 'positive': 5}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_counts_by_branch(query1_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122f99f5",
   "metadata": {
    "id": "122f99f5"
   },
   "source": [
    "### Visualize The Sentiment Of These Reviews\n",
    "\n",
    "To further improve the interpretability of the results let's define some functions for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aea99cac",
   "metadata": {
    "id": "aea99cac"
   },
   "outputs": [],
   "source": [
    "def plot_sentiment(df: pd.DataFrame, search_term: str) -> None:\n",
    "    # Iterate through the branches and create separate DataFrames\n",
    "    all_sentiments_df = pd.DataFrame()\n",
    "    for _, row in df.iterrows():\n",
    "        # Create a DataFrame from the sentiment_counts dictionary\n",
    "        branch_sentiment_df = pd.DataFrame([row[\"Sentiments\"]])\n",
    "\n",
    "        # Add a \"Branch\" column to identify the branch\n",
    "        branch_sentiment_df[\"Branch\"] = row[\"Branch\"]\n",
    "\n",
    "        # Concatenate all DataFrames into one\n",
    "        all_sentiments_df = pd.concat(\n",
    "            [all_sentiments_df, branch_sentiment_df], ignore_index=True\n",
    "        )\n",
    "\n",
    "    # Create a bar plot of the review sentiments grouped by branch\n",
    "    fig = plt.figure(figsize=(10, 4))\n",
    "    plot_grouped_bar_chart(\n",
    "        all_sentiments_df.set_index(\"Branch\"), y_label=\"Number of Reviews\"\n",
    "    )\n",
    "    fig.axes[0].set_title(search_term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4ee83675",
   "metadata": {
    "id": "4ee83675"
   },
   "outputs": [],
   "source": [
    "# Guest satisfaction with rides and attractions\n",
    "query2 = \"what did guests think of the rides and attractions?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b91d5d23",
   "metadata": {
    "id": "b91d5d23"
   },
   "outputs": [],
   "source": [
    "# find reviews based on this query\n",
    "query2_results = search_and_extract_results(\n",
    "    table, embedding_model, query2, n_results=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a23cf52c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "a23cf52c",
    "outputId": "15506f7a-a40d-4680-c6df-1d294167a892"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the sentiment of similar reviews\n",
    "plot_sentiment(sentiment_counts_by_branch(query2_results), query2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4312c00",
   "metadata": {
    "id": "e4312c00"
   },
   "source": [
    "We can see the guests are extremely satisfied with the rides and attractions at all 3 branches with Disneyland California coming out on top with no negative reviews related to rides and attractions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1899ecdc",
   "metadata": {
    "id": "1899ecdc"
   },
   "source": [
    "### Automate This Sentiment Of Review process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "55840339",
   "metadata": {
    "id": "55840339"
   },
   "outputs": [],
   "source": [
    "def review_sentiment_for_query(\n",
    "    table, embedding_model, query: str, n_results: int\n",
    ") -> None:\n",
    "    # find similar reviews\n",
    "    query_results = search_and_extract_results(\n",
    "        table, embedding_model, query, n_results=n_results\n",
    "    )\n",
    "\n",
    "    # top 3 most similar\n",
    "    print(query_results[:3][\"Review_Text\"])\n",
    "\n",
    "    # sentiment of similar reviews\n",
    "    plot_sentiment(sentiment_counts_by_branch(query_results), query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9340f683",
   "metadata": {
    "id": "9340f683"
   },
   "source": [
    "##### Guest satisfaction with food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f903bce9",
   "metadata": {
    "id": "f903bce9"
   },
   "outputs": [],
   "source": [
    "query3 = \"are customers satisfied with the food at the park?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e2ee0a25",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "id": "e2ee0a25",
    "outputId": "dd0cd628-c842-4e25-aa73-d2249e621962"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    Visited the Park today   20 4   and can conclude that they simply let too many people in. Already at opening, queues at the attractions where 1 hour plus. At midday   the so called fast track was ...\n",
      "1                                                                                             I wish they had better food restaurant choices, but the attractions make up for this deficit so it's all good.\n",
      "2    We found this park to provide family friendly fun with a variety of shows and rides. We pre paid our tickets to save time which was a benefit and were glad we paid extra for the park hopper ticket...\n",
      "Name: Review_Text, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "review_sentiment_for_query(table, embedding_model, query3, n_results=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bbbc64",
   "metadata": {
    "id": "f6bbbc64"
   },
   "source": [
    "Things don't fare as well when it comes to satisfaction on the food at each branch, we see a lot more negative food related reviews especially for Disneyland Paris."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4f1d47",
   "metadata": {
    "id": "8c4f1d47"
   },
   "source": [
    "##### Guest satisfaction with staff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5bc36aeb",
   "metadata": {
    "id": "5bc36aeb"
   },
   "outputs": [],
   "source": [
    "query4 = \"what did guests think of the staff?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "08d53143",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "id": "08d53143",
    "outputId": "2abb7116-2b20-4682-d5de-4f45e9ac5a13"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    This was very disappointing on this visit, three times in last four years. A number of the attraction s being closed and queues being very large for some of the rides. There appeared to be. A shor...\n",
      "1    First time at Disneyland Paris and had the most magical time. Loved that there were bigger rides for thrill seekers. Wait times not always accurate which was annoying as you lined up for a 20 minu...\n",
      "2    What a fantastic place, the queues were decent as this is the best time of year to go apparently, we managed to see almost everything, I was a bit disappointed the Haunted Mansion wasn   t open bu...\n",
      "Name: Review_Text, dtype: object\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "review_sentiment_for_query(table, embedding_model, query4, n_results=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5e2cbf",
   "metadata": {
    "id": "ca5e2cbf"
   },
   "source": [
    "Similarly for satisfaction with the staff - the most amount of negative reviews are coming for Disneyland Paris with less for California and none for Hong Kong.\n",
    "\n",
    "Of course we took a subset of data - just 150 of the 42,000 reviews so to get more meaningful insights it would be a good idea to run the analysis on the full dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a5d5647",
   "metadata": {
    "id": "8a5d5647"
   },
   "source": [
    "## 6. Delete the KDB.AI Database and Table\n",
    "\n",
    "Once finished with the database & table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4d39b895",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4d39b895",
    "outputId": "9b1db034-5f34-4f85-fa05-8837833f93c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.drop()\n",
    "db.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c80cb4",
   "metadata": {
    "id": "e3c80cb4"
   },
   "source": [
    "## Take Our Survey\n",
    "\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "[**Take the Survey**](https://delighted.com/t/LSHwNfQO)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: unstructured_io_RAG/Table_RAG_Unstructured_KDBAI_LangChain_RAG.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rt-9l5M4gmSD"
   },
   "source": [
    "# RAG with Unstructured, LangChain, & KDB.AI\n",
    "##### Note: This example requires KDB.AI server. Sign up for a free [KDB.AI account](https://kdb.ai/get-started).\n",
    "\n",
    "> [KDB.AI](https://kdb.ai/) is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications, using real-time data, by providing advanced search, recommendation and personalization.\n",
    "\n",
    "PDFs and other complex document types are notoriously difficult to work with, yet are the common file formats used for publishing important business related information. Since these file types are so common, it is key to have the capability to parse and ingest these documents swiftly, with accuracy, while cleanly extracting embedded entities such as images, tables, and graphs. If extracted correctly, all of the data held in a complex document like a PDF can be ingested into a RAG workflow to generate accurate and contextual responses for users and the business.\n",
    "\n",
    "This sample will illustrate how to use Unstructured, a complex document parsing technology, to ingest complex documentation, partition it into useful elements, perform chunking and embedding, and finally store the embeddings in KDB.AI. After this, we can complete a RAG pipeline with LangChain and query the KDB.AI vector database to retrieve the most relevant elements and pass them to an LLM to generate a response.\n",
    "\n",
    "We will focus in on how to enhance table elements with context and standardized formatting to enhance retrieval and generation.\n",
    "\n",
    "Agenda:\n",
    "1. Dependencies, Imports & Setup\n",
    "2. Use Unstructured to Process Complex PDF Documentation\n",
    "3. Embed Extracted Elements with OpenAI Embedding Model\n",
    "4. Define KDB.AI Session\n",
    "5. Create Schema and KDB.AI Table\n",
    "6. Use LangChain and KDB.AI to Perform RAG!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xzRDHnFJgmSF"
   },
   "source": [
    "## 1. Dependencies, Imports & Setup\n",
    "\n",
    "In order to successfully run this sample, note the following steps depending on where you are running this notebook:\n",
    "\n",
    "-***Run Locally / Private Environment:*** The [Setup](https://github.com/KxSystems/kdbai-samples/blob/main/README.md#setup) steps in the repository's `README.md` will guide you on prerequisites and how to run this with Jupyter.\n",
    "\n",
    "\n",
    "-***Colab / Hosted Environment:*** Open this notebook in Colab and run through the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KCwO5V1QgmSF"
   },
   "outputs": [],
   "source": [
    "!apt-get -qq install poppler-utils tesseract-ocr\n",
    "%pip install -q --user --upgrade pillow\n",
    "%pip install -q --upgrade unstructured[\"all-docs\"]\n",
    "%pip install pymupdf\n",
    "%pip install kdbai_client\n",
    "%pip install langchain-openai\n",
    "%pip install langchain\n",
    "#%pip install langchain-community\n",
    "import os\n",
    "!git clone -b KDBAI_v1.4 https://github.com/KxSystems/langchain.git\n",
    "#!cd langchain/libs/community\n",
    "os.chdir('langchain/libs/community')\n",
    "!pip install .\n",
    "%pip install --upgrade nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "60Fu7hWzPiHR",
    "outputId": "294a4e05-226a-4f06-983a-e8f9f4744832"
   },
   "outputs": [],
   "source": [
    "from unstructured.partition.pdf import partition_pdf\n",
    "from unstructured.partition.auto import partition\n",
    "from unstructured.embed.openai import OpenAIEmbeddingConfig, OpenAIEmbeddingEncoder\n",
    "import fitz\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "import kdbai_client as kdbai\n",
    "from langchain_community.vectorstores import KDBAI\n",
    "from langchain.chains import RetrievalQA\n",
    "from langchain_openai import ChatOpenAI\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fi5RfSrUPfKz"
   },
   "source": [
    "Get OpenAI API key here:\n",
    "- [OpenAI](https://platform.openai.com/api-keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D4ow48pYHqXT",
    "outputId": "2f084d4a-093b-4899-8b33-8fc468b144c7"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "# Set OpenAI API\n",
    "if \"OPENAI_API_KEY\" in os.environ:\n",
    "    OPENAI_API_KEY = os.environ[\"OPENAI_API_KEY\"]\n",
    "else:\n",
    "    # Prompt the user to enter the API key\n",
    "    OPENAI_API_KEY = getpass(\"OPENAI API KEY: \")\n",
    "    # Save the API key as an environment variable for the current session\n",
    "    os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PmtLAqJTgmSG"
   },
   "source": [
    "\n",
    "\n",
    "#### Download Earnings Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0hpV7S8QB6dx",
    "outputId": "8d8197a7-7d63-4394-de6d-9aa954fd6b47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-10-01 01:10:17--  https://s21.q4cdn.com/399680738/files/doc_news/Meta-Reports-Second-Quarter-2024-Results-2024.pdf\n",
      "Resolving s21.q4cdn.com (s21.q4cdn.com)... 194.26.213.25, 2a09:cd46:f:426e::1\n",
      "Connecting to s21.q4cdn.com (s21.q4cdn.com)|194.26.213.25|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 195613 (191K) [application/pdf]\n",
      "Saving to: ‘./doc1.pdf’\n",
      "\n",
      "./doc1.pdf          100%[===================>] 191.03K  --.-KB/s    in 0.03s   \n",
      "\n",
      "2024-10-01 01:10:17 (5.77 MB/s) - ‘./doc1.pdf’ saved [195613/195613]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://s21.q4cdn.com/399680738/files/doc_news/Meta-Reports-Second-Quarter-2024-Results-2024.pdf' -O './doc1.pdf'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YJm74SuKgmSH"
   },
   "source": [
    "# 2. Use Unstructured to Process Complex PDF Documentation\n",
    "\n",
    "1. Read in data\n",
    "2. Partition using the 'hi_res' strategy\n",
    "3. Chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "v1zHd-TiPkgM"
   },
   "outputs": [],
   "source": [
    "elements = partition_pdf('./doc1.pdf',\n",
    "                              strategy=\"hi_res\",\n",
    "                              chunking_strategy=\"by_title\",\n",
    "                              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g5ghZBkbgmSH"
   },
   "source": [
    "#### Explore the extracted elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "evyLF4fgPsQX",
    "outputId": "0eb3a00b-2b2b-4cc2-8a64-561ca68a717b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({unstructured.documents.elements.CompositeElement: 17,\n",
       "         unstructured.documents.elements.Table: 10})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "display(Counter(type(element) for element in elements))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "015XC0HgPuyF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n",
      "<class 'unstructured.documents.elements.Table'>\n",
      "<class 'unstructured.documents.elements.CompositeElement'>\n"
     ]
    }
   ],
   "source": [
    "for element in elements:\n",
    "  print(type(element))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Uk1uVI0GrKEI",
    "outputId": "e1784f18-2e27-4a3d-b31b-49498b98fd9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Three Months Ended June 30, In millions, except percentages and per share amounts 2024 2023 % Change Revenue $ 39,071 $ 31,999 22 % Costs and expenses 24,224 22,607 7 % Income from operations $ 14,847 $ 9,392 58 % Operating margin 38 % 29 % Provision for income taxes $ 1,641 $ 1,505 9 % Effective tax rate 11 % 16 % Net income $ 13,465 $ 7,788 73 % Diluted earnings per share (EPS) $ 5.16 $ 2.98 73 %\n",
      "2024 2023 2024 2023 Revenue $ 39,071 Costs and expenses: Cost of revenue 7,308 5,945 13,948 Research and development 10,537 9,344 20,515 Marketing and sales 2,721 3,154 5,285 General and administrative (1) 3,658 4,164 7,114 Total costs and expenses 24,224 22,607 46,862 Income from operations 14,847 9,392 28,665 Interest and other income (expense), net 259 (99) 624 Income before provision for income taxes 15,106 9,293 29,289 Provision for income taxes 1,641 1,505 3,455 Net income $ 13,465\n",
      "Basic $ 5.31 Diluted $ 5.16 Weighted-average shares used to compute earnings per share: Basic 2,534 2,568 2,540\n",
      "June 30, 2024 December 31, 2023 Assets Current assets: Cash and cash equivalents $ 32,045 $ 41,862 Marketable securities 26,035 23,541 Accounts receivable, net 14,505 16,169 Prepaid expenses and other current assets 3,846 3,793 Total current assets 76,431 85,365 Non-marketable equity securities 6,207 6,141 Property and equipment, net 102,959 96,587 Operating lease right-of-use assets 14,058 13,294 Goodwill 20,654 20,654 Other assets 9,929 7,582 Total assets $ 230,238 $ 229,623 Liabilities and stockholders' equity Current liabilities: Accounts payable $ 3,173 $ 4,849 Operating lease liabilities, current 1,917 1,623 Accrued expenses and other current liabilities 21,914 25,488 Total current liabilities 27,004 31,960 Operating lease liabilities, non-current 17,685 17,226 Long-term debt 18,389 18,385 Long-term income taxes 7,897 7,514 Other liabilities 2,500 1,370 Total liabilities 73,475 76,455 Commitments and contingencies Stockholders' equity:\n",
      "(2,695) (2,155) 81,188 82,070 156,763 153,168 $ 230,238 $ 229,623\n",
      "(Unaudited) 2024 2023 2024 2023 Cash flows from operating activities Net income $ 13,465 $ 7,788 $ 25,834 Adjustments to reconcile net income to net cash provided by operating activities: Depreciation and amortization 3,637 2,623 7,011 Share-based compensation 4,616 4,060 8,178 Deferred income taxes (1,643) (1,137) (2,098) Impairment charges for facilities consolidation, net 41 232 280 Other (6) 212 (71) Changes in assets and liabilities: Accounts receivable (1,171) (1,424) 1,350 Prepaid expenses and other current assets (84) (54) 16 Other assets 54 37 (41) Accounts payable 250 (51) (862) Accrued expenses and other current liabilities (497) 5,174 (1,771) Other liabilities 708 (151) 790 Net cash provided by operating activities 19,370 17,309 38,616 Cash flows from investing activities Purchases of property and equipment, net (8,173) (6,134) (14,573) Purchases of marketable debt securities (3,289) (717) (10,176) Sales and maturities of marketable debt securities 3,233 1,816 7,858 Acquisitions of businesses and intangible assets (57) (83) (129) Other investing activities (12) (85) (12) Net cash used in investing activities (8,298) (5,203) (17,032) Cash flows from financing activities Taxes paid related to net share settlement of equity awards (3,208) (1,692) (6,370) Repurchases of Class A common stock (6,299) (898) (21,307) Dividend payments (1,266) — (2,539) Proceeds from issuance of long-term debt, net — 8,455 — Principal payments on finance leases (299) (220) (614) Other financing activities (106) (353) (115) Net cash provided by (used in) financing activities (11,178) 5,292 (30,945) Effect of exchange rate changes on cash, cash equivalents, and restricted cash (152) (14) (440) Net increase (decrease) in cash, cash equivalents, and restricted cash (258) 17,384 (9,801) Cash, cash equivalents, and restricted cash at beginning of the period 33,284 12,420 42,827\n",
      "condensed consolidated balance sheets Cash and cash equivalents $ 32,045 $ 28,785 $ 32,045 Restricted cash, included in prepaid expenses and other current assets 100 165 100 Restricted cash, included in other assets 881 854 881 Total cash, cash equivalents, and restricted cash $ 33,026 $ 29,804 $ 33,026\n",
      "2024 2023 2024 2023 Supplemental cash flow data Cash paid for income taxes, net $ 5,929 $ 1,102 $ 6,559 Cash paid for interest, net of amounts capitalized $ 124 $ — $ 245 Non-cash investing and financing activities: Property and equipment in accounts payable and accrued expenses and other current liabilities $ 3,229 $ 3,845 $ 3,229 Acquisition of businesses and intangible assets in accrued expenses and other current liabilities and other liabilities $ 267 $ 217 $ 267\n",
      "Advertising $ 38,329 $ 31,498 $ 73,965 $ 59,599 Other revenue 389 225 769 430 Family of Apps 38,718 31,723 74,734 60,029 Reality Labs 353 276 793 616 Total revenue $ 39,071 $ 31,999 $ 75,527 $ 60,645 Income (loss) from operations: Family of Apps $ 19,335 $ 13,131 $ 36,999 $ 24,351 Reality Labs (4,488) (3,739) (8,334) (7,732) Total income from operations $ 14,847 $ 9,392 $ 28,665 $ 16,619\n",
      "2024 2023 2024 2023 $ 39,071 $ 31,999 $ 75,527 Foreign exchange effect on 2024 revenue using 2023 rates 371 265 Revenue excluding foreign exchange effect $ 39,442 $ 75,792 GAAP revenue year-over-year change % 22 % 25 % Revenue excluding foreign exchange effect year-over-year change % 23 % 25 % GAAP advertising revenue $ 38,329 $ 31,498 $ 73,965 Foreign exchange effect on 2024 advertising revenue using 2023 rates 367 261 Advertising revenue excluding foreign exchange effect $ 38,696 $ 74,226 GAAP advertising revenue year-over-year change % 22 % 24 % Advertising revenue excluding foreign exchange effect year-over-year 23 % 25 % Net cash provided by operating activities $ 19,370 $ 17,309 $ 38,616 Purchases of property and equipment, net (8,173) (6,134) (14,573) Principal payments on finance leases (299) (220) (614) $ 10,898 $ 10,955 $ 23,429\n"
     ]
    }
   ],
   "source": [
    "for element in elements:\n",
    "  if element.to_dict()['type'] == 'Table':\n",
    "    print(element.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ONiDxMWGORbH"
   },
   "source": [
    "#### What a table element looks like after extraction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kpB8GotUsg_S",
    "outputId": "afc0dd03-5446-426f-eeab-3c6463964269"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024 2023 2024 2023 $ 39,071 $ 31,999 $ 75,527 Foreign exchange effect on 2024 revenue using 2023 rates 371 265 Revenue excluding foreign exchange effect $ 39,442 $ 75,792 GAAP revenue year-over-year change % 22 % 25 % Revenue excluding foreign exchange effect year-over-year change % 23 % 25 % GAAP advertising revenue $ 38,329 $ 31,498 $ 73,965 Foreign exchange effect on 2024 advertising revenue using 2023 rates 367 261 Advertising revenue excluding foreign exchange effect $ 38,696 $ 74,226 GAAP advertising revenue year-over-year change % 22 % 24 % Advertising revenue excluding foreign exchange effect year-over-year 23 % 25 % Net cash provided by operating activities $ 19,370 $ 17,309 $ 38,616 Purchases of property and equipment, net (8,173) (6,134) (14,573) Principal payments on finance leases (299) (220) (614) $ 10,898 $ 10,955 $ 23,429\n"
     ]
    }
   ],
   "source": [
    "print(elements[-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p34wMxcIOZXi"
   },
   "source": [
    "## Embed Extracted Elements with OpenAI Embedding Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "b8kjpK7FHwNK"
   },
   "outputs": [],
   "source": [
    "from unstructured.embed.openai import OpenAIEmbeddingConfig, OpenAIEmbeddingEncoder\n",
    "\n",
    "embedding_encoder = OpenAIEmbeddingEncoder(\n",
    "    config=OpenAIEmbeddingConfig(\n",
    "      api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
    "      model_name=\"text-embedding-3-small\",\n",
    "    )\n",
    ")\n",
    "elements = embedding_encoder.embed_documents(\n",
    "    elements=elements\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iRgWJLJdOdSu"
   },
   "source": [
    "### Store original elements in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "pww9sgG8Bf-X",
    "outputId": "2a62b888-f389-4631-bf71-6ed03016f342"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>metadata</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7673dd5dd3348ca922edfeb765c4f8ec</td>\n",
       "      <td>b'FACEBOOK\\n\\nNEWS RELEASE\\n\\nMeta Reports Sec...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.057783662762384565, 0.015500455026484948, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0042c2ce77a154ed737cfb0d9b20b598</td>\n",
       "      <td>b'Three Months Ended June 30, In millions, exc...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[0.011829043858470036, 0.013133554749833454, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006ba147b4696dcfa364d82a7cc3ff9</td>\n",
       "      <td>b'Second Quarter 2024 Operational and Other Fi...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.07881357843105635, 0.010791519166744949, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3ccaeebfca3cd0b37f89d9865ed86620</td>\n",
       "      <td>b\"CFO Outlook Commentary\\n\\nWe expect third qu...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.06501258679384729, 0.02305678941583444, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f83ab884e1b7f6cd22bb6fec166375de</td>\n",
       "      <td>b'About Meta\\n\\nMeta builds technologies that ...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.044354494483358486, -0.008683296817018685, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id  \\\n",
       "0  7673dd5dd3348ca922edfeb765c4f8ec   \n",
       "1  0042c2ce77a154ed737cfb0d9b20b598   \n",
       "2  1006ba147b4696dcfa364d82a7cc3ff9   \n",
       "3  3ccaeebfca3cd0b37f89d9865ed86620   \n",
       "4  f83ab884e1b7f6cd22bb6fec166375de   \n",
       "\n",
       "                                                text  \\\n",
       "0  b'FACEBOOK\\n\\nNEWS RELEASE\\n\\nMeta Reports Sec...   \n",
       "1  b'Three Months Ended June 30, In millions, exc...   \n",
       "2  b'Second Quarter 2024 Operational and Other Fi...   \n",
       "3  b\"CFO Outlook Commentary\\n\\nWe expect third qu...   \n",
       "4  b'About Meta\\n\\nMeta builds technologies that ...   \n",
       "\n",
       "                                            metadata  \\\n",
       "0  {'filetype': 'application/pdf', 'languages': [...   \n",
       "1  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "2  {'filetype': 'application/pdf', 'languages': [...   \n",
       "3  {'filetype': 'application/pdf', 'languages': [...   \n",
       "4  {'filetype': 'application/pdf', 'languages': [...   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.057783662762384565, 0.015500455026484948, 0...  \n",
       "1  [0.011829043858470036, 0.013133554749833454, 0...  \n",
       "2  [0.07881357843105635, 0.010791519166744949, 0....  \n",
       "3  [0.06501258679384729, 0.02305678941583444, -0....  \n",
       "4  [0.044354494483358486, -0.008683296817018685, ...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = []\n",
    "\n",
    "for c in elements:\n",
    "  row = {}\n",
    "  row['id'] = c.id\n",
    "  row['text'] = c.text.encode()\n",
    "  row['metadata'] = c.metadata.to_dict()\n",
    "  row['embedding'] = c.embeddings\n",
    "  data.append(row)\n",
    "\n",
    "df_non_contextualized = pd.DataFrame(data)\n",
    "df_non_contextualized.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ys4lbO6WOmGA"
   },
   "source": [
    "### Create contextualized descriptions and markdown formatted tables, these new chunks will be used in place of the old table descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Spm1xSlRu6zo",
    "outputId": "6812aa39-08cd-4326-ab90-62103d7ead72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing complete.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import openai\n",
    "from openai import OpenAI\n",
    "\n",
    "# Initialize the OpenAI client\n",
    "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n",
    "\n",
    "def get_table_description(table_content, document_context):\n",
    "    prompt = f\"\"\"\n",
    "    Given the following table and its context from the original document,\n",
    "    provide a detailed description of the table. Then, include the table in markdown format.\n",
    "\n",
    "    Original Document Context:\n",
    "    {document_context}\n",
    "\n",
    "    Table Content:\n",
    "    {table_content}\n",
    "\n",
    "    Please provide:\n",
    "    1. A comprehensive description of the table.\n",
    "    2. The table in markdown format.\n",
    "    \"\"\"\n",
    "\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"gpt-4o-2024-08-06\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": \"You are a helpful assistant that describes tables and formats them in markdown.\"},\n",
    "            {\"role\": \"user\", \"content\": prompt}\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    return response.choices[0].message.content\n",
    "\n",
    "def extract_text_from_pdf(pdf_path):\n",
    "    text = \"\"\n",
    "    with fitz.open(pdf_path) as doc:\n",
    "        for page in doc:\n",
    "            text += page.get_text()\n",
    "    return text\n",
    "\n",
    "pdf_path = './doc1.pdf'\n",
    "document_content = extract_text_from_pdf(pdf_path)\n",
    "\n",
    "# Process each table in the directory\n",
    "for element in elements:\n",
    "  if element.to_dict()['type'] == 'Table':\n",
    "    table_content = element.to_dict()['text']\n",
    "\n",
    "    # Get description and markdown table from GPT-4\n",
    "    result = get_table_description(table_content, document_content)\n",
    "    element.text = result\n",
    "\n",
    "print(\"Processing complete.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TDRmYwSzgmSI"
   },
   "source": [
    "## Embed Extracted Text Elements and Updated Table Elements with OpenAI Embedding Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "TBcWCfpPPuxU"
   },
   "outputs": [],
   "source": [
    "from unstructured.embed.openai import OpenAIEmbeddingConfig, OpenAIEmbeddingEncoder\n",
    "\n",
    "embedding_encoder = OpenAIEmbeddingEncoder(\n",
    "    config=OpenAIEmbeddingConfig(\n",
    "      api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
    "      model_name=\"text-embedding-3-small\",\n",
    "    )\n",
    ")\n",
    "elements = embedding_encoder.embed_documents(\n",
    "    elements=elements\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FFT8QZswPvfX"
   },
   "source": [
    "### Take a look through the new contextualized table elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z0yGd3NgkmMB",
    "outputId": "ed1e4556-e986-48f7-bb1f-6257c5e2586f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Comprehensive Description of the Table\n",
      "\n",
      "The table presents a financial overview of Meta Platforms, Inc. for the second quarter, ending June 30, 2024, in comparison to the same period in 2023. The key financial metrics outlined in the table are revenue, costs and expenses, income from operations, operating margin, provision for income taxes, effective tax rate, net income, and diluted earnings per share (EPS). \n",
      "\n",
      "1. **Revenue**: Meta reported revenue of $39.071 billion in Q2 2024, a 22% increase from $31.999 billion in Q2 2023.\n",
      "\n",
      "2. **Costs and Expenses**: Costs and expenses for Q2 2024 amounted to $24.224 billion, up 7% from $22.607 billion in Q2 2023.\n",
      "\n",
      "3. **Income from Operations**: The income from operations saw a significant rise of 58%, reaching $14.847 billion in Q2 2024, compared to $9.392 billion in Q2 2023.\n",
      "\n",
      "4. **Operating Margin**: The operating margin improved to 38% in Q2 2024 from 29% in the same quarter of the previous year.\n",
      "\n",
      "5. **Provision for Income Taxes**: The provision for income taxes was $1.641 billion in Q2 2024, marking a 9% rise from $1.505 billion in 2023.\n",
      "\n",
      "6. **Effective Tax Rate**: The effective tax rate decreased to 11% in Q2 2024, down from 16% in Q2 2023.\n",
      "\n",
      "7. **Net Income**: Net income greatly increased by 73% to $13.465 billion for Q2 2024 from $7.788 billion in Q2 2023.\n",
      "\n",
      "8. **Diluted Earnings Per Share (EPS)**: Diluted EPS also reflected a 73% increase, moving from $2.98 in Q2 2023 to $5.16 in Q2 2024.\n",
      "\n",
      "### Markdown Representation of the Table\n",
      "\n",
      "```markdown\n",
      "| Financial Metric                         | Q2 2024 Amount ($ Millions) | Q2 2023 Amount ($ Millions) | % Change |\n",
      "|------------------------------------------|-----------------------------|-----------------------------|----------|\n",
      "| Revenue                                  | 39,071                      | 31,999                      | 22%      |\n",
      "| Costs and Expenses                       | 24,224                      | 22,607                      | 7%       |\n",
      "| Income from Operations                   | 14,847                      | 9,392                       | 58%      |\n",
      "| Operating Margin                         | 38%                         | 29%                         | -        |\n",
      "| Provision for Income Taxes               | 1,641                       | 1,505                       | 9%       |\n",
      "| Effective Tax Rate                       | 11%                         | 16%                         | -        |\n",
      "| Net Income                               | 13,465                      | 7,788                       | 73%      |\n",
      "| Diluted Earnings Per Share (EPS)         | 5.16                        | 2.98                        | 73%      |\n",
      "```\n",
      "\n",
      "This table provides a succinct summary of Meta's financial performance, highlighting the growth and efficiency improvements in the company's operations over the one-year period.\n",
      "The table presented shows the condensed consolidated statements of income for Meta Platforms, Inc., comparing financial performance metrics for the three months ending June 30, 2024, and 2023. It also provides data for the six months ended June 30 for both years. The numbers are in millions of dollars, except where per share values are involved. Key metrics listed include Revenue, Costs and Expenses broken down into several categories, Income from Operations, Interest and Other Income (Expense), Income Before Provision for Income Taxes, Provision for Income Taxes, and Net Income. Additionally, there's information on Earnings Per Share, both Basic and Diluted, alongside the weighted-average shares used to compute these per share amounts.\n",
      "\n",
      "Key insights from the table:\n",
      "\n",
      "1. **Revenue:** There was a significant increase in revenue from $31,999 million in 2023 to $39,071 million in 2024, showing a positive growth trajectory.\n",
      "   \n",
      "2. **Costs and Expenses:** This section is divided into Cost of Revenue, Research and Development, Marketing and Sales, and General and Administrative expenses. While total expenses increased from $22,607 million in 2023 to $24,224 million in 2024, the most notable increase was in Research and Development.\n",
      "\n",
      "3. **Income from Operations:** Reflects strong operational efficiency, growing substantially from $9,392 million in 2023 to $14,847 million in 2024.\n",
      "\n",
      "4. **Net Income:** There is a substantial increase in net income from $7,788 million to $13,465 million year-over-year.\n",
      "\n",
      "5. **Earnings Per Share:** Both basic and diluted earnings per share showed significant growth, reflecting improved profitability per share issued.\n",
      "\n",
      "Below is the table in markdown format:\n",
      "\n",
      "```markdown\n",
      "### Meta Platforms, Inc. Condensed Consolidated Statements of Income\n",
      "\n",
      "| Metric                                         | Three Months Ended June 30, 2024 | Three Months Ended June 30, 2023 | Six Months Ended June 30, 2024 | Six Months Ended June 30, 2023 |\n",
      "|------------------------------------------------|-----------------------------------|----------------------------------|--------------------------------|-------------------------------|\n",
      "| **Revenue**                                    | $39,071                           | $31,999                          | $75,527                        | $60,645                       |\n",
      "| **Costs and Expenses:**                          |                                   |                                  |                                |                               |\n",
      "| - Cost of Revenue                              | 7,308                             | 5,945                            | 13,948                         | 12,054                        |\n",
      "| - Research and Development                     | 10,537                            | 9,344                            | 20,515                         | 18,725                        |\n",
      "| - Marketing and Sales                          | 2,721                             | 3,154                            | 5,285                          | 6,198                         |\n",
      "| - General and Administrative (1)               | 3,658                             | 4,164                            | 7,114                          | 7,049                         |\n",
      "| **Total Costs and Expenses**                   | 24,224                            | 22,607                           | 46,862                         | 44,026                        |\n",
      "| **Income from Operations**                     | 14,847                            | 9,392                            | 28,665                         | 16,619                        |\n",
      "| **Interest and Other Income (Expense), Net**   | 259                               | (99)                             | 624                            | (19)                          |\n",
      "| **Income Before Provision for Income Taxes**   | 15,106                            | 9,293                            | 29,289                         | 16,600                        |\n",
      "| **Provision for Income Taxes**                 | 1,641                             | 1,505                            | 3,455                          | 3,102                         |\n",
      "| **Net Income**                                 | $13,465                           | $7,788                           | $25,834                        | $13,498                       |\n",
      "| **Earnings Per Share:**                         |                                   |                                  |                                |                               |\n",
      "| - Basic                                        | $5.31                             | $3.03                            | $10.17                         | $5.24                         |\n",
      "| - Diluted                                      | $5.16                             | $2.98                            | $9.86                          | $5.18                         |\n",
      "| **Weighted-average shares used to compute EPS:** |                                    |                                  |                                |                               |\n",
      "| - Basic                                        | 2,534                             | 2,568                            | 2,540                          | 2,577                         |\n",
      "| - Diluted                                      | 2,610                             | 2,612                            | 2,619                          | 2,604                         |\n",
      "\n",
      "*(1) The second quarter 2024 general and administrative expenses include a charge for the recent settlement with the State of Texas. The settlement amount is fully accrued as of June 30, 2024.*\n",
      "```\n",
      "\n",
      "This table offers a detailed snapshot of Meta's financial health, emphasizing increased revenue and net income, alongside robust operational performance over the periods compared. The inclusion of both quarterly and half-year figures provides a broader perspective on the company's financial trajectory.\n",
      "The table provided is an excerpt from Meta Platforms, Inc.'s financial summary for the second quarter of 2024. Specifically, it highlights the earnings per share, both basic and diluted, for the three months ended June 30, 2024. The table provides three key data points:\n",
      "\n",
      "1. **Earnings per Share (EPS)**: This is broken down into two types:\n",
      "   - **Basic EPS**: $5.31 per share, which represents the company's net income divided by the weighted-average number of outstanding shares during the period. It reflects the earnings available to each share of common stock.\n",
      "   - **Diluted EPS**: $5.16 per share, which takes into account all potential shares that could be created through convertible securities, options, or other similar mechanisms. This measure provides insights into the potential impact on earnings if these potential shares were to be converted.\n",
      "\n",
      "2. **Weighted-average Shares Used to Compute Earnings per Share**: This indicates the average number of shares that were used to calculate the basic and diluted EPS. The basic EPS calculation used 2,534 million shares, while the same figure for the preceding year (2023) was 2,568 million for basic EPS, and the current period accounts, 2,540 million shares.\n",
      "\n",
      "The data is crucial for understanding the company's profitability on a per-share basis during the second quarter of 2024 and highlights how share dilution could potentially affect earnings per share.\n",
      "\n",
      "Here is the table in markdown format:\n",
      "\n",
      "```markdown\n",
      "| Earnings per share                      | 2024  | 2023  |\n",
      "|-----------------------------------------|-------|-------|\n",
      "| Basic EPS                               | $5.31 |       |\n",
      "| Diluted EPS                             | $5.16 |       |\n",
      "| Weighted-average shares (basic, million)| 2,534 | 2,568 |\n",
      "\n",
      "```\n",
      "\n",
      "This markdown table organizes the earnings per share and the weighted-average shares efficiently, providing a clear view of the financial metrics related to sharholders' interests in the second quarter of 2024.\n",
      "### Comprehensive Description\n",
      "\n",
      "The table provides a detailed snapshot of Meta Platforms, Inc.'s financial position as of June 30, 2024, compared to December 31, 2023. This condensed consolidated balance sheet outlines various asset categories alongside liabilities and stockholders' equity, revealing the company's financial health at these two points in time.\n",
      "\n",
      "#### Assets\n",
      "\n",
      "1. **Current Assets:**\n",
      "   - **Cash and Cash Equivalents:** A decrease from $41,862 million in December 2023 to $32,045 million in June 2024, indicating reduced liquidity.\n",
      "   - **Marketable Securities:** Increased to $26,035 million from $23,541 million, showing higher short-term investments.\n",
      "   - **Accounts Receivable, Net:** A decrease, reflecting a drop from $16,169 million to $14,505 million, which might suggest better collection or reduced sales on credit.\n",
      "   - **Prepaid Expenses and Other Current Assets:** Slight increase, ending at $3,846 million from $3,793 million.\n",
      "   - **Total Current Assets:** Decreased from $85,365 million to $76,431 million, suggesting a reduction in readily available assets.\n",
      "\n",
      "2. **Non-current Assets:**\n",
      "   - **Non-marketable Equity Securities:** Remained relatively stable, with a slight increase to $6,207 million.\n",
      "   - **Property and Equipment, Net:** Significant increase to $102,959 million from $96,587 million, suggesting capital investments.\n",
      "   - **Operating Lease Right-of-Use Assets:** Increased slightly to $14,058 million.\n",
      "   - **Goodwill:** Constant at $20,654 million, reflecting the value of acquired business units.\n",
      "   - **Other Assets:** Increased to $9,929 million from $7,582 million, indicating potential growth or new investments.\n",
      "   - **Total Assets:** A small increase from $229,623 million to $230,238 million, showing slight overall asset growth.\n",
      "\n",
      "#### Liabilities and Stockholders' Equity\n",
      "\n",
      "1. **Current Liabilities:**\n",
      "   - **Accounts Payable:** Decreased from $4,849 million to $3,173 million, suggesting that less is owed to suppliers.\n",
      "   - **Operating Lease Liabilities, Current:** Slight increase to $1,917 million.\n",
      "   - **Accrued Expenses and Other Current Liabilities:** Decreased to $21,914 million from $25,488 million, perhaps reflecting payment of expenses or liabilities.\n",
      "   - **Total Current Liabilities:** Decreased substantially, indicating a reduction in short-term obligations.\n",
      "\n",
      "2. **Non-current Liabilities:**\n",
      "   - **Operating Lease Liabilities, Non-current:** Marginal increase to $17,685 million.\n",
      "   - **Long-term Debt:** Rose slightly to $18,389 million.\n",
      "   - **Long-term Income Taxes:** Increased to $7,897 million.\n",
      "   - **Other Liabilities:** Notable increase to $2,500 million from $1,370 million.\n",
      "   - **Total Liabilities:** Decreased slightly to $73,475 million, indicating reduced obligations.\n",
      "\n",
      "3. **Stockholders' Equity:**\n",
      "   - **Common Stock and Additional Paid-in Capital:** Increased to $78,270 million, suggesting new stock issuances or capital infusions.\n",
      "   - **Accumulated Other Comprehensive Loss:** Widened slightly to $(2,695) million, indicating possible unrealized losses.\n",
      "   - **Retained Earnings:** Decreased to $81,188 million, which might imply dividend payouts or other distributions.\n",
      "   - **Total Stockholders' Equity:** Rose to $156,763 million, reflecting a stronger equity position.\n",
      "\n",
      "#### Overall Summary\n",
      "\n",
      "The table reflects Meta's financial activities over the half-year, with investments in property and equipment alongside fluctuations in liabilities and equity positions. Despite lower cash reserves, the company shows an increase in equity and capacity for operations through new and strategic investments.\n",
      "\n",
      "### Table in Markdown Format\n",
      "\n",
      "```markdown\n",
      "### Meta Platforms, Inc. Condensed Consolidated Balance Sheets (In Millions)\n",
      "\n",
      "| Assets                                 | June 30, 2024 | December 31, 2023 |\n",
      "|----------------------------------------|---------------|-------------------|\n",
      "| **Current Assets:**                    |               |                   |\n",
      "| Cash and cash equivalents              | $32,045       | $41,862           |\n",
      "| Marketable securities                  | $26,035       | $23,541           |\n",
      "| Accounts receivable, net               | $14,505       | $16,169           |\n",
      "| Prepaid expenses and other current assets | $3,846     | $3,793            |\n",
      "| **Total current assets**               | **$76,431**   | **$85,365**       |\n",
      "| **Non-current Assets:**                |               |                   |\n",
      "| Non-marketable equity securities       | $6,207        | $6,141            |\n",
      "| Property and equipment, net            | $102,959      | $96,587           |\n",
      "| Operating lease right-of-use assets    | $14,058       | $13,294           |\n",
      "| Goodwill                               | $20,654       | $20,654           |\n",
      "| Other assets                           | $9,929        | $7,582            |\n",
      "| **Total assets**                       | **$230,238**  | **$229,623**      |\n",
      "\n",
      "| Liabilities and Stockholders' Equity    | June 30, 2024 | December 31, 2023 |\n",
      "|-----------------------------------------|---------------|-------------------|\n",
      "| **Current Liabilities:**                |               |                   |\n",
      "| Accounts payable                        | $3,173        | $4,849            |\n",
      "| Operating lease liabilities, current    | $1,917        | $1,623            |\n",
      "| Accrued expenses and other current liabilities | $21,914 | $25,488           |\n",
      "| **Total current liabilities**           | **$27,004**   | **$31,960**       |\n",
      "| **Non-current Liabilities:**            |               |                   |\n",
      "| Operating lease liabilities, non-current | $17,685      | $17,226           |\n",
      "| Long-term debt                          | $18,389       | $18,385           |\n",
      "| Long-term income taxes                  | $7,897        | $7,514            |\n",
      "| Other liabilities                       | $2,500        | $1,370            |\n",
      "| **Total liabilities**                   | **$73,475**   | **$76,455**       |\n",
      "| **Stockholders' Equity:**               |               |                   |\n",
      "| Common stock and additional paid-in capital | $78,270   | $73,253           |\n",
      "| Accumulated other comprehensive loss    | $(2,695)      | $(2,155)          |\n",
      "| Retained earnings                       | $81,188       | $82,070           |\n",
      "| **Total stockholders' equity**            | **$156,763**  | **$153,168**      |\n",
      "| **Total liabilities and stockholders' equity** | **$230,238**  | **$229,623**     |\n",
      "```\n",
      "\n",
      "### Comprehensive Description of the Table\n",
      "\n",
      "The table below represents a section of Meta Platforms, Inc.'s financial results as reported for the second quarter ending June 30, 2024. This snippet specifically focuses on the balance of stockholders' equity from the company's condensed consolidated balance sheets. Here's what the table contains:\n",
      "\n",
      "- **Accumulated Other Comprehensive Loss**: This column represents the losses on investments and pension liabilities that are not reported on the income statement though they affect equity.\n",
      "- **Retained Earnings**: This reflects the net income that the company has retained, rather than distributed as dividends to shareholders.\n",
      "- **Total Stockholders' Equity**: Total equity attributable to shareholders after accounting for share capital and retained earnings.\n",
      "- **Total Assets**: This is the consolidation of all the resources owned by Meta Platforms, Inc.\n",
      "\n",
      "The figures are presented for two different dates: June 30, 2024, and December 31, 2023, for comparative purposes. This provides insight into the company's financial standing and equity changes over time.\n",
      "\n",
      "### Table in Markdown Format\n",
      "\n",
      "```markdown\n",
      "|                        | June 30, 2024  | December 31, 2023 |\n",
      "|------------------------|---------------|-------------------|\n",
      "| **Accumulated Other Comprehensive Loss** | (2,695)         | (2,155)             |\n",
      "| **Retained Earnings**  | 81,188        | 82,070           |\n",
      "| **Total Stockholders' Equity** | 156,763        | 153,168           |\n",
      "| **Total Assets**       | $230,238      | $229,623         |\n",
      "```\n",
      "\n",
      "This markdown table effectively summarizes the equity section changes of Meta Platforms, Inc.'s financial results for the given period.\n",
      "### Description of the Table\n",
      "\n",
      "The table provided is a segment of Meta Platforms, Inc.'s condensed consolidated statement of cash flows (unaudited), detailing the cash flow activities for Meta in both the three and six months ended June 30, 2024, compared to the corresponding periods in 2023. The table delineates the cash inflows and outflows across three primary activities: operating, investing, and financing, including detailed line items for specific components within each category.\n",
      "\n",
      "**1. Operating Activities:**\n",
      "   - This section begins with net income, a clear indicator of profitability. Several adjustments related to non-cash items and changes in working capital are then added back or subtracted. These include depreciation and amortization, share-based compensation, deferred income taxes, impairment charges, and other minor adjustments. Changes in assets and liabilities such as accounts receivable, prepaid expenses, accounts payable, and accrued expenses are also adjusted to reconcile to net cash provided by operating activities.\n",
      "\n",
      "**2. Investing Activities:**\n",
      "   - The table outlines the purchase of property and equipment, highlight sales and maturities of marketable debt securities, and account for acquisitions of businesses and other intangible assets, net of any sales. The net effect of these activities results in net cash used in investing activities.\n",
      "\n",
      "**3. Financing Activities:**\n",
      "   - This section includes cash flows from activities such as taxes paid associated with net share settlements, repurchases of Class A common stock, dividend payments, proceeds from long-term debt, and principal payments on finance leases. The net effect of these activities is net cash used in financing activities.\n",
      "\n",
      "Finally, the table shows the effect of exchange rate changes on cash and the net increase or decrease in cash, cash equivalents, and restricted cash over the periods. It concludes with the cash balance at the beginning and end of the period.\n",
      "\n",
      "### Markdown Table\n",
      "\n",
      "```markdown\n",
      "## Meta Platforms, Inc.\n",
      "### Condensed Consolidated Statements of Cash Flows\n",
      "#### (In millions, Unaudited)\n",
      "\n",
      "|                                | Three Months Ended June 30, | Six Months Ended June 30,  |\n",
      "|--------------------------------|-----------------------------|----------------------------|\n",
      "|                                | 2024      | 2023      | 2024       | 2023     |\n",
      "| **Cash flows from operating activities** |  |  |  |  |\n",
      "| Net income                     | $13,465   | $7,788    | $25,834    |          |\n",
      "| Adjustments to reconcile net income to net cash provided by operating activities: |  |  |  |  |\n",
      "| Depreciation and amortization  | 3,637     | 2,623     | 7,011      |          |\n",
      "| Share-based compensation       | 4,616     | 4,060     | 8,178      |          |\n",
      "| Deferred income taxes          | (1,643)   | (1,137)   | (2,098)    |          |\n",
      "| Impairment charges for facilities consolidation, net | 41        | 232       | 280        |          |\n",
      "| Other                          | (6)       | 212       | (71)       |          |\n",
      "| Changes in assets and liabilities: |  |  |  |  |\n",
      "| Accounts receivable            | (1,171)   | (1,424)   | 1,350      |          |\n",
      "| Prepaid expenses and other current assets | (84)      | (54)      | 16         |          |\n",
      "| Other assets                   | 54        | 37        | (41)       |          |\n",
      "| Accounts payable               | 250       | (51)      | (862)      |          |\n",
      "| Accrued expenses and other current liabilities | (497)    | 5,174     | (1,771)    |          |\n",
      "| Other liabilities              | 708       | (151)     | 790        |          |\n",
      "| **Net cash provided by operating activities** | 19,370    | 17,309    | 38,616     |          |\n",
      "| **Cash flows from investing activities** |  |  |  |  |\n",
      "| Purchases of property and equipment, net | (8,173)   | (6,134)   | (14,573)   |          |\n",
      "| Purchases of marketable debt securities | (3,289)   | (717)     | (10,176)   |          |\n",
      "| Sales and maturities of marketable debt securities | 3,233     | 1,816     | 7,858      |          |\n",
      "| Acquisitions of businesses and intangible assets | (57)       | (83)      | (129)      |          |\n",
      "| Other investing activities     | (12)      | (85)      | (12)       |          |\n",
      "| **Net cash used in investing activities** | (8,298)   | (5,203)   | (17,032)   |          |\n",
      "| **Cash flows from financing activities** |  |  |  |  |\n",
      "| Taxes paid related to net share settlement of equity awards | (3,208)   | (1,692)   | (6,370)    |          |\n",
      "| Repurchases of Class A common stock | (6,299)   | (898)     | (21,307)   |          |\n",
      "| Dividend payments              | (1,266)   | —         | (2,539)    |          |\n",
      "| Proceeds from issuance of long-term debt, net | —         | 8,455     | —          |          |\n",
      "| Principal payments on finance leases | (299)     | (220)     | (614)      |          |\n",
      "| Other financing activities     | (106)     | (353)     | (115)      |          |\n",
      "| **Net cash provided by (used in) financing activities** | (11,178)  | 5,292     | (30,945)   |          |\n",
      "| Effect of exchange rate changes on cash, cash equivalents, and restricted cash | (152)     | (14)      | (440)      |          |\n",
      "| **Net increase (decrease) in cash, cash equivalents, and restricted cash** | (258)     | 17,384    | (9,801)    |          |\n",
      "| Cash, cash equivalents, and restricted cash at beginning of the period | 33,284    | 12,420    | 42,827     |          |\n",
      "```\n",
      "\n",
      "This table captures the company's financial activity over the specified periods, providing insight into their operating efficiency, investment expenditure, and financing strategy.\n",
      "Here's a detailed description of the table and its presentation in markdown format:\n",
      "\n",
      "### Table Description\n",
      "\n",
      "The table is a segment from the condensed consolidated balance sheets of Meta Platforms, Inc., detailing the company's cash resources. The data is presented for two time periods: June 30, 2024, and June 30, 2023. It lists the dollar values in millions for \"Cash and cash equivalents,\" \"Restricted cash, included in prepaid expenses and other current assets,\" and \"Restricted cash, included in other assets.\" The table then calculates the \"Total cash, cash equivalents, and restricted cash\" by summing the relevant subcategories for each of the specified periods. \n",
      "\n",
      "- **Cash and Cash Equivalents:** Represents the amount of highly liquid cash and investments held by the company, which are easily convertible to known amounts of cash.\n",
      "- **Restricted Cash:** Divided into two categories:\n",
      "  - **Included in Prepaid Expenses and Other Current Assets:** This refers to cash which is restricted by contractual obligations that might include items expected to be settled in the near-term.\n",
      "  - **Included in Other Assets:** This accounts for cash that is restricted due to longer-term obligations.\n",
      "- **Total Cash, Cash Equivalents, and Restricted Cash:** This is the sum total of all the cash resources, both unrestricted and restricted, that the company has at its disposal.\n",
      "\n",
      "### Table in Markdown Format\n",
      "\n",
      "```markdown\n",
      "| Description                                               | June 30, 2024 | June 30, 2023 | June 30, 2024 |\n",
      "|-----------------------------------------------------------|---------------|---------------|---------------|\n",
      "| Cash and cash equivalents                                 | $32,045       | $28,785       | $32,045       |\n",
      "| Restricted cash, included in prepaid expenses and other current assets | $100          | $165          | $100          |\n",
      "| Restricted cash, included in other assets                 | $881          | $854          | $881          |\n",
      "| **Total cash, cash equivalents, and restricted cash**     | **$33,026**   | **$29,804**   | **$33,026**   |\n",
      "```\n",
      "\n",
      "This representation provides an overview of the company's liquidity position through its cash and cash equivalents both unrestricted and restricted for different financial operations or obligations.\n",
      "The table provided is a snippet of Meta Platforms, Inc.'s financial report, specifically focusing on the supplemental cash flow data for the second quarter of 2024 compared to the same period in 2023. This data is crucial for understanding the company's cash flow dynamics and its investment and financing activities.\n",
      "\n",
      "Here's a detailed breakdown of the table contents:\n",
      "\n",
      "1. **Cash Paid for Income Taxes, Net**: This figure represents the total cash Meta paid for income taxes during the reported periods. For the three months ended June 30, 2024, Meta paid $5,929 million, a significant increase from $1,102 million in 2023. Similarly, for the six months ended June 30, 2024, cash paid for income taxes was $6,559 million, highlighting the company's tax obligations.\n",
      "\n",
      "2. **Cash Paid for Interest, Net of Amounts Capitalized**: This entry details the interest Meta paid, excluding capitalized interest. In the three months ended June 30, 2024, Meta paid $124 million. No interest was paid in the same period in 2023. For the six months ended June 30, 2024, the company paid $245 million, compared to $182 million in 2023 when considering capitalized amounts.\n",
      "\n",
      "3. **Non-cash Investing and Financing Activities**: \n",
      "   - **Property and Equipment in Accounts Payable and Accrued Expenses and Other Current Liabilities**: For the three months and six months ended June 30, 2024, Meta reported $3,229 million in liabilities for property and equipment acquisitions, a decrease from $3,845 million in 2023. \n",
      "   \n",
      "   - **Acquisition of Businesses and Intangible Assets in Accrued Expenses and Other Current Liabilities and Other Liabilities**: This figure indicates the non-cash portion related to the acquisition of businesses and intangible assets. Meta reported $267 million for the three and six months ended June 30, 2024, compared to $217 million in 2023.\n",
      "\n",
      "The table provides an insight into Meta's cash flow situation, tax payments, interest handling, and non-cash transactions pertaining to investments and acquisitions, reflecting strategies in managing liquidity and financial obligations.\n",
      "\n",
      "Here is the table in markdown format:\n",
      "\n",
      "```markdown\n",
      "| Supplemental Cash Flow Data                              | Three Months Ended June 30,  | Six Months Ended June 30,   |\n",
      "|----------------------------------------------------------|-----------------------------|-----------------------------|\n",
      "|                                                          | 2024          | 2023       | 2024           | 2023      |\n",
      "| **Cash paid for income taxes, net**                      | $5,929        | $1,102     | $6,559         | -         |\n",
      "| **Cash paid for interest, net of amounts capitalized**   | $124          | $0         | $245           | -         |\n",
      "| **Non-cash investing and financing activities:**         |               |            |                |           |\n",
      "| Property and equipment in accounts payable and            |               |            |                |           |\n",
      "| accrued expenses and other current liabilities           | $3,229        | $3,845     | $3,229         | -         |\n",
      "| Acquisition of businesses and intangible assets in       |               |            |                |           |\n",
      "| accrued expenses and other current liabilities and       |               |            |                |           |\n",
      "| other liabilities                                        | $267          | $217       | $267           | -         |\n",
      "```\n",
      "\n",
      "Note: The \"-\" in the 2023 columns for the six months ended entries are indicative of the data being absent for this period in the supplemental cash flow data segment.\n",
      "### Comprehensive Description of the Table\n",
      "\n",
      "The table presents the segment results for Meta Platforms, Inc. for the three and six months ending June 30, 2024, and June 30, 2023. It delineates the company's revenue and income from operations across two distinct segments: \"Family of Apps\" and \"Reality Labs.\" \n",
      "\n",
      "- **Revenue**: The revenue is parsed into \"Advertising\" and \"Other revenue,\" further categorized under the \"Family of Apps\" and \"Reality Labs\" segments. \n",
      "  - For the three months ending June 30, 2024, Meta reported total revenue of $39.071 billion, an increase from $31.999 billion in the same period in 2023. \n",
      "  - The \"Family of Apps\" contributed significantly to the revenue, generating $38.718 billion in 2024 compared to $31.723 billion in 2023.\n",
      "  - \"Reality Labs\" revenue was $353 million for the three months ended June 30, 2024, up from $276 million in the previous year.\n",
      "\n",
      "- **Income (Loss) from Operations**: The income statement segment splits the operational income (or loss) between the \"Family of Apps\" and \"Reality Labs.\"\n",
      "  - \"Family of Apps\" showcased a profitable figure, with an income of $19.335 billion in 2024, up from $13.131 billion in 2023 for the three-month period.\n",
      "  - Conversely, \"Reality Labs\" recorded operational losses of $4.488 billion in 2024, compared to a loss of $3.739 billion in 2023.\n",
      "  - Consequently, the total income from operations amounted to $14.847 billion for the three months ending June 30, 2024, a substantial increase from $9.392 billion in 2023.\n",
      "\n",
      "The table provides insight into how Meta's business divisions contribute to overall financial performance, with the \"Family of Apps\" being the major revenue driver, while \"Reality Labs\" is focused on growth with current operational losses.\n",
      "\n",
      "### Table in Markdown Format\n",
      "\n",
      "```markdown\n",
      "**Meta Platforms, Inc. Segment Results**\n",
      "\n",
      "| Segment        | Revenue (3 months) | Revenue (6 months) | Income (Loss) from Operations (3 months) | Income (Loss) from Operations (6 months) |\n",
      "|----------------|--------------------|--------------------|----------------------------------------|------------------------------------------|\n",
      "|                | 2024     | 2023     | 2024     | 2023     | 2024     | 2023     | 2024     | 2023     |\n",
      "| Advertising    | $38,329  | $31,498  | $73,965  | $59,599  |              -               |             -                |\n",
      "| Other revenue  | $389     | $225     | $769     | $430     |              -               |             -                |\n",
      "| Family of Apps | $38,718  | $31,723  | $74,734  | $60,029  | $19,335   | $13,131  |\n",
      "| Reality Labs   | $353     | $276     | $793     | $616     | $(4,488)  | $(3,739) |\n",
      "| **Total**      | **$39,071** | **$31,999** | **$75,527** | **$60,645** | **$14,847**  | **$9,392**  | **$28,665** | **$16,619** |\n",
      "\n",
      "- Net income represents profit after all expenses, including taxes and operating costs, emphasizing segment profitability.\n",
      "```\n",
      "\n",
      "This table structure provides a clear breakdown of the revenue and operational outcomes for Meta's two primary business areas over a comparable period from the previous year.\n",
      "### Detailed Description of the Table\n",
      "\n",
      "The table provides a financial overview of Meta Platforms, Inc.'s performance for the second quarter ending June 30, 2024, compared to the same period in 2023. Key metrics include revenue figures adjusted for foreign exchange effects, advertising revenue, and cash flow details. The data underscores Meta's financial growth, showing significant year-over-year percentage changes in revenue and advertising revenue. It also includes details on capital expenditures, specifically purchases of property and equipment, and payments on finance leases, providing a comprehensive snapshot of both revenue generation and cash outflow.\n",
      "\n",
      "#### Key Components of the Table:\n",
      "\n",
      "1. **Revenue and Advertising Revenue:**\n",
      "   - GAAP revenue for the quarter of $39.071 billion in 2024 and $31.999 billion in 2023.\n",
      "   - The foreign exchange effect on 2024 revenue is illustrated to reflect what revenue would be using 2023 rates, adjusting it to $39.442 billion.\n",
      "   - GAAP advertising revenue for the quarter of $38.329 billion in 2024 and $31.498 billion in 2023, with adjustments for foreign exchange effect bringing it to $38.696 billion in 2024.\n",
      "\n",
      "2. **Year-over-Year Changes:**\n",
      "   - A 22% increase in GAAP revenue from 2023 to 2024.\n",
      "   - Revenue excluding foreign exchange effect shows a 23% increase.\n",
      "   - Advertising revenue increased by 22%, or 23% if adjusted for the foreign exchange effect.\n",
      "  \n",
      "3. **Cash Flow:**\n",
      "   - Net cash provided by operating activities was $19.370 billion in 2024 and $17.309 billion in 2023.\n",
      "   - Purchases of property and equipment, net amounted to $8.173 billion, and principal payments on finance leases were $299 million in 2024.\n",
      "   - Free cash flow for 2024 was $10.898 billion compared to $10.955 billion in 2023.\n",
      "\n",
      "This financial data offers insight into the growth and financial health of Meta during the specified period, reflecting strong operational performance and investment in property and equipment.\n",
      "\n",
      "### Table in Markdown Format\n",
      "\n",
      "```markdown\n",
      "| Description                                                       | Q2 2024 ($ Mil) | Q2 2023 ($ Mil) |\n",
      "|-------------------------------------------------------------------|-----------------|-----------------|\n",
      "| **GAAP Revenue**                                                  | 39,071          | 31,999          |\n",
      "| Foreign Exchange Effect on 2024 Revenue Using 2023 Rates          | 371             | -               |\n",
      "| Revenue Excluding Foreign Exchange Effect                        | 39,442          | -               |\n",
      "| **GAAP Revenue Year-over-Year Change (%)**                        | 22%             | -               |\n",
      "| Revenue Excluding Foreign Exchange Effect Year-over-Year Change (%)| 23%             | -               |\n",
      "| **GAAP Advertising Revenue**                                      | 38,329          | 31,498          |\n",
      "| Foreign Exchange Effect on 2024 Advertising Revenue Using 2023 Rates| 367             | -               |\n",
      "| Advertising Revenue Excluding Foreign Exchange Effect            | 38,696          | -               |\n",
      "| **GAAP Advertising Revenue Year-over-Year Change (%)**            | 22%             | -               |\n",
      "| Advertising Revenue Excluding Foreign Exchange Effect Year-over-Year Change (%)| 23%             | -               |\n",
      "| **Net Cash Provided by Operating Activities**                     | 19,370          | 17,309          |\n",
      "| Purchases of Property and Equipment, Net                          | (8,173)         | (6,134)         |\n",
      "| Principal Payments on Finance Leases                              | (299)           | (220)           |\n",
      "| **Free Cash Flow**                                                | 10,898          | 10,955          |\n",
      "```\n",
      "\n",
      "This markdown table succinctly represents the financial highlights of Meta Platforms, Inc. for the referenced periods, facilitating an easy comparison of key financial metrics.\n"
     ]
    }
   ],
   "source": [
    "for element in elements:\n",
    "  if element.to_dict()['type'] == 'Table':\n",
    "    print(element.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MiO6RvlR7WoD"
   },
   "source": [
    "This markdown table provides a concise presentation of the financial data, making it easy to read and comprehend in a digital format.\n",
    "### Detailed Description of the Table\n",
    "\n",
    "The table presents segment information from Meta Platforms, Inc. for both revenue and income (loss) from operations. The data is organized into two main sections:\n",
    "1. **Revenue**: This section is subdivided into two categories: \"Advertising\" and \"Other revenue\". The total revenue generated from these subcategories is then summed up for two segments: \"Family of Apps\" and \"Reality Labs\". The table provides the revenue figures for three months and six months ended June 30, for the years 2024 and 2023.\n",
    "2. **Income (loss) from operations**: This section shows the income or loss from operations for the \"Family of Apps\" and \"Reality Labs\" segments, again for the same time periods.\n",
    "\n",
    "The table allows for a comparison between the two segments of Meta's business over time, illustrating the performance of each segment in terms of revenue and operational income or loss.\n",
    "\n",
    "### The Table in Markdown Format\n",
    "\n",
    "```markdown\n",
    "### Segment Information (In millions, Unaudited)\n",
    "\n",
    "|                            | Three Months Ended June 30, 2024 | Three Months Ended June 30, 2023 | Six Months Ended June 30, 2024 | Six Months Ended June 30, 2023 |\n",
    "|----------------------------|----------------------------------|----------------------------------|------------------------------- |-------------------------------|\n",
    "| **Revenue:**               |                                  |                                  |                               |                               |\n",
    "| Advertising                | $38,329                          | $31,498                          | $73,965                       | $59,599                       |\n",
    "| Other revenue              | $389                             | $225                             | $769                          | $430                          |\n",
    "| **Family of Apps**         | $38,718                          | $31,723                          | $74,734                       | $60,029                       |\n",
    "| Reality Labs               | $353                             | $276                             | $793                          | $616                          |\n",
    "| **Total revenue**          | $39,071                          | $31,999                          | $75,527                       | $60,645                       |\n",
    "|                            |                                  |                                  |                               |                               |\n",
    "| **Income (loss) from operations:** |                                  |                                  |                               |                               |\n",
    "| Family of Apps             | $19,335                          | $13,131                          | $36,999                       | $24,351                       |\n",
    "| Reality Labs               | $(4,488)                         | $(3,739)                         | $(8,334)                      | $(7,732)                      |\n",
    "| **Total income from operations** | $14,847                          | $9,392                           | $28,665                       | $16,619                       |\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TyvJkWMqgmSI"
   },
   "source": [
    "### Create a Pandas dataframe to store text and updated table elements within"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 397
    },
    "id": "UJiOVDnAzQl8",
    "outputId": "cda1cad4-fded-437b-b70f-6a129ccdbd43"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>metadata</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0042c2ce77a154ed737cfb0d9b20b598</td>\n",
       "      <td>b\"### Comprehensive Description of the Table\\n...</td>\n",
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       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.07885341798234707, 0.010770062515672503, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3ccaeebfca3cd0b37f89d9865ed86620</td>\n",
       "      <td>b\"CFO Outlook Commentary\\n\\nWe expect third qu...</td>\n",
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       "      <td>[0.044356841931628053, -0.008697156860572564, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "0  {'filetype': 'application/pdf', 'languages': [...   \n",
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       "2  {'filetype': 'application/pdf', 'languages': [...   \n",
       "3  {'filetype': 'application/pdf', 'languages': [...   \n",
       "4  {'filetype': 'application/pdf', 'languages': [...   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.05788249539290755, 0.015626749069267247, 0....  \n",
       "1  [-0.0004421418779838655, 0.011881716893403, 0....  \n",
       "2  [0.07885341798234707, 0.010770062515672503, 0....  \n",
       "3  [0.06499940353648115, 0.02307817596004495, -0....  \n",
       "4  [0.044356841931628053, -0.008697156860572564, ...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = []\n",
    "\n",
    "for c in elements:\n",
    "  row = {}\n",
    "  row['id'] = c.id\n",
    "  row['text'] = c.text.encode()\n",
    "  row['metadata'] = c.metadata.to_dict()\n",
    "  row['embedding'] = c.embeddings\n",
    "  data.append(row)\n",
    "\n",
    "df_contextualized = pd.DataFrame(data)\n",
    "df_contextualized.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bnqhEll3UAGS"
   },
   "source": [
    "# 4. Define KDB.AI Session\n",
    "To use KDB.AI Server, you will need download and run your own container.\n",
    "To do this, you will first need to sign up for free [here](https://trykdb.kx.com/kdbaiserver/signup/).\n",
    "\n",
    "You will receive an email with the required license file and bearer token needed to download your instance.\n",
    "Follow instructions in the signup email to get your session up and running.\n",
    "\n",
    "Once the [setup steps](https://code.kx.com/kdbai/gettingStarted/kdb-ai-server-setup.html) are complete you can then connect to your KDB.AI Server session using `kdbai.Session` and passing your local endpoint.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TtpzGrEkUEIK"
   },
   "outputs": [],
   "source": [
    "#Set up KDB.AI server endpoint \n",
    "KDBAI_ENDPOINT = (\n",
    "    os.environ[\"KDBAI_ENDPOINT\"]\n",
    "    if \"KDBAI_ENDPOINT\" in os.environ\n",
    "    else \"http://localhost:8082\"\n",
    ")\n",
    "\n",
    "#connect to KDB.AI Server, default mode is qipc\n",
    "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sUxBbbk9Ufn4"
   },
   "source": [
    "# 5. Create Schema and KDB.AI Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "y8cZV9qXUf8i"
   },
   "outputs": [],
   "source": [
    "schema = [\n",
    "    {'name': 'id', 'type': 'str'},\n",
    "    {'name': 'text', 'type': 'bytes'},\n",
    "    {'name': 'metadata', 'type': 'general'},\n",
    "    {'name': 'embedding', 'type': 'float32s'}\n",
    "]\n",
    "\n",
    "indexes = [{'name': 'flat_index', 'column': 'embedding', 'type': 'flat', 'params': {'dims': 1536, 'metric': 'L2'}}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gg_G-EQaQXE0"
   },
   "source": [
    "### Here we create two tables, one containing the original table elements, the other containing the newly contextualized and formatted table elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "-Q-9Un7VUiqj"
   },
   "outputs": [],
   "source": [
    "Contextualized_KDBAI_TABLE_NAME = \"Contextualized_Table\"\n",
    "non_Contextualized_KDBAI_TABLE_NAME = \"Non_Contextualized_Table\"\n",
    "database = session.database('default')\n",
    "\n",
    "# First ensure the tables do not already exist\n",
    "for table in database.tables:\n",
    "    if table.name in [Contextualized_KDBAI_TABLE_NAME, non_Contextualized_KDBAI_TABLE_NAME]:\n",
    "        table.drop()\n",
    "\n",
    "#Create the tables\n",
    "table_contextualized = database.create_table(Contextualized_KDBAI_TABLE_NAME, schema=schema, indexes=indexes)\n",
    "table_non_contextualized = database.create_table(non_Contextualized_KDBAI_TABLE_NAME, schema=schema, indexes=indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 42
    },
    "id": "hbyA6GJH0Lw8",
    "outputId": "5e15e9db-bf72-4e3e-f4c8-7f88af378d9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rowsInserted': 27}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert Elements into the KDB.AI Tables\n",
    "table_contextualized.insert(df_contextualized)\n",
    "table_non_contextualized.insert(df_non_contextualized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "qjIAiwBVzYRA",
    "outputId": "686e7ab2-8f65-437d-d88f-6d0da3d02ccc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>metadata</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7673dd5dd3348ca922edfeb765c4f8ec</td>\n",
       "      <td>b'FACEBOOK\\n\\nNEWS RELEASE\\n\\nMeta Reports Sec...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.057882495, 0.015626749, 0.0125014, 0.016935...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0042c2ce77a154ed737cfb0d9b20b598</td>\n",
       "      <td>b\"### Comprehensive Description of the Table\\n...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[-0.00044214187, 0.011881717, 0.03507208, 0.01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1006ba147b4696dcfa364d82a7cc3ff9</td>\n",
       "      <td>b'Second Quarter 2024 Operational and Other Fi...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.07885342, 0.010770063, 0.032093342, 0.07417...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3ccaeebfca3cd0b37f89d9865ed86620</td>\n",
       "      <td>b\"CFO Outlook Commentary\\n\\nWe expect third qu...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.0649994, 0.023078175, -0.0025019816, 6.0829...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f83ab884e1b7f6cd22bb6fec166375de</td>\n",
       "      <td>b'About Meta\\n\\nMeta builds technologies that ...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.04435684, -0.008697157, -0.0011843009, 0.05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6523c2a592eaef3c9c62b13ff33414e6</td>\n",
       "      <td>b'Ryan Moore\\n\\npress@meta.com / about.fb.com/...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.068496555, 0.012527236, 0.0041264608, 0.055...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b288eb094301be949cb9c4d070135af5</td>\n",
       "      <td>b'For a discussion of limitations in the measu...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.06137703, 0.026085237, 0.07956282, 0.036115...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>69e526bdf29fb6ab25756fefa40b5439</td>\n",
       "      <td>b\"Non-GAAP Financial Measures\\n\\nTo supplement...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[-0.00022395901, 0.037475675, 0.05577307, 0.02...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>75d393ef2f359efa9a296e832490f823</td>\n",
       "      <td>b'For more information on our non-GAAP nancial...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.01794313, 0.016595712, 0.021199394, 2.51983...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>adf7621ac4c8f6206d85b1bba43e591f</td>\n",
       "      <td>b\"The table presented shows the condensed cons...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[0.007979893, -0.012464022, 0.051368486, 0.019...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>a918dcd3136389f1f3321920cd5bfc00</td>\n",
       "      <td>b'$ 31,999 $ 75,527 $ 60,645\\n\\n12,054\\n\\n18,7...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.045977995, -0.037664726, 0.07803254, 0.0160...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6ac0846a53cd0f99bb678c15f19d468e</td>\n",
       "      <td>b\"The table provided is an excerpt from Meta P...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[0.027606387, -0.012659598, 0.04341087, 0.0401...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46348476e1035ccd87ab69a2d1292d40</td>\n",
       "      <td>b'$ 3.03 $ 10.17 $ 5.24\\n\\n$ 2.98 $ 9.86 $ 5.1...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.015753612, 0.004310955, 0.041805614, 0.0384...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b0cf8bf065eab52e08c0428bbaa94527</td>\n",
       "      <td>b\"### Comprehensive Description\\n\\nThe table p...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[0.0070284354, 0.034549743, 0.043274693, 0.004...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>f7f4229f80e00f509426a6d8f45343c2</td>\n",
       "      <td>b'Common stock and additional paid-in capital\\...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.031031432, 0.0055321315, 0.05864805, 0.0336...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>a7a41c0fdf37f0f30e8d8a2bf5bfafff</td>\n",
       "      <td>b\"### Comprehensive Description of the Table\\n...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[0.016377663, 0.03243708, 0.03787182, 0.015178...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>b10e1bd32614ae548f2e08c4d131f460</td>\n",
       "      <td>b\"Accumulated other comprehensive loss\\n\\nReta...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.02234056, 0.01776979, 0.0062756445, 0.04135...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>109f8e55d17287867e2c5007111f08c9</td>\n",
       "      <td>b\"### Description of the Table\\n\\nThe table pr...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[-0.012494377, 0.044495758, 0.034548387, 0.005...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>d8928bcb2a3bb51176df4be51ba74732</td>\n",
       "      <td>b'Three Months Ended June 30, Six Months Ended...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.037447758, 0.031958383, 0.029735964, 0.0262...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cf915cc8ae9c9cc15b396ea448e94228</td>\n",
       "      <td>b'Here\\'s a detailed description of the table ...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[0.0018331239, 0.026139371, 0.046606544, 0.014...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>341be5c61346e8a761bcb3bc8442cfe9</td>\n",
       "      <td>b'$ 28,785\\n\\n165\\n\\n854\\n\\n$ 29,804\\n\\nMETA P...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.027821643, -0.0057690465, 0.0058191307, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>d41baecdce6b423b19c82ed8d8312c7d</td>\n",
       "      <td>b'The table provided is a snippet of Meta Plat...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[-0.005334932, 0.04107228, 0.04553666, 0.00887...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0c91552759dba13dcd139b9b0c9dab62</td>\n",
       "      <td>b'$ 1,507\\n\\n$ 182\\n\\n$ 3,845\\n\\n$ 217\\n\\nSegm...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[-0.014556726, 0.023491282, 0.035078283, 0.032...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>b07080a36773cb7ea01192e93db04f36</td>\n",
       "      <td>b'### Comprehensive Description of the Table\\n...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[-0.010667113, 0.034761183, 0.052321482, -0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2ba38b7e64d61e5e37565e0bb9e46f27</td>\n",
       "      <td>b'Reconciliation of GAAP to Non-GAAP Results\\n...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.008114564, 0.035163112, 0.06455587, 0.00853...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>d08c13817b3961107632d16dbb25b4fe</td>\n",
       "      <td>b\"### Detailed Description of the Table\\n\\nThe...</td>\n",
       "      <td>{'last_modified': '2024-07-31T21:06:06', 'file...</td>\n",
       "      <td>[-0.013586279, 0.03099968, 0.03918972, 0.00822...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5022c9c2aa1e8d5fce278b73f445a561</td>\n",
       "      <td>b'GAAP revenue\\n\\n$ 60,645\\n\\n$ 59,599\\n\\nchan...</td>\n",
       "      <td>{'filetype': 'application/pdf', 'languages': [...</td>\n",
       "      <td>[0.030714538, 0.0024296725, 0.0496302, 0.00056...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                                  id  \\\n",
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       "\n",
       "                                                 text  \\\n",
       "0   b'FACEBOOK\\n\\nNEWS RELEASE\\n\\nMeta Reports Sec...   \n",
       "1   b\"### Comprehensive Description of the Table\\n...   \n",
       "2   b'Second Quarter 2024 Operational and Other Fi...   \n",
       "3   b\"CFO Outlook Commentary\\n\\nWe expect third qu...   \n",
       "4   b'About Meta\\n\\nMeta builds technologies that ...   \n",
       "5   b'Ryan Moore\\n\\npress@meta.com / about.fb.com/...   \n",
       "6   b'For a discussion of limitations in the measu...   \n",
       "7   b\"Non-GAAP Financial Measures\\n\\nTo supplement...   \n",
       "8   b'For more information on our non-GAAP nancial...   \n",
       "9   b\"The table presented shows the condensed cons...   \n",
       "10  b'$ 31,999 $ 75,527 $ 60,645\\n\\n12,054\\n\\n18,7...   \n",
       "11  b\"The table provided is an excerpt from Meta P...   \n",
       "12  b'$ 3.03 $ 10.17 $ 5.24\\n\\n$ 2.98 $ 9.86 $ 5.1...   \n",
       "13  b\"### Comprehensive Description\\n\\nThe table p...   \n",
       "14  b'Common stock and additional paid-in capital\\...   \n",
       "15  b\"### Comprehensive Description of the Table\\n...   \n",
       "16  b\"Accumulated other comprehensive loss\\n\\nReta...   \n",
       "17  b\"### Description of the Table\\n\\nThe table pr...   \n",
       "18  b'Three Months Ended June 30, Six Months Ended...   \n",
       "19  b'Here\\'s a detailed description of the table ...   \n",
       "20  b'$ 28,785\\n\\n165\\n\\n854\\n\\n$ 29,804\\n\\nMETA P...   \n",
       "21  b'The table provided is a snippet of Meta Plat...   \n",
       "22  b'$ 1,507\\n\\n$ 182\\n\\n$ 3,845\\n\\n$ 217\\n\\nSegm...   \n",
       "23  b'### Comprehensive Description of the Table\\n...   \n",
       "24  b'Reconciliation of GAAP to Non-GAAP Results\\n...   \n",
       "25  b\"### Detailed Description of the Table\\n\\nThe...   \n",
       "26  b'GAAP revenue\\n\\n$ 60,645\\n\\n$ 59,599\\n\\nchan...   \n",
       "\n",
       "                                             metadata  \\\n",
       "0   {'filetype': 'application/pdf', 'languages': [...   \n",
       "1   {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "2   {'filetype': 'application/pdf', 'languages': [...   \n",
       "3   {'filetype': 'application/pdf', 'languages': [...   \n",
       "4   {'filetype': 'application/pdf', 'languages': [...   \n",
       "5   {'filetype': 'application/pdf', 'languages': [...   \n",
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       "12  {'filetype': 'application/pdf', 'languages': [...   \n",
       "13  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "14  {'filetype': 'application/pdf', 'languages': [...   \n",
       "15  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
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       "17  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "18  {'filetype': 'application/pdf', 'languages': [...   \n",
       "19  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "20  {'filetype': 'application/pdf', 'languages': [...   \n",
       "21  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "22  {'filetype': 'application/pdf', 'languages': [...   \n",
       "23  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "24  {'filetype': 'application/pdf', 'languages': [...   \n",
       "25  {'last_modified': '2024-07-31T21:06:06', 'file...   \n",
       "26  {'filetype': 'application/pdf', 'languages': [...   \n",
       "\n",
       "                                            embedding  \n",
       "0   [0.057882495, 0.015626749, 0.0125014, 0.016935...  \n",
       "1   [-0.00044214187, 0.011881717, 0.03507208, 0.01...  \n",
       "2   [0.07885342, 0.010770063, 0.032093342, 0.07417...  \n",
       "3   [0.0649994, 0.023078175, -0.0025019816, 6.0829...  \n",
       "4   [0.04435684, -0.008697157, -0.0011843009, 0.05...  \n",
       "5   [0.068496555, 0.012527236, 0.0041264608, 0.055...  \n",
       "6   [0.06137703, 0.026085237, 0.07956282, 0.036115...  \n",
       "7   [-0.00022395901, 0.037475675, 0.05577307, 0.02...  \n",
       "8   [0.01794313, 0.016595712, 0.021199394, 2.51983...  \n",
       "9   [0.007979893, -0.012464022, 0.051368486, 0.019...  \n",
       "10  [0.045977995, -0.037664726, 0.07803254, 0.0160...  \n",
       "11  [0.027606387, -0.012659598, 0.04341087, 0.0401...  \n",
       "12  [0.015753612, 0.004310955, 0.041805614, 0.0384...  \n",
       "13  [0.0070284354, 0.034549743, 0.043274693, 0.004...  \n",
       "14  [0.031031432, 0.0055321315, 0.05864805, 0.0336...  \n",
       "15  [0.016377663, 0.03243708, 0.03787182, 0.015178...  \n",
       "16  [0.02234056, 0.01776979, 0.0062756445, 0.04135...  \n",
       "17  [-0.012494377, 0.044495758, 0.034548387, 0.005...  \n",
       "18  [0.037447758, 0.031958383, 0.029735964, 0.0262...  \n",
       "19  [0.0018331239, 0.026139371, 0.046606544, 0.014...  \n",
       "20  [0.027821643, -0.0057690465, 0.0058191307, 0.0...  \n",
       "21  [-0.005334932, 0.04107228, 0.04553666, 0.00887...  \n",
       "22  [-0.014556726, 0.023491282, 0.035078283, 0.032...  \n",
       "23  [-0.010667113, 0.034761183, 0.052321482, -0.00...  \n",
       "24  [0.008114564, 0.035163112, 0.06455587, 0.00853...  \n",
       "25  [-0.013586279, 0.03099968, 0.03918972, 0.00822...  \n",
       "26  [0.030714538, 0.0024296725, 0.0496302, 0.00056...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see that the elements were inserted\n",
    "table_contextualized.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uZOMkjDY5_3y"
   },
   "source": [
    "# 6. Use LangChain and KDB.AI to Perform RAG!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "W6lNahh94FPr"
   },
   "outputs": [],
   "source": [
    "# Define OpenAI embedding model for LangChain to embed the query\n",
    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
    "\n",
    "# use KDBAI as vector store\n",
    "vecdb_kdbai_contextualized = KDBAI(table_contextualized, embeddings)\n",
    "vecdb_kdbai_non_contextualized = KDBAI(table_non_contextualized, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "6YGeZJtP8DVZ"
   },
   "outputs": [],
   "source": [
    "# Define a Question/Answer LangChain chain\n",
    "qabot_contextualized = RetrievalQA.from_chain_type(\n",
    "    chain_type=\"stuff\",\n",
    "    llm=ChatOpenAI(model=\"gpt-4o\"),\n",
    "    retriever=vecdb_kdbai_contextualized.as_retriever(search_kwargs=dict(k=5, index='flat_index')),\n",
    "    return_source_documents=True,\n",
    ")\n",
    "\n",
    "qabot_non_contextualized = RetrievalQA.from_chain_type(\n",
    "    chain_type=\"stuff\",\n",
    "    llm=ChatOpenAI(model=\"gpt-4o\"),\n",
    "    retriever=vecdb_kdbai_non_contextualized.as_retriever(search_kwargs=dict(k=5, index='flat_index')),\n",
    "    return_source_documents=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "6E855xdv8fW-"
   },
   "outputs": [],
   "source": [
    "# Helper function to perform RAG\n",
    "def RAG(query):\n",
    "  print(query)\n",
    "  print(\"-----\")\n",
    "  print(\"Contextualized\")\n",
    "  print(\"-----\")\n",
    "  print(qabot_contextualized.invoke(dict(query=query))[\"result\"])\n",
    "  print(\"-----\")\n",
    "  print(\"Non Contextualized\")\n",
    "  print(\"-----\")\n",
    "  print(qabot_non_contextualized.invoke(dict(query=query))[\"result\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tTAenauL4eB0",
    "outputId": "7f0c8ac4-5f9f-4ee3-ff85-4573debf95cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the research and development costs for six months ended in June 2024\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "The research and development costs for the six months ended in June 2024 were $20,515 million.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "The research and development costs for the six months ended June 30, 2024, were $20.515 billion.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"What is the research and development costs for six months ended in June 2024\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NsXWMDokAH5T",
    "outputId": "d4b6821b-66df-403e-866d-907aa1450089"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the research and development costs for six months ended in June 2023\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "The research and development costs for the six months ended June 30, 2023, were $18,725 million.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "The research and development costs for the six months ended in June 2023 were $20,515 million.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"What is the research and development costs for six months ended in June 2023\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9RzO0Rjh8sLj",
    "outputId": "2382e159-aa04-404e-fc70-0d1add4c860b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what is the 2024 GAAP advertising Revenue in the three months ended June 30th? What about net cash by operating activies\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "For the three months ended June 30, 2024, the GAAP advertising revenue for Meta was $38.329 billion. The net cash provided by operating activities was $19.370 billion.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "The 2024 GAAP advertising revenue for the three months ended June 30th is $38,329 million. The net cash provided by operating activities for the same period is $19,370 million.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"what is the 2024 GAAP advertising Revenue in the three months ended June 30th? What about net cash by operating activies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "86x-WGhFzFXF",
    "outputId": "2e798f74-17a4-4c74-a853-4d24ed614dff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What segment made the most money in the six months ended June 30th?\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "In the six months ended June 30, the \"Family of Apps\" segment made the most money, generating $74.734 billion in revenue.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "The segment that made the most money in the six months ended June 30th was the Family of Apps (FoA) segment, with a revenue of $31,307 million.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"What segment made the most money in the six months ended June 30th?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8CW2UnnlzbUa",
    "outputId": "8c8a99ef-6af9-4a3f-d027-3fc721b86170"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what is the three month costs and expensis for 2023?\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "The total costs and expenses for Meta Platforms, Inc. for the three months ended June 30, 2023, were $22,607 million.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "The three-month costs and expenses for 2023 are $22,607 million.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"what is the three month costs and expensis for 2023?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9NEqDp4dzqfk",
    "outputId": "f7eddf35-4946-4e4c-affe-953677bb38a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At the end of 2023, what was the value of Meta's Goodwill assets?\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "At the end of 2023, the value of Meta's Goodwill assets was $20,654 million.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "The value of Meta's Goodwill assets at the end of 2023 was $20,654 million.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"At the end of 2023, what was the value of Meta's Goodwill assets?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9mIKbXh3z88G",
    "outputId": "9925bdc8-d618-419c-94d5-621a3cf59f69"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given a sentiment score between 1 and 10 for the outlook? Explain your reasoning\n",
      "-----\n",
      "Contextualized\n",
      "-----\n",
      "Based on the provided financial data for Meta Platforms, Inc. in the second quarter of 2024, a sentiment score of **8** out of 10 can be reasonably assigned for the outlook. Here's the reasoning behind this score:\n",
      "\n",
      "### Positive Indicators\n",
      "\n",
      "1. **Earnings Per Share (EPS) Growth**:\n",
      "   - Basic EPS increased to $5.31, and diluted EPS increased to $5.16, reflecting strong profitability.\n",
      "   - The substantial increase in diluted EPS (73% year-over-year) indicates robust earnings growth even after accounting for potential share dilution.\n",
      "\n",
      "2. **Revenue Growth**:\n",
      "   - Revenue for Q2 2024 rose by 22% compared to Q2 2023, indicating strong top-line growth.\n",
      "\n",
      "3. **Income from Operations**:\n",
      "   - Income from operations increased by 58%, suggesting significant improvement in operational efficiency.\n",
      "\n",
      "4. **Operating Margin**:\n",
      "   - The operating margin improved from 29% to 38%, indicating better cost management and higher profitability.\n",
      "\n",
      "5. **Net Income**:\n",
      "   - Net income grew by 73%, which is a strong indicator of overall financial health and efficiency.\n",
      "\n",
      "6. **Effective Tax Rate**:\n",
      "   - The effective tax rate decreased from 16% to 11%, which could contribute to higher net income.\n",
      "\n",
      "### Neutral to Slightly Negative Indicators\n",
      "\n",
      "1. **Reduction in Current Assets**:\n",
      "   - Total current assets decreased from $85,365 million to $76,431 million, suggesting reduced liquidity.\n",
      "   - Cash and cash equivalents dropped from $41,862 million to $32,045 million, which might indicate reduced operational cash flow or higher capital expenditures.\n",
      "\n",
      "2. **Decrease in Retained Earnings**:\n",
      "   - Retained earnings decreased to $81,188 million from $82,070 million, which might imply dividend payouts or other distributions.\n",
      "\n",
      "3. **Slight Increase in Liabilities**:\n",
      "   - While total liabilities decreased slightly, there were increases in long-term debt and certain non-current liabilities, which could pose future obligations.\n",
      "\n",
      "### Summary\n",
      "\n",
      "The positive indicators, particularly the strong growth in EPS, revenue, net income, and operating margin, outweigh the neutral to slightly negative indicators. The decrease in current assets and the slight increase in some liabilities do warrant a bit of caution but do not significantly dampen the overall positive financial outlook.\n",
      "\n",
      "Therefore, a sentiment score of 8 out of 10 reflects a strong, optimistic outlook for Meta Platforms, Inc., acknowledging the impressive financial performance while taking into account some areas that require monitoring.\n",
      "-----\n",
      "Non Contextualized\n",
      "-----\n",
      "I would give the outlook a sentiment score of 7 out of 10. Here's the reasoning behind this score:\n",
      "\n",
      "### Positive Aspects:\n",
      "1. **Revenue Growth**: The expected third-quarter revenue range of $38.5-41 billion suggests a strong performance, indicating confidence in continued growth.\n",
      "2. **Stable Expense Forecast**: Total expenses for the year 2024 are expected to remain unchanged from the prior outlook, which suggests stability in financial planning.\n",
      "3. **Increased Investment**: The increase in capital expenditures to support AI research and product development indicates a forward-looking approach and investment in future growth.\n",
      "4. **Tax Rate**: An expected tax rate in the mid-teens is relatively favorable.\n",
      "\n",
      "### Neutral/Negative Aspects:\n",
      "1. **Foreign Currency Headwind**: A 2% headwind due to foreign currency exchange rates could slightly dampen revenue growth.\n",
      "2. **Reality Labs Losses**: The expectation of increased operating losses for Reality Labs indicates ongoing challenges in that segment.\n",
      "3. **Regulatory Headwinds**: Active regulatory landscapes, particularly in the EU and the U.S., could pose significant risks to business operations and financial results.\n",
      "\n",
      "### Summary:\n",
      "While there are strong indicators of growth and strategic investment, some risks and uncertainties remain, particularly regarding regulatory environments and segment-specific losses. Therefore, the overall sentiment is positive but tempered by these challenges, leading to a score of 7 out of 10.\n"
     ]
    }
   ],
   "source": [
    "# Query the RAG chain!\n",
    "RAG(\"Given a sentiment score between 1 and 10 for the outlook? Explain your reasoning\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OsU9SeSSQvHT"
   },
   "source": [
    "### Conclusion: We see that there are several situations where the non-contextualized response is incorrect and the contextualized response is correct. We also see there are some situations where they are both correct. In general, the more complex your tables and the more tables you have, the more advantageous this method becomes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zSx44UqyA4zs"
   },
   "source": [
    "### Delete the KDB.AI Tables\n",
    "Once finished with the table, it is best practice to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "s4bCBKCogmSK"
   },
   "outputs": [],
   "source": [
    "table_contextualized.drop()\n",
    "table_non_contextualized.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ay0yOj2ggmSK"
   },
   "source": [
    "#### Take Our Survey\n",
    "We hope you found this sample helpful! Your feedback is important to us, and we would appreciate it if you could take a moment to fill out our brief survey. Your input helps us improve our content.\n",
    "\n",
    "Take the [Survey](https://delighted.com/t/U2RoT32R)"
   ]
  }
 ],
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================================================
FILE: video_RAG/video_RAG_TwelveLabs.ipynb
================================================
{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "fa68ef64",
      "metadata": {
        "id": "fa68ef64"
      },
      "source": [
        "# Multimodal RAG with Video Data using KDB.AI and TwelveLabs\n",
        "\n",
        "##### Note: This example requires KDB.AI Server and Twelve Labs API key. Sign up for a free KDB.AI Server trial [here](https://trykdb.kx.com/kdbaiserver/signup/) and Twelve Labs account if needed.\n",
        "\n",
        "TwelveLabs is a company that deals with multimodal models, with a specific focus on video. We can use them to generate multimodal embeddings, and to chat with sections of our video that are retrieved. Here we will be making a multimodal RAG pipeline using KDB.AI and TwelveLabs.\n",
        "\n",
        "This notebook demonstrates building a multimodal Retrieval-Augmented Generation (RAG) system capable of answering questions about video content. It leverages:\n",
        "\n",
        "* **pytubefix & moviepy:** To download and process video.\n",
        "* **Twelve Labs:** For video understanding, indexing, and search capabilities.\n",
        "* **KDB.AI:** As the vector database to store and search video segment embeddings efficiently.\n",
        "\n",
        "### Agenda:\n",
        "0. Setup\n",
        "1. Download Data and Initialize Clients\n",
        "2. Video Indexing with Twelve Labs\n",
        "3. Load Data and Search\n",
        "4. Analyze Search Results\n",
        "5. Cleanup\n",
        "\n",
        "Relevant Links:\n",
        "* [KDB.AI](https://kdb.ai/)\n",
        "* [Twelve Labs](https://twelvelabs.io/)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4491e609",
      "metadata": {
        "id": "4491e609"
      },
      "source": [
        "## 0. Setup\n",
        "\n",
        "Install required packages, import libraries, set up API credentials"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b754a7f3",
      "metadata": {
        "id": "b754a7f3"
      },
      "source": [
        "### Install Required Dependencies\n",
        "First we will install the necessary Python packages including kdbai-client, pytubefix for YouTube downloads, moviepy for video processing, and twelvelabs for video understanding and search capabilities. These packages will enable us to download videos, process them, and build our multimodal RAG system."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d751f4d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2d751f4d",
        "outputId": "1c846a5f-15f7-4b6a-fe6e-0afc7400cec8"
      },
      "outputs": [],
      "source": [
        "!pip install kdbai-client pytubefix twelvelabs moviepy==2.1.2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d44f231c",
      "metadata": {
        "id": "d44f231c"
      },
      "source": [
        "### Import Core Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "d158fa5e",
      "metadata": {
        "id": "d158fa5e"
      },
      "outputs": [],
      "source": [
        "from moviepy import VideoFileClip\n",
        "from pytubefix import YouTube\n",
        "from pytubefix.cli import on_progress\n",
        "import pandas as pd\n",
        "\n",
        "import kdbai_client as kdbai\n",
        "from kdbai_client import Session\n",
        "from twelvelabs import TwelveLabs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a705d7e",
      "metadata": {
        "id": "8a705d7e"
      },
      "source": [
        "### Set Up API Keys and KDB.AI Connection Details\n",
        "We only have one key to set up, our TwelveLabs API Key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "edadf1a4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edadf1a4",
        "outputId": "8221bb32-8703-42cf-819c-424a94d5dedc"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import getpass\n",
        "\n",
        "os.environ[\"TWELVE_LABS_API_KEY\"] = (\n",
        "    os.getenv(\"TWELVE_LABS_API_KEY\")\n",
        "    or getpass.getpass(\"TWELVE_LABS_API_KEY: \")\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00be0510",
      "metadata": {},
      "source": [
        "## 1. Download Data and Initialize Clients\n",
        "\n",
        "Next we will download our YouTube video and initialize KDB.AI and TwelveLabs clients."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "20ec0b57",
      "metadata": {},
      "source": [
        "### Download Video\n",
        "\n",
        "Now we download a YouTube video for analysis. We specify the video URL and local save path, create the destination directory, and use pytubefix to download the highest resolution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "8c596372",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading video from https://www.youtube.com/watch?v=d_qvLDhkg00 to ./content/video_data/input_vid.mp4...\n",
            "\n",
            "Video downloaded successfully to ./content/video_data/input_vid.mp4\n"
          ]
        }
      ],
      "source": [
        "video_url = \"https://www.youtube.com/watch?v=d_qvLDhkg00\"  # Example: Central Limit Theorem video\n",
        "output_vid = \"./content/video_data/input_vid.mp4\"\n",
        "output_dir = os.path.dirname(output_vid)\n",
        "\n",
        "# Create output directory if it doesn't exist\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "print(f\"Downloading video from {video_url} to {output_vid}...\")\n",
        "\n",
        "yt = YouTube(video_url, on_progress_callback=on_progress)\n",
        "stream = yt.streams.get_highest_resolution()\n",
        "\n",
        "if stream:\n",
        "    stream.download(output_path=output_dir, filename=os.path.basename(output_vid))\n",
        "    print(f\"\\nVideo downloaded successfully to {output_vid}\")\n",
        "else:\n",
        "    print(\"Error: Could not find a suitable video stream.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c7c15c8",
      "metadata": {},
      "source": [
        "### Initialize TwelveLabs/Create Task\n",
        "\n",
        "Let's initialize TwelveLabs and wait for the TwelveLabs video embedding task."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b2cf0787",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'ready'"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tl = TwelveLabs(api_key=os.getenv('TWELVE_LABS_API_KEY'))\n",
        "task = tl.embed.task.create(\n",
        "    model_name=\"Marengo-retrieval-2.7\",\n",
        "    video_file='./content/video_data/input_vid.mp4'\n",
        ")\n",
        "task.wait_for_done()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b26eea9",
      "metadata": {},
      "source": [
        "We can now retrieve the embeddings for each clip."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "938dc881",
      "metadata": {},
      "outputs": [],
      "source": [
        "segs = task.retrieve(embedding_option=[\"visual-text\"]).video_embedding.segments"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "377f2c0b",
      "metadata": {
        "id": "377f2c0b"
      },
      "source": [
        "### Connect to KDB.AI Server\n",
        "\n",
        "To get started with KDB.AI Server:\n",
        "\n",
        "1. Sign up for a free trial at https://trykdb.kx.com/kdbaiserver/signup/\n",
        "2. Follow the setup instructions in your welcome email\n",
        "3. Connect using the endpoint for your deployment\n",
        "\n",
        "Example connection:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1ee7bc6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1ee7bc6",
        "outputId": "986dee25-0b18-4b21-98f6-c004f2508ce6"
      },
      "outputs": [],
      "source": [
        "#Set up KDB.AI server endpoint \n",
        "KDBAI_ENDPOINT = (\n",
        "    os.environ[\"KDBAI_ENDPOINT\"]\n",
        "    if \"KDBAI_ENDPOINT\" in os.environ\n",
        "    else \"http://localhost:8082\"\n",
        ")\n",
        "\n",
        "#connect to KDB.AI Server, default mode is qipc\n",
        "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n",
        "\n",
        "db = session.database(\"default\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b46efe7",
      "metadata": {
        "id": "7b46efe7"
      },
      "source": [
        "## 2. Set Up KDB.AI Vector Database Table\n",
        "\n",
        "Define the KDB.AI table schema and configure the vector index for storing and searching the video embeddings."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f2b16f0",
      "metadata": {
        "id": "3f2b16f0"
      },
      "source": [
        "### Define KDB.AI Table Schema and Indexes\n",
        "Now we'll define our table schema with four columns:\n",
        "- segment_id: A unique identifier for each video segment\n",
        "- start_offset_sec: The starting timestamp of the segment\n",
        "- end_offset_sec: The ending timestamp of the segment\n",
        "- embeddings: The vector representation of the segment content\n",
        "We'll also configure a vector index using HNSW algorithm with Cosine Similarity\n",
        "to enable efficient similarity search across our video embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "42c7c28d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42c7c28d",
        "outputId": "396c1e5f-a2c6-4c02-e3fc-c97052d9f522"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KDB.AI schema and index defined successfully.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "dim = len(segs[0].embeddings_float)\n",
        "\n",
        "schema = [\n",
        "    {\"name\":\"segment_id\",       \"type\":\"str\"},\n",
        "    {\"name\":\"start_offset_sec\", \"type\":\"float64\"},\n",
        "    {\"name\":\"end_offset_sec\",   \"type\":\"float64\"},\n",
        "    {\"name\":\"embeddings\",       \"type\":\"float32s\"}\n",
        "]\n",
        "\n",
        "indexes = [{\n",
        "    \"type\":   \"qHnsw\",\n",
        "    \"name\":   \"idx_emb\",\n",
        "    \"column\": \"embeddings\",\n",
        "    \"params\": {\"dims\": dim, \"metric\": \"CS\"}\n",
        "}]\n",
        "\n",
        "\n",
        "print(\"KDB.AI schema and index defined successfully.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "870b1dfa",
      "metadata": {
        "id": "870b1dfa"
      },
      "source": [
        "### Create KDB.AI Table\n",
        "\n",
        "In this step, we will:\n",
        "1. Create a new KDB.AI table named 'video_chunks'\n",
        "2. First drop any existing table with this name (with error handling)\n",
        "3. Create the table using our predefined schema and indexes\n",
        "4. Store a reference to the table in the `table` variable for later use"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "32563d36",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32563d36",
        "outputId": "39dc820b-fedb-462b-ed58-d272d924fa58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KDB.AI table 'video_chunks' created successfully.\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    db.table(\"video_chunks\").drop()\n",
        "except kdbai.KDBAIException:\n",
        "    pass\n",
        "\n",
        "table = db.create_table(\"video_chunks\", schema=schema, indexes=indexes)\n",
        "print(f\"KDB.AI table '{table.name}' created successfully.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3061abf9",
      "metadata": {
        "id": "3061abf9"
      },
      "source": [
        "## 3. Load Data and Search\n",
        "\n",
        "We now load the prepared video chunk data (segment IDs, start/end timestamps, and embeddings) into the KDB.AI vector table."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2aa5fb72",
      "metadata": {
        "id": "2aa5fb72"
      },
      "source": [
        "### Inserting Data into KDB.AI Table\n",
        "Let's insert our data into KDB.AI and query it to make sure it was inserted correctly:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "af32a380",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "af32a380",
        "outputId": "3184b153-7606-40ec-dd35-361417c69456"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>segment_id</th>\n",
              "      <th>start_offset_sec</th>\n",
              "      <th>end_offset_sec</th>\n",
              "      <th>embeddings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>[0.025205607, 0.0032751139, -0.014859959, 0.02...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>[0.02405941, 0.018603785, -0.02103669, 0.04805...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>12.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>[0.023615949, 0.013139918, -0.022509707, 0.047...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18</td>\n",
              "      <td>18.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>[0.0229081, 0.015123129, -0.028940378, 0.04754...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>[0.0034697105, 0.0023037025, -0.023470787, 0.0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  segment_id  start_offset_sec  end_offset_sec  \\\n",
              "0          0               0.0             6.0   \n",
              "1          6               6.0            12.0   \n",
              "2         12              12.0            18.0   \n",
              "3         18              18.0            24.0   \n",
              "4         24              24.0            30.0   \n",
              "\n",
              "                                          embeddings  \n",
              "0  [0.025205607, 0.0032751139, -0.014859959, 0.02...  \n",
              "1  [0.02405941, 0.018603785, -0.02103669, 0.04805...  \n",
              "2  [0.023615949, 0.013139918, -0.022509707, 0.047...  \n",
              "3  [0.0229081, 0.015123129, -0.028940378, 0.04754...  \n",
              "4  [0.0034697105, 0.0023037025, -0.023470787, 0.0...  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rows = []\n",
        "for seg in segs:\n",
        "    rows.append({\n",
        "        \"segment_id\":       f\"{int(seg.start_offset_sec)}\",\n",
        "        \"start_offset_sec\": seg.start_offset_sec,\n",
        "        \"end_offset_sec\":   seg.end_offset_sec,\n",
        "        \"embeddings\":       seg.embeddings_float\n",
        "    })\n",
        "    \n",
        "df = pd.DataFrame(rows)\n",
        "\n",
        "table.insert(df)\n",
        "\n",
        "display(table.query(limit=5))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2838fd5",
      "metadata": {
        "id": "f2838fd5"
      },
      "source": [
        "### Running Vector Search on Video Content\n",
        "Now we'll perform a vector search using the query \"central limit theorem\". We'll embed the query text with Twelve Labs' Marengo-retrieval-2.7 model, retrieve the top 3 most relevant video segments (n=3) from KDB.AI based on embedding similarity, and display these video clips in the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "978312b3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "978312b3",
        "outputId": "b344300e-bf10-40e2-c9af-07a473589526"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'video_found': True, 'audio_found': True, 'metadata': {'major_brand': 'mp42', 'minor_version': '0', 'compatible_brands': 'isommp42', 'creation_time': '2025-01-10T03:45:09.000000Z', 'encoder': 'Google'}, 'inputs': [{'streams': [{'input_number': 0, 'stream_number': 0, 'stream_type': 'video', 'language': None, 'default': True, 'size': [640, 360], 'bitrate': 56, 'fps': 30.0, 'codec_name': 'h264', 'profile': '(Main)', 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}, {'input_number': 0, 'stream_number': 1, 'stream_type': 'audio', 'language': None, 'default': True, 'fps': 44100, 'bitrate': 128, 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}], 'input_number': 0}], 'duration': 795.24, 'bitrate': 187, 'start': 0.0, 'default_video_input_number': 0, 'default_video_stream_number': 0, 'video_codec_name': 'h264', 'video_profile': '(Main)', 'video_size': [640, 360], 'video_bitrate': 56, 'video_fps': 30.0, 'default_audio_input_number': 0, 'default_audio_stream_number': 1, 'audio_fps': 44100, 'audio_bitrate': 128, 'video_duration': 795.24, 'video_n_frames': 23857}\n",
            "/Users/michaelryaboy/recent-projects/kdbai-samples/.venv/lib/python3.12/site-packages/imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1 -i ./content/video_data/input_vid.mp4 -loglevel error -f image2pipe -vf scale=640:360 -sws_flags bicubic -pix_fmt rgb24 -vcodec rawvideo -\n",
            "{'video_found': True, 'audio_found': True, 'metadata': {'major_brand': 'mp42', 'minor_version': '0', 'compatible_brands': 'isommp42', 'creation_time': '2025-01-10T03:45:09.000000Z', 'encoder': 'Google'}, 'inputs': [{'streams': [{'input_number': 0, 'stream_number': 0, 'stream_type': 'video', 'language': None, 'default': True, 'size': [640, 360], 'bitrate': 56, 'fps': 30.0, 'codec_name': 'h264', 'profile': '(Main)', 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}, {'input_number': 0, 'stream_number': 1, 'stream_type': 'audio', 'language': None, 'default': True, 'fps': 44100, 'bitrate': 128, 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}], 'input_number': 0}], 'duration': 795.24, 'bitrate': 187, 'start': 0.0, 'default_video_input_number': 0, 'default_video_stream_number': 0, 'video_codec_name': 'h264', 'video_profile': '(Main)', 'video_size': [640, 360], 'video_bitrate': 56, 'video_fps': 30.0, 'default_audio_input_number': 0, 'default_audio_stream_number': 1, 'audio_fps': 44100, 'audio_bitrate': 128, 'video_duration': 795.24, 'video_n_frames': 23857}\n",
            "/Users/michaelryaboy/recent-projects/kdbai-samples/.venv/lib/python3.12/site-packages/imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1 -ss 119.000000 -i ./content/video_data/input_vid.mp4 -ss 1.000000 -loglevel error -f image2pipe -vf scale=640:360 -sws_flags bicubic -pix_fmt rgb24 -vcodec rawvideo -\n",
            "MoviePy - Building video __temp__.mp4.\n",
            "MoviePy - Writing audio in __temp__TEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                        \r"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done.\n",
            "MoviePy - Writing video __temp__.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                                          "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done !\n",
            "MoviePy - video ready __temp__.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r"
          ]
        },
        {
          "data": {
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'video_found': True, 'audio_found': True, 'metadata': {'major_brand': 'mp42', 'minor_version': '0', 'compatible_brands': 'isommp42', 'creation_time': '2025-01-10T03:45:09.000000Z', 'encoder': 'Google'}, 'inputs': [{'streams': [{'input_number': 0, 'stream_number': 0, 'stream_type': 'video', 'language': None, 'default': True, 'size': [640, 360], 'bitrate': 56, 'fps': 30.0, 'codec_name': 'h264', 'profile': '(Main)', 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}, {'input_number': 0, 'stream_number': 1, 'stream_type': 'audio', 'language': None, 'default': True, 'fps': 44100, 'bitrate': 128, 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}], 'input_number': 0}], 'duration': 795.24, 'bitrate': 187, 'start': 0.0, 'default_video_input_number': 0, 'default_video_stream_number': 0, 'video_codec_name': 'h264', 'video_profile': '(Main)', 'video_size': [640, 360], 'video_bitrate': 56, 'video_fps': 30.0, 'default_audio_input_number': 0, 'default_audio_stream_number': 1, 'audio_fps': 44100, 'audio_bitrate': 128, 'video_duration': 795.24, 'video_n_frames': 23857}\n",
            "/Users/michaelryaboy/recent-projects/kdbai-samples/.venv/lib/python3.12/site-packages/imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1 -i ./content/video_data/input_vid.mp4 -loglevel error -f image2pipe -vf scale=640:360 -sws_flags bicubic -pix_fmt rgb24 -vcodec rawvideo -\n",
            "{'video_found': True, 'audio_found': True, 'metadata': {'major_brand': 'mp42', 'minor_version': '0', 'compatible_brands': 'isommp42', 'creation_time': '2025-01-10T03:45:09.000000Z', 'encoder': 'Google'}, 'inputs': [{'streams': [{'input_number': 0, 'stream_number': 0, 'stream_type': 'video', 'language': None, 'default': True, 'size': [640, 360], 'bitrate': 56, 'fps': 30.0, 'codec_name': 'h264', 'profile': '(Main)', 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}, {'input_number': 0, 'stream_number': 1, 'stream_type': 'audio', 'language': None, 'default': True, 'fps': 44100, 'bitrate': 128, 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}], 'input_number': 0}], 'duration': 795.24, 'bitrate': 187, 'start': 0.0, 'default_video_input_number': 0, 'default_video_stream_number': 0, 'video_codec_name': 'h264', 'video_profile': '(Main)', 'video_size': [640, 360], 'video_bitrate': 56, 'video_fps': 30.0, 'default_audio_input_number': 0, 'default_audio_stream_number': 1, 'audio_fps': 44100, 'audio_bitrate': 128, 'video_duration': 795.24, 'video_n_frames': 23857}\n",
            "/Users/michaelryaboy/recent-projects/kdbai-samples/.venv/lib/python3.12/site-packages/imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1 -ss 611.000000 -i ./content/video_data/input_vid.mp4 -ss 1.000000 -loglevel error -f image2pipe -vf scale=640:360 -sws_flags bicubic -pix_fmt rgb24 -vcodec rawvideo -\n",
            "MoviePy - Building video __temp__.mp4.\n",
            "MoviePy - Writing audio in __temp__TEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                        "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done.\n",
            "MoviePy - Writing video __temp__.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                                          "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done !\n",
            "MoviePy - video ready __temp__.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r"
          ]
        },
        {
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
            ],
            "text/plain": [
              "<moviepy.video.io.display_in_notebook.HTML2 object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'video_found': True, 'audio_found': True, 'metadata': {'major_brand': 'mp42', 'minor_version': '0', 'compatible_brands': 'isommp42', 'creation_time': '2025-01-10T03:45:09.000000Z', 'encoder': 'Google'}, 'inputs': [{'streams': [{'input_number': 0, 'stream_number': 0, 'stream_type': 'video', 'language': None, 'default': True, 'size': [640, 360], 'bitrate': 56, 'fps': 30.0, 'codec_name': 'h264', 'profile': '(Main)', 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}, {'input_number': 0, 'stream_number': 1, 'stream_type': 'audio', 'language': None, 'default': True, 'fps': 44100, 'bitrate': 128, 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}], 'input_number': 0}], 'duration': 795.24, 'bitrate': 187, 'start': 0.0, 'default_video_input_number': 0, 'default_video_stream_number': 0, 'video_codec_name': 'h264', 'video_profile': '(Main)', 'video_size': [640, 360], 'video_bitrate': 56, 'video_fps': 30.0, 'default_audio_input_number': 0, 'default_audio_stream_number': 1, 'audio_fps': 44100, 'audio_bitrate': 128, 'video_duration': 795.24, 'video_n_frames': 23857}\n",
            "/Users/michaelryaboy/recent-projects/kdbai-samples/.venv/lib/python3.12/site-packages/imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1 -i ./content/video_data/input_vid.mp4 -loglevel error -f image2pipe -vf scale=640:360 -sws_flags bicubic -pix_fmt rgb24 -vcodec rawvideo -\n",
            "{'video_found': True, 'audio_found': True, 'metadata': {'major_brand': 'mp42', 'minor_version': '0', 'compatible_brands': 'isommp42', 'creation_time': '2025-01-10T03:45:09.000000Z', 'encoder': 'Google'}, 'inputs': [{'streams': [{'input_number': 0, 'stream_number': 0, 'stream_type': 'video', 'language': None, 'default': True, 'size': [640, 360], 'bitrate': 56, 'fps': 30.0, 'codec_name': 'h264', 'profile': '(Main)', 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}, {'input_number': 0, 'stream_number': 1, 'stream_type': 'audio', 'language': None, 'default': True, 'fps': 44100, 'bitrate': 128, 'metadata': {'Metadata': '', 'creation_time': '2025-01-10T03:45:09.000000Z', 'handler_name': 'ISO Media file produced by Google Inc. Created on: 01/09/2025.', 'vendor_id': '[0][0][0][0]'}}], 'input_number': 0}], 'duration': 795.24, 'bitrate': 187, 'start': 0.0, 'default_video_input_number': 0, 'default_video_stream_number': 0, 'video_codec_name': 'h264', 'video_profile': '(Main)', 'video_size': [640, 360], 'video_bitrate': 56, 'video_fps': 30.0, 'default_audio_input_number': 0, 'default_audio_stream_number': 1, 'audio_fps': 44100, 'audio_bitrate': 128, 'video_duration': 795.24, 'video_n_frames': 23857}\n",
            "/Users/michaelryaboy/recent-projects/kdbai-samples/.venv/lib/python3.12/site-packages/imageio_ffmpeg/binaries/ffmpeg-macos-aarch64-v7.1 -ss 617.000000 -i ./content/video_data/input_vid.mp4 -ss 1.000000 -loglevel error -f image2pipe -vf scale=640:360 -sws_flags bicubic -pix_fmt rgb24 -vcodec rawvideo -\n",
            "MoviePy - Building video __temp__.mp4.\n",
            "MoviePy - Writing audio in __temp__TEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                        \r"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done.\n",
            "MoviePy - Writing video __temp__.mp4\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                                                                          "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done !\n",
            "MoviePy - video ready __temp__.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r"
          ]
        },
        {
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
            ],
            "text/plain": [
              "<moviepy.video.io.display_in_notebook.HTML2 object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "query = \"central limit theorem\"\n",
        "\n",
        "q_emb = tl.embed.create(model_name=\"Marengo-retrieval-2.7\", text=query)\n",
        "q_vec = q_emb.text_embedding.segments[0].embeddings_float\n",
        "\n",
        "hits  = table.search(vectors={\"idx_emb\": [q_vec]}, n=3)[0]\n",
        "\n",
        "# play video starting first hit \n",
        "from IPython.display import display\n",
        "\n",
        "for index in range(len(hits)):\n",
        "    clip = VideoFileClip(output_vid).subclipped(hits.iloc[index].start_offset_sec, hits.iloc[index].end_offset_sec)\n",
        "    display(clip.display_in_notebook(width=500))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22138db5",
      "metadata": {},
      "source": [
        "Looks like we got accurate snippets! They all seem to be about the Central Limit Theorem."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51c08173",
      "metadata": {},
      "source": [
        "## 4. Analyzing Search Results\n",
        "In this step we will summarize each clip that was retrieved with the TwelveLabs Pegasus model.\n",
        "\n",
        "### Indexing Video Content\n",
        "Next, we'll create an index for our video using Twelve Labs' models. This index will enable advanced search and generation capabilities, allowing us to extract detailed information and generate summaries from specific segments of the video."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "5eda4183",
      "metadata": {},
      "outputs": [],
      "source": [
        "idx = None\n",
        "try:\n",
        "    idx = tl.index.create(\n",
        "        name=\"clt-demo\",\n",
        "        models=[\n",
        "            {\"name\":\"marengo2.7\",\"options\":[\"visual\",\"audio\"]},\n",
        "            {\"name\":\"pegasus1.2\",\"options\":[\"visual\",\"audio\"]}\n",
        "        ]\n",
        "    )\n",
        "except Exception:\n",
        "    idx = tl.index.list()[0]\n",
        "\n",
        "vid_task = tl.task.create(index_id=idx.id, file=output_vid)\n",
        "vid_task.wait_for_done()\n",
        "video_id = vid_task.video_id"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9579d331",
      "metadata": {},
      "source": [
        "### Summarize Clips\n",
        "\n",
        "We can use the TwelveLabs summarize endpoint to summarize every clip by passing the start offset and end offset parameters. We end up with multiple summaries. An alternative is to use an open source video chat model and pass the combined clips!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "35cbdad9",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "📝 Summaries:\n",
            "\n",
            "— Segment 120.0–126.0s —\n",
            "The video delves into the intricacies of the normal distribution, also known as the Gaussian distribution, and its significance in probability theory. The narrator begins by posing a question about the special place of the Gaussian function, \\( e^{-x^2} \\), in probability theory, setting the stage for an exploration of why this function is so important.\n",
            "The discussion then transitions to a refresher on the Central Limit Theorem (CLT), which states that as you add multiple copies of a random variable, such as rolling a weighted die many times or letting a ball bounce off pegs repeatedly, the distribution of the sum tends to approximate a normal distribution. The CLT asserts that as the sum grows larger, this approximation becomes increasingly accurate. However, the video does not just restate the theorem; it aims to provide a deeper understanding of why the Gaussian function is the central limit.\n",
            "The video introduces the concept of convolution, a mathematical operation that describes the distribution of the sum of two random variables. It visually demonstrates this process through two distinct methods: one involving diagonal slices and another showing the convergence of various probability density functions into a standard normal distribution. The convolution of two Gaussian functions is a key focus, as it is shown to result in another Gaussian function, albeit with a different standard deviation.\n",
            "The video then presents a simplified version of the Gaussian function, \\( e^{-x^2} \\), to illustrate the convolution process more clearly. It leverages the rotational symmetry of the resulting 3D graph to compute the area under diagonal slices, which corresponds to the convolution evaluated at a given sum \\( s \\). This computation reveals that the convolution of two Gaussians is itself another Gaussian, a very special and unique result in the realm of convolutions.\n",
            "The video also touches on the broader implications of this result for the Central Limit Theorem. It explains that the ubiquity of normal distributions is often due to the CLT, and the stability of the Gaussian function under repeated convolution is a crucial reason why the CLT leads to a Gaussian distribution. The video outlines a two-step proof of the CLT: first, showing that there exists a universal shape that repeated convolutions tend towards, and second, proving that this shape is a Gaussian.\n",
            "In the segment between 120.0s and 126.0s, the video emphasizes the Central Limit Theorem by illustrating the convergence of various probability density functions into a standard normal distribution through rescaling and recentering. This visual representation reinforces the idea that as the sample size increases, the distribution of the sum of random variables approaches a normal distribution, highlighting the theorem's practical significance.\n",
            "Overall, the video provides a comprehensive and visually intuitive explanation of the normal distribution, its role in probability theory, and the mathematical underpinnings of the Central Limit Theorem.\n",
            "\n",
            "— Segment 612.0–618.0s —\n",
            "The video delves into the mathematical underpinnings of the normal distribution, also known as the Gaussian distribution, and its significance in probability theory. The narrator begins by addressing the question of why the function e to the negative x squared is so special in the context of probability, leading into a discussion of the Central Limit Theorem (CLT). The CLT describes how the sum of multiple copies of a random variable tends to approximate a normal distribution as the number of variables increases. The video then transitions into a detailed explanation of convolutions, which are used to find the distribution of the sum of two random variables.\n",
            "A significant portion of the video is dedicated to visually demonstrating the convolution process between two Gaussian functions. The narrator provides two distinct ways to visualize convolutions, with a focus on the second method involving diagonal slices. This method is used to show how the convolution of two Gaussian functions results in another Gaussian function, a unique property that is not shared by most other distributions. The video emphasizes the rotational symmetry of the 3D graph formed by the convolution of two Gaussian functions, which simplifies the calculation of areas under these slices.\n",
            "The video also discusses the importance of this convolution property in proving the Central Limit Theorem. Specifically, it highlights that the CLT states that as you repeatedly add copies of a random variable to itself, the distribution tends towards a normal distribution after appropriate shifting and rescaling. This is a crucial step in understanding why the Gaussian function is the central limit in probability theory. The proof of the CLT involves two main steps: showing that there exists a universal shape that all distributions tend towards, and proving that the convolution of two Gaussians results in another Gaussian. The video explains that the Gaussian function is a fixed point under repeated convolutions, which means it must be the universal shape that all distributions approach.\n",
            "In the segment between 612.0s and 618.0s, the video elaborates on the CLT by emphasizing that the supposed ubiquity of normal distributions is often exaggerated. The narrator clarifies that the prevalence of normal distributions is actually a consequence of the CLT, not a priori. This segment underscores the importance of the CLT in explaining why Gaussian distributions are so common in nature and statistics, and it stresses that the CLT is the reason the function at the heart of the theorem is a Gaussian and not some other function.\n",
            "The video concludes by connecting the convolution property of Gaussian functions to other mathematical concepts, such as the Herschel-Maxwell derivation and the presence of pi in the Gaussian formula. It also mentions a different approach to proving the CLT using entropy, as suggested by a channel supporter. The narrator encourages viewers to stay updated with new videos and projects through a mailing list, providing a seamless transition from the mathematical content to community engagement.\n",
            "\n",
            "— Segment 618.0–624.0s —\n",
            "The video delves into the mathematical underpinnings of the normal distribution, also known as a Gaussian, with a particular focus on the Central Limit Theorem (CLT) and the convolution of Gaussian functions. The speaker begins by explaining the significance of the function \\( e^{-x^2} \\) in the context of probability theory and how it relates to the CLT, which describes the tendency of the sum of multiple random variables to approximate a normal distribution as the number of variables increases.\n",
            "The video visually illustrates the CLT through animations and graphs, showing how the distribution of sums of random variables converges to a bell-shaped curve. It also explores the convolution operation, which is central to understanding how the sum of two random variables is distributed. The speaker emphasizes that the convolution of two Gaussian functions results in another Gaussian function, a unique property that is not shared by other distributions.\n",
            "In the segment between 618.0s and 624.0s, the video specifically addresses the CLT. It explains that the CLT states that if you repeatedly add copies of a random variable to itself, which mathematically corresponds to repeatedly computing convolutions against a given distribution, the resulting distribution will tend towards a normal distribution after appropriate shifting and rescaling. This process is universal for a wide range of initial distributions, provided they have finite variance. The speaker highlights that the CLT is the reason why the function at the heart of the theorem is a Gaussian, and not some other function.\n",
            "The video concludes by connecting this geometric argument to other derivations of the Gaussian, such as the Herschel-Maxwell derivation and the classic proof involving pi. It also mentions a different approach to proving the CLT using entropy, as suggested by a channel supporter. The speaker encourages viewers to stay updated on new videos and projects through a mailing list, which is a relatively new and effective way to follow the work being done.\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n📝 Summaries:\")\n",
        "for index in range(len(hits)):  # Iterate using index to access rows\n",
        "    h = hits.iloc[index]  # Access row using .iloc\n",
        "    start = h.start_offset_sec\n",
        "    end   = h.end_offset_sec\n",
        "    prompt = (\n",
        "        f\"Summarize the content of the video between {start:.1f}s and {end:.1f}s, \"\n",
        "        \"with an emphasis on the Central Limit Theorem.\"\n",
        "    )\n",
        "    summary = tl.generate.summarize(\n",
        "        video_id=video_id,\n",
        "        type=\"summary\",\n",
        "        prompt=prompt\n",
        "    )\n",
        "    print(f\"\\n— Segment {start:.1f}–{end:.1f}s —\\n{summary.summary}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f27c11e9",
      "metadata": {
        "id": "f27c11e9"
      },
      "source": [
        "\n",
        "## 5. Cleanup\n",
        "\n",
        "Remove the KDB.AI table created during this notebook session to free up resources."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7623dbe8",
      "metadata": {
        "id": "7623dbe8"
      },
      "source": [
        "\n",
        "### Cleanup: Dropping the KDB.AI Table\n",
        "Once finished with the table, it is best practice to drop it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "2076c517",
      "metadata": {
        "id": "2076c517"
      },
      "outputs": [],
      "source": [
        "table.drop()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": ".venv",
      "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": "3.12.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}


================================================
FILE: video_RAG/video_RAG_VoyageAI.ipynb
================================================
{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "fa68ef64",
      "metadata": {
        "id": "fa68ef64"
      },
      "source": [
        "## Multimodal RAG with Video Data using KDB.AI, Voyage AI and OpenAI\n",
        "\n",
        "##### Note: This example requires KDB.AI server, Voyage AI API key, and OpenAI API key. Sign up for free accounts if needed.\n",
        "\n",
        "This notebook demonstrates building a multimodal Retrieval-Augmented Generation (RAG) system capable of answering questions about video content. It leverages:\n",
        "*   **pytubefix & moviepy:** To download and process video (extract frames and audio).\n",
        "*   **OpenAI Whisper:** To transcribe the video's audio content.\n",
        "*   **Voyage AI:** To generate multimodal embeddings (`voyage-multimodal-3`) representing both text and image frames from video segments.\n",
        "*   **KDB.AI:** As the vector database to store and search these multimodal embeddings efficiently.\n",
        "*   **OpenAI GPT-4o-mini:** As the Large Language Model (LLM) to generate answers based on the retrieved multimodal context.\n",
        "\n",
        "### Agenda:\n",
        "0.  Setup\n",
        "1.  Data Preparation (Download, Extraction, Transcription, Chunking)\n",
        "2.  Multimodal Embedding Generation\n",
        "3.  Set Up KDB.AI Vector Database Table\n",
        "4.  Insert Data into KDB.AI\n",
        "5.  Define RAG Helper Functions\n",
        "6.  Perform Retrieval Augmented Generation\n",
        "7.  Cleanup\n",
        "\n",
        "Relevant Links:\n",
        "*   [KDB.AI](https://kdb.ai/)\n",
        "*   [Voyage AI](https://www.voyageai.com/)\n",
        "*   [OpenAI](https://openai.com/)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4491e609",
      "metadata": {
        "id": "4491e609"
      },
      "source": [
        "## 0. Setup\n",
        "\n",
        "Install required packages, import libraries, set up API credentials, and initialize clients."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b754a7f3",
      "metadata": {
        "id": "b754a7f3"
      },
      "source": [
        "### Installing Required Dependencies\n",
        "Installs necessary Python packages including kdbai-client, pytubefix for YouTube downloads, moviepy for video processing, pillow for image handling, pandas/numpy for data manipulation, openai for LLM/Whisper access, and voyageai for multimodal embeddings. Also installs the ffmpeg system dependency required by moviepy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d751f4d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2d751f4d",
        "outputId": "1c846a5f-15f7-4b6a-fe6e-0afc7400cec8"
      },
      "outputs": [],
      "source": [
        "!pip install --quiet kdbai-client pytubefix moviepy==1.0.3 pillow pandas numpy openai \\\n",
        "&& apt-get update -qq && apt-get install -qq -y ffmpeg \\\n",
        "&& pip install --upgrade --no-deps voyageai>=0.3.2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d44f231c",
      "metadata": {
        "id": "d44f231c"
      },
      "source": [
        "### Importing Core Libraries\n",
        "Imports essential Python libraries for OS interaction, math operations, JSON/Base64 encoding, image handling (PIL), video editing (moviepy), data manipulation (pandas/numpy), interaction with external APIs (voyageai, openai, kdbai_client), YouTube downloading (pytubefix), user input (getpass), and rich display formatting (IPython)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "d158fa5e",
      "metadata": {
        "id": "d158fa5e"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import math\n",
        "import json\n",
        "import base64\n",
        "import getpass\n",
        "from io import BytesIO\n",
        "\n",
        "# Third-party Libraries\n",
        "import voyageai\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from moviepy.editor import VideoFileClip\n",
        "from openai import OpenAI\n",
        "from pytubefix import YouTube\n",
        "from pytubefix.cli import on_progress\n",
        "from IPython.display import Image as IPImage, display, Markdown\n",
        "import kdbai_client as kdbai\n",
        "\n",
        "# KDB.AI Client\n",
        "from kdbai_client import Session"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a705d7e",
      "metadata": {
        "id": "8a705d7e"
      },
      "source": [
        "### Setting Up API Keys and KDB.AI Connection Details\n",
        "Retrieves API keys for Voyage AI, KDB.AI, and OpenAI, along with the KDB.AI endpoint URL. It first checks if the corresponding environment variable is set. If not, it securely prompts the user for the necessary credential using `getpass` (for keys) or `input` (for the endpoint) and stores it in `os.environ` for the current session."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "edadf1a4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edadf1a4",
        "outputId": "8221bb32-8703-42cf-819c-424a94d5dedc"
      },
      "outputs": [],
      "source": [
        "# Check for Voyage AI API Key\n",
        "os.environ[\"VOYAGEAI_API_KEY\"] = (\n",
        "    os.getenv(\"VOYAGEAI_API_KEY\")\n",
        "    or getpass.getpass(\"Voyage AI API Key: \")\n",
        ")\n",
        "\n",
        "#Set up KDB.AI server endpoint \n",
        "KDBAI_ENDPOINT = (\n",
        "    os.environ[\"KDBAI_ENDPOINT\"]\n",
        "    if \"KDBAI_ENDPOINT\" in os.environ\n",
        "    else \"http://localhost:8082\"\n",
        ")\n",
        "\n",
        "\n",
        "# Check for OpenAI API Key\n",
        "os.environ[\"OPENAI_API_KEY\"] = (\n",
        "    os.getenv(\"OPENAI_API_KEY\")\n",
        "    or getpass.getpass(\"OpenAI API Key: \")\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "377f2c0b",
      "metadata": {
        "id": "377f2c0b"
      },
      "source": [
        "### Initializing API Clients and KDB.AI Session\n",
        "Creates client instances for Voyage AI and OpenAI (which automatically use the API keys set in environment variables). Establishes a connection (session) to the specified KDB.AI server endpoint and gets a handle to the 'default' database."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1ee7bc6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1ee7bc6",
        "outputId": "986dee25-0b18-4b21-98f6-c004f2508ce6"
      },
      "outputs": [],
      "source": [
        "# Initialize Voyage AI Client\n",
        "voyage = voyageai.Client(api_key=os.getenv(\"VOYAGEAI_API_KEY\"))\n",
        "print(\"Voyage AI Client initialized.\")\n",
        "\n",
        "# Initialize KDB.AI Session and Database connection\n",
        "session = kdbai.Session(endpoint=KDBAI_ENDPOINT)\n",
        "db = session.database(\"default\")\n",
        "\n",
        "\n",
        "# Initialize OpenAI Client\n",
        "openai = OpenAI()  # Reads from os.environ[\"OPENAI_API_KEY\"]\n",
        "print(\"OpenAI Client initialized.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad09d2e8",
      "metadata": {
        "id": "ad09d2e8"
      },
      "source": [
        "## 1. Data Preparation\n",
        "\n",
        "Download the target YouTube video, extract its visual frames and audio track, transcribe the audio, and then segment the video into logical chunks containing both frames and their corresponding text transcription."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "587dacab",
      "metadata": {
        "id": "587dacab"
      },
      "source": [
        "\n",
        "### 1.a Download Video\n",
        "\n",
        "Defines the YouTube video URL and the local path where the video file will be saved. Creates the destination directory if it doesn't exist. Uses the pytubefix library to connect to YouTube, select the highest resolution stream, and download the video, showing progress via the on_progress callback."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9fa444d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fa444d4",
        "outputId": "9a9e9318-5536-43f5-a8f9-230f7d612294"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading video from https://www.youtube.com/watch?v=d_qvLDhkg00 to /content/video_data/input_vid.mp4...\n",
            "\n",
            "Video downloaded successfully to /content/video_data/input_vid.mp4\n"
          ]
        }
      ],
      "source": [
        "video_url = \"https://www.youtube.com/watch?v=d_qvLDhkg00\"  # Example: Central Limit Theorem video\n",
        "output_vid = \"./content/video_data/input_vid.mp4\"\n",
        "output_dir = os.path.dirname(output_vid)\n",
        "\n",
        "# Create output directory if it doesn't exist\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "print(f\"Downloading video from {video_url} to {output_vid}...\")\n",
        "\n",
        "yt = YouTube(video_url, on_progress_callback=on_progress)\n",
        "stream = yt.streams.get_highest_resolution()\n",
        "\n",
        "if stream:\n",
        "    stream.download(output_path=output_dir, filename=os.path.basename(output_vid))\n",
        "    print(f\"\\nVideo downloaded successfully to {output_vid}\")\n",
        "else:\n",
        "    print(\"Error: Could not find a suitable video stream.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d404992",
      "metadata": {
        "id": "9d404992"
      },
      "source": [
        "### 1.b Extract Frames and Audio\n",
        "\n",
        "Defines paths for the output frame directory and the audio file. Uses the moviepy library to open the downloaded video file. Extracts frames at a rate of 0.2 FPS (one frame every 5 seconds) and saves them as sequentially numbered PNG images. Extracts the audio track and saves it as an MP3 file with a specified codec and bitrate. Includes error handling for file processing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "527b2724",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "527b2724",
        "outputId": "dea984d3-c308-41f8-8446-c438c8c70ed5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting frames and audio...\n",
            "Moviepy - Writing frames /content/video_data/frames/frame%04d.png.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moviepy - Done writing frames /content/video_data/frames/frame%04d.png.\n",
            "MoviePy - Writing audio in /content/video_data/output_audio.mp3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": []
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MoviePy - Done.\n",
            "\n",
            "Frames extracted to /content/video_data/frames/\n",
            "Audio extracted to /content/video_data/output_audio.mp3\n"
          ]
        }
      ],
      "source": [
        "frames_dir = os.path.join(output_dir, \"frames/\")\n",
        "audio_mp3 = os.path.join(output_dir, \"output_audio.mp3\")\n",
        "\n",
        "# Create frames directory if it doesn't exist\n",
        "os.makedirs(frames_dir, exist_ok=True)\n",
        "\n",
        "print(\"Extracting frames and audio...\")\n",
        "\n",
        "with VideoFileClip(output_vid) as clip:\n",
        "    # Extract frames (1 frame every 5 seconds)\n",
        "    clip.write_images_sequence(os.path.join(frames_dir, \"frame%04d.png\"), fps=0.2, logger='bar')  # Use logger='bar' for progress\n",
        "    # Extract audio\n",
        "    clip.audio.write_audiofile(audio_mp3, codec=\"libmp3lame\", bitrate=\"192k\", logger='bar')\n",
        "\n",
        "print(f\"\\nFrames extracted to {frames_dir}\")\n",
        "print(f\"Audio extracted to {audio_mp3}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1434ccb0",
      "metadata": {
        "id": "1434ccb0"
      },
      "source": [
        "### 1.c Transcribe Audio\n",
        "\n",
        "Checks if the OpenAI client and audio file are available. Opens the extracted MP3 audio file in binary read mode and sends it to OpenAI's Whisper-1 transcription model. Requests the 'verbose_json' format to get detailed output, including word-level timestamps which might be useful for more precise alignment (though not used directly for chunking in this version). Handles potential API errors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17821cc7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17821cc7",
        "outputId": "1035e2d3-c453-487f-acd5-44d908ef309b"
      },
      "outputs": [],
      "source": [
        "transcription_result = None  # Initialize transcription result\n",
        "\n",
        "print(f\"Transcribing audio file: {audio_mp3} using Whisper-1...\")\n",
        "\n",
        "with open(audio_mp3, \"rb\") as audio_file_handle:\n",
        "    transcription_result = openai.audio.transcriptions.create(\n",
        "        model=\"whisper-1\",\n",
        "        file=audio_file_handle,\n",
        "        response_format=\"verbose_json\",\n",
        "        timestamp_granularities=[\"segment\"]  # Requesting segment timestamps\n",
        "    )\n",
        "\n",
        "print(\"Audio transcription complete.\")\n",
        "print(transcription_result)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dae4d3a9",
      "metadata": {
        "id": "dae4d3a9"
      },
      "source": [
        "### 1.d Create Video Chunks\n",
        "\n",
        "Creates logical segments (chunks) of the video. It groups paths to 6 consecutive frames (representing ~30 seconds based on 0.2 FPS extraction). It then splits the full transcription text into sentences and distributes these sentences as evenly as possible across the frame chunks. Stores the image paths (list) and corresponding text (string) for each chunk in a Pandas DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9HnsPO5HIo_G",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9HnsPO5HIo_G",
        "outputId": "44d7a615-e414-419a-bba9-a91c4f0812f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "--- Building sentence‑aligned ~30 s chunks ---\n",
            "  • 179 sentences total\n",
            "  • 22 chunks produced (avg 35.2s)\n",
            "  • 155 total images linked\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <th></th>\n",
              "      <th>section</th>\n",
              "      <th>start_time</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.3</td>\n",
              "      <td>[/content/video_data/frames/frame0000.png, /content/video_data/frames/frame0001.png, /content/video_data/frames/frame0002.png, /content/video_data/frames/frame0003.png, /content/video_data/frames/frame0004.png, /content/video_data/frames/frame0005.png, /content/video_data/frames/frame0006.png, /content/video_data/frames/frame0007.png, /content/video_data/frames/frame0008.png, /content/video_data/frames/frame0009.png]</td>\n",
              "      <td>The basic function underlying a normal distribution, a.k.a. a Gaussian, is e to the negative x-squared.  But you might wonder, why this function?  Of all the expressions we could dream up that give you some symmetric smooth graph with mass concentrated towards the middle,  why is it that the theory of probability seems to have a special place in its heart for this particular expression?  For the last many videos I've been hinting at an answer to this question,  and here we'll finally arrive at something like a satisfying answer.  As a quick refresher on where we are, a couple videos ago we talked about the Central Limit Theorem,  which describes how as you add multiple copies of a random variable,  for example, rolling a weighted die many different times or letting a ball bounce off of a peg repeatedly,  then the distribution describing that sum tends to look approximately like a normal distribution.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>48.3</td>\n",
              "      <td>80.2</td>\n",
              "      <td>[/content/video_data/frames/frame0010.png, /content/video_data/frames/frame0011.png, /content/video_data/frames/frame0012.png, /content/video_data/frames/frame0013.png, /content/video_data/frames/frame0014.png, /content/video_data/frames/frame0015.png]</td>\n",
              "      <td>What the Central Limit Theorem says is as you make that sum bigger and bigger, under appropriate conditions,  that approximation to a normal becomes better and better.  But I never explained why this theorem is actually true, we only talked about what it's claiming.  In the last video, we started talking about the math involved in adding two random variables.  If you have two random variables, each following some distribution,  then to find the distribution describing the sum of those variables,  you compute something known as a convolution between the two original functions.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>80.2</td>\n",
              "      <td>112.8</td>\n",
              "      <td>[/content/video_data/frames/frame0016.png, /content/video_data/frames/frame0017.png, /content/video_data/frames/frame0018.png, /content/video_data/frames/frame0019.png, /content/video_data/frames/frame0020.png, /content/video_data/frames/frame0021.png, /content/video_data/frames/frame0022.png]</td>\n",
              "      <td>And we spent a lot of time building up two distinct ways to visualize what this convolution operation really is.  Today, our basic job is to work through a particular example,  which is to ask, what happens when you add two normally distributed random variables?  Which, as you know by now, is the same as asking,  what do you get if you compute a convolution between two Gaussian functions?  I'd like to share an especially pleasing visual way that you can think about this calculation,  which hopefully offers some sense of what makes the e to the negative x squared function special in the first place.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>112.8</td>\n",
              "      <td>153.9</td>\n",
              "      <td>[/content/video_data/frames/frame0023.png, /content/video_data/frames/frame0024.png, /content/video_data/frames/frame0025.png, /content/video_data/frames/frame0026.png, /content/video_data/frames/frame0027.png, /content/video_data/frames/frame0028.png, /content/video_data/frames/frame0029.png, /content/video_data/frames/frame0030.png]</td>\n",
              "      <td>After we walk through it, we'll talk about how this calculation is one of the steps involved in proving the central limit theorem.  It's the step that answers the question of why a Gaussian, and not something else, is the central limit.  But first, let's dive in.  The full formula for a Gaussian is more complicated than just e to the negative x squared.  The exponent is typically written as negative 1 half times x divided by sigma squared,  where sigma describes the spread of the distribution.  Specifically, the standard deviation.  All of this needs to be multiplied by a fraction on the front,  which is there to make sure that the area under the curve is 1,  making it a valid probability distribution.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>153.9</td>\n",
              "      <td>190.5</td>\n",
              "      <td>[/content/video_data/frames/frame0031.png, /content/video_data/frames/frame0032.png, /content/video_data/frames/frame0033.png, /content/video_data/frames/frame0034.png, /content/video_data/frames/frame0035.png, /content/video_data/frames/frame0036.png, /content/video_data/frames/frame0037.png]</td>\n",
              "      <td>And if you want to consider distributions that aren't necessarily centered at 0,  you would also throw another parameter, mu, into the exponent like this.  Although, for everything we'll be doing here, we just consider centered distributions.  Now, if you look at our central goal for today, which is to compute a convolution between two Gaussian functions,  the direct way to do this would be to take the definition of a convolution,  this integral expression we built up last video,  and then to plug in, for each one of the functions involved, the formula for a Gaussian.  It's kind of a lot of symbols when you throw it all together,  but more than anything, working this out is an exercise in completing the square.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>190.5</td>\n",
              "      <td>224.7</td>\n",
              "      <td>[/content/video_data/frames/frame0038.png, /content/video_data/frames/frame0039.png, /content/video_data/frames/frame0040.png, /content/video_data/frames/frame0041.png, /content/video_data/frames/frame0042.png, /content/video_data/frames/frame0043.png, /content/video_data/frames/frame0044.png]</td>\n",
              "      <td>And there's nothing wrong with that. That will get you the answer that you want.  But of course, you know me, I'm a sucker for visual intuition,  and in this case, there's another way to think about it that I haven't seen written about before  that offers a very nice connection to other aspects of this distribution,  like the presence of pi and certain ways to derive where it comes from.  And the way I'd like to do this is by first peeling away all of the constants associated with the actual distribution,  and just showing the computation for the simplified form, e to the negative x squared.  The essence of what we want to compute is what the convolution between two copies of this function looks like.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>224.7</td>\n",
              "      <td>257.9</td>\n",
              "      <td>[/content/video_data/frames/frame0045.png, /content/video_data/frames/frame0046.png, /content/video_data/frames/frame0047.png, /content/video_data/frames/frame0048.png, /content/video_data/frames/frame0049.png, /content/video_data/frames/frame0050.png, /content/video_data/frames/frame0051.png]</td>\n",
              "      <td>If you'll remember, in the last video, we had two different ways to visualize convolutions,  and the one we'll be using here is the second one, involving diagonal slices.  And as a quick reminder of the way that worked,  if you have two different distributions that are described by two different functions, f and g,  then every possible pair of values that you might get when you sample from these two distributions  can be thought of as individual points on the xy-plane.  And the probability density of landing on one such point, assuming independence,  looks like f of x times g of y.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>257.9</td>\n",
              "      <td>291.3</td>\n",
              "      <td>[/content/video_data/frames/frame0052.png, /content/video_data/frames/frame0053.png, /content/video_data/frames/frame0054.png, /content/video_data/frames/frame0055.png, /content/video_data/frames/frame0056.png, /content/video_data/frames/frame0057.png]</td>\n",
              "      <td>So what we do is we look at a graph of that expression as a two-variable function of x and y,  which is a way of showing the distribution of all possible outcomes when we sample from the two different variables.  To interpret the convolution of f and g, evaluated on some input s,  which is a way of saying how likely are you to get a pair of samples that adds up to this sum, s,  what you do is you look at a slice of this graph over the line x plus y equals s,  and you consider the area under that slice.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>291.3</td>\n",
              "      <td>327.3</td>\n",
              "      <td>[/content/video_data/frames/frame0058.png, /content/video_data/frames/frame0059.png, /content/video_data/frames/frame0060.png, /content/video_data/frames/frame0061.png, /content/video_data/frames/frame0062.png, /content/video_data/frames/frame0063.png, /content/video_data/frames/frame0064.png]</td>\n",
              "      <td>This area is almost, but not quite, the value of the convolution at s.  For a mildly technical reason, you need to divide by the square root of 2.  Still, this area is the key feature to focus on.  You can think of it as a way to combine together all the probability densities  for all of the outcomes corresponding to a given sum.  In the specific case where these two functions look like e to the negative x squared and e to the negative y squared,  the resulting 3D graph has a really nice property that you can exploit.  It's rotationally symmetric.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>327.3</td>\n",
              "      <td>365.8</td>\n",
              "      <td>[/content/video_data/frames/frame0065.png, /content/video_data/frames/frame0066.png, /content/video_data/frames/frame0067.png, /content/video_data/frames/frame0068.png, /content/video_data/frames/frame0069.png, /content/video_data/frames/frame0070.png, /content/video_data/frames/frame0071.png, /content/video_data/frames/frame0072.png]</td>\n",
              "      <td>You can see this by combining the terms and noticing that it's entirely a function of x squared plus y squared,  and this term describes the square of the distance between any point on the xy-plane and the origin.  So in other words, the expression is purely a function of the distance from the origin.  And by the way, this would not be true for any other distribution.  It's a property that uniquely characterizes bell curves.  So for most other pairs of functions, these diagonal slices will be some complicated shape that's hard to think about,  and honestly, calculating the area would just amount to computing the original integral that defines a convolution in the first place.</td>\n",
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              "            document.querySelector('#df-bf2a9f6e-ca57-4202-9ec6-8b853c28edc0 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   section  start_time  end_time  \\\n",
              "0        0         0.0      48.3   \n",
              "1        1        48.3      80.2   \n",
              "2        2        80.2     112.8   \n",
              "3        3       112.8     153.9   \n",
              "4        4       153.9     190.5   \n",
              "5        5       190.5     224.7   \n",
              "6        6       224.7     257.9   \n",
              "7        7       257.9     291.3   \n",
              "8        8       291.3     327.3   \n",
              "9        9       327.3     365.8   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                 images  \\\n",
              "0  [/content/video_data/frames/frame0000.png, /content/video_data/frames/frame0001.png, /content/video_data/frames/frame0002.png, /content/video_data/frames/frame0003.png, /content/video_data/frames/frame0004.png, /content/video_data/frames/frame0005.png, /content/video_data/frames/frame0006.png, /content/video_data/frames/frame0007.png, /content/video_data/frames/frame0008.png, /content/video_data/frames/frame0009.png]   \n",
              "1                                                                                                                                                                          [/content/video_data/frames/frame0010.png, /content/video_data/frames/frame0011.png, /content/video_data/frames/frame0012.png, /content/video_data/frames/frame0013.png, /content/video_data/frames/frame0014.png, /content/video_data/frames/frame0015.png]   \n",
              "2                                                                                                                                [/content/video_data/frames/frame0016.png, /content/video_data/frames/frame0017.png, /content/video_data/frames/frame0018.png, /content/video_data/frames/frame0019.png, /content/video_data/frames/frame0020.png, /content/video_data/frames/frame0021.png, /content/video_data/frames/frame0022.png]   \n",
              "3                                                                                      [/content/video_data/frames/frame0023.png, /content/video_data/frames/frame0024.png, /content/video_data/frames/frame0025.png, /content/video_data/frames/frame0026.png, /content/video_data/frames/frame0027.png, /content/video_data/frames/frame0028.png, /content/video_data/frames/frame0029.png, /content/video_data/frames/frame0030.png]   \n",
              "4                                                                                                                                [/content/video_data/frames/frame0031.png, /content/video_data/frames/frame0032.png, /content/video_data/frames/frame0033.png, /content/video_data/frames/frame0034.png, /content/video_data/frames/frame0035.png, /content/video_data/frames/frame0036.png, /content/video_data/frames/frame0037.png]   \n",
              "5                                                                                                                                [/content/video_data/frames/frame0038.png, /content/video_data/frames/frame0039.png, /content/video_data/frames/frame0040.png, /content/video_data/frames/frame0041.png, /content/video_data/frames/frame0042.png, /content/video_data/frames/frame0043.png, /content/video_data/frames/frame0044.png]   \n",
              "6                                                                                                                                [/content/video_data/frames/frame0045.png, /content/video_data/frames/frame0046.png, /content/video_data/frames/frame0047.png, /content/video_data/frames/frame0048.png, /content/video_data/frames/frame0049.png, /content/video_data/frames/frame0050.png, /content/video_data/frames/frame0051.png]   \n",
              "7                                                                                                                                                                          [/content/video_data/frames/frame0052.png, /content/video_data/frames/frame0053.png, /content/video_data/frames/frame0054.png, /content/video_data/frames/frame0055.png, /content/video_data/frames/frame0056.png, /content/video_data/frames/frame0057.png]   \n",
              "8                                                                                                                                [/content/video_data/frames/frame0058.png, /content/video_data/frames/frame0059.png, /content/video_data/frames/frame0060.png, /content/video_data/frames/frame0061.png, /content/video_data/frames/frame0062.png, /content/video_data/frames/frame0063.png, /content/video_data/frames/frame0064.png]   \n",
              "9                                                                                      [/content/video_data/frames/frame0065.png, /content/video_data/frames/frame0066.png, /content/video_data/frames/frame0067.png, /content/video_data/frames/frame0068.png, /content/video_data/frames/frame0069.png, /content/video_data/frames/frame0070.png, /content/video_data/frames/frame0071.png, /content/video_data/frames/frame0072.png]   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                text  \n",
              "0  The basic function underlying a normal distribution, a.k.a. a Gaussian, is e to the negative x-squared.  But you might wonder, why this function?  Of all the expressions we could dream up that give you some symmetric smooth graph with mass concentrated towards the middle,  why is it that the theory of probability seems to have a special place in its heart for this particular expression?  For the last many videos I've been hinting at an answer to this question,  and here we'll finally arrive at something like a satisfying answer.  As a quick refresher on where we are, a couple videos ago we talked about the Central Limit Theorem,  which describes how as you add multiple copies of a random variable,  for example, rolling a weighted die many different times or letting a ball bounce off of a peg repeatedly,  then the distribution describing that sum tends to look approximately like a normal distribution.  \n",
              "1                                                                                                                                                                                                                                                                                                                                             What the Central Limit Theorem says is as you make that sum bigger and bigger, under appropriate conditions,  that approximation to a normal becomes better and better.  But I never explained why this theorem is actually true, we only talked about what it's claiming.  In the last video, we started talking about the math involved in adding two random variables.  If you have two random variables, each following some distribution,  then to find the distribution describing the sum of those variables,  you compute something known as a convolution between the two original functions.  \n",
              "2                                                                                                                                                                                                                                                                                                                      And we spent a lot of time building up two distinct ways to visualize what this convolution operation really is.  Today, our basic job is to work through a particular example,  which is to ask, what happens when you add two normally distributed random variables?  Which, as you know by now, is the same as asking,  what do you get if you compute a convolution between two Gaussian functions?  I'd like to share an especially pleasing visual way that you can think about this calculation,  which hopefully offers some sense of what makes the e to the negative x squared function special in the first place.  \n",
              "3                                                                                                                                                                                                            After we walk through it, we'll talk about how this calculation is one of the steps involved in proving the central limit theorem.  It's the step that answers the question of why a Gaussian, and not something else, is the central limit.  But first, let's dive in.  The full formula for a Gaussian is more complicated than just e to the negative x squared.  The exponent is typically written as negative 1 half times x divided by sigma squared,  where sigma describes the spread of the distribution.  Specifically, the standard deviation.  All of this needs to be multiplied by a fraction on the front,  which is there to make sure that the area under the curve is 1,  making it a valid probability distribution.  \n",
              "4                                                                                                                                                                                                   And if you want to consider distributions that aren't necessarily centered at 0,  you would also throw another parameter, mu, into the exponent like this.  Although, for everything we'll be doing here, we just consider centered distributions.  Now, if you look at our central goal for today, which is to compute a convolution between two Gaussian functions,  the direct way to do this would be to take the definition of a convolution,  this integral expression we built up last video,  and then to plug in, for each one of the functions involved, the formula for a Gaussian.  It's kind of a lot of symbols when you throw it all together,  but more than anything, working this out is an exercise in completing the square.  \n",
              "5                                                                                                                                                                                                             And there's nothing wrong with that. That will get you the answer that you want.  But of course, you know me, I'm a sucker for visual intuition,  and in this case, there's another way to think about it that I haven't seen written about before  that offers a very nice connection to other aspects of this distribution,  like the presence of pi and certain ways to derive where it comes from.  And the way I'd like to do this is by first peeling away all of the constants associated with the actual distribution,  and just showing the computation for the simplified form, e to the negative x squared.  The essence of what we want to compute is what the convolution between two copies of this function looks like.  \n",
              "6                                                                                                                                                                                                                                                                                                                                       If you'll remember, in the last video, we had two different ways to visualize convolutions,  and the one we'll be using here is the second one, involving diagonal slices.  And as a quick reminder of the way that worked,  if you have two different distributions that are described by two different functions, f and g,  then every possible pair of values that you might get when you sample from these two distributions  can be thought of as individual points on the xy-plane.  And the probability density of landing on one such point, assuming independence,  looks like f of x times g of y.  \n",
              "7                                                                                                                                                                                                                                                                                                                                                                                                                        So what we do is we look at a graph of that expression as a two-variable function of x and y,  which is a way of showing the distribution of all possible outcomes when we sample from the two different variables.  To interpret the convolution of f and g, evaluated on some input s,  which is a way of saying how likely are you to get a pair of samples that adds up to this sum, s,  what you do is you look at a slice of this graph over the line x plus y equals s,  and you consider the area under that slice.  \n",
              "8                                                                                                                                                                                                                                                                                                                                                                          This area is almost, but not quite, the value of the convolution at s.  For a mildly technical reason, you need to divide by the square root of 2.  Still, this area is the key feature to focus on.  You can think of it as a way to combine together all the probability densities  for all of the outcomes corresponding to a given sum.  In the specific case where these two functions look like e to the negative x squared and e to the negative y squared,  the resulting 3D graph has a really nice property that you can exploit.  It's rotationally symmetric.  \n",
              "9                                                                                                                                                                                                                                       You can see this by combining the terms and noticing that it's entirely a function of x squared plus y squared,  and this term describes the square of the distance between any point on the xy-plane and the origin.  So in other words, the expression is purely a function of the distance from the origin.  And by the way, this would not be true for any other distribution.  It's a property that uniquely characterizes bell curves.  So for most other pairs of functions, these diagonal slices will be some complicated shape that's hard to think about,  and honestly, calculating the area would just amount to computing the original integral that defines a convolution in the first place.  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# --- 1.d  Create Video Chunks (NLTK, sentence‑aware, true punctuation) ------\n",
        "import os, re, math, pandas as pd\n",
        "from IPython.display import display\n",
        "import nltk\n",
        "nltk.download('punkt_tab')\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "##############################################################################\n",
        "# Configuration\n",
        "##############################################################################\n",
        "FRAMES_DIR       = frames_dir     # from step 1.b\n",
        "FRAME_FPS        = 0.2            # write_images_sequence fps\n",
        "TARGET_CHUNK_SEC = 30             # desired chunk length\n",
        "SLACK_FACTOR     = 1.20           # allow up to 20 % over‑run before forcing cut\n",
        "##############################################################################\n",
        "\n",
        "print(\"\\n--- Building sentence‑aligned ~30 s chunks ---\")\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "# 1. Frame paths and helper\n",
        "# ---------------------------------------------------------------------------\n",
        "frame_paths = sorted(\n",
        "    [os.path.join(FRAMES_DIR, f) for f in os.listdir(FRAMES_DIR) if f.endswith(\".png\")],\n",
        "    key=lambda p: int(re.search(r\"frame(\\d+)\\.png\", os.path.basename(p)).group(1))\n",
        ")\n",
        "def idx_from_time(t):   return int(round(t * FRAME_FPS))\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "# 2. Build per‑sentence list with *estimated* timestamps\n",
        "# ---------------------------------------------------------------------------\n",
        "sentences = []   # list of dict(start, end, sentence)\n",
        "for seg in transcription_result.segments:\n",
        "    seg_start, seg_end, seg_text = seg.start, seg.end, seg.text\n",
        "    seg_sents = sent_tokenize(seg_text)\n",
        "\n",
        "    # Distribute the segment’s duration across its sentences by char length\n",
        "    seg_dur    = seg_end - seg_start\n",
        "    char_total = sum(len(s) for s in seg_sents)\n",
        "    running_t  = seg_start\n",
        "    for s in seg_sents:\n",
        "        char_frac  = len(s) / char_total\n",
        "        sent_end   = running_t + char_frac * seg_dur\n",
        "        sentences.append({\"start\": running_t, \"end\": sent_end, \"sentence\": s})\n",
        "        running_t = sent_end\n",
        "\n",
        "print(f\"  • {len(sentences):,} sentences total\")\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "# 3. Pack sentences into chunks  (guarantee ending punctuation)\n",
        "# ---------------------------------------------------------------------------\n",
        "chunks, cur_sents = [], []\n",
        "cur_start, cur_end = None, None\n",
        "\n",
        "def ends_with_stop(txt): return txt[-1] in \".?!\"\n",
        "\n",
        "for sent in sentences:\n",
        "    if not cur_sents:                            # start new chunk\n",
        "        cur_sents  = [sent]\n",
        "        cur_start, cur_end = sent[\"start\"], sent[\"end\"]\n",
        "        continue\n",
        "\n",
        "    prospective_end  = sent[\"end\"]\n",
        "    prospective_span = prospective_end - cur_start\n",
        "\n",
        "    # Decide if we should append sentence to current chunk\n",
        "    if prospective_span <= TARGET_CHUNK_SEC * SLACK_FACTOR or not ends_with_stop(cur_sents[-1][\"sentence\"]):\n",
        "        cur_sents.append(sent)\n",
        "        cur_end = prospective_end\n",
        "    else:\n",
        "        chunks.append({\"start\": cur_start, \"end\": cur_end, \"sentences\": cur_sents})\n",
        "        cur_sents  = [sent]\n",
        "        cur_start, cur_end = sent[\"start\"], sent[\"end\"]\n",
        "\n",
        "if cur_sents:\n",
        "    chunks.append({\"start\": cur_start, \"end\": cur_end, \"sentences\": cur_sents})\n",
        "\n",
        "print(f\"  • {len(chunks)} chunks produced \"\n",
        "      f\"(avg {sum(c['end']-c['start'] for c in chunks)/len(chunks):.1f}s)\")\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "# 4. Attach frames to each chunk\n",
        "# ---------------------------------------------------------------------------\n",
        "records = []\n",
        "total_imgs = 0\n",
        "for idx, ch in enumerate(chunks):\n",
        "    start_t, end_t = ch[\"start\"], ch[\"end\"]\n",
        "\n",
        "    first_idx = idx_from_time(start_t)\n",
        "    last_idx  = max(idx_from_time(end_t) - 1, first_idx)   # inclusive\n",
        "    imgs      = frame_paths[first_idx : last_idx + 1]\n",
        "    total_imgs += len(imgs)\n",
        "\n",
        "    chunk_text = \" \".join(s[\"sentence\"] for s in ch[\"sentences\"]).strip()\n",
        "\n",
        "    records.append(\n",
        "        {\n",
        "            \"section\":    idx,\n",
        "            \"start_time\": round(start_t, 2),\n",
        "            \"end_time\":   round(end_t,   2),\n",
        "            \"images\":     imgs,\n",
        "            \"text\":       chunk_text,\n",
        "        }\n",
        "    )\n",
        "\n",
        "print(f\"  • {total_imgs} total images linked\")\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "# 5. DataFrame\n",
        "# ---------------------------------------------------------------------------\n",
        "df_aligned = pd.DataFrame(records)\n",
        "pd.set_option(\"display.max_colwidth\", None)\n",
        "display(df_aligned.head(10))\n",
        "# ---------------------------------------------------------------------------\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Hbp7ZD4cuHwA",
      "metadata": {
        "id": "Hbp7ZD4cuHwA"
      },
      "source": [
        "For convenience, we remove timestamps, but that is something we can add as metadata, which could also speed up filtering and allow for better citations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "wk9GfX8CuHVy",
      "metadata": {
        "id": "wk9GfX8CuHVy"
      },
      "outputs": [],
      "source": [
        "df = df_aligned[['images', 'text']]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d30e8962",
      "metadata": {
        "id": "d30e8962"
      },
      "source": [
        "\n",
        "## 2. Multimodal Embedding Generation\n",
        "\n",
        "Generate multimodal embeddings using Voyage AI's `voyage-multimodal-3` model. Each embedding vector will capture the combined semantic meaning of the text transcription and the visual frames within a single video chunk."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68787ebc",
      "metadata": {
        "id": "68787ebc"
      },
      "source": [
        "### Preparing Inputs for Voyage AI Multimodal Embedding\n",
        "Iterates through the DataFrame rows (video chunks). For each chunk, it creates a list containing the chunk's text followed by PIL Image objects loaded from the associated frame file paths. Appends this list to the `inputs_for_voyage` list, which is formatted for the Voyage AI API. Includes error handling for image loading and ensures only valid chunks (with text and successfully loaded images) are added. Updates the DataFrame `df` to retain only the rows corresponding to these valid inputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "cf0ce490",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cf0ce490",
        "outputId": "35696e6b-0aba-4634-edf3-4eda832aee07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Example of first input structure:\n",
            "[ Text: 'The basic function underlying ...', Image1: <class 'PIL.PngImagePlugin.PngImageFile'>, ... ]\n"
          ]
        }
      ],
      "source": [
        "inputs_for_voyage = []\n",
        "valid_indices = []\n",
        "\n",
        "for index, row in df.iterrows():\n",
        "    chunk_text = row[\"text\"]\n",
        "    image_paths = row[\"images\"]\n",
        "    current_chunk_input = [chunk_text]\n",
        "\n",
        "    images = [Image.open(p) for p in image_paths if os.path.getsize(p) > 0]\n",
        "\n",
        "    if images:\n",
        "        current_chunk_input.extend(images)\n",
        "        inputs_for_voyage.append(current_chunk_input)\n",
        "        valid_indices.append(index)\n",
        "\n",
        "df = df.loc[valid_indices].reset_index(drop=True)\n",
        "\n",
        "print(\"\\nExample of first input structure:\")\n",
        "print(f\"[ Text: '{inputs_for_voyage[0][0][:30]}...', Image1: {type(inputs_for_voyage[0][1])}, ... ]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d1a459e",
      "metadata": {
        "id": "3d1a459e"
      },
      "source": [
        "### Generating Voyage AI Multimodal Embeddings\n",
        "Checks if there are valid inputs and the Voyage AI client is available. Sends the prepared list of multimodal inputs (`inputs_for_voyage`) to the Voyage AI API using the `voyage-multimodal-3` model, specifying `input_type=\"document\"` for indexing. Sets `truncation=True` to handle inputs exceeding model limits. Adds the returned list of embedding vectors as a new column named 'emb' to the filtered DataFrame `df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "e7e6b215",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "collapsed": true,
        "id": "e7e6b215",
        "outputId": "3cba7aee-39bc-41a9-e673-13067cdc5804"
      },
      "outputs": [
        {
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              "      <th>images</th>\n",
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              "        const element = document.querySelector('#df-236f0e61-27ec-4065-bc72-f5c04905a437');\n",
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            "text/plain": [
              "                                              images  \\\n",
              "0  [/content/video_data/frames/frame0000.png, /co...   \n",
              "1  [/content/video_data/frames/frame0010.png, /co...   \n",
              "2  [/content/video_data/frames/frame0016.png, /co...   \n",
              "3  [/content/video_data/frames/frame0023.png, /co...   \n",
              "4  [/content/video_data/frames/frame0031.png, /co...   \n",
              "\n",
              "                                                text  \\\n",
              "0  The basic function underlying a normal distrib...   \n",
              "1  What the Central Limit Theorem says is as you ...   \n",
              "2  And we spent a lot of time building up two dis...   \n",
              "3  After we walk through it, we'll talk about how...   \n",
              "4  And if you want to consider distributions that...   \n",
              "\n",
              "                                                 emb  \n",
              "0  [0.0108642578125, -0.037109375, 0.023193359375...  \n",
              "1  [0.0004634857177734375, -0.025390625, 0.013732...  \n",
              "2  [0.029541015625, -0.0120849609375, 0.014404296...  \n",
              "3  [0.029296875, -0.0289306640625, 0.02490234375,...  \n",
              "4  [0.01708984375, -0.014404296875, 0.00564575195...  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model_name = \"voyage-multimodal-3\"\n",
        "\n",
        "response = voyage.multimodal_embed(\n",
        "    inputs=inputs_for_voyage,\n",
        "    model=model_name,\n",
        "    input_type=\"document\",\n",
        "    truncation=True\n",
        ")\n",
        "\n",
        "df['emb'] = response.embeddings\n",
        "\n",
        "pd.reset_option(\"display.max_colwidth\")\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b46efe7",
      "metadata": {
        "id": "7b46efe7"
      },
      "source": [
        "## 3. Set Up KDB.AI Vector Database Table\n",
        "\n",
        "Define the KDB.AI table schema and configure the vector index for storing and searching the multimodal embeddings."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d1c95391",
      "metadata": {
        "id": "d1c95391"
      },
      "source": [
        "### Preparing DataFrame for KDB.AI Insertion\n",
        "Creates a final DataFrame (`df_to_insert`) specifically for KDB.AI. It includes a unique string 'id' for each chunk, the chunk's text encoded as UTF-8 bytes ('text_bytes'), the list of associated image file paths serialized into a JSON string ('image_paths'), and the generated multimodal embedding ('emb')."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "c3445fea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "collapsed": true,
        "id": "c3445fea",
        "outputId": "cbd944c5-867b-4456-de9a-212a082a5436"
      },
      "outputs": [
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  }\n",
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              "\n",
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            ],
            "text/plain": [
              "  id                                         text_bytes  \\\n",
              "0  0  b\"The basic function underlying a normal distr...   \n",
              "1  1  b\"What the Central Limit Theorem says is as yo...   \n",
              "2  2  b\"And we spent a lot of time building up two d...   \n",
              "3  3  b\"After we walk through it, we'll talk about h...   \n",
              "4  4  b\"And if you want to consider distributions th...   \n",
              "\n",
              "                                         image_paths  \\\n",
              "0  [\"/content/video_data/frames/frame0000.png\", \"...   \n",
              "1  [\"/content/video_data/frames/frame0010.png\", \"...   \n",
              "2  [\"/content/video_data/frames/frame0016.png\", \"...   \n",
              "3  [\"/content/video_data/frames/frame0023.png\", \"...   \n",
              "4  [\"/content/video_data/frames/frame0031.png\", \"...   \n",
              "\n",
              "                                                 emb  \n",
              "0  [0.0108642578125, -0.037109375, 0.023193359375...  \n",
              "1  [0.0004634857177734375, -0.025390625, 0.013732...  \n",
              "2  [0.029541015625, -0.0120849609375, 0.014404296...  \n",
              "3  [0.029296875, -0.0289306640625, 0.02490234375,...  \n",
              "4  [0.01708984375, -0.014404296875, 0.00564575195...  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "temp_df = df.copy()\n",
        "temp_df[\"id\"] = temp_df.index.astype(str)\n",
        "temp_df[\"text_bytes\"] = temp_df[\"text\"].str.encode(\"utf-8\")\n",
        "temp_df[\"image_paths\"] = temp_df[\"images\"].apply(json.dumps)\n",
        "\n",
        "df_to_insert = temp_df[[\"id\", \"text_bytes\", \"image_paths\", \"emb\"]]\n",
        "\n",
        "display(df_to_insert.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f2b16f0",
      "metadata": {
        "id": "3f2b16f0"
      },
      "source": [
        "### Defining KDB.AI Table Schema and Indexes\n",
        "Defines the KDB.AI table schema listing column names ('id', 'text_bytes', 'image_paths', 'embeddings') and their corresponding KDB.AI data types ('str', 'bytes', 'str', 'float32s'). Also defines the vector index configuration: using 'qHnsw' type, named 'idx_emb', operating on the 'embeddings' column, with dimensionality derived from the first valid embedding and using Cosine Similarity ('CS') metric."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "42c7c28d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42c7c28d",
        "outputId": "396c1e5f-a2c6-4c02-e3fc-c97052d9f522"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using detected embedding dimension: 1024\n",
            "KDB.AI schema and index defined successfully.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Grab the first embedding\n",
        "first_emb = df_to_insert['emb'].iloc[0]\n",
        "\n",
        "if isinstance(first_emb, (list, np.ndarray)):\n",
        "    embedding_dim = len(first_emb)\n",
        "    print(f\"Using detected embedding dimension: {embedding_dim}\")\n",
        "else:\n",
        "    embedding_dim = 1024\n",
        "    print(\"Warning: couldn't determine embedding dimension; falling back to 1024\")\n",
        "\n",
        "# Now define schema and index with whichever dimension we ended up with\n",
        "schema = [\n",
        "    {\"name\": \"id\",          \"type\": \"str\"},\n",
        "    {\"name\": \"text_bytes\",  \"type\": \"bytes\"},\n",
        "    {\"name\": \"image_paths\", \"type\": \"str\"},\n",
        "    {\"name\": \"embeddings\",  \"type\": \"float32s\"},\n",
        "]\n",
        "\n",
        "indexes = [{\n",
        "    \"type\": \"qHnsw\",\n",
        "    \"name\": \"idx_emb\",\n",
        "    \"column\": \"embeddings\",\n",
        "    \"params\": {\"dims\": embedding_dim, \"metric\": \"CS\"}\n",
        "}]\n",
        "\n",
        "print(\"KDB.AI schema and index defined successfully.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "870b1dfa",
      "metadata": {
        "id": "870b1dfa"
      },
      "source": [
        "### Creating KDB.AI Table\n",
        "Sets the desired KDB.AI table name ('video_chunks'). Checks if a KDB.AI session and database connection exist, and if schema/indexes are defined. Attempts to drop any pre-existing table with the same name to ensure a clean state (handling potential 'table does not exist' errors). Creates the new table using the defined schema and indexes. Sets the global `table` variable to the created table object. Includes delays and verification steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "32563d36",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32563d36",
        "outputId": "39dc820b-fedb-462b-ed58-d272d924fa58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KDB.AI table 'video_chunks' created successfully.\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    db.table(\"video_chunks\").drop()\n",
        "except kdbai.KDBAIException:\n",
        "    pass\n",
        "\n",
        "table = db.create_table(\"video_chunks\", schema=schema, indexes=indexes)\n",
        "print(f\"KDB.AI table '{table.name}' created successfully.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3061abf9",
      "metadata": {
        "id": "3061abf9"
      },
      "source": [
        "## 4. Insert Data into KDB.AI\n",
        "\n",
        "Load the prepared video chunk data (IDs, text bytes, image path JSON, embeddings) into the KDB.AI vector table."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2aa5fb72",
      "metadata": {
        "id": "2aa5fb72"
      },
      "source": [
        "### Inserting Data into KDB.AI Table\n",
        "Checks if the KDB.AI `table` object exists and if the `df_to_insert` DataFrame contains data. Converts the 'embeddings' column to a list of lists suitable for KDB.AI insertion. Performs a bulk insert using `table.insert()`. Includes error handling and verifies the insertion by querying and printing the first few rows from the table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "af32a380",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "af32a380",
        "outputId": "3184b153-7606-40ec-dd35-361417c69456"
      },
      "outputs": [
        {
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-31dfb4c0-f7b0-4b17-814b-dfea5e374b2a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31dfb4c0-f7b0-4b17-814b-dfea5e374b2a')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-31dfb4c0-f7b0-4b17-814b-dfea5e374b2a button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  id                                         text_bytes  \\\n",
              "0  0  b\"The basic function underlying a normal distr...   \n",
              "1  1  b\"What the Central Limit Theorem says is as yo...   \n",
              "2  2  b\"And we spent a lot of time building up two d...   \n",
              "3  3  b\"After we walk through it, we'll talk about h...   \n",
              "4  4  b\"And if you want to consider distributions th...   \n",
              "\n",
              "                                         image_paths  \\\n",
              "0  [\"/content/video_data/frames/frame0000.png\", \"...   \n",
              "1  [\"/content/video_data/frames/frame0010.png\", \"...   \n",
              "2  [\"/content/video_data/frames/frame0016.png\", \"...   \n",
              "3  [\"/content/video_data/frames/frame0023.png\", \"...   \n",
              "4  [\"/content/video_data/frames/frame0031.png\", \"...   \n",
              "\n",
              "                                          embeddings  \n",
              "0  [0.010864258, -0.037109375, 0.02319336, -0.011...  \n",
              "1  [0.00046348572, -0.025390625, 0.01373291, 0.00...  \n",
              "2  [0.029541016, -0.012084961, 0.014404297, -0.01...  \n",
              "3  [0.029296875, -0.028930664, 0.024902344, -0.03...  \n",
              "4  [0.017089844, -0.014404297, 0.005645752, -0.02...  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "insert_payload = df_to_insert.copy()\n",
        "insert_payload['embeddings'] = insert_payload['emb'].apply(lambda x: list(map(float, x)))\n",
        "\n",
        "insert_payload = insert_payload[[\"id\", \"text_bytes\", \"image_paths\", \"embeddings\"]]\n",
        "\n",
        "table.insert(insert_payload)\n",
        "\n",
        "display(table.query(limit=5))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ba2d5ba4",
      "metadata": {
        "id": "ba2d5ba4"
      },
      "source": [
        "## 5. Define RAG Helper Functions\n",
        "\n",
        "Create utility functions needed for the RAG process: one to encode image files into base64 data URLs for the OpenAI API, and another to display the retrieved multimodal context (text and images) clearly in the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ce6b847",
      "metadata": {
        "id": "4ce6b847"
      },
      "source": [
        "### Defining Helper Functions for Image Encoding and RAG Preview Display\n",
        "Defines two helper functions. `encode_base64` takes an image file path, reads the image, determines its format, and encodes it into a base64 data URL string required by OpenAI's vision models; includes error handling for missing files or encoding issues. `display_rag_preview` takes a list of dictionaries (representing retrieved text and base64 image URLs), iterates through it, and uses IPython display functions to render Markdown text and images in the notebook output for context verification."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "id": "c6fc48ed",
      "metadata": {
        "id": "c6fc48ed"
      },
      "outputs": [],
      "source": [
        "def encode_base64(image_path: str) -> str:\n",
        "    with open(image_path, \"rb\") as f:\n",
        "        data = base64.b64encode(f.read()).decode('utf-8')\n",
        "    ext = os.path.splitext(image_path)[1].lower().strip('.')\n",
        "    mime = {\"jpg\": \"jpeg\", \"jpeg\": \"jpeg\", \"png\": \"png\", \"gif\": \"gif\", \"webp\": \"webp\"}.get(ext, \"png\")\n",
        "    return f\"data:image/{mime};base64,{data}\"\n",
        "\n",
        "def display_rag_preview(retrieved_content: list):\n",
        "    print(\"=\"*20 + \" Retrieved Content Preview \" + \"=\"*20)\n",
        "    for item in retrieved_content:\n",
        "        if item.get(\"type\") == \"input_text\":\n",
        "            display(Markdown(item.get(\"text\", \"\")))\n",
        "        elif item.get(\"type\") == \"input_image\":\n",
        "            header, data = item[\"image_url\"].split(\",\", 1)\n",
        "            display(IPImage(data=base64.b64decode(data), width=300))\n",
        "    print(\"=\"*20 + \" End of Retrieved Content \" + \"=\"*20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d059e82f",
      "metadata": {
        "id": "d059e82f"
      },
      "source": [
        "## 6. Perform Retrieval Augmented Generation\n",
        "\n",
        "Define the main RAG workflow function (`multimodal_rag`). This function orchestrates the process:\n",
        "\n",
        "1. **Embeds the user's text query** using Voyage AI (`input_type=\"query\"`).\n",
        "2. **Searches the KDB.AI table** for the most relevant video chunks based on multimodal embedding similarity.\n",
        "3. **Constructs a prompt for the OpenAI LLM**, combining the user's query and retrieved text segments.\n",
        "4. **Merges the images** from each video chunk into a single \"super image\" to minimize token usage and help the LLM reason more effectively about visual context. These merged images are encoded to base64 and included in the prompt.\n",
        "5. **Calls OpenAI's chat completions API (GPT-4o-mini)** to generate an answer based on the combined text and image context.\n",
        "6. **Displays the retrieved context preview and the generated answer**, showing both the text and the merged images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7230d02",
      "metadata": {
        "id": "f7230d02"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "\n",
        "def merge_images(paths):\n",
        "    images = [Image.open(p) for p in paths]\n",
        "    widths, heights = zip(*(img.size for img in images))\n",
        "    total_width = sum(widths)\n",
        "    max_height = max(heights)\n",
        "\n",
        "    merged = Image.new('RGB', (total_width, max_height))\n",
        "    x_offset = 0\n",
        "    for img in images:\n",
        "        merged.paste(img, (x_offset, 0))\n",
        "        x_offset += img.size[0]\n",
        "\n",
        "    return merged\n",
        "\n",
        "def multimodal_rag(query: str, k: int = 3) -> str:\n",
        "    global table\n",
        "\n",
        "    q_emb = voyage.multimodal_embed([[query]], model=\"voyage-multimodal-3\", input_type=\"query\", truncation=True).embeddings[0]\n",
        "    retrieved = table.search(vectors={\"idx_emb\": [q_emb]}, n=k)[0]\n",
        "\n",
        "    context = [{\"type\": \"text\", \"text\": f\"Answer the query based only on the following video segments.\\n\\nQuestion: {query}\\n\"}]\n",
        "    preview = []\n",
        "\n",
        "    for i, row in retrieved.iterrows():\n",
        "        tid = row.get('id', f'Retrieved_{i}')\n",
        "        txt = row['text_bytes'].decode('utf-8')\n",
        "        context += [{\"type\": \"text\", \"text\": f\"\\n--- Segment {tid} Text ---\"}, {\"type\": \"text\", \"text\": txt}]\n",
        "        preview.append({\"type\": \"input_text\", \"text\": f\"\\n--- Segment {tid} Text ---\\n{txt}\"})\n",
        "\n",
        "        img_paths = json.loads(row.get('image_paths') or '[]')\n",
        "        if img_paths:\n",
        "            merged_img = merge_images(img_paths)\n",
        "            merged_img_path = \"/tmp/merged_segment_image.jpg\"\n",
        "            merged_img.save(merged_img_path, format=\"JPEG\")\n",
        "\n",
        "            base64_img = encode_base64(merged_img_path)\n",
        "            context += [{\"type\": \"image_url\", \"image_url\": {\"url\": base64_img}}]\n",
        "\n",
        "            preview.append({\"type\": \"input_image\", \"image_url\": base64_img})\n",
        "            display(merged_img)\n",
        "\n",
        "    context.append({\"type\": \"text\", \"text\": \"\\n--- End of Retrieved Context ---\"})\n",
        "\n",
        "    display_rag_preview(preview)\n",
        "\n",
        "    res = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\": \"user\", \"content\": context}], max_tokens=500)\n",
        "    out = res.choices[0].message.content\n",
        "\n",
        "    display(Markdown(out))\n",
        "    return out"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2838fd5",
      "metadata": {
        "id": "f2838fd5"
      },
      "source": [
        "### Running Multimodal RAG Example Query 1\n",
        "Executes the `multimodal_rag` function with a specific question about the video's content (\"What is the central limit theorem?\"). It requests the top 4 most relevant chunks (k=4) from KDB.AI to provide context for the answer generation by GPT-4o-mini."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "978312b3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "978312b3",
        "outputId": "b344300e-bf10-40e2-c9af-07a473589526"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Executing RAG for query: 'What is the central limit theorem?'\n"
          ]
        },
        {
          "data": {
            "image/jpeg": 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8hfUf+vmb/wBDNdZ8Ltg8Tag8kSyoukXW5G6MMLkf0oA4pEdoVlKMFboxBwfxpMiu3+HN5dax4jubK/neazu9OuFmt3OYiAmVwn3V29sAYo8E2EUHhDxL4kWaGLULNI4bSeYHbbtIcGQYB+YA8HHFAHGSwyw482CWPd08xCufzpsaNK4RI5HY9FRSx/IV1OlX1guha5puta7Bc289qZLRA00zpdocoy7k+XIyCc8981b8T2r+F9A8O6ZZSNC2o2Q1C+lj+V55GI2hj12qOABxyT1oA4pwUkaNlZXX7ysMEfUdRSDl1Qcs3RR1P4V2kVr/AG78MNQ1K7Yyahol7GkNyT+8eFyo8pm6kAnIznHTpWrrniDUU+HPg7UGupJNRM9wTdSOWk2LIDsz1wcKD6hcdKAPNqfJDNDjzYZY93K+ZGVyPbIr0PQpG1bTvGHji6a0g1aLZHbyBCsdu8mFaRQMndjGDzg5PesbSbyxTRdd07Wtbiu7a6tWltk3yyul2pzGylk4J5BOee9AHJHhGc8KOMnoD25pzo0eA6srEbtrDBxXoFhrV8fg1qEktzLNJaarDDaPK24242KPkz0IBOPTORyBUOj3s03ws125u2N3cafqEDWMtwS7WzyYDMpOecHp0zg0AcK6PHt8xHTcMjcpGRTa7XSWk1P4XeKjfTS3JtJ7SaAzOXMTs2GKknIyByK4qgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACu5g0yHwb4Is/EVzawXOtas2NPhuYxIlrGBzKVPDNjGM8DcPeuGr0v4nSrd+DvAt7bnNq9kY93ZX2x/Kffgj8KAOKfxTrkjsbrU572N/wDWQXZ8yKQdwUPygfTGO2K1fHU+mXD+HptItBaWcmjxMsAz+7PmSbhk8n5s8nk9a5LvXXeAPDNtrfjPSbTVI91nNG92Yj/y1RCwAb2LL+IHvQByjRyiD7R5b+R/z1KnZ/310pgbNdpp2tyL4zXVNQ16yltZJyt3bbpWia3Jw0YjMeNoXoMdq0/AFzFb+JvEmnWNxNPoTWF7JHbSFlSVAQFJB5zt4zwaAPOh80YcBtp6MRwfxrX0DQZtdlvSI3NtZ2c11M68BSiMUBPu+3juAa6H4eXl3q/iG8sdQuHubS70y4WW2c5iwFyu1Oi7cDGAMdql+Gup38Wn+IoI725SKLQ5540WUhUk4+cAdG96AOCo5OcKxwCxwM4HrV2bWtVeaHUW1O8a9hUiK4edmePOfuk8jrXoHjfxZqOheNdNvrHyvN/s2CWcSruW4d0K5ccZwOg7HJ70AeZRq0rhI0Z5G6Ioyx+gFBV0kaORSjr95GGCPqO1dPoInn8J63aW+lhZGnill1lrn7PHbIuPkY8A55wg67s44FaOs26XXwn03Up7yO+vrTU3so7pCzMIim7y2Z1DMARkZ6dqAEt7y4vfg7qcdy4kFpqdvFASigom0HGQMnr3ria6/Tv+SQ67/wBhe2/9AFchQB1fw4vLiPxvpVgGRrS6uds0TxqwcbW65HtUPjnUbubxXrFm8o+y299KkMKRoqoqsQAMAdqPh5/yUfQf+vof+gtVTxof+K38Q/8AYQm/9DNAGLGyq6s8ayKCCUbOGGeQcevStLxHqdnrOuy39hpNvpUDoqi1gxtUgYJ4AHPsBVSL7bYpbana3Elu2+XyJYX2urIoyfb7/wDOuv8AifeXMXjeO9hneK7FhbSLNG21lbZ1BHTqaAOJjR5XCRozueiqMk/hSKrO4REZ2PRVBJP4V6D8UNUvY9R0cw3EkM9zo9vNcTxsVeVznGWGDgYJx0ySaZ49u5ooPD1xExiu9Q0iCa9uomKyXDgbRuYc9j9c89BgA4DkHBBBHYitjWtXs9Ts9LhtdDt9Nks7cQyyw9bkj+JuPr1yeTzW144XztO8I6izN9uudJjae4/jkZWADMe7Y79aseNL67vvB/gq9ubqWW7a2uN07tlyVlGCT1yMDmgDnb/QpdO8N6Tqk8bxvqMk5jD8bok2bWx2yWb6jFZFejeIdf1mL4d+C7qLWL9J7iO686Rbhg0mJAF3HOTjoM15zQBseGbuHTtTm1CaC3nNrZzyQx3ChkaUptTKnry2ce1dd8NtYvdb8fafY6l9kubWRJt8LWMAXhCRjCAjkDvXnNdx8If+SmaZ/uTf+imoAof8JjqGn+J7h5IrGe1hvXDW0ljCUaMOQV4TI4HGDW34Jne38ReKdKglRtONlfyLGEQruU4VgcemO9cFqf8AyHNT/wCvqb/0Nq6/4XlR4l1AyRrJH/Y91ujYcMMLxQBxKq5hWQowVujEHB/GjNdv8Oby61nxHc2V/O81nd6dcLNbscxEBMrhPurt7YAxR4IsIofB/ibxKk0EV9ZRxw2c8uSLdpDhpOAcMAeDjg0AcZLBPb486CWLd93zEK5H402OOSZxHDG8kh5CIpYkd8AV1WkXtgmja5Ya1rUN3b3Fm0lqgaaV1u1OUZdyfKSMgnPPGas+KbZvDfh7w3plmzRf2jYjUL2SJirTyNjapYHJVRwF6c560AcV3IIII6g8EUDlwg5Zuijqa7SO2bXfhhf6ldEy3+h3sSQ3Dcu0Em0GNieSFJyM9OnStLXfEOpr8OfCGotdvJqXn3H+lSfM+xJB8mfQ4TPqFx0oA85qSW3ngx50EsW77vmIVyPxr0DQpG1jTvGHji5a1t9Xi2JbSCMpHbvJhTIMAndjGDzg896x9KurJdG17T9c1uC5t7mzMtqoaaZ0vEOUZSyfKSMgnPPfNAHJ050aPAdWViA21gQcV39jrOoP8GtRMty80lnqsUVo8p3G3G0D5M9MDOPTORzUWk3sknwv1+e6Ju7jT9QgNjLcHzGtnkwGZS2exPHTOD1FAHCOkkZXzI3TcMjcpGRSV22lSS6l8L/Fn26aW6ayltLi3ady5idmKsVJ5GQOa4mgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACu5g0yHwb4Is/EVzawXOtas2NPhuYxIlrGBzKVPDNjGM8DcPeuGr0v4nSpd+DfAt9bnNq9kY93ZX2x/Kffgj8KAOKfxTrkkha61Oe9jf/AFkF0fMikHcFDwB9MY7YrV8cz6ZcP4em0i0FpZyaPEywDP7s+ZJuGTyfmzyeT1rku9dd4A8M22t+MtJtdUj3Wc8b3Zi/56ohYAN7Fl/ED3oA5Zo5RB9o8mQwf89dp2f99dKjDZrstP1qQeM01TUNespbWScrd226VomtycNH5ZjxtC8AY6gVq/D68jt/EniTT7GaWfQGsb147eQsElQEBSVPIO3AzwaAPOh80YcBtp6MRwfxrV0HRJdamvf3UrW1nZT3U0iDhSiEoCfd9vHcA10Hw7vLrV9fvLC/ne4tLrTLhZbdz+6IC5XanRcHpgDHarPw01O/i0zxLBHfXCQxaFPPGiykKknHzgdm96AOBpQCc4DHALHAzgetWp9Y1V7yG9bU7xr2FSIrh5mZ485+6ScjrXoXjbxZqOheNdNvrHyvNGmwSziVdy3DuhXLjIzgdB2OT3oA8zjVpXCRqzyN0RQSx+gFNffHI0ciNG6/eVxgj6iun0Lz5vCOu21vparIZopZNYe6FtHboMfIx6NnJwgz97pxWhrVrHcfCjTNQubyLUL6z1VrGO6XczCEoW8ss6hmAIyM9M8UAEd7c3nwc1KO5kEgttTt4YSUUFEK9MgZP41xArr7Hn4Qa1/2F7b/ANBFceKAOu+HGoXEfjfStPBRrS6uds0TxqwcbG9R7dqi8c6jdzeK9Ys3lH2W3vpUhhWNFVFViABgDtUfw8/5KPoH/X0P/QWqHxof+K38Q/8AYRm/9DNAGJGyq6s8ayKCCVbOGGeQcevStHxHqdlrOuzX9hpNvpUDoii1gxtUgYJ4AHPsBVSL7bYpbana3Elu2+XyJYX2urIoyfb7/wDOuv8AifeXMXjeO9ineK7WwtpFmjbayts6gjp1NAHEojSOERWZj0CjJNNAZ3CIjMx6BRkn8K9B+J2qXY1DR/LuJYZbjR7aa5mico87ndjcw5wOTjpkk1H4+up44PDk8EjQXOoaRBPe3EJKSXDYwNzDk4we/JxnOBgA4Lvggg+hrY1rVrLUrPSobTQ4NNe0thFNLESTctx854Hv1yeTzW145zLp/hTVHY/brnSEM8/8cjI2FYnu3v1qfxnfXeoeE/A19d3Ms129pOWmdsuSJFAOfXgUAc9f6NLZeGtI1SeF431GScpv43RJsCtjtks314rHr0XxFr+tReAPBtzFq9+k88d150q3DBpMSKF3HPOBwK86oA2fDF1Fp+qT38kNtO1tZzyRRXKhkeTbtUEHry2ce1db8NtXu9d8f2VnqaWdxazJKWiNlCq8ISOiDHIFecnpXcfCH/kpml/9c5//AEWaAM//AITLUNO8RvJLFYz2kN26vbPYw7GjEhGOFyMDpitnwVdPH4j8UaXFLG+nG01BxGqKVJXIVlOMjjHQ1wmq/wDIXvv+vqb/ANGNXXfCooPEuol41kUaTdZRujDA4NAHFj5o1kCtsboxHB/HpRXc/Dm/u9Z8SXVjqNxJcWd7p1wk1vI2YsKuVwnQbccYxim+CLGCPwl4j8TebDFf2CRw2k82dtu0hw0gxn5sHAOOKAOMlgnt8edBLFu+75iFcj8abHHJM4jhieWQ9ERSxP4Cuq0u7s00LX7HWtdhu7e4tGltU3yzOt4pyjKWTgkZBOeeM1Z8WWz+GfDvhzTbJ2iGoWIvr2WNirTyNjarEdVUZAXpznrQBxOcMVYFWBwQwwRSjlwg5Zuijqa7KGy/t34XX+p3TF9Q0O8jSG5LHzGgcqDGx6sATkZzjpWrrfiTU4vhz4Q1FruSTUzPcYunJL7EkB2Z64OFz6hcdDQB5xUktvPBjzoJYt33fMQrkfjXoOiynV9O8X+Obl7S21eLy0t5NhSK3eTCmVcBjuxjB5IOT3rG0e7s00bXbDXdbgu4Li0aW0VWmldbxTlHG5PlJGQTnnvQBydOdGjwHRlYgHBBHFd/Y61f/wDCnL9pbh53tNWiitHmYsbYFAPkz0wM49M5HNQaPfzS/C/X7i6Y3dxp2oQNYzXDGRrZ5MBmUnPOD06Zwe1AHDOkkZXzI3TcMjcpGRSV22kyTan8LfFjX88t01lNaz2xncuYndyrFSeRkcGuJoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOTooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK9mnvfh38R0tr7XdTn0HxB5CQ3Dbcwysqgb+mOnuOw5xXjNeg+LvDNrqvi7VLnwzqem3ltJcMxje5jgeNifmADlQy5zhlyMUAN8Y6BceDL/R4NQuE1vw6yyT2EltIIlmViC4DAHac7ScZ4xjGa0/i7Nolr4gSyi0Z0mXTbZYJEusJEuz5Rs284HHWsLxdqdvH4V8O+F4b6O+l0wTSzzwtujV5SG8tW6MFA6jjJPpW54zvNH1vUh4lS8sp7OTRlgW3aQedHciPYFMfXKkht3TjrQBnXHhSPw94Y0a+m0aXV9R1WP7UIx5oit4eNufLwS7cn72BjpVjUPC2j6P4l8OyXGmXb6Xr0MbJZyXBiktZC4V13bcsFJOMgEg/jT9T1t/GXhjQVsNXisNV0q1FlcW010LcTouAjqxIUnsQTnpjNYFskMXizw9EdSfUL1byL7TKJfMiU+YuFRj97AHJHBJ46ZIBp+IrTwd4d8c3uiSaVezWEFyYppzdESxrkZMYAwcD+9nPtWinwvNj4m17TFQ6tNZWS3enWyuYzdq7AAnHPygklQckjGai8eeG49S+KOtSPrOm2tm14xnmnnCtB6gxk7mPHG0HPqO1vW/EeleL9a1jUrLU/wCyrqxt7a20aeecxPIELBskdNwJOTwOMkUAcrrdjZ2uhSJd6FNo2ureIBBIZVV4Cr5IWQk5DBQTk9eMVydeja94m1C78AS6V4k1ODVNQ+2RyWBWVJ3gQBhIWkUng5UAZJ4PYV5zQB6vrt14eg+FHhOf/hH5DBPcXbeUt6VYuCql2bb8xOB2GMYrl9E8OWcfg6/8WavHJNaQXKWlraJJs86UjJ3MBkIB6ck8ZFbEVzp2u/Dnwxpx1C0gm0m/ma8iuZhGfJdg29c/eGARgZOe1Ns9d03WvBGreEmlhsD/AGidQ0xpztjZeQYi38JweCePpQBnWuhWHinwzq+oaZZDT9Q0eMTzQJIzx3EJOCV3EsrLjJ5IIPbvxdeg6LqNt4I8J+IIpb22udW1iAWUNvazLKIYyTvd2XK56AKCT9K8+oA9D0LSPC+ofDfV9bvtNvBe6ZLBGXjujicu2OQRhB0B6+1VtP0HR/FvhvV7nSbN9P1fSoftTW4maSO4gH3iN3Ksv4g+1aXg62W7+D3jGBriK333VnteU4TIbIBPbOMZ6DPOBzUHh7ULPwL4Z164nvLW41nVbVrG2tbWZZfJRs7pHZcqO2BnP0oAeuleEl+Hdh4nl0u5jk/tA2ctuLtnE+EBLZwNowScDuAM81DH4c8PXa+I/FNot23hjTGjS2t5W2y3Er4AQt2UEnJ5OMfWrE9rZv8ACK10Zdd0f7fHqL3rQm7GfLMeAP8AeyOlUPCeu2dx4K17wff3MdodQeO4tLqYny1lQj5HI+6GCgA9BjmgCPRNF07xlpurR6fYrp+qafbNexpFK7x3Ean50O8kqwBBBB554qxqejeHZ/hlH4j0iwvEuvtn2W5WS83rbHqpxtGQw46jGe9TeE7228AWetaldX9nPqN3ZPZWVtaXCz8uRmR2TKqoA4BOT6VW+G2raYj6v4d166+z6Pq1qRJIx4jlj+dGHvwQPcigDP1WLw5pmj6IP7JvW1O4gS5u994Am3cQAoC5G9Ru56Bh1q7420rQfDviLRja6bM2n3OmwXktq10dxZ9xID447du1c3qFyfEPiKWdTFbC4kxGJXCpEg4VcnoFUAfhXafEK1s9a1HQHsNc0iZYtMt7GZhdACORA24nP8PT5vUigBfGuk+E/CXiqCxg0i4uoZ4oZnimumURIyjhWHJYnJyeBwMdar3Xw9g/4W+PBdteSraPMCs7gFxGY/MPHAJxkVd+JEVlr/jOxutN1zSJbdraGAyG7ChWjTndxwOMA1qaw1tq3xtstc0vUILuzd0k82zmBeBYogGkcHG1VIzzwQD64oAxtI0Tw5rs3iRLbSHgk0a1luoFku2ZbhYzhhL0IJ6jaV9K4PWJ7C51a4m0uzezsnbMVu8hkKDA43HrzmvULrTpdUhu9P0zxt4SW41FgtwLeNrZ7w5yFZiuBk84GASa8q1GwudK1G4sL2Iw3VvI0UsZ/hYHBFAHb/Br+z3+JGlwXth9plklzBIZcCFlRm3bcfMeBjJ4x3rmfEV1plzfOun6bLZsk0nmM90ZvM546qMdD+daHw31i00D4g6Pql+/l2kEx818Z2hlK5x6DOadq+kWluj2n23T57271AtHcR3KuiQYOC7g4UMWBwefl5xQB1XxEl0uxh8KT3ln/aNy+g2oEZlaOJEAPJ2nJY89xjGec8ZuqfDmOf4nWPhrSZ3htdRgjuo3n+ZreNk3Nnpu24PpnirPxAt7LWpfDiafrujy/ZtMt7Gc/awBHIgbcTkfd9/etvW/FdjoPxK0HxXZ39jqNnb2kVpcRWs4eRcIVcge2eD39s0AY2m+GNI1vxW/hmHw7f2dpI0kNrqshlMvmKDteQEbNrEcgAY3da81uIHtbmW3kA3xOUbHqDg1217YJ/blxcr4tgbQGlMwlivj53lk5CiEnf5mOMEYz3xzXEzMjSuUDBCxIDHJxnue5oA9V+C+LbTfGupxKBd2ulHyZO6ZDk4/FF/KvJc85NdZ8P8Axf8A8If4hNzPCbjTrqJra9gBxvjb09wefz9ak1TwXHNeNN4c1XTdQ02QlojJeRQSxKf4ZEkZSCOmRkHrQBgrq94vh6TSOto10lzk5yrqrqMHtkMfyFd74g/5N+8Kf9hKf+b1yd+trZabB4ft723uJpblZry5Rv3KuAVRVbuFDuWYcHdxnGT3Gr2dhd/CbQvDsPiPQm1GyvJZpl+3KFCsWxhj1PIoA8vsLm5tLiSa1HziGRCducKylGPtwx5rvfgZ/wAlTsP+uUv/AKAaqW2j6Z4d8Ia9ez+INLu9UuYFtLW0s7jzGCtIpdzx/dXH0Jq/8Ixp+g+LrLXtT1vS7a1WKQFHuP3gyCACoFAHD3F7Np3ieW9tmCz2920kZIzhg5I4NZdbniTS1sr6W4TUtOvEnnkK/ZLjzCBnOSMcdaw6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK9mnvfh38R0tr7XdTn0HxB5CQ3Dbcwysqgb+mOnuOw5xXjNeg+LvDNrqvi7VLnwzqem3ltJcMxje5jgeNifmADlQy5zhlyMUAN8Y+H7jwZf6Pb6hcJrfh1lknsJLaQRLMjYLgMAdpyVJxngjGM1p/F2bRLXxAlnFozpMum2ywSJdYSJdgwNm3nA461g+LtUt4vCvh3wvBfRX0umCaWeeFt0avKwby1bowUDqOMk+lbvjO80fW9SXxKl5ZT2kmjLAtu0g86O5EewKY+uVJDbunHWgDOuPCkfh7wxo19No0ur6jqsf2oRjzRFbw8bc+Xgl25P3sDHSrGoeFtH0fxL4dkuNMu30vXoY2SzkuDFJayFwrru25YKScZAJB/Gn6nrb+MvDGgrYavFYarpVqLK4tproW4nRcBHViQpPYgnPTGawLZIYvFnh6I6k+oXq3kX2mUS+ZEp8xcKjH72AOSOCTx0yQDT8RWng7w745vdEk0q9msILkxTTm6IljXIyYwBg4H97OfatFPhebHxNr2mKh1aayslu9Otlcxm7V2ABOOflBJKg5JGM1F488Nx6l8UdakfWdNtbNrxjPNPOFaD1BjJ3MeONoOfUdret+I9K8X61rGpWWp/2VdWNvbW2jTzzmJ5AhYNkjpuBJyeBxkigDl9csbO20GRLrQZtF15bxAIJDKqvAVfJCyEnhgoJyevGK5JcBhkEjPIBxXo+v+JtQu/h9LpXiXU4NU1D7ZHJYFJUneBAGEhaRSeDlQASTwe1eb0Aer67deHoPhR4Tn/4R+QwT3F23lLelWLgqpdm2/MTgdhjGK5fRPDlnH4Ov/FmrxyTWkFylpa2iSbPOlIydzAZCAenJPGRWxFc6drvw58MacdQtIJtJv5mvIrmYRnyXYNvXP3hgEYGTntTbPXdN1rwRq3hJpYbA/2idQ0xpztjZeQYi38JweCePpQBn2uhWHirwxq+oaXYjT9R0eMTzQJIzx3EJOCV3EsrLjJ5IIPbvxVeh6LqFt4G8JeIYpb21udX1mAWUNvaTrKIYzne7suV9goJP0rzygD0XQdI8L6h8NtX1y+0y8F7pksEbSR3ZxOXbHIIwg6A/e9qrafoGj+L/DesXOkWb6frGkw/amthM0sdxAPvEbuVZfxB46Vo+DbZLz4OeMoHuIrffdWe2SY4TcGyAT2zjGegzzgc1D4d1Cz8B+GNeubi9tbjWtWtWsba0tZll8lGzukkZSVHbAzn6UAPXSvCC/DnT/FEul3UUn9ofY5bcXbOJ8ICWzgbRgk4HcAZ5qCPw54du18SeKrRbtvDGmNGltbSttmuJXwAhbnCgk5PJxjvzVqe0sn+D9poq69o/wDaEWpPfNCbsZ8sx4A6feyOlZ3hLXrO48E694O1C5jszqDx3FpdTE+WsyEfI5H3QwUAHoO9AEeh6Jp3jPTdXj0+xXTtU0+2a9jSKV3juI1Pzod5JVgMEEHnnirGqaL4cn+GMfiTSLC8S7+2fZLlZLzetseqnG0ZDDjqMZ71L4Tvbb4f2et6ld39lPqN3ZPZWVtaXCz8uRmR2TKqoxwCcn0qt8NdX0tJNX8O6/dfZ9G1e1IkkY8Ryx/OjD34IHuRQBR1WLw5pmjaGP7JvW1O4gS5u996Am3cQAoCZG9Ru5+6GHWrvjjSdA8N+I9FNrpkzafdaZBey2rXR3FpNxID447du1c1qF0fEXiOadTFbC5lxGJZAqRIOFXcegVQB+Fdt8RLWz1zUfD76frujzLFpdvYzMLoARyIG3E5/h6fN6kUAN8a6T4T8JeKoLGDSLi6hnihmeKa6ZREjKOFYclicnJ4HAx1qvdfD2D/AIW+PBdteSraPMCs7gFxGY/MPHAJxkVd+JEVlr/jOxutN1zSJbdraGAyG7ChWjTndxwOMA1qaw1tq3xtstc0vUILuzd0k82zmBeBYogGkcHG1VIzzwQD64oAyNI0Pw3rs3iWO20d4JNGtZbq3WS7dluFjOGWXoQT1G0r6VwOsz6fdavcTaVZvZ2LsDFbvIZDGMDI3HrzmvUrrTpdVhu9P0zxv4RW41FgLj7PG1s94c5Cs23AyecDAJNeUajp9zpWo3On3sRiuraRopYz/CwOCKAO4+DP9nv8SNLgvdP+0yySE28hl2rCyozbtuPmPAxk8Y71zHiO60y5vnXT9Nls3SaTzWe6M3mc8dVGOh/OtD4b6xaaB8QdH1TUJPLtIJj5r4ztDKy5x6DOafq+kWlsj2n23Tp7271EtHcR3KuiQYOC7g4UMWBweflOcUAdT8RJdLsYfCk95Z/2jcvoNqBGZWjiRADydpyWPPcYxnnPGbqnw5jn+J1j4a0md4bXUYI7qN5/ma3jZNzZ6btuD6Z4qz8QLey1qXw4mn67o8v2bTLexnP2sARyIG3E5H3ff3rb1vxXY6D8StB8V2d/Y6jZ29pFaXEVrOHkXCFXIHtng9/bNAGRpvhfR9b8WP4Zh8N6hZWkjSQ2uqyNKZfMVTteQEbCrEcqFGN3WvNLmB7W6lt5AA8TlGx6g4Ndxfaen9uXF0ni+BvD7zGcSxXx87yychRCTv8AMxxgjGe+Oa4aZkeZ2jDBCxKhmycZ7nuaAPV/goBbaZ421OIYvLTSj5MndMhycfii/lXkeSTk9a634feMP+EN8RG5nhNxp11E1tewA/fjb09wefzHepNU8Exz3jT+G9W03UNNkO6IyXsUE0Sk/dkSRlII6ZGQetAGCusXi+G5NG62bXaXWTnKuquox6ZDn8h6V3viH/k3zwn/ANhGf+clcpqC2tjptv4dtr23uJpblZ7y5Rv3KuAVRVbuFDuWYcHdxnGT3Or2Vhd/CXQfDkPiTQW1GxvJZpl+3KFCsXxhj1PIoA8s0+5ubO4kntB+8EMiMdu7CMhRj7cMea774Ff8lUsP+uM3/oBqrbaNpnhzwfr97P4g0q71S6gW0tbSyuPMYK0il2PH91cfQmr/AMIhp+geLrLXtT1zSra1WKQFHuP3oLAgArjigDhrm9n03xTNe2zBZ7e8aWNiM4YOSODWVW74l0pbK/luE1PTbxJ55Cv2O48wgZzkjAx1rCoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOsooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK6TSfEkC6BN4b1uCa50eSTzoXgIE9pLn78e7gg5OVOByeRmubooA1pbDQI2ydbubiIciKKxZJW9sudin3yw9jS6f4kvNK8T2muWKJE9owWG3BOxYQu0ReuNvU9SST3rIooA2b1vDtxqE19DLfQwSOZG04QqWQk5KCXdjbnI3bcgdQa0vB3iHS9G1HVNQ1FLpZry3ntlt7OFDHGsmDkEuOnI24/GuU70UAdT4M1bQ/DWtz6hczajKnkyQQpHbIpZXUDc2ZOCPQZ+tQeGNdsvDuqXystxdaZfWL6e8mxUlVXA+cJuIyCOm7t1rnSM0Z4xQBoTzWBgstOSe4azhMjyXIgG4tJt+7GWHACoOWGea2PGet6P4hvLe8sDqEU0VtDbeVcQIFIQEbtwckfTH41y9FAHSwa9Y3fgNPDV+biykgvTeQ3UUXmRykqRiRQQQRngjPQcVas/EuiW3heLRJYb10s9RTUYnaNSt0wXDRuu7CL0wfm4GSM1yFFAHWafq+hQ+BLzQrqXUvtV1PFdNJHaoUR0AG0ZkyR7nH0rkxRSUAdB4O1LStD8RWmsai94WtJRJHDbwqwfgjli4x19DU+t3fhvWtev9Ta+1WJby5acxCwjJUMc4yZv1xXMd6WgDo/EOoaDfyaTZ6Wmo2+k2Vq0UjzRo8zSOzMz7Q4BJJXuP0qbxfrWh+JdZgvbNtShjEEdvMstuhZVRcBlxJ8xPocfWuWoAxQB1ni/W9C8R3OlzWj6nD9ktIbKQS20f+rTPzjEnJ5+7+tL4q1rRNdtdESzfUo2060isW822TDop5fIk4OCfl7+orkqKAOt8S6zoWr6NotpaS6kkul2y2v761QCVc8txJwevHOfWmeI9Z0PUPDWiabYS3/2jS4niBnt0VZtzhieHO3HPGD+FcrRQB0za5p2p+FtE0m/e5t5tIllI8iJZFuI3YMVzuG1sjGSCMHNc/d3DXd9c3TqFaeZ5So7biTj9ahooATrXYeBtc0PwtrlprV3JqU00KOpgitkC5ZSv3zJkgZ9BXHjrTv4aANLWH0iW6luNNuL5zNO8hjubZIwoYk8MHbJz7CtfwTrmkeHri7vNS+3PLPay2qx20KOoVwPm3Fxz7Y/GuUooA63wXrGh+Ftakv7mTUp0WJ4IljtUUsrqAS2ZOCPQZ+tUtE1600Y6pprLdXugapEIJ1O2KdQBkSL8xUOrE8EkH2rn6O1AGk6aTZiVoZpNQmZGWISWoiSPIxvYbm3MATgdM4OeKvza1Z63oOl6bq7z293pcbQW17HH5iyQnGEkXIIK4GGBPHUd657vmigDoL7XYIPC/wDwjmlCVrWW4+1XlzMoU3DgAKoQE7UXA4JJJ54q7rGr+H77wbpGiW8+pibTGndJZLWMLMZDuwR5h2joO/0rkcUtAG94d8QDSbfUdNvYXuNK1SHybqKNgsi45WRCeNynseDVGQaVZCVoJptQnZGSJJbURJHkYLsNzbiBnAHAODnjFZ9JjmgDr7PWNAj8A3Hh+WXVPtFxdJeNMlrGURlAGzHmZI6/Nx9KXSNY8P2fgfUNDu5dSFxqEsMzyR20bLE0ZHABkG4ce30rkKKAOr0TWtDsPCGsaReS6j9o1XyS7w2qMkPluSMZkBbP4Vyp27jtJK9iRg03FKKAFooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuj0XxPBFoNx4b1u3mudGmk86I27ATWkufvx54YHup45PIya5ykIoA2ZrHw/DJvGu3VxD/wA8obFo5X9sudifXLfQ0WHiW70zxVa67YosL2hVIIM5RYQNvl+4K5yepJJrFxRigDbvx4cudQlvbeS/iglcytp/kLuUk5KLLvxtySN23IHY1p+Edf0jRNR1K/1BLkPeW89sttZQJ5cayYOQS46YwBj8a5HFGKAOr8GatofhnWpL65l1KdEhlt4ljtowWV1wWbMnykc8DP17VF4a1uy8PanfxEXVzpt9YSWEjiNUmVXA+cJuI4PbdyO/aua5pMUAadzLp5jstNjuLh7SB5JJboQDezPj7sZYcAIg5bk5Na/jTWtH8RXUF7YG/jmitobXybiBArBARu3ByR9MfjXLDiigDpINbsLjwEvhq7a4tHt743sNzFH5qSEggrIoIPGeGGfpxzcsvEui23hiDRZIL2RbPUk1GKQxIRdPtIZHUt+7Xpg/NwD3NcfmigDq7HWNEt/Ad1oVxNqP2u7uI7t5I7NGRGVQNg/egn6nH0rlqTPNLQBv+DtT0rRtfs9X1B73dZzeYkMEKMJPlI5YuMcn0NT6xceG9Y16/wBSfUNViW7uXuDGthGSu5icZMvPXriuZooA6LxDe6FqEmk2emLqFtpNjbPDI80aSTM7MzM+0OAckr3H6VN4u1jRPEmswXto+pQx/Z47aZZbaMsoRcBlxJ8xPHBxj1rl+1HSgDrPF2u6F4iudLmtpNSh+y2sNlIJbZDlE3ZkGJOvP3f1pfFWs6FrVposdrNqCNp1pFZN5tsmGRTy4xJweeF7+orkW7fWloA6rxNreh6vouh2VnNqAl0y3W1zLbIBKpYZbIkO0gZOOc+o60niHWNGv/DOh6dZSaiZtJikiUzW6Kswdwc8SErjB7HPtXK4FLQB0r65p+p+GNF0rUHuoH0mWQqYYxItxFI4bb94bGyMZIIwc1z93cNeXlxdSAB55XlIHQFmJx+tRUhoAK63wPruieFtetNaun1KeeGN1NvFbRhcspX75kyRz6CuSHWnUAaGtNpdxeTXOmz3rGeZnaO5t0TaGJP3lc5P4CtbwRrej6Bc3l5qD3rvcWktosdtCjKFcD59xccjB4x+NczRQB1XgvV9F8L63JfXEmozoInhiVLZFLq6gbmzJwfYZ+tVNG1600WTVNLZLu80DUolgnXasUwAHyyKNzKGUnpnBxWBRQBeaPSrRZHhml1CdkZYlktREkWRjew3NuIBOAOAcHPGK0Z9attc0DS9N1R5ra80tGhtr2OMSLJCcYjkXIIIxwwzx1Hc8/S0AbV3rdvb+Fh4b0k3Bgmn+1Xt1MoRriQABFCZO1FwOCSSeeKv6xrOiXfg/SdHt5tT87TGneN2tYwsxkO4AjzflxjGefXFcrgZzS0AbXh7xCNJg1HTruF7jStUiEN1FGwWRcZKyITxuUnODwapzf2VaLKYJpb+d42jiWa2ESRluN7Dc24gZwOmcHPFZ3cfUUpAzmgDrrTWNCTwBcaE8upi5uLtLx5haoyhlUDYP3gz0PzcfSk0rWNCtvA+o6JeS6j9q1CaKeSSK2QrC0ZHAzINw468VyNFAHW6Jrug2Pg3WNFvJNT+0asITJJFbIywmNt3GZBuz+FcocZO0kr2JGDTaUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV0Wj+JIItBn8Oa1bS3WjSyedE0DAT2cv9+PdwQcnKnGcnnk1ztKOtAGtJp+gQyeYuuT3EI6RxWJjmf/Zy52L9ct9DS2HiS80vxRa65Zqsb2hVYIMkosIG3y/XBXOT6knvWRRQBsX48Nz6hJexPfxQTOZDp4hUupJyUWXdjbycNtzgdK0vCGv6XoWoalf6il0s13bz2y29nApjjWTBBBLjpyMY/GuVPWkoA6nwXq+h+GNakv7ibUZ1SKW3iWO1RS6uoG5syfKQc8DPTrUPhvWbHw/qt/ERdXOmX1hJYSSCJUmCuB84XcVyCOm78a5yigDQupLB47PTY7i4azhMjy3QgG9mk2/djLjgBUHLcnJrZ8aazo/iG6gvrA38c0NtDa+TcQIFYICN24SEjjtj8a5aigDpLfXLG48CDw3dm4tZYb43sVxFH5iSkggrIoIIxnIYZ6dOObdn4l0e38MQaLLb3jiz1BNQicxoRdPtIZHXdhF6YPzcA55NchRQB1djq2hW/gK60G4k1IXV1cR3TSx2yFEZABsGZMkHnnj6VygooFAG/wCD9R0rRPENpq+oSXhe0mEiQwQKwkGCOWLjHJ9DVjWbrw3rHiG+1Rr/AFWJbq4acxLYRkrk5xuMvP5VzJpKAOi8Q3ug6lJpdhpi6hbaTaWzQvJLGkkzSM5Zn2hwDkkdx+lWPGesaL4k1m2vbVtRgjFvHbzLLbxlgEXAZcSfMTxwceua5WjOaAOs8Y61ofiK50ua1k1KL7Law2UgktkP7tM5cYk68/d/Wk8W61oeu2+ixWkuoRnT7SKybzbZMMinlxh+uCfl/WuUooA6zxJrWharpOg2llLqIl0u3Fr++tkAlUsMtkSHaQMnHOfUdab4g1nRNR8N6JYWLaiJtJieIGa3QLNvYEniQlcc9jn2rlcUUAdQ+uaZqPhfRNL1E3VvJo8shQwwiQXMTtuK8sNrAjGcEY5rnbu4a7vbm6cbWnmeUgdtzE4/WoaKAErr/A2uaF4W1201q7l1Ga4hRwbeK2QLllK/fL5IwfQVyFOHSgDT1uTSJrmW4024vXM0zuY7i3RNoYk8MHOT+ArW8Ea9o/h65u7y/e9eS4tZbVY7eFGUK4Hzbi454PGPxrlsCjigDqfBes6J4X1uS+uJdRnQRSQRLHbIpdXXG5sycH2GfrVfRtes9Fk1XS2S7vNA1OFYJwVWKYADKyKNxUMpPTODXP0UAXydLtY5Wgnkv5XRo0Sa1ESR7hgsw3NuYAnAHGcHPFaEusWus6Fpenas80F3pSNDa3saeYrwnGI5FyD8uOGB6cY71z+KWgDcutbgt/DA8O6UZzbyXAuby6mUI1w4GFUICdqLgdSSTzxV/V9a0C88HaTo0M+p+fpfnvHK9qirKZDnBxKdvT3rlKKANnw74iTSoNR068t3udK1SHyLuKNgsi45WRCeNykng8HNVHTS7RJHt5pL+dkZIllthEkZIxvYbm3EAkgdAcHPGKoYApaAOqtNZ0CL4f3Ph55dTFxcXaXjTC0QopVQNg/egnoef0pdJ1rQbXwTqGh3T6kLjUZYZ5JI7dGWIoR8ozIC3TqcdelcpRQB1eh65odh4P1jR72TUTcaqIS7wWqMkJjYtxmQbs59q5LucZxnjPWnUhoASiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK6LR/EkCaDP4b1u2lutHlk86FoGAms5f78eeCDzlTjqeRk1ztJigDYlsfD8LmQa7c3EIGfKhsWSVvbLHYp98tj0NPsPE15pfii11ywSOF7UqkMAGUWELtEfqQVzk9STnrWJiloA2b5/Dtzfy3cMl/BbySGQ6eIQWTJyUWXdjbngMVyB2JrR8H+INI0XUdSv8AUUuVe7t57VLeyt0MUayY5Us4PGMAY/HmuUxRigDrPBus6L4a1ybUJ5NQnRYZLeJEtkBdXUDcf3nykHPHPTrVXw1rth4e1PUI2+03OlX1hJYSyCIJMqvj5wm4jII6buRXPUmKANG4ksHgstOjuLlrOFpHkuvIAdmk2/djLjgBUHLDJzWx421jR/EN1Be6e2oRzRW0Nr5NxAgUhARu3CQkcdsfjXLAYoxQB01vrNld+Af+EYvHuLOSC+N5Dcxx+ZHLlSNsigggjPBGe3FWbDxLo1v4Xg0SSC8kFnqKahE5jQrdNtIZHUtiNTxg/NwDnk1yNJjFAHV2Oq6HF8P7zQrmfUvtV3cx3TyRWqFEZFA2D95yOvPH0rlqTFLQBveDtS0rR/ENnq+oSXu+zmEiQ28CsHG0jli646+hqxrc3hnWtevtTa/1aEXdy87RixjYqGOdoJl/XFcyn8X1/pTieKAN/XtS0PUJtKstNTULbSbK1aJpJUSSZpHLMzlQwBySO4/Sp/GOs6J4l1m3vbRtRhjFvHbzLLboWUIuAy4k+Ynjg4+tctQOKAOt8YazoXiG50ua2m1GL7Law2UgktkOUTdmQYk68/d/Wl8W67oWtW2jJay6gjafaRWbebbJh0U8uD5nB5+739a5GigDq/Euu6Dq2kaJa2U2oiXS7ZbX99bKBKNwJbIc4PXjBzwM0niLWdH1Lwzomm2Lah5ulRPEGngRVlDMDniQlcfQ59q5QKBS0AdM2uabqXhfQtLvmuYJdIllZDDEJFuYnYNt+8NrZGMkEY5rn7u5a8vri6ZQrTyvKVHQFmJx+tQ0UAFdb4I13RPC2vWmtXT6lNPCjg28VtGFyylfvmTJGD6CuSHWnUAaGsHSp7ua502e+czzu7R3NuibAxJ+8rncc+w9a2PBWt6T4evLy71E3zSzWstoI7eJGADgfMWLjkYPGPxrl6KAOo8GaxonhbXJb6eXUbhFieCIR2yKXV0ALNmT5SDngZ6daq6LrNtoo1TSCl1e6FqUSwT5AhmGOVlX5ioZT2zgjFYNFAF8ppNqkkkE0uoTOjLCJbQRJHkY3sCzbiATgdAcHPGK0bjW7XXNA0vTdWkntrzSkaG1vY4/NWSE4xHIuQQVwMMM8dR3rn6KAN2+12CHwv8A8I5pPnNbSzi5vLqZQjXDgAKoUE7UXAPJJJ5OKv6tq3h688G6Xolvc6oJ9OM7RySWsYWZpDuwf3hKgYHPNclS0AbPhzxD/ZFvqGnXkL3GlanD5N1FGwWRSOVkQnjcp5weDVVl0mzWR4J5b+aRGSFZrYRJHnjcw3NuYAkgDgHBzxiqFJQB1tnrOgR+Arrw/LLqYuLm6S7eUWyFVZVA2D95kjg/Nx9KTS9Z8P23gjUdDupNTW41CSGeSWO2RkiaM9BmQFhx1OPpXKUUAdVoGtaHZeDNZ0i+l1L7Rq3kmR4bWNlhMbFuMygtnPtXKHGTgkjPGRiig0AJRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFdFo/iW3TQZ/Det2811o8snnQtAQJrOXu8eeCDzlTjOTyMmudpMUAbEtj4fhk3jXbq4h/55Q2LRyv7Zc7E+uW+hpbDxLd6b4ptddsUSGS0ISCE5ZVhC7fLPcgrnJ6kkmsbFLigDZvz4euL6S7glv4baWQyHTxCNyZOSiy7sbc8BiuQOxNafhLxDpGiahqV9qKXSvd289qtvYwJ5cayY5BZweMEYx2zmuSxRigDq/BmsaF4Z1ye/uZdRuE8mS3iWO2QFldQCzfvPlI5GOfr2qv4a1uw8P6pfxE3Nzpl7YSWEsgiCSqrgfOE3EEg9t3SucxRigDSuXsHisdPS4uGtIWkklultwGZnA+7GXHACKOW5JNbHjPWNH8Q3UF7YNfxzRW0Nr5NxAgVggI3bhISOO2PxrlqKAOlh12xuvAg8MXjT2UkF8byG5ii8yOUkEbZFBBBGeCM9BxxVmx8S6Rb+FI9ElgvGFlqS6jFJ5SkXTAEMjru+ReRg/NwD3rkaKAOrsNW0CLwDd6DcS6mLq6uI7p5EtYykboANozICw9+PpXJiloFAHQeDtT0rQ/EVprGoveFrSUSRwwQKwfgjli4x19DUutXPhrWdfv9TN9q0IvLhpmiFgjFcnOAfN/XFc0KdQB0Gvaj4fvJdJ0/SxfW2lWVs8LSSxI8zyOzMzkBgGzx3GOnapvGGsaH4h1q3vbae/ijMMdvMJLZSUVFwGX5+SfQ4+tcxgUtAHVeL9a0XxHc6XNbPqMX2S0hspBJbocomcuP3nXn7v60virW9F1220SO0fUo202zisn823TDop5cHzODzwvfpkVylFAHWeJtY0PVtG0a1spdSEmmWy2p822TEilgS/EhwRzxznjkVH4g1nRdQ8N6HptjJqPn6VFJEpuLdFWYOwbJIkJXGPQ59q5fNFAHSvrWn6l4W0TSr9rm3l0mWUoYYlkFzE7Biudw2tkYzyMHPtXPXly15fXF0yhWnleUqOgLMTj9ajpDQAldf4F1zRPC+uWmtXb6lNPDG6m3it4wuWXH3zJkjB9BXIUUAaetDSp7ua5064vXM0zO0dzbom0MSfvLI2Tn2Fa3gjXNH8OXd3e6g17JJPay2qx28KsoVwPm3FxzweMfjXLUUAdX4M1jQ/C+uPqFxNqFwixSQRLHbICyuoBZv3nykHPHP17Cro2u2mjHVNLIub3QdSiWG4UoIphjlJF+YqGU54zgjFc9RQBpMuk2fmNDPJfzMjJF5tqIkjyMb2G5tzAE4HQHBzxir9xrdrreg6Xp2rST291pSmK2vYo/NWWE4xHIuQQVxwwzx1HeueoxQBv3muwQ+F/+Ec0oz/Z5Z/tN5dTKEaeQD5VCAnai4B5JJPpV3VdW0C78IaVo9tcaqs2lmd0kktUCSmQ5wf3uVAwBnnrmuTooA3PD/iGPSIdQ069tmudK1WIQ3cUbBXXGdsiE8blJyAeDVR49MshKYJ31CZ0ZI0mtREke4YLMNzZYAnA6A4OeMVnUd80Adhaax4ei8BXWgTS6p9puLpLxpUtUKI6qBsGZMkdef0pml6zoVr4K1HRLyXVPtGozQzySR2sbLCYyPlGZQW6dTj6VyWaKAOs0HW9DsvButaNeSal9o1YQl3itUKQ+WxbAzIN2fwrlDjJ25IzxkYNJRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcnRRRQAUUUUAFFFFABRRRQAUUUUAFKDjsKSigCQS4/5Zp+VL53/TOP8A75qKigCf7T/0wh/74pwvMf8ALtb/APfFVqKALo1HH/LnZn6xf/Xp/wDav/ThY/8Afn/69Z9FAGiNXI/5h9h/34/+vThrJH/MO07/AMBxWZRQBqrrhX/mGaYfrbD/ABqUeIiP+YRpB+toP8axaKANz/hJT/0BtG/8BB/jSjxO4/5g+i/+AS1hUUAb6+KnH/MF0M/WxWtLRfHVvpupx3d54Y0e6WLDIkUCwkOCCG3YPTHT9a46igDvfEPxIt9Z1WTULfwno8MsxLTG5iFwXb1zhcfrWV/wmrf9C54c/wDBcv8AjXL0UAdR/wAJq3/QueHP/Bcv+NH/AAmrf9C54c/8Fy/41y9FAHWf8J0//QseGP8AwWr/AI0f8J0//QseGP8AwWL/AI1ydFAHWf8ACdP/ANCx4Y/8Fi/40f8ACdP/ANCx4Y/8Fi/41ydFAHqOn/FzTbTQJdKl8C6TIlxta48oiOOVlOVOzYRx7k1zf/CeN/0K3hf/AMFi/wCNclRQB1v/AAnjf9Ct4X/8Fi/40f8ACeN/0K3hf/wWL/jXJUUAdb/wnjf9Ct4X/wDBYv8AjR/wnjf9Ct4X/wDBYv8AjXJUUAdb/wAJ43/QreF//BYv+NH/AAnjf9Ct4X/8Fi/41yVFAHW/8J43/QreF/8AwWL/AI1d0z4nXGm3DvH4Z8NiKaMw3EcdgE82JvvISD0OBXC0UAelH4jeHI5fOi8C6U5/hheGNVU9juC7j+n1rLvPiVeajeS3l54d8NT3EzbpJZNODMx9SScmuJooA67/AIT5/wDoVvC3/grX/Gj/AIT5/wDoVvC3/grX/GuRooA67/hPn/6Fbwt/4K1/xo/4T5/+hW8Lf+Ctf8a5GigDrG8dO3/Ms+GB9NMX/Gm/8Ju//Qt+Gv8AwWr/AI1ytFAHUf8ACbP/ANC54b/8Fy/40h8aMf8AmXPDn4acv+NcxRQB0Z8Xsf8AmAeH/wDwAH+NMPitj/zA9C/8AR/jXP0UAb//AAlLf9ATQ/8AwCH+NIfFDH/mC6IPpZCsGigDbPiRj/zCNHH0sxUZ19j/AMwvSv8AwFFZFFAGn/bTf9A/Tv8AwGFH9tN/0D9O/wDAYVmUUAaLauzf8uNgPpbimNqTN/y62Y+kAqjRQBa+2n/n3tv+/Qpv2o/88YP+/YqvRQBM1wT/AMs4h9EFN84/3I/++RUdFACk57CkoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAClBx2FJRQBIJcf8s4z9RT/tP/TCH/vioKKALH2r/phB/wB8U4XuP+Xa2/791VooAvDU8f8ALjZH6xf/AF6kGr4/5h2n/wDfj/69ZtFAGoNaI/5hunf+A/8A9elGuEf8wzTP/AYVlUUAay68V/5heln62w/xqYeJSP8AmDaMfraD/GsOigDd/wCEmP8A0BdF/wDAMf40o8UOP+YLon/gCtYNFAHQ/wDCWP8A9APQv/ABa0tF8eW+nanFd3nhbRrlIvmRIYFgIcEENuwemOn61xlFAHf+IviXb61q0moW/hHRoJZiWmNzEtwXb1ztXH61k/8ACbv/ANC34a/8Fq/41y1FAHV/8J0//Qs+GP8AwWL/AI0v/CdP/wBCz4Y/8Fi/41ydFAHX/wDCfv8A9Cr4V/8ABWv+NL/wn7/9Cr4V/wDBWv8AjXH0UAdh/wAJ+/8A0KvhX/wVr/jR/wAJ+/8A0KvhX/wVr/jXH0UAep6f8XtMtNAl0qbwJpEiXG03IiIjjlZTlTs2Ece5Nc3/AMJ+3/QqeFf/AAVr/jXIUUAdl/wsJv8AoUvCf/gqX/Gj/hYTf9Cl4T/8FS/41xtFAHX/APCft/0KnhX/AMFa/wCNH/Cft/0KnhX/AMFa/wCNchRQB1//AAn7f9Cp4V/8Fa/40f8ACft/0KnhX/wVr/jXIUUAdf8A8J+3/QqeFf8AwVr/AI1d0z4oXGm3DvH4Y8NCKaNobiOOwCebE33kJB6HArg6KAPTG+JHhqOXzofAekuf4YXgiVUPY7gm4/p9ayr34m3mo3st5eeHPDE9xM26SSTTQzMfUknJriKKAOv/AOE/f/oVfCv/AIK1/wAaP+E/f/oVfCv/AIK1/wAa5CigDr/+E+f/AKFbwt/4K1/xpP8AhPn/AOhW8Lf+Ctf8a5GigDrG8dO3/Ms+GB9NMX/Gm/8ACbv/ANC34a/8Fq/41ytFAHU/8Ju//Qt+Gv8AwWr/AI0h8asf+Zc8N/hpy/41y9FAHSHxgx/5l/w//wCAA/xph8WMf+YFoX/gCP8AGueooA6D/hKm/wCgJof/AIAj/GmnxQx/5guiD6WQ/wAawaKANs+JGP8AzCNHH0sxUZ19j/zC9K/8BRWRRQBqf223/QP03/wGFH9tt/0D9N/8BhWXRQBotq7N/wAuOnj6W4pjakzf8utmPpAKo0UAWvtp/wCfe2/79Cm/aj/zxg/79iq9FAE7XJb/AJZQj6Rim+ef7kX/AHwKiooAUnPYUlFFABRRRQAUUUUAFFFFABRRRQAUUUUAdZRRRQAUUUUAFFFFABRRSoMuo96AEoqLWvEep22uX9vDNEkUVxIiKLaP5VDEAfdqj/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATR/wlOsf8/EX/AIDRf/E0AadFZn/CU6x/z8Rf+A0X/wATV7RfEWp3WuWFvNLC8UtxGjqbaLlSwBH3aAJaKD99vqaKACiiigAooooAKKKKACiiigAooooAKKKKACiilTmRR2zQAlFRa14j1O21y/t4ZokiiuJERRbR/KoYgD7tUf8AhKdY/wCfiL/wGi/+JoA06KzP+Ep1j/n4i/8AAaL/AOJo/wCEp1j/AJ+Iv/AaL/4mgDTorM/4SnWP+fiL/wABov8A4mj/AISnWP8An4i/8Bov/iaANOisz/hKdY/5+Iv/AAGi/wDiaP8AhKdY/wCfiL/wGi/+JoA06KzP+Ep1j/n4i/8AAaL/AOJo/wCEp1j/AJ+Iv/AaL/4mgDTorM/4SnWP+fiL/wABov8A4mj/AISnWP8An4i/8Bov/iaANOisz/hKdY/5+Iv/AAGi/wDiaP8AhKdY/wCfiL/wGi/+JoA06KzP+Ep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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=4480x360>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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fULNpYWhsvvnm0urFla1sAJSjiLGEocLJJ5+MQy1APPjBD/Y5MfY73M0R+RSXdquh1L3V9HcTTYCqbn6lC4FCoBAoBBYcAnHpzsW/3b5DcMW/XUAqXQgUAoVAIbA2EMA1pnl/8pOfiG390pFNU95itdgt/rWKrUaTyfkKhsDTZip8PeshWBS2EK81U77DHPU1r3nNgw46yMvJCcMHJVuRWUnARLTr28+abCeYQFjUb0eZWeWh+s0biNMZCcbMZj/ucY+DKmH2i3OHllqNzLUB/mqYUUUKgblEoDZAzyXa86iurqPHQ89+9rMf//jH884f/vCHbUu19QofWK57/vOfv9deez3taU+73vWu59MLfs6A251HzVgDU8J/t7nNbcz/Ihjt9TrOe97zHtOy0RqIBtvrQxR2Pz/pSU/yVtPQ+v1agc97mK7t3TXzm0nePfbY44EPfKCf/wP1WiKevffe+2EPe9gnP/nJ17zmNT0zVvvyxje+8Vve8hb78zCxoQPLc55KIUgzpX7CCSc89rGPRdv3uc99dtppJ6vISN0bYJo/VdnKLwQKgUJgsSLQ49999933MY95DK6x4/nTn/60RVPO0ytJqCT8K3rEOPgXiSwOTOaef/O9KNH4c57zHG/Q4rLs91ob7yDtvPPOmE7YbwQl5p+VLiv+nRUYS0khUAgscQR6/LsE41+Tp+I43+d41KMe5XUgvzUh/hWR2QBtjJHty4MPCfa0CckErtGIqVtxnIISVGWjksTBBx9su9JgWdMLyRQMbr/99r6BIQSeauZ3sPiMcsxmPPKRj/Ty2Ote97peW7KtakbaIizyNQAzE2I/lmbK7D5FgwphlfiXGaZQEv8aeNBjyPGZz3zmve9972CpyikECoFCoBBY0AiEGgS5bZ8u+jPz2RplFy+qdWkXsmDfVi0LooOzzQSSOZprmtpeQinFHZl5lojAZN5y245RvzegQuUIq1d8Fi9Ttcba9PzlL3/Zd6/Ve+KJJ37xi180ioASmo5VqbQB6NL3oQkwVQiveNoiXyaxSPZMbS3t5ddlIVAIFAKFwOJGIKSABYp/09HFv4v7ga/WFQKFQCEw3xAQrOFika95Y9GfRL626XeQUFJoWk7oyWUSI1rRSo2QabeatlSU/KTNwdp5LLS89a1vLa5k3mwt1LbaBxOqFuy/+c1vvvOd7+zVXzujTjvtNDPDU4Xe9uaZQ/ajSSykTXMynhnU3HJ6+PQum1glCoFCYJFspqmOnCkCHLEJWQRgB7DXcW5729uaiPSN57PPPpuqOFm/MffGN77RHqyjjz5ajplHpUyVzrSumcpbX7zhDW+oFGPy03gz1TCOPGK2+GoJ9oMf/KCtZhJKaaZzOMN5qB7rlyZwHVZ873e/+9lHBUlbjZ/+9KfTaXv0pptuOvTj0BiXgLegrBDTfIMb3MAm4O985zupcWhdq51pfVp/+bwHfvXpkaaHAdL5/uVMv/ZhS7dvP/uC5hve8IZTTz1VS72fBDE7420iN4vt1a7dd98dl5vLpvwOd7iDXyq0D9ugB9TgeuXk8ZSnPMWT5gtYJOfgcWptr0QhUAgUAvMBgfAvJ4l5X/7ylzf+9aJnM+9rX/vaMcccw2Uef/zxXstBhYI0brYJrKXEXPKvX8K1DUjzP/ShD2nOjPjX+rHfVPKTFBaPfVLrmc98JpZBPTiux925tGmJfj9z4Vva6tpvv/2QOP7VF7OOpFeKcaVXyFRh/xP9GVSoK6vOVrtnyr+U+O6mYNh4zPrxL3/5SxyqRXawvfjFL/Z4IGXvB2tm+NcPLd3xjne0Fcy3MIt/Z72LS2EhUAgsUATCv1PFv2mU0OnII4889NBDP/7xjwt2Fln8iyNQhgEGNmn8q+FBZqpuRS52PJu3deCvFStWoG9FjjvuuMa/NIuIEVOCzeh0zotGokLxL/J62cteJmAUVOqFVl2PuFv+TBOM8XWNm93sZl5FPuWUU1I8ysWh2mug1a13HP1GX/ZsGR0dccQRno18kNL0tNkDzwkNBmz2djdVtrg98YlPNETBv+Jcs/8a7thzzz0PPPBAORmuNPlKFAKFQCFQCCwOBNBityE40YRnSLAxo3eN5JDESl3hbpowMlVWwpEpUzlKORJXNt4k1i2rUjJyIiaRS5UKn901WS18lq8ghrIWG51dJSPS0TYoMDSfzYzXUsGvV76lUarRAkplCShaKyRypMn0G6uIfGWSl0m/oyWkY0MSAVN1MoOAgoNGyunmK9u9HCpfmYVAIVAIFALzH4FGCjEVERT/Fv/O/+e2LCwECoFCYEEj0CKpxGsiPtRjSjarn2Y+E50RS9jlLJpL5mDDieUIiZuFDrm7HFqEMCXOBCSafDQryyoRJXtMR7u8xS1u4WNVMgernt0cVbDKzLNF22i2szmZvYrSBJIOobFDIq1uYe9gwdZwiQhTm8ye/lyao/7FL34h8Dern5wRwkM1VGYhsHARqA3QC7fv1tRyS3f24L7qVa+yTGj77zOe8QxToliKXk4w1IKubG+14MdfYxGvyIRR1rTukeVRgk8F3+pWt/INJyw1UnZNb6JPrx/Zr5yvcczI+wNERI2EYGIrsJ3B0ojKYjmzBlXJUcRsL0mr6d/97nd/9atfrWkDpiivOdjdBimGdUXg+fOf/9xnsWz7HvqZrq5wL+2NJTPLPp9ps5o+Ap3GImPL2AA0ppEm4JnJ2ec9bB0gZoW4O0whQPMcPEg9++uyECgECoF5goB1R788YH+VXTWNf/MSqtAlZGGB0AskXkN69KMfzW3y3llAXatNmEv+7TVkkDR7AmtyaWwjDm/8+4Mf/AA54iwHeupW3ULHaavrluoK4186bVtv/Nt06sdXvOIVPj5tE1W3yLRp/fKXv/zFvm2fCc/wzMOgFnu8jCjwr9GIIJl+h0b5XOVJJ50ksvWSW/HvtPCWQCFQCCwdBEbEv+FfXpQb9yH/8O+ijH8HuzttH8xPTmOxqQR6+eHHVgorGdJ471dI6GemhN4uRZFNoFd8tS8xrx/NsEdZCNxTIki3++pjH/vYRz7ykd6t0Ze+xmH84NXiD3zgAww2GPMIeUgE1GaQMbLm2KRFSYYTavcelAbe8pa3HDpOUIQxoyutu4VAIVAIFAILHYFQAAYcbIhbyR/kQTm5i2vCns6II0pyt5tOSIiAiDnwi7PLzBuIEGmTNs9gztaQ5uY3v/nEEuvkO0jdl6uJDRozaLmc2EBe2tmhUmeX6nXXEbFJiy5C9yJxESu61BCsSoAlZAg455gsd3FZOflhxqgilkRkmrx6KadT61TtUnu7MiRjWMvMZTRIR203M7fqXAgUAoVAIbBwEYhX71JMa4tbyY//b/kScnK3+DdoOBf/dp+QShcChUAhUAgMRaAFg9279j5Zr0SsoVe3iHUFemliDpmoR6m8pqtIjp6wS8LtZ4VcJrrsiiloLVUQ6vPP8r/1rW+ZyLXAOijZLTVP0kPHMM02bRdfC4TliH8TAre7vYSwmkB2/fVu1WUhsOgRqA3Qi76Lhzdwgk9WrfLtSQRgA6vdzyjB6yAoQQFpTjbTrJtsssnrX/9630xy2NjqlrnF4Uonc/nTrGgiKhmpqFuE5lzST2E89aBCemSGIwfvJidMQMbRlVFpL6epinlNmAaSJmGR39DPNjfJJGhuOYx3mDuWKTzGnUjd155sgB6sXSkC5L/0pS/5TLIfEQb7H//4R9zDhqa2FWwotepGJ7SroUqb73ZbALb666uQurWVZYMOdQvfJ5Nwq7SJDU2AyKYxXwoHl4bEbMV/+MMfar7etAxsx0Dy6dSzVn99vvTGN75xU+guGZca7scgrCi3B2B8SxRvwiqSjv5uutXYvUUSSk2+e6vJN+Qj1gNnaBV0ptVAkOg9Y10NTWcSzv6g8vfSjGmWVKIQKAQWJQLxCfjX5pgu/4rKtNddh3VKnsE+G99KtGEIU8frxs8HFg6EZIMovqt5lZYfnbkk0y2SW3FHXfnwb7vbBLplWUKMQn6vm9/VM7vpZsaM1KYUUxv/brXVVrZh2Z2sCYME1No+bS1ZV+6KhQvwr7eKw7/C9QgwQ2+CKxvZY1XO46Bnx7y9Vp///Oe78SptMt1Sb+Nf1VHrDSj7sd7xjnd4eJoB8nv8q6Cj2wQy49iTIiTZgMU8A/SkOV1tvTT5nkwvp9XeEl0NQzMJUNKGpuxJi5KYqohSxMj0DOhWV+lCoBBYfAjwCY6p4l/t5RO4We7dROpLX/pSLnRo/DvoOqiV2VyQtJir8aNLyskEUrfizFtOg7pxUKpoAtEQMe5rKP/GhqYqiegh3/iuJzDOZTOjJ8z3anIOMhI9gVzKFx37/vGWW27pvR2xs71KraWtyFD7ZTri2CUIp0VdQKRpg7afRPArUuecc47PP/vJpqaZAd49/p//+Z//+7//ayYp5WgyUyVEzYz/whe+gG0nTJn8bKdJA6MIZyzPtnArbbpViK26d7/73QcddJAHSV/TnLPEiBrdopxMO8txDDVMvlpG8K+7DIMJZJqSZA5V6JYjD0kMaKXI956fCMiPjILqkpnLtFpmVwPhyEiQZLm6enrcqqMQKAQKgQWKQByac3OJLafbouYYmwNsd92S6cxbUmK7sOjVK9O+2MRhyncWSDb5JEiap/XuDaIh45DPD5uJJRwWcAtP8eReq77vfe9rUte0v5lYs7jq6tJET3m0yYxLj7ZQf9d+3Beb816QdIqQlIjxEuyhkCXMi4CznJRNXSqS6WCVs8sczRKZsZkxxhLMUKlZFGnVIeW8GOxut4i0HAI4GhoxntnRoApqu/Iu6ygECoFCoBBYEAjEpTtz5ghCwsHynFsTmpPv5RNwS6Zz+Kj4FyYXs+//H9AV/7bHqRKFQCFQCBQCXQQGubV7d8x06JgqUdvvf/97pCOCy7kxdVQhKRElmdTrro1G4kfyYk8fwBLliZH9BuDKlSt32203PwLcs0ERtVDey2+XrUUkW2ZLuDs0n0Ar2ISZKq4n78hYpd2aNqFIZFqNabtgn/3aK6SdVPz/zSe3UspaIE6M7CxwnrbGEigEFhMCtQF6MfXmDNrCufu0813veleO0v5mS3oII7uv+NAowhkIg1tEFW9961stAAsC2924cs60JVKKQMuU425ymtt1GWpxTtme3dz3FltsYWoSb3VVddMpwniZ9DTlye9dtkzV9WrMpYZT1TNj2ktltSWHaVwTqeDK55+HlrVKak+Sdtl69elPf1pxsCvSZmCVShudqQ1KPVVKDW1d5EN7BGxTlqOs3c+tiITacb9vjg5V0qurd6mUiXVz5WyObQHNRLY1YN+HNraQk3rpzy//2vD9vOc9jyo5uUXMo0VDlPRqySXhZrYc6Z5Ys58SlUY4pXqSXYUR62mLqpYZJJWilvKeJa2KbilDDaVogAyE00waesKxhJhDulkunZzojFidC4FCYLEiwGM8/vGPv9Od7iT4Cf9yreg17eUHHHE+2OFHP/oR/j3ssMPCU/KJxWPk7JK8c1xW/GFU5Zy73Zxu2t2mR36Xf122u2R6erSCJ++W7artpVN2TOFe2Vm5hDD+hSf+tY2Y5VnxZVigTi0uLZqOWSNvn61ONHD+SgUlrPeNb3wDEXhZSI3Jd9bFbLD9+rWvfW2qGEQ1+YNn33L+2c9+llFZnoHI5L01tG4/WbfrjS5c/vKXv8yurKawy79qb3zXBLqJaXuNJd1Ku2V7aarSWOfurV4VDZCW6Aon3b0VtcyAc4MlAmlaV3hQVeUUAoXAUkOAoxgR/+I1XsVBjL8dEf+GAaFHOBj2vI18XohY7kaMTE+45chfc/5txqSWVrVauhV1765hOpqbcomuDdItB0n56aHjjjtus802s5cLbQnze7UThnzT5m40dHNSpKnNJbH4/C9/+ct+YCE7xuTIpxBPmc3wYpLZXtTfq3TaSxup8S8liNW0tQdDRG+okHCYQgFvU8swB8r204rZ6dVrgstmM8lu7ZFMjltpkctWpCucdvU0dAVSPA9q09x0diVbumkjH/TaraEJYo6uqRGTOVS+6Xc36Zynkh+qpDILgUKgEFjQCMRtDm1CnCH3jjHFmD7VLHLcaKONhLF4xws8zrw6zxnJJAgbP1z/+te/3vWuJzC01mtn8/e+9z0rnSbVvZHrcxV0yvRZim233ZYSL/Z4KYgN5JUVHmI3mofalrpiMO728q104misqhS+8F0PnMjmTTfdFAX7nUOZImVi+FddJOU4Nt54Y/lnn312olqq6KdEcd+m2nDDDWlIXc4+0+WsmQqmsSRzV4uYLQBU9cqVKy1VUChHw7///e+TT3MIJ2CPfmb4BWSsrb1oHUTeRSfM8qht2OayzoVAIVAIFAKLBoGhHJfWhemKf1tfF/82KCpRCBQChUAhMGcIiMXU9dCHPtQOq29/+9vOqHlo7eI+MeaKFStEl+JNe5MEy4K7fBOaHsGgS/PAvsQh4B3Ug/qFgYP5veoyQuhlupwq3620oltELS0+FYROW2mvrLrojAa3hNvmnO94xzuarPYTi4Jo+7XojFhq75rnq2o+iCkwNw/w1a9+tau80oXAokegNkAv+i4e3kCfR8IlNlSZEv3a175mX06cI0/aLeDSXCFnakMPyXxNwfeTTMJytXGsMm2edqZBpinXaEAtilv849ZzVz5ySileGA+5Zf6RNpJIy12W3OhGN7rnPe/5oQ99KKWc5ceDM8YhbY6SPc7x/jRHzPyvaU2zn+p1pl8bzRo7h/96DWyNnSq/CQwmmOHQHIe7NKiuzcl25YMtwzSEQMxmYfJJ0hN5OQSoSkJz5IPUHib2E3NLpg+BoHYTvtplixUAcZi5XWUZoyIErxThNrMsn3n02HgH88yeE3B0DYgZQ8+miU0oq0VdJtBbKfnAd+msitYWk9FaoUZPhQ+dip/dIiBTJ9Lg0ATPCc6WSQNMtMvdGCBTEWdGtgS1Zsa1VAM1Ks9hk5epaeRphjNTnRWxyk4t42lLjlsx1VkOO+EGTzqliZnfd8shxzn1ppn0A1Z68v7ESVmjDaVkOrPBWRWaRptMKDFJ37EkmIPCIe2Zb8aQrKMQKAQWNwJWKB/ykIfwNo1/4xNy5k94D2meBw746zOf+YxIBkVyjze72c0QLr9HJgISfCnXpyDPbyFzwrf+4x926vBL3Av/xvlQxQFygySleVE7kKyqCib5paH822wgH9t4Rb5XjY54MJ6QQvaQUZf1VGeXhg2CT34Pc5FkqiJpmjPhOT6AwCokBQo2Q4Y9HC+gWMJCZ2TKZg2UJgMWZ0UUJBm0GS+BtkIQOAuqwJFDCcx5eD5fLS4VD50p4pLChz/84aomQI9SjuAZNEYgg/QNw4gxWynnFGlnmaqIkTI9MCzRHM+MaN+sgUz2KMikyQfkH2wmowmEldVGOdrLDGIxJtUlBw4em+QzW4Jy5zxXqqZfvudTAvdpr2dDPhn5ErSN4F9PDknDpGCrrEsmgYvBHmxNaIDHQueolWjIawIKJsng1KtSRjoi77F0UO5PwN+XzAZjJQqBQmBxIzBt/MshcA4Bgdvpxb9Ym2PhZIjx8/wPR4c74gw5UgX5Kx4G98VXxwfK5474Nz7NFiVbf+J8FJ8V/qUHufC9XJ8zS4wxxol/17C74aCZObrQ9dQS4IHxAiOZitEYydUTiw8HDsICbHLIR4MEtZqDCCQIaJeCCMvZmEdB2ijXLya+eXViDkXSj0mrbscddwR1nL+7DmWbna3TW05LgNGrUy71XTKbzRIMEyEmnxI5bMNfokLhuUfIm0juusxIjD0tAQqXCIsY+xVMw2Uq4sxs+VonkeCXfozGcmVV5xZOl0lAZuPf2OOWgwaqKFdE1WzDtioFhSkXaWXpUUTtLvVRwHHpwK0wdze941bMi3zOxMgYAnnCTTKwh4w/BBayigFy6FGWvFuqU0RdrBqBPOE6CoFCoBBYUggkYjWo2GmnnR73uMcZLfCc3K9lS7f8ts8rXvEKG6O5XOGbkQbS59st/e6///5o1ItbNiibarjf/e4Htx0mD06eNr9AuOuuu3LUYn+8ecghhyBlHpiL9m2Ot73tbeSbe2+Yy2EAH+6Mhp7+9Keb10V8HDgWlo9YmWSwxAz7rR/72MeyFr+gAPut3/SmN73lLW/ZZ599/ARiqEQrTIPQj48QAQ3ktQJ/+TT1vvvuS7mycnCoCeSvfOUr7Fd7yMKZBlY5jAHA8va3v92eZrMlGn7nO9+ZkR/84AePOuooSvAdusnUt9pRnl/20HZxMYXyb3WrW9Hvax2awxKE1WrpItDSlSgECoFCoBBYrAgU/xb/LtZnu9pVCBQChcBaQkBEluhJYg2rENkJfh/1qEeZO/3pT38qvnvjG99ogtEP6wnuMJQgTnyXWsSJ9773vV/3utf5BIZ3X9V+t7vd7RnPeIYg+u53v/u97nUv+SZyjz766Bvc4AY+2qggO0888UQxoKjTPORHPvIRnxsbYbZ5UZKiWqGu1XMaGCNTCGxm+JWvfKVPSqvOOrivXDHPFKsYnP5PfvKTYvYDDjjgPve5D/2mqUWpgnK3xJviX03IDLAIVIT+1Kc+VTgsLU4XIJ9wwgl2cqtFZNogVUrkrtXEIEO5D4ucfvrp3jreZZddvAn8/ve/388Rt+ZICH4ZvM022/gEjJBZzEsbtUceeaTA+SUveYk5AWv32sVygGROuNVYiUJg0SBQG6AXTVfOrCG+/cyr8nF+iJY354J5Us4RGeTMI9OYswQZC3gmH3l2q78mFq2occp8MdeJY/yOHl/JoZOkRD7/Tq2ZWZqtDXOj5mRdSituThNb4DbuGHkowu2StDL9qle9iv9VEfIwq8tIs5Oq4+WlmSSNGKwfy2GDHLOZVClClUwmsVlxNiA507IWRNnPoYtphyLFgKH5IzIVaQexpIfKuyVf6wACQwjjbEaCC1Co1F34uOVS2zWBMJztW9J2CW10N1wIZw3RZAOC7PjRTDCadNanwAGF2V61ZOcZiMxrOwPNzvKnPOUp+E9FYXEa0uNDLe9m0mD8oeNoU5yFuat4E0tLXTKYPAPY/93vfpd52s6A9CANTCWTgYIma4tbLsHCMHebcg0nr9VMpc1nU0n6sJYiZOhURKultcsiNCXm5U24q9EnuwyDPHJAppYkbR4MqsjLZLyGKOIhSaWwdVcDFWchMWB6TcprVamXACUeJAcNmqylMgnrRE8ga02+e57VbiBCLVUqYoDNi5TrIHaSV1a+qgmoqI5CoBBYCggM5d/WcD6he8jnOsK/PEz4l6/jdpw5HF6FZ8MOIh/fTOLQ+CUkS4AL4oQFY1wWj8TRcYY8kkwuyEGhslzQUP4Vj/GWfH4cFIU8OXv4WzrxFLUOwaSoT71U0YndyGePF9sU//GPf2yEoCB+Z5LE3B9pMseusVDih60QI01OWBM4bfYDAT7hX2mLuNrSRg6gEG0SM5LRLuDLIaaBqIEqZXl17l1dxHAWxCDslgSq8s2tJz7xiYSZoRbdxAx9rdQIQAi4S4YSCZfgbVTbCsqJntwiQz+TMKBnwyXCUjWZxr+R12tsIwAH9uji1Ji6Ak7418tvHh7DAIcGuqUKxT2fnkPyAGEkcDwSlKB+IKvFz0eAEZMqQkYthJUlQ0/jX8L0q0umUiDSFqS/5ZZbyvRoea2ZMGthDlgNYTZhqihkBuTTOgM/z6SHUJeplHl6wUhD1dQ6FNS5ngR3g1hrNVV1FAKFwGJFYCj/+vPnB5wdGs6rOCfd+Jfz6fIvH4LRxF8ElDXa52C5XATBt3B3LuWbcuV8OEBOjLfkizg3wlhAFW7NCv8yhmaeTRXUcrPjx7/k1+SYxOxi3IbqAYJDSzlb/tngBBkBBA7mBFJEec5ZXA9MaXfFceFfOTw8uLhr/IJhb3/72+NfYZcw0ChFpvaCl0L8hSBogANMVKorCdOw8847P/CBDySmoH6UAzF3Ez8OtVzmRMMm30xmuUs6FdEcpZgk7eiWJRzyYhU+ym9BSOMspYyFCCvCTlYx3gMjh7UyNVCLiEVJ1CqLFkkadDX+JUbeM6YgfDSEcI9/jbX8fojM3CXvC6DMgI8pEdoEtljV8MAcvWebTsJqJ88A7YWVTiGpFFV4H3SKa1eeXjodMNFTHntsywydCByaCROLHmd/LKw1ZGIwASippeLf9HKdC4FCoBDgfoHAMQpe0OWTn/xkm4af9axnvfe97+VXzznnHMuWr3nNaw4++OCb3vSm1oa5YgMM3ptfvctd7vKGN7zhzW9+86tf/Woemwb5tvZaBOVvjUY4YZkOPzxlFGSJ1BKAAxXyxhw7JWGfwY5gkls0cOnU+hwGYhLZWcQl7FcHLSTbpuwus8X7Ak97r1VhqPOxj30MfZD//Oc/z2CRNQbXFiZRiz6Yh0dwIoJgvOVby9saguvVqC177rmnJWGaEU1sU1ACYWEZv6qkdnxne/cXvvAFLKmu5z//+TY0q+uZz3wmMsKVSJ/lPgICvVNPPdVWbEyUW8C0RdvEsr3XIkfjE/pTxSAUlVMIFAKFQCGw+BAo/i3+XXxPdbWoECgECoEFh8BjHvOY5z73uaI/r+YK6wSDglw7fe1ytpxnM3QmPwWPmnab29zm4x//+H777ffOd75T7CZaFOqaYLQX2XJhlh2J+bym8PBhD3uYLdHE7HimROBJv1hYNNoNgTMeUGoiGly1ymSpHNoosU1ZtCiSddfiuy3OeUvZ/Law1OULX/hCy5cCYW8Fi5eFtwJhVQu9Tc4rlSlQCZVSbn3TNK/WPfKRjxT/Puc5zxGuipcJex+YwS5JknfESJOuEl57/sAHPmD+9klPetLxxx8vnFfLi1/8YoG/RplAoJyYQzNXrlxpMsGXT71+rLgWidltcML7n/vc5x7xiEfYFS3iNgmsOoFwqqtzIbDIEKgN0IusQ8dtzv3vf39znaRNAnKUnGNKtoRLvtI5ftOy1mc/+1nzmHbAcJfW7cxd2uNiRhIBmFV0mAnlLr3j8u53v5vTN3HpXRwuG0uZsiS//fbb87wmH3lwXt6SG0+NxrCUWVditCEkb9Wo90EPehBecYv3N5d64IEHmiHloxGh+V82M8aksK02PqlIRvGXvexlKId/x3nWFA899FB3iWmCguZG8RxSSUudUSb6aZdrNcEGzA12U67YET4QQ0vOOsIULaCcmWQ9GIfZv/u///u/uIck2rYY7OMWppgpMZnrXR/WIjllzRF7dwfC+khZoPm2tw1DWN+8MAr8xCc+ATrdBHy0Cnz4wNNdJO3WKaecAhkK0+NT4YC5gTmI2NBSGsI23Wed9dnPfrb+Ui9J9nt43ELVnhCt3muvvQgjaeeTTjrpmGOO0eTG8XAjbP3VB0s8P0YS2ugBMG/uyfFSFIVaCgoVAUrzzXorpWqto5aqww47LNPuMrV37733BhFjPDbeefIil1lyT6+1Z1i9733v86yaK4eSHH3hm2HWsBlsdCXT2gMxAyD16kG96aUr4OtKD62XASyxy6HQI2fQxk639AgcTLJ71K3rG3z4GWijIvYzg6lsngr5yi8ECoHFhMBU/BtfyhXEG+Ss4eFfbgTN2dDT+HePPfbgzWwz5UtRM1dpXfPkk0/mCblczMhtEhbMCMxQzz3ucQ/8yw3iX6rQZfhX+OdykH8FbKrm9l/+8pfbWeXlE2/icr8UusU34lnfeaKNB7by+q53vcuOGU5S7CRgIy/q40stwTLgPe95jxp54DnrSgAK7TALYHlsy7FY1bgFsWqOpWVrn9yvF41ghX/JgBcraYXVUGKIFYlYUuX2tUur7WDmwCmEmDNe9okpgxZAgVEgGv7FC+Hfj370o5oc/lU7h2+Y5PXlLv/mm1ujYQlHk9GoPCej5VWqLrRo+gAIoVQ5Xf5lD2vhY2WdVZaQtTrCqnCoq8u/dBotGE0ZBGq18RuFHgYo4UdNttqNfxWBpMfVa1r2exkQ4lmQTsu/+J21yuJ3Iw1hv4cWq2qF/rJ9ipGeWLwpR/PNdBgV6CY1esDsBthuu+30si7zJ+CZNKI44ogjPIF62YvgSJmdzn7s2LvRWFhZbXSMBrPuFgKFwKJBYCr+jR/gD3thTvgXD/biXx9REFLll9MFnjjXLKQIgvPBBQKH3XbbDRVyj3aOCoe9AIOv+Uy8GVoRpfJps8W/H/7wh8eMf+Pbc17zbp30oP/fieaeWjgYDzz4wQ/20Ur7boGJfQS2WEOcLt+Zo0ZPNj8hC3EZ5wwo4CMdH+0QOuFfb/DiX8rt+nJpyOHAv4QJoPKnPe1pbqkOsN6mFvoR9tUNk86GQ5gC2qaY3cViOMKMuUCyZ23vUnMOP/zwZOJHpUZQhlseoTZUMy8RlqGEVR4hetigXcjLcAItaqbA1uyzb2QiWQxIhrwj2jxmhmdGIKAAo4ZjakMLwxUP0lT8a6LAAMzIAbODFNSeTPAa7SBQYTUGtITgQUXcKNLPIKJFzypO9zBDNXG3en392mu9eNk3TsS/EhqIrKlF09qlRQ94wAN8WcR+OP2oi2mgStjLWoNGwyrjQGL6SL0+tWJCw7Ck4t/ew1aXhUAhsNQQ4Ocxi1ZLcPtYgKs0A5nvKNvXy2cK8/lw84pmLI00+FuziLyxgiiDv0WpyuIFM4p8OO+KVQViolRhFJYRqpM3mY/7hLSmK5U69thj0QFqyMjE4Cch82AXUI6tnDGgWN7qqWDQ3GZCLZMP+AhrKI4ykC/DzIUid+vKYky3PvWpTyFci6yYDhdjRkZqrARGRklWYQXLOP3Rj340ATagGK2zlmxymGaBM4jkM8NowYG8zC1QgiLRvZUFjSLpc1Y4y2AP0VDLGDIWfc1mCAPNhLDfLC5I5QtUjdZMnjBPUGw9m0mITNUqGoSicgqBQqAQKAQWBwLFv8W/i+NJrlYUAoVAIbBQEMC8DkGWAE3UFrOTI22O0fSm+U/v1prgFQyaArX0bNbdLRPIQkWhqzlGGsS/JjxNaZpUFMaalhTzyrFFyqypufFMjwuQhdLmLe03Ez/atCbWJinic8mM3vx/Q1IVzTBBND12+NgJrayEuFIgaV5X8Cv6Fg4Lfn10wySqnc0CSVO1ZmKFwNYCzNVTqyIKEwKLrEXrglbxsm1dVgq0SxOUAotw1TKxNVCxbbNHQovMEmTmX4zvE9fkmcFUS5/Wo62HWjO1iU5Bcbo9AFYohLe20pmeNVUuKrcObtOzSFk+PMXItjyJvk0jdOuqdCGwmBCYu50oiwm1RdAWe0z5x5AB56tFLtuZU3aZI421+4Q3J8mzowee1C4im2itadmFbIGNrzR56rUb3IOuLBaKpnhPu1Lwge28EmiAz/XDczZsmX61W8ULLpYDzfBy/Yqry6yuRWKLZ2Y5zZmGbHCS1UGMQsZELSeOt1yaeyVsQc5UKbduN1gWO7ER108nCsGLrMUQZmPNgaITVaRRSGUqnovA6PMgSqPlTT2bufYZDJ9hNs+raRhR09gJDQibjbV8aLbae0XYzpQuZqUTHbIc4JbbMRM9L3rRi/A3noM8bkPe5m2pgoxeo8G3pe3WVdaIQX/ptTCZ3vFClTlfQOkFBrDE+GC05bmLI2FOmzluOZqfwYoahxaXD3kPg7tqZ5uETCMYCc+S3jcO8BT5KhVtHjADGuviuF/TyMiEkoKm75G05wQ9ewZMWGu4BXIyZvOZ4WnxvBn9WGw2KHFL6zTTQb8etxHcVgNKmAQTerxGZiX+tNNO8wCrwiZpwwIbqvSF0RKFajHhbguXHV1WhVu9qlCvWXJngzBKqDLnbkqd/SbcVSRfE6xtOwxiwO4vgs0SGrhy5Up1WZw2jNMR/nbyLFFYRyFQCCx6BHr8GxfadaT8XkBIJg9vvykXxC9xwo1/hYX2l2AK3o/DwXGcJ4fmhVdeRXhj566f/rFAiC9EYt42afzr5Q3+ysYURDwV/3KMOItLRN+oU+3IRUVWKOVb0cRTXKs9SWInbMsbZzjBSbIcL/uWkoJY3n4dDlCN3c2+ZFpL10anowZNcFYR3IwiOH++mj3wYSoGBJ3u8NKLmNm4AlXxzCy3d1YoyCogowBfFPabv/IZjF+MQwS3tvZqFyXcOISFnYoLNSnH1AJsdN/lXxxHg+rE8OPzb56BGeGjCE7xnNilhP3z2Mh0dPnXs2H8gH8JmAXQlYp4hJRVHeEe/5LMxIEBiWeMjAV4D4+RJPqzswoyxmM0eE5UhPvoR9+4XqbBBuER/Cv+twfa8MZMhFBfEZ1ls5qOYAz6RtNIGatasze0MCbJyrrHT43if3u5PGBmAXSBAYO/BU+piQk7zm0vi2E4WsLav5GVwY9BwoywLeFCoBBY0Aj0+FdbuJd2lkAZXR+If4fGv6gBe5rQ5GO5So7O4F9x7oUGgRLeFCBwZRLCExEcX4QgkI7Xb3CBKMOGXVEqclewF//OlH9ZwsGOGf/Gvat0zQ9tzzFUVSrSZCyTMy7WBXgTOfLhuMOBnfGvnbXiKe+9mFBGrxTaLGXzk1dbDTPsTMI1GMHEt4hYPIWGFCSMMsClLpeMMbCRQy0NLsGrLuBjBFX7qQHhtkxKdO5Qs7uZDSuJbr50npNept7XWPawVo8EHAbYUJWO1u/GTjioPQ82hIlV6ZFILc70mLtAecYSBiF4jdl40N41xErYG92GMVPxr1pg5c004afJGZK2eZlqMPbAxfaRi3+xtikaUONukSn0yDujxRZ3e09exxkCGT1mIzjeV7vfZASg13qRLEg9z16Qy/PMeP3lgTd61KL999/fLrTwr1oMWY2y3K34t/fk1GUhUAgsQQQ4Z+yAKfh8nIIs5IhxhFEOkRq3n7tCGxO8RhQ+e2wPtFdTOGduVlSrFLqBnoRoFC/gCwkvoOb3c91CKJyzAzs4U664uE+lDEjxofhPFloupmMG7sat9NNsksFErldPvRArCsMdLEQfBkVoi89P1eTVIkdZMixUS8iRDWhRw21BNreA5ny5ij2ZcCapapWaht16661dSqes4qYXDLqs41qQtkYuhDQTzgCAGIAJ+hTxohGixMhWFsx7GBhgsUyhB+3MaUNVFEzAugbKgxsbUrXzUEwqsxAoBAqBQmChI1D8W/y70J/hsr8QKAQKgQWKQC/IEuXJEd8JA02e29Ml9hT2CoStPltCFdnZMWW7joAuTbbrSSxpgjersTjdHKw41zSjqJOYRV4JIaTQ1UK2Ui7FjLgvUW2LLlN7D8kEgzKpUhF7mOF7TJYdzWeql06T+cJPs8qEyQha2WwJIHuxhNis0qJoVp2qpRW0umqd1PcTxctW6m30IixkNkMuzhUgm8K14U2RlA1cLPE6tOaIcG2zttdOyK9eUbDF6GwAMIVubTelTCMLzNscLyWUi9bZrCIgEzAVbEVVjZSnyYm4o6HOhcDiQGCOPn+7OMBaTK2w4sWjmRDEE9O2K74vM5X4w5YaHpyLtPprPSzrcIkebcYyh2ividU1q7C2gdpRakMMArBOZnLTjhNLbvwyJ253L0+NOVasWMHJ8rY8L2E8kV25XLZaLBByyiEMu1UOOeQQ218sf7LKNKVlaVOcFk3xBJ3mMU3mUujwXQqHHHftqvFBSglUYRkvbwuxedq2z4pAAFS7NUJm2PYatWE+wNolxjCfr7ANCxRmkzUc2ynikLB2aEHXpC2a9CFD87z41eYqt7TauqmJWtqkwWtzEh61Q0tZ9G8aF735tpMqrKQS03CbhOyW03dZmIyFOU/VZHcV1E0EKHFMJdnytQXXGmEgV5n6CKHmkbNh3bY8PYjs6cTWfpTQFLnNT9Zlzfsb92iOIsY3Phxl3dono6jy8BCzt8mUN5QMbkAhxzquB89nqs1fezwUZ63eB47nkHIT9KCgweKrB9XiLpPsryKpUn8Lxh9yGEzMND0YLTB4H8vaMFUsNKVuyRaktmFZ8VWvDQfKOqJKKava1obVayXAvLy9Dr62ZZ3eg22Aon/hZre6jrMxwl+ElmaSnYY6CoFCYCkgMD7/xidzm4ITro//7/KvfcY2l/DzXCi/6v0iHgbFcKH8jF1BOAWN4jsBmE1FohqLi1Yf3eXWvAdiX5HIjadFQ7YZcVld/uX9OFXBknpZYvcM12fLl1rUyNfZ0GwFVBG2ySFGs209KrI/mItDPfwb14q2OHN0g5Q5Utr4zznoa7XDx5l5vDFA4JN6mcqx4yY7xRGEtsCQYcJF66C4kht3SCBcK9DcuGbCE49ri0GF4QdVFKbtqMEQhTzSoUq9yKXLvzpLpYStCtu5RSHMvXqr0hyzBQhtKnIwmE7xsLPGZoWbAbaa4V87liyfpzssOesRm5Y8YFPxL5Y0AvQ4KYgoPR4a6Ml0WIk3NvMisjM6hqraoYepjUmMNNSoF5hhk1+Xf8X5hiuqNnRkYRbsGWBs6U0wLGzrf4YxZLxEpy2eSXE+KqdKxxkYUIuRDSbZ73Oe9nh5yP3Mk7OOM04whtRZHnil/F0oSN5Ikv3svAT+i986oK2OQqAQWKwIzIh/RTFcBJ8Z/9nlX1zgrR5eDlAcC47jCdElVuVS7KERL3CSHBeHZk5TZOe9GqEZlyuuwUc+z+CF2Jnyr4ARsaJ7AwD8i46FJyJf3l69PnVsXtgXL3xql8dmNgHRn1bwnOge13PFhDVtbrpYRWrEmMxIuKpe9jjjRzhoiGjIvDZw+HDRmU8+I1CHhI9f4gIJ7loTyGim0YhQTg6d5gdQmypQuWkE2qCh4YhD1YQNe3QWt69SWInF8JSA2ktNSIFAjjHRiOVTCavXkQYm4cw2pXS0B4kBfoXAvmGx4VlnnaWN8pnn4dF2BEcy9qBR5CX+xYbeLsvcOnq1z9hDZfCAPT14Q/lXvrdtBc4eOZKePQVNlBuEoO8MXTwMImgyugCzs5l5QBPJGp9YGFC7x1g+qwxpjCG1hTH41xDIGNLjrV8yLvUkI2hdwB4H7pZGytjfgApTew4VF//qOMr1uGkiyMzZczhVl1V+IVAIFAKXCgK8XxygM8/v4LpxhPVU9O1VT1yGv5Aj/y+04fml+VKO2m5ml5w2IkDo/DA+9SOBgjtjDOEPf4vuabY/WHBKkrzJSaooUYQb12oEhHGc1c4hKzUUCjIEcgtfUIsXkClqQCVITV0GSHRSLsIyEYHgkI4DRzNP2USREtlLLREbrF77iAkNipDRfG1hjErJOGsOQlQp1pBj3EVAvSYcpIWZSplqwHpGCFFr5AA9BIrRWO7dG6vm2k5YGykkBkbmUQsxC8baYrkdNRMwqFCvg1gdhUAhUAgUAosMAZ7foVHOOMJR/Fv8u8ge8mpOIVAIFALzFgFBlogysacYkJ0hZVt6BK3erRXWyRHnWiIUqQnryFgZF4EqmyKiTpvH8qVkPC7AtBdIkEjG3K/IlwazoF0QhHji4tSV/ExIJqebn7S7CUsFraowfWpuU+1CXQfDKDR+kO8DnTYdmeq03CDS7G4mJtO1QaQprvd2rulT+q3JCkjpNwmgaaJy8urqFmlpFdntBiI7qZQVvSqCvmOkuV+SzIAAI00C+6Sju3aUaY5oOtApzkI1mmeQtkZvLtcMg7vqdW7VVaIQWDQI1AboRdOVM2sIH8fncqw85rQluxxAGP3Y3mQ9UoLr5zT59zh0G4O8DUPG3mjKTUpyu1bC7FV1mHPs1uVuSIsXZsaMnCznji0wIqdvUtUMrClL+5AYo0bToyZb7W01N0rSEqmDbb7qb3JWJhLV/K4xaykNBO3C68hvsIE9YNnAWkAhUYuyXZM00yytdgG8m48gXaol88KDVbjbq2UqHh1atltXN62/HN2cwXSrKJqZIcc5k9fW741suqXaMwkrPWtqXpPtmLeWb/+Zbe6EyTgbXnioYGKG3fdCjHI8A/ZUGTQY5XR16mXbxK2ms8ECM83KkvfgWQPwtNiXbxMz7jcGsqxrv4Lty6o2pDBXbuO+VWeXiitLs1GXVXxT84YUHvtuXTI94dTqQT3SbmmpQYlScnSTLlZ7GtJkKlEIFAJLB4Hm66b1ooOYdPnXl3F5Jx4GxSRk4sdwnN2o/Ixa+CgbVTkcG1a8BdvVhjsyBgj/TkuIXVN5ZvzL13HC/LktvzYS2U4qthTIWZRVtTd2DBKYZ+8X/8wwwRV2ZomDNvkzIp2u8WOm+WGMAxltHCziVrdRBIAGTK1galc+3tubx/bUdvONYVIqeijs3k26l9locVBytnKCqnNL0MyM8C+bweKRyO9LtEp1mUwIgGsq/p3ckHbxya53z4DNdijV6rJdaJ4ov2zQFNKGYW2B8pSqEb2qQkdgUrca/1rg9+ypEYPbEYVwjdbQ+n3ucx+7suyoZjn5dljI1xHic4+WgpkHcbb6bie02N4DqYq0MbbJNDPioZWvp5RiWLMzKLXLShQChcDiRoAj4gS4iJ7/H6fVXf71HiO/JKDgNh225NotSq3P//M2HJRb6uLBsGHmIlsViX8Jrwb/CrcTWfvkgwS/54d9bHtiA49nilMtnLMZWGbw52TEQT6Wz7W6K27ScOk5ICOWaCMz2NbaHpfbY0Z3OWrmCfd6ca6p2KDU5RfysHWmOdGZ9FBn3u3lWWnyoOWqbofqUmNXDAiqZh7YzU4YmPk0SCsi0Z4HneWhip2JQ42yEod6otrhORS92s4FhKH8S6fq8C+ip82eaQMzOc7dek3Qi6bNuXuEHCJoVQhvxd0+FO1lIdMR3UMw6902X+mOhU2VHLvHvMWkuIew5WuO4vQYl+pEncXsdFyTqUQhUAgUAksWAW45BIE48vUQXtTgQZDl7RGhEO8tkMcdQmmf8BD+8KvoEmsYDAh5cI0igiPkYn5e3ORFUD8UoKCZcIRieGDjL50kQ5fq4sPDztJscBnqCX8N7Y5GainCn9uvnF868nkqwSE7hVes8uqLEZFJ0VREuSroTEUS8h251BYvaYu+6Zd20IxHqFLQwTC3WOsweFNcprNt025BQ9jubNhjYsFkrCkRh4GHzdDwcagIXdIsIVY1LnKXziinyqXpC5azysvbbrFh0sYJIx1k6igECoFCoBBYTAjw7WgFEfD2xb+T9Fv8u5ge8GpLIVAIFALzEQFxlkPoJ/4SP5qdNjsqpkts6OsMPkTokw1YydutphDt4DLZ2G2J5T8MLirMNiGBpClTH5ZS1mdHLBcKJH2L89vf/rZwUiTYLauWdhlLXEq0zF6iGwYaM7DHMqV6xeY2oWmCHPaz1uSt928tdAo2lUq42tOWS+Yx3idLTOQSFjVPRLmTi9cEYsxUJglpFVGpmV4YOtTuUFywL2HbtxkAaW333cmhBvQyBc6MESCbp2W5oydQl4XAIkDgvytSi6Ax1YTxEeCdzRKaBuW1lZrKtw4qREvIxv4qi1hmNm1b4Sv5X3OvfoDPdK1vUynlR8Z9/icv6yAYnlRBO027CtmQdTI2MGAqeuB8uXIFJbp2mpr0JSHOXS1WnRXn4nEbG2zCtriIS+y84cFxkrKMtA5Hj9U7s5wII2q7Js16Wts104wqO4EwbY1QYgMMrfiaw232sFkam7rVBYFyDddAGBo6NPluoisvv0tmvVvdUmuebhW1npVQowl6yrMm2q1FWzC0u2kLrOR40iSsiHvSjGPsVNZx+tRUvv18Ntg5fErNxik7DDyZVoW7Oo0qyMONvN0AHssTTzyR2gx6cjZUohnyPqWmdjaYRt9pp508Tj6b7QUyfyk060S3TKbL9MUR8+OeIqsRrTr42zanjYrnSWu3FMkSfh4A9lPY7laiECgElhQCXFCXf8dvOxfU5V9v9SBxnsfBtbprl5XNo5whndya6Itni9v5+Mc/3q2IDW5xj/wtT9i8dFdGmhtPcYkuXyiIfzlknMsM0aaJY0q4UO8j2RcrEvMyCZp2l7ARgq0zFBLLtumutl6ls3ipmSDCCxz4tGqzsGpBl7tmf5MXRmopGhKBh79yKxiGf1FMgGqlkug1s0uLIaCe/KxctlqaNmaEf7VLpwja/TBCuyuhLZ6f0fzbfUj0vsdshx12sNZuv5R1aIMcfR2dMcAADxvapYdGCey88862DIZkiXX5FyfiX4NJy+fWsH2E211vrJmzSBzeTKXNx6Txvq1aetafgycQ8nZx6TgaJBRhqtppU9DzhrjV6znUWZrpaWwKB7FqtypRCBQCiw8Bvq7Lvz0XPaK9Pf5Fr/wbJ+MQC6CAvCqzxRZbuOSU5HM43BFvNhj/8lGJwd3tutauAbzTIP9y4GRsWuVX8akE5hKw0KZeDtknD3nRxL9qF4+wJMGRfJ4/5sUDd6ub3TSrGO9Qnf1J01YXoLz9q0jXEiC7FPzaYiveb7f0ozQ0jGEScw2Fsdu/XW8v39HNaZpnPaGi2JZJD/GsSfNuM7UFXOFfkxV6E0nZQ8aSDDy6AxKZ4l+T1Pa6eZF4kH9jf4Z2dqcB3y8heEcIpaJa9aYv1OiuJQfBrD3KSrHTaMe2OUzKSMMeiWjLGe8LpQ0yfZSlm+/pcgvD6iDVtVv6zgNpgGoAljGYfqz4t+FTiUKgEFjiCISGeGOJBz3oQd4WFoEiJrzJA3t7E6HbBu2lFAI2PfPM3iLm3o0u5ChIWL7wijPffvvtrQFzxUohFBow/ote9CLz5JZCOWE5KAAfhfukcSg9DjnJTHpEvxCgAY8wRlBmbVW9ftpItGUc4nUXC8B+40jUz0hi5GmTMO3QpWnGh4ysIMhnjLZHkp7YQyZFKHHEwpyRGjYxchCKgkgmbQ56SOIdORgHpTJDFUCTabRAORmSBByEIQZDFaHITL8HqMA7Aoq6VQgUAoVAIbBAEQin8PMSxb/Fvwv0MS6zC4FCoBBYiAgItURhglM/guejDPbMTERlq1aZcrch2Ky112stIArcBGiEu23MPDBhH6jy20e+Y+iun5gT//oWp+KWO30AMb8x65ZAjxJHlCiI96VzlpCTW0PPXWGBs0Vhc7nmYJnn9wktg9LvcOn38djfKmr6WyL6zeFTkl1eAlIaRKYi98wVD7WhZVqEtZYhyvYNMi1t+SL9fGQKVuZjRbu0+UZ1BBjQbBhsLAPykbIm09RWohBYNAjUBuhF05UzawhXKNjjZLnpVjLObrTL48qRik9Q8Kpc/+Me9zizn5RYruOvrfuaeKUwe6EIW/0yk2glDw30SIsNZkIJK86SRhLNniTYM9QkPh1nmGZV1hIdVZjDCjQywHZ+zj4bknh83OnXZmljnrPZT9aqdBx26Rkz00sgIx4/iGz52TbcaYujIlxuVbu36Bjoog2STY+0hqtCc4aiRLKX39hOAmJNVU+s5Q8mSDYlg3dbTuvQJJSSUKMz5L2uxGbTzU1eWxwmqTXHEqyRkIdHl2m1HU62OCurr2UCE7Xb4K6sTX7kLQzoVpmeRpkk0xx1qcVmfUooX7lypS9p0WYUpSBtNmzZL0WYGD3kPa4ej2yJlm9Yk4dZZuq1wKAKSsy5N+MlbLFShdVfCmlutywMq86fAMu1S0KXsacJVKIQKASWFAKD/Bt/BYSuTx7EhOPt8q8QC1/EESnIeYr6lOKjKHTEE2JqnMhDdhXydbyQW9l1NJVLj55uwaTFSHTylvblZBMq76c62rROEMvRWXS0PHmLW9zCT+rQY9MqG9QrZuMt1ShzUPMs5qiCV8e/KsovuY9WTl4rxMlKdSX5c83Ewozv5mspebCjD/lD+26qNgZwRehcS1CkihisIpcakkGXdGYNWnM0xC1tcQzlX09LntsUwaoSFOrlbLinwZDME9XqdRdvUpsNT97kNkNhBRqTNv51Fz96bIzlVM1OYCJ3sHiM7YR2CcOmU2CvXj+z4Fky2NMplCtoC5cWef+NfvUqQrMBBrPlO/CvNEn5TE0r6lwIFAJLDYFB/oVAHPVU7joQ8UJd/t17770Fa1yKeM0ts5DxityXTEXwCF8kNBaYcD5dnMlzce6SiXPu3m1p9gyapJSwwjqlMCQMThUx7aKKSzQZysXxkMhX8JsICBfb2cMMv5rn7CDcKlobCW1kKt/Okxsw9Fh1sEaunlXYgQ/v3pXpkp/v7n6Wo40OUEczBNTYLSjdQ68r4Bbz0EGvyNq4TBerEU/R78Ue5NXdKMwwbdGbJCXImIPOFzugJ/4loJebbfZySZPHcT3+jYym0QMZpUJ5tIl/PbH4PbDQaZ4E7B5R3aSPICzhufLwGPM89KEPDXe3ehN3p6dapgTmjf15FNstzKv7aPM34sHD9YxxNIFKFAKFQCGwZBEIJXGbOXzgyhsvwucEPiKdl73sZQ9/+MNxt289+IKDl1jcPfroo022c/I8Ob+tLL+NXITeXjz2c0zuIg7KuXE6X/CCF/iNHT9qB2eaFUQ0yjqkHSSlOWqc4nKc7gipKehr09YCMJopVm/UYGTvu6rFC0t04iAyaR21E1VOBv4yXcpP1aZ8Q/QYDUMhDgXlpJbcks5lK5jFXZJ+B0N1KRJ+yVZvrGQfufGSABBQCqrOIZFd0RLkqVU2AaOEuJWRLjOQI1NHIVAIFAKFwCJDIFwQenIu/i3+XWRPeDWnECgECoH5jIA41yS5uV8TjCJWpoq8hGBityc/+ck+rmyiUszr05beB3a3+wN6wlXRMR4XF/vqsx+V3XXXXU13Wyuk1h4wh5lwn3XIB0omwr/Jgx6MH1gSkybt3Lscmi9mlG/ulFqht6DVTqR8Y9E3qgWwPomYudxIDlUiUwxrdlTALg7VTKvqrFPEIdFKDU2Yd1XKhLno3qc/m4wAlioagKMtQAMFUwmkaa2BqmjpFBfI2zWnOaJmc7ZNZyUKgcWEQG2AXky9OYO22AxkV5ACPgvkwz98NB866AcJxDOaFfVrehZWfUaCozeTiJbcsghn20rcNCLhN80wvuIVr0Bjysb1T2VWIx7Fp5Lhu63pmkilivxUknHxlJjQxJ3M0xwv8u6xxx7W8/yQK2LAEzZdIUITqbThS/xqqneqqmclX12q2H333VHyOAobJkOFaeshICeSvfyhxXuZiuhBGvQgpnS3x4I9+dm61MaY3bO5tV1+ZuGxNQ7WuTZLGb4Y3MQG/YvL7V32fZFTTjmFjBGDh8QG6Gakh5nCxv0udbfn04PhiWpiSUQywh5yjxBtniV9ZxxjXcHTEkk/0OxrIocffrh6taL3/LQmdPUbiDi0Je1yC869tnflK10IFAKLG4FB/uVM+Cit5kO6ziE+GZ++9rWv5Zpe+MIXdvnXxmJRFgfVXDcnSRLl0UYnj+cWz2Yt0NFDNRX1nFhXRsFp+Tc7WihJdbbLmD9llVdv99xzT+ME67XsEdzyz/b0yOFdycdFd6ubxbRWB08O//GPf7x9t+Mo14RxxJpMc/iAapljJhiGFJxb341ZcA3Fpmpjty2j+TcGeNvY685effaCkMdMpi6W8JSmRZ4usOhoiQTeWNstG7C6T3i0sYqwLnPLg0qSPWgX79OgVMwjoF5PuKVuwwB1AbBZHlVNOZ2OZObcuqlXpCtT6UKgEFjcCAzyLzfCtwx1xfLFAkP5V/ybLbn8CVdjv5EY8+CDD+Y/42H4ruZzer6IS8yt5q8GMScwFf/iULE2ujdzSjPLHYRFcyiYzT6I2OJf7eWcySvFqZpi5oTJD9Y4izkgdXDRL3/5y/N14WmVTwVFD7qmp7nxqQo2ycEEbIMAI+dyk1MmlIW0kOlZxR4dJ1O7pFloJ5ZLXWaLs24NjaaUvj7wwANFozjXIbPxb9CgKtrcokobzX7Q4NDkyEi4S8xdsyJuedTZJqb2qLhE7pnMSaXO3nESd5988sktpyVad7QcCUqc/SE4ezfYmdhUHepuHYVAIVAILB0E4gzNOuZFUEuPXsE1tEAQJjaPOuoov6dk47KAOh9T4D+5a35VQY7dmXfFFPKFS6Js4bZf/jUwMF3p/MAHPtCmLkpspJYWN5FUhMMPAzqHDizEPuc5z/EVZ+MKmht9DO0L9aY4ScMeu66t/j7mMY/xyzwI6973vreVYMOMrOb2NCg4QU6X7IrGiYZMDAtZaAvSsYCd16RZ64j8oB5mqM4ciGkHn9UQPFKiaZFXO/OQGhwAK9/UhMbKDOXR7FKN9CTBNrfQq0yTLTT0Kq3LQqAQKAQKgcWBAM+vIcW/xb+L43muVhQChUAhMP8RaGGgYE1s69JGXh9fsJ9YXGa20DykycZ73vOeXgneb7/9xHdCOdtpRGrd1onmzGnbxeQjytKnnXYaDWRos89N5Os7DibqxbYPechD7IoWbCL98L6wUe20qd3ht999JEL4mcxuLb00AUbKFC3a6OwHlh/96EfbeO2XHuWL1uUINt2lNmFmT0O7ZINGeXOYmClZYbjw3AYnM7HKNrGhiUzpC3tFwdrVZJKmjU7NUYX2imrNKky085Kp5shriw1yqjN7YJ6ZTh9zEfya/gV401mJQmAxIbB2d38uJqQWWVu++MUv2pXLMyIbtMHboo24xaEtJSM+tICKVIjxjxI45lWvehW3Kx0ycIvDleZqeeRQi0sHtcRCAzkTTl25O1hv8gmb3DQ1HIGUparJq6tpUCMjVc0wPw7rrRffw+D9/TY6m/1sq1LKooHQhoZHT4xpepryaROtFS3RLcI286doGBEOCgzmdMt20zGMfM/CltPLT9mhmU0tHHQ6rvXiUTZAt1vjJ1JFuqPXnNZHuoxY965buUzxpJtAhJVCvSbijQwML6z1KmWHQfsZC9g6iOlut/IA6+tmfAwgIEdaAvcrYkTSJfXUGzFKTJFnJKEuq8vqZYAcSpRlm7O0hCn11NVa0ZqQfOduTrNHZtLdu61IJQqBQmBxIzCCf+MZes0XkwgFUVu8XONfP3TgFkfUPEkSOXOGHFe71dOpoqluRTJ3OcZB/u2qIuaIVxSykseqtot5Z9dnDn25UCgluOKlfU1fQVT+9a9/3cAjBeM8o7Cb7lbR0ooMSg4tlUxWhX/xSFMymIhw7Gl3XTbNLdEMiNjQzmoaWqmW000gGh1EoY92d3+6qCuzeulY3s5NSezxCMlJQ+Qk0XIkkqkfe/ybj5sSQMHOBAweLDbj0+iEtnFO05CEc6vC40GG8CAyZNwK+RLICM3W+Ze+9KVtBiGGGWGSdJiv91Axo6fNpbvODrUPHu3Jb4YNylROIVAILFYERvDv0CZPxb/CTCGnIhwORxQnxu3wUdnK07yTW12ycOlI+ElmaKUE5Ls7yL9dz8ZVRjLCvfh3n332wX1+lZ5T9QPxZAj76QYxKc3SsTwGdNUmZ8xzMyD6U0rrKM9u2ha/j1bIgK4N1HYvlXXZrSvVDYqNriV3DQz0nWlik7zGJ+MUmUqGATkIdA2WmSJds5MZ+a6wDvKEyNEvEh4h/ItPhZ/hQUXgKSw1WiAm7WC/3WPKqshl+LfZScyhYJ49s9uUZ6tcMyyJWJV9YGAxMkG7ibslmkJ7yL75zW8yKTU6q7TdjZKuZrW7TO0pkpyuZCteiUKgECgEliYCgiMO/853vjNvaSlUbIX3fcjZ8q2lUDx+xhlncJs8MDLlxrlTktypGB+FeReauzYg8Y0S8bUpAsGRCXDHV77ylQc/+MF+FELEpLiVZneBjGIyAqGWQjlqtNjscyGWkEf3QmiFjATWoNN2Z98codAitEEInnrXu96FKeSoqKuN2bFcpana+AqPqFSjpL3EpRSd4kE5qnCkrjQ82mS6+5nPfAY4cjINgrxk4kHv0EJAwtldRJnlcyMfahlm/SI4RJszq0yzaA5he6/hLE3YrS4JNvlKFAKFQCFQCCwCBIp/i38XwWNcTSgECoFCYB4iMDjpl7AupiYkvNOd7iRAy7K1mMvPCglI7cR9xjOe4ZuDbhGT3511VFy0aKOzjzL4FSBxrn0+xASz9jX59JU4FLW98pWv9Bqw3c/iSrcmQsrJoLKrSqBqwVqcaMOYefVxMKQk7WKeDdAicb+4KBL3xYp3vvOd4sfUMlqVdgl7Rf1CVxqY51IoqlSKD0LXFIp/n/rUp7rMT/O1/CQgIyE0Fgj7QMbpp5/uqyheS5YpsO3Gv1bk4eatYwl3rRFYwhAIj6g6VdS5EFigCPx3F+kCbUCZvXoI+G7fhz/8YfOh+OZ+97uftUBuzoE2msKu4/Y5Ch8Z+uxnP8snIiev4xDres9Wiscn4yuPph1pwC7ODgL0RyyXrciIBEkmPexhD+O1uxq6RaKNGP3qxXk2re6yyy6+VLTjjjvy+N/73vfwSitCzCSpdVk00zLXPNGFrmnTfN+8RGmhnJY/mGjgDN4akdOtVDrHVPK9KlCjHvRRqBe/+MVuOaYqODTfTHTrx5TtaZjQOKmTVYMacivT7t27yVHEWERX2jxnIGJ8gL9ZC0ajH4+WQyZhnSjHkIUSj6jMaIt+FjaF+N4joUdiT8/4lPKcyDfxbQZfTl4KVzAHVe6q0dktc+hkUpFEt5kyo7/dJdAeQpIR7t4lUEchUAgsBQTG4d+uc8AgQ/lXpnDFuR12wyBBzC584qC89hpX00U1OdxRS3TvtrS78Zbh3wjHrTUZCXZSFWuNDThqP7brQ1DWF7E2l2szTXN95LOm2HOeTeGg/nYrdXVh6d5q6a4Ajz0O/w6tNO1tapOI8sg7d+vqSU57GWISkB9yyCH05Ji2VASaAaPl6YxAS7hMOufYMFTJIP/aPiWEdka4DvjoVvaL1X1yjBJjKrQo0cWTmCNViPDd8gB0ZZptbrGHTksRRpgS2XKN01G/UaUw3hn1eyytkedTmqmLkm6lUdVyWjO7bWdDqzrm1bkQKASWAgLj8G8Xh6n4tyuTdIt/XcYLxflwNc3bJBEXOqih5XCbNDiPiH+pipdryhP/KpL4116oXvxLP5rmP7nT5plTaXOYzYYxE632njyFt7nNbXCEbVi9W+Nc9sxThOdPwZgKWzJT1d6tYrBpdpWJ8gycDFe6kuOko63pZEAOZVumdMvs6pTpctDslkMD7nPGgwiXcOLfhLrePvLkCN4nOHj99TfddFMb3Hv826qjxEEzhbCyDOASgSoukxg8Yw+6l5BPzN47LN/i7lSUswDcV0WxMJPYowiFrbpuIvntLsnUmIqS7spXuhAoBAqBxYFAnOr4beEPuUre2Iovv+pQljfeeuutkTX2POGEE0T6RheRtDBpEpuMijhq+U972tMEX2TM5/P28fCcto3IXPqHPvQhX3OQJikeT0GzmiQpjLXExFlylGLM6Cbkbtw7symxAdp7ViYfHv7wh9/rXveyzIxhE+bTqcboVBAFpKAaXSpu57cmW0L2Zpq5C0vIimi4/JQlpgiDo4S2dlj2xmsuVWpUI6GIeqk1B4LLUNX+++/v29gabuAnH2JWizVW2sEMZ1UoaB+55qM/iMkHo3xHq64ShUAhUAgUAvMZgZl67OLf4t/5/DyXbYVAIVAILGgEJgOp4ZGUsE60JdzzDq0QUvylpeQf8IAHSFgNNI8tZBOauRQYCiG7UAjlDjjggASVwsDEjBiNKlP3okLhs89GyBfQmb1UVnWOJFpQKYK248iCprCxq3+qtIKtrB85tA3JkqVdZ09+8pN9U+NrX/uaKiLQouyhqljl8OKxT4YRyC5w87TKDpXvZtqVpyI522yzTTe/m/YbjLe97W1VYbe0BXoRsSp6GIKLtStWrFBQ8+0PBFTmCrqqKl0ILBoEhmxMXDRtq4aMRuD1r389mrH+51v93BxSsaEklDBY0Dws8jDV6BYZE468561udavJ+cOJCURH0uYoqTIHSoCfneCHSzahdoNSwlE1WFfLUVA607u0TWqaoMkul2RKNJKEJXxCA2vK90N+WT6UiTjRiYJkzB2bY7VCnPdmUl1uJe2slpYenUijmnxLpJTLfIESpyYn3DyoM03oQhSZbk7ov5vTrU46x6Dy5PTaqEao2uwLjamKjM6nwUEmmruGyXSZo6uEhblMEcXlREnL92wkU9d7Mq3T3+52tzOOSZG00RmkW2yxxfbbb2/I4gciPXVm/+3BGlqdTI3F660uGrqS0tHMZpIh/pvf/Oa2/hsHmEn3MJtVdxhAyPeMqbergXnKevBktlrkuMzTnircSqZLRdTVlZeuoxAoBBY9AkP5d6pW3/3udxe9DPJvc4YtQQOKecQjHsGxcC/enR2qszmooXeTyU1R1fiXZ4tnjh+OTNyXtEzC1HoTN0GUny6yL0c+fuE2uXEhFknuVJzpk1R5qSl6mj1JDDrnJsYqRy6Hnpsqd5kX/uW0u8JdmW5+N51m4lzCkZdO1V0DItZyItnVIz2U9LEVhWNG2j2FuezVleFBV7IJtIS73SbIb7c8YL2yRkpd/m2tyMOGcG0uX7lypc618u3xMENhr1tPSbtUyhvG3cukmwESbLNCT8ymPXfNXKDdPHjutofcM+b5YUDT1hKtI5KTy1aFyxB07qbvWtlKFAKFwBJBYCj/TsU7I/gXJ4YW4524F34J//I5fBeHE8fV80vjgJwijX/piaqu15ITMQk6pRP/Ir7Ev3LcUkT8G0kuncdu8W+vyZEZx7ypZLoapH2IUfQkzOzKx9qWkyI8v5yuPclvwqa5Wzplc9mtUX5PJpI9GZn4l0Kd5aOYkZnpuZmKOh3pl5YZumz16seuJRksJScGyyGcI6o8V5k3sCEsgWeetJRC99ltpuqp+LfbIo9E5kMo6ZKgGtsojryqCXhIKE/czbzUm6YZFYi7/aakKX757iY/3UdDlHd7QRVdS9xqOd2quzKVLgQKgUJgISIQ59ZcXGsC19r1ivLl8N7xnDY0c6pm2k3L2/GMry2mCpmRlBd1JOSQFNGjEluZ3VWWQgE1P6w6h1uCI5xrLZmkV2gEU2TkO8sxUypMiz/nqBWJw2cMQhGqC0hVR1iOu834wUS7y/8rq0Y//ivTDy0a/1iXlUOPWmJeKkK4DNYQt5TSOjMD2M0gwZs8ll1VZLOy+V5EQ9IZRyhF0m8VEs6HmUOgzo6PfvSjSt3lLnfxtWy1KEI/GM855xws5ovUlnsZowq/Smwp3Wvhfg0D4KxlgzNjHJQQlvCTCz6brUbKkz/Y/MopBAqBQqAQmG8IhJhy7tqGGnrOXE7xb/Fv9yGpdCFQCBQChcCaI9BjW4yMcKdSK0Czd1lc5ifmLNcK/czQilXJm41MJCiWHDphKMrLVKQqHvjAB4p2xbD5oHJmKc180imKlC/im8oMBYXVwmRx31R29vJbGxl59NFHi4Xvfe97+/Vjv8WkRb0mD45Jok3kK8D3zvD73/9+OT4aIoQXvLNTkV4pI5aeDccdd5wcu5J6+e3SRK5PV6jik5/8pPhXmJ9IOQKa4EiArGqZNnNnK/b4OLS6KlEILBQEpnRGC6UBZefqIcCl+sS9Tx/hgx122IG/5vo53ExHtglKK7jmT00m+gECvyCAfhzK+ra/70mYK7TROcSDgbhX7pISs58mHJGNGUZ8YBZSPtbh31nL18tEY7ywhBwTu3y67dd2Y1POJJkq4pQJMMwtnKQih8sQGP0EsA6ZSbsukC+NNaPZF7DkREmIxNeOU52vZGmUZlIioY3qzWwywxzEFHRuBwFp5yRUZBeXdrlUCz0t3y0a2OmwH9f3Hf2aHgImQKfqCJiijWEQBh2DKQcgGV0QhnOLfocJ3xjjlrS2WBZN1yQ/M7nUusyhanzWjKc/BWXCEFyMZwNVugP+TXLaBAtjpOomO+RCmhmmoHw4MDhP0aTgxEkt7hLTrjbpTIapgKLEXWdpAuQZ5pDQiuc85zm63raqO97xjnlyJjRO3lLcgoG5csWNAE466STPzz3ucQ8DHQ+MTAdjqDIkcsuMtuGFx0NFNDhHhnlUeZAIa5RbLg8++GB/IIYUXqtyi848Y1qh1KMe9Si7/+VQQlgRMh4DhwRsNUc+hXLcBYsc9sM/8tn3RgAgnkOZjthT50KgEFjcCPAJQ/mXu+ANzEhyj/wSr8WfcPV+1ucVr3hFXBBkGv96DYOT5J0IO6Rxjb2hvCXHIp/boYFbc/AwzSNJu+WgjXfix1SKJgirRaYzb8wMOYjJ2UE/YRTGfqq41rCMtMMqplLOfkZH1Xgcs2hRPuRPm73INMtHuCJb5EiMYXnflwCrVBHPPOgPNVC9FDqgxFrUoBSdlDBGmhkuKVGF0NdOHZ5c7IcxWc5aYu6SoR9EDObSk3ZWnAxHDQ3VUQIBKMUYlapa8ydaO1kRGYkUnID4EgpWRZBRkVrg4CwTFOEC79VIA5ZyOomNc2h+xCQobEAxknI2s1yCPXJic1PLVIeCDplaARMGR4mz1inlrkxWOWuCV5nZiTTtdQ7U1JJRC9we+9jH4l8Y+nDX5z//eQLiavKKM8YDQy3wrYibVjjllFOOPPJIj4eyNKRelsRaRWQ6GGmh+k1vepM3tq3lb7fddh4SlrCHedBWC0Jnj8bSFnugqixMnD0YlMhXBLaKUO4skwYy0m6p2l1E7LKhVIlCoBBY9AjwEkP5l3/guBycIafNvaAA22Wm4l+OiGviAMmjaf6EV/Qjen7/jofhduRzRHwOr8X/cDVkJBz8ZHBWKWeo0kH+pYS3HIx/2a/e7I5lJzG18NIUqkhEyQyfo6AzVbhLz+abb+5SeKIgDdLJd5aWQw8LR7yTAxBel0NmswOZxvGmrLuMoUrDtQ7/3uxmN/P7QmgaAiSdKWcJGXW5BJ0aHWQUN3vgLuMVt8dLPtvSOkUIAAqAGYGoVEWQlEPe9ILiakkrFHRAg7CySVOoFHl9CgR1scHdMQ9lm6SCanepCapwqJflKiIGh3S0tBxi5CGm1+STlI4qd+lxMJV5MmMhMZnPetazjJe22mqrbbfd1l2tCwKKg/eZz3ymPWHy8e/nPvc5GqwfqIKMTMUpB6yfw/Iqkc+E+MSIp1TDdRyBVpciyhJmWNr4ohe9yPu9BnICatjm4WQSzTbnIXShMRuUkuPQcIMZ5lFLmyeWKl3DBpkSlOu+ICCtoNo9iukvOUGjzoVAIVAILHQEeFEHH4gd0CXnn+BXDi/q4EX5RgTEu/Kf1js5c1EPD8ljc5g8pLPVSlB4qYkDN5EeL83H+syz0QI3LlZyiW5UgVuV9amnvH+FqdvYwOZgfOrngI0NVMrxIqyvfvWrFolpMDBQlgbLn8xWUVw3a0d3BDFHTGUG/WbdjUNo8CvAvDptoQl3tVqlbGYYQknaXQigDAMtZ5+RPuqoowyfXvjCF5KEHpTgQ9XKlSt95RqYDDZ1QB7juMUAC8+HHnooDjL1gRDDLyieZlMo5pAPO+ywM888E5O6NGksVvVrxU95ylNQkirYwzzF99tvPz9d5Wthwl7F0y52SozGoe4WAoVAIVAIzBMEJti3+Lf4d548jmVGIVAIFAJLA4GQr7PmCtNMzAo5TRgKsgRrYi4xndAs+RZ/RV52+AjTrHELHsV9Ai7RqOlcISolNttk3lL0lxnFvfbaS45bAmdRKnkTiWqkUzD7pCc9Saa5d5mCRPGg94oF0b7JRUZA5+wu5UI/k5x+Rs9sp7IWHGMYU2faVyp63/veZ/1dqK5FPhIq1hbeCnujjRkOhmmXuFW0K8YUCGc2PrOmPsvytre9TQwLDY0SzqfJzLOv2l1W0QnMaLPGCgftss0Mhh/5yEeYIc7VcLcUFymLZ1/60pfCmST8BdG2mANw9913p5Y9jBEFk7fM4fPVXmOWMHWgCnWlH2eKRskXAvMfgZrWmf99tLYsRDB5ceQ1r3mNTUIcpe/k4wMuksvj+zhuv2J/hzvcwbYVq2sOMlw5fpJ/0EEHHXvssU94whPOOOMMW0u5aYZyphbqLACbW8QBmIZvtQjKjVJr/7H5XKygapKI0FkpfhklSFCuap+OMDupVFy8uVrbmGS6zJQxSWJK2URFFeXcd5w4DTbc4DAkhDVPPvnk3OXNH/KQh6Q6rEMD/susK5pBQhRqu7PWqcXksjSxdqi9pSXIoHZKtEvV0YxvTM4yiRI/FGsm2qS2tUBzr5mrpYS8L04Rw1IK+kHb0BINctxlsFlaVSCkEGfegiKgR4DGYDq1kRJmKCKTtUkryABcqAluyYctPebfpXWTLrBsbwMWefy30047aXi3aeOkFVQp+wljazanlM4KlbqEalOl4WxmEgOYwTDPBgNo0BxNc8DBwVT5LgnDE1W/7GUvMxv+kpe8xGc5GrUraOP+ihUr7EX2ROl0gycPmC1Z5tANPlSnUl1sS9Y+++xjv761ZLY5WAs9YzLm5SEEEWzzGDBPQV+gVK+3uA444AA7t4yNFDSqUK/lZPsLDSDkaI62sFn/agglMj0Y6QuXblFLoRwdmrQixm1qlyBvzMTahlUlCoFCYNEjwEuP4F/NRy7ewuS+8O8nPvEJi3aIgJPhRnxZOfz7+Mc/Hv+eeuqpGEcR3sZLtF5n2nffffm3+HxOlTsVWHI+/BXPj5rjb52VIiAGS3F+zNufCAL/0mZZlDwb8G98ddwmJ8nrssclJXGM0hwdS/ht1fkxWfGYTL5Ovdwml64WowhnjMwMbSGQjVnhX5dcJUlVEOseKuIwwxccOADVQpJhiqjXsitOJKA6y734VxjpU1LGJ8I/nOg1JEXcikM2SKBfG9WFuRoa2kU/O5E4zcknibMYQFUEsrqsOgA6kyRDM8NQPMMI06yNOkK9CUr1l595UjVuYrCVbDh0mzlOmlo9pZlarS49G9vSI5BEK9inqQIR1tPLjevd1S96SutkEqCHTprzLBmEIDLr9PZCPe95zzNKFBsTDtTaaEBlodpPNtOMl4npR0vIdlk5oAoWYgaE9sDRoy8oZzOruvxLoeLsT6eTYbxxkWjfuBQFn3XWWZbVGZmyfvPBPIWKPCpymAR8lrMWm6NykppAIVjckkOhW5qvCr2gijyEQJBDWBMaVpUoBAqBRY8A1zSCf7lxCHDdZk5FFoLfofyb+LfxL8fihQ38awKRBo6Iz8GkfBQucPBX+JT3k8NlpZYR/MstD41/2aYUJyY+4vcwtRw+lkK04tUR74eIhlr8yw0K9CIm/mUJ36h14jh+ktkO1lLCMPlT+UMyMZvPJIYjXIb4XDrwL6eqmY1/MR3+FeMwyVkRRMxOFblFmBtXu2YyUqP4Z0MLARFyZIYaWcgwh0uA4F8JkoFRvuYornZOPg2hShUOTQ7pS1PljH8xCLJjAA33v//9M/iZrGH6E/2UOGu4ZqrUxDoDUlLzcY2KHEifpFrckvC8YVv5bCOv4W5FAIBA0EyZhClnGCp39yc/+YmRniBUNGrbMVpUCvjONnKFf2nTjzjaPmnhsI9celz1r3Y5mze3nUvV4l8jDWYERqaqSytUClUCMASX2uWLl5/97Ge/+tWvVi/+Va9MdqrLJjOTQtYYtEVjGamIspoAf12sIekal0o5U8tgcJFRo7tpqT3cupuRDUDydRQChUAhsEARiP+MV0RSvLTNx5yeaVK3eNp73vOeNuxibR5ebCIKQ0aCfYEqT4i+za6nuFeYBP4EvNZyzDHHWMVE8ejbAINCLOZ7yebG3/KWt/Cxpj35ZzXSrAraLMeiD1Gql1hMRHPpZuz5W7TL8cr3DphvJz/xiU987nOfq6AcM6WHH344R40dOG0K+e1pOyJNxgVoyCStAY/fLE4VaUjcOwG1CBjNJ1h1plbDzeuiJBuUrUYjEaUQmVI+rfKFL3zB0EuTrU8bhvmKlQ9Y2KCsOvMbJjoUee1rX4vOyL/nPe9hqkHOG97wBr99wQzG+40psfYHPvCBd73rXVpkUGEwoAv23ntva8aq1ikGb3BmMzwxrHfLDd7opE1FRUzT9n4JFAKFQCEwTxAIGYV3in+Lf+fJY1lmFAKFQCGw1BCw9ucbSaYE7ZlJ2+9+97t7x1WoK/i1KmoGVSAsHpSOgMU+sZsoVUJkKkYTnVkNtBRoQVxga+5RXCaIE/lSK5SjwbZdc5iiY/PDtgZRKP41hyw0tl2YHrP9b3/72zPDaZAgHhRgKiuWFFlb31Tp/vvvrxaBMKtm2lPCW4GzhXIbi+05NkmranaiYJozLHFmiRlj0+PZ8yPeFH76uqgXhpkkPNc666G2KRMQF4tYoedSCMxa0TElPkhqjV4R7/2aTRU4M9t8rzBfjGyuwJYz88x3u9vdxNFy3vGOd4i1RbXmGQTU1uJF1rrGQiqUmG1a3m9CCp9VQYmBU5oPrpniUPKFwEJBYOL3SReKrWXnrCDAHdPjzF9bjuLrzZaa9zQpaWssZvIbeXwuD8h9WzzjZ/nE173udXaKEObQ8ZYVSmdbW8hbyrJPxYZR7puPVsSKnZ1ParFjxg4t3tmiL09KBoGZqeTxOV/yVuxM17JE1VbOOHHzvw5fkjDn63sYxx9/vIlOE6CmTbEIIylUO1fOd3PlZnVNaNq6bR5TvlpMoaI0+s1gIpusAWsFjkQDKFO9mmPvLFKxR8otFAsQC7TabkX5rW99q6nVoB3eStoZK5ueRmC2aFt+xqz4WH6YT1sQDA62NEgb2k5B885YTVpdJmQtAKNn074I2NlCo1lsc7Us9FFtyKNwTTNpq/kHHnigP1IVOdNvaxHC8+luy5DmtW05UhEA/QaufAuWULLeST/zUKAvfMAcu1uEpt9GYSu+RxxxhH7E+sYHtiUZW9j0FlPHPOtlH+RAzMCEobVeS6SMR8m6A/c76zLa8ryxCjIm7o1XdDq6NcLQ6cA0Ye3Rsp9Jcc1URFtsn9L8D37wgwrqVtWxk1qPxOmnnw4ioygGyLQ66zJ76Xy/BPfTAx+tVtaMtvl9aZhQq9VMtXMLdHDT7zoOpBpi4KKs7lbKU+TMwp133pkq4ypPjoqU9dio1wPmktkezjw/BjSU2JiYndNM1XGeDUvUdPqDIm8xw2iDzbbms9+maqNAz5tBz5577qnhvYdtzL4osUKgEFgoCIzPv+Kf8C8PKc7hbbhQrgnz8nicEueDyPCvTSrcDoLAj4pw7MIq5I5/Mbt8dAAfnlNkZXXQtwBRNi6zpohT3MKeHDj+VYof4wnpQVvo0gou/hUyGQ/gdzzFaxkJ4F8LqGJUJqkRvzAY12Muu3Ce//zn837iOkpUpCBnayBhedWuYgle9JGPfCQDRGhiM/7Whq1p+Zep3LKyvC6epdOvwxs58Mz5GVyG4QuNygYsNKQ5qkOygLIUjZU0R11aymaO2vtazLA9CJdhUpt78K9xhYhawAwTDVcQZ4kwMbLxCd+OGrC2kQAbjDS6/ItxtLHxrx1XRhTq0oN4yg45/As0Jmm1V8WMXvDmOA8wgsjzg8QF+VqnsUiTNsYbFOlH7TL8cA7/Rq1Bkccg/Gv4oUU+yWzJ3PDDwwMiIyU240RL14pYPxZCMxue2gttATNS9ihqjl5GoJ4QoyyhvtqNNFSKfxWBhoV5llAOH08glrdTEAkynpHGTn4hpPGvtusaIxBlY22aiaw9wPhXHxnAsMcUia731XPavJWkUqNB/eVR9DBohQfb42fWwENieZudxhvs8YSkRcYPRn1qZz9J2wjU7q6GtKqTqHMhUAgsPgTG519sy8mL13hIHnsE/yJTLq7xr0s0wQPzkOI+XgsVEuD6kCxu5YswJifMC4mF8SkvNJR/3/3udw+Nf7k+ng3p5M1MrCqK4UX5Ol7ay5l8IBnzueJfjlEchI98W8LWK2Gg5hgqPPWpT/WSEm/MT4pVsYlokRmcfIt/PQBBLE8CimcwJ2ySFz64jxvnP7V9kH+FOezhxjUZvWqm0M8GJtSDs9yyTxfhcuYkTWcbJ4glVSQsNa4w9rDp3JctsKTNSapGBJqDnZERG2SqFy9jNM23+0r8y6WLsCiHrarRmWGPWBJ9GCTYEIbLNB/FIHG0iKxhgsVGPOoQIE+ns8Nll3/xJgD1JsBF9FqEMZ0RDUipZb+zUt7OhYCxBEtE617TZYAtXAoSFsvDU8NVIU5HoEZ30uhVS71fhE/xoBZptdZ5MMK/+p0MhTKxno6Gg1Z7YvWvwZjBjBzhNjEDBgMer115Jo1noCo6ZhuoKfeoMJWYxrJNWZM5hpoGjQJVJAt2T45BC+M92AwDhTGYXlAWxXsm8ThuFdFrUTraM+mJMgQy+WNkyELxr0wKPfkaayw0Av+6VQgUAoXAgkBggiEueXcXKfDSuBUdyOToeFqRmnw56EmLcnbXLWkJs9+CLEQW4uCxkSaK5DZRgBCeLzVnSwMvygOLQ8Wqglw0hNTMveMOM4rowCCEHqXEegiay1WFA+NQ7szVG6WYbOeK8Yh5TgE7T+4WMTKIYBzY02TCCM5cK4JGK5Qrq13OBMxz0mnkkKVutCLfoAgHsV/sxlTUSUb4pmkMw+yYhdnmN4wiWOiMtmgzDYJQcI1MerRUrIqJzCqY7qBEJhoCFDTU4i57WqOUQsQiYpyLpg0eKDTkMMAwDpETuqekjkKgECgECoEFgQBqcMTU4l84FP8uiOe2jCwECoFCYHEgEBYWP9pWa75X5GV+0iGaE1oKx8R6pnPNIYv1xG4kHSJEMax9MiYbxWsi3JCXmNFcouBOeCg0M/doxttaqvDQ7LHlPzPbphbpER4K4kxCmgMX/yb4VZGg2IchFElwR4nqxLlWLW1jEyybUyVmrzBVLEwwPqO+UMRuIiueZt0teppIz3q9fLFkGiL8tKZgxtjkKhvoF91DQCQrqGdtDBOcCupBZ0VVQe0Sw1q7NHVgkpadJgFgaO5UeGtenYwiVr1Nt5pJtrpNMwDp1BxnaXPO5h80n6Q5fDG4jX9mJwTsk9hfSJspAhPpFOovPRibqWqJGQFSwoXAPEegNkDP8w6affP4subOOHqu2fQol2dDrS019msiCXGj2Unu2MqZ9TmzsRyoWUhuMftseGSeWilzsqYpzSTaksW5W/+zmsg708yr8q3eQVHKEqkc3GZN7qSTTrLIZ3cOnTaMql3CS0KWcm02Ci9aVbVPyGZTXpuztlKLJBhm54r5TSugeAsXslNZxtg9Y2lTRWphIaI16Wz1zqwxY0haNbQI6stYttTY/4qoMKI1Ucbb/OTSgVdUp7hW4Juh0Gsm6tIuG8LYo2qWmDsGKTbFGTIRDFQVlynhQCpmeGm2Dyw/wAcr6LFWE0w6260FfG10oDEo2aTFvI997GP4jFXsdyv5NONCS9pKAc2gQQ4cjDNMByNdi5Ro1WYyVZtcttKJJrPdVrfK1GT2Gzcw3my73XLaNbS9U2XqiG222cZ6uSZopr521nxtd7DB2RilFXcXwjbP6RFtYQMZbdciD4zBh2VUT4JnTHPk42mDGM2HsMfG9LRBg2fGsADxkzFPbTOx5yqwy9EWD4C9dF5jggCdEwOuv/zFLmqbpSzNgsLDQLkhiIGCAYoitNGg+dqijzQkNusgT7ipdg+SgYj2ZnSiK42x2KmBHhjPmNGG58pYTUEGsNNqsVuWAfQgPapQqZGfSXwPoR1d6oWAjqbTU6FSw5qGVSUKgUJgsSLAEzrSOs6TH5iKf/k9i4j4lxshz12E3eIAORwMIowJ/3L4LjGXV5Ks5HE7PCevlY0mfK9SXJDzZz7zGYGQvTh00q92Thjhhn+luXEvLAmNcCXHZbcW/mWzfPuT8Jf9SRSyXCZ5dVle5QMl6CTG0/LD6BvfMQZ/Wd3UEGkOXHBIkhflS21mNXjglvEmNzgV/05ANkmmChKj2bgimLhUFz+vXg1UBZOAwwO7pZR8EaC6yCsl3DWiYD/GhDDQkKZ6EaIhBIQBAlWMoFH2DAmwgcbhc9p8NUkCFonZAF4A9viXWuzDsTf+VSRUhVAYY/yAU2w7w8J6DSM4gMm8aQ/FyWiUcZqN4xrCNr2pr+UDh/1k9Bp6siLeFGbwgBnRkzGAvmanZuJZNO2sI/QC9ODmFv2eExvUQuXabvYBD3oA3FUdSjVyUDXLdXcYDeAu1WIwaV0ZaOoSVNtchV7NVlDOEnvdDG8a/2qCLgv/pnWa4AAXtZhavUhWZ6lavfbnGQFqKWEboLXCnwZJ8rqGpF2DarFJS0gfmjZSUoseYYz3m90lGf61HZCRxmANq0oUAoXAYkVggkjG419eURiItvgNTn40/+ILVCIqEc7wJxydsMIMrChJQY4angIxYuJf4Yz5WTrlc8Uq4paH8i8vR2Yw/uVaw24CcPUKZoXSfB23iQX4Ol4Rz7b4FwOK8hCcna+77ror+jPVK67H8nKEWkpx/jw5V8nzhy7zDDS4XNKsaQhOKfJYm9fNVlpV41+GhX/52IwQ5IPxi1/8oioSNho/4GtQIBdnH2hkLaDEaDCh0GS3BDH7yIVRDAACVDXW+EHC9m7zAMJwsaRLRQCIZzEpVRlRaCy+pspdmJNkgLRWYDHsiSn0kZ3BwkyJNHbomQFaoTmBVwKwj3jEI1RBJ96EiVs6Qn85QpqeBH1EobO7SFPgCQEDKuMTnUUeD/p9D8MAozhK8JqGa4J85OulXIBTqI1mSJCdZhpayDFj7u0s/Eu5XgvaHiFhb+JfnYIutVrXG35Qy1RmCFFNfEPb3I4eRH8M03GeH+ToLuWRZLnnxOvEtowzT70mUjwb6FVPEWOwxzihtEhZozw8HmZz7m7ZW+YFOco1U0foaCMfNuNftehQtyRMnrDTOwZDka/MQqAQKAQWEALhCJ4tzh/TcYY8POrBjHxgwlXeLzPYiAD7OPhPMg6NxThIhNs0eCDJe2M3+ajHmdvHETyqgQTl/KeIDzkKqVTBFXO5KmIA+sPILjELTxvl8lVHIVqhjcHOMlWHEdTLEhQgkxkOiWkPShAEviCprOZLKCs/GtpgQHvZj9eYLS0BIsJMYoOymEWTmSpGw2JahN20lCTbyDhoc5dyDU+NbsFTKYgppVI5JiVUQae6CEs76BEtEkgVNLirFONhSAnlbNAER4xXRR2FQCFQCBQC8xwBfj4EhCaYWvxb/DvPn9gyrxAoBAqBxYQAFtYckZRY0jmhlolKmUkL8URe2DlkjaTEXMLkxFwu5VMSDQJVMZqAV+wmRJUWLdIjWHMQk28mU+QriBOHKitmVIWAToCsUvEdAcZEWBgoh3Ixr8lMl+olFjMoD2nKmemhCpGpswidfpbQQFsUqoVhjBScxhL2axdTXbqrSIJ3pVhlDGNuVpEMZmIn/doLOmd6aHbWTIDQQ5Iq2Lor6hfmA5Y2+QRkKq6KACsQVpaMWQIHYC2zutVrdYzvZdZlIbDQEagN0Au9B2dsf3yZMy+psHPXu1l4k8lRyjTrGs/bdYg9+RHVtyoGZfjxUJFb3dpdIgz1ctkxr5Ulr1S77CUinBp7CnuSa3jJBowVRpmpKjCGDnsF6QzgvfwRl/SkmWM2ttU7o1IjDBjnVqs0wt2nqFs8D0M3p6VpCGePwCcPpMdDqRFPSNM5VaIhM9TOXlu6D+dgF+TxYPNgXTIHH56utm56sHjlFAKFwIJGoPmZ/KU7dx3ImPybst2Cq4HJUCUyOcDc6uqcKj8yQ1V1i89Wmv8UJQ6aN63+eONB35uC3Hvrl64qmatRV1dDS3f1rHbHjYlz098SzYzBBEyGUhVJrDp0LiBm5BxGG6ciCol1cRg0Jno8gXpZxO48Tqmenpnyr+Kjrerpr8tCoBBYoAjEwzRH5A8/OWnOLPLv6IhVdfE53dqTOZR/p9UW+3vNSeZMz11nOCsKGaCZjl4YNa1hM60dW5kCHlQbfmfA4K3kjLhFwF2WpF/GNwkNRa2ClDBsaHw6mn9VFzrrWi7TIce5y5KjWzF67JSyTSZz8d1KZ5SeKqjXEUFjRtpKuBAoBAqBBYQAd9oC1Tg9fm+o63PXoWnNe0sgi6GRV0OATChAjopSVsI4QT6FeMGtRGdNv1skmUG5u2SkUzaaSTbN3fzcnfYc+qA2+qMqenKOzUM1x9TYryIMwjbLus6scmlhWwMt37ZGuUUyAhLUWt+VyAq0Ui7VmAWFgEA4NkgwUnXkuwclBCIjf6ipXflKFwKFQCFQCMwrBPjt4t9wWSgs5/Ba0r3+Kv7tAVKXhUAhUAgUApcKAqhKyCZMEwubAm0R2TjGdMmuybc5ySRa2NgEksCDBJIeSpQ9+d4ltQ6ZmbbtEW5a1CuSS3UJnAk4egJuOWTSJiFojZGqMMiRr8YI9Ar2LgMpSQlHuzu0LIGW3xKtSCUKgUWAwMSbAXUsZQSaa0vCh3mSiH9MDNnzld3LqaDrKhkqEyWt9iYjZ6j+OPomNlViUOFUkquXH25bvbJD20XVauhMM2fa2JnKr14zW6kxqxvR/GgYIaCuyHg8Ros1q6ZKtN4x/qDK2XAkmUNHJLk1tI1DM1Nvq2UqMyq/ECgElg4CzVckMSP+XXNn0mpvgA/m5NZU+a3g3CTGHAYMGjPowwdl1mrObAE4W3paY0coHAe0yHgUR+hpdU37xEZPqzc6py3V9CcxwpKZqupprstCoBBYTAg0X5HELPJvYufRWLXam9hgTm6NSXxTFW/6L8XEHPjeqZo/Vf5aRaMFpGrX9kZqvUpH2KaUoyffu5xWoMlPZUATkMhDS+cIq7ryU6Vb23sCa6i2p60uC4FCoBCYnwjE1zmbSGThUNcX7x2B1gqZjqHyTaaXaP42BV1KkMlSLlWO3AoLWEAdWgUxpSLcq2KcS/ThsC7b9ERbKztCcwxTPIaRlOMDXc1OC+HuysxyeNrYWqoKkmQkCDhSNYFojklkYowEEJLunlt13cxKFwKFQCFQCCwgBBoHFf+2Xiv+bVBUohAoBAqBQmDeIiB2Q1gJCWdkZIvyuqValE2t/KEy8kWLGTl0y46fppaGqZSP1qylY1ZEUoSbWtKccQqqPcc4wiVTCCx6BGoD9KLv4uEN7Dlol+1IAZd8ZdItMfRyeAWTub2CTRIVTXVL/lD+YE8rPpiYStug5BrmtOnUNdTTLR6a7+ZMm55peyM/01LTmjFaYMzqRvA3Dfp9HHyIZZp7tEnj3G2qVvuRG9Gi0TrHMa9kCoFCYKEj0PMDLtuRprls/rMlcqt3OR+gmIcm9WCBZy+nexn7538rujbPVno0W02LyWhg18TI1Sb0ES1abZ1r0pAqWwgUAvMKgZ7XctmO2Omyub6WyK3e5VTt6mqYSmb8fNrGF140kmNC3drb5rhbTkvMVFUr2EuMr6crKT0V9YxgK50+ValmVR6Mbl3tVi8x+hHq3R1HYU9/93KqRq2h2m4VlS4ECoFCYH4iwJ3ydeO4u0EZOdm/O6JpXXfdWK953UGdVMnsWjVUptU4+m4T6yXoZ0Nqya1BPYM5kQzT5ZzWkex+oXnQeAKR7JnRcOhqTr1NviV6ZV2OuDUoXDmFQCFQCBQC8woBPpzDj88fbdigjJziX6DBofh39MNTdwuBQqAQKARmHYHGy7MVjjWFvfCwZ/maV0dDU9IqTS29y17VY142/bRpy4x0EnY088asscQKgUWJwPKNNtpoUTasGjUaAU5wqEB5xqGwVOaSQmBykDD8D2Rt4ND9o+um10ZdpbMQKAQudQSKfy/1LliIBmCHqZ6cbnOaTEt07655OiTVo6q1VNeaWzuOhl5bxilSMoVAIbBAEZjKWZUf0KFdEKSnwmqBdv1Qs0e30V04ZGvX0OJznMkYh0pzloj9o1sxx0ZWdYVAIVAIFAJBYKhzbg68UJobBHqAD+2UubGkaikECoFCoBCYGwSGuvoeHcyNJUu5lh7gQztlKeNTbS8ECoFCoBBY0AiE5maL3eiJKmolTER7Tzjp1UNJ2RxTFXfXrVTaTUwlX/mFwEJEoL4AvRB7bRZsjoObBUWlohAoBAqBQqAQKATGRqD4d2yoSrAQKAQKgUKgEJg1BIp/Zw3KUlQIFAKFQCFQCBQCUyNQQ46psak7hUAhUAgUAoXA2kKg+HdtIVt6C4FCoBAoBAqBQqAQKAQKgQWCwDoLxM4ysxAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgWW1AboegkKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBBYMAjUBugF01VlaCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAjUBuh6BgqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUJgwSBQG6AXTFeVoYVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAquHwPLly1evYJUqBAqBQqAQKAQKgdVGoPh3taGrgoVAIVAIzBMEePJy5vOkL5oZ1SMNikoUAusVBIVAIVAIFAI9BGqg0AOkLguBQqAQKAQWEALFYguos8rUQqAQKAQKgUKgECgECoFCoBBYewisWrWK8rURIjWdqWJELU1g7TVz1jWvPdxm3dS5V6jrF2Kfzj1QVWMhUAgsZQTWHo8U/y7Z56r4d8l2fTW8ECgEZheBNXSnKR46XohhEZvXWWedZvkaojG7XbN62tIX05ZtTSY5VavJtJ4dU+209ZZAITCXCNQXoOcS7aqrECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoH5iEB3JnQ+2je3Ns03NNiTY25hqNoKgUKgEFjYCPCcacDo1buLLrooYi0xutlNm0Q7RhdZKHfTnIVi7dzbCR+VLrJOn3sYq8ZCoBBY9AiMyb8zxSFOWKnmh1vOTFXNN/m0aL5ZNX/sSUe3fp8/hpUlhUAhUAgsNQTikBf0FGUbpei7hcu/6YjWhNYvox/ItN25FR8tX3cLgYWFQH0BemH1V1lbCBQCax0BfN8d96zt+mp4sbYRLv2FQCFQCCw8BFb9f+8o5ptlo1qxfGK1fu0RyohgeO1VOqq9da8QKAQKgUJg9hDoxj5zHArNXiPWlqZLF5BGsumj3uXaanPpLQQKgUJgESHAc1544YXOl7vc5f72t79d9apX/c9//iOnNdGXn6Rtev73v//t1pWvfGUJmeuuu678OF5nOWQcrSDPLNOtuOjmqJNoYgsikVbE8gsuuOBKV7pSGptzw2FBtCXWpuNaX+SyZ78u1uNk3NWVDu1tRQjLIeOuI2oJeHicu6pyV063bFeg0oVAIVAILEEE+MYx+fdf//pX+Jd8HGkK8sCTvrn4d8E8PhN8OcmYjRBz2WtA8W8PkLosBAqBQmB2Ebj85S//17/+df311z/vvPOucIUrDFWOYX/5y19uttlmKFgASP5//ud/fve73ylFvnlviXCxMPnvf/+7gFqoKF7eYIMNkPX555/fJIfWMj8zNUFDUFXCXhD96U9/Wm+99eDA4EZh89P4Qauw6j//+U8dFPsHBbo5WqfLSF7mMpcR1Uo7ek3Ws54KpWDi6BavdCGwIBBYvtFGGy0IQ8vIQqAQKAQKgUKgECgECoFCYEkg8P9vgJ6+yZMboKcXK4lCoBAoBAqBQmAkAoPzniPFF+fNTAdne5OJ/t5E8Fy2WXd0q2uXl6JJXXsqXQgUAoXAPEeAJ89hjdZ67dWvfnUG86WDy3i/+c1vLH9aBeRgswGLmMs4XguE8qlyxguWGC0MuxVv7JxEQyOl2uX8T7DfGqcGBh/ttaStmbls5/nfkGahFumpdjmY0EfpprQ9zVdEw5uwzGzCS3dnRdnDQ0zZXqcrNZjTVFWiECgECoElhcDF7Dv5ctE4/Mv3hmShxM3auYW4pYt/Q1UL6OEp/l1AnVWmFgKFwGJFwL7nf/zjHwIZ+5slus0MrTiL9QQ1QuA//OEP2Pbcc8+9xjWuoUiEG/sIiOwVJoCdCTtISl/talf7/e9/nyipq39BpP/4xz+aGUgAqMmGHBtuuCFAEu6NjumCzGiZ8UGgJ6qolTAWmirSHKHT7mf2K+iNshFiuaW9us8TkuGZUoNF4JDHxngsG+IHZSqnEJjPCNQG6PncO2VbIVAIFAKFQCFQCBQChcDSQ6A2QC+9Pq8WFwKFQCFQCMwTBEw6m3rOsr154UxGXyq2tSWHS6X2qrQQKAQKgYWOAE/Okfow1Z577nniiSf+6le/yoJi17tKZ9mPt/fxJ2u6PP/ZZ59tZdeybkPAXUu/vqtkcVGmFdNf//rX1hotl7oVDapTVtoh09GKz+cEBLLUqlFWOq997WufdtppV7nKVawBN6BaYt42JBY66xGWZ2XXpYTLqcz2qbMb3vCGFvv1pufEQV7H6cFuEQIurZSnZ7u3pFtHt0RPoC4LgUKgEFhqCKwe/2LYH/7wh77CiG1tSwpoXCtGxs7Fv/PzKSr+nZ/9UlYVAoXAUkMAXcYha7jtrU94whM+8YlPZCd0FwoyOWSKkkR/iXR8+xn/+gi0gKjJC6xsgUXKXgwWJYmRN910Uxumpf/yl78IHqkS/zb5uUyoulU30yjsspe9rHHFd77zHRvENVATBHoaPo6e1DuOZDNvRIKeqKJWIlF5qkiOshGYSon4VO/oO4ns6tY7hHNOqShMWhV6PB+91mr7m3uRLzGvpYFFwuPhGUjBnKNqtEld+UoXApcKAv/fbM6lYkFVWggUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChcCsIJANWFe96lX333//+973vpZpLe911//UYvXO6qDDUq7fydxjjz2OPPLIvffeO7+ZmVvOVFkhtiT8ghe84Pjjj99uu+3kxMitttrqsY997PWudz07pIeuIM5KW9aSEs0HiH1mkLnd7W73wQ9+8KCDDrISLNOxlipde2rZrF/0nU65wQ1ucLnLXS7dNLhGm9ZZ/N5yyy0PPvjgk046yda6bJVuPRs79fXHJo9tt91WzgQulxxrryGluRAoBAqBBY0AR8pTzpR/P/rRj+633368sbdTuvxrI07x7zx/HnR38e8876MyrxAoBJYCAol6rnnNa77sZS/bcccdfaS51+oEv3nLCNXe/va3P+yww7773e86Y1tvHHXl//znP1//+tc/5JBDTjnllEMPPVSs5LPBtg5vvvnmb37zm70x633grvxM07hjpkWa/CUB2X//b7emTdjrDBlvO++2227CwI985COi+F4MOK2SWRfQkkSjNGfWYjQ+aXm2L+vKfffdV78YeunioUcMVuo+97nPsccee9ZZZ93jHvfwsWfCbkVbZFasWPH5yUMiOe0cMedUIV86516iFalEITD3CNQG6LnHvGosBAqBQqAQKAQKgUKgECgExkNg+UXL/MuRdDdnPB0lVQgUAoVAIVAIFAKFQCFQCBQChcBSQCBrb9bkrOdZ2txpp50sBD7wgQ+84hWvOKL5VkB/+tOfHnjggdZ9bYrND8j6HJRlyCwrWhL2DWkf0/rKV76y9dZbW2XMYuFee+1l07Adt6lXFckfUdf8uaVpbfHyhBNOeMMb3nCHO9xBSxdQExqY8NccH6m68Y1v/O53v/vJT35ylrG1JT3YJCXckm/9/v3vf/9LXvKSa13rWj59ne9+Ec5dYmRsfn7IQx7iK1nZE59M+XkAIunS0dVf6UKgECgEliACa8i/p59+Ok9rNxWXG/5FwQ6fNvSd/mc84xloeknxbxtX5Fmat0RT/LsE/9iryYVAITAPEfD6EIfsFdDtt9+eefe73/18wbdnJwEM6xDL2AZ96qmn7rLLLt/4xjd8PFhcg3C78htvvLGPBO++++7f/va386VkRejcZ599ZD7oQQ+irSs/TnoybLr4pMZxinRl1ChYcySzq60rNjqtlJkBm55f9KIXeRVWMw02ANItpSJHN2cO0mzQNemFGJDz0Kq1Ivn6aIcddnje855nUsJL3fJ9+TvIEGh6Iu/y6KOP9gNZxlrwd+hT5yaviPj37ne/uymUTTbZpFu12ZUm2TVMOvkS5FNRt2ClC4E5RuDS+S79HDeyqisECoFCoBAoBAqBQqAQKAQWIgKiR3HvslUTq7DsF0ZWDLkQ+7FsLgQKgUKgECgECoFCoBAoBAqBOUBAuGQ50w4qoZMfeL3NbW5jIdDqncT3vve9EQYk4IqAtHXQrOE5W2TNpT2yFn1tj25fS7LX1veTvv71r1tETKSWUr2KWhA39G6Em0yv7LSXXZ2UtEuJXHY1d9M0N/lWKpndSofe6mZ2hXv6u7dmmu5WIU1zU969FbVuWTPWTWeeeeYRRxzx4x//WE/pR5KDwq2Inp0It5ct06GOfMObqqmKkHSX2si7pMGXpJNwHno0s4fercxCoBAoBBYHAnzd7PJv88Y43VcnvdTkxRXuN051cfMvGsIvaWkbnwSQqRgqwrPyLHWrkKa5Ke/eSl1uFf/OCuylpBAoBAqB1UYAZfi1BN9s9qtHdq9K3PGOd7zlLW/5m9/8pquTx87nn2UmnLFZtivQ0ry9YMpZKE2JsLrdEmqdc845vhEs/m2ZYybU7neTCF/5yld2bhH3mMXZ32hIwmXCvcaY4+hJk527m56pUpZOqiRyGeXj6FxzGTU6vIwtOAVLkJETq5pJ3Ypiv6GXLeyve93rvvSlL8kRmdJg1qLdzSgiuLURBT0eFdWlI9x1qM65W0U37ZvfnploiPKYZ/d8HjOvsdmNrchoPV2dlS4E1gYCtQF6baBaOguBQqAQKAQKgUKgECgECoHVQGDyVePl50+WnNjxfNGFAm/B93+WrbpwMg51Mfl27/L1JoPS9RJPTkabF/TqW7483+7K68vdN6q76RTq/izM+j09dVkIFAKFQCFQCCwpBC5hVXR58aSZuV2zz30QVnXY82LujsjkdPlFE0z9359xmLwzdCp5srq+7rouBAqBQqAQWG0EfDbJeq2z3/+1yHrMMcc861nPuu9972sDNJfrC8eW7ii3epcFTmn+ua0Rpl6SDvlkckQmdOBLwyn16U9/2s/m+tSWVWGZjhRxJkCDWhShQaXRI8chnYqiVtpSZfJbWZlWNGNthHNW1iZsDcllE6BBexXRlslKJl6mtQiaD1qHyxhD3rnVIkEheQqd3SWpYARUEW0xWEEyMp2jMDa41FiliCkYmXZLKXdz2TKjsJtJjM6WQw88XdLm0KGxU45bJDVWjRGQlkPgl7/8pR9llrYkL9Pqr7LRme6IhVGSKgj4ZJoNdvpRThQSjjGUOGQ6B5ag1z6V3S1CVdolQZgMAyIQG+pcCBQChcAiRmB2+ZezdYQ+OFVp0IUX+NXFzb/ai3TSdmwSHFw63EJPQQMgLmWSCbu5bA+YdAOwmxmeajkS8KSk5bgs/m1oVKIQKAQKgfmPAB+OghHHzW52Mw780EMP9Ss3j3jEI171qlcxPlEMGe7dIUfE5wdwhMxDN0BHTDCV8NA2WZtcU1BZP4j0s5/9zB5r31HGPomYhkKUeru3CIvQ5ag6+VGLmBp/DZJUV0PYqpVKsKysUl0i6xaRdkvVrJXWLh+0lmg2SEchPcScyTvIy3eAK2VJjnMMNnxoKVV0JVWUgFdYygaX2c2sLEmXzk1PjFQcAr/97W+f+9znQgDpa1rENMThYdCJGTP0NOiI61//+qLg6G+ap0p4Dw107npmkqAfLPJTRCK94Nzq6to8lebKLwRmF4HaAD27eJa2QqAQKAQKgUKgECgECoFCYNYQECJORozrCshFvCJbh8jWMrTYNZuhxZPqS/xLPpdyWpw5mZg1k0pRIVAIFAKFQCGw1BAYPWM7Sb4T89EoG01P0vLEUvTE/PjFHD0xSY2OJxn5v0zdZe3RVSw1wKu9hUAhUAisCQK8seI2nt7iFrf46Ec/6gvNT3/6030ES6ZVutzlkF3GLXfryurgZAh2sYC0Qz6v7jwZi03ocelTlM5WGW26tU4p7RNKWU/1RSWLkQ5VWHd0VxGungYCljZdJgdf2HfLBl/DklZX7Ilt7bJrpFvKtpyrXe1q1MqkQavFicyIYVam2ZDlcNYy409/+tPFDNXKX5JQhIXWdwnIiwEStNGvFXKyRq4KajXE1mqAyJ9o6vrru2uBXNx6icqJ/7vVNbKT2WsaJWrptkuOtXYarLBS7stS2clNibJA0yJNJkabBfif//znqlZEe5USQRNjtkymStOvjYTBopkxIO0lQKHvWpFxl061kMw5CbYp7lCQSSnuFlQdagzsjKSEwQTkM0PaXUdrfi7rXAgUAoXAIkMgjnG2+JcvdfDP3C9fHa6MK170/OvB0Pab3/zmiPXss8/G3RtuuKHMtcS/Yb32NBb/NigqUQgUAoXAgkCA37YLVuhxl7vc5bOf/ezXvvY1G6DFwjE+MYgzseSIH6973euKUwStgw3E5iRtkE0Ihoi7UcwNbnADZQVcAjFU1Q3feqoESr2cu971rj4dLXPFihViN7ULnYg5WhUJt3sFe5dbb701ZvzRj36k1aI/ZVvxnmS7bALaJXJMVOtuhi45a7VD2rvTtvN6fdrZ8KOVbdpGJ8aX1/CogqShjjj0qle96uabb+78i1/8ws9fCGabkd1K00dyFGShntKon/zkJy41zWHYcKMb3cj5y1/+MrFBk373u9/B0I55W+FPPfXUrvKhac/MVltt5dZpp50WqyIWg6V/+MMfJjYPmHIGK02ROhcCaxWB2gC9VuEt5YVAIVAIFAKFQCFQCBQChcD4CEx+XXL53yYKrJpYNrYAbXX78hv+/irXXva3319R0Hv1a/5r0003+dk5v1+16vxl/7mpwPjf/8lbtlmlnigkUJ/QsPy8iQ9PrvL2s4uJdXTLuBP5F27mtGr5nybSWdi+8KoTqXV/MXGuoxAoBAqBQqAQWJoIXHTViXYv/8Nk621fXudqG/3GR0L//peJH2S48oZodNnlr2SD2rLz/nTtSZlMqU1s/JooN7FZys81LDPzjJ2XrboM/l0e/p2UXnbRBmjYFz3yWw6TRf6+yiT/sgsm7/93K1vE2/enL7ms/wuBQqAQKASmQcAam8NKnl2qVgG33377ww8/3Kqq70RuscUWt7vd7b7whS/4VLMcTnti1+o///miF73Imu73v/99G7Y23XTTSWc+ETRZebXJ1brsy172ss022+yTn/yklbzrXe96lFhnzQYsC5Yf/vCHaX7Sk5505JFHXuc613nc4x638847W018/vOfv2LFCgI2XltafvnLX/7d736Xcres1z7xiU+0Xkvhta51LVXYP73nnnvapX3mmWdaudSE1s7b3va2u+66qx8yfs1rXuP7THe6053ucIc7qMWqtgbS8+vJ4+EPf7hlTqY6/+pXv3rgAx/4qEc9StXveMc7fvzjH1v41JyddtrpPe95z1FHHWWJ1Dqx/cT3v//9733ve3/rW9+ysssSS6Spl9lMlQkcbQeOr3wxWzSqCvkPeMAD9thjD5a89rWv/eY3v3nDG94QIA996ENtNz/uuOMgYDHbby5vueWWT33qU7/zne+oPUvjVmSzIIoorbY++MEP9t0puFlGPeigg+xUftrTnrbttttqE1OPPfZYDbHUajGV2D3vec/Xv/71X/3qV+HDkmc/+9kPetCDLNZ+4AMfuP3tb7/33ns/5SlP+cxnPvPCF74QShaMH/awh+lZVdtAdp/73EeOjrjHPe6h4QcccIA1e3rYo7Fsu9vd7mZB108821sGlle/+tUqZaoiNDik3bXYTw9s99prL/WCC6qeDb3DeD2uvyzqU+tHqHWKvvO0KOuB7HZr699KFAKFQCGwOBDg4hxrlX9t0MGhS5B/kX6Pf3FW+BdHA2THHXf82Mc+dsQRRzzhCU+Yln89b36kHtve+MY3xk3f+MY3XvziF1PitzIa/77lLW/ZbbfdEBn2V4XRlG+IfupTn0KRxb+L4w+2WlEIFAKLDAERjVeDbEvFCLvvvruwxTZo3purtylWnOuWkESCwz/wwANRqhhQxGenbxcKTp7MIYccIuwV4whwdthhB0HfT3/6UxRPkv7nPe954jjc4Vd3RD0CRjGdWp7znOesXLlS9GTjtYLCW3UhDtGQYG3//fcXpTLDd6nFUyx83eteh2t+8IMfMIwk5RlLCLf3228/seTb3vY2UZgYTY62/N///Z9IWQxIj3duRYJMVXuiOaGfyEsYyDAxvlhPWTHg29/+9ve///0uVW178b3udS8fS/7Sl74kJBTDOhL5gkKMrBX77ruvgO70009nEhzOOOMMn9OWtnVbmHmb29xG1C90VRHhxz/+8R/60Ife9a53PfKRjxTAariZARE6uDRcc7rYslbEzeCb3OQmyNruakgy8hnPeIboUhGqQCqmFvkKRcXp4l/yX/ziF+Gpy7T9zne+sy9wf/zjHxfVCrrF2tjZD15ts8025ge0l6kmBLSLbSTNA7zvfe+jzXyF2vVOM4kGnWsuAt0bFbz0pS8VaNsdLthvMuzcZJNNTFzodz1oeOCWKFjfmWABnX7xeHz961//4x//6AEw/2CiANTazhLCgO3h0JRXohBYSwjUBui1BGypLQQKgUKgECgECoFCoBAoBNYUAfGhMPURj9lM9PiEPY4z0bDJJte1yPq9757zuc997ltnnb3h1Tbc4LIXv00rnjRfIGx2VlAIqvrE8F7blpY3YdBk7L1qcp90NwTtpifE6igECoFCoBAoBJYqApjU1PCj97qV1eUnPu6NP/zhH691rcs/+clP/srXjvv857/7m3PONQX8179MfO4xR+brbetCpiaIXV7h8lcx577uuhdP9aLjdZZPTMH56WZ3XTq8p0Re5iQFX6yq/isECoFCoBBYQwQsv9m7bNOwlU4riyIja5wW56xlWsJMuGTTj8NuYPuh99lnH0VslrX4ZyU1kZQVO/t+DjvsMNtYfUmLgDVLy3uWFZt5vPcrX/lKK6O+e6Qu+e9973uFb7vssst2221nK9I555xzq1vdyhqhRWIboPl/X8yyrmk52QKhrb0MsKZrudFipLVGOeqdIIhLjuOPP97+Y0UsJb7iFa848cQT2WBh0gZiq6FWIrNdyeqmrdiPfexjbe21OGqR0s5vy8YWlWVaE9U666CWYy1VCioZY23VIi4Bq62Yi9mWVFXNSAgwxqK1wNNhidpa+Mknn2zxmAbtPemkk6yPvvWtb7XabSFZgoAPPtk+tfHGG9uGJVb1NWWfm7JtC3uSsblcLWmWKrT029/+tpVvHyez8dpCNQ1Wdt/97ndb4bb72Zo90Gxrtg0aCDrUpm1oWx7WOmvt4AW7td4TTjjh6KOPtphtgV9xwFrn1hAdDUx9RLmzjrMWa6VfH9lK/qY3vSnGpLvpp0Qvq0VZG8ssRdsub+n6kq5YpkW2qnuQYAucbIC2AK8JFp5tNLcp3MZoiZSyYu0Zo01ntaeuaatEIVAIFAKLD4Hi37nhX2QX/rU5zBYue8ve8IY3hARH8C++w4bOaN32L1yJpwyBbCNDW3Q2/vU+D6bz0hEZlG2EY6Bi+5QBQ/Hv4vuzrRYVAoXAIkCAbxe6ip7QkMN8pnhNoCRyEeIJcMRo4jvBlz2sNukKqXh1wZQdvV7+bAjYOyvgUtDuW2khjGBW4IMI6MQX9v6K+ERq+eUckY73Y8Vx3kQVRyvLjJve9KaCTS+mvvOd76TEXmqBqgjRWzoqEo3aPWxLtDGD3beCUIGw6dMEbgTsprWVGZ0JV23m9ratQM+LtTb4iuDUgq3E+GwQAitlGzGqEjAK+ZXVNPGgWkSgwkyRmrhVEMcS25TxGm3AUdBdAbWEkE29IveHPOQhGmtjsUrpFGbm88n2QHvHFWIKMkDY+8xnPlNBMt6bhSeBU045xcQCA6TJaNHEdMOkZr1DvxwmiTGFunaK27us4V5pJi+oNy1gA7Tu0EZBuvhaEW8fwRBckBd1Cja93sw8cxq2I+sjUNh+/ehHP1q+eF8RPeIsbNdeXS/s/cQnPiEE1iOY3Q8WuZuD2cYAZgbEqmY87JO+3/3ux/5Ma0SGvGdGLSJlQb04Wr5WsMoMic3rwGcGZOTD+SMf+YgO9aSZOYkl0VPnQmAuEZj4FFwdhUAhUAgUAoVAIVAIFAKFQCEwbxAQcq+37KJrrrrwGpe57PLf/u7nP/7paedf8Kvttr/yZa540fd/8Mtf/fpnb3/b8b/59QXXvvZVNrjMRauWnb9sud9Csla9zOcqJUxoX+lKV9jgcueuWufcy19hnVXL/ulHiSd0Lj/volXnLt/gzFXrnblq2d+Wr/P3Cy/6yzrrnrd8g7OWrX+m93KV7YGQ+LyXWZeFQCFQCBQChcAiRGCdXy/z76Kr+bfOOhv8+Mc/+853/+/Pf/njvXa42gZXWPb97//jxBM/88njfv23P17hylfa8K9/OW/itxomfq7B1qgN1l//MhdcYNJ8nU022fg6G1/9gv+ct966V1510eXWWXb15at8OxpTn3/hOj/1b/l6P1t3g1+ss/7PV63z01Xr/nLZer+w08zmKISbYxECW00qBAqBQmBuEbBQ5xvPFoCt9lmOtfbmy8RWNH1O2EqttUMbgGzG9YppPlrslvVIC5ASVjGtZXLINsvaW+xTSdYd7S7yRSjrl/Yi53ddNYgSmi2LKpIVPjlnnXWW5VLCVmSt/NnuTN5mXyvHiltctJws0wItDYpbLLQt205lq4lMytJvQ4tCK4g2KjHJorX1V8uKdldbmFTWoqalUFuRJKxcWga2x9ceJnt5VcoSVskk7BPLardCaVHW+rdFTd+O8nUrS6e+OQ0Q9VoVzpetoadea8wveMELLDyfffbZvkHli2JWTy2aWvC2s1yTfePKei1TbYxmhuVza+1WkYlZGLYJG4bKWi61FqtSHaFUWq05amSSIhahfcqL2TJ1AVU2eesva+fMIOOgyi27xv0c8K1vfWuXtkprmp1YSlkLl/DRL59h1nYGWKZVFzF34eOuxXtN0HClNNOqvCeBQA5WBVIfxQSs1V/WailgCbTu0H3a9ZjHPEbP6kG97JCAmDVjZW0+sz6tpyBP2Bo2Sa3oriI3bZUoBAqBQmDxIVD8G/5FAWuVf1Gbhyf866cJMDj68/rNaP7FaDhOWcTKTpxOCfqWxpJd/kWjiExvYltFjKaMBIxkaCj+XXx/ttWiQqAQWAQIiKe4ax9XFkZx8kInG51FeXbWCoJ4fk6eb7d31mZZb4cKewUpXL1XUrvNJy9ItJtWWRGcGE1QqaAEJaJa+QlOkQ76wAu2w4q5BIbCIhGcUiJxQZDISEjFGF8aRlW2xqIqekSg3oy1+9YrvinLABpa2CXC8oMPwi7FffxYbChefuMb38gAasNiyEikacezrcZaIVPwrnYCIndFEqj6sSB6xH3uMt6LPYhShCteE7sJRbUizMhmv3hgNzOdMm3aZoawURHbkYXGQlrbtQXUNoLbXow9tTTo+TS1rdgaJSSXEHrDHLxq1Kh2MENLVacWCZioVEK87GVa+5hhpclCV+8tS7tlG7SKvK2k+0SmABSDa6xpAZ2is+w4x+DsJJyDfgkDA9WRZAagmG2igKRbBGRK2IetOk+OpnlsDF28bs3a6MlZF5uj8E64st7+JeyQALL4F0pawQwgkxeG2yedPu3p6eqsdCGwthGY+OOpY6EjwE81P9JNL/R2lf2FQCFQCBQChUAhUAgsZQSM6/70p39e+9oTL+YeeeQHdt3lySed9M0Lzp8IJsWc//rXv690hStNrOwuW+Xs688Jp0XCInyx6G1vetOPf/x7yy5n9/PEyrG7fzv3P7e4xY03v/H1xPwnfeZbwlehvheLl63zR+8B//1v+SblxFq+GjO8THqh94JWtOYksdBbVPYXAoVAIVAIrCUEQnwml+3iOv/8n1gz2G23XY466kcbbbTMBiaTv5n3t3Nr/c6Mmmllk8jo2DT3TW92I7PPqHZiw9PEZ7aWY1YCuzzyPvK/9Y2J6Xj8a7vbsnV+g3//8FtFJ8qupRaV2kKgECgElg4CEz53ctOwXTuW4ixJWo3jmS0uWiX18X47gK1ckhEx+diVKMnqrw8/W3O1/sf5c9fiBYclQCvEoLOiqayE7y7LJzlZyYRj573p5O0tuxKQT5I/p8c3liyaIgtnd91SkLwEScGaqoV1BFhoOdPaoRoRhBVTAirKWcLaKmut/tJjMZhywlZtfWfLL/Ba3mYnYzAL/eQtOlrvJIOwvvOd76hIPgRgEiOzIG2DuJVOOuHDZrSVNWzCxCy70mBVWEH5jPSNLlWz3wZue6FEnSQ1waZwqKpFo7IVOyu+7pIPqglR5aRd9EfY9m77xa3j2ptuNZqAD11bFGePVvtZ4a233pqMT5eRz6IytNXFKvnWg6GqvawS2zrT4K5bSbukyqI1VK1Vr1ixglqwOCxsa2A6hT1Z/YW/hWqY2Jju8fBpLhvEqXKQccDBFme9wM7kOAPfmMG6su+c+WQXQKx8K6hFbLOi7NDwpqQVrEQhUAgUAosGgUlPOfHSzmzxLweOf7lrEC04/sXa+DcjirXEv1gv/GsggemcjWfG4d88cgYnPmZJw9Of/nSc3viX5XjQ7iv7wMB+3/ve1+jiJje5Cf3o1VH8u2j+ZqshhUAhsJgQEJ7gX1/wFY16X4VXF9FooNc+/XyQn7Xh9nEEry5fBMefu0w828Vhhx12cOkdHuGbyE7oZBqT53cpUBL5InqMYGnSi6moX4wjnkJJYkkCQlTCwkbhj5iRKgwlR4J5lCgiFFWKcvzCAPplEosZ0mzDRFokvMouXtGiKExFBx100POf/3yBqt23lIhnBarUiogNGAhrOOPZI/pLdCzEVtAhiBNR4j6SMlUtlrRl2S01apFJWg3EjNKMkU8zQmSb9VPBOP3IkX6GiX/VBQpVG66QcUkAjdImCtYRcNactCsKiTE7vx3kh5sk5OsUyhXUIzYu+7IyVQJ8IEzMG0/ODEBPRUBzC9S2oWsgU8mYyoAwta0iCa9MM4DZDFaWsJZmTOVurNI6MjqUndD2srEvZOtWsyVNFc0eIUp0rs9jk3dLJnBsCn/a055m37ZN2H4kyuT2cccd50ViPcKqVldTVYlCYM4QqPWVGUDNHbSjW6xlSnTz5yzNqTUbVNrSPXumyp8zO+d5RVPhM1X+PG9OmVcIFAKFQCFQCBQCCxSBVRetP/Fv+V/8W3f9Zde57vJ/nXujU09ZdsGyr91yy2UbXefyv/3d2ZdZf+OrXOn6k5+cvMIGl/37uef97c9/+/M/zv/z3//5+7/+/dd//tvPzvnFd7a4zQZb3mHZvy/492Uuu85vfv3H8/910TWvdfkVK++yzvq/XbHNTQ5/031vfMv/PHTXK1x946/+4U/f3nmXe2xwmf+st74vSfsOtH+L4WijOI1JujcXsBgaWW0oBAqBQqAQmC0Elp/npxKWLTPT/Z911vvHuuv/5aJ/b3bC8ef//R/n3HXrZZtsesUfnX3msv9sNvFv8ovO51/0w9/+6Yf/WX7OBcvO+es/frYuOv7X7878vy9f/yb/d9NbX3D+hd9e77LfW77ez9fd4JfX2OgPT933nv/41++2vdednv7sO256w/88YKerX+4qZ/zz/N8/4IH39M0MU9g5ZqsppacQKAQKgaWMgAW8m9/85hZTrSlKW42zlHjIIYdYwbVv1dohcEQHFgutQfpSlGVgYYJVYUcXN2t7FmgtMVqPzB5W6Sz7KUhSKdqkHVYBXdqea+WSfJY/2+qp6iJjz7F9sZaWLSVabrS11yZmy4TqImCPbwtYlM16LRtwhIZEv9VWBltV9bEon2/0m/WWQk8//XTtlWkVlmGsshHKsi57qFWQNmuZVnndZQwjswJteVKLiLlks1aQJMASkpZvYUKP/cFuKeVsOVO+u2EuOlVhmdmlTIvENKhRJknKrbmm+S6pIuNgszMZBvsItPVUn3aGA5xtPpavCnX5RPcRRxzhq1d2RduNzRgCdhWzVmOzBkwteVVMKp44MQZKEjToMivZvuRtvzgk7VGmQQd5GOiJzSTlsMRmL6rYT1JjaaZBjoMkVJ3lW3Im4LNbzjItMO+3334k99prL5utZeZz1K997Ws9Y5TTrOFdC2XWUQgUAoXA4kNgtviX1wVO3OZ85l/k6HUX5MXULv/iFPajQom1wb90GglARgLNqd2AAS26nJZ/CZPBSgj62GOPtTHubne7Gy4zcLJpG/KGBNq16667+lrnve51L5+Z9EsLmmPQUvy7+P5mq0WFQCGwOBDg//26kW/9eqdFqGKHrl3IwkzBixdNcQSykBb6cfJCY6GWS/kOr4miBpuDEZkgCyB2uxKQKdpKQNRFSfSnlIDLmRIHsiOGSpihFCVJK+VS/IVffHmapCJevPngBz/obeGuTrdcoidnURVtUeiyxY9qEYgdNXmgMNuOvdFKgKniTRrU7jIHOmO84FEpwgn03BKpSROOvLv0q1cwLmYk4C6GhYb90zS4lGkHsBySUE11MmlQLy6WcJlDvSwPjHLSoktu/vf/Y445xmet/SgTC8WtQl2q8LgRxYMf/GBfsLa9+Bvf+IYYXxn6oc0ADUTQsAUjy5VtVQNNz2LqRK843dejn/vc5+p3v3REnh5V6I5WRMKlfHe1Qu3SwmHn7iEfFB4JbYeeWjTq8Y9/vOCa2DOe8YwDDzxQnK4tesb0S94xZm1XSaULgblEoPO9mrmsdsHWFUfA/J7Dmip/zhra7GmWzFnVi6Oi4NbQg6fDZXJyHuz3xdH2akUhUAgUAoVAIVAIzEMEDEVY5duRgsbvf/87f/7zMr94vPXWm5+x/uVPOeUb66zzP8Ynom7nq17tyjvscBchuskL67hKicMF25e73Ldf+cpnvPzAU0855UsbbrixiH3LrW5kav6Tx3/zhBO+uc/T7nLMMc8/+OCDv/zlC/91ntj+xC22uIPNAUJf8bNxUAyYh8iMb1Ka0AZy4xcsyUKgECgECoEljsDVrz7xpcnf/e7HZoCR4847b33SCX8466yJCfpGLhtuuOyRj7zvpv9zLXPQF636+yTdTHxh5dob/+vlL9/u9a/+1rHHnrLRtSb2xt12q+va7nbKF//woQ+d8YIX3dsHMnzq40MfOnfd5cusTGx4tXuabjaJ3MOcwmm5eByZntq6LAQKgUJg0SMgGrKh573vfa/dxlZJs0BoGfIJT3iClTkfpoKAqMfXm3xviYCf8fVNKUGQiMnZIZhyttHH+iVPa6GRl6bH+p+PJ8Vjc9GUWGLM2qGEHAUdEpOkMFFLFmIFbg71WoO0wZdaa8AWO30zyXeSyMhxS9n0juLSirvUHGUJREMEFHnNa14jBtRSYiy0JIlxLGCrXYtEkZGMYTIVj23ELPG6q0Zt9B6OtI3LiSJJUmj5XKY1VIu+xOjP6i8zsm/YAirJGJwE/TmigRKtcNCTnCScuwlpH4vy+aiddtqJNvvRmacUtH3ZS75vKlshzoosAfhrtV7zJTBicMgqrypcOruUsHicSn0Y7G1ve5tFd2vtiliwt1NcSy2E05PNXvpaS8lrqVvOngd4klRjGkWnsnDwkFjQtZBPAxDAoiANNnCrS3yN4g0hbNref//9bUSwvTudoqWBa6L9dRQChUAhsBgR4EJnhX+5X/DQhq34+XnLvxgHCyCmHv+GsrFJiAmP8P/IYrb4F8MCB0R0OuOgGfFvrFKwy79iXlu+aIa293ne8IY37LvvvoceemgqKv4FVx2FQCFQCMxbBAQmvhwstDzhhBOEHvw5ahCqvPjFL773ve9tpyzCkuNbv5rAsWMN/CWucZkAxxmn5Ou/tsOSt+fVl4PFRNLdhiuVIggaa3RvSaOYbk4Y0CeohW8veclL2Ck4EiWJp5SlAb+kiHMSqB+BqgVpUsUqBstRqehe1LbVVlvZEEyzDdB2+mqsyNfeXA1sVYdz00Bna6ZWTt31mwZe+lWWQmc6hfYqEhFraYor601Xt1xmsVXMaDKBtU2/hFI0x9SWr2xakXPL7yVsAQfCtttuK+q0QVmTGU+/t39f97rXYeGjjz5aEc10plMPQoypLcbvKVSd7hYyU3XjG9/4wx/+8CmnnPKoRz1KL0NYV+pEwxVvUKN7OVGrjZRrBYQNXWRmANaUq9QtMsz76le/qqAqbKOn1kPiC9O+8/2CF7zAS78svPvd7y5yFwXrpqDX9FSiEJhLBC6eU5vLKhd0Xf7Cc7RWuJS+JDu/P/7/efYmuVYTzYCWWKvVLTLlWKF3tAb2+jf5TbiJVaIQKAQKgUJgPiPAb/Pnzpe6kbNrw4y0hdGCQDc9iMng3RlVNKhwKeSMgGjEraHITOC/6nIT/5b/y78LL1p23U2u86ffXf0y6y772peXbfY/O97sprf++7nL1l3vAnfXW3eD5cvW/c2v/3bG6d875QunnXH6d75w8tdO/vxX/fvcSV/51c83+OyJZ3/vuz9ZddF65/3j3HXXW77h1a7793MvvNa11/nLH5Z95qQvX+3q69/ghlf/5/nL/vPvZasu3OiPfzh3neV+xshLzxPz8s281gSJTElIOLoC3cuWP1sJxnTtidpkds+D1aXUaJnBUpVTCBQChcClgkBc1qxXvVb980ytnVfGDDPehz8vt2zd3/v3+z/9/Zob/etH31t2mXXXeefb/rj5Zve+yc02/b3J8OX/9s/2Knvn/vmP5V899VsffP+Jn/301z732TNO+szpJ530Jf9+/4dffOXUz//47F9d9SrLVl101WWrrrZsldRVNrrGFf/8+2WfPPGE9S/7y802X+fyV1r2r3OXXXj+JuaxfTaFPSHZZtiYcI0p1tSuYWKOq1tDa6t4IVAILCkEGpNapfPLv9YprdhZybNv1YqdBb9vfvObPnPoc4bW/6wmfulLX7LaJ0dBS3R2/PjiEUnFLS5K+C1a67K+eETehloCtixb5Gvu2mprWwclk0VcZxrcolamUhKKOHSH7deU+CDxS1/6Ul8LtsApU11KSUQyvWZF1sF+i4uWabP6GBlnK9x3vetd7U/yuc0sQFqdJc/O+9///tmqG49Nc2xzjmbLtzZ/f/rTn7b/2yI0KBzWLNmmIDFmEPDFR5+ekg8cQFn/3nrrre1+/va3v22h1ws8NKdREppArYNC9TZYmNRsJkZzbOie9ct73vOeBz3oQTaF+5QX6Ghghh/hVftb3vIWBSnUOzIldK5FVjU6kgPwVksXbcK+13X961/fd7It1mode1iof1lujVZFlLhUEegc8gnc4ha3OPnkk33LilrKo1+r7Sy3nOx7XRaPrS6rnbx16B133JFmH35W1uo+lOy69hFufUe57lORo9vqShcChUAhsGgQaL6dt5wV/vWSCZfrNRUKeeZ5zr82Y/X4l/NnOTrDnigMWaSvseSs8G/Yh07KVTRJv2vKvx/72MfSj86+7IiaUaemFf8umr/TakghUAgsGgQ4ake3OQINvy+EYgR00qIP7GDbqw2pm2+++cqVK4VCyFTMpZR9qzbvkhHfhUHQloTz5z//eQJbbLEFJQm4hDYYuVtXK6KKwfhOcNSEKXF5wxveUBznhwW8V7P33nvb+Ctf0OSWQIkwJXIccnKwRHTmaKok/IaSrc82eQu17GNGT6I8m3Hdsm94cGew4vS466wiH1S2SZox2FktGmU7uMDcK7JGGnY/+9yyUNR+cUWo1ToQ3fKWt1TRaaedhtDtinYrB2upBYVo3fmS7In/E/+qQppYzmldLuWIE70F/dSnPtX3nu1ZR+u55bPKXkt+5zvfaUM2AzRQ7bZu+xkrL5iJytuIghJH9zEwLaBR7DG/Iex9/etfr4E2WKPySJousDtZRakLJpFXl7YI/I2+1D6h95JDq72fLLBVO0zUnvfGye+2224s9AVo+ukRJvsCtN7xvOVhu0RH/V8IzDUC//VBc13zAqzPXy+f6JCIQ3fWjuTnfRQTjnE9XY8zZltTZDUK0s/9KahqCQcOY1UmWN11y+EyR885jmneIhYDi47Tg8450r8IQKLbv2BcxDgskaaN2Ym6vh1riExGEmMqmfhbveQYs0iJFQJLFoGp/rhafkvMGUTT1kigK9NNNyNbZku0W0MTY4q1snxMS3cTTU8TkJOjG631ikSgZbps6SS6AjQ3gSSmuuwpGeeyqRpHmAz51SgylfIRqtxqkE5VvOUPSt7hDhuLtEWnl7nMej/5yTKr+KYnJr+EMrFNClUZ+EmIMMXnzmJU8fNPJg9LBUcd9RGL2WwQeHsJ2MfGLNhL3OlOt9p665t4PfdOd7rT3ntvvOWWN/Xjy1RRmO5uJknIySHNQjIGk+nNNDxmNxAkWrrp6eX0LpvY6iW62qRz2cBsidVTXqUKgUKgELjUEeDHZuTKCPPVl7rZow2Irx4tM87dtdTSLbbY4Pa3vz0jTWT/4hfLbAIzV5CV69QY+3/yk1/4PMapp/7w1FN/Pvnvb6ee+jeT7Icc8p3vf//sK11pYhKcBrRslllixYpbbbvtZgce+Optttlmr70uf7etb7pixQoaMh89TntLphAoBAqBQmAoAl1aeeITn2gDq/lek73WVsVTPLBgh7/lzO0b5qitzH32s599//vf/7//+7++kmU11AKhNWDLeJMbaye2QX/uc5+zcLjffvuJoSw3yrFiamuX2Moirh3JVv4kVCTho78WEWmwNKhel8IuVVs57hps37D1SD8r/+xnP5vmww47zK5fCikfyvWWMHOwKnq01KFSNlvnlskShlmdtZh9oxvdCKcQtpApcqRZLGll1xkUSrHKnmAxoxVKG3mVIuaQYK2CVs1p8KvE2u6TxhZlRX/iQVXYMfyqV73qK1/5igVma5/ddjEyFkp081t6YigzxVxEoKaQAfapE0v4aTVaT6lX10AVY7KEDAsZoFHENAqezpQgaCv08nWETH2RCFcpTSbg0sputpLrdJjAh4XWvH2qmQwcVKpr5Osand7sl9DX9MNN3G2XufbqaEoe8YhH6AvCfmDaV6s9bNaPGCzHcjVjNKqrp9KFQCFQCCwmBFBSa84s8q8XhJ7znOfY4TT/+dfAAId2+Rfd4AhEgLMWCv/6jmajaW86GfmYVTalXPzbHu9KFAKFQCEwbxHwYq3IjtMWniSYwkRCG3uF2fzYxz4WSYlKvABsM6vfqxEqCktJ7rzzzsKiHMIxm3F9p9mbusJM/Gvzq0VDC4jdhicmUkSimz9V+qyzzvJNYt85FmT5jabDDz/cNl8RE3nB6WApoSjqFNM5cjfRaPYf24kr03uqNkMbJIgH5ZAU5svpaptUMKGBNtGZUNH2a0Gc124FhnSKnf0Gkbt43GDm+9///oEHHug1Vx9O9iVs6JkWln7Ws56VL0CbQOjq1/wgkHP3VjfdHSYlX6RpkGAiAsOaNGh7jkkKM4W6wBetC9u9fsxUfQcuMazGouaucummX4+QtKvNFLR8xjtriB70Rrcg3V3k3oqbpo68WNWXwsk87nGP0+lNQEKT6beI7EE65JBD3M0Lw5430wVicNB5p9pW+wwhfJDbl6c9aenfrqpKFwJzhsByT/OcVbbQK+ITuQDkYarL3y3fyhX6G+ZB+BTzXHyQfJk8i3TzOGM2PLOlHHTKtmBjnOI8Dvrhp0zSpSCX5AvzrOJDLa3hOW6RZsZzSba/jKN2qIx2UcU7YxramsxM29sKzlaii9hoY9pdRRAed8xB8874jzGmVid3Df2E4xa4Ikjz44YCbpm1xNCtuJxupTNqiHpNxXodRy0zKriYhNGtFQIPrfNqIzkmIPTrX5gbsniApy1lOcREOeFjjz22+5xPW7AroFJPiz8WLTXo1Ommwj1s2VWv4dzIGWec4Rm4xz3uYVmFhwFFV0NLd5+6ljmLCfodBlsGVUZRs6i5VBUCSwSB/L2nsf6aJHAub4N/hQSiKZ4kC4FyOAF3naX5JUzNP5BxGQ1xidHT1dwFU37oWCnaaJBIka5YS/d0GsAozjXxUdwjDfwPJcYh9EQ5g7lB+Yw30mBh0yYxaFhKOTtiSbMnDVSLHHe7elYvHSVN/7RKmkmR7BZs9iSzXQ7qbAIw0RZOOwFhOjGjCFPbEgYVgZSkAV50jtDcq2vVqnTlxFrsQ3fennP+2le/7dsbukD0e955f5MWmevB5cvXI3PRRed1W9TRlpekL4C/n4rS18uWTQx1Hrrzjoz51re+/rmTvnXTm2/oBeIrXuGaXnH+xlkTn/K66KLL0NasldYWL/t6NgTYVoW1yGDJOFBA7vFGl7n05DCpFeyYMeRpcbcn2eBtBQlM0a4mMpEgpl61G4ezDUqTyEw0wREB566q5E8Unu7olppOtu4XAoXApYaAP9Uc7c8/pnAI/t7xLM/MY/OcPLaPOkibnRTlXVrzcaxiG+dsNtMcIrJgOQ8mLuBXZeLfhmYa1S61tKUHEzQToDk+WXs5cGLj+71BnbOYE+NXzxhl8REuy9yLS2kcJEg3ntGVxjYZt8B2dW3OzPv6LPT5EP3iAyGWB1SUHWZmpZcvW39S+eT+8uXnTbRlefaaT55X/XeGJJzrsyaT7f2nUk/ZZw/U+ZVTT/75z39361vf1BSTH3zwqa0fn/2HyR5fN/g4O/S7A8N6UE3c6UrjAUo8MIYZ+Npz4pnBzjJXD9LJhsz4xDZ/U2zzmCnsAfPcOrOBtV1jSM5YexUoBAqBQmDmCPA2PJKzKVzz4Xe+853tfkb91mvf8Y53YA28YDV35cqVvv2MHzkxG2r97Kz1V+Hb7rvvbumUOzXnz5VZ8jRxZ4+vjytn/txP05okpIfro/bWt7611WW3rBxbnd1nn31k4iBTfK94xSue9rSnsYHHRuhHHnmkuuwGs+7IVSriY4p8OKs4dpmIhnuXVu/zn/98PwdsrIJ00hwtYjmzrU1aB0UHnO1JJ5305S9/mSWa4HvDQjmziyJT1X3qU59SO50+UewuIlOKJMMsr97nPvdhNrrBmK997Wt9HkxBQPkWl7bT7JYFDqVcHnPMMUcccQTjtV3tWThn2Nlnn/3mN7/ZtKG5dIvlFMqk36eeLDw7XJpGYIm3c2nOAq3I0UK7D0rpIAIa6+j2s07BaDYcs/PMM89MH0GV/dabTUf4vV3boOFDoRVrxsPBarRt0LCi7bjjjvvEJz6hCcBnmI+WIUr7tz7wgQ9YB7GUy2AQeSS0xbQ/6NRiG7rq9L7FHb8FbG1YpaFXn92yVK+latEumXrQF9F8NlvT9Jr1e51uVKnLDBW+8IUvsJZaT5GKEKJuZSRT1agrjWHSaulu2ytdCBQChcDCRYBDK/5dTPyLp/QpAl0o/ItnRdOGbfbKo/vGv5/85CeLfxeuYynLC4FCYFoEuGuH2Ee0KKDbc889BSzCEDHL05/+dMGOmEucYiuz4KhpE7KJthQU8YmLfaZXgGmGXMQnGhLpvPKVrzTFKsfv8PgQsl2/4h2bRrwQ61PE3KyIVZwlaLKhmaQidB566KGCI7GSivLrRn7sKLtNhHh21rLk1a9+NTtVp3bhquLCcKGT2EoVCZRalGRf8l577SX6S5ArkLR/RilRqtCYw7cTT0wd/n3uc58rvBWYq90+XfUamQgDxbNPeMITRKbuOiMLX7+GmN26fuWABoGhkI3lWifSF7mrSDBoS7S3W0Vw0qaCzQkwUpr9T37yk+1Xpl+sLfAHhSaIVVkCK8UJWDx1qZmQsb9ISBj85Ug4J3pFtXTayGc7uI1nMoEg01mviU9tI5b2k0T2p3lfWrQrxqdfhxJjuRz6PQAiVs1nmC6w4+itb32rtWM9aJZDQi+zzTtOusBZF2s7JWJ8wg95yEP0o6l1T86LXvQiaVgBxMZCMibblT344IM9Epqpc0XQJsY1ShztC9YwUcrzRkBQ7MVvmSYrXNKTJqf5dS4E5hKB2gA9A7S9F+LX5fwx+0s2tcet+CE8ftMcKF+ZTyP4yzfHd+KJJ/JKzVOPWQdPx2NyzZxCQscxCxJT0AcGspGXjzODyanxPkb5Jiu5Ki/TcDScmhlDU3Lyx1fek0RFfLcZRk6T72t3Z9reVnC2ElrKuUMeI45pDIcOK1RqFtjZLCokeXOTvGgYf5j19jUOYhY+NdaP7sEwc7Uxe/Xct1qe97znmSb2uozqZguBBacHL8Lc7Dy+RI1r1X5/Wd5m83fqufWoTNtx/t6N4YwXmWdwtnq2eVQMPpwNqnxihE5DuqOPPjo7xvwpGWV6rqxGeBiM5zgQwkPrGvORHlp2nEx/NcZqBx10kPn917zmNeMUKZlCoBAYgYA/Z59K8iuoggHDfUEIehIQWpMT9XnnweCBI5JJCWq2vMrhcAsule35qKEegAzvIfDjYRzSnNtQydhJvt0VUPE2fgrH2iGOE87xTt7J+fjHP25sQ5V8y8bMc1e+VUNsJbIa0eSht1qNbCOg7c691g0tuLYzm2HNmMGcQRvIGGmItI2CcIQRF+TFfsCEm9lVy8bbbbedKQPjQ+4d9QgjDR70KTAxwqDOXs4l9uQ3jyYY4dy//9a3tK5x9esZfVEyGTpO7KHXiXoqG6CXLfvnJQV7+i7eAD2Zmw1bExugly2f6Epz2lq0zrrLJgxbNSm5amJRdtWqid+fop/MZBUTO8wkvMMjMGaDlXvDJO/dWf82d2DeQdqQBsPCJ82kuZnUsJ2oeuoHoCvWyqZIuyU/6SbAMGksZhaGJWwzBmYkMfk5WvGetlyOMMmtXtlukaluNZlKFAKFwNwjIKYO/zrnXVNuE4XZUoPRvDPMUfDeInr5tuNkZna17aRE2dXgR7PDOJGzwiOU4FlzCCwM/9KJ33mwjBwyb9iMHO18lEpMwSoOGTHZzJQibjUll1aCJQxDFpiFPRkYjG+MjVPmXrRIgGboooE+zIxncfGKFStMUuNoz4Bbp556qs+WeAbGVz4p+d8N0OxsuIVrnJndNkCzf/k6/5hAdboN0CjVq0r0X7Rq4ny5y09sbpZg/6qLJrZHr7oo26b7kaCRIYITReJfbTFWVMrsvHDSM2PIYWXanLvMCTPm9tALzNAwU2QmymwgE0h6pLvGjH5W59beqq0QKAQWOQJxg/FC4UF0gG4SjHNWXT8p7ZDJq/O0OSubTcAmKrkvU4jiL8uuP/jBD1wiIGqBGM9GOW9sMpOwaJoGbC4AlDADkC8LkEwtKlKQ55eDXKw6Cx7NvykiUwxlRtouauMTC6g0iCVD4qkuSqRlqg4DIjv24FBBmTZaZHXLcmbXTvI5UpzxEtoYAlJK6+gx3mCGSuXLEbabu4BDjJfpQLL006YhmIsqZ2kFtZ2GCDsTzlldTUxB9aqiiUlcbNzkf8EnOim0VV1xqjRQY00XW5F1V1fSGeEoB5S1arZpjkGUKogp5a7i7rZaNI0SYibkpY2+dJNa6FeQMLbVp6hNvjQlMRJE9FAbVTJz5FGxcq+sdWIK9QgZRrJQN8n3CAHTJSUuHWpUvFlViUKgECgEFgECPJtWFP+2cULrU8igAJQksVD4N8ZjPVMlC4J/wZsxhuEcag7/WvHH+MW/7VGsRCFQCCw+BDCLg6NzCEBsfRGacd0CN+EGfyjHZSKXBGJAEI9IK0LAcp4gRfAlmBJ/yRTRSKMA4Y8ZSMGpMFOsat7VXlj5wqVsnbLoqVIRk0qpEg8qmOBLZtBO1aIhAZdlU9t57aMlL5/lnLZvLVueti/FFC4OZYlbKcuYpFmrUlG2S6qUMi8qoXVCNlGYSh1s0xbCBEK4qVeLZKpUw/GFhH1fTHXYVexss5/4lzZigkR3tVc+PdIylcIpAn9N1lLmsQ2wFiJZpWzmadlA0i1VgAgZUaJSxjRAlNVwDXEkncbmUhMknJUCPuQ1gTH000C/M4G0SwCrIlUwKfE7lHLplu6ABjOg6uMv7BQCTzwuk0eqc1ZWMzVEQUDRr5RLtbvLcsGs6FghfQ0uUxwa6LFRI0nrwvBhmEt1mTZRtbIaxZg0rc6FwKWFQG2AHgt5f97kOC8vxHi7xeSat0ZshPWXz2v4k5bJ1Xq3w98278M9xROl4Fh1TBJP5Knl0XicXHI00+ohQ56F3gLxBQLfVNh33315RsbIZ6E3VKz/2aTCL/NHTfOYtnXFeFUK8Yozv9lusaGlL60Et86hewkJx3PE0zYTOIFI93npxwcwOHSrzrqVl/f2D9bXuSaRbVrNbKmuQQlIaE3aiDitedvzuv/++/u9xamg6+ZP+wywh0yKeAI1PxbKdNnSzezkD6ptSpqkBBrzSEePKhxBQM6gBvIE5HsgcWTSXW3SzFPWp1Y87Tb+2q4UsVTRtYGkW46ehnbpFnkWDu2U3CXspwztL9R3FDqa2WlCu3QrNvjzsU/CXLZ3pHB5qtMiXN6Emw1TJVI7srcq7IcjbYPzipXniga3qPLX5MMkz3zmM30sxFsKMcxdB51kmuZuumUSG8ynBBrR0CRHJyLsgbT12Ytx/NtQeXVR7hb9SUjHhq4l3fRQPZVZCCwFBHgS0YU1NjtCbMdxaZDwoQ99SLTgb0Q+vrZN9qSTTjr++OONKxxcTcIz3qz9iQWrwb/0lt+9pSDlcWujQY6jSDzsg0b8j53ZPkOFDe1lwXTeGGahwMbgge9CjkJQRED/aM2Dd2OPc4ZG2shOZq+GqkHlq5ETxGIAGwwDeGOtk+bcKJRwTKVZQW0x3jAFYCP7LrvsQtIgwQu4OhdNUOITX/a+61wb1/SsaJBCtbgrEQOm0t+qJpbDWMXIDSuZC4ChKQNVSNNAIHqUagWn0ix/Umpi2d6PIkw8A+utMiOsK1Rx7rn/FOr/8x/h+olNAKwlqb8M9qB0wAEH4ES/9utNP3dFyMaBXhE2SYE+sJifPaLTCCcdHdvkpBVdq5qpEt3HVTpGKqJgLltONMjvqWIMeVG6D4x54dsfGvqWE82Rd9lKpdJuTtPplnxHs1Apl61sNxHh5HTTXZlKFwKFwNwj4G+fNzMza2pSHCeC8wE/3wv0d8qhcVw+Aej1YHuPfF8Buwnqp/ozn9Z43iMBCEfUXNa0pSLAwh122MEnMWzO5mDNFTrc4pa9I+oH6YwZDMtFSeL3rN41zaMN1lIHk3hvVuF6IOCg0aWa8jlIMEZ8lMGATplRjTD3eScTwT56gRztXzfFIXjXUgu0WOl///d/QWdCwNtleHm1Z11hCDHVOUI06eL0eMCMjPPoJlysZ3L+ICQmJs1wSMFVqyZuRG02Und7yjc/vDG7YsUKIw1DNcMVwp5t3/tcuXKlKXURro+p9AyggVWOqELo4+CsgSQVaaXScJlJpBZqWUuhBwz5Gu76mxI/mnFyGFF0hRWJGWCkP2n6JQZtjnAvvy4LgUKgEJgRAtxL81oCFlPEnI+EM0fEfSXhHF804fImDznhJtzBSydSjtdiQLxWlOeSr4sSpal1y6VbCfo4Q34yOVGidmYYopgFNQgxALAMzI3LVEqI5wtbL3/5y40NaAtrt6jKzCGmo0FdNJPHgFmRZTNv7JBpXZY2+epVKXmZ0AgV0umgXGZMinmpxa1ccuyUx1oJkgSUcmgRSzScBsIS7so0zGCYdDQ7J8ESCbdoo8SZjFIOmZNSF59yqWmCXC1lTLS5nbZI0KCgEZFbGpWzpoFUlxFgScxgqlLSEtFAXu2xXw6DNU1LNae1MeE2nsVrljycU7WCSagrVtHDQvqpdTCJnlRKuRkVAxJtUTC1p+HSaaZEHYVAIVAILD4E4pbj4ZFR8S+CKP4FwtrmX/SNl7G5sVCoOWTdxg/+1op/F5/DqRYVAoVAfB03KwzhA8VQYljkyyu6xSXKER5yhvxwzw0ia0VkhrvJyKEHbYliRDqiG3pExHKICcSk1UWeyxXv0Gm5UC8kQVhM1FhPvlIJvkRq5jB9LmGbbbbxDrB3TYmJE03I+8qyV4LN7vqeAs1UtW6N2cmJKrc0yposOw0z6DSL7htVFrKJkdEEFipIUhPUK05MEOpMPzRkkkxD5BAGmi9J08N+Yvb4SSgeS9QFRnUxWMCuyalC0wBiNV8RVtEjACQpXxGBJKvYA0whZ4Ji1dGZswQz2qXi8nPLmRIGaKM0MRpcJtJXJG1PY921zKFexsQM57SRqWonQLlDwQYOC1O1tutoYs6UwFZxtaeIekkGQ/2rFeQlHGJ29aqIHofilEuoLsXBMnmnToXApYbAzFabLjUz50fFHIrtHT78YLXSt3by6ow/bBuO81tv+bP3d04yDiWGx21N2wilaOBQKPTheh9p7rr70cXjVWmw/mf/xy1veUsvYVguVcpbF3vssYdP/dtoy5FhFzN95EcrHH3X7mo7YHASrzpaco7v7rbbbkDQcDBytSNqd1e/oI04YhudvcNk+dlXtK2b+pySSU+eXUt9O8olX09GBymlCqW6XTyiot6tPAzONsJiTeuy0lOZqoomH4MHtU1VtieZywiPr5MBeQglQNqaDAGX9CS/W1ez012HWy2nlzaMAC/W9HVtDySdMY9OBZ0zanF2q1tFN62II/JM6t5KOneljT9itnr1ZpOUGW5uOUkY/1FoMNH9M1QRhQTStCRaThJNj0uHpjm/4AUvMOKxOcz4yTCC36BBFf4YbdXyrRff7iJJJvU2JS1BiSIOOdItf2iCQNAbendoppYag1q9NowbKiAz9caGJuNSfm61zEoUAoUABPxF8292L9keipRRjI2wYhUxHo/kLmrGL0IsnpBbUCSudXz0/AFidrGHH+HFaPZyhZ6a4+r9wUazTAIONghU7BNauXLlVltt5QOKp59+Oh+F7Kj1dUw/duGDlAmxyCueP/ahaoeazccySSkbwVVnQzAEVBHyHVpkbjIZpo2ODJn82JAAD5KaNrQXWsPhgKEIGx5w6b7u73uiOtErsIZY8u0a179+usiPHFGoIodGUeuQUHz8NrInYxUPCdBcGpwwZrALYuFozZMyE8ZYeRfWLl9nwp70rFrojKmTVHPxujgBMa23tkwE2EponkIPakjYyg8q2Ud497vfXcGE5exUy2RFE7ZItPbS73ApU0K9DtznPCE6eaSgc45Lsif0OFymYNKtiEwmeZg9tPoiYmTkt1LtUsLRtarJS+RWeir6pzqTbLe66ZZZiUKgEJh7BPLHKI7zqoaJSD/y4yUNf9GG3ObmOBxu33SwL+b61XsvqCBf8exq/wnT7CcCNRN7Nq8yZqsNvPlh37pggOE3imQkryXklDjggAP4WyRCW/zzmGqJaY6DlzYX7LfnVqxYgbDoz604RufxFc66pI7gsYU/0POe1Yz0w9mWX0G0HyWwE9qM+c9//vM27W67sBeMdYfvc1CLjkEhsRrtnUTx4h9AQHCGLmEr+V1t3fSIhjB7neUT9OfzH5KZd3aeDEsv0llTlTW3YxjpPW0boPGvg6SZKC+teZgf85jHeIoGy05lZC9/sKA2Tlh6CWgtoZlJKyKtByU8uh5XBtgo5o1xfeGWnvW31oRbFf5YaCbg3DJboslLkGn5lSgECoFCYHwEeA+uhoPiVHlIwwCMn6hTqMK98D8SxBLydD3P/2PvPgAsOYq7gb/du72cpFPOEhKgLARKKCCJjBDRNgYbGxz4cMIGDDgRjI1xwMbGBNuAMWBykA0CRJIEWChLSAIllHO+nDd8v3m125p77+3e7t7tXthqreZququrq/8dqme6pl/ExGwcH5rGk1cpPaamkle87ELE12c2GRluLx7ZjpI9OG2F0tA7Aa/u/TAxU+X1NRPGFvj5QV9yconGQCblyZTL1cxsYRPzvxWCpy3yJYlHxJXOnkOj+pGLiUEELS99vIUQGTWlmFKg5BqwiAEdYxdJMkLDVRbx1kuhT9TXVbmuQijsVkXwRHaRirP2sJJRtEgSoujI6CqyBHLQeBQXBrcpu4qMJC8oqEECyYGJSlk+sURxzksslqTSOfDBTBRsaUIOToRGoZJ4BTFhbiOGTMYrihaDloWouNIBLdWtECq5KoJWYiBJuGsIxxyqItQ05DSz5iURSAQSgR0QAfOh6dd8mPaXNWFfTPsCOu2v7j7R9lfH0wM9k7packBeE7DC7K8m2AHHW1YpEUgEEoEmAqY7/5rrPHN5jPIExBbbGrNBGdOgE7g8VMYDoxmywBYZPSWFqQoh2JrvJxt2AD1keaT17BOP0uZST6Dl8c18612rIsQIUkOUV4I+9C2lKFF2z0d4nDzi6Kh3vetdb3/722kYD02nnnrqb/3Wb3GP4bpmu1PGUCxUJZMCyvWQK0lFxHvUktdbR+YV4ZHN03HJiFALinksBYK9WnRUv6lpfzAzEFyzlEWCQCygQKcgkd6iq2DEhxFxBhaE+XzH284oDmd4i5EcFgeDshRhLeQdqXiNQmFViyrIqCwZEfWgLiFKUjCoO9PJuhGCM56yIUBtrwUkuRIOH1mUK94GB6xkFE8f8ZQngXAPy/J6VqWJguJdB7GEqzWF8csoXq1VoeimInLZWdDc5ODxmEy+dyyKjj4Gk3jYxxzVVAVioy5FVBKJwOQjsFlesJOv7lYs0aBVupnCmbVOcuUA/cUvftGs4agbLtH2FDGYhgxsbCaOmBnRkXGsmptSeUpFLjPFJuUoEZtgDvrBD37guxlevGwJ72fny/7whz8UaeYSTHBFmdBNrhIzSsKsHWGU/JPMBnwNYXLfZLk4AzqcLIT9Vy+dHafknDBzumMdTd+c0c3pgrkeP7jgNg7QQpnIqJm4xIkhSqNEEk2E0iiI6EWRJeJxMm9hfcWoJlo/xMnkuCU5TKO+yvBYYTB7JAj4I6iIlQFbJVI7urJebi0IWCnWLgxb0YdYQhTNgEmNLCLRIvUoEhSq/xOiCLcRSGA+IaktxChXqlyqLF6qjVK04sBLW2Y7QHYrVRZGVBHKco0geyxrpEYMmfIqQpJGxMxUuzLnSsFmdaJZbYc7ZIWe4qEEH0Wrl2ss45QIxtAfj0XeUJnVv7H6idRY4cWr9lAj4l1xFsUiO3x4Fduef+tb30ofJdITj1Dg+u53v+usF9lprsqaUhGR3ZW2tklCSbnEUFVAhCjVV3H4l1WISoV8DDJCjBoi3VqdwJnMkK/KgnhC9CVzhUUq/PV8AmPVAkxC1FfeKB3achFCDQSdXa20rA5d8cM50IhrlJXXRGBKIaDzGxfGkWc/duQv//Ivf+VXfoVlt8d50kknOcwPzb3YpGfkxtDGL8gYA3yTcEURhid+brgmLj8kZPxGxhFGXxSHzRg3YJ2SeO655775zW/22MkOmgocTW2dc8EFF5gTqGd+MMwJLBk3qVthMF2Y6xgOwrkokWnCGWUFi5AtS6iIoOLEqhfcnvGMZ1jUAV8FxUttKbFUHCHQ34yqcdlNPnY+YnnBC17AU80DPHcuh/pbdHmqJwd6mhik+EOsUtqFt5dYdYXm5IwZoVBy6IaO7GLkqmes0y1FbHxb1Y4DNAN71113oH2+q1F6ps9dX20NP25bo5m0vlUlD36r38svv5wamJSl49EBCPzMBMw4xagpVPEIobYYSfgjRiRmEhDRGRBCQKSOiEA4jEsw4xeaUh+3s2JIcCVcT9N7edi7JQ0nUS2dLeIlkQlJBaHlZZrdMnxuZZeKE+E2QyKQCGxfCMToNvytRQXf8PhAyBc4PIoYO7/ww9j56tXXR+oVK9vNGeymd4t8DjQ+QBorUCyjacfnN1Tiy/tv//ZvNPFFjSdQ3yNdd9118SqT2DIvocs8NnJx2Bhfilmuk2mG5GsVM9u2ML8xJdTwqwJ+ImOsDtAqzqaQoBE17gtf+EI2yEOlM6F9W6uafgvLlG6Fo4iwWSNjNUIq5IlyJc1VKMxo8oUSMwIRzOy5t99zZu/C6Nxzz4PLVnOTmj+tm+lcP9Bv8Ra/OFSd61mE+yUiS7IPfehD3kGJtMaLhziE/uOVhcWV3l4vGpueo+Kx+qK/VPYUP8IAcQ3NXaUKUQtyEIEYDT08YhY8n/rci8X0lBf7KIoQL6P+LwBHNz7//PMj3sIgJHvcjpNNSRaJ01sLamBjnalBAsWIkoTwxGpEKFpShkQgEUgExoGAyYe9M8l4fWeGMRla58fUJAkRMtFFuCko4l3lNRchBAwlS0yPkUsSycEQhHghYsq1PjNLJS0ymsNNgDL6uNRbSg9ZCvU2zwe0XtM5/tlyIvjjgSj0xG+mNT/H3BuPPyJpG8xoE2wpBREKSI0ABzOtSOZDTHCSFqWEQJGSzMOuURExAuFFmlvMcVsqi5DF9C4+VCI5MsaLDvFRKDWCqMuvyhgKUSlFhATROIM5XvNiEEIBdioYrPfwiC+5Qp6yvFmNEqUCkGS2JggxmkOqIKNbucQg6hYzSg+B6GCOSFcCxUSqpoRn3MJBZNAERsZgK9cQUm6TSAQSgURg+0XAdJf2N+xIWCiAREj7C5aJtr+gBnvZDVEi62w0RZ9sH1Zpf9sxyZhEIBHYfhEw6Xn3S3/TIKNjo7PMcmGSomphpNqriccsKtXTYjyzeDg1i6I9IkVMEDjj2cdbOxM7UTJGWbLjCTXqRYjEI4bPiWM6bYj74tcPpHuO80JYQU5W4lzHf7euakjw4pfw8H0SE5M8aWR6wYg/ig75cZVEE7RcdmlDDrZQoyCAIR4kMUSkZzdEPMl6KhfPgrhGjJeraO8WBIRAArFFB3kF8SLxSPVU2GSs3sHKHniGwhEfVzGCJ1bZQ2bEq3sdkBDuSm1XwaMxTllc3ZIAH2WF2rKLjwfScJHyMkEQ6cE5siiXVkopL2l1ABJEuuLBEAI90aPjQV690MoNX2dsHq4lIUpLYYjsrhkSga2IwKY9RLeicttg0YY31yVnDvkI5uSTT+bZ6TRoh/zZE4pJwVwTarutT1jjqEtMEzGFbTJ7mVPw+1zGThj1nBXkWL477riDR1QoE9KCJjNuNym8hWF8uVqETOgtNEzToykCZ6mOefzCCy+05cx4APDJT34yu+vI3rAlRVoYgJKrxI+ekJdMBom5okCYybg94YQTED4Psr9IYDgc60vYIogMc8ukMUgc5W26s2TcqXVChj9cXcnXM9k20iwj2Dbuv04ZESM7U0Savqo/S8IghlZOIFOu9Uf4sIrBQIKFCwa2k/+T7ITIS4Ik+hx66KFWPJYFFPCunA5sPJvHymKmDyG0pSRCLmujGCyUlFGMasqoOrCFuSxUEsMY+/VnKxVyYkDxWsBgDMoVTRAE31wuwpgd4E1/3yRoTYUSIp472rve9S6RJFPDiOAS7aMutSCEPjYeJDmGjbRAo6U18YOahmRK4i2hjYiVFwhaMGoUKpW8pKH5BHOM4wFpYx5DLEEkRWpkcdw7X0O6xbLGO3eYOK8OmDC55557nGcZr+AJpDbcyBHoowVV37qWKPz0Dx4quYUYBCCmvmDEgzZx+TTChrSPt7SmxtUVyScW5mqkAyBEkqwdcZJDJWfZUpsCeOQ1DVKYWMxuDZnoEppMjLKijgWQJBKBKYWAIWCAmFsMsauuusqkwU3HhGAo+czGMOGMZfzGgxPmAGccoyamRxJiZh6lBMxKjEnMlYYmTy5i5jfBcYNcn7mImQEEs1OIjbLG2o4xs6mpQs1p5q6QM0pVx1rcaPjBRROKqVo83VGJeuIRIysWjcXMmTZNoXapTe+sD68gSL7tbW/78Y9/zCuafK2PjTR0kRkF1ZUsSSUyinCLmczIElfokRZ6iilZAuRyOyJR2SY+34T89V+/d7/99rQS0S0jC2WidLUTCRMN5whVdpD3M6yYV+ViAx1lICD89Kc/DfPNIkiiDIsvLyJwVguEIggnBCwYhOgMjDWLYzhATC4uUxYe3OV1RbcyKpEoV3TAVepr+cECyiveIWqubB9jJKMQu9pilCi7EmXEQ3lBpFsEToq5hpIY3AojwrhNJ0YVtmkVU7lEYMIQ0P+LbE7PZgausVyLLJLf+973mrT5yBYGxOYMdhNLiKoXWhc+Am0CNAtde+21HgT4KHNm9XDk54w++9nPMsoxX8XstJlKhg6mO1Pc5lR2hLqMNWkz5ygVgZ7nX9B5ae6RxMPL7//+7zsW2uN8PJtQaTNLiUrVW7agN1bJJSNr+KKzn+Xp7O/+7p8YL7eRpCNFX+puvsPQWKybX6jwAwvWGPoDZeJpNLSSK5YHujdpERlXGfWrMKmMoKc5z33YPDbqTvqVKyspSS2wIdhleUHK7DL94MUs3gKAiWSOZcHp+c7zvgdDSVYFUpnXOPUZW/zICcUUIYixYLBCUC8FMfSSZAmzSwEZCZRKODarCLSn0Yip1yjpRCARSATGhEDMq3E15yCCLkLqE7vISBWJ2TVCMKMLAyJuC0MQspu4gj+uYiRF0SVLRLqKxxbPVp6hxAhizN5mbC/uzMmRS3wU4WqqbDJWnJEahZpsgyeYgycY6le5JJneg9ltCZFRPCKkSYq8QUTGYBODpzAUtiKkntQeWVIRHUO9UoWBHHSU5RrNFDFgKWwlvjAXVUkIzsgeihU2sBS61CjE1m/RQuQNlfBQuNAli5ho5RITRF4TgUQgEdixEYj5MK6mQUTQpdZm0UIjIlVkTODVJDvEEETJHrdVcpMhCKlpf4EQaMCzgNMeWVLr+Nfp7d3+xgonKq5eiE1WuV79pBOBRCAR2H4RiBkv9I9HnrCqQbsW61CvY8lVJsww3MFTnnGkRvYSXxciqciJ4jqm4vGEi8EBhd54e4UbO3dePHoEDl+X9uxFVF2BKLEUWniC6Ciknj3Y6jGFDsnlNjjjORFdJEMGjS04Q5OgI4tr/TbMaz2msCFKRTC08MRtYSi56jHNTButrFpKj9uifAip5yoVDLGu7WqUyLqcwoYIQNqLjuLymghsLQTSAXq0yMcgt2djC+r88893pKvTHB3v54QG7yjNYsZ5zBEklsE/Wumd+GImJVPYpMBSOmY7f/Z6baO+4Q1v8MsCXKzsUdmdshcY+6yunQqs4qIgQoZjEF+f5lrYihot8eW2zqAUdRy5rJIRgVN2Ae06ghr1XEFHrhDSnho6MLRcYDmLO4SDA5NW/uu//muGuf7EXnQIoi6qPaaeWqcp45QvTrd2MR37wU3KHiHaWd38XO1l2lb89V//dfu4n/nMZ9zaLwzn16gFfZglPqy8l/jS2ffVuA781u7OXgKL4CgsHvAcgn0+pVJcdbkTcYi3DekER954Ah34uvGf/tznPuflu01Hb4e5OGuU973vfRjUyC9Q8MlW1j//8z9L5RcuCwkO3LJhaTNVDH6Dghw18lvS3/zmNw2TF7/4xdypEfZKHat50UUX8QxzKJd9/fCU+vjHP87X3JGlnJx89eWjAoTf8CWQh+6///u/WwbJAv/Xve51TkHjsnDcccfR39rojW9840c/+tGrr74aIAE7R3/fkNGZc7OBecEFFxibMNfzCXzmM59JWwg7X00pylI6lGy7UtuZrIaGkzupF2d52r5VrkYprWYnmBBnfMrOHZnnIglUorCTm/k1chykZOEvBA21jqLt0Mtl6ClXpIAnrgjZ45culav1KcAf2p63/WY8GtchcLbzTTWR5T3veQ9PDgq8/e1vd4jsGWec8dKXvpRvJSfvd7zjHfa8wQJbMVpf83FN5pynIdTdL02rC51VSo2wwe0v/uIveA84z94OulvzGz21rP4AQO6P9t1hpc/ok0rRYTSEM9sUQb5TbGM7XOThhx9u/sFWr2ABJIlEYIogYKgaQaZrM4lxYZLkFPv85z/fZGUONOc49x2PwWXUIwTIxBUxeoNS8FRcSFCoqYaEusDCFoSCYi6Siw5mA6sF84PJxATL7jAo3/jGN8hhfdyGPrIIRcII8luKC05Tk6DoULVoGER7liJ/BIaWXAFgKIkW2vOKER8Z0UKhEXSL1Ih3G0QRVfJiZouZOXM7o2OiZjGZFXOyCZCxU1mprk3YBv1r67qFzKJAwZbkug7lQVRfClGauOgTdPAHsFGdTV5vueVOvXGPPfbi9TR/Pjd97sXxnRJ8KFB9nRzFsRosLO9n5k+XoI/ihCDCVt5yyy2cC60TIgnhGx7mwNKC/WWvGSMvNVSWwqy5BQNajGWD7seSsn1MvM9srFtYIisfRsdZ6cwuixNf6eiWdLA2INlnOeoLcF3UFziSWLRXvOIV9NGZY4WgLKWTY7HEaKqRWkjSpenJeNHESon9op6MZ555pni3JFhckR+1E7lJPJMhEUgEth0EYswawghD3qzyB3/wBz7sMeStUa3hPfRJKtNvIaIKcSt7iQ+B5bZeU2xxW4h66sg0gWZLDtC0es5znmMGYzsYFM+G5i6ppqAQS4G6jQix9SpETNEwFDYHmqjDTBRN6jyFlhrS2mWWjCGznqUktRPUZi8UPRw/hrBuxIZkQoJZEiLodsliIq/sHq98qQW317zmNR6LPCh5RJXR4yceiKEJ7ygqkjrKL5HteYuqeOp0ybIJYqByervt9p+eetqvL9qp68GHbq4aupuSfV3dPY3+yqWsBPIZQY/ePm73iApPCzaPaQjKqyAGFpD58zUyulTTcx+D6EESD/vIztpR8DQqr3cC7C/vOu8c/PSHLDjZQcba6sU6EIZkxjO+NYxHafogeOfLznqCms6e01lJ8Z5VLReVaHBpbg+YtLXyIZAxPf7445lg1laSfu6ob0I0XJTr2yr6eATG6RmTTKsCPHRQKB6hoJFEIrD9IqAnj2e62H4rvG1oDnZzIF1Gg3+ZbSJLaS+EUFLRIbBcI6ZiGmKrE0G7BiQlya2ZMEy8Rx6vaj0EmR6VbsI00wZ/KBO0a5ET1lNMnaGlmoU5iFKFFraIr1/rMkvRGIKnrkakloJoVeh6xnpknS48LcQIPJJCjVASHTGRxW2JL7oVaVIF8WLqi5OSN5JalJEFQ/AEQ8SEKDEIhQYDWigSxLfElKQkEoFEYHIQMAbL+J2cErMUCIC9zMabxL9Mm5Gl8COEkooOyeUaMRXTEFudCNo1WqQkuU37G5i0XwtcHZOiLeotKyayIEp85BUfSW6lCggxE2d/o6BSuhLbX55Eal4TgURgohEw2GPUT3RBKb8gwLQVzONRMSbhiIyYwlBytRNl6m5JGjlvSS1ES3a3kjz82iL3DtzVNp/3it430s3723h5255ruJh6QUFHHQt/y22JLxkL0Z4kpmMqmSUeEXRci5Ag2pMKWyFGmaXwF6JeRBFSTw26JaYd4RaGlluS63IKXeS08IfNrUcWCUXJJLYWAsON662lz2SW+/i38pNZ6nZXlqErWM3zZaS8nVRvJ235cJblcBkOHyNXKiSMzLM5qTpxmWXsOfGscqSQI3w+/elPc83xOpXbB/l44plkuLLqg6FMZ8Mxt8erZnvkcDFjYiakYEjJkWvRXmK9Xu2pIdk1dp25n9pfjLPBxLSUha2jtMC/XXjHGJ7KPnXi1qNHecftyhvYbp/28kvNgp1vj4X85DgGWRzUS9TfJHFX5c5rq/J//ud/SNMVHXLMmVWSbcuvfvWr/ID1BDE2OP2kMslcsvgMOfmMaxEhths/+clPeuF+xhlnWHP4sQkOAR/5yEe4tP7VX/2VPkNzvtH8ell3O5RwcMtjwCarTU1CXv/61ztNii8yBUjjBevAbJz8+fgYcUH27p40ath25aUkXj/0A77qTiYvZzumRKkmHlefE1CDVxP3X/6CdKahPdE//dM/5aZs35QydMDJZ5p6BZbf+Z3fURF54aAWfKZtuMITwg4xhQwF7Mj+53/+pyLAqxba0Y7s3/7t36r73/zN32BDc0+kv6oZ3fWGsw7TH+ypg93i7Gtf+5rm1jdE2q4I9+U6f502DFVcfWVxDSIYSteiD60wCBzf+Y2pIMXIt2ntwzi/D8IxS1eRUa2dcAZh3mZwk5dHMp91bmFUoo/GpT8fdB7P6gVkUFPS4lKLvPKVr8QAW5vcEFMX0BFiG5s7tXWn/qZD0g1EXKX9LgnfApvrwAfpP/zDPyg3Dpz+1Kc+pcPQx+cWhGP4j//4D93MJwT87+1h13FIOhGYUggYPuprHBnUJhDDwXcgxprz4M3J5iIxxpoZ25zAl6U5+qtj+SJE9kAsJrq4io8Q/EGTgMDsii0KjbzBUJcTbLJLCiH4FcpFxkg3Wfl+xixk2qGbeJNMyCyixEcRRYgkYkPDermRxTXmPbkQZLoK+ENC5K1nDyGupdAS0060ZAxlIi/57fzQVnqwyYsIX6IgJNHQhCZvpLZLEC9EvNWCZvUJjRnY5yJolkJNpZJjso2CXAkUb/VomoWqmBICB7c0EegQMYQgBKLQDKjsinCN0qkhFX/kwjb6wPPJccs8jzU3A6F00kIlBFU1NOEItpJ8879yi26qFiBU+nV38+9nncVYgTA01h7cwfGwYpJOP/10H8zwuIpSlMhaifEdVwiBCR9llo4+Yvix8dBi2Rkm1sqaltOzj8QMJZrAmW8W48UG6bcWDFyvAEI98Y5XZ7kULYm2vg7yOZYViLUKW+nrMp/6MJE0kQUDa/XqV7+aMpy3fGskiPnDP/xDBZVmCrS3r2tUcPvSObVNBLY4AuYTT1LmEA8RZ511loW0dayhHYZMcTHptZRrTovJKuJjmgq6I39L9tHfkmaOZXl9+2GG9zDo6wtGxLNDPIjFofijLDTY6syE16sQ9RKDZzglW5Lq0lqytHC2pCoaQx26Foa4DQbXOiFv3HbMIlLzhdVz9QRqtmdlGO6PfexjjCD0WCISipyO0qAxnPz2qkVdOsoZTsgI8c4gpyfjFc+bLBHm9kI9xvp0R/fgZKzK2Cwd6YAzghiLEEbWw6/qhByifA3ryc6D+fe+9z3+0+9+97s9bjOsnqAh44to3vZnn322R1oC4QY9bs2e5lhnHsmCb+F81xRHs/gGyUOoZ0PL19/7vd/znA5z61je52eccYZvZRWtOt4PsNoMKAUg7xUBmy4GG/vrfZTXC76wpblUPH6cSnbO677F8gmZClonM98e2ynJiOPJkAjsGAjo9jtGRba7WsRUORq1Ta31UM8yyuYrbIUoQtpjIimMlHLN2GgW30u2MHAtFqpFNwLbQylukol2Teoxk6CM4sZUSqg3yiyBfGEuZdVbpNS3sCEwxLXO2U7XsyQ9CQhEu5Q+UBp0EorOIrYKAtnEWwV2hZZRtkkFWibGOv8om6+wFaIIaY+JpLS/BaLNIYaDdziZ+EefJTpGEVUy1jtMU16rzJjn62wd6SI5iclBINolmkyJpUEnp/QsZZIRyPadZMAVV8c86Jj6gq6njqCbLPGyLnhi2Lbzh+T2eDH1gtrZPOHa0/QWN974cWKJZ17Xus4tkkONdmktbCQMF3CGkDpDS/Zy215Q5MKAKGxBh9iILMILTyHqbCWyTtQliw9RhaHlNhhK6nBE0SeEx7XO3MJQktr563mHS63zFFFJbDsITNkGGnbjZ9tpm21HE3tUfDFtHdnjsYdEMRtIvDEQZWaUJPDksJ9kv8qxPd5m2lgKnvpVFhbF7o5dWJwCA6AjeuNph8kmqFtl2bXlSsJ9c5N9lEDGQxHE8uDkquL9KacQRdi4ssWFgRDX2ELj0cLdkzekA4EwKytcajAIIScIdD2QRkKI5aMpqC8lVUFk3UxGLvE8V8Jxpy4HTQ6VYou3nhR6igGFuiCgWldGKbRVKPTqIfSJqyZQqSgXfz17vSx0JFFGa9JWiXYQbeCpTtQoroRoSmz4bRzyMLaJzrdGKZAXqZp2HPnW8F7loCNXS0HlVv/RpqpAeeBTkmuy7UYOQMDkGOSsMvoz/zpAbI7W9bc9qXGxcS+2y4vhggsusNHIcQ2bZQRVgaC7OlGMvz75IsFlB5RnsEZXtIpw38cm1enCytJXKcYbmOuSHUqlYPObjMBXljOA6WYv0yarXNyDuBFwrtVYehchDlC0c+lQZPGUv/LKKzlb816yv2vb1bYoDd/61rfaByUWFKoGBP2cJhQGhSs5TprkB2Azm8JKpNshhxwihlsSDaF9/vnn2+51WiR+e7GckiHghGO9RTw9OaLZ1o0lVMFcoeIFBPSAw88AepzF5SKZztx5nQ9HgZIrCBWkCbh496qIHqJoUOsP3Kr4YFGjJUu5hTxaZRVRIhHUk50QNYJD9KtoZZ0Qsy4EeQQnZv4QGhdWMgIcVnE4tFzkUEYSISFfJMKZnXBDUEDVwKLVtKNImyt6rEj10j0IdwsEmOAhXxKZJBvs2g7C5Ggau93GiHlP64jRw3Uw8fSxxU4IhSkm3lliURdsGRKBqYaAQSQYQa7mMaPYrOLzDOPF9GiYeMYzyox0A00wgowat+YZFsF8IlKukFDQIw0dI8tVMNjNLa5Gn+wGu9FdSWyevIhfpOBWxsge0tBCzB4kMHmCwUs3U6sBTkOizFHB5hpTtEJDKwoL5o2QX4kbeg5UHJUqJYbOPZJXqrnXdIffpCGQY4aJuuNvVujxS2R3xa8g6kXdcbgVSRr1KEwyURgwAw0U5rFQAI/IIpSEUF5MUUYMaZAnx0SKELQaHkmykOBaahd03FJeuXTgJwQxpoFu8ppaxQuYo3SqwhnN44cV5qcrl3IVIbD+VmKmTbZJroiMeim9FF0IDOKbJVR+5Co7jmAy/6d/+idexXyOlU6CFRxndTo3AztSrdDURb9SHP2VG9WJ0kNPV5rIrjrqjkYwlL4m0tsdWukwSF9qca4SIzvhlgF+/kI/J1+Mq5UGR2ed3y0hZDI05Ij3oxAcp3xn5bMfLUsZCnNntCrQf37wgx/4pIo+EDa+fAsU+mjE6JynnXYas+6WgfNRkJWJW3YtCnLSJBA0h4UoL3bLGH7hPt9yIqZFhUoF5q5BDGKT/yQCicD2gEBMOJ4R2F/Ti0nAfGKaNUsY0XFtDu5BmmEymQgtlasPf7nMUWanEkz7wY9gRAT2JXjE0CHUaJHpNpJMxfh90RHWnJWMOTCupAWb6YjVUDr1mB5ZXFskB2dcSTNhYiYhakRhU1+UWyY3qaFYiKKw4AHQlQIRGdeiPzkkm2Bb4sUoKEybJCG0LRkRgXZcoyA8oUyAHEk4iSKhnrdOS8LguVt27r++cSXEGsbKCpuHWfiEQLeY1cUk762IxRivYkmWCpIYLF91OniYn7FncFVjSoQoulxbdKtrMma6q7/R1b9+w+qvnvPF//f63zz2qUf19q0daGzo6h7omTGte1rDn6oJdFMLagOKCVM7lWUBo8QgrHk8/Vl+UFV/0IG1u6+AfPbD/jKg3g94dvOtkR90AoJaM5QckUFEJhuqFDy+qSafuSfESImPmX2Cy0wT6OnYZ8m6ig90CfcsjFkM+b6w1YE1uq9q/TxRQYP+yvIk6xGSBFXwiRTz6suo008/Pdg8PvsoWonWRRrRt98+w2N/eWzTnwSVEorMJBKB7RQBnZzmRsF2qn+qPREIMCumdNeY5UyYZmO0fmJ6Z9EUGgx4SpgITVLmlkWgNNYoiS1bekrbJAKGVbzr1kDoTfInw3aNQNrf7br5Jkj5GPuuaX8nCOGtJXaUZrewbS09p2y5aX93+KYvr24QXuZ4qGl/likDMImpiYC+4a1j1N17V548QRsd4wNEH/MQLe8oOxu24Axikq/jq2PmSgQ2HwG7PPWdlx3eHtUrOLiJUo9KejgETNC8J+0J2fvhz8HThX8z70l7SKZvQTeymcTGO7hO4ONio8gJOo75sTlELJ4QrtciXG1cOWAVbYvL1R4tQkG8b7kqMgMliz0qO0ORMYTENRjiat+L8eDeym/J2a4OzbXzZDPJvpT9VHM6k+BKMv9d7in2wGxKfehDH+JZ4swkQiiPAaEuCLeqwBGkXqJ3sjYLOf6qIP1Lks0wcmzUUSP0iaRnPvOZ/G7tbL397W8ntvAHj0jbcnxWHDIkCVDqSGFCeHDyJ1ZxP1vs6r1wZAkGNaVD2QgMsbbQbO/B3BYm5QtcNthKuR0Jkr0ccfQRN1MewEqktvONNC4hRY7WAYs1nOrwWOJGw4+cxwxHnOgevHU5QDtmmONvx4LaI+kpkucN9yA9CsEnlUA7hWqnsWxtFpxDE+3L+8d2L+9bebku2bsNyU1lq94lBFygg1UA5cRo51s75tAesO3SYGu58kxSa75HxNrslJEcbwntdiO0i0j7xzoGafqVXlokcKU944wzNNw3v/lNTWk4gMIpztqOtjY1oSe7a8kSREE4bjWfrqJonDqDphEQ9VyQ0Z9VnMK/9mu/RjcoGZW2wxVNz7pi9YxomKgCV2DOEMA0WpWoCLR2tFnL0Sq8BCKjojHIpV20r56v7jQkgS8FEFQwEG4pyC0Q4M/fQnYKi6GqIAuQaUuOQgPqcNGg/B/90R+J0dP4YMleBo6M9SIIbImRGmByN3ces+7EP8zeth6rp9nV1nN0b+d16WZcwSDgWoQgZDeKlYi2V61NTRTcAriIwcQQgLN2EUKT6MB1rZJOBKY4AjGgYjQZUKYIw4TBMupNHSYcU4eJy4RjHElyxWaeweCzFjaR26jR1zLpQZVMwWwjyYIBbaiyem4j0rwnhmRDFYN50q2BH0Xgj/khCEWbQ4xoc76vQfxaOo8WMRxlfGdCArH4MQukmWAtTswGjvI1M3AeDf4ys4WGbs1a8qoOWl4F8cWhFSEmbfOhidHywwRo4sUTVzNeMJsDzTZWRziZb2gQFVWgFWkAlOp0YdMX/ZUlFY9pE83DhqrOPuQ4S7gY8kM3MzndCMcsKJFpoxghoFMcmmLU06UkSwABAABJREFUCKOgLAAW5BEBCEKSKzaV4kYMapjw2vnhD39oziQwoJadDqG8dZcfedcBmFSuQk4kDeVFMiuU8XMNcTBkPW/AQm0FEaVQAQOZCLVoRgzqE/Qmr6GPr3os2377t3/7S1/6vM+urCj0IgoTXgoSQ1tFg1FlXVU8sitavFtBJ2R53VqxaD5LIMsh9hGY6qggz1rMrrpgJgTsOFUqakqgYRJWMgqigIFglcWEaUcrTF7U0Sd1P02sxypFkqGkdE0fqQjVVxBltKOPf8RYvdPWrbGggmycNQkeGZXrShmiMCgXP310kgLCJvFMhkQgEdimEDCWTTWmFzOYecaTu7nI2GfCPF7F/BzzGLWNdNOOYMnt+wqToSBeXnIEdMyKZKIt2ktetzJKFcOCuC3BPGPyNG2GhBJfCLkINCmZbazMLcgp5qhdC3U8MRszXiTHzOkXJDx8MVLmWF+MWOebu9qFq6bsJkBzsvmNv288LSJiDmeOzagmUvNeeVaiTOQyOdPHz8hwaKaJ5UHMhMGAxxoAnuyXZxZ1FxOBWVcoANWCP7eiaaIWQ+mVL5d5mDSESHWP1tE0ZbKNUlwr3Dd+5Ak5EalcmHiSBbLFAFVlYTL45qojBTCwCEUCxdRUpZhaoP3xH/8x8yQSCD7ThTmIvNvRBMyQ7FFWUcYtOtSOpM25qjhR1niU//M//3N2X9F6qQAKkqOgwEQbgVFTShVfIA37S1u9USokWS4CZbcw02oyKkhdrKDUy7dAHgbFbFJz7ajfegng54YMBL+cQCxDDAEdj4YOgfaYb/UFZG8Voi00QSgf8jGD10MoxXxTJ+jJXklpAgxSo2kQVFKEtbEK1nUrbVePTDoR2F4Q0IF1b4HCRofBG6vQ7UX/1HOSETBVRoml20yyAlnc1kIgZomtVfpUKxfaFldeE0XFTdSWVbE0yobYYTpD2t8dpiknpyJpfycH522wlJz2J7NR0v5OJtpbqyyt7JnXazoviLwa8vBrgnW7tfTJcrdBBOoTb7G/Vm6FHqvOITD63ljzJn8iMEUQMDPbDyrbAfEIrO718bgDQ/H4ptQOXMmxVk0nKD1AzxBiFuZpYWuNm4tzf22eOSXX/h8PG26yukv0mNiVtD9k58muoZ1Ub1iEkNmuCVdLfjzirQzw2JyTV6StLBt49rTCa8fSwRYXtxi7pHUhsghivLghgVOjErm3cuGlD8cRu3rcpmVUBYFMPT7clzkl27Uin3pveMMbbARSWEGkRV2sUWTh5kufeqE2xuwXhuOmza2SZDM16FCpxKuLerla+tiMLPHYFK2O9llLZAthLzmWSlohkkI3NKgdl1g2bsWQb4vO/hmxaipLYVZW0EEE7RqqxpXbrp9hdWixEm2pclESnBdY1AsdAAhtjjJOReJ99axnPYtPvMOJRdrr1UBg5PdjWiE2JNOtaBK1cA1pGGgr+HFYncq2sVbzE+2EO2LZjqDSA+QiShupKV8iXeVNb3oTOXZt+V0VyTQMmkoIgGhH2ekgo21Rvt32NdWxZKkT+O2h+n1b4KuO4mS86667Qiw5xPJgA75tUb1Oy5bakc/DydVOj0IhqV+pCE/cD37wg5RUF8zqW0okFqdIBOHR3KoWW/6E27vVbYSyIY1NFhJoIqMsdmqBpk/ajrXJWoS3E4EJDUnTsR3badRgU6I+Y7f4/e9/v0gaRgNJCujgIFVD87Uy8H1gQAcezDawaS60lxUxWoejlXFk1NiBJk084SpIGZrz2n/1q19t65e7ADni+YpRTylA5t9g61qjFH2i7iFH9QVJJbWo4eMBbm0mBL8+LHznO9/xqYOWdYA3aU7I5or32te+VmXDTzrGPjCjLgEUaY6D5ZwHW8d7Q0lMJAVblBtXmpTaBVGUSSIRmGoIGCBGhBnMLMpoGuPcbiweOLsgmGnTu+EWk6GJ0Uj3rRHXEDbRsPVFEItAQjAYUGgYupqpGHFfffj8hmQzhkjFsYbYDHyOoWKMU4PaZMJOXXrppWJCiKtAlGGLMJ+Yhcz2TI/ID3zgA29729uYQo4y/F3M5OYibJRBcxIix+dS5hCeMVYaf/u3f/vDH/5QdgrEtEksu+AAPz5JzLSMYigji8mEVfWVkSpwsVJrsyIcCKEDydFJgGayVTvfMrGqHGiYLfKj+pSR3Yder3/965Vr5oz5RzwFzFEmdpOn7CwODy14hlg1pYwsJkbfGsWET0PlooH293//91LhSRQwLbr++7//myM4zaNq4omKq1wiGSapfjCB8L/+67/2AwiKVnfHImoahoZk/AE1TSzGxNh183EaQ2xVKVUd2Z13vetdzIrvqbimKYXwKItkcsLNi3A+TNY5ITYYIBPQhWIiRxNIYA6ij6Hf/Oa3ci9mHXRXekZTBLCKtt5QTcZdklx0E9SdXdOZ9UbBmoeBY630N7XQeRgXhoOVgSohKqIgfZ62sa6guUBbV7AXEy9GlfVAFtAXR36rQTf29RErpmMA1pHPr3rVq+AsHobQ5j0W60xyZKeeUgCiIM6OlIfqy1/+cvVlcKNEV/robNSjlYqrL01kEeQlIYSMCVhiMyQCicDWQqCM1lipmgqMcdOFL4vMsX6fx5eup59+uqk4ph2jXjADmN884JsoTCzMinkmZiQCBVOBGjEKpl/TGisZFSREkhmDfTQHsjv1ipuv+OOaDAln4+pJ6JheXGnoC1hz1zvf+U5W1STPw9izA63opgpmMI82khg4K3lznRUFQ8wKmwNNxXXJtPUdzi/8wi8QYk4u6qmOb5ysPcx4xGIjX418h0wsthACNyWaJx0JbNL2AYkHUkkiZQkezxTsOxdY1rletAmWdVMXtt53pMRGlqgpTrfO/kfAzTWsW6wxHAas6FAsZHrBYqoPgxgxshMV0tgItZOqQa2d/uRP/uQtb3mLNzNWKRQjSjVZq8joqrKwcqSxNvJkfdZZZwEQ5h5vHW9sKeIxnP1ShCZWZdeSN8plf33GViI3k9AftCMryar6VNgTlipodJEhWTWjd+GhJw2pJCb6quWN53QLIQjoHlZTOqFOojVlJ81THgNtmeSBXZJ2kZHJg3nRXJsWGkF4jBo9ilVl963BvM6CBk93KwSvPjSWj6YsFw0itxDTQyRZQRky6hICIYawzPMBmy/lVOpP//RPqaGru0qKRkRQydjxmAwQSsoYeTFEf8AfMSE5r4nAdoSAKTG6vZdUBqBe3TLotqO6pKqJQCKQCOwACJiH1cLV5GwFYgX14Q9/GFFWucGwA9R0ilch7e8U7wBZ/UQgEdjWEEj7u621yATp49WNtvZ6yvkObLFH4PoLqAkqNMVOZQTK0r0QUxmNrHsi0I5AeaPugde0bDPFISxT6vn38V2QdnQyBgL1rTh7oryC+FLYYbLNY3uJKw8XlnCAxmyq1XuYdq7AdtRsyMnu3Urs89XxjJ7n6hBWW0flFo+O+K//+q+8lr/xjW+4tS0Uvi/ezth2KusGZcnlan8IEW/V+f2cccYZn/jEJ5Rr48oWoO06h13ZoOJho5djc3VOIW+Vww8/3KaaImL7zaZUCAw9FYSTcE6ZX/7ylwkshsQul103njrcnqiEX5KgmpS03YUOIXHlesXfiLN1Xb6kuOX3A1IglD0/Sbb6KMDfhWd23FKvroNIu7a2DEWiS7AZLCOZlI/IUKwwIKJc8UEUrSzOeKc5uFFerr12f7W4OmoRuRSkgprDhqs9RV5THKZtl4qEuYO47LPam7S7zJ2LTOoVHeqlFxpPKKPP2J60OvQzsqCALdcom6y8qf7iL/6CV1xp9Mjr1jahrUc75fS0I64gG71FciGiiHp2tVYF3cBpiIWthcBjC1MPV7QkwnUDJZKDjlu0+tqq1OL17CSDC48kPnDYxAg2wvl28ySzuWtHvJ5FcYIYVwqHzjqAXGLIJwT4xAYbzmgLKmHjVsVXgFuDEQcKWklVhA6D1kz1siKvKzcFo5hAY4HjlCbA7CoXfwXVj96IQcCvlVWKMgrV8x3PzOuLhoZVnH0VbC1lxa2ewHtPsxpxOk/U0TXmEE5XPBUMEKXb7Nc6VOKTbatYRr4aWkEt5OWvHAKVRR8SSqGxSdxSurLspvu+QtF89PmQ2S/nnc/3Xb9VC20knpPEW9/6Vg0EtyJB0TG0uSrS4V/+5V+MNU0gCw8DFQ/mAk60Gq1oQkjUsUhLIhGYagjEEDB3GeCu/IH43DBMXEm+973v8UAy6s08ZhuzitFk+JvDWW3Wnx3ndGJyM5rKGK8DaC4ykfpNc4MUg1veKpj5/Zg6eE6HuZfFkCTcrIVGhLS44pdqdWGiZtFe9rKXmWf4v/JosWzgH2xe5cYq1RrGkFcLDKYRJ/pbUYjh18XZ2hzCfGMzcSkidCaW+WZlTKQiTWtUooMzF9lH8w/HLKXTnDJqbdkgmHgxYyPEVMxXyXwFMRNXJEUqxIDDO+fCCy/kWEYxWSgpl6BoU6tSzPCseawcxCsoUs1jVjUyukXHrO4nEXjH+konKqJ2ZNKZfSFZa0aMyBIIlF3D8QPjgeThwbcxZmmA8MeyatK4sfyjdn1KNJkrHc4+qLN65L+LTUaa6C0cs+hMN0EupVBJodpardlQH/kEqiIDEDxCUWyURKgkoz7zute9zlqCV5Ouoh8qKIRooFDDmpYa3P40HFTVWpIAIg5wGsJ5zLqfD4RiE1ErsDsifSNEZ6tZ+sMwoIimL0UEIZXAwkMNyPhagA2ioeU0aRauBhGVoE1hy2/ubhZgVrn6GMdrvcXwCdyUQrLOYKHOwuqfuhNttZrFGw0pIwAf4DhjENFBjCshIuGDJwjXDIlAIrBdIBDj10A28BlfU4RPMc3nF198sd83cI5yfBTEfJjuqgm0q4tV/ed//mezKx9Z8THnxzxZqswSmfTMLexviUSwX57azJAt8WYPnp346z7KUVxMLPJab/uWlYe0D4FMs5bupjVmxVwXpctuHvMbLOZYz4kxG4v0qMjcsPhMLZlR5dDK9KhENiXMhwccOPhVKA9KzJ/Z0oSsLtTGb9oPM4omh2Jw4wJutlSKVM+nIoVgcPV6gS3wgIzTbQk44cNGhNUmTVLUFxHVUYXCbzKnp2dtX2sDP+IVqiztoqaUjMionStpGMIwKQ4mXJk97MCNAfJNsvWVj2eIpUkdEw0qr2ci3zV5ggMdl2sFeWAkRCrzQSCoRXrYKR2gaGupA8Byu0UIfcaR3r7U0nMowPAVnGkLXlcLA/Vlp7Qa4ytGZWPF6LHRQ6KHOC8f+PR7Xos2ZQd9O8R0WinxTpZdHV1LQ4fykKwTJEfphFtpeAVhHeh7PB8P+MrLwsYSDqT8xblEi2TlLWN8tueZ2kMibRnWEEgUQomSfC1AbX1S0DpRqWBz1dCqZtxZiMK8NBndgo7mLvxJJALbFwLGlFWlEeHpybxUevj2VYvUNhFIBBKBHQkBc7LViGADKJZGJudYuuxI1ZzidUn7O8U7QFY/EUgEtkEE0v5ug42ypVSKdVQsqCyxvIr0jsv+i5dCW6qIlJMItCNQ3mrmSr4dnIxJBCBgWo5X/WZm48UvOk615990gN5oINTfSqPDC4S1tv8XhyFxBLRpaodMNj4f9pM4Uti6s01oc5Efj70fPallh2mjMpo3MSm7xjQdvdCaIAgs3pXbuQxR4gkX6TY0lDEI/AjFibHtx4fJjqmtR3tIsnOysaco3m6f7CWv/TCnN9mUtcVlX82Vp47dKZuF9vxwkiYQ4qqyUV/x7cFunMiQXL/WOQkXIobAYCu3tpyFOj8aD06E7UzXllzBDA0h6DFdQzJ8VBnOtjNtyDlMi/OTPUg+XpK47IjkXmMj3JY5/cHl+CWYQMN2tT1U4NPcyUZOYOJeE4dH2sBzThIfJsJNK6UiLRraftas9MdAB33M7GP310Ysf3RuZz/+8Y/f/e53c6tyq4Hqm4KaTCl8cJX+vve9jwTZbUYqkVjK25XkfFZK1C3p41Y/UShna35a9mAKA0Jeu7AYKKCCugGfXUARaGPSDrS9SZLJsY+uXrx+EaQpHQ4gsseJBppcdr71JWzE2ma2l+8MKj1TjThaYVBldae//oMgWWSgYdCpDpWUSAgJtMIPAbQkGioIM5oCdHPoJppwTclFwMawdbYs2HiVqSzhOOlDZyOU0wAHPm0KJQiLN8A5r2t6FeEqoUaqX/ZxFU0aNnpST4uAlK8D/zzNRBNBEXU867SiHXjmOLTnP//5fC+oCmGlAxYRMrUpbcXIqNa6EzdlO80kx/CkWyBgRNuDpwBOeookBBG0W14U9u+5i/FO4Eypaja/+Vw6Fcx+P8JXHA6QIwGS3KB1V37//P/0Ov0qMFd3AmHC3ZmLw1e/+lUtRVUB4GqkJ4CRBydAIEN57UUgHMS4jRjXEZCpo5R0IrBjINAci5X/UHMQVJczzjiDEwnHUN48Rqjxa46ycuCh4jOqGNdyGeNGJecVM4xRHHLqmBAl0oASTOA8aI1EQVmGniRzlznExwxkxjgVSQKG5iz1uKeLXCHZzOOLpjhE0EnwnIbN+XybTFacXQx8U72xb0ZVulFvqWOAK4UEOpBvgjItmDdo5VZQlqu6mE6xmcDJxCDexMtZRwUZLFrRQS7xeAgxN7oVqQhTEGcg/jfKjSoggsYDK5ZXUJBU8VGjyGjS+8u//EsyRZqXpGILyZjR1kg4oaRQ8VRlCGioaWglyChIogY5JET2UIDCblXf9TnPeQ5fdtZNvWDuFw9UzXKRSxMfLHmV4komQkYYMtCclnz8xgmPrzk/M0Vz0uVvFF+eROlRNQpHUJbSqSoMxbX+G+q1xg5/r7lN8tYS8dGd7FqKna3nEEl5ukHbTwGw5rys1CJqxJDRVg/hcQ5MebWa7mE94Lg7PZ/tY3TwiAyjLCNzo/fqeFpHkqsmC6ijBfUELn2nn366IcNlROtocVA79dzAsbI1oHgz65zE8sHiPuhLHt7klspwYwGVoha6t6War4noqUuohVJYRmxqihOhLONRa8qigaJegYC6RyehpBjtWEcGXe8VLUl5mwgkAlsFAcPW0I5gKjBleTS2yvWAYOx7hGcuEZ4O2CMM+Fv0lFdMuZZhHoQ5xzRlwVzP5eHCqwAx9YevYJBLYENdxbgqUYg5ytWHu54ufdRh/jfheBL0AZI1vMnZdCoLZnOsuSueWUyz5lLmldGJR5imvMeNlCIoz8SILzqY4jiqxnn5YZIiyTU0wRwaikHg8f1J8IScojkCjEJwBk/hBA433LiNLCVjRAZEIdNVlRkU6LEsdVFBh9VAE1KuamdOVpAaaV9zO+hiVcCkeko6//zzPdqAyDwfchQkyOUDJL/bAxwLHscYW5gB1jMvExZPiEpkFORiDmRBxBWhCwkhcPOvWkTzMWoWWr6YIhAOygq4Qr5bang1wc3d8eRQ8lKCwiwX3Ng1K0ZLDqJEsndsuq4ii0WdJZzvqFk9OrO24IKbzuPpkukMc4aT4daRLOSkisRmJUMakyqvFyP4PWYaRL5/81YEdJ7+WFuPlsqydNGB+ev7vSwPzvKSowpxNco4RlOSFVYK4R4zS1P6okkWjRKrDq9Z6Bmwy15g1z/DBG8+5ikhEZgcBKL3xnAuJRqqhkC5TSIRSAQSgURg6yJgGWPJF0tNC4+yHqZVrFW2rnpZ+jgQSPs7DtAySyKQCCQCk4xA2t9JBnzyi2OOvbDy/BuveiZfgSwxEUgEEoFEoAUB79vDi2yqPf+Ox3+0Bbsd9dZ2ix0gr0JsLDm81uYNHxEbP3w1xNuY5EZ82WWXcSe1IWRDlAuLSD3J65JxvzEZU0bFeWVjMWEnjGuIrSl7XTyGrTP0ZksNGnIiOeigg6SqRex+aS+7v/ZubZXZ7bPd5WdS7Ts6c9r2YWnN8u6gxGwRIsQWUS23JX6LEM122MgtNWK0kcblcAMiPrh28hyeZDOPUxSU7AUKaHu0FuVOhYxtUWjTClwiBdvnnGZ0DzuLNlM5/tpAJRYb/6R4g9auQKmX3qI5MLjagEfYYrQvq0XwyK4dyUcTqNDICC5B9/N6jpKqYPvT/q5ybZ/bv9SCNhqJCn5i8dthtTeMJlx17C9ye7UZDwds4l0pb2+SVgTaP1ZBe8PiqWc/lQKYwYWBbm4J/MAHPsA/SecXw3lap1IQtydHatGchrIYOHy8ODBR1TYqgTzvZcdMlF6qf2ILHaQSZQrWn8VI0r2BEz55KiLS3qqKq6/b2NhWa2dcEagIMXo130GtQ3OV4n5tbKq4jVXuhpKMF3XX0CQ7h0xZDpgkEI+8JDhLDIxSlU4fIQg8YKEznwlui86TI7mAQ/nhgvayDQ9z+8S22+mvahBDiFd93nLqBTFaAYd69oxVR9Ew1yKGMP9pmovkzqXu4gHlSg7NA0BeZdoFFIJ9dGKj7qoDDf2Ztuqlq+iuKqsJlAgZ8erllrMFTfCEQMJNd8YIlUgWz8NSRgoo1NY1BSSpIH79B0QqpVDxAmlqNxwsGZ8I7MAIGAsGF1vgsEnOxA5LNqUbleZzDq/MjdmJK6cpxdgx6gUD3CSAwUQas6KBJtRRipjIYtwZtgasEWeMm9jdkkOCGIQpwnh0xS8jlWJgxrKBWBOFq+WB4pz97IsO06nsVg58oPmVci0y6g1kEkwFvHsd3sz91DwpixLZGv7W4WZEvikrhryrorHJRSs6oFWWGhRTqEhBjHic5Ec1o4KubkUSGDFBREXEuI3qIApn0G6VjgeeeNBFThAi6QMikz8dwEjJQMwtJAVEaB7akhlqkBDFRYyJmgMWfzWzqIYwtVpZOV/T3HjKKaeoF3Mso+IE6kFeQUo0f+oPXH8YUIZJqpUku0lnkk3XlIxqRrlBkzByKMyjJLT1O9/5Tn5szn42kyu6JZCjyoJUdty6yJdaFGYs1FfzQQlEOonOoHeBVLxq6sP6FS+rcErWyioOVaLYMkZWrjAlamqYKIgQAkFHgoGgFazNrDFwAoQntA/5uM5zH9frSIA2rQjk68Z3EA1GzExVqAQrQnymyOMKj+W6+kqlDIE6P/k+ZELgpLBKyahoIKgLmj7UVgRaaAHHbeDcHr+NxFBvG9Ek1UgEJg0BA9YoNhuYZg1/jpusmwWwIWzgW36zbuZbY9/MEzNtXbfxjRqFhhDZ0fVQF15oM4zNADOMZYBpihsuZUx9FgbsqUdy9tdCHRtRJiizFkPsG2yH77IsKuiBwoKci6qZE09YOvLRpZQ60R4vS50BHRNaOwLyluyFaMnb8TZEtQsszCGtXZPC0EKoODsCFlaGN7APvK1JIAM3ojyt+GJTFg9o7GzM3uLliiL0Adnxe+phznwUKiPb4SlSx9AZ6sUVtUkQym2dZ3NobercQaaN63BHOUqkEuX1ZF3Cw+Cb3/xmBpTZ1egqov9YY+geHktFqpcu5NYzI/dlnyJ7OyFSh2GgXeEmyXfI7B1pCiWcTWS4rT3ARaBI4OiNnqOdKq3iymIcPcNa3vjGWBZvEnCSpiwfxXkboC6KBi97qhUoQ6BUz+Ceu/HEWGOC3ZJgeGo7dlzp2GREWH+qLz01lqddo1XpaKEjPhmZCGz7COjDoeQWn0C2/bqnholAIpAIbPsIWISYqF1D1Zyrt/0mG6WGaX9HCVSyJQKJQCKwVRBI+7tVYM9CE4FEIBFIBKY4Ah54p9rzb24qdO7z0Q/sCdkec7bQe97zHkfp2CcTY+fGZow9J56dXAxtYeLh1+jEptikKQ/bnUV3ipUlQntixHfc/rHPZNPOMXucSvlnU8/GmO0lQijJO4R7hz0q+152whz7am8p5Fto2jmzWWUH0S+TIpxfqCC5hlOjrhiecmvMCOV2gohxFFGUBF29UkFDgHOn3dN3vetdXGdsJNPcDp9NO8gIvNbgaYvONqrTi08//XRIUgPm3NogyQ/VfiThnHi4/Nop5MkEf/7QtvFICwWiuKALOOSEZ7ANQvLR9LFZqymda2gP0gahTsV5yM4o+XYfCwIIzaQU2976JM0J1w1kdLowBzsbivXegv83f/M37WLaUzzjjDOcNcWLiAdeERjMvLhUQdGy0+e9730vh3gKy6gX2Z5XO/v0MKEqUdDwo/Yq7ieV/TyuXsf71olTzqn67Gc/a+eSZ5gzwBy6hhOwNjVtf0IPj/Mm/WausijP+R7ICLuk6qVGgnJ5j2kdW622SJ16RQExctHWVVBr0NENPva/SeBuZU/X5q6RGN578mqsgN3xWqTJIiAgoK0dDg0NVTBADjzwQG5StnXtNNs2JofboioLRrouEcjb5VULu7luVdbwp3Yd8NLKQdATGqr/ne98x+8Rw/Yd73iH7kSISGLVhZch9QBiqBJrbnGwND8wnuh4nAPHh8N580B41atepXfZt7bxrLkBq9UI4czh/C0eHmB8zWtewz9MLVQfv0qpqdbRQ0IIflUTlMhhnTLOhNOZlSiLg0s54oukCW8zWWQ0HOhGT85nQAM7oHwYoOPpUZL0fH7kuh8CwhQgQWV1FQgQ5dqCTN4mAjskAmYDQ8ygM8/40XDj3dg3Y4RXh3meqwdXY3XnmGWCNTGaP0Ua1CyCwC4LGEYYNcaUqbgeZBcpmDRK3ohxGyEEKsusZW40/K0czPmOcjeuzTAEmjrMMHxzKWzWNYGQzBMlNKStCU2MUf+GN7zB4b48XE3yJsYod6iowX9DgTIJ4JFXWmgitYU/bjvGt9SlCOkooUQWUYiAK2JCWv1KJSF4EEVCFNRMrCLhYPZmlJ3Tz1AygrCCJwZ+VDAEEcNttmTLxEAGkhjU3dxuhmSzOBiB1LRvLWF6t1qztmTxWUMKSHItetY1GZmuV6edjrxFLEK5JnxHO+uWim6pdSmL8mrNLf5P/uRPnNzs4ygWwSJEjcJ7SVdnpwIBPR+zOpaBoF46lWrq27q6BaeMMGG/wm1LDCctGdkaSUBjm1z1K8sAtpgm1LNmMy5YZw6CFhhMp3gZJSkLYmqhXSCpFIsoyxX8CjUedWNXtEU7C2WpgI3AwFl2CrNfJCg6gqTo+ZpD3uHAKShtg8T2qPM2CGOqtH0hoNtH8OzABDun1lc6hrYJwXRhTrCydWttb+WsavUpcSJqanoRqFQXbgL00OHJi4ZsqEcJ8wzzYQbzJYxfDzALMc30DOPCKJsbuZaaLc141t6/9Vu/JaPZm0UOnqh1vZRCt5TulkqR2q5bydVOtMhpZxguJkDumFqSQv+Wa3sWOADKzxH4gQifljEHMJHLkkbwZMSqegw054dpiJq6KoidApcPlnB6huVl7rhonMwB716NUi+uKBaRLbd1zvHRln9skP7JvrRIKDgzQzS3cvCM5hN3De2DK/YLv84sIDy4qYVKqQVk1IKlY14xxzO7eA+5UGLxVZw0FpnBjQdY9YKhLF47QIz19FxpmCA86MVXr7qlJGVZu5LMKDPoDLS8OirADTErn/jGzy1p1Aa14tQOJ0J2yyEvEyhGSWvI+OEInZluoFBrNlcqhWnuQTKqb0Uhb4ZEYHtEIMZyGdHbYxVS50QgEUgEdmAErGRiio7l4g5c06lWtbS/U63Fs76JQCKwfSGQ9nf7aq/UNhFIBBKB7Q8B+zAbbcVsYzXYSrpNQfubmwrDdn3PzLYe7aJxjPjYxz5mR8cmmU0jtzZN7Q85nIn3BscmOzc2dbgeOtKvPGnrTMOKHj5B9pAQLCO/iOF6YmvKppFNJr+g6srnQ7mcUZzGSiX7u/bMONnYfMJpB8stNsLtYNn9+uM//mPF+fVSm1U2rlRteNUeT2mvWnvM49zbJAVYfjA28Gz7ffrTn4YMNyAHBIpXF1tuHD1F8gu38cZLTKRUVbEzB0zo8RZ1aCL0wAg3fmA8QW1aO0xx5FYLrMix2+1EJVuDkLdx+IUvfIFDrf5mU9z+LsmOBONoZYPWwVRKCSDFI2x/OnKJ3yofWb68VOJjZzfX6YzckTkEh+8OZsXZIOcqpOvaUPRL8dyYijRseLQ+Fy5eQbRS2b/5m7/hYxQeP3RT1oc+9CEaYtDD7ZjKglM1aWjblS8UXyUyeRVwm7YbKtX+KGzt19KWy5fNe/uyX//61y+55BKFAh9oKstnlz5ANr7s+jtakuvY3/7t38rCZ9epjRzNVVBNYaKrqw58cGoadOyYhgdAbHirpuOZlYJHG/nVYBjSTfMRa59VERoOqipCNw7QKsXLSjUJvOqqq+ijarZ+DXNIGukGPlSpoUtoNSMLraFpRXOVjaYZ7oohWs0OMV8He8y8wO1AG4n2eo1iJ7rZxub75Za2tqgBa2P4KU95ij1+xdFKH5BkP1iNFKQp9RZfL/CQ4OBua5mnsukIjJI0IuGOFgM1sdqXDhwFHN2qOrbboUE492gMgPrc5z6nyhoOnvzXVUr1VRmMvv3ApjXJJEequtDfFc6anrYkqwXCAflahyidUwXt7utIw8GS8YnADoaAnm8OEQwfHxhYEhiwRorpxSTDPcUA50Ti4EkTsjnEqPR1DTYDk2OTcWciNcOAhRBXeYWCUjsdbMqNYDiLiWvJVeREdgwICtDK9G6QystV1+xh0ggvMb4yvvown5tPjGXzgDnTHM4oUJL5k9H0guEf//EfTT6WQKFJXOtFR4wSaWUOL0liBLftWSI+1C78hSipEdNyW89VTwp6uLIgQDep2OjptkW4eJEYNJA29Q2PFQKsuOnwsTOFsgUMnBgfopx++ukakYnxyxJmWl/jOGmSuw+gyGFW2D5BXgsGcyl/IPM/46IPkBCaUEP3aFE4alGg2HzCNyqMi3nb7K2CgtIVXZes0ChX19VJ/uqv/uo3fuM32F9f6egYNGQ6LTUtQS0vGRFCcFowWDZYFFmT4NTNVN9C2lde7IjlCgaYOJ9SR8LDI1xGTleWBHzH//7v/97qiDVnhkDE/Bk7IHUG+TnnnGNtoBOSyYOKHzbksfk9B2xWKQTSn/VnPS05BPxkWu5y3kLTRzVf+9rXqjsr5sMkS0F2lqVj5iyY2WhDQItIYv0d6c2c1TFJOhFIBLZNBEwsvo7guGlO8KBhRHsSMT/H7GqyYn99rWc+MXtYe1tRW/Mz0KoTEx2iZeIdTU1NX8Emb6FHyPj2t7+dGfVIQiVGhCZW0eYZU7EDes1XHoXYGh+RmrXMnGY86pnrVMQzvqmJw27k8gBoyo2Vwwgl1mvHjsftaFQdQebkJ2lQX45pYs/s1iSvfvWrAeJBw5QOT0hqetbEvM1IieckHUqyawyK+d/rkYjhKu0Z0zOUpy0mWD9hlHUMqdEBCmITVE1PYVYUnr432d+0F+Udbs34eu5j1LwxYLhp692Orm6Zocp6AlGYLTk+8YlPsLYe+YHgCQ5W//mf/wk6XzF95StfUSO1Q7zxjW/UFS1HrVp1IRaWQKCxs0aNHugBUyqx5BPiGZ+ttBJgavVei0B9jwc2lWjI4lv/WEkSwoj72TQmXne1QlCQdjEkee1DWycnVi18Ke2bWzHxDbP3GFan/NQtq3xqS3MO3zJaWkxQK6TYRGBCETDHtiyqJ7S4FJ4IJAKJQCIwJgSscKyIIshYiDEJSeZtEIG0v9tgo6RKiUAikAgUBNL+FiiSSAQSgUQgEdgkAh7TGI5NslUM4UqBGTHKLKOSu6WZtpJ6XlEOPv02gZoKz79dPN62dOvtIPIMKiGcUaIreIpGqB5CPKLlpbZtJ1mkInh78M/we7V2dzhJ82spuISQcouQK67ECjxQuVfyurDP1FJEPS/afhV+hBAC7UWR5taVNwkJ9YKiFKm2zfg9h0sKbXlrKYiTk/2zwh9aldtC8MtxarKdV7txPJ9K/LZAqJRa/9M//VPZLSvIBBHXGOoqCIegxcMhmhhtVzKqj1ZfV0liENx37Mk5fMvOIs9aMVrBbh9A+OJobtvVsoyMhixFPskCObKIj4xuuQehqSc1mN2WXBg0vaTgb7lKpafDKW2iO6fTrrna2VwnU6CevES58gC2//rud79bpFpHd0LrSIoOfEI9kcS6iq8XF7clknxyhlOsnnEctFpQqZ5RLWhVj0FH6eLrSUXDiFTTqFGJDyH1LGIqEUNnGBvFdnYpwF/KxwZcvugTPJG3fo1c9ZhCB56AUjS3Bldoi3SlVWGbCEKV60UYLMqlibKooWrURkMVp4DGE5pIomGklmtJQuAngXzZBZJjTKFHQCMk5DURmFAESsee0FII19s5CltZhTMNbxtF+3pBMCKMEW46gmFlsBhQbCgeniVonBxBuH1wQPFZgu8NzDnii11oUT5GmVxMOTauolyW+YsY0UIZiXLhiSCLW15WvpQwURv1VGIsWAr+KxYMSpREThgp/CY96lEYsyy8XpxbydmUeuT4RsURv9zO1E4RIb/o6dYkIGDw9QVjxOWUYZIRMw1dC/MkEwGISkEvHJf9vrxaCCKFolvhFMnoh/LsKawEzcd3jfKqyUkIbvDkk4QAV3iWA9lUDzrOPfztLPDCa81vIHAw4oDFgYmrkHWLIgKHKLSOCTCLSvX48dGk8UzSrErXIbVFFN3SgkV4FI1H1+JGxm8bp1xw+O53v2vxAwr6R63lUl9sFktg4UzMm0pZVkrg5XnmKjugfDOjY7CnPkni/azvQVhX1OVkJBAbVJXLa5x7llIwuOpRRgqCAnqvo6kpJgmwQqitZeUSTz7Xc0Isz3RX3ZsjHa30bUl8vwhRQe1FbUNSoFtgQgLNtSCZw4FTUEoiEUgEtiIChir7axqJbzhZNJMJqyo+rKTvKOrqGfJMs+FvRjLhmARcv/jFL37wgx/knRlWMiYruUaYfslh3fD7zQcTTr2IQtezswXizUimFHMOHagXkwz7YlqTKglDEKY+Yk1K6vXnf/7nPj7xGQm1farkAyrZPeVFQe1zlNkMCPJySJXRR7/t7xbkrasXoibnSjG/NcG11wei7SW2aAUiZkuLQMaaxxyucV2tpqLdy+Qforj/BiAYZHnnO9/Jxdbno+wUWLjeamivaCxLfBpKLDZXeaPcltLb1ducGJ/jajiGzzXe1VA1Sgyd63RJ0tA6jwUGu6l0ltcveFiBgFHXFaNqMuLHxv6K8YyvyowjSyo7z2OmTTWxMYW8jVlAPH4GhEpi+IITCFV9EpvWATtfZxlJM4Lcsrnc9FlPnsri9WHGWv+HPwyxRWBtxVgl0hbgvj6y3vDtgUIRbKssUfehHI//q8NTgKF3DSgeT0sqEdgeEDAMBTOPD108oPmk3PDcHhRPHROBRCARmBIIWKJ4+eN7MJ9aWVVGnXPJsQO0fdrfHaARswqJQCKwAyOQ9ndHbVyvj+Idpv1Qu2y+iveeaketbNYrEUgEJhWBGdNtonTPnt2/dh3C3/Q5c7w3Xr1qVWP90Hu2aV18wuymNPqbf5uvH4F9Ta9rGwXdTXdqflto/par145Z/PQuvlMN8tb3VkLCIyLkF1nhJRFXO0LY6iFiCOnpmd5VvdXnedDoHbUvWXdXz5w5HBq/9qUvT6nn32qzJENHBPQhIfYjg8GDdOGUhK6nusVQ4u0n2eOx3yPY4LE5VPIOR1TlNcV+/vOf55aBzW7WCGsFxXlTU7ZaQz1XC4642g6MlUd7ic4m5PTsBCknOCrUqTx/93d/Nxxze3bbqE5d8kJ/BPXac01CDC8idbcPp6wAs04UBSLJtaAnKQAMENrjI68sWpPbDeSt58I/RiTnHhuonN64hcG/FBoyS7mFGE5+bHsHW+QlvC7EbUnV/epJRThCLexB6j80FNCYbXzKLkQdxSNURCPaHddjbUPGzqUqYJNFKGKjrHqJQeNERK0xk4AutyX7FiHqBY0skFYY8Id66EJExqhaMBRReIRyKzWCSjls+7TTTnOMNKzs5nL5klRnLrk2SUAeD5mya/EQQlrEbzL75jC0FOGxMypIB01W75ZKkeSqVwRRYkIBWSI+9BepOxHS0meCp0iIvHlNBHZIBGIs8LXi2xFjLWZCUzEihgznEi4jqm8GiInaJBBzbzDzOzEnG5tSbZabog2fMsracaum6WYgmRtWMMhbH3QhQWSkcgPl71JuCTf2FSSVBKkCWhUiY8wMpj4OQ7/yK7/iNGvH/T71qU91EDI7yDNGXcpkEkXENaqsIAioizMLKcnWRHydc/LpgirwaMWhJ0AOWIIOrQqnSL44fKrUV6TUmPECOrfcnWWXZCWAWbNKEnQJCPhlA21t7RS5NPSFF154+umnW4A58DgEyiUE3YKJ+JaY8d0W+RoOHV7LJbKjzCjaFVbq4mRrnkwRiT+6n/a1cI2uQn/uVvyi+D9pbh1J51Fr7nchPyoIE47L1JAkSCIBgASSg8dIkZcQqYGkUkTK6JYrG078UgGL4CwlF4IokaGwLPozIVE0Tg5nHAqDJ7o6TjIjC/mc0mL8ki9SCIVDQl4TgURg20TAvGGCMg9TL8as8S7S6GahJMV4lxrTl2uwGe/MLpoDqKt4gYexeaYwj1BlzCZ2s1xHHqkt8Z4ZoxTxMcMEj8gwIm4FSRgQrp7oGRQf2XJmNQObYJ1Vz8PVVOYjnJgGW0qJvN4/kEMID9eYBt0qKJiLGu15Jy0G+BYkUc2RC8VpcaWlgtk1rI8r08yalHqRg9b0sqivK+9e1/DiFWOpo1Bf4zifmFM4fkABeWQFtmCqD37or2fqoi1io3YRGXRc1cinQVpfUGUMoLAaQdM8aopTZ8CppkLQInXOsHqYFSovHu7j8drELZ4rr7xSJHAEbK6yO7MZgUEut1yuI953XBGJU89kZy176EBCSCMQp3aJ33qSJMYwhLOVT8jXdYNflhIiJq4k4JSxpCaRCGxHCMQoyA68HTVZqpoIJAJTFgFzdfuaZMqisb1XPO3v9t6CqX8ikAhMHQTS/k6dts6aJgKJQCIwTgQ4U/UN9K9YVbL3rlrtr9xWBGdibFylOSs3naQrT+jNCcU72T5zETXoW9H0Ti50vZS623QwxCvtSr3eBrpnWqVeER55gyf0DZocL9i9D48dHLsA3Ry7+xsb+vw9fvQIZn8dNalr1YmeIvY3HaA7Nf5QnE4Qob4ZE4niW4i4tQlka8cpdzYmbfM4osnPevKb/Mu//MvRvE+xzWMTy6/x2tAKf44hXTr/S2a72Ihppgw7yHnx2kSkbWxW2QW0LRpbYp1LqsWSbMuQ25O95FFmqeWeWJInjQKidVyHA6cw1LUpiLWkhpC4chXyy61OUeIi45d2/Vo6Zq1mh88vxjpikPtOOYirLryFlksQWRdebluYh7uNvB1THctkr1df8kPw3LX9Hq4epcns+MZuqFz81/0CrxrhcZKTGtnsV5cikIaKiGuJbCk0auEaRPDbsIzbkms4IviHS22Px094e3zHmKJqUaYQLfwR71r0KXmDUz93mJZtcrvpTq7iPsVHoY5Vi0C3LRLaGWKvmmTlapR2hkmIKeWOoG3hKfoUGKMt6nkLksFcTyrZk0gEJh+BSeuKMY0Uj5YYEUoPBVzDn8OwKhOIGPOAKZrrlR8T59JkojYn//Iv//JVV13Fp4cPFjntI7HAiMF8wieGHNJCh5IaRMku1TxWUouGYppqVqe/R2oh3PJucaAv80dDPis+oHJ2r7L8rnocn4mZDkVsEKGJqllsqNQFF1zAVgIHs8gouiXLJN+qAjS48vDR4VJm/Ua3aKy6JgU9SUAGuIwYArHgV51od8zqG6muasoW+0kQX9EwzZxrOS1BgxzuRzyB3DIoIZMockpxdR22IB3IRxWKniKjIu0FRXxc5bK4DR5ZBLSreB0gmjUi9We3IJUEEJEhIQiR0Q/dRn3r/QcnBgEhRJJr9HAlylIfZYRgJhCzvlpUEh+0jLK4rffqpuxqnRwEBoRbAX/kjdu8JgKJwHaBgHnAQBZ8fmytbgoyUZhjTbAIBihqYXQLZoOYZETi90zk0YmTNDPHHDit34cxw7k119EghJ+o2cNEF3NImUnqbIWmjNLLLW3Rsog085S8hYfmIt/73vf6/Rk/I/Da176WBBPs+973PnmVjrPkKmJDZlRfTX1mgzMMXOEvRdRzTRpNZypZ53hKHU2hoba6R9Uii8Ylp5iGIodkdFy9kzn55JOPP/54XuDx5Uw4yqM9s/uoCSfzEcyTiUmUWHTeJIE/QAglNS5zrPqCnhBEJLli1txhPeM2kkopUmU3THRyrwhIw8CAFoMbEvCHnqVzyhidFj/mECtS09ABv1RBfFhSkYgQq5RYEIqUJXSOXEUxGaPESC3xSSQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAI7PgIeLvfNzBr8U5eXHNC8G553WNL5+62i1fQgtf7y+9/kN/z7AUL4t3y2mXL9nnCE+xHV/HhRtzR8YpY8dMdGk1+G4dU+zY8HXhJeHm+eLHtgwdvqjwPHz/+ufg619uAZ3NIrvZ9Bip3Zy7L4WqhkOok6X7XebvzEdjtNrsh63obs2c01qyveCoG5XY3evFwd6be0CnU/U2nCyVW6jbm7rp41WOPVV7U7qqIjQMupRen7Y0Tp9pdOkCP1OIxZtp3ZeSJzZ46EYLsw9nOcbKyzTw8tn9s4cQmUDC0SIvIkCbJ/lD4EhlRbsOVpGOWyDiOa5TlJ3R55HC+cTifaSLkhDPKJmXSh9cs3dR0k8yTxhD1olLAHuVGZLsOAalrR4aID56WvNr3H/7hH+QSlAUHh375+Xhsjja0jwhVSS252m8JL/IL0c427hjeuieeeKLjNm322/e1nWl/1wZn2VAkGc8JJ5yAx6/QPvvZz/7BD37gTDJ9lT5RhaJYIVr0qVBoVrYQkXc4/nr2yDhx/PWyCh2FulVuFB1JRf/CEDyuEcNi/u///i8f8ZNOOkkT//d//7cRas84so/1GjKjr4YmrmMVshX56yhRo34bqG5f1dmKSGbR2zUC0dvrVSg9vxAlNYZJxLsKYiKEJY25F20eNi0zzRYDrDNOQpo5Ohgs8aZ3efE4/hYRLizBX0qvE5KUGzLLFRGRcQ1+nBEfBPl+nuL8888XGYsT9iXOnrR0ERO56tfIKIbp8XziAyExaMwlqc4/yXTgpspUUhdoWwWVyHZligGlvOpEFWQX6sxxG1c8ESwe/EyEIqwTAj0/EqKJOUDzfkZouBASCtQFblmaPkVgoQtRkjoSwdZeX/EFEHQEbPqzMBQx2PGigkCAhp4DVZx1bKNouYIIOWiisIU0QkouSSIjRqSn38jo6rZ+pWS4nhMlKYK8iMgShGuUUuQkkQgkAtsyAgZsqGfwmlXQphdTgRnDqDc5MJRo029LLWI6EonBnMzjmY8sZ2g+mmYJB+7W55OWvOUWpwlciSVmk0RRGGeZf8ihrVt61qXF7Mr4nnvuuRRzSysl/t///d/SpUtJCGl1mUUBPzUQ0ywCQ7FxhWHrEqqssnQbWY0CEbag46pGJEReMRFZeILAA0zLqssvvxxoHMctRXQGGcX4JIkx0g00eh3zkDlBVyrRJGyWItyOpqCoIOYgIkvJiyi0pKhg8CjIbYDjWmdTZd3DghOnISCpCA/ClaoWMHodHrchs7CVW0QZTWglsrYemWW3vgrJ4hHiiw4hp9yGtGALGkPJW1KTSAQSgUQgEUgEEoFEYPMRiAVSWd5svsCUkAgkAolAIpAIJAKbRCDt7yYhSoZEIBFIBBKBCoHK66Gx3377nXXWWTzxuCL88Pzz7eDwZ3N4jRPNLvjWtxYsXnz22Wd7Be3YVpE8uL7//e9fywHaS+yWV+7Fa9mxKRwquBpH8La7/m7eyTWzZg7w/hoYWLTXXi984Qu9M//CTbdM33nhaSef4i33xd/jIFHP0PSl7u2fNn9uX7XHwQ26uzqR2p7FjJ6K6O196umnOl6TX5kCX/nKV/LMvO1nP0P7XeN+DtBUtb8hV0gd0iu0m7PLzquXLKk8nmf2HH/SSX5N+oc//OGdN95UMfPhLrWoxHVVQvw1DwirGKojsR9XdarZ33SAji407HW4LZnyiqSFIXazpNorahfawtzOYHspjo+KPaTY9Wln2/wYmnDfNCnU9awXhxbaC5JRaI/f6jGhlQEcmoTym1R1OIbh4s105EsVtJFbn56IiYkDmKPZKQ8JoecEXc31Ql14OARQW2SA48wtoc5T0MNWWj+6NLbIW+fHE5yuQkgOos7WkW7mqEqZIP56oYqI6pSyIqY9vqUKRT2tzCtCCLFaefRtXdekTodKBd560jZOB4yhf6hagHVbp7fxiqR6icDmIFAfApuUU8YFQojJFiFjuMMifGLxta99DVEcTTjomLpjlmgvLmJCGoHF+uAPycNpFamu7TLbs2ATyOczxIUILZerOZCeXFs4VIlRaHteMUVJNA3JUV8Z6/EdM05mZCgf34CFYh1Lj7pjQAQdbIUOOSEBHYRGtFRwmKhHMg9I4Q8EgTgaGao+mQtOcjqWO0GR4ygu9KRPPW/Q9RgMOMUAQYt3zCVS3w4eEOEUhqtpkVAYZIkSI6kw1NUokSWXGD22Ht+RnybUricVCUkkAonANohAfVDXaaM4BnIQw00ysmD4+te/3lK1YlVb4ltuZefCWyalKLGFZzS3ZebhIdpeC8cVjyykngVnVFkVQqzXCyX7uDUsEjaTKAoEdJuUhh9nyVX4W6pc4gs/BsEnyiUJIcYMDxAfBvteHbNbZlp8YavTJXJLEYQLxcqgRym5BYG4HS57iVcQ+XVMorgWafqJLCUysrvqjSEhhETewhlEyVUIbPFGS/bI4hoyyzCsCyk8QRSeyBViI3sLZ94mAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCOwwCAy+N5414+arr7Gl/oIXvOALn/98Y92GO6+/8ad77unXsz/xiU9wC3bY849+9CMHrNx01U+658917md10grPYO/bvRJv7jh3zZszsHJ15UMcgdNwz7Td9t573333vfJHF810KsryFY/jtm7DgEOXewcas3qW3nG3Q838sKTU3rXrNvT3rd1QeX5Wvs4rhs7HdMBX8yQdbtnf/J//8Q6+OvhZUPqq5lvx6V0O2XECmh9hbKxYbYvnOc95TmNDf2PurH7n6cyYRtVZ8+avfXRJlauE2C/omXbKKad857zzGn3r1f3BBx/0kvzee+91okzf6jVRu5Kj8nWujovme80Du79yv+Zv0N/h3LrHs+zQVDpAj7N5ywZPy2ZM2ctpl1uytCfVYwobIui4thRUz7IF6dGUMhqeLajSmEQFVpGlTo9JyFiZFQQTASFEdrdjlTMJ/CNr1VH5kqUQLXrKVZKKhBae4W4njT8KKnrSJ2JKfAsxnMJbPL6u0hYXPqECA7EJLSKFJwI7JAIto95QitEkPhxiwulKZHBGagsUJbXE1/1FSuQIRLsaLcz1cjFzVy1ZlOWpo9wGZ7mtyyl6Irg+S6rH1Dm3Ok3/qMgImhSGlsqW+KhgXULUN65+NQIRX1LhAaMWJ4rnXIBTz7gt0/Xq1+verrNUoc5f54l4DPXIEejgjGEynMySPZixFfnDFVcYWvKW2yQSgURgO0WgjO5CtFRkuGmhhW2Tt2GChytlk9kxRN52fcx48VnUaITUeUKUa4tW7TH1XJNAt9dxk4W2VGFz+IkKBUJInd6k2K3L0NJwI2heOFt4Wm7r1WlJqgNe6BYe2UtSiGphKKlBlNtgLreFKPq0yGm5LWxJJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAjsgAg0PZjvueceb4+ffvLJP/7u99XRXokTN/gnLGtW2MHPt912G7J/xSp7KNXmOxfnGdMrF2Sht7/yfnb8s98Ndh4z7+RZMxpr1zuSOY7tWMcvuSXwThYUPb127tvqtT/6wQ+CsfJ+njMLw/QZM3rXrOlfufqYU54+KEOZ/Y1pC+b5JeIqhhqNgdt+ct1tP/95+EPvvvvugy+6HREdHtI93byfpy+c7+iuVQ89UuUadOBu7LnffosXL160885L73ug0d2484ab/CHibOw5O+8sy4qly+jA+3nG/HnhZrBh7drZixauWRrwVPKmZkgH6M1t9/Y9m44SR8lW8uKPUGK2EWKKb0FplPaGEBmwbO/gdKxde33bY8adsV3UhMYMp2dpwUJsUg1tPZy0TeZNhkQgEZjKCNQtRX0aEV9PCojG5NYc0kY/j42pFejm0SKy1PXcpIb1Okb29pgxabJlmSdOmSIZATqgwapAF6kBaf1IbAwl45at6URIG0HVqGkwjMBGq012oaI5OSG2wFiSRkOMnKuuZJ0ejeTkSQQSgW0TgTKWC9FRz5FTO2ZpiZxQCZsvvEXbKX5bFjM7MA5bpM+EkC0iageGOquWCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrBFEBh8Hc1ludF45N57b7311qOPPvrH559f+TE3Gs5CfsYznvH5W25rzOzZaaedquOTuRr3TJ82o2fvA/bfbe+95s2cdeedd952880coPd4woEnnniik6EdFH3++ecjnnPWWQcddNDtt9/+zLPPuv66n95/x50b6dzVOOyE43bdfffHHnvskEMOefTRR6Xud+iTTj7tVHn/99Ofnb14J/7Tsh955JGcs/3I81lnneUHn8983vP82qTj7Q497DBa/eS6a5/85CdffvnlzqXea6+9vvSlL/UuWUba/PnzDzvxOJ7NzkS7+OKLG8tXPf3Zz1TQN7/5TcdKP//lL5X0mc98Rk1f9rKX7bHHHiq79KClfl/6uc997qzZs7/97W/zk95p7z0hQIcZB073u9M/vfKqQw899IADDuAXftddd9kH5xv9/e98p/L5nqph0JdlqlZ/Quo9soPFCEWOO+MIMqdm0uQjqcQStnfMVWR7r8IW1D+ataPABKojLBmZCCQC40OAG2h7GIeoCZqaPHLw023XcEfyjJkg6DQi3AI9cNURE98SM44WzywjIBDwjnwdIXsmJQKJwPaOwMRN7IHMJMivW41RNgetQrG4jjLXZLJtRcU4QNd9oAtQiAiTg8M4yhplFmxRheAv13HXa5Tljlt+ZkwEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQGEegbqA487u3nBMyleNaiRTvvuutDDz10ySWXPPGJT2zMnT1/p53uuOOOitnppevW2UPhMfyzn/3MmdCnnXZadYpzo3H22Wf7devvnPO/K1aseMELXtC3fOW3vnIOIZKuu+66ICoJQtNn9snHPfXYY4/9wfe/f92PfvzII48sWrTIAdJ33XDTqlWr4pX7ySefzMn4nptv+fnPf75w4cK7brz57rvvJod39f3333/Xz39+7733crDmP801mWP0jTfeuM8+++y2226NgQYHZYdAc5v+0TfOI+01r3mNYn980UWcnvfee280lRYsWMBJemDVGrVYsmTJFVdccfXVVztS+qKLLjriiCMc8zxvt134RnO2vubiS/lAH3PMMXsesP81V1wxa9asfffd97LLLvu/H/5QFfbebz8lTtmQDtBTtukfr3jZFRsH8biUpIYQGAeMJcuQjPx3shEI7yilFjepydYgy0sEEoGpgUCZZFqIbbz2YadCyWKz6pHbuP7brHqBoc6QztDbbBulYolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKTgcCM6T+4+rI5B+6z6KmHr9178dL7H7z1p9ff07V+4RGH7HPSsfc+cm+lA0/fvoFZc+bcesftq+978M5bb+5eMLex0wIpn/3aOd/60YW7HfnkmYsX7f2kgxszuxuzpq8c6HOc80N33NW3ek2VPc4SqfylG8cff/xNN93UWFXFc4B2bjTJ6AcefWRN7wZO0g+vXP6SV77ilb//+lk7Lfzx1VdKWrZ+7ZpGf2P12saatZUa8+ctWbnioQce+PaXvrri3gd4Tm/YsGH9+vU4586de9VVVy2/5/7GjGkOtOauffyzzmisX8+P+eFVKzA8uHzpgt13Xb56pYLuW/LoDXfctvyBhwb6exu9fcvWrl7X37f84YcOPuSQ3Xbf/abrr8f/yL33OXntRS96UaO/+hlqmm9YuaqxoXft2rVSp3JIB+ip3PpZ90QgEUgEEoFEIBFIBBKBrYMA7+fwg986xWepiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAonANoJA0ym58fBj11xzzYEHHuik5EcctvzAA/yJn/GMZzjzuPHYmsa07uqwZ6dFd3evXLmyMb0xY7fdKg9gv5E4d8YTnvCEU045xWHMPJ4dqNxY1x8nQ1cM07t32XuvKrvQvM7ZZWdFPPzww3F28rp16woMhNvN5+dMk3POOYfL8pFHHvnc5z4Xg7Ole3t7ETP33M3VOdAOfm6sXNOYN9st72fXR2670xWbI5x5PzfW9/lzNHU4RjvfOtg2rF7toLRK8/4Gz+Z58+bJtXDPPRs93Y3Vq9VCJXFWP265rvLGFkiosjQJx05XUY3GLrvsQo2gp+Y1HaCnZrtnrROBRCARSAQSgUQgEUgEtiYCvJ+j+EJsTW2y7EQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYGshMK2LI3Cjt3HXTT8/7djjulYNHmx83aVXnHT0sUvve7DSizdwc5+9v6vR1TMdMxfhOXPmNLgvT5/+ghe84KKLLlpxy528n3kwH3bGKY3efgzC7IUL99prr0Zf5Tzd4JfcDLyZDzjggKD5H1c+x80gbyVzwRxHRN91662XfOPbF154YZW9Kr+7SprRfcghh7jdeeedq1vez81Tn3fffXf+zYv22zs4d91118r7udGYtdtiftI/ueoq+rgNUbMXLKg8pJvnN/OW7unpcV71/vvv35g/f9rChbvsvtvs3Xa94+67Zs+bO3PnRZykZ8yft/fee1977bUk7LbbbmjEzAXzqbQnt+nuONpa3JQL6QA95Zo8K5wIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJwLaCQF91iNilF1+8bNmyu26+ObS65rLLli5devs111S3Te/n2fvssWjRosMOO2zBIQfwGHYo8tHHH981e/ajjz566KGHdu2xmDfz7GbgN/zII484Gfr000+fP3/+oJcwIQ5ZXr78i1/8Ih/lg449eqcD9zv22GO5FD/x+KfO33cvzs2OVd5j3305Fp946qnTdtvZidSXXXaZ8u+77z4ez8edeWZ1RPT82UcddRQJO4Wj87zZe+yxx6pVqw466CAu0U6hVvTiA/c74KjDTz755Co79+5G4+677164cKG8RxxxBN/lo08+uWePXbhH0/ew447jk+2UaVUjX6GrVqz48pe/fMwxx+zxhANdb7vttksvueTQY45WQdrutMfuqu9YaMoHMhVEUy90wX3q1Xor1Lh+tl81AEYRWn4VPXKNMu8mxW8pOfV6bbLQSWCo12sidKvLH011JkKH0ZSbPONDINt3fLhlrkQgEZg4BNiR9qmpRAZhTYwQtqAaLdLadWgpC0NLlhaGvIVPO4wRk9Bl90gEEoFEYCog0G4FJr/WaXEmH/MsMRFIBKYOAjHHujr05Zd/+Zff8573OFdm6lQ/a5oIJAKJwDaOgF3kN7/5zX62+IEHHqiO1Kp2zDu8qNnGa5HqtSOQ9rcdk4xJBBKBRGDbQSDt77bTFltWE/uSHA3J5L33i7/4i3//93/P8W7LFpHSEoFEIBGAwII9duMT3L2+1/bKzIXzHXh8x/U3FmRmHrC3I5OnNb0UHKKMx+zEV7h7+vQTTzzxoYceuuX66/d9whPuvuuuxkNLGj1dBx52+IwZM276ybWNDb2VEJ6bjl7mYtHXv2C/vfkrO/uZwOnTp69cvpyL8z5POIiQBfPmLViw4MEHH9xnn314YD/ws5uqvDOnLdhrr8WLF99+xU8c2Lxo773Rt95wY2PFasc8c5hWkCwErlu+fO7OO9OcwzR/7nuub2ZfMLexfNWeRzwZm+KYy5UrV955550Djy3d6aADZs6c+cAtt1Bsj0MOUaPHHnts7UMPNzb0z9llZw7fa1auWvJAdQy2U58FEpQSLyFlfPS++7l398yZ86Y3vencr3x1Sj3/Tq8aJsO2isC2sEW6rWKTeiUCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicB2jkCcKDt7li9Xlz/wkEOafXCxYNGi5StW3PHIY43p3Y3e6gMMYd0d93Jiro5THmhUhxDI6K/ff40fn3NuxeGg5SuuaczoquJ7B26/5qcR2ZjR01jfPLaA6O5ukcvvuvcnd907mDr0z91XXYt8uPmHuPHueyvREfr7l99+t7/GjO7G+t6lt97pr0qZ1lj70KO3P/ZYY/bsxsrVFBNW3f/Q7fc/VFFCT3dV4qpVjRnT7r/xxore0B9saiosueWO6h9iBwYGna3dTqsyrn7kMX9VajOsW77C39Bd9e/K+s3Uo9MBeuq1edY4EUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFtAIGmt3JjzkB3d+/ArFlzVq5dzYvYuc4zB7qqc5ub3s+V07Mwvas6vzlC/MuH2J/k2TOqpHUbKt/i3oEqsovgyqu40T/Q6O2tXKKF6ra/ci8elBiymtee5uHQQ87WDbd9fY+7XxOyYO7gcfgk+BU4rsyY42xpftIrVtdk8WBu+j2LIgS/4vqbRZIzVIPwru6eOcNBzmtWrqRb14we50M73bkqaP36GbNmrV+7lgw8/UpUoz4O3F0znCA9e/ayJUuq2ykc0gF6Cjd+Vj0RSAQSgUQgEUgEtnME8scitvMGTPUTgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEoEJg9ZrK73lG1zRXR0H39fX19w9M65rWN1D5DYeDROU5XLk1D/oyV9kEPs0ienoa69ZVfs8LFzZWOCa5ycMRetas/lXNg5kjU/CH2/DM6U0n5r7GhqZrclybIis5vJYVFP7Q8sqyfNXj7sZcsQXOzR2DRFpFKsKf4BLxkaUpANnf27tGWU2egb6+DU26v+lXzRM6ePFUbtxEdFfVX796deUYHWJxQKcZgnnqXNMBepLaWu8aa0njyDL6IgZK1x99nu2Bc6LrNdHytweMd2Qds3135NbNuiUC2ycCsRgwO9VXBYVGTNDEVYroCNvIqR2zZGRH0Cao+RLtRCARSAQSgW0QgZzzt8FGSZUSgUQgEdiyCORUv2XxTGmJQCKQCEwQAl7R9Pf3d3xRM0ElptgJRSDt74TCm8ITgUQgEdhSCKT93VJIppxEIBFIBHZsBJrewc0qcu1tnoq8gevwwEB/d/UMN9Df9Hh2mebc5oHGeqdCD1RHQfMmLqc1y716zeBBy8uWV57B4Zk8s8c5ypXnscB1ONwmCe1rRq2rDpgeDM57Jp/UytW4v6nF43oNShjirf51yDTPbDIjzb/TNpY/k7bNoNBKn+Yh0CEScz1IVaikpnNzlUKBnunOluYPPcj4uC5dlSe029rZz90DjRnNv2CeOvY3HaAHu0f+kwgkAolAIpAIJAKJwLaJgIWpV/nVor5TmPy3/JNfYqd6Z1wikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAI7FgIVP7OTVdfHsBNetBFoelDPNDdXbkFVw7QfIW76x7AVcyGodOZuSaXUDkW85Z2nnPTq7jEtxOOf/YXrhlcmXkv8zOmA+fiIcHVsdAliGxqVaXW4wtDozFr8eK1S5Y01gx5YJckYqOmJCBKUGL/kA7NE6BLyuNEFPr4/ZSm0gF6Sjd/Vj4RSAQSgUQgEUgEtgsEhvN+3i6UTyUTgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBEYFgFOvRH4PZfQPCouzozjBt3d3d3VP9BX3ILXDx2NXPjDkTgkoYdEVunlsOTByHpayd8kIoULtcOY6dLVvC/SqpghyYi4jZioQngzh5B1vWsffKg6KFpwXnVxy64yimwWEW7cYkoIOiSUSERBpmBVT52qdDpAT9WWz3onAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrBNIsDvuRwYxwG613HOfY29Dzpwn332ufTiixvhDM1jeNq0yj+4ciZuekVHTJyUzCO5zynLzROjI2bjmi7afTeSly1bNmfWrGOPPfbBxx594IEHlt73QMU1rbsSyxeZM3RxO46Tm6UWAq3oIjyUmd1THfzc0zNvj8V77rnnww8/vPSOuyuZoQn+SkDXwIYNVWS9CPT0aQcfcshB+x9Aq6uvvnr92rXhBV5xZtgYgWGO3t6YKe8SgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoHJQYDjr1Cd/ew6fXrlZ9xozJox46yzztp7330bM3oqNTgi+5PEWRkh8CGu3JGrtOp2epVrxpw5Fd0zfTBXdVOFPfbY421ve9shhxyyYsWKefPm/fqv//q6desiafqsWZVrNe9nQUb+0MjiCe0U5+nTGrRS0NAJ0z1z5sydP3+XPfaYMX9+Y+4szIsXL6btk570pEpId2PurrsQokannXbaO9/5Tnm71ILY6kzoxqz582bMnfPa3/iN+fPnf+e883bdddeXvOQlXXTIMAwC6QA9DDAZnQgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrA1EAjv52nTpvEYXr9+fahw64033X///ZyVG+ubxydzfXYUdG8fH2L+x4P+yjyS+UP7623+DTTWr1hZORnjjFwhq6tx4/XXf/GLX3zkkUckXXjhhYg1y5dXiY5n5uvc21fJbGbsmT2bv/LshQsXcGLmVj13TlVonELtHg/xK1etemzJ0572tBkzZjRWrW2s3XDnzTfTthJV8TRWPfQIr+j+devvu+++r3/96wt34SC9uKunZ/rsWdLXrlg5a9asmTNn9vRUvt2PPfbYfvvtNxDHWlf5M7QiML01Iu8TgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoGthwC/4f7+fleB3/CsOXN6uxq969fPWTC/r3kuc6XazBn7PGEfTsZ9y1fyPxZx0JGHL1myZMWSpb2rVrudvmDe7rvs+uCDD1a3M3v22GefZcuWrXnkscbc2Y1VaxwIfdW11ww0T32eM2/ehoH+rjmzB3g2r1rTt3pNY86sSmbPtBnz5jmP+d6bb1mzdNmaWTMqOXvvtWbNmkfvv7/ysS6hu2vG3LmHHnro5T+9duXM6Y11vY0163kzV/7Qjn9evHjVyhWNDRuodOtdd8o0QIFGY9f9961Ouu7qotjyRx/7t3/9YCWvq3HYkUf89IbrKzK8savYDBshkA7QG8GRN4lAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAJbFwF+z85+3rChOun5qGOOOeGEE3548Y+d/bznnnuuWLFC5C7773vKKadwdz7ppJNuueWW66677qyzzrrxxhvnz59/2tNP/tGPfrRi9aozzzxz9oyZq1evnjVv7jXXXHPYYYfts88+t91221577XX33XeTL+8nP/nJe276ueJ4OZ966qld/QNr166966677r/lNqU8+6yzODG7ffprfu373//+ypUrzzjjjGuvueaYY45Z++QnX3TBBY/7QHd3n3766XvssYdS1h500NVXX9376BKijjjiiIfvvU+5s+fPO/ezX+jr6/t//+//7b333m9/81tm7bzIidE0X7Ro0Yknnvi/n/tCYP7s5z+fV/d5553nlhd4ROa1BYHulvu8TQQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARKAjYCSt0EolAIpAIJAKJQCIwOQik/Z0cnLOURCARSAQSgUQgENh5552nT8+jVLM7bHMINI9Fro565vF80mmnfvO737np2uuu/PHFS1etXLB4Z/Fnv+yl6wf6f/DN87706U8d9/ST9tp/v9kL5t/9wP133XDTz2+/bfnqVcec+vS1Pd3/84Uv/uDKyw494nD+0D/68UW77rH7bXfe8Z3vf+/an/30/AsvuO/BB6bPnEFa9+yZDy9dcvEVl//g2+ddetmlr/u93110wL77H33EoUcd+c1zv/7TSy//0SUXn/GcZ7/o5S97bMXy+2+9/ftf/8aatWsb9bHT1/edr5/Lkfqqq666/Lvn9z6ypDHQmDt37sMPP3zT1dfg58Y9e8/dHPz8jW+ft2rdWoUefvRRDrS+5+ZbfnrdtctWrRTTcHR0o7H7Pnurb2NDbxWTYRgEctoaBpiMTgQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBqY1A/MBonDaUZ+1M7b6QtU8EEoFEIBGYPATS/k4e1llSIpAIJAKJQCIwhMBjjz02ROa/icC2goBloY/i4ru4gw46yPuZe++5h3Jd8+b09vauX7++MX+Og5lnzJhxxCknORb60UcfXb58+YMPPvi6173u3nvvvemnP+tdt+6oo45yDLNc65Ys+bt3vauxoX/unrt5yePQ6KV33RtV5f0vO3rhwoUzZ87c8OAjvJYbK9c88MADjmT+yU9+MmvWrCcffbQSFy9e7Hr55ZefffbZZ5562s0333zFFVc01q5vTJ9WiRoYaPT1z9lp0apVqygWwl1p9dBDDyFm7r6L6syZM2dNo+EEa2XxdXZe9fOf//y/fP/7nE79wx/+sMq1rnfGLjs5stox1JXk3r4qMkMnBNIBuhMqGZcIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAlMeAVtrMHANYsrjkQAkAolAIpAIJAKTgUCY3ab5rQxxhkQgEUgEEoFEIBFIBBKBqYlALAvXcgJ2NnN394YNGxrrN/AYHli/rn9a9+oN63vmz9t1rz3P+/737rr22sbs2Y3Vqxv9jQtWrbzr/vsWLVrkQOhps2ZyRJ42remdvGZDwLhq7RrZl97d9H7u6WpsGHCG9D0PPiC1t79/XV9vY0Y3P+ZGb+ORZUuXrV7lJOYlK1fced+9a5Yuve3mmxqkzZjxtW99c+GcuUcfffTznve8j95yS+WjPKMnHKDJ4SRdaTtj2vzdd19x93277LLLXXfdJZ7rs9KXN495VqhzrBs901etWf1P//qB3Xff/fjjj3/m8577hQ//R6ObhHU/vvjiRt9A9+ye/r6+yiE7QycE0gG6EyoZlwgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCUxKBf/7nfy71/upXv+roneOOO+61r32tPSrxYr72ta8VhiQSgUQgEUgEEoFEYIsgkPZ3i8CYQhKBRCARSAQSgTEhULe/55133re//e3DDjvsTW96k9NwyYnn3/weeEyQJvMWR6D0wLvvvvuE007d68AD7rv9jkZP9yOPPFK5RN/30E033TR//nxOzI0Nq/d/2jHoAw444NzPfraxrm9m9zTZL7300oMPPvhymk1v7H/UURyR995778qpesFcXsYNXtGze3bdddfddttt6a13OvJ57ty5DoJuPLSke/fFe+21l3HhJObnPve5Tmtes3w1Mfs99WjHUd9www03X3alv5e9+ld22XvvR+68u+mcPSMQWLp06aoVK3Y/8EBvk1Y89JADp50hLSlOfVbE0sYjPKQrz+yVa08867mOo77px5fdfsVPXvenb+naZdHAI0unzZqllIcffrhvxcpGV1flWp2hEwLdnSIzLhFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoGpiMD73/9+P7H6h3/4h+vWrbvYWTuNxvXXX3/IIYfYCfvCF75w5ZVXTkVQss6JQCKQCCQCicAEI5D2d4IBTvGJQCKQCCQCiUAHBD7wgQ948vX8u3Llyssuu4x/580337z//vvvu+++n/70py+55JLie9ohc0YlApOCANfhKOeOO+74ny99+YRjn/qMl73o6c97Lk/iZz3rWY35s7/8kY86X/mEFz7viSefwKWYx/CCBQv2P/LIfY89qrtn+k8uufjqCy5c8uhjch3ytKdJ4pF8yimnPPbYY6ecfvpOe+9N+EnPfOZ9993H+9/Z0k5uvvrqqw8/4oh9nnKkcOGFF668777+Bx/1efypp5560AlPPeOXXuatET9ppzUfctyxZ77sxQ8++CBvbHKmzZkdqvb29t55551nPutZ/Kdvv+WWgw4/3BBzIvV+Rx3+lKc8BfOxxx47fffF5M+YMWP/Y48i8KijjnrC8cc+/eznc9fm/UzOoYceeuaZZ+6000559nOgOtw1T4AeDpmMTwQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKByUPAfo/CXMsOayEmT4lGw6bXe9/73le84hUO2ql+rrTReNGLXmQP+Pd+7/cmU40sKxFIBBKBRCARmBwE0v5ODs5ZSiKQCCQCiUAi0IJAmOCI3CoPv4q+7bbbPvKRj3j+Pfzww/mDivHzRzw+PRT39fW5rT+hh6p5TQS2CgJOJedVfMvPf/7QQw+tmVW5vP7k2msr3+gVa9A/+OY35+2+u+664ra7Gj1dn3voIWPKoc7/97VvVNoONL73uS/N2XfPNWvWDCxd2pgx45xzzuH631i9ujpZudG4+KKLLr7wwpkLFjhlua+397xPfraxy8Jddt3tGt/AL1lRSehuXPL1b83cZw/+0xd897uNVasac+feeMklT37q0853u2Zto7fy0q4Gcm8vYv2Kled961tzdtl59f0PNmb28Mn+wn//95yFC7k733XHHZdfcH7Dwc9dXdddd90111zTt3btnddeu9MBB9DfuLvjhhuqEhuNn/7gop9efGmjp6e6qfTME6ArJNpDOkC3YzKlY4px3VqWdRtHv+AzSj3HCmPIb88lvj1yNDqMO2MIH2v2sfKPpgrJM3EIaC/Cx9e1Jk6rlJwIJAJbAYGBUfwkSNfgV5VbQb0ssgWBaK9skRZY8jYRSAQSgUQgEdhcBKq3+Z3CtCqyvl5KK9wJpoxLBBKBRGDcCFTvp8oGjh/zrPZypg00UJFifkZM9jMpp2cH+XzqU59yCNaTnvQkP5zqeB7ez36r1Gab4CdWy/lD4657ZkwEEoFEIBFIBLYWAml/txbyWW4ikAgkAolAIjCEQOWwNzAQT7uT/cw7pMPgv07V9fz7Z3/2Z3ygnUHrENy3vOUtPc2wdu3aFua8TQQmHwHeTY+/hxkYWL5sWWN1d6O/f0Nv0yG4qzF7t13WPPbYynvvm7VoUaXehoGBhx5rzJj28JKlHJcr1+HZsxvr1q2++/4qtaer0de37v6HKnp68/0/ounlvI4TM5HLVlZJjyx75JFlFRGhWdS6ex54eNoD1WuqmT2NtesUdOMllw9xVP/284Ru+mJVN+s3rL7vwYpYu2HF3ff5d/WqtaundTemT2/wke7ta0zv7lu5un/l6oqnu7Hkljv8+5j/p8VqvTFzp4Xrlixr9DWniLhKzdCGwCj8XdryZMQOgwDHx5ZQqmbuEMptEtspAtp3czQfUzfIDrM5UG+VvJvZPbaKzlloIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJwA6LgHeZ1V9fo7/aCGr+2eOpXlNvlXePn/nMZ2ywffSjHz3jjDPe/e53U8PWL+/nHRb/rFgikAgkAonA1EQg7e/UbPesdSKQCCQCicA2gYBn3qZr48bKTP4j8De+8Y3169f/+7//+9FHH/32t799zpw5Pgz2/EuTdCzZuHHybisgoBM6F/nxD9H5wq3va4T3M3UGGmsefKSxod/Lo7VOdI4wc3pjQ5OnedByY0UzvmfIUXZdX/UCSnCAtJPORcff+vXVrcAvuu41Weie7u6FCysHZW+HcM7qacyZ1T1vTs+c2Y/zD++q1z17VuXNrNAIqlT7xqBr7uxGz7RKk75QrlF5Pwvp+jyI17D/5AnQw0KzAyQY/2EUR2ONBs1n/2CWqH7X0OgcPG5jBwBlcqswvkXJNJNv9aVX1RbxixLj01r2CF7TI0LmKEWZYykg4I+8o8wYJUbGkbMU3UZmy9SJQCCWBaV3jbKJR9OsE6HtlpI5ymqW4rb3+paKJLH9IjCJnXC48w5r4A2usWsx2w/ZcfjX4e3IsO3Wr8Opk9GCQ9+nbruqp2aJQCKQCCQCicD2gkC8fxx6GUrrDvY36tK+jkqLvL20cuqZCCQC2woC9cfNRbvO32mnnVZvmL7ffvvdd9e9M2fO5HDsR0I3rFoWrym9M51Mvf0I6XnnnffCF77wd37nd1auXDlv3jzXyVQgy0oEEoFEIBFIBCYIgbpBTfs7QSCn2EQgEUgEEoFEoAWBcKEMK7zz7nMXLVq0rrHT4sWL77q9e5dddulaf9VDDz20bMmSrbJxeemll37ve987++yz/Q6SJ3G6rV69mktJfUe1pTp5mwhsQwg4VrlyLG401m2otOruaqwb+nydMzGHY47Fxl4Mvw3VD48N3vY3f4osBqeMZZXMKXr2LDsEvdWJzs344NnQ388pubs6f7rpx+zK87rTdwyd0KnOhxY2DOlW5YzNiErEwKo1nTJl3KYR6OwAbTItU9hWmVg3rfiU5CiNMsraj6PtxpFllMok2+gR8BGVZQSvZVniOvq8dc5wctVtwptZ0ui7kJ7AD9t1HAqMshRs+WMZ9faaNFqzCoqb7lcVRh20V+QaZfuOWvBGjBMhvFndslrZqLi8SQS2WQQmYixss5XdkRQrU+WOVKmsSyKQCCQCiUAikAgkAolAIpAIJAKThoAd30ceeeQpxz/jV3/1V2+47vqPf/zjXuzES85Kh0n0flauR7xjjjkG4VXtKaec8rOf/azu/ZxP7pPWK7KgRCARSAQSgYlGIO3vRCOc8hOBRCARSAQSgXYE+D0/+uijTzv5lJe85CXX/WTFF77whTnT1qxbt85DaDvzJMQceeSRO++885o1a04++eQrr7xy6dKlk1BoFpEIbGEEOBPHCKq/QUJXrs+uxbt542JjzEkMwrXJuM8++/RMm3brjTdtzN28I6q9oA58GTVJCIzB+22SNMpihkeg2LnRvF2t+62Ohj+KLUc+t2jRnAxa4vJ2yyOg1QS/K+Eab/Y1ujD6FmzRScYiRxJRLQzD3VKgWfJo+YeTM0I83TjgPr6BMQJrJm1RBDQueaV9R9krCn/k3aIaDQrTGUapzJhKL5qPexyNqbgWZpUKwFvi8zYRGDUCeabgqKGa0ox50uSUbv6sfCKQCCQCicBmI1BfcY1sVfNXFzYb7BSQCCQCicBwCDS3l2654Xbpe+1/wDnnfuOAvfdbuHiXO++4bWBN89Blh/c4lUdoXoYTs6Xi/dqv3Wg/+/vOd75zjz32+MVf/MUvfvGLy5Yti/dX3jIJW6qslJMIJAKJQCKQCGw1BNL+bjXos+BEIBFIBBKBqYjA4GNk1/TGQO/Pr78NBHvvu+//fO1r++9zxOJdu++65dF1q1eLLP5Ck4OR598nPOEJb3zjG1//+td/9KMf9fz7mc98xvOvoxvzyXdymiBL2QIIOP5ZiCui/s4m3iaVpOIH1yS6hjirf4OeNq3R23fIIYe85S1v+fnPf/4PN/4DX8rKbXKIsyLypVAF9zYURnKADqexiXBH24YA2N5UCesymkbhWlrYRmmTsJWBHW6po8y4vaG4jeqryfyqo3OX586d6+pkEV93+VGJ0o70HmWLVE3ZPLw5PC9JQ4gRRll5EgQKjJIfW8iPjKPJhd+vRo6GM3m2LALRRq6GubDJJotu41oceSNmy2pFGk22uEwCQ9uob5QyQQV1VB7CBbeODBmZCIyMQKfuWo2UCRqGIyuTqYlAIpAIJAKJQCKQCCQCiUAikAgkAonARCDgIbdr2kC/z0wGGi98+dk33XTTAw88cOctt59++un/9e83VLGCF5v+He3bzfGoSYvyGO6Yn7/6q7/68z//8xtvvPGcc87567/+61NPPfVrX/ta8Hjb453PeMrIPIlAIpAIJAKJwDaDQNrfbaYpUpFEIBFIBBKBKYSAR9tp06f39nrS7T37F15y880333LLLbff8sgznvGMj1337QBikh85ff3r+fdNb3rTbbfd5vn37/7u70477TREaZV4EHYVU56aS2oSicAOhsD0np59DzroHW9/x6233vq+972vOPxk59+WG7qDA3T1tNN80+eNnmkutOeLWTnKNV3ltuX67PC6jXI4YRslZwti3cO8Qh6YyDfLLTpMwdtorLgaZWZPoaenZ8GCBX5a8Qc/+IFfmgDL6NsUpxXJwoUL5QqBBvWYJIyvFaKU0ecdfY1GLzM5JwgBHSkkT1yrTZzkCcJkBLGM5m677eY3a77+9a+PwNYxacWKFR5vDj74YL8v4+uFjjwZucMj0Bxxwy29ql4x1vl2h0ds5Aqyqu0MZVqTVGfwcY4Pna+99tprrrmmPdfIMT4c8vtQRx999KpVqyZiTvNjlN6D0OGJT3yiX4WulBmoVa2rmqgnotyqoAyJwPaAgP7ve8L77rvv8ssv37Bhw5hUTvs7JriSORHY4ggYv7Nnz/au3/OvT4LHJH/zx+/AgA2H8uVk2Na+mkmtr8lrJ0CHFe5qn23q/GOqSjInAtslAjNmzIj1809+8pPtsgKp9LaAQHcPx+aB7hmN3lV7PvmwXfY5+Nz/ef+Tjzj0lhuuWDS38Zu//asf/7f/rNTsa07CWCcsmPy9krWSdOSzs684PfN+VtrHP/7x3/md33npS1/KARqPR8gwE6N5Nq8ZlAnTOwUnAolAIpAIJALjQCDt7zhAyyyJQCKQCCQCicBmItBdHUrZ2zW3MbBsr0OP9Pz79X9+3/zFuz54y01zpy97xS++8guf/4wnzfom5mYWOFx273P8NL1Uzkh/9md/5uePeD+7/a//+q8//MM//IVf+IXiAO0x2VHQkjb5IJzPv8OhnfHbMgKD+wFc6obeOO263z5ve+c7rvvZDf/yL/+iVxuSQnbvbbkR6dbBAVqsZvMWb+3atZdddtndd98dZ9CGG5ZG3cartGOrN/oRNdaWGpTcXx0KG32gjmQ6QNfRmDg6WsFVsNo49NBD9957b37MbtsbZQQ1NCIfLJ5bvkeRt5qMm6Hu4DVCdkkkyBjXkTlLahQRqpbIEQj89Bm9SiOIyqSJRkBjKSKudWLLljv6zjPWcovmY804bv6VK1ceccQRdsvG4cHsGWP+/PlXXXUVE+xg+HHrkBm3awTMjZ5t48cc2ubJyqWmLXK7ru6EK99xEqjPOXUGjsUnnXSSb5AsgMehGedLg/fiiy82kMeRvT3LokWL7rzzzv3228+nTU996lOJ9bNTX/rSl3baaaeKuel6NViXrsp2h4RYzrVLy5hEYMdGwBA46qijdt11V+PXK7kxVTbt75jgSuZEYIsjYNged9xxnn9Z0rEK3xLjN9yao+RqrbXHHrvedtudBxyw7y/90i8ddtiR119/PeN7++23z5kT6k2zeBjobz4ldVdv/zcO6QC9MR55t6Mj4AWU9bOXVzbD8hl2R2/tiaxfbDrMXvD85z//85//fKOvEY7IF1100f777//UE4+98pKrBjeizL5DO1ITodCTnvSkww8/3MFXHuvC+1kpPkbV1V/zmtece+65l1xyyb333isyNAwd6g+VE6FVykwEEoFEIBFIBCYEgbS/EwJrCk0EEoFEIBFIBIZHYGgjrzFn8eDzb6PhZLTrrvnZT6666vRTn37MU572k6uvKPt9wwva3BT+SI5bOuGEE373d3/Xc255/rXDwlfwVa96lYPePBffc889cdxMPvZuLuKZf9tHYFp3o6//SSc87R1/8Re2+z/2oQ+tW7mq2ghohm1f/SmuYQfPqphJnV7pnbUDeJYvX44QKQZYY3K44XuR/CP0sDHhMyZHlmhERRdiBDVKEubqa6NOIR2gO6EyUXHRat6hIxyiY+Uxa9asMRVmtNo2lsvShP9lDMMxrUgUrcsJocxoSi/yR5/FV2WjkZw8Wx2BaNPoD6Nv37GqPXGSS+ekklI26ZQcmpTrmPhjuLnGSqhe9CgBcYIds2vg2zyuYzImUaWaWm005Sb/yCjBJzby6y0yQpaRG0vqJq1tsyCfom1AxDe1IxQ3jqSx+gVWKpcH8hHLCzajBhH0iOyDC5Wx8o91Ouq4HI3IGCOhbSjMhhqAtrdH1rxjKgl8sB566CGtNtbTZ+sCub83b3sp2dc3y21f3+re3lV7773Leef97223X7dwkcX5Wqqa0uoZm1WoYqIu9aSktx0ERp4ith09t0dNOFAKzo4dpfmr1zHtLzRMHbHsGSWAyV/vQu30JOAz+vVJtGm072hsRPDXbWV7BVtixlHfmA+LPgr1Dop7WdSrRf4It8ON3xGytCQxtSpLAcpY9TTpwUNNxETo7V0h19AcvtEHoi3SNv82SqHPUHGbEJn8IwNU8BmZraQGv3aPmE22QpG/Sc66wHG0b9FwZKJF/5GZpbbwb7IWwVDX39LX7xftu+++iLGO302qlww7MgLVVDoUvHo25qYPvPjFZ99wzfWr76/ciwfWr5k1bfrKNeu+/c1v/9Zv/dbNN9y6YtmyKgNO3lpDg3RIxJb518Pyc5/7XC7X3/3ud5ctW+Z7VM93OvYZZ5zxxS9+0etWP/ijpK985Suu9Ye+MmlsGT1SSiKQCCQCiUAiMEEIpP2dIGBTbCKQCCQCiUAiMAICYX/jVVP1SNvd6B548YvOuv6aa1fdd1ejMWPXRbv2dK3t7V9/7te/8Ku/+qsPP/jgvffdLZ7IbodBN3+8bgTx40vyrvjlL3/54sWL/SKfw6F82e4q0vPv5z73OfssxxxzjA3Tu+6iYeUoGO9Ix1dW5koEtmUEBg9HMU77+nd66hF/9O53XPTjSz/84Q81ljZfQ+W2+7bceDXdOjhAR6oX2V7t8X7mQFk/wY6nhTfdo3mjFy/Ek7+G9kbkWPHZKPMobrQgrtG0VF3YQH9Y3bKrN5g4GFtnbdIhP+rSltghIvk7gDIUBZyCJMK+kUY0+sQLQ1yj+ld2wf5T3QWEEJGjyl9jGlOWseppwRRFbbKUkBxso6lI8tfasAM5JnwC9nLdZGNFeeFA0KHsYaLwj95ekEGNqMUw8h6Pxkm4+5gVH08YnhqT6w8x7fw2yZYsWbLbbruNEq66LrFhHF8vbM7mcRQ9egWSv94K7fTokWzPO1wMmS3duMSUQSqm47ciE6HPcHpG/FhLxD+mLKPkD2QM6jEJH6FqMX5DWlyb/pO8jSu3p0htz24+iYmlJcn+t8FrBhA/+jmnRUjztlpvN2X0P/bYYzNn9rj6qWWhmiq7Gz6R6K52/KvXJG3Zx7ZmaMueEROOQMvAn/DyplIBXCcNXgh3HKEjI5H2N/Cpz4cjI5b82w4+o9Gkbu/Gyh+9YjS5xtd/YlZkN9lQ9DgWwFti/FaWN/Rn/xHc3ebM6Xn44Yc//OEPi2k+qqydM6davAmD9jdM7uBnS3WE2q1zZbWH5Nc5h6ULf7O4YdlKQvIXKDoSgY8mGA2eeATMwY/uKLMeGfxiRsMcbISPlT/0qZfbkaaDUGk/WfVldr2AUug47G/HKmTklEXg4Cc+0WkOF5//vcbsRY01Sz2azZs3b9WqdQ8/+MiPfvQj+7Kf/OQnndECHy96gtjiWHGA/sd//Mci1jhCMzT/+q//WiIRcepEXOvxSScCiUAikAgkAtsdAml/t7smS4UTgUQgEUgEdgAEDj7kkOr59/vfa8xZ2FizziaglyrTp3fddtvdPsc99dRTv/zlL/c2vTKrx9JNv5oaDyQkv/e9723J6TXyBz/4wZZIt9SLndP2pIxJBHYYBObvsduf/Mmf+Kn2jxoaTZfLeC+0w1Rwx67IsA7Q8c7anGuC8wo7UIgX6OV2ZGiwCXqD68icRXjyjwao0fNUDTA68IvMsk3XmnGYNuR/JG9xmy5yhiOSfzhkjJSuhp/QHdoTNXYG7Lt2r1y+oiFyGPyHk9bd6Orv7XOdMb2n5CW/v686Qaq1cYeRUnF2xbcu1VmSmwgDlYOpGmyCrSV5yOF+YFOrtgqeqqcNnmWb/C1ATig+GrXqDK5+8aEZorgWHVpui+ef/jZ6/lF2TmXhFEYjGRt/BVmCeTS5Noc/cFBW/Hg3Y1qgaIFouNvicIkIWzwc58jxaoph9BKSf4vhWR0dqHe2yqvihIHKX9W/OoYrpuAcOvG3crhpxIw64KPeNimtUkd7H2UVbsoUenshQudRYtKxviPU2ocSFQ6BCvwHuvtNGwN8jDtYQMK7ujsD2NXo75k+c0bPrGndPYPtOFZ8uzY0c/gIaqCru7J6lOjp6V6008IXv/jFu+570QMPPPDt/+25/faHZ82qXLQbAzOb17DCYSXDdlfRw4cO9RqeOVMSge0JgTDi49A47W+ANjjZNj8eGw2MyT8ySonPOPAZ/fK1CN8S4zcse1jSagHW39/LH3v/A3Z+xStecfCRF918881f+tSipUuXrl9b/UJaV3ev5cBAY9TPy0OnpBSdRyaqxQb5zTAyZ6Qm/8goJT6Ths84xu/IumXqDotA/UnXBOx2YP3MneacfMoJ5557rjm4sX5do7ux5167X3X5T5sPr9P+74cXHXzwk84888zvf/e7YPH8M5rHnnEA6IcI6rnClJcTHEqSj3bQcS2RSSQCiUAikAgkAts0Aml/t+nmSeUSgUQgEUgEdjwEajt3G28q7rTn3scff/Q553yp0ehtrF3DKWdO9YPwfh16YNFOXRdddMXrfvtpT3nKUy6/4rpGY01394y+QR+ZLQxRPPC2CB3Oy3m4+JbseZsIbDcIlFHZXCR3z561x/77vuMd77jr+ls++pd/2axFl9dP1TApnNtN3aaoosM6QMdeS3jnxMRXrpE0SsDkSv4RsBorPsAcQVpJIlZwO0r+yIg5/FdHmUsRo+QM+clfGmg4IlotrjZxqxYZXYu3CCy5QlRJjfiSWuK3FNFS3CbFjtKPrcgZq+bJX6DrSIwVn3r/DIFFQkvTi2+JaeF3W2cIfjEhsIhtURtD4Ymk8C0Ofkkt/OV2OIGFYYKI8ZVbKqJ2m79/XKSNso7JPzJQo8GHndb0HdfCVXYO0M2JfbjuUbV7OONuPEwoFquy4TKOrHnkHZmnJTUqW9VlaGy2MEzmbdSaJkKU246DpBJZ2IqS7TElqSMRoorAFp6xxrdkH/3thg2N1et7d9pp1t13P3Lrrbe+/o9e/v73v//225f6rGMIidELS85EYEogMG7TWWaJtL86SkFjlJ0m+UcGKvEZEz5jhaveYzdj/A4uMJqlV/SCBQt6e5fxeL799ttf/8ZnXX31td3dc9hfkwweHHU9h1sYtFR8lGwlV/IXKDoS2xo+lByTSmNiHqvw8fFTqd6xO8JeIjvqP/rsRU4SUx0BT8ceoU2rPY0XvvCF11577aN331v9yG9f9V1odDOfZTkPoau751Of+tQf/dEfXXfddY7nj842pk47Sqg7duOOkaMUmGyJQCKQCCQCicA2h0Da322uSVKhRCARSAQSgR0aAY7Q/fwoH/difv7zn++I2TWPPdzo6vHoG5Vvbm309/U5eaHxiU984g1veMPtt93vF6f9bF51VlLzqIQtC5NXqe0C8/m3HZOM2ZERqN5KVfXbZ599/uwd77jhhhs+/P5/qd5TVQvm6reYB687MgQ7Tt1GcoA235ndvElsn/jGOusl/8hdZqz4jCwtUjtuRYyQMfjDbQvbWLOPIDmTRoNASx8w6DRBhPYBOCaBhOBvkT8KCWuaPLNHwZks2ysCekV0j1FWILqiXIjwLmrPLma4His+jjQuctrLjY4awksqmRHv6ljHOOAtYkJU8Jdy3Ra6CEHUxZaMIkNUcNbp4K/HBE85WrIcNSceW0c02iNDyGZeOxZXZJZC25UvPBAobEG4Ch2hK7lGTxAVzEWHiCm37aI2ydCepR5TsqtCva1beDoqIG89Ht3SMepCpEZZ9ciN6eon1P0cbzOypym5oh0p3IS3u7tr+kBXtQCrzn4usgZ6xPQ3uvucPDx02nqVS9xQGKHoSGqWVXEXqUNZB7toYejII7LOUOhCtIutyy90ECMwF84iWczI/FIxC4UNXXIFHdeILHQpq73Pl6Q60Z6xnjqJtHavKjhjhuOfGxt61+oXV1z5g2eeeeH9/AF6G7Oa9rl68upvLua7mqdBT6J+WdRmIqCnCcNNVsMJj4FQUosQRIlsJ+QaYWLEH9nL4GqXsMmYkeW3ZFcQS9pS91KFljq25x1TWS3ZN/+WniMAVZJKdUqJI9RLkqBehbmdGLncOj9RcVt0CCLiXUt8yVVnKJEtRMnYLr+Fs/12ZPlSi0y9oh2Hkr3OWS+lMNQjgyZZaJfZwhmdCmeIaknteBucrqUnjyl7R5kklPjRayLLaIoO4R3FlnI7pooMhtGUUvQPQl6hQNSSWr/FVtSox08uHb+NwJ76XM2BK40VK5btsstODzyw5NxvnveN88+rfO/WPlZZ3oF1TYUrnSk+uUpmaZuFgIbzBOe4Gm0nkBXXEYRiCJslb53NbUxZ7RKCs0xoMQRKdoQs7bnqwoejixAM6LHWRa54mq7LEVmUKcRwCpgtN8kzXN6MTwRaEYjp00gM89c/cPyJJ8zqmXH1RRc3ero8/1QPxx6Hqx/nmdsYmD5jhrHb29/b/38//OFLXvSi//iP/+gyujqtHFoLGvt9x5VDy8AZu9TMkQhshIDptKVTtcdslCFvEoFEIBHYIgik/d0iMKaQ7RaBdmvbHrPdVi4VTwQSgW0aga5pPQN9G7rmzB9w6NGGtcccf6Lfbb/x6iuaSk/z+MvFsq/Xy0n7fb3r1je6kQPdP/7xJS972S//x8f+xa++42x5gtgiFe4os2PkFikuhUxlBIrNnaCXOaPFdug11CC/sdXT3Vjff9BJT/uLv/iLay657EMf+lDvylXVrzMLXgj3b/ROeLSlJN9WQmAkB+hQSUds0W2sU17ytwDYcjtWfFqyD3dbb7hNFhHMrnEir0mnnn24IjJ+SyEQDRRv2EtjVc3R3V33sBx9cTKOnnkLco6526S92ILoj12UzhZN5lo63shisOmosQccPTami8gecqTWhUiKDln4pYYcV1lKKkJeMRFCSNDySsUvlOwipboNyZEUtxETElyDLW4jqVxnzJgRGYuQYBYZislVJIeEUDLo+rXwl0iiolIlZkIJxdVVLRWpF1oi1ZducRsM6HKLqIuqSxiBjuwlY5EWMXWZLZxkionIFvlFWkt8+21kH44/Ult43Lbr1i65JUaWkNMS33JbhEe8W2FN78C8mdOqXtGsckyBgzqHU0VX1Z+nT69+Wr1FoNv2VitsoZUi2nO1xESWjpwRWWRGxjo/hpbUIryeF09H+ZjrbB3li+xYBGuo+nVlgrNjQaFAkYMHLfs4TGpH+aF5x2vhL0RHttFE6gLxNTiPqzlzph38xLnPe97zFu125fLly7/z9e4bb3hw3vzqySvD9ovAODpJu02JaSH6fIGifhullNmjJCEKP576bYkfjiiaF2nDcdbjI1dkCX3qqehIchVakiJvXOt1aWHbWrcUq+tcalr0ESPUeUoSIlIjZgS2epYWWi4xRX7c1mPQIbljUqS6DhfkEoaTL17qcHk7xtf5NWi5LUQ9V4kcoaDC05JRfD0JXWpROEWWThWR7TyFuYUoGYtu7Xk7Ftoix23RM4giB2Hgjyy/JUtdeElChJC6ZJER6lkKQ0SWonHWJRRafD17Cx0QiQy2FuF15pHl1DkngY7a9fX1837ebc/Gs599wn6HXL9+/fqvfWH+vfc+Ei0SnnlDVaP+SDhMgs5ZxGgQ0LKW2ZrStWqzZqvFtT17dFephSg8YiKyxLQQLZLrRdQzihcMk5bsw91iLknkjFwXDMEfJbrquhs2bLAeHurDlbBIDc5YKtMnIqUOp15dk6JSEonAeBConnlsJjVm77Lzs571rH/8x3+shFRLwY1Wg/0D/UaurtvT03PZZZcdccQRz3zmM7///e+Pp8TMkwhsAwiYRWMirc+3EUO7EjkmTUv24SRgGJ/kMamRzIlAIrB9IJD2d/top9RyCyPAFAqEFoNYYuqRYyo1BEaWIrYuAUPH+DpP0olAIjB1EHDMG+/nPZ/wxKc+9akXXHBB9fVvr4fitY0uZzGYLQY9E+bNayxfzuW569JLL33iIce+4Hkv/+Z5n/WSZuoAlTXdkRAotrLYxBIzcdWMIjqbYIuBlpex6/v3PeaId7zjHUbcR3k/L1s2ONqq11MZtjMEto6X5HYG0vavrhE+clBFDPWKVjY2w2QhAPnYZFJgvRW2J7rLiVj2yXqbf6MFbnuq4I6oq143Z84cNdNgMQOM3HI2RBctWsRjuAUMDsFl15YcMoVZs2bNnDkTc2yv2mqVK/LGdOQWEX0es6QyCtyi4zYIm7s2umbPnj3Pqn8otOisCKmUJJM0/CWIFOgpRB0VgQgJIY+eAja3NFeF4HQdKnDwX7kOOuiguXPnRpbIFRlbOLfs7erVq0MlmJAcdL0IMXSjSSjWrnnJBZmSGiDEVV4/9o2OIurCg45CoUeZjqkio/QQWHhKKZEqvihQeCKmY/bCg1i7di39CQxlIink03/hwoVauc4fdClOX1JE8JdIhP5DrD6GcIuh1NFtBKKic7bLjxg9pzlGnM2rH/rGzKHO6/z5cnBa17rpjd6FMxpdA30OdR7on9ZVPdr2OMuqqzG7v29Gb393f6NyxejdsH44/GMcrVmzBltRSdHUditVUH3Z3bYo6bgsFawntfC4lRoMcQ0J4skkuZnYKrnkKqmIkrFFB2oLK1ct1wr1JEIi1MutM6AxlBYpSaSJFKTKS0/VRBeGIEKsVCFiWnjI0XkEqS1JLaIm+Za+4PS3dEnfww8vX7Fixetf/3r1ve22B+fMreLb6jrJCmZxY0ZAZzMFGYwmW8PKpIEeLrBlQpik+fPna/pSXumoCHNCdHK0EP08aFdJ+jaXEaWgSYgkkYTTgWQDJ+KpN1yoT86luCJNoUxw8Ax3jXLJl4s+SkcI4oMgRF3KbUTGNfK216XOM3F0TDJ0qANYL06SeoE06j5cFVQZSi2pAYi8I9tf4MgezCREKDq4RdfbyG3wlMZyS0JwxjWyB41Ndgy6RBFbJ7DpQvQMHepJYqQK9chCR3wzfSOGEq9ckvVDpcfqK7p6ZHGlm8D+Fpl1IuTow+vWrQtrGKlFT/KtHFxVrRRal4COWg+XimHx4sUUaKk+foG2WkdS3LZIdive4iSq1p4aMToGy0hnIeQE7UpzMYgQ1SKhxONpSYrbiIcweFetWmX5Wtgir9vgqV8LDwJbXf9SkPhYkJijRApiVDPyRkxcFU2BUlARXlcAhphL0rZANJW3xOpeudJPMfS86lWvgh4MzaM6jD6pxs1KW9JU64cM2xECZgxBEzfbMfpph6sa6aUxh+jGUUF8QcgbNqvkFCMpGNAGtVJKEZErGIiNgDk4ye8YYvCa3IYbI82qdKiLghRRtEWoiEj2tzzOiBSKShSmjBCR4gW3MTvp9vp/ROY1EdgiCHTNns3vefr8+dVP3jQar3jFK77yla+sW77WTyA1j34eLKTLKVgD5tnBR3h9ks397Gc/e/jhh++///5bRJMUkghMMgLmZyU25+CNFj9lTh6fPiGwXWyRFvLLbRKJQCIwNRFI+zs12z1rDYG0v9kNEoFEYOsiMLB+Q2N6T2NdtcVz1gued+llF9924/Xe3IRW3uB4Rd27fkNf9WtI3t6YtapHhv6B9Z/+9KcPPvjgnRbtunX1z9ITgfEhEPY38urS8X6yHjk+sS252gUO+2jcHFrVAOOmMahWY5fDn/jOv3r31Vde9ZG/eW/v0mXVJ/nNx/aWUvJ2u0BgIweUSda4vSNSYKzvYkJIS64SWYhJrtq2UJy6x45LCzgj64ZZRi+UR2aL1GAeDWc7f2gVDTSchBE075ixoz4jCFFuRzkj6DNW/uFEbTI+CqJ86F/KjdtNZk+GRGA0CJgiOG3E7m+1O7vxETvtEmJKsTSJ1QkGHTJcRsSYN1xtzQ75BAz6CWGzQcVzwo6pTWI8bmOSQQscVjim6OTio6uvXLmy6vrN6Uh2kXZqS4wswVxnIEER5FOA8NiNbq9CiaEkOUp0JSfiiXWrLF6GO+20E1GFP4go2ub0Qw89hIfDR2EIzeNaIrcswack1KNh+KvVlVeW0rXRMt+lNRrccOFQqlbXRHaYq3s9snCqmoz2xQPelhrJCxbwjuCHFK0cElqKIE1Lhfx6EjqKQ4QjUbuLWOGX3Qm44UhEYGR0FTTNgw8+iDP8bEqWIDAEPnDjwlJvX0laEywko4Hjise1LoRuglLqkXVaJydhWtdg15IUbcTjudk61ce91a/4NsUqpbdy2e3TEynGi0bVVq9a0nSbrkt9nCZt9ZqVCxfshDNiiRKg6nb9huo6rbtyUBPQgQ8Cz4beDXQjYQT9tS9OPJE9JBAiKGL9hvXTp/np4c7VN4c061INz474k8b1edHCnSti5crShfArVxZXSRpCDKIlqPJAow9Q9YbDoydIMqJpjgjNXd0WCXTjWbhmbeW0UapWUhEyUqm3r1cFhXrSVqTNT9N7qobmbbL/ATuvWfvYJz/xrQsu+tbdd3sT0liwYNqqVTrPpiburViBLLoTAtEz9XaTiZ5p0ggz2on38Ti5TPsx70VsDBOjtRoaQ28NIklkEAaLJCGKMAOE2Yq8YSgNjcceewyDoR3j6PFSO1GMo4xSiI2CECoirxpFUqd8j8fRygzvfunSpWqEqNflcb6NqY51kVGgwMa8W/6Oniqr1YTx2d9QsjIhTS/2+jwjSS0oPbL9hQDEqDHcJElOR/sbEGmjYOBF1wJQMJDPCOJpZ8Av3jUWGMWDNiLFk6ADCOhoU0QJRQE6iLQ+AWPJi9D3FB3Cd9llFzEt9lcnF3TgOm4t8skUA17FRRI5QSsXaCZ5vZctYwVIK9kRbjHLyxIJ9aRC33fffSCy1Cx5CY8gC/0pMFzTyHL//fcrF7Yle5EchNbHECO0JJGvyq50I78dW5w0dyU2lCl56wQe8O62226gJqqeJKMiomkoENLqDGiSQ/929XbeeWcCLYllDOhAEdnlEulKbDRue9eK4uhAgdCkpeiteEtzpc+YUfmDWlJ9/vP/950f/B9zvPxBC5WtqFcWvbkImEV1vOh1BrVbbe06nFxDLya9JUuWGIPRMTDLQohQYkQWOoowOhSBR5L+HwVFkrnIeAwhUmP6RbQEzIJI48jk2TJ+ZZdKvhE0mrrIHvb30UcfjUqFwq6qKdUkGbNBXQ2SpUZF1EKJ+COEbnXmpBOBMSFg4cLXuXfZCrme/uxn3nvvvbfddlsloW1lF52tdDmEQXTOOee89KUv/cAHPjCmQpM5Edi6CJT5s/Rn+phd49FMaj1+c1Rls+pWI5Z5YZI2R2zmTQQSgR0AgbS/O0AjZhXGikDa37EilvyJQCIwEQh0zege6Ksed5919ovuvvvun155lSNm4zdgvVKy+9loDPpXROnV+6TmO6X+xtrzzjvPN8Mf+9jHevuqXeAMicB2hICH3DDEdPZMGrfeNG7BKtjgIJbMeKb2/DuSfO9aBxp77rfv/Q/c3+irdqyeePxTX/+Hf3DVVVf924c/0uAtZTOAes1XsltQyRQ1aQgM6+Shl3ifvjlvXuRVDXLqlYnIekwL3TEXnpEzdkwtkYWIslpUalGg3EauUTKXXFuZGGju3HQNbsNU4xz8A5ytKr36B7eDY0KJfd9qi5ffQpUcu8Vd1QRR3W4cBgbzbhRregjJG8UOf1OXXKeHyzEannrejvwdI+u5xkSPVdpY+Ysyul/0wIgJetzSCKlLK6XUBbYw8EZossUhcLEj2HSX7B90JnhcSNf4TVTHflUktxPd+ese7aDUYuoNWoselpw2cwYfRDPDGWecsfPCRYWvvgMcM0UkXXjhhXfddRcvp8IZJZpqnnzkEUcdddQ+++zjDbvI6E6u7Eijr/JNtKbhNnHDzTddc801a1by+Frl0EsZeUtUQqZ177nPPieddJJ39HPmzav2cX3WyA+12eJhjDhHV9IajYsuuuim62+ItQsrVSnT1WVL2C4yRy5CFu6yeFBsU9FQsklWl5i1vvnNb6qLw3K7N3Z2JPaQQw459dRTcUbdrXFKXvpE1Why+623/uhHP4qksmKL1MK/ZQkrNsEO9DOf+UxeUJZisXVdryD9HVKI4bvf/W49vq7JiSeeqC0o7xruSpGK3+2ee+75lKc8BchchSwZ6xnR5B999NG77rrrxRdfPJz8F73oRTfccMOtt94ajjLB5goc+5RPeMITDj300O985zstkt3i0X+e+9znPvzwwzfeeGM7Q8S8/OUv1xV520R/iEh5BZtGxx57rM17+nfMrsOfcsop1113HU+jkp1i8sKW84Hq68nkyy4yhASD2uuAz3nOsx955JHrr7++Jr85Nzb5eQIdc8wxl158CVH6pGaqenGzL/Gs7Z6x9pijDrz++lWVJ1BXL4cIZXQ1+roaaxrVbDi9u6vxghc+n/Lr1q3R16pEobYHdvwJT9FGQ+A8bq+pyl+hu2v6c57znEsuqUoXU3pjqYgBovV/+MMf1pTfiNTzgUPtjWKbN6Qt3nnxkUce6fdf2lMj5rDDDkPcdNNNwzGc/PRTb7nlFi7aBfzgpCH8TSA0HKrdRjIwCE8/6ZQHHniAG1Y9zeiLmh544IF77LHHT37yE9WvMwSN7aQTT+Yf35K9cHJre9KTnnTttde2e34UnsklOLU0BvSOyutl4J57Hjv8iN1+6ZdOW9X7ZfEXX7DHTTc+MLvyHRWac1VzBdjVXfk4Ds1ekVqf0SMmr1sZAW20vrdXn997jz2e/vSnm7JGUCja70cXXfTzn//c6wGWrs5sTn7iE59oWiOtHl+ndWlT05133vmNb3yDcQwfZWMwZni9Z/bcuUc8sTJ87As5gvFSlxD0koceNn6vvPyKauVQTZCDMySC6cbzvOc9b/Guu7ZnLDFRl3PPPfeOO+4QSZmh2bViKXWhWwzqdiXqdRno7qpmUXOv2XNQl0rORISAZfPtrx+2gx77W694KDwa+2uOskQ5//zzh6tji/3FFkiC1MRokjSHWx60Z8dGAfbXDKmV2xki5vnPf/6Pf/xjrVBaP+K1HcICDEQWAO3ZybckYKF+9rOfsb+hVbChBRL23XdfnZkF1P0wEyUeT9XVmuEZz3gGZ/2bb765o3zZrU+Y7/beK0YLRg+3Cq3KqxnWKGL33XfXOldeeaXVRbt8MZYuhDBwrnUGosRwW2FeGaC6o0lho8Bee+3loA6lt6sXbKYCi5977rnHLZkRqd4IMvfee+8nP/nJP/jBDyK+/apviKxmiWGC6ht3JhAKhPwQDnnKs788+7XOMLkbsmvZ9toZxSTITkOlWzqG2JATBbkef/zxPMj5XHaUb+Gqdpdddtlw4HTMNTGR8VTLwblCnuZmylWr1u6/Z+OZzzxzzk7n+wDpJxft9NBDS7oGpnWZg8L+Pv4srOEqJ3KfkzfVC3qwNcej8NC7nfHkzTzDIMD4Tevp2efAA4477rhdd17cwhV2qrfWaN8979t33n47tjCa0cNd+ewf8qQnnXzyyS1zQl2gidfYN3a+//3vm2M5Kc9sfmYgu489fNQh0rRsclu7fr2RWM9bp40dD1ZXXHGFERfDJPSkktnSCtbsahzJMnRsUJW7/U2L3TLzQJjsunyjmHE5ufn8G/FRzaBjIKumYB3+P//zPz61rEYIJgv4ofmqLjDpRGDTCAyOMj/ni+pfsMduRx5xxH/+539uWOWQq5iDySiU7w16+xqrp3dX82r1nqhpH20YGxovfvGL//d//3fTJY6CQ28Pye28kWRB1Sy8GgHtYYTs7cwZM2UR6NiF4gENJmbaLfU6wrIt+mRcTfVTFvOseCKQCDyOANNahUH7u2i3PY4+vGl/V66vogftW2f7G69fcLG/P7/l9rNf9NKvf+2cKlf1ww3+mvvLzfuxXooBLUS7hPqE1p6aMYnAJhFI+7tJiJIhEUgEJgEB+wJrlq/e64CDDn/yof/yL/9Slcjqeok4aIKrFbut4XDBqE674p3QHxZ23c23XHb0U/c789mnfufb323ye0Ft+54FL2+qx2+LS91HtsXBNtxTcxGSRCIQCNS7k27jjUpBZov3oniO9uLUu1aljOb5d7f9933rX/z5u971rsV77P7Od77z4isu//D7/rGxYk2lpBes5cVPIYr2SWzzCLT6NhWF9Tz9st41S9LIhCwtDBETAutJ7ZxSo8eXLMHfkbMuqoUu2QvRwrCN3G4p9cgpuJWGU8eKNkijmKrOrWO0ZBxkHpJT8W4cWnMOpUa5Q3eb/reFv+V20/mHOIbLOFz8UL7Wf0fD/zh+Q/2zVUoT5PbIzYmpFxpy6o21OZKHyxtQtJdbK70i2Qw7bUWZKlfriB+uhIyfcASGa772gjUc5uVLl+5y8MGvevWv8neJc3OD8/E1yMZudL/xG7/xuc997qfXXmuZIrurLVjbt7vutedZZ51Fgi1ViwyR3tpjiMBp2K2eYw/YTrPt1f/96jk8JGQXaTmi0L322YfTrSSeJZySqx3ijR2g8UyXoXlwry2uS3fd7Xvf+x6xYtTFlU/Mfvvt94u/+Iv0ufaG65UlfnCHeGhRRR9ywgH6da97nQ22OGEo0IhUDpS//du/zYVUXprgH/p0BNmY3lVVPDg5cxxxxBF+elXRNImyKqYJCzZFwuP5V3/1V22lCy1FqYiKc4L53d/93f/7v/9DtzDEbeyRc8BqSfX05WyzAw444A1veAMXmQsuuIC/cguP2xNOOOGMM87gAN2eFDG/9Vu/5TtUm5FuqdTCxoUIQ0cHaJywPfvssznqdfSCDVFqx3k9fJhahIOIB54zDodzgLblr30dE0VC+/a/0rkvv/rVrw4HaDtGdR6pixYtlP1rX/sa8OtViy4hRif8zd/8zZ/fdDP/ACMCpKGhJL3dwYGEX3rpA5/85CfFixTwrF9fnV5sQU2O7EbBvffe3VK1uDWCuAhzv44SQ0hcyaftL//yLxsOXJxbts0q8Y1+oww+mr55W6FdL8Ut5zYxpW8Egyt+Vy4aGLgo1XOhgwHBC9O1OEBH9jrzr//6r//TP/2TbkbJejyaZeG87leMh2t6OrzkJS/56Ec/GspEdkVwkguXUN5veqaOgUGIyaGw6Rtnnnkm35FwgG7Xje+7I8S8x69Phi1KbpVbFZk/f9rSpdXJ9Fz0fu9N7/vgBz/4rXMe2HnxtLVrN9rFVClB3UutC71VNM9CR0BA0xjLJnMd+PamZ9VwzDFUjJ0PfehD99x1Vwubz1F+7dd+zfcw7WMKJ9ukIEXoPJwvGfGvf/3rPkASH6mR6wUveMFTjz/OrB5dqKWIcrt62XL+tVxPTEEb+ipbj18qQl1e+cpXmjPv3fj7hJI3iKgLE2MStmCIBUDptFGX4uAllZt4ZFQLU0RLXb7w5S+Ft6X4loK2+O2Wsr8+T+KjxrzGB0J1PaE3sv0FFPPNSXcEB+jh7G/1dnXNGnM4A9rRAZom5JsDrYvMMHXF6vTv/M7vmCHLJF9PYoDUztmi7373u+vxhZb0xje+8X3vex8TX7etGLSg0p/2tKdZxYUB0kuje0iVhF+nZUC//e1vd3SAxsa929LOWKh/w1PvG1YXPIzDRhStglCE5Z8BwnYPZ4PUzocE3oXFSqwugbaMlyFA58svv1xT1lODPu2007Td1Vdf3Z4UMay/4RkO0C08hjAP41/5lV8ZwQHaolSuERygf+mXfuk//uM/2N8W4SDidqlvsKEjOEDL7hGAh3dL9jja2fRCgVga1TEvzM9+9rMJ13wlpk4Y+xggX2+7OsPk07pEs+tVJ3DPmdOzbNkGTfDa17/5M5/5zI+/W2Fo8mzyTL5qWeLmIrBi+fJ99t3XbMkMeYhrERd2qu4A/Qd/8Af/+q//yge65QMk/dakZFCUyYooz7Buy9LU5GBEmNsVp/OUbwBEWrdjM2+YGXxYYhIM09yiT9x6IvOsrcv5TLH+yMmFOgxHqcvIDtBvectb/uEf/qF9Eo7n3x9fcgmthBYdlKvDK1eSRwlr0a9+9avbzmht0TZvty8Eps+e3btqdWPGdEti3+ltWLlyY/2rdabPTSwhGN/eZZaF1T5W3dB46me+5+y0aPWSpRvnHeedDl+X334rxliIQTHOMjLbFEbAF2VeyDAHFucWUdZmwLCC9YDgBY43eB/+8Idb3uSMCa0//dM/LfxGh/dCVtGeAjzueRHns1sreUat8CSRCCQCUxOBQfs7fUZlf7/59XWjs791rAbt76KdVy+1nPZCJr7/rLOMgWZbcY9gf6XiaWcbQxnJOrURSPs7tds/a58IbEMI8H5uTJ9hj7XaH18/tPnOkFahe87s+RvW9juiQvDKaEPvKpuofY/b2Gk+/f3jP/7jS6+4atkjj0aephUeIrfEvx51PfCyuXW7XARHfP1akpJIBNoRsONvv8lpOHYf7Jh42vXW1P6Ulzxxwt2nPvWpTb5gHK43Ku6tb31rKdSuUHgNeTfLE8BmB2chvzP2b//2b4VnkLDy9DfTini2bc23v/3ta/t67dp88J1/1Zg3q9GTZz+3ArY93le7fR319oJb/7Albx+6/q5fP+vIv8lIc2XJWwi56rTb+gv9dpnBHNeOk29kKQyFKKJGyFV46sTI/CG/zr/t0KG5l8VUCnqoLoP+Ck1Vqy0ep7g16cqKOuWoSVeOEfXwuIWtx3oZPWiYN44dxV0os0kAh3RulbjJjCXDcBKCYTRy6jzDSavzlKIRI/fnOmcLHQIdlcqX0VE9ljtiSBtOgZbsODmiceQyZdc98FrOAeooTUHiY/Osf/Drbftx0+bN63nggSUzpvst4Mas+Q0b6BxJnQ/b370Qf3ejs6tli2Itt7HF2BKZt5OAQIzotavXHXPc0854zrP/+7//++jDDuf2sW71Gjv91rh0cC09JHqR3qhrffHzn686RNOtyqt5Tjy/9Cuv0m3O/fZ5HE8djmvRMKtnBgabvtwFbrn5ZkfhepUf28O8W5y+fNUVV8yeNdsiXt+2vtltzz1sDH/9G9+Qy7rHVaBDUQChXwYyjrnihvLe976Xqtj0Q6XTxMG9NmWdsOU8aTKnz6b1LIXimdMzA4HN0qd/gzOsN+CkFX+yqmpDR11i8O7P9sC53/wmxeYumG955DxpXq22KBQ3s3uaa0iYM2sWV7BPfOITvFWoBxnZI9DTYs5JhIYwb1HFheajvBLFEcQON18QxdVzke/xQ40CGbcFojrbaGgZ603cMQseRdST4Kz0lsg6Q6Hb5culampkD0YrF846gSG6Xz2yIx0Vr2sy+rwtAgkJbdUuSm9Xvpml1SFAZIsC9dtSSonkUdBsu+rszE7gP255m4+ZnjMjZnAsNOWEDhEf/ar6hEDgGwFY3QOtlN4Ngx3SbWCFwWglpCmnytIeglOPrcZLfCswxGS0xhAeLjv0NC4JdGjpt0MyHh/RLULo5kkjXPZbkkpe8YQbsEZonYdi6lVmGMCKoUDwR3YqyRta1fMW4VIjHiFAwAahZyRvImb0DHqxF+aRCXk5KTpJ2vitr58bg+dBjpw7UuszRnVa9ty53Q8/vNyJKuaDJx7V+OlPHCDemDmz0T3YQ5xVqZmreburez39Y+9hoKuaKJo122gURxl53VoIxMpn9doNjo08+bRTuTzqsRx5jS9BcwsI6um3QhiyA/bdj+cHY22Ym+uK8r//+7/PH9QHJywau4O/2QGq9GbuakIg0FWqDyTYX+7LpogYI+KVxSP5O9//Hnvn1YMO7BTewqMkQyNGx87zF0h93nOew0EWIW+UhfZhzAknneRo5wU7LWIuV61ZY1TSXNEMhuLq04mPndTlvz7xidm8Wpt2gSjB3j/3TV+nGDiz5swBizMOLCSoWklorkm6+qvT7tWFR/h/fPxjfNeqPt5cHriWc6D5wloSGL+BZMgfzRW2I9vfAJMomES5oxHbztOSndh6s+InPGAveUdv4+QNuEpexCazb5KhLq2FHnfewIG2xf4WVdsRaCl0lLdRRAGcWN3JVSgS0C1ol6QtQhR82ht6k/LHkWUEmfVqBgKbX/ciIYSH2BF0GCGJBOsBp0EP2t+ND7wfIWMkwXm48bvJvIMMTUtqGnXr1HLXmbPWL1uycuddzaiNgw9r8ACfvt4qy4/jVZNSo7tabPRXv1A57vD42m9TIjanlE3Jnkrpa9etO+nkpx9/4olOMtZdq4OTmw9xnu/AwHS6Llm5glVlmu+46dZf+pVXWKN+7CMfnTVnZr17/7/f/R3Gl+nEz9bEVz0MKPcyHwN4j+o9uydBX5XI9eY3v/nfPvRhtMdkRtOqlUwG/Q//8A+/8IUviFy6Ynm1aGxauviMmCbsYO+69bycDQrWkA/0+9//fpFKjLWE48mf+tRjrCZ82TJrxkzmMp6L11eL8cEO09XVfJZsrgQOe/KTPeH++7//e8uHHL4ulve873zHAPSlh29FZsyd4/mXtTVhVrL6HPlXBaq+6lWv+vznP1/9okvzGF7KGAV0RliiWz+Pz/7KvsMELS6oDjAtvd7znvfEqmmHqeB4KtL+LNKcb7v9qMja1Wc89/mWoN/61rd61/hZpKG5TqcastTHHHXEtdf+zNfCj1vumhIHHXTQSS965Wfsaa1d1RwcjucvC8/lNcaRSN3YrB5tF81X55YakRa3hkmMi2CQZAaIgVnPknQi0BEB35B4V/Bf//VfZlrvHn0VaYJlC/78z//ctyXnnHOOTwHNvfW8+pgQPTCu5pb2D3giiw3m1772tZ4QHffwrne9y6aAvJJ8bOMzfp/S+RTfFkNd/hSk2TJ2GdregTO+rFugNAWh2MGqbICUMZL2d7Bxh7G/8xbPX/nosmc+92z293+/8jnvFhv9HWxszf5ae2IwodgKZqn7G10z9jrooBec9fyP/fu/N9ZVq1PnWjSvsUrdaB5rxo90MQatRa2XrMxj/8X7gZKB2UVrXJbaiyar4jDHwSBjc5X6+FuykjGJRKAgkPa3QLEVibS/WxH8CS26vLe0a2/r/+///u/z4WjwlU3BvZjj5sPumc9/ntdB537lq4PpUjcywt1HH3GsLRIwLlw4f8nSJf2NDQvmLly+allY4YOedOALXvhSjqT9a3pnzl+4bsWjQ6+ImOCwhlvGJpaWLfUIwssuxjoWXSWpPLDEY3XEt/AU5iSmDgJ6Cwdo04L129ve9janq3gt+YpXvMLpSM4W9EZ0uNNSQKQvCWyHB2RPzR1BO+OMMyz77Srar/T8q2cGm3Pu7FReeOGFn/70p4ctYkbXb//Jn6w67UgLy1Xr1n7hHe9oXHRLY25P9d29hahR2fkVVEdFtt1IGJpw3vSmN33lK1+ZUs+/G3lW1dvHk0PcTtwM1fH9QkS2FxrxJUsh6jq30IUniJCJbhfekjFu67k6MmyVyI7Kl5rWVdKn3Q79OMJgSpOzGNsqshI4FNFRzmDO/GfHQkBbd+xLpZaDqdWmQzXN449lq982P+OME2YvuPGRR5ZddfmM++5b3z2jZEpi+0PA6qEobdmx9NHHvBb3OklkvYeg7etwWuXijI4QZsIOrvfvcSCfzSfHmfiR6+uv+ylmixK5vIJ3LJyNIsdN2Q8eNC41X5OYecRbA7HEsoiR11WIqYw+8QpN0dRzleQql2+1Qh8b1V574bRf66S6df19PL2iInqv92jBxn1KEC8QUklo3qLFePfHh9sxeHQ+6inHOAr6quuujQPzCOnta74Z7+tHc6zGb/+Y2vJSO1xqQk4lfQICzckfrEhT4XEXEnJGzt5el0B45FyR2p5XvEgty59gOAmluYdjKPHt8ttjCvMmCXkD2xE4OzKIHCFLJAWPIqKUemR73mBz/jiintpyW08KWvcbHFxNR7fCUDTEUIQUlQpbnRhcPwxVTS78JW+dE13k1+mR5ddTg6Zbebstpr2sYHONOsatEnHStp6l0IUHW71f1eMllTBcfGHYWoQqP/qoI/Mbi3dtvPjFL3h01TePOKJxyQULH3542cCIbzOiRu1gbq2KZLkbITA0vkQ6vpHdYSXrdsRUqd96/EbwKELzqaqcWIce0EKaLdtyCrJxFCEGhYzB7MpmSRLfModLUihRdselemWw1157OYpyI1WHbupF18eL+CKWgXbO7tLly71fqHy5hmz3kIyN/q2E1CY6CkQRCHwORWPg8YikvCLE16dF8sWo5kZCJ+yGJpM5mtrLClhGU7/2vHJtMvsmGUYoumOJI/DXk+RtwXZzpNUlB111s5qpiuJa2LZsiS3CN/M29N9MISNkH03dSwONjN5EqzpCLSYuyfwzZ97M7u51z3jGU7pmXn3wwY1Lzl/oKXjiSkzJk4BAsVm8kK0/WRd9mzVkUJgb9O5z55gSPRXWJ8awUC3qMa+MkRPc/faO06A9FfJp82rep7YegTlAO33TwfBcnz3kxlBqjqNKDIHtMsVgo5Wr0h1XQQ2GXq7I0qIAsSWGMn6tqDL306oaDQ3JyqS2LBhDk5LRhgQNLTPOPPNMPtwq8pOf/RQPmx4aWo1QSdXig8NQpmRPIhEYNwK8nw968mE+s//4xz/e9H6uPVl3de25657eVs2Z22NATZs2Q/czxCxZe/s3WvvpvQuvv/7UF7zgR1/9wogLz02rafhUc8L06T6l82NfPgj0qxduRUoyRRhxvnKnlRdEflOCROPUp33ireR9wS4m+DddWHJMSQQcwGwL9ulPf7ptYF+S6FTepvpu5DWveU28veQ40gKM2ViIyD/6oz86//zzfWvdwlNunXrlydHPhuiTBotruFMbRA6Hjh8iK8xJJAKJwJRFgPfzQYceYtXH/rZ4P3d3Td99191a7e/KdZX9HVg3UFy6Bnrvu+02hvI5zrD8+rnV16JND+nxQWqWs2tjPvRybO+99+b1xfLGhotIU2X87hBbHJGWBIyva3wNEsdklKlyfDpkrh0bgbS/O3b7Zu0Sge0AgaEn3d332+uwww77yEc+0qLzzNmz999nfy+pli9d5cNyD5Xe8Kxbt8Yn8XaKeT/j9+zZ19/n83trfj/wcu5XvrZuxdKmnHCdaBE5tlvCvf/x8seR+R4iLr30UjNnXQSVTj/9dA6UP/3pT8Uzyl4ZcRfxTswjOcNt8SA+jHI9Y9JTGQEnFfpJZ0+jPHbsOepj3r6eeOKJbnkiQUb/0dU7QoTZ7335QtiDc0cGkb4otkbllaT7hfdzvJDxSpa/td/iGy5jFT99up/0fHDxYkccHnDwE5740pfefPenGw891NjgS7/qHWyG7RqBYadFzwzea5vyxv3wIGMEfVRAx3UouvXfjjgGU3nDjhBMrELQri0ZO8aT08I2pttWXTdP2piK3kzm6vy/rmn9jW5/QTuhxt9AlxjnFVWRTJXvbJvntlT+fIINEkcf+QuiunKl9jFw8w9d/pwoXP6cJD3cXztPxOAvSS1EiGqJHNNtXUJHxcYkbXOYO5ZeV68ID/wn6FoGC4JRiVKM8XC4QRhWaAQGbzT6HbnbP6sxMLvRP2egb/a69c5Taegi++3d8/73/fGpT9+3v2+9lxt9XTP9NQZ6qr8xBl0r/7YOAs2xrLnmzJsXe737HHjAr772Na/5zd/Y/6ADY4DbNTU1MBL+dIyqz/T391qLVD2D07HGq+yEP2+pdBuHPR933HE2gP2I+Yzp0+fOnr3TwoVrVq2aN3PWSU87zt/8WbPXrlq9YO68nu5p8vIXjiLsOgcINrS8vbLK947eBlJ0xehTJuGYlCQgREqVC72hUf2t7+vtd8Skzjt9mpdlthMcrGLjSu3KBM71uZwFFGJ1dQylCLe2fi3WCTngCQed+axnHXXMMV0909f3963r690w0B9lVS5Z3dWmshB5XVtuS/yEEnUFJrSg8QlvUa/cFmJ8Yrd4LvpEqEsWU7+t0ZXRHN1fZBpk5sYwJKQ9e6Toirrr+v7+dUOcMTJ8clj+IqbiHJo+g3bocnF0qHqjzkxIvRZG8ZDYwX+j1vVrJLAFNU4qOXiDR4i40JBPxuN/EelqwRh0IepsQQdDdVpiVdNKSMRQ3jl3QZekevaIdF23bnVhi0iK4ayrV+jCqcQotMR0IgYrHdDVENiy5GB7dVKgJMW822zr/jlM8KJFC52B64edF+006z//8z1nnnnUkiXLmu3pk4/qq48qdG3wVy2qqjk74kNOJOd1W0EgxrDR2d/lF8yqv+Url/XMnN7bv2HNutUb+tav27B27fo1fQO91ami3dXXR57b/bLB2g3Vu4D6gpwxWr1uLYO1ob9PqiTEmvXrVqxetXLNakkiez1+6RTTuuNv+swZ2EokutEzvber0TNz5v4HHuh0Sd9Me1GlREafnbUMKH+r1q4hCrO/9UMGkVn06i2EiH/CoU/+5V979Stf82sHH37oukb/hu5Gr5WDF3X9fQrdZBuYjrzyUK7jMa0f1q9aPaOre/b0nln8ny08evscammmi57ttx2kTnPu+XCz9SbL2wyG+uw6DjEt2VtuxyGwJcvmC5zgmXBQX3pGaNF/Qm8nv8QCpqLHWrVxZBmhiKj7uBHYssq06znR8uslejFVv22nTXf+/HDM6pUm08YTn3jQR//jXac/47ClDy9bs7rh7FvbDo3+3q6Bvs2ztcX0dyRCr0hq1zFjxoZAtKmP/7uZwi4vMRoPPPLwslUrV65evb63d9V6Nrj6YrbqGxt6B9Zv6O/tnT93bilDfD2wPh4tmXHXpxx19MEHHoRYvWLlhrXrEH634cjDDve3/z774vQIbB7Qwwdng9pM4KGSidzAsLKnTa0Y077uaq3Mes6ZP88fBsoLlQLNnhm2WMy0Wc2fAUH19Ss6rKSH2fKEW2VrLg111OECi4+f6efxeeZzn3PksU/pmTWT0Q/bbalASb+MJHL2vLkeurt7pouhSZXUDMNJzvhEYCMEdOP4GxoCPXPne2njJzs2rFqxEacbmwi6WJcPQZc+9tgyW1mxm2UQtHI2Gleff/7ue+75lONPbT6kx8IwSmrn7RyjIxsFxgLC6bk+BvDhwUte8pLTTz/dgJJHvOtf/H/27gPAtqJIGPCdPPMyOYMkc86JJBh/EdPu6prTGnbVNWxQMeGaVtac8yrqKgIKZhElqyAqgiKIwCM/Xo6T5//61Nye8869M29eAoTTDOfVqa6qrq4O1en0PfZYe3V2hV3Z6yQ0jN8xswiG0eXoz3ve8+JrgfJCVvv0auyd2wJ+gsDFV/FzvSrV97///Tj9zCpxyG868/jRHZ+sqGZ5bNlK6dYJ5xVc7e90oGVVBGqpY4759PMMvK3SakxtgdoCdwQLtPjf3vnzHve4J57+ozOG16+s3P3sN7fa+9+0npOHk5NO9ryfn7Xbnvs84MEPb3RYcLZqjGDz/G/ZvL6ld129U1+vfvWr46CzWAe/dIyxX3nUUUe54S+c9fvf//5vf/vb7hEU/IgE75zWzepQW2BGC9T+d0bz1JG1BWoLbGsLhEsM51lMgbvm9Lok+8QTTxxbv6GSmMWoJbfctPjaq9esWZX/OifG7XxmSqs+PO342MR5v7xo1z32O/i+JqQ2j/x6bXbQgAxnvlkBJtq+wH/Vq17lSKjjGc9//vMdWs2cTkX7MW03+Dzzmc/0m2A+WPLdpo2kpz/96Q6EuHnXjXKmzLGtHHPnzFsDd2YLmLrK/qc//Wm/8uFHaMG+6f3ud7/r9HMcV5ju9HMYzU/Bu63JWo01lsC0TmYdjzb/db2j+S8aCzgx/43Tz3GaNHgrz3l77LH7rrvOX7lhx3Wjl5z2051WD++8cFFj0NlJzSztstfhb9oCM3WFqlFrTZplbnVwaqSgqkXQfTfBUbAQBPHUt1b6RK8CFk+JhjJpJ74UvApZpUwTyELARo9IZSPU7F4wVsLs+DZBlTXPwCYYWjZNI8uZvQKwQ5RgkAWMpoLM+Aq7V/q0IttiMmUGymSRLzYMZLzGs0w2M9xW8swsYnNaZcrNTbrM2xbOqVSAUKAtS0bm0pmhIDLxVgKRljbkrtyo0j7Sih9gtbIA8PShTGpXzcojRZRYADvvMrBqVePznz/nec9763HHXXvtdX7Wcys1qtlvUws0fbji/u1vf3v66af7/VwAnVQVlTnK3atRiFGvV5Shsa47qx4f3Ityx7NPAA04DIUtPMG4KSp+BdiHib7g972Xhf5gjNqYhQBccKVCWtviIPCSIESira0jMLnFaeokhEz39Jx//vnurqJJVjhTAnKiYARl4dbRZI227rT2JaXPGeWdDvDItAWx0ZVFC4I01tdqIjZL3n5AaBvP7ZdKW8m3SaJtNWmL3Hr1soQMlBNqi9wygk2KKovd5nC5Ccxe+CZ1zgQZmL3wrae8TRLderU3KUG+BF+G6Btdzf/f7zv5+c9/y2tfe/G6da6fn7rzb5NyaoLbswV4PaXs85slS5YoZs6FE6Qw9wTQYMMXZ3dWzouJUnjJ8E2IsXtGCJjwcKaeibfkBLMoNPy1AQDvGe7eGgTJ8DlUFGgmMuVSSbvyyisJ4T0BXikQSYSSObkASI4kKviYJFLAoFSUhLKcSLRCf6u9Ung7pSVf20nydGIjL7lwA8jE0+EzQQUIaRXkZr2WJQRcxmyWqNshcc7LrV/Q28QaWf9tIu12IiT3Km31kWVBp6eP2nG3hXqy97//pGc/+x2vetUfV21o7LLD9uoK2ipTI7efBfhcX/Da3eFDw9cYcakbXiPMcBAt6KNR/+Y3v3HhDRYr+/aBdtllF46MT4/boO0hxSr8VA+Qfv9gshZJjiuXx6nY5kzc8IAEIRPPYAo0/KYngW197gy8orBQw/TZTX5m0Gb0voZyck6mxJLp1XaFGXH495ml1bG1BdpbQLWPv2a0e3os3fz1sj929ud5zdQK/01Lbv7r1VdFK/B0ANpy00RpA7gpxrZv+u0vm7I9xZ3okokoLUKYItsU5IP8+CkwG3JWxtwD5Ay0JoxPKwA8+tGP/vWvf/2jH/3IuP3QQw8l3O+G6UnOPfdcBIcddljs4YHLLXpTydbxdwoLqC3GFXEvmkP/Z5111hOf+EQ7wVddddXJJ5/MBG45TaOO6RfZ8fqdPfVN5zxdxY4N5q985SuIH//4x6uKT3nKU5x+fstb3iIJCgh4I9wp7F5nsrZAbQEWaPG/+h/+94orftvdvajVQm387+Lwv9lH862FTx8bdfiYQ9xK/0sHKj3qUY/60pe+5MfQOVnnq+wfCQaonnott1E6N+OmG8NsBAarb3/729/61reecsopNp4uvPDCGFG3ZqfG3MktoPLwfbX/vZNXgzr7tQVuGwsU3nJybgoeT87OksvNi6+d0qc5W7Uw5OyEdaRyMAV2LwPiRXMnz1QkRgtIG1Z/73vfcwdcz4KdDPHTX3MKPCV51pA5SND62NKc18zaXNiZB9qam8T8wqaSX/+2HPSxj33M/XdmzTw1x/2hD33ozW9+s4U1P2hjkpJ/vqyeDs/a/HdwQquIcrh48WLfILm2/LOf/awabr0F0hqjpwHedCZwXujGG2+MZclcS/n0Mn2swJhWI/YpnajHPvaxqqghIhixqjg5+21ZGlK3ncN+z3HHfeG97/3pRz5y/vs+svQPlzV2nG/Gbc+yrsNlO/8twpvxZaQqUs5hvFpMqeDLNOpHOWTKzBsrL5kl6iKZQiADQB91Oiq6rpZYcMSCEXsGGWQkGhKQVVLJyd1OgNB/lspENhEHkF8n2eOCyXRfTNpT0SO4li2vD090pN0Luxu4uiaSS4uLZAKfrnSFmrzWNM6dp3MPiaOQFoBnNdHSbo2otrFZThYSADz6CjJey/gofXiFiyVzASAzZSSdXyti4bFnUcEYEiqU2/A1lKmoVFZjeytQyUtoolHccMMNNhssgzqk8rOf/cwYhXs46aST3PrjGLTB07WLr6fn2PjE/HkL1wytG1/nqqTG6LoNe+3auPe9d1w3uPxu92xcd21j+XKVwy0pCmZIXjpcxVuHvykLKGVBX2FT8wMf+EBPo2OPPfaI3lXjlJUYQGhpGw0rmnnsKbrq+TvtuHT1KqNbv2FhcGxM4w7ms8/4+V0OPsjpJX1L/KSLhIyY7V01uav/GlhIWi21tmU9y09X2D+ePLBVpW15LzKSRjTd3W7w+tTnPwdBlJBcwPhE2iQYT8fIZhZo+EUHA31N48KLLgK7/kvVto6WLLN+0P7EzsWeBGT0JxJq0eYOgmC9ck68ynUZU8O3lQWKgoiySE+VEGbj4rqtVKvT3bQFfOptEcE3ITY+X//61/vKwpfTbmly8PS0005bsWIZEaZypn/Ll9+yaMGend09K1asWbhj47GP33/16qse8pDGtZcvuu66lT3d6dq/dLtu0Qulvq7ZQjOwaW1qituBBZRgHLnw+Y01I9s5nLJjWGkk74PjZrG2apo9Gm8lNjptDmu33Xa7/vrrITnxSk/eKgQGmeDuHJoYB0rXEoAK2ZZ4BqSFOQth7oykGFFcuemImsm1y0Vn+lWJaUPOphZhDrNwhx36i9/A8kqO286sA2aaaaXUEbOzQNmh5xoS5vWECWQ8Zyeypqot8DdgAQv0Bvn2Gxxfs2Wue/nWt77lwjC9pfnvsqUr7Izqe81fVq9aNjB/F81h+c2r5i9qHHnkfo3GNUce2bjhsp2vv37p1mwt/A2Y6U6jImdnZtrjhwQmJnzQu++++65etlyt4MJ4Ll7MCskMxsCl5vC5LsIx08TlF0KdhLYJZF5sH8j+EHb+3QSzVU7c5ezyaT+4EN0vt+sr4gc/6EGeG0ZGncwe2rAhdutb2TPGz735I0F2RjvTEZflLmju7BxcvcavkY6sSdd/Dsw4Ww1e+TWXP/uX56M3BIVM7rgYgcimWPkVzNNz0jVQW2DzLGCyOjWmHb/n/R+oip520omNrh6fHTRFzVhZm0SVf7smOtdfddVFl/7h6Gc+/eSvfG6i0d/wGyEGxkG30ZJGhbX6+shHPtKQWLMVYeR5r3vdS0MIIg3TeWhLqU5faW52f7UI50p9KaGl6C5+//vfO5+KODqQquj6vbZAcR+VXt1E78Mf/rChiK7V1MnWrzlUzHRWrFihOqlaYS2Lq7kG4lI5kZmmxTk/7BWjBsZxQDNBPyvsgOOzixBkYqNX95qTqEioX2sL1Ba4A1qgxf9acfruiSc2Gn2jo5u15qPP8ZdWdbpsIY87gtWx9Kq//uaSi5P//d+t8r+PeMQjDJit/BBuWO4131uvx9MTPuc5z9Ft3v3ud9d9CW6A9lkUYsdcjj/++GC8A5ZdnaVtZAG1qPa/28iWtZjaArUFNscCfGaM68cbex+4l9MR3zv11PQrnYbxgW+Zq4410mU3eq31Q2v8UKjTzX6OelX+xaTJ4X/3kquv8Gnuc5/73C998iPFJdCboZXpKmrzArMDLjXPKZwXshwa5zd8aOQnj3jkIPatkR9hMNt1htW3+ly2j/9tIVm2spmFzBw5ZMbnmmCSN0OnmvQOaoEYoZnDWn7/+te/fs4553z0ox+VV9XPCirAJLc8/40o66uagJVVdcxKixYR5jFxJirgeJKP+LLLLiPN/Pfss882/X3xi18csTPPf5f+6YpTL708/YRJXo6ySTqYxp2phdbhb9wCqZubZUhzi2aHlYG8dFIWErG6RZXSMwDLNGohjCfYIg5YCAJPXOqiYHkdHK8hP2B4sx3BplQcC4hX9VvQGBBEIKQQkB4U8yoWHGmV21KZrJyFMhzSYMrEkGWabQ6H5q1iQ/mILT+DMszlKacwcu3pNUKW5rUV36RK/2IPmswCkFzkOqLAQtYhXsP+0o0gNghCDsZ4zcJDQkFVfVSSFo24Ur6RaOYMJTNjxgMgEeeogENmCMnPMhdkhaYcW4Gz8ADKseWoSDonF0mUCW4FmBml4gcsjJD84iRVXW3ipls/3WjsYjZoNQEB/F0PvrsNvGOOfoa7TPbff9+h9bxRw9czp53230972h4+3eEIlGaEW0HzOoltbIGiK1DQSjAkR73VM6skWrGuwEYsYOZ0gx6vPtnOMS6j5B1329UJvxCu249exesMojQHmoRfABtDG1X7dVEKeJ2BMUWNT3VQQRwex5DdqMjTyJtkYRNymtEStQlhgOVpuE+ILQQNxLEJ/iu+S5OQfAlNpjvav7IWdSP6Xq5WlmcuxDuaCW7r/OS2ObMisySbWUgde6tZ4J73vKfrSTjTdesG7XHqZNzPpIfRTd3nPvcxCjZOFutI1hMf/wyAj6oXLpzLdfs55m996+1PfvK+euboTaM9Fv3QHbYjutXK5TZMSO8akyPbPC972cvcYOdQhf5WmLl1h5OlOcBZDfTcnJ95Ou644xzg4JTVDVH6cFGe0+Wx7MgsZkWikPwdZ+o1MNOxV/CFs+2Suq+YHG1xLAymnESFPl6jMoOllb5ZKo5evfzlL7ezZcjqQkpZYKXN0qRtQjUyG5zNw+z5GfXEM2MUXG3zus7cYSxwt7vd7cc//vH9739/4/xLL73UjfW6Sp1n+F/VXuU3I+Z/j3z8MQD+d2BBr1nIEUcc8c1vHvfkJx/I/25yUnKHMdcdPiPRuSl3wDOe8Yx3v/vdz3rWszgvPgtGZZjZAhgF9AJ3yUOZKvLjlisdlFR5ADGPnrkXJSQIzJ19jv6CF7zgP/7jP/S98DMr0Bobcujwpje9ya+RGlu20rRiIi28Ms7pC/LigJ11ITC8YKCCcZOuvFV4jaktMGWB5qJTo6uj0d1ruGsDLMWOjVi8bkwYpm6i0U2J2hjq7/azquPn/Ogkzfbehz2+0Vjrh4GLvwnLRBvTbuJNc1bh2xJpzk4/P+95z/N5gw1gN2OhtFLk28X3vOc9NrNt6QXjJnuPtvJr5B3bAslhTEzoS/W0cnqPe9xDjXJiQC2yGgCj2iCoGEGFLGMsbFqQNKfDpX+OmVqZIOjNpL785S8bzDgS8c///M+IBWQZKLPUcG2B2gJ3fAtkv9b0v9///vedu+rtnesnPzcz+5Mu0v3PjmQVh6HHz/nhd5v+d3CL/a8OKrpH+hhw6t+yYo4B2Kn0FKIXdSAmTj8/+clPvvjii3nneqUom6sGKhYo3G/tfytWqV9rC9QWuFUsYGhvgutp+N/XOOqoo371q181RibSvUdbHzr7/Iwnj/mwRx1hlD97eRwuJ2vLhm+1EemLX8tQgvUfS1IEhjvWeXLuYOulhFvscl2dTSv3Or361a/2maXpsI+BnX52C48Zyi9/+UtkuMxEZq9MTXmHt4B9RvVNvXLaR2Yds1HTItcxRa1YwC2E5rnGe564zH9Nln25Z+5MjtPPrYstsVDjR5BcYf6Od7zDdZ9lmTHFjmcZn2At0dcIpuf9PY3eyYNDI8VP4bWnr/LX77drC8yqn1XJFHYslMRTnmCEtpmLWhvP6C7VVPMQQQUV9JiekFkmOWqtRRwhy8Qb7AHEUyya6HmxV6IybwDIQoJXbSCSo3Yw5tQBRW4mH4EPCeCKzMAQm/Fl+goyokLJgIM9TBfPtuxlOdgzLyBe44ksBE7S+/a2uP454V2uYSo7PtYzNtrVGPYXNG547fargUVgy4mJUf/788FR+uZo3BdF1uNInYg7aTzHJr9FSmkxKVZPYTLRpg4ZE8I9lZRnaOgZARckx1l+zaIiNiQgkE0YTwGLKhTVJtOjrMDBW8aTgwZ7hHgNgZ6ZPYBgl5eQEEqGJkmJgr6c08zemm5EBUvIweiVGoHEEqKm483CtzlAB5f3GLWQbHOOGtZJuQfux7auvbqxzp6u/rkTXX0jE10HHHRQj1FOX39jTuPGmxvvfO9PXvqq//zIp2686sr0YcxIukI83e03VSFC3Y6hRv7b5hmoBW47C+gDuqxbGXinsbcLlFN3bf9mwaJFafGpv2+iu0v5OoMsDsZljmPpRsqGv+glOu0qjY51jo3P6eldOGduf1e3QcyBBx6Im5r9c+b09verHlFDogG2VV9UtDsVko8w7D700EP9zKjXCldI03rznxvsDYjw+4u8RL4Gevvm9KXtWxLo4ylIJfxIJFdWJhojCd0dnbwU0r133+PhD34IOSODQ8tWrpDlQYfDOxobxseGO3yP2TkiWX1jkcHIY1ngVsKtGk4nUL5ao7C3xWfK6IKSUaZnD68dFiuTER7sWVorgGD2WUAZIeSU4VbJgSnrMx1NltZKsEn9M8vsE8osmwWEfE/1tKjXyVGCh4bSaYMIME1w6l/V2V+z9umSm38xHpj+mdp8xE4J2wjKySn6jSI29RIFl9mnI48si61Q6oWkmGOnY69wTUd2+8TnhYDddtvZF9JmdK5/9gxtY/w2tMHOwcR9H/TAtUODVhvWrRtes7Lx9rd94WUve+eHPrT45puGXcAX7Qt9uu+vqAkdncOdXWnbPnV3nUMTjSHbrP5un3aotcoWUPrmKUZihx32mMc97on3uc/999//oKLldU00ugaHpv0MSVlHUBlMrwhR9BYUHMVwZTKHTnLUEy1rhlaDkjJBYCkhZIZ62PEKkFnhAMLbmiUAJok13mKmFhKIHRwZ7ujuis+YONDhsdEYOfDaCWh2XgGEM0Xs3BU1nEq0gnaPe91rtz32SAPRYhA+Qy4q6t1qr62WkTQ92+K3TKtKriuvbWVGubeNKiOjcMsVIDSPGuVzL69CmSXgmXOHvZVlW2Ha6lMRPrN6FeLyK+HbVflyWtPBWfnZ5DQLQRxhk/or9OBqpYTJsVnytgLki4bbStqWybFyqlbbOzfn1WEa4voYOG76zAJHBtd0Tgw/8IEP5KzZY8P64XWrG+94x5df9rK3fehDV157w3p3T4x1dpgLTM6GmnOcLKEGbtcWKPmsmKuOm8p1de64y853v9c9D77PvebsuGjDxJi/mP+Ge2rNEd7sYTUc1XunXXfZa999LJ0tWbZ0zeCGvnlzTaKHnMLs6hw1d04huYb0T+EjAo55aVoecpqtq0sF9TmxL+UItCYzYuUzO8rCb4Ymk/7XnGGiMdaZ/gig0ri0Ohp777fvow87dOdHPXDZfOOAsR5uVYodHXnFZkpmITxyOuEiv6afp5sGkhZwm98daQ7kR/8QadEwh1b71JjaAhtZwBgyhpFjxSXQXV1Pe8YzXJe+YdWKRkx+p/cO0dNOPTeSO/nSObpunpFl14LvnHLKPe99r8bO+6Uz0I0NKbpZq9vxVXEqvHatYkeEhgDOc2EexOVDfi/lX//1Xw1QLVVFc/bVogPQTkLbD86beVXR9XttgaLnN8dxbu+Vr3yluvT2t7/dDWq+tmKbwgkkhxAhW8v9Vb4TcHeJm1DjCy5fyDjc7IccTzjhBD8eFdeXZPoQ5ek7HPX5q1/9qtWG3Ffnul2mr+HaArUF7sgWmMb/3nJd+kaOy/OzH9Nlf8rzmvWkz4mMHyOkgWTaLrYl4g5o+K7uU0/+5gPudXDvTrs0Go49FSefNsf/Rh9IegBGoYJXJ7F0XHzrYx/7WL9b675J0zevEbto0SK/zOAMVuusdlLT+p/aAoUF+Nba/9Z1obZAbYFb1QKcoD8OkzfzN9z4x398ztKly/986R+TGunr361Vp7O3b2jlLad8+6Qjjjiir78/neNsTmNnFq1LNMO1NGrzaM8993S01GFowb5SLG0FOxrelqu1ggrj1BCMyYVZyTvf+U4/d2aN6MYbbxTlDjsTnK997Wszp1vH3mktYMfToXnZ/8///E+frllvtw8VM1NPFbJsGVc5fPGLXzzrrLM+//nP++kPlyL993//t0/QVTAfnPvxRpgyPdjo0dMvt3u6XtrKTGAqZK2vXU5GjY0PDI8PDI72D48NNDp6Gp3decDbylBj/qYskHa7NxlUwbwZlusiICpoW/aoXriCzFq5+UleNxSre9U/WtMPmoqQqPS44MF4yUFZJiMkaxWaZJUAmRJZ1iFtCTR39TJLpqwAoUMgg7iVoIwvw2VK+AgV/UMrUYhD4cCUecEIIqeA0B+SKK/Znpkl5HhNQFpYTiELzyu/BBKRuQKY5E3qTAVIxCEkPvSBMT2WdBJS9CzBGGRZJUhcCOIZsZ4W4IqtxPTlUDBmyqlUm1BwebPQDJZrHSWnm1NpErb/N8qaAllOKFNWOCwZ/IEvy8KoIw45gQeXCQgMrpAsqgJ4JUSIyow9TDfLLJTT2lZwKOzpa++HPvShHEn8yCMNv/Od7xjouPLWNXsImN33W4sXL140f9Hvfve7m5ZcbsS2aOfGXns13Eu4xx7popYlN/q9SmfsR6e2y7aVorWcW8UCUYdzvVVFBTXWc83KVUmFrtRs+7p7VAmVpKjOnZohoKygWAHGoMQtJj7780Wgr7VI1s8HMYKcUJk3w0EQxJqeLTHDmiS3YMRLq0xcBZrj+0RcxEVaBt8pSzR2LMw57+YJMDiUCJEFZbxmsSiN/ikv13q/P1xyiSi7FDpAUckCxXnxsIkeOdTO7NsK0FdIIjScWSYFWjsWGJYs44kq55QdsuZt7YDYaXh9r9TluqyDqDJ7OSrDCOgfKW4yF8gqNK2YLDkAyueiLEdVsimqrahN6p9lRhbita2hMmWkVbxGBSvHTAvTJMQ2c5QUVv1MRJcsuakkc1oJmxvRaqLpJJTzPh3NNsRnO+R6GxhJbBNNZp/xbZipiqiLLrrIvc4ugT7iiCO4Wj2MO25dS6nE//CHP+jMKHnBb/6waEH//B0P/OW553R3D1v3H28se+ADD/7LX67YdddG59CcJUvWF7+XNe6kM3p9EPsUvKlf0+7SWoqhYCXt+vV2aYHcj51++umulLOv42ebeBzK6hOiB55OcZ18qgAdHdak/PwCUWecccYFF1xw+eWXm22pVKZReGfjSsIdEIgYTGZgiqqVkphOh4wPGk/tV7oWwvq60uVkCKK/9czEbYGUTEeH09ty7XvZH/7wh1dceWWMKKy4VdxQWwm3PpKVcn+VU4eZ2f9myu0EMPUm/a/1Skc/nZCwjqkjCuPTR47sD5kRKDu/EtNWQ8Rt++TAR3G3ZYScjnc6+jK+kihR0vIs02wZHPUcb6vyW6NwVqasZCUXmSaAnFxWqULQ+lpmaY2tYLImrTndpDFzQhWZs3xtm6OtlDnLpIPMUqnLcU0xnHvmWFV7Pz1/0EEHNf1v6vR+89vfz587MH+nfX593tmNrpH5Cxd0NlY/8IH7X3nlVbvs0ugdXLhkyari/pYkMhXlxtXv1szOZuW9Jm61gE4SUqEL7s6J/lDXF7/46eeFWvv2LCT38GbKkNwTxr322sstONxx38CAVa9yS8/tLiSUX7U7J6Tp4JiaAcAvfvELign8IAmAspyswHQAOeq5GetZV/9Z5cQbac1QM9EIBHrqTOkTJ6FlnzT4sh3IiZC6cpW/YJxOmRpfW2DKAqm2FIPA0fEHHfIIPfApJ53UO2/BsAuifPO31RUp3UNpkLnult///vdPfdrTvvO5Dzd6Bxqj6zZLsoZjaqYFTKldDKG9aom+8LfAZZhtmdTFk6973escS/Urq4buBt4OYL3kJS8Re95555XZa7i2QNkCfIff6nHXg4uZ3UXi1jSbwa4VR6O/rdQ9yJjWcTQGKqtWrbIN/N3vfhcyfBCW+OHgnIQKzJe57sRqv9FOumSuCNGZF5196u3rUFugtsCdyALt/G+jp7sx4teSxxYsWLR69frNsYah4uTU296zxcuuhqMj4+Pr1vK/T3va0775+c82evobY0Ob5X8JiKWe6AZ1WXHcSr9HN7/Q4tXahVsqDbYtlNnrgeeXbT+h0bnx4JuTi5r2TmeB2v/e6Yq8znBtgdvcAryl1ZTiXqn7PeKB//iP//j6178+vcJvi/H4+ODqRqN7+U3X+zDyfve736/PO2c2OU5rOMUXUE6LxoHRMpcLE90WkTGmwBapvOJyaNXZIWc/LFj913/911Oe8pQPf/jDop7+9Kfzwu7czVdLIIbJQmqgtsCLX/xitzy86lWvuutd72r+Czj33HNjftpaW77whS+ceeaZllX9bDLfbdXFYstPf/pTtRGLYNW0YlJCrC9pBVdccYUlGjQVgpkrZDpM2Qzp2orSaxNd//s3aYFqPWibCRXLDrr9IZujVuGtpFjNV6Vaq1Fmt0SuSqGJYPYSMxNzEvMZT72h5ZtmfPo3eIMLLFFP+FSjiz14Qkxm1HgLOtQIfNBkOSHEK2IBDYHoPS0YSdd163hFRWvJbSYUgM8SAJEuGuniojkCWQPA5G2ASD0Y87OsktQF1jNbE2QcLwJwROHyKjlAYEKTyGNgWMwXrvIiCzk2J1cBQnOMDXehjnfMG+/asXtgr74d952zy8oN65avW8PHpT/mSfeWMrU/xwHHRiY6/ZGfeIsQWQbSVsZTwW3YkPI+Nu40ISsUF8ok0mCREbBnqB1PWRZspSuCgGkYUUGZ+IsAKcWwRhB41aNJXRXCG+eng6ysZ1PA1L9hTzorO7wC09EcL4ynumRfP2raFFszIzA579SOIPvBHl8bg4MRgBgMyCHSkhdqSyusF7FRspk3gMTevEqqAmSCrQfoI1DDb1L4YIsvMWrxFfW3v/1tx27+7//+7yMf+YglVJVh9drVbsAaWDh3p913Wbpq+c79/b4Kv8vO3c98wjNO+vIn/uFJD+uSY+chV13XP7ysc3y4yx2TceXz+MLG+MKnPGPPhx/SPX9hY3Bo67WuJWwnC6jAcWnU5LMYkqu5w/765/b5M9SIK0lS01M/OzvceaZ+ulzKn5se/dm12rE5IjHkdV7WyNgNlKoZvZesWH7A3e66zwH731Jcn1y+XKqSq9yitUrtxe2PRjYxuFFpEcczc6mA+U9cOvpXBD1p9m3kaPuaPBfQP3fOPnfZT2NMPVgpRKssIRIIiSytoE10Xrv4+rWr1x1wlwNHR8YP2P+g+97n/vPmLujs7t0wNDLe6PQ31tHlL+xTkbOVr/oKR2DtcPi17ulE2UHx5aXYMHgmi34G0rcNfmGHD+LKKzRs/pjHPObUU09lH1GV2BD1rne96/nPfz4z6vdS91HYGaOA3uA1fuAmp1sG2P99RdANFuLbJPGTn/zE/UnUIzB4EYVwr8985jPzj8mWJWfKb37zmyaQ2MuxJPA4Kq1jpg4iGAGLhSzTBMbBUx+tVvD51ZkYneSBB95leHhDdhbkqEUxNrDN2coeVhocHHaF22c+8xnWo0m4pJAcNmScf/iHf3Cjj9Xbwl8YGCTJaBCwwPz5c9/2tmOPPvqY9evT7bAkSFrgsoeHxwYHRx/0oId89rOfdd6iic+KTwJvfOMbTa0jtkLj1bcK7g2avAe69a7oxtiLX/yC17/+X8t3zxVtbioV/kLZDQ1N/VIwsRFNf1d0+EBzinpjSB6PPfbYJz7xibncIz7yDra6jX1Xp32LEJIjF564fF7snq2IbX36gMGpJvWTeuVYvBrChg3DRx55pH1H7UuKgt7PbXfxFz3h5LPVMjNjyolNA+vWbF6qWpqezzx81eowNEBlMFAx+mx0dnWny/waLt/rmL+gY8GiZUMj8zsbL3nWiy7+8TuPfcl9u/rWjww3hkdHBuYOdPf2+za1MY6jVz7ufvcDH3rE4EH3W7NoYVen7QgDPR+zqVQFkJ/TqFajbxsLRHctbV+d/eY3v7ECZdpl5Olkqrav0obPRTBu/F36G/PzDM3XFauWg4dGBs/4xc9O+PpXr7n26s7ujtHxNNrfcecdAkagYmeWBHRM/aWbJDsnunv97MPo4PAGsdg3DK3fZbed586fY2sr2DdqILGSV4iFHx0dWrlyGa6unk70fQO93T3pesqRkcF9991rzZqVg4Prmuz6io18sdzlBm4gyis5yH/u+edzcJZFOJEi+6mhZtefgNKNmBvhK70V5s0MRg5218455xxrfNOxbo3/5bYe//jHc9Bx0r1tEvJuoQcly5QJvEIefPDBaksZX4bVnE36X52PXUPdTviXzO7V+Ofwww//n//5n4ysAHQ48cQTebGKbsiMFnTRPrDU1znLzgNWaEJ/wt/xjndUxObXpv89UEFkJEB3jd0M7g1veIPP+sEwevUyDeVlwezGGKCML8N2Z/mIovOfRJdhDfDjH/+4awnY2dJHmREM6ViJwUlrVKbkfGnIDjnvgAy7OdLoJRO3Ahzcm970plZ8xlCehfPoIktGYERhaHf88cdn4gpgAOPSQdWv4n8ZDUas2wQ/8IEP8L+FylNqhxyJqpY0rIjNr+YOCpcTL2slNiysLTOsWx+kJUXJVciynO0BOJf26U9/2h2Kzg9pXwZR1kY5X1NgA91OKyId4wPdjTl93d0DZkG79MxduGbD6KKJxsv//iXnnfaWd77mEaPzVo36OKWvY+6O8/zETXLVpRAWk1NZ8xRKkVsA6iT91WGbWqDkNcpyb7xlyY9/dvovL7zghiU3+4LMDJd/cSdzmaYMR3uxIq+Xc2uOdvHb3/7W5zq77b1X79w5rpQm4eB73H33vffye0rrhl38MNY8LpLEVFqfhbWO8Qk/W7Rw3vyTvnXit77xf1J3Ktrf2g3rh0xBN65K2fclsuIvYwgfXrPutG+fPHLexfcYThdJCOQ4YRp/CABBH8/Aa4xCVFosNBSKWtyhObtkxYqcvlHUNqreyQ51uONbQDcYf7JazHP7d1z0hCc8wfJjw0qtKbwRzmRXWepOVfiN6/zMhlrTGFrdcNbKcaiJfe7yp2c9Z8cD7nP3xvDaqaRn5i/FGllpzve9733hzBMdblbnuUWjtUMOOYRnj9m6OaaBlvPQMLweYq9akDG8IcpWd/4lhWrwjmUBx/js+77tbW+TLS7DD/VaqVPB9LcwKlslu+ZELihRD51m9uO/Pi5147gJo1en8NU3g9IySwybddeOCVqmS2sLxd5WCA/5ngGUGWu4tkBtgTuaBWb0v+nuyeRnx1avXp6A6X3u+ES3HeCJhhnxAtdFdzTmFgst+qxRPwbK+6WroDnw7q6Lzz17hwVzDjnqyMbIYHLuhVcPpzkb27p6iW8NStdD6PfAFmQ8fduvWzO1d/TKXPLhD3+4/XF402G9nNF4cNXP2gIzWKD2vzMYp46qLVBbYFtaIPzvRNqqa3QsSj+PvdPuhzz6CWf96p0d/X+efUJ2U3LgUWOqnNz1pNdOrrcg6Ljg/HPvc5/7HH7UUcVhnTSnmCG0nQjEsg8u0xMnoCz4gO1ru6cJvR1kT6vozq3CWwk34Y0p8KMf/Wir934NyUTYxb22AxAEO6AOd2YLxJKIp90HR5Mtm9itMLqzl/SMZzzDXLU8/1XBsq3AzjE79GwWfNlllxn4mfYK5r8wxoRODWXiAEi2i2qgaDHHsJBkQnKtRhPKVLi8xpp7/HaJ54bGRFoNbqWrMX+bFoguchO66/LUGF3YPe5xj9e+9rVgm8EOTapD4NiYbyuiXKtsYwtvfetbbQ+4evaPf/yjdZwQG3KiWwQTFUvqIZMQkyWLOKY6dtocxbO1YL8KJZWC3jMALGR6esUYsKrv1YxIS3NM6tJLL8WuSYgV6I8ecUiAz6ln/XXcpli6ctt1iB1b0d5QBkuQxTMwaHIIvITsGdgkJtxOm4YqUzFby5QAxCgBshav6G0zaLSPetSjnBOyv+usmO1ksfBWZoPMsxxwOaoNYwN4dGx0Tmc62eZSAZ/v/PSXPxfG07nBZCUhABcIpowXl2SXRYEpI/jlNfcTuNbCNqEbL9JSclFSJFA75ARj5MJTUOh2E60LWyzWqemk1BxFiZ6bZHxmrCSX1JhcAU8xit4mscmt4DIem9kW+HyKVOGa7pWdCdTDHnHEETRxEg4vmRQorJTqQIVX6lEN5Bo7SpozPkDqBgHO0qlOYoMRPoSkDJc0RyAhRabOS90ZtbPPPtsSpGN5mbeS9K32Ku+RR5oDqC0X0ZYns9Pdnwpi3nyVn8FHN6wd6Rmh3pVXjp5yyik3Lzv7t79dsmxZ0ndsfMh/nT22M9xBOJUDzURNO/ZNn77lhjWN3il8Dd2uLFDU2Y3qLfWiPoSejp8effTRp57yHb+uqKqouqnhlEu60XDi2cq744Z/93d/50yhrwBVEs2NHBXM+PsFL3iBav/ud7/7usWL1ZOQrKYBou0E7Bks8NE8o6WAhUxcZg84PYv92pA2hSwgSD2bDue5z32uq1a+c9LJNBQDL8VU/zc+u1DU5NQJOKudyEBjY/a25dFZEL9WSZkPfvCDugIZz5oXyhc7Fhsbp1Bhyx8M4gCcU562OiItsiBDYmCc4XCSozXvikD7RWNyostSBJXdEUJkljV4ZGRClpzlQzpBhRF7eJyIYgTEOg1m8Q1fMnKhVUUCYj9QEiytUYSL+pd/+ZcbbriBQ2TnoPRELAkEPsxwXW5QBj7TAOD9BqhRr6oS8jMlR8NLypoi87snoX9Mw0ICeinyyAzVqj85Ap8ldz7CUX+8YilUTr7VKwfH+JKusCfOiYm+vp7rr7/eCSTs7IwGMisJpgaXxKUyI1HGI2h4DXh5xyILjiipiXPn9kds1lzWCHNq1hHh6RyitIx2iBIiuWD3lHFqM4tvLjOyAmA/+eST+/vTmdoi40nhSnDCiV9GwCAUFhuUnuTzd+xfYcmv6Kmn7ChDw+D1DBig1jlgHbmr6C+WfBvnTDedbsaujH/55Vf4uXtkWHIg2ddPLH/JJZeYRBG1dq0anvaqJaQUMuVtAtCHDo61qq7GG1QacfRzYoLPffWr33zJRY0f/rCx+OpG79zG8PrGqhVr0as/fuYqaTuRfiPymye+jfE/8r6bzznn94okciF35ey0Kc5ydA3fihZQ57VotbSoqKmYFJY+R1C48VsEleLL2lXwmpUKQ6DVKPXcWY34cN9USFON7iXzRkKtFT7aMjkINE/fNVnDsr7gy376lNkn4VLN0qAoPDieJll4k3odqXWbwTlGfMJXvmIW0EbCxii8spBaQbOBc8GkbUy13d/Y0Ndcss9HtCYW9tka/6uBG1kxrM6qrXxJuEFBOYoNm2Qyr9QzlbaLk5GtwCb9r+EZR2CeVfYj0g0f5w4J25CtYgODjIdyAkNhhTUynvNSiy6++GIfmZjDyimFy3KwQFKvzJgJIIXsf1GWSz/qhgGJiUbUVYxS0V7UtwhqPsDgR74kDUYTz5yKSYrVtFC+rJ5KLvXwv2qgwYMRVOYKgEr6WNlvO36ItBwu1zNXEhVVZG6C2S2qVMTmV/o4Hi2VVvZM474NjZq0yYbWjPAqOOZrlbCJq/4rX6xH+WxAFNIyaRUlkOwAtCcYvkyGkk18sVMVWnpnMV+XqbrUC3RkxCuD6+7MiH3VQDhRysizxH1bgpOlP9rYY+5cM3eqhP9dvoL/PfYvV/WfeurgVVcmV7towUDaFbClUYQoVgUHYC7Nkyh2CPxtmaU67VYLFPO7MlqT8eoMpvICRDVINb8rlW/EJoLmMB4sKG712bcQRxxxhEVC/fmJJ5+kL7Lwpfe7xz3vafXMFyaf+9znLvnDH0who5/JVYJYMGS0DgIzABZlEQ+N3pgH5FtTkq2h5H/bRBaj8Y7x4vcciil8OYkyfVRdvWjqEosOM9YS4dVzJ/N87cNTHHfccVZitdxQe7J5lwXVcG2BVgtoVmk06HxUukRDvCUjU+C1y5emDzgjNJ1FK/esMVONgZfxie+DH/zQtGi/dnDWEiYJrdWfddZZ9h20CHu9mrB2rYF/8pOf9KmMJm/rN9yWM6xWQiC1ejdgPexhD7P1y6nxApoJcRry5qZe099RLaD7dbDPWb3XvOY1dqYcZZZTA0WrfHpXA3If9Rl3mcibtW2lEQzn7L84xH/mmWdaZEvDlWL9PzugrZRfs9cWqC3wt2GBmf1vOuAhH1PecwsyhXnKzxXzfWu8//yKV1rhv/KPf4qvOMMhzka4/e7DDz/cd7xGvzpJ2xz6sfe+971xTaDZt4Umg1Je3s6+no1Ms35T5tkIr2nutBao/e+dtujrjNcWuM0sUPa/4ytdEmL+60TcIw9buv1clu2JF7zgefZhl9xwg8WqLch7zBfo6SCc7zMtRpnzuj/RJ0mejlX4iU6zDHe0HXbYYdac3Qj2oAc96F//9V8tdpnj8NGOTmGXdF5D2wI1apY7jAVMdR0odT/ai170Ir+aFZtrVsutrhjLveIVr3AxiiFf62blFljAgoy5tnuC/PyRo/lGiSRPt/65BfJrlr9RCzQXHGdU31xFXbEToDuzgW2923FSv4frp21a+aJWIdZjitXZoQfrLrG7DEnUMccco9I79aLSawZoLMRgjCeuEBLPEKIZOAHmqI3FRGtDVoVsy0FalESQFxaDJeSEAiZCWpRUbEu4KklLoIANrWOPPZYQMIHog4WqpAnwGaaeV0ufVjNNtNBrSHSw85ppgiunHq+eoQPhlrTk/TnPeY7dCxeoOLBlXTUSkgUygyULzKJEaa5UlX3nD5A5Tej8sU8ldBZ0sKUB2dE9aQSMDsFMjI13jqfdaIvMI43xZV0LXvvq177o1a8m7b6HPHjNunUX/ubsYjFWunZd0nx1zDU3zit3DY+mC2KcpEm7MYWEMTZ0cNweNrs5yOW41dVXXdVZ3Def9Qz9I79yETnyynSC3scxSifILQezgAPcMpVzHbz5yeDE4gqMV4wU2H///dlKUdovj3QRZCDDGROAJzVsUbMhizE+djrAxyQcIJtCVgCMBZ6GYGccfZJiBu7VVQ0O8au61EAjp0JmjKqCMWN05Wq+IY71R7EajkrrMxQyMcJkylsToKHAwgHII2UCA6ZVylpX90RX99ialTd2NG5cs7KxYMHwnO4NDmGMjV98zejF/7vBt16Nzv7GvJ5GT4di7li/cmj9aleUk9CYmCs7T3riU1z4fu21jY7URutwu7RAca6xu8O9kHY7pzRMfUKzRajf/uLWKE0mNUx9u1ZTkKMU8Lq2qnui4a+nANJ9j8MjflIt3dSIxj2k3V3OE6f90kjHrVpFrZNQqocanZ5cZ+sYaEguCNVGBCpkSqVolVFpPb3SCj56iu7ilEn4kskkIqHmk+ZW/yPk7kUk+VIJgV4B+hlPmdUXonSLPpqUQZ1rcW9mai0uR+xobBgbHZoYH0475umSsMSeHtsyUENv6aM3aU4nV7csymEdvZyxXZDRWUaw6/fC3ZiitEqIXp0vZk/9FS40Oa0AfPcCiT2ONGUhXIOzHXp1g4GySTMBgA7pg5niuEwZX4bNzVQGwQY/gZGoJ5UUjR0gTtxr6FZmhBQYx6KnbOa8l2kcH6ehHhuysjBKoP7Zyinl2/ojwhmwMH4qc/RCFh6xjjjDULUsQZSgcvE1vpQVJQm8BTILSCMNuROkQ4JQpJD8wqJFO9kAg5F3QmCUFCD65ywHPf2V+cBAaiOVQNklS24MZGaJ17lz/SDDap7/2muvblttC/oOuq1ePTEw0CfdivB4veKKv8yfP0/FU3BlgkhOhaF/2TJlGjKdb5NHdiirx+ZY1Ae86DlNGLNo5RjsiKMgrIkrwbLMDCNQH9S9OXMGoiHkqAAkumTJsvnz+bLUF8mjZ8AVylv/NTJoXLdmcO1Vy5Y2hgYb83dqrFy5YcGcdevXv+cThUaLdhvWG/d1G2fPHfY94fDY+M677rbbkhV/OOOMi5733ItuvLFxyQWN0REtN1X+Zk9ZgC7uVz9LlTmw9fNWtUCpR1fhdb/NVpCaw2SrmehUe1XyShuZQc+g1Kx0iVqlVgAjYCGTtApvVLYKMsi0ULHahdakjRNCbFA2VW24CEjo7kiAOH8abMIUJz8wBiXeuQsXzFkwf96CBX3OwjaX4rjsEUelCx+KixuNv/RTU80z0ElCVyclUkqpHk9+KQrWMxS49CATxaR+Gbt1AP1lp+hG2njPkL3F/le+2Jl8fTgPJVSUDdNxQPBKU3caBIFHTz1dtHO0bZ07YvI36X/dIad0WFJBR/kSi9erQnT+27de08lH5iMcmpTrRijpSRrjcME8L+X14TkLYqUiUcIh28qXTQSO8KKs+G7WkHFCDF0csfUaY5gQG08YldaZEkJabRs0hGgjgiEETA5U5X9ln9NBU1aPVhHkTq6VXeZqBUimfNmqQUMCtWXK+fVWrsBg1AAZEHFbGgTOdRnTkkNaRUmvkIpPbKsEvMrXSk7kJafoVXFQGAH36sl6+hD2QZ/VQKbe0g1xpWgyDSFqpleMDBU6RLrWLvRpDOtVFTWwiQ4n8962AFUpNrevsXbd8sXLlw0Nbmj079RYtXJw3oT+9NgPFQfpdtircfPNK29xsehAV8eICtbR6QCBX8fRiNIBP4Ung6qQjziK7JR7psn1jQLffmS1sQU2l35j7vqtrQWavixHpuuQx8dGxoveL+ZBHRwZr6f++llvn/5m2o2AaOBQqk2KINl0tZhVuw3Plc/DE36FwVHmMTONaEeeiTittVmtK353oWh01p5IS3VFdNFIEWiAO+28M94uM+LC05EfciZrValyjZTg+UXlogp5o367Kc3F08nmoRE/XpTCVJMuXuNX3SBThk2x/exMskmFqiAtFnhpmJSN2yjM9afpqSYZ6n9qC6jz6S8Z4sGHPpp7/e2554MNc337m6KmQq7HU9UPlP+mCKeH+ic6Vq1cdc5Zp7zg2c/84uf82FGqoSrt9BzVGD+66goMt066nTdWQt71rncZk3COluUtbvO/DmZZB+AHnc1y9JmvdFTaZ9va0HQT52oy9fudyQKO1PttLmMe3xY6Ru+MgjrplhwfiDovqL7ZMHLI3pH6rbSK6ueMtU0rnwEYsNmE8sG/HptDIXmzGsJWalKz1xaoLXDbW2AG/ztU3ACdtmKaIYa7JW8ZzrcZPemXc7zBpt8SNWKUiJ8xQeb/DctWnPzNbzziEY+48rI/cd4xRJxlz2MCa6+fSzUQ9S2x/tDH/HpIU3vCdW5+aNTygsNVtonNRk24wDxvU8P639oCbSxQ+982RqlRtQVqC2xvC3CNaS+V6+x58KMPW71q8Ko//v7wx+7fbnN+E6qEn87ON1Fv9DLJvvSmxWedc87/O/roL3/xiy5S2oTQdtH8rJmslV4/h+gDJIvt//Zv/8Yjm/Cap9hfcIGLTQqdqr0ANLFr4KePLRzZ0+fxzz777CzY6yy9f2apgTuYBeI6XTsUbogwclN57H5aS3HS0ll5x5StsfiFRvVqKzNOlCsb/HiXYaEl+he+8IWuVDP/rWvgVhr2DsA+qwPQ6qi+T0/ntL5ZhzUUt987ymkFEKwmlRf41Cq9W3RwapvuT/CqQ7SmQ4jdOFuSJDgGijgtnRfTmLI1o2riCqR+VkI+LpGQo2DEEgUj1iKOZiOJkFMWAkOCBSbzJZMibcyipB1N2TETo0lmQQYmJNhDK6+hBoDyTqz6liVtBnR2WgmVqB27IPDMqoIFcmCEgL06vPu4xz3OgikD+kSGKJuyskN4JmPkoA/FCgHpIctar3PP8PR3g2n8EhAdKOYcC/lOCoTy6MMFhigrs+Pu7240LKtJy2an6aJTvL/6dfJGruDIqXSk30hP+xyonczN+JAjUc5P6nhJ6JV0OvWa9uTkggaJrRnoKWD0RENP+jsCjpLjtMbnJ4PFimJPTEnnjQOMrCniIOBBKaCsbRWrOcqRTYKjzEum11BYbLzSgag45EdJllcB7ElTRgiynHiWBhAig2zI5Tv0LCO77bZbiCIz649dKmULBIZ8RsOlvJQ7/dV8FUks9jAOWEKet07IaQG0o3hlqKjYFJYLSBiHoD27dtyFuRhfLtatWabVNHrSef0Ox1yta6yzWWYBYm+UV11ykXN+61YtC8ly98c/OizrKIyryu37TbWRnNNIKL/WwG1ggZYbsFp1cMeq86mXXHyxyqwfU41TtSn67UysV1ex0SB2L5RVquUrV+p4/dqvGuJ0rKEwRnL8qu/kKcai2qsnCMiJ5qOapWZla7bofsWK8nRaItW64jSMuqoH8FSBR4enRvNBrCoGkHULQOoSop4B+jV/vUpLDHxUwvKsgQSSKRANhD4dXekrncXXXecSL2tt9g8W7byThTlfQ8YBTSy6m0qK2+qVhnKkF5JisvzGITAUcJpE76qMyjSMJgojCfounVg5NmCxhKcsFH3mxuLTG15ulCjdpt4vPG+QRblLNNhbeWEYkPyIaiXL+itTFUMSWUPEgQQEMkeFNK8RkCkIz4oCYvHq8zlroRKbhTDddFHwhBSxbXowwsMyCFTIqMNlUXjVImfDysgMh+Sm/JQKgTmWs2M3no7lpaICg1NtLM4jag0ay/r1aRDSUimyjAQMDo7096cKTI6ijCQ8CVy4cIGyK9Suaph1A6xdu2HRoo0u5y4n4JRENC76lC3AcahazCJdQspZwx6vYqkkICjTkBOvGj5iBIzIhwbSE9JTphR6lGxElRXLxKShLEcFTIfdd9+FBVTpIJBQOQutLLcaJvLIdbKtOeFhz3zmupH0LdaC0XSerGss+dlVjX1o25s+W+hbc8WvLfr39O6gkhiL3ec++y1ffg1gl13SRwujxYkYryG2yEW1J7nVslYnNGWBqeaeBtLai2c0jQASPNGpfuqjxObKGTTklAq0JLXZjag8miFRKjZ2Zzo1RrVlkrRJ5jVLnpJSQCHfwM8CljmLD2k0t+gwK0mXX6ND7uzvs/ow2fEWOf3617/uI+wr/nRZ3EhdSSu/ElWWFniau0OIF6Nqzr6o6TTP0rYeYD1mlOvojsoCQ0/6iN0C/4td6RAeBVSWnGE04do0beOWsGdYQNMO3x0lklnKwMz+NyjRUCDqSZkX0isF8gihHBt5h2kbG5T0NPUzAVdMpFUMKDa8Q1sJId8z1KBeYCLvZHollhPhH7MrKWtIeNEfTn6RW47KMGkkq+H8i4LI+PC/xiTaiym/oQ41Imk0AWCkAOt55qgsIWtLLHbKREaCgGJyjdFrpsy8AZCJoFwuralQD7KMJ80rreIp9XJsSEYTZJ56hjSzawYpCpkML835X0CwRBQaeHAYrTWJIJNlvGAFlGnIkXf5ggcHZQbi9TZ80iRKqqsjqc3/Pu4Zz1i5blCvtXB0iVyMd69EcNktfcbDnUMNP/G07so/ucp6zdpUyizD+A984AP027csW2ZoOjRUXzhxG5bn9ElvPI0NulTPiwFprq7qQKM3VfWMyXBUWs1Hc3aUTYfsOlvzX4eeTZadibR45ViGo2wmjzoTc0afBCQ5aQ6dRpuqfxYbLT1642hclmaQDY2mu5/7BwYsuupOI6raXmacy0tIKmNyVhx90yQBbe0SytCE09ElYnTmmwX0EnSw1+VkJ16auE5C5xOSUx7Yra3EGllboGKB4u7nnoXz3Q6lOjXm9DeGxtLp5xzCKZQGqDlmcwF11cBs5S03Y9znLne59q9XlQbd0wrDpX1pJmq+YG1TCGrN88wzz4yRjB4ePhyZsa4FFjSGuFlutKb8WgO1BVhA7TJbd40Of6Ga5UpiU8xlK151qsiin+dckG2x3Yiykcw9lSVUxuHlqBquLVBb4A5ugen8rzEhnzsbBzmdgYpfd4hxYHjvnXeev3Tpmr/+5So7vw99xMN+fe6v9G96Nl51OhkVvFG03yHMSKNoO8heY4BqQ0fIsQBHaoQypoZrC5QtUPvfsjVquLZAbYFb1wJpeN8zf2fzX5/XmhBYwS5Pf7e1Mt0XnHeeyx/TSdM/XbK5wmN6EpNcvGXfai1LoDy8ZS5nTM1WUFoXsggmtE0rz3faxtbIO4MFHAryw9qRU8sscYTA/NdtoYG0QRCTVvsL1h632CbWZD7xiU/YfyHBsDOm29btDSNhqouoW5xMzfg3aIFZHYC23mcNRRW0Bf785z9fTVV1VF8TGLUnlr8r1UgHJ1gcZJOAraRbKHQIWM1TC935ZB+XKDRqP5qQQBpABypRSOwwgdTtvuQlL8ELf/755wOiq0UfIdLKpQAJ9pQQFjv3rrnyIalG5YieRiUqkpBcADDSCi7yUdINUvA76VS1kEpncjJLJIogAM8AQmC8inXNoUma1O2luf9Y9qNt8xyZkkz0sfAaQjxjlujksezHOVSaxC0UkzQTySCNjuLIMoP56LbIV08xix0e89PTaX/Uj/zyf44v/+VPv7z0skvdISOjsu3KOQcGk4SEsZdst7UxNjruIGLg6SPLbpmSukPYfunYNo8cUYxx6JAsVpQUJDnolV3gwyzYnRq0C+ggGhsqiNguYmFCwuDBElyEFJqkXW28kOqbXFNeqcm7uhSU0gLkEApEooQAAkbm5ISdJxNmgA4RJkowCEgAlFnKMp3Js+uj7LCfddZZelKK0bDMi152ylwhzbkEFV7Ru4AcxgmSGBNgD4LMcisAUpRqJAQWmD1ykZsbZNTDieJCYEXr4qINvX2NhYtWL5rLY6wfT2OdRX3pKvTeHdOZwhsby7p7ug964AMuvXbx2C098ONj3VrRWRd+QK370Pt/eenFiwfmxtGBOGY3dRatsDqVNirHW8EUdRJF8aZLp2669rojDz1MC42j+ZqGKpGvufKq/9RhqigGtb48UaZapYvNh4qziWFJmDhS7BuPOBOsAui1bAOTpuFEl5WOCzQ6Jm8BLHZ/yVfldAvRqEnjaBye1reEPlFFo6uJuopYvyFFUakP4WU6U72SilxEJxl9vpoc/WdSsmhx2r678+OnVw899FCNkRCRGn8IB8uCpPNJXARdPenEmI1nnXbvQP+ll/1pzty5OqVFu+yMUocgIdkoEklHH+QoS4PcyhAlEjsl04mSnCwLYc8yWWAYyoGhMn6WcM4Ig+vNGL/MqEO2lYImZznTl8k2CeMimZIV9jjkRLhcEAJoK0rGaRKdaitBVK2K5FayTWJaUydT0dM8yqiSREHfXuHWtII3CisSYg35Aguypl6hAeONdGNWANZkjaFaZQZmYCD12GFbcjIZzYWQnJEZaCaUEAMDM61WR4XPkoMRV4xwvEamsuQAIr8RC45XTxghAJSBD2RZQiao7CCWacrwVD9QxjYaKlgYln3ETEe2MdOt9CaPgzqnicbIwh0ndlq3vnsXs8C+8R0kP9FY6LmuYydm32XiepQPOvQxZ/zpinnrVqgVe+49b689djnhpJcYNL71jZelC0r9Ikg6x6IoOidvB0wDEHUmBslTteJWyludTFigq2FMyHXyJtwiD6szEZSwklIzwRq/8uVMDZvd42UE7uszv6WgbZARzf7aG67fa999LvnTH0nAhTdF6VTHRseG0+xG6evA1990I7invw99KvniNxk2bFg/4CsCKY2M7LLrrob6q1auzB8I5YJasXrVDTenw/c6pX33v4tvMlSaqDdx7mnOvLlXXvXXJz71KZrkRH9vR0+3e51vWb4sJPhpCEFXTH7cGM3/pqW08VQpc++Bho/eZ599DPjlxWSGwrJvyK0/oVXs3LtbCIsJhUwxnWa7dbt2oeO0T2lRUsano6ChWAFZ0aymCAOje5nO/4bwKYaNIZIhwj78ryTK8apE2AdZTjdYymRbDBMl6bLwsqhIcbrYTKmAgqbVOGjaIjNvBYjMQpZzTTgh9FTzgx6mFcjILDP0z7kwhMhRgPC/KiHJMaYNSlGZMfIVXGChLCHTq/YqcDBmGsm1lVOWAFbDs5wMlGki71x5GQmGr2DKryEqlMmaBEE5KqcIELLyQbnJsovKIwvo8QqEBJylBdITPseG/NvwGeoNp0IbH1q4YHznncZ223WJ2cHEHlAjhf9dM3+H1RMTi0avWTUy8eAH3XPZb89rLLdypTqOPeSh9//2qf9k3vT2N/3kxhvWdHQOMAAHLJsx8/LR8Wbmrlq+m8lek7exgP7T0tzTn/EM072oezHzNW8VyqOisTXpANkRhx/OfxWDplSUWaLTwC6/4blMBvVCjkK67BnMK6VQXBPA0Utlzdo1e+y7z+CatV1qSbHwaFFx9co1ztbr28FZZgC4oxFxH0uXLTPJ/eMFv3vuy19MeKWB62GMDewf+Flwo4WKnKh1Y8VKS+TLT5Tau+qZN/ltaqa3j2Wp0EKZnEpdNv12k86QfOoji/VP44UYJPhQhP81EcoSaqC2wGwsoK7+7Gc/22A5V+XJS05lztzEMlCOnR2s+cTy0Re/+NVXvOJfvve971131dWbZMWFJryqFlA04kkmcD4/Gs43IjR/QO4WgqXMOMlf/3PntkBUJ1UrdZsbV63KKC6WI2Y+/fyqV71qk+aMpZhNktUEtQVqC9x5LLCR/41bn4vZWawK8mSzMEXQFE+PWI0qsZHn9HMgTj/9J89+7vMMLK+76tro2UqEmwHGdBJDLOwbb3PWtZ/dDAveuUlr/3vnLv8697UFbhcWeNaznmX+u27ZzWknpqurWCXdhoptvPw7Mf7NE0543Rvf+JXly5fdfL0l39g+3ibpxWQ5RMVsZeY5yzZJtBZyh7FAjOgqk988aZ25ovp1o03awWJp0ORhZxwsgazHjZu03h2YYOrIZiWTqkUOTuJb2lNH3/3udzu56zZfv0TjGBz85G70xjMljBVp+kcLhZ/61KccUIvbi+0Mmb3o9BEX+/0bVcSKBOxS96GMHQLbDL/+9a/POeccVRnSujzivOaY04WMTplwC/f09MH9hz70IedQ3dNz+umnO+Vml6LS5EJOpI4dIEDqzWn7ile84uijj8bywQ9+MNpksIcOkg72QOatQUhCbBK86U1vcvu6169+9atSl3d7HuEninSSwsErKosSFZd7veUtb7ELKzs/+clPSEOjg2CQns7iuEOzCDAKoXZSqZGuMbbV5jeDWMxFVuedearPLPjaxFHMWxGH3Qo1pqa9xWt6yMtpp52mDrj72Vle7ENr17lXmcKiIuMSDYN7xeIVEDlCY0vG88gjj1QKrsdQHJakKZ9ZZDxEhTQwtT3ZVikH+6Me9Sj35Ct67GyS6bEIEi0DSe9CDWSE+1GGww8/XCH6pUKvUeUyPcqAQ04oD4kSbMaO3eW1jm+eccYZUqebWPpnysxelsngMqiwVDmHALBTnjS7bvAhn3ohpCJhO71KS7o56cigtKhBJQHGM6Vuc855AnkoVmkxRnAdbhGZtrt6O1N858SI52AHwwziNbRavSKdUNddXHPNyiuvbPQOpI2Egh26DrcLC6gD9NAMfZDgk4aXv/zlygimqB1+yHZSScixYmlMvYHS5/h+w6GM4lrv9K1F0P385z9/6lOf+tznPlc9j23Rog41bCSnylb8hK5Kot3d9cCDNCKbrFq4KLVCCvZ9/Ubwj370IxJ0rb6MQak6IUiVsqiQuUPgdxyQUrvg8Xb5RiV+sHh8XEZs6z772c+22TyZgeY/5dlAbAbbzf3hD3/ogAshYiPjyLXWJzzhCX7bVFN15ZW95O7e/pRW0SF09bJZjw1hGu51l/1MYGSZQQoVNnHopKnLlvxLPWzMVWGG13gjVlQGKmSsV8HM5nU6aa28rYq10syACfUqSkqd2PxsZQ/1PANoJdhcTMiJvETSm5SAjJNCVmixURkpl0BnIWiy8GApR1UwEZVtknkzSwbInE3xRu4yVwCFkhXc1GvOVFveKbom1Eo2sxlzbJmxbKKm4Oq/QR/Kg4XgqtIV70HQNgpSbH6WraFbmI7lVsVXW/y0iVN+YCD1ZjfdZIB54Wtfe6EPsP/giKnf9Ci60KYdijY1rZg64laygOLwUyoOIRla28bmbpSdalxUwuQcBXBXZ7qsUT8gcJR+K9mnn8jK9dMI2dehvi/lZIMLS25HAEE3ZSrkM56zzjornWQqCJDNW7jQc/WKFTy768dMSdatXctBV6yAJoLrTg1lP/w/H0TAFaaqNZrUlrQZiiHui170ouUb0nGxOEYWcuIAdHjeOABNH8OGrjnpY8LOxpR7MjtjDbejMUtXI312ZQogszEmCTvAQ5qK/uIXvzAOT5YqNt/Sv9s6SDrJn53k6cioPZ1eTBdRGShTTiewTDMDeyvZZmFmn/psxLY1QjkJcFsjlIUHfZBl+rKQTJxj26YbZJtMDtkmaSKhtjpkZWZObjpeSUefMB0BsZG6tllJK/AVZOW1IjZeJSpUKFsxCCrsFRavmnamyUCFrIxvm0qF/tZ81be12HUT6esG+/obv/j5b4oufei8cxtz5uqbpoboZX55v71luazeHR7mO/wqGjf06le/2kKN/E53ALq/mNCG/+3zeU/h79BHuzORJCEu7wyjOQANGI2Zb9GYlDW360P6U045Jb67i6btiVKUax148GDPT55PIzKDlqJr2DlNvZlEzzvr7NzkcwuyviovVilb9wzC8/ohLpJjbk7OFz/1aa99C+aHhHhSQ16sjjroHGr4YgqxNTfu3hACkrZ+isEUOK5ykAXjFfi6MofF6merBdSNXFGtPB/x+MebpVx6wW8a3cWtk7EY3co2I2YjmdNTTlbLsYbb2Z36+uJnPhtfo82SfUrtIonyaxkup5/xGSjH1nBtgdoCtQVqC9QWuNUssJGz62jxv8ZvlUlffo2o5gIIjxbTusK15fWWqS/QEWZsOXerVqxt+t8vzOx/N1K1LKIdTJl26BpXW6C2QG2B2gK1BW6PFjj0qMc5l3npb35dKNfWYTbVtmYzY3yTboZ/hxs9A42RDaeeemqa/372k3HwbLP87AzSW11wPe2dwVx11O3BAnUVvT2Uwm2rw7QHoPWMOdihp6UFcfdlHnvssY7tOgbndJpznPCVapRfg92rJXt7Ql4dSvvoRz/qfGRa0B8bs5heznzuQ7EgTkvqxTlgeK9xR5GD10JwWY6HD8llOQGLCk2s0YPpIBcnnnjiN7/5TQQ0J9AOepkRmR2FSF2UdMfzC5cAAQAASURBVGkISQ0wh2FrwdEB+/F2F2yEhGJZQiTnGdsSISfHkuO2s9e97nWBcXUZgeSEcMhgx0ts+RVMedZ2GuANb3iD1DE6guAsLxOJ6nC80I7GWNrYmOgopoLpwGLH6OStuuMTHRPdjdHBtSs//uEPJq0ag4R0dKWfZO3qGGhMdNkaSWp3uJs1XVJEUHdHX2PMDTXpamex4yOj1y++9nOf/JQkBubMp3dnVy/60ZHxbnfRpfluCqE5scVbeoQ9KUlbJwXjHDDbRtmhzPkNo5EPgBfkzpN9sKtvZ555pkMe9l1gsKMUEEgl4MpTVGBCK0m7Uk5tIUrR2/eKjRw0ZQlJ6eaWJICq5Ljk1Q3QX/7yl0OUosdFFIKcaGIrLBAZwQWQBBZ45zLdM4TYiCeaT0gOYjShbdY5i91+ADOGcOoBQk8AlWAKTVSqcSZWmYZ7uzZ0NVb1LkBwS3EAeo/RNWjWdvS6f7zbDkZnZ/+IVzsZXStWrdNDuM7wez9YufvujZ7edFw2bvIrMooobfgV23CKL+Dq5n1BUHmEwkFfiapfN9sCUdnSdm9Hx8mnneq7FKeKSYlqnA9AFxUjlc7I+vQRiC0r7XGks2tub9/wuvUaGTnCXy6//DOf/5yLowhZd/mf0Rc90ZRWUanwnn3Gzx2uclIqaiBeRPDDg6NXX/lX3TtHo4XCJCFF8wkpUVHBDopRQwvygYJbKIuqk4R4bQwNn3DCCW7Z3/+gA4MrP3ViIS213GLLOf10aWJKNYq2mgEpUnEi/Otf//ojH/1o28mqsfPNO+68K97J1lp0O9fddKNDYD/8yY8Xu1113jwH/HW+CLpm/BnirMxmAWGizWLJxJk3gPyaCQCQYdsoo2znTBP4/NoWaCt5NowhbQb1WpPLCovaZBJZsTLQmsfWVGYU3toLRQ1KxsyiVN7ma2oNqlDxGgTV+yCbXNETJvrIWkjgD5sE+d+NRi8Zq1lFlc6YmYGmhu2psqEyEHS4MiYk5Nf2gqbHZgVIyPB0MuHLCWX6Vq0qCYbwMi+CnErGZ4EZU5Fza77SIY6tcMv+1nalOrM6/biC/io91xVfLXWOpXvTN3R1D3UZad+0cKe+axc3dt+j8/TTxxctauy2+5wbbtBRp3GILsr4b8RHKT5eGu8uuruojS21a2ok2cxxR9S32gs3DbJ1/6Z2nkqla3DN+k987JPOJR9yyCEG+VHxOMEMc1i6jrVr1ts0uumWm8aGFcQ4Z4M5V9elNy9xIvnud7+7r4O4zsDHM5V7EULfr1588dKbb+5JQ+jiLtIxUsZGJ0b5riv/fPlnP/MZ3rO/pzecXdss/vmPfkThTxwnxVImmsNgk0JJ2+LiQHvnzhE1NT5uCoraFrX612ef7/RzIhMK2lD4hmsWv+9977vvfe9LWsxNmtxT/xo/cM2nnPrd66++ZmDBfBOViIvec4puW0Bl622uvFxA5RKZTgibSyuzTEeW8UE8A8sMUVlIGZA0ljIm4ApyOrJWRpicnTJQEVhmbNV5huQqxDmJssCyDhV8RJWVqQgM+hBbEY4yyivLrBBkfFl+RmZgOq4gENtWpcyegZnllMlCn+nEhpxIN+vQyp4xZaDMVcZX4KxqBioE8So2h7YEtw6SDhMNn2x0Le8qFqy8NRobivGYm0vBnby0z6E7ehsdvc7y9ff2+DT/4Q+765lnXb7LLo3d93RatDGevgUxly43rvCkMPxx0W+Fz53MVYtHnvxEpBV/65jhjplKUSAd7hTwKxlO9MpkzHzLBRWYoXXJ/95w7XUTo+l7GG1f4JfDLoDPfvazfp/Bmg88AhNCUfHpDAnR3NSlM396ulWscJ0IJoV0NCxUWqFy6bI7nnuaizNJePHtEzKgdRZPAwNTYBLGRkb8dh7M5H3VxbKq3wr363xuOoAvh8hR3AAdeOtpgJ33SL9rFPUynsuXLn3zG974uP/3JPOImErIZAwqrIgCrECaGlsH89PkV/35zwMLF8bokrRJ71GaBJV1qOE7pwVU+1Q3IvPFEHDPffe9773v/cUvfznhLDMKUXXa1hxIf82RiaakuXkK+FL/3IxKcibDZMP01j0x1BPLUeONxVf+1VruY4466pSTThI1qVKTZ5b/lpMrw8Heipml2JqstkBtgdoCtQVqC2xbC2yO/w2XmDb+4seEw+2mxaamTsV0xZUxvlr3YZ4lIK62tO6CrvDVbTx5J/97zeKr/nr4Yw/57re/l2ZOExJK9ElYCuG14cE5wSJm+kftcKe3TR1TW6C2QG2B2gK3jQWKye5k0pP+rEDttNc+977ffT/78S9aSnSNjCWcwp+2U7IsYqP45DHT+lLympYO42c4M8XU/HcSlTZrOq7886V3vfvdjz7mmJNOPBF+ti42S50GyOtg08TX6NoCtQVqC9zuLDCrA9BOesWCoyVvZ3ajs7P9nI6gFfu1le4vJiQx6fKMtXsAFtsDTtFZQ3dMByYYgz7IyrxiY3sAMtbfsUsUzJCeQVx+wscrINjtExDiyK+nPYY4defpEK11/KDHkrUtK4YlosRSW+peETgi4AyfvQFw5s2iAJWAXXA0wU5DKO9QnUN+2MuURMVrBigQx8SlhV2OyKG5nRh4SAT24Co6VGaeKRcFCmM6d9voDs2J6iwOs0aiXhNQqDAJR0Rh0pSWcxU2Aotb6xAABOZNWhWaJ2UKYhhAvKKMkmJtgFwHXlmQGSkEJmD0Xj3DOFhkHKW0EICDLJ6QYZMyEow9Y4ImDqxDhhEIDxqxQpkYDFOW4LSl/aR0Z20RKKM6hUqZN7KcX0OCCoMsQromttGQLiAMEjSZJeuwvYGctWyBjJE0WEhF0JnK2slOMIUjKj8B8tXoSIfUrV/I1w7zdoi1konxERl3h+7xH3v2X675y+te9buf/ODigQVps3DK0M2ESrYncrYhJdoMt74Bmyn/bf8bNlTOsqG92Xy95He/T1lSSHBTBgZHN5VQuh0noXv7++2Sxq2zUVv65s3VFfz+wt/4S0Jag76mv1/zdwu0SDUKY/P4dIfzTJBDGzasXLG8o8fprPSFg5JNdaYIXsHRAKNnCHzkIp4IFu2ys/7o8ksvvbw4hI1rMrg0K11wkLKg3uqvwOgbI6N9xW2aTbqUKN18qGB7eOmSJQsWLVpd/LBpJmgK7O6aM2dsaFBOfAxR1PxJk5GcmsStFSi89UlROeRkoCwzZWiLAplbzCvBLWNvm4W26of8yHgQBBw6h5yK/mXhZQKFEK8qZ+oYmyEqbSQEH1U3ywx8kzbq52RBQKrtmUCXDBYyslLsWWZFgY2FJ5NmzMxAZGdmmm0YO3vFIlH0OcutamyW8m2TTp3D7SBsXssrPhtbvny1URL/+29vvYfBxmv+6Yzrr79SDy83lcIvDLiFTft2YJs7ggqD69dziiqbeyi/+pWvcHJ6gck/peWvGAxzhAaPqVZ3jLsLmjPUD5TzP7I2/abKhef+qozcCO5s9MxJXjX1TunX1kZIiGaSjlIVwzzPG/5yDd83uHZdY7hd/Y/K0tXocswrfRM13FOcYI6EdthhQZoSNib8Tkv88gPnvZEOzWHF5PGyzskurtwAqTR30UKanHfmWRXe9Mo4JZnzdljk9HOZvQ3LVqNmls96utyiKW15SpLYgg4H18zpbpJgyzWehlOK4aGmid8I3apeK2YjhuJlNjStXDBhK3bORiOqLeVskFvDOxv5lIwkZp9QpU+YTSpBkxMCZOPMzD5Lyorys+SaOelbLbaifNt0Vacymb7Z9GHD4IgPSk87/YWLFy9+8xvPPO+8PxkRJsOm3mujKjeDtSOqZLHofMOPbySkrWI1cjYW8KGRxTSLUVdcfvkVvmVl1zBz9n4ZA9/F96bVM0dDYkkkt7g1q9ZyBH7cLCcaB6BTcfsLmSONgZ0WbEi/F9Ton9MX5eupCnX3dNHBEtPNN6cfXnDGvhivJWHdvY5Dp1sAkr8ufoMoPA4uP5U0Z05fTtGH+sZ7yK647HJ/KdH0F2k7bJII0/fCkiyQBgxc7dIbb+JDsxBRdPb90s9//nPIqK6MITlfF0+SdXftd8ABl112GU32vMtd/ERSqqVZRA3UFtiUBY455pjvfe97a5Yum2wam6JvGx+1LqpoW4I2yM6OU04++TX/8uqD7npXH+23IahRtQVqC9QWqC1QW+COa4Fp/W8e7rbk3Zgx5i7FkJbXTSGNKCdDBowaEyqIm7HFvwX+5JO/88+veeUBd9vvr3++tqCLIbfxYxHd/FdUjEU3klC/1BbYdhaIedC2k1dLqi1QW6C2wKYt8NjHPtZNMaNDa/3+VvjQvJTUnjl733bRFhbtpRQrTYUPbUPT3Zhwb04KP/zud//19a858OCDr7ziisDUz9oCtQVqC9wJLTDtAeiyLWKPxzN2za16g91JHPu+YOPIeGaumLrESn1Gpt3xiYk4PIoXC4JiEjW5zo4r9hWCxWt4hcDbIXA5ChiXABAye2zhBxI7+SEkFCPWcr+kvZIjiv6IvQZZyAn2QEo6UkeA3aXROS3bDI7oEeg4eGiCJmIBhGSZIZAy1AM7Ag5gB7yhGC4BHCEYwZF6KICRBGReReEFB6WoieL3pMd9SuvIVLGc7NQq/Hhxn1Bn+rLWHdAi0n8EyA3K8bH0m9rukSu40snUYutzcvaJHZ5x/TOWbjliqHT2seG232JammIL44tICXgvQmhOvYiFC1VpbqspYKdjFQECVkWPJrhCAhpBLGTkGj4MCKADgweBJ0xOKGDPjAfn15wEUVgiKgNBVn7NjABlbZMsKo9n/OJ2SAj9A450I6HQSkFHHiUKCDKmkAUYdkDMFMEoFpB1KMPBuJXPSCWnFdLKyIApQLfJtFLpdHSrnl327MYGu1LFmzueTt4PNobUtqGOtC03L53K6RxVPRi2Y2ljYsMNN46c8I0PDfc0rrmm8ZMfNOYuIm/qtqRm0qleNRdQUjUovoTzhBcCk+pnMwQ+vVVy0SSo/90MC6Qri5VXcfeY7sGpY5d5z8Cfulr1c3zC6Wdl1N3b5dxUUc+VVNfQyLj6PH/nnVQetbqgnZI2VYrFByTJj8SmcNEQEevY1w8OkoDdaz445VUFK1KZkkbJ1Ou6ubKkbui2YcO6vjkD8xYuHCyaVdqEVocnXGmf7mZO+XX1qaZY9HtgHbjKnNtmqu5qYV+fvw3r1qVPL0ZGPKMuxk1gAaeUXfLV14OgqKvpJgMflEQSKXabhiS3UJ41BGovXLhQCpR3nRj9ZT8niFIR+GyJnXVWGV8ByMEeYkUF4AnvYxXsYtnZK2nwOQmxfsGADlHQFbHx6oMTiuWo4M1PCrNqmSBTAugcufPRkdTLURkO5T0pRo2MB5RVLeMzLHX1yj1q2FW5zCKzQZPP38eQw2dLBU2YSFpMnVJUzYu0Ap/sr4i49/AUGzYMKpOeHvU5GdlIQQd58MEH/uEPlwPmze9IraAxMTDQ7+ZUuZwY5w4IScTz5+0i0ZHhNE6YNFrH6uS+Uyn4IiX8ZnYWkyUTt8bKmrGK4c369SM77bSIHALpFkSR99Wr1RmEzX5e9GSvm6AIpQoFoRSSmDD1dIUSjD5yAKge8dr6VHl8UxR1b1L1JtHg4PAee6Rf1m5lj1xImtpRMeSlyTf1L7HsoIpGpfU6FVcMJLAr6EqdyTT0ob+aGcll/G0FRJestvkbnhytpRYRfdHc0dS9beguKp5Yf64UdsfzWONrX/vBaPcPbrih8ctfqYT6qsTVobc35ku1yFsayzU60keA2lzxbGlrkz1s/FRL1JaN7Flw1Y8tsUDqQax+FdOKdKno2LifXHHEakqW9hb2F+vXWBKR32kZd/WyyqkIE2lRkN565/bBe06xt0JR6oZqRYcZ4+AQQlqq8OPjvfMH3DDdq8ZEmbcKKTBakNlIPv2MmXLrNmzQuFIPNTpS/CDNZC0NGXEAC5kQTye6pZ5yFRTNZ+oYfbM6b26lc2jGT/2LEk3SPFmiImaKbOuhSIKc6HP0TjyUPlY3QoeNO9LUUcLP0v9GX0r50B8v2+r2sfNQkohfjMkK0CH7X46Sr2mbu63xvzQJ/2ue21Y4JLV1rZ6R2TIZTOSljCzD2f+yW5pUNkP2v/wXvNR1xVIp99X8NY8WXnu6VOCNLuIsYNgtnrQlikAmVWqAPHYKUZ4oqcH42W82tZv6N8hIC66IwAgjRN3A3rbslJfiYwHP6couBJaF57RZjM6WEfK4LrIWxK0CxbbKCWQwZsmAMLWaIxeUr/AGvSg6tPpf+kShsLBSy+rl1AEI5DpKPOPLCoClovRbvX+F7FZ4jfz2GnSlu1U4Vb+ZlcYbnenOM7eKpv67p3hq/2Df1lunKIaraRj33VNP65x72o03Nn56us+2jeqi0550qOgnb31OUBpdCeOT90BPblRkEzWBtOSVqFJHFxwFW/3YIguEP1qzahVT+rw+VctSX6SeV6RO/oKQTq+opWIVRxSNOm86rEsps3QVznu8/ItAfY2htes0IHtewRhPciRH7BQy1aDJMD484jvdjURLupi2zOlPNzKgi0PNa1au0h1Lt2cgfenUNsS4MaKSox4d758/UAz+SUqB7OKJJElW47zSLek3MNVXX3/t1Sm20Vhy0/VJ90ST6nZX0UYAdagtkC1QLFGnS64sM00Mjxz2zKee/Yszr7tmcSKYqukFeZtR3GSfqZWoYka5KhtXFaOFZl3NSWVgap7S01g/NZzVS4+MnvqTH/2/Y46+8pOfnPA7lnPnb1i9JrPVQG2B7WqBGFdEEmV4uyZaC68tUFvgTmuB2fnfNDbkfq06TVgPBPi36Y6BPKcZnC5rzbqhcSs8oxuKWH42DRrdq5U9eZKQptKZu2n4RNjo7hv43xM+9Z//+R9vecv7bSvOnT9v3dq1pjVpjXPS1cfamPk1xiy1KaT+t7bAllqg4nCnHz1uaQI1X22B2gK1BZoWCO816dYge3sawyOPe9oxN91009V/+mOTKv3bOeGUTRGmqOM9VmHSEowtGzPf5KVNgYsd22EetMm0saeMvWyrNjkUcLqvbfjkH3zvyccc/clPftJeez3/zQaqgdoCtQXuVBYo94+byHjqdJtHdWN7wLq/EHjMgCyijERj3OlpjwEgyv6fYCPNhIooSCHzBoAMi1XOSCIvdwavZxAEIzhUykJwCbyFKEjs9ic8Ba+i4CMqw4HxLNRJeiYRBSUWh3IihBCwPc7CG6WNwxxQhhxAwCSIDWn2BakBJgQyREUSnrIQgWXwwgiZOJLIwjNQpJM2Y4RAVp5BUKZHyYDSylEZKMSkR2A8y7BXcnJUvMLQk3qeMAEHjahQOxPIMrsxAowQvEEcSZdlZitlShgsEVAGS0XDLCHE5ieugGUcSzxzopmsDERCkTrKmP/bZg5elAA0CMAIFBxMwJ6iIgsBgFHmZ1gmyDxvnZDzC4gUI4/xbKND0RyiQgZNZLZCKS8JMz4+d+HCrv5FA3Mbe+/dOPXUxi23NBbuXGyeVRiK15x0u8gat90tkAsdMJvEcuVJ1dghu2ZvkKpE0QM43yDo5WYIes7WtCgQpzeIylq1kmUMTTQ0Iapx6O8Jo2+RRMihVWZpC6jYQqQYQnK7aEvfBkkH3XWhTJvYbYSimxwJjr84HeJ8z4c+9KFXvOIVbs4O5cvp6GAf8IAHfOMb39h5553L+DL87//+729729sUVkbKOFN4OgFzl7vc5etf/7pL7MSGWQDZMjBPecpTPv3pT0sos1eAz33uc09+8pMzF16BkfA6W3PooYeecMIJFZb8qjN517ve9dKXvjRjWoFTTz31Xve6F7yj8+VYwuXida97ndyV8WUYgd+JpgN7wucMxsldyKc+9alf/OIXKSzKfajBS3kDmHXrhvfdd9/jjjvuec97nu48orIELENDYw9+8IO/9KUv7bff3rpYtRoZx3HQQfuwrrtR58/v/cpXvvzmN795wwbeobFqVXH6eSJRUmxwcOThD3/4Rz/60Xve857DI4NltcHkr18/8cpXvvIDH/iAE1xhUs8gW7p0OZhn/9jHPvbABz5w/vz03U7kMctRfQ4//PBPfeqTTglnZAV4z3vec/TRR1eQXrFQT9U6/vjjW2Mz5u+LkF9bAUVzwAEHaKeVskM5MND3iEc8QgZbuRhZYKJjjz32Pve5TyVfQY/gkEMOec1rXhPjw0mv1Ky6YlWP1772tY9//ONb5Qfmrne96zvf+c7dd999OoLbFZ4RZKpsCvCiRQs065126jzttMb11zcWLJg82BPE9FdJBADe21V2amU2aYFcZBnILK2YHFUBCmc1NfjHGPUBWa4kFZbKq2aYWcpRs9cBFwmtyYUEzwDKwmeAN4t4BjnTRRXqpIceNfvfV7/61eCIKjNulv/N7AqFQQTd7J577vmVr3yFH3ESV4poyvK9hv/N/Vs5NuAt879Sx25UwMHxv/HaKhxmOv8rSt2Ypf+t6B/+V+6e9KQnfeQjH+EddNcGPFkBUY7eelq65aMzvgIYGGBnwwreK16JUq8ydkp2L8YnaDBij9FFqwQY5W741Fp1y2Oz6crOofajjjqKg3aIua1wyOn8r1Pp6tuDHvSg97///a28kQX4FxXBaytNYIwuDjzwQDALV8gYx90kL3nJS1p5mU4I/3vf+95X9tFkdt+MgRE8+tGP5mFVZv5dKMuhvFemM/Qq48uwgYHS2WWXXSAJzKFMc1vBhQE2erRqwiry7jkw0PmjHzVuvrmx445q3ZShMktkLb/OEpD8LClrsm1rAeWlZA0sI7QKF9uKnAETRRndSO5MZlm+uRbOIH+WUdtKVJYzyyzMUr2a7I5jAZOV4ZE5++110EEHmQj7va/NypoGyPt0F0f/tZfUZAoftFlCopFec801v/3tb5/4xCf69KE+/bxZBqyJawvUFqgtUFvgb88Cm+d/UaeZS/zJLGDt+mGnn8HmiWnpMZkg5iPgqbneTCNAdOs3rLnFtGjHpz39MIe41q1ca3Zk5WlKWILqUFugtkBtgdoCtQXuCBYoTgU5RjYyd9ed99tvv1+ccUa6L63TJ+XprJdgPhvAdE9TV/Pf4nKuycMAQdnT2esmmVkuC1rAwrX46qsvuugiy+z1/Hc6a9f47WcBKznbT/hWSr4967aVWavZWy2QesPZhFjdDsrYtixj9N2xsJhnPpXeXKwo25mqV6WGpXXMmEkV06qQIyF4AbHk9PuAkFA5PwcpRCx6lKEkJCCiAE6dZnwQx2uRSHI8QRmpIwiM2MwVygQecUQBqOcJH1xBH2QZDouVabDjCsZMliUABDRCaJ7NkolLQHH2tPCjcSfqRMMPUMtCMtrkPa/F9NJXvOmqliJ3JJPZWdzXYpJbSCuMHHfPRImUuqmKqpE6ZHRlSVLJaBnOXDC5CCSdlQ/rRflmJCAYM3sYsEhuUqcMk5zJsgRJ5JoQyuRnAFgixCvGMktWIACUyDy9lvd0MxJeiDLK8mGChWShIJmsh2iK3eG0ZBBckUTQbKdnWVtwBOkKUoxnE50yCy4iXBTZMd7T5bbSxkQvusaE+yQnNhQ3Gxl2ydpwV4/6tqFLXXMx74rOvvHursYOOzc+/7//5KbP448//cILncNLFw7bHUYZ+4TuKU85nSzPqSqxnbJfi61YYPJGxqIJ6zRTcUc3UKFrvuZYJR61pfjyv1lzxscmhsdcs++KURwIJmma4/ummFTgugr9RcagJDPVPYwZ2wTomfBFRN5hdhfQ5H3MhZhIa3Ri3F8jjqUWLa5En34hJpL0zNdyDaTzqc3rYAuVKEZaoUtTA/QFGBabwoJo5cot/3Z26HOFScbWbKTIbRAcfeZGdRrOADlMIzmw7jG6kUjAHOmvf/2rM7Iz3OB42mmnOaflrFUcFidHEQi6zaVLlzo5/d73vpe0MAV8RfVf/OIXtjCdyCn3h2Wa//7v/3Y4m1ip0DkKiJzQ89JLL535EO23v/1tm5SOGpRlZpg0ufvjH/9IfpyIAkSsvMv16aefDp/pKwC7veMd77jyyiuLE71pTBIETOHk64033vijH/2IAVmVAg49S04Ydh55cNAlcQhOPvnbnrRTWYI9TKQ0CLvoogs/+MHjHYfy+9SLdpi3YsX6BQvnPOGJR2F38Nq3AV/7+v/efPPNhiqRv9SnTqQrk52Tpslfr7z+k5/4/BWXX9Pfu2h8bNJv9sSdbmp9Z+NnZ/wYmRFNb68uNB3fD/2Lb5rSJZdOv+l4XUE9MOBQ2ZxyNXD5+e9+9zu/zB6nkYKx8mR8xqkgpYKFzqIc786JVsi8nnXWWa3IwASXo/N+NdshpxtvXDIwkLIcwXksRWNmruink69Yv/a1rzE+4jwUVASCnC5duuKSSy4hxGlyNZOQKJcsDddPf/rT6667Dn1GNtNP/15//fWO5pMQzaocdZvAca2K/if9Td4QGb1RNEnPCTdDWwEZShekpW5o9bLVC+c0nvz4p/zrf9zVj6q/+53nX3DBn0dHBzhnrdtPZS1w/77ReGfyv26mLPIVMiOLAcdAutjV6EgH1wQVzUWYBdi+YRZR9WOzLJDsX1TFVJQzcJa9jw6r/AMIXlVXv5tQpplJlPUyPk5hSrDoPSZ7QN+mzsC2UVT75pPb1OR1QC0ZKt9AqTYGvca4kexJg1RwG71O5jTlIlFX+Tei3ZYvZf+rL+IgslPLyWyu/81GI0HHFUd+fSWi39bl6tbciVvuw5FtJ/+bi+Okk07SCfN9uY/NuQMgm87/it1c/5sl84+77bYbz3jGGWf85S9/4X8Lhzv1e0coVRUG/8xnPqNny4wV4Nprr0XAA0bpiI18hS9wpvyb3/wmwxIVnXyMSdDACBixE1IRm19//OMfOyMeXBmJvVw3pis7XJdffrmv12JKm9nLQFv/i6AYUcwxMvnyl79cpq/ABj8ZExnPrwAY7PyvnBqblQl8+Sbv/O9ll11WxpfZ1Xmfxt1www3Z/xY2S+1P9hUK/0u4aiyDFSEokRkCca9lmWVY6X/3u99lvSAuR93KMOXpkNxuM+GJovOChYjZ6/hEWlYKyvT7DPyvX5vpTEcD/v4ZT/mn1+zLIP913Dm//OV1IYNFQmZ6LX6hK0kSzKxTKA+zy/CkChV7Fiz1YwstEDaNsXOXu+c2diG50LP0dHNzc5squeqiKGFyoUT3kukDmM4j4wr6ADybHi3xxY19FVHl147RMTVJZ2IunlqKaUdzQJtepw8b61MMABDLS+RIrUxA4o9nGUjYdqE8FGkXX+Pu7BZIv8RVhM55c/7+H5/9ndNO27zTz+pi8S2x73bM2S0s3P/+91f5TaX/dOmluQFOZ+XOiSGD44gdHxpOw97h4fN/+csXv/jFB9//vlf87uL0uytj5f52Okk1vrbAVlmg0jnPXHUzcSYLTH6lCozXwhFUKzAkgopXCvpWOVuVq+mZ2yo2PXkdU1ugtsC2t8As/W/hZmM0mFaHimXppIwxYZrXdHTc7W77mySa2T3kIQ+ZN5AWYFev22BCOjTiUhu9zUZdUB5ANvMTY+p5JrLdO/rJlbU77bZov3vPueaS9Y2OAT8ZOEnWsmTUZK//rS2wVRYIr7cFIsJpbgHjzCxlfcKnc5cBlF38zEK2ODanfiuktcVK1oy1Bf5GLRCrMBt5My9z+p/7ohe6m6wxNNjo9ANy9tf41VhsmfqCqG2WbSsccJe7WL5esXS1G6C0Xzv1c+b2Wqhfs27Yj2cPD8deXpm75JELhcaH16dfsxsb+6X570tectD97vOX3/8hzX9ji38jdctyari2wNZaIHscgMDvzMb1BHGkXZnMbrFCZAZvRYGcVgW/xQltkjE0ycl5zfAmeWuCrbfArPe7m0nlqgNRLirHyNrWnlxlYzkGS6uETNNMZPLfEIjFjCu4bJlXiOFDDfhIIpixxOJLZgkJWecMBBf6SCjvylcymOlDvtfQBCAEsSSyDpDlFOPVE0HgsQcj3lA+8J4ZyDSRVuAzVyQakkMrz4ImbFJ4s6ZLyzLjuLPXgnGSL71mEbMGcAlBnoGcQXjIyFrWGRBw5Kg1KSyZJsvMZBEV+JDj6TUwyAKpEAMTrzk2I3PqgKCJgsvSUAZexYjUycxnuCMKPtjza1YgAwhIxhsYcCQdLJ6CtCJ1NNs1pOynwVa6gt3hResXjjhE0vkgI1VlOant42wb5GNTV25jDOVblRQVyCG3fI2Oj3U3Lr208atf/cpW+nnnDfX3p58OD96U2eL8zUZCtqDybcRfv2ytBXIJTidIwQliUWoTXd2dYyPu4HGu3bVwEZliW2pyHJirStW6AhWVSvUIEXrhKuks3oM3qq5nbmLTVdeZRVIpcjozWY4N4ugIN5c3C9kkQDIabVMGtVYTGK/s5iSKdhpmD00gHdA555xzgqVVMrI//elPYokSCxAA8AB9goNH559/vnNXDBhWLQtxPNrpZyeEJBopBnvQBOb3v/99xEYSoR4YpSVU50JM2xztKovNsMshzioO0cpa7pdyLIAQJ8BCZmsRU/sPf/hDmb4CO2dMvp1UxowsIwi1nTFyjscxoPPOO48ZBSd+6CnW7W8F8RiVnCEOFpqgyToA/Eigc2u//vVv5s+fE4WF0tzVddp3v/vdv/e977kh/Qc/+DnkwECadQ4NJYD5e7rTDdA7LNpF6tcsvnFO/xyvITmblwJ9fRN/+tOfVbaBgcnGmDUJZRzt+vWvf+0EksKBWbJkaX//5CFjr4y2ePG1TiBlnbFXgiNQFYxXvGoF0zFImDcs1kp56aWXQZZPNmeaSPTCCy+S3x13XFjUvslI0hiWMd146vehMksZoAObSF3FyL4YYwR13ioA9S6++GJ1GBJ9mR0GV1n5CgFiQn71qwvnzu2XUNu6VxZ4O4HLuVi+dnx+X6Ont/HFL35nzg6Lrr565Y9/rE9uyHmZLGveRG66xwtbBn30dVlIDWyxBdRJjSLaxRYIyZU85Mxegn6gWfST3f7seVsppS7o7rLMVprZYLCTMxvKW5km8hVPPYOc8r/6WP2hgbTzmhEVyuvEZu9/MQqKA6+nPseTG+J/+cFwAdwlD5KzvL39r1PI4X+5wukOQE/nf+Vic/1v5AujzBrJ8LZ+dcFBWOME5oVknzCsp8Dgrm/EBc42KQNcAP35iExDAjieLPyb3/wGr2JCo6SCLIkuBCLDLu9lmRlGY/CTVcp4gFZMW/LVDRc8ty07SKeHuSGVp8xbhtv6XwRUtfzNRDH8KLOU4RmObiOj+YUXXljkdco/Rsb5d8anXtvcBS+jca8V/xs5NfBQSxlfdZW7SCIr5lXFVjec/4aMFHNsBuRR9smZjiBT3h4AhqJGlHvoMzjaGFk2tPMOjU9+8ju9CxZcffXqn/88+V/fZcp+qsfTVNoZsrMxS9T5qbKbgbGO2uYWUIjZWSuXqABbkEoIwR5h4yLetLzE1aRKvEXnqVeB3lxRIaYkb7KfbIrf9L/Bu2Xpblp6TXEHsoDf3rEysOySP29BngxFnLXia9Q0Hypzcw5Ag4Vy7d20ZC1nZGxiZP33v/99v+P00b9elU5j1wegN224mmJrLaCi8iBZStt6m11Mji2zVHij8meWHAsQVX4NGKXJCzgLb6XZLExr6ytj2iq2WfJr4toCtQW2lQVm738nt4l1FKW0rc2aJFo9tgTBF/PIgyNjxelnZ6i6mi60dOiqxKtDireu/l3nzr3ZGu+3v/3dF7/4eR+89P/samxEWL/UFtgOFgiXV3GmM/tByx0UyXsN21apioOmSWBmVmmzdCj74sxYSbctTSaugdoCtQW23gLhFPlfi8+rF1+fvheaXMJx9dsk2G7AnsbxKfX028JDV199tfnvxGinRVpXNTkA3dPbsXZ9+h3mGNXPSk/p+vnukTGb0S94wQs+etXVk18jt1xzNitpNVFtgdlZIBxcxf9incHf8U1BH05qK11VZs/SsuKhQ6Q1gz6ZflsBNClbwKtgcdizjN9WydVyKhbY7APQZf4opMBUqrUihEEQ+LxnkNnLlQyN7jsTowEbenoii2X9zFgGxAqBISQSzQSVhZ6gzJqEYoil4hkjXSzBFUI4m7K0eC2nGMShp2fIDIHxzMnlWAJDAowQrzkhEiAjL2WFQzKMkLkC8CyAJCqHgsyskixZmNx3i2te2JRWbvIruILDkpxmFwtzk3KKpJJtYyacJbcFChUSY0lm2heECc0zFwKSA48gYgNoFRLSgh5LAOUkQk42jteIjaS9RpjKS0AlG2bhwegZIePLEkQFPuQHDImGDgFAZiDjAxOi8hMy8MEbcDwzzTYHQmf11uDpyU9+suQcMnDfm5GQ61Vc3+XXjd3x6ReuN6xdbRt7cP0aTcMJj7aaNC2fNneX3jLUWLRvY/E1rrEb7x1aNtT1mjf9vuHL8JGuwXU+QR9UNs5h+S5hrMc14xNxR6C6AB07w0ZxbVOpkdvPAg7Z3HjDDb3FeQXFnatx23qom3K+YXhwcNfd0zHNpJWm7K+r4e7l/jm9gxuG9jpwL1FRMQohk11oYJwOWbViBb44nhL50sM7GGFxTU1btOOOaNYPDaZuXweiA0u9xWTbjx6KKIdjhkZGVGMdmVSKhNIx07UrVnYPpMMTarVnJBqpdOsKi80zyN5iXcM9uKJoEuzRNILYKRNIp1icpIQPOZ7OZ2j78G4h8nSKV2wc5C3UTMqEhG3+zEpSQ8bjRBTAimSol1NECU8xISNbAWTy4pnJAvZkfAYkBBcaBJBZQlxLWcbkKEBIwxvsoVsgQw6BkA4YBbLMG7AcxQEaakQqZcqA4QPIz+n0KctHjEzqrCcVUV6FoAGYZIIVumcYJwo3CCItMAmIVSeA6uoJpq0swzt8i0ZVQa8tzJmbVtD87rxzXfDKRO0zsgDsvfee6jx26Q4NptusR4bTd8Dz5kzWScJHxlbBHLj3QYTfdPPVts/c/ZwGJpOFG4cOU8tQUBIKJSN34Hz6GQHdpNXX1yU1MMwsQ8jEIoDjmZEVIW2PPgeN1AH9/alPYB91DJDlyCAC+CBufUoXAVNXjuWFBOUlyjPGcoGsCIFEABlZqMR6RUB/z1C1lWAbYp761KfutNNOBH7qU5/653/+Zwf73J15z3ve0wkzt1Bv2DC86647rVw1NHduJ0OpCI1pjsxFXnRZ7o9tdM9do9U6h9VovP9/ViZtu1JlbszrbqxZNaTqzN+lYzhdqDZuCURoDMfnK0yiBiXKRnFcvrgLeLyxPlmrW4V3YL3hXHpvZ1qvqcO2soCyU2nV2CjEAMCt8pWV1g2vMggxJFNRg1h1XbdmfW9/D2mtvGiEiFKRNqwb7BtI/jEoCQlgcH3q/bp7u5wv0cm3ygkMNVavXKO+dPekDjCQRcMZIBxA/qIdF7ZlL98AvXrZqoEFc3S2gN65Ux/DyJ3K7NfSO/smv9xoFdXT2WXMwFxLb7ml3xck2zmErTyje2GB8AswcVtttiFFIBUWTBnZqmBI8xSVib0yqSf5pT586vQz4u3tfyNHdNAVZ/Wy/pEp+ADyMygzWVsAMTIZLPvfQHrCh/8tKnhv9OchJ2jAaJhl5rQQzzC6IDbKDhDJEYslniEZQWAi9dZnRYHMHg5IzZyu7IiSrlykrnczg0GRihfebZOsVKJkzkUoHK9leJNyygTBqHSy/41Uco5Ct2y9nHoWMvPAA70M6hBCYObaTkDZ/7785S/P/tdPixT+d8Ouu+66YuXaXXdNX7Kl8mpZLQsLx9PgLfnfOXOV69Kh8UZ/4wMfXZ007+9JbrVvYHzVqsZoY2zXRY11aYzXOeH67UZn4XLHmj8kkujTnFknUvTJE+n0vw9OE6a45tdLUQqTPXaBrx9baIFUcM2+V82MwVjIKow8JVbpjwyZcaaC08b9kCiMsvCqrnr6MkG3zIEOzN2orxZFVGIpBq7qtrazYtlKTlZUhFTCExN9PWalaS6gCXSOpV9+I7miRrwi0wZpk2iK+S92+uh2YMCqYjkvzXSq/y5dumzu3Dl6leXLV/T3Tw0upRKpaxHRVZY5IwmugRrytfSWZb19xQizTFTDtQUKC8xpdA02Dxfv+6D77bD3nj/8yCc20zbRE7qKYXR90QANAgxRBFWx0kA2S/JNV17ljoanPOUpJ57wtc1irIlrC2yWBfTqessYNelv+ZrpRoDIVOnwKTmJ/IorkDr5AKL+Z3xmAYgS0rilCXslig+CxEJIllxm3Cy4ogleGAl5+pDSMmlbaRZd03ipDrUFagtsTwtsgf+NniXWhvI6FKSh5XU3LaOsPshStqAbGdXWNXmzmWaPNH1uillMx9DY4Fo/wzAwZ3jltY3zfvnD57zgFV/78scKrkLS9Px1TG2BLbBA8oLFeZJYnATzfRyfGrvJSovYHCfIwAacs1cAfYUYJovKUYHxFCuY2cXkKzTMZFsGhNjgjYTAAURy8WrmCJhh5yUk1M/aArUFZmmBcKOWDHUZAR/wiAfvcsB+P/6fjxYSHIC2eNNX/JRcsQg4nVyOVU9SrPDEmNnGS8x/h0bXD4+mfTvyN+1+Q34pqSVXXeOqrOc+97lf+OSn0iXQRSrTaVHjawtsKwtwPWVRXrP7g48ZaxAEPuaq2X+V2TOyLDDgVrJyKhZs46dWI/VgyfPiVmlbhikLL0uQI1ECZDkLYMGQo2yEMmMNb1sLtGzpbKb4XE7KLFiVX5Sr1wwr75kFYzfgizEfmATLNJ4x4mwVGNLgI5TTgolY0qKeRWwg81MsOBTLQsqUQZClicqZzULKjERFEAsflThV58I1iSpzZVhsTgJNMOKNQXCI8gxlgisEZgmR3Qoy5z3wxAIM+XEBUorhkwspEctqAQRNlj8zkEQVjJ6RfUlHol4DLud9MvVCjVjsI4Fhw7ZR9FlgCPeKSwCUAwwCAVe2T2wSiyqrgSvxlyTEK5qwOSFBE/K9BrFn5AImkgh80HtKOrMEMgjK0oLAE0GODWJI8oX8mom3HyAjYe0HPOABPgIzAPrqV7/qXi6jK2eg3dnMhrHVx0nc4x73choGHubaZaXRU6Ff2GS8kTbq9txzzwc+6lH9974r4f0TN4n3oVmiGpsn1y6Fls2+RoeEFv/lD509PSODjT322tmkS1UfGYyrWAuh9eNWsYCyU2pu2tM2nLpzGl4FsOWp+HItrSiitru/x8U5Ts/j7XCIU42YaNglVYjPfvaz73KXu+CN0JQz1UDsEFv4tnDmEtaLLrwwyCRBEwGgvtmOcjhmwQ6LPKNdtx6AlpaTx1/60pf223//FatX2VcgioQ4k33Xu971mGOOCYFl/R2ApnLR2jrVRVG/+MUvzjzzTFm2TF+ml+7LXvayXXbZxboAesIxZlFewaG8oxKuA3Sbco69lQHqlVMM3cp5KcdmOMjitQzDlHPqtTX7bYVkZCtQEVhJrpW+gmmlJ7CMjNdKKhUh+TXIsGf6sqhMFgCa6Sw5NKSNTF4M4HxqkGWxAWCP0lG1/3rlzWR29zi8yKQOQE9m4YrLb8iJQup4OzvSrdtqc9YwCP78578A5s7TWJzPVv28beSzvHMj5exE2QV767NM2RpbwVAm6MtcFQ0rLLN/zXIIL8ufpQQsWb0KS0Va5bVCXHmdrugrZFvzyv86aPWiF73IfSpnnHHGPe5xD9eE+0DcGqgPPMbGVlrmeNjD7sv/7r333vJ4zbXpfGolwBtHOTC6YKcFD3zoQ7sPvmci6CgWajtWAUcmkv9tTKSzpBPrB33d1LhpOVj1SMHHK9MH0Xq+9x//Am56ZGTobcd+tzNmDNWx2PQi6pjpLRD11shqv/32+4d/+Ic8mJyeo2HF6ic/+qnjU8oUfSrAZnjKU49+2MMeFhMHkpvoqX85Sv6XX/7ud78b96DnFgGp89l7372e8IQn3Pve91bxYISy/CyIW7zxxhs/+uGPYY9mggywasVqNAcefMDf//3fT9d8ygegDTVPO+XURfvsiXhkJA0AQh/jkOc85zn3u9/9Ii8ZnxUA3HLTzb7dMkn8whe+QJmckTLN9oDDsFFw5Ee6OfUApst71ifTZ0wGsuTAVF4zGWAGIWWyUDhjZuZqjW3FVFSK10oqObkKEGRkZvqQH5i2dqvIz4wVyZXXnETIr8SWX2cpMLO0FdhWSGieGQPIDaqtnApx6+vsuWavUmsqm4WhkrQqZRd5r1igrUpt0yKzIrAt2VYiy/7X76Xwv36jQ8eoXy3875hu8MEPvrsJgv5ZL/3nFW0SlKlJ/7vbgns85CGL7n2fgij1Zo3OxDDYmJfyMpE66uHB9fzvxLolKbLwrZ3jqScfn/4AQcloqUsvvXqrw5ZbICpY+ghsdPyudzu44rNa7RyO9s9//vO3vnXSwh0W8E0KTggNDMD4LF+Pc7Ikt7Ij84mvGS6Wr33ta5ZBQgEdQiEmNSIjvcMOO8zMd+eFU/Pf1hwaH7p+72Of+exBd9nvhptjpSVNok1j1aTWvLRKCIwP7b536vfXrU1fOg0ODhlN8KeUocmTnvSk+9///mwTlDC54woMHx2fB/O/7uLNdojY+llbICzg9HO/M9Dz+qwRPfaxj/3yl788aRmd2baYRGxlxTvrhz9+7kte/KBHPuI3550fivl0Kn1KWofaAtvIAjpPtTROP8fviuirhbbiuZWMN88y6ihjyBGCgFiALtpwxdwt5GdeAAJBLJoyI5mQApo8wyozbi6saVva9YtqMmXIhD2UtNILMDoqZyGEx1IzeozZy2xuujV9bYHaAjNbYCv9b/Q15ed0yeV+aTqCSXyz+0rjya7GL3+89J4v73vwIx534fk/iQFB7X83YcA6evMtoHKGf+RSOT7epyKDo4SJOsxhBeyVrxQqxLN8xU6ses7NheR4hvByiiEQhqNs9ZWzTG46MtNJH1zlpJFFEzNm8JW+GZ8MWt6XOstkVaeTVuNrC9QWmL0FjOb1JqN9ft+375GPfOSJJ544yZs6nLT0ZyFw09ISWeqgZghNvzoDSUtUV4ffmbzrAQc+5NGPuqD4QeltMydvSadG1BZggXB5pnt8oqDyczfZMmUPlZEA9MiC1zMA+OnoMy8CxBKKVADxioDLsxjrFY0QLFwhDygq8FnO1gAOEXG+0qoIMcYIxeROcjHkyM63nhFXzLX9XuM4w5bLj1L0VI2Un4K0Oh9VzegKQHRRx9INJWCFHYlBgoMyMGA0UTNgwLEcn6svJDhkggPIAjMQeFVZzQvJ8Qw5AaNBHzrAiAp2XPSHERV3OIFzVNB75rxkZdAIXkkQi0aItOLpFUHGeI28QEbqib85CicBMlIJFq8A8oMrywE0E0pj3CIR+ZpsUUlgR2KESSxxW+pEoR5ESi7FugZHTMN5xhQmiylSLDCzfWARKBHmxRYKw4QIsZHHikRIlJAAAYAyXiuUXkNaJMTaXsPgGMP48RqMkAhCZqso9IEMmRWykFxGUpLwzIU3x0aWsURAFjlCAxOUgKxqTjdskuUEfns/qREzH0r6Qas99tjDhtwBBxzgKKfJ4cMf/nCbvjctXbHkhhv6+9MVfY6WWs1sNBZVFIusxR3hc+fsftCB8zeMDqayGxrx7B5Pv4PcmCi2E8YHJbqwe/z6wc6GzT9Ns7dxy4qlk7sgnX22E6PudRV3X8Wx18ZEX0qiI/kkSXtGihU16tctsEDUT4W7zwEHOLvsZ7WdKrZMwMiiUiFuHCDFPu1pT7Pf6fhy30CPufpEx4S7n62A2/p1X5rj0a5VtjNqGEFCUViTHydgt7HkANZBBx1kl/fyyy6LYYHk9PnqhlOAj3nMYxytNhu/edWK1Hu731ls0Xko+KwOyY9+9KOf+fd/f8opp/QOpAu3kAmWK/bcb9+/+7u/k5frrrsu02eADuoVSl/YQNqQs/6OmEBBZFBSY5999nHUzOlwimX2AIqkJmujg+MM4iBXVMukw/S9TUXOZr2SXKavtIKIzUlXYsuMrXBwZZYsKihzjjI+KOMVjToQo7dWyYFJJm92gNPRlPG5ILJKgJxcmTLDoWS8likx6qtpmEs2s2QgeOOZU8yxrZiImjMn3Z9qNc1TNaYzvIUyh/YD1jLk2yjDUem+vu5dd12IwBeHQ0PDdrh4La/GwPvs22d96vnPf74DDT87/cLFi28c6E/ffWlorKoR2VU64MC7Oep3zeLLDUhWrFQhRyWUHNfGZsFFZqHwVFXxWmDETLmq9NIMuIIm2JvoNv/mcmmlhIlcY2uNzbKyJhlTAcoEYKWm7MretkIfaYX+lagsKoCsVSs+Y8oS2iLLBNsEVnMceYnFR8/9999fNeB/H/KQh/iK45xzzu/v74TRRznw4hevGj175XQn/WNHui3SefixjsbADrvsd7d7rR3fs6ApurKJ+eCx8SWys757LUvu2T/x1x//ePUtpzuJ3z+aaLrHFiX6rmWqz/j4XOD4RLoA2y80JHwRzvvlae9973uPP/7dpV9omBwrMuytY6umLneof5nOnZE777HLf7z5Tb7G+ctf0kcOMwfu5pblyy69+A+VPe9DDjnk0EMPtb6myWiMucKTFgUEA8+dPehBD3r6059+6aWXZtcWeNXjqKOOQva///u/IWQ6TXRKhDzvBc89+eST9VGZrLO7wxjguS964c9//vNVa9fARy3tK4YSMQUJ4tGiiwrXecWfLt91z91WulK6GRx9Fr7xjW/QMJQs17FJeCxd32tJ0dWt73znO/UVclEmawrbLv+2TQgyzN42dpN6lIuslThLDuGZWEn9TfvfyGnZYjlr2QiVLGd8BrCUJbTKzJRlIBKqJNcqJ0urUGY8oKxAWQI4v1aSK+MLqpkGOZk4A5WMqPyBqSiZ6Sv4zI6x7egII7zW5wluyw4ZUUGWZVaAEOWZ8RluKxZZJsgs2wMo+1+DK1/5XnXVVQZpjn4W/vccYzPzEV2c+S+g0bV7RY2eibTEZMbqU9A5i3bZ/273Huvau6AJ/zsPPK9xS7JAY6kOdt/+7g3jP7568XWs2ulXkZiE6/ZrXA1fvpn3rkYf36aPFte+dHakD+2a85I0ExkLK05M3ddbJFc/Nt8CYdaxsb332esFL34RnxU3goTPippZjNMbI0VhBv7oo4/+3cUXX37ZFX5sITc6aZv/Cv/3f/9n6A5frthKHyaeYh/xiEf45pzPCteGVwcOzz8eceRjTDkNBX+1Is1/u9zv3Jz3hQ6Ryfm9/dz9S17w/G9/+9vlbJvL878+qMt5Kce2ws58+yHyCy/4TWfX5IJh0PiMyrcBP/vZzzQQU/sKI48jLxQ2LuV/X/rSl5bzUiGuX++0Foiq64Nf/df42vWPfepT/3jJJWM3LU0GMfxreoOpyWphqSa6xWwl91GOUxXLr1sA+7E77fGKP/959bLl2NOvjtQHoLfAjjXLjBbQHPj8OFtvJJA7/wqTBQEY3bKnnlYw24KEMUQRMn2u+UTxXISX/VEs72fiCkAZxIRX8Fv2yhFgjMuey1kzcOLaTFTbJsS5bFlyNVdtgdoCm7TAlvnfGcRWN0JKpOn35AqvnnHTe+VddGxdjZs7xtfFD87xv8961rMu//PFq5ffbFhQ+99swxrYthbQIsytyOQrec/sQGEyzGGJ5bC4LcScrxnQDTfcsGWaEMs7R0usSIgUuUueWop8aNl9V4hn/yqtnJfgiiyXJcQoIlZuPSkglp7bRIFyQjVcW+BObgFNy+rR6NDIU5/1LHcfDC2+sdHfa2TfsGNXnbqmZjhtqBJXCTcVX6X33tnbN7xilZ9/t4Zz8e9+N2Ry0VloNb3zbiOlRtUWmIUF+DjOLpwOR2NpPWaFFhKDGwF8fExblsedhUfj2tAEXOA2XU2RZf8b3o38WOaVRIjKadEt1GvrrzNZBWh1uGWCJUuWlF8znHPNJpGpSJSSYahMkFlqYHtYYOqUw5ZJV2yGjJ7qmZITYjwXtY1MGPXMKxowDDhqXrzCC9gFg05BwwCjAWRir+pKKJmRGLMoB5dV33hFmaNyvoI9CMSGbp4B46VPMEJKAuwZrTSGxfDYQ0LAwY4xhCALTDAGZWgCBggIxApZAkAQRU7GhxFC50CiyUDQY4kkimeSbxBLiCdNIAWaIQ7viECQEszkM0HVkGiKAbG2WY3b+D3kwUlUivZspOg1JBTpp9dAZnxgCt5CkyZ9YDzRqwDIiPUUssCgieqBjEHQREJBE1FYIIMdUMiYkhOvwRXsUe6RaCiAhnAhUgzhkBkQFaUTmJAQMpVdOdGcXADoQzESymQhp4wJ+m3+zAr89re/tZH2k5/8xI6X3Ttfq5x33nkOQ99yyy1ORTtkN3f+/MUurJqYuFfRJGf+Vs0mMcaxVIXTAWjGdACaWbq73C2kQab2Nex0lQ8MVLDhoXmLUrWcGE9rweN+A7hUTOUsJ7NEjS1ja3gbWWCoOHhn8do5YO3X+EDxRUNQaLkFKTUFIfzyl790U9q1115tKV+RpcJOxyTG9tprLxcxEhIjm2gCCLRC/yPwdK2aOYBj1kceeaQTTmeeeSaB8kE4MQ5AP/CBD/zEpz7Fg/QvWiB1rQ0XCmTlnohW1Hj0oYdiRGyskOoXURMNB5fdQXLRRRfZM4jRTHTgkZFks7HU6MgFOiR9n/vcR50nBHvKStHhOKItI/REk4gLJQNACbB27yovCZFAbc3HebJ1w0M0EZtSSVt82yVkZUJ6vIKb6W46UZTZGtNxZTz54JxKvJbTyJRlZBnOvEGZX8s0ZTiXQkYGY+g8GyFZScTgVoFlHYhVOeOZUwygTFbCJIPESGPlyqE99ljkNKGLe9/znvcoBPVQcqFAMSjo0EoOOHAn95r7vOQ///NNagfPZpiwfl1j/wN2fvKTjzz44IOPOOIInecjHv4EzfDtb3tf6Jwa1cSaPfa4x2tf+9rDDjvsnHN/dskll3zzW1/9yxU3zknnVIWN6hguIbI8g5XQBHPiLzyRdOhcNLcc0wbIdSbiynJEkdCGZ2NUFESZMYsKtTcmn/L+ZXywl4WAsbeVUGFEU8EEbxl5a8I//OEPHSQ999xznSNxx6QD0O4X9ImIzkQ/qf+j3i23rF2//q/3jt/PbVkhiULJhuW1b14TeSz6y+IAdKNjmUwN9q438dvx4L11XGsUVvG1GPndHanG6mSjXsDoRdMz+t4C/73vLf/e916+fnljl30aa5KwqVBQpteKbacoamhGC3QP9PCn/MgFF1yg6Nkzpr5pmNQMSjmmA0rKTaWuKf3TJen4Mnw2u9/fUJEsdsPkwT8BWDyDzFPg7/w2sUN+UhQVQtwcqQdwBNARLhgjAZ/AAZoqTP4rUZDF+jPOOMOFl+4v5xAjDvHoSBoGqIRGAiPjxQC+qIzTHYB2A6We7Yo/Xu7jOhPHnJavm37wgx9QNQ+kwTmWiYSu7m7fnzikxeMzoO8EEEe3LDYTbysgZFJj64UTkg1bzldbVSVXTjRey5SzkRD0QblJ/aMzaU0idJ6NkKxkaN4qcJM65NSDkoTsntry5hQzYwaoTQ0hYzKAS/DaNjbIEFRiW19DSFAGXOHFkvEZKKtRkZmjWoG2lPIYzbxMHwkFPtc3BBUFKqVTVhVXtPdWsYEhKujbalWODVGVpCuv5VTAbWVWaLbytex/DeEM+32CYsvTB40AVY6GV1y+dOddVhy4bFk6urSgTYIpa6Ppo2iA8duaoeh8wv+mA9CNzuQye7pW8L87HbAv/6tT1u2qeumvmFR3FF+HhMVU1ejAfFpCirFiu8rbRpMatQUWUM3CZ9nknmwsRVcRlbP1ALTvlI444ggHoHPTCEoLKaqTrWUu2CSaJiQTGNU4nigtb5q68rPWW/hfdQalylMUeocR4I9+9CMuNUYC8CTEouJYsRyF2Cs5Mf9FOTo8OfCG5AEreZnZIJy42ceFF/6GGkIoicUH8GbQvsSDUe2lYjRiSJClhVZGCOF/fQZsvGrcEv46DJKJa+DOaYHJOt/ZsX5s5N4Pe4iv4k/5+teZonvunNGhoY6xot7e1l0bZVbfstR3+34A7esnnGB1KG+P3TlLrc71NreAhqBLFPTnhPu2RACbRuntbZGKisbisytzHx7EAqMe3rToXve6l5VSa6rof/WrX1mTFBszxKwnFgvvZkC65YzUFRMbqwo68/JpYyv8PsLxo1LEWlM666yz4kZMy5jYpZKFzBIgkPv78Y9/TI5EM9ejHvUo+hvwuBTjW9/6Vsa7gfJ5z3ueyWOkm/E1UFugtsC2ssA28b8xbdakp3qWrdVvUlKo1+hrLL3xGrstk/53fLD2v1tr4Jq/ZIHsjwARTHk4JouH9lz4vvLhZjMaS4hWADhHKwBcrZuhLNHzrTED+ulPf8qXYcyzv1JSVZCbs6Tg8hobneImK3yJiuO2YuAnZE0b7VfaBbAFL941FsV1YyXSGUH5Et8q3zarZV5jA2pbks1kJqFuiULP/5q0yiZVDQn89J/csQmtZpPBGZWqI2sL1BZIFrBucs+HPMiSzvn/97X0Pjjc6K5urCT8JsO2niyPb0ifUy6/8SbLOGYZvz7vvE2qUBPUFpjZArxMqyfCYgIbeA7RbJFndJGinW6+lc81XY3Zq3Ggn5G32OhYmus4TVF5SfNlsJ9bx4LYWiWBkVBrchlT8b/wguVTl0SYsVrwjwk1pw/z4Ac/2JDAlBySE6RM21y05p3M6SjtkNpsdakKyZxsmffwww/3ajEZrxNEDGIfU9bM+i+88MLKBL/MWMPb1gJb1BEXKhTVKT3UXQM4AyblpzhVHXU9HaBsNKx9eBUqSgdvILEEVyDJQQ8jFiwU8ZM0ogITsZIuSMY0GIM59SaOxHkFU0NstC7CI7lIxVMriieZaCI2BHoKoYMGI5S5MJIsZIEkCF6xlBUOgumeWIiyu1DOYEiQOq5IIpREGSGd4BufvBB3YgKZcwZ6ls6i/2CrjoULd7ABw93utsvOIzYtOp1tGJtwk1C6TChZ1a+x+gMkyQ1nnJsZcQxmPOGDLGd5BiCIQ2d1QI7AjAlWBF7xRkIBgIWQXwYiCR2W/g4eY+SdMcEClsyFIFm/2HFkk0wAyPRoRHmWQ0WHpj0Tie7JTg+76aqCBXEQZC6vgZQQmlAAJkIoCR96igVHVAiMqCxTVIaLzE0+MvF2BVRpGlqy/MhHPqLD/fnPf/4///M///7v/+630d3/96lPfcrhTkfalaPCiOlQ5LpVq9Abft7cBYsW7rius8/fqp4Fa/t3WDvQv6q3Z2VX34rO3pt7em605tvdt9hVf8MTjd75rgjcc/8d3/2xx73n449/6b8+bKxz2Cno9Ddhb8TxmYnir8tVqi98+SPf+Jan/NPLn9/T6+dqhxyvblWjxmyZBXr7+6Ph6HNMjPWf3X29Hd1djc5uXcboeMOf7frBYR8xjkGqNjvttJPGkiuwdI0b7G+ZtKswL3jBC1Sqxz3ucQhE6aP0V7qpYoN1dM6cPr3pwEC6ZFRrRZDbiO3TweHh5XssHL/Hfi95yUve9KY37bvvvhGrpSxbseKxj3/86173urilkhqcixPVoojSIFOb7OzYceedpednCOYtWLDr7rvr8tKuLA2aPUmkqHfwNzQyggCxDYqQEE+vvstePzjojPWHP/rRo485BrFziPDk+BuYOzeeS5Yuxf7Xq69evXYt5ctCwDrTjJHuFgfZlF9GZo3c3MrSwkqiUILLUUpEmeqTsStfcsJiaEKUJwspCz12dL9ldnAIzElELK4Aot8jGXuF0WuQZeLAeC1jgit8N3z4vqAk3GtOupUrCSqdMwhRMAF4ym9oKPvwuvfs6zNNRHlmTAXAWMRyQ+qLSpscN6vusEM/vMWy17/+9Q4BcNrwXiVlQOHpirXPfvlZr3zNA999/MOO+fuOox77QJqNjd6nv+P/6S27Ohb9x5uf+6p/Oebr33r3d773oYc89H7//u9veerTH77TLiPjjbXjjVXfOOklJ33/BTvuf1z3Tm/kV31IXFbMxyPp6n1naFJdnnJ25ewHzAIYy/gwaUhjk3LURkmIKILqIbTKKROLFZCXkWAbdv6Yvaxkpsm1KxLKeKLAITC0ZXDDuiCIVHLFgAw5mR1QFqivKCuWYSrpuNQ9uQPTsyxhe8MmdXYBHUOxCvnpT39aLTLrc6s9L2zDUpvKCkQ1jlclXfyNd6beRW0ca4yO9XR0Luveyd+67g3+VvSMp7/eVf7Wdnasbkx0DHXuMLDjnOGu9TesaAyN9ow1ejuX3etu3e8//mXfPuV9//LavxvvaHT3r+voWTvRu2K8Z8VYx4bxzsHR8QF/7/vAU976jiNf/5+PtG4zNtHprKzv6tCPd3RxCQWQNW0Fogtsxd/ZMW5BTn9Graltp795Oyx609ve6m+iuysw8XzSMU/58Cc+fvhjHrPCZfPh6TZuzmFKFVg11g97Rg3XfKLR5VYGgESgRrUWQBBHw0GgXVRCeArsrbwJU2r6Vt4/+MEPvvmtx/qJhgVz582fM3fuwJyBvn61WujvcYFmr+lGzDhcrukvXj1323mXkcEh5/+f94/P+fD/fPBhD37ImpWrelxTOe5rKtMVP1UzeZggFJYybaM3aK/Y1mH1PGxigpn7jbbyxG6u/y3LCbPHs4wvl11WIAPRQzLpFvvfsJv+Oaait77/jbzkjOeshRHi1TDG7CzrFlGWrgDhuD3VVSXlCRmZ8gz7iK2ILQtHJlZdClERFZKDPTAsbKUsS86A2EgIQI4Q9PGMV8+yqIjCxTEFVzwzL6Acgr5Ck5EBZPryqyS8slsltswbNEEABoiNp5YeNodvO05IM0QeqF2HEALlOqTlFcAsXxSbazKKLGA0wVVWb/vBZf/74Q9/+J//+Z9PO+00X4B85zvfMf/VpiTd3ZO+z4xQ1oTzzX+h/PKu3f2t6e70p7dKf73r01/3gL9beve8sXPXDaNzl6wY7x8cnz/UsdeCvrvuseNXv/zoT3z0vu/7rwcP9CzrGx0bGJ8YH7uPIWIa2/lYTm/f0fi3d+zy2a898i3veO4e+5ZVmCWcPcYs6e8sZDFusRRmMDM0PsYX77HvPv/1/ve99R1vn7tg/rquxoaeznV9XYNzel1K19Hbg2DYzx0VXpuNohpnY6nDuiMVWGWI5gYQGw0q1+r8Wva/kChRm2D2DQyYllp7MTCYt88eq7smVjfG1nd3jHd1SjpNYxuNVW7U7enY0NUY6ukc6mz4i2D0PQk1/9G+JBSrPeAIkosU9fmhBnxoGE/NWYWPOT6ngDgyBUYZtV0KAXsN5+sV5Zj5viXKyT8mmISbGtX/3rkssGF81AzriCOO+OY3v5mqeHeH08+p8RSh1RZawkbrF1MUzXmPesdXN/+m4rcIGl2XZv3nnvFz62CHHHFEkjHSnGVPr8oWJVUz3UktEJ2t+q4fdiL5X/7lX0zzP//5zxv7+Q06RhHl6ZMYSGcRfOP6xje+0aavTZxjjz3WvOOLX/ziE5/4xFe84hWWBWJ8WzblIYccQoIeu4yEcaTJdrKBjb3ecpQfmLK+asHBwaw3v/nNNoMdvH7lK1/5gQ98IMY8ZeKZYWNyX5+6QM5X+mnLoLgHJ1hcLUFhW7w2F2RHKmIjSqJWceVrZuF1bG2B2gJbaYHN9b+bk1wa33a6s7kdTzjP8rNJtabRWA3f6cfrdHs2DXrGzvnFqb39cw454kgLOY0RkTZh5zQa9oLTdnAdagtssQX4wQgkmKoIltmvvvrqT3ziE3ZqeK6yZO7VEtBDH/rQ//iP/zDZEcUpm904nOTA1uGHH265w+lnDj0CgnB5Ns4ELjj7OF+iOv9Ejl+okyh8jooUSXZpjqkWh+7I9Tve8Q54GwHPfOYz4/NXr1IJ4hmeaGQQQSThNeZ0smZPyra+VQ7bprG4FwKPP/54zvcrX/mKHBkJQPL+jkQbKsgFrWJCF8QzJF1H1RaoLTCzBezaOvF52GOO+L8Tv5XOUvQUR65Sex32w+bpmf4ibO1tpE05M/7bziX/5rzz73Wf+xx61FHJIyfdihCUzbf639oCs7FAdkZmuxV6zgXGz22ZmXJMbvt65zvfCWm9NNyWGag7ID760Y86YnTcccchdrucD5acSeNPP/axj4WESCI/8UpLlNmoEIladHLr3Kte9aq3v/3t4Xl5Yd7W01T6Xe96l699HKdGbBr+7ne/2w8sc4j/8A//4BVZzkVIi2coGdJieACf3XrZXT7mMY/5zGc+45e9Y38k731kmje84Q2M4FVwSsSsn1N2ISmdpZ6Fl1Ov4e1hgbaTl00nFCUXT5XPuglYVVDejkv62gysDvmaE16d84xBVYgWK4DhMwa9imJkpj1gEaIWYizzBjJ4I4ooLFbzaWKT0oBSM8COBkZl8iwSTAv0ESLReBJi5BpJEEIB4+BoA03yyX9zcqRBeZUKAJeARdKyHDsHoaFnKBwi4hXGK60AGMGhnieB5DCjjIAjIQShamQqkIEhAT38Xnv12oUE+KlWOzEOoP/16utiXzInXVYm2MtPYuUIJuipl9Mtk1VgCghyQWdPCjjHbMJAmlAhjteQn7UKCV6ZXfCK0RMxZCFmUk7OfiBpGAKVflQARqNDqIFXdkJ4RQ3Icupiw4aW8JSd2FxhosTDLCEkqxRIKuGNEDrARKhQEgtPSCTtGQID7xmvt84zUs+29RrNJABwEMQz6zlL3SIvOb9ldg0txXamyy0baYLZMJ90zvX0089s2IkYTTU46CNpxGzrIyGLvOaryoWDaxZ7ENbPrbLAcPHtig7T+QZjCG1HNWZ2XbpqoIYoiHhKRpSnmg+JRkhpTzT0e7pNzcfVknoet1Ga3vuwyeEGYmNpPtqLzgFGB4VlSkKxNqHz1/osQKC33OCTxBjNaDWISTOmcaGI4UvoEOyelEGTlGke6oIhzefUrt3VLxEYeUnaFh1ytFBPORIrg4m9GaTLDvSkg+EXIZbvZa0Zn/6VRDwJie4ibFKm2VYww2oFLDCdwDCCXGhNVk+8CkHsyI6iiW9LuGbmRUZn2uagmJSOgDIz5rQCIzb3txjFwpMDTzfWZq7MkoFMWcZEuhkTALWlTpRUIkVkVI16oviUSIXFa4iSdBQl9jIN9hAig+Qzguz71L6cRzD2qAZl3jJMrCUqOQ2BZEqOPiqkDSnH/d0TcOGFl2JRl9R/B6PBhx76EONsXwW46NTo1oH+Cy64qBDbu3ztkrXrVhsmfelLX/r8Fz7/3v86/cP/c67tore+7fUmAHENgKyQ9pvf/Ob1r7/skEO+8Za3vOXrX/+6r/kH5qQ+UvEqBA7WX3HYOk0h5EUoaw7pFZLCAURsQZjuqwYYseSSLfNmWE5jQCX7kCEzxwIIFypJBwHiefO092T8Mgt8BMgwbKXsIMmUotJBCVi1am1//+SZYJiQBi/d/FpOQhQhsiZUrhUJevJZ2G+OK1zVY926wf7+wieVpdw+4OnyWDbCbDRN5ZRqT1SGhrGioPLbkV27KvveSUkSFZSLqwqPPNLmRMPv+bAqAD4nV1BNvWZ8DWyuBXRTRs4+gNaxlK3KDVlE45GjZ2grNpdIrBEEjfofQEgLGn0pvGJNNWHjAJObUha4Mcls36QiOxYyuC0diDpGeDRJSUQoyyonZxivzcJwZxbxfZxtPKALYoFw1thD+ZwFQFlCWfLWw3qP6O1zcq0yZU3YXP8bcmRHifChrWJhIl/RlWX6wNMnivJvzv/KVORL3gOQI3B+Rk7jVWVQq42CMhKLvOu7OHQVQ2XWgVu6QhPjELFCVHLVSQ8PzuxlIFIXa/hBSLx6CrjUWwLD+BJSvlHQ5ac6SY2QLwuRi5xEvFKmiJl0W2K9aiMcuoSCuEwQXEHmiWY6+WIJR+AZcuJJAgw9KW8QFcKz2EyJAIzdODli4wmPHS+bRNIGNuW+JbhgxCqgLDAD5BAi6RAV5SI25BPOthjD/xoeGPyHrw+CLOf2D1A4F+LM2kYZDfrcJS00DTGp26bdB/yZz/w4akGWk0d3BF5zzS2OGRlGXnlZmuI0xz4zJ1XHztYCbK6eK0Se1wTT7FW1VG8hBVJUYDUzqvF0QrHHTBOviq1R5KLESI5n8AIQR5sKTKYEGABwmvCWwnRoMIgjQKo2GNWiYMmMIWeGitGUkf6lgIAlerbMJSrkSAUQSQcQ2sqgrAWNJzXyWAJxaJWFZLIauJNbwK7Sz3/+83VLljb6utN8VU/XdHm3H8v46NTvj+205x63H5VqTe5IFtAxGiX69R4rcq6Y0pHq4d0uGXk0/jF2/fa3v+23gGILSY9qROowdFxRGXMQxLruSh+rczYMM3wSK8riqjEb2O1TlozczR9J5KckzPc5OEnwVujddvm5z33OVZSVVRoOgj9yftoqqCmYhVDLU7aiLYdKglOTI5dSf+1rX7vpppssrJmghe8wVLaV6+4ryiNwyir2BKnHw1qHLPuRrFgN1BaoLbDNLXA79L9GmpVObNL/7n6AQa5ubJsboRZ4J7cAZ8cC9iVNt+2qgN31aFodLs/6aixu8G7xVZLFJTQ88ote9CLng30a/d73vtcPJpjymOmovSEQzJeZGQlcqtewswNVXKpfeLD6FJjyk/eUCo9vigfvJ3d4YaegDAycz+ZVpQLP//piKjwv55sDdywqBIYanjH/4tA5X1FHHHGEKzMBnDv5HDc4/K9BiMNnXp23trhqwuu+KqrCnHHGGV759xALU4faArUFtsYCPmkwFB9csixNfm+vgf81Gdl5rz1vrwrWev3NWMAUNdxiWWNe0i6V3UATQCd9RZmZcn98GZi74dF+/etf+zEQntTCOC9mA9FJYsfD/ErSSSedxDVzakFclow3XDC/KUSUyfU3vvEN94sRFb6MVmaspskOOvPmHK7pKmLfA0P6lSTOkYs3kw0JiGnLb+ZgL9IOrC+T8Zrn8tHGDHkcG6nYusV+/vnnu0405ORndtkG5I6F0DlY5JFHRuaHyCANSAgnNkvOEmpgm1tgy3tkhSdERVe3DP4UmAptKBklHbFWZ2LJJorTM78GJl497SNa+jeAw4grArx6FkGNlP+gBwsBk4MgtiE1AJpYLVK5SQixKNEI2JPSpRAYuQgClOSo1kjC1jGs9ApQL3OtRU84bZGFzmLBsoAdMZgCAaAUQiBRkVZoEVEwMRAP02EEZLWDHnsmzhjIifEuf9de6xZDF+d2zRlYuGL5+r5e16P2z53f3+G4abojsrtjgvXsytjAoNs4fPpzZ8zY+MToWHeH6w/HVq23B9/rj/zQdpPP0IQFFAFi+VUEPq0ACGX2rHMAkRcE7BBl5ynXOiPTA7Hl8g1iT/SZHSxRHaVCYTG9ku6JEOlGWZBQViDgbHZykEUlIQQ7aZJAoP7IUdi/LCGSFhVkohCTQE4EmgjIylwZzgUKk3MRlSdnMPDEZq5tDoR6kiiKKO2lhfJewYIsgEOTSB0smzNoUsjUFrrXd/Wu7ehe3dGX/jp70p/74Ds6lnb2LOvqXTd3B3/p8qyRHtdbXXHF2v867td/9/TPXXF58dV3yrRLoF0XPDHWMzbSNT46sXqise4nZ13zT695zZuOe/ua0Q1Do43RtIEyGQqVUn1u/s2gYx3VxgKd6TOH8aGh9f58weG04qL5C3q70iYuE+txWFaHlf6KihFtkKAUG2HCweh0fITvN8TxIw4f//jHTz75ZJ2AlW6jGXUpji4hV9NUpBAOHwKKyuMgZKebp+9q1n/t0u+cdPLHPvyRQQsBxRFG7JbR3Y3q2hJLEtpppF5RJt3l3NTKwQLzf/P8xVf8Jdb6EacG3tPtxi9kUq9UaQQ0Eax9UBuxn4767Gc/60K4yy+/XF5gKl1KJOdJVM7OpFm23T9UMvjzSZnlm+mkHnPMMb6c0/uVzwlRydiReiRYjrEnEd2jnEIKIU2PbR3Ej1SKbSsfpatojNsARKHxFMjRc8o7xfxuV1veQLoDRlDu09Gws3N+FCYQTVKuCLEsZYGG/tPxKt8PfehDz3jGM8qlExpKEdKajkt09LSQjhx5kh3SACqt48XusJlOvtypeDy7hEI3Eoglyllnw3RTxy984eMHH7z30qWrGXP16sEQ9da3vvW0k5af+/PG3rs/+Y8X9954feNpz7zry16+6x57XTB/YWOvfcfe9/5vH/8/n/r7Z93nDf/2pCv/ctX55/3qqCfNvft9VwT7s5/5qTe+9oTLft140bOess++e111VR6aT06l5cjy2iMe8aBPf/qjVJpOf1l74QtfqBGV7S/j6rMcWV/TYNvyhkw/V+pbxrYEgfSRpR+Va63/2NUQURQoJ42rKNv0QOPk92GHHQZuTQK7SYi6x/g+UpNE4ilCFuIDSkXfyosKPXalYBJlfbJCE4MNC5HPetaznHVzwHIGG1Z4b/PXuDS3szHsz4jH32ijz9948VdWb7yjc8xAr9HX0TVnXaN3tG9+Y+GCkf7elaONKxePvuWtJz3l799z2RXccjfH6k818tfVbY9C/zw2PjZ84ik3vuyVb/7QR87rMWHsdM+532Yobn5xIa97FItfIPEs/jZKuXDKZUwNt1ggXaQ9+eH9Rb/97Uc/9rFPfPKTi42fedvCqehq//crX/nc5z//k5+dftMtS1r4JxFRddV5fYKKnRtjrtIZyF4vY8oySfAabSc6z3Ls7GE/buUu1VNP+c4tN90sRb2lX4/o4OKLbor86EvbCtQh6/btHPzhD39wU4vTM1Yl3NpCH0GfgCuebdm3OVJa4X/dajadcL51i/0va/gc3HpN2X+VE0JwB/O/chc1wdNYy9jgxS9+cTnLZdjRCl8Kuaautc6wmCElD0WCaqPmGz+QGUHBAXw5+d///d9Wr8oyyzAX4EICgx+1C72oYI/kNCUfMrlLj/wg0MSkGwGx4cEnP/lJw4CyzDLMwbmKAEY5ZjyYNPXcgRiDn4zPQOjg1QK6y4lb854p+V8/n5qbfMbLvlTsfr3tbW/LyAogFXcU8b+R8YgNGC8g/G9sEDJ1hR3GsNDgE3HbgN01SPxvZSOQbgrLrMFdgPyvuYPXivDb/2vvxLA/P4fgr6ktIMOBUwodwx1d4z19w405/rrm7zy6aOebO7oXb5g49r1LDjvmpD8tbyzrbCzvHVveNzbU9wd/Yxxsd/p9Lr7hyx9vPOXJX/jmVy8eWNQw3msphGbK1X/DIxOROsw6TGcBpaVZcrgX/+EP733f+z70kY8svu46Y2OV37oh2/n5EgNZ08bR4suxtnL0BtE5APBqC9ojDN8Hhow25em1rYS4kdr81G8mzN9lp4W77RJTufRzTF2dfhSi0dPdNXdgvDd1Pm0lzICMnrBMQI3II5WEchRYu47eA1mEcnZERUDZylsRVb/emS3w4Ec9cvXQ4CW/uiB1Qumn3YrTz80mEH1T+Rm2Uh0rf9vXhh2NFTfdfO655z7x6Cc3+nuTqv7qUFtgKyxQdKuT/So4hmfOMBkI6VEJNu41po19HxgTHDMIg9V/+qd/Ap955ploTH+sMjmDpb91TxUMSrCu20A3eK04AQxKYeBt/RpWeUUsUbGRtFcBjb1eYq34uaLSop9fgLRvwk8Z4wlBFk8jXis/xp+CNZYAjNb+8R//URIxkSQ/FkvlKyOlaNhpcOviEuNDQ1wCeUN7ycbhLjRBsAVerKxbDdcWqC2wSQtsmf/dpNgKQdmDT+c8w6EHY/eE64/yMHiUs19x8zXnnnt28r+9uiAycphOXiaogdoCs7WAdTxeKTyXT6/dauQVM+fFiwG4OWeteNhwmg4lQ/o0DqPTyeCY0CGwXmHNx7EBwfaBAAOPJkLMm8LNkRYTpVgmslroVhFns+wqIuYleXArIdZ5DAkcgYjVHr710EMPtcHxtKc9zV2YEcBcqnUVjJLLQUICRqcXpGUgYTCAJjYu45wZ5c3snK+I1Rj+3UWYsTnr237EkpZTOoBDYUAdagvUFtgyCzz0kEcvX7f292ef1+iy0zMxOf+tyio6Det92SVWCbbFO+GVv5LU5Tfe5GPLJz3l6Mac/nr+WzJMDW6hBXgfk1C7KhHAvA9nZA7IQxHKJ5ow+gYpEuCMnCGx2WerxUab2a4Jo98NMxFu3Vvhm8jnuwXywZEKDxhuK9x3OHouki8ODAAB94dRirR6/OMfj8zzNa95jZ0mowL6oOHNnY32g/aCW6vNeW1zmIn/v//3/9yUIUWe1JjBs2ygcOjkR3LlKBpK3QoAwLfBMd6QtNPYzvbYonrqU59qz5TTp1UMHsrsNbw9LJBWSbY4qCWCIlRahFhbMZwyfnJczGBRQToPbUSl/qFRIRBHWgVfenhVZcWCEQt2gp2ccwTNOg7GqF4A7AL6SAsGS+ABhnRqlXbltI2q6UM9qcOI8oors4cCWQ48GpQGo8aU9HdYiv6+kpe0eixWCAn0lKIQQiLK8DGagRYl79aVfEkv+7EIFTqr0AFgxAUmRMY9vRJr+Mt6vk1EYCQaXx/CR/ZlGRxtO0yIK5BJbDrZ3Ojrb+y++5yDDzhMRn716wt6e3onGhpnY/7cviLpyp5cygEJERDoC+568D0cM1q+ermP9VeuXCJfiWjGQI2wBiPrL5xgUwSKz2lFJUJn8tEIxIDLwiQKHwSedkAdI5PHM844I/o7JagHhBGIKnJRFjA5+mdbqVu5c1eBQb8zE0qNAdFjJ7mVkRRRwYiY5dmfEF0z41OeNULnSI+EsvKi8JJAqyCQ2fiJN4xOHBJoHiJEBrGjzNUGC3wOqo0oPa/KT6yPU72GWUL49nhKPZKIfFUymFUtA2ULzKASyYnLEakioMxJKEexymjxwfeCnDMwf+111+1yMD80ODqSjrz3DM3RlLoGU7n7KWDbfBM9Sc8XvHBf5u2e2PGTn/zhyHpTUMW30fQssjODVnXUdBZgOiFiVT97rhpNmiGPpA5H642q6xnlGFVClDKKp+Gynizufkbm2yYfeLn5A6XPl3UpPoaGVM+jRWRNWhumJDRDOmgFnjsuWgTT39dHlL5RitbuJUFhSYecUD508ww9ASb8mjZp93zA/b2mGpUTLpyOrjV4Q40sEFWsCBAlIzTRs+mdIoOyQBQatoq0Quqg6xvtT8/u1vySIrMCZV9n+P3vfz8WKdryGFHpOXNRyrKAkmuTQcEX3o6R6aDiTuiIRYAFbNhnHBavbeWzvJ4NOzvE+DXIwDouff6Pf/zjtowhH7vYqDxtySivnqh4/CadQ72spNwZGbdlhNR7+8TWeXfq6XthJCoAZBxAQ3feRImHDhEbAhV91Ld4bX3yJj/4wQ8UgZowOppuNCSYbgL5xPrgT01TOve4x4HvfOc7/ejYsce++6yzLjAOMQUwlrHJtHLl0MKdGwb3e+/xIB7WuEiff/nXzr65M13Ea5dLvZJ3e1H6ukbnGqdohocaa9YM04dt99nnibvvcbkMyha3rvoXgPspO53yt3fryy9XmbcqL6euOtD61qxZq52WCSQn78YqDvqX8RVYdnhkcoRKVLz6cFPZRWyZRlksXboCuwKKgmhlZ0wEfC5jRmxZApjNzcxVs7aVB5fhFuGtkgPDoZx11lkGRXPmTH6WllNRcGRqFEpWcWQFphN16+PLptia1JOcYiTGUDq3jj33JK1vfhpUdw7v4dnTlcY/Xd2DRZ+m4xwfG0njRj/psWiHHZ7/T2v8LsvRj7vfCSf8/uYbtalU+0nYVuptTdbuSLxa2S9+8QuexWI6R5NKpwinn356fBzCn7J5NARAucZChmPCpTJH0QSNKMUKDoGbtFjwRvli2SR9WwI62DzQS1GyZzwlbd8epVcwfXRlbRmDhs7InAPwIZOxvU6AcWCEIJiOd3vgdZLhf2Ne1jaJ8L+hdmgYpbNJ/4sYlz5cLxe5ayv/jup/Y9zFd/OJueJlCwRGVfERGuMzZo5iNDACdlZJYncKRkkFV9gfBruqGCtTmb0MML5XHaAnrpAZT0hugn/kJsRqYl4zr1itlf9SdvCRbo4FBMaX/a1RYrUCT77pe9/7XluCkGBKGw1/Ohp7aW39L3rZEYWAqLaBGemvoUVssMQTBqDpGV2omRlZlgPJ/8ZaXhmfYeXrk0hDxyivjMdIJusZecbIJ0fdfgBKbkNlsgUsMSmXia50e0Rn16gK0NWb7htuTMyX3Nhgsf056jPR8Yk0+ms8+yXJI6xbM+dXv1q8+Mrkf3HDb1v1Ukp37mDcoyH3dnaZ7o31piU7RZZqabHUvmrlymgFbY0UZYFeZdZNcd8xi4SH0QXl0m/LnpHG5Ma6I40J65+EwFuW1LdoYgo9VZhi5TDTbxLAgjEyQv+ghxTiFVARghhShcSIxqt26tW6v85EdZW1wAdlhb1+rS2QLWBB/vOf/3yju5h4xhSvOdHLNLcxoPoX86MLzztv3wP2f/DDHnbh2WffxirVyf/tW6Dso3MfqxeNUZ/8cRN6e/Mac6VYXOIm4opo4zHrbHH3M4I47aTXxUUsCXaIH/rQh1os4hce85jH6Jl9AmeQYMXSWNfaaaTuaZWpbEsJ0cGgDl4sOMZgaKxfUSATh87ve9/7AChzyAQAKzwEcpchJxwfPK0Ei07WXtxA6QM8133JlB1l4/wwQjzL0mq4tkBtgW1rgdun/+Vvc5eY8ptGoL3J/+5/t439b3Voum2NU0u7w1uAl1HTLNSYhZl3c6YWgsIF81Y2JlhALBcM8CW8CSA3avLF32UP5XSU3THumEcjR5RNNIeYHRS2sxk2lAouax0WW0yLBLM2vtWWFgKvEvVqcsdTo7Rj5cvw2K7ijkl28Jqjd1OP1ZKziyGoWL/hkAcMkVDlaSLm4JTRgtkiHWQKu2EAvyw5xLwznwsfjHG4xTEsnyEdfvjh3DS/LzkHVzhruhESOlcSql9rC9QW2FwL8L9ON+LqcKtm8YnF5kq41egvOO+8/Q484CEPfegFZ511qyVaJ3RHsgDfEfNEmeIifdgD4KHgncHgQLnX8MjwsbwZjokjDofFkXFYWPg+s9E4mYDd+WNrj/kQSPa/uLIB+TKODwt2QsLR8+ZSjF2b7Ekhw/Xz5hSDt8FEju+BbeTZcOGvzc39QoJTN1l+BsQaSHDi/LUGznfnKGk5TGJPhAKQNM9R6GXWL9h/+ctf9ptIko4oifoq2HTed1YO0tjMon/mqoHtaoGtOgBNMwUcFVp9UpP8VsgTnvAEeMeA/DBWrKeoXtOVKHZR2oz6RI6rklwEpeJaxHFroHGhqKgogEguqpQnDMnxautCc3KP4xve8AYYVcqNUGKtGXlGSws7hpxWUdLygZ3PDogyhHWBuUosU6SF8hjVe4yASDQEUg9ecH73U5/6lDGxoe1//ud/+sVSA2vSPGWtbIHQHDsu6gE0Nm3puOOO0wwMTF/3utdh155FSWt0NH1PwBLxWqRWHOkbT7wOOmtldz3wwbL/4IceTNRHP/rhz3/+/xhsn7s0Vt5iAwNVOr/YWdC7ERDc6ChM12Hcz4wug5447l3vMAjWU1jz+ua3v5bMPuNSdRjBkz76Iw3bjQL09GMK7uuihqiUULMXyPSQyCLWM4KPGl0twFDkvOtd7zIot0KnyzCIZ6KQQ5SAV4oBwFOYqnoQX09aEHSFvuW2gjBRCpFc1NLAR28IJkfqng6+u0XMAWhH1tyOoBPM8oMFWQCeITAU8/Wn1UZl5xotN4Hp4g13bKhHpZU6+kginmX2gD319S5XUPSM5n5+s5HIl5ojdjsF+ggSEiQBjoTidSsTTdlWtxsulZyIPMwf7exxFeVE94Me8siD99hTmRbpWwhJtbpvPFX1ecNp0rjHjr2WSk//yccTfnSh56677H3YoY8562d/XnZdY+5CxZoKLmwrVoGAZQWkMAtM/ZitBcZGGG+cC+ebPY0/FI1RxVGPP1ydvOTSy97+9rdHlbCQQGg0GQAyLEU5po9YVHj9tv6W49cinHvWeN17p1G418SF0DrYKPHJci/aETleQ37caepy9Tlu0DcumTdPvy2KTMsTUWHK9AHr0vyl7ixdDJ7uJQ3M8JCzKumWES38yKOO1LqvvOIKHaybTQUX3kcnIDuROsYUQZ1G19w5811G7XJTwE1Lb9pl993cxukUiNUB6x2JqvhkHG+EST7Y7RMUkP7QUkiIDwUqSen3BPrIDoKg8fQapeP+bCy6Gp1M0GQJyHzw4+OzjGkF9OrBbgWnHKt0+Dih7S2GmZJDl0qoF8jQMBPo9wKOAXHEekbpOCAlZOIKIEc5dxEVXAET4vyxoDvNiZYJ2Mfx6IrM8qsVJfeOuxYt66PyBoFqqXTOPPNM3z4tX75h3327NSLHlRYudBW34wuG0YM+UPzKF347MbFg/YbVJ37r+287ruse91uqXfiU8FcXnO0KgF+ezQs3Ors6r7jimle88i277cbJNQbmqNMHjg25/e2GC8/ru7DzO2hUZ711+QfnpS772ld/f/pqEE3x8O9UkHcvfX1pCQxN5AKjjIMBUbhTDBtDhiIQ2XQbR6Y3p8M9Y7mtHKvZDgz0OtttIlHGl2H6qBswWX5oCBOAYZgArnAFPbxqX46qwIxzxhlnVJAhTcbh4/RbheCO9CqT/obV/Z6OdfMX3OvIo3obR8h7/2jqx7ob6ZBNT7KEL1nSYGmPrvV6uVO/8dWhiQ3dHXNXrhhav2aHV/zTP5309d+PDP6+GEJOFRauiYl0NKe4ORq+PGKpfXGy6uwDlxReUneKi1F1L3otv0HMI/u1ljUrV+28w47RZjXeqMAh32u4SLGcuC5Lz/zmN7/Z3asOoFiXN7idoQstK5kKs0jdBMR3R6ZyGpGJVZlmBjguIe8cGjMfpJVhwN+99EU+nr7+2mv/7d/+rb+n13GqrkbDjI8QHiFEUTvS9RoZlHEYT6lHTmVQpqzO61iCfQY1tmEUNeTCx+gUE7LC5SR0kkIQoA8yTzAjGB1lDyU7mSBLcLrXpCm/tgJ3VP+rZHn8cBDZtpXsq/yOCDNaVAmxQanyhIXDwQUcrQBxCIFk8Pi4qyI2v/r8RkAZknPpBIaGse4WdZUOmTEA6incXJMrsV59gOQZ0iI2q+dVY59ZPVtWQmu6IcozFukq/jcn4Wh4Ph2eWTJAK6MXr2qpZ3BlXphIPdMDyhmR6/i2rYwsExsu+qijzJWFMyx2J2MEBK1Jl+X87cJ6ccqv9U3w3N7VfX0PfPzj5204VN6HO9LQ0bchYsc6khdudKzx2HE0wSd98t3pIpY0OW4MNjrffNx/HXfs529YsjjJ6mx0ph9emAyWQMJ0imBj/xsEqVilEC/1cwYLaIlp65c1/ZBCcVkPn5umpV1pmU6spxJLLX3K/JPyov6LQqOpxtT11a9+tYWpL3zhC5bXfcaDJkpqBh1cp2lFVZdiJMDpWJhy6cj9739/N3FiJ99zUocZpDSjQisTDctTPqNyUX0IiaXFJlX1X07Wyq0OgbaRZcoYALhiQFejr6ZA5knWqENtgXYWePFLX2rcsm7t2hTJIzedcjva2w5XjHZ9vk4D82ht1gr88ltuaQwVffJtp1ed8h3PAoZDcdZK1vSceniLpZFN/a39Jt+6n3XWWYa7RsV+kNdqqi0Jn6jZWfvgBz9oj8PMTh/um7pY9yAEoy/wbbuGHHON8BFe9d559hQp6rrtklBDEla9LN2o7Y5eibVyVR5Dhqt6/etfH2Irzw984AMw/JSnHGGUEE0AFCCKyzCuE+X0lQUxuxuUgZQ7J7apXXYiFeH1a22B2gJbb4G/Df9ramKO0tHdGB1yS4udmuR/3Uc76GOMyVn81puilnDntACnE97Q0yKPiyQ4KUjWAPiYEz62Ah1+8PWRKZKPizhfHkpAaTUVxtGOmDThwsKp+VHQVpOK4je5tpwESmReSeMZHRgg7X//938dmUAZP7lAJi+PzDKgmeMDHvCAs4sD0KS5ftKVYeae2CM5wu3iOSctO5CEWJ7icBEjAHDuVOWIg8VsDmBzVqyEuOCXvexlLrp2F4+JquEHH/3Wt77VWrErMFF6ja/9I7n6WVugtsCWWeClL32pGeV6818HE4o9nWIK3LJ4tGXStxVXVmdi4tsnnPCaf/93x5+Wuge3nv9uKwvfaeRwRuGG5Nh55XykBFLgXDhWvok788obAsL5xhQy/NFXv/pV7LYdbVvYfQAffvjhzlL7zgcxITwgDzXdgRMEWAj0RAmQFtgz1PM07+YlIQU+0XkzDjo0dP7QAWiMloKPPPJI67dovOKKwPmaL/uFW3he1Qw6NjrJSeIKJOcrmzxvYOJJPoF2CaXF70fGHR3kdo8//ng5MsG3+uS4goTKjDW8/SyQVv2mC1GT2sYqoahPUVTKXlU2grS0Aa/4/+7v/k7tj0ub1AN1PdePskAVK1ZMHLmwyu9cPDkqhxrvaRU+ap4aA8BIuJDrRwbEqq8+4ItVGB/nfexjH3NuhrRyLUSPspIvikEiw2VbXb08+uijrfJrwOgF6XpKN2ser/GEJ0EGnRf0Ib4BpR//8qW7TVlZwyIqGEMUmALgyBSYBJRPetKTNHIWu9e97sV6eVsx5JclwOAq7GacnaT6kzSjdXSttojmN1McgDZ5dFnV8PCIa6ronrQtDEhbwc2UFOjr7rGGNdYYecxhj/H9gU7BQS5n0L//o+/qIwiOdNs+k8DCJp5G1W6J13/R6qijjtJBsAM8mghtJQQ7AiZy87dM6R8BOh0TAD2CvkYUAuyeionmAeAVYBAoNUVmXY8EO0bsb7NHgQZja9KUlHfEosBmQaquE2nmKiZI7K+Tyh9woESGWHJZlETj1cFEBE6oxA21Lu9UCtFrJ/1KLGWYtMgF/QHWB+UagUmRj03NZ2RTB8qeOcXtB9AhMpiTCLvBRxR8BsDTWTWiIjYyq9wADpVmFvkiyhzPcxLZkb4f6GqMyiyrOi23askadW9wdVJnrJEWWD/96fO++tXzhtYmjBXX9Du0I4PqZmf35P3uRVFO7cklujrMzgJdPakVxJcqKnPM/AFMqji8KsBodKmXafZdyq6w+VQauh0YxMYEOmHnrmCUo69BtC9w8Ba9Virxco2aktK8mh2GKDRCDBQm9Sl61GiSmQCNnoi2AFF0ExKy6GMxStQ9c3oko40lt9wEo2JmsU1lplp38JLPfViwkJ0dFixUPxNj0ekRqHuRlhD5QkyHcka2ISzRVmmhSQWfKZM+7biCXqwAnoGmIrny2jb1Ck1+zckFJpLOsdsVKGdQYXkVygp4Vb1n0AExGgSeKkb4tKK0N7KwKuGi0ksv/bMFXNXM9W1Y7HZpPVFtIgn9m07e54xosHBBxuG9zYqjYzziiLv7EKh34uzvfvdn0BXTFcpvpKzOu7kyNomP3IXOG5EWL5QJZADTkbUyzmwl9NmqMysQlNIVUHpGWhX2QJZjM5wlBBCUbZ9YFFklCjKnVZZZIbvjvUaJawVLVyyV8XwAGlw+AN3TtV7/rzt1j9N414C126997ZaLLnr3VZdBxNdHk6flwrZGo2zlqNAdz2K3YY5yzUw+JqY/xZBV1c21t1U9XILi461w2YE2jVKUPiw2nTFOTsW6qUBCVBXdkT6KozS3gil6v2ZXNb0Q7CLR4w1RphVGAnbuDY9XLF1Gf1GeFuv51lZJuAT4ABDLC3ojfLAAT6sYqwR70LeK2uaYMH4kB66kW3kN4lYd4CMq6CtcrfStGCwhoTWqFVOkNlVws2dsFTV7jFQquVOIMIHMcrxu0rOoeBWuzA7YVtkpJ1GGs/ysZ86a1LN/wSKo9p5lgrKqAWeBKFtjK5hMk1OvEOTXLDZjZgPMLD/HZlHlVMTm1zIlZH7NQNY/s2SZZSBiM1c56taBy8pvwxTlSJ9MuGCRR7UZcYrZHKc4AD3eOZLSKg5ATwymOa9TzykUvfUpXxxevPgVvzndb500elOFT/6XQAEJ/0sOoIlIfHXYAgtYcuFW3HWcnFdz7YVVdVwmj7wnR8afem0rPIpDKZs2KhGirOBbGoIxW4z6Px1vFtgs0zTpduDYVSVcpO/q4dUZgW65KWWuViBVraLbBMgXCeRYpjMZxx4VJnPFayQNaTXAyhuABFH0N2ywIOl8gCEBV246TwhNxAooPeM1WLLkGrjTWuDwxzxGVbnaXRKpvWza023SUBYSKzS5xlbwW/I6Mtro7THDcQbLZRyfOv6DSYhfTBqrzh+3RHjNc2e1QFRRvWIYQPdrRdHMRUcaB5HjDLFYi0K2zyz+6H55EOeu7KS4CsctEo466cN1v+6ViAPQueaTbDghZDmRkN4YIBUsMenTY4f3sclqQueCIUJg7LbEAWgslZNPen4HrTivkBlPLIIsEBv5SltIY2N2TMwxwzfRlqi4HdN8TY7sQ9tk8RWuT3H4RJ7I6RDXCtC8LLyGawvUFtgmFtgm/jc8LudtiTw7QoCuowjbQlPS0wBhrLN/7vplN7ojMPnfD/q+wuTHBigVtsHgYVsoWsv4G7YA78ZJ2Zo3eTGVs0Zq+dHTGNXkRWW2I8MrucTN+QG7+ccee+xxxx0nw44j+zlZPi7ICJnBCuSIRclpcoIAByqCPnylJVBHNV74whfaBqKAtDh31w855RxkvKoRQjhrTtMHhESF2CzHaxxopowkkAmQQqQCack3xgC4+HEbTwD0poE8vi9yDSTkl9eWImU4epukDmc7AeYSSsSkRYr1s7ZAbYHNtYDBfOx6pD0zQWvaygalVYcS26Ndkmn+Ozzi6BT/+4n/Pj4lVc9/w+D1c9YWaOs1Ask3WRQ1MYwtPJNQHjC+/CEefOKJJ/qsiMuzcekEEQy8awfNHF0DwUXynnGbzAzqRFpmvmikaOU25r+B51i9SlrbDCGcb2B4Rv4xHCtiM1zfFft1a81O4Ezxkmbani+UIV+IIURIk8EATOHNbeU0Xj15YdfjSsIhQyMKlM55skZMnGXWZP+EE06QR6sEcSIr89bAdrLAVPFUElADFJWnshcVtadCE68xzFKcihyLyqTwPI3bFKrYIMgSssAAosZE3fLpiUV/sBpg6V/FIicYPYWoiJ7grIxXQX0V0GP3ql4GQQYyPUyolDEAyhvjajYkI/Bq/BpDT9LK9JF0fgIQeGLHgtGrnRL5ApAsilnKadEQcqK4AChd81OMmDUVvAIdyBEf7EVs+gFKmEJIEuW6W7fQ9HT1dnc6DprwyBPHRGdfb99NNy7t7DBfXTi0dlFn9/XjacvM+bzOscJ/+hhpYnxszsCK9AnERGOsq9HV3bN05ZLBkbV77rPbsuU3r1q7bNStQp2TBuycvMO3SNyNgSmYmpJvr9eQeljuKKywDKPlgq0URNgkZ6HgqtaiHIuFqXUQsomybK6gibyHEWACiRIgxSgvdiMEkuWjiPMzUs/P4Iq0IKWuqzIPAbM/NaLnTck0ix6Q2QFeqRRagSUUMxBwVH6YMksZDjnBK2kA6+HCIi8hh26hQznRbQJXNPGaMRnIGqqhDfu2HX29Y11zOvq6XYQ7PjZa3CA+ebY5WSLdvts50bW6Z2xl18hA+o1omwmplqodotVQeXTJuIq3oWHDd/SGkZFUxBOpqoxO2Orr7O900KoxZ2zXtX3z546v6d2rtzF+QZIwnjYOh9f/cXh9Y17v3vd74F6/v+wytWtR13zm0srSqky6Q7pjvDN98dNZ3JKe7rVMYfJco9THO9J5dwB90t1bqTRjVjnZMAtM4nE29k4VmJclBSViWKD6MZS6Zx/Ilqcl7OGh9XMGeu12MgsDsqImjz5biekEGIMD0sjxalSB2G3oCosQM22xWCzHG2eICnZAhiXgz93MXR2dy1auIHDOvHlqoEUKxFolxWL7lrTg0uSlJVaPTyKkZgn258OINPEo2ukvzjjjogsv1Mo0c3USWW9XqiGSCM1JjptNQ6vIAlh7FFISXZ09/X2jE+ODI8PLVyw/6KCDZEpmRzQHlas4I64focr2qD5hbUqGep5SlAsh6VYKMKW3STCIMz5Y4tmWPihbhWcJmyRom2KFfZMKBH2QtfKGep4BVAi8wleQBe0UMgjayi9k5hqeSApiBT0lMtg9A/Az8o5MzJ3bt3r1huXLr33e856pdzrrrLOGRjfo/LpG0qcsu+04/9wzl7/uVe+2AnXE4+ad7SOfaxqOvnR27C62Y+JKBX32Gevvfbe+DWv36O/Zd3h4TU9v70SXAzHrO5K3TR2ZGqYmJDgyqMpDdkRfN5W7ps5TCstGykkR8GZ4iqIEiS3TzExc4ktgEE+qV4nb+DVSCVyu3hmZgSAIsRsL2KiUM30GELdVIwimi4ok2sZWUt+2r6GVZ1ltvm200a8u5urY9G5pDBbBb3qkP/dCNjp5Z0jXFzYjG0UFUXW70AzxjJ2NFV29ctdb9FUOk6LsTDsNfoehGIQ0dl7V3dO5x56NefP7Vu+kM+zvu+KiC9d0NQ42OL/+ht9y2D2NeSSM+i+F/8/efcBpWlWH43+nz87uznY6iCgIUgRFEBUES8CCii1i7LHFEv0lP2NM/CdqYvKLxphmSYzGWBA1KigqilRFUUTpbQGpy/Yyu9Pb/3ufM3Pn2fd9Z3aW3QVc3vvZfeY8955z7rnnlnPLee6blNxUmVj45XzLwMhE+0xxU6NYsrbFbLZobGX8RwI8oQfdebzSUvybW/zqk47Q2sKfXL3QS0pi5sJUMaaogjDpvSakiii+2/FRMrvp16Bc2cimW7TDlSqY4oZBqaFOEYxarKHAptOOtP0gjJgZSMp8mGlNwb+Wzvah8WRqmzvavv3Nb17zm98Qw6fVBx/0GAUUzBLFOOYvkwccU184yKE5rjAzMX8AM+Lkh6aY1AInT2BCwug+tTx3JAZnGWUOshAzQ0bErkoVUyYHB0I5PiPMEqjNJRNiXuZcJUygzVKA6Wgjd88ActYzAIFchVCXPMsfqZ5VVF4Dp1a82pha2plj6ooUOcYzi+c1yxZAOWnmXDLDumjBJ3MD1EXb8cgsf7Twqny3yT8Ey2Wvxc/8y0XIcE5FmLMOJuWkWrY7KyZLUs40JImYCftrO2WqBmJ1mSxsivTPXompWWF/GVnxW9nfsLOj4+2tTcNNrVYSvx3ot6ywHratM1rgj6alA7IDPNYOFJZ93n4ppvWg9Oy64aoL1lcqj9lz36M2rLtKXq0jaWhlmj3HK8ZzHSQsfjFMFb+qlH4PR2wSLe0RKZT3hF7s5/i1h8S5WKEXwMSvQAwXDCJm937mlb4St40X/+Z0OR3XESwzx4dH1BF9UYIlnsgIFBjfxtcqh9miZKaBnbJEZap8/WsD3dkztpa9SBgyCCquljxirFtti6gtGQ1s7Fnec8P6FSsT27SnMjbQ12cO0NI1ZzryiEeLBAwQ/MSKEwWbqyKZS/GpaEVQKPLACfyIlMrIioxXztC2pxw8MN8u52PiY4bgGXyiHeIDv8wnyBvPR6AGHnPwwU988rF++yt5P6cxp2TBC7AY77ZDMeH9XG7VuX1uB5fpUEO6QsjlV1/7mIMOevGrXnnOWWdPh96Ib2hguzSQ26prcThUOQfln+RA18/KGYE/8YlPOPg0Phtpjc+8r/bZZx+/22MT1aESsyLSxulFF12EyvDLvpRzZ49qTZKeYkC2grNKcpQG3y9qukbazc2sW3zfwtsJWvz6mZEcvnWimCytjGL3tZxdwAwczmCmDTdyCl6VjlOXS7yw9WOwyuV6FwbCObcTFj91wmpw9T7ssMPsMFsA1nIux5SFKcc34IYGGhqYQQM73f7W5GXWbAb9AK49SKuQrbh5SxGj1jP2sW/41aWP2m+vl7zy97919hcqTR1pLyktsWK1shVd46WhgdlogDkTYnly2223+TU8P0fA+rgvj4cTG+eXFnyKI8YvLXALhsw0f/aznw3mPLFEMluzyStwcGB5LQAZ99iuFH/mmWeK8Vthf/3Xf80iM7guO7v00kstqZhFP9/NA5uVR0iqvEKczv7mjACKlkpYmmODL7nkkuOPP/473/kOU8vi+6FLv8JnnvCFL3zB5Zp/U/zItjs1/d41Dn4v3dPPnrtIzsWFnMAaljc03Hg2NPAANJDs77Fp/Ts0OlKsf9NezAQfQKx+JyMi3rZPa2y+bR0/QVXs5+iVXsfSYKbLB5eJ9Af+J2dXDHE3XnmV9e9LXv2qb335LLs5D5xtg7KhgZIGoumytn566JRTTmFtrYIZGlcp+9D3Gc94hluQ3UAaH8S6QNZ+o1sbeNq8//3v9xt6TPZJJ53kh4+yZSzx3grMdtBK2XUVTGrYbuQcqf/lX/4FttWrpCBz160rXyFbILOP7GbEky2ziph48vyJsngNBPxzTGyoelq2WxdjyFuax7O1sCur/c45tg40XbeKisO361ad1Xr66Iil9rNOtou3a7JRlq0Bb68GJvbB65Kp3ajgSC3DZXxjsWatyrVpsyu/t6gR22fxjF2VOKguk2RYuxFwsMWjBbgD3E9uycj9zdq604Lc3AMtCOXlNWCAVyQml35h2VxWs8vXP2OrMWXJZVSVdbxCU4SzzjqLYz5ks2G/XACZ/JgHlWfwyVlntgDN3c+a6NJuIKYHRw4IhRAbQlABRMo0id3S4jBjaCT9gKnTka9//ety188vvvhihjMyzdIGSfAJVvh4JTk7SEv2m77yla+c8NTH62A+5qOe5qbkZBDIQa4EXlEJfhElVMgc6/y/ueZq3oo+WjL/pj3eBiFnFiAIg1uy4IgLw0lOAb7im0+TxHUCTkp8xQiWUZB4QsMtxwRn8WIkGZj8yidJDHxOa8TbjLNBJgamggRalicAQsLkaiZT45dM/RIcxzKlkHseR2gpZxeAmoUgX2wxoXOXE6g+dcdrREzIiXmGM4cgwdPaxlimeVtWOejyqYoNSouZKClaAVWwCvIyDA1zYvzmN7+xUDE6G1t9/gIHMm/ymRc/wXBHnsSLvDAphN16uhMTo8kzsygLzExSm3UUPCFUcUpRqZb9VSn0ptTpm2+dqykdB9IT/q3FjYP7LN2HW8web3yj1GVjHWqqqf1XjMTmdfPZkuNOPBF+20CzJhc2MtoxzlnCKsEiPlSNm/oSo2KTDI/4MDSQNtMtyM0JDEe6ErV47Rsc9sMQKks3pDo4NCyAox4LHU49IPh0yR634dfcQu/Ax8aBgVF1W9tz+oRjT39mtUtForrlYvhAIg+GQK2ZTBx5+OHGBLnEsACnuWXiWjVokAUSgiURnhghMElUvXOFxUsWEm/d6jV25KFJHezvn79gQVqoTAa0MT6IIAyZzaLSWF3s+FMC8RTWx1tCkiHcsIo2HJ1mktMu+UuAbfJVtEDLaskk4iO1NinjzAYIPjNjZjHKaFmAcuQDhmdfilBIzijLVsUhx8MMUcWo5UCbLHV1FTzqUXtoY+zUox+9nwZmg8lVFnrQ8jtumWyYE24HBnbXDHzwI+/43vcqW/or7T5AKjqLLAaHxi+7+O7LLn6/rB+1/5Fr1qQrBELgkCpy1zjDtFWVaFK2RBH4QRuvYqIv5MhtAmX+ZYZleJtMpkOIjpOzwHM6zFnGZ1aBX/U6SybQSEK22eM/YMwZ1DhD0vZmlxWbgTIHWor4nKrshjsLNsCy4f3YhY45NxnDx4Yem9TSdKy2Nz7YhtByLrFKd99PzAbT2+TIU86lFs75RtIsqWr57E4xuqfiUIUBgaqTVoshyHa8eaYvDI0nYrI3M7g2BLmZsOHIvBRPwYghGJ2wjaGjljBiZA0NCT5h8kzmAcx6yJOfdfuXXAIBCVMOBzDSVDGVBYfkIU9YZ+1KvkGSRYrZvkgBMplNjK3v2GJUJoem96YojC+dxOBZV5jMcGcBVbkQryqmnFEhfh0EJEHoWcbPsCLDiTAdDmQIM6QGt8gocw4gCD2r4h/Ya5UMWararHOOkoQ0mBSlKOebyXNSpiqj7VJYjlEF0+USQtYKVo5RwPLrdKxmji9zKDMswzNzeGCp5Xy3ySEjAwhWhZ9Ty/EzyA8/unwZf9fBIXOt5DNI+ICFwTOCwdk4NsyxtvgKfqq8xbeUUo2iR7z61Z5LRw6R3YIlq4yfTYMHwW9tPgyT7jRkNvm53iRM+vazEXaCBiwqmVpVQ88sS3BMii4slGpSIyLTMrBY60mBmdFSHTU1xfK2IKo42wawgAyWORVM6+tU6dPvPEiSS0vxAw4LulM7QcL/2MUGTJ4tcnbw7vvujUyrnpFplicA2ZGcrWRq7XFhKN7mCeTAjxKAc1nCpEILBKoAmE5YenN3A1v/EsMATlSvobHIrvF85GpAR5lsTM607KKP9A/Yht5ZCtHSBNxyQ83AzsmC8Pi3VM7/7nff/u53H/i4Q+685da6nImxk7Oum00jcjfSQLQZO9icg3k+aT88sRxbGEIN75Y8rl4zkL7vfe8zxnJLcgJlHHYWw6XYT3qyC45I4iuaslaM7X6vUrBAK8cbnx27PutZz3Kto9+7tyXFrYoNspXqbO4lL3nJO9/5TgaFg7JddFcyu/eRDFyiMXQuW2ZVFyazeKKi5dplgWZTy5YXmZ/+9Kc7sXaeReY3v/nNNl3f8Y532LOVHVt28skny8XZtiFiYgOhbgaNyIYGGhrYLg3sVPsbXxzJ38ldmHCbU7Eraspr+Ephu8TbFvL4cG/b3CXDvUPf/+533/nudx908NF3LL9hW0SN9IYGtkMDrO2nP/1p9tcPVlvHMa+IncjYWgQwUg7xn/a0p7GejOa///u/i7Ss8yHr7Bt7GHpP7hDMLqdqjg3cqqzrYyOXEWQlyeDzJzg2dbm+/PM//7PfP7dU/OhHP8pSb0eRSvNhmUZgeTmE2Lr3aw+yc7WkqYUvkaxwTQD+7u/+Thk56phRRF7m6qYKfhSC6wV4u3JvIDc00NBA0oDjmzg3bGqK9a+bLF1ysNOUU/Ttglu14c2W/4HnxcZPfs303XPP/eP3vOegww6946ab6zIkyOzHw7ocGpGPKA2EVWJkbSFygLYkZGtsSHL4tJDk2GOD1DajZS/7y1PIQtVnQjxIXUaOirFmuK1P81dJM2sv2ienasFW55//+Z9/9atfZQ25UyO0UCWATH3w49tj/n6+3eUDzUBbfS9fvhwm2bRwa+G8yYmnCQPrWb7AMcSQlOWBz7hb5Fq2mzkYB7wyvvxCPdFCFhn+SwpramGxzBZbkltEW49Dy9wawK7WQNrQrxtUv7pUGeXaLWNCiCStRHxshfuOTZs2/dKAxMCJmaVWLjIwRcJHG0B+als+Pvv+978vVUMxFwyHs3KmYHw8scJBFmByRrwJJTd/sBBsA85F0BbF59eAlREfwQ4UR1jcSKtD2txBHswBmSrHIAeTB0AefnK6Fsc45OS3IRUH+ZkJnCCZ0INL80ZHYknZ2t5+74oVf/SOd4TAngm56FdptVlkn5KSEOnNjT7Kn7Zrm5MDqRiV8IEP/EWlKf0SCqm8NrduGR3vrTQXV0cXOm8u7uEjcmI11oLc77IqocPouXM6Pvtfn/qvz326uNc5qSjxSXhJlUW2OnkSSHy6gzCkcGNcc5v76kyy7eUJsoas0qPlBJ9EPsEpkRUcUlEiPp628HyGmHWrpmKdoA1AoI3gXOYTsFTeIYYtc/1AU3eQ5aIJAQSYkUuQeIoUE2OTwchXF7YF4asyIZBTFRTBa8icc1Tpgvhw0TZS8//2hUcmjCw8IyYATOI1RMLfa2TH+V7j9ypELkbDAHbuMxdERlkz4JDNU6AvT7VMSXJvc+HjyKibCD3RrO5ILrOL0sAgNbmnu1sSpnryS3juo3JlYbScdAu5u5ndZphugday0psrePlSJX9o+ncpdDohaRrSXLmitq/Cc81Q5+CmwZWtW1T9tQOpg7d2LW3q9zH4UNO+e9/bNN7a2T4yOnrQk4+546ILE3VfbxKDfHxY83iWOkCLUakp3d80VgjSsuf+rStX9ow2FXdfup5QR4DGYsFMV6qnynJXtBzz7VDg3Tt0dnXQvK8maNsUhKeyV4edSl2GzQnEGNlCG0XzSGAGtCVjvpNRHR8rTCBLBespOpq5hcj3vOc9QTJa3NU9ln5hNLUxgKHdP31QXn/xF39hl/wFL3qhStE9kQCOPuqot7397b4Y+/uPfMToOnfOHK1WTaU2JZkwhIgVSHorhtBi3EMuuENrw9p1PRs2XrHyCqN9GqOGtcqiuxWXpRXtuaDU0v2OwdAgYQy0++6z/wh3/UrLqtXrxDzlhKcry+BQv89UDNqpCRUtJy10NOvirrUJLjv1TxSNPsOkeqVP5Yrcy1mZz1FaFEdJy0lluFzecrzh1KAU1VcXh/KlqtaQoUybYbKFwJDJE3J61mWYqTKAChyXyuTIMoC/VxtVRvtgi3POJTBnyAtnWRQIE006M6dRA7Nvi3Bzl70L10w08J7kKToNb5m5dySrV5qN7Ltk7/vmzL93U9+996/94eFP/MkLX7780/9wgCOijRtvv375HV1z+9742uOWLly25v6Rv/rT7z3vGb/61Kf+86qr7mvrvGmcT0WT++HSQPXWt/7hbXf/96GHjj76wJM++MH/XbuGh3RldMjlr2nMkjOrTZGVseIXHYr2awwuRCq6QSGouVsIHE8ihsDaA4D2mNfp2oZezK6p3zIHMFV76uZV8Q/g1WKD0SRGbS4zczOSaP/apxqslT8kjDEHn2gkmaHtSANdqq2aEMrxlIotDjKqwdrlEfKNIsipKf3cvU2JtrHxuWPNm9O/ptTw/HJCegZc/BYHe8zHdLSJeR6JssU9lAmtCG7Fdx++T9F9AUcnI8m+TbSHkaZkwYsLLlnweaMG4dbBffZactd9rGrlzpb1lSEuPwvBbo6mn6EWP+XQ2jWnix/qUGuSsDKU9o59pMYYaB0FXDzMMDWh5rbm7u75nekXAJAbE1qK7gPXa8xmqVp8eisZlCk+jxjIQOFyDk3U7gADFH2WWlzc5WcTf/jDH1quUwbrnNQ49ZswSUEx2AKCyrfUKlpHdv5tqY+JYGBnF/SC3MYS5dYhuoxnZKFBGieTHSxVjampw2wM7VOo1syg2c83FCbJ0km/zh0tuDHT3XPnWSG6mIQTgC0JaMaZTI5hwEaYJG7RGYMhbUR58ZS16YFS+FZWqnijQYiXc8w8dxYQ6o0BR47kidmOhZ5SsIYiy3kR0mwq6iJkK6cGrIBVkYoTs50oUVVqflUjDzf7Wy6RUlDUzAO7skeF5nabS6fs2qdBPtpGNAOpoS7Dfig8ZqSZqhYIE1+OL8fkqikjgO1C6IBk01mYyKrU8msWrBwJzttnVQghP+VEkacTAIcdsb9y1zZCjACqxItXPdf2XN2kmSNpRhaGhVr7q8ZjoChzqOqSuW1UxSOhH4Fy4GAlo5mbUDmXBwDLK6qgLIm2p+FFBSVXOGG8a2x8wUhzr38Tl0D77QShiV00W0uzLBtbBrvh5jH/4le2irVuwkohuQVap/jXPIxpU6WXVWxpH7ZkTQ7Q7KZckhWuVHp9tb6pbYghuO/qbpb39uZ7U/Qq/7ubW4qbgJvSN/ktraNqcKC720KlsoUxx6KPBivNA+npTbAsEk9o09k0MRx/3umnv/3tb/f5sI1mTgai5qRpQGX+SFL+oN8Uo//EylzC4xERFNpGmKIqOyXdcdttGrau4Vsd7UO8PZr/7wMfsCn/tbO+GmfDEMSXm01oKsxZxGu6xkBmAltPVcYcxMItkKd7BrklB8O2ob83GT4SdqYdLRx6N2/GsL3S1DpWaR1P/6Zm/AVHvQZJZh7cPN0BRh5uZyTxaoBNde3HsDhzR2spNBCEBl72y48/eA0OASggKlnQAKCclOGcdQN4pGmgqa11fCjNxF585u/fuXLF3bffkTRQdKIJVUy2tBRdRBXrmYnENJrWC+GJ5Xds0shWND8/Omizb8wOkkVQiWdQx9AV0ds3jNmLCmlHRy/47vde8Lzn/cc999gHSTs8kiLYbzSsggmThvbq0OgI1RppvBcayA3j1iJkrYh3QBuvjp+EnMSIXHbZZfk1gPLAK8YY7gS3CscrNFcICTnJ3MxCMl55V+d4gOshhXLMLGGWwu8ECmX8V77ylWyEGOdB5Xg2iPW5pAgRH2hlnAbc0EBDA9utgTTnH99B+zthZycNapVljRVNZ2s6c988wDvE9I8tdISWjKH/YQrTyzbCpCWtg9Y63LspRY/2fvOb33jxC1/4H3fdNua3X9Mh3SR2YX8nXxr2N2uiAcxKAw6MOBy76y1jM0k8j5lLMZ4cgsMnOCOwlZGaLXhOqgsEmr2jc4qQcT7zmc9k2Bbuf//3f+dXgIvbhHKMTGeTYxkHnF9t/HJIyK84p99jKc5EfKfkkrhyXq5vE8oxZcJyfANuaKChgboa8Otu44Xny/NeesZdq+6/8+67OrrnD5YOODJVtpUBxNlyWuAW5lNkedEcK83CTSahOCAY7R90/DtenAC2xg/C8E7gw5J2GVPIGW0HUCYaGPr2V7/2qte8+h/+9iOJA4GK4XGCW2P9ux1qbaBOtprijlpGzR67/eeyXgrPwW8yxO4/tmKNBhyb8DB9sCQp8COyTFsXDg78OYWMwO47QrXkdFGskOOJxHFUyDHOTcoWv7yh6rwvo2Wg3OMQEtIFuEJGALzmNa8xc4YJwa9ACDnVGr92mZ9TG8Au1cC03lFyVZHlqp1OjqhUqarWqWFsjmtn4oVwlwkYw2jK8YrEazQ1T9s9nnZJABDAEbCq2+7hwLStE6ngkFB8ALnb5ExDyEiNJ1q5gNkVDd20FbmNftxQRWowDGEyPhLxQjqiKCQEEIYrrUm2To48HweiDeTIK7IWE0CwyvAsgSh10JZZiZGLESMEnspja74TqYUMwQoTkXmlmRAmSCb/FkXemk16S7kXfEKM4IM6XqfYFJTyiuoox2fCzFx1ZASpuXIzQgYwlKqZGV9QiReDVgi2sgMHfsQEjmfON50DFf6dgRk5ekYI2niKAWQ0meIvMjhI8kqenGOmDamCPCIDxxOTgOEEc2jRAjP5TgFS7oUmQhgyy8WxXBRBakLIwUFaMjyFy9Q0H0KUiwkOcoCATTqvSCHph8uVXOQrqKb0p1Jcr150dNspUnkfJ+zi5wM8BT3Ic7wltRkDCp4ApBg6w3v26afra009m5LCx5Pr/0g4YI23UWkH1/zW1ltuus6tG86JucPfe/PmSpf2UQwUuGknhinE44Vv3+yGu6ye3QMwUtFbjKJ82iiDerk+G9PKcPQvAyP9C7Vlx4RfVKrVyVEdh2ALkHrjjTciVCOCTAOzzEd2XsO7wqfYkaQ9oA1kuwaY+NTBKycVPpRIWri4F/0x4Ux2H3A0pKmkgh18jlN27YnKVCkUzO4Fiw3d8CPHIGHFnAGbCQk6CEyNNgoO9sX2wGDyv9QIY5CXRE6hzCcz3FmAXOoeoss3sojc6ZDOBaULrdYVIFOVaUWa5CVlTh/oBIKuV1eYoKNAqbhBFlOlFvFVMVW5Ia+KqXpViWGmoxTxjIzAwsz8+TkFWi1mxBsUaNsQyPdPYQu0JAK2BQJgQiIxYAMYl5Szz/6AtvH5//6MKvDjKz5A/PBfXrzpyk0LF4zyHT7ppMPe+ta33nnXzbbYjj6q0/d/GzduWbPmX9asSdaH9/MRRxz6vvf/f6eccsrFl7cdffTRG9YPzJ//v7+9PWU0pyO5NfhkRGbqNMkTwhTTEKYsIRUWTbMEaNJFxNQDSbQHSanjFGdUU8kliKdFKtPkeJ5T1DgOCLHKkQ8M4DdJqVjN0D7rctac5G4A4QWYbS5M0iqXpqv4Ks6zVkipAwO+akiThKoQyJ74oMU/2m0V2s59rZVQs5T1zs0luEUb9lS6aD8txVcfvLIgaMAhDNUFzmxkOPY5z4E2zxGFTxH4YSUWU912eCB9rHjvXfeq65hX021MFOHLRV6xlxItqlYbs5Fhd8LRnq3/mbYwTEldRY+2lw02JPqRE0oTQoHlsoc+c6+HY/hFYpCMJO1K/0Uilzq+j1s3AGjqLrhFdpFXiMSjGiu2L460sxhsKxL8FUG+4tVpNDawJOTMN0LOVTiwwgcddFAmzwA+MKM9BBD5eqJF6LTeAKs7azns8i7qMlkeQFL65DqCGFRKSPE6rOZtLlEei8TTTMhcZlIF1zZ4bKmOemEir0UIDg9D+xsCh3iqXos1PVOciKl6Kpcg1bOspXg1sAOirQLKnMEUjlDtV/GsesU2t8CcVKZSZdpPTgpAdiouBDPBqErd5mtUmU6nWdYiR1mULvJVieBaNDEz2F9MtMNgVZc29CaJGGXdViHXGQGqMKZ5DfurL1fZX+h6OqUpu06aq74sA/3L14QZkBFyPrlQLBT+Mqqrxoy/I0BIlZWZs9ZmlGJHOE9Hm7MIhNTGiuMOtSlmUkupv9RqZjqeT3jGMyiqbUta+bZUUnX70a9JVsk7VrBi2rRx4/jwoKXupZdeqoBjY+18kGST8vIdfPEjOeDC2iSSR2xgR7Rqc5WolDBY2oMpvWf+aUKrY7pK2i4N0WGDopbFq0QhIwQrfRNA4WxWIp7UOKoAJQkIoXmmSw4kpN21NFpKqqqayC5HeiWbLoYKfjlenwr75Zk4F8E0w4dVeZsmc2Pa/OxD4EDeuokmrgorCzqBA0HIeSWBG+GRpoHUSovhprV5z/33t3Nyztlnp53GHV0m1tEj41Kea2lvud3Wwd6uqNKqdvkttxxy2KG/93u/971zz03ez65q0Psg7BrrsF1iNpB3Jw1sb+vdXvysq3KvyZGzB/LAPhsBpsOpiq96nb0wDcyGBhoaqKOBXWl/zXn73QTj39Yh7PzOM/VNbXMX3X/XcpfRvuAFL/jOt76erm5o2N+tdd54e2AaqJ0uhg3KligDmb8YIZu/HD8zgGQGhJlTg3A2ODNk0UhqaKChgQdJA8X6N40tbS17H3CAG+W/8KnPVDraB3s2J8u1U4OdnMHSNUP57KNYmE7tw+xgnvfcdZcvIs587Wu+etZZjTXvDiqzQZ5tWQbKOsmRAeTXwKnaYCwTPgC4vFc5Mzkxttfoz8zQpu7MCI3UB18DMzlAhzRVjaBus4hIT3vitukBVe2siklOzXvrAcgxyHGIUKWRjJY5OFTAXICfBc6vIsE5PoCMmeODG+HtrooMqogM8sywDBAmMIOhVyQCnDhvAOOW0XJ2YuLwIGKq5JkhUtKEiXMXZCrvlCvieCWOvjoKZ2QX/U0Wu7QPHbS+oRVs5UpJDig+4y20pPa8EVkVKEGIUTzDhBfPuH0w0prCazDUO3XwFolRqNBeLmAoxJMGJnhMVlxgRmQ5KSsck+BTxgwYtwDKe3yRVxk5Mw8+5VwgC5mPJK/5mfFxA2eegR9lcaYlSSizBUeMZ6bKMYHpKSkCOPIt42e0nQJE7rIjtlbqNfqsSpe1eLnQZkIDulO54jDbxeJj45tHOhYOu4RysZulKpXFCsQ3OrWzpsHKSIebrZpH/etvGRwZt0eS2mfop6O4g7xpdKRZs3L388hYZ0uza5nHm9MtmwNjqaW1NI35ynsUu+bmnnmdHfPnbNiyx1C6iDPZjJaWuUk/Y0X/qiR/yuFK78LOOeNHHOl7olaHu+5bKu7i4oguNbJexl2wpeXmNasqGzbNWVTpT9e5NTW377Ng4dwNa9ZUBraQsX2RT/RWd++3B5KRgUH5tUx0MGweEWHULcjj4+6cCyce3jzaA1dOqijDToUjXgXRKhLaidaSqqZoMJjwUQZrV9oSRwedgrcHv2oOMW7PAvN/4piFVWrnxSlyaBnbdOSrZgcHb7vttq8WX2h1trSON6cbrRwIQ7v++usx5zsld+41xEOB0LOtGMvckMulzjWUGmC61qtozP54dbPZkB+drlR4w4dbGPF4GmEuYBhFyMIo46oV99MD5NGhYR4bXW3tTaNjba1N9/72zu9+69v9A5w9eue2d8zr6IwiBx8CpBCDYrDbGU/FzGzAlj2OrmmA+XOMnZMCUJw8EqqLqlSvENxwplLAuTZFCoGsRH4u2TWi3FDqkjvjVN1aRW2qGHwg0DBXHnDoNpiH8KrPUX36MuEBBayEaGAA/DMbr2BNjg/9dA5GZFCu8N6eLPEEgxCVB8CqVevnzm3TUJNZTHeTCxKNNLyvOjXs1as3z5s39WnWeOv9lbb7Ky1rWtrXD/cffM1Vwzdef9cPfnDxsUc+XjGHB7YsXdT+zW/ftGjel/zy6e23D7S1nf2MZxzW0zO0/M6hRZ2VnoHKc3/vUL+D1ty05B/+5m97+n/6Z+/+zFOesmzZggnBRgfTpyBNzZx3/aZ2ur9teHAL8fwtbExSgqAvqHF16uJqDTI3A1ySyorSEl57LidN5DH5R0ZUV/YAC0LpaOlN1ftN7Un06r/hGuiC5+qEyfd99933nnvuWbCgWwNWiZPRyYPKKx8dz+nIOcCpuxhYyrSKrEQk17RWr15rChMtn04yf+OPTA10es10TVfp1q3buHBhuis3PNUy+U4HlEUd6b8KTlo65xJKfpHqcWS4mNU0zRlsmjtcWeJf3MLSXPzqQmGEtYVk79z37Abokab24l/1aJDEdj80AzfW7NbJttaOCs7JhhYphXrit0GGwE0cmectm7vHqrZNRXr5kaYHwy1DGkNHW8t4e3f38U+XnPJOwexxfLA08C0ZSRzWXn7Z+uXLK+tuWPSYQxXKwNUxnj4PGxxIboJzmpO5LXrZlKdsYvYICGEp4h7HeG5YvearX/ySNgDQxbRcPb5lbPzz//Gf4fjbu3FT26Nb9ly6jHrUQrlt0y19eopPA0UxwfPMaBoVPcPByg4dDhkHIC+YIrXDIIEfQJGaqjgQALpGtNWRodH2zinDFC158dKlbR0d2lsiKSa05n1saAjgN57k7mtswrARyQYV0wC5ZP6cgBUh5x5Fi1QW2S/t+I1mMw2eXqQ1UEDIQsp0VwRZCDhToCmB4scARU41ZUQC5HxDcgaUozZd5fgMQKi1v1KVIhx8cTNA0U+tcYeGXNLD0/4Sj+T0o6amsxGhw9CbIme1gBXcqEvVgBjAIUfVR8ElidF4pis+ZNZNG8sf0WX+AFmg9Qtod9xxh6E+WnvwlyRgrspMTliocp1mJpDVHT9p5gZ+jgfAD8nx5y4Ze9NZ+Mhd6Rg41acZl2nLMD5wytYnMyHwdPY3hIFJ82DDCGHKbDNMPK23dgIAH391N7P9ZT2hES/b38g6ys5+6b+SytoL+XUTzOWrdBSY5SkDRifkbLQsZFRO2olwyKMJAXTnmOrQG6VpADQsnqtqKldT12DT/ML+9k3c61z8ZkKM3uk3GdK6t5VJm7S/9cRsqrSNVtqseJvNzdo8q5HkVLQl+RrBxyrzV7fPr8Jx2zQc975QbIsFVuvCJcedrDGP9KdPWF30LHWkqZC5oJwzmmYCiwYWb7rjjso9t87b59GbVtz21f+5oNLcVxnaVGnvlDpoudua1u3gNP6yKfWbTMFx931YLarQDevXb1q/Qfdf1L2A16Opfgw+Pzz3u79YuNCIoXtbafasWx+VVdYH66YVFSpMdtlrjAZJpcW0HKCyYnjRC6SKz30EQICO1rZ2l3ur09E0rzPQq06EQL7PTSZO6TcW1PREzpKiHU68VyoasN95jBw9Cx6JPPEvVu46KRkiRl8zTIFhRoi89E1DHAsLWUz0awhBBSAh46v7iEnSFlOFoBXTCI8sDRStTJG1b73mmc961kVxi21qd8UUsPhb9xEodZNyZHwkGZNETdfswvgssPJmR1o1g6IR7vTm95NLLnVtzyFHHHHr9dcrV+vcLh/lTHQ9faHolWmC2wgNDeyABoyuO0A9LelO7w7bxXCWyNtE2ybCtOVvJDQ08AjRwM6yv4nPtPY6/ZSMJXnxIzh+MbWzcy4TzPK2t6VfBt95Y1gaDIf7tlRaOy+44Ieu5m3Y36JWGo+do4Fag1IbUzenWaJl2pnN+sypmcmOAASOJVsVkwch66ocG68NDezOGgj729piw8jXQaeedtqP497ZwaFKV2fFM4cHtlqcXGnam27v6PAjYF1zF87r7vZvYGhofCxd4mY92tHR5uLNnNWOAxf/6II3veUtRzzhCddfe21leKTS4XfgkxtGCo31b+ih8Zy1BtgjoS76dPF1kXcwcpZ5BdoskcsibZNkmwhlbg14V2sgHTPXBrnG5MlTKCNsUyDDMfzgEOTxtHdpszLIA/YanEWiEunQKwI4QpCAAfEMeYJPcA64/MxsRVYJH7JlhOBm/7RYxaXVV5m/1yhOLRU0IuUQhF4dKkRwjiKQOTA9QxjPHQ+4BcOytjPbSMqvdQE4WTNlfEWoix8agBnIGajSTKbNzMtATgVkQkXIpaiLEBwiKWNmAQBk1myousy2zCrgyBEcRagVLJhrDBEcYAeO+HJ2tZzFwPHM+Jm5eCG/AuqS58htZpQxtxcoyyAXh9CHHnro4Ycf7twX7FD8gAMO2G+//fbee28ebE4X0iynmOiQHz5tRJOOHlqGQ2b6F6IiPAPBM+B4OhfPMQD4niKFQBAja3srAGw95R7VEQ404KxPv+jKx4IPjbM6J3kO9Z2dO73jV+cw0gm9E/QTTjjhac961pOe9KTnv+hFp7zozKc97WlHHnnkKc95zjOe//xnvPCFT3nKU445+WRF5ndCPzgH8+3V7e80PgXSG59RGqB8zgdxolkX5rrxm9/8xmipvugqBkCwk1ceP1wcqEKt0adIqeoXrC5cRaaO8OS5S+Hq0bG7Kg7VAXADP+EJT3ArJFoI0Vkk4YOJWr7hhhv8gCSXBUz4lYpEItXTqNva1smthNOtKpavmDgni6fzWkXjGYO/810kHDF/+9vf9vUmn+9yvctFs0mXY1UqOojjaggKpQtESZWFb5NmJgvx0AgpAHZRIF4Emlde8O///u+TLbuyVQngJ5v/4R/+QZ1OJ8/Tn/70N73pTTpIWeyAqYui3vzmNxsT6pIrsvuJ3/KWt5RpqzDf+973HnbYYfQmSIJJbAFAjY95zGNe97rXVZGUX19dhBn4f+ITnzBMETVw4hlDHD7Pfe5z8899ltkGTP6/+Zu/IV4coFblYpUn6S/+4r2aEBcpJBACB3/382pIhD/jjNMGBtLPRARP4/rZZ/+PdvXhD39JAS+66KJ/+OgPXSL5//1//597pPBBq31/6UuXXLt84HWve/ZTnnLQuT+66ZIrbtd6NgxU+ED/+tc3f/7zn3/DG97w5x/6FD4cjD/5mTVnnHHiH5z5mD2XpeZNHnmp+he96EWnn346ILLeWrxh4mGikRMjkkJ4yOqCwOru9a9/fZY8mJSfql5fzrSZXI69vQP6BQHK+FWwQVWoiiy/vuQlL4mxQqFyLhCGh0f1Nb//ftxxx5XxyzCxNbwQL+JDPPH6pk8aWLE3v/kPCUldYoJ/YPLNopNnPvOZsijzLMOGpj/7sz+FmdVbTt25MPu7//77U4LxVkvmuUJmnc6wBojRb+fmWMstukwubAZqMWtjDMjbDMccc8yBhx/+1BecwfIee+yxxlVt4/jjj3/GM55hCPIqaO1qSq/MLa02r0dCDLuWfrT4mmu0UrDB2WgvMDS+ReH5YcTTeX/2s59p7UK5bZsL6RTakjGtGHRTT6dVDSl3ELMsTYsFD6uROSRek+MY5LBoZBBi0hVwtA0IBDO8uKGnvXPCuzck2bzJffZr1KabqqO+Ij5gWZAHE2Uh1SGHHHLwwQebS6Tv4iZDYPoC6olPfKJMlSUHetBWcRATMxY9RQ/SYQn5oDUPMlCgGnnnO99pFA2dhMayDCR81KMe5bJ/w0iOrAJq7S/OuHmqPjXlZzqp0atQRSu7h639DVFf8YpX6OBVYudXjfDv//7vNSENKSIn6z99tQJm3d7//verVnA28TC9Uo5G/p73vEfjyQyrAIMqC1g7nhTNPH177GapF7/4xXJnbvAM8lC1VL3jzDPPxKSKbbzCV3dco8x1My1AwB9DdXfGGWdwoc74keopBn8WCkK81s2C/WW/MlXGJKGJtCTC1xKG/AplRDXGGkBqcSJG8RlK/SvmzyIjL7JZCW7T/sb0IFp+0MZT8XUNLR8C7enjUQUhP/GQgKmOpQtJap+Ub+Zm/gm/NnXnxpj/G1FVmVJTiBFJEQwpYX9zAXdupjuRm1Fom+Goo4467PDDn/miV5x00kmnv+pNz3vpS099xSue+9q3PvfMM5/z8pefYlH8/Oersrzo24ni/c6x0mgNufZDtAqzaM3AUKN5UHK4+7Owep+ln2EqxvzcNy0kTSwZKYN/bcE1ZqOZAAEhtlaptWiumjZ66DjaYbR/T1lEPxJZRVLVR3yq6QNjnYvMVZheIevg8dS2wUZgHxUXe6t4p+lEUPk86YgjjiBtGN+w/l6RQCAMmB5og8YenK5aW5xGzMNOA6NjL/j9l5uI3n3TLRMXdbRWt9gHLLN2q2todVqmgZqR8hT0Vv1U082t9wFnUUXYs3Gjn0w99dRTfRIkaYQ9ZZL0kcluUoXfeG1ooKGB0ECVYcpqqYqves1oDaChgYYGtlsDu9L+EsYU8YD9Dzj88YdbIrG/noLtFzPAmpnpdsu+FYFfcjVfHe7/6le/2rC/W2mm8bIrNbCb2aPdrDi7suYbvBsa2DENFL+N8LLXvca9G/fesjzxap3aUdkx1gX1+Lh9ITt1xz3tqbHytQEO4Opgt8fC1C7NjudiEZ12goqA23nnnXfiiSdOLHhj5dtY/+64lhscHk4aaFjJh1NtPNiy1FxFU08ATUSIAyFjI7gWq3b/EVqOjIEVVTnSq3ghmHvGoUKgebXhLlM5BlrOVEyGoWW4DCDJryF5jgnOwRaOVyGQ7ewDQoxyeSGUM82cgzxeSSvkIpRxqmC/fcr3T5bYukpiStAS3laRE2VJxw9jlXR/nluH0I6OpAspORkWsiXJyzc3h3tUeOdNMk42csJtqtJS/LRm0mRokwsiOAlULVI6tmxpSpwqbhqCV9wOHEqLZ2gxIRR1WgYmEVJdkzMqNBBCw7UwzIj0zOSAJEbpPrCcKknIJGW2ObIMBHKZJKeqwYAxgVCXVRWhNlNuG0iEchGCIc5lNAjBBxD4QVJLmGXbQWCCs1wLTRKGZx6HFT7El1122Stf+Uquw+ZSgiRONpf/6rpKc4urh3pbks+fE/0m34FVKvP7kpMrdzwOx0vmLnCcv7kyzoP2wMrQulX3dLa3K2nvWK9nlKutKTm+tDc1j23ZvI8fSR8cGBoe4qA8PDpIkvam5PrMVzb9dws01d295eC99+hZvwF/Fx459tBCYba1FL8b25Iuy2zuXPCkeUvGOu+HMzIy4MBy3ca14pvGx7hgd7R3uKCoo7l72UF7Nbf2LlvW1rVoi0PHtj0W4hNqdIuuttTekX5qeXio31FH1KN2nnCmWtMOav3hS07V6qilrZkn0wUXXPDUpz6Vm7hGovY9aaMufOmll17ri8BiOwwHuoImnHvuuebKWpEYFeEpFVpSZqWCs6fsnJg6o3UNJE9lE/qglRTAZz/72Tf/4Vue85zn4CCyKjz72c+OGNV91VVXyZH73bI99pZR9Cze1Zyrrr76ag2bFwgmIYNntMZ4DSZgv/PrMLtzzryoejJIIvCVV16pwfPCwd/uHndYOYZCAsFzdGwYT6uOL33pS8qSlx/BJLLYuc+ol1AaIbXb1HSnWfbE0XXdw/iQinKk6uzxGmJ7RqF4TvNoF+oWgW+N3jTdHX5BIpUvDmlrJXRmqSzTMUce1ZG9o+rKAIeonAJ5sWBFIYQXcu4cp+oSioScxp/CPzheqzCj5Sim67/32muRP7J797vfrdVdeOGlYKWjPblhVVhyQOX73/9hc9vKO26vHH7QQUsXPv2g/X6zZsXaTZtv3tK3vK195HGPe9Tmn9+FZNG8joH+ucNDXfstbrt3/XBHU2XA5XMDlcOW7vPF/zl344bhk449bMWde40NVJ737H3e9uZ/Ov30Td8559ku4U9Cjs9XwqYKF4SJn1xYsDBdWxjGWfEpNsSL0iXxJvtgAHAgKH7iNk3g8DEd+bx5c9TLDHU3DcutonmT4K8DqqMZmuhWNJMvzIGxXTHzEBFVrzEompriWYInF0ly6ryeoQQMJGm3eg3kSX7VfzFnUwSu3sWnDdUIO/H95JNPNv7wI2F/3/jGN2rMPL/JRjPf/e53N/fofWxeZVFHe9umTUv40Gy6U6kXtLCx7S5cVo8Dw+kWUsrUkR/vxseBLes3b2DXFFO8gmMV+gGMbuw7dK+lc9qa+lfdFzgxpOQS0ZuBbu68uQe1VrasXZksZuFzj4Mwmq5Cr6zuWcWts7W99ZB99lg3r1VH6BtJboutzR1wescmRn6y9Y9s4f3c0dK619jognlp4thRjPMdo2kWundfmku0jw0R/qSTTvrQhz4UomZhdnsgKoiSfdIDftnLXlYuspjya4YvvPBCbSa/0nnA4sGuzOFiRZOZPAPQtG3eUcw9OytTtR+0AaDilurrAm2gGNzqC4AEAiN70UUXaUXRleJpLmFk+MIXvqBhG+SDefkZwmSZzzrrrORFXdwBL/eM+f3vfx9nn8BwpuFOjSf3xIwQH1fo17KIuUSMJ5lt5rMTgdxTlNTgSY3GLubA7NckIWsyciSJQNuKZkSqK4YeQ8ll+5trCq1Sm1ToXEpdxRw3PDUbqXU5R6RUKnrQ7K9MyR9FILOsVdl04lGLVirkoikyZK8ATLRwe7vQwLQUkYEDjebRxtBUNwtDiiYBn6LMCQPHqxBZ4KmBqQLjPJ9d0mY+cIzDyKMsOT4DSmexI/f4SCY3Swhoo+NgmDmIzLQAsqk7HBBOlwX7a/yUGrSZA5KwaJpWmWcZxjyMO6AcX4YxFHCjxoj3CqATtGCaL+OX4UQ5GXJ8FAQ526rhUYJOAa6yv/CRssI0n2mrgEne6W9V0k5/feELX6iXObEw4fc1IMHYX5on3re+9a2eTWtslsyds4i1Wzw21qdyN9+t/SxsnUM/2qmq9EsNBoQQ9ajK6Py25pXrNtJDNN0oCz0DoA1uWv/4xQs6Rof6Vyb7Cwc3IZeL3rRGSjuwaWz96vvVYLa/mIw1J4vpt2yQzOvueuyCeXeuWbW5d/OQa5/tihS/CzGS9nJStinTsUFfSHa0V9Y2j86d16FQZLC4bR3ejE93V6sm2jU6zxB04pHH/O3f/q1fZ+hesGCof6ovZMF2S4DSBEr+9a9/rS6q7O90Rb7kkks4DUeqiqDngDUhbvTsr9FjOlrDkcmeD3e/9rWvsVxyz5gBs8t/+qd/miOnA3wkoNE6napCaG5No+XnPve5E088Mb5xqkLwqm3kSD+kYBpQO1CYfGrbPu+xNWTbxyCpaDEcaUVCtGfO4rYCrH9NtuEHZ8/phrWcbwPYrTSgQRVTxccddSSbeN63v51KZxwygy01th0vsjbm57+KsXfcBoV+pLVjG/au3LBFxuR1amx9QNnfduutvtA77bTTzvvmtx4QgwZRQwMNDTQ00NBAQwO7TAMPlv1VAFNB6zsTPH7Plk7WSlv6N3tOzoJ3XhkTx+beTRsm7O+3/nfnsW5wamigoYGGBhoaaGhgZ2hg0v4e+oSjHEn87ze+McHU/m29j+EfcJa+rb/lhhsS+XirTRveC329GzO3nbXUjg2c2MNZuWIF+/vyV7ziG1/+Srr+OfJI5Z34DLh8RBQL8NgmylI1gIYGGhpoaOBhq4GmupdEGsWsavgl2Gd3HabdeWcnsddfPrCJUTLKVobLpa2KDyYRmYfadFpTjK3ig79XQaZYiclMAOLzq9Sq13LWGc6ZBmFiXTDBOWAZRb6RY5l/ZOEpkh5yEgBtzgKcpc2RGYAsxCugxe9tFkHM2DS+lhk/M+EAU+And9vWtuKnJ4c9kwO0+JRBCulwIpMASJxfx8fTeaeVJTx6LURIW8ST28TJpyrOcyZ+Zj0ox5P+myvpzNJPuRZxBe3Ej6inuhuv8AabKmDQ5WdZpAwHfu1rVETQSoUWONQbRy+RlLPzmnHAgZxTMxBJObsMaBs5x9wOI4v8xCQOqIIb2kweMeVcUAXPwMmYmdt0QC0rMa5K9bH1+eef3z1vvtfpJKzlCdNFZX7H2Qym7As4mn5iN51wm6txMbn88std1eYQy6We3LDcu/apT32Ki4n4iy77RaW9/SnPeLab9l76hrc4JryjJ7l0dHbNS9oYG3P80D7WzNHBWOHw+GknP+OXv/zlWFub15G2dGW7EwuCNRcNxC+5q8EjD3s85wBH42gHhvogDI+mK9n0Hjx5UZFteHDI/X9+d8Op29zOOY43hoZSy+eajGf/4GbOqRs3byHVvRvWKVpfX3LXGxlPDs1N4xN+gTCXjs/l/bBsbqdjvME194A7W5cQLfRMOsCixemGp7vuvP0zn/nMpjVrjTVtxQcCfhK3VqW7WUxun/QgOCwPlxoqkhRH72WYklPtDA874lV9EFBFg6T5vi39i5cuoky1KZX+C65bjQn6r4k7548N6zYuXLxAM4AmhGJT9aWxMV0BCM7xVWqXSgytKLWd9GPByW9DJFF5DyjCyPDA/O5F4fgiXiCkEDw9Bc1YvIKEK2eoQkYFepI57sbzmpOyGCLJNl5Jxdy0YYM2EzyD1jNj9vf1HfWEJ+jC+gVf6Rw/G4DAHMEtctwKVsZXTEXTKWQkXyot5wjTK5mhUQ45lbRMnmHKV0YcYMorxyPxigP1hv9KTsoAAegZTig5x2cgRJKF7omb14gJhFCpp9zFxGuZVgfnA0T+IAyEMgfIxA5ycE7KQC3b4B+sUvVNzjcqlf4qAbzGjaJqkoB49vWNH3bYgQZJIrnmrcwqwelTpvTNUP+W9Na1sNI0cAgmnW3pk4zmljVmMfxThKFBjUV5E+ypRYyOVT784T/mSP/hD3/abaF7773k3ns2GPa6ulrcZb96Q+VP3vN7pzz79Fe96l3DQ8mfaawp3UtHDGF4qCdJPvnZUrnsBf80ZlYVTYyg+AQLJoFQpgVHeyhHosqsNAyaz8ovWG7fI+quiv/MLDIygCR9fYNsA0AIwmi3pNJ4/CqAujMWaYExjgVO0JLfa23HCRxq8YvKc+Yk5ySDjI8ffGP9ox/9qL19wpEu0Lb5HB4a9RkGB5G41W8Kf2KGlWa2b3vb237605/68uTb3/7229/+dlae+wv7ayhGeOGPfwrnmb/3Et6QT3/t6zkFrivGls6RpAFfJimab30IbDTQE3kbu5k+SlcoZuqBj/JC47OichlrVJEcgoV6FVkQw03H8QYccCgNMKb1+UCiLbUrflc8FO+/627jef9oM8+esdakn4Hk5Jw8EnhCNA/dx0Z3d811pfGS7mLMLLLsqqQZ1z796esRHtsEMw5//etfN1dBG5IAdvsQ+o/B1mTMUEktIhW8rhKiOiiNepmeKhzTqc45yWHO6FFXdTgjEfAxusorRmbIOUlqCFCXQ0QGDlohRBIv0nNkOP2qgz6oTvEXCQaUuQV/TwE5JkHONzWjDfT1tbJbRUAOM/iXWfHd1MZSmxkbmzN3LlaBE8/MSkt2B6r+SzU5cjYAweraXyUKySNHr3QuJnjm3FUTCfXKHFOV6XT2F34o1lOTkEsufubwsLW/JDQgGDZJro2Bs8wBhDbUoy6voUZhM45XLYd1MIZDwMRUEZOggkYVEXAQCc60ZSCmNFjhkH18g1yb4veJkHqpkXqhVdFirgiqVSgnBYxW3YHL8ockRNIxuQwaYKWCayUkgELBLJcrFxBbMNkCiGcwyaw0rehfUmtDRpNUZpsxlU6jlQQgZ7mOwJQjVQgZMlUGyB9WlYrKtBCQYKvuHJMDaIBdCB2SKoQBUAv+dXUbTHBmu4kH0x1jYX+rqinLMx1AmNx/p6Nlc6+44gofZ3J3fte73vWd73znr/7qrz7+8Y+LsWr+0Q8vwfzEU16oQk983WvZ8b7ic8GO4RgwiyI0p9UrhagUq+b0ezLFBw+KSQDPCPhoipo0gygefk4tyw85Xo1aRjbNmNKEYDJSSbs3C5cuUmubezcZ1pbflC4MHh1vlWlLYX/9UCUcIqVbUYe32NzjAG39u3huava6pNSOpuQ12NEyqgo605cCW5o29f7LP/+zEdyE/5HjAE0JAt0O+pXSscqiJQuLiGkfKkK7VSmaZYwwZdSRoVG/hyDEyrGcFNUXI7l6F+hcewAEGs4ADR4QT4BQZgKGj4mnxoanVh0NW4xXCEggJNlGxxcuWlDwSDKX+cAM/ADgSwVDznwG+gaVJfiLJxIYEFlEXiGG0ZU2lDo4BE6gefb3mz8fOml/p/0eFebuHShHUEZfjvlE/CMf+Yga3H2KXLRT1yQbTr941lc23nd/KprZXDTg0fo2+gEUX+sKNaI1RFtWaOqad6HdCQ1LKnebmFP+weu7NdHPfXbdA8i00tL8lre//cJLLr79uhvc6ZU4RIGix9UULkv4QPJq0DQ00NDAQ6QBywHbL7ZizJdMqxi4bM4eIoka2e4cDYSBwGv3tL/NyRvJWncH7W+09hnsF1NrAsrisbDHPfFY9jfNadPxXmV7jPw8FnTxXr0f/OCL//ht55QqOJbhMSW2rd1aKS5cMC1O9vfSCxv2t6SrBtjQwO6mgYb93d1qdLI8NhDMprzZsX/5y1/+0Y9+tLwrO4n1O/s3FpwtLX/2/vf/1xf+e/0996WStEz+UpDbIoVitZhdvQpTmyxtkDan+yzH/vR9R//851f/7LK0bg70RJgDVCcpsWU03nr8U06ocoCuT5XJZw3kSW/MBFrndr32ta+1/r3r1lsnVvSlNS83iBzyzCEDOakBNDTQ0MDDWQOGaGcuf/Inf/LNb37zEbX+3erwr1xDNBKvTFfVHnoZbWbYUCjEkJoHViQ5Eox/WkcVaDKNHfmID+ZhO8EhkidWIpEEQgBl/uJzauDkZ1mYgLHKha1ikvlHPNnKqshZALKQQVL1GvxzdoWtmjQdTekEYuIkJEuZFplhHCeiiryS60mhMEcgKYLOIhnntOEcm7/FNrEYd0Un7MCohLriLdUs9wPP5gnn5wnMgt1EvU/QwWlKzhbjlc1yZNaZ4OZKuofSW8aBkHAmayTi4zWeoeS6aJlJFZAJxec6yrlEqtdIDdp4zXzgRI5iMhxVE5jBJOPPEshUWaocgy1YfFmSjFbLP4QRn1tgZiWyzKSW9oHFZJ6y1pgd17kEOg4SnM4CODS7Rjf5wqrg8cpvrrh000EH/ehb810XNFAcxbaOF9UxlvwXkxPTwMCCxYt1jaa7b/exxD2/vcORmEvCi1A4c7Sm6S+PFqdo9z7xaOPsxRdf7NfNHSckDqNFk0pttyn4dLW1D9z81Esuuth57Zzm5EyjFnHrHkuDVXtT0q0Lo08++eTr77jFFxobV97NvavS1cn1pr0j5XXQgY/hv4W5i/oWLZjrxqb1q+9xV9+69bdt3pxOkbXTww57vEuYRkc63SB7wgnHOTWujA1Xmn0/sEvU/sAqa5dS5ZZQKGTcnY4BeEqqaof5VZJAt46UtJailpMnkxNTx7rO5um/0CE21Zp06MtP2vDhsBktPjlHfMSkVjSeHJ4wERKL6YMuQwwu61k2Da+ISU7YGg+4d8sWQiQe47iVGbqCH4PkH5zJi5KlB/Rw8I3MAyHi4ymem/XSPfbQb+MkO5FFRkGzy56cTjiIcC4h/HRakqSX6TgQ6KRWFiXCR6pNB2ovI1AIp3D3h3Gzq7tMVUzq5SqhrsuEZRh/aJgHUE4KWNUAQrG1qRqSSKWYAccwFf4cVWr3Gi0nmFelRl6pmU22PS29SgD54tHdPXfLluRlLpUfFJ1/7GMfU2T6iYZaFh5W0+i8/fdbMtZ8FyeTsQqVjo+MbRgerPQNVF72sn1bRhf+77dumNeZrGd720K03QvS+Dk01LvHsr1aWpt4Pw8OVO6+a11z07zeLeOrV1bmdO49XLn/k//6o2uuv3F9T2Vh+p0jXoLjA4N9/X3Js6q1Jd1nWfTUVISUOKmxhLm1Lc5lFE8/dWs245Rrp4oPhdBAxnxgACa+VWA61q3byNW4iknoXGQ56wxrzwRYsGBeVbsNBE/ttrt7viru7XX955SXSWZIS3KPxlOVtVdl32uvxTGO1abu3BhDqF8hJyq/ZPKwvzfddBNfWLWjDzY1p19Lv/oH39h3n31b2jZfdNFF6wY2E2DuaOo7A73pBtyBoTHX7/GQ1tfuLRygWXOp9BBBXQPEQNatjj32WB2H/RVTFagdJmEg8MmGw48NDvevaC12X7x2tY4lDc/tWvjsZ1/xy1+Qk64MJk2t6XdIbIhE9QFbW+b4kGnfAx51xde/rnRmGtFyYtLaPqfV/b4dHa2cvXziddvy25tbmsKTRi65DVQJuZu9Kmb0NRpOM65SmE4D6khSbeqChd26Bk+4qgGfFcAVvhrJjUH7j9E7uEW20U7AGSiJUw3iJip4AmLI7Zo7R0GUSHxIiFUkZfpgHpE5owwE2t777stJVGT4CQW3jBOl8NTUNTwttiqLnNeuAOSopthHqpY1qSLk8gZADzOMk0ims7/iw/7qrXXLhb98H4b2NwpuWDYWaXXxWrcKFN8ExjCiHcbYEmjiFRmhsnvSsAEhcxATQXuYzoEMB/g0jxZyFXOEfE8NRACYcGg7Mo1c5O4Vczv1UnPWVYBUnHPtyCgQlB3DEF43xEqIVAAcT4QzzIuCT4wJQVKVNW4ztKsq5Lqv2OKvjFFTuRSQJeGvCITPhapionRmvMipsW4t0JsCygImIPOP4sRTEQKoYh6vWoW2QbwYu+ri7JRIk2SrP8Lozjq135e0VIyunXy4m4e1k7t+/sOhvfe+ec/mH//4x/dtTLZ1zliyv0Nbkv3tGx5bvWrV0mXLxPQUDtDuKAUrXTmIoS5tj0FE5eeVxJQDbQtIdG1eGqNPfaqfJIp7vuFHM24tPoBv72g17nXPaZ972mnXXnetYXzLptRZEGPY4sOoIqQO2NbKd7yzq8vlxJv60lxdb4I4Np4stV+woeG2pnGCPftJxx+6YNnKjesrPX3t3CmUzkbPIyMM9g8tWNStzZvhz1xitaPB1zU39Nm9sCvGBLUTw0vmhhBM8yxVnIHNnZeWqBkhgNxgvGoqBdFWKB0d6atdUToXgfWRnBzxXmWtPUvN/LEKtBADDFnAJIa7qlRoe+69h84rPjPRVAItk0OL1OitObUBPCI0EMNDGL6ipbpAwQ9iTHg/p1TbertKExq58S1GM40wN+xdkt/omN8re+vb/+jT9903UHxAov9MZFTbRXeJBA2mDQ00NNDQQEMDDQ1MasAsnfWJM3p7ymPjD4L9NV2Mi4Hsdps9MsFpEriLzHy2rWMjyf6+460N+ztZ942/DQ00NNDQQEMDD50Gil2yZIInw8tf+UqXGSXv53Z36Nnn2QW/Il7KbjLbqb8zJk6hbROqWk2P9Pad953vvOwVr/jy2nU9G9Yn8lj+Fo7dtZlWkW8zuwZCQwMNDTQ08FBpIJ2O1w22yGMsi918rwCYgLr4tZF5KARskwqOkJmAq0jkLiZCLZr4HBlAVUwwr4osk+SDunJkLVwWUmq8lmnLcCBEpmXCjDODPFVZwyypJ5lXJS50QmkStyr/1m8TnERmDlmYScykvfTJUgmnLAB8HtZB7uCmeeKUbYpzkqaQI3Muk4Nz9SVWBXIVZsRPylNFPUFeHTv5HqwybQYm06f9CzNadWCAc9VU8YQAWagSu4p1Ts0AksAJ2vxaRfggvGbhAYrpkNtBLPcmVzvvt99+YA5YbsBykRXPpHB+IpWjL95L7qPqu+cungX7HnPM4Yc8LnlTbdzk/HhkaMgx7SUXXljZssUVWdyOV95zz6Me+9hjj3kibj09W5xPDCb3mDFflvjp7ruWLLJ1MrxiedMejz7qCUfJt7t7MV0NjqQL837wgx+MoRkeWb5s2cY776yMDuxzzPGnnnrq+o0bHT3OH017LutW3uc+v81DKx+/+vEOmzfee0Nl/h7PPeMMVwcLXXPSzaZrV6+7NZ0xD/fvu2/flo2rb75JQToO7rBJ5GjQNo6KdmkXyVfduur+5vt5nCS3IeNbOqIsmsRkrT0I9fLQZhGNIZr96HA+s5oaigvxyq/jTS2pP0YLT0ovQryq3CiOuAByR/Aqkp80WofNYEBEZhLkbpBEQp4yYSAEcmy9BQKcVGGTeWlCeHoVgrnfdAYEQw2jzBYOhpFLwImsCOKzK17GAeQAy41rbnQLT45MlRF2HUBgJcrHfrKuzcuZN59IvhEzCCYJK24fDrDjtDv4qBr9OmJCOWX+wTBaC88JCLU4GT+SIqMcCYgYz3JkGcZfyOTlpIDRUn5wCG4Rj0TFzeC+EyThfYUkXqv4Y8J7hB9PNCWprgvXtL73ve9t2TK6YEFygM6EYROjKOlmx7b0LUb6xmIy/PEfP/kd73jH9b++68IL/5o/tICVp1tc+X3efPONn/zkJ/mO+t3sRQvb7l47vM+C5JfQ3tauFHvOr6zaXLniinvhR5dpndNmJJzwHxpP7CBnYbzqHdE2cmSoUZIQkWJQRQeJ+PzMVDkmA+UkcJltxgmAgxRgBocSQob/cV3v5xAs868C0IpRxsgrSyIyJJREw1yvVEpZ5ixkXC9aNykiQ7aMv+uAiy66yA3T/AZYYU96+/SnP3300Ucrgp8mV1JdrHdgg+7MVN3LIHY0L9t//1NPeMb+++8/xHHHDbj3rXLr8+U//UlH5xye0ELPpjV77Lk/D2Yf/JQd1M4++2x8mHUdZ83qexYs3DNwyqU755xztGEtn2G99547JT3xScc9+clPDj7hAH3FZT/GwSdSvhMF3Ldq7b57LvUjEgsWL4EfX4T7cWrTibWbVhuFhkbH1qxdc8D+BzzhCU8gEpxwgB5rGuFjfdMNN/iESfFNL6Pe69YLqt0vREk9BRpOLmvFOJIbfBQ5XsuRddE0Wu2HXyxWVWNC4OMWGoawcsWqzq50V2iEjCAXIb9GqphJxIkOBSFYRUaB4BljlFRNF4lUIdPWchMDucw/cFiu4NC7eXPHnDmD/f1dNbYGFVohzuEAZf75NSJ34lPvMPyymzEQ4Vwlv6yVmg2NTEOSKhxJ4kXOxv6WywIWQqsPN/sbZQzZys55Vconf8SwwppivGb9ADICO6uZeRXER0ACUAVVbMuvZAgbVI4EI9RaDINWAeAQQExYZAgy8gqQSwBVHAJH6Yy9nvAzAli+8RQZeiinylGqp0KxQaq+TB6YtTFlDmUYn/xaBWzT/kY/DarIMbh5atWsjzBd8RlW/KXqCOZa5ayDFVp8FD+8MMslinhjFMNRt4IgU456xx9ztVDmv9PhCy64gCW1/mWAfvGLXyia65+PO+44+fqUN6rp/rVrW9e0MlLLb7ypMqdlzwMOeNZTTmTIRvqS/b1rRbK/rpEmm1Uk67lx/fp999//aU97Gs5l+f/jP/4DTtjf+++5feHSffxAE5xyoc477zzDuPbJ/gqUwGE6299wgP7llVeYnPvUVPNj7lesXLvHooVw9t17b6zCAZokphO3r0gf/fZs2XLnffd0dXX49skvS8DhAF08R0w/frt8ua5kvF25ZpXfi5Fjub7Ksu2WsAbJDqomzVWYoYwwBQgZyMgxRmECKNho/NXOzTFOWN1Sr+98ezf3dUx++JcVnvmLKULOYQrAXy66WM/GzZbhMoWpz4oMJAA+4jO3KeICmjk+kDVCbOXlKuiWtmbbAvLSTkTmjDKf9es2dHTONBpXCdB43R00EA06nkW/YNHcoN/si9NieTtRxumN1ANQQjQ5hIAcgo/XB8BwliR9Gzb6QbyXvexlX/785xOJUkd2+kPN9G+WPBtoDQ00NNDQQEMDDQ08QA1kE9zUtP+jD3wQ7C85s1fXlP2dWgE/wHLUI8N0ajbet3F9w/7W01IjrqGBhgYaGmho4EHXAOPr3+QppOzZ32989avJ+1m83RgrxPjndWctTncWn+3U1ur7VtgjPfPMM//jU5/cTtIGekMDDQ00NPDw1cC0DtBWODa7BbvtnkpQbPftUEnqblPmpVSZf5x/R6YQinOFCZcdrzCDCkK8bpdYyDGM7JCj9Todh8ioLFtgBmHQhgzxrMsHfzrMCL4PmmC4fddkFJXVktyemvBrah5r8g1ukhxn5Unxxa3PERNryEnJiwOSpsI1a4JECpJ0VJO+403EicNYekw4skwcqownV4mhgXQ2Njqe7qEcbU6X5XBhyRxa/GLR1nUBLWKi7AVmkjMiPcsx8SpVZH6WyTMCoByCYTmmDEsVIqPgLDVeAZLKyDPD24U8M6uHKjUXAZCO0Hp6bCvkjqB9+tlfSc56JyKbxpvGhzWQ1SvuWbFq5dy9lo709r/tJS++9KeX9g33OnMbHNqM6nnPevq1V/50sKNy5S8uTrod3fT8F5/mLNkh7fqBdc5WtaITTzxxxf3H/PxHP7rn2ivbursrLR2HP2a/M059poPwO+68zXltz+aNDp7f/KoX/sdnPsOJ8IarLmme1zw23PS615/Bqboy3rO5Z/1wZ1f33O4jnnncFXdcu/mOvnOu+BHv7ErH/Le+9U3y9RU8v+0NW/rcprnv3kue/YoX//gH59/8m1+m+u7uPu7YIx0Sawt8s3hunXTSSQNbVr7o+S/9wpovdna0XXnFL0YGh1KHGhtt5oL7SAq5VSh02SlqOh1kfA6aap92IyZV/eQIIEaImHhmbmWSiISZUyFbWHjN3kU5CZBSi1y0Xq9B2JR87hIH1+hq15YdTmkLVEzkBkwed00kTSNwuj0RoRY+kWsx3E3kkjJPaF79KsHEl44pLeFG1ukthYmdQJFV8k9IVcgZqDv3Sf5sHKfjzLlEz0qFrxdIKITkPB7003pY08ahlVbwSI9p8WZMCPK6KJnn1gqvi1snstw2pmPFdyczh5PhWnaSIpXPEF0Vd6Kltqf5mDJw80v13FTp66089nEDd989MrTR7ebukd+UxBg5cPPQpm9888r2zo+86lXHfP+il5z+3G+tWI267+B99tqwceU119zoU6Jbl9/X2lZZsrQyPJTcfdyV79nSvHCsafjxhx246pe/lsWRh7Ved1N/W4tfBeyTOj62gFTNTal9m6YlZVYSrb/yTW07hYkZjvgcxGYYEEUrxwRCFVoZYTbwDK7Pmbw2CzHkyQJkIJOEtDEyKGPE1/IRz1eDHvSCCUOWtDqBj8kMznk5rwcH4GUlVOV1/vnnR4zK1VArXR13D268+0fnd83p4FX5tlf+wc233nr99dfMmTf35jt6uSyd/tLTfnXtzxctmnfjbdf0jfRVWsdPe+Gz3OLMG+fG227k5MehioPyE59y5IU//vG1N1/lJ9Ir7c1zF7WfevozTQC0agaRrtjNl575wk996lN+4eOSn13QOreFGsWwmOOVIZ5bHNd8+PTqt7/VT3j3d7Zf8KtfMgHa09NO/b299tsPE4pVO/zejnjKk1f0bFh7+S9+c9t1Lb+9Zawy3L3nksceeRjPNj5bd99zJ2fokf7hZz7zmTfdemPf4MDlV/x8vNkvO6TetPW3dVW62T1ftWGhdrgWWbfAdeP5C2rb6loVRE9Bm9lirgt4ShLJq8kz0Gq55c4SuWduWRgkcDwjZFhcwT6lRnEgZKoAamNqETikanuY8H6W6qk7lwmjFGIA02VUxXanvxIpsq7iHFLlyLLYESlGiCLsfvZXrYX7XW2zUXwFz5qZAYCmGefmmjHx1DC8yqUu/8DMzS8TZgDPsCCyMBxVyZN5ioc5XS5kqDuFQIJQiOyCW34tR8bHaVmqnQjI1LCMYS7L7JkrL2QCz0Cr2YcBLaZDdXgHk+BQy4eKNA/mpg5lERVqD01Oh7Oz4v2Is1DFzdXLEaOAatlvqdw30nffBT9mfI2x/+cNb/3pz362vK/fhsfmWzb7XaPnv/DZN916tfn2L351WdJJy+CzTz2R/QXceP01vlIwHbL+feqJT7rsssuW33G9VlfpaNlr30WnPPup1157LRWtXr2aTjgrv+LMF338Yx9j03988fdaOyxVhp53+rOsx7VY/tDdc3wGMu9lLzvjX/7lXwbGm374wwvCAf2Vr/kDHzGuXL3aV0ltXXP23HPPAw/c+86eNctX3HPZ1b9Ey6YecuQRRz35WFlbTa9du9pae2Ro7IVnPP8TH/3njf2951xx2dicZD4U/BFlf6OZabFCbVutahjRkT0F+OXUiMSh4FNOmYCDuafqSLSTK7PaTIOV/ZNaLtGvxSfT01peuE7hIvcS4gU8lVZAOcdCVG9TC9jIGhb7G72bl7bIgMucM5VI3s+RhBCQ+Vfl23jdTTQQ7U4zM1wUjW3J3nv7kdyzvvzlYasVMbYdIvVBLHC51WnQOefYTcqvDxjwPapfqnnaKadcfsGFlba0qZRYae6lvB4w8wZhQwMNDTQ00NBAQwPb1kCV/R0fX7LXXi5o3HH7O70tS7NVJ9VVCPbr0j9mcNtC7yhGw/7uqAYb9A0NNDTQ0EBDAzuogWx/eUkV25g2M9/ylrd88QtfsO2S/tkDtCqMheHusjz0I3K2KJ/yzFOu+NGF6bfW/a7xxAr4gewz72ANNMgbGmhooKGBnaKB5G1TN+Tz3bTJt1PH8fJmpay95iAjsGMJz5wUkZ4RH4cWnnbwt1ewYBIMI4soO25ZCXF44DWyCDEyWlAFB5HBMCSJpDKrjEyZcHJSzhp5GJLgP/snwnKmiU8pVL2WUiZACNQcLxkZkMJkfKaSkeBKJomV8SmH+AkfwAIvJRXaCCoFjDIGkLhOVmiGAx/nrJaIgRk4npLyq9TAFB8heEaO8RRffgXnfKviM2YVk8hCpCrLPAM5hMk8AelwsVTqwPfMmGCs4hUQQZEjHltJ+VVq5rCLgFSQyUzA5RBiiJF1lN1taETi6ZeObEfGBltalia3qma3UTqU7R8YEM8j6tBDD+V2E5eoBRNPXpVuw+KR7Adhuzo7eUdN6HNkKAGj6YANlVTcYN551x04P+GoYxy+OXDTr5IHanH/NNcrv03sgq4jj3wCKswTByJB022HHbv0iZ/XPe+2225rbqu4UxNJwvHbr4WLIq9AeSkL5wdymsk5Soy6AGMYeiBACqH66na0iyrkYcGWKrTkUMI2BYIGH1o8o8HEU+TMTELV02URDG2nATLDMnJERhXVIuQYQLDSBlIzmAxyF7xlzMmUrf7m1AxEctVrppkQO7/vYkB2SpHb6QPILQucgdkzoYQQAMl0Cpk9t12BGVIRMkR9AGUsS5UbjF+Mdx3hHnvMN9pAYJdaW5O3k/Gjo7Oy/wELFyxwdeK6zi4DUlINHGPLY/Z9bOvcKw2GnGD8tPq7333CxRf//IpLW9z+mHMxku29TzMX1aOf8MRzzz33jtvG049oF0aEd457RN/0phf4YOPa62+W+stfrWtrr7Tw6S8KWIhHiqlRHdsosrEuJRSS5LyyWnak/WRuOxfINRU6x7xK+MiuHFmG6woT5ZWUmddF+12J5PDEcvGZ1qLmL1zAi27+ggWPfexjuVtpltEyKyPp9np+z4Y+l1OGA7SbmLcq48gYdyu/6uAOcj7QN910EzQ8jzzyyEBTBTSmkQDE2+L57W9/y7CG61uVMr2KJ4YLpNXI8573PHafSxZfqipMzH12Rc7a+PDl2krIR8YLjYU2otl7FcpFj9ecClm91CpQjBo3ERLK5CZp4j0hZCac+TQknLHylFTOtJxjmRU4soYfJJ4RGZx12YjHQY7Bp8w5uEVMYrH1jDdjao0GTK/Y5ki08AkQTII2sdha+Eh9EJ5Zku3Ni8DlImwXOW0gz/W4XbQPDnKujlgcTZdpuVqrcCJJGbGKUIXgVXxtZI6ZITWYl9lqqJkwAxBC1TmmCshZlNGiSURSRqgiDAGqIh8mr1m2B9y2FaRc8LJyqpKmK3KQZEmmQ3tI4tlBls5hvDYzb8Fcs7juhQuZxa2EGU7Xk/NUNv1jf8MB2jJ5K5zBUb+PdOeddx500EGXXnrpHXfcYUBmGd0JDS1aPoBNNHTLkTuste0TjzwqrGTWcIwDnmy06WW6iLqtlTw8oV1IU2njnjBm4ylnjdxaG3LmENl5lmMy/m4PlJsZOF7jOUPZQ+0Zwav+gkqrEOILjUil1cxTVQYhZBXK/sIBZ26Qo9+JcUV05j8FFI6XUsOLOqosnnIBRJKMvGa2kUtmEvJ4zYQB53iAKYSCQAh5IsmrEHzERJALYOahPmfdAHYrDUxOU/2u2iWXXLJqxQouUqmABhztROpW09jf7aJvXLPG9fyve93rLr/sssrI5J7SpAZ+t8vWkL6hgYYGGhpoaOB3SwOT1ufBsb9hzGMtkb5wmtyc2ZVGvtjqMZdoarLUatjf363m2ZC2oYGGBhoa2G01YO+jP10E+dq3vOXHP/7x+tWrk7eJxa9/k6Z5tyk7B5qrr776D974ekfS/avWpnIx/MVukJlAFDPvDu02pW4U5EHWgCaUm9ODnHUju0emBrY6LK+rgrTtXWx8x5a6PfFZtlFUGRMtwuATT3lFqtfMMyeFJEES+YqRKsRuuw36oMJEZF3JgyQQMk4A5WemzTgR41UWkYtDhYBFYihEfCaBgEpkZBpokRpJVbngkGNmAxT46f5mlzUXxfUL7yoiH27hluGkqITflG6FrFQmqriIKUxzUljysJKWaCDzmklwIfzYaBK7yGO8uNe5qXU4ecJWNivi6MgWWMMjydY3N6VLgyYLktytMKkKEAQ16CmpeJtQYBkzUssxGTmTZ91Ge5CdkNmCg6TMpIptvAZmRss4gDLDQIjDJLCkKEUZP+JzTJCU+UsStFiR5AcHTsYUk8sVqWXyMvJOh2VUbrT456xJAnbfaPP4WOtIn0OukfFKZ8voOPfjvvVDm9YM9/a+4iUv+dWvfrVqzZrx/k3tYwPjo4Ojxe+xaqPtgz2OYJ3vjg4Oja5dMz5vXsuW9fPGB/y4scnh/MpgT9P4ss6mgTX33vKrK/bZb589992jaXDzgvYWfCrjgx3Jq3l4rH+0o7uzabj3/ruWX3Pl5e2trTf95ueOk/ffc2nTYE9ltK95dGxhd/f6vqGRvg1+8PWSH51z2mmnNbW2nPe/Z/MmbBrs5T7trtT0b7QyNDjonzL29W7esnlT75Ye98upjPGRoXE3TheFT88UUi9ohOk0UG7DucFkZDEZIeCME/E5FUkkZYRgYjCaOVRRVZF7zTE5r+i5OX5m/g/zVKWI8+8YN2YpLeQYNmeDL4usulr8KjXG6wz4OMwGJzIKB4Kor4iZPW0ZP2BPBY+pAhgrr8EwI9QFFAdaLhSfkuc+97nf/OY305g2mmxfTEI4nt5zz9pFi9tPPPGE17zh+E9/+tNIrrlmZc+m9R0dlfvv2Xjaix77qjM/zqF57coVrZXFxz7ppHvvWX/Nr24ZGmJk3UqeBhv3k//vd/4P/5Uly1r3eczCb36lcu65t49Veoy/rHNrR+XoYzte8OJjn/qM/dZv+uXA2DpD6J139JChacjvP7jnub3Qbxq1jOfRNopCSZy2L0XR4jkDWlk5wbwcs1Pg3DIz/5AnKo4rRl3xROYi1EV4ALIFwwdA+KCR8JtZsGCBK5Yp59TTTrMZsWHTJr5ThfPzlMcMTbpa8pZbbuGwJRAvnrZmlNENqJxWQubPfvazLmM+9dRTf/7zn2fH2WhCmMiFIfbxElaLFy/mg6UrPeYxjxGPj6REMq8Tvhb+gx/84FnPeha/1U9+8pOveMUrUqWMRscv3AXMRZuGN/WsGx7p6+/f4pbYsaZR/xJGER40HT4MM6KAkCpUkQfqiC8/qT1eY/zJr0GuynzfNbUmiNhiLp/qvbj7Fg4OnOrESM/DRbDNfLIMEZOfMTKjzQLkpAQU5YAjYCgEsBXOpDkgT25yVQh8AaUiTyyL4I/Fg0gx8QTkQk3gVHHZZa+yC+3V5qC8NAMhxAsEr7k4tSS1MTPwhxzMM1VVXjm+DKh0Uk0ncxkzy5+RxWSFlzFngEOkWioxte1BRjHUVDGcWQmBjCHaLGrWTJk2UnNSEAaCp1CVb/kVbRR/BjRJkQVhZkDDtpyKJKiq4su5V8HbVRGRV8iWM6piWPWKf8bMQMYRk3nmSEAtZqSWC5vRZuAwHZ9yXg85zPjqR0yh+55POOEERwLsb3z6W5aNlbzuuut4Pxfmt2R/ixlalJShFP71X//1+OOP38r+Tu6TjPQPti+Yr1IspQ3XS5cuZYX5Lr/+ta8NfUoCWLsSgP299Cc/wWe4Mv6Zz3zmJS95idaY/BFbLeInb6OpVOSIZPPm3oGBoZaYfTbnr50ntyZm6hDlUu4+cG6rYRzza1UJo+LqNtSoi0Q+WhkcSdt0dULhvuxzJEmbN29pVTWm98XXSnLENoKKA3TNq3MDNLQcUv0WAXIAnlLj6StvQFpiFKkRX8Qk5HgNC+41h4w22D+UDrcSeRpdI2R8aIIii/E9O3IIWZ7MrQHsnhqwbZaNZnv7Ucccw5X/N1f8YqqwW32Nu7t4QQ+P9q5Z99Of/vS1b3jDFz/zn/a+U+9KepjqfVMaaEANDTQ00NBAQwMNDex0DexG9rc57sosNgjr6SktcIojPOd2I719Aw37W09LjbgHQwPWOHWzsRSqG9+IbGigoYHdUAOGAXaJmwJgvHLkU44zAlx/1a/Tqx259A1wGhDycLHbjA6jff2/uPLKF51xxtmf/WzSwERBd8MabhSpoYGGBh4hGpjJAdogbnCP3W1PIZQCqJr2Vb1m3QVJMAHbJY9g9xxg5zQQwFUkXkVmEq/gOCjl/wGOm1zxAU+Xe+YZ5J5oXdASxxUuhRWDNl7BscUvXzHgkCqeVUWQms+9QoCgjYzECGJQCTjEyYpIcNzik1w8ixMsJDMHHAp+ipkQoxIy/0wrJsNlAHmmmgQSv+nm7bV8HNKIbG9LTtIb1qfVquoqyCeOflMhOWLVCCAmtIcEE3C8OiBU/EKYKYR49cwBeQQxCIODnEJ1XkOrwTNTAYIKZkR6TRkX/h8iUQmUnxGCv1eYmQ98MVqLSPiRJFPCw0EOISLL9VhmIjWYoIrmJBWrwPGMVNzAOd/gmV93IhC55CdJgnnIT5icF7jQTxJMoBUF0G4dxCp+iM1JxXGs1HTXVBEo1d+2tkSrc7mfctGChS7z47XszBi5LlVMHnU096cmTTrcbWlr8aO9znd5E+qe4mFG85ARIaG5ZPqXv/zlo/bfH4LXSJVL8usqHBxFunLSlZYcoOVFjIQTzZRqk1wVcvqOnMx4SsUqxRaX1hSl9EgRj7QTDe0tSl5uAIUi6j8g5yY6qbKEmeEM5Ej4QRItLfiqaJgRIiaeOlX5tRZGEpEZyDiKEBnFUzwcmcZQ7FW8mHhmqt85YAb5o3RR/KwfrxFTLmk5Jnp0xi+j1YXLmAGXudWSVFVBLUKOwW1mVhlzOiBzwCdguee2HZHT0U4Xf+yxx77vfe9z7f1NN93JY4E9DHOf2bqiHuyevze+8Y1GoT9+17+0tydmrsV11a592//9+re169NfcOJNN93S22seomUmhLjI3gjJbeVxh809+eST16+Yc/75v+rZhLbrZa94hm+LL7jgAh7Yn//vr3F1/eu//quf/OQn//KJi7u6Kn5Lu6o4RjZixMDOQ3rmsL16joqemecDSM1iVPGP+FyczDkXOVd0TtouoIo8i7FdTB5kZMYrFKLBrF+/ntvTYPHR0cTdz5PSmLQwuwZYNo7PtKJpOSkxXc2Wwpzifmis3AyNJ0vNaE44SWcH02J2qu9w9tp77739UMOBBx5oAhB6CzH0ArkIBDjqqKPidkltVb5hoyclmviLldwJH00UnBGm4InRPafs/kCokUp9BQEGCFFsQDFxSt6rkgTXhZp6hStbKC06jvWIGc7+++/PS6+ssinFFgsczQYHF0x2zEmzLPwRqsHgr9loBlqLmDKTgDUGFSc7sztVadIV9TiFWdQdcq3OnE0qgaVijlZGSAACAJ8bbuAjmKZhhZBTjUGRzQZdjxpXm2f+wUeLNZEzY/RUHGzFC6GHjPyQAIqmyCQhz8wClBHUAhJhZpKcWsYMuMwto2UgcGaDGfJnQsDMnMuYGa7NKMfgFnBGBsg0N/hy/AxZR1J+Bk/P0GQVE2i5oZZ5ZqoyfhUcPDO5VDFlJhkGBBzPKj47/jqdljLnKE459yApx2TkDNAYNK/KCA7kshqDbeCDZxADbRk5Z5GBYB448ZRRTi0D22RVRn7QYOaPYGQ2SNq8ih2kvP7NYhi+jIGe1fa3aDxpSGyuIKdJZpdhhYzhxLhdGjfkZZw00uKzatWqA/fdj2Wnt1CdZ1FlaWhlEXyYZGAcHBtlpmVtVJzwPSwxJD8OBGY+5s4pXGy3MUrlMu22ACULUbxjjjlmhnKa5KhxwXzJE2a03ug+XIG7F863EGCzpmMiI7TWEZ4sLPurMahi+OpFk5g/f77ZkcYwXb8IzgyfVoE8xqWiBKkIJBFiGqDNZDGiweTXDPhMLpJwAGQ9aJ8LFnRbuWiTSmpWECQQ5KjxhFknsCmoRiUm88nMG8DuqQGdZXKismyvvVxa/9lPfyY5BMf1z7tlmZUuFXDs2it+6TdznvO8517w/R807knYLau6UaiGBhoaaGjg4auBuvbXyVpx+rarxY4dCstF870IO3JeZvFn6TnjrkexPFTk5nStZsP+7ur6bfCfWgQ1dNHQQEMDDQ3U1QAT2FzpWLzw5JNP/s9PfdoPr9k/zetiFIyj5242mNxy1VWHH3748c9+9i8uvLDiTsTJsJsVc7JYjb8NDTQ0sJtroM45d5Q4NrtjHPd0pmIr3K66bXcnAfbr49R8YhVUT0s4BLnEDNj65+9o+15MZGEzHRBb6rXcJMkRB7vtMoUQ14ORp16e1XGRi9gQxnm5s3mlEC8ynoDIPRPn44fwA4hzBZHx6mzJMUAwwVZAjhW0zDBYSQIgFKTGUX08YSoXYXKm0wHBRCqF4dPU1FZk1DE22tLa4kpeuRY3Q4+ntSJPg4JPWqKmuyW3tsFNleTO6/IK8cx1gTn1aA1H0SIiuDRVkrvDsAufndB0tK9d1d/SXhktTtY6OpsUOPnU+KXzegGhEOclTlBUn5zJLzLQi/RqGSIpihyYTvUcEMZhj3iRXtVjbSWilZppA4hIYoQkEBwvgSOjcnblGErN5GTW2MgPQduTe+SCCbTcC4I8C4A8OKhuOI6sINADKtw8IxW++ICDw657yqUQL9Vt2nZo3kr54xMtZzL/Ztc/TxxOpx7a2kZvHHSovatzzv33rfjqV7+qI+uSulPaxkgaS38hKN3ee+517dXX9LW1wzniiCMcpA0NDPpqDsLwID+Y8YXdCxzUEWn9po33rLjvla96VWg4FJL6S2XUWaBITlpyEfPYQw7h5Uym+XPnrvcZw+joirvuJu4hhxzCp8cVrTwFOUC7B9rwQgatIflAj6W67mxpPWDvfUYHXC/dMr9zTn/P5nnLOvdeuswl0W5N7JjXXlRDKvvWWkkxu33YruZXRi7Ds9GS9p9JwoVrNlTT4WCVuZVxNBUtVgNIQ+P4uAap2+qD2WREGyuTPMxhox9Xide//vXf+ta37r33XtLWFuFJT3rSwQcf/LWvfY1OFDyXSCcyhCr+mWee6cr2X//611zHqjwm4Tvt/r3f+z03wtJeLXPc3OrqLtgf/vCH3HyRw4lc4Et1ck+8f/u3f8v5ZgCm8LznPU/Md7/7XVUTSVV196Y3vcklssuXL49BEgm0eAKsu4RvfOMbQVv1VMbnPOc5bt1zom98zqmyiOLwSzjxxBO/9KUvYajhGcrggAVAe3vbU57yFM3j/PMvdLtuJg9AMb/97W+/+tWvVvx0l/2WZBO5COJsdOrqanKdnh8fv+nmzquvvqOp8s299tprfneFl/Pf/M1z3/N//nxe58HLllWe+bSXKtqHP/yT7q7KMcdWLv15ZUlne8/A0J6L5sr3VS/5H1r9+N9f9Zd/eezrXvO8c7999o8vuOeE4w943ate1z2n+xOf+trpp63/6aXL77qrcuLJL9eeiW+wZeuIb+TlKXjEUUf/7Gc/u++++xRfdZN8ZGSIqkNIVwMyQL4hkVdWewbg+BVFTauq4PEKjeb9Yvu5555XzysyYblx8Be/+MXtt/927tw5aeAtQvCnK03r0Y9+9KWXXhrxVU9obg52tfbNN99cThIfDSyV7ogjOIKbf0Z9Bef8dIcxTFUfMZlJvLIdiv+jH/1Ioy0jgM2+1q/fdOSRj+enTjn8d8sIkVfm9nAAotuqRLPoSy65RKfWz8syh5AqVFi9ejULq2s885nPjJmzVMhC1BErqQFzYdEy//AP//Duu++GBgd/TjlGT0895fGPfzxVXHjhhZxraQytVwFm3+bNkSP7rovp3V5PP/103zUltx6OXsWcee7ey1SQiZCR56abboJDBkUwnQBL8hp8MhCvj4QnTRqRnv3sZ/vKQicNDdOeeNpQI6qD3piAWPgY57/whS/QDN2maigqwqsR+C1veYsaX7FihUrX9yk8gtoxLGCFxFOd6g7f+973mMcYjd34uHjpovVrN5hKveAFL4CjdtQLfEEzw19GuXbARmnDafg/hTDwAXDMxJ785CcbLfEhNpG02ygRBGjgcLjXGAyt2VJLjQDhD/7gD0wdfUyiLAQgxq233moUAsgixAabEN5www3f//73dd5ImuSxM/+W7a8xtop1VIFBkv+cqjFMhX0JNEpQfEV46Utfaox6SOwvfaoU8uihWbZcmyHnH/3RH5nSq1DxuVEB1A7yI488Up2eddZZVWWP11r7i0nwCVZHH320MZz5NuBADgsVtNBkYdDQvC+//HL4Yqpy0RI0S+2NJNHMAkEzEAP2ky9auNJpA5Fj8MmsmG8WxLhUxTlejf/E0xR1oirZZIfVK1/5Stf91q07Auy33376r9wVoS5/cw890S37UrNIGSDzi1/8Yr8vUUULIcpCtsL+nhs9sQrN6wtf+EIzEDa0PPkJWvJzjRUUv0wY+pE1hJNOOum3v/2t/mWogRNJAXiaVdLPRRddFNZHDBKEAAEy8TxdeOw1y1wkpoc6OuOMMyzNKL8sXiBgpdcwBHRrUZ+pHp5AOECrSiO2tqpoyhtjUVlgY+PatWtjj0KRNU5zttjyUt6E35yubaZt5Hiqu7C/2lJWbKXNB25Jz7oegHVmf3EzvHgNPXsOjo0bA8UvWrLky1/+8khTRVNXlakp+uK4CEND/UYt1QdTI9HISbhm1Spqb2ppHuodbJ875SxbLsgjAWZwmV3jjw+39H01pTEruGfUBSVTuBi6pDcO62effbYkNcguZBUtWNT9zne+M5BzT/EarAINH2OdlZS5ugmVyMhiaDBZyc09W9jfN7zhDayelgAzJMEkS0IABkVtGhA+9alPAaQKwR+J0dVQb86svYHFGHKjwQROPIPkaU97mjYDQVlyqrwWLVr4jne8w2iDP396TQ4fUwJZQ4NAFXgi3HfffZXF3J4qst6yPJlnA9h9NBAjvyY3nFqyNfvwlt50ADyWDHEO0QaiSeTIXQHUNjYr45xRU7EHnl8D0FumMKrS6r4qcvyuSnPlG1/+8p/83/deddVV61etto+ugLUC1OXRiHzEasCwWR5g6WEntpkHoYs9YiuuUfCGBh52GijZX/uul112WbK/7W3paK1k1R6Y/TW1i7nuLEudcil+snMi55IAs+FgY8lu8eTEAXH1wj8dVPrXbFmaZuDWTQ37OxvFNnDKGtil9recUQNuaKChgd1bAx0LFgxu2JTKOFaxa2fXd7Bnc+vcrpG+vt274Mk4j4x/64tffNf73+/H6DbeeU+U15yhsQbZzat+x4pno7u8U4rZDOtfSY3mtGP6blBvhwamdYA2rmETbdGiSHBU4FhFa+bR4qjA9vc2W2q0ZrvnyGPT3Fm+83i783bVsbJHb68/5IUDyB0AcwEtNHvxDm6dPXhypgGgcgolFVskQi40qgyL9xqpANm5oEVvxMFrHCjiUC4snsgjKUTyhGwPy/EDRyUnAe7jMauGmTIuAoQQPmedAcwDh8C8oxQcK7Q4JKopwRMFzGAV5GAhImnRCVZ392JXyLS2NPdu2TKfO1URAidgT68ZJjxycTTm6Iv+16xaE8cVRXyBHJd6FD/rF6vQKfriwANmKn435KaxNo5XFq5pa7nQWxRg62IU2UfB6TxOUNwjiI+CY0UV5CEnHCFkDnmy5F6FaCHwSU7/zgudV8XNhVG0XNgAPIMweJYjUcmUg0i0qIwWmDnfTEgwWUQDIzb55S7QnnMyhdIjMlUWI8cEoMViojkh8UQuhgawjfaDMGsACamqOOyi16qMyq+hw2gJoSUx+rteT2aH/aecckpvf58iOPt30BvtmVapV2Fp5ojHH67itHCR+rvjPUOHBqwsWHXP69IBn3DM0a961avWblqvS/Lp07NclYq/AMdmRwwy8jLf8qQ32Q0NjGrG2Dq1cy25LJwXch668cbrHORXWpp53jjvJ607aeZ1z+vb0jPWPI7VNddcw+3GMacqcG7nUJ/bdNpd6XiQFL6L6vHhyTY3p8m2lMQMJ4DoUPoCnFTRlhKTHhWZqqpQGTPHi4kgJpgEIFJH00OjT4kEazkZLZChifldCdqtsYsnCvMRDtC1kmvzXBXF64xhvMCKyQ/SsEknWj4vCkNW6hpFUtabV92BE/DnPve5cqT4HOROpfqdoVgkzqHhGNyQc2Spq1uY5AnZLBen0zwnodtuu40vZuQIDbf8ZPRPOeWU6Ryg9einPvWpxo0qJ1qsiIeJEcZPljvsF6MxBOfIyOyju7uDE6348867sNOHPSXriZa2mpq23H777ddff73xT8NZurSbPnk5hAnDh/fn3Hmt53zrirPP/jlrtseeldX3Vc477wcHHvhPZhPz57erHUKO/fral73s1Ne+4al/+7d/+/PLRvffc9kdq9YgP/4pR3ARu+ynV9GAV8gcoC+88OYrf/lKjtR3rajwdj3ggPlXX7tZDfJ2+q//PJ/YBtMk3kDypOHKwFmBjxHPGwOv+LGxYc8CSOKZORgAFa1cOnlB4C0h9+yAJUZ8Oahcxle0pCryQIumxYkKQhknkA22nOemc4BWQchh8topZyomWNEzAThYa3hiyjhgjVDLhMy5sCopXjnPaTxskNdwTMlM9BS/V6BoUk3qIj5kjmddhg9hJAk1cvaXOevoSh15waJFar9KJL5oPmbgFKV9atUangbAQ5RJpW3IQyx0f7+Cc7yjVSOkpzryeYPBeaLsRV40xuYat3nVGILy0BpToPb58/u2bAHzytUyObJjjuSJT3yi6pi3cAF4aEt/73iFMLIzVdAN4RiFdGeuq8mVppjAVxXhkfOqjtwq5yI9PoKWFZRJIQZMFR1K8GpKwwRopVJ58x933HHnf/+Hza1pXIUT7VYFmQtxEjU70jy0Da/qC3+Obpq3ea++wHxw1eXvxR1z9eo1GGJSfIw53N7Z5jOSn/zkJ+FMTACctS7mIyTxKoBNnMy1Xvayl/3d3/49qkiNp4uf3QXORUyrk3Xgq3rzPW2AbJjjTDbD1PHHHw/ZZzmBlvnYWNQgGaNoruJTxk1NRjBP3IxyFGIhYE5OdQY3nruht8xkJwJl+2uYrcuZ75peRpkCITMOIRXWMGWUC6fwB9P+5nFM7kRifz3L4mU5GVDqZWjoPKgCDawVqSZzbB7SmWEmBNTaX2iBqeCagQGEAPio1piQRCpaABzOncYKTqVlthnGgQDGH1N9yDk+migO1hdaQiQRu8zcqxKZfrAv7GNOykwANjSYGORRNWICDf/gqQHoRNPNnYyQah9mZl6lYU6EWDFhELJ4geyV/TX9+N///d8QqYpW5KT9lTJVtEAOJnRr+OXbGpGemQmpSK7LVzlAZ0zKQY5P2f56FdAaH1gK5KpG3WXOIUnkEvaX0UGS2WZAbzXs6Ph6aI4EYK5aAdZ9qq9Ke3VZlckfEjh0wsxpq51zO7SW7oULw/6GKkJs33UwpkqtX2gzFGhk0AV8+aHYgYNWe/bhGRxKZhk10XCKNY5Fw+svPpzAR+opp5xCk3pQf4Ef6oLZ0dyk6k0JrrvhBh8ZcoCGrELZVjqXNZKh3n4NWBM1FhGM6ki1/777kpMwrXO2GsAfEsU+hJnSv/5l9PPtKHWpC7o1hhMpagpw0w03z1+QfsZKLfPmN4f5zne+a/7d2t6SJWdSWSU7GBgmrRY7lpTP6hnx1LVI5OZjDP1rXvMaEyQWWReQXUdnO5M9vzt9xvn5z38eH8g6JvygitalVUAza2WRrba4SvvdGIY1y6D/mUvwmP/3f/93s4XoX8EEt4yWAV+VKMu53/6OmKbiczWNWY6ve93r9FYrAtrgaa21p5bW0UEDMJkz3AzFWtR555332te+1teDMCWFtJl/A9gNNaAhjaS29KwXvsBHQXfcfnsqY2nkj7a6WxU8SjfRgcbPPffcl7/85b4VN567LqFuz9qtit8ozA5oIHaWzJ0MyEZvltfwbhVmUN0Brg3ShgYaGnhEaqBkf52LXX/llenrIysLay+7Ds6TS5sPu1RBkVHJ8m93bpby9aalZT7FYt9WWDK0Ed+wv2X9NOBtaKBhf7ehoEZyQwMNDcxaA4ObNgXu0099jhPhO4v170j+vbVZ89kViLHVU8V50kxvtTdbFzMT1knN1CPj9oUcNn3181+wKTQ+kL6uL2/eZiYNoKEBDcnBjT3Pxvq30Rgenhpocr5SK5mGa1xztOyoO672sTMeJ9auObGVY+P7n//5n+3I2/JGDl8AxGvsg0ck2OY4bsgd9X3oQx9yVGnH/J/+6Z+cJdtVz0yCNvYTMwdjqw13xwlOEezs/+mf/qnzJ0cIH/7wh50fyNEoHAJ45hCsvJIhRmcxOMvdRVxOjPyiveNeIjlOEA8n03qFHGwBgsMqfBzU2et3j6Yj/P/6r/86//zznZegClpoYM8odTyDZ7CiBO4mDi3w+dd//Vdf7joOLxCSowP8gjz5HHAtFqwNIwZPrgjgE5/xzD//8z9vqrS4rPFLX/qKGNf3FKlpEzaWh+pNfOH3BUiHK03j6bIWjqkO2P70vX/u0OLCC87nW+AwWGqxBE0kcJy2pIwLU+eyaZHpLZUoxS9dupjyjz/+BBdt/uPH/99vf3t383g61+cRDQdWynbiNuhUioCDg5MY+fpe2fGb7b+Pf/zj2lVBNWFXE1o67gkmE9oIAdraW5wdOoZxvPf85z/fKe8Xv/hFx3uaBLaJpqAiv4aEhEgRE+SRCw8J5E65kHAC4PxhRJYEGR+0ZWFEiolIdWfLUo073xIM5c7JnCUrtyQ6QxiYshO8FhJNFMQrqSBQvqrXkp2QuX7PqZVDLE0ipA2By8/MjTOcMy2NrXvefAjlvAJnuidM/dcx1W9+8xtiZ4bjTUlLqd9uvXURuWc0QHPU5lj6hfFK4am0eXPf0Ucfqf8qO3J9h3Ob4sQhLhLq5du///77ODKfdHpOF8gJKu6ee1Z0drapCwexxOP+wjFxYGjA6eB1N96gM3Kh4wNkqBnsHVqy52L1LolY6OT1AAEAAElEQVTqCOD0FwdZX/zjy9avXTt3/nw8Q1pXPctu3333Ig/tK6zrxO6/+177QUuWLd28cRNMR3fy4gqQ1e6Ej3I2rNvYNW+O/hGsiufUUFCKbIDb0EDd5pQaWdEjEGszQpmLNhCDv6eOIElFT7S6Mt7k8BhxqADRcVLnLcauyAgfMWAjM0B3C/7YagOeVQJsnUl6gyMAgmEtwswxQRt8PHkwHPWEJ+jC7N1o6bawmZlEqmLGrcaceh2iK4juEK1XKRQnHC8yKziGKfZUEoSiHKkgFBVV4MQl9AMH84jP5EY5CFWRCAMhOCAPtWcqQPAERL3kfAGZPKsFWo4sMynHqztliVTI8aqwxMtFrmKuosPtICMgDxwCU4jRXpIQkhAVEDDC+fNTmzFwcYcN8SRlOSnT6f/Gjb14Ll48v7VtbPWqBDe3cAVIYrI3rj9bvPAx8Ho2r03MWzfySn3nO85U77+49A4yDPXtP1AZGK2sfOurX/WaPzzgb//2/51wUiv3l7e+6Zu33FLZsrmyuKuyuq9y0JJ9XvCS5BF49bVrR0YrQ5XKPotax5rGFFARVm+svOedr/6TP/mTIw8/mdpbmpNj4kgluQUPjmz2NOS+7W1vc3mqWwBvvPH6KCArEwWfLFHqaOAoY5KtuP7NK1hSUFUBk7SiJ0I5BklQ4UAwGME/67mvb3DOnEJZk+T5L0J6g6+akGe24oNcZH//gE4/b1763KiM4BVamFHxVfLnLHp7+3UIA0LEIBHge8qCA7B44pVzj1xohi+RaScb56cOMsPZAMNDo7yQubnov6pvimRihpa0tL2BihjEI446SgfZuLmHGWVMGc2YCWduCsX0Ez4UaMIZhtWsgxIUM4pvXGIQ1ZcBgdFkec3KDDKRihvAvMsCAQnrGdNgRtO5C56oZGHMUX0coCFkAdjWX17+i875cwjM8q6+b5WkI445EszKg50E69f2j0joNdcp+JH2CwxDoyPG+TntHbSqdnyKMNZU4VlCvbRhdKLecGE0cxZ/2CGPs/76n//+Io8lSXDiaW1iSq+iVRmHTpOl1StXqriBoSGNxPAOTeNx9O4ri3e/+93cOK644hcdHek3Yehf1uriE5/4xH//93/jI0fVrVWoqdxg5BVBe9aWXBL5d3/3d+oxIpG7tvKlL32JxmncwxDnCDhoKmLIoL1pFfqsxsAxVDHjVwt0X8jBym3EVmeWh5qZNaPmtPyO29HGOMYIsgVyIaHA+e8HP/zh7bfdhh5OtJ/MyscPtmD0346a7wQmCjPNH8Wpa3/JL0mJqugUUAxVE6mcRLcCwWhSPLHR4kBC8RlzF9nfyKKcUa3kIYMa4TGZr/nMgimvisMhCINVWXhwXfuLA2S0FMIwGSjUptYiUojUyKWsT9yEiA80rxqJsY54jCBNRmoZR3sItBAyGIoBaMPGNC0HNzGhfEwiNbLASpsMZE+v4nOSIsgXAoAkwTynelV3EMTgGVJlIF7DfCMP5hkHCXmi5QBQ5RA4kYvInF3Ee0ZMjoeTkzIgO/EKS7xsgzI3QGgjuKkaBcm0OAchtau76Hqht4wTAPm3nmBslR4ShnjxjORccA2PZtgFQkqCIwSVJB9UhP2FsxXfbb0oe+6/20s7HW9twAr0cYcdRicbN9PKBlaP0fRxr4FajgpFRYIFqVLgI1Kzh2M061m/qbk9fdyidIJ1PW6arkZF87aDbExFB+zdvKWto91Ah1V8eSijYb+h1NS0/JZb7rjjrmVLFkmiMbU2ODz6rGedbLWTsitagQH8+t9cN6c7rRpw3rR+o82kgx9/iPE2qhiVwKnd5sX87u7ykPVIs78qyE9k+ACDjTA40zYd+g5TVeZmYArEpPIsZ9dOOC7tWnzh8//jNxPSNduTwUc7qNSylhz6VNGCalLFRiGRuokYVHJkyn0REeOSvLQH8Hve857Pfv5zHLLVFMa+SdAeJKl3OFod5oAV99yrzXBf/vAH/6azqyNaGnztweeRJgDKAk0jFMnNXd9PDW7rUVcSv3yCffub54T3Mz7EE/9//s//ufjii20c2aIxk7SdYs5gB8/NC3DIqSMghCwXn2NBvvrX1/gUyityIUQybz/ssPRBVGF/J/agAuER9czKN+fxYwIf+chHjGy/qxpQw2OVPQ860Or1P//zPyu9xfSv2a+/pbs8io9kpyxRLvjOLazWpdX5iTntMLIoX/RsK1l2Rl/tPX7F7szXz9NoP/fZdeKbk5uYEkxNvbYtW4sboDlgTZGc/qIXGa7thNvuJMO2OTQwHqkasCIwKhotrUSYD7ZDg2FKQh8xTu6IbmJU3xEODdrQgKmREy6/AxPfqZoY7HjtNHT7cNBANkO7g/0tFHrAgfuzv//13//jt0zN7mIJKWXC9Gq4xeojF3ybtZDw0xaMk8H6uDjnFPDxT3riNddf50dQkq3NSRljUpJSRGZrA3B88V69H/zgi//yfecwngN9ybpOE4r5ZCrLVr9Y3LC/06irEV2tgYb9rdbIw/W9YX8frjWzo3KZAJtN4eLkwrejH/3oR7PB2lHWDxH9voc81rnD//zP/0ysf8vWsSQSqzph2ba2cIV1TjN3dEJzU/Po+Nifvu/on//86p9dZola+H6V+NSArcc/JTli9fVurEmqjqD82mlATA+gJiFqQl2SMtYpL3rB3Nb28775LZjBCh+A/cYyWgN+hGtAk7Bjae9l9uvfug3yEa7GB6H4OrLdb74lPm94RK1/658qxXCW9a4FG9qc8XDi5OtAWXbqjb8ckbfZXvUBZwAmN/TrkN63I45e4kzIjYx0bVs/cGQRZjLni3kE9hITO/729239M6KY2KYXiTbjB4wEEE9JAE+HEK6bJQBWrrrkAO2aE93STjRkQaGqco8jQxkhdxbFBVOp4zt+Z/+caNGGISdGyBnZ4QaIELCnk7wPfOADceLFS8nhh+xkCq2MH69ibCWH5J4UwyOB9zP3iLVr1r/3ve+9+upruUnN7SrWh8XKNDOBzw0gyiJyvNi3VZY3velNnIAd2jmqcU7D0aHATKdxE7IWf7wGq9IzIXAcd2zDYVhBnBJxgC5TTQfjRhL6cY7CHwiaS0wBrqjRqBRfpZcFyKWAGfFOr8G8YP3eIhJHMnTOe16FooUfIZA9RUKLyECQi7bH/TqaMT58+iXFIQRMCIQsiyFGpnCc8aBSXnf2aACOoxxgOBniBjR3ruYzNxoAJkEOQBjPOEzSfrQZDQ+TaIR8UK688ko1QgAZZcKgQv6QhwlJtmoXqToUzpkcj65wGiMnh2ZakqSM0VO6ujo4Ovunen34kNpx8S+mRloCKr1p3bqNP/nJz6Kkjt/GJydOrXOKGyhbko8UtvfduYLfz+CW5FwSIQ56Q2mDRd/sXrL4ml9ddc2vJjGKv3MXd3dy01m7tqMtfT5hx3nt2nXLl99WnEimLCjfoNS9cH7Pxs2dXY/oS7C2UtzOe4k6yvx0McNmfj3nnHOMQtHRolVEEk9FQMRHTPRN3KJZeo14rynES9HpNEUHDPqpo1/DI38CfJwls1kap98tvefuu/n6IQpuVRJOcpr4W+JdlTLT6wOjmolj4Z1DVM01C1ybi/4SZqisujJbdhY57YUCM6vAobpQS5mkDFfhl5PAM6fiHJmSbTrMnHsMm5l/WGHilRtJTg1A5eIcWVQlyQ5nA04M1OXUkITPT5TdkF8lG0KW13mVU06OAbwX1q/f/OiD9vjox/7v6tUr//Fj/2FM43YyVliw1WtWt7W2dc1tldd9Kzb+8Xte9jcf/vjr3/CKpqbfYtvR2jGnec548/qzzjrrhuWVn/6icvzTx3hd3HtvZWOyb35xIj3vWLfiS19asSE5AlXmtVUWzUlzJ+4XxkDez3NaKp/97Jc///kvDw124NnUlgyrxTMYEErWp5glo7HXxKX0WxZiisipeYXUxGcSs0Cv84BTJ7YUFUyCj2cVvtetndZKlIUA6k4VbBVbelGtSukCXO4dVZwDK8sfYpRIJ8CurvSTC/jIKCNntPnzuxCGANNxyMgPIcBgEZ4zKHeToYFh7k39PX3pG6Xm5IBYFgzaJT++OG2u+KfqxipzFyanHI0EE/Nn+D0bN914zQ03Vm6YInQFYGtyz5qKqVTMz3lTYUj54gEQcDAr4wJrcWsmTySD7VDvYMe85OMyuGWguaNl6d7L2FwKN+F0xyTlm3TJd2Bzmk7zjR7o6+/smoN8xV33tc/tKGf6iIJVim87n37CUzkqcUm3NBgcGfZxqVd6kOpp6m7WndyMrr5aE6W0WhWxDkYq9Wui7htRLkff+NrX+Frx3OJaffLJJ2OiF/h6kFlUHQwlJripIyEAfk6cnPQFY4gG47xQLRtMxCDJmfJnFaN+ZZcjMTFeMkNHHHGExZG61k7MeGVkfFYQWUQuQUJaXq11v7+Vo6DtcWE0Xf/e9753+52/xd9UHAeliJl8wS89MNdwYy2kiWaRdhZAVLmU7W8tZ6kEC1/bsgwBI9dZUGU1iikzoU+YVZFlhBmSoM2cijP+0KI5ldlmGA7ZAi1HAqJtAGQRZSmnBjyD/c0I9FNuLWUmwTbzj7KU8yIVybWlLH9GDj4xt1FNFnrQysw1SBbZwk2TFh/M45nRkMxcuU7RMCdGVF8VueKXpc1sM4A2EDwzbZaEhjNmABmn7msV8gyvshPIXJtFpjKwh2Y8c76oAgFg24dydGdMwgpk2gBmYA4Bh8w2gMxcKlqjmXh9x7OcVJXLw+GVkMbq+1et8gtWE/aXWC3pO9sQTxFC2z+/7GdN7c3jrkqlyNHC3jGg4+n2XDrUII2uN1x3vX+JsKlil2Ery1uoX7sy0NkpopaxwWKC5EvkJhPRZq1aJO3pEZ0tzT/5yU+GhiddBLSmpsr8xd0ygsD7uXtRmjstv/FWw/v4UBrGyUYGtHPmzTNkkSrkf2Q+WUnba/RgY4q54QDtm5wrrriChqPF2he2beW7d1sf6tdoQ1FqsKwubsdm6Wm2U4RIjRFDBECg8GgeOAvIo81DMDaKMVAwuO7ad88Cwy1fsonXDHQTN83z+/T8yz9/v0gzhH333yf70uGmuiNfHdaE4a1vfSu0//iP//DzNVJlHSHEjqIhceCGP8yI9yQM++vDA9ddn3baaUYGcwbGOnCCkGAsu0j4lLPHXisoMHNoALutBsYqLfPn8r4y5584/Y2iTjWfXV50jVZL1ho9ZaZBludSMwuSqAoP6e2QMtjjK5s0Vrd/99xz3/pHf+Rrww2l+9e3g2ED9RGjgRghWViDpOZq/lAuupZbHnjLSQ24oYGGBhoaqNWAk8ewv7yf27rmDPX1G15MHZMZLEzVLhpSwgCSJ+0phv3lwlV8HBSWMU8ht7LHtQWYNiYWwlMbTVOIibXSNezvlEoa0Gw0sL32d+u55GxyaOA0NNDQwCNAA2HeWtL60q6I63um1r/l9eesNRE2bQZ0CFOWNHKfep+gY+urh6ytyNIGbKBWo02TcdoRmiFgNq/r4vPOe8vb37nXow5YedfUb0rPQNRIemRqQJOLXcHG+veR2QAe/qXeyt2hrrgacYyhDgliHz8Oqxxv58E3D7JlDiLt7FuYifS0ZHLA74RAvJN1hwFOuO2hSwJnwhimIcdA7FVA4tWZjb14p5WOOW3xw8mEEKAFkyo4Im3xc4zzVIRYK0KT5BXgGbl4xtJOpBCwrBWE73IADpNowEmDyIyMFfzMpCwJ5uR0miUVjNZ5qhHBHc8FAddbtEn4dC+FWysq7rPwXhxkjbl9ghVMXiPzurrQj4wOzela0NHRNjS4paOzK6jGpq60KM63xpthUir+zckZWs7jVIcVt9/29m6aRNLS3AKbcl3UNcb7xV2SXnmjksT/8QqnKvyTD/XYOO0pxZo1653ELFm4x7hahalELbQ03prACf0jKeBU70WQQ/PGjesGB/vUO4k8aZK6pIbSEpCuHkosIl5MoDiJcXqtutWds1gyEJ7PTZxCyQgrgWKLvNIjWhRM8RDQcj7QXCPeTYdJFcXNoGowCKEJaKfkmWweKtog7kyop8c9WK38pVxF6R9851mOblO9pTBBnqAE2iNId4dDcxceGXiNgCPfOGHSEqDl8mYBEvEuCKl1JZVH7UzlWy+r1B/jVpUmN50WqiDenDnJUUkReDlnKq/gcAKgc3BOLV+6hzxSVSKdZ5yq3Q5oLhxvb21rGiuuOOxqoV23HOXsAFywXBiq2Ub8UH9vRogbsOAMDw6ODgx2tidCPHW3Mgc61x50XlWQacsIDXh7NZCbcRCmeizGT0qOcdKP7foG4F3vepe6+OQnP8lHKh0Yr9uY8F373JJuSend3LfHXsu0kL6+3tHicgGNwD1VRoD+/qHkYOW7hcKbJK6XTCOLbIqBV+9mFzB/4xvf6AcKfB7DS+DrX/+6U2RnwM62X/SiF7zhdW8klAyLFj3xTAKUQmkYK8VuB7htk7odzApUOvRXMT2ju3mGhjMrMYEQT/EQPMNCeZZHyIyTyeMAPlPl+CqgljDkCbTa1JAhnnCiIFU8q161lhyDoVfkCMsZZYQADCxwpkPIUsEBZ2GCtqkp2VCq2nrdR9UJUSTX4ra2Ydprah7pmls5/HBXAh/uorclS5vXreUeDcGZKPO5OQ1+LZUNPYnxtdfe+Gfv/Yv77tnsZjeZNjX3JCPsV7Nb22/8zdDC1so/fsjvUfzzwXs8Zv/ugZGx+0aGk71fOK+yfgu/58qm/kpnR2XvvR5z8623bel1JfaySuW+ffc9ZN3GNZt6NvF61hr6hjaOVkbmze1mnp0IYz86Ovi1r33FPwLQhhhBu5Ax+TkJFsUPQ1lYgqKQZb1BSNIXAa2/5ZgcXxVZ5JOQgyTQMq3IGPlzfBnIJGWeOTIA5GZ9GUFkFSymHFnmH2KYPADglJPKJBiWX8toOxeusnezZ+7+2ywhmzU+MjplubYqFpZNLYWlzsxHh4ZbC0d5h/dmlkP9A53FBcMZYQrAinrjXX5jqbWI69DEJyJNIpOZFeNOyk0Dgy61hJGEKb61C6m2bOoRnwQ259Co0jQ2EU7IDOjoMONbv2ZtiqmWfyKrR9ofN/CsWLVSqbVGhoxB3OIzP7P/wcGeLVssnQyDVJ8u6imF3BckumaMwzpTyNfNkC6m4961B462+VRsxb33rmwauWtgs2sbDcTJT3Q8LX90fyExbm1pn9O5cMliWafXpiZmVz4xAqtKcDx9ZaFDuU86XrMsSBCanAemibrAykfvE2nmz2ErBPa0DgrMiEmtpTT+SBL6BgfWb9pYDF/p9ugQhsDWkwZlvYl+oCXCAgg3QHPWgnrnPBQqGEV55V7LNyQkRtZJLksAER8IZbRgtYvsbzCX3QwjcOBAUAQzqLq1UC5CbdnRoqqrloj3FJQx03rNcDD3GpG1eWnPuQllqjIHcBTQ2hx55gDQYEzyrb8yfqTm7ABK7SmLXNFeoSGJZ+oskyGTR4RXITCDahJx6m/0pizAVEJRZOQzx5RTwTkvcGRdhRCvZbbbrP26HGREe8En6q7MM5PMhnlZ5gxHs8m6jfZTN4uc144DY/yRH2hgqlwEavRNQMn+9vZsJjZzlipydGx0bHjCzOWZLHvX1l5hQgsgzLEPibYSJA1m5o9NY8Mj1r/pptKx8ZHBoQnL2zXBy9CTFejamGL9Q3PN7uMtc2Oakzxjtn06mHtUVSLhDAFaEmyqL5Z57OZw7BV4ds2fZ8lpg4tTr+A3GdKpeXPzHnvuuWjpEr82dsc9dy/de6/B4eE+X36r3qK/Gi3LCgrvZ81Aq+YZydLZ22RAjSow82Zp1B00QJADhBTTbM+jstc++zBtazdtfHTnwXboWuekL5Gsjm+6bflBjzvkcUekX9YabuX43rTnfvv69fPhovJaC2b4xAiGm9lCDIYMrnh5aSTgsLkxIkFLTaeYToQMIVKMt5TAEdyNFdB8p+S7fROSnAXM0IAY8wFCQhPjKSmewa3x3EENmI/FVuEO8tlx8gWa9IYNTz/xRHO8X//k8q0YhhWrGUmiJUQL3Ap/B140M5NP64u+8f6W4rcKt2LWnGYjLa32nVq65o/a+XvUYxdR4OOO9IMqld71fovDEDtFEda3RvAphInhcQKjyffQ0i7/yU9e9pKX6CDxzYNubgs00QS7EvXMYFofbU9wlzv0Ms3kMNRsS990R+fdHn4N3AdDA7qwFjKbXjAbnAdD4kYeDQ00NFBo4OFjf5cu6F67qefEpz1907r1P7/8CtKN9KcFb7F7kOzChI2anFuK2Y6ZWHE0Yh6aqApnY2ZmilNhcorElMuyvZa1LreASL/YJiWmxAiDtmyeJkSyvk4/mdjSvXgL+3nI4ZWmttsffVjF9yCLB9PTBjg2GbnglGISIDYn2LMqthF+etllLz3jDPbXNNWU2+hqupuQy3mn922Ehv3dhoJ2i+TZ29/doriNQjQ0sPto4CG3v04xbBBNKHRs7LQzzrDD84uLLqnMneNXt6b90YRip266anCe5lTBIa/16dDo2NKlyZIe+fiDVt5zB/t4732VefMrfVsSNYPG+k3sGE1Y5JE5nSNtrQNz5jSPpJudkgiTaEWGW+0vTS4Qy6LYbEo4E1vPjGqR2Nze1s6Ymk6w/3Yfi1nAxNnHFPWWtBl++U8uOeNFLzjrS1/etKHH1qL1dTqNCSGmUHc+FMeQ5QOYieVu/AZVa9MYqRvhYaaBhv19mFVIQ5wJDWx1ajKDVoyy/Mm+853vuIvX5v53v/td9+AaQ8XbQUdYjKcT54XBJ5ZenvYrjY8A1485ZnBI7+uZ73//+84dbRoiz5s+gDScF5Yjc/ZqeWNc5jPn6lwXkyD/r//6LwwF5EESVJG1Z6QGZ08neRdeeKHdfPLbr3cRo43C2FbO+/hxuI4WT9lhK8gapntZ3GDnDhif/lho+fFQO/4Io+yRXX5W5e4VH+cH3/jGN9yB6izk85//PDdcgwLOtaM1aQmPCsCoEMAb5fvBhT/7sz/jh+3mbNcCzemaX5xBcABIZY2SBpVnIViyauIFovrN68c//shjjjnmoosucKda4BAMEDAzG6a08GKN6EQei+CLLrroaU972r77PsoPYv7sZz+TLKmc7wRBzR+SwHRlsl+1doHNpk1bqJEO1T4lQy8zAQfnzAYtDqrskksu4cXonEkjZKFT3RRtDwIg8IM82EaqeG3Mb8T7fVJ3MKg1cxfb93yaVXfIhkPONDjk3OkNH8pX/FNPfTax3XoeV+jJJdpPIGOSaQNGGzjQCO/gygGSXqMskjJt5I5JJs+57wogZzdL5jtRKpqcJbei9c5SwG2jzTLTbTNqYMxaA6FzT03dCIaOD4qbmN1h7/egv/SlLzlpNhroUHfeeacx2fjAZ+sVr3y57qmTitdtuT5bePzmqquRO1jWfeLOb11FjP6rfwmy8KpD9W3ZYohw/fOHPvQh92MxMbobKoaDm8W5537LL8cZeH/n2kP02azSpI1CA4ByEFm3d9eNLBNWwdPxqdsrq5DrClbFf7rXHaGty7NKtu3VQ12e7hHnW9/Ts573MwQ+za75W7Kka/2GtCzUEjX2hYsrV1554y9+dqOzSEeti7qS99XQ8FC/pW6l8vSj93jiE5+4uaf5m9/8/h2r72iptOgeaXe4udJTrHg39Ff+3wff7Hcqzvz999jTHezfiHyfpfvddvctrZyxW3lUpK962ttbHQPfefddhd6S9SkPsBneZqmzlgo+1YUO8rpJ1aj13oO8bssJdHIGMHMWuRQzo9UTIcUFVbksYmQ9g2DTsXoI4x9Y2bPAO0ie+cweqFV7mTbXaTnyEQvTBtvnp1EADnV8ZuhJG770+9SnPsXGOfhhFstKA5frNO1bjY+7DVRg79JPnAwPF1+LjVsxcX4aWbbw2muvXbL1gisUjk+w8mSaN65LnlKWJ8wrExz+W7lq3HbJjIbnRxjfSMry6FaMOFfsiI+OhjMTb/EYaCIV0Fe1cMREpKdXOZIfE1P9n/78Z2YIJs/sO57Z3KcvVqdO54r1TvGhZ+S4c5/ljOpyVrRt4gRhKHlm5Om41R2sqpCDf10htxkZUlVxqHrdJpMZEPCvknYG5HLSzOrKmJl5XZnrRmbaKiBYeQZQlVp+heA10AIup84GzlnUJa9bKWW2mbwcWYZnqb0ySYbripRTA9imAFX48RqNoSppNtlVkTzcXpVL2BGpkNft6bU8Q10POLvdQNu1OtneGKqmwGjD8atBfoHBLtOiJUvYwdXr1rI4djuZVG7u4jP/Ku1ZjbKSDC47ZVrOLPJ+RsvMCbWGGx/5RtaZp1ewBfLHPvYx61ZMcCuwxi2HbaBZJrs2ws4hNAi1ly5Dlp0n5H/7t38jJMCeIXww4ys1pBKj7CIFkVkP4uUriJH7X/3VX7m1VEZKJ0kIIQOuemJVFdN43UENnHLKKZSfrlt+SIPmvWTZMsJcfMXPbNd8+lOfmhKnVOflBjBDO5mifaCQWTGRUMuxnJFXQbxvBuwwL1my2Q8/mD2aOnZ3359OKgbnjo72jG62cN/+UXrS1uNvRv3Yxz72xBNPdCu8fSrbwvqjbk6g7SrTdjlglUtalYtSu5fjuS98gcFq6rS+Cqnx+pBqYIbqe0jlamTe0EBDA/U18PCxv3stWUyYK678pe2XL3zhiyFuHlLKa44cWb9IM8YyqliFo7Od2WzNyvwxyPY3PgZPJMVFB+lZnBhPbOuGY1aR1OFnEDs65s716yI2rvtcSrVsWdowH908p7e3f9PGwnV50nhGdrglI721oVY6M1jfJbK/fljY75uxv86UJ/zkssQzFjMnNuxvVsVuD+xIv9jtldMoYEMDD0MNPBzsb6ynDnrMY57whCf8/Opf77vvvp/73OeSrvwIWDI3W9un2SlxeKji4iCHxfPnt7S0j+69dzuzbsXa3X0HrgsX8Zeo9PcWvAobCkrWcDIrvw/mtOiuu1b64UuR1rWRwhxPHqhOyOFVJNps0yXEbk/BM1lcO44F9sjQ8EjrmLsU0r2QdsUwnciwppQ3XJ3Wv2rHQthPGdsfc8nWJHbB7MF9KOC8rjnPPT2tf/t7i0+hHlwBGrltUwMN+7tNFTUQHmQNzMoB2j44sZxJf+tb3+IAdMghh3jaizdeh7jRsj1j8zGA/Gq0dZRucxB5bN/zS3Me74yfY7F1S2xlQqsqvH15DI3Fno4WLr30Uifohn4/6Rh+bERyTi+pirDqlSQEsFLyM9B2b6X+9Kc/lWnkGAcG1lTQJEURgoNIQDxJ/t73vvfss8+WafwsKUwBk5AwSMpPDDNP2XFucBrhOIEzrmd7a7OyFxbLl6/Jh69YAno4kPBZS7o/zwKxs6udl3jfloHPfvYzV155xeKlS2mvr7enc37HyLjrTNmuJEbkGwZy8pU+Xf/YPObHe0cqP/jeD2+99VZGa/nty2+79bftnennzt12o8yMZroGZiytfVsqtD1laLEdL+4OvPjCn65d/SdLl+yrCu6++77W1k5fLDW7anqc5DKcKGkhRlKaUJQ93dlm95nbtBq/7LLLbrop/RIrn0hqF5Lok8IHVfkpScV5kvwLX/gCN2KHUlwbLZ61SfEp1yhzYdSDVkxuS2ANjwz/+I//yPPeWYLf+BYTbImRyTNtyBPx4MKJpPWrX/3qDTdcyxOaV0dvL+9tTmssPqzUZhTEM7NKsZNS2RbH5JxzziG26uY8LXfylxst5Mg0mGQ4RHponxPuaYVk25BEkdXIdEiRBGdSM3URZ1n2rN4qJumb9JowS541dI2IHdUAzRsedEatXRdmNXy8YRzwNAKfeuqpJ5100u///u+/9KUvdVr8/ve/3/isg/hS5bWvfa0jJefNTpgcwSIZ7E826LDDD9eLMYweF2OvocDXKevXrnVRv5E5bIqpOcDB4XXXXccVzKLFEGQc2NEiPdT05bGiVpbapj5dT6mizWgUS6sz51JFu83XzLwK84HlUsutNqYqo+19zQxDn76f8iMQleJ3GO5fscU/DPfZr/XZz372eeedv35tpb0jmW92jB2wDHzXu96gxd52y+1a4O233j8w3D+nqc0PZjQPVXrGK0885oh/+bd/uvuuKwaHrzrr3FV7L2ja0jtveGS4rXPwsY+ac8BB6bbmgw+t3LfyklWbNhcr27G+oQ3jAxtkcdSxc268sWekt/L0Zx7wg/OWb+hZ64J+U4jW4vpv04aQ3LOQPC1bUx9MwbhbzDFikrGdGkn0RahtYNNxCpLIvYwjPjPJbMsIM8NVJPFaFTkdB/nGlCMQoqnXIs+SWy3h70TMg1+6XN2/E/p5qIRUL4sXLNy0YaPbdPzsycbeLfesvL+js7PVFZ5DQ3yF91yy9HGHHTZRfRPz/SlhwxSaoNqtixnvaHMTq3fJxtVjS9v27hm8c/ntC29pfqzJ6pLu4cFkTAXcgqE6imoyNU0fLI0mK8Dz2NdEvlMyWzb3hjkSvx7ut1Xa0q/fCMEnP4MJO4tEpKUTHF89Pe5xj7Nq+9GPfhSY0MSbD2fCMhAiEcCBmeJwkh5vbRncuHH9hvXtc7usZsy7bbalIa24NZPGyONbEyllPrsCJnmINx3z0MB0qUFbyyHHUMtDZX+zDNMJH/G1aLUxmYOkrJBAmw65Ci2/UkiGM9ssSU6ChnOEKrT8KjV0CxCJNvAzk4j0FB+pmXY2QFBlzNrXnDQzgLAKIceURa3CqXrNJDPET4cTJNtMnb0wGOK2XfjbhVxVxgf/NXQ1s8Z2ilRpwNVuZ8FrmwrcJsIsMvmdRzEgRBnYLObGvH1ofGzh4sVtnR18jjdv2cIg3n3PPT2bN89pa/fhItNTbfMKeqZHiEZuLGL+7BM+61nPsl1p4Wl9aiMIYlUvKL+qDq8HH/o4RtMFDISxT+hLIO7UuD324IMPe/zj3R+x9z77hDusSBuwcinXQR4GHUrZ6VI6gGmAVbMNTDuH4bmCNrKGEDYaYVaFguApngZ+fc3VBJvT3iEViXi0kgBIJGWqshgNeGdpYNGjD1h/++2JW0dLZXhyelO9U76zcqvhE23dV74jI73tzfsdedjz91jyne+dV+kbqLS3VoZG0tFr2rGenI9ODkzRVOJZw3RHIxwcuJJcS163aUNr2mHye4RJAK1RMH3VPpfus2XBgs37H1RZtKiy/2PSlu9+B7lGpLJqbJ6P5Zub2nScaOchzeSv3kwrW5QM/ygUwFHra17zmtNPP91RCDJZJOLaWyynZVmgb+eh8aCCJm1PKhqPAMf9rsCAo5mG9/OM+m4kNjTQ0EBDA7PVwMPH/va1N+9/5GFdC+baRTH9MuqXbED94jBS9ROmj21tc1tWYdWdQPv51WICYI48OGDuOkG2ZMk8P9Q5v7tj/cYtcZOlJFmZFcL0OyZMZPpRxHRMk2a86WfgfJ/T1cE073Og31oZPviIyl7733f0sZU776xsWNE/NF7Z0pOYh7wFvgtu0tyivXNs04bEKvGZXEKG4a6yv+Gl0LC/E5XU+NPQQEMDDQ387mqgsF0Psf1lkPxjxkYq9/X1PP/Iw565bPE53/1Opbe/fdECXg1MUmV80HHJ7NXMFApz543Pn9+2cOG4s54991+3//5tex5wXfey/iedUFm/vrJoVXKuXrM6LWv9InoyfG3JW9qBCSMrqXvhcPfCwQU9lQHbP5O7AoRwHNzZ7gfPkyGGyXHPmlcJku0uZIzStLUP+55dsdj65D1XcICDpKPDb0pUmiyq/bhYckCbnGdsvegkmSs1X//617/oJWfcfuvykXS11/aE4FbFc2YGkP20e/HcCjGmJWOVzUNp/dvwft5KOY2XhgYaGpheA7NyCIs9bqOwzTV+bM7FAQ4M0ug/GcpwxMVAHzAOsS/pSxExDgNcYRgANADy2LsP/PyMeNuv1jy2NfnO2taX6jW8SCeWPZlgEgi28SZ30jotcAbg/l1J3KZdYJYduHHGMFZZAAjy9QxAPIHhOA84//zz8XQOEZevhHiTeW71V5IgCkNP5xlxe7FMuSkoPrfjIJBLsX87aWmK2CLz5L0s95DWr5FfffXVre3tfAEXLO0mDP6JthRC4MmqSUfCARNkaGT49tvvUHyr2/kLumib/bY+jYyKslezyoylLlmyQNXfOHx7aCM5SYz5rc6J03pOW0XWmSIBMElHANUHdg2zj5YcmlAm1wde0XBC/lBUmbhcNKpTWPfiCHD8tCh3EGqEUw6ZPJQgKZgjV3c85rUcAM2LL1zPJ050EEIOckAulBgwZEF1X375FUVx3CzSoYGIzDkqXZkJuCBKkYJUP6LhphB6oA0xZAAk/e+yW+uKnBuPhgYeeg3oa5q6o+U/+qM/ihua3aF78cUX84F2Qty3pZ/Ts0uqhgaG9VDG5atfOfvNb36za5t9LvKiF73I/T3337dSL9JZ9GIdJ8ZegO6seOJ9F5GAkRFJl1x00b7778+p+p/+6Z98rqBHu83rAx/4wDve8TbX8EdXfeiVsvMkyAPdTmFJmVRU5lmGZWFkE1ObVzHiTQ2JgSASwzKymPJrLVyLUBtTJVK8BlrAdSWszauKTy1CxEArmk2SfMnSNk6B++2/hBH3RVNX15997nPf0rwl5ZIdeuihfmtiZHDUdy/f/t/vXHjpb/ZYMp+L/wte8Kyjjjpq7ty+d/7RHx36+EUa+YtO34Pff8tAy9DIkK/JzjzzzFe//uk+tnnpKz97/BMrH/7Ay23yXn/zAPOyaUule27lxS9+cVvb9xz9fvazn/3G2ef99V//o5owv5icQFjxWlF3cXfQU9asWZu+ayqFQi1RfRMznEis1XCJaCtwBsViEqnlitiKeOsXyLltIMlUgeW1bl4iA7MKf2ve1W+ZJKYiOTni82sGxNfNPSM0gB3XQEPDVTrkSKFr+zDR55F+3f7ggw9ev2EDHPN/YbC3zxT60Y96VBVVftWbzJNNjDmIGJ029fWyj6wkPR9wwN6eC0bTaHz/SH/cLe0ALQ/kxrfoC2bLcdGyWfp+++3nzgNW22zfV0kpo2I4gQC5GBJz5kViHgGLGTUqaJY/T37yk0844QTW3ArCei3nFcS5r5EwZMgNw7g6PDhq5tA6p9OnmxiaSK8fWJuWVC0Tt1dm5K1EeRBfqDSrIuTfZua5pDDByHMpglsVQpltVldVLuKF2kgMa+Or0LzCyTLUplbFlMWTFK+RS5lJ8CznXk4t88w4tQjBvAq5Fq2MkOEqNKqI7hAI1B5r84xfBWSpquLrvkZhy0kRMxsms8EJzlUlisiZyadLrcsqyz8dlfgqwqrXzKEWCJ5l/BlymS6plm0jpqGBnaIB44NfCXPuwz6uX7mSGfXtTexTcZdcs2ml2eNhhx2W82JeNWZUYgIGiGFD4dvkMV1nxA0yLl9gka1ko1WHvQjaiEEOEGRhd/FlL3/5scceaznglQM0u2+NzAL65NK2p59xEC8v+FmYKkCm5BcpX4toHxtfccUVvkEirUmCZ3AIKpKUWRkk49VcIn1ZxOl5MF1EzRqLxznkxwQQcFXujdedpQFKFipd7e2LFg2tWrWz2M6WDy9nbcyB6vCwZumSql/9/Ir77r7bKWuaO/rdIqHG5VdTER3P2Wa0PXjOjPUC20qtPnxLV2kMRiP01I80Ws8FC7Zo/n5fZOnSFpNnTXfRovsd+vatmp88lceTQ7+5KCFDzroO0Fr7dOcL8tWPLrroog9+8IN6N2FMTe0+jbennzubfeiY2PrfBgUh06cXuq1jgJi157l7QWqGTIAJJ+xtMGskNzTQ0EBDAw0NbFsDhfl9GNnfX//kJw419tprGYuwYUuayM28ht12CbfGmDs33VaWrpVMFifS0jRvoH9qGz/b35bmCUer1sJPrK2t3dTXDVpoFy2ez0SaVzKgg8NDeLqgkV1euHCjWcQ++9znGPegg+4xTWgd9tnhYHwL39SUpsGu7YIpyL69s3/ThokjiSwp/gwuY5ftb2wNpYOhjvof1WfaKqBhf6sU0nhtaKChgYYGHiYaeIjtr5NX9sjR7Oion8/yAfzZX/ryut/e3dI9L+2NFBvIw+0D6St0RxKsXXELx8yqY+AQ7rffYttBLkK02XLAAYP8HxYv7rJltGHtXFs9c+YsXL9+Ax+rlP94BxPf2jnsd4YhsLDr169m/mzvkOGWm+7j5Rz7QOw1r7olS7rh8LGyHhwe7ncQ3NMz1N9f8aEwNFbcwnHe3BbkdMti9vYOOr6WxDWpq6ud2ZUdqy1G/Fau3aXdpub2lrHBUbfUffjDH+YArchL991z7cpV6aspoYQ5rTZi38APKAtTk4tp0WdOaJ6TzoYa69+ZtdRIbWigoYGyBmblAJ3WJMWGpm3HIHZObxHijDw200UaTD1h5lCONMob9I2t1k7iAzbOisFHyMgAAZMAsIVsXDO6iWEzPEXa0DRGE8BrkGeSIKx6YuIkg/2IZRUS9iw2N0MYz2AlCX8hOHgVWBQxWX6ELJBFnUip5ay9ZsKIx1nxYdqHleRV1so+NlJsv45Nbb2SYJJ64gcL+C/AHB7jQOyys3byjzX1ty/oHB5tHhlzudpw4WM8sT7MYrS0dYw3tfRu3tTRwYQmsyzb1uaWjjnp1w0Gx/pIPimnesneUyF4soXNTcURSLqI2msyaWy8Is/p7AIjp4GWCSdgsjWNuhWGAid+SWFCdYUZbIFMsMiOwW5qSqpQoSKFiSwLN3GaCWVGUjwhyBehJIQRvFKLoEbEZCYBBBMwIGCYatAiOWoZCT1E9UULrOWAUCTMYhk/broQrIaGehFqy5GaMphsAAQGB6siMjwPlFcxfe81P0jiiUmiLPCjIOLLCgk+jWdDA7/TGuiYM7Eh9YxnPMPM/tJLL/3B987noPmUpzzFhdBxumwY9zmKYvquhrdTlFdf0Ol0z0jyqgs7DLZUsA4x/hiRdBzAmjVrDBEDbW3M06YNGw459FAnx7xLIeNsrPD6tre9bcOGtXysO7smfsn3d06rNGA4UmSmkMO3V6NZHtPKxTGqwDTGShXKSduE0VJjOJTnATmGqW3S1iIQI5iUk0iOYfCMp5gywmzgYhBOJlsWBvMyiZiZGdIhqeDURStEmxjGg+2kHtLburVD7PjHP/5xe6+f/vSni5lAZc2asX331YYH4nPbN73pfS0t74vT4bbmyqEHzx/sWd3WMTynu+8Nb3nhX77/7Z8766ZFcyqf+cz+jzro+MXLlm7acpcbWRcsrux/YOeW3rU/uuAC6liyrPK6N7zyhS8+9b3v+/APL7y7e17lmGP244xIGN8GnHXWWfffu/HRj1569z3cAZOlE79ly+hBB+3Nx/pTn/rUX/7lX37xi2f7+YGyZpQ3DBT8Ap4oZl09lAmjmsSE3mg4OGQcHCIEDg0D4plxAMFH3enXWEFAleMB2RxHJeZ8Mw58SQMDw3PnduIgPkKwCrZGBmiasdmpGAjBJ56ZfJI0/ZUU8sAfGBjs7OwwpKjcMk4Dbmhgl2ogvqscHxk2wmwZHNB6mTw5sokMmRl5uTtUScII8lfW8uEYvTVy7Zl5tWK6Y/nydcnzyiw99a+OhXNxqyLPr/ylENp2kyk+vjj1zSe2bsSE0zs0iL9nuh3AwsEJWPr0o05gd+QFGeAKTH5g3LvZd90K+0hCBoFgniFwFSMd0NxZEWjDx5MWj0pkzw7AHSSTV1HtoldiE5K0qklZohQKIpRzhCBJoL1YtCpdGSFeFQROkJcRwL/r9rdc2LqwAmpmoUwI0QDyEwDBvi396AjaTJmJpkirlv8is90PBXpGYAtSBRTqrbVB5kWyRms1bYooIzkKIUnkJVO54FbOOsPqDi0r5gnO8fJCpd5xCwE8c6rIYBhAmXnknjFrgYyMIWTyi9km1XR8glbbw6cWZ7qYyC4kUQvKHpjiRZZrM3SiEqGVewecUEiQ1GaEDwQVFONeLUIjpqGBXacBrc4VCSOFxTnu8Mf7ZHFTT080eE2X5WI3c6fLQMjjVdOFBt8gwBy7etk3P8yWi5xxDpsbw5FOAS0CclSGIwHshgKDm2UywJaRcdK9CZj76lL8D3/4Q3ffAsRIrRoby5rB3EAkBuZll12GyvXPHLv5nngSg12WRBJPaPAD2TPWjOy7H3zgDiOpiaEfH7/jtjsPOuig3oH+BR3tq9aucbk161z1D7mwlUWMqMbzAWlAs3nmM5951JOeGBX0gHjsEJHmobWYi2qmWrhfUBTKv7TWUhjJsHOt9Q3mDglQS2z6R4bW8Sa9Y968ZIhHil8pJF4EMi9YdpUOstf+9zNDe+2zSed99av38AFA7+EvZ18297TGNABzJJ5Tu/Bb5xe9shwXfVyMvqMrIT/55JOJ5J51SX1+U3GaaUOZSYZn6YAFP0y10SnBxTOYZJ0brnywEZGNZ0MDDQ00NNDQwA5q4OFjf1tHh+23HHn4Ef61FeePc7oXsTUmgQxgFDPmcjtS5GV7dNl1GbHD43rLptgkTyvNwYEp0x7216kvG5q2ikwji8/kWlvTCfXoUJoDL1m6gOk0czR5WL9xg2dLuodyfPG+v7Gof9QhN7HO5rSPfezg3Y865ClP2XL/nUfi09aWTmkdPysREgXp7PLTXxtbm/fJVhXAzkqqa3/7x9LPsMxeAw37O3tdNTAbGmhooKGBB1MDD7n9jcKyQW5ttMKyq/NH7/u/XR2dsUnCTg33DzBVs1//WjCydEuXtbCAnZ2WsFvmL7nG1cV77b0R/6OP3tM5zsKFx7oM7klHdbPF46N7kWG8ZR18ANtn9+jpT3/6Mcc8gen3s5gCe5hsYnMb+8u7LO0lJYc7/lTDcBhEe0EDfensprk1XQfZPX8JI46b3yhWNEBhfNs4S6DEivXv7R1WrpHiF7zHm7fymsAHvjX1pvV+gqnZBTds+p777ZvOjArTPFpan2JeG5DbN/B0E6fU9qbQXy3iRAyRrHnlmw6eTABKJj72H+y5Nda/06qvkdDQQEMD9TSwledQPYSt4mJNIipGwLQrWmwgpjFp6wM5Q5vxF774YAHwmobpYrSVaiCOpMxWZMSUn2noKwI0IRh6RoikMn5dOJAl4eAZsqEV79WTMJIytzTgFqKGSOwBNDFZwswQgCqYBDewENzgC+JZymAYqWLcAB2EERNPnIzu4ElWSUKBeK1tyXRhg9VgIRukxLNYDAcrT9k5d3SvKibd3ek6NwBCBlsIBQbPIscEpgskZVyUQmSSoai0sngywopsIpF4DZs16scS8C9umgz8xHBK/oQZMdFaWlrag5yc8IMEfrwCyiETBqYn+UOf0BQqyANNjNcMe03KKSqCxjKymOBQjglCtBmAFkGkTIXIS2SQi48gBpBTJ8iKPwXCWHLjKNpwxocseA3aKLuYMm0DbmhgN9CAkdPEVAs3ff/Qhz70F3/xF4YRU+fLL7/c2saE+6ijj+SG4oQYwOPZSgDg1VVb5ugHHnggV+nvnPNdnUUfRGvKzv1Ij/aqWwkiPXVn6uqaN++DH/wg+B3veMcxxxzznve8h+vzcccdVziHbeY2yqPrd1erMYBYsVDU0UcfTbF+DZliFb9cKCOtw2nFd45OOeWkDPNBdzT4y1/+MjuyRBLVuX9UvTz/+c93Xug3BywtaDsTkgF/N4O6LE3uTE1OKgN07r5P3kX4I0dFSM+A5e4I86KLLqo76OH5nOc8h7u8RaCWU2ab4Re84AWXXHKJhmR0tQ8b8ZGLhmF9KCOOd2LKWYCJob0dccQRfpaaJkOqzBYCjVnlPu5xj3NlGj0orCzKTDhJ4Py1r33tO+dcgXDh4krv5srddw7ssVdlr72Si54akcVNN9zoQoqN6d/muU2VI47Y92c/+/nrXve631x19wlHV+68q/Kxj/1be9sPNWa1R7+sgfb5pa9sXL68cupzKtrp3/3d373zne986lOfetFFd8vo0p/c6/WGG25QQKet//1fFyFp70iEYUoXLGi3GFZ9FEjCzs408ciSB6C8Ku6II46i/FzqKoD2XMROsCpamujsbNNtn/SkJ/3sZz8rU8lIsHi2nueBLck1deYbZRwwhtCe9axnaTkqqKxY8VK3bOk/4ojU8clfSytGe3ANnm8njAPgoEKopgwaa9asO+KI9EPhtARBY4aTS6HsOJxyyikaNv8StFVZeNVctW3CT9ewa0kaMQ0N7BQN2D/SdG30GDa7FnQb3hcvWaITGYKSdRudmEjXzcso7Z5IrdqozuZyGkbId2pte2XR0qWVofTV4uCgkWO0b2ywtmNmnjqF4Wuwrx8Hg5jx5D//8z9Tt22emPbr45ChlXtWJs9AIpnsXzrjX//1X0vSQ0WmskyG6JtFx5yKnExMfyGkXjyc7iXSow34kPHpGxwQozhiyvi7Do7RgzzE8BWW0zvmwzhG85EUWSsgF21+ZkZR9tcQR8KsiiiRV78SwCnNIKy+vAqhCoX93bW/oQHNlfVnCDj/1a0OBXze857nJ3GY+Kg+ZReoUatjXjU8P6FAt/wImZtgAgFA23SFv6aNXKTXqlxiduF2inK9wNHsIRvhzS3tC5td6DLBtvxUBSeddJJrHdOFUvUC88fK33TTTXoHuxMy4CzkuRn+5JejJho8pAKYFbabCZb7dCbG5ETRjAYkKQsGn/B17W8Wk/KPPPJIr9dddx3kHB9AcGMBf/GLX5AHcsQn0QvxTKW0TPYXeRVtIFAphzxzJ/rP6jUUKKmqVLN0G/Y35FeEzIcqjB4Q/BKLwS3HlwGlMzOk/NhyKSc14IYGdroGolVjqzHrzhZWg2PpNwcqv232HBwaiosSNG9mMTogkuJOgGQEszy6gKauhRvPXdGno+kLvh0yPrDmgRYDBTgIUZc5BI6uwb70bN5s5RV9xCvZdFWDod9AwyTOqDKfIKx64pyZc302nUZo8MRTPGmxfcxjHlNFVX6FD03I3Zz3MyUQw8BiQS2eCQ4SyFsBU4ops2zA260BGrbUuvLSS6x8tpt4xwmsmKKR+xhpv/3c2viFT34qcS3XbwFH9Wdn3B3PeQYO3Hxf9apX/fTiS5khZ7Xa82ixB66taodhNB/7+I3Oi/fav+LOlkMOTXdY33dv5fbbK7de9Tn9uq+3Y5sO0NVNelKg3K3YO72JoTREWJCmQaNSGRqf1Ngk/sx/3bA9M0JOTWtXw1Sgl4kK/ccA9ah99iWVOWemagANDTQ00NBAQwMPTAMPH/u7dN+92d//+Zd/VxD3NHp2zl/IGJmU5rVStlkPrLCo9twrbR9xgB4cTHczxrTO1GNwYos9Md5jj6Xs72WXXsj+ulfKtrC7J0kUxrc5bfC0LFg41zTVTBk+i2iu2MpjenT0iCevQX54z1pb5Xf+dhObees1y9eurfzmisuQc6FO+0sFZswt5y8YW716pL01XYCV8i5CwEyeFbqtWpP2bH85SE3MWCaRZ/7bsL8z66eR2tBAQwMNDTxUGniI7S/L5x8r6JOeffaxf/Klz33eRs/czjmsD52waBygCRmL89msf5k5C9UlS5vtcs+d2898H33cFpH33neX3eN770rXwG3c6Naa+2+7yakN87mIvWtu3yjHWNv6dN3C8+abb7SztG7N2v5+jhD2rxyOWucmv2rnI34eyZPDFEPOSvb09I4MJcO4pc82eGXxom5zCabWonXNmg3FuWi6AdoF02STnSUkby+hpRUTpz9TDtCKzJp7whzY3G9nyV7QOd8+t+KuL4qKGUN5fVq36SS0hGQek+x+4QxdFzEiSZIdoFNRS5OB2ItwVaiCW/+2treMDE1suc/AsJHU0EBDAw0NzMoB2khXpSljlhjjYE4CpIHSOFX4ekr1KjKWMYEfkZ5BFWOop5iqgE8mkRQwzoEmNQDxVZhVfPJr5ha0IWTAwYQY8RqyBYw8xwerXDoIEXIWXkNCQI7EXPBajozU5ALNBoynQ0oWJ0Wmq5oVNjk6tHW0W4iOjTf7XxxYtPlFAmaHSaI/n/fA5FFWaD3xL0xJiiMFfs2tTR1tyVsaMQHGXRidypLESHdJpwumk5CqCFwZ89HuhJDJ0yFJLME1HyJzWQAixtDEdRk8r1O2lXRXdFMlKjEqKLGyEV2kEiDlAo4WknALOHTFohdo9R+kVZRk+pJExb/xZr8BiMGkXIlbyRxin4qcCIsQuQQckTDKAISMHLLl16DyDCaRWpAXmU5kG4UVHWwj3/SMxT9u0cKDbX7SRsCThDm3BtDQwO6jAe1cH9cFOB1+7nOf4zbEu/QrX/mKw2ZfJf77v/87d0l+Qr4gtI3lalvdgWPQeeedZzFg7v7tb397QhdFP+JVY+rPpqRNusL/Cb4jH0Eurn/mUs212kmtQ6kvf/nLchcuueQSy4xNm9aTwSqCMOVh4XdF12RWWIsWJeJf9dKXvtQFY5YfiiO+XAoIlPmWt7zFefl0Dt+vec1rrIs4IdFMrOUwMSJ5erU6eutb3/pnf/ZnZbYZpm2XcJ9wwgk//elPc2QVgL9q/e53vxtONtjGUG9VphK5F/NQv/jii6uo8iv/Zj5AbjLOMVWAG745WNuB5TKVk+SizRCeg5QGUOtEG0OulZKrlD/60Y/yf3KWH5HBBAf61ERf/epX8xvg5WPRKwlO4UzbvMceHevX9dx154r7V6xtaa04ZH3+8079ypd/CKd7/sKeno2r7q+89GVPJf/ffvhvNvdsOObwvYi05r4NPRtXsI4XXnL3fntUnv600/Zatva8C341v+1OP28wOr7loH0q3/vBx/gWPO1pHz35GYtfeeZb/9//+3/nfveGt739wJGxRyGMezC/dtZVXXPm/eEfvtWac3j4ovlz51VY9jQNm7gGm/ych77xjW9cd92tc+YkUz5ZrvibrDD/Ku5lP/7xj3PqRNrkHx5OnLesjS3FMw6A9zPl8K1/2ctepquW9TZJmv7y2Pa0MV2OzDABTjvtNL5xt9xyW2wvRBL+hZIrBgT9t7buAk2v1/A4ehoHMk/A5s3pSlE//KT2lU7Fadi1Eg4NjWq6ZMChTJ5h3mmq/iMf+Qj/lRzZABoa2EUayP0Lf0bwxS9+cWtH+7nnnrtizWpekuamPKJ4i3IKvO6qXzNkmi6S2g/mjckaPM9F3pm+yL/++uuv/vWv+0f6j92UNsVWdre99S3vuH3Tum9961tbbr973IWO0wQ7XzxQ3TAk6GspmH+zO+3ta9etm79oYW9fX/LZTTP8iYl05pTOriYDOYE6ICAMLnxjcjiV8j5hCCZx06A0ubZLcTA9USXD1JJEaR/COxk+z0WPWnDqqaf29G5x6MW68TAzGUg9fXLOn9nuRIBIIVWIZ+8y219Z56ScI/vLgLK/viQJhCRhEZSCRXjRi17kjW9TxGcOgN9d+6tE6oJF9o0KI/vhD384ilz15CDL+htjmZhw1slKYHG4wDLfL3zhC32AFJWOHELG8RqzCzOEiMwI8Wp29Ad/8AdMmA3imHWEADhreCRUd7L+2Mc+hn9Z88En6u4Tn/iE2UUQVj0ZIK3X9CO8sbMMkZd2i/8Xv/jFupPMcF/GQfGr2OZXkwdtW2ePjpPjzVXEhP2t+gAp4wDC/tZ6MGcc9lfHIb9Bo1x8zOH4/of9nY6cxk455RTW0yihJWee+rWeiKE+brxif038smagyYh+1O/v/d7vGZ1+9KMfBW0WIF4NerrGZz7zGRwy8wbQ0MAu0kC5ibK/Z5xxBheKr3/960YJTd1PemufTKp167pVq31Toc3rJiIRFhPvqW0cSSKlGhaMIUY283mYYAN+dC4kYjAvFydYRYxMDV/mqL6xsYCSFPbC54gnn3yyubE1i7kxG1E7vEAOJiFYwCKNOeYGIq0afBari/l4w/w/998YuAhWJkdYFfDBxOBgcDb9vuyyy0xOSJLmCJNZl+8GDm6N545oYL4h2Ue0Q5Z422AT1aeCcj3OTJDxZ0DL1Wrpd+CCJY/bY5/KcOz0VhNF/VelIZ+lMNXsat5tWEfAMK3dxsZXrFy9qW9LDWKKgLNpzQFjA/M72lcMbZl7V0sP03n/ik0r7x7YvHnQL0CYBuiSzlHrks8yUrOHqWfpVhjGTGaWtBlt4gOC/D47YKKjFshRBvthg8V1m7nKZsepgdXQQEMDDQ00NFBfAw8f+/vo7iWHLtvHD9kLToJN1lgc5p4Z8qw77D8A47thw0ZmzWXTo8VZbkwJPUv216+UrGV/V96/dvOW8b7eITMTDtDFBIC5H2ka95O/lZ7NjhUY4mSL/fawqWZ7a/rJkRV3LevbtLDSvHbFoqbNPR1r1w7ecev8tWs39/azY07EBxwIO4vGrViMOntt5bk1MtRXWz1hfy0zGfdY19fibDOmYX+3qaIGQkMDDQ00NPBga6BY2DwI9pdVqms9ldeOTnEQkYzu3KGxBWPNlc19DOPmps3Jj1hgpcJSppftCD2bK1u2DMybnwp5/9173Dp/zsjYkoGBuevXpks0Vt7fu3Jl5f77g+GGSb52jSY2jjZvar/5xnUrVybv58mtl8Aavb+y0X6O/SE7MwBW0p735IfqCcenTMMDIxvX9zhOGRgY6R+wmix0XWnetNHdzS7g9Br/oA8WfEvfPxXv7HproQHMlyzZI6EPjXXMmTNc+E0HygxPOo9gxpAc0IpTrRnwldH+h2lOV/Jh4+Q1hTtYeJClnwKv9KsNG2ZqZSq5ATU00NBAQwPTaGDqxHoahG1EW3oFhuGsbEXAsT6piszsrIgybUTGdnw847Qg4qGJzHzKGYEzw1kCQZK5ZapyTJmt+HJS4IcMdZMCQVIwKbMKqszBUJ3QsgQFUOCnOKfI7mgBwPEUr6wFxcQrOM4tQmNQCgYTj8L4pR8S8h7ag0+TBf+EUzBEkv6mXxQoDloiPiXXCwXtRC6TmSY88clbuihLehYtIZ5lNkhEwiUGNEnxLOMEtxyTECYxg6GYyNprRouk/JqBaGCRb44E5HwzYY4JtGhy4Mhr69T6kpflCfx4xmkZVlFZwd+zzLNMW4YzcgNoaOB3VAMOdJ0PcQNy/S0XUr7HCqLv6g4cJs499zvd3fN7Nm6ev2CensLlxdPPr13wwx9HeS/68cW+5QjYsbG1AUJ9J48hkrwKOg73qIG+vk/+279xtRDjoiAD4Mc//vHkS+EnjDvbhgaGO7um/K6C7e/K01ik1IpPRY7D//Iv/1IZwyeGkqMUYgBUwXn3zDPP5Gpc9jMrl/TDhXMSzEwbqRHDPZ0rHm7OvGVXJgST5OwiVMWXX9/1rnchFyIS2wAUgcyXXXYZ5+mIzDiZXMN4//vf7zVT5aQcyUvV1meUrowWm6H/pg0UOZaTgokY2uMg5TVO9MvMice9j/cP+bU3Dj05R0mCK8mvueZWztN3371p8eIW1xly4l+27ENKwWvhB+d/9+yzLjn//Iv8s2Vsa/if//mfXan4wtOewWV3w6a0fv3t6sq5556/ZlVlXpu7NPZg5R/1qLaenuFzzjnnkksuf/KxHdddt/597/t7pnv9hgH3l9986702hVXCPnvtsXrNxr7+Ld/85je5+RJMz7rnvt8CCGYB2N/vcuvuVat6OFfxfi6Xq3x8ftFFF1144cX1TNsERbQNL1l7ACFI+E793//7f71CyHVXpCffcfr/0pe+BMi6zTjBEJPJpku77dk/PlKR8/4MuEwoJiK1f6UL5uAQw9PNmJQA4DrGv4oAkRRUBXV6LFgwj2cb8qr4jMAt7K/+6q+MM5k8JzWAhgZ2ugbKzcyNjDxHfe7nftaV69Zqw6vXrOGVdfrpp3OAHty8hQOW4Vo8MRDqIJnchF+SLzdcPGxsdD/99ddeW3hxzeF0uOfBjzM6HbvHYl+8XH7z7eVSZA4RiQ/X1dGh4ufPRkbwnNs1x6dKui1Y4OkIAXKMBmVWVTE4Rw8lsI+a3ItpGmB05aXNRUwXy7RVMogXE92Z/bAUGip+wkV5uZtwwDrllFPai/4e7sW1A0XmvLOArOoQ1Rj4gQ98wOyCqVI6BY94aIJBbPny5W9605vY3xyfJQkELp4I6aegSPUogD0p+XfX/hKeWoyxuby1gPnYy1/+8mgtygshlACgEzFar4+jogFACyDz0Z7/+I//OOs8x2fA7MJXQDx0qTdHAgoTma42/4d/+AdCasZhfQIny8DF8N3vfrdcQrYyh4A5N3Ofgl8WDLLsxAh+5wR/zQBcRU5srocCfAGTKgSvvn2qioQZMbKoa38zvkyJ5xWQI6sA17EruD7IDuraUkMSzFF95zvfydlVEXolMHKqgxzCBzJCr0p35ZVXuvFd8auYeBXkaEZUq5ackW8GNB4dJ8c0gIYGdp0GNEWNNiaEE/a3pVkD/tVvfq2Fc4BmQ90Hz2/44gt+rFVDJoyWXNVzIWMlEqBz6VZ6hOk9cgOOV4ER5LvJwsbsvVyo3CN4P1sv+AbAL/z4wJW9w80yx1TZlwmWAzB9QoC5oVIoM6k3lkykmxj45tDCx0et+Piu+Hvf+56xOvItuubECINAKcQEZS6mGEsVJD6v8gWFeMYXw1zwsiQNeGdpwIc6UQW5hWyTc66ybWLOHoGDEWd3H5DPfNxbbe1KK8TZ51UXM7fOpmLLXKfwdQHMtub20TGbShNT4mi30VaZ+875PfrIps097e09boC+445K6+gm+rG4Lk55U1bBuY4ZrivH5EauXqwPmsSa0BImZrM6qWu2pqGrH92+9b59faQiNlzQ2gsVZ21IGSwW+oPDyRnO666o/RmkaiQ1NNDQQEMDu6sGHm7214FuGvzdKjWeftTLaM/ATTfmTxc/Q2XFgjW2i1kyU8r4ly1OmKFsf508MKbFjxYnrpCZI08xMTk1Ux4bT+5TXZ08pmx0I+3fkO6h3NjZUfH7lKYWMkVlyumfrMMBGpPJSehW8jLugomovZewvzjEdRj4D2dBtyKa9qVhf6dVTSOhoYGGBhoaeEg18CDY3xmsZHmDhdmauHSptcXP4LI1FMME+wHiZOqKZd9sjE9k55pKv3brCq3Ozo6bbrrHVsqSZZwimns2ttx//7DfW/CNvOutMHbhI5vI4iEcHk6XPbOMdoRsDbnOKdywxeR/TmZgmhoQLRnT4hjaOTZAiKft64H0mw6xVk2b5O6ZQSUkpDTH4FFQeIsVRxvjLRPxKY0IhWnPyrELtGDRItfPDU7+omBCmzHIiBGHkvjWNfNV5PCT8JPylVJjmiGisf4taaUBNjTQ0MC2NbAdDtC1g0/ExJGA0SdGNHmmcSoPcKUhKyIz2ralKzCCfxlZXiJnwyfjyLrMIWCpdeNrMWNsjUyDJNPWipeZR1KtDMwTnOamdPtU8Yt5xVcrBVnB3FDftH79RieqYdd8YQtxON39nAyhZwtPquIzWbAlsGdaNiZDwowxKqkuQmY3Nlsgp9fJTOwgp49uih/TKxRQXKmMczIxFDKxw1pIwsEI/yRtErUIgIBFCq1uk3PuCa1CjtECD4eCOv1JOCl5slXgLw2HlFBEZjhi8nOCxeR7vFYhx2vG9BrVXIVWpg3YswpnMp/qv5n5JODvRFsCBJOU72RkFb3fhuI5JrLYGUhaAhdUU2dXXouYKtKtXovKKbYGtopuvDQ08HDXAIcSI+H8+XM0cvtWMSo6GNZl5s9Pfs9d85KniJNUCFJrj1SHx4eLAYOnkfFt6sID3S71nPQ9oN4kdby9q/jFluIcenAw3XolzJnT7klNHXMmfowezS7T2lS/Tp2+EKx47miGMdqE5BYw07GDRoeWZ87/HMhNhxbx1AI5xq54io8sJOX4rK6Iya8zMC9jguMVPgC5p3qfDZ/psrAclUT4vAybDrM2PmddVwAFpzpUUoUS/3RdIs82a04/3tfWXvH17a9/ff2++x4M+XWvf2n/QK87KcG9hffOnnt04PPtc77B4WDtxk1zFyQjPW84LWj5/jWNLyD8yjUr8BweSd8F/Pu/Xn73vZW5c3lWFUaCf3bHsmuu21AZO6jFNKHj+o19q9u7Kmp046b7/dOV7rrn7r6+wSVLFm7atPG4447xq9ly7OpKpj+qFfP/n73zALCkKP7/vLd59/Id6cg555xRkkoQEAwIKIoBUAyYfioq+jdhQiSoGIgqIogISObIOed0pMv5bvc273v/T/f3vdreeW/39u5273a57nvXW11dVV1d09M1M13To+7rTPGw9+/u00PLkhC7RDaNHHfIe/GJS5TQGwEC6RHydfRFFrZCLUUeiJfVEyRHk1zHN2xFYsVO9BW2DY57SBjhaIGBtADX5Wuutjouj3FLiNIdd9xBADSBJhqlrPHQGPGgbP343AsvtLS1LWpqmjt/PrWcBXCZKkQVE/HJ8zgCmwjnYut9IpXZe3Ia82Rd/aw33ySuke0MsrMXrLbmGjyuoy3jXdzYzIfDMpUVrCfxZE1nmU2SPGijOfaBrh85gkbZIp0qEucIzZkQAMjY/5XTh9hKzlDkSBRc7MULAVv/wkURmLAw6AkFa21uwy+rL5yAVJGrCeIsIe5obRvpt+Gnqra6+p233rry8suZCLiuoCF04HyHHjcGweAl+oJwTRHAmCXVluttUQfI+g7ilAdXfyUn5AVDx4UhNzJhrJhSICyGlMAqQgAAOzkW64+cUGYI9+Z/NTL7lqxRrWFgMk1DMBBgvXB4GxkABz0kDqsEc9lDBDkjmaL6KzxdBhleO6EtSdqSk6BniHJwgUslCyMJMEq+kJ7bsdAKF7dC9pH33YUUo3qhJoBVJE+R9bOI/igPOwdRMw+MffQ3FMtRg5K5xeiljPAgsR7uVbXky6CkJpzU8Ah1iHC0wPJbQHsV86IPQ5SzgPHGju94T3aA5kLaORR/oQgwadIkgn2nvf2OzfmwkNDBRqmKyomMJAHLCfK+B9MO/o5Tg5xzh+ZYNILdTiLkwEKReY/tmS+55BJ8N+9hgucNKNjZgpqwbN48ZPZDpbC5blPknYfl7Gb+UUi3qqDHV2o+vPjiizfZZBO6g2SEoA+1KCZKiaWKnubYNijjTmQhIRjFilySvPbyK5f99RIc96MPPcyLUtUVlW3NLVzGOHvy63XWVAsxXzoL6HXQpeMZHOo5s2b96Y9/HBzZS5Zqrk43xxf+/iIe9VZkuC51T8L9A20nhLEqv7xwIUFRrVOn8kCpdeTI8TrBq/K5ji73al8uaQsElm/dzk0DRMfZAcBZRgLQpxh0mpTeV5YXHWD1YCtALAF0DyZ6poriAyeuhHl1St3vSRJL0QLRAtEC0QJLbYGh43+nz5l70Z/+TAfwXHrswuftw8uzpe5bOYaOdl2UVvCEOhs8/M67B88kXTUnF1x4Jbd6FVn37BosGz/bzaieLzstfeiVr4UgaevobO/sevsNPlafqakZ7a853Qei29r4nmdFPuM8cj5XxYUkzho/7j/9xW4vjVyc8jzbrkIRiAvG1crbhp9C6s99N+xhiv43tEaEowWiBaIFho4Fho7/nT1z5uWXXuos09nVsXhx9xNeu5P0rrn/pmtvS+bM4o61jZ0oeKozf3YF/qumrpZN3BYtWsjNJS6VZ8x+kYcQIveVBX/P61zrHy66mOfWuc7ClYA9dPGA88D+JtXFCynZG0oqZogoK9Twx11N8CUJMPwQXqhyMV0EdfkFX+6YSdZT/2yc/T/Asc8OP1e7lKnbodulQ58S1Hi32UuIuR74+U9/6i41YooWiBaIFuiHBZYiALpUWurJoBHwhBGYJ3Ei0EwHrKKRCaCWpCpubPT8LkUJgXEBL/8zPiS4Vv0eTqanmlDTVFmLAKZVSjGKUIZIMYKR2LBKxCYWSkfsduzvKzkJRV0kgRzxYlcOfzFoyi9vF+VB57wotUUlXYu+SEatio6sZzL6nmjdG6dwTrgeCodcJhxqtSK2EDZMqQLpNnqWU0JC9hAOmQxvALUpOSl61ULfB1nIkoLVkEYOVTIRgPCmBsI17IWhaFUpgbEYLTDsLMB4ljvg8pSBba6BMU/S4KdTVHGm2MmSOgV4IqaOC29kSBCMWEQZGUiK3E5Ar1ZMIHiDh50xUZjulOoPctn6gigM0jdv2FwI983VW+3ySwglo/wS9Q/plwoulSxMdXW2xm03jPtw8pTfcMMN7Nv6xGNTMhVJfR2bTbojBf25F1yz9urXbL3FxN/+9rfvPeA4FzGIq3Z3ts7vkzN0X3ojmTg+mTUjGUG4Y8Fju/s4xCNBvhlyx+V3yODtXng5Y8i32GLTE0888bbbbrzvvof5oL2NBBhJECgNlNmR2U9RYetFLQbgr4lFDcEGIN1qw5bKIkXQR1U/uxk2FOFogaW1ACcsmxwQh8RSEHskX3HFFQRAs9PAyHFjtZwDnk3N2QyybXEzO0qy6TvBTF0dOd4aosqaI3Bq0003feSRRwieJmCaYGiirxRpykiG8plnnpnLBvILFhCjzLQDwFRTyRoWHyf130YYM3oU29sT+4UaBDZBhnBOED7vwmKVm7fy+XZeKvKxoexSSUAVGlb6WUhq0Bd499lnH77kkDp94CWhBjRwEaIKO2+GQG9dMIDmCP8iRJtauMBjkzARIsbUSe341Vdjy0zMxSyqdx8L7XbPfCHfAMA2wYayfOcKTUrhsDYFhwQhnCKzYmjJEDaCpQKWX0LYXNhx4cEsWxPGZTYBYHCmDL608k1sqPYAwqF8VEVh038AW0FUSmyqOLBt9SHN+hsqEMLiFaYU34fkVJU1lMLHYrTAAFog356bNWsW/pdwRoKGeQGp3b+iryAMBjCu6u6778bjjBnh3sahaW1KpDvZUBNe12HQstOzkEwFALyJChJvi9/E/bFfLB6chvBZXOHTCrW0AjFi8WW4RSS/9dZbxChDjASK7EYPOwSQkUBKPVy8xS67RvMJ2/Tuscce0IcB0OF1Ap+AoC/qF3inVbt7LSTVHfwvFxIKMUEwjaInOYxcaWAlzMUWvFwGgKR3agIap0ZM0QKDbAHGoX/k3b1LpMP4FF4waMTaC7FUkRjqRtyHmqLR6dkHmVVp8PdHsrEMIMCJp6ZXlgID2JcoKlogWiBaIFogbQE3y3ucBwZjqnd+1XlSd6Fb9KihFuDd9aen4gk4YK9LD7oYlBByX2St0z2zKl61KgC6Dafcow1fZF3UITM4a6eS1yqkGlpw9L9D63hEbaIFogWiBYawBcy98vCEJzb5Lh8OkeR56kIAnXlPNtNgszj64fxuMUHglpeLRQOE0GUCuZJzoEW47F/VpmkKZe+FU3XdLZeVt3KQQ/wKYeUYJbYaLRAt0LsFliIAOvUoMJxu9JzRbmMsEI1HjQaHOogXLnvsDiwChACrKBg89AP1bN3kCFBDKfkqSklT2xhDjGjKUkJvyQjA+O74HrkYpkwu495pKd7/dfu4gvdidYTqYgg0cghmQOeKSvcYl/tEb3PHVSD0bLw15GWGYQMKm1PAhKIJ+YID6xnuS7UkR6+dpNWw222akCvp4+i1AzHvHLm++G8VqVY3p9zSQuN2oebFYbdfdUH9otd2rp3FIGi4naWWTaPd7TX3uO77DrQiu1BPMl4VnZWAZEOTDCCMY/CpUOS9ZXd/7kLwtUpUkNL7n1AOVBRhZHHKiXFl17zRSHLvwrprYJFiqQfu3PwL72QPySuJ7j5EKFpg+SxgIxxAsAHMXYLtdFBTRmAtp5wI9Kpy56mH/TTYY6JQWybBAOM1zLAD5LNCta2zIWA9BTB8yCWYKqMsrS2LWVoWFDYFDEhJBs/R1HFMVfVdhJFUtgvgjRcCowlhI+gN8OJD+XyKiPdiC5IJj+jsYh+OZMEC9l2bghD35XmiJqqSRY3t+j7R9FnJGacfsMU2W85fyN6WCRGDvI+b66pwu3R3zp84MbPBmGTPPZN/Xu8+CNjVOA58Z869FVNTP7e9PZdUzGa4V/rAaFwl98MELUIjhQ8++OBPfepTSdLOdnHEQyhokqqw7xR93wtnjRj7meO8cFgoUzrqQgmykmHC1kPYDoFRAizDQTd2hCNTuSEBDGOtlx5061FZrUJpEY4WGCQL8PCLQF52hfzABz7AUzB2rmWvZbZznjV/nvN6ftQyhjkNayoq11hjjXUmTrz37rtRhugoH0RVOKnvv/9+PkwPIwFYSFtttdXWWmstRriNbWKaiWoixGq99dZbMHfeogWNWYKX/W0X8U/tre425N577z3yyCOfe+65ae9MKa5R8WEFpwQ7BoBRWBVx1Ztvvvn1N9zAmZurdPcI2heotaWNqOVDDjnkgx/8INtVpiwGO3KYuBRuRY/Q5+qrr+Z6HzhUle2rTznlFOgxCFXWBQTSIBf4xGBx2tOX3fbcg0gsm/RSLQ5S0aYUk28YADRUX4DRnONLR8AYDVwGG2Ci+gYks2+a0tqw9d5aBD8Y/heZWAAFTCvsowMaHlZqKaZ0U2dTSCiFMTyAO1M8vrSnahoaJVNDgNUCpPSBQBgYU1xliyIuFSI5fQuhtiwjvGLsrVaa9E0TNl1WTh+tl+1piBQvYsNWQgLTMNW0MZYSR0y0wIq0wNSpU/Gkxx9/PD4L/5utyLJHMh9JIGmNCbeL4xvdMILNmHfaaafzzjuPWGecLL7MZrNbbrnlqKOOQm0uwk15G+SQEYXJTMj3EwiAJpR50cLGCucWXGpuahk1ZmRrR/ukSZMQ8sorr8yZOxcnS6NIwAOSkAA7ABiEEOX80H33s2l0yvexiTVVOHE+ueD097fYpo/OQXIaJeZ7hx12OOecc3hsRxHJRsZlwEknnQS7BXNTFZ7j+FzUoyHekiJ83OKkU+e4CYxAtMDAWoDBKa9cek9brHENMv5J4dgOa5dHJeSEo32gxC6DSsW9vIgUc8sIK1GTZVA+skQLRAtEC0QL9MsCPe9EmeoHfLbvGYpcVim8jG7nlWtNuQyle07eU2ERcR3LurP/6EgXsF09UuuWj4NOud4VNwQp08DQQEX/OzSOQ9QiWiBaIFpghVignF9b2oZ5EA4Ld5Es+3Z0EMvlhLa0LfQ+3UUfKcyBpziSrDbJ8bvkqU2dw9aN0vwvLQkZkr3LYG83dwP8LutX7E60QLTAIFlgKQKgQw38TUphomEGJ1FreWEmYqYurvt6ku5nlSoamUmGHphHlgKUm2QAY0kJNAlLC0jnkIunpaa24SEzZQwJYGqgs/Bai1UVLCpaLXjrguiRkMII31sOMRrm8h3M8zyDNSFGL4FOMR9JbPJh5IA5fOFgOVM7JImvHRUXgJFo2lLjaLzrhMsRehY5ZmtRcsiRgRzYxQhGXEaptn1eoAyrDA4BiFUsKMOf4NEzMC2SoDG1gV2IddERGpASa3KcPr4VUUqarGcsEJTKEZfRGFCKh1fsVOlxPApCb8g+eK0qAtECw9cCnJ6cWRrwzIrholTp+bKc3VQryylk6LDTHTNRCEtDq6Ioq2LeEGlkpWYRRjJDllJKExJOs0IuMUcyAiXTWlEx5LWqEAkMXoMHuJTLyS1+FAKgVIiQmtUlzXIAEgTkYhTs0d1VQH7oFvyap3ReCWKH90+AR41KGhuTbGUyfnzF9KntREAT6zx/frLZRplXJuevv/56tl7bYYe6V15paW1Nqqv5jFIn8Q0trclWW23V1PH8V77y5W13vfa73327vcLtyZrhzaCekX84KB/6TBgir+Yoos5dqLBlLJrcfvtNc+c219cXfKVT3Sc0lJJef+/Bi46MXlBVJOz1LzRQyv5lWQxZVpqQdvh6baZofwhgERe5hJMriV21gpGsSywISmvBgDd6jAAmVQQD3qYjyYEmREpyzKMFBtACGtJMAnPmzPnDH/5wxBFHABN6xWOxuXPnshkzbXF+MsIJtCLlOzrZ3/Gaq68mLKmmrpogKo18qcTez8jZb7/9+DY9oc+O3ifYSQxvEkFLhG2xOfTtt9/ORvWMcBLsVNU11BJt/N9rrweGnUkJCZLc2uXirjr5PKmXQ6MEV6HwzClTx44f37hwocjIR40e+fbbb//617/ee++9N9tsM8PDSEN+3cttAk0iBpr8j3/8I73ghRAiu+grktUon1T71a9+RSgY8VV2tkqa08HfjxAIhlZsk/kbvnpWW2unvzU6gAA6mPyUPqlWUCmFEb1ym1uMJsQbsg+A7ocHvQ/KsIp2aSjVloopsrBoMOw6gmBKuZzccv5X+JBXAiVKprAmBKSQSEhhUvQqYnPZxOgBqBKv5aWHxmhEX1Y441aMyClLZngozUplRaWQUgykqZ0iKC1aW6VVIcbIAFCJqj6aMDUMgFj05PAa3gDDO7riMVKt0UgfK3KyA6uoPFQ4RUzRxJaSRUy0wIBYgDGGnKr6anwiPuu4445bf/31iW/uSPL4X7yMc45859snJoGFc+fxBYYLLrjAhQV3dBJ8DFJnPXL4RAP4Qw89FHKpJ/kqQobLIwaamYqNk2+66Sain4HBa+7SCXLtVdccevThxFtvvMkmBFITZ4wonS9GjFj8OB78iUceJfqZE9wTuKtfoqjnz11w/vnn8yoUYdZSw/Lw7AMm5hvKGdNm8piQfaAhUxMAbBF97rnnHnTQQfpYhEng1Sle1lKLOG7CwdklmguPpZr3TFoEogWW3wI6yzS2S6WpVnlp7TJjaG7AZS6zMpFx6FuAAeO8SUzRAtEC0QIDYgG3Qpl+3rv8ggO/VljRlm/t+aiYquWazbhw1W218rJqu/VtWnUX6aTCHxWi/5UdYt5PC0T/209DRbJogZVuAS6VzQ0ZsNK1GnAF1DVyPaQN5fPglimLFCLNBRYcc1DXgy7Ah2AfNH1UWaOhqAhHCyyVBRjJ8f53qSwWiVeMBZYxABrlNDszfdsMLgxP/Lmr0Zy+xEEPL8Qwhs/Q++AKyWQgJJilpIAVQ6BslZBW5XripUkBwQiBQCnEAIOkVsTAwghpTZf6Nl9FgBq3dO47BybTWMoCTgG/Q3NX0uF4/ReFsuzh5naIdpWhD+tZIj7LmYiGckQ9uA66IpcY5Gwv6ZlRM09QlW/auVfa4sAkmVqHybtICMftUjvHyiFJBgD7vaL9u0rZnNsPLu966LjC/dsIDgBZuHNGFkPFjEbRyfQpNFqxMfc+FMQFek9GBxg8mIASR4Eqsbui65croQmbaXqu4h27M53aQl93EO1wgzUh7u0sT9aN8Ufci4JJEhw9MP9dWzoqrlHfNGVqPUxmLMC+pqAtsBQoEsa/0QLvWgsw2umbneDuRPLnjp0CIhjo/rsNApktBlrsCpInK2nXMeCyJmIfBfYEZR2dWszLYnZoWILbWDUvVVfSTD47a9qhMWLJUU6tWGhFGDYSI4BAKpliqgolpDBUSY7h4WUMsBLP3qXhxmPa01TEJrAUUBgB6pGoNU0IIyBSgX2asQDRdUhj4T8kkFVDTKlwtnxjvf/HP/7hwoVNI0bUWGcBCGuYsHoNezDvu9/OG2200X777/nqi11bbDXuzTfnEf28wQZVM6Z3jBuZvOc9B1111XVTprjwZdzD/JZkpHdoZ3/35GOOOeacc4+orBj58eM/u8bqt3/u05M4IboWr4UaaH7VVZdsvNXLGCTTtS0boz7+2Es33PB0bRXHmS00XNgfj5L/8pe/5PNsyWauJt0DDIt9/OZxqNydoFP3QRmPHRFhqDJMSGb0QqaqDKkDR5HjghopMoToCTjHnfFptXAR/GEhmOBbWtrr62vCRiUZdqxku+6ZBCgFK4cYC6BDSCCVCBhlSBBtMmbMKGdWfwZZQxGIFhgkCzAUlST/uuuuE6DvtnT6s7n7tCQ+yRe4iuW8YOmLXOcOY5vEFMfuy6+99pppC1JelRzY8AI4HUhFFfzfJF9ZV3XzzTdDzxW8sRAQBkuO1y9IiKHER1aqqphTOHGKV9WukgAvtGKy+t///ufKPRPa0gynoEMjP+s6QhIV0z8tQkBeW19PlNjFF1/sCIOpSZSyjy65gbN+3oAr3UNRD0TuhGcyS+V/ZXOUZ37GR2DqshMLYqHBd2Mc4s4XLFjQh75MdIpUC2mGuP+l45p1MQguGM0ZqGDAqxd0H8swCdM18GHXDIaGdwMYXWCwGDkY5TIgFqaIBNUCk6DRURCsKwFfk85oV+NQYq1a0vBEHPpwnyojEAABR4EDLWVCIZJA3tDQwCUEN7zAJBhFBoza9A5VU2KNxihDySIWhlwyU0jhyemgDUijFK+1AkCVkKJRztHhYGG91AWksTMssUAfx06SkUYSl3IEwqiHAIwNLl85C3qTo67FPFpgoCzAaESU3AYD8p///Kcky/PqNRvvqwoNyv+qUOkrJEFnLucIO0lzKV6gLv4RTbHU/Rd6GGmXMc9jPDfyM0l1Q81tt90GEeJ1jjgGHsTJCTI9SiH8rzuf+e+So/F45qhaf5HMdtQOWS6l9OHFJxrqbqvIwjtOV155ZbG0hL/ITIldAkOs7tMCHFOmQUwKsOGGGzLB8nmfPjkGvZJZGmV4q43Pgwx6Y/1ogM3LudblecVKGXg6X5SvueaauH52jl+5ngtl+KAKe8NPnjwZs/TDhJFkRVuAY8Rw1bBR2ytl9K7obsf2ogWGlQWGpv9lWRX/+8JzLzpbcr3nVoF1OTi4xi3XStcO2+/02muvNC1etDRtc6VauME3XxkIr06JUnh32R4GXAmvC8r/hsiUqBVQpPXof1eAnZenCY5R9L/LY8DIGy2wAiyAd9DzYU5YXrrmifGrr766AtrtuwmuCnhDnkWWvskGpLZ0pkqJ3WM3/O9rc+YV/G9ZL5liGciinpohMZPhTXuWgdgHp7q2tt0/oh/IhpZGFstGbNzDA4qVq8bSqLxq0ZaO6nj/u2qNgKHXW//0vN9qabwyjsWBSyAJSa4EhqelEFBULQAsxiVeK0ITtm8CQdptkhGIC4FgnMSeK39G1k8AdsmRqoLFiwMmSRlyUihTiolLtcAqGgAe+SwfkljecCscPqnRUFo/YUmm6/ohH4x4S2WCSaWyrSABMvRSDg2AKNXH3riENwVUNFEApBSSIsiQBZihgp2VU1SSScUe5rCbqtz0YljXjEcCQwm7P26K6nbChJRY5aHAEBPa02iMURgOYopMqkKmhsjRx9itKDLlGleiMWJJMEbDGyYC0QLD3QJ+/nOZJkPrjp1TGvZx8JtlBHAHyPxAnDHF3oxD+AjrXtRqqdIoKZImTJjADGl2DuXbzEPgWlnhQiKEICFghMBuXITvgLQigMHWCkdcLFRBbCmkhIYia72KczJexUWJ0ZApAN0gIOEUJFwEYLSHKJah++jPrZqjKyb6QuSNSjCmxJoQlhWJTmP7t9paF64HvXI4UHjLLbfca6+9oCFAee21V6Nq+vR57e3JmmsmX/rSl9Zff8SXv3zyWWedtfnmG5xwwp6XXvrDww/fkVhlxYG988473LB97nOfI+z74Ycf5jB99rPHsJk0ytAKwtlY7p577iEcga9j77///uzZdsABG7e3u0NJQ5xHYX/BlCZziFSFxMhHCOalFexmnUpJAC+HpQMUSjBK5IjdMBRJqEfO0ensdAFY1FIMaSDADsgkAgxYVY7T952cKvCoh+VTTVuRAWMKwGLyAaAh1/CjFyoaAZJhnDJlKsCoUSM4vyyQ2mgiEC0wpCzgzg0/yBnMNux1LjC/cS5bIioUDGefTr1ULwxpJwViQXKa+Ba6z6MCvr6ugl+dz328plqXMinhZYtowsleW1vDr66eM75H0Gf/5ZQVPqjIZfO/phJRntgKR0MCqenIauk4W2lOnDiRDbztWFitAcTganayA6eqoel/6RQJVekRxx3l0RbvpgHJGLN+ARAczNwrI8MVVgkGSd9D4zjpXj4EAASOu63Eix7HJMDC3I7BAdABAqsKATRUCC8EUKYSJxTyNdpDLsHgIdCRFWMpDWI5TPQ9JQR61KP7vUVXS2HlqWGjVqhSLTnSwqbBIJyc3nHxY2RGA4bEsYAMXlnJalEVDDlhyi6wsrZ8oCQS1DUvrLx5kYkckyw9EY7jhgvbcvQxDl5eOqQ6YowRiBYYUhZYnoHKyFdfEBKeHdZBnVAu5+6GqaO6OovHrHM/3nYCWZbL2JcKsLaWiiskphekEBPhZbYA0yC8TLl4lhN8YjfuZZY2IIx4EEKxv/e97w2ItOUU8p73vGe33Xb75Cc/uZxyloedU4YDhIRPf/rTe+yxB4cMTzqAp+TS6oY+jJQjjzzyq1/96tLyRvrBtgBzIyOE4cHVFJc6g91clB8tEC2wzBaI/rdv073ngEOK/tc/2OeThEuf/AXjcl0x8sSAZuV/kcbsihNcekUGhiP634Gx4+BIif53cOwapUYLDIoF9EyS1dtjjz32M5/5DJ+0GpRmlkbopptu+lO+NjloCdcVei/5R/LSBj/wvoNYdMYy3Q9VS4kGH1Pf0MBiBrfh7OFFa0sbdjzgzvrUU0/dZ599vvGNbwx+12MLS2eB6H+Xzl6RekVZYFmm0HDm4q4j9eCPdSwS+nMbadO3WDSngyfh4UiQUYUQVukAEKUlUjAkitaWeIUBqSQrqWrZLCYNyVEGmTQqzekCSgKDAR82BF4tKlcvIICMXji9g9U+MZbN2buZHy1jqiQhftd+FHPZws9tmKxfQUg+w17MbNaWzxUCetSFXNLufy25pKUr35pL2pJMh/t5xTBtUQzCgCtySRe/ikyuMstuzQAEA/ERTGcB1998Ta6rKp808+Pj1Xi3onpeC/Z79j+viYOlg9uljvtQr3nYZVr3Crh2ZUy34uq3kgbg5zoMA6w0T0h0VRVF4SVHpjaZHALkFEQFy7dSnrzTHzxyE66GhDG8kLTFyONnKoFBCCnVumGKrdMdxi37WGMBFozZyFw/N0KggZ6cQUtibCgXMs8hgItjkak0ADjVonV5sIGUhfvRnDuavf1kkDDvjXJZ8f1QMJIMQwswDgdBa+ZY52uGaSJsF3+0++6797F2wmrc9ttvj/WIe9PESGdVJPyX4CrWL5mCSi0gYih5e5K8lEByNtlkE/ZaUMyKyaeKlVG4aJ2PR5flBUkADbyk3gJ9oNluu+2oJc4JgaEckMhn5+aNN944xIcw8yrvodI788VWi8VAEp3MllE+As2FoKG/uoCXIDEhoxsSaFp4YweAnQ2e2dpt6jtTxo4egzKkTIIPzlXg6rraXnlx8vPPvPzgPZNOPeUnzzw6e2R9snB+UpNNTjzh/ccc/Z4PHvP+m2657upr7lzUVNWZqVtrvc2mz14walxSM6ZlZvP0hx57+9vfO/+yy6Zce+2Cb3zlvtqKD3/x65vtui/SZ9SPXDhu9NYX/u7fnzr+li+fccmFF/z15z8778orL3/qqdfd5h+4+9ziXI5dM1vr6ys33Gj9uvoavE4Gl+KT9JT+9I7wr44OLOnOAqos54AyPDj0PE027+YFFDIMQnwe72G3t7tribKJRwMMAGLItCRsNBiftkBuuunGzuL+OkqtGw2NbrDBBuygyYEwJAD66NCwrwaHnqPAJtApXook1Eu1a3JokbGB8osXt6Ke4QWgHk2stdaaKEAUF0JoiyrpmSKOxWiBwbCAm0dSP38xzHnsrg79jwtxfj3uAsqpotMhlZcjdLi+B3mokiRkuUjt+SPi0qnk9eytlRTedOO8I1FMEfS/KGuwE6f9xLtU+vSzuWXwv2ZeZhWmILzYnnvuiRtiFqIq7DhFvDM02267bekcZRoyR8mBmmRVDWX/i6rM2+SEd2+xxRY4GrpJUJd1SgC9pmsYKtW1kIyrC10ehEjBcOHct9lmm9CqRqYhx9UFmhgyBeCDcGEhu7jkDrAwwtEcrlIN4cWHwk5tKCFsAucrUSFS9PDSLx5np6pUpDnMAjtbj3C+lKUBybUZV310EGKjgVf+F4OjHkWS1RoAki9XcH2Vks/VFBhqWYQo+t8eMegmgbh27cBtmBBAMvI1SkM8MAqTuC5FPqajLS4DUjSxGC2wAixQxt8F/ldeWB6n1AszbknLpqTmmfDCO/T7arfQek/niy8u+F//XK//rfc2R/VfQh+Ugyq8j3bfZVU4CxzWLrvswo6PfBhkypQpesFmZXXzPe95z+abb87eV+xzvLJ0CNs98MADX3/9dRwT/k5n0AoeeDrf8d1oxZvJ+F8ArhBSPjTUebBhLHD99dc/8MADK3eoDHY3h5d8xsnOO+/MiUy8IOm9733vrrvuyoMmLsmcz1hWrzG8jBC1jRYYXhYYHv532Z+d9Odo9HqzCXO3/60Z0R9ZRZpumXgrfCUpcNx6mMSFdHihDVwmaebkxpm60P8G0spwDSoq+t9BNe+yCWecRP+7bKaLXNECK8sCPPBksfvggw/m6zrsKsWXtXgCv1KU4ckMifBrnt5zu/fSSy+ZGgN+9Z7y5zgUJbWIX7Tfe9970OTJb44bN2H06JGmz4oHmv2HhvC/I+rr2SAMBZbKJhBjW/IBSbR+2y23PPHYY2yXteJNEVssawGObPS/ZS0TkUPEAuVvMJaonOaskEz3M6xlknRnwvQtpJGpGObQSBRTIUtuFGFnVQyA1TstOoodMueLijOmAaqFxVoxABaDAZCpIoAliVXTphj6A0MjetSguZQokwAgIQCs86GJeNW6chMVCllOWAogJFRVMmnUJxxSdyNgKEjbQn3xj4wpUeopNQBqQsqLvVtcT8hqTSvVC09uKeQDKeFC0hEAScCMYknRq4huNjYkAQwHESQJWKIgRohYwBjS8BwvkJJAi4w9MEjQ2EOO2EMlgaUYuXh9my6wmyodfbUoMoQoUbR2gUFKrClM0yTTR0LefTm9VsdLuyaLleJXJEaHaUW2uIq0VTwPyvw1C/Q2MIxg1QTYI5AImDPOOINA1d4scNRRR/Huo/aBDmlYs8Ti7F589tlnEycUVoUwuwufc845ISYFf/azn33/+9/PHJvCq0gEz29+8xtuXMvWgvy8T73Vgv/hD3/IEhGqhjQ2HtgB62c/+1lYFcKQffe73z388MNLT17dObNC+YMf/IBQm9Rn3BHCfMtsT4gz74+mnI6mI+Zkes1byOuuu+70mbNhMa2Y0dk2+8WXZnJjfN9993Xlk7FjK7fYYvWaymS99eo5ZOzrPH369Bkz5kNG16644k6Owj0PvsFe3givyiTsAP3qm24HYvZBfPvt+f/6178IGNpkk5EE69IKys+aNWvsGsk3vvEJ7sCfeuql887749xZLLVS6wyAgwIgcu7//b//x2J5a4t7b8dVOCVdTitcGZ100kmnnHLKqFEjDenqfKLvhOWxUzXR83iiIrr7LwI/8IEPHH300UVH6qrQjWREHNsdd9yRtoT0la4WDDmjDgIjBjAyIT/xiU/su+++RiB2QuE5NEjAhl/4whcwZkNDrdEYAMFxxx334Q9/WDINbwDnzplnnrnTTts3N7dKsnII4G1tbeNRC/tmSVVC9IwxAtECw84CqREu/Xs7NZaqd6Hk5RHIiVY2LZUyKWIEpjADW1we/6sJmSDRk08++UMf+hAOGocihWVG5cxvuDCinHvTnI0WPvKRj/TW0yHrf+kdOhPY+pOf/IQwaGB9yCLVTRzEF7/4xd7G1dZbb/3lL38ZEylIGiGWkIOF3/Oe9+DBgZFgHhAatUJ0Ndsi7rfffqlGrXjEEUfgI+AlGRJg2rRpuCGiVU4//XScbFhlME6K7z98//vft+asygB0wwVb0QB4GVrEB3/nO98xZApAJfyv9thIVVmRL0jo0i6lPyqBwbOn/K8xCmBosXNGCmmiuGQq+t+GFI2KjGpGZtkqkFwToh46hARmKxYV8L/0jiMLzJsAIdmqBmNzM/uq1vdVrb92CtBxpiyKIabUGhobyktrQ4xElZVWFhnyLhs8SGKXTZl3ARf3faS33377mWeeYQ2Ym3c80Urs17PPPssQDTevXblHnPtlHtFzV172taKUoVDVUqpqmYvhLI0T5+1ZvZy2zAIHhBFNuFR44YUXBkRaFLL8FmCc8AkvzmISJ9GDDz5Izue8uKpcfuFRQrRAtMBgWGDo+18XJLzyUsr/sovXMuviXTNPYonuWopnOKH/RRn8L9dI2nZtmTVZfsbof5ffhgMrIfrfgbVnlBYtsAIswBNybni5keE1VxZteW5MkTvQwUhL7A4e6sknn3zrrbeIDuJmfIn0y0zQf//HYjH7Q7HxE75vmZsbKEa8Hg9v5XxDv7xE+TCSYBmQRHOs3rLI8vzzzy+x6UiwYizAkY33vyvG1LGVZbNAhid3vXHuvffekydPZkLRt2ZExpg2enwDsB4vyjnBwofYcFeEQ5ETXqNack125LBALCGq5XkQC8OsjML+8Y9/nJUwIoEIKmJ+Z2K1FiG25kwgGGTyPBQyXrUnGoaq//3vf0899ZTimGnLJEAsmJwEJbU8SCVx06tIIGbzW2+9lfdaVCtKmsArCzYhRgAAkkVuwqTwkXfdddc///lPjAbe9ddtlIaZXMfBIMQVSTm31WXe4wF4vsw7T0R63Xvvvddcc02RrPtOV9s35vxeyZJT3BPKLZ9gqBNPPHHHHbdmB4g/Xvz7mTNnIdPrRZe5uXTB5UniwtfyWZdn/WaQ7FuBBaqrK+bPXXD44Ydi//b2zn//+9/PPf2aO0bZxUioyLqO5N0e0mxZ7WLT/a69lHzKur5zC7r/AQfyphT9vf6/Vz/++OM1VW4/DHZghL7YazcYsIKTkO9e8gFJwm6EMXGRcf/99zNyiBtTZDBVagdpJGAwkim8iqxS4/xYTma43n333QScjR07Fo8IpZKIXad8AkAax7Szo72+wb3HzDouA+DNN9+88cYbqRKBeHWdAVIhYqZALu+OIKv7dJ8PUhAh99prrzFymv1+kxAzrtScJEgaktU1OstR095dLGATBIba11x99dx5s+tqG6Qqm2Ox+crNN988clQD7aIDQiSz7zzflWNVmwA7rt4Y0hCLkeHoYad5KkFAEwHSHXGpIc171GpUB9Qa4SB8Q71emUmI2vJwYYSYJLYVt4a8qEINm2sbzbDe0DfoRQQLFih7xIejdawjKN/S3Lrd9ttyCj/yyCNdHeHoXXLPONNxB6yX4Adxo2Kw08GAJQvy56NpxekML8kwfUiArLQWCeiWwjOh4b+YBr3sgnBjt+YMI/ZSHTTHhmSCkU9kcG8bUFHbx8RoracaBU9iZsZroAn+evbs2UyVIK134JmlIWCizuT1aqnve4b3cwrutaPVedg2P+Ftvek4fPdtt//n4osv/vXvT2Nu/9oZv3n11cUENPNiKnJrapLV1mRSTZrmj+SCJ5OpcW11refy3Eh68bs/HXPLLbc8+tBsnGBlZTWtd+UWr7/BmJmzFrQ2JxPW4GonWbgg6fK6sGFia1tSdDLJmDGYaGFLsxNW6/fZxLvzKQu+ruFQ7kqJ+LAadZCukYA5oBjQ1zqH6AnLZKJEQzFCERrKGCTTigAIJwqKm3YhIQhrBSOKJDWEoaFSMmFMAixgVFQuTEhWignFhrXgrSiAg05kIaFvXBBWV6c3Lg3llMId7V2HHHIIC5+cv+H1c9GL9eofS0VFzLvYAuw9mUrhuO+++ndX7I6QkWnjP8U4IMVSfdQorabkS7dMzqnVFayBSc8UMcXwPC2tLcVIk96kiT7UtkjZ4y0Onp+ymSLnb03Rh5Y2VBbD/DNQ/rdUfuoIMs8w9ZXax6YjkwAGytK5EeRQ87/Wx7AXGr3K1SneGgawvgfEheMIhucDLHByO88Fhl0LFdmpLwQ9yzel7BMIdBypooQoN4WNLMSAVDElwS6HqE1VhcKXGTb59A54aeWgUqoXKQm9yYeLSyNuYEsHWyhhifKNGEq1JUOJsW/dOC/YOFz+126lTWDfAGrb+Rvy8tmrvhlj7SplgdCDlHa8b19cSr/8GOkTtiuZnC8p4cwFdvrwHTGrLaW0Kp16VhwMwFRCeEtL65ZbblH0v2VeXxwMBYagTI6IDgqbFn/0ox/98Y9/zMzWTz1/+ctf4vIuvfTSJ554op8sA06mYcNnBPjc/N/+9jduasxrDHhb/RGIU2ATSt4Nvvrqq/tDH9JofOpwhPj+w5JALs/I200sXlx22WXc4fL0gMWL/otaBkq1XsrIIOEd+EMPPZSnB6w+lBJEzEqxgI4XawS8xcfaBBexfY+Q5RmZK6WD75pGWULiNUgWv2bMmMETG87u3s61d02XV5GOOO/rL5/eBf73hhtuYK8NRqZ6NAhHUPeYpVeg3U0tj//tlrKskM5K8uh/l9WEqxCfRkv0v0P/kEf/O/SP0bJpaHeLrOESoMVOW1wG91OUtuXirpOArn6yDBQZUwfJXzu4Zy94Pe71uD7kPUaKqhqotpZKjvnfa6+9VpFFS8U+UMTccvIcg609iCG86KKLuK/BI8spD1QTpXIweykSDIfpYx/7GFtZETrIEnxZmohc8RbQ8Yr+d8VbfqlaZIpmAZF9gjh3Vqn730KA5lIZS8R2AwbAKCeah/hjnu2yi5W+wvbtb38bs6aIDQMeLookbYvFl8q/973vsWW61r3YsxA51DLFk6sV01NFJGjCpcgLMWeddRY6MC/zeJRoWoJodfrBBYHxhnO0ieWRB7tMEUNMW8QBsyfTTTfdBMxlmfECkCSTKomliJ6wE/nN0236QuApGOJoCZxyYdN+TYPmTRnTRAAdJDJpjz32QP+tttqGGGgavfzyy1NkqaIpoJ6xixU7KY4axTPY2jlzZ1144e/xFFTJX6B2yO408QjhiX7ebItN6T4hvNXVtYRtnfPGeRzQ6ionIYhtKHh92EOBLDmPHbcaBmcbSPDrrrc6x3H2TGd8kRkxgOlhIAAS2ACMQ4+S7Aj1i1/8gqfbwFSVNZr6LgJoSFxXHXbYYcyzRBJzAj/00EO6MlAtlJJD0WAOGUiO0cIFc4848ugPfvCDLJRy6AmbvuG/140eMx4JYldHaJSi5AAD5LpcdNecWbN233NP9h9FFBZgAFx7zXWpKzxJEC8KMAKBSQgEZuSzwSovurmDV1195ZVXKpQQAogHL9G6OkUTwGoowJQfsaLsTTPpjDDIUvqraEgAnwrvJwDrxESBUJ/B636UPHQsYMNv6Kj0rtGEM6vvvpSeqn3Qlz1SnLlMm2qotDnDAJRlt+aM0jACkN9b9DMEyOxDbG8yYaSK2YbrDeRz/8ZknmoXsRBoXlKVGnL+08+XAopzZ4LTfPTRR3lC/cyLi8888xeLFiWzprgA5aoqdqsa9fY783bffdOf//ILBMSf/d2r4UodGRwQe0lOn55vqB2PMrSFQ8gli4kAI34v0+Aip/kYFNHPhx2xbZKrufHGx+rqu1WeNnXhhhut/q1vfYsb43vuuY8Kr383gde2MM+rI+T0TnA3XTlIZuzDmOWYHA43yttlqVprEYGCDUhR9l1cBn36Friq1ZY1oB2dVc0aQ7+/ZY/XYKvtzs2laYMphWkTLhIw+dJwv6to1ffUCUUxtEkYoNl35+FKiRI9Rh6y/jfsUdjrEC+4bNeo4tEMvWNEySeCCeUYbECp5MHDmM4GDFJby9a7JXKZ2ilK8CnMwPZrUIUPrKorRhoGkU2wPGnFNBpbWRUsEMfV8D3KX/va16Q8B3FlTQtql80duLUcCpbkndKXX36Zi4GVaBOdU1jjkksuIafIVVzfsa0DYrrSMSBNuMsm7jmGPg+IkQdJyBKjnwep3Sg2WiBaYNksMHT876uvvor/Nb+zbN0ZEK7of0MzRv8bWmMow9H/DuWjE3WLFii1AJ81LkUOIKZvf8rdFgkabu64/x1sZfrZL/wviefh6NZPlsEg01vcvPqLcILuCHXr25gDokNvXSaa6+8+DUgrUchgWCD638GwapS5PBYoBCgvmwjnHIqJRUr23OXdFGKA2MiWb57yfTqmSCJ4EA4VOfOjTZEs2QqvdU1CfHg9iM1+WOmkitflYYQYx0MODckx+xRqKzxkzIAowHNAGuWz6WpO7Yb0ajclh2epyEFnYn/RZKuttiKMFZnoAF56SpTYTRnmfdrlxWIY0Z+cZ6CEgG+yySaogVgSbUmZUA1hvHDiqxI2Xtxzz/3WXHPdxx9/avz41bbffscUsS9isRza6JhZFyQfTWi3orqmrbNrwuprdrkIVM+Ur0zyxHWx02RNkieiuSqby1bkK/IZIqtqunJ1SWbEiHETx662wZgx49j+ubnjgc22bh45YmySr6zIjsxm2B2TKHmLDAMw2B1QDMUulbUNIzbadLOFTYs788mmm27e0DDS9Jd6fpiw1xfbL+nn6j3SZcDsn40lSbxOhNHAiNHR+UTREnbzpnM0EGNqviMMQOgzuzhz9METQ1xkLf8XGoQQ/VxZVcvAo2nGHqHnKFDfMAqtONYkqReKgFHjjQDoqkq3hed6627Ans2VFdWjR40dN3YCGGiUAyDEMK63QaKWhPLsNIkZUYAziCGEYnQKrkFNUizVhFQyJOMKjFQW0mBRhjlqUzTeEBAZ0kjg1TRmFItkWpWIxR7CocAIRwtECyytBezkXVpGo0eCwSlAwpeziT7kp5qz4lKxmJJiZ3qxaZDpqFQUSC4qIKYKShHkCYDOuG8xVPj5zF8jJOusXjFtRscfL/zTi889tv4ayeuvJG+8kvC5hQ7HPa6ycnX+ZLMTdtt9m2132Lg9157BG2fa3S/byC9bNbeyZv7iRePrqkdzFeM++JBbLZusUVOT4c4h1zG6rnp1PAkbTm+/U+05vzj72989c70NcDS+H/lMNsMHB5Lddt3j8MOO3HWX3f3+1K6OKdlT4JMgKoD6U+gLf3wyO/QgCgpQGaPBQb0Dy+JDJHCqaBJUpdyQ/QRKuXrDlG2llLif7S4PGY2G7KliWBXCcohhHtb2B+ZAh+yCccTyxaVV/ZEZaZbNAuxbnPpV5vlcS+EXVi2bfI7mUjGGLYawqWSAaiVcMPtQAliiaRtRBljtEoFQfm/EoukPZW8SBhvf28QifG+1fWjVz1miDwl9Vy2D/LIs6lrZqnIKuHvb4k/13feJKiOKEZUSqMlKBIJTBOXaKo+DsW/esgR9s5RvaWmwK1f+wLY+sNKWxorlaU0fZicoKAooT13EapiFebGmr7/WlohUtNGr6VEPi0xyX+KGfx3dTHXCrNGVzXcWP5iWohnwYuhBSmFzdgBh7YCrYQLVStiuYN5/TP3kbcnD7Z9NTghgWG5hUiMwJIjw0LfASj98jCKmKaUl3ioOqj11n+72Ful5A9WfRmFZBq5QsthlAfa80M2Ulxpc+4YMgwyr6ZV7RAa5i+8S8SsgPv5dYqnYjWiBIWaB5fQaA9UbqTGYyug2vC99l8f/9iW3f3Xqe/S//bNWpOq2QPS/3baIULTAsLKAPR0aWK3xJn0nmoOAaJzw67h4wIFVY6mk0TrWwAOi2FIxDh6xopVWoj5ao1cHMc7g9TRKXmYLRP+7zKaLjINkgZ4RMcvUiGY94k3ZcZntDwnWIQj16aefZstGAoiZGcvORzxIpTV4NZtT5AP0999/P5v44mnYIpFAZJAQKEkIcKgjSCXCkAhdRQG2XqbFN998EzKqoBeB2jL2EADW8sDjjz9Oo2zE++yzz7JnITKpSj1mldqmA4xgiFul0cmTJ5MTEPzWW2+98cYbtGuBvMDGYrAUo/t8IB6YRnnHl0820wV9ZMFYygLoRqJKAC3yDT5EoQPbJxMpJVOpVjnEat2QwjQ1Nr799tvz5s3jwIF5/fXXiWJP9RS8kulPETn0nbBvdOYFKZRnH+4XX3xxzpw5VIlegDob8po0ERC7jKlJKG+rNeLCRCTRhzm1KImRCUyDCxrGwLRp0+bOnZtqOuQCpkV4acvPyBWdHR1Yj8NN2Do25OWq5sWLoNGjbSgFmxApLDzIUaPH0SjsjHYEIkGXJsaOhiS1a0JUC54uMG7Z0QQJPE9n6C5udsbv6Gwz4gEHUF76o4BgyzEjmjiNvW682AAgC6AVZGhOQiVgYcjdESom2AHhktqhZBg5uCaEsUrCaCAh1slLJLo1RLvWyoAbIQqMFljVLKC5K9VrTrEUZmUVSzUBU4pMqVe2Uyma3opLFG6MUDIvaWoCyVzofzggvgOQVCcJ71/xis+jj7/yzCsuLrmuLqmq4U0Pt20zF0VTpkzhkwM42XN+ec5jjz0G0m3q3NPymgbxI0yhTIzunZzmJiZSlnqRwBsy/qIpefHFVr6MzGeh5s0z7ZwoXqPiQ8kf+chHzj333JTkbrpyEAZcHhuWEzlYuLBfIbyc7SEqlJYqLqfwfrJzCPrTrtEIsGI/W4EMLwwXI7nosZ3HFwxefl/jnOJwGRj97/4qRckRXMH91cWhxhiDSiNNuQ2qFaxSbK6fFigdLWBKkSlpSztFhAK9+PQQDQmsLbVStspoAFLKLJE+5DW4rFZWuyIB686ydaT/qpbKB2Ot9yanlKtvStGTK/VGvGLw1jvdq1Lsj1ZGU+iD/7MMCsPHfKiEAvK/ACFM7TJIHnYsWJ5EZ3XvjykAsINZeNj1aCUqbEbTAJMmGmbCkK9E9WLTw9QCXNFZWoldYPTqNnkl6kDTzFdownoBsPQBsxJVCluPJ/hKPBB9NB0eoz7IYlW0QLRAtMDQtED0v2WPSzi3R/9b1kQrHRkeo5WuTFQgWiBaoP8W4ORd6ecvT1F4KoXOxA6RM88vMfW/g/2kpEXuN1e6KdR9WUNmIe9nFwaWTIdABgFGDWEGtpUobfktMBQG7fL3Ikp4N1mgTFzp0naP6QYW5h3CQL/73e+yjzLRqAQTM9x5YEquBAEAlNALAGYCBYaM0FUifs4555xnnnlm5syZxE8TFcqeysz1km8spp4WSMROzBBkX/nKVz75yU+Cv/fee3k0SRXshOfqAaUY1Totar6mSKKK4MsLLriA4F02A/7HP/7xzjvvIBMkOYpBIEo1Z6IAiB1hB2hCfm+88Ua2vt5pp53uueee++67T77B6c+X7t0D09A9uHBPQpTdY9TOrra2FohvueUWmjv44IMn3Xn7f/7z37oGt3O2JQgNBlDwOJKZ7Du7XF+I+sX42+6wLXa77bb/VVU5axOmmnW0gO5xredznUUY20Hmc/lcJ4bIjR+7enNj65e+dOqOO+5YXbvw+eefb+9oTzKdua4a365z9hRdrkRfTJ98hp2Ps1VVfAqQMGICqV964fGZM2c11Olwu+YybF7jLWBMHul8lZfvInJ+/vOfH3LIIQyDhx9+mNB5HSB0xjIk6LEkSAASgA4HuVstq6m59tpraZ2tu//3v/8RS81BEQGUxb6L1dmCKgoMDODmzs7RY8YQf/y73/2OLbQRAlwgLf6BXqOliHAXQO7IFp/Fv/DCCwweYt8ZNoSgIRlKaqWwqS12GgVDpwAQS9AzRrv44ovZBJrilZdfXl83gipya25AAPRBrHIEAnd05OrrOXRV9IUAOzqlgJWFCxfV1tYw8tGNkxFtwYcHC94Kd+icNKoAQg2hF0Ys0ChxSra3d7GyiX0IFuSUIVy+ubmN84v9v7UBNnLa2+dNGL86UdGwIwqtAJx8d9BiihaIFhhyFtD5zmlumqXmBMMPX8A7ImIy8rzn89Szz6+77gSmR1z2oo75buasyM6Y3rbDFu6TCBXVjQ8+mIxfI+ngHZZ8MmpUUjOyntDn1tb8osZ3zjrr5VyrM8OmWyazZ+VGj05mz5hJMZdrYNvmRU2NO+88YpPNG6ZObXvrtWnZagKgc3zMIFvRsri5paouwS3wfYu//umBuoZk5MikqbFgUSbJ6urknXem6CiwZzQvkrz7jkKht4PzxwbwirEbreAKFy1o5GKPV9d4dUrdKvi7nn003fCJ1KgY6glMUm1PVlfqbO8aNca9HSca2BnPNAQAC65ZV1kUccRcD2i0iwCWUoERMywswLHjoPMaG5d5uidigpoza25tvbtGHdjEaGlZ3Nowsp6LN9olMTHSRHOTCxMZOdpd0DLYVDWwTUdp7yYLMEI0v5Ve9Bfx76buxr6sNAvwwIGXvZkScXmMOopc0bl5jMdHuaRhRD2TJz469KqCNQ5hMdU1rYWUYZXurN0rbU1NwFSJXk+ZgBFIQpk111wTFyzJ5CigOdOkDWuAPnr9uYbBdAXrdXV1Eu0MvjPb2dzZnlQlXLtW5Kvpu39+padYw7LfuF32huBOgQPNBV57awc7NvNxl8HwvzhfxPIkJ8eH2NzXadzzEy7keK712iuvy3wgOQQagcPSoKue0kxHZWeVVc8SrseaGFdu321yNmUMWLmKqfUhpcxQMEjUIVogWiBaYNksEP1vaLeh4FykA7kA1DMgVHVlwUNKmZVlhNhutEC0QLTAu8kC3IYXn18VurXip/oV32IfR1DK6OlEyjJ9cA1sFTrQdHhohpSJBrazUVq0QLTAAFpgwAKgWWJn0ejuu+9+6KGH1lhjDeJxuW/U3CR1wxkKDEUIFHkJGctOJNbLn3rqKc2kWrCHhmI4owkWDbAmX9aNEMUCw29/+1uEs8hEhCW1YmcNQK2LGF6L9pBwFpxYEmDn5iuuuIJ2WaZiB2iRsZYPoIRkAPUL4SSKrDfQcZAEdP73v/+94447UAY8i2pUQcMqj9gd7BPFEPaxJlVw3eATJDVVhd1zRV+aF9ndohFNk6M8vX72hWffeXsKLVbXuioaUutaSA7VQALWwEIAWL51cVNLUxMGnDNvAWFb2aSmsqI7sLXYnFPEK6/cdQchhH135eZjfzaxpsuV2Ta2sCT4oaCAY3LJKUMqrG1Ll4KSCJk9a/o111yD5VkuYix5QqchYnUQHW/xRtfrgFLu+EIDPZrfeuut2J+lJg6ZUXqmYtO+AK8sxkEHrquvx24EvrPxswv9Yd9lloeqaukItWIvzWkUpFpHDQbAbbfd5hTyVmUYixflxQs9VcDKxU4RMohhf+6551CAyO86HzwtMvEOeO510yng+kjwsc4XtmDXidDQUL/77rvX1NQT2U9I96RJkzg7rLPoQ79auhYXFWP4OWuwwoc0EjAD3OfO1AqCIfh5u+22JsibbvKCxPTps9Zbb+199tnngQceRtree+8NPbPHRhttNGb0OALZr776avQBSS3m4hxxJ5KNIqCYogWiBYaSBcJZS/PAUNJueXVhIiL94AffI0rmsiv/evrpp+M32YkZucx5bKJL7UEHHfTFL35xzvxn8f5vTe3glaTZc1vfmAzJNDAbbTzq2GOPPfmzu/OWTmd7w7777vuD7//qoYdexVZmut/97gvuJa7qWd///vfvu/PRLHtLVyfrrFM/a3YzodInnPi+T3/60zQ9duyiv//tOpzOd779K70WxCTZ3p689717nn/++X//+99RTNOm+Rr1P2xreS0S+ZfVAhxuEgNm0aLGjTbZkGseAqC55kQeyPb2jv333w/ni+vk3arXX52crey+FOEClYOo44gQqQAlB5qirmxDvURD+CnvE+633368W8jVDsTI32uvvdZaay0unBjJBxxwAH4ZF3zkkUfuv//+1113HZdzXNrRENIkRHAoPMJD3AIMJ72n0dWRW3vdibyeykUmF8y82DaARxNRJK4AuX6bOnUqNmFAtrayY32Ggc3AQwfaZRRBhkpD3GhRvZVrAQYJCvg5pzC/rVx9YuvvMgtoIuLmFMfH+2w8PcAb8swKt0hqa2nfZ7+9eTuXeZI3+ZfK/+KdU7ZCoJscMxniUNdff30+ccYntnjJHOHbbLMNSMJ8uSDceeedt9hiiyeffJLiUUcdxWvYvArOq9FEtRK0yoMRfzqkZA/LojdGd0wn/eLxAldDuIy21lzCbgOZpL0pl2RaKyqzw/21Z0U/84CF99w22WxjnmwwzHhUyPO6gT14WJVrvPXWW4/Rla115sWwuFoG+WuvFaKfV19zNR4zgidBP7AKRGmDZAEO1iBJjmIHygLxGA2UJaOcaIFogWiBoWOBOLcPnWPRmybxGPVmmYiPFogWiBaIFhgQC5ijMWBAxA5HISkLpIorskdqOlQghFekJrGtaIFogWFkAb+16vLpy6N2v9rdqo1dCQ9iE2JNQDxk14P4cD7ioTxIJfBaDmftnNANok5ZQGI5xMcEu6hlkmjIeZSPQIkip1F4VYSesFeWrIglZcmBFXdq6RaikKAmyvYSgdSSowCa0wQ6sFwBIyv3wlCb4qVRS7RIx1mggobVhba2ZgK/+fIzcSwdHWyE5tSAmBw5Skk+m0kIcSZs1EWOKtEFlsTofl2Ns0AR7f5iJE/JwVKwKYtDXZksarOM4VYy6GNtdXVbS8uCuQuqK6swXyZXpZ9nsaPc3SKMBE5nsh35pC2btGcrsHN2xowFSfsWG6138OhRo13DmdZ80pJPWpMMW1miQ1eegOqexqB1ul9X35DraGdrG+KmSb6/0taJQXl3nAkO9kYQilw2VLFhxOjWliYw9J0jghDh6RpHhEQVbRmXeMk50CwZklPLUQDgIJKgtOEBGUUIlCgiVgmYFgn8bV68GLiu3h10WkcCxGBI8IoYQLCQEIAnF3Fb62JgQj3QB4HQoICS6OGFgBx68KhKYriChJ61WPQHJrlmBiFJuHJ0IE6dnNcVGHUEQnHWUFy8uHnixImsxbIoS0gWwxv7cHJRhZKojZJQjh41dkTDKH4jR4wkZLmhfiRi6SY9QnFg6GEkYUyin0eMqCOgebXVVttkk01YAqyvr0Ema8+0vtlmmzHyWbf75jf+b+uttgXP6uDaa69N08ihOSRIZ/JBsEoUGS0wjC3ASWdJ3eAk7Wd/YCw9p0Aae/9FGUsIMD1KtxC5RDhUYInEKYJlaC4lwYpmGXrBbCY8323oyrs5n28FTJ48mdmJKiqrq3lFpMA6dsxqm22+zfgx63ztK2ed+dWvb7CBm7U6O5vmLVw0Y/bjNVUTKjJjmhZPnzP3jV132XKTjZmE2WIfozfl8o2PPPmXR5/6a2vurpauRwn+WHejpL0tefutZtxuazt+qDqTr8nm6ztaK+bNWdiyGJRP7jMTuYrKpKV18UsvvzBn7iwip2EvZ8nua4ACb59/Qgn0eonjwegN6FP8QFai3kA1aod+IPXrKYsm/MVDgrfF/xK5woUHvg+q9dZblzeC/n3NdThTImbAaEs/CeAQ6KqDYmtLW2szbpnLM/cCEm6UOHtyRClxbQaGS1maw+0SbsVFMsRIOPDAA998881HH32UoPwddtiBF5PQgQ+YcK11++23ExAGF5JhZHyTk2CMadhZgMPX1emin7/zne8wGAjCu+yyy5aqFzr0fZxcVEHDBdurr77KSyCnnnoqX+N573vfw7glffjDHz7xxBM/9rGP0Tqja4lzyFLpttKJXQ+LScr0v4PwlZ5WIK1TpbVW1R+A01yq9YfYaEIFDNkbkCKmRVJvxH3j6az6y4itrKzJZnvGkrrvDhVdbN+CgtpQPQk3DAAYTW7GsczKI41kcgBSRVUZUsqE9H3ATrRPfdCk5EO+ROIUASxL7L6pXSqfqlJkKSbV6Moqohj36bxrtPrqqwPz5gYekxkMfbbceovtttvuhhtu5HlOP/0vt6jyv+T4XBy65TwQwCPjptddd10a4s2T448/nlGHe91ll1344hlOmfeNuQF/4IEHjjnmGB4IPPbYY/fffz/vHnM42FCfhG1JK8tWA9WuuoARGChurPi/nNLc3nfkc5W12bW3rN7hwLW3Pmj1jfYfuf/7dxq3dnVXviPJLuN8MlBqL48cf9xcX9dZb+0vfOELDAyu9y666CKTyUgwuG8AIb0R0AojkMtFnqIcffTRvEKJw91jjz0YY1zInXLKpwnoHzdhLI+Y+hDSm/CIjxaIFogWeFdaAJ+a+r0ruxk7FS0QLRAtEC0QLTCkLJByvsP+FndIGTcqEy0QLRAtMGgWGIJPk1BpCGo1aEdgeQVH/7u8Foz8w9kCPQJtl60jPMEnsWwDO/FAxEzwlJ9kgRqaj2xpzU9Q7lE+GJ7aKyEBFnLhBRA9CQ2P9W1G40E/MEgAaMihBwNAc8RrEplJEYCFKCSDV7sgoTR6sahIruYgUCgJRZQBjwTYIZYQCGiFBKAkGvRkXR8M6w3UQi8aMaKyGioyFdTwT94KygMjBDLXbt4FZNOq0fcGQOaNUAgPlarICRUuNt19ZwEGAmQCYD/BtFuR7XK7F3VWzZgxg4hnb3y2cOOIODoCv2ApMLpnhsVeZDzgdzKGDIvlXZQ2pN1WckWfxA7YrU1RDlUjR42lCk2wHu2KmE45LcslCGR2gGIgmtv5xuvsGEJGSQMJQKJKZMAANErD1CKHnJVRHXFq+5Aj+eTYilFK8DRcRM8jMGwaCaUJGg0VsUOgFv264JIPfanAfmKkWDbrIryJhaK/fHJ33rx5rJOx3SNbYfFaAWfBOuusQxQLEX4oyaIs0X4EK9M1DMWZhZDGRW6/dp0v7LZOcPMrr7qtnTkBEeXD5ROdPtAghKXlpqYWDMu5SRNUIY0FZjAPPvjglClTWBdk+6vrr7+ebbGwJ+vEuXyO5mQWekejOhz97GkkixZ4V1ogdSJwgnAuhz3lNCHpTA/xpbDIyEurhOmjqjeW5cf3oXwfVaHC/em76FPGDJXHqpp8lMvIP/vZzwhkeeHFl5kzeZ1D9Dj/zrbFRLqwJfMTTzyxxlpubp86vZLdoA8//PA777zzzTcfX2utNectmnHrrU8/99yxixbl5y9Ittjizgt/d/khhxzy/kO/gNtxL44kyb/+1fzggxcceUzyn/8kZ5y5wxFHHPHPKx6/6qqbquuTxYuTf/7z+ssuu76uNpk2Lfm///s/v42rVHA5o+DJJ585++yz2WGOmFjtRRh20Lv7bvq+ITv0S7R5KMcsb0BYWxY2SmuxLFlvSGPvjUB4I+tnK0bft9hlq5VwBtL48eP5AgPOEV/sr9wKFy1cSZ56+ufvu+8+DuVHPvZh9oHGI4dtQUy0NP4a90rsFJ6UnXflTwnkwkczaGmFQQsBgS8AzU0t7CiJ1wbf1taOC8ZxwwIB311hLO26667wsvfkxhtvzItJyIdrqY5+qGGEh4IFONaMBLbSJNgOff75j6vJiYEm5I54O44vaYl6IqR0GOg8Uk4TDEgmPUbOZz/72a997Wtczn35y19mTB5wwAEE8P3+979nviLKkPG8xOaGOIGsYUrS95QNsQkJMqPpDXCHpvC+aHkSHT7q+iOtvIilx/ZTeQkuJS41yBJVUO/IneH87fPIkWOYmpbI2B+C0HQcKUapuDRoTQLtAi+D8iYhBSCQpkmSrFohgUNNUoxDuZjqDqqC6afCpbz9ZBxwMunMZsy4TmYqDjr+l5tNRYhyI8xN6AknfPy1117jXdyl8r+4V8QS/Sw/y2DjGYUeU+DEAZiH9QUGhjefOZKT5abY3fDmckyYNIoyio1+8fmXkrrCa/wDboQVKVAGtzOCF8yBsxn/kCdfePTB9fN+++265557Tm+eDv3E2o14UWf22y8W34VfkfoOTFv0gkFFT5la+NoGfvCaq69FNH6Qbt57771MAuEjjmVulQZoi5zBfNJJJ/3gBz9gBH7+85+/8MIL8fXf//73+aYHr5TznTE94NLhWObmImO0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtMDAWsAeVrjHKDFFC0QLRAtEC0QLDIQFlrz43XcrOCcSz/HZ3AtKFnhYxdFDdop6Lg9BKET0wrBKRAJGAoxCAiuxekR8pNwetQAkSRMMAUlIQjeIvIQRAgI7WFcGoAqZJlnyrRUThRDJJ+6ExSdy5KAYq1PWLqKAoQyFCGbBjFqWuoj7JJCYmG3aBAHAj/0i+SX+x0aN1PSUQK8LHVdHkmxlPlPhtnR0P8dcpOdgsWKEMd1R8z0j1rkCgfahULZ1rshUZjM1SZ5wXjaBdJsxk1CMzXmLsGtRfSf4AT29AjkoXEXlC/ySiib3y7bn3d7Pri/SRFqxATW/fKaaH0s8bLSdSbq6fxyigpLo6UQWfzRU+GUzmMOxCFCez7ENUEeS76Rov2LfC391fF1HPCXraNVV2fq6agB+cAmoqswAVFYk/CQKfg4fyVvDSQPmaFKLGpDV1rBZsSOWHBoSAZQ2PMBImuWwFOi9zsAjGmprqivUNUlDGX6ihMB+UlL9VQ4XpmVZ0HJvaW9vp/JyJY44qTgG3LlJTBXLclqUZR2O5M2bEM3817/+lbOJzYQ4r4lpJmd5ktMKU/CtXvamYotoIqXYqJLzDphttABY4SOaCoEEVbPIx46SxFGxFMfJqPOGKtZ9mQ+I+gJJUDUm1bsTLBtffvnlREWzGIx8in7YuChqGWS5Oh+ZowWGvwU4f+kEpwybxrHIfcoppxA66eZCn6x/lHbbbbf//Oc/kBkyBXzjG99AAkjJDAFgTk9eReBMt+kCpCUa/ehHP3rppZcar1UZcOWVVxK8iw9FAkjlAmA/+OCDYTfiFIDYX/3qV+yXxnRRWsWEQJjdddddxySTUsBaoXdsd5ritSJRp//4xz8IQdDMY3gDmPfOP/98FZHJpEd4H221trqtdrkmOeGE49g394H7n2ayOu64D7V2JqNG12608TrPvJy8+OLchfPrjjz8s5mk4eRPnnXXXQ9OmzYPrnzSkWuvXDAnmT41++FjTznskINffi456eMnXnfNP4ls5lvfXZ31/FrmJx2Nyfpjzttw3P63/zczuvrYebPrmxurWheOzXaMWTQ3ybVlPnDIsb8+53vTpi5+843FeG93UeEtzHcjCHpm9v7JT35y5JGHOsPzIhX+V247X5Hryra2dhxxxGE//elP6ZRjK5fOOeccDhCjKFWJBUCyW/DFF1+cqrIiYomAJOm6zvAh8Nvf/hbvgDQlLKxEES6Mz/axIX0I42WI8N5+++1bWtyxIIkXAHZg9jA+66yz8FkcJg0/kJ6wkDE2DjvssBATwnyg4Ic//CHjP8UV0gwUzKUmLhXHuummm2I3YCTTC6KgCBjFmWJGwlb0khLDHk/KGcFJjX8kQguy/fbbj+8nbL755oS5KJS5cVETfUcgGPwv3UE+SNMZaZ3tXQyKW2+9laNJEDZnAd9sYcYgQguZbH55yy23MLBhFJcOk0mIwPCyAGdBV0eO2PcPfvCDX//m13bfczdmv1deeeVTn/oUcyxvcXzzm9/kjGC0fO9732O3ZmY2qhgPJ5xwAq9nsHep+stoYSQwfrTLKeOTEbLjjjsy3iDQIGEgnXnmmeQMPFrkco4xTHMzp89i9DJ1DL7puu8y7HbDAwPQMn1ECnZYfv/79a9/vdT/Il/Tjvzv1ltvTVGGDbXv2/+K/oorrhgM/2tq3HDDDcw8ehlSSFTFMsxjjLcvfelLzKJGnAJK/a96Ta4w+iOPPPIXv/iFbsH0YAEJ3AXwA8lA+uUvf8lgTom1Ii7gj3/8I/cv3NEY0knw/gsJX/3qV0877TQAWmReJSfJbujPzHnBBRekXjgJ5XziE5/AQzFtIkF42I2AOxfiDhnqiMI+hheAiQhGxP+GLCmaH//4x/jfFNKp6BOjgqujVK0VaZTARxKAIVMA7JzsnMil+nPsGDa8uJVisSIqwM61U4gRjAGpZd9ZXnXACMB0liqvdSHj2H37299mVjH2FMC9JFdueL1S06Uol7/ICJGeXEkyVLhXRSa9IAz63HPPperjH/946H+Z8egRF5/Mb9tuuy2+ct9996UY+l/6iTS8M1MEcyDs7gaWR2SVmSlTphKfipfHvUJDK1yiYC5eRCHomePFbtOctlwa8SIxrwS/973vHTt+TFuLe1VJ5lv+Lq9gCXbonQWqstV11VW1VRXVFV3Z6ma+PZav49eZNPDLVSaNfDytbl6mdm6++u1M7ZRc1eyuylkonMuwTQMDyT26Ia3gLixPc9KWc4qAb67ZDjjggDO//lX8L1/Y4IMbn/vc5z7zmc9gGfzvUUcdxayIO2Za4wRk4HGJixfm/UkGCQMPNSTN/C+jQgOjZXFrS3MrcqBkZCLt2WefZQLB+bK/+H//+182nGYwP//88zzg4wISfZanU5E3WiBaIFrg3WEB3Enq16NfXO+nfj2qYyFaIFogWiBaIFogWmBZLJByvum7u5TzdY/fYooWiBaIFogWiBaIFlheC0T/u7wWjPzD2QIsLQxMYmWIB+s8hWchh1wP68PlCj2st2U5aIxMT+QhtlqkUYQALvSjqNVK0xUMiSJ4EgBLpyxiIUqNaiUVGmsFGtNHLBRJEFAUDLt0kHBrTkLUNTUKAVzg1bQoYW9ra0FnY3cN+Yt65Hsal6stBS4ZHlGkYrHHHskSbnk3jd+/mWVGGFm4EwFNE/QUYoR30stJdWi/7GcSrCFVWVGAlKcKQLlnd5WmmOCy7Kpic1/VSppEORH+gIZydKSM2HXOW1J4ilRpkMCrIgRGFoqCgKKJ4mgiRBiRkbOGJDkKU0cyNEhTc1RZQo6QcHHc1SJIjR+1AjG1VIkLGHxYhQSKYhGxyR9UQCNZIcuox6IaAU9Cog9BUURg/OhHPyIQgfgYTiUIWDlj01PIWMljaQ1i4v4xF5inn36a5boXXnyOmADW6ghZoF9mFs5Klr3Z/rm+voYqgp4hI3L6qquuYg2Y9WBWfFmuI37ruOOOe/zxx1laJgThrrvuqsi6vcBpHVGDao0oPFpgGFmA2WbatGksbBPqyinMOSLlOZE1E4LhlCTIhiiK3vr1t7/9jZMaeju5AATDzps/hGex7zuzAWvnnJuhHF4QuvPOO2mCWrGEtWDQhJ2SCYADhiasZX5A/kMPPUT4iPGmaJg9/vKXv7AlHtOFxUBDo/mWVyyYiIjx9fsfpxXAOEw4hF8zb4TthjDTDsYhOIAAPtMBAs1aKM8uzgQIoqc6CECXN9loa7YDnPzmlIULJ+fy7UyMFZXJ6aeffsappyLk93/6R23tTOYpLlgmT36bEOHXXpvLvs5/+MMfHnnkDYRLH4zR0tJ18803c1nQ2pqss84a3/rWt6oqx/31r9fRLmR1dQnG/vOf/8zsOmXKLAITm5rmEZqzYEETysiWWI/N8ufOndszvgv9Cc52X7GgdTbvJ2Ybbxl2HD1p5I477nj66ecZOXJ2oQVETIAyrYeMwGobgewWTIR0qtaK7e1dN954I0U+olB6BGgLOUSnvfrqqxxcuoy24gXPaGxubuODAPSOYqliUDIyCZ3nQNTV1YQ0EGNhpCGZo8/WyGA4vmAgg1HmBSCyn7FHUXjl0oGcM4vYepxa2daNbEAATkBcLabm9SHa9ZtBok6GgCqCSn/1i18ffOhBxOGhKiOQgDDcLjQMfopwoSRHkGFATsQznvTNN99cbfUJGBZTYEzwJOCGES5yi2gq3Pr8+fPX33A9AmuIkkEBiswSxMPxKpTfOLwNlfDmuGzOowHpZhSyEi3AcGIKramrZkJgSifoiihnzqAzzjiD8cPHPQi1Z4QQyce0QDAfR583BHgVjfHDJP+b3/yGSzLGGF1gaiWqj+jSsDuMOqbNRx99lBw5C+YtZIgSQn3QQQdxgcdszzxJaNdbb7zN2OPCL+QdpvCA+N+///3vnH1YzOYZAMGcqtiQg4V/5/zlTFSVajHaEv0vlAPuf1MHi0GCi2R2VaCq6aZ5lZB3ZqEUixXN/+KDmJiFVx8ZRUxuhIQ+/fRzGIcZz78w2T2BYxDY//SnPzEmrdHUHM4Ou3hALg+QhnBqBQDDTqN8qIGDqBnSZjlJo3VmUeQThGoKGyAaPAiHALGhAsBgSMyihM9yl4T+HEfjFUCjnFBcWRlvioAireO/AFI0CAfJaypcHQGUTfQU9agCMHYxGj0vd9E7eV5VKYcFs+B/cfFGnAKQSTwll3ahTB10LEl/UY9xyzGCgEtHJhkAJXgpMi3gwhAr9agKm8Ay1157bdEbusM3eImRgN/E1OwDzdu8vFeABRghzFf43wvPv2if/fYO/S+9I0GPlTjK6ibfy6L75n85W5lvGbfIQXMoGQYQYBzc+oknnohtif+mlgmZO19mRYYKF3jERjMhoAkh0TQBrDeaEIKhUlYaPJsMrGRTGzvo6ZMNSzrI/gAaGDTKZXNbm9vsgGEJMRbrzLjcvQDNQxg/SRSfowysjoMoje7QBfURF8kTFaKTSUz+X/rilxlCxx9/PLtcY5MDDzyQy07ePePpB+/GEAfPQMIjM5Pce++9zHUYAVGE1PPGUagxY5gzjq2daYvXnKpHVBNMz13DAQccwDskixY0VtdW8ciFAcZWDFwG2BEJhUQ4WiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFrgXWaBXoOEltjP8Ek6D9+h5wE9gFvq8GtaPNYHUBUwBCELxIYEVi3EoicXsXKe/htGihmZAC0ysfJEUcsnCJdYMAJSCoAnmUrQUFS4FQDENEqtlKQI0mSqljxMUhJC9sDSVzsLS3vsdOMaCmmxjOKiQRYW+UwT0RWL3QFqRX63FWVh2dht80xq6ex0MXAYAWFOz6Q2k8105d3e2Ip5hsXDfh0p0d7YLMzQTa9YppO9tcIo05zfIpoNjB3SGcOJR0gmqURgXW0DbbW3t7KMl8+1u8qcD8KjZSfRHXc22/ItemYvWv2UBlgbY6pJOzqO1+/I5dkLmdeQo+D2niS5zYD9SGOhGoOToANjCbFUWVGAY3TDj650H3H2ZGJogKZRhNiCJSyubbc7tV++SirYvLlsEiM5iSbEKADFQEo3gLLshlwigVEuJ+AaCqzFoi/asu5LsALhAixOQ8BKLWofcMAB1dV1wKzWs3jGhlUsZhPOwsI/yC233JKiNoglaIbVdIIGWNVWTBsLunPnzoKMY4EdOB9Z1Fx33YmESrBajJ05yyCGgBgsYMiISINmzTUmog+rfUosQlMsPZrLaYTIHi0wHC3AWcnpSU7oACfLfffdRy84RziDOEc4iShqJuEkJQDlrrvu6iMIiXOWKA32oOX0D62BBBLnHUFIbnqtqKCtkECtEKJBEKrtEZsiQBliiFGVc5ycWss5/dEf9Vi2F5LmjN1gZgZ0COU7tYr7NTJT3X333UxBMDK9qO/WCgC6MVlBryZMvgC6bCE+RgAgGC7Y6TXynYPz6jHLnfe7XzMZfvKTn2Gmf/XVKW+9NaWrM2Gb7cp8x403/ufTJ70PbS+5+JrmpoTfrBlz2WYN1scffsO/j5QsmN9E66ja3px78dkpnW21a625YXvTwuYFLYcfNvGyS5NsRWtbR9LRklQl2Wefn8WnBTqTloULqs/73UmEND1w94JsBvfufNjUybPcEa+uqs5UoR/2JW4kyWcJeu3qbG9taX/owSerqzOEUBCvQ6PWL2BisglDZIoOB4zMYjnd58Bx6A0DIGPW1dXCSISZu9golyZMGEsMEzXjx48pjXf0Ns4Qeg4Bw4+RW1dXLTHI54hTxDUAlMoWATmt40oQBRySYROQs2fPmTlzDnKopQk6Ao1R0imCRcAApCRAQyKw+OGHH6Yq1f2woYGCOQo4VvYCJP6YuG1OCjsoxEihAGc3yhCPxfCDkg4SFMUwY6tITiJiZTjB8c5oSxAzneWgIARfjGRsSHfI5X/RmdeKsC2mY2zjcF9//XXCtsBzGUBDHCxmEmyLjyZolfOX3QRpBeED1d8oZ8VbgBHF3QE54+eaa665+A9/2nvfvXjDgQBljjXjhINOTmQVscuMIkbj9KkzmDcee+wxxYD6KFX3WgUDkrHxv//9T71AJhgCoGEHwzhhwmFAMj6vvvpqkOwUyzcKmEyIFIRA0la8BQaqRTrICUU+sP5XU7Qpaact/hfLU8TInPgARoMafftflBxw/2utC2AO51gzBlBGGBoloSeelyPex+EO/W8olplKFmav3MrK2XS8tpb5u7KId7QZPnCUzfLylV1ahBKkDDPhpEmTGJlihEBiAeDCmI888ghCMC85418SUB5Alxz333+/rh/ACC8a5cyiJNit70YGMWcBxqFKJ07IKJiLCs4yrBeyh2RyoNSSrHUAkpByYdaoeKkShuhhwaHMEGbmx1NgXkkL2Rnb3HzRUEhvMPRU4apSBCpibezJoSeyU5cHXKeFwmkR+bp4gNLEGoB8DoeuDPFTdmiMYGABDEVsKJea+D4aNeHoyQu67A+NX+ZdNfO/kHFM6RrvgYBkG2wmVRwuI8r8L/Mk/hcjIASBHGsIdBbTO4JZydmLF4fLAICLWk4H3DoHnehnuHizkVBppgV8NHn9iDoMa7oNL8Ce+XC4W3nZrbmdq9ukOqnlGreaa92xdKeTfaBJFQsyVRVNucZFnQs7q91lW1d2YUfFfOg7K9p4lgVJVcaFkift7vmSPTdzvEM1oSdjQ+cCdxP/+te/eO+UjfMvueSSI486gnGC4o0Lm/SqBl9awL1yMTZrxmyOOxM4ZyJ2Y5zwlhoDSc9P2D5c3aUK+RgEMq71Ro0aOXL0CMYnQ5odpnlFgXcv8cJPPv4UM5JYuALEHQPrhBUy5tEC0QLRAquOBZj98MI9+os/KZugsuuUnhxlyUuRaohZ2loEYOq2YilLxEQLRAtEC0QLRAu8Ky0Q/e+78rDGTkULRAtEC0QLDHELLIX/De+L4/3vED+uUb2lt0D5KJall+M4eKbD436WcNwqhY9SJddjej1wBy9AeHKSPQnSgyEwIjO8p+rxyB4hakU00NOu2GmOIokiubUrIcq1NCVeciWIJQQaMEYpUcYOL7W0EpIZve+se7ZFYnlCXNIBjGfxKvmdmxWKqloCrUNiB/tdnKl1ChRjkEVTNqdp14RrmdQdXI7hRB+o0T2TCWmGMsmIKCpWsHzRJC5yiFrWY1iWc7oFO2HTCzAIcSr4PZ5pXIyUXZdIPqa8osItZTmiYF2fSh1WmVdyTEMn0pTwSzjwQknOQilkohSN5SY/5HVq+ASLUQpj9AWKYDAYgdRjtACoUeUQiN2KYrGmQ2IhoQcJTE6yRgcDCHtKUzoLWGnjUNIX1mKBtb5L5BNbkLIpFWvVLOWyTkyUFVVEXRBbwNIvLHfeMYmcQAG2sCJogDV1xgNLuaxws95GFYvfbAJKR1gIpmvvvDNtxIi611+fTNOEJhCCwKLvv//9b9bEidfkg9qwEwA9fcZUeN+Z8lZVJWLcJpTuEHc5CYyVwTBLlBktMPQtoMlBMw85YRacdJxrnCwARG2KQOc4ZxZVnGUsnHPalu0dcyZyYKfWeAGI+0EaZzrL7VQhkFMeaaEQMJyYEDBviDesBaMkyYKlmM2uIOVHQkaDmYiopQlN7ODFzvRCdzRxMTkQO0JsAaLECA1J0QbQIMSqTDKAkJp4ZU/V0heaQywE4JFAN5EmmNgXZkhiU9Zff01mqo7OFg7Bppuu9+gjLzz92AvtuYQdqQlhueHf/317RvuY+oR3cHDpBAmPHT8K681vbJ47dzFxCMQdoQF+o6K6hnmPMHI2RFx/4zY2XCb6ea01kkXz3d7JoxtGo8DcRfPYb/VjH/sYLd58y59G1cLqfChV9LTgO3zveeeotbWLPfV01UHYKpHbnZ2YsdBf9ZGuEfcrGAkCUjk09JpaOi5j0roOHKEYxDYxQmTDkNEwHCONHLN/2BBkJEYsAjF4VRWVTg3lakVjMhQuGEYdL7oPpYwQkoHk8DU0VKIDeIL7OWpwGQ2t8CGChgY3sEkUrRaAhHwSAFX0WmSDl9NTAukYSFgVgHZlcCKffvnLX3L+4lWJieF0JudcZqNoYGmOhhATJQM7BmesYpC2lva6hlp6ra65Lvkkg+PH2ZmVKixDDBYt3n333Zi/uroKdrYFZY9AhNNxLgCwIcbEGtCTBs8IQ0oy1jJ93h29ZlSwgSbHlJA+xtvll15x/70PcN2ls4P9njvbu7iEY86n4wwDwv7caxaZDCwMOYzAhRxFL6eC4K2tt97aTASg4UfYFsJhZ+9JNrA84IADGIRE7XNKIgEaKIn5Yzy7yLae4byhtCELa2BwOqAhOT3inFp+/4vHQaCEYygAnAIG59zUVIDZORB4utCPY+c+/K8EQmCSEYtwippghUeykBRTiamb1kl003RL0SAfPfEIHE1pTk4Cr3mYSwg8RYqLogRCQyspAor0dMSIkejGfQnETMOQGRfygeElmlB4imUT5jLlQwKmStTm2AHQls1vZiK4UENHFguEvIKlg3oR1koC7Bwv1GPy5DCJzNnFa04OBjytl0owaVTpSInGKNEW4RJlSOMSYHgDUgQUvXndvZ5VSSaNUkVelhckSZSc3bBTRIJy8DBy9Ok7UwF2oIjCqoUMAgyOYTE+BMYY6mDyIUYC7FY7GACtMMUxgMkJ225oKFzYME1x/8vY5ksU3P+a/+W9IOZJWY/3Q+ggb8ThKOk1xxQ8/rey2l03cl7TU7pDT2mFRF+4m372mef0FGSTzTamFSiZfuka2/RyZIks543sUaNH8okPwmF5QFRZ5aKoezsig2GTQZJJ6O1am9RhllkLXhw7duT0t5unTO5Kcu7UTiqrGRxJR5IfleesYZAkI9xoKWjSknR1dCX+lfbOfCc24aJkkJQcDLF0hJHA0eQjV4yHf/zjqheff4lN1jlNuOpjeGT8JToXe7TOkAMPwBFnftM1GCcCpwxmYaCyZfg222wjPSkC4Lt5HYLtpUVGDD3bS++www7cjGy37XYMKnZ95okNl5TjJnS/qiQJMY8WiBaIFljVLKBrkoKLwZuswAfLusJRvqqZPfY3WiBaIFogWmAVt0D0v6v4AIjdjxaIFogWiBZYKRZYibef8f53pRzx2GhvFiizwtcbaQrP8yO7kNWzpMITJb9CrNUvY1FVOPq1uAWZaAwwliUCkqmGtAoLhiIJXuVaJDDFJBMkrYtAGCuGlOqdCAwGCGGjB2DV0q8GAvp9djMsejnuvA9aynRp2cZ/zrjLmT1T4cIC/JIiHIVtsx1D4ha9WDKjMskrXsnF4ngxroJviRIq42gcO2E7BIGxmO2WQ/JdPqDW7VpQWCYCAT7JBNE8Tk9I/dqk36fZaw5KuxQ4emK3smR+Ecp1xycPOAsvXrioK9dVkfXxOlkWcHQwUY0lT5bwIHRjw6WEUCqaoy1E+B2pXe9cgUrIUsMAfEGWb1E0gE5ioAY6kKglp5alRxEIQ+65C5kxqkxRBKmxIaSIgY0sFAVMW8IIEKXUMEoJCZFgjFG1EKunGo3GKzWsOOCA5CtneYyFMfRhKY6FN2IvWOtFT5blSI2NLnqMdW70JJqBxPolGJaEnVZ5FxpFlUK4EAUL65f0mqU4jgzRA+x/hDRo4IKYqvp6t+gOoC2yWBpkFY9FZWqJCaCJ+roR7ODJgh+Yqmo26cx3dSoSgkHoV0EH3CJRYLTAkLcApwM6knM2cfooNEfhUwSjUKVTDIBazlkoWUEPY1xSXYQMUZy2Emu1LLoDc2KSSyYntVo3mj7EGs0yA7TFVKAWU+1KE9RGOLmUhEZkaCuF1TQEYZXpI2IJEWz0TFZgVGXBT2BoiOCV4447Dhg/Syv4U6Je+Ih5w4gk25XUZZMzzzyTb0+/NcPFs2jL7IoanEWGyRAjw7LJJutoa0b2xUZKZWXTzFmv1FTk/vmPO6bNmDuqPlnc6rZ/rq0j7JuID74gX1lZ3Txrwf0HHb7/jBnJ2DEuqJofQVkNNRWzZreMHL0u0YotndMwyyuvTVl77XHMn0h26rmtLgs9BgMkZAHV058aUoAkkDNCNMCMgIkaU2jkGNK4UkCKwIpILhu7ZnoiP6Wt8YKHLDyyqhI9jDhT86eM5FI59fWFzfBglDRJUOtiD2UKHqScsUEEM6NOLlWGRRPwtCgkBqcjJDCpTVVFBl72RPnaYu/UcXWKPDQ4VQTTEAUII8IpKjGKMrVu5GhO4BxHHxKYd1OiszJLqlM5f1Vc4fY9r+A7I3Q80+kjO/31c1mWlIQhW2Qq49qMPe8/85nPaMyss846F110EZd8RLof/aGjdtllF0KvDnnfIwQ3c44fcsjBDIztttuOC0KiltmMnK2jMQsdZLdUUthTLKPx48ZKZ57xzO68J5xwAkFX5513HrFZ7EN58sknMzjZupIimoTswwXWACBn8qGjy+9/EeVGmg8Ql3CZAm+OPTkZVdRcl3K4qeLA2hBlzP8iOdQtbEhKSn8jA0mnYOFwp4KbjVcC6VcpAWMDr8pnAbh94OcpuUoRK6UOPFtXFxGl7iMGSKA5E2sAdODJZToVqRWGmRDbojYSyKGhSnIgUHeYAMEDS6ZJUFHCBYc5ZBTRn0RHSMIYFwC8iIUAOKwVTdlculmVqSpMqFuqSgQpdkNihFABwUYcVpUiwXDsjAYADEn21OVoeHSogobEuMK2WEbE5NYvACuqFg2hDAkGHObyhjhmRiyutrbWjQrpoCNIVDQtogZjhkQVt8mmA9daBguAnbeP0BkWMKHy8NJ9xMrhArNBLzQ0na11I43zhSoSXJqi2fjZmcyfStIq1dwQLjpPkcu4u/WKnLu/yGWT9Tdad5ejJmKBupp1iNm97upHp7zyWjKiJeF5QKYlgYoLnJZczejaDj58ksGTcNHMjy3Zk/o1sjzX4k6lrstfWje697R4+IRxiJ4ewnZwqmk+JCyeXeHPPvtsDi7Ol7PgtttumzhxIt72mGOOee9734ur3WufPYmw58Q55H0H86rbgQceyP7i+Gi+1YD/1aDiRSOSM6mfnTR9MXj8SMnPmTWXs48hDQtiL7jwAm0yjXB3p9DSQu1wG0tD/PBG9aIFogWGmQWYk513ZocarkDc8gRepm7Cmmvy9gguuLmpiT31Z735VtI5YDe/zM/M2NqSgNbiJDzMRkxUN1ogWiBaIFpgICwQ/e9AWDHKiBaIFogWiBaIFlhqC7gn0jxc5f5Xd8F1tdz98mFDntBOnzqVp9Ptc+a7KEKWKnjw6pc2lrqNgCHe/wbGiOAQssBSrB8wiMMHNwa7c8mfIXoor87p0XxIA94IVCuukD58rG9GkhAjNgACa1c6wK71BvFSDIlNIABV0gHY9AcGCQsprLVehBLEBaWQFEnAnpcna9iqMG+IhjoPdNObNM/oeJVCep6bOWRhDdiDXkMZCtUdsWi8fKKhIUIgeK+OY0klqnzvfIvFYycadQG4Wxsvzdd6XL5gOiIAYHUNKZw61UY3l5Rx1eoXOYnV0hIOh9BxQSwakpNETy56CFjvEUytDhMAmBSlMKJUXjBaEWUyi4juv6oiR3LIRXOksDl4KKZEiQDKbokeEl4sqpK0FHuKa2CLxbZcjAIwsVDCoBvKALM4x8KwR3ZylDmhzAKiLNjfBei7BCPralTlWKX0y3LAGrf8QRoSrOPiZRUQSpqjSJwf6+JShm2pESi5sATSCkffNxizaIFV1wKcU3Q+NbdwiplFVGVnnOFTAATuNC3Oq2GtSStbG1IOBizN+9Y/pVgpsWEM6L+qKeEwSgh4VZHjcArTYJL4TTyTK666C8ox1bgwjg4sbgdokmcihLrm1VenNDR0z5lMrfMXtrJX9Jtvzl1jrWTUqIr5C9gR2X0anABoDgHHkVjnya+1zpqbnHhi9q8XsX7mPh1Oc/PnN625xqh3Zs6btfAdmhg7uobNHInXQU+SlHRtFy4JCs6cKmF8TSFL0QsbSgiJ+1NbSh9ivOkK+oR4YKkXAikCFY0sVFJiraoso0kOGXujBN9Psj4kLLFKTVhDOhZL7EXfYk1aH2RyrDREMh2AQxbwwvRHYMg49OGwp2HvOONamltdMJY/TTl/OQ0rfaBVyDL0Oxhq6C+x3JuKM2fOZJd6tpCkFsAH1bd8//vfJ1KZPZvZEJrwvq997WsYgc6efvrp5HB94hOfIPhPAyYUCyybyN2oqqIqO/WdaX/729+4wMOwr7zyCoFsxD0r5Isd9BGISakqKzAlf2gWNWDCXqNnWBS8xAEDAaIkLdXToe9/UViah7n1Ql2zYv+BojX4yy2qy1O8SMa3ElrM3WeqyoqyvHQQbFUpgeFRgwZixidIEuOTnHOHPCVElCazD0CMNBpKMFjKUExphcBSjFoxvAkBIBkeMoplJYRkojcugLDWhKjR/uSSkBKSYpRiGtuiTxGERRGHmMGAZQHlkg9sZ9+ytdgHO8JJEhuSMcZA0mVuliEAUC2A5+jmWjaVVjoXHeHM4jafF5vnz20ilhc4WTPZc7edwbS0urOMDaAfuv0+XsBmr+jGrvmm81a7b0R8MLt0YpyGfPVLL71001X3cWEsy3CuyHRG3wcge/ZBMBhVNMo0QvcJquMlyQ033JBHLn/605+YYfCS3/72t4m6++Y3v0lsHK+3AXDoYTnllFMA6OOxxx6Lp25sbErpxlkOAWIhpkrW0GbSZ5xxxu67737hhRciny3G2RjhRz/6EVHRzU0tPNdrGOFeXRi+/jdlh1iMFogWiBZYFgvgdjvdUsJO++698x67Mz9zh8LEyFdxdtxxx+bGJj7v8M5LryRaqSg8uVmWduBhlsbH6ZUqijznscdHyygxskULRAtEC0QLRAsMUwtE/ztMD1xUO1ogWiBaIFpgeFqg8PTPPTrNs6sk97/b7bwTTwvZoI2NsXhE+Z73vGfR/AWTJk2aOfnNMPhwmbsb73+X2XSRcVAtsOQAaB6XM3wLT9g9oGfuPNDR4rqe5uikgqxUXeOFRQQQ6/k+xIYULHbI1ApFABLrH+TCAyCBp/8oIIGQCbDnSoglSZrjL3YBDDQu0ihI8BoxDanGCygsf6o5tg0wtY1bkoOiW8DwyhTWaNHd17KnJsZxnzxO8sQH0DG/l2SPYGNP6PdmZu9kJyfjdvdMcu4wZbLBOqXff1fBxwRai01WdZR8KtQ3hgRa8bXKDKZ9z6Usr012Ibd1ZY4UlnfVaiXX6XbzZe8taGpqq4mSTRwJm/XUwCPjY0UH+BUZlHa8Xr5JdXb2E6oOU9HmaCViHXQn1avvVqMRYuuCUo8cTcCrVk1TNOUBBBe18r2g4LWhCsCTWGcL+odCgEk0YaIwrzAMPADbHVPSyM3+1MIl9dQcsFqnKtW6GMGvyESjSjQKoNwAzOs76/oupHSTKQp6KoTdDxECn70EztBCpT+CbomOcg8uXy9LFpvryOUKG31pTDLeVEWoO+S5Yny/iY5AtMAqawGdGqlzyooA4Qnbt5XEBX2KXROXeK3KRBmXYZYfQGbYLyuWShZZ2MdQQ4MNCCWEPQ3w5hML3ooquS3RaE7zDqv7YgPnWdQkyfqXJM/9zffWXnvtO2+784orrmLTWKYunGc2l29c2L7tthv85rfnEYJw9g9+5GTK22ZraPiDHzpw33333XDLJ4k+fGeG2wz4j79m/yG2vVvU2ZZrb0v4BPr7D0u+c9ZnDznkNWov/dNz11wztZrQzLrKzmTRLjtt09LWykbUVKGPVJLaReW7Z+SemAIxbkovq+DrQsY+YDjLmrcsC5TSSiyu1dC43jukMCbHeA0TAnBBENKEcEgJTFWIUYspZEiwYmCp0Vv3B0+H0P/SSlkFyiIHT6UVIFmXzJrcsnnbhJWLHzcDtGc6slXVSS0fZuHjFu6CtLKjhrO+qz2b4TqITSwt9fLyntWXAN0zTEnVoCM4joxzEps9s3Mwa/w0yQaujAF2OyMq+u033+ETMsCtzW3jJoxlT0oIwmtX7noIjy5V1BnHJ6tiFurqyD14/0OrrTGBMC8ip2vqqnk77oUXXmhv66isqqAVaOwOyxiHEaDzAnuGOlvRgLC2NxjismeZCdGxS9GoNoXsrYl+4k0TCbdiH+xSgFyAKMUOHCJTQkqEu3uEYnJ3IkruTrE7ufvZmpo6jRxGkDUEnpEcXrF0M3k1Ct9NkgvAI+OW/deW+E6RP4ZhK5ynHFa3/TZBh8hBLA2FbYXClw02aTIRxbK2MrKwFShT+LAYijJ8Cqm2whZDyrCtsnBKGjSlGGM0ySKz3AhCIJTTN2XItZxwyhoqhmovlfylYg8PgVoJMXLQpfil0mflErvNmzln/c17jg+m5Dqbknc6OmsqGiY056pbMtWrb5AceuwOnGvN7a+Tz5q+4UNP3NfS2VEzomHRAk6KTHu2q81tIjBttfWqOrNtfPFlVG7ErBb3zaiKai7AuRqnBb5JBU29P2rdfpbnEdizsns6Sdr9S4mwgNchzroHXJmKXEA0CCajCbXIl7KYT3CL+FMda7cLaZI8+cRThQd4+OUR7rMbet6laQ32zs6uMWNGp1RDAmTQSJRqeaqJz2Xuuvnmm+mpf9uooqK64o477uBNJH0eBPqQJSU2FqMFogWiBd7dFmDiZQkhT/RzddX7Dj+M5zZ/u+KKxdNnJdUVifuCl3sWstHWW3/4wx9+7NFH7/7fLf1+NFLGbNzvMIczM+tlFQDmbaZokGWoIypaIFogWiBaIFrg3WuB6H/fvcc29ixaIFogWiBaYMhbgL2fc/mjP/4xPhB37b/+tWjK9FDjzXbY7tOf+tRTjz9x07+vKw1RDCn7huP9b9/2ibUr1wK9BkDrQbmtLNpDc5a89SjHrXZ3uT1feaADDJDqSYqXJz6k8JE9jNCITOwQqF1EAVAUATlt6VyCEry1CAwxSAjERQ4SjOglBBgh+pKpqoxYAq2D4KFUE/SUWqmEtiBFptYpKsGiRNE37lTysEMr4KdYLCyHIEcKy5hiVy6Z7vNsLrnAWTSgCp1ot6CG77LbxJ6PbIZruN5uZK5CSy9Mc8WENHW2iHB/aUPLQoAhPgWjLQ/vYAdPjolyiQvgJoGRWZRTFD4semmS73bE8cewR3NGbOw6fBRpixxDwaicKjDKQZbySohoZDTIRIkE8Bq06Cle9UsNUWtJGJGpU5JmlGHT0gdeKCGwI4twqtQusNa9JIEiWllzKwyQ2uTWNWAl6yYGLquP9b1sbYhElBVD2JC0aLAInA5+9EoZq41AtEC0wDJYgNMqPMuAOX8NY4Akp4q9NReSMbMN+AwWyu9Nh7J4MdpUo77TX03vpSyests+KVul6BEiAs9VqGSGY6dYvOLWW2+95ZZbzpg6g1erKLovrLpgC9yWe1WGyL/XXnstFIiqBDeffPLJ+7znmJtvO2qTTTYZNb5tr732emzSfc+99ESmpXmtNdfKNS5gp7zm5uT1119nU6L1J26Ua9txzpzfvPBMJ29qMreyY2tza8s3vvGNBQsaJ05cTZ9oRzLtcgkTNpeCrSOhcUIYenXTyfIJIOx4SuDyF8vKRyUatQGW0nD5GzUJMsjgybeGQkC2NQuHVSsGDm2+EtVYAZ3l+BLHzEBivnInsr/WVZd16NGBK7Q99t95t912W9CymK0TR+fHPvjggy8+8TIEK0DDQWpCh5guMAVx0cs1PEYQkv42LmwiRplhD4b9m+fNm19TU93W2t4woobLVAKmmbuwGG+Hl6on64V4Wllr7TXZyRJ6aseNG+d2oPTzBu9MApBY+6c5Usg43GEbIXTQYOsUGPAqliUwyn4CJg36let/Q01QJlUs250UDcbxjtXRMiqAIbDhIWPKeERJUsXdsO6k1FzK2qGp1Xopge5zDS8AuQC0S1ts1McOtRQtoZWkkYM0WEV4Q0wIp6rUSkiQkhZWrWC4VLe+FShLn+pv3xKoDYWE8BIZB4RA2i6tzgPSdFkhpokBZcmGKZKTi8TzQs4mnMusGQluwmPcXsiuU13uyRIp7CA+y6Ws81yYxZ37vFvY4xR05P5UdT4lxR6KCmFE9X7WhoQDAKslesrHFphb0FBnPUbAF9c31NEGAKm5uWWttdbk2p5ZnVBpXDZIbMbG2KV6QON73W0LFcGPHj0aQ+F8sRzN0fTIsQ3ACOmnfUqbi5hogWiBaIF3gwW4IPdP3T907LEV1VV/vuiipNM7HVxPXbXbFrqja/Kzz//q+eeP/tjHPvbpk//+178uc6+33357puVRo0bxgIi5l8l5xowZU6dO5b3QZZYZGaMFogWiBaIFogWGpQWi/x2Why0qHS0QLRAtEC0wzC3A6hu3urn8iZ86eXF722W//2N3f3ia6FczXnnqmZ+89NKxHzr25M9/7q9/+EM3wVJC8f53KQ0WyVeoBQqfUCxt09YHQmDEiBEE2fBcntUIlsap4tk6vDzZERnP63ncI6RkGjt4MMp5Fu+f7EOY5ZGQntFDqafz0FALMbXCUMXCOfuQgQGARbU85YdANJKsRslhIQFQS5VaFB4MVSBJgmlOQkRAriq4ACAWGQAYtk8Tu7FAHyS3hooAclgd5DveMLKej6xVVbPhTJ7vfs6du6ChodZzSRkFmxZiYZGfTzqqKqs6u/LEbdSN8Hp21nZ2dBK54SoTdnxkDutAuGyOKPpEWLWTWdyd1yMl0y2WsPjkKtlh2s1y3XHDXiA1aMJmB9r1yqlAE47e69/BdgmJC5IAM3b8ajzFyybsosce1X4V06lUTNoHWgPD4xRu4rfydTHctK49qumFF84o4jA5O7sm/fECYPM9iNnBhgUhQjGQBJKlMh10RoIGieMpti5VoQQASQ4N60wAbquhZr99dU2NdrmDDFGi9Gr2yHRwaYVEBdI0GKAnGayBIc5QDfDaIppGecOG84UFLZSRQCRAQBE5AIYE30OJAS2w55nviF8GM8kyXdGAGI5BUFTDHf3guGJWx6YA5UJ4tFe4QJPR2xSFMWwtCOghJ1VXoIDE6dazybKkERktsApZQFME7o+JCzjVc52tzJBMNbhmcuYTkMIrZxrFbcHOnMOkR54SwgoNExQzWKl8UYoF+SkCRdRpPqSVlFiKUsDwFJGQQopM8yd5WAtcFm8CAZjb8RH0i0QxJcEUltigFiM4n+jxzgszC4ZNqwl1avFinBH+wqnjp8BM3nXC+aBsRVJbnxx99Ed0ZFABChdi6QOgcahvT5l26KHv88KdSL22hFfaaKN1P3Pa6eut9/MpbyzM5V4YPXLkRz86/tmXnoCmvrZ1rTWz1/73+08//fS3vnXdTxufXH2NRTvvvHj/g967/U4H3nPfTSgzemxm2sw3Jt31wIYbrs8m0FOmzB45srqtzX1iglnZdcYbH+OwGSu64TDRn6sqp4O7ToCk4EOFMUNR9BhnjaIodyVW9vhC6ckKnislhFq4ICgdcmKUe4ULApOPZq5h76BB0gVeXWOMCa+uUUsCZgRCjD2FSeXIZ+RbIKZYREOjMEJgzQlv8jEhsKWUZBWlZ9mqpUKGckJ4qYT0plI/BdLTflIug1YrhYXuuMOXdSdttoIgqmxnvp1b/4ouN7rc9TLXtG4HaHfh255pXWOTcdvsufnU+W81NlavU7HxC68/3pF0cklc7a51iyncm1ZX2gGmvSI9P+tKCS08f/fEywsSpKo+XzssNjkwf7GGkpThTsp2hXSauOQaqq1zW19zXnDSqeGi8ir1miOcAC+k6NKXk06k4E0Op3M/pfXazAqvYKLAGr35X+sa3aRrJPXXeg0BSMyCa6b7OCmKYSfwqhwLCDA4cFhlsDxsb/6X2Q+xYYtipCE0NyEA0GjeC5HCSyvyUA5wWXzILv9LnmIUL81JAkU6qNtntYhuBEPiH/At1HrF6IVk60xxhebmrvXWW4NB5Ztwjw64fpGe5B0drautthpks2fPhsAb25k34+9H8j2+gOQkU+f09IfAt+lOWIfxzwqor6sjWN+1LhK8EIeGJtUL8L5KTfQwJgT0KCTzHcwpHhH1UseCHnHdxTHl4Pbmv2gLi5EzeFzD5RK9lnOkMmxdnUJ52tUpmeKGQCpheW/8gulTZCjAfTcEoXCjYUgjBAUMEwLUctC5BcaAKRpaRyD9mjBhgkJgy8oPpUV4uFigMMH5J2C5bD6XzXVmW6sqM4u7cpmKyna8XgdfQ6vK5ivbs+zinKHW4QiN5ntQea6hs1xC82vJd7ZlmruyzW7fZy4jq931rbvIZZpktLqTmX9sFe22M3EjKsP1vEOPGzdi7AZ+rsi68d9QXfPcbXNHbJA0zUjy1cmo8UlFW0VjY1euy/mjyrx7P2dQbYtuOEeaQBnaokiiyGlFDuzuXiorRQOS01ZkIHWS9q2epHG6QUbANEUkSDJITn+1C2awe9q3nrF2aS0Qj9fSWizSRwukLaBLG8u7koMO+wDT7DWXXuYo5Uq6WBXBNWVYA3HTdFXVv6/8+z6HHPTp00//8+8uKAiUhH77iieecM9zuP7hKuihhx4qCIl/ogWiBYaJBaL/HSYHKqo5hC1gnhcdgaP/HcLHKqoWLTB0LBD979A5FlGT4WoB+d+C9u729ZDDPsAK3b+vvtq5YxI489EEQlRX5Vvb/3Xl3/Z973tO+fzn//KXv+Ta2ivrajtbCmuCnmfJWXj/y35SxqDHlVaMQLTASrFAj8XRJWrAF5N5mM5pQygVUbwsaPEIiaHMwyJSb46KKsggZg0MYpbrimuZLhxWK4vGC2AwXMAQkFh+JshpwYIFrBTCjjQSGKpE71VId4fmaIIcGpYh4WWlGRZ4EQ6LukwtNEpmBJDQuLZ94hkWXFqfVjwNlNCQjMUAIcMq1iQ6u/g8ZTs6AI8cWU9YkhY5jCsEUIbmnPR8ft68psbGZgLUmloWslKIBDQCb/qHjAZDYDAARRgBEItw1QKIhqIwIYtVAYwYUdvU1LZwYTu/Nye/gyjZEC6EyPgmzQCT5sW7GGKeA6I8B0XrsiKQBHISGBHL+IoM4IgrnAIJsDMYoBSZ5RJlvABIkBDYGbEoCRdm1JAwCTDKkqJXLqSE0CIS6DI5+sAurXQU1K40Bzax0oTFY8hY5ZXF6D5CaEK1olduEiRwZeVLVAMCSytLydhutMCqYwFmG2a83/72txtvvHFvvT7kkEN+/vOf4x9FoOmFnFOVyYc3l37605/utNNO1OrkTclhB+ILL7wQ+hTeimefffYJJ5zA9GUYAUyGILfaaqt//OMfqSorMuV+2Sc6YkgANLHipZdeuvPOO4NJ6aC4n8MOO+zcc8814hTAZcDvf//7ww8/XLwmQQDq8Rri+eefv9566zHxMhsbO8WWlk48+2mnnYaCTMxWJQB9GhvbYbzggnN33XVnrhqks2nuI7GSb3zja5/73GeA9cOKXFnQOfwtmE022fD//u9rRx31gebFRBkWWuCVKxzBlCkL77jj2U033fTII498+KkXf/SjH7HX2xprJvNbEz6Z/eabb/Ix65lzkv32249Ngy677LJXXnlln332Ichh3LhkwYL8d7979mOPPcbY+MpXvjJmTB2umeZol4PioykqAebPb/3KV8740pdOGzWKCzZM4hJK0AWcKX6JrYk4uOuss05oGbMDnpHjfuaZZxqmFDjrrLMYAD7MOm1AiPfdd1/UK+UCgyaoccopp9BBaRWSUUXrpC996Uu40aamZoV0GA0EcPGl2mOPPdaQKYCrPlpfffXVufRKVbW2uusoLI8CVLW0tPr4uRTVKlTEnu++3tIp9YubBsZAWxuxhoV3z6yq0OsOF+LPdSbJZiqNQMslanhZqfTMKsUMXo+sLTOdAYPX6MBK7tv/qjtsFff9738f2GZROk7SQMLRXHTRRcyEYEq7j/9i739cGJGgvWn+wx/+sG//e+WVV8JbKhzkivG/73//++ldqD+eF31AbrvttpdccgnbKvPQwAjAy1bM6p/4xCdOOukkbpE40WAhiUwCt912M7w/SE5M3XWaEABoPvjBD37+859nqhd9yAu87rrr/vKXv8TCdmhEgEDoEXjcccf93//9Hz4CWFXkEJNQiWP3ne98hyYwoykmQBJ23333n/3sZxMnToQLt0suNaglUfzQhz7EvvIqCgOSxEtr0HPQf/CDH3hE+ewjPpWv89hvfetbXBWEfQ+J6TgEIcZgWDD4Zz7zGfwv3TR8CpD/pfsQW5V1BAd6zDHHGD4F0DuuPPX0JlWFBBRg2HzqU59i4uXJBmQpmlh8F1jAhgp94YhT1EACYNSBEbLw/L1nh8MZhdPTMVYm9WOSUaMLv9Gj3eUuHj3Hg73mzlwb70PycClZa621mJNPPPFEpk2AAw88MBmf7LXXzh/+1IEfO+mgo4/+APcLEya4hzPSoWezA18KjdAf6aI3W/WHRTTWUDibUWWm7r+oSBktEC0QLfBus0BXft3NN+W5x/XXX9/dNa7UdPWKy+HXybsx7knRfbfezrOXT5z6OeBsXW03fYSiBaIFogWiBaIFogWWygLR/y6VuSJxtEC0QLRAtEC0wIBYIJefuOEGLNnfdNNN5eWxhYS/+a0Z0XDvnXdNnz796KOPTqoqO9vTa+jl2SM2WmCYWMAt15VNth7G4gQwi5QsgBEJxKfYWamaNm3aM888o+VM1iTcsoR/ws5yAvTAYHgQr2fxIFnnI7iExWBW2linfOmll6ZMmcJyJpRa/IDeWhS7BIJkeQN2ViJZQD300EPXWGONxx9/nNaJH2KRA5k86FejsKQSeOmG8qhBABmL0G+88cZdd92FNMRSq6AuqSplxEUVAMk6tcEGG/DUjO4//PDDIFNt0WkWoDGDZ8KwiGQ91WGAmlsWrb/+6ltuuTWhPHfccQdrfyNGEAiejidzMjM+yirTiCEXN2Uz2arautHIHD+uerPNdlpz9bVvv/321pZ8U3NTpTaTLNHDvV+JGI6D29JS65pu2Wm77XZgMfjlF1/kiR5rj2DYNoeNme3RH0DP5A+r24iHWK7W3ffcYY2Ja3Pgnn722a42wsddR2nDySmsWNOWP/wmhc33XB2t5Qi/w3pPP/nka6+9RrBUbW0dB84fdO15xpFyIw2ZBNV7uzlZjBbixTl8G2200ZZbbkmE36uvvspXthl7ntLRq7UUQJGWyTmOAAR4cfQ5dowc6AHssFKEALJwBDLqDM9A3WKLLVggh+WFF14Az9e9GZDQIJ8ExpKKahSxNAQly8BI4BRggx/OHfbPJuqOWnGJ2CQME6B0/A8TxaOa0QLDygLEhTA1EUJk8c2l6j/LnNzVhZvWxGWTEnMXcTYwXnfddXAx52iOTUlgVr/11luNK6wV8s477yQklxnbtqhEFFUEKOPReL3kX//6lyhtWpMQIR944IGwmIKhgf3tt98GjztGoAjIxf76668rkszwIcC8SqDwc889x2SLWxeLCFCGxBU8a13MvfSd2RgrQQNeIQIdHW2PPfYI1uMqxvcpnM/ZmI0N1RbefPNNb7zxOjFCmvPhRRS5ayWfuefuB1APNgKo+MSDC4LiFiKTtHc6F7Zw4fx77pnEy2OVVS4eWqmlLVnQOJsrIPzMtNkzOzP56kwyb3Hn8e+vwuX9+fw5s+fO/+rp/5g5q60+M/rBu5MXX54yd25yzs/P++xnPztyZMIHxNdYKzNzer558fz2joc5vvPnu+Dy3//+fC6KiFjCxVNEQ5R59NFHUW/RosVg1HFdkyjci0P/v//9j/Bib5LU/qyumxy7NdZYCyclzUtzxga9K3qzHvU0h7uHNzwoIQVm52qQVDBmUAeLjMzVGsN4xIj6hQsX1dbWGAkE7HiNQ2dkdh8Oq/YAVw5cL1Hrd792KDVEXl3NxnhdDBtOEIYNmjCYS9XoKS+WhpkFOKB8ZYYzne292HPSXRpn2WmyPWGOyTIP0B1uH3ynOpPG7Kw35r0wt3P24vbF4ztGLc7OzLEZWGUF+1FCwQnAiEw6ej0RyppGOz3rvBdclmyFIVfuCF+5rS+bkZfof+kUc+xbb72FfN0XgCExQeGbyLlpvfbaa3l9BfehG+GUJvg+3iCCN4VXEQn9978pCfCCWZH+l+bUKJMqnWUG5vLg3//+N94Zm4QTNUWI8fh86IBdnJmH8VN4Z+GtI1OmzLjuuhuy2Wq2gh4xgn1gnVeFhkRDfCNo0qR7uR/Et3K3B5L24fUAfyumT591+eV/4yMJ7OnnHLPf6wAqvBIq8WTiySefefnl13jrwbPrkYiTgHA1MWnSpOeffx5YRVOMIt156qmn+C4WGzyDLz2C0LALAk7WX2D0mDpwN2PHjsVTMzZMZilw9913lyKFQUMSFz+ohxnHjx+vhyqqxSZ8dYqnJVTJ7BCjTyiNifHFF1/E//bh3/G/3IP7d5Cawmsz5NBfXtPCjEgOxRrMAYWdAcB1Y4qG4YHd0Jyc8wIC3gQwxggMXwu408SfRnpAxcVtR6azK1PF5XBbku2sqHKfU8ELV7V0ZlrzFf6tyMq2xD1x8VNHvpYdoDsSfgkPyNqzLXycoSJTkamsntk8JxmT7HrQ9qut3uaocyMZVNdc9djoNUdWVeYYh2PHNfC2Jydjc3Z+a90cSNoz7vnS7PlziJzefKe1eArUlWt2NzId4+5+7L/5mW4y4ZsQTlohFa/Ri+UB/5s6BwdQvknGLCQV3eHo5fQcwKajqGiBaIFogSFnAa53uOZR7r9pmYyoO+Loo67993Vdzd71hBrbVQz3fe0dSU0VD2huvva6Dxx7zFEf+fB1V/2zIKcQK+05jSWUE+FogWiBaIFogWiBVdwC0f+u4gMgdj9aIFogWiBaYCVagLtU54idBhUjGo457th/Xv2vtiYXEtCdwjtZVljYlMQT3Hjdfz7+yU8cf/zxf9PnkroZIhQtMLwtoNW+XvugR+c8PWdVkjUDdvphuyDW1e677z62/WPhimUzaLR4pqftEGuxjTUt4cEQakziNQJCcwjQYcntq1/9KotnyNSjefGihwHheiFkEBM8/YMf/ICPxrKqx56FN9xwAwALcjREi+oDLQJLpqSxrkZ8DPgNN9yQTTQJokUaWzqxIgujaNANAlqUHPXIRIGEktXZX/ziF3vssQfBRqw4ool4RWZ99ys4biIxjJZaa2uTL37xix/96PGsBxOM9ZOf/IQNlYmBNs1hsYT+hBvRp7r6kW7X4ebFo0aPwfJsJTWifjQ7Zf7kx7+qr63vyPn46eK0ZaaDXTA5lRTZ85otcM78+tfXXnvtJx577De/+Y0WdFFejdJ3iEmaI82AAOyHAH7//fc79dRTx6++Bsv85/zyl889+3xF1h/fXoOwC72Bl82r99hzz1NPPYXWjznqKDbjvPXWO2gxPHC+abcmjUGkFbUcF8zFoWcLsdNPP51tSllPveaaazh2kIlFzUhhMCSns1/vESAMcWPbbLMNS84sNt9zzz2wMwzECwEtQgySnCJNk1MrmHDnI444gtB5imzjz+cAGFFQKhZQvKaGGCmCRw4A7O973/vYQowq4hRvvPFG1ufUd2hSHZGcmEcLRAtEC8gCTIAk/B0+iBmjrFmYWHgzhKrUlAILSCYrnA4w0w5wSgLzElEsvJUBQErVil1ROMQWhOy0RZFEhPE///lPiqRSdi4YHnnkEfBSpmwT//nPf8QrGohFpoCbl19+megxYWgubEJIQniJfiB0mFifUD7ExGDxwgzh3XApQFwSyCkSHwwj+yhTW11dRvmamiyXGf/7363M5dgPFvMdAHxeHjn6tgtwZSUlklcwz8frXXTFnDkL5s518mtqfLCFr7z8ivNxIr/8xXnkjz763Jprjq6pybS35rnN4G2ZB+64ePLkRR1tHbioTL6TEN7OKo6s+3oDb6B95ztf/vGPz21szFcQ0JVPFi3q4LWcESMq+cgEVwtoKMfk26HRLJ9eJUKJjfFkYa+fu16qrXX9JerIf5sV5Z324lLui3lCxyZPfjO0akgD/t5778UUNTWFsHLVgoedxNDilTOEg08JEYF/H8z53FCsYDlHuk+PUJ5AZVhCIZWVFYSXgSwrHyGMhzvumFRfX4MEqUSuhKGQiW6EvhEMTRF6kw9Qqk/EDDsLcBz5cgdhT2tt2cBgmDl7LudI2wJOlsZ8p15gc1faLtW51xuYaWv50lNnJzDXqKDdVu2egLOFkUMkVhwb3l6rSubdb1/+l1HBKzr4X+YTwjqxCyNEg4Qqitwp43/xgxBwCwMGvAiAmXlwoPhf4cmtClhp0qRJsPTtf8NpX1xqfUX6X5xpUWVOKz6w0IJDf+edd3AhnE0YB4wF0dLN6uqqRYsauTzAMuiPsxa7zb4yxRVXXDly5Ai+m0TRY5xVPeA2XebaABjhoVXBUEQHbvpw0DK+kPDKMvIa3AtDRuskyaSWgwKSIvltt90GBvnA1jsw1KIwswpv0ejQG7vkUyTROrUIBDZ2AF6C5W1YZOJ/5X3CWoMRDmxGMzyAF5/nfpbJCuWZ09QuVQAEQ4PnvS8IysoXO/4X9Uih5BAm+B7h2CrUQd2Hi9B/5PThf2+55RamU+xv3Rcvx4XxTI4EyTeCsPUIDxcLMBi6TxB/phROl0o3UMsOsKU64khgSFeOSHbYYYcNNvSPLnMjMc5/rn0CzHbbbs6juQmrjWKwsUMBZ5bmonb3DMxdB/L0n5xptrbOnbm1NRP8F12Gi3WXRc+lMu+yNBB5ogWiBaIFhrIFuAJ3F+H+s1xd7vrt8COP5JLvnZdfTSoyiXsvtpcEU1tHUuVYbvrPf077whd323efR+67z0nreSHXC39ERwtEC0QLRAtEC6zCFoj+dxU++LHr0QLRAtEC0QJDxwIf+MAHCBiY9sabCW/5smFbKrmb5WKqyLIwn2/vuPKSS0/70hnb77Lz04897uriLXDRQvHvsLZAr4teYa9YUWALH6KL2ICZjZqImNnTJz1eV86aFsm4gFl705qHkRE/TRQyyxK7+MRyGkvLsEDA4ii5koTAixByyUEHPvO65pprsgzJRkeHHXYYi2esuiGBKlFCrAQLGBZjTALw3nvvTfAQACx8E1OtqEXkiF7KgKSIEFYNJRACYnCJPAbDgu7222+PHBKUsLCQ6QEaRQ65C+Whlmg0D2Pk7AYbbHjCCScRSgUeO2g9sssFOLlavsSrbZthZBvKigwYwKS9tb2miiVhNqes3HW3HdlCct7cxUcecRzrPbbSjKqO1CffqFv7VAm7AtMFmuNL8etMXHfWjNm77roruxHnXNRHjqAquppLKvnx/V5+OffAr/DrymfcrwuLde64y64bbrLp3Pnz111/fb75m/UGRrFMtjPJdMiMLHLxeklBlTz7OdsafxcbMG+x+bb1daNHjBi99977YVtUVXyJ6GVJweoFGAhIHC/iV9CZ5SvC39lJmh6F9IUW/R/h6bUS7AyY9ddff9NNNyWWml2cDzroIBatzVCQIU0DAK2QQZEEHhhpjC62zeYTxohCAq0zAFCJgw4XlGHrYoGLBIwQLA8X7BwvGtUQQpTUS/HGYrRAtEC0QMoCmiqZx8BrbtH0EpIRXyKvB1JzV1iLBAss1tQX1iKNWpvxwirBVDHXkYxXalgRAiY6TYalupUKTGFgQYJkGqCiKIFpK8SYBJCwkLCPfCKw1Rq7AdTCYkUACS8r3yi5QKD7WBgM3eT7AS0tBEbiGd3ymMhkDeTjWXm5pq4OkziXgjrO1+Ygcz8ViXLmTTA8EZcxxMUR2JGtyNdWJhee+8Kbr2yQrVw0fWZSU7nRaiPXqa1YN59M4EJpjTUyvL7FVtlnnXWuwswI2a2pzlRWuB7hUyieeeaZvGnDfpNhB1GJ6GrRQGY9BU8S5TLniEWIxl4oTTZBLHbDEDKOIdWc6GXYUAGTI52JjgIDXJpoV/LFDkEoB5iDRfQzgCQYmSipRTEIdGSFVCspObE49C3AISbx+RB+nf6Xy3Ty62hr32DTDT7w8f0OP/GAD538/pO/9JFt9928tau5LdvYXtHUoUVwBk5bksu2ZKs7uqraOitb2yqbmvLzk8rWpKu9K9/e1dXe0dXenm/Lsytlpi2bYW9od+67RJtcR2c73S/jftksnwfhkp67m8Ivm+/ix1Wzu9wPfhIQ86FsAaYIps2+/W+p/hqNNpkwBxJILzKqNNVYEUCURm8CRalJEjVUFJkmVSiZx5gkTYjx9hNAmlQKATUhCcC0FWJMslhgN/9LlShNPTB0XwYMg2gh406qoaGeyw9mYG7NuaPklsvfDlsLDmAvaSi5F+Oe3t9mdtciBHbFN3djPSTdaNoufkICjIb/RSym425OBjQCeCGgX2DkZQSLAJgEDfG7SGAjZ7YJNxZVUcQC6EzrPDyhLRMugPFAo5gFDSFWStFQxGKh0UICWCgimeHhNer25qqSc1TTwoTswIyoFKa0SOvISeElDROpCdWWNiHzIgH1TAJkKmIfDFhWvhFHYLhYQIME59vS3sbe4PyauxJ+PGdqzrdyEjHYOrOL+bVn2/GC7dkcPz2Jymea/fYk8o2cKTX4VOfKHbf71Bm/Dp7E59t5FTDTkJnTNpnfrA7366zN1U+s23jncdvss/aEjerX2HRkUsfFdLZ2VIZf3agufm1dbvi1ZhdlGjoWdk5tzE1vz89ryzHd4MWRnu3+DRdzRz2jBaIFogWiBZZoAS6T/IP6LA/tR4/mY5J3T5oEE9es7nGMrpyKucOQxII7wkGROrr4Attee+01evx4SjztKpC5upiiBaIFogWiBaIFogXKWSD633JWibhogWiBaIFogWiBFWEB1goqK8eutdYGG2zAnmWuxdLoZ5B+TcH/zXPzywqNo0ySP//5z9z/rjlxorvzLdKoKnUHXbyT7n7gXyCLf6IFhpgF0staZdVjmUqLl7w3v912202cOJF4aD56zjqWBR/DSDFMLH2xIqIlQGpZ6uM7vyAJX2bXQzZr0dqhJEMGjVbFyupAFXu3sJTIDsRIYEMp2CnShK0KpxgV1KLAU9jZZAsCorcJPGJLKulGu+hsEoAlBIAqJMColRsYiWRlwY9lS3qhhUM1wcZLcEFJLXzAiKEok+h5GkgaJQAXgA/Osl6LLp7GUVq7ntfpwH+EYBxkKqqY/gLTIvtZogAAIVMkmGU9uDwxf3skLzzPJmQ0xEIs2iIKipBYmvRg8wUkc2ToAkccBFHI8LIfJ2ZzkdrFvoX6lwoBQ+wyie5UNzQwDOg+xmRIOP0DIdYRWMCTWByFjC2TqSL6GZ0ZPGAYePCSoORYhH0BIzxIABhpmqGC0VAeDDkHUbVqhRwuEgB4VcEoyRic7xQTBk2wGvtp0QtWlMWihsSr3ERRREm6idoMQpbYYUR5Yt2oQrh0o62QPcLRAtEC0QKhBUonmbA2hDVxhRjB4SQTTlCllH3XQo8yoT4hLGlLlLDERpdWQqkOpU2EmKWSb8LxBbrawfuMHu328qQIErdiAvVBC5wSv8ZG8G7djbzoXpwWwCNGVJ122mlcgbz22iwwWk2r8aHSd931KN7q9deT9nyCn1q0GLdbW11RXVmXLFiQnzTJ3bdoJ+nijYmTqWSt4FyKuIIrpCgl6Y4HCtc5RjbYgEagGao/zaGqkhFTNFgAGMkM8xSNURoAcUqyKVbaRFlpETmcLJB3IYy6bMvnqohWHD36HXeF7fZX9z/2/uaM8eGpnJV0TcOD82jkhDpeWmtvXeyiDPOV3ES88sibGsxuqPiBZGMbDDA5ZyJydGWX9WO2paVNkjXSHG9Mw8QCy3OwbGJRXyUqJVDjpzdjaLIKa0N2wdaKASH9EuEUV6q4RPZQHxH3XwK8JN4tgpEAaGBJADAhcmeOzt2qu63ZrQpiYAhCjCSkcnhFA0AVucTqrMSPp+hFQ1726EgItYotxmWLXjKBVSRHPgojv2wTntBlS9TfKENgiVzW694o6V2ocyi8P7DJF3FvrVBrFhNlqtiftiLNcLEAHnPMZvVoW5N3D5ruuO8pcoYZl8oAy5M0bDiVBHChjbSKGiecRysgc13unQqeVkGjMzfr3jnyyUXBuQdovG9AuY+xSlUcnwWjxT/RAtEC0QLD1wL+Ni3DWgNbkCxo3Puw97mvmTU2J1VsbeVv+fxcn3YH3MJ5RvU701DXPH8BH2/kC10XXXABb+LUjRrZ4td9hq9houbRAtEC0QLRAtECg2iB6H8H0bhRdLRAtEC0QLRAtMCSLFBdlbR38Gz2/vvv71jYWH77Z8nwSyQ8IXU/EsVspq1pMZ+yPOKIIy6++GKPYc8IH+iwpGZjfbTAkLVAvwKgWTMg6IfAzauuumq99dYD5gUCPpxKPKiWGbSERm7LBlrtU5QqeAJAiT+48MILEcX793xSlghmhRNRq2dP8KYfQvllMzXBosU111yz1VZbsQninXfeeddddxEaCy9CMC68JAEgQwxrHkT9QsZ3Zn/9618fcMABTz755KWXXopAqsQuFrhME0kQBiRkhMD+8pe/POmkk/iG729/+1uaoy8ig0Ct+xUXt7cxCJTy8lwV6zUvvfTGz352zvHHn8BOUT/96S8WLWoePXoEYsVY7LgXyL43bt6phzOXzCfOmRXeBfNnXHTB397//vkbrLfhDTfc8NDD93EU3OY1bmccF7OLKLeU66MteHEDxVhy8or5QNvOzNVXXVNb27D22mvffuctt95+K9Xo35nPOX6/MkRzILVYlHN7ObuN9JIKJHblOhMa5Qiuv+GGBKATwE0tCeWJ7yJ3lI7XrSJn8/5T8lm346XfXK+LFaeHH3nwot9fwA7cixsbGTyQ0br6DmwAMInuoDnHnaUsEpR8Bfjyyy9n23HiiXkKSfA98cR2oKEHJkeOHQuTqYDjCy644OCDD2Yc8ilqAssUja3mlENvLIjSAJZYwq9vv/32PfbYg/j1f/7znwTioxWR0BicZC2G0gTDTqg3Eed///vf2ZgTnW+++Wb6ot3C1CIN0UGElLJHTLRAtEC0gGah0A69TRfCQ98bgYTYRNc3GcSIYnYiD1uHfYmM0IvG2rKml8jbT/mpJtSQ8r6b6I98Nk12CmcU2ORAnDFRUuwBu+FG4z/3uVP5FMChhx6mt4NctU98ooHU2oLd8MoEP7liamrngqWpqWPBgnmdHclaE+sXLmyGwN1K5JKKymTqzOTJR9u+/d19x40bd9xH/oNLHtXwGu3mca35ZPpUJ7Cy2skkisNFWAZHBl/kqovbOqbsL8uIQB5TcCqHK6RM1S6x2LflxW40SzwQpokBS1RgOQnUfdNwOaVF9hVvAU4jGi1ckeb9Cncuae1sm9k4lcs2zuiWRVMX5KYko5Pt99kYyorKaZwOrW+Nee7BqR1dbdlK9oJs60pa2pLFbUnL6NXGHXL4ATPbn+MmorZjxKOPtj/3yOsVma6GDnYlz3ZWuovmzqx7U45dorMV2Zx/AZK46g4W1/0UQtg0rVSNgDTf3uwuhjnD2Ie90n3mhdM+Xvit+DGydC2W+l/jN1e7bBNU6TxjGAlkWHGLsTzCl43XOtg3IG1TTahoHelDgqfh6gKvWngf1YipQo5yH5zMLZL7bA7uFJolNgGluwUnFeTo0UTBtUmsz71b9XTFe183e4QJsrDYB1yktFhqacDFgOuK1xlMWn4fAgevSrYN5ZdiwtolwrBzochwZS6VecXie+1AAJJMFBKkJKtKZKmqWBzKFuDg8nSFA8dl8r7777POHu7CtIOnQEnyzMxZzdUd/lK5IpdZlMvUdGVb3NMi/wkFBSjz7QX37KmwE7PzwXxBzD3Ncr/qjPuaGN9ZaO/iMw3ZpK2yq4ovM7hB5Z9T1SaLOhcszE2hdffJteqko7prYWdTV8Y9G8z6a/hMMpKnU20Vna3ZjvZMC3TsQt3uWnR7efqB6Zj9IGUI82xqeWO1aTqmaIFogWiBaIEhYYHOXOW40VtvvfXvf//7pAr30+UfzfAWq/MyzPxllCxukZV33+pMXn3u+S033/yDRx/9n39do3feyrBEVLRAtEC0QLRAtEC0QGiB6H9Da0Q4WiBaIFogWiBaYFAtoOWLbIbo5wnrr7vxJpvc9fs/ENfnYhT6Tm51snhT7G+En3r0sXXWmnjUUUddd+217p4Zmap3j00DWf1eMQl4IhgtsBIs0K8AaIvyfPbZZ4kAJgyUOFT2UWP11637+7EPHJ4ErCHQGyGBRfnmm2+eccYZsLPSwIbKxOwCQCZGLT+EGFWxroYC7LSEkLPPPhsARvYzRgFikak1CaECal1q8KwKsXPnziUC+6KLLkIaIbASi0wo6QV52LSpBBkwZOz9dsUVVxAHjDS6TzSw6GEUL2QohjDw0gouFPBIvmNLAPf1t956O8WWlva6OvfBYmM0UQhxAckkZxXCk50N2fERBe+5555XX321uakFPGKJ/17c0gjAQq8aAvYh0U6sE+ATkp2cXO6xxx97/sUXaJ3FZmoqq7uXdWF0vIVD6egLEvgCqY+lQxmsh+naeA6oJeMKZzS6yVF37dAMqbA47RAkYZBArPnsmTMJHX7kkUcWzJvHOpkWSmnIMfmkRlFDReUqUsWB/sc//vHvf/+bjZw5EC74u5gkQQY3XiqFJ6ctunzrrbfed999EKCMLEAV8kVmjALQGQnADBVoaPTuu+/m9ResRwwN7IT+oxK10IgyJcHEQs8O0I8++ihR4wxdhj1cIQuwjGwsKVGxGC0QLRAtEFqAucLmEACbNjWHaGYWjTDKQwnAIjBe1YrShIOEgAkq5IWGFNKEvFZrNKKkaEJC2JD9BMRrMgFMWqlK/ZQZkklaQWa4DZDbaTJ/6mkf5R0wVtH4DAVcOHy/nZwToP5RbF6c8KnVb37rTK6X/vvfm3oKd1HRcG2++cRXXplGVOWcOc3V1bwO5G4o8Dl/vuCSu+6665hjdoDr7G83PP744w8+9vLixa6KtO76LqaysbGNYtnd9EJTGOw4XSocMvC8VYWtEOJpHN6IAShy0F29WhVz8fAJKfqQwFNJoEDnGY1MgOWhEKhVJFe7Bf4i3ooCECLJykNkipKiWhQ+pC+lFEbCU2r0RhzxQ8QC4VEuqxKXYRpd/m1Bf9VXkXzkIx+BOFsxlaqZz2RefPFSk6NhQC0Asx8XfsrddaY7axzeJwH+chE8byC0se+lV6E420HGcGpr7Ug6eCcQkEtcd1/g5ThJUKOA54nZ8LAAx1SKlp1SGCr+KJfvi3h7O+KhZPEjqtT/lmU3XgHkIlNuSMQCq9ibHBrlKoJakYU9CRmBjUaiVBRcyhvKEWzs1ke4aB28zBio4WyuWqqMsVQmGGd/3kXg7rV4pkKvhASSRCEPAHwoRLWl+JCmP3AoR014TOGgSL7RhAKFDLUy2PXLp0BgyOpg2I3e5IeA1Rpn37VGBgCviI3FajVmKKoKSpIVjUwYqiATQWmVBkCIj/AQtwCHktNNj0R4PtPYOBuFFQCN9+yudYFky55KRx2yGGU0zdMVPdjhCQ8JJFOHaylTfCHBXcy75KaF4tirqKnhwy56zgMSFl73hwAyaVm2RVXFPFogWiBaIFpg6FuAJQSU3G233V544YWkkecp7DBTmzS3+gvAEvWLl/fdFe3+fZiK7E033fTZz3526x22f/6pp8vzdvNEKFogWiBaIFogWmBVt0D0v6v6CIj9jxaIFogWiBZYwRbQYkdtbX5xy7777vvyyy93Nbt7YRe+7GIOlk4b4uhOPvnkTbfa8tUXX1w6zkgdLTD0LNAj3rQ39bQwwCIBy/8K4mSlgTBWFja0tuFWDXzSuoIWD9xagk8UWZCAnZghop8hBE1QKUhatJUGYFUZRgBIWrHmCDwFQ/SzaSsyIw5lSiDBytSyqEYR/QHAoAMywZDUtGDJkeYioHbChAnrrrsuvGAoEnyMEAAlWDwXC8ZueQcjkFTlZbKgAp6QZRfDTVVDgwueVqLTwY/gabaOdD+3MZyLbSbCuCbJVhGyVF1dOW/enLaO5tb2xdnK9ubWeUigFVFmCGkqLvaq6WILbyxh3AABAABJREFUjgZtqWxvJ2Y3UzeqesTYuqraqvaudt916whKslLFzjptvC3ifjn26Olipzr39e9O4og7nbKZJFsMnnbdZlWJFvJVmYQt8bqTdGAVuiKTr8h01tVV1lRVzZg2rbmlvaOTWGsXJk5yEnxSMaW5kORsmUwYMb3g6LOPMgtsUIIXPTkyQkxYxcCjlhzjAxAKgwSGIjSMZ2SSQl5ooJRkANWiMGMeegYtwwAhCKRKDal1sSgHQ6IWCYxehiuMCGHkwKjui9I1H4ylUEiEowWiBaIFsIDNSLIGcwvzCdOgZiGK4DXhaEYCzzQl7wyeZGY0WLOQryzUwqsigCYlaCTZ2AHEiA/tTT5CmCqlCZpLpgCTxtb4YdOhfPB0TfJDvGBqATQPA6iVkAzN6T7s1nRYCzss1KIheNPKaAp9Z2e4fFUX29nlksVN7ldVsWGuc7WxY0dts82Wzz732Kabrff1b5zGG0qEO9fWuZxflveCvOtYb4OG07/wyZ123hyx5qb22mvnI444dNddd7jttus/+clP1tcns2dP72Cj2BqOXVJZk1z+t/M/ddqmHzlxfGvXY13Zp0/76jpj1nzZX3Ggp1Nw7py22bPaunA+2Xo2uWVb6FT3ZRwswNFXVYpA3aTXABArhTAY2GUWeI3dKKklWVEClRtxWITSaMRFDqXkhFUg7ZAZpREYIHZywwCUIpFGCuWY2kKG7CKGACRjT9JCgggPLwvwggA/93ETvxNsV0W+g+8W59tbk0XNuQUtmWbClOc2v8FvdvPrc1omj6ivra0mMJkLPq7R3a+T775wcVvVnqtsa6mZ31w9b2F2elv9fHag7OS5QWdT0tXYlmvqSJrb8p055hLO/WZOSH85nySj1x+5/+G7u99he+/zvj3Grz3KGTCXgz5X1drV2d6Zb89lOruSDn7Dy7arpraaTGyC4gaWWcIf0u5oORU1s2lKKbWVuPBQJNFYrvmHIlzMQgCaqJVLlOYuYymVTxU08m4AUlhkqpIE5aXs1goKcMtjxEYJBrjU/0olukCLXCHgYY2lT4ALjEKEoiRwInH6IQE5JHi5rfY/pwtO2z50IALpU+R1joDLEriqaytzCa9YI6HwkrbjL3oNjMTZqCK8pqHaUruSGUoWmR1f4zIAgdKK2z6+/MQNdfBDE1oE091fYwwBp2VRT+FNE/Bhr0Mug1O8hhcAO0MXgSGZLvMgoBZ8mEJ28MYomrAWs+jKUEgIUgBF3jznFlhVRgAesbIqcPS/sttwycORAMw7B+6DJRVT+LVXzuW3YPGCthzvzvNUq5InQvwYaNa7XFLlf8JwAWbPoxzgy2zXyZUZoJ8rssRVd7ax33S+rTVp5QdZZyWOdXG+si1X1dmRbWvJO2zRj9MyL+67l9Xbszn3q3A/92wLVSuak3xLW35RS9eCtq5FbETNUOTxToaXmdyve2YwhSMQLRAtEC0QLTA8LMBlCJci3r3svvvuTzzxRGHRV2+Qu9qe/XBTvn8ZvXgB46rlB7g86ui84tLLjj7ygxtstJFjTPH2lBRL0QLRAtEC0QLRAquuBZyXjP531T3+sefRAtEC0QLRAivHAjhfHssSZzCibo2Ja026e5K7mXXRz0FoQolmVOuWN1XT3tp6yeWXHX300etssAGrGYWf3QUX2PyNcXj7nJISi9ECQ8MC/doBGlW1QqZVSbfIkcuxwgqgFQKK0LCCJQwAeFuCFS+rkiSjBAkxNFACwyIJKbNAT5W4pAZF5IgMGCHGIjLDSBkhWWyGmOZY04VdKhFNKxp0oBY5KgoAJglm3Y7QZxjBGDFVwOQk8HSaVoBpUfIBnIjiqiHEwCIWYOxOhE8O42ViQWkO2rioNaTRq1b6g4SGJAWMBgy61dRVYYHWrlaFcftaH+DrWeCUKOPi6R41KpK7bSN9CWSOyA0iob3RHEH3QfAluum321FF2Fl40SFbmWEFnXUmx1q0ieP0CRoky3ogIGDLbWHoF8kIqAVPUYxQGixGihpjFGlOBOo+sImi1hjBk8ROLhow6COFASBWu562kJkESRMLZAwbkmrFCyxlxAk9SpLHFC0QLRAtUGoBphGQTBeazZhPFixYAIZZnUhinJpcDwQiI4hk0aJFzDMkMGECAxkC+ZQBO9kzv1kteNUyQ4IfN24c8xVJrRsZrUPJDOakl8gXWVNTkyJdIEYZkKYbAoHZCz8lNpTPhI/k3ghgxyOTE8oDFwLFK2XQHN3gLdUNDFy6BuDaIWii24HB7qyY78TUn/r00R/72MeOOebDs2Yl7NbMBcPvfvdHPkTw9juz2dGfDzJAOWZcwqEYMSrZbrtNeNGFDxQ88diU119feNhhh82ePRvzcOiwMZTf/e53efHmxz/+8SuvvELV2LGjt9pqcw7Bww8/PG3a4uOOPeTggw++/rqLfvCDc3fcNVlrreTJx5OmpmSjjdZ8/fUZ7kUjnKoXxZZ1XIwgli7SHbObADDUtrR08qoXo0IHy1fRXUfv7aNxQux1FcYzIQCk1tYO3BHdJ6UOL6amFmm1tVVIC5tWEeE6ashRcyGNYFUxKhgkNCEkxDAy8MhJKeHQSBr09IhvaFRVud6ZcGrRDQwECxc2oR7w4sUtdXU1RkORKFeNWy4/DA+ASuRI0OBBCCkkiPAwsgCDgYTChQFa5a4SObgcZUvUFi6DK9w7jVXuCs2RQVBkcz2WHAf5S0E35CqTuhEETTFa3ClJoGbS2l47cUJlXVvT9MaEYTWCQL+EqYDTGa7qpJ4ht8HaG1511VVNM1qylZw7NYsXt6255pozZ840HX0LMRuiFmDwSDPew8T1cEDnzZsHUmG+GiSMHAFMHfgv6KkNx4/1DUqcL+xM/uRMaCkJIPGPDCGqgFNzEey4MGYqvURkYkOgsbGxrP+lIRiRCTFiy6pHrZrojYDasv4XmcyrnFZIQIHUHGvqwY4n5Vzz07Wbe+VJAEC2t2NdXAyveFWgqsJh4UVVncW+7+7KxwQaAA0JdriwsOjF61tx7g/ddOLj+4xRALwAEGAh4LD7MBqNnhvoFSOJVRUwLZIhAfXI6YWqjKAoxr2RS+/CWoM56MghGQYAfZBPovtUlbUtteo+mqfYQ1Hz589HtzXWWIP5x+L4IYAFy6jXMkXIJR2MJiWfIiwkxjxjQ7UYSsHWJoeirGcYAb5nhUzdR40UTSwOIwswEtDWcn8uDK76YRMMP8Zj2J4GpBugmmusbl4Xby7xRBAEXzfLVCRV2VpO7VCa0UYgWiBaIFogWmA4WaDoB/Z//6Evvvhi07SZTPHuazy8eVo2cclWZOlRD5IF387ORQsWnHfeebzBzudAJ7/2WmV1NV/V7EEZC9EC0QLRAtEC0QLRAkVnGv1vHAvRAtEC0QLRAtECK9oC7V077rnz66+/nnCvmnoEulSqVFW2NS3+05/+xP3vddddN/nlV6rq6zq0pTRytNxR9PhLJTgSRwuseAv0WGPro3nWA9ySQjEBs1oFvQCtXFFUPUiSYCFZ6mNpjYVJLb5qdcHItFxHToLL8JJPLvlaLyyskvmFzLBdIwMIE8tpLMiRg4SepkkCaM4vwfqGi01DJs0ho0XpTM56KlUiNQ1TCnjZbucn/fzSNrssu+aQKa2ArQlhLM/kc/ku2Nl2Cy7WgIkl4lOeHKMqtnRkQRYxbnNov4MUYlHH1eb4L5VpgjXdKjaNzie1uSTblc+gCnREXVRWu/hvepTJscpTQc4vS7CHY9euPKYI4ng4yKo8C7qQZggeRwaBYewZ3dWR62x3jw5zLgIE2e6XIc8TCUJbdI/YaLrsdrrym/DREUejvbXYc4cfRmAdV5bxRitkiC30xP+RQtShNuOHHJga7G+6qpaiDEsRQDC5UQoPWUgArCR6I3b8xeEdNqrRUkoWCqEJNJQ+UAJLmnLhode48r3s0R0IYooWiBaIFggtoBmG6YIpSPhvfOMbH/zgBwn0YYJinoFAUxa1ODtCo372s5+tvfbaoZAQPuKII/iUCRGoNi9Rq8mKfKuttjrnnHP47oEwIaPgQw899Mtf/rLNhCkCZH7ve9/bZZddAAgmUC2iSETAoN7OO+/8/e9/P8VlRbg+97nPfehDH+pNPpQ//OEPN9xwQ4JpLBII4ZpvqT3mmGM+/elPKwTKN+smYQH4kbXWWuuLX/zi3nvvjSYgrV0AJODnN998szO/+p3qqlFtrZXVVWNHjx45ZkwydsL88asvJLCNyPKPfex977zz+h23PXvEETt/7WufX2ed7PjxFf/v/33v7LO/wwsvH/7oXl/96knPPDP51Zcb2RO6ohLDclCczhdeeOEjjzxy7733Ej89bdrC44776J//fAk64G0OPvjAadOmnPeb66a+nWy42ueevHe7Fx+vOv+cm8444zS3vpZ1X1rIdSWdHcnhh7/v05/+5Pjx43ijx/Sng9Kft7o+97lTDjpof0LshCGHjK6RCE573/veB8GECWOJv+KSQBJgJzGKoPnUpz7B8SXEjlqKIQGiNt98k29/+5sAqWQSDvKJYQkmRaMiI4c4QsxIiF5I4GPC8hyXI488MsQLljSUOeGEE3bbbeeUcMYVLaLwxhtvTO84xBTdpr4+SbeGBrdpq2ffrVQ+GIRvtNFGfOJ2tdVWS8kvSx+RQ9ACXIzmCX6qbOfXVZnwIwYZuD1p78iw7u0uRtkS0i1yu4tlfnn368pxMaxrWu0c6XeD5mq1M5d1dfw6KrvaKzrZOnq3A7babr/qbfet3mbvEfyIeN5819GHHb/D+z+z/Xs/sdXBH962Yr1kUWbhomQKvzntk5uy08Zt0NBS1bLLYZvud+SO2++3wR4f3HSP/XeqGZVhW0x+Q9CMUaWyFiDuGTyTA5MYHopJxsiYPAVTu+mmm/KiCxHMVpsCjj/++FNPPRX/ayxwKeExmRh//etf43+ZflOMFGmI2PqvfvWrmplLCcCU+l8jwy2yE97/+3//zzApADXwv0cddRRAqsqKpf5XVRgEj8wOAfhfI04BTP4Yh8sD7gH9NqucopjRvddK19jg+cgjD/vqV8+sqqrt6MAfdYcpow80vP3yta99jS7gyrn+QbjsgPuAHZ9+wAEHYJy6uhG4MM/ub5MTfBlv+xD/nZx00skf//hJOH9O/kwGCZoIHAFvOm+44SZcnKy//vpcXUi+6U9DPEzguHPs5DrVNAToBoYerb76hM985tPkRHLj2P18w8BwP4qdne3bbbcNPqjskVVD1G677bYp41OEhZyOf/7znzeVQkDKcOC4ugvxKfjb3/4245OOEH+PQLjE6P1v11577VXW/0oI5qXvGCfUX+xUYQG+8fXxj3+cMUCRDzelmmZ4HHfccTvssEMKryJyuKj7zGc+E/1vWfsMCyQj3g36bDO/fNLGL5Ot5NlQLlPjfv5MqMhVckJU8JIhXxZLqvm5C1yX3IMmvlXmfkHCNfMrJC6DmSdc7k8qj0UsZ3NXNtOZ7eyq6OrMVPCUCww/Ubpzvbj1tHRoz7IhNB93S9bcZcLEbRrW3X7UxtuuXucm7LaGBmakwgOlQqPxT7RAtEC0QLTAMLVAVQUXnG77Z1LxQr2vrsiNKYfOAM9DDPRtt93GgxTe4YvRz32ZMdZFC0QLRAtEC6ziFoj+dxUfALH70QLRAtEC0QIrygJ6LF9obUTdjjvv/ODDDyc+eM8he97SllWKh6b6ddf6Ba+m5uabb731fR/4QFJT3R39LCIYDDC4iIt/owWGlAXcg/7+JM6lcKlMxZCRWlsABg8MJgRYD2PNTMtmVAnQKUquogQKiQQAybRaE2tNI4pkjFCKBYyqEAJAvKm1KALwSlACCEluSLiAyWFMISmmFPA0LnxKcuCSAkamYsgYVhVq/c4DGMqxZ13skWhCLmnl56WCADA0SnQUq4+gHMxOkayV8vHvomUkn9VHw0g4hkc4AcbgfaR0Qab9oZakomdhK0s/dbrtJ71ZvJ1FIOHdpvFYp4KXzh9EebDHUDFe35TjFo2kKZdVFW1AN13v/HFxnfV9BFCPJE30wpgc64gw1i6A6AGMxQSaWDCsuBseYuQohYzWisRCALDEFHItkTgSRAtEC6yCFmACZDpiFmK6IBJrxowZRLGwkx+7LYYTiCKBCK6C3vDhRASSyB54CbSyqQ97ioac0B+2CbTd+1JCIEAHtqBmKjaW1OGgFhohYRcZRV0GUIX8FEtYJEiLLTBDxrAWmPBZEcgpgKEJJbjoGh0ESHGpSJcxDsl7zPT8DBMy586di5JXXnk1aeGCZL0Nqr/1rW9tsMEG73//+xYtasPgU6ZMQdoBBxxAtNADDzwwadIzhIwT2sWuigSuTZs2DTkNI9EKn5LU1REa1fXgg0/ec8+TRDBvueVslG9vS+66666nnnqKzhIefccdd2D2Z599ExYOLgTED7399tvsWkTQO0EbStRyMUOtP7hFrOs+lzGuSI4cv0Fp92WYt4yTQS39EjvXC3b0ZWpGFyFc8KKSRMMoQJQQ0HcIerMt9BxiWIxR7GFOE8gnRspGiGq9Dg7sTThVKIAmKJCSTxE8jGzUPWvWLE4NKBcvbq2vL8TfQ0CjnBoYh6QWS3NoGJls4JqSX0oZMUPKAjZmOHBuTdqfMIWzxt/lQODHcPf5XjjEfoRTVXZZnFFk3USCY6lKiEHccMQe4BdXuvjLp17+PRGBu+22I6dVvsrtsPvSS3OZAHUiZHjjL0lmzZrXNS/ZZ599CIptalwwcuTIbOOoZ5555o1FU01+BIayBTQS8IkoyTDAQeCD8CCMG6Yyc0PqApMkA4CJurce4X2oSo0uETMFrbPOOkxxvflfWpQDtTFf2kpv/hfN4WJkokBv7MhHBwaz94+lsh2m1P/q7ICXJuShynP6T/Ewx+IFwu5DLN2wJL519dXXQiAEICVHtcifOHENPrOAhaGkI2AgI0FGEZ2pYgJHPtcA1EoxUw8a5EMmgaGSEIPHdG+99dacOXOkUkhAK3BR25uDoBX6DgHHjvlEjYYS6I2cF1UhPoSRQBeknpFRRDc6heVTfjPkBdbu4ymkFRGIf8R09AVTCA8MnoQFwAAYfQqQ3aAXpYhFL4V59Q73ihdGVVJKFBdIDC1rNyWcIrXYvzfzltJHzNC3gA2VFaBqarwtscVd9t+BiP/2OfPdayfZEXfeeeezD73ax/hcosBIEC0QLRAtEC0wpCyw1fbb892tWW+84bTqfmlx2XV88fnnuZXjVbQLzz9/2aVEzmiBaIFogWiBaIF3tQWi/31XH97YuWiBaIFogWiBIWGB0qeg2+ywAzEJTVOmp/XTo/7iIku6trRMdGGStDc2vfDkU9z/nnbaaRf+5tweVAjsv7QenLEQLbCiLeBDA/rRKItbUIVrcpxjhhQgAvJwwUNVLJtBz6obCYAEXqtu4qIIDbCSapEDJRhyBXWBIYERLzkwxEpCaolOcmhO7KoSmWFEo1waqjnIREMuRvAASFMVeAOoIoFht2h+4H1R64gEJctKrArrqZur1Goj/aAvNOXpkZBhv+Ycm0Cza5SjoZJoY/Sij5Cw97NfrXSqkVzgBTvdudyJccuZbDzN3s4EArH9XTaT7XK7W1Ek7hzh0LiEHEKX836PHifU7VCV70Q34rTc7jukoO+uK+jPD2wxalkKsFuelwYBPERfo3g+o+HkWini9SFgJDs2bzS3Yu33rpYg1yRJ9gcQYwjI1OQkuumoPRlFASBd34oDA4Aq5ACIXpRG7wUUGgJJUqMAJiRsyFQCiZ4aVCI2FtGYHJqgSg0pV1EqQWbyqQ3hkCXC0QLRAtECoQWIfWH+wRtedtll4AnxIVCJ+YTpRZMPObUgf/WrX9kUZICJ+te//mUwgLGLcurUqeeeey54yVStCUH+fffdd//994uRvDRdfPHFQjLjSYiKCGG6e8GnUi5hILjyyitDrlLK3/72tyCh0WwsAmO5/fbbaSj8kjVV0h/k7NmzL7/8cgKxcCbWKUlA29razOTJk994/e94jyS3dldSOXbcO2+/yYayr0y679YO74RvvNH1nfhmrPTQQw+99tprFO+99zmmfLzQ3Xc/l83y4+ggP2luTtZbd9x++23DCtwLL0ydsFrFHXc8MGZMTcOI5D//+S9R4sTU1dYmf/7zrVdcfuuoumT2wuS3f3hg/XHrd+VnfOkLv/nRr47YaKPkmefdNpV572BvuukOrpWqqjjQmaeffpp4o9122w0F2BAa74T+f/vb3+gvW2/Lnalr5B5Zcffdd3M9QLGqqsKHejiXKjtATzDfLbfcAqauzm0uDqWv4pgUYp6IXbv00ksh6C0R1d1blfBsg40mpJTxUaaqKos9+2BHw7///e8QwB6SETSGhihs7Ai36GcoKSoe7r///W/ImIKJX7/22mvx8indUmSxOHQs4IeSGwyZSncHke9obxjVUD2hewtJXrVoz7ZWZ9q6ki5FpGa5cnabSvqLSZ8D8ctze8IE5aoKP3+mMIW5jST5eEoHHzapTOpGVy0eOZe2urK1zg7VSWtF68LclHkd89hzksHD91fyNV1d1XOorKyu5I3Gxfk2to7uqJnfVpWrGtNcOSKfbe/KZWY69piGgwU0l5K745skmoWYc5goVKVOgAEgiPN3v/tdao4Ke3nTTTdRRJTx2oQDwHsvuG8INElKjhFQZJZ7mLf5S6ZBMJBB8Je//AVYygBYopYZHq9BMmQKgIurC4SUshtlqf+Vesrxv0ZZCjBX4wLA1+L2eibYaZTPI7z00qvAEJh9IAQGSQAxlwf0gg2MwZiSui5CbS4ueLUA2wLDJZUEwEWaNGkSVRYebSpACRcO7oorroCAIGYLhTRRNPrkk0/SBa6CEAVe8gUgE/abb76ZceH8L3fWqMDbyT5BWVVVycUVDhpeazcFXHPNNWCsX8DWBWAuTkgplrBI78JiKUzvJNzEyox0Fv3NgZYyCvPnP/8ZwCxvQgAwQh+XdhDwZtr111/fm2Tw+F+6z1EwsX0Qx6qhawF91qDwcQN51+5cz5qyvrbDP8uqLDx98jSZFl7h9w7Z9U+nXklPdfrYA8xK9x0HnlJxqvGpNB6EZSq6nBD3LM3z+msDvnnmPnsGxr2XxG/iJvXrbzm6cU4zJ/vYygmjX+bCoKO9rYNp3rfruGKKFogWiBaIFhiuFsgnfBbjoosuSvjkVGOz64W7KuvZm8I1Wk9kacmvqQj9wH338aLXaV/4wiWXXNJc/JZLKUfERAtEC0QLRAtEC6yiFoj+dxU98LHb0QLRAtEC0QIr1AJ6FM8DTBILl3zelj2brrrqqh5KaAFdUWrBYnp4H0zQISx6qpq6XQZPBAhPTb/8zW+c+5vf8HndgvBAFOsWPVqMhWiBIWYBWz/ooRenDStknEUkKiiSAwsQBhgMy2A9OD2xKFnEEiWiREwueiOQQFYuwZCrCKzmxEguUSEvBOCVYKRKvFLGYABJE95qYVSV1QIIVg4ljbLGCaWvcepZFbViNyGQ+YdqYTsF2Fe5qQDiVDWYECn5PuJZxC4E2hF4RgAIJK3QlpfJoVIVws1ETufiEz7AsGVXE6RU0WqQySjwvF5tVo3oIwqRiHeuylbwoXGvkvKejC6wWsnXul6EzXaLDRZ6i11zfNIKLs/oes3qrCFFSZUBwHCJWKunggMdChohBC5qSdaQAMlXrSSTC9DRh0A0YheswanWlVNLleRbE2A4OhJILl5qSUYJHOJ9ZTqITciYRwtEC6xSFtBEpBleeW/dFyUzSTiZaFIqyxJWhXMRxJIQEoC0Ga+stL6RCFwiOwQpNVIKWBNQhn0UvhRj9FQhqlSBUvmilAcEPuZDB7J76z333CMvhif0VzcEurlPxp9++unve9/xVlVUw/lt9qFeMC/PBrFnnXXWeeedN3ny1ObmLngbG9ugZ4dQYADyEQ1M9eyAmIyqTSo7xrBDZ2v7fBh33XXX7373W7/6xX95g3Pa7PkIp3Vz6ITZsY03gcoEQxfftnLte/2lSF+577gjkAU0clIMVFGPwLK1KWKzrQGyZIqsbBFK4woJwqNjIz+lDHh4rTZkF+x70cPVGk0oX0g0sdoIDDsL7L///hO3UAC0e1/hb9dM6s8B1a0MV3pJ71FPboD5HeLZh5Vh05G0OeP4C2SGH4l7FQualN3UNLcSBECzvymBjxUZx5Vtyjc28tTAM4s05kPYAppwGAAcUJt8KJbOHjYL6dBr5umtZ32PTNWSh0IYZiatP00YMQByQvawymBoSFbsA0BUSv8lMkJgLCK2Yqqh0loTbvYXRgcFGH10p2bhxQgnUQWLwZyMdjsPknZDycAhi7QSAVx604bAF05ztRsSgIQbNXgzy+MLfsTLFGEhd/NMP5LUEyEsSzx2oUh4ZahQiAgMo36JjO6YYUM5BssyKorRMBJoYo0lBMRi7H0Q91EVCozwsLMAY4Ax5oaZe6zlJpnu3L32X3gyA5Jkl9nqpuMqm5bjYg2PzJU27xByclVXjeD9Ur4ega+OKVogWiBaIFpg2Fsgn+y42668MzZ/1ix3s8W1sy5u8Rpc+8l39N+D9HwywEYAfE+D5z/nn3ceTkTXn8PeYrED0QLRAtEC0QLRAstvgeh/l9+GUUK0QLRAtEC0QLRA/yzAI3TuRrUWudu++7zxxhtNfNOyuiLp6Ne6Q78aaW+//cab2Ovni2ecwY42XW1tFTU15I6XW+yS0NB+yYxE0QIr0ALln/Rr+cHUoAgcLkoZzJoEsFbOgAEgVi2AdkjiPATP51ARIlEmmZVIEbP8AKD1D5YYKQIr/hhArSNEjNYikoGRCY0AchELCUyVGKlCrEmAAAy1QgKTgNFZMJTQoAM56yIgxSs8ORiqNMt4IQrC1o6JVNG02yOq+B1Y2PkV+uKF0HihiBw/VXU3gWyIMyiYVOScplShPH/d4jdIcjapw/buKZ5LhQ2lEJVP2twm0m6LxypsCeDZHZEBTniGH/Znz8salNTikgvw8PaU0Zw0t7ezS2AYLhwD9xQRnH92SC8dlHN2dnq5JnzoiWsf2Omp541SlyKB5WqCLmM9R9dDMSGcGiTapUzTAoBpSDbn0KhIFQdI9OQglUtnFU2UihICLBrkix28xqTGHg0xGsmpRVvZxARCI0bJQZSEpNqCRgkCKEkwMmDIVaSWotUKGfN3pQV0lnW/H7CkTrJJFcmd08XELOVBSSpiu/8KXzitutG9QoUpsdf6WDEELOBnV6eHZglmjLJK2RTU/8mEyQpRysvKVKMhgSkT0oeTnuFDLiMIkaIURjqrCyYBwOhDMmNM9ZQvHoS8Hk6HUzOfy4C+LW9Jf35pr7iiQPf9hCQ3hhOvvW1qTfXo2++84oGHHpNwriM4AoQvt7cm8+cvnjevSXhN8DAKwCXqxO1obp3+9pTFC2dW8e4QrxE5d+lyfrhOck5XWoMYXtdu5du87pNUNb05df5LL8zdaYdDb7/tU8Q6//inv2Mb2wptT8u3IjLJn/78R97S4rs0vJDELOEEeiF8B4JU7Iu0cz4b0Sood0T+uw1+VtEk0z3VpCjDYm8w4kyyAEzNgDG8kD0VKwjzh6PAXkD5P/AavQEcRBMlgpCMKlGqXeUgDRBvb7m10htBxA8pC7i3nDni2m8y3/7/2fsPODuP6v4ff27dXtQly0WyLFnuRbZxwQVXcMEUA8aACRAghfxDyQsIyTcFvvn+QhJCEkpoIZAECNVgjOkY2+Bu4yo3yepd2t5u/7/PfO6dffbeu6tdWWUlPaOr2Xlmzjlz5kw5U84zz5KTlsxd9jQx+ViT+bflhuNDQWykUMxyTXQ84W6G1GzV8OSkBxkNmAG7n7slnS+p8LOuxdubySAfLzLRzsVywynXT9VXYsFIkM7Hhvilk+lSKQc4dOJEw1Xc/HxpJvPlYnwglkoWS73FREMxPlJKuTk0I4xT2nE3c07qmnc3ly6zFv050BIIqzwGtPAwQhLDBTEeRsrFP3reBeYfCYA1+aEmnCm4VYhVqcolHOnzCkeGwUSwiqwyqkXxiB7e01fSOD69jCLTy6znOH1k61aIVGVRliG6yfSZ7QYoRmR9XsqdR8UoEM5aMa47unuYtTDmjSMWv7Cg/gtPlboww0wiQ1pMWVQ4hBPbNGAxSIyLLM/EXFnAM4pkWh5drKQ4ujcxykzh6upzYLvxXHZupNsNYDnZwxOowvAxjlUxXKcWqrDCjyBCBFHg48JJ4bDoC0Bhn0qkz72KQtWjR4kC01ACVCKfVrBNIG4boVXYpSMMkcap9xWwKPv2Ao2G1/7YObLewVwYP26fWymnoi1dlyGen4OJ53kZPxGkbbJaCpIl1LBhOQADSRZNT7sYelo8XbL382lfozAOHix+ps4ZU4znYDC/bbi4oy/YnA7SDaXUYGkX1OjEKgt93Tda7bkVbRcucpEEIglEEogkcHBIgHfUf/7LX5aPfpPu9XGbVYaY53HcKUwIbGyQo4L+3t6HH3yQr4K8973v5Wtg2ECPBYmeIglEEogkEEkgksDhK4Hd6N8qXTxpOUX6d9KiigAjCUQSiCQQSeAwkkAeK0pM1PL5Cy+88L++/rVgJBc0cGxZ0FI3rh3aCeVR3kAdb2lMfDz20G/v4buR73//+z/5yU/mB4cCPsNbcxzAHq5t40YuksA0k8AYa5gwb3aS4Wxeqz5Tq3gPKVsiGToD701aMegEkgvS2RvCuLa3t7fq2BgKxAAAfW5TA543CaDA1UokQYdOhYUoPo59JZmfioiOx/zJhEyWAeA0DjrijTC84XjEJ14UPC5YXP3S0tICDzpbhQGAQVQuMNbgHCaw3gDaZ+oD0AHeWTmXP44sY11yBobbGUmCLGGy9ljEyFFG3SnlACqxob/EV05jR2M9KQJm9GTOiHtQKxElMaPksiNJWIJRGZUmMMkKGIdnklQYGJeJ5YIhkyNieKIjnyu4hQVZB2CDKwEPFg4DSalVKVSukqgdj+ixgCGSJGLCDgrki6OOBKBHfMDAEjMgCpd45eKzAECVSxsgTBWASAD+ZZfc1tbGozY0TZgVBwXiIQgYvpKIFAVicAIQBkk4EfeNkwDoNDyASSUXnyRg4kHxxCUHYpSKLwBlEfmRBHYrAbW03YJFANNTAgwFfhCo5VADAj6uNlUxGjHqAkymbYA4wZhDkk8dLwsASKqbCoeKHy9VuOMVbUrxZIEkw0O6sW4my+OKLp8vfOMb37v3kaCvr5yVFALWz3xq+7bbbvvbv/0s31b1BAg4gryAFBseLLW2Bg8//OQ1r7yZ1UdDgyWBDkwqFfC2pBvp7ZGA/VwOKi9TiMER7oruR0H84Ac/uP322wczQWdroDmOWVdj65E0ZUQYCsSLmqHvTijKAt/pK9S9veDkhUCsJ+AjfcxUA2q942EpL3KZTEZhGOMd4xsnDsWHU5UdMcAonkCYByLR1GEAnyp4/xgFpq0EfJ1aQPVbCrRegGeuY8X3tfwiS1FuSK4R0UJwzliyTDU8pIyXERRs8HFvNbnweIBR/PSSgEYwanwP2LJ2Mnbk2TMiYHk6BMLM8IibgKwAwihVwEqqBaiNqUKseqxlgxiIKB4No8cqLP/o0cv5Wp+OaXXpiTgKhiEYfDq4HoWOj1MqAZLQkagJF22PilQgDObjUcOjYeAqjkj6LxqzqrMTLzoAEsbx18cQSZhI+SIWTq2Qr/PXkRotaR2I3UWJwu6gyhyOBwYRiqyBtApG8VWR/jFcZB+pwMRJVcDR46EhARqSnO9iPFI0812nUyotTQGiFVbx2W0jUN13bPJcvgRhqlKiO+PIQltSBKBg0/vJv0E81Swj+EgCkQQiCUQS2C8SOOn00xjhn3viyb2em1dDd95xx/Dg4Dvf+c6f/vSnzz777F7PKCK4nyXga3Y/5xtlF0kgkkAkgUNJArvXv6zx9nRjTYKK9O+h1GAoS6R/D7EKjYoTSSCSwH6WQHNLy9DAwLkXX7Rx48Yda9eXr4zYUyZsh7ZqZLYIc/fc8WusHN/1rnf9+Mc/XvPMsxy3sCFrZtYVAIFF/rSVAApXThwSnras7l3GRg1kd0tXBxISjQ4M1CUIX3LJJfgyNoVOe3v7kHNYM5944onYN9M9OIAEQNYqAHNsRnjXrl3YRl9++eVYo5Iqk1AokAoKZxKnn346MECGq0es6swDw9Mzzjhj1qxZslX1vRR4cj/33HMhpUgCRJILDoI42IDUac5h4qxIERfYOeecA/PYyPIoBiAiBy4xUKawK1acDhsUATttIqFA9+d31llnsfVG0Tn6BBJHkug4XCA5UwxOPHF5NjtSLGKtwakLLMGnxZ922imdMzu41i7FCY0dqNrP3XBZYIjB+MKu2okVL7/8yo6OGTbo8OPGGq6YdKdHZ55++vy5c8Hl58jakZJKR+6YliNT7LN5O3PevHlm4R1L5OxOHOMQCkuWLKHuQOGRIiseEYqCTrIBO/HEU5qb25AEP2fXRR68AmLoFB9c4N0dkxZpP3vEGef5fOaEE5bNnj2DQvmMlB05Ui0K43snNqgO4PHnzp17zDHHUPUCEJ/4PMI87IlbHoFXWAzgDw4OUjs0HmpQiEQCCRaVvnz5clJpxdQsTRH5qEkoI3yAZ86cedRRRxHvaSpr8jruuOM6OzuJFw/K3fMgMJr3okWLaDYirkgoQ/DII4884YQTCCg7IBEdNyyFfR4VUxasxOt98o7cdJIA97byqzg61G5+9Cd+iVghlSglGnLJxny8EfP8rN1sFYcQrW70Z5dwjXE8jvmVx42xkQ5mDFr0MA0loLN5GGMA8SOV51MxDBToL8KMZj6JAMMXRgP4EAEdF071YcY6kngdyMf4gIYgiCvg4xVwJI2mjVG7c+PlDmVQRT8cJlL08euaPgiFUdoNnoyWphecY8Av/9B+qVTcoysZlI6OFnrN0FBpcBChNY9wTys9Tve/oWjju+yXXJ0tPs0A39SU3LY+PdLbYNN69ytyE3MymDWz4aEH1nMn+2C/3eXsygHPJogE/TtXuvbVwelnB3/5d6l3/nGQzQUzZgZNyRP7hzqGBu1Vyf7h4PrrLv+7j32EwMhQUMxxDV0wNBwsWQbHA0j0uMXpL3/xc3/1Fx/+2c9v27Z9wzFHJHsGDKulORgYDI4+Zs7CBfNRA9lMcN11r3jzm1+/fPkSp4Ux8uYGa/R+EwXMZNCDXIWbdj/jDb2ESpTvGg531pbf2yFQGZo0vGiYEta4vqRalUzk8LC9/aU6rUr1j7Q9H64bQIeiCmnDvC4VBoA+1UrVo0PD8eEwWYPuG5VPYuYDTRotFCbTdD1iFJgmEmA2NPpzTZbOa/2XblfMjAS97tc3EvTl44VcjLuf8/wmbor1imbtv8T9k1g9oi81UgVF7mzOoZtjgT7RwMdBCqUCl7kXSrlikNdNkyXumuRiyhiDks+XMHo5G4vlCvFsngldU6mULhUbMsV0Jp8o5QnH8u7HusXKErkDLgHGB41Rbmwcl50RUyHBjBkzBMG4hFPkuDgugYGIkQ3lO95ISKNVu9UwKzY8MwrApI8ZL7vxAETcZVLOyId9QO+a8hgmrlTpX+KhXyohBD6CNvrTzBNf2jlMwfFDlyr3DoZq1Cbvs+RyEp6t4wghFkpXGcZ5J9aWdcoan5mJWAJSxJUXPv1VK010nSlnTKmtb9pPMS6SV4ftpzU1MPy8oBTAR4dothzi2egrx4rGZFVr8gFG6hWiRtdNRVwATWfjiRiWryzCMT6spEoWPro6IIBaOkgG0eFYYFbjhJ5Z3uoJOrVESEKwaElaaVUqjzjQtcgNkbSgUgmMp14hyIYPAHXRjUTkprEE3DXPpqTsZzbD1v3wA/Z1inSxckN3gQpAUByJlfjRPZhFWyehzWlB62LsyRwLGRsTzJnaRYlm80GmyK3tyRRL2FQhKc1b7rnEMGFn7EC5xu2XLsUarN+Zs22xOI/0cHZK+KZa+ZewXmkfc+CXSYyMxIcLsVyejzHAmI0RVhaVzllD03O1oFaVjC1fuKzV4WlchRFrkQQiCUQSOJQkwDDvfxybJBPXXHPN1772tdEi+q/7cNjBKM58rXKoMQozuZBpsYp74IEHvv3tb19//fWcp1Ti7C8HWGeeeeYZHBGdeeaKs88+59xzObAYb0YURozC+1kC5dlCJVcemX1UnqK/kQQiCUQSiCSwOwl45UtgYv3L4MpPhyXyoe1HXNGZMLfJ6F+sCFDBp51xxqkoYiwSzjnn6KOPZkvErS3reBNmGCXuQwlE+ncfCjciHUkgksDhIIGQ/h0aHmpob7v4wgt/cMstVvREkgNJt+Y1O0IdsYRFEla5Us7yTUe71VD1gojkytqZe6Bv+e53Wf+ece5LLFIKPZk8+YzTT11x5ooVK1gFn3fBBRdceOGSpUsbuS4uctNbAofJ+teOEifvaieMnLFxfIUZ6OzZszkkQ2oca3V1dWFfQtLJJ5/MXg8xZKFTrvBZl++BIB5xxBFAij6WLgQ4cD3ppJMWLFgAOhau/gwv3AkJk4qtLb2LLERQRPCXOkcknHjnU2VgDTrWM1gAc27NtNizR/ycOXMWLlxIAEiISEpqFhDhUT5H19iqYsMzPDwiSx7Mj5ua0pDFftoJxFAFLCL4xOOTHZmyI4YpLRz6VCgIHR5AFFcEvPMUENqxxx6LlIQLAAFuL4Y+ZLHBVcGF6OnzKFlxuySGwlZZOWOGeLFBjSBVKHiyJEETxjwRAsSccsopxx9/fL5gNuVeSiQhuvnz5ysjwOSEolwQLOfcFJCMqHGVEXMBiPBI1bF0EUF8iIOlrIUOJA4bd1oOTYUk4nFwTlMhHrJ+nSPEKh+acAWTXFIuXB6VO40Zk3q1YWJgFQc8BJWRIsmXXGg5JCleYDQJyMJD1dmwygik0HkEFzDKqwLCAKnEL168GBtowERW+RKO3OEjAXUZfGrfO9+ElCpp+Mgq4XgKBMaDqUKJHqehBPSCBxqBYWpi9qhlXt4IwzCIMcgzxPkGQGMIAyjMsEkAS6a6qSSRNeOtgH270qP8iQ91QJ+Yf43AYYLihLIrQCnCqeEwzGvgrSsfhtb+/gGhQ0oZMfBu2dKD4n7r791w9jkncr/yV7/66VrBAAxxRn3oi7hgeLMG62TCpM6YGf/Qh//g+lddQCQzHc0BxN7AQPDfX/7e1772wQ++74NvetOFW7Z868MffiOGbp2NnfPmYbwbv/bKFXw7hh26i85dOnNGcOyxHaefvrytjZuhzWIJUk+vyW7YsIH1A2Df+MY3/uEf/uG4Y5rSsWDXrmDu3OAd73jHa1/72u4eaxV/9md/9oEPfOA1r3nNrFmdNBNKjfqAZ5QRpfb8h+WmMDAAo4OAqU2dOAZcOdQlsw6ZO3sU8m1rawbAx9QNIMPdwkCcNsyraGEKahjE0G7D8VVhDxaO5707MiWJBkbXYIAlVTFhsCg8zSUwXsuhZr3zRQA4DE8H8c7DTBAoo7OHgGWUGwiUhUcBwE/kBDOGB7uyvayIFc/8DtP9xpZYqiWWbAlaO2O8/xDm0FOOAgdQApPUvywHGEn4jpBXtQS0NpmYeXQ0WcjVQvr2oEC4yREWPEkawWrRPcAe6F9PHyK71b9oENo8kJ5hn7V6GfFKki9IF2e9RhpKvcZPd2GAsFPB5dd6wQpzJXSwPOVwqs+CgHgjUOvUZ6HgcUXW12MYVxkB6YEhyOTH88CjYHxG0MGF4UVfYCCqvGEA4QrM06kNeDqsWFGRtXrQuIzFUJ21uMQrklQHZV4tGDF+fU0pqgBgAP79zDCcKt6gOV7LpMZp86A48VRTDpOKwtNWAtQvVuw4axs27SxvlO0thsNtcoK2VDc73zsIhAFC8TaYhJ1auMDZ1KlCDBOJwpEEIglEEogkMH0lEIudc955zz///K5t2/YFk1XaYcuWLZ/+9Kc5tHrjG9+IQlSOjz/++GMV99ADD9x/331r165lqrwv+IloRhKIJBBJIJJAJIFpIYHJ6N+xSzNjm7WafoTZkaj6hQo2Sf2LCpZ7+MEHH7z//nXr1u3BSUco2ygYSSCSQCSBSAKRBKa7BHgd96mnnhrpd+fmFVXLhue+4HvL+g2f+cxnWP++9V3vLNPP5Z987DG/AL73nnvuu+++1atWjUx4Xr8veItoRhKoK4E9PHZi6qlTQ3zODDghw9aZgzRu1SUb3a2LZeqpp54KJP0NGHyOCRVQD+TkTI7dImyFsYUFV2QxEsKEFHSO9ICBCHNWAuEyEEkM8FgLYS+LHTCpPpLjXiKx/lGkjldJFQUQOZYjFfpwhbUuF2eSKewJBpawDCYSOuHxQmUBVwUHmDD2x1wYjPlQZ2c7zEIEUWDVNHv2TC7WdDkiZ/2MQ0SiRz68CUGsXbGjJeCSKCmWTwneU8SoKBbn1rpcMsU9OHZnVd7ZcjuCGOPawSHMx4L0sYuPxxaLn8hyBo8wZdksgavUGBEjQXdZnWVFZDrVODyUWbx42fwjjsCogyNsZ2lsZ1pQgCUcYMDanZi2LrH1iMyg3QWT1n6WLVvW0d5hGcF4PEUAw9/yRaSlVFBKEWm3FOvo2t1kjPy1COHoFOt5SgoWABScHPGRpz9QB7iM6+oXzgGm7iQuDIg5/QWLZgAi9n/EI1IkQ92BKDACOBWHANWHI4DxAY4wYDxCGZYwiEcCUCCe3JXkcXkU87RzLNe5xVlsiz4UaLrEc2wPrqdMqpyoqVDkAvMwQBKIJOFjd07f0dmwgPF9oEIm+nvQSQAbO66dsq6ULMV3++NiV3ocnTKTH+7L5Psyub6BzHC+mOPqdC7Qdz+jxUVWurQqdPUUN1e5e7XssquKo3mPGT8POvEdtgyjmBiLGNU1oNXKgcEEGIYIAmg0jRUCY4xiKGZUxDEiaRCrNInRv6QKHQphdJ+XtDMDnQAEjA9NBmpGLT7UwCPOo/gAoyXWObwW5WNqA4yZDIaMluIQAJFiakGA0lH2usSBBAX5wJsjSyMfbecqYWMj2OVLgolBIJSiuTn2t3/7tx/5yIdPOumE9evXPvbY7+rxbiRjsXQyaRYe4JaK6Nm0U2qNLc3NuWyiqbHjdTe86U//9IPpdDKfDWZ0Nmn/zpRmLHjPH3zug3/yi69/df3j97U//ODP4kF3Y/OD3SPrNm4rvfsdb/72/379y1/8/If+7P1PPf78kQvnfOHzn3/7zW85+oj2zZsGuCJaU4fV6/IbN2zYuHbNmuefy4/0N6djXCRvScWgqSF1+qlLO9uMyX/4h79bufKx9etXZzKYjJelRzxsh4RjkN5RHMJI1UkJ1cMTNtB5u3SPe/QQJ8UtFtrbW5HYeMIHJ5PJbd26FZNi3vvyxAmgo6l6apAPNYTjq8JdXb00j6rI8COqsLu7V3b8Ph5+qESUNbbsvEDk46sCtM/+/qFK2xhNROcyScOimobR09Mri3MJZBQoCh00EjDdqtkvfaMUy+STcX6FhP0Ykpzyi3MvJW2GMmmAKAZJ90sVA352Y7tubi77dAN1s8p4wgcZClwry63PdrMsvaZUiNvPxXALvH2QI1/ianjmymkoB3H7FRP2K8QIYymZiKWSdvFzMgFiphjMWNi67PTFx50x/9jT5p7xkhNmzk9hQmbXY0Zu2khgYv1Li2IsYgRjrGOwZURiGCGA8yVgJETBeR3k4xVgFJIaZUSqSvKPDKG8zgqYstPohw9l9C/jm/Svhw8HxN4e6F8QoYNS1mooTDMcpryoGMoLvFDCqcRAgcWWW28hK8Zq19UqQC7VFkrWc+hCKKKYrYtxFBapIlLmJ5TRka+TBbjaeSDN4fExCg0Idl20e1tBZrjlSJdayd5pQJmkgFtBtFSk7ajFYGBgYNjxH6aATTYr7mBkJNPbyzYFScCjAal3fiqj3Qnd35/nB5NibzRjFyJexuUU1jFQTies8qJ8J9a/LJ+pXPSvFpKePu2NpoXotJb08QpAnAA+TYjWG866ChIiOBonc0uPK96QjLR8FYp/JJWJK5L0MQqQHStfwmQNWWqZ8AQ8VKFHjwdQAvrwghgoxLl+OaDVFxMFm5XyK5Vyptns5uaKJrVvNahX6M7muH2oiq+Q5PjZ8pWrnS0d56DctxpEX76+iuBj0L3ioTyOoGsrCpOvKOTixQa+CmKK3n7FAI1asI8vudtPyrGOU5jN8SWHeMAnUvgpDAesqq0gxlE5B591JeDJTBCowEZ/IwlEEjgkJDBJDTVJsENCJNOsELb3UR7cO+bMPvvcl/zkJz/xyynqxQ3v+gSB3YNlvz11mkF5bCbAzGR4TX379u1/9Ed/xB4+WxPMR5ngDWcz/AzSZcdk1KkjjxoFppcEqmp2ejEXcRNJ4HCVwCQV6yTBDlcp7styT17/2uWRTll7DewDYrDqsYbrqlF6PP3Lvsqo/hWR0NZcDdUo4sBLoKpmDzxDEQeRBCIJTFoCkf6dtKj2NiBKk5/biW2dNfOMs1bcdtttlgcqjw/ZFtg3tUWo+c45QIFXovb4byKeGxj8n6985YUXXvjT973vyCXHGiW0fL4wmM3wI18uWiP7ZFOj2NzjrCLEvSsBFK6cyBLeu/SnLTXb498Dh4B0EMvmDmefjHcY/nNCRiSOGA7MMMDFkBTinIExN8UBJgc6AUkZn6NNdotAJwxBULBq5bg3fIkvSXKeWygQJjuO9zhdxoiZfIERSxxaEylgIgnIFwN6VFi5Y7FKjpwgwic0ZdNMWBTCPlg6i9VpNGeT8Elh29oaMaPhc7IAkHT22WcTIIazP2UEEQLyYRUYHfsdd9xxXPcr8y9SOQpEYhh/i77YFgOE5XgUn6KMvS+cQ5NzXIyPs9kcNGGMR581KODKp4AcmhLm5BU6AMu+DQCSiEeYbN6pLniEa8c4wVEHJI7MqDukRxhS8ECOSF4EPQVlDbLnhwCPoINL2XX2T5EpCLWPVTQooNdWgRCBIQkYApQUKyseQScVHmBAAhll14V87iCKMu0EXAROOtUqAGhyeOx5Jol44OV7mpz+kjUlhYKSYABEsST+KYKTk0lVxIUu4uQOBViFAaUSoO1RO/6wmXhQIBLmx/MQBQ5hCbgmYV2PcchuOXfWFzzSj6WhXJI1DxzAaqJqMPh0BO9o22rwAo78g0gCdHzMRP7gD/5gAkNS3hjh4hkgGXxUNNd47PI/qp7R4y1veQtE1AaU5H2wuAv53e9+N8CKDAtHMZdddtnll18OgHQWAIpHaaJlyBT0MJYPAwZ9PhQABR9ZG3jlK1/JmA8pPxKGx1s0FLcd12J5bkklCzIiJgzGIyoYykLnEbLd3d0My9zp/E//9E+f/exnv//97//Lv/zL008/zQ3XVQof0YGOdr755pvxYc9nAQVKjWHu5s3dX/rSl3jPkpGcrGXlJjqo/l/84hff/O4jpP7VX/3o7W//Elr1z//8A8fMD9pTwZo1ax5++OFbbvkBpM4++8RHntxx//33n3HGGVQTb2yiqrlk+ug5ZpL83HPP/eM//uPrbv7zP/3Tv378+aHhIJg/xy6B/tznPve2t/3/0Njv+ZMbv/XNX735ze/6wue/t3MHH0Aweyac+L/uuusQPoL1zEtEiIIJC2zfdNNNlCWbLX/iQCMM6Ok0+jTg3TZcWKo+7DKJ3XTTjbx+NjKSQ5H5JAJkx53QTA9oOeH4qvBll11C3VVFhh8R/jHHHMXEEHUZjoc+Mwd4e8UrrgrHh8No0ptueoNmF3CrJAL0CDQs6Lx6d9111/KBB5Q46hsAFSpMJApHEkACtBm6qpk/u0XKBDIBcoLUcBIt/1rnXuEc/cgstqK7J8MyOtBhxpmJ9S/VjQJF/xJgjNI4Qxgn3hn2qWRWOjxqeKnyWcG9613vYrZWW1ZBMoDzmSPYCOtfgKV/GcPr6l/hArYH+tfj0tp5yRMF6mM8kz6GVFas8CatHQZAaYL+1re+lUE4m81TRrA8gAJvecub3/SmN2VzWSigs3wqkBBkWH7729+ODH3ZPQABYK644go+viZpe+IKUHcM7K973etkbhtG9GHYY/oBpI8hjENjUnYCr371q7lKgXC4dNAnCZQTTrCpF4qGGAGEA3w+6qqrLrrssvO1SKxiEgogwh7NhrUw6J4H8Y/v1O9E+vfSSy9lcgKi9K+44hFxMRthZoh8PNmqADAUjXfF67Y9ATN/gD1MzP3cknhywaFAQb/qqnH1L+3zhhtugDEVRwQJ4zCM5pGOQ9umkcCq2gZJAov86SYBqqaqheczhd7tff1bBovsbGu0K5ii3FucW0PZbXtwuU0KcndsjeY1OgjtDidKjyQQSeDwkMDo+HB4lPfgK2VIWVxyySXc+Tjc118uhTuAIsy8ZV+UC7IoR06+fvnLX/785z9nU4VP/nKG4fNqbGtVmFMWHxkFIglEEogkEElgMhKI9O9kpHQgYaaZ/mUt6qXR0Fo5PghF+tQoEEkgkkAkgUgCdSUwpe21SE3XleF+juRc4Kc//WmQywfcL4GzDXtMn/fN7rqyIJdE4u5f/PKHP/whRyqvvvEN4SK38JFrMsci2l13Ek6KwpEEDogERs/b9iB716HKe0mckHHIygYQkfic1GITzJGeJ0s8p1wcjcjxqCQC9AdON+muhEnF5zYjjnvxRRBIzsbCJ6OeLAFOzrBSwgqZvSceQce05bzzzuPUjTAUBEzAUyDAI/QhS+6M1xdffDGRYpjH0047DZZg2GekMR2COFDgU5Q5FiU7syGe2zE8Ym/5c1tze0fr7DmcdnMfWNbRiKvPl0qccGNkY3dHcSDquI1t3br99NNPxcSHonOIhGAWzJ991NHzCkUunTLzWXCVr2dGAdgjnpsa58ydNW/+rFx+OF8YwZ89u2P+/NmUzgReOZYyxpzdD+e2iKSnfzBIpAqlVFvHnLbmjqMXLirkRlKJYN68WdirL1t2XEtLk3gOZ4oo/Y/774JifmhkpH9w8IILX5pMp/gCKj8uvLvkZRdwGbazGKk224VhcSKyFFDSxhRbEuaclRNfVQQ1QqR8Ar4GkQlGbMQAxik1LQ1hkioYbPFpfiSpPSgjUEgld3L0PBCgjJwQU9cAkAQw1IghU1gikhgaiYhDQU5VT9ZkQcvnlFcxtEOypjFACgcwFJSd2Aj7JOGwnwYedByQ5Iu5Hjb9oIeBCY9HpwosepxuEqi0GtqSNQnHHuerE/wMxN6cKmZPPnPJjTe/8oa3nf2qt5x70x9cdv4VSwoNQTEVZJNBjoHELt8qxZIJXuziymcuzismilzKlU3kRmKZge7+gf7+vr7+PvMHi/Es7S1qRdOteeyWH4ZEVAwKTia2deGxTQGAJN7KCAOgAsBlpMJEBgDeDKm0wFEoRi0GQGzvdI3iaEIoBH1GuW3btjFMhaIDXS6IbRODWDjeh2lyjLG81AF9OPHxVQFSYQziUouk0lBxDLDYPzHGTmDAxPiMbRlg2ODWlo4h3KND0MZcN6qjiB566KF//ZcvM08gd+x0K1OSKtYCrHz0hg8mwkrjTR/00s6dXcw9sO35yle+Cql1a7uWnzCbr6196EN/Yjlw0Xsite6F4vwZrS3J1y475iVbdwZHzL3knPNb22bw2YS59977uxtuuGnTroCi8b2Io+YFH/7wJ1/60qspCGNEMsHNykFfnw0THKRRfa0J7qQM5ncEMxoDZjptTcHKJ3bkSsHxJy2mak46eR6XRn/ow+/4+4//BUrElwGBUHdUUH//AI2hqvrgk7aBDZbTPmUk5EAlEzMwkOMb9eDyUQJPsDYAOoIlvmrCwGMqFafRwl4tlmIQLEVD2WEqPR4MFQTnpHr6qkd8mi650wDGw2UWRM3SAJiJeRgQcWCR76ZNm5APb1Fhy05FA6NUDxwFpqsE6GM2PcOV74OMWYA1Nh9AyMWS/Ebi9gtKCa5sp0u5nzBetB/jTUdaFM1SawSutORieH//6yhX5ZzcnZeh6TPXS9sN07HGTOusZHpeNjU307GgKdXhrql0OFE7fNGVtHcITEb/MnQwSNqCyykX6TK/cmFIZwxE+fJKsKo17DMQMcTJhnU8jt0Y1cFXtt1AbYOtnFT2BPoXMPJC9bMsIlDBq/5bq389BEM0ixRmCD6mKgBLvJpFkRmfq/Qvj8iD0sE/KzLCnn+YkazQoSzOmKAm4rbcU6RdAs33UtwXkBAawoE4+heAKgdBZKvJT20BAWZFdvzxx0+gwjD/Ze8C3Cr0nTt3aoHJehAJ1GoZiMMVlcuSDR/ORQGfl5DxAaDIaCgcfCI3Ir2TGHnkfWNEhDISjOJFnCkTlYuJtiLr+uBSO4hRcz8QBQY/NEuSmH3VRSRSUyN8FOV4MIgOOhSQW8YFA8+qJho8zSM836giQvskd4rg4z17TDygs3nzZtoGdQQDZAEYkR44CkwXCbhveWX5iEKahedIoSGLgo2n8kFTkJ4dpI4OFpzQ0b4k2cCdAw3BcLaLKjwoatG+kWRfJ9PPRie7CjoZpOI2A0/FY/zKadOlJiI+IglEEjgwEkB5cTTA2QTzJa44YUaH6oQVNCBv8jDNOOuss/h4I2A4z6Ie6/oeJgrsHQlI7MWAKR07Dw/cdVeZLLXh9v+ZXcjtnexCVCCrSRFx7AL907/+y/otm//wz95/7CknZXOZRHtr+UvEJNuH+2yhGrlIApEEIglEEpikBNChkf6dpKwODNjk9S/LfPZ9pAZtrVhHHSo17E9QqLr6993vf++ik07IOf2bGRgcRS9nPBoRhSIJRBKIJBBJoK4E/KIJyzp2y9mRZqnr17+sgtkl5ojhoosuWrZsmZa6nk7dlW8VjAeOAntBAsnk0cuWcSzyzDPPGDWUMtqWKgxtytYqQPc5ebc2rSxPy5qXx91u5vpNewKJ+AvPP//Zf//sU888/YGP/c0J552TL+UHY8XBgd4gneBUx/aGa7PfC8WOSEQSmJoExjUcmRIZDY6chGG9pLDO1SDClhCOsz3rfWMdIyDnDYL3KUQS5myMsRUfXD961lIAEgrA4HNCyYYX54XAkzuPnAuS5Ckr4DOFJZ1oigHy4pFUUAjwiIMsWJ6IZ8DzDBFOQHfs2AG3YBXyQUcntyoW9cHinu4BzvaGhzPAi/KoxYZjTASxITviiGM4AuS9COREjnDOiSOHrwCQhWfAl0WIxEOWSM4+ET7XTPLEdcY8YvSjIgjSIwKveiBTGIa9jRs3Niw+irFyaGiEJKx+Qee8tsKwR60fkKkuKFDjfi9cc1MS5nkEoYpzzwxMs2WMmRYx8MkJ9C9+cQdhHPlSFqpSnIgCFYEcxAEBHskISEVyxCsDaIDhBOFTLs5ToUYM2RAIcx8uGkmy0MK2T7lTETqNJgvQVRBQwhSIF03aG/BYsCFGYkBBksRgmIULo9SGYR5H8YFXKhR4RHqcK1flWIsexRwsEqAqzfEFCt+KxrTHcjnU/PSg1kWY9sycsqu0lo7W0DiTx3tuXR3w6kTGjL+GMVVMBC1NZmaRiNsbDrR2GiETjMbmdPu8TihgZWCR2TzmC7Q3tWflEvkHhQQ0nvzFX/yF9FFdnn/tHBYk1tBCWo9HWg4mRP/v//0/DZK16MQ/8sgj3EYMsG94YTDiv/rVr5I7qRqdfCqjNDHr16//67/+ax9ZFaD5/e///i8+42pVEo/K8f3vfz/04YQvtpOdwEgiBhuaO++88y5/lGXz+TGjMcBc5JzJDDU3Y0o1RuPziELkduenn37OZ61iIiq6Q2NT0N3d/61v3UpqQ4PdvRqm7dAbKd3nP//5QiHb2oqBrNGnG6KksJ0VTV7O+upXvkcYQ59vf/vba9euxV63JW0Gu62t9l2IT3ziE4K87777Fm7dSvihp7YbfCJoSwW//e0T99zzBPlif/u2t70Wc6sdO/75iVXDZEZkczL45Ce/ATBfKZBKEYfotKbGYLAQPPHEmief/Jzoc8/o7NkLP/OZz+hR/H/ve9+zkjY2EElASQpz6zGzl3/7t3+jXMx3YFiyRYrDw4TtFuof//hnvK9FvJKqWgiPX/jCF2SgzIxFxIkEmNyp9CeeeIKZCfFCFBH5RHZ1dX3961/HbhCtC29VxAX26U//u8hCTQHiaS2i/7Of/YzceQQ3jK4wjf/Tn/408M3NjcB7dA/JPOG73/2uE1SatufjBRn5B4UEqF9rDBr6qseGaV0CehxNdJiPJGezQ/Eh60cHFf/TWrh7ibnd6l8GDT60fe+997KUYAxRthptGLKoXkxpP/WpT6EIgGSk0rAGmEYbHh999FH0LxnVsixg9C8BWgvaNjxO8oIQKhIN9Td/8zeCrKVALl/72tdIZe4XHv8FKR7q6l8BoH/5jsEdd9zhOfcZCZd4vqVASYlXjOcBIfAm7bp16/hIAswzfsOA0PGdsyU2H2EAEa0qoRGjNPbJcJh9f+xjHyOJ12noIGREjj4LAswuSEUOhKsYIAYF96EPfYipES6M5cNck3D77bfXbhfADzB0zI9//OOInTDE4UdZKMx6jU3Gp556BgaAJxIHJJVMgGViQ0OM0jlcU16g4/sAjyggFCglYtcCESkVH0FBHIeCgxSQOOJFX2DEkO9//Md/4IMuACUBhqBoHryaRdMiTAxOqd4n91tuuYUyIhxSwxQ8DAoUNkiSEIgnDEE4BOtXv/oVhtei71F8gMZD1XuyngEYFh0Y4CN9kt4EL8h5glHggEiAlmmjR5bPGeZ5J49XjPJuMrVg2UzWp7MWdlLFDH3sovziZ7/x88ADwuoeZ0ojpBsytDDTLtobgEGyaFuCle9G7jHhCDGSQCSBQ0ECjAYMd29+85tZpKO2/vVf//WBBx5g2sY2CIZZ7BLw+VdW36g8G0fGKutDofwHSxniwSWXXMK6nt0a7n3MDA4yZWHmtF/Zz+Uff/iRHb093Oxz/sUXbdiwYb/mHmUWSSCSQCSBQ0sCkf49OOpzcvqX2txtcfyOwW4hqwGc/t3e0/2yl73sgksujvRvtXyi50gCkQQiCUxRAhdeeCGX+7L+/dGPfsShBtvL2F9xHyWfMeQzxXysmO3oVatWTZFqBP6iJSBl6jTqzLlz+eoj1zAXuOfU383slsBhG2iyHF+97l41V3Os9TUb+/zY/SgWn3viiS/39tBaTjn9NL73uOrBh816gn2R/bwSr2Y0eo4kUJbA3jGAFjH6EsdgMoVh+893LZ11VYmcVGCYAQuMszQ9ak4c9kHkUTFVREQZXwCcxgnMB4CHfhhXuRBPAEScDh09GAFKAQVYwlXlWPsIsM6VIZhIYjaEaSz3KM/BdHlwMBOLwRImiIwmY0iRS29vt6jBHhnBCY8cuXKDJ0FQuGayNrvxYhbMX0gSxl5QJoB5lgKIGfqY5oaFMEqkhNG2XZFFjAYlsCiIM/PSWEVYP3feNYpJiPgAXEx+V+94Ztbs5o2b1jU0pBoaZjjLZ4OvmylZUFjkhhM9dzReNlMmd8dA+aQcYMf/mL1L0IEhSRVEQHR4FE1QcIDhfC6CEXHiecQHjBhg8HlUWBUqsgAACWWSFIMPJI542gnAOvnmEWDFAO+5Ir6ugwI0SRJxEYQTEMVMXawocnpKgFpTU/Hs8Zgr2d3hxdiwVWvgXoSgqXJFZWD2BMmYGWTwErQ1lbjZDrqBwuL4n0/aLKIrs7k7u6UvuX2oONSQL+wc3hy0B0ccn+reZhdPJjGXigXZ/iw3vecSBXp/LlYsDDMQBHOPnfnaG67DNKEhZ/aa61ZtveWW2yzvyB1sEmB80OAgv5Z92hjGVVjw0JBwaocEBEk8ZijYZnk6YQqCx2SBcQxTLW/jEoYhTNZAYmSjkc03dQLOSidNFj7HKlzYkwENAOMVgQ5CEg428EUfHxQNiQQUWZWLIrGj5R0cB6nMbVytOCGWZxpEegrgkoPAkBZdCT3Aay9uyNfFrjZE8wEGSod9Fbpi4cIjnntu9cBAxt0GLZMm/FhrG3UUPPbY2rfe/IFlx8/MZYO22fGdO4stzSNYfef77YsEZPSZf7l74eK1QT44bdkM+mZX1zAWjyBiZ4wyJMC3IC6//PI777zjiVX3MPuAK5JseLAFRTkMHUy53J3IQcrNEUhyPAd//Md/jAG0SoTvCmgmVq4KjFtEq3jBOFE4KZTFgNwYj2z+kYjbKISxC8nuxmszvxNWrY/lN8Kn/dQmge4r1Kf6KiBAjbs7r+HNcgTegynABdZqdd64UPHAo209V56mUikyAR8p4koKx3N0DUxtph4yCkxnCZRipQQfQEgVUbRFXqgzbRoUUomMq30+/gTz7q5lAvajZbtm/eLLpL7giXn6cbYYeB2Jn2bI7lMPdbKjT9Pn8omRXHx4JN6XjWeHYgMjiSGbHjAQOQwCVb3BcCK3fyXg9WbVGOK5YPRgIEIFY8GJpmNECkOSyqIY349FYUTCJDEG4mQhzaMHIKDRCZoMaDLSDdMBmBzJXbf/hhGFqxjRDCNWQVbpX58qRLKmCLIw9kkElEphNQiz6hwZ6Q0DECbJ6b4Es1BejIVPX74QV6PNmu4L4ULRXi12as5ebmlra6E3URHoYmiGxUuqJi1E1i0gAKgnkuqmQg3G4KQ21ccgYcDGgyGeEiFAt8Yv1x0WwqCA5HwbhahJ5496hlZxxlxlwlaJs6qHeT1Sxdpa8akKQITiA1a3+MQbd650VfT1SJ2q7hz/VbTLjxBBAuxgstNNE/VAUFBrB7eKeBiGJBzs0cLD8YSJx0QbHw59UhSYbhKwCqIlBvHGOQlqfDg+zEytPR5waXixqW/ZGUc0HGVzvPbhUsOOkfidQbJpKO/qU9oR3//GK5o+fIDvflwDQGcRUrn9j4e4x/HJYjzJjJpWWeQvE4P4UClTbIgVYsEwzPMmpKVyBwGLadu708sYcb575jgL5SsOVdZQdBSMJLB3JRAeI6uVyd7NaVxqUiXjJruEycBMTGHapqIK9SYbts533nkn1s8oNZaQMMwk5z//8z/f+973innGw2lbikOZMTeRuPTqq3kVp2vjZkpanrG4yVXNDGufS2LLqhe+tuqFxWecesMNN3Apz913373tWWcc4PpynU5cJ8qY1GzyxbNr2itykQQiCeyBBCL9uwdC26sokf7dq+LcB8TG1b+WFwvJqix9jE0awQXAUbBAPfgq9N0+bl295hur1yw6/RTp37vuumvH8y8YVrgvh6lUM1hO21v6V5PCcTIJ8xGFIwlEEhgrgXCfjbrQWNnst6d/+Id/4EqRL3/5y7/+9a+xfmbc5nyZ3LX+5Zo2bUT7gX2/MXa4Z+Q0ZlNb23Bf//lnn4O/fe16k4ms7Iq7X/mN6VI1mnr34hWK953xZPfGjV/9zGdOPO9cDLLjb3wDGyZda7B/4HTUOvOYHJUBUTQpFw43ob2lf3cvhd2XM4I4dCQweiK1V8rE+kR0wnug420FAoMTPDCcouF4JJIApPyjAxwFDrNKLLj4RHJGSABENiXxFRkGrg2Dy6mq4oFXlyNA1uHu5wE8BREnFzmIYGHGOSndH8skHUhzsggRnIAd/TIB5UsuSgIGCtxgdeSRC1et2uSQDEvOZzpBYKu7V5KbjzmnBAxTodbWzrrw0CQ+TJljdUF2drZgui19RgxsKB7gShBLLENVPRPgPIzC4nPLF4KEErZo/iYzA61kJznIF1mqCVzCSMMfjgKPSPF9tRoJ5zw/oCA6xOvjQSESAAVE30fqUT4wOIgrAAXA1GCIIQxlHAFgQAEAX6TCdBQWsACoQQELfQIskoD02elRWciHgnInKXIHowSoX3OBjSTZnNnl0+IYGdD7DHI0L2pfDRgrRwqYt4+nM27wAgmm9jSPwFl6WMNjZploMPORxga74q5xTnDBBRccOXcxLSSZs3Hjy//+7ZH+LOfFxMRTgV0iXwr4JjgOk8TkSAG71aFeGqd1EODVxsoMHozCPcx4plqpOOpLFUfpw4MDkSRhBEMk1kUEAAYm7JMkM5fxJIeFEC9xyoS6LoynpoCHwSYGfYGjjfnIcADeaPxgCZFHUhUWmGKAwYWTFC+feI/oYxTJIw7TK8zPIEs4TNzDQICwHAA465y48lst1uNw8IC1eRmuMuw7y63yBxlkZ3bVVVe9+93v5iIoIEWtuYUP0Gd4f2n+fO7D7iIenZjNjrS3BTt3BqnCIDGpoOGFHas27wyOOy74q7/6K+5F5urKlWsGZjn1qwNTruI+55xzmEuIB8ek8cbUgmuMtJYhSUOEUnmUqTSBp5/ePGtWLyyRO4/SkMwK6PX5/KjpEkkSFL7Ckg+IPPoYmhYx2WwxlxueMaO9bhMSHV1EqleBK+gmGROwsz9jQqK8HHlLUhtWFjxyTonweRRA2Fe+wIiyD2C8xdkzPjGKDGMprEyZkNC8/RwjDDYeYhgmCk9bCVB9NI8cHYCWrFVy3Gwu9y7D4aa7dymHqY3mQkt3yprRZTQyDBqF96MEGKloZlSEH500dokFH+k5CqcSCQDDLxSIJwwdnAcmoCRh1VITJBQAAFLOUwjrXx9ZRZx43yNqYRRj2nes/hURpTJy4sbDZdqAbS6M1TXCFmVsvOE/m9VV/dXbUOCKuPPLsxdyRIORBAXmwIAMDAwz2gPjwIxBBSQ6PQIvzuUDQExVZBiAcBhABD19UgnrEb8uHaqMPF3Njs4cPAoUqDflKAouqax2IShIzwOPOJ+RbOJZJiOEugbQajBqG+IW35NVvnV9nwu4hF0pxojOY5E1+p3qQNtqXgE8qdK/zI48tx5FAcXjMymVlZgQw2Cqu9r4MEwUPrASoHaofWpqxYoVl156aU9uG82AF2sff/zxOx+4l77JK7g0El7/YJyhL7hGMnJged6D3CmCtUOmyfQD+4pSYC9B8spUk+sXYwftPaAfoUQSONglQAcZb7RX0TSSyydmYuCDSxpskqAH4ZlvMrDL/YY3vOErX/kKepOB8brrrrvyyiv/4A/+4OAq0SHIbS7fOW8uX2HmkylBOhlk80EuH9r72b8lRmWk7A1yvg/5gx/8ALV4zTXXDF7QS/vp3rKlPiugjJ3B1geLYiMJRBI4/CQQ6V/qPNK/07fhh/Uvt7OgfPlxCWXN1kLVRLFcor2+yArpX3ZR+ELmYI/p355I/07fNhRxFkkgksC0k4A/3mX85P3SG2+8Ud+l5GyCtfBZZ531gQ98gFFdA7u4D4enXXkORYawe5535MLjjz/evgIdj6WbmrIjI63t7QO9fVpXYvVQdQn03hQDxwJavcrnPISzj1Kw8sknOQLASuTqq68uZXN33nnnxmefGy9fjiJ0ZqPGcyjtII1X5Cj+QElgLxtA1y2GjrhIkrUrDbqqTbOHyNkePjtEHKbUEpl4GPXUfEaKcXdN7Wa71own3FWKQiEjAuxyKuAJ1rJEjFDwAQbSHVXyHoyMkyyGJLkqdCKJicf5wC4DBlYbZWNrJv/+oLoKxT9KFPKRmOIhOHv2bEiyLUsMqTJHcxlx9G5QhIVFwJxF8LalAct4GpiensF0unzOTTyOLPAdTRi2GMNyTvG53Ag/GAFxeDhfKjYkkxRifAOUmDsNtnt0KDifLea+tCw/RIgdF8SxQMamhTAGW9i0uTICbKkUgpzdKRWZW9ndSZtxo5oCXVxZVBAgBKpSkT6eR+8Ehu9TFcMjBDkPk4RFnCQF8CUW6ACDD6MUGSwh0oYBcOf3PodJBSAlymRBeFI44wGV4kH4B1hMZgeyElJ4POQofhISKJntPg2eSrfboqgwd7lkMWaNvxQfpFMg5VgiXhgpts2KzZqf2r49l25hGIyPZHI7dhTaWml45c15brjbvjboOIIjWLsIEJrpRGlgczCcHhlKDafSxVIxX2ooxJuDbDyYd8zcOfNaaGCpfIam0tAZBPT7RJCll3Bm22bMb+rbuK5rNalthVRfbtdAflchRXY0MDOwdq3L7DYqN9UZSuQOXglo/GE8tDGoPGLbuO2HEVoCj0ryxfSpDFmMljwKwMd7SNCNrqPsIwlgVwqwzK/D8eEwWLUEwwCEGfcEo4wI69HlWc6XmFpSAkPXk0j5oFRF2T2aKOREhHAshv5FQFYkLvGHckeHGdQODeaaW9DjuXQ6MZJBDWHo0wNgnIEzFmzaaqbMC4/pOf6k/KtuWPq97z22YL6ZJhdGFpXy6MFiX3c8ldqEsF9YayYgvV287RCkG8w4uymZ7h3KHHv0sRe85MKrrjkmnn7ur/6/gRmNQSlzApBB4Wi873//1uHhzGOP/e6U45qeWDXcngr6csGJi9MDAyxkgl1DwdvfeBF3UF122WX3PbazMRE0NBkq1s4UA/Nr8t26xTjEeIxunkqZVTqVi1SdfAzYOSuMFhpOsRJncnNauNxmKsuQEq+V2UhTT6dLmBBnaodPYxB1R8pkDgCu6uZmpaI6CbB4FhhN14epCyIVL78qxiUaLljkWwWjVPIlIEQYoIEpPvIPDQlYrdN+YvkSrwpRt0NB0OnU34yge2RohmvPXOvooPDtikf3uJdmPjZfxTnfSJMF7ZmfGU2NdeLBWrvrZVzibL8R9DWXvgf28YY8t8xXfsKFrpv/KZcyJn/cEDg6mgk48g+gBBh/NGlnqCGgRw1K4kojj4/RuKQkheX7ASo8cPlyge7HQB9JQPq36s0lDxnOy+foU8N0yF3AdTPyqWEUhaE21u65etlSIcsSj3Y7JtUlhaYc9jWGMkChYCaVYKBZQIQrhvnmZmxt1SPG3bioLXItz1OKgSAM4OPGIpZX1kQ6vTmaTkjrU2FUtJlhM70fS8SeHDyZWL9WXh5Gq0gApBx9/OQDqlBlMXksQXosbd0Q6WMIS/9WQepRvi8LKxrfvMMAPgxZwpKAj4wC00ECqvEY9yMXi00zh+cczbxthJf9k32l5zZ2l9JBNtaXTfdSv4Opwd5EdwG9FuvLo2ytM7veyp5P5cez/Wjp7kMHwDiI0RiSfKoF9pnjjmp+6XwDPz6gwm+EFXcpxyzi9POO6y9tZfxpzLTjP/PbtTTPBCre5tDWf1MlaXNjrhCXxjctzzvIFhW5SAL7QgK13aFKI+2LTMfSDA/RfngfC1KtwqpSD95HWT/D/9q1a7/+9a+/5z3vOeWUUx5++GE+Coxh64c+9CG0ZN2XlMJK8+At/vTkvLoRNqTf+ta33nLrD4b9XgS9hm5S23f2ZXmMK3JkRpe39WYyV+hINz74m98+ef+Dx59y8kUXXTRv3rwHH3xw5fPPZbbtLDOCAgHeHY+IZeLFNSnSK1pJCj4crmgdzS11nU1fmWzlTwWm8hz9jSQQSWDyEqBvVr4WOIoU6d9RWezzUKR/97mIp55Btf6NBRjDfetb3xre1Z1qbclxakDHYfk0tqeARVZV8yIpO7FQCqTFpOWk17TmCu/fysxAmo3933ouZ/CJ7HBHOvHQb+9f+cD9y0458aKLzps370jp3+z2XaNo5Pai9K8oiedqfatnt1U9mmEoVA0fSoqCkQQiCVRmw2FJjB1Vwin7KMw2l+1EH5bOH+9u2rTpe9/73h/+4R9yIQKj6Pnnn8+LJe9617u4C4OzgPBOb7Svux9aCqc/XHQoUZPd9dded9ttt430D7B6zA6hFmPe+pnuUq2Jxd9Y1evi7LJFLAv4X97F1Ro0ZnpWVks+RwNUT6yl4+JTxYA91lX3P/zCU0+fcMrJ5513XvvlV9x3331PrXwqGKh8NRq1mUgkk9xAV2CibTZV7qxHWl8nudKslp3sFeyvdcbJdMjJwBi9yB1OEqhzGrfXi08/GdNV6mWAUmHc5LSPozKApWOqNA3x1bNtmn7IzpgwnQdLHUXqyBBfpjb1srU4ASvVGHVHcXVRSNLEHWCFwfVhRn+sn9va4oODRVn8UBzgPYqywFeMgx9j7c0H5Net2+bBxIl8HzleYBAjrCDw7+hg84RFcl1g5W5suWNdfIyn2dIFmKOtnp7e2bNnCXEyWXPrDy92UHdIjEuZkXZLUxNasD3ogMh4FIhX7SAiAlQZMThQEKmcfxQzVb60LMwD7FDL96sJC594UsWDAj6sR5/qsxMd+WIPGFLDWfNI1sTL9howYuCfdkuFAkkMDhhcGHGCMBTEMDBQxhFQpLCIcbKZgEaUdOAl4KrOdLerTeOHTXC7UDYTLF++/PobzqW1r16/idby5FOrh4aeesUrzjPDgngS+Mbmpu9///aLL7zkmGOO2bBhA40n1ZT/yU9+RjOAjtoYhytQIIKMeNXBrj4d6lfqjKPjC46ZAZ31W7rYc8h0B6tWrSPJxjG7HNMc7ZHvctO4CJNk05jIHfwSoG2Ehxo/bqiW5dMwKpU+psBEkjQmapwHIHF1E8eL98DKYmIwz0YtmE/ygTBl4ImvTfIwBMKpnj6RwgWALonf25uhP86c1cCJ5rKlJ377299eu3YLj25OgiWW9Z2LLzmefvfTn97/P//z+oULg5EBVLZZG6cCM3dG+Az+QyPBosWzzj79JHTEz3/yGzIqZKzTsVDZMdS/ct0LQC5a9NXrrlt4zimdzz/fI06yQZbFyerNpS/8508a4sE733nt3LlP3HXPukXzgs2bs3xaga6MVTRHrcBjrd6c2jl7dqynz6qP/1Tj4sXzly5dumvXwH33/S4JtHMA4wiaQnJ3VVJYVK5inDSqTeskooqpWVl6IiKa3hckZBXw8QTqwocBFAbXRypcS8oDTEBzPKwJUDzZKHAQSYAKDdc1t7ItWrQo3zrCLHTBkQt27dq1dtNmNxMzzXhwOZQ1Awg223zpIRFrYCRRYQn4goTL7iOjwAGXgB9nfEAs6RE/PNBVwQDpq5UkHw4XikhcLWIYNwyvcC0KMbVgxHjKdQF8ahXuePFVYDyORxZ2HJExpSbGOoI545b2D7pMiuvScZB70yOXcNEIh/NV2M+bwpBigrqujfT8gY4LA/DoUwmEUxXGrwgkDDipMLi4SYHWAHlEH6gBKbecCQCUFG7/tUSImYBCXfgocr9JgBljKmFdgN2e9evX9yU32zlHT4GLLWlZ1CzLUpjBVy3vcVvdbyUaLyOGmqOPn8lxTqGpz0o02ExZnr3ns1ZMvVIV2g1CIL4XE4ga8HhSjeL3jgRoe6Orpb1DcqpUfINnpcx8Ve8+sSJmiMBB7TDpBXy7ifueX/WqV7FZ9/a3v/3v//7v/fHwVEUawb8YCahB0hS1Srr++utXr169/pnn9I1dtzWiWeSLyWTKuL6bjMHkVfhc/tlHHuXXuXDB2Wef/YYzz+Domg9Jo1ULu3oq3MaScSuOXapxAHgfw3L0EEkgkoBJgL5Ib4z07/RoDZH+nR71UF7+e/17+dVXczlo18bNsJfL8DEd13HG4VULqP03Y6Tz5oPnfreSX8cR87my9PVnvJ4ZrPRvsau3bEjFS+qR/h2nyqLoSAKHswT81hYjHgMXj/iE2fhi/cukvf7M/5ATGa+48A1k1r/sAf7+7/8+b/+ykFEpvYh43H9j+yEn4ckXyEselOtf/Wr077NPPmXoWj2GDiomT3OfQCbjfKP8qYceeuq+BzqPWMCXrs8671zearvnnnu2bNhgltBF7BIKbChj+iwb633CRkQ0kkBFAvVtZCupe/iXERB9gNNQiErA8Rgmp5GReO1beQDb9ymffYbBbSRFwVQRqXqECLj4OD/yiv4YWu7B5yIioBAtn4AiPZFadB9jmbmbgLu7dxULXFxsX3vp6enB2imbwSLW7JCgo5tW+QOwcBsbAChfG0yGY65iRVpmRlnOBCyf3XiB7p5d0IYHAcDA3Ll6h1KGUDKhsNMbhhYbGCs0CbiasaT2jvQRCxaZ9bZdiEMMuPyoTTR92QgDeJzLxWYAGGqm0019+Z6Ojhn5fI8zuUywOb5bAUoOgFERWHbqUdUHcR4V4zKq9kjytaY5hwMf4yneY4rnCudWfCjgh3HIXU5YAiZGj8pRWEIHlyQ1MIV5BJ4wADghTskXLrl4Osp3SkQi4P0sgVLcJr5Fu185ni8N0gSw3LBLIbkE2rWC4eRQsWWkZcaMhU3cNdu4ansy1xjMXd7U3t7ekG7BwnIwV9g1EjQeGcw5oS19xELOkkm6+4nfjGBOGR9KBLnhUjbGN8YLQ/TIXGIk0ZTPNYzk8930nd58/rzzll969eks5rf3DdD3f373Q9xrP5TuYmqeKqWt0TYVgsaA27no7KWYs6xy7Zq7KJ0bMzjvZ+lF2U1eAnVHFcYKP0poIKoLVpuLgMPxtTGMb7JvCINNMgw1MUYABxaMhbndLR1hjQfmqI7qeoEROdXiy9i3u3to5syZLS0dc2Yv+Ju//csdO7d85T+/zacd0ABmgug68of/4s18X/UNN76Bi3u+9J8fwuzyo3/7uUcfCpLNmzyTH/v7V1100UVHzpu5atWq237wS+LjpUa4opsf0TGTc9Obb775x3f83sUXXzw82P6Od/zFulXPIJpU4lnY7mwO+vuxn26+6aabEN0NN9y4aVtwwtLmnTuHsMCWY/G5Zs26a6459+Uvf/l73/83RIIubcOHbzCAXrHifEEiai8KGKAqjzhi4b/9279t3ryZVStTFAcWngSGZwvh+xVMr6nuPEFlUeWTBWMOkT5AvlUw4z2SBS2tLnzdyCo6iIvBULl7Jj2ij6nCqvsIKZipmxRF7kcJqDUqQ5t/Ml9zjd1alK6HTLYXXnbN+cWZvdRvW1sbi///+PzGpqaEUtXyeP+Yn9PGQU4XTE6iDLqoWYC1jUeNo6w1S0FytI0Tx4NyK6cbt46QXRU9mmSphXieH6n8uM2aD0YSsvsl+QyUTSZpv0Xmqb4Zi5/IP1ASqG0JcMJYITXnuQo/VqHstioBCKN4/btbRJ+7D2jQFjWPXsuth1cgjFWVVPUITU82zHMYjHikQSmIJEzA7wSEGCsX2cWYuTPvMMAGB1ZmB2KrSjqBCVnZxWIpl/Nor6vKEQoOPRxtuQtdtSMAH+lBicHx6ItWFa6khkcng1U8wJTYYY/LHkqK3MWAz9chWr4V+kJHbgQMnnj5YZSqMAAqHQx5fqpgqh49pA9UAYQfJ4Ah39otGs+DD4SpjReGVK1wxgOO4veRBFjVWq3Z/K1YSLK3FsQacu2zG0fypWRzIZGLpVrsa0eorZx9SCWW5StFtF9e+o3lC7rpvNIDpOCsJ4/jirbjhE6nlZOPAeJPAD8OmSlHx+0DLYliiT33eC5mn2JItTS2zWrb1bGNxpxL2AfiCvq+Ss4Gn1S+iZaZK9iYlkhn0c/ap6PILu/wmDBlZiKESAJVEjh24VEoi+fXr8Uv2HfzpoWbP3/+nDlzsHvm/UMY4hCL7rBy5Uo+oERk+EBuWrC7D5i49957H3300de97nXsGHz84x9n3cHSlZEBtw9yi0iaBFAxOCSMz1AcFopFJhJcyN3Z2fnVL30pnGRhYN3uTXX8ZJ/tHizmohzKVi7QmkQtk2llDpYuBfy40sqYbjBqPZu2/HzLrazy5ixefMEFF1x66aWom9/97nePP/Zo0MdmsLSJsQ1OwaaTxoG7ntICOHGgYrlEYFPu2Dhuvn1I0zlhlgkIdY/88kxgj3Bd3VVV2R4SmjQajQTY/ZzppLmLAA8OCSycPZexZUvXTmN3dDvnADMf6d9I/+7/Jmjad3z9GyQTJ59yyrKly7/61a8GqcYgl+VtH94hFZ8V1SmtVUd7Vg3UsbLhhM14i6UefLJ2MNYJ4zE7Jihx7Yy53XfLVDGZ5oO9ytYp897NW395621kMnvxYm4w9fr3iccfm5L+leZ1bIxOMZwWHp1waM6gu59dklQyqHXkIFJ1/Ej/1hFKFHWIS2B6rn/5fsuJJ56IuQXLDSoAiwt8Xrzcvn07gcPB3X///Y899hjr31mzZn30ox/dsWPH4VDq6VlGrzoxKV58zDFf+cpXRvmUfhq7WB5NJTSqpirR5SWltKppWD5jxBq0kjzh37A6DAEm88U0uo4fnLA8jcdZ//7sttuCdOroJUu4ELrj5S/ncg12VFavXZMfHCwNjZQqlknu/AdE05X11r+2JPfZ1pbGc+FhLKYWbkyyR9prgcrsZa8R3C0hcgTGt43dwh+eAGHbl70pAaSPQ/ryIU0gnIHiAcCxwsQnhsNRAoDpMQw/QVjoVQBQwxGpk9eqVE+ffVscqRCRzyOIPLLjLApVuHokSawCRoyIEMBEESuGhQsXAoCCxCdJkJ4Ojzh2jfGVhYoApaOPPnLNmo0i6DkXbx69bmDu3LnEc5Myt04TkGImoCyg4Dn06JYUj3FXNKnas+7tzfZ0P9fZ2UqhZJSsrMMMgFUha8QxOiGvbVs2Yt1FPLvA2E8LxmdUG/ClJqmWMSJFgaRw1p4O6MR7Tnw8gXA8YSgALCd4hYXiKQCJE7rEDphwgalrlSVcUIAUNfkekQA01TzCALVhwNTyfdbAEKks6sqnlkgUc6AkYA1AbcA0e7yUL3V2dgQNvXQUboVtamrsGbDbYZkc79y5M57Oc3srFc1t8QDjuCaWvjbEXc4DARdYMpUkEgA2xF0bsBHMRpBKv4MUDTKRMNNS3jQgd67fwlqamShziFRbBxe6P/jE8/Ro0AHQwQxYsjvBV8sa02oPlOyifKcoAWtsk3OThKwFo6WFI9XwJpfn/oaCNxq5H2PDbE+SFRVWfQ06XKze1RXoaPOnP70TIhhu8UM50P+Q/Xve83/OOmvJXT8P5h5l3fDUU0899tjZfX07t5rSLruXvexlGEnfc+cv/uM//kNRdFV4k0p96qmnfvOb33D9wJYtW044/viLLz7+v1Y9W0ENtnQHM5uDbduGvvvd755++un/9E9//8EPfvjx54eWHEFksLV3+Ec/+hGIL2wJ/uT889/+7j+SATTo9Ot167Z+6lOfwgA6m8Eg23QoQ0eVcBgQcCh9n+PEAV/7BChFXeCw2MPwdYGrIsO4kpKnUAU58SNYkKrFDdNXuBZmYspR6vSRAD3O1zLXdNBteTzyyCN7kiVaNe2HT6CwzKaj7XWe6Up7nWYVQXolBSQy2x9kiwOW6jpczL/6UIUQPe53CYTHk7qZ1w4vGoE1QNVFmTiyluDE8OHUcKa75dwjhlWqj6wbgP5ugYFR34EBFCjw0iPE48JcKYyeVfzwcLaxkSG9nDOpDsNyHBkZoKfoIzy1jNH96UpNTU1CAcDnQgB05skEQKdqBCPKQJIKt1DAkGs8fcfUGnjoi0/v+wBLe088zJ7yIndy6ejoYPgiC7DCMIRBhzEGNNdyNN+zso/HTxW6HsVM3aSpRsJ2LYrK4uOVHeXyTIaxPDNVWB69KhDGrUqKHve/BKhTNufY16J+rVsNlNsD1br/mdl3OdIfKRG9mw6YacwwCKRySR5p/ih/bZS1xG3xzgq6wG0lRdvfK8XoxTHeWQLMt/N9x2RE+bCSAK3uqquuWvO/G2mc5Uttp0Gn27p1K7bOfHWQL5tRHc888wyqilECH+1GjB/AFTjEBgoKyEj4ne98h4uf/+iP/ojiuxHSRkUbEKZBBcHJIenUnCTksKiR+exZs66++urPf/k/7JjUb1QQqNFRIjL5aqoYb01Zop5bYZKj8ayFZAKlwreFSzuef+H7z78QtDWfduaZ2FX83u//Psb0OI5RVj38sCHaucS4jsJxoOwu3OBUF/JoIdtrYj+I7OT0KeFxSUwyAZpye9S8jaXKsc4kM3wxYMoOCvs53xfDc4Q7DSWARuP9hG/94BbjTV1gj9r/3i1apH8j/bt3W9QkqWk4xUezyBcijzOd/v3Wt7832N0d2BvgODYz0V4WZoLEislFVnvgVkfR1ZyNkrJDg4fyKhOsRZkgZkwW4gKmoFQIdq5ac+uqNUFr06lO/66Yov6tzdQdBpv1AEnKN5y7SuRiKvq0lkRtDCNPpH9rxRLFHOoSmIbrX4YyjDTuuOMOZM8Xd/Gff/55tsXcTlG5U4e7/CFZRRSWE+r/+3//L+vftWvX1i1jefSumxZFTkUCkiQ+055aPE4KdGBx5ZVXfvGLX5Q5PhPXSR1ZonsnpYiA2xPNa9xWtkTgn8MbW/bK5dDEpfXPPLP+iafiMzpOO+00TnLPOe9cFr/Mb9lcWv3II2XI8f8YTaYioVW+Zg5hjMpLy6E4sTCpgoewUMH15iohiHGDqsH9NiwoO7jZz/mOW/7pmmDH7Xvd0fGQO65ufaM/yNFarZvSEfAdlQCOTl6FCwyOfm67wDXObz7WpIwbAQoDhA4gCQAHfQ9NGDZki+AjCYjhcIzoQAHzX+yAW1q3ZjM2s44FTT3dI8NDuVIxUSqakSIHSCCGMjErDYmCeD4iysjAoWkmY6Yb9Ua5cLbjhmfObOvr6ycZWalE3Fbjc/FoKojz442NzemGZEMjlpPBihUnPr9qJSewCCCdTrrbrJMwzQuXxVLZZMqKUp6LWy0zQUEXcpckXwDkNDmRSBe55I4vzUzOOWKeYBlH9PGZVTAOVlESiiJpKuGKIxKpCp142WQTSViNiiTCApBfpVFoElQ9KAhQiPiKIaCmQgAHOvHEAImEvZD9YxVlYU3Gh5R4A1hhCpVMuBdXJ4MfwewfCdgV6bQDNp9LudgI0whugUL7pjvjl15z0ZITm6i1TCyHufOn/+2bI6XhdKcdDmUwbIoXs8mRUjoYSQwnYwnGu0w8MxwbCuYEg8X+kdhQPj9iLXaoOFgc4XUGLq1MBokSP+7YimXJKMv7UMXefCFVLI3kirl8Ougv9G0d2MAbF9mifY84WxoeyGQLiQxXPucSMW7VGikN8YICx7SlJNd5uYHI7p2EfxuNK29bWUTkpqEEGBAYavywgI37SSedxPXDTz75JPF600YjGz4O8xrea+eaBM4p6xbnJS95CdQAAJgAPmDyeeT+RSbTTz/9NG+10oxlaUQqwxHDGgM+ufMKLItAnXfWZnHZZZc9++yzfB/W8yziUGBYPvbYY88444zbeAWwnmMQZtOZE6DnnnvOj71VgNdcc82vfvUrlQ7KykVZgLJixQrupvr1r38N54oUOmAoLKwkuSto06b1Yg8A6XryxewJ9rAhnjuvtbEx4Ks+0sWIZ8mSmRywffJf/mfjutWXXTUX9v7jC7djKvHkE13d7n4QTY+Zofze7/3pUUe1r1vVx2uxCd1VQB5OwkgPC2aMrEcKd69cSU/8L2Yfcbqk9cViPBE054PGpuDCC8/+wQ9+AJ8f+vM/pypf/so/XL85eOm5Rz366Ib//d+7jj++7ZTjmj7ykX9G5372s3//7//+7/fcv66l1Tr1v/3bF3kR6aqXr1i/boj7px1Lo8Kh+CxW3/ve9x5zzDEQh3kq2rFmMMgNx81exx23mDcx1q9f5bQhOghlZ/Kj7mgMRxxxBMt+bLgl0lr/hBNOIPKJJ56oTVIMTevBBx/UJ4MlFuLFAPwcheza27kMaTz0l73sZew4wCHt0MOAjoMaReNlMFoObcMTB0xhYGj56zAV37qVsEf3AWZxtG0+ikef8pFR4MBIAF3lnaur4RFsB4OiopkpJwOUWibY1VtYh5KLx9IjBau1OOrYObtFOQh4+ZgfPcz/7MLmyrXNmqoyZdel0ULEB8bd64x+tJ+bmStMIndjOkBHH1IpBTxyOWC61V35zF8Lj+fIyE7t3aF1am7Q0dGQzfJkqpzhfdvaLhDTBZuReiattat4jqg1fcbn0kS5jJd7FL9bCSDtSepfkfL6l1GOeqmlf9xxx1G/aFjIKjUMNp7+hQ00CEMTYyz6FwVXq381rL30pS/FLmfTpk3kIizlQna71b9wAjragReBRK2W/7r6FzDo9/dnL7nkPLbhGISFCBFo4uCFd5wXLJjDC0JPPrmSFwJ5Qc/BGIDAisU8wjnyyCPuvPNO2jvvMjQ1WavWwor7WBEYQzQ6lJ0y0a/yeRUQJULu6C/EFS4CYRxmW8SvWbOG/sUjWUOBAD4akLpbvHgxM5/xVADswQzqFdkKV76IkMTsgm+aIwGkQZJPVRjmYe/uu+/WfANOPIAKwr0a5I6Oq0qCGijc/oX0QBew9z3/2pSXVZZPDQewqGPmRtmRPyt3aKogsEGRYQ8JTKB/r7jiigceeID5T5gmuYsI+hf5w/x4M0/ue0D5Inwx7MtOgBheqGZmyLyUmRuTjXAWUXi/SYD9H+oi4z5IKAOy1Cw7Lc4H2VwJlZtlc6UQT+XL1x7vnq9prJbY7bEbNvnR3XA0Y7pGf7qbC6E7U3zHeWCEZlgKcty/Gwsy2X6miMM9OwHC9JmSM3gwDDAoEWCVHrlIAntRAjt2bEsMZ4+ZPW/t1k3lpWjowKp2BeOH073IQy0pBgdNPNAdSkVv0nd87hrMSfIxtUQOrhhpq3CJ0FMsY9mFUEE8QBjm4CrjtOVWssVnCkRLE5/hpsWki5P4733ve91b3ZyQlR57muoeGpPxE/FU3PbQmFeEcScuNfAou3KO1aDhPXmyxIV9a/wO3XzUShm7fKVV+an8p3/osTvdjkp7C5t7xx6/jFkcO3j0r22btzCZ3LZlC2wPd/dwyU05EzAhmbTP+iUbmSSzcWTvHgwNDSKhYq6RN5aKwyws7dMKoyhjs7Wn8Eq2NlUxSFKrGPnjgY2NZzNNU2g/vRybPqknJoTjzSQnxkfmAEy+oiemFqUebhLQsryvu6chmTr+qEWrN6zVgUmlw5g8Iv2731qFujPZ+R4d6d/9LHyqYLf6d+1zz9gbOVg7YOekE1Ld3Wh7lbaoVPX5SqwtQjxmb57zOV0cByKEW5tQYqhSDCQMnJnm0EDWZgFuIlAKWmqJhGLQkZzQpvM5r39DiT44MPz4Xb+1pynpX0MQWdP7NtFw39cdVanlAULa04yY2ea1UQPwqWhSyyfSvyaFyB1eEgivf9k6ng6FZ51bywYTXcYr4jXEya8FO3hjVDr490P3RRddxAHBXXfddfAW6mDh3CtNaoHDcYzitPGiSmFhiJXR29/+dgwquGZRbc8vk3dTxgRHNkFrm60fU2lbXA4OsIQM8hln7+c6nO1zVsz/tBuMQvXtYTf0WaE6W0ROnXzLGUXJlbtSsav3d3e4htSUnsn16qedyvr3Fa94BW8abN6wkZOU5599liLnu3pHcV2owOwCDVzpkfBr5s68XRxWr3RMHgWj1a6oTHU4CdOs4qPeY7T+rSeV6RUXsjCYkDHarpqvfGBrO4CSFM9EmYB6qbY/wuQF40n5vuq3UEEnjBOWz8sjQpOwnIfhkXiwCHji4Xxrwxx1gOJ3b8FSpnAOkTD8eARVRujQw7GZabQPnGUJcwqYy/ZCBOfpEBYdfCy0wqfXJGG+uHnz6JkunIyXqSeoALjYAxFGDSMYAjzOnj1fqV6MepTvuULUAMPMRz/651itdfds4mYLrMQoEblXMcCjRxQdxmJOyonnWGhwsJ9UCu4r1PDdqOEDfveeGIAVL5r4qlZ8jxXmORwWOjGUDgcuBfGRosMjAWGFCSoMCk55CZIwBScMQWEJIJwvYYc3yjlYOHLH9/BqUVWIVY8AkxdONAkoRmCeBx4FU4UePU4fCZQriFW7+w4mm7Zz586kLQ0VR5iazJnTTvNQPabS7NWbxbwM2qlxmkq53tnITiaxfYm7nTbADKmi2kEPNypQcDQSxh/pZWgCo3h8pjIKC9Hh8n6AfcdxinocApE78BKgNjVEUOnol4svvhhjVk7gNPLAnwdAC2DBg5kL65PawwMR4XZhUDA08ViiwCP0GcyxcMIIhkfi5XyYid3xxx/PHca33357JXH0rwhixIMJLwZYUFOMIMBFX3DGg4nVeAbQdBwsmOEc/ukOo6QrIQhefvnlDz/8MNpHXOHj6D4SDgbW2On+4he/AD08eNJHWC0gsQsvvPDBB+975pnnW1vtHkcR1gBOh8IIGID77rtPGphbWTHFectb3vL+978fA2h65cknn4z11T33/I7wCy+wWgiaW5G/9azsSDBrVpLSDfbcvm5d0N5gtVZh3LbzsqU8enYgE1+37s6ebmLK/REoTK5ZfrS2Js8991yMdD//+S9hck0R2hqCM844CoEzYVi7dhemb5gZffYL3+Jg7IMf/jCQjzz+73CI1oJVdgb5FE4ysR4LLUqKWMgdn6yRD3ZL2AefeeaZyBYDeqWKPQYcWg4WUaBjPrV69TPEe96BhBMYAB0AbwBNvAqoAFkgHBCx7VO+4eIrI2RLKgJkLUeOigQdRxaLFi2i7tT2wrik8ohdFOibnVOM0EnCEYNhN9JjHUgzI4n2IAD5DIOIkTA2WOF4wkJn6KZ03NJN06oCiB4PlASsol3e7n7kICMVhrqrdCzaubl4WjbxtOS9yCoNA2oMC7Tt3ZKFVWDoa7jxgIHBGcDo4GqwTBUY2I84YiZDN7kBY226VPrPz/5PLSlacj4/aiCIISmQCU4MoulirbD2RgzihQyVwpDCqDWe/gWGVAYxKlFWpKAQKXTPCKm83slX41XL+ArQiqFfV/96XMZATHjPPvvsH//4x0SC6JP0SF5h/RtuimH9y6s4VVzxKDZQMWiKMP/KQgCEq/SvZ4Bu0tycwD6bFaU3gPa5UDTUExoEdN4o2LIFA2hDFQA+6O3tLQsWLMBM9pFHHunq6m1uhiNbWJGKoywQef3rX492/ulPf+rzDQcwL2Y38K//+q+hBoqXDwFikPC1115LHX3pS1+CFMKkK3kwpgcI/+abb/7kJz9ZqyOUC6/QoGV494wyGk8VoUGfIQL3pje96X/+538w0WYgYjIT5o280HF8IYp8UaYwIwoehkcmVxs32ieVaCGUHZ6lxEBEKyE9qv6uu+7y5QrjQhC7GWLQ4JAiUAuGfsSCmb1FSsGACYrAKDvMs/PITQyqek/ZB6CGfkf9oiJ9pAIUDVLQRD4od9pPVdZImxisw4HHALoKXY+UDgXNtDbSv3Xls38i6WXU1Iy5bfhMaWlIixbPXLlyC7lTy6TSJhsTjbRhRrv9w9L+yYUmaiV3gzylpqTWifLBohNnzGiYTfyuDbZa7+razgz85FNnkoqxOLytW7dl7drNuSFjEwDDilwkgRctAWacrO8Yk1dv3TRGzb9oyntGYIKGPUHSnuU13bB49YiPL6HTUZTwxjr029/+NgH6Oz7Fl5tubB8C/CBYhMzkhCkQEypG6XChOHFgvof185OPPz4aDwg20CHAdGNje1MzL32NwkwiRNZAsVabPbuNLQV3PsCugmrc8DFrsjPXcZw4Z140c+bMMoim6+MpiL7BnfxeWGfADYnUjBknn2C7PRdecAESSCeSbIsxtWN2tHXrOvxcfoTpW3MzxmGJlhZbcmLgzTvw2aJ9wzNwO8kzj1rYxfHQUD1NTTngh5/d41G/FI3NzSPuWndjCchxwCx1rGOOzQyWeawmfmMTJ/XEVJzD70mBjgVC7DQV8q2afo+Fip4iCexGAnQi1mIs55/dsHY3oPslWcNR3awmSKoLf3BFUjq2BdC/fnbt9a9WKwDIHVzlOii4RbCT0b8rHwvpX4yQTKeUywcFrgqbM2uW21QxPaWjTSlD9IqyELRWYYSl8TtmmDHGomPb2FHp7x+xT00XzUirMKqFQ2peJMb4xkRI/7q0CmNjAHmYov7N9mAcFspdwdHyuayPWmj3IAyHNoIMBdVrGy/MHMec8rqosBfp37A0ovBhJQG2h/36d7TglVFj8nPRUdx9HDpUdRDlkv5F2zI+s83Llb2c4EcT7H3coMaQ5xtBbK1TF4pVAGsHrDXuuOMOjiRQlGMQ9GCrPDN0xkqOusOqAZ3DzYfAJlPYGzS1thU4y0jaBbN5bqThO149WXdbs62fzbaNqzA6Zszo7e6ukK+XS52MLQom4YoGUycdMl4Ri2Q227Vuw2/WbjDgtqZUS8upJ5189NFHn3XmmRDhBWbW4FiVsApGDqx/uzduDq/xy1mMfVOic+H8Hlb9btPGqV30rc+1DlPhKD4ziklAt7OuDMdPJhytfycjpQMLs/sT/cnzp96oHiifriZ0PdYlBZYQSbWu6fo28C7amqmWNwSI1ODrw8TgePROMfiepk8iBvpQUAyPhHECVnYei4GgirKnUzcAZZe1JfKGIpY9nG5ms9zkyulIWQgkcbCi9xbhhe7hSSlfig53FZn5xN0GyKLQ3t7EWDI4xKd7ySbf3MLRFHnx0yQbKXGWY0zGS3ZpR9EOrrCO4jinMDDQ3dbW+PvvvKm1aclg7luZ/IKH7mno2rU+GU/FuP3Zbp0rssknIxQImcQZQmNQjrU0pdtaGgf6trW0pHr7CoNDvZmR3mKJgyBysYwmcF7ywDjpGWHVyARYSvI15U+vQVT9ihS+yOJXxMtfG4tFweeoVscjFADQo3A9M6IgRCXhKxIYHErCE1Q8PvEepSrgCRJQ1mK+irh/rEKPHqeBBOzgsxjLUn2lGIty6xQ8FjPBjLntuThTiEw+3pOLNbMXbdvRiXw2ztUjxRw3RsdTIGdKxQbeTY5n88V8jhecDT1f4lNRWClbazSSru/xl4fKz/WSPDdexorDsVI2bi9C55NBLJEP4jlud+bGj2LMNgfooPwK8UIuyGXjGSPAXbP0Wqf9WbjH07zZBUuWkTn7lATDBEMHl19q3LAyRu6AS8AmpqkUQwQnCkz+GPT+8R//ER97JkxYmLySBJMMJjgisY3GWpdWVJdzYLg2mKULkMLCZ7Ainlywi8V+lAt12H6Cgqx8wqQY61j5cODE+UTt+gdI6Lz73e/Gnk8mO8CLDeIxi8FsAuvbx0OHVUIJs/rf//3fQMJMODIc/rM/+zPY43CFXFR2fJ21QP+WW26BJrIixzBxYKBJ6T772c/y6YKWlkanoMsdgFEcACyAMe557vkHeIWAH0aPw86sYeeO3Pve+zexYmKwL/GFzzzsyHawP04nRj9mBu2uVoATsczjj/Suef6OAlbRDZxAuSLEhmE+XrS7DVobmvlyblNjRzy+IFEcKDBHSOpGonx3D+wF27blP/rRTwG/ayj4v//fJ7mOkXtd/+mT/05Jn3x6zWNP7vrVnffPm7d2eCRY+fS6l112FdZIGFlVZBxgMf7JT/57OtWBORl6ld4Nq25ixbsW3Ctbuv/+ex544LdIhlctkBkZEcbHNA3+N2164T//8/O0N2JwvCCmAH5XV3dHRztN61/+5V98pA9QNKRHXpwK46s5OSmVxevD/+f//B+waBvUF5GeAgJk1vTrX/9aDUZJ4s3DEPjYxz7msUQTn74gmHvvfYB1ILVMy6Tphm+JhqXh4cynPvUp6IOCA8XTJ0AMpx2YvkGNsoQzjcL7UwJuZ5sMre0li8k4VoDO0Oq4U+ZjW9lf6qVymzoWYqW3vW97JjFSSI8w2bIt97jraEHlzWLHNNXMjzd/7OUfp+XU5uQTieNGZ36mhPl6MPNweo3DcjzwjoLrBfjlK5bjfFQBrHj5jhPUJY+0wIJT4y5X81wTEoyPqxfgAmm6oOwq+ptKc05aXCytpQU2ppJ2YOBYKfD2UmU2j3za29ua5s20mKLx1rd9sGdXH59gsVZdvmPM8VwvuyhuqhJgPJSuxEf/gj6e/iUJGOw73/e+9xHWIOOz848oIMB4xDHyaPABbDL6F8WH/kXHoRpq9S+jHMPXu971LkY/iGvmoFzww/qXR3Ika8+eAsT813/9F8Uko6okHoVVpX9DRJgzxP/rv77uIO0zRMqagNMIxdbWBt5++ehHP0pKU1OZPmwqI9667e7u++1v777vvnt4H7i9tZUNL94ERJ1wKS1jPF9Qw33605+wgb1o16Rj5IAPOmM2kxjG9qeffvQTn/h/w4O9fMiJmXXCLplucLlnC7lMOtn43W9/w22RFQp8hY3RpTSMFPjeETA4Xiv65D9/aseObntf0JwRVy4q5g9/+B2mFk7h8rkhMLhT0DYWSKU6Mpmhj3/8/2G3QUQuh3a3JGkTRAHWypVPPP30k8iWFyHzebMOcbmYp6r43Oc+wxwMAD7fJFXoGCjyojI17vWvZOtxFSCLr3zlKwrXAijmc5/7HFJFjDIyJgvF81oaR4w/+9nPeKwiq0dYpSyf+MQnVFi1BJ+XiPBO3WOPPUYz5lHmSh6ACQw5fv7znwedyKpcRI2pF/MZpgFMw9TRhB75+0cC0rzc+UF1XHn9hTSJoWIXTbGUbHiq55ZsvNiXHSI1mx9ubJjFHM6pJqrTbv3C5wefqNAwt3x+ocHWtHYxxx442km8xJeQRpH59EHcaT1lR0qeha5iEuTDHlfR7hBzJ9PS0Tyi+9ld08/B0EHhlReFy4MPIxJ9kc8jsUKnKzNb5oMx9oWJGcFLr7y44wi7fDRRmMGI+q1vds9fPvclrzyZ4qTd/aB3/ahxzXYMoGU/bfqcjEiNvsmwBzUeoXgJ0OiffHrl617z2qce/t22oT7is64fjHYGD7q/AuFhf3/lOS3ywfoKPjRL5xNVHOndc889xEggVepsWnB8CDFhWiAeZ1r7mc98hn0DL23eFuOmKN6FM+tn9E9oNqXll5fBaSefsnDBgu9///s+ZvIBPtJ18aXL2bZ6YVWOaaZbR4KtmZt2t2zXlJdYnb/D+WXPdEk2y5UEepZqRPPUn2M5IPXudDGR2brzd1vvuuTql29cu+5nt96abG3l7T7eT5sze/ZLzjmNGX6h+ASzsmKwC2/egjjT+w0bN2AzvOqJufnm/GDDIFm/5c1vZtNjdJY5hjlNOkO8sCgITf+WLF3KC+Hf+PrXmeeCx+lIGHu34euuuw77Fa4q8BtKu0XxAOwu8honH9pmUuojJx/gPTreM/zmN785eZQIMpKAl4AaOl89eeqZJ9/4mjf85u47M2Y34l53q4z5Hnh/Bg5z/UvxGVEj/bs/mxx57Vb/rkT/JqR/3eYpOGgMepGUWbF42llHzp154k9+8hPtqEh7VlSNKSDp9JLb6S3a2i3RmMjzPcxFx3Gk1fCq155w//33r34uPjhY3ArNRNC7SybFzRWCZZHEAp25sDlTng5AecmSJUoWOyGFV8YK/5m8/i1mYsxGBgZ2YM7R3bOLfZWdO3rYvtCnvAslu5ZC+pcNpnAWkwxH+neSgorADkkJ0GcefWblG17z2ice+d3WwV7KqHM+9VCWXsRo3JgOxZ8+nOwLaWj9K8pcocXqg1tI9kVGEc26EsAS97Wvfe2Xv/xl2TkIhnUl961wFR2n3uzTskVZBxdtVwrOOe+s+UcsveXb3zAADj3MNMD0I5/0TiYbOzvbO2cNs0zr7+3nyLFnBzuufJua61EceDx4w+tPZx33yMO2B2XkJu0KuRz2kFhpG8bY1eUYMpDkFd9Ykn+wYVc79w/n+ocf3vrrK65+RXc+c+utt7a3tmMM3doyY0Zz+4nnH89pSH+G7x1hwTiA/uV8BM3LapFTBmLojBwAkecrr7vuv770pcpuwBQ4B5e78LjP5fvf+54xP3UXrX+nLrP9ilHngHOq+ftBnwC7MH555uNFkEefVJWFh/SBKoDw42RggAesynkixPuwZykc6VPDkVp3+SQfEAUgOUd0e0Q2pnCGxx1adEXicQA7TV1GIqYuNWDmzp2zdesOHRDii7h8n2NtAIKcpqdSsfaONGMHiDyGwUQwHEPY04dzjio5elyxopH3KpyeW8TpI/la1qPSohTliQfoLjGGtRmOeufI1kWa0Zg/tRWMgPFNFpVmEA4QJjXsIFgbKQCPGGbGlwUYj+gDHrEqoMcJ/HAWsOTLBQrEq+hPQGe8JBHBV0b4L57meHlF8ftCAlSZc7QHI0871BiIj2JmdoEPgDqgqlsAAIfr2sKOQple+U+Z7G44dzaOVTDQ86TKSaXglW9ewXUptDKmC/HMbMxo+nfKyKMKO3qcphJAp7DwgDnaFY5mwzmcP12gxkkiElsTpoDhUdGXh1TA8ElVmGGNAA4Y5tbM+cCFOA2VKTVgnPmJcpgI8Uw3zTaiMnwplUciUShQEGPKjlQC0AQRAFogVjIexQeAwboIBrBwwghG8WFfnECHSF7rR++ggAiTLzkSAIAsOPiRFU4YV7yRykGRexkJYOIQSBlKxHlAi2EfjA0SFwHzaZhXvOJl73nPe/71X/+VJNCx5SAAIjP7WMA1jUzZracXChkKjiVIX98wUytPtkzdoYBultm861C5n1LKDyLpdCqfxebJcgf3pKUzf/azu3uHgtaG4J3vfCdXJz744KOUm1RyAgDjyPWbB9h1JEcnD/MRKjVJlXGAJAsn5a7sqGsyomYUcGU3EajgmGQRj+Sx68b4zbUIk6e0fTKZYFYA12bnYnMTu5NJxIUOHROOKzY+j77g4QBVg5TICCbxPRgoVCubhwA3NaUBIwaAMC4AFAR0AsoCPwzA+6w8ipPu7t7GRh1MGghtilQnunF5A3F4mBcDUmQxODgUphyFD6AE1DbsKqwLL8ymuZFqaNaCpfjbH9xe1QBg0reoA8hwiA3rBcYSLzg5Nx5XNHi6D52CDhhP5Gio6QTb/YMGnwpSRbMpTGEMjkslTzrppJdefSEpMoB+9L4nuA+4ZH3TzLoit48kIP2LlkTOVBC5oH+pMqqGdqimSBi9gArTGAUYwGF+rDFULGmEqBgia/UvkaJTRYG8pH+JFxEPgOJjZIYrxXjiBGhjaB9wa2l6Oh7eEyQACvE+CXQiw/rXAwOJo8gM1PiEkQCIBKwTmDNcSJJKDPGEhU7MzJlNCIFhH7tnKFAWh2jra5MzHxEtlbZt2w5hzqIppnb8QBwZKaYajBpYq1evb0ybbmJRbjk5PYLe5CANImvWbkym4lCmFF5K0AfMqbb4C2ueb23p4BFcV1dUgWk9YkBh6uJmL+U5AIgoJRjAuVIkeMOKOQmPkpLouCSjoEfy9QDi0OTilNquXd2oLcCIob5cqn3agopz+pc7tsvzIkdsjPoDGLIgKru6Pgt8NV0xoIzwhUhAWLVEAFCS+PTERUGPnjdgsAKHZ+IBwCE05qU0WlEmBhhhEVaAjQtGP/+oyMjfzxJQvaBtsbUayO+khfeP5JhYqhJjTTb/3D8shRsh4fr5jpkh7jlfPi8CctCypp60QX7mbNvmiuc78TVoMM4AoAsFCAz2mYV31HT3vAIizBoJ0KB2DPayK8sllNtWPm7pbmxnfI9aWo209mEEA4Koo9k/+MEPcrKFdnvlK1/JTgKvDO3DjCPSFQkwN0ATMer6usD6me9dfOMb36CDGFRlOlHBGPOXqQgnpmOiJv3AxAdrWt7cY31GWBPYUlH7ZkxedTnMuOSYaqJBxk2uSVBTy+Rs7odjJ4fVBCe4+b6BNU89zU/x+MvOILXt2ONmMBWfP38W385afsJRaMk1yy9mogUWE0gir7zyyoZkCinpLudCNocWY6IFJJNt+UCi19izY2Qpz3ptHywPZZu175HGR+BM0aUoPc+TDLDyJWv61yThq8CYrNJaqiKjx0gCk5QASx2tSbb39tGVjl549POb1tPTUcCTpBCB7QsJ0K8/8pGPhPXvz3/+832RUUSzSgK717/lmxes5/BGKocD6EqGYNTyyECAGprZPreKph+imc1qxHYvndoFz6lE48yZaXTf0UfHee3nuOMWc9Yz1L8FO6fenhHefx/PscnELmmS29uYs5WSxVKWOds+0r+p5ib2NBYs6GTHb/acmbwwfMLyNgrFGRdaL+9mCGH9mx0YgW1LcatIfPQvfA4M9mlvp2oOA2Skf8er6Cj+kJcAA8nOgfL6d+tTj1l5mZ26pa9fBRzyQphWBUTsf/mXf8nrhczMWVYwaH/hC1+YVhweqsywXuMDifi+gFg/X3TRRXxtkgt3tM6qvymETUUzKimOCbXHtWlsPFi0aCbKcd78Dr7rOH+h7a/u2LYDajs22eFRY7NdGMcZCwtDVDDf5BxFn0oIIujx3WNw3hH6qq2H18SjmC30dHX3dHcHhUTlPcQg0Yb6bUEsnDug4nHLli0zGwx3oCCfol3/utfx9SS+/lS+y7lg1ho0YGlhmrQWy7WLXIiz8+Y5mWogWv9OVWL7Gf5FGUCHOxu9iyZFSyJAvJJ4lKNUxBAOF49H2p8gw/E+XAXv4wlMgBUGC4ehBm8+ZgLiwNSm1saIlI8vFtLaF2PXxU1/i0yIi6V8sTRMvpTVSQAkzvxMDiXeZbLVtG6VYyvNeMP62ZEtnwu68FivWMLaiSvc7btrXPPqLqvjyrcdW59OJ0qzZ/EyYtCQ2pEdeToVnJksNibcAXO2WD6Dh1vNH+JBnowD7q+N544+ZvEvfvGLzRse2PWa1wyM/ObHt/7wmLmvWb7oyHVrVgPg3rkqcrUcTGtHAIasLGUpUS7qdSCZsrGLFVCKlU8qx6rD36wTLgDZ+0ZAQ6HF8KO09iKK+2SOAgXK5mLCPiW2uU+FCy7rsfvAMPowCty9BwWu9DKfn10J5ALkDqKqyYpvvHMpH/AJUFzpECjneWaoqoDzw1wbjpZqhuxaMk3XKI9t0h5nvPgqAIiIzgSkPMqLDMTcDUml2H46uXyR3E5zdNpAwb2LWIwN0GpMtDRra8F20dRwsLMQb8mWcqVEssC1zEGKXyxo5m7GfDCYt/u1rLMXY3GGANbIuTyVYjHWsTHroFnSxuwOK45Xk/FSg/vQsAAcVJAqxZKVn2uH1mQLOW6XTmIQlaePuV8BIxFy4bpKbjKAsXnHzGEfn0U3E5pUdm7il4WZC5oxiaZb0A4x62RzoWvLiI3hTDKsTOoU47ZzcRP5+1QCVBCzXjZKaHXMI5nFaurGiQ4nHJiVaERizNHMD0XMfI5Iq8HyuzEpjhMIyx6FsFW3G8fwmQHzCBbwVD0ZEcBnTwefmSU+c1Dpd5JAAd4rU1jS2y80KsIQBwZnG0BuoweuYJIYiAADGzLPYlpMJHNEoRMWKWUkSxpfCoDJEQcpKACMj4N5T5ZH4nEqmh6xF2drSZEKc9IDA6KMkKA5MGBF7uw0o22KZoh2hVxDqVhIxLPwDsrdd/125VPPWPe0vtXDpY98dTSXN+kNui+EQrOpMd3fPxS4+5gNLog1pFu7+7YvWBCcckr7gw8N5UwgbMplC9m52UIxGxtoaQmGhoOvf/3tq9ff/Xd/9/wQpo+Jtr7hfrhat74LIXTEuFQy/+xzWzZu4tMKab5InsnG12/oTqSDnd0DLc3IIeAiS/y2tjTGuwT4oXyHhuwKBCcEs3hGYJh4uRHGROSdK5EhOIGYLwHQHNjHY3ABh0ggsAdnJdTQlELgSAln4nKNinZI08phwYlBvBqeKVmuhLBpIUkYl/mVm1FzFUcrhRStixjxQzz1jGx5pLLIAskThgLoQoSgdLErmgpolSgiVAdtDBT8hgYzBFS+tB+MqsEVmJo0YASYQ8yda5df8kgqYARc03VCqfBGZOT2nQR096QpUftvfqJBm8XWNvKxETrOYLKnL7ZrpNQTNAU9I5tzid58Isgm+NAvzZu2CoghWiO31kdHNZ/rmflpfqjaNxthzZz565oe90byA5ofqf5nxCbnyrjWUWySy0/M6CZIlctpdpsw87NRhFl4Cd1fwKrTftYtCjm+2MCXHJKbuPE2nmzJNOzguw1AjnAfbTHIsPvBdKCUyaaHBlO2WEgVbBE3kM9yl3xTq/UmlchdSk1KeNowuZJEUGMlwCDMMEIc1cN4xbiEkBmXUMQoO+kjxh8AGKaAIZV4WhrDlEY/0IlBD5IkOqQyuAmLIQswjWagCAYfzQhx6V82YqRMQQcXByS+qMEJOo7hlE+kgQIbZAd9aIosvkZajw41CkI8BWFsJFWTCpQ+FHBEkuqyKg/OJDFIwjNqeseOHaDYlz1L9jYdRYCyiPty8QiRLHqOCWiFGcfzaJu0jmBytSHdZRdADUSXMws0lE4CGTc3N+QKWdoyCg1JM9hDz9akfOwEpWb3GVjPhThCpmjAWI+iLkZKbXzbLT7ANdBnnrH8N795pq29lSsD+PgbKp4ro+ctWJgd2Mw0inpFArzUA5FkwnbQKmv2AoYivATFbJ3JNOt5BA9leLBLFOy1WASVb2pq3rWrZ+bMDu7+4TYFlA4wEgh1xwxHdW1Dky3JrbDEIBbVCxIgRyqOep85s1PKjhhKR9vo7GhF4IlUIx9moVy25mX8orz2K9eOsjOWLGQrYmsd/HciBgqFSDs0oQTsSpiKtI84sTMBwWIBTUnArYvtC3kwQPNAkjQPmFSYABzZMOteBuaUEWLEuCGPkA2d8WSsv3egvbMNJpPpBAQdn1YUsiZf4kEkEp8w8XQm7rMnI6ipZSAZYLmg200ioMDvRW1VkV3kdiOBks3GbVcKcbtqGMp19Q2ne4N1Ca6iTM4dShHP/g5djq6GumSdST1ZnXMfM/WIbxczK6Z8e7qZCLfn8/xQapUuQNxknRsYyTbF+MCMPJ8Z4USdjyml1FLkJ5lx0wqtI7RmDLInmeaXdN8yCorGQ4aldjxoKhT50Wet1SWytpHOFhR61r50RqtPxGPJHNnE+f4RfYPi0Yny9KXh/NCmxBaybk1vpRQD6b6ehh1d6VYoNzEuWa/OIjnbB0BELN2RgnVQ10WtW7hw5EUSmKIEmO7RfIcee/Zlp5227vlVO3LlxfUUyUTge00CKKmvf/3rHDoSwDFdMa3qHI9kI78qv7qRVTDR48QSYE7LRHfpUnvxleNYLn4Gntc+5zsnXNNMY51mFLwrzvXPTFy5QcpPVgGk3saCj6k+m7QUix0dNuefObN96dLFLU12ksJsnfj+Xpu/qWZtDoQrf0ygw4KxGBO/oZFheGaOx+yL42oiSYKC4J3KMjzvynTcMzN/JoRYe/EJhXkdM5YtX97R1sZOi/Y3hDK/k7u7OufMvrejY2DG7Mcp2q7tHdt3btu28ThaJtN1hPay8y+Y09IG/Jylyxqbm4HhVgO0JO3WtKrzhxstzMxMHALsWywx7/jLD0FNixRfZGCIwTFXxAeeJHElnylr6ugF89KxYwO2Yli1jnFa7+dMHvadwypHdpS9e3br7PPOoLqrUsd7hAFXO3bzQtPSY3aM5Wc8rCg+kkCtBGjKrJ1o1rTN22//4bXXXrvrh7u6OV51zveOWsQoZl9L4Gtf+xqfiCQXOrtfaPsa8QHPhh/TfEwU2AMJ7Fb/ImdW9FBmUwY/VuJslHPSNWwcFTKNR8yfOWfm4uOXnZTJmEVE3EBwtt4EDO2DonQbFHZxT/+AXWxx5OJtc+fmTjita9mywrzFT5/Vsr1j1uw1a0aOOupEXjLfsXkBu16F3ExqPB7f6aiZtueGGXRce3sKRbllaz/baLNnz0eDL160BJoOwL5rTeDF6N+wsitktw/29ZSKT8F/KWiHn1L+CJeXab2Xnn3OrKYWArOWHDd7lpmAU1hSAUYtNtjHxBKDfAuYvTi3aDR0N5MEEgdkpH8lisg/3CTA6ECX/s1vfvOa17xm5arnBzJDNl7YfotNuOnM4Z7iUg6YN3042aciYMQK69/JT873KVeHA3FWkayzTj31VF69Zhnr17+o19NOO61WAlqpoVlZhza3bT3uuM5ZMxee+9JLduzcYgrGHUC0zxg09HNWnnRSbNnJz3ByvvGFufOO2d7WupyTiP7BfhavM2YMptMtC499+robeIV4qVvu2TEDX68nC3aEw1kTj8JCg9NO4FbLVV4BQtlxJ6xlGoauCYfXvxzHYNCcaEjz7m6QePqcl3bMbDkHI2a3ErUlHtRYyZomRZEPjQzkdjI73+TOj8hdbJDDaUuPn9HQxGRg0YIjTj5+OXODtDP/gCWW2Dhb+bqFPAzjIIvLOkaHUvbnVe//Y06yACNeAJg+UHRo8sjhDj4jEjHC0nqW+VLfMXPiqaVHd6ZBrCnraIShV15IJhbOWfwiw21zW2defPbku5jVhtvToO6Sy47ZzKNW1hNlPsrGYRja+6dKVNvBIkfa2V5hlcYNKXwaeS1NGuVkZOIoMDNmU2lPmOLk++Mff9O5Fyx87rnnvvXNWyFBx65iRnx66jBmZtQMGZs2MajNn93C3uIL6+/bsSOY0djH9T8kGcz4XZckDQEMRrt2BsuWN7NvtW3LmF0wn91uA+K2iufdYlUBwBIOImE6xFSBTaZGqlCqHjXWVEVGj4ePBNTAGO3sHig/kNB5OS41Wz37/jjSwK9tfiYlluAVdeWFBjAu9OiDocBoeihybND6QKjJk5EnS8tHxeMrhmtlzzvvPFYZqEwMrllp3L/h0aBizjKWavR0YCTARJA5ENM2qoywZkuwwuSSambsZXLGI/FMuVSt+FSxGoHmoGwA8YjFEvG2TeM+WQAWkBAHl+kdj4znJDH9wgeReNm+sNMEpMywCJAKHeBxXCiIriEAFtNTfNo/3AJDJMBwRYBHiOATgxOK0CErYJESDEk4IPGJgUn1F+jDlaNhXx8jHqVDdr6RC4XiEIPPThl5EYZhwqALQGS5dBnck046gUO02277CQ0fMKbW7t5JOM+6W96Cn/70t888vWrz5m2w49ARrwmcIg4O5hsbTSC5XJ4wBz1ocLFHFlg/d7bN+ed/vunGG9+TTC1FxqCkUmUdDxHe57ziitgNr3nb7b/sKZWeF29UEGzwvgQ1DxH3SlUMCzdwQSEvALAPUg0wivCD874+TNmMvooGKYg4Jpmag2KbjG4/wQBwSgVMwoEmwC4cw/iNZmIUysNXedBRIwRXdcoyA2ZoJ5hfxlvsOBCa+I58OYvsiN02BADXKqt0LOTAcidqAU0CmoInlXjXmM08q6enDzGoWXZ1cc+3ndgRrwKSEWEYdjH6/oaNaVAgBkeYuoY+Po8qmhjgkUi44pG2hw8zINJOaLqG7BwMK5dKRPR3v0qAkc3qC3MkM0WkG1juDFDDuWEaHh2fsMyMVJWsyK32Q4pvr7BrNPeLo7G5vKzb0jnVjyzM5jkjKNMMN2zw5kOhyy7fZXcAvmQAzVVZTDycvanZEe4Xfg+XTBgcGBYYHDRQoEQYshiX0MUoICpIDnEQg0/jVN1REQw7UsqMXbRYiYx4HvGtebuxFHiIENYwRV6kQo149C+RhIlhsKLNaxgEVxSIgSUGVbC4443zHnIUAxCET4WVe1j9iVsQgacg0toa2CGOE7cEyAgneOjoER85wAxZwCGPDt58wsqUMRzLYJECpkJyVD8qC3fxXAFd7NkGmCSIIxZaNWXkFbC2tpZsdoTPo6HLhOi0g6kbVwUoU7Iwy2MBGOdmMI1CN63xy18/c8S8YFcXx3LGIcML6Bs3bmyIcSt2nG0stCTTcpKQEj4ONoBhM43TvPb21i2bB9KN1BGVBW3rp8gcRMLiB/7BAgWfVOIJ4OMoCz7FIgZmXJxhIVWn8kwZEdYup+BJhcmRkRzxiBqCqnrlAowRco4cfaRi8H0WgBGGVRkZAwkpfGEBqbBHJMCuKGqdTMECWGECglG+0PQoJOkRibW02WwQJzB8UuUULx94CkvDw/qZ+uURV87AQXBdN1hEik4YNwrvBwmUq4y3BPikWEOjeo29a5EpDwj7gQf1F5+RhgWahI+xgHuikVTHjwHak4fatkd/FA90mT2hGOFEEpiKBNBfTHtXb1t9aulUPs3Uv3lQ65O93tSnwtRhDYuqeuGFF8YTQVQv40nmxccjeSbA7FgyX2IixGc2ocmn7VFPnnjtoKw0hmu+ZsApCcfGzGo8fO3UoiqGAb+vbwcoHDwzV1m+bA6k3JZLcWTIjngFXz64LRtAG3mAaAzcCkMAG+Xjjjvu8ssvV/PAF/x4BlieBwAHRoa55ZrJOZzMm2O5kyqfXHLFbRBftGwX0/6lS7vhs6O1lQVIS/F0JuqsC5jPM5cDRh8IHuTjQiF5GZPOGGswbRMtXvfH98IhwDHQggULnn++vDFFKpNGD6PpqDZYIBVGJMwSFYETz0XdsFSVOrEBNJNq6hoK55xzjhRuFXrdR1UH7YRMlyxeAtt1waLISAKTlADrKwYQdhF5l4C+sOuppyaJGIHtIwnQx7mh0BNnLPLDkY+cTMCPsZMBjmCQwG71LxVRawBdjNkdk7M6jmblOLPjCJQgb1XbZkjcdlfY8TJ95HY+AUBn5Qt90Onu2YzOmjV/FbZTnXOf4g4gbgFCKZxwwgkAL5x9BlsiXduOtKE+P8u0WGyHq1DTX4mEXViAATT+jp1ozuFkshElOEb/TmgA7dsG1MbTv2SKky6OJ7czJ0k1dJFjsWS7GfmMKb6Cu5yCrNmqRf/CyYYNG5CkK7EdbSAZ7u/hsT9lpDCA9g50ILmvKtK/Ue87bCXA+pdusH2wF6NPhoKVa1bRRcLSoIfSZcIxUXifSoDTkLD+3ad5RcTDEmD5yZqI+4yJRNvUXf+G4aWeUIdgtXXu4GrkuXPOR29y8YiUC8Dx5FZSjzuxiO3fokVmStEQW8KRU2NpBT1uYGgGere52fTa0qVLmAaffrqtKzlAsE5XzwAaytJurPhMJTY0oPjopEye+XgvWKNr9TCvlbDWxRBhtYsaJV+udMS8u5CcgRqd23E6QuBYWKlQKxtAu7MVsoMMSpZ5hS8gMZh2cMpDwQnDPADav+YRxjjgQBHXGkCPuBnCcStOJ9/nN64H2E1X+GtYKiM+j1rbor8Jhw2gOW1BdwueLAiM58StxjHCBFQEpkCUPVyW8SgoHrGACzxuyaJo/TuxtCx1rxlASw8hfQLhbIlRpHyf5KLLno8kQJOi8kjAD8ePFxZkmNp4YSiIplotYOPRnFK86FQyHUV18eyO0SLxkYrEQqH41WTNsMAdNLGAmwi3bNnpLowRJN1GP1CcrQ8HtHYvLHfJ2T0z+JSKXnjS8pl/9IevDZIbTz4p07NryY4dGwq5UlNDi8HAlKPP+8yev0otWS47dmxaseKUv/7w6xJNc5YuXzzct/zhO5L5vl0xLprlflrLAwMUs0niHNgolD2+RpfjLlmKt6vvmfd96OU33njh6tWr7/3tqh07n+6ccbTPqxywe4NMTu7RfMKqa4sdx1UTGX2WfORDzT9ClmL6R2tLErgLgM/oZ5lXYEYpTilUoTYlpFFgX3AfkABGIaLQ9JaAu3UyiKWCmbMSAw3YZHCpONfXBaXNQe9ITz7VkUtmCvGReKKUjfG563yBeYc73w+1HKwW6EvcH+NarN1ux/Va+NzLzrXNWeu1NGQulcIMDHNILhih2VrYhgsGAH5cSkeMu46LS7fsIivsId290VzjxchA57XXJfNxuz8v3zBUaBzOxe164Hx8JMPVIp3ZtqMaSOHGOi6Mb+jM03W4kRIAxyeDDxyOjhvTu04OTe44RWACx5SIkwZmY8yrmMMxo6KC8IlkF4VItod45CNczDg137JazufZQ+EVF7U6ZlTQAdhaldt1Agb6GE4RyXkDpDBiFmWScDIGgjjwsrLCGkbUIEJGoDMzZhwDlwkfkGDxKFymsOQIw6AwU4QCE2tSoQZvwIDOrJR5LZAKk6qKBEaTS+blRDLBhUNsuCFImFQimb7jEwMkrBIAmCScJtAIBPpCUVjE4ZwcMXTmc/M33/zmt73tbTDPdH/jxi1Ma8U2M+pZs9qB5xhp7dptxxwzl2k0ZMGFIGZVLS0NLS1cUcmSgDAmaDHE6BSQZQKTLU2dPf2Dn/r8vz639sk5c1qQA8sHJ71cQ2OilYMqRJHqXL9lx+pV8bbWoDjM/Y6DTU1YemUzhVQ8ZTKnRPTAWIr7pLnjeTgXIDokhwRsB81m9qVEOtXa1JggtZC3LUV6Pbnj2FWDVVxjY4sVyi5W5NI7ejkbcOh3NxeJc6bFI+eC1jaws8LuCMYaG5soEvXFJXjIFgmTF/iMMYw6uPkLj8BmnWkAg0SqsaEtEc9nzP4S5kiFcxjo77PWwvyfyuVAjtxJhUMC8+bNpmjkKGByVN3BJzAYv1EdNGZH0NA5hFAYXFDYVaQJ0fZ4pGGTF8WHCDCksp0Kulsv5WkkdBxYJR4HAFmwzCMMCrVJdtQ+JbXkiqOxgcWTCFaio7/7SALW69XzUUCEmTjb5JemxI+VCi8HNuVHksPZRI7bKlOJbK4hY0otEcuQ6D62aAbR3FzOpNhRSjg6ugHavSZAs+VSUjvTwslXjvimKJ2eJd7/lOrAx3i62VS5kGD9afQHJdon82JrpSWzXKYJmWJ1awEiPTApFlZ2fC6GCT23cOXjxVwiw+XQyVhnhhesm2MLjj+qMb2NFn7UgiXg/PKnj+3MvrA1Y4fKKWvsQX+eK0QglGCOob7pKNvt15ZcLqsLRt4UJcDGKwqUoYkBjSEFXcCAw3ioLRWGCEYzUhklGGcIM+YwnuCIQVlI/5InWNgwEeBqNHwA8Bl8oMkFdUAy4EAWXax4ABjN2KDBJy8ok8qoxYgn1QYA6NDnpJ8YkrgYD+0MPI5UcEGBGiMbAORODPThBETYw3EvHas2eAaMr33BG0MiaoVcKC+lszG8vx+yjKgMkk+5M2AiGbfJghzBhX/Q2WlSRuSOEkA9pVJFRmlSnYhySJKCsOxDA4h5+gXAXNmMz+yAqwr6+3vgCmnBIQPznDmz161bR3bQHxoaRDHxgzhKDC0BBPG43l4MO/iaRJOEr0j8pUcfxYQhMWTd7Oj5NndK6+OkTJ7JsakRA5rczod37iwO54M57Q3DmXieD7LQ9ZJJrj2AQineh9/Ph5Ua88kGvgXBu0bwXnazZ8/ctq0L6xNqEuEgKEqKrOh98IbwEfuOHVjM85Ts6Gjv6ukjX6YOZtmWSMydM3fd2o10egQ2b95clB09NcfSnfts7XMNQXtzezo90ts90DmzjQbFpchgO/nQfpjhmP09rCJqMoInHp14baiZO3c+5WXyg4OTY445atWqVRpwaHpYk1O5YMEzkR0dbbRhG6hsv4LXkBoxbl+wYB7brLQrHyYLgMkCsBkzZtKQqFB8yk4uruBwzYIH43L7XAnV0dc3wBQLAA7/mGNQ27BMFjNnzgJ0504mfvbKGZ+xo53w6VqouVxMv9PkgMQBGbl9KoEiXyqiaotuS9CaXjBYxNqST4owu86XRgY4ns0zaWxoKmZQzCVmofz2KUueeLyYjMcSbg+KRTCr3aJM2KTbXIzBunZiOpfZq/2s0+PM5xEwXlp0PzQkS+gytoMZ9fKxAr9kIcm96IlYrlQYRgNnEuhXc/RZuhiq1u4zd83SvqxEHuyPOaGppeLrnmyux3Z4kRdJYE8kYK05CJ4MSs0vrLzouBOT23vuz/cQEx4SNVoaXOQOtATC9XKgeTnU8mcOww7A7bffrmmGmv3kBY4pLRPUW2+9dapycbsRsTnz4r/+9d0PP7DZXkyf4nyEmQwT7y9+8YtTyhp9ony6tu9gDvad73xHuzVhIkcfxSSr85jVazs6mtetydn23fbEpk19v/35p1in7OraBYVZHZ3f+NrXd3btSiWSedtqHjN6hKnVhpcsPha7sW9+8QvNjU3cZl0L4GOc9vNPFiDrxGtey6TuN/f8dkzCJB/SyXOPWnLrJz4zSfBRMFRuMdh11oqTTjppNDIKRRKYigRovXbu6Taf2PF8ZtWzZ516xmNPPUXjoqnblmXkDoQEagf82pgDwdehn+ce69+W9gDVcc45R83q6L7t9m9NTlJs7BRnzGJX5Pkrr+046ihuJ5nz3FNbH3v40TVrdnZvvXX79v4XnrNPX9Z17J20tNrGSF+/XWHDvtw73vGOL37p83WBx4ucjP5NJ+wlqHjctoSSKftGJds7XCLERwm9a29uQf/u6u7yBH0S29E64Sqy5+PMIfg+kZS+YCL962UVBQ5DCdjZCdcV8Q7w6tUnLzv++TWrNH9Vv6enRIvfw7BVHJ5FRoux/uWTR5xoIIHJr38xjGhpG7744vNmzOjm0xm54V4vwGUnsrJsXbd+1/rV8XmPm23Dtk0bOAN65N4fc5STzXH/Trq91c5ZMoPH/+QnXaue+xLmBiMZu/aRr0qPuzHPd8UbGtC/HEDwEUlewCXHL35+avpXTCZicV4AxoD7F794Npf5bVg5+lIo0NZiB/qcYuTse9ejLp/Nserv6jGh1TqWzJSOMw87l+FUCUuOUKmWLzv+zDPP/O7/fqMWEW3OWsAtBzgU4cCGiCDHMwsHC3J7TWH+mzJYHdz9qzvc8xS9ZOLKo4//+b9+ISiEJhOToUHuXMwXXv+KH+FOIMHJED+0YFx17Y0i0RuZwOFepEICXRQgtTf4OsA06FSTLAelxobSHUaWDbU962FRSDhKCsfzAgdohfxahjBMhTik4Zybji14HuHEE1SAcxziceygkcqot23D0xvXrmFABFHH22QRzqWGgq0xOLbnDP7v/u7vzjn9Wj5U8fu///scKgsR4jAwAYUqgnvxMSyovUjWkzoghfK5R4EDLgE1gJkzG2688cYbbrjijW985StfeemVV54ZzMZCwu6jBQAfh5Uefi3DLtG8qrYkRPxJDh21lOvHhAZUeofvINgo4EaZdGB1Ga5PNordxxJgOGWMVY0Qxl5KLYMJMQM1ozepxFChGPgCQKQb123sBYv7V7j8hkjYPPbYY9EUVvfOEQMk5wRSGTwCjJkU1DA5wqELOLzBNoUmjTEKeZFFuLjAvOQlL4EY806mj7wgKMrkq8EfJrF9EcOwwREUKgMlBTwOUqCzByR1Q1jzUYiQBBEKS75sveHInbcYyQUYAGixwPBCJMUnErK8ZQg8WKKPD/OwBBhhwHidDq6ABB2BYADExTyYeX31q19Ff0kO2NamUvZNW1gia64c2LatD7tibNvInRjygho3BdOnMf9CYhiPwQth08JmJGGShyw5nnvuuajZe34dfOpTv1y+fLlTtVYuAHAUHOH88pdb3vve9/7whz9ctAgTrg44pD8iHHKBJYjACY98QYaKkFTBhQjMUDqkAUvUIHcUEa/SkQonEKc2QaEWYA81zTmcpEcqjhjog4UosIQDl0jgXaJd4SN5Qp8w2ZEEZR6BpG1QmxCEeGOr1Y4AhAskj5BtakYSjbCH9RtJoKviaBIUBwpEEoZPbQXCDLj4xNBg3MucGEvZt2uJBFg+KNAHRQ2bMLmIPZJEhDaDPIeH7YUBREGkHEQ4Q6VoOAKkwicccsc2Sd5RNOiDAkEfGQX2mwSWn7pw6fL5S06ev/jEuYtP6uDcSaPEBAxYsz/06ipTuuqqq970pje99rWvZYSky7fPsAGN9umdyeTQK/gENb0fk7z+Zdw744wzGJTInKGGQQP9wriB3iGJgYghVB8jIxUYYmiQaA1QiGGMYkzDERAFxhZgGOQZBoWCOmOkIpVRWigoXMZJBluyYxAmTKp36F/0AjyQEU2CrSLRCY+B5MjYqNzxAdAADi400QsM3TQk6Qhny1smDxiUwYVJwuSOhTScQIEYgAgIVwgQgT6qAWZwwJC17uzRcI2upMgA4zO0kiMjMAUEmDByBp4+LgAi4RCR8voSkZ2draeddio5igIdnTDyhwECjNALF85D/0q24k0MnHXWWbfd9u6rrgjQv+TSnzEDcVBIJYDwIZVFfc/tYKsLzYvkSaIUBNBZ3DKPPbN74wYD8YUym5R4yQWthG021BALFQEWuFQElElFvFQ9vBGJnOGWAI4kOYo8bz7FZ7qVZG4GAKSEi8AJQx91mUjZxABqFbzyX9jDAY/kIYVIAQYLR6ZIjyLAEtySKRQQnYjzCAw1i4MWAOCGiWPNDDzNcmQoFw5L+NKHCBP6xMCqm6DZtzUgxSO5kDuzCzLiNRDyol680JQjAGRKEvqXvGj25CUKQAIDTdEPMxaF95sEED7VRzXRhHCydlb72W88kJEaw25znCTYbulUAVSR5bEqRvDqF/RsBn5khdxwiqwiGD1GEpiqBDqD+LOrn0VRarieKnoEH0ngEJAAwykOfaSyaChWJD4TiQkcKMzQmLHsgRzIiAkdWTCkY/2MY3Lv/k5ELO5AmDMbvPuMyUTQNWnQ9+s58mUOppVtOslHeXl7x6bR/LZt6+Gt8u3bg+3bue+ZT8EMbt/exyM6yBR3wvZzmHrBPAEOhr24FCBSQhO1Wp99J82otVlXw+ZoBNxW/SzHXE7z+VG4qYTYnGmdO2a/cVLYbq9de1OTgo+AIgmMLwHrOUHwzOo16N8ZDY2T6fvjE4tS9rIEpAXk72XSEbmxEpDKGE//kooqke9MC0xLxdnqcB+1LuTsPhS2KSokbX/MO2HZksnWmygvVBuai0Mlu6Dkzjs3/fKX9/GS/9q1W7dt28n+EMoIny0Wp7D4HJipubDyghFRc3qvvA/js5tMAGoT619UMLZZLIftNQl3JpLNcmbE2RCNcQwzXv9CMB7jVd7yD+tnJCZmDMc5TKlUED1G+ldyiPzDVgLuZfU4X7Znc/ioWfMYVA5bUUQFP5wlgI4ILyQ155FSlq+lnPfLsnJKuavLPjWJeWHY+hmArq4Bbl3ZsCHHCwarVvWsXDn47LMb167N7NjRxaV22Dpv3Jh95pnM008PcbEdFLi6h/tS0MsYPFRUVv060SmAFBy6mMMLDlRsNznkWMzu9idcJg9kxy1Xne3NvIvbkNINGAGfiOa13qaGRiI5u8GRbxVDHENwvkAkipV1sba1ISswdDeHJuLWa2FPgRMKjsD8ow+AzJxDIxEf9eR2q2ypwI/DD36YPtvP7V3X0vREJg5gQc7kZw65h2c2E4dF0U0lovXvxOJV6mR1iToYOOH+NpkMpgqjdklzxE0Vd1/Aa5QZz7fpb8VNOXd3KzPYiBRcfHfUmiGsG5I8QQH4RwK6qU5+wm5pLj5w7x3//ZXPPb/y8Yfv+81Pb/1FLJs8+5xl/QMbhcX9N0nOZlyP5YjSTildJ+EuA5LmzZ/Z1b3tU1/+t1w6G1v72Au/fCgTb+OXT7ZkYo2pgv0S+SBl+0qi4Xy7U4coDqFncbnU+vVrB3LPZLL93T0b583nVQyD4TM3BqOfbqEuFyNEJ0zzAITLDE3yT6W2KxKcJFoEdshJoMRti0Gssb108plLjj/zqJPOWXzSOUuPPeVI7ovK8VpwfNh+saz97AJ1rq1ynQ0LBH7OjIJbJPlxhTO6EnMO9BxjAT2Ku5/dz65ZNWAHrwssK1dOWr8b/Tkw8Pjp1mfbdTBnMBol7E7MGB9MHOYur3xxuIAfy8FSPF0KkoVYMpds4CZaLs/MOdby3AemK8EOuXo7+AqEzRNmLmgBVoBXXHEFbwGid3A6WmCK9sY3vpFZHRqT73vquMIXUlhXXnklU0OOfy666CKu9NPo7yfKGO4wQWQWeMEFF2CtxZszAJAKLliY4Fx99dVkx+N1111HjCdOgIMZALAcIoztF0Y8BDTrJcCMGbuWSy+9FLWOI0ARoCMGMPchwLkUd/MAjKUUJkqkEvaO7N7ylrdQKM5CsAKkCHAi3jRDIHdkQhhTKgLabAIGgWBAQzwWSCKL4SDAKgj0oczLeXB1/fXXP/3001/60pcwbiOVJEkG3U4qNlXXXHMpkxFsywCALACwzcWa6FBuBkVi2A6BR9bM7dnjUxH6+oaoKcr7vve973u3vfeyyxZga0VlgeX45+bFXc1NMy+68MpFRx59y7fXz+64bNFC5NyQ4RLKfB7myR3rLsQCTWR19tlnU3xKJPr4NICjjz5y0SKzDMb+CRMrCVZFYO+M3C+77DKuAqJcMK9lAKk4UFhUYP+N3OCHNgYFciQJIsDDA6sFjKrVtKhi1R2IAOBDH1y4GuwdwATwkksuYb4OliPPAikPALmz/OAYDENwyqJ8lQU+SwvEi4EyBcSykyILQFnQtKiy008/DYlhxKamRdZwC3sKXHjhhQDjQId5VR+PBBAUzMtakaIpAH3vEMt5550HD7BKv0AalTVRGQThkD2kIOixosA+kwAH2yk+bsCvxO20idJVr7nwFTdcfNkNZ1/x+pe89NqzgllBd64r15Dlulj7BUOFmF1JZT32oHVMkO229pBLopCJ5Bpovqdc4l7nBhT3UKow3LKR32B+ZyboiTUFjZ1NpdQIv2xDDz/uyGTDPsvNuEbPfryhzUXakmeIfBScsgQY3DT4MIysWLHCbhR2Qw3jBm/JE37Vq17F2MXohwbUIObzYOhg2JT+ZZRDk7IJxdiFA4ZU0LHxJYlxhtGSMceuAa4kQZMBCr2G+gPyhhtuYFD1xBVAAeEgePHFFzOME9AwaHmUSgzRxKOIcQTYFvEanNwZmdEsDJ5whXrC8R5smD6PKH30GjRf/vKXwycsiT2BoXT4KDljNXSYfkATRxLw5A59jJ6lmC6//HJKKt4AYPhFemhkmYxjKcL0A/r5fAmNA5ggQTnzzJM4W4IOqlDElTVhTR6AZ/C+5JJL+dY3XyTSvBcfNTVvfttNb3rVWRcv+sBbzz1jxYqcfZTUDBP5rlEiFS/ygYWO1sRIsGxBcMn5p7c3Fpn+NjemSOdkCk54beeo+fO4NL61KTj9lBWm/e08jwJiVYwepHLnUbqRkcGjj144a1YnhsfikO8iZfPFdGPz0uNPaO9k76/x5FNP51Zn2gyKSfzjU7mUi8kDluXMlBCjUiU6UmU+TjFphJoveVwCzNz0RTySUGHo6zBxmgo14uYMAcJHtqDQilCgBBAvnEN/aCBDEtODMGUYgBka/MmnnjA0mIFJGEg3ljcNBYnGZFKkMHXHFic0QcSHDTKCPXUcMlLAZwEMYRok/QV+7D0xTNLLpk1lKGRC6QTpEaPAXpIAVcnP1oz0BxoEFYcL+JqCfVCBAajchUfY2LUZn41XNHhXb8LdS4wcVGTo+vzQr/rwArxrfc2cnjD3frGmzhYGmJzkitkiu2YmZH5yJu1KOPobSWCyEugJir1B6d6HHjx1xZmzgmDmWDzrnBU3NiV6iiRw6EiANo4ZhO6I2YNSsRvDPg+IzNC8mwydhjTHGcGD96/fsonpDRs4DehCb6jEXLD2Z5u5Ng2NkSkBOH/88cfDedWi1MSMbjpQan13BQpMh8Q8xs2pZCoRb8pmGjatSax5tvXOnwa/uC147MH0zs3pDRte2LVrq3h4+OGH7dwXcLNHM2ZEwVLZ5YMgu05BjF9zza8tllzzxErQWoI4r8rV/TXwFeDKj90x/wN4x5r1yZEcMbWIAlOp4aTWsRu9c+2G+FB5v9FDEpiMu/dXv171xFOTgYxgIgmMJwFrdWxJBY25IHj0kXvOOvNEXs/lDglOUyfZDsejHMXvdQloErTXyUYEJYE907+pZMBXB3gTp2tX4bnnVzptSd8pa2AoE8KHOB/b4scBAmtMvijJHksuG2zZXOjaFjz7ZOyhu05/4NfLNq4+evOazmeejG/fnIrH0rxtyoKMhVe4M0Idav19ud4evjtqKWx53XvvvSqF863z7u63G/2LHTPc2gcH7Su7GX4oUrflSu42Ojj6EIlJ/2KnJf2rwlp5nTZnn4hfks8N8ZXOUqkxKHEKgj6Vxoz0b6jWouBhJwE6CT2YXdmu/Mjmh+6/cMmijmSRtxjtrMgJQ/3osJNLVODDTwKcHbzwwgtaUU6m9La4Q//wQflMPsjHB3o7V69+3r5ZL8ebBMlYX0+spyu19rnYQ/ekfn17y4N3tqxeOWvL2g7OoIaGgpGBIDcc5DNBb19wy3d+t3Fd0u5Wttd7TK85G2DMgMO/Ub5YqKJ2pdw5P3rooYfynM1wIBR2RNX7UVJt+zv0HMvfe+7elOIzC0F6oD8H2Wwuy+Ja+lQZcdhBgHMHphGjTLgQV8txpsbbSvAMDMQZNPy4oTDxPsYKV9m3TQyObHjqGZ47eJHY/dpdeEYQ6whihPVjg0CpWghrdAJrw6NP5bd1EUCb+zXyeAGtiP3Ilhoc3v70qtzWXa2l+CR/HPC02ELedpwf/MWv1z72lAmjWh5V4jmsH2m708vR4l2jdxPi6cXaRNyEzx0ngqtJU2F9dFsbnwAe8o+7DfhOyx4ZxlUnnbqME2ve1cA0is/d+r1CcvGQPkfOYdn6IguAGVibd2x88MEHF3RvePTRYMYpdkkYR5gA4zxuLT+MZpxkr1+//mMf+9irX3MmJ8S333YPpmANifkCBleDi5vw1xKIYiIJHEwS8H1BDZurQbAsGWoYohMlUvbBa5QPKhZ9Zz2HLyO429dUQjpU+NNITFA0RzFIlGTcrBNqZQEMkQ6iNnFPYuiPysgW6m6SBJ/W2d3G+J5QjHD2pQSwW8WUhCMQLEKq8qEqFYP1CZM8fEbjMIxvNozn2JowPlPjtEk1KkFimYqjAeCHccNh6HCIAgyuKp4YkohEASnguVJngSscAPKJFFcKOOslu7tXdKroK54JNAHoEwBXiFIrkEXdkC+GPgRIUio+SVADC0dADjpKIpWwTLtAhw0AeCRezvSjO20CnS4IfcBshm29hpk0es1ufcay52tf+x8u4cGc+kc/+hHAWA5BwTq/s37Diui44/puvvnmn9++0zFvZCFojJb4slszYmltjVHL1B0xwODghCGFHLFpwycVKygYJqnCIJZYdhk2tUY8dMg6nEqYgQjTqI9+9KMYtQN233332QBVcSyl4A2AtWvXgo4jBQbw4VDOmQVb/cIk1t5ir0Kgzl+wyBcwXG0yDJMKtyQRxqdQtEoqDvESD7qwfCBMxEdWERfBMKQHgHPi8ancMMDkw57U5FEiyL0ggViA5SjNvq/QQ/X1DHQ32TG09Vnbcz6sXMzuQWEyT/vvSM6i6Opb5e7g5vCHlTz2c2EZnbBL/u1vf8tor6z9QOQ5QbdSQQzaOB9JwA/IoDDAMlwT49GlkhiBMf8FsUr/hkceUukFRn0s/XBeJNE70BoMrTiI4wMAWZwC+OQOD6gGceK1nqNtnkycgcTxSL5oAT3KFwXYE4fcjEXR8HFhMMcFX05okd5HkWmEl0xIBR1EDH/JggD2vkIXYwJGsEx7Hn/8KYgQDtOXGHmF5u6770b3QYd1qDcQVyqUqb6gfzsaULqsMd2orMkd3sj3D//wPOywV++Yf88997QkE2YgHmMzqqxeeUlm8+bvd8xowgj46ec3sJ2kUosTKpTq27Bhk25iViQA6DEYIJVBjEhKRBWTL7UjGHweYQ/OoUkFEaBSUNDIXFkQQCbEg4WN+6OPPhrOGpoAkAWkqERKR0wYQBKwATPkJFUiBEm+bR3N+Iy0giIeB6kQUv0gzON8+1Hu+DhygQhoAMgHLEyF1LrshWEUFp3a+ChmL0rA6sw1CfeX9lpN22qhzoSuGuywfS7LjfIz4iIoJ6sSfb1GkoetiKKCv0gJbNiwgRdC5rTO2jyw60WSitAjCRx0EmC+9M1vfnMP2NYUgunTHuCCMjQ8gM/kkJl13M4xzdk0UKF6vkZ9rzLZafn+978fBpwAFzCh2+6xUyRsLvl9G82agFGgPIlK8rWQDHvLbtaWZfLJbq67i8rOg++44w4mltn86M4PAkFh4eM0LVNGtVyt2rye3NmUGQoKLGyYSpY3aMKFCYW9uoMUvweefJREsGopK6Y23hPLF/Jf/8bX/eNUAwNDg4888shUsSL4SAJhCdAHeWSvF4tnduB5A3nek8/zNvKh+KGxcLkPqTCj3CFVngNUmD3Tv2xBoZW4//HJJ18IilviMQ4pbOenqkqIoJdRsko8f9FQbJJwSyVHPKWf/vSnXV3DA/1Bfy+vsHPYxCvoJgg0VwWlLBfskbXlQYL0Gvr3lltuKSfbH0AmcpPRv3alFM4Kgomm13sWp70TslDu0r98fsHSalyGL2Q5IzL8MFcKR/q3RmBRxOEoAaapHFNyjwN7wCP9tssypssdjiKJynx4SYA14JTWv2UdW95pj91/32+dvCrHASiwAlcX29tBXMvGoU9mhNN5Lmu0S5SltVlIcnqANRNvGq1evZ7Ial07Xg2QtwOVv33rVg5gxoOtjUdv4nRERSona3zygUcuizRVq3/SvRUfMOXldHGZTYYIHv3Lw7bmrc2sgjgmxYGCu2bNGrbdWPmGl71uPTtG6fuxSAF8MmLIun/l45AlEEYfk1HNg6cwGJT++wffbQsSmkXUANaPcLMRm/90j4TWv3DjWayPd5jG2unUtHI6SLDmP9muduDZF6vifHfcaPShPSpgHcMsqtwdyXTu4WGznQqSdHRglMgYhbrnslh7Bo09L+ygfEbM9vlde8UbuF/z5HP6zzztzMd+25ouNu7akehsWRIEGx0kFCAop6xt2q3ts66u7cuWHfuGt1x6wYqrgo1rhoZ+9Ktn2/M5uq19kTawlxLtpY8qZ3sCJV7CiPd0dfV0NfzkJ/c99/Q6CG7f1sc0Zf78RL7Am9OGRwEJcHlQqE4hjoMTAuEfTIYfFYaGslcqYRUBqgLwWCpjFQUiFS8/TArIyEUSmKwEML/E8JEvMZh+dFfEDZeCoWL/YLE7no+nYu0ZNopRQfGmYjxrP075afaxRIHWjzpNJop8HNti7B1n4t2PW+P4YpN9xymWSPIr0dehbwAQs7uh6ansaLsBwHbe4m6ngGhMpvlZMj9YM1sofr6DWLkU75JGwzxiA+MurzKsWJApFAvJGONO3uhx+xew9rKYqXkoRu4ASgCbHkZOZqgaruGER5w1woqWxEoGaxhclaGJZxtcLoPEigjbFKa3gMlIBSJClO/hqwLQx8IGCyQC4SQe5aAjI90wV2LPUyYArufZZtm8stbSAgp2PN6F6RMJlpKwZGIqDLAAoCOBUBZnimO2OKIpHpQRWDIdhn9IkeTFSJgY+JcPJOEKeVN6OKQEcZYBBHgs0IHpgPRO3kDk/oFUvL2jtam5Yd78GdncAHLl/SPseIGEKcivfPqRnt6tt/78q1yT2d5+KaTKNeY0flDiK6VN6eSCVGJXKr4oVuyFf7HNdJ8A/GChJdsmhCBmjC1zcS6+xOobn3jvoCAREUO5KOxHPvIRTJ/vuefubds2UxjiQYY45eVdKS401XdahUUSAYXxASbfRGZEWBKdODQOnNk6w4XDMI8kfNHH907tjUdQBCYixONUfcrCoxg553yMUABTQKniQXQ8JHwKBgFieYUACfjUKQVEZ0ooEfAUJeBUGArVtJdrP7SgWLC+a92xncf2lrakgtRwkkvReY25lKP78lkDVB+qyn6mqMrzuynmOh3AWd3yy7oPRHCNCT/FBFxzUMynS3yhgVclghbezmhOUvTc0EghwZeMuRSB0cAZF2p+EB9EcTc0p/MlZhg2/iSDkQwvRXPjLZ1uOhT1oOWBHSIGAaQq5aJyVA0LjNIoR7nagmpkY5hF/zJqeVyNYIx+DFA4AnVxGdwEID8MI1IgQgoeNOBLiZCkVCJxYMmnIBAkCbNaxnZwheiz4I0+nwUlIhVHgFLIkZcARF+l9jBqfgIgI7LARJhH4kEHReilYgPz4FjQ3NY6d9Ys3t7h1fyMW+py6JXjhTzUL9NmrDrw3X1b6C6MfdWW8bNQcjt0zBCaikXaOmqIcplQPf+ZkY1trSNBbvV5p82462eD9rav7aLFS/EhNHSulCjGh992w5LWI474xvcG2ht7Omd2Unw0Ep9SwZCcQi2YzUtlwYK5I83JEWb2jFCxeDowuxF44I2pBvSvm3VD1vKlgFLx1ALlJZIAIiK2bv0aqzGbBZEX8La+qMyRQEGAUGAyYGDOlam7MPEOxfq7TGQq80HBVhR0Re2WY90fVZNrGuaZlre8LM1nAXGc9DuBsGABo8GgVXEG47IAsQrGUpNlSMB824BP5QI8ARWzCtdYCTGjx8jfKxLIu36UDqxdxUq2Foil3W5P3Fpp/3AuP8wxMw0ikYvlafWoX9PNXHll704mqUf6kH3g61B1Je73olsxIGtMptubrLKmn4N00X7W61zPtz98Ya0lOOMyu8S9rXUGi7X0UOfvHng2PmivjsRj/QYTMxM0vitj4TJZF4y8SAITSkCvHz1RGujYuPoVJ535k/t/jibzTu1Qj/utQ2rc9jxEASkvrzojgex1CTBfZY7EnAftE54R1c3IVwc1guORqQiQTJP8JKQuYlUkiAnbR0kNj9DnmOd02Mwl6HZg5VkZk6Zwv3OPtnFqP3tVPtxBq8iP++gOMtkVjhdKxe7eHg9HvMJscFigFORHhkvF9swQV0IPkVexZAbQuRyFNo0NyNDIsFDCviU7J+ZGX8sLA7FkSKa4e49PDDOJzeRzZZyxMBM8IYpEPJHlCtApupbOjsGeXkOChHM+ax8oJ9T+qUBgA12bGMVEEpi8BKwH2fFlns60eSR4bv2Ws1cc+8tfP6Y53OiqbPIU9xIk45LvwnuJ5KFDBuHg/MBLOJLVi6/dKelfskPDmJJxo3F3l/qK00SV8dnVUmV8B0EnjZaqN3/Y6GQHg22glmKuZf2qdioxX9wVj2WDUjKXYSk6VKE0pnBE8t4PeJZ7jJ46Ze1jzLujzwn0r7v72ec7yojKU9GuBoBlmYcbLyDp1I4nfLch0r/jCS2KP6QlYJuKtl9SKrEvTAdbOzK8cuO6C05K3XNfjp7iZ9V70r1ftOCkU/C1Eon0y4uWaERgNxKYqv6lcULRrQLpPTM4yS/Fdpac0YXl5FQWH9IdwVCv1DpoDdp9WrnApwzQm3mzc3A6GXMn2xPO5jiGqXQ79c0JGXYqEJ4BsusV3UdTWbfzGD4IqEsC1lj2WhIr9yC5Y/uAjQNuVoBeNq5sRmcGhYDY4VDIsNlKbdGlOF9u4aUqHlxJjRpOJxzloJOPIxIGcYn2oT8m/t2w4e6MCy+QQTMeyiK0gagWXUTM50u+7kxnNGaSIbaUE4n+TNYdfk0OR3xUuOmyLYuKq0RWnqO/JoE6B64HVjA0X2vB1vIPshqrMD5l+YGowlJiHXSGy+5T69KVrEjCxuvk004LincNDQzwvXiuKzvy6CP54psAPJgLmGBtlLDXK23ywNfheUWSq6qGSzs4qV2+fPmvnrXcwmy4I84xkVQS6IyM0OQGKag9eH83EM1YblS2/BjpDCdykQQOCQnQnnGmWs03DU0XYHOcvXUMCDB/tP5VVtzqZZPdeYEg1PCZKFSJSklVkRM8+sFkApiqJNi2XBznfscZGOJxNlJE7sBJgCahZubH1VpeZB5N1av2PYBvPMQzttNWiSGsmpXPEg76+Djf/EgKk2Kcx5EKBU+cANQUTxIGMaSGEUVBlAEmIFxgCChVNPE9HcHIt1wrN0/TxcR/GIAwzHsX5lniAgsmcQQUhg0xgE++SsWvSxyC7szMcgERn46iPkGAW5937tz593//93PmzEDtMg6MjOR4RxnKOGjyiXlQ/uc7fA7mscsufDmRfrIsVkXWi4jiGEzFQZBag0lxDkFSfRn1SCpO8SThwhSg9IpXvAIb+tNOOxk1vau729cgBtDcZXLhhRd+8IMf/PMPfaiSp9WpKOBDGXhVgQCUkQcAmBGDR2Ehc4thvVKZA3gsj6KA4uEWSITg2QadR1IBkxOk94lUuJJuWUsCPt6k4EYt33h8qT2dSQag43OcJEoENlUJuNqSlnHVRjBuZ9W0BJwq2jWrMZPSqeZy8MIjBN9N1LDrloVhBDAcqfZeZaS364ppipEML4gU30uecBUNxhlGGByBcFJ4jMJ6w0ZqZ3wMDERIxadyMZtDgxMQZT/gCF1YfigL01dYpMhdxNUASBLD1rnUqyo+ZNWtiAcLeIjXpa8SKVXMeN48GzCP4/MF+CqOYPB5BBdIwuJBkvS4PJIFOo6dMlhyJS2bSgsGAHAlAQnH4xIgFUe+xxxzjNDDqYQxLOYGuwCj5r6+MDqyJxXeiGTxy1AzNNTU09ODQkTVYuUJWQzf8Tdv3jy7I8B4verbGioOwBDBB1IvJFQxAFcUDf4BkFiqAHhEoSEEOHHVZ7ISDFnAmOgT8DIkFWoAC0xVSSpOMd4nRqX2SQbk6kL8Q5ZBQz7xHpGAAzTJE61wLQDEcYrHF00fgAjMa/qmAjopleGVl3BFJ5y7D0NTMD4mCuwtCSBYcyWrOBy1YG3RWhdjR00mY1pHTerhHYH02mYEV199NTJsbrLv8PSszfJZtkJoC/rwllBU+hcrgeYgeOKJJ65/zZsb7kel1hpLvFj6k8S3AeMwHpMpPoKSb4Omm1MRY4NnZdnrIycp0ghstxJA4EiYqRTTlckA+2oSPDWis1hwlTQxEeB9FecLWX7NTa1QKHEuWqMbq1SlW7yCXYbk9T+o8eo+87SJMw2nas4VjiE8ngbW/C2ImaGVDlv97gsoYVIyiZ58+2RmiwAx4cIUrKqYVbyFH5WpYvJTt36GY7N+ZorOFBeh0cWcMMNZTBh2nE6e3QlpRYmRBMyIxB2W33nXb2684TJ/QkM73/+tjIHFj06HW9Wo7LWlDscTli72guJxSmNvLf0oRlKdpP4ti2ts32As5zOiJPm6qKo1L+SwemLsZzGFa0oz/w34LAC1yaXJsm2aYIuTHosSJFP0bz5XhELR7rvUxp1154ldWGl6yFr9W6VegeRAA54pui99WBvyHq2npkDluT5Lkf6tElf0eBhKQF0Jtfvgo4+/46bjf3uf2Sepi1W6z/6TikYtr1PCg9X+Y2J65BQewOtyJIDdgtXFjSIlAS89tuInr3/BKguwvEHBOXv5fplawWppjG5FZ4Km7kYAUw92NWjhqOxUkM5x7dL4i9BqssqXdTQHXthOoIyJgavKyroafpxnjCJLQapSGLuVo/KiVDnOaeoxyJRd5yNFu+ORJ489qpRBCMeP4leAwaGPJ+Nm/6A3aYlBMvhGcxRh/JCuqd0z62eo5u0T5HbFZbkGx8/Ip5T5UgW62FDQQ0UBL4Hqc1yfEAUkAToS/R/Ho3wfryQ9OpBwuqLlW1eq/MLxFm5sStOfi8XhoaHueQuDC182c/YsLD+eqYab8JmMH3vk6R98+7ZdWx+8575v3/fwZzvnvBAvdg33b8jHSvzArnDIX+OHOK6U1UiRyQxu27bpq1++fdf2+PZnH7z1h4/nC9tS6b4g3l2KdaWCHn58+43fmBJyM1DAPQMULZEdwerZvuSL43izVOy0UMX5rCsR0+gvA0zd3zRiMWLlAEuAibezbA6C3qGh3sGhvl2DPTv7B7szmSGuquOa9Fw+NsIvUxrOBtwSZyv8Ym4kbuvtstGnjRXMHuwSKVuYswngymTDApsC/HChGIwyk+z+qkPRSf2WQX1JMKngNlp6onVG0a8G1D3SsGY/BpygkC0VuUgylioV4/lCKZsvZoqxLD9hFuM5brDOx4CpvzKvziB63mcSYB5G+5mYPBslkxlmvdFMmBrErcU6F47fgzCs1mKJci19YmqBJ4ipSxx4T1+lMA3nnJIoMvHgMpFVEvEEwGNi77MTiov3cRYghput/+zP/qSjs8Xpu3Jf1FSzra0FOn/yJ3/8yhvmHbN0ayoxu7V5vjRsPN6AkdKuro2zZjf/5Cd/dNVV58USW/jZDBohxbqDWCGIby/FthVK64rBjmLshVJ8jfKGGQpFTgQwsH7Pe95z7bXXUsVYaFUGCgMkFfZwQOJjB1ZmzlGBAmdXbPw9+OCD69evx9x58eLFgAkR3337pmGec+985zuBxxQMKUFWMAjNmcRw75997gHf/3j7kgOxZIxxyqb5LEr0cznX8agFo+WceKgDNMUo0fS+JLO3iE+Rlwh88hIwfcrd56M/3ralPSWG+AUpVruaRwb5hlwmOZKPc+dxBiWLZrKzUK7UonOYVnI/+x7C5LOeRpD0Go0EXmerSEm6FSUqzeDXUGQQQa0HTdzrHDTyy8QHRmL9mVgyE8Nulbu+svFChl82GOBt1oHerYVdvaWufn75bhNz5F68BBi4wuPqlAhqOLLpXTKJDS5jbC26j/QBYPw4BiJDXC2WYgBjuOZiaR4JAImPGw/exwNDuQQ5GXjRD2N5UuFAFSk9qiwqnS+X+i86ob+/d9u2XXwtgbd9kDMGD+ggFzD1R/HxUWTEYOfhdGdZjxADNZIWLFjwzDPPUHay4C2sMD/pFKvRbNARNCYH3Zw2x/KXnx1SxbC3RlDFVPyJ5oZnYrGtra39PKJU3fzcLp7mu6WJWM+xi1rnzMJKFB0dt03BsmPyUBgZwXJ9MJfL2qqX6xRsUBp1cI6QMXzXRzxIgGHveJR8FPDV4QEUUO2H28ZoBhOGRJAsaA7Ot9lCFR0eEaN8DyPBQpusnfzL6EYo5MJ8hqLHDcIPlVWbDB1ERDwA3Ktb96camYpfm08UUy2BBPM6N6kr5rKDQ/m+nZm+7lz3zhGzWWouryQLsSI/U7J2Iwg3oye5JNp+ritWUzx0nlmPa0muItGvabp57N/4lb/S4FL03YbhfC7eEAylt+VbuwdSG0qdXflkdjDDDfN89Ylf0f3cZlMpmTDpodfHjBWHjuSikuxtCWDYyI9LerpLuV90bzjuumvSdjJV3lZWbq6D7u2M69FjuEZBK4WlsdRTPcBDKk7KEQ1FqaRGCUttEUPYa0MeBVylLg8pceyvwnCxCzMQckO8yLPu/KGWF18X4dkOYbZQwjEi64HDAU+TmuUGaB6Hhgcwgy4E24rBdukCD1Mb4LAXCy0YBp136ti3YU4lsMm0CvqyKPD+nifO3It473j0s7FCabhQ5P3JmM5M+YIgkNwIDbBIcZ+WSsfc1n4OVz4AE/w4+nXEKjdy+ewnDFim7tNMdpy+Bw58EG3LPE/F050mS8PtbpWBOTmOXCSBvScBWiFTwCee2nzp5ZcTmNFiE8TR5eDey2hiSnTkw1D/7lYmHoAxE/noQIRIpihIjEdNVHzAw0eBCSQwef3r9ZpTNPSV6uEXBcgsCQ0OJA6wsC52WObBDKnoW34O0F4LR40OZ9fzKxXy/JgOlwI+vmu5GII5Px0uB7g5ks0aCKJ/+wd6nfUzYNrecRjje6a/nAafWP+614XhocrZ/hHOLoZxTtQwiubHhlPVT58S5eXjur9I/0qGkX/4SYDtkfIOCT2I32AQsD/7q0cbzr3qGr7IS0z1ELNfZMSQ4vWvxrH9ku2Bz8QNy2PY0HA9Jir0QCojPMs3ArhQShScrAS4GsaLDj04wfq3qnY8lqkdcz38kunwQRK9p/zIEQm/ZFBkQms61U1rmdkSZjueQxgOXHN8/c56oVaLFuBUAb/iRntrJab8V520ogsNke7DYxi5CsWnzuycQRKXMBf4+m8sadbPIMrswelTgryZW8e5LwmTGXqcxS8/e2MKUfDVUS2Bx/owWfUjI9bsmVwWFaxpgLGu4rtNYNsKdgcSineJYzzeGfZzgDEJk39wVhZ1Ga4bqdKNWc9PPq/DElKN87As+uQKzcyb/oYDXL7wiFeSHsNJkyNchtKqjJPd+fPn/93fve+vzN08Z04nySiP0VGshqiSfM/HTOpHP/rR2rVrH3royZ/8pAdbKEYZjl2N9RDz/hF6GhgpBf38hRde4LpoLKW4h+zee81qM1y6mswtAvZEmbDIzpvfOnt2+ZJRIj2HBh25SAIHlQR82w5z3dQctHUkZxzVNOPIpvajGoJWWr61c5wzzhh9udmv7X0gTKd+uLxDPppo+totAaA/GrtXQ+r+jACjow06obLnTKoGKOVZVyZ7lZ2IWB0JUPuTkbz2iaiyOiQqUXUbkkZv+RXAPfw7VfrAy00mPyDrgoX5r4JR75M6w9ejYKogoUxMrZyJefe73/3Wt771oosu6u3t5VFc0PHdjl4c++E3v/nNN954I/ZbpJKFzbWd7oYgNse4Rc7VZZ5IsKBm/8c6bLkYVc4+++w3vvGNul6aa4RYT6LZfa9UochIOaojezKeDQBIwieJAL7CqPvPfe5zF1988c9//nPs8zCk6+9n53GUGT8NELynrAA8w4mxX3FVAP5R6f7RByp49tdHEhgvPgwTDouNcEwUPogkQHVTg7avpE8VY+hodz/ZewtqCQdRWQ4Iq/R0ZiMnv2T54vOOWvLSYxaff9RxL5l3YDYID0j592WmDH0Ta9XdZk4bFgUNyFXwpCrGB8IARPrRPhwfDqubwKdYBUXOk61EjI6xoMgBA1aY2nhh4Gvp1AJ7agSAp8g4gfmkMBa7S1zSQ99H2QFPkjo+2QEvdA3vMoXxuCg0YFg4894OiOhfXunp6WGTfNSJCBQ1P1eC2KDQnIYqR7GqvVovbVgihm84/PM///Pb3vY2SOGaeGM55IgBTPIjrPL6XIghPGvWLE5hFQ6hhnWcraZxpFbaQhkQxqS4xXMYfbdhmKk4y8vDh0kRL7YF6WEUINJJfpTVMAB0vAvHjxeGmspYC6D6rY2PYvaPBNhnP2ZxU7ojOPaUGUef0Lb05PlVuoOKZlVI9VkgcjUSUMNm24375tmLI0Brdy9smbgsXHGRACuSiP5OTQJqRffffz+abvas2aiWiZb6U6O9e2hyFwOAcmOQVpEYl+B2j3yQQ6Ai0VBSlL7/svTm7WKVTAMjvkQEJE4oB3nRDzD7tC7kLCbYaTn//PPZCZkMT1QT1YHzwMuWLXv1q1/Npzx8zAQB19jt7Ts0I7dZAHnB+RcuXrRkApSqJMZ9Lj+GAWaefFTz8ssv5yslwPj2UwVf+wiFvoH+hlT68ksvW3bcUr47aOhjf8LyR9G2jRSYSRXxtED8VCJ53TXXnrj8BIz1OQyWGZawJuNfcuml17/61e2dne6Fi8lgVGDcPIG9qhVnn12JmsrfUrDkxBOuec2rZy1YMAU06Vkno4VHHfWm33vrFHAj0EgCu5MAFhirVq3iS3ptiaB/sNwZd4e0d9I1IonW4aZ/vQR3O3hSNagMDr7Z2RYWL58IixkLMpTopKY92SgwngRejP6torl06bGvec1rZsxopzpUIwKoelSklBf6V2/FAyP9W3CXUPKOtC1HTRWO6veq7DzxfaR/Xe7GQ60ra2Gnf7lC8tqrrznh+OXNTc2ml8ZxFc0s/TzqR/q3VrxRzGErAWa0fFaLj9XP6JyBsWb97rePpYPuYEjRyoJhKrzE2Mc5H0jylDSsNO2ozjkiw/GeReBZOvGojfe6MB44CownAT+NAUDr3xUrVowHXBWPqtEujeKXLF2K/u2Y0V4B054kXcrWiXJEUa/8iKJz2eUzQTDMRcql4svOP+/4RccAZi1hIuvlCi33V/V+0kknXXXVVRhBKI0V/cQUTFE60K6e7ubGpisuu3zpscspjhlWuGW9aVFTmbYdTQe0xyrn+IQG69+rX/6K45cuSyVTrH+n1A4pKVYfN910U9WtOirFbn04AvG8885jI2i3wLUAbBpcf/31VHpt0ngx4dIh7be//e3jQUbxksBo048ksscSCDe73RFB4JUfl9jFitkR3lNszWR7r3vlFe9462WXXzL/TTcvf8W1c3m/ye5WtE/GYGBkPpTJiJGjmIhzN5W90+/usuKK1lxQmDdv4U033XzK8rNedc31p54SNKTjHAazfii/KBA6gPEcuh0qo8n5MZuDH/jgza++9uXNnTOuvNayHhkcSpfyDRwdc/8sd2W5e5KFy7k2v6CU5lcscFUtdz83JeKtOe5HKHFfQkssQPOxMuFn70hwS5cL+JyjQCSBaS4BWqx9PsF+AdeSxRU+8czjXvn6l7/pD97wxne97i3vvum085bSD2jlOfeJB7silU5N/45xMzotnxRb8aqoilGvqBReQ4H1EVss52yTGQPLoJS0n10Obdd1kMLJs0At7EaQ8AtATBZ4LDs3qlQe7K84tw5rYbs7Uz/LD07T/Mnm4n3ZWG8unikkGUs44AoK/M0z9RkZLg7lEjF+XL3pflgD+cyMfuQiCeyZBOgafvK6ZxRqsSCIIx69xvyVMAGWAd5QmEdS1St936ylE475whe+8JGPfGT9+o36xj3k9Rq/TdJLyc2bt37iE5/80he+/twzGxOJ5iF6TNFUJp+1L/LRhPKCwi0rSm2B/Sqz+3AeLqy1oo+eO3cuFs8PPfTQn/7pn/7ud79j6xaDLcoCmF/YwIwzbKYo1ispnWyJCFiUcwihEhyzImKbmDXqFVdcgXm3rWocio05zomaD3uuwoGJU6Hpp/4TQ4ZpTj4MTbEtn8cp4YolcD36lChMPq8IcowEyl8qwI4wzY2q/PKlbCY/khvIZ/qyQT8fUug1HWSfHMnzy8dS/NyWt3U3fvEgYz93deUYynv2UOZHyKZmpVv3jNhUsdCl/HSvpHzFaNYRLzKE2Q3QijTIOIMKF3Sm+PElB/uYQyzBr5Doj80Jrr/p3BveeuHr33bx637voutuuIqxR418qlxF8ONJQKMrI6ofgceDDMf7WmAcDsdPJuxx6wKLICOYTwVeA1o4RkTwfSSBiSmHIRWuS6QWDJY8ZeU4QanRX/biQ2NpKNOT5wZlbvZIMvM2rS1caXDClUKxEC5vJ3V0zOQzCytXrmQ3FrsWzupQZzNndoxliYk0v0wp4ANwNvv2M3Cqoolb8krFdJDnx6W2Ke5bd9NjwGzzMJZKNbUtmN21cG7PUQuGWxs352MNuSBtr0DZhJwr5uydDVbZvAQUroIwA+IfI28VJ5ykElk57WUqez1J4TDMxGFQcILxgbooYs+Bj2mBLkfzxIwnJWpKrUuQSFGrpTkePNREFgA/zVAk/nhYUfyLl0CteN1tH8Ukva/AxwUyM+d0XnXjeTf+4fmX/95lL//9q15y+Qo6mfpCPlHkV76uuLwyVZ/yPenFMzj9KKik5fLCHpP+vPuCk7Eqdaz9N/n6NgVDAmtk/EQ6FeeWkzRbY5nGWDYdz6fjhYY4Ywdy5f5sLquPm1AjF0lg0hJAlQDbNdi/devWU45f3hDE+XqgDqtoSQyg/jdpklMA9KM9gwnrUBanmFSi13BYHaEHaweZKVCf3qBIng0EdDSuilOOuPgAhf+CAakISpDac6iCjx6nKgEkjANr4cKFF154IeeRU6Ug+Ne//vXMD/ma1mTQyZFKZ6fFn0Bf8NLzLnnZRZPBDcNAhPkhB88YbVx66aXhpEmGuYPqlFNO4QiZEx9QbIc41NMVHjWYst0lc9hdkS+B1772tTRO3t9j+8ilVHuiUB1beT7u2GMvOO88fm7iXImd5N9S6eqXv/zmN795kuBVYC9/2aVXXfKy9NibusL73uOFocNbkf/nw39+5Lz53t79EB6dquQWPe4jCdi1MIlETy63a9euc89bTG+k76B59VOmE/emF8PY4ax/w3KjI7NWxRFJWI8eAHNnH1aAO4wJaPRDhoyKOAJVYNHjeBJw6nev6N83NjW1vuc9fzpeRj6e2sHumUf0L2YMBIi54KUvueRlLzVzAvthAM0v77Z0wv3P00AR2tmIanxf6N/RnMbuhKhBsk/IJBmYKv3rNbVHL2uxGp2ukSTSv15QUeAwlwADAb98fmTH2tUnLDuO2a2MNfe/WOjdGCaiR1AurH+ZYBOm4+9/TvZpjqZcnSMXKVwCFJbFftVRNVBhTgBGRCydAEZE2hyQzsWXC8NH4clIgEXNJZdcwmE9sp0MPHIOg7n1r9O/VJb9zP6QaZSbSXHXjCkcprgcxyST6cbGZsyDBvMcSdhXMlFbF1xw0SWXXAZBq2tZGdkH7f2P6bCtT71Ty7GJWjx+zTXX8AFq3gEmlUdtZHnIiQNYKZx66qknn7K8EGRNoQcFbKDhpyq7KiIcyKhNovrpqn/yJ3+iPYQqmVRh1T7y0vJpp512wQUX1CbtNoZiYvbN7Xi1u0a7xQWAvY6XvpQJz544msqf//mfszHFNh34qos9IXSo49gWSeQmkACNWH2GNkRAnQp44n0SAZo4oxIjw1SdZslQYIOsEPQMZYa6u7vZUN4xOC4lyxfDEXdI7Dk56qijLrnmmqBxYDG7TldfwCfvUT9cPRWsXu0JeeaJEavaqD3yyCOxgYaB4dIO7uK69toVz3w5pYWHxw0HyJpRTNRQ/EoipqvLXrpta0NO5jxKOOwjo0AkgekmATXpCZqrpiDFhpi9XdCc3rBhw/NrnqcUoNCVMN3wzvc1AjgAFPAAdQLVZyujIEY8ZOMymvCiQ3DFwMWxrcYTMjK7aLY7mNi4iQ+7COl00hjADMvmLhTFyoL/ojOPCOxNCaiB4e9NoocKLRqwtfNxPsI+cSnZVP3BD37GZ9SWLFkChXDDR/1B+dhjjx3ObWWPlfGBVUO4a6xZswaUI9f283GGIDhl4ozCiIS5kpm5O3r5O9/5zl133XXzzTfPnj173bp1rCo9HcC8Yy4BPzzWptqcoRKvnq5HUJjcszL52n//99133429dUdHm0cnAPO4uo1K7Q0Y3/CADOP6eNHhsQogHE9qXdyq+PFgxGQt/TB8OCxm8KtcGCYK73UJIO1KM3T2c+68Ftu/5ubGWGtmZKTU2BHjttSuOT3DvQbw/2fvP8DsOK4DX7z75nsnB+ScCBAgQTAHkRTATJESSUlUtryW5c/Wrtdv933v7+/5+/v910+7tnfXab1ee3ct2dpVpkRRIsUcQTCAAAkSIAASOaeZweSZm7v7/zt17vQ07gwGgwyCXbjoqa4+derUqapzKpw+rf3W77pnnJ6LBiEsYiaOB3d9TYJryZVdEp91RGD+RVPf81UReKhCZvz8VAnj54Ly8TdEMO8YVVbpp5g17qdoopYYLFcx63UMzMFHACtaxeN3rSAMAIxZrpoo80YT1yxBSI0rEror+1z+0yAGP7EqAkI2YVGFqEgkxqFDh9iJpriurt4qJ82SEYzGeowoGUdSHkxkbo8lNKiwLoMqzJhQi0S0xeU6RAo4oZxQKonO1Tr6BamgA56VOEzQMJS1+i9ghOrUY8fvyKdjp0DPUKjo6JHwQwCVvxDpwwQfka63oxLpZxk7AgaYY/ZJBJni1BIVOdexMYRPzwgHDJ+F1cpvDKsYO0xiaZr+eJwWac91nJGCPoZI4CG1ZpggN1hEI6OYkw/keddCTxqs2vpaXtX4GHImrPIZ4UBdIrV69eqH7vlUa3Nre1c7ODl3QmpzKkXcnAydkXKOiwTpwckKm8Z0Y86HuKJ/sQnj+0jsXdPzCRelJF+xYgX75HgA9R0/M/dAbGKcumDBAl7Eevzxx9krQJbOnz8fy8tXXnnluEwMH4yPA4jTxYsX45+FCdhLL73EBggnqZxlcjseBJyALFy4kLZAGv+X//JfODxev379eDKK9LYs+jkfAMFsetOmTVu3btUz1PFk92E4+2dTiGPIH//4x6fskInTHDZqcAWNamZeCm0nHF9ADuayZPn1r3+NDyp2jYplNnZPOsBzmM83DU46p2VNNeGUlR3v/DOa2Jv64Q9/eLKlMwCZalI0defLqGT3OYZ0OllsIXzIAfoPtiJ0Kg55Nm/e/MkVl7Q07j7aw/tsmIGc0x4FJR9P/audkPF72WWXbdy4kVtYocFfdCOr2Sf/+c9/jqYGADnwxS9+EfF1+PDht956i8+zIJBZsIf9eZwcOE39i+YN6l+aBqkeLHpUaYzyUvODUfXvSU1xz7b+HUk/KVho8WY9rnASsbjqX05/SjiUOvkQ6t+T51mY42LmANsrTMUXXX11U0Pj0d6ec6p6DV/ROFhbsf5lcYGXViaZvK2BumGXmPnqSIHwkWsMKlhFsypZxDJ6lo+bscK9/vrrsX5hWcEjEjULb3h2mKDrBUyfcWHL8uHDDz989dVXUcGAXQT8qWLOWb1lycnrr/CQSQu6gBUVmy1Mcsa5qvLXv4VSifXv8fSv7h1REXRrySlxWyqXGGjTJ0895fWvNjTLVVQwHeb111/Xd48rHcZsWJ1QlWPHzHu/dDP4wGKW2Uhw4U831Y43sgnYdEX/ppMpVn9XXXUVXZePKY0EO2HKk08+ifk145qi5UjoZAKbY7yzPc6WGomYVQZ5cUH9s5/9bOTTsVN0/YtEYv3LrhTAPqPCARhkXcBkL5gcxo/DAe09/nJLoUgkGOF+nGzHT+Zj9nLkGIm98NIrO/bsYiN1/46N69ZtXHb5Xfhr17cq9KqOryJeGTMu9IjZ95YnOGQtuaWB3vWrX/rutZe/3XHkSPehaLzckE05uaic2krhlS1pFJW8FGkGQ9TQnCo7sf37drF5/d7a9skt9+4u9L28an3Zm5VIp+JeNiaGU5WNM06QK7MNG9kI1WzDObHIBMttsuxcT097utbKDVjZfDZdk7XsovzCEHLgo8YBRoeD9ysmdh79XM4yiePomWVt+0Bbzs629x/FpiGZTw56vRUv0fhD9zA4cnhFyVS37OI2WhKZHYoPZvELZfzPmYHD2GHkghrnjsQZX0NDvBLh7Wf8ULqMch1xEQ/7Y/G8TIQkIrzfjFMu8ZJpSgRLhNe5RGAQrQo+8iEP0FpeBNGBjCk6Xr5s5SHYtbKFSM6aYi27eWFf9wE5A8gnmXC07+mnLnCDD8DZUbbg3fiQEz5KonZ+gLBAEO6Frt8DDAmj55MDDOQqxT1OalhyTJpUz3SWSbCfhTjfFMW+oVxyP33/AyVrCnPlDZ1xFKJntflg6cTcIwftz3/2b5cty9xz+x2WywdNOi23zor2+zAsb1g/uy6v1TJ49T1nmZihlFltMgyxa2QxyckfKx+Cn9FEhkc3j3R7l5oSjgUbvuMR8owrayrO1ViZfPe73+1oa2Opw34lc24Sh6FPI8YiRAQnImLI+Ow0kJ3hrKyIRKoPBW415QwXE6JTDni8qy+BU6SSjSpDmcl2sGg4QsmaMGvyp75wNbommhanHR1Hu595Zi1zXX5DGlP0Gi/qyA93rWLvIW8wS/aTCyD0d6I1ImPNifD9BQ64Yvx4uxAHmcPjalz4K99jUVhRx/ITmnXerumnf8VHrrjJxW22+Ql2vFQKC0tMIw4NbOPkgPHLWt3rawEwmpAujrds2XCg5mW8ecrM33hPIOHklvRk+JgHpAQcQKARgurgY84Wv/rwh76nnKGbwSUeESFlhIBl9NnmY0Je32AWZYEzeCsa44tHTCnRT/RbRSsdWMQ1LxhHzUwa7YUm5iNFJSREaSCb7e6d0tBc7htMWvGULE4llPhCEWOQoYy08HKWm2V6LlNoUmVub+FHneeC08y6K1964Ty77MSNAiSlUHIy9uHmdHehryvtdXpei+MkYjJjrwQ25jDCTqVkDGrfoLJVupdJwglNZ/w8fq21AHCavjZsUw6ADwxMFXyFrHH/0UYByWniGXeBFcDzVe7J0vlRhw82q2lk1rYO3jES/LWsJCs55qOlUjaa6/d2oRjbIjVo2B5vEO/FHmPIKsV4sZeBGQbDgSin27ztzDba8LJXxr6ZjYiWlekLW2a4jzeL4Xxdrq7OKvfiGEBclvX3IcB6c7FCzBUPZ2EIOXCyHMgWxfSTZRp2t7tfa0/JPPOcBro3J1IESmUfDEp4+0gpUA1IHJig5Dmn9J3Rwlj7swxnhX7PPfdgd/Xmm29u2LDBLwF73P/6X//rN7/5zeeeew6jK9YvK1eu5NAXZ0tPP/10aADtM+qUI0wm3zUBDPh+plNhecA2hd/lxsaMRT52b8AwicIX1DXXXMMb6WNn0ac6ccVgjgN9CmXdtHz5cnEoM76AcmCJxZW5H6ennERiB/bCCy+ML/cwFI6cy66DQxyOkHEFzQOOgcceWfqUa11Nbf/gAJ32t3/7t7E/wBjrFGygH3roITgG3/gQGYN6mLJxxDiF5e19HGjxnkBb2/Ce2DiyyhtZDDqaj6LHA18FwyDl6JeZuTYZqw9/JVIFGd6GHBgPB+j8nHcyqpkx72tvb2ubcPXVS5986f24OcIBg46Nkxsh4yl4BMzHSv9W1R5pxhsRKAKseXilRGWdP+sAGH97d911169+9SvivHnClxv/+q//GmXx7LPP8v4J8hyFoqKgCnN4OyoHzpT+pdOiSq699lrjCGa4KFqwSq1om5JIs46qf2VzaBjBCWJnT/8q2fL9n2PVoqEfzW/VZmoGsoPM0lX/xqOxoA300NFuhf5jcQxXKtS/w7wIYx9vDrC7wsDnur2jfVZuELewT7/2um6j6Hz7nLEHIaZyDFXC27C8oKhFq+w6Z2ScpYJUoFEXTpPZlueWZT6CFIHM7Sc/+cmbbroJZcoiVzcBIIMVCs5uV6xYwZyfdRaQHC7jg5bV07/7d/9OLTh/+tOfhpr3ZJuMLYW3TSDjl7/8ZRqFl2mxBGDXZTyoguvfB0bTvzQuOMWyz1gGDuFErcn7fqPr36FzmSHgE/+lFvQN7S30DSYVY+cR9WkCNlf8pcpUhPVvbbym8uBEf6hXTTrDO8DLll7xe7/3e88//zzG0LlCHrVL1U6Ue/j5v/pX/4qRfuWVV65atWo4dXwxdiqw3OD9bbaJTuEVYla+vOlxagbQuv7l1ImtEoiF5+H6d9RGM3v3oz4JE0/EAdUTQLG9wjtASPyA+6QTZR56Ti6N4kDil49vXLDg7fKghS+3Ky6rTPL9UhQsqGL1EVfCgQMHfvGLX1x56VQ8HLz6qnfXXZ/o7neRX0PlHPPXRyKyz3Uhnv2p//E/Nokny8GjLB6XXSK+tRSMK/iD+RlOekvFEW1iKMnLIjOmLLo0yWbxju3dmhLMEsZDDnzkOFDV7aGf0SomiV5Zhy3TweNVys87MlLJcsyQOh6ac5GOBMAahJJURzbPTDPHrU27MrQHoszvv/unj/LUiTpuwo1GeCeCM+CKBDgX9IVlhBw4GQ746snP5Ksw7eGkmxR5XjU8uT1W1wkMk9e2tj52Udl7lfuhkM3KB/VYGf7pn/5pf34Vb2cWCpPKbn/ATHoIdMy/v/u7v/sXf/EXP/nJY6cwyaYiBEVPxQn+LTXR+ppKVS4+AGCsbyEelwxr1qy5ZP58UoDnSgChHxmT9rEeGnIuUEGhtau6QvBY9QmfnTYHZGYYFT/l6FCQ8aEsrhhJksLkk+6Xqpfl8c5de217rd9v/WF72uV/tBFI/+QbUKMFM3ItRjTc08UIN2j1Qm/e6PahPElcUQrP4S2dfyg1/DteDrDkUb7BwPHmOdNwWrTKriBu+gCPzvtg8TmjjFJ6iDP26cCjzplzuXJTU8rP6FdKa0QuRUJEcdJxNRH9xS4PfZ4NQUz/MZsQ7zv2MZ9BUGyGN9VNptj84vSWdwf8FD8yTNux7c6I430DXoPiWi7LLHpkAK2+U3QK62LysokG06pIHVnKqaWA1g+nhuHUcvmFEhkDw9hPx8gYPhqVA/ATC95cLl8wW9CVDmveTkKyMaboz2QkMmr2MPHkOGBbd9xx2cyZM8tuhwgQL/PKK6sO7OUVxzCEHDh1DjA4sUfk49oT1qzpLWYrJzoj9mlPvYAxcw6rwhFg533uMYKi003gK0yg2LZt265du/7hH/4hiA4hyc7AP/3TP+EKixkIDq6YirCR/o//+I/62d8gcBg/fQ688cYbOHOiFcZp/RwsUd9Aw5aXJqOX6vQjCFAV106u/Zk42SkUN+dVYGPcMq3ReSTWzxjH33DDDRjhjQFf9Uizo47TsbS+Ca8GVcwhlbbjTY14Ss/ktJg+yZuyVIEjIc6eT8H6GZJw3gYNnKeCE4RVRI59y3YWwwHrEPbMx4Yc+ZRtZzamcDqApePY9R2ZlxRKJDsORNX9M3ObkyV+VLRh4seZA67Zs2KNxzBYu3bzV7/64NzNuw8d6fdH+rlhjg6HUcu6aPSvymcVcUgexi8rfYYwn1/ApoovMWr1FYBay45iucx4x9wESP1EAxMkJCfO+wHG9/+WLVuI+PJzVAaGiWNw4HT0r26AqP4dowNTuv+UiMa5BvVvlenwGATro7Okf49XrtKMrYqYbtYAAQAASURBVHM5WxbJ4Hmqf4PWz8fLOzI91L8jeRKmfGw5gPJlUo3/iaH177tm/XtO+aEDfNQix3g0KvyFnEhd0JUEiNT9ZxQrqhY/xAi06667Tp30axWwT8X8DBu2P//zP8dyDLXLix+8wfj3f//3WOv+m3/zb0YuAS4mXp2bdvT1L1sNJ1tizDg1U/2LTvKzj1xFspvET5WszieLOGxLpilUv6pxsg1Ht8E3HOa89BC/3HFGoCSdStGdMLjihd6qosewZmauSKflpV8OQ+mumEdqT6ZcRTKy4qOSxM4PfuV4v33Up2Mn0l5QfuTIkVNbez7zzDP4w/3e9753UgQrSf76l/fwSQnXv8drqeMa8B0vQ1X6OLtRVa7zcstgpicFCebWD0GS/EQiwfTjxQGji4OZfnY8mFHSjRGhY+X4uU4sGkkPdFqb+zl8sq65Zirf3y0zvgPBtR1+x/pYHcaaSU78zH0Pp+Zad1yTeeXFrbnepqOD9V5ijuftFyDPP0BlTq7bWOyiS+0gu1iINDVOefgrc6+66ob31j176aUCgrwQd1n+agNYfuQ2hpIsSjkmI56M9Dv5tgm17X/+//va1377miMHd/6P7/zkyJE3I96/iHiZIe93JpvxpysfjBoKWjltl6G0C+Wvz/hgh7lQiAvpOGsccE3/5I0oOU6KyEjREZAUZ5NONG1FUl6hOFCK5zB+tpNiK8FP5+UVojycMpKgXuKCAoEUEMrAwZ8ywDZ+ocU1tPEiTRbewhKvz/wk3fiUqqB0xdulDFWNiIe8yhPzR4iQwXicQFls+VcGr4ExKRqzy1TZc0vSz7FRcQv5WNlJu1bd4ZrG6EB3MlFTsFosq1+ye1Gv7Ei9Cpa8HcHnKWTw2jlSIhaDXTx0mjiXStwynsYMhyRxHGGMiowjdwhyAXBAOqo77LNwJEVIV2C4+o98eetH9JFqBz+RCCl+rjMSUeSg0rIqOBlq9ESkgV0ulXNLLpuJI5+DBw+6DkZYgHAa5E2c2NDY2NzQ0HTnnXfPW3Qvc823X99p48HSBM8t2tG47cWcEk7gwV7rlGOuE4/YKctL4BMTR/OeRBI/+9FPeMVw/bsbdm7f5broR1gHZ8SXHBF/9gx5jsOxVpGZPRQw6QBYd4e5aqGkBxnrp/tPQWJ0d6WW/OEtqZ6uXt4UpDiDVvkh12GgU4ppoVxPKffZzaS0VfHnwiT17DLiDGMP6jvRnqJ3JCT4T48qu2U7kWfwO/mcKE7tYjErFyl3l/Yxf+7PS08eLPQX0DPiXgMpUWTQswlj8ICfH2MEvFyHJ5Pm6YkvboSPM5DL0Gk+UeLagrkYKaLD+MfPtvDEXnIY4/781ei1il42heCrlr8VXQUtfDPK5pMo0MvbRF7Ms8xPMOdsqXvMkhcdi3aJH4M2Ts/zYmYyD46hH6XIDywVxGYOoOJOJAgCIOJFJZdX4rsvJjscFoCyVSRfPlrmF4k5BaecgPyo1bo0hVVo1uoTGoqJw4cHnb4iEjReTqHv8XFLOsTLNQzj4IDKDRWR4wA/RRBKoYigONISq8qtkuQjC1Nqg+maMnZGhRmZN4jnZONQrsJWd6Yku8xp2WgrRWNeTSbTUN9iRhvDSoYD32BBEKCD+RUdt5ArM4WNxSpuUxk7wOTc/ppUXV/Xu17pUClv1abfKuenMCtnPDBEcJFOIYKLP3iKpkBYilIzxp0yn5YAHkHFTz1Am7jO0pmIS+kxqy/i9th2np8ZL/7MXCQAKAcHc7zFjKVUJlPP7RjTE1+Vm6JHv8Co4ANuyUWgOVTvE1EYn6VB+JONaytr05xs3tOBP1/lng7NH8W8ReNCw4ok8fpML0pEE0UvXzuhMdM8SHXSfLLIsga7nWxssBShh8sQISXhlpiyxnF3x2LW5tNGgMkwOva7XhjyFtBBosisONqEq0RMSlS/G2b68pA2FJ3IdxbQsFFRYSyTjbG1WQtHvCQp5qNGfNdIYExc1Sgz6YrJmig70a24cjf0KFVCHCklHrniR17WqeYnMCUzc5AluXnNl5q47IZZdtRNqP6TwV+pnolUvqQk1a0E0e/CKAL+5NHvxQgTBtIMDUaQOHbZuIWOckW2ODYR1g5W49L47KUTnEQdx0XRcrO747XyfqlnWSZHsE4mRUkz5KkyKZVi5GEYQg4clwNHBntff/PNe++99xeP/4L+pptUx4U+ow9U+5xRlBcuMlWy6Ef2w5l/VxGKMRYG0Ly6ifsr4v/7f/9vBWhqavI9m1RlCW9PmQNY8RLGzl7VOf1b2u4xE/zs/iM/JRjxn2oEZxCn4L9ZP+CL7df7JgTxjx0XhcPk2HjcwG3Vo4/9wodXn1g89Sn0H2lEZ1b4u+KWvNguv7dhPZRwi0+sbO4kbJFBxSlsFf6TusWFFUfIxyN1bFRYLkL82DCjPoVspjovvvii//RkCQCDnzeMhBwIcoDNI1ahnVlr1er3b7rmpieffI4UZnP0GJlbGtBjlnDBzGcifrKd+UyUeY5wMO50awJxTdBSGcvY0PCI14r0PWeMWvSoHVbwegYBI1fELA5KMFXBnwgp5OVtGayv8KHLyyePPPIIwIr8HFXmoivmdPQvzP+lCVVcOV5nJl2FsAL4+teWFWH1NKwKZ9XtWdK/Wsqo9EM5n26gs/Ea0ob3N6x/fwOLReAzqXSOfe/xBa1+qH/Hx60Q6uLngGpV9q2itnV4MPva6sr6F1sp1r/sEI06GM84X85NKWec7FNA6KtgP6JIOFHiy0hVyhQdrV8nRhcDtmLFCvT1fffdh/rmOzD/63/9r1MgIMwS5ACvkhKCKSPjVZ3Tv6UVHv/lL/lVZVGA4GqHT8xzazaUsNiTJ4y7fLH4zAvPybkI55bHnlBUIRx5y1Rtown6qKovjYT3UzLpDK7Hs/ncj37yY0OSPAmSyu2oxEAkpTBnYM93zdtr+fk4JYtUsFpWHG/Grs7LVRcrkmA8iHZknIkHa1jCyEfjSeFlPwzWabjxAAdhoJBc4fo3yJPjxTn8O8VAz9POR1fT+MgrjzRUyUqK9IEB8ONExt+9lG7Nq6UQ97FpxL/l0SnW89hsSp4OMJ9UpYHEfN6prT02w7jv2DCFRhZuuZwYH4+aT9hojqz8p9CgZGBBdevnvmKV/zk1c+Y3v3nLyy+/zCKQEehDElHI4BWEBMBmzJjxlYfvKltlvlb2rW9lvv8/YvoSLRtBYzMObAw21OHX/sW/sKwjk6cV+AjRD3/4wzLfiwrsIkn8XE1QglU+/TiNG6zI6SMMMVxQHKhqXG5Ni8v2s/wTSSX0kk5AlxOQKuOpAvARs+88HuALBIaxDNlYWiCC4AOvURFJtNY0X9LclIhR91i5lqPcfdu6mJoAKWSrrDKapJIyJGyVkxdI1c4sGVQtiLBS8WBSGD9rHDD9qmomPFwYw5OOOnxvJhs0EEH1HZGq5vOBeUQcDBHm/kMl4D2CM851697jPVeeajoY2tt7r7gizWDhEy3J5BEWfqwM+cy1jPshPMD7ZRERzChxE3z8P/nJI//1Hx7517//W8uXL//+D35kiqhQCLBm58rYJAQREtcacaXKFKrlAqxx/wqAxk3JlYsC86HAppbG4HmtlhiEDOMhB84IBxgAbAwz4cwWjQG0Ik2KeqVv04e50v2kB45Lx54Roi4eJMo6/0rF+HI3squneBTexkvJRx999Mimo9kBK8EmYhhOgwNVk0DlOYk+8zVyyiWQ/Xh5eaTyHICqEv3bYF6fkpE49ZFex8jrgwVh/MRgJFiu6hdS/NEN2WAIwhBHgUrnNGeWwUeg1VtxNcE6lHeGjFoHGwFUMUs+uzR16tz/8l/+NBbZt3nzZgCGJu3yTbcxwqiUjAF/vEfKENbZekYLmJFgx6yalT9jl6iVGrUUFYkAaPWDMFJffxoRfHBRxKkdTLsoqnLeKkHngYdM/mAm/ZAjikJ/77Jly+76zJXQpAbQP3jiKZ3ZnjcqL96C2UZjpVwu5xBi6Uh9nm8w5uF7ZRVBu4iUO/k97ouXYWHNxsUB1rebdmy5atmymVNnbj90wJxXnVMz6HFR+dEHwvWgHKSZSYvqca2TpqvyXbhwIXNs3oDiFvdXDHkZ1GE4OxzQWQEtgmoLljA2z/39DdWDYwP7aBWMZh0nvJ9RZ6FqrEzR9Bbmh1UE+8BjR7BaRoNwlKvm1GMD8xRSS+WS+nvGAxb7t0yE0fsDWVQR3uJPLrBUZ36OQynm51Th5DKbciFe95NPNq+yS4cYlSJofDx4tJP4eaH81GgYT1khzMeNAyy5eIVv/aZd1y9ZNmfO9K27D8ABRpYK/VD0n05/0AU1G+loVU6lWbvwBSQSCbxNgeNP5ACqlnRKIU6EjXcELNbPeOjHSSF5dTmD9TNeSzC7xRk8juT/+I//GGMU4BEOpyaKT6deF03eU9O/vu6A/yLKxzFB8mGC+vek9oVtecPUPcf6V7sl/p5x/RTnvNSR81H0L8oXW65Q/140AyGsyPniAG7neMFl0/ad115xLevfbYf2sxI42ZF1voi/wMv1hW1jY+PSpUt5lReJjSkzApy1wOrVq7dv3472ZFbPuriqLqwRyM5Mm3TUBJ9EfvXVV9HIvBiMH9yf/vSnVfDh7SlwgOYgoByr5jC+uhwVJ9CVdCZOI45gqrKY3aThtDjeYdSPRsCYYfjxiWL0HAgGSpdgVWSPkVu6Ir7iYnHtUWQslsSN1AlDGUdRRh6gf8kFktOZk7e2ttLbwUPQiegJCVAA3XNgOirr9xPxfFScsng/pfOdcP07Kj9HJo7XAJrRpQNMryA6tYYZScG5TPFp1lrQKWWMmRAkQ1NGpgNDItdgbyZF0RIxkj+Iaax4FJ81Bpu5siqL425HnNOVrNpEq/Gsc0x2BVZRgktJoVyczeCUitOtyO6urbu2vDh7yvt9ub412zd2WfudmjnJCb1KrUgSCeaKjzpEgt0kF6sOX6/xmM3S8Z13tuMGsujVbtyyqxxZHE1HC5FdBaSlMUVxcG0lb1kJB8iIl7xa8VXJCqMvZg/Y2dbnfvbWzXfuZqcMd5K1mexgjxuXMS/2lAQp3GYwkx1yK3MVQTUiwFs/LchnwXAWQrC4YLka56kqG0oOkq2E+DBEfMggEhKRRGeB6lNBqfTrVT0PnRCLX+WRkNVWDCMhxpcCu8YHWIFSksafSyHHqIiPin1jyhCzCgY4+tPGBaux7VUC6bP03hjuKItu1PFiLq8il7yKPmYgASVXwMQDFuzhV+nnQ6TjqUc8V8nPjKmKpytNYXixXocAGVMKI/Gi+JXEZ6S4evUiOLITt1KkMCkxsPQu7WDqPJLEEwe/yoCKTy4cP+NVi47KGEekeA4eunIOXxKPFGwvzw5Cz+AX/+235s0+KBq95wq+hfH977zk9Efy2cNSbdMV+DycYbKcsstb2hJAjz/rY1SMPB0a/gFCK2TLIzunOQNPNTp61chyHPgRCM5mwnHqdeaLpL6jBgioCqdTNkWcZnaIGQMDTykC8ejDkMJtsAr6KJgCgMLwSNP97MGIqshgCmUpvI/BpEiPghDzS/GZNX6uq+7YYq5bNJJbBvK7777T2tqMimQHFgzwxmQREdHV1YP2feihz/X3d//e7/1efcPReCwdjYrccLwSn3Voaeng26n5HmtwX3u5WPAcGchSrovHu6zN6HCi0yZOz/UdQFPwniR+phjXqGtGAvgBLmbb8Ty9ZOG0iNvHAISAGB73hHJjqG2wcWtSpJZkIxDRROIaUQAzXCmiopV4isru7u4BLQBKm8b9XCMjppTRx+NI4FFTfPJGfRomXsAckH5ybAj0BN91sUAYTWr3E3PUAzTaAl0TL02ZPuHmpZeRno91c33jtS2Dbk8p1hGJJ3N2HX1SnDSjHY0edCLiyzXi4LXRD4wvVJV8EQWUTJurNK0PNzLCbNb8hGYX3cfV0OyZEtXHs36Hwfh/ZRAy4JkOyHjhKhH5ZEPFA6V8qIFgNH7Fg6zxAM3xmPyM7mPAAxLlqwk4zIyW+ZWdSIxjGPUAjf9borb8LI2LoieRmXLlR2aNg4GPLkRkqiBXnUWYEW2EBTMWPsow9CuLT1ur1HK42JzqLR9kE6HJaumrP1qAXdTGFno0r371QurHfzxxjh6GNlBGfxqmnkkOBIW24kVg+gH5zF4k6VWazgfgETBVT6voowjgqxLP1K1i9vETgR7/VktBjuiI5hGLX+1+QQJ8JogBtPG3GrXQfDLDFC+vTMaRMA7fIDo8b+alNZn+w/u3e961bMDy5RYmsajYILYx40BWfrx+QYwipBTGufgVKfIzDtf5lANjhB9jgfLR4KyDExEbO2wGIN92ICXOTySEjc8qGZUEUFG6HzkeJQpW9dSQAYekLjQot1xBpXEiVfDjvB21rPHkpUQTKlUbuj3mr+KpwI0H6XFgmBEpklOm9jiIL/5kOFZw8aLuMlxYroqzY7pNwrYyZSvrNU6Z4NVvhwvtMdFrxXq7dyBf5NMBfOZe+q2MLlVqrGQjtnhP1y8PRDxcPvNjsckwED/IZtiIpqGzqwYkK5GY8e4sY9SyuuKxaDxWX44neQeHt5z4RAFv+HM8XMYQmCHdzdV2WmRUuXnbK9hOwihi6LeS8nPyQQ/QDnbbju0ZASiaTEIMX9R2FsrxAo11SsIbTHiFBNvgeMGMC2TC5UOOLJyj8uP7RZadcDIJvvIgzqRdXErzo/iYeJKWRKmNOHMXGaAa34gIai1feDDOntG/gh9WEIyo4IrPeLO0kYhkzNY4/alif7pQiBcmWol+aDEss/jiBO8Ow0nb4lsMBoWsGo73yrSMfwmmRI2G148lByrdx/Sj9c++/NDdd//NL37MRNahJxvN9LHkytmqNIdeiprDXY5ykZB6q6e/iFnm1U+Y8Pzzz/PB33/5L/8lUoin+KoEEhU2cuZztmj9eOA1B5HDi5Hg3ADOB29H5QfZde406lM/MYgnGPcBxo7I5wYCYaStQODhKFEyo2V4gGbAcEohGOBEFC9PqazcjjmHVzNowLB+ViTHkKVJx7+CnB0nAiC+BdvxwUd5wtEvqZzjjvJsfEnBCgbjY+f2OwlcIsjExrj2GD8GIMk4dinh048BB/wZFxHpxvQJpL4TL7N79ObrK3EwfOR//hNaIs9sWNIkDIsncxtexskBf3giN3bt2nX06FE/hcHITrXiQefq2DRbB7IEmDdv3pQpU77zne9cf/31mEdjvIXpM6YnmD7zlA+R4zT68ssvxwAahD7OcVIVggU54ItWTQwyczwCkyY7of4N4qSUY2+HVkJBmo4T962lz57+PVbPH0MHqhYzaE3ylfhJ6V/yhvr3GJ6GNx97DqhuRQqgjzc898qXPv3p//jj73H0wc4tqvkkpMPHnpNjMwA1+uabb+K8GamuVkxE1C0mGVkLy7a82dbmygseqGxOJYDEHRjKl/Xyzp078ViM9MY1yWc+8xn9CANIjpXnY1MRPq3mAAqU4KcGmTku3sqRRnUTjNRKuvKR41UJleeaGCzRJ2OMCPCaRReSCjmyxJEYdNHKGtZfxo5G+eiYWIAD7OtfH/lQfUbkGpHgZ2EW6seJjKQh+HRk3B8yIx+NnaJM0yuQfmTsXEqeP0nTDSuuwT4zNgZ9SnGgGg/kRxrmGOu0U6iJ8vcUMl4gWWhmDUF6htLkbzCduPaJkf1J+RCPnzQ//SJYpLG5OnVqQ1dXLwZPWq4Wp31Xe7bSoHGlTqkly9/93d/9f/4g/dpra155xbrxxhs6ByJtbW3AC21kMIFbqkRUKyaiVAyqkigzVo+///u/39Xf9v3vH1wwvcyKETBTHGOPv5Vg8g4PDPa2IAA9x0fealqbWHP+/f/8WTJpTWkVeMUwlLX6L0Ur8T4TqiHO3/0FS9i5Z8morRPoEULR2A197mk+2RKhn21S/tMj6ZZ2jFEz5NuYqnImGse/u8uL5qgWBhSjIx6NsyPD0KjuxlWsOT4p4CGYos0Uc3hWc/w8Z/bJsSVCjLa1me/KHhOBRF63VEdWXKfX15MCHzp27RJHVgQ2oKgyDuzjcb77JNwLfGAChCDxqda4lhJM9wFONjJq5zxZJKcGHyz6jNTl1MgIc1VxgB6oCzY/3e9vdF0CjRVsOx/seJFVq1bxodtPfvL26dOnk5HurM1NhJ1WUvAtgcE038BV5ODRIriK9XM+f/OVViYjviEJ+jqjDBPj9ZYrXqNAhQpm0ajplACS+vpaKgL+CU3yMQcgeepTzi0wpkKVSlXRD6QfghlJJCtXhRc8yLchQeSnV2ELb0MOnAEOiJ/XErsq999/P9jUALq7M8KrBTougkVo7w123eDTMD4qB6rGLwIExsbtuEiSkocPL4dTpBjyRWSgypBR8YSJ550DY7QO4npk8+l4CXYAMFQh0ZSqxKqa+jBjg1XlGvtWUQVpU3jSMa3gqlulPhJSCP6taii0p5/CU301VyKuq/YZygEfpirCUyCPQVsFYW6HFONoz0akQRIIJ05s8Z9oHUHikz8eCTaSM0GEPGX8EvxEjdANqlIupludk1xMNTpndYF18ahMdEuRojfoWANFsY50rZ6BnBWvWM9DzBi97pyRenEXFOQwzZFKRcvNsntQwIaa2XtJxFG86IiJmJF24948uLjZFtbuxBxgQB8ZaP/www+x7NmyZUtfqdoh04lRhBAn4gDzCpbeBF2es0InB4pYpyIPP/wwrz3zzUOcAhw4cABflWp6he9JVc0tLS1kZG//ROWEz88dB4Iy+dyVeqZLujhqcaa5cmbwoZRD9p4ZVl50WJim8fpBLG6VS9ahQ13r16+/+urLn3xrY3NC3jdkfSkvyF10tT7HFWLJjBEVoapc9C8amdmyv/JFvQKzYMEC7K5+53d+B6eVs2bN+ta3vvXnf/7ne/bs0ew4hEYF+yM6HN1VXD3Ht35DnONyz2xxF0ctzixPzhS2cISeKU5erHh037Otvw3j2quuuoo3W3qLJ+1e/WJlzunUyxdrqGACC9uR2NDLvHGklp3sZWEGoy86YuHKyFXrtW3btvEyEitiNC/m0ehrzqF0q98vYiTmMOUccGBs/pttyNGpkM3JkzodGR1NmHoWOTB2446z4I+P/j1pg12fgzBaea2R4BWY4C1xJCmJ/qJFAUj0wUTWmltY7xcxnoiPYYyIYgYAhODnlgibmKRIqSYEy/JR8SSYfrw48OyHgpBvXdbWyg7puAI+bMTrbDzT0OqU++cuuPTP/s8Vd91112uv/giPEgmOriI4iOGHozvOSTArZFktb/5TiSr8EJDvr7122U1TF8645lDm0Z4PCv3TOgYn9pdnRq29Uh0OiyUEjle8lJFlCc+N93b2dhzumDAtsXDhkjfX4d6W1/0LsVid8YXjv81sDtAMlqF2lKNfK9eWcFqT3r5LZjZcu7ielEKPVT8hiX87fuIQS4IhuCI6hzcHhDATxslng+ocXSDJp22ovueo6LNXDDWiKbReZ6+UCxmzcMAEX84QYbeTjmzHxN9YHi+tvLWLG0Te5SeCyQFOmzwrVR8d7HaLsQK/Ah6pECOe21vuwdEj34dgiNLJjWtJscpIuy7+F+k2tiPOG3FJxZXzZzFXsHEWV+TDDuC0rSS/SLQvgl+qMjA85RxUf0PDDS+w0CB+YQmUIm6lpKhKYDQNDSgS/XSDfwiGmvADIR6qyuanRIn3qXJl7GHujI2U4AddNIHTMJcfTirdaAHyu/t7jvQdRsp1tz3NSw6lmq1zP1l3xbJ5zH2TkS6mti//yu1pa4vEU06phJc66IsY718uXqSxog4IHkOVufdFfeWpUHihBb/DXGiEnUd64AltOjYBY/NNnwbxEA8GXyOQ6IviIMAJCRibPP+p4jy2CJGQjG9jXCRjh0PP7i4X++bLLruMR8YMCeMSphARvmoGKr4QlEhEWBa6Hv7mUBzSk/lgC2Mq4fXccuv1v3H7tDfeeOOV7f12cSAeN06h3FTEjtlulGHypQcnfeauuq5csTHteEW+BBF3bRk1jM1iuTxzYulv/9O/7O7tPHp0V8FhNoCYEkMonhuRIl/0Q9QQtC5+1TRCYlVK8FbGpqknEQ1BJKRQkwtxWAbrEMbPAwd8BUTZo3cQJD/PHCP6PeMHWhQcC46YNWfJtK7yuzwteeIgqmlqtLitGI0NEM/FxCTIxT8iWOU0icOkEtG4WyNKRVJRWwXxzmh0GUknFTy7JL6WNaCI0e22vMdThFrxOR3jx8jC7SV+p/l4sNG4PNc6mtKhi3szxhWNubrGK6SoZjke44sK8pPZr2OUcsQa1Di3Jdvih8PnQHaNVvCbGwrxfyRU4jZfQmbmgA9c+RlA3GBLAAA3mFYON5YRiUA+VLPlJE5EnCI6mjcxzAxEoAetfKVuuNUVcS7vRHKRNgrDyXNAxebI68ljquQAlTTHUODWV1JEVGjzcGSJCjmUT/4qjEaC6f4jEBJ8VEEYP7Eqonk1o14BIKJ5iQeJ10R9WpXO+SVbqKgw+mewXJzI4hYaB7biw5b3EZmexxK6iwBXWHkCzFTbsZyk1Zey+5vsvkarlzHL50twxSW9mJWygOmYkrEGUUO3JjezYOP/lZsxg4/hmAGrVeVLDq0t0yc39E9pnJMXL+98/ETBmDlUAtNm6j40tjUfd6OHKv4AVMludpDB4zMKJsO30bGEqR9vDtBPSqq+Utb0eTNrphYZZXwqmg6zY3NfT7m/15H+yWSSqxstOhE+DCD7PXxkiGHD1hiKgOHHTz41YHxPCkd1cSoxAvNPfgT2eWSEytUs+kiJ6MeRjDbRfaTKlSIY2uKgvfKFBPmggSKRK3F+OlYk3XxLwWX/CoHCcDbfIROv1mZBLRkkCOmML6FcP5VAXFCY0tHDgIhiNCIBGeDZcVBFvLiQJF97Ek0qytRcjyMTlEiKlhf+Uf64vza7A1wl6LJaqUfKSER4Bc5afrwWjEfo/lxnseTMmduKlcZguo+RHismODp6+bG3kXB8yIL3OwADm/qWHpKix5UVpuTw8vHigOnUsunDR8AOWG7Xrs2/d88DAzv2flDKV/rox4sfZ7e2LMZl/myc4DY0NLAJQHnMvr797W//7Gc/w8MIJ8S4COLbiUuWLPnhD3/IJgDHvVhoobWBbG9vP7v0hdgDHEBgBu6OGx0n2HHzn8MHHyFSzyFXwqJCDpw3DujcDOtnFDDvk7++et2XP/vAhLc29uetTNTKMokMw2lwILj+DUo/0rlFq7LZjv7VVfDUqVO/8Y1vvP3225yYP/300wB86Utf+uQnP/mP//iPwP/oRz/6+te/DuQNN9zA9xtxKX0adIVZT8yBYHuNAT1OsDEwnLNH55HU81j0OWNvWFDIgVPjAEtdFO0+XBFvf/9ff/rzPVt2YgAdrn9PjZnHy6U6t+opW4iLFi1CEfPSEctbLKG/+MUv/tmf/RmmILx6xBkThtHoXM6777nnHk7MieDh6+WXX+YoClShWKvi5xm8PSO8vZDnrydVwZMCHn8rnCW04ycghDyDHDjm0PEM4v34oNIFm44Kcyx7clVHkbBh2t/X9yDhN6/DAuSmm25CVew4xKeyK++5Vg05cytfwqUkStdwzTXXfOv/+iPL+vG8a6/95jdv4o0osrNj2yufhsDkY/hjsgrPJ7gpWj+4ydf68G357f/4u8nEhEmTJv3RH13/+I+TfOPA4DcTjUCdwMYylNNpLV1fA+IdrN/63d+1Wl+1Bvf/4R/+3osvvpgP3ZEEmHYBRk0vGp0uekjwQdWtPjoGIgh9knEf+fjpOckShsGPV4SmY74BqGyh85fqDVqfePAajo3jKTnltfujKwff1Dkcp8syiDhzFaPDCn6tiH8VAHNkyxX8QBJhxEniUCAeDHL+asLQc/kLuBhPmMEeTD+dOPT4BYHcR0WcLxaO8rnCqBz54AWHjP05EUoDA4XFixffdtttSIl0rAeH0Nkj2Wcfewx2IXaCFVHig1zyi1a2+6WfWmT8SCj3FIqgychFXm2CILuC2D5Wlign5Pl4uiswytsgG89e/NRaX+nBW+2KFSv4zo/uuioqrtwiEIh897vfxQCaxR7wg4P5TEY+nqAulRkRvAWbnpbENVRV7YCBk1xBPmfOnB88unXjxo0+60CeKznYhzU0NPGZxR27tj366KNyEDskCsjo98ZRWwQAAgj9axUB3FaKE9sScUIPQiJg8zGPzBKmhBw4FQ6gKFUAexYeoHO5NpCUomKW5Af6HsG/DSOnxoEgDxnIDGcd0by5BML6SVZdnXWUt66wOnWT7Fi5A6LjgEEaqAQIYjg1GsJcp8MBXwuMioSWIl2Gykd/sDBdxMYINYfTxGBlqaN5JGmIC3onu6jj0UpkPJcSBP0unz/avm7u3Ll1E+q1aYSGgNWitqY+CtYxGAdGW3NUsDH6w0XQB4J8qIpf3LWrquwZvIVvyjqu995775KbpoKcySrh3/+//2zs+YZHyahd7gwSE6JSDtAWfMS7qanp9ttv7/COsKBOOmk+0o04R5Azr2ctDoyo4IhMwsllrqO8TBKyNOSADHDPyuZzuFy68cYbN7/8dMiTM84BzK3wH8muOJtd6Hqu7Fo888wzU6ZM4fbdd9/98Y9//OUvf3n+/PlYP/MNRjQ1DqE5G2ZT/atf/SofEd69e7f6rTzjtIUIQw6EHAg5EHLgXHJAPNwQzLkPsV7HYgp3z90rHnnulcBZyrmk6KIqS+e9MrcZEdCtM2fOZKqDoRULGeyuVq1axe467xAyZ2ZRs2zZsiuuuOKtt95CBT/yyCOrV69GcWMhDQwm0Rs2bAAlSM7lucOISoQJIQdCDoQcCDlwuhzAAolzi8HsIN/eufXWWzc9+6vTxRjmP5YD6GJfERPhwAi1y0eNOLDeuXMnp9KkcH7EtxdYJl999dXYhNAWfA/58ccf5yz7H/7hH1gvo6+feuqp119/XXGrfj+2nPAu5EDIgZAD54EDoQH0GWA6ugFzJSyNsZY05pOj4lSzSh4RkdWzG+Vb1JbjtRxp70rb+2sSWCesHTy0ozf3Rqp+i3V4KUDGG41cURtYMnAegmc3E+QL1jhs5ZhV/fSkM21dB15uTr85uHfvolkPbH9/79RJS1/r/kCd22JKDYVlvN9RNqcr4kMSp7KOFT3gRg/U19VwGDPY25FJ4NWyPR7vjLhLG2pSbV6PUIqLHDmiYeVI4WKO6RmndniTw27q0jkzo6V8T08239eXau06un/voulLn+7al62zsvi0DWjQUZkSJp5LDtDutCfXIVukc1n4CcoyVOmB3zGQmn5M0sncMEVjv8PY9jBW5A1yDoIZCzLyGFM4VscuWdKNNyuGpkpErjHr9s9czcsJXjQLTO5gaefB9wad3jyfE7YGo/iZ8+J4cCKjHFgaCXA8unQUAEPkeDBjpEOzTj25jgF2qo8gv+x6SdeLI2ByBYuraxddOwtzjJ/JPB9NLntWItKMSUqsvr8Y6yvHrO3738t6V+TtnmKhqxhJN09twiNCodTrWkXbxYE2AW4OO9onjgCBCfIkDB9lDtAhacePXFPKeDdhnMPQH6xNTa333//A00+/yOeGg+0GHoLtlXu6Og637bz33uv37kWOqICtABby3a4zYO1flfG2WvZSy+bFIB0CIo4iTjmGN7u+1ZlyX02y0FAzKB73+HnlGG7hPbvoeE2xHf2HXnYH96et9/NOkxVhcKkYoTpBckaJ00ZQOMa4M81YGZKAwR6wmGqdiqQahYIw6ePKgTL6gL4ULay4/8Zo066+vj5sBNku+dX/3rl517Yb58lrRTG+Q4I+Np+Dd70G5pNoCWGY+JLUq9HEopWNYq74mARzfMjfpDqhlExhGMkBGMovId9jiSZj8YFea9b0xPLly/fY/by21Gi1slG1deWuQqGIo92yVY7FcWGLTJMw5OFSZJoGeRRq8CFuhH+Px4Hx9xP1sOg4pVSKL63Q0/zOxlzXiAIzb0QaMFXH+6omFvEQL4FrMe5G4q6DGuVXiEb4sZAW7SX/CWg0UWoBVVlBS6LxLytABNbFxmyZpxWFqOnquVbjx1zFD32kVDq8ePHElNc4fWpHb6lRyPMS5PfwsD4U0KrHGzUyzCRUQIkdD3IIWfg35MA4OSAakw/42PVOf3QX7p/pXQy3vN1bjg+Wo3UGi+nq0bIj20HA0/kDA2Wc5YRgx+dAzK2Rn1fgu2rpmI1D+4J1pBzt6Mi9k4zwunDTzqNvioSiAfCPg3dB/G3HLLvEa8ZGilQwEx+WWscvLXzyMeKAOFEnRKw33n7r4c89fPX0uZsO7FL1Gc6Jz1Q/wPoZVHh6/okJipaNOMyqNI7jyWBZqO/vmBBM1GV1MCWMhxwIOXBuOOBPr/3IuSk3LOWi5ACzNZmmmW9yDlBD23pzx76ly29eNnnye0eOsGpFK4dz6NNs+pFDVVP2mIAqVvwo4j/8wz/0y1pvgrSJ2aQiyy9/+Uv/qUbMCWBVWngbciDkwNnigD+W/cjZKinE+7HhAEpWl7qcCD3zxkvf/MpvLZsya8fhvbrrGq5/z0ZHUNWJ1+e//Mu/DOJfu3Ytt6S/9tprfjo2NnjhJGgKGtk/X/ZhwkjIgZADZ5UDoc4dm73hrvrY/DnxU+1hCHdAT2GjkyUcqgI/ys8+++wP/tt/PnLkyJo1ax555EiwYNCOxEy5UT72OxSwzWJZ6HV0bNmy5f/5f/49yQAYQ88TU4Vzyr179/7FX/zF1q1bUWPf//5O7FSqPHJVE2C+RkQpEIz7rv379//f//f/fXDDe7wexIeHeE9oiK7w70eYA770JDJGOIM1ZJ5UFU4TuQ4BkDBCCWYkiZFBMGB/kMnE43VWvMGKYj3B9JmdrTTOXAf5rocGvnEJKrKDBFaAkKvBViEQhMoidsSqBgvpWpxm1LyVbCf6w06OZlfkggED5RGWj4rZp2HkLg+PtNyReUmnUmMR4kpNwckVL5JcMxk4luGde0yiNV2GfNTiXXylU7EpVRrXIvQpV+hB9J2g3LFoqjzTekHS2MGv/jhQHgNCRoLSSRHHPBvqVMFaBKtcBXzR3NLiwSpfaPUaSRstWNUupIyTbCBRcDQ9zs6vvfZacsmbSCYUi/IqBaMAQdHfbxEJKj7AyKVd3Uomq6gK0sNAQOES8CmlmBnllKsji4w8ggZu5emQ/BlZKc0bvGopSgbp46x1kLYgtjAecuBkOdA0PY1fljvvvJN3wa+88ko8uKBYUan0MT+IXB1T/5xsoWPAM9bGeHpxP0JMoaWZyVxyySW4q+dDZoRp06bl28ScFNs4lXWF7LDtps8QGkvjfqv5j8JIyIFT5oBqMV9DjYoHxUo6LhVZXY4KcB4TOzo68ITxve99D19TxyOjSvWPCuaPr1GfhokhB06RA7YMnJ6eHl5AQu1iBk1v9BfFipO+hwZmeqmy/RQLCrOdiANIOayc4TPzeWCV2zLnL1sNMyx5YwLvgnkrkZBPqyMY8UpASxGYtIxz6n4iEsLnFw8HfJUxYJVeffXVW265ZVKy7uKp3gVcE5/zI2mUtcyIMAb8CNgwIeRAyIGQAyEHLnQOyJaV2UWO2xY+Dm+44Yap7DNX0i504i8O+kYqVlY3BNIJzJn1luvFUd+wFiEHQg6EHAg5UOGA0b85y8J6ijOm+mia9MpBRcijM8EB1aT+dQyUQW1LHMsQruxc+ftXIBkje/go5EDIgZAD55gDOH0Jw+lyAMnOGS3ivlh0OLM4DjrWYKjr4e1RPEsRStmSVbKakhPeWL/mpnl101qWvtH9QlPNPgyd+Mm5B7ZQYvUo0Jqfq2/EIUrFLnpW4fD+lppko73wU9bakut0O+WFljM3EV1kWR+MsPXCwBOseIN2XG/A4yNOTlOpNOAW7Jb6iQe2Fo7ssVpqS3j55CvEUjKesfBVSfls7g6ZY4puE9fQTmOq+bL5Szd/kMfwOund2n048u5b+5uaMlYzpfCrmG9KVcNwwXAAh8X0Cn+z3mwZyHxFJzpERlKqidIFNIyEMClgCG43SP88NpAyKn6F8p/6ES2NcglMpxShIbgaMwAg8UvXPQ+9dd0CPRGnStKlJebkSw5uoGNJ8Q/t5qyWSc3XLF8C8kMde3D3kktn2/dYh/O7a6I1iXQez5TxpHUg39+MC6dYUfy2RrGlZuJdcOJWnmmenbA9BgQ8jYojZI6Y3Rh+AkrGdaXjyljDM5zludFI0nNTFVvGSp1rcGbpOq7M3D3c2EVdBzdyEddJ4QRKfFUjNLwUfmHNo4TceSXLIxeOMI1NpM1ILnhOXTyacL0itcB7rNmbU0+ZOMvkPLUTRJ4rzvIidoqr7fQjRyCSnyPQQ0KFqE3VeIeBL/Li9L2gSwrXSjpe2clTepxS7VjKtVKeXYomSrFkzcGunTLccR0tjZAnKy4S4AqbUaRE8d9H03g4hBYjctNSGW1K5QFXep/0Oxt4sa/Vp6araEtqc2vczyQRvz8GEZLILQWByiARQQSwXrVXENdHii4IoGj9vAqgBYFTkZMIAIGMmlfBPrZXOAMflCcXLBMgL9jo3JrBSG8vEeVaEX7ia5ZbVtTp+fMua2yYPHfOfN72MW1eGSl07EwqWR5oz3YdYaQNdK9x3WbJzuCSP2LlEPEK/CxvIOYN4Eeen2VlhpmD01W7PGVCXzp5IG7XJ6Mlz455dlT20aWvRou4t7Ta6lK9/cmuqNfhWJPFFS5Oo2XAysgihqo2coB6MPYZhNzKaNNqBntmsOLDNISx88cBWkQbSK9jNNAJAbQSitBHG0wco5bAG5kssnQ8WXxiDDwXyaUY/FJUXnd35XJ2tjO6ry/RV7Z7+1JHedGoZ6DdsRYYSNy+ojdKVoIvMogH6Lj0YcQyHZiQ5Bd3kvFIHH0aw0elF+HnOPWxSBxPr/hWtLw8Oy1eQkaNJ19DEdfR2A+lGL8MFVeuUfF/HIu40Zpkbcnl8wTJiNVLesST/bIk3heJR5NkjKGhXFRvOuYWTVnyZOgncZn2SiBRKBcv1EPBtKSTdFLCHDvvuIP6NGk04oDR1Hzynp/tRLlWdLfMChJxN85XXqLlchxgG2eR+LXlMyuOFZGnjHXqVO/EGeGOmyzhLtdzk24u5aSkcNNi1BTvkmlqgxfnUqQmkrapO4blnvziTixq8aWWYl3KKqey3SVMngvlUqTLKfQX2sExaVbNpJaGffsOTWycCP27dnSItmTWwRmSrAJ46Ur6RgQOGTVKlkok8BKmakZ0vVAVCH4WP40U7TOaonESWWZU9cMI37wZEQTQqF2uVb1uBGyYcEFzQKf0DKuhkVXpsYwCJsGq12oKzMClZ7fW1Mk6lFRm1uavGynFvCTDVvt5CTiEiIsfVfxFS0dEQ4qStB3m5kyqZV4tgxd44uI0mp8OcGapMlGVnwofU4Lgk2l9xHP4VZLMn4hVZuZcU7vXszbX1U5MJrbb1hI74PjZB6aL0mP922CEnm9CJY348SCDucL4GecAbIf5p49WW7CqHatuR5YCQJXcG5lFOwbpVdlJJyAJq9OHAJ1ov5MCfSmeRHUWo/F8oXS0bM8GXqXrYP5oSdZrbDfFnXIxGo0XxDW6VZAIE1t0HF8PkxFU1p+MRbdsk5jK2aWCrCKHZw4mIzrd6CbJaznRPL+yDX7kOx8gixUikkeuBCPhy3aRkVa2Ynk+Q2ClGFnEZS6bsMpxFr+sYVlnARwjYzGaEP/IfpChWPSiExhmLIUpohhxi4DJx1IIrP2tEi8+6bgHRPR4JMtXs4GxE/winvzgLSBlVt5RLyFlUSuZgZCd3PWOYhMNKOS7eWYIgt4EmUTwlSQmJ0KDHXdteSFY5FKZX8qJIM9wG5hKWbG6ssWWQr4AM5yoUwKwwfrMw3ccPXiEXcRoOcbnRB/7X2sHWT8XihURCCEJ3lCil8QpnSMlX6T47U76EC3yV/uJJlY9qgLTOpiVhEi6qhDq3yqGXDi3ZsNIlFWvbb979OCK7Qe+eMUNf7H2hSoKGcAE1ZdVj07/lm5GOH08Hy0MKqtHpXnUsTYG/KhIwsSQAyEHQg6EHLgAOZBg9igrSzM1kyminKTuP9ybbqq596H7/umxJ/3VI5oRdcCskZRQ/55+U47UrSMVazAF+JFZTp+MEEPIgZADIQdCDpx/DqB/y9agZa3vOnzL+q3fvPnOv3n1CXaNULhBnesvUI/ZIjkT1LN97e/AnAl8Fy6OE2pSo2yPYTAp5mz9wq1USFnIgZADH2cODJ/ff5y5MP66qxoIrrLIazaBRcmegne56dOn45ituTHJ98F/918+bLVOuueee6ZM2bBpd8UUALR6zD+qBvITP/GJT/ybf/tvLfulq2+99TOd3sGDB3H5g7O3I7I9XTEXqKqm5DXeZCGA0h/49PIpcy6vabmpp+fplc/FcTkZhFcahigxZqZmoQ/Y4cOHOZL5q7/6q9Zl+Lfpffjhz7z11lt9wcxh/ILhQPC4wo+bEzXpJGJhg+W7OdLwr/QTXufiqUZwWcQjfwhoLurnY/PjwAcTSZcud5wAJE8JRDSugMQ1I4RBpz5SYgAOIoMk0hWedH/Wq9m58lTgNZOYEcudBNfi0BH/lHTm2cWplLK3f8/PD7yKByYTKh+wZkBQBEHL1bL0DJdCSQeTPAoWbNBzgXgADAHyGH4OPan8BcBPUfzAM73GGjiYLqVgSj1MeuUhKTyCDN2bE7BjA6h0sAMJGYJhNFINs4/NyV2gRpSg/quIwChZAMipsbF+S1sTptRL0UUx1cKXFZC8CkhUPl9uygWejLiNhhwiJJrrsAiFtlKpDBSP9KmUP0wW0Qo1+lSvJFIuwXBM6k4KjzTiJ3KriT5OHxspBH0KHtNSQx3GPNJERQtCKqh5iY9sTZPjor1UMc2vp89wAI4H4wOfvYh2gyB+CFN6tFl5FOwGfpynQbL9OB/Ww4UeH8PdsWOHjlOy0PnBowivuWZZLL1N+rz0QOmfADAyFJhEUvRKZGTALR/yZ2Q6KeSCDK4gHDHoh58CCVgVhMlSuQxh8EXeqKWFieeIA9q1aBu/1egqSJJR2zGY6MP7GHyKNUVv6ZYgDKYEwar6ieLnSrr2ZyVMU/RpVRYFUPxkIWhHHdlFVXdATCqVwhWlgbSqPLniN73KfGtUyqFklGC0gU+eRiBmFEiTBIDyuQoAwqpSRr0dNa9CQrMpPaAsR0Vx/ES/FmPQP1TW6FggT8kYlYEkImYoBWfPDQ1i3Bwv1zU0NMSnW/fee++8WdP4DkzcSyPZ/vvf/4ACeFsJGOywVXqQ4lPoF89zP66RkSlVANwG8UgRBgmJGgnCk+IDj4wrfDA9mDeMfyQ44Lfv8ailV9dFxZnEgQMHjgdzvtJ1NUTpfBrleDRQweONaB6ZUJkAEz8ekjD9bHAAhldJj6pbLZREbUHUhLYRKTwKtpemKDxgZ0T/Kja/FKXWL0jT/adGt45QfANCJMQoGBFwMpW1rNmKnGuhUKLf+diMKhxedfpgx4uQEZJ8qkaCBZ8CbPCPhKqkKBn+Y9Syj9l/5Kf4YEQ0cdRHVU8VxsfmI/Hz+hH/URXNo270VWnCkUjAhv5FUFQXHbPmz59/+SWXsmXXWjfh6NGjKyes5SWuhQtngyQWEcvsndvbaTWkDZQwX0KDK23aoCAEJojWLz2Y6FdHIz5MVTq3+qgKbbAIjStYMH0kqjDl7HLAdDua4Ol3Xvo3X/nWpN0b2jrklTYRT6ZLMK8dIRTOGEWUS4cMO8AZY2iIKORAyIGQAyEHPioc0BVbzHrttdf+4A/+4J0t2977YBu0oxPPQQ1C/XsOmBwWEXIg5EDIgZADFyYHdPm5ceNGTjEWL5y7fusu/yznbOtgitYwxjbLhcm0kKqQAyEHQg58zDkQGkCfXAdA25GBPd/giSaJGEqiArmeCJ2qZhbNEUf8TVr729e78cvLzs4HHlxgRbZa+f2Rwb1NsR7j/wZnM6BlfxljBTaxxRMbe9rk0h1trKKIOB4u4+zaTFshuzmZXe0MdDQ0eb39+6xc0rE24BmLw62yjYmJ7oR7bsTYVFkduJrFj6zjNTqF2nRt7VXXLfRKe9yjmRsX3vnmSrdY6huINA5EEkkHr3lDp1bYMoIOpOKLJs3bV91p64A7OHVBMTMPh5ePZLO9NZfYfZvbARNI9eRn/EaTIMwZ8iEt8TCccw7gHokyxSGxOb7F5Bd7F47TYrGkMVelL8eMBWrF1zIGuPglMgDICjpgOZstcIaHMyfFQF/EHSO9CzM+bBRiCTHBIZi+KleC1pLEuro6Dvn8FNIjVsVkBw+I2MVSkJjw0sXEFbCc6gHAIR99EBfIxCFYYMR/m/YnoUmLc8sMByeeljEoKTiCk7MZalrmv4slrsdAEVrwqmoVhQX4ZXJIwYO6my/aJX5OTS99PTcwCBWF6CCOE4tWOyTZiZpiEmerXtkt4h4r4tK3y2SfPDWD86QomI2VQwTHWHUMNzfiTMR5UpJUJ1rqLRZSB6LFy5OxZNktRx0vgUfqvJX0ivwSOFJ3I/hjlNF06Ejy0iUJB2/SXqQwOKFW3Ekm3XJEPDjihw7HlriExAEXDiDF3SVuo0UWREnJJZxIlCGNrzrPq4kWessKBm0IDpeCAEt50aQACG8LwjfhhgR8TRoLMHWwJwQ507GvjHjZCCyLW258k2vPx5A5Co/LeIGGdZ1Rp+AVBkuR2kS8puAeSk2zbr9/OjJwsNBOn+k/1Pzmm9sKvJ5JIBdkuE4pFsPHGH7Byp5wWH70I9VCxqlXDd638MQZoxPitMt4CDP5hWbpSDSaEE2cJjbtLHfG9FSEIl3IpBOtdGAgtQOriKb/0JvoQoP92aTpKoBqAJKgccQ7pXOFLSSCE5L0lhTOoXO5nALz9ghZSCQARlD4CtKP0x8MweGtMuHCrDe00YhDrSwvIdBcAwN98Xi0o6ONuMgJ6V8EOqWLz3V+695d8+/+5D/cf9/dCxYs4CvWDBTHwV9+DMASAiOV+9Nv/9tYzctPPPGEZbXQtSLG76yoSQd00TIjyMbxfAnZa8SvSM6hIAPYLbU5RSRRLa8GmPGAnzw6NWAMR/y1M3wLtlviRhzcWjHHK/HWgYkzsGoL+YTnNFpuk1NKW26DJQ6nGW44iRcxqN2SKyUOVXyo8PDveeKANoQ2TT5bSNektIGwg0eN0mpKFxG6E3EkmKaQkRT6cKAbW2BobG5AYCKXUOK5wXxNXQYwxiPwINFAHCtkX3CBkEKRilz1nRZSQAuwij4lkisB6UeikXUiZmMxMkmPAjjOVw1EiEdRbdJrI5HBeD8WyEOSFJyWY0c6e7sbGxtzpTK3PT14g24VAtBJoor3MmL4iAHYLL5NEI1K341W/E3i3pUfs9ZkjZWswU9lvysOFsUdIvoIwFIxH4l7HW1HrliwrKPtMCmMKKoCZr1OmNACsYxcQimfj9fW1bXUodRKtiieqJXjGkvV4rMySkmMQXeS5aYYhIw4/DBTSj3uGC2rqa7mAJEaqz/b31Ir1a9UEqvi+kRzqWA7paaJ82BmyX4X5434kgQkbhiBi0pqS5wfSobrYLRYikJDlJ9UUJxPO6lopDfu8FEYcUzrMZmI8IOwoldkipbP5Vpbp2JHDg/L5X4jK6AhQylRJkVRqwb16EXyPf2YmPdQZ3Fdiw7DzS3qEL+5btFFZIli7ct30/R4nBwo9MZbrHImu6d7ddOspsGuNnReicpJEcIZ4whfJBHhGJ+X5hUmaVuRm1JT2oTK5ZBldCH1wF3pBJJXg2NmnuJANOskaqRvo9i5SkdKxenn6ooS+ZZKyOTHKVKl4YC5dm9vr97T7esaaulvNKvQMKS7h6HD2AXPATwyuziXlW4ga0XpRibQuei+MhG13N//F0f/8a+/9ezK3K5db9KTCfRNrmYEEhHPrMTNrfyll9IbGEGylACr/EZxbiqIRgTjC1bK1S7Nc/USba5gNNhk2CPs6pF7ca+G3yT75/Nq3ljnTYmhfGXFMUYQmROoaAVSxSk3YS+ucOTs/0FdshzI5wqptCzUKBDxwvoUYaKFi1Aa0r9+oq4jVLcq2NnTv/QKykX5iuYqGWksRdoxPhlgAvPtvl7zeq0TTSTi6VSNEBzpRo/X2fmOIw7ehT2vYHaTbLQwHxxhhHhOBZX0Z25RwvR4Wz4EhJpgOMbra616PDTLT12mu5Fiqt7K1GXIAiRDiy8CyUeB7H5Wp6JLmU3IirgMqkKk7BrdqvxxogU3djieuiaVSUVZO7rlZH0a/CWzA6Z7RGWrHy95DS0NJY+lfhwyjebiG00RUfUyLPgwgjdp8gQr0lNmoWsorwz6slXbEI8nG2k7Vtg0JY6UMy2RAm6ThVtS2TxaZgJYpJW5wtjeclF8OqOnmN0YT/AsuhO0vHwDglWoTP3N188ifFjBs1ExkjcWTZM9jkZ2ulVVkUgoegXmHPWZJC1CiHrlIiqzGfmV45cAlUwn3HLBypayZZtpBB9Q4rNR9a5Xb0Xae8t9hWihz+kr5LsO9R3qilv/+o/unT17Nq1f500B/1/+yd929MtnLBAdtleKyie8TC81L0jLnIJgpiPssVA7ppEmQZ5ob5bbqmAWCHioJtmOxpmeFQq5nvZ+PiSL3KxJi6F2KU+1ADDC1QyQ4+nfKtzh7TnggPTWoVbF6ur/feGxP7r5U6xDX7a7pbkZMUOaRvvASJKGco98Mq4USgmuRMaVJwQKORByIORAyIGQAx9BDuAGx58OCfloVpRoORKb1LWlY9XXFt5Uv/XokP6V57qkDfWv8CIMIQdCDoQcCDkQcuBMc+ANr+/Np3/2W5//3J6dB/rZ2zFKWtWu2QVGSw/thJy5oln8huvfM8fOEFPIgZADIQfOHQf0NO7clXfxlaT6T60KCoViJsOhykmE+vr6TCbjlJzVq1fPrrUXLlyIv8k1a9qis8SkkkMO2cgOBE0MJIjpALc4xwLDVVP2QMk772znDKY/OkfzggAiAQsuwjkjqSTaNicf+H77q7968cEHH6xxsk899VQ0+lXOUSzrsBbE0VawRNCSl4AhBUeGnLXgGvO73/3u7/x+ES81779/hBKDGaqqEEQVxs8xB7QXYBdHhBakabR19EAX6yj1ZOZ3FprYj0MqcT9wa3qBnMVilMOVRxzW0rXofgoMgEa4UlBH29FURg6bgdR0YzslaIGkaMhQqniqQSHVMgy0wGAlI0QHkADJLaQSIbtmkUM8CdjxcBRkSFWMcWvixIn5Uh46o8kYOA/ljmouzIbKKfF1vb/3KLA8YjTFjQUT8Zh4TwSbAFBWLB5l5OL/V7Eq2ZSXyYhhmZ9IxnSt+IYnkadkrERqFaRyBQzLKmpnamYOrgLWt1pZipY68mV7w1U/f6ZmuEQgwc8hfU0NLyQMByGJz+9y1GvkiaEWeirSQxO5ZjJYTFZGNyQRpKxMpd25NZZkLt5vsR7QPgMAAcJaWqxFixYhNzp793Hd8V5p9eptYl1GU1MhGgej83xB7FVQO8o2HsEqrrb4mxQyxL91xRDBJ1WIt8TxFQVhZEut4KWykThcBZJbnhbzTiRGNTGalwJI1yCPimKYBRh1wvo5U5smUbMrBiC5JZjixOgQA1ZsEwHTFCKGAxHOgOEwcWoNH7QIzftxvioDYaby84JiBbQpYVVU0YcZFPfffz+t6T8Ckrj2KCIy6EzwUyq3fG/bcdQgBvz0BAXmbQgBwEYkME4Vp2asukIAwS+l6unYt5RLRy0UeZ1BIiOBtetqepCekZBhyjnmQDabq6urxYiTTqhD5mh7ZyIlapTWhBjaC6FHBN1KCk1JExMAEDunoUCWnq5eJJUaN2P9TMbBgSx2XYAoqlw2n6lJi4XQ0FcUSAc52ICh7xG45Qox+XwhmRS/gzziSnohJ/a7UKsHOohhsiudlEWcKxkR+5MnT+5yRZ+WbPmESEdnH9kZJhMmTOBK6YmEfOSdR37IZqmRpW9GeTGbKaU8OuA/r0T6uqzu2u7qVAcHloVIXQT8fIpEaO4VG1+jbyqwbW1thsLJMJAAGR0dHVbnMZgAICMfTlm8eLHLy0KjBey/BeyIlU6nLWM2XYHqs7q6uqg99uVEKB1WHPsBldHQjUgDeVfXYE9XpcmGn+/XWYd8lYUJNgbWyCptOB/m0KFD5UGpOB2DR0Ta27uq6ghwZwem3jIxkIqYxoUbdIFp06a1NLaQq7lG5vNIMp5PbJ0FTMwq0ppHDhxCmJUKw74nSZQOILammHcZtY3VKNbqKWN2NixNK2UBJkHeJ5KpWqxO1D1I1GwLnhWxtYpYmPGRSNA3ixLR4XdKgW873M4EknIBQINDXn/vAClEwvAR5QBNCeU0bhX9MpZNoCcwMPv7q79KVAV/Xm4hm1GDpPZLJ8Wn3E8kQjVHppNigkwkjpcxiCSMn0EOoC5hflNzo//+jy9etDfSZEhySkRecUV3IFoJ5EKR+ZScPf2rvYLVSiRGiZUCoQ2Jjdtm854P9sQiUlHNrEoWLbrs7rvvjsb7yJhKefv37//uU49BPATrKCOeTst7yD7xROrqUr29fKtHFL3W8ciRfqsjCCLxfJvV3thelRqNy6ISLpG96pF/y+CgdPQ7fIYAGNje3l6lm4TPRv/OmzfPz0gEegSzkQ3kRQ4ARu0qM3IF7bRQ6I4znxk40wG4QVlHjw4PyQrCnZW/AIAqP1iQOcb1lUT+UBB84P0aCh1OJXZIHlFNBFHBG6QuPKcWQRhYRzXZBgE55FU4PGIO09NutbvZYEaJD8gcpqEpyRymv6+PXQirzWIyM2WKmD7bpQGu7NFFIkf7+wcRNuw6AC+PzHpWI8ThIUQG36DTR5BEpKpS1EKz6ytJhb5iR6mDesUy0o5k6e0ZYAmTDvh0INEfIGQfVf+Ck+xhOGcc0OkTGzl8I6Gto23dunV33XXXy8/89NwQQJcgnJuywlJCDoQcCDkQciDkwIXCASY7TBWd4eliqH8vlKYJ6Qg5EHIg5EDIgYudA8EV6M6dO7/4xS9+/+c/yRbL7BGxNBU3MGc66C4H5bINEiz9TJcT4gs5EHIg5EDIgbPFgaFDlbOF/+LHy7kCWpAzD64cgYymDlHE/Eacx0gi3lR7Im42Wmp99823lzQ5ExtnHdza9s5K64avGw/Kxl4A40BzAiQKHZMDyWV0Om54HJzQ2jF+29/9IN/Rv/yvlrz2s5+9s8pdvLixYfLlbj/OIDcN5cXhM6ag3MnFRPjL4VkkFrV7e7re3/D2gw98Yvu7T77+jBObfWjytCZMMj2rxra6pRWxaZac1I8ghpOSSMUGps+dN+/Avo4Xfvbu1z5/V9ch6/UnNtfVRetnYxkxvC+gwOH1PHKAZqN0/jN7i0biHBYSYlFjn1T2iiU5OOTcjquaMgsY356uq+OcWE1/FANHcRgwYejDESA9HjBO3VpbWzn84xSQvBzEcuUYjxTGhV9lRkpzaxOJZKkQ43lTpkzNZrM9PT1m+MhsEqrIwgkfYEKwsSdoamriUBP82B5RLvibm5tB5SMHHrNmbjs7OzULlk4gKOJ32PR1PDLKTDhqpVusGQsn3PeZ+zgvLOLhKJ9/4sknoLOndIiDy2KxB+TGybOVKye9UiLjNEB5zItPm2hNmNBqF70U3pGi6WQk3hif0J7gdLSYxmF6uYT5spuKJeoiibp4Plog14BVykVztXM5nrJ6YlhllSJxLB1ta0IiOd3qiWfdeCwRSxQctwsPkVOs+NSGnnixC5dRMRxzOn1JGFLsTeQNmynZiU5qsibv6Y4ZQ6S4ky2VC045Ps2yJuI50s07BTwC0areBDeeAH8hGUdWlAfjg33JkjXJik4Gv5zo42My6iZcnlqJkoeHZ/xgJZFirS0z8/m9GGPgl0s8CHl5fq0TrKYGTL3jjpPD8q7kRWua7brWafhwzkdjIkE48415TszqyLflOCYu9OOoGjMBliC3/8Y1FFfrTqSTpKO1L7/88qHNR+ITo6V+c/AqItCEsuV0l6wEHrzF+V5vb3cuV+RMnBNZPOrSEzAzraurGRjIYuIsGWJeDY0QFTfP2nO4Em9ubaBZ6a70O1ofALoTdup0FXoILQKqTAZ/92IuwK12YBKxuiMjieDW4siomOkbvNmC4TodY9KkSRzeY/4XTUdx/AbB9HwwM0aCXd1U6WN3gf8Eqg1jLzRu0JQj2kPUE+6fp02b8pkHPgXlv/jFL+xIiW5j7B/ExRqWEnS17p59+GfHt2kmjXc8XDySD9s7Omac4VeX6I+W22KFrc3JI651WQRDlaKuiuVaKdc4x1UCSDFcglGQJDRE3EJ0yOaSJNAfT3Gi8YeX88bA1Iv2ZIuHGTG58o6y1epFsdGMMnVAQ4MZuxfZnseLu42gxoJWSQiv55kDdC3CtGlTUXwqlJQg9COyizhPufIIKUQcxcctMjCXw4QogsxRSaW5EK0Nk8RulX5FQI4h97DrUntoUsDDLQpU3rUzlnaaETBsbo4cOVIslJqaK28WgY1CDSa5AAkBDU31EWNlmovlSCm4jnFSbjzyG9qaWq3+PdaCZfZXv3pHl9sJTCkiNjo//M7P0/Fe3iRqbKzn2+64Wp/S4MxGG0h+gmCbNiXT35u143mRzik8Ivfl4kVrlpWN9fMrRfvxb4wLxkSrlW71CrHuAt+DwHd0lGGTmzYrnoi4Tlm+StHX05+Meo3z8Kzo5oxnPF7GAn/tpMZEYyYfFSOtWCY5iFfDmrI1Hx/MQoEVEYMkEeVzLDfDFxmE2Tn0Tqw4yJcK3NJAtJCNGnsjt9iHO8k5VoGXYphNSzDX6VYhXe72euTdpHLaSsTtZqcG2TL0chRwA7EsT/GT6f9y0SyzkcGow4+3e4qRUjwykGm1SO6P9iWi2bJXtqPuAAXNtLKJwcFILM9HH6ySm7ATjVbDlFgxMJwTLbw9NtBX7OeNSr4/MVAedDIRay7OOFF7BSBx012OFDITLLve6kp1F0vdsWjGYe4BbI3VGTvSlz9K36urczucjsIUa/Zc6567l0F5NNLG9cffyXbv6KG5irXSFSUoA3R+x5WeYlstcxOdh5lzWbxtRaCyFXkFqQZAb8slhymIBBag0sWsxon1PW19TNfwA82EMBqL0g/lgXmK9NJuPGFSK/1chSpXNG/VkJEsYfhocIBOY+N+lB+tSwfBCy0/43sV9YW7U65FfjXe4ZZkXQL/ss4+t6Ib/a5PhGEctT3H/PgyDOpOTELN1xbof/RF/N7iqHx8TBGPsPQ5mSRrqFA4woW0Kx9jiSTdnoTTzZsHzbjD9VgwUBY0i7MRlK7FV5K8dNTKRCSStGVUqNDRwTNUBt3cSFrumaIOp4axs8MBFAGBBSPvjTCrR4ygFtGSiBdd6vLUtAWzOZkHohJoIKN/cyhN9C+K2yft7OlfihBCo/KCSrTORemXUoMsgbuKOeeoEZ68BsXnEPh+Qu1Rb7/V3bI+P312QxNO1Z1Y2orVHpqwzpreHCnaOa/cF+O154gzsykyd3I6FniBZ8bURDGXj2NKLZa7SZb2SV5imU/ZzHULSbfIt288qx/dMWVCJmYNxnDSLC6TE6z5ahutSALd1ctkg2lDsvJhn2zMqzX8ESvhaZOxsfZSMb4r4IgP40JfIulGZ4FfXsVJunIdLPeh8WsS6G6QOwxgnKnj7j3CB5ISbipv+B9L1MftyCxWiHnenjH45UJKOjHgFnLy7jUfWrG8hgZpQHFHDX5P9Lt4jJ5NPJdws55diDq8wRWJThEYfmmcUru8apObXGflmnCJPSDfPrKKuWh9DiU+wyomc2BxUzaai54Qr7fqJ0VKUZm9aMhMythH8CUtHQlb1LKdxUszGcvRTn40oWsXmL3XTLKSk62+RLdbGHSpaSRbYvpxCROP4lFrH7sc4rsIK+751mH3wL5CE8iT5R6u7Zm2cp1Ot6xsnAkD3NMfC13TE0QKAmC+bEW9lT3DTFIyTbpmBBge4nzfaPDaGamBgbzvvJ4unU7LS3cO363BI7bBowNkVP0r7A7D+eCAKhLUHkMSffnzfRtzi2bcd8kVm7Zt2lv52oaQpfqyWuucNsF0CeQhre/rr9NGGSIIORByIORAyIGQAxcuB5juMCdik4mvmJRkWVfuieR6o/lR9S+Qof69cNsypCzkQMiBkAMhBz7aHJA9kbUbNk+ft/Dyy5a++e67HMGwycEnx9C/+ra6amGz4XFaVfVXu2wJsjfIEvhCO+8+reqFmUMOhBwIOfAx4IA5Y/4Y1PNMVVE1X8WqaQgp+o+jH+5qalL4t8Nazj+hHwI57l+sVVR3Xnvttd/8w1vx9DJ7St306c+/O8DesnF+Y9bO5oBBzhjMSUQFGycTevDAFcd1/98/+RNr8ku33HHHuzv2ADFg/OlWQEf8gUKZF1AffL309nLAdts99y9YvGzBhHs7O3/19PsJXM2NyFSxy2GfnSqrAQTqHzpnzpz5ta99bdKlvVgWXnllB+aGcugUhguJA9pVHD4xb850OUalseh+6jGIW9rx+uuvX7t2LW1K9wCegJ0x9ijUg15KIqfFWDaTiNWUNj25QHLJJZe8//77jIKevm68AcYzcSyrfKtQ8FAok8UlS5asfPnVZDrhM2bWrFn4qcIKGWw6EPB7BDFbtmwBHuQEOhvYMDwFhrNqisPKB0+Tu3bt8vEQoTpcMYDmSq8GG/QsWLSA7l0o90E5m1Wkt0xuYbAocowUMKf+xCc+AQ3Ag9yN4DorhZH3rGtriRBiMWwi5YCntlY4BnJQUR2CjIJAAKc+IiI2xkOhrk5OZgkwUHmItdaxTrgEFNfRGKgNZRr9LwApbC+PDZlMBITgh048KUIzFc9khglQ8GRGXFdqq/kIyEJc2QVt2LvX1WWoGg48NYC2vl54yy1gBGpNEZRYgeCLzLHYggULSAdMGwtUEyc23XDD7FtuuQXGxrL1ZKyJ15P+dPbpnt19Fqe5BNuqrc/MnTsXidHVJbaAkYFCXZ29+NLLQE4r0Dr5PBdxaUm7791LVyliOwfvKYuuuGfPHkok0IXIggd9nIHt2LYbzBhRgQFWUGvKxa0p3AOSNrrmmmuefvIZXKVCFcghhIzY59FVgASACtLloJx+Tgpg4CEsXbr0xRdfxOUkqAYHszCBfogxBJAQAB4gIUOq9vEL1J1KV3rFhccEyFMKgy1DW9Nw2HzwCQUlnqv02Jh42FV4ri0TJBHgYF7i9A2uIguMuR4wPsDIsrQj+QB+hFw8Cub1H50wQu9l+DQ3Sk9mjNC98QjoF0Qicf/2Y9szT8jGcwxAQ9BS8+fP56sdyLaamgwullGL11133VtvvaWSBJJoPhwiIhvRfUOGWfLGBYnvvvuuTzOCcdmyZW+//TadITuYY47I6ySXXXYZcgmjZ98wGoGG/n3phZeD+pcU8IhjRbp92UXuMRVEvW7atMnHj9xbvnz50qsFIe+3CCSv61gW3m644rDX9CvXm+stXDQTYjAAIl072yWXzADbgb4diHaqQyKSEx0DgB+mT5/e27tN35iKpRPQXFvbOPf6Y2AAnjs3xTzTz6WROXPmgA3TXcqdOGESBm1XX30ZtfAwaR4KsMKI9AicjNgi3qljcZnuiVWAGCNLLm9lKDGQGUdDWY/5i1pHlVx6zXQA4px5DYU5CzGlm0j9RAqkasHPHAZ77KHnJ/F3zpzZcWNsHcwz74Z6FI2xTq5LRGvoObzqNXXq1CDMpZdeKqbPNpZvIoIgZPr0yXOvnBSEIb5wIVD1VYlXf2IairLz6AFqh56nma65zkFxp5JNQEZNW+Mj/7333ms/dNgvN8JUkrIcaf2YZUMeDQcZG1MfgG37rv3klRc2LOvzX3gQmKgx/TRvdkXWr1u//p1NlV1Js6RhArnN2tZzsI/SMSLFDHrqpKm07AebPgADJq1caVYmqC+99BK+ovG6qh6j+egEXh9o2VOTn0JfGM43Bxh9Y5BA59GnfmQM4Avl0RDN0EOP1isjdwzyFGwMgPDRmeKAiCOjf/ft3c+cn1uECZ9QQLy8+eabrFu1IFpkhP61Vf+q7lOws61/UdxXXnnl1bcsRYd2l9uQim2DB1DZSHLkfH19I3L70KEDaOfaWllZQw8hyvs2pRLCnqdOtPLRJDBwW6UCuM1k+mCIilAirMqvv/5wFbeXLKlnlVSVmEzKWph3VSmLeULVU72dMUOW0uh38PMOLf/R+FAYBEYCXLo0ovo3mB6MQzwAA31tJu/wULryyloUOk9lfGHWHI2OxM9q8Ybb5/IIPSXKrrOzsXbK9dfPDeInPnPmNCcq67hgmHtVnElLLtcpb8nGZI3f2ipfkQrCcNvaehAAOg/zBNMWk5ZcPyUIQ3zJElkwViUuXFhDExzp2EY/TKWaWcAuW5FmCgFjgeSDRlxZX1vW9vqI5IWBQCoSVrIa4YoSZEvkww8/lA0bujAyVeYC1hd/59Nc9Vtb6XianT2+2AYHDvFG01Cgg5H38N423q8r5ZgHxmbPmE113l6zbgjkxPrXp8rPEkbODQeM+uRtXTtuRVetWvXNT3+WjYu9ffLq2tkL9HOGFfiZnbL0kMFtJp9hNzh7PA8xhxwIORByIOTAeecAMzD58Z+3XYcOZlORxEj9i3Yenq2eObpD/XvmeBliCjkQciDkQMiBjzAHePW95JYef/zxL3/l8xiQ9GezjfFkrlSQwwM2QIa19GnVUffQUL7sJrGZw64LFjKcsnFsd7yTo9MqL8wcciDkQMiBkANngQOytx6G8XNA93ZRgVVHtuhCjivEkxD+bGwLt328dBSPZSy8q4qnqLh6uwkWhCccbuOxS11nmhst3LziLqup0+o9aGW2Z5oOuQMx8c0iHhxdufIzhgI+BqHEpBRxc2tbly1stloAft+yP7z3vptXrlyZzx6eNCGx2zZWCBgQqI8tcxrLgawkgIsjPs/jzAbLkhW3zbasrNPftWTJ1Gc2Oi0tDdauDkBYugu8+dqmHrtADcaQeLri/eds/khHZ3JCw+DVV11qWc/W1Bbv/MwCrHO27hZHXD61YeT8coBWhgDT2nLRgwrMrTDA4gyD0wt8AXGMgRkKpsDM5+jMdGmmdKRwlML0jiy0OtM+jvroLYAxBIAhkB3DF4wIOfbDqBQwetTs2bM5uN22bRvlcmTIlXNHwDifC7LiiiuuIDtZGFNcKZGjPo5LOQXkUJBHAHN79dVXc3IJWsAITDRvvPFGSAKMs0luOasjI2fDWOEAQC47Wuas97bblgM2ZWYL5OVLeU6v9x3YR5Wzxl7WszF3loks59xTm1upaXNyRrKYdI64917z1Xg0HnWjdr4PwlJO7O4rP9/R3ttYmF3qzzEc4vHMjdPvXNKaKvQU5s+fRblenzfJar1pwYwpsXkZ8+HaBJy2nZvmTYZXiZ5mToibmlra2tqmJ9KX3HRDQ6ZB/dNxiDSp9dLam2eWuvnIfM2U+in1NfWY9Xzy0gcounTQ4kCaeTbsrXdav3Tb7KSTrPfqo/iDrolZNZYztyGJ/+YjjSm7Oe6Uij3lyycuLzWVIDVSEh9mLUlOwTsarp3rHGqcNGFC19Gu+dOmcfb/lfsfTCRSNdbEulZxINo/2L9w7rXzZy3rOgLPW2FjJjo5U2N94poZh/cM1sxptYrpSCxZGuy+9+6vy4y/P5mIpLF/Lubxjjxn1qwpdZmZfLoXayjauqfHu+aae/F7F49F6hrraKOIG5kyfco999xD63CSzZX2pXUI9BCcK9O+9V6a7sfpPt1sxowZ8M14LPVoGnoORvIcDNNJyMhTrPc4zYVOujRNz7k4Ke+8886UaROxMNdugIEU7ctyiDNySiROK3CQv3v3bgpSMQ6TMX344IMP9MgZekjnEB2csJ1eREZy0fnp+aTrOKJ/EsH08NVXXwWeOGCg4hqGC5ADNA0tq83kk4dsodf92Z/9GS14ww03kE4zAomMlDg62s5/+rNzfvv3rt+wbu++Qy8jWjjkLZXthBeLWnGMHi13MGLlbHcAFe2KpzdskAdNJ5D+gEc6fsaXHnHxcGkME3gEfuJisIJRJj9TtFiImmA60pBU5NFwInJSoYyUy+Zf/cKXlt9w1S0//elP39uQZOhZ3gRL/FB24u2P/m58a6LD+SFgz8Y+PNSF4aQ5gLZFRW7cuJEIfRLnf6BADSHlSPHRYSSNiEPYouAQaAgo5Bhge/fu9WEQTaDat28fPblYX0QhMhEFjPeU0L/ckgvZSBFk5D0l5JifF3GKsAUzQwNIpQEhiZLyYciIlJswqQViisk8pWAADeYHHngAVBj8gh9//Js3by6Y7ydg5EpedCbXWxZfW1OIXTPvy5BXHBhAft573XQ6c31RrrxoA0w0M6ll0cKJ3qK02995pGvuxIV79+VvnP/pebPmMf7qY3WMw8FB7/q596EBG0vz5ZVAurZjfe7WeQN7i9bRzBULl1OLeG/D5FhDssae2Txzf3aH0F/GU6R36cTZlNXdsb05Nak+JW/vxBusaHNriz1fYOw6LqV486KJ6Xkt86L90VQ02twgsuKBW75ke7WtkbnZSJo64j9zWjpRPzHRYk+3csP8WTZz8uTmyc1Wp8P0v9wiamjKzd5kLzko7wiREfyLJk5MfH7SzNpFKS+F4pwxcWbxyM6vf/Hz+Ww6OTilZcKkgl3As3NrJL/giktaE625nBEhhQlzGyem5kxpcBsWtorZlptL0z2WX/YAb2NNjolG01CsKTRMnDUpWpvvzdOWtP7e3fnbln524mQPWyjpT3DMtq6b20K0JtdSYzdY/UigyXcsexhzKLe9YVLNEnhV6i/h/XtCy5RifgICjlD2cCqPep3xyU9+sqmuns5G94CH7QePMEVZcslCbpsamukVTKvQs4suuxxV3t3Xy3Xvof2ko9yZm+UGJJKMJ5khXHXVVWBT65m4K2ZzG9dvRNXiVJNEt9lFNaNn0bBth4XPupSgaLorXQicMJbAnFANoIXQMHykOIBTct6khWR6VzLCCpkOWgmk0OIAiPLyYrZX4heJZCORQfwsC5AZHMbLckWhxjyGOp8hwX9zLCqL7gje2xX/ENaz9NcodLPKRv/zY/5gNLMUpxofGRTlic0c8Rijz7NEUIh2PBxAiiIkJ03mxZUIWowFBSm83YGdbvAYA8WK0iEglxA4rAVYi/HWTVD/kg4q7EdZmIAEGPQv0g/xtX37dhWYaBPWRGBramkM6l80Mm830eEBmDxlEhISJYvKQJ35taBo1jUN9Y0gZ4YHEmtXSZU1yNUAmmWsUdlF1ixWpJsieOUTqh68aVHkaO2M6bPLhd3JniSziOtmT25INTSk6n38Vl3rhAW90+OX9wz2ZA/0LJx52c5t+cUtt06KT7I6eQFGOq1TTt8w9/6OQx3x2ikNmrPHumvZV/Y35UsHa+YvuGFf976klYw6k+5ZMW12Zu4EprrkMiupT8wXJid76tEU7BoRH/RyfeXEBO9SweQ1c2moS8RmT57XNI+1bcodnDp5KhOGL932G+Viuri/Zu6c62BIIp+YFm/JNwxMsmZm+IbAUJidKc6smTnBm1QYLEyYuICV+GUTZmLX3uosEhCDv1xKLWiY2FAqtkRarD4+hjS5zpu9sDkS55MNfTNmTJguurXg1ucHbpwzN9LeUlsj+EtOy8Lm2fUL56T66+fOWyxOAbyaWDl21fQ74XCrO2eIBFxElxoWz4scrRH/B+XynEkLjuwvLb/0c5l0f6Rbjt5kamVZl0/CEzz+nFvlgxoFGDT9tsu/wNZE8WDNtZfcS5UT8YnxyOS+WHxqZG5mQDK5uLZnDjN72eI5VzbWtdDi9C66FtWEJ0FzauZp9AreXKID02d8GBjO7YJ5C8DfVNfEHggY2PxBmYI/zv6kZW1avxX9m+vNE7fr5fUAsqNqdxlnCqp/6X70fPQykHRU1b9sEDENIFcYzjsHHMvrt5z+/MDbb67+9D33HvjZj/liR7q2oXugd3i0nAkq6UJs7tEt5YWEgQHGAj0KOcYAoVeQTiFEzkRRIY6QAyEHQg6EHAg5cMFxAK1qdB36Tt4GZ97V4xatfFH176FHfzrg5u1ooo/vcpzREOrfM8rOEFnIgZADIQdCDnyEOSCnrxbf/PK6S4PPrHzxtvvufv/1NTsPH2rgU558npuaYeNi6qfrUlmjnlJgtcvil30YjiHY60Pvs+vC1h/nPsGdw1PCHWYKORByIORAyIFzxIHTNYDGbMjf6CQO1XpV8nUndIyqkFdDEIZcJAYxBFOCkMSrIIPY/NI5ANB0P6UKCbdBsv2nCk/ekRnBqWBkJHBgzxYwJ5wQjrLlYJ4sKEUflUZ8avUWGPBw2ICpJeZ3n94jSHoO79m82fLqhhU0+IEkVGHj7Je8Sh7Hb20bN05Kb8LU8bnnnkMTp2pTHAJVZxm6V2SKllMxbCBWrjx86623Jjxv27ZDdXU3cZxGOxib2UoeId40i/6haLJDMIcrTAJWvfrqVTdv4egOVO+9dyjTOFSSaaNRiB9+fooxvwlGzT/OEn2wqqYB58iUYEHHe+ojVOCxwUZ9CoZR04Oln2xcEYKZvkSTEeGglKMs/I5zlMUZRiwuXoQ5EsM2hUYkwF5meBzHcirMSSoTPgoFkiM3DsYAAJV2PyKkgJOpIR6gOTnDLpkUZoccJ5MLMDok6ZwK08eCxOuJMsas4KdEJpE6uQS/XyInx9CADTRnfsw1KYhcgOH9iIKwxQE5wBBPEdQOMqSISIkIKZxVd246zCljujaN2dbSZUuxpj3UcYjivEiZK66nofb111+HS+s3bZTprCcGT1u3bgWN68mhzqSaZkxtSMRALZ8bIFKfqOFa0zQH37FH2zo4IwcMYhYuvpMTwe4PJG8JoylzOs4AGTogr0NWNDTIUeUbb7whdJrA4SXMQW5gQgTbuaUisIUq4NWSaupY5gCSjBAG2UNZLRLhDFWDe65bhqra2gwAvIcArygaSNjFrhlOjsFP08OTYqGEPfrbb6/Dlh0vyVgAcHwlJm7FPKhgCBmNdLFpcfAjoMgeT3Oy7s6bO59aHNy3GyKLRekG9XVT6CR4Z6Tcnt6D1CWanUqjwFWqkHbFBKsw4OB7G2yUiHiBWiQkDUQn4RYG0ruSeTFHgNWYDsAxyjKGmzYdgEZXG3dQwSi6K1zCcBk6qSZlwfzLL78cYkArBicmUCOVxtqdSKNEehRuv6kv+GEXNGNvDR5KoXQSsTikO4GfR4oHMHLBFizvtY8h98BACiVCEjQASSQYyE4IpoTxc8kBbRG90kAUrc2kcaWEll2zZg1tjU2AT5vpeOgAl+bD+gQ7FavcuGHDBtGBWDsZjcwjMNNtuCVo3iBmLU7SA7obgKrgZyRd4+O/QgDDh4BdzvubxPkf9JBdq0zkhDhHApB3ZOL4SQohT8gBOIzQQLzfd999xFHBZEGLoZHVfsXHQMdD0iKxkTaIaLocZs301ZtuusmH4SmCC8UNAAE5iYBCXaJHmNoBRlmUgtyjk9xxxx2IOD8vKoAOw1MwE6EsBJrRUA10ae1LKA4wIMkZKaVUgXhXfwemD9hSE8epPum9vd2QStEMpVKNnPeUbJF710+fjxrq3Pq+lojmum3+FRjibO9sMykCSY2g7cknn+Q6UB4EplRuRm6/9tprAhPp4JIdtHhXShWKJA6Fq2ffuH79+r7tu0lIuxk4ecOsT6E41h94h5RERF7HusKqpz9/cHQjbJk+qYmZQDI+E/GOs21BExFKSKFGvDxjdKjMt2EIipUI0+lMbQ7Gkh3lu/SyFTjbtqJKvyBYsugO9Ok769+AG1F7MhfYzpV3bMgOQsGfTFIj8NPKcBUucUVbqeJzPjhCY1GiqqEXXnghXWvwuxPIS8sy69i1E2fhxXikES25aMElzDpIlOJNaG68FDa+8cZrcIAqI6lKhWa07Zo1rw+ByF+mAVyZolt2v9y7k7nQB5gVZEt7yOhEHci4dNEK5gDZ3FGeJuJ7SWlNT6Q/7Ny6DSZQEOZitckMvY5eQYmpRJp06og51IEjB5kqpGtrZKbkFFHZsAIwp2i6GZ/hcJzFCxdj+i8EiAdxmVguXbKU7NlZOUQZtyjWWdNnkaLG9GqA5fdzMIATMAhjpkEkDB9pDtCaKgO1FvRDIrSyei31q8Zo8uMXeIQqEOifWjWNXOA0f3zIo2loFwQyKwVkNVdtLMQvk0BEtM8KYFSNon8RR6wLEFCoyGBXZN2B4iYXMrBK/6I6QUVZ4Ff9+6lPfSrY1bFmRtYhNukhBPo8Olo1vq9/KZosq1atAkl/7ChFR2tdtK2+scnaDfwDA31oNF4plUVlpFvot2X36fb5X8VZdc9B0aGsmCjrnoWfZqn1werNAmMCHIDOp59+mgUOBsp8VKeUn8hnEyr6JXoQqFJRZDsFoSkgDEo079TJVzAN2Lq9jURSoHPx4jvRL4eOHOaWiQGQoOUpSll0RMqGh431C/mWjqhRgurfRAv6l48MwEaAqSNXFBMI0e/vb+4AD/wB4cL5t4JqMHdE8poAD6GZaQOKac3a3TRTfe18JjngB0/ZPQRUzJ4Ptvc2PA8GAinF7F7eg+04sA+l7Lp4PhYH2OhHVLwQViP4S7YoIyYerEa3bT8M/U2N01kL09xcUeI81VBfO49qrlu3BoIhFe3pFproPPqlDn9ZSAcDnhKtSL9kdKQUWMH84cNtZlHvNVJNFDc8ZN3K03gsBcIpk2dA3qH9bdBAf2M3gO7EBDLoiZx60UbwQeYJ8TgwtCwwwDPj2rdnH5jjdpwrrACMiZDgNwbQ06bNgldTWmTNDgDMb6hpQLfqPFP1L/jp/Kx/gSSQl8CqXyPh9YLgQE3KGswfOHiAofTwww//+Oc/bhvoZbpfWZ2eCRLpjWwEERhiiCamo8xFuSpuv2OciaJCHCEHQg6EHAg5EHLgQuYA2pUpq9lrDejfL37xi9//yfe7jPVz0PTqNGsS6t/TZGCYPeRAyIGQAyEHLlYO7N57cMqU/bha+9GPfnS0WKiTnSgOatHQUmPdjTnlrWR2YFjw6rEO+2asfNm9YcNEMJug8YuVt2G9Qg6EHAg5cHFwQE4CThh0Q3wkGNKe7U6e8ogIcl9TuCWiiRpXGOIjw8hHilM3UvU6MpdfnBYEgEKCzc/i6yEfxo+MijCY6OcNJgbjoKIgruwCc8DAWQI+eD7/+Ycuv7rv179+qZDbmYxf3tuLaV3MsXEHHYtbchwlHq0koHyhk0WxXbCz5VjBSZRXvvHKgroEOvvVV1995BHrlm8M4DtWizBZjtnC5mAYd3Qlu0wk5rn8Xn/3g37nH//kDxpee2HTqnc5s8mU41ZPYUCc2A45yxHPkZbtOdI0uJFEZccjTsIul5zskfb93/nuS/Pm39S+cfNjv7ac1nbsFHFoF7Hjni2HWGr0rDOIqHFXKc4sSU00ZxqmrX9r5aYPn2qd08J3NR/9xRZcpqaayi6vYwWDyeUnCMqhMLIP8MRvAr9Bh8CHrbsUSRBVVVzzViX6eIj4yIFRbmtEYTRjMHsw73ji5CX4pfhZSCFQ8VHrziMfUiMgqUo55VuQ61DFSubZZ58lTh/O5gawL/nsZz/72GOPcf7KMRjHXUTuvfdeZntiMWNahEM4jFM5n/vFL35BRo7ZODjkCjwZOXgrlgscYdK1b11+C4OCg0MyAkPggBC3fz9/5NEg5V//+texc8Ii1k/ErBmerF27lmEFKmgj3H333c8//7wxbBWLVcr56le/CvEcwwAGVQSMuakC5koVVBFmvdH7PnMvA6qns5++Gs9YpX6r0OtwPLl3Z7tMhD0r2WDNnj2re/bgc79YJ26asKspWOmWprrbJq59Yr1VxHurOT5yrZ7luaNH+7dv3+3gFjZq1SblfHfBlV9Yu3q95eyWtwvp73Xxhx9c/sJzH/a1V0y+IObKG2xmyQcOHM719UWTGUidNm3G7NnFN1551691LJNZvnz5xo2bsYjibFZm6bHkdTekYTJnqC6esBmusVj7ogJWU+ue39GPM6qh8NDDMzdt2r19ywf4YbYjHufckydP4PreW+8MgVhT58zAjmrNmg0dB47UtjQM9PVZvOuwIv32ms3eoCPCBTMkXt64iQPXxs1r9m/22qREvGNGIitum7ZmTXu26zAemy27hFlZ55UlOuSH697Hua2G5hmLFy1KvPnMBqse82SM0doTqf1XXVV6a+3rwpYch8m48raOHu06cvDInvf2pCckadzyoFvXWrPo8sWc47plkSdWj1XXkLr33rtffvnlzs5+j7QI4926/fbl8PD99zfJsMFRZanEWS8n07/85S/pfqxGzEgSAwKOpbdv25WpSUMYkPRVzqq3b90RS+CGTzoPnLnzzjt/9tOfpzLUWYQJeenqHLTv33tAqiPyOoolBJB8EZtcwNCBKZQ9TUqkOAriCv7Pf/7zXCnC9HExdBAMQ4FbgmIAbCg5/HvaHBDfkPDTbCxLfOg3GmJ6CMk0hD48tiFQxHYC0eChRaP8gEE1oyYlYjkou327f9XcsCyb3TUw8J6NuZ5DT406VhR/kzYe4g2wwPPJBjdaxoDQpcebDy+gZ83PkKqFcyUNL9N8KYJRx9k0fz32zx0PvJLxpEKu8GGx/OHA0SfjzoaofY+LtaUbxS7bs+jtzB94HYKuS93pg3imyxs2US4FUUFxsWl6qJQJBFdzK5eTIiMEPlkOIHMwMPrFzx9LpOIIFuX81772tUcffVRvFeFtt92GsSnWDIgXunFuMD995jSMm5966im/RNIffPBBrGYRYmhGLHjYo+GdGSy30HSAIfTAidn08uXLf/Kjnwb9gOPFGYGJpgaAcQEqkKNwg8Y9aFik3GOP/gpUxcFSIhUt5p3lt5UwIEYQ8v0RPM/FrMjy5TL3fuXV1U6NT5rlrmh57tnNFqaM9SgGeYOq/oG5mzYd3H1kjw80c948LIre/PWH0Yl1Th8+7KzGxrl1n5z9yuNGh0a6BLJgNdw/j3dk1r610c9IxLlr/uuvbLTsdqvOwrOj1RCvXbFi5cqtHbldAiY92qqdOR/d8fbgFvzjbWncPXjEapqeu/76xlefNAZMEQFK13RGlze8/Mxaa7Ao35w3A7F8R8pzM2+9/Hqi2S5my0zhG6bVTe69/s0fvFsxYBL0VsOnrlm3bkv73nbRoYXDkTrx0o19Ei6xzXO5cNt49/RVj77rDTgGTBIz907c8kEbhkqF7CHRfUXrsusXN9bVvPPCG1atAFiu6KMpA5e88+JWFCdxHNu6Ocu7KYMf5QN72wTGhBkzHb5i/9pz67mjq+T7kU/N8dubX175ngLoddGi+US2bNkhQojgyLyr5tOTn3rsxdoJVi5nOQNxq6ah8XOtbzy3Ltu5Q2CisnK5/PJt6Li2Awfz2UJjc0NPV2/rtEm33FL/4mOrERlWwTDasu75XIK+yqzM6UPlS+i+qg+rqZdeeJlvg9Ahce2LVVbqjppnfv6CAiglxftLsGv/zgOJdBzd6hTcpcsuZ8aI5TRgaoAV7Of0NEbQYH/2S1/5YgXPRfRHJwwXdIVE4UJgQP9WyCUFS3ej40SZcDBc1qsAGw/NxqOzKBu6fDyaiPIJka5sIbHPjDoGHnmRJFFUWUKGIr/hoN12+H4oZr6iIP1Ef9I9vLxZtAo9QFU6qMAP44h4Lj/Fgf8Qflq6Aa+k6/cZjC9nBdQrlRc6h5I0IilmlgeiKMbPETvPz+vrcEoDyWghbjMoCiaLwDOpNCpXEujMqnmHngpzFT91UdVsUmROq5GL8nrOagfDWVg9/vgT9AwWAmhMFo+f+cxnUKxB/cuiEuX4/vqNEbMLQkNgeczSY3iZaVYZLJxJQb6xmZJKCzZWzdz6619f/1atf7/6G19hiYErXzIyoFiksP5F/76mL/+YNmZx/YUvfOGJR3/Nq22JmFUwq8MHHqrZvm43a5BCXt7B6+vJXbJwKeDbtm4Kdozkp44+/fS7sgBjbctI6rPitx3EGnbHrnd8sGlLF2LSvfqJLVZTyuILZv103pbue1vfeNbox+hRgSxGdt8UZ1JxNOCamuRrbpz6zvMbLOsoizvZ4kpb1kMrnntuq9f7oY9/1rIl6I7uQ4csdB9kZK3UNOfmm5tffPRNgYlSHrqw4ZYVda89/ZZVKDJ0mEmTtu+WVDTSsOnVt4T+pBk6GeuT9yx5FaXWtU9ymXDrZ6euWbOjcPCQPaHV6zhKEZPm2BhDf1h5gUomV1ZDsf/2Ka88DqlG7qRTVq999M7mHR/u6OGbRU6PxRLBtZZcfUVNfc3aF3nBSbSe5e7mknhw+qpVm7Kd7cQjtfvdgdK1n5Qdv50fyORKw4SpeVaar766nlsm3N5g2Yq09N+YWrvaTFeGhuziJQsB+GDzVpF9BLeDS+Suic8//xLsiycwNOddrGjT52a9/tz7Hd09PNWsN1x3FTw8ePhAXx8m++meHt5tS2GL/OyzK4Hxw333Wa+8spIP0bEpy6SSmf+NN16Lleqzz74CDMsgBE4mk1ixYsVLL6zE1JzEclRy3/fAPZhE79i0uyIoY9aiRQvo6s888xxPVf8yQFgR84VZTNjpooyg7EDuC196GIBzNmyF1jAchwPSVQal335o5T7cseE3J7c8uPyOH6x8gb7GuCSYkcBVtZVJCipGTTjRFT3FTAyZw1oDG30WHQQS6Q8kam5uT4QmfB5yIORAyIGQAyEHLmQOMEdSdWmOIqBUtGxFvaHkhvScuJeM8Cigf78wteneO+549MUXNBOQMt8SLRzqX8OI8BJyIORAyIGQAyEHzhAH8ANt9jOsd9a+M7Gu7ra7b3vm18+AG9c8AwNZVbzmwGdo3av6W3T6eAN7HaxzQcj+Htt6BHbhyOyveTUSbomMl6EhXMiBkAMhB84HB445XPQJQHZrIIUIV2S6RnwYjZCo4h41wOEQR+/+WR0pGF5UwQdv2TAN4gTef+oXBwBIuILZ3131wfwIW7GKyi+dR6SoZiIOQooLPvXzagRggsYpy0+sAtNbrTLw4GQvmAhnVPfff/+Xv/zllilbMERYu+ogxxXNzfOBVOAgnmAKtdZbqnDTTcvrrlxxd4GDlue2Dh2NUgQAPm3gEXj+B7aYQdLamoGA+KIj1xUKL6yR767i5gfWURcxUjk2+NhAzhN2sVHnD3/1KwsW3bBg6pZNm37yxg6x2HDdLjDo4Qf8I5fOFrRorqSAAeMS7Fc4/rnkkn4SV7215/BhOTMT+BGUa2KQHFKq2mVkQwPjZ9HSuSXix0c+HYnEh9G8ilNLh0ug8kshQvaRyH0MUrDx/emnENHsWq7fmZXDCqYAZORWq8xVI5qdvHQnTdGMCqzZT+dK0YoKtARMmqgybpOKpXwinsL6mZMtUrCX4uwWYEyNtWNzVspRLiccyiKufOmS7GAjgIoewlNsqTkVo9dxi4ELNlsYwfDULxfiwYlPROagUQ44hwKWr3ilAiG15lANVDg8xoSLDqnYSCcRWzHwwx8tDtqwswEH+GE16QTew+Ooj0SlnBNr8ICtp6Of09O6+hTW2l35QepIFeQ8lYCZUDTCLYLCbqRHYYiAzaOV6+8XQ+RyOV5fF4n2g7ZzxwBW2p2dAw5Siu+slK2SVKLMJNjKDnD6m25pwL4Zh2+QCg+jdSkex4RDEQiTg+pCQQ4kCZ5Hlaks/nPk1oRiDuvqo237d1vxdLKuqdDfa5XzIGdwufkBAUlksD+myfAQBrswUdeMXBnpQq1Twl1tMoFds9jjwi4rLh7cdaYPckymOo4ciaTjoMWYzskVOZ6HnyW4wASeI7KC+IZMp1rFCEzz8g5DJAK2bGennM0iz13827twVUQr1pv0ZYZmFMO2AdiYnCzm3eVogbPwYncPGTntFpNodv4QQhELVvR191kN4t4Pd9XsBQ4MDGIB5na7sRYxMShZspzAxRfyEG5hZkOrkY6JADWCh7RpX88gtaY56I0cygpjjTTI50tkhBWRKG2SU/6AHDCKhi1u2SuWS+m0C1gyLZ+9xtcYHYAOA36qQBZMEAgA00YA0LvogTQiBFM0HZiI0FkCT5pS8H9G/8T8FYsbspORp1o012DcTzxfESpCOF+ln8dyacrjNQTt5RMGjAkinLG6l3QMlY1ewHiURByL+8A+J30Mism/9SHHGSHjKeTlGw94rZs2LV8qbaCXSvMaOnhVCQoJiBmEAV2afqv4pUZDNBEfilb/PQViqlGE98fhALxFgCCFUFKAIM+5oh9JIUKr+fmQtMglscaPCgC9EnGHFApO6hBfSHIjHnlPxhrkjZaSKE0NoFKBhtgHP9bPdAkfP0IbCUk3IBHBy2cBkJ8UwS3Z6TNAYsC6atUqShnsyUIAwMl0BBWPgrZtZGSZ7kTfw4AG4Biv/GH9MxSkjiwKorwAaCyK4nH0L19g0F6qUKACC+ZZmFSIEVXEomgmHtAgBJjJAnSg+xjIoqEDgcRIMikMQ97zJF9CIKskFyhSbAtUsBRVwu1gj9iBgZZZjWUMgBQ/Gp+MfL1UckF+TQIlCHIMoC07zlcRJN22ensNGLovQAWeOOEhT0XNYWNUKmMdRQo8oaE9M/2mRsyRYB0moGKHQnZXajQw4BTQkraVaIgVrTJUDdShiRNW2VBi3ojAMhhNaqcsr2DpKxLUiEm7HVhdkRGTL1pkoLtQijt2AlO5pHanIQtPqYHME5AQfv8yPQ38iUztQM+AVCGexnKKyVUWbagSzfBQnRxIM5nWod/CUjpPNJVCLdK1+PwbPYP5Q9ue/dHadKxe3t2KFMtUXKYBMNXsEnrMNhxHXqgb7iPCNGokvYLXN9S9qGNhbEc7orLJy+shXOkP2s9BJZxk9RGz6XUgJA7ARRYumkqZxhq9cWhKhsm8eY30oiAEdacnBFM+QnGIp8p+wAk6EhX5WVUFALSJqSgiWuEvmkavquzJ3p7V1ofJ6F9kJtOfRFoUH63DlbUbooxHPrVIIVlJIbAj8mmabBZF0Y94J+6D0XZIclGExoiZCOKKqwmCiskXJXIrL/pGkGPDPYEUWX6WnEhUZgLUGuRcg/KMocF7IKxTyjkX82CR0p6F0oEADnUGRbhaqXQjklYjVqRHkkwQER3NOM1oDldt7yGVBaOVkuI0IGYBs2qjiUym2JO3arBRdgEbel75C0xnRwdCXlANzRvR3VYyjdGuKAuqZb4AJhkSBj8LRtsmo7QmijvlCAwzmf5+tKEY5BJ0s43PC+zcaRaYZtSDv+yQ0Ra9zpRFqix5I7y9s0VexI1V9uhAAA2CP53ie2RWEzRE1S6T96iEKu+olGL2B0TtMvnRZXiyAY0DJIdjtjVI+4KEEmvytVYsbaeMxnfRxRb9hOaLN4KDL53jNkBW3DIzSQ7TIIv3trZ0TU2uc8CjmhlmPCnw19SLWDNLQ6GCzkNPqyPRlsaynHou4K+paSgU8xCbkM8RRdDI0icNC/E/QAqopI4yV5T9QNmmMHOY4A4r7zqipiEVQK0iYCxsmV7iQ5zVMdmxW6a70nlYy1QEEjyxLKaU7A/IIp06Ua7Rv2wOBPUvHWzTpk10Yx0sFIT+RSMLK8JwoXHAs5577eUH77rv/tvv+PVLLzJ0aCQa9ozMk+iK2uj0TFVbpNAffI12oTEjpCfkQMiBkAMhB0IOnCMOeNZTr678wqfue+ieu3/57HNs1YT69xxxPiwm5EDIgZADIQc+lhzghIqDl6JrPf3CK/ffffs9n7zxqVdXD/Rn4/iBYv17JhbArHPZBhmVu6yCR00PE0MOhBwIORBy4ILigNgWjyQI+a77mzz1d7erNjf1ln1P9kDZrNcNUFDx2UQ203nKSRLWaRze6Capbo/6ZWl2rhrUrlGJARV79HpABTymmRwbgESfAk/ED9xyuMjWPDRgkcbJBHll495syHK8gTM2sRUwASSkg1NJIg01Bp16ggJOHmG+SQpgehDlYL8gLqzEZkj8HxtucYtLHPZ7XWdmdrChu7czmx+8/77aBQv6rd7v3TztYGnmVOw8sok5bAm7sd5oJmPnhdXk01IoyLXl+CeJD0mnmLNi3/y9fzvtk+jUjc0tW1ZcV1r/Sj/nFpyb8YNiIToQKDgWxcyPkxW3o62NQ5MHvvKN62/7lGXt7Kpr/cxv3Ia73IWRxMFSrh3f02UHeCjGjkAoFz9X+H+WmmDGwC8St1onNd9+x5cta2Kxzp1325T2+JxCcTCSK2Q83PuJtSueXw39pv4catl2OWrMaBo/KGaccnzP7V97KNYaae/cevOnL3vkkZX9/b2modTMgazD3sFhs+S3I/QOWhVPImwO1KQTbllMEjnYKuQGSoUsRzzJeKRUcFwPR9dgwMZQjVjE+ZiN9QdWO5z5GBN5To85lkuaIx0wkogvTE52iJiypPZKOhFS5NZgA4zWxdgWVLQ7WTQXnUrBOOnUvqS3gJOXPsYtMDxigGB3QsA+CWASwUyHxB6F3ghOIKX+ZqSQl0CK4tRb/2icPk8fNhSJQRLdONjtAQaPZvGvw7ZsfpKJBOE0l14pmkARWFYBmE7V8B1Y/D/t3LWdFGyAqAhF33LLLXwHFuI5Esb8lzF4xx13QNszzzwDkVgtQxgfV/30pz/Nl3MxXgFYSV186RKyD/av371TjoRJvOkTN8GKlStXFvOlN157k5TpU2fgWBrHV5zzkRdTe5iG80vO0ui3mMtwmggZmNRDGJ/0hZmIFHiOJQH+pzm949iYoz5Q4a4S5FSE00cO5zi/JAteu8hy6NB+DnG3frAdk687V9zOcF6wYN5rr722dyeWzdYV1yzGxzm+migL17/dBw5OnzeJj/Du2b+PUl56eeU6vh1fsq5aOh+BAYse63ts7+5uxuHsRdMbmpuw+O9sa4cPm97/5dQFExYvvAUWtTRPe+KJJzatfzKaTixZsoRmhZiXXnqpq6MjkorPmjUD2UJGiKTE3VuPzlk0l5clOH6Gwy+++OL6DauT9anrr7/OfMvYImXjujWxmuilyxZdf/31CDH4+eSTT77w1OPT5k69595b4DMcePnll99c/QKNe/2t12lHwvRq4zubyLho8SV8yJieRkb8k617Z22mLv2JT3yC4mAmGQ9sPZBpSi++bOm0mdP4OC/851j6aPv+WZfOhDB4CP5nH39u3VO/jtZFPvvwZ2mmZCpOl9i07sNkfez6y66ZOnVq2SpydPrO6xt2HNjXND1zzz33tbV10hxYJu3ejEmZdd9ddzXUNjBMNr+/edOmrYzDy5cuXnr1MmwLaLjnn39h25ptk6dOwBfpr371q6lXT+YgHE/YGB7fcccKbO+o5urVq7du3U5DXH75Er4XTHVgGomcHPP09ttvh7EoGvrY6tVrwHnPPXdlMrWUSI0gg3F0+eWXqS83hCo+3t5Zuy5dk8LHMxnpzxwSY/KFtpoxa/qcOXPgGPQjwInQD1esWAFynsIxnJ7COjohzikxPKUfrl79VmNjQ11DLYOFognBQcpYg4ekGLtBkQMKc16u8A2eCDFGAJ4XGrRQCBg3K9g3rvopDk5eiWBGwLm+iu1ghcgCq8X9JKmMTYqTEo0HSlqIRF524Iqhs1t2UtjeO0W7OJUfY5x3NDxjoMe7wtguYPBgl49GvSIWCRGPN4KkjeGjlpe3o/yiVqmAl0gbWuWxPhrzOqzV0Y3qfHJM+NEfJtxLY+Ubo+7upINakXK9yAH8b0a8OkxImGHwQevZM/Ht2oPtRmN9nWLBX7XoUKMB0ZuSzxXjDn7MDKSvyqRDUkcv9bRTz3sPPO0anCIC6XPS66J40t3ywdZkPMXHDZjfdnajhja8vuoNHOXyGRBkL+qVzyAgwRA+Cy+9hD6MekJmIn43btw4ZcpkXEXi9RkNiDZBGyKXli27AoGJinnkkUfWv7sB39L48EMHUSJyUsTjm28lUwk+a44YZKL+k5/8BCnKI/QLugO9ACqEIa0DnmXLliEeoQS5x+Rr2oyp99x5T19fDy8DkGv79p3MB6688nKKQC+sWrWyv9DLvGLG/GkIZCyVoYcSt2/dlq5J3PjJm40VTi36d+sHm5m7Lb9jBVQhwFHfa1evcQcHL192GXKVPsms6dWXX3nv7SPNLXXMQyAGClHuH2x6H8Zdc80VVBAdjWB/4oln2vatm9jScten/gWokAgvPv3Ctu0vNjTU3XrNbRAA/dBwoHt9Pu9cffVSdB82wdQIMb75nXenT5+EbEcdw/8333x78Gh7TSbBPAfZjrbFSHf9e5uTqciSy+bjahHOUwXmP/u2vTNxYvq22z7FlyWoO2D7tr9bV5e+96H7mMmga+Dhhg0bmSEumDPn1ltvpb0gGKWza/OWmnTqga98joqj4vlixs4P9yRS9vXXX8lsARqYrqxbt77vyLpJU5qZWbEqQVz/+tfPbF77Dl9FmLdgCZyHNuZp6Can6CRjtPgyOgbWUfSTfXvfTsVTN91zE4KO5sNmbveOddhhTZw0kU/eo62ghLlTuejhw3vFHXfCBBjCNGznri2QvXTpFfPnz2+ZMJlesWfbS7FkMjOhgfnDpNYJaMM3X1kZSYoPcuZC1Br6f/zjH7/xyiqExKwFl0QTcdqOxt25Y0emoY6OcfmiS9GY6EdKZOJ31TVXkpEa0ZnptB9s/CAaj/zmb/4mun6wOPjOmnUfvPNhJGk//JXPo0bJDiu2bN7Sfrj96quuYiAwweD66M9+geJundjCkFHm0wewh4N4WDpSpIjA/2iGC4nyKuXLrQaxfDqe/jWaCOYDIl82wC2Hajs+YsKP1xRQLtHyZB4z2GnuX3//b5mG/ffn37XsorxlGxE9WhVQu/zKovTPYoCk4wfTx+QxGwg2C1qmdpW5h6Gt8hkKqTYLT6E2Ynfwe+XdL/fv3Pl3P2HqwFpTVv0a+HST5fHCxGTLnWg5U+VHDhtLWKxHUeeUAK9Gzm2G8p/GX6nD2QynjJ+er3l1oX3GaUTzgpP5Efr3g00f4oD83rs/xWqiq6fz3XffW/f2u00tjawLSEHgvPPOO5s3fdDY1HDNdVcjUemorCKRtEh4dCVgyFj5IM+6dagVhP/CaxciIZGEfMYB0VTIFS9ZtIBlJiIapYPSRHGzWLjhhhtYVrB0QoQifqHnpk/cSMXRyKg5FBkT9VmzZvKmKxmRh/KJhs7uhsbah7/+6Z4eeSn99ddXbd9ypKklc+01N86cORsV9s477/b1H2Z239hUt3z5F6EfSSuLyvdfydSmV9whaze2wlhf79oqdVy+/A7KRa6iU6jOzr0HZ8+dh/xvnTQR3QfYwQ9f5+UWPtkEGJKcnYHdfM3J8S67+kqUDmpoy+YPmBjsfu+Z2tamu+76LFqJJeSvf/3rHRueTtXU3HzHPdCp+rcNp9FF79Jll91888079+ymRvje7tizIZlO49maBThaYNuWLYe2bq6pr7/htlvhDNpw24dbPnh/I6bVlyy7BFazLQBhlNi2de3kWTNuuOGzLNlQQ1w3v7YqXpe5bbmswafNnPHKK69sfW8TE9+lV17JVAeW8qFSFPeu9c+n6+uXL18OYTTumjdXr1//Fu9cXXvDDQsW3EHLokbXv7OuwzrcMmniQ/f/9pD+/fWmt9+qb6xbOPcKeEU3YNfiww1bYAvv0DIxgM8w58D+g3u3d+dy+RW3L6cPUPFVq1bt2/kOMLAFxnKFEvoJH/FoaKq/+uoVdCQ0KWBbth2BpCuvuoaMTBWo46btW5m9NE6awB4IqJiS0XMokRnIsquWMfkBns6z+cPtTMwuv+Iy0lGvrPE3bd6OA/K6hgTrd7iB/kXp87LcpZfKZ7vohCBhr+bg4Y5EKvGlL30JzkeSETrY9s3bS3n3/ofuo3Uomvnbpg3vb2r/cPHiS2k1OjOs/ulPHnnrzTV8/4HvgdC1mFHQV6kRy2TQnvKQJ28YzggHkOnBGU+7ZX3/+ac+e/sdX7zt9l+8/BKP0CWileXH54eCKuDkFnra4tBMo/vBrwIpfjyMhBwIORByIORAyIGPLAdGKEcUXFDRmm3SGN9vGX6zXOrKZOt7Tz/1O1/50ieuv/bNNW+TMqR/5Qh2CClphKE7c3PCS6h/T8iiECDkQMiBkAMhBz4mHGCTmZoaDYuxTTJhRYpWbuXrr/3mQ5d+6YG5//vxXQm2lMW7S2Vruc6o3P5j9PhJsEq36LlqYM0bjJwEohA05EDIgZADIQfOBwd09VVdsopyTSU+9oYmT8UxmyVWoRzYix8RszHKiSaPOMmoxj50z1NOELlSBGCc0LBlrw9J5BCIrX8i7PWPpIEVIIlcCZgmkJcIpz5cwcCpqkZAAj0g8cNQ4ZW/mFZQKEYYUK5ZQKIma5ROEVSKvMFcesvZOiSQhSI4VyBwCtXXf8DFQoQPdZvzJ84SJGPZKfX3ay7gNYCZQEGceQDG0Q5nCV0Hd3Ye2N1z4MDWrX2ckEESPBT7JEOAZvHpBA/2sTyCTk7OOIfglKKYFV84HGhxfEhe2gUAAnm5QotGKJfALTCY7cJhTnE4ZclnOas6CD/JDkvVcxsIySUVMdmVDE0EpzY6VHEKyAdWqTgp+KjlrIiqAUZBQsEQD0lRVFx7enqBJy846QDwcGBAHCxROqeJJGq/IhLMRUbFxhXKqb4WAQwYKJQGJS6sO1FQzACDB2x65uonailQSETjQXykQDkpEEwLUjTZuWq59AoO9tS3op93ZC1IoTiKAI8+hX7wEKSFTBsFCz0jcUokQCq9DoQcuWH/BNkUSmcgQmfguIugBJBOFegPnOxy0kYuRg3UQiqnbjpmYSDtDloMd/hAMHHyao3Ag1Ept2BQ+jmVvO2220iBgUDCKBgIx+AkADqWKQXTLgJ08pQAYeC/8847OTUEDHiuIMeIhxNZjMNACAx9hhrNnj2bE8fe3gEIY1eKs2ROVZV+5r+RpMUJKOfBJKp9WCQl5V5xxRVg41ATe0axpffE9BlUmt1OivNIcq1YsYKDYTBIlU0vw4CJOEQy9lP1NU6+CGFkxJqKR+LXcKCEkOEoEVI5/6YiTNVhIHV56KGHGL8canKmDscY+CRCFYm1LXVAUgWOXbnFjIlSsGyG5xypUmUg4bY2GU/BTBEcyQNTFmNXDj4vhRsA03BUh2FFFuyHOFGm7jjWyg7miFA6h8T0Q6pAIiVCJ9+DppoNE+shlSNwMGDZRhEAR2QhUQYVMFdddRXNFMlI2wJGIt1p+fLl5bJ4WUukbRJJoRQCYgDrTCKwBToxSk6nU5naJDWiIr/xG7/x27/928SpZk1NCh5CANWk4XC8zVkvGTH7o/NAHsX19vbR6GSEYCrLGTww9fW1ND1tdNddd1F9mEx3BSGdEMtCgJEY5KUUiIdOUJEIAJ2cqn3qU5+iOPgPAG0B6+jSNA380SHP9b777gMA5DRxTU2GLkqAEh6BORhIIUhzXwABFumQvABoOUckwHwtSVth1DaCJ52d3Ug5msnIOnk/CjWljUZGRh/dA43W0DBsvXSOKnCiYujqmG50t7cjDiGV13N8BQvN5KbRk0m0pHjFIwTxqWBnkJIFMH2kfdWwKwgbxs8kB2Ay+guDKpAiQxBfqBjkM1ahPEJT0AMxnEL+EJhFcEXGIpEQR+3tHTxFUJMRhcVXR7jtaBMdiogjL+nItObWJtoX2YsNK0oBeUVDUwSFIuUoBamIhRa6nisYELPoVkQfjzAOQ9pjvIuawAQZEYqrPyDB9lu/9VsMBIQ8vau7q49aYAdDXuBRoARGEykoGhQllGNlQUH0UtQlYhZl1NBc29zcSJWRxl/96lexy8EzJRmx4QYtGoEUhC2Wx2SkaF6MIcAB1FAmE4cMimNqirTntpQvUzTdFWzohckzpzDiqQIEIP/Bhs7ii/BsjsE9NA4Cn1doQIKmQN8xtDEVIvAGjVhY9RchDDrBgOqHXVQIYy/I5grf4GF/bx7kqKHPfvazvLUFBhqxr0/MdsFM3VW/FAsyxaK9vvWtb1Ej0DI2YRrNCgN5ROk40gaMqlEW1MLtVEokDEjQjzQu74nxYRUxV7FtPvCC3qGBeMpoxd8ofebBBx+EtwRydXcPEEGpQQAMpI6pjEgA7IzpD1p9GhrrZ3gCS2lu9HJPVzeU021QfFypKZBWoQSpsJTJCdMh5h7gcQsOFfnGN75BT2OWBSswpp88axolwnkUrnC1UKTXmRZ7gGkenYdE8oIWjjEdgofgh374hjanCjCNCtY119LccAmOoW3pLf39A7Q+KUww6MME7PbohzCffk6XpufTx+hvlEh7UcrFFGjxj3p1tFGMKmH5OXpt6HIAMNMT9/bHho96m1IvhhtXrJ/ZHGDIcHtsFYdXsowIrW8VjM8EInR1vdU4t5pC3ydFQxX+j9wttVCaqdrZI55SVP/CbdQlsg6hqkYIrODok0geZAuapa5e3mzkFtGH+kPy0I7AE4gjlNACNHHX0W7kOQINwaVX8PNWJKIJqY5sR7XxdQVEHNjATG/nEfMxCCAgEkHFugzxiCpk7YaOQKgiP5HS9I1EMo6UJjtSGoSUAnNgEXmhHE2BgJVFSDaLCgA/8h8tpjCAIcDRO9AAWhK7O3vgAIIdbEjgPNPCeBz6qSPCVkU0ug9UVBlhjr5g/iAT4qhN7RDd4NTVOqIbnIChKZDJkKF9UfWvLtZ4D5FC0UroWSYVCHxRkWZhC/HoJmokyPkWWUcP2hACqBfLK3kRolxG88Jq6EfFAMM3JqgmuVCOn/vc53hqGx/eUIU6g5OCnBcHHBfzX8pC/aH3VQ2hWZg8EGAyO10O/rTxHJ1Ogw1WMFuIp1MUCipKJxctgsKlTJoYABQifICZvD1L4wIADdrcrGoJra0tX//61yEJNooaNSbLrGpRhbQI7M1l80xjtEtQHTiGgqN3USIdiT5AuXQ/vjxEA1EcupU+IL0iVyzkizzFZFz6W1cXAHBp7vw5mCPTSVgpwyL6CYVyy1RBdwNk4uRa4IRayGCmQYn0KBoIyplmEAoDMu1RaqkvxTEoQE4KtSBwC/GtE1ognowwkHkULU7T022YCUAMwGE4jxygAaraIGVssJ56SUzV77/jHl4FZo2HHmY6icYYIvWkxSxtPWpzj5o4VEr4N+RAyIGQAyEHQg58xDjAdEsDs0D/V4kafWrMrtg0rqzmqB4zJ15jTfKzrB/++KdMGu+/Y0VA/w5r35M1fQa5Ub+++h5mZqh/h3kRxkIOhBwIORBy4GPJgbJ8QFP2k3sHi+zzYFRw2xWXYqlhdPQZ4MhIFeynhFr4DPA3RBFyIORAyIGzzwHZ9T5eON4RFCKeBSFXttHZJeeKvy6OQDBKYAuewwkOGwicJHEIhP0xeNhe57yHQyPKIq9fosZxtcI2PWcSHAMASYQr50ycFXFwyFmR7sVrLi2RuJLHuQLAnAdwmAQNbNZzy1P25bGQwAKD4xCl1i+UCCkEDh5AAhgO1cjFNjEnTBTHI4wy8QdD6VE7xq+i1YxpBYtgMOAXkry2VSqXsp0dfVRh9Wt9Xe0vXD3/yscf37f+vUOcVewdOOxF4rHGRLmnCE5ywSslG4QuJqNUJtVwsKMvZWc3b12f6s1wKNXX8cAPnvjBQPoDqoMFNMczcewsxauxVNnGi7PncsqBvR5Guyywu9oPl3IDPb1H+7PvzlkgZqxvrO1LJJrdUtSLz4rENuOKStwnU2uzao7a8VhUmgPXXODv6jjszZu45t23Dh3dznHLzn2ZDz6Y3NtWxoiikNvPot6RXLiJjuIyRN1eahXilnxMc+/BRF1TU7k483v/6z0r1tLTY695p3Pbtkh9agtNT8HadlRfeehfSampyYhBiTF9ph1Bq01PeRhh06a0Jl2IdHJpRiHFWF1j80BeGoi+xyN4xdkbwOAhI23qH6gL08xRNHk1KP3kAoaGphvQ03jEMQ8E0IFJJNBYVBBGaS6fAG6Jg5ODLjo5EW45E1JSOROiMzAWOD2l7ShLe6xm11vNC/2USxwCqCnYeErpihPatBTNqKUoJadwVSR+RRQzhBGhIMYOEepOlYFhFNAo0A9hCgkMBABGBLJhC+lkp4mpKSnUFAA4oD0cpjGgOG4EnjoyDAEGQANen3FoRAowjFNQEaEdGXHa4iABklFJdsiAJ9wCRjrtQokQSQrsoiCoQlYQoAoyQEuEUFNTDwHZgRyu0gu5ciZVlx3gdQK+1Wtl4rUxL52waxjLcTcfc5JCcDmd9GrtbF++s9QYaxFuWLliF17SU32HsrF8yhqQ8R/NJWODiezhfNf+/kKXG4/Fk+VaNxsv9Fq9+baaWKZcKNtle1Kd2Mse2rG/1JtHGMKilJ2MlOzuI11gjuN4PWYdPdgB/MFdB6yC13no6GDXANVvrWsp9MncHeBoOZKy05loTV2iPh0pZ/mMi5uAD7mePImMYqqZtFJuzivzkWUyFWyXs8i8FXPiETtKXn49+d483mlL+KgVgISXbN/fAT12MRL34kTqkw2xYryrrbvQXXQHPGpUF60nsnHnJspqTDT1un12LtKcannvjfUzZ8/oaxvwBjgxj3Yc6Og81Nl9oLfrcE80j7dcN1ZI9R3O0g0grNZOF3NFajSxfvKR/e0NmSa8uRs5hLPcUuehDlqzty9nFdxsrpiIJbo7u9uPtHPUzdiiZeEe3YbXQugV2uLIXTDjjZIugXygb7hlMSIHkh6iI1c+PGxM8Vj20BnoG2TB5xZgNDGviNDfyMgOJbfgp+OBHImBzUEukhOcZsDSiwAAJylkQYmAlkdOycVRMGzRvk0pg/3ZWEL02qgB2kgHPnDiOCpgmDg2BzimHfrJBwDQS2h5elwTP6SFfPc6irASbpsAMCJaftyaVkB34T4yaXkJ3kkikY8ncE168WjJbmm0cgPdUa/Ddtpinp2KprA2kNVyFHOLSANfJRgcwMZwOhaZXhys5BZPluD2YhNKRyaU2uJudJK83wFVx86plFqeEDQOAXQHPHBJjdgqpxzJ5doRfhUYSqgAlKOiepHU4lEStW9+gqwMcchqqyPmtjXl3UUN1kuROO8JRCGbwe9miPABh0TJeeyn/xeC+uWVyTc69seteMSNlCMFRpNTjkbsFP1csA2pOUFtOBaaNCgrzsZVBIKxwQI5EhgjpO07t9H4iClkDgIHM1BkDgKnv3cAGKamWC+hN6WZsFY3M2rEF5oFASWJtuRFRgGJKgQngYw8Aow4YlNlEfIQoYd8Q4Rix0MR9EGmT8y9cRaIeiVXKpNEooKHp6hv4MHMLQZMOFwkETCuEIyNFAYxFEHpgsq8v4S1DSlkJNEaKDFlbU7XTGhoKvQPxstub+cA3y6Z0tTS39EJDBFyJVyrtaFxSusEHAbrwgFqURBYcTENAA9kU3FmH1ACqdg2wQ14yDlT1PUunT13+/ubeBQrwx2rkM3BUt4JhBvIfNwz8xYVrCAFwpieCTaGnJl+kI62Ys5TV8dQjMBSpDrWQniphKssWSCDp0wvaRfEPmsLaIOf1Bf9QqGq1GgIqsMjqEU5OmUHQzQAcK/IPU/T6RicAV60xuBge3sXIz+dicFDMuJtVJnPmz8Aw3+0EsOWKW5NbYL1EcUBxsyW7HQS2pDVFvTAfwiDcty3UzUA0KHUiHqRhQiPgIEGKIE8AioPVNSUNqWOucEsupLA3BUaUNk1E1rgLT0QsP6eXuipa64HGxwjHW7gd3lwMCst0j8Qcdy0HV33xupIsYy4TUaiTr5AoToP5y1HvGCixzFwJyMRmO+UXVqZ0qlXMp6cPWPWnl17UaNth9rqa+pbGlvcEiJPhDnM1/kwM1JKBxzO6PdA4CFPsQyjGwN5MQWYTHX0emHUS7WVuVY0GjqLFanoX0YhgynCMrni2YqVoowvgh0pedGSYxfI5NoMzhp+Eauf+VfCPsDkKO5cVcNUse87g4fejcUmxmKbXBEGUVQeHyQwmhr1JFrS969sEFdf6Hj88O8rPzEIpl/L7+SDKWtc2QTS9uRnvixB6TGjyiUzL2OyQLG9GfxivS9NijmpeEMyxiso8tKLUf3wL2VbDJ3dkVibF9lvMZf3ODQvky5v+1lFQVvR+EqVf6V7iLC9+MK56fOUQlAtgHJEWew/uI8PJiD5kHWIU7QtMhPxojCIWbX1JCWfK9DxVeshzBFHSPUOp4O2AJj9B32BnNklwhOhCkJ8BqN3yIXgJSOo9H11Fh3JtCylAWP5DxjSniUGqBBuyE8iZEfwQgBiEEMKpCiSkERmf73dskVARgQphCHq2VXiEXKbK7qDKxSSnvLs1qaWnsNtRHjJCoTzps1AXPMjkuA1djs6dcLEaRMn7d26XSRt2U0nU4d37pv30Ofa9u5H3Vj5YswWLVkeyDYk06z8Oo8wVbZi6XR9InXlpUt2bN9BWR7MKfIWzABo923bgf7d9cGW1uYWuJSKxtoOHGQ1R0XAlozEmtI1g7x5mEw2MuGQzYAY01mwdR06svSSRbxRn4jKl9yKfQOIENKhLcna0I5mIjEyMnIorjlT2+20UX2bD52VHWpESk06w7psybwF0ED70qD5nr5ENN5aW0+u9sNtcKwwMFhXWwuf69MZtNihvfs6Dh2OM+WPJ8o52RJR/Yui12kSlMBPAs1Ku3ClvpSrzWeY76FkUXD0ARLBIELL7KvQIrQ77pYxUMYOnm5AQ2sro4V7untBjmpjGkY/ARU7YxQEQ7gFDy0CDaBCqwIJA5kDoMe1FGgg4F8chASZCZgr9EMMfMAFOB1buw1XWpNvMuSTsvcFZMyJ1dfXwSVmCqV8KRlLUq+jbUcTiVSpJLWDQkhSwtLpFBWn9+qsiZ5P1WhcKTgM55wDMks4fpBuytsOlvWjZ5564O4HPnHFdas2rE2Jnkyyci7ROyUgbYichDahS5iM4SXkQMiBkAMhB0IOXNwcGKnvAiksMs1OMmtfTmeVEfzhaEn2g82R8YurXrtz+SdvuPKKN97bwFYFs+ZQ/17cPSasXciBkAMhB0IOnBcOROX9oxgHQeyiHrD6N/Xsv3z+JQMdnW8cakf/ltlVxXGhUqaaPKDPT4pg1sLhcvikOBYChxwIORBy4ALhgFEFI2gZQ6bziC14zUGcXXh26tnN54OMbJSzt85ZDhvi7PXrUTeHAcADxpVtdDJqLiIk6pVvMbNBrxv9bKkDyYkFh0ls5XNlw52MehCl5SoSNv1BxWEAJg6cUlA0hyucBgHMWcJrr70GHrb7yci5AnHNRUTL5ZbjCrJjckEVAAOYUwqIp3QQYngBHmigFL9cRaIYtCLYHDzxxBMcaWBgWFe3cmIS410rW7Y2bNiemrWMp3aaL+ZyohulaAInBxVsLJijUcw49u3cmYz3gXPnKu8733mq/6g16Fm5yEvz58/q7e3nzEkJ0PpCC/bA6HcQcmjBsQQHRdDc2ZV79dW19c1y9FviEApzkVIPLE3WUCCMElMYOWFiq9t4raZepJQc/MZ1/83f/DP+YVe/ZT3zzEvpemvXDiviPDZ50uREXLwfYbQIbZQuxGNyaFhA40XjYloB9wixCMdF1tPPvYYruvZOfAJZTm6V5C250YyYYlAFpR8kIAAh9GPcQ9th+QqXJrSIyYVCcpZDAH5gIIc5COnkogr0FyJyUCY+bMo0GdlpNQqCA2pAzGdG4RjfJIU2guYlYjBIO1K0n0heCqKVKYuiQaKoiABPnEcEHplKS3byauCUiP6m2WkFuj09kEpxQsm3TTkWomvBf04uoZaW0lxaOnggHvy0HREIxmyF4QMMrCAXAHolBUq4JRDXyMleNWMlu+Gh4qTE/oF+vogKwcWSDAEwQyFdAnMcujRZfIKhEDDlM5RTU05Y/+mf/gmCyQWHSYEDf/EXf8FwY0CBnLoDr2Y3YAMGZsJqjKiwtSIXnKFQIEmEkzQHjUhcy+XrwIDBZMYpGcnOqPzBD35Ah4EqUmARZ8b4OydiDvAgQfz4fu9736N1qCOlw1vo4avrFAc9HN0Bw6OXXnqJcgEmLykQTCMixLgFDAACeZ999llalqIhQxmIWCBANsh5RLmcI/7iF78AXvsJGMjIJ++hgUKpDgXBGXomIwUkpCgrwPA//+f/5BF5yQU3kDyQCgBiU+Un8GTkKYFCIZgS6Vr//M//DDwFYQyE8ATVyy+/zBUAClX+8whWcB5PRkqBDAT1k08+SftyBk+DUi51pwPQRgCQHZxkh8Pf//73IQBnV2TH4IynP/vZzyiuf7APb2fAUOjKlSvXrl1LHLKp0eTJYnv3+OOPqxGVNi6PfvjDH/IUrtK+eFoHnuEJK7R78AjXZlu2bPvv//2/069eeuWlI0c6aG2ccdIl4D+F9vXk2Enk485wns5DdWj93GAR19H79x6ijjAHRlEXsMEcavTMM88BAyTpNXUZBAUVoRPCPcQOx8k9Xb3/8A//oH1PTQ1ovjffWA3TKBEkmM5TKLff/va36UKgol0w0oJptBpxioNgYIhwS71GBujRFoEMfaq3IyHDlFPmgPJ2JGNxUq6JNBntoz2kCoxG5+n/8X98leFfzstnE0aSwSMSGS8dHYO23aQY/AYNwvOoKt0vzpglBGGH45KF/yYMpw7FSB6KVv5CsKIlwphSAKxKNPjgWheEJ+OXDvyrJzZy5aXoAEkR5AwzLjKCim7sYwZGiaogDf+cBQ6o0PjOd75DA2G3izahENrojTfeQG4gW2gOjEdpF+YJGzZs4JbuijkLYLQ7kpyeiZ0u6ZmaNAIKrQ1OjGOIo33oGMxG0FwIYaQxeBonNpLxl7/8JSIOZSqenjE9itjMwFEfFIqc56rTYJSarxTIhTj98z//c9QXspEXilIZPOvHmGYT6GkNDTLHptuQ5U/+5E8oizhVaGgSU57nn39eq0NerJ/RAqgJOt6BAwehC0fIhtT9//k//2cKgtSuo/14LwbVd7/7XWQ734fAGjISsxob61AcqA8Ss9m82G1FbYyh//7v/x5gPKEzV5owQcyvyYg6g1HqHZjXZGAUbIQq+NzXmwP/0fZevuQOH+A/CKn1kUOdEEZ10G5CfKMQ/9ab69A7MJahBHk0EFxlHtLR0cnXBuBk68QGNMvrr7/+zDPPoFipI9VJpaOq5iiRViOFyK4d+9GGe/YcnDFjMiShAZU5KH2aDFS8o1VTJ9bVf/d3f0cWiuOay5bhANqKJoB45l04bef1NLQ2hsgQRoDh9Y2JtsNdTMm4Tp7acuRwZzTGGifyt3/7t7Qpobd7gFYDkoy79+1HWVMjJnytE1rh2H/4D/8B4mVFwDuBvJrY2//YY49BOfbrtCxkcH17zTsoRMgw7KojEbL/5m/+hiaDeOLUkfCTn/zk17/+NVQBUG+Mq9C/zB51DJHe1NyIF1J4CJ9pOLChWOEGDURXxDMls0oIJjuTH+Z4gJGLBiUR5tBqzDHo/3RyhgNgijm8nhcOKP9pd7904iMT/afBCGONvseQYQxae4JPPtpxGQYm0GP5W4tz1qGFfLBijCm9hV3j5JiCBZGE8VPjACKFjKp/S47ILmQ+7YVGQ8QhexGVSHtSWGMSgKe98AlNLjrtc889h9hnPcItsotlEXIVUUYDca2pzSA8WXSwPEFoE29sFrtkHv385z9H9lIcsgucRFiwgA0wWYIlxM6YjI888ggSVRdlEFbIlf7Tf/pP6F9GSmdnB5LbjVqUuMZ8XJt+BJ28TYL2/8u//Esohwyu4AQV0lglKvSTCMG/+tWvkPaoEuJk5B1RRPRf//VfUyJ1YTzK/CHj/ehHP1IA6NQpBDt7yGStCI/wVYxG/o//8T9SQcYywry+sQ6aWSJRUwAIVJAawShUMACQhMlvOpNC+z/99NOQR42AgWDQspkG8wnwFvUEtaxG0eYsXZVyqAUzapQraJnbkE7RrLh/9rOf44OZFErBnJ0pE0qHuoCcXGD78MMtFAcAdYdIMjIJYXJCXqgCgEUfq0VUDDMK6FH9yzQJc3PWp1AFKhCCDS69vuoNmAajSAEVj8AJDwGDV22H26GBxP/23/4b3EOFwSLWm/F4CcIIIGHKATF4VgYDkx9qRCJtTXa2zlCjoKLJYIsqSlbETOdoYl77Yeam1WfLBTCFpCAibG4wnSMjVeCzCaCVXvL227CFjSnIoI48ZbuDKoNEWUFLkeuVV14hBbLhPxE0MpswsJeM9HkI42Mj6F+mQKwdSIFggE07h5cLiwM6K0pH7I6+frrEbbfdVirmVn+4EcfQabvWfDUDAahm0GeM8rAznDFWhohCDoQcCDkQcuAjyAGmXj7VB9raVf+6efTvNjal0nYi1L8+f8JIyIGQAyEHQg6EHDgjHOD9IvA01Cd6+op9fQX27l599j2+lDW4etX6bfuHixhW0cNpYSzkQMiBkAMhBz4OHBjdAHrsmrPlze45MKzxiHNkwqY8RwVsrLOTrsYTbKzz9UmOLjhLYEsUSK6aS48xtAjdLWVfni1+PXwiwpkE5wGcPHE8IycZ5uCQvACzv6/lcuWWADYK5RRBiWGvn816/LeBB6oAIwtxnvp5iQNDXnByrsCVgngKAVjC8TVeHN1hVIH9H8g5KVHilWC9kuI6+GrC9A0HtF3FEvaykdqm+q7ePq95cu2M2lmTZ3BKlOjpxN9iKSe8gkJ4pTQokgh2yW7BiuJPyCvk4vjd3Z7NRxLpq6+9GrCuzgMf7NxvzjXEjA/eQiflkhfKjT0tJ2dy8LN/n9hhwJhCLtl5qAa+pdPJAwe3YxnCcUUhR82hFPMFWCFWqg5u2jzxVeO5Hq5LP9i0v+xZxZI1b+4kmqt+sHXR3EkdbRG3HB8sb48l8uUCFtjCKwKkys8QgXckuOu54t3WdjLTp0zZd3Dnnn5r6dJLaZFctNDf38thnqFWrMrITrnkJgIZVAozRJoGAA6TSOQ0hXQ8Z3NKxMESZ4RYRkpZJmjJNBpdAm7gsw8wiibQ92AUHAYVXpxBQlPS93CtRwq5teghPOJvWOOkQ4BQYmyPOPIBD/7zsFxRmxj6nnLe7zxEwElGylXXfWAAmOLoOVSEgzo+ULtjxw4gKQjk2u7UnFxBekihi9IQDBkg8edHivZAKNHuR13IrtSe5pWiwU9QhEpJY0Mjt7AOh6MccRGBHj3SA4C6A0+5sIWDTyAhVTnAU6rGcINyBiyNQu2ISNPnckT0KW3EWFZWgAcMZKQIuKeUkJ2nxGEjlQUhYMAQuIUP9GcekZFbwDjHhQbNzuDFDoZyyUILUqLGVZ4AT424kpcUSgGPnwKkItHTPrIDoNIDgqGKsqCBjNQIDHoQThaQEJQ8rtSFjLQaCJVIqkxemOCDAeMLMYABI4UAGLdcoR+eg4FEqkxEG0IL5SnHjSQqZ2gLwLSOJFIj0imUk12wEecpnAEtgV4EkZQIAMBcseMkhY6nnKGJeQVDkVNrHoEBypHb4IEVdGnFCR8YaEAqcrpGIgHPpcR0WsYvryuQ/ehRjvZjeGQGGxVlCPMIvhGUjcBDJ7dwGxqUMxBG8wFA+2YyHPqW6xpSsAJg0tM1NIf0N54qqUJGXUoQNmSQG9SUosEGZq6cENPXYBEAyljgqTIwpHAkDAy8JZdyjPGrVMENxQYY9gpaOomUSxYtgq4CKyAbnMRpDtgFJEHJ0Hh4PbscwDkijjfsvGWXrUhn2RmIxXOu14trNroMLmVNW0tnHhjo5tMCiQzvW3zgeE2O12953OC8cYBrqdw7aXLdw1/Ei3z80R/sj9kHIi4u8rALRAzRolHcMcZiuLdPZNKNeILGW70Zr7jRwmcz7ui4yo9bhrF5Kulg5ksNXM9NYLz4BfF6lFHSJdcrenZXPDmQrH8BJ3gTJ07Fijtqe7ipLBnS0qmGPDyzj6Qy+CHGXwmEy5hA1qpJAzc+2jByBjngM5YIchKZI46LjeLzhYkWRzdGzlQlKiRSSBUBtyr3kOTAExBKCoPMJBBH3nLVORIzK+IEbGi4IusQ7ERAQpyA4EXcIfxJBBtXpCikmhSZw2BHyy0RRQuAVMGlLmK+o0KVRFAhhIFEDXGrgekMGaEKgYz1M4laBMZOiFlueYR1MhEEr6qAxJCLfapMUDzAE4E5lILuI67YoASEcIx0EhNmCgkNxLUgKMQ6mVtKgQYizCK4UjtSlF0yIzIMUTAtlLqAgUdgBhgCQEUAhhTloc9bEqFNsRHXoCXilBOCyQJaEPow4AeAXDDQsHool6GWGhG0djo8ecytAoFN+UYKSMieTFXmjbARGAqi1ZQDdDmtsrY4TNZ0cuF8FzfLkl5r/ENTNSMDYDaE0WGoPqFCmUGrBEM8iZChOP1m0kSF0VyUBdNABcEEEkmhv1E7BdDeApF6S9tpiYARp29TR+35WqgSr8Dh9RxxQPQv76SK/vXso8VSXzJddK1ek1IhgRkrjVgbiTfEU1MnWE5+bSozOeXiHtzmqyy5hPitd1wUd6m+9UMvjovcZagu4/+YppeO8REOOAPj50ziZ0f7s0d7LG9JLlvp0pYtQqOubm6pFJu/uG3yzJm9PXt6swcbm5JdnUcymSZkj2V3W/Ygr9eJ6+sh2fURZsiFRLqqA+Wqr399MWIErcgipJav4EaSj8AnC6IM2aWyUc2agURGUQTCUAWX6ggtDsGlsldvgSEvEhgyKIunWhBxAnHSQUVBsbqKPEdKm8UIq0VNEQXNMlfBiCOBCYqHRZauDbmlIK6UCDYkpy6INAWLWCJKLYtZyACGUoKiXjP6xOscg1wEX4OQhaL1ypxBn+pV1ZBWE+twqMXPMWUBTI0oDjDWU1xVL2iKqYoDY5UzVIF05RiQynzNqAg1hSqTCBj0UyJxAgVRogIow0nkFsyKHArhg7JRK06VgVHm8JTArbYIrUM6ak41nTJHAVSpaS5oAJ6gj7RNoQQ8BLoQbIQGAHT+ADzEcNXsCk+K4qReLMwBjtZWMCi1PPV7C2g1bkqoTOpIgW8EKq7peiWdvLqY9eF1E4ZEUnTiBwF0GEilOdi1o+cQh2DlDzQACXLFEF7PCweU+2aydkz5yKH6ZGJf1+HVb6+++xPXxpzsa9t2ut5AIhJzXIfPDRwDHd6EHAg5EHIg5EDIgZADZ44DB452rVz58mduuzlpF1d+sIe92USExS+f+5E5eRhCDoQcCDkQciDkQMiBU+OArmNVm8q3AC0P62dQxafWuy31mwYLPc88/uDd9/IhsrUf7mZrIw8In2kwGcI18KnxPMwVciDkQMiBjy4Hhg7kTr4GuvOu+dhDZ8ueDXGcgnzrW9/auHEjO/XXXXcdTl/wrwMMwOyV63Z5sCjdOtdDGj0v+frXv7548WL8j37jG9/49re/jaEAW+3s+LO9DrDC61Y72DSihz2YRUIGNPzmb/7m9ddfjx0qTmiwbIYSaCOQV7OQi6BxEslOXA4VcKJWX3/VVVf9zu/8DscVf/AHf8CmP4GnWq5eQUWK1gI8nBOkjAc7jB45JwAJxD/6yyffXv16JNlaqSyK1pRIRqVWSyTvAAcZGAHwM6cImBt+7Wtfw5T233/7j4CkRmqaqfSDBITk5QMPPgFYUMoZRkzs88jyla98Zd68OX/8x3/c0dveWIsLZzELVuRkkdI9MSTVWlB7CHbLpebmWpg2adKet9dso8l+/siqRx77cUsTUHLgpGVRLneVgs0feWqMLwd6u1esWPHSy4N88/Srv/EVPin7+7//Bxy30WYA+rX285KRZuWKcx1+kdj/v703D5LkuO40I4+6q0/0jQZA3EATF4WLBEiKAEWBBC+RIilxNByKxllJlJnGVkOzla1MI9NoTTvX/sE9bGSrsd2RSNG0s5QoUeQSIggSN0A0rsYN4ugGutH3UXdVVp77ub9Mr6jIo7Kyjq7q+gUK0R7uz58//yIyXnr6C4/Uh++4g2mVF154+U/+5E9YcoYAYiIUkTcIJKwTWG7G0w+qWymzOIRNc/0QNs06OjD8q7/6K0LYEYg3TUX6wka+KfE94qhMBORNN93EirZcckx93XDDDax3yyVhkmY26ZBACU1zsXGdsF7dBz/4QQzmyvniF7/I8kt/8Ad/AH+MIULapseoSIs0Zxqoztq6hKJiOXHPVGelJRr9+Mc/zlpNX/jCF5jwwxLigahlVRZrT9NBFV0wJuRU/LSplWKVtcuhEeDQJu34OFh1q8gFzyETeEiSAzouadSSE8RI0HFAsQ/6SSPPFphYQwbZMtFjSthbKTlspoQ9lz2Hpj/YQD7VvaBb1ZtDy6EtJH2b7jIITVuOlZJvLUIgCJsx1nHE0GkyNEERhpFj1ckhzWWDAAmKXNt+syqIcUQtLGdvm3WBPaXsTcBaR8yws0fYZBAz85Ahx1o3buRQnTQbZiBGLeQ5JJ8EwiRMOZkkzAZLWz4VkTExcogGCKWWzyGqbKM6MmY2yk2hFZFG3jJJU4uNhG3WLhU5JN8SluYQYzHeJ5y1JKhqn0ezAXk2d+/yOskkUTPKQbZWkCFhpewRQMxMoq6JhUwSloNtVAyXuvXRaiHAlUZdTo0ppJa1pf1ZJHDd9ZfiKMPngrPMWcODcLL4dnHjjTf+3u9/BP/y7JOjnMfqXdiby1nGX/A1w9+ot9oFUN8Ru0ialdbLL3NO3DCuR/7I8Su3Og7ExcKBtWvtM4JtCPC5IZNwlg996EN81/pv3/6xQaPUtNU+Q8vclTXRHITtvhE/cQvpeb0e9NuNa061CTEuA6rEb2vhsomrqm8RH5WoaPIJSb4FxPUsMJ1Qbtrixje0YYGNdla93qrO9FAL35XQljhExsiQH0fEoZ3f0HTi7Fs+VRIKE4eheiKBtnpJcuozExV1uHoJcLVce90lDLSD/7W+4GX4zsYoiXHN//KNf8nbPJ7bO0zOzNf62hdyk09cmasXSMJy+sXGR8C+joZS4j7Jed/73gef//v/+g4rnY+PH850zbr3hg9vSITqSnRMoP7+FlQt6Z2q4UlseAcO9ix6oqEN9a1wxdZnxuG00NOiqF6n5bRZpaGYWdWwKN5c4tMXL4qn59QTF16i9Jw2IBDORVyYa6n+xCEQl5mXzVaRthg3sWejOvu4wnh6XsolvOgE+G5d/3sEP1b0ROnX3t6fnhzmx8a+jZsf2/vkeLm4mF/EF70nUigCIiACIiACq5wA07Usj3HwxBneNHLXXXd1r9vw2BPPjTM9usr7JfNFQAREQAREYOURcN6VSUx+9uG3C1aDOX16/Jvf+s6vfvbuqCv7xPOv26uPWMIKsQo7/8vGyuuFLBIBERABEVgSAk0DoPmN2yZm3G/eMd/Aj90cUmTTCSbD3Ocdd9xBDgu6EAlKTOdf/uVfElHKciP8HB+qNOsB8u9+97uvuOIKJkeJmb7qqqt4l/dv/MZvsPoyQUt4LzSYEvamhHYtzZ5pV4KfUHLzzTfTHOs3E/fMtCJvkabUVk/BZtLUCglLow2zidpkverdu3cTD8Riq7wFmzXGiE+lX9SKzyhY2u0rbn2UCoutFEvdXb3vuuii6667lWDBk9H+f3joHzMbC5sv3TA+MsYykKnyAP+zJh3yhssSdJaNAMPBTeftvGYbrQ9s6iHy9b898M3f+73fo+90il+uMQ8b3ClwC0ERSU2gZIllGwnCMz08SEy85ac//QkicY8dnXr88Sc2blyP/YPrKDjT3b2BCOtSwYWo2lJSlRRL8LF0cxprokq2q6dn+84dQPvwL32M93IWpgefe+6FUnmYZX14PKroIgj9TBgrfjn73Utg+a5gHWE/OX56/a7Br3z5n7/nPTcMDJZ5JfdTTz2zaxfrQe908cF+Fo2mXevVzdW1Q1Oy+8Lzif39hRtuoKesvszbxikldu3IkeO85pvNVXbVOYMhNstpCzbcfvvtv/zLv8x1wmq43/jGNz72sY/xuk8EMJWKiKG51npVA4cgJWYaUFT/xV/8RWL3iYTjjCD8S7/0S7we1Oq6tr0Sq8LeDlHO5cFZu+aaa7j4ufKhd/fdd3PZcAlhP5YTYYbCcMlZXapb4CbWXnjhhe9///vf+973cg2zhjSxerxylHNx5ZVX2oQTVRZxo2n70cUlapu7tGDFcqp+cSOKLMdk2GMJDN1593230lpt92/IIcEVG3JMg5XaybJa5ARt7pqkfXd5e0XOFoxxK1rx1ns2lLAKWoUz6fb8x+Lf1dWPKA1q0clGju0t39X1PbXmQmlc0tIUmQzKQ05IhIqWQNL0WxXSSNKQ7ZHxzbqzHHJI2IYwF5hdAOTYlWnyVHQ3hNqGJBtHiHEKLGGF5FjCKnJorYdMQ2R7Mim1vSVMPmTaYb0YOUGShPU65Jg8hyTYLN8SPqP66eMjaIfWXEj7q4kjdyr5eLHjTuxLXZc5RDEflFot4u9pwuVYaa0j1DJEroq/Mzlt3qiqYRTEjfTV3Q7yKKGIjTR7k7RDI48ACdvbubbqyMQPLXPZ9maqWdtxoygJW0JJuOoS+eEQgXa6345M0BlP2MmN58xKp/J2dbg9qzinT150ybb/4X/831556dhDDz2UzhTT/hqh9eHh0f7+XjzRHXfcccOtx/7mb57LZtd3dRXLeRfiz/0bmQvftadY7u0778BE5VQ+uiVXZi3884ulvu6u0zTKJ5XA+1zUNZ3qHo36prrWZyLel8DSldyeCrxoKV/MTaY28FfIdE9k3B2Kv2rkiL8A81Eff1yk0/jpVM90KROl8lw+KMeGXJGlm9MU5VmVOerjL50ZoV+8z5x2cetc35S6v4z7yxQmI94a4Twyi06nuU+WU338FaiOP6/wJBEl7kPFRY2/ny6ls73rBs8rZXPldYObe7pZqZ1XtGcr+c1InLdjy/joxK7dmQ9/ZPfI6Y8++uijA719k2Pjtuar+0zOfIZmzgCXzcxBXcouHrJDok7Eddy2RFFwNNZEw+vHrj2qJ+raIfktzEvUsntvQz1nK7Pe+PqchrY1FINVIj9xGFTV5wM/nhlLNybvLjv/sfQ34WYy1qC/slpdRFW72lCFpPMUS7l1pr/9WvWS9Tn1/ePMJsQShzNVYueumkmO/741cxikuWm4NDeYGlY7l9UPYy0zyJNI6E9cOSaZkEkcNpSJN7HS0nYnoRct7nKtbZ6zYkNEpjNxH2vYUAv9fC1sUYq2MGZpqDmVxv/aRYG/cf73wou3/cEf/u+vvPwW/pez776q4QbdJcajtVFuqnjjL9y664Insj3P5YuXpbNDqWg93+ejshsR44xTyPbdUJw605O5JVOZzGTecNnpYsUtJuu+3ZkHZMUO/vDvNJE1F4szLhbNM5oMD2Oy6Hw+w9ChXPFfJ8tRH3/mf8tRT4G38tja0gwxUnwV52p3/pe/cqWXv0x6pMh42XvoTJTnz9qt6i9iMyteM6p3fpyvAaWol7+a/nTJja454luj++JbzgzxlytvY63NUqqYK05l3Pgd/+46wPd6/qbyrw+NvnDhu7pff/NMsZh3C9s70xGgTiGVLv7N//NNztcPf/hD+8oaPykOUO2p2nh+O+mGF5hdWravfuRn6wrNNbsIg9qQiCvg2gsVG+qPC5+VdEOzW1tCRzqoZToDjdZNxErdJ4JLjGvQ+9xQQn5bjrDNFhuenYaZwYKQaLOJIH8OJJp1uU1iHRCwFhMXXkMzGma2bjFRhVZsC7U4JJ0QC6VKnF0CuB67FzAS9D9t8Csbfqrw4skzR+/5h89+7BOZ1A33PbGPn7BZwABPM9qpubXroq07T6eNqJ4IiIAIiIAIrD4CbhjIzEWa9Z6m3xwrffsff/iFT/5yJtpz3xMvy/+uvtMpi0VABERABFY6ATcm5ZfY8/LlzdOl3IAb6JYmoh/e/5NP33TT1R/a9A8P7OVn1ik/UMZBaxMBERABEVhTBGw2pUGX+eE+zHXFf8TnJ+/ExpwWM6aHDx8mWJllFAn6ROCrX/0q69eyJi6/kJqeUKu+MSoSM0rQ87333ksAJaGoBMIS/mtBwLTOPB960EBbbCgkzZ7NJmuJHyV69f7773/mmWd+9rOfEU7NYrSEIyNsEZlWF3kSGMDeNlNLE8StErrN+yVZEpKAVFZjwgAkKUKy3mbLoTqtDI2cpuLrr7++f/9+ZmR5gzPh1BdccAF1OUSG9rDE2sUG0kTHWg4B0OOnT7/+85+/+eabRCBhNn1Hz8aNG61rNsfplFTNdsaHjpBgApQcAmfp/k9+8hNiaomEJqTb5zszracuVQtWsB5RExvQf/AQwbdvPf/88w888AC9gB5LF3elrPvV+AarggZrj66RZt/d595Yysw3we5EPxNtRsdhYhHAyFCRVtjMDNvTNWsdAUKHWQoLgPv27Ttw4ADEUHvnnXdCnQW56bgJU5EqtjeF7MlBgOrYT+Q0G3HwZGIJhOkaG03YZk1TyqHZTw5nmcvvxRdf5Kw98cQTBDHfdtttnAuKEGMf2rIE1W2z+GYuGyrSLnWxZPPmzdjMBUz3beVyhE0JCTqCEtRiFTZz0dJ3rjQWq6ZdiqjIuunkcxGiwU66Gb8oeyypWu//MZ20ErbQa8rJhJJVIYHN7NnID8ZQysahCdsemYRAaNTkgzBi5IRDEmym0HSGHHSaAeSQsHz2oSESoV1rhT3tmjYkKWULFeM5pgRhTkFcZ9BjShJtoY0cq2Vnlj2bNYoqK2JvFeNN0wU2b1HVJGSsOatu+9Ci1eUwKAlFoWKw1nLi+SFNwtK2D7aFuvU99TWqbK3LltPmPhhMwqrUN0GOWWJF7G0zCKRDolZSvYztMG4JOfHDkCbfDAhdMMOCSQnN5JNDu6FpTqidU3IotXPHPugxDaHFJU2YDQtpIpyOeiV0M2Q2bMjIBJmOE830NMuPNxSnzT0cO3l2iNPBx8o7Pne68Tl8HvlqwT2Wt1LwUghKua+iB3k2vKS5HrwPpejBl5GPntqnNt5mNY0AF4cdINlAonlWAnvikHpOud9CIq7MNWc9rxkQLyXNqbMSNKOBL2Ngse9m9H1ggJ8B3Gbt8oQSToqnbvCeeCKUG/nWnULGlDTcm9nxs1Mv1qzUDAjy9XAostbZN+RjpUFDIhFss3yAJAQaViezYX6i7nwPTe1SaA6WtKm8oVgcfjwdlDdLzCncsLmG2uZU1bDW6sqs72N9Tn2PYJgQSxzWVwk5CcmGpyMhQ11y6jMtP2gmUW9YvUxDPXElKz9NNxM3k/naTHWUsNVXbJgZF7O68Zz6tNfdQDmSLZo2Pda1ep31OWY+boUq5n/9Pbx6tfDliPEsAyVGWAyRuN9y6u1LFKrsawYjHcZQjF4ZATGSYhQZWqEL9ZdK3DZar3dVcYGgKiRQGPCSaOgC6hsN1eubC0WWQKd9/Ujkh8PQOjk0xMZwjwEgQ35QoL9YnPFKRowRMQmKEA69s8Sc9oR2LRFvPVFkh6gNMjRXL2OlkA9icZl4ZkPb4vobwo9rs3RcZ31pxzmota1jDaFiQ1CUoj/IWKJhTn3mnLXaVJ7QE2wIV1HIaSiZyJzTziCf0B/y44mENg4TOXHhkG4o1jAzVFmKRL2pi2tDvX560U5mOzLtA7FOsecKZ7PbEYn2NUjybBHg1sMfo1N71C0f5bJRdkN3NDQZ/c3f/YB39P3mr3yi1xtXHXCSjg1s2zTbvJIuiTZxSUwEREAERGCtEciVp7NRZjATjeXxv/c6//uZX5b/XWuXgforAiIgAiKw9ATcj28zvzeygpyfqj05NP3ww4+y9OFnP/nhQVyyrcG29NaoBREQAREQgRVFILV9+/aGBhHKzHK2r7zyioUC20+cYU+CX8P5ZZxZTGKMmAFljpNwIiI4v/SlLxFVww+jTHwyr3nPPffYTGf8p/l4OtH65z73OaKHiUAlipd4aOKWCFFCxsKRg3BcA2n8HAHQTMFi1a233vr5z3/+L/7iL1gHmvlUzLOpuOALrRfUYqOI+UWijWmFJthYl/fXf/3X//Ef/5EZWXRSSqOI2aSOqQpmhASlSBL8SujwBz7wAdLf+c53MIYNGWva2jUNNg1MPhs5GAkuYrAuu+yyD33oQwQx//mf/zmTx0yOko8AiUqp4JtjEjSVdetMMSPipiJYBTKbTeWnK1PFaPumHXfcccdtt99y33333f/QT2mFiC5nhFtBl8XdWO+Dza9/6edffZx0enp6gstg65ZNlJ0+feILX/gCmjGA0rGx8Uo0QF2CyShlXS721qmutF9IzJPpymRZC5kgKgKgCeGF4V9+86+Y0MVs5Ok4XQjXDF0mE530mhgs1lGG/7XXXvvzn/+cK8eqc+qZMkeMMxKqc8jmOuI3Q0dduLFkMvnMIv/ar/0a5526Bw8e5PS5vvt5d6viO+Jivu1ccN1y3tlz1ohd5mL78pe/jMJvf/vbXDnImKkYb/ajxK4H8jEeAQyms+eff/7evXshz2LSxNsRAk40P+HsKEEtVTgLVpEzQuvoQSFpeoeR1PrBD35A2P1nPvOZ+++/n8cGTp48+V/+y38hfI30JZdcwtnsyvaEjltfQo/ihyHNKuGsLc3MOkuqcx7Jt+q87IMEm3UNY7CEUgMSukxOvO+mllqWYG9pq8UhCdNjAnStobDJx4ssx6pbOqEBYfINGkXWitkWLHQGsL5qzTxaJ4dScuK9M80t9jRkzSETEkE+3kGTNANMuF4emdA6adNjFUMVyzRTLR32KGTjMF7Xqlt+kDQZu6qt7/UCceF4GsmgP9QKCSuiF9YRV9GtQ9/WZp3ivFA3XA9oti2hgoZCW77IfUaQDGJWaof1+fHSWpWZKN5Qi4qhI0EJdUNmra5jjoDBjGeGdEgEPeSEtCXwoXyoeaDlpz/9abAwyAQNDROY9JGPfIQ7Es9ycAcIMtyveLyEDT2mirHc5Zdfzkr2QYZ41nBl4j64QSFDlSBAgnumeUZuDvHvAOi3W58pNw7cEq0uVnGZETJr0FDOzRM9cc3IsHHvYk+jW7dujevHR1CdUjSgilYwladW4vpDKBW2DQ72+dvXTJwxtdBMf9HATfXSSy/Fg1Cd5RLJP3HiGH6TOCvSZHJ/5m48Y14q/8YbB6JyP40O9Lu3Q+CLsL+/qx9tkxOsD5k9evogpVT3BNLc27t94/gymp4qFtA/UXGL4mdLFZruq4xeeeVlx46OkH8k55534iUCtMjK9v39PX29EBpIl5zCsekRfOL4dDcyUYqFnKP+3m70FybdqvlTxYp5TDqVy0+5TmVd37f1DrAfnxjlchoupdh3lQtcmX29GfJ7sr1Y0pfuRf9wfoynzlj9kl5k3ML9xW2b+/lGcfLEmNOfn6IL+WIe/rgjTNi18zLqptIsupmxhdqpgm2cHb/l1q9fRwewhGeSLJ8+AoE0Hw50svGVzxLs0c/FQwJ02MNlg37XBGfZC9m5MxnSnBr2lFg+XWNDHv1s9A4lfA/0Vd0OPnQQI1HIxqWFMPkIU0S/MMCKyCE/2EYVx3/McbAiEjhfMvmuZdckGiiyjVI2rOL1DgDkubKuLputsJKZPc3NHMRShXyJt1LwUBmfXzoSK2mSTM1c4SaBJUE0ng6ZZynhzlejLWl/IxnlLT8B++Ql2212tmovbEjK26rwydyVfczNAefLcJLRRE+Tz2+zHnBnCP4X92pi3CK4AdrNEeVsiOFiGH3gs4IqvnVTxGeWDz43GbxMKCJh9yLz4Bxy73UOwg8oGCZwA0S/3YXsU88dhieBTQN3JDZudEE/N+eG+vGPdidkaENdU8WeOyT6TU/IDA7a8vkOgAehs3TN/C+jZIQxj7rYb8awp/v2zKfPcVfaiRPvePudd6AKNuzYthMIfD1HuBRVeNYoHznvXIpydLwvnWLUjEckx3zrZM75Gn/3LXX1ZLj/9/iLlR0GnB51BuTSznum80W6350v7dmz59ixE2AZmnTfT3pT7ptAvuRKN27q5stPVHCD6FHvfyenupx+73+3DAzgg3rLzvJ8qszJPXBiBPt5rNflRD3sd64f4J08fKcCy3HWcJ6aypRZZ5qXLfDypJ7t/Vn2mYJzVSPu6hgfyXsflPFDiSi/ceNAV8n5jjxvjmAkWHRexr34ga3inkfasnWAV1fx9DVuaGxshPOfzax36NIlyMAfzXg5+sU1gw0gpZSNfADSR9Lo4ULCtuHhkb4+19zUVG737vNJIIaAXVooIcfk0cM3Qw7ZkGHvwOZypplDrgHy+X7iJPzGJcF1hSokubbx7zRKSVw/ai0HRDhoq2g2xL8eUAVulJr/DZe91bVa/JjA28B4Yu3HP/5xb3dj/2uS9Xu+voXPLx2pF1iiHOu+KY+nF68587/x+3f8Dl/9VrZ4zUnTEhFo9j1qiZprqtbuBuz5eHIz4aPKZpduuIC5byDAng81ikJ+QizRBregq6++yvxvs+/PiSrn5CHo2OgavpLflv/sz/4M1EvQU39qeA+Qm+mtjjhSDEP9xC/DXFaA7ommB3p7y9O5yUp09+234vcfeOChg2eGp7CG+7b/PbnL31pmfPxsQ103Yhv3Z9wuTy7F8mYlreOzsnQgAiIgAstCILgqWmt9L8L3ff3rX//ud79rU5nBCS6LmWpkCQk477sA/xt+4WPuzG24QPc2UpfEYVJacG8Dzl/33ujOOz/wjf/5YVcQ20Il8npSkfstcNq97PK262/ih/0HH7xX/jdGS0kREIFzh4D877lzLjvtCT8a8G2K2vzeSGjWf/yP/5GfEDtV1qKejX+dgB8C8293V7r7/R+eJKboP/zZU64gU4x6M1GhxG+7H73lWsbjDz/0xMnJWVPj+OvqmNlVSG42/rWrmi8VttQmMT9JOR2LgAiIwNkmMC//+6//9b/++7//+zU1/m06M4THsh+7/eDR3fZtDEk+WzyTNL+BMsvIZBizm9/61reYpSPNJJmbSmwn+CN2lXzzm99EFXO6qGXqi1+KmeSjnBNp7QZZb4hzq7SFMJLM3nH42GOPvfzyyxjPXB0/2ZsBJJA3SdMWrgwSzF6jgerYjOQ3vvENJjJNM66aWoaCnGYbyplmo7/E3f71X/812shBmAR7mrAEaVNFQ5bPIaVmPKayBDLxu6E5mmazmUhG2+hkGoKKQbkLNHWTo5WurizTkseHjhOJe/8DP6ZHREwP9FdnDWkCPVE1LMyG5I4bnWUjweTryPBpb3bhP//n/0wLzHRzFhje+x5UOxJ6gQ1euEzHQMS8MDG7KOECIGjPiaVdl+lI6As2GwcSZJK2BCiYWCXG1yxBgzfKLVeJTOisddnaZe+64zc7U8TzQY8WeVMw55GNJtijwZSYsBlDXTJRyLQrtWiOzzxpZAg7RoYr0AQoQsx1x/eXTEtbPpkYT0Uiuvhu54LO77/fIh5MgC7QNTQEeTMDPaaKCwYNxGdg6kMPPUSgJNc8h1hlpiJGddK+xsxVZHpa7JFPlJrldCauikz0xyWxh+ZCr2FlBsRlSFORLZEZDq0oCJgx4TCIBUtIWH9DW1YUJK2Uw6AZgYTlFJGJDLSR59C2UCtoa5agOlWsNCTsMNGWNRSsDQ3FNcermDxiJCwdlySdaM5yTDheZHVNT6KWnTUyg1WkW28NLaFK0B+qB/7ErbS5Vbufch8fOyNmmHUqoYQiNjJrTVuQ5cw1TH6oggaE2YebQChqkYhrCGKWGdCRbz1FvyUQMMOsSr2SwDAgCjmhlQUmeK4jaKAVNpoIrXDrYNl+MxLOWBiKqMUdhlsQVcgPZ4F8yyHB552ALaqzWd24fusvntQMsEPbI4ZCCyqlNN6opRFDBgFu0aTZEAt7mjOdJFgD0tLsqcLejMHyXG6CQ6tlMnzEAcCpt0ycTm38adcPF4ZTY8I4VkKLLO32qerlS93RUayqlmR9IhW5YF+y4s0xuWsB0CZacL9/437syIVkRcVo3743LLuSqX6hMqcxNjY9OkKo1hnTX8q4n78rFR+A5StgHs96pUvutlNKV+PkoGcAh44fp6tjkfsCkM44JrlsL8y7/McCv8P3rkrRf94551Eqn3IB3NRFkhNOAoFnntlXKXdxlnHVGG3QnXQqOnj4oM9xP0YYCRS7P0D0EhqeGh0dK4+OhVokuruzmOr1O8I0QUQyCbY4NNJsfCUI+XY+OLmcOPs+Q138Zjxw0CL5rC4VsZl0fHKdQ2q5xvzGyta1pPs+QJGVImb5p06dCgIkKA1FHAKfJvjsmEy8KF7r7KZXplVnl4laF4HlJMBw0m4s7LmD2R4D7LPJPcQEOLTN7jPsucOwBUmzmXw20pbAx+EiuaOG0kRAMzrDTZK03XvZUx3vU6/fNKOEUqzFBZhmq0tFEvY9IbTI6MnSQRK11EXD6dNuWGR+wwxmjwb2JoxtbOTgc9kzprRSn+NEGNCxT1ecyyS83jlu720QGB0ZmSyXT5060+1v2EXn2aN01kcf+28CWMuAmgBomgOQazrrHohlI82GAPx5VhlxDqOUexcE6hGgOVzk+IQbyJv/LVb9r3NhZj7w+XrAk0bkMCvttEU9/quFe8aGimz8msGwztLFrqptdsiXq3fOTPL+xJ6I54NThXSZgZtVjFWf6Cq53yVKaeehKnUPuuCkeErcFOLEuL5II8nGFycCqn1f7LuNx+RFKbUThA1WlxxOq6Vtb/6RfA4RJsFmZMghQddMMuwRCGlja1+urDpFwDEBSt3TVrXrtqF+rqvQHLWCcsvkGqN6C//L6eME8YRwqBhsU0IEREAERKAFAR/9zICOgV+lHJUzUVcpmh7lp1H3bE/0yKNP3HzTDZ/85Cd5ed3Dr76GP7BBIJ7GBmstNIci7uTcw9nrFh2YKCECIiACIiACgYANqwpEZk1HfamIB5Aefu4pRkDyvwGREiIgAiIgAiKwiASSI9O8/420O3pk7ws3XXc5q3wy/t13aGYmcRGblioREAEREIEVS6BpAHSY8cL0MInFb51MN9qkF37FNg7ZmCtlboxaFvVi87JMB1KdUvJbI6AJa4XFrphPZU/QFfNn6LQljpjbo7mgxNJophZF6LdlBYlDZWEhZpTJpC5iWGJ2WkcsbdWtRdI2q8eeHCaMUUsYK/lMv6HZxELTLRJBOY2aNlTZfDY5BsGaNtuCPNay0RDC7JGkCxb5DVhaRD7b5UJ1SyWWYORHZ6i636uLJTcnmkmznCFBUsVsVDl85E1y4N2b7ULap5n7dCj4JZzZ1TI/hvvfuDmZTEjTFhtLc7HIF/nEYfn+Zrq7sqyISSgU86rkABoxuoOMbaRdjreJE2NnnFasUX5OJ40Mm3XTajkz/CVBPtU5WeQQw0cHWe+Kqwvm5HCIHs4FEGjdcqjobXOayCGNBqoQr0wtQEGPSVOKbHGp0BA5pBEmYWlvl7t42CiiOfQw1cqUsJmEMKrIN3lk2IK81bI9+WgjqIsLFTO45u2UoYGicAoQRhWZbM4IDwpJ8ukmXQACdTnv9MLg2JVDGmFU2T40Sk6LrV4MPcgXy+5skkhoC01QajabDLWCKvIxPjRqCu3Qd6saNm2qEKai1Y23RZrNIFAaFJIZrIonvA6HLqHKDoMxLnCvdnlw1kI6KTZTYVaKvgRLKIh3bZZco4M5ha1rVA2JhJpmGjAp2J+oG/LjqkIXmimMC5MO8qRb6I8XcfG2o3zGvAonzmkvltyFVCRgs64tckxnrSFWv3Pzcf6KoNBt/pq1pMv3H6bqHYBcOlJn1cydyqoZzFoTVVX2D5kBhQmESwi1dZqrda2PyFsCMZSwNWxiVnsdHaCZetYce0tYjt0zSQeTrAVkuJuRiW30yDpFOhiMWLx3yFsrVp09OexNPp4ISqwoCCCDBhqyiuztFkeCjVLbgrwl4kUImElmba2oeg+06jRhBlDKtwUya2K0jhMnw/3Phlgo8hk++CmdJzOT5vEeTHLXCUe+tDJRGovSGQABglxnHl2YuechRR5vUiCcuMxEcaFULPhupXzcUtZf3m6V/Qz1XDVCtZ1m33dShPxUohxTzYiQPcWnwp8O1w4lTjCb4dUKqe5SJbt+82bolYolXuJQwdNztywV+MZmt6pUuodLjQao5WxiScXIeWPySBfpQaaLV0j40pIL0EK365H7SmB0sl32nWTGx1GRUqzmW54/k3hGd8z91/1Tc2eI2TlKXC0mE/bmvzik3ThCc5FoMGeHgLVrOm1Pprkhf46CyqqkHaPHSn2/0FE96STIYQvVrCgIWD4ePwiQQN4EQoJMCyiPq4pXaZFuTaZFxYZFgUnD0uXNnKG6vO2qtc4IJP2gaWmc27yF+co317R8JYv1GbSBGHZzf7BbRNjbvYJDu0VYfsjkkBt44sOLX8Mw2yNJgs0qWhNhTyKoIs0W7pBooIjDev0med5551lobAiAtvx4Q2hgs/xEKYdWij/yRc6NUjdUD7XIqX3VN212TzbF7oZs62YxkrSNwSvP8lALD8mjPSwG7U+Tu774scA1UPEr8WMYY1U31K0Qgm1DJsrT5SlU80iSM9yP2spZUvx5D+PWZk554hU3KkbERr6+bXrCjxfZaNr5X28W7yiiZDpyezTSdjkzhSsv4T1dz3l4KZr2epwAzRdzPEZcSWXdN1TvILP961zCdci9Xwl33uW+u7K5Dpc9vbx9ubBvGt7vV61NVWOXkbQNP8/S0nwvoD59J7P+4iHT4FppSJOApFtZu7aFUTAZ3qSqf0SSw5rUrH8t33SS9qfGCYRWzB4O2Sjl0rXSoJ8ERUFpPB0yLZHoGpKhdUucPHmaX4HCOtOJ6q0PW7TbuuKKL7WLqXpJrXhrZaAIuJuPKCwPgdraV+6RG4NuY7dUZR0uthLhrbg5j1LKs6H3PLXv9Z8fYg74xu2X//TBnx6IpqysxNi39oUvnLlwTw83ateE30LXOMIjJG7soVQJERABERABEVg7BIIDpcuk8auEQbMxtnzw5ecOH3pH/nftXAzqqQiIgAiIwBIQcEPWuLfF2br1Jlj0KjWeYp0qSvlx1H4fTaUmo8pDz78+dXL8zttvv2nn6Z/svf+0rz6GSGz8Sy0XOBVT3PqnxfjoeAn6KJUiIAIiIAKLQ8BmNxvoClNfVmY3fX7ctASlhIYQ4EKC6U9CSYheJXiUnz7Jt7r22yg5CVUNGotlseQRrVgUtVUkDhVViJBPDgaYDXZorVgmOYSCYQ+b/Qgb8i2BkhDRYhWRtMYRIMdXdd1hXpkcImJNDG0kYmY2TSJJJCt1rS0QsZFGM8azYQCHaDOTyCFt0cPMWFvCtFuADpm8qcEJ+/ZdgBRLaPm4KQJ4CNPCLk7L5GSOd+yu73PTzyOT091MLvvZUwsfr9ReWO866FZtrvJkkth1dnI83d27fmAQU7uzWeLO6QWvr6WIvrjfzbHWryjpwqj8ofun5L5wIEmLSDDRTnUkQUcfM12um9Y7JxzbnHxtQ54NJUScE8JO2tbg5DIgTTg79nJaEecw7EkQ/ESO4cJONJBJcyg3bpTaoVWnlI1SNorYhxzESNNT8hGmRQ7Z0x2KrNSE2Ztyy4wroYjqlPIpMA1cPMxAgwLbrAh5KrJnI4d8NhPm44MBVLfLw2Kv6SP2IEwt5IMN7SSoFRcLSizfDhHAgGASmWw0ZL2z6hxaDpkmHNRySFE4DAkyTU+4mDkMpaQBy56NTLofP0FBIaUhTcLS7LlOEmZUNYcZm9psPcJIhnZbJxCmRUPRTkWTR6dhaa2cUuRNxno9L3k7Ha0rxm22tuaUNwG7nknbYUPDrAi1bPGgiobClhknTy00sKcty7cexauHfGcHG+9xrZ1HE6N6kKfcdJITLp54i0GyPmEV7USjB4G45rg8+ejE5npr42KWtk8xaYRNvpna+rpz5vB5aShjHKzjoTkOMYAiKyV4iyAS7i08YmFKrF9BxqxF2BKJhkxJIpPDUAsBKw0J9LORaTohY0WWWa+qWU7thJqDnvVBRpUpt1aChoZdqG+XHMv0hs3SHFQFATOefMuhW14m2etQMSRMnoso5FjCrmNKg+aEgB2GUhbB5jbpMsnyZ9YE0MDmIrH9herS1fWdXTmH/tYKEufdwkazVuSq8ixULF4qyMT1oIn8ZB9qNMLJDXVpDjPNpJBJwowwY7gaLcEpZg1TbtJcRPzVLttZl6Lv14wmw0ImmzU0UxZLURo7qiatSn1+ixxrLsGwhXwosorhUAkREIFVSsC+IzU03t2G/K2m5q2qUva9nZtAcLuhut0ZbG/Vwz7IhIQpDzcTbkTBvyPDId+HSZhYqIU891j7apQwLMhYLSoizBbP59BM8jJ27096gGCYr23Vq/f4oMrUJmyzUp9Z9YT+u1MD/aaZnxOsimmzfWiiRYImjFVCxjxDsCooxJqQTlRpeGgaGE67Uh8AbWL2W7mVukjr2WwbqmqYScWwNRMIvWgo4ME2KGldK14BA+wwJEIpSprpIR952wf5DhJMV3AZcxLrW59TWwdV5tQpAREQARFYfQRqo6F0hDd1z+Qy5lrXw6IV0dGx09/97nc/ddNtX/zCF7/1k3sOnT4aepfmVxh3i581kprzvjqnQNCvhAiIgAiIgAisEQKMpliwws3MViKmaNla+N9E3JUhmtO9zimwRlCrmyIgAiIgAiLQkADul1nV548ePfW97911653//NP/7L89dN+xIf8uxNiv0W5mkl9B3TDYDYTndK8IuEGzNhEQAREQgZVNoGkANPdxglBvueUWu5vbfd/2TKwy88pG15idCmFtO3fuRIB8Mt0vp35rv/vUZUPe9iRqOtxEpi90O/ST30xtKLKEaTB7rC4V0UAXiDQlx4rYm0KKXBu1aT/ymUQ0mYYtIkkVi5Q1AWvXKpK2uihBLGgg0ybIyQltJdJmuWlj72yqWCSZezkvi17Z9Laf40Q3ys3ymdUcWdQZnbTFxopWZgw5xEs7bb6PrAAdNh9zyLNOFeKtLdPaBQ3CxFyTafJmlcnTMKU8MWUdtCJXPe1atEP21pypbbhHBg0uctqH/Bp2LDdhp8hvvjcuE2HTGfaUk29ioQn0mIDhIs1mkkGz1WJvzcVLgx5q0SKbGRBaIR8Z25OgFRMjTSLkI09Fq2X6bR+qI7x7924OLR9hVBFKzvJXfL58vrPFSl1qro2OEzp/zTXXWBQy+qnh9NTOuDVtakK7QWvoJjk0asJ2akjboRVRSlvs2SzHlAQZO0zsTdgyLW3yId+1MZsth6EXVLTSGbU+ANrV8Rt6TBUdMfOQNPsb1PXaqEcRwhbvYocz+utS4fwGSWuUFutkO8lAP1v7Na2/yJNo34ZQyyqGviTaNTFKufEEq+ZsBcmETNBTr9+K2AcbQsLZVn1y09UL+SFBZrDKSdRtZkawJ7RFwrZEDTQjzN4SpNkSMvFDlHDIHvmQT/TGlVde+c477xCCzBMOIb+dBJ99xC6//PKEznbqJmS2bduWyFlVhw0CoBfVfvu0tjq51pydYqQt4TPnrpWuCx6O3x3i/te5THfxuKd9/I/k1V6an7UlueLt2QNR6XI8z1eJPQpSjZyuanL/lH1p8AKxkjo9voxXTcRk4snG8nGJeHp+0vGaZy/NAtVXXHFFYgnV9s3hUajrrruOZ5nCCrKt6s46a60EV2qZfU5XqnWyazaBZp/q2VKr+4gvchdddBFemOWQx0cn5tUZnsPk44/7nletc1G4ff9r93jnv9rb4pqT12PyuLnGmmdx/5pPbC7rSrLum1p141HisFnS3vYQ1qumtBx7e0HZe2erYnq6vKFxnUVWHKmNlEsLvqvHvmwES5sm4l8+mwqtngJc544dOxjAEuWfz7nvw+1vcDtz5sy73/1uRsH2kED7dSUpAktP4Nz/vnT69GkcaOIXgKUHqxZqBGZ+inA5tcWheW2ROxwr2xIW0UR58juP37/5lc3/3cc/88orr9z/7JOnylPu3QeVstvXNvwj+riv8ksIK1OwOgPfr3bt2sU0AaeY5R4OHDjAr2f8hMu+Vkn/ioAIiIAIiIAIOAJMFDC6xEHyGywrO+ULzf1vVMH/Op/rycn/6gISAREQAREQgfYI8OOs/ZBcHZCyyFK1oosncH51Kh29WSp855GfMqT9ykc/deTIkb9/+MdDUam3p39yetJ+c2TMS/Rzvf/ll0li3pgfZNlNfmN86623ECTIRL94tHd2JCUCIiACZ5NAqwDo48ePE9pLCIgZ6LxAbbOwMDvidu88RKy0JjUTqRZyWiSCzobTeJbJns2asz2t84Os5aPczODQGrJD9mZkELD8emOoaIFuCT1INguAMwNMVaJdiiw/UbdhfqhLwtKWYE91Z3/JvaiXNRNd9VIxdI23KHsZO5UOBUXsCa5iTxUOy8Hxu+F0NUja5XtOpsp+Dqey1bKKrkX+d7Xcdwj70uAzmAp2lflCgXlZF1LuZr6xzTaG91W13gDTGTSbhrCvdnD2JYQw+bYnYV8sUG61yK/XTxGZId8OkbQtVAxiJsweATLZWw5pGqJRy7Q9pcHOIGYVESDHbCNhKEItEpZGgFI7NOHQRCIfmVA0NDREK8XZMfSmpOHeVKGfL3PMUuzZswcxMi3fzoul49UTOWaeVTQx62mwyjKtlgnHNcSF462EdLz7cf1BCRpMie2RCUXUtepBm0v4UAPM8/X8ZV0rpmJCVdDp6tUuJFPL5I0pj8vUNM36FwGTMSaUUZG22GbJ1Q6CfAuZmqz7F/lwIcXzm6UDk2YGxCua8oBrzrYMi2ui4pcFql3tpjMQSDSB2oQxdkh+XJJ0EKMJs4rMuJjdf6xWyK/Xn1AbDoN+EqF6KK3PCfJBpnUiyJtJ9IKNTIIwCMBiNX32aKhvqJlaVo58++23eSji0ksvrV9FslmtczHfxq7V2/4SdNA01wbGzRuwU4x0ONfe+zWv4EsUAG2A5uY7B8hlLbbPqe15IUb7H9tgJTexU6dO8bMIzxBS3bZQ2iCx4FC5BjqXNav6G9OytqnGOiWwdPfTTi1a/HrcqPne8vrrr/NJ7O12KyW3s1GL7fDhw+effz4PEN544424cj6/7dQ9F2Xa9792j1cAtCNmI2sFQHf2ieADSEUC7JhjePXVV3kh2IZ1G+alig8stXhfzQ033GDDkzX8EZ4XOQkvD4Fz+fuSd6Fuh/998803G/4+sDyU1Uog4F6xE/E4rXv6ldtrT08UJniLUfH08Onvf//7PDHy2c9+9qWXXnrg1eenZz/7Sq2wJiWPdLJxc2bamB+peFSMQxZrIOTdlg8IjSohAiIgAiIgAiIAATyvTRDhjKuTmXxNikoJ/3vPq/t47aMtLOWmElw9+V9dQSIgAiIgAiKwUAI2CrYxbiEqnBk588Mf/vDaa6/90he/dO+99754+ogLt3Ib42X347/7v7bxuC8rpLBIGeNfnvhlzxw9419idXDWNSn9KwIiIAIisHIJNA6AZq6I36yPHTvGog78oGkTwDYpZXsE2OhW2Ltfu+si/6y0zd47jbWYVxJBeTwRVMWbS7Tr1bjqCRk3jPQWhkTQZs0FPRySNj24txbzZ4ihjV/5rbmgMKgyPYkJACuNy4SK1qjtLZO6NOHSZRf0zGDZ76sh3UjyA7SXt4nnmelnMqlEIDTyFgBNgpz4CtAcBv1WyiS26TSbybT2CDCtmkGd2GaT3qy8ZfJBplmgLVW9te4CMzUzVbx5ZjZFJmDCoUHsMQH2ZjBFlrA92qgYapm8VTElIW3tsmcLVYLO0GKQN50II1Mv5tVUi0zS9uip76kpRwky8VKvuHrhmUlImh4eRbC01Z1zT/WDBw/yUFpowvSkMlXsCQ3YH88xq6hCpu1JWK/jfU8IBMl4rbjakA5KEjpDPqpMm+3Jty3w5zBocwkfQOYruV3tQzFjP5lWJVnRazHlJOs/y748uUPe2gqnCQkyzbykdA1dPJ/q8cP6NAJ2FqyohbwZw94k7VTG69YrRyZuOQIt9FOKctNfrs3bcti6ium3WsGAZrY15BbXXx8AndAfFw7NhQRmWBNmtgmbBtJ2GIRJmLBZSxW2eGmLtGnj8kM5YlxObFadHLxJi7qhyJQ899xzTB4TOW2qQukaS9QuuKXqtt336u+KM57UWraTyLNHsYshKVNvY9o/OBTPn3WfjRdUP4NOZ3wF6KqIPeARk0/Vr/0cK43Xqs+u+FUq6/Prc1pYWy/cIqeebwvhs15kn1kLwNq0aVNn9hBD+fLLL9u9hWsmdtk00qcA6EZUlLdEBBbrc71E5i2KWrwtP1PiPd0TRG13mM8pH3/e2EDkNMGXHPI1oP4bwqJYuBqUzNf/zu0Ta72Oa06enuRxrU79vzXP4v5tZwXo6hsVvKL4ys0Wslz038rjK0DHWyynZnpnemwF6JoNTpZvCG7fUk9cZ+v0HF5jduVz5iqlI3Scb9FEMNPFDRs2ueO2v4QbFR4A3r9/v9Xytdv9Dj8bqo5EYCkIxO9+S6H/7OvEb/IRtu3sW7MGLaje8PBOpIoegPNU5q3yUzUi5WjIJUsPjh558PEj1++48Pbbb++9ZPfevXsPnzrRk84Wy1bXxWPxBDwbQxurjMMdn5zgGdGarojZgpAOifhPXboLByxKiIAIiIAInAMEGA+mK/GBYIM+zSomqNlcZTk6Q+Vy8L/n438/unUdPx6eOHM6Vy65mT+/pf2a0PwgIf9bQ6J/RUAEREAEzk0C8ZGj9bD5+NG8a3z4yWy7TbgHFzpDCenwE8ywyy4/Onbs0ceOXf3qjrvuumvniSPPPvvs0TOnKGAtx2AGVfhZ49ChQ65GFPVmukYnxg8fPWKH7Csl++k3ZLhEc5tnielABERABERg2Qg0jcci4JIbPXt+v7Y5pLhNTFCxkWN7EvEZprh8PB3XUJ9G0rSxsASJeKyYCyKb/Vq9uECwAZ1Y22y6OoSRmUzcgGAkqkwsrjMuWZ8OlpCwDRlTiCWkyQz7kAgtktNws7pWZKeAqE4yMS9ehAClNEFwnddZXd3ZKvqmXcBWCIAmhwBoSs0qE2Mf7OFrAUXoNA7kU9+J+UDqIB9ssIlJlFoOF0xVc9pKUNDA+yODPK0EhYhZxWr1WgHnnRxT0lCVMy0WS4rZ8cOampl/E0oQtipWq3Xd0FbcclON2qA5KCFBEFU4NKQzpvhUKGWuFwGwmCryCb1iEpf3a5DITef5RAThhJL6Q1SxYWf4HNkJYgIDJTSR+EAFMVNlfWnYXMPMhAHWhURmw0MkQWTNIRAStGIN2Z58Y06nEKMvlpjROTsAGnmvIHxxnblBhSZm6vqUNcSa2ZBpJhOqmHL2wQzjiZEN65ryUJ1EQzETQCcdtCqhv/Ua4tqCJVS0U9lCPxVRSxW21mKJJjjs6soG+K2rN9RscfxoiGs2ewJJDtEc9iZZHwBNPk0gaeStigk33KM/NIEBnCn2zWpZvlnbsCP1TVDFNiuiLS5szgVKrF3srO94vR7LoVEWVULhunXruAMEG8hpVuWs5AfDzkrr9Y3On49dijZ4juub8U2Waz0t+ttzTa6+Vq2k9i+/WZO0e5flJS/9miT/cnngx11G/S/p/rzH2+Peg+Ac/W0SVsvHxrXSxtbC2oa1m10PccsbVlxRmfhiThls+S7a/mc20QVQ4Lj57JMwn9IMjqvY5EwldK7gw/Br0gq2UabVCMz3c12rtzr+5YNmG59fPsh8Bm0Vh3asx2Xz8eezj/tG3t+T3UfY6s5xv61rIFSsK1ktGQ2Gby1Nb/9OP1/NjZuttee9YRsq4wHQ8bQt8cGFwjbzg3eiTe9zLc/qWus1G6zEG9FET/ybQEJ3w0P7ctuwqD6Ti9O+atYXLVbOfL1hZ9c/HaGiuc6FWM4bGAz43P53Ic2orgjMm8C5/H2JDy8bSOzTVzuaNyNVWCICeCd+g2EUWPteM9POwWMH3/m7d66//fbbbrttZGzs8ccfL+aqAdDclt0i0P7O7BxNueJWgO7KsgL0dKFADiv2N9A4o1spERABERABEVjNBIhdDiNNl3C/puBP7TtPmx2bPaE9U+ngscPv/N3/+7477vijP/qjv/vedwnDGhoZt2K8L+NTGsL3up80ymn53xlwSomACIiACIjAAgicPHPyf/2bv3rfjTffeeedw6Oj/9+9/1SvrDqnUCzjf1PZDAs/M0fP+LdQKtpXg3anV+tVK0cEREAERGBZCLQKqWwRsOV/0Q5DwFaWup9M295MmBhEasQHk7gWtnbUYDNKbJYu3nQindBmbbFP5MdbjNsTz49rRiYuliiyWnGBuJ5EOl63WpRiLWp+syZ0r8FKV8TyEUTjsVXPi5vErSZnUr5193P2zAB+dsNuKE+wYbn2/ofZpfVH1oLb+6ljfgSoqm45v4MZdDAxmdomGbOhXjhOLKQtERcORaEv5NRnhtKQMCXsEU5YHmQsEZoz4dbKQymXrl29KKEiGy+UJBSDjXRXN5G1fsoi0VjdoZ/VcFHuzs6olK//ocWfs9hUftXquKbap7Z6AVmRZWJMkKwZX83hH3KcAFdcG5tJ0rU2ZGfdEDINotxn7g/Bwvr7QChq1iJVap1qJuLyTQZtnLIg16Jii6JQPZ5A3qqYwZaOC8TTDSVbV6G6CcwpZg0FsXKlmOLZBv/wZOxCiJtTTSPmUrOFrGLdtefFfAgg/aUtu55mV51pIpxEbtfzOl9BhWvCN2Q5QWEQIGEycbF4aSJtfOK3BfssW078IklUbHZoJrHnPTsoN/3NhM/p/KbPaDXs9fxBzXx+ZytM3sJMc1cq+eVkdq1ZR75K8ivWzH1qlqw7qFQvfC6+mXtsTSppT5TxMpYdD5iOh9JWYrfWar7rb6r6YHRNd/N/Y/WbC8VKcP+xo9WaxAnUPst4mhZnbJZXst7adeJvdKmCj6Fsi8Kqx9aKUlsEJLSMBOruJsvY9tI35XrH//z5qzK8ur2dlvng1z777jsAv3WyD98Q7NPdjp7OZEJDnVVfglrz87/zMSCuuZkXbl9fu54q7lrrHwPI+A9GM11xebvfxbWZrWnvW22sVK9nvtdPNt1g8ZL2oSy65MxXlEVX3UhhqvbFpoPPBVX4IHfw3buRIcoTgcUlcI5/X7IbYO1JZ39XXVx+0pYkEPdO8TLnWytRnn3Zh2ox1HDfjPyqGDbsiNecdlXLjz/6yIZ1Gy699NIPf+CDvEbjySeffP3IoQ3dPYxo8qU899V1fQNnJsZK+QI/ULDxVcnVYx9OueW43Oqa0z7pLbCUs0qbCIiACIiACKxQAjPTEDV35X4Q8AM/irzbc9/l+J+w5OZ9cL8b1/yve8+QiVZHiN4BsyPTXszwk/vvf+aZve+9+ZYv//qvP/XUU7yN6p3RiQLTepWIASE+dl1v99DkdCE3Oj09MT09VVWH3povj7nfWWbVyp2ltQ41t1olIiACIiACIrC8BLxjneW5zGea/6p5rhlvxvO4wUBK+XOPC1Xlgl+ekamlrIjfoq1SZTRKdUXZvU8/+epLz1591VVf/NTdU1NTvArytUNHg35+VGSRvK5KuTKZK07lirylOUqxIgY2W4O+9SBuXxKqh1WLZgpnp6zbcwjNrqIjERABERCB+ROIz0Ema4dp4GTB0hzPd2owYYVVT8yTuZFqbfw6p/4W0c+Jtpb/cE7jl9+kRIsN+SdkOjicb8fnlA/Xgxkzp3wHNserxPXH03GZeDphnhU1zIzXSqTbaShRpf3D1srnZSqq5iWPka1bD72Ii9GEHc7ZVrAnXj3obJ2Yb93WTbQubW1JO6Ud62+/4py043a2rzZeq+Mm2m+uTcl5WRLvQjtpbFhS/e3Y0EymTT7Nqq/S/Hn1mnPXpnybYisZ2jnQhQXiXbEf1QX2S9VFYJUS6Pgjyd2s47qdsVqr90/73bczZlarXQ1zyLUsjhdaOp4zy/qmBbOkVt3B8lyf7X9lWnUAZbAIiIAInEUCwTWFadYwPxys4kHW4bHhZ/Y9Qxj01Vdfffvtt18/Pv7II4/wEqtUiUdpK+MT40E4JNz3pXCghAiIgAiIgAiIQIxA8L+xPJeMe2Hc6JmRiR/dd/+mvXsvv/zyW2+99cZK+bnnnjt9ZjhfqvBI0/ikf1JptgqCnuV/ZyPRkQiIgAiIgAgkCeCIY+7SjljuyL1foRQVx3LFvfteJPT5ve997w033HDxJZfgf48M14a9tQd940rntdJKvKLSIiACIiACy0ygVQD0MpuSaG6+M21horo+kdBcfzjftuo1LGOOe2a46St6l9GORFMBeyJ/gYdxtWf9NJkxcZMW2Ls2q1uLZ737bVo7L7H2O9W+JAYkzlHisJmFbYo1rL6Qug0Vrs3MhZzllUas40siVLREOFxpHTwn7KlfpbFVt+Z1fbZStLhl8VWf45ob5M+vv3FlSs+LgD6288IlYRFYgQTshr9Cb/srkFeHJi2fV1rI2Dk+RW0dXYi2DlGtgWrh47YQH7qQumuAsbooAiKwdgj49/757iYCsHBqtdWwXLG9+6/m6SojYyP37338/Ndeu/X693zyI3d15wrPPvvsm0cP9ESZo1Gxq+Lq8ocfrL6D0IgSieWbSft//NxybK7ZZLQXAREQAREQgVVFID6y8F7N7VhoK55f1yHnY723dCtT+tK4150RNzdpPpr9yOjEE0/v2zzYc9P1N3zywx8ujE7ifw8cPdoTRaxIGfwvSuV/ZyAqJQIiIAIicE4QqI1GXWcae83G3UxHvEEOh1ppGOfmtJovNp/rjvz4NzTHr9LT08V7H3hkfW/29vfc+Pm7P1E5lXvjjTde2P/iaBQxou6uRCz8jDwa2NOMvcqQNMHQaCv5EOu4zebZ416+1rrvw6wDn6OdCIiACIjAEhBo6BiWoB2pFAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERWGEEuqPo+PDpHz5439b+DZdt28lqWL/4wQ8+88wzR19/YU5L7bWKqXJ8XjeenlOBBERABERABERgLRKwgKrh8ekfPfpEb/TEte96F/73jg94//vG63MSkf+dE5EEREAEREAERCBOgEhl4pgZuRK+PJkrPvT4Ez/72RMf2HPztm3bfuPqL4yMjHzzkR/F5UM6w3vKffQzOWmfcM9G+UjoIKOECIiACIjA2SWgAOizy1+ti4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMBSEWB+N8zX0kbRL1FVC1LmX59MpYqV6NDU6KG3Rh4/9OZFO3ddeOGFv3PFr+7evfulp599/ujIc/nhrijK12zMVKKsX/u57Kvb7K/mgGt49K8IiIAIiIAIJAkEH0pBbnYhh0+89daTB9+6YOf2iy666Heu+Hjwv88XcnX+11WW/52NUEciIAIiIAIiMEOgtg60W6m5FKXdQ0d+FJxnz18pmkpFU34o/HcvPcnzwJdt3bVjx47f/sivvOtd73rxhRcGjw0fzQ+XykXensRQmnEulcpexYRfsdreqmTtecWESKcavy/CimvD7xkTlRIBERABEVhUAgqAXlScUiYCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACq4UAq1m55atmopdzpcLb77x94sSJZ/JTd733Q9dcddUVV1xxU3Hy9OnTe5979vCpE/SMKeRyVGYy11bAsr7Wwqw1u7tazr3sFAEREAERWBEEcJxFDClHhw4fP3ny5FP58l233HrtVVddfvnlN5dzNf87goj3vy4CKx1beFL+d0WcRRkhAiIgAiKwKgjMHq3iUnlC6c2TR06cPLH/hWfu/sBHr7766osvvng8Kh04cGDfz18ZHx8fLkzw2LA9ekRIdVwB1W2z6Od4Ua1E/4qACIiACCwHAQVALwdltSECIiACIiACIiACIiACIiACIiACIiACIiACIiACIiAC9QSaLhZVLzpXTnNVvOzXhSyzr07K2lRtfIaWnNphPkqVSoVMKvPz8TPP3XfP28cO7bn4ik2bNn3mlvf39fUdOXJkaGjotbcOnMmNTLkYrHTBa2PSdyaM2pvKopVhc207G9xWa8dZlU5H5bJlu3WzXClq5r9Zh+rrzdIVF7KCeE6obEHh4dASsxTNlMUVNBFxws1PzYwqJ+aP4nriOZnaCmShzmyeIVsJERABERCBOQiYxwlCbboe76bcQ0NsKZ4BSkXlkgtdbn+L3+HjtdzilFE0TYhzuYzMaxNjz/343oMnjr374osa+d/cwvzvTMvz9bzmlQgvIOo67/153A/O6J3x9T4vLmQI4jmhmvxvQKGECIiACJyzBNwY0B7dqQ0eXU5tqw4MOax3F5XqaNLJooHq7IlLrrkUq+v2NUnLielHaU3aafGtMMhim4wqp6LooZEj37/vpUOHDl22aRtrQt95553T09PHjx8/evToO8eOFoqFYlSiXby2GWAavILqDvVmefV41kFcsN103F7S8UNU2PeHZG5D3XFLnCKvafYIPqG8oZp2MuswN60UN6peyJnpT3Qoai0fxJQQARFYgwQUAL0GT7q6LAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIisCIIpNPpUqk6cblAg9qMsp1pxWY9w96FdLlJTzeZXKmUK+WC3zBu34HXeg9Er0e9NMGaWOvWrbvrrrsGBwdfeOct4qEPHjtKEPPkJLPGrn5tJnumnfoUvbaw50QIWkLSetSwXw0zE9U7PPRB2a7uPEOxQ1/qA+k6tjYxxZuYAO6wg6omAiIgAmueAG6IOzPOizs2afa2zQaTuAc7t2A+08Rq/rPJW+9n65rzCG34UJwjgVq432KxSNzWvgNv9x54+/XoOayd7X+Pdux/rafBMZGodafakVBk+cibDAlyMJMKaQLAXQDYom7yv4uKU8pEQAREYAUS4KFcHqD1Hi85bHSZVZfk3U3MCdeStX9xRinns/ygsuRHoLP6WtM/K3PmwPye33dVXHg0/7sxLEr9dwNy3ho6cWbozFuvvZ4v5Xdt37Vt27bbPvgBWnz9yDvEQ/OEEkN4Focmx32XqI0bTfFMQz5Fn5zdZnvwuW6sOdOdRJWGh3SKhho20VC+VSZmmCXztKGhTrpRb5WdAuTb+XGgoVrLtBPdQkBFIiACImAEFACtK0EEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEzhoBIomZPV1I893d3fl8njlUQpPHx8ZQZatd1XTa3Krfx5JuorI2BVuN6mL2lknftIVAVfKV8lg2YpnJHr8s5YEoxwTm/pefQS05bO+/6vrLt+y469obaZrFsUZGRg4cOsh+aHSkUCmcdOtjuY2loGmKeWVCpUiUov5SuZSJpt2Sxtmoqy8qEUZViaYKXrww4OK/KoNUrFRO+r2pcarY0MDx/CaMqWM1/b8zaVNspeVocNuW8dFR4s58Gz6aO8hbYpYhLiue0UWIWG2Fqmw6y3w8AsyIT5WLWULsTEPLva0jaouQxQXtbPb19OWmc/F8pUVABERABDog4IJp8FCVKJ1xLyIoFFx69objIquY6YrWb4gKxVPpLu8wLeK30ueincrTJX93Nn/kQokqA0T5lKN1XtXx2Qpn+YtEkR3Snovh8lshquQy3fhfPMJEFI1Ru1LZ//JLFHbqf52BZWzEcG9Lkbgxl9NFThSd5/xqdNyXEA6O/+r2hrhAgq5og88f8VFrfF1xxelKiX5S0zwUpjbe5H8bc1GuCIgIgZl/AAA5vElEQVSACKxRAhnn2XAfbtyUSdkYyXsSixI2KngXt7nBVCrq8/t+HHcUnSkw6MxH0/kzUaa8c3t04ngJP0T9mv/0/srVLVUHjS7t9Ttl3m+xD9KurIyDLPjCNA6yMF0q8D4G87/FXKnc3dX91pmTrx8/sveFp8nfs/XCKy+66FeuvXlsbIwhPNuRkZMTExPDk2OERA9PTdO1kh8FT3qdGF0dEtOoa9ePMelstY9eiF3NpHTRpSyMD1WkzXAvV8EysshByPyvz29vV2tiRprvQ11dlXzB3spEfsKoGcm5UlYxk8nYQ2X+IS739NT8xux1rfT19uVyGv/WcVGGCIhAIwJ252xUojwREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIGlJMBE6dRU08ChNlsm+rm3t5cQZCZirQrzm0wqk7Z506Z6mHpNxEBTpTYf29XVhdpmdZlzffLV52hjQ5RZ37ue2Gu2iy66iH13Xy/BYcOFafb79+9nSvjU0Bn2pUrBzwRPpqIsE7fEl3X3RH1MandFrIKdKUTFImHQ1iDlFg88q32bt/Vrfc17zneWovqD/t7xE7z3ePYWm3CeXdD4yE94R72ZrslSIUxK96Wz+XJxnppm6acuZ3N8eoruL0TPLKU6EAEREIG1SgA3Z4sXl4lLYjPXEqOBk+J+nu0uDgxEg4M9PKe0ebN7Nmd6MovXLuRjorUknrPCY0JRpk5ZTWLOfy16yN/lzf82U9WR/y1OereETvujtZp+koAI3xc4pAX+TCRVjIqVqJCOeHIpQ9gavaeMuhZksAheSf53zmtDAiIgAiJwrhDgMdFiGbdSKeB64g+cxtwSfXVP7VTcgsfWbxaNZhjEw0iMTTds4G8D+bWBrxOZXdtXqlb16Za72tjXCfX7LYgTHp0rVANwGbqy7Tt5cP/Jg08/9XhP1IMZfEPYev7Wiy++eNP2LYzEhyemCYk+PeRCog+cOIzmHC7TNjORPX/owvGap6UUn2rPWZG0Z6HcgJwHltxmfhYCU66C+9+FkC9wq/XZRT/j0LPZYr7Rl5v2WiHQGWxusO83KvE1hndZhF8V2lMzS8p/V0lN5tzvJLWvK7MEdCACIiACCQIKgE4A0aEIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAILAeBK6644syZM6dO1cXdzr/xPuKIK+k9e/Y8++wz1GbK2AVAs7ajmze2yVOndNb0oZ8VZmI5bCEWmhzmZJnTZRknqtRPh1qU8rifwJ2ISsdyQ1FuqHQySu9361P2pboGBga2bdrMfvfu3cQrD3b3oGpjJX/69OnN/bvffPPNwfUTTIsOVV5n+nVwezQ9HR0+E01MREeGJzCX9aCDVSSC2RYTnU5lCOS65JJL0MaC0wjEOhGvN1c66GVNzZ6ewfPOO33o8Fx1ZoyZadfrKZi2cpTr64rGC/1bNhP/nTszzGFlosgq160234Euv49PabuT6LYUa49xtTzz0vM2qWNndOa8tlKtMhEQAREQgSSByy7ffvLkyZHh2n3UvEjNKaRSU+lUtGVbtH59tH1Xkb8rro4mJ6MzJ8pjY5VCYSpFIFLGOdpiPtqw0cVgXX31hS+/eIxmUi4MurrFfZPpbugKas06b+3Wpsb/rt+SSU9WDrEYZc3Cms5O/W/G+d+Bdfhf3vTAk1eHxk5OTk6Op/I8gDRdPhkCsXy0GfHh1aeRaLYvOsqeYC3cE2J8Jbjyop3O/467bwdmX7ynNUtb/hv6LP/bkpMKRUAEROAcI3DFlRcNDw8fO3oCx4FnYas+84pfiPsS5yYsFniCVCYzgc/dcUG0bl104cXRRZek91znRo79vdF55/U8+FO3ZrNtMfdSy/L/Mix2W7wJnzFraBdFW7duJZx3/1sHE+Nf6gXvzgO9x6JibyZVHDlaGalkD7+GpoEo3dfdt3m9C4nesWPr4LsuvuvDlzPa3TDwLvo79NY7BEbz1iaCpN8+dXx8ouB6FbkRL5pdPHjeOVmM72VFav+IEXv7zoDnZUtFGRa73nOBH/9OjpBj3Kzn1UWmvWS7O173kE7tueaaF19+Kd2VjezpLlqd/1ZJp0q93ZyPTbt3sWDz1KkzxZ4u91alhl96gn47F35fP/5lFAwuxr/7Xnw+rqbZaQxalRABEVizBBQAvWZPvTouAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJwNgncdNNNr7766kICoJmgpQNEGDO9un79+k996lMWAM0sqvuruAnUjjeWf0Zti+pMQK7v7U9XorHpSeYuaYspB6LCipXimfHh4XEXWfb0qy8wo7kp08fKUv/9l//Zgw8+uGPjJKtEb98+yH79hbto5fIbuoljfv2dqdHR0eeennZLRxU20+74WIUcNjImJifoUdEFX0XFkpvwvfHGG5lIvueee1pY2H7R9NTUb/7mb/7FX/xFZWJqjsnaZko5F/zxLme/gNbNN9/Mmtw/++kDxMpFXRm3dihbbbWtZjoS+XSZnEJU6ctkPvrRj7700ku8Ujkho0MREAEREIH5Erj22mvffvvtAwcOjI6aS2mgYHCQ5Z+7Bgf7CWbasmVwdHR8chS3ODnkb8Mshswtn/cYEEC8cWPXpz/96Zdf/D/RYvftBuqaZJmb9s7cKTQv4f1vq7v9/P3vV53/3XQenre/vwf9V2y+jq8Qua6jvKd+urAJ66ZzWedxRybxuYPrt+Bhx8dzpAdSQyzrODo5hMx4wfn66667jhCunzzwWJM+zS9b/nd+vCQtAiIgAquZwJVXXnn48OHc5PjpEf9moBCT7ANhrWc8ZYRDxN0kRk6s+7xuHesud/GY7jXXbD558sx5my5C4aMP3ct7hGw0ukA2PL7b8O1M5qzTqXR3JsvQeyKfy/HCH79ZyB3LM2fy+aOnjlZOVV5+6zWM2TbA14PoU3dfum/fvt7p0uWXX45L3blz5/W33coCydjLs7LjpQKZR48eZc8Ql8Hj9DAPBk+TZjs5dJLnoGz0x9sYiAOujn/vW5zxL18G+Pby0ms/pzcL4sZDv6fdlwS+HvADxEP3/KjCcJjxr/9BInkW52rJvkfBx8a/L7/8MkuGz1VJ5SIgAiJQfTuNQIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACCwrAWY9F9ievViWWVjWcdy8efPwyJnBgX6mS1l6yVTHppIbNzUrVGv2JDOrLrE11BDCskZy1alr0+4nJxt0aqI0ReDuG6nS4yPD0cgzTGpvPFbZujW6sSvasWP95Tu2T1aODo+PHzsRTU27UOFCrsCK0QMDF7IKF5HTdJAVmtn3ZdwUc5aQ67GxazafP949ft6tH6o2XSyy4hSBXGwQIG6LfboE46o9mdk9IbZ7Ij9RqC1yub1r862VwUcnohOsdlnXA1t3qtu3ZPPf1qhxKHvNxVKF0DXys1HqTBTdlt5wcvzkq1HUnWdVrzk208MaYGykacL+bLIXc9LFEhyYpZ7I2XphcyhUsQiIgAiIQAsCPsK4MDFRvPji7TgUfOjYaC0IKxVt3R4NDHRf+e78zp3dV183umXnm+dfPD4wHBWKk71jEVE9xClN5lxIT3+/e4PBhk2FUuXY4KBbTTmXy5tTaNj6uoFe8qemctM1R9Pjw7xwE2gr1TIJjWJjeeV699Gp/51+fORoNHKU1ty7IfBNLDOZjXbu8IFJefck1frBKwj8ggzupivbvW7b9vSObvf2hr4DZHZljyOTG12He71+6/bRnr5dN93u+1jG7Q4VKt79yv82PO3KFAEREAERqBLo7SsPrnOv87n6yk3lyhCRt0dP4hZn8SEAmqeM+np5DClavynqdZ7TbZdeFV1zTd+V1wxn+n52023F48cZOJ3asCVzwbucLx4540aR/jlZFzVcrLlURlXdXRGDSIKqp3Mz+d3ZqKuLR4F6GFBPTk2bwyX6mRElbdX7XzJLlfIUr36YvYXVp6cYV/qB4uDAYD6ff3Min81FH+xJP7j/MDV+dPiY1aM3LJe8a+sm2p0en2C/fft2Z0cmy4B3165d7DEWYVww6U19A/jlYm4a/3vphu3jmYGN17/fVNHK+OQk+6KLpi4z+EUbWxj/Wg7D3lBKRLUN/9lPTE7umcq8K1/gq4WNQOcetcbI1AC74fSGKHU6qryn3M9TzU9H0WC+OO6/aZidDfc2zg3j3yATyE9PTfAEmj3vHUqVEAEREIFmBOxxlGalyhcBERABERABERABERABERABERABERABERABERABERABEVgSAhs3brzllls2bdrUWnuY9mM6MyFpRUQJs/xzX1/fNddc88lPfpKZzlxuglCkmnz8pbIJBbMOQ0PkMkvKGk7Ms7LC1iwhf0Cor5Px6fgq04Qms6X83tJepLq79NJLP3T7e7t6Bzk+b/sRzH7PdYVt27ZdeWURFJXx05s2HR3svphJ3InRLax9VZgeQJLusOeQ0Ks07/5l2emNjhjVCdWi16TZiNXu7+8nYoyp4rB1RSkqmUAiABq1zHCbcgQIgLv44ot/61/+luVQCxoBCKtcu4nkaYLbStY702kvZjYC02UXbc0EM/azf/e7382SmQSNoYpJa5Nvtjc9U/5Esd4zetzcdYWXIbt9ibci5/Ms2zk5O9y8mTbli4AIiIAItCZARNEnPvGJG254h7s0LgMXMzJyGr85PT3pXl6/cyP+pX/j0ziaq68+s2XLlg98YBOvsH9rxyaWSM5dexk3+Vz+JPfndYMbcD3d3QOsQPnJT96Nj+BJHO7h5ZLd9qsOCGNwBPiUTevXkcb7sCFFTn+/cxNRygVUjU2OOd9d2uiXWJ7A/6bSzuvhA/3e7Wr+t4f0PP3vBzLdXd4nTmPMehaxzGb7+17DjMnh8wgBL0yfR4/SqW6sQsx7Umez80OVCsTcQ1HRevIJ1XLvcFi3w1tVJju1bpP8r6ehnQiIgAiIQGMCfvhIFHLqhhtuuPKKC/Fx2a4xhkuHj48PDQ1lunrxgLzph8rmmrecN0A0cJQ54tU5L7nt/FO8wOG8HW/gpLozxc2bj0el3bikL33pKufHzwyyz025kVQhn8HP4qxpDv/FlnEOLTWdK+DvcGp4QEaReC4LgMYMRlscvuc97+HtTBjQ2+vGofHNxmt579gtbaVdfkiMTg7dY7s8UNTnHkheN3ACLbt3777z/TfwoDLDamd2d/f6fvesEaNU7CQAmnZJuLo+6LmnPIBYFzHgtQBojHd+ttv1ggWk0cPXEpoDFwb3WC+yYfiboZtoMNvYcxjS1LKNHBJ8peE3hD/5+h/D3w/fo+KMbKiUTLgvKr51zCYNZA5NaM+ePXxZAqy1wt4sidsQ1LkVrflGVMczX3ED4VyxQO+srVBFCREQARFoQWCOH91a1FSRCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIjAWSSQIjjWBRwTlesCc/kjVJcpyHS5J13OFKOphtONCzfYgoDTfoqUFsMWDw4Oi0KF0nIqzV8l5WzuG8zylx0YzvSPHzh86syZM4cK0XC262RqSylbmureko/yZbcol2uDvc2eniq5Cden3nqF6e3xd21jf+TEESZf2RCzvTVn8kxIU9dCsW1y2kpREuQtzezvO+uyLxx5k+lqiqgeGnWHPg67knMB0DPzyUxUx/qeLleogg3IsA0MnWB/KDeGKmakrd1me2NoU86szYkSrGIr+TDsqCtLxBuBbnTSGqxn20yz8kVABERABBoR4F4ev53XRFLFyP1NEIa0ftN0V+/wVP7NkXHeQV8pls+UKziVcResnGK5Q7elXLBQMUXYMEE+0QSLOKcrPdypy7OUmwtg3zCwyFlStpc2V7qjSrCKOz3N+sNYvU79r3MieG28Ca6KfS5VyUTlrp4LOMxHW4q9xBo531KqZPE+IJiq0Efn16aKG7qmu45WcGflk0OHCL0qXnIlD+QcPzNCZFLRr7E5kZpxc/K//tLQTgREQAREoAGBcjnN86TFUhd/UXqgWMIFVtLlfKrcnSoT/ey8nkunMplKV1TMlisbnJaUW5V5Ov/a4SOvlbMHeUhpYrI4PnEsKvUPTnZH5d04tKjS550modK4M7wSsXCMnPCgJLL++SHS5Fi+z3Tpdjdzz91+GNbj91Yz7x0zay+7Q4J3y5USz64W88dOnyTQ+ZJL00WGwBU6QltuyErwNB53KEN3pkdLkyW+OWTdkI9xJf63K3KrNXdTpVzORtmeUukoXz2mJ985fpRQ6Xe2D1DryBE3/kUZ8mVeQuQ313gtENkMY29DWnsEyzKDMBqIEf/Upz715BsvnD592kajiQeGg55Ewhw9mSixRrGfzNLpTQzP35wasVbYmySlCQ0cGk9rN/5AF+NfKqa6spkUr8LQJgIiIALtEpjHDb1dlZITAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARGYiwArJL322mvPPPPMXIJzlHdnmUgu7959IXLf//73ibvNTbOCVjU8a47KzYuZqmRd5IcffaS5yPxKdl90/kOPPEYd5kAvP8oSzr1j47lt2wYr6fHR0YiXA7Pf97OHMH1iNF3JxyaWG7WzftNGsp/8yQONCuedt6F/8IILLvjeD38wZ02brDWx+BRLCP4iMe4XqGYC+Ht7H1nvJ+FbqzWdbsUzvzFFbH+28udkFG3s6r3ltvf19/VPTE3UpPSvCIiACIhAJwTwbtyff/CDH/z85/svvHA7no71IMdGa6rS0fYd0cBA9JFPRn196ZMj5cHBky+/EA0PRwffODwxER1+6yGelJmcco8bDfbzgoJox/YUQU7f//6D5Oem/PKVNWXxf7mxb1rXTw7N5ez+zjqXGReR7B7uqUQ+ltjVMP/7yOMPx6svJL37ogsfesRpQ7NricCjFItSR929rhd0qvp4TaM2WGsaJ9Xtl8LMFZ38hs2vouTH//SzRuLzzpP/nTcyVRABERCBVUuAKN6nnnrq2Wef3bJlYyUaLxSKQ2eiiZn3HLiOZdIRT4/29zpfPLiJBaF5dsd5riuvizZtOnHJldHGjUNvvhYdP0408f7t2w9951uP4YtHhlx8sX+UNSqXYi4VF9ZFPDUOEO9fCq62Oxv5FaAzeLTJqWkfv+wihokGfuxnezsEzCg3lRoY6GZd5EKx1NsbXbjrFx56+Jm4NqJ6e3uiHCHZ+H2GeY02i/zNpDKs+tzb349Vp0aGiDbmHROkH3tkcYbnA339rMb9D9/7B3y7jUbDeLaRUdU8huiwCuNWy6Ui32t4JwaHP3r84QH3JBnPWbXarEXbozNsaLZzAYRb3/c+F0IdLw5ySoiACIjAbALxX+dml+hIBERABERABERABERABERABERABERABERABERABERABESgbQJhDaQ2azBHSMhUiEZqs1YQc7O4LOOYYqK3XKqwzlWxO1OZnBwnJ53t6o66ShXeSOtXmgp15pNIZzMbfJAxzcynXlU2vhq0ZfX3bd20fsfQ6DEXbjXOusa5t/evGz69obt7G+8dHj5ZJCZs+PiUE2YVTLe5tb6abbsGNzAh+mSz4nnmp7qzW3ftWLduHZFwrftrM7KmPh5jbmkLLKP0SGU63eumdEf9+4tbm2OsbD1p08CiXlQJU8tdA/0T+emewYGRWAB03JLW+lUqAiIgAuc2gfb9Lw4tm03xNnlccKEQ7d9P/JQLQuJt86zvnM4Q9OxQ8SjO8UM7Sbz1+gROYezUrXiHY8dOsR8hxspVcWsujk275Q97d17Um75ocrzfh2iViPgpp6a9TPU+7by1v7kPjU2aQ8U9WFneRUI7V45VPX0uPosorv51fV29rSbxW/upBv53oLJpY9fQcIH1JW0d6nIlKhSj/Bits9UHIFm+27ue8JL6CcLCImqhoDvbba7Klyx0J/+7UIKqLwIiIAIrnoBzW945bdy85cDbhyano3eODhPl7BYyLkVZ/IsPcjXP6Fc3jsb9s0ZDo10EAQ+uy7KvTG86cyw6fvgYbx+YHt/j3khQOLPnc58bOv1/4DqLBa/No8Cth43kNN6Pv9lbnseFSwyW4+O5qKunb8eu3VHUaQA0DVcqE2M5XC1PGdGvru7+deu6x8byGcbs3gTaK+BZzbnONikcmU2FSimXmxzJzURJX3TBBUSQB7GFJtKpwfXrMn29+akpizEOY8/5aranuor93bzWibrTWX6ZYHXvVpuVWpC0tR6k+aYEqsENG4dGRzZs3nT8xPFQlDyLoUAJERCBNU+g1dhpzcMRABEQAREQAREQAREQAREQAREQAREQAREQAREQAREQARFYKgKPPfbYqVOnOo4ioqJFJhOTxGJXbx86ct9997npUiYGmQFe8Pbqq68uWMcsBffee+/Q6CmyMJtZ6snJaGRkjAlsXr/L4fBIND7upoqZOC4XfYSYdW+WDndgxFg5jAngTCbTftxbnaaZDFT96Ec/suhn4qrROVPWUertt9/mdcNWdU5tFqxmTbroNL8FC7JRCkQPPfTQ8ZMnOglF78h+VRIBERCBc5KAC8FKpZ544gnnf8u1G26tq4RhEVlE9HNPT4SAD9hykUZTw8MEWnEr5kEdDnFDvOTea0qh48033/qnf/qnQhRClDLOk8W2uKOfVRBeEVCJiMKaJiQ5cgtS4n8XM8Ipipz/HXZfDMyvur23w4zhWSpnrC+LmUpUdHBErhzH6MLEK9ELL7zAY0vd3e67h6216Yo73eR/OyWneiIgAiKw+gg8/fTTBw8eJDa2K+Xcig8YnhX9HLpUYjnnMgs5FxhJpf0CzseOHaO0HA1TcWr0JC9zmJo8+b3vfQ/PjDMyn55wskFbw8RsX+1EGGA2lJx3Jqs787WgEjHen5pyXyT6+rrHJzuPXbbx6fPPP8+3kXkb06QCq1T/5Cc/4btNk/J5Z3NmWT+bagzw5125rkIul3vwwQfj0c91IsoQAREQgRkCCoCeYaGUCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACCwbgYVHGDNrS8CSBeuyitLTz78UjK9FN4WMeSfeeOMNq0M4VOvFJhuqtniqUISS5196MhzysuOpSRcDnU5PYT+TpNM5tycky89e+zWh6ial42Yww4o291bcBW+oZYaVCGPCqVFY6jSey8yz6LFXXnkFbZhGZiyerLGtNlXfbEqctxKPT0/tfcrRm9ekfuPGlCsCIiACa5WAu0tXusql9OuvHarzMFUohXw0OdZdKfYdeL3i4416Cb3KTb1MnBAL8eMgSkQ+u5uxWw254J3VdDn62dPPB6i0MftePeOneMAniDVLEDaEB6k9OUNb/M1dK66tgf99Yb8TiFtmJvp99cGbhNWNGsVBs7391pmurmostTtewCb/uwB4qioCIiACq4CAjY8w1EasTz/7ihldLLEksk9Wur0biofMlv0Aqsu52nJPLof3ylZKfW+8Vl0JGFWT44dxyIVC6eDBmkOnDhse2m+doTlw4EBnFRvWKpd44GofI2mMmszFO9hQvFVmGGC2EppnGQD37t07z0qNxQGPhS+++GLt20tjsfpcuwTq88mZms49+fRTDYuUKQIiIAL1BBQAXc9EOSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKwJAT8tKzTTMImCImv6nialmglJoBZP5joKqKSLE7Kr+HomrCAqWREkyuZ92ZzrsH4duoHYeudO6x0u2CyyhQmEWM8MRHlCyx16ZRRiPEEDLc5M0xsMVO2VOwYXegChrFZB02bpYNAmwnTY3tb9cosXEhEdWiaBUHRNt8Z5VBdCREQAREQAQi4W7TfOY/k463MRfIoDWs/h216Os8tl52T8Q4rX31VPZ6q1ZZy3hh3Zsv6t5JsWOasMyN9wsvMDqVuWK0u0/SQbU7Nd5pgbtcXNgtB86XO+VqmK2hv490GQUN7NZpKYRib/G9TQCoQAREQgVVOgJs8PbB9YoQVOzRPZyPXmUeG/FiWIvfHuwIYB+X8U0dVv5yrPjq6GA/DViljp40xY7Z1fALS+DerbNoWPo7DPMaVjAqNRseWhYoLNymosj4u7ssrOBeoXUQjg7VKiIAInJME4v7jnOygOiUCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACK4IA05ZsZgqTeXbIfiGTrMwBr1/fa1qZAK7OARPS5CKf5h3YFMfU3d3NDKvlmKnx0jnT1inbU91NYUbT7LDQWUuoWSkqTEWTo9H0RJQbj6bGo9yEWwTaR1YRCN0gFjqA6u/vn9OANgXQycZ0ctVIO/b7NjWYmNULVcxC1LKFzI4TKNfsb8f0VFEEREAEjAA30nK5yJKRVf/oA6vwSPzFb9XlcrZYyEyMr5+cWD81eV5++rwawFwU2V8+Fbk1/jeu22RFaMimunDC3v1aFFetknscKfyFzKYJTMRjZjPdNYlqBFXtcO5/cRkI2b7q2sruESOcry+pfj0IhzXz5taMRDbL41tOQ6HgnmVayGZ+Ey8p/7sQjKorAiIgAiuZAHd427LZLN6tkanxcZ+5S5MijTtjJMUDtDid0uR4lr+piXQhhx7ys5l0Dy67wvrQLCNdSVXKbljN1qiVufOwk6dYO64+u4EyenwMNL1YhA1tAwMDKIIhG6YuXKn1dHBwcOGqzJ4NGzYsXFXQsCh9DNqUEAEROOcJLMLvbuc8I3VQBERABERABERABERABERABERABERABERABERABERABBaLAJN5bCEu1tYJ7lg5s5+jo7kQ0kRYEjPEi7I6Y09PT6FQYIa1Y9uYVWWjp+yZTiZcLF/I+8NZKjE+/M0qaHSAKuhNT1dX4+SwkdS88zgLBHxjpM0o+1M0v3llqtAqelDS29s7OTmJKuLY5m1KXQXCBdBZl60MERABERCBTghwu3a+0kfxUh83EtLtq/PrR6dHxkasCqs+l3lQxXngzr0SHs2cmvNHpWqMdfsmxSVN1Yz/TUf5vAvyrlg4mRftzH+as8MpdVY9bqSl5X/rmShHBERABM49AubgGA/6J4jaD1RjhOUGWf51QUYFV8vzN3jbcqnMkHDWszjWSmf0GLgtSjQwrff397Dna4Ft5jqrB53+Mz4+jsdkleXFCoDGEB7ZRS2DzU6NqtYz7CMj7ksR2hbe376+Psa/izKUXmDXVF0ERGC1EGjfr6yWHslOERABERABERABERABERABERABERABERABERABERABEVihBIhGYmNekO3zn//87/7u7+7YsWMhtrrILb94447tg1/96heZCrY/ZoYXuH3sYx/7zGc+s5BZZDOAmVqmaa+//vrPff6zWJpJ9/p85llZ3pJ0b7lsf93lMjlMW/BnfWrcA0xiaeqvfOUrX/va1xpLzDMXhbfddtu/+Bf/AiM7DjU2UBjGlsvlOL+/+qu/Ok9DGotj0p133vn1r3/92muvbSyhXBEQAREQgXYJsN5w5nOf+9Xf/d3f2bF9tw+rYtlI/vBK2aoLShfLqXwUjfq/qSjir35zfsp74GjHtu1f/epXicfi0AVk1VasrK/TZs7HPvrxX/mVXyGmuk35ZmIz/vdzdyOzgAeaZlrgO8xXvvLrX/vab85kLSAl/7sAeKoqAiIgAquDAOGw+A4bJTEe/J2v/db27Vtjpjcb91m+DW3T3iOyVjF/5/k/52937dr6e7/32/7FQXhtC4y2sWRM/XySn/vc5z71qU8RfTufSg1kJyfdw7pXXXXV5z//ayTofgOheWYxuvzt3/7t3/qt32JkvSgKGWB++ctfvvHGGzse/yZ6QPj4Zz/7WWyzcXGidF6HU1NTH/nIR/7gD/7gF37hF+ZVUcIiIAJrlsAi3GfXLDt1XAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATaJMDUL9u6det4OexGv23dupVpwg9+8INtakiIoc1yNm3o47d+Jhrf85737NzJrPAibEQ/o+XSSy9973vfGxrqTC+ToMRgsSLyxz/+cVa1ZFXpzvTEazEVyrTonj17tm/fzmx6vKizNPSIRH//+9/fWfVQi57aUlVEVIf+uhNfO1lBcl4JlpTmyjl+/Pi8aklYBERABEQAAuEmbAlupzH/u6CnhTat35yOMlX/u2NbjHazcK6YSJPkR+9ywcrmf5uItJs92/9Gi+F+o5j/Xb8Y7td9e5H/bfeMSk4EREAEViEB3O762rZlyxYObfxrTjn46Hn1jDEg8mNjY1dfffX27Rt9XQbE4W9eyqrCH/7wh0ldfvnljKk7qR+rMzAwwBHPxH7iE58wXxwr7DDJqHx4eBjz+A6Dzg61xKoxMD///PNvvvnmWF6HScaq1Lz11lt5AHhRBvtoIwydn0qOHTvWoU2qJgIisMYILMJtcY0RU3dFQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoEMCvGeWZYavueYawnb/9m//lnnHp556qjNdxAxR8Zprrr7mmhvvvvvjpWLv0SOjgwM7yezOLjQmmGikoaEhe8duZ+aFWhYQfOrUKaao3atsK0XmuWsrdeWYGq79sXCXW7vL/4XajRMPP/zwc889x4ToxMTEorwb97XXXjtx4sQCl7/CEjspF1xwwfve9z7mvAl0pwNkWn7jzsyVy4wyLxSmv6z4NZesykVABERABJIE7A5MlM8dd9zxoTve3z/Q/bd/+/fd3X1PPf0zL2rep+gck7mgWY5oMor4a7Dhyq+5ds/dd3+UR1+OHj06OLgBoUXwvzu3DQ2fXmT/O1ouFtLlkgszcz1ZQNT3ww/vfe65144dG/HuN9OAyzyz5H/nCUziIiACIrDKCJx33nmshUzkLlHQ3/ve9wgOZlwzZx/MYaVS4/5vIpXib4S/KDp93XXnX3H5nltuvq1c6jtxfGL9uvO9NnPe3pvPqb2RAAO3d955h/hd1lpuVD6PPB79Rfr06dNvvfXWooxV0fbII4888MADBw8eHB8f54tN1aHPw6ik6L59++gvZydZMP9jhr18yyIAGtu2bds2fwXJGvxUwm8Rjz/+OG/YSJbpWAREQAQaEVjojbuRTuWJgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjMImABWIcPHyZMikWbiLX99//+37OG8T333LN///5ZovM5ePHFVxAnoGnH9h2sC8W0KIG8w6PMDS9oe/bZZ1nAmKlHZm07VhSP+iWierffmLVF7QKXhmLRrz/+4z9mapkQsUWZVP793/99gowxjHcKd6bQOsueSDhmzdFGaBwLdHE6iGBmVrhjjPSR6WQum9HR0Y6VqKIIiIAIrGUC3JyXwP++CNIl8r/4kQMHDnR8yuR/5X87vnhUUQREQAQWi4A5ozf8Zjr/03/6T4x/f/jDH4Ym4g6rdWYoffHFF6NKtlzJb9yw7brrrrv33ntff4MHckpBoLMEL9u59tprd+3adf/993emIdSyThETzGsTLrzwQsa/PPbMmC4IdJC47LLL/t2/+3dEGDOuDI/ddqAnVCG2mFcq9fT08JVjgY8Bo5NxNLbZ+BeSCxz/opDrZHp6moeog8FKiIAIiEALAgqAbgFHRSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKwaARsKpQJSzaUfvvb32bG8c0331x4AyzmyCtx//AP/5C4W6YeF67whRdeIBQYVYcOHepsfakwmU20N/0lWPnf/Jt/Y6thLTD6md6xmDTT58yzEm288M6i4Rvf+MYtt9zCwtJBYWe9NmOIWvvWt77F3DyrapGzkOhnqkPvr//6r2+66SaWDDf92ouACIiACHRAwHnfJfa/6ShVYd3/DoyrVQn+l4jtWt78/pX/lf+d3xUjaREQARFYMgK4JJZ85qU9tEAijH/DQ6c25gqeq84QnjNKbt6Vu2DifGH8T/+nP2IUbNHPNupMSrd9zBLLrDrM8BxH3HalVoJnzpz50z/9UxsJLnz8S1Txv/23/3bdunUWAN2q4fbK/vzP//zmm29+9NFH7atRe5WaSvHU9H/9r//1mWeeWZTxL7iIkr/00kv37t3btEkViIAIiECMgAKgYzCUFAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIHlIrBYc6tm79R07rkXnidN9JVNFC8kAIv546effnoRSbz99tvOtnR6UXSyHBThTQtcRituCfPT8aXI4kWdpZnmZxK9s7r1tVhGenHNq29COSIgAiKwdggsnf8l+nmBGHGUxA8tUEm8uvxvnMZ80/K/8yUmeREQARFIELDoZzJJ7Nu3L5R28LhpIk6aZ2vjChcYyEtM9ksvvYR5fX19LI0c7Ow4gQdhY33ljjXEK2KSW/qaV0+kUgkOcbH200R7/+hHPzKF7ddqKGnrPS/u+JfHsImlXpSeNrRZmSIgAucYgcW51Z5jUNQdERABERABERABERABERABERABERABERABERABERABEVgtBAh3Jt4qrI5l0VcLjMBa4PxxHF1cVTwdl5lXmklf1mkOSzXPq24z4XrDmG2d16x8fCo6XjHo0fRtM/jKFwEREIFVSqCh/11gX+r9UccK46ri6c4UmpuT/+2MnmqJgAiIgAgkCITBUXzolJBJHJpkfcWQk5Dv7HBRop9p2vxmsVjszIxEraBnsTq7WHqw01a5Xvg3jUSXF9HChGYdioAInHsEFmethXOPi3okAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAquXQIiH7qwLK3m6cXlsa38mvh3Cy2NzO5ZIRgREQAREYCUTWLH+YtkMk/9dydenbBMBERCBc5XAksbvLpsP7eDsrGTbOuiOqoiACKxNAloBem2ed/VaBERABERABERABERABERABERABERABERABERABETg3CGwwHDnehBreR6049CrhhUbZtYDV44IiIAIiMBqJCD/u4hnrWOP2bBiw8xFtFaqREAEREAEFoVAi4GnFS3P/byFGYvSTSkRAREQARFYOgIKgF46ttIsAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAquSgCaAV+Vpk9EiIAIiIAIiIAIiIAIiIAIicO4SqI+HDjkaw567p109EwEREIFWBBQA3YqOykRABERABERABERABERABERABERABERABERABERABERghROoLLF9a20iea31d4kvH6kXAREQgXOWwFL732bgzlU/da72q9l5VL4IiIAIiEA7BOQd2qEkGREQARFYywTSa7nz6rsIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMDqIqAA6NV1vmStCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACKxpAgqAXtOnX50XAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgdVFQAHQq+t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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=3840x360>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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1xE0kQILKrbSR6ZprsrSsUUqhJ2gnJA7CgDVfSLSL72sWh+VThQxPIz6U9NPthbJLFdWhYuy5nc9sc4A9++ay7prZnjNrG6L5ahw7bvnxyfpSvJbGzjRIGE4Yl5C+QRgYGPz/ADpFXND+yxcNPLJqllmNA33jzkgY6e9LbaHHPLHGdW09BI4TJdjjPfpWfbyWqwTpNAzyMn7pg2NrZHX8M0y3eJJlM6M8WRuUNjNJqVtGK5pQaFHcSSqNY01AnQvKw3fTirc3hyJ5Cx1vS1yoODKRj5QeeK52p7x7Z7lmtYmjhwAFZtxzgZ5+uaOWT6hc6jQ/C8U8c0p1zTk328qgbmPPT096xjpEcdu0y6tZF1IAVZDnn8KzXaMwRBVIkGd5z19KdE8C2s6uhaZtvlnPC88k+vpUxhJSbcrrsFzTtrRb2UxXF7ZN8jN5jSENwM4zjn8agOn2ZAxqUCEjOG3ED8QP6VTtGt0uka6jaSAZ3KrYJ49ahPatOXULmnc6RFBM0Y1WybacdXB/9BqndWy2wi2zxy+Ym7KZwOSMc/Sku2t2unNqjpAT8qu24j8aJnt2gtxFGyyqpEpLZDHJII9OCB+FAiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAorU0Pw/f+ILmSOzRFihXfcXMzbIbdP7zueFH6noATxW22qeHvDICaJbR6vqK9dSvYv3KH/plA3X/AHnz/uigChpXgzWdUtBfGKKy04/8v19KIYfwLfePsoJ9q1LGHwhol5uk1C/1y4VH+W0j+zQfdYH53y7DH+wtczqms6lrd0brU72e7nP8crk4HoB0A9hUdhzdAf7D/wDoJoA7X/hOntONG0HSNOXsxgNxJ/33KW/QCqN34z8T3/8Ax8a/qBX+4kxRfyXArBHapMZoASWSSdy00ryMeSXbJP51HgDtSkHNKB60ANwPSjA9KWkoAVflIIJBHcVrWXifXdNGLLWtQgHolw2Pyzj9KyaMUAdWnxA1OXjVLPTNVX1urRA//faBW/WpxqvhLUhi5sb3RZGH+stm+1Qg/wC621x/301cZS5NAHqXhXw/9l1xNasdSsNU0/T4pbsyW0v7xGSNmTfE2HX5gOxHvXmDSPJI7ync7MSx966TwAGk8UG1VsSXdldW0Z7h2hcLj8cVzUV6JVCXib128yKAHH496AG0U9k+UujB4/UdfxHam0AJRRRQAtSPbTxwrO9vMsLH5ZChCn6HpTRivSLu38258SWLzJJ59jGkV47qkdyyNF88aDjCruORk4B9cUAeevaXaJuezuUG0NuaJgAD0OcUjW06QLO9vMIW+7KYyEP0OMV12tXV/ZaL4d865mNv9jaG7Cyg7lMzEowznlAKn8Tago1PX7i1tYn07UIikFy9wTCY8qYwi4+8uAAvbmgDiUieUkRxu5AyQqk4H4UxoJVRpDDKI1OGby2wD9cVe0XWLnQtastTtZGSW3lWTgkbgDyp9QemK7GG70rQfG9haG6+0aPLejUJMsSg3rmEMPVcgnvk+1AHAtY3qSJG1ldKzjKKYWBYeoGOaeLC8yALO4JI6CJuf0rsLKfUba8t7J7QwFtUin86S7LyMVJ3sh6bCp5OeRiq+p+JdX0bx7Jdm6lNvb38txFF5vyNG78gY4wwAFAHJtbXChma3mCqcMxjOAfQntTEVpZFREZ2JwFUZJ/AV0Xigx6bdS6Np99LPYGY3ZYSEh94DRg89VTH4k+lO8IzQJDrkCsqancae8di7NtO/cu5VJ4DMm4D8u9AGCbaZbnyGglWbGfLZCG/I80klldrOYfsk/mgZ2CMk4+ldbp1xcPomoadeSQpqy6aIdOXKrIIxLulTP8AfYFsZOSMjvWr4ela1uvBkE0pGp2t+8tyTJhobQsuBI2eBncQDzg+9AHnn2efCZglBkGYxsPzj29aFt7p2dUtpWKffAQ5X6+ldasWpnwXrEUrt58uqQuFaUFyu2TeV5zjOzOPatt1ubfx1qNxFIoL6CQXEq4aX7MFweeW3AjHrQB5s1jdLMIjbTCQqG2lCDg0sNtNO/lwwySP6IpY/pXZRjU28P8AhVUkczpfStITMAyjfGULHOcffxn3qXX0jbT9ds9NjUX763JPLHFhmltSD5RUDqoJOQOmRQBxS280hcxwSPs+9tQnb9fSmyRSwkpNC8TejqQf1r0i2um/tOYWU2blfDht7ySNxtkudvABzgsBgZ9RXM60k3/CHeHllIM0ZuQ4ZwWVS4K5745YjNAHMUUUUAFFFFAEvBFdR4Nbzote04jKXOmyyY9GixID/wCOn8zXLDpXVeBk232rXZOI7bSbpmOP7y+WB+bigDKooooAKKKKACiiigAp8XV/+ub/APoJplPi6v8A9c3/APQTQBylvF59zFDnbvcLnGcZOKt/2bHuIOo2YwcZLN/8TVW2aFJ0a4jaSIEFlVtpI+tI7RmZmVCIyxIXPIHYZoA1H0i1j+9rFo3yqcIGzyM9wKkWwtUt1kiurQsXZQ0zk4xjkADHfvmsu7e2Zo/ssbxqI1Dh23Et3P0pXktTZRxpAwuAxLyF85GBgY7d/wA6CrmimlfbTM8mrWIMSg8uRnkDA496ZDoqS3EUf9q6ePMcLnzCdue/SqNu9ssU4mjZpGTETBsbWz19+OPxplu8Kzo1xG0kQPzIrbSfxpNT11JubEPh2KYS51vTE2Z+/Kwzg/SrEvheJ2Df2/pK7lU4aZgRwPauaqxeNbPPvtIXihIACu27nHJz9an2c278w7mo3h2JbdJf7a047gxx5jcYx7e9V30VFtpJhqli+wgbVdj1/Cs92iMEYVSJATvOevTH9adE8C2s6yRlpm2+UwP3eefrkU0n1YjpdC0SDWnulvdRs8Qws6t5m1i2CQMkc9O+ayf7Ms2UkarbIQM7W3EfTIH9KoWbW6XSNdRNLCM7kVtpPHHP1xUFJRak23oVzGpc6TDbzMi6tYvg44Lj/wBlqndWothDieOXzE3ZjzgckY5+lNu2t3una1jdISflV23EfU0szW7QQCKNllVSJSWyGOSQR6cYH4VZJBRRW9p3gnxPqyCSx0G/liPSUwlU/wC+jgfrQBg0V1v/AAr3U4B/xMNT0LTz3W51OIsP+AoWP6Un/CKaFBxeeONJDDqLW3uJvyPlgUAcnRXXDRPBUf8ArPGF7J/1x0dv/ZpBTf7N8Cf9DFrX/gpT/wCPUAcnRXWf2b4E/wChi1r/AMFKf/HqDongt/8AV+MbtP8ArtpDD/0GQ0AcnRXXf8IloVwP9C8c6Qx7C6guID+qEfrR/wAK71WY407UNE1H0FrqcW4/RXKn9KAORord1Hwb4k0hC9/oeoQxjrIYGKf99DisKgAooooAKK1dC8O6h4iuZIrJI1ihTzLi5nfZDbp/edzwB+p7A1unVvDvhb5NEtY9Z1McNqd9D+4Q+sMB69sM/p90UAZ+l+CtZ1OzF+0UVhph/wCX+/lEEH4M3LfRQTWvYx+C9Cug8t/fa9dqGGy0T7Nb/dII3vlyOeyjpXK6trep67eG61W/uLyboGmcttHoB0A9hVe0/wCPgf7rfyNAHct49a1+XRdA0fTlH3ZGt/tEo990pbn6AVn3fjPxPqMZS517UDGf+WccxjT/AL5XArn6kX7tADpJJZW3SySSN6uxY/rUewf3R+VSUdqAGYHpSFQeoBpaKABfk5X5W9Rwa17DxT4g0sL9h1zUoAOipcvt/LOKyKKAOqXx9qk2RqtnpWqg9Td2SBz/AMDTa361bh1bwbqi+XPp2oaLOx4lt5PtUI/4AxVh+ZriqBQB6l4Z8Pra65HrdlqVhqen6dFNeGW3l+ZWjjZk3xthl+YL2xXlzu0kjSOxZ3JZie5NdR8Pvn8UNaj/AFl3Y3dtHjrvaB9v6jFcjFeFzm4yxP8AF3oAloFPZBtDoweM9CP6jtTBQAUUUUAFPkgniiWZ4ZBE33ZCp2n6GmV6XdWxkufEtk88U/2iwiSK9dkSG6ZHi+aNAMABQxzycA55JoA87NndIm97WdVwG3GM4wehzUbQXCRLM9vIsLfdkKHB/HGK7DWbq/s9A8OCS5kMBsXgvFSU7nRrh2KNyTyoQgHsRVjxLqCpquv3FnZxPpd/EY4Lhpy0TRZUxCNcYDqAAF6ryDQBw8UUk7bYo3dvRFLHHrxTjbzCJpDE4VTtY7ThT6E9BVrRNXudC1m01O0Yia2lWQANgMAeVPsRxXZ291pmheO9PtmvfO0KW+GoSFpNyAOMxFx0ygKk+556UAcI9heI6RtaziSQZRDGcsPYY5oFndkqotZyxzwIyenB7evFddb3F7DfW9m9p5BfVYbkzvd72JGd5Rum0g5ZuhwtRah4k1XSvHkt79plZLXU5bmKESfIyM/zAY4wyqB+AoA5MQTeW0hhkCK20sUOAfTPSkSN5HCIrMzHAUAkk+nFbnidk067l0iwv5Z9PMrXY+c4bzMFNw7sE259yal8JyQ+TrcAdY9SuNPaOxcsFO/epZVJ6MyBwPXJHegDA+zzrP5DwTLN08toyGz2460ptbkXBt/s0xmHWPyzuH4da63TZp30W/0+9kRdUTThBp0e0I6p526Rc8fMyk4yckAjvWl4bk+y3PgyGSZY9St76SSd2kwYbUldquew3ByAex6c0AcALa4Pl/6PMPMzs/dn5sdcetNEMzF9sMjBBlyqk7B6n0rrDHqJ8Dawk3mGaTU4XUFhv2hZAxXnOPugkcdK3nNzD4z1GWN1Bm0Dbv8AOXEk32ZVwTnBbeCMdc0AecrZ3TTmH7LP5mzfs8s7tvrimQwSXEmyGOSRv7qKWOPoK7NY9QXw74V2u/mJfys/74BlUvGVLc5C8ORnjrT9fijns9dstPjX7Y2uyXMscJGZbYg+WyAdUDZOBnG4H6AHFJbXEglaOGR1jGXKqfkHv6UySOSF9ksbxtgHDqVOPXBr0i3uW/tC5SzuN14nho2966SgLJdbcKuc4ZwMDPqD6VzetQXA8F+GzLhmQ3CuWcFkDOCoPOQDyRmgDmKKKKACiiigArrPBDG4j8QaWzHy7rS5ZAvrJCRKh+o2n8zXJ11fgIeXf6veNxFa6RdO/wDwJfLH6uKAM2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorsrzRbLZrVt9kjt7mzs4pY4Q2+aKTfGrK7gYYneeOSDj3oA42iunvl06HSdEH9mWsdxfWTCWbc42N5zIJBz1wvToc1JrlppGnatrWkCJcWqtHaGONzJ5iEcue4YbifTIx0oA5Sir+i3FjbaxayanapcWPmKJ0bOdmeSCCORXS2Hh+2XxRD4e1C3gRri/cGdd2TAuSAnOMPjA60AcXSV0lmdJnlgD2tu919viRIkjcRmFiQyvnGSPlwevWppbjQ7XxWbS40aD+zoL6WKVt7bmi34B6jlcE575IoA5WitvWrQaOs2l3NrB9sW6dlmXcCsYOF2842tgkcdO9L4e0+2ubXWL6dPObT7IzxQE8OxdVy3qF3E4+lAGHRXT2Vrp9/pF5qYsysum2il4icxyytKF3hQBgKGHHsKu6LpNheTeF725sYnW/v5LG5gwQsirtIcAHg4fHHHGcUAcXRXRbbKbwzqGo/YIVuLe+ihiwTjY4c4YZ+YjYMGtSHStLXxLf2rafHJbHRjdpGzN+6f7OsnykHONxPXOBQBxNFdQIrIaNoV4dOt2mu7uSGcEHYyKyYwM8H5yM+w780/VtIs9HtNRvIoVkMesy2MW8bxCiAnJ9SeAM56GgDlKK7WLSdMhl3yWMciXfh9tQELMw8iUA/dIOcEqDzng/jWHqltANB0i9SFIppzMkvlghW2MADjPHBoAx6KKKACiiigAooooAKKKKACiiigAooooAKmtP8Aj8j/AB/kahqa0/4/I/x/kaAOWU/usd81JcRxxTMkUvmoMYfaVzx6GtSTSUjs5Jt+dllHcYx3ZsYqGx08Xdv5rPj/AEiGLp2cn/CgCo0ca2kcnnZmLEPFtPyjjBz0OefypIEjcuJJvLCxsy/KTuYdF9s+tX7bT47nxF9gJ2xmV0z6AZ/wqG9shaWdhMGy1xGZCMdMMV/pQBRqW4SKK4kSGXzo1bCybSu4euD0rVuNGWGC4nEmQltDMq47yY/+v+dR6Xpg1CCFiwUteRwE+zZ5/SgDOdIltopFm3SsWDx7SNgGMHPfPP5UkKROspkl8sqhKDaTvbP3fbvz7Ves7BbrU2sy2B84Bx3AJH8qiu7MWtvZOTuM8AlP4k/4UAVVCkjJwM80+dEiuZUik82JXISTbt3jscHpWneaQlpDdyq+RGsBAx/z0G79MUmm6Wt9DayM2PMvPs5GOxGc0AZkiRCCJll3SNnem0jZzxz3zRCkTxzNJN5bKuY12k72yOM9uMnPtVnTLNb67aBjt/dO4Puqk/0puoWS2YtNp3ebbrKeMcnNAFWNUaZFd9iFgGfGdozycd6c6osrqj70DEK+Mbh2OO1a99pEdrBeSK5JhkhRcj++pY/ypdP0mO8ismZyPNuXibAzwq7s0AZEkcSxRMku92Ul02kbDnpnvxzSRpG0UrPLsdQNi7Sd/PPPbirOnWS3s0yM23ZA8oPuozRqVmLK4jiVs7oEkJ92GaAIbGRIb+3lcRYSVWJlQuuAc4KjqPUUXs7XeoXNywjXzZWfESbVAJ6KOw9BWpqOlR2aXpVifJu1hXI7FSc0lhpUd1HZuzkCZplYAf3FyKAMmRI1WIxy+YWTLjaRsbJGPfoDn3oRYmhlZptsi42JtJ3+vPbFWbC1F0bok48qBpR+GKNRs1s75IEbcDHGxJ9WUH+tFgILRlW8hLztAocEyqCSnuAO4pJ5JLi5kmmkaWR2yzsSSx9STWhf6WtnDckSFjFemBTjHAGc0tppS3EFrIXIMkc7nj+4ARRYCDVr6bULqKee4SZxAkeViCbQowFOOpAA570ltPZx6ReRywI91JJF5MhLZRRu34xxz8vX04ptpardQXrFtphgMo98EDH607ULJbXVfsasSoKLn6gf40rICtCkL3CJLL5UZOGk27tv4UwgdM1oahpi2EDS7yxF08AB9FA5/WpINMjmt4JS3+st5ZiMd0OMfjTAzZVjRlEcvmAopJ24wx6j8PWkVY/s7OZMShwFj29Rzk5/Lj3q1a2i3FrdyE4MCBwPXLAf1p81gsOuixDZUTCPOP1oAqQLG8yrLJ5SHOXxnHHpTSB2q3dWP2a1jk3ZLTSp07KR/jU66YHtklD43WjT4x3DYxQBnSoiOBFL5ilQSdpGDjkfhS+WogLtJiTdgJjtjrmp7W08+0uZd2Gi2Y465OKnaxH9vCxL/J9o8rdj3xnFAFCFYmlxNL5abSd20nkDIH49Kjwau3dktvaQShstI8injspAFWDpwEHmb/8AlyE+Mdy2KAM6VESUrFJ5iYGG2kZ49DSlE+zq4f8AelypTHQcYOffn8qs2tn9ogkk37SssaDjP3iRUiWStrn2LJCGcxg+lAFGJUZiJJNgCk5wTkgcD8a24P8AkEWv+9J/MVmXVsLe1tJM5MoYnjHQ4rckthBpOnlTnzUZ/pk0AQ0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABU1p/wAfkf4/yNQ1Naf8fkf4/wAjQByyn91jvmpLiOOKZkil81BjD7SuePQ1qSaSkdnJNvzsso7jGO7NjFQ2Oni7t/NZ8f6RDF07OT/hQBUaONbSOTzszFiHi2n5Rxg56HPP5UkCRuXEk3lhY2ZflJ3MOi+2fWr9tp8dz4i+wE7YzK6Z9AM/4VDe2QtLOwmDZa4jMhGOmGK/0oAo1LcJFFcSJDL50athZNpXcPXB6Vq3GjLDBcTiTIS2hmVcd5Mf/X/Oo9L0wahBCxYKWvI4CfZs8/pQBnOkS20UizbpWLB49pGwDGDnvnn8qSFInWUyS+WVQlBtJ3tn7vt359qvWdgt1qbWZbA+cA47gEj+VRXdmLW3snJ3GeASn8Sf8KAKqhSRk4GeafOiRXMqRSebErkJJt27x2OD0rTvNIS0hu5VfIjWAgY/56Dd+mKTTdLW+htXZsebefZyMdiM5oAzJEiEETrLukbO9NpGznjnvmkhSFxIZZxGVXKDaTvbI+X24yc+1WtMs0vbxoHOP3Uj591Un+lJf2aWbWeMsJbdJW4xy2aAKkYVpFVm2qWAZsZwM8mlmVEldI5PMRWIV8Ebh2OO1a9/oq2dvfS7j+4kiRRjrvXdTdO0hb+OwdnCia4eJ+OcBQ2f50AVLw6adO09bOGVLpY3F47vuDtuO3AwMfL9a0rfVdJh8O3dvFaXNvqUlusRnW6YpNmTLAoAAAVwMEnJArPsbRbyWeInGyCSQHHdRmlv7RLKW1QfMXt45Gz6sMmp5QLIXR7G8vbddTnlkSVFs7+3UrHtJO9mUjd0PAHvUOpSaTLZaf8A2fFMlwsRW83ksruDwykgdR2xxWvr1ta2+hWltAMy2czpOxhClnfkfMPvAKoHPTn1p2heEH1rw/castwkcdqWEqMpJbqRjHsCfwrO4yxPdeFda0XUPL0+10rVI7RZo3SSQRvIrANGqk4BK4P1Bpmm+IbDQdF1HT7KOC+F5b4mluYHjZdwG6MANyFIzk9SfzwdEtLG6vWa/M/2SGNppfJIDlQO2eM5xTtVsoLDWRaW8hkhxEyyEYLBgDnHbrS9krWAZ4fv4dM1qK4uLS1uYgGUrdRl0XIxuKjrjrj2p2p2sR02y1GztbtYpCYppZABE0wOSsfA4AI/MVteMvC0Xh+00ydDMXuYVaQySBt5O4hhgDAwAMc9KisdOS68Nrv1d4Y973Edhyd7LncynGBgBeD1zVXEYusXFteX5uraGC3SZQ7W9uhVIWxgqMk+mc+9VY4lkjbEn77eqpHtPz5znntjA/OrFhZi8t75i2028BmHHXDAY/WpL2xFlq62YbeBsBOMZJAz/OtAI59NlsNXfT9U3WUkb7Jt6bjGfoOtUa6TxLEJZXvfJt7fbcG0EVum1MRqo3Y9T3qnDpCzRW82/Ae2mmYY7p/kVAzMkWNCojl8wFFJO0jDdx+HrSKsf2dnMmJQ4Cx7eo5yc/lx71ZtLRbi1vJCcGGMOPfLAf1qSewSHXxYg5QTCPJrQRUgWN5lWWTykOcvtzjj0ppA7VburH7NbRybslppU6dlI/xqddMD2ySh8brRp8Y7hsYoAzpURHAil8xSoJO0jBxyPwpfLUQF2kxJuwEx2x1zU9raefaXMu7DRbMcdcnFTtYj+3hYl/k+0eVux74zigChCsTS4ml8tNpO7aTyBkD8elR4NXbuyW3tIJQ2WkeRTx2UgCrB04CDzN//AC5CfGO5bFAGdKiJKVik8xMDDbSM8ehpSifZ1cP+9LlSmOg4wc+/P5VYtbP7TBJJv2lZY0HGfvEipUskbXPsQJCGcxg+lAFCJUZiJJNgCk5wTyBwPxrbg/5BFr/vSfzFZl1bC3tbSTOTKGJ49DityS2WDSdPKnPmoz/TJoAiooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACp7L/j7H+63/oJqCp7L/j7H+63/oJoA5WnSKquQj71HRsYzVw2IWF5dzbRbJNjPckCo7a0NxAXDYPmxp/30T/hQBAVTyVYP85Ygpg8Dsc0Rqr7t8mzCkjjOT2FWUtfN1U2isV/eMgP0z/hUdxB5NvbPuJMql//AB4igCvT5FVZWWN96A4DYxkeuKuS2PlxTPvJVIIpAPdsf/XpLSxW5t4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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=6400x360>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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rT5Lf//73j3zkI3fZZZd73OMefUl53Hvvvb/73e9y+dSXCrqg97KXvWy77bZTlmsZeYB65jOf+fSnP/373/++FuRtqiuJA1TTK9RhrWOPPZZ/NLjBxyuUe5d5N+wVutnNbuYs1llnnbXjjjty/rXVVluRypnnNddc89vf/rbjXtOTpFtKhQuBQqAQWLkQKP2r5y/9O6WXtvTvlOAq4kKgECgEhiIwGf27ySabZP676667+lBHzX+D5LTnv2uttZYJI2cfe+6557bbbmsu2fxND22jmUSyZjz3uc/90pe+dMQRR/TN5TV9puFT5T8T/cuKMjj/LQdYU22Coi8ECoE5gEDp3zTigx/84Ne85jW3u93tGEvZn0844QSuG5MUiAZVVfTvzjvvvN566w19E2J/vvTSS/tSmbXZfkW++MUvfupTn/qJT3yC/Xna2rCPed/jy1/+8mc961lnnnnmYYcd1pc07ceZ6N+h9mfVn7YwlbEQmAMIDPGxNwdqVVUoBAqBQqAQKAQKgUKgECgECoFCoBAoBJYdArbPstZZ31p2RSyVMxnachdhmPxssunm6hK0+OzCjHGwRd70pjdVl77sLbUbWH311Zkvm+GymzSlMGmDHsm7BuIpMfGNHV+mnXb2KZU1EXFAdgfpjSvJRBJWfCFQCBQCcwABZwl8nk5nqy76W7qjT5HNVh0pRN91cXrBN9zCs/XtCqVDPSoaAVVIJ9JlHuXy5RZJaORydyjCl+Qjp3iX+NyHipqMLcmjC1tZFPq3v/1NkoBysSVho5xJQBH4RyMnjFvq0sc2kqPRBKrstIlwxIge7KOf9qP9Qyo41exeD/gnF1Ej7VSZFH0hUAgUAoXAIALLTf+m6IlmVZSOa1D/0ket/8cBjbmqAUOIxUQjjNALKLu19ujq07/h3Lh16WcYTnE9vXXDl1ozW2+PCSAbrX+D2zSEaQXJ65TsZZddNkkmUyKeJM8iKwQKgUJgziDgYMmcqUtVpBAoBAqBQqAQWFkQuOqqq1YWUUvOWUTArNaMmGXACZyDDz74IQ95yFe/+tU99tjjF7/4RSvFRNs0/5BDDuH96o9//OPDHvawQw899K1vfesy+mh8K5fx/J73vKdHwhCgxS+LABxcqn/yySdzWfWkJz3J+SIrFCwkrTgELczO0B79dvgI+/jHPy7m85///Kte9Srml7/85S9rrLEGepQtVwLQxvlBD3rQaaedJub//u//HEP65S9/qSGG0vdln9Lj1772tbvf/e4Ee97znucYcMtLKvCq78UXXzzV3z7D0X/+53+ydzkWzkXab8cvwm+++eZcWSlC5A477NDKesADHvCIRzzCUeQvfvGLvIzFbiP1hS98obfoK1/5ilPQy/pdasJUoBAoBAqBFQQBPXPpX20BB1fp38m8lqV/J4NS0RQChUAhMBoBSmeE/jVVMVFymZ5wJ3H/+99fTM1/QQoT0AXbyc9/x7Hs3WyQ22ijjU4//XQcfve73z3jGc+48MILNYSk0e011dQvf/nLlpacBXjJS15y7rnnJjvJzdDJcPvb394En2tm8a06Sy2Cm2xsSWv2+tOf/tT8/X//939N9jfccMO4shL57Gc/u/G5733v+2//9m/8pp1zzjkvetGL2vyX/5H999//c5/7nMhGXIFCoBAoBOYJAqV/09AUH71AJ3JHRZs8+clP5riK2oJPdGKfZvTYFBb9+8QnPpEfSSqJq6nXvva1tBvz9V3veldW6Gi3vtdJdirJVy7EM3f7TMJPfvITka4+yhk+MhTf6la3Is8LXvCCb3zjG41bhKdJ7aMbKmGjHAywPP/whz90+uDII4/85je/qaaGENCjYTkRk/q9732PSqXfAYi5ARs8+eFiauYNs+lfhnorHYzw22+/vXMKgwVVTCEwTxBYOE/qWdUsBAqBQqAQKAQKgUKgECgECoFCoBAoBGaIgD2dTF0MSaxObE/sUNxGjOAZc9u0LW6MfYPMb3Ob2ySSDAKhYWsbdF/VrGCNCUlEkt+VyMhmu+pg9parBVSWrW2QclDOoVVuWK2//vpceHQFEHaG1q6XVtZkAkyfPjUTytG7h0nY0GiByRSxVBrvw73uda+YcZdKXASFQCFQCBQCU0Ug+iJHNXS26W9pmdHd/lRLCT0/GnHqRL8kJnqTEqHCqFqlW+6SRB5JdGIek0s8GpdIHKibaEyUucLTvalyYZyltqRuQCmueL8Sj62Fw1ZWl3KpYQMYNBtssEGjbIUaz6hgJCTz0BMjIbYPJoMfW6aIIRe2GgJuxhWzol7j/WqSrGy+SXVI0upVgUKgECgECoFZQWB56t+mFjO5i0pypw7Sw0e3qtff//538U3/dr1fSUVGacpFWw0FoU1CpUb3DSXr6t+opJ52v2Fr7NAsIyJpSZ936xIoWqQYPCl3d4MBurUNCaJ2ESQwVP9Gs3MJnYrI2y1iGuE2+FlqXrtvQTRJZb1UbkVQCBQChUAhUAgUAoVAIVAIFAKFQCFQCEwbAbNjJ3vf9a53PfCBD3T85jnPeY7DqOGWiTwbgkMy2223nROkXFSw2JuSx9ow7UInk9GXHrbaaqvdd9+dKWMy9LNLo+7NtjB5zrBiaWGpGJ3XIgVXYo7O4uz0zrL7VBXoGB/YK5TSrQXxrNS87nWvg3A3fjJhJhoWmF122eXd737397//fe8Gq4v3QXWcSnLxiCXcruOPP95RYWeDNWhZQiaDcNEUAoXAPEGg9O/Qhi79OxQWkaV/J0Km4guBQqAQmBICI/SvWZJ5jfVi0zruFTiJqPnvaGwnM/91NMAOOrvjzIJx+5//+R9bEUwhhWNwGF3ElFL519CIba6dvGIU5DrooIP4oZ4SQ8S3ve1t7cF75Stf+c53vpP7Ku6uTPlNtE2EFcc8ItC9TjzxRN7DP/WpT9kMUPPfqaJd9IVAITCHESj9q3FjpGUHjq2bhpp8i9M4dvEx58rCzMsA68vTBi08Q020U10W+helLKz9XG5NvrgpUaqOxYJB+7Mt8bT/Xnvt5dMIU2KI+N73vrd67bnnnuzPPGFRtdnRx+8VAHMAIQMM4wqXj1LsuOOOljbucpe7dPWvsQpuy2EhY6oVLPpCYDkjMNMdsctZ3CquECgECoFCoBAoBAqBQqAQKAQKgUKgEFj+CDAhsTpZSeLlnTXqLW95C9PSeuutx+e6OysVN/CRivnJ3tCYnKw/ZSEqdwQxTiXeY867Jl4Wj0q53/3ux8u7T8JafJKUvEyob3rTm6yrKZ0JTODnP/85cxgTG0OY74v69Io1MGYyF4uYjCyA3DNZdbNZ025OWzOtaYkkg2Uqn8MlM7f6j3nMY17/+tf/6le/YrBTqRgZU2hqjVUO1krF3AIY3/Of/vSnBVREKuatIsmSeEndiwWToZBpz7dn3/a2tyXJIWd8hH16lzAMeb6h6rEV3cx5ikiW7v2xj33spptuyviojpboICO7R3J2jz2rFCFlxCQ4d5m0cCBCwGv+UUcdxcbakloLNsGS5BA18H0V1l5YMY2sZaxAIVAIFAKFwEwQiIeIf//3f9fb+5D4+973Potbm2yyyZZbbkk3ffKTn/S9spnwb3l14L7mbdXqM5/5zIc//GGPkqLOnJ/hm8NeFjEWvf7rv/7L1hDqifcHn3f79re/7UvjksQkVwKUJt2NphUh4ByOLZ5k9imbhz/84Q5aWNVLri5ZU3mSfMmNPqLUOJnaYostfMSemusSTyZsYOCrOE94whN+/OMf04nYumSk1HyI3qjjO9/5zjHHHNPGJ4M80dOzhh+pkRZ53OMed8kllxiEEE9kPhA3mLEbk0KHDhKQ3elOd9LKZ511Fm3ezTUYhjAmPm7z/ve/H/hqF86DlBVTCBQChUAhMD0Elpv+JZ6ZoJOoP/rRj44++uiutPSv2Vb0r5nsRRddREfQXEYC6Gkuc1j0VEC0QPSvCS/HytFW4qN0nIblNpH+pfLoX99Js5MmubolDtW/uD3+8Y+nf2WZSIWFSRj20dztbnd7+tOffuCBBzbmFLGLPAcccIAtLCeddJJ5KLFdfXnDdqj+Nc3/+te/bvuLr+ThjGGIgdAK6lZtRNg36+hfn87rzqBH0O+6666a5r//+79H0FRSIVAIFAKFQCEwVQToL1peLgqRXbebfRrarZt9foYNKjIyGRyfZLwhvtGsmBBFvD5T/IopaklVCBQChUAhcGMhQFm43vGOd1i+t2K+2267mR0zp+dsDKnoO0Z1C8TM2twYPfShD3UGmEEbmQHGCLFNkA1IMG+aVLibxdAlj1QVhn2jl8a5TbSjeTFJUtgmjA+yjIVafJe+RTa2iWncWvwkAxNlZGYHna0FREITGfDsCmBZXxITzctf/nLLNKwlP/vZz6yehL5PgHBofKSm6C7DviweAwXKvffe23qK9fdvfvObFoOSF4HhIiHZW5iSkj0CCzeaxA/e5XJi6uyzz27VVJyL3cmF3gJQAgwv2Kqa1QoGKxsbvEiKRtOaVXEpsVtH3BqAiEMgMFhrSYnsZu+G5epeLakFWmpfTGSQ2sRrlAJ9xH0S9smfWouUq8uki7kfVH4vIeij7OaqcCFQCMwNBPQbrtK/U21NoA3NUvoXLKV/S/8O/XVUZCFQCHQRGFe/E+pfY3I9SQzslo/trDP5vVHmv33TjW4VhAnpjsbVTfI4qCgTmel5l3hK4UG2yT5a/wZtqJrvW5F/4QtfyOzAh4X9/OY+ZoshCCtyphvvEyw0pk7deJF9dU8F9913X0cGbID/yle+Yh4aGsTZz+9uauYRq9z7mHSLaGFvgu0NNkiYyKuIjKa0LvsospXC5DeBZLFnD/1HP/rRjTfeWEwkRw+HEKhmatoqlbpPRhilh8w9VcCzG04RfXeUbe7ZkvpyRSSpYZtSJiJOfGgmEqMvNVlm+B42eSpQCBQCKx0C+grXRPPflV3/qlpft6mBxIjv6/d0tuK5VrS9balbuzHBobU1HeSy802k7fQ0C01K2VE6g6XLhQD91772NUfVNtxwQ2Z/h+MY+cnQ2LaMTQu04kYH1CtazKgJN/bnO9/5zrE/W1ZoeclAjUpyBCCRiFuhjWxoAETOFzCbg0tFIrbsrOiqr1yDEFPgxONpaEEjn3HGGd2Pa0pFg7+KZw9kW4CYvCSyN2IFCUfgbrivCklCuSz0L56pNRAE+t4xRTdhImqT06PfWtmfGz7zLfAvg+n5VvmqbyFQCBQChUAhUAgUAoVAIVAIFAKFQCEwGQRiWPnBD37AywN3FU7MymV/J3dUTn4271ciGWWYqJhaXMK5x2QTE0yLF4iNJqacLBdZr7JUxubljG4EQ2NjLoPXd7/7XdtJmcBOPfVUjqL43iIJ71EOADPz+aotemGUyejO9mf1SwBz8e7C7HEPe9jD7AOWdMIJJzh5a5VLPHmYyQTaJSZhBLYFq4Xi1l9/fQ6z+LEialLZJZvhqWWRFCthaEKgXlxmxEdGzlTH+xUau1TvcIc78MYV+sZHoa7GP2zzqBVe/epXOzC83377nXzyyVx7OAgNAfUK827R6g5nMQEhSX33bGm9173u9bSnPY1DEO3YCFpdmmCSlMIbi3bxqZyIxL7WFbVlr0AhUAgUAoXA9BCwvUPGj3/8445eHHzwwTvttJNHy1oe6UcnH6bHdjCX3lt/TldmJ0eX4Atf+MLznvc83wPnmNLWEIrM18XtAqGd3/jGN9LXWV4iT3QWRfOUpzyFOwmP4YO5S5gyldea3GmnnUZHUy7WqLplJSwePbb08tZbb22xR/ztbne7ffbZp7vQNZixLwYfug8fKpvG9LnyiOEuSTyZIUnLb7DBBn15u4+hz4hCPP37/Oc/35Gh/fffn+eObbbZhjOsLv30whYRcc5WodEcCO/y9bk3vOENKDXEaPpKLQQKgUKgEJgqAstN/xLMtlH6zh7cPiHpX+6nzcENAOjfZz/72XSo6R6lxjd0d74c/UtdPvnJTzapbPoXwyi+6F9z+dNPP32jjTaiRJpS6xYqHn2f/qWY6FBKSmqXeGhYdlfKVQRWZKZ/+/J6NDU26uD8CzHXlsYV5KeXB9k2Ubv694Mf/ODuu+9u+mzUYYoqY65uQWIGuXVjIi0HoPb0tK20XYKhYd6fOYwmFfdkQwkqshAoBAqBQqAQmAYC1DctRjdFf0VJRatOg9vMs9yIRc9ceBya/EORbKmzUtasM+mKF3v+rBdRDAuBQqAQKATmBgIGD694xSse+chHmhc738tHkkDWnQ0tmhKMNvEdBd+9UHFz8GY3yFRaZAsEmWRPOKliurPsxoEM3fiWxYqz9XQz/ay8N+02SBzx+uLRi8nVeHYDrXbdyBmGSQtAVWPNmIgVN+Ws8Y7xMNQ4+uUkDztMatdEimmC8IkXaNy64RbZAqEnQ2Icc7KjwIevHBaS1Ng6/GMRX3O3dfykuudqDAcDmoNH9bwkESZNKT4XngkAQdPkMyfWUMI5zZ2MkTPhyJbi2ruRxxEiSQpNq5rHFpmk7r0ltUBLFdONbDIQMhLmHvpGOZ7pehlauOXtI25lJdBl2IroRvbR12MhUAjMJQT0WqV/Z7FBS//SvKV/S//O4m+qWBUCcxWB0frXyJ9CiZMFC9C2t91Y89823dAQ3XDahZx6vMGJwyBlyz5IPCtNvFT9q1xb+7hOtqHOZsLDDz88niyyzZ4MCFwJ9HXjkVDqYHyydKuQGPsl7L4799xzzX+78zgWjFve8pb2MbYd74Mcuty6Ydsbfv3rX6spVasitK25vMukPtPehD3mMstG9pvf/CYeN8yIvXWEcYVt2q5bKQSRJy3onqsrRsLiE5BFoJtrkLgRIGsZG1lfEU0ecoZt7qFv2ftytXhk3XB77Ivs8kTT95iy6l4IFAJzEoHSv2nW9Htsqk0pTL655dVX56JissmN96uJOHCwZTcateW71D426UvV2X3X7ZkTdsd2KJ+JOurQpxayU+6O0dG/lGbLIsAmbK+g43XdQocWNBgp16WXXsqebIGgFSdgr6NPZVOv9j0SwCUSf6rfuQCWcHoZNzGRBBkl7g10DZaSmIjnnmuQTHwiwyScW+RE9MgGafqKSNVwUJGwzT08W/ZurrYOktWHxqGPuGUJw67kYhI5KHnFzGEEJvwBzOE6V9UKgUKgECgECoFCoBAoBAqBQqAQKAQKgSkhEDuLBcLzzjvPhs6dd96ZDylmOD44zjrrrMbK4pAwMq6RYqCxBNVSOblwrDTxjGUJSMWcqS5kLEHYWkniVapljPlG0bxEXXLJJb/61a/szuT96uEPf7gvzCB76lOfeuyxx8asc4973CPWH0VsueWWzcMUzrGOMSEx2G211VZ8aVmocw9/BjtrZrg1Qx7nHZGBqC9+8Yutklru+vnPf77FFlt861vfislJlZuPD5xzIZOxGadEhlihDJHkF6OCAAklYqBxJcZglxLb/a53vSuxCd9isFXTxz/+8c4hO/n8jW98QwxjqDPAmAsDkJGUYGEuErzqKF5A0WGFIIA0zqHn14ycnJoRSVKaJgI0n19pVlUQ72C2jba+8xM+ZVxreFagECgECoGZI9BUCfX3mte8hiOMTTfdVCf/k5/85JxzzqG5FKFj57uwddHxgajb7zpDdAwjwlAKfVIlJqrQ1795x+gjoJ7oZXdJPrZGDD6wHvvYx/JVsWjRos9+9rO0pM7fQpS7QimOZzzjGcL0JvUnxj2lfOYzn5ERf6tc+ZAaMlekRZYwfeSygsW7B10fecgQ9ZTBhlLWWWedJDUvWvajdIWHHj2Fj3ECAGlwYpAnqlB8FCjFF57yEoAYCGi61EgMYfARIPPmm29+wAEH8H6FIXoHh4455hjDiZSrFTJ4wMSlXl21G6DEkDMyozFukVekNuXNM6ub4sMwdzKTQSTmisCWMPZsYUKeIIlSpKubscKFQCFQCBQC00OAgohm5Alrr732etnLXmb+Rf/q9pv+xbk72/WoozbhFYha8di0gMj0/PpzqTr25tKRNldKJl/I2kXzOldJ3dgXYtMJT1LmpDSvOaMJ6Zlnnok/pRAVryDCbLvttiLphcjvHr1A/9IXOPfp33G9sUReAUqKbiUb/cgn1CMe8YhIYkLNr6VvuubRFFIdA44YudzJoHbhEzIMTT850FQFW0s9Jsvd7nY3ZIQ0KaY9xYc+rAhsHw9wEp87PkP179vf/vYuaD7KignOcSUJGWEcSJiA/bhql7B4X6szKkCsQR/1qEfZZyO72vmEXaudR/TitZo7ngL2+Mpoo61xUVIl1VUIFAKFQCFQCMwKAjSUgYG7i5ahXulBuozqyfR/VkqZDJMIEErhyWRZQWgirUEF6Kj+jE8S6Z6AVAMeAreYFUT4iBEhI17GWiuUeCVMIVAIFAKFwIqDgMVZdnjTdt9Ut2xtjTuqjaaLkE2neLT27RiJz7mbCNODJvK0ZIzeUkU6WmPZl80Bw8bBMRVTb48oaSXzZRezP87GKsJhYsZtrCK7iTNuJDH9j+Ea58ZNUliJNL9GzAIgMjZ8/AnmYiQJf0kuYQUxaIS4J8ENR0xDMFt3YoxmRRIwxhqv+kSVRWWFk9HgjXFGHVk8RAInOI9X63qv37LACg0QDFdUCvLu6PEPViro2C1u2sKnyGQJCOIVJDtH5xYvFK04xpwuIChH1IJJB7wROG2BuWswS5qbGGwjlo2wVaIs8moLqWLUyzCVnLJLStsJdNeYPLoUQWz19TohswjiLl5edRdQBXClImlokWhCpiypLsUFKBljFPKYC56IvVcKIg8jHgIMk5rs8BQjFbEYbJWSugsQMsUhE+m3gJuCFNrDaBwlL79aJDsOuImXV1vIoqxwq3shUAjMbQRK/85u+6aDHcFTd136l4op/Vv6d8TPpJIKgfmAwFL1b0AwODdQNxG7sea/1Jb+ihiZbhDGozmOWXO+TJx4BCYaJiDmF6bGIhEgVgvzEfTm18JmHCiXRfuO1r/kcZGchEo3LSIPYcxeTZQy8cFBKrEzR46QpHUluxiBWBXMmEzQMlVUX2Hxmd8J20Gn7ri5MqELc+Ao10fClGKKp1wM8Q/nVkqKHryb0DnaYBJHZvd2DVImhsHEVNd0lQzeN8W5mkiESXUQqwLBIiqaxhCBcITMXXabEOQl9kTzX3xwgwM5ZQdF7AzoYY6PgFRJ4xL1bmmayc9/A3WXg3DkAQuG3dIluUQSPptYgE9+QhLGldRxqroVAoXA3EdgrupfPVuUrB51Svo3amhKDd/ruMcN6UqUEYeJtHC6WVovGkFHLSbjhJQYVu7iI3YCFI1LTNS0gtDot9mNZZTEwozSXenRX2gYltFEKn2+LIjzCB8HBFiSqX5sUyIOEWP0ndL56U9/qhSSq2mrrHhoy+veIj0ypCM2SKCSfERTKYojpwC9bJjhUoUoxxSNM4GbGBHMPbncp6R/lQtqmwoEVFYYCLgJzFz/Rkg1wtNd40ZI8YoAr3pBQ7xUkVBy1y4ChHGhR+COUlLIBOqaPwhM+LWW+QNB1bQQKAQKgUKgECgECoFCoBAoBAqBQqAQGI1AzEMOozqm+7GPfYwl5V3vepdjotZR7DWRd91113UslrGJRezEE09klHnta1/LXsPqJHzhhRc+61nP8pFSFhlGIrYtTisYqg4++GD+pxyvtUMXEwt4/FixZLE9MSRFJMYaxQnbZCmvS5hdyZqTHR6bbrrpj3/841e+8pX8cPkODK8cmOy5556Wr9A4qIzmBz/4AaPeHnvsYQewbZqSiE34HA++3/3ut80222y00UZ3v/vdfayGZc33VFXBIeHdd98dN34u9tlnH+dgcTjttNPWW289bNVOLR796Ec/8YlPZBOEzN577/2iF71o0aJF3/zmN8ns/C3vVCoSo1WsdU4RO8bMIOVRdXiwErZQ9NGPfpRlSpVhBUCwXHDBBb5s88AHPtCKLJFA5zGA5L7JJpuooKq1SJ5BTj75ZCXymQUQbj4e8pCHHHTQQWkLbN/85jfvuuuuNsW+5CUvUYvNNttMFgE13WCDDbQUEHg2YUdTKRxkdMoadGg+9KEPwe31r389x2fwARqb4xFHHJF68ZaFG08cmDR5KlAIFAKFQCEwuwh8/vOfp1zoXz4XdL90Lv40GpVkmYe+4N7RUhDfTDQp3UERUHzWos4++2zKyJkTW3ysStJ373nPe2gxH4fnzgMxhfK2t72NBpmMwNxYuLbeemsq8i1vecuhhx765S9/mYqhkugmH4R5zGMeQz/SyJQXVUJ3cO3k0y7nnHMO7S/w0pe+VIm0Hr1JDxLgAx/4ABW52267Cfh2jSrw9MG1Fv3Lt9cb3/hGqor8tBjFpKBnPvOZ1JnK2hPz3ve+d7vttjPM4BzTcAWZXAYJ3bpsuOGGFFxiKDvKmrJTXwMGkYYZVLmFseOPP552o/fpQVAT0piHyw9LesiCj9EO7yddpcwTJbQRaALaUwBzzWQhisBU+fbbb0/jA+cJT3iCAY+VRQw589p3333dsd1pp520lFy8g/EVIqNPB4vnG+t973ufkZLsxIazQyYQM+TIwEyVvQyAzaOi6yoECoFCoBCYdQR8Ts0Hx7iBNuWks3IMz/0Nb3gDraGvpn/5qKLFpNqgyWuzfp5u0j8ThnYQePCDH0yzHHnkkSZ6VAM/ziaAuvT99ttvMgLT43Zb4kxx03G09tFHH00ZvfzlLzedpNApzXiNpDJOP/10E1Ia0zSc2uUqS9FmcH36l5p797vfzZExbWX+2/TvGWecEf1rPouV2Tp9bbCh9B133NEc0zZNMhBA5OMe9zhzZBVZf/31PZo8dlWSaabBgAoat1Dc9qfS3eaMtKRIIwEXxCg7itigghIklcn1qaee6uwutnR0GA7qXw6yEeCjjoYlIDIRJrOh0WGHHcbyoL50OhnM92lhNVW687fQ43HSQMh4wEyWAFA1R1YvGplOt4nn4x//OEdXRiCya3f6l2AZNiiR/uWgE/7dyoqvqxAoBAqBQqAQmB4CFDelFrVC9xkkmB5iZWghINVIg8IyVZwe/ynlih2eYpWLSEY12R07JSY3IjG4lG7swU6SlYjM5QNvUhEA2ZCjPd6IAg8tOjJL0hAuwkf+ocQVuXwQ8LZ0W8Fj+7F042cuzNDXsltEI+hGzrzc4lAIFAIrIwKsAUYIVJ4JrGk19ZdJdLqs9BLprBJGY12YlnFyw7yeKrTEYJJuHGL13Lo5GzW9z/iPEhPx7PnYGorgwzZicGLu7FFYdhZ19nl2CSsFGbSwXaC0BmHizx6iIGMJGwncc+IFzkpEL6B0i+/W8YnkYmCxjkBUzBWa5Q9iqKP5PrsKSvJjpSylzHo32DrYiV4GBEYXwIn9h/BMEL/85S9JIt5d1YwxpDLCMEGwMBAYUC4DOWslKYKRQQV5lWJLASDM4cYWwagiu/qqO1QxtJdAZYWJhFIuPHlFZ6+IkL/4xS/YK9Agxtx9IuGTxJzFFINJABxRZWVhpaVszFAEo5Asmkk7yqVcDNXUy4BME5NfwDsDBE2ZJhZjJOPuLVUuuLxR2hexGPsKoCSVPN7AtDgQWNvcmbng4xUCjjcTMpZ+vH4oieE1yI6RIE82lOCCHoaglkoYtYgAKs4T+r/927+lXASYqBca9LK7EGsaRdhTIdLWFyIRBjEy8mspd683GUjlTUCQoZqig5iMdRUChcCcR6D07+w28QhllIIQlP4t/Vv6d3Z/d8WtEFgZERitfymLjPxzV8Eba/5r1mPSYcpjsmAOK0wkYbMeUwwxZkxibIQz0csshqimGGZ8pldmTPe+973NL3xiynTSZMS0ZVm012j9m1Simii5m/qZ/5pk+cCVuZX5UXCWqgpmapnXu5vEmVWR3IyJ5PjYpWA67BKPg53zZovCeJrWmWRhJQY+5pgITKzMB1UZdAYAtiLYhiesUPzFoEEZASZCBoEr8zgymP0RdXSVsTLr1FL426WgRLmUIqMk0ppstoprL5KQU7wspo2aLMIoV8bMH7HyJthUgNuI+S8MHaNwKEPd7Q+0h8GsGX/yKAi32Zr/4qlGDQfMvXvmv5nnqqASWYSIrV7GHipof4VH02Tge7ehQR5J3oqJwK/4QqAQmGMIzFX9y5qqZ9MTai/d7OT1b+tFJ9/QsrRLroSHZpckXmdLI+hv6RQak5A6ZzpINy5VFy3JI9u7KiBm57cLkcYR0M9L1Z/r1SlZAwlDDgZYnXmqSedSylLpL0MRal0pengl6ttpBHdK2c66V73qVRSBgigLReOAMhIOFb5F4mDAQFngJns0qVTZG03jQ2D0BCC/LY7EIwkBqE5JOBBVgK40FFFldZHkMVpManhiruLo1ZqoU9W/xic+oR39m4EZbrOlf42IAKjKaqpFVEcjegNJ68SEIZPSrRSoi7qrCAEYxtVCAxljoJdXfNmf2/sz3wLlAGu+tXjVtxAoBAqBQqAQKAQKgUKgECgECoFCYMoIMJ0woGSzJtPSIYccwmWDA662ojK4YMdXhf219rOyo3G65IipHZ8O/fLRIMbRUHsxGac4yDjllFM4VHK+1IlZR1I5mXKMFhPcnLllyuFfiVmHfSdSxnAjzNzGBmS1T5gZyJ0ZyxKaXMxDzHwsrdxAPP/5z+erwrncL33pS861cvbBIxXDkDVCkVxcMd6xBnIIIq/1Nky4dnJg2IllqY688hjCmnnJJZcwpRGeCc+ZZ6ziF4NhznlgezdZ0JynffKTn4zsgx/8oIz8iVhy47ODM46TTjqJ9woxXfudhUyrWUFMxRnj0GAORtKqHZOffasYyq7i3IHxwWGbrMPVT3va05JRW6j7He94RwII5CInR2DOLTNHcvNhSUwW56t32WUXJ58BIoDyU5/6lJPDRGKa5EjluOOOAxepGNHgz6WIw8Pin/vc5zKZ2Y+rHa0jyuI0srCz0KrpOLEAgR3edscWnhxgRZK6FwKFQCFQCCwjBOgIffv555/Pt4X9FlZBFKQnp2TpX54daGfKgiuHF7/4xXp1moLLBs4aKA4eLoTpCHpHN04L0w477LCDTTNycW9BEaRLHyo8ldSNty5llyHvD+ItklEZvGPsv//+VlyoNj4T6Q5anua1DuRCTM/SbjZq8PEkhpa3CkUniuchS14KlH8N2tx+DpqIJws61JCA/qUN6X260liCCnNKwcjhqU99Kj78UxgeqJHiOMlCzw0HTQqWrsC0dmpHp1Os9KlClULBcbFh3cuSFQ1oVGA8QzAAGsPw5GVgkIJUGYwurKwRYk5rk0peoyNFiyEzNxno6V8IGwIZooiEA7XOaZcmc4e2R2dCQMH1BmcflLhtQHSrwQkELFZRxBpUI3K+SRjV5HxTLkMRqtz+GMMbJRLA5iejoDZq6ta6woVAIVAIFALTRoCCa5swhLmI4u6QqqLIqAlsP/zhD+vkqQkKlG8s2or+4uCJpjbbNeelcCmXd77znRSTALVLWz3pSU8yh8LHVI7+NRPkpJIeX6qclJ15Kz7meoqI/jUx556YZsmxVYqejuNMKntfaB8HRD/96U/TU8Smmk2lm/7lFYue2muvvXAzM6V9bOaI/jVm4NJLXehfGhM3c0D6185LmnrLLbckvPkv7fORj3zEvJImPeqoo1SEiYBH5lYXKtLZQjInBkMK9O1vfzsVRh7a0HYNE39CKoLYFCuVZ15p4sm9ppO3CqV5ZR+qf6XGxRgQaFtOrGhJetNsnUhqIZdmggBJqFRepxPWCkwHJLEZiFoX/4IXvIBepn+1moENJ54wefrTn84vJyuHgQ3Xz8TmfVtjZVZO/7aaVqAQKAQKgUKgEJghAjS7aTXNJWD7pnkrvZ9ZnoAkSooapXxDoziBGRaa7EpMoDFMDBmoaUVTo5MpiF6OipwM8TRompzyNlH7+DRwxLONs/ybqps1G2PI3uUASQQGS0A2yurjE8rBUrocBlP7mAw+GlvCczB+aIzhEEO9Rie/sqZR3FC2K1SkSgWQANvG3jeKkITptnu3rbvyELglEdg7b0+24a7IvjbKYyPuMhEOfUNgMnVPlkGGYmRPvAAJW136Cq3HQqAQmJMImN6yDKiadfCojFQzvVC6hW7FHWvkpdppClqSlqHuLYWbF5sFm1azLbgYyc36za9Z2h0UccrCdNvHJ5j9mQ7Q+8YDmzZDgW7QVNrHNpi1zZr1h5b4keHGdOCMq6J9icp3HawLKFq57Cdm4ozb1qadorTeIWnnnXd2toTBHzdam2mdUcVCBuM/Amv3pvDKtdDPuu74B8sDm8Zgl9it6eyG4WnAoL66WaDZGMB8AUNVtiACCoMKoloaSL2cY9EcrApkPvDAA403GEAM5yzWs36AVK2hBAEjKEA54gJwS/zQs6aDG9B8SMP2AMYH3NherG6QQTMBXynwkZcTcJxxgxszC9yWqgUYQ4ATfSGQV2UoXEnSvsxKZLD3IyAg1hC2FihUa6qXZQ6WLmjknBVRGZQMt4CAWEuhlOTRGoeKe13x5NGMnconwbSmYSc+yCxXqZHqM6NB2HCI1UhN7dzwFTQfCEGjIJLwCA8izOliJinisXF5e9FbcrKS4p2BkiLEeMesBOHWymWjQ6ZpYM4EZMRrZ0heMO2rRgZjsrNrEdvOENYzOGsyvwUN7T20pmORjqHJtpAMCbyxI/AcCnJFFgKFwEqKQOnf5dNwOtXSv6V/S/8un59blVIIrBQIjNa/qULmiTSIwI01/zVhMUnhw9cuOIrM9jabx0wlzCxMii1k82hg0RyNyYgJo+mMvQdmLmbcNg9IRaYKMpoqWum2Q+zGaiB77E27XObspmwmgDYr2lZnrmTbgMmRfW5mVciEAf7+97/fTFBFzIXtOrCLz9RJT27CaN1f2CTXow0GpsCqj5gZwdzKTgxzTzMy5gi7H8877zxVtu2BNcDs24RRvL18ZqamXVD1/UtMRsPiHbCRA42ASaW55Ij5miSXySPmmah6ZDYhkrzsAEQ1nSStj5wBhL3FpNg91VRl7d5eP4Yaqzy2ArKf2IBhju8FsNDDcmI7x+D813kKxeGgdrD1qtjnAASYz+78F0NtQVoN4W73qb0fJr/MPgwyqq9xbYmxQQIIdoGaHduaotYa2k5UBqXI7+0dDX6lFgKFwFxCYK7qXwbJ6F86bqn6d9DMvuyamEagKcCui3aGK+ZW+pfdnn7RIUf/Eol+YfO03k030VMomVX15LbNM1diYnu83XREpQTltfONatb/U9nN/uxLlrQDDeuMGGMpPRj7s73rDJ7UgfFMV/8amWAYlTcRCDZJGsywP/cRDM2lImSj75iFGWApQeWiJD/9K4n+ZU5Xa+cNERtjuNsraNeiKiNOKXBDPG39iy1Wdir6+Dc1N+v6VwtqTbtJga8pLZoYGjllIIZB25DvC1/4gnGCJC0CB5qa/jXcYn+228F+RYAQrOzPfS/VPHns38syT6pd1SwECoFCoBAoBAqBQqAQKAQKgUKgECgEpoQAixILERMPC5G1GVYtx1w/+clPsnnZnencrxgMuWGysdWyjVOsLpsRsx9RdtsNEdgMylgjwFLDI4Z4m1mdlWWtYztjhpPETMOcJOBiq2K4EbCexCJm2UyYPS4bRlnWPDI2ycI1hrUxd98dtVrD+KUs8pA5S1DEzqIgI6CCrKvFcEZOTKxIORarOI/4i8FKRQQsQcVtlrBNkNhi6HAsW5JlOZGWLRkcuepQlrAYW11lEXDBzZ31ivzWxoRZDOMdw3Fl9iz7WUUSxiP7o52sDHZWlWzWjC8MOyytqEUMlC7oqTUBhLFlC5NL01gotdpkRyy3JlkGQ2CXJ6CI6rJOBjpHmpn/MLciyGZns6z9pgqFgDsbIvyz49ZWVHbewA4xRkbwwocwaRf8VVbbCeQlEairECgECoFCYNYRoCbw1M87JmHzAY1GJXGTRFmI56DBoo6zAdQfH5R0QQTQV1uxExawIpJIikOAG0cLYM6Q2JnR1G4IJrrr7Wkf2a1voXEXw5eHoi+++GKuQKyrUSIiqYmoPAqIQolHKvqOGqIQCYMJ/xeY0L82SdjB6SABnSiGMrV1RkCVFUHlCVMx4ikgC1qUUZSsNSdDCMcVaDrZRSqUVkLfd/kimRJpeWtCixYtcqrByCGUlosocTrR7hNhGWlGe1bAoiJUJzKohqGxBN9YxFBNtUYWlyJve9vbuNsAIwQc3rApBH0ObJAcOMK4Kd0aFUcbAs7qcFhG7RqrbL/99ooIaAYShljWvbRyTgoRW3ZON6CBJoMl/EmrUtHREa/uhUAhUAgUArOCQHdeo6fF09G77EqktsyznHbjQIoqoftsIqG/zCLpYvo3Kpj6yNzTNJbLSBx04BS3gN2WjkE6pmhniUnfCIGJkdTI4JGbLTH0gs7flg5zQNNt89Z9992XekVgroqAxjR3a/rXNJn+JXZX/zobyfeTwQDJJYVtskf/2pIiUpLiqB7bTLm0zgyUmnNg1eYYRxBdJryIFYq+XbaiyhiFLhKxXZKGMSKJKoZSe/SjH813GGVKVDGGNA5DpkQ81ZHqNPCQNKh/qU6FYuiMqE2x9C99rUUQ26xj0kpmMhgbuLSFc7bgUmVHcw0eeMFGI4bYGoucxhsOgvIcylyQamJF/0JYQPOBJXWku+Epsq5CoBAoBAqBQmDaCNB0FHcuypGONsE0uzSpN2AwtDDkYHg3x6QoDUIoRNqHJpKlFRqbc3ucRqDLrZvd8QwbLml5AwZKsJs0NGys0jcSGEo2jchIOCjnYN0DaYowqDDIMTAwlhDTiNkTfCHjRz/6kR26Uk3DQ5BcfWV1GSKYjAzhM/M72VTBsMSIbqiJY+ZF3FgcwNiaIzI0YJM0SDB5Ub2HmPfxnyh7K6iPPvKITKBlF5OfqgCjUNaqvD/GtLHXNYayCHcfG5NuAAfDVDFGrX4+GfDn3idScmHYsiccIXNP0rL4JbayWukVKAQKgRUHAYMHP9Kc1Ykizm82d3LquNKxiNFdUCtWFlDqu/RC7Nu8X1ncpx9NrilHHZoxiYkz6zSTBfOFabUJsumzwYnjNAIGCY7vmj4zETDFm8VTWHwV8WBluIKnco1hjGosbVsaYCrRbZr1I5bdYrdIE3CDHMdpxDPC8yHljI0ZOis9x0ay6yFpQKcula4INgEVocRN7TlXIrOKtIZI7drjrAdA1zpYAbYIKxFWOnjQNmyjC0RafWBAICdhLNmT3zEVK+xMNy61cHpZdXJ816KMjCw5qg9VTqCc3cWH/cHgBFyYgAiqLCQaAqraS6vFWKF1NBkkQcpMpMlYRUhFzhF1xzOpDa4WGJor3FhL1E5ZakQGL49cJFSoMPOO9rItxLFtknhbfCHMQgmGmiklujPvWOmwLYGTKQebrYyokbxoOI5HbMDpXfVi4AAc3LwD3knrXLKrnVfRAhM8P/axj7FxYWhlhLXN6phDuQ7iIiaVZR17USz9QIYZCkOlKFdLOVHMhRaxxXjVFZFy3Y0M/RC0l1fROg5zk10T5GQsUrrX0oYQDEnLlOf8mNNiWscak70WTpFJtUrltwOoVBnPugqBQmBuI1D6N+2r0xutemb4GpT+Lf1b+neGP6LKXgjMMQToXzXKHubB+W9G43STKxU3l1nW81/zC2X1zX/pLzKY2VmtNqtFY6potmLe5NHE0GTZPMi8wxcZeXey5m6uZ95nMiVghmUmYsZkCwFXUBwhUQeKaK2J+TLVvykoRZjHmbSayJsr2ZSemaDFAjMytWNhsGKuXiaq6mLnP2/Ipmw48Jqx3377+Y61yb7Zq1mYiZVcZrgmaOZ3Jmu+E6YdzaTUCDcBu/SZSe3J15pmiOiVqBFhpfVtcWeFEEmSbDVsmAwNdIESHkrTItXXhXmm5FpNE5BBpLzZ4EFak00zQYs15r+qYG4oxuTU9NN2EdxILguxTWBt9mMnMW/FLfNQuyLROGrRN/+11VCS2sHHS8LiocUZSbwSmJBkFue/SiGDj34xOHj37F0kpMay20T1uUrxXVJ1YReyZZGHLAaZzH/tb7QTkjFEZMOtAoVAITAfEBg9/1159S9rnubj/Upny6Y9Wv/qt9V02s09iNJoVtG/1ug333xzWq9pKHKaIhnhMERz369/5qaQqmWizLcqbUQ3cmDnZK60vx0f9mfKmh2eOZT+ors5WOzan20X1L2Th67psz/nDBqgpqR/sbLtkDrDLWugqg9A8ROpY/GUnbEQGoMKajTETf9afeCrkX6kf3EzNqCtGMMpYlVDLHKG+pcKbvqXQ7RZ1782KDJiM2UzdBvqkN+hSwWJVwUHHl30L7O/YZLGElBBow62bvZn+yHL/qyh5+01/d5n3kJWFS8ECoFCoBAoBAqBQqAQKAQKgUKgEJi3CGQRS/V9f8Z2THsN2X0Yp6zVZSHH8QwrItbe2KSEUTItSWIdY6Ji0rJkZfugeEY063asTnYlIvBpF8aprFBaUEEckPGJ8Qs9o1gsYgi4OWfasyqGTCRbnoKs/TgUZIOpIyKMRM4FkcFSjYBHuRAzyLLoWSy0JuoR85SlIpgQT0E596s4FxoEFqIIyUGVsoitLNJadcshmZjPUFrmwUTASpvKCriro4CM6hJzGytkDuj6gi4DliSGVJLYN+lork/WoIchW6RlMJ/HIZVFQWRKlORScSJxAy8so4U0jwx/nGhYUGTWtBHTyp8dyfbgah2pKNWR9dMKljoyinHCxYU8SWz0ZPq0QMUNigVFoFmlY1/zQVTmMztHxbByqgvcvAPBJwBiC1tFu6ChjmLqKgQKgUKgEJgVBKJKsLK6o2cWsJhhy4jdIbpr6slWifjOoAsoOI86alcrXS5HAnTOVE90Fk+LFBkCJyuwdSjUFn9nAKx+UWFN0TQOSiFGSreQpjhnVyz+IXAgwTIYFWO1jLaVussuu0Tj4OwkDP1oLYrqCTd8aDS6nl6OSOKJjTmZUUZC9wwhpCJDTLVRpsQmjNUdQipLqq+gZORAMVmiEyOeVALdC39wgUgkqWhY20ds03GqQVku7kssYllAQmC/kaVB64If//jHPdqBhIAMYeiYilrYvuNRQVabpJLH2MNim7VDetMYSVmOc6iFUoQNJ2hkGbWCr/k5IESD+y6N0yDOw/j2uH0khERG19PIvHFZm/TxFqVww2EAYxAiSfW1JlQjDMQytslj3QuBQqAQKARmC4HW09Ivul9s9cl6ZgoiRejSdcJmRnpmKiZToW6fLDJzSXPPKCD6ly6Q3XZVPB29473RZI0vLUpwUP9GWdtXKgt6mtp+iOgmus/8mlsKSpxeo/s4MrbHxWSTJDaVyhL1KuCK/kXW1b80Cz2lCipLa4cyMlO+IqViTnGb7qmCGhkAZG5LbJPxqKdoedxs1AiTdsc2VRZDz9qw4qiqzTHGEkSlE2lJipUrMQQ26BhOqKANr1Clo803FYezVMcU3Slld41C/5KKbGbllKnP4RqZ4GYQQlHK4gKaIlRQGMLxWG2PjnmuEu3mdJCS/kWmpirOK5b5vlY+5ZRTbGfxvVmKXvaMAVQfN8gQAH1mxMJ1FQKFQCFQCBQC00MgOoWmo2RpHL4P2GmpJy4SKDWzS1Zchm42W7shqXLaR0EUvSwu4dynXTpdj22yN4aNpySq2fEG2nYyRRDMFJjSFMhAAn/jE5rawRVhnNs1GYaNRi7hdm+BRtBSlWIIYXzieAlzuqGX8RVhUm7ozbt9N4LhwvAgTcAOEF+ljU8ou/gkphXdAomfzN2IiGBs8m1oNDqXRQFGBoZ6BoeMJEfTryypgY7lChoayztjxGXwJsaj9jLigrwrNUobLbV2jQxWrpTS5TC0yVDKmLzuXl2XN1/pXg/yWK/pFo2J8blRoqFsXi2phvrCBuctZpzlAg0nNcxldHVZCcuSao6L3Lt1H/uIPYbDOKfesavkitjCfnoZ0wrHShZK98jT7uEcbglLGhFoqaHpo+xGJtxKbI+DNBVTCBQCs4gAteJ3R7vpjkazzc9TL6HL1VHoN3S/AozkbAiM1Q7A4JAk329g87dUbeKvM7SK7UinR6WY1zMUMF9bODbl122awlv0d+SVNysdqf7TAIYfIlYL2a2Ve2S7kN1QhymAJCbjCAwzMDQZd6iSrYDvJwxJiKfOVq/I7zZDve+cU6D6VSZ9q9ixolPlUk3SccuiwOjqzzA1UllYJwbBLKbY2MBPepctG5G1eB/Kcr7I0I4FhuXB2r2tFMA0oAKUNQgmIOdIiS07HJDZU8EhFA0IB+hpAkX4kAmI7MEAEbsEARRtcAgB+x9sqEjRAhkuKtQGA2zF596VbaIwthMldeORuQhGPHqBxpHqRdJSDCysN5y8q5rmQObcDnVpKMtwhIwwLi+GA97cvHrTnPWlZ9VFvGPMXkVHbX0vDVsZeb+yaAUr+Kh7zDUGdeKNKr1CnLNHNs7Nvaj57Ap7lNeDTclvARkCrCBmpKFc+yv22WcfDSEezpLsJCGect1lYYlis8JcFm84qfhq99p7hxl/jMrs/bBeY2OGQ8h+Mqx50HBajAWJPcq6mJ+JVhuqNCNt3QuBQmCOIVD6Vx9e+pe5pvRv6d851rlVdQqBFR8BMw5C5j5aWqoKgQmXgGnFVOe/5hEWne2gM9rvzn/tIrMcbzf1ZpttZvxvWmS5H3F3/mvKYIbLwp81d5NicxxODcxrCMMbFK8NHEWRzdwkq8/mHeZWLpvhXSZHpoGMw6ZFAuaA5sVZyl9uy9OZ3ZgFk9/My+Q0gAd8k0FeOcxGzdDtZGDptcLOCQWbvEq5BOxs5MPalM2OBRxs87OjwHzNNMoGALjBx9wQK8hkYmsqbaoVKyusTHJNq22AVyio8TfjtkPA/I7nJgS5Rr8JLXWprw0CpaNXd5xN8bSOSI8aXbxdgmpkcsoeYuOBJDNck33zXyZuc0lqUXVMnJkCzP3ZDT796U8DR9vZ1WDpxwzU62Ezg3GUGNNMppLTTjuNNQAIshNAi0OD2YFNwGtmlooD0CYz/zV7Nf9lgXEeASvNZ55uuGJKjhsvZsq144LAaNSIzFyrODtga4QaqQsOJrz2PRrnHHPMMQBv81/OOOxdNFmW0dvrPiX80ddVCBQCKykCk5//qqCewX1F1r+6WR217pq2pXf0tK6J9K8FZd0jRRAFsRxaMAASkr3dMMDZrhQaLQZYlmQDA3pZ/0wwbqFoFtZmWVwCtKd1eWvf7PBWmX/4wx/q/GltSWrNBErB4SZMnen2KQU2bXkpi679mSEUmYqzqdriaO0gHwaIhLlPBIhUGeluBJi4JqJs8epi/EDlUb4itZHxAOOwcPSvERTliCftZosgNcoUbIA0W/rX+wAcBmHMt9hii1nXv8zdBHapEVM2q7XdpxYFlOu8Bou6D2Y7U2CcY2DpFKH2hRv7s4azU9SIVE3L/gy9+Xldv21ifla+al0IFAKFQCFQCBQChUAhUAgUAoVAIVAITAYB1iXGFG4mHLlxMY3JZbnO+RBWFcs8FlrsN7VP0YKWVb0LLrjAko89qba3Wq/adNNNmQK32WYb+yAd3LXH0TkBKy4YcqtkAysOsUM5lWpZRZKLVVEpina39GURRcCWVkYrexMZm1jWzjzzTLskHf0VaX2RG34LQiItQzL2WcIkhmO6FpAEbEMkobVAktiM6JGhzZ5Ux2hRWrBkY0qJhGS39SFWqWJYzSwyWWi0JocVcxJhvvCFLzCisRUiJq0lH2E1IqFUMTxMEV5etjCGJyZCi4IIMCRG25urdHYrrj3s6GXC47WKLwzYimcE5NAKDuB68pOfbCGK2dGFg/2ylpoYKC1EeSQtACEvzPtVtuGSn8A2gxLA4pmirbdZYNMuuKmFHbQsZWgcA7bQaNXNl3gJryB5kTEmMqvJYk0XH6ZepaiXFnQuC6QqokTNmn26aSwxdRUChUAhUAjMCgLp8ylTaznp8LG1cEXP6r0tgViv4kqJUqMBrcFYr6IN6Vw9OdVDoVB2zg/QldZOKBQM9d4umoXaoi5R0hFi6J3oa5y7wtMC1AE9Sy9YNrP05WSFDSUeMaQOJNkzQX/x/UTZ2b1BBVPudAfBCIOzRSk86RHKiCTCxhVUoQCe9ljYeGE5B709LosWLcq3ZVSHEnfslpqmax796EcTxtKO/UlENRqhsyzzWBOKLvZITasXtt2LLlbfxNDmthPhZvAgo/UhENHy1LqAtboMP+hZAdyIZySTkQ8O1KK9IC7CGJz4VA6Bs8xJeJt4bApRZQrXWMWYhKgZApHQ9bznPQ/a0HMpzjqlfUuGH5ZLjRwgpoJqARyLiwhU38gKINCWhIPsNHjw9GJY20u4W98KFwKFQCFQCMwQgcxrqAm9PeVFpWJozwGXhVSGrp7bJn4MKQInNm0ZMRty3o8yNadDaUqrS6dHcNB768apEqqchqU3KaDvf//7FIQunc7SmdOklAVl3RWbllQuNYQD30y0v42GToTSBTSpVAGn8ugRmjH615wOQ3NYyj163zwRT1Ugj0u46V/jB6f7jCVsNsWKpkNGPDTRv1Q5sSVF/xpygIX+Jb+jrfa4kJzuUyLNpS7R77K3y7SUIvNo+AENG2jQK0JG2o0uNsAwCaU0fd8MStnRgiH5EdhhQ8MaVxgGmLpmu7DZKMVHrctLeMzxZE8gKvwpbhtfzNNBpy0woUzF+4KuiijLCUl4OuVIGGDCzSgFN8KIt/XHsUbyaGUjBOMo4xYcTLExZAnJPh6KeLntMG5gVqAQKAQKgUJg7iFAP9LLlAvt5kwC1eb8wFlnnWXWaU8nw+/nP/95eopeM2GUaqJNLVKOLrlmYoZVLm72j2IlDFt3ys4VnM3uDTCUS8NOBnnZTYFpTNIyF9s0TI8bkzBWG43ENq6++E9V7NCrryKiiMnTxySPmCud9qfNmUeME1I1qSGQ0ZGbL37xi+plFCdVvOGHx17Nxy80SgELcFx9dR8hQx9l36MhB7sEfFhs+pL6HpXuMowhqkGRUUcTvo9yZXwEO7EhbZzJhYSdvryuGE572w26HK2xwBSQU+u0oCweg0xfrdNqLSlN1ojFhz7cNKtAwuEpewgUpIGMlnl4sY3bOBBx33hPRuNY28qZoew795OUi1Eog0ajxyabckmC3jvfikuqEv0cxLv8Bp///OczE4USN5FN5satBYJG7rLk8ognS5fN6EbL3rHEJBW3/MZF5sItReSOTAwOHhEQKTEpVLhBlIzJ1cLoE27FhQ9WiXd3hQkaqV3+Sa17IVAIzAQBfZefud6j+2udJEPzaCvy+g0B1n4dF5WdNV8HbPJhBr005lQqna4sP2FGe6b1bhEm/mTQuekY0590UwfDXVFZVyw6M6EzldMRDmTyrqVvpNCdjDVVV6jZOnMBMcims3XIhxMoorKisE7ghkPrnQaLm8UYEpJhMmXxqcSswf33qaee2hWA7cgjGH3gpBsf40bGSxNhOMn+czLitaInyTNk7a6T1+JRfAxZ8VfeeKpL3knvjJcKMZGYkjSi4RnvV0az3cv6lBbUlN5DhilnrrxIfTwZhdi7WKUML50EVkSwooasKCmFBrfC4nWyDcMJZGtVjg8phZnL0Ete77NXiGDt+vrXv27BxSDcYBUHr7GXzeUNd9AI25izWr1UxwKWD58w9JGHYcrQBX0jqEAhUAjMKwT8/PUnpX+npHSm/YaU/gUdLVz6t/TvtH9ElbEQmEsItFnJUisVykY21fkvq6AVZ5fV5MZEwGQk1rBJzn+7ec1izDJMT2zxsrRt0X/XXXc1eTEfMb0Sww5vqd0kBaUYl7m5nQl2FLBJmvWolJ0Gy1r/ZoTjToyu/An3ASvSNM0mAbN4k6wuPas7lHiy4PCrG59pFP5tn9tgjcR0C4qlsctkGuEuw6HZEcRe0UpPFvdsoTT/5ayqm7eNCdVFy5qEQsMBBFsc7R6J8ST1hYOXStOzFfAebpOh+S+fVsY5jORdngaZVjfYn4lhPwOG8qKfzPzXvNsUO/Nu2YOwlRqbSVSBO4/sG2nF2QFiBYrkbTtlkmzkYNbg2gwHjehNIFUqggAgs9IiTYwKFAKFwAqOQOvrukbdiWROz9lSVzT9q+91YIorQ2t89C9z32j9SylbBNQHthotuwDNqOvOrEegr6A+YKXqnA1UmCvbR56Sha6hO9SrfUEh8YyfAkrRseM2WITUvlIm6u2H5k0pg3evzVLfnFZQOBNDjLtaYLjc9K99kpbmycD+TAMuO/1LKRthsj9rQS3SQFNT+0NobTHRv7R/07+NrALzEIEhg/J5iEJVuRAoBAqBQqAQKAQKgUKgECgECoFCoBAYgUAMXk5c2IdqgYr9hWGFRcz5hFj3HAAW8EEStiqb/tlcWKCcL3WU12c/ETvGw9ZmFyP7mjUSXh54mmckQsb3kz36Ntr6qomVFWYjy0XWeyRZ21O0IyJW9RTqgC4Dnw2LFhQRZ+XMedTTTz+dVGSzKuOwDRnsg1Qd389hguScC6tvfvOb4p1ltf0RN0cCWIsYj5xqUIQDtwh85FMua0iWmjilshBl46NTr+ecc47zA87NSrL3kZGL9ZP5ieTOPDB48Qbi9OyjHvnoo4/6AAkJzz5lqyWDqSM0+KspmbmXcoBZEWrEd5WK2MiraJIoKItVsvPtZfHSMtJrX/ta56iZ1WJvjc/+1kwOYyiFG3hHfO2aZXSzZCX1sMMOczRXXiuIqu9Uj4wco7DbOkDlYAnEVJaBzJlnu4qlgt2uYvuJ7RC12mrrLTEkWaHUxM47WVJVZe3I/mvtzaV0rayB1Ms+Yy8AHODcxKtAIVAIFAKFwMwRoBMx4ZEh2tZJAIscwg6f2GovifsJXh25daBkfR/Mrn2HQHTyTobo3p2ZdIyQ1nNoloMn2ocXLcqICtB189xBxzm2Qcs4hGAnhIw0lzOWFB/m9Jc7VU6r0sLOkdKktAyVJ97mG0tlYqh1JwqMAegRx1QMDygymsjn7yi4lEswuzRsyqE+SEVIepA2edWrXkXL2IGBoQ+YGAzQTWKsgFJhhhCcXao+L5NUlS/j0ZV0n51Aak1OSty4go7jFgQHWp4LqqzYecxF15OBo5Boed9VU2vCExi9wYOyKGsV9KU4EiqUCvbFObnEAJaOVk18IA8TWpL8zk4AUwxK2lBZiDWBsYHvucHf+iJIRXKDotUcvTD4cXjDZhGS06c2uICd7xJNCTfDDN+s461MXShcW51wsKzFL4lxhXakoO1HgT9UQefd4I8ScV6SG6pb/xcChUAhUAjMAgLmPrhQQDpkcxz7Ss0iBUwScwROv20WRh9xmOjUOkrqFYHO3DyR1jDfdDd5NIWkf2kxLpxsoNGHm3nRd6ZaVJJ4kzinN+1BsbnQFLJJT13q8ylxNKZalGDmv+bUpr1cNSnOxNmEFH+a2rzMfPkJT3iCo3r2SXBMaY7M65MThrSbL8129S/nAvLS+4rzFVaTaFqe/qV3zM2pWnNeAwD61xCCgqZ/aUzza+rPThrfLDVbtFHS1z5Vk1bFyjCgCZ+A4YTxgDB1aaxi2yU1at5KKpspqVfbYelQYJLQMUJDBRNk6s+k1SZR2jl8VFCAeuUd0hDIPJoMpDICEU/Dmr+zQoDL8IZg4SCGijeRV5yBip1J3Fxmjk//mi+THG5cPxvPmIC7awuKO55HDADIBnaqWbkiKVxkeGopgwEjKJhHwroXAoVAIVAIFAJTQoBmD33O0pgXmycaITDwmvRJdVG+5o+cThoz0FnoaXYWZpNBYwBDFDQic59k6YgzzW/0dDTm7ADuFC7O4dnIWqBl6QZMk9mH6WUjEx40HMjBh4onJC1J6RuBSCK2XJlTh3+XyVLDZMgV+QkpkHA3rxjlGipQ09HRcnUJhM2+zf3Fq3jqTteTmVpXfRKikapGAq2gRLqjTLkBSkzfpV2StxuPYTZSMxTwhZGkyIZbl7KFMfFuGNUYs7kGK9soV5ZA6muAx2LDGmOcZrRpfAUQbziLjbfF4NMSkjcKcejhwH6VAXkfCGkLSUbR2Bpma1B2JGEWGxlDj4+wxkWQpROISfJOWv5AnILS1obxxsO+7msUKoYkeXUDslzGrt4uvl8NLxUtnvMLqzMGn8aWBECfu2Gqn1UyhrmCyCwmMnsljOqt1xDPMNggXF4Ext5GniSJYAoNk9zz6PU2+1CEHxduKFWZDIx7VtyM5MW4EKfo5BUDK3eP3XiPPeoFC7rGpYjqF4FS2CWgani6PBLbo8gwFxbIb5xI+SmhxFa9VFYYscfck6vuhUAhMHMEdAU6Ez80pgPc/MomydMPU6/oU0km4/oNM3S9oi5ax8iMT5XrWLBiH7D8bTZtIqwPMTjx88+npFpBZNCBSCWD/iEdQkttAf1At3NIvIz0nXl6ejMDIcKYd6uI+Tj7eb7VdOyxx5qhy05mWfR+siNjclfo5GvdhJlqQO0IAGeGESAsNTtKNEw94OoSG6h4tJyh0+6eO4WheMSqk9p1cwmnR+2LbI8QgDyaFrMsAo1/1EG0hjfHcgxl3UpUF61pZKUu3ihw5U2DoW+6aGL6MW9Csmhxuo/LeN5XjXutf9G/9GBjKKAscNmo4P2kAY0imNowSdOrOxqosnopmlb18mToZYsFMvY6RRsJ4NzYirRIxFc7exEal1ZTEQ2t4QQ0RLf5hG16yWhcGAg+YKZqjWEFCoFCYF4hUPq39K8XvvQvEEr/zquurypbCKxoCEx+JmiakFnJ5Oe/ZhmmbzIOnf+CIqqwWcb6wBk6/0VjKsH2aK6RCQ7br0UBzh3MuO0BsBbAds32aOZiAoWJKZUZiowktzHbHoPJ17pPpMk/qrsV/9gBTOgy4RqRPUYAn/6yy47hvVGavgmzc1r974qd+W9sCAqSNBGMjVWbjaJcqjwt17QDrTiyCbszmOCmLcw3u2zVxdzTDNT7kMUFMSwtJpgcYJkvm6Xa+2GCqU0ZnE2fraG47HkwsbVv0Jup9RvPFG1CbXcHVkwiNhmymQin4rlPpH+1l/kswwVLEc74EE8TiDT/de4D4LFXpEQvmA0epPWqNxkSIL8dpxhKVTWz8sx/Pbr6iOuxECgE5jYCuqDp2Z9XQP1Lu+n3aBP9G/3LBs5p/gj9awVQz6kbXw5dnx4e1Lpu4wSDkKWWmK6bDV933dW/ZPZC2p0oqU//qnj0r4596EvbpUfQFKJwX9LQ7NOObAW1IYGAEmlYPOlfe/i7zAE1u/oXc+MZDQ03+pqx2rKIbZkKmi3925aMlQV/Az91ZK/OSK/VjsrWoO2VM36I/m0EFZifCPzLItP8hKBqXQgUAoVAIVAIFAKFQCFQCBQChUAhUAiMRiB2JWcSzjzzTMYmC3LuDEwWVGRkZmJnsdLmuIJjAGHlzKqAVRNLIBxbuFiC8GExTF4WqFC6t/Wh+NQQg3mu0NjsiH+jbwE0LEGubkzMYZJsIbWKZvusVOdjG03cY+WRfyiSqFceCcl+5MxwI07ARlsMcWZeZE2L8HY68sPVKL/6tS/7Y3yUesYZZ7R4CLBLenQA2JFpS1M2dLpsr2Q1A2ZskckCTEktb18gVWuRrF0u52/BThhFExJbV2iceRAjbDHM1TL2Bax1pUaqz0kWOUPgmHcCTciAINKaXJK4/2CP4y6EbXRwPSw0dS8ECoFCoBCYCQKUVPQUrRrFSru50jlTHxQTXUOD5BvXURaUsiW6Vm44eEQcjSxMNVgwo4ys33gcVEBYcfAkqWURJoMOn8puWltkO0xIUzg24BIp7KNkAkrB6oADDhB24dBVlImkTeKjKo+5RyPLrqxUgbpxYsHVyJrWjpLKEqBwCJTLFRfvJM6awodm5BMkSXSogOFNHvFUzZyBFINPo8xACE8XOQ1mXFZDs2sn2Y1V2mCjAds+8w6Q0OOAMwCV5diG4qhgj9rFJZXAzlvimbC7llVoSml3zj6MSdJwLbIChUAhUAgUArOIgF2t2dhK9UTn2jLoiqLhZ1lZOm29NJ/FbWLokd5xRRIT4QSaupGdMqK4xUedeRSmF0KZOx+X3Ufh6AXbYV0tKZFmgmIcxqNu6CyDhOg4kdg22VSkyRMOYuy85AMrj021cVchRq3p3zZbN1Vsmk5qNFcy2vyRGWUec6frudbixtEUWx1TTUmZmWYkYKbs0KBIgTZjTaW6rBrDwfGDI68uqGaLbabeXdkaH01jK6dH7dUizfRdeeRBLIGuhSExrVyuGSBmhJAWbHwqUAgUAoVAIVAITAkBQwjKy1kXznRM7mhJ6tIRF65tqGNmXtwcnGCAlUq7iaeXaXxbcmk0SnlKxYVYoZSssHsCRgJ42rtsZupUQ+PJnG5LJfoWMzSQQZEkbqecoOAY1KZMGtnFREBgu4fVMXPeTKuH8hkRaSSjFNwyUsKEYBiSzZVaJLt4H1dHabaOGGXuXeaAtYqBobwCoJYLk1BCQ4DACAiPwMZTyHt09cmgjorAp8u/7zFJIlHCZ7PNNnMQSGQTW6AvCwFEokdmiBIjifpqdHep3eJWonCqaSTmbI+3haMrHlWg6oVhVzGShOchhxzCNqJF+HLVFjYZ581hQYJJWkQWSQlrGvR+JmJAgQ9XsBYpjC2DcJBUNDKtiSZtIV4M/vg0DHG2wmIQ6DUOcUM7wruzzFh28VP15qBHYNR94oknclyOD2ndSasuee0NF7uSeFQisrxp3i7tq2W9llgRSYCHWd964R9ETMRrHCK/u9dj0aJFtukTWFli0Ju2cMUFPSAriHgZFeMTGgEX0FTEr15xMrqUgl6VxSMQ4y6LRzSJEcAwBEFVfFI9hhITx/ASJiH6cfa9dkm3AJn0Y4lPvRKueyFQCEwbAT89P1i/OD+0xqTbb7TIvoBfK0dCXEib3uoHfAnJyUxM9G/6JYMTnqBlcRbXL9pFCSpFktXk1oGEp27NJSy7zgHnvrLySKoI1k3Vbzifw8yCp3OhegzjnHDQU6kdJ6E50EJarsnNx3WhDouK1KXQreSZqMRuQTMMQ0DtHD3Fp1k2RvCkBWgZCqV1hl1iSYDNYC/xaqoI/WTacRAoZEMjk12SgtIuI8i6MkwvDGr8iSq701/udK5hpOZoDNF4H7xURBL2SniX+LdCwHm6MYBAYMHKpVnFYOLtMpBTC+9D9wCwVIe9DVONTl1eUX4zTznlFKXg47Vxt8giCTcAgpGE3iuUUmV3vmi77baDeXeFxdqZJD+BKGV5FU1glxhJRiOpo7BLcRkZOimHISWO3pXUuhcChcB8Q6D0b+lf7zzdQQdFFwgvu18B3YR/6d/Sv8vuHSvOhcBKjUB64NH9sC50qvNfEwrzU7MAU+YuPsYA6flNPWiBdM5dgoTJM1QkpjxWRN6cTXiFTS7M6M0QTYisdPuCsjC2Zr4+x2V7G26m5+5mWL65xSZJKQwWN7sxKmhW5VuMJq2T2Y0GBLCY/2Ym1YQxIxO2OtBnKICqLIiVggBQQ2GUq7FqgeVQ/VaWgLZwJ4lmIraJp3aP3ThksBKfurADMMYynmgy9IwbzODyYiISmPYTWv2R0URV3c1/TVFFssaEmzs0MieNCRd/ZI424GYSutT5L3ocmJdjzNEEKddWDcwx6dqExXgJsTU7HmwC8S5GG2RePzUKDf5qRMg00HJuEcLUVQgUAssfgWnPf/UbK5r+1YNZDddb6sSif622j9C/Omp9qc4fCMsaeT3tlPSvHphJlh1VjbqyZegSbpRUSxJWcUWojs68xXcDffGtk2/dfoj7yLoc+sIoG5O+pO5jU0NN1whoI3fIW4clM83YsqiLa1b0r1JadSDJCh3mixYtsrVyFvVvE14g5/WMAxm0M9hIKpM45WsIauRgXCGgyTRlN2+F5ycC/9zNMD/rX7UuBAqBQqAQKAQKgUKgECgECoFCoBAoBCaJAMtOKAWa0UcMC0serYt4ZFdqlMxPzYYV41RLaoWy4MRG083I7OVqeRtxCzA8NRpkYS5VpHvkEWAhalkSiF2sRQ7K05JaIGKEc5feOlczLo3TMAsu7G5abRySiw3OUYRHP/rRP/jBD2IXRsBE1cgEciK3GwOf1E6gSxzLF+gsIoY+RbNRdu2tDajI3+UsLNW9VarBKLK1i3Art48JAx9v9zmbzdTL+tZHIG9dhUAhUAgUArOFQLd7x7N1zolPZ9764SjloUV3e/vRyyRNn2LbwgoSzmPi3Z2yS1lNAI8t3IYKImUUH/XUsoRb45n4dpe9qdems1pqC6S41CjhMFTWPvvss8kmm3CA1URquboByFg9SswgMn15G2XoW3NMlD30mIQyrdA9dyFjt4iE3dE0fAQSvtvd7sY7iaoNyhkB6l4IFAKFQCEwWwh0VY9OOP1zC/TN4PoeB2XQbw/turvaeTCXmK6OaAQiSRJtIjDIpBsT4i6fbtXw7FNt5OzTbq3cvkDfECWpIvneMv89/vjjo/e7RYem7VNpAfGDZH3FDT42SLt8+sgGmwZifTRLfbTh2HdZ4dYFdqm5iqAQKAQKgUKgEOgiEE1HZ9HgFKUk3qjttY2zgKbWbeh0JIbBnBpl4ubc56UvfSk3jjQaR9XHHnuszZd2QNqbyxEA/8iHHXbYtttuS//yLyCVW8xnPetZT3va08TgwEE2N0MoX//61zPh+oq4T0RwIYGnDb7GALznfPSjH+VVB71jD7ZXUpQysi3wOk0DElhexDvuuCM/AgTD31baPfbY4ylPeYqziFJ5I+Kp8+CDD95mm234wbR9kyNs5bIzM1kTj/MILgwcunAgRJIv0D796U/ffvvt73vf+x5zzDGgwMcGUzKY8HIXBZYnPelJj3/843lYwFx2gXe84x10Mbi6qGLIw5Sq2SHqIIpHOMNKgPsM7hWcJ8GBe4XkosrVYquttrIDG0rYYvjKV74SFFAlraZRI9//wEQdkWkOFdEWRxxxBC/baq2CxHZICRQ8iop3jGTPPffk64EL0Uc96lFw3m+//Z7//Oe//OUvV0cVgQxj/l577aUKpDVY4nwBDr7KaxevUZNzQRmiKEjR2p27KGy7lV1Jw96il7zkJRoCVtrF2+VSFy3Fu6hvmQhYbtD6fgXCWkEzMTf5LXjfhA35ZPQqypgXAERStbi1CWfDml8Sg1jEmGSEDGpNj0MuSTjgo0HxEUamOAHcgr+8XZxFuvIKYUJCxF5RrzFuzsVJTTxhSBvx1EUR2prYAh4lRSpuNd7znvfI60iSojW3X66X5KSTTopgkmRRijvO4+X3fpLovernnnsuZLBSEALELgWpOCiEMVGXZHS3FZ4MkiBGeGJg5c7aBhl8ZEQmgJuw+OAvBh/Z9QkAER8cxPv5ezOR4ZOkZBdWRMsuo6NcoFM05mi6wFa4ECgEZoIAre1UKg50FjfQ6QT8yvxOB9mK9zPU8/jxMpXrhfw8qV3E7MyO4OoW0OgK6DXjioMOOshPHh9s3aW6BBB0metw8pjUblIL++3rAHUmWMneKHWYHnPXp0nVn2DI95BH0rpTuDvssAM5aWTq8sILL3TuV28pBkN9YGR2b8XNbiD8yUbX87s0E+Z90DVWLR5QLXKSAVnS3BDOeaehrT9JbpMniwMpXb2BYlRey6t9W3NIymsmVUM/6EEP0qbazqOGRuZANeeVDhKLpEzRd0//hicVSVUZoVFGSjROo2Xai9fKBSM0AoiX3Eldb4gApWbEKyNKNC4BR8fZdvh8D1yJbzxD0zgnkFRJ5AmrRt9HWY+FQCEw5xEo/Vv6N+rGq74S6V9zbQIb0tBfPDuX/p3zPVVVsBCYewiY897//vdXLxuVWcV1aIb65hSDM6DEmDWwlpsR7LvvvlOd/06EXpsptFntICUdMXT+26U0u8GBPtUnmygJsJsJs1G/+MUvZnA282XkZCXmIoE/LJXFU31VDf8uq1kMY575L3le9KIXcfo/GeYNk8kQo2n0IzCciBXxXGwFLBgwnIhs1uPJHLH7ZG5TQvHeN7iZupqxaiPOqh74wAc2Ic2LGTrMfK2zMIOjMf81Y2UnHyptOKusr2Ir2jy6j0ykUlxarc1/IUMGGyMZbL1XyXKve92LLcVKkHKxTUX6uI14JINUpbhaIOE8jshbSYVAITA3EJj8/Dedw4qsf7VIek4BfSbjof6ZerV43fSvbyw1/avbpIL1qEzWOthl2qDKmkX9i9sInTXVimClcfGkf63ay94UwVRZTYmezlKoLH11abpsFvVvFKuyBDS39REvhtFXn8Ahm4b+7Xt/WhW6/C3guowlUi9JcO6re5e+wvMHgWXb+8wfHKumhUAhUAgUAoVAIVAIFAKFQCFQCBQCcxiBmKtYbdQxy4dZ3vDILsPKg8DeQZFoYvRhAxLJTOMSZoVhihIWCB8Bjzbii0fgwg0T2fFxSXUPqjHiYOgaT7z+JjVJxGjELSNiBImP/UhYasxPyvUYhpikIi1VUjiLQYmsyaAoByj4rSIdq6a7Awg9+gU5HZFjCb26s34uGesZ4KTmbIYveNuYq7K4iWENTMXxJ5WCXCkIzgLi8UnpYoJG7ih7qK22mvhukvi0BTI8ISwQDu4uj+RxhY+YoJfIcZIeDT4NVUlNjJC17D5Uns/YZnUWWfiHed0LgUKgECgElhsCrWdeDiW2sqJW3FtMKz1J7VGgxURZdJOWUThSuf/yl7+clSIGqzkrbLtMoOTqg0i5IrtkX//61+1TWQ7ydAutcCFQCBQChQAEVt6+t0+5LLU1Z17T2dK/SxV1egTTqCCfHZxfQJJenkb26clZuQqBQqAQKATmGAKZ3DH82lnrmqh2FI2zLnxj2e/4xCc+kVcprpQcVODKx7kXDiy4pXDy521vexs/TVwsoeE06he/+MWb3vSmvffe+/vf/z7PU7/97W8/8pGPnH/++dzlYHjBBRe4U2cf+MAHfD8c2dvf/nbeHNiWH/OYx8jOA5QSm0hUniM3XGDsuuuuPh5u4zLfClw7OTjxuMc9jumYQZgkSlHopz71KbNUFm8VPOOMMzgpePe7382dAYFVk6enhz3sYXxdEYz8/F6RgVuoc845x5EMPq14uHAS48gjj+Qm6bjjjsMKgYJ4+LLzmCcprHgc4MOLDDGG96ljm5XjCpxTBnuX1dRmXL7A2K7VlB3ejlWC5TgKYj7CrERwGuKIkboQgGBgZBUXww8RHHw+95BDDlF3PrCAz8/IqaeeKhdnYSqoRbjB4s2Kmyr+ONC/7nWvI+GTn/xkNM4pIeZaC/gEO+GEE84777yPfexjccHAaP+Wt7yFPywHitDwmcXRFdkOPPBAskHepmqPzsN4VdQ3SwataVbGABgdVFNBVYuTDlCrqZYSo4m9CXADLCdoaWirG1xj8I3FOZRXFBp8wAGT1zAvldZhG5HdggvmG2+8sXO8WXCxKT/+pBTktefbJYfHvPxeWu+Yw3J+Wd4NnypxiCi/SsIQYCJsI6q7FRlCekkWLVrkOJMsvnriOLHXQ3EqRWbvQzzIqBdixXl03M6L7a4I0pIQN28FSSDjV+xklHjVD5nfToQhnktNOWLzynHo9tCHPtRbATcH4ZQIOu+wiuDpt6lER6ouuugiL5tU56kcsnrEIx6BTC6gSVILzP1GlLjOOuvIa5Tr52lzv7NVm222GcTgqQke8IAHKIXzOz9AfDSNhtD/iNE06rX++us7tcUdGPdzfkSqg7/uSxLmJOfNBCv7ttW31Uh1Eq57IVAITBsBioYS8cvSBen99AN+s+Mdxr+YkRt/v3S/X86tQqZzoFz8YH3lSH8i7IepQ9C76iuEdSBZ2hZ24dNlrlxZsGr8hwaS0V0XpFsTSFcgrytZQkP+POpt0OimjDFe+MIX0pKqSSr9J8F0+Miw0mXRmERq5bayWsyUAskuC57dMBx0iaBr3XKXbVeAbnzCYeWOSSj76BXUcAgyIWgCtMAgczG6XyMKAwbuEZ16HUqz1MhuEX2lyyumSzDILanJqC6ht0HCu6HWhmS0EhWgvQw7RVJzVAZdgxVHVO50iqYMDrLT1ClFDEzwF+l19QqJTzXxCVyhzD0xknI4DSycrtJHBmyGDV5yZLiFoTC23nC5BHKFQ2sRlMkitUUmJtnbXaCuQqAQmD8IlP4t/bsy6l8qz2jKSI+yo+Do6NK/86fXqpoWAnMDAfZVzpHVZZLzX3NkCsv810jeeH7o/DdzgaHz38wFMiVpd3wyZUjqILCJR5P5bwg84uDq0ufR3YyJkHppE3PfgTBXevWrX21OxKJI5q233lou5bLaoTHHYZDUh0cMSRNJ0i1raLhPnsYKc2pOKeZxyu3m7XscLBqBSHfiDaaGVWhacV3+CbeqDSZFZpYNhvQ2bRwkm0xMt5QmareCLTLcJHURC2W74ybsrjUJxjTK+rr//vvLwlSrTaGKj7sLmaaUZOoqF3UsKbg1ycPZm9nMI5nPNgKBVKFv/sukrFwChF5xOLsrLjz76tVlmHAIGplShOVN9cMnrAbzVkwhUAjMSQSmOv9dRvo32LbeqQ/qxOuyRuvf9IrJy6Dap399S0mfzIDZ9K9Oz/KfR51qM1xPsjvtk9Bj6zxboEtDton0bzdvN8vQcKBQRB9WLaYvPkyGRjb+cKBurMzSv9q3xU8pkCKiTfoQSCRuTek0zpJCnOwJt+wiheUa1L9swtZ/oYqVuwvZoP5tBTWeYcj+LIvlCZz7aPARM0n9i5KQWRBP2F0RLoHu1Y0JIAoSORSxbsYKzwcE/mUWMR8qXHUsBAqBQqAQKAQKgUKgECgECoFCoBAoBKaKABOMy7KKqy+vGEYWqSx9NhQyC8bsIixGqkcB8WKExQgnxp11xiWSncgqHQOisAulgnDOhb8YFiVJMUWJiSQeXYlPuCUJtHCP6Q3f/Q59knIfF1LVWAl8XWcBSqUR2IVYQcjCXNLVV1635Lqx1RaMrbJkbAE3WNeN2RTcsy8suW7J4vGMSK66HBgLx5asNrbq6mOrrLp4gVqrqd2xzj+wh3qMKTClgAIC7kpJDH6BS9EpPVCk1rmjxIdILVIRiPEJE6mSZHTHTVJ4KkXA1WM9XrWEkXkcT7l+ZS5hkSkiGduj3cZ2yaSIBm9LTZa6FwKFQCFQCCwfBHTRy6egoaVcr7A7/w0lS2RTOnmMnhpBP70kys8freSyy8SJx+nxuVFyjcBEdYhk50oEG0F5o0hehRYChUAhMLcRGNcq/7ytCJWNNCSJghghUp/+HUE5k6To33AwyXVUVbgJORPOK0Len/70p0ZcS4V6RRC1ZCgECoFCoBBYwREYqk0yv3N3IaB0bK+kwV/ykpcww/I8RbcyLHPiY+Mmz1DOLtqFyaju4tqJ7yoGZ3t2eb2xn9IBCa6vzjzzTN5/xOBjn65UHp3YkPmi4vOC9ysOHRB/4xvfYEl2wCZucaCHxp0122ya0Z7ngpjuv/3tbxMAQxdvOPa8tniUv/rVr/ifIpUxAA7ZGcw/Dq89vA/wmMN1kTvnXA960INEMmI7z0MqfDiHUjtnQjxyGERgRTjUtN1223EnxOJ93nnn+fquyoq3uxR/QLWG5k9B6R5RslQjeM5zniMj32ExvPPC8+lPf1plJfHUw2+XK3Bx+iMSUIsWLeJdCM2XvvQlAnDfA6jTTjvNIw9ctjhDXi6siO28zZe//GXVURAfYcriq4ujK27IWCF4VnJx8qVGPueOWO0kceFESBuFd9ppJ/KY3RMPpKrGHxZIUynIk41IHgNFq+lKGuC8ieSgAHVrOAGXt+us8cu7BJl99tmHgzbVt+GbbzWOpZzb8Upw9wZPTsp4zeBcad99933kIx/pZfCIuYC8MOTfyjZ3L5hjY7JwniIvYq83mk033ZSfMk0J9qc+9anCDU+SeCHbYzfQ94PF30mwzTffnD+vXXbZJamkfcUrXsGLnHeGSIp45jOf6cf7tKc9zWZrrje8J/bQI/bqElXRsnvZ/NI5tLrLXe7i1+f123DDDdVOlZG5mhjE44GFty8B9VUv72cqggaqqvbc5z7Xa8YLFfdz3qX8ALms4m1NRq89ts5B8bflR8pbivf20Y9+tNfSqTZQP+MZzyCPt47kr3nNa3beeWc8OXTjssRvypEtv2K19kJifsABB0gls18N/lgBn+Rw2HPPPXmrkdGjbor/ON/QJgzJc7VKVaAQKARmggDdRI/rXvzM9Y1+ffmJdbuOLn+/er/KL37xi3pdfQXVI9Vvs0tDZ3nUW6Kh+BwLyYAkHV346wTQtJhu9qHhTKKf9axn0ZIhGJp3vM/rdXr0uE6J80o9ibGKXFStUYqOrvEnofGAPg0rUnXZhrmYFmi5ugGpowkasQ6Qg0I9OXAmk2UojcjI2aRt/FtMMjYcGkE30IgTyd0hdalZwSWpL7WbsRvukjVp89okqUXK1WN6wzaDLpMWDnFoaCUBMV4SCkLAcJTqR5wT1LSMoZS3jmtFAQTgzdAxozh1QYk+8mh0+hpb41VJ4o2j5GovQ95G8SJdSpfklcs4SrxD70ZlXqpcFL0S/WpQ4imApl04CKdogTy6d2NS00bWHhuTChQChcCcR2B56t/0croaV+nf0a9WOu0+GpGgE5l7N7XFJKOuvvX2XbKEG3EeV0b9a+DE/TEl6KITvU6lfwcbumIKgUJgRUaAYdzXF8wBN9lkk8H5bzrqri5g3Vrq/Df1HTr/NaeQ2vr/LufRKKGkUNr8t3Hoy5V4d7MS9nzGOll4uWItZ3u3GN2mPDLiaQrPFmeu1Mdw8oL1CdB9bLMqkSrOOMl6kA3hXbJWdAIjih6R1Jh0OU8U7iMGrLmkhQb2yYmyjIjvk6o9toC8iug+drlFmIaVxzZykEWYhtWUFg7o2cx/SWsRwdSYacUlUnZWZTF9899uQS1s9mr+K0srqJWuRBcZBue/5sIy5kKPTIma1eSXnSe16N4RtBIF+h5lTExeSHfCdLN381a4ECgE5iQCk5n/druOGerfsEo/A88u59HwotRBjda/4ZZ+jN07+vff//3fo3997qVP/xKDRdpXcHSko0ufUmrr1bu5RujfLplwA6cvfvRjt1DhXBNl6SuCYZZGs+xoHVCSa6KMQ+OjjJKUvH0cehzHeZJqkEOSmgZsBImRZaj+NYyZpP4dL/xfakSHeiW0SORpL2EkiQAUoviu/Xmo/kXDNG34KlfL3q2myPBvqSijcwVQhribGgHqPq8QGPLDmFf1r8oWAoVAIVAIFAKFQCFQCBQChUAhUAgUAjNEoNl3Gp/BmCQlPhaZccvM9Tf2GgYadxfKQWNNixHoCyem3VNQo2kitcDQpF72sQULFyxcxT/3VViOxpjtXAsXrL7KwjX8LRhbjbhibAb2Jftrl4wtXjK2cMHY6gvH/ySPrba6XcFjq68xtsZNxnxBm4Ms3rGuu3b8jwDNBsfi1uTpBnpijF8K8r+k3LuBQNbIrkdw/L9Euie7OzwT2eXQzSucpEbmMdlbkhhX32Mi3S3aucuicXO1pAoUAoVAIVAIFAKFwPQQoHbpVtdQ/UvhTo9t5SoECoFCoBAoBAqBQqAQKAQKgUKgEFgRELBrlkck10TCmPc5FMG/jMMtnN1wt+T0vhmiMzkM1D/60Y/497GTOEf6mZ0dh0DpzAyGdu66swyjPOmkkxxZ2XLLLbkW4kgIH+ceMXzEIx7hPAYyeWOT5wOL5yAOp0S2y7kI2ZUSy7YNwQ5JEoN4JOFDx97fJPE/ZTupLaE8GvBuICNvO6rpdAdR3bm4kksM5naCqvtDHvIQwoiUkYcLRbvIaT+oeGQ8Tbz//e/niOdDH/rQZz/7WZ6wTj31VGwJwBtCO4ARaeVShDACjjOgt2jRIj4XROZRQbLg7+SJWqD0KBf7tsdPfOITpFLK6aef/q53vYs8nFsBxOEQfjdIaIvqd7/73S222OIzn/mMupOBqxESys49mUJ5WRLmI+P1r389Z0CcBynR8SSN4i6MTBZbpeHGWQkBeNPYcccdUfLNxLcRP1zES3XchTVl0Ja9xa+kAQ6eNDqQ88KkFuqlgsJeYPvLYaW5HWQ69NBDeQc74ogjQM0flibjK8rb653xxemPfvSj3KV5LZ0K806ed955H//4x70S3nYerz74wQ/aoMyh1oc//GHZnXY75phjjjvuOEUoi4+nrbfe+utf//pHxi8eo/w6xLdrBLxomtha5/DDD/cyMN144f2auKH5yle+cq973Us7KvHII4/0O+XmTIsr/aijjjr33HOVxfWVLLyqebe9gXjKi1h9vT8y8h6lpt4Wv98+qbyfJ598stUlsr/zne+Uxbvtt4bYK+p15deGVNAQ9v7DxA/BDn6p9qY7KPi+973Pm8yPGL9Uu++++3rrrccPF8mxhfCb3/xmEjpo5wXWUppMBaWCS6vtsMMO6LkSO/rooznY8nPghg+Z8De/+U1dgT5ETbnY06wvfelL7RFXHDn9TJ797GfPgXd4xLtRSYXAjYWAXxlNp6PTm+mXqBsardvNdgXj8E7Hos8UiYbfTP2JIYG+yCXGJUlYL4qVXy4C3Yjfr0u4dYNhi77FtEC3RGEZM8zQIeOQIkQ2uzeaRIYDYqlGLJtttpkkneeFF14oUt+VK9x0ffSmgUGcauk/iY3ehVsYCmM1Hjf81lITcA/zPmqcIUwj6/GSpCItb2I8ulDiMAiFGKmJ70uNqLmj6St68BFu3UgDJweQNJYeuxs/ybASe2Dd8E0yyiIZmyTd4rrhkKFXX9nRtyyShEWiF9A0Ge7yvWhMJSzSCxYaxA94wAOMhcDCG6NcakRDhb973jqsjCRpWwjzqCVvF8YIJkn2xIuRMWMq/A1lZdeCBMhbRHuSh6NM47cInIw4e2xM8igmRXQphXO1d6YR35BS/xcChcBcRmC56V89W3rCLpqtuxPZ7Q+7NDqldFClfwNRH1AwBFfuk+nAmyIIyCuj/oUA/et1ojFVx4Sx9G/3J1PhQqAQWPERMJll8mIhHDr/1Zn39eeM6lTh6PmvLtE1dP4bQLrqAyUZEt9mAX24RQaToOjfcaGunxw1Vt3ZkOyUESs9J/K6aAZwFRQpo9mWWUwUEDu5uQynHqymyeLersa5xYwOREi5Up0I2bKQh/maHSDzOPF9SlBMOCTLYOnoG0HmmF2altTHJ9z6IrsZQ6AVTMyn4YikW25fWd2kwbGBEkPfxSGCidHQwjgIeysyDzV19XkD89BkwTMXSBm9n/CEJzAFm/+ayBtRMKi2WnclEanoLHY0gQUCqSQ8mxgpV6r5rwUjBhM2do3Y5r/ivWOgS5ZuQd0wDnlstU7RySXcAomveyFQCMwTBKY0/52h/tW5QbXb26RHSvxEgKfvmoz+bZ2eQPQvvTxJ/ZvSu+pAzGjBugKnUo2+BULjsU//jh5sdCEKh27MoP7tFiecqyteN9xXR1hRNJSI0UiXbPJhHFzow7krqkiPuboMSZjHZJFdTJi0+GhDkZqeHXga+jesumzFqCz92yKbJCF2D3pknqT+zWaMll2N5PXiiWmliPHY9G8qm0jxsiirSy9c1/xB4F8WhOZPtaumhUAhUAgUAoVAIVAIFAKFQCFQCBQChcBUEbDuxUI01Vx99Ow1sf4wx3QvkWw3LgSDl9TwSVI33EfckvrKbY/oW7gFekzGrlu85NrrFl+1eIltsgxJPTOWq/e4+KrrFl/N4ZVLzBXXjl296pJ17jq25i3GrloyduXisSsWj125ZOyqsSuuHLv8qvG/K8euuRrDsSXjf2PXS9/KmyDQ6tIr6YYNo4lsj+Mp1+8lTbjdUQbDELsHzzxKDWVjmMfuPUlyDUYmyX0C2Su6ECgECoFCYPYRsJRlw8Ts850cx9bzL+fAv0p3c9+h/5eYVccWXH8Qoxe9Fq+TY46tDrHz01gro9KCdp8WVs00gfgo+nYXn3W+f4GoHgqBQqAQKARmhsCNq3+nJ/tE21+mwW21VddafdXb9+vW3naLf15T1b8U1j8zL/fQstCVy4LncgemCiwECoFCoBC4MRFwAsFRBA5xnMC3y1bYxYVBHB7x6WPv4x3ucIdFixbx0GSvqrM9stD49jvaZur0gomhR9s6pWajJPUk1czRqRh35xzY888//3wfrd12223xedjDHva5z32OnUGqLEqho5m7o9cQKzGg4GOvLe85iJEhUBzfCo5koEeJAxksFvC8E0WPhvyccBGVbwIEjnkQj5CRXJJIm1DVVCmY48ZPB29QaHz2XEC8gmASkYjBBRIXQt/61rfEcCzFR8+9731vwmPVbT8y8ABi9y2nSASLDDwNgQIxgaWiF0YJMWWJROyoiXiVUihHVO94xzs4LdIiXCw99rGPBYJURSOWEX8MUylM0hYkkR0UTiWp2k9+8pPPf/7z/BBxLPL2t7+dzMggo15cdaAEi8MtisPhxz/+sYNYaqfQ/fbbjxusVi8lOswjl+wiFdqt78oY1vQaWq0Jr14qlTdHQB0B6D3xDWHg86ykvfhaQnDxxRdzegUKZ4SAL977DA1ZOK24+93vDkmXltVMmtULKaAgef1SFMSZFAdMXiS+mbS4Rz8KMvA+5vX+wx/+wPlLwzMZ22NfIAKLJACBvTx+Zd52L0ZcqvH+Rpizzz7bm4NYcTy+ff/739ea3g1n2NwR46AgL4Mfl1pjojqqQB4B8qc6Ah67MsgFBL0ENPhlU180CLxU0PjBD37gdcIfCMhICNu73vWuD33oQ4EGeSegRELgvve9rwPPG2+8Mc9Wbds6f1WE32qrrfyCNBYOwHEci0s4vwsFPfKRj8THL9dv0MtsC7gAYdCoqboANk2DD7YK4gsMWbaeS2oAditV4UKgEJg2AroIXcHee+/t17rNNtu88IUv9EPTKVE6fuwufQ7d5Eetu/Czfe1rX3vQQQfpYVwKpenoLJ0tR1d6S79Qv2jxugv0zkby1aij048lVUBvpiPSIegKUOrHWk8lgEx3rRfSgRADgYKwlUsqScTLIka8Hg8rfZcOTWclnA4wMpBZD08MBMpVF2G1k8u5TZwJoAhjGyUicOGMT/x/kUQvlD4nd1lcrVMV6YrA2ErCWQzoBEhLHpEIXCB62cteRmXj71JBxUklgyxiyOBRQDz0lIJPOCAgJ4aQTAwCukxYjVST2PKSIbLJiI8YAZHB2aNLuSqFlXgtC1XDtrjOJKSYkC31TqSUpUWUNV7FnvdSDMVICnQCHqEhkAtn8pBZny9GV6/iwa3JLJI60L5hqywV5xKRzJTO5ptvroHyDrijoSC4MKMH1YtryzPOOAMBMqmSEIBUEdg+9alPVUe+Mjl/jN7E+Te/+Q0aWJHHY4NRFtLyiemnwQ3r4x//+Lx7wIc8MqnPf/7zJeEvLyaq70pFEk5bEEwqelCTyoWJR7nUVACxpkyJHusqBAqB+YCAHmC56V/9kk619UjRC/oivRMxoJ1uqvRv6d+l6l9KGUrurtK/86GnqjoWAnMPAdOBX/7yl9yym49wZWs8b6BuFmAo7qIx6UddHDtbxue77bYbn+zUqIvSbPPfpz3taWYHJgLmO2Y3JgimbDxKt/kvVggyNchdieEvC80LWyWaCJi8MEFjbt4hUkEEQGnGIYl1joQuj3pgGUmbTjjcMrkQZig23RNgmktfLRxdz1KKs3JTKSDggKG7eEWjVIVBN0lJzT1jBpQGDJkNkV9Sm0USchyn3o25/uCDDzZDZMaUUUGIxacgcqqysAsHGdEAE0QeEZOk1VSMMUymsSb+moa2iuRtItYDaNwccckll6BRlxBoGsRY0VnKEmkaSAxQuDMFYD6la1zkngk6cOGMp6Lb/FeDYpj4cCYAYq0mFxDURZbGB43GyitBJI/C2tSHDYDAjMDE7UWSF+ygxpllg93G1FhGXy+wTANMxhxvoHZJNfGRxVIO27L57wknnADzlMt9hgBWCIbOfw1QGUysaADNJJpULhXE0++FAdxIUivgoOKKUzvhvJ+qr1FUWRJ6kd5hMUTVFlLzksuiraeBP551FQKFwEqKgD5nxPxXN6KH1DstVf927c86Rv2J3u85z3lOV/9GlStRKria/o2WFKOnnaH+Td+oc9MNTkb/6hhVUDV1vALUMfHIoMqkdZFKuNu4CDy6J6Ag3aYaeVRu1AoC8S4cdLmuofoXAVWCvwCEmc1BIS8AcaOYSOJREv6uKDUxffqX2BEy+hc3j7kUPdT+LJJGABfhyUCLaQ74Yz7Jq1f/cSEVh5WLeASTXTwcCOyRchwn7N2UIhWZekVUiKEhKqBwkOoujAA9wVwCakH/GnpZUPCpqrw5PY7jSbL7MJKPV8nukz/nnHMO/fuYxzyGrqTsggMhoWQt1WKrL/34YpbXQ0E4uMvoyiCBkiU5eknysj8vVf+CERPEssiuUi4B2KqOeAzFSAWLGPLDP/QZ4yEAiPdQpCvy1H3+IHC9C975U+GqaSFQCBQChUAhUAgUAoVAIVAIFAKFQCEwDQQYjBiAWMrYsBhTcGBnYcRprBhfhNlWBFp8Irv3GF96NpjxK9YoxjvLJEw2DDTsOOxELrkU5C6Mc5h0Axh4xMHd1chQshbFEiSMIDKnxBB370sW92jsxhwXu7eCiFnCsjCg9WKWrN6zH938pwyIG2w45qM6v/3vn/7617/+v//pbVH96Y+vZgtbfcFDLfnwlqX065aM74O8eq2ehXGVcdlW6Um7ZEGvRr3ADUYoj/i7q2yW7ggDBKzIkEsMejS4hTgcmpELvUKZyZClddzZW2Vn2pOEXnYfTWUay6MYV8TAP49oBMYRGAJ4UnOXEWd3xKDG1rmUMKl7IVAIFAKFwGwhQC3qYG1quc997tPtvbv8W7cssvXn3Z48BN0sLWxhzNoPlRHlold3tdSWsQWSFBoqJo9JdW8XbrQYzUhPoRHf7gKRLXmH35f09L4v1hPvyisWOuWy1i1/9uAHP/g+97npvVz3XsXeFx9+d7zh4gsfaJPHmmvclx5cdfXL5VqwsLesNba4twXEo/u1fFFCZqy3jCQweImXBQgWzCIwmgASybtZQpy6NwS6BEPDOBtFpHXCs8tHlkR270P5JFLeXOjtOgJFa/cRuSqpECgECoFCYPIIzK7+1Wkr2j2KQ8B2CrsZ9N7dSDRdzdLUZbRD4yCLq6mMKCyp9oXY22FmbZ8Q5mLQuBqBsMgRl3nxeOqqJnrXXN3bebP6Wj9p+nfduy9u+vfnP7of7bz2Te8/E/0bSUhlzGBLa9OSkbN7R6nK7l18WnVaIDSNWMB+EWgIMAh0x1GNUlLLLjz6Si53WehfXipG01dqIVAIFAKFQCEwGgHjDfNxx1r4ytl6662dQPjmN79p8mgqTa0zwlPoW265JZ1r0sdRgjMeNKYkyoj2pI9QMiabOLuLNKu1K5R2FsbHGAAHu2DF2NDJsxLfDazHP//5z2n5uDngCwMxOTPwcKxCcXLhI9IE2eiCJMhIS5kSwFbLNgPFJPZnxMjQe3z5y1/Of0Fvqr7mmrLggCH3Q+5Rx2KoZsMhWc466yyPdpESwLZUpZg7k5nSl+rORdEuu+zi4/Ynnngis7wTSm984xu5eyBGz+reufBRO5VVbhszsFfbruoRsWESxMBFSGR2taqOslyyyP6GN7xBE+DPUReEjzrqqKc//ekf/vCHwcWoLovswuiZaHjB0ASgcIGL8CRXTTYckGpWQiJ28a1g4UO5qo+Da5NNNhHm10mhBLvgggvwVCI+8diFA0BcAMnZEhUVOflxSweYFSj4wx/+kAclWHk/0yhpKRhqbohBScB76G23G94+YxABRztqXGiIh61coNCO3nlth8y7B0mPWoq/J20BeaVAFdQwlBEQnKx5mU8++WTbkZ/3vOdpUzRGjEa/UiGMQwPZYxc78d0YrYOtN5k7DK+Wd0nLKk4V5CKVV9TdWBSZnxV5ZFFHwiP2qCwyq11+VoTBzS89AkhSwZDBpyuJcG8ovvbaKgUWNPLKpTjLVYRRZa+37kWJlq6yif8e97gH/26KEwNhfq/uf//7K4JtDQLyYkskA36+scSnsjxbiYcPIbkh4z/LS77jjjsST6HK4sJP0VDFWU01H3mEZRHwSpMEMSZ6AJEJe6yrECgEZgsBPR4tj9thhx3muIUfJo91lJEeoHVcfoCbbrop11df+tKX/Gx1lX6MIg0w3vrWt9Kwr3jFK/S0jnaITEfxuMc9bq+99tJV+iH7vacj0oNhG875vSvCJQsBBAxIBBDoVQxvtt9+e12N/kQPiQmNzHVRupf05LodeQ0JPKKh6DHRr4rXsRtmODLKKEFC6kCnh8axZEXoT/SoeCpI56NnE4mJS0Zh3SOanAjy2C4EisgjSdRLdjII6+jESzWGIRIMocHNn95v11133XDDDXm3DHNi6GlliUjCTARR9PhjQn46CxNhRVBM7uSBTFIJhkZvDDHZkyvSqikyzJWlNUUGcAolWYIzD4PaCGeUKvuoRz0q8uM/+Ys2UXcCyKKmUawY0gt4qr5LNx55xCNTZVnoIJHeAWpUZJLUAmgGUeITqV0y1ISP8RVPbe9617v4sdLEdAQOyPhmVTRvodhqC6/xRhtt5M3hSNT7CQSSkNORJJHGAHSZvCKjwYMAPmARrwr4QBtWpOK0Eef3vOc9Xn7OKKk8ErpU2bvtNNRLXvISv6A0qCzqFQ3rByISB5USib9L3b1vIpWSN8FPSbz3UDNhK36csG6FQCEwLxBYnvoXoLoal76u9K9+WIecrrv0r3djSvq3jUZK/86LfqoqWQjMOQSMt816mPXU7NBDDzWZNd0w1KeUDebNL4zzjeQvuugivdzRRx9t/usyq5JqniV+cP5Lt7rMrbglMtcw/1UKPplW4Gmil7tSdLlYuQigoCxAe8TB/KXNf6kqU6r1119fpHAmg7Igo8dNIrAynTGpUZCeGQdTFUWbQlogOO+885LKTrjddtspVF41ld0000zEjJWQHl2mIVLRmLtlduaxXUpvYQGSDM5/2Q9JSxIcMv/lOMw00JQTCOIxgYCvYpj1EBKZqXoMiQTwSH6Pmf+SxBwt9UrRBjAyggtP2VOWLJlUAgFzlGZzKUKSEmPVNGVWBKXPDxSXZ0zKqmDKBpZUvFu7EWFM8HQJkEGhZtDKFYMhkcSoCA4QFimA0p3MhCdAHvNuqGMeUaqdLCjxQUZgqezPjDNvetOb9tlnn29/+9s4e2+9gUrhZ5xxhlnDm2C+/O53v9u76t0788wzfR4D4OzJmtiSkLkqq+yrXvUqubwwClIKGy98NDQBhs5/fTjEpHu//fb7yle+Aih5kWmOf//3fzf/5XuazLAlDIGF8fGGe/1SKY3S7BjysleosgoiU278hmhKZOQkT12FQCEwfxDQEY22P+umJq9/dZig0/PQvy94wQv0dTrn9LHphHXReht3fVT0r54HvVz6z1nRv3o5PZsOUF/HyDxC/yo0+le/TSoqT69IDbmnO6UphLsvQ2rRYnS58mKiRvrkVERvbGKrdpjYC8/+zIY5VP8io75ltPyn+pjgIEaARki/HXWsxNgqEWgRoEX/qiMmxJBFJGkTRk8A6lUVJInv2p81Ey3GkGtZEL3hhxXzKenfICCjQsnv0Qp4G6509W/GFaGHXlf/Esy7QQAcVEfVXHBwaUHxHskPT0vt9CDle8ABB/hWEIgoQcjIyNfkZpttxhdktJ7Ro7MYFjpZ+33ISnEKpX+tX9tK4Xshe+yxhzfERVroGa2RLS8hiGCb1wBiMrI/j9a/vgyBFTzVhczUvYpgItKLkbbwKAlbDMUQO2FZGNuVLoDeGIy0Aaru8wqBf9kQM69qXpUtBAqBQqAQKAQKgUKgECgECoFCoBAoBCaPAJsL65hv6TAMsbawszDfsKq4Y5K7QOIZlcK5m4p4aHHsNYxZ1l0YoRiJWIvCRN4WkLFl75YlXvZuWU0kdiuGLeY5ZkqcGRBJxUiEuCuVxyWLe5Fsqoq7bnHvkyxji2/uxo7aK3ThH1mOLrvidw9+8Ni+b3/uhg/e8Jwv/eDssz//hEe9+n9/+c2HPORX//ZvT/jFr758xBGnf/Gzv3HA4bqremcMrroSM0tiPWvj4gW9pa9rF/SqzwHWeHzPgBtRASveXbyCmNjI7ACM/ToxmckbymREnCuPUuV1waFBh0AbWQoV4+rJsHgxI6aVNpy7u2DDZBBDHIJSC+TR3aU48eFMZpztQo5JTmqfnCjrKgQKgUKgEJgeAtaNqACn1Gzp0N8OZdJTVePrgu0uoDdu9xB4HLwcO8G5S4wmjwItY4sJhxG6I9lltH/CaTrapzFsTFog3AbvyXXt1b2Tfne+1+/ufvexd71vU3s5zv/K//zy1994wANfdNFFP/p/66/7zGds8bNffvGooy44/7wLnE+56Sr36LFaMn7MYEFv2WzxWO8eB1hLFl4/MunRDFyWyihHC6u2lSRRFcgpXmWzmGoIEcndI+Hk9Z0VMm4y8JE3qMrb+CixcW7hARn/GSFvLrnIbOPLRO/GP/NUqBAoBAqBQmAqCMyu/m1aQ+9NCnd6Ie6DbVwwjUpku0fSqAbhFri+9x//rykOaiWaRYwU38ezmcMOoWQUmctjyMJ81H1x7/joOvf804MfvMqb37rhbW97xflfueSCn37hnvfa8cILL3jgA9Z9+naP/a9fnH300T86/7wf2Q46bf1LWrpV9XmFcBAxc9ImrUBX5mCYe4QPgXALYJikFvANVVoyW0lSfUkuE/ZQikyJuSdy9N0eFLul7UDiwWE0ZaUWAoVAIVAIFAIjEKB6KFzbFs3mfFOdcwf7I512MHOkaMxPaUauFriNePOb34yGS6nXve51jhlwTsEMa7K8aNGiU045xc5RFlr7IGlVE1iegBw/YKoVdmjENk3xNlOeccYZtnI6KMKZhXGCHbF2yn7wgx/0yVOKmEssfOhNHgfe9773CSsxO3oZgWlS0rKu205KKkl2fJqK4qwgfJigCeyiqbnvQY+DSI/kDAhf+9rXUHJfRSQVR3O/+91PEb5zbqbsopTdu4jhry7u9qGyivBYZIT2hS98gRsv2WlzUlHrskSPwwFz1VF3qZLEv//97+fBin+xY489VrxPoGcnq1UDm1ZdyrW9mNjqZVjyzGc+88tf/jJWFhS0BWLgnHfeec7VQJvbBSA8+tGPdlJFGBnrPQmRqaykz372s06wPPGJT/zoRz9KSHAhhpVDL2Bn9lcESk1j3zAXWsqyY1jjEgCBJvDVDUdfbLQlPIaM+c3npnYU34VopQs7deYsGf9KkFc7+LRGhMDmm2+uFQCupu7202syLxtkghuoNat3UvvmrUsqHMT7FeCmNb1gWDlrZCuwIlwyyiVVLsNvZ35sOAb7hz70IW+CDdxovMw2LisaDW4C3t78CmQM1PJqu4TROHREbI8CJLFL3kBRQBOriOzkF/aeoHHHh5BZWJHq8vp5xNOrro7CzrPJgt5La7s2oCQRQ4wS3f0KsPViCAs4EGV3tSqHjNh+R+orC27qgh5nAe8bn274q+wnP/lJv2hFpII4u+QCrFe6ia2NyIxGFpFKzE/j8MMPxxB0fvWqgGC8NleJAaNI9EoniQCG0BDATRGY1FUIFAKzgoCfLT7ufp56J0pKb+mEBpeRTnJ+4AMfMBe2yuyn50dKu2277bZGGu9973v9VP0k5dU/6L4cHnYCBP3HP/5x2kqXQls5qfv4xz9+v/3244HIr3iLLbbgjFIPI5dNApg40fQf//Efetr9999f72f4IWmnnXbSw1P3zlvqLohELeqmDjnkkNNOO80gBz3tqashoWEJwShQd+U6KqzTwPn888/Xk+gJv/rVr/KF5LwKx5pUKl2s5+EFyVhIHRVhUKHfNnTRpTsrhYOdBrpZ2VWKhMcddxxuBOte6dKBpjhiYK4fsyiv90bGKZLqUCVqjQ+IHADm9VKMVGolPR7d/ZznPAcBJG2cIIxUYmDoQKmBHAzFGHSJ/NSnPqXXdTxboQZv4o0iDEu0mgOx6FVBEbpNuY488kiCIcZZNR1/EilJx6sbB4hxoBL14TgYKL7yla80YqECjI4MeDCf0sXXJI1JTYDOcEsvrdzHPvaxJ5xwgmZSa3diRDHhHP2i7oY3xqiIDSmPP/54LUtyr5aT0s5Ekdmbye2UeK5FaR/w0rnk1NbenHe+853eBNhuttlm6N/ylrcwkZEBB2tV4D3iiCMMU11GSvIawXp/LNx4lzhqJA89+KIXvegRj3gEJoAFIIhkN1D0MsuioV0EVhdNrNzoffyRaaknPOEJhtbaVOv7CXjhHXWjzrheVa73/O1vf7vH3XffXRFohLUX2RSBgDMv9Ix7Nl1oVrWji52wmhL+RVwIFAIrIwLLX//qtXTUehuOPOjl0r+lf0v/lv5dGTvPkrkQmBUEjMkN9d3NsPhn55uAqwjWLTMy81/bzs1/Xcb2bH2mIaYV1KhuU+msVSYRPDIb/Itv81/GebMDWUwZMv81hzW3NVk2+zOhMEtloDOtMLsxczHdy/zXXNLk1IRC6WYop556qvnvVlttZc7CDo+JmZq5g9L1WhiaJSE2+zPJNaUy1eK62nQp9kzOF8yS8LcQwPxr/qumZvGKM803+zP/ZW+XnXdm08zMf0loKmT+axpuRoabmpoH9aHNGGiCbPZkDoVhm/9CiVSsdu7m+5n/mv4ku+kbIYVNx57ylKfIJSy7+pKQGOwGnDSZEspu0mr+y7O2piEP47bRi+meLOA1XTL/NYcyrROZIih0MrN8IlYQ7yeYYwVzkzumiTb/ZRCAgLrHG7KGMEvVLphP6WIQJowWUVm2BRNJpbDbd+e/xFC7jPdUihWCqZ/FnpsqZWX+a06a+a/Xz/RcTU1vtSYrsel/vH57wfh9RmCNAH87N0x4FWoqTXhvL87EsDsi76o5plrj413yFpl4WubgnsM7AxCWEz47NERaAUqD81/ioVQuDqA2/zVVx99rD3OTVvNf7wmBWSFUh7WB+Z1RBbbEI6dUvwJvC0u1JF8KIZs1HayEuSzRBJn/yuIH9drXvnZK+BdxIVAIrIwITH7+y5Q6qH9l7+rfrv2ZFmAglYVvyq7+pcd1QXpg/bxeuulf9EyXdCsrtyXyWdG/uk0LtRbo+/Sv3k9v2fQvA3v0L+urJP2hzpANnL4zQmCPHbQ/p62b/qW16TjL05S7JPZnj3GJhYb+pc0ZWpOrq3+NBPT8NBd56F9GYx0y/asnFwMTWZgr6Qh9teEHQ72BjYLYQqk83TU9SykgoIPoX1Xu6l+c2fzVJaqccunan+lftaME2Z+FqT8qzBaCyDn5u2EPNUTtKtouOwF2VzFd/QvbxjD6l9Xa0QOli4/+NZiJ/mWt9Q4Y40lif4aY6lPBHo1DvHJ77rmn1rE9gB3YG0X5Kk4uY0Xq2BqKEw0W/Snfc845Bz5qDQpDSjUVhomBJQzpUGSg00CYA9A7TH4eTuXNr4PalTRa/3pvbXIwOMz7Y90k9mdvuPUaFngNZ3iprY3KNLGRgFHlSSedRGb2Z/J75WhzTWwYbPSlxLrmGwLlAGu+tXjVtxAoBAqBQqAQKAQKgUKgECgECoFCYDoIMJ8xwLHsMNwwhDHcxHYTOw6OWULzKJCkFpmk3EV2TT8RxSEf9ilhKysMWOGASWMesmncrcrg3OXDAsVcJSaRPamW9JxP9RxgjfVWPcevPxOz5zqjF3Ptmmuu8Yzn3Zpd7Dvf//SrX/2xb395bOGaY/c5cZv99jvkN78f23jjT+21zyN9CvdWa/7iP/7jG3+69P9uusbYNVf32OVyxAEjT2ustsqCVVaD5PUJ44eQWco8ZtVQwHoYa6YjPUSyqBZRLazG13sywl+gi0+wTY1CI8YqqRXEPLpbWnMEyIIokFvk9AKRubUynmx8eRyUZHpFVK5CoBAoBAqBIKBv1+1bumuaog+ZpXa8IejLlUeLWPrwoUkiW8YWCGXUTVfphDhk1AFVbgEm5wAnYj6JeMruuic96T4H7H/Ax099gU/3/fi7Y3e+y9iDH/g/e+31nT/9/jsbPGTsrQfe5z3v2eF97/rvN7357LGxi8d55jRdU+hjC3j8GFs42gEWyW1MoWodVIhgNJ1Ie4ZUJ8o0MVLFR+X1ITCiRhaorGZN1IIjMo5OIonmM1oYTVaphUAhUAgUAtNAYBb176C+0HXT7KSiIKYh22AWGiGR9sfYguM+gmYwqRszLu0az3veg2yn+MRpL3rHO67+6ffG7nSXsUc/4o9vfev3fvfb72244af32e++hx32Qvp3732/dIP+7fK4PrxwbJUlC3obPkZcVK0tOGTuQyk1avVKah9N2PbRdMuyWcQuFntcupEzD9uRY/47VJiZMy8OhUAhUAgUAvMBAcrLxUZtq6L6sl3brurQju2ePETYOmnjpt2r/E0452ArpA2atm/aH2knru2VbMu2q9qF6cAM07GNoY5q2J9qn6vdqPZNcohATzkYoxTnHIw3mPTPPvtsB118vN1WUbs2TX4doLXf9I1vfKMztGbxNk0SwJZQmzjlZUbmkoDzC+cf7MK0D9j2Vu6ZnKVxRkK5zmk4k2N3qTM5CFxqkd2r9o8KOJRi7mwvr9n0Jz7xCRUkIYcX6NkiyOzwjzCpbFnGTQzFrfqOo9jVSkL0/F84DaKOp59+uhEUIR0jaSMo1RzH8no8nRJxPgRiMZ7D1vZW546cC2In57ySswNK3HoBO79zKUp0iEWS/axqJCM3ELbwOnTkY/U21wqrAskd1HHqhksmdnWfaueZgoT2qtr1q9b22moglAwLTvvgfMwxxzhkYscqMltXtTVfFbx9paYEsHvVQMXJE9tkNSvO2kKLw9OOZKswquBupCTjnPlReIu8bIDV6Da1x8zCVKKm9sd75WxK9g4wztiFnCUM76qxnK293mF7fBEDGSxsLCAy6PU+C6O3y1mMHwsbjt3MvtzbFkG8H8befhoIjDydPrK/2VZp7w/YiSFgx7lza2eddZZHl0IlMbn4ZTX8HTfCx+unrb0wWspucr9WBZHZ3aOXHz0ZEBAPMQ74u2tKNH713nYxOeGmLAe3EIsXg6cK4uBlEE8AL08TQEAVvKgEUGVoQMZAWunEwEqY5NaSvJkeYaJcMQx06AGFuUgo2cyN2EvovBO28EemH7Dh3tuok1GEC70wdzBykd9RKPv7Cald1E6AnxR8pGpEfPRmcBOWJAAHgvnlYqUV1NrVfrPdelW4ECgEpoGAH5RcfqexYDtb62giDe5A77777qs78tP2y/UTprsdGtGR6sf0GH7XfqF+73oeAZ4WaU8njmhJKhhPgwcnV9FnPZ1GNorwWzYSkEq76Xv9rvFxJEnnTAkK63acwNH/uPz2dVZGERw2OSViwOBEEIWoW9B3kUr/RkLK3SPOOjfdi4w6ItVRNV0iXUlxqI6BB8WBkip3INOQg6MlIwrVUQQBdGX6TNzwIQYh1VpAhyM89FKE6ujV9X68LOmHdZj6Z3i6O4+KoYwqeOaZZ+pFlcUBE5kJpjslGA7OYgFQKTpekusMHVI1ipAFjRhjKnoNpe4RmNDwSDDxukpogEX/bLQDE8K77BAAOBCcnqIrtQKolQ52+KQg8FJzxjAazkCLSE53KzR1H1rfoZGqo/rqKBVDd307sSHgjq270rGFj1SPFJks1AGVLTuFQiSX1lEjoDk6Sy94tURSi3Y7qBRW3hBnkAxfCezctQri6Y01arJ7QQVxkAuYXj/nu+gXR4y8t9qCVHxLfeQjHxEwiNJq8rpjbtwIakWQ2SsdGMkshsDuRtTOopMKT7oVYuKNPw3J1EXV/CLUS420qRahGcGrFbSXd5hsWkcAKzUiturjr00VzcsbkbwDfhECONdVCBQC8wEBPYBqLjf9q3ukO5Soq6QydEGlf0v/lv4t/TsfOtuqYyEwiAAVTP+a57K2feUrXzGAN18wBeY7gxnKjMB0wODcpIx3Kj52qU5zNCN8CtRkwag+qd35r6mK2YGPGchrCqAIatf819TAfMdMwWUuZgJC/1odNos88cQTzWFR2uvem/3+9a+mCchMeE29WerMXs2MWJXlMn0gm0mNSaW+i4WZ/EQlTKYeqmmuYQLCAs+OzebPTYZZsFTzX255TQl5GjI5Uh1FEMDcOfNf0xmTGvFsngQYRCwxpFUi6OBmRtbmv8RTfdMccyvjDRfTunohk2SyRnLMIQMKTjFwAKBCcUMs1YxVlRkSzQHVgtHVBMpYJfNfMynVJwPx5CWhWkvKDAsaoODrwfxXXtNtd0sbyMRblQAs2dLiWsT815c8uNASD0ZzMbBPVOW+eNNM9crivvmjhjZtNxMEsrrgYzKb+a/5IwuD6uOgpuruFdJ2DBGqjMw6BaMoiIQxNG1n2zeTJbPVBFlcQDAX5guDBcDCAcdVLC2yg5TzblgRAEpyKUUrI9hkk00sf3hL1c4baE2EhUeAZYPAipMLRJZFwAUTpQzOf1WTeGzvFoAs4pgCm32nXPPfvOGaMq2Av9I1kHUNOHuRQKRRNKg3HAIawpuPWPUBRXhGA0z8mqBEpD6Q67EQKATmKgI6B1XT8+gt9V0T2Z/1P4P6l9aYSP/qx+gj+le3plfU7UT/6l4oPl2QjpS1TXz0r+6ahyNhAQt5ekuXQvVyg/qXzEvVv/pMMvhQU9O/VAzbOKUZ/auTj/7V7Uf/ImaKNPxQtI5RfNQiVkNbP/pXXQjJ/G5kopsFCPFUWS6dKgs5VNlm+TYScEX/4qxn1mlbmCZnlBRdI6/SiaGrN1bJyiAFhLN4wwMVZ7OVxLwpnmDUq85cFmOnqB4Y2m8Q+zOFKGmo/Rm89C/3VbSY8YkieNTSCmajQ+s7UaRakEodQYGhNlUL1fdS5b1yT94gSWDIGGZQQMZ4MPHuqbsaeT1AR1rjGYbi6F+6UnbxqokVPUhj0qrGewYPaIz0bBIw2kHjMaVoZWPIjAk1veJclhugROca/8AHW5FGR/xkqT7Vj4PqI8BERRC4ayASTqR/DSRIlbsB4bnnnhsdqppWNPBxKYuSxUcRaurNDybuGshgQ0NrDm+F4tAHrrrPKwSq1edVc1dlC4FCoBAoBAqBQqAQKAQKgUJgZULARJ2lY2WSeK7L6osu7C8MUqmoBoorepYjLZXGss6BIMYdFhk0LDUCaJiiZBQTGoHGx9KdRRqPsfcJsOOwGckbtogZsBjCwjkZ2RAZmLoxTDx9NPbs2oPIhohDWOHMEkqYxjlyLr5u3A3Wgit7L92S2ypiyYJLua1asNbYW95ylyc/9REf+tAnPvaBsT/27Fo3W/umd/z+t451TuBn/9Nbq7O7f7+37Ljjrna7fmCvPcdsq7zJQjY1leWmamzJYtauJVet0jMm+nB7tlTKRSRFC7iCD9kEeN9nt1IR8RHbUhNU1SIyM0HKyBCm7mLQ4MzapVKJcYckuydLKwKXGGRw9qF19s3xMns3bcE61h4FgnM3JjSyi8wdDXmEcRZJGKtxNtcSvkV2OVS4ECgECoFCYNoI6FcdqnQIlhbAJDor6xx6XTEI3K2vtP680UiyctP0L73ZaCIPLekEZsKNlcC46ujdhGkBi2T0SCOjlfDpxjQa+kg88RwncEaFJC0XCaOjRbowp03o8UZAWnpQdsorkXu+5e47vnLjgw76xJEH9SLWXP02tNtZn/2C/T2/++PPjCrU/NW7PHGvt9/ygx/86JteR8+OcTJ5q7XH/vH3tdAvHuvpu8U9qrFb3OqWaiHgUlAuMkS3kscaldM4OekRMncyG400ANEbaeCTGAQyqrJ79HgyhiboJcaAwXKUAYBHRaOno6EhHAJ3dVdxnKUmMjQJR2BJAlkJEx+crQ7mTQhl3QuBQqAQKARmBQH97ezq365e8HWvrv6lSpy0tOmhqQBVoE0ysxbpkj003drZeBGa6F9actGiRea/TU8hjvYXgwYT9cKZShKQKlKARqZ/sQqfvd927xft+OADD/zE0Qf3SltjlVvSOJ8/8+ye/v3Tz6N/93jddm/Yf+3DD//IW/emj2yRHLv9rcauueL2FOs1Y38ygrh2cU+d3fLWt4qEwmqhLEK601xKFLAXZLPNNjOX7OKjpvQvgvGq9+pOS+LTk+aGK9pWvTAJPrS2cYXHG0jGfBvNHqyMoFJTNHDu0mQMgwZB44MmTCJzkpquV4V8da2Lcyu0AoVAIVAIzHMEnGeY5whMvvr0UVSzKR6NQ79wGmXDpXiqx1EQVtxMG6MlTRh9ZvbhD3+4aT53CfbF2v2ZXDY+sveaQnZLp76RUaAUliIe+tCHSrXF1uRXQKRCXbwRucxzbeu0w9gJDaVzOiCGOuZlwOZXFgB8CLbDDjsI2KNJ5zrMI96pG2EnMRgKON5yOoXvJ/wdhJCXkE5KyGI3KoM8OR3n2Hzzzb/85S/bPWwzLnWvUpHHfcTV5LdLVWXJFqDc1cKdwdy4wmZlR0RsQYZMNLVNxvZDy46MSwWDAbt+DSQMEgSg1PR+Sre11MEP2PKRpCDZASLJvN5uVKWfd955aq36tgjb52pDKgTUBXGTxAkoaAAKEy4VjG0kaUecWR54WbLd2QAMc/FpdztoMXGJURz8fXbYN42zQ1fjulb2ub/30HLSfvvt563gRIw1w1ttmKdRuDaDuSND8HRKzYZj/tFy9snnbe2x5rVKQ3i7tDViPw2jOI8anUsyj2C0R5xLFJ7dxGNlG7RIr4RP+Lqj9745i8VDiiPT3hMxjgPZQu3cFElsGYe/Pdze21e96lUaF4H49loaMXrD3/3udxPSb0E8YbiQ06bx0KGCvsPslSatMbm3S4CbDxvEvXvef5LbeL3rrruy8nmdLNaopjNIfiBeEnMEsGhxRwV85pc3E/L4wWbYHDGgZMDPlweITChURBaisu8dccQRPr/MsRc0LDBxlsfVCJdqRv5KJAOe3j3voVG9V5QHPfFOQwHBHm5IeiQMgT36jdjLzsuM9tLneFfdJZGfsxIYirEk5HfB6ZtfH8zJ6bPYXni9ATHsO0dvXzu4yMn3nGNXfm7I1CW9X8O2AjNHQAPNnElxWIkQyNig/ZR0R4lJFXK+Ra8l0rtBQ/npdSewoXdH3804iEAGDIPxYvRy+r2hTETSWXRxUrvZZZGxG9MNR5jBXF2aSYa7TIS71aRqiRH9PklujQyfLucWr7JA7pbSTYpdosVML5ARgrwpZbCswZjBgkLTB8ggWYuJGpLLBbGJKqL6EyWFFeHDpHHuC0AP/9GvR1+WoY9K8dKSJ3CpqccU7S5L+yE0PLuRXZ55w7sxCasp/t34VlYih74hXfoKFwKFwMqLQHoS9/zS+7rT0r9attsH9uGjtyz9m1doqT+B0r+DEJX+HcSkYgqB+YNAOk9qhSrp60gZnZjU3DMdM//N/i6P08CnTRYEXN2yBmcHUXloJLF7k62rBJWOQ2YKQyVp2Zc6mRqavRsZZNoM12Of5MRrqd2MSw1nmjZIpsq65W4pjUaVVbw9tkBru8Fcg9jK1aAL/WCuwZhWloBUOESSVnSXYKJwy6VdJnqLRjRZyu1rgsGyoJf5tboPpk4+RnHqmPmpQpNRZC6P3bYIpN2YbkFDW6FL0MLh04prgUZQgUKgEJgzCOhM1MU9v3T3xKSCk5z/TgaNVsQgcXqnCNAtHaXuiN7XB/Z1RKOti41VX3UGi55hDMktoqWLniorfXU6276MeE6kf/so2yM+wa0PvUbQF2jlTilXH5OpPrZCk3EaqgoHyBgQjsAnLe71UAokpypko2/IDJWzry7dl3OwCfJ6kLkxbwGRgy9Pl1s33HJVYK4i8C9LEXO1klWvQqAQKAQKgUKgECgECoFCoBAoBFYuBMzMWQHM9jPhz0Q94ZWrInNMWg2Rtki9tAgLjj2sWe6K0S00IUtYUyaQXF2CxFjRYU4Sj6F7ImNgSmRiJnlvHEKf0oW7rIRdIvNSucfqNB5gGF2w6sLeyug1140tWDh2wAHrOs/w2tftc8IHeixXXX3s2qvXtOMEhxie1l642t8XX7PPPu//wc/Hjjlm5wXXfG3ffb933eKxy68cu+kayh276pqeJXjxAsVdJwvOCgIXDhFPODJYcVRxZjiPUtu91aInwfjqVFK7j5iwzcki0t1jKNtqXBM4uXLH2RXixGiOvphxkt4NASbuWRZNuJUYgjCpeyFQCBQChUAhMFUEKBRnCx0QTcb3fvBRPG/usdfrP3b0hJyoukMPP33B2mM77/ykVa69dLfdvkFXOZzI5+Rinq/qKgQKgUKgECgECoGRCGS26Hw7JxQIPb7/uEU+bff6N77+hPdPmNOc88CDP/W3xWNvfOPTb3nTP7zmNeetsXDs738fW6XnWLLn+cqstM0TE5iQ1w1zzBEEK2ZSpsMrpmwlVSFQCBQChcBKgQAVGW2SLYxs7L4KzgUPdeygjkf2Z1Zi91SHPZa+5kYnhlmWWBndeVvmP4iTKRnxdIWepx6RMTsriC8h8SzPjcCjgnzWm/dq8di6FIeA756IwZMmg7PsksRwaoNhHvnoEXDFJsxnEHc8wi40XGLxFWVgwP2NR3kVxy8Pt1N8QsVCHjlVOXzyOHiXigPXXZJacd19nxGJoyKbicnAyQ6vPR5VJPSEEYNMTIQhgCqzPxAswqBM0fxScY0dnqlO4knuAx7oFc0XFQ9WvhDLpQieyODmHlEVrSlVU0ap6MWksXgT45XMnmwegqAhF4baNCDgAOfk8h1gjp/4ckKsOPFYRZKV9A4cIHhPXvnKV3JTxfsSV2gXX3wxj642ygPzpJNO8iaj8YFio9Odd96ZsyfLJRwzHX744dxF+cgw3LSXDy9DmPcosKA/+OCDEeDAK5NPRnMS503wY3nGM56x8cYb+3YuH08PetCDvL2K8ybwCcXFG9dUivMT2HPPPXl9ijcugnGb5df3ghe8gDz5xm8D3BeeueIigHj+s4477jge65yy03y+DHz88cc/8pGP1KC8Vilao3NotWjRIpJwGnXKKadwxLb++utfdNFFUr1gnHNtttlmnLWpKXddPNB5x3i24hILh3POOYdDOhVE2QQQ4DvMW/ThD394++2332CDDS688EL+rTii4rrXb40vMO+J36YPF4tXIj9ZwEHvV/Ca17yGXzYY8lfFz5cW+cQnPoEnF2Dw5KDqnve855vf/GZ8CEZIv30+DTnAUrXvfOc7YEHP7R03XjzKedRMJAcjz1nCqszHlv7kYQ97GDH4ulIX3z3mJIszLL+4fffdV7m8faHsVqrChUAhMHMEmo5IQDeSQLSbzkQg4ZQltT22wKAYIRtBkKSU1c3e5d+NFx7BrY9ymT7Ouhh93XVXeKqh+zjD8CDaM2Q4OjvhlZhrRB0RjOYTDiNojIKkzrxdcMBKcU3aiYRHkEIbZZ94E1UqufqI67EQKATmJwKto0ig9O9SX4OZ9/N9RUzUhyMr/QuEKME+0LqPpX+7aFS4ECgEVlgEmvpomjeiemQ5ZAZMfMj0bAIJN7Lu40TVjE4JpTs+fVpmkEnKRSZpMHUwZmjRs6WwuiB0C0p8N2by4YmqMELmiYpL/NDUoZERckTS5GsxDUoV72v9LpOZSzVopenyn3yYnF7UyOPuEuM+VPihka0suVq4AoVAIVAIDEWgdRQJTGn+O5RhIsNtIo2DJkmt9MZKzNBc+thGMyIwyHAE8TSSRve6oxkOrZcs0+CZak61slOlH12dpaZOsrgR1Q+HEQRkCM3gqutSxesjaK2TsaK7cVEihfuIFZqkoXUcGhkOrZQ+hvU4PxEoB1jzs92XYa11MSM6IAUvlWAZCreysZ4SVlMinh4Sy6GIKQk2KM9gzJQYFnEhUAisFAj4pZOz6RqPiekbrycyNUKcx5ZrpahphFymkk+eeWYjAFx2GE5GmMnQzGLjLufipiR5mx/2vflTYrJ8iAOjsvLydFHthpePMCtXKRDLSgmghHNfuaowJ6W1uT8vs3MFfoDahTHIpbHSXiLRuFL9RKYFExbfaPJbxiR87IC3aR63eFgXaBiGBr0r5SYJjZiIlBiPZBOTS0ZsxeSgDhoEKU4Yq0Ym/hrndMfwvxrBTddaeNlf/opml93W3ekVL9p9932+eFpKWHXsupuMjf1hwSqLr7x6oT+xq9/kmrHLx64bW/Osky9/3h/P+4+TX/B/v//e0e8bu2bx2C1W51feEQVmrQWrLOy5tSKMslwyegxTdzGqAwECO/qSaiIQkNpEDb1HNE6/5JHACbjL0h4FsB1HXnHXf+8lRUsK5VAM0USAsG00ySUyUgmgdInPFfq6FwKFQCFQCBQCTVNMHgqqxOFGepDWdprxCU+6/FW7vv6sT95hwZLLl4z9rcdnlT9ybTW2yq97f2Nrcyx5i5te97crrvzgoWv85sKFbz/qrz++eOxDR4wtXmVsrZuscsUVV1+3BPUKcdG5rhVClBKiECgECoFCoBDoIGCuR//yRMCxgoPou++++xZb/XXnV9O/txsb+wevkj3aVS6jfxeu9ht/q4zd3OGRVcb+cdM1Vj/1+Jv/5sK/v++Ev//wF2Pvf/fYmibK11w9tuDaJYtX7fm/GuspvjmpAU2BOxBWcAgCXqq0/pC0iioECoFCoBDoINCdJ44bWXsmWR551lprLV6BqGY9aqOJ6dhdjIkzNlLlosTlyofuBaTGWsv7TFdnIZCamPD0yA8OPnwtuTPpx4CfblxBPPsIs2YnRt6UKztiBOhbUuTxGOYec0WYiC0JgVNJESN53RNA3wI35L7+f/GqqVAy9yW1R0ms5bzt8HPEtdBXvvIVuZSoaBnVgrR5BEXkEU8S1WxMBOCPQDxiBEnCiq8rMgiITBKet7vd7RLDjiGyEQuEg4CyEi+jMPdDiiBqlgACRTejsLrwZ/S///u/3CEpVC6ltAWOVlByrSz3iK0uPChxQXXCCSfwJ8XDGkdpPF7xEqWB7nSnO3HbZGh64IEH3uMe9+CPiZnIxSWTZuJ2KsjL9fvf/57/Mu8SL29JguQ+++zDT5bmQ6+g//zP/+S26b3vfa8flFcX/toLjAcddNAZZ5zBJROyX//619rR24IhJNF38QR79xH9fvvt143hvKz7+IMf/CCPMpKHL60PfehD3hwCWPfhrIpTLU2f15h4Rx11FHk8ujiQcmbPaJwjKo3OiZUs+KiyC9sIo17iVWq99dZDDxx5uZTiCA8aa6+9tuLEqwjO3jFV5kGMt7XPfOYzvMjd8Y53lF2qJKVAm28sHqy8lkrkWgslybnlcnqBqMBBqY1E4iPmrLPO4k2Pfz0iyYJeN8LHVk4bote++Ou+ZFSpFAGHsCIbAXBLpbroVbgQKASmgUBfN+WxXeHmsf3cWiBJfY8jSp+IMop1oox+6UofTBU/GNlihmZpqbMVUKNZLwhP11AJJ4ofSjwiMnxmhdvkmdAyRFpq0SFbqvAjCJI0i+1C5oiN80TiTRQfYSZKnUUhlwpIERQChcCKiUBfP+CxXRHYY+uCWiBJfY8jKjgRZenfLmhQmgioieK72ScTDp9Z4TZ5JtFBSy16IlXV6jXJEr2xLcsMA0pshU4k3kTxKXqi1FkUcoZ1rOyFQCGwgiDQlG/rN2auf/u6GrNXzDOH1bkNTmZb0ZL68kJpMKYLXZijSaCbNNXw0NIbEyOH0ZI0ysHARBnHO/upzX+bdhgshYSDkaEfkWswy0QxU2LSiFtgkG1r98GkxIzI280yEbxdmkmGU2Je16WKN5Tn9HINZVWRhUAhMMcQ6OusPLYrNfXY+r0WSFLf4whkJqIcMf+VZagGJM80ChqRZXpJ6ZOnl3eiXHhOBNREWaZHP9VcE5U+yfhJFjdCVeGg3SeDDzKvzSQFG03WWE37lRtRo9E8RwtWqXMPgXKANffa9EaukS5mdM+7VIIbuQKzWnw63C4ggzFTKnAEeuGMW7e4KTFfKvGI0pead9YJCDMoDzU81AQw66UXw0KgEFjxEcggXkcxYli84teChCqic+v27So1KPkgQTdmkF5MZrzBp/GcKJftpJLaRuehDGcYiX+r6WAP38e8CSx+Ipn7sgw+jmYSfDCfNv/BEmcxZuYvtuq72guw7KqplFS8FdENjEuxlNHjLOI2mhVhmmwjKL0bM8d/BH9JkcTvzl5td+c0xCi3xjmjcVt2qd1G1/p5AdJLaBrl2rDeGk5AkitkCFweE2jv2DhJ75aMAk4v2BCfve/aHb1HxxIEQhM+knBOZMoS09i20jFMJEoELklJFWOTfYvx6LLFv3fwZsHCxUt6zJ2RkH3J2OVP2HbMl7T33nvfY94ztvoaHF2NLb7mJlJQkg0fhwTG/uREwdjCsTVutvrN1ljj8s989oe+/r3XXm9acu3xH/rQf9vWf+nfeMlavOrY9b1B+Lv3ihj3h4WVijuQgDjvubsqoJEUyS+77DIxruSiktCEiTsasBBbAKuA756mGefQ25+q1oiVkizj8VO4KTrUAk0nEilC4ik8BXZFWggUAoVAIVAIDCBAm1CI22yzjSN/b3jLHU8+fuyud7714ksXX3HV3wZo+eJY5W9X/GO1BWPO4J346VPXvuvYQQc94o+//sZnP9tTebTSgusWxPvGYN6KKQQKgUKgECgECgEImNxRvi6TR34W9thjj133us2nPza2zh1vteRPS668etwB1jhSPcXqYrMZW7DaKgv/etXVq/zjH6ee+bl1Dhg75JDN/vQ/555+es9NZZs2zmF4QRE05nAdZ1I1rwl8cCg73kxgrLyFQCEwDxFoyoU3n9h1WXEbDnpXj/QsS2yzIcfYKwm9yGbxjn7vKuX0zI3AY2LCP5bebq7GufXqXW4Rxt2FQzIKh39M0yITSC1i+kYsssuqWxAOYdUliISSZHQn9mBqo1HWRRdddMghh2y55Zbf+973+JlKFipJkisiiZRFpBUodSdbeIp38VUkUmqEQSnSXbzs4ZBHGa0poGxgjjPoEbt6EC/0bYzrPygiRiqe7PyyhI/WDPPcx/P1bhtttNHmm2/OMzh6YrfUyNnIVtIA91WaxvXTn/5UFbSCu6p5GS699FLhLHP87Gc/+9GPfiQSStw2AY1LJlBA1VqSSI6WPELSY8j++te/8gMlRqoLvVwNPWtAaQ4LTzhz3iQJGYa/+93vAmYjzuO072RQNN9Paei47sJNcRqUtOGs6NaminbJdcEFF4jPFbIkJWzQLsmyKf9oqTWG3IdJFeD9CkOl8Esl1cvGsRcHVSAlCW9ZfHKhjLOtyKYh4MYRmIw4uOATYneX+LSIpEgO+dTI6phIP4Q0DTnDBJ4CHslJGOto4eOudEwkpTp1LwQKgWkjkN+R31qXQx7zA2zxM/zFjcieH3UrqC+gs+qLyeNE8UOJl1FkH0RTKmUiQPraYko8J0m8HIoYlKRbaDfcRzkiqY9yoscAG0UzEc0k48OKSKNf0aVymyj7RO/AUhkWQSFQCMwBBFoP061L+kD3bv/QDXeJJxkekX2i3imcJ9KzE8VPUp5ZIeuDaEo8JwIk4E+J1VSJl0MRgyJ1C+2G+yhHJPVRTvQYYEv/ToRPxRcChcAKhUCfLkgf6N6N74anKnzyDtWzkoZ2uU29to5U9ha5VAFQprjJZxnKP5JPVHeST55/n8wjePZRzuRxKLZDI2dSyuTzTlTrxmGEbEvNGyYha69N4zyNQCsx71Lu0+Az7YzTKKuyFAKFwMqCQHqYvk4vj+6t/1GdbngatRuRfXTvNFTBjeA2DdmmnQVEQ8WbDMOJqtDXFpNhNVWa5VDEVEVq9KNlA9pogvBB5mo8px1oTBQ67YYeIfC0eU67RpVxRUbgnxuGVmQpS7aVCIF0Ye6tG9Lp3OxmN/PdudTCY1PAmbTYwrISVXCpoubUenBoILRcYiS5BpMaTQvAB1YNLvHJdatb3cp+HY/2J7k75CaQ8GTYNv7TCNjB41N1LaPNOnbztMdlEVA72+YGOatp6bNBWCqmEJjzCOgY/fwHO0bdkW4w8U3RpKNIl7usu8dZR57YeEbsbn1TUFIH46VKci21vpAEFBXcKFsu8fpeSbZjtnohQ9BKlz16J13xUEla3skEMFRi9onajaosG0bF9HFu0oZn3+NkCmo0rTpiGh/VEc5jX9EtY18gfGRpgT6C2XpssikoEvou7te//vXjjz9+8qV3tapcQCbeJGs6jYp0ZU52wjf5E5O6GCvmm7qydF/LaRQ6kywRZgSHCL9UshEcJpPUIALFdttt50vdu+22m8aKH5+WOhlWRbPsEKB3XNpIi/gWvSGxQbKDCgauOjQx6daaAA7t2EaPXows7qHxA8xv0AZ6e+79FtDgkxG+IyXeNxl1uQJ+IArFWRbhME9MK0ggMXlRcUOpRBeepioJ62/xdI2X37s5/IDysqv/umBsyZo3uS3KhTf9n60ec6sPHbfDUUcdfOx7x/i5WueOD3AAY41bXEtBrLrkpre5xVq3vvXt/f3+//5wy1ve4h9/W1sVbrr2wquv+t2Rh1/9wPv+/B0H7f6TC3e+4LtX3/0uY7/7v5uozpXX9U4ipXZKJy15lKVSEIBexEOjFi6PuYdSltSLwDCHYcCRKgZx9IgsyJJxrTVv5qPcqenCVXqndHJWwR29jGpNHldaR4zLI/4yCuCMBjd3DSQX5i6lowwfgIgZz1q3QqAQKAQKgWWIAIMY7jrkKAhhA6TWA9NlHjPEjRDonWET45ILZZQ11dOkROPS27tEUkm3ve1tnU+LngoZBSe7GKnhQyP0tGHnLC4ayiJWsohE6URZtLJIiEMfH483ucn4VOu6u6Fc/eY/XmedsWNOWvPTp97tuCOVNrbm2osX/Pkft7/dHeW98vIr1ltvvbElt+j9jfVOJ95k1V5dbnGLW1OSn/zQXx678aL3fGDxb7Y7/7JLxlZdfclf/3IHuuyqxb+VV+koUy+PMMTdncyUIJkpNbUgc1cnqinijChSd/WSpdVdpDA+wMccH+GUpbh2iXEFH1lctDZizBuN1sFKQ0hNZGgS7sqsLGQKgjmx3cU0PhUoBAqBQqAQmF0EKEoMdc7peIX1ulF2wrp3j00viEE/VP/qsaXmonFcNzz19K9ZMz5dHY0g+iXTMSWiMU3z2DKGhlJ2rN2MkuJQusiuLhMjLxo6UQBPMYi7+nfVtS+8153GDjt2yXEn3+a8z667cOGvr1182cJVL7/lTW8ty61ucUtKasHYLcf/fsu/853udDeGLGOGm/z5z0cffOnG993w3Udf/fvnfP2//vPaK664amzJuor4v0t/Apxb3/Y2OETgYNj0JiEJQyr1cjUyxB779C8B0Hf1JhrZo5Rxlj1t0cUQIMii/RG7Mobp8hEjb5dPaIJzn8xYqTg+hOwK3FqkAkEGRKAzXCmU6pUoBAqBQmBKCOg2daET9Z9RQBjSR7rZxjmdbTreRKJsPbCAKzNNgcZEGLFHGSnZPCYmTFoR4ZaMfWSJ7N67BBRuewxnj41ttyxhSa6UFQH67qqQmKE0Il1KNKn/xje+YYDxxCc+8aSTTkounBNI6SkIvdGXGEmJiRitoDwiE3DP0KVFyivSlXEgDpJyiUwgnIXDX1kJkNOFDEGPxQ301+cfG3vgAx942GGHGVLKMpjayFbSACQNjEERG4jxrToCRHUkpcruzC9GgKkjoMQY17UqJ3v3UV5MMoSTKotSuuj54QRwrLwhyYtSoEvWeAoktRszyTCGSkmlwhwrgcYwgciDZ1cAA/sU3Yi7hYaJambQi8bVV9NAgbnBsLwCrhSRmLy9MkrFqlt6o+xOcMYL6RELIFa1lN7aqzvTEdkdkCdLoAiHlCtcVyFQCMwcge7vd+bcikMhUAgUAoVAIVAITAaB0r+TQaloCoFCoBAoBAqB5YPA7BqampZPgBEs/Fv8iEqhCb3AZKSaRfomXguMkLOSCoFCoBAoBAqBySNQmmXyWBVlIVAIFALLAoFygLUsUJ2nPH107re//a3NHPe///3dbRyxodymnB/+8IfN+xVoTGvdbe940IMehN7ODztIRN7+9rf/1re+ZV91XDutXCDe+c53vstd7mJXvVr813/9lyp3vUT11SXz+bve9a73uMc9fIBO3R3bBkLbvRR6GPqmH4K+7B59UA5uNuUkyU59AYOqyVgKBrlNPoaQ6qWaXJPYBmRDvIvYqYsXwHn4s88+e0TdJ19Wo0ztPCoIUC1eAHRQ6sbUyLKLRoULgTmGgJ+8Xs5lc+cd7nCHbPHUL9EaPtfZFA39IlLddRp3utOdBGwbdaeP3H29s68nEbliXmraFYyiWXfddZ2qUhHfJqUxpcIklW2UukEI5FCTzlkWBNSx7hpD9LmQ2Txq3/DFF18cQMQ0VsmC3s7OsMJfwCMy8a4W33JFBgTdQIibeCMC6c9b62DbODswpi52bKsOmT//+c9TjoSRBf8RPEcnyZvqIBMIMiKVG7Ytdal8QjATYUYXEQkbIArSfJ/61KeMN4477rillq5qqSAOtox7l8T4gfiC7g9+8IOlZl+qbH0EXdy6MiPTauuss86jHvWo+93vfl5mv2Kf5/35z39+2mmnCSB49rOf7Uu8X/jCF4QJ2bL3FbF8HlVEQX3NmscW2a3sLErV+ON54oknPu5xj7PRf6eddgIgWPoGP7NYbrEagUC3Udqb2d4Hakj3pbOlcfS3+dFRTI0ynCkszZdXS4wAGs2qRxXWsxn35rvQGQBL0unJha2M7dyCcl2NuUD3Eefx9N4t5WLuIhW2OUrh12fa5ZvYbUKBEv+ehIuvWbig523t75f//t7rjO25557nnnviEUf84x9/H7vVrccQkPOvf/sHTes0hr7IZ657Ql43tuTPfxlbvPolv79kncVLrrhi8dWLx970phPvee813/Sm5736lR/97W97/JV+7diC3iTx5jfx6e+I5042rAjZ5j50GWlxVnFhSdCgCESGRhYVB7srUISMhCjdw1Z4wVjvK9/hcOVVl3Noct/73lcqMUSGTNjV/XElRmsigGSygwVNA9YREW1HJAI43kCkHug3wC5jXYVAIVAIFAKzjgDVgKcuV88czahz7va91ES69xRNO8iiuxZIRy0ejX6+yUZroIk+xQolm1tyNZqhfOggPLs0ODfdIR4TVyNIQHFdeZSIz/UWvut6xHe5/diBB+733/99xitewcdTr7IGDKp56RWXEuOaa6+69NKeH6tcjGK40Xf4KHrxgrGdd37bGV++63vfu+722159+eVXXH7t5Zf/jSP7v6/i1OFqqyor+i4YYgguzKNMJakCsx6eN5TQEyAxAuiDthjh0Ai4wgcHrNI6ympMBDy68BdGgBtWHgUamXgxOLTI0IRAZDIKROYURJiwbXwqUAgUAoVAITC7CESdpR+eif41JWyCjSvJf2rJqBKaglJrNEpERsGJFKYUoimEG42k0FANKKmwcI6+C5n5mkhJCMJEcdRHV/+uvfrY/vu/4Y9//Oree1Phf8KWMOivuLwn89VXXuVR6ddfSxZYC/vblTTs2GV/uczc+zWvOeTz/7buoYfe8xlb/vn3f7py8difrv3DtVdde3Uv1yoLlR5JgiHm+BCYkCSJ/kVJtpC5ixcjSRbhaOo+vRkh1QtB8HEfr9c/nULK4lIRPCN8aNxbWUY+yhrkgy0alDJ2ZQ6flr0CQxGAGwCD/FCCiiwECoFCoBCYCIEoROqmqxm7xNHmSe3OH6MuQyk1V8sY3TeoxZAl42CJklr20QGUyR5WyShGrujTFkjkIOdwQDaY1Ff0RAR98Z/4xCcsrcYmj0PKFeiiFM6t6EbTSuzGhH/uLb5baCK7ZMpqlN3UrjyNoBUaDt/97netgUKvO2hpNCt1QJVzWfE3ADN486jWLoFWtcEYSX3Nh6bRCzQ+iex7bJR9ucQPxjTimQQa28FAY9utcovsq+ZQGjwbWxn7HmXJ69dl1UfTihuMFxMOjSaBvI1demQuqd2CBh9l6WNVj4VAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFwIqPQGxfk5cz9JPPtazpJy95URYChUAhUAgUAoVAIVAIFAKFwIqGwD93Eq9okpU8KxcCZp4cFrjbunHAAQdwbmWHBw8LHnlx2myzzRzWkmqTtF1WNna/7nWve+lLX8qDibBtIs4kX3LJJc961rMc+V65Kh5pt9lmmwMPPFDtnCQ/+eSTHRTnBmt0Rbbffvtdd93VsTd+Pewvtw3ayfluFpvyHQs/4YQTTjnlFA5Kuvv1kcENyG9961u5jdhnn33Au3w2zTh2rnZ3v/vd3TUZsXn+eu9737vpppuqvp30G2+88YUXXtityEzC3hnZVc1LYgt+H6vsHfcibbnllmQ499xzbRHro6nHQqAQmDMI6BD0lg4qC+iCeIRxaIonQRXUVeoJqRu+F3WevARy7rPDDju8/OUv5zKJZ4oc4PnSl7603377/ehHP8LBpW9xrcj46PpSZUI++clP3muvvTgPUpdjjz02DrAaIK0iqRp6CFA0O+64Y9xzULiOWum3AwWdggBcv/nNb6itT3/607RJF4oonQc/+MG77LILz0SHH344n5X6+XHMen0yLe9SnLBI4WRvwHZFktQehdH0xXjER7xyqcXddttNTd/97nfz0CRpiy22ELPeeutxmcT5F48tcdtk33MrF9k0rkilaHkV7eoySV2MW170ohc97GEPo47POOMMBKl+l3I5hCMqYaL73va2tyn0DW94Q+TRNInv4tykSiSsZH/1q1/ttyN885vf/LWvfW1zgNWIZx4YlAGS3sPb3e52T3rSkxR6t7vd7ZOf/CRHZuThpe6Zz3ym11uNuNE55JBDPvKRj3CAlfdh5sLMhMNgRXBL6xsOveY1r/FC8kt10UUXzaSUpeY1uPLb/+xnP+vn/P73vx+9N59suZaavQhmEYH0CRgC392Pzk9JpMvLnA4kv0Tx7WoCJKbbcInpOuDAJ+2bJDx13XjKpZSUqBt3SRIT5sKJaWXl0T2i4ibcZENm8uVlNqFIFoW6rrnmSu4NxSxecu3V114m8IKXrnmrO37vOc/+zh97HupudtmfVl1llT9fe13P965Lj8mpxtXXrOHP45LFa6+15qqLL1/w5z9ddctb3Pb3f770//4wtuMOx55zzqdeu8d/7b77ty4bd0q12sKbXLP4aqpZFlVoElJJRvtinPoQAIUAVdXOCTcAe2WP5w0CueOjjuJvc5vbtFkD1XTdtdfHQyll5VeccCtdaviP8+7d2qMAMqUIJN49hapFhiVilK4rTlt4rKsQKAQKgUJgGSGgp8VZn5yuWDj6zqPuOsou3XsEEBMFka6+0YRPl6blQi9X6EPg7tGFJvcExEc7hEySvO59udqjgFTXIJ9rrr5Vj2zVX7vtuMta93/w/2612fmLL7t+8NnoaXQEl1/xT/uYQck1Y4uvubanW6+48oqFC8auvGLsZS/49ZlnHr7FU3b5yEfGllxxmTGsXERtl0dXMHQXrwgxEW+wFolBkwuZLOM8ejfh4J/sYpClrEYTshYZdRwouqzCuS8Gt/ARH5kbnxREvG5BFS4ECoFCoBCYdQT0z3i2flhY36sTTv8/ef3b7bHl6nb4uCW1SyOcR3e6o9F0M46T9BRBKIkqEBXTcEiMx8QTO8TXXnuzcZrfuO/yurENNv7Ds7f97t96T38zG71+yrygpyWvuPryhav0VCcx3BcsWUDt0oXU79VjS0zO//KPsR2efclppx289Xav++AHx678+9+uvHbJwrGeqZMFuwkc0BSAR4Rxdw3KnHh3WXIl7D4uc+8WGqmNLKmDNInpST5errxNpKXywV8uZOqOj4wBQcx8vvJCQgA+8xmHqnshUAgUAssUgSigiXraFt9VapFHUkttAUlhOFrmydAMchiRqyUJtHDjMBjTkqYXCEN3q28GGMzshiuM/4PcGjItMEgzw5iZcP7xj39M2xJg1iGaYaVmMbvRJohU03J/RmvCg+9zK3GpUCyVoLFakQOzWIuZvIGTzItsUODBmBUZ8JKtECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAQKgUKgECgECoFCoBBY0RAoB1grWouslPLYwNEuW3Ne+MIXut/+9rf/6le/6s59xn777cfhgs0f9us4xn+Tm9zkiCOO+NznPneve92LBwQBWWy94g1qpaz/2Njxxx/PJcFBBx30+te/3gYyaIyuCJr9998fCPe5z31k3GCDDTjMespTntLNFZcQ/Gq95CUvedWrXnX22Wdn21OjeeUrX7n11lvbBXX++edz/tLiRwQi2CQ36wzls/7663MoY/c/1w9PfOITnVL46U9/+tjHPnajjTbiGUS9fKJwaMZpROalkpHAj3zkI3/1q1/9+te9o4DdC4D77rvvHe94R4JxwuWz293UChcChcBcQkAfmE2ffGHwofOnP/1Jz8D5nS5U//n973//Xe96Vz5xr/fgkIJ+4ddp88035ztGHyuL/spO32wenUlPuNxQVV9HpFLckUceeeKJJ+699970qSokcnAXrBhV0xtDgEehgw8++DGPecyb3/xmDrAoCx0471H0C4jQ3O9+93va055GH73gBS/YY489eHdKXnd8EKB/znOeY98tALklIkwcLbVyUfbppgjGiZUBAE9knGOGZwQbAZ1KkQo37pl43eKeSWNxcagiZ5111mmnnbZo0SLa1lCBz0d8iJei5fKolBHMR9PIiwAaGHKwldoZq+ScGwXHAdbDH/5wGH7jG9/w4hFVFldKlDfhlLJUYUbIOTpJuTlSyGMUqQwDQk/g5sVmKIdxYXt1BOZOO+1Eg7/xjW982ctepjVD361RatFqN5ThlCKDj7tXl+vP008/3eilz2mU1+zQQw9VO7Jxg4V/RJpSQcuBOOAo6KEPfahfDf96HHtpiFkc/KQWAa3VyM/WaOcd73iHji7O7yTNYhu1giowJQQ0kytZWq+YRy9wrm48YpHd7qKPxmMOAOc3i5Uf6Xgh12cUj2G7uqUnssnvUYfmLsYdZXsUoyCR7rpZJaYI8cKKUKd0b1tvvfkzn3m3d73rw7+5hCg8Z6wWytVWXWvBwst7HK7tuTbmporrSdl1m7+//Pc9/ouvu/zPlzpSs9rY2EUXj9HOu71h65NO+tZZn5c4dt1ih3V74gkHDYFISMuIyTlk/ZVfFldWYlxolCjQpBWgCMT3XcHwhshe7cVcX+jiHh9ycrYVnpgkcAP90v8Pfe4RNWE1CrCKXDqXoigECoFCoBCYLgIZwepyXVEEURAesTRzcU84JYihrfTYiMXrtAdpEGAbPghQinFRE01MBK7wQYDYo9TIEDIxciUc7eAxZEvls8bqPWXa051jY9tuu+2hhx71w+/1wk2tRDDFGflTvh57F2U6/oeSvrtmbMnCJWO0IzfCvAnvttvLvvjFo//+x7UWjC0YW3h5VCcOqZe7i4TyQSYFSXX1ydxikAkr1j3hnojjIIihCjEMgVRMXNH4IVOWS6THUHpE6QqBuxgcpLb40IQglFqQzGhI4u7qcmisKlAIFAKFQCEwiwik907nrKPGOXqz9cxiur3xRPr3/7N3HwB6FdUewL/dTadXGzbsXSzPigR77wiKIgootid2xUIAUawUG6BiAwuKWLAi3WdDwQb2SlFRaUlI3d33u99/M1y+LWzKJiGZYbmZe+bMmTP/KWfuvTPni5wo1p7hUYhNaptHXDDtu5Jv5hfpKavhWMGDM3Jc2zYIA0rseMwHCjnT+mN/54tzO37MMcf98Nzm+bdvsM8DaayMjIoe6JqmUHKdPWv24KKFni6Z5iEPuZ3OhRd2vIk94LV7n3HGJ//wq83YX/4MUjVCRASiZFe6K03yICk1GqKEzRVRSBYRgLvK1ca54WiZZtndJm+PnBj6aB6eyctp65y8keNaStkII4F0vIqnpTZyiMYDp9IrAhWBisDKIrBGptMiRKTER2siqW2ORzPcIGV09lJciRDSjheZo/OWpMlHiuQ8ONua5XurjVhjSijMUtvxNvOY9DGJydWTdIOV6uEvRaMnKddC3zAigaV821XHrM0SSZXDs0FWf8NoxFqLikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBisAGicCN8Z3kjVHnDbLz1EpVBCoCFYGKQEWgIlARqAhUBNYgAtedMFmDQquojQ0Bm28SUnFbw22iclyZF4lvfvObiHvvvXe8O2XTufPM+H/xi1+cfPLJZ5999jvf+U5br2683q9UMJvGLrigezptEs2fB2yeQbgj4cnFznuunf5y/fCDH/zggAMOePzjH8/HE8cfT37yk3sEn3TSSTZCEcIZR0/S6FslplBJd7vb3T772c9yejKabQJKNl2pqeajMIdcZauc1vzjH/9ohxaKvXQTCFnZpPQrx9T5feCxpSe7MxKXXXYZ6OjGPdZ///tfkVLNHuZJ3nZxug6rSeaqbBWBisDaQcCckILMnOLmHGbly1/+sjgfT/zRSDVBOTLkCIr54e9//7up8qKLLjr66KN5v5KKvwhZOzqvZinRNoeyzPb84JS514yX1NE1Un04qCxnIqeffnomcC4CZafPP/7xD+AIp512GndIcLvlLW/JkeIee+wRiPj0SeRf//qXE2tm19/85jepCLGJjL5m+nUOSoRt4uqIKytsbuMbJfFcEUfP2LEgdE4dzzvvPBYHvxUFys9+9jO3HCSZ/BHVsVRfhEABfZKhzS+742o8TrpGiGuOzJEGhHhX5Hir7EUupRT+RAp9KiI5WMWz2EEHHcQV169+9aucl5tMWdQLSsA0LoAsV1ZlIkEycsTbt5MRHh5FaH3NmlCQIW3bbbc1TnUJTtx0M96veBODtozJYlly2GGH8eqlcUOXK/VdC8BOvo60Ujv8lj1BiUeqNe79ivwIj2IB5POf//z555/P16ohWRpu8ppXztVHQFfU+umQ+q2+qplMiQnNedcVbo/wlNAutxB7IngiGZ3MMrmhG+MKCsU1ETyC1BJp00PMtZtjKJy6DVVTlqtgHsisq3T8mDvD07mlGrG00zuve8s9zv/Jtad8Ee9AZ/msvoGr/XU6S7kW6e+bOTQ4MNgxVQ7PmDZj8bXmao4Xlw50BqZ1+MfaclZns/7OrGmdObM6nfd94P9+/JOzX/f6J2WOHu40rq+aw8F8aq0I6p5aINDKbeY3UKOUCqpCo2fXstCZ5uoV3CJJ3JDEIxLKcCeTSZMrIULkFVbQRv6Vq4SSpCDEctuOSIqGiOJCIm2eGq8IVAQqAhWBNYtAJmoyRcrcm4l6vOk6zBPwFEMTgbnGmsiVEMOx4q5ZFQjltvD0UMa8bWcs8S23Hhzq/Htpf+e0c3f75c8v/eDRfFbdptO5RVt5Vi/PIx6ORiRLXhGGh6YPc7TRGRjuNF7A3vOeP/3qwrPmHXKrvukLOtPnt6u2IkdjvCgQy0ug2zGh6KGP5mFbZadVksh3S7LQLndE5xX/RI0VdyP/IsrlxrXoWXhIi/CsYdBzmyyFv0YqAhWBikBFYI0jkKmYWJHM7YmX65gltnOFIZRy7SFm8i+pIpnhQ0l8PJ6iSTtLW5R4eArR7fSZ1ywf+k9nVuf8i/b98Q//+L73dO3v8ltHscIfA1Se/kpqExlmdmd4/h3o2t+TPr3kT3/5yaGH3Xp5Z/6yzjVN+sqEolsi7bq04z1sPcwKHZOhTZyApxTU5inECJG00QZQqLt3Nd5Re6Put4J8vixoSA1DodRIRaAiUBGoCEwRArHOhLctbcpqJ/WUzpAlS+EMc8mC3raYN4p4Tx1zm8dzVzYrn1zHZFs/iQX2qNdu4nZLrZ/Kr5RWqaksPVUeLaQHhDV4O7qsSqkIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCKwbhFofuS2horAmkKgbDmyF5y3CyegDj30UP6b7nrXu4rwEfC3v/0tZcXzQimXA6wS3xgidpvdYDW32morDkS+853vPOc5z/nYxz7GMQf/I3Hgkrz8Rzz1qU+9/PLLuQiZWFq2TClURBtdeOGFhE+cZXRqdFbWE57whB133PHcc88dzVM6wOikVaCQRmEZbSK/7W1vO/qAgQNvgp/mfshDHsLHTWqXXCulSUrJtei5UhJKrhqpCFQEpg6BDNKMTX4xDH9x5oNHmFvc4hYPeMAD9txzz//7v/8zN6I7apJZK5Eyoicz/U5chbYaOEtBJVcPww3SwzBerqQWtXOEpri/KfUqpYj0EElOrh66Tc/mVSeoOdbh8eqoo4564xvf+Ic//OGnP/1pMcrz5s3jJIvjLdaHnB4J7ULbcdrmFFCIShHoULITJUjFWYji2FTta1/7GudcThBpzUjg8cf+bEntishY8mJrx5Nr9FWhha1EwsbQ3OY2t3FgKVBLVVzivIZZw5xwwgmWMbxH4W+rjTN1aUdGF736lKL8s5/97C233PLEE08kk5I9FZlkQUVn/OJCKjtmdqmTKQUPfYoEuYxTS0GUQw455NGPfjSnn+973/vSu1wNYUkly6mnnvqRj3zkla985ei9+EUBkSJfZDJatfnXSDzH+y+55JLnPe95Klh66RoRPqYQft8yND760Y/qhw972MM4IcVZYBkzVyVOBQIwF0j2IMMjXilidFdESSg8iYRIiAhKbs1shdLDL8kYETL7JbVRoqtGroiRUG5RxFNEEWiMt4d5TEBSceKX2jftuiH24pfc9OY3v/n7jzjq8r/jmt7fV1wuDqm+LEPDHmeGjGWlcxRoNF/buba/088BFuH9g/1mhCWdJZsM9C0eHD722LPe8559X/nKuxx51G+GO0PT+2fwfdzI7GKgAEKKeokYX4g6f5QM0QDM1IFI55KrRNDxBNhk7Otcz4MGIpkYZImQXN22AQwRpYTCLCLgF0RUVgN1aY3rLnpG1VDqtSJQEagIVATWOAKZdc3PJvMIz4LTtIyY1PZUjCKEgkEWt65tHhIiJAIxmNLbFHQUQVJjBLtPMW7F22xdliZj0Q2lzUCO2x454b/qqquWLh184Ys7nL+/6Y1fXHwFXm9KGzvIcgkqqNxcWUkBcVrjTXLkDSOTNL0zHb8wvetl8phjfnf00U/bf//ZH/zA77BxjkV/ITzl2q5FGKjUZsstCs6E6NzmQSewoJq2wFAoUnObXCoii1S3yRt9UAh3RUx7hadoKxL3ysmFJ00v0pbT5q/xikBFoCJQEVh9BDJ7tydb0zWxmYSTasYuBaEIoWQ+dys1ucKWCb9kMY0zkT08saSSinUIT6Qlb3hKRpxCKT08oYijk0YlFLeLFi11feELO9ttt91L9v9Y1/569TcTQ0KyT+bq2ZP9nb9g/jHHXPme9zzpxfsPHHvMn4OAsgo4KZ1A8qOMqxANEUtZhShLAgo54m2eEFEKjzhi4ZElokaqtGK91OYRF6LkaDkoSYrOOC1CivyNIRLoAoLr7W9/+9e85jW3u93tfGbdbLPNrOK+//3vH3vssd6re8de3qtvDMjUOlYEKgIVgfUcARN4226u59quWfXUfc0KrNLWOAI655jN1EPcaPvwGge8CqwIVAQqAhWBisDGhoBFhRd3U1HrvCGZCslVZkWgIlARqAhUBG7sCEyd/R2NTM8LhNEMlVIRqAhUBCoCFYGNBIFqfzeShq7VrAhUBCoCFYGKQEVgI0SgOsDaCBt9qqqczTd5oWYPtAh3Er/+9a8PPPDAr3zlK/e4xz1e//rXv+xlL0vxGKZKjxuD3MlsVLLlfc6cOddee+23v/1t3jfucIc7cMCx2267lfpd3A1u88A2wcdFxaXEXP008f/8z/9wo1BETTLCBQkJPIBccMEFOWkwOuMEaoxmvkFKFHb8b4sttrCPfDQ/+i+7QRK44jUDIMk4mn9MSphzTQfuYWsTV0pyj5x6WxGoCKw+Au3xyJRssskmvOq8613v+uQnP/nwhz/8xS9+8Zvf/GZTgfkqZRmz5qVVnppKceSIl2O6EZ7NIuHpmRwKsUhIlkJ3W+Ii5cBYj5xSUKrQPmJUOJO33MpS6iuC3k6KQFdedZzJ4fJJxCmd888/f5dddtlnn33++Mc/lvlWceecc06yONoUaW5zzCmmXJz+knJN0XxfComjRwfXqFp0KBUPkjl7JpUTLiFskiipTRVEQvvMWGEoFRQhU4hW8ub8WCkapSgvqWS8173uZdHCo004Cx2z8JNuKGq365Xi8IsQnqTCiRhRkkQSTxY8bUqSSsaeiCyF8oIXvIAjS6YvEgp94kiP/AgM4KPV7hHVk7cndbxbuSL/aU97Gl9R4meeeWZcdup4eh2sCj6E6E4f+tCHOLVsTu53+3DKbZeeeJRv01HUIpqgJynXol67CQpRpKf6bZXabG06yapj1LQZEo9uuYbSaNM6mohYVA1D+kwh9vCnXFfhBz/4Adeoj33sYznAkovXA6D1CI/Mep0iBNJYaQ5XTZC1seJMINbGZip0jeJq6tCsxRjhcSskNRqGhxx0wl0zLUslgTTDQWdDj6MHLc5rYfKWK84Sb0fkElBSoomUxfRkQRQKxVDIHxhYPn36tMHljfu/TTZRi8Hlg4vv/T+dV73+MSeccOAZ35zO4eTQYGbLHLL1EMeBxjCtZ01bPnv2ssH+Hw1PW9BZ1tlh++XXXLGl6kyfNqvRc/iagaHBIUh0Bi/8VedzJ174opfP/c6Zv7n4j7x6Ld1i0+0ps2TpokbDob4Z02f5mz1LhsWq6UnRLOEfVwz0VE2SIeaK4lZwqxYiihNHd3Urou6qKdLoo4ZLZywfXM7lFr9dM2fMDk/kyCLggUlmLXTBraBZSYg0tzjpSbJIg68mnt6/ePFSR52588KAkuz1up4goK3TE0brM2ZS+sZ4WUYL6aGMKRNPoa+m/J7i6m1FYONEIIagOwePTLkx0JmZpRpo7dkYRWBTwgA0txmMBUBJAjkZ/rLjQSkMIl2W5nkkEcxhaLOJyxghrFtT1sCMgf4Rp1SdvsYtSLKPlrN48bJb3Lpz+LtffMwxR//qvM4AFxrDjRfU/oE5rt53WSSkFouvXZRb15mzZzd1675inTl75swZM62ju2qLdM789vzvfeMmB87b5rSzf/eHXzawpPQuAwPcALjJnM0WL1o6Y+Y0NcozFx61GOHp/lPQiAScKDDEWdgsM1htdKlCUjG0oXYbCcmVWxShyCm3oyOFBxRRlXBsMeKscJYNha1GKgIVgYpARWANIhDTUCZnks32rpnMYxeklhJRhFiu0N2at13bPEmKsSDKlC61zdM8zXV/BcEVg0LDI17khMdTIZ5cw1wYIjPZyRgebmnS17nFrTqHvXP/Y489/Jc/aTw69w0t6u9fMtzpdZEcaSy44F0MKerf/QNEok05yztD3/tG5+sP2Pwth+zy/Z/8+fe/aPSkTwGnn+fpBodGvuqASBLdonOgSFkhiodBuSjiQhhcUYQkiRDoGnrhwY9IPgr5wmgeFGE8Od0yGyElL1GYSxEbQ0TdU83HPOYxb3rTmz73uc8dccQRvIFzHe43OV760pfuvvvu++23n4+5YfNmqH9g8YpMGwNCtY4VgYpARWC9QGC0eSoT+HqhX1ViI0NgdIcEwGjiaMraxGndlt5TUwN2lfVZ2YzryeTQVns9UamnUeptRaAiUBGoCNyoEZgi49J+NxV8pqigGzX4VfmKQEWgIlAR2GgRmCKz2H5+3GixrRWvCFQEKgIVgZVCYIpM0krpsNaYp6iy1f6utRasBVUEKgIVgYrAjRGBKbK/N0Yoqs5Th8B1u42nrowqeeNEwFrfT936/duvfvWrRx555AEHHGATMKdL3CdtSID4pJe9/itVqck8CM2fPz8yOTs45phjPvjBDz7kIQ+53/3uV3yClBInI60wizz3uc/lA6tNucE4g6SU5ij7hGFlNZlQ2HWJNpTboJ89+tdRuzHOSvwb9Rx1iwKrYz6nqAo9atfbikBFYHUQMMazncKAveaaa0wOp5xyyjOe8QynTfbYY49zzz3Xbc478WRhil6FWTqzCiXbc4J4jhYX5duSx8zSI6Fk7ImMltzDULaPxBNKSc10lwNIbVWjTNRDbyeVvCKODTs1LeIH6k8++WQOsJzh+exnPwvDNlvijpMpXXDbxqEd54gkp441B5dSWgczNXg5caVVm7ldxHh0PHy1UJIplF1Ql6BR/DCmmqVLdKvbeD1ry48argoqGWURUvRznvOc29zmNvH8hZjsrlLD0JZGDbcK0vcAUuANZ5TMtSS19YmGbYGFDTEZk1riIilx5513vsUtbrFSLiyLkEiIZHUUicJ0aysQhjGvEdUWOCZbiIVNZ+Ab6KKLLjr22GOTpOMVnraEyy677Pe///3mm2+OOIFKSSrywzy6mdqSxdtN0E7qobfFttna+vTwGBqlU2GT2iMT2uhCBI6Z2ib28BOYoH9+97vf1Qc4Bv3HP/5RDvkXyW2Fa3zNIqAJCAR12mK77ba71a1udcUVV8SZFIuz5ZZbmg307W222WbJkiWmZU5aZclYizKcNaBnFo3A8CCaOeXCbLAgLliwQL9y60nKvHrJJZdEDgnolvEi5ESI67bbboveHgVbbbUVUVm6p0S3lKSwiUtFEEXIX7DgCgrMmb2ZIsKwpPO3gw565fz5F5900uA222xH/rULG1drHDzh3H777VXcaFXiJnMGDFjl7rLLdt8/69///W9nJjcd3UPLCprd10zFur+C+gb+dcopP3z0k25+5JFPfNrjT91mm61nzNwcXDQnmX8t1VQpvZoDLAOKcDP/TW960x122METZRxL4eRcg2QWQSqxgiRBQY2G3eVBYIF5IvRxwBg/PQWcEJYl+EhNUHdJGNJYrthIoIxSgEO+yoJ0+rTGg5gkmpCTguS96c22p2qY23KKwBpZrxAo/WRMrZKahnYdkyfEwjkBT+GcWNQNSqgMFYGKQBDIWtHMLITChoqYz1FM76bukoQefjNzGFDCk7y54hFM5oKhajJ32zOZo2BmQGM4cKIoGnMRhSI1K8M4wEIRRhi6DrDcmjracphj9mVg5vz3vOfNv/3t948/ngnr3OIWs5Yv2a6r0LXsC2XEXeXlzLHcNgwrjGBRo0S22qrzvvcdc7+HPvHQQ+/8wmdfDBaW9Lp6dR1gpfQ4wCKcdcMTu1nkuBWPzoSACIXO7bqP1KLlRyxlZZ6MKBTgp71Qog+4gFbKwiCuUlKTV65AWnhgGGXUxZNmnV0LMjVSEagIVASmDoGYM5OzkFJWzf62bQeZEZsJnzlwa2JnjEpFUBgFhkBGDOLh8czYw+Ox2nOlx0yGA49wncnrrgfYjsWL53vwjAMsDB69N9tq4VFHHfyb35xx7LHeN3Z22GHW0kXbsSxXXHkZ+SJ0y5UObcVK6T0RzpGpdtRRJ8597BMOP3ynvXb7CwbWTenh5BmTzIFpzcttxjQWjVaxkoEinLGJatFFvbG/KM2a4fp2s+CDLTxAyLNqkUN4FCBfIAdPuy1QxpQTnEmWC78rBahUvl2miA3+quJgV/Gb3OQm73znO/0aR3nJ6QPlW97yFj8s4dX6+9//fj8sIXQBGXk13X2fpC9t8CDVClYEKgIVgXWJAAuY4s3YIuV2kjqtLP8kxVa2jRaBMfthu5sljq1NbMPVljAeT5t/deIpa2IJU61Du/SiT4m0U0s8KrkWrG5QyR6BN8hfylo7EepFw/VNsbVT/VpKRaAisBYQqNPLWgC5FlERqAhUBCoCFYEeBKr97QGk3q4OAnlmXB0JNW9FoCJQEdiwEahmd8Nu33VVu2p/1xXytdyKQEWgIlARqAhUBCoCQeC6UzoVkYrA6iDgibEs7vP02H6GPProox3X5wvj85///DOf+cxf//rXtnTbYz1miTZn20XtyDGPCfe9731z9vi000772te+5rh1ySLVgSvbju9whzt8/OMf/+tf//q///u/dmn//e9//8pXvuLw+aMe9Shybne729mfzYGUHep+g/dOd7qTiC3IHHyIkHb3u9/9EY94hIj962eccQb3UnZyl/3oKY7mT3nKU25961tL8qO+XHr93//9X5LkGq8iYVgj11/96lf//ve/gfaa17zm2c9+Npn3v//9H/CABwDZ3vQvfvGLf/7zn0tBKv7Qhz6UgwyUSy+99NRTT33Ywx52/PHH85vAY8KrX/1qQiTd9a53dT6NWEK4pfjb3/7m4DcoVOfOd74zryUnnnjive99b85QtKxd3cB04l07Oovu2D9HZl/+8pdLoYnAHzMfIpSEvLPxP/rRj775zW8WNlvtH/nIR2odgeaf+MQnNJzUW97ylrvuuqumJ/y///3vpz71KUcdNO6OO+7Ib9qznvUsWuk5bh1LkPFb3/qWZtWC2gUsmkYum84dcsjBhlzxv/zlLyffxnQ19fPL+QVmNc0e/d12200WfYwEyJCpX8FWTal90kknEa6USEstSj932+7kSa3XikBFYOoQMOIMQONUREhBJgQj+j//+c9rX/va29/+9rwEvupVrzJnmudx4sHJv0YxHyGSY1w7w/OCF7xgp512cuTJJPP973/f9M6thjkhzvXMS6ZN3jfMGKyGLCYiTk+caTG1Ynvc4x7HyYiZzQzJTaH55MlPfjLL5faCCy743ve+FzlMj1LMSM6GnXfeeeecc05BySRjunNLc3O16ZdWDI28QmEjMHGTsyrk9FEBIWelGlBWwGI2LnkR2xNXoSdiissBHoZABdmOffbZJw6wTNcgVTuW7sMf/jAjglMuOquOVDOnGgH/zDPPhMy73vUuJvgud7nLi170ohe+8IXOaT/oQQ/SQIqg/+WXX64I8/x97nOfO97xjhqFB5+PfexjD+8GDCw1nmClxJvf/Oann376T37yE00TiDQThE3mJnz+XzQ0KOh81llnUUCV07hKVARLRNu73e1uRxxxBFNIbRR0ULCeKB/4wAeCEhP/ile8Yq+99mKPtLirVmBr4K8zkKZGLNrcuXO1aU43UQPmRFEbRNxKgiLHxujMdlBYG6m4cp/61KcyeUrXJz/5yU9CYP/99ycTg+WKjpSGUKP00tKIofdc2WWlg6VNV3Glp5OoHTdwpKk+oHRpaxvMlkz0UWWIuc0iIasdaOvkVgXqTo42dasInQ2zpYLGhRLFaKjV2Ef2va3AmHEKkE8feGKwzLj44ot7OHsqqyI9Xj6BHB5jjSgt6ECjjmTlc+GFF5KmlNQIMlqB5prMDKDWONXausVQtS4yit/85jcXBcwbeqnlgY6qS//yl7/MsPrc5z6XvvTABz7wsY99rLYDo4roZv/zP//D/xROY9xSAcgkf/3rXzdeSNBRKUOsIixODGfDH5gOxTkCl3JVkHCFYiDTKPjtb39rfSu7UaPz6966mVZTa22BJ9UvQFkP77vvvqDQeTKuS1KpWo1MBQLthoC8bmO+MlGUsox6XVEwgYeo+WICcqv1DX/jNDNPiPjxEJ7WxGOUCZk90FOEzolTRuXKKI6ud+E3frHp7Xq4EiPHlQRdN6XkqiA8cfOHQQdz15XWufrqRdMGrpDdmvc///3Xni/q3O3eg8/f/dzfn9+ZM3tBU/e+ZoWvjvrqgvmLF157Deuqz3uYW7pscOE1d/zNr2ctXN6Z05nZGehcufDKzTrXUm/Z8qX9fZ3B/mnLBpdvt+30v/592VFHfodb5N33PfUTH7xi++3mLFq0bOG1I3BxgrHVVluYdak0Y0Zja+Bw5ZX/+de/LnO61q3AHpmXYKh2qmzECUyDmjZKrgiGrZpmSkSjOR4Z8fCENTTszHPjY4v84Jm84FU6bFeI6eDJNAgrPOTIiAfs4UGhgFtlYbjq6ivgkxKLkBq5QQTA2MMTtNMuJTVEt6H3ZHE7QdJoZpQIHJ2UEtulFMklKZF23pIUYpuhR5SkdtElNfS2zBqvCFQExkPA00dJsh4zVxcLiG72ZiXb1la8UDLWwtOe81lJwZAU8JjbBZaiFCRitmc4IjlylFso4URhLIYGm0mpayUGrr128aJFSxqTKfQtd7niiusMdMzXlf/ZdLAzfbdndR70iIv2fMbZf/1VZ4vNt7r8H0uWLP1zGLibYFgFpVObzVu8dMmiFX/8YaH0d/qWLF/ir9PXPIN0hrZxsUC45NLOa19z6sknf/ThT9zvayd1+vtuvmABRxrN89FAZ1NaDg5eLn7N/Kso382yOGi08VFuljpgCUSuatrmkVdG1wSchKd1VtA64PJQAP9QMJATyYUHRXuR3LRBtziKseyFQYT9zcMpxfCjpF3aPDVeEagIVAQqAmsWgdhfU7eQ51CGKZOwglhJc3V7Njbbm+HblPC0tWImhDaPVFZAKGysgNtYHEWj42dY2zwo4UHHqejYspi2rsp9S5c0b0oXLWHNm1ess2YNLFt806WLbnKvx/35Hv/zy72edfbFv+tsNusm//7H8qXLLlWv/oHG6SQDmzq6Lmfg+/sGO8PLeaLsDA/56+swQkO8oHeGYuU7w1sTzs30VVd3Xv2qb3z5y8c+4JEv/vbJndlzNu+xZdMGZuBcPjjyvpfmHjbblrRJXb68B0O161l7UBKGbbtJVTwjaw9SupCym0HDbWDE000cuUQOJBlf1x4et4IkV3IKJiIobTkbXrxU0PJD7bx79CrYJ+kvfOELXhV6CQB8fdsLfy/00O9/vwd4Axwcli8zNADu8XPDA6bWqCJQEagIrBcIxCRRpUzXhYLYjo9Wt51aso9mG5PSzjsmQyVuVAiM7g+FUiI9gFhuoVhctelh1htFck3qeELaeVc/XkofU1RbhykaLysrlp7RKmvXtoZjVqHwj5e6ruil4ukVbvM2bF3pU8utCFQENjAE2tOjGaZ9u4HVdKWqU+belcpVmSeDQMV2MihVnopARWCDR6BtcKv9Lc1dbUSBokYqAhWBisAqI9A2MassZAPO2LY1Fas2Gu34BtwBatUqAhWBikBFoCJQEVjLCFhjrM1FV13SrOX2XX+Kqw6w1p+22JA1+ctf/vKGN7yBtwJn+/kz4pOovQO7p+bONnN78elPf5qLh/e9730cDcjFU8Ab3/jGAw88ME6XHCDnqOjBD34wf0l2gXPGdOihh/7pT3/i4opbDd6XOD7gIICfpnve857oXA9wzMFLFN9bHDFwe/Gc5zxnv/3248KDNyjOC/75z39yfvH2t7/9ZS972Uc/+tG2Spwm8KvCucZRRx3FfQlPHdx48WHx3ve+15b0tTN7gsvGfX4ccqyacwfV55CCSw6qcjhSHGC95CUvef3rXw/hP/zhD5JsuT788MPpzPETbXkKc4Te4QSnFDjs+P3vfx9XULDFzBeYZuKVg3wepuxlf/zjH28r/POe97xHP/rRXEgAnJcoLiEwn3LKKdx22NstXgK7xf0Wf1Vnn302zHl/4DPF5u+3vvWtXDxgczrx+c9/viZQiobjXyMOsFTkbW97W6rDb4W8UmV/05vepBs4Vifvrrvuyo0IwKnE2YSziFyr0Ecu3UDLck+TgwTqqArw4WBLS33pS18igc8av8DM24UemAMb5IvbsE4r6tEKVvHkwg8I/18kwNOh+vb+s7VpmwuwNVIRqAi0ETAPZFQaj0IO0JrGzX4mfzPV6173uhe/+MUmMWzZL4gtEkTMKiZVfvfMD+bVj3zkIz/4wQ/MAGZF/rDe/e538++Dzu8Pb1bMCh83vNI4u2JOMJlwnGfS4K3mnHPOMTfuueeeJDNDrJUJkN8N9oI3K1bsO9/5DldH7Bc2TnAYowMOOIA/QTMPx09ymawyvz3hCU9QtHMyDA0JXFAdd9xx7N28efNyoKvMhKqjUsX0FChIa7DoVjPX1Df08JdcJakdoQlYTJ4UQGcdnvjEJ0Lgtre9rVsHlc2KjvSI+1l71uHggw9mU5TFE9A73vEOFsSEz83TIYccwvISxTWYej3kIQ9RTQ6nPvShD5m9yeRtyhwrVQUZJvio3dOe9jTehRhiZsvMz+47VmQ9EE9PsTVahP0iGaqK4P8IpBoOVlySMTS/+93vqMe109Of/nSui0z74GL+4gCLJnjYcfpoDueU9BkVMc/Th27k86SpXEAp9xe/+IWzZyzFK1/5SqebqISiTWO20uvk1dmoAQp2hFWytFAjDQ0xytCToy6QKkLnVKKaqrvuQR++KTk/0m+dVes5YC+vUJqyRJhgRdMkDLnSGUTU5uCJWC6cKKkpGU2tZs2jgeTSb4trnpjLHH7T9GyuurOzWsQKwYjYe++9VR+GLCk0yNHxCLcSU0ELLT0ThWJFt7ZKRXk+yPQNt9ou/aeMyh5+t7RSnAZSF2JztEwuXsw4prSMtGAzHi1a4GyMW4dEJkg1usWPCIoekpFlNjCuuc3SfFYmMMmA4oLKWLvkkkv4k9J8epqGhp42OuGEE2iimSwIrUYsLbQaCdZdIKKVkW7hYTbgFEzL0seaRLeUS9EKsrD82c9+xpWnTiUXJ7DGiL6nCdRIn6Qk+bq6wWXUqKwhr09arhCrP+tI1mNaBz+ZQTi4cUDGF49unOLGA19qDWscgaDtqhEJ13CuaSNxS3TDUMdL78WmyUofDqfbnlGDTZAldBHti01vEU+I26ywpdx0EltYMAABAABJREFUe6k0UairYZvhjA2Pa3jES4jaucXQveVUS0XwN0eIDYRmJtx0y/3224Uvvx/+8N8cY+jbjRr9jZIUa2Zj53xboUns+vZCmz5tesPQ6Vy1cP5ms+Y4BM0rVmfQ/83ogNcpX1zw+Md/d889n/ztb39t/j+ubfISrXieqVqHZovyGTVykxBYctDarUBnuVQ8eVN3cZxgjCbY0hANwkN9wFy8RI7mvK5SejCJhK7s5uI2lHYEvZ2LPubPkmUErm5d2loVhhqZCgQCtWvaqx1RXIhjlls4x0xtE3Wqdru3k3riUaaH6LZdVjs+mrNSbkQIpClrg96Immw9VLWv+1Z++623/8flS5/znNtahP/grM4W21k6X09Z68FitjIduWXOXAdX2OXGDUc3dPq6T75deqzrGd/uWEjvttuTv/nNr8nFJnLY0YQNxReDek8w218PynpTEagIVAQqAhWBLgK8OM7ZovFn9KY3PfiHP/zhj78/Bi7lSbCdFmJzHd+Oco3+n/92zj6jw3f5nnve86yzfslieyaIjfaP4FmS2MFFyxpJN85w49V8pfBONTVZcnkv57eRfNz0CgUlLwFEvPYUvHj0OqLI7749Knc1cr3n4vUKjvXhmW590GG9apQeZSo+PYDU24pARWB9Q6AsGETycsbV3CVQNdeic1lElUh42rdWjz25SvYNONJ8xFnxAa6NxgZc5VI19S1VzlfyjbADFDRqpCJQEagIVARujAhUy3VjbLWqc0WgIlARqAhUBCoCFYGKQEWgIrBhI1Cf1Dbs9q21qwhUBCoCFYGKwDpHoC421nkTbAwKXLcXc2Ooba3jOkTAOWruh7ipcuZfnE+BKGOmK5s5QrH5m2MIDixsI87R5d/85jc2i/MIwA0Bx1VcZqCL4Od0gBcAfhC4ZuAWga8HxHvf+958VHF+wU/EV7/6VTuPeb/iJYFXhRRhdzL/I8ceeyxvRzwi5Sd5P/OZz/CdwQ0T3yXlR3rx89nh9DV/KFxK8XpAEzuWOCbgp+mkk05y0N0eFAyRPEVXPpjspebmiasFW6i5b+D9QeDyg/8FP+CsXJ47OKrgMkzVvvGNb0QTLq4uvvhiDjuaE25DQxwrONkORt4rePRABwX8nQbH8P3vf5//kcc97nGQ1ApPetKT+L/YddddXSHJ9wTkOcbimoQfB4e9y8HyUms8e+yxB58UCkXEzLPDj370Iw3K/wusAM5FBfcQHExouG233TZ5/VoyR1Q8Z/FIQh+uQNA1BKcb6sWFlr3j/Imob/ECwz2NwwnEqhQ3E/hV0DXH4znt4tuCEwq9LkW42ptOAtddc+fOtW9MfTm84JjjnHPOASzoeMNxLDD8cOaUhL8MHaBIqJGKQEVgHSJgmlJ6rEYMR+IO8UYrY5kfIrM6Z1L8CpmCsleyvaQ2RWQ6NaubPPnHiSjTPtt05JFHshScXpl/zO1cF/GHxSsiL0sMDYFcC+2yyy43u9nNzGNmNmVxZcUu8Kdj7jWDcbdkmjWLKsjkyUzw08SyMGSU5LcIhQcozm4YkXjoYOwOO+wwc9173vMezNhMfcrlE4f7yExBlIlDn9S0XCmvjsEhc2BJGh1JTXvoJRf5jIvJliZkAsesy3fPMcccw7EX0JKRJyBugLiYVOtQ2FkRfsT4f7nooos4CDM/M8osMmdYYStTNzvCyw/7Yirm7NK0j598jqiYM/6zGGuWnZswvqVyGIxwkLpyQ8ZLFDvFbxHvh27jdoQts6iggAUG/01xNMa1EDqBMZGyU0kglmHCFodTJnk9gV8nranWfB4xE/SPxxm5pAr8PWlo+KTEoKG4gw46yHoDUDiF+EFjR/QintGcieIVi6ERoKTbsI8MVjBhqXkZ46qJsyR22VqFVdVGaaa0acTmik5Dhp6FTc8pqTF8eilg2VkDIV66fvrTn7LL/DrpfgDn/QqnXCxgIE1Z6nvZZZfR0HBQWf0QwvDRavxCUt56iSGWUb813JhXFCMCGgR2T/EVXa6L0FaSeqkLxOIRTLIsTSVbB/zalWWpI0Id8ZBgWQJhQ5JDgMsvv1wqxDScFrcwi9czrWyUGao6GPdzZT1g2WYYcgNq8PJBVhxgWSTwzMXhGsz1W+oZmyg8nclruOm6ViZlAXDGGWeAiJwAaKTrxsDhIDWuxIrOFkgQ0wEojAg3Xkqtfg0r/rksF6GqBVXKrf6j2wOKUzmq4tdSfLNyFWqZZIqATMABRYpWU21nyYR5zDkhmtTrlCKgObRLWkREWU079fXprune4nh0JI2rR7kVyTUd261c4UmkjCOdxCiTJKQWktq34mQiRrIrCuFhLteSvU0p5hJRRop3JTcsg0NLrl20DPH5+9z79rd90Ctf/uX+4c60aX2bbbmlnjY83Li1ag74Kn1wYNaszY1OqvYPDkzvn7npnE0H+pq5BSoLO9f0dQZoM3/xtSscZbkbvvLKvunTZy1dtvgDR51zyrefvvvunSMPu2L67M7yxXIRO6OrTOPxVnA0GNF0NTQECic9mqKBptaNB6uuY1xVFujTqNHfn6qF6Bo57WsXkMZlGGI33mmj0eYcM96WqRHxhGICyUyYhkgHcKWVUlLQmAIrsSDQRgmquS3EEsEvLpS2CLOrIFU36GEoRSQStkLELN5DHE3pYchtruNJGC0khfaICnEy13Zxk+Ff4zzrXIE1XqM1IhAsQvpkT2fI7RoppQrZIBBg9PPX1CbdphtpjN3Q8q1mzZy1YPlPnrrbLXbeeZe5cz8paXARh7yLOn038JpxmH28vsUb7h/yR0IThhu3GtOn3bnTuazTueZDR154yncf/4SndE7/2p+XLRnuDG7S8DQLEiKaZckGEGLlN4CKrJ9V0HUpNsH8Vvp20T8r3tIuFkjWcqPZwn+D8ovYjSQyHlAbSfVXuZq1I60ydBt2Rh2j2zdWmMimtrec3jdz8fAvnvTM2+600/1e8pIfBIGh4Ws7fbxENybY40I3V1JGriiNx8nun8eJGOLR9nf2rLsN9F02OHjlR47+zVfPvNdjnvDL75586bQZnaFl00gY8Bi9wov0suUjXqSvV8yN5GYCo3AjqcHKqZmPoV6Z+gTgfYL30skfHHxk9LJOqhfLRW55OSHSfQAtKTf6SEZH6QM9t+NVb5Js42WfIjqtBHXJVSklMkUl9ohNcaYFK6WepHpb2qLis+F1Bm1anhTWSO3KjLRq0lYz+6oVWnNtqAjo275Q29bV8wlPN9PzzWy+L7iKb6gIrFq94CPIO8nJATMY2yHZe0qPl/hCbMq4/su0krT2I5RXaK7pG5tvvrkNDDbXpXZrX6VaYkWgIrBhIzDmPLlhV7nWbuoQiP2aOvlVckWgIlAR2GAQqPZ3g2nKWpGKQEWgIlARWP8RqGZ3/W+jqmFFoCJQEagIVAQqAhWBikBFYGURqA6wVhaxyr+KCPj0xZ8FJxf8Arz97W/nFyC+J8onsbLXjbOqnXfemZOIbCm2B8i+T0+ksvN2xBkBbwJ8B/BYxC0UtwIPfehD//znP8cFEtcVnGh88IMfpKWMvGBwpbTDDjtwVcAZARcPcV1h1wj/I5yYCJyecMPEmwD3BzyP8GfBGUHbAdYtb3nLrbbaiislXiqIpSe/KrxHHXjggfws0E1BDjmvIi6Ty5YNUormQMHB7xzYdnaIOwlbq/O4zgUDz1+OYXM20ZbKB9aVV16JGTFyeJ4CBU4U+iMK9tOIE8L5gupzyMJlhlteol70oheBGkQaC0rQlpFM15xfEkmgycEHHxzvVyiQEScH+McffzzXV2lu7ipsEOeiQrnJiC4vhx08O5BMk9DjoUONKJy8soQ5TenKZwR+fkmCg7i95lrHdnO+MMhJ1+LqCxScTRxyyCHz5s3TnTgmIJ9LCz1NJ+HkJd6v4guD1xLCdYZoUq8VgYrAeoVAxntUshUyc5E5xxg3ffHjYy4y1XAghSezhwiGzJ88W5k8efCJHH6IzCHmN5MGO8W5z2Me8xh5zR6mQXaEfytzL5c0Zvt3vOMdt7vd7fgJSukMBwYUzrPi21Euswd/OuZV3gNf97rXmVcVYX4zL5n9uFviRop3GyUSQjINzcwMEAOnOiY6/n1YNK64zJ+kqWDqmEJLjdyqghJTkUyPbZ42UKH3XJMdUdUCDpSKfPsszf8UKHLgZk4GS+TEs9UFF1xgtseMyMcQZlOueMlVZntFIHJjBLT73//+oMYGCg4HeW6K2yZy4iypOMBKWYQo7tJLL+X/sXhTksQvEvdV4CKE+6HUgj7UNs9nLSESS606spTKhhgLYvJn7qXKKHAdhRJ+ugXbSMPD25TeIiPniW4T8PDfxAsbf0aU0c3CzyMVw8rqMYXFcRgcGBoZ73nPe/LlJK92FHQAoUC3QvbIv7TiV4t6bXray7pou+224yCMteX8kSg4s2tcXqJwgCULHNqLBAUhkjZ37lydjZ8mxjGS9TeA3+Me99AfJFn5AFYQAfUznvEMHV53jcJjahutIAwE5dLNNSX28LdvgwMdlBtV4ebWCseo4WBOXA/hPoyhp4bxiBIJGl0Xwq8TIloPkOAKB6MPJXQRGVFUR9wwzPpQ26kOyh3veEeOxv75z3+K88eqy4mQTD5p4pQUQVG1UBAFTWBfO2mctHLlhkIxDt3U2uqUOzazCn7dWJLW18GU9YEPfCDerxA1LsdbqmAiMi5oCIcUEd2skbSLUswbStdME/QWAmuYOgTSf3JNK+jt7c6saPQ0nz6D0xBARAlRvMyNRU8SSmqI+kBmktym+5HsVrwQRdJJkpR4Uss12pZbPF3mEa3Fb3KLjmX/iSce94sfN1ymIlmyGk8uBU4baDxwLV/OZnj8WWYaJCcnPxcsWzDQmd7f6Z89YyY0brbDln+/+PLlzaFgFR/i/YqQX/78nwbF7rs/84Pv+9KyRV3PG13RPdB1DxuPjLUcE6VJeIKPeG6DbdQLEWdACLGBu+svrKtDg1soPWiEecwrsQXqMJRbBYljAIJICWPKqcQbRCBteoNsPQzp7UaK5kiLYBgtSuu0M4ahh1gYQm+nlniJFOYSGZ1L0nj8PRr23LZlkjBeamFb45G22qVe1Ggrs/a1WuPVXE2BQUYPBIVQgDLJrKbkmn1DRSCdJLXrWnLRwUVLFm69ZYej+ZNOev2vf9Dxos5Uhr5GQOiWONTX3/nFzy/lEPblL3/4Xy/8+y9+/sfrTYhrpKR1LcQYXNcqbLDll8mt1LCNtlShTSlsMdDd/tzQsOU6JvN4Qoq0jS1SAVm1Fk83K3nH7GwltUY2HgR0jJ6+wd+ylyo3v0XHu7vPf/6g31w4AkaZstx3DYtOtCr2xXu2weEl7O9Pz/vraadd/rKX3f53P/3Pby+6qtPxfMz9NCvv+bGR3H7YH1Gi/rNeIqALpXvoEvlk0H5rLZXTfF+Z/aDFhRf9qtTg+s+ghbwhRJpB1X07msqAxW3iIuMNnCTl6pUmtoR1i0jRvKgRCt0KZeoiyhKU5douJbdrR4d2uettPIAUlCoy621LTV6x0pqTz1I5KwI9COhF6+FskC8RT3jCE3xbzO/3FLWjsGt42ivPwtOOeKZOaBM3+DhY0qwlMl6VSwco3QBlNHPbARYGYf1xgFVqSm311dx+ScunZFsg8oV6dHUqpSJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhsJAtUB1kbS0OtLNe0p57yA2wiuGfbee2+/fVc0s6XDxg6uFvhZ+Ne//sVDVklKxFYPPkQe+9jH8l1FTk6M53riiSfi4UeAtyahJ6PbeCpxErvtQITLDM4FpPKkIGl0rlBOOukkPhf4s3DLNQbXA1wm8ZtAeedsJ392ejz5k6HbCMVdgrPlNuVAabwsfDTYXs/vhh38nD39+Mc/xgwiLi1ydr19FBPgRQ42DIqQkYsKnlxs446bCX7K4kZqiy22KPwlQkhbpm06bUCya+eHP/yhBn3kIx/J9xknIMkb7w+pi0oVokaU1FPHdhE4ewotypTIq1/9alXQK+KiJdKCwGmnncY/Dq83n/3sZ3UnWeBDZy5OzjjjjCJBRC7ty39Nm1jjFYGKwDpEoMwMzfbE7gmETIni5naKGct2BB5++OHHHXfcTjvt9PKXv/yAAw4wkDGUeQYbH4t77bWXya3YC8eNIpCDG46KuJ7hhJGXJSWaVF35zfnc5z4XS/H1r3+dV50Uanbi0IS/G0nFbEVPUxmtTKScBOHJ3GjCkdEtjzw0SaF/+MMf2CMyecyRN35tmCczJ7dZiqYDDdtVKK2AKOTESAS6jdgSKbN96CVvIoihu6a+ImXWVTTHizgLBUSMEa9PvENS261KcUb53ve+1zQrL2coipZLlZOL+eAPS3XcpqxMyPwHxSsT91LxXFlUjZuYIKn0WFsGgnCAuJZayILtS1/60gte8IKXvOQlznWnWUlIWTE3wV+u+FQyt0d4rhyTwVBSceyFM20UGCmsIK0czaU6o65eTAkEijLK4k2Ji6iXvvSlu+22G5ed8adpx6oSlfLd7343CidLpOk/bmmr6YueRWYiUUN/cLxqvBXLz372M4bMOiqmltc23pSsWEjgzqkIjChXdY+Ne9zjHsdX12GHHQa9sJUexTUkj588u2nlJIEloJEQSgQW+SUS/DWW3qs51C6aE97OKLuFhz4APfpoArCoqewKkpc15x4OsPG/GQCtUgixKiOZwKJeJMcXG85EdD/dxsoNJQxctuktH/vYx/RkYqMqp3XpeFZ6uusb3vAGt0cccYTsVFKETp66RwhpKpW8ocjFlxmPY5///OdDcYWY2tmynMYlIR1YRHYTUYE9WfQWHT7e8VAKvCkaHUTO1JW6lIJqZG0ioN3T9KWBtJquWGY5ypQ5MHFXzD19Vbc385RB3Rab6siiuc2xZeCgmzT0hPSBZEn3CA86ooyRUK4oZSYXx7zlFttaAA93HMvdtDPtiqHlnb323WrTrX/zieN+kVzLl/ctW7bgZjfb+h//uCIUtnfZ8kXLljc+cJuTAsZp/zWdgb9ffe2VGGZPG1zGaVufMc6xY/9fLr48Q27zLabPmXULY2Fw+Gpsnz7+V5/+9P67P+9LJ3600z/AvqA1LyUIdF0xTN02a37AcrWFHsBdU1mUhAzbxPGrmhCcKZkqN+eLuyEWslvoClKXXjKO8I0wN6JEm3+6EWxuycytuLaLYpJo0mWsP97ehW/KLgCHtsYVNLRbywPG0dSadhnPkpZhgt/yhoImYULM8LqZ1DRlUby0cqGMGZFrkpw92ZOrFOq2xMNJN6qK07YnqUfUVNyOLjGU0fSpKH09l5m200B6jjbSeczJiPohYh4rAFWxWs/bkXraa6WUNPkIsuQqopV1gHKbJJ2B0ehee21xZ3gG5mkzlsm1dOksXi/6+6/xhu+Vb+hscZNz3/3sa/tndwaX3Yxi/dOs4YdWSr3rmIdndDVZQod/X75gev+Wy4Z4ohx812GX/PrXB8197Bt+8XPpSxv+vumNue1a4eZ2/FAmSRFcKt5e84yfb9yUBqMVL/fK+mRc7nESokwDaXcaLwuqcdgreVUQaHdvrZaZrURIRBFQhBTgNhF5E9dArLOVkiuKmVNjFXudtZzbSCjZV0XdNZfHKr1n3b7mZE8kCQhwMyhcmRVPCunnE+XZmNJ0HjY3T+ij6w29nv5TumUPcw9bT2q9XW8RMC4EzWeMCJPREz+2jKxO35LrsvT9deGSzoMf0Zm99bfecdDlnYVStu3vzB6YdiXJw0PThoZ5Oja59W+++WbxqB5R10noxmJ6XLtDNdPgQquAa64emN6/9bIhi/nl75h39emnP3+Xx8z77UXysMiCFQjFGnvd6ZbdjYxxUdkyCaQ/ryhrDGacYbaQECYJ0RiCxiLRRMgELjIWywZOg22pOLsWo1Zw9jlS8DMPH/7wh2dMn6WZODqDiFcUK4zhhoYPNPRJUIgkroZByW237v1eXrHvbXsqi4At13CuE2gooHTmXq/2HliDRvm0aVLXjmLKMr0oPeulUug6BKfosD5ESltkEi6woFOv3K4PqlYdVhYBbWpdZwz6pGIwrmz2yr8xI2DCzCu4GBRQrD+zAU2YEraPhn6dsefb38bcarXuk0fALhf9xyS5/nTsyStfOSsCFYGKQEWgIlARqAhUBCoCFYGKQEWgIlARqAhUBCoCFYGKQEWgIlARqAhUBCoCFYGKQEWgIrCmEKgOsNYUklXOpBDgkunoo49+29ve9qxnPYtTKr6lnA9sb1XkGysbPbN9vC3UNiaHtFFue9vbtulTHefGwg48J2Pvd7/73f72t+dR4i53uQvXDPbk2XpS9l9OqRo2Citr66235gWsvWO4p9CzzjoLpDyLffKTn+Tzgj8sPy3IV8vpp5/O+UKPqj2bZgAezLWIIO7aI3/0bY8QbdTmyT57QGk4uEHvnBUOsMJWmj6Rdt52vCcVFBPs4NdYt771rWWfgMfea2CWIibgLDw1UhGoCKw/CJh5yuQjYuYRTBQ5osARFb82Bx544Ite9CI+777zne/gyTSSM5Y80fBgaJIs06nsmVi4y4mh8dOsqa+MknrqTiCiYPZQaM8cFWapPbnKraSiPyK/BnwMZcplXx7wgAeI3/Wud+UHJ96L8KSaRUKJ0JwO7GbRoUgOJjglJbUklewiiKEHPc4g+LcqDIgmVbekhchJEEgpzDUSnyyYf/nLX/IO9sUvfhGD2TVuGvCzI5ldoWove+IpRTyap9ZRrxQqUlQKkf8Xrs222WYbDo9kSWqSkpcaHAnd6U534pYr9KZ5Wk0gSzxnUUA3sKVeKmLq5arirFX8VUXC6Ct+AZ2oO97xjmCPOy0R2VU8PcqtCvLlROEIpGRcOxXvUREebIEmC60EmqSIdukFHwJte83ROwzoYXbljQvO++67Lzp3V7vuuusd7nAHcPEtxScIBuuWkjHCLWyuueaaZzzjGRx1cVV59tlnl0Id+0ldjj/+eB6daKVnPuQhD1ELSaw5Cm2jQ8k1ZoS3Jig5oVpS5dUKVBJRcY6oOAuzSrFOuPnNb64VoIRuPWMxIxcHVbybcakmPOhBD7rb3e6msq6SQNesXVruTQNIENPEisAGGUNJZeN5CuWEE04gitgnPvGJirZeUn1eq9RX0RyHqTUXeK997WujG2dtHKvp5LqZ7DqAermKl9YR16/4DD3qqKPEtQJnZPTEsP3229OEnuj6RumZwKRS0ao0KLbxAjmaBoB6gggJxGZwjZel0qcIAb0a+Ma+SUm30Rs1hJBOqKV0VEmCFscQitR0ABEtbmASEh63Qro65qitrY07bJq7CFdWDsMTJchlWStCjlzJa7ogWegqNeKwiRpucYqQLBeGBfObLn31/Ctus+Mc7tvY0EsvHZ69aWfptTMpectb3twA2Xqb6XJR2cDffLPtu7NH4zl3zqwmL7Y73WGT3/9hIXMxbUnjimiITy0z+aL5i5te36ikLDoPDjXO3c447VrDbZ99nv6d73z5ysv4HzHnNxPynDmNl8MZM2aJL13K0WTjmkQpXfCGZs6cgw5VVSBQXMWDT5e/qXuqZjJBURww4Ya41ZbbaKmh4cbzgiqbe41KbCYHQgR5VURSZrZG6S5FQYhKEcjU6KZ6FZTFrdRYJS2iFPgjJm+9TikCkBc0jVIgr1NpaPHgn8YqbZGIPq/hZNFMZdZNLoMla7/onP4wSf0JT2dYqVwTC49APLo6VSO5ECfOu0ZSQZqgdkaEq7BGJG8wQgIIcABVupPa6YquGitBvEI3RY2eJXSEi2f2BnubYpYopeOJ7UDBpl2SqzCIMDeCJAGPxg2lhydypIaOh7kpt5HjunxZY6daKoyIiZJsh/sF1zR2dmD6FfrS4x//SK6Q//53jvm4xGiO39NQKrvdrYgS+tkp5gazCU1nY5JQXBFxCuFp+uRw86p/oH9TxfV1mtQlSxsLfvnl1/Ds72Xs5z9/zOV/HVGp5x9TqIUHaytkXREGRJY03b5QaJKpOBSaUAwxt/ShHsR6eBBJDhR45OrBEEWtZZRKH/HwRGyu8GGRUxZRdEMJsc1W46uPQLcHNmI0R7oZwMVRJKFoKXERqyztJVVoMnRDepGe4y50nLqTh74Rjq470cQlFeI6iehp+pWOlKWFAZ6lwtpUBrYgpYNCY1ayfF2bOqzPZVneg8XDSFvJ0uV0oXTOdd6X2uptSHFjRHWADHNLazibhwvamfPLpIGTncLZpoSnjQmZOnzmE1fTSChtHgzGhaBoQYkZp5gLW3jMOSxCe3kWhtJJCr9pzJvh17/+wT/5yU/+9rfOwOzO7M42zTw2YxH5A/2NQ6u+/qZHZWZQU3F9LyAUOWqE07Uxdl372983R3HTp22CZ+myxv5eeunV7O9znvOcz372M0PXbIlOGp7Zc2aKz1jUPNcQTogHf50csQQTJq3UPRMsehTAX3hQlCKUahLFtqK0eRAzJ0eUgnJbeFDUXUaNK68iRvPkqdncSKW1P0MWVddJpHT1lA7tssLxkvxlL3uZT97eJeokfZ3GGsamMZXrRNspLVSXMxUHEP1Ej0qHNBb0H3S3rm4zGPW93KIIUl2TfUr1nFh4FNCN05NLa+rbMq5l9QKgbmPtEXBco8nEtdgYUsGSPmN61y6lpcQlbQwIbMB11IIPfehDn/CEJ/jgovNvwDWtVZsKBLxX8X3hpz/9qSmizAYlMhUl3qDMUrqllOdKttI3xBvOtYJjA1wxrKjayv4LyTSriDBx9tjNG7vRTE1zVV9dyPpKpbJCmBiBmloRqAhUBCoCFYGKQEWgIlARqAhUBCoCFYGKQEWgIlARqAhUBCoCFYGKQEWgIlARqAhUBCoCFYENFYHqAGtDbdn1ol42Z5StojZtiNuoccghh8ydO/ee97zn+973Pv6wbEzBRt3sTeGvwa2NwvYGja6D32JFdFzZtUhus7V3PdqoTXh2xoRZEbltZxkd75FMCO8Mz3ve8+yFtZXq5z//+a9//Wt7edHlnYzA0UWsLOU+97kPTyhyfeUrXxmdt63DAQcc4Ji6Xdf2C97//ve/+93v/tznPtfea268ejImVzaM2rVsy3u2xQNfsDvZtSdLkMlum5w0CwiFjcz23np0rcnjRvbT8+eCgqcHYUSUsomnLaEQ25LLJtdSrohcySjVMWaUMdmSxfZ9HnD+7//+z61TBMlY6lLUS8/EE6yS17UwFEqNVAQqAmsZgZiMzAwZvLkatvaVckDz4Ac/+FGPepTDJ3zZ9Gwz5RKItgZ+mSXK8EfkJUeqE7/j1UjRJoEEPHRAkbFnosgEUqYX/GFOWWVyy6ktk+Tuu+/ut+LtazQ1cVzI6Q9DY6aNhOR1LRnFE0imQKnCCnIz/YpLKoqN5pGkiODAuSTz6vbb3/52ETI6i/3o8+bN4zSEfzFuwvg0ue997/uYxzzmgx/8IFtTXCyNALTiCCs5yoqqhCtFRbSUOpayQo+9iM6lsrkFBVXVK1VrZyQqDdf2XBaGHmZzPmUEp8iYudRdQShsBzdP5513nniBPQ1H4eiQW0Ic+iI/h2SArOFSo7SFNYxuxhujdQ420mR3TqPUKLopSIQOpTi3KSIMPVds5KdG0ScMJbulgjPte+65p7rw1qQj8VolVU1piFmJKZSGTCEHTxxlak3LDI14yimn9AiU15qNX60dd9zR4ufcc8/Nvu2cuAvzBApj4B/tt7/9rcMMXEHxb8URlSoEcKnilPzVr35FK6cdaM4B3Je//OW3vOUtv/nNbyLf1Vkpyxj+qng6+9rXvmYxllFcQCCQKJzpP8GZYhhcVd+iCDGqivzgBz/Yf//9lWI5yp+Xdn/kIx/5zGc+87DDDosrN6VoL96vDApe1ehvhN773vd+17vexRlWMMxVZ4iexKY/PO1pT9tvv/3MIUr5wx/+AENq57hXOJOxKI+YNg09FSn9ViS9tOCcSBgwF3qE1+vaREDjandXraDviWhELStooFzTQ8whFMMmotXS1niMRBYqrYmfNHKE0qw4dQBdSy4htUNUVuJNYSsChmREEE8pRRR+pRvCSmmO5g7zOzVr+TKK8RzUSHvKM+5govjqV389uHSO2Xb69NnkXHbZP7n4Gxpa2MzWjfpNRRoJHdPa4FKHfDuzB/sHZm66WV9n4VULOjOnLx5atrRvRjPT3vTmt7jkkkv75OhbysFHp//f0wc223zz7S6+5G/vO/zcc3+635Of0jn+w51pA52h4e44Gt5kcPmswX7FCc0ZYIeI/S1Zsth4jL1QcYCoVOrlNnDBOXMpuiEPMfyqI5XOy5YvWbzkWknkoqgCBgFFKWksEnB2qya9aaxQgm7ooUQTohq+7qMECsDJKdmTVK9rAQGwZ5IsZZWREgoGPUF7aaYMHM1Umg9P2/uVW00vpClFith2PMTRlMI8mUg7u27Wvu3JniQqiSTeZkDPkC/EiaUVtvEiisgEEpTIh6FVEwBHexMYT8haoBuwGlRlU1aQKbeIo7Fa41plPmHxWXnLKvO5NZUpaC0UvcbrUgVOBQKZjgyljCZFGF6MYmeo+6Q5NJtJW7D4kn1fcpNZMzf/0AcXDrDGw33DA39f3hj8rdx0DXTMzXV+LiZStbHvjaVuQt+1nlQ7/VcotdO/pNO/fPFVnWOP/svev77LPvt33vmWxodgXwfzMDPdzVAvFYExELAGy1xXuvEYTF1S2EanZrpGL+/54/1qNa3V6IJWgdKjQzS0WibKgep8g1gFsauTxbyRd0QR4v0GNXqWOqsjf+3ktXi4wQ6zappwlu1dRFoKUFrQKoUoxjclTl3Rq6ZwzbXOEciqTPfQNzy6NY8D3QX+wJzOc/fxCnT48MMv6VvWN3vGJgMDl3qe7x/arKtz99F7uK8zvFL2t8sc+ztwTSNncCn7u/A/nU98ePF+L13wv6/uHPn2f3fl79gY4eZJvIb1C4Eeu0A5FNee5b3bTEReGB566KG+aHtJ/r3vfQ9n87p4uH/Bwm4H6FaOAGux1pNll3pjvhSDzmYJo6tSTEDmZFN04cmrm3K7XkXS+nRmX9o6t5XEk0qlS4ijjAlCO9fEcXBZRBXrbwUieK4sbypS4sRC1odUepaRUiKrr1jgtSLN6wgv6gkHuxcU5TvU6pdSJawTBPQZs+jFF1/sh3YMgXWiQy107SDQ1119LesulKZ14yl3ZV529Gpqs5l3lSvWeL2p6+S+TH0mKM8sTJ65a0xNRl5l+ljTrAjB04ASNHJtgTSmgA2cCMmsJcz2wsS1xVmQn5hzfU5NTc2KHm+tCny3ou0N1n19rlHVrSJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAquPwIqDMasvqUqoCKxAoGw0yXYN5JxGyKYNt6961atOP/30HXbY4c1vfrPdPw7DIOYAQ7Yz2tshrJB33b/Z65ltH6GOt/kDPWqEoah0nazrxyL5+rSRu0996lN+opnTB9d//vOfqA972MPKNt8xs6wU8QZ1I40/CFeFfuc73xktnPIh5oe1DzrooM985jO8YNDzgQ98oCsHXrxFfPKTn1RWKS7IZAsRvyf2j9pT6xgASvBPEskFzJSS1iyFtvUpnAX2NKjjoDYN51R5UaCdsR0fk6FIVrpKqSko2q3WVKy7kVxZcYOSE85tye04py1uiRVCH12j1FRqkiLfVZbE2wJrvCJQEVhrCLSHrZmqDEkD07B1dUTwda973fe///373e9+z372sx0baI9ZZ9SpKmP7fEIY4ttC6mhDUwpNriIQHYW0njkkEwg2dFeB2CKkYOV0JTvIqw73haeeeur//u///uUvf5HKg6FpM3ndyp687ekoAtETisxEQoy24j2puaVkOXnCPxFDzBD88Ic/lKoglRLCmXLFTb9sxDvf+c6Pf/zjfCxyCQRked/xjnfY12u3euyjCT9zfrIzSXT4+9//nltJijZLi5AWcCQFNJEoXNTmPwjRqQaYaM2wSZU9Gtoin13yiPDMEqLoHIE5JiEJnQScpVxxclRfFTAwEHe6053+85//8F4kr0AmHpFcZSx5EcV1OYqR4EAmIYHdbZO565WJYbrpTW/KJRYPUCG6xk5NfkWhvqoZHQJgEaXH3vGOd9QuT3/607UCP5iYScYsC5V4LivMItGf9yt+oHiL44Xq+c9/vqbPOgdEhola77XXXjrnL37xi5e97GUWEjJyraWOnEgWaUQppQ1IkkJxYgeMHEg5D3a7291O9dGTlFx/+9vfuJSSxbFeTqZcTzvtNG6zivyHP/zhfKuBzhg57rjj+DExanbaaScMaU3OrcqADfJp+gCFDRR4dHV74nnE09YwUcRTn/pUKunGpD32sY/Vk7nM43vuZz/7GZWAqVz+3Tj/gu0Tn/hEWCkRtvohsSm0lEKsRnn9619/4IEHnnXWWRpC3fE4tIziqvQc9i5Vi26uMqYni+sValHEJlJAk4RBUKm2nBpf+wjoh1pNl3AVDDeUtFR6uC6nD7QVQ29TdFTtmOyFTe8ip9ziMfX1dDazWTphYSPZLISZNFfdxrTZw1OYuz1o8ayZsyizcNFVBt/V86/Z+qYdHt9OP/2TZ57emTHUdMLhoca913BnWXdiTG6eOZbPn3/ZzJkzZs+expvQ0kG/R91U/JJL/rkMC+6li2cP8CTVOKNcunSWs5AeU+bPHzKjXn31osFBbrQas/KPSzvf+MY3XvSip33rlFP+cUmT02EPGA4NL128ZHlfZ6A5+tGdqdRo2TKXxay9kRKck6SaNNQEiJLMHtQWibHAI4IigrONBjwlmZZLKgYzHgwzGImFklScroonRBIe4GOOTFe3pd2zhMAcsVJrWIMIaBTYZja2VoG8Ft9yyy0tMFiuOBw0n3tONNliCL8mM8q4WbQmjDJmfosWdk0zWWtZmXzpS1/i6pqoW93qVjwZWc9w+LgKmmfgT5wxnafNEz2Tt8TvfOc777zzzpwz0ooNPfnkk3//+99LHbNrkdmThDIZZdpq9MSZWgMf0UrG8oCDSGYU4F/4whdYxujZk2Xt32pZ4zRjVumlygEERYcRpk4xOGy//faPe9zjNBbEzABWF5bxP/rRj771rW+ZT1L0egLX1OGwTiS3DaW4oDOU8ZW1Yrv1tQ6eUDJAwtNW3oQv6EhCehEelDZPKORo1tD1QDzlFnGkrMHG9JCEQmBRBlHQPVCWLveksHTaJp1XvOIVZ531wYsv7vT3cQ47vIyx7b79w8PUyu5WLvMeeyfO8LmyayiuIaIIoSROq6aw/qZSvEkKW27V+e1v/vXpT3/6mc+c+863nIUivejWcHS901oeJLTnHEIKhuGUUent7NCgW9TDQwGTMB6tkyyutEUxnyg6PNh6cIYhNqVLEhTt2sNDGQWRQwhRmFGEUtBajtAhLbWWy10LxaUbMEmPfvSjFZcFEjuLnpblv5ubY09Srp6kikocFpsPf/KTn2AzYzO13tZ6kGRQzJb6BoPiZTXL60HP9U1velN5+i5C1k6kNJ+HXO+R9EDPwp5bxSUJ7X4+dSqlLL3dUFKKmWGfffb505/+5MckPOBz8j51Ra+UZHq2+cfs+WoxdaCZH7x7yXuMoonZ4Nhjj/WlwAsri/yi5JjqlVw1srIIxOKkrxra4LViLCAbOz12Aacs7c4Qnna5GMzebZ5Q2jxmDDymHV1LUGIszpg8mbVKUk9nkFcXZlq5iOe4/Bvfft3vmhddw6xM5rQ5s6elXkPDQx5RFTcwrfF2LaTogFDkx8y5Ll7iNVGWB0ro81MDeGJ/Z8/u/9vf/nnMMae88IWPO+wN3+rmtVDsX7K0WWYsWjziG0gRnlJ7fsIBMoSnuNQFW6GEbo6lVbviGHoobhFTR7mI0haWDZFQ5ChdiYpwHZMHOIYbhnbGG3s8wOaauqimSA89xDAUtD0r6Tw+PXiuZOD0TJ6w/vqXv3/5lC+tENX0t668EG4cV71dH/Cs7T2tChZwgKCaJls/WuB96dy5c0FRnr4lWS0z/X5BSifRM/2SgScmD7PiAdCywS8Q+BkGphZcfoTA07o3w4poI7w2YfJ+wA8/UIby1sDz5s3rGWJtZegp9FDat6sW9+Bv+JvrvId/ylOewseTd3reS7zhDW8gMJPPusJn8jUKMrnqP2tE4UjTW8xX3tj4POGdTx4lJFlY+hUNP2LR86QweZ0r57pFwOxhcjCN+FG3tPW61aeWPnUIxAHWUHehlHjKaswtg5ublbxeeOGFMcc6z3rSf4oabEreTow5E3rYs1ZMdfNPblfEuykrQBlTwkpCdaNkV3F4rpHqwzI9bX0Ggt2kni7NxrH76h7K+qxz1a0iUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgJTjcC0qS6gyt8IEciWFBtGs1PWxk27q233KVDYAmvz6ymnnPK0pz3Nocq4A7APGz+XAfZ62lxra6xIyZKIYwx4+GsoW2az9yXHC3sOO9kaYqcIBltvs03E1d5uRMdiCbQNNzsjs2MSxZEb12yZojYG7g/sOv3xj3/M/0JSMTjWqEbZhkJVR2e/8pWvoEcZ0lZqRw5kVEe9SGgHPibs/0NxNMhJIRH+KeJ+ImwUkFeNSnFPetKTHvOYx+y9994gcnAImBh22WWXE0888YUvfOHxxx+fjPEkklyu9hw/4xnPcO7IRlsbSbWIiCACAVnwiJeNv1EVqpJ69t9oeicfMMjiSkMwQhuzXar27Bb+NBnlo1KuaqrbyJVKYaY/HSiDAT1Xm1y5EWk7wMIG9gjXpulRfjs0YnUb9cJDAg3tY6aknxWVSk+FOoWFHvmIkZNqSgoFQ6SJyJV4vVYEKgJrH4FMLxmGmW1MUI7ooAimAhOIUcy1gQn8kEMOee1rX+vQYBnCFL7ooosyuZkc3BJifnA1sbs6Y4Do6EWqRhSx4mWKSASxTcEmJEtmidyS6bZwYkBxNe27hpPPHZ59uAJx+IoTBxOmeSwWTdxs6TSpSY97LFmo6opOQoSnaqV0qQnoSVJ6SUVJdcLDrqGwvw57MASILIWTJyMiutq2+dEZGlYDqs72CN/85jfNok9+8pM/9KEPOb5CVPLydUJVSbk1b/MOwHMQhOkjSTOpZkxtKSIKy5JIsTtMg8qqNZsrV8wxHkJSIhOT1nTmNo0rlbEr+pQisIGOHW8f/aIVTrYmrcNXlBppgve85z1laUEgtaMJI8Ki3eMe96B/Kuhq0RK1U4Rqnn/++UnVRakqfskll4TixKaWjVZpBXHNVLqKstyGIVcZ1Y6eXCmRVjjRo/bBBx/M6dJnP/tZ3R4IKiXJwWM6K4LA//mf/6F5FhJRlScLeJ555pmycPkBRudsf/vb3xJIgiZ7//vf/8c//lHPLB6pLM8Ae/vb3x4PmbrN5z73OQURSM+IjcI0DPHoo4/my4M7TuuTc889F7OFAVsslZI6ibIElQpQejue9HCRPffck8eTd7/73c79ypKK45QkC5T4Jz3jjDNy3DrtBV6pOoygK1pQoWtx8wO6ACXnY7/97W9rEQexnJZ573vf+/a3v11N+T5TNSdqHFrTpiTz2SqLVQdHojq5hUFOJKZ3WWygkAzJZz3rWW9961vJ5Iq0+LqCBqAgL/Clda973ctgV980kM6mLmqhLpQMdPilBg1qi2MQqGGCckT8u9/9bkpP0zdVqmHtIqAraiZlpnXc6sn6gGZKI5oc3GrW9Dps+DWxPonuVsgQaCuepm9T8KStESNZBDE8IoiuCekkrm7Jb8sZFV/O1ZSs/Z3Zm2y+bP41w096eme7m1968ucu6yzuDMzRQ/mpWrTZ7C2nz5jTHItVYHfxq+TNNmvW1VxMDExjGTv90waWL9129kyr7osb3Tp9S4YGDVQ6XP6f/+q1w8s70wYoP6u/38Q1MgZxHn/sZU98zGN3f97pR77zmk7ff+W86iomYxrvV83Png/3OXGsdgLmLL0TH1WXxn1ecFYoHuPCBJK5AgWqiK4oYIGPdtEQruGRKiQeOYknC/5IUG5mGMxdlZor/gCuILlyTfbReq4FShBQkAn8nve8p87Dvpv2P//5z+uTFFuHuq1m9VWNBBaNQeGGg/9NNfKwbJLn/cq8KlXHZZ74NJw7d65WM39mrVIWdToGh4yWMayeznD22Wfz/Kg/MxP8ITJShHhHwcQXJFHGC1EpqXpF+3a8LOjhLPLpmT7mmq6Fh+aceXG8yGpccMEFFrSIY8pHJEFdGHFOVDnzcgY7ciQJ4rmdQKV2kiyGBqDMVOicYHIw8ahHPcraxrsIRtDKocelZjv71MVTl8jPCgFFxdOlSx21vhWpk7FeNDl6PXX6kMxPygc/+EGvXIBvsaQhdE5LR+sHji+toPBQckp1qMJXE4GVnRKN0xLSJ9MPEYsmYfAmsktpbL1SCoMIylbbT+eStdO5As/B7+rcfMcfHbf7oqEF7hrXAOnPuXaGWfPBRlQTmUygib8uc99yXdB6WWb2SuarrmwkHP/hwSc96rmHH3nRG15xeWf6kv4Bi5mZRcPxyjDcaC5VV09E/AZzjScNPRUkgUBhZdtiAsnrKsnSndndddddTQ4evjgtcho/j/DrSqU1Um5mWm3khxlYpfw8gydZ75yti1796lezxWyEVceDHvQgT1J5f8s0c41RvE9qXwsS7hQ5yYp1lmQy92iJzhMxhhNOOGEtO8CyGrRyaIZcFpqdDn08PltmsMLnnXeerq76JXVMPE37LC8Pzh5ax2RYKWJXl2ZRetJJJ3GwyDutp2ZgHnnkkZY6bM1KSZsiZp2Bnhn+BRyIeS3gMd9ztG5fZomp0GHbbbc94ogjvNaDPF9X2sigA5Gne2+cvHxAmYpyq8z1AQEdL72usRyTsx3pqyyYvy222PQf/7jWMzDb99JXdZZ0Tj7y0G28qGM3u69W9Jwy5NOLutZ8xAT3d23xUHMtf2z09Qx01gPMJSdbM7uIsb9DixZ5f7XgC5/qvGiv57z7Qz95/av+21l6ZX/fnGnTR97mTYBtJiK1KB17SsfXBJps8EmZgXuqGdglibimO4XHbwn4asyBvqcPc3VeFTJqwp//9NceOTfSWzbdu1xVY/1d1cLLYR9V88sZnsQRveR8xCMe4X0yiDy6WiHwSeRBTEc1rNgF5sw7Us+YPlh7v8oBlne25m1fCkDKxsVqiK99lCipXKbE5wYv270rED/88MPLW7i2SpgT1MurA5XycYHVs+BR93bfaOeaZNz7Z5x+Nkzpnuu99RUe//jHM21e2it3knKmlC1qtFtKD+Gt28YJXxM8GkuyuPL2ezXR6KkFsVrEx4J58+Z5Ib/HHnt4krKM9Psl3ld4QcH0WwZojp6M9bYiUBFYVwjExVVWQtFhcaax9sKqm9CXpVPiK2kHlJK/gb7mZcUNzjxd/+RrCZJMlR7r2nNmKZvinjNHbru17h9ugGg+NeXdzchroO79dRdZWnhdR9/AY2NimDoniRX2XLC8i2FDt6IYamCFl37nhdpK9qzIXjdXPbksKkTWjRK11IpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCKw3CNRv5+tNU2wQithrIth9qDaOkuYwSTlPYvtRtqGIOAjEa4ZDCzZz2P9aau94ql/ts3WSX4lCFLGJ1pUTENfvfe97tpnmfLIIik3GrtkLEh3ciuCxwci2SFcUIblswxVPLhFH1jGL4LSZMjzUtofyCU94ArpzOK6RTzfnY6U66oPTHl/uEqQK6iXYasmZgt022RKapAmuqX7biUaY4/0Khn7olesHzlze9KY3FTlApvZf//pXFIWGjtlp58QdvmqOAgwM8N3A45i6JIvUO9zhDq62ooZTRVJ6HDpQW0TztfUHS6CWJViVQiMkV0RQJylsgOKpwSEiOjg0C0NKqvJtb3tbWeyTdpXqKpfDVI5C0dOB2wihg33SdgDnNqUoIm0XDSVROCfSMYjoISKKAJGIurimD/hp4h122EEDnXPOOYgkUEl2IQzaNxEnuDDkfC+KUnJVr1RNag0VgYrAOkHAsM3wNy+ZT0wI7VGZOZxifNx85jOfETHJZKZ1NSs6XOGohrMWmYgiLZOMo6qmCIO9nJknOWPf7NSuLAWig4zUECIBT+iZY+M+AFEqOmk43UYft3wucCuAYobk/Yp6mbjMRSKZ2ZzBiHeqItzkHzmULzKltgO1cxsNxXG2GcTjOYK9cFafLwmHPIurxFRHlqae3ZC8/O/wLhElTdcUoKTTmM4fOixa5NNfXQomjsQwNNEWj+qL05Bgt+LJWDRMI7argEFqGFJ6OyO77LCKVlOFSDPzM3yqliKYnhSR5YSGhnworsCMS5q0C4pKcfOEnrJSLnqES3XG1a0zHm1lUl+nnXle0M0sNoCAjcMOQREO0rgVnAhyjW4sbORTMhS51L0U2s0xcrHGgDMLpawwJ4EbJkeOxZ2NcQ5ENZMKeaIUoXRHkhyeCX8qEkdRCjruuOMMCj5BHCLFkIpwoInNaS49U4nAlKRpnFumIYEGi2PMeEgQ2vqklFz1Dcdg4MmbZ3o7s45fKXQDso4EeQLT7rQt2VVKEZxJceqBqFzrCpFoGCdT1khZH6KnBTP6SI4RpzmdQaEiEWL4O9ckrixrUesi0wglZXcr+3Of+9wsqOiWRrS0ELRsQJM32tLHEoLyKLTVH5zmKr7P5NXzidVnEncoVxGYoyStUt+4KkMvC0J03VhNU9lc5VUdTv0kQV6QpYZ1gkA6fLnqzOMNAep1m3FId0o7rpTC6YHtLIZPbhNxVXohJkmvSw8pnWS0enLxcjX/mqFb3mrrN77xxU6tn/fDazvTTImNc6v+vukGER6iPIfNmGnKamTTJ6JiZPAglno5EDw03JhFGRlGSZTFYghkyKCQM2NG30UXdXi18xi15c3QmtAMt/5G7fzs+WAzQzRBEiGj9e9mGrnI22TvegKiDDTaIfhjLXr2xNuixoyPVzqBFC5lyZv4mELWAlFjKYU1Oeqoo9ipT3ziE5r1kEMOMeOZrAJm6RIT6zNelSfONXWpqRofnVYs/B5yNWi25N/qqU99Kg9Neaj0iPfGN76RP+g8vFPG8olHS68dxFlGPZMHz1133RXFgVK+KjgqkhQHiChFf9VfHQTaPa3ITCSSSyswNxZgmiYdKTwOFavIvvvuq9WsH0LUtXpEuUXEAxx1McRUTR0zaaya/nLJnnG9++67v/jFL/7Upz7Fx9Nll13Gdlu95Ml6tCarQ5mMqmpaQLOw5049oCFSON2DDk5oO0rN6HPFUtZ+q6PbBHmtE6xALHJ4JKGAWniR5fCtJnv5y1+eVy5jttoEMmvShoeAPpDQUzX9s79v2i1ufssnPvE+Rx996iWXXD20fMSC6EtCD/8avOWPl++YRz/60TfdsSnRCJrS4tag5uuzKEhaupuRDH/vYB3C57vwYx/7GO8YebV44wU56zeuIRlfVobjKnbH86YuNHfuXK4utIs3+d5Xe5bkWdItDxdmaWs8VjvzM3wwmBg5ueDlih8NDiNwvvnNb+bjyUNWnjTXZhP7lQgvEKwe24V6n8Cj8T777FPsuAjl2zztuBc1Hp89x+Uht520CvEUxLqx6WCxTvZo70mTKE+11tKrIHPNZvGC5TWveQ1MKEnbKJwiLLf4wPKwrFeg9AC7ZtXwQO2XSDx0W5lYseiQVoB8VSvlda97nbdVa7a4Km2DQcBQzuckp+NvetMZu+32wC984cxQrKmnuJrLPEb/61+Nb1k/KnDzWyutWcFOcaFV/EQIsMulCfIg1p7TkhMl051bkZiGGHROnTxox9jxv/Otb32LG2Vep5k5r1K9sitlj29DCsv6GEllPeb4oQVm0SOPd5isvBcL3DTnlxVY/3nz5vE7zBdkHhK9efDmmQ/HVAnC73vf+ywPPH2btC2KWH9TN0/B3g+z/gwoYMO8dlZKvvV7TiyIp9E9fbO8QhxxjqcJZkkU9mXEKsKLZb8zkVfE5UG1SF6FCHw4Vj733HM/8IEPWFVaGikrr7VHd85VkL86WbyE8RlFg/ZoAs/Yfd2ewroNzV2zDBgPyZXSJNiSaaVhkebVRLZ2EC5isW0ZoO0sUVZKbGWuCFQENiQETE09s9N6UjtaxZ4WfTIxuiYSejvOTWu/XUvNxqXrQpc974h8Chz9V8RvaJFuw060Ss9aDsjNe2yfufLX9X4Fiyy1fa61GaCzxab9W2/hT8RHr+Zv/QvpCT19Zv1Ts2pUEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBikBFoCJQEagIVAQqAhWBtYdAPTW99rDesEvKtozsNcnRfacBhexhtUnF7hMhu1Uw28Dxtre9jR8HxGyItJcHkW8C53XJ4fUJBWhS5eJ55PWvf70fk3ek8Ktf/WrOr0qNfC4YxEdvCrH9kRAelHJiBE90U6i4XaquQjlGEv8O5VSnSDyepJS4q7DZ105fp1ykksZzQRGeHx533oNMWajdFX8DF9npU5xiFG6bcgDo0NRBBx3kh2R52Wj/iqks1E4VynlyKoHCBmJCxOkAAXEuqDiwkCWQOrmkFtyFpCyReGFINWWEA4RF8CdLONNS8TcRSq5pd3EbYW3VFSl1d/r0rW99q6b54he/KCMJmKWqEX00DeZsWlVfR4aKzw50/QS/KvCaYQ9rOdeqjo7gYki5gb3tyoTXG9vNudNy/BVbCXie/exnq5e+54QDgelaClVldJw2YQeHVLP4uVAFzAlFYI1UBCoC6wQBM0OMDl8wxrWDlGOOTV6ieLQxc5qrM67lMqc5rvnRj36U5i996UtdzUVOqme2NH9yJOTH2/Pz46ldJk8/NN1T2RgdklN6uwicHOJgMLPl+L1pVuk4M2OXE56IkZPJlhC2RgUd4Jc3k7MJkKWI/UopZcYjjUwSIoQ0eaOnGTIyMSRX9OypBU9Vzqv4dfEf//jHT3nKU37729+ya20ecgSSQ1SQed7ZV7eQSbniqilvycjQgFQDRR+G5tJLLyUn/DSR6tY8TMkin8BISCTVLzIRUdjfYr8iXBUcodGIn/70p/nCCOCK0AFUHIwkxD+RvPFqhF5qJJVWJNMkhoZY5oltAm9ZIWDDIESfr3zlK1wy7bTTTtwzhRKBeiN3GyyIg22WNEGeiZGRqCwqorZcyaL6iWMLJbkiNlfVEaGnYznqmzYKMQyOxMT9RyQoCMIKsk4gP+dA6MPGhT9sibvqdfqALA6TWOSEnpWJsyugYCJF0B1XNkZwUkaJrumZRVQi5FMvGrr6QfiDDz4Ynoaew1FtZk1DmiTOuYwyXT0oJS9ORWj6eAARMXZUKu2YxnViSohMx8ygZ51AgYxBWGUxoO6ggwYGawBDzJkluRRtmSEiVU21bKBzysvyQGqpoFxOQ6UvRTHX0kOUKKC0AwrEdDyaq5db15yxVEGQ0kozkanrKkvvVVNVTucf3RMsq/Dz/KIU6hXd2oXW+NpBQJsqSKu5psn0HE1Wuj166cZFpVDSVfTGMhuEoTvHNBcMAgaTUg8PThSpYUvGKBN6GFIQHkTXEBPpym6MxZIli7iikrTXC+/V3zfnqCOvEt96iz514lxXVp6EzSXN34LO0iWq0zjGYmRG7Ez3QWfZUuRp/uRNQF7G9VXfwLSBmRxapZpG60jpHQ8CfdOnbb7p7M7nT7zgiqv+/oY33cyJ476Z5JuZ/Rz7gN/p7h7ngGsTiFWGCHiBrGqpnStKGCBQggquyNWo2GUfyeJWalClWBQOILmG0qa343gKg+pEAiRDVIb+4JrSC+fajKRoUwqvmplqPAOazM1vK6VGalqQkbdd95UStaaYy9xLIFPIw6nKejlQ5n99LEYcAz8IKZdR1kmYVyHGBT1enI499lgrE+2Fonb6T9xPJyOKjIm3r01P6mZBbMfbPDcYJzzDgUVzVjOOv3sQZlxi1imWeilOlytskZAr08lNldHh0HUUw1n0vEF92gxQ1Y2D9t3udjdFc3KKgRMTx/V5P1GK21KFdt7x4hkpUVVGoYezh4KzTWkydLPQLXb/zne+szU8ZFBU01URGYnnnHOOw+eOZFO43Wd6Spz8bYruqnA9tbWdRaAVqfWeZXlsAbE6lUUFu+9NTkqh3uSLq5wbGAI6s6B/uuqi3TCDbR9ats1A56ZD05bv8/LNN5n54A8d1VnerCYaS5eR2+14jNRAp29F/ymRFkaRnF46QsbW/C1v/sYJ04a2/uwnL569ybXPfJZlzFD/zOataQZRurp4wjgC1jtyg/KKsG6VowVXEeZkangLEd+7q6nS9dp3NWWtRnbmwHMK23TmmWfqyTw+e89MXp7RREy5Hhg9SYmzzp6AsJkbBd2JFda9xU3j3GRkfs4DOGtrPpdrqoPWKUV4huLh0bNnjFqh5ymbwqEEfKNyvFYw57O/pn3XImTMiNKL2DEZEJWSGSC+OTjURuRzhK/nXXfdNes6mo+nzHhiV4feBo0cLju9VB9t11TNCzSOzzhD4dAEZ+a7SRa9sjXidVof4/FKnywaesunL3lYLvZ3kqVXtg0VAZ1Qz3TVwQRD0N+SRZt6eanKz913m+222uXTx3eGBr0w9DTXPBFcP7C/5e/6KWPfYSYkf20OM48/9IFZfbf81HH/2HSLRU97JoahwQHGgo7NTwUkQ3SmbTt/jU8RAuZbkwbwzasi5n9Pjp4yeuZq9sujdJn3LPtlnDt3rs8NjIh3en5rx0dbPp5M1N4iel159tlnm7qL2p4dVzw+FtpIRFPnr/TVXo714B5EXld6wmIxPf7k0bUHJT+okFfxd7nLXaic6gAqz26MPnh90/fiGp5ZJnk29+bcyiFrAMzdkTqFnT8Djfvp/HpEgTYjTh+gUlY1qd2YI1FPME5png8QvHoxfETRvy1w1eri3TUlfZ0hyhsPH8q59rawdAuldhGlrETGVLWHZ7zbyajKYaiNGdou7yWCZArlqdPvnHkV/53vfMdwAI7x4kphDEVncbmSZTxNJqbL60OA1+B+wIPDTcxWla7e4QgKLd8yJpZTUzc8BHSzqQgbHlBruUZDfR1/iwa6f9P6Fk3zur/5mzHc8Td7sDNnsC9/fb4IrPhby0quk+IyEw4ODy1rfjdk0N/S4cElQ8v7Gc/OtFmdft9psnYs6smSUChTFLkxLkSvtyABnHVECz4/EuI3JfyIAus/d8/dHr/vXo994XPv8diH93f6/OEd828CeEE0QepqJmnl1ZRQs1cEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARWDDQ2AKP9VveGDVGo2HQDbf2ABqr4mrExHC/vvvz3+EHZDO3zp5+6Mf/ahkzy5JxCOOOMLm1/gYyj4V2XlKevnLX87rkwOcr3jFK+wotT3FT8vOmzePNL/mTY4NqTaA2lLsN+fd2v5o34nTDnxjlT0iTmUoCA8GJyRPO+20OH6SxS+0I/JUxWWGTaX2aNrfzF+S3ZzoCnVkwsEe0myptPf0JS95Cbbzzz/fbl2OG+5zn/v4XV+/9LvHHntwsvCLX/zCjudHPOIRqiy73zv1a+ROOvn5X7djBhtGFeEQi7ORdgbT0+GNvfbaq83sR8uVy3uIX32nfzbWuNo/avcw/yM8UDgVY2upk6ikwU2STduHHnqo47I2W0si8C1veQv1Dj/8cPFsDOWzg3sOp31E7O71C654pPIEQSwwqQcKOPBmYhevQyzaRQhK++23H+bHPvaxMl5wwQXnnXdewfy73/2uk7Gq4yAKIoS1GsqrXvUqP10rl63S9iPS00laanO04ReAtaY+wOVZ/HfYSM1BlUIdrIpvLL+oTLE3v/nNu+22G7i0oF8PBgV9HDq1xdwea1D46V2OJOCgmm9605ucStWCmphWepGM/G5oTT3HyVjV0a/mzp1LQlxg6HX27OonGsWPyRNLYakk/OQnP4EDZSiPKJQq57ZeKwIVgbWAgHEnGIYmOkcuGRo7F02e3CYa5o5b5JCAKcgAjz4Mzcc//vEDDzyQLTCQTUFSJZ144olmV7OKXx3389rmTHvozbSCLfsmTCYAxekXc4tDLLL4WW8b/dkLBZm1qGHiihomTNZh9913d9jPyQRKmkPopiwzubld0c5Gmlvwm/lJ87PVZiqH8y+88MITTjjB1PSa17zGvHfGGWcQZXY1+ZjG8TM0D3nIQ8xaZir6xHkQM2TG+/Of//zzn//cvNcFpkEmtWNPze1MsFqbh2V0IEehHBmwjPgz6TlCibL33nsriw4majjgN7vmOIpp8La3va1CAW4md/Ur7s4VYGAguJdiI9wyGQcccAC4jj/+eFcnW5SuRVhJpzV4PvILq44D8X8Ef+0FLr8Qboo2CRPr4CvQ4ABzFQE46xaFeUQC+C9/+ct4soCMWoCIzym5mA91IY35MJlrR57LMMT2iTiIwjSDGpLsjspSIxCxCIRoShiqAjkWGI7p0hmRHYT8Jz7xCUIAgpn1UWvGVATmslhyWJCwPrqQWjvySn+dih8EynMmFYcRrLPGoobm0OJvfOMbNboiLB5Io7YiVJn/C33VykHXdWKTe6NSCwztoJNbZlDJAaQ2XZ/UOuBVBcsVCx7ndvQ99dX9grkjIkpRo6c+9amcncmuidlHmjteqzm0qdLZSid5rD3OOusspWgF7cg4EmUxJu6gl97CiAMBLDnAXDphtCrmUi5x9dI6jjaB68tf/vJHPvIR/j2d59Gp9AqtwwSTbFkowrgTohe5qi9+vV3X5c9CWXCziuO+xJoBsAaUXqqbpdzvf//7eEwOXKKw2lpEkxHI7aZlnmWVzqBjGMsOblls6DbGprw6pA6s1VQZCHRWcd42KQwfDBDTQxREbJZDuhY6VA0ieioCjPvss4/+QyvaSiXEqgNiijauVUp/NmTMJxkF1jyWVfq/A3J6CyXTSzWN0vUE04LGJSoBv85v9eUWtkEJ+AXwFYz13ylHQL9Vhr5kaGsLndmEoKtrCxRX7dJWon2bhpMLUcZis8zeCWlQ40tQhGuKKwIx4HSVlxBF6yc4yUQRWSGpOcCPQUhxuY3CfcPTB/oHbnWHjmHykY+8+spLO7PnNMybbjq8YH5neWdJf9/0oeHGf263lM5M5zD6myorbunSZlwwtsaO2UZoJHemu3aFN/6LiVJTS2fK6LeuqemM6bOlzpnTufivnRNO+OLb3vaG44971x9+27nJdtsxTEOD/X2DToJykjjLIw98GiHDw0BwVVBGROQrQgSbgS8SVWmIOci4BkNE7SKe2wAiHjnUFqehSAnBMGKjfBiIQsSvOHKoZB5LLgxRqQhZy5HSTzxKp2hPfJ5iGGVqQxIgwg1qpYJ41EUQKbeJ3GD2qWBIExRlPOWZw02nvBlm+Ze5OkWb5xOxGPN8x/q0VdJhWGErK0SwqGNaULzNNsm47GDRGfBPBp8G0657u8iXJWa03S4WPFZiGVboRoGWxSleiiDE8kNd4ifLw68VrwUViup0C2kuhb9UB7Go2sOQpMJpBWg1Em+Y5LCwJSmdv61zSUokkiMQs77nGv6eQnsy5rattri8ocBEQ1tTUcwYJDDzQ2ZdeS2BrKksFdquw9FvsNDxGJTbrkXRlgIMtwWklaq4tW6mJssP3RKbhUphrpGNFoF0HtdECg76zMLF/7zLTh2Lw0MOe+UVF3fucMdtly37z5LFhWVSkUiO8PaoGS9zRi7O//73mg984Msve9lTT/3SV/iW56NSFnIy1iJWfDw56xu94BAo1qF65mpPphRgUDy8eFqJDyOUyTRQj+aqswq5eoSsqVvTrECaFylefZvrPMlyDRxiKcWbfyDoPB6IvGEI3VxthlQXT21myHPPPbdUTZK4mbxImHwkQoqoG8yIs/BwDz0mtnkNslKd3zuBI488Mk+URf7oyAQyoxh9Sl28DyHBROEKIo/wIpFgOZQlwegieihFbA+93Jbiwhl6DywKpUDJwv56E1JuS0QLesj1CEw3RE8HQbIwTBxRykr1gdNPP917AG85AlGE80oDHE/QHjomLq6mbgwI6NXtoMrp21tsvvmVV//7/g/r7LvvvocdduhfL+psvVX/9Gkzl02tGz4PjSN+uOYv+O973vOFl73s8Wd845u/+WWnf1YetJvZqa1wz0jcGJpsXdXRFOfRyQdKn/94M/QU5mHKG13v6Hiz8t5YW1jneyB697vf7RtoVviMu1kI3XOcLCY91tAEyPqT1vwYwxCLaR3VzJ8rviVeV0XmKETXrLbS+u12NzHmtk28TsTaihUr7yHL7OoV66677qr6eXymBT0p73ncMylAvOr0iMrom5Cl5jHcm2p5zz777EKRhWRwWRUUK4M4QWWlyo6hRNyWMCaxpLYjPCWVSoVeyi2atPl74kUBXwF8+o+xI0FeVwF/eEpdEEu8SAun26SGIa8jvFsOm68PKKO1TRFFVITkthQ0ZqHtLCUui0Z0W/KWpBKxLNGCNMkrlyifIqwKfCBgxLMQIsq7C7CMKa1Hq8hpc7YZ2vGol6883ngYqigYdDPW3ycPumUFUnSukYpARWC9QCDPX93rSNRs0/HTF+4M4mZ6DL09FawXmq+eEqo2noARWzNtYAvuAqc3H5KWdn8cqG/RyJeF8TKiA23s1PGLG5u/Sx2NedYeoY9OnUDUGk9qOkc3kEyTCQz0iJXs67vr3e62cKtN8M/g26rT+eO5P2q06q63fMZiLLYemmWhwlh4kdskTYRn0nuvNAGREKPfm1zvKwIVgYpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgRs/Al5MphLtd6SFKKlNv/FXt9agIlARqAhspAiY2Mt83o5vpHDUalcEKgIVgRsDAtNuDEpWHW8ECFgB2JHpFKL9r5wrOdZiM6JAdXuI4yUqZwJR7E20R8TOSE4WOFxwTAKRhLKLhWcEG2ftPC4eDTD4oVH7jMteRg4F7nWve9kSKji7wo8AHs62ylrEWW5xvpOck+TdwwZcvk5sc6Ge3bd8P+HnA4KbEudFKcklBF8eAt2cQ+Piyg5m/i+cWbKP0/kKp3TskeV0gH8rdXGYh0CenjhxcLTJ8Vo14jDCZhquEBwzliXnURXUEyhAPR4c7J/m1yOuPfh3aLPZAcxbxIc//GGuFgKX1EBkmUVzNYp7Bbt2+GXgqMImVA5BoMfPghBp9qeqsn3b2LLtWNUcWKKkGinarbyY+ex49atfzfXDZz/72dzys+BsMP8L+al5vh74vXKehI8tPhq4ZXH6RaNod8JJ47dCFmgQDiKw2xLNiUPkR5+0IHz4T+EYwnZhdDvFQc33hzPM4nyp8A/iYDMwbSXnOcL1ta99rc3W9jNxkMHphlxcRTjUTSBpbuMXhgMscDmPypWG6mgsbmVUnIbAccJBBbMpSo8tWWTnEYaXHDXlpEZbAJMC6ORwk6EgcZvgs9NXvIaKQEVgLSNg6ssc6MQFRzz84hnLpn1j2QxsxjYdtXcf5tCFCQSxmAYb8c0nNOd+0RT6vOc9z8+YmzRkNzmYNBzvj7ViO0x6c+fONX+akRTKxRWPh855hsEEaHblioipIpabKraP0WFNTI90M5fSzezBHc9b3/pWM6TphRHhJ8tEx1GOaVOhpnrOKRgUNpHdZCJVxBzOYDlg8573vIfBMn2ZHjnKcYqAcyLZKUN/znFs2ZQldXSNSw72yGxGf8ZCfQk0VaomCVSVqgqEILIy9DT9EsKtDyMuDh/1NeFzUOWAgWlfFdhZxTlk+JWvfIUOnIWZNpVuonb2AHQMCsnpFZwWUVVxinYKwq2IJB7BHCWSRaU0iiKUyEaYtKXKzsUht01uTfUOyTCUGihWUtX4eFK000Tz5s2DJ5C5IbMMiGfDFO2qpqBgDSkGAc4voKGJqcHJkaMRlg26EPm6Qc5wwlnFNRa/TupLPZUliicsRJ4UuEZy0kmLy/uxj30MdNQgnBoai3GBs+bWplxy8D8VZSxyWDrWUBb15euKQMserpQ4aAAm2wdGjrH0IgrLxWq3T3JGTrkyTPBhZ3lqK0QRtfvMZz7jFChDZn0CcyDortqXA0odiQMyOpx66qn8K/lddH64+LRi1GiLR4SjLgjo/7Di1MlBLwKd6dLQGldDGFCwOvbYY7WO6oNCc7CwUSNDrAw0o5UCbuXS5ax2UFh8nV/P18oaxYqL+y3V1x94ImP6dRKrDnXR1oogWTs6Ro4IZBAp2iizvuLb5UEPepBO4rwxF3jqhVkunCy7BQM/IIaq/nbGGWdoXL2OQNBZ6jhCLGCg4Xvf+14NR0m9wuElqzhyrFoNCrfaTvfTzTIoyKEDrDQfp3UGkebT6JpDFiAb/jQ02MWtFgROvrjQ0lWUogrmKx3GrKJSloi8rBqGcX9mYbPrrrtazqWn6TMveMELNEHpSxSzktEu6kh/t8YLhAFbYEesYa0hkI7KUugh5k/PCOm3mY1R9G1zOwY9za1gQjC6TZvmPXR90lUXSkbtKIgLmlVFZJSKxyBicaQiuuqEpj4FpbKY9Q19TEajCYO+h99kopNIFTcGdTapssjulO+c2XO2vcmif/3rqte8acfZW/z8W19dPGfzzjab3YPYWbN/u3TZ4KazbtaUNWd7eWfMbJzXLFmy0NXhOF6eNt98NoGLFy/fbrubDUwbnja9M2f25lRVTdryDaWOVKKDuUChxKJHgYH+mVTdaost/jFw8Tmnd/70rJn77r/tG17+n5vcbNpQZ+nihVsoY9HiqwG42aZbOC0MW5rI4tp2WNAFtSlOWZJSCiI0jC9QFwZPVZCMDpmOSFOKvK7iyYUhM0+AVa7UtC8GRUDS7cwZs3GiyO6Kh5Io2263tSo3t93n3KgUUVN61cQplA7FCqdEFs3CwzMXiIoO+CejGzZZyISPa3IpSN7JZC/FrZGI0lO1KEAmq8GIeBtgOWRyRtHuen5q6vFN39PVmTMdiVlvqyFLfFkiqku70VE8YLpqaFd9wKsAEaI0bqm4SLoEZAJOrrAS5HUr1+gQVGUXpFq9eMwsBzgLfzlJi03FVapUXE8W0Kmtk2dFmox59wIEOoSfkj1dAidiqXLUSHZZRFCipDhRxqxxLa5GbGVgoU/hSd6CRm5d25KDSQpNRlcM5jFXq46SKxGpphelR4hbIUnmT+OOodc0xrWFWXhMyJLCI2OP9yv0sIVhzOt4DAoiGThwAIJukOxUsqS0lnArLnskMOV6iyVW/L9rLBVP0pjlVuKqIWC8l4zimkBXd0031lfBrlsWHrO32wzMtFdyFQYRvUgQwabT4tf6PW0nl/ZtDyvdWJ9sUwjRT5YvayYBR8VdZdGlRUrY4Vb8/y7ee79ZV8w/66tfaMhXXHHN7NnTZs9qFFC0QCxNTGVRW9U8nRl9ypKqn7vVLRPkiklCLMrIorKpLzl4+gcWuXYGd7zJtn2nfOGn+z1/p9cc+P1XvOI/qolZrYMbmca+0FZb3c05GLCRSYx66eGNwBUBzkEvBDLhkxlgBUsHj8U8Mx1siWLZcRa1cSqLZBR0lSXTeEcnqshBiTLkmJokJSAWnrUZ0e6ZulOo6d2TJmO0OjoUiBJZHVFrMC/7y6pygOVpOk+4mim9VCleWegngjfevIpjRiz6e7S0Ns6DVaGXVD0KUdPH1hBrLOsGpKGnJ4uUIGOIroIOIGORVthKBI84Np1k7ty53m/kPUAoha1EIj9VS14aRpnCk8gE3q904NK3ddpiqtoSorMiRNTa1QDH4FV8m40mFEiS4VnsUZtnzHgkj5mEaOowsRQQ2q1J4Z5SfDqxrDJmszqKTPJlV818cdBqWZwkUkofHSEHIOoVBCJtMlXzFUbA32b25grFVwPvCtQi0up1jSOg1ciEsMBYaD72pbTgmDbRqCmjQN7wtBUz5wtECUQZNexv5oHChqGMJn0JHcW1LTk8+rNebaToHoaMa/gj6qY7XHvl1Uv33nezRct+elrzDskMNc3D6SZbNPOPSmGOaSOnW6/moc8MryD9HIPurQoCKGKjFWH+dyu1K7FXDmJff7PyX7bodttucfsvfPqMl+6zy6ve9JMXveA/qkwsJNVXQRRmeV2FiHJVOgxj3VIX+CixXfesc/CUyqalihCR6Jx6paZ4StuFk2RqqBeVyMc2msfMQNtmqdNd4lIVAvijYbvENR6nD8TUsUft1SmI/t4KMtleX+fNs/ecVvJe1HsH7s2e74wiXg+quM+d3mEqDozMmUgAp9VYOoxJvB5jty+PUIgqFhBpHJnXy742b7zL5cHKe2AvLb1IL2seausqXjJbglrdmYq9LDUPt/snAD3xlSzUTgtmyBROkZikMesli7L0T6k94Bg1ul/PpFGEyJXijLVb3/rW1mbexKYvhactTdwYV0p0G7OnIUZPJZIpi0CULFGS+YtBjPzxhJRUGiaeq9Ekomeax9qV6mFrZ1FESokOuTXkR3tkbldcNWmuOFddmkDV0Qlld2vmKc/pvi7JmKcJbDJqrCLKG++ijIawuEqbklxWEWRGN5xZHpg31C6cJbumhH8kp+FQihBv7IXSoIakTmU5KrsPQ17XFzk1UhGoCKxzBAa6rpqmL4+vq2YVtLS/mY2XNLN431LTXjPz8YU13PiKbGb4RuW+hnGjCPe5zwN8ir1kdte18UJz5/Lvn9Bsypo11H2bERzaaHTfITeITVmw48vbadNyY9XGXthMWdmjBOsqbA17wTAxRhPog1Nu1stOv7/vuL34pl0I//DDn1ouLPLx1Hu54b5rFi+9bPqS+YOLPDz8u7OsP/3veua3pcQ4dGVZKlvtYJ3AKLcErVw0dVm5PJW7IlARqAhUBCoCFYGKQEWgIlARqAhUBCoCFYGKQEWgInDjR6D9ti0vA9uUtVa/nkJ7bosa49E35vd7bUzaOIxHL2DWSEWgIrDBIzDePDAefa0BYrJq69CO13ls8q1QcZs8VpWzIlARWH0Erjs9svqyqoSNFoGYeXsWs0fcqf6e3Zk2qbQpHlBZu2zq5SbAnlHQtbewiPPawAEWnmx5RCGh7KpH5I5BLnntg6EAhlzTCuRzKGBXq5Ox7XaxeZT3gVBsbk6qUri4arMlji5wNsHNBNcGNlzyReWsTnZ5cotQsnzta1+zyTJbRW3fzL5MkcJQIjHzNu4c1g32kpbNo4XHbid1EVDUlA6pXWGw8d1JZrccr9jfebe73Y1/FreOIdmLbHdsHGOFP4eLAGWfEGkUEPn85z9/4oknOhGt9LJLlYMVB2xynIbbiFKc5pPL1Y7wd7zjHYWezb7oAiHaizS+XXh/ABHXLeAa83gPCeh04KTDuSm3dlahqOncuXNtJddM6qjWpSwHq3i5ghtfEmUPMYWFwlMioAvOFFaE7bb0UQRg7d8KsCgqy6MHTBqsu6eLee9yHBqPfe1FWjsScNqUGq8IVATWGgLGacpywn/evHnm2DJLZAjnthAzu5oxuMgpeTNRk2OWMN4FEXOLOcEsZOaRZFpwdaiP8yznIuzdZ4C4QLK33gQVHgwOOprITYA5AIMikMYBFqPg8JX5GQWDWYgyb3nLW8h0m1MfbIpU0lD4LeKOQbnmVWpgoCeHODzmYGApiOWIir9CE1SpQsyNpDKzqaaJrtGjWy4nQYIzD2QGAcwcMPG6xag5VCASTle4ATZIurWVk8skCOBhLKhECPqf/vQnDrDARZ/YFFUrByDRC9SODOFhC6hXfE/wRsTFEop6sdHqyGLGmhCOzqMQ+fgxoFCeaTD3sguK5m5JFUAHTNUX0XbwpwA6/oTooOk5qVQi+aSpb4B63OMeByX8GrSAqYk5MmDuFaTKhc5PU6wPmYQokTT+U9ymB/JapRRxaMiVoxrU0KyK08E45mh3m2jIjRRvXKDjEIddAwLo8Et1jEopqX6YXWmbSkmNwy/KF2uIQQMJ8OR30rkj598oCZYsSLhXkz0d1fGtuIuCqj27aVYS2HcB2upCVIp2aNk6TWfWh52cUXdKkswnKeehUbjo1tZTcYJOVURFoFFzyCGHMM0GFD11D2sSlWKRo56xEznhd9UVec6yeNPWWsd4BLJCuaVzsEoPaeNAQ/7UnLZCv81tbkOyVG1BYQ2klQELaiNXcYQYU+ldMlonRBTHGbx0UV7r+6F7gKgpfuXSR6tZJPB1pReV2cYgTZ+xfOKPD48KIhKlUGrD1oBSFlFOjnFApiLt0YqHJy+hVJzCgbFQnDfmGEvP0axSiSoKFJ4aWWsI6Fe6hEbU9DqVsaP/pFdoHUGqnkAfPS1BN9MHMro1HwlaUCuLp62xJW+pBUokEI5HXBZThD4QmfjL7EGOQrFJUkpKl2rMyiVELAZsBrXhv8kmsxwYe+97j/797zvO2clCiP9TEA3VS8Yrr7x6003niCgOg6D7E7t0aXM+0C2Z1y5qlu6K0+dZUfEu46AYIdHKlWRXwYjbfPO+31w4bO49+B17fPnLH/zLhZfRXC6QCuooTs7ixc25OMpIhQMMEckJVibD2CBEYoEs4NcWNAk4rgBBVMF2XpQSjPS0YFByJQdFFliFqESUBcONeRIwmJcQUjQ1inBiaUufcE7dVdUIVxZ8VFm5pSzHFGHo1O4d73hHU2KeFqVORitiUxfzPwRIlisZcy2lrJ1IKTRaKZT1ZBc4wLKWcMqFr0DWpCjDT7Tp3UFcqZY9ZRGCQSN63rQ2IDP9rQhPdj1TRMvq2+labvU6GQuneBvq0F3TdSOn51p4EgEpC8g2gVouzG2ByUs99UXXk1Hcxmrntke+W31AEJFFp9UzRYw+WJk0lMgmEugcO4NeZEafIk0psqTFXfGHQQQgTBUoMOv8KCJ4UmIkyEsyfJIqL4HRCoN42CKTGmrUnh+gIQm/gqxkCBeCqqRAxH+Kvm3JqsUjh8xgGOE4yaRAm5gkV2goFDgaF0P0LKklQlXCTQspgkqZusOggmFwS4LijDVxDnB5RLXQtUaKZLVg7ovYGllTCLSbQ2/XLXUVwrW+q9bBoJlKcVowPJLSPzPDFwYRjYhHw2l0bLKQ1rOORSG53W20Pp7SFSOwsTXdyZhTKaLapSRuUrrDHXZ41rMefsQRn77yys70WVbyzTwwPLTANVloSGzbbMVwKz1VMIIwJKg+TnnNXegphbZECXiS2j/QDNu+wSsRpw91eHDb76W73P3uJ//119dZOknJ7mqklLhC4WMYBmR0hbYZwgmNwiCLuNVCe9aSBUWgFQbFgdptBniEJBdKJARwPDiLPnjok7KIAhdRQmGYogiVSgdQOiVTaKAQpwzvk66evzhhNAnwfSw1bOiTV0x9gbxSWSYvfJU5eQ32LMlx9ty5c129VDeXgkIrszUcJFnX+UkJ3dX71TjAKmWZwMsLecSCZBhYGZEYEVZAagYgzAVjTYsXUYmgawIQCWPO+W1+EuApeKTy2UJxFMaAErYAnjiBEV6S6NOeeULPFadIT3VQLEKS5JGwW/JIQe284uk2ShTPNTW1BuvhRFcLvZ3yLFRMTw9Pj8AiE39ZGySLsjQc3KAtopfS1iJBRVzZWZT2oPPCit9nNi6ro/T/yI9KEZt2VFwiiCqIWXEBHIwZ72WVpcQCfrtSFHMbTCK851qYuSz3OxbnnHNOPhX1sNXb1UcgMxg5eX2kq6DoUVqHfWm3kbm6PRa0tZ7QphCCp62SLqFvx1IQRbim7xluegiKcaRcQXa9DiV9KdJ0M3J0aUG8XUSJ//WvVz70Ybd/6lMf9OY3f8aSf8bsEUu3cGHjdzWSjTL8hHTr1TzeisxfMH+gv/GJs2z5EvobHa5COJWL0u7zRBU5eOIAq3+wmeUEL1pf9YZH3OWeX/jjBYsNFsxKSelhIC2RXFN3cQq4YjY9tlFFUToMixAYFn0ixC2x6pURR5QW7OEJqngAC21iR/MoQkFGX8B3qw9AIKVM3ZXCNA8CqWa3gSZV4AScfFp5ovRKpLwa9cLNTx14TLOq9/rUg4PpJW8jU3o0mVTBK5h6jH+5bUWaCRmMuk3Gwoqs68u/MPS2mQMsnzw4tPJVN5qlG3BP7EPzrrvuqmnucIc7eCXb1ttrTF8HRneStEvpyWyErg6H0rUwECiEx22SCEdMdnG5QmkXWuIR4urFkffz5GcUFIZ2hFgvcrlq9XYdvV1KuS0Ko2QJETZFCIYMCaYsBeUxUL3UXVJPQW4R20VEWqaOxJMFD+UNNJqnExZppehwRreY1PJE0P58gKGUGDCNZRR2X7kodI7w8naFixYf0Mkv0wX50URNs1RTR5J13dJA+GOjzSoGEboaRWbkFAsezV3zBibVLyCncQuPSNRL0U9/+tMtALwL8vms+FdtM9f4hoeA7loqlc5QKGVcFIaVjfSMr5XNvtHyj9kEaY6ug6thzoZElg91Gq9Dfc1KxlD24rJxfTWte6tZrz9J4ihiu/zdPKN4StKNK6Jqpk2P1Ysaizc4NGvsH1HAtvq9eqWQYbkyk48Gf6XkrBFmhsBrc2vXyYCAh/1lnhQ9vcwSKyKqk2CMD/c1dnCVNcws0c5O2mQ0bGep8YpARaAiUBGoCFQEKgIVgYpARaAiUBGoCFQEKgIVgYpARWA8BNb5i/ry/rD93q8QqV2+YY1XhRukb2AvFQNOgajgNh79BvGpDBWBisAGhsDo+SEVHI++1qrfM1+ttXI3mIJGz/MgRRxN32CqXCtSEagIrFsErjs9sm71qKVvAAhkk7SNO4xWe6suS9a+VVOUsj2U+4NsBi1riEAhS0+uNkR5gLQh0m7LNr0dp4ajOB6Gy9Om7S/tvbZSSbCnx4ZLkRRXLG4ySkXB4OxHEW7bjV3RilZlRJGyATTMETJ6H2ckSLVj1U50ymSTKHqyhIGeyrVfJ8LLhtfCIxK4eMGgOYdT0dZmKTLtas3B3UizH5R6AaqAnP2mv/vd78Jjt320dTQrFNfooCw7TV3BmPoWBjKVrjgbarW7rUgYEAOyZnUr0L80QakCIpcWsmd/uQix9rnmfDJ+IdtzoaFeNMcfXy2gk72cJk1e2VM7V3mjpCrY/Bq4aOJWKmngcktC3G2UGvHeJS9+DDgFcUFbyKuxgNlzCKHkrZGKQEVgqhEwJNtFjDfHFjaD3fjNxFgylvlBxOQW5p5xXXjKkTzZR++kx2YGI78IFyGQPyNJZjNb/DGYr0wpJi6uf9qcZRM/Bu4C20nisghRrJw6CN2MLWKyDQKlvpFQbgs+5cwDBqlxgRHnXMlCgSQlEqKrSU8QUSNX06ZCo1JbZ3S1Y0NBIcC8SFDHUs0QGYjYCLcOgrqqJrElC9shlNsozxCUI1WqEB1ylb20V8lVQEDhWanNwGSU1UW7fZO3p40cjlUj1ic44FGoCka+pOSif7uNQgRRbKtqsiAhBhzZqcQAUeb3vM50Qxv8HtDC4JpyP/zhDzuOxQvnSSedVJJKhDL6noCS5QQ99cPElUsHCBAlQu10pzSfLCIC5uiD3xJCKPJFGGuNWKofrdBLRDwouWqgJLVbwVlobjiKBAwJySXeFpUkjVXaS5ezQqBnkJdLLSgcnXOryj/72c/kdavz9AiM9bcMyCLBwUXqFQ3TM1Ou/hOtSFCXxMvgwpMKlh6S7o3ZcamcmMKjpunJekLWNj3LD/xWGmrUllz0Kc2x3377adlPf/rTZBKFX0YhqtbrWkZAv9JGWkH7agW34ijirrqKgVM6uVvqWZ+L6KhpNR1Yu+szpRGlug0lRBTSiCrdr/AQKB42V9IoEBDQrcyjmOyS0K+5+tr51zQHU5csudZ5k/kL/9GZ2XnJKx7xxz9d9MUvdYaXdRYv60wfbob2tY0jl87SxZfMnrnNosWW8c1ct2BBc6qwv3/5tGkzFi9e7sSrwW3FPX/+tcODSxbOv9Lpv6HBJcuWZNR3jxP0NWgMDjfnVHVjWunh3eo3B+yttSFB5le+eMkb3jx8wCsf8uxn/t8mmzi+uMQ5GPT5868GqiKWLWsQW7Z0CSFwJkFN5ccjIKbieBQhlRFRaxVxlYRivAAhOIgL5IQSDDGDHVEkYl3dpi1wRvgKhoZHxkWLlw1M61sMz264+uol06fN7Az3Dw81zU0dXF1pIw8m3fiauURtsoIDtU2MDUrLlpkPVfCAAw546Utf6snuf//3fz26cuqHuUwmyQif0dqQHEhFnvSkJ/Ek9a53vUvDEY4519G51gilVKpIQynxdtFqbTQ5dssbppcJ3F6rb+GkMxPwzne+kw/TBzzgAU5Fcncli56Ahy8wPkDjbFFNQVcki8CEabaq4UbZIPLoh2jW5RFbEs+GDv3GoxY52PSQww8/nHkin49ORSAqBb3tfRWlXTty9t9//0c+8pFOk+pUDlc7Uyqv9uIDlLR0aVkUqjXJnDt3LscTFpZEMVKf/exnzzvvPHHBAV2OF50r3mmnnRz+fN7znqdesUoPfvCDNb1aeF2gUEqqjjXGa1/7WhndtvuAJGVRQ3jmM5/JxTb1iKKb0rnh+NKXvgScvfbay9VJLT5DgUzO7W9/+6c85Sl8i/M7Sfn3ve990Y0LGC5HcRLCWyi/1c6W77zzzqpDpbPPPps3SQtghQoBnz4Pf/jD+U9xcFocjDSPQ1Wd8L73vS+P2/ynEHjzm9/87W9/uyrwU8mdCtxoSP7DHvawHXfcURNzOklhdTE0NKUuocn0E5iYIgwTp3x1A4qZYHPa9jGPeYzeoktw6UJbPlxQHv3oR1stEO7YtramkrxarQ0dNeBwz3ve84gjjuCdkxAQaT7BAsNV7TDUsAYR0LKkdTHuM0VrjszegVpqe2jjRDGs0moZCKG0VUquyEkW7YjYw5PiFB16TEnKDRHFKMv86nynwEuGP2OuiFreP/zGg245o//ux324s3RRZ9bMOcsHGyNLzcJDplAUoHzpeKmI2xJwJiNKJpCutBFHIVKTZcWs/1fKDF3T+cQx/9j/lVe8aP/O6/dXTlNT1aeCP11dLdQlYl1RBHLS4UNvM6Ao3bVgKBINi3pSZUEsxcHHbY8cVQil0ae7akIhpFSNHHGpRTL+BElTGlQ/YJoKFJROQo3U2uzEZcbb3vY2ToJMOGrn6vnRvKeaRdsb1JDx4m6DkyZz1+Rz3aDY1WRQHQ8y3spyBGw2ZpK4wDj//PNBAQctcr/73U8rMH+mQQ4ymK1vfOMb4DLJSzWxMxOxv2NqQo4ZmxHBZtY1OfshBD81IbsZe7fddkMkx4wte1IZHVksBkzshd52KNwuSAdmCFgxXhIIND9D2MOgOGOk1TCEH+aC+rp97GMfy+uTpRS78+Mf//joo49uv0hRWWZI+1KA/S3FWYr4mQ1wceGNGMORFUvhSSSl0EFdUF73utexpw960IPELeEUrcvx6QkE7kVU1gsKGKojs8jBlv7GzqY6DF+ef5lRBtFTBmZ+7j7zmc9wx6ZF8BNrUcRaaSlx/ZbyqTgDDRk23e9b0NzQ89rhe9/7Hg15FNWmlJElxs5PWVBYahqU8eWBgjRoHHPMMXyaU4/xzWsNKxALFb1Fq5GgpVh2DqnFE/i1tBbSuBS24uKghA81SwuPzFyxg52xLk/WKzI1/6YI4+Wggw7ip0bFEQ3DDNI2Z42vKQSCbUHYSMl8HvkmOu3YLmsyFBLkyiwqL5n6Z1ssolQU5eqQCXjcJhdKeMiRHVEqoh6oe9OhqDRnq86ee9/+X5fN/vyJncVXe8nJUDdPrNxa2a3atdQjHm+XDxojzbPSoEfcRsKQZ8vB7sNTKuXa5ekMm907jXrLB0fWDP1d5w7ERqtu6d2kwT/SavDq/mOP+s8Bb/znq17T2f/ZgxbtBjid+xzY7zRfGKndfZCUrynPJJCh1JXTXHTyHoph1ZTfeh8rjlKyiABHCNGVJlBCaZt1dEFedPAGYbepS6QZ3ejYQgw/SrusqYgropjgNSWfn8o3vvGNnllYWzIBqzpW/qrvVe2PfvQjRG9N/UoTE+aRMBNRG41V00SHXbFwayKeJICsr5rPX/nKV/LA5UcyVk3yFOUK8p7+GFwTtUfF4gBLiZ4ZmQYzOaPPgudlNRuRXFYITMOhhx46Grf0PS3LQllXEILZiCDcs5ixwHwQyywysmka406q1mFYWSWuNvFrMj9F8KEPfWjM6uvqDKJHWjaOQDwekHnQZtY17imnnMKxVzsjgdY2DDGz7qGSQdTuH/3oRz29pgqeZGnrYVkqO+VHqlyVIpU+7JfHc4sl5jXTlNLf+ta3+twQnlKWW/HIlIsjNo7LUXRIklXQYtL6xLrFk75x57nYU7yuQjKzDhY9lpW0mDnzzDNl9OhNDjx5fPZzDh5dLSbnzp1LlBWUn8uyhvFKOSVaUVBPLqPg5S9/uXWLWUVeX0A8BWtKbFQCshfRlhlWs5YoFIOPVzEf+MAH8LO/1iqWBNrlrLPOIp96VCU2QRamGc5ZFViBcP+X4YbhUY96FA0j8/TTT7cChLnitDXYLX68ieqZ6yIWAtTQEBYqvljpkO2f8hopu/6zgSLQ18kntubJ2jMut0psbkaTfxETL7Wfw+sSa9qkdBhaYXn3yvo3N62gz8trFDDeGSZJbN7sThA4d+oJK9zrREiPPimlnaProboz2BXTLlfGhrnTvCtr8yce0hiqjarX6LxrlpIKThtu8Fw28g68KWG4zwvGvrm+IXSm/aczTUv9rLNwGjdYsxtnvotusXVn2207Vyz3frOzcD7+Wd2GWdrYRxUesD6b3q3k8v6RlmrQ4EeraZ1G/vofxmy4ovbwkj5/F/Y1K96bT9uKoRkcbvp2P9dgkOzv1rRV11RaX5c6seRSxCpE2OXM4T39drSo642fbkfsqjyacWT0davVeiM2BmMvqammbQPWpZNoch9i/M3vwtQ32P2nf3oXP5oOWez6WzTQmT7QGVjeWdbXibbXfQHqLXzU/chIH5rWZ7k+3auvznCzvXbIiPMdbOT13/VQGSXiOkL42nNHKGOM6Osy1VhFoCJQEagIVAQqAhWBikBFoCKwJhEY/dTjGUTI60ol5alcBHFNFjwJWXRQaFEgynhfEd3a+uBBn4TIXpa28JTVy1HvKwIVgYpARaAisKYRYHGE8aSyTUlKpNyOxz95elvUmAqEIUntqyLaeVfN5k6sJ/ljqjRxrilNLWhMRrfCrBZj8vfQs8hJlcfkn9KqVeEVgYrAOkRg9FzXMz8U3UIvt1MdGa1YZrapLneDkT9xO/bAW7DtoW8waNSKVAQqAmsBgeoAay2AvLEUYdOkHdt2JdpVabtqqbYdPAwVYnv7Y0m1gbXEZWfbykOOXCUUnhLBbDtyuR0dyf5IEpIUBcrmzhBJEBTq6dSeUUTbNF3dCopI9jgxsVXUfk3nK+w9xWNzubzhjzTXUpwsKm4vZklqRwgvR2so1k4SJxMD4YkHhPCIq4JjM/FtgRj9RehjZ7BUOEeNLBSokbzlql6pWpoGPRteC0Mi1AiG5ERg0RmDrbRwUHp8OpCg0cMW4XiK/B7J5TZ1dCti/7TtwvIqThVcla5RIhOPW30DPbm0BehQomSRKVIUFnFLglajfCkuQhTXzhXO9BP8wFR62pc+WgQDYk+WelsRqAhsAAgY7KYFwZSiOuaKnrl9zDqaW4R2EgkomS7Qy4xhhhQIL0ntXO14JKDgFEzpru1p3G0xpmZd/LTtUaMtUByPqbURt0rfPos0Viaw0KptDqjXhmtiZYq0EplAK4iRJihCLUqWdkRq+3Z0nHzm2wFXnBq6GILRnG1KQHPwY7R6N1giObI74lLK6smSg5rYeuhRgJLtFi9akRl+vhV4vnDE9JxzznFkt/QrDOLaWmeTqxyjRaQJmaW4dqUkyRiKvGnKogCKbia1BJKZXfSsN4p6oyOKw+bUit4SlcKjOKHdZ1RZKelUTrCMt3aSXaHNWOqOpgyEWO22NOW2+6dbxRX1qCSeXI7oSG3nlQQuAQJpCAxCsgOhyMGTpBC7dWoaSMBTGrFdtRTd47gtGQnR2YpwEfKNOLDgV18Uaz8Hg+fNm+dMcmaGUpxUclJ0W0iNTykCGSbpAK6Gkmu6k+ZTdBlH6YG59jRTmSWiqtR0icJWIhiSKiJXiki7p9yk5ipVtxGPehGOklvPOn5h/dqFnXvdr/OCF7zg4Hfu8c8/h2XkOSh9dXCwcYUzrW9o8ZKFxlDGgbzdEZ3HpSZXqpmKN5pgHBqIByumLIPHaDUWZCzDDeeWW25xxRVXk3DVlR0ODrgJ2HnnC849mweQkQMIaoEfp4DNaRkRxbmqmqRgRbLbRpUuROFJxh56eDAIMAw+SiFHlQNpeCJZWSKFEvltNrkMUuMxLUJNksOWXFN3TRs98IEP5IzJVKYgzzXOmno64xXIxMt3M58IHrQdB/WwljO0aSzMqaBrgSjx1BekKsVPgdOMJiJuCKauIkVyVCr6RMmktnXDoAmcsVRfB4Q4WuIUyVlZZ1kZRObenOkIriTOpIDgTCaIWGRCVF9eoKE7dZnKajInVPlkLG3HOwb/kqeeeiqHR6Bz1PmTn/wkm4tYFmDOiCqRIyqpFhjk696Kfsc73gFwB1CdW25XQZzmKVGEnijOVIPXKVl0DafVEElb0Z1G9rGpC19mnFs5wsp9pGOi73//+z04OxmLX8hY8PIBDjQJ0fUhD3mIk8ZOCHN+oQhBXsdZnX0NztBIrUsWPImTxtmHSqVeFrSSnNp1CNZMcpe73MXpXA4pMKPnYdzhW8eA8XtLECEqwokkf1VOqIJd0EZOJpPGEQbvG7I4c04HmkQl52OPPfZY3qycT3aKlXCGT30By1uWpoEA+bvuuqvWBLW8ekLwNxDiKUb34L/DWguPEUoyOpzNM05i85HBFayVBsUcRdZ/nBmOl+1MkuroRLSTtOg059xE3NFc528VxEMKtlQ8VzpoAqsXR6lBwUUaOoqrJEGkhjWLgA4ThInVvvqJ23ShyReUjKP5TYBaLaniPQzoCYXutqeVUbqpjtmyhg170w9W9ISh4ebt3M4Pb7zqvOpVr198ZcO7eAkraTQ13pTcZoZP79Wxk1etdUhJKpur1WxP3dERTeCN0K4cDAIdYin6pzc9c3hZw8AzxoIFy9797jMPPviJnzz6Mkf9U3eL0EbpbmgOYDah6cZSM1RdyQylSWwFY8QAj/7IOFEEs0HhWiF5hOA20gqDiCpHbfGkFgALm4ziJTsG8dIxCtsaj6RcI90k2RbOuYMpRd29quWqyYMYTlYjXhXMjaOr2c7ejnMWwMqwKe0eWABpc679eJ7UmAO+J/hKMBtbdZgVUzu+JEzdTKpac2bBKGh9XUKf1DQ8LLC/Zubx1OYBgcHlX4NMvZ3PIxbBQGDIWEnY8j/CTEhSCkuhVyMq3ZT+7ne/WylnnHHGCSecMJ58HZILCUaf20RtxASbtJldV1ppo3ZGzadQhsAqgq333M0fIjcZnHyxEYWTDeKBgmvFmIZCZ7N40qQM4UDgeYpjiF/96leFoSeiuVHAqC5sVh6HmTn+s1zVFPLegfMRCcb4ZKG/DoaT1eOgChSHHHJIxBoOFhXMrrUKz5iaiTm2nKPkHnvsYTnBRAKZYigyysV/BzNnhcCWedhE4XGDpzOuKKyI+IW0IuKcgmtUhtjykqqxwimRJnq+aQ28PFhlmKg7zHmlJMejrsfYLEf1HBaZi0kL11Tcj3PIrnaaGEpak63nZBO8u+yyC7cjaqQzWBGluHJNd+JmTp/kMgNdxZUbBQpbjaw+AmUKSoeJIdCFQK1/FsDZIJ2qPWOjGP49lJ7xIjv5JJc53C2di1jxCCFHktJLaiSHX1wouXQPuVwLRa673a1j+P/vq/ZZfIU7T7LD0waaodd4MV5hcWKL+5Y2b8mYUMWp6YKF/QP+825t+RK3YZ42MIMPrJTuyllDI7QbQlxxd92/UWbZ8kWHHHL2kUc+7UPv+DPnSqXikBG6PFlLNLoJbXDIikWmXpFLgiBjN29DTvULw+hImF1lLKkNgl2xkaZockIpPJRBSUHpD+I9GhbmNRhJryOQfWFkV1nyz3/+85KXQTFBFbfCKiuJpTBJ8nVounOrdmbXTLCQ7+lRRdQEEQsWnWPFG4teRt05LcnT05ve9CaWlNfCXqb1496jHDeLnq9N0XnhbIDrDKZuFlPgdOne9763W0+7cYAFPVaSjZA3lWCs2a98TdbBENlQz2XWFfw3ecbkWYkBJcEzLNOs3dlQT76efy0wODhOr7O2JIfnI4sHj6VpVgLTMwtgKAJpjBFzZhVBju6t6UkgyogrzIng5/WJZGsSTq/0N0/QVOKWka3Bow9YPEDA47ZFDp1ZK+WyuVw44WFAVR8nNCxmPMOqhVuSo55IuRUXyMl7AHRaMY440yEZejrwLMbcZ7TS3MTr6o2NiuTdgoyIgOW6i9X2wMvQq6ynb9gy7vowOTxkEU6fVNyCNk6mvDvib0vP98aAI0utgE4lbJ/61KfiVVOVLUVSl+hmXcc0W5kokR1XC2okQIOvK0/l3in5iqGxvKU58MAD9XMLKksdbKQpQneyjIEhu6+OrLm+NHfu3BNPPFHT77333hHYvmbNRmeAyJtVEyh61nLtLDW+gSGQgaNSnFSaVXRIPVMo9FLf6V2HUN5Uo3SnYmuObmJ3GBY2kWLsCHHbM5m0OW8w3s7bjidjDyX2fjwb0cPcKBZPQq0lxw3qs9YYgn9B0sJk+82b14N9M5qF06z/XmwFNbD1FmzHxTfd/j4PfGDn8sXnmy2XLfGdo6lZN5gDlw0O9Xe8ihmhhE54G6XrpYVjfbp2tZ1IR/Oth6k5g41N2XrO1s02uXb1WnVJhxwhjMPTYl/1aEZQ8o/ueBPJNWSup+X1eTPiumudiRDpydQdhjrMda9EUMasfrd0uekftUe4Wvy0K/NDiaxgHkupbunX12jkrqA0UuPxOcfMHmLyBpgRiu7efcxxu3LgT1BMTaoIVAQqAhWBikBFoCJQEagIVATGQcCqO6+2SrqlvqcPL5qEEMPgnYMF/Oqv0j0pK6IUN0Ekzwve+yWLXCICTeTyyOz9Gx6BVuj5VDGBwJpUEagIVAQqAhWB9QQB1m1iTVg3DK7sHTOX24mzrH5qKUVEoQksbCS3zXchSqLhmEUXaWOmtokprk258cahZOFkEQU9tVA1+AQKlNBFwmbFlZ0YN976Vs0rAhWBlUVgxQ6o6+YHc4JZIvTMDyaHVZ4YIy3XldXN9GXKKjM8ISSUeSy35eHRPCasbBEbMD98hJ75X32hhBg6Brdp3w0Yig27agZIXpjEuE9c2TKa0hMmZp44daXmBD2tSJuMnoW5Rm50CPSe57nRVaAqvF4hwFbRx5NqIm3dbHVt3yaeuSazTEzgaJ7xKKOLaHO2Z7HQo0A2d7Y5xTGbbYsyJa9ISimbMjE7SpHs3ikzyYW5LROxbOQdzYDSJo5GBiBCiu5hVooq2FHaLk4cW/aYpoKyhxj+UpxIJPeg1zY2PZKLnB66rahtSpZ3AaQU12ZIfIIk+1zxtBXDTLHUJdmLnohSy5qypyBJyVX4S6vhnDhvmsPGWWx6cpiL2tGn3PaUW28rAhWB9QqBiYdqZokoPN5ksrLV6Smx571tmZEmENsjYUybVbL3yC/0ngiZa6SCsWuE95woSL3aePYosMq3bcR6kCkyC71ESlKJOFOR+ORxWE3QZG8b96JbIsUkjanPmMQYo1IjbiOcLH3a057mLE1bOBtazGjO28jShrHHqEkt/D3xlNWVd91xuBBdKTmmnoUhWslewG8nFZ1DLNLQc3ClMPdEyogo8JZID2f7tl0clYCQXGPmBVcQm7iC4Um3Jz+hFFrylqpJStHhSXZxGUuuEkkzBYoUIcnpI2cjnRZOLhmLkFBK9hpZCwjAX4OWgjSTzzOZkyWVBwTE0hkw24Bi+GMWkteLIcw9osKQvlFEJYsrCpn52kcINkRySp9EEVBcQ5SlG/Gw0+g8MMNiuCl/v5d3/vbPg499f1xbbDe9M2Nw+FJf8YYG/RK2j3lDywaXzZy1RZ+fah/2GbLfgQYnbfo6Tjk2t/19/Rx5cPDBafCsWZt1Old2S9GlKdkcD5YaHRTnfMuKs/10bkrfdJOthgYHrrq6ORn4sY8sudOO/zzg9fc+7xc/WPzfJlVoqpFntMYllnfHVGoOKBZIpWIb/WEMQzfryOAKW0AOjEkNOK5URS+QlqKdoCPKMUKpAs72JicUIKPEJhJiauSDkPOvmbOmX3stiCic5w7/rlygjAzRPJESjyDVcUyU943jjjvOkUsnM+fOnXvuuefuv//+DoVyDMHzwsRF0r/NwF+AOUcpIg798vjAPYEKElVsVpt/jcd7Khj5RUkRDOHREHlsZOb4sOCHIg6wNEQsjtOqvCSQ8MUvfvHpT3861wnOWDKaKPKi8LMgnp4DaodUtSOLAFWBhyP+HXi/ig6HHnqos7JvfetbTz75ZFA4QYqOjasIgM+bN8+tk0uukhw3dS73zW9+c4RHYUkJ5ZYCyajhPMI7KMs/RaaL8BROJ0UdN9W7nBRVqHGkIjvvvPNrXvMazjicTSWZ/od0j7NydUGIvLGVzvfyl+FQt1o7reo81be+9S1uLxyU1bJ4up12hXLd/pZJTLfnbEKCvA6Ufve73+X8C7PSHQXniOTggw+GUo7dYvv3v/+tCkcffTQHOo6/Fn+OuqIm0C7Qc2CYHyuHYFOe47gagnOrT3ziE9DI6NNMbs844wy1yxpGXbga0b46pC79vW7gL4OPDNXhNY+qFBMi1qHrb3zjG3TjxMpxX0SzrpnHyVvZZVF3aChOFo48nMTWBLx1OG5tlXtmN3AL6Kito9Tcjig3kp151pG45cLCOUuIrqW9IE843UJRSuGpkTWIgLbTo9KBI1ZTZn6AuXiu45WIU5Ca65jNpFNhiFjFZSz3CGwL0eJCKJHsNpIRG0PpjVb/Un/6hMLtUF26qPOGN9//b5d+/eQTO3M24W7q1o2I4caL/XBnYcPfVdKdCB1ym7orwnwlbgibE9zGBpkroqR+XnSWUSiwEDi4NIOlOeofW/yNz237wme+bq+XP+IPb+0s+uesfraOm4bO9L7+//b1L+vv634y6JoLhRIlI5niIvEdEMRSOgZJQlMVNrvLFh3C4Epts0e5FWlLCB0IkeA2YObazhUGV0EREcIsQqDNtsbj9Dchmw/ZXxHzsBmJVwIuq8yEXh2bx8YslJ5j0hHTRiKcRDA6JtKsLsp0KmmC7OOJXWU6PEfnjQJ5F62ap512GgeCTLDAq5F+yFcv54ZcF8lrNjbJc4DFiJgzWViGkvcKRmHM568Ux/MXSIsjElaDAXr1q1/N1jNPRx11FDYLAP4OGE1XvhrTl7h2Ms8DzQSeXjpafxS9iIMnQdx0rbd8+MMf5stjNLPK6lGmfVk8cSudcPaFFWN6qES35OKs4Wtf+xpTYgWiw5PpVflhhx0GIj1EbyHB2WZ+KN7ylrdwZzm6rFBiuJUby64sdC4n2ss5vjUVp3ski/WDXPxIsnesHrNVGg7OgrUBK8YpFejY6OTyRMm1BP8XCjJYTKes5FOf+lQYqog2KuOR8YWw1Y5VUFYFoMCgptTI/BOZrl37eSbXLRxWsrzFCYXlh+bDsPvuu/ORofXV8ayzzuJWklsxJXKDBV4rAStYzrksPFCsuDi8UB2QWogyrwcddBDnJryelRITgQBXLHraK1/5yiwCVafgEJ503Z6M9XYVEIB26R7pANoO2u35uT1rpQjtgqfdYUbzREKbR1yu8doOv5FuZJWHFJz4M5eKdIvefHhwDnqT1O9I+UiN573j7n++5DOnfK4zfSZ/C3O67hWaCTYheVO6UrrEJmdXN7flXH1j6FWf96sVqddzukFORJHQP9DIj2+s4YEr8G862+p0ycnHz9jrKS968eue8trXdq5tXAN50B5YPrjY8/jA9M6gR8muwSVHWaYg051IiEZu5DdM3RD9V9w1/46mpEY9PO3bdi7Zy22jf8suWGZk7jVCJ26pHuGreWtuoYZGf+1rX8s38UpJ03s95NLWXMHLnkh6C4vDKKSyEWhG9UglHv+VSsSJH+aCigeKkqVEij49DOjAay8Bem61qocRjvw8fXsGYWfLoq7InLqI2hGuFqMr0i40fc/rAs+/8DHxenDzVGhWJ8EjcOZn2EKJPcLAVTQjwr2RqjFJRZq2iDScwcrjnsVDnta5anr+85/PTDA0niU9EnrpLbzsZS9jgknT+qVROGnytMtq8OKUiWV0LVDwWzzksY4N5Z+LrYm76qJVO2KN5ynVOsH0RUlmnSFjbRVtDYBTN6CM6quvUeDxP92JabPs4RGV2syfK8ulIh5OxdtFlDjdojNnT4hcbrlanGQxGbHQ8NDKvJYFkg5puYjT86xVKB3EBc/RAvdb1g9Q5XaKSkniTczCjLn3cxrMuiaQi0cqaxW+nq2+4g+aDeUpjO8qSwULBnWnPEfSFjCWXijFkVl6DsvOvxUTDw1+rFKWq1Qlcm0mEKKO1kV083x9zjnnWCV6l2UZYwEpWMKpml7EkZkRipMELkc1E/055Zw3bx5KTzAr6oTeG+TNudQ8E/Ww1dsNBgGDpXlEtaIYbp6OtxqewSvlDkPDCzoLbrdkK/1n22sHPW7woWYKtVwxfn992d9dT7z43FmdWX3TZunzV3NF3ddZyt6y7N3X452ueyy3eulw1+zzitnEV3iZkjTyww5dKEecZ3XjneFm/tTfc3fdtfvMN9w/qKwZzfN6p3m/QE5zUtXeqe61e/HmXcgWvaGuaC60G5vRXQD08wEl0hme3plOwYa1GxZ15TUv7ZsJZAV1xb/8djbRFfVaQV6D/44q0meCvs7MIQO/s5UVWl/nzsPNi4IfLlqkxk/eanPN8dfBy11/vlnzTeTfsza7w9137Nxm+84D7/G3vyzt/PHPnVnLZV50s/t6t9j53Q8GZ0/vXDM0Z86m/dc2Lw1mDDXXJfDUai17OlqPNVjJVRbV9NNuSMRVyITZIxOxSdM5vQ5ZMjTQtHIzmQ93fGfRz5uVsLdJTV8cHdZQ+zbldzsbo0AfS+urrrmmacjmndANhZGKdvubLPYEzrju3ZE6NX0WXRH6dlOLhrP7sUmvvuHQHYbDhnPZkDBxHvyqsLirzhxLf8ESvFldiw8v6u/kb3p/Z2Z/x1uxQU8HtOm+/BpD8ijY+1YM/pkzN+lsvkXnSj+DsEnn6qs6m29px95Q94kAcGOIGovUDFO2fOstvDKjROffVzVcAyNPVXlqGCtfpVUEKgIVgYpARaAiUBGoCFQEKgJrBgEfv7zI8mLTE4c3gZ6GbHHxxs+ThY9NnkS8IvOW2Msr77K8kMwr0FUrm0zZvZTwpOw5YAIhzUNi91UhlXzy86LY+1VayegVKM/1YfAOxEtXMr23pKqX2N6LemOvoAmE9yR5gJKdVoKId6ckkN/DVm8rAhWBikBFoCKwBhHwTcoHRObVF3ZfdsR9IfJZ0K+82KXs94FYQN8QWT321xEJH3ZXofQYU5bOu2hx0hQ3gZwY0OTygcwXK6YfkZFlhS+44AJX8blz56L7DsV0Usx3rrbMYkNlLPE2QzvuZWyKo+RKme+2kDUbj87tK/n5mDtmQTjDnLr4gu/bsaulDri0o7WKb5Q2M/vkZ+kCsSyubKayb8qP64BxPan7mBWsxIpARWDNImCXgs0SJgRzshnVJGCLYKYOz2XOHeTRBtETkPjKlu4TpG/Epmh5zU4rNb2YtcxgJn/bHpLRZhIbV2jlEcmWBsLz9ER55ybsYFxZ9Qo/3YgyAXqCI61NL/F1Emkjlul9PDVKqixMgB+U9XBtAwkYZfHoqgUF0DH6dhF70vRgK4lFyGbdSF7ZZmrro1xWxhYU5qZN36jitsro8zqta7v5pgIE8i3PFMSUT2Z4Otl0s5vdzDLMhud2P18p3RSa8aKm3n5odDuddDaa6GwqbhrxGkcfsLX7zDPPNMPQsBTRxqR02pK6ZiPkC34T0baxVVu7rll9NgZpK20kNgZQah1XE4GJ39iOFt6eZUanriZldYTLK5iVxtPBfDpe0nj0iQUmV7fY672YnkAHWcaTiS61pzlCHE+9CeiTybgKgExQYkkaXfRoSmG+wYi8E+MZCatTxA3qUBkqAhWBdY7A6o/x1ZdgLlp9IescyarA2kGg3VV++9vfOr3D14bTR06hTF4BQiZjAScW2NZkYs6a2oPAmNCNJrYp2ss7rz333NNxOGeVfQdya2lnxZWmbDP3FFdv1wICmkNb5NWkN3cp0XvPtIskkbRUaa92kyG2b4vChUhCVvKumPP1K2ILT0qXt1AweO+TjIgy0lOIfNvyh6d1drovdy2PfO5zvzXU3WnT32kcW3DM4eGmb2i6vM1Z4a6TIO+t3EmNtqnmtIHGddeyZcNuvbsUMGOY1mn2ykybMV2qswY08Swi6mUWlRYtWoAHPJi9hVy85Npurs7V/2kO+Z/yjdfvuusPvvUlNEXLOuS0UlcCShMGh5yyHgHZberVfq3WFNp9TSxVxgCC4jZxzAEEpRATJy0hnOLel/kO6tY1SckiXnhQFITiXRt+aEgSicyVusItTeaEYY7dZrynIoqQCmffAn0DVopDlTlK6mOhd+6+ASiXAw6RlEslbHLJGz2jtnjo5RYl5wbJf9SjHuVs513uchfvCr0iLDBG5pRe2/q0CyqAt4mJ+xbukwA9zZDOl9L/Vre6FWRs9vK9xK41MsHi/CffTxB2mNMbcI4qvGZVwSLQ+9kSx+lbhabE7Dio1hfcttVwIJMPCFjd+c53ZpH17WR33tVBXPE0ZZGZSCSkOVzTM71+9Tnf5xwjooff53NHoO26c/o3AnNNxtLzI7DkTSkUdvbP5xlf6O0MiEcYaPjG4GNM5JQ5oeQteiai9UuSOBzKbTvSbjhsQlJDNx9SVe1OOOGEdq4M1SgPQO9/HZ2lEq8f5YSwV8MqIpfX1q6pWkax7CKEq0sKco203AYlrY/IdwY0nHOObkkSt4Li+sQIcsI5p46VQqBNjXh6PJXQTYlCahFlcqWGnYiGXhl94anXNY6AwW5opwUJT4uXUtK+WrAMyZKEoplyqymTsSd76ULYIsos0dPtfS7N8GlLFi+dsNBF6IlZKQmYEJde23nYw7cxV7z97V8eXN65tlHqGgIc6uxeu5PJiqEmYyRHrDiBQm4zb/t+I8JehDj6Wjp8T1KEm0I5pPv8N593wgmfuOAfw6QtXLgoCneN53WZfJoq1i2wKBTxOo7urwW0NcQGbaC1ecRLW4TermMP5wS3aaM2A7WDSZu4UnEDmTUhhNoCxcgkAYYCNwemUyCoNcc96IwOQ2Pgs8IO4ftaj6LWdAu8eFK7ieuIn3yrfe4VSBCsf0bjtlJ1WWXmVPkGs0OAywMdhn8lyxL8zKtgJ5O4rWDsDlNrduUAy4LE3gWDd+JJUl4IG+ZAhgkkdSd528pwyMgLxsMe9jC7oxQRbWVh7/goxDkx1EVUew1QiCWidHJM+Jw0pbeXpBIpTVwo7HXsly3ynJMK1mxSdQxXxpe5L8yjI22BPSNrNHMo4+mWVMroxrYacEvRlgBe5pXZlSSuXbge00A8TRibzC6xliUglWviItpiSxx6pfeaXmxY5F3rz3/+c2EQsVmf/WWdOU1r+4zLWLDKajNPHDdmVcTSrrBpu0l245KlRiaJgNkpjWvKArIOJqIXye5WKAMwrYBiHLniKRS3PcVJwqb7RUIYethSYoj4TQ7malnQU3qRGUq57aY2VlaY+4gd+Ct8//tPXdS1mf2d/uW8KjQPw9JHbCu25fwrNGfzRxa00mNkOXfOg7MR2i6l0apbQNH5ehE3TZVp3TCJLlzYPINwYcejzamn7/2xjx13/j87s2dtumgxv5OmVaEz/+rUa2Spk+rLlQB5Mnt0QEkR4WlKvT7UKxToxX+F1Obf0blCaUsu5VKjrElKpC1tzcYVl0c2k5UtLJMRHuUpLLAm2pHDIKqilxqVSATagMV4mbc9WqJIDb+5scxsExfdIxDzimejkXw9Z5H4FuQiytM3+eZAdRy9jp24xNVJVaLspd1HK98WbsR985vf5K8QRCbwGBd5eX3SKCJcSNvE5klq7ty59gVaZJo0LHq9uG7LSVyjaFNxz6eMgog2ynwSBm+QylOhJzXWn2HiWoubpNKCHvO9GFdQsox5nbhSo7NYzHg3ZSVmKLb1KZxKF6e/iUsVGNDAyJ4qi1XyPqFornaXXHJJGIqEqBSiOFElSeQGFZ6YgeZayjrnM5/5TBELIm826OYVR4g6Nn9e3pzwtxXvV1kAWB6olBcRrhM8X/TUqBSUCIeYfI3pLRY/7SS7vnQbPkz1ed5Ck6STWEHRx9utLJlCn3ipZoUJhzgMxV+6RLu4Gt/wECidX4ubMfqWNSsQI1HPud12OxiMiLquidQKFo/4Vp2tRBZd9/sEXYM9FjTYkEsRY7GsDM3k2h3ZyhspMsuKVvmJrrjmX1p01Rj0qqBZmbhZNuJBq5SOtmLSwNxjVArX2o1QpKt4c73VLW8F+W0u/cusWc2TiEa5ZvAaL2Omb7alt3ec1TWT9hbTPSgtWbBF52Y360y7dtu73vVWN7vv+YzFb3/QeE3qNE8Tm/Y1NqKLwY1yjE/cl0zUAh6zHw+r3Xb3GqhvedcBFtetHmi4Y88iZIoaM50twtlcz6p9l16K2PTFyfYr3tObHjtru+0WL2/GY8f3oBkzGBQDc3p/88lseMG1zVuaBdd7cdRwTiJQqektUWZClajda5h0pGaRozYZXGOXN2YztZFpZ5sxsImF4u3uu5Me/s9r55tzrrrk0osuuKDNM8n41je/+e13uqclk+mLDlf+/dILzz9/ZBRNpO8kxVe2ikBFoCJQEagIVAQqAhWBikBFYFwErOe99PM52Ilcj0JegvmgaceIxbmXVPbV7LbbbnaV+CLsq5ZPwHlLRtyYjw/jFrMiQS6PSB4AvTV1AHgFeYx/cQoSPEf76Omt3bOf/WyPRb6EOtjpeKcv0VL9lIKPs7RqdqWs+JG5McRNSPKwKaREj2xezY33HDShmJpYEagIVAQqAhWBlUDA50Kf+eyz9eZQNr+3Z8ev92NMkh/es1eK/WV87RfyZc3HLJ+oYhlXoowua15OsvI+M/lkxr4XCWMKZARjB+039sXTnm1LBfbR5iX7gb3i9uLR7hpbubyr5JyLUe5xgEVsJJSCJoh4qehLnBfgPmRQdQLOtZYUWFxhrqbKtQSi5AQKlCw+cao7nP1YkX1BFiq+kPpxW5+Mfe8mzQZ4iytrG98iQSqjdrEZqXlpXENFoCKwcSBwi1vcwjxgI6Xp3RRqljDzmzrMDCZ/Dz52FHhiMtliWAVIZORfyYRDppAJapJyaGLyZ5546WKnnFD45S9/yUVjpmjGyMMXgZ687FFhtiYpdky25oPpNddAg5Jrc0fQmMq0iXkCZZ3LFpd2ajtesPUICSvoseN+rw5iDMFFF12UXbIqyNjZxIuNWVdZ+0LlZTKKhLbYyceV4tdnd9ppJ79lrrjJZ9zAOA888ECY2+3jx+Rsn57S2hmhTjBpU6VMZnxZR9nJ4wWLIZMjS6ugnt6od8lIjlc3tqh5N/Kxj33MziuDSO8ypfjBZj1KZ7AqQ1mFUtZIFnW0M8reJzsM89POa0RsFTIBAs2DRA0VgYpAMaglApPE25SVBWrMvGMSJ5Z8g1l6GHpuJxa+nqeuWl2SK+Z2dAXHkzkevS1hMjxt/hqvCFQEKgLjIbAhzScbUl3Ga69Vpq8+OO0tvJ7ZvHH4yle+4oXL+9//fr/u7lN3HvYmo+HqKzOZUtZnnjWIwBoUNSZi2lrTP/GJT3Q2eN68eT4DoNiTUY4iT7UCY2pViRDQLsBvdvN3HR4lYhhqIPTm0Gqn46WqW9+EvKt1a1HqZRPO8ITicJ3bnCvLqtUHpJz+Io0oqXicu0u7Iwpeg3otmLjOIJI3ODi98XFNko95Xg13i2teEDfuqPzcfTdsuW1nnxfv8MsfbnnG1zuzuydKZs/2NW5ZZ0bHr33zudXlyukc10TQnEKx7cb1en/XzF/kT/LgcMM5NNi3bFHjPoP+Q8N8OjR1X7jwYsp0xQ5vueVmV13VfMfq6wwM+wX3vk7/tM5Fvxr+5S/+stfzdj3z22c2JS6fxd1V4OqeNG58SQRtE2DwJ0Ht8ABHWYJbcVfM+RSnUGjgT6PIghLduso0FxlRBHFI4ifErY+gkvC7hpkQxJzEw6Cgkp1uTjy6KmtFTZtMEVskRM6Y13BK0qYkqFcpXRKx0SoVkcoHROTssssuPhyKswVevocoSzRvF40ooCSE01VFyPcq/13vetfDH/5wCPjQ6OqDq954+OGH48FQ+COk3IqgtG8nH0/GICnu/KqvxdSLzKS2hSepyOfSyGeGBz/4wXwnHXfccRTeddddvUz0VQPPOeec4zCtGjmg64S5VCdVvHP0drVISASeIhHuQ2x8UTkjiiiXoN01bjkB69D1qaee+vznP1/RPngHnL333rsIJ6pHeZSElCgul4o7hy+uRPwiJVVEj/IjVz7wf/e733VrJknXigmIzgWcZNdqaU0RbrxsjHOi9bzzzuNmQichyhlRm+TwKIu0RAgvRYtHAkrqRRSiUExPKNhCT174pNO2RWHI7OQDldkp/OWqS6cWKH4SZI899lBEfFhEB/PnkUce6SgszaOqagr46SYkXjREkRQFclVHi6V9990XHQiuAn5Fi+A544wzHvnIR2pKZ3EjR3PTyov7nt9sIbytcFfSdRev1H0Ai5I9CFzHVGOrjYDW0StcGQLCMitqON9aWFL4I8Lf/O+bXHtDBn7TYxpRn8Qplai2Roa5QUd4bC7hbjPoChvLrqDShdDx6NvpisQKMipo2bLFNqvMGmj8/my99eb+li6ZSbf5g/9+wYvu9Z9/bX7atxupc+b0dYby42bNT7IMDTf6D0xrOnlqRxmU3Cqr6CaiCngYhezdUWjUaOR2QyTEalg2NPK7YyRX07vKqo6tRRf8bOc3vO7ub7jk13/728Itt7hto/MW99lq8yuu6bu8keQYZnfHrYqnFNVEIRk+bYhgqCDlRmdsKHK1eajtC3FzjrE729CZJqpTZgP05uxl14oRRQhp4THPSE2AhrJidlEURO0yTa3gWsP/BjrzRuSyv1xpUK98seviMbLWmnzZPv7xFMBasTtaRH+Ghk7re+3khawmJ3Pp6ZKQAJ4W7JHZJvq6bwTpe/aoHXLIITif/OQnMzeagARzvt3hHGQwyvqJhdOTnvQkLmN82O6R2b71pJMeHpxBIUs6cGFTrh7rh314DPFpk0qwYuwoY6sB0EgozKscIVawFjr55JMnL0SWdHW/W/WUpzzFOg2qrqyJb/DWJNQeLU2uEEtkNM+qUUBnBFkc9njYBKzhIzUl3vve97aYUdlYyQwiWd72trd98IMftPV/ZUsv410HsKHNIPXFnSamgiJKE1tu2UHyile8ojjAog+g9BY9oWc2KxlHR/z82llnnZU+U1LXOJhF8sYcMbgMsbSvBtVGuQYTt0JBXiQUPOYEPIWC3gOjkY7NWDal6ANMrQY17eurIpEZaUThDDNKzCIe8RgRD8Izps8yIczaZHCTTZpffhOWL52jxKsXXvz8fe9+xb+3/NopTfnTZ3S23moTRQwub1wGp6DoRpSI+naLbpbr3TXDcuWKGyP0tCx3tbVC0lVXX7HN1tuxXG0jpYhI87Qry8yZ0xTRPOR2OldccaVV86JFy88449xf/3bOa169455n/Xmwf/6Wm9/qqmuuGlp2q6Flf+nvW4izv795bmUUrGq8SSCHSnTQCvCBm2HVxmekxBWzCgntoLIykgMfMiURQqv2BlPChcjMNRRX/OQDgRBtBB8rKxS9gv0VCX+7xFWIK6gL1BhZ1RcV8qaIMZInTVIFRYzWNkVzjOuYk2e99oMAZkumSZcwWcbtt9+G9+rddtvDhAzMdDm9GtHaUkSV23qmfScr/Yb4SNOUJnmzsXGnoPGQb0ti4OwkM73bZmQ5Z9s3+w4cz9dGhGdw6yJ+oll/B7qsargHBSy2tpDEM6DEsWUdBQG3pZpZABiP9NTVedSyudzeJmWlsbjgtBveU/lo4ZOhTNBpezx2KV2JQsSWRolisZuSrIL0T26nLKj4wLJi8aLAcsjPV6T/9CBc5JQq9zDcYC0y7/WwWRKjG5g9Ftw4xRmQ1QW8NiOqWl4lSUpFLK68TuGpqqiHmYZjltVTdLm14LFVlPUvlBL50Y9+pJo8TX/0ox/N+sQbD0VQuKwS3eIpr7ZK3nbEJAbhMn0Vbds8Nb7BIKB9dcLZZu7O8Dad4YFO/863v7tZaMfZW6jjVpuafjbT5fAsHWje0+rYnhN3XNisKu+34HH6yV//8w896otnfNccM9jfXZF6W80uNy8Cm+DGCLcov261moRVvXrfdLNNt7jVzOaczDRyrUO6Zc2a3swkQ92l0PLudUl3avFrJ+jLBxqSKjQPuV6UeyHZ37wZ5tZdPGF6s5TozOkMsRGLr77G662RhAm9/KzgWcP/0jrlbzrDAd3OUwa9AOy8qn/h0ODQJcObdBZ1PrL5VU2zLbIO7Cy4ZsHM6dP7Fyxa+se/dP45f8Yv/zF40wfe8pZ3/Mudbjd9553/eduH8aLU+dvPvWXo/P0/yxcunHFN4zD08pmNaZi53DIDcF0ouy21oiaqXxBYQVvv/9Wmi7kCGxq+2dJpXLUv8kMk/b4cNbW7qlkr6hNNMw9P7+ssG2y6ZtNxmz4w1NfQux6qGuIaDFYClrXD/X3+GkSVNgJ2t5DrOuAKelJDH+if+5hHT9the6wLpzVj0ArTeNy029F/d9YPmp/xWNCsbJO5acobCrJjufSf/5h/7cJGGbfJNjpvk9qMKX/Thhuw+rvXZqpYUZKxluF2Q8WOpI+2KRx9SevzzDHUt/ld7mwNs2jJQov/zeZsctEvftFtoknK7rL1983adJOt7nf3gdmzN1t6UwZ6dqf/wgvO7zb7ysipvBWBikBFoCJQEagIVAQqAhWBisBKIuBVgBd6vih5h+mb7KGHHuptp1d53k96peaFmKPXvqn5CnzMMcd4NED0eCJXHlJWsrSG3Xlgbwa8dvMuzvuKImE8gQr1dOYzqN8W8tDh/acjxPPmzfPqw9OWd4/e+PkC67OaHTUke/7yVm30U0wpaMyIGiVI9XsD9uHkneGYzJVYEagIVAQqAhWBNYKAz5F+7ca+SvbXvpRPfvKTtgdz38CuSbrnPe9pl6Zfp7ORjNXz/m1lrVuUlIvB9TXcl1YfCJjyySjPDvoKxrbaUnvQQQfZxGKfzyGHHJLPbVJ9OOPpw/5VdG/msyWAaZ6M8B4eSxGibJFlf1etjj0C1+Ctr672ABCo4vkuP6bworaIJY1tDNrr4IMPfuYzn/ma17zGtz+fSrmosEqxBPr617+ulX1BtvVXkuWNRZHvwhYwYwqvxIpARWBDQiDThV8T91n/rW99q/nZz2eK84Lns5QfOH/EIx5hDvHjZOYHO6/MD+YNE0uZZyaDhqnegxJOYk3+Ju1kn4wc28nsQrE11LkPe1E8Hr7zne/0nEUZVz/eabuLRzNTGfNkA0+RPBnFenjkdezLfGh67Elat7cwZ9fM/6ZuJtij4g1WExr4bamydedxj3vc0UcfzWp4btWszsLweMhuerhm6Ww+8YRrf5HWZ1wIX53K2qHklwUFEU/K4z1Wt+mpy8SF4kkWlr1oiFgMfVtI6G1KhKO0yw2RfQRv5EgV4CAJZbQEdAzolgqwSjxyypV68t75znfW24Vs9JIlRbR1wNmU19pjUIQkIgk/DaPSmKmIfv2X1aaSr5kFHPRUgYRkjGLixrj9P3K1FxKy62OFOVkmuKYgEjjOO+KIIxx34mFKv0oFiXLK7FnPepaf+7XZSRVSF6kCsRPUOoViG82jUKIiYQLd2kmYDQQNYZPhBFsKlZUakZ8IIdGhrUk73i6lxnsQWLHBp4dcbysCFYGKwI0fAZZgNSux+hJWU4GavSJQEagIVAQqAhDwCFSeGO3jd+vFkK/vHpzGPE1UQdsAEMgixBsQHky8TVMjD8DeBeTZuy5R1m0Te/2aUekFkHbxMtQrjLw/NUI1k1cbXt61P9h485KvPmk72cOTiqRZnUL3fpBMt15jeW3kjadfhBNXiklABL9egYccMlF8grIvp12W961Kjz6Dg0up5DAnZkS57nzPs/jOe+/hH3/MYzozhh5GwuDgtt6tDA78CcNmc+6iiOFmb3/jhCJnZRWtcDzdA8BO7U6nD/mKfujOD3R2zgsm2qLj83IMZ5R0PqAR1A3e5EgVlXGHW9wWYv+6/O9eVG2y2TWkfe1rXzvggANe8IIlfmRvYPg+tJq7yyPJV31ZZsxsDsWRDxnwljdBvNsomkD0aEWUuCBX2sLLcbVGT9H0hIZbGgKKGspyRNOMikJyU9fuUWe6Kau0F2W8zsu7PAwiJDtCLJcIoBRHH3rSR1mCvCV7IqG7UkBZhaigJDknKRS28SJenMmut3Bjbw8WNge6dt55Z8c1VUdSQGi/oGxUGfVwhA0RDu9+97u52wNCDqD6HqxG3AqQr7LBM8oki3hXXnNJRURCLNfwT3xVNPnhAeDHP/5x774JROkpNLelUAyQh5UPw/e73/28OdUWvkN7b15K9H3adwi+vbxGdwKTjwxHLqWmRkSRqZP7YgE0Ab0UrU3pk4EW/rY+/CXxmsQBxxe+8IW8vnTO00fcFE1yqVRRZnQEmyqErveWblOEUMZ3BZ0zLr1CT1dJf6ZSW6uivAinG7wyGZscW2hT3cOPqNgPx5rIqzgf+zMK2hIUIS/FEinXFJ1rUkuuFKq+gqSSmgj91bFUDcilq+NPLYj1gUcSlXLSlXCpJDsHa0KgrVtt4SpJLtdSbiIpjqhEcpVkpPiarmMbsNFf3gQ8RlBK19y6gTht6aCsiE0WV7dtheUlBEWQhXDTOx7EkqVG1iACgIW5dvEFi8Od9Jz0BF8xNZlhGBOAk52KtS0KGOZ40ogh4vH7Y4VBxBc4g4U0ohSkQ7LRPhS1efTn0XLMPLoBNteUburgACtdWvdg2Rt9+rfEc6s7n8ZPkM8w9753Z9a9dlFcf2c7VRsJTkH6mtJygCU7iowqa7ZUlohgTDFJ6uWbFob02J7u1/TO7uFD2VVKERHlViBEXXRdok4//cxXvOLFz33unWwtmtl3b0Q/fmt0LB9c0LB2HWBZjTAKCopYZCsfawaUhqcb8KipiisLIaUbWW0enyGlZrjRITxGqFwjUrouRElWd0QM+EnGo4kLjzGbVk7fQKdPiRe2lYqYcISJs9BBq4GC8rZh8RCEn55z586FHp0nzj5mqo1cjJQJUIOaITU04Thf/OIXj8k/FUQ1Kg6w2u01XlmGBivzwhe+0ILKSNEzH/awhx177LH4dTPt+81vfpP99aD6kIc8xF5tWNlTPp60/2fvPgAsOYqDAc97b/PlUxbKgBBBBAlMBslkTM4Yg8HY/BgwIhlhghHCZAHGYIKJxoABgw02JoMiCJODiAJJKIfLt7d53/u/ntptjd6G20vSSUzfu9menurq6uqeru7q6ppIJ6RElG4GQmyJ4Ea10T3Vafm+JHCf+9znsoGITsL7hskDvuHh4kUs/alep4N55dPEeMWKEPSLZ2dIoSegWYPe6la3MiExP+GB0RSdcy7WcsGfxZHsxqcxIJg/4GG1Z2IsOjNjTzzxRIWWo1Z649Cvvlowe8TYUZLyOOO11QH0DRN1hXbhiXfci+wdz1kMPiwq8gDSlWXeW2w3FUR5FBF554WsE3eFA7oToYnPgUSrSXHrGnM8ESHzXyRSMkxOkbeLEp/D0uu0YHRUADqPlz1GwgAGICXwByrTYy9pdJ4oKyZj/X3JhfHW4c16l5GH7Ni2tQXhzY8dNnS8859PvcMdiv5j75YIK/aTsegkmRUh3ZbyEQ3qJdGQJh7yV5ZIJ38FZHvHvTISkRoLiq6qpduZtXC8Akmgq6khlMyC31kaY8XzX3Qop0JTI3f0yFgq3VQXZKsn6QQEKZijLPVVltJD2ib8ZUChdyq4UZJdBH9mn6e/f/RHf4RFzL8QHJC4ij8YlcFCO4EMKQEDj7c4A4hgprKMt7CBNHoYtFGFvCrYzsUzbbKreEaiUkGP4kjhHR3tZVdxRGpELWuNH8iDV1FK4OcuGZiBRUGRDtKA6UCUtWQs0qtEZgp3NHLggfuZQtzrXvcyh9W4cOKnq0axqhU3U402RY90NLjuaCmLwKuX5iM9LWYDc5Ub82YMFpHpVGE6PylP3pHyMX+IqR0TNw6wLL5MD4g/i3TSX1lCZM+YFSfRLTvvSNQVNU0mIyoORgRy0v9Zz3oW+zm91Gggi3Wuxb4sAKLTZuTVSEZYTRSHed5HEAa2IA9k9O0u/quO7PFWeuSW7aOpEZ/OzyyDmRXpz80TJleLnltuLqgKtt24XF0kVbOoQijucmJUNspCLe7xIaWr0714zWNRLIWdoiBXpgp7xfNtRrhQBKSiPcWBDCMRtagynuBw9PNwgKUDmGW5Bp+9qsC8p0LOPjcSs4u56XXKTZUD+uFUe6q/6B8pRlYUK7gP5uZ+/+mW0bLf1x4sEols6oKZ3pp67OrJtKi/WTFBYjZ+N+BafeU9wiv+pxd5j3aFmSSrN4sPLASGA6zecg41UHozmusAi4hS3GQ5zBv8vSP7Hniw6+apNHMgJDIx4QCrGBn28o5vHaYiIbGtvDPA9RlJ06i07jc3SJ6vD2htc56oMZrky8TkhCYxWic/Y+UEQQuSwnxjbVu/udgysdUHaccu8qn3otVv6ddYuZ76YO1d72pAWN0ZNCg1h0vpgDOzjv5zcddnHfdoWTE2TpTfNVlzzB2VtaydRs6rL/89P6Oe4onheIbLu5uU6hsBt46X3hzC0U3ZrNcW2HWbH/gOSqNFG2Lv4OpmauZ2M4GapxE0jfSSJS1N+lN+MaX0IpXulhjIhWuXsQvRsERcuwOM37yoGsIwSh3TmjregR3E7x2xeQSPl0JDpLbQ0N1830GkNXjNgZoDNQdqDtQcqDlQc6DmQM2BmgPb44CZPA0DDRitFKcbPF7ZweeOwdJDuv3rxz/+8ZSZ9JCxgxbTdaqDNGnfqWBLgrMt56gVujiCKCKulAnWaHYV2W4df/zxDBg4B0EGoxcqWTZdiLeaoCa1MIF2R8mDSpDLko3RoDPeiyviFqe8flpzoOZAzYGaAzUHlsIBqjB+IZlj0QazRiOCbfQwCGdl/ZKXvIRdKPdY5JqQtgPK/feloJ0LA8NLX/pSG3b2EGnpq3tVc4EjJXR0CiWCZbQXaVedztPWMH2g/QgzhFe+8pW8d8VcYiE8S0mnGFTlv/7rv/Y5OsrwpWS53mAYMPj8sOJ8gTjmQosXrS44hieaUqt95jOf4bvT1EUF7RFjHf4zl9LoYajMxiAQxgEHGXd0DrM4PfXTmgM1B/ZODhgf2B8ynDCuMvm2A+j1Nw5wOMVM1ODPFCTMZUFap+RaLHGIkAsGIxKEzh8x6SEIMpLFI8Z/GWHg4urP/uzPDPiOmYSxBAM5Rh8sPSzoLJfYq1ujgV8c4eJPyTvWI5wa2wJeHPJ6fmpViAkqjhtaZ5HSY+gmI2L/jmT3FRzWbsyHHvvYxxIEJILFr5o6pOOWZUh4hdaaipCr2sSLFNT1KDqDK2eUFsKsksQXIjVWuzAETOStIlwkbxUsx6OgwLMUnAiITiiCpbnKOOAWhkjP+EWCsSKeCtWUrjh7Oey1L0+86pBwBnlwyuiqaCmuHsk7bwAgBDyS5sLEU+lhDQhGuVozQ0Ju4pRvc4TtImCzr+prqCAIwUTVIpJTIpIxuBVUzdVkiUUT56R0OMzJjBswhHkko3GTScZ1IMFEuRlJjkAiiyBFPKfPGwEQ3Jv36byJasrdGwt/89t5ASRGuUFDhnErPR7lxDqyFA7s0ii8lAJqmJoDNQdqDtQcqDlQc6DmQM2BmgM1B3aFA1Y7sbISyethDjh2BWeddy/nQKx4v/SlL2U6swIoHuX0OnI9c8DLyK04rYqNHPpNOhQKFPtSWWujgeiYKD2rhDkG1uVKg00JGHoZGWURIKT7g1MKRaeXnXrXhp+U0HdIgVlZkeJWdgRQJ+XSo1DYppPvps50e9J1YGAZt/do7h0onn3S0T/68RmnnnIpFddg42wayY0TE/29raGhgY2btw30l4dMSixB8+RUOHRI+uU1q9eoRbvj49mtTjG5etW+3//u98468xu/u/CC/t6+VrO/1KmlcjtFOsbcLjpBIfI4xZI8NjbuuwuNxnfRPDXlUITBrejpLc79XnGH217ywIcNfvjjW8Y2nDEwVAwNHPjFL36lt29c3cszTb34Y4dMgCe0P7jkAGTUXdFl6TM7ZABo07CI8kuWUNVhHSROtDpvrHgNRwmFA3bdHAAGDJWrvDbeKNyDtyoOs8PGUuSK0uEJZS7MZb3GNA2Ewf+4Ss+3cFZvpbulGYQZDfKKqEv0JY/AV0PQjzyPwDhg+frXv94eMB2x44KwcaqCJBEUQgUs2JIJmDcCTDoCbK8y87Jry3YKfyCRrn/aZtCOUXpgQFXkmhfh0hPhAZyv2O57GhwfRGKkZ2xRF+VmeI/c2rfwCQ7x+973vl/+8pc9tRvtKsjytre9jft/nYSzqne84x2OwZ9yyimAA5vmU039OWqHAM0tEQDMeOJW53Gr23gqiEdQFmM1ZmfOjjKDo07lwgn9s89n1LXIiJSIBFVdMMHMIImrCMfYAgDbBVnCB5xXPggIVbIm7sIMDyRGDOpUkLZzqFZ968CB7SOPPBLw3e52Ny7ShGc/+9lu1Q48/EFAYFvKNdgVNQIfGGCLXhfYMk4NqhQhMFdzVctCoV6Xc6FflrgqLuIBn/HnSIAhIDptLiLgDbPa1FPMjBTwuSCjU1Qn2h2AsryMcaw64F3jqYw5RURBUmCA2eBgLwdYLqUKWcd3nQO5ybyYdhajrfFfK0TreEHAROvPlbbeC22qrWURQM6FMX6efvrp2d8Bmm2YGR+qxIfEWRyPsrykIX+d+UzZO4Onn/7ddmesaBWfe8ndv/Pd/3zr68/XH0eHvw+g0eCFoWh00tZmp5GOO0Zl56XZhk1UAQfsMPEB9L3vfY9g8vZFvVJxOx5+c0Fxj+MOv9PdfvYPb9m8pvec5FajNfiNr397urgyI9PJjS3xomUKYws5w5CbYdoiBQySpHS9TR6hGZjGioCrcZvxeG1hVsdcr4DJACJaRy70eGcFwlSoAmw3rufoBpBsF7IKgJKzzz6bKGHexOaY3PT0qU99arXnVOGXEucqwjb8Qx/6UOOJnqzu9uc0K6eK3ByLQ6KOWBrXpeDcURj+qiJLNIp4tPIieKxQbKkCMP0wKzZySpE9xBMvb2Yppiuks892uZLIi2DzKASuSIi5GPy7skDO7CCs0lmemQNw06lznnvuuQYHXa4Lfim30av1Ou9XFR7xWgTPl4457Ob5ezKnYoBukqBSplVsOARTzTe96U1z34hqoddDPKReLkjFxWOOofnE9UCtaeYTnQEHMnA14qk3CHA1UTzGZxF2IfFWdpUY8HjrHdR2MS2P/qZ9czfoQrvQrSLYnSg0iF8IrE7faQ5gbLSO5cZXv/rVwONdk+7dce2Sv96jalssJMuq9Ohj5tV5fJOd7OjqWiF/wUTRsntDyehqWSFxLMt6Wn1TzC/6l/W29jn7zJ9MTq/v7S/++qUnfP30j7/19ZeX8jdZw0Bigbl2TXh+uXaLfNXKcs7Qnm6W7mVApjnDlo0Wv0q3spbFO86kZt36q2cr0ly9Kq3r4zaI1J/TONYpF+Rx7H8WOv8945zidje/3YMeccUH/u2aYttXRkbG167d90tf/oZFWIbxksIcb6L6Cl4ckjq/ayDJRDDxIkTVTBK6BJz0s846C0kBIBeOdc1zAk/1nTWsdeHJZekAIA0XXueMM5O9QxGVCgxRzWreuZh3TuDGmAZzIExNOT3NBveFL3zhmWeeyThGTzZKH3fccWBYqARnohGt8ozelpMeyVglb6fjV155zUknnfS85z3vMY95vM6s7dRdoLWwiGP/hzxlza3+TpdYzQitVqP94LtTV1nEFqeaK+JmQQScgZcPOJ2ToyvnuDIYC+aTTz7Z1IV1IH0LOchSUHFChqnGc2KO6AwRLzOlXCK6GfstE3Vmc5xvmmPomSYAJmA7vf4K9tKlkPJdA46GUG5u63gj0JCJjKfSQebeqwsZGf793/+doy6fIvTI1ccMWMlLz/128epXi1g8ruIGxrkwQbwe1fXmdkHqdVIA6/ZO0MXTtATo6Yk3Ol4Z1KogVkjvwrDQrR4Fp6eh2BHJbeqF0mcUinW6kBdtXiTApEdF5gWQSP2yOMBCGev0GykH9MDRyenWUN/LbvcwQvAWhxy1amRqxfKhRk9f0SzdA61dU2ze3CG7y1E6rSYmx42ha9pTK3qGVhxwxNiqAwcf/1Qd+/SffM+U5uKmmYIle9GZmu7AsLuDF6YzMlaMF1NXr18xlaZMvc00mtF5KarRn1wqT69d8fPf/MIHHySl1PQg6elUYf2VVw0dcMD0+OjP1ic/lSOtJt+N9LbpdRoYMhzdppl0vzdsMFqbxk9Ojhse/uya9lH7Np/U15raNn3a4OZLr5z8bXPNZHvyys7w5FTRufmdLDAKH3S/+uo7jJmuFOsn+pc3it/2j15+5jd6vn7VUbe4xe9u9b0/utOdvvtXzy+uvHL0Zz9y3fy+j6vuykna+L6pzjaL4fEZNt2w9d49pU8XHb8LhoqpvlZPu9XpW3Hsk56o6ffXaYrizP/67FU///nEdKtocpjesG3CgWoakDulHrh0UB6fLZntOomqnejHMchHlUzatwwPc2DFtl1njcSZa0P3RUN1ChQrROkIbI2Ojm8uheSGGUW1TZTC+yWYCndkj3BdrDOJC//Zf599x0dGt2zcRCQsCBU4m8XkTtR/QaRzH6QtkslGz1Srsa3V32l2Rnknaw11in67XqLaqFV+NqZdtk7pB0yLXEs293ypFgYovC2rM9LAmMakRm7ieWq/PVuDuXWqU2oO1ByoOVBzoOZAzYGaAzUHag78oXKA3sw6nVqAHu81r3mNLU7KWMywV8vyij8s2q3QFsaiyXWnWUWFRZtqIb9dVKFAAyYgj/KT0w3aM5+780G+0047jTKNopWO9Je//CV6wIMU2QkVWZTlijDB6r5LO7rT9a0z1hyoOVBzoOZAzYF5ORBiizmlrauPfOQjvo7si1AMDxhc/dVf/ZVtxw996EO2rW3YEUwhp4i5iMyLcCmJSxSRUVAA25+yU8b0yBc4zBDe/va3s2W1x8eYivyNHcnYg0CALDm+FHoCxjxELmp/m3E2LJae8XqAVCMeXthimRiYimy3RA0kiyu+2XZkB2Xaw/vVIx/5SBvQIn/7t38rwpZMZQMbbgvgg+HbLaIGqDlQc+DGzgGvvCqwgHXUkeEEy0knO4x+z3nOc6zCnJMCwBzL6gxYdVyNjDtafSMPA/7IlaXJIkhiEANpZ5nVkCUhc5RXvepVFl/OXDCvkmjwF6orpqAt8C+CfO4jK68Icx/tDSnEk4awGt0uMSCDdSAZHTnHxPKK3PzYxz5mNe2LcSQCg16Dv8ACCjx24dtOMC2IiYyayQk4KVBplHiEEiE3iojiPIoske6WaU0YN0pRTXH9ECSJ7BZmAOqur9rEN2OJCUmQLYugIjFXkagdXZm+mLqQm4RgiEIwmR5oIVE0SyFPI4tEcYl6FAwK1f8hgd9tBBiY/+EkeqTA6alcqhymg44XiSsUe1FrchVMdiuvLCYbilCWawTZES+7p5ECp7yK8EgjArZT7GoCEGCMPzWrDwBTkqBTOi7hj6LVyzWOySgRG4N+MF22Z+quxHjKBk+lAMAWZES6K5IyYUEe/jj65LQXr6boUSI6wQiZXb7d6OiW7ChXZU1ZNYBELauSIFIuaKNpRAKV6qs4/mtHwNCqVOAHICOOIUOiWxZQ+AwsyFNlQTok+pKxgnE4/uv5EGoaFcdMSNQ3rKeUjttyQRJMizmhmedhhx3mCh5nghtxjbLqaxcHtj9CdWWob2sO1ByoOVBzoOZAzYGaAzUHag7UHLj+OZDXNiIWWtZCIsL1T0ld4vXJAW1dbeVq/Pokoy6rygGepCggQidCT+FlpLNwq3VCXRLaq2qWheIy5ke0GzRr8EiJc/ieZr1JgNGbUJoEDK2HEkNX0tUxkOGYrgtKkEe/k5RoPcW9732EfcQnPOEDsC0bKLaNdTjTEodk69Zta1cv37T5WnpULQqKohe65j250fHRpuNHjPgV7owG2jrpSncDT6nWSTho3KamUikrVgyhrd0ZTub+RUHT/b4PnnS/+33jf/+jGBtJZw7hGR1LDkGiIiKQhFIvFVE6pqF7EslBehcrsEuWqIindFIqK2jB0HmJqwLMiZjZk9UUUqoPJrCJB0xGLiJjwETpABSkskoM8lyj3Jwr00lB9pSnPOWQQw5xMpDOS93Dj4bmDuCcBZIIUoTQr51xxhm0Zlwavfa1r2U7pX0Vas9V6WjOpSw9wsvSKaec8rOf/cyJzaOPPprTDR3GloBTwVGFpaPaOUj1UjtlqazIXCSRWH0EEoWOwt7vfvez/UADyOorZ9Sa4rZX73znO2OUXROfEwlnUhlGBJK4Dfh8KzHKWogefq/4q+LUQ+QZz3iGo/sZbRVJTpwbgV8IYK12z3veE+ezAyz05O4nL85o3Ii4YpdrYBAJIqtNf8IJJ2DOP/3TP7Hek5Ei1caM3SPXd73rXQz4ZBH0YdkjuBUpk+dvggBTqEgQIBK3ikCwePQW8YjAJp5LCeAoSJaIQEKHqw9T+Ho93XqtXAVa7xgVKYvjLYYKZkF2bxwMTo/bMo8awZ9xBgaqZ5CGQVkixRVMUCJdvKqeDrB4muFFogpV5AEZ47PvX2F+JruasY7XHMgceMADjjQcPeEJ78wpe0nEO81X0Yc/8cQHPvDbX/7sNX29M69bu3JgL4SdV0PQ+eMlEhFyLSLuCkaiSFVEZrA9F6m+oYuUwikDZw0GEK/tQQcd5PNKPiq1CHz1kYHImG9E5c6A+yc+mNg8mRPazQVGDAXaapbtxhlLcavh23ccYfAZgW/2C+E0Ym837+4CMKOAKobxJeIkYtiiGaXJQQ3tVsZqE5DO97///e91r3vxf8HrmRotEXOAQVXFFomGYtNj9hB4pRHtl3Pc6ZtRhAiA6IE7VApg3VVr2vX0OeUdzdsFH3VkjM4XmDmVYIaG5sc97nEvf/nLGXhxahaW9F0Zr89brVwtLvaevQvo1I6EO0GsG4NJb/t887HI7pEZI68xumsVYc4SDnzNrqtPc5xoFrdLjR5B6TJGi5ctPzM3y/ALRbDUhvRCT+v03cWB3Ky7C+H1jOeP//jWXvOnPvXN13O52y3Oe/DGN77x3z791Pve96wvfcYqMjkU8/JVM2J+5n9EyoRrEwFHSkQib06potpzccXtBHIvu9dfRtnFXavDuFtPpVcTd6KUuVksOkzgGcGY/DzwgQ/kAsk6zqdoiTNOncCH4x7joaEJDa94xSussrOznt1Fz2WXXelzwT/84U94dOLLCVplGTlf9apXVZe0c+m/AVOsyJgp+yglk3SrbxEWP6G2Cqq4lcFMNTIB4CQO00wVdojg3JcwJLpEMNyrwQOmQs0r3vrWtzKbs2rWajuEvApMDJkEmkX4SmQ1XVyvc82UxBI7+moXZNwGJIUDSDMTgXqHKdg97nEPop+XLn7B2PGrAshFunQucd5SuhL1z3lJCuK320v1NCKYykuhpL9cpD8KVUGKnh+LfZEoN9BGnPEiy61vfvObXSTFraLNk12zeVmmEzZzBrdUYXPVMhlb8KFaYn6UI6wnc7yO3IQ5oBsIupNrq9mr85hUc0pI5Tzg9MXW5BAqqj92xRUDy5b19Pdbf4KnzPTBAo+aPgnRbi9fudz1gNvcUg+8YNPVdMg9W5ICv0vg7iFOGm3GJ8e5NUITg8d1G9eNT46Jp6oVySsUR5LD06mOE+2pYnJKooC8eEm9mBNcdBGX8bmFdjHCmdSqG9IBFq7HD5Ho8kbvv22rhbAKTW3adumlk8PTNPzT3IuZHYDsHHxwv28/bLrahsfg9JZUu9YacmTzgUddc/XVnSuvtD5qX73lvN7emz38Ab7yvHL/tVZV0+/+aAhiC3C6xpIrN50L1sVYl9ZEE+n7BH2NPj28r5wEWptf9etfF1OThuaeTulfuNxb2b31DwJc9T1Fs8edwR+TunAtF2/YtTqflnlbO23gzAYNXM7W/BXiGs+q8VnoMvfszXb/IsmcDW3l27pd8OsPoORYOtaVnIXNWO2LzgxHc+lIj/zaHdt05cszoy6D4dpcKftNrZ/PZUWdUnOg5kDNgZoDNQdqDtQcqDlQc+CG5YD1hUD9ZVVOP2xr2N6iT8fZ4aXMpK3yrTsqMgrPWDEBvp4JvnaNUG6f2Wa9613vSmV9hzvcwZao5TMKHc5EFQqDSFkisqOkyhWrrVAD7mj2Gr7mQM2BmgM1B2oO7BAHCCybRAKzIgKIyZa9ezuz9pv4maIRpWGWLpBQgIUdwj8XeOkYosSQ+9TyX/jCF5jvEsG3uc1t+OdiaWybONSnhCYKQcZ1bqHbTclU5ch2s+y1AFU+aEeafKbsbKVYRfpSr/1c3jxtK9uRCd7mKu/c1GWv5UNNWM2BmgPb5YBF1vnnn8+9oE1eAyxD01ve8pbvfOc77QYaTmWPUULErbFiV0aJyJsHnMVpC2BX8L7HbG2IPKY7j3nMY4xgTp0EMYEt4hDG7eKY5z7duVxz8ey5FHxY4towOBaUWJ8a/FnvsL/CwGOOOcYnFV/96le7rZIaAnRXmCAvnOxbYsFuRoGMuCWyRS677LIwHmanGsUBiICSOGLGgsXGO0dpPCvRDHCnpRMS8eHqCH49MzboWaKGzQ97Jymys6SFTV/Vnz0CIAVV5J1yfSvXFVVSAKCHHRoAW8A+Hys7JPLC4BF6bn3rWzu34tAKAsJrki1RdjsssgCjBxLUIlJErjg4IyMiZZSimjKqjsriuSxIkmJCxZTdN/DgiRfKpjwA76Bc0QQRYTDMWBqwCQ/6KRy0pkIhkc6C/ZRTTpEIMzK8EQzDWFipBSTo4R7LIyZhsAU3qi0uDh6rURiGQ4yItBG08mICXsXrHyTlvLCJ8wnlS9KXX345qzAAaT/9umoQtz4tyace2qByS8mDJ/QnGhRPWP3FCTiPBGTjGzwCerSg6vPYBTl49AcMktziGA7gmPpiIxhxAxfXeI77hamVxtUVKZegxXM10gFEJMKsHUHCgyTmfyqFADDyGgYRDC1gt16Z6BKaTIqyggMl1fVlHg6krlCHmgM1B2oO1ByoOVBzoOZAzYGaAzUH9nIOxMImX+t1zl7eXruLvGjoWNnGmn93Ya7x7DQHNIe8tBJaxJWaifopsGmvaLIlIpc9Z6QNofsIjyqRSKlEVxIIo1AqGAoaKSJSIrsUapFqueJOSbCsHx1ZxSnV+Pimq666ZOV+xYtffuxXv3L1Od/wZemh6amhVrExnUxJ6uNiYIAP8pHyjMp1bO4hKYlJiRQsfmzyQ9PSbKTT8rRUHnU6Pu+dvgne02hOtcXbrUbS2SXXMVOdyYlQNCVMnXaPwy8yDg/PVE3i6rXF1/53+vQzP/P0v7jd17543sS2YmJ8a3t65nM38ExOjtH1KAvPVTzehcz2hHfWKY9HZbmRlpTOwTH30iPjzLPt/ani2R5sek41Fvi1TpChdEEz0bVlDPRWnBA5FPq6172Ozovy1yf7nH/uasSADxoyJSK6BL8bNoAd38IBmjgZQ1EYWZSY4XOhC0UyMEr49eAA4tRTT6XHp8rU/fRJ6raF8u56utIhiWvGFre5CjmSASJChUq3aLOZAyy747TGKPcItwMDtjiZiduUiZxlOGOsUbRRrlEVs7c48lZLgUqQSxcSqo8++tGPPvvZz2Ztpim1hRPU8VTREarA+VE1EZjmCz21iEOzPqZUBUA/mJwSBESfz8REFdwiEry+AV4uB48dP+YYgiJYCiW1fWVq33/+539mJBfuosBn5CKBKupb5UwVRjx6csmY9IKLwIN7QiCMR+gBjDC3QXO1iGBRroUvutBWUxzbRai6APO+q9Fxxx1Hwws5jbC2U2iUGwi5VuE8To2CkirHxGWkRGY5QQcNXoAnl6vPyGUzA+Z46uop/PkWgLjqwBbx/Cjq6FbpObGO3LAciK61OA1LgVkcQzxdDE86zUoken/bzWJN0VnVs2rsBS87+ic/Wv/tc4pVvQcU0+YMjsF6XeO3NcVn+t21b31K3PNhYuux5559/nm/OP0lLz3u3G/+cMuWsb7Bqxs9VzSmrzXt9eJ4L7Igi3ehiwNuEeua3yC58msybz26MCwdJpcVkXkzzptoPPngBz/48Y9/3PdyAdiGJH/t2M0LPDfREMQzBTliWDNAmZYIxGVAEqNzs2w3JTj2gQ98wCgdTi3tVtoFNEDZ6DK05iF0u6h2GsB+arRpbrvFURknbROy/eKCkAjm5eHv//7vZak2B18PLANIHN/kYbgQnacqghcvYpGn733ve8lfyLUm+f6+971vEeDtPtJLfUKZANoVB1gqnqvGnZmdY5+pVHQ4TLENzCfIe97zHsZe9oa3S9L1CcBcj6tTEyotpU1VxAxWD/Tm6s+cd3z7299eiB4fbyS+uxxg4WfAk4w6MFehevJcDPaqJZrFRQ+PXNF/ukTt3Lw5xa4zz61u0QzDEntvzl5HdpEDWgrb81svEvG4BvIy7dqLxOrTXSRggeztomHNVR6V76wo/Frr/99Jh/74B1ed+c1iRe/yJH+TtNVRY2kWPZbUzkE8flKulYP58W6MDDZve84ZF/7gx1/8mxcc840v/GpirNM3eIWPvVWLCPZF976WlWUsg0W6W5EqZAbY05GdfvsMNToSmULSuRrVDQ6m5RwbMQohCmNpE5PzxWcU261jF5GkkjFELqeGFMfqiHOlz3/+8wxz2cTE0ATg1a9+tUHMt4gzndstaHEAbWTCZ3kBOdshn8tjMkWo3f3ud2daZF1vQFscww34FBOoqjh55IPG+vdFL3pReAHOJOEquWAdytrJxICTxOoiK4PNGyk5k/pwcCCaO1ot1E0cYFknPvzhD2fGBD+pOi+eJSYizBTiwQ9+8Nve9rbFs4SEUvcqGPKkRxeKdFZ9Oi0Nj1vTRZM6werbCTpLUV6xpOf1chVVVzz40JU499ZLMW9Xiex5xj43oxQZ9Tq0IczMhLEpnY8awalSgimBEwh5MJEuZFSkP99nCznAMnnQB2g2TK68UPgWrAtst73tbTHt17/+dV4+R7kZ+VIijP/MH0CGM7ulZKlhbqQc0G1iTNCLbnGLI92uXL2m0TLra4xs2jrZn5ywLytnnj2NYuNVVw6MJsdSGxlrmmcyq9206eLfnKfLrTxk//7ly5ubRlf0LLvnLW67xgcLrlnO6nErT03t6eHZl9vsoM1bU7koryiEFmdedf5QQiY9QDHcKYZ7zDPaU4325smt/QP9zUZzki6tPbxi1UAvnXljYmp537pyOtI31D+5aau9gIHe/rGtM+vKZX39U83WYKdHrbn+MmS1O20LlmanzQRzotn04xppZh9CkaUKIcjec9oEJQqUHP5RdiTZ3VPc7JD9Hn/FUM9480tDB21ubf6vxnCn1dmWIFuTzf40p7rfA4867rhVX/jCd0aLF593OdvNVxx78zs84AGjBx1Bnfu9C7595fr1/3HpBffqu+YZH7ydhtvy0KOLznRxqwN8PaO4cn0x3e6duLZ61CsaqbekRBHlL1F14wp9U22/nt6kvk5bKVPT21pN+y2tVKOitWpt0TtQtKdt/ExsXl/WUYVbAK+tZiW66y2OjOo4n0rp6e1duXJSl7KXMTaRxPNYaZtL/BWNiRlKvDA9RaOn0+ix/5SDbi8+Xaa0G83Cb0btNed9yXnmiyTmVHZSukCSBr+czStHrxibfZG7wHbnbXNivDkxXa4O2s1lU8XQZncjW9u9K5PKuKe/7aXn4s2OVcmBoKiRnJelOXqipK9RbEtvLWvoFeX+0UY7d41ivRfFL+2t1aHmQM2BmgM1B2oO1ByoOVBzoOZAzYE9ywGT91gBWW7YrTv77LMdqmRE9N3vfvfDH/6wnTIL8NiT7V4lLUqXNVPovrqgQlPXleg2NB5z0wOPq4BCesU/+ZM/YQSFQl+Do8eOhZKnIrKL5GugDZzzEhOPMljOO29N51I4NyUjrCM1B2oO1ByoOVBzYHEOhNABQ5qwz7TBRMD51po4E9ZwpmBHmHSzgQU4wy+OdkefLiLLPCIQXU0DuMi0HclBA/lr18/H+exGeRTFhQiO60IEoD/KMvFYqC7z7vFVEUaJrouUFaXkXCClCDllT0eUlcnDQJOr73//+yeccALW/eAHP2DH5WlqztCO7mlqavw1B2oO7H0c8PobKIyixlWDP/PR17zmNT4e9qY3vYllhUHS0zxE7JbhK1Z8adxZYI1WZVIuHbAB3/drGTk8//nPZ8zAJIZhjF1mhh9GbNJhkXE7KIekirwrnkfLrnS3mYy5jyKlCqCURYTLXAzBh6DQdREy5uaNXNLnrVoksjNhGWtl/ad/+qcnn3yyVv6Hf/gH5j1sTgAEjCtUQkSqBc1NqT6txmV3ToeZEJ9KPpjnXAzbG3Efg2fsyqcSf0PsdTkhYhPuli03swEYZHRFDznl/Ajnm2zSmMpoXBZi2j3btDiEwhSZ1RZzF5UyE2AQyyGaI1Tvfve7TVQENLCk5T/LN+nZszEPtnJnbKZRGKIDUCOfQOaTS1k+xeepo2GywOD7vnbJOVGSAt5LAY8aWemz3fWasMNhxy7CAtn0wza6w0SOiTHi5T+LgQ2Ld1ZJzOrYsPkQLzsfEdZZELKfIXY5V5IF/82yqDgYFTDaQT/jdrbBPhHNzic3hANijL7QzKrZi8kBt3cTr/R8CFmDoxaHX/ziFytFWUrXf5xFQvZTnvIUr8Y555yDPCd3NAcDJOVqlNxqtCuQ+HKw7AwznOKBAUkIftCDHsQZlgNNiMzwOYJCraNoE0W5vHrKlSiAiauI7N5TLatcrY8AhmT0J8z/wGhcFsW8sxlqIouTemyYEcDynzXUiSeeyDiQZZTZC9s277i2w1spWl/zOXTGNZiGUHdWVeqCZpVSI2D45jDa0Ucf7fvWTDjcGt/QqWX1Bwxk+sFgDK/0GX1SKTqMhmBrpwj4P/WpTzHXR7lEZlTGH2DVCmaG1JEqB64Hi4BqcXW85kDNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B3aMA3ndvmPZaug9wAF6CsoOiGlPAj1FFc1FhMVbKjLKFZG4yiKvRFeoAmdcpeQswAJSuSKuUaKr24BPELNx6QmymKDLg5aXqj/90wYN3Zvf/N9Fb7FqcJUsPc2e/ma/yiiepmyi/Ni8Ehvps9TpS9QpOhvitkyaSffH4fZ1G9ZTK4ivGFq1euVq/BkaGOrv7XM+h+2NrUr6Gjiy9cx0e5KSMWohPdRecaTun//5Ykqov/iL2zv0QTMF1WzhiTmyCFJyHfPTiEQ6SqrpEuWNXInu8mlcc665COdN6Uoskc0gDFSZYKniQhQUZAcMFz/8TFGTveIVrwhnGXjonC2NYQDMvSo3QmCLOGcK0OIevW14v4qnsmtuMPl2LsJqCsi4xSVaNmq1l770pVSTaNNzJEZFFrlq2Qi0jSKuSw+RMTMqIkukPJqV7hIr6A1VhDZWXXJ2KdSyNMKUjLTtFJeeZj4HWAYOJiz9SqFJ7Uvj/IlPfILaVCNG3XNdulB1FQQMANrwKgzp6Hnx323O2JUlp1cjGQY3vDLerGApxTFVqVrTkHq14y3jjoQ2PBdR7ZYZJ6YFY2MsyvgzQKQE/dXEuZCeSoSwa1iTEhlzhAEBTbGmMUBlnMjQCVWHipmOOMiWQo+sOuoFUhZ5MV+8OjhEFbQOeM47cLiKOVBhDj01yHCfEeVGxq7aZZK66ggMAT5P8eY3v/kNb3gDtXWGrCM1B2Y5MHOS8HGPK2xunXrqJ64roGahtvfXyxLvSzWSM8WjfLujEd2+p7Hfa15zAUn0sIc9wGlTHRudMZjENXBKNHTp+RK9R0IVRjweiczCX0ccdxEWL1TXa7UIDMiuMC9wV6JbuSLR/hOh8JGPfCS8X0k0RMQIPDfX3JQoXbo9POOtCNaF96sYkaTkyNzsC6VoPoOVp/a9bBDyhGWzzWAeRXSNnwsh2fV0bZcZtV1sNhrBcEWBFXZ8begaw/WHKgYpmGOktVdXHWa3i3y7APaA7UDbRLSdTP6yPNhulsUBcFtYHGa7T/HExjAwwojFvIgXgSsTIlg30/GIeDvN28VzPQPwEWm3WAsSiIrW37SaWSWatSyjgUXosUms2ywEQFibIHlqW7cLxtLghBNOkBhuwvLTav/JiYtEvG7k79/+7d+G1cIikPWjXeHAQu1STa/Gd6WspeRVVoRFgfXNmc75oEcUvt/OFU97ZNEc23vI6UBI252QuWa+8/5UpFWsfOUrL2Yt8bCH3Q0JlgbJUQDHEumXQqpJWZeIxDUEbjWlKz4XYHv126XnO8GTXJ4x02jzd3/3d4Qgg5snPvGJzJJ87NeRnqc//emECEjVCW7slit7HQKL/OJ00uKIDyB2Nlw1sS955jOfySrFbIFfJ3N73qmMMxxlGhjVcd6lU67IEiOzvaiAk7MTlFALMD9CjIhxVR2hiusScV5vYCErtZQFuIHXEhsDc+k6oTihzOZJnOUWEZOfbjeSq5wjssTSDOf1BIKJ1NAZWMNb2X3ve9/bLs7FAUKRshMzt0CLTlO1WIdK0Y0psuJRxsmAjHYl3yJ+cZL20FPNEVyNqxkLVUb4rzT4KNRam9TOPVznJ7ujQedORIFp4mozVck2rw7pz7ysmh5xBoUi3iyTorlPl5ii77HAE2rpv0SO3ajBYuw12vz1X/81e1mNrmcaOV09iiFCBQ2ehiNO6n2el1ZwdMMGg+y29esNFDHXXX/ZZV7YwQMOYMrJeZ+31YLXK0k+QyXsOS6Z/7NqFSiOrBFYUrKVRDkxZLg/9PBDfAudlthc5clPfvJjnviEcIsfK0EqJmavgrk6o1vzZ6wYL73e7zmCF8FcnZ+IYyD9JU6uXrPGEE2lOc+SqtFYfdBBjDvVa3DZMuOhIcgSiQpXUC9Gt8cec8ztb3GAUag466ziW99KcyCLzYMPLnjRXbOmGBww4Cw05ixC7Y3rkaFVMOQKvvfLuLjwnZXx8ebAqqLZx8lUYsuMG6ndXDOvUvVtCuy3Of549tnPfNaznvIXf/H4v/zLO9z73vytcRi3a2Xv2IumU3lN0LZrhe6R3EFVku8DK+3kFQVfYZyFJe9Xc8tLNZgoaxHbiAPp09Dece/L3lm7uVWoU2oO1ByoOVBzoOZAzYGaAzUHag7cNDhgBi5YkoeyzqLDd3HoBPi9chLVcoze2PLTnmbXdud2q7/bF63otHBAjyOviEHAmWeeWS7DZ+yIAEiMcl0jqIvIItTOAl77N5Bcez8bm4vEk7mJdUrNgZoDNQdqDtQc2C4HQrYEGPlFC+oze1T6lMPkrxRbVPZMgRFkxHTIuO2i3b0AykVAlE7R/bnPfY7i2oaXj86GxXsUV63LQgRkGJGFYBZKjyxVDizEkCilCglnJC5SbgAsVPqOpsOGgCjObErzMVRmD0/VzxxI47JP3r0l7iiFNXzNgZoDNywHYgQwjoWJKcMJAz77WM6SbOZaf3UNYnOp3dNjCAKMXcpVkDH/a1/7mh1M+7kGf4aUBIFdKk9DPM0lL6dUKwIYtvxoKZEdgt8hYKWDjyyI1BZLoSfDVOuVE3MkMLsyi3WIhgWRfW2GxACkdJUFbF5swf+Mc/EIT1UMUcgaPYp0do1v1GkvdgICN0ymGaQPScQWqFqi/uYRS2PunJwhIuhh0xVPOeUU5mEeMUIgwpig6AlSONXydV6Y+STinIj9jD16SHhcYlpm3nLiiSdSKTBBZ2zm033O6bD40mdUgTEzv07W777Ghw9uGTvd+ta3NvOBxMcRnXViGocA2Fip+SgjSPuGDBtYF9s9hA0Z9ouZ5UjXDz/5yU+qO5y8XNFjQKWaYFy5k0OGj/+xz2FQjWYUMlJig8cK/ac//Sli0ACSzyzkZbawvlARefFBLRitsZ0zQ8Phs846C2cQwKyIjZYisFcttKNN/ze+8Y3q/vrXvx6Y+EMe8hD0q1pYv+dGpNPQH8wHsJ05gU8Gam59QyJTpXBflYG7Il5DFVdfWVwjEjC5a6EHVQAEB+icelBBhMHPpxiTJOZ/zDBiIqfWvHPi8OGHH45v8vJI5fiAWR+S0KNx0c8HGY9X6oXJWI1I6iMtwooDAN4yhscxdcE6SHhS406L6kZ/0yHRhkVcZT3+8Y/nZp1xO+Zj6Vve8hblGl48ZdCiw6CHuz3IAbBO1M24kON/LY4BdnGjvq1yYMesDao563jNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B2oO1Bz4g+IAjQmFhSqHMojawq2r2xzoO+jFqoHyQka5RISIZL5JoaaRXQowt2xZqEjiNoOJhEVOYICfoofmhVImICOvjSX0SCnBphrNsaLngmNuVzzvec//5Cc//cufFU1m+cWY37TzPn0+Re/gik96j6NrpgoFz1mJ/FSvwue9k2omhWYn/xJVjdb+Bx7A27rCxient45s2rhl3eTUJB1i21fhU0WKK664PN2W7qtSljJQG4WxDoInJ9MJip7GQa4/PKv47L9v+JsX3fewWxajU79ot4YlQuLXaiVFMxJKBDOX0JplxoqEtiv4EEAKikq5zRyOLAFQViyhjacBXAXwKGCqicC0O5JEoi7RNJoevPoKGWEUFFe6TgpNqiuOEqhH73Of+1A1nnrqqds9BBh1D5woUagUbaTK4kFbplMkIKtFbzcuF2xUch/72Mf+6q/+yrcFomstklEpUVnX1GNmT+zgw+IhgHPeYBds1RBPpcxLQOwxIJKSUfVZgPmUAcgqTvpiGnYaaupdcU+ppwNbFa1aU/JKrzYxCukfPWLoRrdoDyYyBhie+0oDZSslKbWpRNyLa7RC1zXyBow4IoMe2ud45FSbGmGaW2TQxhoBuPZ3mDZSggDvjoyegtEDo+/F+VUn6yhSJRoWnD12aJxWVDwrRvnf8TJiFIRql4kJ/K5CTqQod9u1ly8lmIyMaDJ4aKipg5WOt3JJj1oA9lIAyC4/xCXa7aDSxXC0xS14SvMzzzwTS5/xjGdIjIAYunXnipEdKSJxitiwI0XL+qgCTfRMhqKgdhe3C+WKnzrwP/3TP+kk3jInBiUiLzj5N3/zN1Le9a530Snn7Liq/6Mtht+Ad8UHiUF/BlYRRXhTeN942cte9rSnPS0/qiPXPwf0Fs3nKixUejyN60IwS0lfMh6ysd0Y+NqaA6563gtu/ZGPnfp/Z/R1RgYnp7Z2itEkW4upDmHZ1i2v/cUY6KXQ38T1WEHVBLRl4kUyqeK6uix2SgJ+h67T7S1Tnc2//H7x6Q8ffPKpQysOLrx0xt3JSTIuiTmBIzvFSSxJnXG3531UaA7eLOUC7npTMp0ihizbY1Evt9VaZLCcGJHqrXiEAK7Gc/ZFIu985zu92jFi23Q06j71qU/lYkOWGEsXyTtbcvqbwdQ34kazSI9hLQMsMWLYCUgzAfuaPnSDyECFV0tEsitguVKKi7A4No3OaIlbKzt5IG3sqQIks7nTX7uGBmejtJ1j+9yBsCpJcxHBRhufUszrAkZLyatdXLu4KtFno/Q0I7Mt3oxn6RFTAuIyyyZeHjKFgUSJuSerbBAQ76AtWzAqGLfII3bFdXviySPAviOkEaVkF6Ume7LYFc6sXoRaCKOIgFHfKMv3EqV4zat5iTCsM1fpSlccThLWKlKF1/MBRxGYDDP5aFuXV1ZEViGl8HICIGjWJ1XHpCXko88ixYTEHEAuOGNOHof2A8873vEO8pdYP/HEE6uYH/7wh/tolU1uJgUhqT3VIgqCR8A6AefVPXaILW2qGCLOPyb5yweWHWj1nQtQp+whDnhnNVD0fK0W/VNk8RB9aS7MDhEp+yLwitB/dIZGY2xqetPg6m/0r7zojW+98zvf+9IffruvKA4yD2VvQL6ZCCc8qdfEMO4681O1clCaWdoHweAsmKOmcU3ZlxwI8Hl/k1Obisa23/2k+MS/7P/Gdx689vDi6qs3pCrGr9Pkl6Nor06/oqfTTq+G98JL3fVeSxSiXarvQqTHNRMLIAJedTVHhrl+IsjwjrP8+OY3v8kdCQMU77LptG/0sQhhWsQpMyMPxBjlQlLsFsLUGk+g4ieFvy0eVdipKIiBkUfIkMjEhPm1T8/5Dl6spFC7u2iI2ZTeODaWpkwKxQdmNC94wQuUbn2xW6q5J5DEi89RlyWqESDcEOtIUVa8Gr7oy3hRIvOyeR1gQQIeM2PWGrfy4oN0/lCsHwkag3+1Cvp8cInA4imGVVD16RLjKAfJy0zAx/f9cl6CxnKPLAt3liyWop9YXYY8jZpGotW3LkHiM0sKDEydONbRad3GtEGE0MQrrAiYrjc3EuERUf3MgUgPCSh+1llnkb+orfKEHZUZiKfVRLfRf7qEJm5H0WFyp2rm8F46hHnjLLerw5ovH5L+3sogg1dWlbXGj+qjShtRsATZAeNKIWNWEPh5KGP1Zb3PeUoUrXR8Y3LH9M13IBGfM+IVzBo9aMDqWPgz5gMDZ7Alw4swZDQBEJgnVtPr+E2DA4S0X4ROq+CneYDKfcvog3r2v8uWnr6e/laDItRpz9aKiebKyVZoqNut5m9/f9Fv1zS+V2za5rzq9Oi2/XpPv+b8s9b/7lubLrqsZ+Lnw1e3jD2dZv90+j2p54AHD/ffcvnafUwImtf+5vKwQ0k++7vO00abnnz2R2c++yuBrNubhEZMMvZf0X/ovqsOW9tY3dNpTI9NjBx48D59A41l01MWP4euXnPz/Q+45a1u0T/Y12m09z9wv5XLh+DY0OhsajWme3ossHsGVq1ce1CnNbR1tL1s5ZpG3+Cm3uZwbzPx6jo07akbr2Gr0fRrt4pOjw9qULK3Vk72LxvrecJI8Zjfrysm2hNbt72/0X7Dli3D7c62TrG1f4vf6vGJ1ROTm6b4JDv/cytXj053ft9ctXH1oXdvXXPP3vWfb05fvWX9Q4aPemz7do/qGT3mt5cUZ/7bLa/4v32Gj1w+eqvimW8vnvy6NBHaMDZd9PpN9fVO9/UUuoXEm0SwNeM3PdWZmmxPNXoni56xotdvn1veYuWJJxz9kD856oQ/PvYe9xk46NCkiCl6ihY3WM3U6GZQHVPDorfTcK2+NTvHGEP08LaxsVb/dKu/aPQWvYNT+64dXj40snzl6IpVB97mtpMrVpJUfctWd3yuBCUFAWRq3VM0/XpHGz3tTvo1y19Pu8fPS+DXLq8R31Ha1m3cMDw6wktdyhhtHjPkEpGZnJ9nXjRvwvSefRnKt7lvamPf5Hizb7ToGe0MbpnoGTjkiKET7r/8Qfdf/uCHH/aA+xU3v60GSr9yDt82ize89BjHrOEHD7nHnY7643vd8UkPu9ld7rTPLY/cNj05PDURo8RoYlVZq/pSc6DmQM2BmgM1B2oO1ByoOVBzoObAnuRA0r6VGkiLIDoryitqRiY3IhSGoZuiq2SHQ/GVgz0sRN397nePbbUqgbDJJQB29ShUYbRb9G+CTUO6u9B30Z7Rg4WWDKQsVVTVeC5aoiJop1n42JqEKhR0EkWo6ZBEe+Y7BBTaDp2G/nBeDWQmzGauKguIRBIk6JQoRAoiQ2GbSUKP7Jgm4ihs1DQ/rSM1B2oO1ByoOVBzYLscIEFILhtwIoTXYYcdZofL1dduyBcBBo8EwohskmLDjvRhvrKI3MmC2B6TvHIRbYKM8LC8YsIaUs+VOF6IThJWFoQpS4QcJFtdGaMK8EiEP4h0Va4UxmY+d+GrTrYCFR3SM9WhDDFtEEVPzASQJJCztvxkV0HpghTZwUeWwB+kyi6CeB/V6No+jlI8ZTqLWpRjr2tkieymBB5htbIyQgDBUk+DNlOd4JKUCMF/uTwKmqWgOZDMezWd0MRmPr66h1QsZYIVBc2Fx0A7qjYTOdTo+poOYznblL7ZjPMKBTk3e51Sc6DmwI2LA0Yng4MhxSjH8ADxd7rTncJANA9l5RDYa0li7WNUtPhiLmJsBxzDWr6KGI6MD8YrkILxx1hhEDbyGLXcKsvYZYhjn7zdYQTCMLqA1prLURFGLIYyRRj/2UMCgMRVEQY63n+svHjDMfwCVpaRPOgEIwJPRMSrATYYAm2MuuqLSFWQOHeMlc78Uo0CfxUVPEgKw4xqetApBSvCtBVjq8QoBTaFzo73M3+Dnrhqghj/QS5Ulyg0MCNGa6JWifjjrIrqRI3iCommBAb+gQ98IDMwzoAIUKXgvETVZPjxnOc8h/ciskOuaqWqcf1HmyIM3ZiPOc5eOVljRoGZlsbf+c530E/66AA6nrzV6pO/GhcY91KO/ABg9GL57/wIMNMPpGKC7srCh8kT/BKx69RTT+UZSqMrWkW4bwPm6RlnnKEsfRVhvEFxm+Vri0oBdt5552G+shjeoI0nJh6m5DrppJOYOnOupLH0LkiYMDnLwzJNOuJZW3G2xRibfGSi45QZCtkg/fznP4dWjVQNE/RzlCAYK1zhYePEOovBP4KViDbqDilMu1GI2+x/CGjHoMCbIHFKhQMmGHqLdHSy8WbN2yW7FSpdEME9zGGrg3uchckFM5rZLfuoZBhZVdtLBVGCXbw7qYgeomis1h/YArH4yse4qrkijvNRWUVUnyIvZn1qhA/Rr6KVdULAuhDOizCycn5K4+IVDBiOV76yDFgueBDjESSBX6KIzwfimwgCVA1btJp2lMgES4+VqF66B+RuMQFPwMDvEZwwe9m1HQ7Do2nYintHjHvxbUU9XAeTjh5nFiBBMMKkmxhHXYKk+jovB1LPqEPNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B2oO1ByoObBdDtBWUKPQ6dA+0JvQWdCY0EdQggj0Ea50Fq5VVKF8qWropND6gae2iEBvEmAyxiMFSVQQgMBG5URLFbeAJdKwQKs4hNGhCEqnIhHxVDpgSP7+71/Rao1+8INXSuRkoD1eum0qPVvxpKQ46pjly2EqCyrPk8Aj73Q7nZyfnEjVUaJSIrgTQaEqeNTX0+sojIBkWNrO/6TQhhnBqbKd0k2YUwml438w0ul6tg2PYiAFmdtNWzZ87GNfefijb/7iF9/6c5/0/YdikleupEROB3QdhFQprFYKtIJcNEdBAIBgCMxRa8SL4x7FX4lEQqqdjDBEJBHWaoFREXEZ3QKWEmhTJcosiJQIISQBIEU3QJJGocZyK12ILPkaiTBLoXK9733vy4XBPe5xj4BUHS45woFRzjJvJMqNKqBfpRAcrQwetngUaKvA82KbN1EuCGHABE7oKUDdBtp54SUu/nShXDk96JyLpwtt121kj7rTGDqfzEO/r3BU04MP2oVXiz/5kz9xwjkXSntLu6qOmA/MCUx67dhVpS/mlMEHEyhYOUiiJae7pKSmVuaSn7MkHj2iq+iuVNWaUlvQdVarEEXn4roi8VSNOIagHKe+p5C1d+t1c/oaMCM2PpV8lABmylMeKLhzUkedh7L4Vre6FZKe+9znOj5KEUxxzw8ILx5U+YYjXkgc3n7b295GZaxbQq4KaPbI7rszqKrgrLJSokNWaVMvDMEc52Bt1XDe4aks4iqoLN808FLQufv0BJYi8uyzz6ardXAdqTqMuK86oJZ/N52T7h61NMU0yw5184IhOGRu30IpkFPvOirsoxPwo4obC6fN+dLSHOA10OMe9zi2ek7nZoLxnHc29RKQRPvv4C7CANhmAG8PTNxmiQ0V7UiPT43O+wY2YosNKnQi4FGPehS1MjCtgBVeXi+mStmKQJsmUAUV8bUN3MB52n/7Rr6MYQPGpzZ8JkIpelG+imCgax2uBw5E/9HHBH1Pia4GBMGbFekkWqYEvEca2tsHQDxgMoAIGN1AxFNX3c+tF6cKIwWSjCcicFbLChgZ4SEvAuHJJz95330vfde73O0z1D+0zz7JwrWvt6SktbkcE1ItyJay9OS8g2RXKRgyzd4+KQLiAXf1N7ceGUZIJU+XGBQEEjEDfQNjE1te85o3PvDRjz/55MYZ/+NYYzE0sDzhaSbhBXMS5Y1yNlLOHKTgQAKYDXgIFTIiHQ+NQlHELEg6ls9xUngNKCs+86TKQ3EMjLp7nEuJikcGeavZM/55I0EDC2bjKrnm9YccWruh7373u/lCkisaa97skZgrkiPSF4ovgmcpjzh3MFTi5FKAdwuMigSfcTUq5boIh41+epoWsRFLuIRnhyo3gioejjiG4OkgP4r3K9NsV5tBmB1uKbwYnHDCCbLYJuTSyK5e9DpDvZZ67WtfG8Ir8vpCkbFdIEQwarvNl0uMiHKVFUxmlKCj+qqSR+Rd2KiZXiqdCOD2ghdF6WywyFMwRBsJaxfcNiSnjaQDoezt+MAHPqCmRJjeawpBckFLjmAs+cK1ig17FemiJG5BxoYrOagsLJWOPILeq80lSmy4ckHFo4SPQWGR0rHI3jmhjAbTAJNJG95vf/vb7U+TZQo1HJFZnEyhlsAFIAs5CDnKVQ0fMNBmOWHq01W2nFXWbrqWNc0wwSboAeeeoC0QA8aLQyCackSjmC8pCBmAOYsxIeGY7Ctf+QqyYcATIhXBiAfAOyfBCgkRbCorhYx+6EMfSnwbNJCnO5HURDaXHCeeeKLPMRlY2Lp5c80Nqi7P7CLLLgCISO7AcVtfdzsHdKqMU3thuN7ivY6Ia4QM4zYDRLwLACSchn1IYhSC1m1u00AlxWseBRlPQAYe6bmsQAIMgDMahLlVwMte9tixsV//538Wvc0D2Yu0+poAensm0qDU2Eqwmt2Zh/f1LUt4yvXvXPmbJGwrCVl1KSaS/FUWsiWmlHKBLCXPGSQiT0XQ2e4keiYnU1c3xSiv6RIre2MpSP33H/7hbX/88IeedFLxgzOH9t03LeQTUDvNlkPaTk4lN4ul664ZiZwAZgP5q15CFA1SinGpykZ4BDDYKMjqFswsjvQ340lllSTOhZGiXjJaF2NdNfuOxnHSwGi0NDZyemWkqmLwshsBTO8NxdnoBGFVmF2M44+FpBAzAdjUWsOZpQjIizbN3MiRXSy3ml2J0Z+1nXUlK5ngfFyrkHtJPDqVdTdTIeN8DOPRLtGv9F6rUasqI7l3cC7ZupAlLZt1UtVc4tRTTyWweP4y+THUsxbSD71fJJSmYeVMKkGi67qyrMIi3Y9B1VzM201hekUAEUNEJzotJwkgrWwqQiFAAFnMKpR4Qhg5aDlJuhHK6CGDSD2mSFa7gGOtjVTSCjdYoRHlCOABUxfypUE8IY45WrXOtWD3SBMHizKdhiBgWBqu3xSnMzziEY/AGfMrK1k80Q8Jd1bgJLVvMKKB6RgYPcQ610rZ6t6bEmoExRH08KPZnMFswaxArrvc5S4xh4FEKcS9uYFubxahFGyx+gaPGBhIf9WBBDFoNlswITFt8IVDkzFZpHgl1R2wSUtYbplWIRIrLNXlfeUrX2kKdMopp2hca2cp6oiHFtHhIwyXEGnOYF0grgkgVynTDPMc2gO2ZXLx0WlSYbJB4+E2gimT/mBMnreDzULVf28SHNApTTwm0vczqY69Ms126SyPDE8h1TFGS11CP/n12NVk/ZbpfhPjI/tvA/6cH56z79C+P/7pT83t7/LcV2emsMu0Njy/c801G67JiXsoQpdIo7hm9UpEGsTIly2T4/F+KTEE0IXrriFVB1Ys98rHokAPNzhsHN3oVWr0JiKNP64TI5s7mzcX+63cQ9QujhbPg+0l+xsa5YBt6/B5cmR0vJjaMpq8ERk3qkhmvBdJ4tn/cY+756ohK7ipqb7f//7361uHFre85f3ud4h3eeCSszghLCpCY+iYY0bWrWvtsw+PwmZfN7GAS92MKudd0Z+tT42xN1+z2oC8X7Nfn7nsst8Uzf5iulzylD1fU+CthogsO8GfrrwG9jTtMSvtTBY+bFIuryAXEFDOGKcmtm3m0KmVfGBp6rI3aO522vchNHaChsWzICmVmybtSmskb2Fl3Wc8YpXTaqWmiiyOaA88xRYsMhkg6/t7DVSDyybHTB6uuuhX85RW0m+voX/NSpP/ow45UvuO7LcfQ+fdz7V5iq+Tag7UHKg5UHOg5kDNgZoDNQdqDtQcmOFAWsyWy1oqTcEGnF0tmisHOOkqaactfwCY8JvkW5HZ+6N8O/bYY5mUgHRUtWuPLy2ZytUcpZ9dOWBWCpb/EkMNaH3HAIniK8pFR0Qo0OgBIm9X80gEAwk1Kd24xaNzqnbQ6APt4tEbRK4ogiYzXNtTOCiLAg1waOe60MJDbUgxGOmKiApannCeVVW/s4qx10m1KwuwKE7EAo0mUKC3tFfYhb++rTlQc6DmQM2BmgMLcSBEiac0Y+KEGq9MdhLtM5J0trpo1aSHQpJgiv0j+1ZkKzW+j+0R0PMip0tnes3aFubY0qV4B2nDjoUJwUd3Xc3I0rV6G3FF81XhKqAEBqYvENqPYwrri01koo05KeAJRFcU2r2iHmeN7BEjHHanzI2kBwAYtKmLyIknnmgmIOKRQLyaA7BGCGsc6RGUa5s4dgFQEong4bS1Z++MzbNdVOldT221+24NpyF26OyPVAFMQpj0MKzilSNkfapkp4Mw5Nndw2SbtlGWq+KY+CLPVqC5UE4HYxvXLUahP6dXI6YuNl41qE09pkE2EO0PLjJhsM9romUSYqKl4nkqwryZ8Zji7EUuVFa13Dpec6DmwN7PAesaqxWLLysaQ5mNFcaBBgGbpzEoGQCNsRZQjAcEhykY97IiMDjEGAUsqmmYEnE1RhmixY3broY1EQVZ7xhgSYGcxYqJCWJkDCRxDYC4Gr6ss4xgxiVmjUZ4tgoGf0KEQZSxyFDsCrMBih8fY7XlJNMIsoylKCSIByCiLiJuVYF4qpZo55dQY3yigtUxluMbeNitISPoiVwEEEOab3/720xToM2oAkaiYZY1ZozPGKWOCIaElQXRoOLEmWteeAaAmqIBMRmhiMHfcg/PCVPEZ3ZV7TOr8DmOGOKbYTCZTtYoEdkEosaFJOPROthCUqhOnH/hR8y60hwgugdvTUx3eEWMr/Bm/ItE0OmppSvjHz1KhG0MhP/+7/+udhqLZMl8Dkq0L1lJvsSX2Kyg83dzS2JjD3JGzmIdXgWj4ru/TG4YFC00J2HSo9Z0AtASsjJijkW3TXAR7SLRnEHHgE2/CuOiqCALKLMFDedkjab0OmCFAzXaDrVUFrgnu2sXQzKHI13z6SqKBpm278vQNRfCGf1ZxRHMdAptuOStdJpG0eisEtZVHJ6ogtNMjHUx09uqRIWIa0fzCkLc7CjnUjQAubSL9tXz1R2FMJx//vmYoILB4ZwlRzAB/xl4yI5g6UgVZMFk1MKj0GC1E0x0NYg3X5Kip9m9lT2/ODJmzCIQdqVIDGayoHZ8SXc688wz6Yj0WD0Ncj1H9zbj0s0YsOGAa0YiIru3WIniznlpUwOFSZFJL554BfBZuwhBSXTgKlV1fIkcuM7gtcQ8NVjNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B2oO1Bz4w+SAM660EqGoohARoTGhlaC/cKXOoL4RqsyhPJJCvZITQ51EByQxtCHwCKFMgRZOgdZGSmhJqEjEIwAInQhiqHEpfcBIkU6545ZeEnJKlZGx4dvfuX3fBxSvevmnL7sofZnewYS+/hVJjVLqhQd7kt+WVSv37WkN0gRlCqldwExNp7Mm/f1JabhsmePHyQOIgmjMlq8YavgEeatvoH95KKTU0Y6ecpXenvbd61QdXzTv7Wv1NaJqSSGrXmhuNXsH+occNg7OQLtiRXHR+cU/vf2zTtld9vv3nPXtYtVy6qdOe8o2YUEZhKTxsUl1pFdyWhoeSjfZ0YkkVzDUlyISMQQlcaaONg2F0qVQjWGRuLwYBRLaUARLhAfNHmlikeCGRASDCS2V9Cg69IaaAFiq6ayyTCTHpQtBHi0YBZzTg47yRkrg36ErbMjAhFyE7NX4DmHrAsYBKYEfB3Yj5q6CgmB1EYl4F8DcW8CRKCIgkt6QlpPmmnKcwjFnCQC3Ik512t7geSE/9XFCp8iiphJpioHJ7vCJ1nEWTu/1VjqRRevKy4NOQu3rmJZc4DU9eNn1JZprh1qrb00uZd6IjLmyvE44/Wt3x64zZxmcQYTZnLii6UBtkytFX4WKVtRVWU6rUo6jk+oZYTaYaYQFn4kASc8eRKqO86Xidju486Botplko4J+dl7CJGaG0ORS4NpcgdwwxaSPGjqcTKkyBa6dHpSfeOKJzAG9Atxq2JOgQfbIPgTWaRQ8POuss2xcqQvrQNQqArt4n4GNjt6t/Q+VzYfnKcFPOOEEXkJsIz3/+c9XFgwve9nLoh/GkAu5g9A2mZwuZtJHlYxLUHkHNR+rPlsvMtpL8Ao7M2zDyVNjgqO2sNllwRCabscjGTeoo6eCWthvQCoDBeeH3cqOz3BKAc+4wesPrbHCzlPkiqvXWcURFod7q4/q+PXJAdLTqOgV03YxMs8tvQtmLsASUxQUwnqRspJoHthXv7rdXa4+6YWHv+Rvv7X1Gug3DwzalSmHssZY0TDeMhyZGdkM5JX4DC1BM3mhB6qXooXoopnaPKrklCVGSHCQhx+5Uiefnl5z9dUbX/ri/3jPe14xNf6vZ36v6Js+KOFpphmCXSGH9FuNlamsZnLYYSQUEsBsIEylINjgLA2dJch1YKR7HyEBRiiDMToZLiLLLKaZv7KruBAw8GcAw503HRIDF4Q5fZGIUQgeX9HhqgAqJeZBb5Fc1Udz+Tw3pQq/i/F5ebKLOBfJnuuSI4sDA8NAosfuJkcSgBGsY1Rz8fggkNHVxGqcaCMFDM7EjdYh07WmjmGfFU7yVxFGXQ1dlbMS9QrijDSBzaTOIFxFu904j5aM3thDcDgVHje8qnIRsjZooVW0WR8JqJpkIumASJIrmKNEwF5Jwtc2PKNzJJltEhnGhHDUCAnkPICYl5IayuqSHQsRaR/UvEWhSpcXS22fe1FmYuIAAQAASURBVBdEcJirDiVCy66CiDe3sfOKKrLVWyyCVIQxbjORJsVEzDxlVEEZVZDbC+lmNd4CBCMDb3Gb5OWAhlj3pWub/aSelyXqG6Sqo1kEcU/+MrNga8KeIx4RlISvItCssUxR0B+yG6r4QiMPYiLgVYfxAfEtbgRAm7mBHWITBrMawtSmPoTwwKCVSXl1ZwjiFc5OaqJcpoT4rOdw2yFFr6gSHDD1dc9xQG/Xgpos2K6ZQiZKzIVKEdfKaSgvGyhgMsCORiAXlOUNJQp1yyoGt8ryvuy3z63MRW91x6tOOvnwFz7vrN+eV6xZa5GcLEsSfMN6XIT8JVbiV6JJQtlTC+T08/10a1VkLyJ/y2w7c2k0klnDoYenjxAWjRUbNmx9+1u/eNppr37b6Lv+5+vbhqaPTkgb28rrlqKxpejgJEcDS5J3KdfeHXDVO/6a17wmhgWWu5YeEjUu4Y72WOJZfFkoSe9q6N1VOS1rtmbs0pcyTl0o4hLRo8/k0vfoCAM5nhjJla6H79GycmWXHtEKQRhuoI0hnVHdehMPQ0BXaSY+2GZZl8kFWKgWhMOxgGWHZH1qpWn1bRVsPkCWEeg8OukAXmRrdtIk5zXaiJMXVugRz4+WGCH1qAU43LTeJGKYKBEiWlmhJh6WogS9dBQSOroigU7JQESSmyYnegs5ZfAhpGRRTeUGZ6RDxWzLnMcymXmfppSLiSTzTTjxB+tc55KKP0okoLnNgscgqSeYM0RnkMtoxkiRiZU5wIknnoh7Xg0OwhigA0Y2+BDormi2NleQ6RnxilEYS2Rb9lp0MyYzhdbn1YXItsBnyuncGrnsZSSgSX9r5KBT0aYKXgTSFn8szxnK02C89a1vVQukKo40N7tQU1MjCJEaL6xCTRVYN6LZC44MHOPZSscArNvIDj9q9ROraVQpS3VicoJgkPhsDkPKw1blm9U6JICNG9X0On7T4EAY/ZWObhq9U83VzYFlxfhhPUNr+gfTBGC81SxafT6uUI7VRpfWVJIahxy4/4f+5b3XDIxx1va7H//sd2PrD58efdADT3jQvc/Qf57wYga+Y7octWmj6F3W6V22bMX++x+4ZsvItmKs0xjMw5THBqz4BT/zI7fzvcLXGd/KLCllvFFs7SmsNqcb7YmeyYvWXXnJpakbb7jsQhP2de3iZgct29ZfGPE3b9125U9+VmacucTiebqnuW10YrSz/JKNW4upbSbNFHZm7cWy3qLVO1ngkw84tDkfSkLrOkPLXJKq6OeJV+uYkM07UpVFTHX6zEVWNZLN7v2LkZv3TTxrutG7+ervrzz41zzSlqwbq7j73K84iJOmzY0DtjUOKO56dHHfE8+bGqXSfMLmzYM/+Gn70LsXJ97vtw/qs6J5w1vXtzYUvY3RAyZH1rc2NfumRg/Zl8X69MBQ0dt/8EjqF9dMTcPGH5JyuGhKNeG6tPRemuJ7fcDYzGpDZTefk4grtvWU0ra32DLYc/WK3k3TPVtHx4cPWFUccZSGKTpTukFny3o9YdJ9X//02ETHpHXX697oW3XAQWuO/yOj63CRFDUrDr/Zur6VI0V7stFaMTG+LaliejxZObRy05ZNxYplxdbNxbL+goOqgw+7YHhz35qVJRVpLh1v8a4T5WAYSi67+qr28IjOUbS98KnjFzqC9cVEdH+jxXUKdL/nQsYdrbl+44b+wYHLB1mTd/ZrNa7q46IrgQyUM5+xcMyFOi20amh0qHes1dnSmRxau8K8c/PwlolWY6idqF+t9csOsOcorzHXHKg5UHOg5kDNgZoDNQdqDtQcqDmAA6FRpH2idrMP5dikjS1KKt+Eo4Ny6Jf+yjIkKx6TJqEo7CFKBEAZFZY8c5lJmRkhr/sCxk4cxaAi8howIsBiWRFgSrROjKWiq6ew0b7aFGMVw7WHo7b2BH1AiF6UKhVhAkhGL7ze825v5466z4bgiSeeSM240LFhOs8oUelUhTRyNG/OZMIpHU7pNI30n1XyIgu+KSJCpNTXmgM1B2oO1ByoObA4B0g0+18h4AhE+24kF/tYny+1CchelIEr/0fEVrYazQiJPPKXMwXGQjmxKwIhGJ4FCCmPshSmV7S35TbSu3LFLUkXWZBHIIrb4SLm7PGRp6eccoqpApscH3ex6caECcEZj0fItneGclmgYtdqX8+cIcME8rhVTRFyFkkhbZnTCNWq2WIzD2EnnGsRFKoCMIF0riKPp5FiioI8VTbDqVY5MkbesKmLXPYZAduqQ4PKVtHGVMHURb1y+kLOPpBaJcNXcDhVYSIlI08odh59EsD2YgYDrPqBFgF2fGxKyqJX2DXwFCTTQTMZpllR8cyNTEwdqTlQc2Cv5UC8sDEseNkFKwvUGiq95swRmTd4xzk3ZALEIIEhAeCAtyphcyIwb+BohvMaQ4Sw0CBAOjB7gNy5ADCGQXklGtkML0b1MDoykhjbGR50reNkEWQnpGCw1lOiNRcbD/QQBEQVt1kyqoIAJ2FhhDRkMYFgwwA/8phPsBpFcIy9UReLO1kYmaCn2liGVmalBl6FVof0bEoaJOUs6qJeriQdGZrTgSlaHRmU5sSuCMuNWNVqhXgUtIljNQeRMOcs8Ft+kiPQqqksGVhZEY9IxF2D1LiSibwZ+r6aEsmL5MPs+ON93DSTFzRgoIozc7Vxz8KTYSqfaKyDJJoDaCBsZHbCQAjawIzCTEmmNrABQK3gBJZOZUWv1RyQgZzfauJY6cHkjEobqSkJqKv4RiM8zFnjPE4gR2FEkCSCIdpRdjTIyFrVqRMnRxaSieBJYTMQzFcdxcnIGifQwgMt/QDms6PW67Rsrh38bHtcCV+F4qR+pSLsgZlOITJW6Oqb+QAtSBhEII/mVjU9UwrkpL9uI4hELmCyiKNERllsCmOaPsnmJ8yNMv6uSPAEhbDp2M7deGvAKFGfMcfwWT6JKIwG8ihYhw+eamjzAS8+6yk0UMUwjUan0FVQvtU6jnR5j7w1JkiweQS5CiIG5RQ4vG2aLppLwCOdDRXylILJzMyYT2uUTE/UPfCovuBRfprLZZflQ78GBF8ZFFhwMY3WsszjYXv4wx/OBR4LKJUNP1nx7mNm1CUYBRsrOPMivGVshktS4lGARblxRUmuXUQyMXVkXg6kTlyHmgM1B2oO1ByoOVBzoOZAzYGaAzUHag7UHKg5UHNgKRygOKOHorkI5YUjZLRFWTEhkcZEYhUVFQY9ZlVpQvsTm080FwLtRokv2bwHWKhKcy4wHlEkhU4kkNOAyEg3F0qZUJFQG9HyzJY+uWLZAQ9+8N3tD33+85fGuQBfpmk2hlPe1ngmiQZQvYS2MxVlEN/mQFDl+ApVDMf0JSEJQjVlR+fYON8ZySUTAiimygoltZ3/oUgKTU1ZhaRXVXH65YmJKdmxjiYonbLYtqWnN53p+MC7r7rf/U5/3OMe9773/XDDNYUDT+nMRaOg8IWTFqvZaCJyYtNY/0DSkZXEzly0C+RxE4XiGOW4gkLfJEJtKgRjwQMTEBA6OBVwKy5FJFBJDCJTpUrNpnqJg3HVlEJVZaYUGaPJIBE8VVa0XZzkDBiPAmEAz1RjgT8B44rnC4DMJO8Q2i5UdKDoVGUULoWqrux76FaNYHaNEPFoLCpmmkfxatHA3NLa83PBRIxCWYrq0AjrchlSLl8fmreavoMU6YE52ktGHUxr6lfiIj65IJKpypi7IkEPhEFGXB0bFiDRfzI89etrX/vafJsj3lCK/nwrYjfCoBFnUHM6bOIO/kUKX1EiXlWQ+nCUK4ISKXELQO2igl7MOM0b2UMbLq6sGPTE9XnHg31A0m4BeHgkvrr82FTkiitju+ptxMNfVcS56BIJYwJvHJK8UKz0KIgVETDSFR0vtZ4pTsFtz0MIAFcNhIFOzwo5MSKsBOAUVzv6bkGK6meyA0xxTvPSOHdld+uEbSTGezEXAFscdQ765z6tU/YEB4ze0Gp3HVhriusA+o8gTnJlKRClg5FFK4MXgM2FAWBk0JqRxdWuUnWskOI9klLFo9yusryAAQNsePhqx8NPOOG4M849453vuLjE3DY3mJxue2syciRFoUF/xF2Nw9UUY1dUPADivcvAKBFkEXLi9iPleVFVuOSSy1esSBsEX/hC8YpXXGqXaHLbpZumNpUYymuTAx3DlDjpNsMlkrQEmLmkOcN1Sy+ZMCORAwiHjSomLWnmUMpfFUFAZgIw6YJ0NYr2CphqWd7okNFVFlUB5sbth3nxw+pI6bZvEawVdA/vrxLnZrlhU5Zetd1I59ILNZZqICOn3VDDYzAzKInW1NAGVRvA1X7bRapvYXWlxO0pp5wiomnmne2EtOJaMeSO7jQvksUTiXjBR7ECTB/QXW3o8hs1t2huI4QuhCpo47Mr0S2jjRCdYd0VAFUTsblZ8jAi79ynUrA6p5O/Qr4V4dOtemsWwVKkmhJxG8lzE2NgMZwamghfMyLsRX+QZNCrvpuyK5pYjCbI2BRX7TnBTFcA0k3CubEQMnxwwzvolVS0jW0hP+X/K+J5ehMIM0COGHBixoJIeKo0ZJg6sns5kCUXtJZgmsCLE0VoBR117voXjA6T22guDJxGYDDRgiBJ0q4x2WgQMJ4KgOGBWTxXMHKhx9MrR6+cnN584om3O+vssz7+8WtGCav2FQN9y1q9yd9iRq7b6F1QCRmPCMyz3akp59Dw8pHRa2UZkQQDspOT6JnaN6UIgQRahEW/tWItURGdXSGlYNeVV25cttzU2oSzeM5zLmDv1Wx+bWRbmueYQvjfv62FHj4fuJzolCluu8ZVZUlRcfCJQY2G25Sr4nrbrRD8SbjLanbhwUMpuU3nhQldBMxV5CW+7V9wBpHIc3V0h/segwwrZ7N6jzBKsLjQtWIIYhDM25HE7aPeEQgISyY1NKKwSNbMUjC7nYxquYEc5xEmHrdVgL0hjjbNFKygzfA9QOtrt5EYlAedFmtMGPMb0UW8CYPDWtxCVXtawMxdTnbl1euYQ8Xq2yOFZoAlMg3l4XYtMgbZSO2SpPGU/GKMlYsIYPMffqslRn/OTwmymLSwQuNG0wo3FqEAMnO6iAx4T8kyQSS6XGQhK/Mq2JvCTaQAJlYoRsU3lyEIcCty8sknuwLG2ygLBqb5AZOv3nSDXjRQSH8TAE9zjUQEKegxvLhyTyPkigQqNDNJj3j1GgTIyI6NUzAc8zRzI1fKU2aj1Yziiqh+Ebq65K9CWtS7haqaWMdvehzQ2034n3iXe+tOOrnV3H4rDtZL0m/WbpK4FR/euo399CXFJoazF268UAcmzijQfvuzH5F9l11z9arBocSflLepr7M9ZTG5qrF+TTFwRXqwBwM5U3qIteYtrtrSWd1f9Cb9WRIr/T2lXOfAiGOnWVkXMxJVToPkssGC7jeqXNbUlxnaBx4Y5LZDV1+61KxUgJzdsVmx9y7oyUi6XvZqung8Xd1TJGPcK9Pcb3NnszlQGjXK0DXWzaRSVPb2Ok6D+dMjG9IQtG5dccQRMZ2QuG7dNZongBUxvWFLwSTXMRvOj4r0si9E1Qz+G8MfVYjRVX82FBedmXnkXNrxMALOsBQfvP2x+LyiN9nvfuOLn5/etK1oTne2zc5hyhaci2TxFPgzQO+KFU47H3DrWxurJ/qGvHojA2m7Z6aPzcC1aMA3bdnQ0+w74aEPtYr83fgWkAO+prLvvlvKT6dkhHMiDe9d0S67/pxnCyV4BQST/gBYfcwxBx99c2Qvayb99rrzfnvpBRckr1g3UNCappHRpqgSwYyxUnrOvAvxp1m28qXrAA8NDej5/b4Ek7ReKf3aVtipRryBql4XW3Og5kDNgZoDNQdqDtQcqDlQc+DGxwFrsZh+R4Q/Cx4ZHNGk/bPusJXpuy8UBSpmbu9Kq2DhZgnmOKWr2b7sXTZpwYVAa6uL6izMVGLdF3j4lnK415lJGKz0c5ZACCYvCqzAZJQSwWLQkUvWPjCfeeaZD33oQ20coNlZa0jQTP8mQu3m+CU/WXZp0UlZZwnpgCglSZSVr/A7onnKKaeIyC6dnt9qziapY95xIBwBiIQ5KAGTmYYq8GeddZaDuJiW0daRmgM1B2oO1ByoObAIB8gRgdyJPSzOpLgUscXGzIyWjLcF32jhFYXYInqAuRLBJJFHBBYNJDVgl9eSanHkIC8SPrcTSmaPYHBl8cLHJesU5ihVeOIsbiPiijwCVAQ9ivNNOx5ViEtSj78Jkt12J3tjXg+qeGxbIJu9q7mBbUdgbKJ4OiAlgcEm2MBSBbenn3567O4FBmphZPOgYaM8Ns5UHBmAQwoHBimBR7q68OmQPxQnPVC5enrqqafC5nszxHrXrrcZji1R3yLKslteLDLNkBGTWVNDotzgm2bCfGXx9WCu4lGk5xJzJBOQI/x1al+brcrCPUWj54QTTrAVmGEiu6pJQQOO2efkpcLEzGzHnEf67W9/ezuPSl+krIywjtQcqDmw13LAIBMvOwqNDxwkMa1nkGmQNz5Ysxg2wwEWAO870wXjj5NB7E/ueMc7ym5EiiGoWsfA6cqIiLO8fAuGCYojCbxWWRO5NYwYi/gqMuoygIQ88ChLLtcYe4kPcQ59TjzxxLCWZIBKfPCh47NqbC8JLIMkMFejNx9YHBBbWMHmyiDEQiwQBn4FgYScMQOTGBWBPx4ZFXlHYrdpVYUkiR4JqolIm4DiARlXQ7TjMBaAVfwexa3FLJZiQtVegkRDgM+28cwF0i3yqjRINNgytpEYpcT1ec97noxwIj5SgrAqTJQrPSKZKt+cY+nKgaO8hDKDcy2ujlpEdgWpoOawy2/F7SAPh1m8VknEc9anDMMIa24lmXPDibxMQ7X0HAcj7qrP8CdFdvvWGlbg7b3vfW8eyhj9vvKVrySdc6NHXrfELsHEJAmdVsEKIowy5hyJIqrZ1VoVdAMiNYN1RcDoD3q4oj2CXDdQIjzicSuuvtbvaY+4EmDGLjAemZAAkyL4Eh7fXqYZ7K8YPFRyzHQeKdEiQbMWl0sK/JBgPrRuI2O0BZKAeTV8H9eExxsX3/bzVBE6DAo1U7UscU9duSfzFkPoXTC30QSAXeUyI1L96I0ABPBaWaUQo1A9/wUveMEf/dEfodBrpWN7xwOsq6y41RPMfDSrNy4cYKmjEGOIySH1iBdE6bzpaR0k8cnle34yvvzlL9cKaiEvhUkgVBZ6YMiFaqCoV5UAZfFqajaoaD7anvSkJ5kQslky19Jv1UIbSXew8aUvfakGqmJQdLza3ICigadU75omkIUBuYoHcGYOYhSNKpSIpOpV9u6rVNXxKgdmduGrSXW85kDNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B2oO1Bzo4gAFhJCVDp5STEihoZBIPeRagix4yQgpLwDRwoTuyVVcoMaKCKUSlY30nAU8NYcSc4hClR5Fx5VqKQPI++gn9x57u3t94P1nr7+8aEDm0AHLmM7GyekNY+NjE5Pxm6BaCp1Xq9nbbPT45XIXjkx32uOjI5sTQGeSW7Dx0bHRbSNjI6MT46Pt6aRTU5eg2U5bWSMOMsYobVGIeVuHNzs8PD6xbWR0i41IH69P36/vFO9+x7mHHdn/6Melg3xjo77I52zAzAm3VqvhLLF0DIQ8FV0GzKG5kyI9UpQrETeo0qS4Df6gBGOlYHWkYyN6AOQgRVz2zMkckS4OAB6YBXxzGzV1GziBieQ4GHGnsew7lskzF2A2R50M5PG9mr7T8SAvl74TeEJhCoPq70T2PZcFPRE0sbYWNITtiui64mVrzFzA0JPalqBFpZqkXvRAonqBR2RmEQy5z0hXRKCAHwZBRIp02nb7tbTnbrW+VrNdoTNHrqymnJcDiosSIxLxqI6MCBDPGYPUUEZT0boNMjIlARlK5GpG6fmlqKYrwiP9P8oViZS4zeVGREb0BEnekcCjrMgCRoqgz9OG58QqEk8zKsTnAGfEVSTqIgVmwLDJ5WnEMzbpAQBSw/m2g0fiARARTa8iOTHnFaFAz7eJ6NLV3VyypcNQPr/OJfJGkkFDJGPriqBzXmZ2gdW3e5QD+oAQvWjegrTgdmHmzdiVuDie6Ce6ZaN/5Gl/edjRt7zdm9/0nWKyWUzqP2M9fVxokVxp6yIHWXKIRLVAqrhrvCwGAbfAooLGh5x9FyPjY2mbanyczGq0h4unPeFzdzx+5V89X9rvy59t18Gi4yoYSfxMD3Zb6WoEr9cnB6wTIjGVWdY6Irtyhd/Ql/lGKBvGSV7OAmL/aVeQ/wHmNbpioIrbxbSHF/HgQ7RgpMR0a+f4o71yRubdzLPYP0WKrUG+Mxxrd0sMRS/KwDsRMYZXc1WLrqZX49UqV9PF5z6KCWcX2N5wGw2kyYIDKp7FbpAXrRnXnNJFuZermhKoXAUZ5zIzuKHbyLWUtoOnin9uHAEKkt5FyVzIOmX3ckDz5ZDlb04RCVkmEsI3w+wcGdoXBm1tbuwKbeAJtCbA0iOlZ/nmRz5+1fHH3f20t3x/dJMVp+TxqfbGufI34KvX68jfVo918Yz8ZaPSTII4y5FqrsXjuue8v6uu2qgS3E1b/05tLp72+M8de8dlf/08S+FLy19vh8BtjBYN0wZ1SNW4UYf8nrJv9rU0FjAcEYZHP4+0r/GcTNGOIieccMJTnvIUjoR2+3ut58CpxBykCAvxFrCw0NPdmK4UJF0/Ze0i2dgV3q/gCTYaqDPlntInxNA9l7HAhO0O7IzVIGeQxATcV4JZTbllS2TpzSJt987cVGEunYrrWuciW2JeXcrlNoeqsPMogwGIjBmyK5LYUYYqQgnmEthofENbpiTSYcgpgU3pmQCPgEU6KR8YqoVmdYHE0PzE0yBAcSI5lKTNYMtog11dNAQSxQEDoIljjoEVVW7E0h7w3MmSxFxEYFv8mlEtDlY/vXFxYLphtdlZ7fxD0Tx2qv+ozVP33/fwv7zzfRuT7bW9A1nVq1Iz/dB3FSYmlo9MXfWjn//s2/+38eLLfsvmeKjobaff6CVXtq9O30wYGezZAHGzXUzyNZUslVk9sv1c0ZfGloStk36Lh+rrsDgkQT7Qaaxu9Kxp9t6qp3HgtukjxxrH9jRu11sc0i4OGWj0bRnxVYeGGS4F2ECrv793YKC36G/5LetpDTbTsVK+otLC38/CPyJr1nCkTUHAiNPgqyaJjM7sWJRefBjTO7gToVoj2btuI6VvqtM/XdxzcvivDl/z1+3i8Vdf1ulffuVY8dz+iVNW9l4+WFzSV/R0Ch+2KKYP9Tu/v/Hb/ubaqb7Dt04XI8lt6PpNG6fsIjSLwbWriwNWF0Ot/cbaB0wU/e1iqFGsmpocufinGz7wmuKrHz5sdOOqi35V9O5X9Ky9ZEXLT67xBuSd1uyMqNlR2yqlN4I4TmrcCNS36dcYz7/J5oTftta436aeKb91jbHJ1f0T+w72H7n/5M3WNo86qP82hxZH7zt461s0b3Ho6iMOTU7DKI5mf/iRf0tnx+TwMOX59MqV6bd85Vj/4Fird7TZs63RHm0VE43mtM+VtHqKHoVtmxrsXXHwAQff6hYH3uboI4+/w8A+q7cVM1sqenWrU8Qv3ilvtA+wJCL9lhyiVa/etHGTWbKMXpOx9trDbnane9/jVsff8bAjb3nMbe9w0OFHFEPe32bR8NgXFZr6Xup+5a9alHHDL6gKGKNHSKsq2PbjnWar0+pvTw10ppe3G8umuXRjRN7sL9q97Snfxj700IOL5B53YqSY9CumzO+n00vpt7w51dsaa3Qme5rbWoVfu6c11WwMtAu/QQDtHWHQ9mmtIWoO1ByoOVBzoOZAzYGaAzUHag7UHLiWA5YANF2U/PRglIo3v/nNH/WoR/ElQbPkEDJdtI/VOdvMa8Ptbnc7tig25WPVIFdgmXv+M2MHGcESPiKxJA/dGg1nnNV0S3dHKSeEEi/A4EESNQVNmo0zFNo4u//97//Upz41PFagkH6AE3mRhzzkIai1MUrnFrT9x3/8h+/0sD2jabdtcYc73AHyeQ/lWgThABgYKFoF1Epx2FiiiBA7IzB3aW4jxa46FR8fJYs4IslsqSM1B2oO1ByoOVBzAAfImhBYBBxXAtydcC/CWQCpRPb5rox9Im4y+BRwGyro2CPOe/GRfSFmwg8VXWuINldSVVgIPtLlCtpCFrsqhZizBcnrCvcfhDWZSKRyPkKO87CQFPKVDSzeVfgW4WEkRKepBXla/WgfnLJDqyzkIUm9IsylDRJzD1UWkSWoCrCgDaqzzz47bGVzdpgFIpu/GDMZExh5cSMDiEj0xVxeM2CIdFlETCTMOjLfEKZ0oZo34nAKShEQIzHIC8I80nCym0HxZcaamj8LdVHuGWecYfqEpb6iAdjebs4emDGN4a5pj6/4iPOIgTYcAG+GBokQWeZSVafUHKg5sLdxwNuaX1gRo6jxwTjjNbeWefGLX8xDjXEsyDY+WL/wgmT8N7bwmGMEFvHUi29Vskjt0sBXjvwBmUaK0ntvziK70cy4JB1Oo7cR2NhYEjgziAFGnhQw4DkY4ujQ11tRhWCJXPIRTxZlwABH1ZQLgI0T+nlNMl65Mm1SWYNeEFBSN2PXYSVINIDx/b8ImUiRJKsGB43DqIUKhfJWAcQh9+lx42cYWEYVMjHGdt6vurJ4Gnh8402IZWbOGBFlqbWiq6ELz7y3gRnBIq4cIRHffAD5OByjL2O+an7zm9/kQpq4J/TZYysCS1UQJRhizJeIq4j/yle+wgCYAyMSSjPxjcXbEQyAhajmXDK0rGaNBlULPecZz3gGJovwR8YDGlti2PQ6E4/Ak1FZxhIxfDAp/bTTTpMul8W1Fgw56Dt8GVjRxHrc6hJuHRfi0rHLvkVepUNrBqKC/ByZNuAPhFLCIRReqRea0UNKknGqADM+4CHM4ne5y13k4mhJX/KUmDYh4QL72c9+NkdOp556quwAouFUROeREm0hAgM2qg5s4X8TndG9cR6RuBGCGDAwRaDtL/7iL8Qhx3m34nqaImBjowWDABLZaEbtve99b+XqluY/cEqnH5BRO0LLfyX3cCiXBRmCoqEChk7ZNRCW8lCmsjq2LIJKBfDcKzw+msstWqhBQOKwRFc85/VblbFCEVitFKyj59EneUCDGYwq65bBAQaKMiJDQa7RIeWNuFuNCAm2m89ghapxx0knQ+ViMqN0Xvz4/5IdM7nBOumkkxDji55YofcqXUSHh9CLrx+eddZZzNpxDItwwBW8noAS5SJecSLRXvBLcRspc7lRp1Q5cJ3JbvVBHa85UHOg5kDNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B7o4QAFB70DjQ11CW0RJEXGJIGki6DLmDR5lVJQaYPKtSNLrVDQ71bingbwKP29cEZQpAS/Lin2Kv/zLv6QMiq+qOBqDhLzn1Ww4iOCogJAcCamLLBHKxAUvoX0CmWukLh1nHuiIyt+ULbDyHA4NESzZECfgActbnkFIRYgquTzRkzjQ01ecfeaF9MW2DIdWIi3BQBaKOayeITGImOXMvGQrLhFWsr3K7bL0hDZC4IwWQbAQWeIKpoonskiJAJVIwCC+WkpAxqOAoQT8/Oc/76tBVG/SdSQaQ1+zoeOj8M3wuysShe4cNtzYlew7V+h2cyFJiP7jGo1FA4jaaLUACBgpgRBkNK50wBSO0qW4BirpuejA4DYi1esb3vCGF77whU7h0vDSlVOw2kiOjBlDFX7eeC4oF4EGIWOopksMyjNMFSxDVnHmeBdkTt9uRMZqcTuNJwrqQgVbNVSJCchqyrxx2UHOfTRvYhWs662vPsokVRPnjc8dZ9KQVW6JxaNFSpkXYZ240xzIbVGN7DS2JWbM7ZsLzRn1BEO6fhjDu07FtMaWhj3FM88E1SHoiLkIc7NnPHMjudCuR9IVKnEhgAwfAFUw8WoISDSLXHXVZiPbn//5fZatiOQp524BuylPLioxHWGMEBB7/9XmItMfo/fTnva02AK010X+miDZcyKa9/4q3CgojLF0t5Nqw88OpW9G+cwO5PYdH/jABzL4JtCjrOi6u73cGuF2Beuus2g3FlF3g11vjh3FEKJBru1KhBmZMbt229GCFofX9DpS2CtY05n/k7Y+qMVs4pxzImtIy240mf7uB9u7l3F+jLMZM+aIqPe8v1nwtDwXrrpqK/n7tKedsM9+8aQ10Ls8w4jAJijatZp+I4qbKaGfOYgmM4b77GGXwa66mEf5TqCvy55yyimsn6+H2i0kvBZKvx5I2guLyGPsdtmydMhFqmm57akXWTAHYEjk1pf0vvrVr1od7/orkN6lWVdoMGeaFyHpBnm0XW4vTtV2s8+VwlVWLJQ9YKqQmQwIhXkfZZg6UnNgIQ7Ei+m6cmDlbY+5rY/rCg4/OP/B0TsRnzLqYGUfA2b53TM05FcMDVkg+GA409IqcosIloVEpnGDtaKvFvAK70fQHHP727O+3TqxNaTq4mI9cP7jP/6jT8hW8S8SJ+/gZ8TpO8bMuGeD+Mwt9/rHHnvLWx977G2dDikD21BHVaXe5ja3EZfRqY+73PWuc3/MN1nR0r3P+MAKOmJu0Eh2rosQtsgjei1h3uwShelOsnN1DoY69OCD99Ei4CE0RIf21QTsWk7SVPiVdrHsfbkPLCaT6//QxCajXr+K/0FKPnRv2rS1uOCCzb/4hYZjIZraa//9k/8vOWHrTKn1IlXYmx8FD42QDIj1RiGojfT8FIB0t64BjGPYa9YkEI46xt0f+UiW9yzy//iP/7jo76U1zr+d5EA7fYslqKIwj7IUGmTAKaWYHuO5kMM2ezMegWcX7lh1AOeXt4uAVJ3QzM+nxe0C7rqlt0kpdrho1lam119PU250toR56+YEoOfPOdw1kz6z4ZU4WQLu6vxZNeHJTQat4EWw5+XVYFV/+4c97HYPfegdH/zAOzzoAcc86H63fvD9j37wH/sd+oAHMLNm3M+4OdFWNm5EZq432rn9dWpR39QcqDlQc6DmQM2BmgM1B2oO1BzYKzlgVWWl4/SjNayvEjry+p73vIdLC9P4WAqJX3TRRTxM3e1udzNvd8TUVN/MP5YAUSdr0urtDlV08Yx2qJGhRMoKSwYneB0Ff8c73qFEFCrIYo2jDadMnRZ2lpgCJM5VymKhwbGFJYkUSxI+vJw9Rv9C5KWl9WzADVHVVIp1TQRxQfpCGOr0mgM1B2oO1ByoObB0DhBPIU/pvxnO/cu//ItvBrPRIr9IsfPPP58zrMMOO4zLCericJpAaIbyLUpZXIYunZIqZOBHgGCSQCCaJ/zZn/0ZR1evfvWrWW6HqCQZWXf/8pe/RDC9PRmdiSG7zz33XLpK0wbuHrittL/Pb0WUAn+1uK54RtKVHrfVp4vjiVqQ2lU8XbfVRxGPXHPTI6Va+kIw1XSaYa12n/vc50UvetGb3vSma665BgGKwFL88TlhkytPeamg6pcYeaNeRx99NEcYpkA0zFyKPOEJT5DXvoPPL4WjjR0lpkpYHa85UHPgBuSAcTVWK/az+Jb6u7/7O26JfvSjH9mxlW404EbKEoY7IUsbKyCOYyQaeL31O/3i71BGxVlhGfktoJiePulJT2KLwrTb6MTkzA4UCg1NVo6eqoWxjrzAUsM+CzQ+d8JP38Mf/nBmq1/+8pcJtczwGOLimhN3PdKFsOt21/FXMZTtcJ3dtEjRRhqXYyAs4kOKOyTer770pS8RlLhkR08Qt261w/vYxz7WaTWMwm3IiQCJAqFvyxg/jfYswWyj29mHFhihIJ0smEtAJk9v0RwAQgCJ2DG/7W1vqyAwsmvHtCVdFBBW5Q6O6X6QI1IVyPHwpUVCsR7XgtQFUEVB0IInkkh5cchVh0Tj9ojYisWydMCIt/pGFYSPeMQjVNBURzryTGwQABi7AKDNLYT/9E//dMIJJ+j8UizwdSoF8az00Y9+FOUolMWL4yPBPiSJVP7FIOR5TXbAUOml+iewoMFTqExa9GcpHunepDMC1FFFJIYPJnx2K1EWtb7FLW4BoSKkhHcwrYNylWKf4N1UcbZ85DUVhPdF3TU0zO973/uU9bCHPQxCMPLC4PgbNnqqdPQIEQGDLWjmc/xb3/oWww+YM3NQslDQXqZkeG5b3CRNw6kajonEVO1Wt7qVeuEYqjAHeSZsqqNoPNciXmGmIyiXyGZD3aVjlCs8KA8GcmKlXbBCMNmDNuquOrihP8ccVVfRXVVWEygRZ6Srl1vzH5SoeyCE3HDnHUESzNJZfciIAIWaNyLAIxUEr/9gkUopVLoA20I8qdODAzMTypodNQdqDtQcqDlQc6DmQM2BmgM1B2oO1ByoOVBzoObAXA5QN9BB5HRxgY6jVNek0wvioSQCA5iOSQCTs0QK+EgEQ/8i5BTpkVGKXG6FyB5xmhq3sgATIlGKoiNdRl/Y7mn1uLZ4tirD3/zt1NDab37gH1dvSbqRgZ5mX9HcklRw5S5Yp52InHZqJrnfctDF53fihMO1lHs0N8CAOiVQDA4NcZKxDh4+MlSTQ7BU9yKpzLjBAkPnRaWFTulROxEVmaG8UfT1tBqtpO+bqXdyTFR8+Yv/d9QRx//ls27/T6f9dGoyHXiZHO8dHWn09tEkJgAfs8ET+IuOewd/HIZp9jn7UfINfi3iSkOkoJIZ6eJplB6UJESlmhWqkoB0C0yuYHW6L32TuRVkDwzg6aEgkUt6lAVzwEdBEa9e5X39619P/3vyySdz6k+nJss3v/lN+3myVCGr8Yy2LHw7TVPNuCtxZe1K9nnzLsSWeYHnTQyqXON1w/zU7ctmFemiOQMHKrdCbndx6fC4RjyXmOAqZm1dTfOd73yHUvvNb36zbQyK+4x8Lp6MsPoI5mr6vPGASUSUBMc1yBCvZum6rT7ac/HtFpoBRLrIzo+Ql+PRgvl2Xsqjobse5dbsSp/3dinA89LQldjVHzyN3hi1mLfoOnFPcKCrIRSxp5ugPc0DFNnpaGb4jizlzmzdooMZrm1dRNpLX130rfrs/3x6aGpTSvCo1RwoiEpCekeonVvThG7HQ5U/sziT3Gl09rdRWxRbjS6N4qjNG4r/O3vixHs+4Tkv+sFb3rKtGJlomlcUq4yXjcZIkv1FGnV9rs+v6+3YLlEpW+WY63bhdxcAaWuqZlvdztYrX/lK+0w2L80NfMTJjtTuKqXGs4c4YAvQLjXTN1fvEflr4sQebg8VV6PNHPCC7+g7nvMuMbKn8S+RjBpspzlgVNeISSYsvJCBPJ4uDrMYDeUqL80cSZ6i5ZqAJTZmVs3uCBfjvEWZ+PNPLtYc/OV3vaOnTbgVfdxPNZsTZelhBprWzsLO0xP5F77mtdvCIOWTzr7pT8M6umgWh29aV3znLPL3Mc998Xmvfe26zrYNU9Mris5g0UHwVKOwvsZqZIf8TXW/cYW0lm61HOC5733vG9ZjDD5Yz4SDEhZFjEjufve7M1Fi3nTKKaf4nFpUkLVHnlzduKp806N2hwbtHQLu4lXkZTAk3cEtuhfWYKZzn/jEJ6SEHilg9tyL3EXS9X+7CAMXeVSlc7tgiwMs/rRaUDW+c7mqGOr4Hw4HLIvjp8oitNiEOwdXa4v+o1YfcPcjbstUsdVYVky2i94kzJMiu9OcTt9H6BD86TedvAi1toytXbbm0IHB1YNrNxZ9G3v7etr9fgcecKhhZGXRGpjsGdk2tm14dKWZA83zQF+xds2ydmOimGhvm+hr9fWWC/TN5byiyv8YYaJXH3HEEewjq09zPCZBWVe3pij2nWh3Lr50eNOmVVNbE1jpnH+81BiP9iW98e8vueriCy5eV/qWJuMTSKlOXt7qGx6f2Hr0EQxMz+1JRqhmMuU1Lsmb/4n9q6YaPe1yYmCRXj5IFciBdSaRmm+3G1FBUxdqcOan3EbPCw9msGgfMDRy4nBxy62X7ttYtXXdurceNHhZf2Pb5EpPV09ukPHyZaWK/gH3wOeeb3xByq1PP33NL35RnHCvI3uPX963sTN+8S3XjV7104uKoaMPvPmxvz5g1WXLimJixerpa54Ierj41On/h+1Df/HcwU7n94+8VzF6fPHN04vh4anLNxVXXsmoGRTtv+uNNJiybhzduH7b+sMHdONiW/+17TvRSB2lNTG9tq+nb9u2la3WQOobnd5GamVz7rSXMrAqdcvpfUnG3t5ND3ny05ubNsDZnk594ytf/KJrsCd4VHaulLZoaLbaHS/JmlZzvJhsTE8N8t+eFEBpttlbdO589C2Hn/4MnWSgSLs5/auWjTemD1iJwFYfLVfa60mFDpS5GuW1WdI8vryv6Icq4dnR4E1/3BOfMDKdnH9NGAiWLesdHu+bbvcXy1vjtojg6ykGlhWjI2uPPHJi28RBY6mUoXghjBMTYzqWlL5OY6jVe/h4z+DWdl/RGJ1oXFQyCBvj1V4CYV5OPO9pbZlYPZG+GDw8mdYYfLy5HjQxPdU32ByfXNM/OHiX+8DZmk7EjTfTjlizk95iWxe9k72tnhX90/0TnUTVvgmkaI+MJWyjxrdlHb0/FVMOFulhHWoO1ByoOVBzoOZAzYGaAzUHag7UHNgNHEgLqKJwtlPgYtvJQ8cRnU50xDStpNptEU6rqabZfdFaO39oR/Lss8/uKtvyJ1BFupl/9bYLuOt27tIjUoIAeKjHLeSdLOXm+Ljjjvv6179OeW4X2yOQVuvOx8JpWeTkpxTmSfTqrFMcJXWyV8StDwj94Ac/eOtb32ofPA61dpHhtkqJeNwiIyJz4euUmgM1B2oO1ByoObArHCCPTjzxRJ4L+BDhfYBbAU6vCB2ilrJdOilM3vmEML8AXAkwwSX15pVKIRN3hZict4qfcwQzBB8b4GmC/EUVhx0AFEfs8izJ1p1niv/3//6fb2MwT+JPAR6KWQCCuO86s/3m3osszjr5KCLw5HIjErm6Ehe6rZI6L0wXtq7buVm2izBnmWvqUEUecZ6/7ODY6Gehoe7s7rSjiPkJhzIYGxETFfsOXEhUMZj5fOxjH5PCjYWdX5/FYiFgDgMyTAKqwJmqOlJzoObAXs4Bb67FhYNCFlZsfriIstLh50iKF9zAYqg03hoBfGHlwQ9+sCsrUKdm1Gsn3vrI4jp3cItH5e5V9/4UNzcGKGOO5aHx38DFSw7zJPLIoo+bm7ve9a6GL2dDfJvNyZSzzjrLUxSqmmUaexXD1Cte8QqLyle96lUK6loqLtRGQVI8nUvwQrl2JX0nSslEYl2Oo0FcwAGrac3HE9CjH/1oXPrQhz5kI14r4wwJ7iNPdrexiJ+jZz/72YZ0zs6QgeeuvJ7ZZyfrIcd2Lp/+7d/+zVJXH+APS9+AQSlR3NyKw8AzlI9I6UvwawXrYqv4P//zP9dMv/3tb806mKIRyna64dfrAhtU8mompVgyc9VkBsIjp26grXls1AEkoioXCv6Zz3wm72acUukqvrVMZv3iF7+QHjABbJ7wla98RencgaHtDW94gyzmNvigdxGIavfDH/6QSyMiUu3MH/i2PvLII5/ylKeYF5GAiuY5izUUUSgFYZxtCTyLYSwBqu/h3pOf/GRFO7JkKkJv4M3CZMBaRH058hYQxpUnMJzUTDxLIkCK2RcavJIChmCdvk3j8f73v59JnhkaJ55oxtgvfvGL4L0UHDxFNX3PkoCWRRXUCIC29sLihipgMm9xIE844QRTO55ATauYdlOw8KXlTdf6aqHWgsqec8453imV9eJruCrDo7h8RSduyOXjiFqTizpMMxP71a9+5ZHOpi40J8jjolR1zEO8vypiwoafH//4x+94xzsi44wzztAEf/qnf4onpnBaSnM/4AEPQB5qdUhIzE/APP3pT/+f//kftVB98FCpskL58Qwndwp9yEMeAgl4XUtPQ4zOzKEVGnh6xRl8YM2uKypXRkh0UUgAYJpOohOaZ+p4FFPIxmd+xLjV449Ma9JKwYAwKZkVdWQuB3rmJtUpNQdqDtQcqDlQc6DmQM2BmgM1B2oO1ByoOVBzoObAvBywxSU9q7QoVgQaCkqlHHebAQDTyFA8UUsFjBRKnAwDMjAEErcgXQWQHskVx/yi3FAAeeoRtBJd6WgcGYrbwO/pk550//e85+u/+MUtpJRlhn2+0uFMLqxSaiOpBWjZwvN6AnSoKLmy6mk1WyiGR5Ir8pj3TE3N6PIiMYGXYTqZ9Dc7k6WHqZ5Ec6SjPDDkukuHyKnlEmCmjkoEJnCWpU4XXHA1p/jPfc7fnnHGC3/6feQlJaMzAela5nOVRcCqEk9qETRjnVvVoe5MVSiDokEGAZES/JQI2FUiBpbIE3YpAmLE8y0YGJQST2niqM9KkhsK9TSAF7rSc1GTUb/SHtorpXWl46s260IZoxt4miMLQf4hpGMCnkdDRFvPZUukBFjmSSRm4HwLVYaJiEfyViGjlX2WhBaSopbekzb2ta99bVdGtznX3EfbfVrNAk+EnOg2x29EkSAbA6s030jrUq1Cjue65Eh+VEf2EAeMwIFZv4ph2W0kukqJFzaXHiO2BhKJRwFTbTIp8TQSI4trRpJLbJYuLt1ClWRKcjh57btpUIpR/eCDDz7ppHu98pWf/v3vD2n1FdNO7M2+BSGvA0NcCSwCKIquEiAeuVyDvCBMKXGbn4oIaiEEhiCYEAzMURfXSImnlWuaBqiuCnFI+ZOfXPD2t7/95a960Re/+Nqffy8Nhgn7jPz1Lqe7CMa8jCSKkB4EiCAmEjNMkOfqaYy94p66zTCyCJ66RnrAuK3CiAdYtb4ZoCtio0sKnL7rK0Icu5LIrpBoEY8iSKnDXsUBPVYD2c5E1aMe9SgGcDYdTzvttL2KyJsqMUt5uW6qda/rNS8HqmO1uPEz5FEAuxWqMJESHUncMFtNiVxGeIninroGQKQEwBKvShGIPGYKz33unf7xH7/wm9/s099fOOWeSTKeKMUtyEiUEoXmUjKFIgSfkChsJEtWKdNlDVJqKbZKt5gJKHIFkngUZZm8y04BAFz9VLQ6ne9pJeUALwFJxLYT63ygvZS/z/vUp0759Y/GLa2L5IByJiSwoMmfSojSg72S4YkgPUOJo6RkUroAQGGkVGGABYsiMWCiaSIlMIuL5IxLjBC+7FRYCIUUZh1iScXyQym0ECxIWHgwH3FWh72RhTy01ssKEo/iUL7EsvYesBsv5VUeBuerPaH6dM/FX/7yl3tHGAz5ULBlODvy0Nhc/5TsuTresJhvjO/UDcuxuvQ9yoFywOQ+KR3pJCnYyM500fkkDm2qoWBqOrmhGbxmHQky1TMdRyOkQ2WJzfjY2pwSHB6iJA0gjRYv+MQ2R3pMOZ/zqKd//KKfsURsTE4ZbYpO0sMvVEeHc5x1mftUji4CCcgqHvHAGn9cyV8Ej4R7HgVGmaVislPqFlQhCfFS258cEF0bfGximq3nRPr+5/ykmgideOKJ7CmvzbRoDK+UhSqy+Mwzz4RcPIRXV77VA8W++67oGduKme3R0fWjW6+6amzDuM/FVihR1VbruOOPJ9x//+NzGeayJ/7Jj35U3PF2KqXiWkGjXLlpU3HVVVkfHgUdsGIVHbvS2fL+6Ic/7OvvX3vf++L5WE/vJb49sOVn15lFdRF3o7rFBz18amwjqtvNNBeN0C4dTtn+cas5tMIUb28mXZ20W2FWh3vNMt5oj+PV8slJtvhDa1bpvVOT/D2l+dlOcKnZN3NkOtpdS8U2SzjAMjX0Sh6wcrWm6eN8TgNNT+oD9kTEFS2U1HHUGtSma6d0gJVULuVWS6JtB0O4Rh3ZuIHh73hTx5/GEzjs+pgfsk4evtOdB/r79ZYjjr6lbn9Re1vut7c//nhW6aMbNxsHGB+7jm0dk/3gffdhkR+ExKRXFZZGV3rPVVnFDT6j7QHZW43k4mqqSNPU6ckU70yneG+nTOdUzKtU+u3FkfTWNBuoneacS1Omz3O0W5PThgJZBpctGy3VU0sjpoaqOVBzoOZAzYGaAzUHag7UHKg5UHNgBzhgNWFp6bimtdiPfvQjRxC5t/jWt77lBKMUh0Kd4HUi1BlF83ZnEa0mqthj1VBdO4gLZvJVsEXiabEwH7xlhSCjVY8VkMOiDq86kev0qePi+etNKHTAmGHS6aefbnXjGKc9U2DOcFpiUJ5YgD/rWc/y5adPfvKTFpvW2l3L7cVpW+Rp/ajmQM2BmgM1B2oO7BwHyD66r/3335+DJP4veLgQd/6fKwRaTTgJLOKYa4BvfOMbIIlp0pCMJos9DflYLTqEqZR5RWoVMsdJ+RyfGwk85D5/BKgiZxn4kblymTkoDg1Ujjw7kLn0ipxZQBI+sPgs4DGTyvSUU06xo82QieTlw4LacG5B12cKsq+34pTFv8all16q1TQu7tlAMZnh4UJTandc5QaCMtasRhNzGFGljaKbiwdPMZw3CpMcrjqkaAhgcztANW8drzlQc2Bv5oDBwU4KiyDOcXyS1oBgfLBV5NagamfH0oZc4ArHHq69Hk70+D+K4cvV4LwTtZNRyBkXH0Mso8ItMsul//zP/3Q1iCmXJDK2I8mCi18bvnWMUSBtPbsFBr9xnvx6yUteorjnPe95ZBbvQqqWi14kMrdqc1MWyb43PMJYa+c//uM/NtST7DjDjMdoL11dDPUEvUQGb5aoVrUSPUW5oR4zcc+G3ec//3ncw0Z843qSbyki4P/+7/8Wb7XgFTwE7kc+8hFreZy3B/2pT32K4NDfbIuH+H73u9+d9g2POIKEUkrwLboHtYBP7vFbxEcSb1ZI4l+JR6rnPve5bAY4hFIF4gmw4ni24mVJ17XiftGLXmQ+kLEBA6P1L7jgAs6hUKWyr3/960kxhHmENmVxHYVCAHo4l0/SQaomCvlg4nsr7fMuX85KgY2cSYWnIUbDQNq8gkGdmQkbaa6vFIr5mKayRCp6MNn7RX3BJZbN/Te+8Y2yHHPMMYQvR2MqqKZ4QlirDv6A1DTiUhCjgmG8F28oDYlSwGgjH6rEQ7RpPmhNlhSh4XBVRbCUAyyV4gRKNSGkTkGPqnHP7TXHSW86tLiKDF1Cq3mzxDU0qlAOGA2L9GrcCABTL26t+MvjBYzxP5q1kbeY/T8LQ9aGblFruxxj42AX23LFoUof8IiyJT69qSn1lpNPPpm/Mw7O7M/SAhmOsNEjjQj53/zN32A1tNoXDXxpsZ1Qnc997nO44dQY91gAMIpDLrXQcPjJf5lKqb4qY+PrXvc6YFoTTng8VRf0u+KzpkctzGohQkmldaDSOVXQhLN2gLVIx/DoWruKxeHqpzUHag7UHKg5UHOg5kDNgZoDNQdqDtxQHLBQvKGKrsutOfCHzIGsahHxGuZAS8K4hHaGpkNgbkK1gVHABJpcAXBmHWBxMIJ0WVzp0XIKZQdFhgCS7kl2eKhI6GhCByS7p3EVCeRgent8TbuVDs1yPqXEjtM9G1audmC0/dGPPvTq3//R5z/59Vsetf/09AVFscInvYtOeZDGsYRGO32Wu+FMTaLf6dtGq5cyLTArd9myFcrkzSOllMcYKOnQiWYEoIqeq9nT4kALVa69zeSonisQT0uVYnKyJa4u8EOS8JQsAiY+uGwIEr77PV2xYhnFEL0W2ImJSbxc0ffgj3/omyfc77NvPO0Rz/l//33lle2+vuV9fZNFazh54Zj5oniPA8nt8lvi9h/9Ws3eTntmQxRzlI5ImqzgZzSK4iSqhVZQTSShRxy3o2oyShFStWZ57hYMAJSLCFrKU8DiKiIiyCKAiXhUORLD10bEkSREfE9cq+XuCfw7gTMY0sWWpdMJMgOLaAg0YPsilGT4KkxXIjyBqgqT2zES3colUN3SaNOKUoBSQVazRBzM3MSdSAk8Xdcu1u0E2hswSxdnMoe70hencBHgRR4tjnMnnl6fZe0EeTelLHP7vBTBkKuaMTiTxTaEQsJqGk+N8xKrzWTMF0gluQC4BkyM/8ExIzwkhpTICLlbIZ6Gh4ty1Gn29SVfDAEmpb9/sGiUg/n0LQAfetjWX/5iuLGyeOdHVv/sB2s+9N7iwfd+wGDP18Y7VzHyaTaGDBIDA/3EASQhQURIHxKhOhZF6UGt+oLM9RJXOgBPc3ArPWpaxWPD2E5S1F3tVJP4i72lVDVeM4UW2jaWjjRb4+0LewcGDtnvsR96z5cf+YTPvumtRz3mTy5Ys2Z4enifRtHTbE3EIFeWi9vXGfFQaGwM8hLWVkuKOUMqYjZ4ijzUqkK0Y9A8+zz9jZSoGvicEvGABBNNpmpZ/soST7d7rYpjlOR2327GPyiA6OQ3eJW9pwJ7KV9tspmq99rCvMGpqgmoOfCHyQGjd664QdiwHCO54ULcyGzYD4kTYGAEcU8N0QvBQEs0CCAhDCkwMwRZ2KbgmhacpZwnR1KS6UA7HS+Xepjbnt7LUupg8aH/uNlPf9D/wfcWd73dPfpb35lopa+TOZbuunzZSiaqhpQoLujJRKbsszJILUIMIaN/oHdgMK1kwVsR9/Wn1W74vmwlv1hpWiLRL6ogBU2yAIYm0IJhOMUSSOmR4rpsmSnMGC/K4q2+ZCly+IFP/td/+fLj//T7b3jTXf7sid8bHbXS6fNrFJOwQVz+UMILQEZzrdxUSmZjcD4DuWW3wcJGJGqH7Vgd8AEmRRBXhVTZcmYApkpzwMglUk3PBS0SYdKk9BNOOCFgWH4wPVkE3qOseRCf6RKVyOJ59+jTTMzipaS+sICDksUz7p1Pq73l+qGQjZHVt3dHT2DOldlepSQn7hBJO5drh4q4UQDXfLhRNNNNkshYR/L1FD91JHVaSdXbOurIo9ZuaN//9nc9/p4neNmtz4mbnnYyc+y0G03eJKm9253xCy9jHn3AyiTZ91m1gtHn1LLpLZunDil6Dl7X7GusavYOrbr1vlt/8IODi+VD40PnnHH2r8775WMe+iByrWg0exrTt9nnoNs8/imP2rjOF3p/NThx9TVXbyidT8V74cqY0hdWOd71/XYUErIsTaVbULOFZfH54he/+G1ve1vZQDFdsTpOC2ROesab7cHp8bGpseXN5LVn40RyrTVeNKempvfb78DpqenVh+w3ecU1qdqkbbswOaCt92e81G+PdFoTLV48p+gOis1jjVWr+BliWF2sGeKlq3/VAcXQ2lkDP367SO6SCpdG+7LLLnvve987e7+Tf1GbkJViLKN4caM4oq//kfsNtbeO/8fBy3+6rnjvtsli5bLmdPLl3elNfoWaw7YS+oti/fLlqzd1GlPLVhx/6YXSp6/44T0uGtg6PFA09puaHrla0gVX33vdWGefy38/efVFzdUTh90Ogyc2Tu43NjjV6L3sze+0AXHUO163fzHxvw87sXjKo5pv+Wj78isbm9enUvy/UYXUe2fVxaZYV1z2+09/8mPnnfczvap3oJdF7Ix9fMx7y/2Xav16UweZmc32FPpS2snRIYvBfWAe3bouvSk+VaKTzOhryty+erK9WUgQ1tdqXvS7337322cgcjIp1Wb6UznjLqa9ddCWrrhmqGokZRTn6m6bnMqlYsxXhZijR75EM9Pn5taJ9kzeaLd4WoIvevntz37+f2ecNTEy0r9iRQCqplC0kld3m1/UOMv7B8S3bt3465/9eP36jZ5OlC/D8v6+dN5sYnxo+fKLfv7zX/3gB5vXrZctpu7F5FSjz2uXuu2iJHgYNLvaeuq5/MLLfnveR7dtWmcXTtsVpXOrctgwLiXIqdbWhLBnUCsbZtKyiNMuGr/Vq41j48Oj9gWRYTdwfGTc006PxUWjr5VcmyVPd2pXmeEnVHWoOVBzoOZAzYGaAzUHag7UHKg5UHNg1zhg2k+P7cykk5biaa5euuQIQwJxBzgdTI11UKwRxLOSPK0yym8zJNVET4+4R5BIl2JdFhnjOpdSW/98ZAAGENcqTOSCjdGRk7dUEEGeUuyzZBos8+kfkjajNDVUrkduI7tbBynV4rOf/axcD3nIQywwHSGGapElj0dKQZ6MclWpquM1B2oO1ByoOVBzYNc5EDKIa6Sf//znBJYQQlMEcnH24R/96EeBJRVZqaMroRIYEUY2EY7klDhJJ4R1/RIJA2//nayfFz5o8EiE+y3eN6o0KFo6kpRuhjAXg6e2+2OPgLsHXh5kefrTn84TJZcTc+HnpiAMEtrvpMDcy4KKhykdCuclLaeDDD8aUgQtpe1soLiy5uL6wcwnY/BUPCl1S9sDjiRsmqxatUpLAWNkyK3Gm970Jh5DuDWBQWL0ioyhjtQcqDmwN3PAIJDJ86ZzI3Xqqad6i8vhIY0PEjmC8WobH975zndWxwcZY3AALGJAkNFiJ4aCjHaRCPyGcUskA4srPK5RhDg8kVc8Iiy0eTYUunByQAyVEd5w1PUoYzDg88VjwcW3F2otvhRkBJsLn4vLj6SolyKE6hBXjWfg6zmiXrhn0y1WnUrP9OeImooz8eK1MEZvt0I0n9Wl7P/yL/8S1ZGO864qC5s44cIfkPhFF11EhsZqlPuqE0444bjjjuNuDHCu9UI8Cakd5MFMkvLi5Pa//uu/cl4RJJlFKCUSYQ6ECpXiw3tCFf6tb31rhowzCCrIUxJSoeKTCFtgUws9DSQ80lkvUzJILE3+knmhOH9VGIVF/ByhMBLzVV5x6RbswNzGVSR6L29c4tXAR7ZQTeFAqnorjo1dKeHvKSeilq+3fCuiRurI0ELI6Qj79Kc/jcIgUrrZ1xe+8IUAkCgClYjsJnhCzqsLidNdBDEJRcl2V4+0vuK4TuNLjgoCo6TnvPNGEBPpDj5447iLCn4qGtPMoFy96VypukrUNKjFdqGKMDNHibKA5CysakeB5rkOp9CZkfCNpcWjz1Da5HQROI1pXd3P9Eb3442rCinOv5WrrpjZHkzAjTPPPDPmop6qphp15a1vqxxIs8k61ByoOVBzoOZAzYGaAzUHag7UHKg5UHOg5kDNgZoDS+QApQPtgytNRKhXIiM1hEBdIohkbKFDyaoKj+jFKMIodwIeQA4Qyu62RJO0XTIGKiohap1qudSylESd6QQPBkKfSLnssumTX73KFwme/VfvvPz3xR2PXaYU1veZnq6IEu2xJXpmfVVE6QqaqUh5ACNupcguLkShXdgWuo2autKFoZnmDvHT0+kr3MuWDdrYot2iyIaeCivKfdvbzvnYx57//Off/8Uv/vrqA1crbroYttE4PZUcdVH3QGVLNNWuVBriD7Ujwty6aiN8ttEYdEoJ4nEp8Gf6E+YyZBWbFGjjmmsUTRO3kOT0rohckTGuXU939BYZO5qlht+NHOjiPzXobkS+dFTRG5cOvzdDzn2z9mZqa9r2Ng4YhzNJsbOSbw25noZ0riamEbkUkXENmOqrLaUEmdlf6boN3wXgpQswR95IcYA2lVUeeGPSsXxF8eS/LHxx5c+f8j6H3cgaRE5MJcwhgJCX4Cv7hWSZ0gNzfjQ3BQAMQadrQMY1JFci7robpaWITGasYd8jV5RVzSsul2vGqRThJS/5xTln/sXTnz76iU9cMTTDn8iXSCiLkmtmchJIIl3ewJbgrkunW09zYjAQkZHlWuwp38zkKsMEz+fCRMpuuSq0WspuwVkj2RUOdLWI3eVdwVbnrTlQc2CPciDG7eooKiUSc7ldt9JJkxJqRgiGcHGdlQvXEXYZz9yILETSc08qfM3sqX/6cT5yybu8rAOvlJC/IIPIXFaZdwZlJOZaBMEyoscjESEInveaCQvRxsqHmYU1I+C4ZgARFFoFi8AMQMRiFNjf/M0Xzj3zlKc+9bL3vefyDF8WpxapIkJOj+xBW1d69VacFYirqkUVXOXKNYXH0wjSBSkBUy1rN8at0LeLLdiOqiqd28219wDEIv66zbX3ULczlFz/DeGlYCV5/Ze7M9yp89QcqDmwOzjA4/wt1vZS5PKsaTZARx2yCW6RXAIAkPsMpc8qrF4+NES5vGzal3i3XjJk3ADWw9SyPWYVfPRhR5O2gOnSYzCZwWNVXhom+mxpz1DB2DQjT9l7eljx+nzoU57ylIc+9KFPfOITmT8ypVXMF7/4RZ+FZ/GZzRyhrdJWxSPua+eDQytYQK458GAmj3xuM+5MRRTFIYcc5Cjs5b/7vW/kDifHRp2f/ujHU7NL7P5VqzwdW8sL1gAfSfJyHXXVFVd04b/2Fn/2pPYYX00MTFZo3a8oJktPP8nOt2gN5uoHh31VFSQ/09I1EArP//WvTS1GL9jka6sT0xOpIRsNX7W95IrLN//kJ7/87S8niokLi2mzosZgeji6Zcvo1Vev/7d/K9auLZ72IF/LxYSRUs9wbX1vnDEsuvjCCzkyS+T74sn42DWbZ3i5UIXSZLE74OpkMboBG4vpsfSwdMaUOkDlNenOtMC9xvKOxLmCBUA0TOVJZ8IuxXQjmZhOh4uouQ6wkrO65pUme7P7WZX824uW3fiKi0vde09rfHi44MwLAaqWaudc3MwuyZZtwxVcCaJ8sYotLc5iE1tGtrRH+Ksqg9EkKar606Fx+3ZLfFlspXWSk6+0CeUt5lSr4OvNp1hsCaFiXm5PTtoRNMGnAUwlNxrbNmxIxZWOwJye3zaBttJAV0V8Jto7kqq2VJISzjrUHKg5UHOg5kDNgZoDNQdqDtQcqDmwIxygh+8Cj9VrXOOsZhUg0iPl4IMPtiB1TvKggw6icLDIjZCWvWWoZuyKO/RIB76IRRl4C2cBDa6RPXx8ZBoQr/Qq5lwuXcHLXvay448/3vltp6/BcEN8yimnZFTVXF1xS3tgTtg6Tdr1qL6tOVBzoOZAzYGaA7vOAVKmKr9CrsU15JSrEPI0issCTuIhhxxyi1vcgpb+2GOP5UiLCPYU/FII40TpggsukBewLFHovBkTBbP2A5mGrsjcjCT1K17xijve8Y7kL1lvq4JzhNe+9rVzIeemKI61PP8vDAvnPr3BUz7xiU84OKBGc5mG8ip5bue2r8TcTBqxCp/jZi+PfexjqaO1KeN/fiJEfvKTn9h/4RbNh5EgETJ8Hak5UHPgxsWBeIWrI0B1PIm3u/pU7arjhiHCxqj9XMHQuhRHgVEiPJ/85CeNISLG/3B1tNtZ99KXvtSyy3jFZZJyfZyJt6xZw7btl2bwtzy0ZLOi3D709Qhh41sr2AFXZrRRNZIJiUeuc8f/YMLc9Mgri9a0t25tG8M+eIl8OXFXdOCBB9o7Dvu0KKLaZ3LpIgvhr8LMGw+08z6qJqLK4l3/IZgEcX2VTyvZhaijdBEV0ccIdD2WuiCW/KoATBYho426VGsUcZAiUWvAMIjn25x9t0SqBS2OEFUAwAd54jkSGaNqAZBRgRHyracRVOoRj3jEfe5zHx8VwyuWnF/72tc8CiTVLDnvIhGc9xROGbP+BLZIXyTjrj/ajUUgHs1IytXXnbT73D6z62TfVDGU++s31crV9ao5UHOg5kDNgZoDNQdqDtQcqDlQc6DmQM2BmgO7xoHQOwQOcSEUka50EPRZQlXTESnAcrGRAqbMnZREUsI+JlJcAwZCQcaAkeg2VB40HTaB7AB5Wqq8ktZsfHSM96vSdL/RbE87wXPppdOPfuRBf/Hnj3vvP7/r85/ZkGjoLJsY62m21qeCm74674jCGmb26V9SDE1PjoxCmHaYJsOpUyJglp7koisZ4l+bktQuqg4giE8lJKBkuV90kppGQFNcI1KmpQtgKcoSn5r0lXL4C1toVLsOAjlsm0pLhxropyZ/eE7xztN+/ao3rv3mt4r//fS5XPNPTwzxLz+dlI09vb2DPT2+jZMUjuUp3XRsGHIhSsFhBbl1kEPVo3QpXO/LAiaCFKHM14YhcbUMAe8azJEWLQgAMFWmlLLodHg4BzjFA3OOzJT0B/znRsSKhUjV7kI8XQhmN7bw3CLmpuzG4q5nVDelulzPrPvDKW5uJ5EiED2ZCeIhSaV4VMqmSYnG6ipMTvEKS5dFSlUKuBWkQwJA9kiZQVI6t/JRIk/jCFuAzciaqQNTls4FZPSm4fFHPOLgN7/pSW95y9u+9t/F4FDRbCybGG8pMKHqDLrADL+8QSSSoqzAmcBmpW0G8Chg5Mp1DMi4EmrwuApBVaQTUlLgEZEeFXetwogDkD3SA0z8x2cVr3356N+/7u7f+dF/XnXRLxsDyDowsa95pYEwflUaxsfJcSK9nDOUsxSFClUYT1VEEYpTKUGWSMlgUVOQYAIsYABXYSRKgSphmQ0ZYOkRWTNwNZ4T68gNyIG6RW5A5tdF1xyYy4HqkB7roJBTBJNIjN6REnmrKTGeR4qhOyPPKfG+k6Q5JcGU8re3Nz5CbsFlhegaxhM8NO1H8hfFJQ1nyHsm7/+QgVe8/Bmnnfaeb32tWLa8aE/3bRs2GSjL6tgEb4yMjG3bNtruJD/IgupUKQySktwqV3yZQuJGIJVC/nqaH0GCbI/KTGmdmB8xuQiJ7Co7yCgLcIaZLZ1MNIFJwkgK4B+eVbzq7y445fX3O/cH/3bp+ZcXjudMmz8kj1SxRgafkYiQv37ELyYrqExJFFWH0Nmyki4i8gbNAR8pmcIkfcv5UsAgKQAyZjX1KCcuPZKrL3v+bJpGhzBKDFTiCHP1CMGZ5qUXVEPuXg7cgE0wb9HzJu7eKtfYag7UHLjeOJDEGwFXdOijrdTvefe7v+Q293VkJLmB4kbalxLavN8kocDDD+lE8reLxvLbH/NHNz9UQkqaaPLMdMsVzec++wXPXDFAXhdTyXMl0XuzQ27+ppNPMWiEvFu2z1o64iZnOk1ug9J04laHHXGHl738sub4hosu+e1F55W0pAuB5Szo8573PLbLz3jGM+J7v6xyzz777K9+9avSffo1A8eg5FoKzzKZHOtM+B144L5HHXVYo9W7ecv629zmNkTtJiqDUkaTowcuW+YYzy0POoyB7AFHH3XxxRf//ve/v/qyqxq9PZzr9PcP7rffARPobbfXrt135crVW4fXX3Xl1Z2eZtFjchCcw7s8rYoJUqZrN0cGJ4rV081zV03+6orpfx3Ytg0JK5fjv48xq/sRG5Ni/JpWq2eys+LLP1L2dJGUHr9KSv7O8Dk//OH3fv7T9gCuPnyfA9fc8RaDK9ecf+bX3rDhp+eff8FjqRrMZ1p2Ooplg8tZMB82uXWs0Z762m9g2HT/e+5zsxUrl+13QTHULDaCzBVO2W4kQQcWEKtGurAJbZriFB1G0KpjcubZbIt2V6mvaI4W7Ze+6AjHrc/86o+/8rVR8BRMzQlbOcVYuRmTcOjVQsz0ru2L3diuc1/OqieGt64fHh4sd4h4esoAXFiJT3FlBWulc7XaacY70ZnadyUSpiYmU1unl7Hoa8qCOGF6QqpEdUSZBM7hywdLvcTsvzU1O7vuFKa2zaLRt6x/27axND3m7nZZMTk62yHCMX0wUekRwtGYOOZ48YWY1ma0Abbw1eDT2+lrNVOlJ5PvtubQJB+yW9TJlhY/e43e4qCDiic9/k5e8Ne/5j/PO3+0r2nmjyOJk1MtL4g2T0uGNDSlybzvr1hWlGNapCfWzXDY09Qv6lBzoOZAzYGaAzUHag7UHKg5UHOg5sBu5cC80+ycmCPVMsup+7TDri9+8Yup+n/xi188+tGPdi73a1/72s9+9jPHd+kN5s1YRXL++ec7DcuLBGxWBF3wceuRLOJxW80e8UUe+RaXk+E0Cfvvvz8FO9s5/j4QGTjnoooUKxTAMrql5aCBXxx+ITx1es2BmgM1B2oO1BxYhAOES4QQcPOKOQAwEJERIZJsSYO8wx3uwF0CUeULCo973ON8pPb0008ni32ggm35IoXGI36pMsy85eanIlF0XN2Skq5yLZKRCw962gsvvJAemx8T5ug+CLHEL/kpCGbyV10I7iole0Mck4OqzJC5VHVxLABKhiWtZjVjNR6PeFd57nOf62slnNRgI78nPvvB5sTOyKc//encE+YWWqfUHKg5cCPigHc/Qgyk1eFUelQkR+LWoomvK185evCDH8zJoH3YBz7wgQTBa17zmmr2hZhAfBhULdOs0azdYiRfCHhX0i0Df/Ob36DWegphFl+/+93vljiYgz/33HNtNNsjXmKWXSF1h/KStuCjUVzn8jxSMkAVuUfzPo3EuBr8n/WsZ3EtzdEhH4if+cxnoNJqBOh//dd/3exmNyMULL2raOeNyyV4VEU+L+TOJVpW+2iWvnT44YcTUvxv6lGaLAz/olBy/053upMagTnuuOPUyMa3uuQSUQgyrjkx8ubbqIVrRAJ+6UIw4DO27UbAQ75dsADIpAZtEnOkC0Oku2Z6ct6A1M9ZQdz5znc2X+I21JlHugu8Wghhxt+FJ6eLePXUBWZI9tybXi1xkfgidM7NlWsdbVHNG48yQPXRXDx1yl43e66bpOZAzYGaAzUHag7UHKg5UHOg5kDNgZoDNQdqDuzlHAhdg2uErFJxS9UiiOQqUIcJYAKYwiJSMsDikazgoPiT8TrA0xvdRnE9jeZoe/z2tyve8pa3/OBH733zmzcEZLkF6NPrcXzg2twzFPLEUWJIW4nlMYY4PRCkxpXmULbZlHnUiJnCa7HPahtzChgaHEjwQQTNDv84oUDDJhGYiJ/9RLrK0FU5bfze937lsFsXL33pcesvvuTcc64ZGEiHBFLeyTG6763Dm3p7wo++owydwYl+j6BSF2B45VZxUXQ8UlZV7Qg4wESigtUUcUHGgAk6XaVk4EgMyPp6E+ZAbv2bcB3rqtUc2Ps5UB1yxWOQR3akG/xj/M8VkR5BSkjqLBrmhQlUOUuCmUGQvDOwC8253HrS7pT+JJ2Gmy5ufnhxyimnfPnL//i2tyWo0ZFirtSWJSRIxrPdiIKERcCCQBVXNfEMScjGrXQYIh7AGSZ4knO5zQBvesO/H3PnFR/96H0efN+zlB80NFuJkkTQoiRl/NVIIJeiiMi+XSSpmLKgTGFkj2s1sVpQHa85UHOg5kDNgd3OgRiNA62VWr41FOcl3iKFptF8vvFc9gjyzkbT34SqItHklRgYrltKgjzwwOId73jHf/zHK973vqK3r9g2nEBI21n4tAglkSWG1BOBjZSMiGuEVHAZZhMSvDCbPONBIwN7JB7yN2cRyUhEqunVONoie8DPkppA3vzGjx5x2+X//d9POO7Wn85ZMA/Xq5zPj5YeCcKUFREsynkjpUpwBs4wMgpu45rTdyiiUJz3ecOcq8Q6M89RqDp65BrzperTnOVGFFm4C9yIKnGDkRqtn9+UG4yOuuCaAzUH9jwHyEuSwKfdWSd3RkYazX562FnZlIqPTzgYVI0M7eHhZm9vUlQnqdQhiUe2Ta094ICrxofpe9uT6Rutw1u2rN1/fwdNB0qr2Q7XjX29PtvaCD84pQT3xdGBoaFiqHHFFVdEFSEXyZKILaYF/iMf+cgPf/jDcL7vfe/jDEsuMOw1YyKRaJtvrJfI8tX5nH32O4CBLEtotI2MTFxzzTXFofsTwPAkd9XDyZH0Ly+/WMWZTXOsA0xe8xZXAyAwKZ4mg06UkZKJyLLiQfT1crUVoejOVGdbmmIVPlDRu88+Su4fSxOh3t6ki0Ag2T1aeslsJOtTjksTcZyLjmzb1pxWiX4fX3ZsuHXQIeecc862S861xTHJ05K9gMmitaxg2s5u+KBlazZu3PjzC9YX7S2+mKHiQgK6MQetmTii7ZJTqMQXLt+W9Q6MTY4tXi0u4AaKxsc/ftGDHrSOjfizn9380Ae++q1v/fyiLbF7s3juJT0t3VSl96mKMc1xZ2fOVQdY4Yj1Ife765e+8X/7LkubOM3p3hLDdRqodIlVothNF1tGcCbvV8JkMbiqWLt22WUXblux7+qt6zYVydeV772UW2acT6XKtFNiswe7ecYtWiV5aQdsx8JUcnWlrZp9Rd+K/hX6+ch4uvcG3uk2hz/9L4887LDDzj7j+295y8cvvLCw8TZVlpBeXj63Ot2+XGNK46m3KfqD9zlo3TGyauiaAzUHag7UHKg5UHOg5kDNgZoDNQf2JAdM2q1DHU4+9dRTrQIsdWNVbv27Zs0at6b0pvexuJuXEBN+GMCLWNSDCV33IlnmxTNvIrSw/e///u+XvvQl62j4169fv3btWlqFKHGRUlClLrEkUQXwiwDPW3qdWHOg5kDNgZoDNQe2y4EQLl0iJqlGK8FTKa6CZFKJ/wsekeiNf/3rX1OtE3DElnSyGACFeWAI+Aqm60SrpYCs3ma4nBgAiyPMuSJyxhln/Pd//zfahPwIwowzJ1Yj8bSEapDdBHH16d4QD9o4/sh8npeq4NW8jO3iQNdtfFztbW97GxcY4rhnEqII2x8mV1/4whe4PMOWHWqLeSmsE2sO1By4YTmQR4kqGTEg5GEhRwLGZqsx4cc//jGXQx4Z9mO1lcHmHRniqUckBSdEshAibokMj+bNUiVph+JR1ite8QqDpE1enzWyVAwMNouXggo9dj/RpqbiuWpLybvnYIIMJIlgexS0EG3B0oWIj/SA6SJY+zrLBq2gLHxgKmbkB/ad73yHTMRVj7pyzb2FPOPPkblgO53CW9Pd7na3H/3oR+QgX2yElNkIIRUiO0oEc9e73hWMBfgDHvCAM88881e/+hXWeRpVyITlSBc9iQtlZXMk8i4EX80eGfccfLWsHI9C3So3io5Hmf4MEDCukeJk3+c//3nTOZ67NfHHPvYxb6g5QBU+UC3lGrmirwYlrkvJuJfAdNW6ehtcvXFV5wbkau0A6wZkfl10zYGaAzUHag7UHKg5UHOg5kDNgZoDNQdqDuy9HFhEsxBaP1f2JaHG2olqVDOK5wAVNUfcokEkFLtcWdHjuM2E9fa1KNqobVn6p4+AF8ULTj5k68Sn/vb554xuYUS/f2+rt7cvOa5o+942tOz/fX275bivHOUxgPIQC/wwT1Vce2Ri0ikjdvvldqOSS23nzO5j1D0eBcHwBMGugkcRiXgioHRflcCwrTPVdAahp/Bl+1ZrW2/PkEKL5ph8Pc3ewf6hnt5hGd99Wudex/3lM573snN/yFnVmGMvnc5Qq9nbajpZ0Dsw0Dc1lQqFmcIU5ul28lfl1vYYCx47Z3ErJcigSI2jRHFLD67usbWGAGCREk9d4YyUsEwCEK2QqF3gpJP0Otz0OBBd4qZXr7pGNQduRByovoYG4XxrZFYLtxFyjcBEyCkBEPCRCEBiF0BOgRfwLNprLVHkkr5q9eZNm5JPjdbA5Nvec+TlG9/2nKf/amqrhP16GgON5ljbEdFOb4k82eV02oOdtttkzJFuZwNskeIaBEdK0FlNCYDZfOlvZIyUjCQiGW0VPmcBE/GcksGKwl7j+Iufs/X005/+/o9d8uQnX9jecgUa21NOns6z70g+Zmwiys0zhKAkEqVHWcHSgMlEVmGARd3nhQnITHYuoo7UHKg5UHOg5sAe4kB1rDYy51E6inMrsVo0+AgS55Vl0rtG+Nkc6W9Cda1kTnfVUI7/10QKvxenvuWQyza89bV/t27b1cVg722mi/FO+WtPpe3vRjqN0l69OlnNBtlWdpaHEq0TYw0YqBJcGXJZswkzMtptfpQjJTH5LkWARWK+ikSowgELaRiJAKQMDqwZHdv84ucM3+PcR37y87956EN/3GpzgUFeV7NeG5dFKAGuMx+ALQMFTAmWEj0KzmcAkYCJp5E3YObiCeBq3qXEAw9PItEZ4iO9VtlxG0/jqmmiCN/NO+aYY3wAtlR3zDP3WEq5NcyNlwPRN/TMG28VasprDtQcWCIHTCAss5f1Lr/yyivHp/enI+5ds39rYjoJP6c6S4HWKL1AtvoHCItJ+mBuY5IzrKI5aD7R6W/2TG7ZuqbVbHWs3Nu+9PvF//0sO9THP+bRCQnPTEyQwzkOIcMD5pYtnY0bx0a3chk9MlQcsXLtt69Zj1rIDT4HHnjghg0b6I3vf//7n3TSST76+j//8z9sNG9zm9v4FPn73//+z33uc9n7VVcdBxpK42gr6aVHpkZ/8pOfUHODmWwMlJBpZuLR6qK44grV/dLAVFI1jISaoRzwZOe3a2p86qc//WnRSbkuu+yylLeRHHeaulzHFRTmhITka6e312EgH0bed999E/ySAxW3gGk+Vm/UjeG3K/fmohjpax430rO8t/jJRDE9WvxuBY9XPf6bz23oo/NopdmKfYh2qsyVDRqPzraeNAlcMTbtsNJwn5YcLQYHfnKfe5+4enLz+e0H97TO6xQfmeZkG5IJDsquvOPtHXn53fqrh/Y/qNh3pWYqRlqHjPZMbRjGwTBqTFwoO0zwrLzb2y/Rr4K3tmWQG7XYND1W8DBembMtUJPO1quK931s+N+/+I473Wnt059120c870FnfuMrH/tYseXqlCN5EGvrujO5k5Ot9K0R70hKSts6Cwc6Hg+DngR7bUgT+xkZXEmeKkt57LPvtqn35+d+eTiBN9LMrej0pxeyGClTynxuERF5KxgSwPbCRPnWzEIlbLxYua5YMbR1ayri9W942je//t2NG361dQOXWHHYTOlludWywuEdGiOSNHupAqHhE5kbGglbmqA6KKHQVmGnKTnfmpgeXz+eNH2rVxd3uUvxkIff/uY3v/k3v7LpFad89OqZRUl69zvtdHqhpLhj66uKX7ogxVVncE3vWqfsyI3rQFZz1fGaAzUHag7UHKg5UHOg5kDNgZoDNQduEA6YtAuKNm+PZb6VfqYkHuXbuRET/mS0NusqK+BjRTAXeEdTaAaScmA2KMUZbEVQuZdrmeqiaBZo9i8wIS1GyrUJIncXVbMl1H9rDtQcqDlQc6DmQOLAUuRLF8zmzZtlJKTWrVsXEio28bPY2q6Y2wnWd9GwXQz2BdJ3LHYqKMvWQAhx8ajXTmHaI5nsEfhixxKpmsu3SMnZuwA0pRmIHX+kx94KSCVyhyGdGb/rHqlVjbTmQM2B650DXa+/8hcfH7z+BgRh3o3Xudi6KmQ1ZFgGFsMIPNvN0oVhibfQct/DlU+VzmpxqQ7lOqsLoYxCV+LecBtUEbhBTBC/XVIXAlgoPUS5p4I2cuurUUpUrhIxs7q2XYQtsi/ydNcfnVuGKh56ALdRbjDHNrpQhcncA5ZbP7p0zluFBxOQroJHkVKFWSgeWfYcfLVcZUV1gkiPImVuukdVknJcK/tImBBotfLS27pKSTUeJGX2Vh/t5fFgY9AfpGbGuq3G9/KK3ODk3YiMQ25wXtUE1ByoOVBzoOZAzYGaAzUHag7UHKg5UHOg5sAfNAcoUAQsyHoHkRy/HlhDD0JxFkFxvOC7Jlv8suyXnPTke9/73ief/D8XX5ruHf2dnKY2nEzkThV+XFcwzrkOwZ3J9sSE7xJMTc6edyhRqWZZMYq2mU2mdFsqEmevO6ZVnMlcIleLOPIBNXpUJ6soRfwovIStW9q+k3DJhcUznvGcW93qVu/4596pCZ+OT/SX7SD31NbhraNjW9EP3tUjyFWZdttpJUF6wNs5U5CndtSidiUthQ28xKLZAFiK7FGE20BlG1IclCucEuM28s2wa7YaHlWfzuKu/9YcqDlQc6DmwE5yYO64auCt4jK8zx1482jvKWDXCDNytPwjVxeqKCuuHvGrOJvv2r+wAQjvV+L/8A9PPeyww17+8l9tXF8QVcJUh1AalzfdzIbAOXuX/s5NqT5dCkDA23gj5qp53SJXSlyjoLnXahH5KfIH+tZsuKp45jOfeY973OOkk4qR4WKsPMxYLSLHuxgoPTifES4UyRjmjUSurkdRlkeRngvqAqtvaw7UHKg5UHNgD3GgOqRHEXOlQFfR847nXYlVtClOQl7nHP61KEnvfPPG057q/PnLXvabdaULgPHJcX6f0SNMd3jNmJyanvAjkaXA6mpZJzvhWF0DSokFYMYskrDM5orbTGQGy9I2p8AT8YjMzZIh41EUEfHRsY29PctGtxbPf/7zb3e725G/JhXzGUrN4ChzZXwpEniqSZHimhPLal1nErVQ3XOWKuYqqirAduM8hsQEzNIeMDJiiV2lOT6TeOtb35rxEB9YYQu7Xcw1wE2PA9VecdOrXV2jmgM1B6ocIJD6GuljD464+HZuOuhC4zo2NjE8PLJtG0HApNj3e10d5gx9bzV7iMmYGxBn5LLPIcQXEYwk02NjnZERalx+p5L8olMeGSk2bYKNxFGWUP1CLxgOp8ip//iP//ja177mC6X3vOc9Wb0edNBBj33sY3/zm9+8733vu+qqq+jer0PD7M1wp4AZkSYephvlj7BL8q4aTBSSfh4Af17yYsGsWFYF1Qxx2XUFmKo537SA9yvqfwBOjPgUxBJDfFmXaThlAszstmGYd/hdVrqygnb58t61a1cBozmnGxdiUqF2IiZX8MQ1IjM0+xNh1arzzjvPR30vv/jiSy+9nILBZC/0GJrs4JvdbO3hhxdcHF111aqDD77jQx4yeNBBMGv9tHlwYw6p75W87a5EZXrW/ci9ttbiPLj1F72DxZZNxZlf2/CMp5992mlfOf74O374wye88KQX3PzIo0IxFpjkKDOFEiihLBOSXmg3hve+972nnnpq374lytLdU4pF5wwKdmNhFVTh/eppf3XsPvvsc9ZZvqucn1VVYdWuIp5uo1eXrZBetmiOnLkrgneZfdYMvOetWJFe0NVDxcMeePC73/3EF7zgWT5F/ld/9V///M+nY/vgsqLRKnTjQDuTvdRP5oLyI2UBiFvXCF0E1Lc1B2oO1ByoOVBzoOZAzYGaAzUHag7cBDhgdR8rC4vlvW3mj57gcI7cBBheV6HmQM2BmgM1B27yHLjxiq2YCbjSdcf0IK57T5OhbbeQlGvaVTXpXSlu02ZHHWoO1Bz4g+HAQuPDIoPPvEPHXIZlsCgCQI7MBd7tKeiPsAjmWZD0dxGwG+RRlVeZk3uaEgVlpgUB11vRO1q1TOe8GTPZ1ZbN8chbvQaSnMttrv5SItcbfC4oCI5rkB10VgEivQq5R+OZvXu0lD2BvNrEewL/HwLO5JSuDjUHag7UHKg5UHOg5kDNgZoDNQdqDpQciOlx+d3aXeJIX5k78FxrirtLKOvMNQdqDuxNHGDXbl/KZkycEsmaHTTGqY8qsQxcBNYtkQg4UsI4PvLCI0gPHY2rp0IcIImM7fK8R3VTqD05xZOUD0VD/ZS/KP7+zQP/+MYLvvq/zg6VJ2IaLZ7oBwbTd9JDfdoshtIGUrPptCmL/4ZvWDf7letrKs7nTE0OZ7IlKt0nsn2gzpeypZfkNJjoOMzi3JDbaq0jHtRG3FWurrgUBCTMs+b+rd6+kW2YU4yNpzFTuqIjI0q3bB6X47e/av/r+3/7mte89ZJTnn/aGxA+6ojM2LiTMjPDdaeYnG6nvCl7sw8Z6bTz1JQDOVFiYPY0zue4zcEBHiXKIqANjBSRTL+4FAeQtE7k4pNeUItor4DMCOtIzYGaAzUHag7sIQ4YojNmQ7fx2dAdgsaAbNj3VGIVxlgdtxksD+aRDo9ACMZgLrssOVdBCKZQltspP6naSKWUXjlmxPqfPrf4s7++4LTX/vbSCzzxwTc4rkFpX/9Eq0VgJVeVRSO5xWr1TPtNlx6y1EWIsqpyJFJSltmjcZECOOqL2qgLABHyyCNySsCNMl+6OMhKCDp5GxF5I1TLkgLSFYYQggHTbm9t9owVE2u+f+62v3vRN0877eOXXvqUf31/0ameJZwpKWHo7VXrcUgCOSQhJWe4d+0s4tozgSBVRNXkmsE0M9NIMBKFDJMBRORylTGavvqojtccqDlQc6DmwB7igFE3YzbCE5ohTI3VxvwYzEUyjBQhbmM8j5QQE5EeUiAP5gHgCj4BzMjfErbTUyaBnVmvNZqF4/ZPeV7xhGf+6gPvXPfDs4GR0da5v0NEb/+tevtHi9EjUubWJS79A63x5PsiybugQUEqUp0SuEVSNcVtpKiaKosnhLNBohRC1gIzZiDxBJ4oJfIqSMS1Wne3gOMqknjY7O1p9SmFG4pibMVZ39zw4r/53Ec+/Jltw4/74AeLztaZUqvkSerpsZLlYmMCz8J2NGqBUTMZSrkZJGVWB0wXzYiUgnJB3nnxgAk+VPPmgrYb+eIXv/if//mfj3nMY+585zu///3vp4KQhaOQyAi5CGYef/zxH//4xz/5yU9+4AMfQEZUucq97RZUA9QcqDlQc6DmwI2LAwQit9MXX3zx5UP9Bx988L77HEg69E61rVH7yi8+DCSV70RzdKo1MDA92Nta1iqTG52mjyoUrVJR3rJmpxDoaR924D6PfNjDcaDTbGzcsnntqtVEG406UTJ51fr0pYTLriBuRgZ6eLH5zfLimqvXV9kFePXq1SeeeOJrXvOaU045xaMjjjjigAMOEHnBC17wmc985qMf/eh97nOfs89Ok4+QniIRqLCne/p7h9Ysn26tLpJGesqURWJ5BS4+YSaSVvrpVz330d/b8hWLhjnFttHp/pDj8OWQlAgDQ2Y7IpVleVn38H6lgvx25Qy7MXLZQNEzvvmQ4fZYqzh0YMX09OY1G0aWLWscMmVeMHlFYxJZB4+nmo42W+1Oe7Cct/VOJ+LW9SfHWCsoUTpFzzW/XvnBt1xYLOtft+71jdamYrpVDPZ1lhXN8RVrj7zkQfdOk8lvftvnPjbf/3bF8ccXv/71j7/+9TW/P79IJaeQvHwlrqbfjTgE8ddO1haqSkcfsQsy0OyZGp/qD6htxXmnN595+o/33afnhS877wMfu8cPv7fm3e/+we/OT93Jb7C/aEwnr1Q+gCJHu0j71J1iyLVZbCpTXGZCu3S9llpuiaHsb9//xviXP/Xt0157/xe96OtTo+3+3nZnqjXFo1tRNnmJShVL2HSTI0sspAq2fKjYOlKsHbqNCeHm8V/f+c77/eljH/HCF75u8zrzZxtWatxq2OEqGiJlxmBrikuvoAoqEus7M6uBysMcbbQHh2z32FESKUZHiuGxYr9Diwc+snjwA/6Ez7j/+sxvfvCDH/1Gl+wUzUbfunX2zVJZ9vNco2Gb6WHys5WxVuexOZ7GpZI3kSsD15GaAzUHag7UHKg5UHOg5kDNgZoDNQf2Zg7kKf1SiNwh4KUgXBxm6cUF5NLhFy+3flpzoOZAzYGaAzUHag5slwN/UGJ3bmW7UtzSjm6XaTVAzYGaAzc9DnSNBgtVcIlgOTv4CDllL4n8gY91GmVuQ0gMttzYmTNv7ebWd27KTmeci2qPpixEZ27BHNkuGdp6IWzbzVsD1BwIDlzHYLdmSs2BmgM1B2oO1ByoOVBzoOZAzYGaAziQ19U5Ull6VaI1s2oO1Bz4g+SAj6L7PrzTMo7oOLfpeEYcMTViCM7JcEJRHSl8xpwTCpb6EgUwy5cvT0duJibcxklUeARxAVMDRhYhTsamY7HpfHGzmJ5x9gHGJ+gdNdm0betjH/VHb3/7zT/72c/+139tOWD/Ymw4Hbvt6V3hyOiqFBxFSaE9NZRKTGeC0ulWB3VKjxn9y4fS6dPpydJJRwmJwgTfTp9wDwdYUjI9KA+AEnY7l6ij4hwPdlTALe6plIjqpJOunWLN6n18VBuiicktSgEgjE+OK3xwYBWWfutbF37qU5964Quf98Mfvut7ZxSrV68c6Nsf/NT0MDz9/b7I3acEcSec1G66PQn5vvvu60PxWBSVlb527VrfCc/nbGFYuXKldNUMVoOUEk3jqXSkSvEUGPwAlBXVh9nTzZs3g6zDHw4H9AGVTZ2tDjUHag5cvxwgKHOBxmHjvMFZiogQKRlAJMZqo7en3lxhzZo14WEhg8EZGeOlDjyBNsHMOOAojwR2kpwqSgdYKVIUmzZffcKJx7/5zQOf+cy3PvKRdNpw9cq16YGDuEVB+icCZnKlA6tuiRVOqcSDHinKqo4nKQuJXNYLTOCRS0Ab8YRg8G49EsQDLG6r15wuC2kVpeNGhpHCvaOpAkhUkdFShN7egmhbs2qfwcFD/vu/f3b00f/yilc8eP36L39lgeO0xDScyMs8JDeVOMO90gEWzgPLZIvgj+rkFBgU7ZZsjfaSEjBdNCM1PGIMDw+DqUPNgZoDNQdqDuxpDhjVcxFGZiN8llMkiNHbeJ6G/dkgRTTDiMsVI/wsSBHyNwtB0lmuawVc1QFWmccjAZiVdU9f8ZjHPPRNb9r8sY9961/+pTj0qGJ66+FXXnnlfvsNkhHKWrly2WRvSXMrXYN+0kR2IiZS3FZlEBiPguYgUooSwUhXwSoT1FoIMNeIB2bCLuBJ1URq6WxaWaRbhldT2gCPIgW8FAw55JDDLr/8cvRbkn7rW5f960f/9VWvet6FF77rG5+fyWrKkJGIzNZrFAFRF3gUhOwMBkaIykZ63GaAjKcqf3OWDCZFHHspEDZs2JDTdyjykpe85IEPfOCjH/3oD37wg9/97nflRTmqXFFoqnDyySff6173eu1rX8sHlqceCQG2QwXVwDUHag7UHKg5cCPiQJpXTLWtQC/fdLmF3tqDNyfh244aJCkQsjjNGUZHO/3lhGNWCocITqCcTCXB2hlYterIfdaYhRT7rO777W/LJ81GM8ncjZs3W/92St+L12zd+Itf/CI5wJq4xvchQtyAEfnBD35w6KGHktThS5oEPPDAAz2iDT7nnHNucYtbiLgVCK9ZAhKdnAwBXrtyLVm53wIOsG539KGybO7td+2ZTuWONFPeVQNDOHDErW4Fw6Uza3ZupnNI+oRiU3KqdZ0QfHAt9QN5hnYdmKXdzFZkHmgMW79+84YtZSEreMlsDPYPpg0FjsiazaGe5K2SIIfBrSuVvyYTUbsq2uErrhi+4KLkNLzRHOkkh0kcMCWAchLIy5hcI4cdBun+Bx2kJ4z+8pfFFVdsXL9eCg9HJWWJV39oAVuqVQ6vVevWT73ib9ftd7P//os/P/RDH/p/v/td573v/ZfvnluMjhcr+MBqFBOld6VO8hW2HQ9X185oq8UsHIf8H//xM5/5/DOe+cyD3veuKziZLQqamd4dxbNwCdc+GRkp+MEdHhmeKK42FX3961//7//+Jv3CKIArpWP6a4GXHlOFymR5Jl/02G3bJijEenqT96sDDiqe+9wn3OeE/p///Ofvfe//fuucBBnVbPHG+4fYGZfO4xqy5kDNgZoDNQdqDtQcqDlQc6DmQM2BmgM1B2oO1ByoOVBzoObADnCgqknegWw16O7gwB5lft58qVK6R0usFlTHaw7UHNjLObDTo8FOZ7wBGbJ30nz9U1UtcV4ZcQO20Y4WXa3Ljua96cEvwg2PbuxtfdNrrxtXjZKtzx9g2BXrnxsFu5gEVc2pb/L1ZWfGAD03DcNxBmf59qYX6Wrfm14FF69RmBsuDnNTevoH3tw3paas64IDN3l51PXC3uTrexOVv4yJGQQ75KbPbvE/L7daDLNbxdS2ojFQMG+dHln8tb52ZrI43I3laS1/bywtVdN5/XDAaRkLLsFQL7jNC5AYNLZs2ZJHDyQ5B+LYjOMKEgWqHPCRy23Q7JSLAEaQEpogKQHvM9bOlSzrX9bT37N1ZGsCKJZZ+IyNXrxl29bHPaV497tv9p+fOf35z9+yj8HK4YaJNWDGJ9eDGR3ujGyFMznj6LRT3qHlK3nRMsA54TM5NspBxWRvz8TE2JbNGz2NMN2Z5lciEZMITiQRc1u3jkhxmkWNxGdht/MXPC4BkssZYLeQTE0mbvT0OsPcGBvf5lHRSf6/8Eb6xFTbd+9jVTc6tnH0yo1r99vn71/2rdHh5Z/9xP889skPP/vsLcuaP2s6kNC5Gd4WxWYsdf4InsnJ5MpKUKJTQ87oKhFOQcq69G3uDjKUVXK6vWXzsB/4js91+2x4o6GmuQUBy46NkeIWgKaHCn8U6ql4HWoO1ByoOVBz4HrgANGZS7EYiXFYSgzOriRCjP8BRvqAiZQQrAZtsjUjETG8C+SCOBhZIKlK9gQcp2+nh0TbHZDttWsO3rDx8gc8rnjHO3q+8Mkj/+6F35oe2T9JhOYVCaad/E6OjG7xm5osxUQjrQ6arbayYs5QLauq1QQ2L82EFvzqkk/bglTlCHAKIdikC5HiqUgIX7IsYw4YUws1VWXEAIMcgMCBF5m5ZfPUls1b9z2w9ZK/OXNwxWH/9pGX/cm6N377a53+vrWd9lTRIXxnwvDWEb9mK0lhJUqdnmpwQFl1FsZZxsaNG+NpXNETM59ZNOn0LP5U2ytgou0CjIxGs/RqZTOGOlJzoOZAzYGaA3uCA2lIrwS3pIbB2XgeQ3SX/AVLmlTH85C/1aEbEkFKCIUQRpGSi1qxql1YR3aSnCpLax922BHnn3/+fR5RnPpPF37jv+/8pr//1mBx/Oi2ieFtVzWbfevX8Trd3rxpatPGyamp80o8aSUb8gXNIm4VapFIBkGayxIJmsEIQdWmTZviVt6q/K3mEg9gaAFfddVVgMXLVWoCVLUs/SMjWcYjcy5dLgDCuvXrWs3ebSPbgDX6imf9+f+84wM3/8iH/+4B93/DeT/qLBs4dnLyV63muoSkkZxpbt589ZYt1qTJzUHoFXp712/ZQquQQJLOIMFsVgttkVmt9C75i36lV9sr5G+JZOYiReuYsXRNWqow241zUnbSSSe97nWv+8QnPvGiF73o4osvvuiii/bff/+jjjrqbne7213veldN8MpXvvKMM87AkyA4R7aLvAaoOVBzoOZAzYEbIwesnIkhjmzOOuus4VvfYdnoxXf4zXlEw37DU1xJ/skxxxGjA/3L+Uia7GnTLw9PjTXGm62BHkv3JOfaspZiz3K0PZ3U3cJ4WpZOXLIZyFhjmqeaAa60+/s3T0+cce45+yxbRgR/dcPFl7dGv3/11RO+x9CZSHK01H57lFbEpXIgtAQ+rmDhLCUkIPEUquZU0OwcIOI00Zu3jZyz5ZKrr756aGVaF68uvV9uSOqBYsSeYqJ35Fe/uXBbf/LI2SjdfbabSQtxTTutpid+8gOziJGG3Uk+OJMufSaUbrgvGRhochWWNPyqDlty3AWAcyjUU2ujbTbDkv7GVERRIjmec0rp8xGNRuMzzd7G+sZHe1srV6+888ZLp/uLF45tWNlXrF5ztNnOt0Y280/0Kx6D6AEaPbD1dRDTmSiV8Wsn0kxv2sZqUfT4Ikcvr1596n5Ru8d3L6aL1sbxkWKwfeVA64q1t6HFKE68HR3N8p/8cusvf7fmnDM3XnllccWGYnJ8c8m94rqeQDOpN7JIarQdCHPdPJUdimeogWsuG3vT6y/51L9+4BGPOOb1f/93W7b97h//8dPn/n/2zgPAspr6/3l1+mxvtKWDVFFEARUQrIgoCKI/RbH3vyh2BVERrICKNBGxKzYUEFSk916WsrAs2/tOb6/+P8l5L3PnvjLvzc7uzu6eMNw99+Tk5OSbvCT3Jjn3XiNuV2klcZyMWVdRneRJu2EhJWaaUJiHtCHjfj99jnZvrhwlF2aXpXwWnUjMqstFP/nL6aeffvPNZ8+fhxS5NORNv/1JFsJwIQPMYqT7d1iiyBbJqNVW8NqVMVNaE609qcVwzjzzhMcfv+HKK61XO37rCEeNfc9GK8rbJGL/sNZS3Pi9Ws2ufeKAHRonve7axZJQc0tPn/3p8+Rh9t7PvP3tBx55xBtvuummM8546Jlnsv29JpZw62lkZX9sCLo9AIUM+QkWCkqDJzi11S7IVEKmWjKNUwQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUAR2BwIyHLq2HKuZfFobJq33FQbgueWW+ot3XJpydSdNuktvSq3IPu3LQdYkyZN2meffdatW8d+KbYQEdheszFqi58xAc3BK7fy25a9R8HfuROvQ766zRRKzgJNmzaNXHx5yVfsqZ58i4stLW9zc7NsMd/KyivNlcqlAbNTfM6cOWw6X7NmDVW2lZVUGiEtlgbMlW1/nG3j+8+43mCzPsXnwPnW156lvFKbbKmkv2I/JUcR+PS37LDc4n6b1Q0uLe/jjz/O5lEac7B7rK5EY7cUBDbN+CudpO8PhZDmxNUTAhqxIhC8EuXFPFE7yKXjkcw3tr7+SjApLe+WPP6y9ZdgN2FHI42MO5l0komiia6jmNF4BzvaZ87pfMlLXjJ3l9apU6dmMvbo2lPzljAKP/VElu46l5lCRZOEoZmJmG0/eZlmu9mX2zrvsthsF2nSZC9tvood0j9z1fFXx98q7USjNgsC/DB5zOFXTG8DMXPGHOaK69Z1H/WqfX78492uvfbaL385zQMu53K7u1MIW3mTYubMXDqVko4Ow+1BWfo2+iwOCPBMnM0MyexajvdsjKLRBUkgP3Edxa1klMbEVIpHV55b8Y5lDxCYdDyWFLODxqxZs46TBmeeeSN98lVXffKUU3563632uAaOCdGWS2UTtvcdIgtKZjkuoMdxsuAGE9woO4dzIILKlVYEakEgOIbSnGpJojKKgCKwNSFAJ8AgEjMNjCzrO9YeftjRZ5656I477v3oB+5NNHOO0D5N0DXwNmtjl5ouyPdCQjjGMNNa4oIQY7CneHbP9PfbKcQnPrR49uz/XXrpu972tt+tfHYdusegU5MoAoqAIqAIKAJjQCCXz+FgMplM8BD33HPP8XbuO99ZcPfdT33wA0/x6BdrTvHAax8Ci+91x5BFvUlkHOSJUx42/bAIh6lCvdqC8l6V+Pz8fx9csMsu91977Zde+9rznnuMA//DnkCDqbYUmifxK6+88t57733DG97wiU98grVO6pQHfN6vPvzww7jEeuqppygLL1dBEiL4/LWllFHtVAQUAUVAEagLAZw3SZ/PcP/4k4/HTKzLNLYl23aPtbAT5oCWGbzonjp5xuQpU3B/xSDLkg2Db1OkmVU5HEcN58VMwI4d1rcP/m1kPLUjdSplXVgyrA4NMWGQNS+22Swf4E3zmkEzWM5LTkErW1Nw4vzoo4/izdlnxNhUy/DU22ud48SdA6xe52uo3xmbb+3ryxneTBMr7m/k0ZpIpjINROJDM5LgvbYpcYBlGhtduUgxwnOOFNZbWDsRTBikgxrgy1v0nnSqt39grTF77TZ1JzO0fDmuPLsYxNM51iPspyMQq6REqhgh8McLEzUIJx6NJ5NNpO2dM7W1tdVmGo+bmTPN7NmrH3+2d/lyvmJhVq0yQ3ZKsC2H0ibKxJgfDh8vwaFV2mReWJb98cXzfvvbeYe+0nzoQ8d+4lOtuG369S/WulZml73dD6MAYTQS5bdGE7I/FfsGq46A3ycvzbdJ9tzzus985rhPfeqf2cGm6ZNnrO3sCDdNLz0mQlp5T4pGZ044/qXs8/zQh74tmoga8RvgrvAOy7GlWP6tlhW2LP8eC2lRUCw/7uiifc4P2MsOTZ588snbzx26++673/Wu85Yvcn6vKDc/Web4xQRjKpAmUgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFIFaEdi2HGDNmDFju+22w2GB3VLjQq041Sknm3v81e/1ke1QbmfPiJ27XpJ8oOUWupL8qOZwypdcZs+ezZ4hKS87iUdNteUKSHnZBgdilJddU1txeVesWDF58uT29vZTTjmFXXdXXXUVFYfPkS23+ipZTm0S+LUS2MW4/fbb05455M8JeQj5HVVKuyXyKayYLQ2Y3y9l5Kva7B2ku9oSS1TdZqrVl5f65ffL/tGFCxdSatlJWT25xm5ZCGyC8dcPnULYodTtZJRfFp2GIOZ/aBILUwjfIIPyXrh2tHX83WLHX9m2yw5W09Pdg8u2VP62RGNk5z3zhx8+9+ijj6VcTU2TmGM89uiTe+09k2bz9NOr3/+RwxibenrWMTDdfPNNd9yxYPmiaH9/rjF2MKMVThtdy5Fd7a4bnxhusEZt2AgQ7Oir46+Ov7V3f9ukpIwgXCWAAYRci7zhHfH8oDxTxDzHg+cFhBO85Vdp8hH7Gfuo/YI9pxZS+dT6nvl9Q+bkE8055xx6560LT/9Uumu9mTnN9PU2pYb4DDWnaXLxiB0BI5HwZJIvr+NniijrI8segsBbHx97R/WwzWKAOxlgSYSFU+C7W9dhFCaxQjPmeqYQ/uo1tDS3MWLmsxaTSDTNSebmlkxvnxnosweZsrl0e/vkRLyFD8IXzxbgtQob2+ik8ynz0wtv2We/OVde/sOPfPxzN19H8TpjDSYz2BiPNuejvZkMzr+s1Rw9xkam1uQrWQsN8kywhSlX+LY65CAEV/sZc8vxMtASRA/XIqPgsddLKqEIKAKKgCKwWRAIjVNlbQjKBAXg06sLR+jQKEBULD87k82Y6Nosg6uTPf5U873vDTx695s+/I4f27QMqlE3E4jm7Um64eGUccoefK09YIyYSpKgzdD+tpI2EZBYaBmCZfiziYvBJ4fhhQvMPMWImixDsIlE16NhsL8hHstn0qmT33zfr/8Sve7aT3z5Kxf99VcUt90mifZyyWWm5XPxXGQF4ESMewWd3dXke0xkvpUpBrQJ1JKvmFOMtP8GOVVkQpJBDUorAoqAIqAIbGIEpOuWTttnHXoGhF8qFuIEbxksCu9yM7uaDAPNap7WBvCWbMyx7zJnn921/Pk3f/Bdl2f6TSJp+GIK2eWjXW78ZRQefo5z9tgn4mAotU1ivQF+qIJDlPCFCOqpRHv9weRC+yQ+CuEiEzvxZcAYCqsB5kA3vq7sw+y73vbfCy7p/u1vP3jOeT+/5s94tJ5mk0TsO0yTm5XLNJjIOspuXRlgbXqPfLYjbxbYGzcdIQvApFAQPl8IoZ3YcBltmkCpgzJoICAgV0k4his6582bh6OrCy+8kORYxXqQOKDHzra2NuYt+MnyWXtiDHlpEkVAEVAEFIGJjwCeaOjqY9a1DG+ocymTes4MtqYyJ7fNSXal17+wBC9U/dlIQy7bNHUK77vbI7Fomte26UhbW2HQlyGQ9Axh7i/LsOi8YDGgxgcz2e6BgbT9ZMKkrtSsoeiD2e4F61Y9mFk/aIYG7Vtxk3AvhZ2vqhGAyRrfOeec47mBgdvzHEEJeCsQzaejiXQuno4kY7zCNqYzbucS6cBkJBU18oJgxGuDCO8crJ6kfacNJEPRGHObvIzgNsJkeMedyKXi2SHWB8gsbRHj7YNNFnGlcGLjfElF7Hv+wUSUAXv3VIrtZpMGzdR4dF18+spYX2JwPahmoslokhkZbxKMrEA0Ujb7AQ87c0hhG/bl7IQn1ZA1O8zZpT+OAzISsjmkZ1ZLsrGxdUbbQGrAPP0gc4Ltco09XT3di57j7bytH1YNertJ6/xyG9YGNDgErNv1iKGlM2sU1ON93ZH/XD9w87+uP/KY6SeddPIHT2u98cYbf/ubR59+LoBZJBNv6OKzINmMrSV00IRwpyU6A3KWU1hTCXBpp9zlTXPERHOpzisvf+DcHx322c/N/f45izr7Ci9/nGGBNBtAoqo12d2Tyh7xavP/Tj/2G1//+bJlVp2YS6y1xtpjf8VuAuvu/S8CCRvsP5KEl3ruvtVdu7maWI9NTXmazeteZ0466ajmhj3+8Ic/3PKt7lSKtSqbOjvU7JRkXD6l/UTaTt7lz2okkKP8NOW28rXw2OKqorKUxigCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAtsgArIfYlspeFNT0/z589lcKwX2+323pvIHCwXNvqgnnnhCCgi99e0Vpox+v7WU9/HHH6e8Qm9l5fUlXeZ29xx11FEUkya9NTXgKmWR4j/zzDNVZLamKDmW//zzz29NhapSFk41cLwBAbx9setxK/vxVin4NhKl4+/W16QZgPyoJGPuFj7+csh8xAbZnXc3Z5756QMO7sL11f/+d8u///3sggVmzRrT2WE+//kn6aIv/GFXU/ut06ebAw9kX+zexxxzzJFHHvnsk5O/9rUfPjrvga3pp63j79ZUm6Vl0fG3FJNROTxVERCTvp0rPxOuhCDTc0Sh3PqrSEoSEah+pact7NgvynHbN2COfHX8vPPef9ddd33g/U8McTzWmK4u0xhPY1IsluSYDP6tvMEoKaY2rgiUwh2CLXKdPfbAgAQpl9uyX2CK/cSK5VzRKWrlWkxa5l+flriWlhY8nwKcdTiY7sEZ1sqVXZImm7PHmnt7e7m6D5LnY9FYNpfhC/HSHXFOob/ffPazv//ud9O/+c1Zp5569k3/NFl76CeOAE+8nI4pujGxZoupotybzQEbfBcKU2RclBSTAyRlHpyD9gvtOS6thdSBUQBZAJEon5ESWzoCVKhv6hBav1t6har9WxYCwT62rOX8JCWUjYVJrFyFqCImeujYvSSuGdLZtB9H33DsLt/97mzG34+/7y70TJvW3tXlzs5VUlqO75WXixyFF0RD9IjNoWQylGN8YQwNRQcwKYmxDNE5fcbUNTwIcfoxY9518j1/v7b5oos+mkpdcv0fhhMVbRjmCAXfd5twgHRsL3xEv9cJIbaJziAaIqNXRUARUAQUgYmPgO/JxVS5DXb4wREkHo9kMvkjj57z/e/vxOrYe997eabPTJ4SGxrKZjJ+fB690KKz7MAhgyYqQpZ4OyEkYWlyMVuUB40QfpATokWnZ4oGztDDmTR5Mu71c5EMD8cf/eh9v/51y4UXvreh4aq//MKL10T4B1VvHpkSgokrcYJiXibIDCqphWZOghiTAYzhbQCY840fXgjAxE7xhOUrAuaG5FWLPSqjCCgCioAisNkRkOdu62ApEmmKNXI7Nd8Ivc8++/D2mMULFuMybtRiwG+bMsW9+M1lefnd1ZWP21dz8aIDLEYQu70gn0eSqxQtkcsz0PR0d/IquGfNGr4z19WQWj2wur/gh6oaAAiXRmNbKbOU09pqneyIE6FEzL5zlhcKcV6Lxw1vum3B3Ycr5H10QyrLK3KYdhy032kg4JUnCm1JXO8U33e72zIXMaxG88qkr8KiyPF4UyyDR7Bpjf22Xoass+y8MxMCs51vIA+75dj3KcydnIMzK0DAbXYy2Wzsp4Os36VoFO9X4IGDMuYGZtGifEvL6tWriU1Onco1xR6opqZ4vN/GWqdCGioiILNH2spgPn/jf9bccsvPdphrPvKRw37+86+zdv/rX996130dJGYiJksnRf9MNTXmYK4uI3y24uDMpDPmBz+465e//PBtN/39gQdsxRHq1ijJKlyHUtmmqPn0pz/+u9/97ubbl3upMeRCA4qaGOtNjQ0trPt39fHNVPsDjTeY95w25/jjj1+x6rG///3mO269mbk3m2hZgSo2utHanvQ2tfUMvghKKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikAVBLYtB1hVgNAoRWCLQ4Bj24XtYnyaMpGQbeJVSsH+Mwmy58/tOgvv1xEB1EJ4VbLjfBzlJZdS/Z7jCSTFWjjJZNLzIYJRIX5IfzB2ItBSIlAtNQbLYYpAsLyVCouwFFYSliqcCJwxlHcimK02KAKKwLaCAF+3xfVebGoqs95+LzpvdjnAfOITLaeccsqSJU/9v48sXLt27TNP2Z3BDERuu2trtutLSbZrp780sG5wybqGJc8MXfunp/fce+X06dPPu3Cvm27/5D+v/ctll6145A6XyCpvzuYG6KiDwyt9owwEEISCqPvHHwYTAp4IOMFhSen5RW0wObQfuEN8MQA9pCqr08t7ATjB8YjbYJSXFz5Rwqk0xiEgMm7D+ihDmLdQdPo5TzDT2mnRhnwl24gSmWB5sdYnDOXlijI8WZLkpcIiJtcMH1YODNwIww+pHa9bb0ld5R2v3LcyPVJNHtJKpUNgVJlKaUfl4x7UKbdnSnLRXrbq92VNQ4N593vNj3505l13PvLxj1rvV7QnBGhoTa2TUkNdHGOwxsfiftIZbHKuQdoYaSTBqFHtqSSAEulnhEAzhA/RSJzjO3ljfwh5fFRlOZyUGRpKZ6znUz4Eb4sYtUcw7MfFU+n+xsbmeDyZSg9mc0OWk8omEpx46aOAqUGzcpH56Gl//u6Pn/vNr8/59ne+etFPTG6gN5XjcELM9an07YBh+8x0qvRr5dbjKlE+YCof8/a3oBKgy5Mbr7rL56fcCYMAVU+rxhxtAxOmTtSQbQuB4NzG/xiFKAuEH4aIrfdnKwMZqRjd4snVkTRHbG0mp37KXPLjw/74pxc+8hGT7p7CSd2+nmQk32Cia8jDjscyJBcNCtrMmVuZExJZxexi0sK/1S33ZSxViOXk7ufeIbX+loSSBbZlsqlsDj8UMiwOILNqNbeMm7Os/NC6k97w6EW/zF74o2/uuvuZl19uBlbZ56S84WjggMMnmudgLwjE55toD2cLvfHk0tzc3NnZafW44KOKjDL/1iJTJpmyFAFFQBFQBMYVAT94oZV1KBnafBftCZ+nH5vgSGypjBcOESJJFoxiM2alU5m1Aymec/P/70xzwdnHXvrrRz566lL0NzY2DA002Yxia4ft4S2ffQqsFkhBtE1YHP5Emnwla25FodcifB/r+Z5AFTRX/0QsUaEsvLwnhgUiGRPJiB4TseOvyU8y+bTJT2NQznR3vvutj138657zf3DWzFlnX3GFHX/bpvJonM/mo4nopHQWR9L2wTkfXZw3HfaAvw124oJOCd5+CE87scKLRKHlWirjOaG0wVSj0jInQQxi3bp1EDJXkYTALshj8IbkMqoZKqAIKAKKgCIw0RDIR+23H9ZkOuZO3/mE1AzMu2LZPL6b9Z7BhtbWpp178o15RrpB09+RjGUT7e1maosd6+T9Ls+gzr9SNm2/isAQI26wolk7rDSt6WYaM20Qv0D5lR1Dze2zm1a8sG98lx3ntjy74Nk7TDev0LPWYdYISBAOjZ4+usYRCr9OhxxyCCPefffdd/hhL5sxY8Z9jzzMZwsZ+Loz5qB99zr00EMfeezx3XffvX3yZMbEp+9/6FH30Tv71jqdP/qYY9LpPJ/Be8UrXsHUa9myRY/cd5+3YdMQUlJ7Hcg2t0Y+2pvbd8c5zZkULsnO2X4qLzdiA/3QQ/1ZLl3R1rSJrDYp1tcOyyQo5vrMQDqdXZXA2EivdYDJ1zyy07qG5vWtN5Obzcqu5re+afdDDnzyySdP+OP15HLvZRdzXbt9++CSJebM773owAOfOvtU8+CD3bl0Y1NjspcGYNwMiS9X2L+JGWg2427YaAsVbtnRrbmQtUwBs5nYM89lP/v5u2bPvOvjH3/TWd9585IlS/557S3X/JXJomnABRlOZO1ajA2CZdTdFR1jSZ5SlhH5I8zPxSbKm1jELH3eXHbRw5//2r6f/ezqZc+ywuPfh5F2zJUkzuN6sS3ebL797aO7OpKXXvZcUzQ+mJPCFix35hcvrO/7QIeC57UiJsLGoGjcOl0dSK9wrclst6N517t3f8Mb3jB//sIzv3bJYw9awUTC9gb9Nh8fpN3525GEL6X9zFYdL/pGatE7RUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAERiBwIhjnyNi9EYRUAS2YQTYYcbWNACw29pqC7Kjq0Z50c+VUJv6jSuFGZR34xmD5o2x421DQMGeusq7seU3pCyaVhFQBBSBDUSAk9ip7q6WSbmPfOQVH/703P7+/ve974r//psN2Xa/MJ9hZtNyPtvgcmlpampyQ2Qbg2Rjsi2aGMIby3PPdc5/uvPVr37uDW8wPzr/nW96U/MPz33g0ksfTfXyieKm3r7hDbLVh0s/WAgxat8rAlwJmEfHXqlv9zIbiNUWkZzChnAo5WwRBSk10ld0aZRyNiUCUhH+Sp9AkIOamEHzI0pivVXC8Ux/S0L7u3VTYqfGzsD9mU/hcEqHoy8cpc3ksokcrqDM/vubz3zmhNe+YZcrr7zy299a2NdnJjWZ/gF6rDieJjhgw7GTTC7X3t6+vquTXggOYXDQHhSR4Hy9kVcOX3vZjD1nS3BmyMEHK0XujmlPBwSPziImwlwL6kZu7pfiBK8iZouTS8C3x49MJmoSHJVJU6hYZMpk406/mphJRmLWQZU9RWONa0I2FsddSL61tbWnp6e3dyiZjMXjsPPnnffIc88998lPvn+//W755lefX7HEgm+/ZJ6JFR1a5Tkj5HxvWQzl2Danp0iciDdYFRiDJ65wGOEOWCIpLEEKBUduuQrt+WFNer81IqDVvTXWqpZpi0GAQW3cbbXDDYNHSWCYEB4jSDKZGBxMt0w2F1/8/w475snPfOG31/7NpPoYd3G8aMc1GRGQJxGj+vAA6XzWe92MQcgzTHMm1ychNkhjDAOmdDXwIewA6t7RcSUvob3OUQlRztWHUBL45MK10QU7ScjLkoFDIM/hSJPP2aehKVN3WL120dln33rPPfd8/4L37rvvn7/5lb5lC0027oqTj8eiMUZtNDDxwFRG4eLpY/v2L2g52XFL8DiLgHDEVF92QQMBAlFyhQjyXaReFAFFQBFQBDYKArw383qlh+d5jX5YnrBcdz5ibUW6cemxifX9ebDPFxnfk/skMtIx+EIsXboUhxW77mXOPPNjx71j1Ue/+POrf2dMg4nniI8PDcrZdPd8WrSP4/g+FHnDLM8pJUL2IIDlDMoyZKNTilOasCzHC0MIPsGywxH9ktYbjKzjDM8j3On9WCLWkM52/fCHDzz22GPf++4HX/rSP575+Z4lzzPfwDXAUDqL6yvr/QoH2JIXCq0ep0bKFbRBOAUZlx+xMIOcUhknuBEv4EOmkkHQko2YpapWBBQBRUARmAAI0Of7MWhKcurcuXMPat2LFbq77795u1nbTZ27x5w5c4YWr0YsHjOMy6txgNXRERmYxHvvWHMynkhk4bvX6Qwleb6t4J4Z0Sk+ku69917oaUk+ahOdM3nylClT4ttPHxgYeC6ybvHixdGsnaXgN8vaEECD28DdGEke/CdNaqVEO+64Y0dHB1nbl9ItLVhLpviG5ps6PILzlTsmV319fTx+Y6SdMORNb28vy47cdnd380ofVWM0YjySJRtjWN6YGsJs5n7YxpcrmImJtdjGtDATz1A7rY2tdorWj2AQTlsQy3EvN3BpRpF7kzmuvOq3tZbPU/xly5bZZF1LrQuiZJKMktOmpSZPbmuLUl/MkIq+mcajSFujDposM+PJLW3UQEdXR2PEArlytTnzG9dPnWHe9raG//u/N7373ZE7bl3w178+vWSxiZOguIISG/ESq1Z0qNLWNvOnP96/y96tX/3qAR9572MykSW9/QXZChvRDGrVG5A75pjYAQcccMpJ57c0msxgjhdV9hdeW8AE+z0ZflYR3t3Re5i+lL0Flpe+dPt3vvvIXXfd9dHHrzv99J8+/pCJ4tM+bvCvlcaRG/PvEqd4NmUNgSY8Dn1HDRmpiCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAisHUjoA6wtub6HZe9WZUAsrs32PpRf6gxYY1i9ecfTlFjRl7ME2FFI+9rFPOJvLwnfFQVwlcBqaqIBaNqkazLBpTXKy/2YHwtxtQoFixjkK5iWyj3ujIqVRvSFrQhSI8qVpcZQc1ClxrmZYJZk8umkfe5VyI2sLyV1CpfEdi8CPif2MYwQ5QHf9EbI5eNrjPv98HWn5V8x3XDNUjOkQyHzroyi6fvaH568f6vf/0rf3DuTT/+8cM9a4uG2d3msku3x7F6IvEldverWcNtNp8bxEmW3c5rr42pHW/4S9d9d/7+Ix859KzvHPaKI3o++fHnOzrWsV2WWGqtsH/dbsfPyNBdfQAniQSrvVwgVtgQKIeuJC+SEltLE92Q/rl6WrHEm13apKsnL4XBKwyq8sxS+bKcejMtVVJdQ9Ae6MKOf3ekgVt7qsGFYBHKZiG5BLWFxERDiFl6W93aUnnlBKsmEuFITI7DFzh34NwHRzva2iY1NvBJc3cQJWLPbBDLlvpgTXFKZHCwoegDwjqJaG2dwuGNIgfZ/FQX4EiTQGbSpEny04Ye6OuPptNzdk+//vXmlJOOxRffed+59te/XtjWMHXmpHw2w2mZXF9fD/JDab7gTRZRjtCYrlwyGU8mGu0f/iyKAQcijU3NZIRMJtPAlZM2RAZ7Z3JvQKk7hWJc38tBHfTD4crJFm6L+ugGXT/ojjdIwW2RRj4zkqqtzR5xmTx5Mt0g0KEqncGrSDIWiecyq1HhkGkhqqGhwXk5oZfmvFIsEkknk5SxIRJJNTQ0T5nSCn+oP3fFT7vnP/WbM8444+pr7/7FL26+5s+5nh7TFEuDP+eWuLa3zOCYUHvLZBT29/eSe1Nji8lbN2GcsZkxYwbHaSgIpoo90ZitHQ4gUTQO9bhrlrymTaPsSNn/aQDY3No6iWs6ZWPXrlvtoQgSqEI+yFFaEVAEFAFFoBYEZFQJ9qAy/gbT4hiRblZcb9gOOhr145QTs8fiWlubmLPjRZFYhAkiE9QDh8BQKD02kgzIXBmRGRq4Mo60ze47+i3ma187dfr02P+d+MQjj5jZM3ccaFvV073WHulzDnPtIEqXn8VXFOplBEGlHV98dgwcBJicEbVxbh7IFQO8DOMvMlIuDCYWezjBKPYw/nLrhUXA34YIm4FDhlEPneSCMaJZJN2IbJ2IMCJ3dXVRaoI1OC82y9UOZPno81xTqe2mTJo5uD73r78kB/p/+41vfOMVN//rZz+78+Ybly9caBrzZvLk/PpVcw1HkPNpvGhxsFAwxHjqC39YWOJzkYKQtTdbSoqYrapi2W3ugaYAPtzGY0kSru9Yn7OAR/gbOYsZ1umVK6EIKAKKgCIwXgjQtzOs0FHjGAtaeu/g+CLjLz4okKHbJ1/fw3sb4DAw0ZlLJ48ePFkwJDFEEtAGPWWHDM+/3/726TjCOOXND9xxh5kxbVq0vb+7Z2AonbZ9vzvTzhXfFW6sYMyqNgQEBxRSTZs2zapwgfGF4gRLgYUU04+/PDwWZWv6l7Iz9lEK9FA6RjefDI6o5cr46/km57LgxSOBqz3l30Mpp82Yms+3dixt/fmF3UsX/eoHP/jB9f/7149+9K9b/7P6+Wet/wLeD3CWn7ymTTfx2OScWe91YgY0V8rCKIwlFATJYEmZD/CkDMfXF0/EARk7p6K6eHCnZniyXrPGvhS1E6Cis2x7O6YQnAaEamdM+jSRIqAIKAKKwBaAgIwf4qCKJ16ep2UF7gtmzn752UfEJkcnTX3PX24yy5ebWx/oXLZsMlOKoaFcl30j3daazA0MRBoiDZOnZHis5mk/wfOrfREfYVIhDqOb3Mds1vYM9vc3dvcyvu+UTPCC3PQP5TKZPTpTOLzqbmpoTqcfbOGS7krb5/eyQYbRslGVmbZ8i5eu7O7unzy5nZFuTW8fDrDyiXhfbyrT08Ow+vT8Z5YuX5bJ4YsnwdCMTO+adShsiESHMjxKmwULnh0YSDMWP/vsM/jAikRIlGvAL3U+JwuSlXMf/5j3D7buGJ99UDNTsOjT2ciS9f1L0gNMFZZNi6zJ9JoID/B5vJNOmzGtZ6p1hD3j+X4mFak8X/HIrWNRIZdLRqL2ExRTkyvmtJkpzUOtrc3xFy1ev2q3q5+dG4u1RPkeRmbn9YvTJr2ko6GpfUpr08wpianP7j7brJiS6UtkFixg2uELxgxQJoGeM0GIcZ/JSJllalttgstMz61Bs5rd2ddLo8UrOyvQQynHNaZrrbnisqFfXH79G9940Lvec9RrjnnLzbf94ZprFi96weGaNgjzI3RKxOmtfEaFVhcKdlZvTLfnyvr4RT/ovfDC95z2kW9e+Ysek2Z2iDJmsSjEA1WZyTkZVfWNZcva3Gp2223O577w5g984Py1HaatkR+1nUszrxUjvA15lNlyuBmvm/SiHv22SFm+s8I02AySNGtiSXPkkZNOfd8xtN6//uWm737vt+vX2PWjBK6v0qzaT3Kv8ng3SA4kwIzhkvrsyhM2t0KQWiveVfuXBmMryz3DjKmrqaZc4xQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBHYRhDQdZZtpKK1mIqAIrANIqAOsEapdPZRsSlnFKFidF3CJEKeIZYtzkUFo/zL/qfahUfRVRLN1go2UcGWXc4l8SMYsg9D7K9FnsSinytF4Fp9buH11w7+CPtqvqEIYk8tKRBGrC75Gu335a1Xfy1mexmpLzGJHCVTHyuEMKkdCCkv2/6saOCkWWkS5FHLFj2iapH3GsiC9uNvx51Af431VbaAnhlsrp6Jckrtb6sbjxi/X2Swh2ulVPCDeVXXWRpbV3kRJq/a8RmDfKViess3sLxejxLbMgLykxm1sXmIkK/rx4swymvX7zOqi8AqsgiVJXQbUlg9NiQ80W5deUcY5TjDm0JrLF0olddYie8FPOEk6ZnNsceaq666YNnKu1/72h88cAtnzLxIGULMkwgqjt6d7bMo4eoG2fj69ebcs+++8RZcsRw3f/473/e+c66/2vb8JJS97G74gBNWjgBBuEJUansS64V9+yFtpSTBzKorD0qOjeaHI+OdT+5NLeUQVYvNwYS+vDUm9D/kSvJeoc9lDESVMoq2UoEacxGIxHiunvDJRbPwYY6aEZKjynjlSggCHjR+o0J3dfVwPiWdsm4deF7ElVXQARazY46m+EpBCT983GfI1MtpoN+IcohX5pNwCBx8JSDjOpNCVcKRCTNftN9ll11+9JOd9t5777/9+U8XXbR64TP2I9sc0+V79UOpfk4ONDU2kJYTNViFgww+nB6JR7BEgjix4heBPbF4w+DAAJNT5P0PFgvTI78nzxfdxUKfFnvgoASrOECL2dxKAWFCcxoWNFyBbDcnhCiBxhJpfh3d6yOGg07uWbvPbDd7pusU7UEL/FKRkIw6Oztz+X5oAs8aK1eu7Ozk1EGEb8IPDODqK5JINGP8Df9MzZv3nf/3haYLLvh/bzxm1SWX/OGm6+xhiGiMr8WbpmSatJ1dnRx0SqXRFsUGFAr469evl0rp6u6I4oQrT9FwcEa1Ju3BJOfChO4azOjCHUccYOWTyUQqlUVVJm3RoFBSLn+FoLzOdr0oAoqAIqAIjBsCDDFeF304nbmMYvS69MYMf/TMxXHNDjGMhn19cnLPDqxexiuBYMQhyODFLTIEONC840Lni1/84i9+s+mNb3zjJZf85ItfXJljmIqYnq4ldPMyd5f+3l8t3x99C+bk7MEkQmiMkOy8LOOvLxeS2EO5ZPzFJMZfLwmBgIw+QWaIBihGecEn+CJONPNC2I/UaCOgNKDB0vJ2vKu7K2/6Jk+auWLloj//2dxyy9fO+k70G99421GHZ773vX8+dKfps4d5eyM430ikGa/j9k1hYTLAMAoNk6vUBQM0BSdrK+SC2IOFCBCwRGSsSYFAKnGA5U4kBiKUVAQUAUVAEdg4CDCOeMX01fIwBYe+mltCYPy1gqnUAA9t0nkTK2LIBMcgOnOCDEAkYZCit+dhEA6DIIPdi170oi9/u4Xx91e/uuLMM1/oWGqSTYbRpKc7l2goDEw2M2uGvbrs7HiBHgmW60Lotsi22jxNvn78FSZmS7kogkwJiortvyLjOdxCo4RUXqeMehSNkqLB8xl54UiRRZXHwcsEiZUr13Hb0hwZHOr9+x8z11336W9/z5x55kl3vTpy/vl/euBO05NmMF0bN8loPMIEKegAi4QCKRkxGeBK7lYmMP4iI7WD8WI/tgVk7EgNHxncjjE0c6tBEVAEFAFFQBEYFwSCfnDwSsMcgOEYT5Frrr+eYWvamg47eWhrZ5S1n0jI5ZiErF27NtO7fi+cVE5tsw+z+QhXbwwD3Lx581AyuHQl15l9dg6T5XMIOJUe7LPDHF+MiEQSscE5jXN22imyYsWKrq7heY7Xs4EEX5Xo7O7mryER612+vLk52dGXmjxZvAsZVgkGB7sqZsG3FvA12dUXnz6duUSms9M0J3k9gbxMGyom3DgRzArwUtqU6SF33pz0uud63JhSWelUKpp1HxHC41WKAm7vXnfYp34fwBwNduKB49SGhmxbm2lvn9M+fcHzzy+Yv2DG7NnoceWyM6hog/1iB1MOW6cNDWbvvWesW7T4+ee9tglOyDzKG1m2vkIyXrgS4fw7VYocwbeep2QmHPAXBifr5q3MXq+//mH+XrSfOeX/Djz//COfnb/8N7/576MPjFBS1w1O3HBm1tvHG6HvXXDxyY8/fsUDd5F3wRtU8Nft1WKLF/DMIMHyUMbgvA4f9F/73e++8NwzZmpba3dPb8xEiYoWlbskMusuTMvtbLWoyJLcxNj6aFktreaNb9zvbSe+lkeMv/79p1df3ZcZMPFG+0rJbt3K2ScLZ61XQCIW0ew6Wo3vncrWtUte8RKwd/P8tCtaphGKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCisDmRmB4M9DmtmSC5s8eCDZX1WgcwnyauPbNDcizrYF9QlyrpxIB0R/alxyyDT1eG4SP9UzPCRJeP0w5ZVTWnhCTvUrsPaoi77Pw+hFmDxP8oCpvZ5CJDPrhhJhepxCVYquX1yvx9ntOJYKMZPOclLeSmOeLPJvaK1mIZDCqXv0kxx6vwRPegFJCNqbTREujQhy0EaReqC/29yMAJyQmTIGaZkn7hFOLPGLIU7+k9fv+K1VZJX6pMaUcilylvoIlgvbtLcgXnWU54M8ePp9pqYyPgiDWt58gf3xpsqi9vAhT5I0qvyF1N77IqLYtEYHGxsZaxl/56SFc4/iLPN0U8rTPWsZf5AlNTU3o30jjL2q9fqzylUW+nvaEM8eef0ayuj2SRH6GyJMQedEP7TPyNITPpUq372WC8p4JITkGOaW0zTRqB4JhM2zKYcGCKUWONxsJm1ZEI3w51m2TLYLmTeLwHW5PHD6yOXVYs4m6w2YuA0pMN8gRrvd9pOHrZ375mmuuOf3jN3d1oH22yaB5ZSBZiIwJRM4eJjY2VqzIRzrth2FTdhf7Q7f2H37QPy/9RfxXV172udkf/uWP7RDJWTNxnRjA2yaXQEmlsHZDMpttnZ0Uh1hbcJdHUaCwlRe+jC+MpxTZg1BUaf+VBiBKEGBwJAntX2RQWDZVJb6kqnQV8wCWwnoZYQYVCscLBImgMUEx+KLBt+dgKqF9Wk/Al/Gulv6kVOGoHLEQk/x8hiTCDBoPGnKLYeAPLfhwGwSKtHAIPl9o7BcOVxK6hm3FvIykkluR8XkFZUK0yISYelsBgQLeDl77Y8/lUtQhlUB1NDQ2uDlktqEhweZ7e8CD35wdYvricVxE2R8CSZAZGhrgGC+VTDRpqYKe7oFsjskkZ0cRmbLL3F2G+qe2Nu0xMLjWNozEai6dA+u4TtvJHHywefsJe7361a+e/+gbP/XBi6+/fjUNj9l0f1++eyDVl0KzzSuNbwsTb2+fTJuMJgqdP5bnMji64qSDPbYqrY7P3UuSTJqnXestCyODLbkCGmE22phPwiUX0SxHfKWtijTniCgvAfdStoem23E9fLAd56wzLAJHljjHYEk+eM+fK5ao4Yo/WdtlETiTwOCWTvejli57yQJzxkcGrrjg39/72aRf/uajd9xxx+WXP/G/f5vBlFm6dlEzvfKASaUtRLNmTW1to2ftT6WH8JCVzWejcY5NdCcbTCadwS70Efr6Ovnx2ZMPrjem1qhBnooyGXpaK+C73Lw1MYr5+ciAiazNG/uAIEZiG4SV1qAI2Kal7UHbgSKwgQgURq4xaeFYG71zzOR4Dmk1uWw6ywHXZFNDK++yli1dFY0kGxsz/f32jRkTt7Uda+nd22abg/czH/90ZP/9c8/OazvmNV+99xarxphmk48n4n2cWWUIsAMb45v1lWjfyeTyblgsvkljIJDxUVIy4BJk6LRDWHHIkPECDgHfH8FUcNatW8cVprzDYaiFJkjyoBJhSl6elkEZM3iqJXg+YtwSi0mSBJpgDZbxK2JnDkxe7MU5Cc4zoPLq2KJn0j1mRY/5zGnJn++35LsXNV93w0evufaSiy4yTzy8Np81zE14YEulWt18w5o6qW379FCD1UTIR/JZ/lry2aFsTl6827yGBpiTsFpRsMdyBrNpO4I7S2xKOPZdazojMhyj5dEPB16kckN4QDKYyiXViyKgCCgCisCYEQi8YWNcsEOD7ZnpkBk10in7556MvH7fb4uwdNH2MUokGCc7O9fIgMgyKU/SdoyLr8e3Q6zNHP4K8+Wv7b7vvnvOnzfp6CO//sAdRbXpyQ3NjT1mVTaVsM6L3RsznF7kstzZhzdng30F6jOSlJ4DwZAKEwJxm6IYGBNDT8RwZIiUoVzGUEniE1oVLqCGf+Uq2Xk+CSVtMSs7/hIrxQ8miUSXW5mc/ZKKybWbXBfPvpZ2IdmQGxhEt0n3mi9+3Fz6gwd//qfWG295z9/+/sef/jT1yF0LM3ipTBkea3Np+/RBkGJSLjEJNTAL6kr+4Z2A1S7B1XIuZ2c1Enixhhpo+7YhkmQuxFTK/YlMMaGVDtKSWq+KgCKgCCgCioBFQN7/cuUvxp/l5SEYO/bdZ9+jViVaOzOTJw829PXOmN9rI3n8buTledERdsy08+GHoYHmnkjT4uX3tk+bOXVmx7QmHF3tGm3iqwwzlvWb9et37x3q7OxbvL4jzacmWlszzZHulki2Kd+ab7YaxZFjLpaO5rLdfYOd63KsCNYTygym8uyc4821WZ6ILI9kZ6Z4V9DIhCmV5SV9OjNkmvg2Q3820z3Q5MtDNGUfnoyYxmyW3VGzeVbOZnvamsyQ9SQVb0oYnrEbk7l165p5Q20S5ONGWwbcwLDuntxHlEOsGsGq98YaNzOb4u9+k+lcu/bO1kRvU7zLDK5ZNj+/xCQbYpks79ZzkXQm09nV+NzSHWfMuL8xy1uLxbs3msmzzfo+s3bt29cnkrGWRYMNDc90Pp1LzMy2dL0wf3Imvm/7rFTn0CM4IYolVsSZGuVzQ+tSDVOiq/88sGx+WzzR09S8JpY2jaZp0E1CHFjSfuotycSRlxlj7fYEGkhNiVz79A3DpXYX5oAy833qCXPWlx/dcedHTzrl4K+f+emFC+++4Yb7b/qPyWfYLmWbXC4bmPmPyNPWAj9fx7NZ8CIsGmlpbco9M3/Nzy664mtfff8HPvALfFele91rJd78FPIc1oLrNDZFrFq1SibYwxGWmsEbtqxZDfXBj83pT9/82yvsbx+/9jhZZ8JvZ7KFeaa1Ab9VXPmlWZrFGUzL2+0fJmr98DY0Gry17bCbOe64tmOPPXb16tU/v+L8O+80KftCy/5ysryLGvG6vttGmELP42guZMSkl2DzKl4dOfJCvnXXrFvzHalG7xQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVgQxEo8w5/Q1VqekVAEVAEFIFNhMAY+vDQGs0YNFQpW72rP1VUTaiojV2uja1/QoG5DRqj9bsNVvpmLLKs1m9GAyZ61vb8Us0B4dCoWT2pyFeXCcbWKx9MWwtdV2FRyH7oupIgTJJaLBEZhNn4slH7xHrtr914JFFeb3uoS39QeS0oSWGRrH0yV7skltclLPKYFLQnWKIxKCxFL6S/VEA4gh65hwyoJA+fJIJnFZlQlNcPEYoK3laPDUqW0nWVF+GNLT98XqLU1iJnQ8pb1KH/bp0I0D5rLxjCtCX5LdeSSuRrkRSZeuVr1+z1y5BX6RcRKhrCBM4dYVgoqjRrBBDmKkeeREBSkZ3kGFKCfFC4VOeGc8ii3N7WMoqxTRwscuSsrLUh47m19mftqBQ4xTasGWawFz773L1PPvnkSy655PvfX2l30dPuCh96HU4SooDdc3wWQrh+j12wBYG+bvPuk/92xlce+d733hWP/+7nP+I4vbXXp/J6hJCyoJ8ZE/gUtyPDttuT/TWIA0zKS2zokF5Qs08oSpz+jTv+4uPJ15e3RMwQGzzNLfZw69tDsHQ+rRCIESivEKIqJBO6RbK0vCEZuZV8y0aNyqS84B/SQNYkhCmEXIWDT6tgfXmZUEaSBPvhi3wtAyupKikM6dfbGhEI/WBlHhesbt+Gafbgz1Z74fj6QgMtnKZLIFMrg5MG/GNFEpEMJwTEJUcC3xZTpqylrvsGOm1PlDLTZ5k99zH77bfTm9+654EHHvjQAw9+7nM/u+M/v+vkq+/utyN6oH2IunNDWEJeDBPYGTTVi40jQdF8SVELTdZcCdJ6fV7SOP3teBFk5FU99dRTp5xijj/+nlNOefkll7x35dLma6+99s47ljz0kIjYX+WqVWuxkPPGbe14KLN+wPicPLBTTfKH0w8CHI7cSDI5SQ3HEZaHJPn6smc5Xmx9Y1lmaQsRJXpVBBQBRUARmFAIxCKt2Xx6YGjtwFB65swX3//A3ZjX3NwQMc04it3/wIZDDjnkY6fPnjNnzvzn//6tbz181y2c2cRjlknZw3cMFf6NOl6erA8shrlgAYOjXjCKsYMgkkGZYNrNSGMSY5k1uFAcVyhH41nEGxYsEfONBx544P/+zxx99G1vPbH9jttf9+C9ud/97q+P3jvr/vtXmZx44ecJLNrR0TGY4iwl0yFASzOW9vU1ZXIcPRx+vPJZ1EyMgL3mVCqoCCgCioAisHkQaOV8vHW8OMRgyLsUXnjwkGX9SLtReK+9Ju3/9v3f+b4ZO+yww9IVt33xi1f/70Y7/iYa8fU8wmA3Ktlz7sEHXkavEUIb4YYRUAZBuZKDY4xgykgq/HpN8GqrJJQXDkg+//zzJ59sXvvax952wvR7bnnL4/OaLrvssofuaX3wvg5vGAQvEFpaWrq6uqro1ChFQBFQBBQBRWBzIRAcvMU1JC6h/VNz0Cphuje0uX5c7Kxe/dhjy1auXPlMtqe1tXWnXGLq1KmvSbXwiEos736RZJ6AJyaIxpzp7u7efdAuRclXchLb4wW7sb09Nrl78rqeNcGMxkxPmjQJzz7NsUZc/EwZsrnzrMuVLLnioZLrQEvLjJaG7oY4AzorA+TFlyKwKpbjSw/W1zMvrvmCGWsHmZh/7YCUfR6fHG9es2YNnjCTsSZeyZN0zKbWlRBTAZ8ZDlbhawyf3vlEIetUinXMwpIHyONjCM0t+TwA9L5kr1133XXdg08sXLOGb0fMnTt3VbqTWli+oJtaQ5sZHOzsHERzxrh5XjIOPqalHRCyS5c+nUwOLFzM8iGZcq3L4M0ljOW2CBMvlDEqYpYsMT8674HLpvI+Z8qHPnTCBz/QfMklv7n1v9b6GJ+YKvh7KhSG32nZgsWise6+rtamNuT++Vez1153nH32cZ/8yD/dHB+PVO51WUFHYbUUR3X8YGnJZX/myJLXqw978YknvvLDH/5p35DNd8i5ZE0U3h3Z76cUVRb+lZdYFBP8qQVypX3NmGFO+8IbX374lPnz53/96394+AG8uYfSjXpbR8Mj31HVlQpM2DZTaqpyFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEZiYCEzMFbqJiZVapQgoAorAloVAcN/MlmX5BLUWHw2hb/mOr6HoZ/PQ2PZPjK8lm0ab38y9UbOrF8/a5Wu0P6QwdFtL2etNUkVeooIC0MHbkD2lUU684hafUvmQwnG/rZRjKV+O63sDvEBwKuyZiMmGPwiYQb7XUImoS7iSkkr8Sso3Pd/jU8lU5SsC44jAlj7+Yj/+SmoEhP2+9Fc1CiOGcjaSsnm6xiR2azVuP1yoMUm9YmQhnmJqSWj3N/OdZOdZpkb5dI79rxwsDw5GzsWM3SbL3ySnZ/Vnv7Ljhz5ywiWX/PRn5693u96ntjS19LCjtmqgc7Obs11gP3Mwlyyet+yOXlyEsl+9MZdPsc32gu8uTCYm/fj7f25pfPtvfmPWLXZfmTbuI7PlMkK/wwcBq7y09/YcGZ6QL6dmmOflhTWq/HDKsVLBcTOkw5Zn5D5gGjMcOR8owiGBIJMo5IMbo8sKBzMNlndU4WDC2um6yos9yGOJTxWyKnQr9oeYVWxDrfSHoOSzqCKvUfUhYL8ibQ9cyM+OnhKfR5l0nk45Eoln8vQedA72KG4m43xbuK34tsPAB1YOB3h8Db2QYSpFnyzdshzk7URmyvbPRVeaye3mJS8xL3mFOeKIqS8+4AhO6fznutR55/zwntsNfpZmzWrg8CrMDFXM4RMOhAR+U3wuHQMHhvptG+NIQN7EbItzdtRX1HGQrtZuC9+fL8Lhc4Nvja3RYHq/YD9vtcT7d/3j5b2/u+zeI49e9uFPvPztb3/X+94/sGDBgltuu+6mm8y61aanxyRbHh/kG+QDhdzTdvgN65Ez2FajC2A8OGT9ErqO2bIo3dBQL16wRMDkkpkUn0mfG80/bcxak+fz5yWlK4jqP4qAIqAIKAI1IyADAp20JcbWr8pDlhttGTf4iw2aWLrghArPkQljmq6JtZjZ23E6bujY44cOPniXA/Z9I293773dfPeGP179x2WMv5PaZ6S71/Ec4ExnxOfPP5FZBxzWPuf8MZdxk3M73SuE4Nw1VPKiSHgYComV3kpCz/d6IEJMf1sLIXNyp8QNcAVlxcHOqihfC91Lp//20o4/Xdl9+OELTvvojmd9/YJc9Ilnnnnmxn/f/sgj5tGHbMpY803GuhwuhEjcNLWvYkTOBp+Do+vCRxIjPL6NzLQwwopxGJtlwuycnsgyhwgHlRaz1H8VAUVAEVAE6kFAxpS63kAG1BfPl9NpM3raJ6kuE0n39lqRSNJE46Zh8m2mybS1mz33NMceZw49dIc9djuSLv2ff1n/lS/8ANeThEmTo2YwlyUBLqJtkLGcq+i3YzFJCAzcbrywowCWi/E2hQv+pZPne6Iospn/tRYH3hRVt8Y+5Mvx/s6Df3/psj9esfxlL7vpHe9tOePzn4knO+bNm/fAg3ffcou5726ce5pow/1dXTigLAbGzOhqC2QwRNdbjgytEfdPbKWd6QSnACN8BoCzDLhcg+Nvsd6DypVWBBQBRUARUARGImDnBcVBndHGDUq5aCT3n71ah4b68CHlnrhHpnF38tDNIJjNpvr719/R2fv4s4+/kBtob2qZno3Onj17YdMMBO0w2RhJtCViMf54b89L9P5odLCNx39jpro5xeJUfr7pyOYaYnH7OL+BwY6d+XxHV2cWZdNnmramnqYm8mUo5ZqL2ofWTIQPJ0T6G6Nm9tRpzS0swuLDCk7Mjba8N2BZsHvtOgyOR60rqFY+nFF8q590g/PaTEf3WuurK2sfivkbB8trKfj1ie4ZA4tjvR0IL06zzGFSduWBp3BXdVzdiwhmMizjWsdGicaGSD7Zk+LP9AyZ/szKSKYhN7iqKbquPxvv6I912zcDIPBcLGfnG3gC421LJssiS/sgn6DIrb/tidxjS6ckG3BSFlm0qmd9YTlV5hk5VmgLL2dqMX8blyk0EmqHOZtzHdVIzUXyVKSJRNN9HflLf9Jx7dX3veo1U0879Quf/NSS3/zm9/+4JsPiNh7YSJQfng1S3a7GA69onFqDH3nXIHI/OHf+b6/e65RTzZ9+ZxtHJG8/FsKyPC1ZJKkM9iqkUgM5XrQVgp1J8vtwq+5raNQ77GjO+MrLL73oruefM0nTQNpUHne5xOdYl3OlsP65oFmLx0DnYN3qchps0zriaD6RctQB+x11++23f+ID/1j4gn0Cidgpa3Cm6sqChTZpOLgfHEw/3Q0L6L0ioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIbFQEZGfqRs1i21LO7hzZiuT3cIxv+dFP8Nu1yyr3O0g8UVZsi2Cy04vCYupGwnNjg7Cx7ZfGJqUY9+pGIaF2iOoSRm1Z+VJmKad2k+qSlIzkWmN7k/qtUbguYzaBcL3AjkGePZrVO6tNUEzNYttBgMGxrt9vvci44VfH33ph22zyVcffws7VL56167ve9a5zzz3nJ+fnWxoMXrZSmVxPH3u4cZUlu3gr2h/sEqFDA0Gendt28y7H7COJJOOd+c63Hunp+eJXz/pSY+P53z2zolofEdTvmZWIuoRRUla+lFnKqWTABvIlI7mGkKykeVsbf6vDIo8GHitGXlwjSZdIwrqA9UqUqIIAv2jOZvT1ea8Kg5lsNJ+LZe1RAA7c2sD+e/dRarkbcU02mETCdjiEGPv3GwyfOJ82LTF3l/add975uONfOn369Fnb4dsqu3L9Nffd99xPf/y3m282qbVOCZv0G0xXV1fBjwYVXAwj8iB352cLp1vwI7n6fCaOVDVud1hKa5TruCktp6irx54CItxy01L+WqebF7/EvPOd+59yyls/+cnpixdme3p6OtdOR2Dpkj7onu6B7u4cblDwZYYzK85jUyPuzE7hisPJoJtBJK3PFMC1hJz/4ImJPx5acxzy4dfn8teLIhBGgPYfZum9IqAIbD4EdtrZHo496KU7zJkz581v3fOTn1yz6+5tHNScPrv7tttuO+20ny1ebF6YV7AvmrCjPzfuDWUZozlx510pFn7sxWlYGekJzMJ4CaU2wi9lek7/YDenENOp7C03P8xf24x/cODw4INjb3/76449dmBy214rV3Li9aCZM3///HP93d3dS5f2MmBOn56MxVKrlns1YyGKhjEQ6xA8FgA1jSKgCCgC1RGQtwrVZWqP3etFbZMmTZq7a5Lr69406+Mfb95n/2k85A6mFtx664OXXbLgttvMukVFfRFOyNvFQetf3oUqT1tuJC4MVaVLEsXBwmoRWpYdRW3tV9IGE3Lrg9cc5NSlmYS1yyNJ1VhnGaapocnce/eie+8239r+ySNfY171qhmHH37QSSdtZ7I7LF++PGEO23PPP81/qod5zuOPdtWVRQ3COvjWAJKKKAKKgCKgCNSGwHPPPcfo1iv+MiskYW1IPg6xpm/NgtaGvpz1d9Mz0NdsYh0dHcvWpxhPxX0nzjHR1sD792Joc69yxQFWz5zJK9evXB9r4/m0GD8O/3Z3dvKHolb3vti/NZZ3x1x7+B9flK2xXK+8WK49U5fSibvZCLebKHQMmdyqdXxDqNkYfGrajN3LkcIsMT9izRHYuwaGcLTUe0/XyiefNOt7zVCm1/TOe2Hec9HIYM4uoGSiMbtGgio70cOVVl5es/PiJZPND+D2dNWqoWefHUKM6ltNnho2FAEqi5km/0m7cURhaRvfVctWLv3b35Zef/1je+9n3v/+133qk3v//e9//9c/l77wwvBrMFxQ0XadzylbbfbjK3aWjiMqfJjZ0D7JdHeZM874529+86F5j/788Uf5Jdo1F4LLfXiiW2g5NqYQ+OxXKmV/ERj57W+f9MADD1z9l4fiUftRmUge9+nWdReOrpwDL9cAC86wMAPvaYVcSP6a125/4okn7rRr/5///Ocf/eDmxQuKGdifndW/6X42wzkrpQgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJ1I6AOsOqGTBMoAorA1o2AHFEIlrHeswfBtEorAoqAIqAITAwEcEdlpkzac33X0kT76rPPfsl7PrDT17/+7SsvNM0tfHN219Qgu6jZgMuua/nwbNKZ7X3c1FgIdoAT5HPEbHHPplPTG+OtjdHun/xgYXfPj84999xE4qxvn92Ld6yddpq1ePGqGvVuC2I6/o6tlr0DLA5+sHGcAwatra0c9hBtOocZG6qlqcDWBusI0bxo/9ie+xzVOXgze+vjCYPDz2isi9387PTHsxW+p3CSFYnZKxvq4XNMw3653R3OaW1p59QNNIc6dtt13+23335wINbW1taY3N4e88jNmTdv3r+uWbpw4cLnnsv398/JpqYdfiD8GclkklSYgBheltLD3pessbvvvntUvrheNN1WfS6fSCT4TLpk9/rXvp7Pa1OATC7rG4Y7Y1JM4/6teHpVzqOMkLU3O+2001FHHbVkyZKZM2fifK0kvjyDUuy///4YE/zh77333uK7zWFtDyO85jWvced57BFlQnldRS6pLJkvOMTkNp9rIVX3+gW/uSzxu1/MO+CAA/beZ9JRRx1/8Euyb3jTK9tb16dNes26+Ry3TqcHkIRYv359NpumIJFoltNWvb3dAwMcui54PMEEMrFur1xW9iPjnNaJc0Qnv26NAYHezmf/deNqa0Uky5AiFhUN1H8VAUVAEVAE6kag0LXnTXOzOeDAfebuvL1p/UVASw8CDMdcYzE8LWaj0bW+73VjtNl11ykMLk3NMa4zZsyQEZmxuK1ljj0Hm5uBtqG+HQb683+/+lmGs2eephuf3JCbu8cOZo85O9m88pPsJe8O7cWWOY57UsjsbunYEnstCV3dHU1NTfvss8+o45cklfGuRE1FRnt7+9FHH71o0SJmgBWFxhQxMDBwxBFH1J00aoc/k97XJcShsOlb9fyt15kb//7oLrvscvRr8zvv/PKDj4gfdsQHTGwVMxlcUHJlPkBYvXo1xR8a6gWr1atX9vf3Dwz2W8eUbk5lTCcj7+CgHY6LobdImHWrGX9zq1f8u3V6qnd9yvI5fen+cVe9KAKKgCKgCIwRARm/mpubecz5zGc+Yyb9KKAo7CciFutgOG5qsoMyoyvXqVM5i84zrO2T8fKMnqlTp3Jtbpplx9/sLPhx81KefK/541PPP79y/pMMZ6+GefDexvBng+vPM3s6cp29RuybPZN3o3Dh+dSNzpbrQ85SkdzrX/96zxpHgkd4nn87Ozt598LgVXgIHWsGPKozP+GFAFfot73tbYzCPLbXoS+y1nrYyL3WJsnb8bd7ec91fzT//vvq7bYbevlh6e23f/FBhw/sd9BxJrbUq8V4xt8FCxZQBLx+cGUK5MbkrDgpEMmS8XcY7Z6ezIwZvUtf+KuhNtzw65UroQgoAoqAIqAI1IUA4z2+eBj8Hp73xIPujWvaTQEqKmE4GjRZJNnmNjRgEjHe1eYTsZV5s3Ko46lcYVmE5PjLscGNqwWvP44Rc/zY4kW84M2t62fOw0sFYnJ2fXCswb24lomB5OtmLQWdOVcueZ8s19xA1q4llGboDI249/ziYEgMKgraeQ5+fPDHOVZDx5LuaezP4GfIpo3lnLejTMIVVsxwVhVe3VtL+6S86wcMf0RGYk/Zb4fYTwaZaCwOh4f9oQxLKNmoeCZFWcEZE1+miJtY/nk397OVN5g09nWMIECdWx14ECsi4hh6qREBN5eL9lg0+aOa8tY9nHMsFR8aiKYHYvfdMXDfHf/ebY9/n3rqiT/6yatwEP/XvyxZON/rp5oLE0IWn3wTZRmEahkcsC1h/Spz5WWPffWs4z72sX/suANfczHLF1mftvLrKCjKJwx/ziOVbQ1MJ3O5Xfc2Cxebt74Vbur8H7vOwMbwoRnb5ni+iObcU0BBhYnH4v3ZQeuWC4vi5viTzXve85aejvjNN//681/o6O+2ck1NyUwa3TjS4k4aDe1Y9OgUtgil/qsIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCEw8BGSXyMSzSy1SBBQBRWCTI8AeR8Imz1YzVAQUAUVAEdhECKzv4sj6wDvf2Xz6Z07/2Gfe89vf2nz7+0xzA50/G2Cr76yv10i02TGFfb0DKbZ25666ig21X770km8uWfKlq35m1PuVB1THXw/FGIh99933hBNO2G233ebMmYPfq7Vr1/J55P/85z+LFy+WPeUcZRyDWk0SQkCmiHEO1GQ4U5P9wAc+MBRptdhGhjiqmsv328OiqQY42UyEa0bOtUTYXm/xR4Yrp0wHB6yvB6qGQ7+4e6DKYtF2K59aiNOl/t5J+GDKpttxBXXYYYcROzQwedq0aVEzC1dMaMAM0tqP2EcTotPbGXKAZfPNWWF8YCFJpnAKASddxVZR2jgq9oMVHGA9+OCDb3jDG6ZMmULb4+rtqU6IAVwpkTeGJBQNmyWIBomtsZewCY3VIPWFAyyUROPrLD/WjfJlKx549tlnlyzvWLFiRaJxGQb3DSy1eLqTG1QQ8o2NSfjRWA4/Kc3NDaBHsApiEc4k05mjnM/Pw4ny9XFCvgUNzY174MvjxusWtE0zPXJCx8ZpUAQUAUVAERgHBCZNjnZ15hjRTjvtNNO6PKhRBggGC0Y6T4tAJmtH5+XLl2Yyme7uPmIZZImi06Y/b2layzWTmgJ/xZKnuc6evdN222338kOmWFXZdqskuxOXiJlsaQ7Y2kGGpwl6fndYrrIDLJtFImpHh+ZmBnSbZLSAAaOJjIjHaQUzQOtDZIKEEgdYuchzFCprXrBFi65k2nPjjY9jbCS+Wgrra03qLhLJMOCCGDOf5hbr91NKZmEvTqVs8qKHM+CFTsbm4uvzpxf+o3etE684jxFlelUEFAFFQBGoDwGecehmTzrpJDPp5iop6dLp2yWk04PckpDr0JB1NLx4Mc9cEVx107dHTDu9dy49A2251BquvMogHP7yPUv0uz59rA6wSrSND+Pxxx9/xzvegX/q2r0/V8kYKBj7EPCjG3BxC3RVUo2Isg6wSDDTXp0DLINTA0LUzlhiydWDg4N33vmQHTTjbg5j4+w7DTKSK5XCFGu//fbDEmgknQgXGVPlNjy+4oiTCv3Zj/9lUutw/22tVgeUReD0X0VAEVAEFIG6EMALUl3yZYTDw1QZkY3NYiiXiZAf0zd2jptdv5u0yHsMudqli7oqE8dnbuJT+KdwU65gNJFiHuWilVcbAsW3Kcw8RyQQP1bxSCKT7ycCB7YLnjXf+tZfdtzFvOEN084//2P4Xr/2mgcffnjV2tUjEuKTSu6ldvCjiuahQfPHP9673dwdvvOdV+20494/+9nlq5cFHZqP0BC8+epXv7Cu80a+tnLWV6/pWW+S+KfD2Zt1s2VDJsfXq/AMh+szvMThZS7fl7XWNkQaP3j6HNzIrl7/5MUX/+Om65jpugT27Z/hfSBtLNAwJ0BnUbBO/1EEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSBaggUDpNUE9m64mrfdYRk7cJbF0hbTGmqbITaYsqwmQylbVdCT5v9ZqqTUbKtUmWjpNRoRWBiIFB734Jk7cITo3DbnBWVRpAtAYj1J/6fOe+H7z3rnPdceaG1N2racRJjYksLX4K2vKS9mGZ3xWVVXcE6WzGm014iLm1+KBLviWU4nj09M7j2qksz22/3h5+c/4fegVP+cqWV0jDBEaA7mlANnhOJchJSzo5+8YtffMtb3vLCCy/g9GrevHl77rknjpPe8573rFy58ic/+ckFF1wAvBxf5Fij9qsb3tJoCU2NjamhwcsvvfkvV9/S328/tc4ufLnabfSyt95tyY8Y9/XsvHU75XxP2X322eLme4zh5G8+Jw/jcTSjx53jsA6YQoFjA/zJTv1Q1Ki31Lt8wd5Lklfhi/fuS/KlR0dGHn/w6ejTyh/BxfUGR2oDchOcjLpTVc3upIT9XXDs1323PFA3FUoAlHIWmLrAARa0O6psz/XIoRFaAgc8CrXskEVSgyKgCCgCisAGIcDwGjHplO1Pz/z6975x1vcKxymLw5WMj7a/LT9MVcl8feU4tOPugWu6soyPqXR8rm6DvMYtmZCKkecp5kLcMsICUcbVUNkJAzII8FcRbQbcZNI5AA1CE7HnGCORuxl/02nDMJ0Hcov6tol8EBqlFQFFQBEYHwRaWlpw88SrhZ/+9EJ7zJwg3XyJej8cVx6RSe4cM5kOl/oFd72/RFMtDHH+WObZuZbE4yUjjqs2UBtP52iQa0hVre9wcAJAyCZccjf+RvocbauqrGYXO3yxLw2cx2fJMZCve6tZMs2xL0By9gUFnipxEd7YZAYHRJuOv8OoKqUIKAKKgCJQHYFsBA/TJs0fL8kLL1aHUyQCQ4oMdAUXOMMi9omdIPxoLhLNmrQbswJJA9I84bsBU1gik3UqrB8dP8EJyIxIXMMN+kmNCShscvIyPMpAmhHN7ipPy7LuWChXcH7l6Lx7D++mCaG8ne3urX4oYqPdWoNYmrD63SVjSwkt79KlfFI8y5YJo6WMSTgRec6XdwGyvJAplHdYJwz+mF+QqlcWTHiF4JhcBStJJDmBW2mTsAk0VEcgX/jdDEu5VWyZMWbyDnTnagrsM6n8wmfMxc90/v2PvzjuuOM++an3d3Z2XvOPP/3nP+t6OqyC5nYzMJCxLZ8FsmyzZeXwdUsTGEinzMzt+k9658vS+cfNlSbqmq20DWoQIhVNZ6NpaVEmvz1J21qWzZnR9853R5avurc9sVsisoAXgVR6Uzw5kLFNgKaWj6X5KaEhmTS5QTNne/Ohj++6++67r1724rO/ds3ttz5jxXDMKm+HXIPNsewmubv1teL7ImlTiGtQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgQmKgDrAKl8xlXf9lpeHy9aQki3CFYWD8tWEqsYF9iKXlxtVoHyyLYcrBfTbuMdQXklSS0Jfv8BTu7y3rQqooq12/SIZlK/FpKB80H743jbhl9rjBSYOgZHeTm+VcFyM7ILzMZuTwJ4gyGMzRYpG2qCqiVneSgX0RagkoPytHoEa2wBiFU7dVETI9281ZuHlK2ocLWLUjEYVGC2HiR4vBfQ90hjKK0lqSRisr9rlh8e2kVge9UZz7rln/+xnZ33323Z7bn+P7Vcnt09OZ1cjKB5M7OdjbXDXAsveiyVBe6xQQMAJSSpLioJ8PorM5MmT2RxszLTGZON3v/tIMvmjc89905Il19/3Xye55VwoCwF7wcFbLRwXI9D5mM1JYE/QyLGZIkUjbVDV5i2v9JA4tGpvb//hD384bdq0j370o7fffrsvII6xzjrrrJNOOun888/feeedP/e5zzU1NfX29noBIXzRPBES0NuyCAwM2MMafT384QPJ+j+yW+rxbCWnA+T0TCHlKMdx+fh00dGD/HDYww/BdYQWlKGfP/Lix8dfwb9SNIprs6CRctzEc2gk+N6i6bqjqZ7tiMLPd/hXXIyWrOXATpHn/w386j3PE9IyuYqDNs+vRMhviqsQXowGGWqTXsATXngMhPN+xTEJKsw6U3HOwPg3FovGs7lsPBbN2Lq0OAA1JeZML4BL0YGSL41T4wQ48klzCDlEYevFIdrS0tjXV9bBxxjs1SSKgCKgCGzrCMjYl0rlGQfxtlAYBMui4gZC5IeDG0DtreufRZVci7yCBwd7Qs/29bwkt4OC0xBU5Bh1XIaHcqZhAwMFtxB1KKhZtPaRt2aVYxaUmYhcPYbgyQAJIDigjEVM0roetn/w5U9wliscxt9s6YhP1Q8OEivzrfIWtrVHe7qHkS8vpFxFQBFQBBSBehDA+xXijJBZnAbXGHhulaHApeCdGOmikWjh6Lnt/GVJOsHDtDz9ZXLdJbobHUck3bNVxA0BBStaXay4XShJ6p7mSh+rS+U2hNPW1tbT07MhGkZNWzoalk9SgEGcS7jx1KEUj8fQwGuBYCoAL1UbFJMaIQlMOzOqHOJJkx5yXkvE9VZlSY1RBBQBRUARUASqIyBva6vLVI9lplFdYNPEujf3myqrTZPPJsxllJlHuUWTTWjdNpgVs0qZC2ZXrMxedvmfr/y1OfroySe/4/j3v3/mn377zI033vjSV+RYf7z0omfdpBFhebFjYqynZAwLl88///xOu0amTLHrLKOGVIr1r2h3X/f69etbWmaz8JXOWAsyrMG490pcZ80wn/3sx75x9sX77Zd83wdeM3v27Pse/t/55//74Xv+LfonTWns6hhel+FVEsE73hIZy/GUEoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCIwURGQPcQT1bqNaVdwL29pPqNtsilNMUYOOzlIybV6jljLZmXEuCIpwnIlObGSvecgKQeehSMC0EKUyouYTw7h6dKCeW3e7OryXgNiJAka7KOE8IZxK8K+vFXs8UqCMkHaC2x6AjN8qJQ7ApWiNh7fZxrEXLIjyjOlvnxFV7IHeWmfIlC7vJyQ98aU1e9jPYGYGBnkBJkSKxx/hSgbpLAkwWw2UUFIKCscZBYFq8mjnGg0U1K5ogFOUE+IRgwwSYhTA9nUhQDJRVVQGD3CFIIo0VxdP0lEkow2trz8fkM2B2+hg+UNRent1ooAjdC3w9IyVm/ApfJj5tD2SOt/CJX0YKr0b9KeQ78aKQhpvdko1PG3EpibkJ81kT67iZU/55pk5sy21at79trPXP2302+44Q/f+Zr9anD/IEfmYpFYV/9QTyLe4j4k7CZU7pu3xe8K12l1ZJlL4PSM6OzZpMtfq8k3ZvvNN7583777nXDV5We+6bhvLnwCM6fkzZCJ9kf4nnU2YfLxetunWFl7e9bxt7ReQW8ij7/0NtLP+G7nZS97GV6ufvnLX3rvV8wciO3v7//Od75DWT72sY+deuqpDz/88FVXXUV52XG+du1aCNHgr9LYSgFRTlkERpwezdHhR/OZBiSjEfst6YjzyyA/fUaPUg1+sBgZVfGL01RTMAlTSHbty5MfnznPpkd4eSA/5AnUqXUsAeU8ZHHmdWR2/m5EcqfXntPJ5yrJ+4SWQL1c5YcjNNYK30rUFpAPltGrJbXQEluXZoQJPv94PO4n1XSyjj/CF0kkkotEY3hWaWhsyA3wOOD6cCuXb2pq4zdFGb026tnazNfNySJiXYwRrKnFHNX7lcdKCUVAEVAENhwB6VwZ/nh6kxHQ9r5ChbQLPxDLc4DrnmHZJK7D9r11ITH8goxlMF0nDPf57nYMFzl8a8fZwcHho3djUDRqEpnVjyq2SQRkXiFXGYX7fb4WZV5tFcdKzw8RiFFNIWaNt+r9qkagVEwRUAQUgXoRkKPjtabCAbN7lJP+nH6dhLm8jLCiQ1w1uadZIsv3+iWj5wixTqdow8drsWcs143t/WosNtk0w8/4ZWcIow6ybrI0AutKluQyzHZsdrlsgagkqXxFQBFQBBQBRSCIgB033BDCKMLLdEv7F7H2LhwyLlaescNxvJN3o1baRYubG1wv+zA8LjI5cZLDL32LQsIZ62NoUYsrkb/pdjYkXI68y4cfdTMesWfIxYo9YmzgXXRRh0SMWF9wLKeTV9lFuU30bxE9Md1lKpYE5h5BU8T8gHvOQi2LnlJJp6zADno9DfKDdFCD0qUIVJ7yuflzTVCG21h60NxwXeeN/7rqkENmvvfDs976zlfvtMfTra3xP11t1q7ABH6E/IzsLylqf7T5T3/w2RkzFh933BufuX9mOmc/QIUEL+d4VpCfrTNbftld0PlB8+9/rr7kxyseeijT2/WcfzOXDrSxz55tPvnhx3bdbxe+OPW/6+K/uPx3D97vl7LY9ZoLer+y+l1Jayqus0YvioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIjBxENjmHGDJkZtaKoCtId6PQOVtIiM0iRhXCSPiyt0gVvhqWeBIbTnBAk+885TuXUZPMJXPPUiIgEiG5H1ar7mSQFAJdDqd5qQ9RHV5L4B+OTw8qrxkhH4kvVXCrHINCteYhYjVJeyLU8USiUIt5cX+cdfvzfaaPaeKVSHhUSWRr7d9is7gEfEqufiD6FVkiPINALW+CEF+MLkXEMKnDcqEaJ8Ee5CvJYloQFLqN6QweBs0o8byihgJaf9BY7ydpfrhBDMKCpTSXg/6a/F5sSHypKUIXkOpMXCC5ZX+pKyYMrcCBHT8pRIr/Rz8j72SgDQAH6vjb/VfhAfKi+H9atYO5ic/Ofe55+768pefyha8zSRjJhmJus21bipVmtBrCFYBYl5SCH8bkne35bvBk07867yn9rnqqrcfd9yfu5Z3GGN96PggfWO942m98j67soRvljr+luLja1wIj1WppOf4JGMbf31yFErz2G233aDf+ta38knkL3zhC9DSABhMOzo67rvvvve9732zZs06+OCDxQGWeL9CLGgzNKl0/AWWGsPIpy4O1ODOwR7oYMoT0hCsslBUjbehcZOsJXeuzomEvY60x1YuoZYGWcYGr71MXBkWGcF1GRYuNeYr5ZLkpXpL+dXlvQbERLLUDHQS5Y4SFVzKutvhJ2gpAKqEH7SB2TKBn4nD3NY1wQnYRMWKKPAlVq+KgCKgCCgC445A6ZBXNgvpwOUaFCjl+NgqUU5Geng/ZHA2Lzzie1VKBBAIj4yj4VxIWqNYICMlFQFFQBFQBCYiAr4/98REtHKrtqn0uXh8i+v1e2J89as2RUARUAQUAUVAEVAEFIHNh4B1TSVvdqKRXNY6ljd8P6Sxxdx7z+r7H1v9pS8tPfjwXae3T//qV2efc87Na1cQn4qYBKlYxcKDfU9Pvqdn6MIL/55MmmyG78fYJRVZSmMJhlBY4rLZ2Hy4XHnlH3t6rWf6qOmXrOFPbkn29qVgvuoVL/3YhxMdfR177LHHCSf8+7nHH/av6hArerR3pF4UAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFYMtHYJtzgFV7lXEUPB6P2+0X7ijsqAntpo2ahdEm8nLgnGv1veBo5tgt9nDlCK7sKpbsguYFlUAjGeTUIi/FRDKYMFR20YNAImF3sRCqy3sBe3o4Gk2lUt4Sl7r8RfRTXgI0obxcgItyf1dLFgiLWC3CGODFPOGzKyVEnpqt0f7a9YtkUN4XpNQMzwnK12U/Gmpvn5Jd7fK17I+nWkUMtb44FAE+hfKcskSwSZQVgClooIrfFxmJ2lE1k1CUV69fUc4VzaK/khmejxiuMUjC70s8XIiR415e9NdSTI/PGOQBh4JXz4Vq9eX1ICixjSNAq+CH4H8+o6JBG6tdGG0iL10K1+pNFM20ZOzhys9QuiPJDlWeCCqBDv1gvZgnSuWlmAgEo0Jll+QI6PgbQiZ4Cz6RCPNbNsRmc+Imxm2VPePrZs8DHzz1xIeXPIV4ayySiEZanbBNLdj6Cgoq9LQTthUUFAvSXjJI4IuGRmQi/fxFzVA2H2O2yPAVzZm3Hfujm+59/be+Y75+hulaO2Ryk+0wFO+ON6RikTaU1N4+Jcfa5aUlB+0spX2Dlx+LCFDeUPMuTQgHmbL8IFOgA08ZH0Utt0GZsrQo32bHX/F/R/GXL18OhoShoaEgUOBD7P3339/WZlvRFBdwiSUyyAuAVCvNgFvwDyZXujoCuaLXC6Cjs6e9SpONGGCk+Up0wcdedVXBWHqpYNPnlhMA0Uhh0ClE0W/YviqSj1Bxhk6MGuQ4QTbge8tZFdQUzKRmWjqpmsURJN8axf1vvMYkdckHhT0t5rlbi4zwg7Fiufwc+F2EMPS3rnOy/X9Bgx1lTAB7UaNXRUARUAQUgXFEoDC4lEwPN3ikq8PGUF7FeUAdGrZNUTtKalAEFAFFQBFQBDYOAtmNo3Yr0Krj71ZQiVoERUARUAS2DQQSsbTJDeTsyJUefSnJ1DL241wnFIKKg7EFfvFxn3cOQhYZITU13Yr+oJ3pEfaMuEGjyFfOsTQmyAlrq8nEDRDinbhPHXFvxQu+kfB0VC4EbRU6EmAFaUk9rL2oLcgJJC1Es8Ilb+lLX/IXFei/G4QAayCkj7j6zecbqOacSWRz0aFe2D25/nznylf/7/rOzs6VD9ybc7/PAVbkWdO2qdw1Ec/i+mpgyP64cnG3jJIxWdbTcmYoEY3Gmky00cRk7mrXN1OZ2FBvlvTsPiQJf9IG7CYioo3p6+v71lfN9OnTd9vloGTu3wggmTbNTpbc6wrycq98661LkQorAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAhsHQiw/rh1FGRbKMUmWybWVrEtNCct4wRHYJs78CxHvqmVsh2QMJFpampKJpMQHIVlX4VPVaU6kWxoaECAPlRCFWGiRB7NnFrn5DlJqshjGK5w0E8qScvVF8ETooSrl4dGOUFSSUG8PEwRhkB/Y2MjBA6qkBdV3FYKHK3HHlTVIi/2oB+1yAc9KVTXT3mxmVSEUklBgyj0iz1YjlsQ+FKW0iQhDkWo0R50Sv3WJU8qbHPml7E/aIzYD2dU/egEk8HBQewRWGirAmlZlCQXb7/4TQhmXZYWe+ptn6iqqz1j8KjlFRu4UqcUk98muUAQKFRZ4z0TZEbVTwUhT3lFP/jUWF/IS11Ul/f6pb5GxZ82TBLKizyBXOQWTpX6lSJT3lH1i6RvbxtVHnyopupmUy5fXu/wy9egElsTAtS1FEd+FKGiCRMZHX+r/2TATX6/IEb/Nup4jZiM76gdtT+UShH91fs36X7RiX6Rn2jjL6PkcBvLm/ed9rb/+7+mj33sd7fdbKbNxNtUEsvtF2btxQrS346Kj2BS9/ibtcrFGLJzHaN1kxOJmvlP93/lK38766w3PfXQsosvetTtErazBX4rDcmGusZTUtUlX0t74PeITq46/o76q9zY4y8G8EPmt8Ysl0ppbW2lxp988snTTz+dPvNXv/oVP0AExFUl9I477rjDDjuIpzxqsLm5md3h+MPq6enhluCaWeGK8Tr+Aki9wXaAhU34I951whxl0l+SE+l9EtGF6yuRsn2UjfPxw4mlWVKbeMASbi4oJorctaBrOGmByuFQq1Jw0+NQZGXpkGC1W1uiekJd8pV+qrUosRXqZuBASkcNPYywew6tRUk9JVNZRUARUAQUgTEiMIYOuZYktciUtzh4jjYoURzyatRcaRQLqlRaEVAEFAFFQBFQBBQBRUARUAQUAUVAEdjSEeAxmRew+hS8pdfj5rK/xtcsm8u8rSDf4m9TFp6GF6cy+UzENCUb+q+++pqLf8ESu8mxFF+Ij8nnYVj+waccKyqs0kdjbEEsfkQkFmWDV1Nz86RJk+LNza1Tp6KL5c58d49Jp0hiv/nCd60cfH4HWIoY51hr0aJF3/3uAE612lruWLvWJCPGxWwFYGsRFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAENjMCuv64mStggmWv7WGCVYias00jsM05wBq1tumhCJwD58oBcntG3PlnGTWhFyPhqMIIiLzVXoN+dIoku0WgCRAEycgTPl+RIYkc3+WWM/PEcutlhECG5GIDYjAlbUis9FbE/LVUIMgJio1Bf1BVkMZsbilUsKTBvILCZenahQUilNRov4cU+bJZh5herSdCAv62VLO3zcuUEl7GE6UyQY6IcRUiGFVKY7NIElWXvG+fpTo9RwCRq1fub71YWcKnLRsrTGQgxH6RryLso7xmSe75pYTXL4QkLBXzHASkmEF5YYZ+v/LD50qgy5o2bRreW0bVLxl5MU94A8oSXswTZcU8U8RqEUYmWF6vQYltEAFpMDr+jlr1wd8XdHV5L4yY0NXlvVh1YX62SE7o8Tc/PRHLZlNrm1vN3gebcy6cdu6ZD1/za6yesW61SUSb8rl8JNZH/xnJtdtS2521sep4+s7KiwmH5FVCNJaP8FVbdvxG8KjivkObb2b3bzY1LWoi1129ZO6M2Dk/fPkt9zz69MOd+bRpb5mJNgCuRTmWIEZwSaw/wSqWEOXldfwtBUrA4UoQrIQG3s0+/mIJTyVYhTG4tWK4x+/VkiVLLrvsMmb4YjACuLKiSTMfWLVq1fvf/36cXiF/zz33rF+/Hv66devwhCXCvjFIGUvRUE4tCOT5wrQN1hNW8TvxmcLjWS3pizKShGbn9diuKdAOh1sgv3KeHJ3jKpsKGf6KfjcwglSxmD1ewDOfU1DMYxv415XdfmMc76s4eOWn4X+84qLOAVZwa1UJj66uLkEbDEWGnw96oF2lCOQVvTNXUqt8RUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAEQkNe2+L6Bli1MmwWWMS+O+FfHG8vsqmsMxZWIsWfuX6RDeC1B2jODBLWGjLwqhyCYvH1tXkvgjT3yXEWJTVsMLHixbiVfcynyRvwrSUSADxSxiwAOgVoIVkRQ54j0ejMWBKRmZWNhCgX4mxI1tE1+uGvW2DuEYmZGzuTyZj0r7HmTgJkzLGWmMuLJKtIUa0i27rLd7NmzZ+yyMx/paWxu5pNL+VicFZzlK1fzzZ6ONd39/f0dCxfke3sz/V1Wb86tu6GVFTjW0d162LruATxoDQ2YXMpMmdTS0dVnJYtWObrWS/HnFd42WWt6lVMEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUWgHALBpaty8cqboAjIOqOuNk7Q6ikxix+aVNbG+MWFmkHotsQWZSgCisBGR0AdYI2AWDo+Tr2y5cJHiE8Zf1uJIK3svEEemlBJUvgIIM/uHHZ3sd2nujzdJdu/kJQrFlaXR62XlAPAwwennfuDkG1eHj72k7a6fsRQiD0YVou8tx+1yJNdyIDSW9HPtQqeYiRX9HPFHvY8IY829j+V6hSOpIKWhIJPJWHPR1jqty756vZ75RAiCTGqfjGbqyBDEg8pzKDOIO3t98LB2FIae8bQPtFTV3uWUldvD8RSraIZ4wcGrPcQ2SdXarbnUF4CG+ZGLS+Nh1RYguUIg7+k9arKEiIv9leX9/rBE8nq9sgvxZcXeVoyTAI4cBs0Rm7liiVIYnx1/T652M/tRpUnF8HT51tKUC5kKCD4V28JpWmVszUh4Fsy7cGXi/ZJ2/C3lQjSSv+MPDShkqTwERhD/+ZH1er6acZekrJA80Pw9pQWx8sjg/06/nqsPCH1xa3g6fmVCJGPRBKDqV5kpk5t/+Uvj7nzzjt/97unuBX/Mtmc9Qqad75B+UIttQD4Vv9wXZVRj2ZqkKu0NCQgpOOCWSaBY4k9MgSEmnPOZLq6zLe/8c/XvuXQX/ziiDcffeu6VYb9vtOnTx8c6q5rPCWruuRplt74SpZTND8eIbxNjb+CJ9UNCFLpQZSkurkSZDyl8dj241pUULKUFuRJOCr+klb0YwPy6JcsuPqJrrewvb29o6ODVGwWP/jggz/2sY+1trb+5je/ufrqq6XuiMJIbxJTKXSKZq6er0RdCLgupa4UZYQ9+sGzKzSSUlHb5hyfq9Sa3Iqkj6LDGRpiQsvDjo2pXL1lsrBpSFAuJdLBfIPNxvNLrXIGjOJ2Suwve/Way8YGmUgS4EyZMoWOlGaP3yv5pUDgD44omr10a9D8dvh9hTQ0NDTw+wr+Unia8D+3oLDSioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJjQIB3s7JQy5vbMSQPJpElEvmuYfCVdVAmSPsvIEIgT5C3yujh7TFXAkyuwVSeRoZ3yKFYmJLEX718WcLmQS7lHEKhKt5gzcCvkE/LF3Og7ad2rNch+xEIH8QeyuI5IYLS8RrcvjovBhEQU6UWqpTXa5PFKa/K8mt2gMU7eTLnrTtX0eBtgCD34K3Q/irypGUNlBf1qAIcH6vEpkRA1pOorYaYiUTNQJo1d2mlUoNcueWPJpqJTpr2sgNejOurKXvOpX324s4Kh3d8HYqQs5/nJIqQ39luq1i33ezFixevWfTcUHe36UvZT1ahTPIrlpDFnHgMl3l2ZafI038VAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgQ1FgPVKv2RZqosFWVY5S/nK2SwIsHxMCGbt667GavLyoqTGVMEclVYEqiAQamBVJCXKL3/LloxR5evVP6rCTSxQ+osLlqg0tuJWmE1s9ybLzndwYBGExhsgGHHlTDhMhiiSlJX0SYQgiSjnCk0ICYRuEfCSQoQEgrcYgLzdDVIMwdhSGimYyIsZcuvFSm0TAS8GUSrjk3sCMUFmVPmQmM/IqypLICYBY6rbI7EIe7EqP3gvQ6aShGtZA4JMUomYJ4KxpbSIkYQATSiVCXIQQBKOJ4KxQdqrEs1ESUJJG5QM0l6tFw7GltKIEUglCbmWyniOb59w6pIXtWTkVZUSPmsRk1vqVzItlfccsYTb6vqlcSKMToIvtddTiUBSAmkJlcS8foRFDKKKMPIiEDLGZTIil1CmbPiThFX0B/P1Yp4IxpbSXswTpTJBDmISQnYGZaClXFwprwzYpArJ6O3WgYCvWapbfhehcklT4arjbwiZ0lvAFAwhBLdSGeGExHwtVJIXPmISUF5dv8Qi7MX4LVdS7mUQkCRcKwl7PqlEzBM+qiwhYgMD6xsbkoND5pvf/E6ur/1D7/5r1zornjer7dVtjs3nGsAH7fF4MpfP5A1DgGzSLavY9lcS4cvr7fdRpSnFnuzgvvlcdz7yhBXI203exnTZmdqQ3cN7ylvv/u9/f3HOtw/96IfOa21t7O3tjLivF/uMStUKx9rv+k+rtTixrCQM38s7A0YZH0WGVB5/aOpXlFTJRSzxCStJoocohNFJkML6TCulErUIjyrv9YukJKykFmECkghgjC8CBPyQVXLrmd54CIQrZQFfktRiT1CJWCVee8giGCW2CdN7KNt7770vuOCCyZMnX3rppd/61rdWrFhBEnmiESViJGq9Zg45ICO3Qf1Kj4YAXQlVLgdOpGps51Lc9B9OLQ74wtyR925ff4Hl21hQBGaIH7yFph9raGhODXbj/0qaY7HrCqoResTEcjjaJ/CEi/PSoRx9Qvi0LkJQgNgQJxTrk1ciapcXydWrbT9P8I6rujk14YL8BCCdmQX3sl6/J4IGMyeBTzULGO4Kw4MhivWqCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIjA6AqxEsCXgpS996Rve8Ibtt99+9ASlEm6RL+Le0eIWite5hcW9qHsjHqm28igLOrwohrDveV3wOXDn6bIExuOMyb1nHl6s4Z08CaNOX9rYBVOxwC1FFtSQkNDb2ysEMoiXzSKft1+GG7FcyVd93GdRSBvJl/muQ1k9wiQjHyRr7IfwsVXSBqNIgg0UHG1Bfs10cO3M06KqPoWve+3R8gIfk2hI0DXboII1ImCbR7FWpBVbjgeahUp+ZNxGjV16bzJxFrZ6Ek2sMuKeCs6sVx2Fc6u+nWYub2paGumjjiTg+I5YJKi4XMb+guIxu2E1uc/+O+y6x659B7Mcc8+N15lly0w6Zdonmd5+ft/kxsotC14Z+6vIdXTazyW6YH8pGhQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBHYZAiw5CF5lS6ZyZnoTWaJZjReCFCn8Wgl5yF25SvKUrA9C1hcPbOsbc4ly3ihvSF6pArEH1mwOqhBufVEpVz87xcBTwdVSUIf5YlKCpWvCJQiQBMtZdbCqbG9jVl/LTZsAhm2nYRykdFTNrEICMFfpfa2IbjsLQCBFFfQlAYhnDKiRVYQ0yKv2r8h+dBtacpRBUJJ6pUPJd9ab0OwTNiZJXbW2GHVWFOhgo+qv1S+ekYbKF9deaXYUKYhMWK30N48WK4gLQUs5fiCV4nyMpuYYODBqiqGBdt5FbFNbLZmtxkRoBno+LsZ8d94WYd+4Jtg/G1paenp7Tjlnce95S1v+b9TXy7er3wBi28eeAsBb3jMlRboxTac8AW3/aF/5QHXRpB3dFL71K7udSsWm0984hN/ufqOm2666eo/3D971na9fd3WsqKhlSwJCYRuS1ONKlCapJRTXQmxW9P4S3FkqKpSahnsBKgqYh7JWmS8cHVCoJbHPPH1M3fu3PPPP//www+/7LLLPv7xj+PZqrm5GQE+js3vjkeb4MhbXbnGjopA0FkVws6/lb2OmnBsAq7TKLybQ4M0pLIV6t08jS2jYCo+zV1FW9nGXMos5QSz2AQ0BpQWBCYhlDu/kRBTbkWQq/OEFU4VUqK3ioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJVEHjiiSduu+22Ki+fq6SVr9yIA6xc2rpkSpl0NfmtPa7sMoEUOvS6uxQJ0vLyvJQf4qBHQohf222lRRPh2/ft0Ui8bClgEiTrbG6bruXaoN6IUnaDvwtUGGveEqjCwqJYJDJp+vQZu+46adKk9t335iM9q5qts7ZoNE71FaSpaLdFQJY1YUoUVcyy/pQW+9mq+JFHLiPMm5davszEkoWE+NXCrZzTwxetitrIfFhzkTni3+EW5tijJxiRWm8UAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARKI+AXcFyzrDkGvLowTGu4PpIeRUBrl86CfAsuQmOXoZy3JpuR0WVuuPTQVRWLGLP2QnaUqHgQA3KR4WCVenXtrYmoLaUslA1gj+r2652Ct988vbD9DKeWUr4KhbCH4ClAfgoSRW6LVWlHEVgwxHwzcwTG65zImsI7k6RX7RYyw+wbKc9+l6WiVzaMdjmQRHC34ZUCR/IvIAnQpLBW5HhKiEYVZZGzPODtGd6QppvQW8glRcoS1TXGUwSkgzdBiXL0qPKjypQVu24Mz2MormhoUEMq9E8L+aJ6hYiFgzVhYlF2MsEac8MEaJcmCJfPVUwNkiH1PrboEyQ9gKeCAELv155rypEVOq44VfPAj3JZHFHVEjpJr8d1VSxqLSwpZwqttclXEXPpowqi0xZ5qa0SvPaSAj4mhXC34ayE76OvyFYKt1WgtHLjyrgJTcqIR2UN2Zcx1+ZyspuV9l825LPtg7FVu69vznjG7nLf7/HDdeyJZqowMNtYbzNuH/hJ/k2syDgjawCCDJeTAh/WzaVlY4/akyvM4O9wHgHN5G83ZTf3T1kPUeb6K3/Sf3816d88cyjH3nq/qVLl8tnqk2+1bm8aXNq17ir39dr70LAwqluSam801nmUmlMgV89C3RtBeOvLyOED2VgciwEeOUhyFTHR2I50QFE1SVDefFaLcSRW7FN6NbW1j333PPnP/85xMknn3z11VfDJ6GklXcx2BnsXb2SECG3eh0VgXzhUE2hdqRfGZ7Nl6R3P+cS7kjGKDKBhwVJRxvwCmQrP71Af1+fZ24gUXoAyefoiaAlZd84hCQ30KQxJMeqkGGYVNYq+b0EswitRgTwDkoprQgoAoqAIjD+CJTtqMc/m3HQGP4SRUHl8BA9DnmoCkVAEVAEFAFFQBFQBBQBRUARUAQUAUVg60CA533WR3gT29vbCzGWQuWHN7k1Gks3uWvarQPmjF1Hy0cCa4JjyaNsmmqP+s0usk8WG12xZroXBqvLahpX5oa8QiFt6B140DRXWbZg1V+Mx2ONmayFncVPXBURst5DknVRRB35ig6uckod2QTWq1E1dK1OCfkIlTv8V1Ds1RfF9N9xR8D/oqKRaDZvv4OdtRUe6YnGTENDYs6c6TvtNPegl+HKall2IN3Ix0aykWgkYxqQYg3VLVPadpRlyT6GFzvb8NJxW/tE0tLYm8mu8rY999h7zz1aZ0x7/L//NcilUmZg0La/LGnyZoQTtNFbTEgidDvuEKlCRWArQ2CMY/TGQyHfaHUXdvXITCBhOdGl9lomNJXhKUsRUAQUgdG21ZUiNOH6w1ITNytnQx5Gxt3wTVBZ9ZZ3bCZlh72+jgJSLCJj4ihiGq0IKAKKgCKwlSGwacajrQw0LY4ioAgoAoqAIrCBCMj465/yIOQEGYQE8aHjHehwdIUkoQMsZW0QzcGriKEWQq5BQmJFXuiJcPV2TgRjPDieCFlFNcHhSmhpSmA8X2KBTsTsUlI8HuPKR15InrWHPvOZDLc5FragcxEbm7cLX1GWLDVsLgTkJ2brohiEloVveKWG+d+UrXHXBiCCNExuRU9pcuVsBQhI/W7sgpRtfmUzDdkTui2bBGbt+itpmAh8KYWMkkJ7ji+gENvc209f/lrqCWHfbjxRNqGoFZnqkpLcy3thT5TVL0xkJNRYCoTHJll7Qm9Y9YzqVVgFhOpRZORNKpX0sBMFzeQyyCmVD3KQrK48KCz6kZcQiip7G9SPAAnLisEUm0MyVeR9kqBMkC7NyMPixTxRKuw5QZkg7QVCBDK+LKEofxuS8WpDfC8vhMT6+g3Fbvrb6tYG7ZEClsoLPyi5ddBba7m2jtoZ91KM+nsP5oiwbx6eCAp42vdXcKpLShIv74U94XWWEshIqLEUCI9NsvaEYuSo8qMKlBZ2bBwy8iaVavCwEwXt+2efqjSJ5yDvxTzhY+0eZ4Jk7ug83wmORKZPN5/61Knp9Mrvf3+IjbWIlHl+HdYy3HjKZFEU83UalAnSRcHhf33BhfBlsbfuiZrkrny2AN///rOvfOUrP/nJfX/wg3kdKwpK3IdzpXhSUqGHs4AK2hCkRwgFblym1fGwOsVmSefVhvgBrZaUWF+/odhNf1vd2qA9UsCy8uAQhCKYytNewBM+KkgEYyWvsjkGk1SneTvjLT/uuOO+973v3Xbbbd/85jf5OjIJcUW/44477rLLLjfddBOPhSIJHzOC+Qatqp6dxioCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKwIQjoqsSGoDfuaatXh2wPLrdJeNiQTHYwFm1gcdCx7Ib+bM67Cy+zrDmcUqktEAG72mhXufOsChsqOpdtmj79uLe9jYXIdGsrBeIjQMSyDm0vhS8+lSmnW6m0fMSgZU85y+J4x9thhx22f+97X3j8yYULFw4tWoyEy8v74CrV5vyuBT+FVSqiHEVAEVAEFAFFQBFQBBQBRWALRMBOvzUoAoqAIqAIKAKKgCKgCCgCioAioAhMGAT8Y5osbbC6IYFbQnGxzPpLsmslLlS3HREE5CquQEQetRDolFtPyK3ICz0RriHzNq9JHhxPhOwBWwwmUF8xPucCYb/5EknG7TUej3PloB7JM1Hr94rk3q1ShA+9UF9RPv4S4QAr15Byvd00CFB1VJCE4M9NaqqSDb6+ICTQGGwrKIZgQpQHb5XeChCgnqUUnhj3Qvlm44kqWYgZ3hjp9qvIE+WHiVr0V1e1eWPFfikyNIGiEeCEfsXqAGuUmgI7kfBElQQiw1VCFcmQTuRHFUagqLgmYZGvRW2pZI32eOWjyo8q4FVtAiJojNBBThUDvJgnqggThVgwVBcWeS9DQk9XIkS5xIp89VQ+1hNVNBPlxTxRST5oQzBhjfJjE6vRqurKN01sXaaWFRZm2ahNU4Sx5YLBEmpJjqSIeaKWVCqzBSFQb816eU9UKazIcJVQRVKiEAsR1ZMUFY/eM9elFmFvSb0Ja5QP6ZdkvTeKAAEAAElEQVRUm+saNEboIKeKVV7MEwHhtKXd154jEdxrc9Odj3a/54Pmne+b8rojfrXuBTjyiDINyph17iqXwk5Z92XZArtcFoEUjkTGiwnhb8OiRXnhjxCLpGCyQ5gHKD5ZC712sfn0h26/4Y7XrVo778LzrNX5fI9L2OyurhTFpus4hYtX64lgbCm9gWI1Ji/Nd9Nz6jK1LuENLAsPqDyYiRIezzzt1SKAPRIF4R9ovQAEX1FmF3gqlULss5/97Lvf/e7zzjvvoosu8jLEvuMd79h3331xgAVzypQpa9eu9bGlxKZEoDR35SgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKwNaNwHitRMTc9uK4u2Y2OWShxTpW+mTjXSTnPsnjFvQ6C1Z5V1Cb3MraMtzwGkkkTTo1lA24J5rqlmTXrxO3RIniQi0GCSdo2aavvWDuSldHIFCpThCPVsZ+2tq16khu0gH7T5676/Tp03NzprFe2ZWxBwDScVvLUberIu6uJOBWjgRIfvyCctaLVlF/Pj+ID6xINBXJpSIm0dzY0NAwfZ+9G3bcfvlTT6155BGTMtF4PD9IIpKg1K2uW8rmxZqqU1vYsixZ+OuGt3CvSglFQBHYnAjk3QgScaNGpMdZoiPI5qyQTZM3fXjZrTKbJnfNZWtFQBvVGGpWf4xjAG1DkgRbaZAuo1MmwmUilKUIjB0B/cmPHTtNqQiMKwL6YxxXOFWZIqAIKAKKgCIwzgjgMYczXHhKEgLt3PIEJ4FbCDi15Mqgj1jwKqnQIHqCt0JzFXl/u9kJsXazmyEGeHA8ETLMVxbVF4tkMT4RsWf3ErEMtPN/FcllE5zyy8RwgBXJpK1DFgIKcxFbrdmCAyzrOCmkXG83GQJUhw+F2nGO51xdVVugp6598L9iqjJUmyjfZGXRjDYNAlLFtBAh6ADGPV+aDfpRSxsbVbmY4Q8Uh1pg2eTYLC2zlvYpCmuRlLw2jXywmGKbIJDJ4BPAusGSnzMmSew25wCrbMUrUxFQBBQBRUARUAQUAUVgi0bA7nUtPmB+4QufvuKKKx54wLROMb0dW0ax2trNgw88d8UVSz7/+U9feN6Ptwyj1coxIeAfZXly4/lTntZgCgGTII9q9pVa5bef7Cbffvvt/9//+38nnHDCe97znrvvvhslbArn68p9fX2zZs162cte9uSTT2IjSnp7e71aUS62B+kxlUYTKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIlATAixV1CSnQlsIAo2NOMAy06bHDz/8cGPch4uc5f+45h73r1b3FlKRNZiJj6rCqmJDo2lK7rbbbvse9qpEIrGyp5/U+bz9JnYVNRJbaV2SRdJo1B4w4DI0NMR3gLbbbrupsdjNq1aZZ5/PWa9bbHBlI7K4zZI9AdCj7x6uYpJGKQKKgCKgCCgCisC2g4BMReTKjKOWgjM/YepSafZSiwaVUQTGhoBvrv4knnC8NmmW+aw9CSa0j1JCEVAEFAFFQBFQBGpEIDi86nhaI2gqpggoAorA1o0AQ0ONIwKSvFvgivccISQtVyDiKkQtcImk5BvMXfhejyeCOoPyQX6Q9voh6pJHyajyorxGzWKVtydoZBW6XnlUjWoPAgQqzoaC66sEVdmQSPAYTiA2m7FOV1KpTDqdxiOLtxAHWISUW6TKO2dYPqosgTB8OSdYViDEVPkQIKHbID5Uk8TSUKk1rjjQEd9D8k4vlFZubd272qfGWeJEodwSKzQEqtAAv6yGIDNoT5BfiR6bvBSqks4gX2z27W3UImxT8lJYqYIgHQQwRIuYx1/ShmTGfItyb4wnatHm7RlVWNR6eSlOlVSbRt63T2+JLz4/Pbpc7ITwseoAy0OhhCKgCCgCioAioAgoAorAFoWAe6KM5FvYcZvNd/ACgY/F/uK373j+2eznP9PLLutEfoaJrLFFyicnYsHkiTjH149NenCKMV1f/9LQ4YeYK3/9tpPf8rcIn66mRKkh+xHcwtejUxOxFGpTPQjwbqWrq0tSyHsWaHl2nTFjxtq1a3m8lAdLnusQaG1txXdVU1MTr2P8sxwPeLi4wtHVD37wg2OOOeaxxx47+eSTTzvtNF7B2FdvDQ0rV66cNm3ascce+6tf/UryGhzk88nS4OoxV2UVAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARmEgItBu73rHe9BnTULRLtsHJjuP8plwQmdXWmOoexIyZJkv2q50J28J63jveefj06dN7+xe2tbVtv8Pk9vb2eMMaFrNamnbm6yz/uEa2g1tknOsi969etiwE3A+JC9u8WcvMR93S9ZSpe+yzz+T992RRclFfD4uV/Qn7sR9af2Azqi1nTY4lHCCpiP3N2s2s0Wjafjc7nkddPNKw3ZwdDnrx3H0OfPDBBwdXrLCy/L5wRWEyLt3wkQN3W7yE7Ciy9d9tFoGDDz6Yb0rhWG2bRWAMBd+Uw2h18+gZCKnBRnY77Lr7lL322qupdWV/f//6jiXd3d3rVrcRm0mX9jdN1dVq7KZEgO0ry5cvv/nmm9n6Ule+VO5hhx3GLhq+DFdXQhVWBKogQLuqElsaNXH6w1LbNgGHmd6aNWvuvPPOMeBQ7/g7hixCCIxZgxyyCmkrvWXGu2naD75ffUaeKLUHToytpRoUgQoIbK7xl19ijb+pkOH+AGT1Zh9KpbeKwFaJwLiPv2MeIicCvBhfe7dQu2SwaFs0PsGCKL3ZEeDRdcqUKc8++yxnOsZgDJ8853sAG/L+isbMEZIas5bfC1cZgsf286kxLxVTBDY2AmPryYPNHg2c1Vq2bNl9991Xi7XIE5j3ctQrOPuFSXI0EzwtRBW1CPvYoLDQnuMz8jNnUvlYr6Es4e0ZVd6a7kJZPaVMZGFikpjH7ahZiP0iNu7yYo+oFbrU5lIOxmA/oaEhwUwMAdLGogmnIeoMtqtRCEDDtN5konyvxYqNWl5xPVN7eVW+tIKCnCA+ns8vET4gQ3hmWUKqTCQRFnmpHeqXJKJf0roGUFbNMBN5r3Pc5VEoTc63n+GMK1Big5QFWm4ryFq2CGw78hSZKqO8UvBgdZdFKYRPdXkR5ir65basWs8UhbRD6Rg9vywxZv2116/Ys/HkpZiiX65S0iC/tOy1zm5LUypHEVAEFAFFQBFQBBQBRUAR2OwIsJ82yzet2D2dMK86xvCF4c+d8WljdzBGOjrXbnbzajRgMLUmGZ+W6hn6/vd//LOfff+oN/7tjpvxikXqCpt3a9SrYhMMAZ4JeS8mW/Q4BiDW8SwKvWrVqlmzZnH1z28Q7CUKPvry8oIA/+ijjz7nnHNe8pKXcHvooYcecsghiKF88uTJ69evZxMDMuTCvk+yIIrnQ0kY1DbBsFFzFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAERgFgZiJvfLwV66Px1ny6LRfmeGLOHY17cGb/8MVVz2WtZED+2D54i85TZo0ad+99ia3xoxdrFybNCwD3b9y+arnVlhnPVtjiMUpo8ELydy5c4fSM1mBSiRTLFEN8SnkTCYRY3lKHVVs+RXvjmoUNqG6LyfbFcZ0yiQSc+bM2W633VjQHMpkGhsbZbkzl6/pY8iVcPHLl6xmkmk8EuGzQM3x+Ite9KJ9Ey0LFy5cvvSFaOvkXF9/aOncJ/SaN8Xv32emxIRHgPbJ2b/HXZjwxk4gA+W3P3EMymVacXo1e7uG3Xbb7ccXnY4fpX//52833XTT/fdU+hieOsCaKLU3MDCw3377cYiXY4RjsInZxf333//UU0+NIa0mUQRCCDDHgBO8hgRKb5lmSCiN2hY4jAXMxHbaaacxDApjGH/HkEuoFqRyQ8xabmtMOIbGUKPmkJE4wBIOOYaiQrdRM8rBzpC83m47CGzG8ZdmP7aWLw1+1Ga/7VSilnSbRWBjjL8bPshu3upgJ3yNRdiQ/mfzllFz32oQ2GOPPXhhjgtvXpnWVSgefvF9wOurJ554oq6EIWF+LyFOldsaf1lVNGiUIrDVIMDPgfdXOGHnR1TjaGJnvS5UAsH/HsdriusVkuMYdNaeRCSD10plDPK9eTVm5MU8EdRWSnsxT5TKCEcEgteyktQeMq4Obc+Zj1malsBV5IWQK5IEX0YEhCYN/LL6Q0wv5omQQOjWi3kiJBC69WKeCAmEbr2YJ0ICoVsv5omQQOjWi3kiJBC69WKeCAmEbkXM11GhOuDWVh2ijepGHiWSyiuR2LpUIbyR5EVtsO2FoCi9JQnzCgGnNLaUs63Jg4DHsxaU6sJH6gv8S3GuwqndnjHor8t+jETe21PFZh8l+uW2FjyRFP0kDMnD8WqDhDrACqKhtCKgCCgCioAioAgoAorAloOAvF7I288789HZ3Xafesnl7/rtH87701VSBDf9lbX+7KqJWKrC/LzZ2dZlEmv5YO0//mQOf/UFl1z+iePfeNH8+SY3JC+j63glPRFLuiXYVOl5aXxtZ4lCHtWam5t5sj3ooINe+cpXsu92wYIFN954I5vF+VB2T08PnDe96U34t+rr67v33nvZ1cfWBL6lzG5veczjHSv7y0nFTkFkMFIckOMwC87q1asR6+3tffjhh7395Evwt0ooAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiMHERyNuvrRYWAwtWWs6aF+262wff3Tq1jR1y8YTdj96UdV8cufkmYtnYWZDln2hhpzJkpJ4VEnQSquzwEwdYqJy9y+xLd90H/W3u4zzLmqMs01zywJrrnjUPDtuxVVGAPZAxufxg3gzFk12sdg1lOilhLLnSxLKZXEOysatYYFnftP6wopF4Ll/4MEwxVv+dwAjw04lEsvzgaOtD+L1qMlOnHXTooY2zZ7dPm7aiozcba6TKhzKsPbrfqTjMGvErk9qX36BV50qLBH9EDYtGc1aDu5gMZwwi0TS/11yej/BmEonn0wM7v+rQXQ7af/78+WvuvsspQZW1zNFyHV5Jl2xcVCAPudfrtocADjjok/GdxPr7tlf6sZeYEXDsiTdCyimTJmXzQ6tWdbDV4te/uYSdEg8+8rd77h/s65LMSr0upjeCFapyLAgwSAwNDckn4vgx1qWChJwZrjIZq0ubCisCwe1SQbo6MrVLVtezJcZap7apFFvRGEbZ4VZXEcYw/srQw+FDcuSHzy0b52SLXY1Zk6RSfYlyYulYyALN0ATpYYQmFwgk5QCkZIoAHZEIcJWNeRDEIubPs/kkEJJQZCRf4VS5Bi0nRxxgBZVXSkjukXzUytfj5qCStlJ+jcYHE0qpgxylNxcC1MXmGn/JmkDB621CkmpzIab5KgITB4GNMf7W8ntk6JEBxROjYoKkaObKEMnA5DqAQicgscFBSoSJZhs8yn12oYxkeOUqwy6pSCJp4QhT0tLRcSuSPHGIjGgjSUgtt6LK80WGK/MWrugUtV5ACUWgXgRo9ri+4gwIjaretJwN4ZGZcyLy1fN6k4s8BvBbCKSVdyN2ak2U41tPH7lcwYkqPx+CtvwAYhOdlD6wFiuli5N6F7p6KpGh/bgWMvymvWwqEebKg1VZgVKmNDOxX5KXygQ5dcn7Nlxs50FN5WlJIlcpMrbJ75eBuKHBnkysJQR+XMPiKCRwH+wKsC04Tg1LbwDF71cy8jqCWQRpL1A7gWaxX7Lw2jxRqspXRBUZSSUC3vhR5aVmfX2NKi+5II+kzyVkMFEE0Rxxrq+Moc+Ex4O/rSwSZiMprpksq1UDeZPK5tL5nJvDOFfUWTfXyEeGPemEsvC3vmV6iHxUWULly8LimcH2QC9EZRHlrxAIcCVUqn1RJTJerRAkkVRUlhAhgdJb9MDk91gaVZYTtL+sQFmmGFOLSWJPLZKS0TYlL4XlCj5Cl0U7yBxbffn+KqjK5+6ZwiEL3368VVKDpXpEoN76Le18QsZIMb1hpblXyrEuexAWebITWgifbyWi1tlGpfTKVwQUAUVAEVAEFAFFQBFQBDY3Arlcxpx++unr1z910UXLW5qn9PWzhdG9QR7lLdzmNtxu78VO+wokNWASSV6CmIsuWvbqV68//fQPf+T9l7kdwIXtEZvfVrVgrAjwhOaf+nhOIxx11FEnnnjiK17xCr7XQdQHPvCBM84445vf/Oa1117L18IvvfTSlpYWljS6urqOO+64559//uKLL8YTFvmLnltvvfXVr341z6Ky/CZ8eSCUvPCKNXv27I6OjrGarOkUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARmHgI9PSwerI6M8BO9IEmd2xDHGCFLPX+OyDKHXwKiYduWcop3RGIjGhlVy3Ho9jWOnnyZNnoH83ZIyWszmAVSzlbse+NSNRwtAFw2JE5mB6g+LGE+0yuW/8a6k+XPYlh8ax1A3CoKvR2cyDAT4a2zp87Ud88fXqquWXHHXeMTJmCNXw+mFpmEd7uyo0WDk2Nl5U0rbxxvz53eIAs+CbQ5Fiss7NzTUMbB0esSfYXnTGRmHWUFfBzZZdK3Y9dfqeBmPGyTvVsYQjIujnnl+r1vLOFlXO8zZVdB+Otdez6pk2bxvGzbH6Q7RM//O5/o0kzdYZpazOxTFIGoxLV9TlaKkmujPFEgObEkFH7qUift8zE+P1OtAbpLVRiS0RAm1PttYZzCvpeull+jLWnEklw5oGo3vFXUtlJpjtniIbggeFSG5DHNq4i7wXgeJooboWDQgoFzWgCQVq6JpgIw4SQK7fQBAiMYS4KH8Lr5FZkpJsSYTISbaJEmCLpE5YlRF6iSOVKZE9Koy1UrlByBKLu2G2IPy63WEWoS5UgRhJP1JVchccdAWpwyxp/621y446YKlQEJggCG2P8reX3Re8tYoxBtTj+QJ7gQWP8IggTPQRoxlBRxS2DmhcWgj7Ka3ApCqlk+JNH+KAA+lEoHGSQxkkloyEGwxeOz8InDHLEQuEggAahUeLFlFAENgQBnLLRtNavXx+cOtaokAZJWhxgbeD7K9FTzFQaub3yM+FKq3eNv9Dmafza/otYbRn/Un0b21CaUC0N2HehNdpDD+wla0lbl3xQ2OdSnShNwjiC+zmeYWsHmYL4QHZC++QQnhZj5BaxSraF5CuJeT7ymO1vPSGlI9bnJUS9+lEuSUjuCcnFc3ymcLwxkp2PChGSti55ejBJJWaMWhAxADGRlGvIDEEJm5lIUDxuSUWA4CcA4VLZyspk7KwDwqqT36CrQykvKBeYoQyKt6RFErXSDyMs2orx4X9VvkZ8CvgH8JSEXCWALESl2ilK2X9FUgjkJQlXySJcQyX30pYkVUlkmEEu0h5sNq45Sb5huZJ7L18SU54hyrnWrh9F25S8QDQqPtIMSttbedxdqyNKfu/BJiTthOyCOcptcZLmepaiXsyTIPUCDSHywgnqKSYK/4sMCbl6+72EqPJKRD+xwgmlklgvE1LCLfpJWCrgJYMEUCApkw0IEgo4oiQo6Wl9WvNQKKEIKAKKgCKgCCgCioAisEUhkJuciCWaJ3d0d2dO/YQ57HX/+8InFi15xjQ2DAxvSi+86yvzfmcCFFUemPuxRN6HpFOGz6Ytfrr/7C++8P1L2t/7KfOrn/W3t5v+nrZ0ZiveKr9xq4InolEfqIKPWzxNySco5fmtelrZdDJqAbx+Vt1wSvXKV77ywgsvvP76648//ni+V4yFu+222wMPPHDJJZeceeaZH/3oR5988snPf/7zS5Ysef3rX3/uuecitmjRogcffJDsyAv5FStWVMqULCSKJCyIsnGHWwzwNlRKqPwtGoGNvqyxRaOjxisCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIbDkIRPKFb5I7k6O44Wk0dqUv7474DmSi01qmvNCS6M1mu62Xnki7vCL3Xqdkl6CsvdlkzpVPbcVnMQWFBJdulL2heMDKRsyAnIaOdpFk6mB8atp0R3bqNEtMwYzaMt6CpECbv8hALt9noh2RmD1GDmD5XDuFiJiGpsZJpaWJRPNGHWCV4jIBOdJuY1F+WramG5Jmu5n7H3poKtHUMntWT9x+/7w5l+M0SBT/UyaSdL++tLHbW/mthoKs0MOWGGk7MF2igmzM3SSyds9rNB+zC7s5fnp8aDsXizSuac2w1jk1b5J77HbozDkPP/zw4EMPm2TSpHJWKf7Y3M5ap4tU1i2dBkVAEdjKEFi24klKxA+8v7+3ucUWrr9Dfuu6hWYrq2otjiKw0RHQfVMbHeIaMnDPDvZYFEeb5GSpHMQiKfNANuzhNYAvR3IAjFCpyuzM0wUO7bOVDg1IirBcxRDJS2i02aOtxYQQQWNR5mOdJjtDFU5QCUwkuaLN8yVHXB5gvKhl+or7MG7Zthe0J5ij0BJLKtSi0zodc09N8F357Ay5NFWBUzmmYpLaIoqZ1vf8hsm1qVcpRUARUAQUgQmHAONpT08PQw+DF8YxEMiIFjKUAVecSPIBABFGkiBicCQVHAiufDYAb+ZtbW2rV6/229qD8pIQYYLQ6J86dSp77OGIQuQZIgleLZLcMtrybgq6tbVVhmxoZAiiKnTFeLwLwaSMyKBfXf+EINLbcUGAdkXTHRdVY1GSa+e3FI2lsCGRtD+QadPb+Fj7lNkPMs+88uIVS5cui8bsORSTlx+7uhQYC8ybK83mbFqbq8wbM99K48XY8hRtXBmeZMxCjww3XAlUX6UaJNZn6p+O4VSSJ4qBD8mgMEw7mhZdkEB4nRBkAQeFoSRBmSBNESQL0SOeSqAleIOF4CrCXINKKtFSLnpLtCFTpZiiQWwWMejq8iIshqHfm1pqDFEI2PlJDE+aJpsbYiGZisIplmSB0xhkcrl+/BPmzWAuP5TNNsPJ5NJcs1H7sJ9zK9elyoMcMQkMkQ/yK9EqXwkZ+IAjMEqbpKaksojy8Eorldqv3gZ8RqQVSfTTjLklBHV6yRAhYqQSM6TuQjKhW0ki9tcuj3nokbQhhaFbkRH5UFTZ221NXkAAH99yysLimVJHNf5+ESYAqa8vX2VwCJIpV2iy8LFwQhUheuiKhfD2CCHtB7p6KSSt2OPzEg3OnMLTlhgjfMQIcIRAg1ciAsErMtxyrW5GMAk0yuVlKakkIVmIjCgMyXOrs9VSTJSjCCgCioAioAgoAoqAIjDREXCz6mw6m+rqzDQ2mTPOOOq+++7773/7GptZ3LBTeT8PnuglKbGvqcn8979333fflDPOeP0fr7ixqwOJPr4rJg8SJeLKmOgIBCuOlbmXvOQlV1111Z/+9Kcvf/nLYjpt9dlnn73//vuJOu+885YuXYrTK7xf8Wrvfe9739y5c2nPrAL614I82qGTtKKZqxDo4WkQvrw8nei4qH2KgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIVEAg75zkxCL28FWD2/2Wd5vcOKvEOauBaAOLI6Zh+ERxJGG9YuSz9sMzJud2ErtLwfVOhVxCbFlwIUe2FbI6I7eVttyF0gZv7SrO1h4s/oEgWLFQ1d7efuQxs2xMbhqXRx5azmk3tmOyehUQV3KiIiDVyr5V/vBo1TvQsnPb3nvvnWtq4zNOq9Z2TJkypb+rS34UXPmRhVrCBhbMqnQOEfIRu2uc1VJaDleOTe44c/s1a9Y8t2ixSaX4qSOG9zmOeiDmMnVH9OVXv4FGaHJFQBGYkAhsuVuAJiScapQioAgoApsBATt7dG4smEAyweOZCyNkMsmVWZ99lDjySDxZ3HTTTXAQrmQlgwIC9lGxoQGfHU5xYTedJCFWJpNstyMjlM+cObO/vx/hlpYWOUUmSnCHwalXNuatW7eOxxamnU1NTbvvvjt6brvtNuz0CiEkIQTKEeaWh9Pp06fDgcAenAvARyECYpUkL3vFSFGFVWT6spe9rLm16Yknnli5ciXlIgoNIlM2uTIVAUVAEVAEFIENR4ABi/GLbeqMgwyUDGFlhx6GJF764XOKIZWRmiQ4fwzmzojZ1dVFFHxGMYZItO2yyy77778/H4ResGABwy5KSIJ+ggyUEDCJ4hb6gAMOQPM999yDNnFAIFmgiuHbCyOPAOPmdtttd8MNN5A2aInQkpdkh82419xvv/0w5pprrhEHXqKtNKFyFIGxIUCLIowt7XilkmZPg+c30tffyy8jlx/k1/edj5/wrW99a/nybrvGkuU3yIJJxWn2eBmjesYdAd+tjbtmUYj+zd6GN1LRNo1aAOSnx+BFgCAwPHEld4ZF/1xZxRgRriIQjCI7QpADDafsmChR6C9NEtIgt1a1K47r2CKM6fB5zpVYmMRC+6sI12i/iIlOUTjuVzHMW1hWPzIUhOf3lqYIFZR0rgNTqQjFlMrCTgK1iQyB6U0mbZ/xs3nryiptF6ao3TIzkLLZKXMcEaDiqCOaOpVFJUgrRT+EXIWoK0f0SLOhxmmctm6LLby6HklVu3x1bRq7sRHwbaNSVxkyQOSpZSFGTYUYwgT0eMLrhOMN8MyyhKQNXkUsZI80vLIaYPrkIsBtULKSJZ7v5UVPMK2ngzKeOSrBrwwkSSt4+hwrJVQHWJWQUb4ioAgoAoqAIqAIKAKKwMRFwM2VZxgzaGL9p395B5PZ59vfuDnN5wqShk/SMpVnHuzn0xO3GEXLgtZmM5FMOv/tMzv+8Y+Xnv6lR849exVOxY0Z3sFfTKT/bhQEeI6SN3T+ATXYkDwthAhz5U2Hl/dmISPPYxDoRGbnnXf+xje+sWjRItYSECOJf+zs7u7m9RmrdB/72MeeeeYZYtnaQmCHDa/Mli9fDkeUhGzwtwiQBS90JN8gP0gjpkERUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSBiYmAbMFz3neimXw2M9TbX/i+oztD0jxpTT6ejzVOnTatd7CHIuQiduN1Pi3HrkZs4LMFdN/tLeHaGB9Yr5GVFFmw4wg0CzQc64IIrrCwe5Zdr8GTLHXtbpYTMNUt8SaNSgTXFkcV3kABe1rGbvd1gWLYv/68/X4Pn3Tmxn7Fxxg+UpSPxPub4uajnzqQJa24/WiL+c7X4vff35lKDbI93u2TFy16nVAIBBqy1HQyYVJps93sw1796qF489qBoc4ue7A/kWzq7RuMsGIZ+MnwE0vQRNxuWl+qPC2i5kD7ibrt5XGcX4kB/PDsjnMTz0YSOZNm2T8R6zCZnQ7cd+bOO/BhoTXX/Yt1VsMHujMFx2rslSXDvPvmNgmDv9OaDVFBRUARmJgIpMQs9kK4QWdiGqlWKQKKwGZAgPmCm4XaQ27BSXt1U9imVbq/q3oSjR1fBKgv16UbHrhe+cpX3njjjeLSAuZuu+328Y9/HP+5DzzwAPvopH5Lc6cGcV3xohe96Ctf+crtt99+xRVX4K2D5MjzGMJJKpK46WSut5eT/9EZM2a85jWveetb38oXK6+//noxAD4PfUji8erAAw+86667SEJangFf8YpXnHHGGRh2yy23iKkkCbUxGhLK0XDssce+973v/d///nfJJZeILy00e8t9QgiC3yUohQrq5KuZ7BjEAdYJJ5zIlBZwMCYoEMKhnul2KOkot2JqXWMudpIKvVUMHiVXjVYEFAFFYFtFYKP2nyj3PXOxey8ALXyY+HR4/etf/9GPfvSCCy647rrrPD9UIexLZx87sXzU+eijj7788stxE4mrdDgMfAS8WBL23Xff//73v+IeC+Yee+zBkPrLX/7y6aefFoXIi88CZPBIxV56HG/JaPu6173uE5/4BGrvuOMOBnRsQ5L5AL6rcOhzzDHHPPTQQ7iMRA9KDj744NNOO42BmJGdW+RFvy8yYy6BKAJRvNR64xvf+MUvfhEl8+bN8/KSSq+KwNaEAD8cfgiJhjQ/kGVLO//6t+sfefr6Z540zbzuTZmWVn5B/IoCb4O3psJvA2WhT6PPrKUToxmAh/SBAoxwPEjBKGHC8Uz6cC857gS5iDFcfY5VcqE/xx4kad4yZFQRDkX5vEL8cb8lI57gGLAI0BJ8MXm+K4U0VCMIUNIaDUN/SJjkIYU1qqpdDP0SSAIhZaRGICh7jVUjZktarrXnXrukV+uJ0rTSqOx8I+/O30VYC04n44MmkqKZIZ/NpkjeP9A7MMjf0MBAJpu284psPkHatLFusNQBVimwG5VTbIC27VFNVATEhucoSuT3y3SXliyNuRblvj1jRi3yG26tatgQBGhCktwTo2qjpfHYQl9hu4ti8tJUvvaRgZYgzQNh0pYmgVNFIbFFNYXeXsZB0T9qWsnO6xdVwixN68W8gJeRTCsll4RlkwdVQaNBrkKAjAzopJXkXCUqlJBbdYBViolyFAFFQBFQBBQBRUARUAS2CATsK56d9zQsvZx51tcWzrM2MzFmNlxl+jvxC8aTOEYufMr84Ac/+Oa3vvz735/9wlO2ZBPf8q3DQl5bsB+Fssibi+qFQpL1P5H0D6jBJDysypOYyLBExy6ZE088kS/SICZPbrwuQYylPmTgszWHh2TUslZ31llnzZ8/n2+7/epXv6rFJGn5ZS0JWqW0IqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAhMbgXxTQ9MOO87lmNNg1G5vi+YauHbutv1OO+20pGddV1dXrNluNWb7HfzDX/sGrqkYX8sxM9omrV+/fsn857gO9uGnqaYQ3Fr3s5/9bPfdd3/Vq14lKWVPY+WdjTXpH18hWRLaNEuiZQsehIuiSS2wW5MakXPm0ZhdyeWAGVf27Tc2ctBdlzvHtxWMp7aI9WWGH7OWfH8fq6SQrZMn77nnnoOxJlwG5BqbWULNxO2Vql+2bFnPQP/s2bMHUtbxXNTtNQ8urdIy4Ut9Q8ut5ZTbj24bT85+aiiKAywnMGfOnKbG5JNPPmkScQ6H0KhQjksDjla280Ghhoabr7uOXFlgxb1BcGHUq/cEmWpQBBQBRUARUAQUga0SAaYNMsfgWnaOESq1iPnHh1Cs3m4CBKS+mMIx7xscHMTz1Gtf+1r8TMGnXjiij8OLl7/85fvss8/q1auZ+AWnlyHzRAPb9kgldcoVPXZ+6BxgIU+rYAMeH57kyfGxxx5705veNGXKFNmkJ7FcMYPnTbx14OuKw1fs1iPVP/7xj8MPPxxh0SytixyR50oWEOjBQubGt956Kw6wmKCSF44/sIenITnzTHIkJWAboXg34l/0E3XnnXdi6sc/+THn0HWUU3Aj0o/3TdFs+0RQe8Clbe3CKqkIKAKKwLggUKlfHRflm1gJZZHhZnzzDepk5GIU86B5guEPL5Dvete7eP2CQ0mfxAuISYwOCDDyrl27Fs+VDOLwEWaw5sp4in5eIuFT8oYbbujo6JBR9d///vehhx7KrnhGWJK7VzsRRk+U77///kceeeRll10mSuBce+21s2bN2nXXXVHFqAqfTLlyO3Xq1EMOOWThwoUrV65ECWMxTqww+4gjjkCGtAQkMYlBXwholGAGAgzTL7zwAq4q3/nOd8IkSoMiMO4ISDtELcS4K69LIW0e+Z51Qzvu1LL9jtbh7H4vXcFX2K/+9UAikZYPVOiL07ognWjC9HJ1NTPpFetKIkUOJvFd67ig4YtAFrVrRpKBrMpDYqnZcGSY8FG1ZzeGksqYSI7kIr9EUSLPkiGFlF2KL1diIUKScEKpgrehspAjyT2zNK2PCiqpRHthiFJVpRxyZ7yupM3zRa2/BlHyMhtIeOXV9SAmudMrUnEY70LBWzdpWZIq1SCpSMiXkiw9cnGqVF4544sALZy2x5Uqg5DqkF+Nb5MiIL8FBGo0QFRJI6B6IUhYS3KRCV5rzHEMYtJix5BQk3gEQq3F86sT8hqzukwwlvZAoL64Ct8TQbHqtCQnoYwsCENL8/bafLOvrgp5CV4sdOv5IQL9xaTWDImVHxdRBOEgA8HVc4J6fKzIcJWWjEKAldigfCmtDrBKMVGOIjDOCNTyUxznLFWdIqAIKAKKgCKwTSDwAkvnF1/2ynlP/+uqy/CLayK5SDTiviZc2wPnhASJ3b08CdhngKsuTb3txDt//JPXveW1/zb5wmctJ6TNW7BRzNMIPGvxJMajFAtdrKvxQTkW0tiMwvfi5PmKWHlwhUCYJJRZntC4IiMPlqJK4OBDMSwYiIx/3uNtyN/+9jeOW4gMq2ssp8Hce++9p02bxjrfww8/TKZkjQBRfMH4C1/4guQl+YpCONgjSvyV3FkjRECeKj1fCLE5xNRbRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBCYYAnJ0Nt8/1NNyyrF77rPP09FJWNias18vnxHJP5zJ5hsSkWQywQbjSHQoZvfYTfrwCcSubbTeeaLZ2E7NzVP+e8+/LvhpLUVjYUX2s7L4IgQcnP5IWuucx51cLu5UrEXlRpeRBSMKy9Gyo446qsb8ZJ2LK0tUnFi7/PLLa0kIAsWdjQbI0ykzlF4aiTWafIbVsuKfw8iwwpUzsRUcDTfxHqf8AMmCTPn6TC3ZqcwmR8D+4vKxhK21WNK0JWfttfv222/fuP3M9axx5rLTG5r23HNvfBOkIwmOKR7/ujfMbGn73913tiYbdpu786pVq7p7qesIHqn8cmQ6Yzegm1g0EUvwm8IhAFk451n218TZRNcebEHjmZzhj59yxCSN3RBMwvZkYqftdnjqoUci8RwLpqyJRjK5VIIPYWVTuVR8Stshb34L2h699RbDJ7cjds2UL3Xb/9wiu+y6tau5GhQBRUARUAQUAUVg60WAzVqtra1stWL+wIRB5iEySQ4WGj6ScmUGgv8F3AzJlxGDYkpvSgRkMx5XfEXha4MrzqTYsyeuo6gmKpTJnoiJYdQsm+J4ipHzURArVqxgrgpTqp4rMvifwiuHOL8glokoPjio8ccff5zvUMojHhmRnL15pO3u7kYP2UnbIC/R1tnZucMOO0izEY6YgU5/S0a0QPJifyCxNC20HXzwwa9//etRTsvEfgIFxLEX/q3IQh428RIi2rhSHF8EQIDT0tbc12NbNWlFDLq0YXsN406Ql8uuvtn0hHpYHndMVKEioAhMTAR8hzwxzavFKtff1iI4dhmyYEBh/GL4AzFGKMYjmB49btHOGMQoyTAqcyTkYSLDOEVyL0zC//3vf3yznF3uCIgYIyAyaGaYQxujKjQuq9gnj0LULl68GGHSQsPEEhI+9dRTb33rW5EkLVEIMDozJopmbuFzxU8l0wMJjK0kxBs7wzTzBGgSEiD22muvt73tbWQKHw0wyeW+++7DqRZ6SI4qElIQ+O49lUUDpgZFYOtDgLbNj2LmrJZ169YPDpnJk1s//7nPXPDjC4aGnsahBw3fvs7Pu8NWkcJsc+sDYWstkVQuPSedc6Uy0gESJVcIktANSlcvSehL6ZzRQDshSGfINZhE5LnCFAFJ64cJRg34wecaEQhdeWLicYyHJnpmOnkfi0m1PMt7eZKjigc6hgmKL3yxGSMhEJCHKaKCBnNLXvgpZi2M/t8r3MQERmIGmfqK82hDCI1AyHLhy5W0nhA9jGVBDkypL58qFItALQEbCKRFm8hDy63o93zKgiRNCDExntsas0BMEtaYpBa1ZWWq6JcoZ7msJtmWQ4AjZYQWndwS+M3lsxaTKP/RzOxPJ5/jv6JYWQOUOb4IUDvS8MZXbVCbNOYgZ/PS2IMB2szGqxak56FPE4X8tKtodp3f8CDo+oEyg2+wjoRGkiojua84ONzSekUnV8lXCH/r7ZGEPrlXK8lFm1yr2C/JSQshwQuTFmNEAzqhfZQn4JNKbJOXmRIFJ2iAiPlUpQRKYIK8WFIqMCpnePYwqujWIcCEhukOZWHmBNaCYKWiIeCdWEttVZIUPTJbErXVNXt5hKk/rpU0ez51zHRNhEkuGiTW2xZkIok8ArxBCLVCL++VQ4h+iFrscfnnKW+99pMLAQPK2uDt8foRpqbk1sd6QsorV8wW/JEnFRXnxTwRlOeHhwyS1af+Pq3XWZc8qaQ9oMcX2RPeHgjsxx6ESWLRKXZk3gBPIEzgPRHlhYDPI4Q0Ibn1kp4QvuADXV0/qZAhIF9v/ZK2lvaDGMWU9ol8FXsEEIRJgj0Q/H65ln3wwGYxnqu8loIAqFD7hxkMArXgjyT160pfmKcGJYO0yJMWe6rLIyMCPJ4JUam8nu/lKSmVK3yvJ2iGpykv+rGKtF6Pjw0RXj/8jSqPVYJnyAB/i6nQFBMzWGKksnyUElsfAvyEdfyVNh+qXH4Cvj+UzjwkELyV3+8Y+mdyIdTYP0h/u0WMv+nM4Pvf/7YDDpjz9rf/TEb+SK6RmQ8lpQuiVwQ9D7sngBG+gCn9uQw31fER+Y0//tqPg+VySd4tsvMcO885579//vPnT/vwgiuvXNAct7scgoFqrb39kDAoX6W8W9n4K1VPXZf9iUl7ABz6KLah8PW23/72twAlDwtBtEM0MmUVerH//Oc/n/vc55555hlGc1oOV5YGb7rpJnaH81VkZh0sp2EV622Eww47DAFmOHxnhqxlgsEt4yNvXUmI5Kg5UhYSohnDfLm8PWgQ2gOCDJmWSvokSigCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKwmRBgO0HFnRuyRiNLHiJVoMdkK9t1WGE86KCDbr31VlGwbt265557Tmh2rBx++Cvuu+8ejoiwvavydp4x5T3WRKz7sCTElX3/rC7VqEZQAj3Ssi5WYyqE29qaScuKVawhPXmy3afEAhMcCa46RjkwowtSNaK9mcRy7PdiLdn02y8bz5w5ky8GdSfs4jI/DU4nsnzJ+nW8uR0OC/HE3vXwg/B7+/tZ6Mzj6sotUNIgqWhb166N5SJ2+xa3pLVMHyIR2SBk2w8O01xAM/GQXFevXj1j2nSSsOrJgunQ0ICl3alLUiDwqle9Cl8Dj95+W0Elv8zsKC2wIKn/KAKKgCKgCCgCisCWjwATA6YEMj+RKQRTC5gSKpWPJOzKI5apPrNZCJlXVJJX/sZD4N3vfjdzPGqBSR1TSiafixYt4ouS1IhsmaNyCbir2GmnnXbddVcIqSyeO5idLl26dOXKlVQ6tQ9foqBF5+TJk+fMmSN8HvSYiM6bN4+E7LHkXDSFIgsUEoWnql122QXikEMOYdZK88CM3//+9xjGhnx5XCKWdoXyfffdFxoxJqVoY288572ffPJJ7OSblwsXLkQztuFpi22BOPjAS6xs1cMScchFWmzgqfPYY4+lEWIGOaINBH7yk58gg6lMrft6+6NxO+NFHuUbrxbGoBkoBO1g2rLMoIDSigDNhnaiOCgCQQRKO5NgbJDWxhNEYww0Aw0jJmMK+9V33313RiJ5dYP/R/hr1qxhcMQ7CS6lCAxGjHEI77jjjoynbKfnlkGQAY4rg92SJUsgxAw0M5YxVDH+Hn/88TvvvDO3uOlndGMkffDBB2+55RZeJDK0+SRoRuaUU05hfCd3vEZ+4AMfIK+nn376yiuvxDAyIqCfepesjz76aAQQfvOb34zxvC9igMazpPivpCEhz7A+f/58dv5DS4cjycmLsRUmEwNKytgtxmDzZm9X3s7afwtjqH1Nsm0iQKOi8bNuwI81lzGXXvzPv//rn0NDpqfbtLXz48IpnvuIwLaJztZSajq0SkWhf5MgArQH+mQ4Xp7mIZ0h3SO9q+cHCVLRCdPPy/MIyaWz4imGVPDpeOm0CcG8ghqElucv0RaMRQMDBGkZILBBlHANyggt+XKl8+fKOOJlJEqe7GAGo0SGIiBDXgxzZCTyREGUzctrHkeCvAjYwDWoFgNczAhmUKCUDtkcUlgqv4Ec0PMaBEkx2PPFAH8F4ZCFPnmICIqNrRRBDaI8pMcLhPghS4ilatyaL2QhSGGR5J6r1J2LsxwJlunKi7Cf5ISUV7n15nkZ1HpaiSoI+OZXRWYDo6gLX0FBurpaqUGfsLowsdXlJRax2hWOmqMKjAEBjz8Nj8DwV/YdnXQCXP2YKP2hXCVf6UnkiipfxUKEbkkCB21cvVphwnG2WA0SqpRL7OfqgxcWJb5/49ZHeYJU0JKWEVZu4SBMQtHAFTN8krKEjM5cGfeRp0SidtSEXtvEejHqzVJCEVAEFAFFYNtEoPYBbNvER0utCGybCDDHlWmuLX7efsgrmxviuvt+5ivfPOCKS266/y4Tce/xIsbO8pnrMz+WJGWfMVAjTxdO8ShujDYcc3Ihu1En9zwS2HfNuUg8wdeSsyazN1nff/u8Ky5+7qvf2uP2+xcsfzpgS949Y/BMAaFfYAgAUy9JQ6KR2P36sRgLb7zS5WPXuPmnIbH0RcXBD+qUdiUPcvARY1HN1Z19bUcsHAI0q31sauE1MYuCSKIHSTLyL4uhyYJbUr30pS/lynte1vxkEZEGw0YZkpNWXjSjQR7/xB5sE8Jf0UCgFJ4TJEQVHDRzlZ8Az+Eo5xabg8JKOwTKPMlPAGR0+/4EqAQ1QRFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVggxGIuA3tuM7xocAp3LNAk4vnE/x1xu1ySS4zneWMWHw1axqNuQRfK4/Jlvioi83aLXCRfJbteFkTy5r4pFTclN/A7zMsEOySP/HEE//85z//5S9/OeOMM/AnxaFlzkJL9BVXXPb+93/ojM9++ofn/2TalHa71d6u02WxFiJGbjbYNYUcexFZtosMOE74IqsO8pZfDA9L1HnPig/LPZz9fv755+tMasU5+VBjqsHB7FGvOejII17HSlYu8RCpZm9nciadN4lcPm7RAAcYLlhYMjuyB1MWMKVOiRECEJCjzscFAclRr2NBIPC7s9XXOimbyppUT9Ou+3Fkcft99h1ItgzmcflmmqLNuXyyJZKcnGzJmuik9slzEs0P33b31GTTDtNnbb/99g8//PB2s2dzKvLxefOa4vFddt/9qaeeap/Uah1XpdP4DuCQIW117tydaa5Lly+bO3fuY489dtjLXsapRVZLZ02bjvwee+7ObSqVnT1zBgu1sUw6kU41m3xbY8sB+x5w3z13Ucb2ZuuzIBpJRPKRZ/KDmabYy04+EeGF/7mZJXZaISvttl1F+FCQa41SRm1qY2kfmkYR2AwI5Olz+AVvwL4FNmlsSPLNUGbNUhFQBMaKAD92Atu92EnFbqijjjpq2uxZXlnUjf4y9/ZMiOuvv56ZM18ipLso3XMVlFR6YyPw7LPPMlck7LnnnrfeeivVwWY55nXky3yP/XJsqGOXHbfslJs0adJLXvISZKg4klD1N998s7iX4uA3zQAOUUw4mZEecMABO+ywA0mQp3ngLJgPWBJFYOaJqyl08kAHDefee+/FNxbzVbbP/e9//yMvBLjFEnbT4fgDYQJtDHlMnTp1KgSPUURhAHNaXGKRBXv/ZIcqqdgBiDstUlEKDIPAZgy+++67EaN05Eha+HJ0XFqj7C1kyx/aeFLKZewWRGTQIEqgN1LAADSPzAWofW4FMxBzMlYcIm0f6SwBjOlBnIJFY8HnC59aiW0VAfc8xrsZO7ujkbhGVji+S7OR5sQ/Dp4RW3O3VcC2mHK7fqBQlZvMaMnUZye9lr/dogkp2sZGVRBjTGT4Y+jBBxaDLJnig5L3MOxXZ5c7MgxSBEYfovjZIoMAQyoDE7ckZxDE7wkC3BIQY3yUUZjRjWHutttuw60VEy0cUXFlTGS8Q57BWgY1RnZckPBBaCRJztsnXr3ecMMNDNkIw8EAFGIkmjGJTEnISyc+UQDzRS96Eb4ymcvhdxLNZIo3Lq6M3diDo/Z3vOMdDNAwxXkloyqvm5544gn0U1I/kUA5JhFbxX3M2NoVNmOJAB6k0cYtBSyqlTfJ3MLOgK4ISKx751wUdP/mI+X7SclI1I7UPyK53myDCNAqaOfdXV25rHnRPjP22WefnV903xNPDNx1E6dRTDTinCZsg7hssUWmQgmYzy+d3pUHh5NPPpmecNQC4StQenhJ7uW5xcXwMcccQ7dM9+v5nqD7JSxfvpyTUPSfOByUbh8D8KXIUhrPTbSxww8/nJ6ZjhcZnzZEoH/BggWMEXTjwSgU8pSEEp7lZ80afpYPygTpv//973hpJF868yAfDfgpftnLXgbBCCJ2+vJiMPIwKQ5Lfow+DDTSYQZlggo3kEat1yyqhBNkYgBRcMSSKjmKgMhLEi/smZ5ThahLOKgH6OQ2aD+c0K3ADn/MGQUzHS9ajKxkksRydYFi0khYZbaE+7PNDM8zxHKOMJGI0rwysXQkbmPj2ThNEQE7KXKtTmyW2Y63H1hCHB+lxJaCAA2gUhMKFQFJOLXLx/ODyOfywzsYchFaVyHQQZIvc2GrVU4NG9smazSmqEb/LYOA1BRIClH2R+q7PknPCMKAxfMFLwMZa0qVysCEKgKauWbsRpVCEHnpJ+XRiawld6KEcDVtBX3Wmax9LqOrsUpdkFiX1D6sQcjVJqsQsIAYUhdtkTcwVpq0lEs0oAq6VAd5S3JbQNehibU+rRDICL9UQ4HjrCWPNIqyWUplz8wPPxlVTOcjtjkHWDyKy5MqV5CSivRwhAiqgTrmbXWIX+lWoOedtTSLSmKejzwBec+pTtCYsEf0V5eUWJ7nIZif1SKMjDTW2u2RVl67PPpBpnZ5wV/ml9VrSgpI5ZJEphFlcxElXhUy4ElLINQCkeisSx792M+1dv1lLS+bnPJKWWiiNCRkfNHKyouMtGehy4p5PcgQaren3vZTi7y0AbGT8mKPVBZPQbb3rBAQAwpkuI5aXwhLkSkplUX7IRWhgu5hNvJSv9XlxRiSCVGlP0GAIMagk8dC9Ht5oqpbhTyYePlhQytQktdGlQd8wbOCCYURTsrF61EmBJUklb8VIKDjb5VKrKU/DCbfBsdf+kOC9Px0lOkMA3eGTuYjHzmAfuzcc+/I9JlY3H6V1uRTgg+xJAG3ShMh3+UiiUzt490Yxl/yIguuhGBVhmgZUBi/hlIDg3b62S8C55zztxPe/coPftB86eMFjuPLI24u1hfDAVbt9tfS3ray8RfYmRVXH0aBVGYOXM8777zFixcL+Bt+pd3ypMqAyEgN+NC86qXVwZddNWQBh0diAvu/2VsDh9aL2ZIKSQJMkgtRxSqS0ESlLCEx3/yEIAswwaqyz+GhtHqrCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKwFaMAOfhP/WpT33+85/ncPKHP/xhznSxJ57j01ddddVuu+18wQU/hKD4XV3drECW2wq42bBhYYilPcneLwZVsgYBAitErCjVvl0EbRyu4Dw5K5I77bwzZx5YWKuURSU+GzWJsoulo+/HqaRD+RsBAVsjqWw/y9DscI3S5jkW1Zewi9qyhiiti/08HFNJR2O0BM5M8uuIN9tPxHMYkrOItCU8BXDLuZd1nR00MM4xIrb02WfhcNqQgy4o5Jjl0/OfYY2SKNrhnDlzOIsIMXPmTBxp4QeBDcScfmQJlVVRAklY5MV/AYGzPcKUTbc0YAzjfCOroqsfeaLPORGw6AS2ibvW5o7mj7ahzibUoAgoAls+Auz3rLolZ8svoZZAEVAEAggw32B6wCThk5/8JK4QxKOQxFdygHXqqaf+6le/wnWsbFRmEhLQp+QmQkBgZ3ccU8fp06dzVnn+/PlM7XCHwQFsnGvgZ2oPF/B2wS47avbaa68V4yStvzI7pRkQJVf4S12AI7tG4ROYNMpxaCaTOOaAJmuuZPr00/ZjpLSio446ioYhs1D27zEjxWfrjBkzMAbzeA5C4T/+8Q+ygCBAEPCdwTQYs5myYioBDkVj9gsTMZSz0w8b2BDILbNfpsE8aeL+gyiJFYJpMIfJ99prr/322+/wVx32yCOPwJdcRGBjXNGPWswbs3JJW9jWGN/mjqSNGbdtMCGNbUNa2jaI2IQt8njVo/Q/E7aYm9iw8UK1ktn00ox6XHl/eKMLIskwyshIXTBmEcvsiMEIJuMmYxZ+Rv7whz+IpNQXV4JMohAgLVc5r0qSJ598EmEE2JHOKyOikOQNEm+HXvziFzP8MczxAgcOm+cZc5FkOGbcJiOuZI0S/LnstttuvCPae++9eV+EBsQYQ/HAwmDN20gkmRgwXvN+cscdd3zFK16B8GGHHfboo48i/+tf/1psIBUBYCkaVzTDZ3KBMWTEuykMwLELIz52ShnH5Upeki/agnRIOTJw5CpRWCiDaUiylluqDCRrkVSZbQEBaYE0P34+g0OcZTZLlqx57Wu3/+43v/uls770wO2DuB3AK5aGLQsBKpSAzXTj+Ip63/vex4MwYdRSvPe972VJCx9YIUk6Qx6icUMsSugJRb+I0Yrg0LHQhb7lLW+ht7zyyiu9Bvj089tttx3v/HlywSMwr/1ZF/ACIYLB4sgjj6TvvfPOO4NR8gBV+iwflAnSSF5xxRWLFi0Kdp4IAMhpp53G8xdOuHgcCybxNEMGD5usejBk/PWvf+UJTn4pXmC8iCCMQRr9/jZEBIchHxW0pyxTBHyUJ9DGgMKtR0mivEBQcxW6VD6ok4QisJFgrGJYlShvUhWZ0iiKEAzumd5exMUNTtdJIoOszBn6i84nwDmZtA7Xcqbg5QoDuEVeaqE0r+qcsdlfXefWGgtW1NrGLp1kQQ8mHYvcluaLMdS4v4pA0ELosqYW9ONI3AVkxAGWyLPE4VLpQ3RZ8DYRU+qIq3+jSGOgSUj2hZpy9Su/fc+3txHbFSAjYkTBJJAchXKF6Srf1nVhh0fgAYHPPMnTAVfESCv6JYlo8MklqvQquSMGIfYgwy1XNDiL7AUOV28qsULDFNoLSFq5lYTQBMSqBACUWB5beB608nW+ICo/ulfJcmuKClZM2XLxJpraLRs1vkxqrroxowpsoD0h/aHbUZWPKj+qwKhZbNEC23jxN6Tu+AHSIZb+DKX7rvSrqcSvZEm98pX0VOLX2AAwg8BwyLpaJVXbAh+4toVibuNlpKlXR0DH3+r4+NhRu5dRBbyqCU7Q7TPDzufczDtfeHPwzlPnnnTSSSee+PXMgEngQC+XaGqxfvToRZH3E/RKRfPtEMmN3fPIQGZ7+dEaP9YiHIu2T5va0NfbRVkSiWkDQ+vee8odf//7xU8/+bFfXszOiCTPHbKtPGJiff09vGGsVMyx8bFhIo+/1O+oSHoBhOXRa1QoSEIz42Uur1lHFfYCDNyCVenwLXzAJNDGeHJj6Y4r71hlF7hXwpZuPqSAqXxsgV1ZwucWe6SwvBrmfTH86j9qjCFgCWUheP1CeI40eLnFNond2L+CkDFbyO2meBLcQqBQMxUBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFIGNgkDh86WiW17MO1aziedMriUT52+m20SQjXawLY41QbeogWhha0HEyWcjdqGwLW2/zDeUYO9e8+qGpGlIRobCp4DCKygua9ZufvrTn/7yl798+9vffvrpp7M0w1Y8tsX//ve//+Pv/7BoyWI26DXFYwOZ7KTmJrcqlGXrXzQfjRtrdDRvN/ClI0PpSG5FU+8Kp9NeAtsf5Ci+xIzvSRO/3OOzrYWo74hUpCfXcJNpWrW+e0pzeyzlNltmTTIfozoyFDPhTs/ETSaXz0XzGZCRTZu57BqMaYiZlN0I0xDLJ7OmJ26aM6a/bEXUYrnK1I7AiBVA90vJR9zPzNUgX7Zm9dFk7Nro3GNez3nC6S/aszOZHLTL8vmMq7NII/L52FCOv4HG/GB6sKNj7cknn/j7G68bHOpPRPL89XR1LF+6+IhXv3qgu/ORe+5mv8dzTz3NCf8pDU3JTK41kWyOxXNDqXjeNJhoz9r1bU3NPR2dZDfU17906aKXvOQlnMtiIbW9vbWnp4sFc7vfO8LSbYQtAh2rV+2yw45PdHbl0hmTtcZj2/qmBNfBAfulnxe/5kiO4qydN79n1VLrAIsS5eko7Dd2nbBDy+1ECvwcHbPyBeWVIzVGEVAENhCBsAvFaNb+4uKuj4pZJ1YRjhXx616ybv3eu+yybPlyYnORwukCn7eMqnY3htvzwPaMgXx2MJPe86A92ELz8B13NUydHB+wc4Ck6/YYe8qGpP7cy+Ky9TDrm3O5xrL1FH5bKMmL9t23u7f35ltvTTUmOA/MQwA9QMz5XS39dbML68gjjxS/nCPmSBWQypn62k8FNVsMO2rdoW70wCwL8HkSoZ+nRtgmx/EnZo8cgabzh/jLX/6CGwu8VODhQk5GBSdmUnH0/Ew1OblNdePT6oQTTsChB6YTK7sE2T4nGSFJXkw18S3FR5oROOSQQ2gDZIcAD31wOL/NZBKnG5iEDUuWLIEzb948TrrioouPaJJc0mIwwWMk2/+OP/54zODjl0ccccR9991HLL488HJFcrEWkyTV2rVriYWW3X3QEF7bgQceiGOsn1z401kzZuezriwmRqXYgnihcSUEonjcgiamYjNTdDJxh+HtdkeO7HFFkgCf60AclCOJjMWBnatNjS3xRBZ8dtxlLp7CHtLxd1zraMtUZnd3Z41r2/LsGXHbs/nEb2+2oYWHQnxi23YV2VhNe8uEbVuzuuoMnNYheEjPszVj43CIOfcKG6mYYGh/bm77OlkwooUyQoA96gxh+J3EUxX9/y233MLwFByhJAmDBcMEA+WJJ55IKoY/XFPh+gSdaGAcZDRkeGUAxdUIjq6Q5z0PSX70ox9B468Kn5IMnbiYxB8WozyjMHvpiWKwZtM+gz6uJ/GQwktXPLMvW7ZMjEcb/jFJQnYMN0wVsIe88GPCkI2zLdyaiLXcooRxn/KSyhdBio93SyYPX/va1xAgOwZ9eZAMAbIht2SEzaIBGv2OIwcD4fvx3Mpk8/bJg9nmYF+2uZXZazYWsWfjeaVFCL5DlicU6TL5KAOpBvv7p06fztQX8BubmmbMmoVTGDuWBwyQ135ijF63BQQirunlo3ZRgmbY3dvJK9KmZpyxmssv/91jT//ulv/aARhXBjFjfyMyCd3Wnne25JZgf/ttkybxe6cWb7vjDvpAnjikr7NdRzEEy8hpq5cfeigOsOhGCn2Tiz7mda+7/oYb6GZ5MKGnIth+p9h9oY3ALb30I489duyxx+6w0048QNF58oiSpklxWC+XO+DFL/7Tn/4UzK4SvWLVKkaZO+++m4y8DA9+h7ziFQNDQ7fefjtDBg9KdP4MMeRC7tKb0aUziMCn548lEkccddSll1xCp+dNRduJJ510+5134teYVF65JwQVUXj/gw/iEax98mSMZ3hCRhJIXj7JmImgVShBLVeYwmegpPhYImj7XOALjRixIuxjyxJWYyAgw52XFIVBNHz9iklesgrhFQarzJsXZJIdwqFCVdEcjAoaGeRXp31xQmIe8LL8EFNuSYINcqXhJaKmuaG1qSHR1NDc0JSBn3CDcVMy0Z2ID/SbRDSSjDTSJtM5+8NhRZhrfzpHE/VWgUawXEFaMpUK8gh7w0rtF44XUEIQABaCVNzGw0SyQD+TSWaM3EJ4prcEgs6EQAOQ34LQ1C/C/irVLbfwE5E+ezVDdFrJuP3aFgQdhswXU3lOE5vBjL3mMm38vEzE9hJ001x5jpbc9Tp2BByEVIHH0tLhYIWivPmi6hPxxuaGWCIapQlQWfGCMJ06MnR/1Gw+UnBWxbiSd2tYNFKGbfpHGQZsj8FbSlvxbn8LLdi9WvQtOeq0odAbk0ujLMaLFfeWMy2tiFhJAuFD2PbAvXRNniFvJkWDM8H6wEKP5GsLYhucG1NcA865snK1RIwRitboAsbHovjxssmLQ7bo8XmNIFzrtSly2bgbNTLufSnJRYx8g8m5HZGcX2LoXm8VAUVAEVAEFAFFQBFQBBSBCYUAM2/sYR5r58vGPg1uN9ecddZZV1zxzQceMIV1Ujfr5dHRPh4UJ+Klc9/ScgXnyqWx48LxWXiirFqxFhl5W/f/2XsPALuO6v7/vrJvm1Zdlm25N1ywjcHGxja9hV5CJ0D+EAgBAiSEkF9+QCCBHySEJLSEFDAYCAGDMcSY7o5x71XusnrXSltf+39mvu+dnb2v7FtpV1aZ0WreuWfOOXPmzNzpM3fU7cQrlccYsuZuu77M11Q+/OE/+OEPv719DQFuCQdX3c+22ijVmAjXNHPBO7MEk4AqDK3oJRAfMkqOk1sfwTLAoziBRxrOKA2AErxmWllJMrwBKrd6lAR87WUByYVWWvADZuGQyS8W50AyGW0SUAD5bNN59rOf/Rd/8RfEokiNIAU01TNFEx+jBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogVCC3CSavPmzWBYqeEOLJZjPvGJT/D9ko997GP//u//rk9hsyk+Uy6zzW7b8Eh/ofa9ylDIXgezImnrYo3Kt1p1Yr8vq1dj1Yn1rEbeEMNJch7dgqdzbtduV9Knfbqs6jVZgRNh9GfbArb6ySkLtqv29p500kkcaxnLZ1m1dLe7eWdLpZQWFjFHqu7EPifzeTsAQLIeiuPEHUcZOUB43XXXcVKFJc6zn/pUTp4gg8ONOnPIkRWkQckucCgBwCCE6HDAIAnig+2QIQQfx/mW++677xnnnvvQQw9xkBKCat59IxfJ+JRGCupxxx139NFH3zpcvJ0LsOTqC761x/gTLRAtsDdYgKoDNasV1zJwmOi9733Pth1D7JNpr7vqCmoD7qy84rrfdXcVnvWsZ7ET45YbbxwbHc36bflUGO2FxNBogWiBfcACvPivf/3rOXJ00UUXTZkc+g9tusFTskeCXbQA9qcbSW+Qmp97Lugrqgmgt8ntGxyu5hopaCBI5RRkYOh8kn0c2ObyLDqfNAToA1JaGcAjZDg6kEi+5ZZbuMoKYm6nggaHKBxkdG4J1ZVVXK7BFR533303N3pAjKM7SkSMgIgdp1jkIxmCn/zkJxdeeCEYYLq17O7DByZq5APgpxgVr4lS6MUXXyx6esjikm9kswEQI/1wJEsl2lOLBQyP6IatlBYLAlAC87nCySefzKl1MusFL3gBr+HNsf0NzRThugV8EUqWLlu4detgHRd/owWiBXaHBXj7cMSkel5+Y8S33nrrVVddRSiXSYm+kYZQKn/cb37zmx/96Ee0j1Aye6NmgjaRDfA0B1yARUNG84FDCJdS3X///bSMXJKITxAYhNBkcPvVJZdcQivM1SdqUmnczz//fDoGEDBHBLvkIBZe4jWtiJTrtG6++Wa0QibEKAOMg1ISgKEHryDYkeZJXEKQKbzJnHFACsgPhYMhamH4zfm29//7//6Qa624TYY7EELipnChpwcDMuGGnLDtbkockfuhBVS+BgZ61q0dPXhZgUvrNm3/xf/3zu7Lf7VxZKQ4sqOPN6hcdhcARbfXWYC6VzpTib3sZS/jcsDLL7+cOq19Qhorovb0CqVWF0CFA7CLdabVe41RM9R697vfTXPwgx/8ANgIGGjgGG3pDmuH9/XnziVHYmkvZC70obbdFVGmZwg4sfUa3vDEwkvH0gaWpM1SpPhQhgpAA0uI4bGpNGhwatQaWZAjLsk3SqJWFFMWGJFJf9ilhokFI8lggM2F2oIU++z5FkWjiTqJVEnAN4d96D/g07B2d7sVqIK7dy7hmm73U3V1ZlduDsPnkXHX2eDyIrKg6K8twhTAshuKwWu+423mdlH/ZiL3Cxx2pgBj4dlOrQqGLwzdxKi+JcXD4pUOlASmm9AKQAUAGGdlwEqFMQqg2iYRXEaJSHxgd9sRU1tl91qNV6jV65NcDdVCSlR8nA0LKH/xaZUY7wCYU3SUCmWuxR5iIAZvxVSF1iQAgHEE3gHo0UQBqLDVWOpxNZKFLBaaUiykqQmcfJEWBPDWuOo1WMglGF4DTI6Q+BZ7I6MkhyzAqiQbiZti4gVYTc0SkdEC0QLRAtEC0QLRAtEC0QJ7igUYBHpVCm7eig8+lpO3vL0/6br5/33ykcrEvUNu9AgZ/eM2vec9JUlt9ODTHdWy31vAFzuYPRnjXtxypXzef13xxrd88CUvTv7nazBbsv2UZZUuPcOJWZ8qaqP1HhJkk0EC5GuwlNJQo6YUksJjQ6l6qUuRNHlEVBNsgNKAUMq88IUvfOUrX8l2mY9//ONMZYrq3HPPJTo29/AJO2GYLmFJD5gx8zve8Q4mkSnelhwBQQwRjBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFtjLLKCN+QW/DKi1lnG+jln79g2LYeAgYeEsU/LBogEFUOHrm7Z7sOokVfw3ILv41Ga1OpRkR8tcV8XaWbbLCYG0diLaUzpDhYc8tMjIis8b3vCG973vfdx4xTVYBx54IIefOfn89f/6GtdgbVq/jo+vLp0/j/NFWqlxyvHlSb8NleVJF0tXppKtLB4vL06S1dqrP+bwfYl7KHsduAEKmK/geH/anpal8G2v7ate9apzzjmnQ0HiwmcpauXKlZ/+9Kc7ZOQ2En3nM5Mvlqqlqv9CEZs/OWjmc4z0u3T5T4FCSO6AdqneMXL1woWcKk84KJHPFEulYmncbd11mRPd42IBt7LMwjIL0bnEZV9yyPNfyhHHteOlJbmuajZXrpI5/rhFxl0dwzVp7Nkvd+W65w0MVouZ3r4lBx58w823lnJ8etjh+cv1F858xtmct8kP9HCdHJdVHXLkoTffcUsxVxlNiuMcD+jKDBdH+BtLijsGh8tE3OtuUiuxqzaTvev+5SeeeHz3/DmbN2/kpRsaGZnT21XkMEh3jsLEdWk7xkaXHXH4nY8+VMpUeetYm+31bzpXarHGuj4p57vzC055QrJlbbJte7J2bVLoScq8Yu7tqzkYcbHU1e0Rf6MF9igLVPO+DS2wsaJ2gmXb1pGHN62ZO2fe5rGxgZ5utGXrTGqbxPb8xNb80570xJPPPXvL2PDq1avv+8Xlz3rWswZGx2l8i915XvyiOhB7VJqjMrvbAhP9wKliDtqOqUhj+B5iAfVRjznh+Ne9+U3bt237zZVX7NiwyekW2/09JIeaqUGFz/lAhiRcNcXhbUgYoVDP2xjHZWD99igTIBq4tBNPeJ0ztBbEiAEYNCEEB8A2PNoIYIRoSAUsGkZ/XIDFgUbwOO5EtigQDiMsuFCyYIjpi0LPIwRI4BEWPYJxcXuX4oUsxGAB9IeejYvcz0WM9JPB4EKyGYd9cjnO5y4NUVzj4yMeoM50f9xAQqQ6qwaQqVbIlUXjA9D39Q9w/H7ZwYc/5QlPKW7Y/sgjj9zyy6tj+zvjebT3CmTE75V3rwOvoPOZHBge1nwI30EGm/VjtGytrp7d0r73WnL/1JyKc79K+OObXqp0rM0tkNxCJYBGsKlKIGmq2PTOVY80ATR/ukIRLoTQtEEAhiZVLbsyETyNGgTsgdfdAYoRYj5FwB1YOlIOI/vnuZZ96dKltMvM00oIbRCOIHhpa9RAq81SjAhnHgllQIoYPI5HpYLogNEch0riFaU0nEGfuBQpMgUHj5TqWsGmJmROe/6COSizaevm3l7XYdgxOjinf06xknRn/SkG/8F46VafSXYyNao5cNnBTL4Nj42SHQv95ZVKLwQzmJwoau+ygG9s6a0FWldcmc9Ue4d3VL7675//7ne/++1v/JLykimXXMnMuCtQ6y62wnVL7D2/J5xwwlve8haq1nXr1vEVivavPzneniCVbqpcDbIMb7UZ9achpwVQTVE5N2XhuxpvfetbueXq2muv1adoRPbGN76Rj17Q4vzTP/2TMZomwqAnGIRbAlMExmiACJw/O3UmzY0GhrKV1FOkVNokRwNGMOgsvHRTEkIMeEtXqD80ONlTcCiB2EGq1QMPrFDlqT2aQACLBUDOQkkFLCDBKEXACAfG7Ap17a7PXMuIMBaCwAsT4hWFZAJDg1NECpJM6dPICE0oE5XEi5BQsuxg7GF0wLBYpKyWJW6h2f319bmuSy4p+5QibyBb5carpJgtdHdlu0pZxuxjlQJ+Oesu90QUfRXoAFAbXuWyNGnlN+qfUl66icw0byVNvCZBCugx5DVTyFzho2wVym8ftfHCElpSeDAAYdSh5EZYqkorQlVsABolgIE4jL1R2i5ikI+j20k3lXcWywiDb5KlqrIbn8KASrx9rlTUL8CCGCSPAPjAcNEJRE7BX33V0+UWafnzb5gj68okTCnxbgFjQgzALzBuVpOsKKIfWoC8IusZklAM9IIrVBmhDKU08oiPC3n1wNo3q9v6o7JBIBj8bC7PQryjB/b5W802tI8NiFD+dGHUs/ID4Iqhd0Li45QE6AjRujyXujmYMqw01gpwrVB2PuqoWcOXZs3hgwl7rFKmje/3NLQJj0HRAtEC0QLRAtECu9ECYY9wN0Ybo4oWiBbYWyzA3EQxV0je/OY3f+UrXxkfmaS2RhGTUHvngxvVMM1XYW8xC71u8D9vUbL87uTLX/7yu9/9yhuvvviB+2xPfCWbif355tmMGTEdUw8WDAaHbcEzHA0HoqIBDyDfMOGjiTIyQlsRGLFGg0xnHHHEEX/+53/+nOc8hyAmnT/3uc8B8GWbJz3pSWwrRw4ftwGDkizLQY+Sb3rTm5jY/bM/+zMe0Tw1NrYodh2I7e+u2zBKiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaYrgVYDmPHp/yJO62clNr+tykXYlIx+uWg9OZa24rHBlVg7bczRs5unXHGGeeff/7xxx//+c9//i/+4i9e8YpXcITgj//4j1esWMEK3Sc++ck3ve41F/zgh2zBr1ST7sLE8pMJAWi/2qLUTU5jyN0RLGvgs2akVbBf//rX9oWVjkT4tTAdV+uQPkXmlqvcllySYnZNkdQeoeHDMM9//tDYWDdLXV3Zo//zP//z3vvHWN2c1u7G5tIjdlcsoDP87NheuPDMM89knX3t9m3kFx+qDqVS0nBgOGfIQcRKwW3P5SAiF8NVe13u33fffSxrioC3g+3gELDP+7e//a3OWtxzzz0w8mqw557DjUhz5SeToQQCgAeDQI64UJ45tYgo8IRCzN5igpBP7OBxYIpuLTddfGA55JBDXvCCFzx25933bN2adHUn42O8kH53MXxpei8setEC0QJ7igV4qdka8dznPtcUuuyyy3j9Fy9c0uXPAoBXNUINYzQAPOJgP+qoo6h/TjrpJHZcUIH09/fz2tO1CIkjHC0QLbBvW4CLG66//vpNGzY8+uiji/rmtE8sVUd7ghg6qxag3qbS3sIVuRwkHRjQ/j26ozySNTpgxtgBjOs3TnYQsMWOTXegBajPqf6hfOMwdmiIkVDiolOKZJyN3YChJBTHLj6OJRM79MjhPg7IiAgkj2p0BOBrOAalRCHHipaLoOyOy0ImZ0F1xMSv9JQOun9EGPSZIJoFCJXQcN68eeivJKxe/RjxCCZ2CIDxcYofgC+5ot66TesWFhc+65nHLlu2jH2P5GZsf2chi/Z6kbVbDirVwpwcrx4vFK8apckdJ48uWiBaYE+yABMvNJHU/OF++5SCagtoOAB4o2kLANRUyafhA6/mQxgk0I7QCsNlTZvkEMSojVDFCw1Rw8VVVhATBL1YeBwcHGRmCaQ6DxJLaG9vLxL4UAEYxaheAY+hIwjFFC9KEiRi+SHlzML1lKoNJTZ/6wGVoL/c6vTTT8fsPf09JPziiy9++OGHzzrjrNJY0t07ocUEix/eIlCzyvQWNNVGekVNyGwnZ0KtCO3xFqALyd/WrUOsYKxfv+3b3/7e/Su/d999yaJFydpVSb/r5Ea311uAuXQuHKR6BNjpxFCRurpp8qCD+oSqFacKU8KNxgCL1NVM9XrVkI2AyBrxYFiAu+SSS7itmO+1aDQkst/97neMy2644QatPjTlddrXa1cA1IYFPyQWgWGkSRt9jHK6gEWE8FA+eLVoCKSZ41FuuvJFL1FIMABYQWrE7VE6WC6ToTiF4uNEkPKxXoiBHSf5MiyhwuAjhCAvrKaDeEVvPoxiEb3hUwC8jeyNmJArFAgsYnxgRSrYhCgJSABjyQH2jxPTRDSvYPJcU+M6MJ7Yf4RpbKTbTV8UM67TMu6KWUEfZ6pUoHRB9UkD2EPdQp1DuBWN6WkClZyQtxF2yfDvY2MQGLNP01Ahjd0Us9xvxSVKH/PEpIEhW3G1wpsCrQh2Dx41KAM4shWYRUl8Mt3yRQnEp8KhguLNIohHfLgAoBcNQfRCIZMlwTOjgk8PTldfUV3pAixGyL6wUd7sD0JnVefXX/PdY4EYi8xOpjPcIPtcTngnvDIXn/wFY4/Obv49gNaDtZfCwQ1OSMoDgMv7yS4krwmdTDDdJ6mU4go1FwwBwHTnIin5TeUrOr04SFWiUjp08hgPzHdipUgTLRAtEC0QLRAtEC0QLRAt8LhbgG79eKWUfOQTp7HA8e1vV3t6EzdRwJeU6z3+ne4TP+5pm6wAwxhGDQx4WHQplCujg1tc+PlfL73ljU97358/8sE/vrXgd+2C5AO3mMDTT5axnz1p1KQxpIZPKhXhpwDcYKw+38dNzIxImWnSRKefn3ILcrNqNjZtH3fccaxUse+KCVniQo23v/3tRx55JJ+yQWHUA4mvfTNclfWud73ra1/7GkcXhJ9V9aLwBgtMmgFvCH28EOkJjsdLjxhvtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLTArligvmUtM5pU80mmr7dvZNSdK04yRednC+z5G144Z0N3puQXcLo5IATa+9pE6MgmOy21sOaCcyH5/PiY5tXd5uM2jlPN7D4/55xzrrnmGshOO+00lpAA+CTPb6+6+pvf/OaSJUuefNqTbrjlVnToDgT1evEb/Y1YveNJaev2J1e6VnAe239G/SB/11bP3KUbBzfenJTZucp2yJm6kkOJ5fi3ToAHSnUKdrg0pogkVHt5O4ogM86mzr6Btf1zM9XsRlgK2UPHKs4u3d2J/3ULom5tLLpZs0DNvLUNqzl/Uo77qjIJBXHxkv7DjuLI+khPPxfNZBcu2c427topCC1ZOrXK1cx4qfKLG65zJ/+L7pTjtvWbqrlkmE3X2cy1d9/N/u/yjtH7rr9+0aJFG8fGxsfGuDDg9kcf5fqAwUyGeygW8SJzVCBJ7njoobFsLtPbx47vx9ZvcGu1mWy+Kz9aqYz4GxD8fvIMh1Q2DA6OlcrgFyxYyE1YlY2beKNLHLzh5WEreeIOeLBXvJSU+WDsDleVVOBKli5eVjxua6k4vm3bpnvvTXiF2TxeS7s7X1i3xoS5Z3dteCKeCEULRAs0sQCNC80Qp0TY7MFLfdVVV7FtAxgH9UEHHQSyu7swx19kMzI0CrF/9zPVvDt8smhgDkedh0rUDdkLL7yQ+7OuuPoqdoA86yUveqA0NPfII1avWjXgX/J8bGmamH9fQL32ta/l3pb777//yiuv/OhHP0ruf//73+dWRwrSL3/5yzVr1tDQ0ORxVH79+k2LFs3V2fh9IeUxDc0swP13n/3sZ6k+ODGb8f3zWd4C1kyJiOvMAtThvI9smeNkIGOucKwBzHCDXXNqI5rK01ZAhADwvjelCZFqPiAGMHyKEWW055Az5ArSYFD0jBPhpblBK/mmHgDdY5AQ4AwPhjYLl4rIFAgBUUoCcBg0a7Ab6tIOc2fX5s2uhc35Q3cFrgDr60ODrZVN1VKVHny5UiWIbEn4kqs39mB2nNuQS4WeNYNrf3rTTRvz+Ysu/GVpx6bXv+2tq8eqsf2dtSzbgwRP1f6uoNhzWJz2d+umTU7vbML7VXWlCdDN5ehVZFwXXbRAtMDjbgFaLnSgIZiyATJKiGn71OQBW9sHwOAuJQqk2no1oMRljDT3VA40wTICeGmCBCMDoGsHJTfo6V4JEUtbxS5G+ZIgGvNTBIbfzYClq6tvgFm4i399DSfqu+YMgN+6pXzuUU/qXbAs6SkMjYxnuzNz/HSpsQAwtMHsqkB13w39KKyHYfFxTA6LfjenK0a32ywQtr8f//jHNf4966yzUEDjX3/phpsNVZ8XnwHx2Fjy3Oc+YzS58vDDkjUrknkL+GQAt7nw2vuOXdWvwrj2Obq9zALUA5/61Kd46+WsCp1WMjRmQYIqaooQ1TJIAFfhdOYUNd0/ambOSaHYwoULO2OtUTE25As0PIS3X/H4U+8A/NxOjTj1o/YInXEEoQOa8HYAgFGrRAJTXLP0qIiIl+USzIIxUQPnesK+jcPHGQZYmghQVtI0YH9gR+oJTI4eU8qDRCA+XIpaI1D5EAvAUE6VujNRprNix4ceu8EoWhEoUjQBgFfWxoeGRzkYweCjiSIFDz0SuCtZAoFBwiWBoU+QkiAaghhNQExuyoDiskiNTEJ4JFJ8HPY3Dek/QCAJSCNUWoH0tO3KBkKgwUOxSleOprY07t6OfNX1mgj1zn3UQdIUkayEDnpEAbObkaU0ER7BREdpl0CQOBVm2ZnompoOvNKCT96J3a3ZBTeTAhNqaUdyCPuoXFGBJowFGmJUpPJFaT4EOB7hlRoCwKjIASj5xtIIwAhSUaO/HomOeglfajeNvVHUDGKIV07pIndImtVF0oc0gsdXvKRUZLKJaMwgSBMmU3HZlM8PcweWrsHCANRSjI4Bir5WIEsRQtSynrNyvbqYwTRGUa0sQGYRhE8W0ELxNlnWW16Qm4KVrcAqKq1kPu546Skl0ZZHJRPFlBAVNtXOLLsLbzSPr/713USPrxYx9miBaIFogWiBaIFogWiBaIFogSks4AaHJ5+evO1tb/vkpz64ZVXCPjfuw2K6xvrfe0gPe4p0TCPYzSQyeqqy/Jsk6x9LvvWtb73jnS9ZesSt6x6RFA3ym0wDTSOSfYJUQzIlhTGYpenggw8+4ogjTj31VHx2XXPzFF8k/vGPf8zeFE3xaMNKyGK8Mw6wS4ZFppNPPvmLX/zi5Zdfjvy3vvWtH/nIR/7yL//y7LPP/oM/+AM2ZfIRA5Q54IADfv/3f5/vivN9hu9973tSUsV796g642mPAqMFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogUaLcCNPAfMmbthx7bBEX/7FRRVv/LV3ZWMDG3cuPHwww9v5GqPYVuwdoonQzsmUfqttOzmc3+B4/aE3/zmN09/+tM5gCT00qVLDzvsMLbsb926dfny5aeffnqlXHKLduzlDRgN1PINeweJl+UedF59wFJCDxpxH1Xfmj3wV7/61Q13X5tLumbq9ivEhqtjpsm0gJ1YdSKN04oiJGZ5zm+SR/PaCm8pLnKGBpp1mC2t7BTlBDunm/JHnnDCmc95ARv3V4z6Q++5rHalhwvuwBQSFqx5R6Bcv22TO3gwNk4xYIssQay3svc3Xz8oxX5uNgSDwbG+ic/mYN5H5CCc7f4AYCi6hOJ4RBTyYcQXJRiEjLiT9tXFixcDqqBKGfYVA+BS1tLrwEV1rLqObd168erVCcetIctzQL/TEzspmfExWiBaoBML8IY2vpKdMOr13759BOLubne8inoAafi43/72t9yeyKu9YeMm2t/enKtPfOXDjRzOsfsCRioO3z4nfIdsZPt2TtV8/zvf4UxSeccQJ+Qg85qka4xO1Is0e74FTjzxRDb/0O/62c9+duedd3IwniaAPTnstzn22GPXrVtHC3LWWWdwgSPFikbn2muv3fMTFTWMFtivLEAlX6+oa+m2BsWAVgYRo3qAokmJChkJogXBNcYIGR1R8IoRWBhJ0yMYQmmb9Ais0BSNISHAGW+oSXtYEtrTWCjExGKPOwEgQQ7ekt+b2pPLYwqGaNiqdimlu0WWg5rcnlDs7+vZsWO0mIwdcsghazdtgeuxe+7WfWSczP3ed77jB9pbY/u7E3mxd7FM1f6uHBsrHX3iMZSTSmWUMvbAAw+sXbuOO9YOPnjphrUbwOxd6Y3aRgvsDxbQi9nJ6wmNNaZGD6AmybchruFIGc03JW4GMCRjlolZI2aK6KiHLazEmnA6+Y0CTX6bIKOZWcASO12xYqSF3b5la76nO5PNDG/enGzY5OT091133XWb16yeu2BBdWxo+/Yd1Qz9k9o9FzBi2LHiOOZiYlpGhomeCUG5JMNkGgY0/HQVi/R7iwXC9veuu+5iml3jX+ZCNf5lXePcc55MOfEduRzvzlVX39JdSE455ZQP/d+3/td//ddn/+7abVuSXrdYEd0+ZYGdrpdUVKhMKDx/9Ed/xJEr1rCYOaF+Vg3TuZlYRKMcvulNb+L+l3/4h3/onLETSvRsRUbVp9qPeNevX88VWkwevv3tb2ctgwNZ9957rz5704p9BvG0RzjUQFsASSZrqKJZFsFhUnwpDF7OFIBF2YEEJkhNAgRQunWUYhEJSqxxAYgRH0YyjuRbkxraDQJ4kSBeZJoQxSVifGJniZNQaKAnXtTWI4AYEaXQUAjxwk4VhCbSQb4osQOixG5cABYvxOhvaVe6oEeaWQ99QgnQ4CQNRsWLj/7gRYk+SjgzhFLbWEJRoUoGQ+/lOFMg1kfhGl9yGLVx6MylVRILF49QSr4AgmBUGTAyAEUBpYh5RDh5p1hkN5Bid3nvc7+pwqghOaQ0tCF6Ei8OIcDQAOBSQqSA0iIbAhsNABKIHQBKfCRIeUuC6E1zANTwUdVWAGGHGN5GdvCw4yMcRhxGQA0wZDopcin312CBSUUNZlYdKqEbTiYirhQMhpRiHwgASJ30MT1FzyPpgoBcwEGTT9zMUp6NEVx6pauvsg7o8hdTljO8767gYW9JQAUnsy5fsUR/91jActPyV/GCdwXaOzD87h59phuLL0i1OirUFnyoNkFKkUMqjnrVOt0YZ4k+XoA1S4aNYqMFogWiBaIFogWiBaIFogVmwgLVSZNW73jXE4fHlv/iYid52G9Zt1kVMHS+1R2fiYgfTxkM3X1SGA+7D4WNjvFVauYLKl/90p0ve/XBH/nrRX/+LrfsMTBnni4mfzx13VPjfuITn/jBD37wjDPOOP744/mewKOPPsoMCNdgveMd7wDP7OoFF1wg3Rmw4YB3bvBpo8E2lqBYMiPz0EMPfe5zn/vQhz70mte8hs9uHHPMMUyxMWX8k5/85Bvf+AYTr2984xu5q4udmmQr+7nR8N/+7d/4rLFJTo02DR+B2bHApEm62YkiSo0WiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFtg/LFDNJ9V8BR/nffftXcBssbu3Ut2RDBx9dOUYd2nUUN7tcJ0zupD9oH0HHTbc1Zcr2q1TnJp0356suHuoapub/SKaxwL5A7rdmVy2XDn0uGO6fu+Fy9wqG7TOZ03n0dtuSzYPJYVMMj6xJVGndtnov2DBgi1btkB54IEHcokVSzYszbg9tmW3yZv4LEoe5SpuCY8rhZzflc0k5dLC4cFje7vO2H4/0c0fmw/+joPn3llek3CzR6WcNFzFI5n1K4ScHH17HaC927mFrfYyJ0KllvxKMpobGa2Ui5yzajSB59GaCrlTdX9ul6Qn5BIjdkriu2TncsUF/cnqJOHYNGQWl0RO5MdkO7NUzL7gDDkbUhhzBGSBFvky2Ty8gFzxlll29JM2HLJ04cknb84XspWk3N1HWSpWxqvZHF9Vpsy7fdfssE7cynvR52dXf/+OUqm7m8MDOY7cIZb8g6BEmSeDquza5sNOFZfVFffVa8fOane+i7xzBTWX53hH0sUKeHWkUq1k82Nlv6zPrm5O9eUL45Viks2zjxtp8OY5jJXnfcqs3baNre5w4crEydUDiE34TLLbQoACqFF7pbJuz/Fgbz7bX8jPn3Pos87duGrlyO23+43kEPm95Cp1QYHTzuSOjEd80e1zFqDMkCaKXCcpa0oMLzsNOpSguCSnkxjb0xDprojSuYv2UVho07QTagq0IjAJTQG4OCzA0Y+BgQKbOj7UcyQNMbsjaHVRrzpI7mTzh5z+r9V1bPYYLZUG+heMbnebhHK+Odgxv5gcf3z/4YdzqpPjR6vnz+8/7XCOlGwb3XHEKacs3FG84Yc/HFqxFfqlY15VTpY0czuhPMani4KPayayCY5YjLiTGEUTCjL2ELnTsMmfWbE7rc/OMVJgxMi9pZxkY5/VC17wgm9+85s8cvaSIF7P8nAx35896fTTrrjiCku155rYihYaQdiJPooiiP7eYIGCrxlCTUvBC5ql07CnnkcKdd63YbJAr1sIpPKl1WP4npqVIMY1DTIaQnEiw+cAITUDgBG0AkLJNC4wsvFveHhYvMhsyqi4Qvkpyjbtb1PJUkO+YgyT0JSlqWIe6Q5boo/fJOlgROH6+vrx6fq7OrNc3jS4RTdN5tzVk73FHTuK4+OF3vzAQO+m4ZHuPrr+peLh/b3HLSIt8xafumbNmlLlWK43ThbfcnRsf1tbXyHKMmXEVLR7bnj79jdX4YRumTuw+HvKKaewa7daLlRK+aRYWvnAOo4zc7iX99YZoTaSc+8jYzzvJlpnPUc/WiBaYBctkGqGWkmz2skI2jQxKWI9GhJAkeLTgEoghyBoZcBYKHjuNeCqi9QJCA0GRUltQ1uDg9g1VS0a3zBq018s+BYaBnUCh6qK3kQJaKVPg3DdieBnsZgeyxX6uitPPu1Jn1ncwzh3/fqVfX19tL9jW5cX169407x53zv+kFtvvZfJ6fk9XSV/ZwQdF+aTOUyCEfv6exn1YBPfmruoMky15/N8wgEhjIkaYq8hTFtLRSvKRjy8uzL+bRTYiAm1Aia7G2k6wUhOSgL6g8EhwezQShpkU9K04p1tfNj+8k5p/Hv++edzAbSNfyvjle6e7iOOPobjKo899iizttl88tm///IjK5Pf/jbh9qu5c5OxsnuhclVnFsDZVjvKf7wsYLVEewUo8BQnahVOXZ188snXX389K2KUDaqm9oypUGqhZcuWcTaK9bUZvwArFVf4SDL12tJ8SGeK91FHHcX1cIxT7rvvPq6nme33WpUGvuoua7Zo5kCiITowkMTxiJ7o05g78DLehIWTZSQEISaWDIKXqp4WE5jkUycjBEA+lNDDBS9C1PiCxJmtkA8vGKKWk1k8lSODACFIkBMjNCiseNUQmP48IgcyooMXOQDoQNWkhIORKaCEjHIl5cUl+fiQ4atLoKiRI61gh5jYcVJDuYlWEkK8kkCkRC0D4gMjQcYR8YYNG2gioW+MHaRcJt+T5Hq5ASyTYbGs2zWwmTKr1zRKbj4pyeQLPYVe1nfLrI1x/2S+4q40IqOUTGJEDvJ5BEAxfJLMIypZKpReQkWAD0b6k3wA6JWDBCEfXip/2K2Jt+RLAuw4DAgljPSsZH80QQFgdEAOEvBxYpdxkIBTjMoChPCIQAVBT+yYXUKUgwrCF5nYUZ6oBQuPArDDQulFAo8445WJeIQFGIcCpj94YHgVCoADiQQoAUxD4NlwyMdE0kqwLGZZo0gJAiOT5nM9lUwlx8wKt4Z5+2m0SwGCuCs7xDCZy9M8TOlif8Nc5GdyfAaI66/G6Xxwr5oLdS/OWKaYz1S5CyvP0Jn8oxyy8k7ohAV5iG53WYCCR7bK6SUSrCJNMcChCxmKY5Hd6VVxuVl2a+sducacZQW8kqmyH8NviHGL306QP1nvL08jytrfRASUk0yjJBcuJaWn6ElC+EryOCHHp4VHCHgHARSKH0oI6Wcbrg0pZzuaKD9aIFogWiBaIFogWiBaIFogWmDnLEBHWZ3m5zz3lBe84Hlf/epXt21Lst1J1k3CMu7tZUzLWALhkIly5yLaQ7gY+ZCcbNaNh1GJR8YnbDPo6Ut2DCb/9m+//NjH3nTKk797+81uUom0c2H3HqL5blbDCgbxMrjCaMJwddSb3/zmP/3TP2UyhbnLV77ylXyOQMcVmBzh2qkvfOELX//617HeRRddBC9IeBmhzd6QjAUG3WP1rW9964orrmCaeM6cOczm3H777SwzoCd5/dGPfvRf//Vf2dn5lKc8hW+JX3XVVRy3QCXNjFjBNmA3WztGFy0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLTCzFnArO0mydeN2xJ5++ulHvOBpAIP+AqyFxSUc3qgefzzfM2e1BHxt121bDRCIy7P9uFI55JBDXvva14YXYLGc9MNC4f4rrvIy3NqiHBuLWbUBZjmJr0OzQMMHom+88Ubt/3PLdm4roV+2q7OkfokUjPfcpkC+LN01MggjK1jg165du3379sTFMJNuN68ZKTqltJbUqVLDKifrt0bF46GHJovnPg0h1XyOtbPlDz4wNDIGhdsNGpIaTwRmxgJm4L6ka8Ghhx568rPP7e/vHx5322FddtS3typ/eQQQrPh5Dcm+fJc7fmDLsuJ1y6zceuZZ5PMK+Kyv7Yu1FIDUmqxhANz75cpJjZiFUcOA9K+/e7mchr4K0FtZzTm1wYcODGQsAbOv+JRTThk9/LDfrF2brF3nims+x5upG/JqLCDrqQ6FRDhaYFoWoNRRJrURvD2j3g69Be0pd09o4xs0q/Hq9QyjwG52Awkn057+9Kc/Z+A42sp169bx4tMWc+SGxoHzOTcWV8H+0EOPhuxO/76+hccd96IXvYg9M9o5s3DhQrhW7NjKubvj+xYA//az/xpyhXCjSmGowa7y8fWkYQR0mO8pLnt0+nfgLHYqxvCMjdgV2oGYNIkY8WWEDpVJSRFvCrmbH2+++WZ6j3TYnv3sZ3POit7XF7/4RXYE0ZF7+OGH0ZC9QNfdevPS+QsXHLzkuuuu6ymkj0uYhaW5bLKbUxGjixbYny2gd7CpBdq/j7zg1MPUjQDqSQI0lSMk0jh1KZg+cJt4QyEc9dQBVBomWGhlOHQ6MDCgkWNIGcJo0kZ+ez1DOQZLGow4tQW2UdZoWgGwpJQB4zvbtSYARoYG+AzLCQJw7XPR9W02bdrEeBxiJIDDzpzjc/tzc8nRRx/9jFe8EsTWoflQri8ehWKb85fF9rdVRgivvMBXvsjg7VkaQ8XbiN+dmKnb31x2xf0PrHjkkQMWDLA5dvPmQdro4ugOV/bc7dbOkRC3IzqOyGSO6O/fFlBtMLOvQ9O6oikyZXvVUSkkj1LPJJjOAkQPTHthvDzSLdcjfXXDG0DTTAtLO4IzJA0K4ziaWoZ4fJyAmUOamzAWUYKRSim72aO47NHkdw7A2xhv5+whpeQgEMfBgd7e+WeeeeYZCwuLFi0aGjqSZJJYashyqQ8DPrAwu3r16q1bBs2YpAWHNvRh5i9azL0eCLdQLsBSXMhhEBTG2xQ20zUNNaTpLAzRhdlkZJ0DpF3EbaxqNJ2LbUqJtsRiEQGAQTiA/KZcez4ybH/pl/KyfOlLX2L8y7UyjzzyCKlj/Pvb624d6M0OzF9w5ZVXupsTMsm8eQNnnXXC5s3X0+OrlngrXb+OLm5Sch/giG3xnp/vs6ohxYZXmxWxww477Oqrr77//vvvvPNOum306qmX9Mp0qAAXDN1xxx3cSG43tXXI2AkZhbYVmeoNVKXtQPONGzdSYf7mN78hOVzDChdtzS5WX62iFp6occDEAkDsvJvyeSVBUrujG7CUYSgHIBZJkA8lNDSXVPI4hIgGUSSfRyZIwWBeBIaMIoMACbzaSABWFGEs6KCswWIIRIjqQ7ggBsMjcUGGBBxIMDiEQKMRqGKHV075AgGM0ENG1IyXUQNGMMKLjEco1TFQqNTDx0l5LIDjVJ20wocSlUg7UUNGvGAQCCyB+ERKjPjihZ2mFryIoUd5tKIkKJkEybmIvUPPOlj7rZPU+j88ogwCsQxdFGzGoxzXFZkdUAB+fCUcGBYU420SRiohjSDzU/rzKGIIAEg7PnHBAiwJIpAdwPOIw1Dw4pv9CVLyUQBpaC7rKWpgRQEZOsNoToyESn8YlQU8EpHSa7xgYEdDYsf4PIodMughozxDwOcriFHs+OBxUALDiw8NDuVxBClqYMSSdzyK2PPVeA2ecQB9iA59lMut5CuzoIEg6/cw8MY4w7rsdTj9OAwf7KpUKIr43JAEPp9172w+62pazOC8nJ+15v6knMt0JRm0hER/N1tgupaHXsWGYk2O8+jyfY90YdJCJWtwvchZikgX5ZZHHAnicfcnK72is/s1iDFGC0QLRAtEC0QLRAtEC0QLRAu0sUAu21UqjxfmJs9/KVdob/2P/xwuj87LJnNKySq4CoXalxXpWLcRstcEuWt33ab8UmmcQQKTUSSLi8OZORkdZoff8AX/nfzVh055zR9csHxFcXTjVjdkzvTvNambCUWZTWB8xageXwMqScVcYNgc+cMf/vBJT3oSyC9/+cuf//znMaZuvwLDDM4999zz7ne/+xvf+MaHP/zhH//4x7BoWodQJm6YZGGOBrhzhwRce3q2aWrIx8wXhxxWrFgR0tul7Cxc4S699FILZSaIlKKhUtc+on3kFbDERyBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaYB+wQFWb09jtys44B/N9XCWLbYBJZtytg5WS7kryaM5dgFUuL8Vf3XNvcuBAstldu8MhW79E4nbXVdhK6jaQTiwLVoMtoHyVlyUVv6pSeqA4lhww8FjZ7Y3e1O1Wf47ZXt3Yn+kadftu3Z7fZk6bsP/zP//TAt1KHQt19uwAnryAKkdJ3LNUICK0LewYBVPKLCpXy4/6T2XPnTs0lGw4KknmJcmtXlCu7Fh6kzn+68FOn1LiuIgJfE03CQWbck6d2XRsX8zztWMWzhKsph31XXMSbhWau2BhiQ+Okky/g1NKKCcqWffLqStnEP/HhUPkFNu8+e8+V4vLr/ijd55VybiUZvILHn54+P9+eKynyx239jv/e/wFRfo+7mwmcF+UzUk4khW+CzlfTkpZ/65lCu7lm7P4gJNOmn/Asq6lS7ew6D40XOjpJ1vYdl3mn9/DSi7yrrH92m3R9Xv0eUcp1V1+D7YroGXyle3cyHXlFjKWMt1L6l9JEfslSzA1V1/BZJssAtz7m82x6O/fK0/iD+u5L2lncl0lAvxnosYpRtWEze81KZwl8LplfMIoaKllU2oGKAfdWRKnDMr39C0+7qUv3Lpi1frrrye1JMxFjsCSL7PVCsWzJjz+7AcWYCcAZwI5NaHFd1KsAwyUXpUlysyUZqAwQwwlxxUAkIBYJGi/AWce2gghSKHsDWDDAyzsHICdIxwwThk1XOxkIFIA5OgomvRpxQux3j75nJUlUqJjBwJCli5dikFa8TbikQa7kgBMEpCDNCiFbGQJMVKVAzMAWHrJknnosH37OOe/ONXCETvcj5Yu5TAMWmFmmp6BgV5qA8jGRkp9fd1cY7d9hE/Gj7iWJpfli/ZztuU2f/2i7/zXhX3z5jlN4KEJ9qd9uJjjlmLx3nvvlW03dLkKkY/Ou8aGK/u4Ec9TSivfaLmqr2lChNQBFehxyCTvoBcLfhsjmEwYSb5sQsmh2ODa84oYRgARU3pVbl3cU+1REbt87AMjysiRBNiBCcU3OGQxWBHJAiDRRCeRpqWASZtZ4Fe/+lWjwJtuuqmG5MhQls8JlvsLfV29c7OF/pG8M2aXa76SpX5b0KaCy/qxokNhIFJX62F2UCE4KdHtERYoZ6u1v0Ad1+H27b07DMl1AKtXruTTmmCEDCgjOGGBSv1iGqF41N8ExfShpu2vNv5JmOqilGDVMFbPUBMCQ2ntLxURj9RsBDWVEAo0OWp/Va9CIJkhZQpGeZoh2l+qfQA2H6r9FW+KOHyUwsinVqG57LD9bUyINE+1v2EbFEbaFJYmQfu7gKbW995rlT8ENMFgYEeBKqMC7t+o0NDnOGQ6f37/eKmcy+e2bBt1zU4+6ZtL5796x0WX3HLRL7H/0FilOj6eKbtjyYVc3rW/pUpsfy0v9s/2l34tB4UZ8/ceeGD/gUeu3HRld7YwNl6d0zPAO5hJtlPqNmwb5e1gdEh760Zx6fkWM2EEogX2Cwuo/fVnBFx6Z6T95UVTv50LI3C0m7yAIBvbmpSJUwS0vO5t9UMSDSJoU6jzxWXE1tS6psQ7BTEIpd0hdnSg1XCVgCeAHQAXxm5CdMkC7R33MKrpRwfYIUACLADwytejyWnE04zi6A1qvtcoDUipEQpEExIrA+pkAa1kI72JCnlReGTYDTmgx/HIaINWdsvmbdtyxe985zvXHHncbbfdVqm4yyxEpoEwR0Joc5k4p5KkFebOiu7eDLGPjo4PDY3sGHkUUSYWAOPgYyhnK/eZiZp9KuUMCVcoqaAnzNUwZIdyE7KUU20sJEmmAwMxcSEBdqwtg+NDQ4pS7Ho0PLFo/As7SHxcU5YUEkaiQAGmLLggmyR409WGrinipo/SkJ6bpj4ofjhTDEAJacorJM03vMSLGmBgl0yFmqg2EmYpqOn493rmPOvO26rIBSSlXK5vwYLS2PDQ8MhYLn/4CSf+zaffz81EX/3Kr3LZpHdsqFgcYo3GVTvVPoxbnfGvdtRVir+70wIqnNTV9Mk5KsXrM2XsvHFUHdynxlzcT37yE9HzylP7dfjOWhQI4a3/6U9/aphpAaqvKMMhF7XWkiVL7r77bpQM8U1hJOCQwAt75ZVXisbXPbXJt0YuGlw1vvgsS+1E+2sVgiLCRwEqDZo/GixVPtDgVq1aRbuGhqRF1X5KH4LgIvsg47Ymqn0IRIlhBeCralVNHlZNqtmIHQnES+xcAgUMJblJlQgvoVIGYjkeAYTER39yH4flRUwoNSG1Ij5RK5niIkhaoScR4cOCI3aUJy1gXMNUrdKakDrYIYNAyhMaOjOg7AYlEkCiuciIi0fYw8IAhlBpThC8OCUBJCxKPkhUwiA0K1JeXOgGFxhuOmKOn7ba/xUwQ1JZiNxKdQSCctm15tVKOalyuxmJTfJlLiei7tQMU61RRhkiUsIdfTCDwSNqEw1A6FL6k2okEKNyH0rshhzwOAxIECxgcISKHaSSLIApC4oQuYDpYMcIpBo7EOpT6jwYw1wAg3xYUJ6yhw4IJwrwXFSKBEIRxWOoPLAwSJODjFjw0QcuFIYAAEopoEghTvHS5sKCAjgpDyMKqNRhOomFHSTshOJSyszsI9bDGpR8xKISCgAQdVj8FKMzFMPfLB/1cZmSUEhJYK0mc5akQ4SfyfeS7Krv41bG3dR6f1eJnKCUOYNkJqo4li5dwfKJxcfBTixusdT9tOyDQRbdLFnAWd4vtJEdlD09Ki4VRb1fKpysnntqV0Qpp8DttdIOE4pOCzLhO53Mrvjr1eq34/oy4xTgc1Jow4I4q/DUbOjlFt9RnsJn8ToSQvwr5kjQydE5Yqg9i3sBHSMCicj+IHXxTogymTVARsAPWdJE7Z6nXj1txx3DogWiBaIFogWiBaIFogWiBaIFZtkCDKS5AOvYY5M/+qM/+uuPvW/UjSUnOrEMjMOBhEYRs6zRrIufGKxqIOGHNAwbqklPuTj6la985TP/+J7vf/8Ld25k63nrocKsq/n4RKDBIXGHo0cVg6c97Wl80YIPej/66KM/+tGP/uzP/qypig899NCtt976hje8gW9fsI3DaEKBhpwpQMNXclb6U1BxmtZpE4VUEm8bshgULRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtsK9aQAsl+KyttE+jKNkdCpnbTOd3RQO7bXnOue22O+em3GAo3dwOyKzb3ypNtDUW+OSTD8st2sHe5YVzFxCaKzn/vhsfZsvr1vHNLALaFteJFLrdrDun7C5zlZJTTjnuRS96USk3yAmx3r7xAw44oFxey47nhQvnuiWundJMNpFyrP8ihstAlCUkNOsuNXE3OGlD6OOV9F223eMpQBamtE+YWq8MNi2NcgrhuOOOW3bkcRCs1IVlfv90uVL7TjWqqxjbe+b23Od9KMdAvHO5711tjb72ctUWQEGyG55iTxRa74YWZdj4rlVRgoRHGDSEsmucUPa+erwyv6YGNPBCA+Di9Aq43bbuzXBihTAfgVzP5ULrLKwF9x5+1EWbNlUfXV3ZujXhyARJJpQ/AQ1CTFoE9jELcGSC4wocvTj44IPf+MY34v/mN7/h9KCVsU7SSxlbtGjR61//+qc85Smc6frZz37GcQiQnIigAFPIG4tlSiw0bHo544wzrr766h/84AfwwmhcVsxTXODnzZsHkkr4hS984Ste8YpHHnnk61//OonSm5Wi1yPC1QYB4ODlMAlR/+Ef/iGf7/rv//7vaV2AhQ4cf2Inxvve9741a9agPDro9A6vcFMFQiR6crSDc2sf+MAH+vpyHLy86aZb+Sq8pR1iGsTTTjuNI2FHHHEEB+fA3HLbHXfddXd/fw8wh2jItRe/4uUI4STbr37+8zIHcjhF44/onHzyybqXhLeedKHhww8/7F5z71CeiDjz/PSnP/3000+/+eabyT7lHWdsijuGoGplfAXp/NKZZ5557rnn3nnnnT//+c9hl3kVRVNfMsliAOpG8o7Y0e28886DnkxpE6kEQoCRyTiIn/zkJz/jGc/gEPjFF1+8YcOGpjG2QlKZP+EJT0B5XgHOx2IcDIJM2b+pGgoyNRYvXvzqV78aFozPOW16BZSoVtHtUfhc4tod0sghrq0j2xp1I4j8JWmUEk7lcZEdNOkGppEtYvYeC7znPe9hL9mFP/gBFw3sPVrvO5ruXPvLi0ktJB9b8Iam2l/Egqd6pJLkHW9jL8mBIGx/rYoTQERNJYBv2v7qGGRTFiHVMqI2j7vY/iIh1f4+9thjtKo4NUOtlJcmkAXtb5dvf2+mg4+BRYBP+8sY4d577zv44IMWL1yESdesW7t589b+fnfdA/Jpf1/+8pfS/l569S9/8fOre/uS7dtKme4SZ2tPO/FE2t+lc+fT/u7Y5trf+x95ILa/Ztv9uf1l7Kn2t3dgIOO7rBQt3tzi+JAb+jEaoxeX5YCwG9pFFy0QLTDjFuCNY/xLNc4g6Pd///cPOuggvsr8y1/+kkZhWnFxCdHrXvc6jX/52jTNCq0SQ5tU46vGCF8Nhx6Bce985zsZhN5+++3f/va3aUBdrztodlOwSbD29wUveAHjdwYgX/ziF4laHQAjU1okBF8AkQIw8KE5RnPGv+vXr6cf2OoCrEaDSA7sjE8Z/jP0Y/jM+JfkK+GpVIQSxAuGxB57zBOInbHtf/zHf9x0800DcwbQjSDkoAyN5rOe9aytWzfwSDMK/q677rjzznvmzOmFbNu2UeaQP/CBdzLyuvzKqy+55BJ/K0fGTdP5qzHQAQcl7GQ3ztSQDjTTL3/5yw877DCOS8C+efNmKKWAUbYCKDa076985SuxwH333aepD+Jtz654Vflr/HvWWWcR+y9+8Qs6G7j27CiDBBJCKcUxLfDMZz6THIedzk8rVRvxCMEyjH8pPNz6ce211/LBcqKWuRRLI5cwkMFO1h9++OHkDl1NXpw77riDTFTZa8W4J+AxGmnk7hfefXKBcrt4wbzt20e2bNzyu9/97h//8dZf/vLWtcuT7PykwlCeu8irXG+X4d50l3GxLd4TsnCGdHjXu97F9NH555/Pu9OJyFYvZqqe70TUTtPQTLzlLW9hpuuf/umfmJYxOdRCz3nOc5iBJDmGbA/wFuxOzaUMkarmAeBN5B2k9qC6pjIBAw2hYKgYCaV5ojptWiGDxzFRCS9CLKUIgV6PusIJgco4yz4pQIw45riIiPueYIQADACxowN4tRrSyqKAXVHroih8HqU8LMBYFc3FJTyScZIgdihFrKaKICkAIwBBJI26FK6wzZIEosBBIyVJPg45hCrtgptmLsJ9ut3tUaSaWHBmIuQQKW0QifJtUe3eSSRDIyUF4JuTVvhKo/N9g8tcuwWFAASoIcuTBB5RFWmYXRIAeAxZDJb+KA8jRvBJ7yFFMPI6IMdy31gMQCbs3ni1a7AQIgkgSTgOyfiQ4YwRWMYEgEASzIwiQwEwBKEAvvGGgCkgIfhkIgpAj7XRX8oTF6KAAWQQE+LU8s5yHwCxkOEIQXmkiQaMMc4qgLZkmco8EckOYJrbwX8AjGKIklwAhs/t4N68mqry3eOKqyWQQ2jS5Tob2cywrOcSW+sCudeczpqmuLwBJl4x0TQvQ7Nqiyi8rQXIPsLxQ9eWY2YC7V0g3l2RKDmhECXEyluYLld6W7tGUa1pa1VrG4JU0NSrjymG+BgtEC0wexawCqiTKKZF3InARhpVYWFF1kgjDMoYmQGtiMGH9Dy2YbEgA9qI3ceCLMkAgttkOg2JOqDt7UmohEigLBbCjTbcaXqndNumVKGN8ht1iJhogWiB/dMC1BKuishUxkvuyqvXvamwcu3lP/xu0tuVjI9yDTZD+nYtSOdGU0XUIf20iDuUmSKrJdztqSI2nmpTBtgjz+3kvaMXXvDYez+w/XWvXXzntRvHK9UevwFLQlS1pgTao8TZYxtiCzLAuB53gFQ0HT6xCPStb32L+SlmB9h0qNuvWAljuqFx6wnpot1snEEjdZZkAMFNM52glD3bWEbEEEhzGHFt6C3IaeAbU+gFWFAj4GlrXmOoYUwgmCllGlcEogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBXbeAmwDrXKVjtuo1l92PoeuOGDBpk6WTAaTZH5pZF1uPfhSTvu53SZvvqTLemCuvlPUcfgLbiZ9T9Jv8KvfcgFTlbt9+PrkaJ4VpWovn+b1joDZcGP+IqGC39DsvtXpFHauyH1OPPn1nYFq9jmnn3kGX9asVv901KV9U5dL3Vuuu7I0nrjT29jE++win7SF0CVtEsJTea9+IdEEZqYgn4CNQ+sHDujP9uW65uV2DK0qFcbnzlmQ7+8rj4/4VKbXrdzNVeSN251bywqXV2zkrZKHmQJZ7xa83NeS9fXRBb3l+T2l+f0Jp9e78snQGMVg3G1pJ9N9NgO4TZb+TwXCB0avuQWqtXLi1h/d3qFMrpT15mShmUXJ+Qc8+fnPHxpYUl28eFWGNxHDdkE27o++syGfZ779iiMf+VUW6pPNyw5Yysbudasec69RmTX65NBDD129ejVEbID3h+eq4xxh9qfUiGvhwFy2hvPaSRP3zWNufKMk+L3yKKcCjTAIkMxrwWdmuUALCS5q/zEhlwSU8UcFAHCssXKEgDN4yx960G80d9vlKRi8dDknCXUcS8mXQ7eBPJPhZqBqaaxcqT7l7KcNnDJy1113rb3ppmTEHdUg4r6eXg6D+VLmENHt8xZgFwH3IrE94MMf/jBXOHHigrNVL33pS7kLaVppf/e73/3e976X4scp4te85jXcogU7wjmANKUcNi28+c1v/sIXvsB+Bg6kQf/d73636c6HRlGcGEH55z3veV/96lc5vazTLJ/+9Kc5E9JIHGJ4F4jOvb/VKmc4OTN2/PHHU/hPOOGEt7/97SHllDD3B33+85/nEigEcpvMm970JlLN+4hkYmnFTihBHEDiwC0fM+P0Gicxzz77bK7xWrlyLUd7jJHLTU455RSUBOCoDNFtHxrhe2bdc/pd8qvVV7/+dRyfZvsHxucek2uuuIKahkNOHITmYC14jtlgGQ76/va3v7377rv5eH2oG6dn/+7v/o684/gc541//etfEzUGLGXcfqQ2DiHQc/CbksP1H5QcIuIINHmnhCuNjRLAy1ErnnTSSSi/YMECjs6i4T//8z830qcwofBly5b98R//MWeAuQCLM8yf/OQnU8TtH7m+6rWvfS0HgNGES7g+9alPkR3oz4uAhvA25qBhsBgFhpvLuH4Llic96Un/+I//SL5MWfbaq7QbQkkCCvfkc+Mj449u2bp1dCzJzSHeoo97TZdrNcq+zzkyWk7K44Viec6cgeEKTYML8X709nQL1PfTOT0LLktdxwCn/gB9UFyhr5e/rr5ejqC55+h2rwVmr/3lHVf7C6CqrFXKWrW/VBGtWAy/E+2vKh/fX3W1K7rNePurI6BEQRWtiExhA2SThvb3rFT7Cxl2oGtBS+fOrHblFy1ZXEoym7Zsq7W/SeaVr3+D2t+Xveb1rv298ira33ymZ27/wqc/1bW/3V3ujPfcJ7r2t3zPfbH9tVzYP9vfKldquC7aUDK+bf2mtSPD25JR966NjW9zlpmfJFxrUBng9Ugyg1TaFUaB7k1xgXnvOyi6aIFogV2zgBtM+SHq3/7t377//e+nOeMaqZe85CXLly+flmDGIDb+5TalK6+8EnaNAQHU1tDYmcwQJlKGThr/0sTQXvzXf/1XqvEN6SUEDA5e6J///Ocz/mUo9Na3vpXLLD772c/S3FhcKQAuYRiuAtP6H3300VybdeKJJyKN2SQuRU2xtH+E5SMf+QiTBohiDIUBMSnCSYsS3oYdBaDE8m9729uGh0fPPvvcV7ziVQ89/EChq4fzINWqu9mBMS8jUy7AYrTFuIzLpkkyF2ANj46Pj5Xzvbl3vfd96Ezo773s5Ru3bL7uuhtIIk0/8XIrlsWOMty2iZIprV784hd/8IMfZPjJ7VEPPPAAA2TIZCX5JqER2LhxI+wf/ehH6WtheZLMDWKWd6mIjB28nI1/yTu4uEaN8S8GMcqmgLRCAqEwknZ6UCSffPw//+f/NGVpheTYCGWG+6+RySVojH83bdrUpvBIDsRKI68PpR0LkHauAPvMZz7DLWCYolV0ewh+aKzoBvjZMa48OapaGerKD+0oJV293KVx3x0P3ncXbXEhKWQr24rJnLmLDzigNDy0dc2qfJ+bPc7XRsl7SFKiGi0t0PTQU4qaKTLKMD6VDC+vQvVmhe++gsDbvFBKDkHQi5HaacoaIMXe+EhEjUjDMNtmsAG8dzhWBHh/W416jFgAVU0YkZIcJhwbKlEpxl1/lIlQFcvL+BjZFGAmkPuACHXrJuUyxKiR0gQkU21wwU4OmkpmfDDUS0ggRyxUUUAjhwUoAAiBEgxR4EMMnrEhOsAOBi7hAXCYl6hRD17IMDuhqrdBEsQIlMZUxGJHpukvPPTEK4cEiYUMbQlSKHiyQG2ZfAlBARiJCEClV60PQpBA+4XmACknCbDg4MVulBYYcaJEuEJhJ4i2EmXCEhIKLFe6KlUmlnr4K1VcS80CL6u+/som2qZSOfFfPsqx8sZNvqVKJq8JKIQQC7FjGVJBSs22PEpJzK60hDGa/uKCAPsgB/3BGCXKozZpkTO8ALhwKGBlDwlQogYYaDC4ehqQpSSgpxzRqQC4fAraa0RZiYU9FTWPCCRqWGCXEFhwJI1HbA4N+qODMgLjwyI5AhCLDpKgJCBKqkIGFzZRruGLESHgBc+Sj24kAZ8yQxSkBQ2JFCAVo1OVgsF9DllngWqhly5gsVt3hvppSv/pq9z4uCtD3IOW7c6WfCiWcAbMu7FwZtjBfoG2PM66J9smcplyha0Sfv9EBTq2Gvio21WkKd3i465YgNyXU0nAp+Dh60UgyIST+yCFN6Toy/40OMRihEYAokSPL1GhQAnhXaEkuOqSouj+OTSL6bAQn4P9lAp4iQIDnPX7RkymAU5IfbVdCjsJrparORNSR7i3W4l0gHcIUWiKmEdS1DRISLgBXEq806NkimBKf4pRxJT8ex2BmdiAVknAjtCITH4rSuFFD9wJMWTTko9MoxfcXhmjF1mKhccUeyN9iqDx0fQhqFFgin5X5LcRjg5hRFAasQEhQQhPS38YRS8JUwo3eqdQg7VDNQyWfF5kw0wJhCpNSbxzBFK+kySEyuwEfRsWCwIgFiUEGAdsmJ1LYIrLKvEUvvFR6TU1GgkMY3oKAG+A0bQCRIkv14oMvOkDPCWx0Uvg7NFPaU+ilsLSX/pEf1+1gLJbOd4+jany3J6YUCs/FkV7lmnJR6bRC24v3OhFlmLhMcXeSJ8iaHw0fQhqFJii3xX5bYSjQxgRlEZsQEgQwjunP0PFpzzlsD/6o5d+6EP/unld0js3KVcHC7kFjPYUI2JxFpHDNljbQkMALij3tPY31DCEqVeLpR0s9G5al/z933/985//q59e+E+33z7O6MXI2idc6RWxs1FrK1kQgNnWMXgWw1i8uwJM2V6YcOkvHeDiEadQJjv4zCb7rZliY6/eO97xDuHZHMn3S02CgGOPPfbUU09lUcd99rPBSb5LqncN4TUEgcSOz3MIN6U3gpBeSWhKD1LCoYdMUYi3E/r2lKEO7SlbxRXx0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QLRArvHArYcM93obBVpuow7Qc+CS+Oai5Z42PnHvnp2x2by7vhTl7/8Sztlzz33Sddcc+uCgxexFbi4vkS8w1W3R/+hzetZvqxyoU99IWwnVNpFFnYbs7l59dqHAArd7nAX+6RZhusNPsyz01FgGY6NHXzwwX/wB72cKBsbzbOP+fLf3P+rK+9lIyNrb+xfjjt5d8a8bl0xm+S0iuo2jpf65xxxzDHZ+QceddRRW3vmU7qG/CEHbWxl/21Ybt27VtuEUMNv2bJlwZyBc88992crHkEfrUlzCGHlypU8Uowr9a9YI8dFVypxSm3NmjVDoyPCQIZYyjOK8RawTx08DjzE7IYfGh6hjBHql+x9AuqXEaCtU6m+og3MGfK7l99HvHyA2fCISjkohYGG948jNAcudtaY29WFbtvvuj8pF6t5tyW9XCxOa6tAKqL4uBdZgDqHM5BsKnjuc5+rw7RUOxyFveWWWzpMhYrcs5/9bMotlSElh9uaLrvsMsobd0a0KZChfA5PQs+5HU4TcZzyggsuUOiU7DQTxMLdQ1yBxItJeeY2Iu6TQpNQfggTEQ4MRzVILGk/8sgjqXJ5f3nkAqyQuD0s9Thkyy4LYtywYQMnUXUZ05SaIxk1eJ2p8zl6ivIHHDAPmLSsWPHzMF50o4rAX7Fixdq1a7moC52pcAaHhseGRwcWLjrnnHNoiVRdHHfccddcdjnsmEKHXrgWCq7rrruOi664Z4q7PFatWkW8igI9yTuUx4E5+eSTOcKKKbAqp2rAyFYibvSJhfqHY2Cwsx0FUd/73vcgQyyMTY0ggarH8J/85CdjNPKC4sclYu2jk2TE4kSJHTAa1SYApahRw/YYjk7BpbNqWJhdNBxpxtquyZjK0RnAnhgc42MBNttwNpsLsKbi2yPCvfXcMTAuIMN61UXuRGW/LxTzi26f9qC/Bmto2yCt5ND9D7or7aaxfXuPSON+rgTvyJQWOO+889hOtmrlSlqBKYkjwYxbYKfbX2WuZTEV72y0v6TXomia9p1rf6lwdECX3uYstb9qDdtX49SBnbS/1O00EDRSbKSkdeD7o6hNw0ebNTY8PrBwfrr9vcpdgIXd1MjS/q5fu4H29+mx/W0oQ/tn+6uCl3S5LdlqfzP5uRSYpZzizeW2dm/ETpXiMl6TkbEVtL+rH17h2t+xOAHQUIAiIlpgFyxA150mjHEfgyONfxkCnHHGGZ1fgKX2kfaXqgxpvNo0B4x/GdBxpxKvcCfa2fiXtoZB0Pe//33aRyoHhCNQUTSVg/Lc/MuYkeaJYR2DIAby//7v/65rCJqyIBaZOHRDstpfBi8M35D21Kc+tSlXGySRMuhmjMn4ncaO0TQq0fISRRsuJYpmlEjRmYaVGzlIBcZ/6OH7x4vDuTLXuLhZYuRrJoEx5kMPPUQGYdh58+Zy/xUXYBHFE5/4RKwNJQ0uwzEuwAJJMvHtDghgbItZGk3KrdP0nbj9Cp8rpDlGoegQCFd7R4z0B9TQcwM+N0kx/iUKxdKUV2aBBQBf41+mDmBh9N2UJUR62TX5/GAcDK60A4SU7WF4IWCakfJGfjGS5VAJY3kuwOok4+BFApRMfZAKso984QYuzNg+3j0hlMKJG6+MM+Rn7oWEFDPuIp6Kv7xGGmb5BgHlZ90G0rVh1cprrhkrjtbuE9kTkhB1mNICqgHak3HV/vXXX3/PPfeQy3ojjJ6ybRgDLLQNoHq7DcEuBt1www0f//jHEUKDFYr61a9+deutt3J/H2P5aSkcCknB7auCFHEnjyiWchpMUYPprZT1aJHBg8SnDsc15iZBNF60IDhxqcaG1zRRKJJDdhQADxJ6JADwKDWoynDSEIyQokQmePlSTL6iAAaAAACj0daoEeQRgUgQrxQDBoNwYlcU+GBwENAg8lEKCcQHCX0qI6CnvlLUhOJEBq+iIL20KYJD38VRv3YKLosdWAWeGHHgwWBJujTqw8CFTM9du7wGAijxQdbUCyacQoNLAYihzLhOjXOmldIuJAaBEUe80BuNAVJA+QUsC0hbdAiTL4GKSL4SCAyjNIddApEPQE6Bl00E19Llo4cAXggsUgFhFG5puFBozHHTX7Er3/GhV4wkWVpBCTs6gFeMIa/YwctBIwcNEgjFyZ7gRUMQMHiTM0sACkgNellYhtSZAuBNAadezi1PFNwKZ5ZiDGa8dluPn2L2N/eTMRgfMkpClmufuTjMrYEn3JuFqKq/hgh2IsVcrGYA4CDDhdEBR7c7LaAswFeOkDdkoh6tPJCDcpRMyKRePftqi+OSQxCAfAEihj18FFIsoS88vmJBAeAUr+SE0lIw9HCBrDFOfpWczDrGUQajHlhw0kGwPZpAAaIJfVGKS5YJQzuB97sLsDoxSqSJFthXLWCViwFtUqqaBYJOiEOymaU3aaaP4krV0Y0JCekbQw0Tyjfk4wg06hNiDG7UkCCFym8kMEyKbPbo6w12u2k+tErpY3pGIFogWmB/tgA1Q1jPFzPJBz781NtvHv/h95J8bs7IIB/c4+bszdVSrfPtutd7sLOa1oA2ykLjhq1Uj/yb7KpVJpozY8PV/jnJD7+b/OGb5nzgw2e/9a2XdzNurjuNZ9oYxHQwoM466ddCAUI4zJdJDPWHkL6Oa/IbymwS3AEqNfj5h3/4B1aJmFxjxpYPbtilV9p+LXkMODVp9bKXvYwlGT7rkYonJbONGUNGS7IlKgwN4SkJQmKDpQa8HeoT218zXQSiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBfYoC5T9mt5QMpZkCkPZSlIeTnoXJCNbkuzCu6sLs3n3AdVs1X1VNcs/Bzt/VxzrJh2usOxKLCGvNPafa02yxTJbFxeMVPl6cabkdjlXMu7qjSX+dOdfnXFM5SlHjWTd9VLbsy7tD23ccMklv5zrd/MNe0GlfGX79mRtbduku4GIFUTt9uvKJyMdnTgLtesUZqsudkMxDhiMjo2682NVd3UR65ikIvg6T6cCRYeELr6ImyHNq/nGz2tfsjCTGUwyQ9yrkoyPX/+7ZLy4aH6+f0tpc9VdgYWttArMXWBZ9xddEws4s3AI3e1MzWQr7Jbmc9SV8WTegb2LFm1fvODIZz2zd9Fh24rF7ePu+9QVPl3NNvGM3ybbdpFdW3jXr1/vTlkgkxuvXG5UH7pvecZFUS6NjnGykLtn+PxwaWx8/rx5Q6Whx5Y/AGPB7+ceG3NX1XSR4e4+N3fWojfvvnrNGQ8KEkDC5vzx8Z5CYWykyAlpSpbbv15NOB/ICcCRkXGWd4mdMxLdvb2ZkfHeajZXqiRjLJojMeEL3E4hX0YyrnhQSEBQamp4CjBbkrePj1OCKwvmHvfsp/etXHnr2DjnaUY2b6O0JfkCOtQ28vo01jf4+lfUC4vevmEBLpyiIFGifvGLX7z//e/nEC/fyuJI2HRTx5FdDvFykgFG2CnVamUobLgppXGm68/+7M84MsGZ1osvvhhe3hGEtGdEMm8N7wLXdXECljOc0HMGjNekPaNCiU5K3nzzzbBzfJRTx8Cd8IY0HDx+8MEHOcjKudxLLrmEa6oU2j4VMgvG59gw7E972tM46cEuDg6wpQz2W+8sxiuvvFJwKdOd7+3jBOyPfvSjT3/60xw7YVuI07+74DYMVaucyP3KV75ijIgRjG0NCYBAjn/r6A55x50sYWh7mFg4PYhA1Jg/f/4Xv/hFYOor8GQffiO7slWHIsg+TsxiAfKO2o9bHjBaI0sKI+GuzaV78NBDHFfm5C3nt2+//fYU5ZSPHPfFShwgJ9WPPPII53gltqnmjdIortyKwjYbziJy9RWHwLWpqZFyD8O4AkBvigsPx+fO5/arFd1LwHRX3Ptb6WPjWVLOlTDFkjkH3DeeOfnEkzdddU1h3PVCk0xsBZwZ9nxHZU6B5NXgNCYFG4UrvkIt+QNmejN5dyi3qmfPPPW0Rx99FLJWb+6en+S9TsP9s/2lgFG3qJjhz0b7qzaiTXlQDd9J+0vTEPYK7rrrLvroqE1nvdDb1aT9pd+Sdc0fzbHa33zVNWq//e3l+JXEHXUOFYvt7/7W/pYyXUXq31K2q5Kv9iybs2RsXc9SisRQxne9qswAZMaKp4DpSgaXj+eOPOWk0m8f6Rl9ICw2EY4W2K8s0GGffFo2YdRDj51u0qWXXsrNwlygwFiMywqnJQTicPx71VVXoSrDUo1lOlH76quvfs973qMBKbBuh6QSaK8GkhlAoTPjX9oarlknRobSINszIlnCkQBA68bA8wlPeAJDyN/97nfteRtDuT+dS5O5SGjp0qUXXXQRAyIlpJEyxChq+p90U++4445nP/vZjPwY/N52222Frt4wCYTiuIxI7Jdffjk604Zy20Eu76bsGP9y6Rj5SPf1pptuEpnMzpgujFSjTmt/RQPLC1/4QkagiOI2WKYCQpb2MJFisTe84Q30Z7A/zT3CcZLclFeWT41/uTgM/K9//WusEaa9qQSZTgNVxr+MoLm2m7kXLN+Uvg2S/g8255AIrwDWI+28ESq34pK2oQTFjo9DVfKLfIcLUQylKUJuanTPdlqJoBM3WClv7x8Ynb9w65yjUXks243fWz2EbO3PbMOk/Yeu3drVtWzevLGbbxkYciVwzM06RLcXWIBM5NV4+ctfTgeeXro0ptACyAfg9cEpiHv0eAu4CEePVvIp0nwuglk+qmUYwfOCi0Y+SIbYxAIlV6oxp8S7CRIH8bJDD+W14k3hlWfNCCEoFrKHMK8/q0uICt9BCIiRKppajigQEo5HjJ030WBqMz2iGC+m4XlPqWqYKGOyTq2MBQGgNg4aNMdiKMOcpFIRks0I7KzjzwBiDZzqTDAYikemBH24yyyZgsdUvJBhXpEBEyofE+GQQJvYyKVslUx8WHBGBoB53c2KPpvCXBCj6QAljNgHKwEQKp/JZ7hQQJTAJtx4BQiPr1gsLt1rBl6ukaseUsVoxAsjjtj1iPJIMAVCdhghEzsAXJDRWEOjIDCwkyhCceFEOkGQ4cuRTQDYSpQAzgJV9wkWZCDNf8ylhCgsOT7uvuzCTThSxoc6UQihsAGAoeARCjs+MsGAF735IC0IAAK0RYJY9KjsUygYHKEwSrKJUjGTQGgwAqIUyqNiV1zAxgXgJNYvWiIIB5kRIIeIQBomBDx5LdeEh1j251EaSrh84+WRzMWHBp804kSDTAAohUQZAB6lg4JAqr9hAmcDUFx6kdGBSGlAeQzjUi7n8j25fG44n+npzpe6uXEsN+QmnnE9+sGHEx85o9XR/Ji7QamHPRLUUeURRQSsAs/FZRQ9HjGOnLtmzTY5NM8KyKObSQuQQRgfX072F2wvCEgrDwQRPUFwgVR9wmOZVfH6+wVBLUeDoRAY8LCrLEFsyVBtTplxSF8lEqQY4YJeCuBLiBilifkATguvHj6U0IsFGGnUDsZuoYjK5H2j74XCaE6xSAJIE44oHh1jkDoR4ysIH0Z8nAV1CNRq2w6p9wEys5EBbRLlLFrP4zZkFmTEnm/qzBA97J3Td04psdLNImr6KGSKPsViNCFgNJ1rJRZjDKU1whJrfiNBCiNKIZtGIaQFGb1hUgJTj0ZmQIog9Sj55qdCGx87FNvI2DlGDX+H9BCrZuxQMSMzoH1EIVkIp7gsCCDUn1ovRZl6pBqdkiZkET2xyIVBTeE64fToEQVDU4FCms4AVu+LZUpGIzCgTUQhTQi3YglpQrgNPWRyrWhCPJThY4T3PQtYFhvQJo1WcjohRo7IxNUJi9GIpY0mCpqu5FB+KNzwIRLY8IooFdr4uBP0YjHGRpkhRmqYHwY1hUWpoKZRCGlBRm+YVmKFZybiOS9OmOF6//v/rLgdHIM9tgjQEXcflTUhVJsGKwr5TYWHSOMKkTMLh+3XlJIdcdXvcUzXizRsoBi+sCOXcpP827/925e++vZnP/vyX/14q+Yp3CDEt49tEhUGhXBKMQsCCPW3dipFb4+7of1VXJZYHtlk+ZznPIdZXSZJKTA/+9nPQn2YCdIkDgmhzHz0ox/98z//8//5n//5p3/6J8jU21G6VIqUdnzSQigAzgQaIOSUBhG9SUAmmA55IUO+WOCaMi4IcHDJmaptACjbhMagaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogWiBaIFogZm3QO/cZYcdNpblaqft/fMX4Fe753GwZMPMx9Riv93kiKa7XMJyDAKm2LPio9CuD9v7MTla98RSFA6g0FVgJzcfqD/ooIMGc275Zjjr9monPf0c9bn6ulu4h4hLqUCUMg5/z0ObZ+/2K+Rzswm76jkYwJZu9A/XntokB8ZpuZrYjPuAsz5vM56Mby+xJOw+gcveT74YxK1O05K5/xJTkLjLiQ2mJe516jn+lFM45bJxoI+zaqu3ulty2H9uRV1l2B5bGY3DMIObNnOjBJRWmDmFqPMkLLxyIkLHD9xFaaOjLGsuPfBA3ui169dzPGbu3DmcNIOMtxsWNv5ybw5nVABAUvKRv3XzZvcJcY/0y7NuKzlnBdnCq1eDM2aIRQGWesEAO+UnbTVvoj70OAIgRnOcBPItJZaVUeCG31w+uno1q7ZQOKNFt69bgAqWYkAp+tznPsfxxWOOOeYnP/kJ56Mow50nnUJ1/vnnUw6POuqoSy+9lP0JKmZTSiBqaHh3fvrTn775zW9+0Yte9Mtf/pKruEBSRBEogjZyIKMMc3HSa1/72te//vUclOUoMq8DyDZcCjLh1OpvfetbX/Oa13Bi6oILLpiSMSRACHZ729ve9rrXvY43FztwExPbVKgB2tuQ1CGH9ov37k/+5E/e+c53Llo05zvf+c7996/oQPcJFahYOOr28Y9/nJtuOL7LUWRJhqJVLqTw//3tb5OK4447jqO85B1VFo8pmon46pCsRzWF8T/84Q+Td9dee+1ll10m3inZEYOeZNO99977yU9+khu4OGH4wx/+sBNGJQ1e4kLb//f//t/pp5/OI0W3rl2nvxzQgv2Vr3wlO2c4uI4xOSmETOrbKTVR/pLjaM55wiuvvJLiR80sy0zJ3qmKezBdmEaleg9Wdj9STe8g1SBncSmWr3rVq3QItvECLBlFLX1PkuUV1vHX/chYj2tSd6X95e2zqmaW2l9s0/693un2NxS7N7a/rurLuK25je0vDZtreusHUxvLl+MNXGx/97f2l7fG8p9eEE5FovYxYF88aoXEV82pAmO8EYgW2D0WsIZm90TXNJbZ0IFuP2J5Af/lX/6FZoiBG4Mgxr/TusSH15P2lwEIdygz/uXSJWCSoCmapmkRUiliHMGlz29605te8pKXcEkxu/qpHxTUhldBan+5OInxL+wM39CEmaLO9Ud5a3/pJTIEmzLSkAA9bfzLlf3//u//zm0vpIgpBSRjB5kiZAlh9GSk/JGPfAT9uUKZ+7O4j8ZYOOcREocwwnEM1spJ9c477/zSl77E6BvlsUBIJlj1LbkMAFeK4Jvf/CYTccxdYHwmlhkSmgIpyvBRGcQUH1x/9Vd/9eIXv/jGG29k/AuNVy0dS8grGH2IiLtsGP8+9alPZb6Xm7w6zDuikJJo+7GPfQzr0dWnGIDvsOSgA5S6o5MCzyQMA3m6o7JSeyGyIT4j5a997WtwYViSz/iXGU56RCJoTPKehQla4VAxXlseSRGp6666yxSyvVkuHQHZ3iyhkAjvCRZ4YPly7md/3/vexxTNlPqQ77yAvFbksjJaPvNC3KLFK8ZV0RTvpnIo8BR71qqQQLGBxl4B3g5uY2KGkzlG6joWDsA0CqEqIzourmIA/u1vf9vYBTDQuOeuu1CAaUYaqUb2MEZgRLnvEORyzEaGc1lMkTHRymQjc4aHH354UzkoQB1Ofcht/qiq16Ep5U4jSRSvFeyaakNDYJCoDYYYqdZolWidgZkgFXEqOmhQjxlUFFZOiYCmB0bkYG0cMOwIxykWfMSC1/oLciSfOhBYjlAyFHZoIBav5ENMFMinqKAAsQNAQ80JASyEAuCDlGT5YjdfCqA/ZEgzPDBJJl5EoQwwsackEEQjS0kgdnQgLggoQsjEbkgAABOqbfKRjFhsDhfEwnMTtxKr9ELAo4RAYHJQFa5xvqySzcoCY12k3jknpzKMR2qgr1aH+Y8OyCmW3AU3paq7AI4FK/4rOQghCnjJPpSHFxixxC41nMwGRxA06I8yCFc4eYcoHgkiUQi05KNGKMMekQM98RIponBSTGWeUJySYOxqHCEjCEb81MsoOSQWB2yMIaBQlXlgVEUB6YA+xK4clAQpgI80fKIGjxqwQywzgkcIQVgAMvAoBgyGIEVtQKjJzMJo1SgwFa8Si4+DHkfC/a9YJySAh1dWylRcXueredLLB8DgrfpL/B1QrfIisI5a8TP2maq7KotvCyHTHSOuO4cMFjR5rIdM/EpaSDYRFqEOLIBVzZFTWJISSMaFJlWJJXcM6Vh8TQWlc0yCeCcJkIEzYgCDEW6wtIMPYhhxQPjgQeJ7PufxiI8CYgFOqk5VETtGX2vh48ATC5RSGB8ksoxdQlSYOMDMI2+ppKGJkl+j8QJVqp2QwDkdvDN8LSLfC/V8riIVEPBNAe53F2CZHQ1oZSFMCY3I5LeiFF70wJ0QQzYt+cg0esHtlTF6kaVYeEyxN9KnCBofTR+CGgWm6HdFfhvh6BBGBKURGxAShPC09IdR9JIwpXCjdwo1WDtUw2DJVxenPZcoedshpgYxCZ0AsNA56IQSGohVi6Xqsqbs6GNkBjSlFDKkb59epEEMF4DsAwwLj5gijEKPhlS/gV5OSNMGhl6S29BYkHKhveYihgZA9CHGRKUA0qVsBRCvYsFHCC5Fr0fwajl4FH1TMiOGHvl6nD16VCLvWuksVfFFY/pIq+jvexagpClRBrRKI2XGiuWUxAgRPUAnxEYv4ilZIDB9BLdSW3ijDx+NpTG6RnojbgWYPhA0Ckxx7Yr8NsLRIYwISiM2ICQI4c71h5Jqn44ylcknPvW8W+74zk++nwwM5N1GrGx3Nq9ZFT+5UK9JFLvTxrsw3law9FH70p5LlFRZEM96+8snr4cHyWHUVrzMGtWTQOsArsr00EUXrHrHn1zxd5/6/Wt+/XNmByBQi9kmIRBYfduGDFGQSRrAntb+ohLKh0oyCcuOwyOOOILVjl/96ld87JRQHJSsxNAZ4KgAH3I59thj3/KWt/BF1i9/+cuf+tSnZDQKmPIUMgAeYZRxYAd25q47J7TuoAEtSoPrgelfCLAnvuQrWFxp0snP0OA6pBcxURAXuk2WNPEkgaJRGifCIhQt0MICtdq2IbTVhMLkLri6nX7mpUFCREQLRAtEC0QLRAtEC0QLRAtEC0QLRAtEC0QL7AkWqM2/BKowmuW2hdrBDI93I9vJruX8y2SyffKJKaY2E1D7ZJJjoqIFogWiBaIFdtECmYQD6ZVSxrWomaeddPrLX77O3bpQ3ZQ9fB5fIB+ZM/+EE+4fGSF0XtGti+UzK12MaqS1VoLv/vxtNfIdBU7raO6LqXUYIOta8voiiw+a8Ha6EUd7OgnqFeBPeQ0PxMTVV+YTr444l8nq+qoRvwhVSdzFK70Vl+oDMxs3j2/uuvfeJ+Ry430Flnu25XrAj1f7Tjg29/xjz3HG8js9tLjzhd+W/+f7P5hI0kxAklzxB7GOP/qY6ngxX8hgR7KhWixlqmV277IukK8mrBqAZTGKz8vziN/YmyLh/JHrZAI5pKzAz2WG2bDCZku6E/nCCiLNdW1F/dPOOqRvwdDwqNvP3dVz3K9/fe+tN3sJyUCS7JiJ9O0tMrSqIj+97ZU0UAwmp0TlsT8pjyblnp5lhx589JEckDvgyGPnLFo03N+9sVIZcvkGJxllvG6/Wb5aBKGlw8kya08snrKkyOI4kUImSlbwQSJrycJFrMBefvnlFJKzz3jq1VdfjbBDDjp4fW79+lUruyrlZ5751AtWPNqbyy4dmLN+ZPjIQ5bBO14pD8ybe8ghhzz44INnnHTiTTfd1NOV71u4gL3dHJjBPfH4Ezkpd+Qhh3FQ4b577jlq6UE7+gf4Jjw69VUzvUm2SymmdMlItduwHJYt4051pdIXypL3x/2xk23sFu/vnXv0XM6gnOy2ko89/Lubtt9/f1LUuhakJEVlOYehJw8H6rG6CHbJyYy7JCIyT98CHDDgtIm7bW3r1q9+9auc+eEqH8rYdPd+sMeAs69UXOFqe8Nb2UQ/8p1Czjv1gx/8QPcfuTeqVt6a0KdQRMcZWg69cEqNA5xcKsfpEVXaKcqmj9KQxF5zzTWc3tTpU73XTembIrHemjVr2Gih07yYFP3RoX0qXCJ9MmnX0JlrmDiGw3bOAw9cgBpNI2qKxPJofsu11+X7+yBAppJPKnBNWVL4fKHwnW98gx2xbBCBXTs3nG4t2FMyKTBcvMXNZTDCRVZKfiqWFJcesRWJveKKK8g+2Nm+y6k8TgI3JQ6RCJeeAHfdddcNN9xAKJuFOs96SYOdGu+LX/wimUjhDyU4C7R1WJ79NmvXrv3v//5vCBGFKajMAdry7SmB1UyWv4r/25brdmplluCV8yvwK12ux9WdccV4JNddyXVzl6SjqbrDinKVqnqYvrms6phKrfmpk0z6nbHWYpLU+JC2gErg6PBYtbf6v//7v9wpwLWtEOkCrIrrq9ZgcdJvwd11623c69n51Qnijf6uWGDPb38pS01rQpWxXWx/ZTpEhe2v+h5qR6jPecSHBtdUk1T7Sx2OWA6maldtqxYBUZJGLVdvfyvj49UDD1zk298pKn+ioF9dHueQ28hdrv29sdBPFepawK6cu1SXM8KVknrejlYplU9o+IiqtL+u4aTprLe/IUF7OLa/e1/7S7mgDOQ4p1uuZHP8rfdjsfGca1vzpaWUolx1AXB3wo0kmR1ZpkuYLfGhycQRJ9pT/1K4cWsl48ezvm5XgclXp3d+KrbOslv09x8L8PoweKQK5TL9T3ziE1yjAIbxL+3atIzAWIDhM20Nu+uRQLMOe4dCuMMCxgsvvPDHP/4xAO9+qzYrVIlYeMT37Ub+Mu94JFIuNOcSBBGrjQsZDZYEHmkouS2FW5AwhVpPkG0YTQIAQlLtL+0pvBoFiyCkT8FQYnyu0OICbn9hQWXZIUcy36UGkzxoaIZdS8oclJ/OqjWjd91+91233e1IM0lvv2v9zYVZIBifSC3tUDLq/MY3vkEuAGMK7Km0yzdRjQAEcC1evJjpPsawPJJxOAkPo2jkFQYrcekYExc4YbhEjN5IK3rDI1zjX3pHXLimb5yjgJBGNiVA3nFlGBfuyDjog/5wIWpK/YmargujZu7AggVGCg8vQofsU+o2ewR9unwnk4xnKqWkwN/mvFt32Jpbin9g4uYfxqpu1r1cdbehLSxsS7o3J4yU3Uvn1la8eaq5qmtzqxn/sgOl+nlJuv1VnaK+4PTqFy88etOyAPfBQM+NVBTygw8+uD0vFSZNAPdP6arokJg5+e9+97vM6lPguV++8b0AQ1HBcb0Ur0N4CJ0gndHmrBaXcHFKi+qCViaUH8LUhLhQgkIZkvBuczcidTtf7whZDA4VYz6NIb8aMtQ2Gm4E++xnP8t98WCQmaouVAkgh2qQ268ggDcQO2nYYjKnCyAQW6keJiLm36g3eJSheCT5TErziIMYHQgN1Ki1TSJTm4VAU0OiwJOnSCAKeOWgESVInCRYk0coLFS/VGKEIsHYCUKC2AFQCV5CIYadORNqQnglEJ/KUGqjDM54kWAOMhKLg9cPOWs3Zkq4hUqCcQlQRP7qnzElBC6Sho9KukBKkVrsMAoDPZGiPI0XLBBLJiXfR+2u4uJuKP64JaxS4fotrSaLnYyjlXQW4B6tri7mbHO0mYODW52QinvjyEkfqWsIuCsMVUtlV6JGi+7isLGiU5K1snKmykVa43maSydf7Q4+agzuGMT40pYgc6Y/oShPa0UBBhaBi2J0lKSRKOQo4SkhPBoGC9DWox4AeedT5C47g1FIBAIjHBYVGx4BwONEA6+pB0DsiEINQn1UtZtrCIIRdhSDV8bHh0YFQApT8MgO2AmCGGUkBF9qAICXBFiklYJAIgoHL7ASArFC8Xe/QwfTUNryiKODSCPI2nklmynxUadcpph1bWg1KeCrZaRfS2JZSU8yhVy15JZGku2kvFLtcTbJzHP0rpNSzRVYHM+Nl/J+MZd8p9V29nIrlRB605GFGKtuAV5IFwma1DHul9F4+Dgl7CNJC5mSa58nwCzmVIbDgqrkgwcJmT06lnKR+oLsp4i7IP5cl97vPnU55VC82M7iPuMQ4h4D5yWU2FND9UQojkBP7koBJQWxxAtSQQCmhjBOjbrjJSJUZRgcxFQXjj0oNsIrGWgNjVebfT3uBq6a77A1V0uwf5qA6wIlTaQYwQHOIM5JKQV16Gs+qEPifYGMJoH2AGMBtE+PaPCpplUgOqGHRtkQ5lNTRsmn5FEdU2ja00NGUUNnuKBUpd9UrJCkUfQ8IhwHo9E3JsfkQ9OJPpChA/pMV3+Up+1J9SlNsRCQfNQmCrhwYahgJYogaERP0pTYpvkrIfLNnh3qQ1ySOS369vqHKbL0YhySgwtDQ1jpRTIphSsMmhKmGyr2NvIRAg1OfVYiaiwwjRFBxlgC/E7Q++S2TC8GoUyigzad0KMlFpA44go1QWceve6uDwQ9MEB7+YSKBXr6VUSkx1ByI2z5ZeyNNMJAACB6JE9ZflAAGrgob/T7GQoC48CTFiQ0jQj5UOJDIPqmZEIqgcqvWaWn8uRFS2VTqBiJ4lFqow8ZHYZGeB+zAEVar23T+jlMLGUGGvzY/oZmMVj1CW96J+01ZGrveNmnrH8UheRjf3hVXVjUBui9VgUiel7nGW9/KQaqD1/4wheeeupJr3vd36MAE3blopuJ7u51V6SjJmpIH6v9XKjvorTS3xICYOmlfJqEkMBgpZe4dkP7m8u6xk6vAMlBsd4et/5EngBXqm74wewSFvjXf73yggv+5nnPc7Nymj2EDF7ZxJQ3ALzqf4D26cUge2z7S/LRXz5KMnn6lKc8hY2qzLEyDcq3CD75yU+CJNXQMN156qmnYis6NpiIbwXwWS2mQbED5QQCygCvCekVvRaoyGXsI6RMajaERTAE5BFiwQCrhBhZCoBANifjRIkPY0p4yCUyej4ATB4hoT098lGbRCEktr+hJSMcLbA7LaDaYHfGGOOKFogWiBaIFogWiBaIFogWiBaIFogWmCULxAFOG8NOPgnfhnAfD4qFZB/P4Ji8aIFogWiB2bRAdds2Vkx6utyqVl+OAxqVQrnAyuBsxtlOdpv1mqZsom++e6MpQwdINoUvXLhwvLhVGy1YwCoX3DbZ4bFhThFks7183RqbgcFc+FoL60DwNEje+973Ql3KjLBZJT/PfXZ4vDTMWlup7LZ30/RPQ1ZnpFgSx/eDkX3ooYdy7qJYnstFG4NDi2688ROdydiXqWTz0PKYq0UfbDzJze1acsAxuJNPoixV++ZSVFg0xHV19eB3XmKJQkuiRId9TQEewevcF3i+nc7yvTLAALb0s7KptX4AjtlwOIODLqzqnnzyyVyShTQWQFljpczjoMGxrMvhwKOPPpqCzelENGdzPLfD8DqQFooiJ9/wpYDXrbZfDlhrt+wdbuoI1yujUGJHJnIOO+wwXqgl5dy9c+duXLV6bN26ZJzTBW5TcpLvYhtBjX7ylbhNo+gQaWY0emFkZENGYJYswIYBcpwXAfnYnJLJ7gLquulGR/mhVkRaY4a2F0WkFHVKoyu09S0HsFDZSyX8pjKthHDgh6h5AaV8K3rwbRxbIDACmyVoc1ED4U0jbSoBLozGS8rrTMOkqgCVptV8+/2o7uD0hg1b2EfWNKI2yMJAbTso8VLbtKFsDGIziTv0tX27Em6GbaQMMTIRjOQd1oOXXRmUJVLdufW080RmRw425xRih5YXmXzJCctPqGobGHaKLtUs5S3cYtqGxYJUXWsLDUhSbYW2wySYqL0UsEYEy9Nd8fm+lyZl31GbXCAx+IUe11vGcUOcCmfZoQn11UvYdfVHd1xvYRY6tNIh+k0tsMe2v/ZqN1U7RE7Z/lIZ+lLn2nEBITuwWhAAGgKqUFdG6116KhbqZ+pYaEBCo6pVsOQQCg3tLy047a/rOdfPqjlBnkuUbXzf/rprOzZs2MTR2jaUYRDC0QuVegbc9kWios9PZ0A0Hdqw1v76myNgdFKUzLaaQ0a8sf3dN9pfZbp8ShTFXrA7AlovEhSnpj2ckNJRRxctEC3QsQWYeOHNwmnsFr6JyFBzM6UwRj0a/zISqb2PU/L49xr5Gv/CKCe+DuOFmFEnUWvYDgwjMKIsfvSZUhoEtLzUpSQEv2k9I4GN0oiLllftr8ZQsCOQxpR2WWMiU8YAqaR4oXdzDlmuYi6sWvlId88chWL+kF6GrQdZSO3aofkL5m3dss1HVzu5QJ6KyHIEDLA9KpSmHy6SwDCQL4sHcqcAkcNgH51JKVqZYiYfwJBNZZFw8o6oAWjKsSR5157F5ECGfBjhwoEHgzOCTgAi5cMARMqMIoVnWoN3ujoknAMpildRW9o7if1xpnH9rHaOtOAoG41JAw8n/nQN3i6+GDbTFliydClVEDPtXD5FPur1bxUJrxLXGhX8ipJolL/cSccbevfdd29gPry16x8YYA6NcO5Dwg/LxrJDD6WSZ+p+7erVD3R1UcE2iiEKWFicYB6NabSQHWLqKAZHLHiRBJLTyK4YDU9atm7eLJWkj4LmL1yIJg888MC6NWuobY0+BLieBxYUoFbBD4N2HZZAfAFkCnUOo2DNAWIEYJDURfg84htxKnbw8FIDY65UEEJIJqFiD0MVLz5BihpiCNRUAYMkXrE3bS8glkqQoScKsLIjWAKR7MpS4ERvahBCdFoPIkYAaQUeGkSBxAGAUZDxCgBP1ChJDQygEkVdJMlKi3gb2cGIPZznR3Mkw0iaAIhdQiDGWex6xCyE5jPOJ3Z0EE217Hi5AMs/unu1iApihshi9L4b71Nx4kuNUsWlGovJRyDKIFkyQaYceBihkf4mBzJ4MRpO7AQZr0kDCQGPWvhgTQ398cHARShpR7isB7sY8a2qV4xSHl5FobgUteSIMVRAEqCEAFVVSEASHb54wStq2OUkAVjKKHZYiBoWhfKqSh8wEIhGLCG74KY+LE3xHSLDuEgLuuHLSQJFlEeSjAMAabAAMHw3yqLjcwuoBB3E8JI6AJKZ8Sd6uQkJSkeAq7h7YBxQF0vPyEnzxRakmU7Cp9lFMo2aAwhXvM2D9yFsmMWNyVL5wRQAolTuAOsxZAFjRRd8xTE5LvfDBVh1p0yneEAjnBUDxyXqQK6rUOrtheiDQCfBCoPhkZH1KxfC1GOuvWsmDUbxisBxBYuVvqDVvjAFAaJMjh7BwCIJyORRsEUqwB7FziNcGEqOm+Km5Zp0MqbFv9cR047iUJtqAmemTyUEm4KBEoCKGDjMyxQxj0ZP1axQVxbqtYgBISMCVSXhowkSpEyokhjxFTuUiktlLpSWgsVC4wdAKlLKpx7Fiw6yDD5wSmDqEW1RBkrJn5IedskndWrJmupgsZh8AKKAC2ehBhAKrCBg6UOqgfGVcUYMgCjzUQAa8guk8CFlIwy9yDz51O+Z6DskllYQoxKuPZc3hqsHobTy1qhwU4wkt5dvjJARC84wbQCj3An6KfVRhYiPcPmYl4LUqA/GQRo+ZDhKHY8QN1IaRrEjWQC+BbUBjHhKehF0Xn5CyaQRR3La5zXCKe0q8CSER1jaKE8QZDgBirENPdJwO0FPs4RW8LYSLuN4XWo1bSvKiN8HLEAVjSMhvJi4VgVDJQ1KAJXq9q+w0Vt9GBZplbGU9RAY29+UTewRe6p9B8B67uVv9grL7AoCJr8gpqYCxlfGmUwAZYR87A8Ng+qmJUGSua4Yx0CRWYzCnOJ7Pnj8xT8oXHoJhScZGiqyxTTfxTesiJ/qnW/eukJFLPLBstKAHEUXqtEUhgyHSjjBTclAIhmnNFp5a0Wcwktye/nGAlmSGS9XRvJ8CZrLlxOSyicW3IoHZoEsU3WtG5MRzFNcdsnoRd/t/sP3jF5+fXHrmq1cgc8+YyfK39nMpwpNrGP0eSojy0QurtaOlpRAfNXnsJDXwCkOCQQp+1AANMiXtilieyRqnKkBbEFtAM9UK5koRnFFwllnnXXmmWfCRejD3rF0QXkQAdum+QQrn6NkepQvqLhr1AcHaV6ZM2VpCnZgVIXY57DzVDgRCJ7HlD5mAXEpmbA3JTZeyREZSB6V5PYJlzLQK+ECTGYKkD4oz/uFko2aG70iVZJJvuEj0MIC7XqSk1k66jZPZtmbnvQyYA4AkqpHV03UHa8xqxwQMH6e/Obs45apG8D98n61eftCyghHC0QLRAtEC0zXAtZxSnWNqHjlJBA4JODR+quNMe5F9TY9+75c0lNgaTMZqiRLupO1YwnL3dU+FmuTipu9buqWeOyGpmGPF5KOK46sQQHlnTI3zDjpFtIIDvGPl/4x3miBaIFogf3HAtTM1NikV9X1jCQ8rM9nRKAJUTvSpt03ylaARrIsxGryqzcpVBKmTaq1dd80W+2LaeG4OE0y1bPmM5mimYpwTwxX8z17GbonpjnqFC0QLRAtEC2wCxaoZFwDW2b9KzOe91s5D9q4/cAHVx7U5y692jB0F0dxivns9mu3n9PTw/INS4Ss72QLC1vE2TDnzJ5QzgD7LRBsEqEPwwZ6fOZr1eASutDTsIw1d8vYlrm9yfaRcCK7k16EzXsTWcl/UhWxd97/4D3PfifniIaGN7NGqT1I+uqOuhVZY7PECFPvAtCqVvPuBFR5zoHZarbSk2R6J6+a5ZNtW8U8f8jtXaeH4nbAbx96yD8krBiybih4F30+M4OESuI2Sef63NmwSmauWxLwX6DN8+1iNu2S9jKXCtQmI1yvIDPGdEwl768UybLR2elPboyTxvqUhft4sXf5HDKdK7lMo1h433vs/93m+14bB0cryar3fvCdJ556GQf2SN3mzZ0e0p4Qt8dAzkR1Z90nkILD0BpV1a3wVjM5lhzYuO5891zfL51ze2bdpUyY2AEslhc4TN9/9Knc6LTksMNYz93SW9jGPiJEc1q+xCc5+WIwy7I5lzOZxOWjc4Hp/bO8jC/byOWl4CWis5qp8nHX0sjIkLviSiXffT7Yb+RPEtYlPWN2ZISsJ67xQj6/eOHCTatW5Cvl5XfcdtSyg+65775sNV8cHeZMzPg4m+9LQ0ODd999R7ZaZj28t+CO9BfL4w8/uJwLsI464tDFC+ddf/31fT1dC+YdePfdmzduXEcUIyM7CoV5sOeSCozd/j0qUJ9MpMMNXupOsAqZg9lbWw+q7SsYq2SKo5U5T3jCacccs+aRFXxFaZAzP48+mozs4AiOuwwLy9NXd5YnTi7G8jDxVt1WdZw23aqMT+Qx4RZTfdkolcupx4A8grNrAd4OtgrgU7ypWKjo7JXsMGLyjkqJRkqZ2HlWEpERUxdadODDR8O3AUxnA9oQp4LQgehIPrw4veYApluKPnyEjKYZSrYiIIR3XzMGnew3gBdRigWYb97z8fhCoddVSR24gn+pnAz+V9z5EFIwsnUbW3w64J4g2b5xEw+9bMSROjWx/meCqgmEzjSIClAqqLT0qKQ14ZmMEhc4r3yn25lCGTASF4VWQjqM1ySQZdrmBKN48acse4pUQoDlvIAJIRbFHguUMrSB48Ws+0syPuMyrrcxt7gAfywZz1Vy5WoPvZlytSupzh0bewx8NXGtm+9aJpygBMhUDqb9qOYfo9xVfc82KR0ETTa7GT90vh9UQ6gdCkMjPCMWoDQiR76KtB55VfWu1prqhsh4/anEXF82ut1ogZ1uf5XFaAow4+0vrRjtIMLVnLWxh8qYlTQDjMUwBkhne0R/qlz61bbn0HhDwDQxRkKBtQlQPu2vmYUgXCghBStU9MCc+aXHzhbZKdvfST1pYoDZt7+ZanZo21Am2+VNh/Vc44T8SkbvlOq8Sf0cXso1Kx7L9zIc5LWFjPFCbV5d1G7Q3iIV4OvvdO19j+2vbNXKYqkC8Lg8urKRGe0qZHOMABM25XKQp5ItuRLCQBbnT/1SEB6h5Gzr2syr0ZPtSbLlUjnLhue+HOfeEzgZeg6xOJNxsxG4kg4PV13lX5tjYYzmypMLneya1/BhqZ5MH5/2dwvsCS/ULOmAWAZrvGWMgq3tmFZ+w8X8qsa/wHKIxU0pBxqNuKkWYKSNMy4DEEJQKCoMouGj6aQXIbIwSCwpXpMDHmKFagCl1kSrsU25jN7iAiMHFwBTdLSGZT5ensmOFf2NM5Pmfixy6i2fIqbVGED4mq9ULo0NF5NcvtDL2Zkhp4Cfo9a8nBtceG4AN+PqsJ6ZUyM5lxA39G44Iy7dIHa6BS0pj1IFpDWaustJlE2TLxb5IgOG3VphHslHEx7SN4XJbrIPX7nflKY9Ej0puuoaSefOY4eSfiN3YMHOHViKqHN2DI4jUrmQvXMh7VM3S6G19rGSDJRL2STPX8H3uRaXNhFjJePGSdnywWTK+vw84KNosivHDY7dnu1OmGhgpoo/NoYUB0t8F4B2E8pRV/QpZ84Dwf9s1c2kMaXqMc6jL4dfYnZ6wqktdrz0ZPFruk0QpN/9ICSCrSzgrNrb37NjB7P+5I/7mojPMuzvsoD8EicZROnV6wMmlyN74aU+ceEEAQwObqF65DVHIEiVbXwccvAdqbszaCjXRQ7D4g4lAdTjKm/atB4CLmnJ513WQ+BDHRdOEvz0J0sR7uIYrvfxeBcqSmi2b99aKOSY7Q95HUXdURMAQomjTsj5ix25z4vHOolbm4Cdiq67193Ya/gQgEX2MSvVQ2uNRf1x2r+K0cQqITS+zKDS+EocdoYAJHPRTM3xqJbFIhMXPngIYCT7WPIzAh5JIBUaVSsOaXJGIIAgrU4SSjVIHc6qJWajPgRPqKJOcVkSIFAQ0QkJF4NHNOcRmTziUBKMKKU5QcKQQChhRxTxmpKwcKkWvCggydAQGmoi+aq0STsS6H5AIFvBBS9IyKSM5EgCsLSSepRtlTcpwNVMEj4+jhFqQqQ57B5AEwbdlWImPzRUTarjpXLtoxF1DZ2qbrWM9bKcu86MRyLNVovZSqmrQhVXrlbm5Gm/MwnXbXGTgVMp414K3huKKDqUSuNVdy27qse6YP8b6k8yLQw8ZpQFwBM7GBwANLIhUfBIFDxCo/caa/MIpfIUPFljEky+AMgAoLS2T7zCIxyrEkoW44OUg8VsCA1KQgZAqYMgJZlQgmAXpUkwMghwEChqREmOdMYnFF4cvMRLqMUuIbvum9oSZfJRiVygOwoAElgEehRmPJvJd+Ur+YQ/Kq1KjnGte39HvK/5yfGyM0tvqVqsFLMJ08vd5WwvzXC1a54zSOK6u64HiJ+ju7ljLMtqa56l3bFyif0VlMsC3UTXcDt55YoXBzVFkRaeOt8tFLu2AGEVniifOTcCHvE7B7So66gdCZSQeZ+iRLfU/bnz1C7MS5AoPUYfC1ACVTgFAIPESi7vfJmklAKAxFdBpfkls9x6OvjaH1wsvzNhkqnQVcuoGINx9SGMvAKSgO8AP4tSLZecHP3BTzn0+wdgkRqO0ketXEOQVBVe+iAZJ+Uhw0EjgHEOENIo205OXX8Vdd5tyJx6vIPUAPWtrVR/4HgfEOQABNbXzpzq3kkreIlXMAANIX+E+5PyNcpOfmorc52Q7hs0dAXUCpIcy9HGpMmy4DE0VT8AmdFIZhjLFVoLyxshTZQRCwCv4kIjoWJhURgQSiCbqaMRTsOD30qshCMBArop+NI/jN3kh0gTCFdTgkZiWKDshB5eyTd92kdhxKRUvSVhQh1MpiHJXEyk/NWbaUGNAALJLMiQ314Z8UKvbJoWfXv9Q62Qr94e5SHEt4GxfJvQpkEUhs7lI4Ei2jk9xLDMOD1iMSOSUZ4GlReBR/mpNFohARANJkWf9vmrUOjJX5octTomKhWFPSq/4GpTPkVs8tUDm7L8kF5SBy/Kqz5RcojLYk8Bll76eaRXGZGiST0qvfid55cSMi165V0bzaUq6VUSUkrGx33MArH9JUObVkeUf+V1J+2piPER1Qk9ko2l8/4MLLy5beo3ybQiuhPtLy8+9a2qO5MDQLvsrOR60ZnRMff1y+c9o/tZz3rW2Wf9/ribxEhcqpM+V1ezDcmlzl0OhT1cmHfoRn3eXv86rfuFfg9sf13ym+1TpylBZ2civ7rgq9nkc5/73FU3/J9XvOIX53/V2celyvdgMUKlWpvoEVJ42AmZsj5XHQ4LJQeYrAFu1UpiRsTi46Ch8CBfelrUKUChaLIT7S+iNJmivhmfCiFSiSLez3zmM3wWmDYUBwEOY6ISBKEO4Cm6KIwmUEpzyqRaagSKWI1UyGgwLMiEYE9rf9FKOWWqpgAzPkkgvSQ/RRAfW1mA9Uteq4E581jZxYyyJMTq0mBPXCveFN6IOaFgXEzaApMpECPc8CEvh1/Cx6lh/x1LyCirqkPEElSck2RYosDq3de701e7mrDEiwMNh5EWzptP5/yQw+bx3t18y01MNY8MJ3PnJt2FuWDGu3p5y5LELV0wl+bEauEWI/pRA/KVwDCZwOGjog4xk3Sd/OCi8A75Lj7/KN9LdZ5H1yorEYtAlwbW5NUWmGtPqZ9GZSSBxFJP8uphnxRLfIwWiBaIFogW2EULaCWVVhInUarPgWvV+OQ2RXW1aERAFU2bEjYQJgcAPN0hlqlov6yREkEbX7GoqYJMEVkUtAtg0FwdUWs+pJXRe/nhuuZEKz+Wcwtmpcp2oshUFsA4ML55x46Rgw5Kjjhi6by+w2jZb7rpto0by8VulOfYX8EpX+XIUNLbxafjWZyaAyZbOdxFl3d41EAavpyP3Xn2aOqBUSgYIwthQ3YC1OLzDTE2YeZWe9QMDyDhpoDFJU2M0qITpfAMWHAWFIFogWiBaIFogRmxgNpf6luun7BJkhmRbM0lrZIaJsSqSt85+dZqqHWgWUdhJEvstGTqAEaxNDyvP5cvL6C1ZTppdKx00OLk4IPnbxt0n6bcumXH2nVbmSbjNoK5A3Npr5nIdCmp3ZoVRljb+xKiUrBaOrjph6h9TBG0eWQ+ilClGsD1BLxT2mVnEIoiJBDZlL4xpijBEykzDPRzNGGYIoiP0QLRAtEC0QLRAh1aYM2jj5533nmDG9c6+kImGfdDUTZ5ssmSdo0BKYPTcq/z9TdJ7sQIuobWvK5Wgph213jW+W4r4SQahA+PuqYbdKcz+pPi1oO1levWrfvrv/5rkGwAlKZE2NfjZsIZloNvPODRKK7kLzhi3yuOEypTOy5oSJIbR2uktMs00KbS1OytKX70ox8higuw8Eu57RBWMk64OPK+z6PjWHYBFulkews02bzf7OT3y4o+THuXu82pdpZG8sJQ0YdXFSiP2TvT19fN0YDNm5PjjhTV/u1TyFTO2faK6+3tWbbsiONO4LPtc5ccSWeYzxnRIRzzX6KuLXr6DdBskXP5CPt0HIUKaXR6kXzIIYewAuLOJXtHEB1mhEGgmRkAbWAgnAEF/VtiXL58+ate9ap7ly9X+Qy5pAhcQvKqw3jrrbfytafnP//5CxcuPP7443lEiC2cAdMNhgxe+VzN5dLV0inI+aIHIEaRK15kkcBjjz2W68PolnMsZPmdd2y7+25Hw3o9q6jI54+qqVR099BHt29ZgGLQtgjNZGpTcREvmM4joAxb6e2cq5HS4k3p00jZCjMttUMhMIpXPkEAu83+pgkx4pwqHdsfSliQIF+am8BOAGM0oBOuRhqKQedqp9jFKB81LFEpslaPsMhBAK8cmFb0ey+ejRCbNq9ZuLD3Gc94xgMPPEDTRmePFo+k8g7W+l57b/L2Rc2nWw5p9/dFM+xlaaIOmW7G7XQKU3ERLxhJs/5he+E7p6rFYsLVjstHpj2qiW+kN0YFhb4FdQLAWOed6AbvSqKQ5vVPZ6LwoUokbYyLJ7rcWW4GCDU9Qoq2sAk0bcG05UgHGqMBaYrOnmP725mdJlGFfQRlXM2vt6fp3KxUunp7x4bYeZgdLjLGZMuhE0ieh6ImxREfogWiBVpbgFYm7PPojdvFynDn2NXaijf14jcKBGM0pr9azNZpbReiRlYUJrCRIdTEbGUtNfQ0ZMILaUo2ijJ2gJoExhJlZpMq27dsyBZ6UEnVmhJrogBwoSZQEq+W6VmQ1S0zYYzQ22MIh0IgSD0aSysgVEPZB+VO58J0YzetdprRJJhNEBUmygjaAI2xm7Q2XHt+kBVgM8iSgxduWLuZfQ+HHLJscHDV/PmFzIIFK1euq1/T1mmaJttHJXOifHYqJdJ1ZoHJ1u6Mp4FqWi+1YrRi0yBsCsROMzbKnZG0N4rdRYy0wqSqt6m6tZNHRtbmJXwcyxyNG2xkHxPCCgUNli5GUMtFjcS6DDLVGIVGMF4AxQtARMTCgg6MIBEFBoBkolLIHibcJKgCVP2PHHglU/rDAjJsUqGEgKQRBSuV6M+uKiEVF/QEaYknjDGEFTuqEjtCCAIj/dEZhwTkgMSFjIJBQoOJFJfWcDwt1nAXMykVAEKKC/li5DIhB+SyzlbjI/jSvx6RG0cXcu4imN4+JwBAQZAho07mhKC8VjkrWUcPbdOMMxYBMLYyEXhoUpobuxhFQxrRBzuT9dIfLijBi924UoAJgYsg8YIExidDZVVhhDQLSJRyBzLxCikfLm8HfidcSIBuECgKfByh6IwPDA8pUkJCrtmGLYEUdRLFbAY+SCv5egSD494v8Lz/epSPhgDed8oCkhYHeUeQRGWceShDzvL1ZHpUUMbIEniZQnKxcI9a3aQmyomYiI7VTCcNKfjahViXbCrV+kWGl6jot7GAsg+LWT4ahsLAK8AjzghctgYOvM8ll8vQK4TojIVQ8YKRGlw4RY6HlKaeiRKBHi00BHiJ9HoiBwBKABQgLsgUF74JNN4UgXQTEhrJ4RFAsEQZO4CI8dEhhPV257lf01c4IUsbeL+7AEtmkkVkvjbWUTaLoDEnGhklHB9iuUaaEKOiAwYAnywPQw1GlGDI0FnEFtoUEIvoGwlMYCqI9ArTiiBFT0pV2jqktyKLnE5YkN9heqUY9GZDGKeMwuwzJaXkW4HpnF5RTIs+ZeQ2j9hTDV4bmlSQZXEK3/QR4vCQfFOaEGn2CZFt4A7pjQxjksX4yFTZaBSOqVV1ikbE7e1voRDjiM4wjfJDDJSd0Js0k2yYUJrBll4AsoAoCDLAyEJASXba17u2YWgrGOL2mqQYTbEUvtWj9EfzNrHohZVkeqV0CltJi/h9wAK8tpaKKYtTWObbFCETKOH4EMtZUFOA8q/ip7dGcCMlooSEDJ1F3EgWYsQi+hAv2ASmgkhve4IUPSnd29tfjeQbTcq2Z291RmZ0qSt88fGjH3/n93/wpbtucz1snK/n/FVYeq75E6NBEMos/FYGn8Rap08h2zySX7Pd/raSb0UF9Wij9Xj3nTuuve7KP3jLS8//6sXbBre5TyA55woVV4c3GoGJlU7ad3tPw2wCaXgfy4RnERmNYSaIAshCkY+DyzABVRMQSntnBRxxxBEMzpleYb7mkUceueeee2AjiOknAJCEYlIwigW7KVJJJ14eFTsEer/ASIgAUaZ8BIpXNPCmCJo+WlxNQxuRHYo1RuiJAr+NPWU3S7LxRqC9Bbi4CYKufPfZZ59NJUZRURmgjPX0uIv5QEIwf/5cfMxLLlAsVTwogX09vaq6ZX/R4NMFgljIE088cfXq1WvWrJkzx99Y4ecXlFP4Aqa1sIQOFHgYjzzyyIcfftiidrE3G3pCyYtCKA5tFSMa8rgg61LXO/du7rZ68YtegcCe3NMhOOJI92mIsfHt1C1333nvFVdcMbiVzwf1D3cvIfZCsoi0lIpdbv4xM+Iqn1yX29XkY4cdp+IKIHsCKHYOG9x1113AnTtEkRH4iMLmRK3qNIwFaRzSwM5UEQsWLKgJr31f0D/VLdAYL8I5xSGDECpVZVUShRkOOuigH//4x8r0RvaIiRaIFogWiBbYOQuo4n31q19tlwzSSKn6xTcA4dTM5hREMw1gVbeQKTUOPvjgFStW0GPcvHmz3bGVoml8JCKQNDoKMjUUBQ0Q7phjjnn00UcRC410NgVE73ldtxNJge/ArF8T6Sodhpw53XfQqXjja0+loZk3p4fG6IknHrVq1api8Um0Pjfctunee++951H3ba5S5RUoVu7a4iLKjsFLk4i0TPYQfBnHWl4XjXdKCyDNGe0vOuteXemc0rzGM/2fpUuX0skhCvfRLf+RRukjXwZxavvpcflEIt2MMhUt3Q+uZUHDn/70p5bLKZr4GC0QLRAtEC2wcxZQvXrCCSeoTlZVvHOimnIxLuP0II1af39/ON/VlLhDJHLQluHqypUrGb51yBWS6QKsnq4+FKuU76PZOu6E+cw7HXrI0Qz0Fi5aunbt2k1bhugw3HHbJkbZlWof7HyHDVeZ9LE+N4Hg7shq7Wi/CGSYjFu2bBntL816a/J0CDGq30KScQTLByDI/MWLF3OAnxaTbzwaQVpWi2fJSQUihL4E1kBtxuxNaVIs8TFaIFogWiBaIFogtID7jCV/GbeG1T23V7dfnXjqKbTgtDLbt27j4pvbb72V0HyhwLfsuH6mxs6nSutj8Bqm6Y8WDLXzyreJk6hq8wnlqsbinph2VEw2xp/E0uIBFj7pq8DBrdsvbyTjUz7u7i3JbgxugZkGeTG8wAvrzVS7fOONN7ZQ7nFD87lc5dhLfq/50sbjplnbiMkUwpUvwBQw9QAtpwxgAkdB8t3UTcV/epOd8hTjDNfCeQAz8MF2rmyojiUHHJAsXTx33rwlS5Yw5XLAAQfRPSuVu1ggHCq5b/+OdjsJpYzbHavikfMllp38prU0ZCnIYYL3JdQTdrrWaEjBWLJ4MWuu1XHXa2Wqis1bJsoAJmqY+WGFxq3G5rPc6Vbqyix/7GG+hc0nZkq56soNa0ZLYy5qH22lK8PfAYcexA0jTIYtXby0Z25/tSv7yOrHtg5vv+fB5Zt3bONswabtW10UhVwpW31s3epqPkMnvHZhHLL0ypsSk4Ban9xbuw6TQt8bH/aL6iV3vVWmwne6sW3fHNa1Tj32sHuOPyq3afPaFSuS9evcBoUNG2u1UR/XYHH5rd/LwNY14qpHP+l1dzlWM7UAe6yTT9IyPuxmC1h27Eq8CCE3Z0TUrqixi7zob7VTh6J2IskhSwh3GGMj2a4LCSWEcGNcKcy0iFO8M/W4i9WIldtdlDNTyZkpOZauqQSGczWuR1qt9PK9ctez4zOM+R1JfkuusgZ8LjuSyy7/2SX/csstt3ztvO/fcsvd69aspvWgcncVfGmhM2D5YBdd1q0BRbenWaDxotU9TcP9UJ8ZqUIR0vH7PjM2pt9IW7nrslBbcvCBcUqLHtsbZ+cUCGXW4bAOdKOVNg4Nc9SN9RGNo3RSsAb7q9znaUeL7oAJ9wI2dsaVOjgA8n192cTtY8SSfOJ0cIc7PSt91F+udZrbqBLq0JZsVgNJy67ID22yK3L2NF5LlykGRid/GS5WuCe8yoiTO7Rd2av6FRO3Y5DXgc97ZbPbcxBD1JW4O77zVb74Vcn3FRbtGFs3f34yUuITuUmpnGQZBGvJxUejFzIszRY7wKRxmQ9oukMyZIlwtEC0wJQWqLcjE3MdU7LsIkFYvRA7jzstUFNAsE9XSKr9FTvK8A1DNyvbgSu7UwuZbKaLtp/Krnf+/JGh7UyQMVvnpGXosSLPtYR8bAmAqs4xqIHUdfaVZOHiBRBs2rg58bNYNXrH55xpIdiJrbeb0lEYjOAkT9+MasEtlr0UkHFCc3WYkJ1g6VDyrJLlWEepdvNH25qKqJLdRqIqmSFmNjPVI5Pq3A3rN9NgH9GXnH744i9+6TPsWPjkp7/96IPrMt3uXKdugM5NFDSu6GDZg39uWrj+KYuJE6Bqo7PBHolsVS12k2nklG7xsRML6C2ebsmkBghZEOKKwXTGOGFVQGeeUQDahjLbK+9qn3r9E3IhlmFFe14LRQK8pGVamhu7AMVuyqRCd+4RmRIrgI00mAgledRZEnwehZRvEYnRHkVJGjneIiMTxCN4thLpdAwssoDxCoCGdOHDQiw4JODEiy8yfAGhGVVCpDOMEmJW4hFHKA4A+UgQYJoLgEDCLVSPYkEygGjwjVewkgnMwpNKBbzoAAYHHAImNhSCfaCHF0qKvA9yjKWSO6WFSnKSUy94cFCuuPPelS4W1rwpGIOM1CVPDEKKOZaPct1z3KePuhifuJlDTJEp8tWgUqZSzrt481kyX52EEiti6F9OStVSsVIqlos5PyaSkkoCLMgBBsA+2jAGQT129ysalBeZHg1vj2KBhsJTT13tJbW4BIgSn0enodcB+WDg5RF2URIKgGI48MYeagheeddmh55lLlGEvHokVGqwSU+w0ZhuRK3YCYKmUQ6YlBN9Ctn5I7Ho/CnWAO7r6wOw2JEDEgcG/HjefQqolHffzqRYVLKZctbd5Tye8y2pz09vy2oXpa2aLVeKdCYr+X5OTidlZ+RKpR96HvArSbGSz2UYIhfJILdInPPn2vKunCLfZVy57Gr1+synK2nqQ8LuJTgT8SEj5/vBsPKXR3TGhx52fJUrMDziAOSAXVz7tAvT2yqhzij+9bTcB8DJjCEXGBHLtr7oUieTw7w5/t2psgRN3eB6/36KBJ8xgitC8DqiSpn+mxsauMUH5/yxD4oEcpwWADW8K3jsNXW8FED+Cy+tKCCO1yPxaQUE8xajOTBOgEtJLicCkLDjKxXoT9Aksb5aCJHQ8yguizEExI5AorB3WTEqCD+MyJBNgXSPtinRvoTEapYcZYw9NgKYFZrOz88gXDkt3wQixGADoLFsBiDPwFhoChABPnjBKYKmj1A2jbopMUgriK0IUniV8hSyzaNK9rTsiVlkmTbGsRhDfdoknCBJA5BJ2xCbcOWXHqdFr/ZgSv1D+RZpe2C6+YU0TNSmV5GKDmLZJ4Vv9TgtYoR0SA8ZZQB60mtmBDA4pY/hATqJQrkpYqLosLyZ8M7zl4gUl/yU2vaIzupbAIRpMdgoU4Cp1F6+uEQ8pcwwik6MmaLv0DimUsge4X3PAlQplihKafvix8sIzbTaCwSaCyMy2ADIKM96UwD01ltoChCByr/gFEHTRyg7eRONd7r1edjemZA2APJRaVr2xCyyDOZqI1lBoT5tEk6QpAHg4MVPY5Jav9lJzlfe/Oazjz766He968u5XA99bnG5SVfXL5+Y+BBeyih/gTusgoxe7J34080vZGKiabW/YYpaqYRBFMRB9/e97+Kf//yDb//j477+78v91Ve1nqfMm5KAcBXpFD71aAWe9JocA1LE4WOHJq3lpn8fiaLD8ibh8JojaswLO0JwXF5A1uvmeJV5zThgf839QW9JM7VhN5sg2ZIJYLARpwAIxAtjKqjxUcRTygwZTbEQ2QaGnrLRPoowNITbiI1B1DlM5jC1NLd/Lta4+uqrS2XO3jjH7ER3dy9lrFwRZupqU4yNPnl36RW/8TIZ+Lmhn01k8EXvRvpOMJTLBfMXUDDQOczuSn1+xIQQGhKgwfz58zSNDj4/5D6pcMaTkze+8YVPfepTOeb64x98idnG//eZN/3lX/7lvPm9L3rRi556+lkbN2784QW/vmt5aXviNobiFvb3VSs9W0e2hklAH947EcjXGxQq8PSnP/2qq64KaTqEES45obSQ98wzz7zjjjtIWojsHJaq+FQ+cJH1iojjxM961rOIvXNRkTJaIFogWiBaoEMLULtyd+Hll1/eIX1IRo3dqkUQ2Ute8hKuTwpZZgSml3766afT4ugyqenKVM+yO1k6mmx62nH9b37zm084oZeDFj/9yaWPPZZ89V//v/e857yenuSP/ujMs89+CQm86Z5LL72eM3ff9xFtm250oqfnzB0ZtPKcN945CW24nve851122WWa8mpDNt0g2l/sPF2uSB8tEC0QLRAt0IkFaEO5OImD6J0QT5eGfUtsoKRdYGBlc1zTFWL0YXPPYHP58uXtW39jbArM6567bWzwwHnJ2Wcfe9BBGSzwkx/fuGNH8rznn/Db397DUPacc0592tNeyGDw+hvupqEfK7vh7U7PBaA81rjvvvvoPDTVpykSrtRkLxhRhmk/8MADubRLeCNoKrARGcppDOVytBlv1htjiZhogWiBaIFogX3bAjSmLoG57AEHHPDJT37y/e9//6b1Gz72sY+df/7511577eDWrS6U9s1aWQNcQFuXWtyst5Lawul2D86gYweiu5Yo/8rXv/7Yk09mHoDuDRdA33rTzeXNGyeU7yRGEsvsspr0WsPemo1EuHR4uhlN0ZOf/GSL9c677wCuH2Vx6HzVrU4y1eJgh3COyX72XoKrEO7277Jpt6WR2wRJWsqvsj+8tbQU8R77qJ6V+mNawkj1tfRI/4reLDCr4T4tPptZdNBO0+45PUsXc4FsYeEcblDtP+gAZkVYEkUsTHQp3cklV9odr+SAAfZbY13x93DNh1IYH9EkDw21guMU8Tvv2TrG6s/GtWs4st6TcwuRtffX8zHhg1aAAPCy+MJdsdu3b8+yvb9c5lMld955J5vFYSGUlVxFJuGoARdvDY4VXCYAWe0lUdu2beM62scee4xOMly6Fl+KbdiwofaaTNJ66gdvnOZkXhm3pZvvpjA5hvLcyrfsuCfkn/707atWcvX82tWrRzdu5Js2rriPl5xvi1xeLp7bwWAvY3ABVtMoofR8TQMjcrdaYH/OiF1JuyvyVuCnk2O7EmkYz67IEe+0krAr0YVq7yK8K2qEvCG8iyrtG+yu1XMXuKjxTeiEFrqSbduS//3fh48++ks0RjfddN/Kx5Lenn0juTEVE3kdbfE4WsDeuMdRh1mN2iZO6cGmEpt6nJYa8O629tf3V5toR4r4bCQ9/OHhHQQrOaaVA+o9YQXRvS/k88yi08lnJZrTnuw047ymRhxNIghQu2KrQMyugruiRsgbwruq0x7Pnyo/KiE1X8rXWt1gDMVdAcPDfFyUweBoMekeTQYHk94BtxXaDWtznqHlTMMeb5GoYLTA3m+Bna7EdppxRmy2c7HDhVOttRNqiBEJ8AJXRnd09c874YknL1y48PLf/MqJrjWgE5UazaLoYQGwqPvn9MEFkiV79qc3VcaIm4aGYpsSRGQrC4QZ0YpmX8Dnk0yPG/9e+PPbTjvvvJtuuumaawa7cwkFjl4fx2OsCFliwagZr4+hg9bciCIwOxaQ7TvpSFv8Lr/8HTdkaIhsX3UYpQB4iVQSphV7Sg6agJEPMC0dQlEmYeeEEG8oIZS8KzBicSydsI2H8SAwgMe525cApK0AwaaGkLItMPQMo6CR2XkEkBOj+QDmtP4COzlF1EjAEQXsOEUh31gEIDmFodGBEbzY4ZIzSh6NBaSVLvCo0XQDWE2EX3IyXgMQgkNhNJc06KWDGKEkFfg8GpcBYhcvZBgPXgwJAdfKSAJ4yEwIBJImAFhB2KIxyMtx6iHBcbmzmO6CJOAcH1bBvJU+Ej7GJZe8aPURsWQSShCMVKlgcBajUiqZ+OhvQZDJAkqvklCLPbBASK9QMbpo6s64JAp0yCUqpR2YrBfGaGBX7MYuAnwwkFm8xmIERgMAmVHyaMRC8mg6hOzgFS9kMhcYXCghpJ8pWLEQIwuUrF3i4ygA0oFYAEyNcta9aMKI0YK8nk4ptZieqUai4pTjiqT6WVQAnHg9Sy2ZiOaRGsFxOqvXKpOMzxliBg2FGD0NutQWef3Xf5wcOaOxiACwLfim9q/zxV9n3jCLZRGZ0YxpZoISOCzwFiSDk/uGUWaBB6MMUhCwkI2PUsYLmdRPc3hfQkwBUZoogEYnYuEVFxgegRUkZHtf9E1pTLIAfNIVJq0pVwppO1JS+H320UyPvYBb2Ut4lScoMYf8VnYRPQJ54YH12J4lJQpicaXwqaiJAgeyFbHYkcZ7otowJaHxUSzWGPCoKIRv6rsU+hpWaZySHiGSDxcwXGJsKhykF1+7TQDhemwkljT5hEKJExnyDTZGoyQUGIKmtYnR737AdG5jH0vFTuhvYg1omkZFAY30Ma2aEgs5rfIDS0hPRLhWwi2ZANagmhFacYFXFgO0EW6hELcnaxPR7AVZMg1oGpdCd6IwK2ed9VvbnxiRj7Ni0Dm9GJvqLCQEFhrChozAvmSBsAgBt8px4WP72yrrsQ8OA+q1Nau2ogev+hYu4M7fX4iVTWJMyRfSgqDEiYYoDDYuoyQU2JM7eqXCGAEqZT8Ay4+WislBy5KPfPRFF3zvyrvvTLrrEwFOQv3y2pQEydk533QOVUqJslRAPN0q18QakBKuR0UBjfRpJDYdRA+B7Ln8luR733rkQ//n7At+vHy7O9Q2UbuGQoDRnPfL5IehKZUsmQZAoBhTlKlHaHAiTgWFj6JBfwFhUIcwjOoYsL0bFr4nfNhhh11zzTWkEbzNvwCDwSnVxGjdCbZWhGQWr9nZAAsKAYUiOUR2Andif+QgHydiHkkvro18oxclj62IJUcE7WW2krD/4cllbtwuc9cVG/d5iUplPgg9MfXQ01NgfpLjHpQH2Z9vXLM/Piwe8+c6jEwHDeUQGqQV/ZeuxdU/p5c89jlXyeZU1bsQ5PCuwAtsGQcsaWAMTmEIKpZLHAxg3k1BEuJ8e/YAlLgQR9EmXlZOFy7MDw66Gc0XPSf7p+99G2p/5EOfveWW7ZtKyaIk29P7N3fcWVw3Urz6yu+d9KQffPjDHz72iSd85jP/fN3tzkBMHm8eGu5x31dzb0pPLhn367CckeCdVXQoj+NF9tGNSg1eTw5F4DPtCxkvAjT40DDDC4YUgZEEr/sk/S1IBCmfz6GzGUtCyiVnvYGBga3bNmczE9MjtRysursXiRfp0HAbCI/AejE5fYEQaagohE9FFx+jBaIFogWiBXbdAlSw6vKFojiQRhtnXTuCqL3BUFEbGa0Jm2jt0WhCjCp2cQkO2ylRzp8/H8k0Q8a4aNEi2oUQAw2bd20FlwYCfcAYCwA7k9iyic6kCEd08+bN2bx5W0gzMFDYvn18Tj4ZKiXjybpzj0m+ff5fr1q16k8/9B+0vxtKyZIk6e5718o1P1y1ffCa91931hOu+8QnPvaPHz317/7uHy66YRuJ7+nuI4pctt+1m1xKyedA+FxINjtv/gBnFcO4sCEtslINMQBp58BkSNOYdrhSN2SRrs2bN9NK4uDFx/KkNJTjlgG7+IyNc8ITV4omzEHZp5HG8p0uGclEGnGZzDDGCEcLRAtEC0QL7KIFqIr9OfSWF1Sp2t+5WGjZqb1V21OlqxnaOVFwIcraIMlMibJQEZtvZIz4GLMXMsl4NeH2q4MOSN78hjNo3S753wdp0DYPJ/O7Bub0PG3DmgeHyuOrvnfbXU+869xzzz30qBMuvfS6+x9IuvJJsXZTvYmcNhCOLjthNvsb0Ngg0o6TLiYfGoM6iaINzS5mWRvJMShaIFogWiBaYP+xQGVk1M1TlyuHHXTwPTfcfM0VV5L2/I7RZ576lMt+/nNuGGBqmBGkhuL9lSwXz0xpHM3a19Ys3TS4G6XaopnuUZIU+UQBsZ+0nlJ2C4Iqs8qsHpQW9y05+6Rz/vUz/0bTfMEFF/zT5/7xV7+6iC+8+uhRg3ikHbDXapI8MNwglfHfh3VwTfNJNKkHl4JyUoSykqltXk9R7PTj//zP//zfv/4brhn6wj9/ncuJvv/9/8529fEBYgb8laqbsc/4a5UqnEOtO62YjCVj8/sWbR3e1lvoGxsfqwemfifmbVIBLR79V1Gd0abL2ELerKHpcdGzpQDgiCTsgAkOZ7Eg0MwGgM0mAYu9WiiwsZoJIFx2YCHd8lNOOYUudN8Bi9hVX+lysYx1uzUpDhM4sfyRB+RONSlkymx4HvW7XPM+p/TBlZxfAandJqavT9feKbewRdS4uv7u88FMECGZZRo+SZvk/I1cXZXtxa3bddwODsp+Ptlc3OxKd+C6F3dvHt2cdLFmxhqt+/h1oadQ+8htlihrZwxqHNnEhWbcoeZ+9gNwWqDiv9RNYSuVevLZaqXYk+fVcIUNIKNQH6U7SZAkrNPUtA90aALW19dqQX56SiVYvuqB0S1beqrVvoEByMaGh/r6Fhx06CH98+aOj7nyPD4yyhVdGzasczOQ4+7bS5WVK5JhfyFuKsrWpdUKhsxuxjcgJSk+RgtEC0QLRAu0scALXvACvqrIAsrFF1/86le/+uSTT7755pv5mhendi+88MLi+Dj7Omhpx3t6mC/S4v6EtOw2B1ePpiUpdG9g71Df/HuS0QcXB13Db3/+15CMZZNF+WTTaLJ4PrU+myjAbXbNT26Tl6CdGJObQxcQXbRAtMD+awH61Xt14umaUm264YZzlYUL3ZI357hZ6T3ooINe+YoXPvLwPevXFbmtmP44/Vs+IAgL5Ph0hJ3v+9uMJlgd7u8taH8m1+ZuH6qdp92r7ROVb9P+Moewbu0GCkCFrQ6ZCpsB3P7AgUUpo6mEyF+/fmuyYzzpXchXcLdn57qGtmvjNgrX/N5RTpVnBuHtKvQW2efQP8+NtP04tz4Q1OxOSnx8jBaIFpiwgHtnfP9V66dU0RNhEZoFC6hmw2e6j+8muGHIwBwmcfv7u1/2shddfunPGTZwZTwZgWOaruwnPDPMMHOHZMbVaeDpSCxduoQr2ml/77/3gZNOOTE1qTgLikeRe4EFXv/611M2eKO/8Y1vvP3tbz/ppJP4wuVpp512ww03/PjHPy6O+A2K7jbnbXTA3KRuzWmsSsvKVzzcfvhRP9G7Y/jRpHdHMrCES3rWdo0xl/sXX7jMcfgpX7dAwWiaWc98vup7enk/21mpXeAyMf51Rdk7WGv1S33nxgSmHuLkRzd9C5iFqVuAyV/qBABqGAtqKnXblsFCTxcLCrYXVPSqqUaHx+YtmNuUESQ0xEJVNjYynutyCx9Cgmd757o167t7C0hmhpzJFrZuMmHeVBT0FFpUFbv57AhlF+v4eLFQcB/AAN8sLbVJdoLcpE1fH2oMDw31+INjFh1KouiSAw4wjAEkgdjRAaMRC2teaIs0I5hZgIhwyEQlOcWuWBRE7AY0jR0W8MYoYj0KJlS2UkKExJdTe6EgxSWBcEHQNEbZ38ig0TmdlHzxhmQiAC8kuaMYm0ZkSKILhYQqQaMsSyHDR0UhnYWXZDGC90JcSlEHmC+8ABOjnMfXVvEgRhpFosuf2MqzekVZ9atmkikfGhzE1Kt9fe4F7O/pZs0uw/qsGwXjcqUiRbRSHePjMVX3SR/2wlXcSDmXY6TMRnEwNPgq5043GKUVPpIRgf7SR5GC4ZFQOR7rYI3XHhsBlQHDS0keFSnyFQWPBoiYx5A3RS8dhBSvYPFKf4vUABMCoBRJDnEZO7z2KJXELgL5oTVM+OwBqCFNiJe8JiIAajzBJMEIOtfBJ6TqJGT6MP0Of3/VWGlA0pCTyw65SDMjlXxC5UodwjuFy1bcJVls3HSRVty0dd0yLnJfxVao6dyDqu6qy1liwitV3M1u7rFucHhJi+WCuPCVNEmWHAUZxij3Q0AZgYl4/fF5xAh6WSz7ZFLwWAxr84idAeSDlIO3Drpf5YV8guACKQvDC6AgQ0KQz7s1jqzXiUGDiM1n/wbE8OIEEITaMKKwYicJiMXHeTFOiCKSHBi1fxUAR5AAfBHABRKHBHzwprnRCMBHE9kK2Mep87ZwTFRrEtvGdy/hfuWwmtLrctK7psknBDymbEMTMooen8zAEdQJo7imJA7JjNiQoRopWPqLJRXU9NFkGtCUzAQamQGt6IWHTJTy2xAbmWOou0Z6QkIkj1b6xRSGAqfojThF9jg+moYGtFGmE5o27J0EKYoOIzIyA9pHYWQGNKW3UAC9XJABGL4pV0gzJaUkdEjWKrpWeBMLYPq3Ik7hjd6AFIEeZQ3aDDUJFmNTYkMamQEWlAJEYGQGpMjs0ehRTLpZUAogFIzoBacI4uO+ZAErOQByTVNHEPjY/jY1DkjZR749tiI2vBncGC0oBZh8sRhjUzJDQmZNalMWkEYMYMSNSD/er/YUEi7AeuMbD6Va+9KXfgR3/fu0TnzINVOwiTWgjeROaNqwdxKkKBojCqtKQkXg/Exy3nkXvfFt733JS5L/+Vo6hpCLMB4nGNO0E8+i4dmAibCpoE7km4ydkC9eGBk7UZz44iIYjaDaJ9bSLsBuv0IOEpgmkGQTYoDwKV9C9sD2N6VnfJwJC0zMISJNBYP6yiRTGuUUpJG8ypXRAICxYiYaCnBNmuen0stlcyW+NjD51ZNwiWIOwoSAh525AHUCLS5hCBUGGiKChgM0YOh+cSQmm5805QENzkfEhIvj43FwcDubkYaHR3g5Xvqc7Ic+9KGtm1e8//3nLV/lzhbM9/eMENfChd2jq8ZGxpJLrys/9v7PfvTjb//Slz73t5/6l8svX7WjmPTz0e5MV7nIctqouwtjx5CPqjYDoojA6FUCcHH7lgIYtZUKS0sKgMbrXPPEK6Rg/MbHlNkhIHZqERNucpgRtJwypGTiiz6Un5IgluhHC0QLRAtEC+y6Bahsd13I3iKB/cMkl3OLTPA//+TcF77wheuuu/QjH7lw9ag7NduXJHPnsjtqwl1xX/Le9/7dB/7ixX//93879nc/vOSy21jvd1d2Zl1XeYJun4ZiE7xPZ29MXLRAtEC0wO6wgG6s5sgO7pSTknPOOWf9+pU/+9mj3H5Fa5oapENzx52lhx66/OnPWvb855/d07PmzjsfZm7Jf3Fw1rVVvygcis56lM0i2K+6Z80MEHHR7LXe8AABAABJREFUAtEC0QLRAjNkAd/4coCWne6LB+adc/pTaZTPO+880ByKnZNkc12FbUV3PpZxH8gOZwdsMMym4hlSdGoxJ5544k9/+tOhwUcz+SWLFy/293FzYw4zz3zEBTUY07ffnjdpxn7q+CZTzGDfgBMLd9xxxwU/+C4xPO3sZ7/uda/7/ve/XSnucJtv870cxmKtgdzQFV0slADkc2yRdFc/dGV6/fpFaUZnJDrM9skWeTyenFkyGSzgS6vTwLpMLDSwQoRvAGSc8WDFhzkcPtoBJbvnCeXKdfxSl7vs+7DjTnDXXfXOZSFGCRrmNjAKEztY2bbvI4IRyaxygM/xDWm3eOpgcVSqLl9qF2CxabruMlmnajavTasTG5kc0u9VRTg7aC2KLvbmg89KQnAvSF1g+IsOqImGXIAFzAVY+CJAvtdworSTNEfvFUF7yMhvyABslY2oVaKcNfxkF1/YhqDqU+qK4M7OHIpPfrZe0FCJ7bfIZ4aN1wGtuC2X76iwrJwfmLts2bJCwemcKZUJLY6NsE5dHOQ6gPGx7TvcnuCRMQ4UVb3Pp71R2F0a4AG4cEoC8g0AxmFtfAh2OjleTPSiBaIFogX2LwuceeaZ3/ve9zj6+/Of/3z58uX0Uqiouf0K4IADDnj00Uf7++Yde+ITuCSL1hbT3PnAYMpAVLxgqHupyfnUx3Pf/oynbaHbNslxARZu+apbuWd8LDmCntKD9w9NoogP0QLRAtEC+5wFrHo87LBlfJGU7V5UlQ8++MgDDzxAP1Z9dTBs6rKk028HZtgAL45QbrxasmRJb3eeHjLdafrPRhyBvdoCbdpfBrlcatzfN/DUZ55Ec8zdHBSYh/yNkakkg9fA86ijjhx86Uu7h4cZivXwdVJGaPnt8CZFznbmRwc3MZ5C4K233rpp3SY//HODN4ZWzmdSYmfHgyl94mO0QLRAtMCMWIBKSY0gLeDhRx7NCGJ4eIxbiq677rpXvvKVzLZRv0GglhSASEEm2W7qOibqqBt7CgVmopghJAif2365a4Y2tKure0Y0jEL2Xgtw4/N//Md/vP/972cUfPfdd9O/OvDAA7n9imLDesTDW7bMyRaefOapDIoPOeQQkvnIQ1tSiaXIuRKYcTO90DzpnHMyO9ZQ9gpF30nLroe+lAxQjEfLBb5PWRzeQektr94QyvElPESk4dg0py2yy8+qK+RjXrL7Fa94hWqJ9rKZHrnkkksoIeS42KEH4PFFL3oRJUod+KZCKGB05uG96KKL6IZJCJSq5QAOPfRQ+oQHH3wwyxyE4iQtVQB4ZBaFSRvi1QS4JLAmyLrBKaee/Pu///splkCfiXWEa6655pe//CVVK8ThsILHl7/85SeccALycQHvBEi8TPjQsfza1762YsWKVmQTDDsLoQzOK+I0CQGJFEHoCy+VRC9Y7FhVgMiwsIzMI0KAFZ1oeJcF8JrTysBLwiEQEl9OQWqGhCFGqeR64F5t4cWbwlj7JRZRigaMKW94A0L6VOyiEW9Kgh6lCQONVGhTRhJeGyn4g6msw4hLxkQUJQEfaQDYkIW5gj9RxdVXWKBSGofeAc747sWBGDLuPnKfqOlzF7709bq1vFzWrb6JoFTsJ1FcYUXhrJYcr7vk0vPSyufzBWYdq+PuHSEI4QTByyM+GAGSJozhw1DIUAYndmgkkEfIeJSDoA5OYEBCjIMYRwAwvvQRHbBLhS8/YEQmP1TDaVDXASEEiQak5KR8Tz6hs0JTxHoMYwzJpKpoJC3FnopxRh5RRs6kKWoe0Qcr8QiBhYawIVOAsZgoANkQSiFzXV30A0u5IiUN24LMVcuUn9IOv/5YdUiInW4lV5BEA5nFlWHR1H9jzMt0K60iMzMqIgmRNE9ZkwBxKI2g6LCAywi/yi9fNsFWOIVaPipINsQXgdGInfoHemCTI/pQiDBiBCaIUhc+gkEvfFEqIhb/yVPJAS9HkMha+SZW+uBLNxUPhEggvggkVtIUaoxGY5YhCBr5YhSxURpvG2Bif0MbohgULbC3WMDqBQPaaN4JTRv2GLTnW6CTLDYaA/b8dEUNowWiBaIF9jQLWBVqQBsN29IwHisPDyUHHZa8+0/e8t3vfnP5nUn/3MRvbncd37a8beLcR4JaJX/e/GT5XcmXv3jhu9/1zht/958P3MOgtpbkRpZGzIxbp5MojMaAnVBDYyG2VgAwf4oESdPoLiWQWTyN8JkOIIhpPr6WfNZZZ7EewBStP4qQ4oiP0QLNLaCCx6SfBVPkKHvO+cMelDRAkLgJGra4l8ZsIgCCipvjdNf/Gw2ACEJMCmamlQ9llGtfM3MnAZgEYcLULreCnjMATHXp6kAeiatxjgAMb4GC+Eo2rrunhxeBqQmCwIPp6eUT30WiesbZJ37qb97wu9/97l/+/mertiTzfOL55Hdf15zVj1FFLyl0rR5i30mS3L86+ezfn/e2t73tw3/+oaT8uYsvXTO/N6lki+Vto8ysD27bUc0yjzYpmcSlNGIuwfhyYMw+AOGjWPRSC5Zv9IZElMEGCOkF5nwu5NzH0Wvfq3f0SNZaiwDolTVSLJQZwmK0WCIQLRAtEC0QLTBTFrDq3QAk02DxKAw+FTIYnGpsRa0FA2FCmlAxcak+NyEQSLIoRYMcQyIZ2B4hEw0+QnCSwKMkhHJogEQMWYoAsnnzerduHRlOkuMPO/hv/+2tV91+yd9+8pIN3H7Vc2Ayum04k1+TX7C+t29DX//Q9kHOM85NSg9vTj7x+Sve//5T/+Kjx28auu266zcuWZyM7ehmg3HFHbXliKo7Y2lLJqaS1JA+od0a06W0K13iMiEASgW+GPENY2TiwkcUftO0i6bRPuBTckSDL7wUM5oIRAtEC0QLRAvMrAXCdiEluU1QipLHVtU1QkxOSGPIlKhGGmG8mNrqfkgTsocyaY0Icufl/aAYmLaSPXNnnnHyow+vuvrKVYPuW6f5QrarVBkZT3YkWfYfF9hbwlX0fJ5vcDj5xc9XveQlJz793GdvWL9lLV8pr4urz8s5jaRJqI90MIwjqi/2OwlTORjVkkIoxhSHkCY/DCUIvPwUPnwUHEpIyeRRLqRplBAx0QLRAtEC0QLRAp1YoGdgzpPOOP3qq68+9ITjtmXKH/ib/zu8Y8fvvfjFzHtzn9TvvfT37sfdedcw07cT0+0tBbv93dN0HUhtJzHPrTVJpTdTeOaZT1p+yw0vesYL2PT/k2//59aVyxcW5m4eHzxqmTut9NCqx7oSxvotXX0W260XsPZAU9uSNAhgScL1ZerMQcjOgy984QvXr3dnXTgqc8ZTT/7Wt77mbu/iI6LsmSzSKXIRcu+Q60lVOdvsgJEy3/Vw95TJ9fUUhkd3TIzk6/hd+K3nUqaCGrsgZxZZ6UlqsoIrNsIv97B8SRDlWd0nNBDMrncFcQEWjITSx8NXby1hRieTrHngXuhLWceLEPxRPu9bLo8kbv3CLd7UiwpTQMDM/uDLjVb9gov/pjQzNyDzwQVYvf6L1nN8X7gQXCBF7MzbsMTVlcv1sp7qTwLAW/KFbCznYix1kAVkGHqO+UMCnWSY28Jdra4f3uEUqCdKCWnqV/1tVWU+yT2d96VRVMErp5dNsGhGMm6hrTKn1/m1YxLJtm3bMDNbz0d3bMcymq3i7MKiXG9uQR+P+QPdm1u754yVJ9bg/FEHMgtHRnNqiJzj5L/bre4dVsK5VFQqRxx+FB9v5vVyK41NXQeWb8oXkdO1gF5DchNG4Omy7zP0SrvsMK1EwSLenZDAq8TrQHQGTCvqSLyLFiDLyD7LwV2UtnvYaUw5frZ27Vpq5h07dhx++OErV67kQlX6Y/Rn1qzeQHLWrFnDnYbz1qzxPZw+Uyzr7idNCuUC/ubtbgPGwsVjH/rwm140/m6jEVAuUCSzI72lbHbtaOUVn/70tV/5vGudk/KBjiC/zpM5aXJ5fwGlq9Sdc8fhJrlafV7v3kwKiw/RAvuvBawKwgRqQfZPW+xE6ylDWe29ExKo4uicIoe6VA1xbtJmNoL0l+mqDnVVK8t6S8ct7t60ksFwZSzjar9szk2tF+nKTnZ0ien6bly/ZtOmTXSkcVu27XD1J//x9+OOVmgnK/x7Ucmfqv1dR+u8ec1aitOqDev4K+UOJskZv9OSg+WUAFY5QHSX2MWbLcxdctwpZ2zduhX6aqVQLHHo3HUIGQOzz/LAJRspn0sXJIPF4qb1V1J48mV36zSzMd6MTo7s6QqVm6hw+JLHUbahpIMDhkIKJMroRwtEC6QsUHt99td3hOSrfnD1ha9JsE+bGiPjNo7hTXz5WPSZpAeguzKGIce7SklmRz4ZpJbL5yr9fZWlBy4udA0tXtS9du0mTsTMXzK+adDnA/cGcvef20xO1ZjP5jKj46Wh9ZuYK6L1NDW452jz5q2eoYlnSWgSFlEtLKC8Ngu3oNqz0Mwicifp6tWruViNrf6UCq7yYSD8jGc8g2HvhgcfJfShhx7iNMGShx/2E9QLSAAt70QySt0Utlx+AEx/7xFPOLZ/vfuaQ2mQxReKfcZd9JyrumnPxUOPcr/SYYX8lvHKQytWsuOQUwYu1M/cVrNMHtOwun4gs9iYscTuibrL1yN0b4pbR3AfS4huFy2gsjo6PLZk6eIPfOAD3AbFmtqUMrl37+GHH77jtjv75vCZlgn33Oc+9/TTT7/44otbHZ6yWuXJT34yn0i588476diT6TjWNTjDki/kzj33XCa6OVeyZcsWZl0oSBMRBBA3T3Fc661vfeuXv/iV3n5XT8oRxXFPOOaNf/DWm2+++a777nfI2myJW+VTmSkn7uI/361LXvqyF97/2EMP3nsf93+NjWmtzY0+mP857bTTfvazn6HAsF8T6a6XQELB4NPJRNVnPu3sd7zrnZ/62N+UuXlLQr3wgBzaGXBkVuiQqCUAkszaEO+XfACcxYf+GBkHBnbXMfbtsnx4tSQhLnwwgqFUFAbAQowmHErFAkaZKAwweGAYqTdQAAJFJ3rzRY9vMQrANxqEoDzxSoKUUajI8EmCRSqZEJja8ALDLgniBaOEQw8AEgCnUFGKC/09q6/NBHnfN/KufUcBYsenuOKz9ENGIBO/y39qJeu+UcJQJMEaxKCkoBEwy0Hd3b3cRNnb0wd5LwtsSPOpL5fdR1AqZZetva4n4O6fwRRcsoXPzm3W9rJJuVDIVTLuJCMfTfEpcGZXwUAHJ8GnXQCps6QppUQHl7IPHzLwIAFkN2Cx4Cs0NBEwscCIL+uJDEpxgRdGj/IlAV9REwUwPnIg0CNBYb4bSyNgQqSeosDe4IFDnY2XIMEARIeS9gggaxiNcc0UgFZyRNEmFkfTYpHeN61+ddZVNS4truYhpxJn7bGeec73l1g5Gcwgl11F3VUZKRdYMoawlzfZJbxaqoyPl8td1exYLs+WhAp4WKq+7GWrKvkTrTAEyMlz2yXZVC9XwEoFAgF4hEYFABjXJo1Q7lcOU8gaAmQxlTd8HnEyCAYMKYGxJEGp8uwN7F4Z2JkqkRBJED0woZImgRaqd1NvivShqgGp2pxaQUjoJYqGNJQgOUgmUgjwCXUJqNeHxi5K8yUEMgHwAofaKhWSJh/eMOEmAS7BUkM6JPQc6y+4RdoGqL38bShiULRAtEC0QLRAtEC0QLRAtEC0wKxaoN4bZrjlOv0f/ODr6CJ/5SsPZ/LJ6GjD8visqrIXCh8cdEqff/6at7xlwfve98oPvueiQncy7r/HEA4MgOt23gsT2UxljZGYr2fPMR85gURTG+HYiSSDBKNJIowgmLHf3/7t3/LlgW9/+9tuy3J9INosnoiLFmhiAd3crwCKk5v09JessRfDTY776ePwjdPLaD5BBiNEj2CcI8jP0IIXgqE+BRiYgupmKv0+D0VNjRnG4irQuhPMspNmOngR9BYQjihdkgXs3xc3raa3oK+vl4l+ZsaY2e7r6xoeKp791OM++MEP3nDD/372sz8bG0zmdSWVYo5l17GkjEw2tSBhxN9+xeTZQUsSPo/xfz/xjf/9wd/xqdWVj/zz7Q8NH71szuDgEDPlbpLPJxxboQOR8ogDII16RA0h8ZFsocAsnLA1CjJzsrzMAhKZXBCG/kYAoHSFopDJfKvobdbRCWGPjXNOH0ThUA08DhYefagLCggcXkqGviijHy0QLRAtEC0wUxagKpYoq415BLZHAR4xgTQaCzWMpMlHuAj0KCGiNDIhLUih4aMwoZ5qO6AxIQCKS4zANE9iEZVvEhPasiQZOee0k//hH/7huuv+4SMfuayrmhRyHHfMVhgYVbcPb5UASeZknHO0rZ/85Gc+/59nfuYzr3/ve793z73JXL9nQItnYbyevOZZElJ6ho+1yPyP2IQJ5aTkN7JAHCKBhTFf0kxyCLShERe+WmF7jEC0QLRAtEC0wIxYQNU1ogyYEbEpIarzwyisVg+RxqUWVr4hTYgBJgQayVGQHhWqppQv0hNEW8aQmRM4z3ve81asuOGyy+7mmmQcd0S71XfunHaXSzhHp8SN2R3C/fz4f3/1zKefyTc2f/HrSx55eANbbgjQqN3TOiGNOiNHeHwvVbo44imdNbt0JFLEKbFNQ0VjQeFjCKcsDD2hZlVgqWEYExiBaIFogWiBaIFogelagC+UHHTQQV//+tdHduzgQ7xi5wPmfLT5KU95ypVXXrlj0+bpytxt9NY+Hn/88aw0XXrppcw833bbbRu2bfjQ+/6cC7y2PDyo1atk1WO+C9Gy0a/UDqI4At9T6CgRYfNtynTE2ZqItHDulNMLXOT0N3/zN3fcfsu5z3oOh2ou/slPnnT6U/NJ8cYbb6nlk+vnJKxGzu/NHnHEUdBc/rsbD16y+JTTnsQ3vdesWdc6kmmEyGQtDTcNSbNISqeIxQv6jfTQNm/ezJEPIlNvjdNH1mXiPJIpQW+KziePXNIBDGBkwOXEb+bUOQwOBrtzGrK6t4T/HrW7fszKCgssCKEzqg/GcqdScQwN3Mli12n0E2vBBVg1Xomk82pyICZTKyyg5HL9/ShWHOG6rdryfZL1s1C1gyiWlGaABGpCD9jkN6N1OFJKvOiMj5uS3h254qqoermYkt4JbebCDnUIy9pVf2inXBsMOAuXuAhXVmsmTTg3YiAJTkP33+smH3vWNiMHSAWxsEXJ0VJga9ExZHYtoLwgDjLCDQODEdDsRtxCOnWCqdSCZHbRUsB0COuoVhGLWKaDHifYhLRiNLxRGmBBEdgNFlCW7YaIZjCKb37zm5zGvPbaazm6yYnHJzzhCVyGxaFfDmdef/319DNpjteteoyj40947avcBVh9R6Rih4C3nqNoADj6M8n43BRNjobYHTPjosaEFv/22139nqIJHxVK++eQU7UbIWOEowX2QwvY2xTbX+W+qmIzC49TlopaneO7LtDjwMifklcE9Hz8EV/4JlVurFlv2rxtxYp1DGR27Bhiu1ZfPjnkkEUXXngh9H19ydpN/iqjjFvIpsodced5Kxx6Jvby2BgnNVm/ZnSJNpWx8raxrY/kHsnm3SH26EILTCuzQsbHEW7T/nLJAkc6mRt54IFHGEJRVChgTVUl4cKvW7eOuzwofgyLuD6G2qCScyfS6ZFDMLhjkMY3Wypu2LCBBhgMBGwgZMxMeaO8m3AGXF6mME64K9Puc5u+Xz31y2SSIhAtsN9ZgDfFXsn9LvH1BMsCnZgCGpjwBcAop1YUJI/cA7Nhw8j6NXe74UAl6ZmbKVfLjzzyCBPFzATuGBt3c4e+doR+bHiYqTtWXguFHghWP7ayXCpR19GM4riEaPny5X5SsRajVCaWuu7uV/GGmN0DowZR7564ZioWKeys6T/JPFNid4McTruwYnLddddx488Tn/hELoCmcaQ1vOeeex588EEaRy7f3zo6Mjr64LldXW4Xvd83GCpGWwk9GFpniiIT12ur49ghdQEWxW9gfD2hA0ce7tiDwgYxCIorEqrcbuWc2t+JT4oyyHZYvxuzVjoCCZ4lejtpge7ewrHHHkvH6fbbb/f7S6eQc8MNNzBnQuUDHdmKr6rjkEMOueKKK+iw4RpFKJfBk8vMt3DVmiZeqJFA0knj2iw6/5TAH/7whxQ86CmKWuxISSOI2In0pS996UHLDnRDAxUh7yOZOZxbbrkl1+2ufalfgCU9HYKDKg7twOS+++4755xzHl5+v1tPqRUsF3LWWWdxhRa6cS3XmC+STOfLuQMmfMDdO2pX1u8OPfRQ3h2+uTPkPk3ras4a6Uz/kGTfU3UVtQGKBHU4eoNtiR0/jNnySCwIkdPbpIRgcLiEkf7QAMALQN9bMUKgl1008kESCowCyBGjkNgKXswoOdIKAgGwyEEGvZQHYwSQhWVJUUs+QTyGUUt/8GInFFHESw2m6MQuWOwQwEXsaA5sQSisR8YLcOGQiVMtRMzArhn2qUYHiPEZrSCHkmw+K3DQsPzjeEt5vmVCcqhFURyMxY4CwHD5DHT3WjkuphRxJaderuJ8DVCKXLSPPi5x7iojJ5nv2KAPF1/6q77Ao7YT4V9P0kIoDg1hFJ5HAB6lvEwnNWAnVEIQCGwSxJvykYDe5LtESTI0JJOslyiL2hSAQNkN4BJSf1R2CAkxCgC3d7CbKCgtCoklaiFNsZQ0qW1IuAwDC9IQbqEzCBCROYnlEUDRyfKdRKcE8qUptOViSiUTn9lj6Y+QPAenuQDLfZ+IxeYeMhr5MHZVS7wdXe5kH+cJXTmp5XW56GGXfczB8F+ayLZ+sddZBucJXCiA9BcxsPIuhTQyCdx/fNlB6VUBM9+/+DXPzKVsVTbJaPg44c1uYMgFcg88IkymCHwW1QowlKEOPEKD73LenyQlFHZcXY6TaXIA3Fq7d0ICIh8u6IEhBpYmEiKBBAHgxOXkeFh4HgHgwheBSRBgXDwaHCoWMtbluLiMxrhaAX7PRKvAiI8WiBaIFogWiBaIFogWiBaIFph9C9BF9v1aN1xfdnTy2jec8am//cr6R13EDIhz7pN70bW0gL+ePsFc3/r6Le/4k+OWHpmse9iIGWZMDEUMu1cDjN9wJEE+C+fc3M8BRSZPn/a0p5133nmWOgqVRl/Mzvz/7J0HoGRFlfdvxxcnR3IWDIAEkaggImLWNeMa11X5dINrWldXFDPqmvPqmsWIggSVIKJgIA1hyDMDk/PLnfv7nfp317vT/V5Pz5v3JlZNT71Tp845depU3apb8fqBk7jYjcHut/e9733MUkHP0E5cnsULCUCwwJgWiC8eADOXxICciqQqBEbTqZ5XNKppqpZgmCGF3s8sMKkKEhbG8yDxoZSPfPGyX5M67OszwPTpHEIwIUoLTK99rAMam9FgbowpML5XwKylEhKZtnuiALGsn4Kc3tVTGsl3lyrJks2+Tu+oZkrF0586561vfsum9cvffeFlLF7MmDWDfBXcklWhVBhKVkeyFX6Dbi4vVa6sW59cOK9z/fqhN/3z+y+66F2f/MQ73/72D470rZnXHY0M2acDBjI2+8wUb+dM29XElBxBXGd3NxlIsWfF5R1jslTDjy+WgMlmOvFZzWW1o7urE3rlYs7cWRwrIl/kGiQ+OeV5Z14aApObSJBTLAYSxbGi61VqU+3ITKdYDOiSVS1ndScuzK7ZXiT39NjVYLA4yTYZ1N3dgWRu1eO73W5Kxy4p8wVRl7SD/pI0KaHnDkovJBMsECwQLLCTLBCfIqdb0XoejTPNLz6NNi7eFEPTgHEkW4wuJAcu16rToNp1inQo8SyKiyiISYj2Fp8geE8GrJ6C9VHWBenWIcBHrKdBYbFY/+vkpOjjTCYdfdQxbSG8PallRx0c/e8Hn7x6yZc++7nFHP6dd+C+jz66sjNaU8zwEaDooAN6O6JUVzKDnshhXR/5XYmefKLyn+/4yyc/eeIH/vsFH/7wpX1rc/RQicp88pKPNqEMwnFeGQCPAYivQ5NNT0YSOIJIUEfjMZ6GcsHBBY0ogcGQR08DBqfSgQyHKcDEacDgwICHAF6CwF4IgFJHDkC8747TBDhYIFggWCBYYKotoFaafkFAO8l5YvUmDSzI8XhgnCeIwxLiRfkoY3ASBOA3sHuMABsF86/C1hBOzdtomuHirOnR+f941KOPXnfXXQP0YJyxsJF1gquSS1yDxZcFy8kEvyjqJNt05IXyUDLRm01Vb7jxL53dXc957hlf//ov6DPprKsl63ArVT4MSL7wLHdKGnwDQJC+T75Xux0AFlycUjYUUrCSc4RGCRDXxPM2yPF4D4hA7JIDjPMEAQgWCBYIFggWCBaYoAVSyX94yUuYy67NbtelPLJ0aalaWbjfvn+75e81nN+0XqfZFf7SsfJewbD/xCcdv3TZw8tXPMSCE4PYrih1zbVXn/ikJ/YNrxsY2MhZETc85tXD5m9b9KAV92ai7dHtZXB03N0e/bhUvJC4wX3q6KOPfvGLX7xixQp9QpyJFe4je9WrXnXo4YczEv/bTX9FBDsYeRP459e9+mtf/47PzuMe97gHH354/vy59957z8aN68dNaY+LoBrgmKnAJqwpsGrJssVWcwm9J9NsyZYso7NJW+JdyN3LNAZ+XFRTPWnjgSoPDTcq0fCgjpvcRCMSVrFqvxYymEQr6iKqqVZIduOxdb+tGi3ptvbqIj93edY26TfCCRNXFZpKq4UtQtRkWoAHmacYn8aQZ9nPo4KZQDIaLk2Ml+Rgx02YXRImwB5PFxg5CGlfE08sXtlN7HGM8GP6dEYcooOYjR/xefUxieNIJe1TmUDevTQp7IM7GFAWdpb+NELUfCyPAjhvz/aNsP36t5+WKLnoCif4hhtuaGBnpYYnOtvdy0FHohrWGgaSvSCzHY8yyTNcnFuqlLLVyzOVaVG1v0GOvd4log6a9XLUX95oE0ZRB5ndt7IJyn43scN6i7fYkPsqGG9MmNEO0I26beoZRtkCFCywB1tArQ1+6H9di2HvABS3N0s7Re+JfSsEF7Dw7Uig/RcvpaD+l68KImNmrjB/ZsdxC2cfeuihi373pygTZXlbX7th86MbZqaiJ8yODuqMluc4JRzli8k5qe5KTxd7ujLTuxnpdLnbft02LWbXy9PnWDuM8GLZ9oBJKx3MI7de4Xa0nQoab/YJC5fBJ8a+5/W/1YptLaCcqc4FjpAn3NUF9ItuxoMQ9aicsGHt9GqyWiiXEil+XGpVTmUqkX3dk9gy1zu7o50rNvekszMOW9iVnbY6Kq+0+Yhy334L9x9YMwJ3VyXnzT5YNJh9h1T+pD0BvMzY+wyKVCvVUoKPnLDf0ZMHIFhgr7aA2j0m+tg8D7xzx7/bXxITboRhxKGAOk1rOFyvhA9esAiSVbuppKyr0p3GigUUgXIBMl1libl6Xjk68MB9F1bTy5cvL0eVdH81PdBX/tQ3XziztGZD9AAplqK/pJ3MRDbKsx07V+3szOfWrhlZyxUfNHVcr8/4hR1T7ENDrOujrQkDlm4+XQHNeKnUwvcS4G1B1iIKCbiJscMYlzxhIRNgjLOgBrMQDVsE44q1gJWFuLQWxJMSxa09OIm66qqr4jJRo4sFFWZNt9hhZyT1D2/YaHSELf6JSpbPg1WrD2Yq/QtnDW/awCRYlHLXskQ2RmY7RCKdGOg4FJnlRG+hMj1VrNJDz5iXZQ/kSMe6DRvs9c865ELW+vuq3VnT7c400NVTm9ikP2tWT0e2hw2NuQIvArj4uLj2pmroLZzGy27im17bO9WUyhbbHX1kAFpbgKaDwxHcoKdK3lBdeeeyOtPkVLf9S7viWcPjHxJolDixQSx9B80UEy8QcOCiQThI5IAkaj1HSoaGuKCqKSkkJvkd8NjHvOY1r+Hu8s985jNcZA4ZYwb8F7305WefffZ3fvD9yy+//Eho7SYj++lb57TM0JALVKKxZQ3rvFe/4gc/+MGNv7x81qxZTC1yy+rcA/d75Stf2bHv/C9/+cvpvr587TuzJgXnZvMdNJWetwwGwWIUCj5dMIVCI0/KZAH74GNVnKcnSmUBABcGhwteAKwKJUiiOIbDpl9GXkjAxdlVvlAiR1twlS5CwICHHZ9GQAkBawQHI3KElA+l1MYnXaTRICCNCkAFgwu8uNABYiUtZZQQciQBXvJOkFiIpblOM4ERIxJw0gEuWBAidvZLA4gdGuh5mVHS+OQFH2JhIJD+YGRDbbfG59FAjmyYiFzL464TqpTKVG2uGHJFwbEgflDVzBVVs/ardEW2mOW4qhn7gA1tn+tMSYgYt8hL6dAmWjbkk5wZhJ1nAtzObTTEhqhNlLXGTQ79TabzMbjKUZkiqP3e+ORXTlESY8k5B5JEocfHIRA0sTiC1CjVIoKiV6yHYcQExMJI3UMaQRKl+AQrURQQ4BkBwMCIj5Py8EpVYhFI0sgBQ/YJNgsBCS866BEAlnyZy+Q640h54HjqE4aRhiZSDFhBFRMySQUMgIsBGrVbixThspEwnx1K2pV/1SKfJqKqWEIMbI3RtX40AYBl28Noldm4KnTN3dUcL6PZRJn9iFU+72lcxQIqVlOuXpWLyjkqWVEZjdUK2GVVJ95aPqLivo8FDzFRDW5MJDSS00C8Wwd9TmW0eF7AeAceWOaKG8EjATwNBFhYxAA4L1b0Vo6uRIQX0sMEVYiSQIVEgnuIR89cKDlYasLdjhuPBIAeIZJjGjgHnr9KiCgBDZoI2dr3vAiE3QfFpSTwwXsYAMeNgK0lN8Q2vd42xIdgsECwQLBAsECwQLBAsECwQLDA1FuAF1m98r7iFYewEP63vy21Cc/a6/TUJ79HpPDVr/7uuf8w893vPvTtb35YGWJAxBhKI4o9Iou1TJCj+ADpkksueeELX0jcwQcfzKSY7rSqW6DKzBojNybIwMA1f/78H//4x0984hPf9ra3/eQnP5k7dy4TBMx+aiKmlkD4EyywNQto0lBUmuHCJ6gZAfnxiQDqnh/YEwslGDkvBIDaqKk9YLEjFgAnYiozs3hKS4w0niDjGCTgUAcuaIjCwU6QpOVLQxhhZ2IWMjBwjeRG2MfEnDpPzNx50dvf/vbBzYWLL76YE7xMOE+fPp0vdSANRte0WEakgAms2CQ7APdUPLox+vznP/+Jj7zn4x//zwvf+7FHHmEyrpx1N5PAwgVYShFR0ONgZGYTJA4NkU/QMBU365ruMNhN4oveEZpHECFMawqDHAD5RKEbDgCBWKBUsu/VQCDLAICEGF/LMBIiPDQ4ChoWgB6uFnF6EiuZMAKQuucCgDIenFJYiimJHZnulGYqCA8WCBYIFmhtATXjogHG0RQT9C2/kF6IWmyQHtNA4PEekEAfbAFste3dKoEXTmcFca6vj41Lc/aJ3vnOfykW+y688PKlKyO+Xko/RW/DN5bot1mvpG8aTzLd93vf+6Vvffd173vfCz76gb/CWK3aSmclZUsIcIHxiQKA8S6O31ZYVvW29cG4MT2SFD0M0EwDRgSo4YFtVSnQBwsECwQLBAtMogVojceUNh5+TOIWSMmRTzfhKb18DxAVJwBWrwHeCwGQ83IaAIuto4AksLsjeslLXtDf/9CNNz6yeaMdqE/THbnzijZudfdUeLExASYIlfnU6oxZJ7zudc/95S8v45u9xVK+s6O7UpJwn1o91bq2Cktsg8xR0olCExDoWbAJcNzUaOFjBROMYyaqZuALFggWCBYIFtjbLfDYo49m7eYLX/jCFoZIJk485RR2tPNRaDbzFkt2gmIXdOyQZmmJGYnDDzn8RS96ETvsezp6yvmhzkRmpFrkAinWoZjW5vYrRud6IRjjtWCXyRgzBrjnPve5Gzdu5KSB0ys5bca0gb6+jWtXk7szzzzzW1//KngWB3p7e1hrA164YOamTZs3j1S49YlP1IDkI95r1q5js/0uk7MdoYjei/CpD7pio0WqDS9RDUExst95VIJbwRkNskIx+spcQ7NHGoitr/jsG40Ra1NoTFotLk4zxq76mAQDeT10mDhXA0ljMLbnu00udzyad2smr0Zf2BvFWjg2ZBgrenJxaBUTWLN8Q47qQaldo7e1qpprKq96hPvruKgGtu7WOuNbsIXAlFiAQRADQJpuWniaRBZtKRQceFw7SYoeH3bo4QJuhzFOA5emTBEyAXZEwYUE2NtUe8zUQcZz0aYoyHBaQoUdRzAufzxYZNhcH5agCLS7Yzz6ZjxbROiGWEanI6Mc45PMzcRjYtAB0+EABI9J1gIJ13aWHew4n3eUaZFcQ5R03h79sZtORWJJTgkiH5kNqbQObo/+rSVPONZnAd3GE0JFJdZV2HqT3kDKe029KLwcJGMxCLXNwCcEptJZuwzLSnDbTNiQcAgGC+wVFtDTFPpf37zQHFHwmEVNejuVAGLchPtfNYMkF+9/EVgsVY877vEvOOPJM2fOPPmQI9Ekm7DDurn84D777PNwaTN3Jf/8uoeWblhPe8hesnXFkWkdvQMbNvROn85uTATiTCt3/REwZDNnz2Pleu3q1RyAZweX5S70v3ti/+vrLeXu4WaAauYJqIe8AnEAGDJ2GBLFDR34091Xx6g2K1eulAS2CDLT0lHu4dWxVBzwYqtZ27JIEF8OdoJJrvnAqS/fxncbLzwAwQJ7pAUYQ+kWA3KnMYg9K22PAvSg4cd7rm01FM+pekCEKPVtlQA9jHret4lXycEo/SXHS2ghkCivKgC9W10Ba3PEePLJJ77gBS/IbuhnhDWntxe8XVCQSq3bcBdN158eueNX164i4zRog9wk1MkeaRuBs+n6wAMXdGQWIiSVsDYNGnwcEhYtugu8HEJ4d0Kgk1y77kRkYNpxyHGCd9r4FwUYv6Oq3wK34/WXEfbdd1+uG9um1GVhSgeH/gQlqh3LTx2Nr8ntJKHS15AWmB63BRfjXnbJU5nJbL6Lb21GpeVR18EdQ1z4xuVHTFXX+l/7y8ibyen+/qFENIJ9qMUYp4XwELXDLNBcEKoGzQqM/Ti4EyMQm5x6kVIlaI54linrZvkQI4qorVZOOqOjjjqKG9OYjxpavcyrxDdXcPvttx+zhR7ZAHDkkJp8IK3nggX7778/xAxYQDLcoJ1k2HLAAQcMd9ltU32JxOzZsxvYd0CQtloWwFA0+4y50BCAXOOjADbEUCCByYuI4/YkFoclocEUXggYWIiiEycVWITBRyZRYIgFwAGIEVMAxPGkyFonwvGB1TLA4nUQMUnAiJP+yAGja7C4w4jJMYI4hCBBvDACCE/GsQC5BoALJYmF0vNKSZ+o7ABSNoEXR+qwU7JSST6iREyFlAV4p6gDHBEyK6EDCsvmsKMAQQRCZilW7S4nWjqT4y7zJY84jAEvDrxXTJjxfMia27yanm6ljzNgKBPXnyA3oOGbDk0ODSFGGfKOzmQfSiHxZUAA8o6SDRKgRB6xOBhxZBlpBMFLB3EB08iDNAs0OehRAGn4mA4D4tCZsiMKFvCwAzdnHsk4eEka+8MYJ4MedioPNAAEAbwQKNEFX7zoT0LSDhrvwMDVpPV2IRCOMRGLYQF4xAD0oKGPkpOqZK2aNjvDstUkYcGhL5RcyQYLuXYYYxVAWxsXBQHPEz7WMxMV8/g84KZhwQ4Pcm8YGKZq8BECXu92FbspyxiN0lUtwQRxlp5zwJYFh/ES6pF79V8rqnHceHaBPB4l7ri1iSUojPwGepBxIQT1FKhoVPckFkYAXxu9HFAQK8pLgwwYJzIvgSBIgoqKE3jYS24NQK8K5skQC0b6y/dREwBqT/4EOANLsECwQLBAsECwQLBAsECwQLDAdlqAl13ebnl55s2ZG68e87joZa848+vf/PCdt9g0aLZzi3tetjOtPZGd/VJcQ5PiqyFMaAxuLn/lyz99//v/45iTPr3IvsHMwMaGJXtexuvVxnLHqPuaa6658MILP/GJTxx//PEf+MAH3v3ud/ss6/iB1ueYxDzttNM+/vGP88Gx4447btmyZYym2AEJMXJwnisAwQJbtUDBfTdMZEzhadrXzetzJbXN07FCXK5PfULGvRZDw3zsc9QxDQeNu97fkLSDBPPFQqIIbMds3XqBTYVU+BKZ42Oj/dDgCD9NZDicTcsODdmks4L4fFMBTLFs89dUctxwboQfKSbrpyaY3+e5oNrTAmv/fjFn3xCYxpqiE5QrRq98xVMOPGD6mz548aIlI51dPYMDTIYNbR7Ms2DFPGsuKg5VbB4NITAivMCX06pMpXUXCiPzsuVb78t94D0Xff/733/9G0989/v+jn7ddlO/ZaWatyvnNGOCjxvMjeAjRzOGmhtlPYPvIjg3zH4UJu9GckMKy2d1DBpgk+vyMm/ePFQaHBw9E5XPm7nIa53RDGWzdUz38X2lqIQmmczASA45TJXWyLLDrnRiX23qynVgMRKqy2EzqyXKXL3HAICJB6cIxlbxtFTKWG+KkgtigwWCBYIFdh0L0Gh7ZWiWcVqeUcMoTLxPHA/jhQB4GjHSnJJKw8WIBHHqp2h1ae190ItyJEYDBl9BfGkoMiGRT1qS05kfZkmq0p0pDxWjysjcKLrg/MeccUz2Zf90xR1uD2e5GvGaQffiunrrxeiMRnKb+YFXciCJ4Dc8lOwbqnzwP7//uc997tnP+/tPf5rftHEFsdW0rR2SOpmVJvKlDxoCxPHNdiYhjKxuThaL04NRplQQ+MLE8y4ufISgTAsauOipJUpyGtICKX3i+J0Io+qOeQHYiXkMSQcLBAsEC+wWFlDfEVdVGPlqq2stthu3ZXXgwR2oeMrpB2XTA1deef/qdRzjYQ9UtWxD5ST7AuyTawzBQDHJVv85VvbpROWSDQm59eGGP9zMVyUf//gjb7rpPt5Xcnm+mczYk6m5xnuT4xpODI7naGIS9gwu3ijiL357RqZCLoIFggWCBfYuC6SSixctWnz7HeQ63dNt32927pWvec1tt922/NFHuUOK3e133HEHt0tOmzGDm5h2Qftou+2//Mu/ML2fzPC964iv/fIKwDg5l88/smYVG701g2y+Teu2ckldM9T2HLOOhk7WmHTOnDmHHnrogw8+eMghhyxevJg5+YG+oSiRnTN/wbJlK4444rFd0+YUhvp5JTrjjKdxFOGMM07fuLGf1K+99trVm/qXL1+5eXP/mjXrmOXfIdPkrSy5U+L0ZsIm7NapYzFe5zQ3AqVe7RpYVFNY/cWplBsIGoK8sxrGvdlucWNTrSRUpWyzeN0pBZtosrXkrTq3tMRa1VYJPQEpq2a2XRkqTAbVXrNbqiSBCI8ti/lkJwuwDeu2ZrSlJsmYqZyxlVzNsEzimduSRRQtfCoAealWavfdW31o22QtxIaoCVuAkyRMvtKkMz+J06OqYmpHJu0AzsrUlSPsdBOC22H3NEz8AutEyrayM3EKC2qQOo5ceLFbBeCCRhIA4EV/5WirvGKEHhty4gUdzILbkjoSpAC8bOfgSE87iXoa6DnbxtyyLCYFiB2zmfVccQAW2GU6KR+f345TtoCZuyYWO7RfbSSNdHGamYeXsotL2Gou4EVOXH8wCGmhajxKacHOKSZ0wP5bTTHOLpia4/Wn9kqlZrIdjFF9INHmHMk6Gde5FVKdpYijQtUMjXlzd+fb/2qUjnr5Klg+1YnER7pNbMXBPDre4En38pdwPq+FXge0cNnXu6F2yu1ge4TkggV2UQvsef2vmvE2za0Gc2L9L7y47e9/1RH4/rfkXnbXpaNF61b3//k6jrYmR/K0Zl0jdih3RqmLFO/JP0w/v6o0jV1cC/bZl6uQu9LlSrKUSdjeKi5SRKbsoAuwsAYSBobsOPTmjRvJLy0kBLTG8f6LLphgm6bzZKH/xRQYfNfpf9Eny0c0ExFfF+Gn3WyZ2uxIiYFfOWnvqJ0VO4A9mErzK6UzxWpUSmep0oXIYlNRhjqTS9hr1ezuwuDM2dnor+yTLmZtV/lIdSaf1yxU7fIUuTLfJDEudxg4Gt3bgCZIYH+mPSx2R4fv1+uc4W+wwN5qAfbi+rEbDbtrks0WsdfXVqZR6+3pacAn9nxNuA2nK1HqKIzyGn62rwNtgnIKoLwjAbg5z15m0rVOfL5QNLDT8OZyeS7LSKYyHW78laalqVavv3/R0HUdSx56iNE9G7LA5Kt2Mwj7o/GKPR0PR1FHJdOd6OwbxuKdUXW4o7c3P8IdkcV87h7kJyK7nUROevrMAvDuxC4vtIUAsegP4MmkXgsfYnixPCwynVlgrLy3EELUhOcuSJf648ePsn/rtOKxsEuCyg4LECRTcZoWsGzFzA971ClBNtK3IB4vKj7+5fEZj2xXwNfnBaxus/eQES9b5+3SKoIUOqZr1JJdkRwiwE7Jkagrl+iOemez17+YHeFjIOX8DPbNRz39g2vytgxi39ruoGPO29PDkzBSKtoufErDZpL5OrWDoypk5hBKeanIfBuiKMbUdQDNnFLVaQ7jzJvY4uDAKGWAttsCeiLGFENUM56Cpib5KOq/YI+BhUeymXFMDPcU8btt8X0XXfxprsddvn59D3MudfepL33h5jtuu+mvfxumyUraz97ookq9Vhsdn3WnNWMx8dJLL7151VKWt0idR5sHnOf05kW3D32nXJnZe9dDD8xmk3CxyNsgv5y9bEZdo0lZcEod7bYuXcKfNm0aq0j0wiBJFD1pkey8TP2eGgCc14dHhhzhsDbs4gWGnSimsnmaGI5Z0bjJVfWPBMEjBwBiKKFn+pR08cGo/YeF1JUWjNDEn03YKVkcicKl1Ht7ewkCIwFiTE0UY0ac5KASLMTikEnS0ODIghihJwoa6JEDIx2KshxPXdLwoUQIvDipQRAJ+MTKZ1oYWClyFAlpOEdjCZEKGZfmaIIcYJmFFLkACysVc9xwxDCFRpI2K5nL83pA9vFJBdtS7/hRZPqloqrdy1YpFzE0lzhh7FKxQhtYMbJ0VLXVJHKJZK0rZVXr7BKq2h1YKCCd8XHYypSJPUHejLI8Po6MgBevTA0LRrAsbPlZZchwJAeX8q5yB+lTIVG4kAmG10KE4IDloJSdYaRuYDooAcCTIjBlh52RgHwqErzO8KO1F0qcU7yLpxVlEAiN5Aug7JCgF0Lw0CsWSgBYEE5alBqwZ1RC6E+iDXYTzXb6iJVxMDLZJzn0kXN5MiUB0KocWW2E2Kqi2/eufNHU4GRNShoGThiSObhMeff2Uk3aJVaiTyStRvG2acFqpexuZAOulG2Em890oVKlYAcGSctokmbwcsmWb5JFe9uEHT/J6TiGyu4Rqzi87AMZsTgAPTXCeN/URclYBSCItuTOI2F3VHud5yxX88h8Q9CbA3MpVgCm88SqPJ5RLARFoGCDjxDJ8QDPIHIIwohPuYjAMxJUeSlp8Co7kYkXZJxLKoGUMkR5wIvdKkAqXo4njqcC7J0naB/YpV+1289GoAwWCBYIFggWCBYIFggWCBbYTS3Ay64b7pn6b37zCxgifuc7BvP+n+MKFuZHazOfhgyu2QLcfgVSl7D89KfRe95TfPGLj77//jtzm9lax/cxbDTCgKGZcXfHaHDFSIxh+Ve/+lXu73/729/+j//4j3xU7Etf+hLTDUQxz7Vw4ULuxDnrrLPOPvvsgw466KMf/ejXv/515V2WgUyDrt3dIEH/HWsBm5KQ03QAT5mrk6wN8ddcfdrKqGzQ7taMDHYhJo9gtBkqd6k/MJhs2nZviN7YHCO+Zh+cVOfV72lyJLX0RCx2+Z5RZPEpDCa/FEuTizDoeY4sC46UNuWEI6PnPe95n/3sZ2++9REmazVJCkFJB36ZhHObkJhw1JyjBIJkFrIYlXOFaF42uvPB8oUXXvif73/eS194y//9ssq5j1K/bTFhsk4PHbAcCLOGa6zwNTcXP0xRqdodW047poJrDRoYjIbmKgJixUifghXV7EHjcscNKcYNjHNyDCZ1eHEOMypZSOYAObAsYvyaiYzLcPA639Lyzgv3mKkA4qmgBkEUZmIXDTGsFIvTTIUOQWawQLBAsMBOsUC8cQPGqXdTSy4MvtdtPIwnAGigISiZcZoJwOo11N95dqUVTxQMQWu6q9HcKDrllONf+tJncWHrbfeVZndGG3P0PqahujZddUhPS3+HfBjle/nsq2JfyZ9vL9KDv/e/X3XllR8fsRePKMWnXuofd/LEzQBqyEkrEcR1bsB4CaL3lCoOHxvn8jRxwFMK6aPAA/vYuJwxoxooJzGIWZo18fJbx3qyAAQLBAsEC+y+FqCh207laUXHay3B03H4gUw8LQ97wKshgQR9lABEAcj5WHVM8SSUH8h8837EEUdcccXvl660TZo4WNgwAgEwNG5Lk6XlnaOykJewelXp2muvff4LzvvrX+9zsTAix4T5/hp6whIiGh9sQPrYFoCkxQmaMQ2xIsD3ansMlA3sIvME8Vhgubj8HQCrqviEGoIeH4BggWCBYIFggd3GAvSUmrStT4BL8x9+97t0S/wu+8Uv+bKELsZignpXy5dO+AzmRxY/eL/XbVoyM1ApdkdJVqwefvjh2IcZHEnjGNfzudE7Lx42/c4sfQu6GosmyenT6ZRjUiYCIkHvBhs2bPjUpz6FCDAzps/q69+USHaccsop6Y7OG66//vDDD3/Oc55z6U9/TOwVV1yB35Gs/ORnv0xz7rQSLZg5jReqa6+/3u4qn4gWew7PNk0r+beyhvyrBmhSZOu1AWZHVOOKy6rNqzTHxDGC42xbwJQ1YXwtZm0RN05ANRPfA+MQjqKRz3IPDgA3GtEEIRMcfs7dMqNgE9UoQtLwPWVr+cZZ1Rb82tbkUVlTADER6aSOrjlOQSJB5DZYgMMYHDfab7/9nvSkJz3wwAN8SYvjQ9QZVRtfi1pIpBoTS+MJ49KlSzk3IkwLloYo0jrzzDNB/vnPfyZFgu2kKyF0l3Pnzn3sYx/L7ogrr7wSpNdcgMiafaXC6FVrr8985jP//ve/P/roowsWLNC3iJpZGjBeT5I+9dRTOXjGFZZs1WggGy8o9Tgng/EPPvhgyLgNczziZjzsnDbhtAx3OB599NH33HMPZYfzp2KaWRowSGCMifNlhzLSqoGyRRD6Jz/5yRDceuut7ZeaBDLnP2PGDPI+c+bMm266Scj2hdD7UHZx/bEGwRbaEqUM+mqG9XC8D/iyg6BNHTh9jeZef5L2YlvrMNWxpr/rVVpbQzndema5IN1bFeoqm9myXTN6XU0bPaybKNkyU9+GzUYyfjfr41QQU22KID9YYFe2wJ7X/2JtNSytH3DRbE//S0JqTCa3/7W20zWe99y/cs2ylfvt15st2jgvU7Wjtpw+5ZjrI2utfe2PBjoSdmh5//33T3RUb7nllmnTp21ctbp71kw1mHRw/gIs2sZNmzYhhyhaTnbXGeyCof/dw/pfK9ptdNRkaggOwOZGXBXk6Zg3bx+qDa8ZBxx6aGGpndvn7gOLrcyyAWTsZadQtLtg+jetw7epFdcHOzlOspPJApLw26hdIA8W2DMtwDNEK80cJiM4rsLXGKrNrPJw8YRqwOXbcEbBPKTtS4ASOfExFMF2HlL3aNs+4VmzZvkxSJvpejISUld13nnnbdP4V6lLTq3/Xb7+7jtuS3SNHnRZub5w6ZV/mpHlpio7xMGdIdOnTacDXblyxaOPVku9ublz+VijXQXysle8+O67775r0V8Y0OW50rm3fi0U0+JulOptgsI4pQvjSP/wgv0XNsxd+Ny1BiSZ7J911lm0sdwXw/iR8WRrLh8rC+DH5y58bJsADTuzzZTgddddB4tyJ8mtJUAJGQ79fd3zt7e04IVFCQEghOkOZrOZ4V+0aBHGb8HYHNUw/uVR4nFoJtuVMbKGut1mPc1EFXtAVOXmzJ8PUE70448kelIzZyY75pDrRLUHCZWi3d0TbV7Hpn/upZw2LXrtq0/l2M6SpetuvPHGB++rVVqfCrba7czllQ8AFlDxqQoRpCY0m8XHqpltJmjA0AQxDesWDO3Mhnd0TIwvWGhjxswjGwA+68LNODgmRe+8807aNPbfUlc52MKjvXHj+oceeogLsAb7++cmU3a6ZYc7rIEdUIYuEj119RIws6b4qIpWPEnQkAWcDaDq95ugLOw47IwEfKahJIeOQDehQCwJENuj6g7U4AMLIwn4SMCRLg52iYUGOaJEDlwEJQetwCMfByN4xn3w6gIvYJBoiyjaUrRCIGYnafDACMGHS6oCgIcMRhwywSAfXvAEEQULEqQGGF/ZgGUB6QA7AMJhRIL0BGAenvYZIZhTWQAJI+yki4MLB7uzQc0IpFsp2z0yUdkd4KrqzJGdS6Kdw0dICwcBeqKMpyFpYPCGtD1jSUoFNbj2CoW7u8wgOGIlXNkUe3N3Bg1cOK85rxPwIhAW5OCTIjUHWNmHxZcdlPASJQkClC5cAFrpRgcqJ6K4FFK8pnys9GGUDanAAMRCpuNjSMbySAMwY7pqIyMgAUpScYav3YCm1CUcegAc6UIJF74w+OSRILzKAumihmJFiU+WqTDilSV90l7OBAAJQSDyyRE6g8FHE9LCIVM+BOWOIpWq2tVpXPyvv63F03VMNY9a4XiVUytKGS1pKSRTuja6YnVbSaSSZpmMu0yNYS7EsIABbVmuWg1MlUbfZNJVuyErKtrFWFHaNJd8yIDljLH+7IDBhsqpdCZdkEodlUDKR46iRLbn+T7LAA3OZxYjYA18TOSREHsYgKAsJhhrCyMy2daze0Bi43LEjq9KAsGonPoT6uklXLtHoJRYFRk00kcSvBAAnzoAxJ5S0rzwCQNeTjxR1Kjw7a+YAbcqf7R+b5U0EAQLBAsECwQLBAsECwQLBAsEC0yWBXhn9a+tZbe388jjoue96DFf/NylhRFuQu7i/TsR5ScruT1UDgMnbOeGuFEHN2GVR3Jf+tw1H/v0c37y8zvv+kskw+6hebfBoWoRACPDd7/73Wwl5Dvb73rXu5761KcylaNZQk20MYPJjvybb775T3/6EwaxgdNY48891VYhX9thAdc8iZ/vgelnl7Db1CF1D6e6RG2sfWTbEYNnUsmna0EmGWthd4jFVd90yqZKPZmv0h6juQbjdWRMYeAS7v4p0dQwTj6pQBbHFMt8rJQPt1gSNovhjs4gs7Or0+ZJkckMjC1JVKd3dTHhtcGtxB2zMPr6Ny64+fpbfvDdh/hYOarzOQYIC0xu1DQzgaYtudxi0oZvv1SZj0TMcCHiE7c/ueq+pz/rnrf8v3fcdvvF9y9hQRcePu6STiVS1YS7rcnJL3NKxqYzauKZlkuk+doffQFqk1AylbTZXiXuz/8gq4ZxE20+CKVOPRPLpLErIH/shkJhCq+MSZxAv/piDamTbDogqiaNj7a6uUWEOGlwuVlpmzi2BftSaXBnrImQcs2hnqaAmZHX3Df5krZ1kvA3WCBYIFhgz7SAb6vV7uELU2vAXaZpDz2ZrNAQBNnMNR5NnBgaBSVWMDrg1Cxr3QLfE4hGTTTsdcDeFhJ99JnRwftHF33g3B/87MbLfltKdh2yyT5ItZTVKJbu+HTHyAhdkgljUR9eSZYahnWuM9qXvz3Rykt/ef+5z/7LN75xwfnnf3nJUr6SVulMplmnZO9sjdT9QQ4OUD6aIxafVwVPRhINqYyJgV5kPjYuRLE+iuQwlKdvSMuTeZmewMsZMypONrmwVHXFa2rzPSL/DUBKB6OxisnoY3ITDdKCBYIFggWCBbbHAmq6keB7HHV2asyTbt8c42G+1bfvftE555x5y9/vXr8u6knbzpKyG2hCOapAbeg5ivBQPSHrOm+9Zfnhhz/wpn9+9Ve/8V26haq71d93iL43aeL1iABsxQKUDkNg/361RRlthTVEBwsECwQLBAvskhZwM7/SrDKSG1VRK1t0xcwPp9LlivWpvv0fJdsFIP+mgS7oy6h9pFJkoplP/C669+7x3yDGUt1R4/nZ77GIRnGerP42Mhq1/RAyuf2KlTREse7GJ7j5IPIvf/lLSbYe2W3z5WIUZj1YMyHjGzcPXH755cXinr0suRXT6uWE17/WbynENrjx5KbdgklsOWg8QldUVB43nTM6pwN5bZVqC5yTEsPEXnvHTID6gLPXaQq7Pv8zJmUcCUv7xKLUdvm4kEmBTQ97smxs4EC/VKfQuH5Sh67rXCZAstxoImbBmgRXXGNJa21hJx9zUSvGYg64HW2BtWvXcgHTpz/96Wc/+9kcvvrEJz7x+c9/nlMl7XRDnMHgMgj2J/B5g1e96lU8NT//+c//4z/+Y1vr9gc/+MFXvvKVzDdeeumlb33rW+3p2xb38pe//MILL2QO+Q9/+MNb3vIW9khwMAPXQoaqn5qmpz3taWR/33335f6pf//3f6cXYP2xHV41FAcccMA3v/nNE044gXQ/9KEP/eY3v2nB2xz1mMc85v3vfz/Xb2HJD3zgA9dffz1fPmsmGxPDobj58+e/853vPP300zkY8+EPf/inP/0p1lNzBEvzUxbHQHbooYe+/vWvP/vss2H/0Y9+9I1vfMNPAo+ZYjOSgnvb294G/ne/+91///d/NxO0wKAMR5exOVXokUceec973kOOpDxRAHFtG+SgMMdUMDufi2PUzGE/vh7HEe4GsoZgg0BObl900UVcgMVyM2XHCcMG+tZBDg+/9rWvfeITn0iFpyCuvvpq5PBQtObabWJp/2mk7b0nKqUWPu28BT1nfopCoVioY3lWYGz/AdF2HMWagg0rV61atemSn/LClk6MQDdmTn0RmKDQC4xpo4DcayywR/a/NM482i0ecD34+LgJ9L9wUUFohXCT2P9+5CMf+fa3v82YhnFNLlntmZkt93Qe98znV7rttPD8rl78dLnwf//3fwMJu9SjnIsWHDz/w594J90QQ0N6kB/9/KqO6dN4d6JJ5FYL3qM4cYmSaAtm2aMrORybsW659nXJ0P+G/rdCd5pg6qdYYX0mTd1hUx+7oK3v5Zj1hoHNHEhfvrH02BPOKhxysC24VNnJSLSdO+WkvKoWb7/5zZt5ibr1Jz9j+2amVILdHkAbxhXpkVMRazb8s1P9enxMSHDBAnu3BRjz7rPPPh/72Me48p7GmYHk9773PUzinxEPjGkn2nYeqC984QsvetGL6B0Yg7zjHe8Yk7IFkq3+b37zmyFg9MdwTP1FC/qGqKc//enc5s9WKA4FMBi89957gfWY4zcQKyg8PmmdeeaZX/nKV/z4l8MFjMTH5AIJi1yFreO4ZOKwww7jQ930gMsefIQe8IrLroRsJJGmsVk9axaiXvnmN6xcubK7u5PRVjpT4oXn+ku+/0g03B31FvoLM+f1f/W7Xzzs8Ccymvuv//ovhvCMODZv6qOpOvbYJ2isQTPHcJ4rCe6+/Z5sz+jwqn9z3+wFc973vvedc845sH/yk5/8yU9+Qvvpi8wDPjsoD2yau/cTmk0M/prXvIZqwPXZn/vc53JsJt8W94Y3vOF1r3sdHDq1sS2sNtXP3VWc9aAUqD+8fjDPzHuChKAqSo4nkCi4TjvttDe+8Y1UPHa48U10bsFuZ97Dy6RE3vve9zJ7wNVvTGLccMMNPqodgIkjjX8pGgr6b3/7G2bUXsF22KeORqW8nfK5B4PKMpJiq39iUzp14nnP7D3zTGxetjM3leFqnuIr5uyKnJmpbqpoV98Advjptz7ITCrz2MkMne7mN7/13z//2W/ec3s1zWc8mUWtVz/TMFEwz81nJxK1q0yczrpCzk2CJW1JaP+D+6dPj0o8cpXowbtG6/92ZnCPZP+Hf/gH2lImo5iTpFC2KY96pqjAzMg97nGP44ngWnaaCCaXWjyJSiKeFs8gN/IznUW7R4O8TTpwDyP6r1q1gp7I5l7qjonNM84445vf/r/LLrssiubX0Vv8JTnUoGoxl/uMN76ascwN3/uJWk6Qxx9//D/+4z/OfcyhzPGuveFvNJhbMO+QgB5MlMSeKEajQXPH7B9OBuTJAgCJgwAny3v7i5cgBBLi9hLbTVhEIR+fqVRyIzkIhJIyVdLg4VIfAQHyYedsnc+9Pn5AFE5CAOBFjqcRIA2lPD5i4zQ0BQ30EAjjZFvSAAhhtpk2hCgIPA14xTYIUVCU+JDBK3boiUUTZiNJXZdAkXHwMgK5kM5mVneDGFEAWEBBpV4o5+GCBSvhqCf4soAjGLtLsqGGLV5z+1+FmsX4o1Dg4iFaLe4oSpe4hCuRgcKVAmXE/WWWNHnHRw3laMzM+ixD5s0CPYxkFoxgyLTuABmw/DEFxuVgOnRAAophNDIODBfW4zIsp+2oDKLglS9L4uPA4ACohH7to7UCxMqRCxIgIdh9SkRhf2R6TByA0vMKIFZlpIKT2pCBHE9IXGD7MFqRBPRSGIuhgPKOT16oS8SKzKoldK7LE8s2KYNkuPCRgN29fVjmBTNs5++4J46ayY1XVny2gaKS6s1XqVhZd9FVTc+KzcbQzVrhclmlc2gogQCQyXqKwkdPb0kyJTxkOOUXjIJiF7z3+OTa2wrABwEEYwqVtQqRIBb29gEJGQQ4ABWxfGjatK2EQExJITyVsfbfgPpzRBTy5cf1ASMkvldptwPs2QguWCBYYBexAG2NNPFAC8XaoWnBHqKCBYIFggWCBYIFdq4FtniHrkbpTPTWtx7NDe4//vH9jBP1Ts8ICmCq9fRdqgdapNgOTQv2qYnCRHwFO5FJdXT3Rr/4xd3Lly9/6UttZqpSHzrtkmpPjjH8uBFxl1xyybOe9axnPOMZf/zjH/kuJesK11xzDUt0zO2+4AUvYLOmbr+iXsFl9nE+VXEHVLPJyW2QsstYgEuj+HFhE/WH+SzqUtkOyepnUxs4qpj/UcfM2fYMHtfabCAPJhdU8e0AfgDMmSpoMP/crGIuz4xijhhNWEgyPul6eEyASY0St1+ZBnbFu4YZxuX4AMy5eROmX+15cLMts6JoZhS9972nrlmz5ktf+lG9CbEtm7aQ5B4ZKHVTFUFm7jR5xwQK2SGLNp2XSDE9yU0eLDqxJvyFL/yG6zbe8Y63TOusPXQmwTkAOUIoySzOlkGbD1IngHmJcveOjVq1nglNGCpkEpi355MYzpplplad9STYNlQhs56OMXKAylJJ0JCOSobamce4BMiPS6ZEmPDd8QsiaIur5ccyWzrqqKNYy2cxnpxif2K98p4sAMECwQLBAnueBWjr1Nx53yG22WvokjCUMM1+Q1SDSdWXSRlFxWFh6OwgkxxgOYL0XgumRe95z0v4sN4ll1y/YiCqjAxXcwP0MvRZHBTh9ivItCxC0HUFo32BhOPnoiF+LGjz+8hHruN8zjvf+YreHpM/UipoudoTA6CMlJRW9Cl65SDoHUoCN3DFg8ASgt8AxMmIQo58sYi4gSYuAVjBOI0UA9OgVZxm6mBlQQef2Dn35S9/+cQTTwQZbr+aOpsHycECwQLBAt4CzZ2CjxoP8EMn16Vs0a0QYLvkcCk66aTjYH/44VV0u2rnx5M2Jt4lYVOX7h7/xA033MARxxe+8Bh3+5WN14hipKy+O67PmNJ2ceQEimB7ciSjNUjgLO4LX/hCbciTPt6qDZQhGCwQLBAsECywG1ig9V1JDGXdpxuUEYaUU5IjJlp9P90G0KDDDu4cG1Kf+qDZnImCuGOWgFzni4WhkeGrr/4dFJwY0DrukmXL7bMTU6/WLpsCL5NUIqYIMFELBxlm1CQMtmUJo9mVCgV+TObww9pb/YlSXFtIK+YK9hse5+dit2AYI4CSpXKBlRRNZKF/O04W2KbCwnq8ObOIwzJHCwcBzi24uXfw9tNglq3dX4lPvVSq5Dof/5WqefcrsgZViBp/1Upxwj+Sw84YDdu2n6FAORUW4AzScccdx+lfTg1xHpIjUow+2iwXjo5QjoxZuAWJtWOqMfsWqK7bqufznvc8mgieOAAOgCFTh1Ikh3rSQuDs2bO5AAudWVx+ylOe8oQnPAFeVnJ9ozQmr+oe084AZPnII4/k6CaHUbED91aMyeKRksxhaTDozN1VXIHEuvCBBx545plnerI2gSc/+cmojRyOizz/+c+fO3euOuc22TE+RuOQ3pw5cy644ALsEO/bm4V4s9Ck4GgByTXmoiU/77zzJnB5E9tUbF0/lwPgiB1XfngFlDopNqshDOmeffbZlCDsrPwy7UzRE4UE74/Hq7LD4Pvvvz80j3dOjOOxeLw0pJKjMGvNTH2D4RiwJ2gT4JAkJ5CpbxyhJ/v+MFWb7LsBmYqO3R6uRLhfgxPa7MvipCXbKjjKvn79eu4sI8i8EGakrKGkXtEIjNmpWWfmujPIcBO2wPbwTjjRwBgsMOkW2CP7X1oAPaHjNf7gaX4n1v/CSHepMcVU9L8Usd5/yALy6dnVq9LcPfzww/RQXPnBqVWWpWnAaPl5feLlgQ8IcRcGjRu8OL3PkEEcvRsvVzSPSIu3fqH/3bP7X9sl6fo4/GYXb0mI5YlocNQiaKg/VBvernnhXL16NZWQC1Kpk1zKgNvsHAC3r+JT02BBGlULp8qGD0bJKcoH4zoEOFhgL7QADfWxxx7LAJbnhcEXYyjd/swzIjemTfSo8oRCw+jvZS97GRiePoaiXMg4JksLJCM4BhE4AJpEnnfeCpAsFrUDwGjokV4ar9ncoMToj8ec0evJJ58MLxKkoSdrAJQ1xl8IZ/x78MEH07DQkXGXFmPJBuLmoNTAdKTLsHHM8S80jC7p+LiVCcnAXDC9dOlSbg0mia5ua9lQkhMNYkd/LoHavHGoa7atN0+bZpcKoR4OXmyLfebuMwdl4KqpVI4YgnEsAko04SIq9i9Br9xJyQblFQVSwrnCifE7TSsyGYnD3kC/1SCDVsbOOADmLnhVgMWn4tnHVIZEGTYyhKfIqIRnnHGGxr/iGpMlLhBi7h3jqh3uAWEY2/4AVupRSahsvMwsWbJk5syZiPLC2wSY8UBtDM7Lz3Of+1zVOrv2Yqe6MR+T1hpRELJJ3I+zUNXpguPdrjpfqjeDXw58MRCmllINbNLVDnxFAwPRd797z7Of/ZIP/NfV0+fVhCEf9erOrtYSHE+rGeY+uv/5n4t+9avPnHfe4c2xARO3AA0F9ZnioEGI4z082np4VB2gCaI5pXWiKMEBM7nBuz1wC646t/1VafKGBhd7U2mykLnVIva1TgkR1FRSXDIwjzkOQPS2BXZLB56MU1dp2JHATCbPNbmgWePZJJYOjmEIJkLDNnO0ZQqTEKK5kJRaLmIiiUIrXAsayD2jKH3QS4Idp1gT5/sLR6ESETFFA7tn9IDnkiis56MAQKqtiyMF+7QAoJGTEPkiIF3UgIXUgQHiWil1+c1JgPFR4+kPDVEUPTWQOsO8rhwdDZWBuoEjltTxgSUTHyVp6HBUEhwA3TQ1mSfCayViKAHkFEXFowGE3vsACKGfglimgAZiuPBVcMq+MA1J1KTH/shcSlrsscga6OULiBP4KCQorQYhIInCwSWCOLuIvR+PEhxnEex9Tyz5cUrPq3QJEuvNEmcETxAyAZ6RIFopKCEKNgvx0iYGIDaedFxIc1pQykEG4ImbKYnyVgWAACdAPk8HjrpKy6bGjfocr9gKUr2p8NRwHK/xON7HeDkBIIp6ji8HWR20vwTjGGtnY0+NHhZSRwcUE+A0qnmoJIV9Hvd4gAJVPayVsfvjcx0vRGDvVND4mEvEkhMXBSwnMijlECIW74MXTJTXx/P6KIC6DKtUOM8o5SGAS/Tj+WKUPx5NAz7OItin6ymbMT6qNWDX5gUXLBAssItYgCfcawJMy+KDAuLIOHEDWQgGC+y+FthqP7r7Zi1oHiwQLNDaAk996snnnH0udxWtXhYlU1GxPMQkQ9k+8VNJRVM7LxzvUuNdrVc4jowTe4KdDbjTdKlksTyYLkUbVkef+OjPuezput9857pr/yrdml8qdrbOk5Y+NURjIQbeZJNpytud8wkwhteeS8b2TLtDzFxS3Cxw7ZLF6nOwI4DQ/7ZjZaoKJ2T5D7FqXTJhl/QxjyMDOgKrTprrYUnRiwVZY6lPWmkSCEZVP3Glk7VdFwQhoNKyYiRGUSp1xFpabi5f9Vl4fBgFm5J2qZOpalNRqTT3aqEYX/zGB8PkLDuVjJ5rqjpSyWxpeKQyLRE997n7nPucZ7/iFf+1fm00ryfaPJQsRJWOVAZ9eMqS/f0ASGCSl+kz5RQMRmCuFmnJbFeUHq4US9x+VYkKmc6uux8Y+fhnfvihi/77n//laR/85LW93dVKvsrMLvGMfsgG7FgFVbGRKe/GRExmAkgsH4fhAiylVZtUd18ah5HcYRn4AOB1oiQQbrMtXODRjZvGSAIHhimgRJSFBTxzOHCBrDcMRgMGR2ljNwCSSPFtGofHl2Ss4ZIuM+vI5Kqx7RBHpkiXpFiHZhWf/fdnnXUWi/F8x1jp+wqwQ9QJiQQLBAsEC+xQC9AG+vRcU23LojjhhYnTqO+IY+Jc4oUGR9Oq1hVieh8wio3T0OfCrh5HBMCeDIy6V9phNcUs9PhYLwcJ9D70+HzbFmRPIs3ZoOlR9G/PP3P+E1L/7//9+OHB/SrRiihaQx8uLtZhR0ZYZKXHYe0hIujFoq3SEiYT9QHIKI8siT743zd+5svPevPbFlz88bVcM9mdyhTS2WJNqh2eQeeaPtYP2nuC8u7flqHxGJ8WZPGMW4pufRrfZd3eRoBxRHlHEEaMDA3soiE2LsoxGRcABAK8BAEIwbFOLL8hduqC6KkawiaqV73qVWzmZi8am9VuuOEGPn02dekGycECwQLBAruCBdTXqGVurU+cEnirLKJXA9ta8pixyPepeKCZki4DJJcqu0G0DabcsD7i+4OnPSm1z4LH/u///rBYSDmsjVITdjkybouOjLCyw/jSxeIZGUjjMGdd7Lp1xZtuuvWsc2afdmb0p+tt0wCjz1QiaddnE63bpyu11wwx8p1y/apJpetkuRFui0wZUUsn24pEcjzGA15AA4HHx4E4F7BcnGAqYFJRKbMxkZ3QTJIcdNBBjILZUHvXXXdpFqJeLjtuVD4VOQ0ygwWCBYIF9h4LJKuR/XyGab/Tqajku1cfUQf8Djz133X0pP11YielF0GIlwMw2q9Pmq47QRBT+SzUxhO26fuqvd7QBeeYtOcLDe4OLIgo1i1I42x7B8zhCs7ecm6QQ1+tc6wXmNY0xKZcNdqyBMZm4rHClUcfrS3JWG0Z07l1ljFjGpC8lTExwhxRfBqngaYhyJswb2s4JnlacyEcMnxYxNUgqjkIMci430zjMVgbBfClCQDOx44NyGLjrfs4u8nmcfayW5KLY9qHGbMccdhhM6dPX/rwwyzFtc8YKCfdApxR4Vwfp1Y4AMAGA87BshjKBoN2ErKFVz59VCxyMn/+/PlUPN0WgczW7NT8+GMCPbcgcQ6B82Yw8oB4dsio+aSi+u/xHoBg2bJlhx56KEcduJSHRomZ3vHqvPA+aY2dSZTre5jo5pwPEjACz76XPx4ADWd7OHdKXsaj2Vb8ihUrkKn8tsNLkVFwFBkLuDBy6A5lWtgqLlOpUIIk6s88yyBxsm2FOYCHViqseJF5m8cFoqpucABJKWB/aTVe8cV5gSEmy6ozyPEbDBrIxgy2aaUxeT0Sm+OkMzWfp6Z9+3shEwNkIllYeWnKEf1gko8w8mPTgaWSsP1C7BFwvs3SpCpZfpVqDz+CYzi93qWicqKH39qBAVIppLqhLGUAOamZSifThWolVaikO3t65syf1THH5CQKMWl6nA3D5ZLozAOOoSqRFXeMbEuwas9gKT5rFItP1aenwLUSEmMJYLDALmiBPb7/bXg8aTN8KWxP/4scXnG5TcMakm1wo68WdSYw+lVWrHiEu6qSyQ5kVks0esxdz3ho8aaN6++koxweGKThenD6PbxgzJix/8xEYrB/zVBfcdXKTWRk/Zq+/k0jerdnoZcbsPo3b7ZBhJtRN4G4io0c+WvKOzuE/rdeChP5uyv3v5kqG75TmUQyxTxC0l5fy4namgulX3FjLusRq1EpkSxzHL4wzHn0riqbxqkqeoPl/uFcpqOjXM4VMzOjZHpd1yG8R2Wr9rWwpBswV5IjwAgvVsrZdKLn0IOqrnfOxwbBefX+bs5mejGnKmopV9zIK9H0rl5lwyM9OCtIwQUL7PYW0AMyXjYY7TLi40IQmmgGIBpDjUfcgOe543lUG849vDxZjAIQ2EDWHIQxjuQdgBEcGO3wj0d55QXgN4+kGH2jNsNnRkAAqEF/1JBEXKaHRaMxFJIZSXG5LSMpRnCephnwmpDKymWrGIHGaarq3m3UXq32dvXncsuWrfzmN79T5cvI5fK99+TvuPXebGpmOrG2wxaly6uXJosD8zozIzN6Zy15cEU201EZ6k0xhI8ymzbmBgbXky/aeSYZWCRtyHsim2TMxZCTpCgFrEcSDTRx3RpguDhnQdEjmdInFdJqoCEIGT4EzVHNGAbjTGWwpVxR8KrrJ9isGFFUGByxqIEyAJgXB6B0AcZ00EBAjRX7BMa/2GpMye0jqbQ41MCn7mF/cqSXuvaFTIwybiKZQj7SmEO2aWS7/z9dYYMbPzdaZKehS8u6RobEbNyPkpzJYgBbSiX4dDSlTB9tNT9tM/3myFoHu/oryXw1lYzSA1U7w9VP7x1Vi26bQ6aYKXZms6lp1VRPOsrNOGA/SyUZFSEfqhQ7uh94sLuruzwyWOJGIngzSbuZvV4lOMIAzt4CxnJOB9dO3HlL9K9v/+STTnj8//7vg7xUOOLaLouxGPdq3O9+97t7772XdowWYzxDUASqPxB4AJgnlCB3fH/729/mKeaZwtd0IiwN0qA0l7KNnfEpR3A8DijAMUAaJYJijCfaIIogZFoxfODOOz/z0Y/SFm1evXZej8raauwl3/3en6+7ftFdd83mfn8qLS+QtrwYe89jz+306Zyk4En81a9+dcWivzMvmlizgXlRze5yL9iFF15Y6cjcd999B86f39vTk68wHVMhXa9ks2JThFGKtBVYT0mYBWJ7j0HGtZL9hVExQQAS2K90NKhKrFwcL15YiEIaAC5O0ADXZdRKnyAPLy0nQAOlgnG85/UANMCkKwcsBbBDQ97VRMiHrDmtSokVME5tcZwpzRolBAwy8BO0YfYrphKMX9mgVWWODjuxU5lEOzuzpEg1T6fKqSTNZKkjk+7qyNJEwp0fyfEqMjg4TH2mu+YbJSMjxZERvlaCIhQHQw4O7NB4pspMKVZZGWJ6DikIz5TzZa56zueL/PiiDTdfjeSKyClVbNtzKWWtmT0t1bI2qrmiG629Y2a/IdfIAaPywiYYTXaDt4EdMhkN31R3DlgSFJSPNAbpRKEp0kAC+/L1vF4+EhqEIEEYGCUTIO7iyYGHGN8jhXE52CIXcTLR43t9GtgJejV8VBxo0DkeNTEYgVgpLtbDFBLNqFozKhXj37Q7bEhl4xyfFQaL6cm0+54Pp+Zqj3m1ogOJ8GEds0/KnXqjpwbm1ik8JmpIpdZIaD03YS+6HH7DMhxww08kO8xQpdFetcy7W7rMLsR0spPq58S7RMqug3DVI1kpwctLK5ni8U51prvdwFs1ga4EBwHOssnzoxKkHXBJU4eSFbuoemLG3B25MAV2lkN/AJ8LXxOEiQeB45SCvY9MLwpK/9DFJUtCXA7sOGqJ+VZZzCHKs0OMA4kvJJRW0JyFdI2/YzTFcI675onY8wKIAAAXpxSsWOUCXoA4GRjH18g4JrJZeAMmvP81GCQEgwV2vgX8w9zQlKCZ2oJm/M5XOmgQLBAsECwQLBAsMCEL6GUXVr6owPcufv7z0dEXUbxU409I8DYz7e79r3s9SObzlZ5p0c9/nnvta+/kAyP+AqxtNsdYDLvIG4iG1ijo9dHAqWE9iZqDY2GGaRr2aMIlAhE35M+PwJGp160GghAMFmiwgNb8Em5ZhblyZt6ZB6R2qVqymsskviqbKhWT+0wGAStI5aRaQkb9lGTYOcmJWE0uUA+hYUkVOQDQiB2MzY7WZyfB9/b2QBzHiIZarcoMO6mTXKZq13lwkRM6kBCSoXHTl0U0mdaVKhQ2nvzE9Nve9ravfOXL998fzZmThiCf72JnU9Rpc82Q4UyImwEBFgAZGUEHBdGKebpUIlWs2sZNvily3V9y//u///vOd77zl1f85c67hno67H4NtDb5hSJc6Ie2NvPrZlTBIJzjFPjQMEsnnVGboFnMTeTJqnxmBvp02qbwoCf7sirykQkN5OhMGRUKNnkHMY7ZbGIxCzDX5JEuFjbJzoEhO2AQRcaRDMZK2V1lIsmUl5M0LEr4IKvx75A/KMA+7Ouvv55csJNAxb2DddghGQ2JBAsECwQLjFqAps8H1FbTgINRXylMvE+EnuYxjhGNFwLAQjgOGroYWlHadoIS68nAAIO0HsT1C2DYuOO60RoVGGLpXhEiUWD4NF9diK0cgyGhUsH6F12ABYZl1JectIAv4n7y12968MFo/iFzh/r7OzJ281QmYa8BbDkiF5CRHOumBOsyG//OmTUTFDc44mc6hhcvXvatb33rjW984zVX38RCPl0kXVv8AixOZkJJRugukY9x+AoWyHifiIbQGF/9Bigwte6YCOdglHrkTjbk7Qi4Hm9/ocHJPogiRwTpW4E9GRizj3sZUL8mGk8AIH10Eaf2M8Vjpw722SFptn389a9/ZQ8WF2CxrYpE+YTajlRm6rIZJAcLBAsEC7SwgMYaap+byRQrvO8ymsnGw8TZG2gUhU/SSKab8Bgo6XxpoqVVsxBx1fqOLLuOGBSOnlt+7GHRWWed9Zc/3cZYsJm3QQ2lhc9VVuzoa44VhkX/1as33HLLhjPPPH71ss18+7Sh02TU6Jf36S7VjzRLI18NbyNxmtbaNsQqiC8rNZQOeNnQE5CQWJSi4DhNPDau1aTDUgm1lSK7Ldm4Sf9LqelAsggmPd0gMFggWCBYIFhgB1iAWdzRVFrcfjVKFKCdaIGGSW+bBOfNgo6YtQ0/pQ6SWYBxX5J2ovpTn7R/F2XGgOn6E044Ybx3vAZd9JLTgIwHd50LsPRK5l/F40qOB/PayUSWGFvnFBoc9IjCFzCeWOElUL6fsRmPxRMDyI1H6fFa+2OrOop55CgwqRdgoRJDBnLNcg+OKTitP4IfTTFAk2QBCtSPH8cTyRVOixYt+vznP3/++edzmvQzn/lMww6E8RjBM2ZhMpPrevlQ2QUXXEDJXnTRRYwK43PILdh91Hvf+94PfOAD1IRPfvKTf/7znwHQwdfG1nWe469f+cpXuDX4mGOOueSSS/7whz9Az3yyv8OrRdVi7Myh389+9rMAj3/846+77rrLLrtMU8pet/EA8ojj5qwrr7zy6KOPPvHEE2+++WY0GY9+PPz3vve9Aw444JnPfCYKf/nLX2YMyKE75l1RYzyWOP7OO+9kPfpFL3oR7P/zP//D4TeWUOME48FMbpMEZ+R+9rOfveAFL4Drxz/+8YMPPthmul7s5z73OdbZCf7+97+/9dZbsYmiMLtKkOIYrwjAkzrZ5wMMsGN/RuJecmuAIiOzlBdH/o466qg/OefrTGteYqUSCl9zzTXcnob1vvGNb2yVq4EAdtTmoxFMvWA9dYU8EQ1kUxEcz6STmZZvktnO5g6Ga15CF2Dl3VGmEoeBq1WePmosEz4LFy581mtfazqMcwGWdjt84QtfcDQ+AQuN5+gsfAswWr7j9VbjSQn4YIEdbgGqK1V3tNKOpcAe3//GMy1T+JfeifW/EkKzg5zJ6H9/GO9/N28e4PUD4bRrvM9wsxBvCIsX/SlKZrLdPSxfr6r2DwzkZ09byL0hndnE3Xff84Mf/ODFL37x+vUbeQvKDw2l3ZUQ5JqXW5wuwKJbRCZnhsEDkAXecEL/u0f2v7Xn3fVQqurC6IsgdI3mOG9eH/JQNxwqYVtB3Oc5mW/gRYgjxlZnzLOqjoMMfKrqTly74+7+4eLdY+7c2Wx1eMUb3gCSU8dy8HIYnlYoWd0APKNo93Vyv4NnDECwwB5vAf8C2ZxT+iA+9sYY8PWvfz23ljACZVTrRzHN9A0YnspbbrmFUTNbkugvGEdPYAz1oQ996BOf+ASSeTBpEpHJ6MwaCefsEa7DHvBqQPyRj3wEtQ8//PBf/OIX3P/CYITRHMpADK+nHA9g1HnYYYcxZL722muvuuoqXuPh9cQNQhTEpwXDZbrTjOA0/v37TbfEx7/EDq9ZH03vwby0bMX8CL1npVLI56v772/bvQjSPzJg//73v3/0CYdit+9+97tonsub5tymRJvGpRwdHVm2cvVt4iqtXH6okO0enVFnqHXb32/92te+xkX8zAN88YtfpFNucwgpyzz0wAMMwBnAwshA/v57720Y/8aN720SBxh9v+51rwNDwWnuIl52ZETEFFOcSzBvGj/60Y+4Ohz7M5C8/PLLm2laYBDOkJkXSOYumIT5zW9+45NrwaUo8kXq6Eyip59++m233UYpbJWrgYCyY+zM+JfZYMzY4ilrYNz+YOuKPaa1x0zUW8z6VesrXXm5h6YeZQGicHqm9HRUuanD9eUwsrGRWPZOHHHEEW95y1vAFzMjWKM6VKBHnjNtf5oU9uybfdyD9c1vftNkKuCu5bKrYcZ3M/eJ+vsHfv3rm08+edqtNw2PTxhi7Pb8pUuXso+CD2nTJniL+GaBkvJIAQ0YNosy+U9ztMXi3ZY8qhtUM3iB4xII0qeAYUclTKIUQZwsLs9XV4gZd6AqSfN4RlEuTkYziKMWQYZzt8TE422/EFlGedg5cEJGEnmbn/FNOm3FSFRhgrGyto8ZG0ThthAx9QEZDa1wtP9kxOW0MWEReF/RBAHQWUC8n4rzi8BnzQNeiJcQ52qGIZMjOTN43UkrUieWpg+0z4JXiSiRESttPSC8j61Lrf2l+25NAJ20amAcM4i1qRUon+2w4UM2ayeeAHCiRxQ60/4IsE1kRdtLVi7mAcgdvnJK3w0NrR3qiZgemQqJHIDicI0RFncNFoxlqiLDFwhKtVpmzR89ITIzdqWR7T9XfSAJxIJBsrLvLQlSTgSKhayObvwrAvmeUfltQJIEmjTwS5M4ZQOBlw8lapAF3mTwwYPBeQIAePEhw+xeGWgEKxcqo2ZNPH0DoKBLqnYyDgxBkkAgzqcryu33EeiqTM1DYSB8HMIFgFG6YBQ7XrrENkcJicHGiwWvquuW32tksipRJM2pRfyMu7wykxpNgo8l4PTM6JotyHCVip1YrJWFu34LJEEZkALlKRDMcIAi9gYnv1CKV0hgGuXmTO0ZGIzga288R86GY29PxUQqxzFL08zlLqhSwYlGpo7LB46zIxOaBgJXEPZ8UQMkTUUTZ2xg8WRQemIBUAK05h0vtplRCuNLgTEZx0Q2KDxm0FrPvcr5EiLXwPFgsx3iBK0pxdsOTXMq0mRMvEd6yb6l8FFjAqKHeMya4aU18Hq8BxoIGoKezAMNBA1ByDylBxpo4kFHPurFo8aE4zLtYY71JePRiyXOOCalkJ7MAy2IiYIs7loTi55OQq2eCk7snhEksSDVzNGeUsQ+th0AXpF5oAWXT70dYuR4Mg+0EB6nb4DjXOTX5zEuFnir5RuX0yYswyIct1UWkXm/HXrRtBBOptDBk6kaEASpoh+Td0zkVvWBwDN6oAVXnCYOj8fSDs2YvBNmHFNaQO46FoiXLHA82KxknKA1pXjboWlOBcxWGT1B6H/HNGCz/Wmvtto+c2F0ucxBbho3G8+r3XvsCdEF/9H1zKf+cdBNu/GBARwzCfidnRwyT2tqQyl63xeQxwCAjLt41JgwxLta/+vb/zEVbkQmKp0dnbn8cLnCdBLXMXMZOZMj0b+95Yprbn7+z6+KFt3EWAhktzN1OZ9j+r424CTvjdJceLz+l8itlu+YAlsjUcwXWWtKYqFEvXbUQKwcLNQfL5k3KDBEeYwMDhJM3PjQhP7XWykAdQvU5uNq76iu2lBVNN0JUvVTxAoCq8rhewdS9ZNZT1Uzgkwe6RoIzZvQNIHBSQI+AoWppQ7KTYaCbMYgmfrMyqhqNTSIJRUeCIjXrh0ET2ucY7EgEeXyuZHhaOHCjo9d/F9/+MM1H7l4+czeaFUllcuVcmVWCDKlvN1Pkc7nR/K5lN3FkWSeljVLPqfARxWkG2KRqXyBKlaZJM0M5avp7ohW53uXPHT8CYs+8IHXvfSlX6xWRtIZLrxPV8t8s8juqM/waTZ0K3HlPJ9IYDGDpS12n5SHhmyyGLuZZL43ULZnmaCWMTC861PM7P4xRw55hwwOuHDQg3F5l9lRko0wpi3zrjQJZIRlY4JwydmysZvFRizC4UUNMmifOKxLJgoafEqKJKCJS6hLmvy/SogU73eOBE477TSC8UthJj/VIDFYIFggWGDHWiDeotLEERQm/tE/+jhab3WUiqWnY2tOvE9Ea33pDiBOQ1vqM8R+C5zHQMaKOHIkWWTIbJCDVnQf8bTAsE+XVUJY6KnoVUZGhkboX51TeiOPWA+VSRXoyLK5g4D7CkvmTO84711nXnLP/33mc8OMgwbXjKSKXfnCWj5MysqUdXpVvjfYBzHLgkwErl27IcHXjJK2IkUnJR2qfGclmVi7ycjsc1dRdNihhy9d8+DXvlo45ujHXX1R9WUvu2Xx5kJpqLDJbR/hw0fQpLOb+wY2lwrlQilfyBXL1RJBfsDEypGvhrxjeY8RDWpwKgbTQQwGQGXhrQoSI3MIRyuX0Ks46EbjNmTlDBpivRwY6W3jNMhRKWN/YkkCYtET3AHu4YcfZhsZKXL7FcnxGoCPVuQIJ012pD47IMshiWCBYIFgATVrtHKYohmON3qC6SnoRoFd0zjK5dmJijfg4pIva4vXwyKmg5BkyVGrSztM5wKB1CMKGBeXySdnnSiGnqhkIAcw+SjvGae/+sEH+u+441HGskQZr4uuNG+1dEfch4cHB4f6IaO9JwlOxQMYk9voqW+v8e2zVDK79P7CzcncqU/Z+OMf57i2gB1V+ZI+uarbr7TeX+kb6OMDlTZy5cfOUlMjqrpjk+oNlYu45SGIO59rIZvpIUDVeCwWo28VJXgIhIFMcxFxYi+fPhou0XuBopxSHz2lKtsWr776al4JOGIKhjG719MrOaWaBOHBAsECwQLBAttvAfV/dJ/01rOmz5g5ezYb0Ldf7IQlSJ8Js3vGLU9cevQWgM6PbIHaXQJ8UrvB6RuzDjle3qcuv7wJsKu+4W2Ed4Od/j6ADrwvYRUuMGLeQHNKIHmpkyMKJcE0mLNFEHosDEv7XO1b3oTHXu9bqLGXRrmarxo+hv2bLsCSMfMs0rfnqBUQukJw4xMHg6EWdXTaaYr2xASqbbYAYx9mI/W0jsfM5CRR3N2AG49mPDySefzxuQMCJzISHY/e41UlfJCr9HA+CKA6hm8tgmtw8OMEHqaR5ATy85//fGF0aI2ZWwXhkhBP72sjGKZbYeegGqeXPQGAUo9jmmFoGK/BzoVf//Zv/9ZM0CaGA7ecvsaJHoFo1XAKt4UoKLm5yV/e1Lqs43IwFMYhuV87p6j20/WiODrL0XEfpPTVZzWYXSZtNizGf/vb3y52lImvUHiZYwIsTDBhsnr16gsvvHBMgjGR8fqAMuvWrePWsDEp20R+xzkRT8B6baYSJ5Nh5fvsMH2ho2gObw9gVynbVepOVnv4JdxRH03kzKjY874pOgZ/RrlU5YP3UTe/eBIG62nD5zcQza1WF+ST2c5u6vzGjnWkmy7PMSpiE4kNA/0s/+QqyXKq44dJzV2Mrka5madEmsvVq9W5yfSCBQty6emWRMH6BdvmZVJs+qXm1EtUS3xOhC+pMQPGBSHwJm1zChfKGJV2RIDEkWUxAtckhD/BAruABUL/q8cz/mBOSv+rRx6/vf7X1oXHc93dHfH+l2CZkQjHVTjoSwKJgU2bBrLd1l7RBrGzjcnszp7OQnUw7QZn2a6O73znB/zySdtQx10dRunmpe0sO5u6XOtUNj4uNLJWkQVw7koI/S8G2UP7XyvljnKiq5rtqc7tLK1NdtiXPnNJ66dSlV4egaJdsFatuCrSwT1qrJRkqiOFwWJqE/hkeaHb8ceSBbt88yzBpNJs2Ct3JtanE+kK9Yhu03V6FduQCHe5p3dOMtFRKibvTNjeBr5HSoqM6UiLXhZTD2++gYsPosz+1v/ylRP6ypLrf+mZgV3NRD1zFqyy/pPq7u7q7aHPhd20ivW/6aiGBC8mxzcKe2QAggV2rgWs6o5zdpqKbQ9LxxZdgF6k29EZdsh47efyIH9/UDujADH6JP74xz+eeuqpPkisCOSjP04wNBZXf+MVC5cmP+95z/PsKNBM42MbACzDVS/cgOzx8XVSkA2pg0E4SABebxhA9PUN/Nu/vZ1gbaFY68u2y6saTeuIKkWucqH5S2c74WGVm3tdNmwYALAvCySiYnnkoo98oJLU7qwUA9hE1m2OStoMH80dchgVZt1MUbbbJh69Y50005n9unNCtjP5IErEkpHO7m4WXnFCEvTCAURjGak7Yeoh+/t35+IYYE8mXh9sKDiaaG6g9uNfxrMNBA1i40FkUlKPPPLIxz72MY/fKrs0kVY07PS/cet5OW0CFFa85ruuxpV7m/wTIiOPPhce8ONfdOBuFRNcoApO48YpflzTDCJdnmd+YhM+exqTdLNlbodJV7PpcjqZzdlXvYdT0ymUtLtEgzEuJWmyKolSIs2PrpXeNlO1K9gSVXvTKxNK8XHsTZVEenkqWVr5QF9hpnGUpuNHCBjk18fr5EDaNv/PzaS4JDqXNpra+DcaxmTMRRlGrupuMK9Uo96u3mk5vjb+2KMzzHLss0+SC+b+/hdLPW3vlaObH8BUt7wsqSZqz/3DRITqW0MWwYPharz47VdgrGFxjlKIsyAEDBOGNJsUPVF0BzQsPIzghYnTe1jSCPIY8igBIAokgHz38oYMc+JCGsKZuSJIpySk6Nn7mk5G/M5+8slvfetb16xZ81//9V8jQ6OqvvH8V5177rlc8f+lL32p66jDMxWOHdLk1raV8saGHLKMfMZEL33pS095+Qu5D/H33/0xjwaTwJyryWeSF7zrPw7tnfX+97//mrt+1ZnJ5gp5Dr/YZ2vhZ7ctOo0m6LWbTADLkAp2oJjQiqlOYPU4FBAYLElrDx4YSsjwcV4J6OWIgpLuG7NzzZw28IhMQjxZgwQY6SagIQm23TZ0GRQ9UUoXApJADmKlg0SBAS9tUUCqYnm4UAOnXcFSwCsvXmi8IyHpz41pYpQPgZIQQOrSQbmzlRpGD4VibmBwOJFK+8ulynayqVgcqVB7q8OZyhApYtt01r3/pO3YFFUVGslh1MC+rEKuu8CtWMkuS6XIqSBKhw8xRoVigo1UuVK2WEkUS1YoMktHR5otX0l2pSXLlYHhoXxp9rQZPDtlu+6qUsqX8sN5LvsjI4TMAnZ4ila4tina3hlsHJ3KJLoK+RT7w4zEOTILJangSE5Bqep9GQQWaDAywfjbGtlTlErNS4ZdSaj6KYgEAB58hOidjb33BKkAMILE+XQFIIQoOQodJNlEJj7ECCQKQHXD6+B5AZAAPTSihMUXh5CkDo0aKzGKCx+WOC8KEISdhIiFHR8kZEgQPf7EHJIlROwE5aQYieKo8PgQECUyWKx8XT3LRmm6RrYRZuxXoI2pVHPWXiUy9KEpmht4nDWSCWdnqrXtmHCVwbVmTmrtvRcuelzoSUx9cqo+AkVMyh4Jzu9ZA152X1KijnmVaNNQLJvqNwNWrD0xYnqEipUCt7EZxl2A5Qhq1svwAORyPJ5m2EyWPNDU8jIKDfTKsNmI0XHJWpJkpoMxNGcIJV+p70a+L0Svs88IUYI9xtM0A75KNAgUHuvB4n0h40LE1UKIlVFdH2Aoa7pxeNNm7VxnpjpZrfB6V6sHDuOJpQCMFC7ScNJKgAQKtri6g10pih201xwMtR5GIaU/sSDxPaUHREkQB7H94dJyJoT4sBovBrV3A+Tza2yFlKit8exVjk1UtPWyly/IZguo8CCmSMyqMes3E4MRvb5aQ+sGlzBjEsfpEe5LcTxi0UOGPjTTNI7AOClGrAdAemJg6IniTQ4f5+XHYSFNnKMXuyMfpfeMHvD0UAK3Tw8xQtqn59FSjyhGr4AAIfHllF+VL8+MdIuzQKagCgh6YdrRB0b/VaL26Vvr73Xz+utVwMvXKwVBr7ZeOMDAgk8WiOKosxoLn0EvWYDw6C8uL7+BzAchw0GvZ6RNeikD4yTSU47Y0Klj9RkNsQnyx8uvUo+X71b1QZqXj3n1ygXGW6MZ8PTtlK+I8dGftJDWwj4i8PoTpHBFT5bHa1gQThTlpde4FvKVF6+/glNHjz1xJNdsQ580gM+vXojHIw743d0CPAKh/+VxayjH+PMI3P7zCOU20etJbF9+6/ZN0vDl1L613f+WoGekShLM1MA1bPM40etedyBj8vvuW93dw3uVvQ1jK+cnad9SqdrSeLMN4yYVF/St9fcsXv/dvf9lH1Zls41nzD4JO8OW7SgvvucRPnH5r/969KtvuBMTMuls+Ky9/Hd22sska8Pj2XP37X/1qux7c5flLJbR+4Pyq+4yPnknvO+PCIb+1z8mAYhbgLqBE4Z2A4A6xgR+R86m/ml56h8niVgGsElqd1mS6NNDCV51oJEE9vTQAIJh/pOhOkKJkhCqoolyF7dRe4dzI8SSjBKGBcdOIK8YmhjGTZEgXIxgbNsQCuVG0ik73VooFpL22QCb+MBBaQELcliXjKBzxAIAs5lf+co15I3DpLlSnnQLLBGx2TIqM0nChViki2S+ygeAWETpmZICUh7fKVyzFarxIr+uP+JzN5/7ynvOP/+Aq3+zfHCwyspXrmC3X+H03sgkKKqQGQkvFcsRJ4CZ+rT5MXaopLEqUbIhACu5YJhqI7tCMviVVsTKSUNmWesIS8vpbBimTgiyuFIsIbmmMPhcrgPJvkAdJucsP3qAgWakv38Q0zHtDAFXGePvMOfKzuboqS1ooqBSJ8s7TI2QULBAsECwQLDABCxAz8UvVcmVotKsdMSRmJ6e1Fe/eg1v9Z22tDo5jgV+1qGKUYUPHr78S89+4QvnPHhlbpirJPe61YDJsedWpehVBD90xFu1VSAIFggW2OMt4Idsk5VTNa34zZI9ZszYLRVg4ow7nG3EyeHfM844lO0pv/3tbzcNDdP9ejlbsmwRYtjFaoZQza29MAyHGZOyU467Dx7zWG4rjq6/NmI7DwNq7cDbQuL4gXb0GZ97ixjNMEggSrpcbEHQTlqefQvOnRRoNv5OUiQkGywQLBAsECwwEQvQp3Cy6LAjjmCsOhH+yeOZrMlct8t0K2q1fy3RVgTt+OjYgYRa4jv1Aiz2xF911VUsNDPD3847zA4wmH8zEcC7FosUPl2QcT09sSdoAUDMyysEcQkt6Ilqv6ZJkwb1Wgvfu2JdzfdPd2MRbHkBFmbEYZ90yg5iteO8QA+ICznx9et2RAWabbJAg8G3iXcXISYLrsbZLsHtV0nDw+00i2fXg7D9Wu2REjCODNXCSp7GW0Cbn32wNeALwpM1Y3xUA9CcdAPBhIMt8jthmZ7Rm5SaTEJKCyQbxlj9F1n7RvBixwVo6flxu9U4C/Fen/EIxpR8wjnngO8pcDg44go9lw12atScXaYeRXcuvpcvcAz293f19LCfBEwqtgtFpD6n25S6eIMfLDDVFvD1c6oTmjr5ZMEeT7dLf2KpwDsxxp3IZa0rObc9cqPKkxFmm5nvRjFvFkezhabK73h4SCFojt1CxGQEfCrSZ0yRnsbHhv7Xm2JMQAWH3drpf70x4TL6+vlJL1lFoz1vdIMSji8AMuobszciwxfg2RsAuCCwKrrlOFrbGkFymjdl2wxHjj7nHF6npxU70SrtLs5IxfZhliO7gOOexbeH/rfBwiEYLCAL6FmbImv4xz8uX88+D2wcOR7c5mAZmc0Cm3nH00dNzXg6GD7W9ZNWXA5Bu3SlKTvIhAw8Xw9GgHapbz2hVkqMHWfpb4ubAL3yu02MHMFuX6lm65Fim8m1Sda+MqJsX4FtlezlA6C81RDnCJJofPzbpmRnAZtWopt29W10z7yX4ErQkqEGQiNixTp2T1g7B8p8eN8oblzoRDf+7S30Q5GLhi0BrsGqu6S7YOvee27j65qD/dWeadFNVxW/OPeLc+aULrtsXSJh13jZrZdOn6k2eF2pXe4vnzhlcQS1GgpiPEUxFI77SSppO2XmyUAC87kR7kLitIvHNzeDPoqtNv7VDlFIQAcBkiaV4r54kck72Lx5VoLNDl7chg0buNWur68PYoKeDPXAc8CQhaEa3m6D2eJ5RzIXysO7fPlyvk3C9XZw4WbNmqUm9K677urr6AXD88JrJ0mYkqOJ+NSmFkAZkua9VCdc0AFAxgfgcArHWIgFD6XMGFcIJA798f2nDuIEOoMDo5yiZCtlGTw0lIWi2Cvl2ZlrQiYK4HtiH+sB1FMqyggw+pME+lNGFAGUpOJ9zyiVJBnf66CM47vMWa5lAc8YB2DEccQpfmEZ566ohFyARRaKJbvXSXXSdobh7GANroLdpAM+CqM52iYTeQRiefThAiuQ3OWFhHzBfPBg0A0aPgRMTsGgD1GW1nCObqs328FDAQ2YkZE8ynO3EATc0maUdrbI7sNSomCgVI6ENEo3w09CUCJfQdF432sOmS6SkyYiQA07AOVcXDjZjEtQndENFaQFOWMiMmV5Hx5GMTICkrQ8VxxAMbgkUzb11oAFRukPPF4WyKxqCI+hJMMCAC8KIA2AIEl4HRBFWiCVawB/dQYwTgZEjpVx01qJeEU5AR9NcAjBMjgAahFPDXBcGnjMWOnoJnZEPM7ygGSEWANiDJIJwhFYBDQx5eO0RiNK6/3M+aALmRC7byilheFY2fGMIBNt0cFfgAVP0l2AxU3PZmR3cRUAlPi4ZCYLCx06dUMJCI8PDQ4kNrf6Vilbrq3kude6psze+ceVY63UVF7edA0GwYwNmPGCXo5Y4knA4grCaqbYVUaepVmmZ4cRYlFK8ni80OBUe31CAJR+g3xJkFaih5GgfAFKy2MaJLQfDEde2rdVoAwWmHIL+IdfT3hzempxFDseTTNXwAQL7EYWCBV7NyqsoGqwwIQtoHdiZjkY06U7o1NP7fqnf/rnt7zlfbNm9mp0JMmC3dWiE06qLUZaHjU+8pt5dvH+N5VOZDv41J8GM7XX+1mzkl/5wvVf+MJnzjr37b+7urxg+hFM3iW7VptV7bwfw00NhpuzuxtjmD5A+/hEFfWnYcgkmt04k1Oj+niVf2pS212lMvDntqg0M5mZTs3llatlpkmpY3LM/7jTsLVphYZ8Wktis7s2hcHjR5C5gLT7Ah7sjPyprn5OWc0OSB5X1wyCsFkiw4CLTRhRpQ3jHHKQ70hsSkxIS5Lb8dPu42ZQMHWFGskKU17kp6srzTdPN26IznjqQS97xdMv/MDF9y+PelPRplLE1YNMKEoKCpNT++oFWyrZf5lI5qruBK8TDlLqEWKekxl4u+HflLH0MlEns8t8YOb2uwY+/clPv+c97+lb/5nf/nY1cpgL6WBumOk0m6jMFvi2G+u7JJRIZpPZTDLNBWFYyRTeck9VLGsCa77mFl3iZgqAVCpr+68StZlZcg6SxKwttDkWM1eNi+/c2MxLzc4gSdthTLgVVKKCbsAsCeG7pgafs8wkZTQQ42oBQ0yV80kI0B1/U5VYkBssECwQLLALWIDmjgaWFts3gCgFxvsARMkJL62B5TxlHWG8ch4DOxgfBKiTWDenWDACmtOK09QZa31xPWh9B68QqfLcSsSX0zbyadKTnhydelr5e9+7/p47jYo7sFz365b0UMe+EMgffDkAo5CSANhESwt06/qBdFdrRiOlQsm6uWjJsqUf++IN73nP569b9a+/vGaI3gyaaoI+jr9oaD0iX3uxgPMtVh0bkIOVWRcaFyNK0XgbxuU0ICWzWbLkeMYGAsnfNX0y6NXeNTUMWgULBAsEC0zYAvH2jebOyxEejMaD4OOUPtbTbyugpjUu06feDDSkHk/LxtCuT+aWaq4jGBmODjs8efQxR95wzY0bh9yuyhQjZDeoi7M1w/WO0mbV4r86K6ryzgIf48dVK/tu+2vv6ac/c8l9Vy1bQWc7g4hEtNntCHDaQEZP7n5KSsPeeH7jcNzOXjUReGvE6eMw9ARF5olridbxPuiFx4FmaWAakHH6HQnvOprsyFyHtIIFggWCBXZfCzDXfc0119x0001hpWb3LcSdqLneZJoX/naiSj5p5meYpUHDhvmr+CtTHPaMLQD7yAmunTdVSam/l7aQ2RC1rSo1sO+5QXttd6/2Y2XR2dm92LtY96JvkO4pGYujAaeC4lNVzfan/rj5ugaOEAwWGLWAqk1z5RmlaAlNmHE8qZMucLyEJoZvX732Kadak4nJnxiXz7UHJiZnPK4pEqvkJJwzkHTBnDTD8fkxjlFxbJIznHYCx/Yt2Fp/XyaqZqJiwn5lPhqCq9ghq5HKY/CH0z34ndEj+fTgULrCz3ZI4EYnwOgSXLArGuLX8cDm3qWFdUU2igwnDmAypzcx+gEtgny+sBJ1up9J3tLRVSSGkgV0K2dTUbZ32pNPhSDJJ9M4AFy1b4/l3LUy4uLKTwDWkzbeemvUt3kkk40KplzJ6a/+osKnzNxSkd6uyS9C5EtI8IMFggUmxQI8WciRv50C3ftebZl7e0RthzK12ekWqZcT2h0W26fl2jpeVV0LY2vNnKOnqeR2IoZBBNHHsM5BI4BvHvrmVAozZALAmgrKr7FNwZ+plj8xlb1WHpiYnPG4pkiskpPwrfW/KepAtpLuqGaylbmZ8pxqYpgvfRYSadv1ULbNvaVEyTpN9gtG0UA6zS87XE6l08ORXY7Q4eoNLMhhAwO/6ekMnftw1JtNZKlD0ORrvaEd+mXLYSFZzaS6csnODanukVSiK91RTtggHUq2LSKnp+PZ5VRqc+cR6dkLFpyz1DLCylalwkl3/EHXFxtxFM0u2uF2+vf19L9rV4f+11kleMECk2kB30z5/mKr0sekHBPphbeWCVmtiYjRtcNribr2w3dwCKhPCNXnitgvrc5WF6w7lmr9W4XW/MBT36ZVKtutCiD4QnCUKLEVnY8qOaUm7d3DSdtmrx1rjCd0e3jHk+nxCG8u+nZSbIfGp7JNQLM+28TeDrGUb93/amM891sUuA4mWeFXTdplK5VokyVRtkuOKlGKkSzHIBguVzMdfL26yy5pqRaTvSA7uLbFXtLMz9iAupLn2qRU14bkbD7CDdKcVcyaK6Z6bW8/9/gkuten59TR9b/04ezaTxSQvzqTiLLTZjz5NOI0KLe9jcY4OvCeXR4gdma1tPn++6OHlwylOqOu6LLvrwKZndVdyJlW5chyxA1QpItzZqm9iILfG1xndwdXk/Dyw+Uj7eSXQuTCoI6urKqQZ2EKhfmTJUuWzJ49G9jjPdBcq2mpcOARJZ8iAMDBBZK0IMCXEA9AtnDhQlIZGhzu7Or2SUDMRSqZVGJ4sP/2v9z80aVLENW3dq0EiuyqX136p2uv4X4rTr9M6+zt7+un9ST3vMF5OcjHGiRx/fXX//RP16HJtK7Oed1dKNCR6Jo7Urnm/36k67E63YftRyrlQsIuKIKS6Xn742VNDUAKKMOGYcqOXBNEZ95sOS6ET5pcTsQFQLoDCAwAxNJL1hA9SIh12kX3GcX1hQsjmxTHLvuLHRYA6QCg+6f8YSWEIJmy4BYkABTwqcd1AAkvNDgEopKImYzS7VcwKgmxSwFVCRiJ9dNWXgdUhQy1kYZMp7vlXbw+dxJiirkFm6HBQQwKvTOs3dRTrozAxYkfG54mUulUkpOV3FOUpAFLsXka3174kc8lVywvFvL9Vgol2jm7cgsFckXTJF+0+TqMCBKVqBnlKsOV5HCuUImGe3uT3GxFG8rgg2NSmXIlZY1cojCCSRhSWItUr0zu5iZa1iQUKEvDxQZvmmBzKMzT5y91UjbRDSmk63MNgDjHYZUHgLRAYg2RAWAEKhW8+KLxGkiOgkQBqM7wsFAQOpUGI2rgYAevEvQKSFUlR0JK3QsEryxDj2SESNW4ApIgRoRDrzuwlIS0Amnmq83i1m5iQgi88iEmdYrIcwnAhwCHfCUEJq6kJ5sYQAYxFI4LoTAXACZi5hllJJCkBZBoKZlmvhcb0bBwDI5Kk63YvHSZw3C2+99mg3E6gMYMCoxgaH0AUB6JgiVQvpdvQe1UHJ1xAWW8QhTdyYI4PRPhxJbtkcU6Zd/RJqsF0uPKMoh5WcAndWsGneGJAk52lFKd1n7CCoF3MrLVN9dqUmSZqIOSq5Rrp/NIcY9x5NosU79y1BvB48kp8Hj5FT0SRE8lx8WJqU4EkS+rxqPiYsXuhYjYFapxEFQZATukeRIlyfhKVzKhx4kYn1gRw+VhKAmKHl4Pw0jQCah18YrC96KIlRwhJRxf0rxMJSEaYJynbA3UX0NbU+1Bsby9+YZP5m6ROQgoABriFjTxKNkd+a4Itl4GkJFE+/IhpsZ4/eNJjwmTWVhoX8aMbUZCDLJ9fSDeVv3J8jbJx/70qW3aU/n19kE3GJuz6THQb5M+kta+/tBvk/4oRr+ID6OUJy0lKp1B4rz+AGQBn9cF4ePEcTLBxOLarw8Q0/q0n1/pMLn0KIANpb8yK/nt5BfebcovMumD9RYFY7MBmzHbWr4k0do+8XypPustEzwOrVooxjgE/VvLb87ClNJ7ezanKwyZAlCmyG98EDUeS8DvvhagiNtvn6kbPF/t10/VotD/jlc91IC0b0/kbFP7pvaq/fKFnvaBVxre5IeHmbOI/umf/unmm2++5HtcyjIYz4VvjNkz3L7+1Idt0p8Ud/f+l1GQXgbUrnobXvqT6A1vuP2iiy78858vXLPhEWaoRjZuJJYLsFvbM/S/of/1tSgADRbgXZHGiwks2jF8GhyelwYaBdU3xaOE4S1TL/QEcXplBaDhQjJjH1h4lvU4gyToCGuTdMDCeMnjYfQgM9GEEGgQLko3r2XcSgMybr868CBriq+99trf/OYuopig63LbkJivJcR0mxZereFGH7oJt8olsVIVOcQKxudcsUvI3cYR8ekDyzWMf/jDxnPOueX5z3/+Ndd8baRo03iD1epMl31ltlzlWo7aEACBiCVFoiQZuLXzFqN0KCyIaSEZAdXyXn/zdMZhbs9uvJKDUIDveuoxtUElAsFIPmJ5jZU1pM+Wo7TWOk5OLDny+gDLUJMjOkgJFggWCBbYJS1Qb6WtH2x2vp1vjtqlMOQCffBTUSpTzfRkel796nP5/NQPf7iYCHfLYlv6yhoSNR4Dcyy883dk6bCiX15157nn3vamN73pxhs/smp0D8B4rFOOVy82KcnQA7auG5OSSjtCVLi7S1VsJ0eBJlggWCBYYKsWUKNHU7ytDTttphpwMfpxVkOKkCkJDzQQtBmMj9fcFrLoWc96Fntr7rhnMWvkjHoZNmphqE2BkKnZH4+ecSVi77538PGP33T22af86Ec3FYo2uvZL8gzV2dEyHnsDPm5nHzWmTUQpmjjsMa3VhgyuMWnAjxflVdphwJga7rDUQ0LBAsECwQLBAttpAX2omQv96YvD3oDtNOZeyM6cPDuMWZ0Z811opxhEbya8KbGCIAXiL7dxPcM7zE4poJBosMAeZgFaEjUmNDuTkjVJmxRRu7uQyTJpO3YY0+zNyGZMO8L3NpoFCxawV22j2wfFu+Vhhx3GdwH33XdfMOwQyHOwNz4rtD3WYV7HPXZbnR6JvwlsNcG1a9dCowuw8k0XYHF8jdijjz6aQ1YzkxEv0tqYl3VXfnBknths1ZZ8HnjgAbKMNA7RacMJyOCCBYIFJssCtCRqTCars5C0yVJvB8uhlaNl9aZQXur2MV2I0pJBC8WgdxJq8/8tKCcxakyzNyObMZOowx4jamv9r9uRWD9ISVlj1QbDgjTHQkm9p3Yk7S6axC0pOWCoeHG8h5UEY3a2iNOl4lYsWwGX1T+2U7odlQW3DVIsJXcBlvrf+VEl9L/ekgEIFvAW8E+ux0w1MGaKYyKnWpMx5dOYCO9VAvBIzwJGBB4gio51vOaL2GYhXtr2AF7P7RHSJu9UpDUVMtvMzphkO0yf1v1vqbjNFWbMCqYKSc2U03tdA2U8yw1RY5pIyNr41+1aHORiGbsAerTrL7kLsGr97xOOoP/t78tz3ClVGmaYP637IIRkq3a0X+PfdevWMf7NF9o9LN9Csd0oipWRxYsXP+1pT5s/fz7Xf29V86OOOur3v/89O2Ea5gqWL1+Oqe+//37O9CGEUo6XqcTGm6Z7773XB6HEUT0g4/2KcsQBgxQgdu8zV3P44YeToiZVPJ4tOtxshb/PPvvgOzFV8hUXwnrQ5s2bueUcJFF///vfYeeNLr63Z9GiRccffzy1Ag3z7hp01mqcjuZhsbvuuovqNGfOnMP23f/II4+87LLLvA47BsBW6I/O6IOSwOSXN1IwAPjMX5FTyNCWy4AEx3UjSmRwwY5PrM5vIhMMPlyw46AEBgkNyamkJAGMYqHXRQ2ehnRxxEIJr9iBpTD0wAiHhnPrFAHFSumTEGrwhCINGCd2xMoRBAAPIzLhAoMPDBLhxuPOJSGEfc6SU+fe4i8sKECi+P6UH5fiGlHCDl5hYwOdkVN2tyQXGJmfcD7pIlxZqJRNVFTthB6k8UZWQChpCtTPc4H3Dnoyzl1IKGDVNWUFl8hbc1Yt1m4NA7a5QyyvB8RdeqScIpJYu4urXhnItWDDJxJM96loCMYd7DiiLCdcbui2uwFAo5wCoDMEXprY4YJAsI8iC1QeHBKIRQcYqX74OIJwicX78KqgiYJANBDDThT64BOEHgKfkJKWDvKhIVFgnmgBnt4DEtIQlCZIpnRgJLMIl/4CUA8aMKJU0oK309dz6oeQ2I3laZASq8zK52PVDS6etI+Skl5DADIrSiF9EECxnhiyOE0cH5cgOOlaW1kmNdrNsl9RLa1RcTxQylui7qHHgsAUsZ5TKSAMxQcxXFgDP6cjhJU8QVoFRZnQPdFhAVxDzlrb3xOLV76QwN5cCPH1lliCOAEiJgiB6L0Q0UAARsQQUNYEiZIvds+oID6FKxYoiRU9GFFKGS9B8r0P4AVKB0V5gbCD9w68HBiABmIh6yRt/fW7bduiDkTBAsECU2oBWhPfiPjH26cIJo5U0+NjAxAsECwQLBAsECywu1iAyQGpylv0054TnXZm59vedBvfpatht8yG6+/GXp7cknDiod29/2VU2fCS4G3x5c/d9NNfnfT0Z0S/+vHKzs4ol+Mimxm16R5PFIBggWCBVhbw7Y/N3TA+53FjHg1Yc3Z6J6+N2O3zAjaVRtBNOphcnWsl6DA2valL3EVT5Ip1ozGHQK5/Mqg+VWKTg1G1wNoUSbvr3iEoVfjSiiMzUpuPwOlGqgTbM9yNV4aqb84wTJn1ImPhwy2ppE1J820D7pnKdESbB2329l3veO306b2f+tR3CiW+fBblCknm6SDj9uVkVZMGNmnFFAnmSHNBB1d0cDe9+4CApvP8HCKi+XhCtWh2wLlsdjHryDCnI5kYGax+4qOXfP/7//X/Ljj6E5+7c3ZnlM9FHelqscz9X3yfwfqBUrmEjsp7mnybNTD7aJZNbt3FppUcnSsgIklXPhlhZlzkIOPTNCDJAkg3yGLqVpMs8tWsmkwXyx++WsgMkWlIovWZYeZ96qXlKGvESm+KfZ+WAGV5itMM4oMFggWCBXaOBdR609DF2zpguk7WUYjF+VaxgcZ46p0C2isojDITxyDEB+M042W7HZo4L6ufI7lCV2YkX8xvjIqfed85hx509vv+85/LeXcjRtp9niuRi/jRvVRrnWlcgmD1X8AAtqrqFp+0IuuJ1U3nR8qdyfTSqPT/PvrDS3/+xje/8bGf++JiuvMN5WhaTyffl6l/itDzjQF42wrAFzAGqUN5G8YJQHq1t9VucXoJR3IcGU8owMECwQLBAsECO8YCvvNVgzxe18DYFH3YCcS+Gd8RNBCP16TTx2kjFIywqAtoII4jG8R6O6QSM8vR5kTSvknP8PrJpxzQ03Xg977zVRFoxDserxeyTQB7P9hHwvdSr7rib29801knPXnGH27so5tnHM54PhWV6OMro9dhbUU2poZCQ2+vpwwiTo8cMyik7ObNFWcREjIA4bmURCcqPRkvXT5WAoMfLBAsECwQLBAsMDELsC+Znp2Oxr8YTExO4NprLcDCAa8lu1r2x3tT4m1qF9R2V7PeXq9P8/6IcacE93pbBQMECwQLBAs0WoDPbnGKknfLK6644oILLli/fv2xxx7LvgXO6nCmcfmjKznNkS4Nz8r2RBuXTStuPNwJyHJPeaUyFA0QqmSX0F/PrJbndWQOrB45f+SgaPoZbkvVQKVoOxzoylO2XlPd3H/IzAMOWPPIwanq8Y/NcD5sJLuhG4JMZZ2mbpDGPNi0adNmlXNHT+usDG3mjZcZFR9bSdgMz7r+tZzeTGfTR8yf1T8txdGv4ZI79Zfh6GBFO0lMsUplJFE57rjjpifymeJg94xerttMa2950TarHNMzy46uVQocCX7KU57y7ne/myN84IMLFggWCBbYfgv4aWFExYdf1orZCIcj5UD2HktjxeQ/zR3z/wRxwHF2IYO/h1mgrf63XCgOru8t3rIg+/DBwxlqRb7QhR2qCVsqyuUGrJ6wWFIqHbLpgOnVxOqh++lD5w3MoVJVItvLl0xVmETifDp17CD6wWp1xvDmjnJHybrxSp5tDkZkx9dLlQK9/4Ezevbp7bjv0c1Dw0MdpR6/d8JtkUxWBxfPmjVr+UD/kfsdt3HmbO7g6C/lOcZcJZp6W9+TiOQh1/92p8qdhcHu6d2h/22ovdYGuB2SDfgQDBbYqyxAi6Q3bz0R3gcNbLGxPc3eMiIjSDNIG1UujfTMmMWtFeAZvwz293dP6yAWfoYV1hYGFyywpQVe9KIXMf5dtWrVLbfc8spXvpJLkI855hgqD13h5ZdfvmrlOvq1jo7srJ7u5Mb1PSNDswcH2WAwPdlNlctUO6hp1Ctuq8l2dVJFn9Z14P3p/EOrHkVCd4fdaJN11ZoqTO9MJeTOoIVzpmUSG0Y23s/1SYyMkUMsSqky8/o3c+ZMuu+Du4cHNtyHHF2NQSrIZzs+lIPFoo1/k9kjujMPsRkyivrdlSsdCbuBJZW0Os+GEEa16eowV1p3ZdfPTxSnd3fxjGRL+3BwIlnsnsb4d8aAjX/LRT/+1S4U2Pceh1Wx7Zo1a3/961+fffbZ7OjYat6vueYaLsxyd5S4i8fqDFdeeSXS3vCGN/h3+HrM6F+iKHdq19VXX71p0+Z02m6VgksViXmYz372s+94xztGGcaBqBh33nnnH//4x2ynHULBIQSfvKx49NG//e1vTONwO5WLGfVcZUtQgUkOmAiu63rwwQep4lpk9KS60Oo1r3kN50S63ER7Xy0dI5np8r0qP8QlXA899NCXv/xlJo7AkzpiOVDj5UwpILvxZGFPkuZRpdora6rJEICHAAeGIM4bXLqBJxYyAHzlQj7EYrcXYydBRiPWsukcBF4msPCSLF+MEo4vJABqCKY49IJNKjjO5hCUTMhwiPVOeC8f4ZIDAC+v91QhWCAQBlgKIMHzigUMLMCZZBrYKlD9zmU2j1tOE25cQCOli69AOUeFhQuDOd9U48AlOudzZupCfhixsIAvu4NXiJY+LgXzYMSBhIYgXMBguPAIhVNJm83jAiyiAKDBJ5aycTSmFSyO0cpLJjICV3wEccSiA1oJSSyUIPHlgJU6gGLlE6tEEQIBPhhRejKC0ICHV+al4lH9fFstXsiU1pi+lEQmFUCUwAiUTDA4BQGUFkEAkUmmCIRvSAUklE6MyZE0yQEPgALKBcrjxI4yvtPBgA0ytz+ITBwKqExJjqB8CSdKiqFwkjNl7IosM7qtpIqclSNHZoFK0h5zKqbzC9RHkMqLbNWgp+zjoxyXscTdmDaMEwAn3aED/EYJdvYNZ22C8iWD1x5xV3xSTwLRBKdqT2ZBElQUZLU2QuE9xccgGEi5IbPA+CB9WQN4pMjMoO4pA4BSDi45Hm0ZEDxk+HAhhPYTAmD5AD5WYkWGDwsSVPNJgnooSv4QCztBr56ipIP0lAQpA170UkNkEMh5TZBGFLVdEsQLEocm4iWKtPDFCwBeSOngMRILLwROhgH1NLfhr3b2bgNDIA0WCBaYOgvoMdZjv9VUJvbMb1VsIAgWCBYIFggWCBbYARZgpMk4CP/CC19+2223XX7pit6Z0eDmHZDyGEnsef2vRhSJdPm66x649NJLX/va5//qx7/K5aJ0cpYfnIxhiIAKFggW2JoF9HDRaDCw97Sa1SLIcF2OWOYHGaiD1Ls9QT/pAA2w5hCZ/yQIGRimM7o7bbcHTu0Si0NMFpAoT67kwIVwxYpSU+HCIApK0ejLGqQLkqWOns5e6C2thJvQ5Moscybj7KfNOPfccz/1qc+sWBl1piJSiCr2fQZ4jaLqLuOwDUwmh2Uk5YV1JxaT0A1nYuuOjABCCa/wTFWjVTk3YgmWiqtWRd/+9rff9a533fSn/7j99vKcmT3QlypItavgMVoqmSGPWKMjY9OsZkYu/XKXypM1ANPKOWdnvudQ5kMNpEsqYASgP0FgJKAGAL4YgSFjUklIYMRm0p3xXMAOQaJkM8vIUTbxOzP2zQfMDLGm4EEyCc6yglTaKT75kpKkDuA0rGV2p+gTEg0WCBYIFpgKC6i7ibfVYGjAtUIgPL4Ar4AwQsofU47IxCVYZF6OugyCEiLAwyKDBi71UXSM9c6qJsN6YLYDp1IzZnRXhqNCVHzeqZ3PeMYzvv6/371nuV2fyfdRWKyjm6P3xy8W7fNDdqdktQqGDSh0NzVZ9VcOqcFuA2jolOMERIHHOJURW/bLFEqPrlv5zW9+8+1vf/u111/4p7sGeEcZGspNnzb2ZgiS9mkB40jC51cYTwAAxvuiBAO98KIkKEcQQPQe42l8rAARIFMEwQ8WCBYIFggW2HUsoMYcfdxIbrSTatBQPQIjLHolOriG2NZB348AaKTTmn7MWPSEvTM7O1/aCME++0UcHfzNb36zcVOFUT39b6Vo2wvG5J0wko6Lvo6ddexTYlvheeedt371PXc/uIwBf2d99Fy/bLrdRGRnb/YWbO3QtGb3lveiZH+Pb8EeooIFggWCBYIFggXatADdShjrtWmrQOYtoJeTXbbm+Hcnr3ADsFWCBvqpzum26tOg3k4PTrV9dnoGgwLBAsECwQLBAlu1AH3BXXfddcoppyxcuJBF/7vvvvud73znD3/4w4MPPpjDjcuWPsqExjOf9ay5c+c+6aSTuPZiIDObGarqgG3GyKXs0O/GTpvU2ieTAj9vXoXrMKZtutWlW1v9hyZZdqcQK9M3bNiwqi/LNgZEcRg4u+AwYjuiWdDzcquFJLpX4Pnz55988smcF0UsDgLrdlPml9MW7B/sP/TQQx/KF9AtV6lygDOaNQN83q22cKyFQ8JRfph8ZUc2HXHEEbN6OtmnoSmsXg73JRIzKukFCxakiiMc8uTks0WFhRQsGFywQLDAdluAtqUQdaxZs4amhlaO7UiItEasvkINlqBDMAFeO6lLLA0aLSSMNGJhJnm7y2GXFkC12Gr/+6IXPYW+cv/996eTesKBh9jJzzJ3ZyToMvHZFsGSx3B+ePXq1UcdddTw8PDAYILdDoWhOdajJW2nRCJZtg0Y6aivr2/+3PlcsbHfwgOILRbwykU++4njI52pVDXhjmgm0wcddNDM6TM2bdoUVdNIQ0/SYscFaaXzPdTtxf2Zxz3ucXeN3AE8HJXp2TunW/9rHy91F3DQ/6Zyg/S/UX6A/ndudzb0vw11Eas2YEIwWCBYgKYGx2o4DwiND2MQADnwzfZh5EKblu5IP7rkoSjT8eIXv3jFihU3XHPN8KaB7LQuToc7ltE9Ws0SAmbvtACd6e23385Wf64Tmj17NjcH/cu//Mv3vve9I488ks505Yq1vIM94xnPIYoxMtsI96UTZSd/JUs9TJXtTiAANhOuXLMa+JBDDuE65oWz59KrbkjakQRdgMUOdMjoYxnJsl+Rm4mAncGt4+UqDAebV8dH8+bNI0UqPxh4EcVTQN9s/XhXF5j+wcEDDjigI5FEw3zF0iom7XnJJexsgugzxQ3kolJcxg7Jmb09IKvlfdGtuzINCdOTKxllpwq52Ph3r+uPzLaVqKe3m2N33AbFi7cvi/EACpF5CTPmlt033wH/3e9+d/3111MfxuMFT4o43pfsvcw5gqKnaNi1+7WvfY1SHk8CNDgqBj4KOMoaO0EU6+rpufHGG3mr1F6UuBwwOLWosBM1NDDQM20apzZ4EETptLNzHOTluuuuo2mdlrALO+IXYPWO2G0dm5PWLGONwsBgusf2yqIHmDEa6LgSkwGTC0uobjeCSFW+QOohgkCOnIrANHQsnhEMNJpfwgI4HwWLHOwgZS4wzeorOVKHvTkW+UKKl6CSEB4YPArwEFOUlD5CsDlIMOAhI2kvxKsnmSRNFCwA0EsBTwx7XOEGXhdlupXUFLmESBpRGAli7IhKGe7Hckhg8C6zpk+pbFduyQ0PFyz1vG1dKxa4ht6GG0jWhBoVQmp4xaSJS2F0K7WQcEEGPUpICEEGMcBmOJRK2H03ZI2RDpbzMklU+YUXZ6m7C54kTWLx487Js6YVSi/HE0iOx0PsYU8DAJIoiLEA1gBjSjoNiZIm8uNcgoWHHkbKjjyCgUsOGoLoBgCmgV0YSSDjIhuTRnJEH/eFl6qe0dcZCl0Z8VGTCJBoa+E+vwDKY1ln07LWx8U18TnygI8lCcGeBQCkgj7W07cPeIFxljGRniCeHAWNw8Kw4OJlR9Cz7JEAGZQpAChZHFVOMHMLVAxPACwneiiJwmEunJpHmQhjAuCrOUJOa9MhBALShR45SCZI18+BVNKqlO1aQBwCBeCjCTT4EOOjAEEpo0RFrFj5ngsAYpwA30rDLh2gl0BPQ5C+AF8WAO8BhMCIDxd4WQ9KgnLKHVFtutFTu20yBLJggWCBqbOAf4A90CKtdmhasIeoYIFggWCBYIFggZ1kgdS0aZ28Q3M907nnHnns0U9/6Uv/CU2mdc0c3Lx5p6jku1QPtFCjHZoW7DsmioEBCTGCyeWjiz/x+z/+8Wcvfc2vfvazqDLU093VXY0e2TFqhFSCBfYwC2imlEzZpVBZXQtlWdRMruHdWJ3huu2ccBOpHskCJ7MPBHk8IWAGAQLIcJpiQAg0cbEQc40Uo30cMIN/gEwqzbVQZbfBCCQOCXDpqdd8AZLBsIPSIZMQ2JcPkqYwBHy0olKqzJ3XwzbKgaHo8UdFb73ggl/+/Oe//OnfpnUibQYJJVPZVLqLj1tUqswMasI0adlPZqJEulK1iVm+t1Z119IjtrlhrEZ8YJSZF5sNUSy3/7tZoCKoS3608imnL/7Xf3/Vv/zLd/r7hpii4YMuUKYSabufiwkQGjA2q1S52cou/EqlLZZZGwxC9wEsBwZt0+miM6Nd14VhcWTTuJwjir91DptbIQqfa7ZECXFXV0dPTxeiPFm2I50tpDGWiJFsX4RLmbWhYeYKLows4dh5B1+ARepxmxNEK/mqCfFYn6kABAsECwQL7NYWUPtmO/7rDpimT603AG07Rw5onDX5LipoaMk9DUhJiNOw54PlfOTjaD+hJwhBvF9AMng6YjW2yGG/CCnGeyXRFIs55AwNqk+xd3L3Yk4fR0Mddbsvmg4lykceEH3o4tfdfMePvve9u+Z1zByO7INspcqgli3o9t27fJJPoNKPKi8T89n6SXc5VEmxwfTK36448nF/f+97z/2f//nZTYv2sd6tWki4j1nVXjYwZ6LCb8as6SSXtIu5zGL0d1gjnnf6ULFIK6xBEKSzor3qYNU4C2RYFSQFBAwl1rMdXfXSkRxsaFrVT0Ejp27V0d5f5c5rFS9O7LEQ40700XYnph6SDhYIFggW2CkWoH3G0QDSQeDTwhO0c3pNjladKI8m6OF2ALowWOhi6C84Y7BVFojVE+F7YpDqd3KFaMbM6Iwzzly1at3DD610o0wuW0a9BD79D6paaJIceWWkm0p13L84d9QRxVOfMnPFmmW5QRuucvTCJcJH36DKWNAunuZnkwa8O/AfteWwMHYgC7KzI5hCj7RIUddYj5fMmGU9HvGk4zHLpMsMAoMFggWCBYIFdooF4i8JO0WBkOjuaIFdudpMhW5TIXN3LPedpfM229991XkMbavjzR2FN9sxrBVQwQLBAsEC22QBVhy4Roq5o3322YcTZRzNZdWAaQ3mLlatWlUs5ZlT2nT7Hbmurkw24ozo6oECazEdfTbpsn76EhgLI5WoGB146KmwHPSU2ffee+9ld09zOtQml+gOZnTYBoNDExvh6k5feuCBB/5h8QehGUnMwc9Ubc6KKCgRmMvlOJfbce65v/3tb9GBIMscTiBrLiazK1VG1WndXXPPOWfJnYu4FmTdwAgzP9WU7UPIl21dhqktm3lLpY4//vjpXZ2cCB3sHyAJskYqCXfZR9mOyDFplmD150lPehLnV9PZFMkZQWw6TkkHP1ggWCBYoNkCvq2gbaKF0Tsrjc+LXvQiVom/8pWvFIo2ta45c02cp6plVry5nQhpUNI29nZ1M6Vc4iRzuTJ7xkya5fXr19OIsbcMGi4VIpWke+2dxLn35rwEzA62QOv+t1DOdXSnv/GLkQMOmHt0dDkXc1Q6B7u6oo7EY9AzWRmmvqWqtveAj2iyzLTPw39evHjxffffwdx/Z4ft7rCKxo6CSonYRLnARRinn346ewNu+PNfWCpK8XXRzs4E1ZHtlGxhTCY7kgn6Uy7yqJ588q23337XXYvZ0sfOhGTWLobortpR1Z6kbe/MJzakTjzx0YfL3NS2YvMG5FfT7vioq9XqfzuS1v92d1v/Oxz63x1ct0JywQK7tgVoTHBsnLY+1PVudHSonGInMjvWps+m32Q8wjs5m4vBV9lfXY1K/GGvsntFB2A8wp0uvNgf96RjV69ZWRweYQmYu/nuu+++NauWcTMH0iELb/S7dl3YOdqp/2UzHhc1cnmQH/9Sqbj8sbNihxru+M3VxLJl/qqrrlqfH0DR7pLddJOudOBXqgOwU1HpSaPbn8EYeXDIRqm5aClbJivVXupe2tVYkNT2xzzmMYy1uV1oeHgk05nlPiy2U1L/3d3OVlHpOm3H4Kmnkpy28KGV9mQm3dtlppJ2499pPeecc/vddy1durSvb5Dtgqms24fPWQGXENdadXKA4mlP65lWuumyyzYNlLu76cpnWKz7WHW5Yhs7GTcz/j3hhBOqlXy2o9fGv3qFhW6PdtbmOMerN+XCyxKFjhPSx3obQONhgGaCaTN6eafiSIq1V/VdmnEWwchB0uzZs7iN1MuRcIqDN7H4/ttmdqtO7oKkWJSdhbFSTURdXdmuLrvpm0kRZCIQJ+Git5rmjn5I/87uDu7AQvOR4SHsgBB3PoVI7h+0Czjy+eFszurtZqvsNdfNxllmb7imKZdju2rP7Fk6yCOZdapJ+yv9EU7eUZ4gT5ykK0Vls7nsoCRKzxSUyrgYJVPFBI2QWB68ouyVuD6P5NNSlBKFV3jYhVEsSAUV633RQ6yahjJKF5+0lBxNEDrYu7Tb0CUCYDlE+US92HqkVU4qHkFP46OQA9ysFUi4lDRlDAGUNlJI2Ig1nbETQ9ls1WHM+DRk0JQr9p5fLNj1MZTD0FBhoJ8LvLgRq9Z8IVYyNVatRLVS8DpnOTVEL292SmS5VpdDU5UoXa6SbTCZZKVcKndk7EQSzrgydnCJO7EIFqpWoJyXTZXS7CaX4XSBDOkqp8oXfAQRCK+PAglGvgCRQQmA87wCvI8QTy9K+RB4GpSBBoc0HHh8tI3zemKxQwCXiXC1WlxEIQRfeB8Ui4/yBJ6sgVL0ca44BhhGUpRAggA4MHaPXlcXmhNsYNn+IIkiVkkrv5ZqPSEA0lWOajMeXBBZTSQL1jF1ZUyfctW201MTgaE0jJ5Wd0GbuCRHBJLmbQsSdlLhRRNY9JIjH2SDk3p24M1G0055R2FCbCTuWuCEjY4TVStN0rJXVTlnYTUWPpsWk4jSnR0j1UqhUqbGpypl7l7iKKITUuetidij/pBBHCZSqZE3ABxIysVM56yKrQjilHnafM8FMT0OGIhV0LBAqZ4RZGt7wQ4BZDS2yBE9whlJuATtSUdguWI+lPg4To+ChwyMNMFHB4TgSJpY+aI3lnpT06wPieLQBAnKu6fxqUAAjByvA4Bi8YklKLMgQUJQSTXNS9sqoF22WyULBMECwQLBAsECwQLBAsECwQLBApNjAeZYR0Zy2a7oggsuuPzyy6+9lhfuqJ2TbJOT/F4jRaPUe+6Jbr755te97pW/+MUPS1E/I5es2xOlgc1eY4yQ0WCBSbaAJsEllBkBRuYanPNkMVx3swSGBK7TdDCGJ1YjeXwN46GEADzE7KqMt4TwsnuD5R/mXgEggItpX5sN0ePtRHdk02CQRkjJiQYSS44J1UKGOT7kcLaWadWUofmIaGXjxgF4XvOaV7Eu9ZnP/GztJs7d8mWJYURVIkTavRIjuRFWa5koIVv4aIsOOClPQlLeKWJyBeCTIkHpw5wyqXOkGAxJMMdRKFfe856v/fyX//n61x/70YvvQI3ugk1wMEsDS7liCzPOquVc/2B3Vwf3cIFBpYZzttgEMuzjlDJLMpVJKgTBoySwlAQjfUhd+kMsowGwV2Zz32aWooHlOoZNZ+QTRCV4sQb0iHUYm6yhL4MGsyCwxrYD/5Cu159MoaEmjwB2oBYhqWCBYIFggR1nAbV17Fz0SdIMsgFC7R6xtMw0yKy4+0ViKMfsO7Qq7+WwCwTngwC0sWDizTsY+mg1vCQEDcsApK5+QbzQ0P3xoSOnkm3UkFPDTOeKg3ftRju+8JrXPBfiD37wxmx2/iMDa3vSc6174ntCrleiY2K9iNUIRklI46uk8UxJDqLkUAwa4IZekt0qJIHO8FYrGbTtSEUf//iPfvfb95x11uG//dvqcpSbF003tZqcTKQLsIg0fWKXf5FTvgLXYB9YwEOmEoFLGC+bssOqsiGU6rZQXr2tyMgFXCprMMpgg52xuXIaf2vyqex4ACV3fKIhxWCBYIFggZ1rATX1dAT18VGCkdF4Kqm/oEemP9rWNpOmXgO68YS3iadnGRhaD/GJJx5P/3jZZdeXcgw/6WhcP8sBnKkZRjEGx0TpVHTNNde8/o1nnnHGgsuuWJNxIza79NkdqNDG6BYZwYBYDwKs18LOLSRsU5Qf5vMWsU2MO4w4/k6ywxINCQULBAsECwQLTIUFNCqcCslB5h5sgW19n9yDTRGyhgVCfQjVIFggWCBYIFjg17/+9YknnsitVUz43HrrrYcddth///d/n3rqqcylcPgWJJ3FunXrWNApPfDA3XffnUt1H3DAAeedd9b++++/svM+1hru71vGfMui7/6VsxhDJ5z0yCOrqhvS84967DHHPAEaWbijajsEFv3q0nsfXnbg7DRXbj26et2B+8zn2ql9991XF2CJ8tJLL+0f6GdS5dFHH3344YdLldIJx51w0kknKVYXYP3lj9cw/cIiC47lj6WPrpm7z4KnPvWpcxbsA1nJbkiPOP3+pz/9aWBgw8qVC6N5c9euWzm9Z9Zpp52G8sRyABg/05n5zW9+s2zJEt6rEQWGbOIHFywQLBAsMJ4FtKCsWJoOTbQ2vFQTXLlypeauoYQmmbG2Jc4LjXN2ndD8OXNpbPMjI8z/c6OQvpkUxvvjFcEeg2/d/2Y6becGji543Tr7kkrnjOzpp5++//zTbGNAicuwujauXUXH/fAD91OLuDRtyZIlQ4PRkUft++STzuJrWMW8bTmY1pkFf+tfbxoq2CYQ1krW9vUvnDXz9LPO3m+//Qol2z/Z09VNr3rLzTetWrcRGkTR/7Lp8EnHHsslVomMfYSss1zgsozrrrh82bJlI1U7O5rLlZavWn7AEUc85SlP6ejtoVz4CCh+vP+dP3/u+tD/7jFVNmQkWGCKLUB3iWN3MS0ezZoWW2kGWRPWQjk9owXdejTwE48//slPfvI+ByxgPPKXv97ClUAMPdasXNk7c8bg5j5uGXD6uk1mU6x5EL97WeCKK67g7qfrrruOCnPttdceeeSRH/nIR84444y1a9c+8MADVC162MG+dWyxYDi8+pGlUUdq1gEHPOPkk7jEeXDjCP6KlfdznujWW+/m7hq28NFvrtvQd/BBh5/8pJO5uJlLpqil3Rnrmv/2t7+xzW/FihXU7Y3rRmbMzj75lFOOOOIINhBiNF3DgSYIYfwL2YoV6xmPHn/8sQyTtYdEF2Bdf/V1sKxes2b16tVoxeibYfuxxx47f5+FJsddgLVo0aI777xz7eZ1d9111/yFHev6ygvmzeBWuCOOeCw0tfFvdq6Nf5cujqID3PjXjjNYLP/3Dkcb4jNKWVNSOI9pAETsWTzgyQb6Brt7u7TPU82Uj/IAr21cZ0btopShRAgunihBTzwegBCRNfBCT9VSdUImsTiVaVwUyHiwq6cTLt0C5vGw83YnxbiCAxZ3C4ePNwDJ3e7brkObNqW7u7eIm+xAXRPTvEF/orRLVjQNBCDJndOfEq7dDdJQOmIUEhhifJwy4YnB40ASJcATeGJh4rHC4PvUG2KRryQkBJ9gXKAwPi3JQYhoyJ1PQoDHeCECmtPVl0c4Q2WMevZd1gAhpnzpfLkAi/+SXE3Y2R+k4VASs+NIDmd5cM4uBqyf/eECLCN1PoxxCyCf1M2vZ0QyoSH3Sg6YFwB7TLIZpua4AAu8XRLkHOMR0qFh5JYY8EpdQvAlQT5JxIPACPAYEeML8HgBEuvUNE/IBkoFvS8ygpgFeiSgvOcFQxRBfBlKZMI3+D4IEJcQx/t0QeJE1kDso4QXi5Ck7ollFgXpgxrUFv32+8qv1ySuTLNwi3X1By4cdxShFXp6nSFQGcFbq2+uXsXbPU8DIEZYfB0QJcJ96pZo3YkeH8cJN6GBSRWyuMXgAd+i+zQR9fuMJAcJvGDEk64nu4f/xf6YwvsAcZsr89hWTgUEDXjI6JiwmHvwwVk54iMNJA5ifCjBg/R2lPy4D5fjsCuohIdPKYoRH0cUvknb8gIseJFArGuITBRpiRIdlDpBaeLVAIALX/TSQal7GrjkhFf2iQXpaTyAHMikiejHJPP0zUBY82i2ScAECwQLBAsECwQLBAsECwQLTIkFstlEV1d6w6Y+vj739LOjM8+Zc+qJNxVtOpQLsJjk4nV/StLdW4XOIeOZaMNb3/T1G/5+/otfFv34m5sq6U1RZX4qkSxV+xk0MTxxxtH8iOC91Voh38EC22KBUtm2OcoxPYGzkXnV5kxxxbx9MqDCNRb1No2JDOYOGMBrxM7nUrqL3WCK5RpLJe/mWGM3W5mcYnFoZBgALk0iFIq5Yjlfl2ppkXSxVKxh3J8tMFFn0bRIV6JUNSL1ZLlqd33niwNs+HjFi/d7yunHfPri769eF01LRSNcvGHrRckyd21Xk5lEphKhUClVtskvm7+IaLwtDc7xEmXOAMO4gE3NaIbCNKs7oyILNqWC/Gha98yosnbl5uhb3/r+m970ppv/vOx3f9rMui1HepkmzmaquUI1V+Q4cimdjDAiF5cXy3YlVnbYvltSl2p/WQ/LQc2Hkko1PFaSJjqsizJscIFSSOXCSovrsZjGsWlmm2ehGCAp1yajLTuFfKlYQNnRVhEWJo+Iqjub8yVnbmrLZmNIwv7sKEfqTI9KpXiWd1T6IZ1ggWCBYIEdagEacNq9hmaWSXOcWkKicGoP45rRVEIDL0h1wY5pi7sLEY6DV4yQQRPHgEeCnJdDumDEIp+gQxoJiz6ui7F3bHUOyegg4O4kXdryFzwr+qc3nPTSl3140+aos2sD+EKln47V1sFY/aRPAlULurf0+jd3ucoSXcxv/Nktl+7d3jhrztHQXbm4SrFUKGejNZujf/63r3z84x9/yp/f8ocbo1R5VpJrNROb7eotxmKseSXcGhtM1t1bBsmXN6xsKEw8+2Qc+4PBhwUyZ1QrtZoyUcRmL7ar6gozUeI3mBEMaVlZ1uWAwSldiWKtmu1iIH3J+iR2GNDd3c1GNGnlbYI+0lzK7zBlQkLBAsECwQJ7gAXUcjb7DVnzzWy8pQWm76BfELHgOAH44dwa/CMfO+u445/wrf/9Xm7ExrE4vr6LYxQMbO05nZjbYuIQBjc6622ds07W9fGut61RNo0HGS+SDt/b2thX/d1vb37a0552z+LfLFkCOV++p+NlsxICuSDLdmc2OGUWpPKi7oYgGfSUHukBH9UMeJt4yZ7GYzyNj/KAkpAf18ET7ABApYwOKKAbM3nTY6jeQu0doFVIIlggWCBYIFggWCBYYGdZYJtegXaWkiHd3dwCo/Na7WVkjBd7Y3TXlIwvYRyu8RlCTLBAsECwQLCAt8A9zvkgl0YBcytHHcOaRbScs7mV/NKbb2Q2pquU+/d/eu3VV1+xuu/ByI6nZQ5JH84B3Q9d9iGuxvjp75ePjMyKok3PPPmEffaZz34DzuiyHsFCBtd2PLz/gZWVqxcPVx697f4Zc2bO2X//E44/mBO8HAddtWoVnxzjYqzzX/uKiz/5qXIyuuqaK6bN7uF+q+e/+LncCMNeEa4AIYl58+a9+HWv/8QnPtE/krv8xj8jnE0nZ53zdA4wc2JYOxw4U3Tm6ScUhjfceMOfli25Z8mDfMYre8zxxz75tJMRxSLLuvVrjjvuuI3r1//j+ed/+MMfRofrr7++oyvLDEk7L0h144S/wQLBAnudBeJNBMuaNE20GizgHv+kE+fPn9/RY6ufv//97wer5f3nzCWqpysDTcGmz1lEtj+lyM7mpats8U0mKsl0IjlSyA/nc5w77qWNq2QWzphz3+LFc9OdI5p8N6Zq7QCwkyAdErY7q/aWXJ9zt1SC210s0Lr/zbBbuRxt6r6Pfvb3192RSCWGNvc+5ZSXXH31lSz0JzLD6YF0JtP1mre+7n/e/7He3t5Fd91eTdjg69gnnjZ/4UI6x/Wb16v/5fLHNUsfGdw0+Ld776OrnTFr+rwD9z/y8UfSG67etNH3v2c/5zmLHnhgeX/f6j/eMLO7my9nvuAF6n+HN7n+tzuVeOEbXvPRj350YLh89Z/vKxeKI1HxpFOOm7tgxpj975Kl9z308OJkOh36392lTgY9gwV2mAV45a5UinSjM6dPpw8lXTDav5S2cUcxGTEaYBsyvV8SspFCIZOdYzutE/YZxVJ1LYvDG9ffc9/iXFfmnCWLH57X2XnMIYcgpIcvIW0YPPiAQ9ZusHXt0lZmk3ZYjkNCu5AFuB8Kh0Ly77jjDmBuYa6pmE1szg9yR8Wj+b7lV/6GjW3s+PvQv/7bjX++cd3qNUO5kWV/eZiu7UWveNVt97x/9rx5N93+14GRAW5gPu6Ux82fvy8vaY888ghVcWNu49Of/vShzblrr/3DupUbH314ZbIrNWPOPs9+zj9weeWGjUOMW6n83M784pe/+tMXfyqVSf/++j9nujrhfe4LX2ZCUimGyZ2d3TNnzvyH173hS1/6Ujk58usbbmSjQ1QqPP6kkxbstx93bPnx7zkvfF61p/Paq654cO2q+9eM8B5xyOOecNrTn86llmwGWLduPePloaGV57/qOR/9yOJ1mx699oaVme5EOTGIzonKXjGzim1xFDS7MQUrWCv6cf5Ao3fv5nhukiKWuQ6izIzuRb2BjLSY06Ad442u4eBGnHJMXhFIybiqcVhJe1Et5HiaBoEEhYHA3SwUDaQb5/P7MzYgQXguNwSQ7qbdbqTx8icFIC2Mxksvjq0sPT09eq1liwva0l94nUnOb7wREpubkvV7c4REGlxxRpFBCRKfFKGUAfFxEKjg8AlSfNrZS3IEIRYgGB8hOC9W+hOEjBkq9BcLGKmh5IC9/iL2QpAp5wSbB6Wn8UgBXnkAaEACUOuUBRhtR5C+POz2ShEbY6RVS3d0WC3NZIyLdE2xaofpVuXKrEqp2FV0Y1p3WmuYQhC7ETiHQLf5qyYXIchUFMeucJ1uA1kXN28xsKkkuNYoa1vLk6k0rV06laDIEqRMuXVMt3vlSBUdMDRJ8L8ji0psKk6VCtCnKmxQc6WgzHo1fN5JEY2gkQ7gRY/viU1yzA6iFAv0Xn/Ri9iyWWcRvYKigRdGHBoq1vMCgFeQKHE1iAIPDT5ykCBNYMHFeSVZPhJEpqR9lJA+CKBaB+ATBePzyIPm8XGu7YTpoehGmQ9Bf5xaS5pEkiNIijhyJ8Dt248q7m5IMl8tFZJFiCocgnN6GhkHCJ09zFP1wlJkNmWVwmxudYYnFIs5ydKfqEyCIbRdTMY+e5DUJHwlLUCUaOUtSUmQYsGda0uWTDKc9tbqEuZwALF8U1tCgJmfAeaRYy8jpUIx8gODZMQ6MvcFbmvAaEzYocdDBHp0o6Z02FN9NaH4vtb5nGJz6gY+GPmKknHM8iVrhUSGMYmlTJ39zaRgCAovH2JoIMBJlHxEKUpVEZFcuycWTyYC+SClj0ThIwFfSBirdpcp33G3SihkxTV0nh6k5/Ja+bSMxW2vZdCjLa/+CK0Y4z5ciMJHBw9bg0qbZte1mQ8ewNwW+QZdc7VGuR4Mf4MFggWCBYIFggWCBYIFggWCBabKArwu87LOqCfTEb3vff/xk5/85O67bbIet+VbunDBn7gF7P3fDuYxcRDdf2//97///fe854W/+vEvRwajSqfNJsTudZl4KoEzWGCvtQDXOfm8a/bBBufMSjE7EDGPmWYJ08b2dqdTbSzO+B9YY3jwBBnJcxsdGB5YpNlEVWqLEbpWSTsytY8SQAZNXCYnZkFuibEpAFJnthSZLKQaQSLR3dU9zPNfd8yDnXTCfq9//ev5LsrPf71oZleUTmSjfCFXnziAxdKyyTW7hN70rs+pCXYzY/XJu7pY/mqaQyywg8EaiGImmSC85LojY58u/db3Hj3kkN/yFdaHXvP2teuZXIv6hiO+n0p2ilGJ2UNNL/veASFIQwjOJ4iO3LnhECYcvHxoPGCWd1ECPDsJyVmTiGRsYIJsGoW0QHJJlidW6j5dJSTdvIaeOE426TCpKDuaxkK+7UujvJ198DVDNOnpBoHBAsECwQI73QJq/Xa6Gu0oQBfTQMZ2JjB8hWx2ovs///Mfv/zlLy9alB/IRd099EF+bbuRq0HIhINKXey//3Pf1VdfffHFF7/mNe9cv8G6Tno/W5aaqsQnrHVbjOrcd2Td4A2B/d8ox0fz4irS/+6YN4F4ogEOFggWCBbYvSwQb679MC2O3P7sIK1ZIGvo++4766lPfSo94EbX9/mEIFYHaEBspOkJthPgiisNsm9dNLJgwd3Pfe5Jv/jFX5cvt2kC0qWDtrFocO1ZgA/P0vmyo4g+l/2yVCH2urXHGqiCBYIFggWCBYIFggWCBYIFggWCBYIFggWCBYIF9joL1Kd6NDdj2eeWDZDM8HPaNl+0iYXZs+YfdthhXNvCiT6dv4WMVYBNmzZBxq1VOqnI8d26NNuNsGnD5vJBZfZysDQPzZIlS7g7hrX7Jz7xiZaM22zACj6xTF9wPooTg/fff//RRx8NDKVo8CVTKu23337s3AD57Gc/mxs9Zs+enc6m2F/HV7w8PYev7rvvvnXr13GEkguwPF6A17ABH4LBAsECwQLNFuBzg7QkzInTEv7p2hvtJGRHOiqUembNOPjgg2d3TaMlZDKW079xXhpAXD6X49h4d28Xbdqm/j4wHJikSZwxcwEs87P2MchEbPObEUzB3HtcsQDvghawulLfxRfvf3OlDdb/zh7tf4fL9mVQHP0muymoRfH+lxUB6g+i6FL7NvVHB9rZ3Yb+98Rjn2hpuY2OOqXc3P/OnT+7oR4SpK8fq//NWv9bPxKPYqH/deUTvGCBvd0C1qi5Zo0mCFtwE5/dsGHbwWuWmTu3dleCyJxfZeO0GjEhtS/rkUfW8qWfYn7mfQ/e05Xkfp91NIw0dyOlkZUrV3K3Ab1w1V0lubcbPeR/Wyygmhbv7Do7O3nlu/322xnqzpwzm052xqxZRx11FPcyM84dGBgw8SXrWBmxsv7OYJOKWs3lDzzwQMlRva3kywsWLOBmK/CMVR988EGGqzwIRx55JAKgodMkSG0HoB8Hs3jx4hNOeBIYCEZVci+EVG+0ov+94ooriH3Ws55FtUcBzpVZ/1sd/UI2+wFs/Ltu3ROe8IS1Q/0QB6cSGTXpJFlEYhuEKRXKC7x8yOQ8JTRgfHDCwHhCtj+nkjwZOm49c2iL03kZvUnyDMLGI8YLKtWbp0w+AHh0g15yHat5PEpw6QXY0yBNEoSBEQcs9ngZAYNEB93ihBwoxYsQYvHRAUASlLp8SUMBuNABAEcUQTopGKW/pV0/TCRGJQqx10c6+KB4YcfBCxcY8eKjLZTCSwgYEgXGh4UWo5g3BTKcZ+JspCMmFsfhJJdTVOAemdrBH/DVKAN9tWitEw79QSp1y3vFkgMgCgVInXIwL2XWI+/INDJO5bja3eGU7bTnwAhwPBbQdHaZnTnThRyQJIGPztzChVi+kozlEQJNtmzSCu42HGUcSgFweYyTrPuGIDenKOkJDIFHKkpI+aZfMin9FSvFKHGcst/MDotnp66ibQMv1oMXMm8uhEh/+WZwZ0/pjwSIwUDPTCm8OusERk7yIZaDGAcSH4x80UAPIAlIU1BR+GIHELvHTwpAJURt7IY00iWPpILv7QNyVCWrlZR9F1zVhBWoLgFPZkyIOzZXLrjPS8uGllvEuouTEAsXQSiRIQKfU/Ju2dcdSZDWL6EDqdQ9pYs0DRGFfPA5d+MWTwEYE1IqpTvtNJ9OyYndqccrp9GjKJQQoAbSgL0+EEPJmwMOmygIRmpAuac6zEIeZQ0eZ3tX2ZqDRQ7COmh/CWI3TAos40uSCg68yo5YYEUBeBgyNBF+TN/LVFoNNGMiG2jGDHpGAJJopvF4qSp6zwW9zwKwx4NEGpnCV8abJTdg7CEJLlggWCBYIFggWCBYIFggWCBYYAdYIJEs9w8MJzuj8199ILuI3vjGTydtRt/e6XlXr4/Td4Aie34SboSwjnxWiwuiqO9Lnx145Uue+ZLzf/ndr0WVchdTNFHEsW0GsTlnC+6iwQl2YPCCBYIFahaIDde5YVo/rj+3CSJun7JJKyZx8JlZ4LnjPmzw+Fxlzd6KigtyTZVFuTaO9s61erW7tLUGynwG06eSYOyOUsN7iBnha47JUnSuavds29Vaul8DHPMiPgg7vFzDZUlyybebzzLhUZ7pLBMYRTNnR29/xz/mc5n/+/YfTGwUDee5ZRzdykzGgmFOAZZyFS7O5Kb5FILltFIuuRUmJhyMxiEtrfoMBTDKCBNHgmHqEWlcJZ8f2Ti73Dt/ZvfyzWs/97kbnnrWs9713vMv+tAPWFDDdkyd9nR0JvNMF2byUTS9K8sHBf2ZYOTIaRqFtGQr0hKAyooSBiQzI2A8WRy2wnLXhKGa0VuwdmEZsAXrYj0gOZQPmVZCYFxxQYIzlql2pMdGNHbh8HW7E044gQyylMik+VlnnYVBmOv/+9//zjoisDfFVKsU5AcLBAsEC+wwC9C4+bSA6RSEibfFnqAF0CDHC0EOsHdegjBKhUTBxzEiA0ObLBrm5x3S+UbOq8Cj+Cx5fvazL+xfM+drn101UKR7rSQ69k1FK+xCS26tdDysaSGHJSUT6Ho6rXLxjW9c2X1L0BHWPMhqatcE1PAQOyVtkMWXZXgDqWY6K6kc30+96BOXHvm4x77j7W/99Ke/MrC5nCnvPxyti1L5VJJFrM5kskNfpKnlwUmJ512ShVFikABIk5oySrumi/1RDx5DbAMom4vBy4kjt0HWdpNy+8bxxx/PmtYpp5yCMK5TYbcNfe7dd9/NkZjtFh8EBAsECwQL7E4WUFPsW+YWqk+s0ZbkeI/TIglFeWWUonhnzIyeeNxRfHXtzkW1W/hrctzWE43uDeO6eOfH5gFqpBP5Q+9oH72K6GAZc1ZuvmnpnNn7Pen446vFRSvWlOrXU/KtNRtfOzd2usqUzxqUyl0cUxMwSX8kPy7MYzwQj90BMCdMcHPnzuX8FfuumFVmo9WGDRv4PPsOSD0kESwQLBAsECwQLBAsECwQLBAs0IYFxlsh8i/8bcgIJDvWAgxwtmnIKe1Yi2SC1Gsal7DVEROMTCQq3Tijl9YmAC8OOZK2VS6IPc3Ect3MLplbzbJnFOC5mD/ennXMhhw1pDJekBQ1lT0eQQs8KZJZ5RdYQfktuBTluQhSB6hCAPDGYwW345MLeLcpI3EFfNJCtpOip4GFdLUcPwF2L2cnAoXCwMaNq3TR1amnnsph2o0b+glygId5Bq8YK+8bNqxdvXo1K+/CszRf7czmmeupRGCmzeidM2cOt2j98Y9/PPzwwx/3uMf9/ve/Z9YCCfaVtPqZEORQXsxd3HPPPdCTHLs+Dj30UMiwJDA+N8hQK1hl4PYrjv4SxYckAWDE1MyBVFIc1SvZF8QSxeGRPvsNDwwN9bMRpVg2nUkOBxDcVFsgbucpfQQkPJ7cVGdtB8sngxPIXeh/ZTRvuglUQng9F5uOBgeH0mXOY2ee1DOblq06VMimejet7Xv4a5fclB/ZZ3hkoJOv8RUrqYxnTLiDu2VWvPnEomviOEeJzFy6mqsU7uhbGQ0ko0PmRKtWdbi7C6aXrI3qs/uLbKfcBJySltrACsrfqjTPBWXof7dqrqkgcAeGbdWjUBjcuHH1Fv3vRut/15UHCtWC7VZ0rqdn+vr1a3B0uzpsTEcczZ6+OVmhzwNJp0lvS/97ww03HHbUkb7/LbHZscoeRnu9ZPdzOpPOVStLVtb63zvvt/734EMPTSaz6UShUmRRxm5wG7//5Roajram8vnQ/6pkgr9bWoD3zAa9aRVpPxuQ7QfbbHvjAmHBgVGDrChh4mRbhcVCjnjG46K2yrj9BD45Jc0WbdstzjZtuzcyGaVr9zsQhDJRyI3w9eFikVs32MLMjrZqssD1kpXUasyQS3AhRqKcmWdaJWYNF6qr7rwtmr9wZKj83Z//JuKS3H0Oifo2cMNHVGLTdJQpDRhl3aFAc5nWIxv/Sm33BWUrAgUbiVqGYfElZWrXD/+3ZKpF+hTjQtphnCyahnSVEZDbKl8ssG+T8bc1lUmkt69+WTY1P2l9K9fgsDGe655ZZH/yqafccccdGzdvpodl/Otuv3KXCVR4l8vce++9XNnMtSlw0ZMmOjtKmVQ5GQ1VSrb/sWRfaKbj/tGPfsR+uec///nXX389Y1i7AKtuVy5InTV7NsIZRz/66KPsbL/zzjvpx1/+8vNVIqrAyUwvALy/+93vuPqZ5Bj/nnfeeVZMpVJXb2+hWOKmG64JsrF3VFm3bg3EABx74HJod9rMbOZLp56+IYObdAvo8cFX8Sk46ak0CCQVla/85tgGTDxokzZbuhpmO7q/LeW1CqEwDlvJXJ6UoRavnbyOAkAgn1iaVjKLgx4CorhbB4A7VnhmhYGMWJ4sHkAeQ3iRAwvtkpcPAC+jM/kwIkei2HIMJTNLSIAdUQQhQIhShxdpkgA7jnYAUfiQ4cAgBGliAYMohICXGsg0Nnd7CwlBLAwASGjwoUdzFAApOdIkngVSBIkE0pIvmdCDKeTsBrEEg1N6w/qhLZSBRQ7JXCLEf+kcJezmrHI1U8gP0XIkoq4S1+tVOhiVJiIaPdpJVKu9FMGCJEsuY+xdPT2SA4GuvupyzWqXbe82RywvWQAZLjvC+AxEEpbNbLKaiSqdZJoN4lZwqWw1na+Wk9wOzf29lQQ3j7GDm+Rl8Hj2gVWCZFY5InVyQK5xGAHFyDsAeDNCbF6dKFIHAy8wlcds4RyU/CUIFwSIAgAjvGiUHD5mpxQQIjw0iFW6cOEI4jfwigYyGU0ASISgP8QkStFbaTgngFjRkC6MOMkhCIDvdQBQrkEKUJR8CYljJgtGWanKIBSZUlI5UhIyhTLFONTULlWsnLJ5LJmMSpwiLFPeLkdYnHvXjMWVIKUIPQ4rYGXuSEMmz4n5roCwMsRKiLRtzqXuMClRMkUtdUcn03HxKoSq3yxfIRNK4sFW7Ro7u1sOEIydXqSIWaQgITuTaB0svBwTNPuz+OXUA2NqF/LlfK6UG+FXLRaqDKitCbHyHdWzpu6e9kfWVq5oh2mfW2dZsbIMcNxEKkOQsio+YjGjAFV70VCPVKAQgAGWk/C6TxnUrA1GzgmsIWER5HWARmkplbgPpaJqzK6J9Lnw9c3HegAaLxMkSXhfNHGxUgAMusXxXloLIFyA1cI4ISpYIFggWCBYIFggWCBYIFhg8i3AXP273/3un/70p4sXR7p4afLTCBJjFpgxbc6Se1d89atffetbL/jd77686uH+7o6ZbqAaIwpgsECwwLZZwGaaNAfFzCxjcuaqvIDaAN7WYMwxOaSxug3dmSNwyz5MTzB6Z+7HEZgXnxqzMPf9FwqsROIrCIZJhHKFu5lqSzkAzB0I4+TUJg6g5LYpJkYSbDxyUyT5gq2M4jh6y0Hb173uuQcccMCH3n/ho2ui7gR7TUwOStqVV042c70IsduvLK1RsU7GKAYWnIl1u3UVFAZuJS0ks3gk4dirKDNjxoyZndGmvuiiiy762te+tnTJ+ov/52o06elhos16BtbAmNIbGXF5T9jXkCSHKMTikA8GAAXdPJvJRg3742YbzXrJJJNNfHVQSHx0wDmuGm5UrCsXlQ4E4B2lTe7JKVFgJQdBLcJhgM1gU+9Qg+lg0lm4cOE///M/k03clVdeicXOPfdcvtjDYqQ+pDP1uoQUggWCBYIFdpwFaP1wO6alnfRcuc7KpL73vU/hE98vfcmHVnNUISpnooyWx9iPSaytabhuji5G+a13RhYDxnU9eKMdEHhPA4Hx150kKMTlt2zBchj62ShXii6++OIvfvGLJ5100n0P3BRFbg+yW+MVl0R5fSREyLp4k+bhNgEJ928IbXK1JiP7XmxrysmKZZ349a9/vTYWsLmHr7W//OUv57tzF1xwwWQlEeQECwQLBAvsqRaI9x1qwNUptN+/b7XNFwEGFMAoG/hJTzqB75H+7GeX0HcxaHM92OhLRVyrSbS8+lUJBJ7RHW0ajH772z8961lPPeKII6LoAXcH1hYJajRa089lIN5p+i7P8/jMesxUALKP/PZLarI0IV0ZgY1Wp59+OmNhRru33nor91FyEyXfmGUTrUp5slIMcoIFggWCBYIFggWCBYIFggWCBYIF9h4LTGykEx8ZAbOYKIsxatP4pYUBoSFWIx2ImRy2Zcu6hPEYm/WEBSTSWDj2a6PjsYNXEkrdkyEBRzCeIx/bDECGBJxGYfjAkElIM30c02AZH/S8rXWATCxxMjKFAl5CPLnxYNlNLPKbxY7H24CPszdEjRmEXsp7AyooOWOyxJEikxAYlfc4QQsYLpwIlCi+x7RgbI7yBpeEiQlpFrsjMWRB6+y9vb1M8nPol7xwHpgDwDIOynDOj8eK+YeNGzdyaxXPKUjo8cmyjD/QN4gcnUjkng7w8+bN45yYaPBFpuJG1P7778+pXa7ztou03I4CoqAhITkOBu+7776s8sMLwLE3O5ZWa11GnzKEkCgOoMH+DUHkBLf7WkCl6evk7puR8TSfWAbjBgHmCZJ8niaCrR8BaCCGBkrcHtD/ts6vtzyZbbC2w5gp3E6qof3224fbAF94xLG0KoWBIW7FomGkVVyyaQMTsH29D/3f/105mDNTSxSfa4K3I2tnqrkKEJ+TnkQNpSLgfGcnts3MnLac9vD+5eLyvjSp+6YjjPZnW5zYpUw7fNArFTXIsCgoOVuVUNe2VnPUtm+VSwTwit0n2r7aDUmgvERJghfbQLYrB8nCeP2vf5apVZDRUfLdC265Ej11knyRZeV9eNBO/oOkpjX0vxBBqTJScW9P/0ty3uyIDf3vrly7gm7jWaC5rdCjNB79VvG+CQLYKrEniKuhZ9NHjQeIJZ6KkhZ7XOB4EuJ48YKBHT8uNk7WDEMJC07NFEEcN1rgcz0Go4PZM6dzwYFkMjCHjBd7YgFwNGV9fYw1uM/C0gZDFNLOPf98ethC50IGI1FXP61ZV+dM8GylRuZl3/3W/TfdFBVz1vDVB3HSDQkuB82ajoERL702XIzcnbBaXzYG9VgoVCJTxEgUcpS7sWgbceRUXPjAEiKgkbRlWEJEIrgdIVDi2qFsmXgtUqKc8a3+7HaOUiMLunyEDpRRJ0He9NztV6O5YfjJmyHvcvjCamw7SpGMGDITpKLSU3O/FUIWLFhgBG6MTEKAmB1bIZwRK4/A4Yc/RlwkSqxqEZTULoTst99+3BANHoAHwfr9qtVV1OACLJXg5s2b0Q0HAKWcWiDJNMw2NEg1CXvGn1ELTFl+KCkKAp8Upjo5JUQqKnrlqSE4ZRmdNMEojLlw1HMccvHJEbWa+s8pD9Vn6r8eDaLAQyYu2KHE8STyDNodOm4OFh8y8DwmOOTAThMto4kdXtKSfCh5psDgwwsAO1EEoUECLGCUOj5BOcGwQ4aDHlhyUIYUaUzAIxNACiAHAjmioMeRltjBQ+ljFSQVnGaVYSdpESCWXNN6wA4gCZBBjCbFbAeaJzKWLnrABT14sujSJITZzQgEDZ3ptkm/oSGjd320RIlR2Veuawqo1NJpcqr2ULG6AKvT3TLTWb8Ai1TUI6czpr+MYBo4IWQHVcBnnYKkDkzqcLkmuXY0SfIbfJcXMyAAUUiGnYzL4MAyHUkQS3IQ4BN0wmsVAP0xgNghACAIIDliES9RONkfCSRK9pU0eNIiRfAA0KMDuQAf51XqZnC7WMkcGOVTqpIcAsGIHYFSyRKuJ+0VUOrKnQgQgoMXLnyQBCFwaNNKxHEWMU6Kj7aSwzOrDJIXr790IAhQuwCLAwCoV6h9/gT1MBaMUtgO+rlH3ldFhBMFQYO2kmxiHYEAfGVTCihdUXp2CMCLDKQSEiO+sThSncaTPsInXQSMBCHBV40VLFG0PFQhbEIUonCC4XFS91jP20Q5xAgq0xYZjrNgLk9pVaJehwX4KABMiq8nSBII+tIE8LC4oAfj0wJQWqA9gZcQJ5McMHIi9n4d7SqMCyBWSGnoKT0QVwxK8HFK5VrEwkOjZgHGeKwXOCbQ+JyMSRSQwQLBAsECwQLBAsECwQLBAsEC22+BJCP6RPTy8+eUqsu+8NnrOGpd1ZjJ3ua3X3yQ4C3gX/K5tzzXPzAcJaNf/uSR1/7TGW++YNYH3rEp1dkXuSmsOoNdMRNcsECwQGsLuAG8TSBCpjm+VNKmdzW3qwE890swGmf6lbk2TX4RZMTe1ZVlHpJZAIJQAnBRfqGQYV6OIAQI1LSpS8UmqpjyA6M5OwiUrjDl0miLyYQvUZq2gAuYrR74lXIaZCrZBTKTLmXT5QrfFuiMBgajs8886pnnnfa1r33q6uvWkRfu2GIdiGlnKFGNVVfaZrLQ09Ndrth9/2gCkUnu7eITgGQBbZlDJwg7UzkQgMT3AIKgx5etmPFJsNOJr6xV7duA6XTnYK6czqZ7UuXrrx/6znd+9trXvvzaa6++exEXgUQjfeuwMZNjPV3Zol32FVVKER9h6Mh2dWRt7yn5Sri+I5PhWxPMMI4wbco0IFEohsNcFApB9CGqYX4QG+JYacPHktBDnE5tQaYSZAUHCSiA08wspQZsdkoksDNT5GScMpL9WbBwtFPryQKk+8ADD7zyla9sTgzdiIWsOSpgggWCBYIFdlML0LCjOe2bb5YJ0oDTOPsekFgadtZa6m2y5dWaeufELgw0OIt2jm4CBxUtJ44kcGCA6yS2IA1GQRICMJ46RnjHl7LvjdEDJt0ZhmpPVO1OpobpTIr56Jwze85/1QsvvPDCSm7+nGjTxqgzGXWkkj38WGdECB/ywydTZE2NOZ0u0sDwymG+u26SWKUon15MHR9AA57uLO2+KVTKj9BFZbMsEvN18nyyFC25r/Tpj13ywf9+xaaVd/zyujX7ZqONxai33N1dKfErpiyzdm2nyykp0rMA+7yDidsZUyj7aIuD2JnHrGoinAPJZhrICCEHhxAcQI3ClSkEvhfzNKoAIhMXGAC/0Ogl7ACArTyveMUrmhNqqA/NBAETLBAsECywJ1mA1p7sqA3HF9Aig61pxmMXl3oWCaebaBBFUJ0UBPQOCnpGkD290QknPOn666/fuNFk1Ed4rl+Lthg3wWUU2+2Qk0q4ntS+k4ZQOuNEYaTcEVU2bSpdf90dz3jGmcuWLa9EA9M6ouF8LkryG46i4dpdmE4BZWFMXwoS5TPejsrQQ+YFqgS9KB/bgPGSeadiEkBlEe/fPcEUAdITv6+v75JLLmlOZZuM0MweMMECwQLBAsECwQLBAsECwQLBAsECe48FmL7TdOLs2bM5xM5pPZb54oOjNk2hsSdDJE2KxkclGlu1kMOoFnZYWDllvhcY4q1ySUklBAtBWPDlWiRHFDRKUQmhgDCSIILWEnysT5RpXpRBf6KYaeeYrpazPWUz4PMIJQuscGG9eF6aWTzGqwqGZXEsz7ETUveWJ0devucaE6ACkC76N8ylx5MYkxEkvOQUnVWLAAjyESavRjMjYuUUBYzmVD9GuEzdE0RzotrRXzT4OFhkwOYUW2BIDqNhPbJP6shRWbQuO8hgjCsJhlqkcic7im2R7q4W1dc3xCemKDge/7XrN6F/IsrwfQvmOnxRkjvMwrl01gK4XGOfffY59thjuYlDdhNZd28XdqA6LV++HEoO7r7hDW/YtGkTsTishGMuRcd0n/jEJxK86qqruACL+k8VcvE125I0SKQdc8wxl19+ORZ7znOeQ7q0Eqm07UOQTFqttWvX8kGOpUuXUoGpfux2QFVvYWTudsXhld/1ARUZevoWVQafXJuz7MUTiky1UaoqtHutn9Nd33o8KWo5Q/9L/aF8qTyUmvx2ik8s0Df3vwjEtRDlqyg0FEQ9aHdh0ECx9quWilZx2bJlxcFhKiHNEQ1OfzrJKdsly67kG0s9US/I/sHS9BkzNvQ9vO+CQ/r6NiTTtlIMnjOdZMYuBKHt5Ayl2yBXWbHa5t/Ze8b3oNjfVbKF9ZHhYc4TK8uaiPfWEHJMn8oT+l8sQwli7d23/x3s61P/y8vD2o1LyBE7BdT/1qtlVHbbLOlVH3nkEfpB6OlAff8rss5uuwgAU9D/zpg7J97/clTUzOT63353Tcb29L96uEgr9L9jPpgBuVtYgMeBFpgm1D0ZtspGU8zjQ3Cb9Oe+Gz5Fo4cLxjbZPRkAzy/9CwowHkGBraYuXh5AKNXNqQUAT1D4rQrxBPBKB3glx0e1BsQILw0XiaK8w1TMjEm70YPeE/MiBBoc1gap9zchnU8/GCVS3DySHs5bdmi+mIt48KFFSGPPEwbJZDZB+YQnPOHhhx9euXJlxP0OTBSk04Xh4sy5M0ilkq92Tbe96JC16dDHZ5YRYLfTs01ekaGqsoOG2ndHIbYpgaQhZvSE6WDBeujDawyvte1LgFK5QJoktMkrRj4kDDuDPi5DRBOQZAeAYmotR3aDl0TJu6ocQXgZTbfm3QVjyQJ1koIg+9dcc41pWL+SMq4tjzk1DVuRX2525htU1HPLr/vQpm12SNrGRWoCBCtWrLjrrrve+MY38vaIcAyFKEwEMUNUgocffjjBa6+9FlGwAONgjAq5RHcv41yC1JCTTz75sssug/e5z30uSVNYwCTa0ZnonT4NyXrEAHg3oFyQoPGvm6KIqx/gybcAZYRQKzkH6LlQMnF4shKm2lDQPGUkpyaXqjsVCU2WwuPJISOojeMJovLjQ0mQ2kvjQz9IkJyCx4EhSqJgBEOucTwdzBFhDQAImBwgikeYVpRb2hGFBHycSgcJwFASRA6PKvT4CKElBOahgxdfqSMKYjml7n2Q9Gtw4cNOUFE8ntKWhEhCjli1qACoTULCA6MzDT4SxA4XxQoxDiUJkgWfKIDXX5qTd1kPORiB9gF6ukBiq0UzGglJDoyIJEfpjCUqhaUPalgfneyEuBINQ0/zAuxpAEDiUACfS7OQUK7aTWEogCM5ojqtO010ua5Dl2FJc7p907xa20CFJgQlX0mgAKeuECLjZKvJ1p2IeKGXQwHJQSxNH+0hBOhJQjgfBVL6kAqa47CYfFPPVTxJAI+hoMeBF5f3QSITBxlJm/KOHYDKo4IDgwLADexwEYUENIcXGAUofZKj5lDxcMiRqWEHxocs7kgXJ80pfcqCWMgwGtmHFy6qgZfjlK3Vz7icSYSlsLIs06EJipFTkHKjyVXdVvlqwgoubS8PEJuSzpIyUaVqzyyZIiM4eMkOUSC9HA9DJmIlByWAyBBOVNz37EiDUcl5GtJCGdixMFNCMNKEGGWtja8SWymaPhgUPKVAUSpdQzonIQjHOQnWCpEEbmqLwedt5wH/n73zALCrqP7/fWX7JiGEEGroCCgKKIpgwYqKYkVR7NgQsWFDfyAKitjl70/92TtiQ0CkSu+9hRZKCgECSUiy2f7K/zPzfXt2cl/Zt5tsCsywTM6cOW3O3Dvnzty583CA/EBjAWg4CaSK9eyCprpKyNC3kiwnA9PRXEJ0gfCWSxd4JKgLwNglAZkZ4wFnmyw0462oqw5ekq4r0w4GUaiAXRLQBcaQqhKmOkcOEtRG2I1AGBWFd9IDAqNsDLgROaYGHhiXT8dFjNKJ0euya2DzhKuwB+ETZh+TEfmkMclCgvBCD/FrCx6XPeMixsLJph+vEyZgz7hYxkU8Af+Ml2W89ky2P8dr/3jtWQfyJ2BSZIkemLAH9FgPe+PApDvdiJun53FNtjUjX5OWSY2/4xqyaC8zkOZ9i/BxxVPa29gtzas2SvMeK6LbbZc/8sgjTzvttLl3JZ3TkmG+8Fo92cMITw2Czf7GhsmNEI+XvrHY1a0bdynVueYKE2TajdIAaKzW6AUYDUBIU4H9dNKtKPtUToZZh7njtqV/+MMfDj300N/97NesPPsaJmA8sQAyy6pMjMUS5qYrRNaDx0UsIZovCZb9oZAUJqyCJVVbE5NikaK1mE+2/LVoahS1tjzAhVdZX/AHHrHcpqVJdzH43yFh9UdrBNIITNKSEBjYtXDg0a1wkRjYWbYg11UNGfQkt6jqF4zIqaLoMLnRWRVcxAWtMkCAKDAsSBWG3YpkYbiITNWyJMh7oq1mJSeeeOKVV/3+979/iLVGFpmLw25ZgFUKyGgXXGxOQgtyWHlkRMVOHYBFS0FilW5bitQ6eh9VlVNUqy0XhpyUS9wyHP86C0uY7ai+8Y1/7brLdl/84udO+tp3Fy1ye5ehwWx+UvXxJcuwpDXjTrgAg4pwhJclwrPyj/3OZf7dgADk0CJgMwYAerE4mpwjcD717wmMjKb5JT/XL5gNXjTeT64HSVgi52OVPOybNsaqlqlYQ4CORh0pJcddTCMpVRWL0QPRA9EDG7sHGHvt5ShtAWb4ZfICTBU5ozeRS2OyGquBWpiQRjHLaCCDRsMnwQE5vIELacBAoImnxl5Y0CWM5IhmeKgfRczAQCpmEX95ifzMXZMf//jHv/3tD88/f8Xg4LS2TFumnOG5ARr0pmOnJNbPUYEZetqXBBkfcggjGyAWAAERj1/o4ZHgwgsvOWC/ri984Qv33/N92rtsxeh+KVoHpcyq9qHanvIzSFwnY3CdaEyp1+t+IkleJbxiEvu6aEjoQzCafWM8LNSCASAokyshRDSEZsweQa//f8PGrn9rogXRA9ED0QOT5gEf5tworbF6XHqMt5qrgTS4SGIhfDCDq7c3CDIIPLkDiBcdncl73/veW2+9lQ8hOKSSAx4lyMcZJ9JLdrgGBkj1BHJ/+pUzXSYR+oEXPbKc7yHf9ra3/f3vpz+wsC/c6O2NgdqpoplEFqItDaFIFRhapPgrygp9pRUTMHAcLLKHqE24x4xxcE4y6WR03CSbHMVHD0QPRA9ED0QPRA9ED0QPRA9ED6wfD7CWyLIkuvlajzM4WHjUV1XjXdbTTG277bZjcsf8CHZN3DQ9aTxJYUrl3z9meRnKnA5RSMAYvmMZ0ynw8s0JTaAhfIwEL6pBNmZEC0uyWIhh5Eig4VpaR2lja2tKZmLIeQSsjdMQ2PWdJHPGmsQhEmIS9rDAy9IuEjADe4QkD4lDmCpaCiNa6DJ9i8VHX2BEZkDIVRNGDg7caqutYOfLTHkGymYk0FP0OJ7HgXy0RpFPsKRFxlcLEZ5cVwjG4y6948AAGkInNqldirAfA3Caik3mZgZKd9hhB/qdtsOLAfRCtdkpsbDjKCixlrZzAWCAFufH5E2J2hCK06dP4Zvz1772tc4tWbe7YMams2gRfYR7/b6phFuSu2yPPfbYdtttWYOisbgdJ+BAms8lRM7VyHFU3IBvectbdFNzYdO51157LZLBk+M0LacwXCxdupRjrWbNmoU01CGNSwh69KICzN577/3AAw+8/OUvx0sU99lnnzvuuIMPfekshgt6jVNCOCHrrrvu4jgPaFDBB8O33HILMNI2xr7A8o0o0aFcIbiaLpO3uXgMHrMhsLhLzicDVAz7jq7XVeGvRneTAjB0cNWNqWJDJojxl0uFzl2/8ZcrlmGEK4qhSQMRIxtjCwdtcL7P0stugGCw0Mtl2pa0dLR0LM+6l7PL2/mQ1Q04PoTN5Ay+nlVPPLb4sY52NxLymSTSnnh8ibv8WMMmLOc5Dos/ty5OiNM9AvuqoX6GslnbbG3xF2pqU7dDzcs4xl+65ikQf6dOn078fd3rXkenFzMruRQ33XRz7ouR4dGNkJtsuimYVPzl+uEBTPEXVyj+Miq++c1vHnTnrWUt/iKBfZnI55rh5ABUpOLvUO/44i8Dcoy/Ne/KpwNS0bmZMWpD9gZ3AZcxAzjjOY+1TII4R5W7jAF5XGYvWbKEHbzcWdyD3IzwIllPuQ3kyIfQ40ZuZCzR/JEiVapt7GG4sJb7nZxpFKLQC7KB0ppV6GIUxXhOfsTsmjQNkOgN57+tLa3EPrZlE0z5RV/EUlQzEYJ5wJymC8wPD3NwR/8Q5+pOWbqyp7+/UOaYvkxmzoUXzjn//KScTzbZJHnssWTLLZOWTLJs2Q3bbcee6eT+ue5zDnZisztuWifdR8fR8EcWPppvC18pNzC5UoV76TWav+XWWzOBpYgciuqCMfkhhgaPMczK+WIkH5NXTz4o5WGD6w12EtdAM7yhcK4ZnoeRxtQbS5pkl51Mo7hytt9+ew3mIJFAZ415CUHJMz+UdC5XDn7QoyyY0LaNBdYlylSUri9l3Clg06ZPp0/NfvA8xd1zzz3Pe97zOK8KPNc2XLNnz/7nP/9JFbAjzmVwKd2BKFwBF27Zd999OeUZmD5CMo+CwLh9m222gfg1r3nNzJmzkEY9XmXxK9/Z7UQVi/QsZ2zxFPqKV7wCBCzPfOYzL7/8cuBC36q29u5VS3uGZg9hA6fCpee//PIYR9CNLAc5gTFNggfkYa5/XQDcFFLSpOcho9PFYkA9MyFGC2SMeGhkvy1qGfy5rijW49oA8TREiXGGK1/baIVRA1n8AaBdWsNRFUWQaimMjFTiZewCppYcSqqg0UDKiOoc5Ud1cy80+AQy9EoIxCRgkpYyIOBW5QaEzN2z/tAiqZZJ3OMM2soB0E5CBfTc8tgJmZLYkUCtEjBVdBzq0Mv4Tw5SCTmYPVJyZqgJhkEINLAzQGGAvEeRBI1Tncliw1C/+6DJPKax2rU94z7z4XgfbFCLkAams9PFfQ6VdLlnpC1SikYBymV8t/tAqp22k4OhigOwEFV9ABbyaBGL4xgDGcSQkVCKZC1YO8O8k31NZfGZ2rDtoqGZADgN80jyIVcCwrkvwGjyjkYS7GH3SbXsx3tYjgNlvCilUQSwO/P8TSfblKMRpFSjF3sgg0XEyIGMJM/THMOAhIYcpBjJYcQMLgBgnr5EDBnsaCFB7MT5BIxeWLABLlhgpEilfAs9vPQ1BNIlRvAAYEKkqtZWLsnYD0DOvYMD1XynNbyEyq75HVn3HMhyiAxz7vJRlebAXvBfE9B3um6hcb7w7zvUUnGRO2L/VSB4OZa2y0vQyx51ZWgGMLWwK1HUjYbZAFRhIZNkT+avAe885GBPeAAWGNSpCyQQGBqSuwe9ExAIgCWOYLU7aUT9U/dfmkzDm28fDg+JjZ2etS4DiZO570DifAEgSWBgRyN4XSd4HgBeki6GUL7HrCYZCRIFmQSCQR0weaoWGpNMLwOHwlUVYgQjSkDznqknqlq4YcY9CTTOjRQwt2J/CKeak+okI17reBMoY6xo9oR4amteDdaQavZqjEkWEMoPq8DX461ZZTaEQgyuFlVTiJlUk96kGVBNZhjZk7LKaqslpCiNQIAYQ/Ym6a2/RB9KkOTG+JQZoTGpqmbsgcUMmAx6hJtYA1J2hk1onj7kMoHWFsMIQHW9qhRlzWLK8lCUVRmS/jWkpFkVRVUZBsCIDbDaamOgMTKrrcZQVS2kJpkJWXN6EzUmUK2rHovZbEA9yoh/OniAK8fuL10S1ddSeKmkBltcNCa9ntXkTERV01sVgMkXspm8nkDjrdcuCBrYU10b+sGEG2BmhE1oLB9eiHmclZAxiUVmimSPWWV4A4rDyXve+45M0vHj//cAy50DffwwAI+8qYdjnGCNoDddoUlLoFRj0UgyS0bFjUDUCky1d6Q+/a/RhxUN5ENWzSL6xlxNykdItXzxerw+RdsDK0qZu1mG9keNJ//7oyff9qYd3/vh7U744gMQt7Qmw0Ms7jAj4I0yk7HA7yN2mJZ6ZssSswf6epQjImv8a0JUVy3B5EMArCJ5M/Smz+itUVZlgBM9IjZEGmxAtZBqRiMGWHP6UFpjuFpXPXqz2YB6lBE/4gE3POJhVhAchjeTLHH6IzAqU31/AJbWelgGohbfQs96BK87wwGqrW2ItaBCseBf/WQQCBevcyxCwcirICTAxQkXjFlc8R3tXf19bg0XvFJrv1tcFka6EEIq8t7ZnWrV3sY6ZLI8lwys6k2mdCSf/cz7W1t6f/PLs2Fv78iywFbgWCq/VoidvUN9Tl3ZLTdTxACKLihkXStYQeOFotY+aC+1mA1A8lzu9oeRoiwhRzIYmqYFl1Kpx8lyb24ZdPhpBZrm0sknn/r973//Qx9+85e+dMaSQh8vu1Yse2Lp44/2DCXDg4Vizi1Do1ravVjneSSTeMPOek5SduNedzcWuWU1948/tB5jAKiyJDnYo1cmLL1C0NvnXG2pVC7I+WBoL21HBfQioFmsKaEaSwYG6A4XtvzSE7TuhYTJmSQAe5BMTksnSUUUGz0QPRA9sEF5QOMe8YgNjmYYIzxDuhUBGM9TGF4eEyUb0yBTv8JtZFIkpUIy3loUMDL9NJMVoUEdP94DhvNniRHZQkuu2Nrf2wfmM8e8+s67zvv9bx+ctzTZPDed5/NcaR55kplFSMRML8eN6kP8ds3AAFGGmML/mEETiqXhgQH3ZnG4MIgWRVjPwula7ufReMkBIIzhwQwP6lVEAdFesHuNzfsLzgBZOjT085/9e/cfHPjxz+z8+S/cPJTkBnvzhb4MfytLy01UYWigt29A8VpIDhBZsXIFTyhGgwFj+oc9zbhakcs5x78NJdqGcRM50FAFgHDIANhaijdMF41Sn0ojNJaMRuxWXDfAelG6bpoWtUQPRA9ED6Q8wMAbzkQYt1MEYRFiihqrBVitkLArFjCQijhFYAMsSgm+ELsp6kgKWUYEsguklG9h309yyCGv54Hh+utvXv6khTJnj9KIZFclWDnxZ4SkgqfYuJlGD2AmabLvpoteIs3jEaGclP79n8ve8IaOPfbc9YGFt0LPftMkIYhXAis/+Eu5d2XfUHG1yJ5NMn39faOWQdREokVqFLkMq26I8KNmA430mnjRwyOWZtbh/NpqmzBkskg2BBsmq21RbvRA9ED0QPRA9ED0QPRA9MB69sDovKM5Qyb93VBzZkSquh7Q9IH5zitf+crjjz9+s802u+iiiwBSy6p1+Ucq+MbvE5/4xG677cbxNCeddNL8+fP5MGakcox/OTGHb4+/+tWvvuhFL7r99tu/8pWvcOQNC6es2Y7B6X/gBxrmdD/4wQ8OOuggjvH64he/SM7yaWNeP8lzGbNpvt3lvJ4PfvCDzPJ+9KMf/ec//2nMm6rlBfSHPvShd77znXyLcuqpp/79739HbPU0M8Wlol4fv/SlL/3Sl77Eu/XTTz/917/+NQ0P135rMtJxWgqAmG8yP/7xj9OKc8899+STT+bLjZos9ZC777770Ucf/YxnPIO+O+WUUx599FHW1eV8GtJ4gkkzt956a9p+4IEHcpgRrXjsscfGbLtksqABJdcM5z5waBHr27/85S+xIVxdqWdziOc0ba5e2M8666xLL700rGoMYwZcXDaHHXYYLcX5F154YeP2hgJ5JYGpe+6550c+8hH6DtX/+Mc/6JQxmx8K2QBg973H8uU9p512+m67sZMq6ZqyCV05Y9OZHJvul2UqKzO8euDWmDfvGdzasHCJ8iKDgYL7l++PQHL79PcOrHhyJT84xjfV+IFLce7cuVxOnEgFC/da36p+Li2q/vjHP2655ZYAnGaFIoAFCxagl6+LcabWW5AzZ84cfW8sR1133XXXXn3d1E2mII19JoWh4nXXXM+aDNr1DSFG3nzzzZy31dbhPolsvjc3gI7YyEygC3AvOb2Mq1VUG7gLmryLYYS4ZsuRgEwlOpdLgt06fIIEMeJRyLeL4XJcTSEbOFLXJ22M8Xe9x1++KPXx93fEpUcfXcIAwpFV7OVasut2PB5ce+MNRFjOemC8uu362y677LJiYWD6tKR/6eDLXvmKjx7xUU5RPPfCs7/5zW9m8sPFEp8+uu9ad9l1R3cB5x28U8dmxNYH7r+vu6P7yWSIBe9B/+pA8XeXkfi7+NFHs+3turPGHL64fWL83djjL2NUb2+/xd/OqWUXf2fMICzyWa5dAwyAPODNn/8gX25zTTFsWvxlYATDgNmzYlXPirnETQ63yrS2hPGXS4U03DvAL2cCVMffR+aPxl+enxlaG8RfbI7xdwMPLs2Yx2VjF1gz9DVpdO2R16zdkJFcwzLv4IMPPu6444A5qoYf7WNONy6zd9xxR6Z+BIj777+fudjixYt5ym3SIdzITIKYPr/qVa/ihJ2jjjqKya89FDUWwqM1pnI7//CHPxzX/Ndah3wugHe/+92YzV3PBPCSSy6x2maA6vlvNuHoAK4Gjn5IhoucYpWdNn2GZpSYCsDUiSPDmPz29g8yKXjTGw/lh5qmTtuG+PvTn/8BTGFwAR/RvOJlL2VuyzDI2bvbbTWbGPqvf/3r7jtuKGVdfGR3FLaXciUOCcJ4pglMwdhZzR7pZsyGRlf+5jNn8oOIzH+ZpDB9Xr5sGad6UCvPN7478D+zGOaPHFHETOdb3/oWW7nGnLzLPFxBL9M0ps90PdOob3/721w51vXNtAIjmf8ecsghTMT+9re/8VjSDJdoaBoJ1UjAt3/9618vvvhiBGJYM0Jqzn/10N4M+wZFs2TJk0zet91uO9reO9BPJ3IiKqdKcW/oMmBo6+huv/H661etXMn1ifG4Dpqrrrhi4fz5Gb48ciHYne0y/6F5f/3LXziEhWsDrxKCL7/0UhbE8CmHKHA7ZDmfJklO+9OfOPcZeoQ89MBDaMGGZY8/PmXqdOY1PADwY91nn3nmE4sXUzRfIequW2+m2NLRxbEfAHfdMucX//d/XEU2/7315huvvfa6lg53agNGGm8EJs8D3LOsYSJ/4cKFdgfpymleab3OQg6J2v5+NsE6efQslxYAl5keAsFIEZQCxswrN3lwgTR1248ptyGBb4qzUACPsiRWb7Dfqqjl4ZPhBSSJInelrQaIFwweEC9OII5DqRBDPAIGww0IGYmifEsu/5CDhwYJzOnIdftAQFTijoOAYZxBlRxKlFqiCDHaUQclxsMuLdCAJ3E9iBF82C6ZIe1Syp2LEEyFV42lVrogRg5IRNF8IQWAJGE52mmFtINRK9jABVl/0sOXQm4hxg8CfLzEUZRJpj+Xp+HJcCGTb8kXS7kywTrjZgTs4u6a0qIDsFgXxQ9ol6lhE4C98hyqScRK2ivPcwAWqc2b2laxt2I1HdpfzBcwhckwfc0St+vxvNs6nslnci2gIW3t6M4Pl7KlwSTDL0no605JGM3VTDwGQI738ANOwFqIWACUhfI8PiSBl9+ogpIxGeer7/AYSAhgFyUNR7JziE9CSj2U4Ggsen0/uwyB0ICHRqIAwNBkRBlGEkwOKjADOeQIgR2MauHFWumS2eAlR0gMIHHZk8OOARBwkZCDASAXxtgproOE5SQUCZB2YVazpOw+Q+ObOCjbk3Z+1JprtFAscWAkKPU6VzyMtIUbST1L0Tmh6EjVFpMP4LTwf4kJ8yAEQ/4qxavqVnLRODKfqCLhczkWvGgcmbtNMkMldwYfX9wxXYYDggoNNvkPBjHbpWKpOASNc7hE0Zsc9sYf52QN9rmz8DjEy1rheZ4WmbxBU/HbhBtsvPQL/WVyEM5lj9vxNjcRRUvQQEwfUQtAF9MFrvNGEgRgROZxLrhIuAkBgIBcVwi5u1x8Asm/qiVX4nqWeGOkOFLp/k0Vw6oGtY25UkJSxbHfnqYYnlZFuhzn0ltNurhJMvPhBOgnwGLqxgQQPi75+EeX8piSRTAB+eOyZ1zEmIT9pCaNF33zxKIfl0nNEKdoUv5PFVPWilc0jSlDxnHRo2It0ocGhw1nbJWiEInNqDYWAFLzzQybHMI1JQiJ/JBScDU9GKOsNqmavqZMkYWiqskUw6rx9TBmVT2CFL7a+BRBqoi1xFcecFP4WIweGNMDzdwXYwoZk2Bc4z/XP2lMmSHBuOjHRSz/6CEVjc3w8rwbtndMFgjGpGmyseHAxbM4j8JbbJ2wpnzyye9Z8rCTUeLQf9YZ6iSetP3DthvhSVApB6hnoRHUEVkD7Ztb1wYxpNSltKSKKR3wNiaAHgIjSxGrKiWzuqjgWI0Hw0jMAkKp4Jz5+MKEfYTvPPz1v/71jxbcy6JAUhjyL7r8YS7hY5HMCBtumGoL0QKSZK2oaUk9JIxWJSFWTAFORxBYqU0VU/QqVlsFV03KEOk0jVx1oR9CGuAYf1MOeWoXdVXowtA4zJocRR54SLoYMny76nddMPCK0nIuJLuWQArvRx/3CSzsDNc4UCO2ail6ntEgRK2SuXoE4eY1cEEvTCEpcqHrAA6nzF/1hx32EnaVHXPMMffcl8zaLFm13HPlKoOq4+U/v2iGPW49jsQxemRFwz0AAQAASURBVK4p7gm80mpnkzNcNFrmkPEgcYXWWUQDBgIwnslzuc96eUTEIKedBU7+7rqnzCv2Yz7znne845Zf/mkeS9+sI1ML0XC56FbfvGTnpxLtYoUOhDvyQ553BdqauJ/ckZFUAZAM42lc5hozsljjRblfDOaFguwRGQQkbCYXi1ZUVetd5N5AIF99gz2OwSeQIquXY4A5px5NM3h16loRVU/dOlBRT3XERw9ED0QPpDzAcMeYHyJZiLeiYF4AaPw3vHsBM/LSSGOaaEJR4EmNh1PWNxjjiYommdE+hUGCyQGg1mR+8bOH7bPP9scc8y32chDBkLZqcJV/BjB5isUOZ1pgVxol8hAEzmIfCyFwsVrR3/9QjBFjoWs7R3Mm7hgP/mg1tL1Dw228iU7c9nmOdjzhhBN+/H/vO+KD9/zo130F/xOFSM674EycdhHNtZ13bc40/nUBGI3ZTNb96tZIkjEjJfevs291rwojmtBsYGMEFpch1TqQRkMP8mhB6+h0xVyIjd7I1j2wIdiw7lsdNUYPRA883TyQGsw1Pms0DsdqDYkhhvG8nq801NesrZYTyhSLJAsfDsX9fcn+BzyHud7555+/YsWQyQ9pDCmAKkW3yoTTY0EyRVYETNGPt+iNZGqd7R8uXn311W956yte9MInr7lmvuQQ60Yjokcxy2RjTKUWmPlpJlv08bcSF4P42LwxPB6kiK13XFt9AmOuFkDYBYBAOVRGnJIWi9ED0QPRA9ED0QPRA9ED0QPRA9ED0QMbpgf4fIKJDOmjH/3oc57zHCZHbFy54IIL/vvf/47LYD5A5RNWPtzlNAe+I+VDVj5w1dc4zciB94gjjuAEnDe+8Y2cfvX1r3/d5lmN2fnchaVazqd4//vfz0ovn/1/6lOfQlT4BrOBBM4UmDZtGt++cuoWJw7wmxCf/exnx3sA1s4778xHy+R8P3ziiSdyhBNfQWMAXm2gWlW4iE9xODdqr7324ks2/HA951XffLN/SzsGNw2Hgu9v+fz1Wc96Fl15+OGH33rrrRdffPEYnKtXv+Md7+Dzbz65fPnLX84BZByyQEPCKfDq5KuVIHvta1/LB8A4n5M4PvCBD3D8WfiSYjXq1Qv4h8UT/E/30Rb8QO/jvdWpxi5hPwe38dkbwKWXXjo2Q0DBm3eMx3K6nlPM6LiHH/Z7tgKaeqA+neL0jZ122omG8Bkzn0Bz8dej35DxHR3tdPoll1zG52x8otbnf2Qll3ffVvFOxyznBParrryalZpNNp1G12u3A32nlRju2SnTutiEMHfu/XPvvd+4ALqmuB0Oluh6vvd+4oknYIEePAC6eM2BCoYRLiG+auMsrWuuuZZFoM7uDmg4PKu9s23Lrbfg+2SKdFlrO1/al+666y6Kg/1umYtjaxjEMIkPCx9d9Bj0IGOaJA/QayQu/iOPPNJU/N///R+dQpFephaAvJnBMEWDEK49pyCT4cJYubxnm9lb77///ghk/fDhBYvoZa5A07sxAjH+TlL8tQXbxldFGH9XVOLvTS7+trdgGE8XDCabb745VxonXCg0cAAWjwpghoeSxx9P8qVhHpl4dqIrOUaEo/f+e+m5XMncFORct1y9uVb3qR5FaPgdRPJMv17ruycExd8ngvjLkwAXf+p2qNkQyGL83djjL1cInUv8vfKSS1o6O0vZnmKPf0nRmmRc+HXvPqABIDhec9W1EHMEJBcYD10KdjoWEDyBkoMDHpj7IH/hBdM2dST+clSHfy61+Mtvk7KfjzG6NeNOBFD8BSD+ch3WjL9EXuyJ8Tf08NMQ5hpoZozakD3D6TPcCByFwNSJYYRJxIc//OFrrrnmiiuuGJfZxx577Ktf/WruF6aBjOfMf3m4bVICPmTuwNQPdk4jYkLBHEpjwpgSmMASWZhAMfPiWYj576c//WmOkuT+HZNXBOpBZtzMoXjwZip6yTgPwKqe/953Dyf38brWXR4MU4xRzGdJahQ5NmvgGhwcIrzifA6x6h9oYRnhhpvvvu222zafsdnixUuYndE1RN558+YxLgHTX8wTB4s60MSdj9DfNwD7AQccwHCEG5kLnH32OU22XWTve9/7mHg+9NBDnCB20003ccYul4RG3THl4P83v/nNhGBaRxNYeWBrGUF/TEYI5Hlm7nQ9TxqzZ8+m6/F/k+ymgusNt3DpYsll4zkACwk4H6ftuuuudBPzX/xM4tlJtpmKmkC9+W+Tl25NmesL2daWv+++++bMuY8fyM615gr9flt7Nsm2VE5AwDB8wi129WXXtE9pG+ip/DpXti1TKpTzre5AIq5qvx6V8ydXupCNJ7mQuDY0IUIIJ3yAAeAa4zhUYKeL+M92h5ZWDlaB8tGFC1s5VWeKm2KznrNi6RNtXVNgGeztybS0zdxya90Lff1PbjJzGtPnRx55hLO6hvvcM0NLp+6sLPPfxQ8vaeuOxyDglUlMXBV09HHHHacBn2P4/vCHP7jzzvzUVfdRgzuCKrvXDJC5uk4EcyVQ3HnnnbbZZhuAO++88/HHnpgyrZtBb5dddmFI5KDwFPsktnmNRdNqksQIplEkQzK2sCBpxZRC4cnFIsDuMookE57ipRjWCg5z7lkGQzBK0NvNizpg8GaqxkCK3Pvk0sXJRHpCNky1DeosjQ/kyDQaZArmSd6Q0Ic0whP+hFeVyWTqCsHyJ5YIYznRmCkD28W5YDgZhmaqsblgvxM2M8PlSQCZEosoA6RXRXI1XK1wZCVHSfIslQAHLAMA8J72V7uJsE+uKlgnD0x1U2OKvt7BqSQtGCDtFIF5QoCM4ZGcolgkU/TCA6touQAcglEIdGau3moJMXaEk+ASQK0kyBguAPqOS0IGVOcQK1EFLwkAjOyng6pZDAOZYKyVCmEkEFHYQOLaMBaQxmXIyQOsOQLkTMEVpb694AmXGEaVGkJ4NTt1AJZ6JOwOiMOmIRAMSQDssEAAoA6Cl1qQRiMbICDhKHJhoBQxua9ksyI/I+oOwBJeKhBFAs6UKkpRBBm55FBLUKYTMQNGT+4fJ1Qd8+Y8oP6y3oEJZ6pPK73j7316kIsHDFVKomQGpF6T/5EmgSAhUA+Sg6QzVQVecgRINfIRKxXkKqoKMiXYefzTaowkIBmAZJJHaCf4r8weF3Pd0WdcUjZG4sZ+pzsh4P4kV+vqdZLwkJEYm+j7SaXHMOSjC73kKc8LY6YCGJyitKIIYCTRXsmntgEjVaKnvXJUSpoVAaAkl1jkj+kfiCUfQGO9LGlgD5TcydxaRsNNqPuWqnoJw3R+qgiMtya92stg3Yx/kNA8vfSK3q4fijXNEDE26FY3QBrJ67VCZisC0QVjjhRGD3Hz9LJ/LdKn/IDZ5i4AwaGjwMiTcoiK5pyQshqWNPMMcoRRLnrZY7n8ia+gMduq6eGFBi7ovTtXWzoJ6UOrwENsugRAYEBILJhHChSRGpMZo+7fZuhlJNc/M6hmhGMkDiHnTmyGXibRXgEN2iiCmG+kHgiv9hCu1xy7H+sRCK8LpkliWIy+GRtMBcR6aAPTDKPoxV4vNzmYRDxi0muUVmUYA2Q/xpC4f6FsQGxciqdWHBOoOabV5OI2Z3DABuyBAJMa9AVmQPOejyWb7/DH3//c7exRqnlOiJ+1uXpCom+jaylJLAao6MlGn6dVG+ZGVg/QkEhttWRDWusMoEo9UpOLWijJx3x+QAgSJESDv7lRSOWIqpkgRpHjH/FPQOb2P5UKo/2Crp/+8OEjPvbYu9+XfPPLnNFMPWfnZ7Lu+BsMdjL836iwYrGPbTbQQSbDRusCTQJFo4YDV9WnERJl9sNSj8uUioYiSXBaqC9Ty78i4PrEscKIOIRDdvATiL/SolaH0mrCPA9A34yLZGSMvzXduB6R3AVcJO51o5/asFSgDtVl4AyrDFQO1LOTRmwmLGHXcw0whIKko8ErByAcaFzVfaH1a2ES9/K0csI3MoGdNr9MhijdnnBRhVgwOf8DaP2cfpes6Fl5X+9w8uIXtB191Hu/871T/3vZ4mndnB7FLxG5YTab81/5alHM289IgFKEIB8EX9hm3XBRGfSkl1w3Fw5Rlaet3HcyTzm1ao4j8AtkSEJFNnFvf/lBQdDtrckZ/3lgypTT/+d/vvHwgs+ed8XitnbO7XPuJMAwOPG9sf+RGIS5V2P+t444RZETsWyo8SOa3/EJjRwCwHqi4wkSVVgOAoDlPnfCF3fl6gd50BEwQhA2ChY1BHpa3dbW6WeUbi7gk1kSKKsFIhMJklyrfsPCbSx2blhei9ZED0QPrG0P8ATF0K3R22QzJhOqCFhgGIcZWslToxbFEANcTQN7aliGxrQIgMAwigWEADEanmcD4EqE4hG9I5ctsjdk+btfv8kRhz3/5B/+7IrLk5lTZuYGlz88eH8+YcGkleOp5s+b67gqUpyWMjGqBEAUrLQIe/h1IB1JCYFitDfJvZRSi7xzMLvy5gkyaykxlyIS+wf5vRh+YyjLrw/lkxLxezDpvvKuVSd//x/HHnvyA4uOO+f8ldkcP4s0UCpu4iQky8hbsmUoi0lrPsNOMudtegNFmaTFS3a+4jgt8jBhHil0vjBG4zrGJ8MAyPOghRQgpJGBRBRF5V6Gy4wgAtED0QPRA9EDa90DqWHWQgyKGI2JgCEBGEVnmaHhWnEzJCNyUSRBjECRKYcRPDlcYKi1GSVcwhil1hsr8+XcljD2D6yAeYcdN3n2s3e74orrFi5w4aySnFQXs0puRuj+VW7SFOmKBF4XOV1SsIPP/1xiS1fnFNor4+FSu0RpQrCh6COmi5BwOqUk6QLlwtjDj628+MozXvKS5y9czC+au8jPX6a0iaf0u76K7EQl8lYsBM/PrHEglgxTcOe5B3xFvOeszvCe3p86yhGvVvcgGAiUA/CyCWK1SFzAJCHpDq0DVKuLmOiB6IHogeiB6IExPaDIMiZZJIgeiB6IHtgIPZBeH9sIm/AUN5mv3PnwnkjEl5BM65jQMV2awOwGCcghcZYQ8yNOv6q8UR1riZJZFfS8gmQmCwsflGIA8y//ztHtF2ocJanFZl5Sw8uyMEL4aNbmcTU7D43Cw8u2dYj5EJFPVpjYSntNrgZIvlrha0N4ocEA7CFHi96JN2CkCu3Qz5o1CwB7ttpqK22caNxqGJGPl/hW6p577qH50PPNFa6jQxtrrK5FCG0nAfAlLR+e0SK6MtyvVc0lDN7GYCjxIcbz+Z9Rmp8NEwIYTGL1Ay9hNlU4DXZdCSHlmDCXCs7HG1wDYxKnCDjKhMZiKkKQQC1m6AtkzEsRp4pQgsFp6KX7kDABA1Iy16SoT8vGI6Hy3oRFFZajYGzraGNZKikP+3OjNHqziaHiB9aiWltBOjwXrRSpyfQglwGw8mo/MLbgH1yKZHMsACm8zCjSF2LngrTjq3Av6lTU6VcUoSQxesgSI9blBJlhRBDzteUBrYDRp+oXDs5j/DzhhBO4lb73ve/x/eTChQu5jtrb3QCuW2Ogb5BD05YvW8EJZfSyW/YslHWMGv2lE3+4QqB314S/9dACL10MEnjnXXc6+uij//rXvx566KE68QEDwBv92mrdupQT4y9dvNbjL1eXBoExuxLtDGUzZ21VKGWKSXaLrbcdHC6W3SkI2eU9vUSFQqZcGhx44J67OYtDZxY8OOfuzbfYlquuWOpgPbpQgqEwVBpY+eTyfFu5cwrXPKG8xOvm4dIwL54Zx4ZXreIyfrzw+BZbbLE8n+PQrMG8fwjxI2t1/J3S1cUdoYFR90K9hsT4u/HG3zw7EZR4r8HLhVK5vYPhry9XzLV0VuKsi72VlzXsjCi0t7snJZKePRj9wodMhHA9O4z7kGX1VCi2cPgGini8J6Lr2cbl5fZWN0qHifgLAdctACqoQix5jL+hl54ysCJs43GmurHQGwuAhFSTbeAYpjlECi5y5q08fvAAQ1HP/+OynIjDHISnX6IJrli8eLEmZc0IgZ6HVfTyPAO9wkGT7HieaQvEenbCAGDdrWOqRi+UWM6pW2gnCsM7gfkjTkvNf0uMVe4dcgtPb4wwU6dvutmsLZifcjIFowpzrky+pVDmVwZb+NHd/qHhzu6pjy5+Yuq0ltb2wfaOx/ItDy9eUub3zO9fvHjL5cuvu+VuWlcsrXy054lNttms/45hfqaYlHHboQt7P2evKbyV7hts88f1drQyho4vYQ/+p+04hJk4z67NXwD4DUvg4vgbdT3FJtXTKC48EiM24Z4zRxhvMcNuqybl4HyU0vXhTKpJXhjpPvoFpZihKTAmga9nhvDkHLlFjlKI8QMG6IqCvUnta5ds/PNf6ffWspmPda9KdE3ynZWVBJrGiVWaAfPb2KXhkguCxUoorNjvf1S7wHkXgwUeHHkmXG1jYCkZHnCnnVKFhNac/8iUPf5g/HzHdHlpPFYy++ZDpBLPlxAN9ve28/PYbn8kSt0plj0rnsTivlUrETDQO5xJ8sMDbGtszXVWon6OJ0w+hFjWg6kZr4hivd6k6qmaJrvJXO264Jn/cu/87//7CQ7/yU9+gj8ZyfkZVm4KYC4hOrq/d6Dm/Je7hvult7ePrS5ZPylAJlwYD56k3uEuY+7AMdPMfzkmjxv2G9/4Bl9twP6e97wHlhuvv0mPZ6JvnPMcOJqctkpaDT+CXIv/qmnkaheAmikMOUnq5BaKGoiUh5aoc5EDQIIAb2sENjLwusXAQAkNgFdS0SJ1YqcKAIxk0oOGVxU5SZarCoGEM6ZgaJEiGQwNyRRBTBK75WBgh0wY2aactiOWoVjPA1AKgBJYNgsmdgOEciAg9fb38VfMZgqZZChTHvaH3oKHf4izgQpJbricGc6XW9uKpdZSoaU4zKdBNNwdAVEoFwYGB7JsgMa1+kv4aohw6/6cOpfrz41gzBDcJEEXrTsTqFzIOqsyDFls6/I+Z4fccHm41229ToaGE4xYWejnkmZ3dWuRIZGFwXw7R/fl84M9g0PllsHicP9whieEYaJQkZUiJ5AkryLTtcV3JQbIz5qz4Dp1hBGLjDyVkIBXZZ65FPl4PqQEAxk5SCkilxmGlEaRUYtkZmfqFBVDgYLBwwUlCUrMpitBknMNW3dDLAIAqfMcjgUaiBl2wEimAPHKQgSSqFUusrWVm14JxCTZZngBwodKMSbrO63EqgnXw0AWgJsoIdIW+av0NQ2RQJOgVrgFwZFRwsSaUjBWax4DGRIYFwBOt1oYRabcqXHP8pm+onszJS6qlChSBZwpuq8ddR1K1IA6lAuJ65Y28pqADZfuZvItkqCnUI4T1kprcKMcGEoLhYuAWnqW0Z5OISdxFwBTC54EgV2NwNwmFIVUkZyrC6RuIgYvtIiGKuSECcnIRAU5ulSEzHW9vwBkIRcAtdz4kCGKKmiAqRUBRUvVGKsCqFlrF1hIGcLSGGIqoSVERRgPuA4ZSeNyyAhTs5c79E3KF2Xz9BJrl2yTWpqnV0ubpDezDWhsj8ikAsoxubj0IcMY2cNN1Vi+ZCJW9E0Smz1j0pt8sYxpv+gtbyzfbNacXMRj2sbNr7GG4akZezYE+pT3rI0GVDsqZGHsphXNJ/nfJFRrsSrpRTIYyU9V1ewU0dMLSo0NQ4LIABpThrWY0YxwY2mSXjLV2CbtERkX27joIY4pegAPcLFZLkCYlHNANkgpYpNjLDaWqqqa3jDGIsDwjQGIGxOo1u7HZoihQaxYBDepRcRNqhBZk43VbY4zZQnDRWMtMzad9d73vvT3v/9H85bzfI58TfnUkJq8NZGNjbFaeMWu3PApwK6ZMP6maKqL6q/Gkp36kQvGABNVjbEqAxz/iARDGmCWgxEla4XvfOfbZm3JHNtRldyuG2Y+7g+HIykUBoskkIvdJAsQkloRTCD+4qWUzMbFcdFDjIWKZeTqkXo5ekUJUI+mGg+xpmHVVdUYiM2e6toQI0sgBtmkPSKL8Rc/TFKiO7hbtAytCx5FIJnhc+UXS0XuJu4hjotiwW64WOgb4Jd4+geHh/oHB3gTPMRW5MIwf4ahFi4oC6ViwS9y0d1IckWSx1BEBWRFFidLRdb3B4bcaqCzpDDMGgSLwixQOvJiEUuQQBEA2wbd6VcJi6pcFS/cZ+q3v/1tfoXpT6dd35ZL+vqSnp6E86O4vsQrFUQvBTBa4ZIvOQN8gtKuUiQDs0iHDfIDOQlC6LV8SU6iqEQtAv0fe0tZABsuQFjEzmTqVDeh+/Ppd/NLyyeffPILn93OO9dVI+uuvMajCXi1Jdua5Z2ZOz+LZWX3olozQafYJ9apBTjbhRnZlqpiKqfL3CjIQIddqyeEexe4jEZJILkj9st59CkmiQxWYDxPe1cXU7uEECpwYO3q5rDm1RS5+iiFnFhRdk6MN3JFD0QPRA+sLQ/wUMQuHMbbaoE2TAFocA5H7xRGgzkDdU1RofDqAdZGbAHk1UIUAZHjaNrcCA/mJfvsd9RRR5111ll///t9U7uJd1Ondk2d2jp1SseUqVOmTu12f10d7meO/DO4exK3oOOQroUu0wOeK/n1T1Q4LSNJNL5EOKv8OTaSj5giHPIPBsM8qiDTnUTp3kmwcPO3Mx7jp7pOPPHEvfd0NhNbu/Pd7q8t4c8UGVDd9hFDJvKvt7KSVfOHtYKtB2UGvVzNFTHRA9ED0QPRA+vGAxqZNRRXj9iyAbwLWj6lrKIKTCqXHNEDSzi5yEQvPEgS4YDgRQLoH1oChn20u+yy7Ste8Yr777//wQfn5VtY6qlodvHPp5QlFEMLmXJaMkpiMRteSYXiUJF9oEz/x/xj/9LoH6fQuz/OxprS3UoUv+++5Oabb37LW17cOaXGwc1Y6swI/mias9Nj3K+oBqdempHVQHV7kQPSKIGFSVHiAaNJAXhbaw6hnBRNLEYPRA9ED0QPRA9ED0QPRA9ED0QPRA9saB7gvZ5mOr/61a8ee+wxvuS80afx2nnuuefy0e+WW2552223/fvf/0YmJ2qNKUSqyc8+++w77riDTzH5EJRtGyx98ya3GXb3FU1r63/+858rr7ySDegc3nTGGWdoqliPPZy1oZoiU2YWq1kB5uvTiy++uB5jPfwjjzyC95iAT5s27be//S3SkMNnzPXohUc1icNieJf6m9/8hobDhevwg/sapH6Ci7cDWMthVcDoPf/88zmbg/Onrr32WnqvPmvtmksuuWTBggUYP3fu3Msuu4y5LS6V8NoMARbim2666ZprroGdU89wPkfhYF5A0ghkKs2XwxdeeCFEsP/tb3+jIY0YatWdd955XAYkgFr1jXB8+cyly74CzL7iiiu4Hbh48GojnpHVEr2gwfn0HVcRrrvrrrtSywuN5TxlaulxPNDYb9SGt95Tpu1Pw4bQj27N0b81Y/zhJnrjG9+4zz779KxY9ac//YkxgSMV9t//hQwjBx544A477ICLXnLgi3fccceDXvvqnXbaiXPuDjrooP1f9EKOn6CKnJGfAUSeZGRjsZHBXACXDffUY48thn2PPfa45ZZbzjzzzOc+97nQswVlY7/dYvzlWlrr8ZdYoBFpzHuzZvzlutVIxdWFBEIqgYkL+/LLL+eUkAsvvIgwzWXPVcrFSaqOvyCRwEvb22+dc+/drMPPf+D+efPnLeDpCEauW2pJMi8Vfwsx/j794q9dDA2uWC6qerXNPHGJvTFlyowGGutZEvHRAxuRB7jgGeR5gPnjH//IYzyztksvvZQzhcfbhH/+85/MPdnoxPSN2ShPL5tttlmTQrjLbrjhhquvvhr2RYsWIYqZYDO8MBLpmIag8aqrriJsaQrGg1kz7NAQ/hBy9913I4G2MxUlzDXJa2Sp+S9hEUvwAJJF8/DDD1955VVMjoiexHqmmQ899JCqoMHmf/zjHwCESyLprbfe2tNT2fZ29y23/PwnP/k3D3z/+Me/zzjntD//BTshI/nHT7dR+ZabbsVjPEOiFx+yp9oMaxL485//zGMYNmMb03/eqWMMU8Jm2InmtIspMO2ldcwleZRthlE0KGLZhFbTibxbB2DTe/PsomT6zIoBFw9rIOPlxfnYzAMJzWcNhK9+kKC1C2yrKQ08QYSHc649mo/NeA+zeUyaN29eTZaNF5lyQio+TqBdKYHVEvTMWY2fGAaDlSbGHrkaeADHqpbwceCBBzLD5ZRnDsDiXtD8l3sEPPNfKBvMf3t7+rr4Zblp3exM8X98gsFnGG6Kzf3F9UA4YGY9e/bs5z3veffdd98Pf/hDps+MGByq9YIXvIC5MAMXlkBJamDwhlCl659cgIZxGS+MjGQwBNClW69RvrnucxUlWIQxRvCSFuahFuHRAqP1JkgYCTFWG1aFosDT1+TINBoAxQ6QJKMPYZBoVBST6kob/D+sn+j7mpr2m1WwQ6krRAJRLTkpk6gFY4nRXvLBQE8ROTJG0sCnrJXSVC6BKWRYNI0A6hpyqcNvKCVRhQ0UOR+TIG7ewBKqQmmCJaqeZyTTuFKTnVAgihAiq6C39qKXIpRKJioFiF7WWpWK5LQROSbTCKoBGYDzGSuohZFkqqkNWSQfjAhQUe0HkCkukxaKWjdwypKaSnUZkwuQB8JcfWp5WGWw1QIYktuQxKWuq93wIRDWGpmkQQYGCRjmJVWkgTeCUJ3EwsLFLFHU4vyarY7INfcA95cST/7cPiQewnmO5blUiRBpCUwIiwBiElwkE8KgQZLkEEA+RXJYnLKR42tFQ3MAlMRrDaRo8LoE3OkkT6vEvIW+YdChd8dsOHcpU7VwcKy+V63nABhquUpsCEZ+M/ThAN2AHmlIlnxJNtWphshghiQeCmHRBce1myKjiAQJUY4ljEr2arnaGEkQMTCPU9BoKqiqahYjhkD+sfZWE1fLx5h6ZCJWzsDKzSbLaS8RDpi2h9pDeslktMZFzcgXL2J18YSiGsDWX6bCAOMKLRQ9/gmRRgkAOwmzheT9FtcDMFbV7N+QFxj7ueyZilsXpwhSRbV3PdLTWEwixyG0GuOFodfwUrWj6HroIbaE8ZKQalrNItdzeL+bdiNGLLDlGIDnbUFkXPQmswHA+KMWoagB2bqs0qSLC7UZpRBzV0LZJD3tZY9FM5IjzUbqAd0jTRrP9dAk5Toj4wGRS5pWMDby7F5TL2OCYpCGKYYLiroRUvTgNZggUPcIwRQM9ztVpGp3gYGApJEfM8CQUpJTRQSOSROyIJ8iBnhVbuUCCQ3aS5Xir0YqrFLbkVARm9EBS27he2Bw1cc/vXeuvMcp37isyZFBQji8SPIrMsf6B5PGRY+8xpHROXr1+CtHoYg0ljluGKR/CWQIqUkvd3kl7st2jFH/0groa14PKIUeMujx+bhuGbjOPbv8lkN2P/6kVx51xEWZZKtyMpDNPmGPivQ/8ln4AhgYcD9k0dPjrnk0jtlYaNx83a+RNUOPQOIF/tQlAUvjvoNgAvS8AlG8HtN+CGL8bcZLT3MaLle7YvlFEi7g/oFeYfAMRS7UlrbW7CCHnrunJt3ISTbDOgRXOEjdHZWzpUa8yQ3GSVj8CV+p5ecC+PUAjopyiSOfKAA7sRxY5+5VDt1K3DEWTqu/R/OZbHGIb3FL3Ld6aHtiZc+sLTf7zDFH//e/V3zzOxdCNVhMOvJJv6/OtnSwouXE+8T+Jf++gYbw4yqtCSfR+9VDPZAy7UKNhQmxaEis8PtJh8EGhLe20XuLOcqKgbS4dJmzhnXWrx73ux/8cLMf/r9Tf/Xbv11187wRCaXBwiA/f8AnvrmkWFmGT5L+of5skprnqtEjfH4SOlrwEAbIBnJ5TcdghWQ25zIkfjAYRrqV4ZrpM6EQh9CtjJYSa2QGyGMM15DpidqqBIix0oWpuvEX15Yc07x2zTOxEYgeiB6IHmjSA4qnrBgwkIYsjMMM1yAZohWhwtrGcCgKWE/gKNLcR0+zoQQIQKKI8V+jopbLwiGXx0higZ7eh5K+wtDwtjvce8QRR1xzwSNfPulS9zw9yNmNS5YPrAglC27JtiNKknnCJxE6EZVrcRMrLHxy+dKO9i4wWEiRHWbsUWttaYcSLniZcWh2ZsKxGQwn3kLQ3uJOq1w1uKqzvbtnoIdcE7ZcW1ch6SUS//D/3Tht6i7f/fb//vHPP3iyj0i7wMnxoW/zzbtp+MBgV9/Qqk2mTAW9vGdZd9c0lJL0uIEump/zP9sFEjvlMfWdE+X3EslIeVJIL0Ggy+Hi1S8NBE+QBRNKGKULIFhwAhrHpAyYIhg9ED0QPRA9MD4PEP5gYLAlpjDqAhN5GXhZpSRgkVgU1ZAOAUUCRwojGiSYYuSQEEIVSEVY2IERpWR6KRqjYChJwOgC4IsxBxcHhooLt912B36R8r65t13035sqs+KE39pFtHttjxyU8mNkJhAACbKt7DZslGmXmYp8TrzKud++aiHokPTUAVkooSZcTeOMzmR6+4dgHupPrr2qMGXKY29/+4F33XX5g/OQsbymnHrIlpzri5GfOB2lCvXiZLWaavWXOo4cDMbgf8NQhNf5x7cOeid/BMYTVOEZQjliAUJFo+o3ZghfWfM35nZE26MHogeiB6IHogeiB6IHogeiB6IHanhA008mPqeddhrfwW666aZ8x8hCK6uRNajro/iElU+ztt12W74F5UNQ5r+swjY5P0LXAw888O53v5szs/gyli9hbPpZX6GrQT6TMoj5AaH3v//9e+21F+uxOglrTEbN9SBjvsP3Y5/97Gf5rgxvPPjgg415q2uZEZ900kk6Pgl2uZQDpDjaqZrYMHKOzr1C9WWXXca5MBxajWG4zsgaAFhOgp5vUPEA2z84iAp6MORNOh9KPhvGbA4vowdZiGbaqzk+whtoVxXO55Nvmv+c5zyH64dvm1nulgENeLFN5jHf5Ng1Pro+55xzEMUBXrCrSnkDIVb197//HUZei6BdyDENMF608On7nDlzmNTzITfXHgZYbT1AtrFWj804nw/mcRoN4UtIrvx6XBssvo6rtRODC2ktG15H3VrWEsVNngfoQXWi1osYPRj2+X5+/vz5P/3pTxmB+Sh33333fcub3vra176WIeUHP/jBMcccA8t3v/vd733vexx3yEEPH/3oRzkPiNGScW/xo4/vuPMOnKIV2gwjQyg315InlvJTOQywDO99ff1bbLEFQxYjP+Pkxni7hW2M8ZcoMxnxd8aMGY3jr3ohjL8zZs4i/ubYo1Xk/xb9OiLFKdN49byMj9s5k23RwkdyLUlf/5NuVTnH/rHhJFM658KzH1r0gMVfvtFxvzThXqwPZPNu7T2fcWvviqc8YtHp/gZyGaGqOv7q2SnG3/BOCWH8RvEpE3/DpgVwJf4GmDHAMR975DflNWWlJKSKNVkicmP3QIPrYWNv2pj2M8YyMuOBX//619dffz2HlfAkw2SKF51j8oYEF1100Tve8Q6eRjgDiPmvzaFCmnowkwgemd71rndxhCgnEXNclEb+Me8+zOYpCDK43ve+92n+y2NSkw9FYqf5tJfHM+ZBHId0++23671nPWur8dXzXyTz/aZ+Q72UZPmB4baOTuykXeQkhJAPDhez+dZSUvjDn39z7Q1XEhk5xqt/YKitu5XpGIaxtcypa+PlOzvQWkCuWLaytaMll7hW84vGbAjP5BImjzwlsnrADJRHxGoLG2M4few973kPaxese8y54w62O7sAPfIWuDEvvmLBBO/ttttuPNOyfIHz1czGjNSq7zgAi/kv53bBRd/xEdaYjCmCU089lcsP9jvvvFO+TRHULNJH4Mk1/2WrNk/vXIokfcVTk0tI8WI/HcTsmyPA6DtWD5gCYz+1zZvRQMuGUBU2RK2uaVVIVpNASEkYi3jccb+Bxlg1eR7gjuP251md+S8jMIfgM/hzO3BGc2r+e/zxx9eb/3JJbLrZ9GVLnpy9/baHHHKIrNUEgdUkpifIZExr62j970UXg2Gwffazn81A/fCCRVtvuxUwa1982c0wiD2wc/+Oq8mrbwUaF+tEiHULkJO4EWgpLWLQwJOMJxjPIE8VMFX4FoAimhyDB2imUcKrxPDFWKTBEwyMyuVJWIxdRoOhSmLRiCXwoh08RRKTCwhQRIIXvLWWIkjCE1zsiGbBEF5g2GGRPQAkiYKXRJEcIdIiIZgNOywmHADjGYExAMCzjqoWGRKowgDI9ImNtCMTLjBcMMjkWyoXxhJ+7t7/ldm8lCuWM0OFUv/gcNtQadD9LHA5h28yHDHBvmeniEmrcx2fZ43wup8izGX5Biub97uDMu7rrZZyCV0EO/Ud9rjHBv9jwxm/hZr96dijxMdMfNPUM0i7Bga9VzF4iBMJuFZbWtsyrtNbSuWBYbfDarBQ7B8a5E+nXOJqXKeGy4HF/n500d3gcQLe0FY6KGk7PUKSt40R9oolEPmd83CZ/RICgfodArGDQaNyJMBCThUJGmeqPzhSeFkIBiHkJKwS0nJEAUsgEoDJYYcFGDPEiDPNBhlglgBARsKBJPmBIuxYJdXwApgEqqAkX2dJ3TSmOv8lWkKfl5N8v9+xj53mzFTDkWatMJoxVawDAvUFfS3zMBvnk+N/5e424rnX9/s6sOcpo0I3i3U67dJdYL1PlRJ+BgkMAQlK4XUD0kEQiJ1aiuTcLCCBSYwuwohXlOTisiIqICYnIR+8qiRKxQ0qT48+G5Rxk2EMs3eekOi2yryxOR2pXg+ZrJsNoHay6UMDasIYQITg90AYYphjczlyXVZT2mUaGm/2WytSjCI2FgCjNMBYjMwwTcoP6RvDqOAhg1dBtJeepb2ss2uEram9sbS1WysDzC0GmBaz0ACrqgZonR4sFMW32247LmPeytPe6ieJavaNDkN75TGcw3Pb9ttvT5EX8PX6FzIlu7DZOAIjxTHbjmRW9LhfoEQvuVQrFzvCAZSHMtcWvbRINffv3nvvjS56lstbSo1gfQE8yvPoHLa9piVYy4VKNzHssGQ25sWJk9VelucQOKb8mkojcsP3AAvQ3CykmvGXy4bExQAB8xbuR272DaRRGCZLGgA2YnDxM0QTj8hpAncBqbohiNKlbjL5nJuZOc2vdwugAmISYwKwwly15DXHoMLaiyJSiFEVSAF0luKvnje48QFwgrEkmScdZWkm2fa7Pvrxj3/8rHNP3H775BnbvlBLyaOUkujz0DkQMPho1QPhaDQXGa8A44IeSubwWFLT/1LlG+daBz3EhgwMqYD0C6KQafGXCta16TXsqaY3DIzAKIIYFfX6V5bLHsZM1n3QxfUg+6k1h5tkiYUMeu4XxTupowogZKGNCFEtDoF+5cAVbOs86qhP/fjHF/UvmY0HktyDcqMYlYNR/G1rc08azcdf1I0r/koRzcG8Zto7Xnokyy3ym+UAYaLVMf6GDolwPQ+w1YzncC4YBmSuGe4OltrZV0SRa5giVez0ZXhk5Q4hBD49znEbcl+TM5gA8DaUJyuKJFhIvG7cZpttuGK5yGHhbkUOs1dEcWvkM+7ng6Z1u3gBABc5IwZLZgySwDKYOxobeEmQy7gdw9nMCgart771rYi9+OKLn793W0vbDkjo6pzBC4NSpgMzWMOFslTwyx8tTjJF2kLCKtnc0+N+fYIdVNgDHntoBT/ohDokYDnNhFI0bN4CgFdWMXrQBDAkxkNYoMSe4SF/7COvbMvlfHYYqzpaVmD56aef/qUvfeltb3sbL55XLM/C3tvr3oDyUO+MKfe6sbfchgG5Vvd7OyysYZIXn9AWpzTjmkACRjgJYgEgWRMgMW3Ew54qwe38yi5CkE8OFz8DRZ9ShAsa2ktjebLFeDAU6SAwzoelEnLoShj5sWhsQ5fEWg4LZNBwqZAo4rF7771XZtME1DGtY8+xPGaMDYAUJTJJ0IPHPAEhjbpVOZTQqFbEoSLJASMymgOlGEOyCEcPRA9ED6xLDxBwCTcMleF0kqhEYpiyoUzxNxyyCCKKtljLaMbIJhrGXrNfcUFc0CCNaMVszsRCScAFgy6CLHgbKk2IAMMXc27x5K1vfRMC+VW6lzxnywcedXFz05mzkcOLQUIAiUADI1y8FBIvOS0lGqqqxFGXpRL2kBSkcAW75Hka2XrrrXkAoIGSQHyhaZhnJlFEDudwgeF8TCyxKofBPlIuISQtWbwQh/Dd0atf/epXvOIVt99+f2t+X2hKmR7yTadvQWTvc0eNdSQZ/1ztjK0kojA03d2dGIA0EhU0jdBGqA37gmU9EvZDIzfSKGgcz0giztI648JmyRGXqGgXfYrBxFxaABKPcWHgjREx8d/ogeiB6IHogbXsAQIuEhl1GZkVazTqahxOhZgmdTPCK7opoiCKVC2K2mYE5nNuvbSnp59o+/rXH8SeWnZR8yVmd6f7jJnFMyc8cT9dIGlSCiz56JVqIg4YTeFFSfwFSdDhYYAgRWKmb+yiqZdLeKrWKcq4+SnfB1F1771z2fY9e/ZrFi/+T2ZgO08sIxXTNbf1uY/CEChQIoF9C67o47JnrGShXvqOzmLSCpear5aG9NWwSTBdBHpNh7kACPSwmDOr2TdeTDPO2XhbFy2PHogeiB6IHogeiB6IHogeiB54mnuAB37NjJji8eGWm1UVi8x0xusW5rOcZsJrPhiB2QfC60K3dtpEQilzTD5h5QtSFm+ZaTLDAqnJV2MB0EDPQihvXVlzVlt4CzzmuijyaTsJgKkc1vJWFBhpjTWmapHAqixTZg6f0rI8EtgWgj9TlDWLaGRdl/kp310ztUQOi9t4D7zoDTB22giSnITlqAOg79DOLNUW2KGv5jUhBmA/zucTPj5eZZUeCQikF8BrrQPAiKsBDOANBc4/88wz6W61ujFLaBXsNJ/jz7CBq4620xa9BUBXSFmt2jC8TOcbZhjxHgIbazcuAChRgce4dGEHBkMKaRrbgDrWQ/j0V53IdZhiD0VFOHrgqeQB3S/k7JP56le/yilXnCPAbpZbb72VU+H222+/qZtM4Uw6NpzwfS/7STjd4MrLr8r8T4a7lUjBmYO80eO0CO0d4vN73fgIJHE/MqCRMzK0tbtD5RiXrrri6l1324WTs/iceMmSpew24naDeOP1KvbTRoaRGH/Xb/y95aab2FDF57xh/NVgTjhuackPlQs9K9zGMJBcqCSO9iB2tLS6n/pzz05Dw23d7uea7IIEz7XJB9RZ95Gxu0rdhZ1zm9mMBmSMvzH+brwjWLR8o/ZAOBBt1A2ZmPE0nxGbvTQ8wPMYz1E+zOaYwOqZpHmZzJU4P4sgzpiPNKafhDOmdc1IwADeq7Ld6LzzziM0aNoFI7YpADUQgjqei2z+S1QiJDUz/4UR4cz4UMekD2svuugi2HmDzOy1gcZUFUJqzn/B0y4E0gq1CJgkvTgKO4FxFAdXrVq14uabb0My+8q7N+lataI33+ZWAJCMEPAw+trWKbP8nnD3+8guEDNnpPnM++68fc6UafN7Vqzq7G5qzQF2S3iP1+UP3n//lGnTps+YQbhHXavO3jKiOoBsYPWAI8wwlUbR6UyHwdfhGEXTfOjxNrNvTpGmFSQx0nbomhECGdayegCL3lCPKhgLkhZsYDO2Wq1LF73qssYGwA4X/ufG4RmGqw6YRo2lNtZHDzwVPMD8lJudbwGZ/37yk59knssh/h/72Mc40DY1/2WrSr35L8tujHK4Y8GChZdeeikAdxB3H7ceowFVikSMUei6+cZbdt51p7e85S3HHXccR2JxYPQZZ5yBUm5hrbVyS5I2CudiJ22ksQwdDCMacMxy4gINx8N4ALJUo1SEFwIocQ7EFMmJLIyHDKrIhDfFaPINgAB62WAOhx2M9EJAMnoAYZSjkd28OJ8YKo1g0As7tpEjXLzGhVgSwQIyli7pVuwP5SNH9sMLpVXJDBhBwot8yBjzIaOosRdvwE7CDDHiFjGaGdBTi2r0kmg1MQSZ7GqGmK97qTVr4RK7LFE4lijkYAAdR6KlkOX8vmhOtIQgU3SrNyTw7gAsfyqZN5vMEVhCDmLVcdjPkxhi4RIvAEnEAM5Obz9K1XYaC4AEHCJea7upAJAQZGIqxLIBJwBz+yCNKvkNjFPplZKjEQy1JABEYTAwOUIkzRRRVBeoUYavCUgFZlOLzXoYQIIkowIC5QBKUKoWS3QBwEUCDyP2k8s2MYKHETtpIDBtIQFsCAkLMYachpi7zDwwWF7TTqOpWQtS3YTkegQp/MTosRA5OB97lFNEKXhyUqXPfCsoppTG4oQ9gMOV6Dhd/BIFUoC6Q91qSF1OVAGojyAgccOpd5RTCws5VWJRThFe7iNqgVFEjhyK3F/wAtRMYp9wYyfMOPpVzIRFbFyMdIZm4BrcU8bTW3QPSHVSqrZmUfRiqUmQQq4V+vCaq3npcKkp6SqnyaY3ZY+KCDGCEKgWbrUmBxqRKTd8SrIxhkA1S1ibElVddIp9UkRU59JkKHX7mbRq3nWDkQHYKHUGmHaz0ACq1Cjl9KMRC4aS1tFMEo9BFKHkkYLaUAhcoiRXMjkbCyAPqCHcsCSuZNoOfqRN6SeV0GnQ8Lhj9I1bDTE+JIcMISI2wHhFQDHsF5GFuehFrLwZetMCMRczCV4DrHYtAkwteMC1RjWQTOuYP5DLKjW2Hn1oNm0h1aMUXjRjkjUWEms3fA/wHM+ohZ2M2NXWctnYuA0l0+ZmrsxqOZOKsStfl6sVQ6V2PdNYWsQTodoV0oQwQtRSZqfggWuKFQtVJEY2igChnLUIS7IsISeZcFMKINjiLy0lQWn9mCRu7treMZ0dY62tSxjA3/vBvR+Y/68ffOu2GTO25NATGqJLwuSnAERxMegZJrSk3nCBSYxRkgk9MOwpmWHR6DWyUaVGkZNCLYKRiUnIJwGzqM3KjnqcYihZRYgBQhgaYZAf0luRWoxhZPYrL84e5IM0AnFJCDA+Rws5ziSKqflgUvSmCzyrCVxsmVz3OWcse/tht3/x2Od/4/hreweKW0x9KS8nypllEENGAkCR4q/fCsi55H0122vyBcCF/TIDOFWbKkKA5SyTSanlKTIVdY1hBvSyRHhZayzIJFFUT5GHd42IUyziRSY+JMFugIldKwB6WZPCpBh/14o/J1+IPjp1tyGXNOoEcIXzXESRdVKGQTqUd8NcyeAh4BLlxuFeI8lCLicwXGBcALp0wQBrjGIw0QWJKMQysCAERi545LPxiHuWt6FgWrLuzmLXEKZAgxCKPM7xMwScGK/wCibPD+0NrcqXBzPZlSC32rr72GM/2945eNxxx9195+DMmZvkhxnEpvX0ldq7ZvA7BvysQVJwy7JcnCwRF9xv+lVexNIE2UzOR7PkHDmBagAUIRzzsJ9bUsaDJEFJDg1JbaFRyDQMDUcdLcrlaWmZ0zWAW1umFFpz7a1Turvy8x++66hPHvvTn/3oK//z+e9990eLFs3dfLO9nbTEvTsvFJ123N/bW+jw+8JXrlzupPnI636WgWCUcy4CUIIXf5KriMGyGe+5E0fgzXKiFmdntWYzbj0XUfI/gDoOGnHRlWoI/sc/NBx6ABoFDBm9Vh195DSukA996EMHH3wwLtI1gBbYFengAinPyM4xc4gZ35xnRi7RMVkaE0iU0dB8NmUef/zx/JaLmmBVEYgeiB6IHljHHmCAYphljJ08vYzwDMiMq4Qtgi9jrMZzjcwK/fW0M0jaOClT99x8wde//vXluYFjvnhcz+AL+RXdWVu66dKKFSzCtPDIIFGMtNAD84wgCeSmBTjrnkFG5yAGG82EAdpF4mBNdptly7M3mdK59PEFx37hxx/++L6nnPLV4756Cq+Py+VZOGSzTXfKZgazuQGiIzHWaSy7hwElXniN1wYY6U1aRwAlhew8jTjxIyEbQHaGNNUwbjGW6lowhx9++Oc//3keY2rWrhckQZ/EVUdOG1P2cwViMx9CrBfbotLogeiB6IF6HmCwIik+EjSNTHhyBjTGZPDKAcK4ZvQCCIIiEyNFiFP06LJYmWJXEY0AjjdbIH7k25YdethBxULu0kuu5qfptth8ttYnR+LXqM01pYFkjpaqkmG0l+cQRSUplakpg1O8NC2FqRRdQzPZZGuKfT2L/nraFW9/5+6vec1z//XXmzg7euZmnUwec5ktvD87vArXzMQfcl2R4LcBDblIUmD90ZACQr20iEQr8LYX1ahTjB0JEgKX2ptS0UzxZS972Wte8xq7GJphWbs0akLzMgnKp5xyilsgrddxzcuKlNED0QPRA9ED0QPRA9ED0QPRA9EDG6QHmBZpjhPOasdrqbYriEvv6XiHyDxCc6560kRAjgHQM5sTFzOReizVeFQLqaVyeHlDXU1WD4N25mia50KDJbIZfD2WFB4WzfJkgFqhN6QpyppFzax5P6tamoMx0l7TBswjSSO6rO/C7oOxJm+1AaJkjozB7h2xX6ZmAg6lXAEGddWMwlDFR9TAsl9WgTQ3VjNKmmigRzU0ylkYp+0UZVU1b00M6yRcNlRpcaYmTQMklpCwBKVcPKFq4AaM1MIo/6v5FBvQx6rogaeGB7hZuE8ZMbgFAF75yldy/571r7M5/YodXy94wQuuvfZagJXLe3i3pZuCN256z0VR4xu8IHGIblt+ae/Zz3526B9+lY19lZzmr3Cw5PGlW2w1i49+0cInxx0d7ufZSCHLxgjbGBIO4ONtSIy/eGzC8ZeXke38RoJ/zxvGX43tfD/KBc9OsWxb5TtYraiz1s8rYS5OepC+y7rTD3kS4GHG7aTCHvDBVqxKl+rlL1Uq6xrm9onxF1fE+Fu5SuI/0QPRA+vEA4zVjMkKvozDJCawNj43b4LmINAz/WQo02SqGTnQ8JxDiNG0i+K4nmpS81+a08z8F3VQ0lgMVuhRS01a8w1HDtKgt/irnWO0Qm1Rc6BBnWDwJJ4DoWT+yJyvq8s9KFKLMa0d7hwxGSB65VSRwGfL7mUu4vl2HfNpb3un24pM3rzZUEqsk8VZWp2dSCA5+b451IJvLBAaiEUvgexiasySqhWveUxCxtRrQtQEed6QTQJoURvJJQG3j2vur4vc3TP5vJ7SJVNWNd+KJg2OZNEDG44HttpqK252Roj999+fz0OY/3IMLt+ehPNf7m7dUExjtUaE/dzszIUZarhlenv6pkzrTjLJrrvukpr/cvtwfjQnZ3FXEqH49Gb29tvyW+lf/epX2aTBAdPPfOYzOXrv+c9/PqcWXnHFFZyCt+E4p6YlGhbIaZEGChYwoaQ5RECxgMEt5Az1mg5YLACAl6EGShEghCLjP+Fb9DgcLiI47pUouEhmj2wAAwGhh45TuGRJwWgAiE3gEQUlLOKSzcDgSZChDuNRpA+jwICHgP4iAVNFchaMyAEAg3xqsVnLp6FqkPCSQwYeaWiBy2jAOHH+LCcoaaxbbFm5kisNwCn2h3CJBmJZRQ4GUQC0Hfl8g8x1iOs6Olqxx9dzpfU7i30viFcs5DQfCeTU+kV+Fxy5BZCGaqTpAKx85QAsdxKT0nDJBZrBYg5KuoWGmxC6jyqKXADyCZLlN3ISEshRRMIGZ5L/ZpY2Qk8tkYu7QDcUjaLXMA8yNFEr4Y5rJCGNKrVFOIoCqAJPEcC5wMsJDRAMAarRhfHe2aPbuUFiPzkCEYLeUDVIJCCWKhjRpSZLO9KQTIIAMpLYQwkgxQ4Z7OiiSC4JwLodTAKK1H0i2BBymoN5WGLXFTB21rSN9hpeV6AVGwNowT+k0HsNWJqnl0AMk/2yHEUSLo3QkGQ8gKoMaGBGrJqAB5yvfaIvXJf7YYo7C8CkUa+i5XZfUAUZnaX+AhbGi3SZYSQNCcYLHJKJYAPJXZh8WiV+eUM/wfR0aDVXHokHxKdDY9VGnlToX+49u1Gfem2nT2kgAxnfIfNI9Mgjj4DRGFSvse46CEa6emQbGl7jJlZhPDCTIvp33rx55H4oruzDaGD2Rn0Z+E6b3PuXh3I9iTbwoVVpmjouep41m18upL22QmpKI/BU8gDxl7Pwn0otatAWDbn3339/A5qnWBWP1DWfN1pak2E34U02n5Xdd993H3/8N++Zk3ROebQwuLT58YRhnyigmWEzftPz/bjo600yQ3V0q8Vf5th8jcyyHQCxRj1uxBRJEJMAwLP4YrVrHaC92ND4SSCldPsd8wsXrjrppF/87Gdf+8kPHrz+miWPLrilUKKrKusFIX1X19x8vov2Mqo3GX/HZQy6xhWPoOeSwPOhkQ1ggosW8hrQhFW+9yYx/tJfpOav/xh/w97ZEGCGC1bteSJlCd7s0UkZVgRg5dEW4CgyGnBfjHlrcC4SP3pTT04GMQnbjXL9pUHWa1kDY3zpauksF4d7S8NaTwLfnuTKSZHVRwi6csnHjnwX19t5F/zrkivdiiQ/aJsk/CUtObcUUPSLxlz22FbdCghSiXEvfLqjiDf4rR4WW6FEDgMFrcA54U3KeROsCGsASQlMFbU8wbIfzTnttNPY2Pfyl7/8xBN/N3feTTS2u2Vqz/DKFEsm4cgvt3TYOGk0Fg0vafBzOPIvWrTp8hXLQgms3adW//khIJazQxqmJ6nhffvttw/9ExIDo/Hmm2/GMwwCwORcGOQadsi533EgAPgUb80iQhjf6DsJqUkzMSQy6S94eRWR8sPEBEau6IHogeiBiXlAozejHIMSgzC/kBy+ImVk5k1nGF45ltFeHktj9VhdTbP11luH8Rd1JIbrUDKxzFRjVVhlTQsj6Rff/3ri7y/++IvLryQiX2M0zQBt+U4OpAwpOzu6+/pXGWb+/G5+LGvRokWreldalQFGNqXb/ZYjTwXCuGlJkBRppnVNebK3p5x05JOOQuJC4XOeVz7ssMPe+973fvKTPx8qLgbz6MNXrOi3+IsYorTyUXHTpk5fsfLJ0XKSVMdNfuMaPyu+iDL0mGGIraF7q+Wk+pSgueWWWzZ+un7ooYf++te/6nIKjdxgYX6FUg9XG6yF0bDogeiBp5sHGJfWepOJAkT2McVqNh0GX43nChaaOgETX5hFfegjr2P+cvutd86b93DT60ZjmlAh6Gjv4oM0Eht3QMmMxsEljGirqcn4RS2/olhKepYs6bn66id5IHn+8/f6939uXfAIjwH8LfcsCuCa8a8mo8kCy2L8um/jQNmMKB6xTAitZupKjG7MSP+yK5FnKqaujR3VWM6Ea+v6v75EWHgODB9X6tPGmuiB6IHogeiB6IHogeiB6IHogeiBjcwDE5gjNNPCZqaHkoMBEJPMEmGoNUwzGidME6pGSKo4plhZazZDL7ObNL7mBh7mX02yp16ewsVks0leaxpNNjgEhK9XG1IaLO9hQDNcNWngJZnAcQFMXcdFX02s9RYMUEOqCUJMTftDgghHDzwlPcANokUthi9gVh0/85nP3HXXXSx27bLLLn/+85/ZLcN3iTvtsuOuu+7KQtle+zxn9uzZ7Gfb+7l7sWdmn332oZZPTw844IALLrjA3UeZhFd7yDR3SQUCOSSrMFRsbW/ZZvbW/KDLnnvuydopLzS/+c1vciTWeI88MPkbCEAzJ8MSDU3NDFAYABnJLBEGqwwzGRaazFA1yFTRyOoBstZshgyYVI8+hW8y/iIQw+AlJ8qwlQmA5FW5nMDBJc0GXlaJ+WLXtHgqbBp1JhirFVCNCfH1alNCjMWbFONvTfdEZPRA9ED0wGoeYMBcrbw2Chq0xzV0m9rJsMeEh8DEzAslAGMtcpSrCphksNGDrH6xCNLPYStnPThiHx5TtqWKJrMaMNXVVdWYitg6/d+M0po0NZHV2uthxsUO8bianFKa0mWihE/VpngbFCfM2EBmrIoe2KA8wPcgTGn5/oUZwbvf/e6a819Op2JGsMszdmbeCuW2220za9asbbbZhm8Z+ICCPZxMb5nk8nNwbFOx+S+3IUMle1fYQ8J2Vr6VYArM1xbf+MY3kHbQQQftsMMOR338E3xwOnPmzPe9733MnflenoG0eoDdoDyGMRphyLVQSU7zZaSqmJGBJNFS9hrZWqhqodSaAwQwkotXn49RhJ4EL4nvkkRjvCJmdJJ8Yxc+zGHH4RCQjF0AuYzUB3rSAq8+g4IeA9Crk6HAQy8h4FFNEUCiIBMg1cAQIJzLAxYZDxAaBi8dDSV4koz0c1L3fQ0YnAYjZL6+xhlM8JIkHGJ/dJT7jUBNZ3GbbNCHkLRR2gHAi0vNx/3ShQSZlDoACy3Y4CxJ3J6igaLLh4rOMBmMZFqKq6UCIbIf+WC8mZVMBLB7vGuZtkwjB5OwDT/AJcuxShKUizfMkQMXGFQDkCMHJAl9cJFQIQy58coaWECSo4gLT22nStq5DJx9nh2k8QKoSM61AQFa4BUBSBIYjFGVMNQiFtjkiAwkZMDYgA9NCAC2IQe8uMx+AJlqotYvIPNoBWbQnHrGiKxebWP8eHnHRS/HWg+aJaEQwSHGyCKwJh7ApfhfdwoXDzCDAEh1hy4n5eZ8AeRKYicXgCiAlElgJIS7CfkUubPASAIAGA0gIkuxr9/i0+4ArOpbsV4H0FskehEC5fUowUOpfAOhl6k0VoY1sDysUnut1WFVNSzJY3omZGxevoSPaUlKuz0xqJftzgxtMFgqGtMYMcDE6CXB7DTAJFtLpUIE5AKMLASoUkR054n699mNHSUVoYSNCDb/0OrmG2KUemgbFy/OaeB8uc7kq7jW6SV2vPevuMaV8wRvz9ZjMkIMzbjomx9vkTwu4jGtjQQboAfG1cXcZWPeWRtgG82kcTXWuDZAIDXcmYWp3rH4awQGFIZYIxhun5oc9/U3P/Zo7zn/djU5HpqaGH90GZAz/qCRx2sT2xiQPeOiV4vIBdSUT5XFX3mGlYWalNVIdr0wFUcCjKQGWuDFftYOTHg1vZdRmWlwsSGc5xk7hAVMA/nUwr7oYefMc85K5n0+e9Qx217/tiWFEqse6Zk2j7c8CENf3aKaGKNECyapvTUpU8gwHiGkgf1ihJ7JVUpIWDQhAPhTho0pVhLkolDa2oWRH7Z3TOEQQxPj75iOmhyC0Zk/1w9XNd0BQHfYHSq97W2dgwNu3ZnaTOJWB7o6OovDheGiu9dymWxep015ArsUdTpbKXEjg5CsmXL7c3kbDRpJGtDKCWum3FnZfJJlcxzbWll8TUpus52DUcQWpDxn/2dK5YRTMjbtSo488g3P3mf3E044oaVlMyxpz7UgrZS0AqMTvQV+gcCvtSGEvUqsaGgtI+N+USDLpjpaqnbRMNGwlKk5lIreQHeVYjme0QIoOewQ6O4jVwLpVbsStRoocu6XbvNtbR1oT4p+ubY0iMS75tw/f97PPvnp9/3P8W8+5aQz+krJjCnl1p7kyeGkLcnn8AFHSuXb4GW1E1Hc7E54zqkold0qKkhppAm0C41UkYDVal+CfoTXnR3G7yjlHe9qC+yesFxZlqWA/Sz9cACHPGbnd0idp14twxLKKL3cp9XqNpKCeW8jsTeaGT0QPfAU8YAGH0Zd2sNZV3YElZrnwslIDJ3sBrckWf6GkxIRihBKRCnn3BvKUC9xgZd5YI4//vjXHvD40Ud/vWfGSzbhSK6Ci1AdJfdUUHm2CGKK2lgouxrBHR1txVXDxbKEu7bzhEDEUVwrloYpsnWeje8cgEXwJXLBWImDidOlRIgEScyjmPO/6MtJmsNJIV+hcRp5VvbE/Zmkn18MhOOuO9uP+8oZnz9u/y98ed+TTrwB9dM6pvLCtrfc556r3TN+uZhpLTmD+cMTTn51BARDUgT0Klz0JAlWLptDTDUsLnlGtYYBUMPRQl+khIeirvYpxGwUMP5JXWMbhdnRyOiB6IGnkgfcoO+TGqWRVuMwGABGYPIRqsqEiypCQMgiDMSiVxWjXINIIV0M8LBAjwpxCaYWXpDknCHMDqL29uSZz9xj6ibFf/3r/L6VMz279i1VthZ59tE5nS8ieRTjPkHziVmhgCB3lCQmyP0DvRV8NVXA0By4VKqw9uGHNpl715LD3rn9y18+68rLFw8WEjfNdqm96EKtcwJzWZ9jtDMbg6kiZ7Yud6mWPHQXU1eSltEMn6I3xgYAQohKJIWncOGiHtc9PtWr3WDxdDS24SJz1wZrajQseiB6IHogeiB6IHogeiB6IHogemCj80D1RKMa03yjNH9pnn6tU47LeIir6asx9YxMzWTF2Dx7tdg14ZW0NZQA+4QlrJVJ65oYUO3PiIkeeOp5QPcIi5PshGGTDD8L96c//elVr3oVJ2Gdcsop/GAAyJ/85CfPfOYzeQfFKzl+hu3CCy8EyevC3//+9wsXLuTT39/85jfazsfrPD4AJucjXnylMc1GNjcaZNx+EgZ2PjnmlSh6+SFwTr+auskUuJ567l3HLaoeb6sxzZu0EcVfmjmYlHmrW6qsIbt15nzZ/WhfMjyyAu3e9ebdcnN5CHq3641LtMR2L9aZ3fr/cKY1k80USqXB5X3s6Wrp7NSPFGqZesRvWmnXOvYIrta/KKiFHgduDSW4Jk7Uhhh/x9FPkTR6YAP2wFq5lzfg9j1lTbMHp/XVwmbCh9EMZN3LXf+uulzwUTjvd4XxitvTEDcJuG6/ViF4W53NuMhLBK7VxprIWoQRV+UB65eqmoiIHogeWM0DHEqlD0n4qdF689+zzjqL30Dda6+9+I1zJsLPfe5zf/nLX3KXcfrzt7/97c033/zuu+9mMwkzDmay8+fPlwLmUGCY5/L5BpTMsstT3Ek3TH4h4FBptpbkW3IrnlzJSVjnn38+P1XLzlh+nW41+zbUgg0yfkNN5XMVkNrsRJMBqFLzwStRBFCbRImX9HUSLPgWFmpBqhavkkCKnSqLjJKjT2PYX42rTTJkUgQGdnJZAl40CJF8ADDkbA2ilt6RfImFSw0JGTU1hl2UYicnUUUOsehh51NKDJAWaVQtOUXoka/VD8HgaYj0SjVkMj7FLhVqHTlpcDCHA1vZUeU+rSoCt+RKmXxmOMtOs8rmakRRy0lRfX2DA35fd5HfGR52yy9mf8bH7qx2apU4CbqS8BPQUKkVyhIzaTaZlcpZvt2C1OcIIfFNFTkEI3/ZUsIfLJo+u1m4I6Br2IiVybCgpLbTBQCooNY3x33bJQwOtCpqlTBDzZHPzX4V4ZUbESjMCN/otiiqvOfc4VnINzIAOR8WQxq7LFRRn8rCa7ViQWCoV5eKaHQxKEcLSGCSCUEjRRlv2qn1VM51G1QyC5u0KvRekyyTRBZargsJD6NL/pdS0YSUk2TMU0ZsY1/Jt+QkXeHc/gAqMvRxRzAOUNQFrwAq56h3dMsYC5cTgzZFvgSFy25bEZPrNoQAIUjmxiQQU5QcWMBLmq5M0YMHMJgiab300egK5npRv+6VErfU6zU9To+SsMoAyIBd//geUh6abfRCqmhkBhjLWqGXtHomUasqGmvqBJgZ1UBIYGYbYPRGJiAkCGGjBxClMYIxSgOMPkUcchmNAcYOGbcZPas4R9GSEacACMBoIDA5KZqw2Ay9aOAyQBJMvgEmWZRGbwCUIlZu9BRpJkXay+/G6OmWVjjqWoMIApVgAahJY8JDAGKKGwg9DWS4VGepIZVWeSNDs1MwvnJ+8XdxqiosqrHmH+hVa4ARG6VhAEQW5qo1YgHGEooN4RTBuO5f4x0XIJemzKspATshVhVATbONEYHqL5oAckz5kiZiExKBp54H6GIuHq6Nek3TpUIuoPFlVk/IZOND22paWH09i6WBYWMSGK9RmmoDjGZtAdJlGlNiTS8Eir81KUEyH4d3zz2TIz9y5MFveuWKpcm0TdxlUJM+pcWzu8cwElXk6BWcolTRrLLBrXl6xVbolRAIEGqhWB1/uar1lB9SCpYNwDIYdmuFIVNcUg2j1izsfqluMhhDAkAp+cqtKiU/LHIj0j7OvPrMZ477+a8/8sJX3nLNRRyVXVnQsejKwM8cB/kkme0U+xRKq4bHG3+hJ+FMZKOLhlTLBKMG4naIIdOFVJMeORCTqBWx2E0sVQYbICTyYQfpG+qAtZhQocGwSeFqggyo2dLQNmTG+Bs6ZC3CdAQJgbrq7KLSdUjO5B+kaPz7SndyH0kHYOmK4tZGgmjIQbJwACMHNiFWywcgkcMaIoBoKMJIEQKHYemzXNIuolyW46zc0Rtg2IvEeVDI6Wpv2WyzzRYsfBRd7373gW9+85tPOOn4y65c8YbXbbvJlGR5z3CuiHB3eXNbI7BQdguL8pXMoDnOTr9MjIUc5CECVj1lv9EbV4VgRA5iqUKFCJRTFCZkBwMxC7+0rqsr71a3y0OtZeeotraEU6XOOveO4eL3OEZk5dLy9/73X8uX93CD6mtkaaH5ECOTIjDuogjACOckjyQIvH6nDgK6Bkrxtra0Uusw2RaOFHHS8EpR80pcIec4QPaHuURhvNbcdRxh2EAIYooeiB6IHogeWBMPaNwmMiKE9+sM1xq9JZPBnASNBmeQBE1owqE4xEAGsTChHGEYz5EGnqLEikY2EB9JvIcnvAG42OGPgUCdNVBrdF/5ylfe8IY3fPN/9r359mSnl5ZcdCv6tZGyPxYz7w6P4EWni8b+FabiWjhPRjsqykXXLr/HePSBHHzRLY85A2QY2h1xZbbiqsweV8BmafcbpDiRs1AsVLYj+0CNChiIphjT1qYQ3HbZZZedeOI13/veiW25vY874eeEucHyoJtA+JM9oUdsqeg6hUcGqQMDoI7AGKfYJ9Uqp1YEVqSlpGoaaxpV4qqJoQo8XSBdIY3JlEaqACAz/HoHwiuwnjGiUTPr0UR89ED0QPTAevGAxlXlGKDBNrSkGqPo3PxQbKM6gzfTuHB+6eHRpTD2DLH555nP3I2fmrzggt898EDy7D1m8quSirM+2DobnXkKq6OGrhaDFJKcnZXgNkoHwu2D8mfQd7S7c5CpUxurWzrKNrICEGLc1qbR5OazTGpRzez7ofmP/vrXFx155MGZ8v0XXXLviBVuytxix2GN8jqIZQHydLNWp8FCMzIVdlcnXK0k/8MoIOiOylOHRBnBaswbecE95vnTz3XRWts38mZF86MHogeiB6IHogeiB6IHogeiB6IHnoIesOneumzbhJWu4fSqWu8aCgydVi08rBWcokkVq+lDTE3iNbG/psBQ45gwEtbEgDHlR4LogaeMB1gg0v3CuVdnnnlWV5f7icr+3gF+sI4vci+55BK+3e3p6QE5c+bMc889l4aznHfl5VcBbLb5DE6wAmhtb+EDx/aONoihBEOue9CKaOFjp0WLFn3zpJMRzq/kdHS1d3Z38JJObzDX/MZHb0xrxQPrpS8moNTGeQeEK+z+Va9bmw4SNNrjxTXJSjjqtJgvIa42kzngZS/Llkuc+Day6h7wTwhsplEpmlSxsdqaxGpRY8Z6tTUF1iOuiUfCmhhQU2ZERg9ED4zXA/E2HK/HJky/5sNmqLp5ac1ThvJrwvVE1cMjREFTBLrY3Itm98J7VIOxA1TeaNd8rTzKsZ4hGWxmj9caMY731puYuppcprpmbao59Wjq4VPssRg9sFF7oGtKJ5PWRxc99uD9Dz344EM157/slmFGvMmm7mN2d75PuP/FNz7X4raU8POunGAlb1BklqFtkEyf+SyFKvYDMzZ+6Utf4tQtyNo63KccnAt068238cd0GC3tnfyc60aTaCaJZjKFJwHTRo+rZDSQpIIBGltA0k5f7zcbB58R4SVth8bziLUdp5qvwQUvQsj18RE0QloOIDjUIqThVYUcicUSqmSb2gKBaIxFEkCKGEb1KUUANhQBgDQh+tAGetNibUEUMHhytULCYUeIEoxmiWrDXLVgJB95zldJAZntbe5bQv/ZkPsgSw2EErHQtLV1OCP7B9E7MOTcm8m5DeowOlHBAViZ8mhvevckwz6yl93maydN7QKAXZYITxFRSmCUKAJUcv+NGN+P4SI1WU5AFDTAEqgiWkyRJCCEhoA0mTJGRWgoKkkOGBGTA5OghIAcDDYY3mpFAwEJSuEtN4xtpZME4Y1FSDXQeCUZ+4UnD3e1ySrMNsshQI4SMMJDUesXDo0J4WqrsL8auX4xKYOtaMD6Ne8prF0XA5c6dwHN1FVNziZScmEAICCpSA6XRgluDfDk9BQ5QkiCdddACTs5eBiVYIGdm53bDWJLI/WVf+EiqQBNqnYdF0e/0lnHijd8deobukod2dhgaKxTG1Oqdg3pZZtEVesNa5sxxkyqJ3BMIabRAGORA60IUG1wWCsYOc2QQSyNTRKHimAkVZsX0oRwM/QSKKvGZZIxSiNFANmWkkORgUYEev4LjUzB4hUxVTCCIRkmRR8WoSGtd3rZEBo2LphhuhkJrqnlMt9SMoKbfDDAyoWU6yw3yjEBhMBlZIIlOcSLINRoLBGIHnhaeaDe3bGBOMFu0rV1/0qgHk+bbCOqJ9tL1a1L2ZYyoJb9jMAwlTs6k/d/ePvrbvvxZf91v1Ux1L8VT8+t7T0pgeu3qOZYowBSHqA4rvgbigImZAujvPnGmhnVjGCUZBiwc/dISqmQHNFQlUk6WX7IDA/ceHUyb96Ct799h2suesh/+d7t3qb4j/PprYw7EmuoZ2V2k+kpeWMUxxt/TVy99hoeIFzXMMYQgIY04onRdRaQkMkJgkMuc06IjHD0QMoD3G46YYl1Tqq4bLhNOJRKz5AUIeBW4m5kFU8LeZAVSm5xQQvQnsAz+oVaatnExqXpbmA4hzlLya2QgnF/ZfcLkIyZuqTBsJJR8icxcdI/vwbEyAQTJ1/BW3C/HJCsWDm8eOWj226enHLsp/Z63jNOPPlrN9748JTOpKVtq97+O3NJp1+qcL8biaQRg73ukQEEtMp8ZMuZHcAA7ltff1vZaA/AyohudjVKXMhXUlH3mnLUgYRFRePi82Ka5lpecl4dSobx6TDfAJc34RyO8y58rDz0488f887ttu348amn3fdImRca+o0EuFwbcIBzFUIAKj/4gHC0kABYsMbJskc5NpCwn+4bGmZBBx/TSZjH8ohzpmsyq6icGVJZqHVIrQSpFeR0Ou9v1ArNy8K1oVCdYIwxpITMmDFj6dKlhhSA90QZ0qdoVBSBrMIMyaxJGdI3INt+++0POuig3Xff/YYbbuDHW+BiyyavmlAUzpIaqIhV0QPRA9EDk+cBBnPW9Amm4WDL6KcBkMGNcbum9nDcqwcjU8OpcqIGAyCDfEgv4ejIZRioC7y4bM8PEV2cPa3TEgeWd+juOeqLL3j18x/+zpf3/cmFST7TsXPSXSp2JGUXhoZ9tG9vbxvkcEkCjXuEcBFHkgVwgCNFtctFfIKbry8Tpfjjp4Jc0cEuXvuQndPPA/KQnOEMjCy5BJLneSGYyRIRaYg7cQtMW2u5b8Ad4uWCqX/V6n57yKUiL2xzLU51rrWQyV9x+dDnPvav33zzdfv++LCvfO0vfasI0MmWm7Q9tnwQBVOn9C1d7tlGstJqx3mMYFf/lz4aM7qFHBCT4CKFfUGsBIOlymFxFD6yh+zCIwFK4REiSoogw2tJBF7haLwWcpJyLJEZO+6444tf/OKOjo4HHnjg5ptvDp8NsGeStEex0QPRA9EDa+iB1AClURokgKoMswaKbPR2MnwErAhjBC0VNislhNcVoHItycFv2GuT6Z0XXvzbh+5POtrYkUNkxxIf4jSUlv1ZV/yWXq2EzSQ9SriIjPWVCaXDEzJcHPUhqRb3GuD86c88Izjhma62fPvKnoFLL5q3z77bZnND11z9UE8fwt1TBK30ubKwFc1GClTATFsEKA9kpkEIcAOJCuWi8Dg9OzifiCzNXKsMpTaT2RppSnItplEc7GuXfvbs2VtuuSUbK/kBzBtvvBHh9pyJLp4Jx6tx1NYIRQ9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA88hTzAkpreLvGOT28S9RUu22l4d8lbRVa9aO7KlSvZfx62m70WomRJLZd378igCQlSsLR4Ya5GC3HkLFulKGMxeqAZD7glcrY28ZtGrH2XB3jP7N47t7a2d3VyXtuDD81FSK7krt5M2f1WKBuo3PXmF8phgDnf2rpi+cr9X3rgE088MfeuuxYtWHTkkUeuWNF7913XewPClWpZ5KTFFD0QPRA9ED0QPfB09oCLjuxoJs9mCmxPdq+Z3c7k7qldvJd87LHH7C2kf//rXkx7d7GDSDvE2I3uHv9e8qIXzZkzZ+G8eTvtuutRHzuS3bx33DUH0dnKe9raPrbjtGpXR2z0QPRA9EBDDzBAcfSzHTtVc/6LAAiYCDOBZY5cUx5fHDC9WLFihdW66Ybf8sH4xs5PJDPRppYiiWmICNjqmm9zv4QKxswwIRs+gNkkmkbCCeS0FAyWKwcDrFwOCRslGhgBSPBqjw0wZBIFDCMuEntKCJLBkJPEJfkhmSSAF4GqDAmj9Y5JMAAu0WMAlGaG0+dVixe89uVCLHpqQ3UUTZQsVC4u2FVEGgCMJGu+qhALEljyhSRHMpS5UgFHJK34KpvPZFtzuZbWVezOnTLVzXxFNjjAyWIty5fRVXC1ZPhlYLZ3kw8VieJcpV6+OzKmnHU5gh2svtAONL+xu8wR5v5KLnFyVtb9cYSWp3f+4UCaUi5TTNxu70KmyMeZPBqQYzc0lZjORvIkM1wosyeaB4bhgrtyJFMNd5T0uN/+zfdcbmLvJDgaLjfVkqPDYZJMcSi93wmr3adpmcoPSztpfHum3dd+O5xrlnOn86fa6Aq+SCawgh/ZnS4jfZXPeEJxHqqYJOEUReb61HeXo2B/HI88/pLALpmN9fwIcqbk9se7LXo+MU7wL85g07dZhQe8SdDSDPa+V/7EQk4D3N78yp9rufsbTb45owSSVmn+CJfYR3hDbuSsJs3JlYNGRFDvbtJRhdWQF2hixiCuZp8EDObKw+ZnlNRrSMXgShN9r0+CSU8NkebDsJeFVK4bxMi4U4D1LSEsDInk0IA0h0iUlqCpIml41Den7otIH2HFQg4BubiQjBxgIi/0FmK8mAqj2FFNAlYOl7QAhMaYVZMNxJXxMTxsvSLAio3ZjMyAtUvfWNoa1mKzmW3AGsoM2b34yo23hvKr2bnl0CUV1bWhGYKboQm5GtMzBIhA44IssTwEQpnjghsbkBJlxhgXgMEp4prFDYE+NFiwrArxNY0HmeqIemTCW/eZZANEQNFoQlEiSxFDUI0JuSIcPRA90NgDT7c7aGLtnRhXY883U1ut18ZbsVNkDb7kz1Hac8/dP/a+9x38ti8NrErau5NscXT+2YyudUNT3aKU3jEJatKHXCGcIg6LkIWUgkNMSNw8bHLoGeYtrMDQO53tyaqe5He/O++73/38P//6nSsuYvrNigLLIu55xnWin3szjxrv85UYMa8Zy43Y6Ku5zH4Z1rjhRmxk1QKtKgLRA+PyAPcEVyxXFHN4AJ/cMqtdmWDYBcSSDjcOya26jSS2vrEq7ZAjtwYYDslyxG4BrkQOO6KyTolbFgJ2BD7BmC/5T14zlQ9WTQ60LXl+lyDT01cYTJKtpme/9a3/2WKLLT73uc/ddFNfe3uiHXJdXfmeFU4maxZIQ5V7x+D2LFWmZrJNqsnBszji3lL4oyewEo2iYRgBwAmIgpJcVcDgJV/mSRpIWBBIEiWAVFDFp8VaT2nJthZY4HXNL/IaA5rWbDJcSs6/7K5S4ZRPfOIT3/72sccdd/IdDyVdLfxIZkdvrzufy6lwp364c6NwKXY6XYk7nIvE8KVWQGkJShJFcjyHS1jBaW9rp0VGgxC3T3EY77kffEAOa989q3ra21vFCC9VJCj1ogIyNdCEhICqTC+AdjfKXSwPaaQNbQjZQxhFGNPb2wsSWFUIbKC9Ho2aRu2hhx564IEHPvzwwxy98alPfeoDH/gAh2HxJbDJl4SYRw9ED0QPrEcPMNBpqDQbCFI2bKoKDCOqEQCw+zyFqaZhrEuNohRTGJMJ3r2PKycMxctXMRS3JkOcTjHc3rXFqae+msMNj/nUDy6+A3IXR4hB0LsXY4zY/nArMJjEizuFVw3dGF/yx1FJi3sVUXbns9tbCwKNMJx9BV5xB4A5l0byeiHAaR9ZHAPmx7V6+laCseT4/QMLPpAQE3XtTTd95jM3/fCHxx933Ee/+MX/W9mfPOJPv2pJiH3Ded7F+gcTzulCGl4lN14aVO1nDFAyRTTEetBMMmCEvPJvCk+RCiERKJlGY/gUBnWp6yFFsI6LRFtC8IMPPkhbPvjBDwJ89rOffeSRR9axGVFd9ED0QPRAPQ9ogK05xoYsNiCDDOGQZm3BjP2E+hzrV0l7KVkxber01xy8G3Hn3HOvXOyHT17cT2yo17S0TmByjwqT2jQJn9aZu+7mOUuXz3nVq1654w67/fnP5zLt4zGCxwYcyDTauXEk/DmMK48juag/noRV9L5y8cnOMS+JUInYYeEJBzwPSPTXuCTAJb0SK5NCFdVwYzt33nnn17/+9bfffjvGHHjggfvss8/555+/bNkyPko0FQDjNbLajIiJHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHtioPcB6GguGWjlck4ZoiayxhJqrXuGyWGP2WBs9UO0BLmB+Hon12BLf8haKCWe0rRjsKRWf9axn6QCsFAsXoZbC+bRNq6O77LrzAQccwAEcLKXef//9rPGG37Gn2GMxeiB6IHogeiB6IHqg4gF2lfFK139knhQG+HHjFU+u5Odp2TC8cmUPG7QgG9m17TjsWZH429bayq828iu2nZ2d06ZNi/G34tL4T/RA9MDG4wFm0ExBQnsZ3EhgyJliG2wTXgA+pgBv42HIvrHAGE9SMy03QK2DgGma7ZkRrKJ4oWfLk1wkN1KU0yiKXZRGD0vNJCFWJXqtb0gvVWaAyCgKCHlhtKIAyIzS5Aswq0RJLozhxWhIkVEUQEvVahML3hJIKGmC6BElAAKqyCk6y/wHWWw2h7K1tYVt3m0dWaJqV1flJ+pR0d7mvgYa7C+wl5vPqpjwtrmDlpwIMnyOKA5fkrVmjEdWvtxhtu3w/GYjWS7HBUySSZApgUk1B7yz0F8DfLTpgNFPl5w86DFAgCtXJSR4M/3+saAWfFBKg+JCnVUAC2kYAY3lpIjDojFW20+jpFo0EKgo5xg9SC4AklmiKuUm32pD7RsvrNZtmPabbSnnb5jWblBWmetqWkUtLiW3W0BFbgEwwAxBfFvBAAI7GJLkQKC+YHUOgIQQbhly942J4xL56LZMquCF0iyBV/Tuu0uf4AEDuwByZFLkGxCJg0pylJuodQb40XadaYuKogcm2QN2OxkwyQrT4sMRIV23nsrra3BZW80NXcq4iVhapNSMCihDCfVYIEtVVWOkuhlpKVGxGD0QPRA98HTzAENorrWQ4QCSXHLU5zY549xLzz/P+YAzsJJkQT7Hg7Kb6j9Vk0UQAQQOxa8m2ztmoJFYywEES36qKGQokxWWUpHFi2ypMDWbGTjjr0Mffn/Loe+aecXVT5T7ekp8z1/0E4RyW1IG4HwZZjju63aTHKqr1yhoQqUNyGpWpVSEokK4Jm+INJtDZISjB9bEA37hgOXIDOuhyMn64/D5cR6dVAWmzBFO/oAJVgfCe7+jo7VYdOsFjsbn7e0thUIra6YiYxGhq6uLIuxogQaAxD0LC+sIJbcuwaEeHusOeGJ1g1/JSLK5pHtKNt9SXNKX7P/cGV/72te6u6Z++ctfvvq6vk2mwOqO4cokHflcN8MwxXyeHzPIdXS1s9IxOJRDJrrQyOtSMP29fV6ByzAJezwLv0jgTKIIpRZqMVsLH+QQg4SAXARg1ASxgwcDCw0RAXgSGPBgurq7O7o6cSPIbM79bMWm07eZ0j21sHJVLsldeNWqVT3/e+KJnzjl28f86NTfXXXVkqTYP3V6kunjG9rhcrbdyeLXDDKlllwb0Sff4tZ3EIt8egqxwI7Gp26fVizvYTnbr967dW36i+ZTL0q4KGIGi0eIAg9Nd1d3jgM/XB84s+lT5PPSGZb+fs5AWX0R2usKM1jwiTAA8GLI6173uuuuu+7RRx/FgJC4AQxjX1/fpptuqo1lJrMei+wPcyjholNI6MX+97///X/5y1/+8Ic/UHXXXXedffbZP/7xj/nxRnRVr7/XUxTx0QPRA9EDk+oBjWNEK9PCSMjOICILGI3e7AECw9hlNMRoTgwMMaIRl8iQQzIMiigyQgpDWCHlSmX+ALOlTEtmUwLEtNzM3uSRTGYZwWTz7ZMTT3xWsWur//neqefPSbbdbvrAwqHOtvbWDveXGa6cFIWcjvaugbahQnEI+bJZNpBbkTa2r+KxwbWL/VDk06dPRyOtgIZxe7PNNgM2gxnSK8k/sJtAtZrmkEBy9hbRLZ+pTMQ4fxJke2d328BQeaC3rYODIAv8bA1vcvmbusNmKLpuXtdhn7rglycd9JufHfP1U763cGGytIcgODyldbfh3BN9+rWi1qXI2XrLLfntXzUBYwDwIQcphp7n+C0MoOHQQyAaFcEo0XaeK2gaNkNArr7wj0Cr0WAelIsXLx5hrfEvloC1XAAYroF999336quvJp4KKRcpryHIo6iFOKSvRwleokRcTWb4b33rWz/5yU9+8YtfQMPRG5dddhluPProo8ViZNUSIiZ6IHogemCD9UC9savxwDhGc0bfwnOee9LSyqz2sf6+pKs7ecWr9unsmH7NNdc88RjBxU9+M8zgWM7ijCydielngv6378bQUr+aQ5frVzZbU/GMDrEaiRQVZhc5nZ2r+rr4mb0FD606/z+3H/S6Z7/xTfudd+61PBEMDvTxdENc9L/1KCa1ay0Y1mwDJkRnsZUGzpw5k2n1kiVL3GGgzS0bolPPPyZHVqSK9UxLkaFUfn7Ri150xhlnzJ8/H8aFCxcedthh++233+mnnw69XcAG1BMe8dED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9EDT3kPsJqk5ak1bKlWpRoLSa1lNSaOtdEDzXiARc5CscD3vlttNot3zZtvvtkNN9xQGhzIdefcnlDeorr9Yvxw4jBr4Hn3Wwz8aKpbiKYGeIvNZm6+2cytZ8xaPmPJIw8uaC3xw43uL+v3kepds5MSU/RA9ED0QPRA9ED0QMoDHD05ZcrOW81ioxSPk7yUXLF0Ra49n/GfmZfzPgAX3V7lTOIOfMkm04BKids7vdmmm7Kpaevpm5V7B+bffV9bMeHXgPnLs6Or7H96MaUrFqMHogeiBzYwD4T7LlKmadrLLFs0DIAicLMSn1L0G2ORttBAywGUaB8rA+yVBVC7AKDUpytgKEJpvGC0PVhIsStHDkimeOTsoYUSsWI0CQCmBS5guAD4JERmQEAC475NGjlyCzKQYjSAIsLhIgFLCLaFSo3RCfUSYJEccdnHOLCTvDBnjxEjENi4xBuKNWk0HBhe5TJeciQ5n8tjW3dbkf3Sm3TnOjtb2jqGOf0qOAArU+IzV/xWaB3uW54rZ1rLRY7NymeTlf1up1nFMPycyRb8TLnkd0pT5SzPuM+m+C17cnZ3e9j1bDaX5w9mx++OwczRhRknI8PHZbl8W7bovvnK5pwznUt9x/GJkzPeHfw1emACitT7ACSq1HHkwlCLEH115ZSNJPlBJWAVjV2UQio3XkMKkASrHRHvGgUSnwtD0ehNghEb4JzmUygwvISwkHroESI/AIMx2LnXX/bkCFEOIHukOhRuqtc9sIGYMXkNr/T46EU3eaqe+pJxJjcC1wxJVzvXNs2WkzUIgFcVeF35BoCHUmMgoQR6P5ZX4ojch2TRk0MvRbAwmBA+dAvDKEskEBgAGuFljHLJXPd5ZeBb94qjxuiB6IExPaBRxsjW72BhZmxEQMqBjS0fF3FjUbE2eiB6IHogesDNakvJ2w9PnvOc5xx11M/KlUmuc8yGP96mLJxY/DUuA5q8KjRpaZJ4AmS0zjfQTUt4TbLsycdPPfXU4058+cFvOOuc003e6KkBhmoe8PKbJR8XMULHS9+sHZEuemA8HuC+5g6CgxVHYC5LZvgVjF+DA6lkUqklgRQGgCJcfX0DrB2AZJkAjBYUAJxwTwyNqjjp3xJvM92aQilpa81OmdrS2zu4fFmy/z7bfutbx/M16Wc+fcyd9w+2tyQcJ8UyCKudiHIHgiR595p0cNCtWRQGWekold3536wFs4TBgQscEZLLuHUTJRmgfAQ3+q/VSgK5lhFtVRFSkMoBoKddJAAoIQMAT4IG8yhiBgQcgMWRFj09PSB5qTsl29pdHr7m9p4TTjj585//5HHHHXfSSSedd/kTXYNJ19T2crl/YMi5izViL96/NiiPvh0Bab0DGUlkynEClqxatYpDOmSJaFpacnyUiz02Jus8jkH/qoBVJtmPzYOD9E8Q5MRflZtw3mrvscceM3x617vexQEcb3vb29COJdAgsIp1FCGPSfWyZcuooCuNxVQYg+gNL/nkJGjoBZKI+YHHQw89lDOwMOPGG2/k9xuf8YxnmJwIRA9ED0QPrC8PaLzSQMeQxQDIiUVmDKcpMVYzCIP3Y1uGX5zQj04YDTEuhfEkA4zwRoNMkg3mSOPdp2F0AFZbZ7ffSsSOo8r7V+xpybUsLSZ7bD/rO//7UuLvl770rTvuSAZKiT+gsAOzZbkpqgaMwLRDQ6TGSLUrYZsTB1j6ROTCNiynBCxeuahasjCM6sOloZZsq+IFXPUowSNQYslXPrQkaUtKg0OL7370+OOvPe64L5900oknnHDC0tt5dV2RIQOU8whBwjzJwTnyYU2NsNBGLFe7UiYRlAnc1KpbEYU/Q/9Ajy78QGhO8VYXZZ7wiHr961+P2MMPP/ytb30r+Zlnnokoas2NBlSLgrG6OfXowaPaamGUcwSYnJ122ulVr3rVb37zGxp42223zZ07lyM5aBf9hgGh8dX2REz0QPRA9MC69IAGtOrcbNCQpdHPkDWBZmhSjH76wqjo0ISh4SE2+iTbzt5k//33Y8zkJKMVK4rMcNrbMxxsmOJtvkj0qUlsBtcjqMk1YSQHQA+VkwcXPn7uuRe97GUvestbXnPOOef1rnLy2AiFKxpOGSesti6jmk+ulIpNIGtyGl7hb+edd95qq62e+9znkn/jG9/gMcAumJrs9ZCIhVGW1KMJ8RDLAHFZ/GVKzgmb3//+9yG+7777FixYwE8uG2UoIcLRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9ED0QPRA9EDa+iB5pez1lBRZI8eCD3AoigpGS7xDdzjpSFeFvf0rCj1DbTN6HjyyScz+Vy5j21n/kM7jr0aWXf2TCyruoXfRYsW3Tv3gYGBVraTzV8wf9dddmVBnl8zeviRUE+EoweiB6IHogeiB6IHVvOAi6psZxoe5hWkf//otoWzBavyI4IcYjVYcj+GuPprbf8a1G0Tgmve3PtndHSzg+iRRx+x+Ltw0cOrqYmF6IHogeiBDdUDDGhjmmYTkDEpN0YCzapSOQ1h8yrOITSQtJtXu2TZOCpiaACIGiQoycFAbJtpPavLRAANCRZhzFdgDDYASmBsgFhnSFEUpXZDSTVICIwLQGYAeG3uWyFSSCBY8iUQFrMBGHpkGgFVwNggOcAYwG5tclK15BCDHLiQICPhRb5xVYpJEeGtrW4Dc2trls922tpL5O3tzgykQV8quo3H5Snt/JJ9yc+LORYayaVcmd3LyJFS1/aRtoKU5QC+RU5aLttCEYCcPhUQ5mxypsoSYqnFPHJMQT4E5itnkk8CREyu5JWu1jXgJXmEpNKhEkiOfMGidA0cOX4ODFWwG0CRRBEab11FWkiTUgdxvSRR4oVGjBIrFnrBtAB45f7juJGrFzPEaOySGQox+ZIZ87XrgejetevPamlc0jiZXF+LAHB5g+Hu4AsC+T91F0NAQpTIYIFYEqBkBAVWEg1kJFjIwZMzDsCCRmCZJHqQFIGVVyNFvF7yeADWenF7VBo9ED0QPRA9ED0QPRA98NTxgB5zw/bk25JCf3L0p19+y+3/uepC1XTwmJ0kffagHNJHeF15gIWA4STjDg4oFvMcGdDZlfz3vFWHvfeBo4464NILr+p9khp3kks56WRVIUlWJNmeJKHvYooeiB7AAwxiOX8URXZwwP8CT8bdLxzfwAecOqqAu4ejMajlr8QJVSOpt3egr2+QszNAuP+TpLVvECSAVui0qggcLih4QluJcKVcpgQ/h3HwH7rz5dKjSwYB3vnmPY8++ui59z/0ta9988FFyfTuZGAw4ZeEsIYVjlKSLTg+spxbziwUOzraWGsdHHKnfbOoAU7LqRjPkC6TaJFLRddGTnwCT0kWioZVVxJIcmEY4bEfDAAYDfgUQZLgJaEOj4neSfZrvuRU8duDYEouKw4MF513kqSnNMAY1JlJ7rw7+fQxp37+Cx/4+jdO2P63//uHP9z1xJN93R38tgLHZpF35cqt/AZCgVbx+4Q+oaUll5c9XpjLvNV8U1wEgBHD6D7OAtMhIyJTo0rlQmlkLR0MqeB/eQkaFpfoMiTQLJZ6tASETrFX51hCw8lp+7bbbnvEEUdcfPHFnEI4depU1tRhpMqLrSvBamE57LDD/vznP3MgCLyM5FSJPaXXnCDDoAktxOG0CBaW+D/+8Y/ffffdYCjusMMOO+6449VXX93Z2ckbBXHVlJ9SF4vRA9ED0QPrwAMaqaTIBQ7/KxNawQfJkMgwq4V40RDdDKMxsJpGccG4IAsxOgCLYVAHYJWSoUJ5Cci+VU/0FwvvfnXyP//zjquun/e1r13ck916sLCc05keX8Gb0OKQe0fgfgSD3BvjxtgQ42T6wR8a2kKSzWhnfJY9vFcF6SKQD8GEkkLJxVnxsi2qAuifpBLBJQcc9LlSHsmSRs4+5lxm5K3siD28uaAGMliGyplh97NFZX5ZcCgzlGTz581pPeew753/83f87NSvfe3kn591/oKW/vmdhf7BpDubZIuZNmehO7Cr8kYEIUiTD61R0AhDbkjfLBeJLBFb5S6EQEZfiAaBRgOBvEFuyGaAQw455FOf+tSVV145a9Ys6OfNm2enX4XxsZ4o7Pnyl7/885//fPHixdhZj0x47EcmLCQw0KsJBoiM0yc58RNiijNnzpw9e/Z1112Hi1Qb8+iB6IHogY3FAxrHyAXUNFu1jI0NaGoygmQoZdzlZGcGSDdlzCbb79C6337PXr788csvv3nVSjepLBG/hviVP45Qdr/1N65kthmX4oLwIGU2TxRGMC5ATbaGm3ABI6J4WZ9nepp3DZ1GZF72RN9/zr7luc/f6pA3vurCCy+c+0DSPaW8YgUR2i1M4JURRv4dIypBYdrFtbrqQNJaAqUOLYqDBx988L333sszGBvF2PJlSswVhqkJMF097bTTli5dShhtxnJoREYuAzQZR7iKf/rTn3gGUFDG1O222+6ee+7BsPE+WtS0NiKjB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB6IHogeiB9a7B1gUdTbk3e8q5Fe634IaKCZTs8nWLd1tj6/Ypty6cri/v8TvF7rfMGSZ1O2Pcl8mZ8qZck9xB77KTVofT1q7b5h3j1ujzxfvnXvLZz/3yWSLLaZM7UZazu83g1GLseu9vdGA6IHogeiB6IHogQ3EAy4yure55Tz7nwf62gvJlLbszM1ntq8a5lV2S8G9z2arcgGqTFJsHc7ls4Wy+xFBdoQRvof5AeOO1v/edZOLvy3lu+be+6nPfTq7xebdU7t5lZkbdNvJYvzdQPo6mhE9ED0QPTCmBxixSZD5cd4d3sRQT+LDDT4AAc/YTuIzFnbUMCODTIkq8fppWoWLKiTws8FsiqaW4pQpUwDYAAMZgHKzSroqEv0ZKBxqzN4Y7b2BHkpoQLKZBzOAjVcAAgUgBKXwksDAAgaAuSHGE5sAZJLEistguKBHPgAfp1itGQweYpJpFA05SHKxc16VO8gq2LuL37QHWPSQkbyf3W/MM8Mll+WYTxVkmFHK+q1l3pMI80yVXHKUQ8kE2dX6Q6L5F8ltrR3gs1nXfezuBgIvsXKso/eNJUc1VdiAe3GaxDpGCEaahv24prrhkNF2KEWPEBLqnJs8Xj8gjWRRkodJvoUSpCyRqeY9qtioTN8ZpQDotdXZGenVSQJFmqAUbgWXCqmWtaiQBNjFC0C/Y7/IqO3q6mIbHsZLqRiVi4ZckpEGu299pSupkmQIYAlz413HQMrydax9TdTJycpDORtvi8JWbOCwnKxrmBsBa3UxA+sOqrZfLLopRKOxl9zfSu6OC3tTAkFyy0AgXSmxoicXIEugl3zDp7jWZXF0p+m61Bp1RQ88HTxQc1B4OjT86dBGhm/FjCYba/QGNMkYyaIHogeeJh6YvJAxrsFqbXkbpYX+8ns+uOs222zzkY9cHIhl+s2TcHplJCB4uoO4jrRuvIAilh5yLcnQYPL9788566yjDj74xr/+gXcrSiVOdBmBN/p/Y/zd6Ltww2uArcFhmtbUuMycmXx369crWbIMlx60eKoVAbVGXEPukCrHWCy404gQApfGAWDO2si6FU63EgGRP52KVcjKJ6+sc/QPJ1vPSj7ykcNf+cqXX3rppd865Q8r+5JZmyaDg6yJV06zK/W7Q5dQx4kTNsL4NQ4nlRTCSHcYf3QXeJJrk1vvcDYERTc+gNEKJrDWTwWwhsLCCrVSB1e4AkKRhXvooYGAJEDCkUCC1+Uebk1ypaTIUSNtbckTTyRf+MJvvvjFpR/72Me22ebfv/zVBY8+yplXjg45nrx2JoGqA1bRa8zSWM8Lv/ts2J0ttlpSzILWnc+lEMZXtLDgUhxLTPPHpa/GU7NgDTzTJ2g4auqZz3wmrw34faeaLDWRuI7Dzm677TbOqIKXFWGW76GkUaET1EblkiMCaMIrkyrYzznnHNHQqI985CNQnnLKKZx+VU0ssphHD0QPRA9sIB4IR7lxmRQOmGMyuqOvnCYXHQkSwwVe3CZbbdp9+OFvPPrI7f/4xz/++P8eWNCTTJ82KqnC4vkcq0suH0G4kIoNqjKkJxul8cXVuBylP/RKlGPmnt4dzWnqYA+PyYBA4ckJrplKpWmd05b0LfnkJ3//hS+8/Ktf/epee535s1/8l8AzmAzmOJnTzRkahWCT6owZSSCtC8AZDUhSiFEVSKOZAKDXpZdccsl9993H8VVIePazn21y9NBiRXTJXYYRgFVvf/vbb7jhhvPOO08WNrBKrSBHNezEbh5+4CIE80xoR6hcdNFFYAi+0HzlK18h8h577LEpvbEYPRA9ED2wUXjAhu4GI6TGxiab42egFVpgd1ajP09yi62SXXfddfvtt+EcwzlzHuxzB7y70ElgGS7YilaTSlYjQ4jSalgfsEZqKhMuig1CQIq9ugg7yMYS3BGTxdLiZb1XXjmXIPL6179uzpw5d94xv+BjJkGxsrTqi5rD1hNojQrNlg3VtqUwRlZPeIo+VTSNp59++qJFi97znvdsv/32zKlZRlDsM3opqqeF7VPEUw7AMvoQMC2GBMOON1QQZLXRjV1fnDgJAUGfdOedd9qk+MADDyRG33jjjXG92hwYgeiB6IHogeiB6IHogeiB6IHogeiB6IENxAOa8VXP+5o3b014m9eyYVI23/bmKTfMlkarogeiB6IHogdqeoDh3eGzuRmzZk3pcwdgtfrXlO73CN1Op+zs2Vv2DBWXLVs2WPSUfvmURVp+ya8zvznbmTJTViBglX9Ly4IqAnfbYSd+Tm/OlRfdeuutTyx8eJNNN+XlZrX2p3Nkab7tzVNWezhiogeiB6IHNhAPEDUYzcyYVNHw6xJYExvW4sjMGQ/8Ml9XXx/vebv8bwlnsm4LN/F3q61mDpQHly5d2ZpP7E03ZvMGs6O99YUvfOHKgV48tjLnzshQ/N3dx9/br7j6lltueWLZw9M3/vi7Jt1U83Jqvu+ap6ypKCKjB6IHogfG6wFtRNE4z9YURnu2spDz6Qc7YcATKThAig2lULK/hVp2rmr8p5YEMVUQAJAAOAQKMtXKHgj4SXVgkNoMA0ARvMjEDhd7bzBDtRoSoXezxdZWtANjD3h4laMRYmB42Yejs7ckmSoA6BHOLiBagRxjBLCEGRCjAgzaaTssFJlOSim1SCYnVsp+5ZKAAWiXE2i7KGU8BGqLtgZBhpBc1iE57gotuNy3ffT3aKlSMuFIJtnPEFoth16RfFWSy1d6Afs72qfAWy675vAFEv2FztAeSYCRVpBTCyUAeADlDsP/fhpO3r+qFwLbtCzng6dFIMnRS9+pgVZrolAEmeGd3BHJANTCTvfhClhQ5Ovd12EIpO8goB+FVCeaZJDANIEcYvkfCQikx0mwixchkiAzpAVGtJCbAeDNTmQCww5S7OKSHNkABgDjuWCwnySMamVeqFfwZOemvYEia2YDmppVDYRPWGZNRWMizZJqvWCsdkw5kWBMD5gz5eoG7rV7BBrdNbr3xdhAEWTwSrKIU0ohCNlVHFNsyDJ5cDwAa+37drxdO176tW/x6hLHa0+T9JDpxhgv/erWVR6hUshU0e7AFH6dFa2xaFyT9qZ4U8V11pz1rmhdduh4nTxe+klyJmbY/dWMu4x+kuyJYqMH1twDXMnN3192/TepdwL0zRvTpA1rQta8c9RSdDXPUo+4sQdKJXf0hhKzSmah02cmbz/03f/62wV3X8/30C1ldwyLaDZJyhx3vXyEfP3/a14yU1KNTRWNbDIA66kmlULWJKWsZT0qoGfxgk/eXc2NlyZ/+/OD73r3QXfMOevum0XLUoufKZQraxzCTkbuGzH6GqyBClGGXRY0Z5QvRIbwKMVaghBuXRZaVU/8pBpTT2nEr7EH7BYocRqV+/MpnOQDK7kafwAWCwokkCImp/fDC0BFctYydWwEl5CuIgFanhBZpuR2G7nlz6TckmM1M+H3AFasSlgpfP6zp37qU0ftsccep5764z+ddh36NpuWDA/mi8MFfmwg7065yLBoyHKk1+4WN5HvxbolUVmIqb7WG6njObzdRuCOiKL1I2dkUClG5Z42nXkVlftasDmEoloHu1ZSYCZ2sDQpjVjoCFivz+SGykUcxIF8pGImO3PLKQ8/suK4k866dc7cz3zmM3vts9+JJ5543Y2uOl9alc8hAScNZcs559iyW6bJ8WOFSXakJ9y/OdakZRr+4JcZXJD0/eCUjCaH9N0NQXnk+2I1mRq8CMxqrdaBwVBE3Sh/Hcgpc+qcP7fccksW4ukgfoJDn+NaVR3uCtoUQc/yMcXGjEaPOl4YsAbNJQGsk7MQyg42HcL1gQ984EUvetEnPvGJO+64Q0Y2tiTWRg9ED0QPrGMP2ICG3noDr4bExoZlShbfGeU5ZpE/jeHuZ22p5S/rzopyYdqdK1HsKCcDpWQAooEkecYOyddPfvXs2TM/+bk/nH7+g0nSPjO7eTEZzCTuYIqsi78MtAVin8uJZS45aQ4LpuQwtGW1NBKCFTEJvNAQXhynErArVaKwg5pLOMQdbVl02lMczhj/6hFA0Rnj+ct5TUM+Di7pXl7MDd/dk7z36xe/6eJLvva1r+37gu1PPPHUy24Z7k+GsZI4m5TcLxrRHMl39haz7q88qpFa0EaTskRFTCWJBsBoQtiQjUUZGQCxktYR6RTsZsyYMXXqVCOgSk4QRgZUawTDFvDw9I2UnWI0sQCI1ct1YN71hlWCeSXM5gOc/853vvOQQw553/vex37xMEBXs0RM9ED0QPTABusBRkUNjA0stJGdgOGOUPQnryt4uCnZyEHsbhD2cNEzMKPj4GjStrM32X///dgZc+01N86bt5jz3FtaFRup9DHS6x7TDE9VI/MtcHMrZ8DqiSrhVVVNsDp5jZKOqaKCR4sa1c5+/tzHQpmEIFUuDs+guHJV/zVXzlv0yLyDDnrx7rvt+/e///2Rx9wjglrrPObPqvb26HnDrxyECsr5com9Vg7PWdeuxh/oHJI0htXYCXsV9kceeQQVLBczFZU0kymfywA5OWUMawXsfiOl8GExxUj85ahoEejxRtNtMFSRVMWFtNdee73uda/79re/rb2AocwIRw9ED0QPRA9ED0QPRA9ED0QPRA88PT3AbGXC0x95LDVDWXM3rnWBa27SOpOgtq8bD6x5168zt6yJIprJKn0oQRjyELm24KeJV9eWu6Kc6IGnswfWfLhY68FirQucpP7lJSOLrg2Eb9o9dfPuqe35Nsb/rQf8d9SlYdaXy21tLaWWJZnhbLl1kXv7zQYzdpnxYxSFJx5ZvON2M976sjfcs+0m4Je1ure93d2drLK28o3rzJnPW/mcgZU9yxY9Whou5DnPg+Tfa0M23rTmXT9ejeuFnmbG+LtePB+VRg9ED0yeBxQoU8N4qjh52idJcvPRn1FdryDrWdJSKLWXM7lytqNzyrSC2y80tW8IlkKmnfi7MmntaM1kO9qWDS5b0V/kleVwvtSS5LrbO/Z77vMWbtGN2GX5NvLuKaPxd9/lvQPLVy55+BEdVMEDgIvAQcIqugCE8qBmFJy8Pmree7IwZfyoiZMJ0Xzz0lrXs15atNZbEQVGD0QPrHUPaExm0NbpUXwwwscy5GwQZTMMQwf7WsEwtiuy2F5TdraIl5xELUKgZPMMO06JHULKYGAA9r0wyiFKReXgRYlG2OFFAgBIDd1MKtlgw+ck8Or8KQCJhUBWQY/9MHIGEx+8wEuiSixwkcDomxTtyaGogdGR+hOUaC9NwAzkwEsTpk2bBqxjsKBBGgTSLuEyg5wilBiPdmxQ8yFWlchwncTiCxQCe41OUS7bls20lkstFNzkla+iErfbmXkwJ2VhKfQ+Z35bhIIvhGCn8VhFHXTZTI4DtTraO7Chq2saGM4EIx92h0etdmqYa61P1HozUOH2m7W2ujO8QI4m72esA8OGbXU9lBTVNQ4/cvyT19tFi5xh/gEASrkaMjpdHxMBW5Io6PE57HYAlqmAkn1c9J14uRKs600IAPS4mouWxKZr5IgRyeBJshxeGWa8MEJDkb5Tv4f2SzK5sRujAHiRgH/Qq9sHXa4zfRINHkCpVFcbkBI4rqK8BIuaYJ4xvFVVixVxSBl6xrqvmjFkkV6jMZmwh2RGABBqCfHjhU2+AZKwtuSP156nDz29TL/LzzW9TY/oSuBuNVjjAPTGkuo4HEiV0dfzp7EbQTXGqtYQCC0M4XpiG6171uPZqPG4vhnvQyP3cd1oNByz1bAo6lj8aKxoTeib6drQYGtOtUkpUWpvyFsNm5BUe6spUxgNvuafVG11EXrdlqoyvUYpTKoJVjsmgP1ICFU0ZpH9jenNJADoSRNor4TUNKZBVU36FJImN2/PePt3sulTbWm+iNMaXyTm1Wb6y4ibbG9Ijxk8HTawvLGd9RilAl4S/Wsa69ELT2OhbJJYlMhvLDPWRg808ICuogYEqoJsw7zSZFWTt0x1M6sZU82kWE1TLad5jHkypahJCcaFHFKKSxijSdVqoPvQh1659957f/7Y492ZIW4NhAGkIqfJxppeZ0GVDSmla6XYQEuDqmZUM+Q2fn5oIKSm6ppICalZVY3UiD7aibnkRz86919nffrFL87ffbM+U6/0l+JdAwvrVaF0VH4tomqroKqJrOYOyUJYlKZXVTRhsuMv/VttRrXZGEaCshlieQNKWKpFRcx694COrpAZXAA8g7nLwH+IyrlLwlRv8VHX6zKAV5dChcyfw80uIy6RXKbqm1W3gub+WlvcAVhLetzI+p5D9/3kJz+5YsXS97///bffW+zg9IkSRzxgBMb4RU+nIMOyJrpSswAWPlylH12Vy57wU1y79txxT/46NIwazuDGWiTr2hQBaDUEAKwwhg+lyJd/xEVuD6LOAm8DGK2cIk2LMpA5dXjHEwAODnKjrdx004RPWf9xxt233/6RY7/0sZ/+9Ke/+tWffvObK/oGk2Lv0CZTuzAjKboF60xS+UUFf3ZJ5RtX2YDkLAdhcT96LU7RyBFXIsYej8Qf7gZULl7Laaaros7/wWJVDQDIWIdlvRg7OYaD5X4Wjtko5kzSWrm/69XqBnJUBRkLu1p6bsDCbzAecMABz3/+8x977LHf/OY3K1eu5Cvf/fbb74orrvj3v/+Nw3UgyAc/+EEO4Pj4xz/O6Ve0DnsYPBHrHLVOAvGY7Y0E0QPRA9EDKQ9M6uhU9CfFutOdVk8fef/ORx555N13XfqBD3x3zr0Je45akinhg8Hq5Ou/5CJaeko3DquK/f1Jj2sm5xn/98ry/Ycf/71vvOf73//qp77yf1de/dhKJ6nyzrtxvAgjnUIeOSl8RCF8g9ETRWiia8IaJCKdorZkpKSlbBCNwl+oEzJe9m+11VYhMoTDqxGYdm277bZvfetbOaj0ySef5HANiL/whS9gzA033HDeeefxMKDjNg4//HCOniQ0E3932WWXhx56KBQb4eiB6IHogY3OAzbMGmBNCIdKQ9YE4PVDsZttcfpVLp/suus2/Obt0qVP8Nu2CxeucFGaqoIjCCcrngvucQcOxnksIQYB6LB4M0zSJrw+ZnLGC7RnOgfK/asGk3vvTR577Ip3HHoIU7a/nn7Oww8vZv6rRNv5L2yv4NDVITxeG0Qfyh+vBHjNAPWOSQjxIEVp9gvA7SwykFQ0XgGhBBEQf6dPn/6sZz1riy22YJsUARdKgiw/s/zAAw/ceOONdiQ0AfrAAw/80Y9+9MQTTzBJB19TRUpjLEYPRA9ED0QPRA9ED0QPRA9ED0QPRA/U8wBziuqFzXrEGws+NY9bL2Zrqj7Zqm2COdmK6snfEFxdz7YJ49e7VydseWSMHoge2Ig88DSPvwofDcZbftuHv+6iW/ceGnB7uvjIQGvd7CXrbXWfj5KItgjhJSar5O2tuYULF5588skrpufdEnzWHY+VlAoO3mabZLvtknOuSHLZ1pZW7cZsoH3MC2lNeMcU3gwBBuCRZig3Ipr17tWNyFfR1OiB6IGUBxgSxzWGrN8htOYYTrzjXeGYDalJ0PzcE0qiZz1fIZyTJB568MHOYfeThNNdEM7OLLoTHIqJi7b9Le6AD47LIO/Iu63XhbL7EdwnH1z+3e9+tyfT6/olk/dvwYm/pWSbrXz8vQZMvqUFCQrcqe5rpliz4c0wRprogeiB6IHogYl5gIEXRnJiAYGAkZ/dKSSOBNIpVPoGhChg8sPfUxcSdkngFCGxI4oEUsGIcCNKNqMKKXqTSZFdN2hUQrsIFDdBsmEG4XxjAgtV5GGYQ5fftuMyDIaLhoCEBnvYBEuiqAgVKhWMQCX0Qo8i2OEFKVGQ6TsakYWqqRINjKiW/ZgBEo3U8gPw5ERGtIeMwGFRoRMMKqBvnBDuyLw1eEJWgZQNmAGGZwEnv+x+dRjh1gWOccR7oQHg5TQROAP8AVhMzOHNdrvjL/m6x+GDBDHyaTW85nzVg7cm4z19LAa9KRVMLr24ywCQ2IwchJBTxezemhDolw+cDbRdieaLBQCl2IwZAKFqCCiSwyjL1XHkYLBQRooGMh2dJi5VmQRoMA9G1KnfZbOX4fbE1zRbraN2bSUpRVroN9lvraBWxhuxaReXNc3wBkhIyJgSTlHsa71pZkME1pcH1LPq8cb9a1cIZHZJYHaDS8saFWppht4Y1zoQag/hBoqedgdgyRf1vGN4uwgYZxnErVjtSlh0hVHFSEqRgbuazDBrSI8chmaz08SOCdRjMbxaQRPGtF+6YMQ53DnhtmBzRWiPqUA4BOOi1zOcSUBsCJslNZGhDSkYM2Ah/pHzmJWqrVeEnqoG9EiTBwAYStTeJv2JZPwZPrOCQU5oTL1iCh+ypGCawGOBmp+qCovmH5AbAr3ZppFaTgZZ3XCrEmAEBpioagCaMa//kKv6+g9rq2Ho9VBYXWUYbCBZsTEQUgIjnMT1Zk5owA49jW1AULNKGskF1KQxZEjWDD23DPYbewSeeh7gyiSNeTFAo7YDpJ7Jqn0SElMLfTVNiEnRj2mM8cLILdNg/DdKA0yXMKmikQGoirx5exgMGc8b2yOxyMQtrBEwONSc2oWWGIx8EYcmhbAowYwiM+7hJ5vsVEoK+fb5m22WHHvi5t/85hvuudXRZrJ8PO56h+4tl5dnchwQ7jo61WrZDH5ULAWfrHONZqTG/Qu9tZci9tckC1mAoRGjgLA2ZUC9YgofSkjBzLcbx1MzBs8Llm2WpwQavWygaASqsqIAEeCZbCbPd4McjoPbikW3UsZHfa5fisn8e1u+9/0fnnrqd/52+uefXEzFEwpInNiu4Rlu1EljKN+0SzU3i9EYENKnYCfRJ2eM70ryahphRGkaxZIiFtKEAHC/aMtFNaUwEluvNoU3yeCBffjNqX9TlNVFKQrtr6apxkijeKtrU5iQTIwpglSRmyvG35RPmiyylcco8SFPejic22TYjS6ZhOVdP/YaTZLPlXMcYeXvVij46byWPBuCsgX98I5bhs63deVbOv0wlm0bHkJgsTxccIdKtGWTTK6Ubc+1l3PLHu9JnrFT8ulPv2rf577qjDN+8pNTb35ysDijyx195b58dWdocRsWBoscyJW0ZN0iLzcmN4Jf3GTAdHd9S6tbrHSGjNxxkGFtmVro/QlcrawVd7RDpudeKBnNIFN7mbloMRHhSlxOrhV+jZWcomRCRmJ8AyN1MsYI0AsBoQouYhaSEZhvy3GAVZsfgBBMW3DgYDHpXVVkwg167gPJt7/zq7e85S1vfMMrnrHL1n/64xk33jLYs7IXfxGTsvyfd8YM5buS9o7MoH8x7Psjm891TekeKgxjQIlBN9EybqFcOeLEBSxX5SKX8yGNcpgyEkvdbR3APKSABB5q7aeNuIh25UqJ+/MqLFvt2C2PRSwRXH7QT2EIRo4UkQsDOYCQJlCAt62CY36dqpUEcmTidn634eijj7788st/9rOfffnLX/7vf//LuVccg8VZG5/5zGde9apXXXTRRUjAk5yQ9eY3v5n3H7vtthtVJ5xwAi8zQntSimIxeiB6IHpgvXuAkFS97FY9coZDGRGFP45B9Ma72JdNBrJE0MrxGTNdJOQ3fMquiv1E+aS7lJSmdj420FfebTYHGL322S986f/7f9+/9OLSggVJK//xSzjJCg477yxv4s6gcKkNYdjGOEw+MqpX4gtIAhREwvMKlheQmn95Xre8TPzigHOKHOZILgyDP7zlglvPAXCJzcU+IcoXfSYp/sWkwrGLJkjy0bY111axx0c3GOQucmBUsNTLQ0kx65eM/GPL9Nz0VflVfYTplpbBUubOx/rfc8wfXv/61//olE9eeumlv/7TBbfew28CL0d+PpkynAwmuc3cEmc7q5x4leiZtJU4OwuaDH8lZ0uu4B2lvvDBlvrRhNOwxAwDEDxKEczmQmQDOAydKWk8INF2eOUZ5WaAyeQNLo8rHGVlGAFIo4qzsSQEpOTTs6eccsr555//1a9+9S9/+QvR9uqrrybHdWecccYb3vAGzqCE+EMf+tArXvGKN73pTYsXL955550JvpyHlVIRi9ED0QPRA+vLAwxoSjIAGCDMKzGlyr6QplLpplY+GvkyQZHfzdNBU8DUMRDzC3v87h1rVkThTLafqSpBo1hInvGMqXvuuedWW+506623zplz79JlbjnLJQgUeFX0Oaqxqp5hAWEaJLwyU9P4z3nJYQJZ4qcAWzuyhOUM62j8VerRr8aG9AZnKnNMh6BpwptzjNEBWX4FmLDJwZvoGII0k2fm6wImdfkk27cy84ufn/Wc5+x00Guev2zZsptvuerhhxPWC/r18ZFfafXrDUVn24h5aOQpwD16VJ583NOIeypI+Y0W1UrmRix0PhhJXmylUIsvjYNdoiTE2EMAmFqC6YiSyi9O4YQlS5YQrIWHTH5TzhOUxV+qSPxE5Nvf/nYulSuvvBJg9913Z1Z71VVXbbfddkTbxx9//J577kEmc94DDzyQeTHOpOs5Ieuvf/2rjJScmEcPRA9ED0QPRA9ED0QPRA9ED0QPPG09wHv/17zmNR/5yEduvvnmv/3tb5xWz7xDU4/GswYtVrMyyhn3n/70pzlsl1VBhPDqc1zOZJHwwx/+MFPUC31i7XHzzTdHGkIaGyAtHAr80Y9+dPvttz/99NOvu+46kKw/Y1szTYAG+/mlHJpw2WWX/fOf/+SlrRjHbIK+kqKxBx100Ete8hIazvqnfgBgTF4j4JXrIYccwnzt0Ucf/d3vfker8QMmGUEDAAPwFbwkmvznP/+ZvgOj+WNNRvMnbQRGwnOf+9y3ve1tmHHuuedec801fJRVk7Eekp5iKkot5y9ffPHFAJttthkTT8yox2J4DMDUj33sYywsw3vTTTdRhUksShtNPUAN2XTTTV/60pfyqwxMilmLrn51Aru0iJ4iGkkAWMjSN67j95MWLFhwzjnn+H0F9RTWwGMq7LxrZqH77LPPptUS2+T1U0NiREUPRA88zTwQ4+/E4i9rqqyRErCq4y9rslxE3bzEXLFi01mzCMqrttmM3wlgIZaxeunSpe7XF90L5STbO/ymQw550csUf/9A/O0v9eSLmVnlGXvs+axVPcOsoHJIBz+8B8DjTd7HRxdTfPSM8TfG36fZcBWbGz3w1PEAIznxl/0q73nPe5gFMAHkt2TAMAexKUOD1jL7IwztuuuubExlBscE8LbbbmtAX7Nq6623hp0qJhHMgAHGNYdi/gs7LwF//vOfz507lyMYXHjyc5ya6kIklLSUHx7eaaedmMCeeeaZTCRDggYwsy1mOjXjr3/lnbQU3DEcz9xjT/zJpxqY1Ft2x1swUSoW2RDFvq9sZkXf4W9/27P3ezFT1z/+9XTib9eUzsJgoSvbsddee/WuHGCLdWubW2Eg/v7qV79q6ezEZoSQox3gRS96EbMwFPGW87577uFskgY2UwUX82vWB7B80cKFz91331e/+tW8Wv39739/9913dzQx9wzlT5069X3vex8Ypp/sRuZ6oO94Fas5YOOOwCGsNjD/5QeN2Mw8SfPf0FpglJIANP992ctexgoAD0Xs4MKZKeLGRXy49957v+AFL2Di/J///IfZNK+zx7v401jFOqulp2rqkq9qVkVk9ED0wHg9wMDIPaXlWWC+6WAgYtBgNCaSEilAkiwMudHKj1cgQ10UGXsJXkggNEggEqDROMagFG5hNV4FDlgILAQCclRTqxEgHAeohUwfxUBgloAkwYhSF53a2ljApJaVW36XToq0BG0sph2zFX1gJOjAi3Y1jRCGWCiZroKp5qUKPAlenEbuDSEkOvsxRlowGEtovgJ0UuKjnuLg0GB7obVQai2Wi8NFt3N5aKgLdr4Mdk0uuxXXUplPrTpKGT7lwtdsaytlM0MtTIYzbvNSMWkhbPAdFobxZZZ3/KbY394+xUkouj1OfE7EvnHtW6Ir3DY333cQ8A2Y+wKsxL4xtzk9l8m15lv5bsjMVkzvX9VTLhQLfHBbLOpJBlYJkUPUcNpOAvZNcOGMRJPhgoyoKic4w4JEFeokB0C9jxBI1MtUAdMQammyKMEIL4z8zxWL8TIGArUCY/A8omSGuKhVghEyPIbl5AKwgVo0Yip4ABL2jzBVVKuIQITAgl4MUNtBwitGtRfY2NcKgFLJVIukEcmyRwCwiuSmFD8AY5WS4UVDLXhgclUZLxqBVRQgGDyUGCOW6paGGLHIAC5NAVLUKJf5o41wl3GavoomM/Kb2WnKWPYesL6o9kdYFV4JBlezGEYXGMWUkIB3tWdaI+OKE6xLL6A32Y2A4BpvRNZkHV+UJkkXxFy9usKBzVQJAW+NBdPUC0JxPj3zlPsaOwHnhgNHY2JqJ0A/psw1JLDrphk5EI+Xfrz+bMaMapomtRBHx9tf1boaY/APg0Iz9kA2Lmc21luvVk8Y9WpTeIjHNaKN1/7m6ZtxYMr4iRWbNwn5EE8q/XibMAF7xqsi0kcPbLAeYJQY13glehufDajXQOjHdb/Xk9MAP96BTkN0k1wY3ySlLBwXcdgofVZdGEiOP/7IRYvu8t/zuvpU7yCf+CstNXVVIyWhGh9qnzA8SWJDe8YVf0PGSYJpMjcNwnEsVwdPK3wvRvHss5MPf/iOU09953vefpqpXjfGpy4S014PgL75jpvU+zfG33p99JTHs4hmbWQBjjVTf2exwOP+Za2NFF6l0JPC0QwCMNyAyOFeRAi3GxgeM2HsanULpnxYS14ablvRu6It38qa9VY7lg877LD9XzydF8af//wXHnggmTp1Zjdrf8NLR1bDEra3oqiv3x041T80yDovS94s/GnJUmZPmTKFRXNWDKGBmCuZZWWWIEF6253xWm2kCgy2QSmMisihSC1tp4qkhWMAVhxQB0CtyLBBy6yiwRIW2Wmy7mXIUIcK5TLA+aulpbdvgIPCEEIVzYeyr/9J1jk55wtXPfbY8M9/fvqc22eyEfzHP/4x76HPOfO6hx9+gjEN3uEWt0SLEcB5ziLxTwtoQQgtBaB2Svc01OENDAMAAwf/83ac4tDwgBoCBhpaxCo3sOuZkZ9EkM1DQ6xgN5Wg9xJce/E/ftBVARLDgFWUtVYMRdMcirwtDpEhDJdEYTB4Tt/gauFNLfDtt9/+rne966c//eldd90FzR133MHLV/A4kN+Puv7663/5y18ied9992VnNm8jZEwoPMLRA9ED0QNPHw/suO12RIfepaWeoZ4tt9zyve991UGv2YQzFE466aQLL1w1bcpm06Z1Zkt+Id6/4OmcNo1hk7GdMEcoVPwlBCPEO82NyVOnuljmD1WsOBICxnyN2EIRkYlB4tJxFWCoQiaxg3hKERbk+HDkABehRlJFrg+dsHBgR5t/gUj44AkBsRVd/hUXr6Ip8iYaaxn2nUwvuXOKe0lc8O0iAoKEjbzFHd01dUp339///u+V8/59xBEf/MUvPnneeef985xice4DK/ozm3Rt2pu432jih5owdeQALGfUiIFOqNfjQiEJI121TxhAGimNTiThNeTEgOYjmulKsWAYrli+fDk2hzbgUqI5GOiNl+LnP//5Sy+9lM+0gE877TSC76mnnko4/uIXv8j2Ne38e8c73vGLX/yCUznYk8cONg7jYGtUKDzC0QPRA9EDG50HbCQEMFgjpMsZLZtoEozMQdkhM3t25x577LHjjtvzFSXbph99dBnnPU3p9kdZIkfHNpX1Eak7E5ONNQ49oajBTh2GeoQQxZgXy0zFAqIk0QoCIScmX7xN5ugNnw2KpSKrd0xY77zzgeUrHmI37Wtf+1p2ol9y6QPs4yoUk8727NCQe9Kol7Q9ZQLRlMaqvXJFPfkN8GJvQECVuzZGes0aricEHmB4XFGotaoG0l72spfdd999+sabaHvwwQffcMMN+Ir1BJ7iiOPw7rPPPmzlZy7MedA8ifGYN2/evAYyY1X0QPRA9ED0QPRA9ED0QPRA9ED0wNPHA0xP+ADyO9/5Dh9hMr/gGClW+ZiM8GKxGSfAzuLhj370I1608V0rn/J+7nOf00ykGXbRfP3rX+cLZGa4HCPFp5jMcTiigklTM/MyyDh7i/N/eb/5nOc8h9/CYX7ES0/7gKqBGSx+8ooQ9mOPPRbtCOGTGx3i34DLqtDIbJrzhb/3ve/hsUMPPZRpHWdkGEEzAB8eH3XUUTvssAPL7EzPWVZduHBhM4zQMO/js2d+4GfmzJmc7sE5UF/60pdYTwgXnFOirAq/YTN68djznvc8GjJ79mwOUMaMFEvjIsa/8IUvhIaZO5Yz8UQUnhHXmD14xBFHcP4XSrHh+OOP512tXt83VkotCxdcJC9+8Ys5PIUrkFOoeElxwQUXpBhTVxFFEeAH7OTb6WOOOUZrIBRZ9k+xNy6yuP3Od74Tz+P/bbbZhsuAHuRCYj9AY8ZYGz0QPRA9gAdi/J1w/GWYZdBmFbQ6/mqc5xmGLVMsgc6YMaPkv3vly1NeGbsDsHzC+Yq/2+6o+NtK/J23yMVfoglxratzU47k6OhoYymVwArx/AUPiZc8xt8Yf+1iiED0QPTARucBIgiP8cx/mT0RNZgFs5uF96G8LR2zLYQPQgnBhQDE3JMYwds3fn51TMYUwTe/+U1mkSD3339/3tYxjSLWMIcac/YEC5M45k1vfetbMR52tDMNt1leSlGqiHyaz0YdftCO+TITWM4wan7+y67pevFXijCDqRkNIWcLFBZOaUlwLHN8ETBf3nu3XbF/IMsPBOdmbbvN57/weSioJbYeeOCBm0yZATE/nKQZPb2z1P9cnxqIZHqNn8LlzClepDIRY/7L/CvVzFQRj2EDW7lYppg+YwYzUKautIUHA7p+cOT9eIqrXpH5I8sm1PK0gPc4BA3/Mw1Ei7HQ8JpdSSuYffNLvVCyCDMZ81+zQQCWCEA1faf5L486IHkNPYH573vf+17czioE3cd+MHotbHhKeyxGD0QPRA+YBxiOmMQx9pIMmQJsyArxIBlw4IKdXTQkAJAafBh+RQyNcVHLIExSVCInwQixyCgqNIQsIQyvioiCWLmEGFkIQECqxoCUUgZeNR8zkEPQZ8aKFtpFeCKFvAZjLYzkJORADOBVjerylav9lDIhj1Quu0+3aAf/01iSvtQZ5TQ1I4AkK8dI7WxGviwHU9GeyyCfgE4OK/R4CPkSI3Xskna6RxK8CLHOgpfawuAAvCMko/8KSQ49jAAk63ejI6gBUwUZeajOaMCbBNng/cA2M/ezwSS4oCEZSwggGb0kekH01OIEcooAQqbYVSSHseIxb4aaj0aSrb0jIdSYghEiG3TNU4RXV4u0pOjXSjGUDKyicuSHLTIkeDUE92KhXQzgDRbeWKA3yQBiF1KwUUom9wsYhFgbRaaiabHaCDz1PKDeV07vhxeDXS0beKuxOTQ1hOtZXolw9aqfenh8RBqzXdCE7muSZUyxIUEzMmvSp2wLacaEq5WGzRyTff0SVBu/fu2pqT00csK+NSEG1NQVkRupBxp068SumZTAVLGBl0TZJD1kEzOvgQGx6mnlAS4h0phNhkZXGo9imgo2YDGBzB94WIdevCYkxVtNLwLDp+hNGvKpqic2xVWzWK0idUPp0TOFTIkyIeEsJUUTFo0eJE3QbCckSMGiJ0/JD+WkWKyYy7olmGzLg8Xh0hvfmbzuTV3HfOque66v1AcSmBXTxUzCWU1oalQRr9kfiDLlDqjGNyMfh4eMBhuwmo7JLDTQWLOqJlLNqVklJLdJsTzY1TlluEAv8y28X8opap2LytKyh5Of/2jFT3673SvelPz3X2owJ45XNuRNpgNGZde0f7S6CmqevgFl41uvSmcFkRKYKtbjAi/KJukhm5h5DQyIVWvFA+Eyq5YvCUP013DiFvL4FQE+1g0DGfThKifdCgaCoYFBf1UkhaF+XiL29/XkeG/JrxmU2jnyP5tbwVrocDlhTbc16X35q3d65wde29nZ/rOf/Psvf7m9OMQRFB3Dg9neoX7PxBJmwgFNy1f0c0ezLIg9pWypuGqY/cr8xgIGWNuBeeUJmY357FiChiLxggQlL0rFxbqhyFg6xEbGZJFRS0MQRQKJNIooJbEqTa6rl7VLuGgsBDKAKgA8IBrl2IMcYCgxvq9vkF8FgK7oxHDweLJs2UrPJRl4B1wyXEjO/M8T5577zfe/73kf+tCHDjv4dX/84x9P+8vVD/cMFpPBjgw/0tCHHHhk2+Yzt1rVMzTQXxoeYvk7r8M+ULqqd2Uuu9qhZlQVS+5HCdQumszLzry33B04kmTaWwf6+wdyHGLvH3BYfNVfxT7/z6jHQ2ywlInf0E4lOSrQBWBuBM8rahKvmbU/Hho2DYPnO3C8yjo+7qWDYHnwwQdxu/SYq6HnLfg//vEP4dmuTUfzs0v8XMa73/1ukGxeJ2cXO98G81KWnyZmezdI9rTpG2Mxbvg5HsBIOXPDtzZaGD0QPbC2PDCRu77M821lwM76X4bIFrbPFucWklIuIQQ/wnCSTWZBMX/hfIbxLWcmB++X+9Kn38TP5/z6J9f+7W/XPTnUOZy0tHe2L1vZkyTLfVsgd8dFrexZTtweGhooJkViGUM3u3wIJZ7GBUGmrYyu4QFYNMFFnCBGM4ZDI4wOwIKRcRs8o/pwaYjY4QU2lfH2loA7MDRA5IKRT25GfqHF2VMsl7AQG/r6+nk5rjBEjjqTniuVnT1MHkhl15b+lq6+fObvV5YvuOXXh77ugA996NiXPO/Kf/971d/+tHj5qhWFvPvB4IGBPXjQGM4/Cv0KFyGTntZSedPOwSfckw8nlriZ4erv+cCDsUTREkiDDQCpwd8wDYCaEmrSI5NEhNVpmEbDD1fSU894xjOIpIYE4KlJp0kCSwvswNCfeOKJoiS2AugwSn5AkmuDfXt8UcbpG1deeeVWW231spe9jGhOHI8HYMljMY8eiB54CnggHBLdwKhBOxjO2Y+UamYmcfty2BlUKCdbb5mZNWuLffd7IXJuvplPPufxzWlba9KSSwYH+J089xN2I+yVvbzND/UjjKv9S7BjhoUQoi0prKOKYgoZEqxtmJiLp0bDvXOfD8EoemBe6eGHr9l771nsJn/jm2dyutOcORwcXeLUZxLh2jnG/e8kENMzSVtS5ltTF389krypz7Y9/WiGqNHCWoJolRLyAMiZ/LJlyia2YPhulpxYySZvJr/ASnQHKxKCuRhkHgC7vS2YIg0WTn+mliMpCdk89sDCPmw2YbO+wcdarD+wFsHPGlfkxn+iB6IHogeiB6IHogeiB6IHogeiB57eHmCOwIe7TBaYNTCn4GNgJil81tvMkiy8zEpYP3zmM5/Jizbk7LfffnwPPN4DsHbffXfmKUxRAWBnoZgFXszQvGnM/uGcfd4qssC74447YgnLj6xGwmXzr3oS9MaQrzdpO8QI2XvvvZv5AJhWQ69ZMwuhwNiPFr5iHe8BWHx8y2e9TAB33nln1lfnz5/P19d6f13PbMMzASTBjnaWcGk7Lz0bn5+FqcYOwAwUm1kEoLuZdI/JHvIKxud0FjB+gB2XqlhNWY1h0rrLLrvoemNuS/NZDQEpI3FyNYth1MUcuMaWAGa7fAKNBK4c1pyNBiDVXlWBpL1IgIXztu69916Mx3vj/QAY7TDSd6x74we0c5aWzdxDM9YujP1yDlcg0381R0ADp6lKvORyTjW9qqrxa7cJUVr0QPQAHojxd5LiLyNY3n3om+TaO/K5PGGFjVhPPrnS/9hDZa2Vdd+99n/R9K23feSJZcTfnXbb/cGFD3d1tTGGr1q1kj/WmXv7Vtx2+/0823A26A47bqcDsDQ8Wvwl5C1atCjG3xh/G8RNVdWLv1YbjoqhNMXlsDbC0QPRA2vuASZBzGGZP/I4zRyE3cI80rNbprFkux/hhQt2MMQywgS/BteYN1XLJEL7bTSHIpRAwESGmJWirC7y/LDnnnsy7+AncFDNJO7OO+/UuBGOHtWMYGgvicDH3IG3hwghhP3rX/9iPlWT3pBIprHwgsFm4NT8FwISUbZUKD7+5FLiIxMuMNA7fIv7PUWUEn9nP3ffxbx9LQ12tnZuv8tOM7fcQosPPSsIv73ZJNezasUDDzxA/MXPnJJ8/oUXSghKib+YyiyMXUw4EAK6kvkvKqg1a6sBFEPAT6YzcWP1gOk/jLSdyeDj4zwAix7X1jIAtldxeDfCuYRCA0I4NIb+Zb0FYuawHNM5GfPfUF0I2/wXB2q5hhPMxzv/ZcGEFRtuFi4eriL6FGnkoaJJha2juRS1GKVrzJQaAZhUFZ1CT4EUXhezMRrgrtEqXquNQPRA9MC4PMANxb1GrhuQMZABEAnCmCjVkpMMCQ13K2MXwZEATdGqRCYMMIBg3dcmhCJjlD42IXwA2IIhklODAFxKKS0ghQFg2DG4Ql31j7EDoAK9KLWhEjsRQqOIv+wOwiEALE4qpIa8YsdmPIYEElxoU0s1jrlmj7hFAPVJYbglyxdafQPDuVJfwhR4kM+6ysWWYraj3R22xVZqhPNDg6Ukn0la+PqKz21hd98rJfQXsZtwDwkOpwXZtrbOjo5uZsf0hnoES2gXxmj518f3yuorcmggVfACkxBULrea6yha8mSOsWYcQTVVSFAOABlIsRsvRVaV8SHOFDKUbxIASEigL3CmEUs+VcYFoCK6dNnAAiCfUwtAsabNoRDJCeVLLLklapGGopQ0CMQOAI0SZBBbwga1QsSmOlU0/HgBFOmqgxGYTpcEMwMMeBOrJmAqBoQ21MOrIRJu8oVEBXjlarsJ5EKqVgGxEQTwuns4QWlME/ZA2HfNC4GrecbmKZs3YM0pm7Rqtbdca641SogeiB6IHogeiB6IHogeWHMPME9gKtj4aUZTBWhs2mD0BoSWjJdevJIfypkMWHOhmmZXq8M51cjGGOSPKdwIZExjgala+VbIE044gp1Y/zjtiWkzkxWrfRo8yqSZmGkcraiCoNEUjppm6EUW2lMlMiIqR27rxBbOwdIKTjkpZrLJ73935qvevO33v3/wc/51Dp5SVXRZ9ED0QLUHhgv6fNTVsAWZRcNCkSVd1ijz/D842MYZTjokQryckj9kv7HDUpc/t57FWvdR6kjyS7dwu+Ol8qV8ISlwhhNCWRg75OW7ffCId7Hx9O///uZf/jL3oQVJZz6Zvkk3elkU46isyqpjmQVxd4/bIqnGQ3Iw4diICgZVJeFZBFSRRTFZpJVQYC3OArCITI5GEgAE5HApr4gL/pFkBGqtTZJBAiBBhIoItN1kgocGlkLJaaFIBkbLlGBItlrIy278wwfSv/jtjZdccuOnj3jH4Ycf/rx9DzjnnHP+dcFdT/IB9cAwR5awkow8GFFNAhgx0y0yetUVvJPuD7PwhO4cMbVCK6RaCKT/EIBA3rTb6VdibJBLDgQAvlEOUEeAlDoAqkQpssMOO0y/1kv0x/+8NAV41rOeddJJJ+E0iFlTZh8zVRxZxU85cTVqL7gsofazn/0sb8e1X5w3r3Pnzr3//vupXbBggVwBzA9CYglFfoyIjd28Hdd+AgnZKHI5TY7dKAyORkYPRA9Mngc0IDSWXxlpy5WXTzz0jhwLNcrHmP+Kl+x69CdfxKh4/eV/+9a37nnkyWQGm4wyubZ8G/t4/LxJEtzbJuIUW4c58JKBulhwL0SJL254d48HJJevjvFYRwHN6APBCI3DcMqi41QC9pIc+XiS39gMi9v1wpOFO+DLJSefYVMjZ+VBwlf4ulF7UOdoRMErvKyzpzzoCHp6kz//7aqzz77qa5/b6+1vf/sLn7vgj388809Xu8NLWp58cnhgxYg8Z7BXNSqWLiDpMWCEzEVGkkwyZE1AZOQ1ayeGlEy6lS4g+B533HFsV0KUtNCtvGvnFxQBeKdus+b//ve/H/3oR2FJKeXn7nE4256I3YcccginT7J3Cq577rlHrUba61//ergUuPmYja3kJjYlLRajB6IHogc2Xg9Uxmp/hpNGbQUD4f2602h0UDN337Vz//33Y3icM2fOTTfdt3w5WzpcDbFo6tSulSt7PVzhqsgX5xrkFqcAJFPxyKKSAWugZOKsRDziP35oZ8d0klx7w+I5cy54wQE78Pu0BxxQvuCCCxbO98JXD4w0xEKtf9qprAGO14615eTGejGVj6v5PjxFRog86KCDCKaEYKowhnTLLbecffbZhE7gkP5Xv/qVrWBwbDT7trUUwP5pkUH/v//7v8aiKXCMv+aQCEQPRA9ED0QPRA9ED0QPrF8P2NP4+jUjan86e4C3gY888giv0vh6lmnpTTfdxLQCoPGSrE1MmFzwczXXX3895yjhRs4SsqPzm/cq851DDz2Ul4DsuuHjYVQjVgaYomppqiKHndeI/OYNh0BxaDLG0yjoG8xqxcuaJ/MpbOZXczi8iReFl112maqq1YUYSSZX83EgK+owXnHFFSFZMzBfzOIxXoizlErb+ZyYL3g1GRyTnWVYvrn9/+zdB4BlRZUw4Nev40SigAQToGAgqJhACaIYwJzddV3E+Lurrrq76qqYdc1h1TWsOWKOGBAYEBCJoiISlDBDhskznV7/X93TXXPnve7Xr3sCM8MtmppzT51z6tSpunVu1b11HrX9khCWCy64gAWsNNs0nMxoYNDYDT777LMf+chHMr5d3AiHPW29ZQLhlZ/whCfAYCctr0/LNFPBdpgvuuiiAw44QOQ1NtR3XvJO2+8hTSukv//970cddZRKmULbAdOaDhcJqmYo4ca89XB02SVRU+k5Ff66667Tau+s1ashJDuJnTclpuLacLy+i1bEbRKXMJ30u9qDdyriKA0lyzJzpRuufyWhskBlgbBA5X83hf81cZmHw5X4ZIhfcylECJv7fKg89ibzv1dxIrfeuvxXv/qVnzs07/kSyclkW7X8hVecmb3J//Kklf+t/G8eHk1AdqwBNPnfSZEhwTBGbACjaeJqqqK6rCxQWWBGFrAGNL3zApZg5nY/nmoV47m6QyFY/vCHP/gdGj8843Op0047babRr1SERSBgADXi+xxw6+c3k6pkcli0aJHwu5Yh1KA/Ro2KJfCkLBmpmaYUXMIWi/3EDpbSubQNELOQfKr1ryKJQShjlcT/WpXDqE4CZOGWXYT46FdDLADjgyIEFqQ/+tGP5IpiVWhfgn0yIzm33XKLCFbWy1bf3oRecsklPHL0ndJM2QTk2gfmztVqOx52DyC1PUJZNdG3v7Tue+pTn4qG5X2HHJ92M340s40aWBhck8Xt8kmVft88699oDmszlOcZDedZPBR9+9vfbt/S1lI7P4au5yL9ax+AnHgf3Uq5qTHaoupWa1Mp30fg3PWhDyNAtnJNqm106KRFFbKyQGWBGVnAfRf05VuykzsRDV73e5ZQrjeQaKTAZ8DNDuMyeFGKkcSz8C/BVdakLLMVJgSLXGotDUxIayWwPpLSl9W+qZ74wpZKuLDQR044jNTEHpWiAZAQQpBFjUFczuFd1h2JLL64dgJo+fIxe628Kgk9PSNy0yY1uuvJI6saPYFRUTRBKVVh4hPtJHAiIQhiefFbyzUPHoSHDsHO7FFF0dhktOCO6jjKxDux2w8pldkDEzl8FGUAnrRAZhoATDi4cAqhSRBEaYaxBwwITeQZmckykDqvsIYcJXzUHiwZE0h5uepAYkEMliatCAs8UXl45NqjOowBhP8KmD4aC55UJvxGSaqmmIGXtC/pDxmYyNUVmLjpNESjpKxDxiuS4GEQZHYACdlQASODDyGFvCTQEMpwFMnLdY3DU96pmakCtnoLlPt9q29M2wbMOIBCW2l30sI8XAB5Zmljiw2hD7FZQmstFGhT2krfhIl5f6rZP0sOINeV8U3SWi+DsnP6VgntMZwKgnAM7Sk3Q+m0g6HJDptB7XCTTfW2miIIsj4d0meyDLRKDkwQZLIMTEqvNDTxdJJVmpRyw5HRZXlgtxdIsQ7tGXLaN3PSurL8WfBOKrBCVhbYxiyQbw3AtFOutm8IfZguS2i1ZIdTRytjYLZ2/2tZpyHDtcbxr5iz4w57PvW1n3e57GZL3OZjwFNZYCPipx0MTf24qZ2LpuX5vH0zQ7GsT5OerbxBkMky0EoZmEnpJ077F4fSG3cX6Obtb732Jz951utP/OX7Txwe6xodHL5hTvd63z1MJX/W+OiyDm8irejQnuVWz0i3LH9ak85IbEW8DVvAUCnu3NjqHKv19KdRNGZbze6RvxG/pleEcrJpWNDYqhM4P0XIGqunvakuoej87F732CpW6moM17tqK2opttTdd6wdeuh9n/q0x3lVeeZZ5779HW+59PLamtFab21hY6T75qVr+nrndPXdMrQGV1FZl+hZaXuRAjSAUj314i4rD+mpVnxJm2KzLLiQxaQUyMjJD3ymhEFfTrhcBkFIiBwlgHZBHIzIJE/7UQq2W903kHbYkUHaGCQshQVrpE+07b7S0c58T+/cMc5neDhMf8XVtf96y7ce9tBFT3vyY1/+khOe/pQb/C7xaYv+vPjm9GX39rXaUswrbh4YXjHPDzqMJUON1MYaY6O1xmhfd//Q6BBMkbpGh0f6enrXDI6IDagyHaQ/e7p6u8aSAsyc4qSM6ahad7JxughOOc+XRK+fNCoQmi/lwtxGK9nAJ1tMECP79Kc/7delkEGyipfuvh74+te//oEPfMCPNiNgIkd2ESjybhim/Gu6tu99XgbpOzY/l+THoJz19V0ajO3R3Asu1U6IjXjwVhf9is4TQwVYpcoClQUqC3RigWIq7koz/667D4/Vl8TcnK7Har3zbjzu6F2PPe6Zzvlc9ufB//iPdy29qc6XCAO5slFb07eK4zfde+0WNfWOpgCRZtH05mlU1p0CWG7KZNIzb6eoVAGkf8dTrtZ1UmU0KQleh4+nkeI645UngRPSxp9YMg8gHjUAY2Jo9dW6PHzM8+5xbaNv7WDtvZ/54//94KYXPPshb/nwJ19ywWknnXTShade77HmokJCT7+gnrUFYzv0D85bNZxcVb02RF56XJl4q10QJh0ixWXOITO86YCwRuSf/OQnv/e97zXVdfrpp/O/v/3tb8t4uuVWBK9Lzpq39R3bbbfd5mNB580cuAqkcRLN8a05z66LwnG7JBaNRwLvKctVVHBlgcoClQW2IgvETEjh8qJmUv3Nh4VzGuvvqa218KrV/KTu3nsvuM9+e/tM+Ybrb/n2t3+zem1ttPAAfp8vpYaIwEPd9YHYb+yqb0x3m5ZhxdtVeVY4NwemjM8EmwuwNLby5HJShZ4A+rpqQ2trp53yt4svuOUBB+z/6COft3T5Zb4z/tvfRofjaSYRJuqxrhWDw1yMqNEpjXHCaaW+ro0FeovIWFtYyabvy32BJ6YzF+xHm8taGj/hQHGh4XYB2gWI75x8c2wg+fI+vtjmcx3BTS1f/x2675KJza68XEUFVxaoLFBZoLLA5rFATOBNdZmuMwZBhrd2oNyWchs3dbui3nKNAZf12dQ6hHw1ltXYPJVu0lrKtt32WrdJTTep8GzPACalqZCVBTa1Baw1vFA7/vjjjzvuOEdMv/nNb9q+yxt6U9VengG8aHv7298uhq/zn2Lix3u3qRgnxb/pTW9yflLRV7/6VdGg4lPYzufPD3/4w7icvbHCAsQqKV7CTlodZNx0VNVSvzCkyY7CCuMlAhdMuXVTSQghiE8++WSwE8jOgp566qlt6CctEjXjrW9964EHHsj4FrliGYdunTRfG23G+gGDRz/60YCf//znlo2s197rhfxQxhvV17zmNS9+8Yudv1V7/BDUpHpOhfQS1jFapd5QW2/OdFubznaJd999d5GkjEP2JCrrP6kRAolSr3lxLLccdhZX301KX9Y8tx1Agh3sD37wgwJgOUR9xhlntB8zZTkBO9otJvU+++zj8he/+AVNJCvxVsqNjjHaBdvyutzqXltc6npALPbDOHIp28T5qGi+vAzQWcNRUtIAiB4MriADo4GXsrSN3qJKYGWBO6EF3HeV/zW9bFz/a+LK257ck1lLiueKeCmZR1qr/+3uHsA7Z07P1VcvyWQAvtKrzDKm8r+V/91Y/tf4NLQ8PHCygGLApgNx5gdH9OOhovK/5buvgisLbLgFfJdi7fD//t//E8bX83yEAfJCzX0dt+SkVeQHY6XgN7/5zY973OPEcvIuzyO0G3ZSrqmQJ554ouBHSi1FLQPjro95YCqWMt6HtSJDCZ3sMx7s+W1gKFmmnBT+zGc+Y/lzyCGHcIU/+9nPLCUmJWtFhp5t1r+aYPqy7uA3Qxl5AFmaHYNXv/rV1r9WoF6PCmhlXcPyWHIssCAWIjlzjQP1uohXb3jDGx772Md6iLKWtALllLE31VJmLHerwN+vf/3rX/7yl/tO+He/+52q+4qPt8v07WE/1ksHNOxgDQ6Ix4w8jbdhpyQuOvueSts3z/o3jKNqgErf+973xvqXK481YBuFm4r0yPvf//5Y/xo52DsftE2iZnSpFt+h3X7r0rnz58SPPKk6P+8R5dIjZSC9QA/h7vRouzzrCUAmZUzWJIZQGMpN4aN0Q4sjjg0WQjJlBVQWqCzQiQXyPeVuldx0Dnq4Vd1NnGbcg3FngYMmxMYdij3fpwCeJWiU4pJCvjtdCkpFkJJSooJGkRvZxBtIVQdeHloh4MeDUY4x8pApj4RYRTacMXK7WZ/YaguazBu1QwKiiA62oDWfHHlUh5dN1A4ZlaKXwLhMQQEgcyknBG/QyOmDUYoqENdrI6kKJ4GGh9eubdAWSzErrkG2elWKbxU/SxwyIcnRIlyspzTkp1OWEy+pAcjQR68RSDoMtZMCjfS3du1QCAzbFrzj0aaK56u0o4iRBGRqzAqH8FAjtysDSlWBRQJE10cpTGgll4oak92k0CHXohSxdpFgPmdwKQSGcMrkGgEhJDABx7sDfiEu5dquObkVwdUqJOqVRwqCqI5WAKLKXGVYEYLUtmLgKXLJmIGUl4dNGzllmZ3DMSSYy5DId1ywx6U8xgwg8FQC05YyZX3gEcBTOIDMEoC6AGlkFoAcjBKQJROSfTFYaq2ijAnGKq8ssA1YIN0Md6pkQSilWaQ0P+bpgCkyHAAPZ0bI00obWxFIMgJAFrL56VUdTeNF6OMyT7WhW5NKWdUA+FE+ySxZtk9mCRo5g5h2LbMVsY/5elL6MuPs6GO+VgVRUUVrRfShgCcS+qB0Ga2I7sg6hATskThOZIhdystkk8LIZtG/DNVm/JAZdQHYh3wNCT0zvqxMpkfsCSP00b/WsTDR9jJ9K4yFkGnHM4Pk/kI/o/7d6PQGZDydyClmSGiF7otR0dTG3JUBeLQKIOOb6OMyDEssmeRPZZ/AEwVgn4ULF2LvvL3oEXtGjFE9qSaEhzLuX5MPy+vfaPik9BmJS2NJ1oTO+0stLDlVe7NwALH0oR4WtYSSZYIyjIxY9Cjja5L29IQbz/THVZZTwduYBdxiksFQHg9GS25mhgOo/G+2DCBsIo/5Zwv0v/Yx6Hnv/Ws+5vvSlz9ts72rtqC/r7+nb617nObR9XJ3euV/y51bhhlqk/vfkTHjZ/mKpeqNESU3XUcf9XfvuWz17Zf/6Xpf5j3/H55x6qnfOO/0mi3+FcvTctotrPt0aFnnkBOYcSH9/TwRuFzUxBKXMRsQSyb5U/mjwBMY47/yv5MaM6xd+d+pjLMZ8IZo15igSPZA0/NbUWPsrtrSatjfatIBjQWAm0WUJayr164ZbNRMj+kUq7gSo7V73aXn8Y9//POe97i73OUuvz7lx1/+8pfPOfcWu162yHpHawM9A8MjwykyU0r+8VQ6XkP5VlJm66+p6nzpHowpOmMCKLgmxBUoMlM1LWlSZFDFDZ5zQMBZeMYAyDcJGMBS3OkUi5SrCPYQXuwTpjZnxVwwdHe95gj16b+7/szffeng/esnnHD86173uuOefKN3tF/44g9uXpXiUoUCpI3URnpqwo4xUDJRrqioIlnGJqNgIE4Z+y2IFCSreEFY/MvYiQuLSuOXIqznQrf2ebTC8i1O6gaxmR/gR4n9jlPAaochP8ziFW9ZLF7neP10Uv4oLV4hl2kyrEYyPVZpzgMf+EBjzBfPHukR+PqcRyDHozhLopRLVh9KcUlZzpYPhG23fD0rDSsLVBbYAi3gi5wIrmFyXDhQe+5zn/T8fzrEZHj2Oad/5jNfOfO3tXn9tfl94++o6M9JRCvyzBOA3AQeQam2tGZ6WMgqdSWfKa3n/vh5+udU+NVxDq5hfS85ji//s2zZyI03LXnHX3/gdwhf+9wn+spq7T83fvzjH7//d39afDWfUuvqSc85REW93Gh9bPxde1lOSYGEzvXCl8k2Eaw6FYX780zSGg7SN0xSxqMMlx36ZCXJsU0NGe7+YQ97GC/vvFkgDzroIL8rtWjRIlWgSU+DJc8bNJuogZXYygKVBSoLbE4L5FkxKl3nh0pKmNx5I9Phgjm1Aw/c94AD763w2uv+9tOf/vq6JbXe+nj0K0gOZKyRltXWXyTztknMhHfI/iIhN2oK17BRRW6oMI1Oi+HCAB47brttxW9/e67f9T3sUfs64mtR6SzTHy65MYV1Kh5YWMt6cM3q8S/VNp2tNrRhqVGpVU3LW47SR95SfELdWgtfzHsi45dDQnjY/fbbT2NZBtLeo8Nj3kDFT09vgd3a2q4KU1mgskBlgTuPBWL2tguatyJjos4+Ky6RZQzjuCwWmOPuY/ObizIUoEZ5VTitGqFzkEXDp2XZQIIwVF51ZmkMHlvEGbNJAQ3PznrzNHyTNmdS4TEkFE01kifl2mKR0U3lm27TqaouFUWullxpxmy6qknenC3tvCExnNI248SSp3PeinIDLcDm9jAtMZyEPP/8860jRNQ1i8Yqo73wcn959eYEr040AerQmIfbs+dS9A4eO4cZXiMkKIUvV5HpM4AAjN7RxPgtHBgTviVhpmkPkI/dy0d7vCJn+b6RBAbJ9UYVUwlRKrGV3/UJ/5g/2pyKpQmPnbYCb0kkmFG5D8gmskkvKcnOPjEVPVnsqt12283bVRh4EnITJuVtQn72s58NjNqbitpcRhXs9oUvfAEZS2KP7uuwdktX4+3Xv/51psee4ansEARspa8N3dNPP11oEki1576birfcHAbH4pX6Oeeco1472E7ElQnawKGDrhe47U9/+hPAQdyodEY2bFPFpEXqVQtt7cM78W5LZMcddzSAtUVik4MPPthd7AeitOvyy69weC3LYa7QkBDE8C4ll96bR4sgo0grAuPSsbJ73eueOnpzPkpltSugssC2agG3WOV/N6n/DX9klpt0CMG3+l+UaVqcgiXL0XeV/63878b1v+VRF47YGPOY5As3bl2EHW8KKi+c78EKqCywIRaIOdwkZv1lFWAJ4A2dT5o9z3McbSS7T+PxWO4+tXa2/gXHnhigfCO3kRNChGH6yEc+EjVilEJClLZhV2RysPz51re+lcmwZ7g9QFuVar7QUfHbtLF2DgXa8ypNirZd/9K/vRkJ8QmxBeCvfvUrlNoCA+ik4Wg8O1kH2buwebHLbrvddMMNvlmnUifsKkIm6b6PfexjAIyQLgOIHKZ98unUpz71KTQmavp0XjsW7bWCE/VM7VFLue1TKRDE7de/pLVhjyJyyutf/kX8r1Bj2jx0KK9/bX1Azqj509bSShD1qouqe919TyPfh9/2QPSa5S19/C6ycNgCaotn7bZyL9vVCTl5YU4I+iwcFzgGapPRojkc7hFHHKEWH5wPrhnqHkhr6tAkC6mAygKVBaa1gLtGMne56eT2nUyA+YEW4C6T4J3ykJC5VbFEHrxK3exynituZE7EZa49C1FFRgaAnkySXebSmAGCQBGxphebgVEvPCDymB8QUEAypSDLVZtqNEECQBIrqQV7MKrdJfUw8hfg0N/kEwQoJfJDAji0kmcJikxrGKWogjRwUIb8sGEg/eI9ZO9YighGYzuu2CkpbhIdamMpWGdjNAVUQab5NFcvlTSwt5H8KUb5WDp15HeQk3dWr133oq1D0fy0S1iwh0qFGdfCBG/oZpYNgAQCI00Updrh05goepYRclEZQOZSA6knjzbCUBhjqCSPoihVUZYAExrKVecylAmYbSVyclFmBKhaUdSLIDTh9BXh0mQygwYQkjM7ejBkEAAkmPBKkYcQBIrQUzu4QkjAwc44WNSi1RqrCAt66gV7rnejACRrtVo0Vr2GDUwetKoIGFJpwACXWORUAkhZGTA8nYkNa0d3IIAPgSQoJU0CS7kUe5gIknDjWREJYQpwpKhxPB8/WJjqnSiv/q0ssLVa4E4XAGtr7ahK78oClQUqC1QWqCxQWaCywJZqgZWrbtpu+9or/61v+dpTP/zeK4eWiUKytgjqMv4J+Jaq+J1WL4vqEUFdbE9ZNNe6Gmnp3FWbt2DlqjW329P+3MfXnvDyv/7DC2vnnWmBLvDK7iVLFZtlY8VPnXRVgQtLhqnAO5kF6gI5TKRilyntW9lMa9R6+rrFD2oMFcGREslYT2+9u7e7PlgTISttZQox0W1XFMJ21Mjo0GiNLLfTLjuN7n/v+Y2xtUcdddSznvZQb1V/8oPvfv/7v7lhSW3lYK2vuzant7a60TtcGx4bSa/c6nD1OSOj/SNjK7u71tonS/tjje6GHyawW1WPyFgpj2TPK+nTQYrNL1xoIw+mMpwxrWRtatBipbENV5ZgMy72XhmTQJt3SsFBP7ElJ7gwlezipY1vW6ku7ccmalvhKVRVfUU97f011q5cdGnj96/93MH7/+Apxz3mKU8+7nGPP9wb0+//4Nc33DDSXbu4NiqX0iSWTFVb01O3u79uq7FrbLRufzuVjhbWTKG1utN0mfo9jlvTqlc4srFhcbfIoAxMbKhHntowkRTRuVA77dQH2iXA/qM8Pkcu47FkLpTaxUp47W/aRk+78IWJUnuLhDjYy/kDHvAAvzKt7b729nNbtk39eFQQPO1pT/NhkPfgLgmJLeAsJCxfFlXBlQUqC1QWuGMt0EhBMEaTp51IQ36lp0topeI6PdPygfX0V/ga83Y5ZZ9S61pV6xrqb+ysdLj7lpR3ndY7t3bIg2qHHXaX5zz9X3zUdeqpl332s19dduvC1YPJRa8a7GkMLzT9mq+TC2gsU1etSyCJRm0szcYUk491NdJfAtLVWBcXVfyVgySmB29cCNZL0YhAmZMLh5GuVJPySIUnwesqeQgpvWtOL181eZym8Cwhp4zpCt6iIGQWDnBcMiH4kqyJpIYQkmqvc7Vj9cLwWoxywWhjGfdYX6u0u3Cmtdpd19aWrh2q/fb3C/5w3o8POuhPT3zC3Y968bMOO/5aP3a36LQl111Xq99y6rxVV/u+RpPYq6fW6Btd3Tc6Ek9HUR/fLviklOqNtvhNJpWPd3v6x//+uuClorSr0ZP+LHAmUhGTdOJiun/Dnpkqao88IwEwfGW8rB3XrVAyU4bBY/gdeuihJ5xwwgc+8AEHfh71qEf58szJq5AmOokoWuTEJZllCYHMmLis8soClQUqC9yBFohZl4NLOoS7KubnmAl7GsXCbYxjKootn9JUnj7/Sdem+DQ5Zs+VBChIZfxv8X3Vve62x5577vnAh6SPev9w0ZUXX/yX5UtTVOhEMFFnWtqmKZdTzmssMyU3mC4LTUIzbJ2ueVMF66ek6cTHKOWSqfBlms0FF6vYcfuFjTkRq9ux225d8/3v/WHnHa868KB7PeTBRx948G1+7vjii6+85SY2W7J67coJT5o6USjM1CgbFFxb9FTRSNYsHFDgijbZuki27y5M7VnBH/+VjQwoOqlt+wuZk1MoalMaPB7hLH5jo2BaYqGijzvuuNNOO02gKwH9+d8IG20dzR1bR/sh68lVqbCVBSoLVBaoLHDHWYAXsixKrmncrY+r0jTt55Vv1jQIIg/2XLQZgKx2KNZ5jRTebNpmq/oY3VfIVKUnr+qcDIxdjs2miZVyLJbp0NSznZtuC6QMA+ZBaPceJrbZtTcMbkM+gM2mf1mrWVe62cYGDaOuyI1P37uzWHy9PWv9O2fcnC3tRKuYIowf78u8ImSKPEN2wl7RbCwLmCElA5L94wzqjOau6EfsEpVcSoAOxxtiXU8BLDF5YgwJ7RuIGKXbR7LF7RBUltCesVxKZ3VZSdFBDCMWMI8RWKZpA+NSSnks9HFHzzQGFgUwJtvNJPhUqBSmU6PLiFwWQjqxXm4U4txT+QbsXAJelUaXhZzOeXMryjqEqKzeVECwyC2EHcuhOcpQYCqWwJfVi/EGA7jpppvCkp0ICVG4JGOgzNg5e3s9pyqlrZ12h6R22WUXb72FxrADYOCht930yEc+8pOf/OTDH/7w+973vpdfdgXCHAMrGh7mHVqbvoAamJsObEdFgIDtSDBm2FPr2Hbvvfd8+tOffuqpp8Yb9qkUq/CVBSoLzMICyftW/nfT+N9w0G06hfMyy8mlmPQQmyq9ey+4in3peDPblebYWi2dd42EjHwzpMdXR6Y9OxGlKGbaCapp/i3qGp+EKRCTcOcS0NNcnuV0zptbkXlhQtQ0So+bKFVa+d+N4n/zM6epgH/3KV10xPKlKw455BC/eHHttdcCIlqoomk7qCKoLFBZYFoLmMFiDpfbiIgn+Wm5ygRuRjMwDAl5H6NMMC2MMRwHylnc2liCnZwZTf6Ii0eP8dAGhBBFCDVCzrTKBDEhk65/CVTUvvlo+E2Oj/FVGvW2Z4lSuknJbRfGF0ilZ1bRn7O0mHijRZ0oEDQaHh8+AWakf64iuLQla9KJEdBgYb3sfzvhiioyJXYYlwC/w1Rexmb12gC4JBYz7NuQbfQiu/qjww0hv63Zxbqy+WPb3zCw/j366KPf//73H3744da/l136V0cMsmMNi4XRxLGilRMOEowUewjRnFBYu+DVdb/73c8XbqJPCnLXP6cPDfxGb1QlsLLANm8B96Bkts/v6dxlMNFwu1h2XyUEHoABptZcGjTBrhSjNKnFlJIvJwe9G1YuuW2DRRFGmGCPCTxglUb0K0gJr5RrCRYY8s2WfBavkeWYiuEhJVVEQ6I005CJxiabPOtv53nt2lVFLamuwcGVIyNrHKlKn3L74jqdxYk/Oovf5NzNiiwQQE75MpoftZPmBFCS3Jvay8JsokjiLalRT/JJHQ8hFBZjKKthUuvdDvUkC8AMpVNEtd7u4jecRqm3drixtmukr9sRsYkd++G1iVoYqrWrV5tjSZOUJgWkWEdrzLBDYH4u2G8GN9Y0UhROKqAEUIz+DEhOMEWOAIBGHvqEAdFHkVrgYy0mRwYfqSwBGSG4zPzZ/gC8iNmHWKVoYKSQk3MYpcEYpYoorFtD59zvSgmMqnOODKwonovIyQA8yaEAAgqMaz8hJMyoCBklOSwwCRmPPtQmCtJlFOXaZw2oJRJtJVWH34QMmVGqunG64p+sAz0pI88KuASH/gSG5oEMneWqUERS9JQci8tgxCtBMjgk3hjbLIAgRAUwDk+MQcgqVRbY2i0wzbJqa29eq/7mdCnf2K0EMXHAB2B2MCO3kk2KCRYzyKSlrchNQU9mbp2WujTRS1FXLsrKBN5lAEEf01+myUBmDyCm0c7bOwv6EB7V5dqzPhkgWU955Ir1M8fm0qIrEzQBmklakHWuf5hoRvRY0Adjkw4um1qkv6hUxjcRNElAr6d0roZrb3vi4EXf+XieaX/Nwj606sSe3LMGRu7pJxoO02QQl9nUAaDpRH7IYUCPAuTj7cSYUUXn9kTvWYR8qVXzwJSrjmfB4vZdF5d3KkZ4nUv/zturfzWTVdvILBfRh3r0oX97+0Qr0Ed7KdaePmqhed5nKddbwduMBdy5UpvBYOREYwNw/87o/sLb+fiPKjYufYz8aIKWuoz7N+pqbXjg0QcQ9O6XkNCUZ/YAZjo/z4I+jBPV5dqbtHJJsp7ihjTZ/uljHnPga17z01tuqW23Q23lstrKVRzxenO1ZpJW+d9WS5YxM+2vGEIzHc9q1HcS9uyY1q653jm4ufNqq1fV3vWu8z/84WN+9PU/Xn754oXzdkCjlw1RLIW28bibPFooEID+RTkjf4Sl8r+FSSfPKv87uV22AKw90KyF91jujkgjjbQ3Z7aTmyYNbw/sYBtQMWcWXGkbq5gSa6JfSTttV9txx/kPfehDH//4x9919518o/nNr77vK185+5Ybattv71BKbW5vrd6TfiJ4eKR7ns8rBxamO1dord7ennqXJ7rRsbToiEAYRRVJvtptramdhhLHFEVyl4mr9Nu5MKTFPU7VmPxD55iXAhMSyvgssz0QcwWVkNkKpJUEhpfgXVIJELBL+tAwi6VhGBkBFhaI3Uw6w4iXgXK0OMrb29+tJXNrY3+49JbL//KNn/zkJ4864mFe7z3zWU/227NLl95y1VXfvvSa2hwhxHqSee2kq6t3MG0Ke7vtfz/sEz+SQ+bQ4Mi8nnn0sXcfh7RT1Wk/sz89G4+mHeTRYps7qxrAei6wtPrD6Nb2kzu77rqr19hiVF1xxRV04E9Tt5YoA6YPQNsD8MKAoeK1a6q62CuPoqg3ciaCPOaYY975zndecsklXnU/7GEP8+1vdPFBBx2kdj9gFYyt9ZZFVXBlgcoClQW2fAvwd+FNeISY6Jp0VtqEyZdPecpTnv3s/oMedBenYj796U//6ldLhHpYujR5lNGa3/qZs/3C7b12LLvRzLslAJrGL8sjZZVcguWm+oBz0UYHFi5cOHzr8IgIYDU+vfbHP151+V+v+tSnTn3BP99dHIpnPn1Xh6b++MfV11335RVXpMAoU3bGRtesA4FcankxlV1qBsoyYnTlojAvAoCkIxRJL3nJS17wghecccYZPC8L+PaIL0a2//77H3DAASeddBI4vglAHDJDAjyMvEqVBSoLVBbYiiyw3gy2/hymSEPkrZObLyYf8pCH7LHXXtddd93vf//jK69cecMNtXlz0sK5azQF1po7MHftcPpGZ9yHT4S7ahW1FdlqU6gaG3UsdtttK0877Q/+jnrsvne5y11e8IL9/PTu4Nq+4eE/XHO1j6VS5ZwMA0aXlS2ZMEVPWWKCNpYvUoVUyJ5l03lJKeS0ERFbB494xCMOO+wwn+Hecsst97nPfXCJNI3Luvtud7ubnYEsIVTKik0rPzNWQGWBygKVBSoLbEQL5HnYQt6KO7Z8Y1Ef87+6wo9EkR1Re5gw9mmlmPzt6SIjSlK0EdWL2kMg4VmyWlTticUrM2rwMrloWiAYQ3MSpqXfEILCJEltx2DATsXY7nZpeW734653vSs1NAEmjLwhdWXLlw1FoEtdKdd3tr6FYrHD7zLTt1YaEppUakPfKmGzYbKq6a3B8DCrirZprGqmZGwYrpIRkjeU2rQ9pFF+AxuLnc3DCCEzS+7QMiQYnPFqJsZqh4yzJgsNqc16apd7ERY3ywZaoxOVVKe9uY86Ydk8NLQyqAR4zR26eeqtasm3TBkwFOOywzEZxOYB9jQP6E39KO/cvIiD3fyZNcHeXg3qSUYOMtO+SSmz5KoRZLgNQIJ3kV5c0sT9WH4n3oZLUTRTriJCzIEd1ogXveSuxOWLXynehLavMZfijUqjRnIkcFxmsjYACbkUrPku2SGQ08oJdm1XL5aYxzrkDTI5IbperusDGWIzwVQA9VBGToeAQ+C0mmeZQRnTjk4Pabm0DRBKekdg+CHzHtzID2TWpA37rItyvR5sHP2l+fbbb692g4cRXN7jHvfw9kewjPPPP/+5z3+OjwR81RbVxV2pm3y9Rm0v7u9973vbzL/yyivdOyTrCKL0JgKlWsRZsImDxMg80sxa7YqxskBlgVYLxO0MXwbyLBSzUytXEyZ4K//LDlP5X0WtxoSUTHfZ/1oLhPHhw8iAMmNxOW5+cMyocR1kJuGm3pn0MsTKcym48r9h/GyTNkCYblvyv8ZSLKi12rMQz+sNPre+/Y7b+aEpW/0+ruOU0cg7N1QbG1ZFlQXu5BaIeTiMEPdUGdPeODEFZZrwvy7zvQnIpVMBWYj7Go1bO2OmYmnFY7HnaQLBrrSTekMISu4PO0AKXnI61yH8nRw7rqn8b6vOGRP14o3Fb9ReViAkZ8w6lzmxglZ7KIDXmiVL7hDAa02EmCksf1J8kSJ1yI7WynGdehM+PWPay0G2gevfkK8V7SuatJTy8NFe6ztA52pjzP4Xb16/T1rRxkKGejvssINPvr31tlClAwMa+R7eOM299977Oc95ztVXX239+w8veP6f//znvP6N1hkkuHxDjuWe97yntwZ+UCr2johKA6DYVUaMAOXIUNoOuuCCCyyT1W6QRFvCaBurXZWcygJ3BgvEXRPzbcx7cnccjKLIAe47yZQSN2NYJnjdg7HJ7Gb3Xi/uR5hwZEEZvLF5la0aUweB5IS/UF3UiBJZTKEq9dQNgzKIs4QMwJscvOnL/o4CxMrhI5ETSPjMCMALH1/nxpTrEj4CYIl8i17toRt8qJ0luJSUajtGlCqK5gQNWFGYTl2JuijA4l8BrUIrRTZcGb/eleqKnz3GG+Tynr401/WkwIDjEZ8HR5OXXzvaxbwjjgkV5yiL1uFLReRTHKB2pZK6QofQjacJAJ6SlJfMrpk9SrP+41ylfwgnFn1uNVWjatoqiqrBJAc+uKmBCwypdnBqY/F6HaXLMEtqWvp1gdQKCR6c64dxGewEAuSQkSsCkAaQAo836lUEgIyKgiWXxl0AmWWizPUGAEPnqEIDDaHUQUWIKAQY5QhyHlwbK1dRVCeXVKQ5Evlyl4EJfcBBEzrLqV3WJFrHSsHuMkasS7yRR43kFPWMvyFK1RQEYcBQAMalFGbHHvIBVaossE1aIL1trVJlgWyBmPLaT3ztS7OoCqgssM1bwIOFNnp0aH9TBEEQb/M2qRpYWaCywOwssKX5X+t2OxExfU3Mcmm1ZsIr8mKVVeu3ghJTWwDsge1XffGbL/7juTue/O2fWtMtu926Lm2LVGlLtEBXY6wIExPhybILS0vj+k06eHDV7vaUvvWZ2579hCe/9r9WvO1tiwdvSd9v2Z2op5W7xfId3KzK/97BHXCnr76r1vDXU0/xNcZqo17/DA+tNRl22Wcbqy3oagyMpD3o/uGhLht3I2MDvSM9jZU7bDdnYd/w0mU2/mq9PbW+3pXCJs/vrR10SO1+99vvCY974j777DM81PvLX/7yh9+96MIL/3rtTTWHd+5694UG/K23rhytDdtCbdS6Fy6cf+utNlKXCR3hZu7qnjMwd76bsqdnJz0zVktztbfC3b3pCFC6wbvSZq5NMSnf72gCEwCCjMn7tjCCQGVH4FLSWE3DSyubnkEQ7HIflcLLVR21RxEyYm1xkmASgaSh7dqgic04nzHBk0kCw8q9mYuKippTRoj9SlxKEcthNCq4xmqJq9GVPgtujPbSs9evI87tq42uPP/PK87786++9LVf3f+AnR/0oAc99pjDPvGZA65bco3fhfvtOVddc02tvvYW1u4pJrfu+iCzCY21YMG8xuDY0uXL691+06HXrnkyWn86CzFcG+2qd8+dNzDaEGl6wNfPo4M2Utft+WadywDFogsc8/7Qhz6Ey+kF4TDe+MY3vu51r1u0aNEXv/hFX+2UWcpwmA7Gi0zG0Viw3PsALjtPzRmI0nPPPdcrB7HV/Lzthz/84cMOO+x973uf95c77rjje9/73r/85S8hBzH15OVB4rJKlQUqC1QW2HIskLxvmqiKVACj8rr/xVxKnhlQb/T523HhDmazvnpyFj31XeeKprhmSXLNC25fuqy2+3ZrbxqsPfSBK+5+97u/9FkvdjTiyitu82NlH/nan88++/dXreBueuYs3KW3N70x3X7hQl8aeanUN9DvEIRvLc3AjfrOabbsWkGV+lh631kvXMDC7eaMNtJXIHPm9HetGMW4cLv5/uJlW7iJBQsX1nu8elwXi3zBdvNq9fReMFomx1LvSSdkCslp63677Rdw6BJfoCFxCZOe5wtHb/r2staDR3jVEKX5LvmuAGguFoaicl077rC9pxd+pGhmcsE77bDjjtvvoBWFkKTDdgvmdXd5J5deGeIlbbsFCz2FjHWlp45IK4ev719Ym5MaMbymvtA/a8CN2ns+ecv/fPVHDz54t+R/j7v7/o96/VWXnck3/eHcocsuqy3oXVOf47mo4bTIEPX9OPyCHYaG5t2yIr34rHmG6u1duGD+QO92I6PFx2pFvw8OrtJQqqS+1w+8dvE3rkrxT1hznU0LJM0vvvjiION/P/GJTwgL4uOel73sZWXeqWDxST29sEA8ijSREQ4T9pELcSXulQ+P/v3f//3Vr371i170Im4XgZ+gf8c73iHUC5iRcSEOUcELLiOjqMorC1QWqCxwB1qg0TU6JiBkzFXFPJxm3uQBI0+LqO6i1ERocdE/HgNi/CMVEYOHxmpzeupzF6QQAHe56/a+Qj7gwHtYAM6fP3DmmaefedZNt98+WhzLrfX4Jb3htKrtTv69tmbI8jAFnraulk8kNY7xSylPYbLStCml0qTSNIuyEMLjBEvkcWl1yQ9SkvdJEq30BgZ85hVVwCMI9k2Xa0Byb/ICmIB9ohUtLYomfiGwUGPcF3sYikcC330xxq9Ovnz+vMv3vfcujnoefPDed9tr+yWLb7/mmmv++tcbcXE+luO1Md9I1Xrr1s11Ww3aPtQovL+FZurVIqXer/UUex3pecsWRBF5mo7hgouxMEHc8m+S3Gj4uPbZz362hw2XvqN9xjOewZJnn32254F42mnhWw9BggTlkSY9HrRNVrhPfOITbUo885nP/OY3v+k3aY899lgcHoFstohHCc6eVxe3FVYVVhaoLFBZoLLAJrdALIKOP/74fffdl9u1Y3zeeed97nOf22+//V74wheave1q8gK2cAUxNG+fcMIJYvp7lnCK47TTTvNLEo4+KvrMZz5DV24l+e8ikSxN2gD+CH6q0mBpLQ0M2UGgLvvkzpNcddVVv/3tbykfMoOgzA6TeQEWiY95zGP8FsKZZ57pkQNXlhmS2+TYEUuTym9lROb4Crf4T//0T4997GOt+i1pkdl/vuiii7jIr3/968JAhJ9tZS9josZcdaFFsyZlrcq8YDbXp+JUnnXWWdoe7EGTZZbZIaM019skcMu5pKEBaW/BkLC/ZBMexpIfxjsEP4ABz8K5ma2aR2Obml++LLO0EpdLwQhsNcRTazyJlQmIRSBBRhVgGnqGB8AEUimkBycDW+uiKPKoIpPFZZYGmF0iMDTxsGez7pGPfOSPf/xjb0+SQhM3MoJphWfiJsoyr8bqESkAlG4TnRWvupoYN9ElPUOlDJQriiLd57a94YYb7FiabSg8KXGZsYK3QAvozaZh2Xk/onQnalSrhPYtNVqcn0ETcYjKxOV7oYxvgvP4jBPILjfDWjjroL2aQHlekueK5neoeQjJFuvE4+d6MxDscpV6DoEnx6WUaaYFssJlrjLcKqFcGitfQgJZLmplbMKUWbIaGWgi3uiXFv5x+NkrjM1WqVbsscceHnjSe5PigwTOS7hPQ8gM7/dOjjzySC/Hv/SlL9FNYDh7C8aYzvX86XnMHr4fi3rCE54glDY5fJ+nI2a0HeQ8MHoHq9Dj4t+vX3xD74JeGzC8bdxoG92GlcDKApUFNtwCef7MotzUHU5KKLdt/xtuIlsmA/Dhf3k9M55cchgzfRA3vmiNJUPi8KFWYdK0YevtdBBgT2VF6sTaZU0yXIit/O+EHTv+d1vyv3YtbJs0+d9lty/njpfetszgdNjeTbpkyRKvl1asWDkwsMlf2XTcDxVhZYHKAneABcJxyONlLudFidYngRlpZpIhpxNfNiOxbYizD43mZEqX4NCkgIutb081hf8tPPVY+kFm34iN+eBsxG/6Un6gry99m95BCukkA9TCdiNFMJGoNGrvQMz4p+CdUJZpyI+mbcj6tyxw88Nl/5tr3wwjh5f01bcavX+3BPYZW0SwEibyPe95z+Me97jnP//5H//4xxGgtK9igOX171FHHfXDH/7QWxWvV2L9e9lllyWCZSvJFFRLozTBTqmPCnje7t76jTfeaP3rXMOBBx74uc9+vtuRiSIqR25yBVQWqCzQoQXca6Y+95cNK/dRTIPmQA/A+V2hrS2Y8At5Hs4TC4x5Ho0cWTolVNyPCGCoEfichwTSwmUgCx0C7zI+gg1kOQ8dQmaunXxyIM0JCGIDUKnJx1N6GCF0Iyp4mywDqflqDzlDQ2tWrVpOHoyWhXy+yJ89Ud8ZaWXx3VJaaRbqabVPhgZEW8IAE+0CI5ATrgrJJcX6+/oZtncsBejXdMQhh53wDg06+NPlYBCY/na/e/uSYvXu4reaig+wERA7lmpzDswJMcH+faHsCBOQkPTlUl9P+v2AwjnTcsTfmG/CG6O6ymfQCIqUVtNSY3SoUffpm5YNrl2TSxMJHYaH1tQUUtgnY/4f/0QvlY41hjUp/SrxWH1kuLZ2vc/nEoEe0XDFWiQluKt4aZ54xw3i+SEp4Leglfi8W3UT+97ORuFCihhfamvokJx1ku/AGPzI8NquWi/9wdjpHNZG0NBZhfJ1X7PTVCrq9USVPv1L175B84maK2dRx4ONrlqZzmQpo4De1Rep3fRgwKJbSWY3oJ4J/YeH6h7UhuKr9ppDduk9eypVUVIy2Vka7xFlG5DGxYbw4ss3d1zIUwSIulprCEZNAOSGIAtzQcYMYMRCIiCnVUgrpixKaciJSaCVuMJUFtgaLJBmJ3dSkQdcgMVJ2IDK+binKaMquLJAZYHKApUFKgtUFqgsUFmgssD6FohX1wk3sYIaGm1YyA17f/CODz7a4t+H6ZllYuGZERWwBVnAHoHU1EfpMu0C2WLQ12LP1F70olecffE/vuhF173zP24rVhfFtw0FzRbUmEqVygJ3kAVWr1ltP86up1dEtnTt/HV39aaPHUfH36ramOvtS1Gcal3L7M656ZYubaxIe561OSO1ffbZ4bjjnni/+91v113T28dL//SXr33ta931BV/+3uUIFtZrI3W7Y7Ubb1weXwq5Q22Z+qpj7dqhFUPW+eNL/bWDQwNDIytXr4oNABt92G1Mr16TjulK1JAwwtnlDKQ80RQhqOx/xfYZAJmUadDHG+KMsUOdf+oW8pZbbslFAF8kE6I6gEaVN+airiCGJxmNDTiYvHkXNNjh5axKmQmPk1hRxrEHgFTMY+kHZ3K7GGGsb27qBaesBTopfpmwp2vUyeq1q9beuKx20xm3nHHGL6762x9JfuThhzoT+/wXzHWW47wLL3FQ9o+X3LRkSW0oubtab23p/Lkr+gS5mjNnaDRtrUq4bGBTiZ3J9/uulBwcGuzxkw+JqTlpXnlLJtpCc2d9HTGK5kOyFeF2MzXEgAHHRmeTuKLRqR5f8bJzdAQWmkCSlu2QGUlbtGjRoYce6oyE95HOF3mL6dd+VOo9paGLoMwVGmb2OwPAqrNoNS7GmQXjncGkVRsrC9wBFmh6qK3Vbrt1CTW6awO8SX/vnOVrrq/VVuzYv9Mty2oPfMDOjz/6EfeR7rWDCfBPF573sY99bNXKvkXn3h7Pu6tSA0aEhTBJjjRGfbwRnnH+wgU8FxYzcCOFShR4KR0crY+lwEwRAMvkIJqSHzfim/wkksncXM2TTszqycEI5AETL0FdSpSEKU8pqshc9VrauufaCq70Opa/8HFnwTqe4ZW4j8hzkUsK81OC2IKp5+sW376AM01Pbx3GW0RiU9MaDZfShM7Jj3EWRe0JTu0WCCN0LgXAivgXvKQ0mty7RUW68AZ5xZpV1518/SmnXHjRpXOJOvKw+/jg5sX/eN/rr7/+D+dfevLJJ//xj2O3rrACGevrGV3QWHbzzctYVkSOsVp6XTk8nJ4HygGwFiyYk+TPPLHAkUceqZn0x82qjrX4XXfOvBNh/P5ee+01YZlmDqaDCueuIs8VTmz6DMj5Gd8MnXHGGc5pI3DM2EAK4shDUGbUO+UOitIqryxQWaCywFZhAROhpYxfHAxtuQ0zI5+wYKDH/L/XXrtbidx1rx3NeNffcJlQESbkv/xlONZf5uW0mitWweXGbqIpMc/AZflmbx4cxlRPSYAEQx9FGS6rt+XA6funwr/Lws+vXFW78MKbLr/8pusWX7p8+Yr99zv4YSnVfYJ29dXXC8pw/ZK1wo3xtKkVvLs/kcziGUHub9JV7kzaHHZW1//8z/+wYTxReDgRHsIyNlay08ojJCJ4YpyWWOtEnLT+9ZgRv1vLd+O69tprsYcEmkwrpyKoLFBZoLJAZYHNYIFwryr605/+5Hzjc5/73FNOOcVOpunaSu173/ve61//eiGTnNawnuKd+ZFzzjnHdrrABN/97ncFNcaLQLRH/tpq10KP1+Cy4SMHlKf9chG4XISyTQrKLBOliu573/v+53/+pwCLwtNYVEaEmqDUBARSaxViIr/pTW8S0ktbWkvb6KAo03uIclnWp+kSpYcZRhMA4m1ve5vF6Uc+8hG2dbKFZzz88MOFEoOkfH7mAVB4KgXUSJoaM32ZEl6CUa8UQCgJttugVFDsd77znXrzAx/4gGcA63GiJL3GXAiiduyQuMJ6GYbZMpNmCofhrJFoaKJf2TzRIg8kxrDx/OQnP/n0008XRyOCyGhCGCqAsFVuVy7qEJ8ZA8AeErx3cJZJfwkM5xVAE1n5MlhQSnohDkQhYHZ3mUhtGuIhKnelolbdAkNUU1G5ojZwZjckqOFSvDYWi42aaFEb9s6LNIo048qejF01jbXXpDpIc4j7l63y0yl8FJGfdYCJAQkjxRCFbNJBUWCmKoKP1MSYL5WCyfHeSuhYXWOSNAH6DMMbNAMsU1bA1mKBNjdR+ybExJhp8ujKmEmBGKiTFs0CGZXGsJwF+0xZVKeuqNScgH0WVWMJrg4t1qQk3rIaSmcqJyTMVPnQuazMTOsNXnKyqNlJKOuwUeDNoIZJ0o4Tt7X77rsLTmo+DyOIRMlHf/ITnzrsUYd6I8RNe+njlC/v5umxeJUz4lUI1+BxxR6CXMhOb+HNt5wFsfyF6dfNaEAmeE4f2A+v8Oy4Nop9KiGVBSoLbCILVP63c8OaqE2bebrO76BNfU1PI2WZ6AuW9JCMPR7mPay6BCvKvNkxldnLcBBgKatRCC9TTQNn3mmrKwtqJZ5pvSGNnCxqdhLKWm0UeDOosTn9r3F11NFHWidaYr/0pS/9xS9+cfttS32nkPzyuoPlG8VylZDKApUFtiYL5Lk3lI6pbwMnwPBrm9MKuRUAU2u40aYWlWnKzaSt+BImw77+PoGQ/ArRTDUnOZ6astiZGjDos4YdKhD0eDPQIWNFZhPVR262DQXAuvLKK61e2dCwsSj2kuXjH//E4x53jK14HyvGDyQLD20t7CN/blSyFtbjMEbL/vvvb/2Lcoedto9IOrFDaxVsKW1lbQXt/Qs5KiJz4XYLMBpvapSqvqgsUFmgcwvkOTbuHfdsLJdM42A3JoKmRHjQlGtBg15yh7qjY/1FJnzQx+u2oIEs36pxKSdWQgOWMpDhIMj1Nj1voyc2HaUpVoKmFJUGMUAiJ/M2AYoID80dACpK172XDMa8licq648rRJnwkA0Pr4yXjGBJkRx9qConZM6cPvlAfa7LsUb66CvIbDkXzU8vwnzdhtF+ILj4neU0s6k0XRXBxVDWUzCrcVu5jOrUJVYTyq74ScPi3FbwJmQhQZ6qTKlAFZmLkJBb5FJJnAaKIpcBBLFLCiQx6bPz9Gl360uikIYri82YYIyiEBXSooqQBpbgXUZjccEErzxYsEf/ymmiH9EHWUgGB6bMC8OeCKIUL4NnyYQEsTxSVA3ONKFA1B6f8UcRHSDlQSyPWuRl3lnDoQ/9oyKX7BBVQBIbFWU8TBAHY9QLzgoEjCZY5AEDQpQcJuDgcgmAkTJXuacgpVxFBVQW2IYtUL0d34Y7t2paZYHKApUFKgtUFqgsUFlg01nAeTBr3doe9/rbU5/83ne9612/+Om6T2mrxdSms/uGS7ZBoYOm7iMnwMdGh2u331r7yhd++8Y3vuX97z2hdp1qdxAzp949WETltl2VPkOvUmWBO4EF1m2wiuKQ/ooDojYVnQjtrQ/Zxux1TLRW6+/3mwDDfu59YKAussGytUMpor09x1ptbl9t7dDg7nvWDnvkfgcffLAPLh01WbO66w9/+MM3vnnBOef8+aZbaoIYHfOI3f08mTtwUBAI4TfW1kSkN83a7XO/2cIU637hvIHa4MrYn0NTT0eJe4XnSGeFJ5LL3nrfaHGv2v+yYyjlfcmgcinl3TFIl/Ly3qhzDjbK0dh/DC4Y77Fs50mKXObtswBUBC8P+thZg7FlmXfZYjMOQcaACYQHUCNEhXq5akV0Qxb6BDGMhNJldxGGY2xU3JF0YIOk5UND7NBVT/vgrpX7WzVSWzq8y89+eeH3f/njvfb48f3uu7sXdU960lOOPbaxZuWqyy+//Lzzzr700iv//rfaqlWjg7UVa0drfTV96jcKar3dqTsAukWkj9HGspH0ewiinwzrC9ublIyeAsS4yT0FE0nrWMNvd08gxv+lpFS2SSaAByuKtgvA8aMf/cgnuZDsEx03KaNXI2jiPFhIs1Fu1IHjm12GwpjlB02VVxaoLFBZYAu0gMmq+BUcTjVNtuH1usbm1BpDtdEUaMOPz5iKB9euzr/x0l9f21Ov3bv3r7vuWTvioQ/ytcfd7y7b+w9XXH3BBRd8/Rt/ueKKKy9bnIQdc8wh19VOA/BrNVNno3dw1UrRr2DkAfRNeLc0644WYbIUp9k+ubyY84dGeEBaCMOYECONoeHRtSN+dyiKoZKb41gdhS0uiszc7sVWGaOKIqVi3kZuuoZJdHyBX8wpXvEmt1G84fJuz6M9hyiOFdpCaso0Jflx/qrwUGRh9DNEE8EtEg2XBANQEvITBlxSulA4rfskKwi1JpXT5XgzElIsyK6uoeK1bhH5qlZvpJ/oKeryYzuNNcP9t1z/oLPOPusH3zp3rz0W3/te33jIQx7y6CMOfurTH7Fi1ZILL7zw7N+ec+ONtRsWLxvpr9UHNdvjz7D+7l2pCWI9AtWeXmT0jg70pu5eo/qu2kgPZ9i1pqd7cGQ00YzrBGpJ1PFxT0an7q7V/vrXv8o9YGT8VADLiCPpaCtiosJ7NhGzS+qXri5BJxU5oxgE6j3llFMC5rvDfE28yVqpjSmvUmWBygKVBbYcC/gJOmufSeamYqkTL5gjAKIZOM3dyXnV7rqwJlrjXXZJZwIdMvRz3MuXjS1ZctUPvvOrm28eWz6c4jvuv8+uPWPX+6U2jmNi9jbBTromKmbptKojXcVZnViowhTaBL60NG5jxjzfZsDkHDBAykutVGcxOUO2Ebgpi6KZEzWMfwpWXK7vNQo9RZBkn/HngZUra7fc1Pe3v9cu+8uFu+564cLtenz8eu/9dn3AgbusWm1VesON162xslx6s1BfY90REb8w5mix6afBvtkarftFwSI4WbENMqEHe/iGzLPNBGKyf/lEdL6+LReKTlW+nBY+88wzfVw7LRkCi2XPRfGFt4/e+G5rfPjw3Z1IqGgqC1QWqCxQWWCzWYCPiH1dYZi4pMMPP9yyN35y3P7zz3/+c7GixBG2mQkpGCKHIqCwid2+qOhRni7kGLk/87xk5o9PrjNGFeBYYKsr9plj8VtuZngrpZGCKwhUSoLcJXwgkamOOxNg6M9//jMYjaoR0Fxu3xWNehVlLjSSsyKf+MQnqB14BGRGEZaAo7qoK+fokQVXaFWWnMkCQImGwFe/+tX/8A//8MIXvvCkk05yasVbCVvKX/rSlwTGYlsaMq8DLbjQhzIBh0qqCK1CJZeBCfmhT1AGjJgQYrl+edgci9KLL774q1/96mmnnQZGo1L7Fch0h45ziTdEcd9Y0Kg0YAQhJGqRN7V3c15G7aEPJX2c7ZjQJz/5SSeOfvKTn/jdCzqLviGm0oknnuhNkKivDpCjp2S0i2VCSDSn3LRoCHwUxWXwBkYOGXkIbKIh3OMQez7taU8TJy4HwCIk5ERXBhc56ME7FMkTFFWNYfj73//+X//6191rb3zjG9HghZQCCHa5yyxQaRmf4fZAmd1gEJpNd3/5y182REPhqCUrENJCk1bJmaW1yHhjFowHHnigjR21wLhU0b/9278JGSbUlF8QCYOEVlFLlpmBEI7GCM9IxIGHkYI3IxVBBkEggyYT5FI0wWt0uV9EaREK8JWvfKUtPvq7YTN7SKvyrcIC0b+5uzvXObNkoHPejUtZHqIbV3KrtNzYDLTSbGbMLDTJLBmYkc6z45pRFZuOOI+WzdkKz1cCbcdjpGcz3pkapnRzqW8APALd7W5346ZNqvvuu699qnve8578HUBkVbMr18Ad7LbbbswiNpbJFuDrCyeEPbOZ8KVoF/exYtnK3XbfFf0BBxzg0XTTWbKSXFmgssAGWiBu21nMRZklAxuoyazZ84w6awmdM2psLP0AVjSWtCI+YzepmhWTJpYCVqw2h1MyK8qhPOWm5TB6IWVRWuJZW3jSFi7B5FkQzzKbhf0zSwZmVPfsuGZUxaYjzqNlc7Zic/pfQ8sA48f57nPPPRc8MKffqIuv5jadYSvJlQUqC2wVFshTXwa2CrVDyfC/AXOd3uHabLMqgdEc/jnP8BON4l7tQaV9VBgBjM4/7+LRxuDxx7/KwuejH/vwLPxvVBECI5+oa3P8u/lr3IitaumdjSh7SlHej9iI9imarVTfhNszpIZ+9/T12Mc+FptTCV64WCMDLHsNDEVepsT610oWrNTC2UizNMbCz/rlDJe6gzTDMgSCvaO3rLbXLQalRz4Piui36l6b0rJVQWWBTWYBt5IUr8BUEi9cAHErxSuJfAnpBgwW+1pBk+f2DCDAmHlDd8TwcgkGLFdvAFGFIkLyvewyhCSeIgXZVOzw6M0/uTkhHGtmDCVzpSEqCBQB5MXbw7QI9cNG8rofI04zjx8qTuTg2uCIr6/TV9ON8Q+fxhrpdWpUHU4SWaHyulaHZHt6tvvmz+9XS7+vndik4NXqZLIioBUbJ2UcAyo0J7ZnNBmqu2fIe91uF/X0Xszit8dqeGy4nj5VH+zmly2HfSXtv/it4OJ7ZopJMP6wE1zAgZbH0pi0Lt+Mp8+cJ5Ky1IT0ijaaksxYTkGYMUn7iS7LyCYAi6Y1IV3ibUWO11oYIRmkSEFGTgCBxA4DDuEhLeCgnJQ3JCCLzgoJKCfVBDIEAnLtQYklamziDWQQ4wUE3Cp/Fhh1hT6Ul0IypBuzqCfZOZDGj9FlvElNGrbWiwA9zSMvN0EtUakq4F2GAlEdUS4RqAsvGE1gWmupMJUFtj0LxPfJ2167qhZVFqgsUFmgskBlgcoClQUqC2wiC1jG+U/UjJq4ICec8Ly//OUvn/vcHyYqgx1f9E5gqn+3LAvouMl2D+wXJLz/7M5sv2Pt9ttq//u/Vz3hCRe95CUH/vtrL07heWq+Xy9oGsUm08TuxpbVvEqbygKb0gJ2lGwtddvOFOshbaeKypQ2rGwYLiuiYYjxMLIsRX6Y311buLC2zz57HvLQezvq8OAH399vgS5bfrv3RuJufOELX7j4olU3Lh3XNZ0yqdWUi7WE9y4L5123PIkbHUv7o+63PKvGfhaaSOqWAjmBG//XjQxKs/VEKpMFDi8g9uAATRLiMkonLYIs6k/qAOSIQ2BcBlfUFft0UZr344IgGMvsGV8Goq5yjeC8fxehOigRwosOSi1CY5dv3JJdAj+l2E/z59e8v7t2sb8lJ//qO9t/6DsHHTR/33vtfcwxx/zrv/6rjli7ZsTnXFdeebVTIn/+041/L4Wrsofir69PyDM/9Do0uMaOtOAiSdNUdTJ77p+y+uNwIpkwV7k4tyuQQVYmAEcP+prnQx/6kFxDIOOoBvYmYpiwfBRFHhIMYXugSl2qqJW3SdQ2fDm7ts+Oaxs2Y9W0ygKbwQLuu8lvPV9X9PZ4RxdzL79sOhRxY8895x3xsIN9z/HEB9xX3li1TJwjJ/fOO+8vtAqPAAEAAElEQVRPKxu1m2+uXTeawk1KXSm4YWP77ecsXbrGpyEwaaqcCP9kqvRqClKep01AoUzij2BPAAk++WzRMj0niBtRTyc2k48ozdBokquY8OsAGLhy68zMRUps4958AuXfhMz8qVoVJsqY4QvEeMbzFiEwxi9VhdFzQbmuonI/VpReNGbhgPG3tAVrulxf58CEXCIBbFGInWjYeJ3xD/XECk2nCufOmTu0Zujvixdfvrj2izMWfeFzi/beu/agQ3YWG+UNb/h35rrq8hV+qu4P5y8+//zz//QnUShr8+bM71m7dnQsvcYeTR54vZRNm1ScLpUbjlZ3Fx1U98WPA6LTcaee+vjHP7548eKi+e2qUxGapupC/qT4UD7yadWoCCoLVBaoLLA5LTAxQSX32ibFHNxbr+220/x99t11xx133HOvXXw3uWz5dYJBiMB7ww3DS5akmI7xdVKsPHkuXpLXjSmVkLaVtKl/NkXRtJixgz/Pw4DA+zQqPhuFoS2vMZuaNi9P2fVHzSlmRPGcJNDkjTeOXP7Xxb87d/Guu9bucc8uH1IfsP8DNHZkzQ4c3C1LhpwYuebqZYMjg/EEEs9XU7UAo8eH9R50piBlQNaLPQEkGKcgnBz9gx/8gASpE8bcZVbKWZyqQwIMIOMroLJAZYHKApUF7kALmNXDNTgfa0UmctBTnvKUhz3sYUJfidME6YmCL37Sk55kIUZPvtjZjP322+8b3/iGU45OZXBkCDxsmPMt62AkXC4BNuHTgrde96GzTdSlS5faDiWHEFzZHVAjYPKxpHV0oRhGGCnYObIgUyqYMnfjeImHHHU5LqJIrEaSqY3F3jJ9PAsFl1Jc8C5tTbz5zW/WQMQ0d/hEEfXsOdCQemA1QoKbEjmEKFJpCAzGkN/E4tKvsr/0pS/94he/KJRz2hgpknYxkVhUz3rWs1TKUNgh5ZTRfEpavKuaBBaDUepxQg7WTHjNpL8GYkejFIyAPko9Tjha41KRb3BZm0z9K2AZu+2yyy5RpAoVoUFPslbDkKN1YDRgkglkKBgaNhnkDrmkRmiiaZSkw0c/+tFDDjnkZS97maGrRcwILyoWy9Nf7AzNZ2q5thhjrKS9kg6Jlka34iJZHkUhPLcRL4yEGBKN3GUoA4YHq+WnP/2pfRUBzmAkVUuKGJa1dUe+xGWgsr+h4gTUBz/4QbWglLTirLPOcvAJMSEotVenR71xCVZdjA2SkdFHkQQmKqShCWTkkCEwIwPjZYeTVMcee+xrX/tacbiUUkMRc9FcXSEcRpHcpQQgX2nUCBOtg1FEQhDL4eXIhIT7v//7v5tvvpnxdQeMx2AfORhvWEKUlhqrcYYfF2SIAktgXATm6mgYRXL6KFUEYDQyIYMy5JPGaEGPADGYTDQANPoIHrub5Tvf+c5Tn/rUww477OSTT2aiaF3wVvnWYoHy4JmpzjF+ZsS1IdW1qYjYWSjTRmD7oqhrc9bYpM8GmjGzZ6BJ/ma4bKr6DjTmZmisKkybHlFMpPwXz2uaZQGtFpjj85//PLfiwwzz/KWXXuqlP2IY7s9Ma7ZHjNLLCASe8TzbwIhyNTA3PY+RExM7SjM6x9s/J0Vc/drXvtZk5M3T0qqWygKVBTq0wIbcobOYMzekujYtInYWyrQR2ElR1GhSjQBYQh7EjwnhTUXFmgxAN8mjrKd7syKMANamSvOwcMCKPLsGC3jSelvxrZhJGadCZvYMTEW56fBNVYcxN111d7jkzeN/NZMvtkqyCcOPG2/WX7F64s3vcCNUClQWqCxQWWADLVD2HbaM7IMVDjZtI/MjXG0ABZnMs8G6CnfeeWeRAa+84mpuV3BegX1JMGEWDrp5/20d2wRUrnoCV/27FViA/7Xs1df84E033tzTm7yk0SJ8sx8VMIT++Mc/2vMU6P/vf/+79pTXv7ZwUfrBButflLHrvnrlGutfo8gOqwc33zq6xGgfErFNVLv6YM+H6G16qzcc8VZgrErFygJbmAXcUzSSS+bqSOCymu5ulyb/mKWLmX89Alxl+qngEBtCylVkdhMC3tAkgCDLBJNKRhNk9GyljKIyY5OvidZBAvi7eN9hSpEg5cELlpQGfaHeuN28rjEN1uvJ2ZUrCjjsZlb0jnLu3PRac+FA8Z3SyGq6WbGaHknA631aakkjvY9DZn4bGR3zYqh/oAt7vQhwBK/UCy41MpUqYKJeOZiSZESuFNCqEkzQRBFGl/QMfClPb68ileUEjCXYAWGTMo2iuGxCZpYyoAqaByZymABCcq6rTAMZeMRqkcpNCEpIQBgtiF1CguW5lrLYJjhzwZfhuAwJZXyIpUlU0SRto1xGe1kmXG1o4lLVMQAAkPLQQS5F1WFnRVJgSFOKPYBAZnqXUV3QaJfL6JSoC4FLpQakXArJWX4IrPLKAtuqBZrPjWyr7azaVVmgskBlgcoClQUqC1QWqCywUSzQleKxdAlhba/zoIfWnvNPOx560EeW3ki2B0uL/Hmpli57BLHeT/9WaeuwgE2M9MVC+hBh2VLbELVbrqu94vhf/vrcQ05ZdPEvTrq9O2LzoCnOxFs7owx462hgpWVlgQ21gJ2p0ZGh5aLwG/0uBGaoj9a6iyOfC/pr++7rbMN83/QcfNBB8h122Em++Lobr7jiih98/1THPH7zm8vtFo4IulFoMr+nNjYyf1W66eYt7O3vmbP7wIIVwytuGrGD3evHRbv9JECxV1Xsngr939fbEKijlHxsGX/lCTf2thrFTw2MFZE5bIRJJb7x3VKU8LH/1URQJm4DhwQEADlRkeIyGAOTt/OCrEwQ7MiCPnIEUlkrcCDlwRKYoBf+g4RuPwZRtLS7XhxkShuLvQKUcU+jKXhH98hYffnKnpWr11WVXNpQ72/PWHnmGRd//SsXb7+wtt9+d7nPfrs7JfLcZz7RxiXm66+//pqrl3h9+Nvf/u7vfx+8+dbUiaNjQ4PDIbomJtr4ue11gieHQvmwRm7I5KTrYzMxXi8dNdZuphRUUbo+R7qCD8NmW+Wq8zuMVq4KU1mgskBlgS3KAo6GOgjaWDPYN9Y1VEQcLH5OpzZ485L5fbVdF9YGBmoH7lG7610XHPrABzt66itbn2vctHzQm8uPfO+HAhqe98eY9/1Q0Ly77rHn9aPXN3qLbyJFiajXltV3IL6Yx/kXjrW3NpZ+JE2KOZM1vFWKF0tySLNrJL4rOaXkl5oTshAy6dMyxvB7QTO5iGaR665Dt3H5BTN4XXEBJQVKzkl0LqmJpnypVKMSUSIb9y9lgklh/jelWjpVkj6HmUhj9XR00I8oFU47WXf10Ep/VhLClIXbXLysdu0FtVMuuKW3dsu+dzvbU9PDH3RfP8B+3JP3edkrnrdm8Eof5Vx97SV68LIrRoSo+tuVtdtX1Vav0kNJv4Fazdv45YNDy1b+uad/bJhvTmmdDhO6TPkvN8o/3nrrrdl6GZiUxzdDhXFSYdHscSdbJk7GKF7oBlAuCq4mTEZOSt9KXGEqC1QWqCywmS0wp7vW06gNFavJtPjlE5NbFB89ecDddqvttFNN0CtxEHzyyP8uvZ3/vfV3Z622hrr2xtvK2pqgYzU77jbHFtTGEBAdrrSdkyrL2UQwFyCZjeWqkPumit8vYzZR1RtVbJhR1IBxM3fXt58/b4UwIOu89EjtxsW1G5dw+jeMDd0gPPTBB+4jFsb+991FIICBucOikFyz+BaftN6SvmWt3bK05mg+JT1YEGrx63FMlE2bFegHh1P80DaJJXlbxGFMlLNwebNgaaNSVVRZoLJAZYHKAne4BUzssYXL23p+kP/qV78S60oArNNPP12pQ7aCv/zmN7/xe+aQItRYuAnTY3loHWdXU6QhGLkTF55DxIgBiG7gPKRvlK0u73KXu5CMGBmnJnKWWD8k+yVzOwxqDCNY4oE5POwPfvCD1YsGEqNDIPyXZWkcKYEPFprbMT7uuOPIFDfHiREEd7vb3fzEuschLHZuHS/xLgB97CTwhhKuJz7xifvss8911123aNEi4RUe+chHRhQe51Ie+MAHaqygWl4lxKe0WGiiOgBRmkOlvffeG6UDTmJp/fjHP2YKckKxIAMDpKc//emEU4MOyMjRUnhCYM477zyUDKjt8nvf+94PechDEFx22WViQISXRyYukqZ5rmPbu9/97hSmJAIKY3fI6tBDD8UVwZVOO+00phaY7GlPe1pEVmJqXaYLjj76aLbVNJd0UKmYPn5wnjIwGElAsMcee+hlav/61792qUeYgk10qyawRu6FaPIdktOfGhKdH/e4xz35yU9+73vfK8oYQynScHidJXDbhz/8Ya1gvd12283zFeMYnIaHVutBw4Z9ouG6QMSN2PM3ipiUBEkDdZyGC8YEJkS9AJ/vswx6SaUwBDrO5NLIT09og4Pw5DiCrlTSm4aKge0TfwIpBk6/2LJ8ud5XKmwW2H2H0ah+4QtfiIyEMLscATn0kVMy4tAJkqVrcBFOGvWIighoAJSqi4a4JCSKQghidUHSHK8Re9VVVxkkrIGFemrXZIZCELyhNhgvezICA6InlkzNJwfgUtWEIMNrilC7jrBhqJsItL2DRpEJ4Y1vfCMJuga9jURG0EHmkCVLllAeoyFNE83UR7SiM2LyXaoOhvFpglJ12KkXdxwTwdMHUsLFbhjdEepiMfVqkdtNK9wUkKpWKUYAXknIvxNOOEHoAbqpArta5FWqLFBZoLJAZYFsAROj894uzepmV5fmajnY1CplSrMx2ASeMVFqHoYxe8vNt3PmpQdCQlyGz+JbYYILGT8CDoJAVnllgcoClQW2dgt46PV+1oOoR9zf/e53FsK77bKz33vyPjY9fje8mDXvLS+mx/GQr6bZvr55VkCeaUEebuNpNh6tTa2QW7tZKv3bWMBg2Gz+l8/lkWO510alqqiyQGWBygJbkQW4V/s8dooAZjn53/72N5tXJjw7RbEFpDmxDJEPD6eYvLvskr6ds9Nln/YBD3iAvdmFC3a0r26GtHllpcMv26raiuxQqTpTCxgMNurtGVqWDsxJkapikBhFNhWlLNBQAUeekYBYBduKBHtgi/VvsQ25jio8r2vCbaUGYFiSb6yuo6ugygKVBdpawK3kxvEc68ZxN5mowQDzPD7TdVyC40aWB6VcyviAEUttKxwvDHq1u5YHENKiInIAAcvLYjNvMCoFBGUAbbxMmZIcl6FQZsw1FkLiV09SrCmfZVk8sg/6UMa7G9OUPTrTVLxtaYymt6VcXJaWhQNyXeo1oTG7lBaqvekXR4aHUsAg7Ep1BeKgzy9aFDEJrZAFJYIAmBwQdUVFJBdy0vtWGA1tFI6XTR0l6/FNVbcP68btVshJolL1qX9TbEEVwRMilwghEj4kRyk4VxoAAQGEsHJpCJEXiiVDAeQwiOXBC8YVMIIsIegzccYHEPiMDCFFD65zQKQFHhl6KeijlpAf9WY5TUCTPrn5wRXC5WUhLqmBMhM3ydzAy6hOrjlxzwacVQUUbR1vLPcaY5g+8OCgzNbATiVNoLZRLTe85VkgyiiFUaNcwhI5AAEhWCJHAy7bZAObXLFXFtiSLTD+Zc+WrOLG1S3PHRtX7DYgLebibaAhVRMqC7BANZ6rYVBZYEuzQOV/p+qRrXG+ck48/Vcs5z/wgYf4OnmidVZZ46u4CUz175ZoARsLpd2YyTVEkDc3nC74t3/b/xcnXTrqoy8fOKSuL7p//e42mKXJxVXYygLbhAUM8MHBYd/r7H33nW6+5dbttqvd6x57+mHqPfa8q++B7nWPdOh3YKDHx0A333ST3xG98sq/+V7/phtHHA1YVWxx+iyovyfdXHO7UvCslSMpHISAFHMH5vqWiBBnY9asSluBsW0qnBPL2d6Vw9gXsxtrnrVJW2DSYRvbZJLfNcg2dpl29Ioax1IwivGUCQCBagJclvfCwBJlEAevy6AJTFxG0bR5miCKDfomSkgYAovy8Q1KlzbmAp/po7EqLbb10tYhTOzfhT4k2PhGYOM7MLGziWY0HatOBsRr61CDentrfjl+eI2KcDR222mHxtjtPnn1Y4SnnHOzv53nXrzTjk4B1e61z64HH3zw3ve6jyM3D3/4IxH39i/w5vjyv11terz67zemN9BLhuPYlFAjPGTYq83bBkJyuyYFwixRFG3JZHYtNYQEQBitiSBThpCmulyil8pVZJYKqCxQWaCywBZoAafduDaKrRxcs8v2e5jqd9kpTfgPP2wn5/oOuf9dHbTbtbuX/73soj/86U9/+vznv+5839+uFxKr9tfVtRtvTPEaioCQLY3jYJNHEHWyrzbM75i/vYbkRNOLqJgnI+c+irkzZeBiai0m+6mffoO+IC5cckvlECWZqdhl5JkrIlhlMkC9YErURaJeT3d6OaoVmOspJPF4iueBMQEq0rP7JnxKL9Qr3rfRzrNEUX89BbniFD1IJNeoZfSh5PBog4rFN9OJTg94+zs8Vrvsmsal11x3xjnXze+qHbB/TQ/usZfDtNs/+CH3fvzjH/+UuemA7u23pt9cOu+c5YbEX69K+aV/u2VeV82zWctr30KJmWfR3ZPy6fSm0uivqYgnxU+FbJI8FVmFryxQWaCywGa2gKnPh4zDjdr8gRRucqft0pF1S2D5/AXJgW43vx/BDTdeaSW7eLHfeK8tWVxzjnCXgbSG7av1pLjBJR9k8QvvG6EOG1KeHtvMuh1Ka0+WnGwR7QJQrrfMVTwAlBGbEKZDoUaEs+zUYiWFigeV4npcUnrSKJWnl2gJ44Dn+edfsWpt7S4L0+Xue9YFw1q4w26iA8yZt4NV5/LVae1pCSxdffUNy5atcDK0OBYqosE00a+KJoxXal2+qTtxveZNcVFWKZNMisylFVBZoLJAZYHKAhvdAuF2iQ3farl3ySWXOIwhKNLnP/950WEEvbKvLkjTe97zHvBXvvIVi0GhkX7/+99jCZftPI+IPCI0PfvZz8YLeOUrX/kv//IvuB71qEf90z/9033ucx9i3/nOd9rCPfLII1/96lcLGvW5z33u5JNPVrXJPxxT7LV++tOfPuaYYyAp4ytVYQ4E0xFi+9vf/vb73vc+8sNZYBE6wdkkT0Qve9nL1OUn0IW/ed3rXofLcSNFr3jFK0477TQBaxDH/i3AipjmBx544Itf/GIsv/zlLwVFEkXrmc98pnhSF1544ZVXXqm617/+9f/8z//szULaqSglTbbj/dCHPlS7fvrTn2qI2oXc+vd///c4skJ+NCczibTl0BQT8eyUTzvko6PMSBTTsQOBUQs5z33uc3/2s5/5IXdhj57ylKe86U1vEl3o4Q9/+Gtf+1qme9e73nXuueeKTi6ylShF4jo5ZkO+VouUJAqYVflrXvMasZCo8YY3vAHS5oz3HSeeeOLHPvaxRYsW2Vd/yUte8oUvfOFtb3sbDZn6+c9/PhNp+L/+678++tGPfvvb386womuBjzjiCC0VC4kOYjO9/OUvf+lLX3r55ZdH7AnNjL7ILd1sQLYwu/ku2SPxM57xDK8G9Ls3O0aO9w6U8aW+D+vFCGMEgP0rDfHm6AlPeAIyY+n000/XWOPh/ve/v30tPaJDmXf//fd3UM2g0jUGko7TuQDtdTsQ6xc7dKLamdpt4oD6Ix7xCDTqNeSiQ0mgJ1vRB+a+970vOQBbKPbNbKA5IIfAc527Q38973nPE8NLh7r7BCATboxAQ4LmNLz44osRI0PvFjv88MNF1NIXWqcieFya5mYhLaKhoRTiTY3ayBp03muvvTQBIxbmQqAtxEoIFKmRZQ455JDvfOc7WhdmVMTIWsHIFDOY2VNkE6OXDWEe85jHgIk1OC1GHLZXkUFLrDHp0jlArXB/CWVFVeHbDGAxsMwkJNCZTFHYdJCkUepiHONW1bqMcNIYTWNZ2w3L2rSVHPNmHIcMI2S/82ZEwZMgcpawaFqqme50OeFufOEAdIoPKtxl9DTU3TW61exEN3OI2HbwGLVFdY6Wab5WiElnZqCz24piVdq4FjBadNyGyMQeI3lGQmbHlavYQPaQQ+1ZaJ51mB0Q1t6k9XYiPAyIMoDQanYtmgVX1DsLxlaWWYvacMYwXatKWwhm1g3M+rcfFe1Ls5AKqCxQWWAqC2yUm5SQqeRPhd/AuWsD2UMras9C86la1CE+Zq1p6/Uo69nVY7zkcd0DqtTXl5alJEiWLeAiT6e4PUNZ7XrI9QCPxYO6taf1pksPsR6qwZ6Zg71DVTcdmeZrwkaRP2tRG86oCdP240Zp4+yEzLqBubo2fRRtl0sGoTxzVUBlgcoCM7KA22cDZ5INudlz7RnIyk91X2dK9YZbmYX+WUiubvMAoWo0bSodcqMA5jfbSiKi8MhB7wPwECIXGEQeO4p8rh0k24P2wH/5i9/cdvtNz3rmc2yi2i6z0WQnatIGYg9lJi1tRUbV8DPiCjnBGzVmOa1VbMmYUD6avwU2wRbxprZetNpjYVTkMobrpq63kl9ZYFuygBvH0qlYPSX/68615w/wBiSm1pjtNRlSHg+6cqsqbw3CFEHjBrT+IsElApchoclc5ASBSpG5lKOBjARWe7CXhYQCuABJ4wIIZLDLQwG6lRnhc4q6XALKElxiyfoHPdWIEqvK/wJVWUViiaKghzcFxfsvS87hoXS2SAApNKbA3JwJaePxhrBIJDBgzr2aQaY5vm3r708/dsIkwVjOyddBSSk/0Vz0CIP7xElduRZKEitFA9NXz4k0dV/obxGsamxoCIT38BVtB/t0OkRFjjWEA0LhjA8gSsN6BEarCQ8gi0o6FCm0mtAtlaNUQoKUVcqMAQQ9BdDAoA985oXJVeeiACIP4eQgk0NmIWBIedQeVQRX5PAA9DnFZZQGLy5ANDyXumRYXCRECpaNlatRIt+AIT/XleVDZp3B8MaPPOwpD4Iogo+2U9tTHMpoVNkgQa8jEJd5o5ZAymlCMbn7AjsghEAqrVJlgW3VAuNOcVtt3oa0K6aqDZFQ8W42C5jQN1tdW3VFMzIU4i3TBc6oFZuuv7YQNTZdAyvJlQXuKAtU/veOsny53pj/5QGsK+Jv0+LIcq5nbHT4pf/vIffae7d3v/tHtcZ+fnq2u7bjaM0P6FXLp3UGC2hGLuMO9L+x8h2rpU+0dXWhtiHQ/eH/Pu3Nb/6vV73hPR/72GjXWjva1ucW6s7g4UgERTPTrgemsYaoAQW4abIZGXPTqFBJvVNbwKA/6ID7vPAFz+4bSBudffU+O0crVq/yPfrvzv6jcwK3377M1/Nr1gz57U/3khtjfk/Nd0FzBtK25tBg2obrKwJaeT1U3O97MGijZ/i2VasGtu/pmjNan9cY6V3bNy99EK+KoeXL7eOm2Xgs7ZkKiWV/26aveu1wpW3aIsWtIcCEZAPXHpkE2dWT6vV5vQSTO88lmTbmVBFIGNthqaKJVGwFj7/EChxM0qG7m8zUkOIsh61npc4SkAbvEhxbbKEVDOKoPTAOh9iYjs0+jcDuc38tggkC9LiShSfeoqFxKUXDkUkuiYWJS0BvUrAvNIEMgJDh0T7CNQ5tb+88lXb39M+dt7B7ftqCHFq1etXakflzdt1uoZltuL5sWf9Aat2N16++fnHtsstv/OlPTx6Yc/Jeew0Mrxl1XOFu97yHkxKPfcyxqY/6up2yWHb7qPfKSxav+cHPf5raUyRAzIwTiFn+yxqUzMyaCVNGgnNpGSioJimaCl/mreDKApUFKgtsORbgERxLE4vQkbz7PvhMc/ic+o7y226Z48uen/3qEsfSzjr7b+b8lSvSidA1g+lRtj6n909X1eb09BfBJD2+ekjt7evv7R/o9jdSvBDtr6XwkXsMrF3Yv2b5WDp919ddvAHtqvNT4V/CDiZ8SaU8HU9TTMXppd34G8ViHu7vn9Pfn/wbyuHhQX5E4qpwZWNiD+elLnJSjUXChiZmewitCK4IaBU0KoWUu1R1oUOa5Lldl/KBtQN1jn8iQfB94ZERJwfZ10c3xBMkYkGmpDo54YqirolHhuTHKCyRAJYjKxDjFY0VQa9GG6mNXYWtw2P1jA3A1Me2T/iiwjkD8wb6hQBNdqsXKw72U9oY7ee9+7rnEjuS2tf43VXLVq9dc961td6zlt79Zxej2W3nBQ6F7r/fPXxv/cQn34eSPQuG5Tfeuso5xiuu6P/JyZdNPLKEYmlF03nKNokumJQx00xaWiErC1QWqCywTVrA1PegBz70yCMWLliYvjfq60m+wHlsn95eetl1Vr6Dq7no1SuW11avTRuCktl/u75ad/9cvA0L4EY4gaKMRyhWSLFKanSN+rPQ5Uy7isBY40TFPzHrlqflwJSc2DryKIrrWCGuK+sY4qPRqnHC0ZOaUqsAyHCarUV3OMZOXaFD2HhcnWgGB601VpbjbWr0FhdWzXWfBdkMXLK6G2bx5f21emPHudf29Fw/2ljh5P897raHfKednPrf66ADD+WvxcDycdjtt6+4/Mqrp21y2YZleFrGDSForShjyoNqQ6qoeCsLVBaoLFBZYNYWKE/F4Xm5bxO1KDPCJAmyI8CTHQA77ULPxG7A//7v/9pP5ooERULMa8sd+BEbSNSYj370o0LDOCD01Kc+VYwhgXh++MMfXnPNNWJpOYUrFI7tgp/85CdOBL33ve8l1iZDKK92Pt2XpoJJORokJJP4yypVi9BU3/zmN1Vk20E0H8d3w5XIUapaLCfxdIQWsoZ1uOgf//EfbVkIrWWX2wKfVqogXykWtVAY5iMf+YigRfbhCaThf/7nf5ImwtfHP/7xH/3oRxyuwEMCRYkSJVyRRX3YKiRYth911FGiU4lddcopp3gk+/GPfwwQM8jTSzzJ5E6JplEPFxvKCYFUnV0Cuyhqh8Ql2e4m+c1vfrMAQ8R+/etf/973vsekIjG96lWvEt2JtnjPPvtsphCIJy3eR0bYU9AiphbSiAICLVmk6zg9+N///d/sjAyMkj310ROf+ESmpiH7sPNDHvIQZGeccQbMV7/61d/97nfCnP3617+2xhd1SFgiYbZECyLwOc95jq14IaJQUkNDcjM3G1CuV2MlaghLJMiayGWeio1PLaWPHpdHd7OkfgQzmkhnxhJbGQAnnnii1hnw7CMWmPNpBptRilIIpGOPPZa5yBFPiil0IiuJlKSnjj/+eGPeEP3Qhz6kyww5cZfUjguxWGMu1a4jYuvGQ7uhSOB+++2nRqGUdJOAcW60r33ta8wrRBQFIDEKDoUXjYpEetJfjswJKkcxjRWGTNA3wZtcipmlTwk0ALC76YwoEbgccVdKMcHUhHiz10S+8SxuHQJBzY4s4tC5K+3tqCsno05Huy8ElmJb1lZk/Bh4Aqu5pxYtWiRYmDHjvmNSw4MpvJKjNuu5E93swsZpiOBTbij3u1J2ENBKQ0RwM/bgDXW3gDBeVDVEBb3SUmYkRGQ6Y88kYxpBqe/iBhGWiwHf/e5322ETocywp5VAe6GzgHQEmkDcUNRmbbeMBrpTjA3dp2dVRAKxrOGWd6cDVErUBz/4wc9+9rO62CDXcD0L1jQ0FCBQpdqr9+lpGOjWME42XQVsuAXCpHfIxLKBytOc2tJMR0WZPoQEhqgNVKk9e5YPMJjzZXuuaUvJKbeoTF/Gt1a3OdseWoWqUW8ZU9Z5WjgaEk2TB9DauknlBFmoMSlBRuqggEN+xufqOhGSuTYD0KEFOtRkWmnTEnRYUUVWWeDObIE8n2x1RqC5SUBqmiGnbUiZPoQEhqhpeTeEIMsvdC7vGI//vKJ1R0z7N9983RVXXGbZao98jz12u/LKP69cede58+cMrkkHOBtjjfRLU+kHmVylD8xwSbfdupRkS+ye7j7P2PBLbrjBwmb5ypVgKTWzeLzPmgQyX657g17Qb/SsaDgt1i0kAzOjikLb6LIkq6VFbaQFbyeVsmfICflZZq6uEyGZazMA0bSNVVF7aeXSMryxaq/kbJkWMPhzdwOabo0tU+ctXyvTvv2HDdSz3DUzElXuxzI8qRAEUi7Kk+GmGAkdtqhDMjpnJQMoGmJnOO13RdIRXWP1tYNrVq1allpZuOgFC+auXrmUO5g7kD47K3hHGqON4ZERH3cP+y6rsEe9p3te/8CqVctHRgavu+5q29QD/fNPP/23F130Rxt3tvWS8CKpK3y/Kzqk18QTCrT3v0XViTSAckcEnAkm5E3ybyc0k7BNgSKtrMYUVOvQTXq2NiSTTqVnZiFqKposZPMDM7JGG/WmkpNbnQHjqo2cqqiyQGWBbIHyjGFO9pbBxO4OkrwAkqP0GqX14R8jem5a7uWg9yMhM+5TvN5AYZTQgInKt3CuFCAlVzJ3rhxc9vt440NikglsZQ8WvLhCTuiAGKBS71ngMyNkwJBgXNSmf8iBDLzLrH9oHnj0WirN49gGBjBGdUpp1+sT74G58dbJl9SDQmANDQ0PjwfjIwdxyJGrQlJ7vNYprgSO7K7VffvU7WcapVqXwz4pTLNYRmO1wXT+sfCMesXRrDHfSI11DY/wzj6F7qUbSs9NQg01utJ5ya6aA1keojgFhvWXTNQ/psinVuMuvrfus/Ck4WA9nd4Sp0uuz0NDCusTeRityBEUjUZRdJZ6C+XTYbGgZDoNV4qe5OiLyAvOdRlGZswjJwqwAIIx4FLt47wYyWf/pERhzCgISlySGuUhCj6KXKIHF3ypF2DyAIhLmOh0OUrIkAMvZRowIeVhUJSPv2xVu9GLHZCrJodMhgoFgr6cE1i+nAVMciRd0Mqu6tzwckPKgxNXVoMol9TWFnmYNJAhPCjhXRLoMsRmCUEWRlBL2ISoLDkI5MFikGdMBVQW2NotMO4htvZmdK6/27g8QbRnLM9B7Sm33lLWyDbJTwxbb3Mqze/kFojxzAiAeOabhUE6nyJmIbxiqSxwp7VA9jWdWKDyv51YaZPSxNqpfRW77lHzpean//dpZ5/dckytPWdVekdboJMVvVV2doh+VtZHuj7h/b//+581a9tpH1HM21FMXZarm5qkKqkssEVYwLfs/NQVV13kuMJNS2665ZbVaTd0sLZmVYqiYYFtu9QPBuyyi2jutqpr8+f1+CZeUKyVK9c0Ru3lubmKz4nS2VItSgcbRrrSreUsiuQYSL0+3DVnIHbHYq8qeUbBIib2nWOWtm8VW1dun/E7qNitKuO7fIlUBJZCUHavNr9CcogiGQZNeffTCQFJUU6BoZiVYxNxppkU8BgQSSk1Ag59wJDUAJQ1hIShfBYYOqMMy7jMKYRQSXKywr6eIm1BDCbWfj+a2M4L/eXe+/b19KZ6R4vNxOER7RodWTM4NNLXHy8JUuXFBnRt7dra5ZevrTdqV159XqN2Xo8AHv01p6UWble75z3vccxjnu4A8MDAruh1wobum+Y2TwDaNQGmDg04A7moAioLVBaoLLBNWoBz1C4+yKGvT3zipOXLazddW1uxotYYSZPzqtt5llr/gKNxvElyW9st6F+5cjBmzjxVpg9sC7cSLixKY25Fky4LlCylidc/CSxSfmM0Tpz2ndJsXFCP05RdqqnaJeJIIaQpD95WAi4MJXwiEFSqXvcWLYipkSstSgM9nmOpcfwTCRaGNC4PTJ9MPUEyrn/R+FyYgEwAiHYl4UV7ASEwaLKtojTlRUEIGRdVoDAm0YUmXfX1qoBUqnWNnvGGz5sztz6WfiiYsBUrBq+5ebDv0lt+e+alXu929xoMtYW7Ji98yMP38e5z7txD2n+hVWjUUUaTqejSg8REKRhZvpyKpcJXFqgsUFlgG7CAaL9c50UXn+djpltvXswnm4otddcWDke4K144fTFUq+0wkJZOPJXvMbLnSi4mfQw0vkKacpItWao8u5bhmHtLhOMgmvK0HCxTEbeyZww3hEvKAsPtBgF8eNJwiJkrgFlX2iyn8MzryZwwHWTU0sQy7SWulBJ7bEGs44COrtGo1Iepr3zZNaYLLW/T8rxreMWKm6+75ub0iNGVNjp23GFnnne77XYQocAiXr+ve/JYJ3iLhgpjbNEaVspVFqgsUFngzmYBTxqazO+IuPTIRz7yoIMOWrRo0Z577in+i6A5QjIJaiOh8YAhsswOO+yAhZuW+5T53HPPFadpp512sn703CI8kIcWEXPEvfr2t78t9o2deZvAAt8IroSdI7Ok5fs49zC1kDe2Hd7ylrf8/e9/F9lH6BxfVNuCOO200/Cij19uQBzrVgAJFKZDJAeN6PnJT35SUKE//OEPfiaB5tEutSSXWqSokTRX/BEgolFj/81vfsO3OjbsdJNVvGhKdA56OWJVU/LUU0+1MYJdQCU2QSngTlQUVeQ8KiVZ8gxDuHg9cnYQgInaeMlkJVGQRNvRXnbbZ5996ECru93tbswFVjVGlJqMJfZn5IjFGxLiR6im888//+KLL/7FL34h6I9IVWIbRTwvNhEF6YYbbvC5vBpx0YpM+njhKKAVzYU00ndarSKlKop9AIzkCwaES6mI2GGNaGDYJDd28wAqjXrpD6Cq4SFmGdPRUAMhQxNtCQIRixBr8hve8AajKwJUCZfGaO985ztf+tKXMjLjiBh19NFHi86GXlCkL37xiwDj0Nh+4AMfqC8EolId4YI6sY+QZ4z8gQ98QPQxo07UJB33qU996r/+67/+3//7f6EeBZjXzSL4FDOKOBa9SZToVOK7qZfaLr/85S/vu+++Ik/94Ac/YG03Ef3BaNSoXv0uLttrX/vaf/u3fxMNTalgTCeeeKJSkcu++93v0vBZz3qWDqUDgUJZvfCFLzznnHMMJHGdxOcyHty8oj6RQzFGk0d3A8jRKMaUI1ajwQapjeymaa94xStIE59O9CtyhMMT0Arl+9//fjcaSuHAxIxDbCjqCDGwRK16z3vec/rpp1PGPCBKF1VPOukkVauXBdxfeFV04YUXip2nRe50o9rtIJoY/XUECW5nXfOlL32JAmYkEtw+j370o1VKTwHvBAU74YQTzBjEssPrXvc6T9Gf+cxn3DWig1FMAC92c3eYrA477DC9j1EsOZf01DXqNekdccQRInCJiWYGE2aLDmEHGuomrTYlqkK9VdroFogbdnZidY1xOzsJcQvket0L5OTLNoB7XCn2uH3aUE5VpCIJe1kCzFT0U+FDSORT0bTio6LM20owU0wIzG0BME65lqkEBs1UpZsOn1XNCsBkuMN60Wc5HbJsLLJQ1RxFgTtKh43VlkpOZYHKAnegBTZkHqv8r+l3pgaM2dujdzF5J/bs+b25jcdMiztrFutHT6TWOCIUb7/9jujBafGYHkBSKlxAemzwpKq0tzc9usfaFjBn3jzLEEsPD956KkJiwaPMvmNCmRm7P3JmnSgQvLnlMBnuUCz6LKdDlo1FFqpmG24ssVujnDuqC7ZGW20DOptPrPG3gYZsM02YxcwZbc/zp/WsRbRcis2fbJymOTlfBm/c+xmZuToEQgjiDHTIiCxazRUakDNVoP2UZenKYw7M7UfW2522x/nNK674O5e18847cKO51cWmYNIEy9oVw7vsscNdd75rbOLZjLWBxn0rAuyx511s8dlKmqqBhHTeiqxAlpZ5O7ck5xV9PaOqc40ZyFVnTCdAWc9ZSAiWLdP/zqI5nVhsUprNWdekClTIygJbkQViruMy7P+bjV2a6k2DZhK+z4MNhyLxhjBxc8ljvtVMAGKl2C3EAo/LJI+dWKUBR5G8fIe6VEqOtz8kgGFiEg4bulS7vGzSfBn06lWRBJ+LCIGhCbVJkJMQVWcaGBIK1p4QUghIv2EfOoRB0GhLwZViPAGi3jIArwa5Igk9gYN9w145rVqVwmD5Iht9kwLokbEeQI6RMt46ArqLYFU0VIQveLFHmrhMLYoU9sHokhYFY+qd+LbKv4rSVdFlOUcM39dXxGMaS/UOjsQXaOO9hkBbQnjkMPkyxOKiPM3lYSg5teUMKA/jZy5A1gRLpKye0qhCL7CbyzzwyhICJge7utQeMuExyvOjRbSRGq3sMHglvCEhKwbAKE3KmMkm1C+OdBUjOQ+edNtMDN2gn1SBjYukcKu1y1UoDWWi1eWiMpwVJhA+7CCXSJAysY7LMDzG4M34IGZGvDEYCjEpC8YMZDkVUFlgm7HAnTEAVvvOK9/wpt32xNtGaW4yfx9wxrRvILKYSc2q7VmCICbf9jLLpWX6aeWXGbccOJoQzafVVteKchdMa9UZEZOWzTKt5BkRZCMbz5mxvW7BMpU+mTdLDrEZn2vZ6EB+Upm2LrqhCTJ5k6pNigXBtGRNXLO4zPq05w1tc2PbE1elW68FYjy00b88biv/28ZQithqU/tfVUgtajgKFaus0cbY6Fvfs8dQ/QfvP7G2zz71/p4i0Hj3zemUW6RW7hZxmw6R559oxWRtWVf5tINzHenmgmak0oyItQC97w2Gh9NPbzUlRWwlqAu84252UWy4+Z5hdNW8E99wxTOe9cj3vu9u//LSa9K5xuEd6uL81G9OwzGNCqi06ralYxk/OtKod/U0xpq3yZqqi36JSpuKXOZ2BVkmyPiM2ehAdknT1kU3NEEmb1K1SbEgmJasiWsWl1mf9ryhbW5se+KqNFtgzpy+Sy+94jvf+eGtS8c/8tll/nwbpytX3WY7c0HfnIao/UJFNGpLl40z3b6sdvuylcVFnxule+IR1Q6tHai1Ize43SJWxNKVg7evWDtGgL/hFYPLV8zfYfvGmrWrI/Jc+qmAru0X7ujn9epjaWs1EiFSbGyJ8CSBYez3IaiPddn2dW7By85QKEYIGq9C3bBKY7cdMVgKMrnSzJWR8ZWSkUOC0qhIaVKicAHFTuN4dK2oC8ZG5Lg+xaaw8wmOE8TtEIOQhqpGo1LSIIMFJldNT5fqpSpkND9/NRVkNvYcNxkVYmys1hiOKSr9GFKiN7mN37OW+akWVehFdXU3lNSEvqoPOdPrKy47tn42gbD0C4diXQ0Ns30KrSIwR224NtSoDY3VeodqQyO1m26vXfH3v//61A/uvEP/ox752KKidAaYinJ9Vo4MEkpW+Z3WAjHmZ9T8WbDMSH5FXFlga7GAe+H8iy485ZRThiOGpFeTxUe39F87tnZsZGxFipFl0uYcuhurhuvdPcUbzPrQWBGuojacZuMxb2GHrrz8UpTpB3i8n0uBK4Wi7PVXG/V4XB9ucBAeZcdDNakFgZyf4ua8C+Q7MlJRpHBeq1eu8TN93pOODKWHgZEhtaU/XnqCsFabNw4WbjM5GjKlwIbk8MXJm4lB4Zf8urocnws36hPj7q518bD4mnCUnKNNsKFhctb5TW8fSR72An00+UEuqahqvK6oMTDkADhE5hvHFME6tQtyzRrnb0WnSs5X8sy7ZMlNhbsORLKVV7su/BRSoOShR70Wj0CpdHi0wRj+0MeqUZdond898uQz3BXhrqJ0hEk0v2twqLt7wc1rB/WTBxTxsboafj2pv7G2ce1S3rhx7kV/J/mJj99dBZ6swJDyWJukq+kSZaYjGS8vU5bhDtkrssoClQUqC2yNFjDdXbN4ycmnLFpP+eRVxlP6hLZR67GeMlEXS8nkWQXAWptKrEeLkgT6P6eYnUe6uv3xBcJEdxerXXD6W+dMkvfKSVGCixCKMQ8X+1EJpyAw3IrVHyfrMjCpuLMUrhZt8CZRE5ucISqtHyeiSyNDkIkjXHV2KVEUpevybIT1jKF8vEbC45OphCpSWvwW4cPSE8GkqWSfifIClbYVxhPeendvvSv9IJ5VctpWSM85Sgs9wOpPwa/GdEZKo6tl/DBIrUUrE9r1msHadUtu8QVOV9d1ngQOfMADff7UGF337JHotoAU/bUFKFKpUFmgskBlgcoCk1vARJ19ZVDAcLtnnnkm/AEHHCDoj9M7S5YssT4966yzhJs5/PDDrYvFphHCKVisVfluy2eM3inbanbCNvaTFaUF8tCQoDPiAT396U//4Ac/KJqP6Dl2kklwOld4GnJUh9gm+fe//31FD33oQ31fKywOHSAFZkKjFmLzZ1ToactHS2pXCwI/mCFWzpve9KZ//dd/FcdHJK93v/vd+dEiFC7n6tUce92qo4/GEuWT9GCx5X799df7LN4lSkldYFGxxM0RYeqVr3ylGmHI1PzQLRRDHBUpFUxHnKz73//+bKgiBGrUdvTCFe23335CAglapDlMTQcRjkQFuummm7SXSXfZZRdKggXoQYOdZAGVYFibHJGDGPZJT3rSq171KvqfeuqpgjoJeiVc0dve9rZ3vOMd6MWxeuMb32hvgZ72VWDCYuIK6dPXv/71goXpC+y6j3oINMFuBk1UFFzRxZBagYAy8s2cypWCoxX6iErUplvqp2I4KY0UvYZS8Cm98Pa3v53dxEtC/D//8z+MzwIiJS1atIjNRfsCMPuyZcscOxc7SQMFwDIsP/vZz2IU+opASMP45z//OQs7l04OQ+kLZvzGN75hEB566KGnnXYaMoqRJheRStfoX3pSmED3xSWXXKJzCTTaDXUqiaKlQzVBNCg9YhBiUUSUkSmK0wUXXCAml+Fq/JAgRNTTnvY0saUuu+wyylP785//vJc+2m70HnXUUarTcSKX3e9+9xO7SqOQGTC61T0iRxmDVhVRC3pIOshhtEvgKiwGFa1oKMDcFVdcccQRRzzsYQ87/vjjVWfYkMyYInYJpkYHg02TDTDWi7HKOEJTkYlS04znyy+/nD11DQWMPScDWRgBaUwhZJjmuzVIoOqVV14pcB5l0APcemyoveivuuoqlmRDtxtjiqJ17LHHajsh7lZ4iolTpqO/8pWvuA0pHwHsyBFQgGKaQCW3hoqe+tSn0lNMOt2qvSwQPa4fWYZZkpnuoPEfVW8zuf4yqg1vd0SMOsPGHQEZY6/DlpKD3V0m100dcmUy94jhZ2jF3KiL3V/GXiaYCqAkYsNDrgk0NzgBU9FPig92DQdE9LdJyaZCqlqrY4iGd4CZiriML1sYjBcjC7hlykVlljKsj9y8d73rXTU5moCd0dg/zOISoAgmwyHBJSDXwp25AbXdRBEEuahc46QwyiAO7xYWmJQySw56l+ymUrm73u1PQijWhr21qKh/jDW0VGolaI/JNZKT4WDJl1lh+IwM7+/S7UNzRTqCGu2r2wZKszWyKcpm2QYaWDWhssDmsUDlf9nZpG0muQP979iYN86+8krucs2aUbGqAB69PbFbR7xs3t0tTDzAe9blqihcn+OBpf9by6791re+ddNw8ji+fBrwQ4L1+R6J0yXPW69bOPNuC7bbbtfdd02t6/Kyvucuu+3q0dezNGePMjnhetf2O+wAOTBngAdJFReOe/3fSypoJ8sKVdMyQRXUq/zvZEbapnBphBSp7H+3qRZWjenAAvlZHW01Ejow2CQkTf7XqtM8DGklOwn11Cj0sWo2/WKfmnCSEusIbkJyX3MfVq/cTTjETO0yXE9TR2NRHV54cCx/pnUBWWwZ0ASM1jKTrt/znFNmUamES71Go3WrVTxMmaYNXHAntQGF/MYOO+x4ww03LVw4nxo8YX1u7f73u9/tt6advbGRUcjeevcuO+0sWAf63Q94gH1g3pijTD9+PFzbftftWWPt6LK7HvmIA44+Um9GFaNdAxS7prZGRPUHbL9z4+tfX/Suj1Ns1/SivnbD2OBOO+9cNLwXO8kxvXbof3N7E2OxgR+Lwakajj4shgAcpsOCN7QNRpdTSWjCkxZDDtBU1MmlijzMoCSEMoZZ3i4oNy2Lysg77fo3m6ICKgtUFpi1BcxXksnHKzaA+cfcEtOgbUxzEW+o1Iw0viwqdizDTxWsKSyUtzA8JiReSOzk8MVgimGMNVGeTmP6UopR4llMdwDOSxF6lXptoV5y8FJDijZGFXJJjerFGABNokZc6iIHFzmhD3qARA5KNSIGBC85LgupiYyrKuocj+mjSHwq/+OFL8QkT4E38GONrp5u328P9ffNSYayzhzrobKvttELBiVPPrL48JgQBx5Jozk1pNSKgf5eB4G6+yxaR3xt7M1jbc1oz8Kh+srBrt7BdAAorVK9lMLu4Wak0d3weVTXPD8L2NM1r7c7Gd8XVJ4DqNhTH60Pd3eNOLzlYylk3f206+qaO5ZMRCdCIo31JCOs6k5+cKDwXUnVNXphrWYmCR5piq+5dETarU5fqksWufiS9bL9YfF6CIHPvUZsQZ+ypGHhYemAJsYMdilsGxKCRh5A5nKZRQGwMJ3nLuOEtChCbPCg1O8wACqFhCZeLGH5AAhBKYX+GDUBXK40lCRNiqppDogcUtdjIURdLINXIkcR3sRWWEBpwJGXFdtA2KkyNRLiJX2rKNWVkSiz2cv4Vjg1o0hNEsYPJa771i+1C/sE+bj8MEjg5esufdNXkGe9usa/nF9Pz1Z9Kkxlga3CAjN+C7tVtGpjKdnh7LOxqtt0cpqmxakq0t48901Fs/nxMVlv/nq3yRo7HAnRdsSMvwXaocMbM4/naVvdYUuznC3TLFtgT1UqVRaYtQU6vM1nLX+zMeZ5o32Neb5qT7bZSs1ykz0P1Hv80ESXj7mTIg8/dK8nPenY973vU/bWrVFTrJYqTW2BDkdCCOjQK01d2+xLwsGNHymcEAPpE4e/XbnqE5/4wmtf+7KvPvRzZy8yCOwciKsT6+r13i4YPCFn2lZ32NIsJ8RO6FX9W1lg81nASM/7QXb3fMFz09Li+55abUAshhTaaWxOd5/Bb7tttOMJsZFO7nrYdgeN30Q2/qQRX9t2i7SUQi6lLU7JrYaweDK3uwcxPksXu4rz585ddzcVD+/lm2Wy+Xyd6UK+JoTYdQUdQ/mWD0DV5RRiYLI8MG29WIUJ3SK3RR40LqM5KAMu82bhkGW4TKMXwlZBYFd1dIQZx40MiTg1udg9V5daerrSdq09Z/PXmkFb0OY4UTzSvrYQVxiKyc6bg7QdGJdyNNtvrxW1m29LyJtvH/SO4W533e2a629QWTjGphk161kBlQUqC1QWqCwwIwt4idUJPW9iYg9KE74Z3ltHzi4mf/gMpJKJhEAqrrynLOgnAmBBhldKzqVIJGQhuS4LOjDnwo/nUlUQm3RYV1VSwHUSkd6jpr+CwAuqeK5OXnJCTqpP3BB7c1F74ilSwTL+yF2IGhsVfMLjSHAEUXGiJtU1/ioruarEOB7ReJwoYwBQpCVMSeO4TPgJsb5vpmuJJFUbRi8zTpCv+3fCGknzeMGWwnCM+V4rSRgVnyN9sZwatI6naEX5ciqYAuuxTUVX4SsLVBaoLFBZYBNYoGkGdhk+SQTiSWsrY7MznZyyWD9OWtQG2eRK2lCWi+IpAm+4MwD/G6JCyXhaiFKYci1lsrLMDYHHZY772NlLCjlyzx3hr8uedry09IzUvkfKeljXpz38eIYqF1RwZYHKApUFKgtUFpjOAuFPkx8pFqqRi2px/vnnH3LIIS960YtE+YEUVWHRokV2XI8++mixZsRXEjAiQtXE6tt3t7YLuGyb9jw1pAW1b3w5KS8uxbs555xzHve4xwmgIxiNiDZk8vi4+DuJH4wHAIw+3j3iiCMcwRLgBotDRIpCQzvnKINFriKXcnJ8tK064Yoo+ZSnPEX8Gqo+5znPed7znicyjuokLAEE7MtdvKrbeeedReoRgANe5B14TZOLkRSUrBSbIXIa0u0Tn/jEk5/85Ne97nWnnnoqHZ797GcTLhASSgqAAXJqA372s59R47GPfSxj4mVMdYkTBN5tt92EZPrmN78pppgv4N/61reK3SOA18knnyywyAknnMCMO+64I6vSM0xEVQprMuEMotK73/3uIiL98Ic/3GuvvcQOe0GRREQSTusJT3iCKGYPeMADXvKSl7z85S8XrYm2mkYrn/iz2Je//OV73vOeb3nLW773ve8RqyE6kUDVOYnHPjpaFVioDVYv42BkB8a5YxMLaw4jiPQUI43R2Acy8CymVCsMCWHdDj/88OhZyrMA2yITuEoUJxGdtJFAw5u1NVnThGVBhtgpBVGfDEhydA1DeTMlTJXqDjzwQDGeWIY90zv6Wg1ApvhoEQArxLInwOhCYxSBVa1nDW8sbKv3I8HAI0Cpajqwdgy/KFIdeqpqMoDC++yzD8n6iyhVG4EY99hjDzTaq5mC2px00kkPfvCDjVvRr1CK0kVPOmCJUQqIpF54RnMpJ8q4CrHeUEOKWvXmN7/ZLGG4kq86dkbpGInb0A1uSDMgtY0WdVGDMqyqLUrJd59CqldLSSCWfBLYWcPZXFKq1WoR2ItltBSx2lVBPe2FQax/2VZHqIIaCHCJN2cYhB2UEiu6jdL73ve+lFQ7ZWJskOkyLA8jVt1vfvMbNwvNqXTGGWd85CMfYQFiJRpKKqU8HeSBr/JZW0AnRtAxhmVSXaD7jHOXM5KJRX8ZM7rbkJgRr36MFFM3NaSIhNWhHPVSGDt6w75DrjIZdkIMewOeTcpF7eHQ3ADmUwiZRe3mB0Nak+POcoNwTOSQ3L5qdy5V3VmxVR7E2fiFFdP0UtzNyRuGwACUZuEa7m5Vox7Uj4FHVqbJxE0AGuw0kRs5GqIV4Da8oQA5BpuZR/PBzE7PfKc31dL+Ul1E6f0ZdVzIDP3BmkD5qRQI0yHLAFh1MU9qLwCGY5rp+MFVpcoClQXunBYwh1T+V9ffsf7XcsTR0RiBu+yy3U03LRNRxKUwVfL79F7loZ2Gnlpj/dg10MdtXbJHWhp7buJzhYBeudrr8tXz5ywM1ymPFQEJ3T1dgmcNjY5wr7ykR4Wyo+E+brxuce/cOctvvW3OwuR/w5sApk2V/2U9qfK/0w6ViqCyQGWBJgtk/2siNcPbkzGZQFrFNFG2v7R6MsOb1W0KWUe0J24q5VZUjT0mMQp0LgELXmsfezuW3lqRF4BNtbS/5K0ogIZ7ak/ZWqpSSDm/pnapvEpqpW/CYNRkpvNDvIsX32RjadmylTvvvMOtt97OyYo+v2bVattWKcpVsSL2vOR3EHDZDyyqHsMr6sWgH+hdfGsSvqB28QUXXLzkWj/t68EifbY1WuxI1AdPuec9d5u78Ibf/z59hZZS8vLzbE/dcOPAgvkrbcnOmwdTiE3FnSTttVtFVX2B0SVt2jCi0WWZIC4Nm1jyz6jqEMJ6nkPAM7V8sFNb7fT3cEKOzTqDv6kHmy6DsVr/hh2qvLJAZYGZWiCmFFMlZ2fiMol5MZHnGVOQBB/TY7HOGp8zY/LkpxAjwI4xeGPiNZWhV4oXTHI41rKGUa8Zz6xLFHazGVG2UuU2AzFK5MTmaihGFEACZC464IWRVEE99ZIfEoK+PKtThlhOHyMhFMAOxhsSsODFwgKFVJWm/dusf0gLyanJxfxPQmCKxqafI7Kp7JEgJITRsvyglOPSXokm8cPD2qYi7HKi1BUJCAMONbIEl8RSA3FSJtGk31MhJogTZXLdwnIl1lRQpITqTRZopHhc9VEnkYrDSuSwUJGn2v28ojyxSxPvfdAnhSd6gQ01QU9xZBiTuKKnCo7EGOzjQoqHHOxhf10fxkem1eoigSjEmV5RU9JSEvCqOlqtxqCJvpOzOQnwBGb2QmoK9h3sMQzIQaZSzzZhtOjNzBVA1id6LQvRdkVhAWNGvdH8qKsMB4a0LCpXgSzDswNC88hbJYRhy/gYk2VMe3hSDcu2zeyBjDEc+pR5p9Iws1dAZYFtwAIzewu7DTR4q25CJ7NSmcYcF44nt7ppTi8TB00rJvO2AjMibmWfFhPyO69FYztk6ZBsWg03OkFTBzXJbzIF4nBaAEmpvIllC7+cqcJBb2A3maKpmVusNdqr3dQKl/nZpRNGNEEWeau0jMlk01JmFkAmzkC5tAw3yXcZmDJNK9w6X7XSVJjKAneUBToZw2Wa1vHcNN2ViaNRrZg2jZ0RcRs5bYpaq6h3eWisj/ncuqe+816D/+81B5zz22s//sEkY2x0F3NEAia2AKaSHATTkk3FvunwTR3UVFGTwoi3df87viuRG75mpc+Lr/vEf488+pE7v/LVh1+z5JSbr7vZqwobA3YwumrxZii2CfhoTyPjm0FNlryjLnNDOlRgU/vfDtXIZKH/tK3IZE1AljMp0DpfTUp2J0em/cb0ojHlLDZv7g4MMqcIqFSvzW14j2g7crSnCJekJI1/Mf+nNZpndzSC/8tHRfQfG/E6M/0mgZd29W4xmUw1qTd7it8CKIjNP8UGWUSKQJiiWFAppSIwRSwGxhF4O5iZ29NMNfAyPupKqhSJhgHIs2QwtcsYZCREihkVgUssEkoEAWekyxACk0UBygTjzBOxtAhigL6e/jUjxa8a6ZpCK7+oa7t9rFf4stpoI70MYGRBsgYJK2rtbdR6u2o+3fJ6wdTmb1ybtEmahgGytDXtt4VX2IhP29QjRYc3urqXrlhFhvGivBg4hcQqqyxQWKB5uHZgllmwdCC1IqkssFVawDNm+it0T8+bxaWrjCy3yr0TvobLTMRF8qssyb8ULsYsPWniIsI1kxBCJiVrQqIMTHioptJOLkPCpO6viT0o5RloItiIl6rIKrUX2xhLL4lLDhPcamOY+Bu3V+FRdU96ne9/Twd+rCeJKSW/4SSF9ETjT5BKmOJpKPq2UZvTqPUV0sY59XVJRgVWFqgsUFmgssDsLTDW1e5r4Ji1Y5aOOtJRzolUBBOeuGj5N3nwYh5PQuriK6YVVMpKviC8ecImNxGzflwFqliGBUhawRiLxLKQEkM7MLNMBcDnonaC7uCysEn0TIsq6VMqyPg6edzkyaxp7boeSxGZMzncdUYvyicu/dvd29M//ht0LfVUiMoClQUqC1QWqCzQ3gLh1vMeb3h8Xy2fdtppRx111H3ucx/hdXz5Izntc8EFFwjPJP6Oo0rxzSu3hcVeMYL48tUZJJ9T+/oW3te04uD4WBYgxpOwWf/yL//y0Y9+VJHPdtWCBUxDCoQQ34yKR/OoRz3qO9/5DqQwQ2LWkBD+MYjRB6BSX4q7BBAI3nfffVUh0pOTw+ITnXHGGR/72McEeLr66qszL3q70KFtVA0G+BRY7TR3KZ6IM0UCSGmOEDkUoExIkBMosJSAU9/61rdQirzj02Enk4Xdufbaa6+44orYZs/0Qup88pOfFJbqH/7hH77yla/48tghHxXJfQGPWBgOZ5XF23rMYx5DrFhUahfKKlQSwUqAp4suugg9PZ00c0zufve7n1KXAjOJU0bzD33oQ4Jf/OIXv0B273vf+6CDDjr44INf9apX/e1vfxOBS4Sgvffem0psJWmjpu2///50xkgrOhxwwAEiaKj6uOOOE58LAVVd6hq2FXJIP+o1po4Gqv0OTxpOnxtuuOGss86iNrMYsb7nlujMvJTXO8985jMZMM4baBFAhzq2JxeDTPgqQvJn3JBMpFQzwXqfHZjOmPz0pz8tjJQB8Nvf/laRLjAyGVwtLKwTdSsTkY9eETk0pABRchilaFxSj7VZFWUMMHj2xIJGzLIIqoWLBEmLiAVEdVSiMxZyNIHycY/A6Eoxqly6T+XuxzCReHBPe9rTBLgRtOtlL3sZozlXr0Y0UnRl7lkqESWHdxuS7+N+A0CraUJnQ4iqGu42AUgoqcdubgqASwqrDgv2fFsJc0Ms0+FVpLECkOkdeJIpoDSsp42EIKMejCrQkwkP41AiIDqaqnSDVJHaCQGjZxzS2JAQEpgOBg2Y5bGjpIlLid209Ec/+tGiRYuE3nMTHX/88dT+7Gc/Sw0pFIiWBqbKN9ACBlWMcz37yEc+ksf56U9/ah6Lgde5cN196KGHuhEEWxRMMBh1LrHTCsEritNhhx3mvjAO//KXv9DKeJiWEUEMCYMEL7d18cUXix0Zg60T9pDgZjH583RuWPMYfTrkRRbERjLXLFAddvO2AT+thGLId6mathjdGmaw8847z/0bnqW9hLAPXlVz2e4dFnDvuL8yI92Q6QJJX6DUreCmBsJzPfvtt5/ImG5qCrids5D2gNmDQBEAeUmzma4njf56pKmWLCcaLkepIo5bwEezukB7HKuRMBVjlgDQHKbTHMSq40aJEuVzpuOWAU3Ohj0D8inU7nzwmNjNmZTHa+QbeC41qqxnBVcWqCxQWWAqC1T+lwe/w/3v3nvfvfC/exT+d7kH0nCSt93mkfsP793jmt6dU9SqBfMXLNx9J5RLrrv+b3+/ddebl60cXNM91rv9/Pl77rkP//u9H/0kOcSx9Ba4q7t/3WPE2Ej/nHm9XcPzF85LPxzpKyyPRsWY4L8cPt7vwPX8L5c61YBpwlf+t/K/TUOiuqwsUFmgQwuU/e8RRxwhcL+tV0GXOlmClasg5+EPf7hVgO2+2PxR2uH614rD4sUaxAL2d7/7nT1MjNhjiZdrmXRZhDLW79aAQvzbHObCUE5KnEUFQH4ms8tqAW4Px+rbxlQTZZtLEuznWMDa8OSMTj/9dGsoDj0kZ/lTSUBpP1DEdtuq8+f3+oUGayjezzae+POCSNx8840DfWm/y7ZUV70rtrYKmkaExOpO68jehx7yCAvY//vKl1etWT1/bM7qvyxu/Hlx//x51vVpN2NO+i3nkeG+2l9v+eufFon5MVC8th9MZq7ZELz7vfe1PavvWEBXjqvddh8gaHSc9bKNXAtYHWfT2x6CPbFpPXiwsxgJu++++0Me8hBBqKlqnx/7VObK+PL61wLWDh41LGCtSTNNJ4CnLyb1awE2HyzA9T7FygOjjZBq/dvGOFVRZYHKApNaIPyamQfAd5jueFtzuL27vH1nVow3X6YjyVQTbiU5giLcEkqMwYudE4SX1GhSldtL5FhhorqyJjDmTC8yMILlLk2DIZNKqrOPSgcJ0mROFAkhSk7bqN0LVjB2FWlRriX2URW1bgVndrWo1HISe/CG/lFL6B+vTkT4p5VXKEzBYyKmGGK/B6RGgFL/UtVi2vztyI7L0dFVsZ+JJSsWgCqkYMRFz6I7kvWEZ1TLmrUjQ8Njq9cMDw7xxsVvI9fG4uDV6FhDbKt6T3dv3/yBOXPnzJ3HFEThCuH17kHso421E6vYeoPn9v5IXuvy7geZSzp3DaRfo+mdk2pvFG2JvitChPkdydR9DF/0Y40pqExB0dL8YZGYTmJJ0qgRCqDXKLxqCUw5x4VYrmZdLyEuE+huYw+7FH48Ny2MhleN6tV9mR0xIZEDUOLVTWXJwU7P4CXEWzDSQoHQP97xIdBe3Ydd7eWGIKa/irL+gKgOXtLdaBiBAlnzshqbAs51GaFTVRrG3IDak4Wb0lQy6VBWw0G6zFjGZ2QFVBbYxiyw3qS2jbVt0uaUZ0kETZdlFlNALs1AmaAJnh39Zpto2jch1OAw4iGmqWmzvoxKCQ/58nB+UykTZNNW18Q+rdg2AkNUk8BJ6dWSyTIwKWUgy/RtyDZzUdY8A1MpUNZ/WmJCNh09yVnJsiZlOBOEGooABhvfD5YyQRugTFlmKcOZPeTH5aQEmRKAIBSTl/FTwZl+KoIKX1lga7RA053SdFluUdwvgWlDlllmR9/h/ZhrmTXQvgmhxlbhf6kqjTbSx5qPelTNN21PfsqbBA8xxcJ3NM+WjBhmaW+cIE/CJ6bxDJQkNYNl+uayO+46a56BqXQp6z8tMSGzow/J7eWTHEoCir2qes9AzVGBk77/X4ceesp3v566vrsu9ui6s+X5zDnJhfCOxsUEcaqt4Ipq14PHUUV742GyiTgTlAHSwj65LeXSVjjTtxZtXEyH+kSl0YSNq0AlbXYWiHvCi0DbkQv6amuH0knP7tqQuPzuBuPfT+qIRiWQFfnTBsCy6Wm/tPgRgDTae7rt0vbbpJMSu026rhTmKa3OiooJRZbH/7RNwFsOiTUt/aQEoUy5qAkz68Vj3AU51zT6RgPzDeKyXHWHMDmZspiU0lmRjAkgWhHbyqrTCudpyjQ2aLtsMRcoll9vp3x8akxxN8wx+qr4cCsFzBoudqXt3S9fuSJ6KnXCeKiWsvgKrixQWaCyQGWBTW4B07sU/mV2lYUEebC390pBhibTT1Vpk9hMFvVkOWV3lmkCCM8ScmAA7XVrYp/RZdZnWq6g5D+npWxDQEg0fMLqbWirosoClQUqC1QWuFNYYJO6uVYLTrizVFKGWymbME3Em841N9XbySXdUipWpnl9mnYkJlIqLKW47GQ53smTT0lwBVYWqCxQWaCyQGWBZguEK8kOhQ869dRTRfOJAzk2dRXZjYd0ykiIJRibrtlzHXHEEY7Q2ON99KMf7fyMwDE+BxeTwrEcX9D6BteZnDPPPFOkCaeAzj33XN/d2gcmIddIIZ/h+pbXKZonPvGJjoGJ5kCIAzlORjWrW+zPxye2hIh84fiT80uOYPmm3DGwY4899sc//rHoQsJqOI6l9lYJdHZaTBMA4m48/vGPx24h7CCQECpCR1HSsajDDjtMLC14iRD0dKaqI2rOO2GBfPazn+2rX8QqEqkKhm7x5XGYiG4f/vCHNefFL36xBgqTIcoGIf/8z/9MYYbVcIAvzn22TiYdwAR6ISgMFrs5GiRXqrHq1TXHHHOM0EJMDcPIdHD6SEM0n/UWLVqk6oj6pL8oT76TdT5xFmUGIy4SyPGltZNvIjqxpCI1HnLIIXqHEBHQbLDrRPRqd75aF6OkgJhHYZBWw24eTBhWzs56Sv7Vr3716U9/urhFIohpV3zW7xQBo2mpM3UGs/A0IkB5JaEhCHy3bXTpF8fGHGljc83XHfpIqU505lxz5Mh8F/773/+eEANG3wnoFp9949VHBqq6DDl9wVzsbISoiCVJpo9kkKgXL5sjVpfv18GhD4xajAQdQRND2vh0s4SpYcihEjJn6sDkuOn0izFg1FGbQLUTqJS2WKgBoznUMEK++MUvvv3tbzcSnJr48pe/zFbO+5XvQY0N9eSsKraaNkI61uiStoxJMQbxffyznvUsbdQF2qV29RKl7QawcahH6KYIjdqVUp5iAPqQppkEEsUmwpapgqouMQYSVxhNu7QFi7sGO5gR1EI4YlzBkm7RRkNHU5JBnEt337lxRIehiSazhs4iFrvaCWd59yx2GJdyJ/HcDt6/u0kZ4bnPfa6oYZ/5zGc0TYLBSFoMP5aR4AOo8llYwEAKG77whS98xzveYbwdffTR//mf/2nszUiayer973+/28fpXwM7eHWWITGtHEPiyCOPdGtwTwbna17zGseA8eruNrzR75GbG9UuaJ1RJOSiG60NY2uR4fS+973P1G3oiiH1rne9q5WmDcYNIgbiK17xCsq/5z3v4WrdBdM2PDTHa657xjOewf5uJRPOu9/9bvEi3SxtalREZzOJW5LNGV94R/OVuJCvfe1rTYYIyI8qmNG9D0MmU7vppLJwt/8b3/hGnsV09KlPfUoQK7dkmaANrJl8NMdqXlKLIcTNmVKiaozlWzXkwARSF7OYieJ1r3udBxvN/9rXvibKYZS2qVQR+aqWIzZc+URTE0fwP//zP+0Zm0q1VNxGkTFNofR3ftvUHeaaSo3cNAqbFTX/qU99KiTH/fnPf97TS0zaTRVVl5UFKgtUFmiyQOV/GcRMu2X436HC/77z6qsX+9LJslsMDrp51hWahJ6chWda0/sZp5+5ePFZq1evWTVY235OnyXJS1/6Uv73sU84lv9dtiqF7nWM2NKGd0Yv7q7H4O6+Gu9w881LosnycCWV/638r8FQpcoClQU2swXK/ve9732vLQiLIJO5PZAZaXL44Yfn9e9TnvKU4LXymnYZiJJbwWIRZGcJ/OpXv1rE/FiDlHVoWo+E76C/BYu9Sj9sYKsw1r/24pqIy3IyTEIm03ZbczaIbLZ88IMfzDSdAJaTJ5xwgn1j7bWKp7w9IsIl7LmKJlFRatVm/WsB9Y//+I+Cd9jifvOb37xypS27FMdZXBEy+dy8G7DnnnsSbhkrHxkc9vJ2YF4v4//bv/0bD3voEYfbPbj19pvtOtT7+2wjCwrG585ZuECAJ3raGfvrJX8kzfIx6VO8+7X5YPFuAeu3E+we2LMiXGF7/RN7LUXlsPq2/rVhawlp8W79S/5UrQ6uyNEYHnpN7Xa9IHWcVKaZCqZbXv/+x3/8h01IXW9X2Qp0KpYmfLTOAtY2dezK/upXv7JbaATqEQvzJvp8GYwuq/VvtkkFVBaoLNCJBWL2kBeTcIqCFMkqKV6+hJB4H2eel8yTpm4OUR6MiF1iBAR78JKJvew6YyYvK0ZCCAkkmBAJlzyQIROv6R1SvTGfI0aglthTlYNtq6IHRAoJln4m5CAIruwRkKX6ivaafrFTHmXWp/zMEPMwXqmRwhmNJzSgaIjziYXMFHURBp77M42TKSkKBSJXCyAnVYf+BSa5w5Hi9+012fQul8r6TNSfThIFr4aQoBZF0QQskEwHGZjIo9J6CmKV3nOlVATewp46tzvZFmNBnH6XpXso/baTBpGjOdjpjsklMgkmeg0cMg0bAEesR+JlH2WQZd3wYokUxld7sBf1pkzDEai6iTcISIgUmoDZIfNmW8UrwpBQaJ5IsGSdVYFXHoNHKSDkeKJDRis2pDlNAi+HL8ymMI38aAgYXtWFuexa9Odxm/XBqzpkoXwWuHGBGB650mz2plqoAaMUMBVNmSXoo3Wd0Jd5A54dV6ucClNZYGuxwHqT79ai9EbU0z1/x972MW3JJ00bt6WkRXtznjFRUZ6Um+rNujXhXfI08nAYrXlUhICEyMPaWYEmAE05KSWzjAm4lSs0j1pa6dtgiFLaJHDSy0yWgUnJMjLI5DkpyvBUQDZ1eyDYgwbcnrhcmnXIek4FEBtFGZiKsolso9OHwCw2A5Pqk0sBRsWkNO2RGKU2NCEWTQbaEOeikJlEt03o9VcmcZnhaYG4C6Ylm5YgD5hpKYNgpvQdiq3ItnkLxN1xBzYz7rU8gJuAjahY3MjR3pyTX7bAVPdv1qpVn03tf1trdETKFCXg9T3uPfShj7z5a9/45J8vrHUN1YYG03zYSt8eQxaCbJA2QCbLQBviLLZceyDLmFY4m7o9EIxBA25PXC7NOrRXPutfBtqzUCMIMrCx6ENg5PUuuyfzuoYXnH9O39e+9u33v/+de9yrNjKUApCn4Dw1MWDSn0fCzjVv0lNFUVcTPl9mt5uBXNQGCJmF7HYZCforU7jM8LTALMb/pDLzgJm0tBU5U/pWCRVmdhaw6ysE1ty5aUvR/qK1Slfd5OhXc4Z6u8b8KI+zAsVfz4CocVP/9de7e7vFvbKSMpmkUWxb0O6wzTuSG6NDrlOofAH+7Q9ObNe65WxAd/f0d/f0Fn+A9FP2xkOvnWp/afe01xtrGFvA/mbazLSJOJHslsZmYs5hohAGYCdasq+tFvrbfJRg7DaW640P+t0sTSlotB0eu+ZqINghBJc+vEaQhzrAHm5gghGGNcoEYLWXMOnWXrkqaMDpUsLl7MFoI33QbKM2pzn9A8Vf7xzbv32984q/uX0uE57F100ThQYiXnndvGawNjSSol85K+InIEZSYLSaMdEwPTcxhN5VXlmgskBlgcoCM7cA18AH8RTByl/wFPydxHEkPzqRsq8Jz+LtV+bCiyoTZIBfyDQIwJJS9HJ1SYFsUnyizvRaLqpDlmn4RPimjc3bb1cXmSRTJtEuW7YSHH8eM5T6Xdz007gTCZUfGvKHA87/ty29nfPygzkuU1HhenhJTwzdNQ8X43/aRcN8Cbj5lpst64qQnaJ2pj+iklAtrY1xYcSCLTZH/eTQGO82NjTSGBwelRdUKUPvIghyHsYko+mvQerEnw+81qxd0RhbG+5Y7gVwkffUu8aPMbOAP02V4vF8xYrVXgdHK/xYsBWpPmVtMORYbWRO/xyvaZuePRJ/lSoLVBaoLFBZYGNYgCvZGGImkWHtlt2riV1KHrf42Ct7WED211EaeUaWAXVYqPoemmTSykLaw5MoNwUqO3pAofKEzoVjzC5vhOcs/qw6uWnBqSmQ4l8XfyE7VOIHu+u9wyODzvEODa/lptEnliJlLdrr31qaGQFKeWDe2/5Akw5lsjKcnhZCAc8H9v1Kf+GjydR831p5cikzVnBlgcoClQUqC1QWmJ0FLJytQy3rBHkRQ8pRIo4GhqPkkYRY+v73v3/VVVe5DPlBL/DHWWedBS8AhCgMDsO4FAnLqSSe2lEuO8D2pSG/+93v2lLgv0ICoKwnafBqRCnSkMcJx4V+/etfo4GXysT0cencjuAdtuXtYIu+YVvYEWLHsZxHEpbC2Z6f/vSnYqk0VUSUsBHCfEhHHHGEQ02aIDjOJZdcogniQDkSLO6VE03CBlGeYrEtr0ainGgSsMNpJWfGjj/+eCGK/v3f/x3NH//4R7E/onXycOIAvIzgfJrAKAKFfOITn/j0pz/9gQ98YI899vi///u///qv/2LtaOOJJ5540UUXCeAiCoxnEixUFc3EPoPgSgKjiMsTh7Wsvk8//XSvA5785CeDGep5z3ues1uMwHonnXTS4sWLEfgJpZe//OVvetObCBEZyiksDRQhhU0OPfRQmxUOL3lme+tb36qzxIdShccVIUggxVf63ve+50WJKoh9zGMegwv7kUce6dPtpu4od83mhNO2SfHUKjrV61//et3KnjqUqoYEu2kmi33jG99A5oSVXhZ3zBhDIPSJ7hAiDZ4c59ycvDKEHDjXBLZ1SZSwODrXzpJvyt0UDkvbAXNKjRB48cWMbSOBMdl87733Zis3ztlnn+0JjcEjGUVkYmFYFTEyXmNPxCjGNEgUkU9ho8LOm/hxxHrvQ0maSBRQrwHjoLsesSGjTx1cFDfHoTW1E6VIHBnEJGiIGnUiRokc9hHgiVj3C1UNfjcjGrVLAMm9Tw2dC8NiAMP4ggsuMJaEyIk4Lwzi0Np973vfn/zkJ5deeunLXvYyNEY4uzlG6O4w/Ix5I5YOdrfIJIpKdNA6StKH/SnMwtpISRVhBzA4MnYwqmnrElno434kEzsy7aUtPPlOzeE9/PDDlZp8VOQWEMNIT+kCbXS2kMJibOkU9lG11ukIXcDsZgnbeoTrF8LdR2YARiCWicxjMdVoEZW0SHQtMvGqXSrMVmWztAD78xHGgNEFcM+KovigBz1opuIEPTTs9YvJ34wX7G7qDuWYcp0gpYzcADA8DNF8U0wqJEoNAHeiWdHAMxSNPUdeJ6XPSCwGTx4/7g6zFo9jdGE3WWXKDgFzjjnZTY2eJ2JMdohB214CTYx8LeUmsGjRgx/8YLdA+1GNTCKfhwIgduu5JVnPid8cfSxXjSCSJkePFAJSFjR63CRJGm/uDK0bkygpE2RRrQDJKhWAzCxKvkCWqpCCt70Ek5IZles3A+gFdlB7axWTYswGhKvReCPBfG6qMQwmJW6DNB3x9SFKSEHG130upam4ojQIqOGJi8V0n7vGbDkVV4WvLFBZoLJAkwVMs5X/3fL8b+olri0mee7MpQdULt7ylqviKB1WHRqq9aU4WT18bvjfBx30oCcc84ThwdGx0cTLKXBMfJxHFGtMz/zkYCc5VVA8+csr/8tWXHnlf4tBUWWVBSoLbCYLlP2vidq+x8Me9jBbOjOtXgghDsI8ZgmT17/mtE7k2ISJRZAVkGUdb+KRwGI2Vm3tJahREyx8uCQLWCuRo446CrI9l9LwQeh5KO21grP2V+8s1r/2baxAYxPY7h87UD6WUW3UCCXtR9k1sv7VCsa3mLJZJJyFv7lzx4N3eEnsY+P4zPjv11x96+23xc5YsQOUGmI7ziYV5bE/6UlPEgaEAixpN1gX+D2DXXvnX/Sb3w5ed+P9dtljx9Gx3Ya6+mpdvb6j6kp/rGdLzY6TAGQW4BaSoXaHZrQRZwHrIcEmmChgapTaNDyMHzQaYveABIAHCZulufb2EsrrX0tgxrcAx96eq7XU2DPgGd8wsB1qF0Uv+ISglTJjGJa2Ei6wkUOZav2b7VMBlQUqC0xqgXA6crOfXOKAImV/l2bPifkzVl4mGdIQlydkNErlhZjxuEiJcwIfsKkphGR9CJFiBgMgIyG4QhTYdGo7FICrzF6wpgxSAtAtc8GENBjzYdSYWbICAGS8VWCCheu3MJT4TXiuPFLW06X5llg5JPYoysbR0mis2kmQ56oBMJFHpTmPNuZLNGXh4JAPDwi4TKyWJgm5tAzoP39rutPfyvrYqu7a6p6ulMP0pL5DHGprHTisIZcYJCzDVUVdTTW6lII9VwqT4SZAkRqJJdyoa02tvGE6VUh42ZlMyEiQWCLB86Q6S1FTveVLxNFqZNjLRRlGk+EykPEZUBowmYCQXGbZnHAMEu0ChH1ac0WGVnuaMtdM6cu8ncOb00pVXZUFNrUF0iRVJff/pEbI+AxMSpaRmSwDuWhSAFk4sw7pp3IDZeFosrTy7J95c2mZK2BzaCYrl07FgjieUczUZfomGJlEiLw9ZStj+KppubKGqiAk8iZp5cusT37GKpdOBUdnTVXaikcfzwEM21raiumQrJWxc8yMqkCcDdtJFTMSTiD6aXsq6o0BwJ7x4DJtR4Ta5Adlh63I8psaOxU7fDR5KoIsJwi0IsZDxjcB+YbNAju0T8jBHvQxvJuEly+DQC6V8QHn2puKMj2gTFOGgyUIJhXeJLO6rCzQOn7CJhmfgfa2ymQZmJZ+RvNDJ+O5fGvk25kambeNblPNh1OxkLmp/e9kBiyWo43aO9/5Wq+rP/nJxaPDKW6LlCa3IshHbuxk7AkXVsr6T0XWhJ922m+lr/xvk03Kl1ONtzJNwNn/Ft4idnyGP/7xvz7mMde/5z2Pfv7TTpn4vUzPgUjGdyWM2+QhnX7vLFX+N+zU5n6POytun2zUVvp8f2WaCthEFvA+zBGUvu4eb9dGx1KciLpQEhOpUdww664n8K3/TlCmpWjQe83pQ3ZfBdl/rBc/n6Bbm3bu6o3mHcDytVh0TWmsPq7Lv/zLvzQVbZRLr1dPPPFE26b0tBPNA8bgLCaCtJowk0Qrys6xfdUM682rz68d/vEFeXviWZQ6w+Cchnfb1KYVl0FDciasl9a29ZIlR4v5bbxFXeOb5rEQCEb0Sm0fO4Lix7LiEkYCV6myQGWBygKVBWZtgTyRxk+6vepVr2oVFf4lT8gZaKWcCuOtM79Dzix4m2QWc3/KhobWWLL5GIhbn+kvKJZldglgNVki2Q8V0jnUpnmk2lj5uaCZM6sHCEcmwDGigOVcua/HuMjR0aFm5vHr8eeKUmmqcawr6dlYr/aEn9Am/cvOq1b9aywxJp59StIKTbrG42iU8CFjLGJrTsgjrkjesPpozAnDX5/6i3qKTVqlygKVBSoLVBbYyBbwRazffnd8ZUPkclLYI+e5AvbRMP/uQxkOYlLhPGl88zRpaSuSfMtSuUO8nK+K+LsgA0TtmcslApThSTO+PeCEJ22xMMh6MtfzgEmGUi7Plpc8q9EknA6SBalmcmfCWBA+013QJpmtl46kkuyLXh8Wj3v/VqJWTFeDMXv74+O55JepKu8uPH53dwofsHqFuFpVqixQWaCyQGWBygIbZAH+JTtKfgr8ta99zTrat7+xeLRPLhbMl770Je4y14SMn/rIRz4SjyhOAeHl7CxpCfTFMAKxLa699lqXnihEC+IHy3WFqMCEWz/llFNE2oJRJLzOhz/84VxdBrInFQfns5/9rFA11KCh6n75y1/SUCAJzwmxSR6fdLtUGhK0SBQq1QE8hFDsL3/5i+NnKpVQki8JWhTb4zC5ak8IhIuwI57Ovvvuq7HeI3jgEWlLcBwCSZBHXWCMQg45lCX/zGc+w6oORPnAHY3wOmohCmx7nLYnn3yyQE5UgvHYQAhrUCw9DPT2MotdeqJQ6gvSonesx7/1rW9RVWJzvaZp4pGJaSW+iacaeNKIdbjoPe95j0q1iECa00enOD6tFmZUo1aHZc444wyWxE43mNNOOy0siZ162qV12aTZPpsZoFLowERf+cpXvMI48sgjxT67/PLLHSHwlCuakoaITYaA0d7xjnc4KygUlMNpTp3piC984QuInbtzcs879wMOOMArJ13jbQubeDJEL6AVdoPN6PJALkCYeuP1t32b//7v/3bw7N3vfrcdLc97LO82YWpxlBztYysn3HSKvvPI/aMf/cgdIQgXQxkwVHL40JlDrzOI/fznP3/MMcfoOLeJoGYsLESX/gW86EUv0hAdrUcEfdMuHeHoPoxhQ0912UfS0Q5yC9R10EEHOdjm5ctzn/tco0hFgq2IE2S0A3S98FUGkrERXRZjlUHEnsPllVAEilKqpWKoia3z2te+lp4GjwHz9a9/3YD54Ac/yFzif2k7pFaDmdFZOMFoiBLJi7YA+qB3wI89BcIznE4//XRnNdUlwJbbCovmuOWFEKKYelmVEZyvE8pKbzrlqNesAoT9Mm71hYBc//RP/6QKCyX3Fx1e+tKX6nQRzXSx13ASxrDVhz70IexgfUF50rRdF3vJCO/gKLEA+jjQGDFxSP7yl78ct797zc3F+Oeff74eAdM5jBYGrPLZWcD9y4ymU73p/o0umKkoA8yYwW4YcDfBHtNUG1HqldzaRp1BboaM+w6LqS+muzbsbkDshoRb3mAwctyGbjS3mDmzDWM0GXuQcR9Gl9pNJmaeNoyTFmmyAantphfxE7Wa/NAt6CnZyhjTJl53AYXNRe59Nws7SJm9lRcm9I/7gmRzgrub4yAt6591QBb0ZNJNApQVg1E1A7p/9R3LB31r1ZO2wu1s0mNA84+wlXqTNJ1CSCs9TBaLwDMDXh1Nc/rTZCquVlEoNcQDAOdoSsebB14r8VQYpjZn0lniX2ju0kjIJpqKEV7Vmqnr8WoIdiqB8UYbc0vbCKmKKgtUFrgzW8CkYaKo/O8W4H9XTfhfYWeT8wpn5LHZs66lnIndkwYvP7R2mEuPVam+y/739qUr+F8YlH4nyRMRILmWbj/vNNToGv/VQ5LDR6AEt/pfFSc3OdljQ9OdQsPK/7Jv5X+bBkZ1WVmgskAnFthY/te6iY+wkMnLEJN8JwpYR1hESAALWHHwuUJLwk54rdpMfbwSesso6xHuiUPJa8M2QjRcpbZrrH/5Gksh7k/89DYskxZhtI1j9Wf9aCOXP9IQy+Fpm0ABe1zWfdSgOWvZfND2BQvmEag0EoGhXs6HJgJMC2+CJvvfW5en9S87QLIAaXYOAd2r0y9H3rD4Wo31M0hJbPHDitEce3Ek2zy3gsvr30RTrF/9o3TShkMqtWkQ61+a+yI6NvSCZVzEFMwUQ4xXecBEWYHq0Ck41kMTboAxtYFnU9pI0JD1KDq70PtCd+lE5jJ4AO2brGnROiY14DlffU2Tav3bmb0rqsoCd3YLxBxi0jNvRDJ9uWyaMF0GRo4s4Gw7mCyHrwGbi5QSlRNMTFaZC5CETqTAuyIt2AEZGcRyLlWepQU9jHql2KmGLNdFjgSJrCkF0pJQsgUdDitoFEVpmQWGZBUlNbpS2MHYuLZdymjpp3KTB0lfKxXc479nHLA81CjrloWTSRRzFaVJW94E0iXJEsqQEyzgzMtr0xy7elHmItXRU04sZCrtSr3gZ4HlfokwZMpHi1NUUftwYfVQNUQRzhn19ib6eNPa35d+E7p4fbrujTDioI881CvDgcm5IrXkilxKuRTQdFkuAodl0JQlBA1kjJNphWR2u81Mp3VNtbSRkNXLQCYOleSt0jYnJiuWgUlrb1/ayjJT+lYJFaaywJ3HAlUArNTX5uvWLjeVZHwGWskyZnb0vBcHn4U0AVGvXCKfm0TAGbhsosyXQemSm0HfNNEnQSXemC4jD8khPLvqXJTlA0ICyaFPMLahRE8gslg3lkU1wSE5dCDfc0OIzS4z6MtNgEGDJdobFXksKKvXVItLEtAHWRv7NzHSH2ZG9KGPvKkjmiTHZRBPWrSxkDOqAvGk3TqVMjMSTgj66MqmDm2STwcjTX+xf1Th0hNhE1m+JE1i8Bhv+stlLp0KUEuWjwYLzFSMYZbc3iCeSnKWRm1DDnG+cZpYQmzkUYQegH4qTbIEXPSXQn5ZSKbJAJqwpzx6AX2wRB6UUalcgqdGEAOYtw09spAf+sjLYrMaGVCKPhqbkRVwJ7GA0dXaUkMi4zPQSpYxs6M3MtvM51GvXCI/btu4BXK9TUBQQhrPcZuUCQpJ6xpLptLIASSHcHlTUZMQl2UHl+nLZGDVRY4AMO1tGPShg7W5ZfuqVeutewv5Fu5rjn1u7ehj1z7j2G9cfyXc3LFh+9G31bvXdHft6Doca5irYGnOVBT3u1a0sX8TG/1hZkQfDZerqEla62UQt+I3ImZGVSDOY6MTHWYknED0eYS0kU8HXRnzeW3M0YKVY0WInpuurr3+1d/67k+f/bhnnHLyd2pdOmfEMPDPqkJaMridnDHRY9bfPZm0LjT6NzeBYjChXit9CGwibiXLmJCmFZva/8aYp1j7JtMn5gcagiXAVEM0mkkggHzEcsRRRbmikCNHGf3FpC6nvfEJifsxW6wCOrTAj3/800suSUGOvJPrqqUFS1c99RSb27mLSSeCW3UisIgfmO6uJKerK56IvH6bM3/epOyzDoA1qbRZI2krGZMf+9jHDCSvqH1874NvTZDCGgHL1YI459NWit1bT69ODfsYz1hCzrS87QkoTFvyQ5pXm07n0G3dzvEE/0QwrHRtf1pOmcwYTQvauDHtj9uG9rpUgk/W6WAODAlVXlmgskBlgcoCU1mg7EQcVHMgs5XS5AyZ3UQGWimnwvioyJGb/KA1FdmM8AsWzPVU4FGB++ARQvgsXMOkAbD4ModmyczHXMNDpbZP4tPWKZ6cU5MyRdipoFDkEyLfIYXLazLshJT08nv9VLzEzaGu1pWVvWuCuUhPCyF26gBYIb+8iCvkjDUHt4omU5ULvuKKK4pqy1xleJ1OFVRZoLJAZYHKAjOygDd0nK9ptvNNuUnlh3fOOcA0zpFJvM+kLLND8rw+Qi2+JF7PEaglap+d2MzrGD8JHHGz2m3971SVEivZBPDFFYNwamGQZuFT8U+HD5kOJBNbpm26LBdleKyWPPJII32Dbvcoq9RTT5t41GaEVVUArGyvCqgsUFmgskBlgQ2wAC/DrRReMW0jx/rOJVhRlDqeFATZi/lWxOqV08cbSC5VgncpfI/wN6985Ssj7oxVP2nxfXNIzvq6VAUvjAZjHMLxibP9+YAzZQaw2FXGpTqUzsM4CSN3aU9bqZWvS/RRF8rMC/B8JVeXE1COTmlCXMqpIbeZ4DhQfCiFjBDOlxAyxU6CEevEs5mGu7Qj7UEiKoLBnpPPi1nDHjuATAfMaKsK++3hzbHHZ98UdoAtTgEJxeKSWM0hkNEA2OOcD0ZIcgCSuqKIAsiiInajqiZQVXVURYkLjVaoxbElrSPEsTFFrIcMsQ5FFhpSkvJKsURjo40hJLfxjgJCGbXTkNrCpYn9dMQRRwhEZaNGJKavfvWrWhd9rQm//vWvxVQ6+uijEXu6Bju1peuZVyQyx9WY3Z4Jm7g8/fTT2UEpRhUxiKNub3zjG52aI1zXwJOsRiGcBG9Cee6554oMbviRrysdXFcjSkf79AsDfvrTn6Yby4O//e1vn3rqqQ5x6Qs0WGy40YdMUZaMLrULTABWkd60UwQQvEmcF0fpkAnPpFR3U08YL4fZDA+66XS3qoYICmZ4u4l++MMfYhfRyU+/OFFPYZdUYjfW0+kS4fqaAlrx/Oc//ze/+Q2MUuOQNA0XnEuoKc+0gmcRbkifc8456hUxyjgRwItuLvfYYw+jURQqloEngVb0JJbadGNJqr7jHe848MAD2WHRokVoTDhUIoEZKaxq1vve976nabQikIQzzzyTzsylUbpVdwsuxg4XXnihOG4CgZGsdVgcBfz4xz9OPl53HGJ9jRIvq7KbOHR0A1NSHxn8FBMISTgzZJpgSGgC+yMjkH1E79Ip1KChy7gpaF6lDbFAjDH3qShCTGow6IWZCvzf//1f95SwZW5bgyTY9VF7OVG1O8vdISqcmGhuQJOA/lXUnj145caGu8nL2ac//ekGkl1igyrwk9aeGYPGgAS85S1vEbvNvPHOd74zCIidlL0VKfCWmcSUZaB+7nOf48taaabCOHbL4YoN94//+I/uze9///uspxfcZVOxwIdu7rVogqB+YkEyGjvovihVFAm9exAxHxeWgc80St3y7jhPCO61s88+W0Pa115WzBRklpY/6lGPMgOYMUgmv0zTBDeVmmpE03vEIx7hvjYIdXro1sRVviQh3DFis6UQnCZAamMvk3UCe49vin7CE55g/jF9Rd9RQGrSs1WaVpsVTzrpJE1w/PvnP/8567FzK2WFqSxQWaCywKQWiHmm8r9bgP+9S+F/f+eR3IO5zqqLYlWr3bx4+XXXLPcYPXdObdma2vVL/gzmn30qZbbnrLP/PY//PeM3Xb19w8NDI10W2rWhNWnR6rtRDqXYO09nlgv3su7potX/OvWbhko7L5rKpcr/Vv43RkKVVxaoLDALC2wU/ysavqWcENLl9a/VVnt9omrLRh4Eu32zH/zgB5YhlmOWNtY47dmVkoDdus9miwDidl0++tGPYrQl1Z43qkZj/8fmkvDxxx9/vF2yD3zgA+0ZW0uF37IRJM67DTTba7HVZjmWq2hlgYlSW1s2sqx/n/nMZ97rXnt84xvfsIayIZCcpmRLjCHSxtjYyFCKygGnqEt4yuJLZgcGFGX/yw5nnb6oJ+0VNebUGiPLl170Oz9vkH65fvvtF9y+chUJjYGe23vH5o4mxr5if8Li3eJXHPzoO3tl2FPtRaisApgyK/tfGxc2rJBOsE/OlVuBTFssHj376fozzjgDQKvJ2UpYNOX1L5uL4G8D85vf/GaJqiNQ37H5cccdpyOMPRsgjG+7AHPo2UaK/QqbJ/yv3OZPtf5tY6uqqLJAZYFsATOYZAI0a0GaasyEFlNNc07ergz61hx9JHIAsflm+nIZKdeYAULAaCQKqAKQSwOAlMBBEPXGJT0DiSD056y5YL4vk6EkE0FUETWGZLlLRVRVSknzLTn2ZqMInOmjioIx+SMG476Kn+/1NjA1t8b5dw/JeUXPC7ZtixhZSX4oGXkWWIhKCuQU72d518KMCc1x5wePkENI1k3zM2/ob9sZWSBDPnZFcgkvlpGu5M0Hu9LKl7FgXM7p7ur1XnSsMTKUxkCtno7yRV2uwPw4oGi1D66YtDHoW++a31UarnVpYBoto6NrvR0VPgFs55gmk9o8yZ9IaCS6MT4uaJcThelfEnIby+0KGhgAFqpGM8OGkNrlaSpJX98gwSgP3txGlyixxLgt77qjUZQZAwhlgkup2o29JppMGTpMWlohKwtUFtjmLbBuh3Gbb6oGWr+Zf31qAAZECri1+aZXCx74+BiilaAJk+l9IUFyU2nrZdDzLl4Qho9ppcnOIKb10If8qehJUBRcvJefPOIA/LIcJF/JeQAiRV1BGcJRxkdOFrf0R5ZbAc701AbLJS9EbSL4hoZzClFBlvMsH4Z8n9HQAX2WnCkDCE3APBP56DlgMP+nKL6hKesfXCR7QkLpF9LkVvhxGS6zqYq4pEDoQ1ob+5d5KWAlDzNT+hgPKiIhWzIkwwDkkmb6sMlgC0yZIOAynnFc+hAB4LuE2AwCN8nPjBlwNF3/IpuWMuxPsRgPWcJUAHrjQemM6KkxLb3e1/Uefw8++GD00b8uDelWZaJd+pc9fYHkLvDsFchW4jJGSz2gGzMxPsssZRhL6q0iRXvbjP8snwT6GM/0sRXSZmRm+expvkLpW664c7O0SQEaZf1xuZyULJBZH7XEfAKT78rc3hCCpmjumPlHkf4K/dvQq0V72YfmsdlHSBt9yI/5CplPTOL+bd+ENtKqoi3TApX/dftEig6KER4315bnf4fNV7feujSPJQ81cRM//NC9vvDtRzjr9ebX33LbzbXerp3SDTu6+EEPuufw4E6V/80WawW2dv/bGO1L/TvHl/Sj3rBst2Ptbe/b4dGPfvSzjvvhpX/0LsoH+t4qpQ/6vYHhIOYvmMt9G/OtpmjCuAuy/8JYZinDuOJ+kW9L/le7cjOzYw2nqaXR6sr/No2ZLeHSyLbYa96H24ia5Wl3I8qsRG1GC7iv4xbejHVWVVUWqCxQWWBbs0B+RgJIthfitei21s47XXscidlUz1C+wc7mbGyyWnIVFVBZoLJAZYE7gwW8grFvuRW1dKtTmG199dQYS0bOwNZi8O6uunemy1emd5pVqixQWaCyQGWBsgWcKilfVnB7C9hHtepH4xWJDwx8hyD5cgBSkeQSXP7YACVksCBA7Buh+HTEpVLHinxC8x//8R8i0YgM4nMI7FFLqzJREYEY48UWDGnENhE3SQgyNEEZOQWiFSQQ2CTBZVSHTIrSVrFBIw+A8qG/S9sjYLxRxBfHxx5BkGVmyXTAAq9pYICE1zOeL3DoDJY0nwSaK5VjIQEccqIhCCLlS6XgkJBrRFOuCB6B805e7RGoFEbVUZEcsdrzUxyMhCVyxFFKf7yQUdEdm4d6dAh9mNEnSb4i23XXXTVNj/iixtE+BEHJ2j4Dk/usS+6rMB/QaxcW3xp51U4Cs3s7j5cpcDELi8mN7ch1CiBkIkAmBg2xpEEiJpwQhvI1f0ggObqS6eCJgocMMhJoK8dODTkJMBTTXyhhKOYdJaSq1YWR/mSqVNtdqktOCLy3vfbu1OUSUkUkEKWxJBCYP7cDoA/rwQNwPehBD3LPvupVr7r22mtVQQIuBlFFEEMKs4UYnjIumV2NpPkwDA1KDaGwxqo6vr7THA1H7Oha2Nz3Wkq9uNco8kMgOS7BEjJ4ioVt4UUfI3a33XYL8+o4OtCZPnpB1UrBGk4xvU++RDFtJzAu5dRDTCB94LWRhcVgEoNGdZIukOtNeHXRRGwsp+x+85vfYC+LyjIroGwBfV2+zDC8SGo+vbv66qtZWN/pBbku0MvyGGPoy0ZGiSyQGZ8BLHoKr0GORl/Lc8Kb4QCCMXJcUgwhlL6po4Yh6jRmE1f5Ei8WmOA13uIuMGwgqdpaaZkdHGPVECUq6AGSkVZmz3IygBdZlkaO8WmES4CYwZRm+gwEC16YQKqIwu64LFCjXJpAsvxJgWDHhZ4Cmm+SIc3tE7yKolRzsjIwYBYrNxASDQluSXhysvBJqy4jTTv0xx51kez2p5KELKor02ekIrXIsUR1Ji6xwKZteEgIXjB2NlepMRNd2VQdSimQWZ8MYDTeeCvjNlQyikL/oMmUISGLchmwnOkMeHNptFpRmLdMHOzl/F73upeAcaeeeqoay/hpYfevr5IuueQS0QDLxNX+f9kaFVxZYJNa4G53u9v97nc/TyNzB9LRidbUNHVkgsr/him2JP+7rOisHrllNG/stAQlu5MT82GwB/j+scbgmjUebIS+6l6bTrnWRkfM8z6QSwEjwv+6vnnpbfPnLYQZbKQlZE96aKqPdSdnF2+BG7W0ildEcuQANCSsWbpMVC2OPBxHr49Qp0uV/2Wryv9ON0yq8soC25oFNp3/NbmX52ewSSZSGV82qDWI9a/n/1j/tlnFlOXEeiGqs+AlwZ6JHRV4ZNk7lCsCK8oYlKomQY3BwiPEWbxM0x5QCwlWcFn/2K3KXLGWQSYlt1ek0CFyBPaIVKoEAd8Htk00lRHIyU1IznFdsmM2tvPOOzFCWKZeSy1V3pTXx5IR0mZESv30D/9L2o1Lb1s4J4UU4bfRjNXTUnesyw82r91hl51RrhpOnTswnEr7i99kXtEzvrO6yt4Fn9vfhyXJLWoHZG1Tbesn1rb0U7tVP0Dv545AGJoHQEhOISNqsei2AFTk0sLfUlTz16+k+YpY9CGcwTVKl8WWaa6xzIOYQXKRS6WRq1rfCcqvIVbfsYcQlEFQlhNwlMrH+4je9braYy8xaMpVtEqAYS7R4oTtPv3002PHZlKySZHl5+cyQbX+LVujgisLbDoLuPe93hWEQcj79utfU0Ekt7k3BSYcvsaMEXk8upt24hJl6Ey+X08xE3qRYX4zy5kxwMiwkGOylfNZtg1NuQRijByxOc28KueMzGyxv5onQJR47dZip5WdQwAhsRGH2JYgL6xeQgChA65oSOhs51CCoTwg3F8oLyfB2xBVSzSHUXsoEPpTQNV4qYE3zBIzauis7YRQoJCZ3K6VqbyvP+0YhwUG+mk9tzDGHMIooy7TI16toDh/FMqrWinrkdDd3cd66qU2TCEqxfAqTFhbs2YVIYhZz5Su+S5Dc4aNjqNtUW9/tJ31CiXHMwoEIwAvFn48pUbSQfdDqC61OsZDX9r/7LLyLQJgqWvt2nTKsrcv/H46YqnqtKYerJG5cuXqJHIsbTvrO5rMm7egoEmxO2LMaD4AsYQx9Cczxg+WsHyYUb3jqhf/sDm7YSQEL5nBrgoJo+brAjaUSAirIqNV2J/R8EouSaB8SEAZEmK8FTZP779CDplGC7UlvCEEr4QrNbnQP3SWS9EQjEqR0UHuFYAmFE1PAUaiaSSoSI3RcSENUhJXzVAvzJvoqRraZkYAeuxqVBcWGHXJo720Tc1spHHieaQQsO6ZLeRs+bnRueUrWWl4p7UATxfTl9wslO1gkeP+jalGnvHrTWoZuw0DjMI0UrmNMU+VMeCYoUxqiOXl+S5TZiQJkkuUkXJRJg6ZcZnpAabFYo4dn74RxOyZgdCWQECerEPOtDm3YULPMzJ6cqQmRjVGMzMezVR1ldlD81C4jA85gQ8amCYnmuvKQKZkQ5bhTvQXX1UeyoibKqJ5DG55FnWHAKGYPAYDA2pRmBEAL29VrKk5rQTbDEZLo7FswkRmIj0LaN/AuKFibIABkU9qTKLgc0XtJWcy+oDdiXGvteeatDSLmrQ5FCZcmpQ3IwmZqlGZZhMBqo6B2l4++0huZM2MlmJsZbmjWtGqSYXZoixQ+d88UZT7xf3itjJLZGSb+7F8x2GMhLGMDzmKAJlghv63m/ut1fYrRP1d7qsz+cD2tSOPmbuw+z/e/h/fuu3G2nbb94yOrK51ral5qmxsVxDfYVlYQG5qkpvQtD2mNQBMGKRJv+BqQm6Tl1oajWUTJpqF//Uhrrm/t3e+74fnzRu4/dblH37n2Aue/v7XvfWUl7zk9tHbvQ/pqdUt79nP54nrxvOk9izrA+Yf3QWTUk6LzKLCKzXRbzb/60lbKt/ITZrkSy1FSVt5vkOjNEZp9FRgonVBX/nfbMM7HEg9ZZ7xNXNPX7m/KBaXjeJ5v3iHOANlmx+IrYYmWTckgfVG8y1Wvu5qeTQbqzfLnoFaHZBmIwAmnWw7kHFHkrRap2zPWWgWnmgWjBVLZYHKApUFKgu0WiB7Fm+wMtxKtg1jWj37dI3dQD+Wxbd6yChqedQY50gPLus9/4yVNZkKztUBUo1dRU5SuaCA48GoLKeZxC8hFhKmeIRqJq+uKwtUFqgsUFlgegvk5d70pFsGxbSvYDaZmu08VPtKG2M8YGLPQHv6zVVaPAlM4VQDPTrWqKJfba7uqOqpLFBZoLLAncICtlUt/D1+xDu+aHNsBTQ9k8QGrJcmvkANgniNkom/8pWv/OUvf9l7770/85nP+KA5Nq5DeKspQ7hKc70w5S/qMktQehMUAuHj8yQYKslDAYplOFOWhWQ5kBhzUWiYc/isEpngoMcuRY0+GkZfFpKlAYLMjkogXSImx1vjMhc8pDxYwphxCVOmzO2CD1iplOXjgo9SObFx6ZxbKKlqD2zgYMz0ulJRrjSAaCM5SsmBxJVpotI7JC/rAPYJhG+7fcZNGdb2hZ7P3MOk0VJN81OUShH7BA7ACAaPT6vFVApLRgMFUYpSIxBxNiCZUWnOSRDCyckxjGpBKceL0tfeBkywy6X47i6s5xJlJggNicIbOgMizhRKwkMyXp+ihgQsEjJijRai4lKudvhILtGzDFX/P3vvASDLUd1794SNN+kqZySBkAARhECBZEQUGGOw4QEmGAPCxtjwAH8Gg42xjTE25r2P98B8YHLGQiQTBJgoJIQFIgghCSQkIQmlm9PeTTPfr/o/c7a2e6anZ9PdcEqruqdOnVT/ru7q7qquUQzwaTVMquBQxD7qAEhPvuGGGy644IInPvGJn/jEJ3Rq4xqndB4+LOTTBSRRIYd/2GGH6bs7fY+h14bYIXLcyQVegEKOoOGTI8NHpNiRij7hIAwiF5JUkXQg0MUXzeRIyTtVQhgm8uQcAqowTtNIAAKYBIAk6niEoAhNTjcQhyJacLhAYYcw6CQQCJNwSqLqWc96Fi6+9a1vUaV2ScDzeSJgRwTY+VpG1sQsbxldnfVxt0ddx7rADn0AXRIy8o4FjviWLVsKtFSlTmK9EV/mHZqEGA3pZocqdSQ6aiyDd4qoF+jG8gSgk4teGisWW1B4NN8wl02cYi2235GWOlWAoPg54yiargnQHDubZIpi3DSKCHOZlW5c1dF1zDR3aGGH40iLBGAsFtOyT67Tn5NataBn1mL5Apqwccp1jOPINRDJvoJHHqdc0ExXOAgQag1D6IKEGHaEgMT6DaPAuFc5Ao7AKkaAawUXEHIunjYWiFm+1ej6+DuP8Vc3AGHcrzCEjrDyLWA/2Qz3n3Ueryen2MUqqUxPTtUmp/ihoXC70qxPsXSND3z5VBfN8emJ6lQyPDo6kYw3WIoWls41WaSbVGtYS29Gwl1ulfsRDncwH4YrMm6LNe7U14VxH5lQVS7ZiBks+/jbfmroC8NySLuUI+AIrEIEuFZ0HH/7aiqPYLyoQcUuyFKX5QJTCHDdJjF4afwyxQItqlAkJ3gef0ySIm+HeBoyTk9CAZCzfTBh8DQkyzJerI4kHnmOo9X2ohgjvMLK4NDRDuoMr1iQr3QES3gWw4LkmWOF4KeG5AhasTVqYccoGhkeNpPGdPoSd2ySd+BTg+sGpprTTfa8qvCXoDrVmGIkHlo/yA0S6jXqmslkPbjggTlYZlxPE9sYmwuIDguyAndWUjPRJma98CQkxa9QZ0mnheBR4376QowtPEyGuzi2vVBtN3UThpAMfQ+n2irFwIzFOtIKg6PGEWfrK2RoC3ZIGDHvMtjRQszEO61Gq1/F2IjTjoAjsIoR0MWBXBcZWqrJHYq8ROUCYlcbBiOehbmuUmWXFOSlC5PEC08uOwx2THzEr3AxwgBELerUkuchVZUemcnxpT2JuPxSRIvAGI4xAo0vs6AAUEcASfwSNupc/CWDMDIUEZB3xa8cGQTknas9MrggWmiEZQEa1zSNWjgoAliqHsJoTFcnxqemp5q16kC1wkeFeEnY60rLqGQhVWEzLFxxJWek48Ez1FAk5zpNBe5IEHD4PDG95gegGJNRJ35iQIBQg5VoREhlwsANnwglCdMSN0IwaRe1yJBUpe0sp9Ixvd6Y3r9tK/cqDDqVidZwjxiOwHNkNOySRhTBb/qUTM6ul/CYtiV2SumozUwT8037d+3aQcy8CCfnaOORyKExSC7CwqNIbCQIWoE75FN3QZ5E8AGadtvhqPmyg4oIXlwjQxXHS6aQpBbLAgcmCaYIJAUFWnQYijSfnCK9jj5ALQl1AIQJjSI5MhiBUKgYpxaaxGkCyAQgYWkRfyYAWcDIoiY1Uy7wSHFp/C5Uo1qHf6HMuR1H4IAiEN5RrqmkCx/XvjKtRpgLqHIuVXkVY4qYgzyDii7fWJAR5XlfcAjG5DsKwNRVVbVY1p0KF/2e11n8IowKqZtx8RUhYsCom7CMfN4XHOQZDtFlYMvIdyvSWFQ0EHaTEV8hET9PqqEBacqHYUaQlwyHrHw8MlggLwEOE4RcQOAIvyqKsDBEKHhovR7K1C5skTu2gvgzvnTHU16ehmOhQD7GR/hzyMq0mg5MQhgL3P2gSxEiE7CK8gKqyHPrI60CSeRJyNOftaUowuKQd1TUIQMfaok/6KeScR4rIq/2woQmxbUxnalV5yGX5VgyQ6NI/IhJuMAFihJDEpUyxlGRffAEdnIZUQz52DL28VIcj2qJRAY9X60IcL2lM5DKNHAO42m/47WNp/RAdULlHcNbg+MvazvbULSewB/2sORP//RPX/fq191xW6jZtWuqkvAgGmgGl4GBjZzFSvnLQhBKEyBLxsffNiQd/l2G4y9vlCYm9zf2hlcnO3fuYsC/+eYdr33ta1/5tw9/8pM//5kPDrCMgfcuNr4xnnKydzun1EPIScggudLH3473wx0Obcqi1ZwFZe5/ZAFhuoSPv93wXHo+nXawEpbsTExl70V1K9P1Pm+BYs3fMOU5s1z1qJ4l6wUQKHWn4kg5Ao6AI+AILAkC3e4nl8S5O1lJCPA8wjTwSorYY3UEHAFHYCUg4APxSjhKHqMj4Ag4Ao6AI7BKEODGQ3NGRqhhFMWxok0twVEVObT4rGSAuPzyy3/0ox9B86EURUnKwjzzEI1NhqW2VJQXGBl3GWETy2ihKE6cx0wtxWHCCAsmY9ZMEqJbkhZ2IMyIhM2gamHGls2gxFSUgDixtVhGc/GmDkGtGhKLqVHWNKrMILRmG6mN7SwT2kBgyk89TQFrOY3aqMhpBcK03VbaQFOFjBouMU0dIkmK26hacWQWGhkWTUHEHEniTsKyQzFjIVNUVFIhN3ULGxf4IseguYMgxaZMESOSZHITgp4gdfiaBJcv1FEhIcNM8Ze+9KUXvOAFT37yk9nDDnxY405gyLOllMTQQkXC1rsIQL6oZboWWsYRsBVlxkRY69lMBWGMWy5dokVACSMcXCLBNbVYYBEjRSJktYOCVJUZQVF2lKuWPMaZImJ8x4J9jCApFxKGef/73//0009/xzvewYI32kKKbTo9ZwSEcF69Gz8vWczJHP28MD0nz1wQThnXNDMfgDg91bsFiaLQm7OFbpbnwCeSuYUx5w5QXnFugc0BhPIqCklNKNkQxLhmciWEWIYtKt92l3QEHIElRqDbRaYbv9/wel6R8sNfvy66yZdxTTPzASyf8ZdIQjDN1rfNfPsSN3b2jW1cE2g133KIdHyYZSGrE5Xn3AHKK/Y8QFE4S0QqJDWhZEMQ8/F3iQ6Pu3EEVhcCHS8yC3hh7Gmqp0A3vONLZTeZZc7v1nYOSnxceLbq1hAsdFsB1bIQvaBOrc6yjNl8DHBi791cd+PL4HwsdLO8BHxDA8LovvzOTasvFy7sCDgCKxQBXRjJuWlXzvwCcwfp9Wbm4YiJCV71MwekF2ualaDJqCgxQQDTJhHiDbAQQxeb5BgnSSVGDLMYpwomRphQ0PwFkkw3wCFXAHBsAIImTooIsGMR6hImkvCcmI4mVCEAny9G4ZuueZcFBOCoVuoYlwym2P2K6RtTMQIZhLGAa8IjHCwwO5TqZl+k00CCMbNmBBWMkNTklAgjLLaVo4JivJ2CGUEX76gggHAmfuGptqvWFCGY9IOJLjQ5NJIc6EqtBR0Nx3g4FhN1NrtYty5scwmoSJLQIhnBYYEGBORRSvnhQOtoE4kS/LRq1rtZvOMLXQVgP5uBCsI0DegAR+6kHqykyWSIDQHtgAahFkmXHGSQhE9CL23IjAWwVYHZNAiJKWBCQh1rJOm21KI9sCRATiuQBwFZQAVh+CR5N10nHAFHYE0hsOY2wMpcLosPtq6YlueFM9ZMUkQZeS7HGiDzwnmOxpVieQ050sW4VChmQs0bh4MwxpHUINFRJmYixugCJ3ZKMe9LnJJmzYUGyI4GTSYmsM+oBoccj9b2WEa0AtYQmK/txikwKJVMM7GPI6LSIcvDknEk8GGapBGxfdOiVgIiRFttR0KRdKzKMy2efFVHTk/jitC6Afcl6j8drcVMqYC/5M1CLGO04YA8Cb5xTEZEhi/huCojkFE3fBArlrT+pqVLxcJWC6GQittrUVljkTcjVhsTigd5mkCKqwpozEpYxgtcmH2sqRUFwuaR84W1YlZ0YvUhUKYbWKsRjpPxjaDWaIhYOFMlsQyTonXp2E43WudX8fmini8LGJcKxYzrji50PiqqjgIZJvYXe/xNkl/gtJJsIG8m+w45InnP+//805/7X5/8+C2tYJpJo30QeCZl7IXv428LnNw/xZ0nI45wmW5jWj2NyxrdRipzGH+nkjCeqlfzTiFE2Gh+8hMX3euBR778pc+85BufuPOWJLx1Cd+d76PfMLYUDC+Z1tnJggtVZQSspSIMH8SKJZds/NX4hbtMqPkiAaevpVqSxSpqHfI6dioWNFntFZ6IQRQIW2w+/hoU5YlJXom2L4DltVzSEXAEHAFHwBFwBOaGQJlbmrlZdq3uCGSnkNuSRS+Rwk8UzU45xuzqDiX57ealGx9DuOp9N97BobMcAUfAEXAEuiPgQ3B3bNZGTZeBvAt7bWDirXQEHAFHwBFYaARslkQEtx+a6cj4ET9mZqbGpE7OfA1TqKzKYGIFesHvZ2QwNmu0NSGOM0ObcIafKXYTy/PznIypuChhcgNZNDIxEavEtBoYc0TLbMzPcFSUurlGniNFlWrzOQJpZbj1UG3sYvnQio2142qgGqXw4GiOT1XqtKLRMsLaIo4VCwhDpkAmrpozgKYowoqx8QKaFqGihksXfDInLyvvscAJy7TmhRde+IhHPIKfidYUJyoCFjtK8mWmIOBbABg3Gi9xlSnmmaZihGQwrsRnA3A0vYsLYqOoVjDnjlYZm2ZchCyjjq4MQogpg0ceeeRHP/rRn/zkJ/xOO/yMuhdXGQJLdojpt/FpslAw6hQo04oyMgsSFY5KnpjzDGme6gvS2HkaoQlzaEVJeOcZm6s7Ao6AI7CoCMzh6je3eJbZ+MtntJl7y3TutRrmeKe5s65Wtbq00Zp1Hcq0ulFRPbfEbOylm2JEGtVmLd2no4H5ajLzFUBrZje9Z7dZ3nmCP0/1TIsOSJEmzKEVPv4ekIPlTh2BVYYAFx+/mBzAY5q5+IdRs50q6R6UjJUz77naVfbvVIWvdppJJYyoQSz8PwWr0dRGIdlR2xSNaB39ub5oysRvZp1wBBwBR2CNI2CXR6Y5SLz2Nw7IiIbPVIiqbCzWq1oV7eGK+Qi0+LxLqFKLBRSZKyEhZsbNjiSpwgtbEbGHEQkLTJUiDJM5CFmQevyKWLT4bN5kwTMhgllcYIdaXGOHBIeEWZL5hUBGfNGomwAW2P0q1ZgZ+FJdJnRgs+tC4E9M8LkimzTxWzJq46wfBSEw7MgmjlL1MMkogsAQoEgY5Mw9wqfp5DQdphBImbPChiNrZhwO8oofLTxShX0I+NRamk63rOJBOLQhHZWb01ON/WO4RwYLaSRVtr5CEZmp6bHB2mCzETai4pgQVDquE94YE1DsPYVYo1mdBIZmPVUJc9zgijUiIcGEVsAGAjKpWDj6EIgBhYKkCAGfJE4mlyJMrGkTKzgBvrTbkGMtbj6cjAWKMElST8MOMhCiCQZCwSOWUaeK2OATAASuyQU+zGAiFSCXrrXdmp8x6EVHwBFYlQisuQ2wuA5yIMtc6bg4culEUqnn4Z+bPFfhvGU85pnYZ4VWR/lYWNd0cRCmCXFtMQ043Ua1joqxL2K2Yt4ptVgm/r7sY6fk8ZJrDXKEapF0DNsEEON+zob2bsLG1+5F5eWxnz+UeY5Fm8fNXC8U0ZcLhC22MgH0ZRyDGNfx7Wkcy7qJkQu0uPvppmVdEWEd3zzmphs3sFs8eXVpWXvNo5nNEJInNyIjYMWML8lbbTFht3Q9tSyMnpKxR4R1CtDw4guR2Y/VnXYESl7PAYouRDfjdFDqCd3c5Dt2Yzzm3WF/DY6/wiG8K08aST358IffumXLD17zmpvSNyotkLil0rOwj7/5bpPh2JCR4XcsIqwLacfaPLMv46hjvN/xN3aqExMj27dv/4d/2H7JJee95S2/94fP+XQzHZk5h3g1we0neXiZ0yXFDewWT/58lJa1F4HYTt6VasmNyMuIk/El+W7CGX758VeKaTit1zdlHCHj428G8zVfLHq+y591THH2iViR/U6m+rXfyYbzHAFHwBFwBBwBR8ARmAcC3X78cB4mXdURcAQcAUcgvEFyFBwBR8ARcAQcAUfAEVhKBDKTNbi2G5I8ocDyfC3Glam8wUVqjjkyYm6OrDnd1E3AiG6S8BdKpsBFQVW/3svIF7g7IFV2uI2IVyCoRZr2RYCixHq2tKdAycZ2s9ONX9JsGTFcqMkIg4nhU6DLZwDXXXfd1q1bWZiBGCcyuU0KQ8tmN2uL1CgiV/DyK1rBkFsReg7J1NU0LIizYcOGyy67jB9FZ/crcCi/zGYOMbjKUiJgR3wOTot1i2vlDhnraXEAZXTLyPdrx6KKjS8GTWBzi61MMOWvPIsXQ5k4F1YmRbTHQp2F9ejWHAFHwBGYDwLzuQIX6xbXKmZkltv4m4adX1k2H4yXWtfH36VG3P05Ao6AI9A/AmVGyWKr87dQbH8t165BbOfZ5Hmqr+XO5m13BNYUAjynkPjyna/dtXMCcwpwBAJM8WHyXZj4urxoCgm+wcUeWJofQSAYTXcCYncq0RKTbnyBkjtkcMS+B3hhZkEWUIFJTi05zPy3hFQRtiJHWLpIGgda8zXimDVFRa041DLFQxG+mLRFxhGIE0yK1vBUTL8Xoq2vWhtgyT45UUklnjaSQYygjjsaTuS2AVaqFXaDIirDxzzKmvjoyggW2D4sPiixbhy/aBmxHCMKDw42NdsFTaIqkxDBCFWKEAJfqWiIlq2f4GsDLMUAS8JyrTzIpxbAB494Rzgj0DKbikk4tTzT5SiCG5tYASMx65DJiJyaBYpmXKbM3d69e0GPGEjIWFQQaCmZdzNC2MSMCgLk6ntSN/sQomXZdJeSOICu1UwhsJRNdl+OwPJBYM1tgLWw0Nv1q+R1pKR8N2vd+AWN0k0Jfs11N2GGCo2y3QSK+cRmLjCVEVYVo5oIk8yIxUUZnE9IsbVudL/xyE63+IsPkLSKZVRrMkbIb75o8hCiu7U0E3m3Jpg61kzGCKvNE2XkMxFiVse32D5aiNF5yEm4jvM4ErOPQWnpBqibfWRUJUVoFbFpRIamaF6sCuGYCX+hUhxGT5uCpafYfATUzEVq7HwCc901iICdHSU7ZEn5bta68QuQX+nj7+i6qX17k2p9B8+5b3pbcuYjrzz3rI/vu4vLI5fBkPOXG+0L8MhW6YjYcclWR2XANzEjovpAFh8gaRXLqNZkjJCjfNHkIURnQsoULXIjMgJWxJrJGGG1eaKMfCZCzGrIKLaPFmIafyVprzNUJN/2m+Yf/N5HLvjC0/7+nze8/lW7CW9oONm0aWT3rqp+DjcfMByLWYFhhyRJIyjGtLQkY1UIZJpmAvMkMq6LrYFSX2EgPDf5vrSKY/baeSLQehtdbKXVqQuFShkqtOCVjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwGpBIDMVYlMwmrih1gi12KaKRJDrN0VK4iFrJYURy4RXXtEl1zIC6mbdOttq7VR2thYcems7BL9/vmnTJn4JnFNYJ7LVmgXZzPNNYFGJxfOLZS2zlIv0p9zHDz/8cGbbteaEdlHVrQstaqvduCPgCDgCjoAj4Ag4Ao6AI1CAQOtnjsMyz5lvXAvkvcoRcAQcAUfAEXAEFgyBsHFGmXXqC+bQDTkCjoAj4AgUIMA7fBI7+JBsXwU4qJBrnyA9N4XHp0qFjZaogtCXfbIsC+xApKJmPLHGTAFmJYmKbErGcviSxJc8Im9TD9AYUTymEhNUYUGbHyFMslpViYOMIoEpPhwSwvLFLkjQVCEGgRa15NRKLG10+AyQpCdKOVLYfCCYFmcmRBCTHYmpKFo5ZmGiDpHGEJqv8CmqSsFDS9iCSeWDGaCmluAJG6AUattg2dlh+bKoIPgAc6BRYdKnWsMVwYyneZgSAh58yRGScGhCqG1tdBU+UQQzchkkR1gNEYciCQESfHIsmDAEDREsiFFEQPLQsR1V6cCxj5Um6UyAkBBQghkX2+wQGHyaKUfkShLGryRhmooRJkOtJI2DTEddGTcLTjgCjsDqRsA3wOpxfLlQWuohmlabMEQZ+cWWURglgykp1i3mMur9xlPGZrd4yvD7jUc25xzVnBW7tUWDerfaPN8CMCIvYxxkJKbc+N0IEzOim6TxS9o3MSOwkPeS4QAOnAzTXHckTNiIjmLGlFhJYbTimzAzkiHMmhEZAS86AmsEAU4BS2WabMIQZeQXW0ZhlAympFi3mMuo9xsPu1/V6sn0VPL0Z5zzohfd87nP/cCVP+Ixl4tviGL+GPcbj9pepqUdUZqzYkdrMNfa+Bu/a4jb3phKfnj5XX/1V+9829teddm3P/2lz98wMlK7/bax0dFRMLd3EN1gNL4dICOsqiMhsZLCWFjs8TfGp2PAMbPfFy59GY8dOe0IOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7ACkIgPycSc4w2gqYZDRHPHBl/GTa/W2xx/HHY3eRjmcWgu8XTzddCxdmv327xOH+ZIKADmjmsFOMOw2QuxaGhoXgmepnEv3hhGCZG4IvPDNgLTB+HLJ5rt1wSgfjQlFRZQDFOigMbwPzbEp/mBdY6tpS2l1Q3y3yBYyodbZrk6iPmAJdA6AZUGYNlZFYf1N4iR8ARWAIEuLwsgZduLrpdGLvJL0O+jYbFsXVsaclrO7okfZlbr1fDb+nah6wH8ugVt3jha0vClXcMXB37eRmDZWTyHp3jCDgCjoAhoCt2x6uQyRxYottFkqgKquYWc0ccylxpCyIpqLImZGRaYczeFAKZuTVqPlpEwpN1R1his2UgiuUXljbvBlHPgIsDmKd6sXGvdQQcgbWDgF2UmOPgwmLXFvgk7a9Elb3AhJkHR3tUyQK1Nl2CtXhXJilmLJhHtLSVkgkoAFznPYqDLjLkKCqxcRNVMJXwrjkLilQhKWGKiKlRCp6ihR1bQAYBjOMKPrszpfnM5kqYASV2W0ptz2wagAvpFkyaoBJ2mkq3aqL1WIZWbFKniBHEqCKHTr13yAjSuJK3YnmiHUmDbc60m9nUVMCVLbbSPGyANTlZgcYmXvKOxFEwRMu0URxYPhI1VtZUC4eEVqbtsUxsR/KGErHBEVAQ2q+N/dE6hpEeOPy0jibysix1alE0ZuzUaGSgabXwgZYu7lCEqSJ8NccUi82amBOOgCOwchHwDbAO8LHr9zrbUx4BG/bs4l6+kT3tx6b6Eo4VF48uGZJQUr6owSie8o507OwIGtExSKuFEK3chOXdisuNKB9epl3W2GILGa2ezS+21lO9QADLBDMH+3YKF+uqpchIjLy47RII0u17yoLgraq8MJJqr6kYYdZiQtFaY+Mqpx2BxUOguFvm/faUR8BOvTn0557245D6Eo4Vy9ObNx2zfeet9zk9+es3PO3/vOVj37wo2TByxu6xbUnlho5XjpIhCSXl5YPpVxL7JLTI7aAUG5GYCRvRUctqIUQrN2F5t+JyI8qHZ+0SoVcSGSbvFj75vsbjH7H5fR/6y8c+9iVXXj49MjLSanKzykuIpJLuI96ceROUVOIXVeEwLRJE6gBzsG+ncLGuoDAZuStoS0+BAt0yVbJPTpK8ER3VFb81tqOMM+eOwCIu6Jl1BkURhlexi+g28uSkI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAjsNgIMJFRPNMxzwBszmuedsqol2lIGZkyvhZK5kDFc6D8LhRubkcI2PnFATU6BidmMl9JkclolrNr0X8suSrpuPnWQJh8SDA2NiYORZ/JNXAWj8h30fjoQPd1USqQl9mS1kqKzQcWuYgbK2sFrqnKy1sMeYPF8lLEoK4AZgeiwEssJjrjRWF0FOtpOd8EVDL285YznI5GMjJ9FQsCACi5yxgsUOkmWV7FLHTzbgJOOAKOgCNQgED+shNf/Pu9whTIy2zHq2U+vJJiecXyHLmIGyvdAtdU5eXNY95gsbzWbWKQr4/5Kho7lSR8SsYnpWmefoJbrfLrp3v27GQFYjUZNV8iGpVw6z45GVao1et8jFprL1Zr/7hupJCG12MtW74JGOjRisiFyI5GclJ9MAoCoPlylzFXoNJNsryKWejm3QSccAQcAUegAAFdvjIXHzHR4gpToNtXlUyZ5WLdjFgmvGLdvmqxzKun2F3Hi2pmY4WOLgqCLKjClGrjGGSfSBrNsDK8Wg0bjmglePulEBxuBqZSrano1RkjclLjSw22U5+e0s4RZYJP7YdIgps5HfSOuMnsPHPhkzcivmLO1HZTyYiVLM6haXNQKRmMizkCjsCyRaB9fQ4Bdrw0cWUgqRYCeRKSJBHdFOFzJZduMNF+9JAWufYDQiyf2B8KedRJ1EoXFfj89AjDBN7zWuJIGEUZoSh+GnKI2ZJZkDV5hAmhEQojUkclNkItMVDFaJzys99DacelyUntCdUaoRQAuuxvRSuSJP0y0YJoE4SNJA+42A/bTIX4hX/Y/indBirso4S48pho22iBFpTT1Oa3xm4rdiOwjJ5qRbBrFdM9AYYmRz/cgTRDVJoLS6YmE/aYqlZplKWgbkagwZCiHTsLfkaB6jTRTBLyNr8GjbxU8rlZMEKKGIGDSePLjjqeaKsyAnm5iBWh0UJGuhCSMS0RUlEVx5EAzAiKeJSi2RcnNtLRbCzgtCPgCKxoBHwDrEU5fHap7Wa9p0A3Rec7AjECa7wjqfk971QQ6ykTo+r0gUVAt8sHNgb3vnIR6HlV7Cmwctu+sJFv33nH8ODB733vK++6665//dcf7t+b7J/aMTq4aWxyYf2sVGvekaIj12AZwwue/9ef+fLz/u7vnvvCZ39h+/btrIFAwFGKUFoBpI+/K+AgeYiOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7A6kUgM7XEMo8Mp3zT56xY3oVLOgKOQBkEyp+Mthq+jNnFXgZWPuwy0ZaUoVHM2Kppi93AkiGtHTE+nNB0Ochz9EkxAQ58AdLtA48YJRQp5g+f+FTJcqwS091q+cqFzz9iyZimCoGYMzda3jMNxxQf6vC5Thmb1syMcLd2SSwPl/gckfwHLRnLcRE7SnLXLZhYZQHpbm3kymbfHeXdddNCkrYXKJopPv0aHx+3YoaQd0EBOJlaih2Z8HFtnaogyG6t6/fY5QNzjiPgCKwRBHz85UDrMssFGUK0jv4SjL9c6rli84k0HhUAOQxiYXyZnBykNt0ja6Y/auAgzqOOOnz37t3795e6Q5jRX2gqRiy23W2Ekkw3LWp9/I1hdNoRcARWGQK6hisvbhrXf8R6SnYT4zIr+wXXWwS61drtQccgcYoAVYRnjiRZcujkR75t5/G8C0bAvXv35vkZTh6cbs2JFTvKiMn4O7JuJN33odUua6ZZSJ1W7CnPnvBSKFrbiOjdhan0SygY8mJFOwrt4FsHpfjYFdhEcQ7Pv5mjYMjgKFMl193CUysKwqMKGW6c8kH2xKrYrNc6Ao7ASkQgPvFj2toCU3xyXY64/nCNIlfqeI2SOgOBtCyHjzyXIOlip5s6VWjpPSGExYMuKqiLgI5rJSaOrnJc5EmmDiHXjLO4sCqYMiUOOUas1tSRIbF9FfIKvtsGWIhhgdpMePJO8KmRmXaZC+R5dx0HkNoJl27ppiGEARTCtGIaJhtnIQ8CAsHEIJAUdDEzT6vtGKFKOYoijJNugGV7nM0Ek7cGB3USrtUKcTKS5i69hSCEmQMndeRpeIGF2CDqaFnMqjJ1qjIYSgB5vFOLFwsApgKQa3KElccejcaCBMwCwrIgy+QmDIF9BBSPVUF06B+xWj+0RdKPkss6Ao7AwiMQLh9rKunqw2WuZwIWyRhRrGJiwrOnsOQlvHi5XW2L46F2PvFIvaAVPQW66VpgEAVJ6tbYbtaMjymjl4BYbHdCprghjOKkYpmlry2JjMSsmRkt8TNM2mLyRptkhlDDY/kYiowwRZPPELFWTJtKzCxDW5fOxxBzZMq8GNHNRU+BbooZR93EzH5MQHdLsmON7WbW+SsdAR3ibt0g5tNSFY2Ia/O0iQmivEDMMRkRi5dbl469d6SJAf7cIump2FMgSe6e/gX/GzZoSVytkgytP2LqjW8b3XTI1he8+M1jO5IkPE7eum/iGiKN/4Ja2GVcPzehUlFeIp4i9X7rFtsd9nu6WB3jb1fk0zuL5vQgix5f9tIPnXKPh73mTduHD08q1X0Hbeb1wXToL+ozgW7/pbgZdCrlXbSlZv6VTKyY14o5Jhkzy9Alz1+ZMi9GdHPRU6CbYsZRNzGzHxPQ3ZLsWGO7mXX+ykeAR/6+/lZ+i70FjoAj4Ag4AisfgW43MGuN30hYcdzX33QjWdS/8sGEMGYeAcrR7eDLe+ksufLPAG+BI+AIOAKOwMpCgCVEq+xvZeHv0ToCjoAj4AisJASYkihO9uBvrdJEW8m8X63y8pJUeMVNUK0kzT6ENQ0i5q9lOsakDL1CsbLeu0Lj97AdgbWAAB9L8AHPE57wBPuUxT50gTjxxBMf+chHTk72+LG4RzziEUcccQRipivoDjvssN/93d+FZoDoBubGjRsf9ahHHXfccRkZvstdt27dueeeC9FN96CDDsLp/e53v5NPPtlkuPLQKD7cRf1xj3uc8Y1AQLSIe9/73ljgUqxLFgTfnPDRL18FP/ShD8UILkzFjBhBeKBHEUVjQhPDve51L4zHfBMwAjEgwoVxRPDl0mmnnXb22Wdn+FY0s8973vP4kTb7AkehEvyhhx5K/MirXcpN3YgzzjgDScIwjogjjzzyQQ96EHQ3Rarw+8AHPpCjbMFIF/BJVJGLk8mRR1cd7B73uAe18kIYSiiec845MktVRl3C1G7YsOG+970vtRymPXv2SAw+6D3mMY+Je45MZezQ5x/2sIflP/8msOOPP/7UU0+1APIxIEOQIJyxjFO8gwmHPuPOi46AI+AIxAhwufPxd2nH32l2OLRDAP5PetITJibGjMOlniPCDQvj1+bNmwcG1vFntSnBB9VVvkeuJZVtd2155tP/x8jgEHQ1qVa4iaAuSfbt23fIIYdo/OXzYv4GK9WBpDLYTDJ/Pv76+Du7d3nJEXAEFhcBu5/Xo8RTn/pU45hj7uQPP/xwnkC5nzdmR0LPvzw28lBgjwMY5MHqyU9+MirGzKvb829GhmcTNrZ4yEMekg8sNkJseD/ppJOQFx95Rh+eB3nCetKTniTvmYdrmDIL/+CDD77nPe+JL4KHqTAg9u/fz+MzT1hwJBz7NVrPiajHOxGjgmWeoXgIyunySNj6azYHq9WRRzzisfU6I+xQpTJsVVNT1VPvea+zzzwnaUxWk+lqM+EvDLLtvNqcqDTGK43KEx73hEqjVksGoKth7A2j88Z16w8/5NBHP/JcloxJt1t++hlnHNzp+ZdjB7BqZq4JrdbzAMhDOrl2qRB05EAB/vScboqG3gMe8AAeMykiSWo9/aavL4qff1HBkT3/rl+/nlcWMkIV4D/60Y8mMHOk2KwogvscnvGlRW61qN/tbnfj9QXxdFREklcWqJ911lmxADQJNLhxomlm0AlHwBFYxQhw1ndrHRcWq40vMtB6+GWcZagiH44SRSV4XMfSPZ7CK0pZkC5MqhCTDLQR0JYwTmJ8xJ0FCY0AVzASdqRILsJ0IahFl+s5SUbQVZFcumjJC7ksiENsZgEtwrZEUfEjKRdEkf7hMftXr/NZYi39a7UAdfbdQnVoqM7fxo3rN23awN+GDevWrx/VH84HB0PIAwPs9IQiuzhxmKCIpDY4CLCEB/jr9ccbd9ohpnJkCIZWSiX+8Erx015evSoxBpHaJcIICQEQVtsNfEBIaQYXdpquN5sDjemRpLmu2Rglr1S4ExiqVUeqleFqpc5fwrAeRnZFHiBkvKfV7B4W/YHiTEoP7KCOIwgTgHmHBhQS0uo8CEieXFXk4nCIdcggUFTS0bdOIskZ3wMDcCjKFCpYIFcAorEgmbxubEdBkssIFiwYGVckomWKthAYtKrI5ZHcEChPdNOyM7qnKSQXNfUMwAUcgVWMwMx1bRU30pvmCKxlBDTqr2UEvO2OgCPgCDgCZRDYs4efhURwmpmApz0teeELX/ja1/7vX19TRtVlOiCw1sbfm29O/vmf//mVf/KSF74w2bsn2bE9vN2r8J8nR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBRyBFgE9KSIsBhubmui1UzXvsVz5vwTmOgCOwUAiwOnyhTK10O1oov9JbsYLiZyzQZ5PHHnssW/boKwsbp/gg9pRTTqE5PbsoexixARbyfG4RN59treJiR3rr1q18gsvHlhkv27Zt47Peu9/97nzd0VER5vbt28n5zIbvbPmUBVrjIB/h7Nq1i29v+Dq3o64GQatCneApKgcHdGkRUeE9vzuVKULgiyAJXrpWBef+978/3ycbnlYVE0SChaOPPjpmQqPFrl75vSEyYmovzeSjl7hRBF8GfKwhxteqdIMM/nwALEgzHuPizp07+X6bXdIybeS40KJjjjmm47GTIw4QBGHzmTSOoEnY4WiqT+rYqYGx05jmOx+6LjLsfgWMQgA7tAg60xtjRdEcaLRoKSpxLYeSz3c5dhl+LANN6+gnfCQf8zlktIIehfGY77Qj4Ag4AjECXKN8/AWQAzj+4l3jr12uOSjQRx11ODtYMTCxhJitQOKjFtMIU8yMvzB9/AUEH3/jruK0I+AILE8EuI3nKSCOjTt/nmJOP/10mJmnm1hMNM+/DAHs+5ORLPMIZs+/GV2erU444QQegTXEZJwSnpK0eNSKL7Y0h2GLrRCOOuqojGJcxDJGGOPOPPNMGhtbYATkuSbd/7HHT8LjSA87OI2N8wzFBlLxrlhxbUyzh5QNvsanXWzLxcMUdggSfkcc4PMUxmgrC5KEuXP79vvc5z4QPFIak2I+dXv+pfkCRH47egc0vbvIHDs8EhI7WXTUshio1V5RQIcKCTsln39lOX7+RRGmcGBncIIHGZk1j3mCnpMHHzH6nnpRXkUcLOOCPsaLI+PAhKYVvDiKu1M3I853BByBVYNAfLmDVipoHVcerlFcxLRlD4QlLp7QypHhYmKXKcxik1yXOKnE8mbECOxjRIroYkrqaCkhmY8BpmoljwoJ2orYRMC8ZAhqSTCJU7pxDrPlu/0PnI4JI/AFIxbUdmKQHvaJnEE8n7SvE+oWcKyOTYWHOikTPEXZR4aEBQUg72oIFpCRulmwoghrvhRlJBNPbFliWEZGSTGbjAiYJkN40B0TFogQAQUs46KpQoVaIgwtbBtRq6VlfFOxkCBUW5wjJl1y6YqDljnqSGRcm5E4EtmUZBwwNE2DH3vMAOhFR8ARWAUIzFyXV0Fjlk8T9CSzfOJZa5GsHfzXSEszzcwU11r3XnHt7fd49Su/4gDxgBcVAe8//cN7R5LcUa0ltXqyedPJI0MnYOERj514zWte+I//+Hef+Whsj/nsrlPakls7+K+RlmaamSmGg95aAsc6yKlkMvnQe3/1j2+58DV/8VcP/q0kCXNAlZGRDe1NtFmRaX/qL54vLgIdjlehw37lC415pSOw4hHgxXnHvxXfMG+AI+AIOAKOgCNwABDgq+aefwcgLHfpCDgCjoAj4Ag4Ao6AI+AIOAKOgCOw9AiwUJXVwHxnq7WqxTmfxPDNT8/ERybMcZB6SkpgbvJgpUW0xTGrlkW3CBPS0iO8Kj1mFj1TXObNjANWqGuqM4SzcU6d33Bb5sd3YcNTf+7YqzsyF9a7WxMCXLQZGviIkR1/+HCCCzg53RiCT0P5MlZiyBQgxreX55xzjj5DRRdh1JF/yEMeQk4RL+rkeSOMdwwfZ5xxBp9iqjacRel5dNZZZ/FZMvsQ5bWwhgs5IuAHPvCBRAtHiuwqgsf73ve+xR8Am9n7pYmidbxNmzbBw6YabnxTMQKnfCvL17aKM409jIBsC8UXvAaFyccEZpEHfJoPH1ochmy+qUZdMMYqHWmOHQELZATQ4mZD4HeUj5mgxxfIfDErpmKA5qtmvn9W82HGKkYTJ+qnnnoqh97UZUE2u+0dhgxw0VgssH0Yu2VZS/lkmoYAvjbAMl95QrGBMzuFsUeJoMMO/Lvd7W7szCVOXtE4BM+xI1TCgCl5vLMnFzbpmQUWEKMVfP9MN0NXrcY1kQAmhw9dc+SEI+AIOAJ5BLiMcNHw8ZcLPuDYNXMxx1++JZ71OfFhhx15xhnhPodrOMeCsYD7B44Itx8Udci4m8n9DdQqg9VkiL8HPuDBmzcdWqsNViqsC+VWp+7jr4+/+ZPdOY6AI7AcEOBSTyKS3bt3Q3Abz1MAjwPQXP+58+c2ngeQs88+W9FKuFvkGzZs4PkXeTZTQAZhDWR6BKPIUwkcMTNGeEQqfv7t+AyFTZJ2ZoTgOZ1HXbxDY5+bCnYvevCDH0xgGXeZouRpOw+bNJzxDnfkjF9s38xTDMyOYcd2eFQkwbHhEprNo3ksjTmxCjRmiZM88/yLR6Li+YsEbioy+CJqeToWj1Saw5Vmff3opgfc7/SBGmNxjb8htp1qNrl/uP9974sYB4NiKp8fwQOn2/MvNyRsIMJ7e5pAPKRM/Crq2Nl7BonhnadCMOyoEjNpnXZP03Mr6nN+/rV4CJjtq3gIxaaY3YLv9vyLOuCr43XT5cARPF440HhBTO7I2ZOa/kBt3FKnHQFHYBUjEF8oMpf9uEoI6OKf2SypZxEtJS4yXHiR5xKtxPZPbbLzv1yNUWHEUeLSx3CJBRSpYrjsmHizzYOMFNvOZ/7FguyQ5xO1SqrCCGFbysTfOeiIK3XQwz1hK2bydeuIf2T9+tGOf1ThNt3iKXhG13IMEp4w7IkegeTjh1lGERkQUMOFnWgOBMV6bbhWHRqoj0AMDowODa4bHlo/MrxhdHQ9fxwccnbZ4oBzv5D+zewkRfxYVitoSJxURa28yGPc96iy+Hu2Im67+k+xSlyrGKzJqMdxdqN15C1+i1xHEGuEpERLMwmtmINH5FGUkfnn+dN5/jbdgiPgCMwBAV44enIEHIHVj8ACDuGrHyxvoSPgCDgCawyBRvrSdduO25Jkz8FHJm95y1uuuebCf/2XpFJLmv4+dn6dYW2Mv7wmaIatYprJG15352mnff8tb3nOs571kduub73f5zVCl6mQ+YHr2o6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4AisEAWbNWDB655137tmzR98jdVw/apNrC7tWdf4gdYy2o1maoNW9LMyVgDWqo7wzCxAAdiV9S8Ca5gLhA1vFUSYRLblFQtHoRSUy31osqq9i43HziyXj2jJaSwYmgS22L7U3zg0NXHP1ywRAsQxEZsSJMggYpAxJIMyXtHyyeNVVV91xBz8jl/DpIwInnXRSzw9o5YvRjY9dkf/5z3/OB5yckrLPRkI333wzMhxWchyRpGI5n2owMvL1EZ9NyruqsMaXsTt27MCgCRuBfRJf/+ALm3xwwhZUt99++65du5BhAwt2zuLL0o6bZ5kRIzDFFloEn1oNGd+lsKkTdpDhc1a7yOTjh0P8fC18xRVXICwBLPAFKep8BQptjowwJpZpOztAqUp88NfXv8LNtLoRhMoAwWfbEkCLD7fYvesnP/lJbDYfPLWARvCAf/3118cCgE8VHEZzwWgxWxgM97SR+NlGauvWrcbHNZ+UA4uFZFVG8IUPBsEH+2xiRRfiW3Q4HHF0OXb6/tbkuxFEyPe6BE8k9AcsoMh3uXyOzqdr27dvRyAfuazBByiCJ07aIjEIwN+3b5+6MeodXetKRdtxZNYg6Ips6UVbrM90VHemI+AIrFkE7Irk4y99ADQO+PjLiM9Fm+0TiYchgEGBC3vJTRx8/PXxd81eyrzhjsBKR4DbeJ5ZeNDbsmUL9+08DpCzsw8Pp2WaxnOrnn+vvPJKhgweGRhBsMA+vD2ff3kO4kGJJ5fM8y/PzoxBPA1hsONjIF5IupFg82geOngE08Myj0I8PvNEz1NMmfh5vnv0ox/9/ve/X23nWZLnJoa/Qw45BI49GWEKjxmDQAdc+LrkkkuQtFB5qORhnOBh5odRwrYEeoz+3/3ud7EMk5x47PnXHKoq411Fxl82a+ZxlSK+SOznxYDeUTjD7Pb8S/M5Lgjnm2wWCAkZdZ6dO3caH9ccO71FMaYRaOnAgQzQATKH/q677gJzOPN5/uVhFuPYBA3i5ykYm+Y3T9DrEAar733ve7RCx44YOBfIaREC2mQtr6vnX552edMS12KHbo96N8VY2GlHwBFY3Qjkr9twlGybHt4BwsnjwHWSizlV6UU9jIOMR4hBMEihxZUWIwjo2qUqcl20pS4V5BGGhgltriG4WMHRBc10IUQjzxjNdZIYxCSXx1jXqoxAkURRsUHjnSQOHvFLSORSaQvPGmFh6u0rRkio41oBY4GEBRIRyo68yCA3A4SNCpdiBiPUUxcB59RY2ERJOGATJrWpQNAWTT45GeLBuFTgqO1sOIYuAejYmWJQjhLC8gJ6Cp5KmJYwC01ufBEKAL4ih0AdpslDEAC6xEZOjChmwqD5oIdrZKARIKFISHAIXu2CKUXy1FQqlzIRRhF5WAQgdVmQESy0pKN/MIIwCYMiqJQL05JrTCFMirRnkTFuVEheAWCBpNjMAgS1IBPHjBEO/iy7XnAEHIGVj0C4MHlyBBwBR8ARcAQcAUfAEViLCFTC22r2LUpToN/9vkeNbLj++Y+6JNnPw+dRKT9dpFVJf1LSnwcFleezEEjfkqhvTCZ/+IxvXHH1S9/z3sf/zuO/sr+xjZ8g4Y1WpXlQM/SzmQV/swx4wRFwBByBZYaALmld37Mus2g9HEfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBJY5Aqx5ZTUqX4mwwwXLbVmHSsCsiI3DRoAiudawkouIZQ44TUNIxWEgQOQ0Uy0qFl6DtQIwzsuAwDdI1113HWvZrVf0PBBlzC6IDCHRmclJLMXGJoQsL1mQmbNpQdo1NyPq9sSjcxkoBAJnPYvRM4BQXD6Rx+3NxBlXLSCtfoIvkAEfCJJ1nthRR2Ys4PQcEABttMCWrzuU8w3taaeddu2117L9ED2Tj4Ef9rCH6aQuY58vLu53v/uxhxS6GOcUeMlLXlJGEXk+cWHU+K3f+q0f/vCHpvLUpz6Vzzi3bdumQdP4RhA2itTSBAi2TLr44ovlmo8wH/OYx/BJ8K233mryBQTqCJOuueYaGoIkNM3RF7AFilQRPyjh/dxzz+UbYIow+R74rLPO4qItnPMWDH+q8MLnsgR80UUXYYoW8Unqgx/8YF1G8rp5DofsjDPOuOWWW1SFEayV/AAV5Bli+H77V7/6FerED7DPfvazwUHIAw7Rwsz7hYkM7viA+eqrrzaBs88+m0NQ5utrueCDWw69vBDP6aefzre7HHozWEDgBfDpt8RP8Bh50IMedN/73pevrxUAQXaLn56DDJ9Po/Kzn/0ML1hg26xTTz2V76akRV7gHS/00oc85CH6fpvmcCiJn0+R1bQCXa9yBByBtYmAripcmnz8pQMc6PF3++GHb77b3Y5jCBsdXceIwNDPCL7lzu3VZKCaflhb3Et9/PXxt7iHeK0j4AgsWwS4jefhlyemCy64QA8R3Mafd955eqVTJmw9/7IBFsIa3M8///wyijwmMPzxGiTz/PuUpzyFYYUNjLhD0DNpbA0XxAlHj2CMWTzyaL9jOMjzDMKDSZkHWIxgikfOo446iudlNZlgsKDduxBQi5THYahqbGyM52WeQLUHNEx2owZPSRY8B+kJl2clnkDZgeurX/0qHAVz5pln8lyZBsPr8Q57TMRhsNsXAX/yk59EF7iwxtO3NoCIxXJ0eBs/NFDdsG44//wrHAieVpNAlZSxwM4j4hCtnh9V5PmXjU05djpGGS1Zo4rjThXHiA2g6TlwcDqf51+sETCvDgDfnn8VA2BmwqCoCHnVA1yKn9j0/AuhR+C8ljgIQHDo2QCL5l922WXgg3cmO3geh+7osZs15zsCjsDaQYALnS7v4aravlZAgIByXV7I40soVbosQ4jGCIMdl3pxUJc8ObokOORclxBAGEk9cSsAOPBhKhjDHyZaGEERJttLsaekmJJBnQSHnFGgYKyRBc3fSR4LEKJRJ35yc82wIBoBCMVAzIwLhARBbrr4JVFFwghJDUcRv+KTcyVXA1FkQKMWIk4Ywaz8xjnu2EJKHIxjRN7RVREOKVYRLTGzGSJL4RIHdbTg0HaIOBKzLzu4p5bwaBd7eaECP9hq7xsFQS05cw7mLo6HQTZIt+WtysI2QlUKQKasA5iWCKyhRZJZcvjEaRZEoA5BLQmDikRV5AJBWqZrtSLUNNViBIPQELhGACIWkE3liCmpiHcFQ9GTI+AIrBoE6qumJd4QR8ARcAQcAUfAEXAEHIG5IJA+hNaHkje/+bknnzz62te+Yy5GXMcRaCPwP/7HO7785de8+c1Df/mKz9fqvLZImo1KugFWW8L/dQQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARWDMIsAiVj0z4uuZ5z3sejdav0bIQltXJJNakkrTWliKE6OUDT3sZbViCzELqgsBsuW2BzBqv0sFVznEviQaw81Uenyexzl5fTJVUXAIx666s71cHxik9wfgU4S9qJLGvRXXUl3FAQJ4V6hDsBcNmLqxZF9PsKHLhs3xasdiRgIkQ0PcDXBL5CoK+DR/Xi+3dwHcCBEA77pN8+sinPjDpk/o0pfxlyvAMh3C2WauaG0GEGMzopk6yzLgtki8Zv9kXYcWM047FjNO8LpyMjNmhimRFiExjS8YvC5iSup1iseWedMY18hgsDiATfE8XeQEL1QiTKXZtYpkYMsONMCnA3+xASMwiQZcUCxTTeQCL5b3WEXAE1jICXF7iS5OPv7re9nvVjbtQXjcDckbY5NkuhGPBc+7kZNiZgiIDkD40jVUKaDkitxGkQDhflR8+MFU8CFrweWslORaqEaZY7NrEMjH4+GvIOOEIOALLHIF4/I1DFT9zcYsFiuluZou1OtZ2NAWTK3bmok1xbgHLheWxEZg9x0GTNyJuSEemBPKWcRfLZ0aT2Gye1oBFLljUnLxYN07GNWJEotRNpScf9QIZBWwCGeFMrYlliFgLOqNlaGS0OhaFmNCLzXYURiAvA6df2Dsad6Yj4AisYgS4SuhCkSd0/dF1LHOFkQqwQISrT3q1kQVVGa2iZGJ50eRUkZM0DaFcHHLUEdB4qkhk0AQgqCXxwIgueVwV09YQRasq2RcHGjumQptMJmKGYOIY0CXJuMkjYDKxTfHJURGfXAkmBBZM0ZxmCHQzHBVpvnRjCwRGke2rJNNNl1rElDLGicpaB42MBNri4V+ZFQdLCOQdUWuKsQuME7lUTCYWUFWGoyJeUM/7ioWtFqIdYVw/Q5eptQjzwnGVjAou4xMAacafU46AI7BaEOg68KyWBno7HIGVhICNux2DzozENpyLyNR2tOBMR8ARcAQcAUdgFgI84vGomz7o/ckrkz9++egL//A9n7sgOWjklKS5NUluS4UPCnmztad1ylltmY+/cz2iegmlhen8CijESDK1+SeX/vr8537mbf9+31u2JP/3n3hfljQrO9kEq+1FDyAUA4cJm4J3YW0V/9cRcAR6ImCnWE9JFyiLgL8HLYuUyzkCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI1ACgYMOOugb3/jG29/+dmSZnuu2ykXrVuPFviVsL64IoWoVcs/1vosbxxq2/pa3vGX9+vV0G77K7tZzDhQ8xMPWRcQ2MjLCRl1xJ4FJIjC21FnU8JYbJjSWhjMRDBocMmgQID/ssMM4tQ0KOHHkOsus9gAScVSLEcb+/ftxQQKiDRs23H777XwmQfNjcBbDr9vshgDHQlWcyxDs2EjnZAMszug57Linjr2Ao5iF1zN+XGdkeupK3sREzOdkNFMZy5nAKCKZCVi6ZgEMjc6r5zlmcD6nUtx2eRdHHwtlnPYVXkY3U8xAQW0cSUY4LsZixBMXY7GOdF7YMEQ+X9vRiJgSjgHpS73Aslc5Ao7AKkbALho+/gqK+Vw5DUx1mEyxoBcdeujBU1OT+8Z2VWtc+ae4Pd+zZ0+BfL4KXxrFfPzNg9ORkz/QhiHy+dqORsSUcHy4+1IvsOxVjoAjsFoRyD936BqS55dHYGGvPPE1LRMDQVJrAvmYrSqj2LGoR7w5GLH2dnTXkdkxAEmavFrXUTIwm62XafweOW/a2KeLzy+IpFod0JYcZidJJtmupKud2RXWFrExIk7H59/Zqh1KGWuxBJbj7b30sB/Lx3SsmKFjMWySMgJ9FbGmDtDTTkZAYcDM95++AnBhR8ARWAsIcMXjoqoETdKlQzlFQGBqgO/pMpeaGByESRKGoAqDJmCXI65OzDvAT8Vbb4mliDwvurWPlSlShS4JDrr8SkeIL/cTHXpJjjrJdDOE5oAyTCsqHlkWM/P9oK6rVFnkisospGGGQYqGYAc+tOKXSiaPxwfkpSVFs2kEtcRjMYgvg6hASEBGKJoiQ5vitD2wVKtcutCZhDocq1VROXwzboTUkScAGi0orNYUJRbzoc2LCDgKWMJqckeneTtYgGnCsgNHB6KjdxmJY8iYNXVkSPlazKoqrhUtPir5kPJ2nLPSENB3kWW+jgzdplkNJ85s6bg7zVwtxW0k4ykgLY20M4eatk7731Qo8Nv34W2G/7ukCPgGWEsKtztzBIoRsEG3WCy+20CypFaxTa91BBwBR8ARWOMIvO51z37b/33Xf3wo2XBYkoT3HmsolRxJffwt2SeOPi754uevPfE+1/7937/4E+/58I4dY7zjZ581vZCJXifxZNhjU/CSHl3MEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUdgOSMwOjrKglT2wFKQtjqWIlN1StDaMUeEJJdPTpAEEy+0VdjxwmjJIBYzl08Tlkkk8dEvGdK2bdtYf8/y/Y0bN2o/mjkYKemrXzG6BLGdc84597jHPYjKOjOE1vFDrFu3rl+zK1o+/qqN1f8szT/88MNvvvlm7W5A03T4YqzU3vj8OoAILOr5qw4jEOjMXBvpJ+9///vp5Aewye4aBOiQXGTYrI2PeXbv3s2RouvGnbkvlFAn9aUyf2F5pCF2hTSi2LiJGVEsv3i1MWhxQ/ryGBspr2juIKQlNMiNU96aJMsrSjKWL3ksMmKZYnHAGWF5L4kewpI3FyUVTd4JR8ARcASEABcTH38zF+Sl7Bs8yOCOhyyOgm57oDNX+JLxzG0gwJeab05VJDdOyQBMrLyiJGP5ksciI5YpWiQdiYywvJdED2HJm+WSiibvhCPgCKxlBHQNyVxGBMicLyay1tHmgkOd8cIDOy4yzPJO1eTMU38Za3YZNyJ22pFpAhn7cREaXXIS8nk74qtKwUuGd0qxiomZ044EYlKP5eGQYk5H3Y5MGSzQjS1LOLZDbVzsRmfEMsVuWh351gE61vZkqqUF7e1pwQUcAUdgdSNgVwkILjjkJAYdEbQdwhDQiMY0iphWZUWIWBdFXcTMAo9yMiLLyEtABLQSMhCmRS0XUpIx4ahWVeJLV7npZgjkleBDmBEV0Y3jxyMBxhYQkFYmQpMxmwpDRejUFKWQZES0QmjTrX8NIjMrAvRm64b4TZcqFBltmTIQU7XkzCIRAOoZPiqxBWjZN74wR0tV4qvtxpRN5VS1a0OTM7NXVCEmF1LHrFJsRFXkBppkKLbFu/4r41R3s98OLzjMiAVWOzxzACemU5FWp7WGmBYc7Ete0cYy0jVrTqxBBNSd1KU69mcJgIxkKum/1vHT2rTGWGsQxGXcZN8AaxkfHA9t7SFg19PiputajDADNpIQJJjKi3W91hFwBBwBR2ANIjC2X7/LtD5te/jJyiTZmua8I0/u9cDkm9986+cu+M5fvwze+g3Vk/fVbkiSHS2BGaLNWHX/MoCWaZOPvzmU4l2Sw2KIJNmdVBtb7koGh5O3/3NyzCF3XvyDF59//tu+c9H4poOTXds2IVFJplLJiSadzzfASrHwzBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHIHVjcCePXtYB8z2Nyx0iZfn2jydiEzt8sGEwLQsxwImNi3aURXFzZs3b926lXz79u2x2PJpxXKLRNOvZaKi57BVEKuc7beUy2gtjQzHmr1y7n//+1922WUXX3yxnKpvqIEFzdQH5/k4+VXtPHP5c2ipGsvJbjjwbQDp+c9//sc+9jFOkLgVEoZjG2OVPHHGx8dLSsbuytMWWHmV8pJx5Dii7W984xs54nRvikpYg4gly9t3yQOOgL7HINd14IDH4wE4Ao6AI+AIOAJrAQEff0se5cxN5r59+1Dcv3/P9u1Txx0bPkXRdsMlrbmYI+AIOAKOgCPgCCwsAhqpeSMEkbVcaSQV9trgnVuDD0l599Zs8kXGtCQrlfB1RjNh/xT+ae0TEVmYtdVIxHfSEXAEHAFHYHER4JKeSfiDY16ZPaGoma8OF/+2HDK8bZYpDRYSLlBpq5b6V5bNfimdfoSwLPF0RjU0JIqfzxIZufhrzSvFhplQzqS4ticdtysvTAzgPzVVr9WIgbEyTASn++RAEyExs/vSAH/1+qA1QXZoAQTazFumikE4DMXhj6RisJn+haIspEhQDKGxn1VbQGKpaqLvNMkbqZeQi8A4k9USUl7h9iDgFv9Rw7ZcaOEl0TyFpirEEfKgGtvpRkul40wHVUroQnS0AN/6rQQkSR51gI6qwWasS5EUG4GWkc76zl15CKhP6ovjHtHTGZpVbomrU+xolXan0D2a+n0yXUk4p1jI0eCEUc9ptL5lDl4ajdbVhjO+0jrjgsdUPBCkajh56WSKKqVD2dOSIrDmNsBi0QBXbVJPmLn8IUzn1g+R9bwactHnB6AY87CsU6LAhaytX7/eZIpVystjB2GCIWx+Pg6akEg9m4wK8mhBYIRksWUIbCoRP9cI7XOZkckXMav29iVP2KxZwUveYMzBJpFjn5ayIoSEO4JUKzq2RfGQy05HGXOBKWjF35c8kVsYKMqOmTUCPgn7O3bsMGYBQbTIk5MQI8cRWEGAgJjiWy4M6aI0GQGT6eZF+OAFoif+GJG8iPLyxccX7woPMZZ80T8h9DNrHHEhgIC1xeQJBgHwJFetychgJpeifm5U53ssH9NSRJ6EfapwUb69yDMzhHzepizDxzLx93X+ooL6yMgIu8n2FQ+OzK+Ijrnsq72olG+vji95R7PGJGadv5zsxnRi9SHg42+3Y8pptQTjb6O5Z3+T9wHp1lfNNK+GfLIxfvSxyQc+8LLrr7/+L/7ic3A2jB7JMFqpr/PxN3/IdJUmJ1FLziWRqxyEj78AQmfmks54sX3nXoam+lDyD//w2fs+8InvfOdLn/jod9z0Kx7+dHvJm57WEI+WhlQff/P9zcffPCbOcQQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBByBFYoAs2msl2AuKY5fk27iaGZEeSyzTOh8YHCYHaMJEFoXweY+0Lt27aJI1TKJfBmGIdD6CkxdhV5kmPelvqjC6htEuGXLlm3bti2qr5VrnC3wSPrAPt+KsbGxPHONcFgRx0WD/kNaI032ZjoCjoAj4Ag4Ao6AI+AILBME2L6ZJ1ee04eHw+JPVsOOjU2U+UxgmcTvYTgCjoAj4Ag4AqsJAb1j7NiitCp8fMEwrdfOfGULk+8vxOyo5UxHwBFwBByBA46A3vzHOSFlnrm4npeZHZAR1MsIH/CG5wOwsI3Iy3TkMPCRQMmmmCGYLrRiRy0xBZryjJjBTm3miMSSquWb6JhpNHNbBbomZgTWoDvGYzIxIcm2fH9TSGh1i61tMHbVlUZYdWaNwwFHRmCaQN6EajPuMsW8ljgZ+/JCzoHrpuL8NYiA+gk/R6MeYp0nbGDVTvSZNKknh5yeqW7cFvF/lykCa24DrGV6HDwsRyBFgCsp/yqPCcGT4aeX3VnyEvPcEXAEHAFHwBHIIDA8tK6SjNdrYUFwtbYr1FbHeeQcXZe8+4O/NTp8zBOf+Op9O5LBoWRy+k4Wnrf2KcpYWb1FH38X9tg2JsNWpOyTvHH44Oc/50tf+MKb//e/PeYP/uC/Jnfv4NUH2x+HHZDDNsnkA+19kxc2BLfmCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCOwjBBgKaE2wNJKRCITkVkJIz658ZdPG+Klk4qKIGGyxtqWWfPTR9rKZxnGv3yQnPPxFeDLqiHWE4iNH67jV/3EyfQWOr+FjSSnA0UmVNkrjSLJakVkOBQzBjPyi13MxNPNnYIkV6KZJAXPrxLyQ0r79++PdWU2TCzPXi8XyxwQumR7LTbaa3QBITHlICOCDydI0OAguLAQAyKxArNetdwQ4JDp+PqxW26HxuNxBBwBR8ARWMUI+Pjb/8HlkSTckXInvnHjRh5YSNyRrlu3oX9TruEIOAKOgCPgCDgCi4lApfVDC+kNz1CzyZfgA+lX/ZPVKhtgsfXGeJKEnbDCD5Q3eUnVko9i0hYA8PNVkZSTjoAj4Ag4AgcCAS7vpAPheWX4ZLqEZ1WeWCEASrMn2gISvqXixnREWKZssqajhXBsKhWb+8vIoEtipg+ZTNUyKRIYEcbhxXTJILFQUrKnGN5JGOTA9RTuKCD1jlXOXKEIcEzTyENeq+tV1awfVMu3C41GhQtCI6mHOfdGLexppb5RqbAdVnjlhdb0dJh/ZxUHE9H8qFna68KUNBcVbpt5IcZf+Mq5kVTTifpmLfyjK4PumrEGR7UQnpYYgTW3Adb4+Hi67qjHCaDDgDAEP7RF12/12sLjo1Ua3X6srJvqwsrHoe7du5dzkuFcI3q3AIyPPLcC5X9UjQsBqby8fvCwL3k24ywvzy/FIc/x0g2NtasboV9cXFj8M77AB1Tbl+D2tS8j1C6aWJvRx7+6V8MCzTc1GYxzekJ5PLXKqrw8jcV1X/Icr8zaJgs+JmgCJyBI4gJ52ljmFof+gFj5eHCRATCOIU8TDPLl7etetri9aql89Xv+0ti+zpc59P++2gs+JY+v2svxGhjgNZynVYuAj78Fh3YJxt9Gg8vPdK26L4RRSfP08fNDH37gve997/uc8uqxHTwkJBPhxmfXnj1DlWof9wM+/maGD4oB5ihfa+MvQyrPhsyRcEu2Z1/yR3/0mg9+9PnvfOeDX/JHl/NYyMZYmUQXim9gMrWZoo+/GUAyRR9/M4B40RFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAElhUCZdZfLauAbdYPQiluAvOAzM6cdNJJmzdvfvazn71hw4aXvOQlrN9gcVostqxatEKDCVOQ6Q5ByzB+OgaxEZit0IMTxxl3BiRZsUOtVEw3lpe6tEwsFlieNAETucJWu1QkZ+JYK/oykcfIxHRGLFMsL4liHJLFZhaoRcb4cmS15tc4sbyMm0wZQuqShFZRh5hcRfkiV7GMWZdZVgiE49pOyyowD8YRcAQcAUfAEVjFCLTH3vDvKm7mgjdtx44d2ORTGm7XN21s8k0En/UWf+mw4DG4QUfAEXAEHAFHwBHoFwG9SkKL10ekftVd3hFwBBwBR2DJENDnb/bEShHXmedWk+kWFfI9ZbrpluTHEdooU1K3p5iMI6bmS768F0Y6hEk8t6JLETskzIZRsHAclJhydKWiAJSrCj5EzBctPjnfyFstRWjiYSKYyT6zYAIdCbSkaLWZovFjIlVqZYQfV4mmrmPk1Ma60BldcfL8jJjZifnyCPI92x7HIAtwpNWvrkKVlqKKQ3J6xSGgM9d6YEqEM1qHGIJU0Cjk6dT8ry07KrVAcJ6izp4W/K8NsMInzOmlI52+1yRssMyOWDPGm1ya2CVrhoHZYu8zok4tMgIzV95FduTmHQFHoDcCujLa9dEIaeaLXH+pgq8q5b3duIQj4Ag4Ao7AGkOALT3Tu/bdtLs+nEylv6v6/o8/7mEPefgznvE37H5Fmp4aZS67PpAMDO2YmlwXWGsmZYbRzHiaL/r4W9Q1mnrvE25R2LVy86aBq38y+Tev+cI///M///vHf/3cP7gjiX7Wlz2Xi0x5nSPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwKpDIDP7tpzbF5ZP5lYJK/509WT41dAjjjjivPPO42eHfvu3f1s/7Vb+p1+Wc9uXW2w6FsstKouHnqAVsR3jpM/AVxW5fh+O5bbxuUCPsqIkraeJiJnmtyPTagsIFElYlnFJihNrwaEYy8S1JWmpB0+z1yvLOEY6ehEgpoIMSTP1EMYvjgFJsy/JYKWtDmHqMW3MPBH7LamSNxJzMEhLSWKa/QUxHjty2hFwBBwBR8ARcAQcAUfAEYgQYE/e2pYt22q1ytRUgwXGu/fsnG5Mjo4O7d+/NxJz0hFwBBwBR8ARcASWNQLl3iD59xrL+iB6cI6AI+AIOALFCDBvwu5X+mkZk2RWhYk55ozIjVlAaMRUbhMxBfKZKhSlq1wTfMSgPbAysWV0KcqjdFU7hxjyZp3jCKxQBOxsgtAMKTmpUp0kZ/uq+GTJtzHU1sKGVhOVfZDV4YTN6NgAK7VWD+dj+mkzv1bGJaJam66FGfkwszw5ScaUfY2c6wez1tUk0FVNFzcDjUqx93w8zlkkBHwDrEUC1s06AnNBgOsjaspjQraMb0WTz1RJwHNHwBFwBBwBR0AI6M6be3rGGe1+9Z6PPPoRj3jEk57011dfHYM01GyOa81ozF31tI2namlmVM0XTT5TteqB6reB9KVtWycr1eRrX9syNvay//jPp77/Q1/+o9/fOjIyuH9iIlgr9aKpX7cu7wg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAjsOwQWOmzkDZFCLLQ6Y+FJiymvO22297+9rfD/N73vrdv3z74LM1c6Y1dbr0HwLVp1HJecqrjbsvciVnRqufES/DpJ1TRSchJhnZMi4mu1CkaEYupp+Haas1aMWFdNF1J3FkWR+Xtx1HJnEIip8pgiT1lYpYFMaE5lcitaQLQitihlhR76WgcGQmbOzjEg0E4Cqy4mcgjIGECkKKs5Y3HvlRrUcUqYsKJO4Z0rSqjbnacWCQEOByWFsnFYphVzHZGL4YLt+kIOAKOgCPgCCweAjb4QiyelwW3vGrG34MPPphH2qGhgampfePj4+vWreOmYsOGDUly24KD5gYdAUfAEXAEHIE1joDd9swHB3uFFb8HWFn3UfNpvus6Ao6AI+AILDECy2GIIQZGPebTaLvmszSkpsyw+5WKQiYNuDXpxgyLTbIwEyQ6npGx2mJUEUvNtqSkFTstVqeWsEv66mkqI7BIZjNevOgIzB+BTF+lqNOZnMQZyrZXMCvhXOEUbvC2qsApMs1q+GGqgYH0B6iGwk8NsZAgKFcwVdUGWI1GmFvnrCdBc9EYHAyn89RkuH5oG7tKsw6nmr6VnJ4K8rq2NKdX0ovKAqxWdFVRJ1jRDfPg1zICXKW40KxEBBS2BW9Ex7ZQWyzQUWslMjMHlOJKbIXHXBIBP74lgXIxR6A/BNKda03ldX931mPPO+UlL/nrn1wmXr2SDLA1VigwfjYHTbIvInO57kv3wAprPLVR1YiOUVFbLNBRayUyMwe07PW50hgaHpiYCIv+h4Yr+/aFTtWcqFx28eRLXvSRd77zra9/y6ve9KaJZDxA0kwOrld4HtmyEvFZZTGXPb6rrNneHEfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARWCoEhoeHccWqQTksmHErqFqqYDv4yUymaLU0zbE5tT179hC5lkV20HfWPBAAVZarLs+OYc1ST1Co6hjk1KrnnHHGGYcffviWLVtOP/30m266iV+rPuSQQ/7rv/5rYmJi7969YTFuW5JmSmVkZAT67LPPPu644z7ykY8cm6Yf/vCH6HI2jY2NYf+oo446/vjjL7/8cqnHECke5QpSZpHBHd6f+MQnXnrppddff30sI5wVOfljHvOYK664YufOnbFlWcvksRGqkIeDBQhOFlJG3oqSRFiJNc1waN0zn/lMwvv1r38NHyge+MAHbty48Utf+tJBBx3Ex/n79++HPzQ0BIG8dQ884sts4gUx5VhmiTNVJGRQoShFqlAUTS3yyuFQFSa+h4Y4Ups2bbrf/e53++23X3PNNVgwyyLiFlGbLqduXe5UlRETE79Kpu7EEiDAsdAhXgJfGRcdu4HJxLVEaMkERMRimap8sS/hvHpfHHzJHedOrCimODQqrlpwWqBxWhl6sfcFd+cGHQFHwBFwBMojwAV5sUeBbsEUjwVxrQ0f+VBjsW6OjN+XsGnNjcCX3M1h/D366KN5uGDj2ampZHRkY2O6etddd7ENlj4UTCqz7mYLwhNuPv4WQORVjoAj4AisSgQYgBh90oGo9QqIseAAttQGRIWRGRkPYGC6r7DbjDgSmDaACkm1glxaGeEMky/82VWj/ceL99bOILFWSs96SM/VLgwjE1tslOaoq1iHgcj3FolJUTjERpx2BBwBR2ClIMD10C5iRhB8z4EJYbuWxkZiul8Q8hfbYguxfBx8sdZi14KA3WIYjDAtZQKwyNsEM7kclDBbpJfT6ctj/URNqZfVwGJ+2zZbs0gZ15kiwiZvREYmLtKiuLji6H7jL4PJ0oNAVCv8OCwBZmXv+afab5ZAlZRGFs4m7mE5J5mBZfKfP+Z10w2wkiQQlYS/tmLHxqTnyVTYN2t0ACOVWpgaxqwlDKHIdYOTl3ljZmxb88ME1GDjvHAHzi+a0WMrjTCnTBTIT08E6X379idsfxXYSWU6jbkycy+trbJkv1Ns052YzpsjAjO4z9GAqzkCjoAj4Ag4Ao6AI+AILG8E4mfIN7zpYS94wQv+6q/+7UsXWNCzNhbkpt4qnHAE5oAA/Y1HPhRZHmG7VrIs+DOfTF772tf+6Z/+8V/+5YxVHjVnCk45Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4AssVAa3OtLlXCCXipQqamS/y5Rr+Co4LVNvrYpdjK3T0LUL1AesJECR2qmKLNL4n//3f/336CTthnXDCCWyrxO5XNAkBVuXy47Sjo6OyQ75v3z6KbD71oAc9iEn89evXn3baaez9xO5Xd7/73VnLi+LBBx981llnIUxK/QSgSLJJzrxtzEEGRRxh8MwzzzzmmGOQsUQtfBIEe07BZ9spdn2SQRODEMf4cmFFSWIkT8CMk5mSJLERMDPL7HL1hCc84dxzz4WPPLtcHXbYYSpSe6973Qs+wsxHgxsC+l1fTVJDyxpVUgdwhAkP6ERTxCAf/EsFSarIJWYNUS322f1qcHAQXbYD4EDILHw5EmgERhITeelKIJMjY8mqkIdpRSeWDAE7FiLsQFix30gyBlXs10hJeYwrYHOaKcIvNmWKRhTL52tNMePL+ISU11pKjiJZSo/uyxFwBBwBR6AnAjZMiPDxtydiGYEYwLjK+CXHX26z9TUgS4a53cUUe85ycxvbnButSOam61qOgCPgCDgCKxoBhoDlGb+NkkYsTZwGiPyWHKO7xWbW9NZLYsbspgW/jEyBulc5Ao6AI+AIlEQgvt52o4tNoRUrFgsvdq2C6ZkvQRjmQi8QFJKYmfBMUvNWmhJi3CTBoUhiFskmkky+gMCpJs7wpfkgiAL5TJUiyTAXpGiTWQtibfkbmed91PJv4GqNUKcA5yCTsDr7mB9nWlYJ2hLT4kxPl0/IYwR5cjPCCy4l41Ary8aBiL0rKsIjLd4Ju1qP7+K1yz84XxRs6eJ9jWGLEkQ/RucZcE/1jECmmI80I5Ap5uX75Sy4weIAlthdcTBlajMBZ4p5CxmBTLGnfF5gZXGWW3sXO55+7feUX1mH26Nd5gisuP5mAWvO2IrFOHPHL4FO8qNp1b5I4IhkelOluWt0aHCsftMb/+GEl7z0gexA9Mn3JrXKhunm7lRyslKdZO9caY2NTY+mZjrZl8gc8wU3WBzHErsrDqZMbSbgTDFvISOQKfaUzwssFEfvj7AWXi40w8LrZhIWSbOr8vveMT49+cm3vvW99cqr//71W5KJuxqD4ZdHonRUSt+V/hrJVEp3Xg25fNqr4Bc7nn7t95SPMHfSEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgcVFQEsGWVWMG2YxlIuIHWuaaflMc2Qi1MpagswvsYXJXjysnoSIWzRPOg+FcRQbc8cKBgLXRx555KMf/Wjoj3zkI5nvlomNDZVMfZ6BHRD1hcV2KZtA5Nu3b//xj3/MQaGfsPvVT3/608MPP5zzgjA4L+Bz1Fhfy2Fi7yRkSFSxAPfaa69lkywk0frOd76DqQ0bNrAD1A033AB9xx13XHTRRUjSDbCDEdQ5yhDYRIv1u9pjS0zs7969G8Vt27bdeeed6hKoU0tChSoVyXH0jW98Axl9D0BIENjEF/tw6fMAybPxE+qoKInZLoV/49qYn6GFD+pHHHEE+dlnn/3BD34QGTz+6le/uu997wsNbhAACDi0RWGgCEEugwSpaC0SVj9zCA499FAQADSWPuPi2GOPBVUQI35cULVjxw6qMGIBsykAlsPxmJyk1TfeeKNAQx6b2EcAd7gGc5gkLKAOAUfxzCG3AOag6yrdEIhRhbbuIXmKlrpZmDMfy+oPEAVG4gjpQhaP+HGtxY+MJAvMZqrQJfWrlTGybIsGmhE0lmgpLtuYPTBHwBFwBFY3AroOq43QmQuyXa4z/AXBBJs+/nZDXvz9+8OzQ7Mx0GxMVav1SqWW3sA2kkp4GGHLWYn1zOPjKNrH356guYAj4Ag4AqsAAa72lmwsoF2iD1QDCclcG01IxlwyQk6JQYRgIef+BKZyY0LAJFl40DCtKELP8pEYr57sDxHUO76MgokpSWZMLlgxH61Mh1Z1SkIgrpEFOBYTNEwSDTfmIhH4spBiF3GEkpFYLOO0I+AIOAIxAly1uFzEHKO78SVALbqiYyOiTVcylkMoaYyAhpApCC5Z7frwr0UCEfONFt/ECi6/qFDbUcBCNTtzJWoMmymcIddfarw13insyDjt1XtgWgoIjLns9VxPf9wEJNKJsGrr6DCbNLaP3ayY2wrCkZEOJBNA+I0ba4jBFBQd1NosdEntUuvfPIeKEGU7IYDldqnzv3FInSUWn0uQakscPG7j+Ds2FpluDYx14xZkXMRVc6P7DWBuXlaLVnyaxHTn9k1WwhfBU3qxNEB/oHtzYCtDwwnT4bUBXkA1+Zi9li4doXqyOhVmVwfTaehKj53Z6QmVOhbSm2qm2vGCufS0J7K0n4Rzh53ryDnKzUaTEziZnq7XwoWrOhVmw7m8hNN3OpzCU5MhTxoT/EZToz45BVlJ756r6XfNjZkTM6mkP8LUDJxqM96gKcWkdaL3iB9dT2UQiPEtI+8yjsAKQKDbiLgCQp9TiKu+vZkGZopzwsyVli8CC34nunyb6pE5AkuLwL7xm//xzSc+73nP+9M//btPfiD4Dqswtb9QGkn76hq2o04ZfWdtC30rrlCFVd/eTAMzxYKjhmR48NNTYiTH791S+uB7d2zd+sJ3v/evDznkgpeff22Yj4ieBCNxJ5cUAR9/lxRud+YIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AisYQQ0kbT6AGAvHrbmsZmyvhpYMBOXrzIOBFM8whOCJZuvfOUrL7zwwuc85znQH/7wh4kBGQTWr1//4Ac/+Jvf/KaE2caIHX8yEUoyw/RieQSErclbUQvQr7rqKjaiYksp+om2WPrud7/7rGc9ix2X2FAJmd/85jcbN25kM6bDDjuM3Z04RqeffvrY2NjmzZvZ6YmuddRRRx188ME333zzaaeddu655/785z/fs2cPezNRdf311x9yyCGnnHLKLbfccsIJJ1xxxRXr1q175CMfyZZbxEPVpZdeeve73x3Lu3btwtrVV1+NZfklTg690jOf+Uw2xvrJT36CkXvc4x7//d//fbe73Y3tojDyqEc96mc/+9n9739/NqI65phj7nOf+1xwwQXnnXfej370I9p11llnfexjH1PbreEUY1o4SMZyeadIreYryVmxQPPf8573vOpVr8LXrbfeigA9lpghfvu3f5u2fO973wMKNrECFhoCsFu3bj3nnHMUHruG0WqYIMBuWRTZ6ApAbr/99lNPPZUG3nTTTZwRIAzs1AIyTWZHMMBkiyuWUhAYu2WBGAj84Ac/gIMwxwVYEGP3sZNPPhnX7IqFPAcO19///vePP/54ggTtJzzhCRyLK6+8Ui21ZsaAWJUIz5cSATp8x2NBP4Sfr1rA2HA9f2uxEQVcEHMsPH/XxRbwJXfKTdiKxCm6IGDTcsIRcAQcAUdglSHAEJC//sPx8XeeBxpgSRhRbtZUzGNuAhCZ2kwxlnTaEXAEHAFHwBFY/gjY2KdXTJmR8QDGrxG24z0PVSSqSIpQYZPDz8Qcy+RrM8IrsUirSTEOK7EVHrMj4Ag4AjECdlmDGdOxzLKlV1zAhqRFzi2BEhx+3gW6Um3tlM3kTqUStoQ2rQKCSStG3rwwTEvd1IPT2V4oZjimizWjnThQCHDItHHSgQpgdfhVr2/1/XT3K52Mg4M1ZsnZAosE0kwH0+05I8YbTTj8qhOcRq8NsJBkczwMcm6SswEWHHDTmWXnV6hqv/uqNMIt9/TUZNBqprtiNatoTadk0gymUJSKHQI4flYaGktP+OfmS4+5e3QEHAFHwBFwBBwBR2BREKhU9iUJf8PVymjqgEff25OBa+/zkNv+8W3DL3zRU/7y1X/3yfcmyXQyUDuEO3MLIn1G5r1569W58Z1wBOaDQDrRMtOvDt54RpIcR/f7woXJ6//6nc9+1l+88W2VjYdnPNyWVG5jAXMFudAhvU9m8PGiI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7AckSAVZLsOpRfHFkmVrQKUkcL8RJMFl9qZSdbXF1++eXsWMSuPWhhU2InnXQSu/mwWdJBBx30jGc84w1veMPDH/7w0VHNKQcxSx19ObMnAgDYTUaHgH2vRkZG2KqJxbtaLLt//346zCMe8Qj2sYK/adMmdl9iF6fnP//5mIL/0Ic+dOfOnQijyDElp4oFuOPj4ywOZhsmdoDC2vnnn4/3pz71qewVxQZPHPrf+Z3fYbso9mA69thjEX72s58Nk6PP5k3YYZcr+gMu2E6LALDPrC4Jy/QitpciVMwS1a9//WuK9BP2vXrkIx/J5lxsKUXPYccoTGEZs2xBpY204g4pNLBsSeBY0Qj4oiEUBjEcffTRxx133GWXXQbnsY99LFW0nQ2t2McNmjCwz0ZdbDX13Oc+lwAQo8nExi5U9773veHTBGImsTkXO1WxdxWte8hDHkIVICOMCjLEDODIvOAFL8ApLXrhC1/ItlbYJyptEsf5AnTshHXiiScCL7UPeMADCPIVr3gFuoDDrnMoshcYa7I5BE960pNE4CtuJpF7Wp4I2GGKCUKlSD8h7zdsU8wYnIMdWSCMbrqxi450ga5sIhCnbo4K+KjjWnksFscT8xebVnMsJAVGcbH9un1HwBFwBByBvhCIhwmjsWAX8L6sxYpmDaJfI7IjC4wg3dRjFx3pAl3Z1GhleTdHBXx0ca08Fovjifnd6EaDO18+NRzkgbJSGWw0uM/laaXeTb4bX22xkBQYxW7yzncEHAFHwBFYfQj0OwYtPQIW4RKPU+YXYv5jt6y10eOT8PgPdlyM6bbGYv4bt3R2nIvp1W07Ao6AI7DMEIgvgFz2LTr4RhcQ8bXUxGI7xixJ9KurOC1ajVxxVEbHVSWDmbMYTmN3HWMwJl540FWSx1qtwm47o+uGR0aH2F2HP7bd4Y/NsJLKzDHKhIdHElNLJGaLmAZi0ieTqJKYvGcsrLUiUIDDWmv12msvp0zRH/Or/LHxVL1e5a82nAyuqw6uqwxvqOtvcLQW/tZV+Bva0Bxc3+APYuPmodENtcpoIxmZbo40ktFmwR8C1ZFKZTgh568+XIv/akNV/RkzFAcr/A1tGBhcXx/dMDCyvj68rjo0WhkmmOHK4Eh1eF19cLQ6tK5WHUxqQ0l1IPwlFa4SSaPC+7P236zvnOP7bdFrr0csZovB1JMj4Ag4Ao6AI+AIOAKOwGpAwFbV8qwet+d1r7v/H//xH//Jn/zvj7+HTW0TtssNz+FNNhgKabaseJ47AguPwLZddzKzUmFr5SR5z9u3Pu1p57MO+K1vPbajp3iFdEcBZzoCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCBxwBm9VizyBorXXuN6r2YuyZfzFFsjIGbd0wTGg+TcYXv1GsJcXspsRK6y9+8Yts0MOmPO9+97tRQZK9e7DDBkC/+tWv2GLpda973XXXXfejH/3oAx/4AFsFmf3YF8x+4188+WUVTLdmgl63Kvg6cGxZxfr4sFJev0abKvz3f/83x+KSNLGj09DQEO1l1yqEzz33XLZbYourW2+9FQsc5euvv/62227jiMO84YYb2MqKNffXXHMNNEf2yU9+8g9/+EM2h8IYezbt2rXrrrvuuummmxCjq6C1fft2tj9jDylcsGkU/olEm0kpeFx8+MMfhmYnKYQ/85nPsJSf/dTQZScp7X5FL7rqqqvYGAuBU0455Wc/+9nxxx/PllLf/e53UVyQg4Uj+jDbXZ122mn4Yk8rLAMde4HRKOgbb7yRALZs2cIGXo973ONoIND94he/ILYf//jHbEr15S9/GQGK7BG2Y8cOCPbPYpcuej77amEE49ih+NOf/hT0gP13f/d3f/nLX9Je8KSKs4kcF1deeSWcU089lUNw0UUXERvyBHOf+9yHDbPwiwu2AwNnTj02ySIAjh27g3Es4GNEiR5iCZQWBKi2bf93VSGg7qF8VTXMG+MIOAKOgCPgCCxjBHz8XcYHx0NzBBwBR8ARWNYI8K7DhtHlEyghLZ9g4kh4uaQUM/uleX0nlWXbzI4tUleJO0xB/OpUHe040xFwBByB1Y1A5toYF2N6KUFg5FpKdwviy7Bi3JFBOPxsCYnJMiVomJY6+rW2M5XGnBETQ8wQkYyA1siuWdqORiwA8wVRIOlVjsCKRkDdm9zu/QYG6ixUYB5WOQSzqHGCr6KI9EwNKsUJMQkw/8vyDNmHI/VMHvORtIRfaOUxs15PuHjwx8maNmRhpr9X9JE9UMEXLb84UDG5X0fAEXAEHAFHwBFwBByBvhDQ40GzGR7LBwYmKtV9Bx3GUtrkqJOSD33ySWc+6AnPffb//uxHk/XrR6rVgelpttvdniRheShJbyTI7U98zx2BBUagcnNSuamZHMzPCY+OrP/WRclf/8UPzn7Amy/82onH3ze4Wr8pdZi+Ims0p9isrf3GaYEDcXOOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwJwRsAXKLHxktfS6devYV4jdhVjuzGZAhx56KAImMzcvLJjWmmkjsGNfSWGcbYlYssmGPvApQkOw887jH//4T3ziE6z4lF/UN2/eTHjsyMNUMhsGsWvShRdeyLZKz372szEYAvX11gJrEXKwVSdhcyWOBR7YtsmODjs9sduU3P7+7/8+ezxpm6e73/3uMFVFj2JnJY4gB4tDCZ/V9nBOPPFENmZiLycEMI4i/ZBadNmkiQW7CLPrEwGgy0F/xCMeQSdBEo42wCIwIlGE5PRk1H/yk5/86Z/+KXuopYt6K/RqNuRiKy762y233MIuUeyrRS07PT3pSU+69NJLaQ69DqZah4W+Eo2SvBEUCe973/seO1t99KMfZYOtk046Sd1YgHB+0ZORgaBdbMXF9wbf/va3ifCEE06QDC1lxbM2ugIW1AHkzDPPZHssTlL2CwMT2rtx40ZkjjnmGNoFFGDIPlY0U8HghWD+8z//8w/+4A9AGz5GUNS5hjp+2V3rC1/4AgujCeD888+nir23nvrUp2rXLWErJLFJ0cCJaWM64Qg4Ao6AI+AIOAKOgCPgCBxoBPhVXf48OQKOwLJBoDKe8OfJEXAEHAFHwBFwBBwBR8ARcAR6IaCZF3Il5oaYmmE2h8RMjZLZoIiYFWMCLRLqSkw/QTA3RzICWmLKUYeIjTjtCKxBBDinON3IOb9sVykIJmGZt1Wi2CZn/mXqmQLzsEzOMtkKUZwQS8/sGsLQxQlTuBwZHR0YqAUPg/XaQI28yn9DtdpgtT5YrQ1UYA4MDVTrSW0gYYqYaXM1ZA0ex2XS5DBP78kRcAQcAUfAEXAEHAFHYEUjYA/MLPGkIfyO7467mqfeN/ngR/+YZ4CnP/3Nl387tI9tp9m6OhVe0c314Fc8Aiwspg0X/eddV175nK9d8siPfGTdC59x7XXXTVrD6KXs1ObJEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEVi2CLDQmWWaf/RHf3T66aezmRH7Db32ta9lIuzVr371PGPWOmnlGVM4ZTUnfs8555yjjz76nve855133vmwhz3sjW9842Me85izzz77yU9+8gUXXPDhD38YRSywP9G1114LvXv3bvY2uu6661h1yuZBbJMky8hokXeB00wMS1bsiMCSeZ+/I2GrnM2kOF5btmwBfDZdYtModndiQyVoNmZinS7L5VkBjMApp5zyta99jf2t2OCMrZeovdvd7sZBR4XtnPbu3cv+ZXD27Nlz5JFHsqgXg29729tOPvnkHTt2sB/Wpz/9aQ7orl276JP0E7avwi/ENddcQxe97bbb2CUKYWgIOoM1k6UFX/nKV/7sz/6M/ZuQZ6kxu2hh5Jvf/CZ+tbkbLvB72WWX4Zp42AOLgAmJMOZ8sKSoTvjIRz6SnarYh4s4Wd4AJtrTDfRYC0H+m9/8ho2raD7tesc73gFKcACHhhAwTSBUFAkbbEk0gVPmtNNOQxdMiJnEWcMXCzT83ve+N2fEu971Lvb5uummm7BMQ9hUC1/YROD666//+c9/zsmCPHFiHz5n3Fe/+lXOrG3btrEZGbvLcYw4y8CBrbs4fNp6TNha6zKEwe6EI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7AAiJgkzJM1jBPlFpWHva1oai8o0d0USEnISYiowJTMvChO9pxpiOwBhHglCFx3oV9pur16mDYFUtpcDD8ptcgG06R6ohUOXcgK2HXLLagClqTzSnyAtyoRRgBiQXNLpvZmZFqeoJWB8Kp2kiCF7aw4/xtXRHS0xwmDIKZmprGfzinUw7G/QQ3JJeS8A2wlhLt5e7LT8LlfoQ8vn4Q6Dlo5Y3NQSVvxDnlERDguvLobqO8rks6AqsJgTmMv3aDbTjohKolx7Mr7VTllxP7G09+RvLmN//FL37541e+8r9uuoqfjd2N8NRUs5JsSrX2p/kes+CEI7AgCBQNps3R1MUY+dT0xKaNh4zt33HrjdNP/51vveENz/vC1x/w6ld/5LMfCSIjI4PpFumVXFevNabrtbrvjJUCOdfMx9+5Iud6joAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCOwhhBgSkUzuZn5r7iIwNjY2Pvf//4PfOAD7JhDYgMj5rlYHMnGPQVgmRG5ICfBjBPqGEyXgYbfO2VDIlZWIACfvYHYF2l8fJxdrthw5z3veQ97+uDx3/7t3z760Y8SEpIk5trYEoidgNiuiCLyl1xyCeqvfOUrf/zjH6MlGXIFUBCwV80HATusHII3velN5FhjnyY2loKgyIF++9vfzpGi/7zvfe+jeMstt1x/Xdh0ib2o2OaJbafoA+9973t1HD/2sY/Rx2CyqdO73/1u7P/gBz+gD2zatOniiy9mGybMIszWVJhidyf2Y/rSl77EWgI2hPrEJz5BziZWn/nMZ5DEoJpGbyHRtehFKNLf6BX/8R//sXPnTrZ5uuiii6hlAylMIX/HHXcQAwLf//73caFuKTt95VI0dQzSiq9//et0ZqKl+C//8i/0Z84CNpm6+uqr4Vx++eXs/wWTPae++MUvPvCBDwRAIiS2D33oQwSDDKF++9vfpmnsVwXBfljsafX6178eDNnN6uabb2YHKyTRwhFQcF6wGRm1GIEvTNJfF6sgwJnC5nGYJdFeAkMFeB/wgAeA0o033og7dN/61reSU/uzn/0MAkXahYoA4XghBm2cvoByYUfAEXAEHAFHwBFwBBwBR2AREJhuNrmZn6hUphsNHlLY8pVtbYueZBchBjfpCDgCRQjwo9i1WhLeBHlyBNYwArxgIQEAL1V437KGkfCmOwKOgCPgCDgCjgAIMKvFnzauEW35LHy4c2BehhTma8LcEfM1YT6LaSBSKLdncGappQXVKodhkkZIxcTyFpzjCKw1BDi5mGAlMUmaDCZMmFbWkQ9MD0yzCxZrHMjrQ0mtntSGknod4fDHWchMdaUelkA0kmZSaQ7WB3Xz3w1AbFGlc5m8mTQr1R4PzZX0qbrCV/W4S/BaSaab1WZ1iitJuH6Ep4z6ICsrkuHRoUptnB2wuEpM1yu8JKtONZPWbnhcUybTi0qNAKrRc0kIKL0wpf96tjAIrOkNsLqdA/EgJJlukvFBQMvEjIgFMvQiyZtr2deZjGvjZ8JQkVq1WmLFwpKUzTLykuyrvYqnOAxriIQphktg+lIjzk0sJqRCToKvPBbI033Fj3osb9YKHEm+QEA2zRREsXAsabRUeioiEMe/ePIWD+4syDxhYkYgUxCVrCEgGeV5szEnxn/B5TEo+zayxq5j2lzH8ggYPxaO6b7it3hktqdxHM3NfhxhRy/GlP1Y3unVjYAd+kwz6QnGkUw3SRODiPvPAZQ314pnMcbfuLHCypwaIPwe6badv9y0OXna0x72L297HMus/+b1v9KC1VplQyNhUSY36txf677a9OZC4F1h+PjbEz4dqfzxyigKUhMzIiNmxTnLy7LUzVqeMDEjkBGdF4Zj3TKWnyXJ8yGdrxIe82r1UZZrQ7DX1VVXTPzxH3/oTW855kMf+n9ePvL1D37wirGxiYMP3rRt205MyWzbTlhhXB8odf8mXXIff9votf4FE1Fga3RGxouOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIrHEEndOZMAABAABJREFUmEmJp31tVsUmxcCHTW2Q0X5DgoudcWxyavY8V284WQnKokkm0eSavXXunqZjjz0WPmZxF9aMpnsb/SJN7NrDNkBsRfTBD35wYmKCKjYGij2xoRJ7JN1+++02a3bOOecw4/aqV72KnYwwiDDGydXAfmOOfTndEQEgFbZst0SKZdiVSUUORKYWzq2/uZnaO++6PVaBpg+whZOY7IHFBkzQ7HR20003iYkuO0ZdddVVKtJDRGRy9rqCg6T1ba34xw6djY5EYl8tyeBIfYMqVOhCmIWgu6prWc+haHTGY3FRWhY2zWTbqe985zuxlkKl74uJzBVXXCGawNh5SjRiV155pWj2uhLBTl4igFq+2FwMDucdRbMJB1Mk+eK0YicsIJWKxFS89NJLZZAwuAiAHqa0p5hOWKlIBsQgxJkzRDLluSPgCDgCjoAj4Ag4Ao6AI7AgCOiOl5zlhNypcg+sZ8MFMe5GHAFHYEEQSF8C8TC5IMbciCOwIhFglIoHqfhly4psjwftCDgCjoAj4Ag4AkuFALcNPOQqTbP/c/hsOfxyiTh+U7FUx8H9rBUENLVKzixqUgvT2fWhMGHdGGyQiz/EBlhhtUPIORdhciYG+XRatpZ+7cpmU8WQcQqjiExqJOiEF1vdE7VVdtaqVGrpd82N1vNFWCBRaYStr9hll9qpWtggj6leAp9Imvz8UrUSJsTxgjtdNyr6JL85x6nw7jF6TQcE1twGWPGwZDSdMsZGXZ9anTxx1WqlBQVn4HJroAKzI9UxvOLajipLxszE3zHUTPdbsthWn6OO8M6tmbEpP0BzwxAtXVIyZ0GMZ4zznL244kpBID7cRsf9gYb4+FvyaALg9BS3LvVmYyioVMLPtx580N23bt+6beeVhxyT/MtbT33WMx77pPPedO21+5t7N4+MHD5WvZYbe/0yU7PZWkQbu+PRQHdDekbQMbIjFUsaXVxrYgeEyMTfMdRM9zsgca4Opx3h7dy0Svg1YN1uNiZ59uO3cwempxJ+iHjbbTv/5Dm3fv87n/3bN/3O7zxt4wue+61td+5MkiObTTr5tnTLtj2yGZ4Yp4eqleFGYz8Hsad3rTPuHM8q5fr4u0oPrDfLEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcASWFAHNJyqP56QyHFY9rlu3TvNWTNMwOUUueUl2DNqqJKn5HfbZMWGZujZNxjQC9fvf//73uMc9RkdH2a6IPXfuda97sZ8RRFhOWq9jSr9Gc+qpp27ZsgVF4sTXySeffN55573jHe844ogjnvzkJ7/rXe9S5GZZhMKLW50R8GJ5BAxhDgFaWo4Y9xBoEkct2GyGfZcoBslUnqJxIOgYjWZSHxhOlVIx1gQn0zYrin1EWI+r/dQQM/Vgf/bCDIoIIwAhpxAoYk2SRCVHBG9iqFDLYmXJICAORclAKGVct9lF/+KRJhC/AiMY2ccUSY4gFBVFRYhFOMIQptEQihMBmqCTwjjY0QmLIvvNsdcV8kiaI9FyAZMitLyT65xSkSr2IBNuipwmwCRJUbTnjoAj4Ag4Ao6AI+AIOAKOwNIjoLtc8pgQrWC0bDil+YCQv/Dk4skRcASWBQJhEbUnR2CtI2DvXtKhLLy68TQnBBjffYifE3Ku5Ag4Ao6AI3AgEWDwYnZGoxiPq2GmJk16em2X0n+5VQhb3IS9aWb+JiaY8BqvVMMGN83mJIKIMVs0Pd2aaZplwguOgCMwPwSY5GUednDTANOm44PjFX6GaZiJ7UaF3a2YwR5sVuuVRkpXqo0m53H40HiqkZ7Z1UqYJe+9zQ0PBOkcrOaFe87DNlM/jenGUCVMbTeqqQc+pa8RSZNCpVmhNhmsNqYaQyODYdOrgepwc2jP9smB4XXj+8OcdXOqUeXCMtFICJB9s2BFc+uKuZ62h822PC0IAmtuA6y0U2Whi1/gUmdFFiVwsoljzKxyVDYZCKOj+ixpMkZkJWaXTcyI2fWtktVCWHuN2VFFTE5LneplhFFBTJJGFBiXvATKy9takGLLqqWxdrxid910LQy1opuY8U3MCKvqSJS0b9by8lbVzX5HfgFTBovNmnocTxkVkzFFM9WRMDFT7CgGUwIZ+Z5asWI3yzG/m8GefAsstpanEdP52M2gVKzW5OEbM2/WOMhIzAir6kiYmLQ6ysRMEzPFuDZPdxMzO6j0vLfIm3XOykXAxqO4CXF/gG/FNTj+qu3kImKUMrRk8nhu3c7Plu5+6KOT//W//ufGzbv+6IV/+42vBNVKso5llyxGDctgw3kHzhmTHYo+/nYApc3qeYzagjP/SqWkog4xykbMGOpEmdny8lIxxU5WA8/EjDBmNxXxY/nukkiFxFjApsj89PTYxNj73/3Lm+/4X+94x0s/+9lHvupVb/jh99j0irdU3I3rmQWavh/WK5N6BmMyOl9U7BaP1ULY+WXMblqKQWLkIgqEM/LFkqo1m6n53mdvNzGzg1kff8sg7zKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIxDMsQoN5FpuOgWZeidltiRkfQmL9Aqi9fs466yx+P+bmm2/+6U9/ygpRpm5t9oplUSSMszL7fve732Me85gbb7xxx44dD3/4w1GBYAMsqggJFcRYWnraaad95jOfWb9+/Z49e+55z3u+7W1vu9vd7va0pz3t4IMPfsUrXtEtQrWoW63z54aAUI3n4mOcdZQrLLJNU+hCbTexGHzYxoHAYC1dSysLqiJXQjjmo468jMBHBrrlOiUo0n8kQK2ipQitcIwwLeNYYDFHWiVzFEmyLGsEI134ZsQE4MR4dqTZ1oqzBkkzxfS0rKmZZsSaaYQ8xq6hrWiExMy+ipYjZo7MYEbXhJ1wBBwBR8ARcAQcAUfAEXAElgwB3ZRyd829Nres8W32ksXgjhwBR6AYAT1O+iNkMUpeuxYQ4FzQ6x0/HdbC4fY2OgKOgCPgCDgCc0aAW4V4RoYij7pMHlWTMG1KCcvpUzB8Kmcmnubs0RUdAUcgRoATjRQWNFSrbFDF4ofpgfCTQtBwBgaa0EzbQrMBFrnO2XT2u/W5KxPhWIhtdqNTV0Gypzwnf/CY7rNFAEEl3U2PqwG6DTbFCr8vFfw0B3RZCL/VpJ+MGhioMgWs6wa/thSuG12iw5RfVQKIC5TW3AZY9EKDzvq0EVYFQT/jjFJv6ygQC0vexIzIyMRFLJuYEbFAhi4pb6Ykb+01fsasFSUvsWJhJKWFmCSVm6k8gUDJ+M1yJv68zTwnXIDS61qc58XgWDw9JU29r/jRkny/9iVvTjMENmNOsXAsaXTJeBDrq71zlrd4Mk2zgEWYmBHwRWckjc9wwjikhU3dJGNdtVdDFx2JIikWyNDUYlaWlWcE4iICktf5WCBvVbE8powfm43pucUjsz2N42g+9uM4oc1d7D08RUQX54yKF1cHAvEhznSDTAPpbz7+ZjCxYjgZk1q1Ug+/+8petZXdoaq5LuTV3f/P3yWvfuXb/vM///PvX/9fN/yCnx7l5GW552+mm3u4vrH0OoiVS7oelpNNHzl8/C0EK77iFQgipuutZOxM6aYyZ3mLB3fdjMM3MSOMmdeSTD/jL897vC4Kz3yNaZ5sK5s3bmZF/n99buJRP37H29/1e5//4ptf9hevufBje+rN0JObE4el/+5ik+SkMlGpToZ/U8TywYijWnIffwFEB8gIFX387dZ5nO8IOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4AmscgXiCW3NqHWfWYLLhVDesbIImL0CVDGpynF+LYYOqk0466W/+5m+2bNly/fXXP/3pT3/ve997ySWXoGt2REjxu9/9LmLMr7H11dDQ0Fe+8hUmeeMgsXyPe9yDvX7uvJPfUgqJ3bLe8573jI2NManHrkA///nPxV/OuQG1nIPsGVv5VkzbBG5YmBQW4Oo3Y+kEHbzAbLH7WAxgduLeUoZZXt6s9SSwKXDUt3vKz0EAF1qUbC7scKhFxrcGGlHGHcJmISNvdvJERtKLjoAj4Ag4Ao6AI+AIOAKOwNIjwFd+/EBpozHQbE5UKoOs3eZTxPQnS6fTYMLziCdHwBE4sAjY4+SBDcO9OwLLBAHOCD8p5nEsGN81xM/Dhqs6Ao6AI+AIOAIHHgGeVdkkRIMaRPynm4WQMzHEF3Pk7FzDnG+jGX4bptkMs2nUptvZhD0EECAhGXS6T/cc+EZ7BI7AckVAk6TknGgklivw+15KU0MTyWBjenCyUm9O15rsHjVVnw47YNXYigoFXka1vqTme/m0fdr/B9Gi753xgjC605zXmikvEg+Ga6nYdGoWl0E9NdKspvuEpFm1Xq1X6pitTFamJvnBKOw3K+hMTFUbtWr4QbTw/qw2XZvms2a2zJqu69LBNaRGINicxjKxd5rTD1F46g+BNbcBFj9tF86OsEVca7MkAKNDxrDRCSmSszyInIVKyIsZi2Vo+ijyMOcgr/MtYzBTjO0XyNMWhcpCJcUPh4tFgYocyT7CrHyivRnvcRFJipLH7P79+22nklgspjHLPYHw6Use++Fodccfy3IE7NiXsBwRVRxDTFs8tALFnuCgq/ZC9CvPgSCq2HtHmiudjhe11tmMkIqQF63jS97RWjcmLnoeX3QNHwJAvq/j25c8lpXipmWCF/JEQvAcKeQFFERGUkV1CfU3NaT88eVMYVNGLOOO1NG+mDpe2O+3P6u93SxjUH7ndv5ivK94wLav/kx4fR1f5FlqmTlSdjioBQflai/GuyHj/FWAgI+/3Q6irnLkXLh0/ekmCZ+LT702yMVq397JcCvfulCFfz77Xyfc+973/sM/fPkPfpDc8evkyOOSbXewNRW354Ph4j8xhn2NmcoLL3IJKhpSu10MdfLi18ffguNlVeEQ9Lq/QhhUNX4Be1/XW3T7ktdgRE7HsyAzhHomkRD8Io2/mG1Mt35wGHcMGYzC1cGJW29Kfu/3Pv3Wt97wnvc85bGP/ezrXp7s25dMToVXS+FJMEkQgwYremlBE7AvPDW4Z8ajuL0gj0E4Pv7GsDjtCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwKpHwOY9NVtk7S2YhEKFhEBGxXQhVFUgYMJyxGKPe93rXp/61Kc+8pGP/NM//RPzXB/72McOO+wwmcIdhHKqpHtDmsyOiNgvU2mnnnqqdrnSfD1zYRdeeKGprFu3LrZsfCcWEAEQJnFclJe1zNylZjAh+G0szfGjnJ/ebS0C6G2YACRkBEWLDToOUrRJql/19jEnidjvnAz0UFIryEVIOqZ76M+pelERm1NEruQIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwMpDIPMOp+Cd7cprm0fsCDgCjoAj4Ag4AouAgCZoyJW4eSBVkrDfDhte4VB8eaZqEUJwk47AWkFA9+rkLGAgQZD0TTqf8fIJPLRyCATYaiUlwgy1dDgH4WgDLE5H2M1may1ENxCDbpq0/xRilLoJqzbMtWN5OpzvRBmY6ZZYhMwFoVILtQMDIWcXLAJmA6yw2UX68TINUpC4I0Lkk1qIsNoM7aUInw22IOCEq03KRMDTPBHovIPMPI0uZ3WdQuQEGfpZmoywyOlh9DmKEBKGsNqOhCxTNQf5nsZlVmLmKB+GCVDFOU+iaeRcKTq6sIaLQMZS3rhxtH0A56EIqVhtR0IyhgzFjmLGNHkjcGe1MSGbcCQZ5zqCsTC1FAUgNAQNVzEW60ibmBEdxYxpYgrJ+N0Ii7ybAHxsxrVqTszpSZfxghGJ4U5X256OYrOiiyMxeSMyTYvVOUYmliFiMaORgTZJOxAm0JGQWDoC0ehZnU0GM1qyL62OArF8RrhA3qqkghGIMk2QTL/x9Nv/FZUFGbcxpk3MosrUqig7lvc0GxtxeiUiEPcHDTq0wghrET3Bx19DwwhgAUDunsMVapqLczI8dBS30c3BPWwG+LK/3PfiFz/7zjv2/MEzPvfzK4LS6NBBu7Yk9epoOLMGb60PTu4fb23FyL1P+/bHzHcgUCThNHNJNFGqREsyzn38NZSMMHyM05GQmGAX3VHMmLHZvuRN0Y6j2TRiacZfIgntTfdQxzVr7qvV4XXravv27XvlS3702U//6C1v+YeLv7/77//+X7560R3bbk8GBpMTTzx4x3bG+jor8vkSgC2uLOYMYc00IiNgRQRESxIagsCMb5IZQjLKewrLOMI+/mZg9KIj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCS4+AJndsikdTOTZb/cQnPvFP/uRPdu3axeQOsTF7ZdOg4sQTqSzNEtNsUpSA+HHr4GuinGVXW7du/fznP//1r3+dOa/Xve51/OLRli1bnvKUp5x11lm33XbbN7/5TRTNpmiZ1Q/GaEaPHHUt4oodQfNrRtdee60I/aAa1pDUFBuNokrBQMgRhKcFRMDg7dMmvyfLFD/rl1rzmOXVe3qUgA433UkETAijy7tbZZIGTk8YreGAZlrGLCbKGy+247WOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjkA5BHjZ2PqkqJy8SzkCjoAj4Ag4AisMASZfbP5FNHO709PhU2V2vkmnbsOeIaoiZ7aUnFomy0hWpcmyFdZ4D9cROEAIcL6wUgLnWi/BOgQlC8fOOBHwIUItO9KlqyY4B3VaslmWqnQ+moUMYQblN7XU2iwoI6ki8pWwOqM5yHf4STLdCMJsVkXOBjzw0x+josDGVrUmV4VpJumrw7UhLhIV5Kr1oKyYJ8MKkOmJ8Etp0/ubrMQgePLKZLiSVBrhY+Rmal+uPZ8PAmtuAyx2i2BYCudDYeKUo/+FHdqShLzMiEX35bRcbHkiYWuAbvGIr5xzBkmi0qKljiqtUy69XtDedNOBqk65AnhkSvKIIQ9tpjoqogLm2O9XHkXiL7Bv7VI8XLCQxxFR5SORMDkXEcWj3IzkVYxDA9l5hCLG+5LviadcKH5BZGAaIZlMUcIWYRmCYMrEQwOFDB6RL8BfTiWPJMW+5LHcUx4Z7OuwcnyRVw6/Y5N1dBQ/EJHE6ShsTFrK+av+I49wChQVOQI948cFYtbTJG9+MwSSOsqGP47KNAEtgifvKx6LKhNGvohl+j8nTnn7SKrhapHZhClahHKEaabJOLEqEViG4y8drwzU9GGuD/TVgpMx35/R6mv81VWFPHPKxBGOjY2lArUN6zfwC737xm8+9Z7J61//N/c/69qPf/zjf/saNrnl5ptrTlJtpE1rpqdhuDLzKM7lhZvy8Fcm0Vgffw2ozEGZw/WKzkbiqms2OxJ0JLv+I1/cH7AgeSSh+5Iv09+Qwf6ijr8MK7VqGFyqldajIE5pC/khhxyyd+/eSy4ZO/fcv/nzlx/xd3/3V0958h3/9E/v+8l/JzfeuI3V3sDZbO4WjMRJEh3nMIUnTOET18Y0kjrKhj8xFJzypovW0oy/HAh8kcx1R4KwkVTDEY5hMdoILCBM6mjKmY6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AisNQRsLoYJIDaH+upXv7p7927mVpgv3rRpE2hoLkZi0HCYXRLBlJeWVcBEQJNTquoG486dO9evX48LBNA9++yzL7300jvvvBPXbIn1X//1X2Eerb2RlqZ+LEL8yhEcoiXBIY99MQ108cUX79mzhyYQCbnURZu6qZhx4ywHYnlGNQdkSjZEh2l4ZD2TlQMDQ+T8fGxrpt/mCpkbhe40Q0pgdqAzdBxzLKPA4IhJlysZamywI41B8Y3IiHXjZ8SWeRG41JCFwm2Zt9fD64gAR1+p+LLfUXc5M2mO0hIH2YYz/LvErt2dI+AIOAKOwApCwMYLRqsVFHbPUNvD76pqVM9Wu4Aj4Ag4Ao7AWkDAxu7l8Ky3Qt9K8eJuhUa+Fnq4t9ERcAQcgQwCXLRjDsNfx2t4t0faeLgU3VE9dpGnpUiOl0w8eeE8J44hX7t4nDn7zShStMS0GiBQJOwZ5mx6bigtIA4KbwENLrGplR7/EsO1Itxx2YkPq4rxtYjFCTpxYFriakMKyxfSFL7Mr9dbzHrYLQurFCvV1uoFFJN0Oyq20gKWJlPj7SnmbigpKonFEUqekGLFEBi7W7HpVXpZZuI90KnHRpJeqJuhmfLeqDSIcHoqfJI8PFxnCcdk2KennbTooj5A7VQSLik0LQ0jaHGZkZwFQDGOhKJiFjNTG0s6DQJrbgMsWwBUfPjVb9TJoFUsVqF2CeTp3HiJu3gclfhWi6Qi76Zi7QotbA/hImKzGVr2sWlgmqOMpBVRwewc5HVBwb6ZyhCYNY41s5u8hC0nHiRJhpiZyhNoIQm/X3lTzNuMORKTC0VIrRGSzBQlnBeLzWZomixHGX6maMcLYcmTZ2TiosnD7EteyNOQYvtqqbVXHcmKcSTQsqkwkCGJkxHLFCUvJrRSgSIChIEABC7IMwbjosSQ79mfJSldM1umCQrDjm/JeLBcxjjxWDONiBuYoa29EFThIhYQE44IK5qYEbGW06sAAZ25xZ2TZkpA3QBaxZ7Nn4N8N+N5j3B0/uLFemwmpHx/lp1uKuYFgoSYzl8VY+NwKGKfxDUkbenU7r3bj7779r/6i8Oe+QfnjY9fcf5zL7304kZ6H84vwI4g3mjd4O1Ci01jJycGMWIXql6PAMF/6ih7/oaKdlJgKlkzpdUWmflXwpbTWCRJhDcj1IVCC0kq+5U3xS6GW2yJyYUipMIICWWKEs6LtSx2+kfH1xQ7iQQegODLOgPyGdcZRZOHj2R5eSHfU14BK8cFgZFbMR+MhYEMqczxZWeqhKfT5oxZFHnk2z82McUOYNP8mHbyz2+441sXfe71//D4T1/4rs988S/f/OadW25sbSU2MjLCxnC0nYR3PMqpimmEdZ45G/VWF8rEbEW0TIUARJdpApKSh1Aymx0JyaBCUqgdxYyJvIRFkFtVRwJhDpPdb8QujDYCCxjUYYVGt6NNZzoCjoAj4Ag4Ao6AI+AILDYC3Z6Letz8RWHp29Zu93Pl7UQmF4vkdrTnbe1i+Xa7joAj4Ag4Ao7AIiAw/3F8EYJadJM+oC86xO7AEXAEHIHViwCPhHoq1GwFuQgxl77d8mshxWGwddQ307QEUbEsEi9MkEFccskln/vc5ygKmdQ7S1PCUkjCazPTXyKN5k813SMZJn8UM9NKTMZu3bo1npJVrcmkjmbmkaU4t7wd29y0u2rZQdm/f/+OHTsWyUtX93OqIEjFaV2Log6fNafYsNTD8gBW43a742ybqKSvfugdMPgRWrHlri0y618FE0cChyQhEeSxAFUmMMtW6YKmL8kzdlTM+CptdRkJqgmZ1i2j+DyU+SGgI5vvwPOzOqNN/7E0w01n8zvyYxmnHYHFRkCdEC+ZS5z4rDOBWOwY3L4j4AisTQR8/F3a485TJJd0fkeTlb38GivFWXsrL20w7i2s6tQI6+Pv8uoNzYEQT0U/UJ3Sik+c5RWrR+MIZBHQ9cSuKrrOKM+K9lPWE0FsTZevMjYsmDLCCyhTPsK807ilcS048NJAtfNrF0Mwf3pNR87Tlh647LGLWq5CGqzjEJx2BByBVY9At5Wh3Ro+a9+lbkLOXzwEMsONFY3IuNYgApNxJJYxvuTjKjgUM2MxRfGlKHnTgojtm0yekDvlciHLMd9oU7dg5EV8YyIfG6EWTkZXwrJsVRlJ2UkH3JaIuZBk+jxLfZ09dPijSLvDJBpTbsCbjrM4D39hEg6ZsN9CmjPOBgBTIUT5a7C1D39qUSv+2qzzC5kgb2O1NtMJXhBr/eEdu2nMahyOQ4KZ+WsN/GlAEi3OI5vFgvOqJRz0O/pSVd56N35GsqPNjsyMYsfinBU7WltjzLTntyeLaXurj/KyKNyPqmMGGU6L0LsrM5/EVuqcGY3GED0kmapOcVdcG6hNDUzVa9XpWrhH5te7KuyFxWfx/ANRTcjTEywYwhzWZZQjiN0UechA5DuSHeV8VarYynAcF6G16VWIP7QiVLYspHRjOm1jOs8+UA9NqjWqU1P8Rlr4BbLB4bCrT9ABmf0NlpFMTTUq07xJa9STJgWaPl3hK2iuBnwizQnT4OqcXgFoXutCnWpXaarwVENocMrPZt1aZ83PKHSTz4gtn2K3hmQiXHMbYGXa70VHwBFY/ghkrr+Z4oLHj/2SF9AFd702DS72AV2bqK7BVvfVkRDm5jJW4ayn2O3cl2S32p5oZxx1lNeiZCRNOHYnPjIwa/Xk6U//7X99x4O3bdv2r2/9Px96T5JMBJOsi2Z7otCILnPP6fNmR+dZJk8VWZaX1yQC1hvV+kxx/pBgkKc7cs36xH1+fHw8rUqdVJLvX/bzF73o57/92yf84flnffe7h/39q3d87KNfpI7dr+IwYgvi8wzMyT7U5aSIdedD6zmWhpB62pFwT7FYALNqWrF9kzEX3eRNEi/dZOIAnHYEHAFHwBFwBBwBR8ARcATmjwB3oX7zOX8Y3YIj4Ag4Ao6AI+AIOAKOgCPgCDgCKxcBm79Ynk0o+dDK4y0TW7QlL9/Xky9GSPqxU7a+GhwcZKeqH/3oR8ccc4zwGR0d/bM/+7Mrr7zyG9/4mqAzj6nzWXNSVGn2JwMySytZVKoFma31oulKUrkonteSweVwsACKdk1OpstGrRnLIbJcDIQa82IMy3cPHc39Y3uqtcEGk6GyydGi7fzpsMV5WGg8K1lXgRvTs4RCTwjJ+oyKyNAKrV7IyPdVxJrJyzLzv+YirhJdEKcJz5kQpKiX91Jest+oZDnGp5sv+LFYv45cvhgB4FUyMYqcDmLqvLCiyRQQLXMl/ikwskyqOjaiY2xIws/LiwmAhqEkuxmRBQnHKkG//VFBR11nLiUCBQdxKcNwX46AI7CiEbAhw1oBh0u9DQTwrWgyBYQZ7EkUGFkmVR2b0DE2JOHn5cUMY2cbUkl2NJJh8ggQ3b9nKr14IBEofxAPZJTu2xFwBFYIAt0uKXr5wPDBevsyTUESMbQw2PHFRTc+WlRZin0ZU0RcZbSq4mFONLkIBEy4mODFF+9jOwZfrOi1joAj4Ag4Ao5AXwgw1jBI2YgDnZlFkjUbwhCI7RsfpmiZEh3XxloZ2oSNyAh0LJpxI/Jic6vK2+nIKTDeUd6YKcizNnTGlBIoIqatZ8TJAG5GuhE6fLMOUjfRiI8Xbjxg4LTNbhFwqLVgjDB5cdp561aqbWSJ/sW7PBkh3CgaoSoTMKIgROkWCFCVt4OWwDHFvIxVObEgCOiyM3Mdk9F0vYSYvFLioKRvliqDg3UIlj3AUa2q2AKOomQg0hQeKMLEd/fU8eCKKRPQEMrNjAnAEW1VBQSx0btCSGGrrLCzLRwWcjSm+ZdUsanzRj2IsTNByOsNGotK6M+6wtdS3XqYaie64DHttBhM+3xRe4Pwak/xEREtzLu1e81tgFUMRwamWDimM2IU81gvkjxmZVl5JpJMlclAGJ1RUdHiNzEj8vJUIU/OiWu1BfImMwf5nmZNQCGpaExzHRNWSzzSotaYsaTReePl5WWkp7xiKBaL45G8OIRnVcWE2Teio7wMgk96VQ3du4y8yZSXl0o3eYVhESIseXFMK2ZSRVFhB+k0iWl28oRMmbDJw8kLw1Fg1KpLWyQdhU0eYRlEXop5eQlY/CbQLRIJWPz9xmORl7Rv8feUJzBkrJmxvNEiyAlD8iKUW9udWDUI2KEv06Ig3Eh3daIvlVBoTo8n1UF1p2JH6vbkOtE4odvmudBBKuc0b7EZ6Nj1mRf6LKiV/TJd1GTwonjkNzhoDAVOZV9wwPLURlIfIIi9U5Ob8d9spPzqeJBkH2m8N0ZCtM39bOAzcnDypCc1//xlv3Wvex331y+/7Wtf+9qvfhXMtFOd6x87ArWL4d92O4a4ojebNKHNmBGaAVihEvPQ0KjoGakcZQJqmorGzIkHhtVycTBAjNlRJW+8vLwM9pRXYMViFhvxSF4cFa22gDD7RnQUlkHwUf9U8ztKipmPp4x9ZCTWzb7Mml+TN6dSVx6LKWzJd5QxYTOFL5bva8G6jRqccWGvY06PNHHCcDrcdlPynn+78SeXH3af+xz9mn/c+4rXn/OWf/nepz6VNHYmCb+lNBmWYleT9dMN9lzXDSr7taM/0WzQ+3eyc9z0VLNCjU6C5lCwXQnnWpoCt33iwwzPnz1TBig737spCh+r7Ya/CYQWVasTExMCE3mS1eYJ1ZJnDk1eEk6wNdtapthRy5mOgCPgCDgCjoAj4AgsPQIjIyNse8odTmbWav6R2C3rwMDA3r17ybkvnb9Zs7Bu3Toiz9wEWm0xMfPKNf1pP4Qr/CgIt7ppOig8TSbbW6VQqKc/S9ReiTbzlCcR3VhrJsWUjIjvA4eHh/fv329VC0hwZwvgzOiQxx7n42IBTc0nDNd1BBwBR2C1IsDIyHV7bgPZqsRkU/rKifF3YKDK3gu0kbdLDNnTYTEGg292kfT4dLivyL/KSYVnELJhMeYbbcSMQpsyxTaj9S8qHLV8LXwSQsozWmWKKDKgI0nO5gXk3j3K4OYyjoAj4Ag4AhkEGD7iESQ/Zkl+zgNWxl2ZYscYFADjnZ5kNQ4iKT45N0tqCEwIxPAlAXIUSYjZck9FwpsH3WJJC8XDDjvstttuGxleVw3zvMmb3vSm888//6UvfSlVuLj22msvu+yy6SlMhWWjzIIRQjBFDlVtzV/jiIRNaiDiexBKrLdM7wKCXiaJ362We4qMfHGxm51uWt3k4/kraMTSRiVHHXXUr3/9a3BQS7uZPYB8fr+KhbCZg048CrivsINwbcBmS+NG8Su5cRE6/FZs6BTih16KOkDRi6BVJA8ybXBVhAOhmKGZlERL8atLS5eqOSRzIV3MasVFhi8XCszCm4O7YhU5zbguVplbMGBebJZawgBnCOUQhx566J49e3SPTW1shDD6CrundxewI7vgwGKZY0dO6hdnaSmPdbvxY5nFpi0GEIOWu0xHnUMMhj82jY7tmF9zGtc6vZQI0LF1vYr7AAFwaObc55cyfvflCDgCywEBu5h3vObPJ8L5XIvQtRTHYEyImL+UtMUQX3uh4xvF8vFYQ2RWF3ZzoVr2PKG5Jpk60sz0rM+Gyzt1yXkioMOEkbgPUOQYUaXDN08Xrt4fAk3WKHM80ie+5rpUV6szxFHtQq426S+82dLV0j8YnJ7syeR0WKLMy4HZZry0qhDgEKs9RuhKopyXkNzzh6X7JV5rYIe3Rjt37uRVD3RsB1rXKONDmEdoUiwvztLnxMBLsKEhPq6JF2eVCsTaCBErxPy4yaKpjZmxYkQz8raXhSV6v8RRw4sc6QjCVxV6RkQ2ikjkMRV7KZL2OkfAEWAFSBcQuo34fV9SutjPs+PzPabzkuIoEkkuXlTdvDu/dQ+viz/XfwYdtkehqJQfbTV6qlYLmG34YMwVH1ihGbkoQtuwEuvGNGIUxUGeoqmjKyPiS0Y5MiRqJSCmispRiZOYeTH48hLrQse60OLEMhlTVoRQ2yEMnFQ9aMdi6eQY3+jS85lIreuv2ag1m/zxgaECa50XaWPRDh/bJgm39/rj3AHoKpOT9SZ/jYFmYzDMyzKOttwN1MO4PB3m5QKY9WSqyleLzUYl+A2rubEc7q6bTPOlf0ltullttKc7hYMagg78+I+jG85eDhlNS8d75allqc7Kid/KBANQ4ig2qypPdLxBks2CKuzLu/wqVzDkamwmBlDqeP8pYVnIqFDsGAOu85Lz5jAKZMzardos2x1bN0tiaQvdRq+ZjtKOh14mEgDpyykd+najGrZ24sRI6fTb9nS2vpFuC9U6NNUaOnoiaAzwc1y1Zn2K3js1FDp/c7g2Xt/XGBpIhpuDNWa8k2qNR056AkcwqVXZYQrtdBspun/wFc4P/jfEOd3wrpMuDWwmawOe3h6ThScd05NYqpvGLzvissKTxJILcrV8Oh3rhUOlMcWMu86nqWSKawYxUl+B4spDK4AptTldmQhytJe3acl4ZaTJz1k1mGSfCud+rT7I+VKZ5FTkZEyvGrxyw0e61zCnNXQaSMJ1hkiaU+GIteioJ7ebSWUriRPU2w93qoDTFgnujF4aIhNMsVMePCUQn8hxi6ADemkD1RZ/T1EMqdc6AmsRAa4R8cWu52VoseXzx6A4pEw8efViTrHxvG5P+Uw8/crnPRZzVrr94tZ5rSNQjEDP/m/q8VXOmEbMvhVssTPnsgn3JORLebitDveaHZS4Wee2uyUWFi0no6PcPK/fueuuTQcnj3vcGc99cXLCCSdc+r3vvPa13/7BN1ufJa9fv55PtdHiDk+6HUxHLzhyrjuE4vNYHTFcAmamj/Xsz4stn29ycUiZePLq3Tha8ZyeGq0OGXdm6PAA3GRqLbk8Td/+UfLylyf/9m8vetnLrnnbW350wcf2YplToMGjYxKebtJnVJ4Jg7XYVLcAlj9/ztgu/6Z5hI6AI+AIOAKOgCPgCBQjwH5MmgZjrqtYcs613EjiYmF3vyIYntRKhpSfcMhPlLVnmoLJo9ePbtuT7p4sB83ENnZNGXl7RWzZUL5Iu19hPNytp7OPC3h/bqYyzynGj5vmtCPgCDgCjkBfCGgDyr5UVr3wniTZkDZSu19BtsZfBt6weiB7o1ILG7Gna6BmQ5MZtqwY3+osxliGzYU1u7DWZoPkJUfAEXAEHAFH4EAiYKOzERr1wlDaXjIY5qTS51zjEPHTn/70Jz7xiZs3b04Fw7O5CCQzppjeVS355z//+c985jN33nmnZJgH27JlCzPCbKV08803kyNTrw0yU8aKSWjEMEtOShd0wptJgdk9scC4Y2V3rfxS2I4GFp3Jexs1f9E9zc8BQRJqjCdFTNIH4q7S0wkWSA0+QB0YGt6wfoxXTBxZul+7B3a0IBfWBYo9UkvCCxu4H3vssdu3bydI9mCCCYcXRHpRRhFfymmLiI7eYyZmVTQCXVJ8xxvLrw66JDjWWGGiYrfujU3D0BSdOCAIcCAsdQuA40Xq93zvZm358NUuchBYyqiEpLwvpV/35Qg4Ao6AI7B8ELDBt2AM0kjh42/JoyYkuRGNsY2YLTOgWtKgizkCjsBKR4ALAk0YqLa+QV3pzfH4ixEoGE9VxXhKKjaiWntLWUY4I8MogxflBSFltBa2SAC8pJrbxzK691AeRwWHotqlMyuuddoRcAQcAUdgLSPAoKM9YgBB4wVEZhA0PoMUVVbshpsEbODuKW+uUbFgZDzW1dOi+H0Z7xbnfPhxYPOxI12s0SJufs2sNbCMcQ4KCUlyUGqmW1RRFrOMheC3zxfsfUVYJob5yBB/t8YapHOz39FsR2YZ+34bVgalAhkhH+dct5CHQ4rhZe8riiT4tcF0P+VaoOtDgdZWs1xtUpH0TAkGZiWFAatbPHQt9S7lBfIdjUirY1U3jxk+uhhRG9PrN6WQ4ChHnsYill5eqslkjQelemWIHLBg8nSFJBuKQcma9hiEiy5LPcyOduSKo6UqE4+KsUzM6Sbf0chyYOYb0jEq3wCrIyzOdARWEgILfnnKGKRYfEFZenkOT0FUmXj6PZYFljua6imfiadf+Y5OC5grzn4en4LWeZUjsFAIcAOZN8Vtc/7+kG13Uslue+DmzRRzJsLS1MZoKsTvwDSakyPNymC1sjOcC83BcOceiKQ6OLF/MhnccNf5L0ie99zn8vO/l3xr+z+94f3f+04yui78/qrcsA5VROZUEtNyLuPpxSFMVnlaKASKMZ+Dl4zB9JB1fZbD/tLLy2m3u4JMPOUR0OOf2hu3Wo7gxOuh2fF3180H/9Wf7vv0Rz/+h3/4h+9+32Nf+/ob/+kf33/ZZcmvrx1vVsbD14XhE4BhnvYqyUHsEJ00akmDDQImo/N+PPwacufnwfKBL5GkIbOw/jAbG8wU4yqnHQFHwBFwBBwBR8AROIAIMAXC3eB97nOfu93tbosRBo9afONnd6RMMyzUfRGfC+7bt6/bB2xqi+5Iq+l92eyntfBbYTcPh+fWQ9Odv87cVWVHrQeOHHz88cffwS+HJMm+O+/iO9htm4b4ZcVfrxthHoWfDEnNDqV5yPTcy++ikFo/GhLIbNKNN1CPjo4u0h5YTGiBRpjPaT/IzB9njhoB33bbbZdeemm2SV52BBwBR8ARmB8CWvB0yimnzG3t7/ycL5F2pzE63VQifRs8Mh3GrNr23QcffPCxhx4JDocfcvjWrVsrk1OMlXcNNMmvmdzL0DYyofVhtcwbs27jb0aMAVGrHIS5cpMxIg9KwUiKFomYeY3GfZQ25YQjIwygeWvlOYzpMnjttdeW13JJR8ARcAQcAUegJwIFQ1tP3TkLlHEaD53pGBuGVAjTRYDR/OSTT37Uox71jW98g7cBDMGqZYKLWw49CytIhmaK7GQFX5Nfv/rVr8bGxqjdN9aa870hTXGjpqYnqtU6f2hxt0CuGR4b3yWcFsP9TJgfi5LuAtJavSeI6lrkrAkjq+73rmH2vJOZ6Uq0b0+yArGddku7RZ7VPVBljrgOBzk0vYI0z2DOPvdRbIg2tnNbMjQys4gh/OZzy7A8hs7WnIbW/Wf4PeI0GPUW635BrJ2gLYXb3WOPRZ2XTPe85z1ZewB9++23I4C4cunFdNtSj38NBGySMhZUS06o+doepld4tdrL0YkhWuFtWnnh0yFJ8VEQx3KaFNM9W4gpkxdtHCMkkDcF31TiWvFVFfMXg8YLPZM+qf5pbYmJvF9qYSrOjKS12ggkMd7RSKybp/MqzllKBHRE8h7jI5WvdY4j4Ag4Ah0R0KWDocHuguKLCTRaxuloIcO0UQYt0cYxQgYzinJkKnGtTKkq5i8GjZclGH+JXON7rgk8P9qfKnnysr+cuDOWEIGCfqsq8iUMx11xFuknUdK3E5XwGidpsv6fpI8xsz+RklZ55ggsOwTiSwdvY0iE2PExrWToMpgZc3WZ6mZWtflxFj5M8WU2H4NkZCFDGzOvlecgnGeW5JijjBEVVVvSVBcxYuNSowh5goYQrRwlG6nTK1IXK852BByB1YiAzvry5/58ZyhWI4ZL2qZuI0XHIDSCZFSQjPnQDJSxuuTFN0ljxhwUeQw3juzAsZFXVVYUIV+iY47FkNGK7eshNDZo7wFideiORoxPrQVgTVAtfIbF2Kno1D79nzkpRlIIGs5PziTNWrptVVqdWkAA75Dtk4X61l9g0YR6NRmo8blvMkQ+OMgUbZgLDPNx4UBMTIY13uPj4wNVJKth0Vj4JBmLaCsP3vmbJpJmmCOLXo63aGZ89cf8MUSqjnKr1SljJgsVacKvJRgpFMZYeAIXMmqOxEljCZmOkWSMCVEmlGKx4tpMVGXclZEJB5LDlp4gs+WjAzi7YkWUOkUvHv2Zrh2Olya3OVkCCDpj+PA2/CZXujaSzd9I9cGQcz6k+1tBVodCbSXsBFWpjYSageEBTpn6AChWq6ybqLa2kQrCYRIqvA1LT7KuH/Xq0Cu3jpdGyDnVSqmRQBvRrunKpGJ2vwwPRJxY5I00Fp3dYjaSWqhKH/pDvEST7mhV4RPlOpcUSmxuFdrSqDVr05Vkip2wqgMTNd72sRkWYafizUZKT02GBSRTE002BIAIz2KNsCdfJb0YVHI/Yxa3WjgQVQisy5klfrda6S5snoGdYhnvsYzojJ1MkHrnkmF60RFwBBwBR8ARcAQcgdWJQHyrdOBaGJ60zTv3aqQ4MG73J8eT//edf/Kgh24/8sgjv/Cf3/jEJ6685GstDVZBc78/PDzMfT+fEKNrproRSHarcr4jcMARKNOHLUgeg7duu3N0ZMN3vr73O1//t//335K/+Isz3vjGv2VZ9kc+/B9f/eodv/hpUhtIGpPhvOBpU8bj86tlilOw96ljbg8kQfB9QXQgY3XfjoAj4Ag4Ao6AI+AILDQCfA7KvRAbYF188cULbTvYe8hDHvL973+fGYX169fbRsPzdMSOGCMjI2ecccbPfvYzfc7azWD5DbD+8AnPYuurQ+7cjc2HPeN3L7nkkkMPPfR3f/d3P3LJ168iTU2AUmNgIJ3z6HsDLMLj+0ZiftCDHoT9btHOh3+ve93rpz/9KXM2PMlqFd18rEmXx+FNmzaddtpp8zflFhwBR8ARcAQMAb2CYCwLKxAGBn7zm99Y1Soj4oUC7aal67yjDbAee/qDuUOojE+z3eSJDzrx1ltvHUwq7J55/a+vA6Kt+3Yw0HfbAGtsInwOES9fkhcQJtmrKgiGYLa9iN/fIiDhmNkOsvVvp/hbVWgx2rJT1ebNm3fv3s0ob+6QMOMZg+WLbK114oknkhff55Q36JKOgCPgCDgCjsDyRMAGUIgwfrcH6Ey0DMpM7D75yU/+yle+8tnPfhYxU8xIdisyqq4b3YCWEgYnJicG6gN6fMY4/mFSi/H8PcDswA7k1FcXhLq1uyu/2M7s9nY1sgwr+u0bD33oQ4877rgLPvTBZHxfMjTKgtak9W0DlkI3ExQQ+cbSbQ4//PA777yTDiOxjIzpbtiw4cYbb1Rn27hx465du5iKxWY3xYydgmLer2LO8wuMrNYqO3zWQGApuPk3MSccgcVDwM5QP0kXD2S37Ag4Ao6AI+AIZBBYJuMvYSiw/PNmJmAvOgJrHAFule18WeNQePNXLgL2xKdrPkXjrNxGzTly2u4n9ZzRc0VHwBFwBByBMgiUHGe7PYvF45TokgYzsaGrlOGvyiJgaicZmpxpYJuT5WfEKIJznPQDhOTVetiMhY1+yKv79zO5pvXtEg5rxJjj7G0+77ADpx1th6qYFTwuj0QkALKaprpybelwaEN/6+d77eUjTyQcMnWztBfNLIegSKO0LRT9iz+KJP5hAnog/Zidc0HMIDzU4OxIauFbj+pwggxzzSw6rdakGBylv70S9sZCiyKJMwm6oe2f0m6cMvs+gdAKsUWJplFqWctdByLBWaS0ZrHSguywQRVYQQsx0Tqa5DQ/cKbDQsrqWPodR50FHmG/LNLU+BT05ET41bTJdCux1rcq7bZTa37ljqIaFUcVi0GbpPhxLmsSiLXMS0eiX3kzYpEYQVV5v2anI+EbYHWExZmOgCPgCDgCjoAjsJoRSO+NZxpoRSNm6trUvO+96tUKW0fzGRU3sPuxWqneNTKS7Nub7ONjKH6MlWw4Wb8++a1HJy9+8TPuc78N3P//0TO/z8rUn/9s31TYG7qVmhioTY+PY2q5PKm2Q/N/HYGFR8AegXQOpps9N8cndvEwzEbsV16WvPAPfnjqqTccc8wx7/7YI179N9UvfPa6b33rh1//2tjevcnE+NjUdLJ7T4hqcDCZmOBfnTXhaTk9H8PTuCdHwBFwBBwBR8ARcAQcgeWMAO/x+fqOJyN9jMf9IYkJEmJm7wlFDoeP9NjiwV73w2GyhNp4awb2sGAPiLix/ALPHXfcAWf79u3is7HU1q1bY5mDDjqIWgy270grcAhJ8UgSDt7hIEaCRphNQ2KZdevWwZ9leeMm7GjuRflhhx6Gr/3Tk8O15JDpZFeSPPmI45/4xCcODI1880sXvevKS2n4V/7lL1768heesD856xc/ffqDHnb/pzzzPR+/4Lodt/13wm8NYX7AXPDsCCAT4TmUh81s24lnLzfN7WQxxxuBscMUW360RcK/cIg55pTB5+STT7799tuFj2DEDh7teMlyT19s5IF3jqlmwqDZWisOxmlHwBFwBByBeSLAVZqBTFdsTHFljscyM54Od+GViuTteg4fZpxLRrmGAGi2trz66qs7alE7t8QNA4mNmX75y1+aZUxZqIoKjsKIZSTGh/5yPZIkB4+OnHvuuZN7x6784Y9+fecd3HYMPfT0911xyVHTldHR0aec+TDG0Nsvv3ksmd6W/sB3+nvf0u6dE4lBgTR7XF533XUdcS6wFVvIiJl9hku2jJ9Mf3Uwj0NGy4qyTE4SaNIVR0zuoPoN2Ow74Qg4Ao6AI+AIrCwEGPvyAduNhMZHHtVPOumkD3zgA0hKfv36ER6677hjC+stR0YG9+6dqNfDDUD6O5+z7OnxlvGaOxkqUsvVkeF16YLpIMnrDe6w0NW43FbmiZ8UnvdnJ/Fn87y0MhHYNzZ28CGHnPeMZ1305S8nu3YkQyO14XUspedVV9rNqq2DXWPJbfjh7vj3t+hOJ5xwAi+46Fqze84MFsjQ/bivu+WWW7jFRYxbdN60MOVacKeXOmrd8M/YalOqpYQ1o9uVnf+lzyNZUriziTlx5bEbOHMy2Z+SArCLSX/KLu0ILAIC1hs1Hi2CBzfpCDgCjoAj4Ag4AlkElmz81c0nOQmnDPcpSQ6pOWqeOgmPrxCZbIbDX5h19uQIOAJCgI9VIeJXRFp0zENleK6shNrWntUOmSOwQhBgOCARLG+HVkjIazxMjdfhkPHWjb9ycPi74nI4uZQjsJIQSK8GTZa0cGsynAYe1tC2Zgeq22fo8NEiSVePlPTMEXAE+kSAW33ulMK+V2mCHh4ZIZ9qhnm6qelpcmpMJp0Zy8zn9unSxQ80AhxQbclEIBzf4nDUQxCD6CmMqWUlT0vbrQu/v0VPp9hi1sIzQr1WJ+DKQPrmKBWu1JvwK/Vx8qS2X6cGItXBARZCJKxoIA0OUMvuV+QywJe7QT4JvyjW9jiXf4UwoSrI1FnIzJbREihzREwXQlo2RY61Gh8tc9Sm0+3A6BbpD5WZF0XCig4cQacfqqRYNcahq7XQJZQACAE8cMFoNusYqjfoXS0B7Jv3OB7RiipVn9UhLQyJ4QiCXJJmBw6SGMnwTSBDlJdXYBgnYUSEwpBN8TP2OzIzMpmib4CVAcSLjoAj4Ag4Ao6AI+AIzCDA3ZXuwIyYqeuTiu/kUGUfK3a/Iq3fmPCkcOqphzz/BeedddZZRx47dv3117/hDe++6KLkjutbPoZHkump8AAwNbWfda1DQwP79rFctVXr/zgCqx6B+PTh0Ww6WtI/NZH87Kfb+DvppCuf9rTkJec/5/d///ev+fnN3//+9z/4gSuuvjo56LBk613a/SoZqA9PTo2nL3ntcX0FgBc3fwWE6yE6Ao6AI+AIOAKOgCOwoAgs6r3QohqfJwxs33r/Y4551tOfxfeKn/r4x3+z5bbfJMkpR29gD69DDtl8663bv335FdddfsX/fP75r3rVq975znd+7zfXs09yPZmOJ4p4kq0yU5KEOaTMNEb40ZVVkeb/tL4qYPBGOAKOgCOwYAjY4GjEgpleCYZYKnjQ0ODDH/7wG2+88ZdXXb09aTJerk/C1gDVkYH9eyYn9+398re+ctb9Hnze48777ne/u33fzqmoXbyv1SunZroJe/pjXa1qBiwohmMIFkzY2gIjIjNOOgKOgCPgCDgCjsCyQ4AhW6O5IotH8Be/+MXf/va3tbM2Gzc/5znPeexjH3XkkUf++Mc/fsc73vGTn/xs06bRiYmpdJercIfFH/cFLDjElOw0WCSd/qBp+BXVSg2m3YkZEQeAEHzFYwLLDrKFCEiz4ekaymhqcCEsL72NkkeKw9qYbgwOD3PE3//+9z/s3Ef9/tOe9strr/3p5ZdP796RDAxzQ8lEqfoArQhE+r2i+gNrEMTUbWe3ZqaQsuRg6LbbbmMZLvtecYMKwYrku+66C1rRyqZ1trw1C6Nk6/IWDizHwm6jt3Th4JGkA7F0Xt2TI9AdAa45um7QM7tLeY0j4Ag4Ao6AI+AILCQCy2H8JQaapJybgYKb/4VsudtyBFYaApwdJFuGwZnSmK6R84YnnD4V32BipR1Rj7f9Pb8/AC7PvsDFhUMTLjGlU1/Cpa26oCPgCDgCjoAjsKYR4BGAEZnEgwBrrYeHh/n1I5iNsF8QU3lhyxuTWdNIrdLGc+hntyxTbFWmfaRz1Wz1AyyfCVGtS4MP6xzZ2IpEfw5RDqab5NbSPaHqYeZIXx8ktXQWqR4mqasDQYYU1Nj/io8RamHOmk92xQ+nBp8ukNLPFDCN4VBMU+oo3ZupEvi6ldW9L/Ud72zhIxnMpoTyjhyYJBNQ0fKOxq22mxZ8FC2HUCIe+Kqq8JNVzSaT7+H33adCa/S2rVYdgJhK0l2o0j2yauGiUmMnPfjhCw9u+6dbwmgFc+0HgRSo9HCkIRrfCMljJ+ikW/KlgtlMdrLc7uWe8jQfbXIaomZ2N5atIU5UUMxWdCqvkq9cOjXNeY6AI+AIOAKOgCOwVhBoTE3nfsygyhJhJpa4MTIUAoc9U6fYQDa9KW9XpPxQiIUphvtRVOBm7vTbipl/C2/apsL9emN9M2HzHf6SkYOTww9PHvno5OEPv/tpp93z5JNPvv4XU1/8yts++eFbb7ll/46tCXvFVusJN7mk6cl1SXNwcqqRVPdTZOfoJAk/GqNbxiDhyRFY1QjQ1dunZ4NfGmtmV7lXRoY3Tu7e+cl/T7766Y/c+94nnvvYox7/+N999nOedt111139y8994xtbv/X1ZOvWZHLXWBsnzTq3S4X/tl13ECqoqlbq0w12Y7bLB9ciXnSx8HzmoiSLGOH5MN0PGobJmzu1Nqtl1bMJhDtKGtMI6SFf6rlxthcvOQKOgCPgCDgCjoAjcMAQWPCHoOhWc2EapQjJRZQwGt+hTernQ9H6800nveL8V/yfn196wQUXXMVzLC/yp5Kbx/aON6uTlTqbYd25sXblrumLPvDvf/+iFz3y5S/+zrvefvWvbuaes6oPH4PjcG+peQK+YGSSyX6fBD5FIuRGlNtRUhD35AgcOATSZ6KwI8yBC8E9OwKOwCwEGBq6veqMBzjRMWeWlahgMliGtqLREAs+HpmXKJAOJG9tcT2ZvjB+UG3dwx/68Fv27PvxVT/fyltZ3uRWkltr04+vjhw0VdtWneSXMrckybVXXf6kjY845cwzdl5z1R133FFr8ktm4WsHNYd8kpdCtCj3mkdtLG5pt7BNq5sAbTOZDu3sk9XNC3xdtBfQV5+hufh8EeAgHvDDtxximC+Oru8IOAJrAAGulnbBZPizFhsTzqGHHnrKKad86EMf0tD5ile8gm2sX/nKVx5xxBH/+q//8uY3v+l//s+X3XTTTfv3N1nAyU8i1SIzmJwOE8HcNjCBxPx12NqoXm9MTrKV9VBqsNpka6wk7L3FbQV/zdZHlWgFfhqSP9GnMKyqjInDMFhP7Nr1jc9ceO5Tfu9hD3vYiSee+LlPfybZtzs0tD5YGx6a2h+WDXDDmf40LP0j9AR+MpYFBKyS3duc3k9nq/NLxUGjlUI3CneojWqlNtXYv59f3gqr85vToS+xJBlix65dgZP2MJtBJBj6oPV8EWmvDIbjqlBGN5XngSI+cVRlWlaVMS6xxcgt/sUw7jZXJQLq290ejVdZkzlBdI7YSbrKGujNWa0IMJpwkqrfqg9nWmpd2mp1amfErKjaAhnsmE1pFQib2TJEsR3V5r3nLHfegGMgZRtc2GmEOwJuJMJo30jC3peZdsmyMREQp2PeTX3+whkLxY66YRjzCywUVCmM2E4mMBV7WuioFev2dBELx9ZiRWgTi2VEq5ZQ1R/yAgeco7b4+LtkB4K+wGRys1lPLwPkoeiLCZcM/xXqyAYU4ud6km9F/kKkUzsvKY5qC2TyF9gC4W5eOvKL7VCb8G1CmqpJZXCgxqILnp55lJ6qh3x6ijGUe5Ew0NbCqdQaYcNDNYs7KuHr3FqlieRIOMuWaPzV3YDaq7dQ+ycm+AqXp3oxLdfhaxBXmoyfIfL4xwKoyk7MhI75BRYKqmQwtpNxoWJPCx21Yt2eLmLh2FqsCG1isYxo1RLq8hl/CYZEeHEr8pE754AgoA4z+9DobA7nN9/H86dazr/0FTFncesE12FNmcjOXA/YmYCL0AFpjjtdKAR6jr9yFPecmM6H0e5FnS/jyNOdkIkVzWC+KhbrSZudjGSGrxG2Gn33N5GeChpna+mLb34oFCPT2l0jNVethC/1ppIhEEua9HxemIfOz2eFaX2Qz8evljabQTfs/DGTxlPkuQFopbyuKhj3Sa139SmLNSSkeuvsDHSmgYHVPXVzhEYjGaaWDXnS5yZ+YjU0j4yPFQPdygNYfXlMdVtZXrEN0czVRpxYqyOdUTQZU6ctxlxYIoMhHhfP18JG7tbmi0CFb4b5jG+iUq1X+dXgWrXO0ukB673hdE1PHzh0ivb4yNUm/MHj9OGvte8VU2fs7TM6Wlm/vr5+43DoupVxLpBJpY7p6anGQH16sFblb7zJ2m/m2bi/brAlTPhrhj/OTZ4mWEBml1Tey4nGm5wpn2/DXX8hEOCyb9cKu1Klhu0AttxIDBmN0T2dH1j5dkdvhUnMaaessK0VTajVQytIoTrd3CqpBz4DKR8giI9G2OlqaChw6ulOTul2VLWBKmw+cEC+UgsyyENqHA97O/G8zGZYLHhIWC7BGTlVpTKcEtzChtpGOnLxtAREfDbPKcQCCmiG8MBpnbshNO4BdBggYEMPpGc7Ve2zKvBJjXRrLUylpZST2uRkpBBst5NIecn4QoR9vLDMxD20va4UIMQvgkXvodWp8iCnO1p6+Z7uCDAxHjbAmhgKo/bQYJWfTJuYmCCvTIW99CoTodVT45D8Ylp4Sw9BTsIOLuTFjgJMkmqNNpWYr9pFykN87VDxTpCKCgK+cnEUgFohsb5C8g2w+oLLhR0BR8ARcAQcAUdgOSIQ3x5ZfOGGKb3bMw5iRnckCu6l5v8RJrd0fDB10MbDjjhm+xOe8IQ/+KNjRkjr77r11lu/9a1vveY1X/7WV9KgwsZWIbFRTjuF21mWnE5OT/Juauapt13t/zoCawoB9vkNuyGnZ3OlGRZjQY7t3xsml5Nk+9bkkotv4O+Nr7/0zN9Kzjvv+Ec86u5nn332y166cWxs7Muf2/fFL371puvrY2NT5U+lcDHpcvUouGjwKMsGWLMPTbr2Ln0nMJsfSqkp/ISGmdlufvPqznEEHAFHwBFwBBwBR2C1ImC3RtbAPMeq5kBgbcFvurBJ0txGXyGlj40tjRe84AVXXXXVf37hP/ex+xWJO99K2LXqsMMOwziMqV3T3ANze/ypT33qiU984gMf+ECY7IE1wRRQUOA+OdxbTrOoA+E0xfZh0HB71JXNoLbSEpEv+BFcaRis4Hjt8Nn5Yl1xyQ6rxQCOS+99BR88D33NIGDnhbXYOPHpo1qrMuGYsFoI0SJEIwmxgOe+uYhj6EgzrtqLn3PPPffGG2/89i9+xjpBXtMy/NZHBzC1c+fOEFsYgUOCvOSSS84666wTTjiB4i2338F+7azSSKsQzC4Gha9kjW0zZv0b1xqdx6Sb/W78WT7KFfDezRpVSuUsudRyRCC8t4w2naQYr0dZ7IjpP7jId+zF9uv2HQFHwBHoF4F4KORSSeIKBlN8aD1iP/jBD/7+97+/Y8cOvoHcvHnzi170oiuuuOJtb3vbddfd8JWvfOVv//aN5577mf/v/3t/PZ3DmuIBfWaZZRpReNJvhcYNBpPJY+PhWb7evutIL5vtQr9tWF3yGkFWTZvUnfLNoYPBpL/RwTYefviu22/75pe+9ItrrjnjjDOe85zn8MM/P/rpT8a33Dm1O/2spRZ+hJc3UKiEJcHhvjYo87oH+xiZporltuE+Nr3lFYF0WLcQ1ukKVSTxq9uDSk2zma07W6pS4yEqS9KycwG+cUwmT6hpeb508/wl4BxA10vQOncxfwTU/3W1n7+1ZW5BZygnhVq9zKP18BwBG3dEWDGPjKrg2zBkwhDGRMAkoTkRGBZjjizH8uIgQ4r5FKnKcKwIIXmJIZknOvqSmI3dppX3lapnY5BNvpMxC8QIEzvhKpfebCo2MRVw7AXaWiG+tUXGpSjaJIurZDP2kvdrHLMZRyL7ZsEI8wthitAIGAKxsGTIOfTkJNXGMlLvZjnmSxIjGRV5z0hmirFHWYgFVBvz4ShaCFI4mmmCFp9SLE8xFQw/RgKhYl4GzgFMPv4eQPDdtSNQjICuG3Z5sWJeS1Xw7RJkwhDGRMAkoZfn+MvVPG1ywgeqe/ZOph+9EjbbW9TYUopv49UEcn1tyz9xA2kXCzxYmNFuaTrELOH4mw7ys0IikrRFrbsFaIYPbhLaERJyS16SoZweqUy7TN4ISSqPhRHw8RcQSMtq/NUxIioOWXy84uPo9HJGgGM3OMgePZM6iGVC9QNdBqVlKGPnqQgrdgsVAY615YhZ0VRkRMV+x19pxTbtMYoq+c3ImLs8YZIipE6ugUNFVcW57IT34XjU2BxXt+nwCJzuR2WvtlODsy56MoWGWsGOE+KkeeumhaGcTSqQiU8iU4yZbc8d/o3MtuzAEYwmLVPiGzNPBFNpk5iTYvsMG1xiydTIzI1W7Kib/WA2SpkiNcbJNLmbwVhFdF4xctgiY5kCy3nFmBMrirYcsdhFrOX0qkSAQ6/EOQ5BR06bGXJd/bq12rQQ074z69atGx0d5Qtgqph24w6fgZiEgKXURTeTzl8xCHBAu8Rq40mrXi/iGCa4sHTXmjG23OQV9uDQEE2opLtLqreze3NoVC19eTgYThZSWhWmlQdH+Lo9bICFDHw4bIBFamrfR34PrP10yQZYuOAXRYGAeW8kg3CoZfoakMNzaCPdR4uxDBl+VinI85timE03kwqF2ZNWqY1wCkPwf5BMU4vDP+0UBNIzvc0I/4YbjLgc0Xl5cYIbUtorWpyUgWr6byuYgEka81AzPFzHG2CxPx5+67WwDdZkJWyAxQgemjwZriSNSpigB6NQy4+ksRkWzPYeWAIT+XCMopEaMYvdhGHGfBNYPIJQSbJPePIex2k0BCk97qGl5UNqr6Mpr+GSjoAj4Ag4Ao6AI+AI9IvAzM7r3Z4EzCK/S9pTxoTT3Y5ZDTzNzXbYv1VzSGk1v4vKBlhhNbFulyG4r0SgOcVOy+y4zN1euI0OKSz75NacXOVWHjjpvS18bla73WJhKq2Vtcb4WG3koB/XR5KpsfB+ZOSQ5NhjkwedmRx1VHLPex55zjnnHLz5OLZr/c43r/7Od77zoyv23357ctctLDHh/VvqjD2j0xSFwlqTielwK8y9bLJuXbypfCTlpCOwJhAYGGDD54kx2hrO0HS/55SuNBvD7DGn00gvJ39wcXL1Fbe/6//+et266ukP2nTmmWe++GVHv/RV5+/c/fMvfOGSX/0y+c1vksv/O9l2e/ihh2BkIKkO/nLP9nuGQjDBKdnjyWp6emJwMJz7eh5L9VpZfWB6ciqctwopXH+Y+W7wc8vhs0mM2wWHX1nWttDUh+tRGj2/q5R+VUkegjPh2EUnGuGRTvz08hIqQiCpFzWtfRnspOM8R8ARcAQcAUfAEXAElhUCevfNrRRRsZqQfSgUHhze8isZByZv/CnydKXZEdThICaZWJIqe6U+Pq6nxRkpLMwUulN4tGRSillFYt67dy8cGUS4Pljl79CJMHlzZyo0NpCM1ZLHN5LXveSv3rLruk999VNbx/a0NlVNf2NseGpgzz52uApNCz+aUg/zN7+a2vv2z1/wjic+55mP+x///uEP3rZ3y5UBpPCrfySt9MCvUmCliUkRcSweYQi/LRLmrqDJMzImAIFWRzsZmUMOOWTbtm1iEjNoALVNfsDPxwOHFMsYwhCxfadXNAKsD9CpQUeyw13yvFuohtON6VQ41aVgib0vVCvcjiOwSAh0u+SK37E2w7SijSZwMokqOJbPsy2YkgURVhSz/buX4RWMxrwNgwM7xibPaibnPeRxF97xqxvvuJEXT4y//PFDnQOTDcaj4UM37+P1c/tFEf9uaza+fNn3/ujMRz7g1Ad9avd3t+3dOU51hV/znuMgZXEaEbciQxuY4pMbeqir1qr6IuRd1qSYjwdOhtmXCxc+4Aioh7A4j0ig9+/fL2JpAlPvYvDlzJLrvvzS9xR/X1ou7Ag4Ao7AHBBIh7vWNYcXC3q3gB1WM0MPDw/zsytHHXUUP8Hyrne9iyKPM3v27Lnhhhu4TG3atIm3FhdffDHyp556KvnUZHL0ccmmQ8J08/6phFcamzYkk5PJxI4jt2y9fbA2NNVIH/vTmaHwIJ/+0rheYXDjQTB6Qp/WLFL6a97tme7Wa4M5tHEZqhRf56ldhjH3G5K1QiOaFWM7NY755FR1Kj24tfqt1/z81muuftDDf4vudORxx+7atevHV1yx7aYbkukJ7lmbtXrYH51d03iFxcLdWjXhF2LpRFgEMWYa09c7oRz+0v2wuNfll2/bNHewqqSbhZW/FFFkCQS3t2lFtAYjhKmHd3uUjiMvoO3lW4HM4lUVdy382oHQcSE3zuJF5ZaXMwJ0ANKB7bfLGR+PzRE4sAjoKq25ib27942sY7FQmMjgOzSmIZh94PyFo7OYHHmElWsUE1Ot2L9v/KCDN/F5CYo8pY7t3b9uw6jmJlCxlqKyYcMGRmF5l31yirgmJ8VmTRFCN4cS2D82PjyCqzBFInXyWBEx6cIkDIr79u3jFlTq69evR5FoTQsZrO3csxv5ySRdUsi/BLZv/F73TsZ3M/nCFqvJr29JDhsZZ1fWiX2bwmhfDb8nP8UaykoyPjGBEdTR2rhx4+7dwZTFIIJct6MQujYiY6HCBBxuhmOOKUKIj4owVBVMOTJ3cmHX3tiaMJQpEOD2m8QNeWzc5I2glkNPIraDDz6YHF/dMKThKFJLLyKHpreYKRSNNgL7OpQ6rBRJhEo/EY2ktY52qYHiUEVCTHmsaBwZITcj0B0xDLba39qJNl0R9BkQoC+pVt0eZNSXMsIHsEhLSdYHDmAka881jx4zV7y0+XrSnDWxvvZg8RbPQoALCGcolw6uZqt7/N1UnRl/x6ammcuemNg/Mjw0fMS+vXvDL1tz/T7hyKFbb93XmB4Ek/HJaXahGa+NsnSjMbqTtzoI8DXrFEM9j+ljhzabQztr/LIZv6KyNWk2RqdAclHG34n024sNlU07d29LThpPdiQDRySTu/kVl018edCkzIDeHpt0NOGwr3Y8dkDbgffx16BY3eOvNdOJZYKArrfxyZg009v1Shid0y0DGtONyX27JjldN1a2MOPdbNaT6q3pTzXxLrk9n71M2uNhzA8B9YeC8VfmuarzWAfNR2rkaKGSf3ab//MvlklywS0Bfm3qRJHM4fkXIyQCxsK+PWM844szzbvvSrJ/w3h4k80YV0uq+w9pMMROrCOGWmVbCKMaprkr01PHH5/cehcPaAnv1BlnB6aHCKyabjmh+1zefKMFAsOjQ2qCnt2ayX7k+eCHnEfmsKfH6DiT5zyyT+9LhjfwIn1sELNTSWNqU4C0MrBhw8b4+ZdoiWF6YC8taDL9zisKloHuTWoHHY+hWnMHAnhUjiRPYdBaxWrPPgqJWhIP5nqo5HmT1xSYR56n91QmDPabN23iR1l277q5OpIMDicHH5zwe22HHZZsvWso6Dd46q/Ukl3T0w22y5DrwG8n3jDwWE3OEcS+jqMFIPm4iJ4FD00Vz+MoQusdQttw+DcjSdFqrWr//vGNGzfo/Yw5khhF+jANVwdmPQOP6loDbHaMQDi2b3yI/D0M7V1uz79xwE7PFQE9sZLzx/BHzlgJwWWKvgfNZ8K8jWGnHs5uMTnf0015wumMAKcLRPqXC4K+R2JVKYu0awNj9cHa4GC48kxMT9QqbGezs17bM1hvDtY515rpNTh1bQZxneiP31MkJkJqnRGsPhOVXlCpjP/wQJH6mCk6XH49LSoCXBLTZ6ZwedHlMeWEi1voJ1HS9YfcxkFxIpFZ5HKTJ7hGMhkG34FGpVZhSji0lO2rBmqDo3Wu82HYDb+01NpDX43hZqM6MKnzglq9Um5yOtTr05Ug2WCiOk3ID4Qn4/AHzXSzTgBOxPSXnRj30x2dquFTiHCehFMybAswEbYC4IQJtzG87ORST4KhZMaJlbsQ8gp/6VijHDHJa6urWbo4Cl/phhTzu8pjPojKfpqnIzI83bTgMdyrUKynUw/pZgjsvgkn7GgQ+ka4N2Ms5ualWQ/P4qFNtL/Gz28wfY+ZCq+fkQQewoCg1aHYXscOLZzJobEWR06RRBVMUorYDFaqXaQcd1jWzafFRoQkqsgVjMTEQT6tby16KRmYb4BVEigXcwQcAUfAEXAEHIHli0B6ixSeJ1p3xumNMUz2r4IDPw49yES3v7OrWiVuxNIbwyAY/sI9YiyoargtJnfa6Q469dHhg4877rgjjuex9tcPedjRLHp+8NlHEsno+mne0Vx66aUXXnjh9y+bvOyyZOdvUt1qMrgO4yGFgNMEQRFSjDSGEEJoCP+1xSTsuSOw1hDg6UhNzpyV6VnDSdQ+LcPTaXgM5v3qHb9p/OqX2y/8+Ff+9h+T+98/eczjkkMPTV75yufxrmF8/ygyl13ym8tIl2y/+91P2n7n8bfccsvE5L7wRB8nnaAz5kPdlJaex2JtOjyMhrfN7XJ6Rs882qXPbu0zndM6vLjnKtE6wXl+TZMEZkw45Qg4Ao6AI+AIOAKOgCOQIsAnpppKZ56bKXnusphQZxJFU+O6laKILFPXmmGSjCYeDEXdnpGTWLJG7UEHHcQ9GHPeJsPOTdRijftGmNDIMNEuXxLbvHkzfN7m447l4+kSijDfQFRmBxoVxOwmkLl8ajfuDgv098shv5QyUH3l815Gu777te8iec+T7saSBbSIbd/+iWOOOQZhu0vEhdn/3Oc+98d//MdPecpTLrjggsHK7snJBnM7zMBWBwYJPo5EKvl4QEBMs8kSf1qEehTzTIu62ZEvm9hDDA7JEDNkjCOZHD7BV0ZGHCywZRK0p1WAgA4lHYZTjNNt69atS98ovoxixczS+3WPjsCKQCAea+YZsJmCyKR5Ws6oYzzD6Vg0IYb10YnJ37rfw7ka3HjVjbt27WMBArcR+tkvLlC6f+ho9sc//vFZZ5112mmn/exnP7tt366OjubMlEcb92M7FowR1HaUjLXmT+PO0vytuYUDhYC6ij4TPSAxEAB3zumC3Qq31tu3bz8gYbhTR8ARcASKEeBiRUKGsQ+C5fLPeMYz3ve+96XssASfqnPPPffmm2++7bbbuFuAzwPs4x//eB6udY19wAMegMxPfvIT8iOOTt7yln84+V4TfLdw+NE7eJUxPbmRa+CFHz7kDW94w8TU2JGHHXX7Xbc96MzRP3/Jn3/sYx/71jduRiusdgyfTdptC7y5J90i0abUZrCTzkHzoiMQSibTKqdzW2IaR4TsZJh9Fc0pWmYt/Yn1zmZ4N0LqXLe8uepIcYx0KitSGxdTfvqbt60tEtImj27g/cgPLv4W3emkU0+55z3ved5557E1w4033kj323n7bclEuoGajE5MJUMD9LFgFmR5nRVyXmml1XKtKhgIpUl3vAQz3US0JdmWDRJYIVe0hx56KEP57bffnqrOtKWl2OkfFJU6VS4Rj/5jMYjGMU3IuDeO3gSWb2PGjhdXOgJ26Ok2K70tHr8jsCoR4Nxk642NB20YXT/CFZsUZmf2jG3YtN7mJmDqqg7BzRs513+GPEYxkwGcweGBHdt28oUtt3CYZfcrJPfu2cc2VYJORqi66/9n7z4A9DqqQ/Hf7UVdltzlXnDBFQwY29iY3gk9QAghlCQv5aXnpQcS8hICeX9CCiQQSCimBVNsirENLrj33rstW1aXtu/3/809u6Or79tdrWzJlu07Xo/OPXPanJk77c7Mt3xFHJQFQ0ZML3pjGMR6FcKDvnxMvQyy5Q8+jBFeWLR4oa9IdOWBDQJBUhBnxmRG+R3EghX70aNZ8cijnV0dvtqAg4u0dWvX95S3gKVthSW/6L+/dPKhhx46tG41k1auHv+rv/qv664pVq0rFvYmkyY84HRueeUT+SHt4YceCVOTnMm1JpagRyBAssRiWtWHjAnnVHNRCpiIMj7IchKBSoQP85cglAzOBAEgyz5kjO9NHBi/q5EJ0GQtkAEDLD64uARg8YFkxk/nw77+XsJ9oQhejs2WED6dfJagV6xx+peiMBUQAWMAhIefoygV4iTJxL8Y4zY3AjNXFhIGiOMgdBAEJR/Gp9IYATI7shC8AXMC57d3plPWMBRxu5dIcTeZ8eQ+sk1gQ5MHnlyrnt7aWx1eO//pXeKPM3eqxzOi/x1I+xB0dmn1ZnzcR+S+vp5Xv/rVH/j1fbWi3V3zNaE/OP22f/iHz7sMDKanzx0ZQ3pGl1y8411HvPCFL1y16lG7WUaHdvrUpz51302r2nsWaNfSzRkd5Z0c5dFWOzG2U/9rAPCud73rvt6LldfoeHHDDTec8f9dYwNMXjmAj/43+vfxwv0gKSNN1QNZ3f9ygnZS/PTuf5uKvn7cQTwQ1W9KY7yw6SbYcphZrlLXY6cp/fT0QaoMM/S/UgXTjZhVadLl3KPeanvMf8nXfZAspoUuFTKUglmS+w7t5yznv2UOGhs3DMxfMG/BoondlbID/5KXvOTYl+5J8kibm6WKb/73xbdddtm4rtftkj09ZU+a+ti3vOVZNmysH36AMcsfXvrtb3/73tvXzl88zy/Z44qwcf36/rlzYw0hxsBhanuaF6fpvDtA1q0uDnzWLu9+3z7OA+rb5evM7153xhk3eNtSXuP+68L8d3lvf38eSLOTiv32W2bT5iNt9zNs6ZIDzj777Iv++8q+pTuPr1+NgMfCDBaassEoILPIPOucsLL0YZ6Yr1m11pJFvO3pUGMKKUePPHR7Z8/i3fct3v/+t++37yEPP/zwgoXtP/vZz77xtdvTvc+lPZYH6MIgFyVjiqJTo5c969at99WA/EgNAyJTSja4IKUGnPMLYFjMJcPU4A3KDCNTiBQFY+Aj7unpXrt6Xcx/ceUkehH7GMdCgyu8q1atnjMnrdIEWYgSexQAEUio0kjK9RA+1hDijUCf1dXAM8EDSlwFziGy3Ci3gkXlqVTAKfwRNE3xhJCyEuZ6SAWyKUTUqKemB5SmMtWKmkZFBfDYdAHWUzNnm6xONba83GqsI13LWHRMrJzLcndfugCrLa0gtuULsMBRz10hlRIqIa3DliFL92qAy7P4GZep0ptSiirXbycZ/dsYn/gh83i9NnFOA4U90yROjcZi+DJ12ozYnEEAwhzn/h2y/I0ps/2Qv6l3C8Epg5PjATdgpS6vo1PfNDqWhgRFuh4r7aMTxzw94tAbOQ2lIQ1ZAGJwdIIAIeO3NxC62Ex7tjCyKQ6TIhcyWJq2qebM3rbmdYrZc9aUtQdqD9QeqD1Qe2Cbe0CHFz3cNpdcC3yKeWB8QVGstxBR7sSMwV/ErUMXF732Wp9x8frmO16H08JuW3mHfbFW9mM6OTGWS9dIwaV7VQUwWkkhIWgiHi33hcb+kBSjs/qEvVyDst940aJivwNSfPSxbgof3W33gZNPHp/T/2JbTjva9rj//vvPO3f5HXfcceF5d9533+r779ls92nSPV4Mr7MslX57rRq8C/kxmceatpHyd1bckpP2W+TUGqg98EzzQH47qvM3ThhvDMdrXjokXtc0MV67cueicILa+941uGLk4h8X/iwj98z5gl97OPFFB+69997HHT/3xS9968jYA0uXLh0cvv/cc8fuv6+wkeyqK2yBKu68PcWOGQgNYgARU0pP+/qy7fCSlhSlelFX91DbQGpeyld4AjsyOjKvf7fBgTUlesJIE/Wx8aK3Z87g0DrrIiWp1sWBg5QhH08TJk2DN5M/IXHTPwg0FnOK8UUJSOc0J9bKS/YQSwbAH1HoB4vxhUXbRgR1/7vJkTVUe6D2QO2B2gO1B2oP7KgeqA7/3Allq7p9DL52x55ygM3u1eX7+LRgAT1nKGjyYwaCy87FUGErQPWzhFS6JAUZwHK8/W1VyT7Y44KRWuVlZ9YCiKusshzDSnLWjqZhnsOJwgvHO15w/IvHe7r+5T8/e80jd5nrzp+/00MrjR4N78xvfeF4ZLytaywN9gTbFNIxgLGxNKM8Y2jldV/59Fnv/92+40+84dLvD4yMb3A2cXhs43C6YKvR1hF78sA5NNnjUU5b85XpA9iiHGRoqnKIFcgPCeHnJhpJTfaEnNigkxmDi5MJCTmRVMdPUQ+Yidi5onqoMLvuuqtNOVvMSExepiNTK+IVm46gFW+nS2utbiWrMbUHnrEeqHRb28AHIU0cYRtI3HoRcYOEmyctv24se9T+1euP3v+Au3vGv3PhjwecbdO3RkdTbqSzcc7uSsssfoazaEzeLOAOgXIh6PLhVatuvOrEZx/Vvd+BP7v2CisuK/0gWUm99aY9Fg6efCxsj4NnsvSeaL2Pw+SatdkDUW10qS5d1XUax6bTL8MWV7e6GyV6uq45hmq5ihq/VceHRuahzjCg2b76ufZA7YHaAzuSB7RyGskDDjjgz/7sz171qlfdeeedP/rRjxioTXMHkCOF//Iv/6JB8+k2JqoAZ+AdK+ju7vnVX/3Q17/+RbdUL1u28+/+4at+dNbX//urV7/1rSf+7u+f96u/+vyzz7r6nnsG7rqlb6yRpu1uv/rDPys+9KFfOOcHN190yb0d8XVprMfooqMtRiDWHNLnHZ1/XNMZGyi3tktOQnwoKr+A+xxtS7Y8ujwc0lHHODLg6IrHGGWkb9QlS2Irw7YafcT6iV+aIZ8l1m/S5uFJXU39CM36lPyJHBwEEzbt8P8wuBoiOzkLigAmZ3m8kS6V6PLb034IfcFiPbUSt8k6bYRdu+bmSy66+fIrlh1wwIEHHrjPsr0O2v+A0ZEhKyorHn1U/PCD9xVDI/7WrFnj57nS8dpUmGUARMXylbRSugwbTXetjacCUJ8MlxUDGpVjrNyKWt7oYeMt/4vJolo9d/eWtyPtym0JOWvVFIqqjxmeDp8JHifAmAjkeHNtWBcAoTdiRQBAFrrAQsCQAefUx2lPzf5U8UCUeKr25Rv6VDH7GWtnda4RRSauIp+xnnm6ZtwbKixesii+KURZK/Sdd93ZpUXe3LIh39Tv6FjdISVesWKFngtcrR76hQW7LPCFKLjifKZjujo7GIrCjeTTqF8OTPQO8UstRjU777zEJypiaRcyF16MS3dZQj48Xc6EINt9991jMRxlDIqCKxjBodoWpoceXI6LMeQwnmEsFEKymA2SCmdlXGU1Oh+mu/1+8QmHnLvXIQ8UQ7ekD0q9b/r0p4rrB4tlC7sHBtYk3jJb8xbvvHz5wz3l75eQSfuSnXfiw8gdsjCDAbxHC118yP7IBYIIDOYcX+sy42TKZv+SED5MBk8GomjkBKNrEohiSfZAUKHPPoy8Dw+O7Lzr0ljYn8GH2BW3UnPJCNs4cDofGtUoI9mUR5RY3BiFmISZ5fuu8cADD8iFhQ6eCVNVBlkI48OH8FSIsw+ZFN83M5kcVRkDHzEbciA5C4/U8CF4l112IZMZ4aUojsy4x7Ldly9fHkksISSKu0laVe8TD7OWUiax8InX/szTaBqo6fDuc3hqVRqNwba03dpVcZrQyrbDaVyjsuWUKDJxFZlTa+Dp4YFoT54h/e+Dy5fPL6/V2HXJEi28rmq3pbse+ry/M1Nc1Fl4YX707Z7urmJspG9stG2kc+eVAw8XPY3epTu37XTd3L12X73worbe3kM6nzNn/nB7394+Rq8fuEmzPNYY7e7pXdI7f7v2vzdv+PHcZx1cjH2xq7NzbtdeXeuvdv/l2jV39Xb3pma2bGlz/2uwsXEwdVt1/9v6nuY+FMBFQpXm6dT/VvNVw0+wB2IuQGmM3DQy5ehxwopAlg+GRoZJYuunQx2doye9aD/X7d3z4P/cccf6tvaNI6Ori8a8tLKHqm3E5rJJrglR/inrsP59y138Jp4a2gE8sMX+N7Xt5cQzjFXQxmOLFy/eTvNfWvQdhKu9pooxCFRXo7p6zHO3rZr/ErvX3svcR2zehJFw07HRwbG5PXNXH36b1DlDu4kX7D5a7Nwo1j5aDA33L9x7w8ZHOvqKPfZY0Puc+V2HzOku0l3Au926e8eCzqJrpG/OnKGhtDLQ3r5OvMtuO1szZ2G06mKmmoKl96a9cKml0D1ndOku7Xvse+fe+xY333zzXvvuu/vui/Gm4zbpooq1uoLGeO/icvYaWU6pEXYZ3O2oRXMW3Ts8vKaj467GbbcVO20cGL2ru5GsEmTKlFPplN5Kqw26Eo+KDMZjiGGGmZo5qUfXdZnSrnj40QWL5o4Op8uXx0bH0kCkmDM6NjZ3cbHrXiv7dvrhvkvbe/vG9117/U5nL127alVn58LU37eljawkiz0QG/LFPAwzb97cKMRS/nwDniCADAuxIAtYEoD9rr5iHkABsbw6qQxd1JGAUVAPrdvYhGAslOUTRbIhpfFViEUZquNXM0MvOYClS9NCAclBCUDs9nNt4YIF80kO7wV7lpMYJ9dhqmsIvIo+iOv46e4B70mqV43GkHcX3Jl+Qngi0x2NdDecYNXHW2IVyiexolF2sqmrFQJWp8rQPtqW/hptrhUvF9Q6OwbbirHOjmF/Wo+O9pGOdMK40dEoL2tz4LAx1tlo62yM+fjpr8M3ZJ9lJw8KUpA+xJWaVG7z7+pfQk+8EyVFHT3hHohWSBMtxLqreqBle/pdgGU7pKyNdKYfIG9ze5NtkeXadffclPHxjoRx1FVqLoTU6tq9ILR1uMXJu+XnJoo2vwveMd5I/VOHgehkaG+kt258NP3uQvnZOX15Ll+W9C6l10zUSF1G+Z56R7rHfdUe7R53TZ0Od/Jep2rTHaWDxR6HDjZEB5d6idTzhWaMSW/Z4Acc+IijJaAiIyfoS0yV3h6NJMdmUaEBDvmpS02nfdMzTNLVkT4y66LKDr1Rdp3j5bViqWGRpc4i5XisvY1HdMENfobvHE+d6SiXjYxIJCB33BmWKSHnGlcEBJPgJkdBVvGZYDsBoUvM1dlC1sKIU1VppJ+Uiw7doxCWRI5madWmyjdLhqccGXdEeMpZXhtce6D2QO2BZ6AH8ly3brqf6qU/2f0+QROvkfGRIb+h0vrXsGyVVq78DY6nP8BwoxgBjG36GxhNh40NpQypu7sLv/s1b16x667FbrtZESv2XFa89nVLX/yS/lNeVrz4FcWH/+bED//1iX/1V8f5+/GP33nGGe//8pc/+J//+b/f9raXHH30sx94YMO55577r//6n+9+97dPPPFf3vjGb3/wF7/xf//qyvPOWX3nren2q+6epKIMabhuYaqzvc+8KE2H6lB7oPbAdvCAH2uw4m1zRluR3j0r4319NlsUG9cWN11XfOZTt/7J75/1spd869hj/+0d7/jOJz/5Wa/wgw8OHX30wW9724v/8z9/68tf/oDX3Mseb73XXyOgKTjF38t6XvWahdOZvHZtMT5WjNlbbmlsdNOfDViNYt14sW6ssXa8GCj/NE4j6zasLIoByLGG1IHR8cGRsaGRsbQ6HDPD6RRtC3xakcntNmBbyKxl1B6oPVB7oPZA7YHaA7UHtrEHqsOVPECKNXGDmZhVBY3FdJgI4LBDks8GHqsh6Gc2NOuamSynVunTmn0loPGEQJhET150am1/vDj66KOdpz3//POvfeSuBb1dfko0nTRIKa5gTV8sfAzIijIQAn1WuvPhRz//+c+ffPLJzk44jBCMmQwwqXTi38AEe1NSPFZ5q3AT8ZRyQmzECPJjE2/1cUo5rXqDrIqv4aeuB1SMVMkbjQ996ENve9vb1IeZ8zKbd3ZmCa2pz3rWs/78z/98npWgOtQeqD0wlQea3rt4FFdpM7KJuEoTcBNlfmylfDyYLHYGe6oZ2Gv3PQ477DAHwO7duE7vaZ+CcUUMG0iIrmo6e2wfvmPFQ3fddZdmZNH8RTZY6PPS32MKTZZXH6eTl2mmI9jm+Cde4zbPQi3QXqVwwiGHHOLqFrCtnwbJUbhb6x9cU7I0SWsicybz9a9//ZSMNbL2QO2B2gM7ggdMUjRc2kZrCxrJ/fff/5//+Z993/nABz5gnBAWnnDCCbfffrsfQ0ITrRx6Exwb+mH+9E//1C8kffCDH1y/Pm1w9O34P//zavdCX3fddVddXmD89rcHrriieOD+Ad+SDj2q+NwXX/nOd77zF3/xX9/73m+tWVNsHC02uMVofOLIAQm+JPuzyKHdjb+tclRTa92RdmKmj+NstvMwRLlIIbZ601J+N0/ZCkb0aPQVzp64mqm3t1um0/bOSqiOgGJAFHGFZAIMSkscoZpLY7UD3kUDiMKfTYyxsBDIKQma6HfwR1mIHInVMcH2VgFQNEZdEOlchyq3ds2jaaY8Ojy6cePIgHMd9sg6Yjt87803nP3d079/5pkXXXSR6zNcLX3wwQcfd9xxr3nDG17ztrc878UnP//5zz/o4IOXLFvWvWhBx/y5RX9v0d3peHvhR+/dkGVvhD8Xn03Edk6MFgPDxaBqN5riDYPFuo0jGweGN2wcGhjwNzw4ODo8HEY6/GMAzMOMDz9XS6cK71ClENbysO22yc+l/dyrKuZHs4CJUigPAnlBcImny8jToCpOl7Uar3CjfLe4WlX7akfzQLyzM7y5O5rBtT2PwQNeT8Mtfd/4aGN0eMxXFhiHQp/73Odq0r220eaTbM67bNkygF/ocL2Rmzd9PTnyyCOrSrEcddRRZspkDg+NEDg0MLzffvvtsccejmgGpbOdRilHHHGEe5eqvEQRSCzh69aspxeXVabUgkwOv0h+3vOe5xEv2xy4HR0Zc79qyMGiJ3IQFMaYE+Ax28+wjs52FrrA2pCJbSbyJtQIBNKE3Xbb7dnPfva6DQMIJoOlrSKdlnW4c10xBBgb3n9/p0P7BwfTL4nmsM8+++gSjTjIkXcvjjzKbCZgtosvqaAO2XQ+lDWMXBR3PJGTs0+UR0HSFn24etUaivbcc8/wYTaj6kNIZogt7AcBlhl8aNBOr1PNiNkwnQ85VpI6wBWIsWCcjXwFp0AVa/LhYBr5K+688EICjOzIFDtlcAYfYnQrFl4DEpZUfYiXDxWNeo5MFZWaCar1kApKVftY8AEzjH/Ez3nOc7DjUk9klkw1p0lRZPlJjMtspVkGg59EM55RqtUBtUutC7fnerW1TiAES8Rby1vTP1U8oHqoMM+Q/te2Ep2yBvORFStMzzWquob+jnZd0IrB9OO5O++8s5/N1ljppsuOaX333Lm6cp+KtOQ6bvEjjzxy7LHFXnvttXrlytHhEQ0vTk3x9u5/jz56rmmvPsWrLcyf33vE857n6K43NL/jk31HMr7uf+v+96nSCj0t7dSublW+vMhecO/yJz/5yb/5yN8L73nPKyEtZ21RTt1Nb9FFOybBFvtfnVH0R+Ym7ufV8kfftF3nv6YYFJkOmzvTnvq4Muj+HvP816RS57tq5Wr2A8g7/vgXmNoQnoPJ6bL99khfSvp7zcKK4Y1z5xZhg/7a1xmMbDjppJP2XLZs+T0PVcvU3Nbr4+4k0mJKFXM3tpu+u82Jqw88cN7ee++tH/dO6R0IlMfjj18sv1KRDQ8X1smb5r+hham+FvEGw3SvhgTPfelLdf3R/5JAu6GCGT0jV69MNIjliGFJ9GQw7yMf1+DAEFPZ0N3bYTanQCHdOlb+auqwxf8jjugwyWUqyWQKISMaFgUkm8ccc4yKAcYrUMROU9QN6zYyCUspv4v84KURXhHsu+++RiyEV8sXWXdvusM6eXJDuqebqXkNodSQMHyoFNCYXJMgy2bck/lL/1Jx0EEHMT5MykkmzujTrWTpqpS0GZhzGFC14fDDD99tD/U8Td6poF2OSAtR4clqPSS8HKqlNQQyq67Oemvgae8B5a70vQhCAOKosZIiTOmESIraVa1jUZekZuQMQqaUXCN3ZA8oX0ETJNbIaKyEqD9PiZjNgpZwiyEo9XpVerBs5rqdAZVcqXks3ZNeKEDEgZEkxLtQfSM06VrsKPHAB6U4GMUZU5WQ4eAlRwBXVWwtnNmz6gxUbQi4VXgQN1Fm+yOVipzfJoMxolG1SEYmFaCYrDbEGkKOrRsYhwiSIjWSEANaAxosEQMiBFmGJ9HT/rtV9EEc9pDoERw1qhoHQbxB2VHyPvuQ1iufxiGqQs4gf/kGkx+3CMQ3jyCL2jYzy45Gf/XVV4fBhoYzWx6pcUEsWGZnk980WyhDfifjcbr4MdNPJxC+aqeNYkFpvDszS3Ble5rkTMeb/bO19Mmbk9O56YTDV+XPQJaT/KqPl9/jdMIDX02NUTtMFZkFNgHZP7OkD+Fhz2zkZ/oZshAmRQW75ppr4jHX59Ayna7qLwZPRxMCI66+v6FxZq5sP7KZKUN+9qfH2bwy1157bTZvNvTmkEH/GOyZjf25fs5SfvYPq6ryq3DOIEB7Fd+M9eUZPx1xJqiBHdYDrf3vSMelbX1FwyYKu04MOCfWuKbOwYaNtimknQpCIuxJOzkXLvSTZUVP76iWL/5UFkO+zjTq2/jww4WPSSHWqo5tHIiReTOsarnZVQDYoQGfln3cGVv+liwhiOH7+8Vdy/baQyVEIAuGVgZhcxfubMi4ckU/CX4sTRO0cuVKsavJYb79Pzf4RnXLzfevWNG469akpQwbluy054pHlxfFFP2RO7AmQ4zgN9plmk4zzzrk9mSW70h+H2fTmLAiy5/Boqyao3gAJaDuf8Nj4ZzsIsgoApgqcjr3Zv/Pkj6X79bSM2Bme6LCPJH9b9gzs1W5P9qi/eHhdeu0O3cE7O47gLVOFbb8q7REg8XoYHHrlR0fuxJJvKU3F8XNRxx79pIlCw46eA+/VvH8E3aSFpP5V7z6PZqI8Ub67aORsVXigYHUMvjpb3H5SwONe+8ZsoHKYrLtYmlrejo2kICR4dX2mdmjLslPahu3aqm0SNooyMHBhLd0DEhtpmIqf245tW+puds8pJ/mTn8RfEHY2FjTGF+j2WxqVXJWMxAs19z80xOeZ8/WZlu3Zy6CzS2on2oP1B6oPVB7oPZA7YHaA7UHJjxgEBXjqIgNEQPYooPmL+5buXKgo1Hs0tt26pHH3nnplf9+zc8M8dYM+mkgc9h5M0gIFYagaFB3Nop/Xn7TJT/40ude856PfuKjXyrGSZjVuvAMOuqk2gPb0wN2/JgFv+IVr3jzm9/87ne/O1RZYZjh9fGFLBZCLdrkFVGM1nO8d+J4I1qtRn/ssceecsopX/3qV2+66aZMcPHFF7/yla/8vd/7vT/7sz/LyBqoPVB7IHvAmwX2YgoAr2cAmWCLQPWNDpjMCKXUCclZ/hYFPh6CZH95P5Xz7hZJukcLN1gcd/Qxt9x408V33abTTItHnek3vDQmjPQ0bX7LtZpY/732zlvWjgy95IUnnnHmGR2N0TlWl8aSo0YmF21msDnlv/TtDDQ5KRyIPns1A000+XFrgSwQEIZlgCjwZOkl59ThKeoBO4BZbrfrC17wgi984QtgxVr9PDdDvqyO2vMaBHbP+LqKUVWJ/jfXHwRRc5YsWeJztvs4zj33XDfCZMkPPfQQgre//e1f+cpXMrIGag/UHqg9sCN4IDdlACF9DBofP/3009n2j//4j2YN7hU677zztIGnnnrq//t//88mRWRSkQEca7zvvvve+95377rrkte+7jWDA75Bd9xzj6/GRf+C4tf+18lf/vK5Bx5c7LP3YR3FuemTuNPpe/f811deuNNOcy+/5IolOxcve7mfFi8efiB9VHIz1cbxIYMwX4o6y76XJl+FGuNJqYu+sbe1+/niZEB1QNHUiYeFSVk5BHI8xOVX0Zw7NRH4auzTFS2+dkF2lx/Tx0fSCoMfJm1vjK1bPzbHDz51pM9bcRtYDAvKwWL61WIBZ7KnHHdVDQstUmkvf0jVz7PaANBGVKdP9uVpjZLed6vkeTDvTgJ+hzadQhGqOQqZO35cNRtsB6MZsV4SHH0rQH1LVW7Mj3XNW7Rg4bw5c9duWDNvTl+e/HLIaGdHe2cPYlke27jx4btu91d0dvfM7/c10xERk9/9997Hj6v3P+e5aNarheWUGYsxgB2oNlPp0PkwHVMZGoRfPTTgrAs/24eGRpK7Q+BHy9K03CQsjP5/ZFQqYmMABOVX1+kHzIlvYpiBK4cSnfABBD7gmWMj+S2G9GPeQvop7YDUtMSmKollXJz32smvXMRWY6XA+bHhYV6/X6QfGR4d6unsRbxu4/r2tq5SDO5NAW/59qWMtJe/67sprYZqD9QeeGI9YD6iqVy4cGHst9FN5zbqiTWk1vbEecDVVM5Pum/Upj77iDTm+jiHHnfaaaeqEU5d6vgEl0WavS5aNAbjDOdtt92WySw4w9xzzz36ha7duqIvUJ10prfccouONSbCixYtcqLy+uuvz4wAM2t9om5d56j71pXA6DtiK11Qmhc77Ula206pr1c5sdCoupoa064PopTlJDguqwJnFXp292voK3GJWeJwrArP1EyDANluuywlfE5bGiTsvuRAd4HtufPyoXU9cxcd7DvT8rv2PPLQZ12891wj1Z2WDtpp1td/hKPRexyw+733PjhukFcGEnjV3Zp56xotTr06dkEjn3iUHXlhP+dkG9p3asco148++iiPxdjGOVWFcuWVV4Z/uAgvH1YX50M+e5QgLh4QO2BMF+dk+WS6WUw286CIDxWl2I32HM4nxjDkC9XFBxKYStQKOzvLc7xRT3ipyYfOIcuRekWmmBkG9nybbVDNiGKqERekspAjPkS2ZOlOSo18NLKgHsJXfchyoyYaVVdkaGSQtKoPxxeMk6yq2JjKPGUhR0TJ5s9+9jPsVNDIFRiX7X17dZcmGxgmFY1KLgmjGqs6ZfupJkqOJvbdLSrk1EFxNijfqrTMUgPPAA8Y4o8WbbYtjo2Oj/mB0Y6u9Y221Y3G3CItkG851P3vln30tKN4RvW/Sk+jrdfT5ruSUvd0953H63SGBtfttNvuC5cuecFJ3zvz+3MNMLo6XAKyeO7Oiw499PClizvGhnddsuhFmtbutjlL5q/bacmi3r5nNdoW6Bq6O/bTRB90QN9d9z0YnTsV27D/9UlK/7vrwt49F5+8dvUqjX9f58E3r1x8zD4nP7TraPvYnTIVn63077n/nTMv9d11/1v3v0+7FmuHzpCRm7ZFs2B0akZgwGydz1sZRrup1Ie36jJYI/1+U1rpMkzs7+9dsGCfpTvPe3T1XX2Lfnr0Cx6+5fpFq9c8XDTml2uyaRWsXAZDPosP1Tu0n2rjJjyg/zWTuvfee3U6+hdjMPMCHYrpQ9VHugCzIQN+81/9i17ATESIXzIISm2decHdd99tutG2tE2/ZjpgGoXl1ltvVRVjambCQpoz2h6zCjaotLotGNMNgAsiEVSnYBhZVTXMJC4mlXn+q9Oh0bTowQcflJEs32wFJfroIr0m+iapc4f3TfHoIvGyeT09Bz27fflNLOnt6BvoauvtGd9r2YG7LJ03MtTT3tneGE3frL1ZJjt81dZYyM6hoXTJNeHyyIdybXZGNTsZ3Dc84n3E5WXcY7d9dlq0dzGy59KFzypGb+GlsZFdd1l6zIL5d0ltdFhFH+7p2kvGzTGr81/O2X+3nmXzF91556NLFh7Y077zbj2NZct2vnb+3HkjXvCJgIzzlZGli8im+Z3yrfqQb11QhYyjsJVkY+y/96H7zRa5RTENpO8po/09h/d0HrLXbi+yxLHX7suGN7S94hXH+vQ/Prpu0eIFPZ2L5dEahYmhjEyaUJiuQnI+yZwj7xbqGeZXWzINLe6ZYqdpY5CZk5qx8qFcMyMME7tVyqy8Or9W5XheUMScRjVGu/UeeOCBLJ9M8mUQLyQy8q0qYLE4UzaJaRgG4OoqI+K4HUzWECg1sem8CpybUDSmxhzLWvaHUvVKrs1/keEKZB0/EzygegsKPWqyV0qdcW+VR80X2IV1JWYraoUXZ2w8/RKzAG4+tvdMcOvTOo+6CdVDrHC1TvEox6msJz877pgOYHaEskqX48YZDZa1iW/67eUlj+3j8hghDyXl1BuEMmfZY4YpAid1RXfRsPkgvVONxqaeva2RLoweH+uA9XU5yfF5esyw1mp0VxqvjvuYm652JTaxFt0pGu+h1XlYSG/vRFKpK9nj+29ZOmmg2+bUBQMmzZvMbxICX5oacNic/JPwEyvtgUwyg7KF3gg8ySn3Y9ClAoyVv8PBS6WM9DNi5Jc2lP82UvfX7mcHxgzmtSrJDPYRkaIi+SEVTAokp9SoYCnumCgycJJVZqGkjJFAIi7NTw7P+ABgZCEYxYFspc/46YAm+dORZXxYEr1qwFUJYUmkai0RSPXIVICQ5cwMPM0vwGrKvBHVH/7hHxr0e3OMXaoD5SbK+rH2wA7rAe2g+YwK/PKXv5yRKrOh+Q5rbW1Y7YHZe+CLX/yi3isGDbPnqimfEh7Q/37iXz4U/a/VEAsTytpQJo9XABHK7AyLG+PDBjedXUPGhf1z0iLU/PlW4dxlNYjRFVVBnwZA42nzq5UUKyPtbYMxaAu3gK25UedOGBgsgU9cRZqHkOP/vo4++OFiI3zRlsbibeUB4e5izlCx/oGH0zrgbbelBZe4AMtyT0yAYXbd9Sg7S/bb99l0dRS7lpJTm7z7rsdZ1dq4cT372zvSNlDIFBqxgJVWo4q2cjNKOVgvGlvXkm8SmARtOeS8b5l0dhT8xgmCPsgSJCb5tUIUsx3emJ2YpxJV3f9uv9La/hVmpDS+XIIfj+l0OQlqi8XccoLXiGlRGXfchn50bK0Xbd3GO8T9c9Jc68ILLxSr/Oq5zwaALqcki+KoYw5ILUnfEnjHHsRtRa+57Hiju93qmLbHSlna8G8Km5ZZR8uzj16fBI+m9iHN6ssAKF+slCS0t83Tcnb3lOZPWFjCkxEmNTNY8I6Opmlhe1u39I6OTcvlk+RT//uXf3ibfG3/Uphae42tPVB7oPZA7YHaA7UHag/s+B6I+VTErAXkkI3PGEBGzhJYsybNDecXxetf/3pDu69//esrHYstdxb7BmKQF986xB6b5FdHcead/vz6+GWXXbHh2Se/5bVv+fx3vhw2pCFpGWZpUiYLdWLjTMgQEshMMxvAgDl4EYeo2XAFzWNQN3vhNeWT7gF7kuwo+su//Mv3v//91lvYo7bYR2UqlHc1TWmktQiLRWgs+9iegsvrYGuCCqOOVV+NzO6X6F74whf+r//1v37wgx9kZAB/8Rd/8c1vftM1WGeeeWZTUv1Ye6D2QPaAl2u7tsnkC1nddgJSfxSiGw73pPDC446y2/KWW29ZXxSWidN9EJLKFZsgzBzxuCmu9PkuPyekcfgRRx151P1XX4Im1oI2EW8Okbk9Mlu6cIpiSrmezPfmhkz7FOZFXGXfHmZPa0SdsD094GvLG9/4xi996Uv6Ynqs8/vC4hys4eh0atUEXa2OWzcdNHnPqFdmSi4VhljbVY8//vjzzz+fhGoV+s53vvO+973PLtX8GwxTCqmRtQdqD9QeeNI9kNuu//7v//7d3/3dn//5nz/vvPNe9rKX3XzzzU4OSDUBydNeu/Pf+973OjXxi7/4IYOKE048+tBDjvjsZz8/2nASIF27c+GFxUEHpLsSurvPjd8yWbt2yE0KBx98sOb0l37pmIP2fo3TDiseWuJcykUXXeZaBwcitNarJw5A2UNabuksv1jRW7guqQzR2zeNp5in+U1frtL2zTTWKs+npI2j8+a377//3rqANWtW2MJbtKV9dJ0dCxwquPGGm3bffcEeuy+79trrHn206OtsGxhu+DXuYddgdRQL5hTrNxR9vWk01TO5i5LA2FFJH0W+0etSYKwHxHfj3AuEGbvsMueoow654oorHn103In9o47qu/jia9GsXz9U/g5gc7ETJS9Cc8KO9zxLI/n56KOPdtokT2BTaZahMZ4+RyqaZz/72YPD6TicLRbElkXZMVz+WHv6HJlvzhpvOEYy3EjfQ50GUR0eefAhdcyhkTRN7kq78NU9Gp1jAeO1f8MXRvQj4+mX5weK9GPpGCmCZ5Ivl1KrF2AtGksfE+MyLOpuuOEG54WwkAbPGPSRd3A8wjw5IelPzkz2+CttY59Hb9nrXve6GPM47sJ4WUBtuOLltS0E1/w5CxwDu+6GG7w0Q6MDSuH6G2/gmWpeIoOR3yq+hp+KHlCB1YRcmqn6VOozvNStyhf2kOZFJnz2vNMRZ9tmLyooIyO5bZkNO13BNRviTJNZchYyBs0WDagSh0yYjJwh+5kGl3fZVnCri5o7Gk1zpDa9udngVqBJS9gM2YRvZdxxMPK7Da3dJtK2WPSP03vkm2/6ZQUVL05yKn2HYw3SqpLt7dHrOUarY4XX1LvGwtjDsCeTWWHGeMwxx6hIZbE3rDy7bokfHKFEph0AO5Kqg3jJS16SGQE63LxpBy8JaOCrZxPMo11udeqppxISvIZeTKLFUU+w7kmXzTAq9NdVXmdcTzzxREgGS3Uy2So3SkPHbAa9TnsObUhL5Xsv3d9tX0PrbkPwkY98ycWPPf1p+HfvI8Uxx+x2wgmvcKJ13oK1VtR7+55tx+OcJf08Njgau6pSX8ljLsuOPjFUyBEPywLDeBuN4YQzrhybbWChM65u3aJdvvhBknV+ABaZxUWmfe9MfdWrXpUZAUxVmoYiYmRySiMhQibjH6Omk046iUABmcBUAuMQdVQDXw3UiqZ2W+FSGmd3w4dHHnmkVRFFnOUraEYq3Kj8YtUJAVdkGiqYqgjkFJIWjUzy4Zw5b3jDG7goyheeRl8iYr0l2JkqNW18Lb/6AeQImVqR5UcFxhgXYHEymRi5RSXhfDVNYAb/vPSlLwVnXvVQ3u2eDc9YrnHWlwPDpCAjkLusz5DAh2J4lZP8gLO0JwaglK8YHOqYGtYGRtxUjlu0Cnvwbm2Opst+tm2LqpsIIiPMaMLP8EhXcM1A05qUWXIWMgbx7A1Qk9MP+rb5SVE/5DlRr2Zgr2pRl1TOuv9tLZ3HhuHbx1zxssYZyi7TPB6A/Gda/6vR1q7q1C644IIfX3b+vHnF4oWFb9o7LzhC/3LCCQdqq9ePpJ/RXb5upbXu7531r9/73k96lqafwp27vDjllOfrTbwm7Z1LdSI9nfvrcfbevaH/zW+ccidkm/S/mh6m3r3m6o997GNr5lylrDeum68PPXHvvZ076yrugYnmKfpfgxAVb2Qs3dJY979c8bTvf6ttuMqg7gnR+DQlSZ1NwBXdUJW9Ck8nhN7WpCmRrWRNmCpX7hObaJoeqyzZ2uCtJjVxtT7iRR8SMtBKljEogxjG6Nq4VzdqGGycbDV40aL5xodWq5INE/OGzLoZYLjoO92hh1lQPWpobOVll10+NnaAucDkj39vRlx9CO1NedSwwwiz8d5saKoaM5wznjGPByCNwY9HQpX3cUrbhpZUrWqCFZNpiMG87w7uHTav0V5p3k844YQqpd5Ee64yqFrypf+K+W91eBBzt+c973lhOTLdk7qkV1ItSSMZPRUmzmbc5ilZRUxP9B0qLTwJ5h1iMjMN1WYZMY2VRCwVGM2bYv5rAEkFXVQgjql6sMsjRtrlAhnDzNOvuuqqK775fQRzRxaJD+5K/e/yuzboc+f2zMM+WjzK1G9+97+kjnavEXfdt8hU69BDd0vznSJd1DU09IDJdXdHr97W7M9Ui83wJn2mYMMjK8JFpmkLF86T+uUvf52c3j7tZLHrorend23DHlIbHUtkqq9nXzNBNzo1zX8fGrrMzzEO7zzgje5sTzPoVxz280qtegGWuZsCktP0RaZItxKDGROPMIKxh9YAI10CV3R1dcjLi170ItdVhwPXrhkxyd19n3t961+7bjn7x8aHrQCc8NyXvvjFL160YD9tSzG2CrsykmUCQ7iY5z16nbVC9ALE5HNFpjFHsGCuoPESLhBoEKgGyjjGZFn5GmKEqa4PGFEodNNPXAB+5gr+r86v1R9kkuSIUgaoKtYZSFbr1DFIqSSoPKprNgwAE/0173EdU1UANaHqQwSslSO5Iy2slWsyq6KeMDgMiJhPHoPeLKHKSxQ8TE6Nx0xTxeckALeIZ7AkE2dRVWC6VGKFKmXA6HPIqVXtranIphSV2TOQeTOAEdxE4FHpqyRaOATW+TyqG14ogJDpM5CFtJqKJpBiIVPCZzNaYXrb/CLQVoaq8ClZm/JbpdkibxAjm07IDElVRU1wVe90koOF95p4d4RH9jNM+9NUN8p8bapaU5r65OYoe54ZQjTCU9q5CVkWQZUeLJUoIVVaNby9eZQoCU2keo/iDfJoL0RZ4hPFmsjG0q8+WHBC39aZXhYEDEMILncRpAuwYLyMMOSKRsfSd2cfb7XwYU+YJBYkCcTqSgBBkMmCppQ00YwEHPhMloFWejIDKbb3Aft4eS2tzARXWAuOEPITmTA2lBwymr7pt7el6zUn9GaJZQZDRWIsc538otaVhzMyIeEZzkBGZiAnKQVCSBZnZJBVMTlpSmBr6UNyxJGpVrFkCmFe9Mhh5OytmnBiq+inH4anDPRtYjDEMbIJrz39slnn6GnvATVZ82fycO6553rhfaYyj4pRlwb0aZ/9OoNPYw/ceeed6rYhyNM4j8/MrFX732iytF3aq4j5BAEYsHpV2lzS0dGtj+5sTxdUDQ6kUeyqla5VGunp9UPij8ybl77NuyvGWEeQOjzUhX1o49DY2MqR4U3DtbZiPsq2dGppgHg0Ubsa473azKGB+RpSEkqNaZ9KXE0ltRw3pyU5v11qP0OjbTc1s6N9MZr2jh62aXVtmCKQnLUbNohdcZXGFR0DSWB72sOxfp1fSx2BNOTYsHEVTHlU2Vi1PD3SVtbztpUJnX5DyfW1+yR41iEpelIDL8maVS0zCiMrtgAUWcrv5EXpT6qB2165ClD3v9verU+MxLhgLq6c63gw6WyzccNLuWkxd/LDbkKXpx2LzvZ5qnTP/EPSKGs07X4uxl/qxfMO+7tjVTmxL5Kcay4sG5Pyd33VE8F2cbyjY+s0FH55Ueydhe/uTnuY2rtuSy3SaGo9klhL7Z1z1C4ADHzJNWZCnhqf9NMKIXNico4sB1oIRKPlTHBP2gTf0ZmWANo7Hs1kMwN1/zuzf+rU2gO1B2oP1B6oPVB7oPZAOcCb+GzJG2lwNhmycyYRaZQINvwTjOsyQQmksV/lp/Y8JAKTSAm/vO/hb1p25D9fdPYlIwNmjGuMEstBahrrlXQOjfq3fEpiIkyqSHpH2xoO9/mMtGqk+Nezzvz1X//137rpxh/detX1ySR/Ni+WBkzyzuZfeUEWcQCR09nwVmlw2ZiSJVSTtghn7VukrAm2nweUgiq9neR/8IMftGPJzRfmMaq08Ed/9Ecf//jHAxOTpqyaJdYfPNpq49qO3/7t347fcMNlcjTzuuJPf/pTv5SOq7rzJmftP/7jP/7P//k/P/nJT+ynIT/js+oaqD3wjPVAfv29F4/HCTO8VqFilvKDLFuVTZqOPTrj+HjWUT6ANWkaNV3tKUt3P2bRnt+87IL7y1/Wku47m1StSrZqasnRKEqzrtxo6OTWjg5ccsuNtu69fM+Db7vvtgeLMfsgRtOC0GZNaDjR7ggAGeIEtzSxSW6lC87ZDKBJZlNq9bHVUdXUmWG8YQayqkb4CDOz7/ip1Uw9A621JdrGCfe2RN6t6r/lLW+xK9emXhhFnH0S1QBGV2sfsJ+cjQuwrEPqu+H1v2ALm5kyfBtCbiyDX1i1ndem/KpkL9rFF1/82te+9tprr63is+oaqD1Qe6D2wI7ggZhlxGzF1ULuwHr729/+X//1X8cdd9wnPvEJzWa0YJo+jaHY9Vi/93u/d8YZp//mb/5af/9ctwCcdtpptuv3zS1+/u2/esetV6+6v3h0UWGr/UEH9f9seZp9rF9b/NPHl//jP77n61/5u699rTjkwCv23bd47nOeb4P+L77/mKVLX75u/X0Oh9xz72133HHP7XcWN99c3Hv/yMYNvs5u2lnR0VV0uAsrXTiV3GbD5Fg6I5ACzMR4pByIeLQjQ0u/3367vesXXn311Vffe9+9v/ALb2nrWPn5z39xwZxnOUfx9a/8h5nXb/7vF/3Xf93zza+vTd/Re4u1A+Pzeovf/u1f5o2//dtPuych3X7l23gZl6oMr2C6OvxMaeeGnl566fK7V+lbfEfnmIMG7W09jBkfHV60aKcP/q8TP/rRyx5dVex3wLzXv/GEK6+9dt2qoY7Oor0zMfrBqvLCriTS1zYYtzONp9/Q0uNsUpmSi63eMl5ybeNIHkmMGKCbi2oDVkminmSVHk1mL7vsMhvuy8RNBEEpXjg/HcVpK38P1l7gHAbKH/SKx1gmokjobE/3v1Cme77nrrsWLVhgWp0OsXSmcpGEeHhsFKXHYGStkDDlWRR4MAwg4vGyznTGr2H7cVvfI8sPmjZaxGQcZYQwO8MeQ8Jk+vb4NzklZWYylD+7q+okXENNks3yrVQWdoqoRXK1ZKclzk3ZO2pU40ih41JetOuuu85hMG+Z4YrzPzvvtJsbK3bffQ+bW4aGxvrmzH/2YUddfvnlE8r8+JHsiXNojMPYPCK28yWjpwP8OrHt0m0KtZx4IEvbuktO9451+5HfSRnSlUb8dLG9z3XYHh5Q570dqmu1Dldrb1NlbqKc0qQmdjTx3lXxUzIGsomsatgMXJGEuErfJGqL7Aiw48pCsgRAhnNqFigpkOIAMjGaKpxZMoA+CLypVeJwWiYrgebF7Qm9jZ6i4XfC9A7jzh/qoEcHhi655JKH7r3fGb9yc4ef5U49hddIw9H6MoXNoatqbRUfqTPETcTx2ISssk8YX0VV4BkYK1SbgVWPVdmrOdqMYcaHmc2rstIVZQfZVIiPTXVV+MywLNvr8tWvfG3OvP4N6zZ2dqcTlZB+gbXKaPJrKnrdNddrxu2mYbDjpm58OOOMMzKZ2a4LjH70ox/pN/Mis1sjZUFngcwwz5jQ6c2TTz75y1/8SmYEwF955ZXm13jtAKLCBZd6ELPdTKaXMb4y4yaQfEZuXD/wtne81WJ1XN6kIdLpp+O7c+ee/9MLunvLMVXJbzXbLzeMjoy1x32mow2/T2ku747ULP/QQw9lwI9/9CPyh31wahR9/YesmbPup6fvbao+PrC3b1Bzux/duOrU2x/4sSss29rH1g0V/V23OS99xHMO+eb/nO4A0vhY2bF2tL31rW/1KxH6+izfIVXHdxNjOUhgvwOuvvh873vfyzRK/13vepdfnuBt2U9jkqJIl23Nm3fppZfa/sQ/fOWSJvd9fPGLX8qMgDe/+U186NQrAmFsdPyoo4/k7fPOOy+TkWP14PTTT48RCBtGh8fe+KY3GE3lS8T40LFkPbtF/swIoF2OfBRwMpadssYYPrzpppsymeIWvvXN0+ctmLtu3XrlSP67fuGd3/72tzPNwIbBF554PAMsYkAaF6HZedelCvdrp329b04SPjQwDLDAQmP1AizHbo2uZZOR7GEDOzEq3Cxfxt/x82+Xa/dYcbVah9jJXtX1xz/+Mcls4x/upehb3/oWOPOqJ/x8z133eh1iK5p6qOKdddZZmcYgR+DD+FrHh2Mj4+9459sBmeYJA5L2uv+tuFvTpNBz6x3vmnRAhnNq5pMUSHEAmTh4M+V0gHvGtHDrN955zHMXvuCEPVzBfO/dw53daxojB69cMax7TYyxx7IiYkJv3f+WPqm+QVEK4apqWVSctwUwl+kW6Moxm6YgyALIGjOwRSGPjUCWnwn9b/vrUv/rZ2ktZ6w3Pe/rHW10XnXV1Tfe3tZVdPX3d6zZOHDUYaM77dR++dXf08V0Dq/Xj47O2bmv0XnpGQvGBgb65z1749q17UXfnPbj737gW65QbzMOGVhbFNfuuf8Bxx05/5vfPN3vgG+P/vfA22+fv7Hn3BuuauvYvVwbekT38OyXn/WDb3754Y70kb29nGS++c1vzv3v0Mhg3f8+E/rfprc+NyNN+Pw4+0YJy9Y2Pq09V+rMKl+mshkZkMrmmWky8eyBsDzpnlH7lALDRdW8V+EqS8bHipykB5ffs8eynd/95ndrVA3mLfOKDZBKK4zPvKhp5SgL8eNN1rxcRWvX2Mhwd2O8/28/+o//9E//dOkNP/jSfxe7zrnbxzvHqtI6UsnkuD5eq8WbJCT8pvGG9jzqQPRls89+UMpRZCqA2bM3dVvZvK0C2Jw1VmtFtm2rpMlCljZ7xshI0D8G9tkrCkpZNiPzw0Kh1w0TPX3dvlN8zeeESoj5r+Y9Skfbvt/++5pQnHPOOZmKtaYP5m6mG8hMT0wQfPk1v/BrB8hiUuyeI/Pfr3zptOoyytvf8TYTFlMwNCY1YvMO69vVuZubrU455ZSmmdRb3/4W81/XQSKWBar95IBZ3gXnXWgun237uZ/7ue9///trV6/r7U/XISF2FbALKB99uFwyGl+DcuyYNRseXnP+j89zos9KtV543sJOjDd8ebn6b+acaMYeXNR/9V03r7vzjrtcJ2VuNTq4es999zj2qGNPP/3btmqSP7hxiA/9UimvDg6M0TQ0tt6VxSe+4M2mchecv4/vNp3da0eHhw897KbDDuv4n9O/zFfm3EMD4x1dP6vOf03BKDX/PXSPeXeedc+qzgOG15oR9+y69373Pnz3rT+9eX15LRcagTEozUZj4uZk4cGHHGT+e9GFm9YQFi5e4OcbrSG4uFim0txzdOxNb3rDRZdfZkHAm07O8MaRhx6807zyggseam9bAONCS7POZbvc+oMffnd4bTFv52Ljqq6xkZGXv/JlfvihOv+1oSWtinz3zFgnYRJvv+lNb/KLrcnEMqhjD5/8sKOCUZ3UQOsSS5bupI75yasVK0xaE50qFGsICiIYxRY3WKLGmnJa3xgeHFm8JF1J9rWvbZKveXv9G1530UUX5Quw1Cj7AC0a/OScn3Z0pQZKRpWU+7C++93vZuGAV7/61dbqgxGZ4C6w6dcQ1mPx0UOOXv/G10WLV5X2GOB4v4IxC5yh/YkkcQbwVoVs0QZ5DJqQEDAJ+TGJbuklYYIGEKs0QZN7osxSBcBCNik/BlBNyiYFsmpkhtFklpAAE3kPfE7N0gKIOORkyqrTQloTe+Yt8fpBHFYCOSr9CWp7f38vMs7jB26xQARIaVGnswjrnIWPQL7VtKe/Rlf515nihPfZtXfcb9K09VpkGxvfODbeUX6c6xhPumwFV3+pLHxiU3h6d/1WyN6kiPRKIVY0J6eVWdiE44omTBOv1FYa/MElFqosm8zYpGRTYQUOSyVxouCqmAy3SmNMIEvNybb8smSuAJAFZRN+hlTSFGWVfjrhVZqtgplEZmla68eErZL05BDP3n4f+RF3NLo0y77Ot/scnGp4hwXmjjGn6cvQKE/cV7ICm55GU63wadR5/Ub5RX/UTVEpbHKaV6VhVp22Z9o+0JZ6NS+X2BI2ZR36OK9q2l+hyqSkJKH8Zioedxaiyz1kE+okl4EC/6ZudISZQnoMi9JTGYLGy+kp4MBnURmo0qtaTfSNtvT9No2pU5sw8RZ4cQ1FSYhKEvLxAmz+bOeuRrfMaHKM2w3oy/rZnSSUr1Ta8pAUpaXR0hd8xPxNTsu2BZDEtrQVmSbsj0c2aNDCniq+ign8dHHIiS5jOpoqPgwLLtkMB1YJAkbAMKmACLM3iYTN3vZW6U91TBSwXARg7+af/umfPtUzVdtfe6D2QO2Bp6sHLIJoroWnawafOflSiAYl8huA/ve970jrDu39xbiP7xZ5traQu9IvkvmpWJtrJ+fvk0A6j1SMDZdxWj6awNtnmChLReDtEGQwVtxKrc0KDE/LS2qLnkZ5RU5z+lP/Oeb8TfmYEtlEUz/WHtghPRAzxjQ1LTdBNtkYk6amlx3L1rZlTWK38jFsrDJtI/11/1t1ag3XHqg9UHug9kDtgdoDtQeeYA/0p6luj737t912m836C9vaHzWhHB3vdJ9VeeHpnL456wY2zN6qro7C4QenNV73utfd98/3XT+wYva824ly8stFytF03zm2VnWWGesPW8te0z8GD2zD4sva3aPt/MynP/1pGHXD4Q3bYgDOZsDYP1TdJRM0UeL28QRNsPiwHSuKqr3rtByzsREna8mA36Yj3AwoMCHKUoZHr8wf/uEf2kX0jW98I9PXQO2B2gNVD+Ql3yryiYS9s2zYVmZYMF4yb06cfs+n9y20lJcGpL0R1AlblcFb77jVScWjDzggXSrUWF/+amCzzU0y7egQNESO94WuIBDLqSTI1q6zSchWGTlL4mzGLOlrsu3tAfVh26qwI9lR1ahdDgnb26cz9VOrjuK0KlJFpdqObC+yI+VBwCTs9hVJOvHEE/W/fudWzYnK0yREfw3TlITRAJgcx49jU3UTV/1Ye6D2QO2BHccDuTt2rOi9733v3//93zuJETvsGVk2fhPDhg9/+MPOD+y3X/wGdTpd/7GPfWzOnN7e/nQg/wtf+rJNknfeWTjsYdhQFBsjj9dfX3zyk5/86Ef/5NprP3LjdcWNNxZnnHGRJNda9fcXBx5UHHpocexzdn/5y0+d49ei+vpWrx11Zc8dNy9yj8+tN62+++61GwaK4cFJh7Ulrp7+bq10fFgvxzZFT7efOndkwxf89Bldpvzq3ndPvw7bKafcu2DR6Lk/BKaLGGzFvvzKtY6yoNEDYSHBuMQpe6dZnCUwqXJlIjl+AN7RBjcOgasjJ7d6HfOcJStWrLznnvHe7rS5U6dh7uXUzdDQiC2tN95wj/Nd2DFefvmVLsCKo/dhG5j8yanbZL6eIv/q2gS1IlebKQ13qsR5lSmTApkr2Aw0MyQ5ubT6kSR/0/UVM1A/pqRqzW8SwAMwTV1/E008BuWUSY8Tafhi3GIFwCnWtWvWuHitMT7i3geLZobrzmIZAhn8uPvD+MeZHOMcYxLrBn6aF43X2PVYg4Ojrj7ZecnSlBebTlRie79T7srrq5i4aUvKFKO1qAlTZkSSIClXlZkrzJRCauT28EC13qY2cHJeFrqi1GbWW5VQpZwOn2lC+GxUZJYMTMlFo45gi3qrQkIOlgiRZDoADzOllip7rsYxz83SMk0GqqJIzoxBABPT4Uw/HZAMK9Mck7PMqJt26HHFg8tji3jTTv2q0laBVRsYEAQzZKEqAVmVvZo0G3g6w6bDt8qMnFbpZ2lP9nMskFYlk5YXUav4VhhleCwbMPsSbJW2VRjzUAMUt1/hchXR2tF1cUmT7PCJtWXTWMfIY6HYqSVkjl/qYdUWZ2SyLjk1J7U6xHKxjDj7aq+jR2M5ZPEWWKnWQbRtOpybOgR9ig6dotIPMA1TY9dFMSCXgk7fsMcUmEAEkhxnpVHvM1zeZwqjLKxpo3TOs1ocziGT093jdPFIf3+fm7MEx3GrpWNA6NFlj729XfMXd61fPziwdq2bW5NbRkcHig0jxVDH8IDzrvc8cjtKv9QXeadOjrq7OobTNZGpc2OheboXSsj+MXDljcGBoZ7eNLxEI8uQ7BwYmBiAdnd36THT72gODKOJ80WcKbC5MSd1eQ7/cRRX9PSk3yXN8vlQMW36ENAouIIB3JJppF5vxFyeFi7lpyQ+JDB8CEk+UbzKr5kR4F5ulcQlIyxx+NapV+pQqjOZTHbS69BeuP0KMpVIZxuP5QwGpYw3fXegNy2ktKffDVXEyJQXDN+GYcGoHGV5YKMBdMFFSlmOuLosuIl7rHr7emhU5WSKk0lzTpjnkZHm3C+fUCfIOAIhhIvdW4SGzWIGyJp6TpSxd6YhytW32OMFiTKikVi2CSirMjPjEwNUVcsCe3IDxYAwb2ZLqhKqlNPhM00In42KzJKBKblo5Oct6q0KCTlYIkRStDwwU2qpsvNYPHJacHnEmGmqAHz4NusyJPZDNSe98NBb77767//vF9TA+b1dmtAqVxPMpJCu/tT9b7WAclk0eazpMVdv5dWURFq1hW9KrT6mUihLORuQC7dKtj3gZ07/2+N2D7dv+DXdvj63H2pI5/XMGxhK9zv40qz/VZQKfWB4/W5z09Rw+eCg/nds/RoEqV8b3+iyZP3jijX3J8qJvq/NOOGOO1Z0d/sqtL363w0Pr1xazDNcaO/pcV5j3YZ0e+MuC3dZMXQv27ob6QWP/ndTL1D3v8+M/rep3VATBM2ImixJnBuowJfpW4hyK5TpiBLyYxNAhdTgauVtIs6PrZQxpEEQSWTqfzP9bIDMmMydNMk4sLVxrkpDGY8ZyKmz6QVwUUG1K2XtTnnZS1699pFH//iP/1h/evNdt7qkJktrBfCWK7WF9S6DvV/7tV9zE+6iPU+56aZzHri5eV9NKztMtpmprdZmh0zJ24QkKqRF3JTa9EhyE1noQsaMDDdxTfdYnSY00YSWVnVBlqt3axFjmWX/S1TWAt5a45sMnv2j5trQ3TjfdVExkaFa227k5p5fsa4Kzd133623MuVJ96eVwdxH7eJnM8rA2KBIlEllItNhdQyYSXk0lXAnFMxgefBtXce6uA86L/qr+xj1YqS5HRjl2OiQLwipj1OOk7Mrovwqg5kU+vCVW3pN1c2kWD46urG8QKJQh/WVpjBVHyIzi4FMlnjVetKkj2FhecRyJKc95bXLKoPs+9kMZG7QMIKNC7AGOjZ4mwxUXSTd7gZKF0h1+MYxnH7hqbz9isHmhswzPwUDmGH8qxUx8QT39fSLGh0bDOt17tybbsgaGvIWMoNAGrl6eHCQDQKkDF5++S1+z3HUfz1z2tsXhMaV61ZuaFuXs2B+SmC4Onwryym10moyyRQPLtYxPBozujZLQQ+tH0wO1JOPJY0sHx0Y6+prjAyYyY7NWbDAZ6NhxdhTrFudZraEUBdf8MERfDhIBeciTuVW0qj/5Ffnpyhb990htoZQekzlT8L4CqMyqs5/MaKRRAUW81+wSXe6aXpkoqJ0dLbH/D1qDt8CxOqwi8lM6tUfjDKoKKsLOJTChNNMmVMuRhuqpS6AlmRTGYjiamZYrMhJTFUoVbJJ8q3+tyqkCrcKilSxwB6htQlq5coY9F4xvDwJBuSkAAIjziHwiAGBDEZwxgSee4O4NQ72Kr5JfjUpw0FTfayaITUeQ29Ve5OFISFrjFTIKiY36RnZBGQzgjE/xuqc/ocB2Z6qMZmSwCqvR/QRaB8bs/Y1UZ+bVAdl8EZShqf1eNbaAmQJIaRVQpUgaBgZYlqTWsQnBPpMGbwRR93LSVPyNiERw0QcQBM7V+dUBFN6PmQ2MQayKcbeKoHxYX8T8ewfw0JCwoZscBKbf75mc3HVCrl5yhP6xBthrZi12arpjAjv5QwiAwvqtourtO2jbSNoxtunFZU1WloWxtJVb8KmetqeOi0lnWJrvaERrM1mnh8KKo1MP90UL1R5UXviIWU8/erJZu1GEjVZwSRp0gMjzpIDI7VKOR1ZxgcwaUPijTCZlZQjl98Fkq5oyQFCVVdbI+YjKb88J8mIDI0nmAnPjKeroCQJcRw5AYlik99KltA2RZxTMzAhYZpWPZNNIWsqVNScLVah9EZsHjBWkfQyLBcuWo8RNufbwlNacH96B06JQgI0fXt4eme8zl3tgdoDtQeech6IlcfcaD/l7K8Nrnpgyv53vFyfqZLNFrYXY01aTku7DDYPm4Z4m+OrT7OhqdK3wpsGsJvS4GZYK58YST9db7/ihjxj3+SSaZBVghquPbCjeiDe8ol1qBYjp3zZp2oYWji3JWK7Kaz7321ZTLWs2gO1B2oP1B6oPVB7YLt5oDwaOiHd/gyfWGInh7mJLwoOsFXXytE5sWZvPTIbEXzYDhpfMU1Xs43p947nz8+PaHAhjn3qgfezZtblJQWjVQtHPT3GIKpKgwuyugTtl6lKgjTOXLBogY8go73p6/6u/fPYtu/cRRu6upc+su43P/S/Hhja+Pnvnr5yj6X2GfQMjPXO622MDBM4d/58W9r9cBmN8kt1aBT7wZC4AGj+wgWOp5qQQcZXhVWLd/m3i8//02XL3vy+99z6uU9x18rd54/cP7TTyBw0KzvT7T87LVxU2OcxuetFvtwoJMi+1AjyLtdNeWcVr06SFBwo12ji64VS4MNY28k0dplwhazBY0csI5BVH8JIxV71cxMNyeFklPEFK6RNOT/N2p8qgMxWP8vtaGZzNbd7m2ziiWKqvkpTWosejZK1maaJIHaPqUh+792Z0nPPPbdKgCt2q1RrWibINYRJkFGLcl3yq3Tc6Cw6f6qTGc9mj3a9qOFgjGFY4D2qRc7EvuQlL3EBlir69KhU2Wk1UHvg8XvAq+elJidigNfHiXGx7skrE2+WF01/7eWF9JJGyxZv68TWvdmZ4jW0iTB6lqyRzIBzOxB9NHUo4+2WFKn0lD8PNqEvVny6yyFAp0wUfqZ7/NQXnPjw8OAlt9w0Oqevt3e8GEm74lKa/qho/g2xzvZ2f5JKGv9u5g1qkt6+vktuum7pq/fe5SUvePRHZ/UWnY20P2+op9wTMTKxcyGZhJYeP/qXfuyr/NOtk2h3A0/yoaaMV0MLbwQwoSUJSL8OWv47bRTsNn3yfDK3rY1LhXTuYsaAkj9z+zkj7VM1UR6ZLtYfcXXsDN5hM5M3m3qhVDOlM3MnpW5E8UVXKF+pcpYb16JwwQcddJDxoS2kkesgAKt+5IszBhJMoMASsBBcKDPsd2J194I6Bl8d0lAa24IRg7N5hJApL3bBPv/5z7dROMSiCaCOaw/UHqg9kBuZHccVGjH7+88888x3vOMdv/iLv5gN03JGGwt4wxveYL6/004LxebLlghsx5eXDeuK3//d//vQ8jQ56hgpfuMDX1q5ckNI0PCNri++9ZWBU0994Jfe94aPfvhbq1ZOyB4bKdatKa64NP399+cfaG9/YLc9ij326DzgWTvvu+++xx0370Mfetdw4xG6aHHi5drr1q1cWVxzlZsgimLDxNfq7t50aY/2u6NrdMQBk/IOH2MPk7urr1yd7Okt1q5buXDpRgcn/Cbp9dfdecB+yYD1g5e1d6+f10OjG7vSvs45vcV9d9926cXGfsVwo1jYX+y0mJxi0LmO0WJo1EZYl2E5HNq2aNfi1W/Y91v/s2LX8eKhe9DbnV/stV8xOtT9wP3p5k8/JDs2vsKoK90i0V6sXX97Asqru7p6001bg/YVtDtFk4ZuEXQxAjj6lB2nhkTpT1g52fO2GtlkMK4mTJbAKxFSZsvsK68cRid22GZEWiAiCrqqdOlC95R16WpTUllFyex06KcMkNURxeQW3k0yq1B4HotQxYMDWcUTn2ngo8gyZjoAF+KIp6OBn/RGMkN9ETYpS6nwRtUx3vcju537HXSwd3DthvVtGzt7O9oHBkfce+UIlhGys1imM8ZLDjWZUBgQemH32GMPY++777q/vbNjrNHRN2fBirXLB8eLux5ariYWXd6Q7nTVnDwCyrWFibeLKS6YGBlpjA6nWrtpG0lno8OBJJW5tHTCgWXRN8qL5dIv/jYc/urq7Uk/Bj5BNTH54j32xNJdrPjNXFJJUR22xgNRXSM2gjUu3WeffQiAcSGaSmISqmaqJ7FCFYvM02lA40RZlBouY92QHCNkXN5EqZBSm4TAmDmKvbbVt0YFiFc1RDVxVR/xIg5eMJupE/I8uko8JYyXE5iNiwRdHqUCCbIQmPADZJMEqgWphAgkYMcFg9GjfAWLxwAICTjoqTZDCe2cTymB+qmNG11F5J2KOX28B1XlDacwujoWrVk9stOS3q6uOS8+dUVR/KR77sff+d5rvv/VwzrGlo+3DY63jbnFce7c1CmnjMTcvCKGDfEUsxWNA/Mgw/nsr9A2g2yGEpNc9Qx29kPyQ3V+FPxBSTJAu0RROvbZXp7VLK9tQhbVr1lf5TmrI1+Je0y5m1y6B6vDFfKpQYyaRFy5jDKdTGkbI4MZWQVUWlwUCfDMkIVYKYqsVYm3IWzFhjS/i/7g/b6JFH09fYcccog56Q9+9H0fINzvo/Iw4OUvf/lFF13Ejc43Jns6ipe+9KU+f3zta18zcEK2ePEiy0ovfOELzz77bK+/7gCZvBx++OFi5yTTfc1jhYOXp556qmp52pe/qljhOW3nnXc+4YQTLr74YjdfXHHFFe4zcqHV+9//fseD3TRdXvDUo1iPO+44R9N/8pOfqAx++4SfPT7vec9zb8jNN92iP3Ly8znPPZbYvfbaizrBGVSLQqzC7miuVuXKK6+UTbdOe6EM/775zW9e/LNLWHXggQd6a1xbY2p/+eWXs//gQw4i57zzzjvp+S8695yfXHj2l+YvnPe8Fz+P9j33POiH3/neNXf9xLu0bNmey5bt4lPLfQ8sx3vX1XcuWjT/BUcf7QS+8JnPfIYohSuDylTPaOh74/U3zVsw97DDD8XFh+b+tMi4R6vxzi0r90svvRRGhTnp5BO1pez3gxO33nybo87PPvJwV0wqF5UN2YXn/2zRTgv9Kga3P/vZzz7ttNN+dsFFzh77kRiVlnAFwUXc4sPBscce6x3hUsVEPvg1r3mNU7IANNddc33/3L5nH6HQDucNDkRzw/U3HnLos9wFIBekceClF1+mgF7xilfQqCVXiILz2Ke+9MUyKJv8YFEC0hVjcqTaGForINLmz593yimnqOreZYexIb0XFjHckslalcGXCHr32msZ+f/zP/+z52F7qgbKqG9O72tf9xrEKs8Pf/hDWXAXFVPdxa9keQzyysuvWrBovukDjUcccYQr/AznVYCTTjrJBzXCmeQurYceXH7wsw6i1ElvbkFzxWVXGifvs8/ezFCNL7zwwssvv2LRooXcEhg1kxnpBHJbccIJL+R8RcC8iy68mKv33HNPng9XsEHhqvO5SdmGb+t0okJXxPyvXOr+17sTTZCGgmcEtWIb9r+5LEiORhsGUB7X7TjmOfue9MJji+JzPQtue+1bRn7yk289+OARnZ29RVt8L6773+y/BPBhPKu93tNwad3/Pv363wuuuXKoozjl5NT/HrBP6n+vueX6eXPm7nfQ3nqcffea6H9vuuWWZfvt87y9jv7JeedpqB8+56cXnvf9uYvm6e4H1ut/D/vOGd+7677bivHOPZYt23vZYl3n/fev1P36LQn975Hbs//d78j9Tir737UDqf89+9bLFyyZ/6oTXqbVPezZR+p/L7jgAp3Cq1/96rr/fYb0v7kt02QZURiQ2CwRYwAxjJ5Iy2aEIEY88/wXvZpjXG2YoRfDoj0MRgBR8Ghym5m1AyRFqhEjmpzkEWxmFEDGVwGmCsZmhlgAGmUHgIZSo82AqyxTwsjChmAkBBDGiCURxQNT8sqUvGscInes1WsHr/EnZOArvDGfTYgycXxwcIOLa4898VnPf/Gzi8brervXvvstc3784//c44A/uOPBR++4615rt5aI5GniTHwpq9E2ZAmpvehZu2b9XfcM7br7nm94056DI+cvWjDv5S/d+Qdr/OjBrePFYgtHFqgWzttpzZqpj2xlnwdg2BkFF25nIU9WjG8GpYb3eCnSAoBXH0w9Wv2GPnwipgtlTJZ1o5IElnC41qlZWeUZWTzxtvIKM0jwCM94EoiFrzA1g+iRqT+ApjTVeOb5L+PZILNRW+hFr0cIOZJmVt2kbvaPef7rwqkVD99O+75772sy9f0fnslj5kT0ypFtV+ZoaqC5CRqXZJmesNAUQF01kYE7+OCDzG2/+tWvcpTJglTBcqgiM/0xYSHKTNP0RInYNIXFVUrq3M47L8Vo1mlyfdUVVy9ektacf+mXfsmk41vf+pbJVHjmkMNNzQ/BaIJseuXWIdNV0xxG3n7rHQyj6PnHP89UyPtLnZmO+RoDzD35nxxmIOMc2bGzyyzvO9/5znXXXMOqo449HLuJDKXnn3fBQw8Uy/bc02xO1vzoyPnnn3fjDbc2ujtPfvlLGWnu/5///el7b18zp6ftWQc+q7trjt8/WP7wgzRecf+V5oasMqlXfHpDeeSiU158it1iZkn8c8+9N+qvDzxkL1OtBQv2Z+Qll1xy1dUXM/stb/m5mP+ec845F55/vnWGU049edmyZSScddZZt5Y/MXXsc4857LDDYDSz11571U+vXbvr7ru86sSXu0wZpfn1Ddfd6Hbj177+NWqRt8ZLYRzFh3vsufvxxx+vprGERtNks2meV6y2hpqcXnXVtfPm9Z900gt95TdTuOb6G0zeH3jgoWX77aVKmC0qwR//4Kwbr97Q39X3qle9inDelmvzSlXXQMUygtLnIkhlxANvfetbNWhMheRMVULN8YKQ/9Of/tSMW3HIkbJAptx/8IMfYFRqv/zLv6x2WYpRl8y4Tcxf9epX4uIljHHD13OOO5YrFBlF6uf5P73A/NdvUsqRIvjKV76iUpnDvvFNb5B9bzEboh4qiJj/yoLhivmvKq24zXYVzc9+9jPTWLVCL/asZz1LxWCSz3aybw3HioTMcpHCvfzSKxhmHcZ8U2mqXXIU89/Zv4OtlNGgteLlAlKqNyjDQeYxkLIJYGrgpxOVhWOMgAuvNyU3uWhCTibINE3suEJvEOACBA3e5LdKkBRP8LiQxWPwViWE9pCTuQAZrppBlCBJlgWw1MgL+aFlSkbIYAQIwZKVEpXNC2AytuLKydLFlT/f0FzL1pm6gEZjYuczojCgSkkOJ42lfrgxMtYYa7QNNtq62zrELvjpGO1M33/GrNsXI8Pj6ze2DY22jTbSFh375oZTabsLqAPz6PiYb7PWon0AskebVAupYX9rHNkJfIZZEu4qTUqVB4AmzBaHf6iGj1CVDINAnGyq1L0mmgnO8h9J/g35TfjQm4uewMCENHgYLGFk8EpKiidVExtk4pwFMPtDSI6xZzgDwR6MuALvMYgDn4kfDxBGEsgwtQ4gEJjilguwIimKaYLm8eh+fLwsF8KkWUrq8EVV1uLtHE7Fh13wsbLcsDjxWWei1UjvSVkJ2wMRXy+TqlgoUcvBufjIabdtEtLvYqGZLNZS/rh6BkmjagKI0D5e7oTsSJ9U/R7RJHraf8Oeht2XKTTTZ0syf/r1g6QvPt5tog/K1l2YIX90otwn6NlPSMSl5Mh9qort5WXTLsiCb6/WlvQNn5cmJSRXJE+6AQzeO5LkJI+UPziWHlLQDgWQMxJK0QeQ8UEWcTYsAPHW1s9MP2FYVXoFDu3ZBorAZYFO5MhjtiFSCZxZJvHBUtFTtJZLNbWGaw/UHqg9UHug9sAT54Hc7T1xKmtNtQdqD9QeqD1Qe+AZ74G6/33GV4HaAbUHag/UHqg9UHvgqeEBpxeyoXZaePSRyVYAC+7xgdOX77z4jtJSOAwaGwt8Ps801TV0OxVsmwix6GP9HVd1GR3Sh3zsMWqSJNDepMtGmcBIzXZOCk+fIsYLl++s8ytboLsf2dDf3/HgugeXD4298fiTba341Kf+5ZHR9bb0DJe/Lz00uC6EdLYXjAQzYHBspOoEyJDv6yqbgz7iVW2rVq1dba+JPQe2mFx42ZXrOwf99ljHxvT5dm15AVZnI7FXL8DyKMT2ppAjL1Pmvem7o7NDKLN/eIydVT/zjM034Z88+GzyIY20481ykqlr12Z6j+F52lUAoerqSBVX6T0+hYLNIlXn72iWc7vNLrFprFq4M9gZZREVuInMWxkYh2rUsdiXlmkw2hnjMWqaHTZ2rtjbZI8OjE3GklQAPxAHiHql5oRVXvaQ1uRMBDa+2HBsd4tNNnb/xC/mqUVC8Np6ZeMOmU/dWsT4OtQe2K4e8L5k+V5k7b/uqel10yF6VQUEgvcr3kqvId6QkF68MmRpGQi81xAXXoAAGbwBZGLdX9BkzCwB21IOPzS97178dX7NdOOIzUhDm/ZUJDFNumYjWSOJ7Kc//al9mQftfdBVd9/c3t4bclI2Wg7ZhhaeqeqSoziUW81aJshAkjfrkD2JSzs5G76sKIgxVu2ZjYSnBI2KqkNp6oZ2NMu9R3aaetGa3rXp7JSpSNJFql3VqqIQI8n2UNW1SaBCtxFWd+mAAcC5WRh7Xh3OhPGjvt64KesPjXks2upMQuzoteFVp2+TK2LtRnTfjGEeFbYsT5edGl97oPZA7YEdxANaMyEa1X//93933sCu+mwbfCRp3xwnEMd0IxP09bmft7ji8tu7eowx0t8NNziQM5EuyYkhk6R/+qfPHnPMs/TVNoR2lSOERlx1bSdmucUR5f33+hu95KIH/HS3e3X22uv7e+5b7Ltvsf/+OzsY8OrXHKGRX/Vop6MCd9yajrJc/LNb1qwZGxouNqwtBtNQpVi0kzWEBDz44Fo2zJ1XDI66zKd78eLe9q7CnlUGOKwxpz/R7LprccghTrns/IIXLD3zzOv7eop3vetd6zes+NSnvrfXrsXxxz/rgQfuc36grX3xV7/6U9uAscQY5VWv2uk973nPw8tXWw04/WuPdnW1/9xbj9BNDA/sOv/U+d/8n2+7ooupcVSKRkcd+vuLob7iqKMOWr3ulr32WnjAfid8+tPfndjamWyZCFytLCafdtB/oz4wLgBxAFVzYSIjOWlTvtK++onB3ub/lAIcd9s8JFqjtRKZGfXauv7o/SEFAwPjCsDm3Olp5muVsIRtmyycFEHmJPiE/puqQJmPuFcqdOeNwHZMu0/X0SArLZYXDEKMbTY88ohxjjGJc00GJGqmAYyzMcY5cuFkkeULb25KenRtDFe8Rw+tWMmHzl+l91ZwCpQDnYHs6V60665ODRlMWp1E0xgcTjV843qed2jM1TWJcszlcBZASnNVd3/kRAmUpZay4Xaf0VF2xum7KKBcTBmYMOAJdfMzRZniVoLqhtYs5nRybk1ShYcXKxpVCKDaOLI1nV9UMAfALHOhRBMshMN4xBiv0qYy3VxQEFiwUh9yimqpBSYkY1qBLNC9OShph6E01tYiC61crRjGx4pu2K9uo/F2eJUCDnwrY5ChzIN89MEiL8Eld961KXkDyU6A02iyINe4CHQLjAnNzFyc7ToeRq5fv0q3YrVQrKSctevvfy6l2Wxiw6pWgTxGHTx67YZqQLXswIdhkrKcJnaUMEFcpcHIEi7VvMTb3cToUZ1hKgLs4SL0qb0qb86CkdTK1YqhSPMe2q0nyAKY8SZircStGJW/1TPsZxXLpbKqiSt0qTOyIIkuMWJmmGZqdQmsVokm9m3ySCkt1qBYKKdOXV57fbrfkHCVnxkWgeMtcAdQZ1d6iWTEyViGhf38rOa4JskhTwWRrXrnO9/pyiRItbGrt8s1SXSZtDolG8s1xOJ60Yte5KwpGrocXpXkvVNkIT9UuHLIMVRXTuhxXH6kZBwvd38TFgc72T86PMYGjY9cYEnDyJFxL4IXX59C0Q033DinHJM56qkOi1ly68Ct9BLlEK8rsVQAh2OVO+0vfvGLjakcJT337J+4gGnt6nWcQ52bpK67/Mo4S7z77rs7MUvvJZddyQwvkbJzjjQGt+xX+ro1jGRa4NIu/fTc89atWe/rgNV1eBhnUBksCx4dVybZb7RwwuDG9OMT3kq9YWoG0xB3BBlGXrUsf84557R3pnYVo1KTd/eIXXPVteqSEnHdlfrjkinE8sjzRx55pJaEhHPPPVdFYxnJzlrzhiO7DsF65BMZ5GRFrN4aSGMhCpcsyNfG9QPIFPG73/1uRqK85eZb9cA8rIAcYOZJwwPO75zTyf8scVTbJVZKVq6RHX300RY0aDG6JkoeWc7hnP/d7353aGiYaiXycz/3c+rY3/7t325Yfw9TifJInY90GiUafebgBEqd7nYAmGQWcr7i1obzGCN9JmOPH+rgAfirrrq6r7+Xx9Q6KmTcTASZ9Zvf+Z3fYaqKJ79uXpN37nr9619vAYe64cERR4LVmZ//+Z93S5cSp47zGeYyX6evSaPr2quvkyOlKeS34IkBZJAxKn/d/6o/XBFFoN7yvzqmyAKeoWiQCYo+igx9sGyx/yUzmm6lT7U28M77bl6423LVyQDYjRD9CxbMUA1Ka+v+N81n6/73GdX/Dtx6R+5/99+nuf9dNNn/GsKtX5X639Gh1P9eftWVt5d3eZT973Fe7Usvve5J6X+LrtTjR/+71z771f3vM7b/1byryXoKA85o6qMr0S9E1wypGuspjDpicDJljxDzXzRSo0MXoxfD5I5mSl6pdOm/DEhYkmnYwBgjKAaEnJzUBJi4xb2xhIDV7eDFFR1cE/2Uj4aF8MEYBLlLZSH/GMVNySjVACaNdScXDXgjnLZF7UEQigwIDaHn79qg6NZb/exBCmY0xDbpzWK5q6+730yno2fQIJ8EbYuBHzyZXDfh//bCSLWzc2Ky1iQwZxmLglAKGNHAC1QH3GRDPJKPBZmiZ20gA4CfoeywECuU0/x0bzJemTXYlsR+GA3UlEqbkBrSGL7CG7oofQYQYh5kGqI+6J0Z08SVH5VdjJcyBiBfvEGUVDKrSU2wwgrhLKdIkOvAUN1EvG0flTgHqmlmOvoX8yZDOLMJg3wG8J588cP4qEvER8WG+s997nMlMZWLsMuaaY7Wz4QrbDPd+LM/+zMTE1ceu2sJUqHISExRZXbcPS+d7aYzJn2mQghcHmQOwl0Eqjwhh2qFaNLnNiKX8zKMM9Ew73Wve53yuv32Oxi/6tHVJkQGD2Bmm1CbgilQeWEJO5nhuiLTZBMieVFbDLdUZrqkKl8vJsnnpwsce5hBHYw5lxkrGtkkigqFYqZ85WU3dBbp6qiTTjzVJ+8LLjzPdYcdXe18aFJmOkkm+90s5q4JkyPS+NYL9eMfnb165Zr99usk2evGADcuubqHanNP11Wb/wptbfclh4+Pm3PJI708ZkYsUMddbnpyEbb5KUY5QsYYMb/Jmsrjaw5TrQO4AItPtCTPec5zvFxoXOfNqnhTfuEXfiFN3Bjf0b5uzUZCvPiEYDGPGB1Mq/38oLgpMhlc/kBaGePhU045Jdxi4OFWMvTxxV81IM002SoHs5UUjD0zHIJLLOPmv2ag8i4VIwIaiTW1hKGRDW9/+9vRfPSjH41qwGyOZaqF9LvuuNtqAOdYnbBMYdSBS+Vhoaup1DHTcGQyqClWYd7ylrcoPntseAMgX4qScBXD4gP5Su23f/u31U/VmKhvf+s7yKhzgbX1kKiK7Dzw4APMyuWOh/lzdbEG8oMf/CC3KBQ+v/7aG/gEMmx+zHFVQhWeWSCHcN3MNFOm4hJkPEsAoKRakAQWR2o8VuXARCBBmwBGiTEkTCY2/xsqshzJ2L3pJERSE0HYkOmrAN54DL3KLrQ30YQFrUgFhzGEZBpA5D0wEQdZwFlFBrLkwEwaldFTAKGFAeH80gNpmUtAPW6L2eioLaqaIxhwuGjCjFQmychsPEDeMW4yKX0snijBJvXB24qsYpp4k74yVGlCuziAalIVnmSdon5WZW6yvMxFZIdkonISACY/SiIhaw+WqANVmqoxTTCyTJkZm5BNGpskPIZHNuNSoNGKhrrQPt0FWOjDzuB9DEq3CUt4m6lhrcfsvSnlIwvLy58XUlibmqnJHxxN5Zv+nwz5AqkSMTEajMQYikwSTvzrC2gqoI5kydjkdvGUVr0calIDGhdgsWostROaqSmaiyb5M9jTRBmPj40+XYeXwqZ3pDQvoVgrgwkqg7rD7dra9NRW+eXR8qIr5NAcVbIgTKHkK6NJeBOy7K88UhfInJSBTeyTUE4CZMZIzEmTtFP8m2kyMAXRJKqJpkldPIqDHHGESe7Z/vuMuACLa2brj5qu9kDtgdoDtQd2AA/U7fYOUAjbwIQnphzrPn4bFFUtovZA7YHaA6UHnph2u3Z27YHaA7UHag/UHqg9UHvgsXmg+qnAFyaBHLExjI+I8SmxOp6JL4toAFukCZOstvtwBa7KgQn2oBGnL5mbnxhEkzG+9WZKqjM8PDacPtaUf756DA2OdY4WR8yf/4pjn3/593/85YfuQjmvrWe4sBFf1tIJvvShs9PpuPaRMV9J0/TXLwJlgQBKE7K0uYpftX6jnxI6f/k9O1143vuf88L2q2/61oMbutx45X9iSiFJVIPYTXwpV+VX24zKrqtiIKtlEXD2GABGyBi8dniIo8gkgQlpkuMRS9CEupIkIeMxc8EEMYDk1uxn+qcQYAeJ2x98FN8xbY5SUD2Y57udX8yz9cRek2rpTGl5bHWaMglSqj099rW07gazF0pQWz75yU+eccYZxx133K/92q855SJWSU4//XRJdjjZH6MCeHOjXpEJgGnSiwClzUnOlnzhC1+wcc1ml7DKDh61KDLiXI0fuJPUas90WajxtQeeUR5ofeVhWpFew4ws01PkNczImZ3WRBaPIaGVka4I5Ff731bKorwmwHFzSTYU9Hf3Lttjz1tuvuXOVSv0rLZupM0GeU8CqrLzycZsQXipjw0CFnsBnZo75eBDHrj7nrvXrNVslrtEXEmwqUdzLSZtm543tzgylbVLzDAVQZuBzVmbnzBm3klv5Xw2E+fnWQrP9E85IHwitoVXr2FDqrBj5iIqlT6RebaEuqMhui2luUWD9ZhClSxvvrHx14HV1v6O2EDaMIpALwlw7vfEE0/8/Oc/b/NrWBIy2Warq52sHo1hck2ratQj64JtCHaRpR6WXufoMFYNMwRylDT3v1nO074eVh1Vw7UHag/s+B7QOuUGyvGDc845J9uc2yuAMQMyrZ8ZsFYXRjw46ORSw0Nfn6HCuLFI+0gxp9McOY0+Ukttpj5adLYV119R3HLtLRsH00+Ajo3PtSbg+pz2Ntsq1/vFzzarA7aiNtK5oLFiFFPHcOPO24buvK240G+ndj7c2bmyrevCXXbpPerYpU6SHHDQ3BeedOpf/vXbnJq47/7bHSm54vI777+/WP5gsWaD3001wLDtNF3HY996W9E3PtbmV4nHx+ZLGBtb53at+fN2bisevOueoXvvf/jjH3/FBT+7/vY7iptuufTUl+091la88jWL1qy55acXjd9823XHnzB/3O/RT9y7OEDmj85a97KXn/Wzi269/fZC0mvecPiyfcb+7/+9aWzjTR/527e8Ynivb3zlnvHGsBMggmwNDbmwwGVbu7z0pSf++Z/ect21q9/4c1e5L2ykvG6oGO/JDn9KALm2ZCDMrj7mmtOao4nrqCbGfSm9ekGVajFFmBwlUkGyWG0EBIweMCGnQjmFnKlQwTtVyqZhak7NBmTMDOyZZisBefE3MRyKDMXqkbqUFtB6uxcs2WnRkiVrBzZ2dHbMXbxTe2/fyLp0Ouuqq29Yv36j2cGDDyzfsHHDuvXp3k/4u++53yrH6jVXWHHaOJ7c1bVyrXhodLx77txhZ9iMx7wh/f07HbB/OtbY1+McjkFOmd+0Etjd2UnO2HA6j7Hnww8bU1lFMVK6z2V5ToTiFawZquhWV5K51LSnv8LdWaPrB4c7evpG09uQtBOLPL3yqYWow/byQPhZkSl3w1Gn18BqgiNbis9LBOkUlrNV999/v+KQmkqkfMtabVLcTpQ5uOX0mnpl9cl5LedR474SA2kVhNhQ2sROuJubBNqtX1VT3csW2ltfpRBlRmPIbUaj1hmWG8M70wVmA8vNT2OEX5XZBNPONlrgmU0Ch8Bgd0IshARNaGz1gHw5Gic2W7Fe5/gcq0jgDWY7n8awsD/nIvuBnzGyH4Cd8xFTx2ze89J4nHzfN02CJtkdvrU23rjnngf3WNbmUpgHH7z3ta99bWPj3j8+84bukTmuwntg+XWj4+Ub7JbtzrlNeY9HHaV5Cmud32M2S8h36FFxqADKXU2Qoyl5UWJX+iZQcXgyyGAc55OkxZDBVl6M8sjbuCjiJeXoSKSqAskh6gPt2WNNEiY9kJbcEWPnPUdeATQSxR5uVJSyQ351KtckSiWJ1KquEMhCoYneY2inGsxI8tUTFY8llDqNSabzh/HKtLJvQwzvMYM6NVAJMkwd5nCqZccRUG7s6kmLzPKodsmOcZrcuY7KrVjOSCtZj1WT5ChEcR28XgYQrwCAOtLk18gKI3bnKnkAgbqqEaCIGQIM7Wbx3EJOr2tEy0u4nOfEDlY63b2J2NtKlNPvbHZjkaJUiLRoQ/QVNJJJmn6Hb+XOeWCGEOKAKyEY0wCzvd2VZAg8smfpLkvkwh1YFJHGthHXQpV3lRrFPbxy3dqNIw+vWDlWHm8yb/fakm/Wj15wuRLfsuTyyy/XODgLTW9kzWK+tyOcw0iVTUNBe6xXuJFK4GRmi+UII9hLZAEBLxbEJKswjIdBQOOaVWst0WBRminjZSfIM3LBgQpO7gY3PoLdgW12cjsabdWuu+4igxyozjOVLgagVxMYTwVvuMeKUhUAo/crvVnt6fYr7kLJBlrKpiZd6EOapPQ6pE44fduimlX0+ibiUSpXsM1rTiPK7u4uyx2I5QVSLubM7aeacC8CQPbT0fGu9BMdaDQ4rmxQeYiK0kHJgffdc78iQ0OCBlBmtSGdnbIzqDjQaOVUGIB6Qi/tKBkfgLxL4j2lmfxjrFRetCfmbVlLrujt5R+Ayk9+uDrRTb7XgJT37RzkggYGhHPq/pc3nrD+l65qEXu/VIkrr75peOBrxx2/eHT0oDO+edv8+c9a2O8K+8G6/219FaL2wnvjeE/jo1mo+19ueXr3vwt2Wjw4OjJkeaKzWLl2zTHHPXdgfPTehx8yOuzqbJ87f/7CxYsf2Lz/tY4TLTzArM8aT3sx8uij63TIK1Yst9NBO/gE9L897oBesOC+u+8pxsY72zpGBkf23GsfbW/d/z4z+19tV27E9AXGz8aTBhtmoMYnWjOx4av2zdDC2AnQ2gwGBrsxj6GvltDAgz/FJJCpfzfWyoqmlKASmisJWMyAMk18kyJkSvbov3RbRlDMowWl4Ry8EY6RlYGNkTAhU7JnLXjDAHk3djIoMvqSKi9yJNdEGV+xzZAvc1UBXIZqcg1gv7Ei1xkVGzGSHIxT2ZDmsyh1r+Z/g4N9P/nZ2Z1dba89vGdsbK+Lzr53fPxZD825c9DGlfY2n60tM1syKnd3WSAuV4bahsfGC6tcd9+1fL8D5914/fUf++h5PuGNrj74s5+9bJf5pwxtXHTvAxdbJG60p4lMzJ8tUjeN7WQ8z3+RyayButEpD0QWOEQuqlnOsHzxsCEuAgWhuCVxKZmqjVqRKVuB8AkV7qbhdhLM1whRgvymQBVuK1cVE5QKWuB8I3Bj+BhEsZ8Q7mWMEowyrfJmmAFRslGjAh8dOplCpqwCjDfwpiKQDND7i9ksLxxCWnijyrUN4fCemJPVc2sXyot82ZffMAxS6nhauxo3L4vxibdDrXarlYv3TYgkReGGQBK8iemqoDlpuqE4BAAXidGYxsqjSmLEbhKBfp1f9Bwv0GOMK7HknRY0MNi9C+Fhhikgs0WmulgJr/kvStYySS4SY38PseZW8eqhkQrJ2+YvaiaMAEDgKzkW35QtlXa2pfkv5wfj4iWLlL7phWaNAWjWrRryi0/oVa1HVjwwPLLh4YcfNLEeGEj+oVGrxTYE+mnmMYwEU0izXbkDywUPEKV2IUuEfvest1euAfLLcs6xBlimlhTpp0cWolEDza+1qHSh4QrquFTTgRFmxYpHFQdAkfESRWTiUhVVJ3gwG/jTtdo+04cx7gsz0587d77F7eXLH3ng3vs0E43Odndu7rJk6c033EiRHh87a7wg9qt4y1z15fYrhnFj2MAnVhVMKqVqymjncCwA1UOlQmD+6yvYhnUbg4zH0HCO4uBzTR9d8iJWEISTLERLIi/aHV+wVEIlomKQzFGyRjguWsx/OYc0SaR5xBXtALyShVdAYGLVZ+bRzgbsWhulhkARqF2qCvO4nTGEoNevRR1W5STxqhmxisGlqWXdRhPesD9V0MnulUnx2BSjVLHZJgCYgUBelFQUFsaQhqCVVyqfB2PkES9KGckBTQRyAOIcCAy9YnKSE8oAH9o9hVKMCIIREHIkwWSzCaEdElfYAA7VQY9SCDlZLAkw8GGDsggJWABSQ0iQoRQCCY9Lrpkd6vBKyqoRRAjtYQmCkEyM1BAbVpEjxPoP2VLpiqSQEMRhQPKMEmk0NgyOdG8c6rGyNDTWuWG8Z1QNT25UWi6Is8S4fv2IBtLfwPCIv7H0YVdLKXtJ6pibCcfH2sfHOsbTgjOl4T1AfPULpMemAB+BVYzE5RENQAggMFInSMt/spycdwReCo+SotwB3CiW2SovOMgAISe8EWTBjlEILhj0ZAaxOBgjVVIE+DAbJV7IUkYCUEbusoQMZBWBISHY8Ya0iKWSkLkATYzVpFnCYZUYvTqTDaZx82ubkryELEP4AXE8Pilx+CTcywCP4BksidTgioxEPANLubVB+lRiuyZ//qfC7xXw1GaAqr5Znt4UqvAElnYzkChi1ifshHc3sTVBM9nTRFo+Pjb6STem/rpJqlez+tV+ogKUL8VYo33zox4TrO1dXueQ06Z9gA2x4+MTX5G8cUGq2xI8ql0ZGfRRUlVksIij6sZjLtbZ18yo/1naDECrcJjQnm0IaRF7VYUmghnk56Qt1YJMWAO1B2oP1B6oPVB7oPZA7YHaA7UHag/UHqg9UHug9kDtgdoDtQdqD9QeqD1Qe+AJ9IDle7ft/OhHP6qu19vJlLZulN/DIvZN3YftKo2P9zBSfcPzLcryetBUV/Nf9apXnXnmmTk3viX46G7LS8YAYOjKq/MAGHtZqrpgQrvU+CDxile8omJz+iYxd9HcNSvXF/Z1OI0wnI68HdYz59XC7gd+5B//+oxS5bz5O/mVah/m2gpXVY04W7HT4gV2C1n35wfXYO2+626uD3jjG994z333dnWkLQj45i9cYKNAKaD8uuTXv9v85N/gvGJ8QUfP537zN+3J+JXvfdlenY0+IvuaNJ62ai1YsHDNypXtk19giPL7Y2eddVZsWwlps8m7jRosxB4fJwDhn7At5Jxyyim25odj4YUpfTilnGp5KVOeDwwhNEbdAE9kf/NPOBm5gwPvec973Ovkd9ts4NhhTQ2HM08Nd//Uv/7rv5599tn2jsDkpCbjbSvxQ+WxoaSa5N3xS3exHerrX/+6LS/ve9/7gsA+HjL//d///Stf+YraqHxVla997Wv/3//3/z3/+c93DRZf+QVC21/cVKVe/cqv/ApGL7L9Q950G4b+9E//1E/A8aSarKqcdtppCOzACHY7adjvbbWLixBXa8U3/nid/To61TZOqYpVg2u49sAz1gN+z9OrWu3vYj9TOMRnaY9CdBzRFGuZY9eRVGRSvYmQ1YYa/vDDD3feL2hyUrUxAXtbgxcQXUxTQaAhn3m5X4Cxme/WW28FIJ78RbKSr7wAa265h6iraNt9192fdcB+Pzn/J3pFuzVH7cNwpC3dI1Hun/C0+aYUvfCv//qv/8d//IdNdZGdUBEx3vwIsAcL5kMnv0r79uUrL9Dp2pNA3sRWhdIceiL0tHf6QVEt0obBAbwMlF9BpmStSf4k02z/PfDAA+3/tquPzYJc2ChZ7eizoCDwSHUgs+pME8URNOqGEgzPZ4KnBJCcPLlbyyWMJ5100uc+97m0022qA8k7Qo6iRLxW+tN3v/vdP/nJT7yVrI3XJ+elaqpxsh2rdos2FZAKacSYdoUWxQc+8AG94Ze//OVgjP73/e9/v629xmx+MVXHqqN/zWteY3Oqmu8nZx0TVZf0xX6sVXeJUY3yO7f2CHov3vGOd9iA6wAwO5mXD81i1wXroI0Y9cJie5ovvfRStmXzqHvXu971d3/3d039b2slzNmcMuM5tQZqDzy5HvBLyE+uATu4do3Gy1/+crvn/+qv/iq/5gG0vtqtmCc3d9ngJsOirZYaBJNx2r87aXC6gsE+6njUEXXaAd1mW3C6otOoIMYdqGO04CKpEt3vpqzxtD5g3r4Bb7sjTk4vFOlQ1nhb2pTc2Si3oaeTkzaQlru5S7qu3iScwr65xW67F/vvXxxy6HzDpH32PsTly5ddcv9v/MYn16xINhRkNBobB8f+z5+89uDD2t7z7m/3tafjIl0d6x3y+sjfP/uii6497QvFooXF5z73q7/3e/98+23FyS9a+vPvPu53fud7xx1X/MqvvNOdBueee+5pX3vITQ42rsudQxby27949JOffMtf/sXXbr4+3fDzpS/92V33nvXtb184MtjlapIbb7r6tNNu/7d/f/lXv/qDs75b7H1A8Td/+9YPfOCrO80v/uZv/mbjyAU6i0suevi669LVYMnOMYspxV/+1e/DX3nlteQLlYLYtLsa2RMfGKNfMwj0W+W62m9+85tKihlVI8Pais1bNhP7lEQzCwmlaEyZzY6VZgiBF8CZPQNTaqkimxirSVPCQZ/0TQavg8A/fiXeUZbgkghgRg7xOKXMKjKdofFGlNumvRLE+FP3vC22vg+NFO0LFu+7//6WAU35UXKCcciGBx/SBFlWSkY1yoMNk8NC5SXEBIcxDo6lMzpJSzmQn7eTSzLmukp74QJnyfbefz80Y13pp85jkaHDhRZOQHV3wzubKNCblJTBCMeC2IbhIdofcduF82kDg2wqhte5tqcYSYfHhD/6qw+fc8455jKLnG8sQ+DFZlMpTlflb3YVWiaoAR5QFlP6AV6/Y3RqRNpKELNd/pZkruQUlkGyQ1YxaYI30DWutjKMwGBYQUfJiqvS4tH8tAmPHpcjWEQRYjGqOr/OEqQG3sqnekWIoE4iUHtVWotjgKYpAJqwPFg0Qex3bk0rJAtSaZdECAnxLmSNAaAJArGuGWMM13M2WcVmlsR5SOpCozjzBgalN46FpgMO+JlNcB2l/MmBcVdO+Adjkxn8o1lgMzsJQYYAu7IjIU7TTHaV0WEmAcmUVHCp7AYHF+6+67I1624s5zXrvPfLlhR33VXMa5+vWB/uXLVh1Wj/ooXMi5vouisTfvYIlArUoVdS5lOmTlwa/gF49zctvyf9mwJ2HsMusw7dSQjbxPC8ZwYkj4HfxFaSeUQTpUO15sJ1aSZu8GGPco+6wZIwRhKNEQOk8rYklLFOS6CA3SySzDjcqwQ5J/MCclBFlRobsHCRIL/mcZYpsFCBIFKxhOrM61HLZgaKJXLNEnWGQPhwIPx+++1nDUETFznN7FsE1IFTTz3V6Wh3NExJTHjgZVblX7NuNfvlOpaFvTgk8MzgxqE999pDDQ/b1DR2KhQxAkDksapC3hUHsZyJxuOG9Rv75/TRiEtMEY9lh4QlikOJRzaRCWTiXb1yjeOaHEV1UEZMCAJWefXYILDWozogFwBk6pU4Soco2fS+SPWOINBo3H33PeX54i4WkjZvfnpnAeoD4wc2DrqJCRfV8NG4qc8A+WIA4XIhSakBYDBCRkYAMFEJwXwVuX74oUecSZZBmWXG8uUPL168iElSiY3XWWVW8dAwQwYJly/0KgaZfMshMPTSuHb1OqdqkcGg15rhRSynKPFGjWKJ/nmPZbvff+8D6LmLByTRyGBAZIoEeHkUVAMyOQSSLgRy4XWOBtP7Ll9R0Exl2Mb1A04Uo+ExxBhzYI82Ci9RTIJHjwu7JI8ATghpkU305LjYSx2GYaQspBOYjWKPPVMuVAyM3mK6iGKPLBPOPG7BwjwBDd9yaZw3DnXwqWZu2CCz9KLBKMvOUYvpxY5yeGhEHWAJSmRY2B8Squ1GsIurANg5YYs2Foj6e+eUic1RmNeMLfvluv9VCuHqJv8oKcUNKd5B+t+OLif8i4VLjBkcR9SAFIvm7KOyjo6nO0S8HDkLHoW6/+Wcuv9VK7R4df9b9791//sU6n+ZmttzsMGYyYJBQgwkNO86ppgKebsNRdAImSUDkIYTBjaCoYtBrLGH1sBwxdiVEHhjG5LhM1cVCHrDSLwkaFSxIADQC09OlT7gbEwAhkDBLtWwh96wSnZCWuRIahOjIZA10pjvoAyyGOAxxgDP6FFeAlP2es220Cu/AmtjiG7Sxx43Q5n/MoBGJlXY4gR76v0JHBgYXbgwXVDbt7Prnov929N9ze1jZsHFne3z0LSPpQ0thIjDgLbyG3ejGHAWvr8r7Ypp73qUu0aGh10QtMuc4sEHi8V9O7Pn4c6O9atXt8/px+iaF9K6J+ZMyRwyBbkTCBGzkxzeCwAXD5gvyAvKxLN5kIpGZvMghz/JUWQkxJSH2cEUxm8uIHmA0yA5yvgfI7M9KndJAr1iJlUNyEgjf/SqEL1YMCo1MBYjf8sRypRkKqrs2YYou8i7vAjMNuJ1u1OwIEjeKed6OSPYyfdIr1m/GCMLuU49R2zUB4OAJT76+zr/k5/8xHuU9c4G8NbMMH4OCaw1VWEAvQNDaf4Ys2+ZBcAzxozG/JcreAZX+IFbWChVNk21CMkmgXnSNEd2IFHKWhSBR7kgQWe3aPFCfjMbpRpeBeZwGQ9iYrGETPSQgruTFi5OmzYJ4RwEbPCOyCnLwZERkyCpUQEQg6OA0CNT2kuW7IQGgVmSbQ+kxSwSAQyD5VSteOThFX39vSon+eK77rp73rxUQ9gTWQOHDQASQov3HT0JWGI6RinjcXnHCefzlStWmf+GtyHd8aQeIo4FHIyqDRg+PBCxjJBDOLfThcUiA7GIhwdHzGdjGsjykIkGo5coHBLeIwGZIiONc1DyW8z4kHGF2o5LIUbJui3LRX4ut4J0jTWnySAhbIiSlQuB35Qgkx5e/oiZKavIgcfFRQKM4DGQuUSCQJKyg9ywYeP8+anV4iVaCHzw/ocUulwzmLW0UE2IaqOOYRnYMLhgUbo0sFoo7Ax3QZLMAFxJWvlGQ4b92Nmp4oW1Him1foVFI+x2MCv0sqlhUSgq50MPLHeXGeFhLTnoWcuHVACaglSfd30+++EPfzjz/Ld0T4oUgcAS9pMWYslPpVyZzqNhGDKAklJeYJlS8cTBiJcBPIOR8YqbGyGz2cHOk9jJwcgASOwYUdKLFxcWBoSXogQRBxeNLMkwPMuFUISXEHVS7RKHSeRjQSmm17sTZnsU8Ib9YQNXU83+MCMcRQ5KXCoegwMO+zM7IczGpc4QwmyWBCAJF7+F68iJ7IfxUsN+LLKMJbJADu1yJi7vME/OVxl22WUnZvT0Jr9pG4IXy5o1qTNau2aQkNWrk/1yVEpIfu7uTMtBixenVbi589OVlwvmppvTFQP69WvWijasT18BVq4uPTCQqnH5M8epVoTxuHq6Um3BGH5gQ1yAJe/J/vJuZaqF8D/e0n4c6ZK+8EPKfjlIKM1L7Qx2GVf6OSYBkvxEnMrOv6nuCfwfzg92NLIv4I1ADmOoliRwUWgnwUstDgkhJMhYG7xhOXZCgjfYceVaB8gGsIGdYTleQnKMnSKMArMDEDOmzFEaNwLEUfeyEMDoSPqBE6Lki3xGhp2IBUrFDIuyIDMw5MAzPmwIFjEtubxwJeK2RJlDsIuzlsBkglYgCLKKbU5PYzh5wuBWCyoY2oOMPeAIlfRmEAGUOOpYwM1ElefHQI+FSUokDAsJFZGbgY9BPn5c28/+bSI/V6dGWd+idCLO8mf2TLhpa/0TXGKvT7YhI1sB8r2q4rANEDDKzA4IaV4uzUW1hfECRpOCPnj9nJy3OJqReCtDTnrb61B7oPZA7YHaA7UHag/UHqg9UHug9kDtgdoDtQdqD9QeqD1Qe6D2QO2B2gO1B3ZYD1jmzrbFkrfHKjKnBtBE46NIhLy8HuzIYrncIyAvymdpoSIrCrERV2kynvxMTGMSW6TPitb702+f+ETYKHZuFGtGi5cefvhrDj/2I//v4zeFoM50VC9tQ2qIHHBFOvHNLCwnJgRmvawFd7anX+62g0no8AXQD3h2tm8YHrfT4cG2odO+/a1f+qVfesP1l198wy3XjfMYK5JVPv3HpqnEVnqSNPKz8YHMcQBNGc+pmSsIxD5dSI1AbGzmCOdnmsyFLOCMyTTVLIMFxRSp1aKcVPVU+jc2fDj/+Tu/8zvufXBnkyLIH9iexJz4DhrFZ7MXI11xwphcdvzvuLJNgS6l+sd//Ef7G3zFn9Jau3PccROvlYLL5UW4DShxAZYP1fbPTcnus7db5CJpzz33dEoK/PnPfz4wvnUJATskYJeP72T2SzHeHhcbMlhuCwLgBz/4AeRLXvISF3agZ4bAHsTuv+NwcMghwT6SXAkDWce1B2oPbNED3hqvFbLWxlkLsEX26QiyNMKzilbiIIMPsipBHD7vKE3oSr1qIdY5HnPgYc6enXXpBWC/z8f2ro6ycyEkCUoyiE3g5M4e390Tdksh9pH4Ho/wWxec+4Y3vOHUR1dcec/Nq0vGUvCWRGyeHg7UOzDANh1ANmlzwq1+igzmbGaxMLRoYCNpq+U+FRhiLHHssce69/Azn/nMdL3Yk5gVHahOUP+YC0LXaYsqa40ZXCKp/8qddaudyu7II49UeXSyua9EFltpg16Jk9DKG+MQYxJXWWHXpdrNrEpfeOGFtnahVz3CYw5C6OURUGdTL2KvVfTpdp4dfPDBflEW/aGHHuqopN3euORC10zv1VdfrT5n7bFT7Wlc5XJOa6D2QO2Bp7QHopkSC9FaAjSnMZswNbdBXQYDWcZphaGtRPrpT1NtqZMji+QJMO5y5JHGCJjLMct8O29jDcFBkrSUkEK6uDrJLLrHGm3m7VAu0RpvjDdclANsGyLLUZQuA5t0AZDd4unaHsOZwbXFHauLO24ofvSdtUVx9W7Lrt5996/29e40uCGllva4Xwpp0d65xlEjwjT7dLkiZNhvuXft3tV2R1uxodsFou1jDluZg9mj3tk1PjJaXHBhcf8DX3zta/c55cUvHBq59BvfuMctAaVMWUkS+vvmOoKxYKdizUoXx6x/5JHlOofe7pF//pdvWjBwf4GhYn+/XdjFgoWFPmKXXYr77i4+8f8+cvCh3UcfffRzjnnDH//xp1euSOalPKYQfkryy78tDq/KhZKS88mN8ljrCTBD8QlqqaDjFseQ8vHbQCz7H7+cx++EOBiQ6kH8nLDr5NILla6I8lKoJ44RdPX3NTo7rLMxeLyjM90z39trYaK/s9vcf3zEzfRjftQ+iWizHOe4Q2ejq8O7mszrTCfxig0D6XVasLhrl12cjXQ0Ys5Oiyw1WDrk0pH0291jbb2daqEDAcjXDabN9I4XituH07kOwx5nQufvsY8/ozIvlzUK2nvG0mmEFffcZoA3/OhyB1OQzttpSVu799p25nL9pLKtOS72Td6vw7b2QEwNSFVeijVWL41pjV0dl4KHVPTO7TgYbIjuLYhXIOJ4KZDFo8INQHEb4sYROGNm955gt4Cm/hDVmgly1BZxDJIBNoIjM2dxtM+A3IsMKYT8JgmRC+bRG8d7rG4Z/JtWsN+ynmN4ME1cHkkLmWKKVFEDe9IiI+YUaiwMMsajJw1lVU62h/1xMocNLGGzC48cKzJ5cesQ9zYxVoXIdawuEhJzDRMHa33wftTBuSpaQlH5qmVWi9jgLkn9fe2r17i9K53wXLp0D9qvX9ehH7n51rvlZWS4s+hJtz0KWotkSaX3CLycsgGsjGi/88472a+wLGAyQ46UYFbcBODiGUFV4UOpVHC+7GOXIwCZgUcc7BlATAV2Z6XAmgWVR59o/gXmk+DN9NjBKRcloLychIxUyPChdVRB0bMHu6LJh3lCezWOwkVmpZTBMusnDRhjEueqnVBUpW+CSeY99ssyz2NHwHWmhAzjyZDfxLVNHsMPEat4yYwitcPywi1UyEUoclTVSUuORcarkPKlbgBQ8hK/hQ+DXqzgQkgQewd7+1JVDAL5jWWijCEQHLNmQITgTVWovCsqXv9wqZiQUBGvXkgWU02O1KbmAgtRApocA3p706nmsBAgjwIgFDFbUogCRFvXtBrPWurCMxjRiGHifQQLIS2aJo9yBCOQiZEN2WOQoY7PwfH6K50QIrOQYPIRAEJve2dCBhmZAGQCekGSoJgEQGqQ+1OulQKTuFHrnSgmA0Z4jADy6UIjjirBPypDKXhCclZBAMkazGqbyT/kSMJINVG5fMGCpKhO1RgeGTl44wbGcKzCLRdXNuUCe25hZBAXTLgFEKrF7PdOhQfAUZSR/aq1fJLLN2ppT283l4aLYMjHngs3rAql4lBH7/YO/MMMWiIjdf/7pPe/xXj36FCx4r5eR751SveuuXeoMx14joN2m9eHuv9NHbE6XPe/WgwNoxa77n+9I1whAKIhrfvfuv/dMfvf3NcHEKM1AwlDShstDA8M3d1da0gDGYOZzbuATU8kIDOI0qEb3ABMIgg0iTApi7ENCZsYNocMAIzHDAB8VzKeoc73JjMIk0cwyeTjaBqZZPtjFIE9RnEaIhc2xbSX9lCFWKiqzdJoN16KmQsaLy+Bln1k3ww0hrWIhSYJYRIks3GZ/qDhPT0CdmsIMi4LJAuS0LdK4BYX76wfGhtv7177iJ8RGLuvr223nXYb3LD78pHlxfjdsu3rXdXyEi4XiRu9nR3uwto4ONSY1zNnzeqVu++x2BD3pjXd1h/uWD1vYMOaov/hotNPK6TPebSnkBa9kjEZw2NukIlWy0CRw2P+y/jwrRxF9jNjsIsVHAca8MsIYoEGVz4ZQhj0Kk0DztY5bLAnW8pi5bTSD/0xd/PrdOx3BRWXxvwxKLPSbAaACvabuXMyy62BmIrGjWYx4mUSCzNLFhIARkB1/qvyEOhbp/GY3DXRx2PYIyaWcyBxKW7WMkN2rD9gt/1pYqw/pZRtgWSADHIypWkdtL1dXkKp10HGGenGn3gHUdKJBVmkioWmbHKpEJTiyGY4UByP+jU03g4q+DyykucsHpmhLgVeHIzmVuxkHiECPJPCgZlSBY45TsxQYkKEWOEGjclm0BAlhPGMQUNLTPFQ0hJzdqXpEUHMlGkMwyJHkmRBHNIAnBOYqD/wkGE/4WF2zH+ReWvIZ0OYFLoijquK8eZQdQhG6y2RRGPMZ7MrFEqYJw4z8Ib3sGiXgpFzuAVNviIcYySRz3JO8BoiE+AJiTKikR/kRQCzH6PseJE5LWpLGBDSxFF5AOhxESVEKkYwvJiTsw1YGJ/NlpoXIjDyEquIQhCFS2MyqKwYUfohH14gTUMdGYGHIVCAiUJnBl5maLgQyDvxIYEuAUv4mWf4R4jUkJbh7QSkPFR6AZZ7LHOQfpND7iIvciELkmDCFcE1nVVZAjLliB0gBG+VCyVFGU8LFZwQ6uAjYIERpAY7fJgnFjwGDSBrD72ZFyAJcRCUfCnySDLzEMQjk8BCiA04x5A5hKigJMEjXgEQNJkr5X8qDyCglCqpyZryxnPVVePAdemzaqpXEz9GyDOESyU/iMXgZH95jeR4W9/IcPfQYP9I39zBjYONsZ7Bjp6hQYrdWt7VGJ/b0927ZmTj2KgfXRkfTZXN+0JFqnVENdkMGZ5Jlpf9M12QTQEXXkipQhjmEVfE+gJlwfIgq7IHDS2EILBgHOziKD6vktQqI8qQkPXmRxghy8dISMiBlASGBFcFZvoAELBKCPag9yg14Cb6KR+DXeyVpxQNWAar5k3J+NiQ3M62lPmy/tM1kcHNL8DKwhEI+XEGgECpsyRGubX0zGa8UualGQqlaiEVvJp1VZNa4bCHcEDArTRVTNDMnh4vlugCtof8rbXnSaHP1WPMr4SVtYUZkGGMOPszU1Z9nuEqfTg2J00JbC19FqL+ZEtCSCQFHC2VHrnaZGX6LGQGoHV2NANxnVR7oPZA7YHaA7UHag/UHqg9UHug9kDtgdoDtQdqD9QeqD1Qe6D2QO2B2gO1B56eHvARyBcCq+2POXvV1fmJrwKlrPRJafKDoG0Ii4vi5JNPvuqqq259dHnaRzOnzYG59PHGV8aR9g7/jacPS6QJ5EyIqnxTlBqfqSbSPKevgukLJZauto7hdOa17Yrbbjn8kkve9ra33fqRj8RHsRCFLDFWBMYjXpIiBJwxgBwmSSYshM8sQRMfxgIJ9uXVN+OquizhMQBUyKs4Pnc9Bgk7Aou9QTYR/t3f/d0f//EfX3PNNUxS96JcnlzzHLJ6xSteYa/YBz7wARdOxQVY9m/53h+VhJHvfOc7v/a1r7kdI99R1Wqz1D/4gz9oxVcxtoLZ0FbFBGzPge9exx13nM2C9orZdeceK0m///u//+lPfzo2rCAI4k9+8pNZgt1m//Ef/3HppZdmDADlX/zFX6gzYX8kyaDHqEhimYJRKNW9L1UhNVx7oPZAkweitY84mnexR2TeqXitxPHeBT7ImuRM9xiMkToDY5VsOlHw6Vy7Q+lFY2F5I49fv1y5enXsJIo92Ey1yQlNudlkgp7ZGgdIvZh4q8LwyLALgPZetuzOe+7cUIyOp1P5m4Wmx83SNn9gmw4inLl5yjZ4IjbyGLJkWd/6eAZC28Cm7SkiKozd/C5z/P73vx/bRu14zht2t6fyLciOPfrOCbzgBS84//zzzzrrLCOo2FkVnLqzL3/5y7/8y7983XXX5Q2vrULtVT3ttNNa8RljQ7btobHPOyMDiMpgq7rD266pYpKrryQZGPz4xz8G2L3qHD7Axtyzzz47uMTeEf2+HeoZE4DuO3rwJryKlzFOC9jbmnczZ3wN1B6oPVB7YIfyQHU0EhO3atdcTWW2xxj8RBZMu23MNkryV5mCb5Y/c3npo+V11eV4xAgkDUKExgRy0/pAiZxQQZH5MY1BTYUmthyRJcDfhMZS+4P3Fg/ei/CR9i5XaW9GoNORL1uyu3q6jARIMPyBSdP5LkcI0gGnOASk3YZft6H45V/qv+iijX/zt3ftu/ddH/qVF/f13WOKljMI0N2402rXXYv77ynMHI88ZumaNbev3Fgs2rlYuLBwGYvGPx2ZGS4eeCCdS3GA6Ljj/Lb3Tl/63C2nnXbOX/zZgsWLi9UrN8ks/VFH03qgWutSJSnrYSDF8Sieln9LCbPkDY3R14PjcUuyH2N6yk35WsWSm4UuL077oiVWErIBhjfeEZVWDTay4hXK3MA2MQbG2TExLMHC2pRNVbmtbf6znmW8tHSvfZcuXer+Gvjh9nSgKxYNxidutUv+jDyGxriEN+cafZxckiqo3uj7Gum0xu4L0zJL1+B6A1EjcHeJeiPgi860dz9CljOJqP/d9h5QLtUJUVlJ0vbx//N//o/FJXXpxBNPNLP7yEc+kl8BwJRFE8NsNcTY+JRTTvngBz/4l3/5lxb9fu3Xfi20ZAmt2UAQFSDT/MIv/IJ5gRN0bp+P1KqdTRIQBMa4mgRVy0XwlvXULnII0XSHzVNaDsls7TA5ciF2GvYTn/jE9773vX//93+PgXr5Kk0s/2btWRq/RZAESYjfY7Ca9+u//usmDl4EqTlrmX1KACVjuP2oo476jd/4DY9Zy5T0UtkmUKTgZNxMqmjvtib51x/9h6uvuLxjblpjRyCeTpSJj4PWwpvf/OZvfetbJkR/9Ed/hFjZcSZGZgR7q4TIF++hdMmy24RNi77whS9ce+214jjvh1eoeoCceBRzuKDsLITCX3zxxf/wD//gCLHfHjABtCTbqjSk4YokQgS/ZOD24W984xvmjAgcwf3c5z4XxTrDErq6oZ7gfdnLXvZP//RPpn6/9Vu/JfvWYD/2sY+ZFToJ7KAm+VOaEcv+GtiTTz75f/7nf0y0X/e61332s59V/fjzf//v/40xZX47hCntycgMPGbNIYH96lUWEtnJmKyl6p8ps5wpsygYJZgfA2glC3xU4CpxK281NeApLdkiY6uuKaVNKRyyKj+yA1nNVxNNCK9yBSY7OR5nH9NFRbyzwVXVPns5mbKJnfCcFEArBh6yiRGyFdMkarrHJmnkNGFaGYOmVemU1gb7YzavVftsMAq9WtkUGduUe93/Pon97/DIurL/PW5T/9vc828qWxVGCQp1/1v3v6pFbkAysKmubCUUEqJByKzRduWuIWuBr8KZPgM5tYpp7XRayYJeDc+MAbTyNhF4DGub8FtkbNUVEpqkNT1mmqr8yE7VOWFVlSYYWzHZyUEw+5hSGuv+N/zAb02VasqCC/c2Uc7e54+NMlsCEBSZQjd9+9Vf/dUbbrgBxi+3+e7zb//2b625yBqRRaq5QCySvPWtbzURsGHjT/7kTz70oQ+pWqp0kGWuKmB6Yprpg1dMFSW98Y1vdHm0604+9alPMSkmoSQ0+adJprmSTSPYTQPf8573mJGZhluNxB6MTfTZBpSC1PhMZrPKV77ylS9+8Yu088kMs6cQaMZqmYgKMylyfNT73d/9XRcK+wEbWYCvvgtZaQDZpOSl9PsAjY0bx9l/8AGvJETV6envj+w3MXrM3mDAypUbPb7whS+86KKL7Cwzfvjc585SdumyDRc/tzRfWRq9sWjmZpw3velNZ5xxRp7/qgYmcdnCrC7zAqLUbKrxIfI1r3mNK49jwcRXSw6szn+rXAFXBXJR/HbdlVde+Td/8zc607//+7//5je/iTLIshlZDsslybtSA1tGO+CAA7761a/+9m//NmKWq4FSJ5a4M9vmQOv816SV5z/84Q//9V//tcm4yqBYN2dKT2GPuiHYTHXCCSeY/5o+m//amxQVz1R6St5WaY8Z09RKV12ajQzhVcqoD9Va0ereVpOy8MyYMU264FsFNiGDoGoVIVWBVQNapVVTA27izUZWKatvIpnBkoGgDF1NGpseUbZiqoqqcFPG1dVq6nRwk2emI5sOP3vzZpDQ5NIZKKdMemxZmFKp7FTxfBiYpmxWaZjU9BhGYmmtG01ypszONkdSmoOamTPF7ICny0LVktY8BqYVn3UBpIrJqcahThLtOQ6uqsaAA1/V0krJz4GsxpDkh2r4yHhVPmQ2D75JCEwoDQuDEQsg4qpJkdoUZ2LjDUm6bMMGJvWNxnWHExdESl2/Pu1UKW9SwjQRUuXxMz4usmqMMN4Sa/rANF7uaRkfJqcj/XRRMTqUfn6gSXV+nBQ28W/GMz7ZX/58S3gpJ00JMEAHRIpUjEl7uX8MkORME9BHqjiAzA7IekNsVUbGAHLIBFVpkCzJwjNNE4CAOqFKmZFNxNM9skRSlhOP0xE/fjxFVSEVdcmM1iA7FZrW9M0ws6cMtq2iT1W3fE2iZlZ9vpkRmz8E1+a4qZ+CMsTOxrCtpQ+ts7cnbAj62djzlKDPGUm/gjbZ6AWyyf5MOXVpTfI+Nv9MJ7MVX52/VE0KWFVkgHYsG9/0frUKbMLUF2A1OaR+rD1Qe6D2QO2B2gO1B2oP1B6oPVB7oPZA7YHaA7UHag/UHqg9UHug9kDtgR3LA7aqZIOcIxJsrxFbIveRz1dGnyqr3xQDY48LwOo5mpIp/cRxliNJyMvrIQemushuyxfG6ndEGLpwZTmBoQumig9dm/QFQ1uxxDasU09ZPN7x9TN+dHd70dfXvduQDU6jY71dncN+ESPdYBVm9vTIWufAwMTvHPptnvKXgkK1OGSXj767OYsxMupr4Zz5/X7Xb3h8fdHXc9Ujg4/87OwvHnjoB172xvu/900MD7anczttxZg/h3DBHY2irTy5B5b9MDPgprwnf5U/JJVpEMQuosgsy8PPmQCAgAMDg0AIOVVfwWD0WTTLCZrqB63AKJ34josyuBRHVd2OCcs1wyJ32cLvfOc7tkZ96UtfguGiXBUzwZMCuJbLhjM/sejHNg899NCwQf3keRaK5eJnP/vZxz/+cceo7OuyoW1KO70XXlI3aNhw1kSgSsT7gvfnfu7n8mOuErE9jvBXv/rVX//6150RRfOLv/iLVTnOoHq0TY1tgo2GFKkwzK6SZbhalyBlkG12ocXHeLXIJjm7ElutzRJqoPZA7YGqB7QGXkxvkDfXG61liIZOHG0+fAYwNr2DVVFTwtFgNjWbTZRkTkkQulz+gD72+7i/ygaiuUXjhc8+Tqr9uPHLoakbtK+oPMgQN1SViKRHRgiPDMppk+otPm4oxm+4585jj9j1iN33fWD5HY0xPTBtE7/jpz8ICVREmFIgGwTaBTsGciMJOSX9zEiKMgE4hMigAA5nKjK6NInhw0z/2DRm9h0NsAXfJaRxtRPbqp55Ek3lfDu2+V+PZoTDkigXZQEQ2Okotfuk3ve+9/3rv/6rQ8iQrfYHZSS1psLonfEeeeSRit5jrldg/anYMOCYY46xiZwQJsUZAPgI+adlJxHpX9vT77rrruj6q/gm2GAvhm3VCqaX1//mfeFhfxNj/Vh7oPZA7YEdxwPRa+RmlmFlw9V8KhI+UZadb8y2jTc026PlHHzcDNwv0zbK7bMxEGnrSslm63amjmuN9dpaaZjy5qrGiJ67szHkYax9Y+mN0DjZESAssdHZT4xtxm0FTjLSZlcJDXdapd7EoxFJw9Bnctzz4hfvsXTx4X09oy88pbjnts4HH1wtacnSjoULD1+8eP2BB/x0/vyetas7lizu2nvXkfnzdunr3u/YI4o9dzv21JNXDW28brfdiptvXPvI8qKrN2khWRgbKc77ya177r6Hlv/+ux4564fXLlj4nNe8as/7702TxIeW33n4YT1LFx97+CGjd995/oEH9i2Yv+fzn9c+b27fi1703BtvvEVPeOvNKx68PwlMmYzslXl8ykW5tqQq8USFslpOqAsDshlPjAkyGxozsA31licLivbKGxRZVeXHxxp7Ll2yaOGCRrtfsXbPm5emGEmX0baN9vZbBBt36b1xdUfniFo1MlYMu7ZNNVNlGw23vnV19i1c2Lt4idt/9jvkcEOXnrkLDZkcbzB6GVO729pH071y8ZKl+i5fHWUlHW835/C6TVzWI/vjjXZXbyUrUkUuDOkGyrdjZDT9ZHffgsV9RTG3MTRvbKy7o92tRQPDQ6WKUmiq93V4gjxgbhualJrjvjHMNuh1pPPMM8/cf//9Y5Cs1GL8PJ1ZBrTPe97znv3sZxtvWzczxHUX0oMPPuhQrkE+ZNxOhb31fTQXMOPbc889991334MPPhiN1Tm3xruHwvqbJLVuOr1VvEG+a480s+eee65DmNa+ZCcm5lt8GR2CZYBrpwDu23VxoZOfnOD+rFDRajZ8GGZ1znqac7MHHnigqcHdd9/t5KpjqC6W4kkGTMlbtTxgdzDtsssuK1ascB2/mYhLoO666/ZWshITpZZWxdvah9rbGgvmL3Js+MQXnbh0l6XnXvijS68+d7e92q++Ym17MSfRNNowROvRJFAFUDRix7ZPPfVU64pOvbpT2O1XzjArOEU/g/95yd0fS5YsMYdiPw+4+sohZNcuazo0I9hjEtSkNx6z5Je//OXPec5zrK6oZp/5zGecpzX1w54JWtnDMJabiCl392cpL/WN2c4hu4CMNKlCKy9MSFa9HTV/7nOfaxGYB5x7v++++5SgCky+oApNyQ4ZxWr+SALjKYpZoR8z+OEPf3jSSSfF8sJ07NsJP4PHtkpj9luuvbOUPEuy7MDZWNUqM1s1G/ZMMxuuVl3Yp0RmsRlANhsVrTS83YRseswqZgmourOkfPxkUzqnFTmbHAVNK2+rkVPSNCGn1NhEE5KnpGxVus0x1bes7n+f5P73pW9O/e+j99x407X77Lv7sw7Z86677isHsK3FXve/6TOHV0kFrjBl4sUAAQAASURBVPvfav2YsnmpEswSrrYMwTJLybMkI3P2jV6rzNnzVvM7G65WXSRMiaxKDhjZbFS00tT9bzgwPDMbb09J04Rs9fN0RTklZWv5bidMmO0CI3NG8zVTAB2Be2zNC2ae9oY92M1SDfjNf12EdMUVV7zqVa+65JJL3AAe81+pZkPIWrMJI0gyhzIHcd+uaYtplAmI65zcYWR2NstcmwQRYgHHBMR+ErNv08+4eXwGCbRLRSaw3/TZ/NcPwPgq59vczFc4hVjTLpNWlr/oRS8yHzR7co2U+a+bsO6//36ToMjgDDZ0uJi9KF756l9kw8bxOy+97pq9DjpuvP3ROYt23rDi4aKzq9DPpDls2paDstHY9JG60Tbc0Tne37/w5JNPPva5r1y6y1E/Oufr199yTdGzyl/7yJhxcNt4sqG9bQpP6sXMAWXWDyiy3yJAnv+yn/PNXmWh+t2wmpFYXpBqAvvKV75S3mXBFJgb1ZwtZjyLUnZuPYtH9y+DTaLDsEzTBMQInxY2+J2e448/3rdLaxGm8L6cKkq+ik4kdyVVCeHJmP+aO1fnv6qNLCh6E/DgpavpRZC1yJ35L+3YVXKbDai49dZbrZ9YvQnnVJVuc5gNTTIjX01Ij5kyE2SglXgGzCy5msiaHslvxTx+pVUJUT2qmFal2YYMBE2UbBNv62N2aWtSFVMVDj9LrlmSEdgkP1RPiaxa1crYqnE2QlrlNGnZHo9hapN5rfZPp3rKujEd8bbCszYCgQEwYxKX7uSKADOdRhmMkAk8gjFqxzRQAHFICMmZ0iPiGOaFEJRZWibOQDB6JDNimEyfxQKCBZleAEEmDiDYg5f2aEWRhYSgycZ7zJRgIdgDzkmRixAFGWQohRAbyOCKOJIwAkI7f/izPXpwcJjVZVxaVe7NJoFVgwOj5TZweMtTvsya9rLQY1Lk09LwyFj70MjGAZ9viu5x2+HSt9eu8rPlyCDh7WOjjdERG66Q+6ZLglJIff2kDRM2JtPLkDOV5JchcicOLoxoPMoLzAR/6ajyuy/cREAZjppEpH+b/F9NqsI0ewwjAw+uPkKG6gCCPiinjMPaHFeNz9lstbYqivbgKg2ZqD9Vggw32ZnxjxkI1Xpz3oucyntoAVTEVuFN6M1pNuGng7YTPYPD7DyW26KjgkCWt0hZzcsWK0OVGDx7+jAj298kZ7pHXFvl0qcK/bifP58q7Dj2h9tb47A6yj1qlziCJPZXQ8aUX8enyPDEsZMpUmpU7YHaA7UHag/UHqg9UHug9kDtgdoDtQdqD9QeqD1Qe6D2QO2B2gO1B2oP1B54mnogVtJ9ObO8nrMIGd/SMmarAOz5g8rE4n7JPyHTt7lGcdCB+9sSdP5XznhkcMW8Bf12EaH0/cwP+vmw2tken4rTBwxbxByzcQHWlm3omDjKwoDq3prezsL5q+9973u2De152YX3LE9HVnxiplFI0OML+ctEklZ+n8iYLDgw8QmcH4TwvDjTgAMfmFYC+NLk0uwSzrw7PhDZCTvDUeC/+Iu/sJ/vL//yL8G2kcWRpB0hLzYpXnbZZbbu/cEf/MEdd9zBJJVTzQSUpZd22ilTB/D8DuSv/MqvOA+WzZbTnMFgcf5N7oIgPk9Wa4gzVPYF2iYu+2gyLxqPn//856l25s2Wtde//vXehb/7u78LUbaUeXGos3sSBllcXKXyBy9M2CmV2fFzjnYivuAFL7CTz71jToIFC9V2syGT5dlvqQwz6rj2wDPZA15Ar1v1reeNeIvj7RPHWx9IlJngifGb4+jpvHsl7L90L3tSz7n0onUjGybQ+i5/NhHbOdTmvHnqGEtL2bsZb0XMFGD4IfFMMsu1zbI2vzqye/HwqkdWPOL4u1Q2SdJWauaS8sntXFMIndxshEaYkmCrkK1CwlrFlJOqTfRWCX+qEOsmHGlm7dlnny22PdowaQdp/O2xts+ehYcddlgUTfZqFFCqP42GXeCOFvs9bYeiAxMx4uASx/Z0pZlLNkRFf6cD9SPYNrijVPPjPUWA2CZ1wKWXXqqL5Bm/d+0kM/rvf//70Zs7DG90ioadOnpHC+IXtnWmIS0UiT0SSD4z+NmrZxv6NddcI4+ZBqC/jj46IzFmuAZqD9QeqD3wFPVANGUp3rwDr/bn5TjKNmjNr8j9Ve0uoXSxjosJXZOTsBPtYQm4eCcREZk2PQesAY9m1mPCRPNZXncVV26V+6JtKk2pZuHpn3JDth8Gpr28XHsi9a677v+3f/u3nv7BRx4pzLASYwMw9kd/9HcrVhQmS2NjQ3/2Z58YGvIjycUZZ1x0/oUXOYz22c+ex9j99nO1YvGfn79s7pzNtmHS8K1vXeV24tWri+XLGVD8x39cdsQRLFhyzTV3rl2Hceiv//ofNmwowPfcM/Abv/F/TQpXr7rvyiu/2J1uA2s744zzpXZ2hItK8+toazwQa01VjhgYNA0PqgRPAJzei20dojanF6MMBjlpZaB8UyBKjek1s/IAbm8fljo04FjC2LA6p6aOWlXoTPfDzZtn6G60M3/X3Q1yBsbTfMeIiMe8d+mt6fDOuf4qvVTTZQRB1cPg/BgsYgHZyMigpIHRdCK0vbsrFjfKHNTRE+cBZVFVplxcXGVI7NSu+6cMX9/0pjdZjLJeVCWbAbbWhNf8wrVZLiGyDOUEqSqXak75O/MBtEpgiZrg7DFdl19+uYpn2OzKeKdqjbS9zjEIb2Jssl+q06dU4FXDrde5Npd2eMJbiUNaxhveU+fsK7xrOKy8uUGJ/fkCrKCfMo58YXdbvRO/3iNzFkodouZA04GsZUp2yCAwX+AB55adhuVDE+rFixfwwMhI8xVgmwv01MZ7Z5111tJddrnyyiuvuuLqX/7lX773vjvb+5NklgjTqfZ2U4DMLcnOslKqGXEAW8ZjEhQSpmOXQU0Ed8m4BdWoAIidneZGF4FNWXY5y1msyqPWaQoY4PQ4H8qRGIYBmawJCNtUudtvv53zWe7uM1yu4mKMrEUFaOLKj9zCcm4nh+d/8pOfECLMnz9f2ZmplZ5LlUfIXBmQcTNHl16hjPJSE1QkGZdrB6G1pW4Hy/TbA2gyzGOUdRP+MajOb2uTqKbHkLxFZCtBK4aox4ncYjanlD8l1wyvTKafUlorcjaY7O2ZhefUGYBQV7W/1YAZ2LdhUpPepscpFU1JMyVySvZZIqvOaWXZ5upaVcA0aVEB6v53h+h/H7q90v/unLrf0YlGNZfj5mXnqe5/6/431Q41oe5/82vSCmz+4rSmb8LM3EQH3ZTSWpGzwdT971a5dFM5PSZo5sJtLa/HpGTrmCg1dTLyN/30I2GAt7/97eZiprGzscdc6frrr3dzFq2mjS6iMguAjPmv2jVdluHNI8xY16xZ40MVIeZQHl/60pcCdD5mQLGAs8X8+KaG3m4Wl2f52ZsPf/jDtMsO+bIwnQFsk2oe5+ouHjBpshHFFVovfvGLAcYkW9Rrhk4FX+G10cUEyk3WbkFyiTP2madgITw8bMqWPrEN3mzqTc68JUvWrV5R9PQVY2lbTjU0lYgs8N4FF1yw254Hmz8ynseUAm9Yemi3QFauBlclVGEuIuHqq6+OSVye/5q5w6MUy9qUDmQwvykvG3gOOuggnifNpNIkVAVQmiwJIVWNrbBVC2bDA1x8pvoRZTWG3lbiwIQTEJhy0s5a9n/729+mzj4i2ZEUzm9yV1Uga911hYDB1fmv9UATWGL5UOFWWcAhUKxymrlzggrv8miU8m74JNfYzamVSxPvtn1syprHKYsp27xV2luFz8CeiQOYzowmCZkr8E2PMyCb5LQ+TmnAlMhW3inNeMxkTYzbVXjWNcucZnrALA1rpZxO1+wFZjOmY2nCNz0G+5TILLkKTGdwlWYbwtRVNQYs1kBFSMnlHioxvVowASAVppqvgMVBEAAyLbA4bG5iqWZEAxVkGMFiIZQiAwcxaZkyFKHJZFkgUZklCCIOgoCJisekaVJ+iPJIQggJTJUyEwcSWTWVVZkgFEUcNGKpwpRJoUtMZsA+B1nJtM6MUXcZntGtGBWI00eichVdXoJ+PL4EtTek6iAswI6Oj0rtbE8XkI2Vyv1WZEmfdqgGV9jGqqSlLN+clIHS6k172TI+JEykhqDSpCAI50gNsSE/XBTqspx4nBQw8W+VMgiqmEycRQUmZIqbikYqJOLsrqCHESRlgU1AEETMsVlslR2MQNwqp8qOQEG00oSorYpDHYcIZAYv2+Bb5DdnLXiDeKuUbldi5TIb+Tnj7Jf32bO0uGVa1lAxS/ogFge9ODDTSc/0kd+nH33TBVg7Wn6VS5RUvMi51JrKK96OspltSplowwObcjfZfTTR1RdgNTmkfqw9UHug9kDtgdoDtQdqD9QeqD1Qe6D2QO2B2gO1B2oP1B6oPVB7oPZA7YEdywOxUB42geP7mdjiuNVzHxqF6sebwEiNkGmqcuxHEUJmfCGwZ8VGq4ADbwMTUbhijV4SjFDV5bFJe/BOsJQPTrA6rrrfQNHdVvz+S16//KJrP3H9Bc7YrVu30bdmgM9IfatWr1+7thhN1/r4CSF8Xd2FXTK+7raVj1XjQ0WO28bLL4t+eKhoDA4PbRwcaBsuekfGHh4tutePfO22GzrvO/Ijb3rH3/7zJ1Y5stdWPNo97NeIipHyW53vRm3Fms5ioLPo2DicZUa+WvPOpZkGHGWR/ZMdnmkkhRCxgCA8X/VhYDghywlMPIaoXKaQUUYwVWOyxh0fsAHud3/3d9/znvfELigZYTPn7LCWZ9t4nv+da/Jp/73vfa+71Wwsc1OGLYmMr5ZXZoncSa1+JI6culDjD//wD/285He/+12YYIl9jR4d2/vzP//zoPz0pz8dgJh2W/HUz6gJYo/skWSfoq11KlK1dhFrN5szbP/yL//yuc99jhm/9Vu/9Zu/+ZshECWNYGZ89KMfDWQd1x6oPTCzB+Jl1wJ7wfOLn19Jr5X3ziOCaK5Jy8DMkjMZdgExURnZxBs0QdZKg9kdWOPlHVjDRbuNs8895PDbr77uxpXLN3Z1N0bK/g5RuYsjNRplG1zSl8JK6do1SmPfjfT0UMYBTGwNm0Sm/jrgRmO4M/223+UbVjz00J3v2O+I768464bE3dZe7jPJoty55W+0kfrvCeZSQkRMYErOZjxKAlSoZgKDt0oRvOEusIwHTTXmcwEXskxZFfLUhe0/9qvUX/7yl3VbuoPoO3aQ7HC14wFCXFPFqiiFMC8KCKzPOu2005xmdyTbEetqFtBEeeki5c5jU9bgA2Nntiu0bMp3TBom1f+yuOPXrYkVgtIm/gBCsg43LsmyH915bN1rpLqWy/nwpnF17JGy7d7uc7/ATIuN4E5EhJ3ZvD322OOHP/xh1pLxganj2gO1B2oP7FAeyG2UVjHDgNb9eG3urSrHDFVKeRn368Ha5zaXSonLDXuNLjOlYpdlu+6zT/c+e2r8N64dst967SVXavRdzWMzc1djDd7O9iFb/1wBYg/qsAFMW9HVGGuAU6ddnm4iNMYiCVGqDyD1KbjsMndnFotsj5ZQbuEddwF3ccdtiAcTbZtLgjZ0dKZruB5dUzz4cML1tRVrVxd+2pi5uhZXaS1fnWCqejqLq1YSXizoLbo7inXpNtGJ4GbRRx4sVj1SmOzGlcjDA8XVlzvDtQIFzM3XlyIaad1j7aPFaJoTp7Dq4aKzK412YObMTewhNHVrFj78pUEb5f6ytmCtxrPap1tleGLgpvqw/ZRS5ARgdPHbT8vWSjbO2VqWGelTKVufGi/H8Q11s6PHOKpr3oKit6/o6FLph9vSmb3hjvSr3O6Y658/f2T9wMpHHmnM6R9Wg6xutbXN3fcw99Q4r2ipwYVHBlHrx4qHB4bG27u5cawrvadpVBa3XyU41b7ytVMFY/Q1YQnKjkbnJFJqwpcD/TSuHgseFVcLYPGuI1Ya05itrWNsvG8O2tntMCe1DtvLA4a47usxbFYr1FiDWGc4zz333NnoM/p1OayA2LlZI2SniN1mZQ3QDUGQm3ccquCm4G1VSdS0OAJqzG/gbeTsAndc8e4EO7JNbC2Q8fl1110XaEttBvMxJg+ubEALX0LQbpUyFirF73rXu1yRH/ZMSV9FxtKfXNBOI/bjjz8efM455zgIOrPeqhzec5rU3UkC1V7JVatWMF6vWyUr4XijorVP05mu7o5169d8+t8+CV68eNHVV149p3/B/HkLNq7fqOtr11+md7dVThKmi/G3du2q73znWy7r5/8XvOC4q6++onr5V/gwUW8erHtoN+Td3IofHKNSixzElX0HkjVKHFtmYaaCI1J+o7AcJ/Z5wgonJGlxgndmH2JUb3/0ox+5w8v5YewuUjn99NND4Ob2bnoKmSxnv6rOVHl3c9kll1zi9wOI4n9tY17yndIDylepOeZt6KLsXKC8fPlyyN133/073/kO+zfp2/5Qe4xttr+iWkPtgaePByZbptzIbDp/WWbSaxV/2zvLdf/7JPe/a++68+4rU+87p/OWW2/YeZfFq1Y9VHZerf1m3f9uehvq/jd8Ufe/m+pEDdUemKUHdpj+19eluM3HSMAY3sjfFzTTitnkQzdh3iEgPvjggy+88EI/gmJrh/mvJGLJBMQYA9Ak0yzJPPemm25iA8DMy7TCbhDTq5j/NtFP+WjqIUSSCZTOlCJhSuIqklXmbib+8UUMo8uvzV5dDVwlmw6O+buZmrmbabubj+RdFs4+++z8dW863sDHPHb5I1c/eF+j6CmOOeLIe+68cfGCnnUPb+ydO3dwQLo/GRGnvjjoi7ZNe7SsGJhtfe7f/59U896HH3po3pzORQu6+aCkt+g1tR/kHa/sm/25QtpihSI49thj3cZlSitV76Z0ouDC2mrMVwikuv9aflUAF1eZP7o6Sh3IaxdbLAXz1rhrDCAjsfQBsBRDuFBV2gTzPzPOP/98Hzpjumr2+t///d9NZE2PITMm7zH/tWDy3Oc+19z5hBNOsPph/usec/kKxlYbZMqtW1RbIxJj92tG6q2agFEWDGiblG7Xx7r/3a7urYU/Iz3Q9vjnv6kTqnRDYI1JiZuIOFYbqyHV3wkAIVrdatuLWnsVlGBcEcMImkGB5Gh1AxmtltgjmZp6QCCDFzIA0uAFBoQ94JDpUUBGu7ikSv1ClRcBfJPSoBTjQow9MKExkGFYMFKdFzxRlmonotBFgme5EIMhgzHrDflSYcIbWQ5iwuFduYmA9ghgPaoLl4aGRpKRpQhJIaFMTRF12ENao1xpTJ9sULWND40ONYbTfq+e7vaOcT+xaHbMFXZ62Xxtx1f6UOWvq5Q9mvZkFx2dXR1tnY2xtrGRxli6cksfOvFHaOrkkxkJ02bt2hJyCXR3poxHAXW2kzCxMiPV3rKiS91os1leh0w7JX4o2denHDOY0Mi1nKZcVYL8GgZknyT9JXGQwAdLxgdlPEZMghD0AUcMgzjKC4xYTBpAGYVXM2MWmDEhkISoG6FXHHIitcolCTJKP1IfZxwa2a+7Z5UPBOGKUFQRnupVNeRBVwaqqU80XL5PKkZkh8da7N/Monj/xhJ9e/lTXBO3tm1GtPmD3aAQ4RyOCmBzks2etoo+iLUxFbPLLG0msvqQKoVnBTdZl55e9JuP6NNXrM3yO8Vns828s53pS13JRB1oMqy0zXMYqRAnrNXBpqZrck4z2a9p3zrbOzVxubjjc381CwGn7/R1qD1Qe6D2QO2B2gO1B2oP1B6oPVB7oPZA7YHaA7UHag/UHqg9UHug9kDtgdoDO6wHJr9SJAN9vopHsYXyasj2V5FNcBNN9THEoq8iM3sgmx4hq5i8Ih/4LCfhxwu3bb3s5Be5MuC//uu/3BwV6/plJia+ySXVob2MI4NZyAxAYixDGOPzUnvbmIu5fBT1Xdj+LTcNvfaXf/V5hxz1/RuvWogyKQqOia8O8ZDllCQhLMU5NVATnJW8z0CjvHKRIYvPG49BTrBUGatwNmmHBaJu+DlBm58+8pGP2ARmI1S2NlLlKGN2QCAXQdxw4QcS7WxzjZetkJJs6VPTstmteYn3KxMAuMKpqje+8Y1xAZZ6AulmjdisFlWlSp9hm8niyg/bMX11Bqvy5NvcFkZmygx88IMftIHfRsZddtnl4x//OHzIt5vNITE/K+prqB+HzPQ1UHug9sAWPZBf8yrgTYyXKyOjfSMtY7YoORM8BpbMG4DezkYTDZR338tu/+6GYkM2MjYAx2OVkd5kduorU7P8WMzAODy6fmh9ukhowW5H7XX4DfdclwXltj4pKENVexWWyDwBUMVvFRy8VQkB55LyWA1V4fAecyFWk55ysJ3TdrHblxxnwqPT2ZFzUXU7WFATGGwnvZp88sknf/GLX2yyP8oLMiibUvOjg/FOINjb7Vepc/dNfmyky2RVQBePUscdfX3s5LY9LmhsUtcXNyllDJm0GPrquL2DBgwwCoIBYtvZnQy3eTHft1XVWMO1B2oP1B54CnlAb1nu1kv9uoau1XK4yf3AMbpwMY7t0R1Ff39X31zbeefvuocTYl1772FeMzZUuEXitlXrNa3zV6/QZjaGBqKJzu18UmFBIf3EcJryMyDkxwVYsaV5UuNm5oR54iwK3N9vq3QSFSxiMj32x47CNEuz1zxp8Yeyt5EuDhKQuaYKxiOu8Y5SQjlm8djbm/B9fQkJ6O5JQEjGS777rUImuLsrdmyXu6vTXu1i7lx+SFoEZHXYogeqZYrYka0tHhqssoBzxYh6Uk3dovYZCMgxSFC3zd9byegSYhSRNbaSVTFBpj5WkWBTfpdYielK9bIMhHd419K1a12GH509/UYg/fP6RxcsKHbeBaXLso1VDEisCVgxCFPRlxfRh4zNYixNY55IpigBqTXYrBFozVQ4xEuY5WIxxBIny+vwJHkgSsqo1fWsRqf/+I//qC5pgS2oRuls0a5c9CYarv5xX7wKiV2xkmCxq+m62KrAqHhRN9BT7UDm3/zN38DkE6RV+tnAbqtXpUmLrGHJFs7AHsQmTZ/61KeCjIQZ6CMJF7IIYG78zGc+Y7IgX7NRmuVbD3TChNu/9rWvxf1Nc+fOWbECsjvnIhNPCQSZjLsBX79Euz30U1K2IjUODmArPiVl5ZAo65RxOGeLuQi9LrFSfyxUfvWrX1XiDgNrWCjaIjvXoXd2mse0VGeeeSbMggUL9P4xbyVhBg9EEvtlnEbfHcQkUB1JUxoQSJSU4nV4WGWzQitASo3Q6qiMCdtChbve4MUqHrO/8Y1vhA1b7ImytBqoPVB74JnpgWhD6v43/FD3v3X/O9n9bjahaGoc6v63ySH1Y+2B2gOPwQPR77g9yuxD7/Nv//ZvbvAxmzCSN3s1kw2ZGqXZCDeD+MEPfmD875dOLGlqpgS8piRTSoh5IhsEX52Qub5KgCdEmI3STEMXOZ/4xCfocheVD8EmlaXsTasumRggKVsFhon5IyBsrhJPCeOK2ZZUDjSM+exnP2saKC8xf5ySq4oMveZKLlEf3jBy5pnf/943v59+c6CjgwPdmF4lngEOOTF+GC/SZR9mcoGcjkseEYgZbAJr8dn895/+6Z8UPTyTtpiFkED+zTffbKGPUvtzACaVyk7qdKoDTwvvmf+6ewsvB9puJAmvKXD+5jiDkDDAuoGCQxa/fhclEnmf0oZARhzzX+Vl3cbqTZgtiRDZV6OIbRUSwiOO+a9rs2L+awUDY64VMxhfJ9UeqD3wNPaAdkPQGqTmvQzRnmhk5FoTkVuJSBUjEDSGVfrsIvSS4pFkALJM7BEMH5hIzcR4JaEPjDgkxGOTnNBODmNYm5fygj3izI6M8GDJBPEYwknwGPQ5BsALWQ5dYNIEjBEDCEcG4MkQGEjEobfkGDdcCWkZGcZAIshFUPJOrFqTQGa2ISwJdjF7QgJeEnSLyYy2nsTV3osgMsDksAoxmgSHQeWPFkOiDIL28qqXMFscAYGAo9rfeQw8GuxhPGR4AEaQRGyQRZ2Jwgrl8FKDUowxqwsgTApiZoe0KjIkhJCmCoA4JAPCAHJi2JAfAWh4DBBkITwESgIEsTgbACYq8KElSgcy2DHCC/EIz28esz3wUQqEBM3jjAlP5V4G4wROno3AbGHkdDYs25sm+3A2isJ+1WaWXJHN7Kgtqtgq+iBmCXuimm1RfhBE2UV9mA3LU4d+6rq9ve1XEFEWW3RmvH3xOrOqiataqdBkAmRgrY2KJCCL1OnUbeoPpqOo8bUHag/UHqg9UHug9kDtgdoDtQdqD9QeqD1Qe6D2QO2B2gO1B2oP1B6oPVB74En0QHVBHByPOS4RaSk8WxgYj/E5Kh4zMsiqj1bVIWGyhEwT+BCOLIvKlBkTQiLOqQDf8ho+bY4XL5u75C0HH33az849d/0jjfk9a9cOlT+LMkHbMTbqz96i9N2vxPloGBbFs8/fbCz/JCMJa1PcOZY4xssvnuOjfjloNA6+unIL7q553Tctf+hT3/vqW9/91p/78M0PDAxcNjw2VH6KTbrKL7QTsioeCG+Im/I+aV0yMdOkh8nP7ZklkPEYlDDT+VBSpqECLDTpggktrU4OXdsqJr+q+nGKDVHx8caFEYcccsirX/3q3/qt34rsPE7h2FmbDX78Zs8gQVIUSrxW9vO5euNLX/rSxz72MRu8mjLSKidjMoDFjjq7Kp/znOf4YcmQ4F4wt3JEpuLDWNBnd/mkbUucM4TosyipiO1XazIjP7IWixNobrw6+uij/RylD592RfhxTrpc4+VnRW3Uy/Q1UHug9sDj8UB+NzPwGKRl3gxsrRA9oG7Dl/NlRc+Ri3f77i1Xu/2KkGhhAM6np+40/d5U0G7SkJRO9MabuqdNyVuEys0wazsba4fW//T+2/3877JV99+3blVvUta+sSi77aQ1bEn993SBJTkEDZ7piLcWnyUD8ObHrAIQSVsreVvRb0MD7H3fb7/9bGjeVrY9WXL0X25sfN/73uc+KR1ikxm5KJvwTY9nn332O9/5ziVLlsRVVlL90LEt4wBbTMTk5J43eHWazuGn/febB2WkM90ct+nJxne/hKyXt0vp+OOPtx3clnpIMSLXY7kKMy6+3MRTQ7UHag/UHngKekBHunn/bAyyWX9tupKy1dZtYFG099u+Xcxf4pjUsmOfq4Ft9M0b2WPPazrTHT1+YHisZ073C0+da9519y1u4yhWDBRDQ92jAwSkKbHhTeq3Jy7o8cPAnkpEQgoT0/qq/rKfl2Q7vd3pJVVEljgsWbTFL7tODAAaaQDF1sZIaEuUeaMvwEw9mC1oUII8jSP8tXelhzI0xjti2jg8VN5+YjnDKkVpUjKv0dnjAuXBRNze3k2vfcWd9pP7jVC/NNuxmrRRO5ld8ZWkJ+HJfymrpRtDxzMs5qNZjsqCTM87nYeIklSVFnDGZCAkND1OJ3aLeGOMUD0d5awVRTWIWF7USBWlo3vO/J13281il0NnQzT52WsVp6O9zf1qjva1FX29fb1z5u/qMFt5qM+AxKlORyXWF+0DQ35xfbzo7jPQGku/ud2I9bqx8pdpy9W2qIdJ6cRjQqR3QW0nf+LF6qhsEy95I7MxrGovK3C5YJfMkrTB+UZrem2jG5KmEb/eHS959TXN6kJUHW+VBxTNVtG7ecrwWMVwf71BrHOzmujFixfPZrwaS1jUefti67bTmAEbdRtpaxVjpN1qkhoSL0gQoGS5UxYoZ/1ebCYVex7nz9IJFKHMIVR7bJoUbKZm8oHBcmq9LqzF5TCte+X4BEaAmaSd6d+4dIzbEUUnwm99fVPefhXdzUSnUwpNK916NHHvxNsXrUQyoCSYeGfLPi4Qm8U0xukjeVHujO/omFA9g/38E9kky6HxWJl0hJioLTZ6oZ6EOJtBjuBR60QjB6pCACFnYTOLN39Q61BSKiOEbNHtiAlI0kv5cXg4VJPgLRALaKbTTotUNpMQosJ+9ALe8KekOtQeqD2wg3sg3uJtaOTWCqz7Xx6LoBQ0oeBoY2cuFC1t3f9yUd3/1v3vzG9KnVp7YIf1gNb+ybXN4F9XYuTPEhOZmESYVkRPNLNtYXzEZnAaIvT59iswIRG3ZrMqnzoBjeuD4Wc5h6raFpPoYCQkppNVglY4bIMPs5sszKmtjIHhqGD0aOrnCicTH7P++ObVJG06IfDtY2nJdMnuS83FBjuHFi9eODw8unr1uva29s60zsynFodiMlud+YbItGigBvm/sy1dWJbMTr9MkJbIivb4ybqJGxyabMCCWKCX31QAE1IjiuhNIm5iqT4GO4yMK3GM3G7mPsuyo5e7xOjpUgMBHgGcCSOkjG0p0KvmmDVjFGQ8DJuON+MBQsx/o/Lj9S6k9cDy9hOpUypnJHw2LwDI5PPJ+W/mlakphdTI2gO1B57GHkiNS3n3hyZFA6VlEOQ3YkC0G9FeaXA0eunLYBnCLRqiaFKiYQyWYA/h4hAYDSl6GAS5IQri3ATFY7BkCZAYQ2MgYQTGsIrlVqSzouCVCmB/PFIHQCwASBPCBoy0i7XSwRIqcuMZ9GL4kCMOOB5RxsAgaMQCA0IOABm9HimNzisTUB2KwgNhdtgTeFknqTRfjlJHO56uvYKRNb71p3viAGvCZT9b5sViMzcXPty0j463tY82xgfL3zLpis65FGfzqa4gjG/4rElQ6s1SCANSNsqMsF/xRWZhEIyGEWXliZqTU0umFJEsZqu8R+mHZJSCshPDML1EJFgID2R2SZwWopLQyQAPhI8AxisOdrDelquRpSKv9JUIBPZABleUOziHEOURZRgAriIjO1QzHnvUJQJhkOEKXph4DEykSkIvDhsyMUWhLjAIZhnCMFxRl0LOFnlzdli1ReInhoDloWg2Hsj2q35ba15ric8sYZb0Yb9SmI39oTFygWVmA3LqU4Z+8tK9bPkTmd8t+h8BT0Yc9d9jhODNMSTLxdEkxis8+/KadrtDk1/qx9oDtQdqD9QeqD1Qe6D2QO2B2gO1B2oP1B6oPVB7oPZA7YHaA7UHag/UHqg9UHtgBg/Emn4TgTX89G2mrXjta19rG9D3f3quT3/p85svfBVSNImsgpx4rtDMEvTBcfJjVuHWLDu9BtcNXXjVdYcddth73/vej//zP89Szo5JxslT+nm7WusbDKWPWQVem8Bs/yLhHe94h8cLLrgAHGLjM89jFo49JDweC0N7iJpBTlOSy6r8wuEHPvCBD33oQ6r01mbB19OrrrrqC1/4wi/90i/dcMMN9gXCnH766eTEh9VQF7nz9Ss+Xfuw7TarO+64w5fRuIZDkpfFhzEYLMia7CTwM5/5zPvf//4/+ZM/cXDRjSF07bXXXvfccw9pr3nNa+bPn//PT/H3YmudX9PXHth+Hmh9Abefrhkkp+/nZbAjZ5elu2gobMAN28TRsGiFkUTTl+DH0c5PKGv6J/X+bdfcf+eyZcvc9LfqnB/p+pO6hk1SW+5TpvPkdPgm5Y//MbwU8eOXtkUJyTEtRTAlcouiWgn0wnvvvfeKFSsuvPDC1tSnBIYror9j7fXXX3/33XefdNJJrRdgzSYvRGH/2c9+duqpp5522mnB4lItP7AcMAJAq/P1nn4UOo4QeKGCJliqjFWMn/527+cb3vCGhQsXXnvttU7CSyVHXk455RSdst9GrtLXcO2B2gO1B3ZkD+TmcWuNTP1beSQpjTdiC2xPT7FgQf+8eQcffLDZzYZGh9Z1fGSdWVVbuTPaLnndYueCBQN7793Tsc49Jj0DG8x0bIamPS7EsamYsOg8AZsWAZpv40r2RipiywXT2W9XcMiJG0tKLjt90/Ul6fxU2qPsnkTbEzfdh5WVAmJBI4TnPj33JnmoFZ2MnJrBlZLTsbGxsfLikokBXJnHyWu2prP26Y3PlS082fS4xbxzaS6CGD+EBMicFPgsKkoq4kAGSybIAjNmmwNV7bMX7kDgWDHmDTPec+H1isF0zC9qo5MLzHaznBdq1do15gVzFy4GbxgYgKfO2QCn7oxPutrTyQYEkJ0d5cLC8MQ5kBksCYPFaPgz6W2hzu8FjUI1PT1WMOSEqCpNDT9OD0S5bJUQpaBotLraKOt4KpX2SrWxfDRLOdiVe2qwy/MnqZqNjIi3aAwyvKHIApfwmKsEOcIsDc5kwSI2XHcQFN7b4VKSINiiMbFh3UrdvHnzHJ/Wr2HfIlfWDggDZBw7WNDjlMt9sz09goWcDRtGXSEFGBxszJmzFdvjlVFZWGPz5vUrwaptM8P0yqn5pkJnf5gxM0tTKpbQSA5Yrl27ZnWUtCbK6R6jtVFpNYYYowbOxv9otH7ERpUz0SNKHQjeLdZb1gr8psTxAkz9jG0INMH0OJ3BNb72QO2Bp7EHtth0tOY9Wr+6/63737r/rfvf1vahxtQeqD2wzT0QTQ2xpp967eiFzQJMQ3LSbJRiCV5yYj4Ss4PZCEGJS4zRrCG0Z3tmoz1oQoI4jAfMRnvmBZjFmPIDTGZNaiJpljF1ISFPomfDGBbK8sCAX2t7xGKDhaVHH10FbxmgvW1Wc8BQLd64cci10fQODAy7dCUM2KITMCp63a5c+04HEHCF2NnkYs2aNRZMrHXgUnBYZs+LmLoq/f/P3p+A2XVU997w3mfuWerWLNmWLHkesbEBY2MTA44xkwmQ5AUCPOGGEALJzYPJzb1PQpKbm5cMlwQIvB/hQkhILibMBsxkG894ni0Pkq3Jmls9D2fc+/utWudUb53T3WpJlqxhlY931161atWqf+29q3ads/7CCGku7aoOdUmMHWsnpdKm+lyWoKixAYiR5PsvdubYurbr33/pPvzp/v0XO+reHK3Nvb+maQgYAocNgQO+f6nITISfmuFxoT7rc4PnpJaqkFNmATT1hxM8Enko+YcYcs17I8nuU5Hq6FCqR0oRksggpC5yzfsjpd4+OqpMqdfUtjiqn3qKQusTnrrUQq5FekRTbdIRb1+nZt+Kb4sq5P1EQJ4qJDJNVaiLnCPJV6ch6tId7GgtLfUIUyTmXEVqaUJHkaH35PUbTzLJrUs08R872hf5KlS+2pXVkf9HSihS/0P3TSurGXS0LeyT3D++WCfAkupuLPTIKdVJqq+l/PPGlJKoy5Ei3xFvua7pBk4BR1NLvSmtxZ4w9sUNl7Qtse4sS9tu+LRULXBUBa2lP9b1RZjVGZOGaJqEBdUkj5BT9cE3ijVv32d8dW1LG6WuZqirZpuMcKqeJxtCghwJGbWgDqgp3+J+ZZJeqTWO2so+7VBXdXxmn1UOtYLCiD8emX22eOQ4j6sH4P8+O3i0KsxAgHWouzPHKwc1/wTAJa4ixo4jST2klETey7XUKyDHCM8ZniezdGpOb0ez1LciQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFD4EhGgL1yvv7ULfVp/USBnXT9Rjmppt90UjptLRWio7vwqOl3MCpns95J2MQPLhyML7rsdavOPvPP/uzPHg2CtjBYQCDr5KDs8fMFp/t36WnX02HwPQAffnyTTY8LUdZ0qVQRr2ia7y3dlwV1JdrNZtO1qvuSFSupcAwSrCB4PAg+fcsPvv9fPnHtFb+y+dZfTATBTleD4Cq+HM5OlgM+iQRinPm+k0HS9H2D9le/jUCZpsn7UzVGRR/3goVp7WhbicblN08A4r/wSBYhnFae1DnIPPZ9x/nVFP/0Fr9744drSbN081//9V//4A/+gB/le3myohdqhl+P0Sl+gPXWt74Vjqf169cjp5VZqjRZmOkUC2qEo9qcSXOfcvwhoQYhFBkGjnzyjtBSb4ce3XnnnW9/+9vhdtmyZYuXzzGj99fXv/71173udbBo/eM//qO/EsQPB46aIq8Z7ali7pW9TvKnCU0+QBHyP/7H/0gKYb/i9KKLLrr22ms//OEPJ7uZVLO8IWAItCKgd6KXJx8+zFx6J6oOX1ST0fsreVP7ujNlqEXSUp9JKrvyKZ1kkebzTEqEwQfRWctWnv+yC2655ZYSv2iNg5yb63j4TjO1Sw1J9WcOp42HT6v92SRh2AHxRBCOoRQH5XTqu/fc/onXXxusPPu2oa3y+9fG1O5ak6lTFF1bzLN+qqV0plZmKZqpyn7JvX0yOnB1TPbLyv4oa4tAoVcLVw4/rmLqJEPTydaRv/e974WBkaDo5ETgffbNJmsRBgxdFOSJwKud8mpHY4buPPXUUxCbEtvM+uTAprB77rnnjDPOYB68//77FQT9R4/BzY+7ypNIIvG/4SbfVKT6ySPDdP3116PWpLlixYrVq1dDv8UPxFW/dQSTdixvCBgChsDRhUDMO74sNoSJCpYqnK+l5KfSQaYDJo+gZ+H8M87ILF6N4MliKmzrq4TZPcVwrL1XdGIJwpmUf3M46FmRb1+y5oRTV7Tv2rXrwV+Udu1KR+UgDvPu3wSupeRVX3cDMim3neDWDjOtIBrrGrGcTPxi2Z9qzJX7AXMqCvg3jPk1d71QH+aREA1N6aOgxfKcd5RVOokQvObk/OvIsihyeQGEUjdtVWSdFVQoiYMS9fjwq8ialIdxnaKr/stm6lLJEWjwE8ej71eOipuiNPcjtfQXn4JIY0SBLrHfootZVxrLmOq/LO3UBXCtxRFTWksl6oOqJXW8b1qFU5rzCr70Rc8oPv44F/tcLKjV3MUQh+7OioNsZ1fYMW+kGpYcZ1AxLTqss+XX+YW2VFv7gu75hXkLwrZCWzaX7ZLdLRAGGfZ83OabBDaMT06w/xPLP5jNDp27biN5nXHGxDX8dH+mbgGnhJmU7uwJfZbzaEq5cY8wjNDbiTyQxU+VG1jM1ccOh5DxicMcbUw1IO1ZOnAEwJj3076+Pob0wK1Yzf1HYOnSpftfaarG4sWLGyc6CenwHcwgyr3WSMl8Qzb938b9PH3psSlduHDhwXeMQGg1wj24ZMkSHq0LFiwgQHq/LPO2S8Uzzzxzv2qZsiFgCLxYCBCIyw3IXcxXRX7rZo7Gbf6dI1AvuprNvy86pIfNoM2/hw1qa8gQOMIROJj5FyZimG3XrFnjmQuO8M4eM+7xO5lZ+5J8q51V0e0RBbHQE/Mbr4SqexcOlaNZrSUKZ8wej++zM4JxKAv4ulPNswF18skn+6+296tN+KP5tvecc85Rfq79qmvKhoAhcPAIQEN/3nnnsQ11MKZ4EearBPmmYO+NaIQ+oaO/BdKfW8i3EC7xANHbHwnKfFWhnlCoGYRk9MgbOsn/wll1qKh1VQ0hnmhdPVJKRo8qwVqTt9RSIRnNc0qGWmhq69TVLugPdbQUodcnj2++CV9LJZgikW+SUx2h+qxFTQpaCx00SZyigCdU4VTrkkGop0g06anKnefSOjXUAsekPmo+oSd5NyM3vqMTk/pzOL7tJK8QqwVGTTLuGyMlzPKmRLNBywVuqqmlkeuQKmgfm/yhyCf1Vi8PHQWU63ZcR/wwIaSo7lIDNOyo3Bv0GR0ytYbQ+0BGBxoFWmeOowk/vlqqbqtxb7Apg6Yqc8QHVSZPL0jaL9FwSXvkLVBK0ioc8YGjgqA6WgsdMk5XDipUhePqmERPoZ5L94FrLmqHQefA/D8Mjr00TbjnyeFsOnklJPPT+sBg6dOAOw4Fvd78U4jq5Elqx1+NyVOtqDfstE2o8Oj7acgsnbEiQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIFjDwH/r83TNf7lPb7K5QstOIn0e0FK+T7Mf8GGDgFsSChFh31zvvfiV9rk9WswxYcgCkz5PXQ23DnlV2X61Z3qEG+j/+awfOHm/u0aalEl+UUaJEHo0DoVCfLXihyxRhobG6TiqlWrrrnmmttvuWWgNjm/Tb7rxeeurvEokNDWyAXsoYyrtVoZT/TrHNrie1NcorlisZwEgVq5jNjRUBDfKBkk2MnU5CdQk+47P9EZGVmcDwYHJ2688UYIIK6I4ptv/8XSvkX0ZXutiD4/icOhnKPi0i9o6Rf2aZpSTOES/34dwNa/ynVNckpKflfBKQpaxanIWPBjO1VDE4PYoS9JnJHs2bMnaQcJLAxJhgWtQi2SaipKyba0xYM/KrCvfOUrwerEE098//vfz++N9JsY39xVV12FG/oP+vkWm3S8nAwXLT83XO7Spz/9aV/k4dUMA5G8irzaLBkuMBxmKGfRmWMR/qsb9BfwOVLRd3laI7fffvtHP/rRU0899QAIsNQgA33TTTfxa7BkW3SKU8VT1WZxQ4sODIHdu3d//OMf5+cs2oodDQFDYH8R4D71iZuRBz7PeWYunnjclRx5QPE8IY/aPo3r7YwmswZGOE3+0BM5EhKPCPI6F3DaZBY5EpgZUmHqnNVn8GvRdevWyQMtLZqt+mqKJw5lagqJaPLbrGyWtnBG5XM9iqPC9UBMPdl0LlsaH3/yySehK7r/yUGWIkxk2pY2zVGXK0n76OCqPtnIoAMUZHxKKs+SpyKJWhxBRqd1J5MDFSniSEOaIa+ls9g8FEXqIe7RzXPPPZffi+PSDTfcoGOtLaqHzBcUNT239WJQNfz3ffGunnTSSUzot956K5LWUq92xGbwWZP38Jlnnnn961/PCvPxxx/3wjlmMIUmP+KERYv1p9bSy0NXccnrgdLkpTJ7E2pZdbTWTPrM/j/96U+hc5mL8kxGTG4IGAJHHQI6R6vb/imhz21/Sqk+TFR+xPZxb4fFTR6ubjVRX06o51MPRjrPj/zy+d7e3ollK0lx73KmuTFHwRO5f0NYuLF4rRNmbJIQRfFYZnLUF2r2NHaXSumhAeSYoi5HfWLjTPJ1UD1phc49/rHd/OaI225RUK/h1GQRQhOuX9IjPxzimVvToeCb0G66ovosrG2Bitb1mmRwW+l9HF+WlKgyixGX9wBOnTpgHSoNu1LtKEwK1FwcV8A97FThSkiui5C4gZhaYDtgxXZTKzouyJNDpoOrK0PNS02XfKMsUNnHQOadId9kXKtMe2zS5NSnJn29klWITlPptKe4hF8UyeXIFRUFy5Yt654/Hwn3Cwtp3kHYRGrj09bW0T2P22fRshXUYhVOc+qJAoI+mbTbDcvmc2zITJZGRaL/0LdD2fuABU0qaYLOq2lGRwR9PZWuTd00dd3koCDyyvVi+3PQCAA7q+VTTjkFOh4d+oM2aQYOMwLJUOGWW2g/fNHdbq2QzM9uoh5sNruSlU6LAA80fbxzG/LPErABywbRHJ9yVEGTOWjnzp2887JFMG0TJjQEDIFDhAD3IEsUbmGWQ3zpowtChDQ397vY5t9DNDqHy6zNv4cL6Re7HZt/X2xEzZ4hcPgQOPj5l82QJ554gm/Q+NnG4fPbWto3AslZdXZt965qBFizg3RElrIPyRTMOy+3Ie+/nCZ/Uze7y9z7pK1bt/I7srPPPvvCCy9kKT7HVffslq3UEDAE5oIAO/zcg7z2ctvyj2hy/86lFjrU8prk/T6/ypOlSTXNa6PktZYq8wBBovnWh4CXJBtC6BtKmvItJjOqTNNo+m8ueOtXHVrHFEWkpA/arqqpnCNC/82ICr3nZLSKmvWlesrRV0yqIU9qJvO+ouok/cdVb8SbVf+9Bc1wdJqqLzvMKkdIhkInSTZVzzs1901lo9A1JAA2/mmTRkGj43pZuIpJI9IiMIIzjfqxRqL/Dovijw5pyuLeOa1L05rxY4cWXpGmrasQUaSNojzTRQ50lDYZ8afaKArS9waPm+rTdGtFJMmEcSzoEblegdhRU75pMkiSFckjoa5WIaMd160q7Qu18IEi1ZwJilazSUlru8nSoyjvO6JDPxfPfZW5KB9qHe/M3P0/1C6Z/bkgoOPlHzhaJTmI5P3gktGkaprXZwsSTptanOvE3FTNTg0BQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIEjBwEC6b0zSa4K5PrtF2FsSR4NrzxLhn+ZnER1NtlJ/ltAhL4WG/TQauj3eXyr6OQca2NjY5OTY3zN19mZv+7c12SfeOHan/4glwtH0lBsRPOCFNQc3sgcM2zxN7724zvj2SqpaVT4LC0FcSkYDuKJYvAvj9xXeNX5l6w69Ynbb314zy74t2ptuSCbJQaQz/CI9KteNwy17/q9At0kASZfGfqGNbAQfNQrIAIHUvKrCE6xw5FaWoRakx0so4O8qS1+JOTb8hmUyYs3jeSLku164QFk+KKUf6mPxA8E+aURlxMS7Z239t73vvfb3/62dtwLm069nAxFpHe84x1chErAkSwlr73hKtXLTBGbY4+Sg9JkVi2rcHZr6oDq0GWCRUmt1pokP/nJT2CveOc733nzzTdrEXbm0py3g/NQyCXJ6ShqdVUlrXKvPG2Rb2WmDCMLiwo+H1j1mcya3BA49hDwt7Z2zZ/6e5MMiTuaxxePOzKc8uQ8MCig5nH2pn6IQ4uzWBPCqTioMsESAo8qynFQjqtX9J1YGCh9+fm1Y6WxICtfi+tPiJJeqVmqNH+LnlTaz/wYPxVLhQsqMc7siSEOCB/f9vzyM0+Fhon5rj0OEfEvSDpP5RC5n5PN3ogCwlHVxOH6T7Wm6vnSKVEi12SBEowkyqeyM8mnNA5Njl8WQk8AcWdfXx8ZZkx40HxT2uVXvepVsIl5oWa45DSDjqo1KVx88cVcVJ6rcVqdpipH2qkOn/eKn2Aye8LvdgAEWGoE0IDXI4x9hXH2q8g7MJfMLKagEG1iEZ2LQdMxBAyBYxWBpscyp/R0lmfIS46DeujdaJ1OHRVmUA0cz04qKwQ9qULET+e7l7UtO3F48fI9bX1xezcWSvkuHr9l+d1vFISZIKqEFZnic7VAFlNRO/sGm2odI+la9+rzVqwJhx9+bHTHjiCuRXBepXg3hzCrxoyekfUGqMmxvqbZe5JvrCAoT9J56A6G8G4lk6xN4qDm6LDjSPRljGCzCmWFBsMnEq2hrFUpx9zFSivl3quo22icn5XLWKLP/9SLI5HUVzOt68SEl9JE4/frKqZpJByb8EfzGEqCDStZ+hgGkInnJkoDgIYwnY3SqQ7W2gFXlujUOy1Dw34OycHkTkW/FtVgg3V60Jq7cjfMYjiI2gptjHBPTw9LU16KAZYlsa6K2ZHglCOUUps2bQJwxZwjVlx1sdaUtDQpnEkzqUNerLvkdqViOoMQTja53lyq/0nkw4BrLYy47LizMnk5Ftoqbe0VqNe7u0tBuaNQ6JvXDaFqV08vbqRzBY5hW4F+VeUSlUSvi+USC6HRnePkkdBCLp1hC2jZvF5WWSMjE8A6OSZU5mRQECP1Fbi6BiuZbIIF6Qw+cLUKbg0SeTToBUe1jL/aMZGoNV9UNybaMjZOwd1k9QK9o1vfYhr17O/0COhg8ULBKwAvAn7RO722SY9cBOrzlHNw77lq/3xunXLmUj85Y85F33SmEOCRqCe60c3NWH8eTqnMlkOZtGHDhueee242PSszBAyBQ4AA9y83oH7lxC3M/cusOsd2bP6dI1BHvJrNv0f8EM3goM2/MwBjYkPgKEDgYOZf7Z6un/kRDnsaR0GHjyMXk7PqHLpdJ8Bq0YTUXdLcrdn7bAuGh1jAb+fYfeK3c+wlcj9qa/tcSKPJ7iLfifOveT399NOc8jTYZ61D3BUzbwgcdwjwD4bp7Mm/FOtX1LOgwK1K8gqab6qYVEAzeUq+SRkFfQf3NjXT+jTwdfVZkTTrq7QKkfDtj/6OC5u+dfl2wyWVoEZSiT8mfdBSjq1Cr9+UaTLoT5MWqOLlvnqrhCL1U4/eea2uBqmlFfWYtOa+2/EPZy3R06kdD1+rbkW0RKf+rSuZkG9wpDByldj9rHeE76Ecv5VUAENXKpm9k/dZTLiNF/3eh7wqkmlCxhug1xRp3xF6U+QRqoWkHV+RDHJNqpws8nkUfN5naFHlelQHaDqpnMz7ik0ZdEjeeS1VIUevrBJ/6lunRV9XO661PN0kpypvhUKtYSqZaWqIIjXom2469fLWzNw1W+seConv6QE4dgBVXvQuHIz/L7ozR4DBqbvjMDszx4tB1Rg1P3Cz+5k0S15rkdGkdZN5lRgB1uyoWqkhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGwEuMAAxT3oNCocApX1npka1wfoSdVECTUxI6+v2Wrzs+Pu7zEFQlT5FzSvLfmSHxBEAISURM8WsVKnojmmHnHTc4Nsn1m2I4F/iO/Mtf/jJtF8txpSxqo20wHRVVPyIo0X0fOTY+pib42o1PW1t+dHSsUoORqsaXpvQoab9cDcrVqusFHBpTqVCQfpUlKjKohUElFY5FqfEoLjkGifFq8C//8i//63f/+KJzL7r7sfvkG4LJMm7hzcToaP3rvilj+8hBWsS3if4LRbSRkJJQaIwZ8YT+qw4FM6kzUzPJLoO8jinD7U3NVPEg5Yz7Yy51dXW95z3v0S4gTF4eixYtggDLNwRj1Jo1a+bPn//ggw+CAH2kKNlH/Of3CtB28C/eY8dXTGaWLl36vve97+///u+TcCUVmvIf/vCHv/vd7+7YsQPfklg1qelp0plpFVRIBCzOL3Bp4cKFIEDXuMxmss8t9vDDD5933nmz2JxLkdpvuo/m6LPa3y9l79K2bdvIU7epaa9gGUPAEPAI6J2ip8m7hoeGvwF5fPFY4FR/oUWem4vkjcwxo09RlLHj28WO5vnxUNKOCPU3RE7Kj7eoVYuDpQsWI7jtttvGgr0mbvSbJ+ykuUS+WhYWCZ0FEuJ9ZTGfYP6qVSpiJKjecccd57z5DURvTowXmxDxUfdq2mE2ta7QjjPbKivBvpqvl0s3XUf1iNRnkhamFSYVDlteu7lnz55bbrmFnq5ataoJeXWVuWnt2rXeK6ZXeAqYrfinrb2wKYPllStX8tNhJuimIj1l7nv5y1/OADEQ0yo0Ca+88spHH320v7+/SX44T1m5EQwMpdrBN6qrCOD1y+amqwIAD6AVjPiKTQa9NR10f2oZQ8AQOOYR4K73c5l/ROijwJ8Cgj40kpKjBZmkz9ILfXzybsgHhpxCYcGSJUxwcdd8pp5yezsrFmZ3epdYOLi+ylJqKpwJsyx+Fi9ezMbC5m07R4eHM2V56S7Bqs0jOpZ3zzmmaddArUKxCv9QIzFqmmVw3AjKSgqvWLDpFAFBUKUiedZoug1CdTrFh65DEkR12ElRKBWFf5vOOfvivlpQ/9WThkTg06ZpF2HDhzn29UhU0ytE0Gh0ssVLCusycrLZEgWnndHGZtTG5xlo4bRyREpTOpgiTVVzoHHBtGfbqQWArM3RZrDIqyaL99NOO21+bw9TP2/ELJMeeOABqA3YbOGa5N385JNPZolFraRl8kiSDdWdcH+0VKsk5XPPq2W/qvcZ7qPWRmGPDYgwAKywRt8IEsNhek2k2dKTVrKfke/p4i6LwwwgTJalU8XREVZ6RcjjuChd12QziwSFXKNfVGeJtaBnwerVq2+55XbMkuTtpvGD/lZP3B0hHqZSadZU5ZK8vKjBJmUggtmMItngs3RYENCNOHbquAz8JXpYWrZGXkQEdCpVg8n8/jZxYHUPrNb++nZs6uvjkb7xoNU55QD6yYNdH+8HUNeqGAKGwAEjIIsfF5fI7EmGtF+mbP7dL7iOVOXkDJjM76+/B1b3wGrtr2/Hpr7Nv8fmuFqvjg8EDnL+BSSmbB4CLL9ZRScx29/XYbaGktUtbwgYArMgwG6qL+Vf3ePbal1Ce+E+M1hgQ5JaeutxI3ML+59J7LO6KRgChsBBIsAdxw3I7MkXAUyj7PC3/iS4qQnuU73TmbvJ6D1LXZJqzv4SPXtpU1vTnnoL6obX4ZS8L/VylehKo0lhlqdNqx21r2ZbS5ua4zSp70t9ZloLCGevpdWnreu7r6XTda2WGKMpqkH2P7xXeO3zuhWyd2m9U7TF90Cq6R2mXWlU91EaVtQZ1eE57z0no/nWf/hE5Q0DU38xkiyaroPTjL7WT1bkSpgyunfO4+PFvqJ2wZ96hWRGSz0gySLyTXX1NCnUDmq/1BNfmuz7tB3HvvaLUmrNpJN0CTVGxHd5Wre9A8mKTXlfcS7KTXWPtNNjoAtHGqQH7c/+bckedHN1A3olzOV60Ot/LprotKp5CRluYVLyOem7M7Xa9iLLGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAscnAv7LrWT3ZQ++8eVlUq55dvOjSOgtlrpvCdNROe0i6JB8sHv5hy58/XXPP/ST3J5qJUCJL0v50jsVZDhGkg1S7tsKjpxgYOrbVLWeOHo31Jd0SOxolh/VtP4Orf6vD4nNeCQTQJHl4kWDLalgy0Tl3l/e/Y63X/vLdU/tmhwdxb5r2rcTuX85kCMRr7VYfuKWCvb6nZzXnEuGn+wQZKhfLurXHuT11FfXfvneIU/mvZpmtMh/g9hUus9TIt/47dE+1bwC14P+2IgwSPqC5/x84ZOf/ORPfvITr0N05de+9rW77roLco1/+Id/+J3f+Z3e3t6mf12TH0wQsXz55Zd/73vfGx4e9nXJ+L6g8MY3vhECLBqlm0mdafPvfOc7f/zjH1M0E6NHshat7NMmCmeddda73/1u6EWuv/56+vu5z33ukUce+cxnPoOpaS3w5RPkMvhMiOzzzz+var7dfbboNTWzv/pN1Q/m9CVs+mDctrqGwOFBwD+mfHNewr2TvH14QiYphPyUmnzsyxzQ4MzydfUJ483SkBY1HVWN6mS8canlHpn606R0FFTi+IKgcPW5l/yfDY9sSg0x3ZJCxxagP0VSOxxJWMMUGe9MPZN4DjNxCE+f+9FY/djAQityxIjOF5TATIDukBA2xulIfqgKw+VYaeSEZ55/yysuvffmWwcnBjcHUSUd5mP5wL7ZsCd/xQGdtgkWrdXyjTLskMWo2E241yjf6y8KmpCS4Uh1zajEG1Ghsyo/72MQfUVf11dEcmCJOcXj02SBIh1Nf/FAwcDvC/nHNpUyklKcZ7a95JJLYL/yPJJ9fX1vfvObCWjfsmXLBz7wAeZESCGT3dSGmJSxQJFfADR1B36Es88++xe/+EWTYzOdXnjhhc8+++xMpQcmh8rqiiuugHQSokxm4Y9+9KNkcGnnzp0zGaRHZ555Js43rStm0p9JrhyUM5V6ub9gvGT2DPpeYaa6yYFI5n1FyxgChsAxhgCzLh/31st0Vp/73OOi6d2nfrqvue5ww+N/vk7DiYcc86z3pJ7TvkVhVl7v0+2i3bMsvXBhsOyUaMnqbHu2lMuNBrlUrjAZAwQrG/lkqlG1Fqaq/E43HnMP0bF8lbrzo/b8gnnluLRxdGJj2BUsOrm4a30AaWNFeBtTKaVX1r2FqWcvWwHeLRZB6rA71uUOedV3z+BGVZYbjr0zSGcq+M8/WoxmOpa5WLqjP+EW4qo4mxG6q3ItOO/M7NNPl6plYfYBDTYoaCPt2pEgoNqCcrmUzQ/jD8jwe+4q/0gyuxAoxLLvoInoPCiEyPMrRwcpXtV/ppzJZFmiyLkU0FM+4kq95hH/BwBJ/Jra9U7HQuBG6HyX/jpedHrlJFEnnatGI+B5wSuDP/iDd7D4+cxnvvPs2mJjq0nqudqyJqSOMyWtaIIwpK2tgwsJtSiSX9Uj51it4QNM6bm+vsU989pYS7AMY8WlQRooMMqkzs5ONhlYmFHKKW/9FJFco+qzOJBMWuolNEdej01FXkczqiN55yFMaS47tdZ1/5K2G2ts6pXBdUJH4nG5BMI2yOOXnn8+S6kT1qxk1RcU2lgcjlfLg/2DlWqJ1ivSe4iy4lo6qHLHNXyDHiuTyyi5le55ZTra+ln2l6pL0+HJa9awwiyXIpZn1XhqFZ1yhG7eSezHsfz4VqzCb4VvTsR+Gmi7Ja57HmgcgwNP7gIqoBdyy4gokjsMI5Q4ZSmdSomqU0LLzQUBnhsMPdcwylNX2lxqms4RhEDyx+0zBufMwd8D39meg3FTmQ0BZb/i0YiSHmfT3rsMfYl7mDkua291OzMEDIEXGQG9Z5Nz6FzuYpt/X+RheGnM2fz70uD+IrZq8++LCKaZMgQOMwIHNv+qk2zjsJ/j9iIO3Gt9gMy9/lyWB3O3dixqJmfVOfQvlp2xaVKodvbT2jSGTPRiIpBcKvN1rTfNfaFF+7xBku+8WoWjZrw1yxgChsChQ8Dfbtyt3I/8LnT2tvTuVmW+QOHXL0y+7q494u7cZNdm79QxWTrt47c29Y+TCHlZo+PJfeO50oD6bza9nWSLfFOqxn1p/VfcjSb9X/32x58e0kzSw2kb4hbwDk+rcKiF3kPdDd4vZ/RrIL6Vi+Iq3zzDRKZfxDWMyIi4vHxfjgL/omYtku+PWDlz5EtS7Z3qe0/m3mWtOHd90zQEphCYcf0/zVfGU7UOZW5/b4GDvP655WmRLWUy3MvkSb5/9v7jobCMIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhMA0CTRvr02jMIILPaPPmzfAieYqiqe35GaocBvFjjz124oknvuMd7/j81/7lMDSn30z4hhTM5BcVvmgumQOuqMaV/GLZsmUrVqwoFAq+Rbg2tm7dSrTkww8/7IVkWr+h4Wd/0GR88pOfVDV+zfDtb38bSL/0pS8tWbLkyiuvhI9jz549SSOaxzjfmG7fvp0vbHxpq31f9JJk7rnnnrvvvjvZNBQks2MO1Re/6li6dKkSYCXrHkAeQGZv7gBsWhVDwBA4eAQO58OKhwDNkfRHKt55JD5Ppmly8UXFWiUThK9++auHhoZ4sKPHo1yj5b3OnJ4ziS/UfcX9yrS2sn79ev71yPPOO++OX94xbXz7ftmfXZnWWx2gigoB05eKXuOXuLPbPJhSZb+a51LSDr9gYKRYlmzcuBGd5KBDXpDUhKcJpsjvfOc7TKb6m6d3vetd8EFASQk31po1a/iV4bQdgdYB5gIWAJ5gK2n20OVpF+Oermv2huj+d7/7XaWykqtfOSBmvQj5B0jBgdXFQRJgze7Y4Sll4A5PQ9aKIWAIvLQI8HDzDiTzXtiUaRBONYlftNMDfvYk+iHO6GmTtalTijs7O5YvX7RoUW7+fN4f+UfdOVJRFjPQQDVYh4ScCBG/ZvSVHW8LIplNxgeZKHnlnHf66cObu7Y+8UQ4PgFfkU4ZM4HiDOxVOO1c6TWy2WBiPMjn4KKChVO6FjZ+f82PgKO4VqoF2RQdCiYng8my0DV1dwSXXXbZunU3RS4+C9/5hXghA3OWkHS5Yymfy4+WhFcbswiJ2JJ38Ujy6iHtHieJn1Iz7BqL6Kc/cAY3+XBFSIIiTG6Wq64+55q3rvqXf/laNhv+3u+9+YbvrL/5509JuRCQub/ujypznfjbCsuQWBGtwapJW+FIykIlxr8yHZRYPDy77gmWRiy3XnjhBV10sdchF2AUQfQ5MjLC+koN6pHq0uSck3dm9hqoYVmOe+vt1Wijtw4jKOLQTQeLVvb09fWctJqbAqpxFnuFng5qbdq5a3JycmhyXBwOhcE8zOTkJ+z8x/Xn/jVviribUGYppYnGEQ5VKmwT5bP5bdu2nbjkRG7V59dt3LRpE0Tq2PcOYqfus8MEOyRwo91UmCODJkdthSylvq5kKKDLe4nqJ8inE5vMEDAEDIGjD4HmR9/+9+DgLex/m1bDEDAEDAFDwBA4uhE4+Nnz4C0c3Qia94aAIbD/CNhzY/8xm73GtDtGjQ3o2ata6RGDwAHfF1rxgKsfMQCYI4bAsY8AO/n6FYNs9TfSsd/tY6eHyX9o4UAoZpq/0Nobmdavf2bSb9Xc25Kd7R8C+vWc1klOpj6vGW5bEsqc6tEr7F97pm0IvCgIzPTVcOMfk3tRGjmSjegvRfm+XifWJleNAKsJEDs1BAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDgiEODbpiPCjzk7Meg0+/PRZD44vRq87tJL15xz1mc+85nnxrdP1hr/vk/dmsR+ztlwXdF9ASeYzBEYGvBtSDV+LuUQ/dFE/9r7b/q3q94Tn/2qv37il+hEfGXCJ05+xbu/3s2o78eRzIv7laG3PGPbLQVEKvLl5cqVK3/zN39TIxglHjIMiUDmexTYMeCugoTC10s6zLcsyK+55hoqPv7446pz+umnn3vuubfeeutv//Zvo/zpT3/a/9OayS9WUT711FOpeP/993vj02YOoFPT2vHCN7zhDeR/+tOfesm0GR2dpM9gAlwQjnR1dRE6O5Njzz33HOGy559//l133TWt5f0SztTKfhkxZUPAEDgUCOjtmXwq7rMVCTIngt89PPepfDAKGWGPCEpu2uuNgleffeHoom7o/FKVWlsmVyIC3f2DUdJEcgZtxKInH30H44av2/ooK7pfLD8/OVx87ul3n3nRxWvOGlz/SKEcjGbk4ygOfG2X8X76TKMc43MZBdS0X94ZTsnrqbegpw3bh/AvMyCkAAsXLrz00kt9M+oG1wmJKZgZh0zrcKiTsE9CScA0rbUgO2D+feihhy6++GKopr7xjW9gH8utPYIiioq7du3y7R5MBg9nqq6OeQfOPPNMNPc573trnsfKW/BF02bgd+PmYnWxYcOGaRVeLOEc/Wlq7sBqNRmxU0PAEDjmEOC3pLyR6o+J6z8pdg9P95ra0tvGRN1SMIOgZc6cQe8gxN4ln8GYtqvzQ90H5gr6lM0LvVMqX1i4rPOEk3Pz5tXmLxxv6ypn2/hXZSthG1MeTD78K7MQJNdgPorS/Ku0cVwUjp7QTTdRiIWxIIW4xjtrWyFXyOcWL+4IS8H2PeH4EHWyQToSHqypXs00UalvDEHDeYFdH9cUaSnkk/N7gzNOX8Vr8o6d6wcHg5E9sfuHb/m3N8WpzlywZFmQywUdHbAD5detH3nDG0657PJTH370pl07AmYk/pnSM04JFi04mXltsrQHMtL27uHXvOa0/sFdu3cHy5e1b9w40dYVdHcHmzcEIlnavmrVqgcfEF6nWmNDwnmoV4UeD+QH6FOIHFm5tFwq7l9zrQ9aymEbtXGayqQq1fEgmKDbV74xeNf/s+rb179w689koLLh1jddu7gUPXXnzTIQOl78w8RweadTHSwJSNxNGOeYy+VXrz5l5849ZKJIFlf8ihsrrKZ+4zd+4y//4q+eefq58cndjCAXADsYGEEHC7oSg5xUt0coJTOXOV1XQTThM3NEXZqIZE8m4skAEZgui7kC9DKln/6Cpm+l8aCzJ9W7lHXgyWecAYPq/NWnsV/BZtbAxMSejXtYTNYyAUxek2kMAA9op9JhkBLL3FeO/srdndwGggjEWBkudoFI/pVnbkwUKzC3pdZteu7kk08+7YzTqlF1aHRkslrCLIl/PRo1zHLUPnImltJ5Rg2wZGfNgcaNWYcujjPiTGPAhQtOdOcIkakdGAIK/lyu3gOzb7VeCgS4dywZAoaAIWAIHNEI2Px7RA/PATpn8+8BAmfVDAFDwBAwBAyBA0HA7VcdSEWrYwgYAoaAIXBYEEhuOJPnWxW+XKBlv/9/WLywRl5EBKb+CdsX0WiTKSPAagLkRT/Vf3dKv5tL3qTakJdohqPetnw7SJ6bl3/JmKOXq9qL7qQZNASmR8D9ZmCaouPmvYA7l6/dOQICd1/TDWgEWNNcGyYyBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEHipENBvhZv2sl8qZw643VVLl77xjW+84fs3bNq1qZh2gXXTEVwcsP2Dqbh4ce/GjS/ccccdUCNBgHUwpva3rvuSovmLiv014vX120d/2pRpKtVTjnBwwJcBMYp+/am1+FGCr07wp8+TednLXvbggw96yetf//rvfve7nFKdWosXLyY69Atf+MLOnTsREofZ09MzPk7ManPq6+vj25pt27ZRgBvNxY3zOV75s1hIFmHtNa95DbYhwErKkVDkJclG6Zd2jd4tWrQIxhDYrxreTfNXg2PBYZoyExkChoAhcNgRWJDtgmUJbr5dQ7sKbe0834qVkoTQEz+vVAEJl/xjMCF78bO0oo/Zvvm9/f39+AZ5YrD+kRe/pYRFfrJDo9ouYu9DQuWwZmGn4vdDmzZtUpbJpGP8nEhdwUmSd2vp0qVbtmzhVJXXrFnz6KOP6ilqK1asYOb96le/qtWhuFK6Rl/dZzo7O5l/IUTwkoPJJD30dlSoR981rkOvoBnc0FKv06TgT1FAmVPVnLbRkZERfgICvZevZRlDwBAwBI5wBHiakfT5RuZF93Z/Te6vvl9HzFKRIlXLZjMlPenq4l1pwYoVTFUTHV28ajX1XX9l27pKEXyYC9LpHPw9QTAvJ8fOuFIul2EsCtrbmeDkxVM2GySJHdc2zDycek/Iq0uzuC31XWprC/74jz/+gxu+w3Ll6je+5t577x3aXXIEWEGxFhRSwfLlhYtecfL116/t7i598IOvfWHrT+gOTJRtbdnu7kx7++Tpp+fOOeecu+5YzwR95tmrli0bevyxQYiK3nLt+V/4wjc++MEP/vCHP0zlJj/ykY/83of+enh44pWvfOUFF1ygBFh6begPHHGHjjAPetjrLh61f5Lj7nuVFNIzLpLKWAn+2Gve/LI3vnXhP//zDffeHoTC5RTc+MOHSlHw67/+urHBRx55qD+VDqIag89bO1sZzYucyWLAMml8Ys/43iUPPfzAm970po9//ON//dd/rcsS0BZSJ4eyrh4d8ikuVP3BdxPeTQ77Ut8jzcyk5vWTGVXm6I3wmCAv+xJQstVKQSoXtLV1z5s3f9EZJ5xwAtxX3d3dw463i+UffNz9A3voSCmOWG1C7y6eOzor+qjGscbK2DeKkKQIhI4Ui1O6rxIYr7QUstH57fPZEXpu4wbuCJpgtwdTZNDEGs2hmckUOKUlJO4ulIbcuEiDrmTqH5GmOjRcHCExq9/7omXJEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMASOAgRkn98lvizQjJccBd6bi4cdASPAOtSQ89U539ZxG3KcqS1fqner1+eUG5mj/5J0JgsmNwQOCQLHPQGWv/v0rmwC2QiwmgCxU0PAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyBlxIBNrXZziZIjC+W4N+55JJLCOzEIQ0bIzaPPF9cqRp5It92796d9BguAEgBiHlDmYA0r5MkNnrVq15FQFp7e7taw/iCBQsGBweTdgjjVHIBjJCmDU7DN3TK5UnaImyQ6pedcc7KYvhfLnzNZP/QE9nyotdc+q7lS5AXi/JtGRnfOyJVVcJR7VSrBMmGUSid7eubjz/0gqbpL0GzkCudccYZp50WET5KJB7RgOCzZ88e/4U6xmFZGhsboxb5wIX8NeJhRZBOS2BelBZsny4RM5u+8Xeve+aZZ9afd3K6PJrOCxpU439YGyA2wg6tZFL1eEhXOnUAvYGBARxWEZl58+aBhnZKhVdccQUDhBqnOoJ42IRzo+/VpvGaaikIGB38IbKRiFDGEYOM8rQj4mvhBtApOBdeeCHecp08/fTTyAkYxlsfkeiraIYqNHHaaacR6AjUEHAsX74cwLFGd+jFV77yFVBBmeNtt92GY694xStuvPFGYnevueaa73znO8py1WT2vPPOwzLKTfLWU3wjjHN4eBhkzjzzTKo8++yzCF/72tc+//zzTdQhrdWT+FPKICbZMZKl5DHrJXJtOMIURYbe+YGbBeodO3agv3r1avXEW/OZVg9NYggYAkc+AsyAJ510kj7reFDgcPK4vzd4Uh+zkDXwROWZMxMOsxQ1VdGfCmVicW/ZkmUdY6VrzruY/ANBZdFpp5dmaGHu9rU5Iu1JPAmxzDSUdwlCJcWkySVOZ2g2yHZ28EifLHQMtqU/fu17oXMaWrgoLJYrLRVS9Z/Mie2WwvpYtLablJx88skMokKNn3QZzJMKs+Rbn/lU187ywCfyH1OtOq0GmXlZvTD6UDAoARbrmVY1L0ETkkqmXZpgHcIiB7iE48Mt9kD+9ttv98pwU8I0yvz71FNPUQWuDdgtWdJ4BZ9RAkpWR14ye4ae4gnLAJYNzIPcBeeeey4zO/Md7k27eEBfa3FkMYN9Jl8PkSKPjpd4B3QhqteSWqBIrZHxNr1+U6bVYJOCnRoChoAhYAi8iAg0XnybTaqcd30yvCbyLp5NFwKm8lo6XLAoe+IppfbudO8CuHnKhXb+DdlimMNEJYLiJ6zFQS2GsSdgsRWH1YCVTZgN5Ny9x2OE6SOUaXcilumpFqfKo5O86eZPWLVyXoHpqX/TSCBkSJUgTEWB/NReVw5p+Sv11V180x/kO2/rwnpPGkWcZrLB6NjuWrxrdDw+85zu7btSz61zFWuBLiMWLuy7/FdWvbB97bp1wS/v+8mOncGDDz37+qu2PPhQZWiAvYvgt377gttuu+eJp4JCNjjlzA3ved81v/u7P1q/8edvfNtvIPzzP/vshg3BvAXBJa98oW/xxKOPBcOjz3/mc7coeuxY4EMIMxAwiHNMr/KHCVH+HL1Jfroa8h8jWB+Rxq/dOZehjyFO4m80OraHXr7tXfPe+MYzPvcPP378UelzW15gKQe1m28MwtrGj370o//n//yfX961JZUJOtoyrLUWLVzF+iSXa2cRwlYGcLGAYQ+kUGhnGcNig+uHhBGWZL/4xS8+cd1/+7u/+7u/+J9/nNy9UZBVjY0LMioRn+s/vZWry3VB9nbIex3yTQm1fY6amkItCh1DKAMvWMg6KubqjWpVlo0xGy+ZthNXsjNDT1esWkkfw64ePChH0Wgcd5SLpXKpmk7Vgpi1Fzar4lpYDtgOcrkwwKK//lOhkk+xNubWc33LyDYIvaV7kfQpqlUAoAaTVqU4OTk8PjzSsXzp4u7OdjiwhocHsa4LYxZ4EGVxmk7JPw0dpPMc+SPN1+m0hO0Og+jTkuZFxZIhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBzlCOjXHEd5J8z9Q4/A3t/K+vZmEPtyy+wHAtyMfAHXdEvOdIomqcl6k3JTqZ0aAobAoUZg2nvQCLAONexm3xAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUNg/xBgO5s4PeLE1q9ff99995GhvsaYKa1AEwGW0vT4NuBN0EA+vqxS0gHlYNK8qsE1AHWRi0OT7xPRhGKpyY4SM0nkm+PpmHaTHR1M1WryL8lkMhJiV5goQpMBXwNBiXeNDBD9NtGWoy4EWNo0miQk9EptElDY0d4xMjGGK+gT90eLCxb0wvsAgQL5crm2ePGC3/7t337iiSd27h7MZ+/m355Jp0MiACHASnIx0IvR0dG6pIUAK5DQv6C9pxt8usfG77nnnv/4wO/CKAEtFPnRybqH6CxcKHRg1Sp9T2XTOR0C9d8fYZSgdTBUCZ1qJcCiCIoKBRYFlA+GAAs0sKDRmJBQgB42vT+tGTQhrfjDP/xD1O6++24A/MY3vkGG0VHkW6sgoRYXzJvf/OZHHnlk+/bt559/Pv5DgIUciitCRoFXSWG0+vvf/344OK688sqHH374+uuvX7t27bRmqQKBlxJ5TKuQFMJ+xWX853/+54Q0n3XWWTB6rFmzBoVrr732ne98580335xUnj3P2NUvM2IuW5IWgSruaX52SFsMCMcHKcku16pjEkPAEDiKEGCe5aZmmuD5o7OJPhb0qM/8aSeFWfqYfORiYcOGDRxJM1WZpaipirIHlCrl9nSWZyxTDMef/PwnG4My86kjg2yq4U5nnTtaK1x66aW7du2i1yADiVHOEWBBqDTTA3OmjuW7u8YHBgeCgJnirRe9mvmXdc6GTRshrGxuNPHAbimTRUuzfss5Y7d582ZPOOXwnsmv5srJ8dIyXQgxU+hkAQ7NdVrOYaR6/etf39/fz8THYgm2BWbSFq1mAZcfNA2srO644w7mwRUrVsBvhdLFF1+8ZcuWJu2vfvWrEIMyBdMK3FiMEQq01cR1Rd+ZwT0UTUZaT1nMsAbAJv7jM8MEKyWLgX/8x3+Ej1IXok21dETAjeTvDtjHVI3Wm/T9KT31eUDGDhaQYISkeT22DjpDTBEj4i1YxhAwBAwBQ+DwIMA83PpqpZOzf+TLImpeJzTB0eIVvCvxxOY5793jlOSe9V6WyPiJnp2QKCox7ZbLwxWZMiYqJWp1OsrI3mwvkuKe58fgm86leY8VE1ApxTHElc4+B5HNlLSUo+/R8HDwy1/+cuXKlZOTTzGBMifmcpBYCplWT3dmfKLKDgCz4Sc/+Qe1WubrX//6A/dvwziTbH+/6CxdGsC79PWv34OQCWrPnoDXWFjHAYOFJf1Y/7z4wux3yy23XHXVFY8/fiuz5K7tAeyRrtOy89Pq8j66ISaPjuQXDK3uFnIF1le1uPjWt13xznee8rd/+yXYrzI5QZWkFXNtwU0/Xd/e9v33ve99g4P/9PTaIVZNUMa/7PxL2R4pl+FYS7NQ4ch62NGDnsfQsG4FQF1jULRp0ybyH/vDD37u83/DRgd5BpqLioxviAxVSJrRIRF+qsZCRZeCrJEYPlVDsykl5cl8qxobYtJ6NhdzoXAV1yaDWinI9nY7FvilJ6+EF3X+/Pk4P1Epc2cVR0fJ02W6RobkV03eOELvrRfihuul7PloFXToPkdOUcM4mags/9ozHHV0szRRgQKVRR27fFzMoMeVTNPgrBa0lliocQizGaHB8i2SV/tTIl9mGUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQOGoRSH4dcNR2whw/5AjM+A3RjAWH3KXjsIHWbypV4u9in2nVPA7hsi4bAocfAe7B1rvPfhB5+AfCWjQEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQ2AcCxJsRJ0aQGzF1EnDZSISxEZNGEfvduuXtv39qqEhUHolS1FTIKRk9qoRAQSgDNOwNiRrUotaja0oOmTArjsVTW+upOBtVwpoGBlbh2oje2rVqdd/q//Xjb/5yZKA/F+Q62rtqaQLnUplQguhIOKOfRpBhOiVhitkwXYtrEnUnUa7C1FAqVapRkKqKkAhGouy0R6WKtFeLYoTouDpimOQkRMg6h1w7kWQbKRTGio4oE02O7wmCPQM7/vfNN3zwgx988z0PbhqvPdjQ4u94jaZreFKDpyuq0PdEYT1LWzjpUSWjrSeVkUBIkZTA9ZCke8AWFZUWSkcWCUOjEZW+Uar4ywB9DPp2vU5r5pRTTvnJT37ypS996ZOf/KSaPfPMM3/84x+3ajZJIP/667/+axVqcyBP5ld+5Vc+8YlPJJURPupSUjhtnkuXCwBOmWlLm4Twd8B+9Z//+Z+EH//Wb/3Wv/7rv0LDwb1ApCvkXCh3dHQ0VZnptInWrVUNtEGeoWwtUonGgs5USiwoo8MNpQrJsZ6piskNAUPgSEZAH7889Hhk8XB40V1V+xJb7uLMp7Wvk91MzxOdkDLEygexm1bFRjGuXb70ZJ6N927ftCUoy2QaMAvKn2lSIi59mlIVNSY+wtYhJSwmiIrKxSLOExk/zdTo6rrWpzHcV4lT6dxwrTxcHlv7wqZVq1ZdWOvas2njeBSzXpEZmqnGGVXLre7PhElrY0xPCqMvmnvdVk2uBIRM1tiEatPbbM3AWgVccAS8613vglvzRz/6ETowiC1fvnz37t2zzDVqirpwfTKvqv+6cGL9c8EFF3zlK1/RmdQ3CsOX2kSNZZReWk3sVyjjM/7PkYCStiCahH4L4q3LLruM/Be+8AXchoFLqR5xzDuQzKjDHEnIccbP1LiNRFHlqBkkSvWlRhCq/3raevS1fBE9IrE28BLLGAKGgCFgCBxSBHSOpgl50Ot8nZgTqi7PeyqzebWaDiErWrK8sOKkUu/SVDY7lslXs4ViJsc0EYVCnVMLMvKmLXSH7ACwomEtAMMQB9kOkI5QECpnFqRWvJvL6301lUeLNzde4liO7N49WC10BrligHMhOiWmvYhXeFYBQrg9lRrrGme17qqUyqzlCLBU+5yze3//93//j//4Y+uegQyo2Nvb3de3/YUXxK/JCVkWtrV1XP9/1331K585++wML6e33vxlVmNt+cUcTz094GV38wuPLl4qlscmg96+YHhk58RYUMgtyGdOpDM1R+g0OR797MaN7/zN06761dSjD2+lB/AdCa6xEHbTEfWKLG47z/fqi+gcXSlkRBzo4vYUYxQy2bKKeqBfqmUGWFle9aYFv/nei/7iz//uqceBOigWZdiFFysWTUEpDG743kNB3PW+337FF7/4043Pjtzw42/+4hd3sB5gVYB1rh0WHqzWYEvfsmUbaxjkrCK45FgRUQSp2a/+6q/8w6c/MzoqzKGuSBfUck3764T8lMv4L+fySeNwEHR1wYyWG9gzxHpTTFDN3x5SXk+6KJLS6RKl9aJ0hs27YILuoZkJOha1L1t24oknnnzyyXQkKmTRLNYieTcJc2EWqqw0+tUwE8TpOJ2qhQGfmPsukvp6rWjTekxxz9EZaSzOu124MAM9lnDus/qaLBZZKFZk/60UVchWa1XZbwRJjjBlZTLpwcE9AMlWzJIli+bP79m4cePw8CCnWHA9c/0AiiCM4ir7e2BHg25Zj4JAVsO1Og1ZhN8gyU6dlDV2txgdqTQF+nSQmcwQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQODgH33d7BmbDahoAhcIgRmIrSOcQNmXlDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUNgrggQSEZIJ181QXukMWAuFkzYHHxImGYI4UOYtMspCQtqhCLVQejVyGNfiRW8cJYMmjiTTmckmrQeUifqEh3n+DdwBo2VixZAsXTvvfc+9OQT81Lp/jI8VhNBdxfVy9VKSqLbJMYPx5JtFWuVTLFYVjuugKg357NEv+I16jhM7KJ+9zavu31oZILQPmndxfh5azSEp1iXOFYXLJrKBNEUgYlEHtI6pe3zeieGBn7xyEMn33LL6897eV+6PYgm6nZiiaeV0MZalEqHxO9pu74VzShTgy8igwSXkmp4A78DHEkqpPv0i5TUgbRCx4sjCiRpfe9EFdVBrC0iwbJnxdpbXdCj6X/7t3+DGkPZry688MJf+7Vf+7u/+zvVJFxzFhoOHz6qFwl0VBBqvOlNbyLQ8cEHp4jC1FvIMiRUsjGmBJRimdQEhXJzeLiaHG465Sratm0b7FfIV69e/fzzz9911110FgaQxx57jN61QuQtnHvuuT6P27CQ9PX1LV26dOHChV6OEZ/HFK7i9vnnn++FTZlbb721ScKpdp+6jDu9a1UwiSFgCBylCPC81ZuaDF3gZm86zvFR5rvvn5BI1KY36HWSGdWZqRWdQWGNFLMyqcqsSuJx9/jjjz/w1CMdqdxQVMYIHBTTJu3RtEVeiEGZRx2NFJMCiwaRuEc9Mxn4ND3kfUUyoEYTSf/FyzgeGh0S52WGDtZuehqz5176KtYMk+WxujxpZe881fcW7ONsLn3ch4lEsXNfqCeZgIj2h9rJz+wJLcnCYMXxmmuuYWaEcZJRYFaCv+Dmm29G0qTsT5PekteJiXmcPE0zPW13idaTVSj18y/D4Y00YYU1xg5rvu4sGXq3bt062K/Q6erqWr9+/dNPP81646/+6q/27NmD0F/ASSM0TYK2FQ/VScgmdNrt7e1V5iz1YWhoSC4kl5QPFH4K2MHIY8F7rhk8xxlWFMkirYuEHtFrdJKeWN4QMAQMAUPgUCMwF4KaVD7P432SOaC3t+rewkYzGR7d6hsPeeYCjpr24TDvyLlcuq0t7witYD1kJmqPJ3gFG9m8GQtLlizZnU6PD/Sz+AgqZYpjNgmYaxqrIG3WLWr2aqrhjtQj6XHFihVMLmNjQUcX7EtF1ipLlhT6+4uVyWCyGhSyAe+VV1y58p/+6cE776xeeulzsChXKkLDvXJlcMYZK3/4w43f+c6dJ52UWdwXMy2eeGKFxQCWeaFm8bB0aTCyuxv745WxsYkyb9bnnHPOj37wKM66ZU6dIUi91G0QdQy0VHi0Hxkvx/Ek/QAW1jUML3ToE8VhyK2uuvpl73rXq/73//7fsF/l2nUw65r80RFk2s/lghu+f1s1bP9//98Pfe6zX7z7tmAo2CF6ibR589ZVq07csGFzQiZZLss/+qM/uummmz7zmc9UqqOwaAJ+A39RcAMhGZ/wEyHDIa7HQdWtp2Cn4nru3z2gyx65thPLGK2bvOA1zxEE9MhlTGIZI+uZEkzy1SDf0z5//sKTT4MiFvsUsYKitH98FIMp3aRyO0ZUxAgKZHRZRV63p7Rp5LjJkVN3l8ntBuMU1lJQjbm1Exa4bkmTwnwFibzsZXHvkIeIzfkpy85crqB22I3hjuAy5h6B75UtGphYuUHy+Txq4MBdxxF/VB/76oz6oHlxwHnFqXrudSxjCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChxoB/b6SVnzmULdo9g0BQ+AAELAfRB4AaFbFEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ+DwISCxai6RIaIMIgBCxciTNKgsGVqGIqUkMhp+pnXRUaGekif2DAt6SgaJP/VClXDUTD7fVpkYqwXC9SOxdEFQiavEAHJeSGXPrlU+fNW7Noalz9/03fEgGIxqRLwVMmEtiEvVCg4RWkjYHqGBVORYDSCiConyzKTSmXwuVSpq4CfF+EXn5CNftElDeqrNwn4lIknqheb1mKrrN2QdHZ2jw2P1M2dqNBtVS0F1aAAhbFVf/M63Xr109R9+8MMjX/zCjmByI52pBeM4G8btcZCthaWMhBdCkLRjx15xlXXPGg3xt1VCxSQ1EgqaEpUkLpGko+OHibFL6rTmiU5UtojWIiQM7mWXXXbxxRf/7Gc/u+6662CAwvJ/+2//7YEHHphWv0lIZKNKaIWK0EhxSgTvn//5n5PBWy2lL5QSIYlE+8Up3B/kVSF5VOWkZJY8jBv33XefKlxyySXwfhBvCSZKXEXvkuGdTXY8yRdyqixbtoy4ayJdlYBDla+66ipfSyI8g2DBggWf//znvbAp82d/9mff/va3EfqR8goMnI6dl1jGEDAEDIEmBPxjE7nmeVomhU36etqqUIH1geRolIh6r5ZKme7O0vDwGUFw9aVX7x4dvueph5hgR6MyWszBSoDFVLpfaeoZ7h7muNpUHcdESGmiSNWYHqRUW3T1dHYQUoBGhUxMiH/MhHrrs090nXPqRVddeccPvk+U/kgoE1/NzTBLHLnAbp0JHemDmsQIOnPpUSoI+YiyW3IQ8x+mmjvS1C9/qgsVf0qGVvkgp+nixOTE+HirLfUQ5TNOO/2E5SsgBbj6ql+FIABkbvvFrVu3blWDrRVFrlAroNATuBUUYrrA0ohGv/ed74oWrJ+a0Bf4Q2hJtYOs4bS/dYXGH3EbTfdpyJr/JhV27dy5e+cuNPLZ3PKly4YHh7BPl7ds2kxzuUx2QW+ftzbVF8YoDN7x9l/zpplzobXidH7PvOT8++lPf5paOo7pMAVnKdbe/rZrfUWfYW6FeIupecOmjYqDRxgL1IqEglWqWzIEDAFDwBA41AgkH7bwazLXM1XRaFWm2aAqB5KbtsMsS4H2E05ZsGrVrvau0TATptphTK7GuShMleIsc0ApZmsiVQlg0JbJ1S0QoBdijnOLAFm+6PumK2bujYIq87iuB2TfolKOyrwepoJ0pnfh4lznwpNOfvCeu6OdO4NyFHS0BdmMTA41yEA9yY4I3GQrjk6f3NLm3nsfW7LklgtedvoLL7xw+y9e4FVxoB9ibPoklVj4PP3084sXv+LyS9dkc+H9927Zvi3o6wt+/MMnT1tz+o7tbIEEt/w4uOCC5a9+9Umg9MKGbffc8+zqlZ3Z1MrHHtl62inz1harw43diV/eMd7bmx8bdgRMbD+EQSpdcoCIq0nOK9ZWMnkKEbl+xBcn0a4oXJo/Mo9cLThMkmWmfjhx5Jz0q5/8q6/IvOXacz732S8/8oCsduBRnzaBsFunxTd+b6I8cdsHP/iRUunzD97T0IVz3X1oIt82AIuZI49qlLI91d7/0CM3fPUrN02J5pZzG2xTquxLuLVwxtHNatfE7abkFtX0nauWEhkmtjIk5xKXsu605HqXQyN11muuhId03klryE84Hnyqh2yVdc5nWwZ6KVnfUzdMcSuxy8TmIFSppajGDRhluCXZQJOrRJOuzDmS2KyRLYtKKapUSuXiuLDYR+wr4gyJVjiiIPVrUS6FMrt8cY3zOIbgXfemcBlLIyMDGzZMssY77bTVnZ0Flrul0jj7hPJOIDt4Ec25sXZHWYyHEYtZSdJ9XbZGbkHL80IamHLZaU13qD9/FObpFExmCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFwBCIw0z/ceAS6eoy55L6zc1/Nynfce33Nxql8R+fkmjnG+m7dOXoQkK/Op0n+O+9pyo5B0Uy3oRFgHYODbV0yBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPgWEJAYtUSCeof4t+8oKkUORviuifu1VRHhVoRhiMNddNTitBRNW9Zo+DUoAolEm+CSEQJXdMgy87ODgk9jaujtfK73/qek0466ZZH7hkMgmGnwDcRk9W4TeJjJawtUw96JC9xbp5gohrVhNWrEfmGJh/XunzVppGf+JbJZPTbOL6RQ44DRP1l0iHRe95nYvPS6VRUFcRUis+jqQYBlvOaWhU1kU2lKwLuhg0bLr300tf9yut+eMsPg+6OYGSsNjkZFGO6miMy0LFBYScJIPY1DtA3rRL/BaHK0RkZGUnqOJj3GlDMqmVf5CW+oi/SVjht+mLSa/rM6aefToziv/7rv15//fVeCInV4OAgpwDuhbNn1Dd0br311i1btjQp4wkSPTYVNZ2qz7jUJJ/2dGBgADl9hL7qnHPO+dSnPsWp94S8uw6nrRr86q/+qi+gyn//7/8dAqyPf/zjXpjM0IQLPk0R4XzllVcmi5J5dLj8oPDw1GC+FAskf2oZQ8AQMARaEUg+vjTPUTOtykj0qTKNQn26EyImfZyWmGKi4FVnv2p8fHzHwK5SIHQUaRglGlPqtPb3QxhCtNX8fbrzfWrmTVrDc3HbcTOp//4oGfe0xPt0AFtTMBFXn3nmGR7RF5170W2P3c9iIJtmohd7+lStP1yTj9hGsy/Jg5cuzGW+W7p0KbxXDz/88KOPPlqqlJXmqbuzizGCryoJV1NegGt0UIsUvaeffnpgaBCJXyklSzWfBEkl/oiRucNF++oEvJ+rVq26+eab06k0Q9PZ1j42ATtoTSdobzyZ+ed//mdOtS2IFd58zZs4/cGPfpjUEYXGuaKxe/fuv/zLv2zIpv5q31kNIsLaVEEjR0PaVkNgfw0BQ8AQMAReMgR4IDPZSPNQ3uRyCxcuhF+41tbJKoIXV8TJ10Ce8CQotDi6jQF35L2y6WkvmwEukWETpFJhj6BSg0Y7KpZGkPBqxrxcDioQbHV1dQ3vEgJHTWK5kaSxqbOGtPG3vthwp+SHhoKvfe2HhXwASfL69WM4lYp48ZfiAsuXONi0aeJf//UXSFLpoFgOMqmA3YIbb7y3pycYGg4KhYAX2Z/+dFNQ28SLL1seWBgcGNuw4Va2IqhVK8tq6PLLT/7lL58fHBx+5plhbCIBNpLmJZdIszif0DrKsu3tIS/XixYvYJulvXP4/PPPv+rql8N6+ciDpUXLglqpm42UTCbFjlbV7e2wHpBJPywzlqmwMH9+LkwP3PSzp1ef2vmhD33opyu/9+STO5cslA0QCNmoCyv3aaedtmLF9p07d9IQtbhUOMJo9pOf3HTWOT1sjAwNjSDhyqUWbmCfiokLdWrtgRb4bnh+u9PPkc/nenbt2lUpZ5F46P2yJCmk1KugoB3R3ZhCoXDiiSfiSe+Jp7Eebj/hZLaqRuNY9zpcW7Kuhq5Km1AJeZwkpbNZ0MEgeSyLJJ1GB9557xKn5Ok7t09Mq+wClYrkWVlRsWlZq8q+Lb8qRQ3jlGpbkHY98cQTOLxy5UrudDZS+vsHsJnJCYbqjEJRb92t5Rg9Tb7RZHP1MvtjCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChwUBvq3TL/UOS2vWiCFgCDQjMPs35s0/2G2ubeeGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAobAS4pAknuCiDiSj0DjKyiVJB1En6SRdfodVasOAX4IKcUU2+hEqXFKraQdlfiAOjHV1kZ8ZyqWkL+si6qbiMtBXH15EJy/8rxTX/ny7/zoR4Pl4vKeRXuGdxXC1EQYl6M4V2jP5orE2mXz+aT9ZL69vV2D95zDEuDnWk8HVcL5QuLzOEWHRFF7Wx63ie7jlKhXCYVtpI6ODhyWuD5Hl4A1VwkmK0nqc3c+n87nJ8ulQja3q1JctmDB3ZODP/vu13//9W8pDw4///AdbW3tewq5KpGspaCcJYQvCz5EJ6oRf8SykH81GJ20LXqR/FoCf1DDJYQScxhLR1pxRohZLGjdVh0Z0UwG4xTRcTQZQSI5k333jmmGQES4Np566ilOqYIPeHLxxRfffffdo6OjTcpzOVX2K5pGOdnHZH4WO6jhML2YRccXEQV63nnnQbn1spe9jJ7ecccdFK1evfp3fud3PvnJTxIRqoh5/WSmyR9OmyRNypRKuGYms2PHjmRRU54LoGlwnWH5GjjZKcWHurM02mTZTg0BQ+CoQICbmhtcb+39vcH3qd+kwKl/mCg49UD2kjvLhTzWJ5i+C7kFpXjFyhUdK5bcd999nUsW1oRcyLFHpEKJWnch+T7qfu44a+viRINpa451vedNPWqqLgxeYYr5gOfq888//2unvay6sP/B3ZvTUao/J8/PnRlx3XFB1KsmDVK5yWDyNKmZlB9kXtHEuKQEi1OrWXgBmHOhRYD9ilIoBnAXggN6OlmcbNVPSvyoqVAZr5T9KqlGvj66TdIZTpmnPN3ADCp18fKlLIsWPPr4Y0uWLOnr62MVAfvVwr4Fb3jDG3784x8PDg1OTNcFddtD1NQEFGBIHGx7cXh1dnSOjo+xNpgsFZuq6KlczdMVePxnvxKmq2oyQ8AQMAQMgQNHICX0lfLW6qZoNx+GQrgDsyX/B9l2nvXBvMVtvP8uWMonnWqHEaqa75hMpWtZeU8vRvLaW4L8KobnOpSnPK/+8FnLQicVsHgR+9BNNWa5eotwe7L4iYV0ilL4dHLtQSaqhRNMBJPpiWIQZnuXpMN8bccWmKvgOkKHVsSoHmlq2hlF2mskp0APo2pQrMExJBkswS2qyw7oq2Aiks7jKZWioE16E9QqwoE1PgkDqRTxycB7hU4YpMKA119hJ40g7ApqlIbB6aevfP/7fyud/trGjTtqlfHGVKeNiO/JZY66zaKv4eVR/Vd6waKArkI21dsrq4sTTjghndvCTsUPb7hz65bxCy44hffufHYJ2yxd3W1DQ0MTE6OUQlzFSgZKLFK1AjdWJpPuPeu01L13PdDVVn3Fq049ec38jvwrKU2nhQCLzZCzzjpraOgkVmVKgMUrPxZI7CRw0ZKhIa6fMCULXi5O0kzgYhYd2N45BnEbamsfH7zxxhsff3xjLluAGApJHNRJ6kXHLXugrpKbhGvZjR5qsEhVqyWOS5cuWrhwCWSj555/Pm6Mpbvo0M6a3BjjQdU1J3eWmmJRLwlHsZRyRFch11GYSYUsMbUjQVE2pkJc4C7RSwkSepfoeKVcBECAkw2NSomeYjmTSnOtqQ4ZPtyIsfBc4S60dbQurOv0Qq7IEFJ72YLjTs9kAwZgaLj/ybXCMrbmlJWLFi3avn37nqFRFNhky2blQeEApRLWuDPEorYledbG7q1KO+jlljEEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIHDhoD7ZvCwtWYNGQKGwH4gMKdAi/2wZ6qGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAobAi4pAd3e3t6d5iV4jbM5FxCFpCtWDdAl9jWrTiLJWHSQk4t+Il8MO1jhtstPZ2entQKVEPvmNlwS/xfGChX2QQJ3Vs/JDH/rQt+/6xWOPPXbRFa95+OGHl/UtELPVMtF2xPVRlyxBfeo2p02JtjCIPzgcRUJope4hoQqEVpw2VfGnyag58uiTKKVnmseyKqddzB2nyIl7RHnRvHmASUTl/aR5S6+++ur7wvG1a9f29vYOQi5WqgpnU0o05/eIpm+UjLbFUYVkNCV1yGOqv7+fIlqhae1XUke7BlDoKMLT6jBS6GCBI/mkhWnzN9100549e2C8evTRRxlZghI/8pGPbNq0CT6pAyDAwjdaSR45TV4PvnRaZ1So1WdR8EV/+qd/+va3v/2SSy553etex/Wj3Fvvfve7n3vuOdivGJSuri6vPEtGr4RZFHwRl7HPt2awA4Y6Rq2lc+9Xa12TGAKGgCGwHwjwKNYwengVgzQPw4Xd3WefffaenXt4iPXIbJeGrkEMTsWY74d5VeWZVn+suUl0v+rXK7r5IjlHeLm4Jif1OYXpjAUGJAUXXHDBpvsnBgcHVaHOG+DaRlWqNGbbhoIr2/uQbHHvkoM9U8uOvkD9n8agQp7NZNetWzcwMLBixYqNmzehx/rhmmuugf2K8dGOI2wQe0xjZ5Yir40n2pyXeMte4jMsohhJrhYl5PLyaTNXXnklY/E//+f/5DgyMgJtBGqnn346fF7wcMGVwCpi2opCyrB3gdeE5sCXqNvqLRdtW74wy5KmyWCTEU6TV4UvtYwhYAgYAobAYUNgaubVaTqbXbCQtUl3R08PmxITEVQ4Wd5y/UsZz21e8HFPHv7MG8IvNTVH1N1WUzLVuUlDFcjzjk+VvPzMLxtkaHp+W5l3tGhsTLcLaHciG+xYv140mWorwh9EIi/LI9n22KuF+on7Q5G2QGuFAgxCvHrXy2GwohRyJKkuHKTNRlyHhLXTF6GBNf2IL26OhMobm0yJW7du/eIXv8gkOzQ0DnOWKnvfOFVnVIKRZJFoH+WJ7vCZnKxCP/3zn/+cjaDhsacnJoJtm4PFy4JdpY2yaxQM0stsLnR7NXm2Mlgikq848qaoluXFPJvp4xobndj82c9+ecEyRi2oFp+mlsLOyz17U9QaHi7nciL0MEKWrlRXjJcou8tKr0R0kDQlipBzZDdIKjqdpYtO2rZtG7tcQQABVp2pqqki156/5rR1LntNUHtfeOHFbBCFbnuHilzD2UIXVVguclXrLaNXrzeLkMvQn5LR3SGufDVLc2RqseyqsdOnZrEcl4vYDMolcbUmu0mYSvhGtt4FKmrTSJKsa1TBmui5zUPyOI9N9mfY7+qdv+iUU045oRKxATXUz6U9Il1zCR1MSl1XmwxijpYMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPgkCJgX8wdUnjNuCFwSBEwAqxDCq8ZNwQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBA4WASGhoa8CeJFCScj6sxLyChhhJeoDke+wVJNMk06w8PDmCUajUg2jWEjNhWhxqOqKUppy0WsYcAFjlYrqUq5khUeqMpksavQXli78+LOtvf+xtsefeDOv/nhN3NBsLQ6sbkyNrFHGBMktjUIBnbupq1yXEs7ciqaIyF3f1wGeojiPGkrjmhGPmEAU4Pzh9i8oEoY6tAQRAkkKk5Olgi/S4cVTqFDohPSjEtIYHcqVyOJM3WBdSgoe1dDJci3EylbTZUqBAXunJwkwO/B0uDa0tBf/fw7H1uz9O+uuPZ/PbT2Z5u21oqlShRM0JCrWZmYTA4EMqINCSZUZDglwylJe+eb27x5M5qcSn/jmHhO+gWqXoEhoO8aUihAh6H2nahOr4M+rVMdCSNFhtLkYHlNn0HhYx/7GKRRMFAsWbKE6NBvf/vbd955p1fYrwwt4pg6oMem6tMKkzrr169va2sjEjIpnCkPdQi9+x//43889dRT11133Z/8yZ+gSajwV77yFTKA3DQWM9kBVaBTbGfS8XI66PNNGSxoaWs3TzzxRCi6NmzYoFWSRlqVm8zaqSFgCBwVCOi97J+B+Ly/d/fs+lrapKOn/pGSdRMdQe88G1P5dh5KqclSZ67t/JNPKe0e+Pb9t2eDVFgr7QlqEaQN4uKBQ0vT2rr2lEfuXGxpFZYU4rOLcfe1kta8ELVaVMtEcWV84p7nnh7MxK952ctvv/32UlThuT2WdewIFZlbxRsJwp+a61PCMiHJ44MCp3p0JXsdsJVysfd7SQ/oRJtIp9I4P60BhmZweOjLX/7ym970pgULoAFZCIcUlFhPPvmkrmGmrTWTUKel1sFsmq7oYNLCXidBwBqDpQ7OjI7L2mz2BFHXSSed9OY3vxnqSdKv/dqvoU+GodGKc7weUJ5lxleHwbBWqgHm7C61lroOxlBhsr5iTdWqYBJDwBAwBAyBQ4OAvhPF8oYFRxS0UMoelMoI51Sm0L5s2byTVgszTue8gWpQbu/grb6cLkTpTDnM1ULegVNRKk3FKOR/N9eroyGzvJ/NHMmQ7wCWY3m5D6giR5n+oyDNhNtfiorFUkc5ZEugJ9fW09PTm8vtnKzGG55nTcD7OocqvgkBZcTaIY5l7k6x5+ASc7ruJIh15wv+oVssoiDNpyKIs6hZp85yQpZZ/OfWAH66jVmE4ZQcSW5J4tyUk5D1gu5xFMvQcwsB1sBgpf+e7d0dwQR8WN5Icip3zqgPCQUxfjSnxjLNDSOTf7FYe+bpF7RHmaz83QmjVMAeCJ+6XEvHxkaKxeLERMmxMKlMNPu5yBxu2zZ44VSmo628ZUu5Mqdl7FStOeZGhzYV8p2FXFexPJZJ52SY3Og3VWcBxjXGuPvtHy49thaeeOIpurNmzZpTTjuNKiiwpElnxEQYR1yvXEsOL1n9hsi514SPSu4EuZy5psI45ZCMq7V8JhtC/s516HjwA9ZX3B6w5XKFl8vVUqlWFtKrTCBH7GOTIvJkkkm3rZzPlHMVqiYqtKRJmbm472m/WshlaGNgYPfE6ATrzK6e+UsX9XUU2na7JOtA2f3K1PsiV3XdThjIXmUKyOJYeiP3qnwy7iP3o+BQb9L/UYnu8nmhZQwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgWMMgcwx1h/rjiFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIfCiI6CxasTISaZaJUSvt6sHYqkTly2+9tpr4eP47ne/6xttCVXzJQeVoWlpvRF+JxF59TRl1kfxoafhdaoypSFyCfYjzo6MWFM9F2cJY8Vr3v2R//q7//WZ27719NPPNr5CINTOMXokrbiKvq6WcKrJK4IMbFYckw350gPLNBqRv7Nb+P73v3/rrbeecMIJ0ENAKdVEBDZ73dZSmiNMcVp5q7BV0t/fj3D58uWQa7SWNkn+6q/+6hvf+Mb8+fMhDSkUCp2dnSjAIaK8G8Rk7rPv3iBjTfKnrRlvymeadLTXM5XiJB7u3LmzqZadGgKGwDGGgD4Epn0MHp6e4oA+9SO4IGvxgoWLLr/88srmHQ888IA+mmGjYLaaPv5+VhfrdmfQwezB9Hom4xBa4a0+n4cqY/BDtaUzr3jFK37++EMDwwMS4A8ZQINyYgbXphc7EGRyx/PpNfZfik0/4zIHzWQAHdg39gwOvLBt649//GNmfyaIXbt2wTyVTWc8FOrhTEZeRLksdYJAZ394MGe3LKMcB48++ujGjRsZl+07ts/rmccptehIqVIWnoLEqml2a5TCfrBPHTWofk6rLE26XjSV0i96BMUna4OmIjs1BAwBQ8AQmCMCc5zfmb+UGQcOHm+ZeVFeDZkT+ZNxBFhdXbx4LlyyhIlyvFAQQp90mry2wsyCHZK3sN8ZrcvkLlN8KWgwHetCBYIkMp2VcegRx5YsiUZHg7JMEPCD4kM6FjIg9QQCLN80EhwTaiz4uCKZ7B1rkOtaQ0kres99hklTZk7XIYGiJbkiaVSrqA7HXFpm5wmYtqEvdZRYVK1bcHbUph7Vqs9rF1qaOpoE9JReg7ZeOxzTGeGHymZVmJU+xrIT07egk30M2M+hEGUfoKenkyuKnYFMugN699GRWjbbUaoIOXsqI+RiQlfmEnAVCuGqVau4KkZGhEKdpBjqKOv2gLuU6sirM463XNX3OlKXKloLTT6Z1Dz8nBiHq8tdPaK+17VNqY4pyto05/gDmzm9gc77oYceYq9j6/btS5cuXbD6LLluY9iyakkirbqJhmW5kuGN4p5yXeW6koW0Y7tiqcYp1QEqijirJ4QQo1OAf6y9SRBgYcG73WhCOuDzMgIJAnQpc0madokzr8xiDEi2b9++Zet2lsHd3b0rV66EgZ2x271rkFVoNpvXSr5KLagxlEqbq6tiHg84L/4pyvUG7Y8hYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIXDcIdCIXjnuOm4dNgQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBGZFAPoJPhKvRyCmhIlGcTkOiPGL4EIYKJXbg+C6S65aFHf+xr99fns0SeghMX+VMCDEcCqodNYWDl2hjz4kji4R4ioN7sgQXhmlK0GagpA4xjiNdixul6rFP/rPL1933XVfesVbvv3Ct/9xdAN9nHAhq+lIggY1slWNR1UJoyX5XtCWNJeQEL+HxEfx+ZA/X+UAMkn7M1WnIVUj7JCkan19fXv27Jmpylzkc2l6JjuEd1J00UUX3XHHHTPpeDmMIarvJQec+cUvftHb2ztL9bl0yuPZaufkk0/G20ceeUSL5mKt1YhJDAFD4AhHwD8E9B4/sDt9f2sxkzLpyAwVBDUXPF+DjzEVtNXinkLnG8+7qLRz8LaHHxqKJpmfwlRYDWOZqA4sJWaupIGpGS4pnTUv3Zy1GsVhIMySdNDRagaDQTAYF9NPr73kkksuPfPcBx988PnyaC6Tbu0OhnUinsUFZT9IA0kcpJ1e65pk9rHQyXp2nZkcgP1Ki7Zt21apCf1lOkzBfuVXAjNVnEW+zy7PUpciaCM4QhiBnVlHRszA1cVHcgzK8JD7K5jLR4kcVDSH49q1a+egVaeNUN84Tpu0tKlo4cKFcGBt3bq1SW6nhoAhYAgcnwj4tUqy+7M/ulvnR2XGSVqAXEhoc1Iyo+pigVpMkbUQGieWJTkhLsrkgu7u1PylYc/CcnsPjZYL3alCIcp1VNPpOJ1nOqylc9W4CusRkzsTdBhBPeWWAvXGdAZwHFR1ibQjWSHEcRlVYfmAJKpyHI+zkB4V873VdLWjowD1T6Uy3t29JJXvllfgwZ1xuRxHJZqqRfKSng7KAohbLnEqiMleQZSG6zOFBkSiJBYp/KcTpq4jyNN2YlWihaJcTzpDChkSqroQqZcwe0KeJPTNajMCNtcRVWvaqZhpnt7PGbjR9pH0Vy8efySjTEcca8WC81SuRzjWZFziKsd8vn3Zsu5SSSiu4FGCXo337mXLlnEKe9SegU2pdHscCcEoVyj6ZTeyYCUXSC2olLMT48HkRP26pUU+Mv4tw4dB6nOs1leOZEnJgXT0bfXhET+DYEQ0wlwadrVYR34vu/jj1GgROxTx4Z7oSKdzqdQkNxo+T0xM3HXHHUuWLVu4rR/SqEWnnZnPpEfprzhazUDHJdtxAYtI+ogBbpwUK/44YoMpDLlsK7K8rAJltTw5Qb5Y4RaoVGQLqp64xEETSMQfqsNgtZeb4mO89yUrOIU16tTgqhW/MSxd1ocDN4d8SHDLYVwo5Livo1Q2yAXp0bHBkaEh1pwnrzypuz3b293FncjaWJyKSlIpXeDIT3WFh4tHgLzpyA93M/HUR+626ZLeONOVmMwQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDh2EDACrGNnLK0nhoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKHCgEXv0cUndiP47aO9trIxJt+9SriD7/4xS9uK40TmafhoYfKgf2xS8QcHw1M3J96QS6X3T4+9KlPfer6//LxX/mVX/n6Lf+5a3RUQgUlVHKGOLz9auCglefohgb0pomZJIyzWlXqq4NkvzpI32kdT5YuXToXO2iilsvlCGMmowGivqLEfyZS02miREYNIhXsJIX7m8fILE3AroV7O3bs2F+zpm8IGALHAAJzfybT2TkqzwYLT7OJYjZInXfeeVgj+H+0Mp7DsATVH3iSpxy13VQ3ZaXxpGUqmeUZOKW//zkaVcupIGxvbx+cGH3sscde+5rLTznllOeffKhcFTYB2KPUMMH2Evq/n0lrcaSe4q8tzmUskjqhxNwLIknhTL5kUmnUaCiXyVZcL5Q4YCb9Qy0fGRlh/cZsNRN802IC8kkCUUGycUnM0eHdu3fPRVOHdSbfZrEA+xXzO72bRceKDAFDwBA43hCYdp5S5pq5Q+GNuIxMxGRc3tlwfEOy+nAFQTqdX7iQ982oayGzecalai7H30riDVqnQo7MJnvNJ6lUoJsMzvZeB6rzQUFJr7SMN1wRZjnmHTdXRy3lXh5ro6OjteJIW1sbbuBtHBXHxsYyVbfYqEirKeiDoOBx1Fe+IZHDCuQS02UqJT8jhNNHFNzkpO/Ujal8rz0B6tEhjq5KfSrbq3e6vHIiDvKZjoCJDjXVktZbkvrZIj5qBNP2cVqhdqm/v5+JXobSXXLM+LApQaUEb5Tj+JZxp6ip/wj4UIshY0CT5eBM0qPkoESTQWbQJYmpvTiWkmMt14dcvY21aJor0A1uqwNiOZGo6M7ETy5O7guuKBzL5/PULZbLQ0NDO+687dlnnz2/ErG9Vsp3dHV1wW9F72iR6uqbXPcN18WTOM6m06ABWRVHLnV2TsqOejWWy1L7HnMb0G7a9Y7q6q0YauSRQGWl/vpSWkQi2g4+TrWKZlRIE+qYnjoLaj+kR2vXjkOD1dU1n9TV040OnuLknoFRMe46gk2p1TyAzpIdDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyB4xUBI8A6Xkfe+m0IGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChsBRggDRcUlPCRhrkiRL55gnOE6C5RrhgESvqaRRXTbPK0G6JqxWxJ2mg0yROMLh6hiy+eWgMjLx/15+zcXnXPxfv/e1daXBYVeN8DWC16qhRIsmo9iisMYHEc1p6unpGRgabLS111+tiF/0Ea8kXM8RWZHVoDsE2hDhciohds+b0JA8FCSYzokJ3SOrSfW6xiujEzGY1jgX1aAQBakoKKBUrEwGwabi6F/e9N23v/3tf7T9lT++7+e/AIxKkAtqnWEw5kzMq9vba1ycDAl+arEEDRK1SGcHBwfpOKccpVOuW6rUdFR8tO/JIpUj0Q6S0b7PYgodFwksZl5a6iscwO0NGzY8/fTTSoBFd5BwFOcSqVgsEhTKUT3nmtTCabvJBZzNatxpsx1vUpvwdrx8lgyEGuPj46qAt9u3b9d8q7cqv+yyy+ja1q1bm2zOpN+kZqeGgCFw5COg4dn61NJbW5/AmvcPKITEtNMd/+xVNSTU9d3UPEfNqLzpiVFzjJIyc8Zx2k2C6ZjJMMpOFHNh7lVnnrO8p++eRx/eMT4wFgRZZjsmwnBv2kediWZ8Okqz2i89yrqiMXvVXdVTouvdk7Yu3Ncf5yz0Es16OlFHTp5y5ALMl12FdgL1S9VKFMRjE+P5IFi3Z3t4yy2vetWr3lys3Pfc4zyL03E4TOdxhr7EQSfMA0HATE1KgpZyPdXuElVPacbhy0pBVF1faJbOxs6CCBsIaF6PalMxIa8ZV4QJmA/qkmTTlGq73k41kuUBdatVydC7epHQWzTyXvuQZerOx/HO3buAd82ppwQ/+fG0reEwxKZ7cT7IGkkHU2rgtDI+6MCiOVGUQYBiBBKKaW0iVB2tPpNOk9yjw9oJbgvG0N9fTZqczu/r3bp9267+OdFstVY3iSFgCBgChx8BnsyaaDq5DGh51tUfh7w16myMvmZ0GuGYWFnU+8EUI7PU3gUIKIYsp6lFrcP8Wq/c+KNeiR1XUa3F0Axl83iQimQ9U39NhyiKtvJdkPrMW7G6a968avfCoKenmO9i5VDOd0SZzERa2ItKKVnWFOOQ9+QSyxWmU/nARtWYaGgLcirmHXHTMV7xV06b+glTD2oyt2bLQg9UyeaiSmW0ElRrcapYhBWrlCtACNQeZqJCR9DWUa3UaAMqoEwOTqsoVRPmHdiB8EpXDukUZEHVTCw7D6kaLstMLXkHY7m+zyAoCX0Wpuov8iLBAmrsftSHEoIiN+9T1JwS+xXNiDdUdXwbZ9P/zWbzjp8LT3iVRmcmY9NXf0mlbmlbXxE1xn0Gh/xiiTUGL+YMllzbcSwEZ7Xali1bZNXqFlpByB0i1uqXq8NDh6hWoxY8UyICqyZ4GX+Vc0yFjDoXiTdDxqXQkaC5LK3zV64dMHetxEFJJO7adipyphmc0UQtMgi5pmQ9KDxU0q4mLYLECkdRKe3pf/DnP9/Y17fwxJNPOumk1SzbcumJSq1WrRYzGImzco9wo4jBlFvNxlzPEfdBCqDkdqhUdAmKRa5U2W0hOX1ZgQNjDdo4Llq5/ilRf50b9RHBPVen7i4+436kV6+sxKWK6Gue4lgufm4KbinnVZnuUZjOBDDA9u8ZhQsVhYULF3Z3d8/rnrdowfz5PXvwtjI2Ka9L4kiYEUo6oSLjBuL+1GV8HSP7YwgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIHLsIuK/sp+me/ArZkiFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIXDEIgAlkPcNdh7i/SD0SacJ56sRToakVJLoO59Uh/g3dsYlsC0IWnW6uro6OzuxQLgaCWtIJAjNpSiWzXO1U3J0D7WKa4KQzlTQ29lx6aWXXvbyy771rW9BT5DJpIUpKwg6wywtUquzkFWSC0LzRN7ZieVyuUYrJJpDMjY2luTU0LZQwwLxsFqLLpDoI/2liiuSyDqJuXNBjCpxQYWIJSFBn7DcQiE/USwVCjkqQqikpRrYR1uYrTn6pDAtoZhIOjraC9KJetqxY8fPf/7zP37zO6l++z0/qFUEmWw2NS8tPZpXEbWqQz4karYeDyl912hMKRbuMOk7Cf81klAlnAKCqLiktRp9lw5SRXpecc00dFRCRfqoLWr1o+KI24wCRFHnnnsuVxrBq/QCz5Mdee655z7xiU9Q1Nqjabv8mc98ZteuXa3KBykhyPaCCy742Mc+NjQ0xEg9+OCD3/zmN2exedZZZ3EXbN68WXWSPZqllhUZAobA0YUAT6FcLuef5Djvn0v+sY9E5zUy2js3H0lWn/mzPB+8Na3YdBSDMXwPcXem7eyzz57f2fPMM8/wjMoH2UIg0xMKmjTPUcmM6n40mXP6KkvWwj1Ova73lo574Yub0SZgDWvLyaoj72bhweHBtWvXXnzmOSwYfrlpnVvhNHgLYH1wk6ebBvfyttUxBV+P9TWJ612yj621kqVNecUHYVLeaqFJospa10PapHNIT3EAogEumFNOOaVQKDAXI2nyhBXIDTfcMBc3tOJ3vvMdLj+wxfJcas1dh8v7ZS97mS6TWDM89NBDs9Rdvnz55OQknsyiY0WGgCFgCBzVCCSm5b364dYFENlMzdoU81hmamx6wmu11pkLiejLVsFeSefNVCajVfTI8oZdAKniCLDgqmFhU0lD3ZOOO7sWLFiwfMUKpphKVx+alYxQUJLQJ6l1FktxmME3WtTjXq066ZSEN2X1jCN5MeIchcMS5qOavCOXK2XRqVWh42IWC4rFaFKYGWmXdUUWpqA47lm4sL29fXDrlrLjSxKXHLMVi6opz2DMxOGaOJVyOwNk1G36SA0pcKlpLed16JAWYX+qC/KeWz9rYJAsPMC8onqAlV/SakCo7fsMpw2ZEjFJuS9VeBVSDzXXGxcSp8h1WwnkOSVR0R3Fpt4dSCgl+YZ8c04i8tDt2JDxyTvgJI0hbBTThGT3Z0S9QXXSn2JG3yCQSCbXxqVeHR7eOTq6c9uuJx599Mzzz1uzZk1330K2hiYqRXTSjk4u6ZPuMtFFlkNgQme1FTKYJanjdbf1EdEQ+iJXRc/qLxQKmjNYB1aLkagpLGtDrhFpRfbnXBJN1wRDJPlUhl0saLDY/WtrG6UvPCLYDoqzBXqUguc2jkujJTqiY8qwSrvJTooVS4aAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGwHGEgBFgHUeDbV01BAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEJgLAqlAAgJ7y6lMObWVcNeIiFeJVV1YDLq72//0nEsvO/+yP/nefzz49OO7OzPVTC3ozgWT5WKxAk0GcWx8DkXyMXWE1BETJ8d6mmoNASFzBLESaLd3cN+UjuaqROkR8uhiGMnzGUt8YzDckb3xkftKpcn3ve99X61FN992823V6mQ5yuSl9khGetgZRymicEWgIXr1+F4RHLJEOCIdnIt5oJiL2uHU+Y//+I/rr7/+nHPOufvuu2lXR887AI3UTFQXTX3R0xtvvJG6RFFCWdWk4G3ub4aQy7e85S1f+9rX3v3ud3//+9+nOpwgEGB5+z58VCWwX5133nl///d/7xX2t0XTNwQMgaMRAX0Ua3x48vbXvJckFZqKZuq1zL6kmoTvC4mVPMklpHx+kGovdK4+aeXy3oW/XPfUxi0b05BLhpl0Ok8rUbkIx5PQPAmpg5rY9xGzTc807/lU5Tqt5JTgxc3RX0gnILfAbMlFyve0tz+xeR3LiVWrVl0URUIvWB6La/Ewc3QcD2VFM12dfoJjYqfUA+C74xYGmUqtql3mSEKT/nNMpnpgv2PKSMpVXxYOLVWSasl8vYnGXKynSYVDnU+2+PTTT1911VUnnHDCunXrQEM5F7wDIyMjEErOcXVBrfXr13NkxvQWDj6DtVe+8pVvfOMbmdzvvfdenP+jP/qjmVYFNLd06VK689Of/vTgmzYLhoAhYAgckQjoG5/MVsmpR2chHpJOqNTPdQUkfl7zVTSjT3gvdDZZQkDtXOXYoK4WGFQHu3LiTqNQn/YZXKlEwoBTC7MURbxz8+7fMb/aMT/qnF/L52vt81JCgFWIM5lStg3LlVA4NCthqhYyk6eqkG8z6eN9LRJmK50ieZ8OaU5PdV5OQyolCrBpQ42NGtsRnEJ3BddVVHUSt2KCAgz3UtkgHwa5At5nakI8Xa5MMNPlIaruzIedY0FqoloZx0IqrEIRBDuW66YsoFCWfgYZqJDiqIrEnbI6YalRC7K0XAujKlxK7DFQhFmFiIpUJZ+Ka+p0DFe4GhPc6mb449TqfZ2SulxSralo2lNprjE00yocJcIEOs7jOHBk63Fyl4PVry6Dp/oE4rCVce7Ab2DtynVQWJsAKXTqDCxiHU0nkRHhUx9tqSI+cMdINrl0dlTsIpw27VWq94VyqO+1IpIrY++EBA/xh6NPei1xSmmaC56rS672KBgfCcZ3r71px5bHHjvlnHNYDy9as4rNrcGJEZjxU3KzcKGJtWrsCNPDbLU02dmWHx8Zoo8wSAXcbc4HjEfsWUkV8TCGNhcQ5C2jvloGBfep++/ePYRrPsM+VxRlIJ6V+1SueQyyI4iy9IWjyGMeB5KRJE8RHb/6KMZVaTqVKrTBfAepVXVscGBo9y5qQJbX1dbJMZNKc8x29WG/tGczypq43adNrcM1rZoJDQFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyBoxqBRDjLUd0Pc94QMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFD4BhFYHJy0vcMSghOk8wFsP9MTEx4BTL5fB5htVrVQDUkrTqjo6NjY2MaF6d1VdKwI2Fr43H7eGk8KFWDbFYIsEJhv1qzZs3FF1/8rW9968frHy+I5arEYE6U0a9U+JRdWy4Sr2GLhkjVSAJYCWmjUQIXy9UKn4ZKQL+oWHOBf8TOkdqmagkdF2deefYM1FflaqRxd5OTZSqOj9cB1EhEYnGLExJjWS6XXCSg4DM5vheGu4q10XLpnkcfaPvPtr964zsxctMTt9LJasmFJjqCsHB8fAxwXLSmupTNSu8b7okLrhM4MMXQ5PyZOkVH+y6xhY1EZCNqyVFWHcoZ04bW0fSXcaeD99xzD04vXryYYzabLRaL2gftFBcGHU9e7bP3EJsooE91kl7MyUt69urTli5fvvyrX/3q3/7t3yr71Wtf+9pyuSxh4W50fEM0raNz9tlnI/zlL385rTUTGgKGwLGEAE8DnjD6kOHG57Ggef/01kcEXfZPbyRJBFQ/KZkxL7HrwvKoCtjJZ/KnnHLK0oWLnnzyyQ07NxEYXp6c5EGKV0z39YjzGc01F3hPyNTzPOX2JgbwdWjC5w9FBrhK5an1QFSQr+/vWv8k/Vq1eBn5nVuek9VItdG4gKqTfEMyw1/tWimqZsMM1mqNeHrXaaHLVIU6Am6GrUscQwF5P4J1HTfGM7R2RItZh+zevbunpwcvXSfqk6Y6rdMxV7L21wMye5f8lT+72hxL+/r6Xv/6199333133XUXVU499VTWpbPUZb7G2+eee24WHSsyBAwBQ+AYQECfzE0dYQojIaRU83rM5vMqbKrVdIpOKpXlMZ5N56iYJMBCSGL9ofadNUeOE8Jiw7SY4s0oDFmAZNvybTAR5xYu7+7uxj5JfXAKKpAjFtQmbDg6vzjqHOHRoWi2pIsofEFT10UcczmYt4QMS9ctYZVVVyrOIS84WqtMUV4PdelSC2vM/gsWLEBSHA5KpVJWmHyg73JNQx/U8EE7C8UVkrpQ+JJCeImkqAbvVpWJU72lO95tVQaw5IqPvDQjdEv1KslSX/cAMhjFHzF9rCego5t6pK8NnOvIA4JKFAqv5pFxN0f9wqO6GxE3oI0R0ZWkGjmkWPom9C7gVO4vl3y70lMnw1N3X2SDsB3aqdFdux564IGtW7eeOLQH3u358+dTTxa0NQjkQslwabILFwcsmTq6uiiVru6dPCZerJeQXsatpRhBqJewXPwN8FVTmkgkjCBvbderaWepIT47+jzsseyMyjWWpqkADr1cd/s8tgfbsllq+YqJRixrCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgcXwgYAdbxNd7WW0PAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyBOSAgsYW7e2uT1YnOXB4KqeVBMD8I/uJtb1u9evXf/+i7v1z/BLQEo4SguvjT9BixelPcFHOwfyAqPiJOo+4IvlNJMgrPh9jtFZm3r9Zg99IwWBSzEvAXTtaq6VRqQxRtuOeOzs27Pvaxjy0+c81nv/bF2+CrCoJCWiITk6HC+2rhQMpxw1cj+JCkYYoaFujR8DpHcgZSNgKPX3jhhR/+8Icf/vCHb7jhBiKQWx32lFitRa0SEAAiMGlCplVz7hJ8I4QbGo5/+Id/UN6r6667juBSb4FGVV4oFPD20ksvhf3q0Ucf9QqWMQQMgeMBAZ48PH/801i7zKk+pfWIEB3SPgFJ2knLdBqka26WIcrdxcH3Ztpfc8FFhI4/seG5p3ZuEp5FItLDqEK5zF4RnA/7bEUVtC3vFacqkdLkbJow57uTkB3C7LbKBNH8EHjcu/GZ4YHBSy65ZN68efc/cP/6YJL5dzIlPYUmQD3wHWl1CLd93xp5qeV6LBnplxIkuPWEFOlgORwUlilwWhs4qiQ7dux46KGHmLOeeOIJLqRZ+jVLUbLHII+mHpPyA85feOGFCxcuhEvlTW96E0eWDTfeeOMszsAHx6JiYGDggFu0ioaAIWAIHNkIyPs4fEc8aSPlbE64Cw2STmFkMsJGleUNBRKZIC2ENVG8b5pIpsA4jKuQX4n9qYSQ1mopWK4kSYFwXqXjMM8zORWmaTHMtgeZTL5nfntPT37RCniwhiDCTmdr6UI2na2k26hYCnPpMF1xlFLVKKzFQZXJOwxgwWG+bbx6k+HUkf7QVOtaBgfolKSa48ASKq4wxXIgyDjGoFRULkOmE2eY3SfLtZgXzLHRAJLlWiUoFNpg+erszETltrauCiSY6YmApVMQFCtl1gCpgE7FNTTdqkMmNT66zKCnaVlFhDWolsXfTCYtdd0Kgd5pRhYS+OMYRJVQCewVM+RS5E6csrOiUjvOhMBeV6LAh6JCPVONpNyPC+tohogRmZgoMZLoNAZFB2SqUt14U7tT5XPJOXq4uSi6vtAiiZUYNbzDZNKO501VooB1Wo2dNu67yvDQzuHBnU8/8exDD77i1ZeedtppMEbByFsuTkSVClc4dqpcq1Gt0N2Zi4XrNRNmuEmUbw4EUyGkusAo3G9RKMRhXL48VrjWxWuhzKpfz+5UVsgkuY65B0hQYbl7TeVUl1IRi7NyWVNbGODcdpWzxj0kDyjXGcaCDCpyd8vN5f5gnDedWiWqRmPjE7u372G/5WWnLFNuL22CWpYMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIHjEwEjwDo+x916bQgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCEwGwJEsE0ODQW5bLlcRm9pKnj/+99zQt/yz33uc78cGRpDRFAbsXUuRE4i32Yz9uKUEQun4XDaFkeVJJtWCe0lhQfZ/IPbnvn7v//7d//Wu6/74Mce/vfPDRdjqEnS+xHqeJDtH/XVfVAloZ4/+MEP/u7v/u7MM898/PHHD75j9eshQVZykDavvvrqu+6665vf/CbX/65du55++umlS5dqK1gmQ1+ILYf6ioTkLW95y1e/+tVNmzYdZLtW3RAwBI4uBHgUkNRn/4jwQl/kJV5nn90kpJwo9SnWvSBYVOiCG6gt28YT6Zkdz2sRlgleD4ifR11ZnPZpukVhyiv3FG0pf2kEU14FwaaRXel77331yy++4GUX7H70/rGoLP0VygAXtD+rg9iR5HT4K4i5lKykQh7udaHPJJWOiTwclOvWrXv5y18OyRRkWDP1qQHYTOVTctWcu/5UzRly8Kv29/ffcccdo6OjMLlwI3DKACXVmX+VkhKFs84668477xwbG6sPYlLP8oaAIWAIHEMIyNzVeLXVp6JjmhFiLDLCfZXNqoQnJ6w3dH12AiyUk/CI+b0ftmItq6TQ0gT8ThzDtNDxZLN5mqvlOtrb26vZPMTBYaHAwxkGHiHJSglXVhXP3JzLq1/NEXTCtyO9aPCE1luXdl12lsnX62gdpwnJD2eVqhBXwQUqJRBR0XU4i1GAMKtQCGpw8ECUXBPO5WoRR+BVhDkoVy0xj8QVmU1StSqAVauyPMgIM6RQJaEpZl0H5BhmpAA2IhAQFiF34ngkySHkqOA5LiwYfsQaFvSoplRA3tIhRcBj7luBKNMLGUsv9xkVeh0vnymT1JzW4EwVkaNP0gvMqyFRm2S8UCir4KeLgmqNVT8XJJ/K8PNrf7Zr9wMnnHDeeectW7aslhfOO92pi0JZNUHPLUbc1aZXpjaqZilCyPOBWtwwvi2fUQdwhozvJjc7CqmaPG3kboaZtyp3DRlO1SZHsRnFOFOtCTW5f744UyjWquUKee4mNUL1uOrW3hVheK9FaQTr168/55xz1L73yjKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChsBxiIARYB2Hg25dNgQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFD4PhEQELdlLIqnSb4jbg1DZELCIML01GYFlYrF/VJDGm0JFXoH5k4IQhOSrd99HffT4DrJ771tftHhsaDgEi43ijI1NK72iRULz8mEWwSLSpJwuT2Tpjn0yhvlKXqMa9yLpFyhOTVIolVdS4SR+dEBNFJTCt5FzUnzfE/zXFUCXF+DZN1CeWq4+WzZ2gVc2kXCbiAGF9nH6PC8xUE96aC+wY3bP7m197xjnf8xwf+K4RH35ocmAd06E2fNOhPzFXDGh8NWsZbJNKpVIrYP19VJQDuO6h99wq+FjqqTIigKpNJqh2ZeYlwJCDZpa9//et/+qd/+uEPf/j3fu/3vLfaHX+6vxmqk/a31rT6BI4+8cQTcGr4Usg1fJ4MDTEKKvnIRz7S19fH9cApFZUSS4vsaAgYAscYAtz7+gw/sH75uk0PK32ayBTkJgiZCCBYiOJFhc49xbFCEKyet/jiM89Jp9K3PfbQpoEdzL+izFQSxTUi43n08V0359UwBREWD0LYJObwOGxyQzqVCLyX04N7qPr+OktTDqkcfyU5cVJThNBV0K8wKDUqPTG4vXz3nZdffvmbc6++5957ngkme4J02blXdoaYFqWeI8aCpkKMuMSAuY+E+dfb5Y/rpnZf5lO34CCCXyVaSm2fqdtSgwd3DSRNHf48q45nn30WbqlLL72UifjwO7DPFln5wDu5efNmr4kkuVhC3tbWNj4+zm1CL6Ayueeee7yyZQwBQ8AQOOYQkNmt/tbR6BuzlcxkLjGJJd8ZoZoi8a4oryqz0kSGqTSarBqwWnaLhjCdQVKJeWGXJIYyQoCVyQrJDvxWtJVp6yYTprOch/mOIJcLMvlyvi1OF4J0oVpIwTxVSeWDVK6MBMIeFjRhWA2EQIpZm/fhCusb5n45sFihZ5B4ptx6o87a4xrf+4CmI7py+m5xovsX9BFuIOH7cu/UcSaQxUMUpDNBvhDkg7CS5v1zolKh9ZIQ9WTzbZ2w+YRRNaxW09VKXKkEURWddKVUqVSK1QozTliL4owsJKgFPRbHrFusgAXCkDJ5E6zwPi/rBpdk5RbJmcqAU/B32CJ0naF1Mk0juXc37eygEQBtAdphTgZ7DMTIyAh5PU22oJLGANVLmtSaSpPV9yvvzZIh+V2RJiOyhucSE/9Z0rsrR5bEcVlux6wscvNtwcTowFMP/WLLliXLli1fswYarHxPF8Wl0iTHeelUbzZTLJW4dCu1UB4EbmcNy5RCm0tGdvvk8SCEU9w82kfZdEvL08AnnMxmCh0dHZOT4+gU3GYOzFfcKdwybBfKNiJEclBjcRs7b2GSo7RUKdGWbN655OyLI9IcG1k44cZIJFX3pHLUWnQQ9cHxUlQuVqN0Lc7grIyhmpFnhSVDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ+A4QoAfBVsyBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEji8EHO0DsXAu/pQwOz6SJJNMRJud27sErqKdL6z/1Of/vw1B0AnPhigTPBcSECvRnY0It2TFueSlvUZyrUv7LknIX13iREShOodFJkWON8Nru5jSuiFVo1x1GuYP9m9bW27Lli3/9E//dN2v/9b73//+0ftvve+Bh1xg4n5YVucbHk5VbPS0/pcCL/FKvvta5HW8wpGcwWeiHJXJAvaNz372s9ddd91f/MVfQHWB28RDokCGPr7kvbj11lvPPvts/HHhmsGSJUv+4A/+4DOf+cyOHTu8b+VymTxFH/rQh/76r/96wwZui8DYrzw+ljEEDAGPgH9iq0QfLL7UZ/QZqKeaJ/IcFoozFp1w2mmnQRCxdu3aoaEhZmTHdgWdBPQRsF+56dBbOYCMn77dQ3jKgAbeN85ncrtRPs3fZI+S1VUuT3x8d5MoEhU6kZa4YjchpKKgI1MolUo33HjD6151+WuveG3uuafWb+GpOzVfMHfQBD2Qo1h1dp0Nf9BW6u02nvB1oTulrlR3OJDR+YiMt3AMZCYnJ++6667Xv/71P/zhD8nPRMHwUvW0v78folVtHY6Vzs7OK664AkrK9evXq5DRYQlBntE555xzfvrTn3JhcHqMDZN21o6GgCFgCIAAzz1oYxwUMh9xyvsUR008KjnVJDquNEjJXEdWJImUnOMQY8EdmDuFHUpLebqSMIhCKp8jk83laKW9vV2oo9q6kVRqMkVG+Q5qpTI5ybt207FUrIkB4bjxLWM5kYJIOKDdBgQzLB/tnU62rXOu6mCND8selGXxo/xZTsjLI/1FIjTewpmDD5mqkPvkowzkRzBvQWsF0Q5yXFJPmF/aM6JTLRchVaxOiucxpFjVaq0EVVedRyntSK4gwCLl3KIjrlV4peW9VvBx/ErK0qgAip6sQ6ZSrYa6nMIYppCIp0mNKV3LvZgIKO6ArrgzLljXvB59Y5xSykWLRNV8kWZUoTXvlVUhaZai5Cl1OeUa0z2EJnJt39xeVdwdgQQmV/yCLEqucLYgoKnKdgXj4zvWrduxZcsj8+e//s3XwA0KBxv2hbUqm50sFnEAA3p0rU+tmX0r/j51utxb+pypu8Mtz7U9ODg4OjqM29wpExOwyVXkHnGXNY8FKkKERcVqxG1dow1pUZ5YQgjn+4UI33K5nBRGIRUpEp0og7VMKHLHjhV19HTQRJMn3o5lDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyB4wcBI8A6fsbaemoIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChsBRicD8+fO93+QJbCM2jKA1YsY49vb2+lLNICG0DB0JmnPhZ729PQEUGkJdVU89YXpeKlOTsLV4gtDRVFDIZQq5dHc6R622CrGd4eqOvtdcfe2F5527fuPGT//gW3uCYMGaFYSSFirSbirbmcpmV7gmKlQg8k2aCOb39c3zLsUSbte3sC+dzZRLVYLfSLjU09NTqUb433AnyOXbcvlKKl2l3TCQALxcro0PvaQp4v2y2UKh0MGHokKhjSg7+ugkRfLeDpLOzoi+I8ESOgv7FodxPQZYY1JhWMimc6VKBSQ1Bm9R3+IFfYs0NJDAXfHB9au3NIHxMK4SBFha1rFtz55//P433va2t/3Nr7ztoXkn/mD94xuHNm5I1fL53HBBRqG7d1E1HEjlhSCpFopXCxYsoJX5vfPodWlSQmo5JQLQszygk3cJZGiUUxIKJB07lXgd1ACEIpUQi6gKR/JRO+J7B5/UBz7wgT/8wz/8kz/5E7oDwr6nXuel6g4ufe1rX/vEJz5xyy23nH766StXrrzxxhuT7FfesTe96U0rVqz453/+Z71UvNwyhoAhcOwhwDOKO72rq4tpK/m80rwP1UaHJzzd1zmIDBISavqg0xlH8UkGmlcd1WMmDuXRX41TmfySzh6eMCf0LWTO/fm6J6ErmmxLVcsZpm2MZqFUCIiGh+0pjOTPjGwKOq/tNSIwVqRSeIJje8lnOKHL87q76YOaUrfJ86F+qwnMyqwdBDzeOdJxMgIRvJlMr3FdjiQTZqIwSBdyEjSvSSyGHT3zMBI5EgrEE2FYZfLOt31r7QPnnXfeqa98eddJy/ufWj88PDxZEO6PwSLzYNjpCMGIvgfLquPAijOpKO0cjIJMTsYFMg/8IWnfOaqT9dbdH3U+qrp1BRoNlFwXWrubrHqk5+n47bfffskll7z2ta+94YYbcJfO+qv3Jfce397znvf8+q//+sMPP8zaafHixc8///xzzz3nHeM+YrzoxUUXXbRw4cKHHnrI32hexzKGgCFgCBwbCLjZR+ZTpZLRd1054z2VPxTwQMzJmyyadbKZMMViRYhvpoXAFcmsJssS1GDSybGaqKS7UeelWQp474boKpvhNTOd72ZJkso6jq1cIZvPl8Mca4BSushSp5oq0H4lm4d3J87lw3QmSuV5aa9kC7EwUGWYXKr4A8kUTYVBlZd8Ye5kccCLfm1q9cBOBR9P1yUsPKJVT/Up2E2+LEO0YyJ0eMCkQ76+nyA0WHFQhp+nWq4iLJUnaawaZgOazmZYK8Hck4qjgiAatrGvUChMjI7k0mwo5GHiqZSqbDikykVmFpyXpULk6K6q0A+xYonEPIuuGJYtUBf2H5Y5qZR4Qi85DSNZzYyMDIJMxi2EFi6cv2jRItZR27dvHxsb1U5hRlNjfVE/tT/TIlC/Gjxqcq3OvBhrLFbVlF/h+CrcNyTkMl7Ojs9QRYs4IiQl/UGZpMKmolmEWKCWdwbL5NH3wmQeRihK4Y5y+qLJPcSd6uxjxMll+R8HGYjow4B9qm3D997+C5ZM3csW85Iyvz2/J5cuCSVdnI3CWlTjfqB6re5Cij2XKOUW51yt7iLnqBe8nnJrc213dHSM7BmYHC/y8sESHrJRbidN3Bf6KlR/8rjNtUqkJHriOLcypjOhgEx/tafcuqzq5b4IU0G1gjtse8F+J/eJGwvq8UCaHByGYC/Ad14dsq7jzlvHAuZQkUPi+eBlljEEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBI4tBIwA69gaT+uNIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgCxxwCQ0NDvk9EkY2NjWngGWFhnBKhCkGGVyBDQBoSgtkodSFzECqlnc6+A8aIdN28Z3heEJx18qr3/NpvYOrf//3fISDYWpooBISFlgmBGxkrEfAJCwH2iWSjiUqxwlEJsPB2ymHHmJHPpQcHBytlYcHQkEIi4kbHhgv5du821UmNSDkvPpAM4OAnDXHEJvh4f5QAi1aQcCTMEZdoA/dIEmgoYXtCREWibqU06Vg7hM2kFleHhyeerwZf/Nq/5V9+xYUXXvieV553/fXXP7H+mWiyXKyNE4jYH/VPDO8J8g5nR4ClTesApYT3Q5ojeZfIw/KQ6Hs9QtFp7fuAn14JIz5/pGXwjYBJ9Ypxue66677whS985StfWbduXdLVl7wLXAYf+chHLr/88te85jWbN2/+8pe/vG3bNvjaYFpJ+kn+U5/6FGxZ/f39TXI7NQQMgWMVgfHxcZ5R/jGl0wSdldnEJST6oGPK8BJ9SmstpieVNx+d2XyhjSYyqSzPnLPOPIv5YsOGDS+88MJobZIZTRqRiUnoKakulFnNVuZ6jsN4onZmr+PaFRqI2dWaSulN0rhHTORTpBdSSS17BbUzPjYmmXIdQzpagW8CWo1UcOcdd5xy6qlnnXXWmp4Fjz766BNbN4lmbvqv+/Eh6Qa9BtKkEJvaNBmxM13yRVRv8nM69SNdxpX5ve99773vfe+dd945MDCgmNAvMi+561u3bv3c5z4HxxlMr7h07733Mv+2tbXBv+B944JhXQpJ1je+8Q3893LLGAKGgCFwTCIwNfUw5fNkdNMrE5m8UjaSn6dqkEq6lJz7mmDBAvowXYmOs6NENhBgib0MBDjZnGOHUgKsIC3zYyotJJIkrU4GXeyQHBmWZuW12jctvjaYuCTr6opAVhSO9Ip5BzmEOKK49zssk3Jy4SFq0y15tBbKqsASq1wKSqWgOsnKDMod5KweMA1tlU5zHCs1iLBqYaUoJF/OQyX9yWViKLGCaoG9jmKxKAu2EvxgsuiiezBckad75FmKAZr2GYngJhsjHIREbMGCc3bv3r1540Z6jSkIsJjIMOgJsARHS4cMAb3YMM9YaJ6M5jll6DmyFmLLy7vAWDOgyBli5Iyaji+nCLlOkJDXOwUJCTsMOEJtyJtqzaCcFKozSFTuHROdxOK5UUvUG9UTdrDJhy7E2cGnnhjMFoKlizp7eyFYZ8kEbxVVYJmjX1Vc5JqPIHiX9wjMVuCd4i0gkhW4K5QDcvZnuFDh0gKBLVu2cMXCvoVEbgR3v3N34AynzpRUITmYhRCOIrnToJmLHXk9JF0Npl3pA7S2rh+uytTQuLtJy2VHEeo6nFH7mKJgb/DEviVDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ+B4QGD6X8QeDz23PhoChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIHBUIaKiYukrAHvFp3m2KkGhkmhciIRHVlpQQAKcBdBp+tjUe2x03mDhQJCJuYiyeqPVODPcEwQfOu/Saa675zz0bv/SlL22OIiQTfDJBuVgZGysRK1oNMlE5jmrFTLaNKLUKUXRBvblKmbg6FwAnzYsQ6qtqBRHxpnEqEr4JdZhgQu8heVK1VnWkHhJMSNwdCUkqTNXiiDwRfSSKJouTNEAfVZLs6cTEBEJCW71l1DCip+oWEoLrRBJFBEHyF39qfJxS6CJdXcQdYZAiQqNUqaUqZTR2B8FAEPzlA7eevOupv3rjW//7e3/jjO98855H124oj6I5EI5NBFG6FEBzpdQdtXKpWirWKmKoFkQ07bpVrDsg5gnUdX1P0JGoTnLc6ZSgUa0SbAngFKmOM3CUHYil/NGPfvR//+///fznP/+GN7xBvSeoUi/sZK9fko5t2rTp3/7t38BZryuiQJX9ykdj4hXUXXBzQMDxknhojRoChsBLggDPbR4L+ozScG3NJ59ayPHNz0qqhkTlSU1hZyCkHOqHIGivyUzUOTaWD4LzTjj5oosuWr9502NPPLFtfCAbpGq5DNMTeihFqZDp2lWqh8R76iYymvcSbDYl7zAZTU0K054y3aj/05Y2CdUs+n6aQ6I6LiNy1aEjqVrIk39qDeMUw5ooTJllnUPXBS4hudjyzLNjO3ZfdvLprzjljJVB/pkXnhmFGSsItmaxF+SrQS1iyheyjNF0UFHOyUpQROYAZ2i8YzI6iXZ8i8nOJocSr5JFXv/oyjz55JMPPPDAO9/5ThZ4dIfJV1dWc+yFIrDXAM2x5hzUIHi97777WCkx83JhwJ6gviXn39/6rd+CnvKxxx6bgz1TMQQMAUPgWECAB68kR9zM85BnoybyiOkhEySPZfeqX388x6n6IsH3309hFGELaxWooDmmxViUygbZbKqQh+YnKBTSHR3MeHEux7SHhbJbdtTSWWpCSQmbTpgrUDfOtUXZbC1XqGSyUTaPMtYgm6zx8h5CQCWzMxsDLIJqMkWngoj5mXNmc4T1rQNy9USR8FHSmwjOKmGwIuFA6OZyP2Ej0bkbUk3dc5BtBqqkgiysRiFLK2mlUmLeF/+ZyKtsnkTybi4rgAwWq+VKNq525KRHmZC1WJhxmxTpqBBkK2FOXrrDUpZjIRS2o7ZqGwBmnEtRTSwp0DTMujBkqVYFL9miWfvM00ysnZ2do6OyOXD33XdTHawymTpfktalXbXgAEbmgJG/lqZDgOu5sTL0xGqi5+Ej664Kd9G4K08KJak5zXOpQ2jF0oI3fZYZCFlv6CqU4eDqII8OpezOUBEhyjpSeso+BmsVSNPUrBZ5BRUmj94Brc4R416fjFeIHHk6S2OESbneLV5NLn5Z5IbVIteb2+OqTAabN41t2/rjPQPz5s1rb2+nj5m865d7XACBVIKiil24QLY4alz1PAUEtBh6OEBY3NtHx9ErjY1XJibzULwVspVyJRUJYa7cyZDsRxmos1LVVMyeXv1+FEq4OuaOTJabDU2k3BTcaFJNxoW3gqkdQsdKx3ZZFeI4ueclCbH+8p4FnblsOtvGTapPDx4jVNYf9foHgNi0ZAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIHNMIGAHWMT281jlDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMASOfgSICfOdID6NiDvYARBqaJxKyHsdFEicEkimck6JYnMBpQhFkSIJO/WRpxK+KfFqpy874Td/8zfPal/w85///Ev33TwQRS6QlFg9qSXWakGqsx0mJ8mHWULsqEi4oMSEOh/wJ0xRaSohIUHbRbwfCWVOp4rnnKPXJNS1q8TFcgpz1VTPHZWVuqFWiWelrWymEaPo+k7gInQe0IalHJkUmoJPI44RDwGHhJ1qVHXxukEulxafM8U2x8IxEQXPb975D/+/f371BefDIvGqV73wuRt+sGXbLoJdh4sj+QxgBgWCbwMq5sbHx8UZzhKO0ooIZ00A68uJxiRx6oW4R/IKR1Gmu7ubUNLPfe5zN95449/+7d9+4hOfwPne3t6dO3dqL17CfhH5yeVBxDJOEt3KBUZwLIPIkTuOSxcPP/rRj1566aWveMUrhoaG1GE7GgKGwLGNgE4KPAR0atDO+rw+GRAikehxN1fyHEvO3b6KluqpPOvcrJRyofqrFp9wxhlnUHT77bePV8qD44M8cTJhfW7VVmgCBVdJbci8rML6+ax/6tXVSGOFMEsN9Jl0tFOzqLUWzfQYVzlHz19Av6aUaY8wew2hd8/bvSw7CRV3Dw88/PDDJ5988hmrVp166qn/99afJdW8ZbGjU21aGSmTWrPlcQk3mjTE5/2Buqn6EXIKJh0dHVxgv/7rv3711Vffdddd+zuRAcKh6AuAq+X6Ms+1wrKHa48jbuv1f8UVV3CPfOpTn2IVcSjcMJuGgCFgCBxpCPB45LURHhudH3lp5dRJeDTWuXKUgAb+R+Q6f7U+qSmia1pKRU4jiJ/CEK4cKcrAeJPJt7Xx1oOExJQr9tGAu0bqhSxrsJBJyzoHJY6sD8SOe6GWiXPaqdO9ANfnep1BZAngPpiYS9JaM2lSSrs63fOyjGMp2deQfQ+O7vU5qDmGbVZPqFWrTDeVqrzi5VOdzudY1znaA94EOztlfyNb62RKqoyNsG8AOxZdqFbkZRwKcPIkdCKh9a5C7iNTFcxeqRTUQ+gzWl1dXTQEkhyZs2o12b2R6q47HLHgJPxtHa5peiucQsndhGlUjmmRQ0/JTGXE9arQ0VdYHS1UEgLgZUyBWhOnPT09q1ev3r17944dO1gO6bgrE5ZWRLJ06VJK9UZjKKmr9w77PGiuWLECFk62d7hT9IJn/JKNvoh5mm61hpB2pVEBgZuRS53LG376amXXrt0jI6zz2KDrmtdDXzK5HJp1Px2NFUS6ggY13foK+319fcBCj+gdrN90mZ5y4XO9U6p1tVGqSF136SYdowmwVRzcJS21VNOrIZeKznnuGxTSKXkiIQdercX2C01ri76iZQwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgeMQASPAOg4H3bpsCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAobA0YQA0WLeXfJEhWmQGELymjTkTNXIk7SWP3ojxISS+mqpHFRWQdCeDjprQUcQ/PriM1/99le3n7zi0Ucf/fhDX1u3cXvvgq6x/lGUy+gRcRkScigBftH4BMFqMVlHjYFlnyjVPBmfkKg/PqN2PEsFmo1aBIDSmLhYr+KKVMEbnHtGzXLUKlM4unOg42+zjleGAKvBgFGL41yVTzBJpB6fXFAOgodTwc8feuRHjz/6jne846/+ywfXrl3705/dvCUI7qoG3UEwXo2LDjoJh6UKbbvmtTltuu6VE6knKtG+a97r+FPUk9W9/GjJKNHVtm3b3va2t33rW9/68Ic//LWvfU05OBScl6QjCinRy7Bf4cDg4KB3g3BQ8qrwG7/xGx/4wAeuvfbapILXtIwhYAgcPwhM+7yqz26ujIcGp62Paw2kl3k1jtuFeTI4pXfZqlWrlvQu4MH4+HPP7ioOV+G9ClLldFgJ6zMR82irqYNBG2uzGPQTzexqMzlAdV+UbMXLXbB+DL8AMfuZVLoaTRE+UhGIhO1RjSjNQYPsoOTsrguL6zY9tW77C8uWLfvtq98Kbvc/s3ZgYmBPEMPJIdxIcVwo11IlgRfbaQgrWBbVqUKmfGulktD+ave9t65NObRKfNFRkcF/5jjSZz7zmb/5m78plUo33XTTkeB5E7B6H+EYkzJHXfSeffbZDPdXv/rVPXv2HAk+mw+GgCFgCBw6BNxTUWarOjuMY7YiT4KGRqcqjkq9I8RIjnYTSc3R3MQhL597pVj2Dxp8QGEahbDQjiSXK4i80C5sOQUhwErnc3BahZk006Z73WfGlnyQFs1SqsyTOZdtw3pF7GRqcHUGHKHjgYwHR5h12TKIoYmSk7Qw40C2FYmXKUdglJiFhVwLXedqQowZXr3k6NmFyEOBpKcUkeGopEjsjUS0WoOaG3rsQBZQDpgQd1hsRUE+6wwKnXE2EAKsXDXHsRpXhBlL/MNXR8qDKdYL0sVqUC6xFElVqpPlCtMlkrBWFcVqncpKnMZ3mHzgMc3CJiYdhkYsnc5VS+V0Rmh9qAWXUJjOQRUmaICEGyy3qBM+IGcjyjj+IBQYQY4OjtYDK8e9FkutGsegRHdjIP9iuPlkM0G5Ur8SAAqoEnA1QQf+3C8MIkluDcfoWigU4HuamJg46aST+vv74TuD85phYmjOO+88KLlZI8EDdfnll992221oQmE2MjIChRnMWbBEob9gwQIyciW4prE8C+xJl8jradMoq1CN+LzPNMk5rRfFjvpKNulkn0IwqVWCyVpULU+Mj+YqJfqZjyPua2X4SuXb5PHBXZLNcnViZaQ4CQjz5s2jgzs2b5InQDbb1tFerpS5WPVaTcdCkyct8gCgaW61MFWTK9Q9YeSODlI8W8i4xJn7O4WJ7j0q1S920OWWFPB4ARCeXxqgclVgrJRrxUl5kgjLntppvuDjKcPaoB0NAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIFjEAEjwDoGB9W6ZAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGwLGEQDL+jQA2oii9hEA+AvYkqg+qiEZCQiKwrCEIqFKtElEpAo0aI8YsGwZEr03UgvN7e6+++up3nnHh5s2bP//5z2/ftX1dEBPTVizC4OQSlvhIzKmzSbRazYniEB1M1aoVH5ApbEGxEAb5VPc5qOFoWEsTP4c/rtRZ0xym6w7DgSXJCURBg2I5JVqPhIQuoEPwHKeZdCbZdw0JlvouUVdadwwjDZnwKUiVOAY7EcaiU6u7FFBEj0gCcgPUcjkqZ8tlWnU1Aud+CG1YEGysxF/5+jfXL5r/2te+9g/+4A+efvrp8Xvv2rBpO9h1EseHWVDGYz6uu3QEy0mffU/FnEsqwYeGoB78jJy4RYI5seB8lPBBr3PUZTZu3Aj71XXXXQcBx7p16+jXS9gFf0/N4sOaNWve8pa3/NEf/dGTTz6JGiHicrVbMgQMgeMJAZ4VPrX2myKE+mTmOU+GWYlJh6NX5jFO/Hx5khjvYPn8xStXrlzSMx9OvVtuuaVUK00GUUeQGXJ2qOLaqlclL9OfO3pr+5VR96jCDIJv/rTViLTi5iAy+N+qMLskOX8lNb1NFepjv1VZAJRge10OJA2I6+5cisYnx7ds2fKzzTtgJXjlK18Ju+IDzz41WZkcCSLWMMy/UcNz7Sx9IXlzyXyTEJYFpzqljMTrHBuZz372s5A5bt269amnnjrSeqRD468WrpO+vr6Xv/zl999//4YNG440b80fQ8AQMAQOEQI8Bkk8A0OhrpF3QH3V5agTqE6UPDNFb4Z5Skpc0oxYcyRZnPIqzZsMlqmPTXnT5l3bvQU3Kkmpvm5CAgUf0GRV2KGS/ZUpu7HyiRwlF0dSDQJp3nndfBqxfpG3bzeJJyZisUNLOtlKZmralSJOxQ/131kQqUu8NqpuoorMHcjhmOIoCnD2pPm0t3VRZ2LPtmh4uJhP59vanHv4I2RYMP1QChEnyPAuCix4jTjFMg4CLEfCCAjIU47MK065FYJrlwERTedJFMkGAUxJHNOB0C3590TqCrNWYmWFQWB0je/7XZ6mgEFxxvhxlbjiGZYqVGwO8GCiyOWQyma5vGbEQfFyxVyxAwMDZDECgLBZQVne3d3Ndc71DAE3ErWDwumnn44QTtXOzk72uCDJYoAgzKIig8VociTBkIWyjuYBDAp1Z/R8/wrcfYE1LmC5TVwiz75VtQqnFf5ncjn6smTJErwtOd6pQnuBSzTKp5H3LlzAdfn8889DAQYgXJP4xnWLEP2kL0j0VHW0C9P23Wsmq5NPGlTo0FRl/YtNHJalu7sfm6rbqSFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhcFwhMPVr4+Oq29ZZQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIEjH4EwSsmnHt8p/ubS+XKqQmQa+ThVJYJVAlUdLZTrjsgJU+WjoX8avEbMJuGfEsEJK5MjbzpzIrUtDl6xYOWpp576hldftmnTpr/4wX/+7N57tqaCdDbI1ySSrr3QPjE2LHXCoOBiTwupTEnONUl8LEGDxLARqBbHaQ1sc84ITZVPSAiprVTjNGGjLlyQU0pT4dQWPRIS4bSEwBFVSinsQlQkyJAqMFhxCmMIiaK2QhsIoK8SRUOba29vJ3AxVRI3iaCDP0NaT0GFIUm4Q4JA/SGEkRA+EcaiQ3SllLnwSFrEDeTlyEXqAmkuncvk8kGp2+kMSXAs1BsdbWG8O4h2x7WNuwb/4xvfuWrVg1ddddWX3/uHcDTc/NBDL+x+4ZSx1HDYtakySr2JIKCNeQxpEIwlIh8zmZhPteqi/1x4ofY9C8eXsGbJsZBtK2RLlWoJJQ1xVG2KjroEvD6yEaB+8zd/0w/EkdyX3t5eGGre9773CakZ95GLjNXr5Eh223wzBAyBA0OAiUg/VGfqkA9TE9QK+peZ2U0f/ihqoTBUqIQZhDzPOqYqCSZ3geQ6B/EMYWpbdsIJixcvfudJZ+/YseO2tU/u2bNnd63EnDWp85WbCxITRaD0ChjfZ9K5eJ9qKMiEMnPSUqzx0J5Z60BK6u02LM/uxlQDCW/boLcIgp3pKKhNvBAFDzz78Gn9i2EueMdrrty+ffvTG58fmBhYFmdHgwzP62JQDYWegmVKCk6LomOjZCxoV8dlqom9c9OSiQCIpr11j44zPPeOQgnxzW9+E9aD5KTsS1/aTNJPPOFWgq/h+uuv595JLvleWietdUPAEDAEDggBplT9UHuW6ZXHNS/O6VqKN+5UOp2PHA1WTeiwUlXH8CSTkfOARYbMaG6WrDpyq9gd1T2Vc0So+UwIQRPTKCQ+IcROfFLpXCFbyLLIqdaYL2Ux41gyq2GQYQ9BxNWRyWJ7z7wozpQrQTkla5uavMtneFetMhu7NQ8GYMku060wFfEy7YixnJuOjorFFDKkfBJzurzvkuQVGfpp7LHUcppJHaq4JZYQaSluYkoMSU0h0KbVjFujYUuWa24ZBzF3PMFSgFqpfBCWg2KpFJfD6oRbm0m7sBu5xoWeiiaAiPZBSrYtZD+hBvuStEQhlKB02vXBLUNoQZxAjfWeDoIu4mArxwVBAwotdj/SWYEcNd3DEbeDbCgYYsdRY+G+rCzlHVkaEAWfpPWIVSXtTMP6pGOKclMtX/3wZGhdE/31b/oH0zTDISiUyjJI+UxQKARFIRcDVgFfrxA53QsrUXAJZ/jL1hALbPJc0izIx8fHYVlSBivk4M+6wnv70EMPsSgiofmzn/0MOdtKKKtB2LrJsCGgxFgKuwefIp+nOfLqgNbVvB5h1FKhP2rFho6Ik3W9WlMGDEiul7CjaaGDggIo8odH4VfDp0rbSFwqz58/v2t+r2x55bK18TGu7V6oeGvxjh3bQYQttUI+S6N0GUzkFiTvniHpiKvO9U17FHGPZaoxe1JwkiGXUhkOrnV9WZhmNMRT5wsdJc//7Amm3S0GIb/sq5BoOqqU85k0C3Vs6f2lvPjaOffiVb+/9X1KK9rREDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBA49hCYiq459vpmPTIEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQOAYQmNc9z/di3rx5hK4pBU8Ulok06+7u1uA9pyMhZEg4VquRD71TnSLRspRWJIjswgsvhPrqwiWroN743ve+9/S6p7efuGBhLl/tayP4LetC3dq6u2kIoihabJfY2LBt/nxoOwg/xUIqzCHp6OjQ4EBqkZDjYc+8eWR8QkK+XC4TeUhCHwnxhNA8eR2i8iiiOWxqDF9PT4/EwVUq9JG6nHplMhT5pO1qKVA4RyTKEYoijrTlfJZyDZZTf0qVCsGNagqJCjmlivjgYvxKpQnqRmFEN+e3S0+7BcIgJFhPUBCcszkx0lfrp8qWLVv+/d//fWDNhdBw/P7v//7mzZt35cONGzcO7BBCrnxbhr7QEZzMpKeiAxt9nwqb1L6PDgxRS0P+tFMcaQXh0Z70GqYvAEKnGF+llzpi+7VgwYLdu3dzAXgPCY7lUsF5L7GMIWAIHA8I6NO4rb2dzuoTwD8HeKbptMLMxeMCgkhmOqLKedBVSmUPzooVK5h/+/r6tm3b9tOf/pQH4FgGEsm4q6uL2aGWlvjzarHIlIDw0D3zccy71JrxTeMR9AG+j62aByNhhqWDB99HEB4ZGXnwwQfDE1czgV555ZXQE2wbGWT5VAmDYrFYyzoysmzWAewoROdAgJXsGoPCaHIECjkS63+UJyilSEdyJ/Qi5PKAsQI/GeUj2VvzzRAwBAyBFxcBnRx5aeKNIwVzTSPRStO8yWmTpMkTLXVacqhW5O2ejD5mdYrXPPNyrVzmJXrXrl28HUOANTw83Nm7kOdw/+4RZljegpkEqYs7/KEhKiJhekUok6yjqSLDa5NY1ke3O8HxKceQOzYo4cPh0/RKpaWtclWuW2lMxNT11VGgrtBIubY0r29w5Fn5wI8NPRCJtVq1wo5J1c0tkIfjP86T4lj6kqpBYxXDBMY6rd2x76Ar77AlmIWEWki9oO9+RSByl3wGawjkKPReLuMUqOUYsRDJyiR0TD/Uol3KtTo63o6rVD8gJKlmUn6E5Fn9AtSL5cw5L7+QVWjPkkUcH3zk4S1r1waZnIyvu0IAQRD3w+EyCHVsAFCvcwTkWaLzOs9iWxcV1JNr2F3MCvVzzz2nNxnrPRDWLlCkGS4DStk6YwsLEivtY6MpVakfk0K1462hwfK7SSF5upehOZ5IdxP3ArUUEAdDqVjcuXHDzp07l55wIluCZ52/cuHChbtGxoFivCz7bABCDe4G7yrXXZNL3n/Fh+s5jIXZrYHNHB0VNUw5Ej/I592eW+KZANRY1hHZD4umaggYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIHHMIGAHWMTek1iFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMASOHQSIyEyNjgpDQSqWDe3xkV2OdkHIF2qBMhekBwYGAlFMpWCuSoXZreNRJc7GAaGHUExRcuJk3FceX13oWbp06esuv+K0007bEBd/dsdt/9+W71N3XU1iFOeNpIbKpWC7UDVpSu8eJFOLaaY8oqKB4XqZ/MkS+UlMrETExUrtkQlTaSSkhFpAgNzQ8BBUTmGQ1qg5SsfGR1IT0iOVwFVBtFu5UtSKhPHtGRzQPMd8Nrd9+3YakrZc4FyFwNUaDCGSVKjKnBKyWJZw1spkSawR6yj4uKQEWET0DQ0NVV24I2Kqo+B1VHPvYzQ5VhwI9iDcKSHHQbaYwkhbKCQapUEZhV0O5wGCYycm73zkjuDxO1524ilnnHHGH1742rPe+f78zp233Xbb97c9Qb/K1WI8UYVBiyEcTkl84sj4KCGc5RKFQS5OEfg3EmcGB/s1LNYNdxCVqlG5WgugRiEuEM0wGwV8xIOjLQE4MZZ4TUaDpcnPiv9L38P+/v5WJ3C+VWgSQ8AQOJYQgByC7sBYyNwaQOYghAUS7l0dG+OYT2WZYorloj6o0UxG/E8wPTFHFIs8svvShRNPPPGSU84kYL4t13b37Xc/MTRQjau7mRlJFWaEqDIhD0YS+jz/Y0R6Ki7UQ9uZ0OXR6eT+gECMODYKjl4+e2bu5EeEzTNTz25tv0rrDgNORZgxwbBckVVECkKJxtFTTDlug0R8vGup7HqZVoBYqNRq/dUJSr71zEOFIFy5cClB/m+54sqVS5YNDw0988wzL+zZPVabZBFSCWqsDACQ9UcmzEzGrHEEZ+g3tAu6Tki505onFKBsCnVhDMHCXIFWu3bcHwQcwFLBZ8gbJ8L+QGi6hoAhcIQiIDOIS/jHYy3hpTAlkSisZ9LZTD4fprMQ7qRzWYQ6xasC3Eh1NTIhdYVgCQW1krTLbMWH2RaeKt4zeYnl9Xheb++2XXtg5OwfHM21tbXnSv2btp50xunbtjHjZ9oz6S3PPdt5xhlM0hufW3d6IVedGA02bQ5OOmkyzGfaOmupXDWqRhneXFO1dAbrtVA8hDALOijlbcIHMlB6SjfZxxCH9VODSFtOmeDZzxAQeCXm6F6sZLmBkPVUQx+HRdm9NvMWHLsFifaajvF+LJaZt91MjjXFQI7IXSnWINykvXQmFWWjuJoJMrWoFk9O4Dl03zBlZrp7isVJyJvmzVuwe/sOnJjX07Zz+/blixdORrVURhot1artnR3F4gRDAHMpEnxKQ+fjhlK8IJMS7GNdu4jAJ1k1qA6ZlHgSxykow8JUmNHVpgNK2MScpuAT1cpuuMnXrxaYn/QjjWuikBVgnTS7Ljucf/SC9Bcnl6u+6Tf54BU001Taeqrrsddf/assQYdrZbin/5/feu/Xv/71zY88FsCiXqtwFbmRdxeGIium+V/Q5wjAGTQZHzYfYGQDslSKTSeO+CwKDai1CyxHvRuU+rzPoEZdT32lRjhqxqtNm0FH5ViYVqEudERp7tKdTWuqTG+HqXPlkuPYEEXcbpmgWNq+/rntQTAxOgZRO/dwtlrtiKF5ZzdGCOi5PXzKyTqX3URgAya5r7ntPB68+vAoyRcBj2vVVUpLj9wNEXC300HZrGpJGIMMXjTlBhEN2pW7klTf00o+A6czIRVb7JrAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBA45hCY+gXDMdc165AhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGwDGIAHFiEi/G0UXP1ZmDCCcj4KwqQadKiwX1FWlVoX3ZsmVXX3ZFoVBY2d5JROIzTzx55513Ds1ru/WhB4enQicDFNLpUcWLqEFC17JZCXibLAklFk0RoRalMhKn5gLt0umsBK0RYRjHxTpvldY+VEeJjnOhg/VgRSJeYcEiqC+RYMEgJQSHNVtt8JUQxPfohnUbN248YePA7t27333ZZW9/+9sv6n4rPEqP7try1FNP3f3MBjwDS8aN8ELhg3JRg2VqVms9cZxNZSuRIM+AylH6LBqCANUsGQKGgCFgCBx2BJjyHKuB8DTA2FSLKulyhSlHpsjEfIpfzNHdhfbe3t4TFi7uI82fv2fPHiYFpuyFCxfuGNwxGtSoIrRPQmsgqSKx+fUEUYXmdNZriKfi2b3kwDJNZg/MyJFWC8zbA0ixohd2b4t2b+uoBVB3nXrKKaeeeupZ3RdAnQD4jMK2/t3wbU0GtVJc9YRlhPnLCEJDASsHw+fC/qftINAdk+hN21kTGgKGgCFgCBxmBGSloclllCaStz+devwEpO/gqswbIhmtpn8bZ2LI59va2pQ5um/BYubHzq55EBUxOcL6xOTIazV5KKeXLl3O22vc3y+E0ewG9PePj493dnYGS5bQOlsEySYc2Y2jsGnw+8hLqzALkeS9FRIi/siHcyWxwic91W7qUdxX7p6kVLyfZulDdZ9QaNKhlBdsZnUyWsqprKsgkQ4zcabsOHpgO6oWCoJnaSIYG6u2d8QTE8WJidF8ns2EaqlUzItlQKCPo6OjXV1dZAYHB7s62iH7TqWE19t7QX7uWxAeQHzQlZ8iiBH8wQ7YoSPoOSYhcbKpj77hIybjvJXecJlNS4B1YJ5iSvZPnnsW4iouwiuvvPLnk6UXnnkmyOZkAwVYfBKI6heGIgyMXNJarnhy7SLEVT2lSNmskGjyxjhN5tWgF/pTlehRhb7WLJm5a85iZOaiBib1LjROXYUtz6zdtm1b79IT58+f39HVhecQwYn/cOfBst4AR3QdAOqqu43qjyAkXKgklDmiOPX2wkI6cVO4BqcODVMQ5qc17xquj4VKprQtZwgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIHMcIGAHWcTz41nVDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMASOYAQICUsRv9kIvktuZ2eDMhQPKf4PgqW5YKAcLK5GxJ/NDwI4M16xcAWkG6eccgacG50rlhL4t7tahHfj3x9/cOvWretrMbxWr37T5c/wB96lRnKxqlMBcgQGEgNHofB8uMA2wjDlbyP8shbUiHAjVk1C5lRJGD+mLDQMv2h/JUbOBfLp8UWzu29DTaF99BFJU09FWKlH6Apk/D8YV55b0f39tXf+x3ee6QmCC09eARnZW654zRUXnP/f94wTQLv76WeJN368NERU7cbh4ngQaIxmYWJ3JVJ+M0ePwrDGMrIMF1hPZCRGcSAbFP0INi6SfXflCNBoGr6m0yPAQXPBEDAEjncEJKg7Mf/y+NW5uBZH6SBmOm4PwloQdwWZXJgbiifag0x7kIM1sj2X7+7uXjhvPkQJpFKpNFKrjO4avOOxx0dGRnaXx9JBeEp3YXsgTI2pIKU0V1U3n2jeQ8/02jTNUNQqaX2Etkq8TZ+pz93+/OjPTIZKFznVk+Gu/NqNg889dl9HprCkZz4sn6cuP2HN8hMKVTguJnZt38H8OxLW4D7onxgtBcy6suKp1gRkiERZ0EwDNyouTTVjucOIwFyu7cPojjVlCBgChsABIMBsrx/qNs38MsUwQcNEI9N0WiiW9HVbaKRc8tM3UxV5ffVGV9Tr746iRwEVk1Xy2VwYxROjY/PmzRvZM1AcHVuyZAU7Az0dHZlaeWx8lN2D0R1bakO7J/O8YQZdy5Ys7OmqpLPjS5divruz+4xzT8DmRD5Pu7VUNgrSlXQuSqUroRBkV11fKo4mu+rYrphOnduOwSquuu2CrNBg7UWYoz66qVemYRwX3ixhsHIEWlJcX/oooRWYwfqdYLvW9Rntyo5ESgDBgiSHbZgR1i1RZwVXFXqeTJbX6qiWQT0fdLAGqGbKQZjNpNMVlmbF0tjQcCGTqhYnymOwZYXjwyOs66JKdXxktC2bLU0WK6VSHl6wBGmpa65xcAiIM9OleldcoeO+DsK0oC1ICfcQOy0yckFcy2ZCmLDiOMclwa4LCGfCVLkm7NhHcmL9rDxKMzkpF61LeqnPpCZyp1iKaqPFSTjaWMXdeuutV1xxxVvffu3Xv/71gXUbgqyMJi0KE5aAJjsk4EeKU26HKhWOTU5oi3r0JGV+OaGZ5HEml1odlobcQKvxaSt6HUpVeVq1AxbC6LXPuvWLEUWBJ1sbHd49+vjutp5lK0+CU4zxAlvuupTjW+Mq5CFElaxci9zXYr/mni3aTSkOQ955UHebhcIHLD4IsXvKMbZNeSRVGreCVqc5uQt5xDUKHCxOqX5pTFW3nCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhcHwikIwYOj4RsF4bAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBx9CECcsbAzhF/j3FNPLBaLq3sWkD9t8UpCNE9Od0K0sXXrzm3btt10w3fHx8d3VSY3xJUR4ewIOoKg6LoL8YYEXlbrfScmTcPS9NyfaiSaiyaUqDmnIzKCM72ORLwd+kSMnQuQOyTRgy+K+woIUbX4yQGbHR0dJy5cWhrYPVSrPvT8C08+/8KGRx8eHq1ctWrVwoULX3Xq6Rwv6W0bHh5e98JGjtsqE4zX4GQNwrL1Q+VWr9Rsq9wkhoAhYAgYAocaAUdZBQdWuj0D+0G2J1fI5XLL8/m2trYlfTILd7d3QK40MTwyNDT01FNPCQFlPAbrw7DQF8p8Sx4nhd+AjxzE5VTLFBo7SYt4xrnWTc370XudTPejwpGvKsuUvbxkMtXzsWpx+57t0F8MbN1aCSondS6C5mPB/N4TTjgh09cDFNsG96AMEyU8GJUwIDj//mfX7mXLTgwBQ8AQMAQMgUOJAKQwmOdtlwzTEClOZ1ljQCoDa0+YETonLdUZn8lLMxx5KVfX/GKApYbn+sEIyhjcvXs3au3t7S9s2bZixQpYIJEvXry4v7+faZE1THsqZD2TyuQhydo9MAQtTnt75+rVq8cqMDGJhUwmI+Q1GfmVHW0hIUORtC78VJLIRnFE647USkoocLxXLq+njawUNSWVePYrr9+q2VRx2lNqsTxwCc8VE9xmCYcsDS0Xa7BcrtzXJ/jziaLa5GR6Xjfwlctl9Okm+yrzOtuH+vsz3V3UZYHH+3txfMLR/zjqH/fur63M5agoOQgdq5fLiQMOTDKSwkhHU31AAIMqq5S52D8qdPy1Oru3dB+cuSAVh82bN5+0fMVb3vKWf/+Xr1bHxqJiMdPeXtVRBhxHt4Rlriqxn0op1OTdYAlDUwP8erN62iRscskb8RlVWLp0aaVSYakP3S32m0q9EeSa5thlX/HFzshl2rCZCorFbZs2pfL5+fPnQ5pf6GgHHC2tORKspkW1FtERMtwFcRQrmCrRUgEZfrqWhA7KdJ+KmqStSG4uZcFSfFr4AFsMmcAQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDg+EDACrONjnK2XhoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIHIUITFQHzjhv1eV7+gk0LbSlCEMl3pKw1a7OnjyMG/kugsfS2TxxrY9t3Uj03U8fvJcQzUd3bx8ZmXhhGvYkgUCD0qKxyWwlKEEI0Ug7d+5sZOUvAWx7hVgS7MYnSkRdVktJfc3HUbBr1y68imrKsiXioeGBeimxpMTDRkH/nl1IIhd0GuFDwo2Gpv6tH0uVMp9169Zp4G6lViWSFXf2DNYte+3B4SGf18zuPf1TEgnZC4jyVYk7kyyReHvF5WrxDEdqVRIeS+CvOBPXglrVxRUme/Pwnm3PDe+i30EhV6tKSf9ohUjZBzdsCPjcdx+SMxYWiDp++coTOL5mwRrGej7KtVo8OULkbXF0kKBKMo7UrB+GjsnaYkZ8WaVjspx/MpjAwkR9UMlaMgQMAUPAEHgREIgzqe6++WcV6ywPzGs9PT08ezNtQnoFOwWSsnv2QnjF/Lt2+5bx9eNb+mV2Gw3rYfDkk+Hm6tau4ti4zGCUwKUlJiRsXsv2dXRECzMqYQevtFiDyZNx6TNWm1sBpiSy3SUoGJSF4cDtN4LwmX8nJyfVLJQZoMI6IekRMHE614Z0Snb1R1ljpMIS0GZS5ap4PhLIwmjr2M5gbGd6syh1QQWSbV8wbz5slYsWLWKVla1Jc2t6F9FBqjH/ksgPVop0f3t5gukYlhCpbMkQMAQMAUPAEDhoBJgHmbt59cMSEw2Jl0Ehi8lkKMpkssyAUNIglym+FlWZK93bKxLUEEbuHTMS2h+ZLhFSm/95tWQKwwgLGKirOtryS5YsYUbr6epivsuNjqdS89JhLQubTbUyuKe/WK3I5J7OdHe2DQ3tGRkZXHrKGRFUTKlMOfr/t3dnzXFcZ5rHM7MWVGHnTkoUKUryjJu21LZDHocnwuHoDzCOufONP4g/lCfCvvKV56bdM75x9IXCssLmUOZikeIKsAAUqiqXed7zViUSVSiwQAIkCPyz2YmTmSfP8susBQjl40ifp/pEVVn/9PmqT+d++MTsRUq3ifVbsH7J7+UWMJRZIFdiaTv2We/r0cez7Zwg8899a330NSecp4icYVU/y1qbWIrwtWd4SN8i1EJoRH+1UFkmalP/QgWZ6I8IeRzV4yS3nLG5PB8kc612qx0PelGzVTvfXFxc3H6xnrTn8n5PmT25ZpMk+qIyt7Dg4PrCoC9+6lUz9cmU47I9o44mBjq+I4xSpHa9NCTlPFnkkMV12TXVtnqoNzRSSx+zrkVo6UL2Y7ytd3Bbc/RMK01fy+QMZKKlVasnuvt1X/b78825jbX1Wxtb+oPY//wfv/jyyy/v3v5a38Cj/kDf9xKlmjUagtJZjXCnyG2rs6FNXWlrq/w72OjbssP6oX3W5Qi9UI5Wf47TuJSBpXPVVLl/rKlp+8eqHd2m4yoKLnQhGmW9NexO6/d0kz/r97bW186fP693iVqzoTefRLebbkyl9Ipu+BtNeJWNhqhrF9fr4eUQou6C+ejg+E9H0yXQWVpb+/6rSi3Rpar7a3qvG2C8IbYRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBA4NQIEYJ2aS81EEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBd0pAz9F9+umnP/nJT8oALD0wpuct9QTm0yfPtf72wVMFMWxsbat8v/tCT99t9zM9zvqDH/xAD6R19MRnUXTCX8H9kbV2eIa0Fh4z+9cff66anU0LsdLztfYYW9wsH8/TiUYV4q6G5UBX2JOXesLP2suHUVr2vF84qMfo7HFNjXl5edmeRRwtiT+GOtr0XkKPw2cdy36H7eyuPzov+ulPf/qrX/3q66+/9ieEy/2zF4phh3ZGeOg0Wltb02OuEitnMUtrbtgMTXjZzxrYQ6xFL7fnVLuxHf7uD34g5/UnT/Wg5mJ4ODlOB15H16gZD/Qc4EojVlliirha+/s/dJUVgKX1p9/5UE/YfvTBlXa7rQeYNevBIFHNqPmhrvj//uIvj0L61SwDpg4CCCCAwIEEPv7445/97GcfPrVPSb396n17e3tbQRIbvW1tPl9b0/uwArD0+aW3ZS2qoPftK1euqLxg+Q87n4zWr4VBRP7I9/Xr1/W5s7m5qfWgZ5FMZWWrGTb908o/p3ynr3c9gL77RDXuwRZeU236qGwouwcz9plbbX9aWQ/G68NdAmpzWp1Z9od0DpuvDDVgfQLeuHFDgQXlCGdpZM86g0DTqYRCfHzhspKqXmxuaK0vSOrCLlJRzGeKwsj1jUgOc+Gbkj7HtV8Jm9ozn9QVcNaqN/T5u7q0oshR/7rSa+hn0rHos9qdEOK55zDYiQACCCCAwIEEwkeTpedY+FRYvBDHFqyjLyBa66PXP7v9o1NlfRhp0SH7kEoS/baYNOz3Rx3Snnq9qbU+zvS9RZ+A+iz75pv7+uz75ptv9MeENLXPQ2VaaVF9Ldt9HcwXVpbto7DeuHbtmn51Vaq1fjnVdxV9TNsYQmyQdRfH+hDXWo34fhXSkFe0E2E5+uKhz1frRot9fwhhWL5Ha0u9qew56BeMsmVvX+tyjwpeLgvK3LEUrJ0/Bti03Sq2P5ckWVJvterFQNPsbW8rp6fVbutC6BTt8W8QmqI21XS1q0rn4WC5Pb2gfq2Fifla26ML7RdXNVXQALTUksIvxPSG35kjLqD7UzeSB0hNG7r+QqK/dw0y+xanL95abw02Hj58+N2PP/nxj3/82c3vKSH9z//n/ypYPe/1lRk+3o6o/U4YP3AI2+parzjFYPl01KJfwUNo+uiaCLFW9tLTP+WpacRp2t3YeDAY6MXebLd0j126dEnfgeO8poO93H7N8eH47LTWfLWEb++pf58v60wOXDXDO5L9vVHxWrqfvR3tn6zMHgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCQAAFY3AYIIIAAAggggAACCCCAAAIIIIAAAggggAACx1FAD5L94x8PvvOdzf+Vt+t5vZXNN5JG3D4Xz8f5RUvXWNeDq3neWmq36/XP7OnXpFYM9CxZb6ur+bTDA7ELIfQqDc+X9UI8hK8vrqz+6L/9dH5+WQ/B1mJ7qLUYBVpZ2R8UzP3xztFjq9o/fE7NA7D2QFPveghQoSHVp+D2DMBSTS3eRFkYbk4JwPrzn/+sBx1//vOfqwtNdjjIPUYxdddo/FZBj+5pra71GJ4Su9Ts1NMmDnjoVT3AJDs8kUdfpSFkQ3EnGuRiGmX//d8GoeNe3XrshvyPLDxAW8vsOec5PXBbr0tJ9df64dq125rd8+2tJ1l2P7SvcdZb9cW6Pdi8sNbTNfvi0T87UWTRLJrF8LFEK1eGY5ssCCCAAAIHFdC78bNvH68uLH2R2rtsM7L382g+KdqtXs/eyZsXlvW8uz06Hj5H9M78QaNuwQTKJ1BMQoi78k4TPZOf51nfIhVq4SFwyzL45L/oQ9v2h2itTLmSYSk/DaufVt7OLGsN4JNPPimfRddIysVP9/bLXmZpszzx888/V46GGp/9rMmaHoDl+zU2n7VSNkShzcn6s+/Rp6qWkL8RSVgtz2XRJ5ffr4dvMnEIoIwy+7Ds+EemVUmarTmhbXY2lL+ggpYhTpYr9uKfW1tJktoHtC361pDresd58eDBA7Wj8ABPwbCOWRBAAAEEEHglAX2wetCSzl5ZWVEqkD6JtBS1Rvid1yJjag1FxwzDrfRNQzu8K3106nR9hmrpd7c2BwM1pU80RdbokHKvOp2O9qgdfT6q5rNnz9SymtOnWp5Yg5ZKpTCsOInryfK5s4pZet7pdrPi0gfXBnHysNOpLy7qg1DdDXSORXEpNCdWgpaCsNLwG2jqv+eGz9YifC8KWVch8Uqf7PpGpPXwEz5WUJH9tup/BND+PHyp0D4bsc/Jhj4qh492beojV4sKw196Q8fDrxT6HTqca9VsNlFmwWGKDrNyoUOhsrbVuTWTaDur1RWDab/Rx4njx/VmbakRb29s9npR2m+uLusybL94EToMOUEith71LcD+gOCRWKFH62dsUd2xPZObasR22rCtAX3RDNvWuBZv0tY1/U0hTgodlX2iKYRZ6Aawr6CjxU/1k0b7jvdP3XX60uUxo7qfdXOG+2tn0Fn4W8q1997/6Nr17cKCaHVjq5ougPnk+blz5669f1Xn/ue//adu7L999dXz58+tgu7wcMOovlJWleOmS6z9ZdPqSHW0qXM9/FSNl0f3KaiRK5cuqVmvr1jYJ0/01xp7AdoLavdF9y5Uc6ywT/tHeii81nWn2VtHXKT24ghKGp6NsJ9v9rsba/pNJVbi3dzZs3PNlsp6jSsBS7PT37dEpumYndZqJC70xy27GxO9ynVZ7Ped4RTCa8TKqlnTX7qSRkPYFrqvXcMbf/gXv9EpwzP5gQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIEYHEPIIAAAggggAACCCCAAAIIIIAAAggggAACCBxXgd/97ne//e1vfXRJ3FQhL/oHGqzO0XNmeuBPz6ZNntluN7vdyd0H6uH0VrbnJnc/e7rzYOVRqqyGq6nHNLeOshfaRgABBE6zwB//44///h//rsgH5RxZ8kGIJxBIM0rSKNcHp+UXnppFj74reqPff8e+MOgpfwV86tl6Fcpsg+3DuGr6AqBQDf8acBjt0QYCCCCAwOkVUKSMFoUBKa/n7NmzCtaRRaqoJlsii+8J4TL6FFZZUcuK4PFMHx3WiZYsU7dkZk/bCek81qAaUZyWYoZardZGd8t2WXaN5dR4WWcpWst2Duy3WB368MMPszv/vHv37vzK6qVLl7rP1tW48m9URxVsOCHjpkgseCsNUUxZCGLKhpFNow9Gj8LRWSF60j4wVfa4K4veCf+0xwtlbk4YhFYHXoYthI96talNH4/KGnqtpuFam2EWZeMBykLENReL+CkKm2y3G+lKzM8r/SpfW2uurmi/8paCgLXvFGUjYwVv09flof1PKat5wc8tTyk3lQel4Y1Vfkc3NSlNcGNjQ/fYD3/4Q60nJ6Istna7ff/+fa2zus1dN7Oq6WJpUcyb4q6+uXd/dXVVt7GS477//e/rBdLV5dOdHGJP9Rq5evWqUlb9xWL7dT/orzej+1nlzz77bHZV9dvf3tZLwK+OWvvDH/6wtram15e6UGvlVVP5HVjCq0NvB0Lx13VUZCopSqzZal24em1paSluL/tENFl/jfi1852Sl4lebWYSAsX8m7HeL1TBnfX+Y+8zFuLXCG8VdqoaUT6wt6nNYe92hAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEECMDiHkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBA4ZgJ6dk7Pg2lQvvbRHTT6ys+aDKvw5yY9qml3+lX5SNoYh7IjyqVap1r2CtWa5SkqTNtfrfPule0KvY25rYV+feUPGftIKrspIoAAAgi8lkAWEpN61saut9jtWT7RRvkP5dn+yVv91Kx+LlbL1UHv6jgcqDZcrXnUZT0eP+j331bvs8xu0srNB+FkX3s71W9BB52R96KzQpKHZXjMMjbqIIAAAgggME3Af/FXhs7CwkKn01H61ebmpipnIV5KK1XILdE6pMaEpBnt0aJYGV97y8qh0aYdt/QZ+3xTTlYoRwoGUkEZNJY7k+eK09Jhna6vH8/X1t9///0zrfl/3L7d3NhcXD1z8drVW7du/e3evQ8++KC1vKIP0G4tqiVFL84GUZzmlgblcVdplFqPYWyhQ2XejMKtfEzhDxoat40nCWmiKitkZ2cJYVVF+B5kOTw2ybAOBbW9s0flZ6v/AAAiCklEQVRRVrZlS71u1Tz0qtqYQsMs+iq12EsN1ptSS+aRKW5HexReqR0WKCafMCJNrlC2VNZXIM9Wr6/j5y9fUo5SmubR0koeJ/VGPenHvY312kLbeg8DtIKVwgjDxqutBFj+wacaA6Sr5Q2WRw1Xd0OhQSd2a5QaKqo85RtJ9Zvnq43wiM7SvDT3b7/99ve///0+XSh+1Vg0DdVuNrWKw31SC26KnbJoM4WsxVFzrjU3N9dZX6+2dv78+adPn1YMqwcPoazh6GWrESrdSb0oDeoQGj3KJpRIFwjDfWF3crib9dLwbDh7ydid1O9u/vP210mj8dFH31EA2VLL7tLB9rbO1dtIXMSbdbv/7H6sZ3mmvLFy4oq9SjwGS5dIyVdJLao3lF+WROHlrBbC5dBNrFdh6D1EZR3lpGkbAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBd0xA/8O9LAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIHCOB8mGwYzQmhoIAAggggMDpEOBT+HRc51efpeIBlEny6udzJgIIIIAAAkFAeTH6qZQZffdYX1/f+QYS13UorlsQ0ihQJrbKIRLL8VRZGUDKlLE6viush3t0eBQx43t0MHSnPJqa0oJ0tP/4iVK3Lp49f+Pjj7/+6isFP934l5uffvrp3YePHz161Fg9v7i46CNUZeX75DUL2FKnaiq1UKJ4oPQhDaAW/tM7ZRWpR4+mCp3ZcGwzDtFUia097kr7bWyjiCuVfQI+4Gp5WNNaCqer5uif7apVeqy0Zoe8i3BWOSTfP1rbyMOi2am8vLxcP3u26HW2trYUaLS8sqJxrz9/nqTpwupqNgixqKNz9dNPVxOlc+XgTtGPln35Ae9xp9KUGCCdtX/j1RbelXJ1RmMsPgXttJtG63CnDW+TQbe8ycu8peGUldm0va1/HhzmsV+6XfWy0qLb1cKWQvpb2d1wDMP7rWx4OmG4VRKdHwplO54uVw0vG2ti2NHY3sPY1BheofH9TtHUSgnh5/k333yjd4D2mXN6u1hdXVU2n+YrUiVbSVXvDLYUidr0pZyWx/PpoBe03yqEwx4Tpmukd6FIMXMsCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILBbgACs3R5sIYAAAggggAACCCCAAAIIIIAAAggggAACCJxQgWpUgz1KO3zkVD/tIcEirG3v+OKPEI7vZdsFyocE3wrI2+39rUyZThFAAIGjFhg9o/2q/Uy8NfuD+uOP61sgxH7L/kf3O/P0HZu0mvZIve+frD+LWfWsUVbHLOdRBwEEEEAAARPQFwxL0BnlLmmPyoqJ8T3NZlPloVRc1860UApNnmaVbxC1UYVQz/JnQrCMvnqUzeYWHxSWkJYV8qLCJ1gc11Q/TXu9bnvl7OLCwmI/ffLocSdp3Lx581qtfu/evS+++Ot3v/e99z+88fjx4852t7kwPyhS/aVAayXdDAYDxdYUeRiPpXEpgyf0pZUm5fv9rwrq0DYTC6jyOknDymXEju33L0yhMCyGH1ariGLfFSbiCVZef+dEHcqst1oesrEslisqBjsxWz4eq6+YLg3G/xmp0IoiSAor0/+FoLFioHSeF50XRZbW59rzS4vPHn0bbW8n7dbC8tLzJ31NV7lAOjNONL3hfPOQGqSd6lyJRFqrfRtJWPziWm/h0o9228/qTpWrh9RHdVPlTHNQh/4HHI3cBz+s5F9Pxk8Za+Hd2tS10aySRkPiaWzBbUl1fkFY+8YmpftzbI82x2wnK7zOniNt/HUGts+5sb0QKku4feRsu/SO4c6DnvZs9bvbnafJs8fKhpsr3l9ZWirmWoNB/+nWtiKxVCGOe41aUYvzvt4kCr2KFHIXFTW9d9kVq9cV41fUEr2L2XXR3Ws7dRvnudbhjrZO/WZX/9rQFS0valmwgbEggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwagQIwDo1l5qJIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJvVkBxOSE1JmTNVLr2nUpf0qKEqRDRZCFKyjwKy95RMDpLDWqxOqNQLW2Ozgr5VMOjNVVWs8rYOnv27P379x8+fHj16tUbN27cSZIX6+u3bt366KOPGo3G//vH/a/+8pf/+qMfnTt3Lukreisb5JnOTZPUBhZZ/FOmSCJ17b0Pw7Z8oJV5VYrDzCuFFtlgdsKhbLPc47E7PlovV2KkXGE4x0bDNr011Vc6mMq2GVr2Nj0yzMtaW4VRtSyzgCpjs8VODxlJg36/v7UWKUEp6y6ePbtQazx58iR9+DBaXT1z5kyvZ3lAWir5XT7K4dD2/6EeFf1j3YVF7ahsrU1ZbIS7FwUKTe7cXeUEbun2k5Ilf6lQ8dbdrNnq6Ixztis9wp/xlNmrHV3Ls4/hKGqGeRX5YJAPNp71+y9evLh8+fL5CxcWFxeXkoZeLmmazs/Pb2xu6t7217UpF0WtWdM7iXZq0ZuGrqCl5ul9KSxJHHb6q7Uy7vLlUBYqBykigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwigQIwDpFF5upIoAAAggggAACCCCAAAIIIIAAAggggAACp1PAn630B2d3Pz67sy+Pdh+ZLlV59nB6pdc4MvVJ0Ndos3rqUY+/2tfu8viDrLuPsoUAAggggAACCCCAAAIIIHAyBZSjNDc3pziYcnpKeykDX1RQZIzH+qgQ9ttvrtU0pKT6q2zI9NHvzqqZhegZbzaJhr9P+w8dnWu3Nzc3szTtRdHSQvvDa1fvPXj64N7d1XRw6cK52vz888eP7kT59evXP/vXT5WN9be/fqkArNqZVbXaTSKLvkrmojxJMxt8Gtc1vDy3CCeN10aY1CKF4PjY4vBXhUKjiKPYI4rCHkVl7YQQVX4vLkKdXYE4IdDKo7KKMInhiblNNa4pSseSqmynEqmUbzUIAVianJY0ZPEMrDg8SxXSKOtbHUUmZVlDZZ2Z2RjqQS6Js8H2RtTvzrVa55bf63a7z54+Tvv99uUry8vL3RfrL5SEVau1V1dbjZaOxnmhyyME68UuUD68LmGwCvjx/VoLUP9fG9Uc7lf6lQ3drpwq2JxGYVjVUCc7d7Soi+rmaPfJ/6lZ77rnw4xlMcvM9XIbc9tlOFsjYx2pBV2sXe2M1ahslpe1LFQOvm5xxjG8ajf26tB9rNs31ys37eVFev/23x89uK8YrMVr751pt7v93qDfrWV9vafVo1q/n6V5UW/U681Go65b3t7B7JWgyDx7HVhL1qaSzIqoFi5gTdFmeu2ENzi9oyR6Iyn8n41akVn2wyP29H7CggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwOkQIADrdFxnZokAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIvHEBpb2UCUeNRkP9KyZGkVLVKCWPx8otN6nIh7FQLwl/sXCZEL3kOTtlOJDtDxlMymxSSI2WTqfz5MmTq1evXrhw4dtvv93Y2FhcXLxy5crKysqdO3e+/PLLc+9dW11dXThzVtU6X39dX14uVpY05jzqa5BpPbUpJI1MgU21urqzAaqs48p4CmOIFIClPR5Yk/iwQjxWGMqIvBzjKN8r96gsG64H5UTDYCxvNOwPkViFoq88yko1m4rlCmlZdlZoM0uHBfWkYVSPqoI2paq15erYWkSaXffBN/Vz565cXN3e3n706KGir+ZbbRFtdV6sra31vrX0q/kzZ1RfFbQst1t2yUZjloPSfnR0p2vbsMUvwWTZ92itdqp1/E4ob5KyWrWg8atHLdWdb73sI9cw9hxYdY7lUPesWR7d85Ty6EsLavzFixcD3S26Cn4hdAOUaH63vLSV3RU0JDXr691H9tgam91rTmePDt7YLlnZe1Ghm7W/tXX39u3o4f3Fy5ff++Bqq9W6ePHio0eP9K6gN65aw6LxmnNz5c0wDMULQx3tHL4pjfmoyjtM9MauBR0hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJwCAQKwTsFFZooIIIAAAggggAACCCCAAAIIIIAAAggggAACIwF70HPvZfqRveuzFwEEEEAAAQQQQAABBBBAAAEEXi6wsLCwvr6uIBgFxywvL+sEpcBoUXSM1pPnN/KadmaVFBnF70xW0x7VCy3Y0epv9dZ6HGdF3m7UtczVkjRNtzc2l+cX6+fPPHq+tvbPe6319WazefPqlcePHz//+u/x2bNnLl66eeXCWhJrz2avo+SsNFuvRbWuUm6iWhz1bAxZ6FE5UOq6VrdRhinkngQVW7xXEdv4o6SpMeioBdz4LD1+aDjjMN6Q+BXlIb5KcVrl4jU1p+EpOscCtpKa9d7IO1me1fO+MqFqRaqTimzbY3S01si0tsQuq6NQqayV9rQ5N7A6iaUhiSZVuXnhTLNZz5493Xj6NO9uLrTbZ1rNVpR3Njb6Gxvzi8u6cAos29raKtK83ZqvhbiretL0YaoFL6g9G8No03eGPRbvVY21Uk3dBv0sVaFa38sOo6F6C6qp6Q4sWkyTV3SXWQ4jt7SpSn6C134b6+osHOFtjGK8z17PblQb2/iRN7ddalSv8v7dl6fsX01HZ2/zpU2NVXAyveq0P6Tc6RaLdePpztPdmCo579atOy9enNV7xccfX2rPP+m/GKSDOInrSVwrMr3sNTYtOmVsnLqVrc1waye5vfpqIUhO74BJpM2B/oX3sygJldJ4FI0Xbn47mQUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIETLUAA1om+vEwOAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHh7Ap1OZ3Nz04ONFEelgXjYjZKVlIFVjkuhMKkq5XkR0qQ8UsqPzhKOU4mPiiw7qVbL46Tb7arHxcVFJXBpUcbU6uqq8mvW1tbu374d1evf+ZfvrqyspPGm9jx8+kyhV9nK8pkzZ3rb3V6nE80taKzRYlOJTVHSt7IvPuxhqk3YqZUdrYe1hVVFtTnPxrIzFB2lzCzbWbOyliIE3CgoR3lPHoAVQnPskJZqAFamkC1Vs925zlWyVb+rWKlB2rP9IQArykPZqoRFp+ifaqYWj7Wh9q2skJ1hAFZiR9LlufqjR4+ytae1ubn5+fmlpSUR3b91K2o05xcW5htNRSkp86e7tdVuJjq6udlRa7ViKK3WvDddRy+U69Gh6jXREGxTl6bZbqmCb/opXr+61n5ddAVg9bvdso5O0U4tvuc4rMvBlIXXH9VI7/VbGt1IoytlLap8cEANSRPcf2B7CvgpuuiHMZkDtLHnYKadb5X3SgvTq0sBWJqCDlqOW6ulFnrPnj149OjB/fvXrl+//ME1Ta2XKSKryCyozW7a0Is1NyqHHR6qFY7qlOrNv7uaV2aNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAqRMgAOvUXXImjAACCCCAAAIIIIAAAggggAACCCCAAAIInDYBf4pt12OXQ4KdfXs96XZYTju9zNLiUY5klv6pgwACCCCAwJsQ4PPuTSjTBwIIIIAAAsdDQJlTy8vLN2/e/PWvf+0BWI1GQ0PzFBhLloki5TFprSyYZrPZarVULS9eEppjwTQh2acsjE23GqHlh4q4psoKxlJHCqzRWmE0isrKkliDyVJL+cnrc9rTqNV0SJs6MY9stNFLxuOj9b8AvGTkPpiwrtaf4a8HcahTHYnviUKcVqXd3cXJlqt7dsq1Iur3+9svnmmdhK9rHueTFBbxYxJahj0qSWn4hS4p9v5PEJXstXsYB95yxw/ee/9P6j/KFEhk4U26KrYomsjumHIYB279ME7QODQAJYVVb2nfqbXf7eUIbdAzL+VZM59hFatdSF+N+GC8kWp5sv3Ju9Zb87O8vM9gytdLWcd7l4xeXKJwonIkKvgYqmlQ5bnTCupFhzSYl46nbH9yptMa1369HdVDlJWfPuwldKr3E+t1rhm35nrdrbu3/r7V2bhx44beqRSi18uLwWCQ25x0n9pLQ7dG2VGSJ9qfZPYuF6UDzWFYrieRdurtp0gb4fZQlFY4q/ra2UkJLBukgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwAkT2Pu/Pjlhk2Q6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACb15AoTF37979zW9+oxgsheBoc3t72wKnsszSYkY5SuXAfE+Wv2620WQAVhpSZbLIcog8AEtZVyrnlnaVeMRVP6ppbNa3orJyOyHzgKdq7FQ51p2Cxwd5bM1klNBOvd2lan0v7z4+tnXEAVj1INOMUuMoLKFMUqHsYwvrUQBWObSjDsD621+/un37trrTnVN2+hYLfn+WA1Bem4g6nY72+KFyrRyxstrxL8xw/73KJJRtp+Xx48d61b/K+cf2nKz/5O4d/Ysa7dX33mstLS8tLSkASxfdk68UgKUbw4efRIleTc1w/8YhBqtemHc+SJWedmynyMAQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBB4YwIEYL0xajpCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQeJsCxdTOj+gRv6n9cQABBBBAAAEEEEAAAQQQQACBUyVw7969L774wuNgPMaoDDNSwVOoqiBjGUPVQ9Vy2Uh157TyTr9FUj1RfSmIK5xlaTWZpRgVUZbbj7Df19OaPTH7g4nJjIJ7dpTyYejVtL+f7B34lUfT6s9q5u2mvf7i4uJCu61ooVoYn85XlND0v/PM2v6h1FOOW7vd/sUvfnH58mW/kzVGtazAIx0qu9Bd5PvLPYdVmNbsNH1/GXq4W3UMe1/FUGOWl0A5jDL1Sacq+qper1+6dOmXv/zlxsZG2V1ZZ3IYZZ09C+WJ6m6WUXkjM/aiBstFJ5YzKgveWtl1GiWqP4jtlXLm4qXr16/Xmg31pfA4O32UoKc6fmIziXW0GZt0I9y/yr/68PpVbe+Db+9JtlhUHwsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAInVYAArJN6ZZkXAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIPCWBba3t+fn5y9evKgEnGazqdEoxUaRMZ5l49k0nhEzuT7EoXuKjdZxZDFKarnsrhqAlccWfFOzldU5ZYvn8Hh00k4mz9sNwMqbcwsLC1GRKgDr+FwO3TxaOp2OxvbHP/7x888/v3nzpsKe7L4Z3V2qoAFrPWMA00Fn568g787X1RamBWBV61TLO9d7tNfHr62yMDqy38/Jkdy5c8fCoWqHE+E02f5+oznIMU3TZzrL9VJNBWCp+X4UDwaDom7RV1GIPMsys68nSTUQzYadDqx+uI2zPLP6cf6nP/1JQWm6cw4yUuoigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBw0gRO4X+mc9IuIfNBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBtyWgbJe31fU70W+apop38bAeJcIoCEbZMb5WBEw1ceboom0cytuPLZXIAq40JEXPqFzEIeKnsDgbjcwOjxYf3mjrxP80gSSaCMAaztv2KyBsYpmMTrIq+bCdieqz7bALFGom4XopAKu8Fn4veTPlztlaPYRa3qOvu93u+fPnnzx5onbn5uY0MA1bS9mNqmnxQCUVyv2HUvCOXKPa6aE0rkbKAfv492/Wh6Ga1avjp+hVX77S929kxqOzjGfGpqrVDiQp8Cy8GPJwnw4iu8p6Oysb3LkJwi5h1nJ734uyVDuSeBhQtry8rHdIHdUSKto7UhqOJuEdafTqOpz4sNDFiVr1er0TNR8mgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJxWAf6Xo07rlWfeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMARCzQaDWW7KBzGImOyTOVqWE81y2YUAXNUA/L2k8iiaXxI3tMwtiYE0FQjbLzaUY3muLZbFBbBI6FygMOonhBotVcA1k7N8hQVXjMASy0o+ke3ii6L1klsay26cFq8UO3uDZc1APWo9CLl77TbbWUYWf5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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=6400x360>"
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================== Retrieved Content Preview ====================\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "\n",
              "--- Segment 17 Text ---\n",
              "Technically, there's a small assumption, the distribution you start with can't have infinite variance,  but it's a relatively soft assumption.  The magic is that for a huge category of initial distributions,  this process of adding a whole bunch of random variables drawn from that distribution  always tends towards this one universal shape, a Gaussian.  One common approach to proving this theorem involves two separate steps.  The first step is to show that for all the different finite variance distributions you might start with,  there exists a single universal shape that this process of repeated convolutions tends towards."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
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          "output_type": "display_data"
        },
        {
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/+wrR+1aWPDII02cL9t6fa++z/coA5qlBxWoLzRcc6TcZ/wCv3/7Cj7ZouP8AkEXH/gb/APYUAZmRnpWrbeIL2KFbecx3lquMQ3a+YFHopPK/gRTPtejf9Am4/wDA3/7CnwXmii4jMml3AQOCf9M7Z5/goEyQf2BfdTcaXN7Dz4f6Mv8A49Sf8IzezLvsJLfUE/6dZQzfihww/KmTXmim4kKaVNsLkri7wMZ4/hqP7Xo//QKn/wDAz/7CgRVksbmGbypoJYn/ALroQavDw7efZ/PKNs9dprZ0nxbBp92pQal5OR+6e8Dr+RWus1v4l6XfaXFZ/wBluUjGB5cir9O1Ae8eRng0VuyX2hPanGj3AmMuf+Pr+HHTO3+lV/tej/8AQJn/APAz/wCxoKMqitP7Xo//AECp/wDwM/8AsKPtej/9Aqf/AMDP/sKAKlleSWF0lxDt8xM43DI6YrS02+uNS8YWN5dSGSeW8iZ2Pf5hSW91oomQyaRcMgYZxd54zzxt5/Sm6MyP4ssWiXZGb1Ci+g3jAoBXe6NZ/wDWN/vH+dJSt99v94/zpKACiiigAooooAKKKKACiiigAooooAKKKKAClT/Wr9aSlT/Wr9aAMTxD/wAjJqf/AF9S/wDoRrNrS8Q/8jJqf/X1L/6EazaACitk3uieRbq+lTSSpEqyutz5YZucnG0/nSfbND/6A1x/4Hf/AGFVyk8z7GPRWx9s0P8A6A1x/wCB3/2FL9u0Qf8AMEkP1vT/APE0coXfb8jGororbVvD8fm+Z4c35Qhc3Tnn9MfWpY/EehxZKeErTJ7tcyN/OjlDmfY5itC10bUrxd8FhO0Y6yFSEH1Y8D862z4vt1/499LFofW2eND+fl5/WqFzrVnekNd2+oXDf9NdQLfzShpdB3kMOj2loM6hq9tGcf6m1H2iQfkQn/j1OGp2NiP+JfpkbSf897w+a31C8KPxB+tQ/bNG/wCgTcf+Bg/+Ip9zeaK/k+Tpcy7YlV/9Jxlu5+7SuLlvuUb3UbzUJN93dSzEdN7ZAHoB0A+lVq0/tej/APQJn/8AAz/7Cj7Zo/8A0CZ//Az/AOwpFGZWpov/ADEf+vGX+lJ9s0f/AKBM/wD4Gf8A2FaWkXWkt/aGzTJlxZSZ/wBLzkcf7NA7nNUVpfa9H/6Bdx/4Gf8A2FH2vR/+gXcf+Bn/ANhQFzOq9Y6zqGmqUtrphEfvQsA0bfVTkGn/AGvR/wDoFXH/AIGf/YUfa9H/AOgXcf8AgZ/9hQTZPcsG+0i9I+1WBs5e81mcqT7xMcfkR9Kb/YP2kbtO1Gzu8niIyeVL/wB8vjP4E065vNBbyfJ0m5GIlD/6Xj5u/wDBVf7XpH/QLuf/AAM/+woFZrZle802+sGxd2dxBzj97GVz+dVK37XxBFZAC2iv40/uLfkL+W3FXP8AhLrR/wDj50SG6P8AemMZY/8AAhGDTVgvLscpRXVx6/4efzDL4Utx8hClLiTlvf0+tUW1HQ26eHdv0vX/AMKQXl2/IwqK2zf6Gf8AmAuPpfN/8TTTeaGf+YJOPpfH/wCIquUXNL+V/h/mY1FbH2vQ/wDoDXH/AIHf/YVW1WaxnnibT7dreIRKrIzbju789/rRYabe6sUKKKKkoKKKKACiipbZo0uYnmXdGrguMdRnmgCKityS/wBANw7JolwE3EqPtvb/AL4pgvtDB/5Atx+N9/8AYUCuY1FbIv8ARB/zA5D9b1v/AImpV1PQV6+G931vn/woFd9vyMKjFdTHr3h2K248JwNJv/jupCMY9f6Uv/CWWKf6jw9Z2/GMoEJ/NkNFg5n2OZhtp7lwkEMkrngLGpY/pWonhvUFwbtIrFO5u5ViP4KfmP4CrsnixpgQ/wDaCr/djvti/kqAVntfaU7Fm025LE5LG8yf/QKAXMSC30KzX9/eXF9KDylsnlp/32wz+S04eIJLZgNKs7bT1zxIib5f+/j5I/4DiooL3RRPGZNLnKBhuzd54/74pJ7vRjcSmPTJ/LLkqPteOP8AvigaRnXE81xM0k80ksh6vIxYn8TUeTWl9r0f/oF3H/gZ/wDYUfatH/6BVx/4Gf8A2FAzMrVI/wCKUz/0/f8AslN+16P/ANAq4/8AAz/7CtM3OlHwvu/s2fZ9txs+19/L6/doA5rBowa0ftWk/wDQMn/8C/8A7Cj7XpX/AEDJ/wDwL/8AsKAM7BrUtvEGoxRrBLMt1bjpDdqJUH0zyPwxTftmlf8AQNuP/Av/AOwqSG80YTIZdKnZdwz/AKZ2/wC+KBNJ7knn6DfkebaXGnyHq1sfNQ/8AYgj/vo0g8PfaedN1GyvAekfm+VJ/wB8vj9Cajmu9F+0SmPS7jYXJX/S+3/fFM+16R/0Cp//AAM/+woC1tiK70jULEkXVlcRd8tGcY+vSqVb1p4ggscfZIdRt8dodRZR+i1bPjCGQf6RpYujnO65eOQ/mY8/rQGpy1GK61vEXh+a0/eeFbRp92PlmdBj1yuPyqg2r6Cx48NKo/2b6T+tAuZ9jApep4rZbUtDPTw+w+l6/wDhSrqOgkjdoMg+l83/AMTQLmfb8v8AMraWt7bXiXdqil4843DI5BH+NXLGW7ufGdjc3rF55byJmc9/mFdr4S1bwhbgSXOlSRkDobveD+YFZ2ralp1/4z09rFQsZvI9qjsN4oKRlP8A6xv94/zpKV/vt/vH+dJQMKKKKACiiigAooooAKKKKACiiigAooooAKVP9av1pKVP9av1oAxfEP8AyMmp/wDX1L/6EazK0vEH/Iyan/19S/8AoRrNoAKK2vtmiLDarJpM0kiwgSstx5YZsnJxtPtzSfbNC/6A1x/4Hf8A2FNITbXQxqXFbH27Qx00SU/W9P8A8TTk1PQ1PPh9mHvev/QUNWEpN9DExS4rpbfW/Dke/f4WjOUIG68kPPb6VN/wlmkxjFv4VsoffeJD/wCPq1FgbfRHLJG0jhEUsx6ADJNacfhzVZYxK1m8EWceZcERKPxbFajeMmZCqxXcKkYIt7pYhj0+WMVly6hpk7l5rC6kc/xPe5P57Kegry7D203TbQ/6XqqSuP8AlnZIZP8Ax9sL+W6lGs2tk2dM0uGKQdJro+e49wCAg/75/Gojc6MAM6XcEn/p8/8AsKmubzQT5Pl6RccRAMftWMt3/h5+tFyrGdeajeahJvu7mWY9t7EgfQdqq1qfa9G/6BVx/wCBn/2FH2vRv+gVcf8AgZ/9hUjMutTRP+Yj/wBeMn9KPtejf9Aq4/8AAz/7CtHSbnSj9v8AL024T/Q5M/6XnI44+5QDOaorTF1pHfS7g/8Ab5/9hS/a9G/6BNx/4Gf/AGFAk7mXV+z1i/sIzFb3UixN96IndG31U5BqX7Xo3/QKuP8AwM/+woF3o2RnSrjGef8ATP8A7CgZKuo6Zdk/b9LWNj1lsX8o/wDfByp/DFB0izuiTp+rQMe0V0PIcn05yv8A49STXeit5ZTS5wAgDD7XjLdz9yo/tejf9Aq4/wDAz/7Cq5ibDbjQNVtYzJLYzGMfxou9f++lyKzcY61s2+qafayb7axvYX/vR3+0/olaH/CXIwxNZy3I7i6lSbP4tGTUh7xy1FddF4l0Nw/2nwraP8h27X28++0DH1qi2s6C2f8Ail4h/u3sooBtroc/RW22p6CTn/hHmHsL5/8ACkOo6ER/yAZB9L5v/iadhcz/AJX+H+Zi0VsfbtD/AOgLP/4HH/4iq2qT2c9xE1javbRCFVZHbcSw6nOBnP0pDUm+hQooooKCiiigAooqW2Ma3MTTLujDguMZyM80ARgE9BRg5PHSt19Q0DfIV0S6wW4/03Ax9NlRm/0Pr/Yk/Xven/4mgnmfYxaK2v7S0Mf8wBj9b1/8Kmj1nQY2B/4RiNv969loFzPt+X+Zz9FdcPEmgR237vwnaCbd3ldxj1y2efbFMTxhBGVMGkRW2DyYPKQ/n5eaB3fY560029vmAtbSefnH7uMt/KtEeG7qHm/uLOxUHkTzrv8A++Fy36VNceIoLsYuY9UmHpJqRI/LZVL7XowJ/wCJVcY9Ptn/ANhQGpPs8PWfLzXmoSA9I1EMZ/E5b9BQPEUtuCNNsbSxGfvpHvk/77fJH4YqKK70Xz0Z9LuAu4bh9rzxnnjZRNd6KZnKaZcbCxxi7xxnjjZQOxRuby5vZTLd3Es8jHJaRyxP51CrlTlSQfUVo/atH/6BVx/4Gf8A2FH2vR/+gVcf+Bn/ANhQMzcmtPH/ABS2e323/wBkoF3ow/5hVx/4Gf8A2FaYudJ/4RlmGmT7TeY2/bOfudfu0AcxmitX7RpX/QIuf/Av/wCwo+06V/0CLn/wL/8AsKAMsEg5Bwa1oPEepxR+TJcC6g6eTdoJk/ANnH4YqP7XpGf+QVOP+3v/AOwp8V5o4mRn0y42hgSPtfb/AL4oFo90TfbdCvDi60yazc9ZLOXKg+ux8/kCKP7Esrn5tO1i2lycCG5zBJ+vyn/vqopL3RvNcx6VOF3Hbi7xx/3zTPtmkYx/Zdx/4Gf/AGFArdguPDurWy7pNPnKHo8a70P/AAJcis0oyttZSD6EVrwarYWsge3sryFh0aO+Kkf+OVop4vIUK8V1Oufu3FykoP8A31GaA945WjFdcfFOjPCPP8MWkswbrkJke5QCq8mveHpDn/hEYF9lvZQKATfVHM07bwD61tf2noRGT4e+b0F4+KQ6hoR/5gUg57Xrf4UBzPsZtlcy2d0lxAFLocjeoI6Y/rWjp11c33jCxu7ty88t5Ezse53CpLfUPD4uIzJoc2zd82L0nj6baj0x4X8W2TQDbEb1Cg9BvGKClqab/wCsb/eP86Slf77f7x/nSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFKn+tX60lKn+tX60AZmvf8jBqP/X1L/6Ga7HSfCul6nYeG5W026WPU1mN7dpc4Fssb7TLgrjAALEGuO13/kYNR/6+pf8A0M1uWfjOOwbw1Jb6afM0N3YeZcblnDs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            "text/plain": [
              "<IPython.core.display.Image object>"
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          "data": {
            "text/markdown": [
              "\n",
              "--- Segment 1 Text ---\n",
              "What the Central Limit Theorem says is as you make that sum bigger and bigger, under appropriate conditions,  that approximation to a normal becomes better and better.  But I never explained why this theorem is actually true, we only talked about what it's claiming.  In the last video, we started talking about the math involved in adding two random variables.  If you have two random variables, each following some distribution,  then to find the distribution describing the sum of those variables,  you compute something known as a convolution between the two original functions."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
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            "text/markdown": [
              "\n",
              "--- Segment 16 Text ---\n",
              "Normal distributions are supposedly quite common, so why not?  And you could even say that this should follow from the central limit theorem.  But that would have it all backwards.  First of all, the supposed ubiquity of normal distributions is often a little exaggerated,  but to the extent that they do come up, it is because of the central limit theorem.  But it would be cheating to say the central limit theorem implies this result,  because this computation we just did is the reason that the function at the heart of the central limit theorem  is a Gaussian in the first place and not some other function.  We've talked all about the central limit theorem before,  but essentially it says if you repeatedly add copies of a random variable to itself,  which mathematically looks like repeatedly computing convolutions against a given distribution,  then after appropriate shifting and rescaling, the tendency is always to approach a normal distribution."
            ],
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
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        {
          "data": {
            "text/markdown": [
              "\n",
              "--- Segment 0 Text ---\n",
              "The basic function underlying a normal distribution, a.k.a. a Gaussian, is e to the negative x-squared.  But you might wonder, why this function?  Of all the expressions we could dream up that give you some symmetric smooth graph with mass concentrated towards the middle,  why is it that the theory of probability seems to have a special place in its heart for this particular expression?  For the last many videos I've been hinting at an answer to this question,  and here we'll finally arrive at something like a satisfying answer.  As a quick refresher on where we are, a couple videos ago we talked about the Central Limit Theorem,  which describes how as you add multiple copies of a random variable,  for example, rolling a weighted die many different times or letting a ball bounce off of a peg repeatedly,  then the distribution describing that sum tends to look approximately like a normal distribution."
            ],
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            ]
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        },
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 300
            }
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================== End of Retrieved Content ====================\n"
          ]
        },
        {
          "data": {
            "text/markdown": [
              "The Central Limit Theorem (CLT) states that as you repeatedly add copies of a random variable, the distribution of the sum approaches a normal distribution (or Gaussian) under certain conditions, specifically when the original distribution has finite variance. This means that regardless of the initial distribution used, as the number of added variables increases, the sum will approximate a normal distribution more closely. The theorem relies on the concept of convolutions, showing that this process leads to a single universal shape—a Gaussian curve—after appropriate shifting and rescaling."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "multimodal_rag('What is the central limit theorem?')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f27c11e9",
      "metadata": {
        "id": "f27c11e9"
      },
      "source": [
        "\n",
        "## 7. Cleanup\n",
        "\n",
        "Remove the KDB.AI table created during this notebook session to free up resources."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7623dbe8",
      "metadata": {
        "id": "7623dbe8"
      },
      "source": [
        "\n",
        "### Cleanup: Dropping the KDB.AI Table\n",
        "Once finished with the table, it is best practice to drop it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "2076c517",
      "metadata": {
        "id": "2076c517"
      },
      "outputs": [],
      "source": [
        "table.drop()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}