fact_checking_fever.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/claudio-bon/fact-checking-fever/blob/main/fact_checking_fever.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GM9DBN-Qz3k"
      },
      "source": [
        "# Assignment 4\n",
        "\n",
        "**Due to**: TBD\n",
        "\n",
        "**Credits**: Andrea Galassi, Federico Ruggeri, Paolo Torroni\n",
        "\n",
        "**Summary**: Fact checking, Neural Languange Inference (**NLI**)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tO_-4CZeRCO7"
      },
      "source": [
        "# Intro\n",
        "\n",
        "This assignment is centred on a particular and emerging NLP task, formally known as **fact checking** (or fake checking). As AI techniques become more and more powerful, reaching amazing results, such as image and text generation, it is more than ever necessary to build tools able to distinguish what is real from what is fake.\n",
        "\n",
        "Here we focus on a small portion of the whole fact checking problem, which aims to determine whether a given statement (fact) conveys a trustworthy information or not. \n",
        "\n",
        "More precisely, given a set of evidences and a fact to verify, we would like our model to correctly predict whether the fact is true or fake.\n",
        "\n",
        "In particular, we will see:\n",
        "\n",
        "*   Dataset preparation (analysis and pre-processing)\n",
        "*   Problem formulation: multi-input binary classification\n",
        "*   Defining an evaluation method\n",
        "*   Simple sentence embedding\n",
        "*   Neural building blocks\n",
        "*   Neural architecture extension"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGDwg78PS_uy"
      },
      "source": [
        "# The FEVER dataset\n",
        "\n",
        "First of all, we need to choose a dataset. In this assignment we will rely on the [FEVER dataset](https://fever.ai).\n",
        "\n",
        "The dataset is about facts taken from Wikipedia documents that have to be verified. In particular, facts could face manual modifications in order to define fake information or to give different formulations of the same concept.\n",
        "\n",
        "The dataset consists of 185,445 claims manually verified against the introductory sections of Wikipedia pages and classified as ```Supported```, ```Refuted``` or ```NotEnoughInfo```. For the first two classes, systems and annotators need to also return the combination of sentences forming the necessary evidence supporting or refuting the claim."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Oa5FpVpT7p4"
      },
      "source": [
        "## Dataset structure\n",
        "\n",
        "Relevant data is divided into two file types. Information concerning the fact to verify, its verdict and associated supporting/opposing statements are stored in **.jsonl** format. In particular, each JSON element is a python dictionary with the following relevant fields:\n",
        "\n",
        "*    **ID**: ID associated to the fact to verify.\n",
        "\n",
        "*    **Verifiable**: whether the fact has been verified or not: ```VERIFIABLE``` or ```NOT VERIFIABLE```.\n",
        "    \n",
        "*    **Label**: the final verdict on the fact to verify: ```SUPPORTS```, ```REFUTES``` or ```NOT ENOUGH INFO```.\n",
        "    \n",
        "*    **Claim**: the fact to verify.\n",
        "    \n",
        "*    **Evidence**: a nested list of document IDs along with the sentence ID that is associated to the fact to verify. In particular, each list element is a tuple of four elements: the first two are internal annotator IDs that can be safely ignored; the third term is the document ID (called URL) and the last one is the sentence number (ID) in the pointed document to consider.\n",
        "\n",
        "**Some Examples**\n",
        "\n",
        "---\n",
        "\n",
        "**Verifiable**\n",
        "\n",
        "{\"id\": 202314, \"verifiable\": \"VERIFIABLE\", \"label\": \"REFUTES\", \"claim\": \"The New Jersey Turnpike has zero shoulders.\", \"evidence\": [[[238335, 240393, \"New_Jersey_Turnpike\", 15]]]}\n",
        "\n",
        "---\n",
        "\n",
        "**Not Verifiable**\n",
        "\n",
        "{\"id\": 113501, \"verifiable\": \"NOT VERIFIABLE\", \"label\": \"NOT ENOUGH INFO\", \"claim\": \"Grease had bad reviews.\", \"evidence\": [[[133128, null, null, null]]]}\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nex_8UM4VWuY"
      },
      "source": [
        "## Some simplifications and pre-processing\n",
        "\n",
        "We are only interested in verifiable facts. Thus, we can filter out all non-verifiable claims.\n",
        "\n",
        "Additionally, the current dataset format does not contain all necessary information for our classification purposes. In particular, we need to download Wikipedia documents and replace reported evidence IDs with the corresponding text.\n",
        "\n",
        "Don't worry about that! We are providing you the already pre-processed dataset so that you can concentrate on the classification pipeline (pre-processing, model definition, evaluation and training).\n",
        "\n",
        "You can download the zip file containing all set splits (train, validation and test) of the FEVER dataset by clicking on this [link](https://drive.google.com/file/d/1wArZhF9_SHW17WKNGeLmX-QTYw9Zscl1/view?usp=sharing). Alternatively, run the below code cell to automatically download it on this notebook.\n",
        "\n",
        "**Note**: each dataset split is in .csv format. Feel free to inspect the whole dataset!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RbH_8errW5MH"
      },
      "source": [
        "# Classification dataset\n",
        "\n",
        "At this point, you should have a reay-to-go dataset! Note that the dataset format changed as well! In particular, we split the evidence set associated to each claim, in order to build (claim, evidence) pairs. The classification label is propagated as well.\n",
        "\n",
        "We'll motivate this decision in the next section!\n",
        "\n",
        "Just for clarity, here's an example of the pre-processed dataset:\n",
        "\n",
        "---\n",
        "\n",
        "**Claim**: \"Wentworth Miller is yet to make his screenwriting debut.\"\n",
        "\n",
        "**Evidence**: \"2\tHe made his screenwriting debut with the 2013 thriller film Stoker .\tStoker\tStoker (film)\"\n",
        "\n",
        "**Label**: Refutes\n",
        "\n",
        "---\n",
        "\n",
        "**Note**: The dataset requires some text cleaning as you may notice!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gH8hIK21Xrl0"
      },
      "source": [
        "# Problem formulation\n",
        "\n",
        "As mentioned at the beginning of the assignment, we are going to formulate the fact checking problem as a binary classification task.\n",
        "\n",
        "In particular, each dataset sample is comprised of:\n",
        "\n",
        "*     A claim to verify\n",
        "*     A set of semantically related statements (evidence set)\n",
        "*     Fact checking label: either evidences support or refute the claim.\n",
        "\n",
        "Handling the evidence set from the point of view of neural models may imply some additional complexity: if the evidence set is comprised of several sentences we might incur in memory problems.\n",
        "\n",
        "To this end, we further simplify the problem by building (claim, evidence) pairs. The fact checking label is propagated as well.\n",
        "\n",
        "Example:\n",
        "\n",
        "     Claim: c1 \n",
        "     Evidence set: [e1, e2, e3]\n",
        "     Label: S (support)\n",
        "\n",
        "--->\n",
        "\n",
        "    (c1, e1, S),\n",
        "    (c1, e2, S),\n",
        "    (c1, e3, S)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E46flIz_zQy-"
      },
      "source": [
        "## Schema\n",
        "\n",
        "The overall binary classification problem is summed up by the following (simplified) schema\n",
        "\n",
        "![](https://drive.google.com/uc?export=view&id=1Wm_YBnFwgJtxcWEBpPbTBEVkpKaL08Jp)\n",
        "\n",
        "Don't worry too much about the **Encoding** block for now. We'll give you some simple guidelines about its definition. For the moment, stick to the binary classification task definition where, in this case, we have 2 inputs: the claim to verify and one of its associated evidences."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsiTV-NVdgsF"
      },
      "source": [
        "# Architecture Guidelines\n",
        "\n",
        "There are many neural architectures that follow the above schema. To avoid phenomena like the writer's block, in this section we are going to give you some implementation guidelines.\n",
        "\n",
        "In particular, we would like you to test some implementations so that you explore basic approaches (neural baselines) and use them as building blocks for possible extensions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJBQm47fe7iE"
      },
      "source": [
        "## Handling multiple inputs\n",
        "\n",
        "The first thing to notice is that we are in a multi-input scenario. In particular, each sample is comprised of a fact and its associated evidence statement.\n",
        "\n",
        "Each of these input is encoded as a sequence of tokens. In particular, we will have the following input matrices:\n",
        "\n",
        "*    Claim: [batch_size, max_tokens]\n",
        "*    Evidence: [batch_size, max_tokens]\n",
        "\n",
        "Moreover, after the embedding layer, we'll have:\n",
        "\n",
        "*    Claim: [batch_size, max_tokens, embedding_dim]\n",
        "*    Evidence: [batch_size, max_tokens, embedding_dim]\n",
        "\n",
        "But, we would like to have a 2D input to our classifier, since we have to give an answer at pair level. Therefore, for each sample, we would expect the following input shape to our classification block:\n",
        "\n",
        "*   Classification input shape: [batch_size, dim]\n",
        "\n",
        "**How to do that?**\n",
        "\n",
        "We inherently need to reduce the token sequence to a single representation. This operation is formally known as **sentence embedding**. Indeed, we are trying to compress the information of a whole sequence into a single embedding vector.\n",
        "\n",
        "Here are some simple solutions that we ask you to try out:\n",
        "\n",
        "*   Encode token sequences via a RNN and take the last state as the sentence embedding.\n",
        "\n",
        "*   Encode token sequences via a RNN and average all the output states.\n",
        "\n",
        "*   Encode token sequences via a simple MLP layer. In particular, if your input is a [batch_size, max_tokens, embedding_dim] tensor, the matrix multiplication works on the **max_tokens** dimension, resulting in a [batch_size, embedding_dim] 2D matrix.\n",
        "\n",
        "*   Compute the sentence embedding as the mean of its token embeddings (**bag of vectors**)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gypl5z5ElJo1"
      },
      "source": [
        "## Merging multi-inputs\n",
        "\n",
        "At this point, we have to think about **how** we should merge evidence and claim sentence embeddings.\n",
        "\n",
        "For simplicity, we stick to simple merging strategies:\n",
        "\n",
        "*     **Concatenation**: define the classification input as the concatenation of evidence and claim sentence embeddings\n",
        "\n",
        "*     **Sum**: define the classification input as the sum of evidence and claim sentence embeddings\n",
        "\n",
        "*     **Mean**: define the classification input as the mean of evidence and claim sentence embeddings\n",
        "\n",
        "For clarity, if we the sentence embedding of a single input has shape [batch_size, embedding_dim], then the classification input has shape:\n",
        "\n",
        "*     **Concatenation**: [batch_size, 2 * embedding_dim]\n",
        "\n",
        "*     **Sum**: [batch_size, embedding_dim]\n",
        "\n",
        "*     **Mean**: [batch_size, embedding_dim]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhVg9ZLymOUc"
      },
      "source": [
        "# A simple extension\n",
        "\n",
        "Lastly, we ask you to modify previously defined neural architectures by adding an additional feature to the classification input.\n",
        "\n",
        "We would like to see if some similarity information between the claim to verify and one of its associated evidence might be useful to the classification.\n",
        "\n",
        "Compute the cosine similarity metric between the two sentence embeddings and concatenate the result to the classification input.\n",
        "\n",
        "For clarity, since the cosine similarity of two vectors outputs a scalar value, the classification input shape is modified as follows:\n",
        "\n",
        "*     **Concatenation**: [batch_size, 2 * embedding_dim + 1]\n",
        "\n",
        "*     **Sum**: [batch_size, embedding_dim + 1]\n",
        "\n",
        "*     **Mean**: [batch_size, embedding_dim + 1]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nd74ULgpnJrc"
      },
      "source": [
        "# Performance evaluation\n",
        "\n",
        "Due to our simplifications, obtained results are not directly compatible with a traditional fact checking method that considers the evidence set as a whole.\n",
        "\n",
        "Thus, we need to consider two types of evaluations.\n",
        "\n",
        "**Multi-input classification evaluation**\n",
        "\n",
        "This type of evaluation is the easiest and concerns computing evaluation metrics, such as accuracy, f1-score, recall and precision, of our pre-processed dataset.\n",
        "\n",
        "In other words, we assess the performance of chosen classifiers.\n",
        "\n",
        "**Claim verification evaluation**\n",
        "\n",
        "However, if we want to give an answer concerning the claim itself, we need to consider the whole evidence set. \n",
        "\n",
        "Intuitively, for a given claim, we consider all its corresponding (claim, evidence) pairs and their corresponding classification outputs. \n",
        "\n",
        "At this point, all we need to do is to compute the final predicted claim label via majority voting.\n",
        "\n",
        "Example:\n",
        "\n",
        "    Claim: c1\n",
        "    Evidence set: e1, e2, e3\n",
        "    True label: S\n",
        "\n",
        "    Pair outputs:\n",
        "    (c1, e1) -> S (supports)\n",
        "    (c1, e2) -> S (supports)\n",
        "    (c1, e3) -> R (refutes)\n",
        "\n",
        "    Majority voting:\n",
        "    S -> 2 votes\n",
        "    R -> 1 vote\n",
        "\n",
        "    Final label:\n",
        "    c1 -> S\n",
        "\n",
        "Lastly, we have to compute classification metrics just like before."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4LJ2yPxsUOV"
      },
      "source": [
        "# Tips and Extras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uf80UVRNrXve"
      },
      "source": [
        "## Extensions are welcome!\n",
        "\n",
        "Is this task too easy for you? Are you curious to try out things you have seen during lectures (e.g. attention)? Feel free to try everything you want!\n",
        "\n",
        "Don't forget to try neural baselines first!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "COXeCXdYsBEf"
      },
      "source": [
        "## Comments and documentation\n",
        "\n",
        "Remember to properly comment your code (it is not necessary to comment each single line) and don't forget to describe your work!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ejv6SDE8xc4_"
      },
      "source": [
        "## Organization\n",
        "\n",
        "We suggest you to divide your work into sections. This allows you to build clean and modular code, as well as easy to read and to debug.\n",
        "\n",
        "A possible schema:\n",
        "\n",
        "*   Dataset pre-processing\n",
        "*   Dataset conversion\n",
        "*   Model definition\n",
        "*   Training\n",
        "*   Evaluation\n",
        "*   Comments/Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DR70uh7pabo"
      },
      "source": [
        "# Contact\n",
        "\n",
        "For any doubt, question, issue or help, you can always contact us at the following email addresses:\n",
        "\n",
        "Teaching Assistants:\n",
        "\n",
        "* Andrea Galassi -> a.galassi@unibo.it\n",
        "* Federico Ruggeri -> federico.ruggeri6@unibo.it\n",
        "\n",
        "Professor:\n",
        "\n",
        "* Paolo Torroni -> p.torroni@unibo.it\n",
        "\n",
        "Don't forget that your feedback is very important! Your suggestions help us improving course material."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jc0gNWU2pgKQ"
      },
      "source": [
        "# FAQ\n",
        "\n",
        "---\n",
        "\n",
        "**Q: Can I do something text pre-processing?**\n",
        "\n",
        "**A:** You have to! If you check text data, the majority of sentences need some cleaning.\n",
        "\n",
        "---\n",
        "\n",
        "**Q: I'm struggling with the implementation. Can you help me?**\n",
        "\n",
        "**A:** Yes sure! Write us an email about your issue. If you are looking for a particular type of operation, you can easily check the documentation of the deep learning framework you are using (google is your friend).\n",
        "\n",
        "---\n",
        "\n",
        "**Q: Can I try other encoding strategies or neural architectures?**\n",
        "\n",
        "**A:** Absolutely! Remember to try out recommended neural baselines first and only then proceed with your extensions.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MT1-aJ4YbhKs"
      },
      "source": [
        "# Initial Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgwHLfIbhSfD",
        "outputId": "a41bfa6e-67cf-4301-ee9a-615419d03a4e"
      },
      "source": [
        "# Ignore warnings\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "# system packages\n",
        "import os\n",
        "import shutil\n",
        "import sys\n",
        "import time\n",
        "import random\n",
        "import gc\n",
        "\n",
        "# data and numerical management packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import math\n",
        "\n",
        "# data for preprocessing\n",
        "import re\n",
        "from functools import reduce\n",
        "! pip install joblib\n",
        "import spacy\n",
        "from spacy.lang.en.stop_words import STOP_WORDS\n",
        "from wordcloud import WordCloud, ImageColorGenerator\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize \n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "from nltk.stem import PorterStemmer \n",
        "from nltk.stem import LancasterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import json\n",
        "! pip install pytorch-nlp\n",
        "from torchnlp.word_to_vector import FastText  # doctest: +SKIP\n",
        "\n",
        "# plotting-displaying packages\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from IPython.display import display\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# debugging packages (progress bars)\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "\n",
        "\n",
        "random_seed = 42\n",
        "\n",
        "random.seed(random_seed)\n",
        "np.random.seed(random_seed)\n",
        "torch.manual_seed(random_seed)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    print('Used device: GPU')\n",
        "    device = torch.device(\"cuda\")\n",
        "else:\n",
        "    print('Used device: CPU')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "Collecting pytorch-nlp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4f/51/f0ee1efb75f7cc2e3065c5da1363d6be2eec79691b2821594f3f2329528c/pytorch_nlp-0.5.0-py3-none-any.whl (90kB)\n",
            "\u001b[K     |████████████████████████████████| 92kB 7.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from pytorch-nlp) (4.41.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pytorch-nlp) (1.19.4)\n",
            "Installing collected packages: pytorch-nlp\n",
            "Successfully installed pytorch-nlp-0.5.0\n",
            "Used device: GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNvVvFbQufrk"
      },
      "source": [
        "# Data Loading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvVn1TonbtP7"
      },
      "source": [
        "### Dataset Downloading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BspxZcRjW0NG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50b7a325-2c7d-481f-ad3d-a83edd08cf32"
      },
      "source": [
        "import os\n",
        "import requests\n",
        "import zipfile\n",
        "\n",
        "def save_response_content(response, destination):\n",
        "    CHUNK_SIZE = 32768\n",
        "\n",
        "    with open(destination, \"wb\") as f:\n",
        "        for chunk in response.iter_content(CHUNK_SIZE):\n",
        "            if chunk: # filter out keep-alive new chunks\n",
        "                f.write(chunk)\n",
        "\n",
        "def download_data(data_path):\n",
        "    toy_data_path = os.path.join(data_path, 'fever_data.zip')\n",
        "    toy_data_url_id = \"1wArZhF9_SHW17WKNGeLmX-QTYw9Zscl1\"\n",
        "    toy_url = \"https://docs.google.com/uc?export=download\"\n",
        "\n",
        "    if not os.path.exists(data_path):\n",
        "        os.makedirs(data_path)\n",
        "\n",
        "    if not os.path.exists(toy_data_path):\n",
        "        print(\"Downloading FEVER data splits...\")\n",
        "        with requests.Session() as current_session:\n",
        "            response = current_session.get(toy_url,\n",
        "                                   params={'id': toy_data_url_id},\n",
        "                                   stream=True)\n",
        "        save_response_content(response, toy_data_path)\n",
        "        print(\"Download completed!\")\n",
        "\n",
        "        print(\"Extracting dataset...\")\n",
        "        with zipfile.ZipFile(toy_data_path) as loaded_zip:\n",
        "            loaded_zip.extractall(data_path)\n",
        "        print(\"Extraction completed!\")\n",
        "\n",
        "download_data('dataset')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading FEVER data splits...\n",
            "Download completed!\n",
            "Extracting dataset...\n",
            "Extraction completed!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMdbRZ-Jb2rT"
      },
      "source": [
        "### DataFrame Creation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z74gGxTlbl9c"
      },
      "source": [
        "# Dataset Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205,
          "referenced_widgets": [
            "2055e25ad59e4d3489443b730793cc2f",
            "2fa10b89c72249b98085a9b8038e8a15",
            "6bd604fefa6741a1a82c20689734c585",
            "3af5b0e4e806427ea943ccd5c8396c76",
            "dafd9bb1f01941d7860b80a3c1927d53",
            "fa568710d3a7463aa7a1d4779c484622",
            "b0a1cefe01714277a3a95cb158fb5e21",
            "7c22764a007249e59fa6cc20fb900a3a",
            "17f758d619584c0282401db91bf27ea6",
            "756e2b55ba1642439b73d3d5571c5788",
            "c63733f0ec6045aebc9ecbd71a7b321b",
            "47e597621a8c4c579e3c779257623f4c",
            "9a73d073df454044bc316d9a1f3426b6",
            "cc9637567cbb46ca9adfc91f54f7d749",
            "7d8497410c584fc3b3db5a756e776196",
            "c4c9b72735a34819aebfb8803252713b",
            "b1cd0115e2754be78d5041e6dfeca8c9",
            "6ec37e04e7b6491ba9a66aafa1b8f618",
            "0628448f280d4a4fa4d45c3dc8271542",
            "c20672a764a242dc9792f3b7c9bf04f0",
            "1d6e5a3ea6b7494e8b4d797f30212352",
            "9689bd533b064155a2701f915015ca53",
            "0457dbbfb5aa491e9be104992a73157a",
            "909127969be4494098a9efec83f2c0f4"
          ]
        },
        "id": "FYz2k4pdj1Ua",
        "outputId": "97be6052-3252-4575-d39f-81dc3f292a92"
      },
      "source": [
        "def create_df(dataset_name):\n",
        "    dataframe_rows = []\n",
        "    file_path = os.path.join(os.getcwd(), \"dataset\", dataset_name)\n",
        "    try:\n",
        "        if os.path.isfile(file_path):\n",
        "            # open the file\n",
        "            with open(file_path, mode='r', encoding='utf-8') as text_file:\n",
        "                lines = text_file.readlines()   \n",
        "                # Starting from the 2nd line to exclude\n",
        "                for line in tqdm(lines[1: len(lines)]): \n",
        "                  # Excluding empty lines\n",
        "                  if len(line.strip()) > 0:\n",
        "                    line = re.sub('\"', '', line)\n",
        "                    # Replacing the possible starts of the evidence with an special identifier\n",
        "                    if re.findall('[?],[0-9]+', line):\n",
        "                      line = re.sub('[?],[0-9]+', '_SEVID_', line,1)\n",
        "                    if not re.findall('_SEVID_', line) and re.findall('[.],[0-9]+', line):\n",
        "                      line = re.sub('[.],[0-9]+', '_SEVID_', line,1)\n",
        "                    if not re.findall('_SEVID_', line) and re.findall(',[0-9]+', line):\n",
        "                      line = re.sub(',[0-9]+', '_SEVID_', line,1)\n",
        "                    id =  line.split(\",\")[0]\n",
        "                    #claim = line.split(\",\")[1].split('_SEVID_')[0]\n",
        "                    #if (len(re.findall('_SEVID_', line))) > 1:\n",
        "                    claim = line.split('_SEVID_')[0].split(\",\",maxsplit=1)[-1]\n",
        "                    evidence = line.split('_SEVID_')[-1]\n",
        "                    # Most of evidences have repeated information after the last point\n",
        "                    repeated_info = evidence.split(\".\")[-1]\n",
        "                    if evidence != repeated_info: evidence = evidence.replace(repeated_info, \"\") \n",
        "                    id_claim = line.split(\",\")[-2] \n",
        "                    label = line.split(\",\")[-1].split(\"\\n\")[0]\n",
        "                    evidence = evidence.replace(label, \"\") \n",
        "                    evidence = evidence.replace(id_claim, \"\") \n",
        "                    # create single dataframe row\n",
        "                    dataframe_row = {\n",
        "                        \"id\": id,\n",
        "                        \"claim\": claim,\n",
        "                        \"evidence\": evidence,\n",
        "                        \"id_claim\": id_claim,\n",
        "                        \"label\": label\n",
        "                    }\n",
        "                    dataframe_rows.append(dataframe_row)\n",
        "\n",
        "    except Exception as e:\n",
        "        print('Failed to process %s. Reason: %s' % (file_path, e))\n",
        "        sys.exit(0)\n",
        "\n",
        "    folder = os.path.join(os.getcwd(), \"dataset\", \"Dataframes\", dataset_name)\n",
        "    if not os.path.exists(folder):\n",
        "        os.makedirs(folder)\n",
        "\n",
        "    # transform the list of rows in a proper dataframe\n",
        "    df = pd.DataFrame(dataframe_rows)\n",
        "    df = df[[\"id\",\n",
        "            \"claim\",\n",
        "            \"evidence\",\n",
        "            \"id_claim\",\n",
        "            \"label\"]]\n",
        "    dataframe_path = os.path.join(folder, dataset_name + \".pkl\")\n",
        "    df.to_pickle(dataframe_path)\n",
        "    return df\n",
        "\n",
        "# Creating dataframes\n",
        "print(\"Creating dataframe...\")\n",
        "df_train = create_df('train_pairs.csv')\n",
        "df_val = create_df('val_pairs.csv')\n",
        "df_test = create_df('test_pairs.csv')\n",
        "# small datasets for faster applications\n",
        "df_train_10perc = df_train[0: round(0.10* len(df_train))]\n",
        "df_val_10perc = df_val[0: round(0.10* len(df_val))]\n",
        "df_test_10perc = df_test[0: round(0.10* len(df_test))]\n",
        "print(\"Dataframe completed!\")\n",
        "\n",
        "# Saving the evidence and claim corpus before any modification with preprocessing\n",
        "corpus_evidence_bfp = df_train['evidence'].str.lower().str.split()\n",
        "corpus_claim_bfp = df_train['claim'].str.lower().str.split()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating dataframe...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=121740.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Dataframe completed!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuWT5vKjuwhS",
        "outputId": "b109e546-78fc-482b-9ec7-95b93364c3a8"
      },
      "source": [
        "'''\n",
        "Checking the correctness of the data\n",
        "Some claim and evidences in the train were noisy, very hard to detect in the process of df creation\n",
        "For that reason, we found the samples with problems and according to the number resulting\n",
        "we decide to delete or apply more preprocessing techniques only to those.\n",
        "\n",
        "Note: As we found only 33 samples in the train df with problems, we decided to delete them\n",
        "Test and validation were checked only for  purpose of information\n",
        "'''\n",
        "\n",
        "train_Iderror = []\n",
        "val_Iderror = []\n",
        "test_Iderror = []\n",
        "\n",
        "for id, data in df_train.iterrows():\n",
        "  if len(data['evidence']) <= 1 or re.findall('_SEVID_', data['evidence']) \\\n",
        "      or len(data['claim']) <= 1 or re.findall('_SEVID_', data['claim']) \\\n",
        "      or len(data['id']) == 0 or re.findall('_SEVID_', data['id']) \\\n",
        "      or len(data['id_claim']) == 0 or re.findall('_SEVID_', data['id_claim']) \\\n",
        "      or len(data['label']) == 0 or re.findall('_SEVID_', data['label']):\n",
        "    train_Iderror.append(id) \n",
        "\n",
        "for id, data in df_val.iterrows():\n",
        "  if len(data['evidence']) <= 1 or re.findall('_SEVID_', data['evidence']) \\\n",
        "      or len(data['claim']) <= 1 or re.findall('_SEVID_', data['claim']) \\\n",
        "      or len(data['id']) == 0 or re.findall('_SEVID_', data['id']) \\\n",
        "      or len(data['id_claim']) == 0 or re.findall('_SEVID_', data['id_claim']) \\\n",
        "      or len(data['label']) == 0 or re.findall('_SEVID_', data['label']):\n",
        "    val_Iderror.append(id) \n",
        "\n",
        "for id, data in df_test.iterrows():\n",
        "  if len(data['evidence']) <= 1 or re.findall('_SEVID_', data['evidence']) \\\n",
        "      or len(data['claim']) <= 1 or re.findall('_SEVID_', data['claim']) \\\n",
        "      or len(data['id']) == 0 or re.findall('_SEVID_', data['id']) \\\n",
        "      or len(data['id_claim']) == 0 or re.findall('_SEVID_', data['id_claim']) \\\n",
        "      or len(data['label']) == 0 or re.findall('_SEVID_', data['label']):\n",
        "    test_Iderror.append(id) \n",
        "\n",
        "print(\"Were found {} errors in the data train\".format(len(train_Iderror))) \n",
        "print(\"Were found {} errors in the data val\".format(len(val_Iderror))) \n",
        "print(\"Were found {} errors in the data test\".format(len(test_Iderror))) \n",
        "\n",
        "# list of indexes without errors\n",
        "l_idx = [i for i in df_train.index if int(i) not in train_Iderror ]\n",
        "\n",
        "# Creating a view without the samples with error\n",
        "df_train = df_train.loc[l_idx]\n",
        "# Re indexing the dataframe\n",
        "df_train = df_train.reset_index(drop=True)\n",
        "#df_train = df_train.drop(train_Iderror)\n",
        "\n",
        "print(\"{} rows were dropped out from the train df\".format(len(train_Iderror)))\n",
        "# Saving the evidence and claim corpus before any modification with preprocessing\n",
        "corpus_evidence_bfp = df_train['evidence'].str.lower().str.split()\n",
        "corpus_claim_bfp = df_train['claim'].str.lower().str.split()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Were found 33 errors in the data train\n",
            "Were found 0 errors in the data val\n",
            "Were found 0 errors in the data test\n",
            "33 rows were dropped out from the train df\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzyDcEOob6uM"
      },
      "source": [
        "### Definition of the pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDkL-ZQAb9Uj",
        "outputId": "1a8334b5-d4aa-4b96-f7dc-7a4d636d86c7"
      },
      "source": [
        "# Settings for the preprocessing\n",
        "# Some functions were inspired on [1]\n",
        "\n",
        "SPLIT = re.compile(r'\\w+')\n",
        "DATA_BW_PARENTHESES = re.compile('\\[.*?\\]')\n",
        "GOOD_SYMBOLS_RE = re.compile('[^0-9a-z +]')\n",
        "CONVERT_PARENTHESES = [\n",
        "                (re.compile(r\"-LRB-\"), \"(\"),\n",
        "                (re.compile(r\"-RRB-\"), \")\"),\n",
        "                (re.compile(r\"-LSB-\"), \"[\"),\n",
        "                (re.compile(r\"-RSB-\"), \"]\"),\n",
        "                (re.compile(r\"-LCB-\"), \"{\"),\n",
        "                (re.compile(r\"-RCB-\"), \"}\"),\n",
        "            ]\n",
        "try:\n",
        "    STOPWORDS = set(stopwords.words('english'))\n",
        "except LookupError:\n",
        "    nltk.download('stopwords')\n",
        "    STOPWORDS = set(stopwords.words('english'))\n",
        "\n",
        "def download_file(data_path, filename, url):\n",
        "    '''\n",
        "    Function to download a file and save it in memory\n",
        "    '''\n",
        "    data_path_file = os.path.join(data_path, filename)\n",
        "    if not os.path.exists(data_path):\n",
        "        os.makedirs(data_path)\n",
        "    if not os.path.exists(data_path_file):\n",
        "        print(\"Downloading data ...\")\n",
        "        with requests.Session() as current_session:\n",
        "            response = current_session.get(url, stream=True)\n",
        "        save_response_content(response, data_path_file)\n",
        "        print(\"Download completed!\")\n",
        "\n",
        "# Downloading the file of enlish contractions useful for normalizing the data\n",
        "url_contractions = 'https://gist.githubusercontent.com/Sirsirious/c70400176a4532899a483e06d72cf99e/raw/e46fa7620c4f378f5bf39608b45cddad7ff447a4/english_contractions.json'\n",
        "download_file('resources', 'english_contractions.json', url_contractions)\n",
        "\n",
        "\n",
        "#######################################################################################\n",
        "#                       Some functions for preprocessing\n",
        "#######################################################################################\n",
        "\n",
        "def lower(text):\n",
        "    \"\"\"\n",
        "    Transforms given text to lower case.\n",
        "    Example:\n",
        "    Input: 'Many victims were said to have been killed in USA'\n",
        "    Output: 'many victims were said to have been killed in usa'\n",
        "    \"\"\"\n",
        "    return text.lower()\n",
        "\n",
        "\n",
        "def convert_parentheses(text):\n",
        "  '''\n",
        "  Transform the sepcial characters LRB, RRB, LSB, RSB, LCB, RCB into their\n",
        "  respective parentheses, Check the Regexp\n",
        "  Example: \n",
        "  Input: 'Roald Dahl -LRB- -LSB'\n",
        "  Output: 'Roald Dahl ()'\n",
        "  '''\n",
        "  for regexp, substitution in CONVERT_PARENTHESES:\n",
        "      text = regexp.sub(substitution, text)\n",
        "  return text\n",
        "\n",
        "\n",
        "def remove_data_parentheses(text):\n",
        "  '''\n",
        "  Reeplace with spaces all characters between square brackets.\n",
        "  Most of the data between sqaure brackets are non useful information, containing\n",
        "  most of time the pronounciation of words \n",
        "  '''\n",
        "  return DATA_BW_PARENTHESES.sub('', text)\n",
        "\n",
        "\n",
        "def normalize_contractions(text):\n",
        "    \"\"\"\n",
        "    Function normalize_contractions\n",
        "    This function normalizes english contractions based on a predefined file of contractions\n",
        "    \"\"\"\n",
        "    new_token_list = []\n",
        "    contractions = json.loads(open('resources/english_contractions.json', 'r').read())\n",
        "\n",
        "    token_list = text.split()\n",
        "    for word_pos in range(len(token_list)):\n",
        "        word = token_list[word_pos]\n",
        "        first_upper = False\n",
        "        if word[0].isupper():\n",
        "            first_upper = True\n",
        "        if word.lower() in contractions:\n",
        "            replacement = contractions[word.lower()]\n",
        "            if first_upper:\n",
        "                replacement = replacement[0].upper()+replacement[1:]\n",
        "            replacement_tokens = replacement.split()\n",
        "            if len(replacement_tokens)>1:\n",
        "                new_token_list.append(replacement_tokens[0])\n",
        "                new_token_list.append(replacement_tokens[1])\n",
        "            else:\n",
        "                new_token_list.append(replacement_tokens[0])\n",
        "        else:\n",
        "            new_token_list.append(word)\n",
        "    sentence = \" \".join(new_token_list).strip(\" \")\n",
        "   \n",
        "    # Removing special contractions don't matched above\n",
        "    # Ex:  \"Mother's name\" -> \"Mother name\"\n",
        "    sentence=  re.sub(r\"(\\w+)'[a-z]+\", r'\\1',sentence)\n",
        "    sentence = re.sub(r\"'[a-z]+\", '',sentence)\n",
        "    \n",
        "    return sentence\n",
        "\n",
        "def filter_out_uncommon_symbols(text):\n",
        "    \"\"\"\n",
        "    Removes any special character that is not in the\n",
        "    good symbols list (check regular expression)\n",
        "    \"\"\"\n",
        "    # First let's split some words that appear without space to a \". , ;\" \n",
        "    text = re.sub(\"(?<=[.,;])(?=[^\\s])\",' ', text)\n",
        "    # and then we delete the punctuation \n",
        "    return GOOD_SYMBOLS_RE.sub('', text)\n",
        "\n",
        "def remove_punct(text):\n",
        "    return (\"\".join([ch for ch in text if ch not in string.punctuation]))\n",
        "\n",
        "def remove_hyphens(text):\n",
        "    '''\n",
        "    The main idea is to split the hyphen compounded words \n",
        "    '''\n",
        "    return re.sub('-', ' ', text)\n",
        "\n",
        "def remove_numbers(text):\n",
        "    return re.sub(r'[0-9]+', ' ', text)\n",
        "\n",
        "def normalize_whitespace(text):\n",
        "    \"\"\"\n",
        "    Removes duplicates whitespaces.\n",
        "    \"\"\"\n",
        "    normalized_text = str(text)\n",
        "    normalized_text = re.sub(r\"//t\",r\"\\t\", normalized_text)\n",
        "    normalized_text = re.sub(r\"( )\\1+\",r\"\\1\", normalized_text)\n",
        "    normalized_text = re.sub(r\"(\\n)\\1+\",r\"\\1\", normalized_text)\n",
        "    normalized_text = re.sub(r\"(\\r)\\1+\",r\"\\1\", normalized_text)\n",
        "    normalized_text = re.sub(r\"(\\t)\\1+\",r\"\\1\", normalized_text)\n",
        "    return normalized_text.strip(\" \")\n",
        "\n",
        "\n",
        "def remove_stopwords(text, method = 'spacy'):\n",
        "    if method == 'spacy': \n",
        "      l_stopword = STOP_WORDS\n",
        "    else: \n",
        "      l_stopword = STOPWORDS\n",
        "    return ' '.join([x for x in text.split() if x and x not in l_stopword])\n",
        "\n",
        "def strip_text(text):\n",
        "    \"\"\"\n",
        "    Removes any left or right spacing (including carriage return) from text.\n",
        "    Example:\n",
        "    Input: '  Hemsworth has also appeared in the science fiction.\\n'\n",
        "    Output: 'Hemsworth has also appeared in the science fiction.'\n",
        "    \"\"\"\n",
        "    return text.strip()\n",
        "\n",
        "\n",
        "def stemming(text, stemmer= 'porter'):\n",
        "  '''\n",
        "  Stemming the text with the stemmer received as parameter\n",
        "  By default Porter stemmer is used\n",
        "  '''\n",
        "  if stemmer not in ['porter','lancaster','snowball']:\n",
        "    raise ValueError('The Stemmer is not valid')\n",
        "  if stemmer == 'porter':\n",
        "    st = PorterStemmer() \n",
        "  if stemmer == 'lancaster':  \n",
        "    st = LancasterStemmer()\n",
        "  if stemmer == 'snowball':\n",
        "    st = SnowballStemmer(\"english\")\n",
        "\n",
        "  return [st.stem(w) for w in text]\n",
        "  \n",
        "\n",
        "########################################################\n",
        "#       PARALLEL TOKENIZATION + LEMMATIZATION SPACY\n",
        "########################################################\n",
        "\n",
        "from joblib import Parallel, delayed\n",
        "\n",
        "nlp = spacy.load('en')\n",
        "\n",
        "def lemmatize_pipe(doc):\n",
        "  '''\n",
        "  Input:\n",
        "  - text: not tokenized\n",
        "  '''\n",
        "  # returns tokens already lemmatized with spicy\n",
        "  lemma_list = [str(tok.lemma_).lower() for tok in doc] \n",
        "                # if tok.is_alpha and tok.text.lower() not in stopwords] \n",
        "  return lemma_list\n",
        "\n",
        "def chunker(iterable, total_length, chunksize):\n",
        "    return (iterable[pos: pos + chunksize] for pos in range(0, total_length, chunksize))\n",
        "\n",
        "def flatten(list_of_lists):\n",
        "    \"Flatten a list of lists to a combined list\"\n",
        "    return [item for sublist in list_of_lists for item in sublist]\n",
        "\n",
        "def process_chunk(texts):\n",
        "    preproc_pipe = []\n",
        "    for doc in nlp.pipe(texts, batch_size=20):\n",
        "        preproc_pipe.append(lemmatize_pipe(doc))\n",
        "    return preproc_pipe\n",
        "\n",
        "def lemmatization_parallel_spacy(texts, chunksize=100):\n",
        "    executor = Parallel(n_jobs=7, backend='multiprocessing', prefer=\"processes\")\n",
        "    do = delayed(process_chunk)\n",
        "    tasks = (do(chunk) for chunk in chunker(texts, len(df_train), chunksize=chunksize))\n",
        "    result = executor(tasks)\n",
        "    return flatten(result)\n",
        "\n",
        "# End of Spacy tokenization + lemmatization\n",
        "\n",
        "def tokenize(text):\n",
        "  '''\n",
        "  Tokenize the text using nltk word_tokenize\n",
        "  '''\n",
        "  return word_tokenize(text)\n",
        "\n",
        "def lemmatization_nltk(text):\n",
        "  '''\n",
        "  lemmatization_nltk tokenization applies nltk wordnerlemmatizer\n",
        "  Input:\n",
        "  - text: already tokenized\n",
        "  '''\n",
        "  wl = WordNetLemmatizer() \n",
        "  return [wl.lemmatize(w) for w in text]\n",
        "\n",
        "\n",
        "################################################################\n",
        "#   SPELL CORRECTION\n",
        "################################################################\n",
        "\n",
        "\n",
        "import string  \n",
        "!pip install symspellpy\n",
        "from symspellpy.symspellpy import SymSpell, Verbosity\n",
        "import pkg_resources\n",
        "\n",
        "\n",
        "max_edit_distance_dictionary= 3\n",
        "prefix_length = 4\n",
        "spellchecker = SymSpell(max_edit_distance_dictionary, prefix_length)\n",
        "dictionary_path = pkg_resources.resource_filename(\n",
        "    \"symspellpy\", \"frequency_dictionary_en_82_765.txt\")\n",
        "bigram_path = pkg_resources.resource_filename(\n",
        "    \"symspellpy\", \"frequency_bigramdictionary_en_243_342.txt\")\n",
        "spellchecker.load_dictionary(dictionary_path, term_index=0, count_index=1)\n",
        "spellchecker.load_bigram_dictionary(dictionary_path, term_index=0, count_index=2)\n",
        "\n",
        "\n",
        "def is_numeric(text):\n",
        "    for char in text:\n",
        "        if not (char in \"0123456789\" or char in \",%.$\"):\n",
        "            return False\n",
        "    return True\n",
        "\n",
        "def _reduce_exaggerations(text):\n",
        "    \"\"\"\n",
        "    Auxiliary function to help with exxagerated words.\n",
        "    Examples:\n",
        "        woooooords -> words\n",
        "        yaaaaaaaaaaaaaaay -> yay\n",
        "    \"\"\"\n",
        "    correction = str(text)\n",
        "    #TODO work on complexity reduction.\n",
        "    return re.sub(r'([\\w])\\1+', r'\\1', correction)\n",
        "\n",
        "\n",
        "def spell_correction_text(text, spellchecker= spellchecker):\n",
        "    \"\"\"\n",
        "    This function does very simple spell correction normalization using pyspellchecker module. It works over a tokenized sentence and only the token representations are changed.\n",
        "    \"\"\"\n",
        "\n",
        "    count = 0\n",
        "    if len(text) < 1:\n",
        "        return \"\"\n",
        "    #Spell checker config\n",
        "    max_edit_distance_lookup = 2\n",
        "    suggestion_verbosity = Verbosity.TOP # TOP, CLOSEST, ALL\n",
        "    #End of Spell checker config\n",
        "    token_list = text.split()\n",
        "    for word_pos in range(len(token_list)):\n",
        "        word = token_list[word_pos]\n",
        "        if word is None:\n",
        "            token_list[word_pos] = \"\"\n",
        "            continue\n",
        "        if not '\\n' in word and word not in string.punctuation and not is_numeric(word) and not (word.lower() in spellchecker.words.keys()):\n",
        "            suggestions = spellchecker.lookup(word.lower(), suggestion_verbosity, max_edit_distance_lookup)\n",
        "            #Checks first uppercase to conserve the case.\n",
        "            upperfirst = word[0].isupper()\n",
        "            #Checks for correction suggestions.\n",
        "            if len(suggestions) > 0:\n",
        "                correction = suggestions[0].term\n",
        "                replacement = correction\n",
        "            #We call our _reduce_exaggerations function if no suggestion is found. Maybe there are repeated chars.\n",
        "            else:\n",
        "                replacement = _reduce_exaggerations(word)\n",
        "            #Takes the case back to the word.\n",
        "            if upperfirst:\n",
        "                replacement = replacement[0].upper()+replacement[1:]\n",
        "            word = replacement\n",
        "            token_list[word_pos] = word\n",
        "    return \" \".join(token_list).strip()\n",
        "\n",
        "# End of Spell correction\n",
        "\n",
        "# Pipeline for invoking the different functions of preprocessing\n",
        "PREPROCESSING_PIPELINE = [\n",
        "                          convert_parentheses\n",
        "                          ,remove_data_parentheses\n",
        "                          ,lower\n",
        "                          ,normalize_contractions\n",
        "                          ,remove_hyphens\n",
        "                          ,filter_out_uncommon_symbols\n",
        "                          ,remove_punct\n",
        "                          #,remove_numbers                       \n",
        "                          ,normalize_whitespace\n",
        "                          ,strip_text\n",
        "                          #,spell_correction_text\n",
        "                          #,remove_stopwords\n",
        "                          ,tokenize\n",
        "                          #,stemming\n",
        "                          #,lemmatization_nltk                          \n",
        "                          ]\n",
        "\n",
        "# Anchor method\n",
        "def text_prepare(text, filter_methods=None):\n",
        "    \"\"\"\n",
        "    Applies a list of pre-processing functions in sequence (reduce).\n",
        "    Note that the order is important here!\n",
        "    \"\"\"\n",
        "    filter_methods = filter_methods if filter_methods is not None else PREPROCESSING_PIPELINE\n",
        "    return reduce(lambda txt, f: f(txt), filter_methods, text)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data ...\n",
            "Download completed!\n",
            "Collecting symspellpy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/99/af/e71fcca6a42b6a63f518b0c1627e1f67822815cb0cf71e6af05acbd75c78/symspellpy-6.7.0-py3-none-any.whl (2.6MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6MB 22.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.1 in /usr/local/lib/python3.6/dist-packages (from symspellpy) (1.19.4)\n",
            "Installing collected packages: symspellpy\n",
            "Successfully installed symspellpy-6.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vcDaOpVTKFq"
      },
      "source": [
        "### Applying preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sos-dtW3TKXi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22f8a159-01b8-4359-cab2-ba6bbd85ee0d"
      },
      "source": [
        "# Pre-processing the dataframes\n",
        "\n",
        "print('Pre-processing text...')\n",
        "print()\n",
        "print('[Debug] Evidence Before:\\n{}'.format(df_train.evidence[:5]))\n",
        "print()\n",
        "print('[Debug] Claim Before:\\n{}'.format(df_train.claim[:5]))\n",
        "print()\n",
        "\n",
        "# Replace each sentence with its pre-processed version\n",
        "df_train['evidence'] = df_train['evidence'].apply(lambda txt: text_prepare(txt))\n",
        "df_train['claim'] = df_train['claim'].apply(lambda txt: text_prepare(txt))\n",
        "\n",
        "df_val['evidence'] = df_val['evidence'].apply(lambda txt: text_prepare(txt))\n",
        "df_val['claim'] = df_val['claim'].apply(lambda txt: text_prepare(txt))\n",
        "\n",
        "df_test['evidence'] = df_test['evidence'].apply(lambda txt: text_prepare(txt))\n",
        "df_test['claim'] = df_test['claim'].apply(lambda txt: text_prepare(txt))\n",
        "\n",
        "########################################\n",
        "# Applying Spacy parallel lemmatization \n",
        "########################################\n",
        "\n",
        "'''\n",
        "# Uncomment this part if you want to apply parallel tokenization + lemmatization with spacy\n",
        "df_train['evidence'] = lemmatization_parallel_spacy(df_train['evidence'], chunksize=1000)\n",
        "df_train['claim'] = lemmatization_parallel_spacy(df_train['claim'], chunksize=1000)\n",
        "df_val['evidence'] = lemmatization_parallel_spacy(df_val['evidence'], chunksize=1000)\n",
        "df_val['claim'] = lemmatization_parallel_spacy(df_val['claim'], chunksize=1000)\n",
        "df_test['evidence'] = lemmatization_parallel_spacy(df_test['evidence'], chunksize=1000)\n",
        "df_test['claim'] = lemmatization_parallel_spacy(df_test['claim'], chunksize=1000)\n",
        "# Enf od spacy parallel lemmatization\n",
        "'''\n",
        "\n",
        "print('[Debug] Evidence After:\\n{}'.format(df_train.evidence[:5]))\n",
        "print()\n",
        "print('[Debug] Claim After:\\n{}'.format(df_train.claim[:5]))\n",
        "print()\n",
        "print(\"Pre-processing completed!\")\n",
        "\n",
        "\n",
        "# Cross-Validation of punctuations\n",
        "punctuation_claim = df_train.claim.str.contains('['+string.punctuation+']').sum()\n",
        "punctuation_evid =  df_train.evidence.str.contains('['+string.punctuation+']').sum()\n",
        "print('Were found {}, {} punctuations in claim, evidence resp.'.format(punctuation_claim, punctuation_evid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pre-processing text...\n",
            "\n",
            "[Debug] Evidence Before:\n",
            "0    \\tHemsworth has also appeared in the science f...\n",
            "1    \\tRoald Dahl -LRB- -LSB- langpronˈroʊ.əld _ ˈd...\n",
            "2    \\tRoald Dahl -LRB- -LSB- langpronˈroʊ.əld _ ˈd...\n",
            "3    \\tThe island 's geography comprises relatively...\n",
            "4    \\tThe island 's geography comprises relatively...\n",
            "Name: evidence, dtype: object\n",
            "\n",
            "[Debug] Claim Before:\n",
            "0        Chris Hemsworth appeared in A Perfect Getaway\n",
            "1                               Roald Dahl is a writer\n",
            "2                             Roald Dahl is a governor\n",
            "3           Ireland has relatively low-lying mountains\n",
            "4    Ireland does not have relatively low-lying mou...\n",
            "Name: claim, dtype: object\n",
            "\n",
            "[Debug] Evidence After:\n",
            "0    [hemsworth, has, also, appeared, in, the, scie...\n",
            "1    [roald, dahl, 13, september, 1916, 23, novembe...\n",
            "2    [roald, dahl, 13, september, 1916, 23, novembe...\n",
            "3    [the, island, is, geography, comprises, relati...\n",
            "4    [the, island, is, geography, comprises, relati...\n",
            "Name: evidence, dtype: object\n",
            "\n",
            "[Debug] Claim After:\n",
            "0    [chris, hemsworth, appeared, in, a, perfect, g...\n",
            "1                         [roald, dahl, is, a, writer]\n",
            "2                       [roald, dahl, is, a, governor]\n",
            "3    [ireland, has, relatively, low, lying, mountains]\n",
            "4    [ireland, does, not, have, relatively, low, ly...\n",
            "Name: claim, dtype: object\n",
            "\n",
            "Pre-processing completed!\n",
            "Were found 0.0, 0.0 punctuations in claim, evidence resp.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAiI3le9v83H"
      },
      "source": [
        "### Preprocessing Effects\n",
        "How the data changed after the preprocessing? <br>\n",
        "Some comparison before and after the preprocessing in order to see some statistics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dspBqoaDwJv7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e255125-c288-42b5-913a-9aa57c1664bf"
      },
      "source": [
        "def get_vocabulary_corpus(corpus):\n",
        "  vocabulary = set([elem for seq in corpus for elem in seq])\n",
        "  return vocabulary\n",
        "\n",
        "def get_uniq_tokens(text):\n",
        "  return set([x for x in text])\n",
        "\n",
        "# Before preprocessing\n",
        "evidence_bfp = get_vocabulary_corpus(corpus_evidence_bfp)\n",
        "claim_bfp = get_vocabulary_corpus(corpus_claim_bfp)\n",
        "evidence_sentence_bfp = [len(get_uniq_tokens(x)) for x in corpus_evidence_bfp]\n",
        "claim_sentence_bfp = [len(get_uniq_tokens(x)) for x in corpus_claim_bfp]\n",
        "# After preprocessing\n",
        "evidence_afp = get_vocabulary_corpus(df_train['evidence'])\n",
        "claim_afp = get_vocabulary_corpus(df_train['claim'])\n",
        "evidence_sentence_afp = [len(get_uniq_tokens(x)) for x in df_train['evidence']]\n",
        "claim_sentence_afp = [len(get_uniq_tokens(x)) for x in df_train['claim']]\n",
        "\n",
        "def num_distinct_tokens(text):\n",
        "    tokens = text.split()\n",
        "    dist_tokens = set()\n",
        "    for token in tokens:\n",
        "        dist_tokens.add(token)\n",
        "    return len(dist_tokens)\n",
        "\n",
        "print(\"---------------------------------------------\")\n",
        "print(\"\\n\\nStarting analysis of the preprocessing...\")\n",
        "#Total Number of Distinct Tokens for the Evidence\n",
        "p_evid_reduction = 100* (len(evidence_bfp) - len(evidence_afp) )/ len(evidence_bfp) \n",
        "print(\"The reduction of tokens in the Evidence was of {:.2f} %\".format(p_evid_reduction))\n",
        "sns.barplot(x=[\"Unnormalized\",\"Normalized\"],y=[len(evidence_afp), len(evidence_bfp)], palette=\"deep\")\n",
        "plt.title('Total Distinct Tokens for the Evidence')\n",
        "plt.xlabel('File')\n",
        "plt.ylabel('Number of Tokens')\n",
        "plt.show()\n",
        "\n",
        "#Total Number of Distinct Tokens for the Claim\n",
        "p_claim_reduction = 100* (len(claim_bfp) - len(claim_afp) )/ len(claim_bfp)\n",
        "print(\"The reduction of tokens in the Claim was of {:.2f} %\".format(p_claim_reduction))\n",
        "sns.barplot(x=[\"Unnormalized\",\"Normalized\"],y=[len(claim_bfp), len(claim_afp)], palette=\"deep\")\n",
        "plt.title('Total Distinct Tokens for the Claim')\n",
        "plt.xlabel('File')\n",
        "plt.ylabel('Number of Tokens')\n",
        "plt.show()\n",
        "\n",
        "#Size of sentences for the Evidence\n",
        "sns.distplot(evidence_sentence_bfp, label='Unnormalized')\n",
        "sns.distplot(evidence_sentence_afp, label='Normalized')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Size in Characters per Sentence - Evidence')\n",
        "plt.xlabel('Size')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "#Size of sentences for the Evidence\n",
        "sns.distplot(claim_sentence_bfp, label='Unnormalized')\n",
        "sns.distplot(claim_sentence_afp, label='Normalized')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Size in Characters per Sentence - Claim')\n",
        "plt.xlabel('Size')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "\n",
        "print(\"BEFORE PREPROCESSING\")\n",
        "print(\"The vocabulary of evidence had: {} tokens\".format(len(evidence_bfp)))\n",
        "print(\"The vocabulary of claim had: {} tokens\".format(len(claim_bfp)))\n",
        "print(\"The largest sample in the evidence had: {} tokens\".format(max(evidence_sentence_bfp)))\n",
        "print(\"The largest sample in the claim had: {} tokens\".format(max(claim_sentence_bfp)))\n",
        "print(\"\\nAFTER PREPROCESSING\")\n",
        "print(\"The vocabulary of evidence has: {} tokens, was reduced in {:.2f} %\".format(len(evidence_afp), p_evid_reduction))\n",
        "print(\"The vocabulary of claim has: {} tokens , was reduced in {:.2f} %\".format(len(claim_afp), p_claim_reduction))\n",
        "print(\"The largest sample in the evidence has: {} tokens\".format(max(evidence_sentence_afp)))\n",
        "print(\"The largest sample in the claim has: {} tokens\".format(max(claim_sentence_afp)))\n",
        "\n",
        "print(\"Analysis completed!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------\n",
            "\n",
            "\n",
            "Starting analysis of the preprocessing...\n",
            "The reduction of tokens in the Evidence was of 13.92 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The reduction of tokens in the Claim was of 20.36 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "BEFORE PREPROCESSING\n",
            "The vocabulary of evidence had: 33617 tokens\n",
            "The vocabulary of claim had: 22883 tokens\n",
            "The largest sample in the evidence had: 95 tokens\n",
            "The largest sample in the claim had: 55 tokens\n",
            "\n",
            "AFTER PREPROCESSING\n",
            "The vocabulary of evidence has: 28938 tokens, was reduced in 13.92 %\n",
            "The vocabulary of claim has: 18223 tokens , was reduced in 20.36 %\n",
            "The largest sample in the evidence has: 94 tokens\n",
            "The largest sample in the claim has: 56 tokens\n",
            "Analysis completed!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UhBisZFdwqqE"
      },
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqDGe2CYws83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "60ff3803-cec3-449f-b819-24b08b3856ae"
      },
      "source": [
        "#############################################################################\n",
        "#               Data Visualization \n",
        "#############################################################################\n",
        "\n",
        "def return_sentences(tokens):\n",
        "    return \" \".join([word for word in tokens])\n",
        "\n",
        "# Word Cloud of Supported Claim\n",
        "\n",
        "txt_claim = \" \".join([x for x in df_train[df_train['label'] == 'SUPPORTS'].claim.apply(lambda x : return_sentences(x)) ])\n",
        "txt_evidence = \" \".join([x for x in df_train[df_train['label'] == 'SUPPORTS'].evidence.apply(lambda x : return_sentences(x)) ])\n",
        "\n",
        "# Create and generate a word cloud image:\n",
        "wordcloud_claim = WordCloud(max_font_size=30, max_words=1000).generate(txt_claim)\n",
        "wordcloud_evidence = WordCloud(max_font_size=30, max_words=1000).generate(txt_evidence)\n",
        "\n",
        "# Display the generated image:\n",
        "print(\"WordCloud of Supported Claims\")\n",
        "plt.figure(figsize= [10,10])\n",
        "plt.imshow(wordcloud_claim)\n",
        "plt.axis(\"off\")\n",
        "plt.show()\n",
        "\n",
        "###############\n",
        "\n",
        "# Word Cloud of REFUTED Claim\n",
        "\n",
        "txt_claim = \" \".join([x for x in df_train[df_train['label'] == 'REFUTES'].claim.apply(lambda x : return_sentences(x)) ])\n",
        "txt_evidence = \" \".join([x for x in df_train[df_train['label'] == 'REFUTES'].evidence.apply(lambda x : return_sentences(x)) ])\n",
        "\n",
        "# Create and generate a word cloud image:\n",
        "wordcloud_claim = WordCloud(max_font_size=30, max_words=1000).generate(txt_claim)\n",
        "wordcloud_evidence = WordCloud(max_font_size=30, max_words=1000).generate(txt_evidence)\n",
        "\n",
        "# Display the generated image:\n",
        "print(\"\\n\\nWordCloud of Refuted Claims\")\n",
        "plt.figure(figsize= [10,10])\n",
        "plt.imshow(wordcloud_claim)\n",
        "plt.axis(\"off\")\n",
        "plt.show()\n",
        "\n",
        "# End of the Word Clouds\n",
        "\n",
        "print(\"Dataframe structure:\\n\")\n",
        "print(\"Dataframe Columns: {}\".format(df_train.columns))\n",
        "print()\n",
        "\n",
        "print(\"Total samples in train: {} - {:.2f}% of the whole data\".format(len(df_train),  100* (len(df_train)/ ( len(df_train) + len(df_val) + len(df_test)))))\n",
        "print(\"Total samples in validation: {} - {:.2f}% of the whole data\".format(len(df_val),  100* (len(df_val)/ ( len(df_train) + len(df_val) + len(df_test)))))\n",
        "print(\"Total samples in test: {} - {:.2f}% of the whole data\".format(len(df_test),  100* (len(df_test)/ ( len(df_train) + len(df_val) + len(df_test)))))\n",
        "print()\n",
        "\n",
        "print(\"Some samples in train: {}\".format(df_train.head()))\n",
        "print(\"Some samples in validation: {}\".format(df_val.head()))\n",
        "print(\"Some samples in test: {}\".format(df_test.head()))\n",
        "print()\n",
        "\n",
        "# Some metrics\n",
        "\n",
        "print(\"A short description of the labels: \\n{}\\n\".format(df_train['label'].describe()))\n",
        "\n",
        "print(\"Distribution of Label in Train: \\n{}\".format(df_train.label.value_counts()))\n",
        "#print(\"\\n{}\".format(df_train['label'].hist(bins=6, alpha=0.5)))\n",
        "# Plot of label for train\n",
        "pd.Series(df_train['label']).value_counts().plot(\n",
        "    kind='pie', title='Labels Distribution - Train', figsize=(5, 5))\n",
        "plt.show()\n",
        "print(\"Distribution of Label in Validation: \\n{}\".format(df_val.label.value_counts()))\n",
        "#print(\"\\n{}\".format(df_val['label'].hist(bins=6, alpha=0.5, color='#86bf91')))\n",
        "pd.Series(df_val['label']).value_counts().plot(\n",
        "    kind='pie', title='Labels Distribution - Validation', figsize=(5, 5))\n",
        "plt.show()\n",
        "print(\"Distribution of Label in Test: \\n{}\".format(df_test.label.value_counts()))\n",
        "#print(\"\\n{}\".format(df_test['label'].hist(bins=6, alpha=0.5, color='#00bf91')))\n",
        "pd.Series(df_test['label']).value_counts().plot(\n",
        "    kind='pie', title='Labels Distribution - Test', figsize=(5, 5))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WordCloud of Supported Claims\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yuhA73kUKRsPou3H8yIqt84Qi6X+oitAYhcoSimGfsG2sysoYdFlQJH1Ullj1lrBS2CLpU6Yxrh/hRTUrjXQ9nGAkZtYKrX5xUrqkm49sYwTDrZQiKVGlRjqS4XaG1oZkJyOPC+KNFF0aylTRWYgZbmeoQuMGDl7kgoAiKTHa/uwsxCSdnLSbk8cFg/UErQy6NKjS2uYMtzOKVOEOS7av9RBSkMclqtCkoUO8nTHYSIi3Moq0pEwURWrF/Z9GpEWPUlt7F61L0rxLZ7BIqTKMUeSloDO4SVEOKRFoo/aIjrrDTsYYzSCxKi4hJKVKKVVKku3YeV7nJHkXYzRK5YAhL2MGyRqO9MjLGGM0Unr4boWiTFDavotxaqUAShcMsy0EEiFs+wFKlVE+4vdWVkLcdhW3HmFKhc4LvFYF4UpMoazKqlQIAN/FbVVRwwyd5ui0oNjoYvISEfqYUlF2Y/Tw1pjprLDnndFcG3jIKEANM8puvEcG/Jk2Tj1CRj5qkOJPN635R1GgBgnlzgDpuzjV0NrI3gHhOXiTzT11lB5m+K0quhZZlVWcoTqxJTa7bcvtXF72hqANwgrmMKVG9RNUbwgCqyZzJDotUHFGud1H9RN0WpBeXcHkJd54HZPm6KKk3OmTXl3Ba9VgtHaVO4docx4CH0p4ZBDiVmu4tTrB1LS1ORj0yVaW8KdmcCpVuzhnGTKqIH3fEqAixxSWRRZbG3hjE1TOnLeSGte9+yStjZ3wlWKXiRulyNfX8Mcn7aLv+5Zwua61DarV8SemMUqhuh2y1WW8ySncWgOtCrvgftIWWmNtdtIb1/GnZ6hcfByktIbheQ5aIxzHkhCtCY4dR9asqLHY3rZljBi8ThPMbTt8nWUUGxvoLKPx0ufJ19bI11cPJzz9viVc7TYyCElv3iRfuEEwP79Xh3DdW5ui0d/CkVYv7Dh7C6XRyhJgoxFSIoRAG2PPFTnJ+5ctgfubAilH5MUgHNeSltEmAGFVRAhpFwejMUbZb0erW9+HEFY9O7oWIfcmLKMV0rHvx24Zh2KvKAeB2P/tCTmyIbsdtk6ks7dwCSmRrofR2kqfpDPq28ePtGsJyfaN/l7fdm4ODt24DzZSBhvWqHX7+sjA3n42B35PdnIWfrJxZxG3cPt9WEnTze2N/efh0HZ8mjFMb0lkC5VQDBN6w5VbFxR9hun9qRwMmm68eOB4mvduO97ddy4r+mRFnwMP4I6/O4Obe7/n5aNZID8MbqtK5fwcbrtGsT3AaIMQ1pYHpe23WVoCKAX2+9fGrnN5weBnV1H9xKqn4juca4RAONYGZ9/PkZ2PcB3ESEIZnp3FbVYQrkO+1rH3jj51nWTWZsa5exQaGfpE5+dwWzV0klsiJYS1xRG31eeO5gSl99oqAw+dl7bfemSXVN42F41U4mgNBpIrK5T9IWqQMHj9GtUnTlJ7+jSxlCRXlslXd9BZQftvPUe+tkN2c+PjIzxq0COJ+yQL120HR//TxZuky4t7u1CUIk8S8rUVuwBi9ibLdOEG6eJNq1YyZtTxw2eFYmuDYnv08YyuMWVJcv0K6c2ro8OjMgA16JMuL+xv29JN0pX9bfvEQUq8iQnC02dwW639rFsp8pUVqk8/i4oHZAsLGGNwwginXkOEIXCIuHgEt93Gn5rCP3Yc4XmI3UX2LtBFQbm9jaxW7S5r0Cd+7TWixy5SfeppTFmQLy2i+n38iUlqzzyHOzaGToao4ZBscYHo3HlqFx8nPH6CYnODstdj8OYbtL70ZbtzGQzIV1cQO9uftfXgUGhV0F9+n6knvkp97gL1YY90Z4UiHRDnCbNPfx01krD0Ft+lNnXqQBmOFxA0Jhg78yxBbQytS4q4S7K9QmVintapp6mMz5P1N8n6W8TrNw5tixfVCJtPIaRDHnfoLrxN1t+mefwiY6efRamCeP0GZdLHaMXMU19D65IyjekuvINRJXlvmyLuksdd6nPnCVvTdK6//hGP4ofA3OX3j+O+hzl/hJ8Tdw7wJ2DAjcFo8CZbOJXISj3u0qyyM6D3k/cZ/+ZLGK0pOzHpjXV0cojUyZF4E03qL54nODaBN1EnW9okW9wivb7G5O98lejcHOX2gHzdqpedWoTbqiLDYM+g+f77ASiDN1YHQMVWypTdWAdtGPs7nyc6O2tteNY6xK9fJV/bIZifYOp3vkL/px9Q7AwwpTpQbv/Hl6k+fZro4nHcDWsDJHwXJ/CsuqtZ3dsH7kqZogvHkOGHr10PCnG3RROO4vA8LJ78jVO0jlvd/sv/6F2K+BBjxZHBt1OvW1WRlJTdnnWtVyVuewwZBpiyRPUHSN9DhhHC86yHmwGTpag4xhsfp+z10MOhLdr3kVG05wGmhzEqSfbO3wnhebhjYwjHRcUDVLdr29ZsIQMfYwx6MEDnBWhl2xaMRLC9Lmo4xK3XrV3VSBVnihyd5/iTU4AlrWo4tHZdD0BAHU/SmK1w6ovT1GcqRC0fx5eoXJMNCjY/6PH+t5fI+nd3+516rMmJz03RPlnj7X99k/V3OxTJvdtw/PkJ5p4aY+pia++YAUxp+Pb/8to969uFkJKwOW2lXqqkzGJ0WVgbuNq4lfKpHJWnuFGNMo3RZY7jhThegCozynRobXpcH4wmjzuU2RA3qOLXWuiytJ5XRUqZxgfa4FWaOH6IcFzrjVdkqDxFFTl+tTkq11CkA3Rp7RuCulVdWxXbEGOM3cQIgS5z3LBqSXD8i7PjOcKHw29Yu48yGaDz9OOt3BlJH6R9b7zJcfQwQXX7yCiwqpHSeupYqbEDroPJC6Tn2r1rOlLtCAGOdc82+ei781xMVux5IJmswJsdmTVs9zDJx9NfGfk49Qpuu4rJyz3zAeE6qDhFD1JkLbRtLxXFVp/g2LhV/RRqpNrJEa6DO163xrlKgwAZ+LjjdZwoQAYu+VoHNcxAG4JTU7a+vEQNM6virdp69tROxhrzq06MzqyNTXhyivTGOibbP38J18Ft1XBbVYwxe15R5baVivrHJzB5ab2mkoxyZ4Dbrlmbm8in2OiN1geDN9OmWNvZI3LCd3HbNdxGBV2U6DhD5yWmtEbV0nUwo7pMUSJ8D7dZRbgSFaeoQWrVY/eJe8XhOSI8jxoC/tZ/9xwzl9q4gcP/919/l6ST7/MG2X/9LVuNA1KvXanPSJq1R4Pv16175Bn3c7uBC/Fgdeo7+rrbxzuP30e1YdPn7C/P8tg35qlPR3iRg5DWuybt5iy9tsWP/+ll4q27T3BnfmmGp3/rNHNPj/GX/+sbXP3uKmnv3rr8c1+b4/SXppl/cRLhCCpjAV7ogIH/49f/lHjzASdUIQ9ROd0pot8PKVxcGaBNgdIlhkPG79By79aGw56jQAoHbe5U+T6gfkY6ePWmJXdFTjkc4DVa1s5POpbcjeJ1qSTep4Ldq9H1LNEOKyOC5ozcW7W1ccozyv7+WCTC9XCrNZyoSra1jldr2g2EEBTdbaticBy8RstO5HlOMejaMBWH1O9EVZyogti1QdQalSboPEUlw09VSIXaiccQQpJsLlHGfT42iYgQOGNNS0YAk+X4Z+ZRnQHl2ibuRAudWjULRQm+jwx9hO+hegOrGilKVHcU8iIaGbLuzmeui6yE6H5s3bBdF9Ub4J+YxRQl+cIqun+4ivMjgyP3VDb3hdvVWw+LO9cDIWy5P0+ZUox2doeUIXfXqdvbgJ3z71eaJO/os7TaIODW8d0y97Q2D9aFnzvw4BEeHLrUqHuvZxb3IiR3EoQHnWwfkGDcFQ9S72F1PiTpcgKH1nyNr/23T+MEDt3FmBsvr9NfT/AiSz46izGq+HCJkQHKTN+3W/Diq5t0Fgdc/vdLBHWPp3/rNFPnm4QN/4H7YRtw2LO4d1sqXot2ZZ4432KQbZGrgxKc+yY7cOgzkMLBdyqk5Z0L4oM9LyeqMPaFrxOMTZKuL9N9/UdMfuU/IJiaxanWMGVJtr7C8Mb7dN95jWLnDpsPIfBb40TzZ6hfeAqv2caJqtaLcRiTb2+QLt9k8wfftg4Lo357rTHaz36R+qVnWfx//zHjn/8Vorl5kA5r3/5jTJ7hVOtMfe3X0UVJunKTrZf/gnR9af+7KiX+2ASNi89Qf+xp3God4XmUw5jhjSsMb1yh9+7PDiVKn1RU584iBGTdTRAPF3TyoeBIoqcvglKo/oD8ys1bc4AAd3rSkiGjMVmBOzOBrFeRgU+xsEKxtoXa7uwRHm96HH9+hmJpHe/UMdx2E9moora7yChEeC75zRXwHNTOgwXmfGR4EPUR3J1UPFAZBz3gfm6Sdy+ydNg5w4P1/c4yDl0vHrDMB8AR4XnUMPD9//1tGwwNQbyTfSoDjn0SUJ+KGD/TIKh7XP72EjdeXuPKX66gldnb3OjSkA/vrV5aeGWD9fc6OL5DspPtRdy9F7J+TjEs6SzGCCk49flp2sdrhI1H0bMPR+Q1qQUT1PwJeukaVb9NaOr00lWa4exIKqMJ3Rq5GqJMiSsDOskSvlsh8prsDBf2lVn12vhuFVcGDPJNpHAIvSYTlVMs996i1DlSSBrBNEnZQ+sSISShW6NQGYVOMUYTenW0KUnyLskdRMlttKi3xqnMnSTv7dB/73V0liKjKrWzF2k8+SLRsVMs/dE/Rxe3efAYg9cep/7Yk+g8I776LiodosuCYGIGf2yK5lMvYLSi+9Yr5Fu3xZQSAieIGP/i1ym2N1Bxj+rpi4y99FWrYs0ztn/8PapnLuK1Jxn/4tdZ+Tf/z77wGGMvfIXKibME08cYXnuPMu6jyxyv3qJy4izh1ByVE2dY/bM/tKRHSNyowtgTXyBoTuCEVYSUDNcXSdYX6F19045HpUE4PsPYE1/YM/Qu4i5bP/suRWzDNbi1JuNPfhHpuKz89Z+M7qsRtCZpP/45Nn/2PbKdNdxKnckXvo5AoIsMvzmOEJJ0Z43tt35I3ttGej5evc34E1+gdvy8VatOzKLynHRrmXRjif6NdwEIxqZpnnmKrLtFODaF3xjH8UPilesM124SL39A/cRjBGPTYGDzte/Y4ZYOTljhxN/++2y89lf0b7xzcOHSCuF7yEqEqIS4k1ZlbrIMd2qMcmUdnRe4E210PET1+naRk4JyfYvyNuJilEYnGfnCCrJehaLALK8hPJdyc8fW7bo4foT2vYf95I7wNwBHhOcjwHD7kx+y/NMAr+ISNjyEFMQbKf215KHGtkjUh9rs3AmjQWkNIy6lS/2xuhUrXVDqnFJn5GpAxRvDkVa6FLhVpLARY5UpKHSGMYbAqRG4NXwnxBUHJVFCWhWZ50SjOkq0LjAYylEAN0d4I5JTH7kXa5QpKXRGqXM8GeA5IUoXh24mpWsXHF1Y0mIjoGfWE7BSxR+fJpicwa03KPr7VUtFd4fBlXfQWWoJR55hVEGxs0nl5Hncao1w7gSDq+8crNhYb8V0bcn2o1Ijmj9NsbNFvr3B8OYV3GqdYHIWf3z6lpOAlHjNMaK5k7jVGunyDQZX30UNB2hV4lZqSC/Ab08QzZ3Eb09QdLcRCOonLyG9gCLukW6vWTVab2ekPgLhuAStSeonHiPdWsEomyLBqdSIpk8gt1ZJd9YQ0sGr1BHurcVaSAfp+njV1mhMredcNHmcor9D1t2kiHsEzXGE41KdPU0Rd20E7CwhWV8kmjwGQpJsLFEmMUV/hyK+RSSk4+FW6jhhBZUOGa4v2FQMnU1UYlVC5XBA9dhZ3Ki2R9ikHxI0J+6uiteGfGHVepiWJXqYUiysoOMUPUzIry+hdroYpTCpjSK/69FkbV+G++z8dH9AMbIrKdY2UR1LFIXrgrbfpfBcyijADFNr5/Mhn6qDS0DIBHN02SJlSMbhHqQSBw+fcWaI6ZEQk/Mx20Qd4ZHgoQmPcASOa0O6S0fsRWMFbFj20gbqOmC7IsCvuAghKJISx5dI17ra6VJTpAqMNVZ1g1FeHG0oEnVXSYl0bP4b6QiEc0uvqTXoQlHmd9et7ubikZ4k7eVWDepIXF/aMPG7KRuUocyUXfjMwTLcwEG6+1WHRhvS/od/fPv64gqkO8oPtKuTxXpkGW322nGnJsN6LAvbF8fmFtpru7H36VIfPhYCHF/iBQ6q0JSpQvoSxzl9NA4AACAASURBVL1tDIxBK1tOkamPRD++r+9SUJ0IqY5bjzStbHqAqHVrIdfKkA8PhuyXjhjlWjoY72k3XspHBgFe6NiUBHG5l/ZAOnJv/HWpcX3HnhPsxYDR+pZIOldD0rJPVg5IiwFVfxzPCZDCwXMipHABQS9bJSsGCCGJvAZVv41AHm7vA3sh9o0xFDpBlJK8jClUjCtDHOlR6pzQrQOCQiW2jjJGmRLPDxA4CG6FjdhXvuOghjHDpRv03n51n62ODCvUhSCYeAqvNY7KrPH0LrL1ZbL1g1HY0xXrrhxMTBNOziJH8YBuh9GadG2JZPkGRhtkVKV65jHKuE98/TLJ0nW81jgyCAlnju25ygrHJZqZJ5iaQaUJnTd+THztso3/tdsn6VA7d4nmky8QTs1ZlZo2tC6+wODGewzXbhAvfbB37e5HK72AcGya+onHuPYn/5AyGeD4IY1zT1M7dm5POnP/EHi1JtnWCvHi+yQbS9RPXiScmKN6/Bzdq2+g85Ri0GHn3R9TnTsNwM67r5B3Nw9VoQgh8NvT7Lz9Q3rX37bPS+zaVRiSrRWa557GqzRw/JAyS3CjGuHEMdLNFRtf6gDh0eRX90sX03eu7v2uOrfy75WrH+7Orjr9vXvu5/r7gYtHjRbnxTNc5x22zdpdCY+DS0SVM+ISy9xgy6z+jSA8EmcUsPGzo6F4uEjLUjB1ocn8i5NMXmgxdaFJbTK0uWsMdJdiFl/ZYPn1bd7788W9eVEIiMYCvvk/v0hQ8/jO77/BpV8/wfTFNvXZCsuvbfLDf/weeVJy/lfmePa3z6CUobsU853ff4OdGwOK4X51hBtIpi+1ufStk0xeaNKer+F4kmQnp7cSc+U7K7zzbxcYbqeHLtLnf2WOs788x6kvTfN//vaf4Vc9xs80ePY/OsPkuQZu4KA1dBcHvPp/f8DiK5v0Vm9ZjDu+5OxXZ3nx9y4w83hrX9mDzZR/9vf+nCy+vzw6XuRw/PlJjj8/wckvTFOfCnEDBwQkOxndpSGbV7r89F9cYbCe7lu469MVxk7WOfvLs0w/3qIyHhI1fYSAeCujuzzk2l+v8va/vkHSzfeNRXU85MI3jvP5//Qx3v/2Ej/6J+9x7leOcfpL04ydqlEdC4l3MjoLAzYud/nRP71M1s9t5NlHiPkXJjn+wiTnvzZLdSK0ySF9S/xe+E/O8fzvntunA96+3udP/8efsHWtR5ndGovpS21OfG6KL/8Xjx+o40//p59w5S+WH1jic7+IWgFf/i8f5/SXZ/g3/8OPOf+1OaYebzP1WIvFn27wxh9eZ+2dDpe+Nc8T/+FJ/KrLYCPlL/7B62xf75N2D7cRUbrAd6rM1C8SuLVRSH0zkrRolC7Yiq9zafobdNMVlntvHShDYAPHKVnQiubYGS4ghMRzKkRem9CtUfFbFCrbZ+CsdGFjK41KUTpHCpdmNEfWf//AhJh3t+i/8+qBOD359gZFz0Zl9+pN8u0N7vcpqGRIvr1JZf7snsRj30usNUVnGz2K/aUGPeuhMowpOjaOjM5STJkjPX+0qNvcfbVzjyM8n2JtmfjqZTD7W5WuLeG3rUQjnDtB3t3CDFOqM6dY+/6/Yri+tHft7X0Ox6YRnk+ytWJd/pWizIb0PniD9vnnKYcPmnjXoIYD0p11kg1bZzHo4FYbewTqQWGMYbBwmay7eYuc3r6b0opkYwljDPUzT9D74E38xhj1kxfZfP27FP2dB67z0wxhI1l95iGRHOcMHbYY0qfk05n89k48FOGRnuDsL88x9/QYlXbIcDtjZ2GwJ5lpn6xx8ovTtE/WQMDV767sW2CkK2nMVXnyN07h+o6VrDiCuafHeeLvnKRIS6Yea7H6zg6NuSrtE3We+52z/OifvMfOjVsBiJrHqkxeaPLC757Di1zKXLP4002MMoRNH7/m8dRvnKI6EbDwyibXvrd6oC9CCqRrQ9LPXGpz4qUpxk7X0cqw9NoW0rUJBZvHqqhSk8X7H7wuDZtXerz5R9e48cMKXuhy5iuz1CZCHE9yP1+HTUzo8vn/7CKTF5rUpyKKVLH61g5lrhAIonZA2PQZO1Unj0fSgNswdbHFyc9NceKlKYY7KdvX+hRpiXQktamQxkzEU79xEl1qll/fYu2d21yKBciRdGj2qTFe/PvnmXt2nHxQsvlBn7V3OzSmK9SnIhozFYQjeP0PrrF97dFmSh/uZGx90MX1JU4gqU3a+maeaLP+Xpf+2nCfd1S8mTLs5AcMkXurQxZf2eDlf2Sf3dipOtOPt6lNhfskkR8FdiWEUSuw77fn2Ai+iwNmLrUphiXTl1rMPT3O9s0+lXZIa77GM3/3NK//4XWWXr21g02LLhsjQjPIt0iKHgKBHAUE1KakUAnaaAQOvlshLnZIyz7aHKQSSdEjK4c4hYsxmkKnlLpgpfc2adkjLwfE+bYlN0JiMDaSrkpsUERgWHSs8bQQGKMPlSSZoqCMD+78jVK3yIS8FYl9FzbIaYPKibO49QZOWEH6AcJxcKsNvFZ7X+DG/TzLYFQxWqzFrdQGWt0WdPSWBG2vZiH3kiBX5k9z7Dd/786CkZ6PW28Cwrr4SxezKz3V92GQf7sx6a6xqhC39f+Q+0cBIe+EVuU+6ZPZUyk9/HttVIm5h4NDumWDDDbOPMlw9SZOWMGNqmTba6gPSRN0AFIiq5VRFH2D8D10mlmvONfB5Ln10HNtLkThe+jEqsJkEGCKAhH6eJMTFGsb1t36Iw4qW1IQ0+Md8wopw8+8dEcgcPGZFicpTEFG8jeb8ID9Vm349ZKtqz0Gm6nNlOo75EnJsWfGmbzQ4sRLCTd/tH5gR+1XXCbONbnx8jrdxZio5fPUb53m2HPjDLcy0n7O8s+2mCs04VM+889P8PofXNub6KQjGDtd58SLk0xeaLL8s212bg7YuTlAF5radETreJVTX5zmxEtTlJlm5Y3tu+6gpSs4/twEreNVpCNYfWuH/lqCEOBXXaYeb9usytn+fhht6K8nLLyySVD39nL8BLX7H1q/6jJxtsHpL8/gV1zypGT1LduffKgQAhqzlnCUmbVHOcyeRCtNf33I5vs9hjspSa/AkXacxs7UOfHiFMefnyDp5PsJz21ozFaQcpIiK9m82qO/OiTrF4yfbjD9eIuJ801Of2maGz9Yo7cypEwfnaQk6WRs3xDkIyne2Kk6ZaqYeaLNzo0+6+912fzgVhTWYliSDYoDY5F2c7ZLQ5GU+FWPfFjSmK1SmwwfWVs/DI4nmTzXZOnVLTqLA7yKy7O/fYbpJ9pUxkLSfs7qWzs0ZivUpiKOPTPB1e+t7lvIrQ2PfV8LlVDcReQOVjXhCI842yItDieiNj1Axn73QcUgt9GCSzI4zBNsXxnpqJy7L/JGa6v2OXjmNg/ag2THH5sknJ2ncvw00gtGkWVHma7DyEpm4K5ru404zW4A8lvHdyUWhlsnRvULISypQiC8gGB86q79yjZXKYd9TFns9dEJKzhBeNvCP1JDj9zw0dr2ZRQRWzgOblSzLvq78bS0Yjei9m6YAen5uFHtYF93o2rvw+EDYuMnCUsm7hECwY7P3Z9nEfeQQYRXa+E3x3GCCrrIKO8SXuBuEIGNDyZ8D4Jd1bSwhs2+b73iNjdxGg2EZ8dL+v5eXDFZq6Lj2ManmRij3OkiivIjV7gYNAU5WxzcMH8WIXHwCanRwMNH8uCSw08qHorwqFzz039xBZVrVKFR+f7dgffHLt/475/j1BemOfNLs7z8D989UIYx1r7nlf/rfdJuRmO2wsVvnmDyfJOF3iZ/9ftv0l9N6K+mgODirx0nqHp7thB+zeP0l2Z4/JvzbFzu8t3/7Q02P+jt22zVpiMQcOaXZjn+3ASrb21z7ftrh6qXHE/y/O+e48f/7DJX/mL5roTgMBTD0oa2BxDw1G+eojFbOdSG5DA0j9d47u+dpX2ixo0frnPlL5ctubsDQgqE5FBV0s0frrP06iZFUh6w03F8h+lLLaYea3HsuQnb1n93eFuiVoDR8Af/zfcs4YpvTWgX//Zxnvm7Z5h/aZLxs026K8NHKuUZrKcM1lPWR69L2s3xQvuK7iwMWH59i8Wf3ofOP9ckeUayYxfdsOkz/+LkI2vn/UKVmjf++BpbV3t4FZfHfu04rWM1hBD8u//qFeKNlOnHW/iRy+PfPEHU9HFc+ZA2RlbisxVfQ9/Ffuf2a38+fJhEQz/QQggQTs1Rf/xZWs98gWxjmcEH75CtL5N3ttF5RjhznPq5SzQuPX+Pes2obQ8o7RASXeTkm6vsvPbyPS8tex3y7jbSsaqqaOo4RqtbNjyulVyZoiDrbhJNHcdvTuBGVQqjcfyQ2okL5L0tioHN2q2yBOG4ljz5ASpP8WotopmTD96X22DKwnovBRUb2+YhH7vOU9TQeq3V5y+gi4zB4pUHC4kAuBNj+CeOUywu405NIhxJub6Jf+4MTqOOcB3il39C+LjN65d+cAMB+FMTuONtnFaTYnUdk+XgugckhEd4NPAJqYuWzUX2GVPgPZyEx9jFaG9+uQNFUtJbtuqH8dMNpHuQIRZxydb73ZH3i42R0l8bElRdBusJ/bUEow15XDDcSq0xaMXFixzygebUF6YYO1VDl5o3/+V1BlvpgQ86jws++M4Kx56ZoDoRMv/CJDdeXkcdQni0Mtx4eZ3FVzbZvPLxxXJwAklzrsKpL80Sb6Vc//4q7/7bhUOvNdrcdY7JkxKRcugzUbmyUo9r/ZH0ybvrhq+zOGDp1S22rvUPSLO6y0MWX91k/sVJquMB1fHwkau1PitQuWbr/d4oaaT9e7CeYEpDvJEwWLNJKYtUMdhIMRjc0MGruqjOg8d60UaRlf1PrYFh7cJTRHMn0VnCyr/6F3tu4bsSjWB8cp8X06OC0Zqiu4Nbb6KKnPj6+/fOEaa19YSSKat//Se0H3+JiWe+wtTzX7e2dhtLDNcX6X3wOjq3xECXJXNf+U1s3jSNLjK23vhr8o6VrJkip3/9HSqzpzj1rf+cMosp4x5l3MOvjz1033rX36Yyc5LZL32LcjggWV9guHaTwcLlBy5LZQmd915l4tmvkm4ts/nad++pBjsU2mCUxmk1QQjUICZbXMGZGEMPBqA0/oljCM9HD4eonR38E/P22u0O5dqGjeAcjAIVfgjhcXAJqXCcc9RFCxfP5vIyW3TZ4l5kMiBinvOMi2lcbr13q9xgy6zR4d4brzM8gU9ASck13maKYzTEOE3GcHAoKUmI+cC8QUZyYJPi4TPDSVpikpAID4+CggE9+maHZa7ec2NTpUGdNmNMURNNJDaPXklBwoCO2WSD5T1DbZ+QWU7REuOEVPCJEAhOiYvMc36f6rog5yfm3x+oXyLxCDjGGeqiTUCIg0dOSp8OXbPFOguHzlEePmd4EgH06bDKTeY5T120iaji4JCR0mGDZXONlPuPvHw7Ht4tXQiacxVq4yG1qYig5uJ4zp6nzeyTY0StAOmKkcfQ/tu1MlYdsZsvSxtUpiikoExveWRpdcvTS448whCC5rEqYd3Hi1xOfn6a1vGa9fC6vXO+tRWy6iVD1PTvIQ43dBZjku49oiJ/BIgaPlErwK84bFweMthM91Q6DwIBuKHL2Mka9ekKftWSw13vtep4SG0ywvVtpOK7BU/O+gXd5dhK7e44X2aKtG/dkd3AsZGHj3AojB6937vk2oDKNEVSkidqz+7IaEM5CpxoM4A/vPj400p2AJwgRDgOpiwoeh0bo2c3celI+uE12498V2+UIttYIZw5hhNE+ONTpKsLHxq002hF3t1isPg+blTbM6TO+zvk/e3dqyiTAcnGAtGxE+g0RaVD6yI+7KG1suomxyHdWcNgEIFHvrNJ0etQxj3y/o51Nx9lvN+5/Arp5q3EnSqJSbeW6WDzt92OrLNhyVlZ2FQgo3xouyiGPQYLlymGfVRyb1Wm0RqVxpiyQGXJnnTqQaAGMSyv2rGVwkpqypJieW3PSF6EAXJrB5Pl6H5MsbZuiY02mLIcpaBwEYE/SsNzN3IqaDBGW0zSYpySkowERYknAppmAkfcff5SlPTpIIwgEBE+AW2m8Ahw7mPZ9AgIREQATJnj1Gji4pEQI3H2/ivUgVGs0aTBGONiBoCcjJwUiUNEBV/4KFOwxSo5+1XHAkmFGtPMUxUNAiIKMvTI604gcQlw8NC3uQwYNDkpMX306GqfgIQhGUMUt9akkvLAXCMQ1GnRYpK2mESjyckwo3ZXqROIEGVKumxSkB+43yfAEa6NM2U0EVUMmoR4NFouwgjUfbs6HMRDe2lVx0Pmnhxj8kKTyfNNqhMhbuiM3G0d/Kq751ZuO7QfWhtr/3G7o0VpXa7L/LYHoQ16Nz+JFHuu2rXpCK/q4ldcLvzqsQ+NkaJyfctl/TAY6K8lj9Qm5X4QtYIRERN0l+L7ytF0AAL8ukd9qsLpL80w9ViLylhA2PDwKpaIOr4kqHuWMN4D+bAcZZc+RApWaiv1MdY7TXqfHd3uo4YxZiTduTWOu27pt0vOjLGkHjMiPH9Dh9RoPZKe2FgsQrt7xr1utYHXGMNrtB99vaokXV2gdv4STlShevoCRXd7FM35lr2PkBIhHVSa7MWeUXmyF2DwbtB5StYpGGzdpNjesLnnkhSnEiHDEKM8hOtRDHuUZYoObLJlNRyg8/2Lgkpjtt/8wb5jZTKwpGr9YAbyor9D0d/ZU7fdiTLu0bt20JvvUIzC/Rdxl2LQRR2St+3DoPsDm/Lhjt1WsXx32xh9c+mu5+4FF5e2mGKWEzi47HCToRlQktNikppoEFG9q6S7pGCDJTpsEpkqdWEX8weBxMXFY1rMU5ic3CSWRCEJqeDhUZLvk54IJA3GmREnqFBngyWGpk9GSoUqbTFFgzaeCIhNj4Ji3/2235PMcBIHh4QB22aNnAyNxieiTouSYp8hskLRY4fY9KlSpynGqdOkyyY7ZmOfy74Z/ds/3h4tMckxziBx2GCZoemRk1OjSUuM02YShCAzQ0qKQzdoLh4uTQIqVjJkOmQke4QqZUjJw0c7f2DCIz1JZSzgN//BF2nN18BYb6b3/myR4bY1Ns6HikvfOsHcU2O0T9QOL2h3or/zsDZ8mAmCAPyKh+NJskHBm//yOlm/RJd3vzGLS6tSuIt7uDGGtJ/fV5qCRwk3dHBHOZry4cPFiPGrLi/93gUe+8ZxWidqLP10k61rfbY+6DHYTChSRXU85Ln/+CwTZ+8dKrjMFGkv+zSlDPrEwmhzcBy1+fly3XxGkaws4NbqVCceY+LLv0a6uohKhjjVOq2nX8IJIvt35S7zyUPClAWDK+8Qzp6gevoCk1/+BsH4FPnmGsWgt+dC77cnCCZnWfqjf07Z71iD4/uEEODWasgwxGuPka2tUX/iGXAkOk1QSQJaWwNjz9uLEfRJghNUaF98kXj5Gsn64Sr3+8bHMLlMMEuTMSQOV3iDTbO8Jw1Z5SbHzGmmxHHGzMGYTrvQKDKGgBmptR6s3QEBITUWucIK14n5cFOJMaYYF9NUafAer9Ix6/ukOB2zxbiY5hSP0xZTGAN9bGiAXSJ1lqcoyVnlJlfNW/ukM/fqa4yV/hkUAREGSM2QPjsk3JvgznCScWZw8XnT/IA+nT0pzhrQNpOMi1lO8hg9sYUxMOCgnWyFOgUZO6xznXf3tX3lwNUPjgcmPK1jVeZfnKQ1X2Pnep/Vd3Z4609ukI5UQaq0+YrO/NL0AdfpRwUDoyCAVkq09NqWjdFzj5QBujSoXH1kbXpYqMIafiMgqHk4/oNt8YUjOPPlWWYutfGrLh/85TKv/+F1eiuxDbI36nN7voZWh3l43IH78LI9ws+Jo/E9gOH1y6CsRCyaO0k0O49RCl3kpCs3UcMBRikmv/atj6B2Q++tV8g2VqievoDfnsRvje95TOmyRBcZ6eoiOkvubeNzD0jXQ0Q1gmkbqFXnGSqOUcPYeor5PtIP+HkMlR8phKA6e5po+gRBaxK0/v/Ze7MYy7LsPO/b+8x3jBtjzmNVZg09N5tNstikaAmiyKYMSbYIC7Jk+NGyDejNNgzDgA0Y9oMfbAN+EQhYtCTQhgZKaoEWKbaa7Ek9VHdV15xzRmbGeOfhzHtvP+wbNyMyIrMix8oq1l+VVRlxz7DPuWdYe61//T/xxs0Z9+h5hUBQYw6fAEVJz2xT3NNWHTNmSJ95jhxyq49y0woMim1z576ihveiwTwBEYqCgWnvG3dGzNgMQUBEnZDxLOAJiajSxMFlmzsMTfeRyj9mz98Pd9wN0cLFIyVmxGBfG/uEIdJITouL1GiSWibRgdvKSNhmbU/J7UnhoQOesOlbf6Oqy2BtwtrPOmy8c7D4lPsUSx7ZMEflClN1UblmOG2f/rghj0uKpAQDlVaAFz3cVyIELJxvUJ22XNvvo7vPgqE6X+IGzocS/YBPX8if4kAICVF9Wqo2UOYa6QiyibKzQtNDdW4gszbNIwH5RGGMQbqCxmJAkmlk3kN3rrGwXEBXME4kRaophj3SjVtIz2Pu4guEzQghBWkvJundhmyE5xl0+xq+k1Kd83A9gfE1nu7hTFapz2mczKHIDWU8Jr55mayzOdPhKeMxWWeLyY1Lu7R5LPLu9lRBWWNOFziVum2D19rybiYj8t42usgfmqxrjKHod622kTEYpcg7bUyeUcYTdJoggxDpB3b75X6phY8M03Ke0YqkfYdi1EVlj0YYfZYIRDTjyKTE+/SicjIykzzV2NJg0GhiRofKsgBEooqLh0BSo7kv4PDwbSlu+vfdhGoXn0CECASxGU+Jvc/mOgqpIHFIiSnYL0lRkJNMvwefkID7SYQYCnJihk+Fk/jQAY9fcamvhCAEnesjtj8Y7FtGOoJoLqDSCp7IIPfBGHq3xhwd5tSWI5YuzrH5fv9jGfBM2imTjuULtE7XqbQChBSHfuAJIaivhPiRS5Eobv+ks4/0LKQtnTVWKgd2zH2KT3EYuL7k5KsNmis+uoThdo5fkaxfnjDuZjTVm3Rf/wFh3eXkry6ycWmMKjRRw+NLX1/hx/9ig0n3bfLX3+Wrf+0Yl5DcUgGdW5YXk7U3yLsbVJKf0TxbwQscktUEZ5DTXA6YPxGx+fo/ohkKll6t01j2aa8m+NnPiNbe49xFh17Tp7+esnH1Jnd+/3f3jD9dXyVdX6X3+ncOPL5yPGR06S1Gl956qPOyM4e4b2ZUa8bv/Oyhtnn/nfHsJiTGMLlzdQ8HSIhnPIZHhI9VmVem2BfsgCUl30ucfdKwAY+6L19lPwQ+IZ7w8U3IF8TXHri0M6Xy7sDFw8e+c3Oyp358u+ETYjAU5mCvQ4NBUVJS4uHjsd/rD0Cjp+fs6YhJPnTAU6SlzR4YQ9jwiFp7B+4Gks/8lbMsvtDEqzwdb1Kj4fp3Nlh5aY6Vl+b43F89w+Z7PVSuSA5o6Q3qnp2RZuqZdmAdBkVSMrgz4eYPtzjxpUXOvrZCmSve/cbqvmWFtIRtIe5q8Rgg7mQUmcKvuFSXQpxrknJXOvDUzy9z5heP2PPwnGTLP8XHD9IRtI4FLJ+tonKNdAXxsKB1NKCx5CNdgR9ZPlrcKzjxSp3rPxmwcXXCxtUJm9cnTHoFni/ZvDph6/qE3tpe1VrHlbzyq4vEg4Ii0xy7WGXtA+uLlwxLTnymQTYpmfQLbr45oLeWcuxinYUTET/+FxtcfG2epbNVNq4+myzEL369xYufr3D65Yjf+e9v0d0oSOMn/4ypzTmc/2yVo2cCrr0dc+mnD08afhxIB2pNl6//p8us30j51j/pfvhKB0EI3MUFROCBUpTdPpS7OpWUAikRnouMIvxTJ1CDIWWni04z5FSwUBeFzVabqXBi8XTVlh8N5hCaWPfCdiEljLnJBw9cMjXxfduzDyIWfxxgZj1iTwcPHZEkvZztSwOMNqy83EKVVhtGa0NQdakuRZx77QjSEcS97KlledJBzvpbXaoLIad/cYWXf/0ki+cadG+MyOMS6Vrz0bDuMXeyRv/WmGvf3rA+Uk8Q0hE2yJgaqTq+JGz4M1PUxfNN0mFuRRpLTTosLP9oJ/AyMN5KuPzNNRbPNWidrnPhzx/H8SRxJ6PMrY5LUPOoLthzefnfrpH0M3RpDUW3Lw84+tl5akshL/zqUYw2JP0MDNSPVDj+hQUWzzcYbSZUF5+d2vBHAgFR08oV7JjCNo5W8KfBd+NIhYXzDbJRMTOrLWJly4pTSEdQWbDWINK1lhvRXIDjW1+zhXN1woY3k0zIRvY7vVeA85MGKQVR3aM/FQON6i633hmycq5KfcHn6o/7zB0JrHmqY8+bNgZdGlxfElZdsrGiLAyOJwkqDkHFIRntflkZ0nE5bYQyjNo59QUfObWAcdypNIUxZBNrDCwEBBWXpTMRXiDJJs+u8WD9ekpryeXUxQjHEU9NC09KgR9KoprE8z+aWYuQUG04hJXHkKMQAlmNrNM54PkBJi+s7s7Evrzd1pxVX3YcnGYd4dk2dEplJ2w7psoGdJah4xTV2RuAlRQYYzulBGLfy1/iHKq9/HHwKK/tknyqfC0YmM4Dg5aSErWLK7M7M+LhPcbx3Udg7wEoyG2HlfAPXFUgdrXil/fN4DztEO2hz0jcTdl8v8+km9E6XSdoeDZ1mGuiVkDzWIW5E1Um7YwyVU8t4ClzzdpbXYyBI6/Oc+YXV1i60GT78oCkl1mdmKpLfSmidabOje9vsvrD7SeajhXStmcfebmFG9rWby9yqS6EeIGD9CTHv7hIOswpU0WZlrSvjZi00z0WF5N2ytU/WefFf+8YrdN1TnzRCiX2p0rHWhmqCyHNE1VUrlj98TbpMAdswLP5fp/BnQkL5xqc+aUVtDLEvQyjDUsX5qgv2Zd3++qQsHlwKvGTAukI5k7UaByt4AYSN7IWJmHDajAtnG1QZopsVFAkJZNOynAjJHKCzgAAIABJREFUYXDb3oBCCpzAYfliE7/q4foOXsWhthLhhpYDdezzCyQ9G8QWSUnn+pBJJyXJn10K+aOAwUqobFyZ4LiSpdMRvbWM059rsngq4nv/7x0WTkT4oUORKdKxVf02xk6KgoqLFxaUoxKjDV7gEFT3BjxaQ+dOQlBxUaWhfSvh2IvWYqEsNOm4JItL8lRTZtZKQkqBF0iWTldQpbkngLLvx0rdmVlOOXIqszPtjo6Hiqgqcbypy7kU5KkmzzRa2XWlBKUMjiNQyqBKQ54abrybUKk5nH21sq+kFdUkchoECQFZolGlsd55oZwFRzv7K3K73QfB9SRh1aE25yClII0VZW4zYLWmPUat7TjL0qAKQ5Ebqg0HMbX2E1KQTBRa23PhR3fL3NIRZLGmLDSqZLae4wh7HnbFOpWGYxvKpseXjDVK3V8cdefLEJ6HcK1flmyFmFJRbLdRsQ14nGYDp1kHIZGBtRiRYQiuY32zpEQGPqZUlP0hSvb2BTy5yTHC4EwDG3WPdoyLhy+et2ehITcpWmhc3JmH1WEzNSXFjD8TiBDfPM67d6qBxU4N88HISXFw8QiQOPsIxxIHDx8Hh4L8QJ7Ps8BDBzzpsGDzvR7/+n98nZf+4kmOfmaeL/z18+hSM95O6Fwd8i//qx/QOFrl6GfmOfLqk9fO2EHv5pjhesz25QGv/tYpa6D51WXChm9fapOC0WbCu/9qlbU3O8TTrMeTguM51Fcq/Nb/8lWc3dyYXdfH1/7zV2d/N9rw+j+8zJVvrXN7l0mkKuy5+8Z/80NOfWWJkz+3xOmfX+bc147ieNaBPu6k9FbHbH3QJ+lmd1v6DXSvj/jxP7zM7Tc6fPG3z/PCrx3HcQWq0Gy+2+Unv3eF7o0x86frLF9oPrkT8BzCr3p85W9f4MJfOL73g+l38vJvnuTl3zg5+/Wdn7a58q01fvwPLmOm+kL1IxF/8b/7MrXd2bBd3+kv/2d3v1OAn/yjK1z//ibXv/vJ9tpJRyV/8rurs3vo/e/C4skK407OzUShCsNbf7w95XmIqbGlXfaPf+fmHkmIb/3uranx5t596NLw7rc69uUsBFoZ1t4fH7jNHRSZYtjOePOPtmeaRrtRqTv89t89SpZoPF8yv+Ix6JR4gSCdaH73f7rNr/+tJU68EOK6glrL5Yd/2OeNPx2ydSvnr/2dFRrzHlu3M5aOB2zdyrh1JeWH//r+9jNCwF/6W0ssnwwII5tt/t43uty5krJ43Ocrf2GOoCIxGuaPePy7P+hz6Sdjrr/74G6e0y9FnHop4hd/s0VryeUP/9E2V34W090s+E/+2xNkqWbYLVk85rP6QcLN9xPe/cGI3/67R6k1XVxfUJtz+MbvbDHolMwve/zCb7QQ0nbDt1Y8/vj/aXPt7YTN1Yzf/rtHbdAjbGDYaNlXhh8I/sP/4gi1posXSFxf8I3f2WT9esaw+4DyktaU3R7+8WMI36Nsd/GOH0H03VlZSg2HiMDHP3sK1e1bgcGixJ2fQwS+DZY8D6Ni9HhEsbVX9dhgGNKlSp0qDZY4Rpv1PZyWKo2H1tV5FuiySUiFFksc5Qyb3Dp0h1fCBMe4KFGyyDG00PTM9oEcpgdBo2fdYY7wcM2HK5x3zRZLwqPOHE2xwMj09+jl1JljURwDYECP0X06tJ42HinnpZVh4+0eo/WEoObiBnZmoXI15aTEjDYzOtdj1t6cEHcVwvER0qGYaL77f17B9QWDtT5FopCOh1FV3vpnIcONy4y3e7hBFVXmdG+kJINNbv+kT+fahCK1ss1uWJ22hxrirsdbv38Hv7pJWK+gVYpwPDAO2XhCHkM6TO+WkXZh9UdbbF8Z8OY/vkb3xmiaOTkcVK4YbsT8k//yu4dLZRsYrsfEvYOj22xccPsnbdpXBrz3B7dwPGlVqo0NiopUkU8KskmxT09ocHtC0s/pXBlaXR8BaMgmBZNOar+XtQn922PibrZnFpb2cy798R223u+RjQtGW+mBgeFwPebqn67TXx0z2koO5Es9SXSujZh0Mtbe6tjjO0Q5Mp8UfP/vvceb//TaofaRDQubDZser8oVo/WYb/zXP/hQiQDfq/HSma+z8e46ozU79U2HOa//w8u8/werDDcSW1rE+mr9yf/2FtIRlNndkz9cm/D+H9yi816GOzrG6bkLeK062igmaZv+8CadwVV2fyGVcIFm7QSN6jEurf4h5h5ndMfxCf0GZ4//Ko5jZ3lKZXxw4w8oysfktuy6Loy2WZc7H4z3cOOsvcneC+je6/V+eli7t717ZnvQNnfQXUvJEn1gsLMD6QhWP0jwQ8niMZ83/mTAmVcqLB7zMMB3v9EjrEikFFz8chUMnP9Mha1bOY15j7IwvP29EZNRnyzRD+TpBJHk9MsRCMHln064+lZMc9GWvY6dC7nxboKQguvvJPzsO0OOnAk4djbkwpdqHxrwJGPF1bdi3v/xmGPnQo6cDvFDh+/+yy5zSx6X35zw5p8OiUeKJNakE4VS8K//wTaOKwgiyYtfqLJwxKfWdEkmCunAW98bcePdmJVTAScvRIRVB9cXSCn46beGbKxmrJwM+NKvNWguupz/fJU8M/zwj/p01nMa8y6fe61BYz7mR3+0v5Fl9xephyOy/KYNYPOCstezrudTqMEQnaSU2x1MUVg1ZWOsQKG06SQhBEZpdJpaxeZ70GOLGnNE1DglLuCbiIQJioIqTRpinnDa7XQQrCKxh4NDQERIBaYu4gEVIqpoFAqFRj0CV+dgDOhQMTV8EXJcnCc0ERNGM67ODjG5LlrcMlf2dIBZ3aCEO1xlmRMscRxfhPTM1jRTpHHwqVK3goRsHej8XpAxMUO0UCywgi98tkyAQSOnM78Om3vWabOOh09IxHlepSM2ZkKPETXmxBJ15uizzdC0ifloLIkey0vrfs7jAFoFqFyS9GKM9vArEW5QQRUZ/VsCXeakY2v+JD0Hg0f7asloU5MnEse3DwytqmTDiI1OhhAVXD9EyBDhuOgimwY9FcbbMWxrpJNaUTBZgJAIA8JxUIULwoF7Xg6TTma7pB7lNBgoU8XqD7Yeaf1921OGuJtNW8rHD7VukVgX9cn2/gt4B/m4PPBzVWhGGzGjjQe/DO0+rA/UQZDCoVk7xSjeoFSHm5U8CNm4IBsXDO4cnqCpS8tp4vKj7dNopt1uH25SGvoZx+IKybpLMZKz/Xevj/Z7jBkONKQtEsXgTowzEiy2GlQrCyiVY5PJ4kArBdcJiIIWjdpxDkw3T7WUtFEE0sdzK3huhJRPnrOQx4o8VRTZR8dfSseKIj84SN+NYbckCCVaG7Zu5yyfDJCOj+MIXNe+3LU2szKWmMa7jidIxoqN1YxBu/xQnSrXFyyfCChSTWcj5+YHCY0tl4tfqrFyKuDGuwl5ouht5axeSshTzakLEXOLH/79JGNNe326XqY583I0KzO5niAeKdZvZgw70yyLsOUox7VBgtb2Gq/PObieLW2lsaZ9J+fWpZQs1rz05RquK9i6nZOlmq07Obcu2fu5zA1R1WHxmE88UmyuZqxdS6k1XV77rXmGvQ8nD5uixBR3n28qTQ/4vERPHj04z0gYmR6hqNCkRVMsUKFGSYFHgEYRM6LKfiFLZ/rqnmMBBwePgIgqAtt+3RDzOEaipgFGwpj4IZ/X90NBzog+nglYEsepigYe4SwwcXBnHmEO+7lUJQUds0kkagRUqDOHEGJWGrPGEiHG3A1e7oWiJCVhSBcXnxpNlFCzTJGipGP2BjwZ8ex8z7FAnXlCUUVREBDhE03X25gGnh8NyfypsbbCxjJ+pWE9ZxyPaO4I1fnj5MkIXWRkkx5FOkIVmQ1MhCSb9Aiby3iVJumojeMFVBdOUmkeIZ8ua3SJ0Zq4v27dgKUhrC9iamZqNKhw/Ap53EeXOZXWcbQqyCY9VJmjsmfb3fBnCa5b4aWzv8V71/85/dEtnvve1ecEQkiWF14l8OskWY/Lq3+EFI7tWJhmMe/Fgz5TOidO27x79fdp1c/Qapzh+PKXn8rYJ/2PXgqizDXlIZKNRaqn2VJjuS+FJTyHFclrf7nF4lGfJNa0ljxGvZL1G9PsXGFIJor+9uEe0q4rmF9xGXRKxgNrxTLsljiuoNqwL6neVsFk+lmWKKvGfAgy8qhfkMUaraG3VeKFchaYlYVhMlR3gx12uDeSX/+bSwhpeT2tBZf2ek6eFbYkvp6TTqzoYxorHAeiqqTRchm0C/Ip96i/VaBKQxBJ5hZdepvFlJcEg05JVHeIqs+H7IXB0GaNiRlyhos0xMLMPLTLFptmlYKMllja188UENESi1zkS/ds0woDNpifzTP6tNkyt4iZcK9kH/u2fDj02GZEn8ykLIgjNGgREKHR02b6lL5pk5Pu48ooSrpsUpqCJgssiqMschSJYwV7yRkzQFGiUFNC9854LTSajJgb5n2OiFM0aHGMc+zWyLmXCG4wdNhkZPqc4gJzYokmC7i4pCSM6NE329zh2n3Pidn1z9PCUwt4inSIdD1qCyfxogaOH6LKgjwZorKYMpvYslORY1RJmcWU6RjHDynSCUlvjfkzX8ANqyiVkycDyiyxJpl+iF9p2LSm1oT1BUbbN5GOSzR3hCIe4vghjhcAGscLkI6L0c9j6+InA470Cfw6lbCFlE/e1fqTDIEg8ptk+ZBRvEFR7s6O7b/5R/EGk6TN7c0f7StnfYqHgx9KPvtanSIzvPPDMa9/c8BrX29Ra919NJqHVB8vcsP6jYzj50PGvRIhYH7Fo8j1jN+iH1HRvLXsU6mnOA4sn/DJYs1kpGbjvPdyacy7XPxylX674P0fT1i7lvILv9GiUr+bHTiASkWWavrbBS98rkJYlbieZOGYj+MJJkPF1q2ckxdCbl2ymaLmosu4XzIZPj/XY0nBmD7v8TrCyFm/iplqvRgM3zd/MC1L3c1QJoxIzYRt1j50Hzu6MfeewSu8hTQ2+HuUF3hJwRrX2TA3EbtyMTvBid51DAdhRI8xA9bNzZ1c8Wx9g6ZaWeZE8xeohC2yYswkabPeeXO2vsHQY5uh6U7XF3vWP2i/Bk1GwjXemR67uOecH7weWN2g96ff05MqDx6EpxbwlOkEozVaFRhVIqSDkJIiizFlgVYFurSdRlqXUCQkwy2kdNEqx2hF3F0jG3UQQlLkdj0ESOlijEGVVhl1uHGFbNJHSIcyS1BFMm3tw64vHco8wahPA56HQb1ylCicpxIu4EgXYzRK5YyTTYaTdYoyRgjJ0cUvEPoNwqCJEA7Hl77EfOMsO7dnu3+V/ugGAL5bJQya1CpHCPz6bKZQFBN6o5tk+YhS2fSt6wQEfoPFuQts997HdQIateMEnuW4ZPmIzc7bKF0gpYvvVVhuvUJ/tEqpUuYb5/A9W6cvVcra9hsoddesr1E9TiWcJwzmkNKlKBOyfMhgdIusGM/GLxA06yepBPOEYQtjFEnaQxuFMfe/iT8MKwufIfSbeG5ENVrE9+sEfoPArwMwijfpDK5SlglSeoR+g+X5V3GkCxiULri59r2HJiXuIArmCbwanlfFcyPcKd9nu/fe7DxL6bHZfYc8H6P0J68LTSubuXjhc1Vqcy5FqmksuDiu4EGCwkLY7qbzn61w8Us1Fo/5fPaX6qzdyBhsF9y6lHLihZCTFyKiukOt6TLqlXQ2Hi8j5riCUxcjvECwdCKgs5GzefP+Jfki1wzaJZ//ms/5zxmaiy7VprPfpPaeSzidaDZvZUhHcOGLVZZP+PiBxPMl437GnaspL3y+wktfqXHqYkSl4XDt7ZjVDx6/lP0kYTD7bA5241638Z11DIr8MawN1B4ltEeDnvKDHgU7x3C/9R03oBotsd1/n6JMKcr9lQ+DpnyEZ8ujHvuDvqcnhacW8KgiRRUp+eRg24k9MBpd5uTjva2FSf9BdmF3+8uL9C5fIhu1d30OB82QHwbCd3EqAcJ39/Ap1CBGFyWoB1wQQuA0IqTvsvsJo5McnWSYZ2xUuntcwpW4rRo6zdFZicn2Xmyu9GnWT9CsnaQWLeM4HkZrCpUiBx5x2psFPHONM9TCRQLf1sPnGqdpqGOzbSXZgP7oJmBw3YhqtMRS6wJR0LJy+xiKIkaZguF4nTKZ1qudgEo4z8mVnyfNB7hOyOLcBSrhPForJkmbdu8DtC5wpEvgNzi58hWEECRZn8XWi0RBCxDkxYTNzjsoXYARONKj1ThNvXqUarSII33yYsIk7aB1QTG8gdYFIPDciFbjLM3aCarhIkrnjOJNsnz4WFdXs3aCevUovlfF9+q4XmWaJVsAQErPBm8kSOHg+zWWWhfxvSqO9NCmZHX9+4/sfRYFc9SrR6hXjiKn58+RDlk+ZKH5ApVwgcCrMUnbjLRCfUzb7rU2jHoleW61bwfdElVCMtH0t0u27uRobZhb8jj/2QpFYchTTTK29+eoV9ry0y4ICX4gOfFCxMIxD2Pg6NkQrS2P68Z7IyZDxdIxn/kjVVxP8uNv9tm4kTG/4hMPFVlqnx1awWRYPtCKRytDlmhGvYJq05kFaN/+511WLyUYoN8uSO/RIMoSzfYdG7gsnwio1h3K0pBMNPFIkSWayVBR5DZs1wpGfUVvq6CzUTDul8yv+LSWPQbbNoPTWS/YXM1Ixpql4z7+OdvS/8e/12bz1kfTbvxJhJQejrj73hFIlC7QekpAlj5SOJYSAtPPSgwaz4mmXY16amli6R7aKJuN9xpE/hz90S2UztDTTLHrhAhxNztT6twmJDB4bgVg1zatYKScWqbsrGfdvwXGKEqV4TrB9DM526bRVibAbtPM1jdGobVCm6eTnBAPMpMUQvyZJ2HUf+4F5v/Sl2j83At3XYyN4fb/8a+YvHeL/M79FUedWsjxv/ObVF89hdu62xHQ++bP6P2bN5m8vV9N+VnAaUSEJxc5+z/8Tfr/9i2GP7rM8AeXZp9L4bA8/wpHFj+PMYrLq39IUSa4bojv1nAcj9E0w7ODwG/SqB7j8xf+I376/v9Nd3DtwMxD4DcI/SaVcIH+aBWtCzw34tTR16iELXrDG1y9/c3Zss3aCT5z/j9gML5Nf7TKdu8Dkqw7yyYNx7cBplmSJb548T9mknYYjtdYb79BkvXsuL0a/aENunyvxlLrAseWvkRvdJON9lv2JT/3Aq36aZbnX+bH7/5fJFkPgeDUka+yOHeRvBhzfe1PyIuYxbkXWZx/iYXmC1y//S06g6sMxo/mIi2lxxcu/A2SrMdwssadrdcfuHyrcZbl+ZdZal3ke2/877OH1YHL7uLw/PCdv0eW33Vsnm+cY2HuBY4vf5k3L/0e1WiJ+eZZ5hvnuHLr35CXMfP10zhOwGbnbdr9R2SCf8yxo0Vi2FszEsi71/jHwG7hU3x8IKXHQuMc843zeG6E1orQb7DVf5/O4DJJ1uP40s9RjZbw3SqO9Nnuv09vdIMk63Lh5F/Cc0KSvE8UzBGnXeKsxyhe5+j8Z2nWTlCrrNAZXGOSbDJOtmgPrnDu6K9MM94ejnRZ7/yMweQ2WT7k5dN/GRBkxYgomGOSbKONohLMU+oUz4nw3ApZMcJ1AsbJNjc3vsuZo79M6M/ZxgnhsdZ+nVG8QZYPefXsX6VUGUWZEAZNRvEmo3id3uj6I587Y8x9yXBPV2ryE4Dkyjrb//i7DL7zLuGpJcLTS9S/8uKh1tVpzvY/+z79b7+Dt9QgOrPM3K985imP+CEwUy299/oQSOnZrg5jSyelyiyXqpggEAeUN+6+DB5EPMuLCaXKSLIuZZnZxKsuaPc/4OzxX8H39ndNSOnQHVyjP7rJJNlCm5I4OXgGYIOgO7T7lxnHW2hjx57nd0tUvldlZeGzdAZX6Y9WSdIO2ii6/asYrVief4VG9SjGKIoyZaF1gUmyzWB8h0nSwRhFd3iNUmcsNg93LTwQuycdh3ppPrk3q9YlaT4iyXpgNL4bUa8cJU46ZMUI362w2HwRKZ83kbanD4nEFxEahSt8QlllUG4hhMQRPjXZYqJ7lDpHGYUrPJQpZhwEgUQiEcgDhdjsXaJxhIemxMGdbrNPYfLHdouWwiHyWizUzgCGSdYlKWzL+JHGy0jhMMnajPOO5Y0JWKyew5E+WTFikK5TqIR6uEzkt3CES5z3SIoBcb5/ojdfOW2ztSohdOsoU1DqnKwYslA9h+eEgKA9vkqhU1wZ0KqctOVklZAWIwbJncc65k8ajNFMkm02e+9Si5YI/SYr86/S7n+A54YkaYc7wx8T+A1q0TIrrVe4sfEdfK9KqTJbElcJShcoXaJ0zp32T8nLCa4TcXPju9OOWsFc7aTNFI5uMojXCLwa9cpRAq/Ora0f4Hs14rRDZ3CZUqUoXRD4jenk9Ta1aAnH8dnqvcvy3Mv4boVatIzrBPTHq4zjTQK/TqN6HN+tstZ+A9+vE486dAZXKVSKUvlTLZ1/GvB8CMpRgk5z8q0BOisQgUv9kOuaUpOubpNvDfAXG5av8kvPv/2AwZDlQ5vVcQIWmueJ0w5ZMaYoJjOOzaNuXQoH36tTi1YQQiKlSxg08ZwQR3rsmS4L+58k65Fk/dnNoMx9bgohyPIRcdpBaZteN8ZKae3AkT7VaIkk61GvrMy4KwDVaGlW3nGdEKVyoqBFf3STJOvN9p/mI5yk/cj8mecF2uhZMFtMZ1pK5ZQqoyxTyjJFShcpno/um2cFTwR4IiQQEYkeIXHwRUjk1LF6LB6RrKFNgXKsjYEvQyZqQGZiDAZXeLNt7JgqGjSlKQllFYOemlsaIMAVHqGskpiRzSo9ZlwrhYvvRFT9BSZ5Z1rWsNdr4NqnmDJqWprQiCnJ1pU+xq1R8VqMdEHoNah4c8T5Xd7aQQi9BkJIchUQujW0UeRlTJoPsfe9iyN9Kn6LOO/hOj61YJFJ3kXr8oGZyg89Vs9BOFNirTHozG5LuJLKSp2sF6MyhVHatuqHHtKVqLxE5wohBdJ1UFkJUtwVuiw/2vtbm4KijInTNsZoQn+OSrhI4K0TeHUKkeA4AVK6uNP/g+381Logybrk9/Bz0rxPlo9QuiBOO2hT4rkRUTCH0jlpPmSSbJEXE1r1M7hew25TOiidkWS92TZ39pcXI0q/gTaaOOujTYkjAwKvge/W8JwxjuMjhYvnhCjH3g9SSIoyJc66j68Rdgh8GvB8GJRGK41OC8ruGDV8OFKeyUpUVlJIQdEd3/dh8TzBGEVveJNG9RjzzfNcPP2bdIfX6I1u0hveYDxZf2SiruuE1CorLM+/QrN2YnoTWMuGwKsfqDsDkOWjQ98QeTkhKw4WthJIy/fxahxb+uL0pXKPKB7guSGO4yGExHcrlGW2Z/9aFxRFgtHWOuHjC43WtnkAY9BaTYnd02zdTm3+aZlEPaeIZJ2aM4+LR6ytuJsQgpZzdBrkGjzhU3fmrUid9AhElQ2uU5QpCoUvIurOAnPOMqkZ2b4aU5LoMQvucTSKVI8Zqx6hqOLJwN4L6smcaylcpPSQQtIeXyMr7xLP02JAoVO2RpeYkfOFQ3tynXqwTODWaEQrTPI2ngwRCNYGD3aR95wIV/p4TojEQUgHkJQ6oxvfIvSaVPwWjfAIhU4RCAK3xvXOv6N4DN0uIQV+M8QJXBCgS0WyNQFtcCOPlV86zfaPbpNujykmOdKVRIsVvHpA1ktItic4noPfDIk3R1bw1bPPpGKS2za2jwhlmVEqGxzkxRghBL5bJfAaVIJ5ZOQRhfPTZRPi1GbetFGUKt8X7NwPYjoJzcsJhbYT2qKcIIWL61jFeaMVhcoO3KY2hf2z8/wwxk5kpxmgwG9MdcOgKMbkxWTXONNnEuzApwHPp7gPtCm4ufE91rZ/SjVaYnn+ZVbmX+bUka+yuvEDtrrvkmYPLw9+5tgvU42WMabkvev/krwYgzHUKitcPPMb9x8P6tAzwB2C3oHYKeMBl27+f4wmayQHHEepcoxRtstr52W/77lnpmP6GAc8B6ki38NV+bMJMwtsAhnhiYBINClMYk0QpxkvKTw0ikRPGJsBiR6hplm/mtMilBU0CgcPKDA4VGUDe+2UlqivOjiui0eIi89hvIsOg1xN6Ce3ScsBR5uvkBQDJlmHQbLGvd9vPVihGizgOxGZGqMpESICoNQZQn14hk/rAiOcuy3MRuM5ASv1C3huBaVzuy3RtDlco8jK8WNPGNyKx+mvv8T49gAncKmdnuPK771JMTqYQO01QpzQI1iocurrL3Pp77+OW/FY+MIxNr5zneaFJbyaz/aPbyMeP9H2WPD92ozAGwVzGGNI8z5x1mGcbJPmA9baP50ube6eywMmcg+C1iVx2qEaLVEUE0YIQr+BNsWuAOfhtBSsHliHSbJFd3id9mCHA2jQOxP/Z3xyP1YBjztfw19qEp5ZxluoW8ddabMoxWBCvt4jubSGmuwquQhBeGoRb7GBvzKH26wgPMt811lBvtYlu9MhvbmNKZ8fDQkAhMBtVYnOrhCeWUZGAcKR6Kwgu9MlX++SXt+0adp7V3UlMgqof/k83mIDpxJgjCHf6FMOJofqENO6JDcTTKwxaOK0S6N6jKW5C/RHq3sDHsPsZhNMpd/vuTmEkFTCeRzHo927Rpr1KVU6nYm6PPhB/2TuDGOsDEJRxkjhorUmyw9SSb2b4SjKFEf6uO5dby0hnFkq+c9a9uOwEK5LdPYFgiPHkJWI9OZ18u1tis72Rz20Q8EWVs00oBlTmgJtypkC9k7vjEbbUg96Oju2185IdUn0CGfK19mtm+KonnW3NlYmIVZDcp3hCtcaSN6TCfYWFml+5RdBCNRkTNHrMn7rjQeO35UhgVejFiziyhBHxNNuGchVjCMDlmsvMMrbNisj3GmWda8G70H+ZQeh0ClCOEjhUOgYzwmn50riu1W0CXGm5fCdkPqJZEeFwAld0vYE4UiCuQiv6tvy1L5lIVquEba+PeoVAAAgAElEQVQiwoUKfjNEOIJ8mDG+0aP5wiKVI3WMNpRxYf3ePkI40qcSLnJk4bNEfotSZXSH16elqjGeG7LcegWwGaC0sOWoh4XSBeNkk1plmVrlyFSdvUKWj0iL4Ydv4ABoVRBPS2phMDcbZ5r3SfPh9P3xbM/vxyPgESBDn/DMCpXzR6h97gzeUgPhOrbOmhXk3TGTt1fJbm6j4l0y8wLCsytE547YQGm+hgg8hJSYvCRd3WLy1irlMKHYfoAHzEcAd65KeHqZ+s+9QOXicZxqiHAddJqT3twm/uAOapKSb/T3Rd5OLSI4vsDcL7+CvzKHrASYUpGubpOutsk3eogHvKgd6U0nCZq8jMmHNylL+zA/1/q1PbwXmIpRTbMdjuMjpYdS2d5OFgSuGyEQTJK2TdUacFyfKJx/KtYH+2FQKidOe4R+k8CvIRMXrW3JQiCR0qFUUzd6o8jyIZ4bEfoNq4BszJ6fxROakX/SIIOQ6NwL1F79HF6jwSAIMfCMAx4xjVzMNDDdxQ/boYlN23anP1iZDAwKBdJaUeQmJVaDqV2N3ZZwPIwqp+uau6q1wtqwx3rnRTHd9+yZZD8XUtqyj5GkZgLm/uUHtzlH67VfBSHI29ukq9cZv/3mA2fcjvTwZEjg1ih1SqmzaYsxJMWQyGtSDRZJ1QSDsS3IRiMQaKNQKrGcI53PAqUHIZtmAhzhkanJrH25VNO256ldfa7iWft0oWJ4Ejw4ISgTy9MrY1u2cjwH4UicwMWteDiBS5mWhItVgrkQvxEiPVuuLUYpo5s9TvzFC7ihR9aPUenzodvmOiH1yAYh3eE1BuPbZMWYJOtTCRdoVI8CEKddDHrKv3kwBaDUOVk+2FW6ViRZz3ZLeQ2CylGEdNnuv88otqbIuzXSdmCMIi/GaG1b0PPC0jbyMsYYPRtn6Ddn47QWJyVp1iPNh4/JCX04fCwCHhl4NL56geW//hreYoOyN6b/7Xcpe2N0XuIvNgjPHwGjKQaTvUGjNtS+eA5/sUHZnzD66VV0nCMch8qLR6l/9QLB8QW85QYbf//fHpgt+aiw+O//PLXPniY4uUjvj96YEaeDo/M0f/llKi8dp/bFs9z8n/8pJt1F4nUdap8/y8rf+BW8hTqjn14lubpB2R1TeeUktVdP4v/5z4Jz8ENMCIdW46zNhKiUJLXBURi0qERLpNlw9uDcQalSsmKIMYpG9RilyhiMb+HKwJJitS0RlWVK4NVo1k7QH93EdSJqlRVOLP/cTCTwaSPLR9ze/CFnj/85XDdEa8VwcgcpfQLfdiZsdd+znQiqZKvzLotzLxJ4VcbxJnkZs9A8z9LchY8FJ+sjgRD4i8sEyyt481ZXKDpzjnIwIH7/3WcyBOn4SMdFSIkqMqTjz3SfAHYcdF2/SplPLO/AcVFFSiITMlniehFOpY5X+OhJF8evoFWBkA6NpfNM+rcp0wmqTEE6OK7lfalyd5BgkI6PUQUGcLwQVSQElRZhfZlx54YVRn3CSvBZOSIrxwxTy7kzu6SYu5MbgEAKuadU3B5f3lc63h4fTo6gF99fZqMzuc5O/+ZujLMP96w7DIw2LP/8SZzAJWhVuP3Hl3ECl+qJOZoXFpGeZHCpTfuna0RLVUtylncnKrrQFOMMvxmSbI0Z33o+Jr9x2ibNh2x239732Vbv/vfR5dt/9MDtdodX6Q6v7vv97a0f3Xed91f/1b7fJVmfa2vfsmPNumz13gNgdesHs2ng3ZLbfrx7458/cJxPGs9/wONI3LkaS3/lFxCey+gnV2n//g8oOiNMqexDynWQ3/ZQaXGgEODW730b4TpTd93cLiMEo59cRVYD/JU5Ki+ftDo7z0FVS/gulYvHqb56CqM1m7/3bYb/7gN0WoDWCN+lHE6ovnKS6OIJqq+cIL2+SdmzM6zqyyeoXDyOO1el/Y0fMX7jGtntDqZUjN+6SfUzp5j72ivUWvtbwMGWpDw3pNU4SxjM4QjXJuR1SVmmXLv9TeJ074NKa6t8fGP9u9QrR5lrnMEYTVFMWG+/wXbvAwDW2m8wVzvBfPM8reZZyjIjLyZcv/OnnD3+tWcSQBQqod2/jO/XqYaLnD722lTIT01b78fT8RqUztno/AzH8ahXjvDyub9MqXLitMs4aROFW1Ozz6eLheYLVgiyegLfr+NP1ZG/+NLfnnZj9Flvv8losk69eoSFuRdpVI8ReHU8L8L3qnz2hb+O0jl5GbO6/v09JbonDmPIO23y9jb+Sg+nWiO5dpV848Pl+h8GwdHjYAwqTSn7e1ulW8demYmZ2ZLrtF1cOnhhnXTcJo97BNV5HO8UINAqJxluEjVW8KMmk/4dhBB4YZ364hn8qEmeDFBFihtUdwU1gqAyR3X+JH7YIE9HYDSqSCmLhMbiOYpsTJGNyZMBtbljOF6EdD6slPu4MA/gvu3/7HE6pT5kFE9sWzKM8JeWUXGMmowB23m1/fodsm6McCRlXFAmJWW8zXt/7weotESlJWVasPatqza75gjufPMK6fY0syYlRmnGq30Gl59MIPZAOA7hsRPoNEWlCWr0aKWj5wVCOAjHsdZCQuC6AWF1gVH3pk2wSonR5cwpQatiavvkIaWDdHzSSQetbKbOujO49h7WytpDGY0uCxwvtMsZjeNF0wnLh19jz33A41QCvMU6/vF5kisbJNfsn90clA+7RfP1+6k9T8g3B7jzdbz52l1hwY8YwnOIzq7gNivk6z0mb98k3+zv6RZIrm7gLTWpfSkgOL5AsTWYBTzBsXlb8nMEk3dWSVfblL0pT2UQ485Vye50qH3+7IH7N2jSbMAk6aB0iev4NngpE7J8xHCyRlneSwi0paLu4BpaawK/bvU1ismeZceTDTAaIRw8N6JUGWk+ZDC+zVb3PdLs7sxK64I0G7DReYuiiHlQvVdrNVVTfpsk6z6QG2CPJaY/LdOVKsN3I6tToTLSfGBT7tMZcZoPZnYVlWgRoxWTeIskH2CMYpxsT8f3aDBousPr5MXE6uEcAKVz8iImzQfk5WSmamqm7bx5YdtMra5RSVFMSLOBVcNObNreHpOiUClal1YpemqNYVPStj20O7hq29N1QZr36Q6vPxJBXacJ6a2bYAyyUiG5foW8+4ReJEIgPJ/Kiy9hyoKi094X8DhuYI9b5TiudYjGGIzROG6A4/o4boAfNhBT6xSMwo+a+FETL6hilMIJIhw3xPFCyxdxrXKs4/p3Ax4BfqWJHzWm25NoVdiHuevjRXUMVlFeFRlOI8D1Q+DPVrv/40K4Ll5rnurLnyHfWCNdu41JBqTtyezP3ceEoSw1Zbw3W5N1k5ncxU5J0Ald/LmIeH1E2p5QTp7yJMZxcCpVqi9/hqLTJm9v7Ql4jNHT+9M8sw6mx4UbVOy1LyRFNkJIB9ev4HgRrhfi+lVUmd31QDAaL6hZmylt7xUROzCVEHHcED9q4noRqkynvFBgul6RTVBlhuP6qCL9ZAQ8bqtGcGIRGXjEl+4QX1p/eEuGaWeOEGJXUGNr+2oYY7LS8mOEeMAr9dlBejbDIzyXcpSQ3WpbvtIu5J0RZX9iS00nFkku3505+0fn8RbqGKWJP7iNmuwNTsr+mPjyOgv3CQqM0bYFfXTzocatjaI3vEFveOO+y1g9nd4s47MbV279mz0/F2VCMb7D21f+8d4FBcip5oYu7THsdAS8c/WfHnq8g/GtQ6kjG6Np9y/R7l/a91m79/6h9/eg7d9Y+/YDl+mPVumPDqfMPY43GE/r7ofB9vQYVJ6T5gN6w7sqp4PxbQZTNeuHhSkLxm+/abkmTxjCcXEbDZq/8Bo6nhBfvczk0nt7OC1lNrbBnCqQFRchPYwpKfPJtH05R0gXN6xTJAPKPKYsUsLaItL1UGVKng7xogZISZGOSMdtpOPhuD5Ie0+aKX8nqC5MvQBtyYsyRzou0vXQZY4uc8oiIZt0qDSP4PhWVPNTHB6yUiE4cYrW136N8c/eQCUJ6aDL4P1tG6Qc9gF+TxeTVwuonWjS/dk6aTd+6mRl6Qd48wu0vvZrxFcuEV96j/Tm3fvOGMUwXgceZK/0fCGszFNfOIMuMwbbV2YlWj+sU2kepdo4Sp6NUIWVJHA8G9DEww2S0fZdit0UftigMX+asL5EkQ5tsCQcXC/ECxsk423yZABGk8Zd0B/uxfXcBzxOLcRbtCJZZWd0N1PxEPCPzROdXaH6mdNEZ1aQtQAZeEjfw4kChGc7n6zqMB95R65wJN5yE6ca0HztZWtrce+ghJiRtmU1sB1rU7jNCtJzUaMEnat9OhI6LSh7j98O+lFASFi80OLcr53Eq3h85399sA3Dp/hkQoYh0ZlzSNe9b7Gkt/7e3R92FMWnM8Qd4rABRh1rOWL/NYzaN2arGa0o0iEHPhiETdHvBFntmz+Z/n7/ojPi9HTZ/vp79mIWAqOevmniJwXBkWMEK0f2/E4Xit57W48VpKSdCVkvtrSuZ/Bc9ObmCE+efrrVzGeMHRFZ4Qm8wGqqRbUlXL+C61kR13w6sRBC4KrKLGhRZUatdRI5WJ9WbAS11nHcoILRpe2KdUO0LsiSPvFwi2LaXdtYPMuwc+NQbJRDBTzS8ak0LSnYaG3raa5PkY0pixRVJNTmT1HmMdmkR2PRlkrKPGYyWKM+fxrpeDZqbV+n2jwGQjDpf7iMuE3OTHUdlAZ9+Fqw8Bz8Iy3mf/2L+MtNhOeQXFlHZTmmsKqbtVdP4h9bwG1Eh97uU4cQCMd2kRXtIZP3HjDDNobkyvqeQHAmG6Pvo5tg7vP7jwUEQkrcwMELP50d/1mF9H38laPg3P8a2JPivvdyN/dZ7qCf9f0epeqe5Q5POrbls08J7w8LrzWPO9fa9/vHzsgYMOrZPROdWh1/aZlPUsSjVE6RDhkP1sjTkbURVQValZaPIyRlEWNUaSfq0kUbhS5zjFaMjEHN6A+GUfcWjrOB4wVEtSXi0SZlkWKULeNbro9Aq2LXeg/GIQMel6i2aD2UyhxVpriBJbxqrVBFQlRbIk+HqCKl0rAReJ4OSeMelcZRXC+04kaDDaLaIsaYQwU8Rpu7nVOO3OM6/mEQvkt0/giNr7wIAuJLa4zfuWkVkycpKilwIg9ZCZ6vgMcYTKnQpSLf7DP47nsPXLbsTSgHd1tajTJTpcuDfLKw0ulyx9n2cJCuxK+6eBUPXWqkJ+0kWRkmnRRd6tlLJGj4SFegC4NXdW35yUAel+TjfFaGciMXv+LihlOF1EJTpiXZKJ+9C4Swy4XNYCYdH9Q92046G5vAq3oENZ/h2tiOQ4AbOIQNnyJVZENbkxdS4PiSsBkgXTmb9OejgnxS2OMAHF/iVTy8ijvTFFKZIh1ks/ELKYhaAarUSClwQxfhCFSmKLO7+/wUTx7C9/EXlxEPCHg+afi4TlGeJNxmC7c591EP47HhVKp484sf9TCeKFSRksY9JoO12YQ6Tw9PxC6LvWrbyWgT2Om29JgMNlDFfkXu4iH2caiAxzKj/3/23jS40uy87/ud8+53v7jYgUav092zz5BDDkWRoijJTHmNHDu2U46dpJTP+ZgPqcrXVMqJK+WqlMqJLCllSanIpc2KFYtaKFGkxG32vXc0gMYO3P2++zn5cC6AxjSARi/DBkf8V/X0NHDvu7/nPOd5/s//n+AVRowYXdzF9krG4n24qsmSAa5Xwh2/SJYMhgfq0Jh+FscrIqWFpV1Gpp8zRKxjHmQ+iHezF069hF0tcLxYDqyiT/1nnscq+nS+f5U7/+fX0dHH0sdS3sOPedLQuSLZ7OBO1snDhN678/AAni5Zd4BKc5yxAOFY6CTbl9GRnoNdLz6QXl5posC5n57hzJdn6K0NqEyXsFxJ1En4i//ldXprxqsG4NLfPENh1Kez3OfcT5/Cr7qgNPPfXuajP7hFb20AAiafH+XsV2aYenEMIaG7MmD5jXU++Pc3SHrmPjkFm5lXJnjxn1zCr3kk/YzNq9sUxwpELfMkFBoB537mFJf+1hl+5xf+mDxROIHN6MU6L/zji9x5fZ33fsu01rolh8aFGi/908uUJgtYrkSliqt/eJsb31iks2SetZGzVU5/aZq5n5jC9i3SMGfzo23e/n+u0F0xNX6v7PDKLzxLe7GHV3GZfnkcv+Kyea3J6jubvPfb149/gX+M40NKrKCAf2oOYZ34qvyP8bhgWbjjEziNsSd9JI8Gy8Ku1U2H4acIUX+LqL/12Ler8oTm6qNzJeG4GR5p4xUbDDqrSMumXD/F1vIHqDxBDdO9na1bu50ju5PrLllYUqxM4pfG2F79cFgaOx7xON3sEN5aR8cppRdOo5PMlHiOo4osBFgWKklRUYq+W3lTCGRgxAzd8ZO1YlBJRu+teQrnp3DHq9R+8mnaf/nRsZWg44UN3Ik6wdwY5RfPMrhyh3RzL8B0GhWKz849sEJwMBJQmSnx2r95n6iTEIx4zP3EFBd+bo7l19dZeXtPUC6o+1RPlXntl98j6afoXJOGGYPtCGlLvIrLS//0EstvrPMX/+IHxN2Umc9O4Nc8XvhHF3nz1z9EK01posgrv/Ac7/67q7QWu6T9jEt/5+DusuPgwt+YY+RclbX3N3ntl1fJohwNZMNjQ4DlSJ7/R0+xfbPDt/7XN8jijNGn6pSnirz8z57mO//727sBGcDEcw3aSz2+9S9fR+eaLM7JjhAts4olaj/5U/izp5G+j05Tlv/tL6Hi44Xyk//Ff4VTH0HnOeH8TbpvvU6ytp/cKFyX+k/9LP7MrBHBu3mdzg++g1ZqtzvEm5zCKpWRQcFksZKYPBwQr64wuH6VtLmNGhzPi8cZaTD5j//5kUnDwY1rhLduMLh6RMbyABQvP4NTG8GqVHDqI1ilijluz3RhOaNjlEsVgrPnj9zOxn/4XaKlhWOVxYXtYBWLFC89gzs5hb1znWwbnaaoOCLZ2jTq0ZvrpBsPrm5rV2t4k1MUnrqMXa0hfR9hOeRhn6zVJNlcp/feO+SDwTDrmyEc54H380lDOA7u+ATB2Qs4tTpWpYpVKCEso9issgw16JMPemTdLvHyEvHqCnmvi84Ofk+scoXCuQs4jVGsUhm7WMKq1XEbo0jHtD0XLl7Cmz2Fig8XrstaTVZ+89cgP3rsFJ6HXa4QnD6LMzKKXalgV6oIxzXPtFKoKCbrtsnaLaLFBcKFW+jk6CyusG1KzzyPXathFcvYlSp2zWSppGfEW/2507jjE5RffuXQ7eg8Z+XXf4V80D8WHUF4Hu7IKP7cGfzZOaxiEel6Zj6MY/J+j6zdYnDjKsnaKnnvYO/BTxuOFfAolRH1NkmT/m4Xgsni7F34PD1aLTGyPECYDokH4I+o2BBs+1fu4I5WCJ6apvLqRZKVbXScoYe6NHalQD6ISZa3UeHwIcxzsu0e3mTdWDRcmCJrdhG2hSz4+KfHsMvB4e3oAoRlIWzLWDUUPaTvAmAVXOyiT1b0jcFolkH+MW6MFEjXBktiFX2sgocQ5mdW0ccq+ehcmT/ZHrlYpxnRzVWSlSZWtUD5lafIWgOyzsCQqwHpO1gFD6vo03v3tvn5sPQXL23hnWqi0pTSZ86DFMSLm6g4xa4WCC5M4U7WH6p8rHJN83aHqBUTtjzKU0WmXxqnNb8/Y6dzTdrPaC10iTsJWmmEFGilcYoOhdEAp2ATtmK2rrVJwwy/6jHxXIPpl8eHpScLv+oS1Dzaiz22rrXIE0VvrU+h8RAaMgJqc2VKEwXm/2KJrRvt3ayUtCUqVyYYq3rUz1bRGvobA7I4pzRRoDQeUJ4o7hMsA8jinLAZ07zVMSUxscc7O/AwLAu73sCbmsEqFFBJbAisx4Q3Pok7PoHOMrJOG+m69+5DSJyREdzJaYQQ5IOBmTjKFbzpWQoXLuGMNJCFAtLzDac2S1FxjFWqID2feGWZcP6GGQyPemeFQFgWTqOBGAr9IeVeZ+QQaXP7nsDsOAjOXMAdG8MqlrArVRN4OM7utoXjIh0Xu1w+cjvSP17Z2q7WcMcncScmCc6cx200kEHBBCTSMhyCJDHllUKRZGONqFIluj1v+AnHGN/82TncyWn82Tn8udNYxRLS9RCWhYojsnoDpz4CQhLN30T6ASpNsOyTldFyGmM4o2MEZ87hTc9il8vmXDx/WDY3k7WKQ1QYkQ/62NU60i+QrK0QrxxMa7AKRYoXn8au1bEKBWRQwCqW9j1TVqGIVTharDRxvft237qT0ziNUbyJSbzJaexKFatYxCqWEPYwwNQKlSTkgz55r4ddriI9j3h9lXRz49B7Lix7GNBWsYbnYJXKprKwcx5+gOUHOAdwk3ags+xIvtoupMSuVPHnzuKNT+BNzeCOT5hn13HN85UkqCgk73XNe16rk6yvES0v3Tcw/FHHsd6ePI1orn2EF1RJ8pQ06vGgFeUkapOlgwcny+aKrD2g9Y13Gf1PX6VwcRpnrELne1fI2gN0mmNVAoJzE8RL22z/8VtGoE9rVGICh+LTs/inxqh/9Xn6799GBh7OmMmc6DRDhQlW0btn18KxTVBRLiADx3hx1cwL5jQquFMDtB52PXX6qDAx5SMASyJdG6dRMSWkkRLOWAWkwCr5uBM10lYPHWXkYULW6e+W23SaE15fof/RIqUXzlL9wiWEFMR3tslaPbOqHSnjTtZwpxtEi79jhBiHAU94cw13skbW7FH70tPY5YBwfo1su0dwftJ4a1WLPGhLWhZlhM2YwVaEViag2bza5Kmvncar7J90k15Kd3VAFma7hMKdv53ApjpdpL8eEm5FpKG5Zts3WlRnijTOVxBS4JUcvIpL2I4JWxHpIENIwda1FtWZMrb3YKVIIQSl8QC36LD0g7V9nNG7uTul8YDCiE9hxKdxvrr7mTxRRO17W19bt7u0F7u727jbV+wkQNg2dqWKOzZB4cJFyi991qz29FB9V2u0MIGD5XoElSrB3GmipQVUEhNev2om8iOg8px0cwPpB0jHRXiuKTdZ1iPzbIKz53BGx3cnUACdZ7uTkVZDfZ37DdbHIQlLC392jvJLr1C4cNFkVHa2r7XZl5RmkioU8aZnyLsd4uUl1rc2yXs9dHZE15UQCNum/PIrBGfOGeI1gFK72xeOi9MYxW2MEpw5R/Nbf46KY5NNCE4Q19CyKJx/iuLTz1K8+PTefdh5ptQeEc8KiliFEs7oGP7ps9ilMn3fPzzgKZYoXHx6SDcYNq1kmelG3fHpGzbRHOVmfuS9GKJ46RkKFy7uZQiVsQrRd58DppVc+gFOw5yDOzVN7503abdbh2Z6hG1TuHh5mJXaCwDF8PrB8PlVat++7jmP4wTSQhg7l9NnqX/l53BGGuZ65UMPNz3ct21hl00Gy5s5RbK+SnT7Fmlz22SQHqAx6PFD7C3EP4Ex9NjLBa1yon6Th6XOGbb2w7Vfqiih+c33SFabFC5OU37lKUa+9jLCsRBANohJlrZQYYwaxLsXKu+GbP3H10z68/IMlVcvUv+Z58m7EclGm+0/fJ14tUX55bPUf/bFe/ZbfHaO6hcvU/vp58w9kHJ3dd/4u58zL9qwtXXrD16n++YN+m/PA0b8r/j0KaZ+4efMd4YaQMK2KL1wluKzp81DDkQ319j8g9fofOejfRpDG7/9HTrfvUr5lfNUv3CJ4uVZZMFF5Yq8PSDd7NB7+xZ5J9xX7tJZTue1G0S3Nxn/J1/CPz1O8blT6FwzuHKH7us36L93m3P/0z97oM4EIcT+7MaO8/gBm1BKkcXZIU/LcDC5dwf7y2wf//fOtjP9sYDi3q4zIQV2YO3/utj9zxEw++xvhtz4xiLv/85+Ho7W7GaFdpAnOfmDakP9ECFdD29ymrG//feRnoeQFt333yFeWiDrtFFxZAa/6Vn86Vm82TmwbLyZU4z/nZ9n8Zd+kbzXOXwg1Jpse5ulX/5FdgYsIQR2pcbIz/4nlJ9/6ZGOf/n//r9M0DS8ddKy8WdPM/EP/gkA6eYG4cI8zW/96ZGBZt454hwAq1TCn5lj7O/+Z7uZg7zbpf/huyRbm+TdDipJsIICdq1O8fIzuOOTWOUKwYVLjP/8f07zL79JeP3qoYO1XalS/fwXKT33otmH1uTdDv0rHxKvLpM2t5G2jVWp4o6OU37pM9R+4stmew/YaPCJQkrKL7xM+YWX8efOGGPi9TXi1WWixdtknRY6y01GOwhwRsdxG6O4Y+O4k9MM5m8wuHG4ZUW0OM/CL/5vwzEGQCBth/G//48ITp0GoH/lQ3ofvkc4f69Fwg50nh9aNttBsr6K2xiFs+dIt7fNfdjcIN1cJ+/3TZDqOrij4xTOXzIZkyDAn51DZxkqz+j84LsH3vN80GfxX/+rPckTQNoO1c9/kdoXvmTO9fYtBvM36Lx5uK0DmvtmWt3RcfzTZxn72z9vSq9ZRtZu033rB2TtNnk4QCepWfxMTOCfPos3OY07NoFdrmBVa2z8/m+Tte/1ZgTAspCui3Qcsl4Pq1A02eMwRAYBOkvRaYoMCrtlRun56CQGaRkxzruz2Voh/cAsVLTGCgrDbLGNdBzi9RWEbSOkKcNJ3zfZwvDhhRgfMD/6BFetuSJa2iTrhkSLm1jlADH0gtJpTtaLyLa7e+UsAA0qzui8dp3wxgp2tYhwbXRisirJyrYhRXcGDK6vDsm9e1+PFzdp/fl7DK7eXw4/XtgguYsnk2336H+4yPK/OdrTBCDvDoxb+8dsMXSak6y36X7/OvHiFtJ3cJwCE+4ZNrrXifod8s7A2GUMB3LLgiAQaJmRt9t0/vgNBpUAu+Dge4Lecpdsu4M36LH9b/8ItdXG2uyY7xUEaQpxdMhg7VsENY/ieEC4HeEUbEafqtHbGBA9QEdSOsjo3OlTHA0IRnycgk06yBg5V8WveWzf6qCVJu4lxJ0Ev+rh1zyc9ZA8zRk5W8Eru6jUnHMa5qDALTrYvim5Ohdfuj8AACAASURBVIHN2OURLHcvu6CVpr8ZYrkW0y+Ps/ruJnkyvG6ORGWKPMnprw8QlsCvuBQaPp07hsciLIHtWeQnQKvpgSClCXQch3jlDtHCPP0rH5D1eugkQecZ6eYGydoq0fwtal/6Cu7omMn4FMt4U1PEy5q8e5S/kEan6V3/MoP9/Sab4yDv9Uz6fzhhKMsmv2vQ0ypHJwlZp3PkhHDksUgLpzFO5ZVXsYYDdtrcpvWdb5NubaDCEJUm6FwhbRvhucQrd6h85nO4k1M4I6N4k9P4M3Pk3e6BFhrC87DrIxQvPb07eOf9Hs0//xOSjTWyXhcVxQgpEK5LfGeJrNWk9MJLex1pD8i727d/y97Ntul8ry3e2PPYMCzXScdFqxyVxFh+gBo+I/u2JSX+zBxWuWI6LMOQ9vf/imR9lWwYGJrtm4xWsrZK6PvIoIAz0iC8eY28c/jzpLPMKA/fdbracWH3HprnTQ36JpA9bDvHyBLEq8ugFGmnZfgs/R4qisw9H2aIhGWRrBuuljc7R/XVnzSZ06rR0+m89r1DJUCMgvJdz6/jmOtz17mqKDryPO4nJSIsi+DMOUrPvYBwHPJuh2hpgd7775KsraCS2FgxqRy5uUa8skQ4f5PqF76EOzaBFfj4UzNmsSMEWXP7nn14YxOmLCYkTn10l3Pojk6QDfpYnodVKGGXyuTh0Eg2KJL1uyaoUWpXj0prQCmyQQ8VDtB5bniJO2OIlBTOXsQKAoS0SFvb5OGAfNBD3duodWycqIKwJWwc6SOFjeSA1cwAGGj0SouMFrHqk6rDuUMCiSt8WOoTL3YZ6BRLOMid1KJWSCHIm03617ZxRYCWw/ZqIdBbCdHWCv33FwGNLVxAkOmDyKUCiYUnC6Z9f5CR9pv0lnqooV6HxBgX7vjVONIbOhMrMp0gYHh89q5jsQpz4oVN9JJ5gDLhQ6FIv3+dUO0nmtk2jI1bVGsmcOl2U9L5BYQNwobcFjipxrcEThHyDz7Ats25+hMWtbqg1dSsreYHLoSFENiuRf1MBb/i4td86mcqtBa6hNvHd7zNk5z+Rkg6yAhqHo3zNaJOQuNCDSewWf9wG600WZgRtWPiTkJ1toRWmqSfUhgLsD2LZBjw5FFOMsjIopzGhSpRO8GvuBRHg11FZnPDob3YRTqS8WcbJP10l7SskpzBVkSW5ETthP5GiB3YjF4awXL2FHV1runc6Z+oktX9IIRAWxY6TUjWVul98C7Rwvy+z+RAur1FsrmBf/oMdqmM7fngmJVt1mrdJ+C5FzsljkdG/rFM4ZDAu+/fKjeD5UPuzwoKuKNjBOcugGWRddpEi7fpvvvWcJK9i684/DtZXTEt0paF2xgz/KiJCdLm1oEBj1Uo4o408CamzKTS65JsrNF7/x3yKLwn+5SKTbJuB3dyGrtcxa5UHurcdiBdD8svmhVzEpnsh84BYcqQUpINetiFEihFLm2zQs/VvSVNIbBrdcMfU9qUPm9eIzmEz5Ld9T2rUERF0dFlUq3vKUcpxN57pwFl5DvuDrQfBlmriYpjc9821o94hppkrSZ5FFJ+4TOG51Mo4I5NHBmI3htoi31cGa21KTs9wnlYxTLe9AzeMPuVbK4zuHXDPL8fu845xrxBrCybDGVQwC6VsMoV4+3V7x0Y8FiFEtI3AYhwHLJ2E601Tr1BHoXm+R4dN9yqyGRIpR8gfd+UZLMMGQTmXJV5pvJwsJvVcco1sm7LlHVtB7cxhuUHu3xAE0Q/9CUCTlDAI5CU7FFmipcpO6P4VgmBdWQC91rnuywN3j/wdyb4KDIbXEYiGeQdWtk6NXscTxaxhEWsB1jYKBS5zijICqmOyMmxsEl1TC/bpp1ukJMy4kxjCYeV+N5UrC0cClaVSf8cNh6dbJ1OtknVHmegTCrSk0VSIuK8T64zpv2L2MIlUj024tvEekDNnqTijGLj0M2bDPIOg7zFmcILJgA6godQrUl+/h/4jE9atJqKm9cztrY0jVGBJeHmjZyLlx0sC7JUUx+RRMNsTqUimZiUvP6DhD/5es7ggKxh3EsIWzGX/uZZKtNFLNci7iZ881+8ZlrNjwmVaaJOzJu/9iHnvjrLT/33ryAk9DdC7ry5zge/d2M389JbHfDar7zHC//4Il7FI+kkLH5vFXVXKS5LcjavNbnxjUV+5n98lXSQ0d8IWXlrg8rUflLjtT9aYPRijZf+6dNc/NpphC3Ik5zrf7K425aexzk/+KV3OfvlWV7+Ly9j+8ZZubvc5+afLdFfv00Wn9wS1mEIb91kcOPqPcHO3VBJTOeNH+DPnt7VO3HHxg/lWnxaEJw+gz93BssP0FozuPYR7de/D/fhgHTffgOkpHjpGYQQeFMzqCim9/Yb93zWn57FP3PODOBak6yu0Hn9+2bQPyQ7kLW2GVz/CKSg8uJnHukc3dooweQpnFKdpLVJHvVRcYT0C6YLKYnIBl3c8ghWUAQpiTaWEdHR77YRr1aHC51+7JyM4ecJgtaoQZ/kGB2JKgpJm03iO4sE5y4gPR+nejjZ+IcCISlcehp3fALL89Fa0/nBdwlv37on2LkbOs9offfbuOMTuEP16sK5C+SdNuGte8uEWaeN6/k49RGyTmvYBKBRaUKyuQ4qR3o+yeYa0jGcTpUmCMc1wYpS+NWaKXfl2fB7a0jXw66NYNdHiDfXAI1ba5huxU4TnSuE45BsbzxSOQtOUMDjSI9LtZ80xpV5j814YdjyfvgL1Ek3Dv1d2W5QsuskKiRRIY70mA0uo7Wim22RqJDAKuNID1sYAudGsoAnA1wZYEmHdrqOJSzGvFN0syZS2MMV0b3IdMogb7MSXadij2IJm7ozhSM9pLAAjRQOPgUyldDPWqzGNyhZdWzpMuLOsB7P40ofC4f58B1ynePLAqPuKbbiJWIVIoXF6eD5g48h02xuKlaWTbDgOIJnn7fodkzW5upHKafmLGo1ie8JbNuUv9IUNjZylu/k3LqRc1h3tNYm6Pnev37HpN2FKRPlTYdA18ARRFmb23+yhbQkaRIjMwct9lyYBRZSWFjSpvlRj/fv3Obab68Q5z1sfJJBStbLcWVAplPSMGXpe2tsXmkO3XY16SDFcq19WZbmfIf++oDrf3wbrTQq02RRxtWv3yYZ7E1acTdh7d0tvvk//wBp79gNaOKh8OAOWre7vL95nat/dHvo8SJQqSLppWRJPtxWyuu/+oHh8CQnXzV3dwV+FJQia27vW11Lz99nXfJphDs1gzs+idYaFYakrRbZ9v01RVQcoQYD1GCALBSwKzWc0YMF5ex6HXd0fDcoSNtNwvmb9w0S4rVV7EoVHjHg0WlC3u8jtACdYwdFZLXBYHkep1LHLTRI2ls4lTrCssmjAU6xQh72yT8e9ChFvLyEU69jlSs4lSojX/lZBjevMbh+9VPd5qzzjLTbwVfK8BotibDsx5fRfFBIQXD6LFa5ilYKFYYk21tkx3BfV2GIGvTRUYTwfZzRMaxDMolpc4us2yZcuHlPg4BOU5KtDdId/s+u3P/w/7VCa4iWF+4ititTuswVKlml1W4ajg+QbG0Or6XerbgcJT9wXJyYUUwg8K0SrWSFbrpJK1k12YwjHqAoP3ylYEsHW3rkKiXTpi3azvt4skiuM1IV48nicM9m4kt1hK0dNAoBxGqAJwsU7KrhJOiU5JACoicDHOHjCI9MJ0hh4UhTArOEbQwGhUAOy2y+VcYRHjkZUlt40kcgTMmLnFiZAUZjArBetk2iQqQ4/JbFsebalYwk0UghcFxotSS9nqa5pYgimL+VUSpJXBdOn7XZ2lB0Oop+X5PEmq1NdSRJXytNb22/uV7ZGcOSDgjzcKuuRAgHC5uKUyPK+8R5j0zFBHYZKWwUOWk0IEk1SkKmHFJyBDYlewxPFuilW4RZh6S/Pxg5CHmcEw7bwz92VfYf/1APaKczTNoCy7MpjRfIkxwhBcWxAGlL8iQnHZjPOUUHp+DgVT30UhenYGN7Nu3FLnZg4xRsipNF4k5MOiyVnTSkreb9V0jalCfu7hgx3JFPt6u3Xalgl416vJDSEFKP2aLrz5za5cYIxzYdcFLeU6LaaUvWmOBDRSZYuh9Uv/9YsiJZ2IfmOmnfGC4KaSFdn7TXRiuFdFxUmhA3NxBCkieh4fJk9/LztNZESwt40zOmtOX5RgjSdbBKZZL1NfJum7zfJ+t1n3DnzzEhBFapjDWUIJB+YEizljWUWjCZXqtYMhw3yxq2lt/VWfSEYNfqSN9IdQjLonj5mWMLGzqj47sOBoaU7Jr3/eMWK3lmypCHLYiz7L6cvTw54MtaoTNFftci61HLlIfhxAQ8GshUQifZoJ2s0k7Whz8/goR4ZEFvGMig0Ghi1SfMu0x659Go4U/VcM87XUPmNztlo1j1caSHJ0vY0mUtnqeXNw/cW8GqUbAquNJjkHd32wAV+e5xmn0oPKuIJV086RPm/aHbsoHSGZn++ACzZyMrjjjvKIS33kjvDa7v+vj77w4dbF3wA8l77ySsrqh7gvKDr+jB77UlDe9ID20cPauIa5myYWBX6CabdBJNpmKKTgMpLAZZixRwpE/BqSGQDNImtvQou2P4dol8kO+W/z4pSMfCr3pMfXacxb9cJktyxp8bRdqSuJuQdBK0huJ4Ychb8rj2h7coT5cojAa0F7v4FZfSVJHa2Rrt223ai12y6BGYdZ8INFm3Y7gi9/tkdlcLrMAMfiekOeiTglUy+jFCCITvU3n5FSpHCMEdCmHa8M2KP+XuDLUctrIDqDBCxffhsQxhyJqPlsoHyPodsv7Bq/60s8fZyAZ3Z2cOYegrxeDmdfyZU0Pdogmc0XGc0XFKl59jcPsm0e1bxKvL6IV5w+HIFRxTcPaHCiFAWkjXwZ+awZ2YwhkZwRkZRRaLSMc1XCXbRu4Qv0+QpYkQArtW29U+Er5P46tfe7iNWfbwj/VA6v4/KjgxAU+qIt5t/jFnSi/R8E/RT1vEeR+ls0Mn+GayTDfdPPB3nXSTTCec8i8TqQFR3idSx1ONvRuxCulmW1TsUbTOyfVhkadCCkkgK1jC2Q1uQtXDET6gSVRIwaoBGoEmkBVs4aK0OvQcYzVgK1nklP8MmU7IdTYMog7H3QHLYcFLksCf/lG0j/z/sNnYKOtScOr4VplY9gnsKpawCbO2aeNW6bCsB4qMTEV0kw0Cu0pgV/DtMlHWxbUM4TvOe/TTbQZp6xMNdsAoLIc6RNoSpMD2bMpTJd7/ravUzlQYf7aB5Vjc+sYCwhKc+copwu2IoBGYkhhg+TYgiJoRjUsjpIOMwcYJC3i04R98UiunH2kIYUT/nHsFHB9yg0OiJftiBbEzcQIqDu+r0rsDnaWoJ3bfjhgUspTmt/+M7ntvUXnps5Rf/CxWqWy0Z849ReHshaHoYET/ow8I528Q3rxOdkR31pOAOz5B4fxFKi9/DmdsbM9ncJeIbPR4dBzvLhgsz0O43iN1zT02SInl+Y/NV04MhUSPo2H0o4YTE/BIYTHmn6bsjOLIAFt4pCq6KwtzL8K8c2jAk+mEQdZmKbpCrnNynZHrhEQNSFRErlNSFdGRm8NMEIR5j0RF9EWLdrZOplMcfDQZnWyDVB0u/d/PWiQqpCu3yHU25Jdocp0hhbXbndXLmrtZn37evuuzkKiQVrqOle1lkXKdEeZdlqOrw7yUQmKRPkpv3hAPkmUOmxE3v7nE2vtb9zgTJypEp4owa5PmIVvhgtFnGAY6uc5Qw0Cxl2wO3ag1ST6gFS/TTTZNILmbVRBorY7swHtcsAObwqhPabJIeapIGmbkaT5s29UgIA1TqqcrSEciHUkw4lMaL1CaLFGeLlI9VcYtOYD45Mc/+QjZlifFMfhRgNjpmNVD64DbJOtrD7WpvNc9UCjOyMk8xM0b8h1O4r0zWi8tuu++Rby6gjPSwB2bwJ87jV2qIFwXy7IIzj9lAounLtF7/x2ihdtPnucjjQBf9dWfNJmqkQZCWsYqZGuTZH2NrNtBJ4lpTx+KA9qlEqVnXsA/fRZxgMr5E4Ews5jOM7JOh/5H7z90VjC6s/hY5CROIk5UwFN3ZxBIUhWRqgHZodkUA31Ex5JGkeqYVrrG3UutHW4MQKpj+Ngm7m45t4WLLcxE1su2Dyg17SHREUkeIfLOfUpte4h2Sl93IVf7a/Ua07Leyh5u8H1cSAcZ2zfabN+4d3WmdEZ8V2o+O6Dmv4M438uy5TohPOKzPwwIKZBSkgx1hHY4SirXJlOzGZJFOUKClIJwM0RIQZ4q0n6KHLasq1SRxabdMgs/ocFCCITcU559YJzACfPEYIdIOSRHRksLDK4+nGGhztIDJfr1cB8m8JEPEPyI3UXZiYPW6CQhWV0h3dzAro3gTW6hswynMTr0IAtwqjWcWh1vcsro54Qh8SOKyD0qhGMbL7NzT+E0DNE82dwgvH2LePkO8dIi2ZBIe3cA4Iw08Gfmju0H+Yljp0UfM36pKGRw7QrpAa3lx0EeDk5m6fEx4GQFPN4Ut3vvsBUvsh0vPcatP9xAX7Mn8K0SlrBpZevH2s5xg50H/eyP8ckg7ae0+ilv/Mp7uz/b/NAMFM2bbZo3hwHeXnYbgPbC3uq0fbuz95mHvaXHmc+E2DXM/DEeI7RGJ+muOaewLPJ+j2R99fHuJsvQaYKwAoTr7vk03QfGy88+8fddZxnp5jrp5jq9997CKldxR8coPfs8pedfwgoKCNej8soXQEjk1Q/pv//OEzteKyhQffWLWOWyySpnGdvf+DrRwrxRGz4UYsjhOSH3Y+jztTM2CNs2vnWP+fn9NODEBDy5SpnvvY1AULCrdNMtMhU90aCgma4gsh0BxB/t4MSrecx9ZY6VH6wQNaNPLgvxacVxbv+DPCJqf6lWOu5Q9fjwlZWwbZx647HV6n+MPaTtbZxOy5hh1uomM+F5x3awPw6MW3gH1w+M0FuhsGsBcBR2DSd/xJD3u0TRgGTdiF2WnnuRykuvIFyXwvmnAP1EAx7huHgzp3bfvazdJLx5fZ+K94Hfsyzscnmft9uThNaadGsdYVtYpTJObcTYPjjOjzl7H8OJCXhA4Egf3ypRsKt4skCuU6Nue8g3mvGdQzk8jwM52Y96nAOYso1f8zn1pVO0brRM2/SPA54nBr2jIrtbYhLIQnHYtXN4wCNtB3d8wphy/nXGx2X2pXzka5I1m2TtNm5jDCwbu1bHHZsgWlp4xIPdQ97vk3U6eGMTpiuoUMSpN0g2ji5XW5UKdrV65GdOJIaclzxNiVdXcMcmSFvbRt23UMQqPmAQt6PLAoZzZVuPFHSIodkmUpr2+zg2OjBHkRstC+n72NXaw+9b71/sCEs+WteX1iQb69iVGnapDLaNOzZO1u+Sbqw//HYfAsIS1GcCehsxSZjv+7llC/ySTdhJydMnM7GemJFTCknJrhPYpnNpzD8DHF32udr+q0804Pm0wPIsgrGA2S/Ncu33r9FbPWFKp3/doLWR1r+LpG1Xa0Zr5bCMghBIf6h14pyY1/aJQA8nUr3j1WTZe6W+h+QpJZsbOI1R9LkLALhDV+9o5c6BfJwDcR9dh6zT3ifZb5fKeNOzJFsbh0+yQuDUGzgjB4sZPlEcoDV0GNSgT9Zqkm5t4Y5NIBzHOIg/ALTKDVdFa0CYLqlHCRSGJrdm43oodHt0mcoKAqxKBafeeOh976hS77z/wnEf+Frs36AmXlk27fRj4wB4p+bIwwHp1tbx+TgHaO88EAQ4nuTUi3UW32rSXY/IM43KNY4nCSoOlQmf9EYPlWdYtjHjvltANk+NmKPlGBEUlStUrh/psO7GiRk5M5Vwq/fmA5Hzeun9lVB/DKjMVWhcagxd25/00fwYOkuJV+5QesYoZgtLUn3lVVrfSQkPUUe1a3X8uTOUX3oF8SiD46cBWhvV4F4Pq1gyHlZTM0h3T8L+QTG4fhVh2ZSeeR5ZKBKcvYBdb5BsrhPdnkfdT79ICNzxSVQUmj8HBK7x0gLStqm+8ioA3swsNd9ncP0KeRjeOzENhfBKTz9H4cLFBz6nTxSWhT99inR781iiiGLHAX7c+E6pweC+paN7oBR5OEBFIdIP8KdmGNTqD1260bki63awyxWk5+OOjGIFAbnKD860Whbll1+hePm5Rwu0lELFEXnfPL9uY4xsbBvhuseWKth/Ipree2/jTkziTU0j/YDyi5/BLldQcWRc6e/zTgjHwRkdJ++0DUn7Ia5nacRj7FyRz//DOaYuVdi81WPteo/Vqx1mn68x+1yN1sqA5tKAoBJw8cvj1Kd9or7xQvQKFu/98Spe0ebyV8aRjmT5gzarV7ps3X5wSZmDcGICHoWikzxY+u2orqmTAiEE9afqlGfK1M7VKIwVsDwLaUmSfmKE6m61WXtzjTw5JBIXJmgZuThC9XSV0lQJy7GMwWac0V/ts/rGKq2bLZKuuSbl2TLjL4wzcmmE8kyZ0qRRkX3+nz1P3In37at9u81b/8db9xhinvqpU1RmK2itufEfb1CaKjH6zCiNy0abRuWKuB1z5XevEG6G+8tkwqQxZ39ilupZc8xu2TW6N9sh7VttFv9ikSzK7mlzB3AKDo3LDWrna9TO1nAKjmntTxThVkhrvkVnocPWh/cGvZZrETQCpj43Re1cDafoYHkWeZyTdBIGWwO2r2yz8e7G4df8E4RKU6LF22bAK1cQto0/O0fllVfxpqaJ7iyiwtBkdRwHZ2yc4NRpM6GmKVLrEyV8diCGx85daX8hjSAflnX8rMkhUHFMtLRA4fzFoWt1lZGvfo1oYZ601ULHEViG7Ctso34c3Vk4VNlYZynJ2grNv/oW9S9/Fem62OUKI1/5OaLF20ZWv7lNHkWYrJJEusZN3iqVjKVEY5TuW68T3rx+YMCTD/okm+v0r10hOHMW6Xo4I6M0vva3zXFvb5H3uyAkVhBgV6oULj6NOzk1POfIZDWeNIb3duRnvoYKB2SdDsnmOlm7ZdSjk9jcX9seXqMS/twZ/NlT2GVjWxCv3iG+s/jAu04210231+wcMggoXn4W6QcMrl9FxSEoDVIiPc8QvfPceEodAJ0mhLdvUbz4NDIwJpf1L3+Vwa0bpJvr5P2+0bgJCsN7cQl/7ixOtUbWahq7j4d8D7NOm3h1mcL5i0jPw5ueZeSrX2Nw7YpxD89SU/Z0HITtIGybwa3rh743KkkYXDNdhbUvfAkhLbzpWepf+Tn8uTPm2er1zHMpjc6OdFyjMF6pYZUN92frG18n2Vh/qIAn6qY074R0NiJuv77N2o0uUSclSxStlRDbtRiZMyr2bmBRmw6480Ebx7fwK0atfubZmlHx7mUMWinVyYDSqPfpC3gMPgWEmY9DQPVMldGnR6mdq+FWXCzHQtgCrTSlyRJ+zWewMaB9u43O918D6Uq8ssfkZyYZfXqUylwFt+KabI0ClSlKkyW6S13at/daxm3fJhgNqMxWKE2V8EeM7HhhvIBbcVH5XsSfxdmBvOzq6Spjz40hpKB5o0l5uszos6PUztawPfPoZFHGzT+8SST3a+Y4BYfybJmpz09RnikTjARYvoXKFGkvpTRZor/ap73QJvq407qA+vk6Ey9NMHJphGA02N2fVnrYDi5RqTow4ClNlahfqDP96rT5rm8jHYnOTIA42BiQRzlbH209kYCHPCdrt4jXV03L7sgoVrlCcPqMWW0GBdSgPxTEc3Enp/Emp5G+T7J6B6c+ggyKj5YGf1yQErtSNQrCUiBM/775ebFkOAVDWMUizugYwZnzZkBXauipow1/IuybSeYYUHFEtHgbf3YOq1BEej7FS08jPY+02URFA4S0EY49LJ+4pNubh1s5aE3WbTO49hGFCxdxG6O7E7VVKJA2t0m3Nk0mRhj7Cen52OWK8ZKqjWCVy8Ybyz54EtRZRtbtEN64ijPSwK5UkYE5bsv3SbY2yDodhJRYhSJ2rUbxwiXSbptka9M42J+Eew4gLYIz50Dl5P0+8dqqCQjDwS4XTdim1GiVKgRnz2GXKwjHIe/3iJeXiA9wlL8fkvU1nEoNd3IaYdu4E5OmnIlARQPjtC0l0vcRlo2KwkMDHpUkRAvz+LNzpmvOsilcuAS2TVKpkve6JjAoFHFqdYqXn0VIizwMSTbXKRaKD91AkHXaxoB07qwJ2CtVipeeMcfV7xvTTcsygpi2DVIS3r51OMdPK+L11eF9OY/TaGAFBYLTZ807sbVB3umYgF1Kk3FzXexqDbs+YpTGC0Xkd7710NykLFFE3ZSol9FcNpmcnVJU1MnobkZMXCwhpUBaAjewaK9FFOsuXtFCWoLqhE8a5SSDnN5WjFeycfzHt7g7MQGPFBYNb26vpnoMtJM1+tnBVg8nBVppxp4bY+TiCOFWyPJry6RdQ8Yee26M2S/OUj1TxSk6vPGLb9xDJi40Ckx9bopX/rtXyOOc/mqfW390i2g7QtoSf8Rn/IVxsjgj6e1lvAYbA1a+v8LGexuMPjPK2DNjnP7Z01z9vau059tE7b0gI+2nB2ZZALOPms/z//x5sjgj7aZc+/fXdonQ9Yt1wq3wnuOunqny4i+8yPgL42x+uMnSXy3RWexQnikz8tQIp796mtJUiQ9/80PmvzG/L9iStuSFX3iB8kwZlSuu/4fru+frVT3GXxjH8izc8sGiX2d+9gynfuoUxcki137/Gv3VPukgxR/xqZ+vY7kWXs0zQeMTgs4y2t//Dnm3S+2LX0Y4Lna1jl2tE5w9f8/nVRKTrK6w+fU/oPalr+BPzyJrI0/gyPdD+gGVz75K+fkXzeA8HKAPCsaC02cJTp+l/uWvmiAnTdBpih5mvPofvW/cx4+BrNuh89r3KF582kwMQWHX2uAeDAOq/pUPSbcO5/ypKCK+s8jW1/+Aymc/T/Hys1il0t52nzrGgWl1ZPkg73ZpfffbOPUGcOVgVwAAIABJREFU/plzuBOTWKUyxWdfoHjQcUcR3TdfI+v1qLz02RPWraURno/t+dgjjeN9I0sZXPnQCBUu33ngPQ6ufojOMvwz54xxabGEVSzhzZw6YF8ZydoqzW/92YHbUlFI583XCM6eH2YJazhj41THDniGwJSO3n+HcP4W8fKi8VB7SOHBeHkJFYaUnn0Ru1JFuK5xLR+fOOBAjSFo89t/hk4Pr2rk7TbhYMBGFDH6N/6WCQaDAG9q5ti+Wqj8kfW6Dvz63WyKIdUuzxQqU+Sp2jVftj1JninSTk6eKpbeaRH3Hl+n2YkJeBzhcan2pWNxeDSaOO8z333jxAc8AG//8ttIV6JzU5LZKR0tfnMRnWsalxtMfW4K6Ui4iypguRaNyw2e/6+fJ2pGXPmdKyz8+QJxOzaZIGE6sK7+3lXSXorO9p60pJvQHDQRQuAUHIoTZjjtLHTYurJFuLW3o8OCHTCZmvrFOjf/8CbL311m9Y3V3ayIkALpSOJWvG8bjacbTH9+momXJ3jnV95h/e11WvMtVKaQlqQyV6F1q8Xlf3iZiZcn6G/02Xhn6OItwPZsGpcaNK83WXl9hWu/dw2llJFfk4Ib/98NVKZQ6b0Ti7Ql5VNl/JrPrT+6xbXfv0bUNARhIQWWY4Fg6Hz+ZEuiycoy7W6Xwa0bVF78DO7YOHa1ilUo7QYE+WBAePsW4Y2rxKvLpJubpJubONU6zgkIeIRlYZfKOCMNduX4j7NoGWavcFzQ2pgfBoXj71gp8kGftd/9TfzZ0wRnz5lsTKmMdFyEZaGSeGjQaTIQxzXgjFeW2PrTLdrf/yuKF5/GmzmFXathV2omcyCEcaWOIrJOi6zTJt3cYHDzGsna6n1dqnWWsfWNr+OOT+CfOkPx8tND88cAadnkgz7p9hbx+irdN18j3drEKpVItk8TnDl7/Gv0SUEbcbvV3/x1vJlZnNFxvNExZKlszCdtByzLmE2mKXnftKYnayvEy0uEC7dQYfRQE6uKY8Ib11j5jV+l8vLncCcnTbasPCwvKYXOUvJuh7TVIjkqizQUTdz8j/8v3tQ0hQsXCc5dMJIEQ9uIPArJe12S7S36775FvLJs7q/Whi/2kMGBTlPSrU1WfuNXKV5+Fm9qGm9qGqtSG5r1CnQSkw/65L0u6eYGOrt/NlqnKfGdBVZ/6zdwR8fx584QnD6LXa2ZDM7QYFRnGSqOSVsmc5lubxEt3CJaWngkKYY806xf63LxS+Ocer7O0vstok7G5KUyM89WGT9fprseo7KDr9v869t4RZvzXxiltRrSXg1pPXgi8FCcmIAn1xmrg6scxKoVAiQ2nlXAtQoUrCrtZJUof/C63t1j8aEmmQ/f7HEg4vbBD1DaTxmsD6jMmbKT/FgqMRgNKE4WCRoBS99eonWjRW+1ty+wAeAAhXat9G4QolK1WyrL05w8ycnj4zL3wXIstj7conWzRdS8v91D5VSFyqkK0pJsfrBJe6G9q2QM0F/rs3VlC2lJCmMFyjPlvYAHUEqRRRlOyTElvxGf/mqfLBlmkY6Yt7Q2QSWY0lbQCEj76e5xp5wcXQqdZ8aGIEvp2fawnBWYVlmt0XmGSpK7ZO7b6DwjvHWdrNsmvHXD/Lxz7wSr84z+Rx+QrK2ZQU5rk84+4sEWlo3l+pTGz+InHs5WCkKiVIlQFohsx0js3wWVxKSra6TXFwjqU7QX3iMNe/fwwe6HrN0iXnnAFb/WpM0maMjDPsnaCsIzpQwhJToz2SOVJGTd9rEDHp1lu3wHIS2SrU2sQsHcmx2/La1RaYoKB+ThcFLcWDPk5mOQpo2woSmr5IPerm6KkBYqjo1mT7tNsr6KShK01kN+R4iKI7JW8xNRzrZscFyBVpCmGs8TpKnpkgmKkmigEBJsB9TWHYQVIvqrDO6U0I659tK28AJJmihsqWjUM9bmmyRrTaKNbUTcx3UAR5ClGsfdP+YrZaqiricIB9qM/9Icj22B5aS4eptC+ypZuEq0WKBQLxJFZtyTIiftheg4JO/dz7fLlDJBo7LU3GvP3xV6VENX+7zfI15aIOv1TJZFSjqvfRerWNpT1n7A+6HzjHR70wTKWxtEi7eRQTBUUze/V3FsCM693rGMZmHP7kNnqQlqmltYQRHpursND3onMBz0yXs98l6PdHvDBDuP8FzlqWLp3RZB1SFPFGErJYlymndCtNJsLw3YnO+jck08yOhuxMS9jLCd0F6P2Fro43imvBX3MwathEH7U5jhyXTM9c73DvydEAJH+JScBlV3HOmfYSO6TZgf/DDvdhqy448zXHgO7QGkJUBDkiik2JP1MC+yQAhBGqvHNp4IKYwOwZC7I4TYbYlUqclUOEXnnhJLcbJIcaKI5Visvb12cLDzQ4DWmo33NuiuHM/7pjxbpjRVQmWKqBWZ8lfd3/295VlDF3KNV/N2CdVmZ4aX1L3TpThRZPTpUcZfHGdNrxG3Y1SiTOBzyJyic81gc0DciWlcbjD+wjjSlrRutsiiHZ8s/YToYncTpQQSCbki7/fof/T+od+SWGj2DGbDm9cJb17/2Pb2Q2cZvXfefKCjs9wAvzpG49Ln8QY21p0uwnLwYx+XAGm79wQ8Oo5J55dIEp/yUxU63/o2g+076IfolHoo5Bnp1gbp1gb36/kR0kJI63iWAMPVf7R0G5ZuP5ZD/Tjyfs9MpMcg76pBn8GVDxhc+eATORYwQUWxJPF8gVKQxJpqXdLrKrJUU61bWDbYtiAoCFrbHbxODzsUhM2cNDEjru2AV5ekfYX0JWMvemwsxOTbOflAUxux2Ikbw4EmKOx50EkpiCKFlIJqTdJuKqRljq3bUZTKEtcTuJ6imNxiezkn7CiKYxZxM0crcH1BFoNtDye4++nGKkXWbpG1W4TXrx7vYilF6y+/+dDXegc6y4iXFogfo96T2bDeDWSixfm9nwtpeHZgyq/iLpNbbbLoxq/PzJE774q0bFSe7m1DWne1sO8a0aFzzZ33OsNMukBloPKEQTNh9crRmc+7sTnfZ2cXjzOuPzEBDxzRdaUhJWaQt+mlW8T5gLOll1nov8tqeG33Y0Kal7FQtckS0//vBZJBN8PxJOWaTRwpJs/4uL7k6htdyjWbNNb02hmVEYfp8wGOJ7j5Tp/OdvpYLEXK02Vq52vM/uQs9afqeBUPu2Dj+A6WayEsozeAkR7YfTmDRoBf89Fa07vT2+3A+mFCK00WZqazKzrexQgaAcFYgFN0+Hu/9vcO/ZywjDu5U9jP91Cp4vv/8vtc+DsXmPniDF/8H75I3Ixp3Wqx8sYK1//DdeJWfGiW6trvX2P76jYv/Dcv8OJ/+yI610TbEbf+9BZLf7VE+9b+jNMnBYnEZuiOTY6LR0pMTo5LQJkaGSl9OuSkCCQCQU6GjTsU3cypM8GAHjEhGQkCgY2DjUNMiIVxa89JcXBRqN1tZCSow6LDj6E6e5nGU5/Dq4yydfV7RO118jTBCcr0NxbIokMyqnfJz2gpzYt4zH3+0CAkpakLoBXd5Wv3//xfQxRKkp/+mwUWb2YUy5Lzlx3Gp2xWljJW72TMX0v4wleKjE3YBEXBr/yrFmOTNuNTFtKCsK8pVSRjkxYTUzZ3FjK6HcVQ2w8wWZu/9Q9LRKEiy0BaEIUaS4LrSSZmLN78TkSvp5iasRmbskkTTa+Tc+2DhC/8dAFpwQdvxf8/e2/6JMl9p/d98s7KOruqz7kHMwBmABAHz+Wxy+Uhc7mS1ntZUigckh3SC0c4/Gc4/EJvbEc4HA6FHVIobCtWIe/Gissllwtyd7HAkgBIgLgx90xPn9V1V975+/384lfdPT3dPdM907goPAgQnJrKoyqzMp/8fp/v86CUJkiWZfDI4y5BRW8kzyTNGZssUfQ6ghefj0gT9SBuBb9SMC0bvzpLMHUMKXLyeIhTqmFaDkpJ0nEXpQS2G+CUakiRM27fQClJ6/SztK++rO0XSnWq02dIwy5SFjilGpblUmQheRICCq/cxPbK2K7PynsvIIvDt8k+iDiv+xIeZ0pPKzwIZJqQtY82z6NQOXExxLer2ObOEc3WvMujn63QmHWJx4IkFOSpIksEtmPi+iZvvDDQLo8+KGnQWvCwJ6VVw4CpOQevZGIYBu//fEQ8EogHrKoYtsHUuSke+93HqJ+qo5Ri5ZUVslFGHufITDL/ufmt0etdyxvbSi8lP6KqhEKTsUNsW2scFNko49oPrlEk+5dio/WI4eJdzF/BeHXM5e9dZuXVFVoXWrQutPCbPme/fZaZJ2a49de3aL/Zpn99d+ZN0ktov9nmlf/lFVoXW1stttNfP83MUzMMrg248v0r9K709tQBHQVMLJrMkpJQkCEo8ClTooJEkpHg4OLiYWETMaRMHRePlJiUBBMTB2fy2nb9okYTBxcLB1A46N9BQkhKjI2DRwmPEhusEDM+AOkxcEpV3MoUo+XLDJcvkw51sr1pORTp/j40cW+VPB4xXrtO2l+/p1v0RwLDxG/M0jj1BEUafeIJj2GYOE6Zxy78l2xsvMtouEQ4PoLrrII8h9WlgkrNZHrWot/NKAqFaUBzxiKJFStLBaJQlCsmrVmLWsPi5tWMmXkb24FBV9LvZkihW2SlwGSrqKBg0BdEY31BqTVMgsAgTRSjocD1QCpFuWwyu2AzHkmiUJLGirOPubiewXgo6bYFZ867pInCsmHumE0SS6QEp2wxGujlhn2J5xtk6c4LmOfVKZVaTM9exHEqGIaJUoLuxvsMBrdIkr21oa3piwTBNEWRsL7+JqLYv8Xfmr7A7NwzWJYLBhRZxHC0yNrK6wjx4T+8mrZHde4RZJFhGi5etYXtBoSdRYosptw6iSwyrR+SAiXyOwK69dO4YWhriSKPcUo13Qa3XfrL7+H4FfzaDIZhEvWWMMIetdlH+DhNX9+X8HjTc3hzx+54Rd94ja3wNDUpOem+0SSkHpnn5P3OkRMe0D9407Aw79L7uCWT5rxLuW7j+SZJyWQ8KChVTGzXxHYMLEu7QXolC9sxqE072LZBHApKZQvPNzFNg1LFxPVM0kg+MOGxbIvWRd1WcQKHpZ8usf7GOnEnJhtlFHFBabpEabq05/Iy1yp20G0g0/4IslsUh35Yl7nU4myhCV46Svc95/Mw31M8XMQFg+sDxktjoo2IbJxRP1Oneb7JzFMzW15CexEekQnibrz1b/1MnbgTM//ZearHqvh1n/HKmOHi8AMjPAYGHj4ZOg/OxMLDxzBMpBJICi3CNgxs5eARUKKMa2i9TUqCgYlrBFjYGGr72Dt4uIaPhYWBiT35GSslSIi13s0o4eBiqoOdM4ZpYToeluOTDjfIxj3y6GAlaJFGiDQiHbTv/+aPAIZp4VVb+LUZsuh+mo5PBizLZXrmInHcJUmO5jMVhaK9UjDoC7JMsXQrJ4kVtmNsaWjWlgvd7koUQijGIz2E0V4T2+/LFEmscD0Dv2SSxJI0UQgBUipWFguSiTYnic0trU4cScKRZNCT2LZBryPYWBfEoa4GTc9ZrN4uGA70e0ZDofcj15Wc8VATHtuBcCT1OkO5R6XAICjP0KifoVY7udUqVlJiO9samr1QKjWp1o6TZSFm+x3uRe0Nw8KyXBzHpxRMI0WOVAXrxpsPfaweCIaB5fiTtvTEpdy0KbKYIg2xHH/b/dkwEHmq77WWg+V4WI6HaTnYboBSCtN2sRxtCZDHQyzHx3b1vUwp9bFMXL8v4Sk/8hjVJ5/dfkEqkBKrUtt6SRUFSmn/A8O2QSmy7gbR9cuM3v3lke6wY/rU3BmEyhFq5xeap4rBRs7qjXSixVGMegWVho1pGUjLoDHjTEiRRaVh0ZxzCQcFvbWMk19rMO7njLo5o36Baeo+cP6AonXLszjzjTP4DZ/Oux1e/V9f3TPDaj8ikwwSslGGYRiUZ8s7fHYOja0Img9+FDsZJGRhRu10jc77HaJ2tMtf6KAQmaB3uUfvcg+7ZFOeK/P1//HrzD07h1f3uPaDa/dcfnhzyPDmkMW/WeTK967w7L98lrnn5rjwjy5w4/kb5Ec48rhr3xHEhEgkHiV8AnKVT9pbPoIcqSQFBWWqWNhIJSbLjXHxCVRFE55dx02hlCQnI0GgUFhYxIxRSBzlEjIiIzlQdcdyfUxbP4mKPJucL3dFxO+z7N379WC4cz33W8fhtmlaNsH0SUzXh2hw6OX/c0ESK159UVcsorGgsy52DHDcmTyw+fra8nbl7+aV7d/SjuXuGgL5+Us7qyL7JXK8/dq9L7zdtthaZvHGtiD9fkMnpukwPX2R6ZknGA5vs7b6GkURYxgWaToky+4lbteREErd/2a+0X6bjfY72LbP+cd+m3Jlj5HzDxFKCJLRBrbtIfKUNB5g2d4dVRxQIsO0PV356d7GtD3coIFXaeFVWliOh1dpIrJkUskXKLU5eayvSSJPCaYWMC1nQh4/Pvb+9yU87b/+AZ2Xfrz1Z3d6juDUI/hzxxldeot0dYkiGoNSmI521Zz6/K+TD7qkawefJ7MNj8fqX77HDVmHi3pWhbLTYCV6n6jY+XTfWUl55Yf59uVL6VbQU1+r45cBqRgPBL/4cQ9RKLJE8pM/WkdJPU63ditFCqWtCKR+gnnYDA/DMhCpII/yna0dA9yKS/OxJo0zu9tZAL0rPWonaohU8MhvPULSSxgtjraqPgdFHudkoa6iuHUXJ3CIuY9V/kNg4+2NLb+cM98+w/LPluldfnj7gCIptia8Zp6a2deHZ08o3T7rXesRzAXMfmb2A/XhERSss4ScPAPGjLnFZitlS06/+WzJgA6bdFROVDiCgpQIAxOJ2EVcJIoe6wiKiahZa4UiRsSEGBhb298LzXOfozx3hvLMKRw/wHIDTMdl4blvM/eZr8PkQjZauUL32muMlrYFneWZU8w+9RuUp0/tMCq78qP/i7i7/+++dvxxZp/4GoOl90l6a2AYzFz4Nbz6jJ5QyjP6N9+kf/Mtkv52qKbllXArTWYufBm/NoPtBZi2Q5HF5NGAZNCm/e5L5PFoa5pl/plvUWoew6/P4FabmJZDaWqe2rFtQx2RJXSuvEr32mvk4a9G9ecocSdxuPNaeD8h6Y7lDvHew2C/5e69PoNS0ML1qkhVcHvxRcLxGkJqbZy+ee9/fV1ZfpXV1ddBSYoD6VIURZEgZXHoycWjhsgT+rffxjAmQxBSEPfXJq0rRfvqy0yfeQ5Z5ISdW9TmzjFYucRo/SpRdxExaXdtiZ7vgCxSot4y8WCd7adr3fF5EP3OB4X7Ep7NbJhNeLMLuM1pxlfeIVm+RdbrbJshmSZWFDK+8jb+wslDBd4ZhoFvVe9dgTAMCpnQS5ZoJzeIxc6SuxSQxrtP1tXrCb01E8OAOBTk6XaZMwnvcBzOj7YEp6RivDymslAhmA049qVjjJfHGJaBW3WZ/cwspWZpXx+cIi4YLY1YfmWZ6YvTHP/KceySTf96XwuITe1ZU54v07/WZ7wyJg93VyzSfrpVZZl7dk5PjHkWSihMx0SkgsGNo7vYD28NKU2XGN4ccuyLx7B9m2AmIN7Q55HpmDhlh+rxKt1LXT1BNal8WZ6FP+Wz8IUFsmFGOky3jBEtz8Jv+tRP1xGJIFy5S0Rr6FH+1uMtvJpHuBaSh/mWq3SpWaL5aBO/7jO4OTg0cTws7iQbWn58r7HS3fuilxFwF2mJGJEq/V3mZDsmuDaXU/cstmvkUZ+kt4ISObZXotQ6gV+fJRm0yUYdbQyIIu6uUMQ7n3rzZMxo+TJ5OMSrz+iWUWP2nu0A0InvTlCj1FzALTf0jSON9YizORknF8XOm45hUJpaoHH6KdxygzwekY172jHWtLSmpVTTk2F3/JTSUUdPq6Qhpu1ge2WKJGS8ul0VlCInGbR3TZ99il9d2LaPaepORJ6HCJnrSsUBlhUig0Prb9Rd//2ooCaTVtvn+p0Ti7JIiforgIEsMqLeMkUaomSxbQnC/p9CSXGwCciPEIee0rIrNfxjp2j/9Q+1L8idzo9SItKY6MYVKuefwKlPHWrdQhX3IDyKXKSkImSUb9BJFlEHFJesXL+/d8wHAVlIupe7tC60KLVKnP7madpvtDEdk1KzxKnfOIXIBOkgxa3trlbIXI9n3/qrW7QebzH3zBy1kzVWXl0hG2Rg6SpR6/EW1390fYsc3I2klzBeGZN0E2Y+M4MTODglB5lLLNci7sRHSnjGK2OcSw5rb6xx6muncMsulYUK/at9bSzo2/hTPrOfmeXyf7rMeHm8RXhsT5Ojx37nMcL1kPHqmKSTIIXErbiU58vUTtTYeGeD7qXuju0ahkHQDDjx1RM0zjToXNIGizLTE3C1kzWajzexbIulny59NLESR4BoL+OlB0DcWyUb9zAmvfiZC1/GqzQZr15luHSJPBoBCpGniLtEy9moR+fyq9heQO3EBWrHH8dv7ONQezcMA782q9tnWUT/5jvkYR/TsrG8AJHGyDuuK4ZpEbSO0zz3WaL2IuPVa6SjDkoJnGAKv9bC9srIIt1BlML1m8SdJSyvjFOq4TdmifvrrL39wva+KEmRhsjso7lG3A+maWOaDuakNaCUJrf3JpYGrluhEOmkLGNgWe7Wk7lCIYpkkg6+ffva1GuYhj1xvN/UaErdai2yu95vYVnOxKup0OLcSbtHiExv07QxYFLlOMT2pJgIe4+GJFiWh2lamKaN79cnQmIT2/bx3CpSiS0CdHeFxzQdLNu7Y5xbIWVBUSRHtn9aT2NhW+5Wi8iyXKTSJELKHNsu6TaS0lWjg7TVDouwexvL8bGcEvFg9Z7Vrk8iDj+WbpqYtoNVChDRGLXr/mpgBRVtnnaImIhcJvyy+4MDvPOjZskHRxEXXPqTS8hcMv/ZeU585QTnvnOOPMwJ10Iu/eklxitjWo+3eOZfPrPnOsLVkGs/uEbUjjj2a8eYfXqWi//Vxa0sqWyc0X2/Sx7m5NHeT6nZKGPjnQ1e/J9e5OI/ukjr8RbHv3wcJRVRO2Lt9TWu/fDeWpjDonelx8/+1c/ovNNh7rk5pp+c5uzfO4sspA4Q7cWs/WKNwc3BDpJWJIWe3Foa0jjbYO6zc3g1D5lL8ign7sS8+0fvcvslbcR4J5RShO2Q8cqYyrEKZ751Bq+mJ5hkLgnXQ9ZeX6P9Zpsbz9/4xBKeo0KRhFuj5pYXUCT6Yp9HI5JBmzzcLQjfhkJJQR6PyJMxIjt4i9QwTMozJ1l+7Yf0rv+SbNS95/ttr4LtVzAth/6ttxguXdIVnu01bu3Tndh8j+WFFFmMFAUii0n6Rz9I8UHAMCyarUeZW/gs1eoxXLdCFHXobLzPeLTEftdCz6vx+S/9D9y4/jxJ3MV1Kxw/+RU8t4Zp2mTZiMuX/4zR4DZ5Pjn+lovnN1g49jnqjdN4Xg3bLiGKlCjaYDRaZvHmC9uEwDCpN84wO/cZPL9Ot3OZEye/jBA5o+Ftbt96kZOnvkZ96iymYXL50vcYj5ZJEn1O2bZPqdRidv4ZGlNn8bwqluUhipRwvMZgcIvlpZcnepqHv+afOv016o0zlCvzOE7A5jnz2c//d1vvESLjtZ//a+K4g7ijDTM9+wRnH/k2vj+lPdryiF73ClcufZ8sO5qHD8cpUa2d4OSprxHHXdJsxMmTX2U4uMWgf5ONjXe58MQf4NgBeTbm8qXvEYZrRz7tpaSgSEOK9GjCOj9uODThKUYD4pXbtL7yTaJb18g21iYaHjBdD7tSpXbxWT1psr5yyLV/csjMQSELye2XbtN+q82lP7mEaZtIIRGZIFqPKJKCwc0BG+9skA73nmZSUtF5v0O4FnLzxzdxAmeL6UuhwzjPPe5w5jsN/uY/7j0pU8QFG29v8Nr//hp2SYdporQoeD9/nxvP32Dl1RWcsnMgh+WdO73zsztlB9uzteBPKkSuk8uTboK4o5UockHSS3j7372NHdhYrp5OUxNTK5ELkk5C0t+53OY2s1HGjb+8wcrLKziBoz/n5DsUmYA0wywKjp8wWb4pKAotTH/yWY8bV3OG/YMbTparBr/zj6s8/2ch6ys798Vx4eyjLqWSwaAvuXGXoNMvGTRaFmmi6LYfjnh99VslgsDkR//pYS5SD/HbO7RjgiILeweeBCvSkHTUIR13mb7wZbzaDHF3iXBjkWzYPcBT6CfvumJZHqVSk9NnvkFRpAz6NxiPVjEtG8+rc+z4F9lXDGpoUtlonCUvz2KaDuurb6CUwDBMPL9BGvcoxPZvelO74jpl+r3riCJFiBzHCajVTzI19QgoyeKtF7du8oZh4LplSqUpXLfC+vpblIMZms1HMU2LJO7TXnuTxtQjtKYvAGwRHjlpfThOQLdzGVGkKFlgu2UajTM0W+cxLZub1/8KIR5eA9Juv8tgsIjjBFQqCzRbj+K4ZU3iimTy+QVJ3Efe5Wjc717j/eT/w3YD5uaephRM7//dPzB0hcsvTaGUIM8jlpZ+Rqv1GDNzT1EKmrTX3qRcnsPzG8wvPMetmy98JOPtn2QcnvAMB8SL1yiffwJ/7hhOpabL3QoM28H0S1jlCsnyLZKV2w+0UwYmluFgm86WGZtEIFSBVAVCHcxi+4OCacHMCQ/L1hbs/XaO7RrYjoHjmjieQRpJ7QUUS0pWgSsl9GJ669rXwjAMWgsulA3yJKPzxirVuont2Fi2geMZDDYK0njiJTTMdpjlmRYEVYv6tIPKFM2pBrWmi2UbNOcdbFcTmu5ahigUtm3QbBoYIkL0tWC7v5FTbzkEZZPKoyXSWBANBeFQX4zGy2PGywez498TE6FwtH4/D9ydy4hM0Lt6f5GzZUNjyiIKJXmmx17n5kxEEaN6MVZk4Lja42M8koyHkoUTNrUpk6CkR1wdU1Gtmzz1WQ/DhNXlgnCkx1krNZNa3cQwDQY9QRIrikJx7KQNSlvtn7vg8NKPd1/8TNOg1jC8x+tQAAAgAElEQVSp1rQHSbli4Acm0ViPzp444xCUDfpd7SnSnDZxN88hz2DQ1dvLc8XCCRvLMlBKT9L0OoJSYNBoWuSZ4ux5B8fVDrdzC9oLRQrYWBcEZe2c67oGWaZHgaWEldsf3W9IKaVH2bPkQHb5SuSkww1GK1eozJ3Fb8xiuT62XyburpLHQ/J4jEgPcZ59zOG6FcqVOYLyLO21NxkMbjIc3sZxAuqNMwTBzH3X4ft6GCKK2gwGN3XVwjAISi2yLNyht1BKIkRGGLUJx+vkeUhRJLhuBcct47hl6o0zLC+9smMbpqlbUlk2Io46mIbNVPM8nt+g37tOnoX4foNSqYnn3THZqwRFkRBFbcajVYo8QogM161QKjUJgmnq9TOYpn0khCeOOiRJH9O0UEpSqR3TFcP+dbJ0rFtaE4Hx3Xq6LBuRZWNMy6FWPY7rfnABrrblIaUgTfpEUYfm1Dlcr4bn1Vld/oX2xTJtypV53Zb7FYZtelimg2no9m1SjB+6jXdowpO2V8j7HQCCM48RnD6PXakABjJNKcZDRu/+kuF7b5B11h9sp0yXqt2k7DSxTQ8Ti0wmxMWAWIwmgaEfzVObaYEfWHz1d6Yp1yzSRPDyD/o0Zm2qUw6NGYepWYfVGyk334tYuRrz7G/WmT3hoZTBT7/fYdQvsG2Dr//BNIZpsLGU8vZPhxw/X6LatKnUbOozDq/9ZMDK9ZjO8m4W7wcWpy8GfO5bU/TaOfVphyyRlGsWX/hOk3rTQRSKl/6sQzgoKNdtfuP3p3F9k2go2FhOefVHPT7ztTonHyvhegarN1Ou/HLMpZ8/BMn5EBGUTZ77ks/V9zO6G4IolHzzuwHDgfZOmp6zaM1qp9j33kx55/WMJ55xmT9us74isCxtinbmnMPXvxMwO29x42rOlfdyFq/lXHza49kv+tg2vPpSwvJiwXAg+Pt/UEUqpc3MDnAalismJx9xOHHK5sp7OeOh5Ne/XWJ9ResiLAue/rzP9KxFrWHSnLF45W8Tlm/l9LqSf/CHVUplg6JQLC8WvPTjmNPnHL70GyU66wXTczbDvqRas/jWPwio1SzSVPH890LOXXCZO2bTmjHZWNefOc/h3/+fww8iiunAEGl8KIHjePU6UWeJ5iPPUjtxgerCeaYf+xLpsM1w6RKDxXcYLl3mk1jN2QulYJpm63GKPGJ9/Q26nW2zxCwbk2chjakz91yH45YZDpe4fvUvufN7GY92T9EpJcnSIbdu7IxLiMJ1bFvHwszNP4tp7XRFNwyTQqS0194iz0Mct0yWjYnCNuPRCmk6wPPrLBz7wtZ6NrcXx51d2wvDNVyvhmpJWtMX9pwIehAIkU60/wZ5Hm+NU+d5RJYd5EaqkCJDTpb7oGCYFlHYZqP9Dlk2Jk56KCAM1xmNlpATwrNQO3HfAYEH24F7VK4+5AtG2WsSuE08u4JCsTJ4m6x4uFbb4TU8SiGzlN6rLzJ4/WdgWhMTQnRS7SSUTBb5gUL0tmHgmSVOV59jyl2gbE9N3C/1zIlpmPpHKRPGRYf3By+SihD5IVd7TMsgqFqUKiZLVxPeenFAZyWjtdDAcQ1uvRvx0p/GnH+2zBd/a4reaplrb4S89dKQaCD4rf9mDgUMOjkv/mmHUbdg5qTHN//JLN3VjM5KxuL7MWs3E379d6eZmnV44Y83du3HE1+u4TgGP/tBl/btlC/8F01OXQj49T+Y5tLPxwzWc7JE8t1/Mc/y1ZjBRs71tyKKXOowPsvgzBMB0ajgyi/HFJnEds09p9w+rrBtbUa2cttkOND5O8dO25Q2JJ224PK7OX/xpyGPXnT56jcD3vxFytu/zOh1JBeecrEs6HUl4v2cm1cLnv9+xJX3MvJM8Y3vlqlUTdZXCtZXC577oscXv+pz83rOyy/GdNvao+Sf//f1e463zx+3Ccomea744/97RBRKLMvgb/8y5olnXap1C8OA1qzFoC+5einn5pWM3/2nVU6csbn6Xs7l93SVTkod7Pj5r/iMR5Ln/yxkZbHgt36vzIkzDn//D8v8/KWE7oZECPgn/6KGbUOvI/jJDyLOnndoNE18/+Pji3FwKGSe0r36C3o33tjO/Xr0i1Tmz1E7eYEbf/X/kgzWEEcgQN4yNzVsCpliGhauFUxe1w7XUdbFMl3dWlYFtuVTiGTrmtQqn0WonCjT7SPT0K1oIXNsy5uIcwss06GQ6aTKoOE4AaXSFMPBInm2s3KVpSPG4/vLBcajFcJwlYclgZukYFv4vPP80cRBa3uUFBR5TJYO9fSTUhQi1R5tByQveR6S59EOofV/ThAyI8/1MZdFRpFHZOlo6zwTssC0NoXeR4vW730Vu1EGwHCsiYGgQmUFnT9+ETH64OxMdsIgcFuAIi4G2KZ7JITrIbK0lG5f+QGm55Ms39KTFeqOCYJDxI5bhs1s6RxVR4+yryfXScRIq9QnTrW+VcazygR2g1n/LJ10kXHeefCP8ACQQhGNBJd+PsbxTE4/EWCY4AcmUsJ4UDDqFYgC/JJJuWaTZ5JoUBCOBH7ZwjAhSyThoCAcFkxlDkHFYmAbiEKRRoJwUGC7Bq6/9w/eD0yUhHAgCAeCPNMhe5WarZcfFuSZolS2Ju01yflnfbqrGb21jM5qjiwUjz7nUWs59NYzlq7EDDc+WeO5mryBZeq8Hsc1sPTE6ZYja56DV9JheHmm20R66EVHYBS5blWliSKJNLGoVE0sG6JQv3bzak65alKp6rbUaCh1PtB9Tu+gottiRa50260A01AkidRd+8k1yzIN0lgx6k+cZh2DMFR02oKnnvMY9AXdDcGNKznPfN7DMAxGA8lwoD+fZRlUayZRqBgN9FRauaLPJyl0zlGWKaTS39UnFbLIoch1inWRY1qvM/XIM9SOP45XmyYLe7sJjwIli309RPaCZbi4dkCtNE+ajyhkhlQC29Q3YQODWmlha6pUIclFipT5FuFx7BIuAaZhTSoIm+/VJMAybWzTRyEJ0w5ZEW1NnpqmjW37hGJ9V0VByuJAbR4h0h3i2/vBNB0q1XlKpRa27WNZLqbl4PsN/FIT2Ee5otRd+6inmHZe+41d1QPb9gnKMwTBNJblY1mO9koqtSgFrUm0zieRnD841OS73Pw+FZt/LtgWy236eB094ku3cY+3cGYbpJeXUIXA8Bz807Mfsn+gIi1GgIFpmLjWttD8YXB4wmMYGJaFM9XCm1nAqU1hlytk7dWtUVLT9yepxBIRHaw9Yhk2c6VHsE2fqOixFL7DMF+fuCkrDEyqTosp7xjH7SeYKZ0lFqMPn/BI7fVz672IR54uc/6ZMmkkcUsmolAkkc7e0t46OqPLsk28wEKxndklcoVbMikVFo5vkmdaLGvZBq5vUapYKAViH68YKbQOyPEMnEkchhRQZArHNfHLJrYLRS4ZdnLiseDUhQDDgMFGTndZVw0++02b4+d9QPHez0Zb+p1PApTSic5BYFCtm2wm0BsYSKF1OEWukFJ/55Zt4PsGfsnA9Qw8zyCekKMi05k75apJmkiEUJimgWloMnHzWkFQ1iGFrquXNUx9PtzT1XUzSFhCpWpMAg+ZaGtMPF/iTiouWaZIYl2dAYgjyca64OxjDu0VgyxRrK0UpImLYYDnG3i+TpoWhSLPNeErlfVreabJmxCT/xab5nFHdeXSN6Q7n94N05qEh6ojK4Ebpo1h6ZFiueXRoxBZwnj9BsH0SYzjBrZfxjD3uqQpZJ7p6oztYrmliTB1cuOYVCbuhG25+E6dRnCCKO2S5COSfKjHxCdVHt+poVQx4bwGw3h1x1O3ZTo4podtaft9NXl4MzAZJWu4dpmy1yIXCVkRUogUMbnRGYaJYdrasO4uVq2F//evxEpRbImD7wfLcnG9GlPN81TK8zhuoEmP7eux7H30Imryz67Xldzz9e3tefh+g2bzPJXqMWwnwLY8LNvTRMv81dan7I892uS7COUHh+itG8gkwyy5hG9cQyY59lQFd775obfA0yLEMh39kMHRtO8OTXisUoA7Pcex3/uvMSxbZ/B4Pv1fvgxxiGFZVB57Cn/+OCIK2fibHx5ovYZh0XAXuD76BZ30Fv1s5+ioQjLM26QyIhURFxtfxzfLh939h4brmcye8nj6a3VqLQfL0TeXvW4hWSL52fe7fPZbDX7tt5tkqeTKa2P67RwpFN/9b+cpMh2H8cu/GTB7yuf0xYCLX9SiuGtvhty+tHcJ8fJrIY98psx3/tk8a7cSylWbwUbOT7/f5Sv/sElQs8gSxTt/N2TYLQhqFpW6Ta3pMHvSJwklS1djgppNteFQaxWcuhhgXYk/Mt+iwyKJFZfezvju71cwTehuCESuycrdME3dXvr8V3weeczh+CmbW9dzLr2VsbosuPp+zpd+o8TTn/d4/eWEV15MeObzHs98wWd1qWDxek53Q/DyCzF/+M9qZKkWQqeJ3Nc4EthqkfW7kt/5x1Xef1vrjb719wNOnHZYWdLCY2uPX6JtGZRKBvW6RZ4qZhcUF59yefeNlEefcPlH/7zG7VsF9YZBe63gL/8s4rd/v0ypZBLFip+9kHDucWf3io8AhmVh2i6m7eP4FWxX6zPccp08bugKTJ7u8aR/eAQzJ6nMniaYPsF45Sp5PEaKHMO0qJ+4QDBzEikLwvYtRLZbuKykIOwsUTvxOEHrGPPPfJPx6jVdrjcMsnFfGy0W21o536lT9Wd07pkSmKaF71QZpW3K7hSeXQYklqm9UnIRM07Wd3mDGYaFZZgImWqzOyAvYsJ0g0Kk2KZLJqKJNsNCCF1hlTKnyGNs299VlTItG8vxOUo0W48xPX2R1sxFFm+9wGjlNlHYJsvCyZTWOU6f/caRbW927mmarfNMNc9z4/qPGY+WiaMueR4x1TxHs/kox0/+2pFt71McHKbnYE9VcWankEmGPVXBmZ/CsK1Nq6QPAQYVr4VlutppXuX3JNAHxaEJjze7QO3JzzG+9Dbx7RuYrsfcb/3B1t8rKUlWlwhOn9dePAfFZn9S5Tt62bvfJhGTsvFBjQePEnkmaS+mvPIXXezJyPOol3PrPROlIB4LhFBc+sWYpasJvbWMl77XwfX034cD3WoC+OG/XUOhqzJpLGgd81h8P6a/njHs6HZXEu39XfTbGe+9rElLFgtd4ZHQ38j56/+4geUYk5ZXwaPPVWjMOvyH//k2w07O/Bmfp75S49SFEldeH/Pz53uMewVf/K0mRS4/MYQnSxXXLmX8h3873PqzbWtRbpYqwrEOHXzvzYzVpYK1FcELP4p49SVdoem0BdGk1fO3P44plQyEhEFPkGfw8gsJ7/xSV8I231fkij/6N0OkVBQ5OA6srew+RnmmeP8tLUhWCopc0WkXRKFuqf3J/zPeSnEeDiSX38mIY91WA/jeH405ccbmq98q8W/+tz6DvqBUMvn1bwf8x3835OW/TXj/rYwk1mSpyLVW50///RjLBiFh1JdceSdDKcWgL+l1ChzXwDSNh+IgwfRJynNnmDrzGQzDxPYCLC8AYOG5v4fItAGgyGLa77zIcOn9B98YIJIQWeRYbonWo1/Q7FXp1o4qcuKNJbqXXyXpr+ocsLsgRcFo+TJetUUwfYLqsceoLjy65TnSufQyRTTcQXjGyTpJPsA0LK1FQWcFFSKjEAmjpA0oTUaUmrTed16PNkZXdbo0IJXAQF8vJAIhc+J8QC4TTarkTnfpokhI0yHlyvwOsS+A65QJSgd3sT8ISqUmQXmGXu8qvc5loqij23NS4DgBnl+7/0oOgXJ5Bs+r0+tepdu5rDU/k3Ruxy3jeh/cJNSnuDfS2xvIvMA7OY1hmahCMvzrN5DR/iHQHwQcqzTR8R7dff7wFZ6gije3QO+VF4huXcOp1ne+QSlEOMKwLQz74E+XCklUDLANB9fcOz0cdG/dt6vEYkQuj96DwDVLuGZAYNd3/d0gWyOVIWkstyan1FblfudNb9TTWh6AdCnbamHfeezi8SScD/13SSgYbOSsL6b01rJ7ar7zVDFIc4bdfNfNa30x1dtj82ar22VZrAXLmxEcBvrvikxqXVEsKLJPzpSLlBCOtb5m+zjsxrAvGfb1l9mO9yaQG2tCa3KM7ZDfTlvQaevX7zwWN6/qp/B7kQYp0VqaOxCOt7cdjXeK7e/2uV5eLCiVDc6cd0iS7Uy3KJLEsWLQkzvCEzdx+2ax49gPetv7EEf777ASgri3ymDxXdLhxq6oBcvW1c3pYy5RBrZZ4FgpQigcI8FSPcJV3d5RQidv6wwdgWmBbespM5mNSLtXKdKYoJRgtmyG3QLLMSYkRumg30mVznYMZBFTRB3y4SLSn6Iy5WFaJmE/Je73iXqrxJ3l/cXKSlHEI8K164gsptQ8huV4KCkmSe/xrimdQqYUcm/9y90+LfshLe7dzleqICv2XleWjYmidWr1k5Qrc+RZSJaNMEyboDxLpbpwoH04KAzD0u1IpZCTlqFp2nh+nVIwje8fzjX/wNubGFgCWJaD41YIgukdI+wfD2j9l2EYk3+3Y03ufUO+Yzm2l/sQSyWHhkwzxDDCmWkgxgkyDhGDUD9FPSAcK8CxfJJ8cM+ChmU4mKatKzsTrRwYk1bww+PQhMf0fOxqnej6FUQ83pPwqDzTra59Cc8e2c9KspHcIrAbKKCbLu/KAzIMi8CuMeOfpZeukIj7m5YdFhVnmmn/FGcqz032dPt/X+/+OWvxFeCQA2iwr6Thztf66/mW2Pig69/3pntHK3jpSkKRKZ77RgMhFO3FlNd+on1uzj1d4dzTFaRQvP13Q9q37yVyPEh69oePQx+LQ67n7tc/rF52e1Xwxs9TvvoNXT0Z9AV/+3xENMl/O8ixPyhkkdG5/Aqdy6/s+fdeyaQ17/CN35/ir/6kzXB1hX70GvFYMH/KpVy3WPtlBMZEkD/U++i42n8oqFiEQ4EIbzO4vIwUiuPnfNwTJd762ZhyzUIUetlSWU8LKqBStxn1Q/L+ZUbpVaKR4NyTJUzPZP3dmFG3OPDxH61cYbRy5ZDfzEeDOOrQ7VzixImvMDPzJL7XoNu9hONUaEydYWrqETYTqo8CRRFT5DHlyhzl8txEaC2Zap1nauqRI0/6zvMIKXLKlXnKlTmydASGQWPqERqNsxNzv48DNgmLhW17mJaLYWg9mTNxopay0K3bHcdCa9tMw8KyPUzT0e1N08GxS6jJMnfHbXwcYLoOdr2Mu9Bk/Opl8rUedr1CanWgeDCNZ600R720wFLvDTKx+wFjE55TwbNrlNw6Uhbag2dCFo8ChyY8ShTILMWu1pD5HjdH08JbOKWfqsK9n3BOlT9D1Wnd9apmzjV3lqZ/goXSo4yLHkLlKCSW4VC2Gzimj2nYdNNFhDz6iaJRtk4qxgzSVWzTo+HOM+OfmajEPzgoBe/8bIiSD5/Qfje6axn9ds7VN7SHgRRqS+eyfC3RLQ60kFruoX8BmPZPUbFbuGaJS8OXjnYHP8WeGA0kl9/OuHF5u6JU5GpL1PxhwnIMTMvg9rWUJNSk5OSjHj//yZDGjE1r3uW536wxNWPTbxe8/1rI0rWUR54MmDnusHIz5bmvV2nMOJQCkz/+121ErqjOOXznn04zNWuzeiPjypsRX/7tBmu3MobdAlEoZk+4VKcsSmWLm+/FuL5JOPrkiOsfBHkeMhoucfnSnzI98yTTs08wt/BZkqRHv3uV61ef5+z5v4eQR2PLsdF+lzjucur01zn7yLcnQycFg8FN1lffYG3ldS48+YdHsi2A1dXXCMN1jp/4Nc4/9g8xDAMpMgaDm9xefAnPq3Hu0e8e2fYeFDOzT9FqPUZr5gI6k0wTF1DUG6e1iabIuHHtL+n1rpImulZ77PgXabYepd44zZ1ZZuXyLM3px/W4fh5x9coPGPRvfpQfcRfc+SaV584z+rt3EXGGVQ/wTs4SX11GZPkD8bN6cJzjjadZH17R4vx9wo1du8xU5STHGk/x3vJf4FilLR+eo5jYO7zT8nhIsrJI7YlniRavY/q6/eTUpnTGVlCm9uRziCQm21jbcx2uVcK37y5ZarW/RFCoHGXoHp6tXHSvXE9/CKUdlytOi2G+AcXRTmkVKkeJEKEKTCwc06OpjgEfLOEBTTg+CCgJQipEsXv9muDcf7sVu0nZnpq4Xn58y7G/SlAKhAARf/TftcgVaSwJBwLHNQhqFs1Zh9qUTb1pU2lYjIeCLNFj8I5r6mcyQ593ndWc44/4hENto+CXTKpTNpW6TZErumsFUirmTnnYjkGeSkSuaB1ztPHaQBCN5GSK0ST7mLReqzQoU6NCjR5txgxIORoNnBA5/f5N8jzGdSuYlkuRx8RxhzQZcvPGXxOFbdJ0Z0O0yBOuX/sRUbhBmtzfsRx0xSUcr7F0+6e4bnnLAy1JusSRju+4euXPSdIhm+XqKNpgZemVLTE2aO+f24svEUVt8ixEyoLRcInFmy8QRduxN1k2ZjRammyvogmPkiRxjzhqE1ouXP5zivyofV8UcbTByvIr2HaJPI/u2ZIKwzWULAij/U10lRSMRysTl2aN0fA2RRExGOxPZqQoiML2VjxEFLW5ce15hsPthIL22puYlkOS9FFKkqYD+r0bEzfmnce9RJkyVRrMMKRLyIiQB+iCmAZYJobvYJkGpu9iNcoPddk3J1UxY3PCZ5/1KKUtaFwrwLUDlFLkMsY07CO55TxAtESfePEGtSefm2hPNOvy5o6hRIFTreMfO8XonddJVveOlhCq2Lc/Hsr9f6CZ2J7AsAw9HnrUUEgKlVFMTsJUhJPR+P+8Uban8K3KRx7r8Sk+GhSZIhlLhj2t07Fs3Vv3AhPbMZFC2x1oAb5ECn19SiK9TL9d0F3LCYeCLNHExbS0T1C3nWtrB8+kXLcYbBT01nOSSDJ70qW/UZAlkiJTlCrmlo/V5jY+SpSpMcMCM8YxFJCp9MgIz+bNOY52G48CrC7vHboqRMrS4t8daktS5qTpgPb6m/u+Z/HmCzv+nCZ92snOgNkoau8gNqDdmqNwJ2GQIiOJuyTx/sGx4fiDCXlNkh7JAYng1r7vfQj2xWi0xGi0dLj9irss3f7pjte63cs7/pxnY/JsPAmP3QmfMlPMcIrHWDKu6zDlByA8KsuR4wQr8FGuwHAskNqA8IPGlj7KsLBMl6yIEEWGYwccxa/90IQn21gj722g8ozK40/hz5/AAGa+8V1kEpMP+nRf+gnhjUsUw73Tlq+Pfv6w+/0pPkQYGNScWSzTZpwf7ELxKX61kKWKLC3odzThXb6W8t6r4db/3w8339u++b/10/EO8f7qze3l7qxW36lNuvxGpFu8d4r+7xBlf4pP8SmOFsmtNvnGkNpXn8SqlhDjhN73X0ZED59pdj9US3MEXoNMhAziFZQS2KaH7zQ+mpYW6ImO8ZV3iZduYnoepu1q/y4hkHlGMRoi0w/LgvrjCT3pVWOudJ6SVZuYJxmkImJcdBnlG7STG+zFWg1MArvOlLdAyapRsmq4VgkwUUgyEdHLVhjlGwyy/Z+CDAya3glq7hyBXcczAyzD1m1DmRMXA0b5BmHRY5i3d2x/2j9N3Z0jsGp4VpmyM4WBgWsGfGnmD3Zt63b4DkvRu3t+nqNAw52n4rSou3OUrCpgIJUgESPa8Q3GRZd4DxH7tHeapneMqjPDG70fUXV0RtuUexzX9LUtvkwZFRtsJDeJigFC7a0Nsw2XwK4zVzo3OSYBBiaZjEjEiH62Tju+htynP/0pOJB4f8frm92GO5d7AFH2p/gUn+JgcBeaBBdPYTUqGJaJ6btUv3yR/vOvI8ODVS9rpQVqpXlalbMAVPwZHKvE4wvfopDZHm1EA9OwKHtNCpHRGV3HNCySIiTO+kRZj7x4eE7xwNESIhpvuyhvWoDfazb4iOGZZQqV7Xtz+ijhmWVq7iwNd566O4dl2Fup747p41g+nlWmkCmjfINC7RyvNw2LujvHlHcc3yxjGe6kfaeF3SWrsuXiKmTGeI8wVXsyvj/jnyGwGxPCBObEsdI1Fb5VRqEQKr+L8Bg6rd5wJmm19taIoDE5Me/GUano74aJRdmZYto7RdlpEtj1Hdv3LF3qdLMSvcwgKnb2tT2rpAmOd4yme4yaO0NgN/Ct6uS4GPhWBdcKkEpgGQ79bHdOkWuWCOwG0/4pGu48luliYk2OqYdnBThmQCETxnmXVD5YyJ1pWPhug7wIJ35T9ydPetxVT4DkImHTQdhzKuRFfFfenLZqt0yXQqT7eln5Tg0wyEWC2Kf9/Ck+xaf41YPp2li1ks7GFLpvbHruoQza1aa3FArXCrAmOXKeXcFRxWTCcNeWKURKmHbohjexTXcyLajIRXwkvnuHJzym9hFQd/p07EV0zIm+5qhmhnfAoOEuMC46k+T0jxca3gILwWPM+GeIij6DbI1ManFczZ2nbE8x5S7gWxWuDH/KKN/ZILYMZ6vCIlXBOO8QFbp6YRsuLf8EM95pylYDE5No9ItdVYWSXWO2dI5TladJihFRMaCfr4ICy3TxzICmd4ySXdtVGVEoUjGmnylGeQcDk7KtS4pxMeTW+E3uJliaMB092XVMn+PBRRaCxzEMg6QY0UkWUShs02XKXeB4+Umqziy26XFr/MaePwzTsDhdfZaSVUEi6aZLpCLCM8uUnQYt7zglq8yGXduT8FSdaab905yuPENU9BkXXaJCe0pUnWkCu86xYAHPLLEYvsV6cu2BPq9teczUHqUfLpJkQ7Ii3PF57vSl0FCYpoNjeXhOjVG8qp2BDYtG+SSDaIk0H209UZmGhW15lNwGYbKxpaXbNMXTa5TUyyewDJthvEaUdibE69O6yqf4FL/qUJOwUJEmqKJAbU7uHsKHZ5S0SfIRUdZjqnwKo2Li2iWitEsu0l2WMyhFoQrCZINx2qYfLTFffwLP1uP+QhY7suYeFIcmPOyLvxYAACAASURBVPWnP0/rK9/i+v/xr1B7GXAZBla5SuvXfhPDslj74R8/1A7uBROLC42vcX30848V4TEwcUyPc9UvYBoWG8lN3ur+GMl2Ho6BwVzpPPOlR5nxT9PPljEwdlRYchlzafDSJI9GItTO5Z2xx9PN7xDYDeaDR7kxfl27vd2BklVj1j9DITOujl5lLb66gxQZGFvVnrtPIoWc3PR1RcnE4lzt86AMEjFiJb60i6F/EK7XvlWl4S5wsvIZwrzHenKdW+M3ECqb1DAMbMPlQuM3qLkznK1+jl56m6gY7qqaGZjUnFmujl6etK76m0lKOKbPU1PfourMUHPmqDmzjPKNrc/kGD6nKk9TdabpZyu81X2eVG4/cRhoO4XT5WeY9k9pU0yV0EuXH+hzG4bBdO38ZFIG1vpvkxcRluky03gcy3DJRUiU9uiNb9CqPoLv1snyEeOkjWN5lL0WJ1qfoxYsECYdxkmbMFmnWlqgHhwnzYfE2QBDZliWy8nWF7AtFyEz2oPL2KaLa5eZ91oYhk2YrDNO1hnG90/p/hSf4lN8cmH6Lla9TPyz95H5djSMyg/TqtdVmUG0rDPmMHAsj+vtnxJlPcQepsF6K3LLXyrNB3os3TkGKDrj65MK9oPj0ITHsGysUmn/8pZSKFFgBRUM9+hzfHS7xZ6UyI4mUOyoYJsONWcG1yoR5j166TK52mwxbCMq+gzzdWb80wR2g8ge7CA8CkUmojuC+XYuL4UgEWPdijGDfdpJOnDONPRovW06JHclLAty9juQm9tV6s63KNTERv/DeNoP7AZ1dwYTi266xCBbJZfxjkwVqQTd9DaWYdP0TtBwj1GonKLY+YNSKMbFBuO8S1wMd06byZRh3iaw61iGjW+VGecdFJpc19wZPKuMVIJOcptUxoi7CFVajOlnq0z7p/GtChW7+cCERylFkg3IZUohUqbKpxhE2oiz5NTZGF4hLcYUIkEpSZINMAwD1y5jYCBERpqPKUTMKF5nHK+TFSFSCrIiJM66eE51Yuql21uOHZDmQ8bJOmk+QqpZhMxJi5A0H+E5FarBwi7CY2HhU8YnIKCCg4c5+UciyMnJSBjRJ2KEPAAx9ifjtT4BHiWsO4h5QUFGQkLMiB45d7WDMXHwCKhSpoqNg40z2R+JoCAnY0iXmIic/dt1BgYuPg1aeJRw8TFg6zP16WBicpCMHxOTMjVKVCbfk7v1mRJixgyICUnZrVM4zllcPHpsIBGUqVKmhkTRZ4OYMTERsxyjRBkHl5yMDmskRBST33mLORpMY2GxzA1ixvv6oWzu82kex8YmJ+M21yi422Dv4WFh4VHaOu4Oul28ecz0EUsnxyxEsP3bbTFPg2lyMtZY3PH9bdoF1JkmJWZMnw5rd2zXpsksNaaICWmzTE6GiYmNQ5n61rGysTGxkJOzKCNlRI+EaN+pPBMLD5+TnGdIj5AhI/pUmSKgsnWsNu1YcjI2WCZjb58aF58KNYL/n703+5Eky878fteurb57eOwZudba1c1u9kIOOYBAihyMSGFGlEbQkzAvepT+pQEEQXqQBhKghYKG4mhGFNFSc6aX6qru6q6urMo19vDdbTe7Vw/X3CMiwyMyIpfuBqQDJDIzwtzclmt2v3vOd76vejYEUFKSkzLkGBv7WmPxZaHzAhVl+O9um5JW1Z2VH47QNxQe1Bjn9ySfEqZ9CpVSqutQUQSB20FajqGDVJ1brxuvwOERL/z94q8FlrQRjmPIzMu+VHjIVzx4UU3g4g0wtt90SOHScFYrYrCi1DkNu3thCNqWu+BV+LKBt8QEVVEihYsUEkvYWFiV4iTMr71GI4XDsntR6JyknC7KLS1nHRBmsFGe4XX89pYpAtmgbq8AMMmPmOXDCw+0RjHODqlX/JqWu844P+RFswaNYpr3ScrZheyPRldk5QJLWDiWb8aXNq7VTWcVR3hoFLlKqNmtSn7/NFzLr66pNjpT8vW8gJIqA5MVMzbaHxJnI7IyQkqXSXxAdsa2IC1mWJZN4HZBCJQqyIrQmFQmJ8ySI+b3OS8i4mxEzetVcvcAgqJMSYuQJJsYsc/Ksy5Oh0ziPdba79P0Lqrfuvi06NJihRZdXOGdm6xyUhIdY+NQUpCSXErqNhONS5sVuqxSF00C6ljYzHW6CgoSQibaAKiC86aCDi41mqyySZsVHOEtAI+ZVkpSnSCxsThhSnluAp2HQGDj0KLLGreoiyYePhpNTkaso8U2Z8uBy0JgUaNJlzVaokuTbvU5gUYREzLQHlOGDCkqgHIaq2xRp4XGvBfarNAWPQQWtrYZ0Ueh6bFJU7TxCMjJKHXJiBNm1bPQoM2G2CGgzlSPKMgpuWi2evb8t7mHLRwiPWOPJwiKN/7GcPFp0qHNKm1WLoyhgqy6ZwZwJESLMdSiw7a4S0bGUB+dAzw1GqyKTTa5S8SUI20vBTzrYoehPl78zsYhoMEqm7To4AgfBwcLaTiPFKQ6wcFhzICCYukYkhXg2eEdDnmOwCIlocsaTTo0RKsaU+a+ZtqAOrO/88+IhaRJhx7rtESPGnU0ZtGa6mSR8Z5n7V8nVJJRTEL8O+sm26JNl4CwX33fST5hHB+c8aW7OgQYXiUKpUtcy3sjPNHrA54KYBigIRCWQL8IOoTAsh1q995Fuh4qXY58t2sf0LigtHzdw7AWIOC3LaRwaDo9BJJV7w6r3p0rtxfClGRs6yIwtJBmAnfWaLnr1GQLaTlGfwh7Qdw1mYqLA2GW93ka/oyuv8NO/ets1z5knB1yGH/JJD9ikh3/1ncTebJOrRKojMsJqVqu3B0VY9IyRCBoOCvYwruwjdaaKF+eSkVrCpWhtMIWDrZwOTUVsWjYPaRw8GSdr3X+6KXHLYUhlL9OGNl5Y94osEwXpC5J89kbLR8qXZLmE3b7P6LbuMv2yjcZzJ5gS59S5WRFVBlbnkv1VSFY5xbr3KLFCoXIiQnJySjICarMT50WPTawcRhwyJjl2isN2qxzizu8j0CgKAmZkjAEBDY29WrFLpA84fMLL88Oa6yyySZ3FgArYkpGioePi09brNDWKxzxnAOecczFTJxHQIsuH/F7SCQFGREhMSEOHgE1PuQ7jDjB5vJMtoXEx+cb/B4+dQQWCREThudAVZseMSGP+ZxDnl2YQG0cuqwRUK+OZUqHNW5xnx4b9DmkTY+SgogZHVa5y/vUaPArfgroKsNxwA7v0GGVkoLkEsDj4NGmi4tHTMSUIdkb0xc6H6tsscY2XdYoRVGNoRk5OT61KntoAOMTPqfPIUNMRjwjJSaiQRv5wnRm7lILjSagjsd5j0YLSY0moMlJF2CpTY8em9zifpURTImYkZHi4p0bQwMO2eMxhyzXnJuHjU2TNk3abHKHAvOsRExxq3HZoL0U7Ng4NGjxNb6Li4fG6OskRAgsPHw+4FuETE3G8zVxQTEOSb7cw27VFiBHK/VaJp6D8DGD8PG1t9dAViZM4n2SfMJa8703YiJ6LdTgdHus/8k/wm62sZttLM/nzj/9L5Y2ZAkhEJ5L/OQr4qfLiZsdb5Oue+tCeeJ6IbCwXrqq+k3EvLtJYADHND8h11cbnMbFmFl+qhZtIfFlg/fbf0jd6aHRxMWEg/hLSp1R6gKlCzaD96nbHexLJtZcJUyyI37a/yt63i1azjotZ42g2aZQKakKOYi+YJDuLm3n/m0IS1gVsNVXdizNr4vGkLqXC1JqMpVcq6RyNoQQSMt0qaVlyCDdpdDplaM2LUPiYrkG1XW/s9O4Q0ffRmvNKHpOli/r+jKdWN36Her+GnWvR6d+mzA5Is1DwvSElcZdmsE6k2ifokyp+2u0als0/XXSfIIjA/IyYaVxD99t3fC50hyzT0yEi8dEDygxruEajYVFhzU6rLLJbVbZoqRYCniMPuw2W+IuJSWH+ilDfcyMyQKYCwQSGw+fsipPvRhj+mQkzBib1bfOURgunMAioM4qm2xzjyZdFGop4FlhnU3uIJEc8ZyhPmHIEQpl+Ai49Nhkk9t44nKz4xZdtriLT4OJGDDWAw55hqKsSqYWTdrs8A4N2tzjfWaMCJmeOz9bOLR0ly/4hJApSpfc4T26Yh2PgBW9zhd8QqpjJDbv8y0CUcfXNWxcCjJmTLC0NIBHrJLrlCOWi+P5BPTYRCCY6dECYLyN6HNAQsQxe0y0yXCcHUOtKuO3xT1W2EChXwA8ockM4S3KeQAePh4BY/o0aS+yfxFTqK59jQYpyTkwN2FQ7XfGhCGFzhfjeg4w1thmi7vUaLHObY7YvXIua4oOAXVKXfArfmpAnU4xppgWElmBy/DCQrROk3t8iIPLmD5DfVyNISNKZWGxzjYrbNAUnde+H97WCvXvvEdxPEZlBcK2sNcummm/3dBVJnqFVrBZ/eTXJDyoi4Js2EeVpeHw1BuUUbiktcykv8p+QvzsEcnhcg7DfAU+TPerh/r6JyKwkMKm5rz+jX0bMb8paRkyyg6IyylXnV+u0nMK0q6s0XR7dNwtSl0QFiMG6XNm+ZBSZ6gK8Kx4OwRcXjbRaAqdMckODU+gDEnLkMBu4VoBDXuFVf+ekfLO5W8V+Xsehrs2v3aCyzXJxSIDqa94LExm5KYPzSmPqtAp4+qeXpVlKVVOpl5tNVyqgll8jJTuwsYjTE5IixABpgtLnZYjtVZkRYhIJUWZmDZ0ZV7Ok/hg0W6vVIHSJXkZE6cjo6ybn7a+Z1UbPFB1dZUoXRqeEBie0JKlY4ohb1tIIqZLrr751Ca38UUNV/tLz7tJhwZtHO0x4oQRJ4zpkyzhtCSYDNyyO52RLpg6IdMLk0dOhkCwyR0c4RLoxoV92DjUaFAXTUoKRrrPiBPCaqIEKkkCix6buARLF9USG586LbGCRjPVIwYcLUpM8ygp6LCKJ3wCXadOi5ycmNOMZpVXZ8p4MWHPmNDSK9iVttaMMRkJNi4xIQE1JDY2DgWZKcURkhDh4Cw4Uhd5QwIHj4ZomwWXDs+d+5uOlBhVlfZCJkvK1ub/W9zDFzU8fQowC3IybY7fcG0ccrLq3y5CCyYMqNGo7kewuK4SiYNLNM+MVJGTVVwdRfQC8ATIMOW1dW7hCIearvMy3wUHF6VLJkwZM6jO+XwTicS+MF4NwK9V90Ix0xOGHF24HwNsajRp8PrARDg2MnCJ+xOswMXyAyzXQVivnmTw7AauXatK57NryW0k+QSNQln5tSU6XhbXAjzFdMzxv/pLZK1O+1u/z8of/DEnf/vXF7u0tEYrRT4ZobIU1OUHGBUjvpj8gELfTONDYOFaAZvB+zf63K8jdMXb0UCqQsbZYcUnuX407C4r3g6ebLAf/Yrj5DH78ecXtjOkr5dP3oXOGKa7DNNddqsyWdfdpuffYTN4B1s4OIl/TcDz6+VNKV1Wth4my2IJ+YKmjAlbuIt09nJRq1cPrVl0yRUqY5IfMapA5NsIrTPGyUOK6jRNp5ZibkFzOPrs3PZZETKYhVgWSAlF1VQhgOHsicl2CRb7mMYzJtHuQjZrHv2KEzQHd3FmRtdcVWIc7TKOLmYDSgriJZmWeYRMKq6KXkxIy6LLGg3RQmvFAc8YMVgKdoALROWzYQilhlS6/LMpAw4pKapSwsUM6bwU5+CREJ3jwZz9njkIcnDxuZjlcfGp0aBBi4gZI0yW6MWYl7hc7eGLOm1WyHRyDvAoNFlVdikpjBwF04rGnTNjTFl1g+oFz0VhIXBwSKsSoSH/DunqtYra3SEjOQcy5iTiBp1FieyVPJmuGSXlpVwigIjZIktn4+CcGUOGPm4+O7+fMWF1/xw0igHHrLKFjU2NBkNOFmBHYpPq+Nx4MVAnu3ScFeT0OaRYjKHlIP5sWEhycvZ4RMLF9mrDT7tI5PUICCpgmlSlxeESn4sxA9qs0qBD44oS63VC5wXlNCbbOyF47xayXccKPISUxmfrFSwmmv463fodZukR/dljsuLy+z2POB8R56+eKV8WNyLClFFIPh6S9Y9IDp4vb0uHl4oPmkxFeWOwA/PMxfUm+193lDpnlp+ggwf4sknbW78x4HEsn0Aa3soo22eSLzOtE/iyiWvdzNBUUXKcPKafPudZ+CnfXf0PaTlraDRPwo8v/dzZl+HbEhhcFkk5IyyGNJwV6rJLJuOlwKzmdPDtBqCZ5f1LfdpeJTSKWX5C193Gtjw63haT/ITyTVvaA+2W4PYtm3/yj2v8N/88ZBZqmg1BFME79x18X/Dxp+YlHCeaMDT3pdMW3Ltj8x/8eY1/9l9PiSKN6wpGY0W7JQh8gedZtJoWg5FiOFT0ViyGI0WcaPJc49jmM64rkJbgow9dokjzgx++/rWck5cdlhMPBRYNWngEFJScsH9l99Trxpx4PCcOixeyRQ3auPgoSsb0l05E8wgZ4+EvBTwtOhW5VDNmcCUHJiVeTNw1mhcmUY2qPl+1CGMmXgMDTKfX2XOYg595yWMeBTn7PKk6xmpscpshR+d4I216NDEaX4fsEzN7I+WE1wlz/oZDc3YMmYKWyXbMM1YATbq4+CZDxwkRIS4eDTqL7rsA0ywSEV4Kri8/Hk1BhqI8M4YuDwM2E0YMuMncNe/mAhaZocsiIWTGmAYvGnPfLNLKWqIYznBWOwjbJj8aopL0lf20mv46G+0PSfszxBsgVr9q3Jj5m+w/Z/B3f2PAziuqKj8Lf/4aE6dG65J++oy4+O3inhQqY5gdUKiMwG7S825zED0kV8sVbaVwULpcrKrBZDWKqrRgCfsCOVsKhxXvFp6sVxmP5ROvhURaNvkLk79Go3RhXOFRVefX1alKrTWFynEsH1cGi5bjtx1hMWScHbIRvMOKd4tS50TF+Ny1FFj0vB0aTg+tFcNsn1S9fPVw3VC6ZJQesBG8azrBvLscxl+RlLOl2SYpzKrSaCjd7PlwbIGUMBiWFCU0GoKdWzY/+klKqaDZEHz7my7djsXBUckXDwv2D0scR2BZgv6w5Pe/4+F5BuR89nmOZZr7UMrs35ZGE/TubZvtLQN2kkQznmju3rZZ7Vn8b38dozVY13wvuRVXokELF6/qgJIV186UR2zhXAJ2RNVFVZHwyc9M1q8W81b5RsXbcHCQVafWHAB4BJd2tJx2dWkysiuPpcSUCpe9zuwqgyAQtFmp2ruXAzmPYDFZy+ranQ1dAZsXf2b+cVUhF84enEYxZURKjCNaNHUHGw9FsniujAN8E7Rpe39zZqiXh4NbZZzai8yLGUdWVe5xTInqhetSVjIFihIXFwdj4VPDSDTMW7xTImxhU9MNxOL7zPVOiC9kBC0sAhrUqzE9lzeotOYRiKqD0LoWL7BYdN/dbFzPr4PZR36ljICiPG33fo11qXBt7G6D2jfv46y0sAIXlCb88Zevvk8hEUIa8cDfoBn3jQFPPh5QTMdXg53LnACrGKRXM9pfFgpFP336Vsi2FvK0Ew2xsIUAKrsFjzmvYy4MOH/ZlDonzIdExZia3abprNF2N4iKsSm1VKQ3ISwsIfFkg7AYUKh0UZ8sdEZWTdiBbFCTLdLSpLaFkHhWjVX/DrZwuOrhcStLhbiYGFCl58RNgSVsPFnDEpJS59fItGlSFRkxOiug5nSqY66E94SguJa2ws0iKWdM8xNSFdJwemQqZlb0DT+mahl3LI+Ou4Uv66QqZJodv/EMT1gMiYoRnlWn6azSdtaRwlmoZ5t7al7Lvt0gKWekZYi+qbO8gKKEo74R32o2JFsbEqVAWlCvW7RacGtL4rmCfl9xcFQiBBSF5qSvuLNj02xaeB4MhgrbNmWu/lDh+2KRxdnelGS5pixBKc3RseKd+zZbm5K//Bcxtn09kXQDdNpV99QKjnCR2l5o8cxbZS9vlxWVzo5YTOqvUy508PCpmdZterjCRWrnDIgQC5C1fNFVNUUIw8lQLwFfqiKzLguJXHRT+tTwhP9SIHdKsr643WWwplLMunK/Z/cxzyYFul5pKAULtRuBoE4TTwSUumDG+K1m24DqjrVo0KZDrxpD8hxono+iF0NVRb2C3JTntel49amBMO71oElJFucrsHBwF+XMjORcFm8Ohtr0qjZ5H1vbFXg9tdhZBsAuC1UV7m4aVjVfgK6I01eNxYug+FXCCjycjQ7u5op5Dt6AS/p8vjxd4L88ZOU/qdHLu2tfIW4uPGhJhOuet5Z4cRvHRVgSlEJlb2N1oHkefvbyzW4YAsuQemXN+EgJh6a7hi3NQ9Ry1tC6NKx9nZOVMWkZVuKC83JbylfTH7Fd+5DN4F2+tfJnDNLnROWEQiU4IsCVNXzZoO1u8Mnwf2eQPFsQ72b5ACls7jS+xXb9Q1ruOvVkBU1Jze5Qkx1azhpRaWwNPHmRdAmCNf8eH3X+mEl+TFxMSFVEXsZI4RDYLRrOKoFscRg/5CR5+tJrM0h3kcKm7a7zje6fMs6OyMu4Ah0+x8mjygz1zUWmIib5EU+mP+Vu81vcqn9Ez7/DIH2O1grb8uh62wSyyTQ/5iB+yKzov/H0e6EznoU/Jyln3G98l693/4RJfsIsPyFTEbbwcCwf327ScTd5NP0xz8JPSS8r+V4ScawZjxWDgaLZsNjakNy7I7l7x+b+XZu1NYsvHhaGvDxTeJ6ZsKNIMxorBkMFAvYOSsJQsbuv+O63zdj95Oc5f/A9z2RuLMHdO5KffJKTZZp37tusr0naLYuy1Ny5bb5vPFFICeUl72kLiwd8nS49AhoUImdMn4gpCXGVrSlx8LinP6hA+sUQb7BQus09VisxukLkREwJmVaCdeZ4FIp3+PpS/s5N47o0eHNNoivLY2DeIQkR8RWcljcRA46wkGywwyqbHFeQx8FbZMZmTCvS7ttbkQsE9/ia0V2iRSFyJgyZMiYhWtwzic0DPrrQeg4G9My5VOaeCuq0yEgXBO+YGfWqF9DGwa8yaoXIyXRCcYavs8Ft1thmhXUKUSxKRREzk9GrwMt9PsTjerSCObfq9eLqkXYqVPt64ay1CD7Y4fi//Rt0mr8RwJNV+l+B22YS27xctFmwUr+DJWxKlTEIn/z6SMtno3bnAa3f+S6TT39EcrhHGb7A3rck/tZt6vffR+cZ/e//y9c+yF9XeLLO3cbv0vNvL1C8FM7C6Xy79gEbwYPFwBql+xwljziMH57bzyjbJ1MR/fQJm8F7+JWAnhSSUheUOifXKc9mnxDmg4qTZCJTMaPsgF+O/ob14B182WCn/nXTYaNi4nLCz4Yf49kNOu4W60sBjybMh+xFv6LprNBy1xYdP6XKKXVBWobshp8xyHaJrkFY3ot+QVJOiMs7tJx11vx75pu0Ii6nS7Vv3kTkKmE3+oxMRbTcddruJuv+g+osSzKVcBD9inF2yCg7eGtcg2l+QlZGzPL+4p72vNtGZFKXlLqg0Cm74WeM0n2KV1iRxIkmOy4ZTRRFDs+eF/z4pynjiebgsMS2IU01thSUSpNlJoEaxZo0KxmMFHNtMK0hzWB334Cu6VTxV/9KURSavIB/9l8VRIlR0v7JJxlCnNrfjceK/+F/LilLfSnYcfFo0aXHOkJYTPSAL/SnZCTnWtM1UKcBfLB0P7ri96gq+zkvb91YPgCLLqv02KAuWqQ64nNt2n+LquvmdGwI7vO1S/ZkyKNKm3LvXDn6srCxF0rQL4YR/yvQQjNjwqF+xoyXkzAVemnL/ZuMESe4eKxzix5biwm9xyYOLqmOOWEf9Ra5OzYObVbosY4tHEI95nP9U1KSqqx5es9cfB7w0dL9aBQhE3ps4OJjY+PiE+nZgt8TVd1pgjkvpoEjvBc6+QRdVo36suiQ6piH+tNK3yY9N4Y0mju8+9auzTxMybRYjMXLxhqYjOJVmlDXDZXmlJMY/+4GZZigczMW8+Pxjfy0zsYoeobSBbdXvkOY9o3qe3kVqNcMw6f4TpvA7fL+5p8yjQ+ZpceM41dTsIdXADyy1sBb36pcVJedvEbnGU67+8ocn99UzI06r7venBUD0vKiPsqca1KqrNLVaVbOrzZK58b6QKXM8mFV+jidVYyab8og3UVjQJhjeWityVRsTD3zQ7xySqEycpUsLZ3E5ZTj5BFRMcS2PCxh2mjn/J1cJYzSfcJidC3yeFqGjLMjlC6Ji+nC/VZrbXRnyvFL9/EqodELEJipeGGpAYZfk6mYUbpPXE7I1EVCX1iMOEmeVgToMdq2kNKnPJN5nCstH8ZfYgnJrBhgBw0EJVqVlElovouEiRpgJY+MrYfdIKivkGdG1TjLZ8zyAVExeqXViFLmT56b5yZJYTqrZA7Ss8+SvvJzwKILK0lOf5aP1eKRPPvz2ZJJLR1c/WKbt1w7uCQVcXTK6MJEbbgwXAkaykpWX1FW3Ik6MbPKxuB6MedUGFIrhMwqrsopN2W+3Wk5a/lzbngfphPKJ7iybOFW/KRlkRIvykFzpembkmPfVpjupphERPj4eDpYiC1aWGasM+RtNodIJAF1bFxKisU9e5HDZcaOXpSTXgyTFYtBG97UnFtTkC+ud0ZS8bHmfCkDjIzO09wTj2oM+QgsIkZMGRNXXWKnIbArWYK33cSRk5FXWUEPf2mGax42Du4bWHjqrKAME5zVFrIZoAtz7sVwhn5FwDMPS0hWGw+ouSsk+QSl8ktGmCbKhliWjdYlUTZYlLVs4V5Qy79u3BjwWI6DrDXIRwPTev5iKE0+GmDZDsK5Odq0kBWR9uVkWqWLN0qezVTM0/CTN7KvUudEZU4U3bz0plHMigGzYrkiLRgAMsmP2Yt+ufT3cTkhjt8cx8lwWQaExYCD+Is3tt/rRlgMCYvhjctmo+yAUXaw+L/XXsMRwQuARxn16XlHnJA0Nh/goCnzmDgJwbLQlkXhaPbCzwGB5Xg0m++QlUOKZEoe/faQ6JetNd7k+mPuEwRi0SmzLCsxb9k2ujWX+7YlRNWk69NmhYL8xoDHw68mOsM9MbYT51/QvcnbsQAAIABJREFURvk4WBBil4VR+s0r8msTuyIev5g9FAh8ape2JcfMFl01hvzqV9mry99Zy77nbYSqiLwTPWCDnQXJu82KEdnUCRMGb/VYBFYFLgy5eMZkacnPrrSt5SVjyGj4zFBoXFyaVSdWVulsgwE8Bnxq/Ep1WWq7EvqbjxHTvSWrezRlvNDkORsGCNeuHENvKhLiijSuF151l41F0y14s87dZWEyPCF2t3kunxR//uyVR4Nr16l7PQqVstn+OkII4mxMXnlGvhhaa/ZHP0OpglJlPB9+jCsNEd2W3gWvxOvGzf0ZhEBYFkJa58nJi99jevWldUOhIoFrBdxtfJOOu0XDXkFay32i5vGr8fffGED5/+P/G9Hcfg/L8UhGy+UCpF+nvnYXv71GOjkhm5lyn2VJpONi+3XyaGyk1lWJ1iW11R3yaPxbBXjedpgJ07yI3Yp2+mJI7IoPceulXSOHPEOj2eIu9/gaAos+B0RctBNxK2CTky1AlgFNCSUKD2fRpXM2LCQtugtDzMtixpiQMQkrBNTZ4HZli3EqEWFjL4wrA+pL+RkxEWOGDDmiyzpb3MXD5xlfXth+rt7comvUfcnfCAH1qoiJ2OcJa2zTooOHR5MOA46YMHzrZTWFqjJwGgeH4JIxtMoWm1xu06NRzBijKHAJ6LFV7TsmIVx8V1ZZRLTpVuNVMGV0BoDqRUlWVCrMLwIaC0mdFu/w0RvhgL0sYmaLcqPx99pAUXLAed7lGlussE6D9mtnnfLDIfnR6OL8/hrZnYa3ylrzHUCQLZpwjOHx8tA0vNXK10/Trm2zO/yUpHg9AcwbAx6VphTTCcHt++jHD8nH5/kfwrKo7dwHDWW03PtoWUhhs1V7n467hW25DLN9Cp0ZOwS7zVH8ENeq4ckagWzxJPyEcXY9jZugtYEqc9Lw7aZoXwxLOqze/jajw8/J4rdT8pl/j3R8hLDe6vdc+v2Oi9daw2+tIWyHuP8cVeRIr4bt1YyJrIDw6Am2V8dyPKTrI92AbNonmw0p0pjGxj2kZ1560fEzyjzFkpL6xn0s6ZDHU5LhAWUW43c2cOpthLTJwxF5NKFIQpq33kdYEpWnxIN9yizBa67gdzdRWYJTa6MuWR1Iv47fXqex+QAhBGUWI10fhEVt9Ta2X0eXOTEWoEAryjTGCZpQvRgbmw+QXg1L2hRpjCVt8mhMdPKcZkuwtS3xA8EnP8lpdyxWehYbmxa7z0rKElwXtrYlx0eGvDzon75kHAdW1yXf+Z7DaKj58mFBFCp2bkuEgJ9/WrBzR9JsCQLf4uSkpCzA82B9U3J0oBj0S0aj138GcrLKrTzHxaPDKvf4cJHRkNgLbR0hRPXCvnz1OWVkOqyE6Y7Z0ndo0yNkshCAs5AVLTUgI+U5X5JUnSum3dr4PfkENOlyh/cq8nSBjb3wU5LCrOyvEowbM8DWDnd4nx4bBNRp0yMnrSTrDIcprfRvLju3kAm7+jE+dWqijqNvE1CvBATLxWTvVLYIHgFf8ElFFH67gKes1KgTIlxxKqA31cO3KjR4+v3FwvHewaVNj3t8WAkhKmTlneYRYAmLiNlSvSNddWEVFARC0qBNQnzB1qekIBbhovU90+m5DI9GV2XQiDpNmnTY4V2Sigdmn1GnlsKuFKtPfffeVsSE7Okn3OODynjWrTzV8gVQbrOCwKrc2F/ThWChcP/m5spB+IQkvxlYCdw2UTokLyNawdavz0vrbJRxSNY/Iti5TxnHYMlFx5awJJbvE9x5B51nZMP+S/Z2GpaQ9LwdpHBIypB++pS0jJDCwZU1jpJHeFaDut0FD8bZEak6y58xxqW2WwMNqsxQpTmuenubssjQqqTIE7QqEYDt1UFY6DI3P9cK2/EQluGnqDJHlQVaFdhevVK91ebnRboAGgBlkZpttcL1jeWDZbs0V+8xGzxdAkSEmfytuTlbCcJCFSlKlTieQb5KFZR5inQ8VJFX56Sx3ZpxtM5T3KCNdALQqvoegWW72ItjM9dCq7e0YhMWthfgNrrYQROVJ5RFhuM3sGwPy7YNAG7H2H4dS7oI20E6bvVxiQjH+J0NpF9HqxJVZKSTPlqVuPUOdtBAegGqSEnHBX57Da+9RpFGqDxBl6VxDO9umeuWp2ilycMRbqOL1+qRhxMs6aAu6TAUlsSyXRy/Th7PzD2p6kDSC7CDBirPTlc+WptxNa8VCUGwsoXlmFKPHWRI10NIm+jkObYUrG9YtNsWn/wkZ23dordq0e5YnBwrGg1Bq22xfcvC8wVacw7wCMuAl/fed9jbK3n+rCBLBZtbEikN4FlbN/t3HMEsNN1erZZgY8vCdQVFoRmNXr8MbBSWQ2aMqOkmrvBYY7sqIWgkNgE1Yh0R6xmFcKsS2PJISZgywtUeLgEeNTxhTCPnPJg54JHYhHpybvWt0dXxjHG0SyDq9NgkI6UgqyT6A7RWhHpKgYsQl3MwQqYY64gNPAJjQ0G9shWwsaq26SHH+LogEMsBT0rMiGNGnNDQRtBwTWyRVhYYCoVTacxY2mj/GG7I2w9ViRlGzGjqDp4IFgTgy0xF3+z3l4sMRkO3cYTLGtsLqwm70lPKdEKsQ9NFJi4aLYMZj3OdGw+fsFKefnGbREf02MASViUGGJ/LtpkxNMHXdWqiQY91MtIK6BofNzTMtCm/BaLx1jM9GQl9Dlllk4A6DdHGxSWrFgK2NkT/GWNKnRvA8+sVxX9phGmfML0+HgDBZvtr5GVMViaV7c2vyUvrbKTHB4w//RG3/qN/SnDrHtmoT3ZyABpkvYHT7RHs3KP//f+D8OEvrr1fC0nLWWM3+gX99PlCq6ftrtNyVjlOHqN0Sd3ukqqQjeA+ShckVXrMkg71zi16O98EYNp/Qjw1BnMrO98AIBisMx08IY8mCGmzeuc72E5APD1kfPSQPJnQ3niPoLWBJV2i0R7J7IQ0GrF+7/ewHA9d5kTjA6b9x9TaW7TX3wVhMT3+kmR2Qp6GbDz4Q5MhKC9vQZW2S2/nm7iB8T4pshBLekz7j0mjIRv3/x4IQRr2mRx9SXPtAeFwl3hygCpzulsfUeQR48Mv6O18kyKPqwwWWLa5Ft2tD0FYzPpPiMb7JLOLkuRvIlSWEJ08J5uNqa3dxm32EJZElyVx/xnpdIBWiq3v/HtksxF5OCLu7xIP9ujc/QbdB98mnRyjlSKb9imSiO6DbzP44ofMDr5k9PgTgt4tnKBJY+MB2XSI9A1YPf7535p7u7JFc+cDyjQij6ZYtk3v/d8jHuyRTvqMn3xGNuuz+sEfIuzl3LIimpAgmB0+Jp2eVNkn01UTnTzDa61SX7tjyNpL9yBwmz3iwT7p5JhgZRsha0aiARgOFXkG0jZvo2/8jsNwoPir/9Xwif7hn3u8/6HD0yclt3YkSaz58kwDYJbC469KHn1VsL9b8uRRpd1UgFu1qHdXLKJQ88WvCnaflfzFf+yzfUuy+1xx565kMlY8ffL6gMeI8qX8ik9O0+m6TUO0FsrKJ+xzxB4JEVvcfenK05SSJkwZ0WODpu5Sp0mDJhqjQ5KRMuSECYNFOWQeBTlP+YIhJ9zS92jSXhBYzecM8DjkOTs8wNGn4nMvhlHFzfiMH7HDA5q6Q0CTgEbFWRrznK/ISFhli55ev/I6/ZIfs8I6bXqssEGdRqVNJMgrx++YkBHHxMyWt4Jfbde07MuvtckJ+9i4eDogIa6IusvMat9sGMXrnIf8jB4brOrNSvSwVf0upc8+JxwyZcQt7lPTl/sHJkSkOqFOk5DpBTFB469mMldKK3LyC6KKBTm7PGJMnx3eoUkbnxoWkoyEMX3G9NnjCdvco6ettw54SgpmjPicj8+Urjr4NCgw0gtP+TkpCR4+2+L+Wz2eX09oomxE3Vuha9fJy1cxGr8YNwY8RThFPfuK3f/+vyTYuYezsoq3sW06gPKUrH/M4Ad/Q3q0pGX9qhBGaChTCVl52slQVl1FrvCN54mKGaZ7bNXeZ5juL7azbBfp+FjS4fCrH5DFI4qKmBpPT8iTKf1nH1MWKVophCXpP/sJtfYW0vZorT1gsPszHM90AO1//n9WJo0C2w3w6l36z35KONpDq4L25gc4bo0snpDFE+rd2zRX7xNPj5mePCJPZ2it2P7g313KdRKWRdBcZXLyGK0KvKCL5do0V+4QNHr0n/2UIgvx6ius3f0ek5NHuEEL12+ShCcgBLosUKpgdPArgtbaIivUXnsXJ2iaY0smBK0N/MYqe5//65ve7mtFc/t9k7mxXYp0BlovQIEqS1RpMiVCyqp7SKPLwnT6AUKazEqezFBFji4y+l/8WyxL0rr9EY7foEhDk6GqrqXKEsozyxghJNJ2yeIJuszJ05CTz/+O2kqV8Skys29VIvTNqWvXjfl5mT/5uUln+5bk7n2bWzuSd961GY8UjabgL/6Jz+e/LMhy2NstCQJ4/FXB/v7VwMSyTPnr/gObRlPwzrs2jiMockUSmy8+PFDYtsD34csvCo6P3kyZxPYbBN0tgkaPyWTEYPKQMp5hSQ/peEgvYDbeoyBDOA7jHuTBhGgWcpXxtrFh6DNjvBCeO12uzotXRk2noKCx8YA8npJOTphzMIYcMWVYEaXhVNSwqBRUCvZ4zCHPEZWi8rJQVbbjSz5bCCmafakK1GWAZpdHHLFXcYpOFzn+O++i84y830enKRM7ZGZl7Ge72H4ApUYVBZbvUmaJMXl1JEUGQvpYtqQMQ36pf4JteVDzqH3je6RHh2S7u0Q1wS+KX2C5HmK1Dbs1rLJAaXgef8kBz5hbMlxnsijJK1uPNyPydt1IiDjgKcfsLUQogepOF5X2jeI5X3LA0wvcJtuGO/dsDnY/51n8sAK42YUMT0LEIc8XXlRz7s4HHzk4riBNFA9/VaBESuIM6DcjHg1AayPW+Y1vO6iTjGyYU54U7POEY3bPE9GlhRVUmfVSMS1n/Fv3+6iyoJQKO+hSTiOEZXQg1OxMJk0IZKeB3etg1XzSx3tGBwcQjiTOEvacfY5kH5IC4djoUlFmKbrjoqUkk5ofez8jPjqk0BmWW0clKZbjgCVQ07efuXuTobWiLDPCdPCb0eFBKVSakBzuolWJPeojA0MA03lOGYck+89QcXS519ayqEwaBdZCnRSodGsyfLtJkRsCk9IFtnDPbafLgiyeEI52cQNjQogW5OkUXZWg8jQENI7fxHHr2F4dVWRY0sFxagZEaI0qcvL0NHMEpuRUZBFFZlY+thMgLBtVZJR5Qp5MkI6P49WJJ4cV4HmJ3Pu8hFUWKL9ECIF0PLRW5OmMIp3h+E1sr04ajai11vHqXcNbSaZkyaQqqyTG9bwiidtuDUu6545NX2Hk+rphOS520DDlRPQiowHgtVcXZaps2keVBdL18LobWG6AsB3SSZ9scoJTb2NJx3CRwhHYDrbn4zQ61eU6c7+1Pnd9yywmnfZxmysIIatMz4QsHCEsSW11x/CJqut7s/Pz8VqreK1VnFobt9GlSCPTpdPZwG10sWwHtz7PYOjFMZ6NJNY8+qpgOFCEoeLpU6jVBPW6YDbVPH9a0j9WeD4cHSrGo+XH+ejLgvHItJgnseaLX+X4viAMFc+eFEynmrgCPHvPS8JQ47kG/EzGrw94pOPjNrrUeztI1yfPY/JEk8QhtVYb4QYoIBungMLSkowM6eQUtjGD9TsbSNs1z0CZIYRFkYRk4bACJWecpC0LJzDXHa0pozHkKbZwaazdJY+nSNslj6eURYqQDnazh5A2RTwjjyeoIsdrruA5Pr5lkccz8jSkzK7mGc7LPldvs9xoUjYa8xNAF4Vp5sBMhEWRg7AQ0kfnOaIRmEYPYeGougHMeQ6zkJSYTORI28KWVjVZCkS7QTwYYJHj+l2c1d7CDyQ72CePo5fKZQtMB5mDQ0HJiP5bJyu/GAaKqpeKMuaXXGfXE3zjd12+P0qYxJef79ygc/49tmNUy2/dluQ5HOxX7elSI90Sp5WQjAuKHHIEOvDobGsIFIcnnNvXPITr4GytGhA7jShnIcVOh3z3CF2U2CtdZLdljqYoyeIUyhLhu9irHVAaq1lDdpo4SUo5miIcG3t9hXI8RWUFaVGiZYGzZd435SQEpRGORNs2sZOid1rYro0IPMr+GJ0X6DT7DQMegSMDXDvAEo4xNr5iW9cOKikVIzIrljVJ3TBeealbRiHRs0fmIIQwD/RrhNFbMfYFjnVa6zdaMykNZ5W0NC6zzpKTL4uUdNZHlRnr934fxzshlgcG8FQiYlI6lKrAr/fwm2s4XoM0rMzcqv2VSwitWmuKLEGdBQ3V9mby1ISjPaTtUu/szDeoNrnsATTO8lpXf14AJEIIw22p3LLTsE+tvYnX6GG7NY4f/5A0GpmXpmVjWXKxvUmjgFYKtCYaH1Dky/U/hGVKLI4nUaWmyBVlfrPUoS4LhBAG+PgN0BpdGgAXrGwDAlVkhEdP8BpdpFfDbfagp0nHx0THT0nGR6y8+z1s10dYFkUamv2oEum46Ao0Gb7YPEN0+rLJ4ynRyTPq63cR0kZYFmUWkwwPCFa2aWw+IGt0TCYpuoLYrbXJJClj72D4STX89rppaa+18FprWFWnVtDdwq23sKSD11o1x6jKRRfXPKsEGBLyD86Or8oFXZyflyzr6nnqk49Pz7vfV/zff3u6z/lLex5Pn5TwpHzpPm8StlfHa6wQrGxTpiHS9irwYu63dHzyZFbpNFWrtDReAEBhWTQ3H2D7DYSQFOkM6fhEJ8/IwosimEI6BN1NWrc+RBUZs8OvyMIxCGis36dIQ6TjEfafk0UTbK9G+9aHSK9GPNgjPHlKHo2pr93DbXQMibq/Rzzcp8xOnwtRZXH0Qm7PmG+epunMv+f/m3OILlPRFZ6L5flYvn/eyl5Ksv19LN/HbrdJd3dx1tawggDyHKSknE7JB2eMJoUAKSmmU1SSGhDY6VIMBqg8RwiBs76BrNUQUqLiiDzP0dnV2RoLSYfVKoOeMOLkjQIeIQwRHyHQSqOUUe62HaP4DUY/qkp4ICxxbqGQZ+bSzcvARaHR5rVWlXE1zZbF9/7A47NPM8YjxXwqkrbZZ77kEggBtZrFR7/jsnVLcnJU8uSRBg2eJ2g0LZptC2u3WgSXmrLUrG9KGk2Lzz7JcRzz/hTVuSHAaro4dzaxe23ywz75QZ/gW++hKvDjrHWRnSa6KChnEcX+CSrVWLUA793bZM8OsTwX2QhgZ91kvxs1/I/uk+8dUxwPKUczCHz8D++DtCiOBmTPDhG2jXAkwrJw760jW3Wsuk/2/JDicEBx8utvaDlzxZGWQ8NfpeGvGzcD60Vqwfz5MjYUSTapsjoa32lhJTa8ps3JKwMeK6jhb9zCaXex601GH/+AMo5M6cKeZ0UUXDOzoHTJIH1OYDcp9Sr91LTdhcWQSd7kvdYfsOLugNA0nR5hMT4nNOfVu/iNNRort5G2u/DDAohnJ/j1FW597U8Z7v/SEI2FwKt3sWy3Ah43e8hH+7+ktXaf5to7ZOGw4u/MGB5+zsaDv1dlhOIFkfm6EY72yKIRWx/8cVWamTE6+AVaFRRZSB5PsN0aZWnIs36jR+/2t/DrK6A1aTRk1n9C0Nqgs/kBadgiDQck4XJNn61367zzvQ5//p/f5dHHE37yL4754V/ezOF9svs5070vzEq2ut9es0f7ztcZPvyR6aDKYrQq6d7/XcosIXr8CdlsiFaFeVmgOfzkX1cA2jpDsBYMH30KWp2+CLVi+PinnGXmzUnOu//mfzFvIV0RwTGgavT40zMv0ssBXR5PGHz542oTs102G3Dy+d+ZLBOiyh6a38fD/YUAI1oz3X9oPqc18XCPlxEvqo+dizcFTN7WPtNZ35SgXZ902icZH1ct+ZrpwZf4rVX8zuYVexD4zTXCk6dk8YRaZxNh2Qi5/HUkHb/qQhQcf/7/UCRTQzwXgmR8SNh/zujZzypRNE2RzDh5+EMaqztIv0Fz812Gjz/GrbdRecbhZ/8X83s0jxoNOmKVLXGfAYeEekJOSo0mMTPmGi0FGYmO0MCWuAMIIqaM9BHRC7yXcjpDxQk6TbG73YUumYpj8sMDZKuF5Xlk+3vIWg0ZBBAEqCiiGI/ID05L9rpU6DhChaFZvDVb2KurOBMzidmrq+SHh+giN4DfcZbLhpyJgAZrbFGnxYwxQ45ems26SQgBm9uSv/hPariu4Oiw5MlXBb/4ec4f/QOfO/fN/f7rv4zprFjcvW+ztWMzHJSUOXi+4H/65xHvf+Twre+6OA78m++n7D4vmE40/+l/1kApTV4pjv/9P/LZ2Cr4+Icp4Uzznd/3uHVb8j/+dxezGmsbFvffcfgH/37AbKKxhGBtveRwT/G9P/S4e9/mcK/k0Rc5aWJA2slRSatzmpX40z8L2NqxabUtjg4LPE8wiDz+5Wcmuy9cB+/eNjrLEYGL7UjsjR7FyRAVJqgwRngu5AYImwyQcTov+mPUJMRe7yKbdXSWo9O8AjxT6n//WwhpofICXSrslRbFYIKKErx3d1BRQjkJTfbQdciPhhQHNyENv9loeGus1O/w3tafYFXNP1qrRdOOCUGpMtJ8yjQ54njykLq3gu+0iLLhb6ZLy3I97FaHlT/4Y+x6A8v1kUGNyS9+agCPJQlu38fprKDShOlnH19rv0oXHEZfIoQw5pBVhMUIEmg76/i2ISDmKmMv/AXT/JSEm6czVFmQJ1MsS1LkMUW1epv1nxBPDrEshzQeIoTEmjrEY0MA1tWSocwipsdfndMPUqqgSEP6zz8mi09l4Ys0ZHLyiHh6YsBInph9lQUnj39k1nxlgSUdsiUZhbLIOH7yQ/Jkasi68Zhw+LwCSRnHj38IWqNUTpEZk8povE8Wj7Esu+K0lGaCfv5TLGlMTdNohCpSk/GKR+jSdHmVxfIX2cq2z+aDGrWWw+2vt3jy6SvoHGhdqUWfgltVZOTxxHx3dV3m1w0hqq62/Pxsr+dKwGdBskaXS1okL2mb1ErxYlbN3N8rHhYhcJpdgvUdQBAfPMFfv2Wuf56ShxOTcZM20gsowillloJW+KtbxIfPKNMYEDRuv0c6OiafDEBralt3UUVOcryL0+xg11pYjksejs0+bQfL8SmiieHHnRFEtBwPWWvSuPMe6ck+2XSEzlP81S2KaEY6NLow/voOlrSJ9h8TbN5BukGV4UpIB4eUyZtPY89LivNnZ3Evqp+/LP2stUKp0vDQyuLKublMI6L+c8oipbX9PsnkmGw2Ipv1F0DT3Hdt5AqCFrZXQymF1GoBpFSemgzukrGQkpDoiEwk9NUBGQkCi45YYw6sbSQunjHWpGDAMYXOcIVHT2wT64fnuDLZ7vNF+i4/OUFYRnWaskBlGXo8RqUZKEW2v08+GJhtigKVvJCRVSUqScgO9heZ2/DTTyhnpiRXhiEqSRYZd5UkpiR2Jtr0FsRxIwhZo0UXRcFIn3DMq0v2L4utW5KtW5LhQPGLn+WMh4o81/zhv+Mx7Cv2nqXMZopvfc+l3bFwbMGnP0lpdSTNpqBWF/zu91zqDcF0opiMFB9+w+XdDxx2nxd8/lluSrQa7r9r0z8x74/f/T2Pp1/l1Ovi0vX2eKTZe17w8Jc5R4eKvWcFjx4W5Lnm+ZMCrWB7RyLl6YLlxVfO+pYkzzQ//ySjt2bhegI3yki/fI5wZJWyEliOTTk2IKgMY3SSoYuK5xenUCpUGJH+8nGVVa5k0osScTRE2BJhS1ScUs4idJYTf/ql+Q6l0Kmheug0M8A4yxfPIUqZd8HsN8vdafg9Vhr3SPMJh+PPibIhpcr4aPvPOJl9RX/2GNeusdZ8l6yMOZ4+xLFNpScvE4S43FLjJnFza4l6A3/zFv7/y957B1l2nuedv+/Em+/t23mmJ2fMcJADARAkSBCkmJcWFayy5LUsybLW9q5rXbW13t3aYG1Z0q4c1i5vya7VWjQpySuatElTzCBAACQyBpNTz0zndEPfdPL37R/f7TTdPQEzA9AynqqZ7r73nHO/c+4J7/e+z/s8Q1uRQYAwLZz+IYRlaVFCAWYqTXrLNpKbCXhIaEYLSLXWLTmSHq1IUg0myNu92mI+8aiFU2tsHTQpNdTcm6vOzNBbBK/RnYmvbDvo1Nbd/ILOVZkQpZBJSGdxes3LUkaEnTpht6y0ejut2pIb/DVm9jKhU1+5wSxxg1b+7rCarAkQ+U0if21AksQBncUZrkYSB3q/rxrb1TAtgWlrXoBlCwzz9vQzyiQibNVIomCZnAy69CQQeoZ+w9K/d1o7SWCYJk6hrLMG8xO4pf7lYMcxLT1Ww8SwbJLAQyQRYOD29ONXZiDwQIBT6iP22zrgAexCGRn6+AtTOMVerGxBq5Cb5gqR27SRQWd9kCAEhmWRKg8RtxuIdgNlGNiFMsBywGNnixiOA9OXsfM92hbDMEkCPY47x966arimhZXKYqWyOivjZJblG+x0DtNJd/+luoGKXPl3rWsF3f6fBB3yQ/s05y0KCVsVZBwhTBsn20MctDWxP1fGTucJWlV9/nevI5nEm/IKE2IiIiIV0qHZNdJ0MDG7rdBL7eLaaTtSEXW1QIhPQZXJGnmEWquAmzSurWWjgoAk0Cn6pNWE1rUnGypJlgMc0Jmi5d9b19c8y5CjLAYwukpCFja2smlSp8Ht194p9hgUSgaNRcmFsxGdtiKdFuzYbfHKiwGXR2PqNcnHPp0hnzdotyRT4wmWJchmTUwLtm43CQNFu6WYn0uWS0oIizdfDZmfTTC1OD/VhYQ4hkNHbH1tCahtYpES+IrFumRhLmFmMmF2OlmWgKhXJelMwu59FteimaQzBrVKwvRETKHk6PKWSkhqm3e4ycbG76kwJl6orwQ710E8c1XH7ar1Yu/Gyz4pMsRdg9ZbNzfdHI6VI+OWafrzzDcv0PLnSGTEgaGP0PIajaTOAAAgAElEQVTnmWucxTYzpGyd4ZIyxjbTOnhXcZfv8y5weNyBLRTvfpjmmbdonjmOmUqz/Vf+1vL7Skq86XHyh45ipjcyttwcm/ljxCrgcuuNG9vIpg/3tSTXay97k1i3ndv1gL4N27nOPs6Pe5TOtwg6MRderTN36fbMBJKgQ3v20rrX/er0Bku/y1CSoL6AW58HYRAuVlBJQlCdxZuboOfww8gwIO408eYnl7M3RleU8HqlAwCEIDuyh8T3SAIPt2eAJPB1K3x1kmhxFV+jCxn6hLV5wvocYW1+OYha2t5GMEwTTYjUZPxr3rFvB5YoJoaJnSmSH9pLqtCPlcmTG9hB2FlECJPC1oO42RJ+KkuShGuCOx38bP4RTrZEujREprxFc9VW2WAFzQpOrof+fY+wOHkKw3I0vyVT1NIIXa2qt4uEeNUDSHN1dPeYQHaFA3Wp8w4f59sAC5u0ypImhyQmwKdClUucWRZCvJ1wHIFtaUL+cklVaI5MnEAYKGQCpq5AE0WKOIYwhG7FklTKIAwSAk9LOrz4rE+xR3NvwkAS+BLT1lyx6cmEdEawY5dFqcfk5Rd9Thx7ex1ngi6PcrM3gThSRJGmZoWB5hbd8iP57fq/vI31DAxGxB7qLNBUiwR3UHvJNl1s02Vm8STtoEIsg26HpEQTBQSdsMJC4wLFzFa29z5ApTlKLENtnnqbcPMZnnQGu6eX5re+QtSoY6au0rFQChX4euZqbywS9R5+ejA72qE2FXDy2QqhLwna71Q+4KcM3QDGcFI4xd5l8vHaRXQgATqLaRfKuOVBUuUhIltrcbg9A8jQ1wKFSuL29CPDgHCxSlivaH6FEHhzk1jZvOaPxbq9+XoQpomVK+L2DGDYDnFHiyO6Pf0YjotTHtRdOu+AaW/YrlO7fGyZnI2SRO0aC+dfWibPJ5Gvyy8Cgsa8LtdIiUwiOvPjy+Vkb3EOwzCRm2RfwlaNyGvSmrusBUHjaHnZ+vhJDNMCwyQJO1pM0DAxxk/qZbpcqyTyqY6+fs32bK1p0mBE7NE6LsrHx8PGQbu6R2TI0aRORMg2sb/bHB9RVTO3RSfkTkK3UU8tiy0utfhHhHdk7KPnY5IEPvdzGXbvs5iflczPJfzg2z6H73Z47IMpfE9x9lRIuc+kWLoqaFTwwrMee/bZHLnHYWZKMjEW0VhUvPRCwCc+l6XTlnQ6iiDQPLXmouTs6Zjdey2UhHbrxvdLCF2muvcBh30HbXbutrhyyWFqIsFrK558Os3QFgvfV8zPJLipnzJ1v5uERHJFnb3j2R3onmvL3McVxEmAIQxsK02YdPDiBjk1QNbtZ65xFqWSZemI23G0b78giRCY2bzmBG6iaPsefnqQRAovivGa72wr6k8jgtocwrJJogB/fpKo3dDiiXMTmgsV+shQMyRVkpB4bdoTF4ha9WUz0vbkRaJmXXNxlMKbGdMaHIGHvzDd5ZMoonYTGXr64b2RCW8XSkm8uUkSv6O7RcKQzvRlktAnCXxQEn9+EmFayMAjqMx0szr6zrKaE3QzKBQFW7ea3HOfzcs/CZmbkzQba/lW8qrMiZJyTdfTasRXBTOr7T1UEl/zdrsUfC6vsyqgk3GgXxf69aV3kshfF/gl0bWPRUzIIlUcXAKlzSalkpiYLAsfCp9A6WyIJWzd6qyiZYuJn2Zs1EZ9J+F1JHMzCa+/HNBqKhqLWmphsS5JpQTzJYMk1lyaTDYhmzOoVROUglolYXLCYGoiQSbQaStaTUWlktBpSbyO5s5EkSL0FbNTCY1FSToj8D3J5HhMoyGvmeAOA7hwVks8NBa7Mg8dxcRYTBDon1MTCYs1SRgozpyMmBhLiCJFtSI59pruCqtWJKPnIhbmErzO2zsHbFwKoqfbKahLo3W10PXzslBILKEDb6UUhjCoqwUEBlnyWMJGIEiQNFWVmAiXNHnRQ6h8TKH1jULl06GJid1VbS7QVHU82ivt+l3D37TQFRpJTKB82jRJkSYj8ssdjTERDVUjvo52kzYBDXCszBo5mTDxMA2HtF2iHVQwhNn9JwjiNqawMA0HqTZzVb853HTAo+II6XtY+SIy8Jd1H7ShqIUwTNyBLXomdzXx7j28h7cJx+ny+OI7l8DwZseXf++s8oHrTK0vzckoREYhi821HLXGhbVmts3LK2rjcVvbfmgo4lad60IpOtOXl/9MvBbNS6fWLNLqrHA/4vbt4WH0lA3ue9Dht/5Wln/4D5pEb0Q0G+9y9m/TL36DktjbOEkiQhZV5So36qs4F6s266vbp0bspgRdWhe+p96JJN0dRxzD3EzCN7/mLe/bUmmruhAslyavDkoWrhLHrC5ITh2PME3dLbWE7/25t6bZzrIgXzBRCs6eiqgsXDtr4fuK42+ufVAv1iWLb0rYIDB87vtrA+YroytB/Pzs6mvjZiWxwcGlX2zBwMTCQqLw6VBmCBuHmJCcKBIRkYgYlzSeamNg0mdswcbGwOoGtSFttUhG5NkqdrFIBUvoDsEmi/iqo/3vRC9bxC4mGCVWK8GwS5qS6Kck+lhSvG5Qw1Nt0iK3PE4bR2tEEdBW0bWzp0lAGHuk7OKqzixFELWwTJd8aoB6ZwLXymObKRIVEcYdbDOFYVjL7em3ipu3lliYpXn2LQae+gytcye73RGQGtqG2z+M09NL8b5HaZ45RufSuVse4Ht4DwCf/HSKVkvx+qshlcp/yk+D//TGbhjihmhKf1HwbmRqPvdLeQ4ccahXJf/6n9VZrN3ZEsM7jWSDWPlm/Smv3sbVgdLd9ztsGbEo9Rh89z96y2rjNwsTq2v7wTJPS58TCgsbgUFCjIPbJbuHuGS6+kWqGwj4XWaKXtbE0tnATTIhbRqclZqnmqVAVhTpZRgblwCPKjNkKbCoKrRpsEscIicKOtOiDM6pY9psVeTZJQ4u/+2SZoEpPNkh7o4PdLbPV22yorTsVQea51UQPfSLLbwlX+zuP8vrVdUsNaWbJYr0khY5+hjCo71GZXzd/gVV6u1xerLbmVk8tbzFSvMiQ6XDDBYPUs7vJOv2aveAzhRZp0ytM06ldZn+/J5bdoHX+3eTiGoVmmeOY5gWdqmMXdIdIz0PPIoMA2QU0TzxOq2LpwkW1ncPvVvYdU+Bhz47RLseceKHFRbnQx745AADOzMoqWhVI1769zPUZwNMS3Dw0TJ7HyziZiySWDH6ep1zL9WpTvmbXqRu1mRwV4Y995coDjhke2yctEkcSLxmTG3a5+xPatSmA9q1jU+OIx/q5cCjZUpDLmdfrDJ+qsWVt64/a9/xvjzbD+fZ/2iZ2pTPxdfqHPvueu+sRz4/zPDeDOWR9R5CjfmAMy/UOP6Dm/fcSuVNCn0OBx8t0zuSIlOwcNImSaRoN2KaCyGXjzUYP9XEa9x4+UwIbZj54adSzE4nnD0TU+2mvd/DncP8rOS5ZwImxmLOnY1ZvA0O6+9hcxjm7euQ/M8VF8/rEpRtC8Lg7Z+vGXKkyWLj4KMDBUmC2X1cJiR0aHY9tjS/JMBHEiMwSZNDIHDJkCFHRNjlSulMydXWGAKDNFl6jWGk0oGULXQwpXW/lHYcENpqI1ax7gglhSVschQZEXu74ZW57DGnOVqSQPnLwdhqrA5klmDjImDZwHV18G9gkKeHgigjSUgJ7TGmHRKujXZYIW4EVNpX8MIlmRZFtT2GZaZQaHFBP2x0u7bOoZCknAIZp6e79LuQ4ZGBT1iZozM2Snr7boRl4l25CALdydJq0r50jmBumqRz/VbJdwrFQZdDHyjTrsc0qxHVSZ+7nuhlaG8GJDSrEZVJn/FTTQxTcNcTvex7qEgqZ6GUfqA3FiKCTrJhsFIacukdSbHn/hL7H+mhNOiSLVlYjq5T+62Y2nSAMASTZ1rMXGxTm17P3cj3Omw9kGPvg0umouqGAp7B3VkOPFbm6Ef6OP9ynfkrGzPuS0MuQ3uzbNmfw7QElmNgOfrn/JhHZfLmOR/5XpuBXRm27s9x+IO99G9Pky5Y2I6BVNBZjGgshKTzFoYpmLvcuWbguBqmCeVeg917TIwl1db3cMfR6Sg6YwnjY/+ZktjfYcxOxTiuYLGWEN+k0vl70KhXJTdQJL4uTCxsXFz0g9juutrLbodeQoxHG4fUGn5XQoyBgYt2nU93A54Aj5gISdLt5lsb8CxpIuUo0KGheStYK/5cLGl/r4QxwLIQqtEthOnwJqGp6stq2TqrFN9wsLDatW6jYCgtsmRFgY5qLGfCVsa5OaLYI06CdaUpP26w6E2DEBTSQwRRi5Y/x6I3Rdop4ZhdTTEZXceq6cbw9kjLUtIePUt79Kz+21iyM1hVpP0phBCC8haXox/pI+gkbDukSVmGJciUbD7+N3dy4pkFAk/y0GcGadcjhCFIZ03u+Wg/rWoEAk49u1ax0jAF9368n7s+0MuB9+toNA4VkZ/QrIRkChb5XofylhR77i9y8bVFTv2ownf/5di6h/7MaJvzL9fY+2CRnUcL1Gf860ozCEMwvC/Lrnt0kHTx1UUmz23MLxg/2STsJFQnfTJFm54hl0K/Q3lrasPlr39Q4eCjZe79mQEOP9GLMEAmiiiQtBcjMkWb4oBLeUuKnUcL7HuoxMkfVvjuvxojDq/j8yMgnRG876hDNiswunobV9skbHRsblDOYrlUc6cyRhuVgtTyfze//tsd581s53Z85q1sY8Nj9hc8BvjBN+68O/l7uDHovruApYvUxsXCZoFp8pRwcOnQIkUahSIkwMFdDohSpDExMbE00R2fkJCIaDlgWo0cBdIii686TKhRshQpivJ1XdhDFXQd4JtcUadvUVaga/NBiEKQIsPVXKQCPdg4eKrFmLpAL0NkRR6b689CNdl64/v9ojfJoje57vWUKtDwZohlSD410BW3vTXcdMCjlWEdEm/VBfouBTmmk8G0XFQSkS4OkoQ+7fr6A7cMAem8Dj6qUw1+5y+9iu0avO/DfXzol7eR77W57xMDzF/x+NLfP82lYw36t2c4+FiZJ/7yVkYO5mhVozUBj50yeOzntvDAJwcZ3J0haCd85w+uMH6yydwVjyRWmJag0Oew8+4CH/21HYwcylEaclmcDTj9fJVmZSVjNH1eH9eP/vUd5Ms2AzszbDuSZ+JUc0PVUGHAzqN5BnamyZYsokBy/pUaU2c3zq6d+0mNi6/VMS2BEII9DxTZ91APT/zS1ps+/um8xfC+LB/7GzsoDri06xE//OIE51+u0ZgPSSKFMKB3JM3IwRxP/8YOhnZnMS2BlIof/OE4UbD+3DEt+OVfyXDXEYsdOy22bzfp6THo6zM5eo+Nbv5buRD/2q/UGL2YgILePoO/89/kOHTY4vvfC/jyFzublmP+198usG27SRgp/uav17kZr9trIZWC/gGTz38hzZH3WfT1GeRyBp22olqTzEwnPP9cwPPPhSwurh+bZcGnPpPifUdtjrzPprdfr7swn/DqqxFf+bcelYUVz6DV+Dt/N0e5V9BuK/75P2nz9Mdd7r3P4e57bFIZQaspmZxI+L1/2GR+TuKvSug9/oTDR55K8eRHnOWgI0ngn/6jNm+8HnLl8rVvOJYFP/NJPe6j99j09Rl4nqKyIHn55ZCvfcVjbnbjcefygsNHbB5/wuH+Bx2KBYFlgdeBmZmE8+di3noz4nvfDf7CBz/v4d1FmwYdmsucEc2tNoiJ6dDqdkPFTDDaXUMBYjnz0max20a94vq+kp9Zf79r08RUDoPGtq4Egv7M62VO2qqhu7SMAnvF3Qh0uW1RVaiqze2BTCzylOgTwxQp4wiXgijTUFUqaoaWqlEVDoeM+5flGFqqTkst4pKhLAZxSLEs6HkL3mtLLefrZ4CiG+RIZCQppIfxo0WSW+z8vumAx+kbJLNjD61zJ4hbTdRGzmzvECwng5spIhCYTuaG3NkNQ1Cf9pk626Yy4WNagrlLHeavdNhxtICMFe1axOgbDaqTPjKBbI+NkopM0SbXs2J4ZpiCdM7i4GNleoZTBB3J+ZdqXHi1zsKYR7MaLV0LdBYjokAycijHzqMFSkMpjnyoj8mzbdr1aDmYCT1JsxIxM9pmYGeGfNlh+5ECU2dbyGT9nV4Ygm1HChT6XJJIMXOhTbMSEnobXyxRIFndTew14k2XvR5yPTaHPlCm0O/SrISMn2py9sdV5i518FsrPJvQk4Rewunnqxx6XPOT9j/cwwt/OkUcbdA6qmBhQXL5UkIYgm1rM79aXXLmVESzufY4eB213K3he4pEQi5n8MCDDl/9M29dwGOakM0KDt1lYTuCixfiG8643AgO3WVz3wM2Dzxg0+4opqYkkGAZAtsVlEoG/f0mlr2+m6NQEHzggy4ffNKlWDRoNhW1eoxtCSxL8NjjDp224s3XI958Y4PSao9gaEh3qnz8Eyn2H7DIZgXjYwluShsuuq7A99eTQOfmJMePR1g29PcbbNlqcuCgRS4nsO1rV+lzOcEHn3T50IddenoMWk1FvR5jWTpweexxh8BTvPlGxGuvrh/3B55wOHqPw+HDFvWqpFbVDwzThHRaUC4b9PYZazN3wlgjYCiEuTwLFIa+tVl2CsO0CdpV7HQRAURBG9n1ohOINSklIUyUjBHddKLcwEz4aqQzgp4+k8P3uOSKBq6rSd6dtqJeSZiZijl3IlwXqO0+YHP3gylyBa0/E3iK6cmYV360OeH2iaczRLGi1W2jHtlpky8YWLagUU8YvxwxeSWmMrf+mi6UDHoHTA7d7S6vs8pFhziGhdmYl571aNRXLspMVlAqmxy6x6XcZ2JZgiRRTI/HXDwbMj+TEIXrx/v+J9OUyiZvvOSTyQgGhi2Gt1lksga+L5mfSXjtRY9O66erK23FPHY9klUBy2Ymq1dzdK6HJf2nWTVBRKADI6U1kgxMIhUSE7KgpmirJhEBs2p8mRhdVXPYOFrBHolHG4mko5pMc2VdkKWQhPgsUlnOEsWE+HSQJAR41NQ8YXcsCTEBHgE+DVXrBkEhSyafsltKux4EAtcukEv141pZLDOFKSwtDqpiorhDM5gjiNpEiUcQtbCtNLaV7ralvwslLXdgmNJ97yecm0YGAcm7GfDYLk66iGm52sIAuJGWwLnLHuMnm8hEu+A2KiEzF9tsP5KnWYmYH/OYv6Jb6tv1iLnRtubxZE3S+ZVDZrkGubLNnvtL2K7B9PkWr31zlolTrbWZCwV+K2HybIs3vzOPm7Ho36EzRy99bYaFcW9Z8E8mmu8zdrxJsd8l22Oz654Cr/z7GeJw/UllmoKddxfI9zmEfsLlYw28ZnKjnq1vG0JAvs/hyAd7cVIGs5c6nHi2wpW31svjt+sRUZDw1vcX2HY4z9DuDDuO5kkXLIJOsi7LIyWcPh1z+VJCuVfgeYqtIybjYwn/4Ws+U5MJctVXXKvK5Rtmq6UYH0vYt09y12GbXE4/cFdnFWwbBgcNhreYjI/FHD8W3dYk5dF7bD71mRTlssl/+JrHlcsxjYaiXDbYsdOiUBAbCiBbFgwMGnzh59Ps2GkydiXh5ZdCxsYS+voMdu0y+eSnU2QzBrYNp09rY8OrkUoJCgWDT3wqRa0mmZuTnDkdY5qwdcSgUDBoLKp1mZZLF2OmJhNefTnk8BGbR97vcODg9W8RlgV9/Xrc23eYTE8lPP9cyJWxhN5egx07TD7zuRS5rEEqLTh5IlqTWQJ4+uM6OLNtwZe+2KFakSSJolQyOHzEptVSa0pdQhiYTprVrAPTdLSYIWCYNkrGOJkSTipPHHZI5/sBSJIImUQYhq1tPoSBlDFCmN17iacDJsF1Ax4hoNRrctc9Lp/42RzlAYtMVmAasFiTXL4YcfxVnwunwnUB5s69Np/42RyDWywyOYNOS/LGSz4nXg/wN5iECAFPfTZHHCpmp2LiSHH0wRQDQyapjEFlXgcQr/zIpzrvrQkiDAOGt1ncdU+Kpz+bpafXJJUWuClBrmAQRYrmouT4qwGn3gyWAx7DgP4hi72HHD75hRxbt9vYjiCOFedOBPzgm4I4DpibWv/w/9DPZNlzwKG5KCn3m+w/7HDkPpeePpN2U3LmrYAzxwO8TsJtqFa8DYhVEgTvXsSVENOmQVtdm6s5p1aqF9PqyvLvVbUx77JDk45afz+WSDq06KiNqwAhAaEKaLDecDoioKFu3oRUYGCaDsXMMAOFA+TcPlwrh2nYKJWQyIhOWGe2cZaGN03Dm6EdVsml+klZObyogbwND7WbNw+1LMxUmmBhBhm8uzo7UdBGNOd1bVAYxEGHGzlxW7WQ+txKmmMpq4KCxflAE2q7iEOJ19QH2nINbHflSdUz7LLrviJOyiAKJNXpgJPPVZHxJmNQcPpHVbYfznPo8TKpnMnIIV0mu3J85WQP2gmvfnOWXfcWGNyd4fAHyqQKJlEo12zbtATpgsWBR3rIlmyqkx6vfnMOr3HnxcUyRYverSm2HtQ8qLlLHc6+WNt0+TiUXHmrQdDWHQaWbbBlf444lOvI20rBhXP6BlouG+zeYxOFinpdcvpUxOjF5JoBylvHQgoFwcPvtzly1CaKWFOO6SkbfPRjKdIZwehowjM/uH1lEseBbdtNRrZZ/MmXOvzJlztMTa4MdjVn6OrP3H/A4uH3Ozz2AYff+d9bvPTjkBPHV77L/n6DsbGE3/jNLA8+5HD2dMzzPwrXHYu+foPt203+zRdDvvpnHhfOx8uftfT5Gx2/KNLy/q1mQjYr2LX7xgz79uy1eOgRPe7f/70WP34h5NibK+PuKQsmJhP++q/pcZ84HvH8czoAEF0i+sFDNr4v+fNv+Hz5i501wZgQ3rpjZqfylEfuRsmlBRVJHGEYJkkc4LcqdBansJ0sRqaH8tYjGIZNGKw8ALKlLWTL21AyIejUsOw0ufJ2vOYcQadG2KkTedd+COWLBo9+KM2v/70y3/t6i29/tcX0ZIzjGOzYo7PBrebGc9Nnvtnhue90yOYNfuW3Shw6em3OxhL2H3a4/7E0s5MR3/96m/HLMXGs+PlfLfD4UxkefDzD3/7FaTptufw9D221+Mwv5HnsqQxf/WKT57/XJooU23Y5/Pe/28dbr/r85IceX/+T5vI6S8rDv/jrRR5+Is35kyH/6h/VaDUk2ZzBX/mtEr/wqxbnHwn5vf9h44dgT5/JJ7+Qw3EFF06H/Mvfr9NpS3bstnWZduba1/KdgmnYmIaDbaaJ4g6JDEluo4XBe1iLbKqXcnY7B4Y/Shh7BFGD+eZ5osTHEBa2maaUGWH/0JM0vBmuVF5hrnEOv7neePtW8La6tKJ6FSub146/GwksvEMwTBvLyWK5WUzTxm9XCNrXjz6XCMVLUFKRdAOJOJBr3tOlkm7uSKy1JkrnLHqG9E2qVQ1pLoSbBztdJImisxjTmAvo25Gm2O+Q77PXLBNHkqmzLapTPsUBFydtsvueIlfealCdWgkO8n0Ou+8t4qQN/FZMZcpn5mJ7Q17M7Uah36HQ5yyXFI4+1c/Wg3r2uSG6Qc7ArkzX0VmRLphYzrU9iJZcufUfS69de2yjFxPK5YgggHvucahV5ZqAp1AweOQxh7lZydiVhNnZ23e8okiLl7Xbksc+4HLhfMxbTsSVy/rGfq2x79xlcddhmzCEUyciLl+O1yzfbitOvBURhrpzbf9Bmxee3zgDEUbw3W/7zEwn1yV4b4Sbif+27zA58r7uuE9GjI6uHbfXgZNvRfi+oqfH4MDSuBM9niiChfmEwWGTRz/g8srLERfOR1SrasPAEABhYFoOSdQlQyow7RQqCUkiH781j+1kcTIl7HRR206YFoaxcq0J08I0HRLl47h5DMvtqjrHRH4Tv71+hns1SmWTQo82unzleY/RsxG1SoIQMH5JB31xrDbMuCqlPaNaDUngK+Lr3DuWYFqCOFL84T+tM34potXQGc6v/0mTj342x8H3uYzstLkyGuK1dWZs32FnObPy/W+0qMwn2nqhGXD2eIhhCLZut9ecK4YJH/x4lqGtFvMzCf/6n9eXM0u2LcjkGnzkU1n2HnLYd5fDlQsR4VXXv+3A4FaL/+cf15m4HFGvJSQxTF7RZfx3MtixTJe0U2K4fJSUU8S20hjCZKr6Fo32FE1vBkNY9OR34No5Gu0pOkG121n0Hm4F+dQgffm9NL0ZJqpv0goWdNcWUneaCRPbSrOt514cK8dw8S4WmhdJbvOxv+mAJ2418KfGcfoGkbGWMpLRkvP1BhfsHSzOyiQmiX0M08Jy0mtuZtdeT60LTJZ4JBu9x6rZ8eq0uuUYpHM69R14kqBzA1+OgjCQeN0SlpuzcLNrZ9JKQmcxpjLu0bs1xcCuDCOH8lQm/TUBT7Zks+2uHJZtUJv2WLji3ZTGza3AzehxLwUjpSGXfO+N9YwHnQSZKAyxcWnnVrG4KJmdlczNJezabXLmtLnM/XBT0NNjsHOXxbmzMTMzydsWKNsISsH4WMLpkzGH7rJ54EGHQtFgcDBidlZSq0naLW06eDXKvQbDw/pc2LnbxLJYU7pzHEF/ny5npdOC3l5j046mMFRcGo3pvE2p+5tBT4/B8LD+Infs1B5Gq8dt24L+fs1tSRLo61s7binh5IkIYcDgoMnjTzj0DxhMTyUsLEjmZjWXa/XcSiURQaemjUFVV27ftJBJSOQ3SSIfw3RIYv13HLQw45A48roeaYok9PQ2Yn1NGXGATAKCTp3IbyHj65uOJonSJpKh0sFPKcHrSOpVbXVwI5FjEuvt3OitMo4VYaA4/nqA15LLx+X8qZAHHktwHEFPr8HUuMBDBzzlPhM3pfVpJq+sBKRxlFCtJOQLBtn82pPJMODAEQfHFczPxJw5HiwT+4UBx1/1efTDaYa2OmzZZjF5RQfjq6EUhL7i/OmQuamVz73NE/frwjRs0k4PvYXd9Bb2YFtpTMPBMl1qrTE6/kpwm3HL5NODSJXgRw3ku+bl3cYAACAASURBVDip/4sC18qScXqotq9Q64zTDjZOTBRSg/RktpFLDdwWocGrcdMBjz87ReL79D3xUYxUGn96kqi20M30XB1EyDtKag47NcKOLqOUhg4u37iuByUVUm58d1Fq7c36WjAtsVziSkJ53TbrJchYEnWXtV0De5Msx+VjDbIlm8E9WfY/3MP4ySZjx1dS8sUBh/3v78EwBdPn2lx87Z27i1iugbVU3lOa/LyZmOJGkIki9CTyDsRncaR5Pa+9EvL+x1x2nrJwHEEQKPoHTLbvNNmyxeDffjnkyqXbfzP70XMh09OSv/ff5fj051KYpqBe1+WaF34UcPJExMLC+pOsUBD09glcF/6Xf1C45mcsNiTpzMbvxbEmbzcb63k6dwL5gqCv38B14X/8n/PXXPbyZe1offWt7Etf9PjQk5LPfyHNr/9mllZLMTWV8OwzIf/+33WYmZZryOpR0GLhymvX/KzIbxD5DdazGDTa9clrd3XeAGYmYybGYmqVhL/6t0u89oLP8Vd9XvhBh3olIVqv93bLaNS0v1O7uZbwX6/q4N0wIF8yMa2Vo2w7AkOI9Zkmoa9FJdcHXIYh2H3AoV5JGL8UoSQrJGcBU+Mx7ZbCdgRbd9q88ZK/bmc7bcXptwJ8T97Jue91kXZK9Jf2sWvoCaSM8cI6iVyklB1Zs5xUMbaVIZsewLJS1JtjxEn3uSLEjWlK3JDWhVhvrX5DB2iD9ZaHdL31V/hutzaGm4dpOJiGTaU5ShhvToWpd6ZwrCzl3K6N9SluETcd8GR37qf30Q9j9/SS2bYHpeSyvcTV8CYuM/WVf33Lg9wMqVwfqVwvTrqomd6tGwt4bhfiSBJ09Czuan7PtWDaBm532ciXm5agzr1cJ1Oyue8Tg4wcyjG0J0tpsEF9NtACgru1gKCSirGTDc79ZHMOze1GHEji7riVgpe+OsMzfzR+TbO+tVD47YTkDoms1WuS7/x5wCOPuuzYafLAQzYvPh9y9z029z9gE0WKF18IuXDh9kcEtarkjddCfuOv1bnriMWBgzb3P2jz2c+n+MSnUkyMJ/zTf9zi2JvRGjfnpSxUva74oz/sML9Bp80SGg3F7EyyoWS/lIo4fudUe1eP+0t/1GFmevNxN5t63FcHYlOTCV/5/zy+/S2f++63uf9BTZj+xb+c4lOfdvnRcyFf/5rHKy9HP1UdPUkMLz/b4dLZkI9/Psdd97j85b9R5Jf/qyKvvuDz2os6+Gkt3r7aTRBAs7E+e7Rc/rsqE62UzsYcPOqya7/N05/L8uoLPnGk2LLN4t6HUxx/zefEa+vJr/mCwZbtNnfdm+Jj/0Vu7ZsCCkWDdkvR07c2wFpCEisadXnHmyiuh6HyEUrZbQRRg+OjXyWIm6SdIg/s/6vrlvWCKl7QQzE7gmFYCMvGyhbY+dlfw5sdozM5SvXETzb8HLc8yNBjn8RMpamfeYPq8RfXvC9Mk55DD5Ie3E6qbxgrkyMJfaJWnc70GNW3XiQJvHW+GcIwyO06TGZwBLc8iJMvY7iu9qwMPFoTF2leOo03O45KNujePPQAmS27QCkWXnsGK1sgv/sIue37MC0HpSRRu8Hcj79FUJsj8TcWrn27iBIfP2phGPaazsqrYRm6SuBHi3ck+Lp5a4lmnfboWQzHZU1P4wYIq/Mbvp42C8s7disQZhqZxHQWZ5AyJg5v75d0PYQdLSwIkClYZIr29UUCBaQyJtlue3u7HtFZ3Pih22nE1GcC5i53GNiZoXckxfC+LPXZgMHdGXq3pTFMwcyFNvWZgM47VM7SY1sZ92oTwKXj8W7D8xVnz8Q0FhXFbqfPT14M2bHDZOuIyaXRhEpF3tZy1hKkhDCESkV3R1UqkonxhNELMffe7zAwYPDxT6S4fCleE/B4nqLVUoyMCM6cjrg0mmyqLhqGuh3/Wn6a7xQ8TztZb9smOHdWW1Fcc9wbmGMmiVZ39jzFW8ciajXF6ZMxp06aPPVR3cH15FMur70abRjkvZvwOorpiZgfP+MxNhoxtNVi136bLdsthEhhO/Dnf9a6beOWcmNO0BqsmrwrpTNRp94MyBUMnvp0jn2HXUJf4biC0XMRJ14POHti/bUbRVBbSFisJZzb4H2Adkty+nhA4G8c1G2WTb8p3Lwf5xqknR4Mw6bZmaETVIkTH8vcmCQeJyFxEmJbaYQwNK8rClFRiFvqQ4aBruldFZQYtoOdLZAeGMGbn0JGayfgVrZAbvt+8rsOIUyLuNMkatW16bZpkd++Hxn6tCdH8eevzjwKslt2YueKCMMkXKwAqquL55Lbtg+kRJgm7fHz6/bJsB2sdA7DtinseR/CsrEzOcLaHMKwEKaJ4bjIOLojvNxOWKPhTdGT3UYnqJLIiESuPp8EtpmimBnGNtNU25c3FSq8Fdx0wBNW5mkE104lL0FuUs7KO31kzOLNfvQqaAmnjuHjxW1a9SmWhVjeQXithPqMj5KKdN4i12PjZEzN5dlkKG7GJFO0yZZsUDpAaG1SCooDSWMhZOJMi75tacrdrqjTz1cZ3pulb1sKJRUTp1vU54IbLqndDrTrMa1auCysmOux6R1J3faAR7HCb9AqyzeWuQgDGB9PmJtLKBYNDh+xcF3Btu0mfX0GJ09EtJp3vuQzPyeZn5OcOhHz+msGSsFHP5biyY+4fOmPOqyWmV9cVFQrklQKalXF5ETyjnBwbhWNhqLSHXe9LpmcTOi03964lYKpScnUZIhtQ+8LBgcP2mzbbvL4Ey7/5++2uCVB2TsAKSHwFcde8Tn+GuQKBvc8nOLnf7XA/sMOvf0m3/7q7Qt44OZ9hWoVyei5kP4hk8/+Yp7dB3SXVKMmefbbbY69EnDp/Pr7UKsh8dqSKxcj/uyPNu5YS2JFuyUJbnDyIAyxEhAvl3+Wdkxf6HoSpTl+wjTAECR+vOZ9JZfSWYBUeruwISfBsXMIIWh2ZkhksKEA4BKkipEqxjQcvX0pSUIfb36SzNB23PKADg5Cf80zx0xlsPIlrFwR//SrhI0VXpAwLJxCmdKhB3CKvfjzk3SmLxMtVrFyRdzyEIXdhykdvB8ZhwS1OdQqkT2lFHY2j5KSsL6AX5lGhgGmm8YtD9Jz+CFUoo0+Nwp49BgMrFSW3I4DJEGHuNPCm74CQmC6KVJ9W0j8NvIWxf02gvbIWmB77/00vBkU2ildCzRq/auMU6KU2YZCMrd4VvOs1NrEiJQxsXz7lZyb79LyOwS3mO4aydxFf2rXhu8tXcjXIiwpJF7SZNaaRYpZotBDJhGy26HxTqE+4zP6JvjtmFTOomfY5cgHezn+gwXCjWY7Ag49XmZ4XxbbNQg9yeSZFrOjm8vKV6d83vjWHEc+1MuWfVniUPK9fzXG7vuKbD2YQyaKY9+dpzL+zu03QLsWUZnwmT7XZnh/lu1H8oTeIJffatzW7IJMoLGoMwCplKBcFjdsGyElvPh8yKOPOzz8iFYb3rHDAgTf/vPwHQ8m5mYlF87H7N0b88STDtZVV9/ZMxHlssFTT7s89bSL47BpF9ZPEy6ci3mlN+Tpj7s8+WEXy4Rnf3jr444imJmWnD4ZkcsLhofNO0BjvL2QEhp1yXPf7pDJCN7/ZIZ7H0l3A/V3N3jde8Dh8D0uX/6DBt/+apO5GT0x24z+IRPFqTd9dux1KPWajF28Rb0qQ2BYBk5PmrgdohKFmdYTP12Pg7gTYmVtTNfCTNnYORe7mMJ0LWrHp0mCGMMysHIuQbWDYRkIQxC1QpyeNEoqwrqHiq/KvggDlCKW4XW/BcOwMA0bqVYIWCpJWDz3Jk6xjJ0rkd9xgOal02uyOOnB7WS7ZaPm5dP4lenl91IDW8ntOEBuZC9Tz3yF9uQlguqKubZdKBPU5hh85GmyW/cQNaq0xlYFLkoy/q0vbzheYVrYxV7cUp/+/E0gLJvUwAi1ky/TvHSKxsXj1zkStw9pp0QpM4Jtpjk0/LT2+Ep8wsTH7B5vx8yQyIg48bHLabb3PsDVJKP55nnOz/7wbY/j7Xlp3QCccj8Ig7CyXuL6QuMVxtsn1rwmMMhYRYbS+wCoBGO047o2DUNplr2ZJ2v10ONs4UrrGJ4bkjiKTGFQe5p4i3TqU3dql9YhiRTtesQr/2GWI0/20TOc4kO/PELgJ0ydbVOb8pczE5mSzZb9WR7/xa3agsJLeO0bs1Qn/Wu2kXcWI8ZONGlWInJlm96RNPsfKdG/I42TMmnMh0ycbtKqvfMPxtp0wPN/Osln/u4eyltTHHLLPP1rOzj3kxoLE572HgMsR+CkTHK9DsN7M7gZCxlL3vjO/HU5PEGgOHsmotWS7Nhl8ku/nCGT9Ql8BQIyacGLL4ZrSkOr8erLIVtHtOryF34+TV+/wexswpuvhwSbtdDfAnbuMnn0MYe9+y1On4ypVSVBqDNU27ZbfPBDDvsPWLz0k4j2VVmQy6MJphHyyksRH3zSZecuk0N3WZpnpAS2o3WJDh+xOHEi5jvfCqjXbk9WzzB051c6I0ilYGTEXO4C6x8w2Dpi4PsGga/b48NwJTs2diXB6I77sQ+47NptsW+/xcWLMUoKLBt6ew0O3WVx/lzMN78RUOuO23VhZJvJ5382TRzD2FhCZT4hTnRX2pYtJk886aIUvPbaerXidxt7D9kMDFn0DphcvhjRaWm+Sr5o8NATGXoHLEbPhxuWdZZ84dy0gZvSStq2LchmBZ2WIArVbWvbtiytidM/ZFFdSIjCdRWZdUgS+P432nzul0z2HnL4a/91iTdf8lmsS+JQUSwbjOywUQp++OdtOh11zW2m+rJkR0p6v10LwzRQiUJYAivjYKVtonaIU0ojw5jmaBVhCuysg1NK03N4iPrZOUCQ21aidGgQlUhklJD4MXY3CKrW1pNig6hFyimQSw90xQY3R9rpIe2W6firWtKVxJsdJ1ysYmWL5HfdRXvi4pqAJ9W3BbdnAG9ugqi1iFzVspbqGyLVO4RKYryZMaLmWsmDJPDw5sZRUmJlC7jlobUBzzWglCRqVHEKZUx3SYxz8z1sjZ3Dm5u4oW3fLuigxqIdVBDd1lylJFIlCKEtUDuiDkhk9/WNPNiXCeRvE3cs4HH7hxG2tWHA04zmaUVrd8YUNq6ZpRPXiWTAgq8DHqlilJKYhk3KzBPYHUzhsFTCUkphOS5x2Om2m75zUAoiL+HcT2r0jqTZdlee4X1ZDj1WpmfQpToVEAUJlm2QKzts2a9JxijF/BWPMy/WaNWia94k4lDRqkUsjHm4aYNs0WLfwz3kyw5xIJm77NGqRZvr33RhWIJsySaTtzBtgWEIDEswuDtDcUDXsm3XoDycYsfRPDLWKtRS6pleddJfZwPhNWMuv9lg7ESTob16O4ceK2O5BtVJb9kjzHYNHfCUbQZ3ZzBMQWM+5Nj3F64b8MSxzoycPBGzZ6/Jzl0Wj3/AIVz1MHjjjWjTgGdiImF2RhKGiiNHbS2JP6X5O7f7dBFCP1gGB00OHbIpFAzaLUkUsdyOXSgYzM9Jnn8uoHWVRUarpctYP3wm4EMfdin1GNx7v8PwVhMltap2NicYGjaYmpLcYHXvhlDqEezZa7Frl0UqJRjZZrJ7j4UQdBWQYccOkyCAs2diZqYTZma63kFtxdSkHvcTH3IoFAT33e+wZauJXBp3VjA4bFCpyDVSBPqYCXbuskinBVu2mjQb2m/LNLVIZBzDlcsxL/14vcjiu41U2qB/2OLAEYe+IYvQ1x2gbkpQ7DGoVRIunl0/7oFhk94Bi4EhEydtsG2nTbFsYNnw4AfSLNZ0ieji2ZBaJdEB/q1AQBzpa2bnXpsoTOkuL6WvsU5b0lyUjI+ucKSkhCsXI86dDEllDHYfcDAM3XmVxIp0xqBUNmnUE1164to5LMMyMNMWTs7FcCxdgpIKlUisrIOdczAcCzNjEaul111d0lI6SDIdC2EZuL1ZzJRFEsQkfoyZtrEzDrEXbsgf88I6lumSTfWRS/fjhw0MoXmUOvA0ujo9PeRS/ThWmmZHc0OXkAQdwkYVtzxAqm8Y002T+Pq5Y7gpnHwJ003Tmb6sA6FVN0srk8fK6g7G1MAIVr605mAZloWZySMMA9N2MVPZ9cfPSWGmMphuGtNNde1PDIRpYmULmF0OjhBiUw4dShI1a8SdzXoX7wyCuEXDm7n+gqshumfUql1pBQu3NI47FvBk9x7ETKVpnHh93XsbeZVYwmUkexdz3ii1YJpauDZTk8iYUHqEsoNEsj13hAVjjqpRQcqETn2aKOxwy+y2m0QcKY4/UyFbcohDydGn+nn8F7YiI0UUStr1iHTOwk7pLi6l4MpbDc78uMqx787fWGkmVpx/qUamYDFyKMfDnx0iW7KZudjm7I9ry6KJ14KbNtl5d4GdRwtkijZOysDNmhQHnGX9nGzJ5uBjPfRtTxO0YgJP2z4EnuRHX56kuRCu4QkFnYSp822e/TcT3P+JAd734T52319kzwNFFCx7fxmGrtsv0axq0z7nXqrr166DJIFqVfLHX+rwyCMOH/sZl899Po0Q4PuK8fGEP/7S5m2Oc7OSsbGEifGEffstXng+4NTJ+I4QX5WCakUyM5OwuCh58CGbUsnAMARhpLuT3nwj4o3XI772FW9D/tDCguQP/kWbyYlE+3E9qBWMpdTZroV5yauvhF2NnfVPf5nozqEbFbFbwq7dFj/3C2k+/dn0mteTBJ78iMuTH1kheH75ix1+9GzIzMzKbKtWU/zBv2gzNhZz330ODz3i8P7Hna4mEMzPJbz+mlbJ7rRXxh3HUFlImJ5KOHTY4pH325R7TOJE4fl6f7//3YCXfxLy8ks/fQFPuyERAkZ22Tz64QzpjOaUNOrapuHVFz1+9N3OOmPa+x5J89hHMzzx0fXaAgeO6GPdbkn+79+t8dKzHWanVqxnkniD9vIupNS6QEqqtTwfBXPTMXPTMV/4LwtrXm82JGOjEaffCvjDf1LXWaruca5XJd/6d01Ovunz+b9S4KlP58gXDSxLUF1IuHQuZGE2JgrXE9H1WFcmJnEnJKh2KOzqJe6EqFhhpky8WZ8kTEi8SP+cjoiaAUG1Q2ZLAaUUfrVDuOghbAOnmMLty9CZahC1AmSY4BTTyESiko25nPXWOLaZYkvv3WzpvZdqc3SVoKyJbabJpHrZ3v8Qpdw2PSldPLcuo+AvTGHnCpTf937sXJHYa5EEPqmeAexCDwhBc+ys1qZbBSuVwUrnEJbN1o98YeMvrwthGBjWVZpyQuCU+shu3U1mcBtu7xBmKqOtUUxLLy8Mgurs5o9ApZBRRBL6N+Q7eWMQCMNYTj5s9uyttceotce6u2J0y6ibL6+XW9KnU7eNwHzHAh5h2Qj7xoQAAYQQZK0ebSK2iTcIgFQJYdIhYxaJWxdo+BfJlrYyfOCDoBSdxSkWxt9aJTn/zuC1b85y7qUab3x7jns/PkD/jjSlAZfigEvoJ9TnAuYve7z5nTkmzrSYu+zdcHpeSsWb35tnaF+WbUfyOiMjoDLhcex7c9dVdwZI5UwOPNzD3U/3kylYy6TA5WAEnYnp25ahd2u6q3AMKP35b31vHq8ZsZG10NkXq0yeafHCn05x3ycG2HooR3HAJV/uzghbkqAdszgXMHaiydS5NqNvLK5VtL4Ojh+LOHM65o+/1ME0l1yINaG52bj2/n/3Wz7PPxdgmYIwVIR3qBUe9IP/q1/x+I9f97EsuqrSerCJVMQRRPHGwoNLUEqrJD/zgwDbBtNY2V8p9bpRqNaJvAH8/u+1sGwwBDdFyD72RsTZMzG/89sb++ushuerDc0ilYLvfzfguWc04Xj19yS7+x5GinDVMySOoVJR/KP/o4Vl6azOcnDcXS8MtOXFtY7Zu4WxSxFTEzHf/0Zbj33JukPRFSRkw2P1g2+2ef57HX7/f7pG0K8UnfbKsVYKfu/vVxBCH5er7x+dtuL//b/q/PEfLK5Zz7Tgv/3fein0mFTnEv7+b84R+DpbKwQUe0weeyrDfQ+naf6K5Ot/2lxjPlqvSpqLARfPVJbPLdAZoCTR+7mRp9s/++0qpi0IfUXgKxQ+UTOkfaWmCccKMAQqWfKygFVW4chEEtQ6LKnd63U0R6B+erYb3ICZshh4dCcCnUWysy7RVRIlC40LxImHYVgMl9/HcPmI/kgh2Nb3ALJXotCVhHprjGrzMguL59epLHemLyMMQfnII+R2HQIhaE9dIr/7CKabIWot0rpydg3heGnnlJQkfpv5V36gW883QeJ3iFormmpmOku6f4QtH/5LqCQmatapHHuR/5+9Nw/S7DrP+37n3P3be+/p2QcDYAYACRAQF5AEKZG26YpN24pk2ZblJLKTVFL+I5VyyklcSSpVTpxyYpfKjh0vseU4liwnlkXFWixa4iqLBDcMCBDAYAaz975+293vOSd/nK97umd6ZnowQwCU9FRNkejvfvfe7y7nvOd9n/d5qmEXXeYYrRh78iPUD9+Zv2NheFimZdINkI47Kk8J6mNzFEmPbLiOdH2MspUZrZVVUnY9pHSpyozG+FEQkPZXrR2UEEjp4PjRiINrtxcIgvo4jhfQX7lkldSNeiCe7j0DnvojZwgmZzDG0Hv12/hjkzROn73njsOZOar4ZtpMeA4y8JChR9VPMcUto7GByhTU3DapGjAo909deTKg5U+hjcLxQwI5hpAOg/Ur1mFWFdYAsDJ7SlyLF4b85j+8RlBzuPSdHkW6qztmNefNr22SJxXr11PW528+jKqyJaVf+ZnLaGXYWt7/Ypejjqprr/QpUk294xE2rHWCKjVFpom3SpYvxQw2SoqDqDLvujb9tYLv/NoKixdiOy4Yw+rVhMH6wXRJ0kHF976ywdr19MB6QTuHN4atpXxHd+dWVKVhsFlQ5opzn1/j0ks9wrqDHzkIKVClFWXMYkVvNWe4UdJdze9Ds8dOjFVlSBO43wxentvsyDuR+TMGssxmn0Z/eVv7uXnO97ePW3lBB0VVWc+nW8ts94sih+I+z9uYW8/7PUbUuQuUskH3/ZacityqJd9RFfEOiAd3eWkMpLEh3XUtPc8GNI+/L+Ct8wXnXsx483s5qrzZ+RhGtlx16IjL5KyD5+0NwrbLXtXdjr3fuQ5vf+eM0lT7ZCbvBJXtF7Ub1C5SsjGG3oU1pCOp0hJ169wCGKOIs02WNl+lVBmR3yHwGrhVaF3GTUWpMobpGr14nmG6sq+lhC4yymGfbGOZcOIQxdYa6coNajNHUXlCvrky6k7e+7tVkaHLHCE7pKvzVHH/jp12pqr2dEq5UcPq5YR1htfeYHDlddLVhT2ZGqPvLAWx9zo8+DgopENz4pj9XVVOOlhHSAfHC/CjFvXxY1btvMrRqsQLG7hehHQ8sngTv9ZGVQXbhOSg1sYLW6gyI6jZ+RwgH6xbk2AvpDXzCG5Qp0z7DNavve2Exj0DnnD2MPVTZzBGM3zzVcKZOcY+9AlUEnO3C+c2WlTJze4j4To4kY8/3bJs+UG2J+gxaNJqQOS2aOsp+sUKylR7urakcKi7Y3T8WXIVg+fi+220UgzWbd++44VI6aCFg9nVv7p6JWX1yv5R9WCjZLDR4/K525WKtTIk/Yov/l837nWpqArN5mK+x/7hYSGPFa9/dZPXv3pvf5/9kA0V539nk/O/c/v3hQDXE9RbkiIzJMODDUhC2BV5a9wlSzRxt+Ktb3Xf1vm9HQgBXiBQI27CgxJahWNfh9vTvTZta2y66+77cD3Q6o5inL+P9xgEOIFreSLaoIrKZg7uZxeOQHrOHSbnhwPhSryGT9HL7+tBd1xBvSmZnnP53ks5V98q6W5YHy1GMg9lqUkTTZFbleb3fCvcLTCVZnjl3uNiUQ3Z6A8pyphmbZZ6OEHgNQGB0gV5OWS9/xZptkmp9p8rjFKoNCZdXaB++CR+ewInrBGMzxIvXibbWN73/qgsQWUJwvVQWUIx6GL2S5fvAyesEU3brEiycoPeW6/uySAJx0X6AcJx98ksPXwIIamNzWG0okh6pP1VG5j4kQ2GJo+T9ldGvFpN0BjH9WsjvR8X6fiYXS7uXtgkbE6RdBcJmxM4ToDWFflw07bMuwFhYwI3aJDKZYab9yNwuxf3DHgG518lnb8KQJUMdwSX1r74K3cVKBp//lN7JgddVKisJDo2SXBojGKtz+C7Ny3uK1NyZfAdTrc+xFjzGQ7VHqdbLFNpazDmCp+mP0kg67gy4PWtL5G4CYXISHrLRM0Ze5EOYB76+9iLsCY58VjAj//HY5z7WsKv/vzBgpYgFEwf9vgv/qdZvvSv+/zqP3/ngh2AqC45+4GQG5dLBl1FGj9YkFGbOgJSEi9eYXcwL/0Av9Gxrtrd/cU0LQTNI4+Rd1fJu+v8IGUqfq/C8V2mfugI9dkm5SBn47Vl4sXBwf1lgGiqQfuxSVa+fg1dfn8C3caRNo/+5DO8+ne+Rtk/+IKqyA0bqxVJrHnsfT6GOlpDGmscV9BsS57/4YgPfSKiM+7wS/9scPcs0u8CDNJlBvdLoN2FKo3pvfkSzWOPUTt0Al0WuPUG6eo88fylfb+TrtzAjeo0jp+h9ej7GV49T7J09cDHFFJgisL+2x3sSAcnrFGbPYFXb1L03t6C+H4hpIMQDo4X4UctwuYkIEbyMCXZ0HJr62OHqfKEIumilcLxQpzAQzk36S5a2zJV0l0kqI/heBGOE+L6IUGtg19rUyRd0sE6ebyJPmCguB/uGfCUvS2qoRWcMlWFURUqTUnnr6HvQhJoPdVDejdFg6Tr4NYDwiPjpDc2MNXeYEmbiq1ikYv9F2l4E3S8WQKnTuS2d1KOmRrSzZfoFSusZ9cJxqYJwkmS3jJBY8IKNsWb3DrRiCjCaTRwOi2Kq9dBSrtiryqE59rVuNYEx45RdbvoJN35vQiBcOTO335X4216l7xb03q9Jfn4Z5p8/hd7C4yRswAAIABJREFU5Kkm3S1nJGy3Q/P4WZzQOrT3r72BylOkH9A49AjS9ajymDLuU6VDmsfP4IR1vMYY+dYKZdxDVyWd00+ji5xiaK07pOsRTR8jmpyzPIvV61RZipCS9iPvp+hvkm2ukG+tkPfX35FV10FQd8YY9w9Rd8Y4P/ydd/t03nX4rZDaoSYzHz6KUZp0dUg4VqPYymidnqB5ooOUks3XV8i3UlSuOPTxE7g1D11UrHxrASEFY2emOPSJk/gNn3hxYFurmwHxYp9ikKPyisOfPEnv0gYqq5j8wBxO4FEOctLVIb23Nph4/yzRbBMhBRvfXaLoWUHTI58+TZWWuJHH23nTtLbK1v/4Z7asi/pJj7/4347j+yMOXAXDgeLVb2dcu1Ry7sX0wBne36vQZUGyMk8x2MKtt2g98j6K3hbF1hrlcP9FX7axjHA9aodP0XnsGcLxGZKlE2SbK7Yt23FxowbR7DGG1y4wvHYelVu9O5WnJCvzhFOHaRx/DDCk64s4QY2gM0nt8COWM/NOGOdhy2Jl2ifpLlOkPVSZsX7t3PaHCOngBnWk4xFv3iAbbuzQS4QQCOlitEIVKWDIBmsUsR1bB2tXbKZdCFSeUmRDpOPulOz0A5Kt7xnwmKrcM2CX/S7J1Yu2XHWXvt6q30UG4a4djUhnQt4kct6CUmf0ihUKlaB0QeA0caSLRFKZkkIlpKpPv1gl1wmOynFMRNicwnF9tLpD5GeMLak1G7iTEyOlzpEfghT2ZmiNM9YBKdCRVTAWIxVPYww6v8Wu+R5ojzuMT7mMTzvWkFRb3Z63XrMaMq4neN+Halx4JSXPDK0xh1NnAuYvF6wslHQmXWp1SZZq1pYrjpz0aXUcorrAaFtT721ULFwr2L49fiA4+ohPGmv8UNLqSFxPMOhq+lsVKwv2YQkjQbPjcOyRAK0N0rFpb8e5yw+6C4SAyUMuZ54JierS+udsaeav5DvnJqRVoD10zCeqSaS03IfVxYqttWqHA+GHglbH4fAJ357PyBfo2oWCQV+Rp4YwEkzNeZx8PODU2ZAnni2YnHHpbSnWlyu21iuydNSlMnLRln5A0J4k760jvYBwfIa8u4YuS1vC0grheNZxuyrs37br3cYgvQDHs8+zcDy8egu/NWEVUbUGozDa4Hi2w0arwj5X76EkjzvSsmp6E9yrm7HtTmMw9Ku7ZbR+sGEHUGvtXvZzim5GGRfUZpvUZhoEYxG60LROjhOHA/qXN+09DlzcyKNxtE26PERIgRt5qFyhSoWQgmi2QdHPUFmFLhXNE2Ok6zGlU9A6Oc7wRo8qK9FKUz/cIpyqE3RCdKlpn54g76aUcYl05U7rttUrOTg6HUEUCYJQ8Nq5nCI3DPqa6UNWddx20Nl38PKFguuXSnoPSdfp7UBKmJiUhCNX95WVh3sujvTw3Tqh30ZKFylGA8xdsDm4cosFAmA0Ok8ottZw/BCv2SFZvEKVDu64uNFFRtFdZ3D5dVqnn8IJ69QOncBvjdsNpER6Hl69jeMHexafKktIlq/ROHoaJ2pQP3oar9mx5aEgQrou6coNxOyxB7k8B4fRZKNsy7adU5HcHuhJx0VVBWU2uGsZVlcFGnuNb7WHuuOc/jZx/27pC1cp1lfuGuwA5GvLiF0ZHl0pVJpTde0yXHr7H9q2nqf0ytV7nkuRdMHA+KEnyNMtVJGw3yBusgxTFAghCU+fstklIZD1OiZLMUpZeWvfx4nCkct7hWw0QCt0kqA2NtHm3hyObRx9xOeDn6zzwU/WLbGxNGSJ4e/8jyusL5fUWw7/+X83zd/4y0usLVc8+VzEX/jLU/ziP9rkN3+pz+NPRxw95bN8o+C3f2PARz7d4OwzIYeOeVQV5KnhlRcTfuXntuhu2Ym12XH4wz/RZvFqydiUy6NPhTTakguvZLxxLmXlczZTNzbt8sSzET/258cpcsOwp1i8VhKE90dm3oYjBWeejpg57HH4hA243nwl43M/u0l3U2EMeJ7g2OmAz/x4m7ljHp4vSVPNb/+bAd/56pDleRuMtcccnng24o/9uTH8wAam0oWf+9vrvPVaxtpiRWfC5flPN3jm+RonHvMJgjZpoklTw7/7NwPOfS1m6XqJLnPi5cv4jTH81iTR1BHbHSGsr83qy19GpTe7krb1KfpXvnfzBRWS4dIV6rMnkb4NeKTn2TZQo+lfe50qHdoASUiKwRbJylUGNy48OKno+wCN3peMeSuO1Z5CmYrXB797S3PlIMdUmu7FddKVIcnqkOGNHic/exbhSfLNlGRlyOzzx3HrPpuvLrP+0gL1Iy2imSbjZ6e5cnGDeGlAsjxg6XeuofKSoBMx+ewcTugiXKsMHk7WcSMfXWrCyTpv/T+vkG0kSN/h5GfPYoBsIyFbTzj0iZOotCRe7LP+8iLpekw03WDi/YcOnIUVAk6dcpk7bK1U/sUvJNy4UsGvJTsLm93DmZQ3OXn34sNtCyZu72Ob/Lz9Gdzcx+5tdx9z+3jb2wL4Pjz1lMvsrMPmhubzn89vO97uc9u9QLs3h08QeE0mmieZGX+SwGviOgGCu6/yvnXhnxBn+zfQxEtXcaI6Xmuc4Y2LlvJxF5SDLdbPfYUqHVKfO0k0c8x6YBmNrgrKZEiycImiv7HHlqmK+wwuv0YwNk1t7gS1QydpnnySKu6TrS/Sv/QqxdYawnEJxqbv0JGurSfYQ1iEGaMZrF+96za20vLew/1bSxTFbRoD+6H/2rk9/+116vjTLeK3lvHaNUsSfEBURUJVpmSDVeuudYcn3hnr4E5N4U5NogfDnXZ53euh4qGNlOs16z6b5YBBRBF6OETnOTpJEL5n0yoHDHiunM9ZXSj5l/9wE2Ng7rjPJz/b5LkX6rz+Usr6csnqUkVYk8we9Tj9VMjSDXtd5457nHjUZ22pZHWhRCv4yq/2+cLnejt6F5/5iTa1uuT5P9TkN/9Vl90Li2eer/H6uYy//pcWKXO7/bYmjh8Inv5wjR/9j8b5m//NEuvLJa0xl3/vT3Xe3k0QMH3Y42u/OeDXfmGL1YWKH/6jLWaPePyJnx7nX/wf6+S5YWzK4c//pSl+/u9ucPXNnKLQPPfxOqefDPnRnx7n7/1VG+AeOenzmZ9o8/N/Z52l65ab44eSZKCt8KGBlcWSX/6nW5z7esJ/9b8e4v/+W+tcvZDT3VCUhaGqDE5Yo3HkUdywga4KVJHiS6uOZpSiivv73MuDdTCUwz69K6+Rri8y+dRHSVZvkHfXyLbuHaS/m+iXawzKzVF2806/UxCIiMhpUj6AZ80PKgTgRC661FRZhSkU8194C8d3OPzDp/DHIlumyiqQ4mb8cWuTmcFmkqUERyIcq1qtK022HtvM0uiATs2jHBaotELnFdd//U2imQa16TpVVqHSCpWPjGQPGEQbA1tbmnpDMDlpx9ooEkxNSX7sx62O1fy84hsvFmxuaj7+gs/7329Vk7/61YJr1yqWFm8f61ptwdkzLs9/NKDZgm98vWRxUXH4iMPMjKTdltRqgi9/KWdhQVOrCz772ZAosl5xi4uKz38+59OfCjhx0sVxDF/8Qs78vKLbNaytGsbHbh4vqsEP/ZDPBz5grVhefDFn/obd9j/86RpRJEhiwxe+kHP1qiK9g13MVPs0k61HOTTxNGAoqpi8HKLvsXC/2+fbrt+6LOhfeo0q2d9rbA+MoXfhZfpvvWKrHVKy7e1hW++17UC6ZWwyqmL9pS8jXpa2FXxbXFCPAhmjSdcWGWkW3HbY7uvfpvfmOYSQqOKdtSF6L+GeAY/b6uBEt4tjHQS6yCm3LInYaIOptPU62RhQ9e/MiXGESyAbBE4NR9rgpJsvoUw5km5wbMSKDeut58ldziNNqVZX0aNMz469d1ViytJyejwXEQRQ2Q4b4TiYsrS8papCZ/k9s1q70Wg5HD5pMx5VaUtcR0745Kl1KFYKVhdLag2JHwqmDrlcOZ9TVTB71GNqzuXyGzndTYUQMHPYY+qQS73lUBaGR86GKGUoMnPbBLayYAOl/pZCq72LwtaYQ1iTlKVhZb5k2LcdG1cv5swePbhu0m4kQ8XWesXqQkU80Ny4bJVZn/pghHQFNU8wMe0yc9Tjo3+wwWPvC9HKMHvUo9VxGPZvvqCDnuLK+YIP/0iDqxdz5i8XXL1QUJY3lZGNtmTMLNFoZchSTRLr27gHQkjcWhOjKqTr74ln9guOqyzGq7dpnXySfGPFqqUKQX3mOOH4DCpPCcbs//rNMfz2JNILEY43WoIayriH1xijeewM+eYKZdK/q8hXy51kOjjBjfQNSp0hhcOYd4imN8Gw2mI1vwLAhH+YQNbplxtUJqfudmi6kyylF2j7M9ScNoGMUKakV66yWdr3xREukdNiNjiFFPZ1r0zO5XjvgkQg6HgzdLxZIqdJzWmhpeJM46M7nZKlybgc7xUSHfMOUXc7hLKOFC6FTklUj165Rq7jnX37ssZ0cIJCp3TLZaaDkwQyQgoXZSoW0zfJdYJ+l51BjTH0Lm7QONqhMdfC8RzS1aENblxJOFHHCazCL8ZeGZWWZBsJMx85SrI0QOUVyVKfsTNTNE+MUXQzOzGNAhazixBtlKH75hrN42PUD7dwQodkeUjRz9ClYvq5w6QbCW7kosv7uzbGsKeb5dgxhxMnHV57zdqa1CLBxz7u8+Uv57RaEt8XvPj1goUFdUdtqyyzpabXXys5ecplfEJSbwhqNcFwaFgblaeffsajVq/IMtjY0GCs/hTAM894NJqCJNHEQ8MHP+jjeSXnzpVWX2jX8Z56ymN6Wu6Ibp494zE54fDNbxaMdSSLi4oLb1asruh99Y620arNUQsnqFTG/Pp3yMshShV3bA0fXUHKan/fSCFdopljSM8nW5tHZfGBlf6Nqm6Twzl60uMz/34LP7JBdJkbLnwv59yLKb1Nu7Gpyruerb7LAsUe853Vpnsv4p4Bjz8+hT85s+svVuxJyL0ZGmPMTsQLdhVdDro7AQ9K2zZ0Y6iGGSre7+YIfBkSOg2a3iQ1t4MnA7TRxOUmWimkkIROw+om6IzK3DvbZLKcKsthc+ue2z4MOC5Wbv59IUceCcgzyztpjTkEocBxBaoyrC2VRHVJZ8Kl1XH47osJWsPMEY/xKZd4oIj7Gi8QnH4iYPaYT6PtkCWaiWmXZKjx9ilDrS6VbK5XNwOEXW9JvWlVUpOhIh5oVGWDh6XrxYEUm2+Dwbak9/VOd8fmasmw7zE+7VqvoFDS6thA6/ijAZMzNx+7NNFsrt98EbsbijfOpXzmT7ZxfUFUk5SFYeGKIY31wcX0tEEVKUaVO8FNlcZoVVrCXDq8Tb2zHPYRjkfQmbYaUqPn2Y0atitBOriRtQZxghpuWKPKYiv+pSowkPc38GotgvYk1YgMfberWnPazEWPs1ZcR5sKKVwm/aNMBEdYy6+xnl9Ho+h4szTdCQqdUWpJx51hNjpNv1yl483QdCcIpX0vlKnolisoQOAQyBrj/mECWcMVHpUpuBy/zN60hCB0mox5h6i5bTwZYIxhMji6MylkajjyITIIJK7wGPMP0XQniGQTRzgUOmNYbWIwbOQZGoVA4ouQ2fARMjWk1Dnj/hyhbOAKD2Uq1vJrFPrBGwMcx8eRPq4bUhRD68V3lxKeMYZyWFBlle2wMtC/soXfDgknaji+VRPUpaYcFuhC7dRx8m6GUZoyLogXejSOtNClIl2NiRf7tE9PEnQEbuiRrsVUSYkuFEU322l9N0rTv7RJNNXAawQ4vgsCim5GtpEw8dQM0nd2SNX6Plvmd2N2VnLihMOv/WrGxobmzFmPT3064GtfKygLQ6+nuX69ot+zViz7wXUEaWq4fkMRBIKpacnkpKQoDAsLihs3FOtrmj/0mZBe17C6pkgSs2MXgoDHH3epKkO3a1hdUbzwiYBO5/Z7JAScPm35Rv2+ZnFB8dwP+QgJ0rHaTaurmoUFe4y7Jd/r4RS+VyfNt1hcP0dRJQcq7e4+GduEI6yVQ9ggmpxDlTnJ0jWrm/MAJezWmMOzH4toth2aHQcp4cu/PuStN/KdgOf38eC4Z8DTPPM+mk88c/MPo44mp9Fi27baKGWXElJahrXRFJvrJFcvEl98HbAZHl0qqn6KN1bHCT2K9d3O2gJPBDzS/BDj4REa7vjOIXMds5i8QaEz6u4YxxtPo1EsJRfYzN9ZE7SDYOqQx3Mv1PngD9f5r3/qBmmimT3q8SOfbbOtGF5VhmsXciZnbWCgFFy7kHPisYBjjwS0xx2GA01ZGuaOefyRP9vhl/9pl9/6XI88Nfzhn2gzeWj/jEyZc0ePqu3k1u5302CTV2/7dd2HViC2dzyC1rCxUvIP/9oqF79355Tq+nLFv/uNAS9/PeaJZyOefLbGX/lbc/yTv7HG+e9mLF0/WMeTKlIG187f5NLcMhitbd7u8RYvXSZeumKze7tWQ2sv316qyntrDOdHSsK7Rtr+5VctQ1uIA2UEK1OQVD0i2SQXCUJI6m6HVPUpTU7oNEhUD19GeDIkVl1cPKRwiGSDE/WnWckus5RdJNNDGs44uU4oTT7af85GMc9GMc+4f5hD4aOMe7O3nYdBs5RdZCW7RNOd5InWC+Qq4Vzv8zaTegtCp86Ef5SZ4BTL2SUu5d8hNwnTwUnGvUM82fwE36h+mVzdXCH7IiTyGjjC5VryKpkeoo2m4Y4Rqy0UD74CHe+cZmL8MeZmn+P8xX9Nt3eVJL0z8VplFYtfvrznb/lmwsIXL7HAJaQrbflp9PisfPO6zdDsuiRFNyNZGiBGBOPtlvb+5U22VYJ3vwuDa7sInsZyd6792nnbQ+FI9C5Rvdf+/jdu+/7bhecJfE+QptaJHmO931aWNV/4Qs7hIw5/+s/U+Pa3S966WPHmm7ffj498xGd2VlKrC65eVSh1k1/T61rV8+0k+rVrFcOh4c/8mYjXX68491LJ975X8vjjNba2DGlqyHPDL38uY3V1/3elVrPZ8CSx+/2t38zpdjWLC5qf+2cJz3/U58d+vMaLLxZ89+WCXm//C+V7dZSuWO1dIK+SuwbBt0FKpB/QOvEESJs1bp16Erc1RnzxZTZf/8beVNrbwGsvZfzFH5un0ZT8if+gw6f+aOOB9vf72B/3DHg2v/4lei9/c+e/vfFJorljBNNzxFfeJF9dQqWxzfp4Hm5Up/X0hyi7G+TLCzvf01lBJaD96CxIQblrEAFwhcfx5jM0vAnSqse1wcskqsuR+pOMB0d2tit1Rq9cYS46Q9LKqPBBSJLuIqrMHprnxoMgjGx3VxprstR67XTGXZ58LuLSG3ayr0rDjcsF7/twjbEpl/krBUvXS06eCZiYdkhiW65xXKuTk6W2hFPmBteDR54MabYdbly+Pxb7+nJFkRnGp20HWXddUatLzjwd4gdvoy1dQGfCZXrO/ltdrDh5JmTqkMeVN3NUBYOuYm25JKo7HH3Ep99VrMzbwCWIrEv0dnbI8wVRXRIPFK99J2VjuWJsymFsymVq1t0T8BgNeaqJapIwkrBfOeROy75RCYrbJhOzN/UrxP4rN3NnzhhGH3iCqkxBonqETgO32kKjcIVPr1pFmYqa06bQKc6oHJWpmLrT2smmrmSX2SqXyVSMQdE36w/0Dtx62ndK+UeyyaHwEVayy2wWiySqh0Gzll1BIJj0j9LxZuiyTD4ScRNCkuohy9klBtUGalSK7pUl6iFJ3t+EGB3zwVT0dgcfwI6dwX4wt4xp9ytgiNnnePehB7QNx4HZWYePfMTnxEmHmRmHN8/7dHua775S8md/KiJNDVlm+PrXCw4dkpw46XLqlIPrcldTWjUyRp2bc3BdgeveObHheYKoBu22ZHxccuSIw3Bo+MpXcj7yEZ+jRx1WVySXLyvA0GwKfuRTAUeOOGxtaVZXNd/8ZsnZsy7ve5/H4oTixnU1smyB5z/qj87j3lqJWlcI4SCFvG+3RdtmXWP8/R9F+gEg0GXO2rd+i2T5Oiq+tx3LQWAVxzVFrg9KFf2BgPQkjieRrkRIQftYC6/uIQRsXNiiSiurnK0NTmDV+Y2BYlg89H6JA+nwlL2bpSAZhEg/ILl2keTaJYqNVfTISEU4DjKIcFsd3PYY8hbuj5ACf6qJURqd712pS+Ew5h+iNBnDcoPNfJ5MDZkMT+zZTpuKrIrxZYhUAq0rvLBBfewIusop8yFZvPmudshY52FFlhqe/XgdY2BmzqMsbg6IWsPWuqLRdggjyepCSZrY1lPXE3Q3FVVljQDjgaK/pZg54vHsC9ZF1w/E2ypBZalhfblk/nLBsx+v09tQOK4giOTbk+ExMOzb3/H40xHHH9WcOhPguoLz51Kq0v6GwZbm9e8kzBz2cD3B1nqFEJAMNP2uIh7YwK097vDY+8Md6ftaQ6KVDZqSW4QF81xz5c2cY48GNNoOc8dKlq6XbG1UZNvkRSlukpiMfQaRlkzqNCNMpdB5iXAdVJLDtj6UFMhaiAx9VD+x+zEGU1jtJkYEQ+FITKVt+7wjR5PerknxHs+hMiW5TvBkiCv9EU9NkSu7Cg2dBr4KMRgqXWBQoyDElpaGaotCp+hRdkQdoMT7MOBKn5rTIXH6jPmz1NybhpRNdxwpbCnNwQNulqoqnROrLpXZ1YViHm7r6fcVd6d8vCdgjPUeW11TIGBjXTMcGvp961R/6JBDVRn6IxJxltmS0/KyYNC3pal+f/8Zd2VZI0VFmhh6fevHpY3t59jc0mSZQSnDuXMlYSSo1wXf+XbJ9RsKreHkSZcvfSnn8mVLPB70NRsbiuHQUFWwuKCIY0M8tBmgtXVNFNrXr9vTrK9rBqPM9+aGJh81ZaysqH295baRl0NCIQn9tiX93k+AbQy6KklX5pGeh9GaKhkQL1ymHGwdmLtzEIwUUg5kE/GDAr/uU5uM8BseW5e6eDWXaCzEDV2KYUmZVtY0NqvwWwFu4CA9h+7lLkVSPlQxz/vu0vI649ROnObGL/yfVMOeJf2OsC27PTj/Xab+wB/DH5tg6xtfAUbWErUAb6IFWqPSvQOzQNLyprgyfImN/AaJGtk83HLjtdGUOsORPlU8JMmWaThHmDhi2fdxd5E87WPUOyz2Jrb7NDVrixVL1ytOP6n5yb84RZ5qelsVl17PRxVBg9HQ31L4vnVQv3HZ6umoUcv54rWSIocsMSxdr1i8XnL2AxHPvVAnjQ3X38pJE72ndLXteaO1wBg5cqXdnnwNCElVai6fz/nqrw34k//JJFmm2VqrOP9yRlkItB7VvEaT6m3LoZ2Mx+hzJFfO5wSh4MOfqnPkpE8y1Lz53Yyv/eaQqhRoDb1NxS/+oy0++1PjPPdCEz+wl+x730p59Vsp198qQQgOHQv4gz/aZmrOw2g7cPe3NFfOFyxcLdkR5jGGZGD4+hcSfvSnx2m0JHFf8Vuf6/HqNxOyxN5/GYwIxQJQBhG4COlgjCE4dQid5qhujKwF5PPr6KGdnKXv4k938CbbpBfmEaFvzf96MU6zhqkUplLIwENnNocvowCd5Fb2QNkgyFTVXSfCSpekasC4f5hQ1sl1SqEzUj3AwaXmtEicBkqX5OL2Fz9Tw51MyTsFgcARLqGscTh6fN8skDYaVwS7HI8tKlORqoezIj4YfvdMHAeF1rC6qvniF/aPAK5eSRkZXO8Mr8vLGimtL9/d5MbOn694802bRbobn+5f/ELKj/5oyPiE5Gd/NmEw0Dz/UZ8XXgj43OdSvvBbtuTqOHuPNz9/+8E31m2m59aW+dVVu497nTPAIF1BSpdm7RCuDKyx5U4m9O7PiFEVVdxn6Sufu+t2zig7prdtO4RdYG03TmlteNi8YWGbAJHOti4AO2VUre59XRzX2osIwZ5k9nbQvN+l2bYiErumCq0th2q/2C+aCJl6apLGbJ2VV9bQyiBdid/0GX90DFUoqlyR93LqUzWCTkDYDlF5xWBxSNZ9eN2i9++WLiXC83GiGiqN9wQ8FgInauAE4Z72IFNZ/s7ml18jPDqBSm55GYVACIk26q5kMiEErvTQRhE0J2k1TwGShfNfxBiN69fw/DpVkTx00aI7nBF+0GB8+gwIwdriK3h+nVdeNLz+nZzW+BRxf4kyt4TkskioRmlrIRz+9n8/QGBIkxwpG3zpV3K+8uspUkbkmYfnB7heyM/+9S1cz0NIh6q0n2utKItkpztha63in/xva4xNnsHxmswecynzAWm8jtGK1vhx1pdeZWMFvv3bIdcuTpMmmxjtUmue5ltfSUmTAY12g6pMqDdmkNIlSzbRpsIPWvhBkyReIxmuYghR6iz/+//wEkrZ9KN0tuMhD2gxc+Rx4sES8XCFxesN/t9/MImUkGdbVGWGH07gyCM0WkvUGjMs38j4mb+ySFQfx+gR4TWapLteIqSm2Q6I6pMUeR+tSq5fbPP3/2qAUhl52mdrfbDHWLDxwceRtQBTKorlTcLThzFFRfc3vgVKIwMf2vat3V3+qH/gUWQj2hkBvJkOTj0EbTlrTrOOrIfoJENGAbIW4DRrZJcW0WmBilOyCwvcS+CvNAXDqsvh6HHq7hhCSfrVBsNyk6Y3TsMbJ9UDKlNQ3SqAhhkN2u/0pG5HOoPhtcFXGJQbZDq+bSulqxFpeXfq0LyDnVi/94Kdg+LWksmImnkgbC+s7oWXzpUcP+7yZ3+qRlnabNLnPpftOc59aLnetu39CAsvbrxMXg44MfM8T538MbrDawzSZbKij9IjQtM+SPKtA/N9fvI/G+PEoz5vvJzheoJHzgacfToiqguuXCh47aWU/+/ne3TX1UMpWTWakpOP+zz/qQZPfzikPe4QhJI0Vlw6X/DKN1O+/OtD6512y89zHIjqgj/x58Y4+0zA3HGPzrhDnhr6XcXi9ZK//9fW2VxXu8yArU7a3DGPP/Wfdjh+OqDRkmhlePkbKS9+KeZbX012KUDsAAAgAElEQVT2dN0iYPLMONF4iFGa1pEm7WMtorEQVWr8hkcZ28Cwc6xFf2FAspZQpRXScxCO4GHivgMeFQ8p1lfoPPMR0vkrFFvrKGthjfR8nFqdxmNPgYFi8yZZULgSEbgYbUgur9rywS4Yo0mqHnV3jEKnDMuNfVeOvqwx5s+Rqj6FSEAavLCO69eoipgi7Y30DN65Va90PJS6ydIPwjZlMSQeDLFadRN2YndD+lvXEFWMkBWN1hxCSIR0aXZ8/KBJWSaURUxVJmBKHNcnjMYRcgrXCxFCkgzXEEKgVExZ3JxojLEdV0o5SEcihMFxfIzRKF0iHQ9rxlYjCKdZWbhKVaa4XoQfOmytLSAdn7A2RjqsLBdESqLGFFKOjDXRhNE4edrFTnwuWWpu7x4RFY6T2NZPY5DCQeCRJbZFXDCJdBVpPKDMNynLmFpjGtdrI2VEd/0yjuvjejWkE6C07cAJa+MM+4sEYZuo3kY6EZur1xCOh++3qcq9Jq8yCpCBjxYl3kTbZmS0wWnXcTsN1DBFJxnezBjC3ZWNkNKKYwoQgYfbbiA8l3JlE39uAqREZwU6zqwmhtKoYUq1OcCph7it+ojMf/dJV5uSXMe4wieQNUqT2WfbpKMsiY8va1SmJFP7t8g+fIzOWbDTlbX3U40yFYXOcIWPRlPo/YjoZrQbZ+/f3sE4JAoniKJJWo3DOI4VQq1URr8/T5yskuV39n+TwqVRn6HZmMP3G7iuLdFXVUpeDOgNrpOmG+g7jDXNxmEajVkatRkuX/sCUroEfotO+wRB0Bop/UJRxmx1L5Ok61SVzTAGQZtGbYbJicdZWjlHnvep1aZoNY/guSFSulRVTpb3SLNNev3raP3esDDZxvqaJs9L+j2N1obBwLC1pd8VtsHhiWcYax4n8jsYwHNDOo0jVKq4K+ftwvy/JSsPoK+DpRlMzbp0Pt1gdcGqVn/lNwbUG5Jjj/g88UyE1vAv/3H3odh3KGUV+zsTDudfyUcabYaoJjn9RMCzH6vhOIJf/rm9Om0IOPm4z0d+pM4HP1Gju1Hx6rcy4r5GOtYjESAe6j20iWZb8szzER/7A3WCSPLqt1PSROM6cPKxgA9/ss74pMMv/dPezXtsYPW1dbxLXRCCdCtj+dwqTuBYNwMBqtQYZfDqHsUgt/1PniTvFxTDh/tM33fAUw37pAvXqT/yOEiB2+qgkiEYg/AD3HqTaO4Y2fI8+erSzS8KgXQdZOhRrPSoBntbUI1R9Ms1ArdBmxn65RqVznGkP9LxiDCOpu6N0/KnGZYbFE4KUuA4Pn7Usu6t6cEezoeNqrS+IEII/LA5mug1Qrj4gY/j+HhBgzTZQFUFRmn8oGXN31wfz2/g+3XKIiZ3fIZlaq+Z4+NHbTy/huuGo2Pl1gvsTi/qqLymtUJIh21yruP4SOnhehFe0KC3eRWtCuttYjRpskEYjVNvzlB6wx0moB80bVBXZZRliuP6SGlLQ1K6OI438jrZdT5Go6p813VwcD3LRRFC4vkN282X9SmKAVpZrxR7LWqWZKjthCCEtIRD18f1Isp8SFgbw/PrCOmSpVv4QROvMXMbydiWmDSmsC+O6iforED6NvjWaUE1SPAm2+yeiVWc2iDdcUadiBp0QdUd4k2PocscHWfoYYbMbTlOFxU6TpGBZ4Uq1b0HNY2m0rnNZMkQTwcM9SaVKdBopHDwZURcpQ+lbfsg0COekEDiyYBSZzsFzO3gpzIFqeoTOS0CuUUq+ihjszkCiRTOiJvz7mVZPLeG59UJghad1glcNxwtFEo8t4aQDkqXlOXt2SnHCQj8BmOdU4x1ThH4rdH7J6iqlKzoI6WD1hVFMbAZzltQiyYY75xmcvxx5pe+QeC3qNemmRx/nDDsIKWHAMoqJc975HmPasR38r06zeYchw99iDTdJE7WGOucpNM+gevWcKSL0hVZtslguERZJcTxynuiaWMbcWyIY8Py0rt/Tu36EZqR7U4UQOg1Cb3mPb/nOD7cx5wb1STHD7ss3yi5ejHn8vmCqCb4A3+8xYlHfT7+Bxt8/l/1yTP9wOWtqrKczN6W4o2XM7obFUVuGJ9yOfFowNxRD88T/Mov9Kh2UR+imuDEYwEvfKaB5wlee6nkzVcyFq9VhDXB2JRLZ9whjfWerNr0nMvZp0Oe+1iNf/dvh7z0tZT1lQo/EEzPuRw56TEx7fBvfnFgNdJGt713de+cXAx2vSu7E+D3yyZ/G7h/a4mlGxTrK+gio37yMRqPnMGpNwCBLnKqYZ/B6y8zeOMV8o2b7by6VNYt/egEwWxnX7f0q4NznO18gkPR40yHp+gWSzS8CRzhc6TxJKHTxBMhrvS40PsaTmecoD6GqjL82hhGG9LBGu/sIGuoymxUZnLxggZhNL5TII/qE6TJOlUxQOnKdglIiSk1WbJJe/wE0vEosj5pvIYQdhBNBsv4UQc/bBFGY2TJJlmyhVIFZdanPXHyzqnWWzqIPL+OHzSIalPUmtNorSiyAdNzz5DGa9atdpSJAduVUG/N7ZQEjbHnKoSdyOL+os0ERR2i+hRRfYo0Xqcs7szNsPs8jFLFqGVdk6Vb1OqT1JuzDPsLSNcnS7skgxU6k49QFglalZRlQq0+CYCqMsamHgOgKBIbRN1lydj/7Vfv+FnvizfF9ware1f6yct7XY/LXZ8X8/tLzSMFtbPHqXoxanjw4MRgKE2OJ0NCp06cdndKu5UpqDkteuUqqR4ceJ9vFwZNOuIF+TJk0jvKWnHNlpyRFNpmmZKqx3z6Bo82PrRTsupXazh4hE6DutNhJb/yjpGo98Oh2WdG2SXBZvcttFb4Xp126yhH5j5MozHLxuYU12589bbvjnVOMjlxlrmZ58hHWZStnm1fr9dmqNemmRh7lBsLX2Nj6yKbWxfveB5CSDrtk8xOv596NEVvcIO1jTcQQuC5EWOdUzbwqvZ/Zg7NPIc2ilo0wcbWBYrBPEpXtJtHaY3+ddrHeeW1n7+jUN7vdWwOrpDk92d3YIy54z2543ewPJZ//g+2WLpR7nBa+l3Fhz5Z5y/8pQlOPxmidcbq4oNFPGVheOPljDdevjW7mjM56/LcxyKefC5C3uKeceb9Ie97LuL4aZ//+b9c4Y2XM9ZX7n0uP/JHGjz2ZEB3Q/H3/pd18tTsrC3joeaP/WSbFz7T4IkPhFx4NaPfPUCga+7w/79PuH8OjzHosqD33W8xOP8K0vWs9g5YWWxVobMUlSV7GEzSlTg1n2BuzHa1mNvT5EnV5WL/6zS9SSaCo9Tcjk2ZG0XDHSepemyWVlMkrrbwEo12NM2JExRJD63fnRVlVWXEg6WRq7tmef5bGK3QqqK41h/JkxubBVDVTjYkjdcp8r4tiWg9WlXfZOiX2YBBmZL0l3b2YbAckjzf3u/t6K5fvEmihp3yXppsUhXpTpbF2VWKiwdLqConGa6SZ7Zctb2aFwiMsXwaBCO+jiHPusTDFcoivq2EaIO/JmE0RlXEJEXM2uLLu/ZpM1BS2glJqZw03gAMWpUUed+uVo0hHa7t1YIR4mbVRQiqKkPHBXk+4EH1MB4I2pBeWrTP9n0W6W05t4MvIgpt79G2Rs9UcAKD3qNpc1BMByfpeNO03Wl8WcOXEa70+PDYH0dREVddlrK36JWrO9e4MiWL6Zu0vRmO1Z7iWO2pnfb51we/DUCuE9byawSyRsMd55H6szjCR5uKyhRkejhSid7ODb3zcJyA1bXXWF37HlnRA2OQ0sX3G5w5/ceJwgmmJ59ieeW7FOVwZwFhg5nHmBh7lMFwkas3vkqcrKIqO7G4bkgUTXDq+KeZnDiL43ik6Tpptr+wqZQOJ46+wPrGmywsfZPBYGHn3RVCcn3+a+R5746lsTDsMIyXeePCLzGIl2zpysCS822OHf447dYxGvVZGvVZhsnKvhmr3+tY6b6xU0K8H9xvwJNnhhtXSvJU7yHwLlwrmb8ysg466rJ8Q7K6eN+nc2B0NyvS2BBFAsfZmzo5ftpnYsYhHmhe+05Kv3swjtKpxwMcV/DW6/mOxdE2Fq6WbG0opAPHTnncuFQcLOB5h3H/AQ+AMah4gIpHK86dtt87BxvGWOJysdrHqQX7bqtRDMtNKl3YFY3bRgpvFEgoUtUnrXr0y1W7cixiiAWOG6JViSrfJe8fo2964wBlfjPTUeR3XuFqXaKLO39ujEJVClXd/rtubevfjaraX9hPpXvT7na/9j4olR/onPbkHXW1Mwnst50QgjzZoiwSjK4o90n7q1371Ls663YP/krdmVS4s73R6OLdl0432f0T5Q2azXKBRPXRVDtBYa5iNovF0WKgv9N6XpmCXrmKI9xR+enOKHVGqgZIXKToIRh5OhmFRpGq4T6lJ0OvWkOZaseeQpmKTA93baEpTMZmuURpciLdGqmiK0qdk+t4pzTGKIO1kl0mUX32EzL8fiDP+8TJGsN4eZfjtaAohwziZdqto/hBk1o0gVI5lbLa1I36DFE4jiN9uv2rDIeLo2DGXqNiZEvQ7V9levIponCCen2WLO/dsaRkjCFJ1+j35+/KG9oPlcrIsi69/nXyYsDuezVMVgnDDvX6NL7fxM2771jAI4Vjs+5OiECSVF3UaOxu+FMIJAZNqVKUqQicOqHbJFdDq5KvC6p3yK/t1syXFA5SuDu8Lm0UWpcjAvPbh6oMSXy76nOWmpG2mlW7f7tGzbei3pRMzrrMzLkEocDz7b/HnrRG09Kx+dfdlf5G28HzBUmsiYcHV69vth1aYw5KG/7wj7X2/EbHgZOP+UhhHQVc791Z5NwLby/guRUHYaEp24o+fH2e6OgkKt3/Qa9MwbDaZFjdO/2oqgw9LMiHm4TNyT0T5vcbAitcd3cvlvc63s65H+w7BoNSJVubl6ydw10zL/fa54NeYzEi3773Vhxgr9VS9tZtf0/1gDQfsJzvLa/lOmElv8xKfvm279yKrXKJrXLpntvdiu138NZj34puuUy3XL7rNgZNpodcjL951+0eNvqDBdJsY1ewY89GqZJu7xqB36TZOESzOUeSrVOpDIGg3TpGELRQKmdt/XWKMmb3M2iMpiwT1tffYGLsUYKgSad1go3NC/uehzGGwXCBYbxy38EOQJKsM4gXyYvb+Ylptkma2bHS9+s7E/g7AUf4tINZGv4kngxYji+QlF0qnTNdewQpHJQu6BdrFDphPDzKVHSStfQqcblBWvYYvuMGtcLy4twanlsj9C13r9I5RRmTFT20Vm97rDAaslTflmjWygZDVWUII4nnP3hA4PuC2cMeP/RCjWc/FjE24dBoOdQbEj+wem7VPlptYWhbypPh/RHI/dBydQ4f93jimXDfbbJEE0bytjLaewUPJ+A5AITn4DZD6o/OUvYSio0H5yM0J05SH5sDIGxMkvSWWL38jQfe70EwN/YUWTlgkC5TqHeGTPqDBKMryvz7zzk5CJrhNJ3aYVb7b1Kq7P48dH4fP7DIsi3Kcr9305BmG5RVghAOtWhyx6QYYUtavlenUjnD4fK+8hZKlwziZZQqCfwmjcas1b3a59kyRjMcrtzhXA7wO/LuHctlShU7hGnHuV336PuJUqdspNcZlhs0/SlawQw1t0OmhmRVn61skbTqsU1192VE5DS5MfguWlfv+ALElQGh3+bRw5+iHk0TuPW90ilGM0iWWdz4LpuDq6TF/XsvSgfqDbmbUQBYvRvPF/iBoN9VpMmD//Y/8qdbfOD5iLPPRPz8393k6ls5a8sV/a7mw5+s8+FP1vj4Z263qEhjK4DbHnPuS2w2iTVXLhTMXyn5lX/e3bdqrzVsbVRsrr03x9h3LOBx2zWioxM4tQBdVFYQ7gGRDdeoCpuqzOOtPaWk7x/sCmGq8Sj9bNkS4X4/4HnPQgqHRjDFTOtxNuNrVsvm90DAI6RL59gTpF3r2K6K259R6Xj4jXFac4+yfvGb6OoHSPH4ANBG3TGzqFRuFXIF/z97bxZjWZad5317n/nOMWdGRM41d01d3Ww22WyyKZGmRZGmKBm0JHgSYICCYMGAX+wnA/ajDT/Ygh8EGAIEWLIpS7YlghQlDmI3m93sKnZVd9aYVZmVmZERGRnTne890x78sG9MGRE5VVZ2kay/UJWV9557hn2mtdf61/8jZQDsv6E8GSCE461pc5KjtkWp3H0n5H0zK0pnD+ffdGhfi2O7wPb25JAw6CNt4hEgaEYLBDLGEwHKFERe1XVtWh9liolq+P74291/7KNnUB4VvhfTrC6xOPMKtWQBpXN6RW+Po+NJH9+LSaIpTk+/RCWe4ebGdylV+lD7GkaCqRmfMBKHykjzp30WFt07b3tD7VnpfBKcfyak0fK4+m7O5TdS2ttOAbvILdW6pFI/vmx2Z63kQjekMmmXv71SPtD+rN0oObUcUGsIVm+UaHXMnWGhKCzqHs71P0o8sYBHBh4yCTGlmpiM3j0TcU7pFoux+lB3h8Qj9uuOdGadvUSqB5SZ+xeYuGB/+hwOISShl1CP5yl1ipBPbAg/xyPAlzGVsEU9XkAKnx8VgfZJQwhBUGlSjLooefyDT3g+YaVBY/EZdq69Cfz5CnjuVQo9+NIVQhy+LA7N+u+1DkeqdzKM9+JkWFcmeUQBGmMV5gmrad8PAkHiN4i8KgJJqgeAmCgYa3wZEXoVLAZjHJF997vIq1GabHJcT2bykYRNGpVFZhpPkeVdBukG47xNXg4QuPbz0K8xU79IJZ7G92K2+x/RG90+ZA58P/i+08VZPBugFYwGzs7j/DMRyxcC0rFh+87xAY8QrtdESndNSulUkKU8vgei0fQIIsn6SsHOliYbW4SEqRmPU8sBUzPHv5vu3FLsbDoxwhdec6WpjbWSorBI6Y7BDwT9jjOH3b1sb3xUUK1LFhZ96k1Jv2MocosxTg8ojASeLxgP1WfWC+yJva3LzhirN5BxQDBVdZL7B/ivnvA5XXkObUvXjZXf2vs88Ro81/oZ6sEMxmoG5Tbvd79Jrkd7qq1CSrCPhwh2L/gypFlZdCnwT2hM+Dk+fdTjeZJd/5y/QDC6ZPO9P/pR78aPFEL67rlwDKT0XfnHMul6OqDBZJxEgxAevh+hDpD7D6wd348mZSx7zwzMJ8auk8tnDNZaAhkTeAmecNmLTA/p5Rs8O/VTNMMFcj1iWOzQzlYodUaqBpytv0q3uM2waDMqd57Ivs63nmeqdhZjFO/c+P9Ii+5d3C6H1eD7XFr8BtP18yzPfplx9rvkD0FkFhL8EP7jvzfN5TdS3v5+RlIR/NLfbLJ0LuDaeznXPijo7hwO9IRwgUZSFUSxxPMgiiXVmjNGztKjhqLtbcX8os+zr0RcfC5k2DNEseCVH0/48Z+psLB0fBXlg8sZs6d8Xv1qwt/5r6b5o3875N03M1avl8QVwfxpZ9T82/9sQK+zr7T8h789pFKVvPKVhF//b+d4/Q9HrN4sycaG2Xmfs0+FTM15/OP/pc2orx9KCftJ4YkFPI6kbGk+ewkER9zSPeGzkFygm9+hONB+OxUtMR9foOo32cquA4JGMMuZ2ouM4pws0gjh1IDTwSb5yr3rrr6MmK6dp5mcphGfIvAmWSVTkqoB/XSdYbZJe7Sy9xspPJanvkg9XiAJmyRhi8BPWGg8w1TlzJHZ1/dv/Aa5OshfEUR+lVZlienqOSK/TuAn+DJCmYy8HDLINljt/BCl8yMpVIHk0sLX8YRPocbc3HmDxdaL1OMFavEcngxQOmOU73B960/I1fDQOjwREAV1FprP0YxPE/gJngzQpqBQY7JyQHe8wvbg8eqmCCGZrV6gFs9TjWaIwya+CLEYlM7pjlfZGd6gn905cQYrhU8tnmW+/gyVcJo4qLt9twqlc9Kiy2rnLdKit9fxMVu7SDNZpJEsUo2mCPwKvgx59cyvYqw5NDZrncvc3HmDu98ongxpJotMVc8yVVnGkyFMOk62+tfopqsMsk2Ow3z9aaaqZwG41X4LTwZ7n+1yRfJyyI3t7zEqdihPKIkm04s0l5/HCxOk9FD5mO7KO3hhQtSYJWktYHWJF1ZQ2Yida29QDLtEzXlqCxdoLr/AzkffY7S9Qjl2hNfawkUai08jvQCjFV4Q7W2vOneO2vx5gqSO8HyKYZvuyrvkwzatM1+gvnCJrL+Jn9QxqiDvb9Nbff9Y80QhJAtf+Om9UQ0qTXQ+nmRTJOs//D1aZ1+gMrPsJBKihPHOGr21DwkrDaqzZwgqDawxBHGNtLtOf/0aaXvt2LE6dvyiFkM/Of67eIbAT7BWk6bt/e4cC6PRFmFQx/ciqpV51+V1V0AjpU+tesrdeyplnG4+WPPGnxNYDDvpTbr5bQRy7/7VVmOs4sPOH+8tp41CW8VItclGA1cCs8UTzVrVknmkDNjqXSEreid2Y+XlgO5wBU8GtKrL+9yuB8SgZ/jTb6ds3i4593TIT/5cjaTizKDf/G7K7/zzPsP+4fvlaz9f5eUvJ3zlGxWkFDRacq/76oUvxpSFQZXwj//XHT58J2djzY3bv/6/+6xeL/nZv1rj7/93cxjjSMPXPyr4p/+ww7MvRvzS32oe2Uet4a3vjtlYK/nlv91k6VzI868mRLEgzyyjgfOD1KZ/6JLubCt+718N+PhKzl/+5To//6sNKjXX9j4eGtrbirUbJWXx2XV7f3L1GGMxhaLYcNYPeni4nVkgiL06ltvoiXuyFD5Vv0U9mKVfbNHO1yb9NlAPZsntFrpoT/Rcgj0+z0kQSGZrF5mqniEJp7DWUOh0Tzgt9usU/pDCO8oF0qag0GM8FRD6FbCgdEFW9g/NFNxk7O6zbfG9mFZlmShoTLoXSqzRSOkTBw1Cv8Iw32GQbRwrZR77dQIvIfJrzNUvUYtmCbx4T8lYCo/AS45NEdfjeRrJaaYqZ5ykgCkmMvSCwHMpzchvTFyE7zmED41GZZFK0HL7ZhQlGik8fC9kunpuEoBYeunRF1noVUjCFqeaz1OL55HCd+2jE4lSISSVsAV3dWAZq1GmIFdDwqCCZ0KsDMnUwI37AV7HccGGQDJXv0QzWaQWzwOgJ8GUJ0Nm6xfwvdC1LY9Xj/ze92KS0EkqzNTOI5AkYWtv265LxLne36t7LYhrVKYXGW7ewBqNLlJ0mRNUGvhRhSCu0V+/SpAUCCGpzp2nTN/FlDll2scLIqeTNWmZ8KMqYaWBH1UZt53Q427mK0iqbr1xlay3hec8UagtXKAY9wgqDaLmLKOdFYwuMWWBSocnl2mEIKg0KFOnoh3VphiOunhBTFSbdmOfDsh6WwghiZtzSD8kbs5N9qeOF8Tu2HWJ8AIqM0ukndsPHFhUKnNEgzWk9A/JHAjhUaueIgxrGKsZpVt7HZ4Wy3B0h2p1gTCo0mqcJ8t7d0kjCHwvotU8h+eFZHmX4fDOZ7YL8F4QAl54wUcbZ0szGFiKwhLFgmZDOEfzgevmuXTJ59o1RZ67sstTzxju3Enpdg+noISASmPE0rJP4Atu3FBoLahULbWqotcrqVQgjCDwfdLUkmWWooSZaclwaBiNLL3e43sY+V7kZDKKwSTQOn7du230pU4J/OSQv96DwBhLNja8/1ZGremxsaYIQsHWumLtZsHKteKIwnJnW7PycUFcOXlbxkB7S5Nn+/t9Z1Xh+xmeL1hYdK9ypwNUcOVyRr+jEcJxau6+ZYZ9w82rBd/7wxGnzwQ0pz2SqqTILeOBYWdLkY0t+oBlkFaws6Eoc0Oj5TG/6FOtyUkZy9Dd0Wyul0c0ej5LeKIEFFtqhu+fMEMTjsNTmmyv6ymQMRW/ReI3uDm8TDu/hUCibMEzjZ9E5LcZ53co8yH5qHNfDo8QkuWpV4mDuhNX67xNocdIIQm8hGay6GTS7uLlGGvoZesM820iv8a0Pk8cNBjmW6x33yUr+webVin1UW0aX4bU4wWyckBW9sjKAcrk1KI5GvECM7XzZGqIsepE7xZPumBreepVSp2RlX2G2TZSSCrRzN7NeveDd7p2jvn6M0R+lbXuZdKyjzIFoVehFs0gZTDxynrMJTprqUXuBZaVfQbZJsoU+F5EPZrjdOtFhJRI6R8b8FSiaWZqFzg782XGRYdhtk13vEquBpPgr0o9XkCb/NALbVx0KHVGL11nVl9kqnIGX4bc7r5NVg4Pze7cWB94WCPxvZiz018iDhoYa7jduUyuR0jhUQ1nON16kThoEgUNeuPbx77oBO6aWmg8izYFpc7Y7H8IWHwZ0aicptDjY9Pqu5BBRJDU6a1+gMqGjqemFVF9xgWuZU772vcJqk2S1immL75G//ZHFKMOqhgzdeYLh4jIYX0aL6pgVMH2h68TTy1QnTlDMnWasDZDkNTBWtrX3yJI6lRmlpg6/wrdm+8g/RDpBwy3VijHPewD6eIL8mEHlY2on7rEaHuFsDpF0nIWIKOtFcbbq1igcfoSYbVFZfo0aecOFiizITtX3yBqzFJfuEht/hw7V/+UB43Ka9VTVCpzhGGdohgcOL8RzeY5wrCB0hmDwfqBDI6l11+h2ThLvXaKuZnn6fVvovXuJMFNxOKoycz0s85cN+/R7a88MkfnRwkp4ad/JiJNnc/V+rqh1zVMTUkuXPT4+Jri44+dS8pPfT1kY8OQ5wbPg5/8Wsi3/6ig2z18LQgB8/MeX/96SK0q+M3fdFovp05LlpfdOhdOSZpNSbUq2N4y7LQNw6Hl+ed91tYMa2uaXu/xZYB2u9e0Ke979Rij3SRaOFHUh9oOAs+D738nPVK2OgnvvZXx3lsZv/UbD7Uphn3DB5dzPrh8fGv/rY9LXv/m8UkAYyAdW771Ow+n2ZRnlq07mt/+Z5/AwukBdPs+LfxoGLdycsDmwAFbVxM21mAmL5D5+AKJVyfXIzaza44MJyJyPSaQMUltjnpd4Xk+Vgiy4Q5F2jthowJPBjQqp+mMV9kZfsyt9pscnLv2B6sAACAASURBVLUh3pychLtPhGWYbeFKUzVnQGcNuRrRzzYY5XfXoY+eyF66zps3f8N9MznRTln5A6aqy3hewHTlLN3RrSO/3UUcNAi8iLXOZW6137rrZS0m67y7HOZRCWeIgjq3u29zq/0WhRpN9G93yZquneBxz1Atlsur/3Lvb5ODZ7fTLQprJEGL6eoZ7lZ78UTAqcZznGq+QFp0eWf1txjmW5PAZl+9VwhxJKuVlf3J2Agq4RS1aA6LZZBtMi46KH1Yl+Ug4qDOYutFavE8W4OPuNX+Ab3x2t42hRCkZZf5xjMsNJ5hs/8h/XT9iICaFB7N5BTr3ffYHn7Mnf77B1SgBXTEfbt2Rps3WS8LFl/7BaxW5P0dNt77IxACXeaUYxdoq3SISsaE1eaJnBWAsNJ0CtlDJ6Kn0gFq4j0X1aZIpk5TmVkkqE6yZrok7+9grXHK4WVOMWgfGbN7weoCU2aTDFWGiQpA4AUhrXMvU51ZclmrpI4pC8rUnTc17u8Fa6bMXWbsITVm8qzP3PRzzM+8wE7nKlrnBEGFZuMs1YqzeGi3P5po4+wf02i8ycbW22iVs7z0VZ5/9q+Tpm2Gow0AqpU5kmSGSjLD7TtvstO5wmi88VD79lmC1vCDH5SsrGh+5VdiPnhfYYwLgF56OaDXs2xuPfizwRj48EPF0rJHtSq4ckXx7LM+Wll6PcMrrwaUpTO/HA6dnsyzzwRUa4KbNzSLpyWehCsfPL6ApyxHeGGTeuXURBDx5HsvDptUoimyovfwnXWCvyh9EQ8PIRBJhN9qYJVGbT4Z/tZBPLGAJ5ipE803sBb8ZoIe5Qzf2X+5WyypHpL4DRpmDmMVs/E5ADr5OmbyspBCTuwmFNILnIOxNYDdK00dD9f9VagxcVCnVVmmO15jlG9PZvz2AZ7jR4Mhe8xn99r+0U8tSudkZY96vHDPmrEQboayNbhKocd3BSgnp2jVRHtmqnKG7niVQbZFVvbcvn/KQfbxDwyLsZAVfZKgie/dLWIlqMazRBNzv63BNfJJOWp/hydB4z33f3JudgPMPY+xk3/kezHNyhJKZ4zzDqN8+8A4u1Ttzugm9Xh+T99nXLSPKsZOAsluuko/Xb9rHB5s3FUxZty+jXn/j4mbC3hBRGPxabAW6Qf4sSuLyTDGCyJUPuZe1hoqTydlqwou6IiRgeO4qGxIOe6R+QHbH35vMmwGo8q9EuL+mD4E7KQd2R5oYRWCyvQS0vPJhx36a1eon7q0V0ZzPzvoB+f+fND3iLWOk3fr9ncmGjmnadSX9gxvPeGxtfMene7HdHs3jz2m4dBZrVgstdopgrDGdNTYmxgU5ZCd9kds7bxHmj75B/fjRJ5bytJitHOvXlr2DgnWNZsCz5MsLXksLXtEkSPYLi66v3c6hq27AiJr3aVoJi5CZ8664Ofg5N4YVwICGI4M4xTCELa3DRubj7d7a5Bt4Hkhreoyc61n6I9vTwKa/UmIlD7T9fNMN86TRC3ag5ufWHn5OMQXT4MnQTsrJixYbbClpvLcMiZX6GFKudPHq8ZuWWXIbmy4QfMkwXSd+NJphJSo3ohyq4fwPYTvaAleo4ItFXqQEi7PuvVYKO60nRGywKX3lKbc7KInZt4iCvHnpgmXFhBhQHHrDibN8Gen8GoVrNbYvITAp7i5BtYSPXUWhIdud1HbHcw4JTy7iNeoge+jttvodheT5SQvPeP2q7OfIQrOnCI4NQeeh97poPsjzGBI/PxTiCjEKkV+9SZevYqMY0QSI6sJaqeD3umith7cI+3hAx4pEX6AX6khPOlo6SfUOW1RUPbczsjIx6sn2FIRNCtHZqIWw7DcIfZqiFAihKDqT9ErNugVG+w+lHbdo5UtMcIipLOZx9r7PhGtNQzSO1SiaSrhFDPV8/gyIFdjlM5dEPGptUkKPOkTeBU84TpE5GTsKuEUUvhI4U84FcfbxtpJS/4g33yIG9GSll3SokMlnJ6UdyJGRUyhUifE9ymKgEnh43sRngwnxy0dD0t4eDKYtIoz0T3ZP+Y4aOB7MQZDP7vj9HOeQKuKJ30q4dTkehgdy/EZF20KNQYE1Wj6+CB1ks0aFx0y9QjpXyGQfogXxpTjAWG1hRfG+FEFlY+Rno8fV4gbMwRJEz+pk/U2neN8mBBWmnslsbDSxKgSlY+wRu+RnsNKcxI0Wcp0gBdWkEFEmQ4BO8nqFI8/9SyE4xdJH6xFFWN0mSE8f+KtBo/amlSqlDRr0+uv0O5eIwobaF0gp3zndq4gVWN22h/RH6wyGh9PPC/KoVNeFhJtSpJ4miisA5aiHJJmXdrtjxgM107s0CrLMWm6Q3+wRlmOHoqkq3VJnvfp9W9RawwwQtE9JnmtVEaed+kPVsnz/kN1iyUVwey8x9aWYTyyKAVbWwaBc+LOC4sxhqJwj/lOxyCFy8hICb2uwWhLcIKNQK9vKEr3ndGuHFKUoLUrcWlt9/ymlLZoDdWqYH3dsP0QGaUHwSDdIArqNKtLzNQv4smAUba9x2UUwpU6ZxtPUYmmsUB3eOtTCXi8RgVZcZyisjNAhhMvylITXziFHuWU2z10f4xXryCTEOFJ8lubWAPCkwRzTeJzCyAFYqOLVU5TSngeMgrwWlXMMMPkJeHpabxaAsag0xwZBi5rqzQyDNCDdC/gQQpkHOG1GnitOqYoMWnuAiBPusBMa2QlwYxT0JpgcR4MyChABD7FyjrB6Tm8VgMzSjHDIcb3EbLEq1WxpcJOnBZEJcafauHPz2DzAjMaI1JHEvOaNWSjBhZ0d4DXrCPjEBGFiDAETyJ879MNeLykQjg9T/PVH8Ov1pFB5Iw0jkF+Z42N3/kXAJSdESYtKDsjgtn6keBEm5LV0Ts80/xJFpJLCF5hrPv0yg02s30JfV9E1MNZMj2iKEcYkSOkdyBQOBnGKq5ufovl6S8yU7vI0wvfoNQpg3yLznCFtc4PKfT4U+ke8GVIJZpmqfXSpNuo4bqlRLAf/MA9j2FXn0jph5Njv9N7n3He5uL81zk782UsllKNWe+9z/bgGsN868ROoU+KSjTNdOUMrcoStWiewE+Q0kfi4024UuOizcEgTyCI/Rq+DLBWu2zUE9LrkMIn8mv00tt3lb72YYxyfm9GEU9I6HfDYlHGnatHeWh6QUTj9FPMPPVjGFUgPJ9y3Gfj3W8R1WcQ0idIGpx+5efxwoRi3GPjnW8CjmjcOvMCyfQiXphQmV5mtH2L9vW3KOvT1OYvsPjFX0BlQxdoGkPa3UB4PlF9mqXX/grgsj6j7RXa13/w0Pu/OwrHf2wYd9apzJ6hOnuWqDGLNQqjFKbMOCkD9iDhT7d3nW7vOiurriW/KAYMhmusrj+8Arsxim7/Bt3+jYf+LUC7e5V296hlyINgnG4xTrdY33iTv/13aoSh4ON/eHS54Wid4Wj9kY7vqWcCfvVvVvkf//suWepG9zf/1T7/8KBwHsA7bx9+Lv6Df3Bv/seb39+/7n//9/Nj13k3TtKc+aTY7l3FWk0tmWdx9lUWZ191TRTKHYMnQwI/AQTDdJPO4Aa32z/k4TOa98/eqp0+UXWeYLZBenWN6MIp/EYVGbjJuylKzDgnv7WFLTXh4jTRxdNucNAI3yM6vwC+xOYlVmv8qRrldh89zklOL4PSqN6I/NY28bl51E7fvX/bA6ovnkePMvI7XWpfvES+srW/+2lOsbqO7g0IL57Ba9YJFueR9Srj1y8jQ5cBEqFPdPEMtijR/RF6p0uwtED1whnK25t4zRq2KBn8/nf2D9yTpO9+RPzMeWQlAQHR+WVEFKDWtxi/+e6hcRq/9R7+wiz+7BTRM+dBa/RwTLl6h+LGKpUfe5no6fNk73z0wKfnoQOeytlLTP/4N8g2VhmvXMdk6YmdJmp4YGZrLTLyOf23vgZAvt6h/Yfv7X1t0HTLDd7t/gGhTAhlwkC1yfXoUKu0sjn9YpN2vsq43yezbqa9OyO9H8Zll+tb32Wt80MayWmma+eohtMsT7/KQvM5bncvszO8wSB7fDX5WjTHVPUs52e+4nQpsk02Bx+SqcHeS3O2don5+lP3WZO9Z8niJBRqRHu0wuDW/0szOU0tnqORnGKx9SILjWcZ5dvc2H6dXnr7sQV7gRdTixd47tRfnuzDmLXuD8nVcGIOa1hqvUgzWbr3iibcrieQ3LkLj6MQ70qtjwJd5vTWrjDcvDnJXoq9Tq2w2kJlQ4abN9i68ieAy17qfOyymOtXGW3fQnoB1ujJbK7EqHLvOzERiQMQ0kOXGaPNm6Tt9T1RUGtcyt1qxc7VP6Vz4/ID7781mvXLf4BRrotw9fV/hcpHFIMdRtsrqGzExjvfRPhuH/fegtaZyA6lt/dZmY3o3nyH/tqHJ17/vg/PvxRQrUmGfcs7Pzw50yEE/MIvJ2xvaG6tKDbWP/kbNgjgv/gvG9xe1fS6mqlpSWvaY31N8d7lkk5b8/JrEWfO+cQJXL+qWF/T7GxrfvGvVfA86LQNb3wnZzg0PPdCyMuvhXTbhtNLrsT0tW/E3LhW0u0YtIa/8isJ3/1WjufBN34+JggFm3c0a7c0H75f8tN/KWb+tIeQ8N1vZnQ7Bj8Q/NJfr9DvGWo1ec/o49PglN5vnfcKdj4J19VaTWewwjj7l5yafolGcopkIlkBUOqccd5mp3+NzvAmw2ybh713//k/6vKbsURre08H8mK9TbndB19ixjm6P56oDTo9OWsMKDcQxVaXsjsg/eg2dmLqbNKC3rfenmjPWaxyJSqrNBiL2hlMPtdgDIM3PsSJQlrQ2m3buBJauelKTbuInruI13CZFD0YgXVlTpTGKo2VCqsmWbEgcM+IYYZViuL6KvnVFXcPj9xn94OIAjAGk+0H2l6rTnh+Ga9Rw+QFNi/3s0ulwgzH2EI9UFXnbjx0wCM8DxGEFNub5Nub6LFLfx8HU+wPpNUGU2hUd4xfi45t9zNWMVI9MjHCFwGZHh0ptShTMFA7KFNQmuyhdWNcK/qY0uQuY2IKhuEU1WiGudolWskyxuj7BDwPdyPU4jmayWmioM567z0G2QbjouOySUbvZW7uv9ZHewJZLNqW6NL52uRqSFr0yMsBzWSRJJxirv6UIwU/UPfN/RF4CVOVZeKgOdHbuU43XaXU2R4XR5lnsFYfIwpoJ0GRRgix11L6JIIeaw3K5K7cdoKKtpg4LQvhUersng7Zn2BHMGWOKY/L5tkJv6agTAdHggCjihNtIu75nS5PNODVRXqsPcW9oLJ9eYdyQo62Ru9tX+UjmBxeEDiF2IVTHn4Ig17JcGDptOGpZ33CyGB0xvWrgnTs2qYvPR3geTAaWdKxIakIKlWJwDA9I6k1JP2uods5en7qDUkcC+pNyZnzlrUVxXBgKEt4+rmAKBJobbl+VVGtC+JYECeCSkXS3tH0OoZ+3/D0cwFhKDAG5uYlZeleEHlhuH1LISS88ErAG39smJuXtKYl779TsLWhiWLB088FtLcNee44S698KWRrU1NrCLodw8YdzeIZjyQRzC94bKxrhgNX7jt7LmDljEIpkJ7g1g1Fe8c5YD/9bECjJfE8QZ4bXvpixM62Jh1beh3D9pbGaDhzzjvyzhBSIH2nmqty4xy3vf37zxrH9/ECd9xY3PKBRHoC6QnSfokXuPu6GCnCqo9RBpXvnwvpCYLEo8y02+Zd29DK4IcTygLQWkwYdQrKTKMLs9tr8cDPBW0K0qLLTv9jxlmbKKju8QeNVSiVTRSYdyYl64dDr2PgAagBdhI87MKk9yhDaoPVBp0duC+txdwl63KQBXFw3cet3x7wdNPl4ee9DEO8WgXZqLl4wvcm1WW7P9iT7ehOH6sU3nQDDJjhCJO7cbPGHG5K8n1kJSZcWsCfmULHY/dnd4A/N41/ao4oL9GjFJlErqw23USMM8cZOnDsVpsD+/NweOiAxxQFatCh6OxQdrfR4zEnXXGHJLmNxWQlo4/WiZenTzzJuzLqJUdbu2Hipv4Y1Dmt1YyLDuOigycC4qBBIz5FPXYtsyvtP73Hbw+2Md8/xKzH89SiOYzVrHV+OGljP6AbYxS+DJ4IuX+3g6k7XqU9vM7F+a8xXT3PqebzrLS//9hKW4FXYbp6FoGgO15lrfMDtD2ohSL3goa7rx+Lywhpo5yoZNCkJ24/0n4cJCkLIe57voxV5OWAwIvxZTTp6Dj8EPNlhO+FSCHJ1fCJm5FaYzBaYdTj5xf8qBDFgrPnfb781QghLas3NXduOzuGL/14RLUuKXNLkVnurGsaTcFP/kyEELCxrrm9pjHGTZSTqmT5rMfMvMfNa+rYgAdgbsFj6ayP78P3vydYX1V02oYvfzViatoFL3mWsrDo02xJWlOC1rTHzY8Vt26U3Liu+LGfiKhUJcO+QXrOiToI4fo1xccfKV54OeQv/ULCD153svzWWL75u+7Z9tIXQ77wSsi//c2UnR3N/ILHf/brdVaul9y6qbn8VsHaiuKpZwOaTUm9IYgiZ0RpLUzPSs6e9+l2DTvbmm//YUaeQbUm+Gu/FqOUZWdL02kbvvHzCb2uG9O3f1CwcVtz6dmAl1872vkWJM6ywI88ipHCDyUycNkEIQWqMJSpJmkG6NI5g/uRJEg8/MjD8yUqH5I0A4SAgbZUmiHFWKHyidFpIAljj6QVkvVLFzCFk20IgdGWIlUkzRCVG6y1nHquyea1AeN2QT5Se0HRw7z3rDX0Rrfo3aMb9tOAiEOXpS0VqE/hefGYJoO2LLFYRBggaxXXAJAXSCFcVkgZt0xeUN7eBCzhhSXQdpKpyV2Dwl3HKUIfr14lWD6FN9WEwMefn6G4uYY/P+PKZklEubGDLUpsUSICH5nEGCkxWbGXZdpNA1qlDgdDD4CHDniy9Vt0v284/Vd/zc1AixyT58dGW9nGGpu/69qSReDh1WOqTy1Qdh+PW/rjgrYladmjn60zVTmzJ8Z3HIw1lCbDYp0ujpdwPyUD1/7tBP+cDsQBFWQZEgcNWsmZJ+p0DJCpAYNsizhoMl0999AiW/eCwAU12pYTI8EDwQ6SQCZMJcvEQf0Y3SFLN11jvnyaVrLI8tSrdEarx6pQ3w/K5JNtC0KvSibuTSAu1JjNwVXOz/74RLDxFL30cLB1qvk89WgObUu2Btc+Nf7TSRhuXIfN6+4vR5wjn3jt77HA9wXNlmR2XvI//Q9dygLmT3n8yq9V+dbvZ2xtaJSy/L3/usn7bxd0OobVW5p/929SxiOLH8CrXw5ZOO3RbEns8wH/5B8NGPRPHo9bNxXrq5qPPij5tf+0yqlFj3/xT0f863855sx5n8Ulj5/46RhVQq+nee/tkvffLvnZfy/h534x4eZ1xbuXS7Y2NMO+5e//Nw2k5xJuRc6eAJvvu/tq0DOocv8e832IIkGamgPLQhxLBJp0ZEhTi1Kuq9FYF9B5nnvm+wEkFclwaOl3zV6iTwhHSO7sWLLUZb/+n/9zyPmLAVMzknRkSdNdH6Sj43Lxx2eQnqC3kXH6a/MY7cjJXuTRXIjpraesf9Djpb+yRJFq8lFJPlT072To0mCUC1CaCzGNhQQvlGxeG6DL/RfgudemSZoBOysjzn1pGiEFRhm8yKM+G+H5giLVNBZihts5g+2MIPZZmmSEipFi5c02RluON3f9bGHmP/kl/NkW/d9/nfQHV/bKU581ZB98DB9edyWkQ4GZC3gQkH8kJmQsd/HkH99ypbi9tjwYfefNQ+u144xynNFb3zrwocvSjF+/zPhPXYluL3sDpO9ccdm93YvUHvgdMH797U+/pBUtLNL84lfJt+9QdHbQ46Gr6R1zzZX9fZsHqx0r3KtEmFIjBw+iqXG/o3nwC92TIbFfZ3HqJfJySK6GTqTPWnzPBR1TlbMonU00d46HsSXDbButS2rRLItTLyOE50i1QuAJn/Zo5RAXZlx0qZTT1KI5Tre+wCjfplBjfD9munKWajSLMjkhx8vgfxIkQYuZ2q5Q4g7lgU60OGgyW7tEErTojtcOifd9UiiTM8g2qcfzTFXOuHHNt/G9iCRo0aosYTAnZke0LmiPVvBkyFz9KS7MfZVhtsUo36bU+Z67cTWc4k7/A7JycKyI37jokJUDBILFqZeIghrjoou1Bk8GpEV3Qpp2KPSYrcFHzDeephbPcWHuJ7jTe59Sp047yG+w2HoJIQTd8dpEg+fTN930gwpBWCVKWmiVubKa9ElH2wjpIb2AIKwwHm6hy2xPJA+cDYLvx5QqnfzOw+hy4jXl1uN5AarMUGo/eBNCImXgBA+te4k50umn09FXFJZ+z3XwGOOeqXHi5O6zzKXSw1AQVyTB0DIc7C+7u0uViqDRlPvPyHs8IlQJaerKSdITzM5LXvtKyMys52T6M4vnO50aVUK/Z0nT/SAmSZwybTp2AcREHYNqTfCVr0V84RX3jHv37eLY/dnNtvzcL1YoMos2lstvFtxedRygn//FCqsriqlpyaBvuHld8dWfjhkODKOhpSzg5seKUlm++lMRzZZkZ9sw7Fve/F7Oy6+FzM677NStm4qtLc1gYPi5X0y4c1tTb0oXaN01LkZZipGmc2vMwlMNV1aylrRfMg4l0hfMXnAlj2KkSLslaa9kuJMTVX2qMxFJI6A2F1OZjsj6BZVWQDYoYTvf4+VJT1CdDhFS4AUCP/AY90u8STmtzA1pr8QaJzkSJh6d1TFWWxoLMcbYvbb2zzw86f6VTyKP/wlgLWjrqjOHhtYe+OOucpI1Rw1WT7rxjouwd7ep75I2KNW9X/GPwO18hC6tKuHMPMMr75Bv3UGPBie6yersrjroZBBkGCCjo5sWSGKvhi9DPBEguXfn1Vh1ycxRG4jj4BRWQ1qVJUqVUujxRJ/GTBSMnabJMN+mn66fuB5jNHnZZ5hvEQcN6vE8RW209+J2JZy1uwKeNsPMlcxalWUiv0ahRnheRC1yXljDfIskaDzQsTw4nNhiNZqlES8Q+bU9TR6LnVhaJJQ6pT26cSgL80lR6px+eoeZ2gXioMZM7QKhX8WTIZFfpRJOMc7bx3Y4AU6mINvCk8FeSTD0K8RBnVJnk3b3kCRosjO8QSFGx0qJFWpEWnQYFW1q0SzaKJKghUVPHK7toYBHm5JRvkNntEo9nqcSTjNTPU9pMiSSMKgTeBHDbJvOeIVCPZxS6aNCCIH0AuJkClWmrr1fSPf/0sPzY+KkhVYFBVDkTqDRDyp4Xug64/wIzwvxvIBysg6EQCDx/BAhXVu4sdrJBUy+s37kuDdGY3R+Xy2jR4VWkB+gLCll6bYNUzPOTNEa6PcMw77LiDSnJKcWPYaD/exGWbqgpMgs86fcROSkLE8UC6amJUtnPFRp8T1YOO1zesmj3zWkmd0jyhrtrBd2+dVmsi/NpsRoS5wIdtUxEIIggEoVdrY0qyuaorDsbBkOSA3R6xpuXlOcu+AjE8FwYPnw/ZKdLYMQPguLHlEi2NowjEeOE/T0s4Fzs/Ys16+W3F5VeL4gTS1xIoljy3BguXldsXTWp9GUZJOAsd9zfJznXnQcpSK33LimMPrw+Iw7BUWqSXslg82MIJZYC6N2QTFWSN9xddq3RozbBWm/JBuWjDq5a1OPPRcMjTXjbsGonaMLjTmg7ZP2SoQU6NIw3M7wAomQguFOTj4okZ5EK1c6k4EkiNzf8+Hkd8rxiP4MJHf+bOKzMK6fwj48Aocno+zs0H/vh5TdbUz2YOl84UmE71P2U7xKiJfcneERhF7C2dpLTIWLVP1pfHnvLNAHvT/i5vDBWmaNVeRqRFb2qUZzNP1lwon3lDI5hRqy1r3M9uDqhKF/PCyO9Hxj53vM1C4wV3uKs9NfAtzLMi27rLS/DweE6NqjFXI1QkqP+fozTFfPIaVHVgzY6L/PKN8mL0dMV85yv7P8cOlbS6FGTrTPzjBTu0g4ab20VpOWPTrjW3THa9zpvvdYtXhyNWC9994kc3aGmdoFTjVfIFeuS+1O7z1GeZuz069RS+aPXccw35poCPVYar3kyN+tRaTwJ3YN6Z5dhT6hQ6/UKdvD6xQ65cLsV5mrXZwYgTqNnFwN2eH6od8Yq7m6+S2mq+eYqz/FXP0SvowBp659p/c+28OP6Y7vb2T5uO7ZspgEVs1lPC9E6wJVjvEnwoGeDAjCKvXmGUZegFI5RhfUmkv4QUI22qY5c4kwauB7IXnem2R0MorMlfmSyjTSc550QdTAGkWWtkkqMxhrUOWYfufmYzqio3CTx/2XWL9n+aM/yPnlv1GhUnFk5TffyLl2paTRlPzCLyecu+Bz57ZmY12TZYaVGxp9TbGzZfiZn0u4/GbOG989JgNn4ex5j+e+EBBG8O5lR5IOQsHsnEe9IUnHxunEHPNeTceWP/2TnF/9j6rEiWBny2CU05IZ9Azf/17Gxh1Nnu9nn/74m4d5ieORZWWk+D/+9yG7vP3dZVdvKbf8XZPpG9cGewHXQVy9Uu6VunaX/81/MUYIVwbTB26Pjz8qj6z3IFbf7u79/0ff3tyfcz7Axdy/k7HxUR8s9NYPZgsPr+POlf7e53v7cVxF9uBnAk4/3ySMPW6/2z0UQP2Zwmc8yfPnGeJenSRCiCNfVs4/RfPlHyM+fQY1GmLyFFMU+6muA8h3Nmn/8e+5dXkSrxox9fXnACi2BvRe39eo8EXE042vMhUtYrF083VyM2ImPkM9mOXG4AckXp3Eb1D3Z3iv9016xQaZfnAukEAQeAlCeBMCq5vh76q66gnX5EE0X6TwXSlABHsaOnbSPZOr0ZGxEEK6zJUM2aU6W2tQtsRaR84M/SpKZ0dVe3EkYDkh+ObqwbJau0fty8kMX3iHSLvGmj2S+KchsAXgyxgpvb1tW+ykO86VSXwZIoVHpk4+j1L4+DKcnDd54BnoSmL36pQCl91zhqXRnvAhuONXJj/Rz0qKAE/6eDLY+43FoEyJU5sG5AAAIABJREFUMeWJ5ThPhnuiisVjIjU3ps6TVGcQwnNCepPz2W1/TFKdxQ8qCAFlMabI+uRZl7IY0Zy+SBQ3UConCB0J0VpnWqvKFFVmFHmfWmN5T88qjGqUxZBs3KbXuUlSnSWpzOL7ERtr3//Ex3IcpIQgFIQRDHq7JHPHV6nWJLtd6lnqVIGlhHrdZR6UcpkdOyG8ghO1i2OXxciPaXSr1pznkZACz4c8dcGKEE78TmnQkxfqpJuXsrSoEipVQRAIRkNDrSGdArpyJZrzF52h4tUrJTtbmgfozP0cDwEvkBP6iN07Pz9KhBeWqLzyNMnLTyOTCBCYNCO9fJX0/Y/Jr7gJwuzf/Q8JT88yev1dgnOn8KebeLUKejBi9Po75NdWKT6eTKCEQFYT6j/7JeLnLuA1607Q78Ztxm99QHblBrozYPbX/wYmzen/9rdROz2SL1wifuEiyavPsPW//TPKtU3wPWb/8/+A4uY6+Y3b5B+t/OgG6wnBWntiSPnwGZ48p+y2EUGILQuMKifkpqMXn8kPiFj5HjIOsKVGJiEyPFzKkEJOBAUHjFSX7WwFZXIqfpOK16Kdr+KLiGrQwkSaUCb44mQbhuNgsRT64dsNj4OxCqMV6oRusiPbtoZSZ8cai+4iK0/yAYPykffbugDqmCDqSUCZ7J6dmsUDtMEbqx5ouZNgMWhr0A/Z1WSsa9N+WFKyI6c/Xl5Pmbv2c+c3p/a4OGUxctwxb4C1GqNLtMon7t6Qp13Hy7GWIuvtc3GkdJ1exi0/Hm3tBQt5lqDLjLIYYo1CConWLmN0HDwvJIwazJ56kX53hSxtk6fdY5c9CcY4Jd4Dj4y9YKbXNUeyDBpo75hjtFn2n0NlcfILcTTcY0AiJIc6+/PM3rPrdTzaL+ntdYBNJnyrvnJE4oH5VAT0/qJDl5+dQfVPzRBdWCQ8t0h+dZVdvSx8iR6lUB6e6MhqQvT0WdR2l7w7REiJN90gfvosMgwoVzexRYk/N0V4ZoHo0jKq3aPc2AELXq1CdGkZWU0Y/Ls3XBeTEHitOqrdw2vV8BccAVxWYkQUgjUEy/OUd7YfuqNpF8L38RtNZByjR0NUz72nvEYTIQSqd9e97vvIMAJrMem931v+1DQydEmAYuNkKsnjwkMHPDpLybc3yLfvLcwnpESN9zMRIvScmNEon0hlHw54BJKK12Q1e5d2vkq7cNHunL6AtiWDcgttFWPVQiBpRadJdZ+henBZ6c/xOf6sIh3vkI6Pl2NQ5ckPlSxtQwr36+BS5cnlOSl9VDE+RGg+CM+PqdQXOP/Mv8+t69+ks/3hQwc898UJu/44xPHuTg4+VKBy1/a3Nx9Mi+VzPCCEsxnaFd5kYuCLEPvMdjFx7LQH6nm71kX2OEFaMbE2ElijHFdNTGySjMbRsNxnboJgjr3QwqV5wrOnCean6Pzz33NCfYA/3UREAXpwOBMvkojo4hKjP7lMcWsDM86ofOl56n/px5DVhOEf/wBdKoKlOZJXnsGfajL8zmWKlQ1sXlD/6S8SPX2W8PxpRt/5IWYwBukCHoRAVhJkNaG8s4OMQ2Q1xowyvEYVU5SonsuiC9/ft4Syhr3ZhDWTLqmJIGkQYLVGBCHB7BzB9DT5nXUX8EhJODeP8H10ljqRwcmN41Uq+NWaEyNMx+B5e3xBpDi0bDA9g99ogjgQ8EhHf3GdYRqr9eO50XmEgKfsbFN2Tua47CKcO4WQ+0GNyUrKnSHldh+vnhwWJQIQrvRSmIzc7D/AjVUoWxLKhFyPKcyYdr7K2drLdPNH02b5HJ/jLx4e/YHR7/75T4N/js8mktYCQaWBF8SMd9bwgggvqhBWW2S9DbwwwY8qSD8gH3QmopaWqDkP1qDyMf3VDw6tM0hqzrw2iOjc+CFhpUFUmyFuLTDeWcVag/QCkunTZN0t8mGbYnh0Yi0CH+FLLDhF4DTHloqif3wjg+4Nyd77mPTtq5iRmzwM/uANKq89i6xEBKfnMGlOfGmZ5KWnaP+Tf0125YYLbIDeb32b1l/7WSpfep7ktefRo9T5as1PI6REhD7CE+Q3buNN1QnKknKzgwh87DjD9FwAVn3+RfzpabxKFdXrueDCGspuh/LOOmo4xJYFza/8JKMr76N6XfRggN+acgciJeHCKWovvYJXbxAunCZbW0G125gspfGlr6AHA8qdbdjapPL0s/j1Bl5Swas3GH34PuXWBqrbRQ8HyDhBRtFkTAPC+dNUn/8CwvfI126Rr9+m3D65c/ph8Km5pdef/gIyjNjacDNHrxIRTNUQviRoVtBpQbF10HrCUJgUXwYEcr+VQZkCZXJq/gzKFFhc+UsKOeHg3B+yViG6uEzy8lO0/69/8+kIPz0hiMAnWJpDtfvYrPjM6jl8js/xOT7H44DrMAyxRhFUF8BaBneuUlu4gBfEWGPor12hMrNEkNSdWryxzhblGBKVylOn8D6ZkCdTS1ij6a19gC4yavPniGozaFUQ1loYXRwb8GTXVpG1Cv78jOPJrG5Q3t4iu3ITtd2Bu9usixK11XEZi11MLB5sBCIK8Ro1RCVBSEm52XYWCru/V85LSg9GBHMtzHCMCAO8mabL8gC6P6LcbONPNRwxvdSo7Z4T7pvAazSxWpPdvI7faCK8COEFeNUaKnDVF6sEfquFDI+hjRiD6rRR3Q4mTRm99zY6HWMLZzSc3bxBMDWNjCcNFZUqpixRvXX01StEy2eRUYzqdo/Mw6KlMwStaUyaosdD/GYLr96g91kPeILpWbx4X1dGeBIZ+a6c5csjTHWLJdNDfBESyv3flSajMCmt6LQj+GKoBzNoqzHHNiIfhQwD/LkW8fMXjhFU+rMFEfiE55ewpUZr83nA8zn+3EN6gsaphNZylWKsaN8cUowUUc2nvpA4hd/QQwgmKsCKcbdguJ1j9YNltvxIElZ9qlMRUT3A86Xj9lic0N1YkfVLRtvZfbPrQewx91SdsOIz3Mlp3xxilCVpBkT1gMpURBDJvdYlVRjyQUk2KBlu3ZsTWJkKieoBUdUnrPjOjgFXhdClQZUalRnSXkE2KB3n5T77mzRD4kZAVDu8TqMs+UiRdguyYUkxOplHJ6Rg6aUpvEBSpIr197oEsUdY9anNxnvrFRJ0aSnGimKoGGxnqFzfdx+FABlEeEGI0QprNJ4fIv0QUxYUow7VuTN4YQxFhvA8TJkda6PiJB5CvDB26wwjdFmwS9wSXoAMQrS6N+9Rd/oUtzbwZ5rET58lODWDTCJEGJC9fx3dHexlctyAGswoO1rd2CWMeZMsje+5Cl2aH5F8saXClgqZRKitLrJewWvWXMBjLLo3RLf7hEsL4HnoYYruDrD5/jiIIMDmGarbxavWXFVQegg/cF0CkzKhjGKQx8uGmCzDZBlWa5fJOVBKVP0efmt6j7Yi/AAzHqGGA4qNOyRPPXOE0rILv95AViqYIkePhsgompS3Hg8ebE2PoMAr/MAN4ARWaUxWIish+Z0eqn+Yd2CsplfcIfKrVG2LXS7wWPcZlm3O175I1XcptVowTaYHlOYYPxGOsBiPHsuDLucWOPTH8cvu/ucEXYiD2ztpmYOL3GPfRBJT/dLzqJ0uZpzBOL3P+u6xX4+67D35II+q9nv3Nfaw6/ikv39UPOh2H/eYHXdPPuoxH1zXg6zjruXFgxisPDrCis+Lf3WZr//d59j8qM/v/8/vsHGlx+JLU7z6N84zc75GYz5B+oLBZkr7xpBr397k7d9aJR8dL4p6N+rzCaf+f/beLMiS8zzTe3LPsy+1r70v6AYa+w6CEEWClERSlikNZ0ZjhRWSY2zFKMJheyLGczMR4wtfKHxjO+QYXyg8Mba1DkVJQ3EDBEIkAGJrLN2N3rururr2qrOfk/v/++I/daqqa+sGGgAp470Aus7J/HM5mfl/+X3v977HCxz9hWHG7u8jVbCwHIM4FFRvtFm+2mD6zRXOfX92zwk6N5Tiy//j/QwezvHB92d54X85S9CKGTlZYuLBMoc/N0xxNI1h6QgpaS753Hy3ws13V3nv2zuXDzUdxh/oY/z+EiMniwwcyuNklX1D5Cd0KgHNZZ/aXIep15a58c4q7dVgV6KvZmiM3ldk3yP9jJwoMni0gJNWwotBO2b+XI3rry0x+36FuQ+2vpGv7ZflGnz13z5IpuywOtXiP/zOTyiOphk+XuTkL48zeDSPkzUxLJ1ONWT5coP5D2q8/59mqN1s79xirmkIoRpEUsURJViom+SGD5NEXRuDDUHBmlebm86T+C3iYCu/zUoXsLMl7GwRJ9dPHHiYTpri5L20V2aQSUzYrqNbDn5tgaizgzp7nBBcnCK4fAN7YpjU/UdwjkxQ/vxDNL//UzrvXsQ/vy55IYVE7NW2J1l/7mvbPBk0xWGSUhJX6pimgVkuYA33IeIYMb9KuLBK5ukH0LUMou0RLVfUXLGGLi9Giq5NhJTq/pVClcZ0XWW/dH3DfNnbgVv2ld7+rO9id50ev4r1VkehOinl2ue9/3f/Z1ldfaZQZaFmb9629M3tYM+Ap/9zz5M5cuKOB7ZL/fiL6xwbKSQiikkaPlYpg+6aRLX2eteFjLjROsOa0eUamuEyYeKRNgvkrD7Vwpy0udp8k3ZUAV1Dz2XIf+FRrNEB9FwGzTAIr8/iX5mh88bZ3liaZVL+jS+h5zPoGWVPX//rvyeaXSZpqPpm6tQRnCOTOEf3KWJVFJM021T/9AeKed9NU+Z/6SnFQu/4pO5TLYmiExBcvkHzx6eRfojm2OSeexjn8CR6Lq3Sl35A560P6Lx7EdHpvkGYBkYuQ+Grz2CWC+hpFwwD78wVgisz+OeuYvYVSN1/lNSJgzhHJyllU4iOjwhCvNMX8S9NEy+ptKs10o9zdB/pB46i2RYyjImWKjR/+BpxtQmJQLNMCl97lqTeBF3HPbYfPe0SL1cJrs/Sevn0tgGXZWW477HfpbpyiXp1isrSeXU+SgcoDxxjcPQB5m+8Tr1yjUZNtWT2Dd5Dqf8oppVh+soP8doqPWmaLoW+QxTKByiU9mMarmozD1usLJ6jvnqVdkv5tfR+Q81g8vAXcFIlkjhg9vqPGd33JJncMI5bRIgY36vSbi6wcPPNXmfS3YKm6ZQHjpPNj5HODeGmShim6kiIY4/66nUqKxdp1m70lKtLA8c4cPQrLM6eprp6mU5zAYChsUcolA9SKO1j+soL1KtTBJ5SJx8/+HlS6X40Tefy2W8hpcCy0hT7DlPsP4TjFrHsLKblksQBYdCkWZ9h/sbrqrtqm2Pef/TLWFaaOPa5ceVF+odPkS/tI1cYRzdsksjD61SYuvw9Qr+xSXnbMBycVIGxfU+TyY1iWg5SShrVaZr1mbuq0r0TdF2jNJ7h0DNDTDzUxyPfPIhhKW8nvxWRytkUhtPkh9KMP9DH2KkSH/xgjst/v7BjgGKlDI79wggP/aP99O3PYaUMdEMj7CR4jQgnazJwOEffwSyHPzfEoacHefevbjD1+vJt6cBYaZPSRIYH/vP9jJ0qURxNYzoGIlFt7JahYU1maC37VGd2lpsoT2Z4+JsHOP7FUdychW7qJJHAb0SgqYCjOJ6hMJpm9N4SR54Z4m/+zTvMtFa3D3g0GD5e5P5fneT4F0dUBsbUEbGgU4/QDQ0nbXLgiQEmHirTWvY5+52bvPutadqVnTMfdkYd7/FfHOW+r04wem8RyzXwmjGRlyCFJNvvkCr2M3pfiXueH+Nv/s1pVq838Wpbs9V+bZGgsdwlFK95Z6z9R7JxAq7NnMctDJAfPUZreRornSczuI+guZlzGrQqrFx+Q3UqxhF+fbEn5CmSqEdiVn/fhkyJEIQ3F4iWVjHePEfq3kPkv/QEouNvCnhuB0m1odzAE4k1Nojo+EpxGBRBOZ9BL2aJ3lglWqygZ1IY+TTWUB/xcpVopYaot9Dtrg9VuUA0t0LS3j1zKKKQYG6WzMlTIAVJu4VMYox0BmdolPTxE1jlMkYqA4nAn5kiqlex+wcoPveLeFevdH01IXvf/Vh9/SSdDkm7jWaa2MMjuBP7kCIhqlRI6jWscr/iExWLoOtElQrB3E3MUpn0keMktSpRvUZcrXC36hh7BjxGJoeRzuLduHpHAxvpLBufMiKMEV6IO1pCd6zNdUy6LeOig5CSjV0OgoRQdFj0rlAPF9DQiWVIO6oQiwA97ZB55ASaZRItrJJcnFYdYtUG8cp6p4hmmejpFHGljpxfAdvEHh/E3jei3Gi7AU9SbxHeWCBptEEKjGIOs5jDnhwmuLGA6BLS9JSLnk1DkuB9cB1N11SNdqW2zhESgmixgghCNMsCKXEOjKLns1hjgwSXlYGdWcqTuv8IJIJgah7RUSq40eIqSVW9XQgvIJxZRLNMnKOTBNdmVZan5RHOLSO87gWta7gnD6GnHLxz10BKjFwGTIP0g8dpvfq+SrNqqnVRz6aQfoh39iqaoSPaHvFqY5fMl5r0bbeA7awrQ9t2hnRmENvO4qbLeJ31h4xlZ3HTfZu6iXTdZHD0QbKFcdx0mShoEcoGoKEbJn2D92CaLpaTo7Z6ZcP2NUwzpUTydItk/BFsJ08SB7Rbi0o1285hllykTJif/ilRdHfVkHPFSdxUUYlHelX1MqObGKZDoe8gEoGm6b391lAPUDddxqyv89PcdJlUug/byZJK99FuLqyZh5NK9WHbWcJwbRKUGKZNoXwAy850A7sKmgdGV0G51H8Uv1OhWb9Jp7W1i9K0UthOHsvJMTByP+nsUNeeYlnxJHQLXddJ4pDN+lwaueIExb5D5Ir7iGMP3+uQxCGmlSJXnETfIfV9V9Gd2Ccf6qNdDVg4X2PhYp2gGRGHAitl0Lc/S3kyy+h9JUZPlakveDTmOyxe2vqW7uYtiuMZ7nl+lL7JLEkkmH2/wtLlJmEnJokElqtTGElTnswyfE+BkZMlqrMdYj/hxtt7mxgXR9McfW6E4eMF2qsBy1ebNOY7PYdw0zHID6WoTLeoTG9/nTo5i+JEhn2P9pMuOVSmWyycr1GZaffcx01bx81ZpMs2qYKNaet4tZA43D7YHz5eYN+j/Uw+0oebs1m+1mDlWpPKVIsoUPtmuyb9h3MMHy+QG3A5/MwQy1ebLF6sU5/bvjNQ1zXsjMmJL4/h5kxWrjWZPVOlXVElI8PWKY1nmHiwj/yQS2Ekxf5H+xGxxKtVt4wnRbJNl9X2kElE1GnQWZ0hiXxkK2ZbnTkptpS6bl2qqwS1zTfrMPtLqusoSRSxOFFeUqrNmu23vecxCKKlKuHsIqn7jpA02sRaBRklOIfGMfuLECWE02qekHGCjBKMvsL6fCElMkogq2MUs/iXpjeVtPzpKWQck7SaBAsQ16tKAqJawb+u5nkRBkRLS0SrK8g4wp+6Rrg4T9JqErfUvRQtLyE6HfRUirjZUBwewL8xRbi8hIhC4kYNa3CIpNUiDgJE4BHX6ySehwx8/Jkp9GUX0EjqddXxFUV4aIjAV1yePVrb7wR7BjyaYSCjiOals3stuglWqW9Tv6eME0QYY5UzgEbihVuupZ2sDRIZsexPbfudkcqSefI+vPcuEVyewb/QXc5Yr5HDmg6QTXDtJuHNJZCQefQE9oExRMcnuKSyEXGlTtLqqKhaSJxDY2j3HsaeHCFeqfUCHgAjkwJdo/0XLyoujZQqDbjm5pokRDcXCafmej4hRjGHnnGxx4Z6AY9RzpN57F7ab5zDv3Cd6OaS2oBp9AIP0fEJLt8gqTfJfv4hvDNXCKfmSGobBPs0Dc22SJ08SLxSo/3GWaQfYk8Oq5TrA8don74AG+rKZiFHnNRpvfIuRPHmNOQ2kFIQeDWMrndTbxwrjeMWiGMPy05jO7n178wUlp2h1ZhDiBhdN7HsDEPjj2CaKZLYZ2npPGHQxDBs3Ew/Q6MPYpgulp2hXp1S7aMbf3fTxXWLGCP306hO0W4u4HsVbDtHefA46cwATqrIyvxZ4ti/LTHJ24GUEsctIAGvU6HTXCBJQtWanRlkePzh7m+m9QKeJAkJ/Dq2k8Mw1tXDHbeAabnEsY+zlinqwnKyaLpO4NfoPX41AydVIgwahEED36uSxAGpdD/p3DADQycpDx4nScJtAx4A3bAwLJe+4XuJwzahX6fdXEDT9O4+2CS3nC/TdCiUDjA48gC6YVGvXMVrrxBFHoXSPtLZIdx03105v7tB05Tn0tCxAivXm7z5x9e4+OI8QSvqdddOPtTH/icG6D+YozyRYezeEs1Fr6f+uxHZAZfRE0WOfH6YsB2zdK7Gub+9yaWXF4j89bLV0LECB54YINvvUJrMsu/hPkQkuHF6dc9yWWk8w9FfGEbTNa6+ssj0WyvMnakSegm6rmGlDIbvKRJ5CV59e52jVNGiOJZm6FiBJBDMna3y7remmT1b3bR9N29RnsxQmsjg5i0aix7JdgGPBhMP9nHg8X4Gj+RpLPhMvbHC5ZcXmHln/ZgMS+PAE4NIMcG+R/oZf7DMgQsDJJGgPt/ZobylYdo6h58Z4vrrS0y/ucK7376B3wiRQvGxho4VsFwDK9VHbtBl/6MD1Oc85s5sDXjuFLHfprU4hZ0pErVrxOFHKYfs/uOaQ2V010FKQbKs9t0o5jAHS71MzR1vUUqiuWWCKzNkHjlJdHOpy7kJSd1/BD2XJu5yh0gSZBwjvACznEcKQdLtxBJ+gKHlMcp5kqpqcFmDP72edUpamwVf42ZjnVO08fNtgtFodUXxdwxjk5R358rmMd39h0gadaLlRaJaddPY3tXLW8YVnbYaV9e33ZePgj0DHhGFRI0qrQtn7mjg3JGTPZY2gJFxsPpyiDDBHshvMQr7UNA0dMfGOTRB9c9+SHBlZv27bRjySb1JcG0OGahyUzS3jHvPga5CpoI9OohzeBz3viPIIERPOegpF+/MFRWAbEC0XCFera8HO7BJxEMzTdx7DuCePISRzyDDGHOgSLSwivC6F6CuYeTSOAfGWP7DP+9dsMAdd5NploE10o85WMY5PIF74sDaN5AkJM2tN2BwY4FofkUFO7DnBSaFoN1aJFsYw3GLvfFtJ4ebKrKyeI5Uuh83Ve5+p2PZGWw7R7s5j0hC3HSZgZEHSGcHWbj5Fkuzp2nW1387TTMI/QYDI6coD97D6tIFmrUbJMl6Kl3XTIRIqC5fYObay4TB+ht8p7NMuf8oo/ueJpMfQYhI6dHcFUguvv9nm/7euN9uqkQq00ehvL/3eRz7dFpLFMoHe1YQoJFK94OEytJFsvlRLGstgNRx3AJR2KbdXOzZiXidFc69/X9t2S6gSnpOjlxhnHZjZwEv28nhpErMr/6UhZtv9sqL67j1rVajPHicXHEC3bCZuvR9KssqOAVYnn+PobGHGBg5heMWdjlvdw+zZ6pce3VJ8V027qqEG2+v0q4EuFmLB/6zfQyfKGI4Oqf/fGrL9LXvkX4e/MY+NF3j3PdmufrKIpdeWtiyvcWLdfxmRKcW8kv/+hQj95YwXZPX/v0VIi/eouOzEW7eAg3++PdeozLdUiWoLkQiCVox02/uLvPhZizcrOJDduphL2tyK/xGxNzZGnNnazsmJzRdZYyOf3GUsVMlRCz5yb+7yI23V1id3lxSSyLJlR8v4jcjlq82+fzvHee+r02ABjdOrxB1tn8+SSFZvtLgzf/3OtdeW1KO1xuOef6DGme+M0OnGvLIPznAyMkiU2/enS4ckEgRbyljfRww0i7uvYdI3XcYI59VnBgvIJxbpvbtl4jmPtw+BFdmlJaOY5P74mOY5TyaZRJcm6P9+hm89y/35gbZCdQ8dmwfmqb1SNLxUgVzoIRzcFxVAvzbE8j9UGqZye7zVFyvIjodRBTdWfDyMSh37hnwNM69i+G6ey22BWFlWaktdiHDhLjWoX1hjmC+hgh3rvmbmk3KLJAyc5iajQRW/KmuK7VE10wlCLXR+WJd/HRbSCG7LX5ywwqwzpZS0Xnq1GHMviL1v3wJEYRYYwM4hye2/6GEXA8UboHmOpgDRbLPPEjrlfeI5peRUUzmyVO3ZFC6olk94tdHiWbVWKLtdXk4G2wApETGYlOGClAX6x2oGEsp8DorFMr7se0MoOOmi5hWCiFiaqtXyeSGsJ0cmmZgO0pDAyReR7nMp60M+eIkUdDCay3TaS/dso2E6vJF8sUJ3FSZYvkAndbCpoAHDZLYZ3XpAnG8+WYO/QZ+V/jOtjObMid3B9v/RhKJ71Vx0yXMrmcZSJI4wPdWGXTvxzBddN3CThXQDZMwaFKvXlclPCulvnNyqlyXhPjerVmE7bedJBF+p0ImN9zNIu2cjhciYmXhTC9o2evYMrkRbDuLSEJqlStEkbdpea+9SrN2k1L/sW23d7exer3J8pWtGZs1eLWQqdeXufeXx3FyFrmBFJk+F68ekERqpVTBojCapjShTIMXztdYurwDORXwGyFLl+sIITENDTtt0H8wx/LlhsoG7YDWasDSxTrVG+1du5x2Q3PZp7GgsjWpgs3+xweI/ISLfzeP34i25+jscG4M22D0ZIlUwUbTNIJ2zI13Vmks7ZwJWbnWxHJN4kBguaoEN3S0wOz7lW2DvSSWXHttieaStynY2XRMS+qYQPGoTMdAN7WfcX+szfeUf3GKcHaJ1qvvdTuJJCQC4YfEyxXEBmXj+n/6MZptqhfkW+aM2rf+DkxjnQ4hJaLj0fzR23ROX0CzlJGv8HySWgvRXH+Gh/PL1L/7Cs2/P93jcAK03z6Pf3FacUv9rdWUTxLB7Iw6L3eodP9xYM+AJ1xd3CQgeLsIlubRjPXhjbSNVc4hvAARRJsuhjVo6GTMovLLsvpJGXlM3SaRMbVgjoQIQ7fJmCWETPBFC9lt9dOzafRsev1iuFVz/hY2/4aN9khwesrFKObQUg7BtZvIJEFPOztfLFLuWKfVLBMjk8LsLxJX6oTXZ5FplXCeAAAgAElEQVRCknns5OZMkZSq3NfxMIo5ZBghvWD7YwC1L0KgGboq223cHSGUNk+cKHb+3LJqg1w7TsPYmjWScseH0raHjCD064CmuCOmg+2qyTuOPDrtJSRgWm6XM6LKOEImhEELKZKe63cUeURRZ9v2T9+rdl3BwU2V0fStl6oQMV5nZUuNP0nC3pi6Yd11folhOJhWCtNKYZhOd3wdXVc+VGtlq7XuBZFEBH5DlZMMu1eOkyIhDJt47RV0w1KcJTuN7eTV21oSEgUtNgbnhul0z6mDYVhd/ytNmYLaaXTd3ON+VfokXmdlR6uITdA0bDuHbli947i1vBhHHYJg52DhbqNdUS3nOyHyEio3WohEYpg6VsogXbQI2hFJV+4/VXJUm3hGZU4Ko2lGT5boP5DbdkzD0kkVLHRddcnopq46kszd+9OCVkT1ZpugHW1xJb9dBO2YxqLPwoU6Q0fz9O3PIuIhVVqa6+DVw54ruYh377I0TI3CGnE6FviNkPaKT+TtHLT5zYjWik/QjEiVbJycSWEkxeyZ7YNqmUhWp1oErZ0nuLATE7QjpWhsaOiGcmD/JAMeDU2lvOj6sHW9/rSucrOUAl3Tu64hEtvOEichQij/w6TRQTQ6rLVTaZpSbxYi6Xo1gkRpxsWzy4C27me45r8oE+KZFdBArDnVAiSCeHGVeHF3npj0AqLZZXT0biZYQ0cnWa0jVhtomqJFrGnWSbVH3WUltp4ilsoXUCLQ0DE0E10ziYTX22dTs4llgJCiNwaoYzY1uysWvMMLVufucXA+KvYuaX3IlrDW5Q82/e1O9JF/8AD+zVWStk+4emtHgoalO0xmTlFyx8iape7PpxGINjda7xEKj5SRZyJzL4KEhc5lamGdaHYRe3IYGUX4H1xTo5kqKt5I1toVGmiOpTJBQaQktU0Do1zA2T+Cf2H6jo5fM3U0y1QKnJEiz2m2hb1/BNFSkTrQi+ajuWVS9xzAB8IZlVbXLEsJaEUbHhxSIrodYLprk+gaPYfpJCFaqapsVSGLNT7U83jRDB097aqy1kdIFUqp3uhFEqHrNo6bJ50ZQNN0fK/SnUjVd6l0H06qiG6YqpPIbwAS3bCwnBy+V9lx0hUiIklChEiw3Xz3YXLrviSEQXMrP0fK9S6lNcn4uwaNVKaPfGk/ueIEmewwppXGMCxF/DVMQMffYAORxAF+exWkprhOqSLp7CBx1MHvrOJ5q0hkj9ztuAXV9RV5m7IwhmGRzgwwMHKKVGYAxy1i2xl0w1LeWkZXjn0XSCFIkog48rmd1z4NMO00oCku1DbdWMq5/e61ju4FvxnuyHcBiMOExoKnAgypeCXZAZfGcndi16AwksLNWb0Xlqd/9+htb1+1RoOTNdH0vQKemNrN7fkut4vYT1i53uT1/3CFL/y3J8kNpiiNZzjy+SGWrzRYutJk+i3FwVkjcO+0Pd3QKIykMG2dyE+oz3t7B2JSndP6Qgcna+JkVHZM07bfjJSS2lyHcJcgKgnFpsyU0oL5OMUNNkPTlKGxYTrdAEZ2AxTV9g7qhcM0XaQUCJFQyE/Q8VbxgwZx3FEvF10z47VmCg2NMGpj6LZaTyaYhk0iYvViYqUJwhYaqgssjjvYdlY17YStO+Yaar2AxCXp6tRZukOYeOiagaW7hMLrBlo6iQwxNQfZNV4uOaO041pX5iXA0h1SRh7XzLHiT6NrBraeomgPUwvnCYWPkHFPHNjUbLJWP8v+tR05uD9L+NiEB53BETTdwF+4CUDn+hJRpYVmGCSdQJGWN+6IZnMo/zg5q59OXOVy/VVasdLfGUwd7C0XCo9KOMu+zP1UjTmSxhyV/+e7ZD//MPnnn6T4tc+DrhFOzRFcnaX90/dvb4eFJJxZJLq5iHN8P0P/8reQfoTwfPw1Y7g7QFJvEQhFQCt87XMAqj18fnVLSjOcWaTyJ98n/5WnSd17WKUwdQ3/g2sEl2fwzq53KSVtj+aLb5B96n70X3yUpOXR/ukZgiszxMtViGJq//HvcI5MUvz6c12RR42k2cF7+zztdy6sZ5A+DKTA8yrEsY9lZ0ll+snmRwFotxYRSUzg17DtLJn8KLajattee4UP99TfjUDNthPwxwXTSpPNj3L4xK8igcCvMX/jpwRBgyQOECJmZPxR8qX9mwK0JAnx/CpJorqa0pl+svkxfL+O51UQSYTfXsWwXDK5ESw7Q+DXCfz1rEk6M0i+tJ/9R58n8Ou0m/OsLp7D92uIJMSyMwwMnaI8tLuEhEQiP5TX027l1o9air0ziFgiduEASgFRsD7paxqYrom+YUK1U0oTRq2gykY7dTRth/aKT+Qnez4WRCKIgo9OmPeqAZdfXmDpcoPRe0tMPtTHkc8PU5rMUt6X48izwzz9O0eZP1vlxulV3v32NEkotpScNF3DTisLApFIIn9jmX9nSCEJvQQhFJnZyVg735pSZXB2+40+bdhWmpRbpq/vOEHQ6AY9As+vkM0MY1kZPK+C56+ud36aLl6wbvA80Hccw3AQMsHzK1hGCtN0sKwMcRyg6Tq6ZpIkAbXGDaRIyKSHKBUPKaJzEiFEjGk6hGGT1XArgXcvuEaOkj1KO66SMvOkzByOnqEVV/CTFmHSoeSMde95lenpxHUMzcQxMpiajaetv1SFwsPSHTRypI08RWcU18gQi5CiPYqu6RiahWtkmfcuEQqPT7Vedof42AKe1MQBJdzUDXiEFxLGQnmPRAky3nwz6JpBwRqiHVeoR0vUwyVC4RHLzWlRIRPCpIOlOxiaCYkgWq7SefciRi6DZqvMTlJpEFfUxZl0fIIrM6qFb43sFSdEy1Xar71PvNbpFCdKFrxSR0+5XbHEEOn5IOUmQrF37ipIEB1vR36P7Pi0Xn1PqW/qSuE5abQg2Zy1kUFEvFSl8+Y5tJTTVdrUiBcVKXojZBgTXJsDNOWGG8VEs0ubhKWixVVkkpA022qs7jaim0s9zpFMEjrvXkR6AcmddhNIQRS2u51aWRy3SODX8b0qIAn9Jrpu4aaKmFa6l4npnRoRE0cdxWcxtr8EtW6btKbpRGGH7Zmhn+yNZtlKB8eys9QqV6ksnadZv9nLfEgpESJWafFb9lOKhDBodMtPORy3SKM2rdzOpeL+6LrRPWcpwrC9qY0/nR0kX5zENFPMLb1GqzGH314lij2kSHBSUe+hdjchYUN50O4ZKm6ErlsYxt3mSe0M3dB6asDbQlOlm436nSIWm27TJBabMhsffH+WynTrtt9rIi9m+UpDiRDuBsmHak/eMoyEOFAlLJFIWss+y1caFCcy5IdS9O3L9jR4UgUb3dC48OI8jcXNwqRSKkVmKSS6qWGYqgyzFzRNLaspP8c9FZzl3b8U7yosK4PrFlUWxHRJhIEQEY6dxzQclRHRVLbGMtO4G55HazBNF8NwVCY6iTEdB9NMI0SCECEaKgOYJKGyQjItHCeHEAIhIoSIelki0D/8daKpKogllY6Zn7RAgqEpmyaV3VEcI9G1cLJ0h5SWx9AsNlo0mZqDpaew9QzSlN3gxwCU1yVSrm+ju7yjp7ldm6c1GCZkMhrf/K0si/MJ1y5HnH3v4+f4fGwBjzsysclaQsYCuY3M9xo0NDJmkSX/KvVwgUB0uTi3XAQSQSwidM1UJ1lKpBfgn7myzajddfyAcHqecHpD90oiSCoNOpXN3INwao5wam9T0uDi3iUuGcV4py/suRxCIDo+nbfP771skuxZ2xXNDmGzQ3h9l+NIBP7ZO9NW2ogobGGaLradxXbyBF6VwFfBWRg0sewMjlvEMB3CoLWpi0pxQepYdhbDdNE0Y0sq1+qSjTVNIwzq3fr2pwvTTJErjKMBrfosywvvK6GyLjTNQDfVw3ILpCT0G2iAZedw3DxR2OqKBEoCr4Zpq9Z+w3TotFZUoNeFm+4jnR1EyoTVpfN0WoubuEu2k1Xn625rHktJFLaRMsEwLAzT7batrwc9a/IBnxQMW8ewd+YpqXZvs2eiDUqJeKPNROQliA0vXVd/olrGf7ZJsyroqd5oU73RZuqNZQYO5xk4lGffI32Ytk667JAdcEmXHRYvNfBq4SZStRSSsB0jhUQzDKyUcTvxTjczZKDpGiIWhJ2dM6s/22dQwdAtdN0iilrdACUmESG6bpB0g5Eo9pQOlpTESYCuW93nlLpulNimRpIExLGn+C1SEIQN4liVkXTdIoo7SCkxDMX1C6MGSaIywq5T2NCscueK6wJBJHyCpKOCGwwSGWFoZo/Rk4iw90yKRECYdJBSEBshhmEiEb1OUFOzMDQVhK3xZ6UUvcSDkLHyt5QhiYh7y97WRbQBpgG5vM4/+50MZ9+N+NEPtZ/vgEczzU3WEnuvoMiAQia7Tm4aOpZudUlWPw+31j9M+F4N00qRyvSRyvRRr1yl01TaL563iu3myBUn1rk9G9qfw6BJdeUSEwefI5sboZ0foVm/2fte03QGhu8jlekniQOqK5c2d2h9WtA0NN1QpOgk3BLsWFaKfGES2y10id0bIWm3l7C658xN9+F7VQJP6ey0WguU+4+Szg4CGtWVy4Qb0ueapkqTcRyQxMGmYMcwbBwnT764H924g3vuNtGs3ySdGSCTH6Vv8ASV5QtE4Xq2M50dpFA+sMsIdxeZskO232Hl6nZdZqrrpzSRQe+S+kUiqc93iMPuOZNQnWnj1bu/n6ZIy/mhFLXZnx2C5V6QApYuNVi61ODc924ydCTPQ7+xn3u/OsHg0TwHnhhACrlJIDGJBatTLSI/IdvvUp7MqtLeLnOtpmtYrkFpPINh6fjNiNXp1q7t+D/rqDdnqDdvsvWgt54ILwnw/EqP1LyG+cV3Ni/n7y190WiuP+d03cJ1il3pMw3TdIjjO+PCBUmL5W62pRmt0IxWNu3nTv+OkxDPa2w5Jl+08IMWq8EMt2KnserR9ppfP4vYM+ApPPA46YkDSClY+dH3cIbHKT381J4DO8NjhKtLey63BikTmtEqOXsAIRNacWUL10ADXCNDv7ufdlwhxAfLRLdMVarqBslSqrcXNA0ZRWiOo54OcYK9b5yk3iBptpBeoMjNpqHM5gL1t2bovcyS7BKC9ZSL9JWZm2aaiDBUniOmgea6ineTCJJGUy0bRYgwQretXhlNM82dS2A/Zwj9Bkm6j1LfYaQQRGGnpwoceDWiTAcnVVRlvyTC66zrhgR+nZWFM/QNniRf3IebKrO8cIYobKsOrnSJ4bGHSZKQWvU6jdoNktvpKPqYEUcezfpNMrlhin2HECKi3ZjHtFLd9vmDXYLj1jcVicRvr5IaOEomN9zrwIojr0sEX4aBY9huASQEfoMwWA8qVNC4QjY/ytDYw7Sb8wR+DcvKUCgdIJ0dIo68DTo/dwuSeuUamdwwqewgY/ufxnZz+J0KSeyTzg6TzY9t0F36+DFwOE9z0Wfq9e11TlIFmwNPDGDYOkE7orXk0VoJNonwtSsBtbkOtdkOxbE0Bx4fIAnFz1XAswkSKjNtlq82WZ1qMXKiSKrokCrYmxZLAsHsmSqdakhhJI2TMZl8qI/5czVV/toG/QeyjN1fxkoZiEjQWvZZvFC/K6W6TxfbUq53WfruHq8QMZXqla4kSXLXXuo27udO/97tszsd9+cJeyst67oKHqR6w7RyeVITBxQ3Z5cL/k7TXEIm1MJ5slaZgj2MlzQJRQfHSHfb1UvYepqMWSRr9VEN5gj1ACObwRwoK2sFoerKwvfRMxl0x1L8lA1OrkZfERmGSpYbMPpLaLYFmoYeRmi6DrrSxdEsC+kH3ZbxPCRC3eS6hlZrdK0Zsir4MU0VXLXaGIUcUgj0OOlxaNby6zIMu8HZz+cFs4YobJPEAbaTJwyaqszRJRBHYZs48tB1C5AkSbQpIyCSCN+rUl25RDY/guVkKfYdIokDNN3AsjNIKWg15mlUpz7RDqDdEMc+zdoMxa6AYLF8CMctoGsmpuWimzatxixZbWxrWUtCEKg3KstKEQYtkiTslYZCv6Fa9nVlQRJFnU36Ql5nlVZjlnxxgmyXDB4GiiullJ8FzfoMtrt9W/VHQRg0aTVmcdwi+eIE+eI+Uuk+ktjHtNIIEdFpLZLODnwi13VhNM1gtz27NtfZxCdJlx3K+7OMnypj2DrNRY+Va80thp9JKKjOtJn/oEZxNM3A4TxeQ4n6VWfahF7cK2/phoZu6Zi2akVH4xMNjjJlByttYJg67UpA5CdbFZQ1ZbJqukaXl6N4S8ktXEkhJO1KwOpUi2y/Q3E0zf7H+kki1TW1ySdLg2y/y+i9JSYeKKPpGtWbLaozbTrVT1fb5R8GJGG0s3/aZ7j72DPg6UxdIVhRKavE76iJWwqqb/x4V4+Tvie/cEftz4mMmW2f53jhGfrdScrOKPVwkYxVxtAshtKHcPUcpu5gaiYXvB8T5CTmQB/ph+8jqTWUhk2SEC8uY42PYBTzRIsrxPNKntvsK4JlwQa7eefIfmWyFoQ9YjFSgGFg9hWJlytEN2YxB8ro+SyaYSJ8n2hmHmt4AHt8BO/sRUXmSlQgY/YV0dNpsExkGHWNSrvCUdUasuPvqU75s44waBCGTTTdwOusKPJtF1HYJgpbvUxHHHubAh5Q2jQzV1+iNHCMUv+RrrmogxBKr2d5/n0qyxdoNbbykKQUymNnx5y6qolL2f0t79IkHAVNVhc/IJ3pI1fcR760j77BewiDFp3WAquL5/DaK4zwOJlu59rGfQo6q90gBzqtxU2ZoMCvKx5A97MobJFsCHha9VmisI3tZCn1HyOXH8cwLAK/zuryBdqNOXyvSqG0f+fz0j1v4g7b9KVMqCxfpNNaYuLQL5ArTGBa+0AkVCtXWZk/Q+g3KPYfVtv+mIOe8kQG3dA4+ZUx3vnLG3j1EBELNF1j9ESRA08OcuiZIdCUaN61V5e2nZznzykftCPPDtN/KNdtuTZ560+uU5vt9FrfnZyFm7PI9rsceHwANGitBJz+s+sf63GuYeh4gb59WdyCxdQbK9TnO7RXg55+1pou0NipEiMnSpT3ZZESmoveVr0iqdrcr726iG5oFMcynPr6vh4n6vrry2pcTcMwNSYeLHPPl8eYfLgPKeDaq8o5fU+y9qeANXPvrrC8em9d+2xdjm3PR6/WXU9f44B11xXdS3vj5a3r625CG6e7ntG4pkT/N96S+gba1EedBnRdjSfE7o86vavOoQE7GbbrRveYoXfcQu79CDW673Zr52fbccQdhQNb9vnDrL8d9hYerK5ArVsDFkIFFO02nanLu1rd5088gO7cfnpdImjHFc7XXyZr9TPoHiBvDWBpqu0vZw7QildZ8q+z7F+nFVWwy5NY4yPIKFYlJUD6AnNwAM22leVDEBLNL6Fn0hilPEbKUSWrLkSjhey6jhvlghpHKk+SGOVhpVk25mA/otUmabQQbQ9rbAgjm0UEIfFqFWt4EMN1EJ6PMdCnyNRBiJZ2leNtEKixTFP5k/2cBzyBX2dp9jQrC+eQMrmljCOprlzmzZf/AGBHrZ0kCVhdOkd1+cItwoJyg0vxLaR1ETN1+QfcuPrijneh79UI5t+jsnShG2DcvXMtZczN6z9B01/rCZSp4Go9CGs15lTwfEvg0WmvcOXct9F0RdJONpH4Jdcvfo/pyy8AbFGPBkVsnr7yIjNXX14XpUQi1oI/KTj96v+6WcBsA65d/F5XN+TOyZFJ7NNuLXL57H/clL2VMkEkqrX5zZf/QLX3fswE84svLRB5MU/89hEe+o0DBO2IsBPjZC3cvIXlmmg6TL2+zIUX5jj/wvZWG40Fj6AV8/3/+X0e/c2DlCez3POlMY48O7zexSVBt/Su4KD69+KFOtNvffzWBWtIFSyGTxS45/kxHv9nh0giQRwIOrVQ6b9YGk7OUhkeR0ckkss/WuDqq0tKkXobXHppgeaSj0gE9/3KBPd9bYJ7nh8laEb4TeWWbqfXzqdB2I45//1Z3v7z69R2MA79tPHrv5nh+EmLgUGd//0PGjzypMPjTzscvcciCqFaEUxfi/ijf9diaT6h3dp6D5gmPPmsy4OP2Dz5rEO5TydJoF4TvPJywN993+PyhYioe+t+7Rtpfuu/yvJXf9bhpRc8ZqbUtf/N38rwxDMOh49Z/G9/0ODt1wOWFwWaBv/iv88xNGIQhvBv/1XtQ78f5As6z33J5ff/ZZ7v/k2HF77j8f472xN//8l/meHZL7jkizr/6vcrTF9fv0dTKY3RCYOvfyPNw4/bFEo6jquxsih46/WA998JefF7/racrQOHTX77n2eRwCsv+7z905D/4nez3P+QzcCQge3AwlzCD77j8cJ3fRbmbu/ZoOvwq7+R5gtfdpnYb/IX/3eb7/61x+rKR4t69iYt3xLS+otz1E6/2vXF2HnjYXUVw76zVlWJxE9aCJkQi5CKcbNbGtCRMiEQHYKkTSduqG6t1SoyiomcOaWinIhuIKF15bhBtD2EFyCThPD6DJrrkFTqPUHCaHax63grSGr1rmEZyCBQJa04RgYh8rwKYGQUIaOYpNFUwoCaplrNpXorEq02waVraow4UXT0KOq62saqlHYHVg53AjOVpf/kU8hE0Fm5SXPmNjrEPjRUC7bYQQdHymRTW/WOo4iEhATugKMjkpDd51TlpxN9TBo9QkSwDU9nDUkSwLb7J7f/TtdJP3YfRjaD8APaP3l7u5VZy4wJdt72buW/21JW3g1SbKuKvb7tT2YiXL7aoDLdIg4TRk+WcLIW+SEbK2UQeTG12TYr15pceGGehQu1HbMRUii9mBunV9EtjYFDeQaP5CmNZ7DTBnZaR9M04lAQBwlBO6Yy3WLubJX5c7VP5FiBbumtTnFctaDbaQM3b2FnzfXQVUJ7NaCx4FGdaXH57xeV+OIOXWdJJKjcaHHuu7PEfkLfgRyF4RTpsoOTtdTzS0JzyaN6o83qdIurryzRWvYR0c9mLctNaZT7dA4esfjGP82QSmuEoeT0GyGuq5EraJw4ZfONb6b54d/6vP9OuCnYMC34lV9N89DjNqPjJgtzCdPXYwwDXFfn8adsXBfenzD44d/63WyRJI4lY5MG6bTO2s09ud9kaMQgl9fZf8DkgzMRa91d45MmtqNRv/nRXgwCX9JqCGpVwf0P2Zw5HaK/F23JhOQLOpP7TUbHTc6fCwk3PAYsC556zuHBRxweeMSmupqwMJ+QxJDN69xzr8XwqIFta/zohz6d9ubf3jAgm9NJZTQefsxhaNikf9Bg9mbC/FyCZUOxZBD4bFl3JxRLOkeOmXzpV1KYBpx7P+K90yGdzke/7u64SytcXSJu1ncNdgDClcWuv8idIZExXtLES7bvwNi0bLVOUr21G2Z7yDgm6mw1UItX1pn1u11+SW3zm1JS2fzAi731scPrN/k0YLoZ+k88hYhj9CunP+aA5zPcDjRnTS17l+BL10mdOo411EdSb+4S8HwGUKWaubNVGvMdklBSHE+T7XPQTY3Wsk/tZofpt1e49KOFXS0TQIkYVqZbdGoB5ck6+x7tZ+zeEumyg50x0XXwmzFBK6JTDbn53ipLlxpUZ9o7jpnEyk3cdHQaix5BM/5IVT7FFdJwMiYDh3OkS44KeFJKWyWJVKt55UaLpcsNFi/UmXprZc8kXqcSMvXGMmE7Yvz+MkPHCvQdyGGnjV4wuHylwez7VRYvNVg4v0OQJ1W7e32ug6ZrhJ2YOEx2nSLWjFPXDEs7tfCudH3ZjkZfv8GTzzpcPh9z9XLExQ8iiiWdo/dYjE+afPGXUly9HHPxfITvdduxTcjndZ7/aoqhEYPAl/zkRwHzswLX1RgeMfi1f5zmiWdc8gWdV34U0OlI2i3JypJgYNDAcVTWU9NgeNTAcTWaTcHohEkqtf5d34BBoy5YWviIAU8gWV5OuHg+5JnnXIaGDTIZjWZz/YfXNBgaNhgcMnBTGu+9HeJ5svddqU/niWdcnvycQxLDKy/5LMwndNqCQ0ctnnrWZfSUiW2rdcMg2bYkViio8zs0arAwl3D1YkyjITAMOHnKZnFBjbkT1vY4ldYY32fw5LMuR49bvP5KwE9e8jnz7t1pWb/jiERGIUm095ti8/x7H2qHPsNn+AcDXcc9fgjhBwQXPxm+x/8foBsakZcw/dYq02/t7jV0u/DrEXNnqsxt40J+p6jPdvjW//DmXdgrhU41pFMNmTv70fdtCyTrDusfdgipdI7++L957bbXaS37XHhhjgsv7K15dicwDHBceOPVgO/+lcdbP12fq/YfMpmbTfi9/y7PwSMm+w8ZXDirZu/BIYMHHrV59CmHv/zTNi9+z+eNV9azmZYNjZrgV7+Z5v6HbR59yub1V0LqNcGNqZhHnrBJZ1RQY5iw/6BJpy157+2QYycsMllVEjVMjZExg8pqwtS1jxYIA8zPJvzwOz5f/EqKIycsTj1s88qP1vfbMOFzX3AYHlNB1ovf82k1VOBhWvD1X8/wwMM2cST5n/51nXPvh70gEHzOn4145jmXX//NNC/9wOPdt8JN5bA1HDpqcuGDiDPvhPzR/7FZsuBvv71704kSbFXbfOJph2d/0eVX/1Gaf/9/tvi773l3LdgB9pZHdIbHSe8/opzPNQ3NsjGyd78T5E5RskdxjeynvRuf4TNsD9PAGhsm/dgp3OMH917+M9w+evylz/AZtkII+MlLATPTm1MRraZg6ooSXczmdMrl9U7K/iGDhx9zlNP75ZjzZze/1CcJnDsTUq8K0mmNE/fZOI5Goy6YnUkYHjVIZzTSGY3JfQaWpTE7E3P6jYCxSVXaymQ0RscNbEejUZdb9u/DoFYVnH0vZHFBMDFp8tiTm2kkhg6PP+0Qx3DxfMTqctLzjzYMjYcetdENmJlOuHAuJPA3R2Bn3w1566cqgLr/IZuJ/TvkSDS4einmpR9sz/XZDVGkSM7P/ILDN38rw+HjFtfARuwAACAASURBVN/+sw7f/tM2Vy/fXVrCnhkeZ2AIq1AiWJqHKMIu9eGO7aP+7ht7lrU+Lmjo9Lv7WPFv9CSuP8Nn+FmCZhhYw32Y/UWlvfQZ/kFBM3XcwSxufxbdVnzBytszvc6pHdczNMyMQ2ayRFjz6Nz85LhAHxZWwcUupHAHsmimjr/UonX9zjNrmqljpi0Kx4ZoXFkmrH4894WUMHczptXc/FvEEbRbymLEsjRsZz1wTqc1hscMdF1lK555zt00vWk69A/oFMs6hqlR6tPRDcVLWV5McFMamaxOoagzPGbi+4LlxYTZmYRUSiOf1yiWdQYGFUe03RJUVj/6/BlH0KgLrl+JGBgy2H/YJJXW8H2JaSr+zNiEyfRUzPXLMRt9qPVu6S0RKnDajsRdqwpWlgVRBAODBvnC9jmSJIZ6Vdw2KXkjHFejb8DgyD0W4/tM5mcTXn3ZZ/ZmQnyXxZf3DHhSo5M4g6PU3nsTNOWRNfDcV2iceWuLH9YnBQ2dicy9hMKjGs5++HEMk56z9qbcoqa0h1At0FtYYLpyupVrvYCbBtV6qrg9yLU26Vt6Grfs0Pq6PWLzDuN96PZfTRExldjVXej101jvs9S2NzaQQnT7G+X6Ohv7Pru2R73zI245T5qmXlUkan8NA0UcQL16GRv6UaXYuv4ajPVe0d721pa9W63UXR0nPeVi7x9Dc2w0Q+91EUJ3U9v1WfbOj7ahr5Vul71YPzfbbVPT1o9D05RWlKZ1Ca3d9bb7rW/dVm95sf22Np57KdU5Xf8Bt//9tmxvrV/4Nrb3MwrDtSjfP8bgkwdwBrLops7rv/8XJP7ub6S6bZIZL7L/Nx6g8t4cN34OAp7MRIniiWEGnzqAVUix+OOrXPkQAY+RskiPFjn6z5/mwh/+mEr14+M6VitiQ2lGQcr1lmxd7/qtdeG4Gn39Oraj8ev/NMM3/vHOVinVqsB2VImq1RDM3VRO6IWizvCowf5DJqvLKvMzN5uAUBP62KTJ4JBBGEGjLqnehYAHlLfZm68FPP/VFAcOmfQP6izOJWSyOpMHTfqHdH76k5gz727OWmm64vAszCXU69sHKlEEvifotAX5ot4r292KIJB0OvK2ickbUSor/s+zX3CoVgTXrkS8/KJ/14MduB0Oj26oh5rYLNz1aUFDeXzcqXfHrXBLwxz85d+lPX+dxo0PqF5RMuGabpIenODAl38bTdOpXHqThbd+QBKqtxE7V2bg3s+RmzhG9fLbLL7zYm9MM5UlO3KI0uEHsfNlzJQquYWNVbzVeepT52jNXdmhbVejdPghigdPYedKXP7rP8RK50gPTFA8eD+psnKflyIhbFVZfOdFvMo8cWdvcvdGDNz7OXJjh0kPTlC5/DaVS2/hr27ftrsXNNfBHOoj//wzWMN9GIU8WmprZ17njTN03jyD/8EVsEysoT5S9x7FPXkEo5hHT7tgGCSVGuHMAt77F/He/QASgWZbWBPDFH/teZJ6E+/0OYrfeF51580uUv3z75P7whPYB8awBvvxzl6i9eO3NhHHNdvCKOTIf/lz2PtGMYo5sE3ihVWCi9fxzl4muHQXODa6RvHXvoS9bxRzqA+jVEAzDOyxYdKP399bTLQ6tH7yNu2/f4ukvuH3ExLNsnBPHiH7+UexhvvRcxlEs01w/SbB1RuK0HyLC3Xh61/AnhjBv3CN4OoMzoExUg/egzU6BCIhrjbwzlyi+cNXlRFuF0ZfEXtylMwT92PvH++qhSeEc0t0XnuXcHqWaH7dEkTPZSh985eRYURcrRPNLJD/0lNKzsF1EK02/vlr+Bev4717fst+6tk09sEJMg+fxDl6QBG6E0G8XKH58puE03PEC8v8PCBuBcy9cJHq2QUGHtvH2FeOf9q79LGhfn6B5rVVVt+e4dh//fSnvTt7Q0IY3JnGzZq9Q70q+MF3vF19ncJAsriQ0GpKwkAydzPBa0tKfTr7D5kcP2ExMx0zP6s+n7oeUe5Tk3qxpHFzWpWW7haSBF74rs+xExZjEw6/9PU03/qTNoPDBs99ycH3JOfPRrz71m5elrtAW3+f3AlxTI+Hc6c4fMxiaMTgxe/5PPaUw6NPOPzuv8jxR3/YJNjaZ/SRsGfAk3gdEJLCqUcJK8s4A8MqKDhwTOWxdlvX9/Dnbmz5vGSP4nxI/s2aPb1+h+Jpt0JKiYhC7GyhF5gAmKkMVraI6aRA07FSOexcCW9VBTy6aeEUB5SA24bjTw9Okh6YpLD/JKabQcYRfnUR0DBsl/TABHa2hGaY+JU5wuYtBMT/j703D5Iry877fvftL/fM2gsFoLAvjUY3epnunp4ZznBIcafEVRZlKhhBORyywhEOiZYphxUKWZItBe2QaFmibIkSTZoiac1oKHKo2Tic6dl6md43oIHGVqg1q3Jf3n6v/7iJAgooLAWgh6SkL6IigMyX79338uW7557zne8TYJgmpu1heQX8sVnK8w/h5CsIwyBorWKYFoblYOdL+vg7CIFNx6ew6yDlvccwHI9gY4nu5dOkg+11Ou4EYVvYMxMUP/k0Rs4jXlgha7+HcBy8YwewamVwbHpf/CbRhSskayPdEqkwfA97zyxZt0/aaI9UwgzsmQns2QnMcpFkcVV3ximlFbLLBYycRza/i+Dd87h7Z7F3z1D6/o8iHIus1UUOQtwj+4gvL5N1+rqTzrZwD+7VAcDuadJmh3hpDWSGWa1g75nVSt2DIelGa1Ou4J6gdIde1u5i1ir4jxzBLBVIG23Cd66Z26ooJr5wBXnDsYRjYxVyFJ59DDkICM9dQigwq2Xs6XHdtt7sEJ69tGWcwrExSnmcPTNYU2NaXG5lg2RlHcN1tBzCILiWjRQCo1yg8Mwp7D0zmMU80XsXtaSDITArZXJPPow9O8ng+df0dzcSoxOugzVZw5qZwNk1RbLeJFnd0N9RpYi7fw+G56LCiPD0BZ25EVomIv+Rx3F2z2CNV4nOXUZlGcI0MCsl8k8/ijVeZfjK26SrH4zGjT9Twp8uUTw4jhACu+RiujaDKy3Wn79EuKGd6/O7KxQPjOPPlLV5bS9kuNim/c4KMr42UalEIsMEmdx68rJLHv50ifEP7UFlEpVKZCp1Gew6OBUfb7JI9eQshm2gMkU6TKh/4zxJX3Mo7ILL5EcPIIAsznBrPkIIwo0BGy8v6BLRDjKVVt7BnylRfXgWwzYRtoE3XmC43KF3foPW60soqVCZQsYpaZDctmSXmy2T312hsF9f32QQEa73aLy8iIyvZqxBGIKxU3PkZsvYJY8sTOm9v073vTpZpLczHBN3LM/Yk3uwfBshBFmc0Xz1CmG9Tzq8/e90pwnbOFK0W5Lde02WFzPeeCW+5QSeZRAGiiRWmrCdKNbWMhxHMDFpMjtn8uI3Y9otSZYpFhcycgXBrGNSKArW1yTdzoOtjrRbkiuXUuqHbR77kMMX/yCgNmZw/GGH995NqK9mxPHNGa92U2JZgmJp+/nUssHzBJ5v0OtIggfQGn4jrlxOeffNmN//dMBwqNgzb/GxT3i8/HzEhXPpfWvvXI87Bjxpt03a75KfP4RdLOPUJrRuyJ79ulRxGyTtxrYBT8WdoWRPjFxadwaBwBTW9o7UO4CSKUm/jZ0rYjrXBBItL4/tF5FpgjBNhG1jFyoEjRVAIQwLO18mDfpkkQ6ChGmTn5qnOHeY/PQ8w/oCUXuduN8EBH5tBqc8TmHXQeJ+C5UlJIPuLQXaDEvvLzexB1CEG8tEvSbCtLDcHP7EHGkUoG4Z8Gy9qobt4pTGKO99CLc8TtTdoLt4lv7KhXvmYQnPxZ4aI/fYQwxffZfo7GWi85e1sKJjg5zDnKgxfOlNHUjEo7FKqR+aUuogqdVFDoYgIPfYQzjzu/CPH6A3UUWGISrQD3thWwjLwsj5DN84g1nI4x7aS+6JEwxeeoO0sQZJhv/YMazJGma1RNZsY5YKOPvmyD12nHStSXxxkWR1HRXHuEf24x2ex963i+Cds8hhQHZfAY8iuryEWFrFLBWxp8cRpkmyss7gm9dazVUmtR5TvPX7E7aFkc9hTY8zeP510o0mMohw5+fIPfEQzu5p3EPzRJeWbgrMhGNjT48jk5RkcZXowiJps41RzGuhyyC8lnExDeyZSbwThzBrZeILVwjeeg8ZRgjLxDt2EP/hw5ilAsnquv7+rtM0MislhGmSuTa9r35bZ6mkxN0zS/5jT+LsmdVB3nuXRiVIHdTkHj2KUSyQNdoEb59FBiGGY+McnCf/9EmEKUjXm6RrjQdXYrwOhfkaxQPjFPaN6f/vreGO51l/8TKtt1YQTa0kXzoySfnwJP5MGYA0iLFyNuF6n+FS5+7HJsAdy1N9eIbqyVmyMCULE9J+jLC2TjD+dInigXFKRycxLe0DmEUpg4Um/YsNsjjDKrhMf9dBsiAhrPdxx3NYeZdgtUuw2iXphqgduL3bZZ/KQzOUj00hTM2vqZ3aTePVK2RRSuuNZe42rS8MQfHgOKWD4xT2jQOQhQnDlTzDK22GK9cWVoZjkttVwS55OLUcYlR+jRpDhku6xGflXUoHxykfmcTyHYSp0wxJJ0BJRbrwYL31gqGivpph2w5JrOi0tb7N3SDLGH1W83wmp0026hndjiTLYGUxpVw1qY0JPE9w7r3kJn7R/SIKFUtXMhYXUh5/ymV80mBqxmTPvMVnPz2ksSFvqmhLCWsrGRPTJuWqgecLolBtub1LJYNKzcRxoLkhNzu8HiTWljPeej3hxW9GFIoC9V0e3/fDPo8/7RJF0Otu1Q66H9wx4OmdeYNofYXKqadxJmewy1WEaZKb23cbaX+NyHHYTiWn6swy5s4RZgO03ODdf/kCcZ1a7L1DZSlhew1/bGaU4dFkCadQxcmXidp1DNtFYOBXp+gunAGlMCwHtzzOsL5AGvS0hsr4LJX9J/Fq0wzrV1j4ym+TDDubAaEwLPLT88x+6AcYO/ohlJTEvRZRZ/v0venmmH78z7D8wu/TW36fqHWjG+1dKOVed9eWdh+ltPcYtcNP0jr3Kp1Lb9G+8OY9XzsAe2YCa9c0wnPpfeVFkpW6tswABi++gQwiyj80h1kukfWG1yZ3pYjPL9A4f3MgnFxZJffUSZy9u3Dmd5G1e6TBtRbLtN5g8OIbROcuY9XKCM/BPTzP4BuvkizXEZ5L+Sf+DEYhh1UtEQO5xx/CO7ofYTs0fuPfkzXbm8FC+O55so89SfF7n6X0yQ+T1ptkrXvLeF1F1tAPbBlEZMMAK02Rg+GW0tCtIBwbGUa0/+3nic4vbI4zOn0e4dr4J4/gHt2PeO6lmz5reC7mrinW/9GvES+sIHu31okxXIfS9zyDWS4SX1yk8auf2qITFLx2Gv7ij+Ae3Ev5hz5O8OZ7W943fI9kaY32736Z6NzFzUAqfOss1swE9swE3uF9dD//dVQKRs4n/5HHsCfHCc9eovWpL5A1mpu38PDVd7EnNcG7+PEPMXz1HTZbSR4gZj55GIDT/+Q5kk7Irh84zviH9rL8+dOE6z0wwCn77P+ZJ1j6D+9y/jdfJumETH/3IQp7qsz/5KOc+ZVvIKO76xwxbIvKQzPs/clHefUXf5+g3sMueez7849t2U7YBtOfOIhT9jnzj58jGcTk5yqUj02x72ee4Pyvv0TvvM56FfbV2HhpgeUvnaFzZo2JZ/ZRPDDGrh88Ru/8Bml69zNDYb7Gvp8+xbf/xr8n2ujjVHI89AvfzeqXz9J9fx2V3d3kJkyBXfLY++OP0D1b553//Y+I2wETT+2leGiC+T//GOf+5fPITNt/eFNFLn/6Derfuki41mP+p0/hTuSZ/YGjvP+rLyBMg/yeCvv/yyd443/+AuF6H4Rg4ul5xp/aS253lfO/9uJdn+fdYG0148VvhHz/j/g88rhDpy357GfujlQtJVw4l1Ao6mBnbs5i4VLG+poOeM6+l/Lx77GY26N1nb7ypfCBEJZvxOm3Ezxf8H0/4vPd3+czPmFQLAqe+8OQ5cWb79ksUzz/9Ygf/HM+e/dZnHjE5u03ki3cp0cfd3j6WU1TeO3lmMuXHvzvUko2hWS//Hl9bcYnTP7Sf1XA8wTDoeL9M98hHZ4sDAjXltj42hcQpknx+CmqTzzL6uc/hUpTDMfBHhsnXq+j0hQMA2diirTTIu3devLopQ3OtL9OppIdue4awsAyXE6N/dBdf2Y7yFHJSSmF6Xg4hTJxv41drGIXKoTtdQzbBsCtTIIA08lj5YoYpk3ca5AGfQzDorz3IaxckWTQoXHmBdJosCX7pWRK1N2gceYlZp/5EbzaNKXdR1i/RcAj04T+8vsM6gvE3e3Igbe/XipLUTJDGCbVw49T3f8ITmmM9vnXWH/ra0Td+y8ZCEu71AN6iXN9+je7npCr7jjezY8Nhsj+UN9XnnvNeHUEmSRkHd2Vp+IEOQjIOr1rZqxKQTT6YYzsQ5w9sxg5j7S+oX3MbsiqZN0+yeo6/olDGDn/mjHOHwNUmiGDkOjClZvGKfv62lgTNW1we+Nn44S03iDdaOny1e1gmjj75kjXGsRLa/r63YD48jJmMY//6DHMSmlLwKOSlKzdJb60eBNPJ+v0MGtlzGJus+ZvODbegb1kgyHpelMrml9/SyhFslrHKOWxpie0EW92jZQfDVJe/8wC579ZB6CzEhD2dv4AFFeJ71ezIFLp8Y/GaeddCvNjDJc7BOt90lEpqf3OKlbOYeLZ/Qjj7hda3nge0zOJm0PCjT5ZmGJYMc03l/AmR2V0Q5CbLeNPlygdnOD4X/sESipMx8Rw7Zt4E0k3ZLjSoX9JPxeijT7ueJ7KQzM7GhtozoqwjNHvFX29s51nHgzXorBvjHBjwHC1S9LTC5/u+xsI22Tme45gOBYyiFFK6eux1iNq6KC88+4qY0/sobh/DITAreVG16TMkb/yEWSUgdAlOIQgqj/4ztz1esZL34p48VshB47Y7N1vMbfX4uL5hDjSj4WJKZMDhyyCoeLX/q8+7bZEZjpwuHwh5bu/z2f/IYt+X7FRzxj0tZXEhXMp3/fDgulZE6Vg6UpKq/ngA4flxRQ/J1hbzfjwR12UgssXMy6eT+l1b/5e0wS++AcBu3abfOjDLr/wtyp88bPDkUigYv6AxbMf95iaMnnhGxGvvRxvGzg9aJw7k/DPf7nL9GyFp591mZ4x+Qd/u0OvK+/be+zOpGUpUXFMEmtF4rTbIguGxBt1VJZi5PJY1SpJq4GMI4Rl4UxPk/a7WpF5GygkiQzpJetkamcXUGBgG+4ds0t3wlXyr5IZhuVg+UXifgfLy2N5eYbrVxCWjZ0rYRcqupTm+lheXqdWB11NZDYM3MoEpuUSh0OC5sq2PllZHBI0V0BKbL+AUx6/7diizgZZOLgnGwolUwzbxatOUdp9FNPLkwy6dC69S9hZR8b3zwSTw0A70UuJvWsKGSdk6y0whC4pVYrIQDvN36gyLFwHs1LCGisjHEeXq0ztOOce2HOtK+tGvZWrisVXu3qyDBVttTjZDDRHnzXyOR3IKEnuyZM3BTP23DRmuai7qRwLYZmoG52ov0NQaYoMI1R4s32DyjJ9X1nmTZMg6PNWQagDpTsEbMI0MPI5ZLSCGmzP+5D94chHzsLwvS2q6SpJkFG8/TjTTB//+mDVNLTxrmtjz0yQv47AfRXW9DhmMYfh6vtBxcnmuJRU9NdD+uv3d98Olzq4VZ/xp/YStwPc8TxpkBA3B8hEYroWpmeRRSkyTlGjyT8dRMgk25xw7xaGYyIMA5lkmvujNB8m6V5n/AmYrgVCkAbJljZ1pRTpMCZpXwtgsyglC5LNbjCZZKhUYlg75zQm/ZD+hQa1R+eIOyGGY5KFCXE7IB3cfaZIGALTs/V5Rtm16xbEZGGCVXDYpFyqkYt7nG12+abDGCUlhqcXmIZtYlgmKpMEy12y8Fpwm4YJg4UHL8B41Wvrq18KeeIpl5ldJscftpmdMzc7u3xfUCwarK5km3YeoG/31ZUMyxb4OYON9YwwVFepidRXM7IMHEcgpe7OupdupjshivQ5nDuTcvS4zaAvOf12wnC4faCgFKyvZbzyUowQgpOP2Zx41GH/QLehV8cMLAsWLqd8/Y9CNuoZ8a2dZR4YhgPFpfMp33ou4tSTDgeP2Hz4Yy7Pfz2i3ZL3Ve3esdJyFkUknRagyaSG6+JMThMuLqCUxLAdvNk5ksa6zvBsM7pURghh7DjYAR0sZSod8X/u/cxVlhF1mzrgcVzsQgUaS9i5EpZfIOo1MUwLyyvgFGsgBLZfwM5p0cW43yYNhwhhaDKyZSPThHi0z5uOlybEvabOKLk5nEL11oOTGXGvcc+eWzJLsXNFSnuPU54/wWDtMr3Fs7Tef/We9rcdNNdiAxnG+I8c1SuvJNUckCP7sGenSDdaZK3uVr6JaWLWyvjHD2oOSaWkgxLfQRimnlhv9fC+2o4PoxZ1Ncru3LjhtZYC4doYhRxG3mfsZ//sbU4oQxiG5rvcxqvqg4SKkzuQptXotLaZdDOJHIZ35pcIMbrOJoz83W45ltF7wrN1IHg1AIni24zzutb6q+MUBsJzsMpFrLEK/skjtxyeDHSQJQzjjhzBnaL11jLVh2fZ/xefYLDcIe1HhGs9+pdbutM+76Ck2pRtuAphGghDjIKUu3/mbH4V17f8b7shZMOY/oUNzv3ai9u25xvuyBw5uRZQ3C/Cep/6ty6y58dOkvQj4k5I3AoYLLaJNm5dEt1u/EqOMmXG9tdti+jHSJVgszI/Wtxcn+mXSUbcDrj4268QNe/Ooy1NFFGkCILtaRJX10lBoIhjRXbDdUwS+PS/GbJwMePRxx0++kmP4ycdLEt3Ia2vZpw9k3D2TMJwcG3ilRksXckIA0kYSM6fS0lGfmNS6nJZt61bu7MMmo3sAwl4QHORXnkh4vBRm25H8cqL8W17i9IUvvrFkLPvJgyDHM98xKU6ZuN5gvqqNg9945WYz/9+sK1/oZKa8B0Gakdt5KPHN2GgO93S67hnUurz+J3fGOC4go9/r8dP/IU8595LN3lR94odBzz9s28zeP80Kstwd+3Gm9tD7tARrEpVG23KjKSxQRaEt1xpvtP6yn11lUuVcmXwNr343ltYlUyJOw2yKMAwbbzKJGFrTfN5hCBoLGMYFm5pHMvN4xSquJUJ7EKZLA6J+22yKMB0fUzL1b9dmSKT7UNgJTPSMAApEaaJYd/aWFUpRRYN79l12ilUcUvjVPY/gjBMnEIFtzqJMK0HZlwqBwHh2Ys0/59/R+1nflRPYmmGjGKStQ2ic5cYfOs1nQW67kFWePYxvIcO4j98hN5zLzF46U3SehPZH6CShNyp41T/wg8/kDECqDAia7VJ19bpfPa522Y/4isryAfFjrtXfABk3Rv3r9IUGcU6GNxGRgDAyHmb78l+gIyTTS2hnZSg9QckahiQBCHxxUWGr7xz600ziewNts2S3i+8iQIqzbj86TdY+9r7uiPouhgm6UV0z9Y5+HNPkZsuYZc9kk5I7dFd+FNF2m+u7CjYCNa6pGGCN1HAmywSbfSx8g61R3fprA46ezW40kJmEn+iROX4NN2zdZ0RMsAu+qSDD2ZZbeUccrMlFv/gHdrvrjG40gKlbtt1th2yMKFzps78T50iN1vGqfjE7YDK0SlKhyZovb6sy1LobJA3USQ3V8FfaBGs9qid3IXp2fTe3wClCNf7RM0hVt6hdGSK3vvrmscDWAVHV962yUD92/93wL/7reGmIOCN6HYkb70e80MfXSNL1baeUErBKy9GvPZyxG/8yz7XNwMrpT3AMsmWyV1KuHg+5b//b1oYpq7wx9G14ysJf+cXW5imnvQ+iE6nqxCC0RgUiwspX/1ScMcAIYoUly6m/NNf6vLP/5HYjPWvmqPKjFuaNV84n/K3/4cWpiE2g7y7QRzB6lLGj31PnSyDbBuy/dpKxi//ww7/9H/rIpW+pve7Btq5u6eUmyuvtN0kVLrtNF5f0xwepciCIdng1nVWyf1p+igUa8F5omwHq5BbjCQZdjBtDztfwinWMCxnM1MjDJM0HOi6cqmGU6hiOh5JrzUKHEZRfKbvfiGMWwcVwsC0dUpcSfmBOaaD7hqLe02C9UWEZePXpvHHZhk7+hSt91/d7C67Xxiei717lmSjSXJllfjiouahDIbXWs5vLCHNTmBWy6SNNsFrp0mbbV2KSVKMYl67+N0nIf16JKvrCM/VHUf1hi7D3IKMKYfBA+TvKE28NQzNHfkTBJVlJItrGIU85uSYFhO84aloTU9gjlXIegOy3gB1XcCz4+MlKfHiKs78HEpKktWNW2aIlFI62PkAAj9hGjhjeQrzNfypoo51kozW2yt0z9VJBwlJL2L1K+ewqz7zP3UKmWTYJY+4FbD6lXObZRi76DL93YdxazkK+8Zwqjn2/NgjhOt9wvU+rTeWkHFG/2KTtW9eYM+fO0naj8iS9CbBVhlnbLxwmcL+MaY/fpDxJ/doSlom6Zxeo3tufUtZ54HBEAjHonZqjsJ8jaQbISyDzuk1BostBpd16aj2yC6KB8dxa3n8mRLCNNj7E48QrPbonq0TbvRJBzH1b1zAqebY+5OP6hJgwUGGGatfPks2jBGOicoU/YUm+bkK5icOYTgWuZkS/cst2u+s6pJXIhkutbnye29RO7WL6sMzOjg1BP0LDYZLbR0c3YAk4Y6TrpR3du3OMv2X7CSbJyEMb7291pP5YBczpgmlsuCpZ12WFjPOn9MGnnfzU1IS3QkV72yMSt77uSl1u+9CUJw6CEIg05iwtYyUyT0d53rc2xNshGwwQCYJCINsONg6WXzAxM9e8mC0OpJhD1EwdekqX9HtoHFIGg502S0KUDLFzlew8yUMyyXut1FqNEEoRRaHKCURpoXp+KRh/6YHtjAMTNfXAU+akD0AHs3tEPeaC0Wh4AAAIABJREFUtC+9hWG5mI6HPzZDef4hBmuXiOTGLTNRO4HhuTh7ZshaXcLT5xm+/PYdJyqR83U3UhDqtuthoCs1I0FCq1q+51b57ZAs1bGqFazxKmbeJ41iVJKipO4YwTQ1f0jJkdLzAzqwVLo8M1JdNitFZHjdJJ9lowXCAzreDqCyjPjSotZLGq9iz4yTtXv6miAQOQ97dgIj75OurOvuu/vIuKg4Ib68jLN3F8Jz9X6H4WZgI4yREvVVteYHrbgsdDbDcEwMy9Bqx3uqIxKzQtgmwUqHtB8j45TGKwvUHttN6dCEdv9uB0Qbfdrvrm52Lgnb1DoyRRdhCIK1Hv5sGWGIa1o9UhGsdGi8vMDcDx5HVjzibkjv3AbDpQ7xiJujpKJzZpU0iJn5niO4tTwYAplkDC41MExBJhUyzhgsdkg613F64kwTmZc7d7S1uB6mZ2P59uh6mLjjBdxaHsO1NPfIFJsBj1Px8WdK2EWPuDVEphm5uYrWE1qwdEkryWi+sUTl+DS1U3MIQ5B0Q8K1Pq13VnRbvWmQBgmtN5fJohSnmsObKJAOIoLVLr33Rxl7pYgaQ+rfvMjuHz2BXc3pEqwBcWNAWL8/SZL/WCAE2DZ4vi6ZlkoG+w7aHDxi85UvhFw8n3IXPt9/MiHA9osIw0JmCWk0RGaJ/ksTZHpv89d9BTygsxqG61J85HEMz9t8vfPCN4iWPzjp8AeFuNvAdH3cYg23MoGMo1HAIlFKkoYDkl4brzKFWxpHmBaDtUubKXelJGFzdTM75FWnGKwFN2VwzBGJWBgGadC7ZUv6g0AWBYStNbqX3wXAsCyUzCjPn2CsXaezcJrelffu+zjC93D2zBK8flpP5lftDW43tmYHNTmGPTeNf+IQaaONSlOsyXHyTz2CPVlDBtEDCwSGr76LcB2c/XOM/dyPM3z9DMnSGrLXR+R8rGoZs1pCBSH9F15/YKJ3Ks2Ir6xobaG9uyj+mY8Qnr6ggyylyFpdnelIvvN8IRUl9L78Avb0BM6eGWo/9xMMvv7yiKRs4j92HO/wPOl6i+4XvnnfZT45COh/41X8hw7h7pvD+bkfp/fcS8hRh52R87HnprS4XH9I9/Nff6ALJsM2qZ2awxvL0zu/weVPvQGAsAxyMyWe/Mc/zsaLlwhWe6Cge3ad7tnb/z7j5pD3fuUbdzx2tDEg2hiw8eLlLa8vfW7rduH6gHB9QOPlK7fcV1jv8fY//MMtrwXLHYLlDvVvXrjjWK5H6fAExQPjCFPw5t/9giZBC020fuzv/TCGZbD2tfMArD73PqvPvX+HPUL/QoP+hQaLn92+ZJkOY9LLTU7/8nN33FcWJvQvNTn9f3xtR+f1nxJcD2bnLE484iAEnHrC4egJG8eBP/pCwNuv/2mNdgClaFx8FSdXxi2MM7b/cSzHJwm69Ncv0107f08L4/sOeAzPJ3fgCL3XXx6pMutBZIN7LzdZwiVvVcnZZWzhbpawEqmzIqawkCq7b+IyQNRt4pYnRxydPlk43BKMpOGAsF3HKY9hF6rIJCRsrm3ya1SW0r74Jv74LtzyGOPHnyHqrJMMe5tfiDBMnNI4Y8eexrBswtYavStn72vcO0Hn8rsk/TZOoUJ538MYjo9MYgarl7ifyEL2BoRvn8N/5Cjew4d1uUiNOoqimKzTY/Ct14gXVzf1aQYvvE660SL/zKMUv/dZHSSlGWmrw/Dbb2LkfLxjBx7YhKfCiODVd0mW6xQ+fAp33xz+iUMI19Gt7UGI7PQYnLu0KXL4QI4bxYRvvkd/vIo7vwv/5BH8U8chTcnaPfrPfVsHe38MAQ9SkjZatH/vy7h7d+E/cpTi935YE4WVQvYG9L78AvGlJcL3Lt6/Jo5SqDCk9anP48zvwjt6gMJHHsdwHTAMZJwguz2SK6sky2sPvpxlCMycrctl13EFnLKPN10i6YTI2/gCGqaNaXuYTo6wW+fG30yuuovK3DFW3vnKPfPuvtMwPUsrOifXSh6GbZLfUyNLMtLhHw9x/z/j7uG62q7iZ/9yAc8XhENJfU3y6/+iz7tvJ3QfsLjhdxqG6eDka3iVKWzHJ+o3SeMQ2yswvu9x+huXCLs7Sxzce8BjGAjbxvB9DMfZJEOqERPsXjosDExKziR5q0rRHsM185jCJlMJjXCBlAjbcCk5k0iVMUhb9+2Wngy7yCTCsD3cYo1+v01ynd1CFockgw7F3YexPJ84iUbt7Pr8lJJEnXWCjUUMy8If30V5/mHiXpM0GiIAO1/CH5vFr80QttYImivEvZ2b790rsnBI2K7TvvgWY0efwqtOUt57nLC9joyDe3pI23PTOHtmEa5NslLXHVObZQ/dVm6Ui3qSV4qw00elKVmrQ3xxEcO2caemwBRImSHbfZJLK1qpOUhJu11UL0BIgeoOyL59HtVoYcQShEVabxIaBma9oTNCAFnG8MU3tDrwVSsLpch6fWSSELxdwJqoYRZyCMdBJjEqiLQWz8o6MnqA5FClkIOA6L2LyHYPa3oc4bmQZcj+UHOXrl4vKQlPnydZXEWG25c6k6U1LYewVEcGW7eJzi8gBwEqjJDRXU5UaabLVSO9H3Oipks8UpG1u0TnF7TC8nWt5ypOCN85h7GQv2UmLL64iOwPMfK5rfo+UpGsNXRJKM3Imh0tBWAIZJIiO70PTGVZZZJwpUthTw1vssDkR/aDAqvo4pQ8Nl65gqHyOPmqFgxVEtsvYzoeYaeOV5rAtHU5OuytAwLTdvHLU8gsxS9N4vhlhGHh5CpYbh4hIBq0kWmEkhK/Ms1VU+Co3yRLIm5ebOgOOr8yhWHagCBLApKgCwjcwhiGaZKEA9JogO0VMCztKyizBGEYJEGfLAnwy1Ob/Id42CWLt3Y6RY0hTjVHYX6MyWfmkYlEWAbueJ7BpeY9uaH/Z3xnkSSK1eWM578e4jiCfk8HPG++GtPrylsSjW8Hs1jSUiGmSVK/UfD2wcCZmUUpSbK6etvt3NI4hmWTRgN665f0XJ0lIMTo97RzbuQ9BzyGbWMWipj5AjJNsSo1hOMiR436aauBDHdyxQWO4TObO0rVmcG3yoDCECaxDLAMByENXLPAlH8QUKwMz953wJMOOmRxgGFrBeXulTMk/Ws6D7ojq4VbGsOwHJRqEvevaz1XimTQobd4FoRg7MiHmDz5UaLOBlG3iRDgj81i5yuYXo7W+68xWLtMMrw/Rd+dQZEMe2y88y3yU/P4tRlqh5+gc/ldwtYaWXR3bZ/Xwz95BPfgXoxygf4fvaAFAEeTrXAszLEq3tED5J9+BNnuEl1YRPVTVJSQLNXJljZIjRyZSpBkmMJBKa1vEV1cJ1ExJia2sMjqXbLfewWpUkwpMYVLenGF6OLW9L9KUtr/7os3DzaTqEHA8KX7U5e+F0RnLxGdvXT7jaRk8PWX73k/0WvvEIlbd1Lc8rCDADkISBbuzjxWhRH9r96s8nw9gtfP3PrNOCFdrpMu12+7D2E8UBoXKpF03qtTeXgXpUPjlI5MggQpJUk74OJvv4pr7iZX8eklIVkS4Vem8ApjhN11ctVdmI5PEvQQQmCYNk6uQnXPSdKwjzkKOkzbJT+2B78yjWEIOivniIMuMo0p7zqOYRik0YDOyjnCbv2mhYYQBqbjU549iu0WRgHWBv36BUBQ2XUMy80xbC0TtFbJj+/G8rSAYRoNMG2PwcYCYW+dytxDowBIlwAGzXDLRR0sNLHyDuLxPcz98EPAqH08lSz87hv0LlwLeMRIEktKHjjnzDDAdsT2XVMCXPcW7/0xQXtpC0xrlJxOFDcmaYWhx52mO2vV3imGA8XrL8e3NQXdFsY1jTMxaqK5Ktxqj49jVSoI1yVZr+vXhdA8R0YdmlJeE2i9ujgxzWt9/5Y1ouOpa9sYxmYG2T96FJXEtw94hCBfmyMJugTtFcJOfct7puMjd6AqfhX3FvAIgZHL487MYZXKyEEfuzaGXa1tbjKIoluuVreDLRwOlJ+iaI8xSFucbn+Nftpgf/EJpnOHNreL5ZBmtMi+wmO0zOV7Gv71iAdtkmEXlcYIyyHptwnb1y5uGvYImssI00bJjCwaEndHOkTXobPwLsP1K/RXLzJ25Enc8jj56Xl9jF6LwepFOpffpXP5HeQfB5NMSbJoyPILn6V68BQTD3+EPR//86x8+/N0F84gk7v8roTAKBXwjh/AKBbofu5rBK+/uxnsXIVZLiD7A3KPHEH4HmYxj+zrMqdn5KlZM/RkC1PZWMJhypknURGZSkhVTDdrUDYn8c0Cg6yDiUWsAjISpp0DrMYX6KZ/Opy1P2jsO5HD8QzOvNx/oIHCdxqWI/ByBhO7XNYXI/qdB1ceknHGpd95VXOornNmUehJXlY9bL9Ede4hgvYqpu2RZVrUsrt2Hq84jl+dAQS52i684hjNy28QD1rkqrsYmz9Fbc9JQBEPWsRBh+ruEwBEwzbNS6+Thj0sr8DU0Y+y9OYXNFfwOhi2Q35sjqi7TqjWkFJiWo7O6MRDsgsvkx+bw3ILlGYOYedKdFfOkiUxXmkCw7TJjc3hFcdIwx5x0MNyc0wdfpZL3/7MFqKnTCStt1Zon167Jjm0qSsjtxDHd++zKVVNFi/G9Nr3r3Z7Fbm8YO9Bh5/6+SovPTfg85/ubb5nWlAomfy1vzfJN7/U54uf6d1mT985nHjM59ijHk99PEehaPDl3+/xO//imlik4wmmZi3+6v80wYtfHfCZX99efPePE/7+A1jlMkY+j1UqEVy8QLyyQtpskg0GCM/DcrUkhXBd7FqN/MlHMDyPeGWFaPEK7txuwksXSVstVJJQfOJJksYGSb1O6amnwTTJel2C8+eRvT7evn24e/eS9ftY5TLJxh2e3UoH8VFvg7B3QzZZKTrLZ+4pE3yPfaaKbDAgXFzAKpXw5vYgo4is3yPt97CrY3h79+NOzxIuLWgBwjupvwqTij1FK16hE6/STxskMtQt7NdBqow4C7AMF0PcNwUJlKK//D4Lz/1/YBgM6wtbOqhkHBG11ln46m+DgiTosd0yx58sYHoOUXeJ+ht9DMvFsLRyaJZEZFFA3G+NotJtviil6C9fIO63MR2f4caVUdr77pEMOix+4zMoJYm7zW23ifst2hfeIGytIrOUYGMJle1EMUqNSicx1riNd+wAWaNN1h9oAqplYpQK2NPj+CcOI4OQrN0l7dz8wHKEhyFMbMMBIFMJiYpIVULOKGMLh6tae6awcPA3bRXEn+7y9I5h2YJizeKhp0ugFK16wtqVCAEce7JIedwmV7SoL4T0uxlpLDn+VAnHMwj6GRfeHjDsZUzMukztdXFcg1Y9ptNIaazEPPqxMrYrSGLJhbeGtDcSkmjrRRYC3JzBoUcLlGoWpiU48+0e+YpFqWZRrFgMelpywjDgnRd77DrgMbHLxTAFl08P6bVTkkhx9MkCjmsgM0VjJWbpQkhtymbvsRxxIGmvJ4C2FHjkIyX8gl5hrl6OaK/HWI7BoUfyOJ7B+lJM/UpEecImXzRxcwZ+3uTy6SHNtYRea1RmzyTqFpN11G9hmA7FyX0Ylks0aI74OowerGpTLEEYJsKwkUmETONNY1XDcsmSAJklqCyhV7+I7RWwvDwyjfTrMsMw7W29AGWWEHbWGdv3GChJPOzQ37iM7ZfwShPYbk5LWiiJMLQQpMwypEyRckQ6NiyU5WgdrywhHnToJGe3L1srpYUM73DvzR922bXXprGWavPIBxWHCoEwBI4rMK2t10NmMOxLfvc32qwt/wlJ7wBXLsYEQ0m7kfKjP1PGsreOW6CzJo5z8zn9SYGRy6GApNEgWriMNTaOt3eefrO5mem5CpUkpL0e4YUL2BMTCNPEmZkh7XSwqjXMfIG03UYIgVWpYLguMo6RYYgQgvxDJzZLY9HCAmm7Re7Ew3cuRwlwC7rEvC3usex9zxGDiiPSOELJDH/vflTSQ0bhiAehMH0f5bnYwTjZoH9HTo9A4JlFguws/bRJLEetlzecl0IhVYbxAAxEryLqbBB1tuckaMHAPq1zt1cpzs2WMD2bztl1esvr99RaG3U37svnKotD2hfeuO02MokIGssEjXvPjqkkIV1rYOZz2FNjuIf2ag5JmmorglIBa6yCNTVGvLCs9W+u451IlRGrULf2CwOpJEHWI1R9UhmTkeIZBWIVkqqIWAaoUYSjUIRZj1T96SRVunYJ2/QIky5pth2PY3uYtqBYsTj8aJ7WekyaKFprMQioTNiMzTgUKiaddYMolCglqE3ZlGoW4VDSb6esXooY3+Ww70SOXjMlCiRRIHE8g/0P51AS2hsxtmtoteEbxybAMAWlMYuJXS65okFzNaY6ZVOZcLBsKIUK09QP/JVLEdN7PabnXZQUpIliYymiuZZw4ESOOFT0OynDboZhgOMZlMcswoHEdgSGCX7BZHrew/UMwqGks5HQbxs4nkFtyqE8bmE5+jlQm3bw8wa2Y2C7gjTxSFO1GfDcDlk8RKYxpu3hFgzC7jrRoIUwLCw3h+nkRsRlT2vFyBTLKyCzFNPSVjfZKEtqmDaG6RAPWsgsRRha9kKYFpaTI4uD7ZWAZUYa9XHzlVEwk5IlIU6+ipOr4PhFon4TMZoslJQopC5VKYkAMpkikxhh2himTRqHBN31m6x4/Lwglzcols1NUew0UayvpEShQggdZI9PWRw85rDngMPF92LyBYNBXzLsKzqtDNMCzzeY2mVx5UJCMtJx8XOCUkUHqVcDFtsR+DnB2JSFkmC7glLVuGnuyxW0RUO+aNBtZ4SD7eeOXMGgUDLI5Q2EgCRWDAeSdiPbcc+D6wtKZYNC6WrpBqRUrC2nxJHazJy2NjIGXcmwL/m+nyjdeocCCiWD2T02jieQmVYjbqyl2nLCBMcVzO62WVlMCEZ6NJ4vKJQMHFewvKBd1h1X/xZyeYNgKIkjRbFsIDMY9CXN9Z1FoMK2ddKi2yVeWcaemsYsFG65rTAMnflxXaxiAbNYIlpcxN01h6hqM3EZBAjPw66NjZIffe2zOTGBGhsn2dgg2aiTNpv4R46Oni+3HSWW42PnyrjxNRHeLE1u4qPtBPffpWVaWNUaSauBVanh7d2PmS/QffUlZBRSevQJopWlTTLzLSGErieqDHkbMoLAwDIspLr/Dq0HieqJaZySx2CxTdwWf6LG9sAhFb0vP0/0/gKFZx+j+MmnMXytr6OyTBNQG22Ct8/R+/LzZL3Blog8VgEbya3bbwE6/MdZrtoz8STjpUNcWP0ajd4F0uzuSomOa+AXdPbi1T/qUF+MCPr6Kbx6KaLXSvn6ZxqbD3phwEtfbDF30Kc27XDsyQLt9YR82SRfMvnULy+jlA4yqhM2Xs7k3Ot93vpml07jFnYTEobdjLe/2aU27bDrgM/BR/IUKhZZqnjhcy12H/IpT9h4eYNHP1YmyxTNtYTGSswTn6ywcsnm219qMzbr8NynG5x5+VpZZ30pRmZ9Hv1YCdvVWZrDpwoMuinnXg+58Na1B53tCF78Qov9D+epjFsceSxPkkB9IWRjOWbpfMhP/3ezxKFi6f27u8Yyi0mGHSyvoPlkaYqTr1CaPoRXmsDxixQn5omHXaLuOhMHnx4FRQKZRnRX3qM0e5RcbYZ4WCDorJGGfbqDJhMHn9beWNGQzuq5bfkHQhgYlq2DKsD2CuSqMximgzAM7FxZW59Iua3atQKibp2wu8Hk0Y+SBD2SoEvY2xg9W69te/C4y8knfT72/QVMUweyzXrGv/ilBpffjxAGTM5Y/KX/tsahhzwqNZOZ3TZJrLhyPub1l0L+8He7FMsmR064/NW/Nc4v/vwKy5cTTBP2H3X5xA/pSfT//Lt6ETc5Y3HslMdf/CtVolDRbWdcuZDgulsjnsMnPI4/6vHUJ/JMz1r823/d5lP/qr1lGyHg6EmPD38yz8knPQxTB1ZvfTvg93+rw6C/M02t3ftsvuv7CzzzycImN2fQl/zK39/gysV4i4P43cAw4JEP+eze57DngM2gJ3nvrYjf/GdNeh1JvmAwt8/mF39pil/6m3Xeflnfo/OHXJ76eI65eZu//9fXeOaTeeb22RSKBg897nP69ZDlyzEf/p48w4HitW8Nt5TU7hZKypEzghxl2rbPuDizs9jjExi+T9psojKJUIqkXsc/cABnahqrUqH34ovYExO4c3MwHEKakA76xGtr+IcO6WxinOg44Hruz22QpQnlmcPU9jxMHGjO67C5RPPy7Rf1t8N9BzxKKVSS6JVQsYQzPYPp57EKRVKlNJnprvaT0Y3XKTvTAPT7Tb16uQ4C8M0CU/4hesnGPSstjz22i8qxaaonpiHTUupJP+S9X32JpB9dU1MtuVRPzDD51F688TzCFMTdiOZri6w8pzUqvPE8e//sw9QencXybfJzFdIgoX+5RefsOvXnL+l9FV2KB8bY84PHMH3t2RPWe1z53BkGix1krCeZ8Sd2Uz48wXCpQ+XENG4th+lYbLy6SOP1JfoXty9VfaeRtbuEgyHxxSt6xWCIazo8UuryQZxoYcFbKBv/Z9w9Bp2UhfcCPvfrdR56psh8N8fiuYALbw23GBkCTO1x2X3YpzRmM+ymxJHEGMnaR0NJr3ktoEkiycZKzB/+zjq7D3l87MfGOPtqnyvnQoa9rQsP1zc48UyRQsVCKgh6KcJwkFKRJvovjiRZoomOri8YdCVxKEkTxUtfaDHoZigF3YYe1+1gGODlDMJ+Rhxe23b/wzkmZl3yVYteIyFNFcIUkCiGfcmgm5FEkp0qdkf9JvVzz+usYxKhVEY8bLN+/iUMQxuBprHOTAoEUb+JHOltCSFI4yGNi69gmJb+GaTxpqDl0lt/OKIOSWQaI7dhs9pekbEDT7B6+mskI/HSqSPP0rj0Ot2192kvvbtV32vUhaWUImivIgwTmWl7n6U3PqclIpTcLKVdjw9/soDtwL/6Rw0WLyQYWoORRj0jinSwsHw54Z/9Lxv89M9XOXDM4Td/pcX6SkowkISB2hGXx3YFjzzl8wM/VeKf/J11GvWMUsXg4z9UvGnb02+ELJyPeeVbQ/7GP5i66X3LgkLZ5L/4r6u88eKQX/qba/S7klMfzjE2afLTf7nKv/mVFtFtlI9vxMqVlC/9+x6f+1QXpaBSs/jkjxY5/piH4wneeXVnQrHj0zZvvRzw2d/q0FxPefoTeSZmLP7sz5b59L/eGa8nS6HXkfzmP21y6hmfR5/O8b/+who//9fHqI6b5AqCYX9nAZk9NoYzPU0uO4Hs94mWl7GqNfyjR7FrNQzPI6mvYXg+QgjsahVjZCB8NXmRNJsIx8WuVpFxRLyyjAxDCqdOkfX7ZP0+aaNBePEizvQ0pWc+TNpsYObzJI07dAEqSXPhTQzTQghzU+j3fgV77zvgkXFEuLSASlPSbgcZRxiuh+HnsC2LpLFxV23PUmVsRAtU3Vkq7iypigmzPr5V2mxXL9hj5MwSeavKWnieILs3IlvajwnWenp+lgqn7OFUfYrzVXqXWpsKqLWTs+R3V5CZpH16DQRkQUK4MUBmuq6fDhO65zco7q+hpKJ3sUncCQjq/U3TO8M28CcLTD09z3ClhxwZXjpVn9LBcQzbpPOe5gtYORtvooBT1nL20cYAYRoEq70/WdoYmURlMVn4p1jc6k8RbM8gVzQpVS1KNRshBF5OLyaCXkauYHDk8QL1xZhCxcIvmIzN2Nj21gWVkltFkw1T4BeMzf0WaxZuzmS7BZ8Q4OVNKpM2Sl0zRNdlf3Xdv/Xf6kJEsWJRnbBBQms9IU30ZJqlagvB2ssZ1KZsdh3wqE07jM86ZCN+z95jOWzPwPVNeq2EYsUiVzYZn7ERSuF4xjXCrVTITN1TiV+XlIY3vZbFw5toKwqIhx1uTCNkcaC3vUGEMw3v3JV5tXxu2t6oVU0R9Vuk0UDvN7m1OWx2Aw9Pcw1vYCNfh04rY2zS5MBRl0FXsrGW0m1nROG17yVJoLGWMehJolDRWMtYW0o3y1Y7QbGky0WOK7h0Lqbbzhj2LVavJMzN21u2jQKFTDOEob/PG+F4gokpE9OETjPjysWEcKiovBfh+x5PfiyHucOZzbJ1Cao2biGl/nd1wmTQM8nld97+nCaKdiNj6VJCu5mxcD7G9QyOPOxiWTujoKSJIgwkywsJR056+re1mBKPSo+OKwgGO7vnZRSh+n1kFJJ1OiPXhJikXkf2+wjL0q8FAXI4QMYxKtSOAtqweeTJFwQkVyVpsoyUJuHly6g41vvu9bSMhmFoWZLBYBQM3Xnudvwipu2PuLAClCQatEYyDfeG+w94woDhufdwp2c0S7vVxHBd8oePYRSKDC+cvSsjwEylrAXnKTtTlO1JSvY43Xidgl3FECYT3l4cI49lOBgYbIQLBOm9MeCHK13CxoC1MAEJlaOTTD69l/KRSaJWsBnwTH90P1mc0np7laUvndW8nJEaqYx10BJGA678h9MUD4zhlH2ufO60lnm/zmTQ9G3yc2WmP7qfb/+Pf0Cw1gNDsOt7D1M5NoVddDcDHtACYIXdZU7/38/Tu9DUGSdTPHjJ/f+EIYSpvc+uuqoLAym1e44hTIQQSCmR6qpnmtAlh9HnrkIpvY0aEVuvh2nYo231MaRMt3z2euj9Xtu3UkqXd1UGKHIFk9q0zfyxHMLQfJirhnvt9Zh8yeTkR0q89XxvxGeQuJ5BvmyRRJqrI6V2JU6uy5ZYjqA8brPnsE+hapHGtw4YlIIolFi2wHYMlNQZIsPQx5OZzvIkkSQcSM6/NeChp0pM7bFBwXCQMexpV+w4VKNFg0auaDI+67D3iE9l3GZizmXYy7jy/7P3JkGWJOl938/dY337y32pfenqHTONWTAzAASABBdIlIxmEs2oA6mDBPGkI8100wEn3agDzLSYUaKZBJACRRHgYKMGBDADYKZnQU/vVV3VwgIXAAAgAElEQVRde1bumW+NF5u76xCRL/eqrKyqXoD5t3VVVr6IeB4eHu6ff9//+38fjfjiL7aYOeszNe9z/6MRyilSlf1AFt4mbUlGhiwt+mQnrJeUnqXnh0dc+xQWl85iBut3acxdRSoXozN6qx+RJYNTXvP446+/HfP6l0N+7m/XCEPB+28l3PkoJRk9m03VQYpGs61wXOh3DN1tjc4hHhlu30j5ws+ET3RtP5DMLrpsred0NjVxWYzz/scZC+dczl/2kfIIDtoj2tqadHjxpwK+8NWQPAelBHNnHLY38jFH7Ekw6Gm6WzmdrWLtW3mQMzmdM3+2hnLEseNSiMN9l6UFX2o4MPS7mn5XF7WvYovOC4PniWAtebdL+vAhentrH792dP0YWQl5b3/ikVIIBCaOi4yrslSO7vcZvvWXuzdRjlnd75HcvXPyMSwEYWser9rC8SrjeXGwcZdoa+nJ7ncPnj7NyRjMKGJ051ZxM0rhz11keOMD9GBwYkENi2GYb/FB50+ou5PMhldoerM40sdYQ92ZppetsxbfYnX0MaO8e2qeTO18m9ZLs0x/5Rw6znEqLm49YP3795DltlUoSfPqNBs/esDDP7q5a2xYxpV/T4rqYguvVaF3a4NkKypk3KVg5du3mf7yeZS3P+ynk4y1790n2RrtFhp8girNP8Gj4TlVJhuXqIdz+G4N360RehPcXfsLch2zOPlFfLfOWudDHm69zSBeo+K3aVQWmJ94lVowU2TDWEsUb3Bv/fv0omXirDDABQLPrXFp9ueYaFzAUUWK84ONH1AJJjDs58gIIVmceoPJ+mVq4QyO8hnGG2z377DSeY/BaI3ttYzuRsbdD6JiDOqikrG18MH3B1z/0QDHFWSJ3ZHO4Ed/1EFrxjWWjC54Mm9/pzued5LIsPRxzFqZ8WVtsaM8al5KRoYffqvDj/+0uM+DFY6NgYe348K8K3VbNh5sFPXEFOjyutbCb/+vy/umhq3VjO31jLe/0yv5JoWnwVr4v//ZElIWVZzz8julgO+6heGzt612jzfrt/6npWcu2vw8YXTGqLPMqLtH8O056Qy89d0R778V881/2ePn/naNv/tfNKg3Jf/L/7jB7Rvp2Ig4CQ4dKQry7Y5hCnsX8T0ZQLZ4nqd5RkeSksXpyg5Pzzm88bUKf+8fNvin//ghvY6m2Vb8yj9oIE9ZtuuQ4VISw3fWrCNvWYAflvWT98Da3Xet5KfvO+fxBOD90N0uVufYLD25QPABY6fy4ks4zSY6GhLfunXYkDlux3RSWMv6ze9RzKYFv3fq8leeugbk6YUHKxWk6yE9D1mpFrE+KQul0AsXyfs9dP/JXU+pjuhaTaIjPBmOd+EWQ2aKjJ1YD05t7HgTFSa/sEj1XJvbv/Vj9CineqZJ66XZnY38LoQo4uDPioNysDilMWWGxIEBaykUc5/QoxM0XK79R7M8eHubwWZCMjhMPvWqitkXGiy+0uJ7v3FnnyfKqyrmX2wyd63Bm795h8VXWkxfrjP3YpM3f/M2/bWYNPp8SOc/DkJImtVFoniTle33mWldY7JxmTQfsrL9Lq3aeZTymKhfZBCvFZll+YDucIm17g2sMUjpMFm/wFTzKp5b5cHGDwGohjM0Kws0qvOsdT4kyfponVGvzhG4DVSZhg/gOTXq4SyT9YsM43U2ejfRJqUWTOO5NS7OfoMP7/8+uU4KobxkN3S0g2JCLNz/O5OhtYyNn72w5ojJ1kKeHr7uUbCmOPYgb2hvW/YNcVv8YQ4UQD8yS3rctvLAMlPMaApv0J5Nu6a8X1u+O+Poze6XfE6qPBzGJyCmFFaKHXN3W/PmnwxRDly46nH+ssfS3WyfwVMI6FmqNXEo1JnERdZQraEIKwLHBSkFL7wW0GhJtjeKh7C1rskzS3NC0ZpQdLcNYUVw+UUP9wk9KKPI8OBuxtSsw8S0Q6UmiQaGSy96TMw43Lqe7PMePg5+KHCcwpMyHBjy3OKHgiuv+Dy4fTqPV72lmJhxmJp12FjNOXPeZXquyGTTuSVLLKPIUq0XITPPhyyFKy8HTM4+Zll+SiM+XVkpQlOnrZFnDMmD+6QrK8XE85x2FY5XKSQghAQpcSuNgtf7FMqkpzZ4hFQIx0UGId7U9DivXiiFU2+OCU5PCoMmNRGpOX3q2bEQ4NY8wrk6Xitk662H6ExjraV+aXLfoRaLHmUIKfBb4ZiPAxTby4PGiCm2rtLZCWHsfq6THJNpVOAiXAmy1J9ohZhMo+MDL5UtduVHZWI88vakwK+5KFcWAmtHwHElrfmQM6+3efM37+x7dxxP0VqscOb1Nt//l3dxAkV9OuDCT0/w7u8tEW2nPDsRjk8frgrJdExncJ9qMEW7dh6BYHtwD8cJCdwG1WASEGidkWQDutFDBqM1jMlRyif0mrRqZzFm9xlWvDbN6iLWGrYH9xnG6xiTE/gtoNBK2YHnVmnVziCEwyBeZ3twj0xH5DpmqnGFVvUSvlfHJBpt0kfOLQfngCfcUD2XY097jvSLDZRO42J3uZPFWf5cXNMiw0pBkjca5fnouCyVMlaChaNWCKGcIlXb2v0T6M7PO7naO9cqb0JIuatM+znG/DmHIJRFyrW1ZKll0DNHqil3tzX9ruHCC0Vl+O62ZtDVdLeLMOJoWISpzl/x8AOJ1pbWhNpnyAwHhu6Wptc1XHstYHsjJ6xKmhOH+WJTs4pGS9GcUHi+ZHJacf6KRzwybG9ossSyvZHT3dLUW5IXXvUZ9DSXrvkEFckHP46fiFC9w5GJBobzlz2yzDI9rwjCwwbe5IyiNamYmnUIK4L2ZNG2UVTc3w50Zqk3FBeuerQmFeevelQbkpvvJ2RZEVqOh8U582ccolcDRpGhPaWKPsyf3/jSw6erToC16O7zF1R0ghrK8RDKQQhZEvCfTpPp1AaPHkWFYrCUOI0WwxsfYJIY6bo4zdZTV1h+PhA4gYs1Fj1K0UmOcCXBdI3Wy3NsvrUnNqgtwwfbSE8x/ZVzLP3hdayxCClQgUs+SvdNDCbTWGNxGwFCiX0CZ6OVHpX5BpUzLbx2BR3nCCmY+tJZku2I4YMnTys8CqNOypu/cfsxXXDy3dSd728y3EqYuVp/ri/gp4UkHzBKO0TJFr1omdBvk2URg3idcDSBq0JCrwlApiMyHTGMd3WStMlY795gon4Rz93VsaiG0zQq8yxvvU0/WibJiwnmwcYPCbwmk/UL42MrfovZ1kvcWf0LOoP7xGkxFta7N3BVyHTzGs3qIlpnjD6T79TzQTAzjwoqjFbuY5IYoYoNlolHCMcBBDbPqCyeR48i8kGPcOE8o6U75KMIFQQFMdPosTve7qzmQuJU65isyJZSfoBOk8J17rlF1mlZO8DEI2RQaO/YLEUGISaOn9q1/mnjb/5ndV58PaA1qYoU7L7hwZ2M7/1xxLC/33L+4K0YIeAf/pMW0cBy68OEH3w74o9/b0CewfpKzh/86x7/4L9u43mCXsfw/W8PaU/txoOy1PLuj2Is8Kv/dJJRZNhc07z/VnwoS/mX/l6dV94IaU9JpucdvvG3alx4wefWhwnf/Fc9Ht7NGHQN/8//0eUbv1zln/z3UygFG6uad3844nf+ry5pcvL5avl+zv1bGZtrmv/uf5gmTS2DrubDH8eHQme/8Ct13vh6SHNCsXjBo94q/r7+dsx/+GaflQfFgnzzw5RKTfK3//M65y97RAPD9XcTfv+3esRR4XntdjR/+G96/OJ/Uufv/2OHflfzg+9EREOD7+8Y9Yfbuy8sdpg6+FcEohDbDGqFzhWWwfodksHWU3lAT8/h0RprDPnWBt03/xyTxKVBIOm/8xZ570kXcYGvKmT6sLryM4O19O9sMbi7zeQXFvjSr/1ddJKjk5zO+yuHvDYf/+ZbtF+bY+Zr55n/hSsgIOvFbP34IQ//6KN9WVPr379P6+U5Xvxvfoa0E9O9uU7n/RXW37xPPsro3ljjxj9/kxf+0ZdQXsH/0HHGgz/4kP6tR6eaSyX4+n91iR2/fXM2IOplCAFSSf7416+z8EqLxddaXP3ZGb79v33E2s0+w61igXz5l+dZeKWFE0iykabS2M2KePXvLDD3YrP4LNJU2t5RTTiEl/7GHNOXalQmfFxfcf+tLe78cJPO0ujI4x1fcub1Nhd+epKwWaQy1yZ97v3lJvff2mbleo+v/pcXaC1UkI4gbHjc+NNVtu4NufYLsySRxgsVlZZH1ElxfMlwK+Uv/sUtvvCfnqG1WCFsuDi+4safrrL8YZfeyqNTGI3OSs0nizE5WqfkpuizQqhNIEQxaSvpUfEnmG6+QKMyj5IeUiqUdKn4E8RZb/x8XOXjqJBhso22uzuSNBugdTzWmZJIHBkQ+hNcXfwbGJPvE4hTysNRPoFbR8lnoCr+OYL0AtxmG+n5hQhfHJEP+1g/pHLmIiqokHY3UUEFm2WFi95aqhevIf0Ap1IjXlvCJAkmiRmtPgBrcKoNwjMXybY38CZncCo1rM4LA4eiAK4MwiLRwlqk5+NU69gsI+1tkW5tkOU5fM4Nnt/4n7fxPFHIFVhKQjv0u4dF++59nLLyIOf7fxphTGG8JKPd4pSdLc2/+80e3/qdQVFzS++S2/eGllYeZGyv57z13dGYl5Imlj/41z1Ge0Jov/uvevx//7aPVIXektFlzaoy5ASFIfDh2zF3byb81j+XheiiLq437JsndsC9/1bM3Y9THHeXP7aTjbbXePr9f93jj3+3j5RF+M4YSjkGyygy6Bwe3kv59V9bHyfqua7AmF0v2k7b4sjyzX/Z41u/3UcqgTUFkVs5Yieay7d/f1Dw38p7+53f6OKW6s7/+z/bwprT3e9nFX59CilVId2QJfS7q2SjAWCxRp+q0PVePN0sasvsFc8jmJ0b74pUWClS2Z6glpYSDvPhNQbZJiPdY5hvP/6kU8Ckms4Hq2SDBK/uozNDPkzJ+jFYSLZ3Q1ej1R7Sldjc4FR8hIQ8yhjc72Dy/bPCcKlbkEONQaea0WqfeKPUCbKQ9RO6H67hhO7Y4MkHCYN7HdLurpEwLK+dbpfk5hLVts9gMyGNNPXXAjbvDfEqDtOXKiBhuJXQW42RjkA5spjIBNSnfGqTPsqVPHy3Q1B3UY4kaLo0ZgJqUz7KEePPHF/ihccz9YSE2mRAbbrwZD18t0Ol7eHXHM59cYLO0tEMeiEEbqioTngYbXn4Xof2YgWv4jB3rcHK9R7rtwdE2ynSkcxcrRM0XKYu1qhO+PTXeziepDbls3y9y+yVOs25kPp00ZY81Sy9F1FtezTnQ4QS9B5Tjdce2B4d/HfZcgDatXPUghkqfpv+aLXUhRD4bg3PqY3vsYAci2gevL6xdpd/VoZphBB0h0uk+YDc7A9vWmvoDh+S6aMNyc8DBJJAVJhQ8zhifwpybjO29SojO9inuyWkQCgHFVQweYoeanQ8wp+aRYXVkj/oI10P4bpIx8WpVIt4v7XoeIRJE4Tj4Dh1xiQiIYtzgwrKD5CeDxTFPwtYpB8WvD1TlLYwaTo2ivRoiH1ERchAVAhEjaaapKPXie2QxH56z87BJZR1WnKakR0QmT6R7dHbPvkuOc8gz4qQz1EwGgY9U5SdeAR0DlFuiYb7wxKDA1TPx11nB2liS2Pk6TlPxbUev5gO+4bhYzKq84wxd+lRsHan344/JjqgMr23b/aG0I6Cg0dF1mjJGYa2x8j0iexnoybZcVCOh+NXkcrBrTSLjegekU6Tp+j09O/TU28bheviTc0QXriM9LzxvJGsLkP35F4eR7icrb7KenyHreTBczN4ADrvr9J5f/Wxx+XDjO71dbrXH6/6G68NiNcGbP346JINOs4Z3u8wvP/oPunf3qJ/lLiggN5qzGA95vLXp1j+oEttymf+xQYCwebdIVmiufqz0+jSGBNC0D5TxQ0Vw62Et//dEgsvN5FK0FoMaZ+p4PiK/kb52autwqiY9I9tn5SC9tkKlZZLGmmu/8kqzfmQS1+d4twbE7z9zaXHZut2Ho5453eXmLxQ5YWfm2XuxSb8zgPuv7VdZDdIQRZr6tM+k+erIGDjzoBmHDJztc69H21RnwpoL1Zon60SNBzWPx5w409WaC5UeOlvzFGfCfjwW482eA419EAdmb2YalyhHs6SZAOWt94hyXpYa2hUFmlWz5Sn75xbGE5iJzXjEV9rAWM0y1vvlJleh2e/wutz+i2cRKFwyDimjttzhkRSEQ3OOdcIZY3CBJIIJCM7ILMJiR6h9yxcJs/HInsmTcijAfmgR/XclbKI7wiTpUgvKIxGpZCejx720WmCSVPyYQ+30UKG1V1pDGOK85xSHDBLka5bGjGiDJcVsMag4+E4PC+kIut3HlkXMJQNJtUc552Xuc07bOqVT9XgcYVPS81w2X2dTf2QtfwekT69jslP8PmBJwJaapbL7k+xmt9lnQdEp9Su+8QgBMoLCOpTeNV2+avdOTQbDT5dg0eGIdVrL7P9nf+AHvR3J5Yn9bEJSaCqjHSPSD8bTstfNWSJJh7m6NSSDHL8mvvI44WExmxAFmuSQbEr7a2OiDopQgiasyHZKCfqlJ+tjIi6j+aJCCWYOFtl/lqLSttj4eUmIIgH2bHhrL3orozorxeev3RYaNy4ocKvOHztH12iPlO0tz7lM9xKx1lhaZSTRjk6McT9HJ0ZlCeZPFdh4aUWiy+3ufL1aUAQdVM2bj0lMW/nfilIxtVgEikdljb/kjjtYKxGSY/Qbx0IN1kyHZPlMaE/QS/aNbpcp4KjfGSpimasJtcxcdajGkwyTDaw6bMN5waiyqSa50Xvq/zF6LdLT8ona/RoND27yY30R3giwBUuM+ocVdk69pzh7esM79zYFfAr55POuz9gbLRZigSC8vNkY88mRgjChfPoaEi6vcu7yoc9+h++vYfLZveLBEpJ/corJBurpNvrY+2t8fedpO9s6c3js1P+xlhT/P8Zac9P8MnB2qLm2sHKBZ9FRFsPyeMiMhK2F/Aqzf2p85bdor6nwDPT4XGabYTrYtNi8dTRAJs9WRXu1MQUefenFD84AK/qcOFnZrj75jpJPysKLS5U8aoObsWh0i5CWkVGVNEGv16o2A43Y9zQobccsfHxZ2RHtDPP25204EdPXtZCtJ3SmA1RrSLdIGx6+JVC6nPYSalO+VRaxYxeabr44aOHhDXQX4vpLEdsPRjy7u8XHi2TG9JYP3Y92OtE2TnU9SWLr7bQuWHlwy73f7zN1Z+dwfEVypX7D94Dkxt6azGdhyM6DyNu/lnhidOZIRk+HZt/f5s1uU5xVEA1mKY7XEJJn9BvMdd+Cdep7Ds+Sjbpj1aZaV0jijcYxBJjc+YnXqMaTO27l1HSYb3zIZONy0UxUQujdBslXVyniu/W6AzujwUInxShqFMRDSS7AoifPCy5zejZLaQo/E1V2cKn+ugCwEd53A4SFvfy7g7k6qcbq9iSl3OwPYfy+negNaOlO0UR5J2J9gm6faC3Sc2IjlknMj1S++lyfRI7YiN/ULYl/lS9TT/BJ4vERqzn94lMj9hEpHwenr0lSwYM1u8ChZp5oRpe4KgadE+Cp6+lZQx6FOHU6kjPw5RGjsnSJzJ4CuHBbRzh4ssKz8LxJh1JfSakMRsy9CQ6NTQWCt6IV3GozQRkicFqW/BvLHg1ByEFjq9AwKjz2ScnSkdQm/Jpn6kQ1F2ac2GhpisEg80Eay1hy2PmSp3mfEjQKDLVBusxM5frVMrPWgvFZwDKldSmXFoLFfyqQ2shxOSG4VZKby1msJEUQnBl3DuLNWl0AiPjSJqMwPEljlek0+vcksUaqSTqEU4snZkizLeZMOpl47akUU5ykracAIUdbImSLaRU1MJp2vXzCCSuU9mjsLx7U6Nkm360TD2cpVldxHNraJ0Sei2EkGizO6bSbMD24D6t6jkq/iTWGsK0hZQKRwU4yqc7XEJYfaq9eSAqBLL6tN3w1LAYMgqenECS2RSLeWabm6Og49NJW+SPIlU8BhkJmU0Y6ueftnsSGDQjO2Ckn43H8yf4/ECTfy6fvdU52ajHcGsJnURFSZVnhKc3eHRRQ8tpNNkrWqD7XUx08uKe2mY8jK4z6Z9BCslmcv+pXXAmN3SXI868MUXSz+gsDZm+0iBPDdYUUviUxs6OGm20mQAWt+KwcbPHcOPZdfZTYUwPsfvWVwv4VYdLX53i/Jcmmb5URyrJ1r0By9d7vPPNJRZfbzH7QoOf/9Wr9NdivFChM8vqzT6Lr7eZvlLn53/1Kr21mKDqgLGEDZcXf3GOsz/VZupCjVf/7gIbtwes3ejz7h8+ZO5ag/kXm/zCf/sCAJv3hixf7/L+Hy4/5j4OEoULo2ntZp9X/laRTTZ3rXhG2UiPn8vYrNhjX2SxYf3jPv3VEVMX61z86UJLaeV6l+XrPT769vGuz/18m71/723ZbltXOu/Tzs9xdupLzE28SpZFDJNNbq18mxfc+r7sqn60QpINqIWzLE5+sVRaTrm7/j0ctY6rwvH3J/mAtPcxnlNhunmV6eYVfLdRhrr6DEarIMSpAxEV2aAqGnt+c3LJ/Z/gJ/gJfoJPC+ng2RfKFo8StxNCPH5mFCXRT8p9zmmTZY8k9x2ERNH2F5kNLhUFQ4VDJ10mt8m+1N692E4e0s82jvysaFshpifdIuXPZKYIkextqD0Ymi9+EEqiU43Jd42hTxNBwyVPNVZb/KpLPMiQSuBVHEbdrPCQ+BLHVRht0LlFZ4XR4FUUyisUq42xY2KwGWU02g7KkwwGlulJSRBIXAcco1netKS5KJVRNYtzkoUZxfd/EBGlEo0YV+E2eVElO4uP4aAIcDyJciRGF8cJCW7ooBxBPMgJ6g5Slqms46hDoX2URjlSCZxAEfcyvNBBKFH8XFHIncw0Cs+Pzgz5sdW4BUo6SOliTIY2GVK6KOFgseQ6RgoHKQvBqyyPEEIhhcJRfqn8bbHWkOm4MGAwZHm07ztcFRTXKI/PdYoUEkQhZLhbp6uouyWlW9TUQo6vb61+ogytCTVHQ07SkJNURQNfVnBwEUgSOzq0iRjZIev6Pg+yG2iOfs88AtpqlraaoSmnUMIr7sembOsVNvUyPbOF5mQeXYHksvtTzDhnkShupD9kUy+f+HwHl4psMOucpyWncISPRJHZhJ7ZpGc2Wcvvk7Pf/S0QtOUc17wvA4Z7+Ycs5TeP/R6Fw5Ra5Jz7IgbDll7mbvY+Zk8fhqLOtDrDefelQ+ffyz6kY1bpmsdUhi4hUVREnXnnEqGsE4gKrvAwaLTNSW1MR2/QMxv0zNah+4PiWdVki1f8rx/6bEMvsaGXWNcPTtQegKpoMqkWaKhJ6qKFFGrclnW9xJZeIbK9IzenDTlJW85yzn2RD5LvkZLgCo9FdYVQ1lDCBSyRHdDV62zrVTrm9PyMJ4EvQiqiwZxzgYps4AgPBwdtczJSEjuipzdZ1/dJbXzsu6FQeCJg0b1KXUzgixAlXHKbENk+fb3Fg/wmmuxYPlddtJlQc5x1X+R6+n0GpoO2mjPuVVpyGk+GRdKBTeibbbpmg4dHjFtfhNTlBC95Xz302Vp+j029zIY5eR0qj4Bp50w5n0zgCA+DIbMJ23qVNV2Ey47rmx1URYOWmqEpp6nKJq4opE+M1SQ2Ymj6DE2HNV28s0/Le7Nj+fXDeHoOj7Xj0NXTNFMKh9ngEjV3Ak9VcIRXFHS0GeYYoaE4Hzza4LFlyGVPVEpnJzTCxOP5KJ8k4t7uYhB1ionO5JY8KX7ORppspOGIRSONNBwoCSEEzM8qrlxUVCuC5RVNkmqc3OA7Ai8QXF4U+B5MTAjuPYB2ExoVi5KCLM55Ih08C3li0IlFKZ9KOEGS9tCxxQhFNZgkGfbQOsNaXZZlc5HSQQqFyQeFARebIiRS2gBKeTi2jsgdyCFOOnuoHxK5pxjOrhqyRZeGzt7PzJ6+Mzbfp+pprUbbQu34UP/mR7mMbWGoHLD/jqMkH2zP08Bag7Y5MRGO9VDCRQCxHZYaV7sDO7YjMpscOckIJI7wWFCXaagJQlFHCokmRwCu8JhQ87jCJzQ1VvI7z09Dq4SLz5RaoKmmaMpppJBYDDkaR7g05VRhKODxML+1bwIt/HUGQ05VNqiIOoGoEtujPdESRVNOURF1RnZIbKJDvWTISWxEz2wgUXgixBNhaay4yBOG7Fx8qrLBnHORupxAIsvFJUUUT4KKbBSLaZ7SM0fvfg2GjJRu2R5HuASiQihqeCJAnXDKVxTnLbpXqMkWnggx1qCtLmrFiZAZdQZPBPTMZmlE7e+d3e+v0lLTAPiiii/Cwmi0GUo41EULVxXfl2QRiR0913EUiAqTapGWnKYu2wW53Bb9JpB4IsATPh4B22aV1B7t5XfwaMgJJtQcE3K+2MwAOSlSOFRp4jshlsLYjGz/SMNQCoUjPEJRxRMhLenhCp+mnCreN5uDsEWbRIArjs6iNbYwRnaevSs8/PGz91HiZGOxONdn0blMQ07iiwpSSHKyIpSPz6SaR+HQFYVReNTzkih8EbLgXKEqm+PnnttinhNIQlnHFQGeCFgz95/7mvuZUTOTQjERnBmTGLXN8GUIHF9J15EnE8k7FT5Dxs7zgJSFwfPG6x5TE4qbtzM+/CgDC34gcJXg0gWHmWnF5ISk3ZJgS6fdU3BflfLw/Tq16hx2UCxBSnnUqwswFGRZVAgCWoPrhDiOj6N88jxG27Rsu4PjBAipMCajUpnGUQEgMLYQECxE/CyO448r7SZJd1/o6a8icpsxYogpvauOcHGEi8RjUz88ZNzkNi136IcHvMKhKhqccV/AES7a5nTMOokdIVGE5UIWiipV0WRLr5LY6LllgwgEFVln1rlAS01jsXR10Z6cnKpoUJdtQlmnJltsmzUi09/jCbGlF2iLmmzhl+0+zuBRwqGhJsMsYZcAACAASURBVJFCkZqYvtk81E+ZTemZTfIswxVe4QmREwSicuQ1j0Moq7TUDGeda8R2SGT6DG2P3CY4wsMloCoLAnqx4B09QWlyYjtkKfuoNDZqtNQMgXoyHpcvAtpqhjPOVQyaxMZ0zSaZTXGFRyhrNNU0gawS6hodvfZI2YOWnBnzMzt6o6yHaPBlyIScoyGnqMkWG3oZbdZI7fMyeARV2WJGnWVCzZHYiK7eGHtxXBFSEVU8GVJTLUQmjvU4hLLGpFpg0b2CBQZmm9gMSYipiDoVUacuJwndGokdFbUgjzGedtpWcO4qVGSh8D4wndLQENRFm9wmpb7XYWgyRnbAUnYTR7iEpQbPkz57R3jUZJOzzrXCw12O8cRGOHgEokJLTROIKqGp0TUbJHZ4qJ8ULjXZYtG9AkBqRsW7YgsJCFd41GULV/hU5F6awfPDZ8bgSU3En63+n090zmcl5fPzCEfBGz/lU6tK4sSSZjA3o1hd1zxcyfn6lwNGieXm7Yy337M06pJWU+I+OhP+sWg1L+D7TaRQzE69gutWkNIhjrs06+cwJseYjCwf7ibP2D2pwQjCsE2reQkpHe49+HZRyNN1cJ0K7ealPdeICIM2rltFSY+1jfcYxdto/RnhZT0H9MwmPXZDKFXZIhA1HOGxnN8+JPD3KNRlmxe8LxGIKg/zj1nOb7Ft1hiHfZFccF9hWp2lrtpccl/jVvYOsX0+JEmFywvel6nJBrGNuJH+kI5e2+NSF8w7F5lUi8yrC5x3X2Ylv70vhJPYEev6HnPOeWqyyYQzx2Z6WDtLlqGKlpwhsRGR6TGwh4nIY1Jwec956SWYkHNPdG9tOcuUXADgevoDembzgCFWbAUDUSMnJT8m/GfLkMOmLbh0ldIrN6nmT9wWB4+2muWq+wYWy53sfVbyu/ueq0RxwX2FGecck2qOC+6r3M3eI+Xod6utZtk2qzxIf8xSfnPPGCye2Zw6z4Ra4IxzhSQbPsYwOD0Egjl1gapsktiIt5PvMDSdA2EZgUQRyCojc7RXBuCi+1rhhUHyTvIdumaTdJwFJ2jLGaads5x3XmTBvYynA+5lHzyyfWfdawxMh22zxq30rVJGYGci3NGvOnrHaTCkNmbTFuO5ZloIRzKpFk7eQRRj8aL3Kp4IuZ29y7q+X4Zly/A7Lufdl5l3LtCU01xyX+Wj9EelwbuLQITMq0soHB7mt3iQXadvO+w3ikXhfcI9MkT7rPGZMXjgr68BIx0Pp9Zk5ou/yGj9PsPlO8SbjyH/PiWyDP7gWxGeVxBio1FRRDBJIEkt3/rTQv7dmEJ2XimB6xbF9La3NU+iOLAXFkAIpPLI8hFpFiGEwFEBSrmlQrGhWpllGK2TZUMq4fS4xIMQgmplBsfxsNbi+038oImrCk+go3xyimrovt8gzYaQDtEqRUq1T8TqrydO9o4FokZVNqnIOj2zRdds0Dfb+863GNbye6VnpcWEmuNBfp34ObzGRcinSVU2iE1Ex6zT05sHFirLtl5FophT52jJaXpiY8yhAsjJ6JttUpvgiYCmmCw5U/sXtYqs05RTCAQ9s8nAnEwbzO7584lQKm4XZx+lmVLcQeFBe7LrP2lrmmqqCKsJxWp+l57e2rOQFzBoVvLbVGQDX80z7Zzhob5JZo4Oj6Y2LjkxDw7cW/HM6rLNJItUZePEYbfTYv8ccJRWUhEkjc3w6BAUioqsU5VF9e5tvV56v5J91xiYDuRw1nmBumyR2tG+sXhk28rxtpLf3scV223XyT1fp3kNK6JOTTYJRZWO2aBrNhia7r6rGXJW87u01TRNOc2EmkMJl8weMFiEGIf5ivF7VIquJbNHG/BBY5o8HpKnBTfSq7axRpONTp9F+fQjS0ik72OSeKxnIYOgUCd9AtLyX2tIifJCaguXsDoj2X68svPTwli4t3SY1wPFYxyN9NjDsleXbe/Pp0GaDQCB48Rk6RBjigXL9+pluzRaF67xLIuK/91oNxRlIdcpcdwptWkgy6LinHK85SYtuGUYkqR4OaR00Pp4d/CTwK21SonzBJ6ytstnFYEICUQVB7fU8RgeuQMb2T6pHWEwBKKCg4dCoZ8xB8MpwygOLn0bE5k+GYclIxI7IjZDNDm+CHGFj8IZT6gWQ2oTRnZATTTxZQWPgJR43+IWiArVMqwwNL2xB+d5IbfpeMGoyzbG6iOJ5s+bIwWMuU0CQd9sk9joyO+NbL/oa5mPOUIJ0ZGLV2pHZZmNw1IBiY3KUKvBFf6eRfL5ILVFvUYHl4acxFhLakdkpAf6+mgy7g653MUnsymR6ZHZ+JAhk5MSmT6aHAevIDTvGYtHIbcZsRkSmU9H+y0oOVYKh8h0Sezh52mxjOyOxpQdzxMStW+cFKTkwlD2RUhDTqBNPu7/XW7dEXKYQuDXJrBGjw0evzaBydNSl+d0i9DTKy17Lv7sHPHSfWyeg5S407NkG+uY0el0MP56wu4u6s/5hT+2BcdruB3582nQ7588S2AHcbK7u7YYNreu7/t8PfkkNU8EzYuvEnfWGK0voeOTSy98nlARdUJRpcii6ZXGxWHvmMGSkZHZBKdc9Bzho49Y2J4GnvCpidaYfF2EVw63x2LRFJNqKOq4wscT4ZgoWRxj6Oh1XOURiCpNNcm2XttnQIWiQVNOAZQenuc7xgamiy8qTKuznHdfpis32NTLrOjbZDb9RAydHYSyTiCrWCx9u3WkYbmDHb5RqGqEok4iRvv6egcD2ztW9NBii4QA8tK78/y8sBZLR69Rk20m5BxXvTd4mN+io9dKgnKxiD8KSjjUZbsg8tqEyHaPbPMOST41IwJZQ+Hgiwr6GM4cFEbk8yZtPwpV2cAXFSyWoe2iS77NQRhsaaRnBMLFlyGpifdxrzJSts0qs5xnUi1Ql20e5B+zoR8wMoNHhrCEUITNWXQakw6Ld68ysUieDBluPzz1RvPpDB4hEH5IcOY86foqWmuk61J74SX6WUaaxD/x8pwAJo2JNx7y8b/5dYzerSH0E3zGICTK9Zl87Rt0b79L2tv6K2vwuNLHlT4guOC+XKRmH0P4doQzDkMo4Zw4M+lJoISDLwti+pxzgRnnLNfsl488VgqJg4co85uUcA6tYZt6iYqoU3WaTKuzDEx3HJIoCMINKrJBz24xsv1CNPE5olfyP0yZjtxUUzTVNOfty+M09PX8PgPbfW6k8B24wsOhIOtlJkE/wiua24y8NIg84RfFYY9Yy3ObPjZ9+ZOBZUM/xFjNQG2x4FxhwbnMvHMRbXO29PJYZiGyR3tZJBJfhAgkTTlFzWtz0X39yGOLbMZgPBaL/jleCyu1o0+1n4osMA+J4pL7Gufdl48lEzvCRZXvusJBsH+jntoRa/l9XDym1CItNcNF92XOuddITFSm199iZPr7uF9u2KA6dY7G/DUqE4tkceFdzaJe4d15Cq/6Uxk8TqOJNzOLNzdP5YWXsWkCQmDyDLR+enfAXyNYo8lHny9FzL9ukK6H15pCBRWkcvfUY/qrCDEmR6Y2JrfZ8WGqPa/5jrv6+bSmmFAzm5KRHOlJONieke2Xekf7MTIDEhuhbU5dTeDmHsIW/Ioig6sCWHp64/jveYawO4RTvYxBU5ENKqJGTbbHaeGBqLKuHzAw2881xLb75HeYQ4+ax3d9FWLfmQeP+gzVFUMzsB20zslsSk22CWW1rHI/gycCGmaSDfOQrl4/0gDZyfzU5CQ2PsDfOYDyWQ1Nt9SUO74firJBn14/iXEJGktiY3KbHsElKrGnmTvK6Qdh0GzrNdIyO7IuJ6jKItOwLWdRjlNqZ22NNZh0npD0N+it3iSPB+Q7Bk8S7SszcRo8lcEjK1WcRgtVrREsnCm9E5psYw2TJic2eIRUCOUgHReELEhlJXnVGoPVOSY9sMOSEuUFCOmg4yHSDxGl0rOORxSCdQrlBQVdKs8xWYI9wjoUUiEcF+l45XeXAy/PMTorKykf0W7lIt1SRCkrZPOl6yEct3jxBUXpjTQuvDb7+kOg/LA4ds/CafIUk2VYfYJJVgiEVEjXL+6h7DesHfedTkYcqj8kJVI5SMc70N8WjMbovHh+B148UZ4jpERnxQuuyu+Gkpam9W4/P2uD95h2Yy3WaIzW5Th5xPfKQptHuB5COqXNsnsNq3Vxb3v6THoBUjk41QaVmbNFGzwft9o4NDayYffo+xYC6QZIpYr+2hnfWhfjOzt+wlRBpWij0eg0Hrfn4HWOfNZPgZ27GJguIzs4UeZMbKIjDYxn0ZadhSC2A4a2R2QeP/n1TedIgyUlJrYRqY3HE7AsuUcNOYEvAgyGrtl4JOfiWUKT07dbDPMuVdGgISexTqmWLYt/SyELvSn9/NL/7QEj5tEhpl0jx3L8Yv14w+mTRWwjYhvRNZtMyjkaapKmmqEuWoRqnoaaQuYOkelhrD50Xztej9Qm9M1WGfJ89P0ldlQaBsfDYj/VbrJ7/huYTuHdPEhGPgI73smjMLAdhrrLll5hSi0woeZLD2qdOXmR0NRxtU/PbGIo1o9RZ6VYZ9KYfM/ab83TzS1PZfCky0tkm+sky0ukaysFh+dJIQTB5DyVuQs0L76C15hA+SHS9dHxkNHmMtHqPdb/8o+LwmHlQAtaM0x/8Repzl/gzu//C+a/9isE7VmstTz89v8L1uDWJ5j9yi9jspTBg5tsvvPnjDaX9i9IUhJOL9K48AqtK6+jwjpCKnQypH/vOv17N+jdef9IA6R+7hrtaz8N1rLx9rfRWcLkK1+jfv5FHD8EIUl7W6z+4N8zXL5NPtx1kaogZP5n/mMaF17Cqe5K/3du/CWdm2/Ru/3eY7vOCaoEk/NMvvYNgvYMTqWBdF1MEpNFfZLOGivf/T2yYReTpbv93ZqhsnCR1qXXcOttnKCK9Hz0aEi8vU60eo+1H32rOGfPAlpbvELz8usE7VnW3/5TrNZMvvo1KjPnSs9eymjtAZvv/QWjjaV99/vUEAK/OUV14TLNS6/i7bTbD9CjiKS7TrRyj9UffqswHo5Z+IPWNMHUIq0X3qAytYB0g0JVO4lJOsW9b7zzHbJBF6xBegEzb/wS1fmLeI0J3GoThGDipa8w8dL+kIrVmg/+xa+RR/sXYiEVyq8w/cVfoDJ3Hr85hfJDsmGPZGuF/oOP2Hr3LzBHjDHhuCz+3N9HSEXa22T1B/+e6S/8PNW5CwRTiyjXJ4/6RGv3WP7u75H2t55JGDm1STnRWbpmgw39kP4JFYOfB7QtdtJF9kuPDb3Emr77VNccmg5bepmabNGUU+S2EOybUPNF+rfN2NBL5CeY8J8lDJq+3aavt1nSN6nJdpkq/Apz6iJQeNK65vkkN2Q2ISfFJ8SVAcqMSi7HYTjCxcEr2/QIr9tnFAbNulli3SwhMkFLznDGvcqEmues8wI9s0FHrxHZPQUsMSQUJOXERmzpVR7mH31mPFhPg5SEvFSF7pg11vMHx4b2ngQWS0bCsr7Nsr6NR0BdtbnmfYm6bBGKGttmlZ7eLDcYAun4NOZeIGjOjI3qwfod1m9+79TtOJ3BIwTu1DQ2y0EKpOdxaqKZtfitKWpnr2LylMGDjzBZijEavzWFV2vTvPw6Oonp3nyr2EHvNENKnLDG1GvfIN5aQccRlZlzTL729cI7k2dsf/ADamevEkzMMvHKV3n4nX+7jyMz9erXqcxdIJicZ7h6D51EWGNxwiqVuQt4jQnC6QVW3vzDI2OHQghkUKH90pcxeYbyAnofv118ply85hR51Ecn+3fHJk3YfO/P6T+4gVtt4jenmHj5KycOk4Qz56gtXqZ15QsIpUg66wyWPkYnI5QflkajR55E+xSDsRa33qZ+7homzxg+vIVJk6K/m5O4tRbNy6+Rx0N6t94h7e1f5IRUeM1Jpl7/WXQcodOYrQ/eRKhiUa8tXGb69Z9jsHSTtb/842fncbAWt9qkfv4aVucMl+9g0hhjcvzGFG6tSfPy60W7b79H0jkgUS8ltfnLtF/8acLJBZCK/v2P0MkIozMcv4JTqYOg4OWU7bY6Z/jwFklnHa/eIpw5S/3sNaK1+0TLt8lHwz1NNEXm1oH+qsydZ/KVrxFOnyHtbdG7/R46GeHWGjiVBpOv/AzKC+jeepd487AmDEIgvQB/cp7Zr/wd3LBK2ttmtLGEkA5urUkW9YuQ6IEyKHtTnHd364+fmEemTyz7gKAuW/TN5jMp6HtaZDZmaDpYoCKrVG39eNnqEyKyfTpmnXO8RF22Se2IoelRlQ00GX3TKXfkn+5CNio9WV4WcM59EZeAimjQ5fkYPEPTIxR1aqpFQ7RJiI7V1wlElYqsYzHjjL3PKyyWvtlmLX+AtjmLztVCZVsM9hk82ub09RZGaTwRUJetT7HVzxZD06Uu2ggpqMs222L1uXicMhJ6eouH+W1m1Jmy5l+TgeiCzUAI6tMXiPsb9JZvYMq1V6dPN75ObfCoSrWYiB0HVaujtreKVPQSJk0KHs8JkMcRaWedfDQkG3TQ6QiT52T9bernrhHOnKW6cIn+/eswPJAtIQTK9Rk+/JjM74y9Hml3k3xrlcGDj/DqbdxGm3D6zK5BISVOUKM6fwm33iIf9RksfUw+6mONwa3UcatNnLBGbfEKbvW75FH/MKFYyCK0YTTZoEu88ZB4ewVrDEI5BIMO2bB3yENkjSbeXiUddHGrDUwaY82XTtj/kurcearzF/Gbk3Rvv0e8sUTa3yKPRzhBFadSRQXVwktzYMev0xFpZ4M8jor+TkaYPCNtTVNbvExtsU114SLD5VvQO7irFygvwAlr6GREtHKXZHutMD4rdYLWNF5rmiCJcIJKaRA8mzdGp/G43fmwSx5HmDwlbW2VHphJqvMXiVbvHjJ4pONSO/sC4dQi0vMZPLjJ8OEt8niIzXNUWMWrt8lHg/0lJYwh6ayT9rfJox7S9amduUrW32a4fJu0d0Cb5sD4cOttwqlFqguXSDobjNbvM9pcRsdR+dkCbqVO7cxVks462WC7CE0dgPJDhFLYPCPeXCYbdMgGHZASrz5RbBLyw0q3xubj9FpX+Cg7OpHAV2IjRmUqekU2CEUdT4THLmiFVJsi59EchdOiSP8tVJM9ERb6L6JSZv4cVRajaFFRSuJoozsr09NzUoJSdTmUVRzhEZluaWB9MkkXO9lJR9UT0+TkNil5Ira4t+eYyRmZHrFsYrHU5QT9kjN0kJtVFY2yv1xGdkBmk2cuR/A84OBhyI8cFzkp+R5PlThC6M+gx5mLDg5V2SQQ1WOzq3bH4pPp6HwaiM2ARA7JyYr3QdQYMTyGtD9OCzhy3ArEsYKCOx6fzCbjPhEopJBF3ULHw/Er2K4hi/voMkLxtLIipw5pCcfBm5hC1es4tQYmGmKS3d1tsr6KiU6Wmjq4f4PB/RuHfj9cuok1GuVXqM6dR3mHa4iYPGOwfJv+/RsoNwAhmF+8RP/eh3Rvv0P//nXcRpu6vEZ14WIhbAdIx6c6f4HK3DnirTU23vkO/bsf7At3WWtoXnqN+tlrVGfOMVy5TXYgTCOkxG/NEG8uEy3fZuuDN/d9/qjQlM1zdD4Aa0mD2sk4L0IgXZfmldfxm9MkvU2W//x3yOPoxN6UaPkO0fKdQ78fLt0sFuJqg8rMORz/CHl8UYRZku01+nc/YPO9744/kq6PdFymXvtZvHobrzWNTkZH8qZOg9HafUZr949o98fkUR+vVnhgVHBASr0MKU288lVsmjBcucPSt//NsdysfbBm7OWyeUYwMQfGksdD4s46aefRu+zCgLyME9Z48B9+qwj1RbtjaDQxRz4aMvulv8lo/QFpf5to5c6h67jVBkJI+nc+YOOdPztxdlhmU1KTUFVQlc0iXHEo7HB43I3sgIHpMDQ9GnKClpopVYrv7ztaUOiSKOHgi5Ch6T12Uj/ICxHiOKrrnvsgYWC2GZgONdkeC56t5/dLI2sXElG2p0Jq42O5R7qsgzU0XUJRp6GmGNoeEklk+3TNI+r0nejeHsd/2UUgq0hUaWTZQ32scKnKJoKiltkjSbJHtOjofx2NntkkMDW0zZhyFunbDpHtM9qj/CyRzDrnqckW1lo284ekJv7EDMSnwY7KcqEfdYCnCEX9KVkDSgPowPgyaAamw8B0aMppmnKKCTXHll4htsNDz84RLh4BBsvIfnJ+0gNvGScZi5HtF5w9MyiKv6pZNDmb+uER772DIxw8ETAwh7MHFS411aavN0vdnYPtE1RFAwcfay2ZjQtHhFPBCeuF4yGoETRmyZNSyTwdkUWnl4g4ncFjDKO7tzFpis8C3vQcTnsSm+1aclm3c2KD51HIRwPSfrGDl8rloEveGk3a2ypCWDIreCMW8mhA2isK7JkkxupsTHYFUJ5P4/xLCKFIu5sM7390yOAYrT4gnFhAnC/CEnFn7ZDBs4P+vev07rz/1Pf7OEjHpTp/CSeskw06bH34fXQaP7PQkU4iku4m1bmLBYn8mBDI4MFN+g/2V+y1RjPaWEZnSRHOq7WONFCeB3Q8IuluUJm/iHJ2QqxFu71qk8rceZQX0Ln9Pr27H5zM2HkGqM6dx2tOknQ3iNbuloT6XSTdDTo3fsTMG79EOLVAPuofafBA8S5svv/dJ3LrbpkVpJC01SxXvC+MJ2pdFm4s6uRssKVXDu14B2abD5M3eSX4BpNqngk1xxlzlZEZoCmKSPoiJBQ1HOGwpVe5nb17qCSCg0tTThYp60KhcGnINi5FgeBJOY/r+OPU5dxmDE2H9ID+iybnw+T7XPJeo6VmedH7CovOlTH5uEgB9vFFSFU22cyXWdV32dDHaz9pm7Om73HGuUZVNjnjvFDee4eOfnzl7oacHIsbOjjU1QS1MsRRlxMIJKGokpNj0IXi8BFcqLPOtf+/vTf9seQ6zzx/58R+95t7Zu3FKhZZJCWK1EItliW5WxZs2JhpG/A0ZjBf5uMM5tP8Bf1xZtAYoIEZGI0GetDoadvjabfVtttabMmSrJWUKO7FIllbVq43737jxnrOfDiRNzMrs4pZC7UQ9yEBZiVvnThxIm6cJ973eZ+XOWtlklYxOhrTpDUQFXxRpiIbxHpML9+mnW8cOZ9AGD8cSzjYWPiiNGl/EIgKTWsJgSAjNwJRndFRmwcoVk5GJ9/gLf0iT3qf5KxzmRX7PJ18k0yn2MKlJMvU5TyJjumoTa5lr93Tr+dXBRKLy+6nkMJ0Rh/qLklhGiiAimxQFnUT5dMDdrJ1hvpol+33kldYtM9y0rnI4+7HTWRMjxjrkSE6ODjCo2bN0stbdPJNbmZvfWDnVhI1fFGeXPtAVqgVbuElWWWGpUkUVqFQOqOtNrnz+d7ON4h1xNPeZ1iyz7FgnWKou4RqgEIhEPgioFREAbv5Fu8mLx9qLVGVTZ5yP4NGTUTiu99TG9Prqy7nyHXOSPVo5auIIMCrNCnPnCRPxliOT6m5PNmbo/4WvV844QHIMrJOG/IcIQTx2ipqX78BNT7+Q1k6HnZQIZg/gR1Uka5bVANZeI05vObCXirqzv1Xm8omo24vqpPYrdoxzFxrVazXPr5rWTiVOsJ2KC2dYfGFrxza1y3Px59dKX4OJtVIB6A1Os/I47EhHh80hIVTriEtmyxNSAcd9H2KVKXjYvtlgoVT2EHZVA85pvrKrc2YKIaUZs3vIvnIo/BwlEFrk7rTGiElQj5ai3hpu1hBmdL8SeygYqqVink7lSb+7JKp1LtDqiIdF7tURQhBNh4eiLB80LD8MsJySEc9dH6wUzkYkpjHITpLzPfArxw5jlY5KksLnc7xr3ekhnTYYl2+R13O4YmgaLprUj1jNWRIh6Pe/nIyQj3gVvoWNTlj0kgEuJZffJ92nVI1oR4yUO1Dni27pOik83jRB0gihKQkqtjCiF0b1jwlWUPpvOimnHAru0KiDm6gGk2kh2xk1xmpPk1rHhu3IBh6n1+INuelOu+rKVHkdPMWS9a5SePGkeoTqfBYfiiL1hlKsoYs/nGKTttAUd5uHGZVoaUaqf6RhGesBoxE33gDFRuJOSeFKDqnd/JNdtQ6XbV1ZAoBoC7nWLDPILGQCBNdEEGxSZWR0jR9VUWZuCKjF7cOnetu1+2b6RWqsjkhTrpIqQkEPdWil7eKHlJHt5T4VYMhlG1K0kQWqrJp9gutAbPWYz0iUYagRHp015RopEN28jUUGbPWMjYugawSUDngXTNWI0aqd0AH9EGgaS0wZ50wqSEEtnBxCw8gX1SQ0qYkqpNrn+mUbrJ9KCKbkTJSPW6kb1K35ggK9+U7v/e5Ton1mKHq3DU9OFA7lITplO6KomJa72kKu3mLoe4yUG0yUmQqSEZmT6vMnTa6yCwmGbRxgiq2V6Z5+iOMdm6SRaMjCz3uhYfakfLhABVF6DQlabcmmh3hOMeu2BKWg1tt4s8uUz3zBE6phnS9YiOzsVwP6RSprCMjctpEN3b9C4obTSt1RyrF/H63BFwIieWXEJaF15zHqdTuHHiCia7jLiknnRuB9C/CMFBIgXQDUxWVZyaVdR8PGmHZOOU6/uwKtbOXsUsVLNdHuh5C2kjHxXL99x0nT5O9yq99mJAvAcjjh/SPM2+7XCOY25139eC8bQfL8488nrDsyTnlafyLIaYFjGWALErGj7hOWpty+iwr1v8ua68U+h4WCXdDQoxWHTaya2AbkamJrDioIn+e30V3o9FkJKbpqDWgrudoyEVDmiwXy/IYJtuM1cAIgPPWoY1YFOmlqpw5NPau867Exhd2cek0uc4mZOhOZKS08jXjaUJCQy7gEmALx5AlEhIdMVI9umqbSN07yqxQjFSXUPextDHb66ntSc8qgcRxgkkvN601WR6hlDnPkjQd2oWQuE6ZLIvIVTrxybGFe+Bc7kYKBqqDQJDLnJJdK4zchHGzJSFSIX3VYjO/ua/jvcCxA0CTZmYtXRGYc3dOxwAAIABJREFU+dzxPdhNR0lhEYjqvvPPudMwDgzZHesBa9m7zFrL1OUcVdnEdn1TMZeN2clus5NtEN7FDFGRk+qkKG0+aGAoXBvh2EjP+FmpVDJOhljC/kBsDfagaetN8kBScmycvkYKG4RGaVM6PlAdhqrDdn77rsQSTHfygW4T6RCrXqY0KuFlbrGxqyL1mDDQbTpq667tIpTOSXVMqAeTru0PAk+UqBx57c29aAl779oLQW7lONU6yXCAjg86kadErGfvEuuQmpyhJufwpD/pdZjomFD3CVWfbt46kvAkOqaVr9GUqmjz4mIJB0VWpJNjOvkmXbXFoEjl5llsCM64T3nuFEplZHFIPGwjbAev0iQoz5LFI/Pc/EUSHjC6hmR7c+8XlkVw9jHi9dvkw/dntKWlM8w8+Unqj32EuLNB791XiTqbhZg2orJynurpS9QvPHuPSTzom4VAxRGjzRt0r/7sHsNrskGXZNA5/P/AKMh/4SaLRQjjPlNZwcJpGhefZfbyp4ja6/RvvFUInjvkcURp4RS1s0/SePy5e46jVfbItDnHQTB/kvpjH2Humc8RtdcZ3LxSaF7a5HFEMHeC+rnLNB5//n1GUr+ca7UbLbvHR+71/5VWqAcsN09JaOVrtPIjKsCOgZyUncKBFl7Ftjya9fOcXvk0L7/5HfL87qkMRU5f7fC98V880LGPHjNjpHuM0h7w8GnklIRX4u8e+r0hMVUunPky5dIcIEjSIbfWf8hOx2gOfx5/GylsHKfEU+f+gJtrP6fVuXJorPdDW23QViZN1fDOkucxUdInze6m1TINd8+f/iJKZVy9/jUAbmZvcjO7d0fu+8FYD1jNBqxiztc/fQqd52StHvn43joyYyi3c+R8gkun8R8/ReljF5DlgP43X+RHf/03j2zed4NGs+GtEf3WSZxll81/9R8fbjxboJou8f/4NGt/+i3Gr1+77zEGusMg6zx0uuu99BXeS1851mdl4GE3Gyz+z39E+0+/xegnh4+dkbKV32Qrv/lA84l1yO3sHW7zzvt/+BAEXmWWeLiDkBYz55/D8cqEnXVuv/J1li5/njyNyaL7i5o9MOGR5QqW5yFcD6tcMVVbUiIsi+D0WfLR8FiEp3buKfyZRfJoxO3v/iXpsGuM6/IMrRQqzxDWo++eq1VOOurhVpvk8ZjR2rV7bOCFod4vSPdxL2ilyMI+WuVIx8WpNGB79dibeO3MEwTzJ8jTmLV//M+kg87EGFErhWoufiDr/bConr5EaeEUKktY+/5fkRZER+cpWim8+txd563ShLTwxrH9CnapAu37n8ODWKdl4yFOuYZbqnEkoxESy3GRjkc67N73F/iXgSxP6PavM47aRaPXDyekdGjWz5JlIVutN9jpvm0aIiYHr5HSGUk64u1rf0OUPHy6dKZ5gSjqkPaiexAeTZ7H3Fz7wV2t/z8IlJ45Tz4cEw7H5P3RAxfkRe+ukW53ia6tM/MHn+dRRYKPAxXG9L/9MsJ9+OecznKyzoCtf/PXZNtHa31+pfEr7Ba/W9lsOR6lxhK2GxS6Hg8hrDu63h8PD3XFNWDZNu7MbKHf0caXxnWPuZDCpLBsF5UmjFu3Tci++AILaWEV+p5HDZXnxN0WpcUzRhvi+cSd9xcp/rKhVU4y6KCyBMv1CWaXjQj3mG//u6kgnaWMW7cPOGIb12YXp1TlF/kAOg7sUhXLC9B5StS6fSBFJKSFtB3s0tFpyTyNSIddtMpxq028+jzD2+/ef6Sn0GuB0SjJYxDDdNjFrTQmFgdpkZraheX5eI15hGWRRcaW4VHCtgNcu4TvNSbfSa0Vg9F60T3e3DdS2HhuDc+tIi3jFJ6rlCQZEEZtc85IPM98xrJchLAYjbcPbbhCWNiWR7m0UJRPC5RWjMZbZFmE1jmWdAn8JrYdoFSKZXkIJBrFYLhGrpIDc3PdCr5bn8xNqYw4HTCO2pPP2XZA4DWxbaNb0FoRJX3ipD9JQR0HUth4Xp2SP0u9ehrXLpGrFNvySdLhgfvGdSo4TgnXqWBJhzSLyDisG5LCxvebuE4ZKYt0VR6TpEPGURspbGzbJ/BnqJaXce0SWudESZMkHZKmoTk2YFsejl3G9+tI6ZCmIVF8OPpsrn0Z3zNmmUpl5HnMMNycaIOk5VAtLxfaR4UtPRCCNB0RRm3yPEbYFiLwcFfm8M4vo8IIFcXYC03y3gg1DMnahgQKx0YGHu7JebCKVFmuiG9tosYJZOalUoXRRPag87s/u2TZx25WsRoVhJToXJGPxqTrO+g4RZZ9rEoJqxoQr26jo4KAWxL35DxWtcT49etGV+g5WLUyznwD4droJCNdO6ynkmUfWQlwF2eKx6BxoY+vraPG8eQcrGYVq1rCqpWQnkPeG0F4R7RTCKy6OaYMPBCgooSsPSBr9yFXZs2qJdyVWVSUmD87JsWbrG6TD8bo5P5etmWthF0rY8/UiuixQMcp8c0t1HgvpS8AZ7GJ//gppO+CgKw7JN3qoMd7a2kV8xO2bV78ooRkYwc1NP0yg6fOkrUHpBttrGYFd3kOnWZEb9/CalTM2FKSrh238lEz7m1BockddzexHI88iwnqi6ZUPbv/l60HJjwqGqOz1BjOlctEb7+FSmKEbePMzKDTY14gYR7AqHyfb48J/1teCbc2g9dYeNBp3n3+acxo/Rr1x57BrTapnn6CpN+5w7paFP+KglB8MG9SQog72OrdyYbOM6LCx8Wrz1I98ySt175PXrRGuHPuRwpclTLEcvKg2V3vwBCC5uIjOKtHD62V0UqpO+btBjjVJv7M4pFEOx+PiDubqCTGn1tGZQmdt39q8r/7N2shijDO0Q9grU3rCo2e+EQcOt6dlX6tNZxynWDhFMHcClor4+K8q8GoNKmcvmSE/70dxq31B16fo1AO5qhXTrE49wwU91mexVy98TVG49YkHeU4JWYaF5hrXsTzTAl8HPfZ6b5DtPkiSqVIadOonmZ25hIlfxbXKfGjl/9PsvygJsq2fSqlBU6f+By29MwxVcr11e8wCrdI0iGuU2Fp/llqlRXGcYeSP4uUDkrlXL3+N4Tjncm4jhPQrJ1jYfbyZG5JOqLVeZu1zZfMpiwsysE8S/MfpVyaxxI2mUppda7Q2nmT8RGE4G6wbY/ZxgVmmxcpB/MIISmXFqhWVuj03qPVeZvhyKSfquVlatWTNOvnKPkzXL3+NTZbrx4a03HKLMxcplE7jetWkMJiGG7R6V/n9sZPJmu2svgJapUTqNIC1fIyuUrp9K7RH66y070KgOc1qFdOsjj/DL7XoD+4xetXV4+49vPUq6dZmH0KIQRpFhKOd7i2+i2yLDIkzK1x7uQXyFVMnqeU/BmEsOgNbnF78ycMwy1EycNdnqPxOy/gXTwJSuOeWEAlKdGVm0RXV8naJo0nKwHuqQWav/cZs8khUHFC+8+/Tbq2Y6JC9wFneZbysxcInjqHdB1UkhJfX6f3jRdJN9o4C038CycInjzDzp//w2RDlb5H9Tc+SnDpFLf/xf9tvLZqZYInz1D93DPYc3Xy7pDb/+L64WMuNvEvnqT2mx8rtIgSneW0/u3fkqy3UENDaL3zywSPn8K/dBpneYatP/4q4U+vHhhL2BL/sRUqn3kad3nWkMntHqOXrjB66S3UMEJWAvyLJ2n+3mdI13cKElUGS9L5y+8SXb1939Ej7/QiwZNnKD9nqg6xJFl7QPvPvkWyuvdiL4Sg9PR5vMdO4C40wbYIX32PwXdeJrm1Bdqkv/wLJ6h/+RNY1RJaadLtLv1vvkj03hp6HDPzh19g+JO36H/zRYKLp6j/zgvkvSEb/8dtvHPLOItNhOvQ/eoxCY/WdG+/gVeZxXJ8urfeQDouQWOJ+oknaN98lfQBouEPHuHJTd+hdKdF9wffNREerUFK+j//KVnvOBdIE27cwA4qlOZPsvTCVwg3V1FZghNUaT75CSzXJ4/Dop/Qo4NKIgbX32Rw4gLl5bMsffLLeM0F4s4WWTRCIHAqdbzGPE6lya2//5OjjQcfCAJhWdhBGWG7OEEVrzEPQmD7Jbz6LP7MUtFXKyFP4r2ogNaoJKL16j9SO3uZxsVnOfu7/wPhxvXJ3G2/jFNp4FabbP7k6wdaS4y3bmEHFaonL7L4qd9mvH2bPAqxS1Wal57H9stk0eiwl80vGeOtVZygin/mCZY+8WXC1m3y8Qg7qNC89Bx2UCUbHz1vrXLScMDGT77OzOVPUj17mfO/P0f/+utk4QCVJdhBFX9miSwa0X79hyTD7iHik8djws2bqCSmfOICTqWJ/daPIc9ASKTt0Hn7pwfukf6119F5hj+7wonP/zOGa+8y3rpFGvbxm4sE8yeNqebqOwxvXiHcfLB8+d0w17yE61a5euNvGYXGM0ggSPN4n4mX4PzpL5HlMa3OFTZbr7FLyIBJdCRXCZs7r9MZXGe2cZFzJ3/z0PGEkNQrJzi98lnefO+rZOkYKW2a9XMsz3+E/nCN1Q3jVRX4TVy3yrXVf2AUbuH7TRq1M6wsPM/a1kv0h6ac/GxxnJ3uVTZarxbXxczNpNSMePfC6X/CjfXvc+3Wt1AqZaZxgWp5mTMnf4O33v3qsdcsSUesbb7EdvstlheexbJcwvE2WztvGtK778Wi3XuX3nCVrZ03+OgT//zI8aR0uHj2txnHHTZar7DdfquY/V6LziQd0umH9IarPHPpnzMcbbC5/Qph3EbrfBLFAhiFW4yjHTr9a1w8+5UjjmjW49Typ1Aq4/Wr/y9pGlKrnqRWXuHcyS9wc+0HpOkIEFQrK2xs/5zN1qsMw03mZ54k8Gc4sfhxrl7/GmoQEg1X2fy//hOL/9M/I+8M6P/9T0k32+gs34vQWBbBE2eofeljbP3rv0KNI6TvETx5mto/eZ7xq9cY/MPLx7sIQiAci5n/+vOEr19j41/9f+hxgn/pFO7pRWb/6Ets/fFXSW5vo3PFzB/8JnbjJbKtDjpXlD92EbKM8ZvXTUcAIGv1GP7gNcZv3aT5+5/FXZk98tClZx7DPbvI9r/7GulaC50rhC1Roxid7V378OV3iN6+hf2jN1j5X/6bw6fg2tizdWb/239K+y++y85/+DtUGFP53DM4yzM0fvfTtP/sW2bpyj7eYyt0//oHxDc2UVFM5dNPm8hLJaD/9RePt24Fqp95GhWnbP/bvzUEp4i26XGCTjNEYIEU2AtNhi++zegnb5KsblN+4TLOQpPal56j9e++jnBtvLNLzP13X2bzj79KutEGpal+/hlKz17APTlP72s/JusMQGns2RreYyuoMCLvj3AWm7gn51FxSrp6f87geTIm7BS6Q60oNZfI45C1V75R6GbvX9P40ElM6bh4KydQcWx0O2GIdD1K5y6iVU68sUY+6N81fTC8/a7xe9FQWjyL11ya6Eni7hZ5PEYIydyzhx+uDwutMrrv/pxk0KZUuBa7tRkzV12UKmpFOuoeSLU9LGy/hFufY/apF6BII9l+GWk5eM1FpO0SzJ9EK0UyaDNau8Zw7Z0D0Zrx1i1QOTpL8GaWCOZWTC8xlbMraFZpYppT7pv3aP1aUdXkUlo4jd9YME1flTHYG7dug1LMf+yLj+RcHxXCjesmdeX6BAun8JoLk2hPMugS7Wyg8pSF57505N/XecZw9R2kZePPreDV56mcetyslyo2UG2are6mrQ6PYYhT58pP8eeWcUo1mo8/t9d0NInovvPzA4RHZQnj1hqtV75D/fxHsP0K1dNPmJSCtEErBqtv03nrJaL2xiPzU5qsW7SDlDYLs0/Tsa8xjnaI4l5BdvYqF8vBAp3ee3QHNw9EbO6s4NE6J89T1F2a+DlOGc+tE/gzLM8/S56nCCEJvAaOXZ5UOwGmAiMbMxpvk2YhMnGI4i6NyinkxNJAUA7mGYabdPs3yLK9dJGZm8a2fDy3hu83mW8+QaW0iNaKwGvi2EFR9XO8lhqTuemMXCUolSGlRZ6nR4qztVYolZKru4fXhZBUyov0BrfoD1YPjLN/fbVW5HlSjGmOf7QgXKNUTrYvJXnn8QK/iVI5SRoSJwOUSgnHO1jSYW7mErblFoQHlEqIkx5htGPE0nEX1ynjBOYlDIVJLUQJ5Mp4nkWxSe/sW1K7Xsaeq+Esz1L7wrNmY7Ut7NkaViVA2EfYetxtzRwLu1nDnm9QevqcSQdlOdZMFatSQnpGNqHTHBVGpBtt7Jkq9nyDdLODd+EE+WBEsrovoqC1+XycFBYRRyPd7mDVy1Q//RTjN2+QbrRJt7qG7OzfA3KFTjJ0dHQ2Q5Z97Lk6ajgm7wzIekNIc+L31rAbj+OdXWKX9Gpl1jdZ2zGpLqXJtjq4J+awSu9fNXsn4tVtnPk6lRcuE77mk663ybuDA4QNrVFRTNbqkm60UeOYbLuH3ahgLTYBsOoVrFqZfBCSdwaTCF309m1qX3wW4Zq9O2ubPd6erWPP18laPbJ2H2exiT3XILm1Rbbz/v450vaQlmMkCl4JabuTZ0F57jTxoLVHgh4AD014hOvinThF3u+ROg5IC7tWx5mZMwsaR6hwdNcy9Whn3XQcF5LqqYumOaNSZNGQwc03SYc9hJRUzzxxoAxa5RnpqEfca+01FVWKPI2JO9vGI6YgCHkSm0aN3a1DD4jR2numD1EcUjtz2Xjc2C5K5eTjIemwT9TZNOThjr+r0ph02CXulu7Z7frQmtlO0avrDPvTV3HPfDktv0xQRCqk65P0dkxabd8YSb9toj/xmMbjz+FWm1gFadJ5Rhb2J1of87IuDXnrdxg7t5CuR+XkhYnuJYuGDFffJhl00HlO9ezlA+XbUtqQK/JwSNzZNOnMQp8BTDZqnWfFNbOLViN7fZfBlMUaL4bCwqAQn925ge4fW+ucqLNl+kb5FcqnLpi+VwjyaER//dpkPWpnnzq67Fxr4s4WoeWhxzECC7feRLgBQkh0lhL3WkXEJzWhXGkbg7I8MWW3WpnI4I030VmCWDyDXakZTUkhtDcprr03dykssmGf3juvYLlBQbZmsb0y2XhI1GszWr/G8OZboDRS2gfXQmvycEQ26O5rxrqbAt3T5NxtMx+FW1jSYX7mMnkeY1kOQlqMwu3iOCaS47lVcpVOokCTw9+nc65j+di2j2W5E23ILtIsPEBYDOGJSAoRsFKZiQhZTnH9DRlznQparzEMD2rsducmpamSsiyXUjCH4+xFg/M8LUTGvzyPGCEknlMzKaXooFr+g3Am3l2zXKWkWTiJ0KXpiCju4XuNfYTSRMmSdDQhQKbsPsOynPs6riz7WCUfGXh4Z5cPkAPVG6FGx/dmE5aFVS0hAxd7roG/7/LpLDObp9LFHpMS39zEmqnhjCKynT7uyTnCl7smLXOfSNZ2kIE3iZII30VYkvjWlokWqePdS9Jzsaolst7IkMPUPPPSjTY6z7Fn92kOc4Uax4aUFDqevB8aPzPn/rfp+Po60rEJnj6HGsdIxyZ1bZK11iTiBaDGCVlvRD4w9g1qFKHT3NgFwOR6Zr0hqiC8CEjWWgjXwS4Zy5isPUBrjT1Tw6qWSW5skneHOAsN7GaF5OYmWXf4vvMWlrFGsd2AoL6EtB3jrQYE9YX7LkO/Ew8f4bEdnJk5sm4HpzlLcOYxrEqF/k9/jIojas9+nGTzHp3UtSJcv0a4fo2tF79x1+Nc+X/+1wN/Tnot1r//Vwd+p7KE4a23uXrrXx74/fDWFYa3rrDxg78+cuykaBHQefMnxzjjfeOuXmW4evX9P3gHdvsgPagzs7QcEBKdJow3bxK3NlEqRWuN4wSU6iukyYiwtwZaY9kuwjaCr9rMWfI0ovPqD+653m//h/9t73jColJeRHV77Gx/m7Xvf9UIIt0qlnSNPiANSdIRUXuDzW99FSkt0ixC57npjSItpHTwvBoqTxlHbYSw8P0GtuUxHG0c2Og9r45leaA1YWhyyarfY/z2m3Rf/RFpGqJ0huuUEdIxBEBr3v6T//2u5+TYAXV7ESf06Pz42wyH5phGo7FIlsfkeUyemgdzpbxEpbxEq32FNA33UkC9Ef3uz+i88kOSZIDrGqOxNB0hpfGnAUPUyuV5lMoIwx22X/52sZ42pdLc3oafDpHSIfBnsCyHwXBjciyd5/Rf/iFpFpNle5oWKR3jHwIk6eCu1VL94W0Go3U2W68yP/sUc81LnF75LFfe+2tG4WYRzXm0AnWlUsZxh9eu/Nk9Ix8mPfR+KeLjzU1rRZz0effmN+n2rx9/sh84Hp0X1f3g+CaAmlylR0aK7hsCk7pY32HjX/7pwWjCA4yF0OSdIYPvvULv63d/Nus4YfjjN6n+xkewKgHJ+g6yEpAPx/chkt1Dcn2D5OYmox+9SfkTT1C6fBbv9z7L1r/+zySr26jBMTsIaI7OChxxO2ilUPERWYRCcHy/iN64Qfz2Kv1v/4zqZ56m9sXnEJ5D699/g+TG5mR+KkkP6zzF5MCTbMeR57BvWul6C3umjrPQBK1JNzvoOKH0scexGxVQCtV7f/1WnkboPEGlEfWVS4y7m0QD8xImbY90/HBVrA/vw6M1OstMRVW1iru4iFUqY1eqZFoZx94PACdPW1z+qMvcgsX3/n5MONLUGpLT5xy+93djsgwqVcFv/FbAP34rIigJnnjaoVSWhCNFe0fx6k/3HsZCwPySxemzNkFJsHojI4o0zRnJ/JLN7Rsp67dzXE/wqc/5fOeb5piuB1/87RI/+3FMv6tYXLE4+5jNaKjpdcyNNLdoUa1JgpLgb/8yJI4e/G3Tsn1mli/ve0CZ8LapXMmJx10s28Fymri+qSCx3TIIGA9aOF6ZeHwfRoXCwraN8Vq1uoTnVLh+6zvMNB6jFMxNKjqSZECShliWCUE6to/n1VnbeAkpLTy3SqN+ntFos6is0Qih8T2T/ojiPmk6wrJcgmCGSnnJkKi4j4YiauAhpcPK8sfZbr1BFPdYmHuacdzFcQI8t8bq2g8naYE7zsTco5aDJW2ThtIa329SDuaR0sb3G2TZmK3WG/tSCXvfascpU62sYEnHCE+lxfrmz6hXT6FUTn9wi2bzIlHUZhx1yPOMcmkRpTLSdEySDPC9Br7fKM65ZzamdAiYuXhejSgekKZDPLdOtbJMrhKq5RWUVmzvvMFc8xJaQJaNqVZO0Np5i1G4eSR5MNVPgiyL2N55kzyPadbOUikvEMVdsjxCFxVUjh0w07hAu3t1cu4m4nT8tyrjHRPhWD7V8hKjcYs0Cydz2U0BHRda54TjFlI6zDYvstO5ym5UanduWRYRxT1TGRbMEcd9xrGJpEjhIKV1SFj9i4NGq4xRuEngN2nUztLtG68WUTRKvHM9lEoQwvjsPNARlWI02mJ5/qMImjh2iTQLKQVz1Kon6A1Xye7hnXTPsZMUEMhywJ1pwqzVR4URsuTjnl0mXW+hRmbdZck3ep9jVhupOCXZ7BgdzHwD99TCXrTGtpC+ayJGGnSRJqr91vO4ZxYpP3+J6I0bZJvHF6rvx25EJw8jRi9eIR+O0WmGd2qBvDM4NuHJ+yPS9R3c5VnsZpU08FDjmODyWaTrEF97tAUK+yHLPihNPhwz+P5r6FzhnpzHP7tEur6zr2Dl3shaPaxGBXdlDrteMZEqpSl99AJqFJH3zVqkG22cpVm888uk6zukG22E52AvNFBJikqPqX1VOUopdJ6z895PybMEVbzM9deuPFBl1n48POFJE+KNNeNEPBoRb6whvQDpB9hSkvW699364DhIEo3jCMoVSZpAloLjCGbn5YQQ27b5s+2A5wtm5yy8QOD54lAjdynh1Bkby4LhQBHHmiAw41eqghOnbLodhZSCuQXJbpcJKQRz8xLXE/glwbkLDvWmJChpSmWJlBBFmjzTzM7bD83/hJS4Qc3obopUhi6cpvMsNgxZayzLQUoby/GLlhga2y1h2R7yqBYZd4EUFq5bwbFLeG7VlLhiSmNt2zdv1ipDCAvXqZgIjkqxpINteSYtJC1TiiskWR4VkYpdi3ExiQABhvB4TbIsJk3DwkHWRK4cp4zjBAR+02zkQuK6FYZF2kYKE0XKjwx7alRhlqhUSpoah2rHDgiCGfI8xS42ZCntIyMmlnQoBXMolZjS52LDylVmHGyDWXyvTpIMjDGjNn1nTNrOgqJUWmuFZXkm7ZBFjDERClOtJyfpPNv28L0G3f51XKcySd24boU0G5OmY6SQxRrbRxKeSjCP61YnLVe84uckHe0TLSs6vevYlkutsjLpxK1URpKFjIoyZilsqpUVHKdEpbQ48alJ0hFpFhGOt1EqJYq79Aar1Con8bwGWR4jMGLgJB0QxffXC6c7uIFjB9QqJyYuskrnpOmIYbiF0jlZNqbbv4HrVmk2zlFK5orS78Sk0sIPhvBUyku4TtlcH8ulUlokqQ1I85gw3EZpcx+0e+8ihEW9ehK76AmYq4Q0HTMMD/bFCscthLBo1M/gOCXipF+snUkJGI2UKXF33QpKZTTr58myqPjskDQLGY1b2JbPTOMCeR7jew1ct0Knd40sezDCk2zsIEs+wZNnsWolsrbRdeTtASpOSLe6xO+uEVw6hbPQMGkQIch7Q/PZtvluuifnjbanWcUq+zgrswRPnTVRma0OOkpQ44To6ioICC6fxZ5vmKBDmpMPQpJxbDZurVFhTN4bYVUCgidOM3rxitHM7INVK+GszGE1KjhzdWQlIHjmvBHY9kZkLXNfuiuz2HN1yI2O056toZUm748OpIO888tY1TLWTLUQ9y6beYcx8eo2KsnIeiPim5s4SzMIx0bFKd6ZRbNWV9qPTBd6J7xzS8jAkB6tNbIaGM+g3ujYZAdMCX3eGZDc3MS7cAJnaQatFd7JedIto/0ByAchwpJYzSrjN2+QjyIs20K6DllveMDJ+f1h5qx1jluqIW0PAdheiXQ8eKiObQ9NePKRScneAAAJ/klEQVRxyOjN1/CWV0hb26SdNsJxqDzxNHa9QfjOlQ+k5cLWhmL9do6QwvxXQHNW4jgCKQVCaISk+BksC6QNcawRApw70tPSgieecvnR9yKuvZOiNFy8ZD40DjWnz9ncuJYxDjVSCuSuea6kOA5Ua5KnnnW4dS3DDwT1psT3BX/3X8ZICSdOPwpDP7OBap0Ywa1WCNtFZTFZGhGHbUrV+cmmlaqhIQBaY9leMenjCziltAn8Jp5Xx3FKk9y/0QeMyfOEUbiFbXm4bg3fr5t0k8pJktHujFEqZzTaZDTamrzxTwSfeVIQA4FluXheje2dt0jTvV4pvlsr5lAuPGAku52pR6Mtkt00j3SK9TkcTs+ysYlooImTgSEetodXkJQ8N9ECQzoEu1qZ3WNJy8H368TxAK1yMmU+Owo38dwateqpCQncfYM2+h890aQk6RCNplo5ge/VyYvIgxGsphOR7+7a27bHKNw2hNKt4DoV0/A2GdEf3KZcmi9ImgMc3tQb9XM0a2eLtJuZVxz36PavT1JkAOvbLzPffILZ5kXmZ54ABHHSp9N7j3DcmqzVicXncZ0KrltBSIuTS59kHHUYhduE4220VgzDTW6t/5Bzp76AbRnCLYRgc/tVeoObBeExkcmDbQRMdyelsgNC+43tV5lpPMbC7GXmGo+DMI7H7e67E6+YLI+4ufZ9Vhaeo1k7N/Hr6Q9X6fSuMQr3ucEfF0VvPqXyu6aI5meepFJawLGNv85M4wKlYJZR2GI1+SFJmqF0xu3Nl1heeJZG7QwLs5cBQThu0e3fYBgebODY7r7HbPMiy/PPkuYx7e5VeoNbJMUGXq0sszj7FK5bxfcaaK04vfJphqMtdrpXSdIhuUrYbr9Fo3qKk8ufRCDJsjFh1Obm2vfJ89ikRDXFeu/bBIvNRh1hxDp+/TqlZ85T/fxHUWHE+PX3GF+5xbg9MH47791GjWNm/vDzSN8z1UFSMPiHnxNduUXeNjq00rMX8C+dnpRgB5dOYdfLRNfWGf7ja6TjNqQZvW+8SPm5i1ReeBJRCJWz7S6jF6+Qrm4f8PCJr69jVQOCp8/T/rNvTbyBduEszlD/reexZqo4Cw2k69L4nU+TrrUYv3VjQnj8x09RevYC9kwNnRsxcdYdMn7r5qQkHaDywlM4yzNYtTLCtik/fwnv9CLpWot0u2siIIOQ/t+9RPXzH6XywmWQkqzVY/TiFUYvXZmsN0qjk3SP/+wWzmT5A6UGS89exDu7hFUtG9PcMCZZazF+4zoqThCeY8ZP0oMBCa0gU4UExYi8060OvW++RON3X8CqGm1p1uoSfuNF4vdMlEoNI7TSSM8xOqEohmqATjOyrS75fei3DAR+bYHy7Em86tzkRWfYusGofdiC4dij3sulUwhxfPop5cF8n2WZgKf64Kz8n/uUx8opm7/68xGLyxZPPuPy2S/6/Kc/GTEea+p1we//UZn/8hdmgz111p5EdrqdnG9/be+Bb9nwuS/6NJoWudK8cyXliadcLAuSBGp1wZXXUywLvvJflfjqn47odRWeJ/jD/77Cd785pt1SnDxj45cE7Zai1845dc4hTTWeJ5hfsPj3/2ZAOHq49TBNOYsxNKZn1aSyTB0UE7Pvx32fPW5bCM+tsTD3FAhJ4DewbZ83rvxH4wHiN8nzhP7gVhHxCUizkEp5CcvyUCplbf1FbMdU0bhOmW7vxoTwBP4MszOPU60sMxpts73zBkqluG6N+Tkjso2iLpvbr7E4/wyuWy7efE9z6/YPCcNtTq68wK3b38dxStSqp+j0rpEkQ9RdtCNLCx+jFMwyjnbYar2OY5coleap10xaKorabLVex/caNBuP0aidpj9cY6fzNipPJ58FSNOQtY2X0FpRrSyzsvRxev2b9AerjKM2gd9keel5LOkyHrfZ2HoZ2zbEsFpZIfCbDIbrbGz9jMCfYX7uSUrBPGHYYqv1GlJYlErzBMEsjh2QZmNur/+EkyufJAxb7HSusrz4MYajTaK4OxH/HrhXhLWPHJqbwLRDOfzGdedndxv97SePhvCa5pHGt0hNmi8eJJliYrC3CzUpr95NSZmo1/65CCRCWiZCto8E7EbT7jW33TEPVD6hJlG1B4E55t7x7oTpw7R/PfZ6+t3ZE+o46ztZg0mkz6ybGVfdsRaYty2YRHm13k/ODFmXu33ACq9wdYfXmCWdSSRq/98TQh6+T6RAWNK8QWpMpWiuD+pApDBC233aE50V1ZCF4FfY1qRUWkhZVMTqwiNs354hdo8n98YriMChSIVlnP6FJU1k6c59RwpzXCHMzxT+akqb/+6OZ5sx9h/PpM7SA++JwrGLfoH7zqEQUu+PBCElwpZ78o475194/QjH3jNO3D/forrsfjCZ24Fz0Ohk37yEQHiOmetkLsKso5QH049SIhxrbzyl967pLmwLYVt74+3eB8p0Kjiu2NvMQ3Ly2a8w2HyP4faNiT+e1nf2yDwMrfVdRU+PjvD8EjC3ICmVJTevZZRKgsasZGnF5vq7KVkGrgtnH3O4ec0sVrkiJt+BJNasre5rZidgccUiKJm16rYVjaZESOOJaDuCXkeZ1NdZc4wkMd/D8487rN/OiMcmjeV4EI010VhTrZmb3LLBdQXvvGXm9uuCXUdchDT6HCHp9K4VAluvSI8MkcJCSKNH2I1CaK0YhVtIaWFJo+1JkuFkI7AtD89v4Nglo8OI2iitsKRDEMyyW6YbjlsTQS9C4DrlIlI0plJeZDjaQEoH1ylPSnDvtsGVgjlsyydXCeF4ByksbNvH82omApJFjKM2luXhe3Vcp0KahURRF6Vz81m3xu7GMSoqhxy7RLm8QByb9EOeJ9i2RymYM5VaeUwYtgodkVsIvh3jaFukHny/gW0HxRxMZZ5l+0aYXWg9hqMtKuWFiW6lFMyRZiF5ntyXNmaKKaaY4lcWQrLy1JcY9zcZ97bIExMhUnk6+flu+NASng8C+wnx/t/dT5BKFMEu9l5SDo3564f78THZH1J62GPeOc79+ancfWR5R0nw/cz54c9PCBt97K7Qj+acp5hiiil+LSAECxc/Q55FZNGIrCA5WTwkHhxuB7If9yI8v3pdIn/JOIqU3HfLpTuCC7/eRGcX93MSj+qEjxrn0Yx92P/kF3t+xyc7j+Z4U0wxxRS/NtCQjHs4fpWgvtdLc9zbfF/Ccy9MIzxTTDHFFFNMMcWvFEyFsZzo1AB0npK/j8nvNKU1xRRTTDHFFFN86HEvwvPBuAJOMcUUU0wxxRRT/ArhnhGeKaaYYooppphiig8DphGeKaaYYoopppjiQ48p4ZliiimmmGKKKT70mBKeKaaYYoopppjiQ48p4ZliiimmmGKKKT70mBKeKaaYYoopppjiQ48p4ZliiimmmGKKKT70+P8BDb6RIC6bucwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "WordCloud of Refuted Claims\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Dataframe structure:\n",
            "\n",
            "Dataframe Columns: Index(['id', 'claim', 'evidence', 'id_claim', 'label'], dtype='object')\n",
            "\n",
            "Total samples in train: 121707 - 89.45% of the whole data\n",
            "Total samples in validation: 7165 - 5.27% of the whole data\n",
            "Total samples in test: 7189 - 5.28% of the whole data\n",
            "\n",
            "Some samples in train:   id                                              claim  ... id_claim     label\n",
            "0  0  [chris, hemsworth, appeared, in, a, perfect, g...  ...        3  SUPPORTS\n",
            "1  1                       [roald, dahl, is, a, writer]  ...        7  SUPPORTS\n",
            "2  2                     [roald, dahl, is, a, governor]  ...        8   REFUTES\n",
            "3  3  [ireland, has, relatively, low, lying, mountains]  ...        9  SUPPORTS\n",
            "4  4  [ireland, does, not, have, relatively, low, ly...  ...       10   REFUTES\n",
            "\n",
            "[5 rows x 5 columns]\n",
            "Some samples in validation:   id                                              claim  ... id_claim     label\n",
            "0  0  [the, indian, army, comprises, part, of, the, ...  ...    98304  SUPPORTS\n",
            "1  1                      [recovery, features, rihanna]  ...    98305  SUPPORTS\n",
            "2  2                        [rihanna, is, on, recovery]  ...    98306  SUPPORTS\n",
            "3  3                        [philomena, was, nominated]  ...    27307  SUPPORTS\n",
            "4  4                           [vietnam, is, a, mascot]  ...    98312   REFUTES\n",
            "\n",
            "[5 rows x 5 columns]\n",
            "Some samples in test:   id                                              claim  ... id_claim     label\n",
            "0  0  [anxiety, has, been, linked, with, physical, s...  ...    16387  SUPPORTS\n",
            "1  1                     [firefox, is, an, application]  ...        6  SUPPORTS\n",
            "2  2  [keegan, michael, key, played, president, bara...  ...    16392  SUPPORTS\n",
            "3  3         [google, search, can, find, stock, quotes]  ...    16394  SUPPORTS\n",
            "4  4  [a, good, day, to, die, hard, was, directed, s...  ...    98315   REFUTES\n",
            "\n",
            "[5 rows x 5 columns]\n",
            "\n",
            "A short description of the labels: \n",
            "count       121707\n",
            "unique           2\n",
            "top       SUPPORTS\n",
            "freq         89363\n",
            "Name: label, dtype: object\n",
            "\n",
            "Distribution of Label in Train: \n",
            "SUPPORTS    89363\n",
            "REFUTES     32344\n",
            "Name: label, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Distribution of Label in Validation: \n",
            "SUPPORTS    3611\n",
            "REFUTES     3554\n",
            "Name: label, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xKJWrJeAmYPPIUaS7LQP2qFX6bosdZLKpsFqUzrs+h/Aue5HYHgd26bb54nVYQwvSU8RfjMpKsmMT4PJ0qu2uocJqzReBfWKHEBnidYSDSy12kMmiwhpFqVw9iDAPt0gWHQycEjvEZNE+rBGUytXtgduA9WJnERmBE+aIvyR2kE5TYQ2jVK6uQyirV8bOItKCF4A31yp9f4odpJO0STi8c1FZSX7MJOyE3zh2kE5SYTVRKldPAP5P7BwiY7Q5cHbsEJ2kTcIh0nmtriVM7yGSR0fWKn3nxw7RCSqsBqVydT3gr+hIdsm3RcCOtUrfo7GDtJs2CVf3JVRWkn+zCKeWKxyNsFKlcvWNhPcJds1BeFJ4x9QqfYUqLhUWUCpXpwNz0auCUiyLCZuGD8UO0i7aJAw+h8pKimdd4JwivXWn6wsrnd9Kb72RotoL+HDsEO3S1ZuE6SwMNwG7x84i0kHPAzvUKn0LYgeZqG4fYX0YlZUU30uAU2OHaIeuHWGVytUtCMdcrRs7i8gkcOB1tUrf3NhBJqKbR1hfRGUl3cOASuwQE9WVI6xSubodcC96+410n7fVKn2/iR1ivLp1hHUKKivpTpU8H+bQdYVVKlf/BXh/7BwikewMvCd2iPHqusJCoyuRU0vl6rTYIcajqwqrVK7uQI7/u4i0ybbAcbFDjEdXFRaQ0H3fs0gzn0+nAc+VrvnjLZWrr0GziIoM2gj4UOwQY9U1hQX8F5o6RqTRx0rlaq7253ZFYaWjq4Ni5xDJmK2Ad8UOMRZdUVjAR2MHEMmoT8QOMBaFP9I9naf9USB3OxhFJskutUrf7bFDtKIbRliHobISGclHYgdoVTcUVu5eCRGZZO9Nt0Qyr9CFlZ5Y4tWxc4hk3NqELZHMK3RhAUfFDiCSE8fGDtCKwu50L5WrPcDjQG/sLCI5sWut0ndb7BAjKfII60BUViJj8W+xA4ymyIV1ZOwAIjnzztgBRlPITcJSuboRsBBNIyMyVq+pVfrmxQ4xnKKOsPZBZSUyHpkeZRW1sPaNHUAkpzJdWIXbJEznq34C2DB2FpEccmDzWqXvsdhBminiCOt1qKxExsuAA2KHGE4RC0ubgyITk9nNwiIW1j6xA4jk3F6lcvUlsUM0U6jCSt/AuXvsHCI51wO8PXaIZgpVWMBewFqxQ4gUQCa3VIpWWJlcySI5tHPsAM2osESkmVeXytXpsUMMVZjCKpWrGwOl2DlECmI6GZxLrjCFRQZXrkjOvT52gKFUWCIynMztxypSYe0YO4BIwWiE1UEaYYm0145Z2/FeiMJK3/C8Q+wcIgUzHXhN7BCNClFYwDaEM3+ISHtlarOwKIWlzUGRzlBhdYAKS6QztokdoFFRCkuvEIp0xqaxAzQqSmG9KnYAkYLaLHaARkUprI1iBxApqFnpSYkzoSiFNTt2AJECy8woK/eFVSpXe9EcWCKdlJn9WCP+oZvZu0e63d0va2+ccXlp7AAiBZePwmLks2c4oMISKb7MbBKOWFju/u+TFWQCVFginZWZEVZL+7DMbGMz+4GZXZN+/SozO7qz0VqmwhLprMyMsFrd6X4ucC0vBv8b8IlOBBoHFZZIZ20cO8CgVgtrA3e/BFgF4O4rgJUdSzU2KiyRzpoRO8CgVgvreTN7KWFHO2a2G1DvWKqxUWGJdFZmDhtqNcingCuAbc3sZmBD4OCOpRqbdWMHECm4fBWWu99hZnsA2wMG3OfuAx1N1jqPHUCk4PJVWGbWA3wEeBOhIG40s++4+7JOhmtRVvalyTCmsGrlDAb6pzEwMJ0Vy6exclXsTNI6hyWxMwxqtTnPBxYDZ6Rf/xtwAXBIJ0KNUdf/8g8thB4bWDGdgf4e+lfMYGDlDOsfmEn/yhn0r5hJ/6oe61/VQ//KnvTzmfT7DPpJv7YeBnwG/TaDFTbdBpjBwJRprLDprJgyzVZMXYuVU9Zi5dT0stYUVq01FZ86hVXTDJ82BZ9m+HRgGjDDjKnAzPQi+fMYHBE7A9B6Ye3o7o1TuPzWzP7aiUDj0PHCMlatmsHA8umrF8LADAYGeuhvKISBlT30r5xpyxsLwXvoX9XTUAgzGGAGA97DwJTGQpjGiinThxTCVFZNncrKaUMKYS3DZ6BCkMmRld0/LRfWHWa2m7vfCmBmuwK3dy5W646eevVjG9lzN4RCGPAe609HBy8WwnQGwuhg/IUwBRWCdK8VsQMMGu3Nz/MI+6ymAX8ws4fSr7cC7u18vNF9ftqF6wJviZ1DpMDyUVjA/pOSYmKejx1ApODyUVjuvqDxazPbCMjM7IMpFZZIZy2NHWBQq29+fqeZ/R2YD/weqAHXdDDXWGRmZYoU1GOxAwxq9a05XwR2A/7m7lsDewG3dizV2GiEJdJZC2MHGNRqYQ24+zPAFDOb4u6/BXbuYK6xeDJ2AJGCy8wIq9XDGhaZ2TrADcBFZvYk2RnZLBj9LiIyAZkprFZHWAcCLwCfBH4JPMDI0ydPplrsACIFl5nCMvcCvHc46X0GnepLpFO2JKk/EjsEjH7g6GKaz4ZggLv7eh1JNXYLUGGJdIIDT8QOMWi047DyMtdUDfhfsUOIFNDTJPXMvJcw9ydSTdViBxApqMzsv4LiFJZeKRTpjMwcgwXFKaxa7AAiBZWVaaQAFZaIjOyO2AEaFaWw5qO53UU64U+xAzQqRmEl9X8A98WOIVIwSwgnTc6MYhRW8IfYAUQK5k6SeqbOmVCkwroldgCRgsnU/isoVmFphCXSXpnafwXFKqx7gEWxQ4gUiAqrY5K6k51JBUXybikZOdFMo+IUVqD9WCLtMTdrO9yheIWl/Vgi7XFz7ADNFK2w5qBT14u0w5WxAzRTrMJK6ouBebFjiOTcM2R0a6VYhRVcHTuASM5dTVJfGTtEM0UsrEtjBxDJuUxuDkIRCyup3wE8GDuGSE4NANfGDjGc4hVWoFGWyPj8Pp1MIJNUWCLSKLObg1DUwkrqf0TTJouMxxWxA4ykmIUV/Cx2AJGc+QtJvRY7xEiKXFg/jR1AJGcyvyulyIU1B3g4dgiRnFgBfD92iNEUt7DC7A3aLBRpzZUk9UdjhxhNcQsrOBudnEKkFd+OHaAVxS6spH4P8KvYMUQy7n7g17FDtKLYhRV8M3YAkYz7TroLJfO6obCuJUyfLCJrWgb8MHaIVhW/sMJ/Do2yRJq7hKT+bOwQrSp+YQUXEOb4EZHVnRU7wFh0R2El9ReA78aOIZIxd5LU58QOMRbdUVjB/yNMnSEiwZdjBxir7imspL4QuCR2DJGMuJMc/j10T2EFX0MHkooAfC4vhzI06q7CSupzgYtixxCJ7AaS+i9jhxiP7iqs4ETghdghRCI6MXaA8eq+wkrqjwBfjx1DJJIrSeqZPIVXK7qvsIIK8HjsECKTbBVwUuwQE9GdhZXUlwCfjx1DZJJdTFLP9YmGu7OwgnOAu2KHEJkkA8ApsUNMVPcWVlJfBXw6dgyRSXIGST335+vs3sICSOq/Rqe2l+J7gILsAunuwgo+g96yI8XlwNEk9aWxg7SDCivMSpr7bXuRYZxFUv997BDtosIKvgzcHDuESJvVgHLsEO2kwoLBHfCHA4tjRxFpo2PSQ3gKQ4U1KKnPBz4RO4ZIm3wvfVGpUFRYjZL6OcAvYscQmaCHCS8mFY4Ka03HAk/EDiEyAceS1P8RO0QnqLCGSupPAUfHjiEyTmfkdeqYVqiwmknqVTQHvOTPb4FPxQ7RSSqs4X0ayPUbRaWr1ID3kNRXxA7SSSqs4ST154H90TQ0kn1LgYNI6k/HDtJpKqyRJPWHgHeiGUol244iqf85dojJoMIaTVL/I+Gg0txN2C9d4TSS+k9jh5gsKqxWJPWfkeN5sKWwrqIgszC0ytw1cGhZ0vt9dMiDZMO9wK5FPd5qOBphjc2Hgetjh5Cu9xRwYLeVFWiENXZJ7yzgFuBfYkeRrvQc8K/pOTa7jkZYY5XUFwF9wMLYUaTrLAHe0a1lBSqs8QlzY+8JPBo5iXSPF4D9SepzYgeJSYU1Xkn974TSeiRyEim+ZYQDQwszc+h4qbAmIqnfj0pLOmspYWT1q9hBskCFNVFJ/QFgD2B+7ChSOEuA/Ujqv4kdJCtUWO0Q9mm9Cbg7dhQpjMXAvtoMXJ0Kq12S+kLgLUBX7xSVtngYeAtJXSdGGUKF1U5J/VngbUDh5tKWSXMr8IZuPnRhJCqsdgtnKXkH8K3YUSR3LgT2JKlrSqNh6Ej3Tkp6DwW+B8yMHUUyzYHPkdQrsYNknQqr05Le1wI/B7aOHUUyaQlwGEn98thB8kCFNRmS3vWBi4F9Y0eRTHkIOICkflfsIHmhfViTIak/R3j/4aloIkAJbgZ2UVmNjUZYky3pPQg4D1gvdhSJoh/4AlAhqa+MHSZvVFgxJL3bAxcAu8SOIpPqLuCIbpl/vRO0SRhDUr8P2J1wKrGlkdNI560k7A7YRWU1MRphxZb0bk04aevesaNIR9wLHElSvy12kCJQYWVF0nsk8HVgduwo0hargG8CJ5HUl8UOUxQqrCxJejciHCH/3thRZEL+DhxNUr8xdpCiUWFlUdJ7AHAWsEXsKDImTxFeAfwuSX0gdpgiUmFlVdK7LvAp4JNAb+Q0MrKlhM35r5DUF8cOU2QqrKwLR8kfD3wceEnkNLK6lcA5wCkk9cdih+kGKqy8SHo3BD4LfAS9mToLrgDKJPV7YgfpJiqsvEl6NwVOBI4FZkRO043+AHyWpH5T7CDdSIWVV0nvlsBJwAeAaZHTFF0/cAlwho6nikuFlXdJ7ybAUcAHgW3jhimchcB3gLNJ6k/EDiMqrOJIeg3YCzgGOAiYHjdQrt0EnAFcRlJfETuMvEiFVURhB/2RhPJ6ReQ0efEC8CPCZp/mU88oFVbRJb178OKoS4dFrG4RUCXMCPtLkvrzkfPIKFRY3SLpnUE44es7gP3o3pHXQuByQkn9Tkek54sKq1slvdsQims/YE+KfWzX3wgF9QtgDkldv/Q5pcISSHpnEkprP8Io7JXAWjEjTcAy4E7gtvQyh6T+QNxI0i4qLFlT2Hx8FbAT8NqGy/oxYzWxijDfVCim8HGeNvOKS4UlrUt6X8aLJbYjsBmwUXqZ1aFnXUQ4dfsjQz7OB+7Qm427iwpL2iPpnQ5syIsF1nhZD7AhF4Dl6WVZw8enaCwmvXInDVRYIpIbOgmFiOSGCktEckOFJSK5ocISkdxQYYlIbuT1aGYpCDNbCcwj/C7OBw5390VmVgLuAe5ruPvX3f18M6sBiwlzqkOYNno68Bl3379h2ecCVwGHAlsD6xAOvZjf8LjTgE0JszUA3O/uB5vZ9oQT3M4izOx6o7sf27ZvXMZFhSWxveDuOwGY2XnARwmndQd4YPC2Jt7q7k8PfmFmew73BO7+rob7DC01gEPd/fYhD/sW8A13vzy936vH8D1Jh2iTULLkFmDz2CFSmxIOXgXA3edFzCIpFZZkgplNJcyYekXD1dua2dyGy5sbbvttet2cNjz9RQ3P8dX0um8A15vZNWb2STPr1FuPZAy0SSixzTSzuYSR1T3AdQ23tbxJCAz3lo1W3sqxxiahu//QzK4F9gUOBI4zs9e6+/IWlicdohGWxDa4D2srwnsMPzrO5TzDmrNJzAaebnLflrj7Qnc/x90PBFYQ3vAtEamwJBPcfSnh7NafNrPxjPz/DmxmZq8EMLOtCLNKjGt+djPb18ympZ9vArwUeHQ8y5L20SahZIa732lmdwHvB24k3YfVcJdz3P1bwzx2uZkdBvzQzHqAAeCD7l5v4akvMrPBwxqedve3AW8HTjezZen1x7v74+P5vqR9NFuDiOSGNglFJDdUWCKSGyosEckNFZaI5IYKS0RyQ4UlIrmhwhKR3FBhiUhuqLBEJDdUWCKSGyosEckNFZaI5IYKS0RyQ4UlIrmhwhKR3FBhiUhuqLBEJDdUWCKSGyosEcmN/w/k6IxCCY+I3gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Distribution of Label in Test: \n",
            "SUPPORTS    3606\n",
            "REFUTES     3583\n",
            "Name: label, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faGGJWOVw1mZ"
      },
      "source": [
        "# Data Balancing\n",
        "After the visualization of the data, we could see that the train data is umbalanced with 89.389 samples for the label SUPPORT and just 32.351 for the REFUTES.\n",
        "In the following section we will tray some functions to balance the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gI57GDlow4KD"
      },
      "source": [
        "### Definition of the Functions\n",
        "Below we defined 3 methods for resampling the data.<br>\n",
        "\n",
        "Randon Undersampling: Undersample the majority class to the given samplesize <br>\n",
        "Proportional Undersampling: Undersample the data considering the number of samples and the proportion <br>\n",
        "Imblearn undersampling: Some functions of the Imblearn library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcW55vQzw7hN"
      },
      "source": [
        "# Some functions to downsample and balance the classes\n",
        "# Some functions were based on [2], [3], [4]\n",
        "\n",
        "#######################################################################\n",
        "#                 Random Undersampling\n",
        "#######################################################################\n",
        "\n",
        "def random_undersampling(df, column, samplesize):\n",
        "  '''\n",
        "  This function downsample the df to the samplesize received\n",
        "  Inputs:\n",
        "  - df: dataframe\n",
        "  - column: considered to apply the downsample\n",
        "  - samplesize: final number of samples for each class of the column\n",
        "  Output:\n",
        "    -  the dataframe downsampled\n",
        "  '''\n",
        "  df_train_balanced = df.groupby(column, as_index=False).apply(lambda array: array.loc[np.random.choice(array.index, samplesize, False),:])\n",
        "  df_train_balanced[column].hist()\n",
        "  return df_train_balanced\n",
        "\n",
        "#######################################################################\n",
        "#               Proportional Undersampling\n",
        "#######################################################################\n",
        "\n",
        "def proportional_undersampling(df_train, column, proportion, n_items):\n",
        "  '''\n",
        "  def proportional_undersampling\n",
        "  Inputs:\n",
        "  - df_train: dataframe to be undersampled\n",
        "  - proportion: to consider for the random undersampling\n",
        "  - n_items: total of samples after the undersampling\n",
        "  '''\n",
        "  # Sorting the classes in a descendent way in order to have as first the class with majority\n",
        "  u, count = np.unique(df_train[column], return_counts=True)\n",
        "  count_sort_ind = np.argsort(-count)\n",
        "  classes = u[count_sort_ind]\n",
        "  n_class = [0, 0]\n",
        "\n",
        "  # if the class with minority is inside the proportion\n",
        "  # otherwise keep the same number of samples\n",
        "  n_class[0] = int(np.floor( n_items * proportion))\n",
        "  if n_items - n_class[0] <= df_train[column].value_counts().min():\n",
        "    n_class[1] = n_items - n_class[0]\n",
        "  else:\n",
        "    n_class[1] = df_train[column].value_counts().min()\n",
        "\n",
        "  df_train_balanced = pd.DataFrame()\n",
        "\n",
        "  for i, c in enumerate(classes):\n",
        "            tmp_idx = df_train[df_train.label == c].index.values.tolist()\n",
        "            np.random.shuffle(tmp_idx)\n",
        "            #tmp_idx = torch.from_numpy(tmp_idx)\n",
        "            l_idx = tmp_idx[:n_class[i]]\n",
        "            df_train_balanced = df_train_balanced.append( df_train.loc[l_idx] )\n",
        "\n",
        "  return df_train_balanced\n",
        "\n",
        "#######################################################################\n",
        "#               Imblearn undersampling\n",
        "#######################################################################\n",
        "\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.under_sampling import TomekLinks\n",
        "from imblearn.under_sampling import ClusterCentroids\n",
        "\n",
        "# method = ['random', 'tomeklinks']\n",
        "def imdb_undersampling(df_train, method = 'random'):\n",
        "  X = df_train.copy()\n",
        "  X = X[['id', 'id_claim'] ]\n",
        "  X['len_claim'] = [len(x) for x in df_train.claim]\n",
        "  X['len_evidence'] = [len(x) for x in df_train.evidence]\n",
        "\n",
        "  y = df_train.copy()\n",
        "  y = y['label']\n",
        "  y = [1 if d == 'SUPPORTS' else 0 for i, d in enumerate(y) ]\n",
        "\n",
        "  print(\"Features shape: {}\".format(X.shape))\n",
        "  print(\"Target shape: {}\".format(len(y)))\n",
        "\n",
        "  if method == 'random':\n",
        "    # Imblearn undersampling random\n",
        "    rus = RandomUnderSampler()\n",
        "    X_d, y_d = rus.fit_sample(X, y)\n",
        "  if method == 'tomeklinks':\n",
        "    tl = TomekLinks(ratio='majority')\n",
        "    X_d, y_d = tl.fit_sample(X, y)\n",
        "  if method == 'centroid':\n",
        "    cc = ClusterCentroids(sampling_strategy=1/2, random_state=42, voting= 'soft')\n",
        "    X_d, y_d = cc.fit_sample(X, y)\n",
        "  l_id = [int(row[0]) for row in X_d]\n",
        "\n",
        "  # Undersampling the df\n",
        "  df_train_balanced = df_train.copy()\n",
        "  df_train_balanced = df_train_balanced.loc[l_id]\n",
        "\n",
        "  print(\"\\nDistribution without undersampling: \\n{}\".format(df_train.label.value_counts()))\n",
        "  print(\"\\n{}\".format(df_train['label'].hist(bins=4, alpha=0.5)))\n",
        "\n",
        "  print(\"Distribution with undersampling: \\n{}\".format(df_train_balanced.label.value_counts()))\n",
        "  print(\"\\n{}\".format(df_train_balanced['label'].hist(bins=4, alpha=0.5)))\n",
        "\n",
        "  return df_train_balanced\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iN7xMcLIxCBv"
      },
      "source": [
        "### Applying Undersample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmbWnh-vxETo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "c849897b-b84b-4b5d-aa87-96b49cd86a87"
      },
      "source": [
        "'''\n",
        "IN the cell above were defined 3 functions for doing the undersample\n",
        "Feel free to try them below uncommenting their corresponding code\n",
        "'''\n",
        "# Applying the proportional undersampling\n",
        "#df_train_balanced = proportional_undersampling(df_train, 'label' ,0.50, 60000)\n",
        "\n",
        "# Uncomment to apply the random undersampling\n",
        "min_samplesize = df_train.label.value_counts().values.min()\n",
        "df_train_balanced = random_undersampling(df_train, 'label', min_samplesize)\n",
        "\n",
        "# Uncomment to try the methods of Imblearn undersampling\n",
        "#df_train_balanced = imdb_undersampling(df_train, method = 'tomeklinks')\n",
        "\n",
        "# Visualizing the results\n",
        "print(\"A short description of the labels: \\n{}\\n\".format(df_train_balanced['label'].describe()))\n",
        "print(\"Distribution of Label in Train: \\n{}\".format(df_train_balanced.label.value_counts()))\n",
        "print(\"\\n{}\".format(df_train_balanced['label'].hist(bins=6, alpha=0.5)))\n",
        "\n",
        "\n",
        "#Uncomment to work with unsampled data\n",
        "#df_train = df_train_balanced"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A short description of the labels: \n",
            "count       64688\n",
            "unique          2\n",
            "top       REFUTES\n",
            "freq        32344\n",
            "Name: label, dtype: object\n",
            "\n",
            "Distribution of Label in Train: \n",
            "REFUTES     32344\n",
            "SUPPORTS    32344\n",
            "Name: label, dtype: int64\n",
            "\n",
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6rKo4Ejy9ST3Jbknyd2t9vIkO5I82P6e2OpJcm2SmST3JnnDQJ9NbfyDSTYt7i1Jkg6lcZxxvKWq1lbVuvb8cuD2qloD3N6eA5wHrGmPzcB1MBc0wBXAGcDpwBX7w0aStPQciktV5wM3tO0bgAsG6p+qOXcCJyRZAZwL7KiqfVX1BLAD2HAI1iVJGoNjFjm/gL9MUsB/q6otwERVfbPt/xYw0bZPBh4ZmPtoqx2s/hxJNjN3tsLExATT09MjLfrY5cezcvKskeYuxvTs8kPQdHr8PaWj0ezqsbZbOblirP2GNTs7O/Kxb1wWGxw/X1V7k/w0sCPJ/xncWVXVQmUsWjBtAVi3bl1NTU2N1Gfbjdfz2J47xrWsoV24/rTxN53aOP6e0tFo51VjbXfNrgfG2m9Ya8/exKjHvnFZ1KWqqtrb/j4OfJa5zyi+3S5B0f4+3obvBU4dmH5Kqx2sLklagkYOjiQvTXL8/m3gHOArwHZg/51Rm4Bb2vZ24OJ2d9WZwJPtktZtwDlJTmwfip/TapKkJWgxl6omgM8m2d/nxqr6iyR3AduSXAJ8A7iwjb8VeBswA/wQeBdAVe1L8hHgrjbuw1W1bxHrkiQdQiMHR1U9BLxunvp3gfXz1Au49CC9tgJbR12LJOmF4zfHJUldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktRlyQRHkg1JvppkJsnlh3s9kqT5LYngSLIM+DhwHjAJXJRk8vCuSpI0nyURHMDpwExVPVRVPwZuAs4/zGuSJM0jVXW410CSdwAbqupX2/N3AmdU1WUHjNsMbG5Pfw746ogveRLwnRHnStLhtNjj16uq6pWLWcAxi5n8QquqLcCWxfZJcndVrRvDkiTpBbUUjl9L5VLVXuDUgeentJokaYlZKsFxF7AmyeokxwEbge2HeU2SpHksiUtVVfV0ksuA24BlwNaq2n0IX3LRl7sk6TA57MevJfHhuCTpyLFULlVJko4QBockqcuSD44kzyS5J8lXkvx5khNafVWSH7V9+x8Xt31fT3LfQP1fJplK8rkDel+f5B1JPtvGzSR58oB50+2nUPbXPtPm/lzbd0+S+5Mc9uuOkg6/JP8hye4k97bjwxntmHTSwJhnj0dJ/l2S/9vG7kny7uert30XtP73t2PdBQP7rk/ycJv35STr25r2H8OeGdj+9VGOZUviw/EF/Kiq1gIkuQG4FLiy7fva/n3zeEtVPfslmSRTB3uBqnr7wJjfrKpfGpgH8G+r6u4Dpl0LXF1Vt7Rx/6LjPUk6CiV5E/BLwBuq6qkWFscNMfXmqrosyU8Du5Nsf576zwC/D/xiVT2cZDWwI8lDVXVvm/f+qvpMkrcAW6pqDe24mWR28LiZ5DY6j2VL/ozjAP8bOPlwL6JZATy6/0lV3XcY1yJpaVgBfKeqngKoqu9U1WPDTq6qx4GvAa96nvpvAr9TVQ+3fQ8DVwHvn6flMMfM7mPZERMc7YcQ1/OPv9/xTw+4VPULA/t2ttquMbz8pwde4/da7Wrgr5J8Psm/338JTdJPtL8ETk3yQJJPJDmrZ3KSnwV+Fph5nvprgC8eMPXuVj/QBuB/LvCy3ceyI+FS1UuS3MNcat4P7BjYN/SlKuBg9x0Pcz/ycy5VVdUft1O8Dcz9ION7krxu/780JP3kqarZJG8EfgF4C3Bz5v6biPmOM4O1f5Pk54GngPdU1b52mfxg9YX8XpLfYe5XON60wJq7j2VHwhnH/s84XgWEuc84RvFd4MQDai9nET8WVlWPVdXWqjofeBp47ai9JB0dquqZqpquqiuAy4B/xXOPPwcee26uqrVVdUZVfXaB+h7gjQe87BuBwS9Nv7+qTgM+AGwdYs1dx7IjITgAqKofAr8OvC/JKGdKDwIrk/xzgCSvAl4H3DPKejL3H08d27Z/BngF/r6W9BOt3aG0ZqC0FvgGMA28s41ZBvwysHPEl/l94INJVrV+q4APAX8wz9j/CrwoybnPs+buY9mRcKnqWVX1pST3AhcB/4v2GcfAkK1Vde1B5j6V5JeBP06yHPgH4Fer6skhXvrTSX7Utr9TVWcD5wD/Ocnft/r7q+pbo7wvSUeNlwH/pX1O8DRzn0lsZu54c12SLzN35eQvgP8xygtU1T1JPgD8eTvg/wPwW1X1nH8EV1Ul+SjwW8z9pNN8uo9l/uSIJKnLEXOpSpK0NBgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKnL/wMjSU+QRNZXogAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrnFnaXUIWQz"
      },
      "source": [
        "# Encoding the Dataset\n",
        "In this section we can encode the claim and evidencens with glove, word2vec and fasttext.\n",
        "Feel free to run the section of the encoding of your preference."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UeIn69SMcAdj"
      },
      "source": [
        "### Encoding the Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmKzsPj5xl00",
        "outputId": "656799c1-7ed0-4b86-8822-e15e98026332"
      },
      "source": [
        "#Encode the labels\n",
        "#PAD = '<PAD>'\n",
        "#labels = [PAD,'SUPPORTS','REFUTES']\n",
        "labels = ['SUPPORTS','REFUTES']\n",
        "#encoding_to_label = {1:'SUPPORTS', 2:'REFUTES'}\n",
        "encoding_to_label = dict(enumerate(labels))\n",
        "label_to_encoding = {label:enc for enc,label in encoding_to_label.items()}\n",
        "\n",
        "print('label_to_encoding:')\n",
        "print(label_to_encoding)\n",
        "print('\\nencoding_to_label:')\n",
        "print(encoding_to_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "label_to_encoding:\n",
            "{'SUPPORTS': 0, 'REFUTES': 1}\n",
            "\n",
            "encoding_to_label:\n",
            "{0: 'SUPPORTS', 1: 'REFUTES'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaUAjmwyCY77"
      },
      "source": [
        "### Encoding with Glove / Word2Vec\n",
        "Run the code below to encode the claim and evidence with glove or word2vec."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZN43u35BcDqb"
      },
      "source": [
        "#### Encoding Claims and Evidences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGDLqEqwprLA"
      },
      "source": [
        "import gensim\n",
        "import gensim.downloader as gloader\n",
        "from pathlib import Path\n",
        "\n",
        "def get_embedding_model_download_path(model_type, embedding_dim):\n",
        "    \"\"\"\n",
        "    Parameters\n",
        "    ----------\n",
        "        model_type: str\n",
        "            Name of the embedding model.\n",
        "        embedding_dim: int\n",
        "            Dimension of the embeddings in the embedding model.\n",
        "    Returns\n",
        "    -------\n",
        "        The path to download the model.\n",
        "    \"\"\"\n",
        "    download_path = \"\"\n",
        "\n",
        "    if model_type.strip().lower() == 'word2vec':\n",
        "        download_path = \"word2vec-google-news-300\"\n",
        "\n",
        "    elif model_type.strip().lower() == 'glove':\n",
        "        download_path = \"glove-wiki-gigaword-{}\".format(embedding_dim)\n",
        "\n",
        "    else:\n",
        "        raise AttributeError(\"Unsupported embedding model type! Available ones: word2vec, glove\")\n",
        "\n",
        "    return download_path\n",
        "    \n",
        "\n",
        "def load_embedding_model(model_type, embedding_dim):\n",
        "    \"\"\"\n",
        "    Loads a pre-trained word embedding model via gensim library.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "        model_type : str\n",
        "            Embedding model to download.\n",
        "        embedding_dim : int\n",
        "            Size of the embedding space to consider.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "        emb_model : gensim.models.keyedvectors.KeyedVectors\n",
        "            Pre-trained word embedding model.\n",
        "    \"\"\"\n",
        "\n",
        "    # Find the correct embedding model name\n",
        "    \n",
        "    download_path = get_embedding_model_download_path(model_type, embedding_dim)\n",
        "\n",
        "    #Check download\n",
        "    try:\n",
        "        emb_model = gloader.load(download_path)\n",
        "    except ValueError as e:\n",
        "        print(\"Invalid embedding model name! Check the embedding dimension:\")\n",
        "        if model_type.strip().lower() == 'word2vec':\n",
        "            print(\"Word2Vec: 300\")\n",
        "        if model_type.strip().lower() == 'glove':\n",
        "            print(\"Glove: 50, 100, 200, 300\")\n",
        "        raise e\n",
        "\n",
        "    return emb_model\n",
        "\n",
        "\n",
        "#Supported embedding dimensions:\n",
        "# Glove -> 50, 100, 200, 300\n",
        "# Word2Vec -> 300\n",
        "model_type = 'GloVe'\n",
        "embedding_dim = 300\n",
        "\n",
        "embedding_folder = os.path.join(os.getcwd(), 'Embeddings')\n",
        "Path(embedding_folder).mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "model_path = get_embedding_model_download_path(model_type, embedding_dim)\n",
        "embedding_path = os.path.join(embedding_folder,model_path)\n",
        "\n",
        "#When the embedding model is downloaded save it to memory so that if the cell is\n",
        "#re-executed it's not needed to download it again\n",
        "if not os.path.exists(embedding_path):\n",
        "    print('Downloading {} ...'.format(model_path))\n",
        "    embedding_model = load_embedding_model(model_type, embedding_dim)\n",
        "    print('\\nSaving the model to memory ...')\n",
        "    os.mknod(os.path.join(embedding_folder,'{}.vectors.npy'.format(model_path)))\n",
        "    embedding_model.save(embedding_path)\n",
        "    print('Done')\n",
        "else:\n",
        "    print('The requested GloVe embedding model has already been downloaded')\n",
        "    print('Loading {} ...'.format(model_path))\n",
        "    embedding_model = gensim.models.KeyedVectors.load(embedding_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCxxXy7kuDMB"
      },
      "source": [
        "def get_embeddings(df, embedding_model, embedding_dim):\n",
        "    \"\"\"\n",
        "    Retrieve for each token present in df its embedding taking it either from\n",
        "    the embedding model or, in case there are tokens not present in the previous two vocabularies,\n",
        "    generating it randomically.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "        df : pandas.DataFrame\n",
        "            DataFrame which will be used to create the vocabulary. Each token\n",
        "            of the vocabulary will be then associated with an embedding.\n",
        "        embedding_model : gensim.models.keyedvectors.KeyedVectors\n",
        "            Dictionary-like structure that contains words embedding.\n",
        "        embedding_dim : int\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "        embeddings : dict\n",
        "            The embeddings of the tokens presents in the vocabulary generated\n",
        "            by df.\n",
        "    \"\"\"\n",
        "    vocabulary = set([elem for seq in pd.concat([df['claim'],df['evidence']]) for elem in seq])\n",
        "\n",
        "    #Out of vocabulary tokens\n",
        "    OOV_tokens = [token for token in vocabulary if token.lower() not in embedding_model]\n",
        "\n",
        "    #Build OOV embeddings without the co-occurrence matrix\n",
        "    embeddings_oov = {token:np.random.normal(size=embedding_dim) \\\n",
        "                      for token in OOV_tokens}\n",
        "\n",
        "    embeddings = {**embedding_model, **embeddings_oov}\n",
        "\n",
        "    return embeddings"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_b71UDY9ufkI"
      },
      "source": [
        "#Encode padding\n",
        "#embedding_model[PAD] = np.zeros(embedding_dim)\n",
        "\n",
        "#Creation of the training set's embeddings\n",
        "embeddings_train = get_embeddings(df=df_train,\n",
        "                                  embedding_model={key:embedding_model[key] for key in embedding_model.vocab.keys()},\n",
        "                                  embedding_dim=embedding_dim)\n",
        "\n",
        "#Creation of the validation set's embeddings\n",
        "embeddings_val = get_embeddings(df=df_val,\n",
        "                                embedding_model=embeddings_train,\n",
        "                                embedding_dim=embedding_dim)\n",
        "\n",
        "#Creation of the test set's embeddings\n",
        "embeddings_test = get_embeddings(df=df_test,\n",
        "                                 embedding_model=embeddings_val,\n",
        "                                 embedding_dim=embedding_dim)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jo5teSbfyJ4G",
        "outputId": "db2c23cf-0e06-4754-eea2-392350333121"
      },
      "source": [
        "# Checking the OOVs\n",
        "\n",
        "vocabulary = set([elem for seq in pd.concat([df_train['claim'], df_train['evidence']]) for elem in seq])\n",
        "embedding_model_2={key:embedding_model[key] for key in embedding_model.vocab.keys()}\n",
        "\n",
        "#Out of vocabulary tokens\n",
        "OOV_tokens = [token for token in vocabulary if token.lower() not in embedding_model_2]\n",
        "\n",
        "print('OOVS {} - {:2f}%'.format(len(OOV_tokens),   100* len(OOV_tokens)/ len(vocabulary) ) )\n",
        "print(OOV_tokens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OOVS 2458 - 7.840010%\n",
            "['federatsii', 'elflman', '1946september', 'supraregional', 'transluminal', 'shophe', 'wwfwwe', 'owuo', 'iteru', 'telanovela', 'athnai', 'donalbain', 'chrononauts', 'bandamax', 'varayev', 'commisar', 'sily', 'productionsongwriting', 'johnt', 'virginiaplaintiffs', 'sarcoptes', 'injans', 'wildings', '33million', 'lubnn', '513000', 'febreuary', 'iridioides', 'georgeville', 'rivele', 'simza', 'vocalistaccordionist', 'hulandes', 'chymist', 'charactersrichard', 'brche', 'lymelife', 'nitroplus', 'damnesia', 'veeraiya', 'teavana', 'antiheroic', 'suseenthiran', 'yakthung', 'dolcefino', 'zootropolis', 'gaarjya', 'vended', 'dpartements', 'actionanimated', 'delevingne', 'graceffa', 'hickenbottom', 'romelle', 'greeance', 'qubool', 'willkapampa', 'pashtodari', 'agrotera', 'wechat', '348347', 'etpt', 'nutsa', 'mcmary', 'muthucharam', 'ubertov', 'szeles', 'dhill', 'kukhianidze', 'baahubali', 'experimentalelectronic', 'chainsmokers', 'tunt', 'gonzlez', 'writerproducer', 'urundi', 'ouron', 'dhirajlal', 'lopoldville', 'uncombined', 'marekstarkiller', 'romona', 'olmpicos', 'paglen', 'dainard', 'latajcy', 'ogghwa', 'peoplekm2', 'wonderstone', 'antiheroine', 'nikhilesh', '5196', 'patriotpublisher', 'rnning', 'kinuwa', 'kimovich', 'solbin', 'ellyse', 'joshy', 'ibrahimovi', 'ltdbaby', 'lindstrm', 'aftershow', 'falcn', 'patzzi', 'brona', 'brindavanam', 'tarly', 'bojack', 'libertatum', 'platzman', 'championsheavyweight', 'hubbel', 'dillagi', 'dastr', 'nerrukku', 'numbencore', 'dimetra', 'kurahara', 'mrui', 'eivissa', 'raupaul', 'shensi', 'dezs', '185329', 'pyrnes', 'adaptordirector', 'standartenfhrer', 'langpronaudio', 'pandaria', 'cutforth', 'knigreich', 'inputoutput', 'michoacn', 'shanowski', 'araneae', 'jwoww', 'yanger', 'nordoff', 'rustamid', 'malfunkshun', 'puzhayum', 'krnos', 'bassistguitarist', 'thenali', 'demun', 'knan', 'boradway', '185430', 'anglic', 'grrm', 'rolihlahla', '4550', 'smartwatch', 'knste', 'mjk', 'trenneman', 'ternt', 'bcas', 'frostnixon', 'dmographie', 'pinisetty', 'korsh', 'mepris', 'kyam', 'gmailinbox', 'eore', 'sashikanth', 'hireann', 'hasheni', 'pertuesa', 'wolfhard', 'essos', 'bhrat', 'chgoku', 'dhada', 'concrte', 'incio', 'abum', 'sarbjit', 'sqmi', 'salue', 'gunasekhar', 'garca', 'julayi', 'zanti', 'stripclubs', 'lafur', 'bdiyat', 'prevalences', 'drkn', 'bondagediscipline', 'wojtya', 'lavenza', '6126', 'chareton', 'diathk', 'akuna', 'iatas', 'repilot', 'elasmobranch', 'bhambla', 'wheb', 'imprio', 'saroo', 'makhon', 'igpx', 'canadaunited', 'adienne', 'fifantasy', 'rihmeek', 'turquotte', 'lofranco', '1980searly', 'britannicas', 'lactobacillales', 'bhedabheda', 'gofundme', 'mngke', '470469', 'cambrige', 'federativa', '5450', 'arestrup', 'scre4m', 'qurayshi', 'calvano', 'stika', 'plhza', 'balmores', 'chapitres', 'gravenhage', 'harivansh', 'uachtarn', 'woglom', 'shahenshah', 'gmez', 'rayfiel', 'lenworth', 'demigoddess', 'maattrraan', 'qarn', 'gadon', 'harmenszoon', 'thozha', 'chesave', 'poryes', 'soilearthrock', 'bogdanovna', 'afzelli', 'banjaree', 'achsenmchte', 'dreamville', 'yarlagadda', 'afnistn', 'supershows', 'lnqi', 'yorin', 'turkmenia', 'pravitel', 'girasoli', 'panetierre', 'meisje', 'tenitra', 'chyornaya', 'fhrer', 'lustrum', 'shsaku', 'cwfc', 'deimatic', 'emayatzy', 'hammerback', 'lszl', 'misere', 'constanzia', 'adaryll', 'tholins', 'isayama', 'toxotes', 'lfcc', 'rossif', 'kingsguard', 'lummen', 'sztykiel', 'blackbear', 'traue', 'tessandra', 'afzelii', 'winnpeg', '5240', 'progressiveliberal', 'preuische', 'tgg', 'udayakrishna', 'aristotls', 'akamassiss', '156430', 'freistaat', 'eperviers', 'scanland', 'montral', 'malmdy', 'schnerr', 'wuotan', 'provenal', 'a7x', 'asshai', 'mozhi', 'rjavaa', 'birdge', 'adorra', 'infans', 'xealand', 'v18', 'abctbs', 'kabinet', 'smithed', 'mayersberg', 'cardsharp', 'comedydrama', 'musicalromantic', 'dottor', 'ultimacy', 'exmachina', 'pmw', 'fliegauf', 'croftlily', 'larbey', 'noneducational', 'auamendiak', 'nonsporulating', 'nties', 'citezenship', 'trilogychristopher', 'dhoondo', 'harleen', 'tugalala', 'satullo', 'rovero', 'kawliya', 'blcker', 'suebian', 'solverson', 'yeddo', 'bassbacking', 'diety', 'thr33', 'ilios', '196465', 'behbeit', 'preventatives', 'samaire', 'miliken', 'sense8', 'szkely', 'kucherena', 'rseau', 'roboting', '14771481', 'canfari', 'gonxhe', 'inducteee', 'aashif', 'anoai', 'sjgren', 'iwjeaqk', 'jawaani', 'shenaz', 'meridiem', 'adventured', 'gcnra', 'gendry', 'benjamenta', 'lacel', 'libnniyyah', 'bajrangi', 'scabiei', 'bearu', 'bzd', '544th', 'farshchian', 'createdco', 'rapsung', 'kumail', 'taissa', 'titn', '1901december', 'mildronate', 'krt', 'jovovichserbian', 'braa', 'pholegandros', 'jelline', 'kkhh', 'rashford', 'dodeknisa', 'keyboardistvocalist', 'jamhuuriyadda', 'conquested', 'samuthirakani', 'sathalanalat', 'chingkwake', 'terraria', 'tontnt', 'communitys', 'kordei', 'vadnagar', 'qualley', 'tuaisceart', 'nikhla', 'gunnis', 'cheaptrick', 'makkonen', 'kroitor', 'befikre', 'rvoires', 'constableunited', 'valrian', 'bhanji', 'penlope', 'brbeuf', 'gourang', 'mclennen', 'taoisigh', 'belaru', 'stormaktstiden', 'seolgungnyeolcha', 'waksberg', 'cuarn', 'erythroxylaceae', 'sextiae', 'alianovna', 'mesophile', 'loptr', 'minnesotaontario', '3140', 'childwickbury', 'thenmerku', '1971september', 'udayapuram', 'bokma', 'craseonycteris', 'runnian', '9153', 'mumolo', 'schferhund', 'cortzar', 'lyses', 'shilowich', 'gumb', 'kimila', 'iustinianus', 'rockfunk', 'baaghi', 'respublikas', 'eifa', 'rheon', 'ad65', 'manasseri', 'encyclopdia', 'prankstars', 'fdration', 'radionavigation', 'pardubick', 'gryptonite', 'mallemala', 'grimhilde', 'techniscope', 'harisree', 'sidushq', 'retweeted', 'pancholy', 'mawlaw', 'jashni', 'espaol', 'vierbund', 'tezaab', 'castlebeck', 'borussi', 'cretton', 'devineni', 'nederln', 'candleshoe', 'rhne', 'komarnicki', 'b612', 'buttrose', 'mittelmchte', 'writerartist', 'csngs', 'corsier', 'mironoff', 'zaslavski', 'doahl', 'raajneeti', 'ishid', 'marvelton', 'brodbin', 'glssa', 'talion', 'belleforest', 'paeksang', 'catreus', 'pudhe', 'sidse', 'sp100', 'andarivadele', 'nilotes', 'hoytema', '500000', '18674', 'rockfolk', 'dotun', '74274774828', 'ignmar', 'unindustrialized', 'seperation', 'marselha', 'athelete', 'googl', 'gangaridai', 'moonblood', '505990', 'umtshsdpa', 'tekilat', 'marconigrams', 'rgion', 'releasesooraj', 'mbius', 'cairnholm', 'aodhn', 'tenpenny', 'phirman', 'miricani', 'tubelight', 'wahlburgers', 'ewwy', '8611', 'uncast', 'vertires', 'truwisha', 'summerscyclops', 'kardi', 'stageiros', 'romcom', 'inoperability', 'dhanoa', 'hyoyeon', 'spelaea', 'bfca', 'lomenda', 'cheir', 'ehrmantraut', 'elikolani', 'emperorempress', 'spivot', 'windust', 'chokher', 'tormund', '1964march', '932012', 'sreenu', 'hotell', 'chindians', 'gayborhood', 'starboy', 'eidgenssische', 'sorceryadventure', 'ashalata', 'bloys', 'pewdiepie', 'harmonimix', 'reemma', 'sagarmth', 'prncipe', 'mevlev', 'genisys', 'sssle', 'sporulating', 'pardubicki', '197377', 'bjrk', 'toln', 'raakesh', '2815', 'nephalion', 'tsinchuan', 'actioncomputer', 'kaaka', 'rajeshwara', '19268', 'takepart', 'nelsonfatty', 'mkena', 'extremophilic', 'sabh', 'weebly', 'pinkman', 'syriacaramaic', 'policegiri', 'vietname', 'medlocke', 'kozmic', 'neerad', 'acronymed', 'radaan', 'amricains', 'euphan', 'bentayga', 'mccamus', 'vangsness', 'lyce', 'includedmalcolm', 'srisaila', 'portayed', 'hverungr', 'odsseia', 'alphabetum', 'sephardiportuguese', 'pradhnamantr', 'velsquez', 'trinit', 'noncompulsory', 'rakul', 'belgisch', 'end2001', 'sakurazaka', 'pramos', '198082', 'ikwadur', '570ce', 'chandranudikkunna', 'auerstedt', 'ministr', 'aimovi', 'rgine', 'mrket', '90377', 'hephthlite', 'fancine', 'hypercarnivores', 'ptisseries', 'pithamagan', 'mahomedali', 'rhythmlead', 'comedymystery', 'rachanachk', 'shqipris', 'horm', 'gardenzio', 'cofound', 'zbornak', 'jfj', 'mthq', 'anjez', 'statethe', 'rockatansky', 'lodie', 'alibar', 'sabbtios', 'guiol', 'othelllo', 'damodardas', 'wilusha', 'lakessaint', 'intervencin', 'provena', 'crocetti', 'mayerunited', 'darvette', 'slatoon', 'zwemdokrock', 'venturella', 'pattadhari', 'madnah', 'lawthe', 'jmike', 'tekhnologi', '17751783', '3160', 'ireann', 'hapalochlaena', 'sleyman', 'liebesman', 'garbut', 'heckering', 'heikegani', 'odontoceti', 'bawitdaba', 'magyarorszg', 'kentar', 'wertgesetz', 'e5c4p3', 'felinae', 'mehoz', 'kushano', 'hulkcomics', 'antiquarius', 'maslany', 'frumious', 'potnia', 'khiljis', 'gunday', 'isagoras', 'metalrock', 'latna', 'pavonazzo', 'callaham', 'prascovia', 'krouac', 'zhylaw', 'suzrrounding', 'fargnoli', 'iaro', 'vitamers', 'pulimurugan', 'pirineus', 'aguna', '2780400', 'jtunn', 'perazhagan', 'goundlings', 'guynes', 'sphadikam', 'invasin', 'laadla', 'albumdvd', 'wrnw', 'megadiverse', 'guzmn', '14522', 'waale', 'muntcha', 'euescreen', '064942', 'diocesi', 'matel', 'televizzle', 'saswata', 'nayvadius', 'collge', 'allbum', 'fatts', 'akiro', 'lincolndouglas', 'lnai', 'ciambotta', 'soomaaliya', 'incognegro', 'okovi', 'actionsuspense', 'ghilji', 'predsedatel', 'complicatedly', 'in1266', 'oitnb', 'revested', 'protg', 'titaness', '2918', 'comedymusical', 'astrom', 'hartswood', 'lebris', 'tsagadaina', 'autnoma', 'flotus', 'algrie', 'grentel', 'lawmangambler', 'eqalussuaq', '440s', 'ratm', 'votoms', 'workthe', 'marths', 'snegithiye', 'downington', 'archbasilica', 'alchimista', 'phsis', 'benh', 'dmocratique', 'colllsion', 'leiderdorp', 'phillippine', 'govindudu', 'msw3', '193527', 'boomin', '13676', 'filmsestablished', 'aiso', 'trgovite', 'kylo', '20122013', 'pholegander', 'panthon', 'windstruck', 'heartlight', 'mnika', 'lesane', 'talamh', 'innse', 'yevgenievich', 'eurodisc', 'parasitiformes', 'michiganontario', 'britcom', 'statesstate', 'feautres', 'stefi', 'coopration', '342239', 'televisionseasons', 'nobumoto', 'dramathriller', 'joaqun', 'arbresh', '247365', 'mtley', 'grettel', '18300', 'kospiah', 'megatall', 'charactersalpha', 'sonyatv', 'fictionaction', 'twisleton', 'dezerian', 'earthroad', 'tchin', 'ppth', 'mccubbing', 'unmetathesized', 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'nrnp', 'sarrainodu', 'sheev', 'salky', 'columbias', 'leati', 'susheel', 'varietysketch', 'freki', 'chosn', 'ignatowski', 'modelle', 'lexifier', 'wtan', 'shophet', 'liqour', 'capetonian', 'chomolungma', 'sarayburnu', 'resund', '042', 'trkmenistan', 'machupicchu', '6897', 'junstrom', 'rossiyskoy', 'saala', 'dogsbody', 'byleistr', 'ramn', '20013', 'roahn', 'mtys', 'isced', 'liddi', 'weard', 'spiriti', 'jameka', 'tiein', 'tuinfort', 'reichsfhrer', 'transperceneige', 'gigageyi', 'keneti', 'malliswari', '195455', 'hesher', 'hskd', 'onwumere', 'stageira', '1906march', 'giselli', 'stabbington', 'sheyi', 'andreievich', 'octopoda', 'pirinioak', 'scle', 'mouzakis', 'ibhof', 'siculoamericani', 'siculu', 'culturology', 'hdr10', 'superchannel3', 'dysthymic', 'alloduohangouts', 'ssaro', 'unphotogenic', 'arizonaalternate', 'grosssing', 'coloradoconsolidated', 'rhaeto', 'andalasia', 'nonpersonal', 'rajmund', 'protagonistantihero', 'trksoy', 'itfa', 'preetha', 'tbily', 'oakenshield', 'waymo', 'federaalka', 'qubec', 'borreliosis', 'minnale', 'barlen', 'chamis', 'bablyon', 'quvenzhan', 'xuejzw', 'arige', 'ouehbs', 'pornhub', 'vayadhinile', 'ghazab', '19601961', 'spektors', 'faraldo', 'markovna', 'gadot', 'plinko', 'arrowverse', 'deewaar', 'medc', 'vikander', 'wcwworld', 'mgmua', 'leondis', '7445', 'rockpop', 'popism', 'yedo', 'shqypnishqypnia', '199697', 'apaharan', 'aahey', 'olchin', 'zylka', 'tigrayans', 'monomously', 'wiktincubation', 'stucken', 'marsican', 'pinkava', 'imitiaz', 'shqipri', 'talaiassa', 'jzef', 'shellstrop', 'organizacin', 'kazynski', 'rochen', 'anons', 'mcain', 'ozploitation', 'quinzel', 'dallol', 'hyn', 'ashkay', 'shabakis', '198592', 'srbi', 'giantsbane', 'bastn', 'blask', 'olenna', 'kaminey', 'herohe', 'bijng', 'eminm', 'waluigi', 'skurge', 'pennsylvaani', 'maxiseries', 'palogene', 'helblindi', 'chacn', 'metroxylon', 'babado', 'vavrincova', 'fnke', 'hoofdstedelijk', 'aeia', 'boyega', 'nsai', '64946', 'girlsquad', 'khwarazmian', 'findie', 'broadchurch', 'lngdo', 'rbhip', 'zapatal', 'kniggrtz', 'farcques', 'muammad', 'acacallis', 'lukaenka', 'syncopy', 'palc', 'terayama', 'assaulter', 'muhteem', 'repbrica', 'crimehorror', 'stahelski', 'toshir', 'hasaan', 'devletleri', 'ateker', 'herveaux', 'pyari', 'shqipnishqipnia', 'tankbon', 'machaao', 'mahershala', 'cognitivistic', 'mevln', 'futuresexlovesounds', 'gyal', 'rayana', '6838', 'varenberg', 'whalberg', 'waynebatman', 'cymoedd', 'hopr', 'khatron', 'strategypush', 'vneta', 'kelor', 'szkelys', 'ruco', 'piqad', '1893september', 'abeckaser', 'unnarrated', 'heidkrger', 'rossitano', 'weltschmertz', 'hotaks', 'pictureseight', 'entertainmenttalent', 'wjib', 'belgi', 'hogbonu', 'estvez', 'microbats', 'bwaa', 'novacane', 'ogaklkikekl', 'sbastien', 'minghags', 'allhfull', 'dybala', 'stairsteps', 'harmanpreet', 'keery', '20062007', 'javadd', 'krokidas', 'meganesia', 'sikhi', 'velaiilla', 'siracidis', 'morrer', 'flvios', 'bendinger', 'quackser', 'deutschendorf', 'qsim', 'blackagar', 'cntv', 'worldroad', 'sonuga', 'thrse', 'hatalmak', '238m', 'mellark', 'metathesized', 'matanitu', 'bieberfever', 'oggktn', 'okorafor', 'philosopy', 'ayiti', 'eileanan', 'metalhard', 'kirthar', '931987', 'hallstrm', 'mawln', 'gyllenhal', 'kbenhavn', 'keymh', 'kenzari', 'peders', 'instax', 'ysd', 'outgrossing', 'makedona', 'silkscreening', 'ichnology', 'vansanten', 'hshim', 'bhal', 'lovehate', 'latore', 'shqipria', 'kiranjit', 'filmtv', 'numspa', 'carthg', 'junun', 'supermensch', 'l4yer', 'conation', 'antemasque', 'svizzers', 'nalam', 'normani', 'tardigrade', 'szchenyi', 'atyat', 'a2016', 'dmokirasyaw', 'shintoists', 'tholomew', 'shqip', 'buchannon', 'strus', '1902october', 'dabistn', 'khaanat', 'sambdromo', 'chalkidice', 'nbcdirectv', 'lamna', 'levoamphetamine', '1648195', 'dyssomnias', 'dsseldorf', 'wdan', 'keppa', 'birthyear', 'diskssd', 'vishk', 'molaro', 'ferrah', 'halfman', 'varr', 'diaval', 'amrica', 'kapii', 'saldvar', 'elenchus', 'australinea', 'telsa', 'unerotic', 'vooruzhonnyye', 'passanger', 'galazy', 'dechristianised', 'discothque', 'krabb', 'lucasfilms', 'odeyer', 'stva', 'namastey', 'stacpoole', 'deadmaus', 'turkicized', 'boulange', 'melanija', 'ringz', 'hannibalic', 'brnice', 'raghuvir', 'skodowska', 'uniwersytet', 'angrboa', 'nonflowering', 'pescow', 'othijoth', 'epiccbs', 'prakesh', '1884december', 'prasthanatrayi', '1926february', 'corendon', 'foodstirs', 'stigeweard', 'somniosus', '132524', 'skarsgrd', 'pawe', 'shahjahanabad', 'menama', 'fredspris', 'cryptonym', 'endhiran', 'chiquis', 'paxaxon', 'kaakha', 'singerrhythm', 'phedape', 'muungano', 'fashionclothes', 'supernaturaladam', '19485october', 'tokuro', 'saagar', 'oann', 'popdance', 'tramar', 'doggumentary', '0914918327', 'dragneel', '112077', 'herv', 'localram', 'mesothelae', 'seasonseries', 'wasalu', 'storeapple', 'mahaan', 'cdmx', 'semiticists', 'gabonaise', 'kimitoshi', 'colsefni', 'togolaise', 'hippokrts', 'jacelyn', 'chasta', 'friman', 'ruvuvu', 'hamikdash', 'kelela', 'popcaan', 'saamy', 'checs', 'fesitval', 'resolutionlibya', 'dramasdil', '14159', 'sakunthala', 'ebenazer', 'tsentralni', 'peopleplaces', 'vwalt', 'mantee', 'aestheticization', 'svizzeri', 'yetiyoya', 'gahnawiyogeh', 'abcwb', 'saniyya', 'elfont', 'rigoulot', 'brko', 'ripuwlika', 'douard', 'leian', 'trkiye', 'thysdrus', 'raynold', 'deladis', 'mizuo', 'terreur', 'snowpiercer', 'ganeshananthan', 'poblacht', 'skyrim', '19492', 'ltel', 'ghorid', 'landress', 'umarji', 'burno', 'folasade', 'azrbaycan', 'pizzolatto', 'sureyjar', 'lvovevich', 'heldenbergh', 'uukuhuwa', 'jall', 'wahlberd', 'volodymyrovych', 'dalongdong', 'kinwa', 'ichir', 'dohny', 'oorelithe', 'ladinisch', 'theatreland', 'estn', 'mustan', 'paruvakatru', 'whinehouse', 'thuppakki', 'truva', 'aramayo', 'onak', 'comicgraphic', 'weho', 'athnaia', 'romrio', 'mclarem', 'tawantinsuyu', 'monoethnic', 'quertaro', 'thonglongyai', 'peterare', 'duvre', 'vaako', 'benzies', 'rpebilk', 'swoff', 'lootera', 'lducation', 'venutra', 'fapp', '1908january', 'writerdirector', 'vavrincov', 'unresearched', 'pirineos', 'franais', 'atherectomy', 'geeska', 'missguided', 'hunsrck', '424423', 'dnde', 'bnin', 'nnedi', 'portugus', 'levarr', 'in1971', 'ephthalites', 'andulus', 'victorvictoria', 'frdric', 'natchios', 'parlophon', 'smniyn', 'zwittau', 'poprock', 'breakroom', 'kalvi', 'rodrguez', 'unadapted', 'udden', 'parasomnias', 'ramrez', 'heughan', 'niccals', 'adventureromance', 'kallstadt', 'characterscraig', 'gulln', '1960general', '1895october', 'nickelodean', 'dashev', 'vdova', 'malayalm', '1757july', 'risqu', 'behna', 'mcaloney', 'bloodhoundz', 'cefuroxime', 'delevigne', 'cipramil', 'fogell', '200lr', 'hexalogy', 'extrachromosomal', 'girn', 'tumbos', '430s', 'uviu', 'abccmt', 'onika', 'alxandros', 'hahoodzo', 'mikros', 'pantherine', 'gth', 'abcupn', 'andaluca', 'uluslararas', 'leukos', 'hunagrian', 'fantme', 'dyokovic', 'pennsilfaani', 'stamatina', '103000', 'anastasakis', 'raindog', 'fhrerbunker', 'wuoronos', 'catalanism', 'eastern9', '7290', 'annaeus', 'damul', 'mlanie', 'cludia', '8000m', 'wenrich', 'visegrd', 'cinquime', 'aznaviyn', 'izombie', 'lennonplastic', 'jasarat', 'crochon', 'lycanvampire', 'renesmee', '6789', 'worldteam', 'khadoos', 'migos', 'erdoan', 'yoroiden', 'benzos', 'alecx5', 'thieblot', 'asmweek', 'androgeus', 'nonautonomous', '4dx', 'maganiello', 'ghorids', 'adetuyi', 'skesis', 'raavanan', 'eleveneleven', 'craseonycteridae', '1917june', 'misre', 'zivago', 'usab', 'brachyura', 'mrityudand', 'ds2', 'ojibweanishinaabe', 'vanderwaal', 'swobodny', 'pfaller', 'mysterythriller', 'franaise', 'parasomnia', 'millworth', 'bejawada', 'ramathan', '17075200', 'nawazuddin', 'nx37602', 'chunsoft', 'kouchi', 'stige', 'diemon', 'kror', 'adle', 'kiteboarder', 'vasoline', 'castina', 'nickki', 'gwenevere', 'kmida', 'medianu', 'kandukondain', 'shymalan', 'laufey', '1887december', 'gedei', 'zrich', 'downloadsstreaming', 'buffay', 'philadelphiasan', 'wodeyer', 'mpcv', '1911january', 'bennes', 'contanious', 'draenor', 'bthory', 'mortdecai', 'thirumagal', 'lmites', 'barood', 'kaui', 'amoxycillin', 'bredeweg', 'trogneux', 'programsfacilities', 'madeon', 'kanthaswamy', 'hivju', 'dhoka', 'filmshighest', 'chryses', 'hoechlin', 'cordozar', 'cyclobenzaprine', 'luftschlacht', '187611', 'haster', 'muoz', 'roghachd', 'seavers', 'chornobylska', 'spordes', 'tympanist', 'iikhanate', 'hanala', 'dietes', 'uchronic', 'malayam', 'screencasting', 'khlo', 'temjin', 'sovetskikh', 'pebdani', 'denizi', 'drinkware', 'lopold', 'huffam', 'nokou', 'zakhm', '47000', 'pareia', 'necropolitan', 'dzayer', 'ikwuakor', 'duvemla', 'tekhniyon', 'cild', 'constastine', 'metrocolor', 'paradiseinterscope', 'limbuyakthung', 'ittifak', 'quatoriale', 'penfro', 'kweens', 'rajski', 'splatoon', 'kiiara', 'amrutlal', 'kalaeloa', 'inglfr', 'laumet', 'javaad', 'shmucks', 'witholds', 'dextrorotary', 'multistarrer', 'graphy', 'montorgueil', 'kaluuya', 'curaao', 'auzire', 'caniforms', 'adegboyega', 'lianjie', 'landnmabk', 'katastrofa', 'zaytoven', 'dinkley', 'raasleela', 'flvius', 'pilts', 'vfxlos', 'parouse', 'ruvubu', 'anarchos', 'strabler', 'ocde', 'startford', 'ciminella', 'laroquette', 'hatien', 'gopalkrishna', 'espritu', 'colfseni', 'compono', 'droc', 'prouvno', 'gangaajal', 'zeik', 'obergefell', 'hernndez', 'hermeticus', 'shansabn', 'aisthtik', 'pybrika', 'guanhumara', 'boniamy', 'lewgoy', 'jetskiing', 'systrom', 'hssh', 'kwadjo', 'pachaikili', '3222', 'dippold', 'necrophile', 'singersongwriter', 'allh', 'ncpo', '80md', 'waipounamu', 'annhilative', '118111823', 'navrtilov', 'gunasekar', 'feihong', 'koninkrijk', 'margoyles', '2200s', 'vissarionovich', 'ilgan', 'strannix', '264mpeg', 'meridius', 'flexeril', 'fentanil', 'shofeim', 'stormzy', 'ptros', 'nishikanth', 'zootopia', 'intouchables', 'igos', 'sriti', '187020', 'amphisbaenians', 'enemiesclassic', 'driveand', 'shinichir', '1929september', 'ntas', 'fraternit', 'adventureromantic', 'pranker', 'ryhoravi', 'mccallany', 'mensinger', 'kalbagh', 'pirah', 'cgcg', 'dumberer', 'ogamntril', 'mcquarry', '1658october', 'kerbal', 'mcdj', 'potenze', 'constituio', 'abdomenbelly', 'jedo', '2011the', 'gaaffaa', 'lancme', 'rickricky', 'chiyaan', 'csks', 'browngardt', 'dwards', 'actiondrama', 'tequilana', 'yevna', 'furnier', 'kadannu', 'stateslist', 'caraven', 'roiland', 'kultgen', 'gaulanitis', 'kinrick', 'koreyoshi', 'jamphel', 'akhmadovich', 'atlantiques', 'sthetics', 'negga', 'konungariket', 'padm', 'jnior', 'primettes', 'maracan', 'buress', 'gewest', 'levieva', 'rakshitha', 'ahliyyah', 'naayak', 'coathangers', 'nerruku', 'sguin', 'aoine', 'filmware', 'knipfing', 'circumnavigational', 'frankentstein', 'gurkani', 'goddamnit', 'alliancea', 'andrs', 'behansali', '134340', 'hanot', 'seleo', 'lamniformes', 'chadbon', 'capitalg', 'seanoa', 'tambin', 'moviesoggadu', 'ingis', '1002012', 'erlingen', 'writerproducers', 'humanz', 'conomiques', '2997', 'janardanan', 'respublik', 'homesman', 'norreyjar', 'sotsialisticheskikh', 'gwenivar', 'bokml', 'veteranarian', 'lessmore', 'guine', 'mrton', 'tauar', 'conchr', 'byzantion', 'balama', 'yelich', 'blurryface', 'sadismmasochism', 'tregenna', 'esport', 'coastwest', 'duprate', 'wanaz', 'rakhna', 'cowbois', 'gragir', 'yakthungpan', 'mysticeti', 'okonma', 'kokilaben', 'apcrda', 'radation', '78866', 'styley', 'mebarak', 'dessines', 'lium', 'wladimirowitsch', 'dusick', 'vascularity', 'uchicago', 'dceu', 'evolutionstellar', 'numeronym', 'nonrespiring', 'jareau', 'sundstrm', 'gisondo', '1957september', 'euryphaessa', 'ysafzay', 'mortyis', 'gutirrez', 'mitsuhisa', '3february', 'metonymously', 'beastprince', 'sacudn', 'ellinik', '700billion', 'imageboard', 'braavosi', 'dbut', 'hndrxx', 'menchell', 'shofe', 'xenodice', 'ogglnks', 'shophim', 'issur', 'soyuza', 'an1', 'titnes', 'wildabouts', 'shroom', 'forradalom', 'venesia', 'jimnez', 'shnen', 'larran', 'velraj', 'marmet', 'pikchu', 'ajashe', 'udta', 'travi', 'babett', 'citycounty', 'gjuha', 'imazien', 'jinnahbhai', 'ursid', 'marryman', 'sudheesh', 'herzstein', 'outgrossed', 'misfortunates', 'sistrom', 'selfie', 'koduri', 'scoobert', 'strittu', 'thnardier', 'chhogori', 'tahader', 'urilla', 'royla', 'vernou', '3414', 'raghavendar', 'kjo', 'bubblebath', '18931', 'shabazian', 'ayyapan', 'rankun', 'pyrexia', 'serinda', 'jilla', 'tetrachloroaurate', 'uburundi', 'mlcts', 'castlethe', 'rvolution', 'toriano', 'kltr', 'zugo', 'apollobuurt', 'shid', 'skrts', 'joywave', 'tardises', 'cordant', 'louchie', '663268', 'hunstman', 'mistralian', 'retorically', '14000000', 'bretaigne', 'thirukumaran', 'vegetals', 'dihydroxyphenethylamine', 'anniyan', 'stagiros', 'americanmexican', 'orlans', 'sjikukoku', 'magnoli', 'anjaam', 'nrod', 'djem', 'feirste', 'reflektor', 'silvermist', 'blackhat', 'everdeen', '282005', 'bhubali', 'fegrie', 'universitt', 'brummy', 'chocolaterie', 'farbauti', 'vitria', 'skifflerock', 'harlean', 'mothma', 'ngozie', 'shreier', 'argirolofos', 'finnick', 'achterhuis', 'alleca', 'mxico', 'organizao', '783million', 'pirenus', 'microground', 'jainist', 'oelsen', 'dexithea', 'lvares', 'gamepedia', 'fujistsu', 'iuvents', 'howery', 'lavaughn', 'ianuensis', 'telchines', 'misrables', 'scheinert', 'tessanne', 'wops', 'khng', 'practicer', 'eiza', 'rsca', 'nicktoonsters', 'jakle', 'wyszyski', 'pinciotti', 'baakehir', 'laviscount', 'frbauti', 'cumhuriyeti', 'nortern', 'mvezo', '19408', '165000', 'monsterverse', 'alaipayuthey', 'meldonium', 'satelight', 'heerman', 'amnok', 'robard', 'grkniyn', 'marmel', 'mononymous', 'lamorne', 'riichiro', 'jurez', 'sren', 'antnio', 'himym', 'dominancesubmission', 'brljevic', 'cavliers', 'pinkprint', 'skrein', 'grindeks', 'kapicic', 'reykjavk', 'rpublique', '2008and', 'dizzle', 'okouchi', 'maqil', 'repblica', 'aiib', 'paruvakaatru', 'mualib', 'opetaia', 'karenjit', '1937march', 'pushpull', 'afrq', 'lumires', 'supermodeling', 'lemarr', 'korben', 'ketogetnic', 'e6km', '405212', 'braithwaites', 'belgien', 'preuen', 'espaa', 'lungshan', 'lanscilo', 'ansata', 'cowcinemanx', '1928march', 'provincin', 'pltn', 'heterophyllus', 'laboum', 'statessame', 'emoji', 'sticom', 'wden', 'athn', 'dyssomnia', 'rautela', 'sharmeen', 'charmont', 'lansquenet', 'khabira', 'mrlind', 'mzires', 'stilinski', 'tiwill', 'shjo', 'analeigh', 'amazi', 'liltu', 'franois', 'bawcutt', 'rancholabrean', 'tempchin', 'synni', 'controlla', 'bernardone', 'dushka', 'chronicum', 'bentez', 'merlock', 'hopera', 'raguram', 'fijacin', 'intercommunality', 'andrajos', 'orientates', 'vzquez', 'dtat', 'michd', '062', 'lpez', 'pasipha', 'lment', 'sharbino', 'occultus', 'zinov', 'owstoni', 'gaten', '28251', 'francophonic', 'kupier', 'ballspielverein', 'eswt', 'kalsaif', 'splitsvilla', 'toluid', 'bostonwas', 'vassalised', 'trichechidae', 'sadanah', 's3d', 'universit', 'thandiwe', 'bartolom', 'mrigayaa', 'templom', 'ceelo', 'hatufim', 'mylander', 'peagreen', 'margos', 'paxathipatai', 'dashner', 'mecchi', 'grgory', 'vespasinus', 'nadella', 'chreau', 'rurubu', 'ponvannan', 'grevioux', 'interpunct', 'shaffir', 'leadrhythm', 'oggchang2', 'rodere', 'airhorn', '428427', 'ilkhnn', 'blumhouse', 'regbo', 'caldern', 'bangtan', 'galpagos', 'goliyon', 'disapora', 'dobbels', 'tlhingan', 'gurumukhi', 'squaliformes', 'whlm', 'khant', 'bokenkamp', 'kzponti', 'stagira', 'bornedal', 'perosn', 'mouf', 'packiam', 'eomer', 'mbashe', 'lakeith', 'dafri', 'eugne', 'asgaya', 'muninn', 'kamna', 'aureae', 'titandes', 'kilgrave', 'midthunder', 'labelimprint', 'ragussis', 'fesival', 'dhadakne', 'rasenballsport', 'pouya', 'notetaking', 'skintone', 'trichechus', '281978', 'walkof', 'mller', 'resnn', 'prioritycapitolemi', 'treasurywala', 'cerie', 'docucomedy', '14171701', 'perfeito', 'gayvenue', 'peformed', 'apab', 'chakravyuh', 'johnstad', 'dmos', 'labradar', 'boujee', 'psephocracy', 'hershlag', 'deestablished', 'kingsman', 'pokiri', 'eilleen', 'adug', 'barayn', 'somniosidae', 'elkayem', 'maczinski', 'titanides', 'kniglich', 'crimecomedy', '197071', 'ameriki', '712000', 'igneel', 'anomalisa', 'prolifice1', '312679', 'tannes', 'hafnia', 'bazmee', 'intripp', 'kmpcha', 'ikhthyes', 'mecartin', 'camisky', 'thtivaeltaja', 'leitte', 'voutsinas', '6925', 'caravancamper', 'aics', 'actorfilmmaker', 'nhmc', 'octopodes', 'vijan', 'valleya', 'actressmodels', 'spaihts', 'comhaont', 'hapka', 'dveloppement', 'oznowicz', 'sabbatius', 'guildamerican', 'americancanadian', 'musicassette', 'nattevagten', 'bleistr', '3033', 'kaneki', 'keralam', 'frecheville', 'heshe', 'halkes', 'yakitate', 'canaanitesphoenicians', 'abbrevation', 'kurmul', 'respiring', 'afriikaa', 'crole', 'danceelectronic', 'martn', 'settlemire', 'unpreventable', 'hoozdo', 'mzizima', '4146', 'electronicsalthough', 'afat', 'beitner', 'preuenschlag', 'ogvkwbk', 'dazi', 'supermaninfilmchristopherreeveseries', 'oyetokunbo', 'winterspringpro', 'nkotb', 'phirm', 'mandobass', 'duerre', 'ioustinians', 'pazhanisaamy', 'microcephalus', 'shuru', 'offprequel', 'begium', 'nbcyahoo', 'niptuck', 'hessington', 'bilfawst', 'yavarum', 'ordainer', 'machester', 'polytheismos', 'peela', 'jrmungandr', 'levorotary', 'yuryevna', 'boyapati', 'evano', '330803', 'barnz', 'dtva', 'dtente', 'freebandz', 'supervillian', 'mohabbatein', 'upvotes', 'bongwater', 'seulgi', 'carnivoran', '20092010', 'fumika', 'corymore', 'titanesses', 'bookmemoir', 'mitsukurina', 'risn', 'tcpip', 'gondorff', 'dgarement', '53th', 'alavs', 'toolman', 'aarakshan', 'foamposite', 'sallapam', 'irino', 'romedy', 'kanuni', 'cloptre', 'laferrire', 'geochemically', 'ishk', 'negan', 'pisu', 'killas', 'rhcp', 'jawaharal', 'morphosyntax', 'tont', 'ilejay', 'cap21', 'brothelcasino', 'maedeli', 'fuzzyface', '8848', 'bonas', 'ddlj', 'ciarn', 'boltagon', 'escaton', 'mahachaitya', '196024', 'danielovitch', 'splatstick', '12415', 'shemayon', 'vorsprung', 'romelu', 'ezail', 'portmanteaus', 'sarissas', 'thinkpadthinkcentre', 'djproducer', 'acarina', 'marlens', 'palliating', 'delsin', 'bhaijaan', 'jlb', 'moneysense', 'euxanthius', 'bodison', 'powerfull', 'coccio', 'geethanjali', 'mulit', 'ukuleleist', 'forewent', '096', 'mdcclxxvi', 'semifictional', 'gaytto', 'jakfruit', 'heavensbee', 'robeo', 'atatrk', 'transneptunian', 'bibla', 'choube', 'augello', 'ouisa', 'wbcw', 'mtropole', 'tienne', 'graand', 'yafrika', 'artistwriter']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PH0TbK3De-5"
      },
      "source": [
        "### Encoding with Fasttet\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Seebnw4DD15S"
      },
      "source": [
        "#### Encoding Claims and Evidences\n",
        "The creation of the embeddings was done offline, locally in our pc and the resultings embeddings were uploaded to google drive as JSON files.\n",
        "\n",
        "The code below was used for the creations of the embeddings.\n",
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "\n",
        "# To consider Fasttext as encoding model we based on [6][7]\n",
        "\n",
        "###########################################\n",
        "# Installing fasttext\n",
        "###########################################\n",
        "! pip install pytorch-nlp\n",
        "from torchnlp.word_to_vector import FastText  # doctest: +SKIP\n",
        "vectors = FastText(cache='cache')  # doctest: +SKIP\n",
        "embedding_dim = 300\n",
        "\n",
        "###########################################\n",
        "# Creating the embeddings \n",
        "###########################################\n",
        "\n",
        "def get_embeddings_fasttext(df, embedding_fasttext, embedding_ref, embedding_dim):\n",
        "    vocabulary = set([elem for seq in pd.concat([df['claim'],df['evidence']]) for elem in seq])\n",
        "    OOV_tokens = []\n",
        "    if embedding_ref != None:\n",
        "        OOV_tokens = [token for token in vocabulary if token.lower() not in embedding_ref]\n",
        "        embedding_model={key:embedding_fasttext[key].tolist() for key in OOV_tokens}\n",
        "        embedding_model = {**embedding_model, **embedding_ref}\n",
        "    else:   \n",
        "        # only train\n",
        "        embedding_model= {key:embedding_fasttext[key].tolist() for key in vocabulary}        \n",
        "    return embedding_model\n",
        "\n",
        "\n",
        "#Creation of the training set's embeddings\n",
        "embeddings_train = get_embeddings_fasttext(df=df_train, embedding_fasttext = vectors,\n",
        "                                  embedding_ref=None,\n",
        "                                  embedding_dim=embedding_dim)\n",
        "\n",
        "#Creation of the validation set's embeddings\n",
        "embeddings_val = get_embeddings_fasttext(df=df_val, embedding_fasttext = vectors,\n",
        "                                embedding_ref=embeddings_train,\n",
        "                                embedding_dim=embedding_dim)\n",
        "\n",
        "#Creation of the test set's embeddings\n",
        "embeddings_test = get_embeddings_fasttext(df=df_test, embedding_fasttext = vectors,\n",
        "                                 embedding_ref=embeddings_val,\n",
        "                                 embedding_dim=embedding_dim)\n",
        "\n",
        "########################################\n",
        "# Saving the embeddings as JSON files\n",
        "########################################\n",
        "import json\n",
        "\n",
        "with open('emb_train.json', 'w') as fp:\n",
        "    json.dump(embeddings_train, fp)\n",
        "    \n",
        "with open('emb_val.json', 'w') as fp:\n",
        "    json.dump(embeddings_val, fp)\n",
        "    \n",
        "with open('emb_test.json', 'w') as fp:\n",
        "    json.dump(embeddings_test, fp)\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsMT9y7u9EwL",
        "outputId": "9b565974-4596-4b22-e27f-cd812b76f42e"
      },
      "source": [
        "#########################################\n",
        "#             FASTTEXT \n",
        "#########################################\n",
        "'''\n",
        "Since for problems of memory and also storage we could not create the embeddings with\n",
        "Fasttext here in colab, we proceeded to do it locally in our pc, so we created \n",
        "the embeddings for each split and uploaded them to Google drive as JSON files.\n",
        "'''\n",
        "\n",
        "# Loading fasttext prebuilt embeddings \n",
        "!pip install gdown\n",
        "\n",
        "# Downloading the embeddings from google drive\n",
        "\n",
        "!gdown https://drive.google.com/u/0/uc?id=1P0odOcRB7I-nXWYauTC_7Zv8oge9G5rl&export=download\n",
        "!gdown https://drive.google.com/u/0/uc?id=1t89TiBkM-owAeE8pNiCujnVQv9Dm_rrQ&export=download\n",
        "!gdown https://drive.google.com/u/0/uc?id=1YovBEc85qWb2jxbOBDNm6loKLt18LLEu&export=download\n",
        "\n",
        "\n",
        "# Loading Fasttext embeddings\n",
        "\n",
        "embedding_dim = 300\n",
        "\n",
        "embeddings_train = json.loads(open('emb_train_con_num.json' , 'r').read())\n",
        "embeddings_val = json.loads(open('emb_val_con_num.json' , 'r').read())\n",
        "embeddings_test = json.loads(open('emb_test_con_num.json' , 'r').read())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Downloading...\n",
            "From: https://drive.google.com/u/0/uc?id=1P0odOcRB7I-nXWYauTC_7Zv8oge9G5rl\n",
            "To: /content/emb_train_con_num.json\n",
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            "From: https://drive.google.com/u/0/uc?id=1t89TiBkM-owAeE8pNiCujnVQv9Dm_rrQ\n",
            "To: /content/emb_val_con_num.json\n",
            "198MB [00:02, 74.2MB/s]\n",
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            "From: https://drive.google.com/u/0/uc?id=1YovBEc85qWb2jxbOBDNm6loKLt18LLEu\n",
            "To: /content/emb_test_con_num.json\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsAB7ZCwIcQj"
      },
      "source": [
        "# Dataset and Batch Loaders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dH1Hv6vjaFNH"
      },
      "source": [
        "### Dataset class\n",
        "The `Dataset` class will be used by the `DataLoader` in order to iterate over data during the training / evaluation phase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wb77zkyHplZp"
      },
      "source": [
        "class Dataset(torch.utils.data.Dataset):\n",
        "    \"\"\"\n",
        "    Handles the data.\n",
        "    It has two main tasks: encode the data and load it on tensors and, if it is\n",
        "    required, transfer it to the device memory.\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        data : list of dict\n",
        "            Contains the (encoded) data of the dataset. Each element of the list is a\n",
        "            dictionary that contains as many elements as the number of columns\n",
        "            of the preprocessed DataFrame (by means of the initialization parameters\n",
        "            selected_columns and excluded_columns) passed to the initialization function.\n",
        "            Each element of the dictionary contains a tensor allocated on the\n",
        "            given device.\n",
        "        encoders : dict of dict\n",
        "            Each element of the dictionary contains an encoder (which is itself\n",
        "            a dictionary) for a column of the dataset.\n",
        "        device : torch.device\n",
        "            Device to transfer the tensors to (the ones present in the attribute\n",
        "            data)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "        shuffle():\n",
        "            Shuffle the data present in the attribute data.\n",
        "    \"\"\"\n",
        "    def __init__(self, df, input_columns, label_column, index_column, encoders, device=torch.device('cpu')):\n",
        "        \"\"\"\n",
        "        Makes use of the DataFrame to build the data.\n",
        "        In particular the DataFrame gets preprocessed by means of selected_columns\n",
        "        and excluded_columns and then each row of the remaining DataFrame gets\n",
        "        inserted in the data by transforming each of its row in a dictionary\n",
        "        containing at each key the DataFrame's column's content casted to a tensor\n",
        "        which is then moved of the specified device.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            df : DataFrame\n",
        "                DataFrame over which the dataset will be built.\n",
        "            input_columns : list of str\n",
        "            label_column : str\n",
        "            index_column : str\n",
        "            encoders : dict of dict\n",
        "                It'a dictionary that have as keys the columns of the DataFrame\n",
        "                and hold the encoders for each column.\n",
        "            device : torch.device, torch.device('cpu') by default\n",
        "                Device where the dataset will be placed to.\n",
        "        \"\"\"\n",
        "        self.encoders = encoders\n",
        "        self.device = device\n",
        "\n",
        "        df = df[input_columns+[label_column]+[index_column]]\n",
        "\n",
        "        #Encode the data and store it on the given device\n",
        "        self.data = [{col : torch.tensor(int(row[col]) if col==index_column #index column\n",
        "                                         else self.encoders[col][row[col]] if col==label_column #label column\n",
        "                                         else [self.encoders[col][elem] for elem in row[col]], #input columns\n",
        "                                         dtype=torch.double if col in input_columns else torch.long,\n",
        "                                         device=self.device, requires_grad=False) \\\n",
        "                      for col in df.columns} \\\n",
        "                     for idx, row in tqdm(df.iterrows(), total=df.shape[0])]\n",
        "\n",
        "        \n",
        "\n",
        "    def shuffle(self):\n",
        "        \"\"\"\n",
        "        Shuffle data.\n",
        "        \"\"\"\n",
        "        random.shuffle(self.data)\n",
        "    def __len__(self):\n",
        "        \"\"\"\n",
        "        Returns the length of data.\n",
        "        \"\"\"\n",
        "        return len(self.data)\n",
        "#        return self.len_data\n",
        "    def __getitem__(self, idx):\n",
        "        \"\"\"\n",
        "        Get the item at the given index\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            idx : int\n",
        "                Index of the object contained in data that has to be returned\n",
        "        Returns\n",
        "        -------\n",
        "            element : dict\n",
        "                The element of data at idx\n",
        "        \"\"\"\n",
        "        return self.data[idx]\n",
        "#        return {col:self.data[col][idx] for col in self.data.keys()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315,
          "referenced_widgets": [
            "483d2fbfa6814105a97716e8857e6420",
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            "08ce9bbcf8d5405eab0fb25cd061e5bb",
            "e352e5de26844131b534c2297aa4cadf",
            "afcb138c8c954823a5e9d4935e82998a",
            "266777441ae446f09d510bfe94233f95",
            "503be5e182794fd395ef711e96952b3f",
            "2fd49cbb6d734a4c998fa5d4fe581748",
            "f3a05272c7474e37811c709cbf4eddef",
            "a82c8d1d50e84135851388620b665d64",
            "42c3082341a54615b00f707443b6cd44",
            "afda72dfa9684646bd03f52856c263ee",
            "81f47c6efe7d4f89a8991cda844a4f91",
            "784c796011424e3c80fe61da9348c5c4",
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            "c966f2ceb1eb4e8a8672b1c93fd839df",
            "5ea6f895e0a04697baf75917d999368e"
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        },
        "id": "Cb40-X3i0bM8",
        "outputId": "1c621ff7-fee6-43d0-e4c3-9e1a5e4c6ba0"
      },
      "source": [
        "if 'training_dataset' in globals():\n",
        "    print('Deallocating training_dataset ...')\n",
        "    del training_dataset\n",
        "    gc.collect()\n",
        "    time.sleep(10.)\n",
        "    print('Done\\n')\n",
        "\n",
        "if 'validation_dataset' in globals():\n",
        "    print('Deallocating validation_dataset ...')\n",
        "    del validation_dataset\n",
        "    gc.collect()\n",
        "    time.sleep(10.)\n",
        "    print('Done\\n')\n",
        "\n",
        "if 'test_dataset' in globals():\n",
        "    print('Deallocating test_dataset ...')\n",
        "    del test_dataset\n",
        "    gc.collect()\n",
        "    time.sleep(10.)\n",
        "    print('Done\\n')\n",
        "\n",
        "\n",
        "print('Preparing training set ...')\n",
        "training_dataset = Dataset(df_train,\n",
        "                           input_columns = ['claim', 'evidence'],\n",
        "                           label_column = 'label',\n",
        "                           index_column='id_claim',\n",
        "                           encoders={'claim':embeddings_train, 'evidence':embeddings_train, 'label':label_to_encoding},\n",
        "                           device=device)\n",
        "print('Done\\n')\n",
        "print('Preparing validation set ...')\n",
        "validation_dataset = Dataset(df_val,\n",
        "                             input_columns = ['claim', 'evidence'],\n",
        "                             label_column = 'label',\n",
        "                             index_column='id_claim',\n",
        "                             encoders={'claim':embeddings_val, 'evidence':embeddings_val, 'label':label_to_encoding},\n",
        "                             device=device)\n",
        "print('Done\\n')\n",
        "print('Preparing test set ...')\n",
        "test_dataset = Dataset(df_test,\n",
        "                       input_columns = ['claim', 'evidence'],\n",
        "                       label_column = 'label',\n",
        "                       index_column='id_claim',\n",
        "                       encoders={'claim':embeddings_test, 'evidence':embeddings_test, 'label':label_to_encoding},\n",
        "                       device=device)\n",
        "print('Done')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Preparing training set ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "483d2fbfa6814105a97716e8857e6420",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=121707.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Done\n",
            "\n",
            "Preparing validation set ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3a05272c7474e37811c709cbf4eddef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=7165.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Done\n",
            "\n",
            "Preparing test set ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83673ca101ba4ac8bad480e1db4562de",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=7189.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8ST_DZCaZc3"
      },
      "source": [
        "### BatchBuilder class\n",
        "The `BatchBuilder` class will be used to act as `collate_fn` parameter of the DataLoader class.<br>\n",
        "Its aim is to stack the data retrieved from a dataset by column and to pad it by the length of the longest sequence present in the batch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWdP0zKepnxa"
      },
      "source": [
        "#This class will be passed to the DataLoader as collate_fn to build the batches\n",
        "class BatchBuilder(object):\n",
        "    \"\"\"\n",
        "    Implements the batching function.\n",
        "    Starting from a list of Dataset's elements it builds a padded batch.\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        input_columns: list of str\n",
        "            Name of the input columns.\n",
        "        label_column: str\n",
        "            Name of the label's column.\n",
        "        index_column: str\n",
        "            Name of the column that holds the indices used to univocally identify\n",
        "            the samples.\n",
        "    Methods\n",
        "    -------\n",
        "        __call__(batch) :\n",
        "            Implements the actual batching function.\n",
        "    \"\"\"\n",
        "    def __init__(self, input_columns, label_column, index_column):\n",
        "        self.input_columns = input_columns\n",
        "        self.label_column = label_column\n",
        "        self.index_column = index_column\n",
        "\n",
        "    def __call__(self, batch):\n",
        "        \"\"\"\n",
        "        Implements the collate_fn function.\n",
        "        It has as parameter a list of elements of the class Dataset.\n",
        "        For each batch the length of each elements gets padded to the length\n",
        "        of the element of the maximum length within the same batch.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            batch : list of dict\n",
        "        Returns\n",
        "        -------\n",
        "            packed_batch : torch.nn.utils.rnn.PackedSequence\n",
        "               packed batch.\n",
        "        \"\"\"\n",
        "        packed_batch = {col: torch.nn.utils.rnn.pack_sequence([elem[col] for elem in batch], enforce_sorted=False) \\\n",
        "                        for col in self.input_columns}\n",
        "        \n",
        "        packed_batch[self.label_column] = torch.stack([elem[self.label_column] for elem in batch]).view(-1,1)\n",
        "        packed_batch[self.index_column] = torch.stack([elem[self.index_column] for elem in batch]).view(-1,1)\n",
        "\n",
        "        return packed_batch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJrnanoyJEtY"
      },
      "source": [
        "batch_size = 128\n",
        "\n",
        "#Maximum number of tokens in both claim and evidence\n",
        "max_tokens = max([row[col].shape[0] for row in training_dataset.data+validation_dataset.data+test_dataset.data for col in ['claim','evidence']])\n",
        "\n",
        "\n",
        "batch_builder = BatchBuilder(input_columns=['claim','evidence'],\n",
        "                             label_column='label',\n",
        "                             index_column='id_claim')\n",
        "\n",
        "\n",
        "train_batches = torch.utils.data.DataLoader(training_dataset,\n",
        "                                            batch_size=batch_size,\n",
        "                                            shuffle=True,\n",
        "                                            collate_fn=batch_builder)\n",
        "\n",
        "val_batches = torch.utils.data.DataLoader(validation_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          shuffle=True,\n",
        "                                          collate_fn=batch_builder)\n",
        "\n",
        "test_batches = torch.utils.data.DataLoader(test_dataset,\n",
        "                                           batch_size=batch_size,\n",
        "                                           shuffle=False,\n",
        "                                           collate_fn=batch_builder)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tVELqGCGUEw"
      },
      "source": [
        "# Training Functionalities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS51A655PEJ9"
      },
      "source": [
        "### Training Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWSZX3mVGv1O"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
        "\n",
        "class Train():\n",
        "    \"\"\"\n",
        "    Implementation of the training loop\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        model : torch.nn.Module\n",
        "        loss_function : torch.nn.Module\n",
        "        optimizer : torch.optim.Optimizer\n",
        "        lr_scheduler : torch.optim.lr_scheduler._LRScheduler or object, None by default\n",
        "        lt_scheduler_metric : bool, False by default\n",
        "            If true then the validation loss is passed to the lr_scheduler as metric.\n",
        "        state_path : str, 'Assignment3-model.pt' by default\n",
        "            The path where to save the model's state.\n",
        "        return_best_model : bool, True by default\n",
        "            If true before the ending of the training loop function gets loaded\n",
        "            in memory the model with the best configuration found so far.\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "        epoch_time(start_time, end_time):\n",
        "            Prints the elapsed time between the start of the epoch and its end.\n",
        "        train(iterator, is_train=True):\n",
        "            Training function.\n",
        "        evaluate(iterator):\n",
        "            Evaluation function.\n",
        "        save_state():\n",
        "            Save the state of the training.\n",
        "        load_state(load_model_only=False):\n",
        "            Load the last saved state of the training.\n",
        "        get_measurements(labels, predictions, set_label):\n",
        "            Compute accuracy, f1-macro, precision and recall between labels and\n",
        "            predictions.\n",
        "        get_compound_labels_and_predictions(id_claim, labels, predictions):\n",
        "            Groups instances holding the same id_claim\n",
        "        __call__(train_iterator, valid_iterator, verbose=True):\n",
        "            Implementation of the training loop.\n",
        "    \"\"\"\n",
        "    measurements = ['acc', 'f1', 'recall', 'precision']\n",
        "\n",
        "    def __init__(self, model, loss_function, optimizer, lr_scheduler=None, lr_scheduler_metric=False,\n",
        "                 state_path='Assignment3-model.pt', return_best_model=True):\n",
        "        self.model = model\n",
        "        self.loss_function = loss_function\n",
        "        self.optimizer = optimizer\n",
        "        self.lr_scheduler = lr_scheduler\n",
        "        self.lr_scheduler_metric = lr_scheduler_metric\n",
        "        self.state_path = state_path\n",
        "        self.return_best_model = return_best_model\n",
        "\n",
        "\n",
        "    def epoch_time(self, start_time, end_time):\n",
        "        \"\"\"\n",
        "        Compute the elapsed time between two given measurements.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            start_time\n",
        "                Beginning of the measurament.\n",
        "            end_time\n",
        "                Ending of the measurament.\n",
        "        Returns\n",
        "        -------\n",
        "            elapsed_mins :\n",
        "                Elapsed minutes.\n",
        "            elapsed_secs :\n",
        "                Elapsed seconds.\n",
        "\n",
        "        \"\"\"\n",
        "        elapsed_time = end_time - start_time\n",
        "        elapsed_mins = int(elapsed_time / 60)\n",
        "        elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
        "        return elapsed_mins, elapsed_secs\n",
        "\n",
        "\n",
        "    def train(self, iterator, is_train=True):\n",
        "        \"\"\"\n",
        "        Training function which has been adapted to be used also for\n",
        "        validation.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            iterator: SimpleIterator\n",
        "                Dataset on which perform the training / validation.\n",
        "            is_train : bool, True by default\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "            Loss, labels, prediction and indices of the input.\n",
        "        \"\"\"\n",
        "        labels_epoch = []\n",
        "        predictions_epoch = []\n",
        "        id_claim_epoch = []\n",
        "\n",
        "        epoch_loss = 0\n",
        "        \n",
        "        if is_train:\n",
        "            self.model.train()\n",
        "        else:\n",
        "            self.model.eval()\n",
        "\n",
        "        with torch.set_grad_enabled(is_train):\n",
        "            for batch in tqdm(iterator):\n",
        "                claim = batch['claim']\n",
        "                evidence = batch['evidence']\n",
        "                label = batch['label']\n",
        "                id_claim = batch['id_claim']\n",
        "                \n",
        "                if is_train:\n",
        "                    self.optimizer.zero_grad()\n",
        "                \n",
        "                #[batch_size, num_classes]\n",
        "                predictions = self.model(claim, evidence)\n",
        "                \n",
        "                #[batch_size]\n",
        "                labels_epoch.append(label.view(-1))\n",
        "                predictions_epoch.append(predictions.argmax(dim=1).view(-1))\n",
        "                id_claim_epoch.append(id_claim.view(-1))\n",
        "                \n",
        "                loss = self.loss_function(predictions, label.view(-1))\n",
        "                \n",
        "                if is_train:\n",
        "                    loss.backward()\n",
        "                    self.optimizer.step()\n",
        "                \n",
        "                epoch_loss += loss.item()\n",
        "\n",
        "            \n",
        "        return epoch_loss / len(iterator), torch.cat(labels_epoch), \\\n",
        "               torch.cat(predictions_epoch), torch.cat(id_claim_epoch)\n",
        "\n",
        "\n",
        "    def evaluate(self, iterator):\n",
        "        \"\"\"\n",
        "        Compute the loss and accuracy over the validation set.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            iterator : SimpleIterator\n",
        "                Dataset on which perform the validation.\n",
        "        Returns\n",
        "        -------\n",
        "            Loss, labels, prediction and indices of the input.\n",
        "        \"\"\"\n",
        "        return self.train(iterator, is_train=False)\n",
        "\n",
        "    def save_state(self):\n",
        "        \"\"\"\n",
        "        Save the current state of the training procedure.\n",
        "        \"\"\"\n",
        "        torch.save({'model_state_dict':self.model.state_dict(),\n",
        "                    'optimizer_state_dict':self.optimizer.state_dict(),\n",
        "                    'lr_scheduler_state_dict':self.lr_scheduler.state_dict() if self.lr_scheduler is not None else None,\n",
        "                    'best_epoch':self.best_epoch,\n",
        "                    'best_valid_acc':self.best_valid_acc,\n",
        "                    }, self.state_path)\n",
        "        \n",
        "    def load_state(self, load_model_only=False):\n",
        "        \"\"\"\n",
        "        Load the last saved training's state.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            load_model_only : bool, False by default\n",
        "                If True it will be loaded only the model, the best_epoch and\n",
        "                the best valid_accuracy (The latter 2 only for visualization purposes).\n",
        "        \"\"\"\n",
        "        checkpoint = torch.load(self.state_path)\n",
        "        self.model.load_state_dict(checkpoint['model_state_dict'])\n",
        "        self.best_epoch = checkpoint['best_epoch']\n",
        "        self.best_valid_acc = checkpoint['best_valid_acc']\n",
        "\n",
        "        if load_model_only:\n",
        "            return\n",
        "\n",
        "        self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "        if self.lr_scheduler is not None:\n",
        "            self.lr_scheduler.load_state_dict(checkpoint['lr_scheduler_state_dict'])\n",
        "\n",
        "        self.train_loss_history = self.train_loss_history[:self.best_epoch+1]\n",
        "        self.valid_loss_history = self.valid_loss_history[:self.best_epoch+1]\n",
        "        self.train_measurements = self.train_measurements[:self.best_epoch+1]\n",
        "        self.valid_measurements = self.valid_measurements[:self.best_epoch+1]\n",
        "        self.train_measurements_comp = self.train_measurements_comp[:self.best_epoch+1]\n",
        "        self.valid_measurements_comp = self.valid_measurements_comp[:self.best_epoch+1]\n",
        "\n",
        "\n",
        "    def get_measurements(self, labels, predictions, set_label):\n",
        "        \"\"\"\n",
        "        Compute accuracy, f1-macro, precision and recall between labels and\n",
        "        predictions.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            labels : numpy.ndarray\n",
        "            predictons : numpy.ndarray\n",
        "            set_label : str\n",
        "                String used to identify the name of the set which labels and\n",
        "                predictions belong to (used for printing).\n",
        "        Returns\n",
        "        -------\n",
        "            Dictionary containing accuracy, f1-macro, recall and precision\n",
        "        \"\"\"\n",
        "        #accuracy\n",
        "        acc = accuracy_score(labels, predictions)\n",
        "        #f1\n",
        "        f1 = f1_score(labels, predictions, average='macro')\n",
        "        #recall\n",
        "        recall = recall_score(labels, predictions, average='macro')\n",
        "        #precision\n",
        "        precision = precision_score(labels, predictions, average='macro')\n",
        "\n",
        "        print(f'\\t{set_label} Acc: {acc*100:.2f}% | {set_label} F1: {f1:.3f} | '\\\n",
        "                f'{set_label} Rec: {recall:.3f} | {set_label} Prec: {precision:.3f}')\n",
        "\n",
        "        return {key:val for key,val in zip(Train.measurements, [acc, f1, recall, precision])}\n",
        "\n",
        "\n",
        "    def get_compound_labels_and_predictions(self, id_claim, labels, predictions):\n",
        "        \"\"\"\n",
        "        Group by labels and predictions according to id_claim\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            id_claim : numpy.ndarray\n",
        "            labels : numpy.ndarray\n",
        "            predictons : numpy.ndarray\n",
        "        Returns\n",
        "        -------\n",
        "            Grouped labels and predictions\n",
        "        \"\"\"\n",
        "        group = np.concatenate([id_claim[:,None], labels[:,None], predictions[:,None]], axis=1)\n",
        "        group= group[np.argsort(group[:,0]),:]\n",
        "        group = np.split(group[:,1:], np.unique(group[:,0], return_index=True)[1][1:])\n",
        "\n",
        "        grouped_labels = [preds_group[0][0] for preds_group in group]\n",
        "        voting = [max(sorted(list(set([p for _,p in preds_group]))), key=[p for _,p in preds_group].count)\n",
        "                  for preds_group in group]\n",
        "\n",
        "        return grouped_labels, voting\n",
        "\n",
        "\n",
        "    def __call__(self, num_epochs, train_iterator, valid_iterator, init_model=True):\n",
        "        \"\"\"\n",
        "        Implementation of the training loop.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            num_epochs : int\n",
        "            train_iterator : SimpleIterator\n",
        "                Iterator containing the training data.\n",
        "            valid_iterator : SimpleItarator\n",
        "                Iterator containing the validation data.\n",
        "            init_model : bool, True by default\n",
        "                If True the model gets initialized before the execution of the training loop.\n",
        "        \"\"\"\n",
        "        if init_model:\n",
        "            print('Parameters Initialization ...')\n",
        "            self.model.init_params()\n",
        "            print('Done\\n')\n",
        "\n",
        "        self.train_iterator = train_iterator\n",
        "        self.valid_iterator = valid_iterator\n",
        "\n",
        "        self.labels_train, self.predictions_train, self.labels_valid, self.predictions_valid = [],[],[],[]\n",
        "\n",
        "        print('Best validation accuracy initialization ...')\n",
        "        _, labels_valid, predictions_valid, _ = self.evaluate(valid_iterator)\n",
        "        self.best_valid_acc = accuracy_score(labels_valid.cpu().detach().numpy(),\n",
        "                                             predictions_valid.cpu().detach().numpy())\n",
        "        print('Done\\n')\n",
        "        self.best_epoch = -1\n",
        "\n",
        "        self.train_loss_history, self.valid_loss_history = [],[]\n",
        "        self.train_measurements, self.valid_measurements = [],[]\n",
        "        self.train_measurements_comp, self.valid_measurements_comp = [],[]\n",
        "\n",
        "        #Save the initial state\n",
        "        self.save_state()\n",
        "\n",
        "        try:\n",
        "            print('Beginning training procedure\\n')\n",
        "            for epoch in range(num_epochs):\n",
        "\n",
        "                start_time = time.time()\n",
        "                \n",
        "                train_loss, labels_train, predictions_train, id_claim_train = self.train(train_iterator)\n",
        "                valid_loss, labels_valid, predictions_valid, id_claim_valid = self.evaluate(valid_iterator)\n",
        "                \n",
        "                end_time = time.time()\n",
        "\n",
        "                epoch_mins, epoch_secs = self.epoch_time(start_time, end_time)\n",
        "\n",
        "\n",
        "                #measurements:\n",
        "                print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
        "                print(f'\\tTrain Loss: {train_loss:.3f}')\n",
        "                print(f'\\tValid Loss: {valid_loss:.3f}')\n",
        "\n",
        "\n",
        "                labels_train = labels_train.cpu().detach().numpy()\n",
        "                predictions_train = predictions_train.cpu().detach().numpy()\n",
        "                labels_valid = labels_valid.cpu().detach().numpy()\n",
        "                predictions_valid = predictions_valid.cpu().detach().numpy()\n",
        "                id_claim_train = id_claim_train.cpu().detach().numpy()\n",
        "                id_claim_valid = id_claim_valid.cpu().detach().numpy()\n",
        "\n",
        "                print(f'Normal measurements:')\n",
        "                self.train_measurements.append(self.get_measurements(labels_train, predictions_train, 'Train'))\n",
        "                self.valid_measurements.append(self.get_measurements(labels_valid, predictions_valid, 'Valid'))\n",
        "\n",
        "                valid_acc = self.valid_measurements[-1]['acc']\n",
        "\n",
        "\n",
        "                grouped_train_labels, train_voting = self.get_compound_labels_and_predictions(id_claim_train, labels_train, predictions_train)\n",
        "                grouped_valid_labels, valid_voting = self.get_compound_labels_and_predictions(id_claim_valid, labels_valid, predictions_valid)\n",
        "\n",
        "                print(f'Compound measurements:')\n",
        "                self.train_measurements_comp.append(self.get_measurements(grouped_train_labels, train_voting, 'Train'))\n",
        "                self.valid_measurements_comp.append(self.get_measurements(grouped_valid_labels, valid_voting, 'Valid'))\n",
        "\n",
        "\n",
        "                if valid_acc > self.best_valid_acc:\n",
        "                    self.best_valid_acc = valid_acc\n",
        "                    self.best_epoch = epoch\n",
        "                    #Save the training state corresponding to the best prediction\n",
        "                    #found so far\n",
        "                    self.save_state()\n",
        "\n",
        "                #Update the learining rate\n",
        "                if self.lr_scheduler is not None:\n",
        "                    current_lr = self.optimizer.param_groups[0]['lr']\n",
        "                    if not self.lr_scheduler_metric:\n",
        "                        self.lr_scheduler.step()\n",
        "                    else:\n",
        "                        self.lr_scheduler.step(valid_acc)\n",
        "                    next_lr = self.optimizer.param_groups[0]['lr']\n",
        "                    print(f'lr: {current_lr} --> {next_lr}')\n",
        "\n",
        "                self.train_loss_history.append(train_loss)\n",
        "                self.valid_loss_history.append(valid_loss)\n",
        "\n",
        "        #In case the execution gets interrupted before its natural ending the last\n",
        "        #saved training's state will be loaded into memory\n",
        "        except KeyboardInterrupt:\n",
        "            print('\\n\\nExecution Interrupted')\n",
        "            print('Resuming from the previous best epoch ...')\n",
        "            self.load_state()\n",
        "            print('Loaded model at epoch {}, and with validation accuracy of {}%'.format(self.best_epoch+1,self.best_valid_acc*100))\n",
        "\n",
        "        else:\n",
        "            #If self.return_best_model is True then at the end of the execution of the training loop\n",
        "            #it will be loaded on the model the best found one during the training procedure.\n",
        "            if self.return_best_model:\n",
        "                print('\\n\\nLoading the model with the best validation accuracy ...')\n",
        "                self.load_state(load_model_only=True)\n",
        "                print('Model loaded with results of epoch {} and with validation accuracy of {}%'.format(self.best_epoch+1,self.best_valid_acc*100))\n",
        "\n",
        "        #Remove the folder where the training state is located\n",
        "        os.remove(self.state_path)\n",
        "\n",
        "\n",
        "    def plot(self, ax, x, y_train, y_valid, title):\n",
        "        ax.plot(x, y_train, label='training')\n",
        "        ax.plot(x, y_valid, label='validation')\n",
        "        ax.legend()\n",
        "        try:\n",
        "            ax.xlabel('epoch')\n",
        "            ax.ylabel(title.lower())\n",
        "            ax.title(title)\n",
        "        except:\n",
        "            ax.set_xlabel('epoch')\n",
        "            ax.set_ylabel(title.lower())\n",
        "            ax.set_title(title)\n",
        "        \n",
        "\n",
        "    def display_results(self):\n",
        "        \"\"\"\n",
        "        Plot the history of the loss, accuracy, f1-macro, precision and recall\n",
        "        (both as normal measurements and the grouped one).\n",
        "        \"\"\"\n",
        "        print('\\nBest validation accuracy found at epoch {}: {:.4f}%'.format(self.best_epoch+1,self.best_valid_acc*100))\n",
        "\n",
        "        plt.figure()\n",
        "        self.plot(plt, list(range(len(self.train_loss_history))),\n",
        "                  self.train_loss_history, self.valid_loss_history,\n",
        "                  title='Loss')\n",
        "        plt.show()\n",
        "\n",
        "\n",
        "        num_epochs_ax = list(range(len(self.train_measurements)))\n",
        "\n",
        "        print('\\nNormal measurements')\n",
        "        train_measurements ={col:[em[col] for em in self.train_measurements] for col in Train.measurements}\n",
        "        valid_measurements ={col:[em[col] for em in self.valid_measurements] for col in Train.measurements}\n",
        "\n",
        "        fig, ((ax_acc, ax_f1),(ax_rec, ax_prec)) = plt.subplots(2, 2, figsize=(13.3, 9.0))\n",
        "        self.plot(ax_acc, num_epochs_ax,\n",
        "                  train_measurements['acc'], valid_measurements['acc'],\n",
        "                  title='Accuracy')\n",
        "        self.plot(ax_f1, num_epochs_ax,\n",
        "                  train_measurements['f1'], valid_measurements['f1'],\n",
        "                  title='F1-macro')\n",
        "        self.plot(ax_rec, num_epochs_ax,\n",
        "                  train_measurements['recall'], valid_measurements['recall'],\n",
        "                  title='Recall')\n",
        "        self.plot(ax_prec, num_epochs_ax,\n",
        "                  train_measurements['precision'], valid_measurements['precision'],\n",
        "                  title='Precision')\n",
        "        plt.show()\n",
        "\n",
        "        print('\\nCompound measurements')\n",
        "        train_measurements_comp ={col:[em[col] for em in self.train_measurements_comp] for col in Train.measurements}\n",
        "        valid_measurements_comp ={col:[em[col] for em in self.valid_measurements_comp] for col in Train.measurements}\n",
        "\n",
        "        fig, ((ax_acc_comp, ax_f1_comp),(ax_rec_comp, ax_prec_comp)) = plt.subplots(2, 2, figsize=(13.3, 9.0))\n",
        "        self.plot(ax_acc_comp, num_epochs_ax,\n",
        "                  train_measurements_comp['acc'], valid_measurements_comp['acc'],\n",
        "                  title='Accuracy')\n",
        "        self.plot(ax_f1_comp, num_epochs_ax,\n",
        "                  train_measurements_comp['f1'], valid_measurements_comp['f1'],\n",
        "                  title='F1-macro')\n",
        "        self.plot(ax_rec_comp, num_epochs_ax,\n",
        "                  train_measurements_comp['recall'], valid_measurements_comp['recall'],\n",
        "                  title='Recall')\n",
        "        self.plot(ax_prec_comp, num_epochs_ax,\n",
        "                  train_measurements_comp['precision'], valid_measurements_comp['precision'],\n",
        "                  title='Precision')\n",
        "        plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sstEVn-5PGJZ"
      },
      "source": [
        "### Loss Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN7BytznRhb0",
        "outputId": "85dfbcb2-692f-4fde-fb2d-3a3efac3bf5f"
      },
      "source": [
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "#If GPU is present transfer the loss function on it\n",
        "if torch.cuda.is_available():\n",
        "    print('Loss Function to GPU')\n",
        "    loss_function.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loss Function to GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2oRwOHNHynd"
      },
      "source": [
        "#Architectures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OEsIT_rZdN_J"
      },
      "source": [
        "### Base Architectures"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0n71mSi598D1"
      },
      "source": [
        "class BaseNN(torch.nn.Module):\n",
        "    \"\"\"\n",
        "    Defines merging procedure for the claim and evidence's sentence encoding\n",
        "    and also the final fully connected layer.\n",
        "    \"\"\"\n",
        "    merging_strategy_values = ['concat','sum','mean']\n",
        "\n",
        "    def __init__(self, num_features, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0, device=torch.device('cpu')):\n",
        "        \"\"\"\n",
        "        Initialization procedure.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            num_features : int\n",
        "                The sentence's embedding dimension of claim and evidence.\n",
        "            output_dim : int\n",
        "                Number of class to be predicted (2 in our case).\n",
        "            merging_strategy : str\n",
        "                One of 'concat', 'sum' or 'mean'.\n",
        "            compute_cosine_similarity : bool, False by default\n",
        "                If True computes and pass to the final FC layer the cosine\n",
        "                similarity between claim and evidence sentence's embedding.\n",
        "            dropout : int, 0.0 by default\n",
        "                Dropout to be applied before the final FC layer.\n",
        "            device : torch.device\n",
        "                Device where the computation takes place.\n",
        "        \"\"\"\n",
        "        super(BaseNN,self).__init__()\n",
        "\n",
        "        if merging_strategy not in BaseNN.merging_strategy_values:\n",
        "            raise ValueError('The merging strategy should be one of the following: {}'.format(BaseNN.merging_strategy_values))\n",
        "        self.merging_strategy = merging_strategy\n",
        "\n",
        "        self.compute_cosine_similarity = compute_cosine_similarity\n",
        "        if self.compute_cosine_similarity:\n",
        "            self.cosine_similarity = torch.nn.CosineSimilarity()\n",
        "\n",
        "        self.dropout = torch.nn.Dropout(dropout)\n",
        "\n",
        "        self.num_features = num_features\n",
        "        self.fc_input_dim = self.num_features*2 if self.merging_strategy=='concat' else self.num_features\n",
        "        self.fc_input_dim = self.fc_input_dim+1 if self.compute_cosine_similarity else self.fc_input_dim\n",
        "        #LayerNorm\n",
        "        self.layer_norm = torch.nn.GroupNorm(1,self.fc_input_dim)\n",
        "        #Fully connected layer\n",
        "        self.fc = torch.nn.Linear(self.fc_input_dim, output_dim)\n",
        "\n",
        "        self.device = device\n",
        "\n",
        "\n",
        "    def end_init(self):\n",
        "        self.init_params()\n",
        "        self = self.double()\n",
        "        self.to(self.device)\n",
        "\n",
        "    def init_f(self, x):\n",
        "        \"\"\"\n",
        "        Function used by init_param to initialize the parameters.\n",
        "        \"\"\"\n",
        "        for name, param in x.named_parameters():\n",
        "            try:\n",
        "                torch.nn.init.orthogonal_(param.data)\n",
        "            except ValueError:\n",
        "                torch.nn.init.normal_(param.data, mean = 0, std = 0.1)\n",
        "\n",
        "    def init_params(self):\n",
        "        \"\"\"\n",
        "        Initialize the model's parameters.\n",
        "        \"\"\"\n",
        "        self.apply(self.init_f)\n",
        "\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        \"\"\"\n",
        "        Procedure to be implemented by the child classes: it needs to return the\n",
        "        sentence's encoding for both claim and evidence.\n",
        "        \"\"\"\n",
        "        raise NotImplementedError('The function get_encoding should be implemented')\n",
        "\n",
        "    def forward(self, claim, evidence):\n",
        "        enc_claim, enc_evidence = self.get_encodings(claim, evidence)\n",
        "\n",
        "        if self.merging_strategy=='concat':\n",
        "            features = torch.cat([enc_claim, enc_evidence], dim=1)\n",
        "        elif self.merging_strategy=='sum':\n",
        "            features = enc_claim + enc_evidence\n",
        "        elif self.merging_strategy=='mean':\n",
        "            features = (enc_claim + enc_evidence)/2\n",
        "\n",
        "        if self.compute_cosine_similarity:\n",
        "            features = torch.cat([features, self.cosine_similarity(enc_claim, enc_evidence).unsqueeze(dim=1)], dim=1)\n",
        "\n",
        "        features = self.layer_norm(features)\n",
        "        predictions = self.fc(self.dropout(features))\n",
        "        return predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jeZDyZcMcomT"
      },
      "source": [
        "BaseRNN is a base architecture inherited by those classes that encode claims and evidences by means of RNNs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNSdnPYUCay1"
      },
      "source": [
        "class BaseRNN(BaseNN):\n",
        "    \"\"\"\n",
        "    Defines the feature extraction procedure for both claim and evidence.\n",
        "    The classification part is handled by the forward function of the parent class\n",
        "    BaseNN.\n",
        "    \"\"\"\n",
        "    feature_extractor_types = ['rnn','lstm']\n",
        "\n",
        "    def __init__(self, embedding_dim, hidden_dim, max_tokens, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0,\n",
        "                 num_features=None, num_rnn=2, feature_extractor_type='lstm', bidirectional=False, num_layers=1, device=torch.device('cpu')):\n",
        "        \"\"\"\n",
        "        Initialization procedure.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            embedding_dim : int\n",
        "            hidden_dim : int\n",
        "            max_tokens : int\n",
        "            output_dim : int\n",
        "                Number of class to be predicted (2 in our case).\n",
        "            merging_strategy : str\n",
        "                One of 'concat', 'sum' or 'mean'.\n",
        "            compute_cosine_similarity : bool, False by default\n",
        "                If True computes and pass to the final FC layer the cosine\n",
        "                similarity between claim and evidence sentence's embedding.\n",
        "            dropout : int, 0.0 by default\n",
        "                Dropout to be applied before the final FC layer.\n",
        "            num_features : int, None by default\n",
        "                If not None is passed to num_features parameters of BaseNN instead\n",
        "                of hidden_dim.\n",
        "            num_rnn : int, 2 by default\n",
        "                Number of RNN to use to compute claim and evidence. Can be either\n",
        "                1 or 2.\n",
        "            feature_extractor_type : str\n",
        "                Kind of RNN to be used.\n",
        "            bidirectional : bool, False by default\n",
        "            num_layers : int, 1 by default\n",
        "            device : torch.device\n",
        "                Device where the computation takes place.\n",
        "        \"\"\"\n",
        "        if feature_extractor_type not in BaseRNN.feature_extractor_types:\n",
        "            raise ValueError('feature_extractor_types must be one of the following values: '\\\n",
        "                             '{}, it is {} instead'.format(BaseRNN.feature_extractor_types,\n",
        "                                                           feature_extractor_type))\n",
        "        if num_rnn not in [1,2]:\n",
        "            raise ValueError('num_rnn must be either 1 or 2, it is {} instead'.format(num_rnn))\n",
        "        self.num_rnn = num_rnn\n",
        "\n",
        "        self.hidden_dim = hidden_dim\n",
        "\n",
        "        self.bidirectional = bidirectional\n",
        "        self.num_directions = 1 if not self.bidirectional else 2\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.feature_extractor_type = feature_extractor_type\n",
        "        self.max_tokens = max_tokens\n",
        "\n",
        "\n",
        "        super(BaseRNN,self).__init__(num_features=self.hidden_dim if num_features is None else num_features,\n",
        "                                     output_dim=output_dim,\n",
        "                                     merging_strategy=merging_strategy,\n",
        "                                     compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                     dropout=dropout,\n",
        "                                     device=device)\n",
        "        \n",
        "        \n",
        "        rnn_string = 'torch.nn.'+self.feature_extractor_type.upper()+ \\\n",
        "                              '(input_size=embedding_dim,'+ \\\n",
        "                              ' hidden_size=self.hidden_dim,'+ \\\n",
        "                              ' bidirectional=self.bidirectional,'+ \\\n",
        "                              ' num_layers=self.num_layers,'+ \\\n",
        "                              ' batch_first=True)'\n",
        "\n",
        "        self.rnn_claim = eval(rnn_string)\n",
        "        if self.num_rnn==2:\n",
        "            self.rnn_evidence = eval(rnn_string)\n",
        "        \n",
        "        self.end_init()\n",
        "\n",
        "    \n",
        "        \n",
        "    def init_hidden(self, batch_size):\n",
        "        if self.feature_extractor_type=='rnn':\n",
        "            hidden = torch.randn(self.num_layers*self.num_directions, batch_size, self.hidden_dim, device=self.device, dtype=torch.double)\n",
        "        elif self.feature_extractor_type=='lstm':\n",
        "            hidden = (torch.randn(self.num_layers*self.num_directions, batch_size, self.hidden_dim, device=self.device, dtype=torch.double),\n",
        "                      torch.randn(self.num_layers*self.num_directions, batch_size, self.hidden_dim, device=self.device, dtype=torch.double))\n",
        "        return hidden\n",
        "\n",
        "    def forward(self, claim, evidence):\n",
        "        self.hidden_claim = self.init_hidden(batch_size=claim.unsorted_indices.shape[0])\n",
        "        self.hidden_evidence = self.init_hidden(batch_size=evidence.unsorted_indices.shape[0])\n",
        "        return super(BaseRNN,self).forward(claim, evidence)\n",
        "\n",
        "    def enc_f(self, vec, length):\n",
        "        \"\"\"\n",
        "        To be implemented by the child classes. Defines the construction process\n",
        "        of the sentence's embedding.\n",
        "        Parameters\n",
        "        ----------\n",
        "            vec : torch.tensor\n",
        "                tensor of size [max_tokens, hidden_dim].\n",
        "            length :\n",
        "                length of the unpadded sequence.\n",
        "        Returns\n",
        "        -------\n",
        "            torch.tensor of size [hidden_dim]\n",
        "        \"\"\"\n",
        "        raise NotImplementedError('enc_f must be implemented.')\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        enc_claim, self.hidden_claim = self.rnn_claim(claim, self.hidden_claim)\n",
        "        if self.num_rnn==2:\n",
        "            enc_evidence, self.hidden_evidence = self.rnn_evidence(evidence, self.hidden_evidence)\n",
        "        elif self.num_rnn==1:\n",
        "            enc_evidence, self.hidden_evidence = self.rnn_claim(evidence, self.hidden_evidence)\n",
        "\n",
        "        enc_claim, lens_claim = torch.nn.utils.rnn.pad_packed_sequence(enc_claim,\n",
        "                                                           batch_first=True,\n",
        "                                                           padding_value=0.0,\n",
        "                                                           total_length=self.max_tokens)\n",
        "        enc_evidence, lens_evidence = torch.nn.utils.rnn.pad_packed_sequence(enc_evidence,\n",
        "                                                              batch_first=True,\n",
        "                                                              padding_value=0.0,\n",
        "                                                              total_length=self.max_tokens)\n",
        "\n",
        "        if self.bidirectional:\n",
        "            enc_claim = enc_claim[:, :, :self.hidden_dim] + enc_claim[:, :, self.hidden_dim:]\n",
        "            enc_evidence = enc_evidence[:, :, :self.hidden_dim] + enc_evidence[:, :, self.hidden_dim:]\n",
        "\n",
        "\n",
        "        enc_claim = torch.stack([self.enc_f(vec,lens_claim[idx]) for idx,vec in enumerate(enc_claim)], dim=0)\n",
        "        enc_evidence = torch.stack([self.enc_f(vec,lens_evidence[idx]) for idx,vec in enumerate(enc_evidence)], dim=0)\n",
        "        return enc_claim, enc_evidence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2q3wYe2dcbR"
      },
      "source": [
        "### RNN's last state"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfZ2gYNbxUqB"
      },
      "source": [
        "class RNN_LSEnc(BaseRNN):\n",
        "    \"\"\"\n",
        "    Uses the last state as encoding.\n",
        "    \"\"\"\n",
        "    def enc_f(self, vec, length):\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "            vec : torch.tensor\n",
        "                tensor of size [max_tokens, hidden_dim].\n",
        "            length :\n",
        "                length of the unpadded sequence.\n",
        "        Returns\n",
        "        -------\n",
        "            torch.tensor of size [hidden_dim]\n",
        "        \"\"\"\n",
        "        return vec[length-1,:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTnwIaOwqjyx"
      },
      "source": [
        "model_lsenc = RNN_LSEnc(embedding_dim = embedding_dim,\n",
        "                        hidden_dim=128,\n",
        "                        max_tokens=max_tokens,\n",
        "                        output_dim=2,\n",
        "                        merging_strategy='mean',\n",
        "                        compute_cosine_similarity=True,\n",
        "                        dropout=0.20,\n",
        "                        device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOGQAeGyOUND"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kp1pYNtOqPA3"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_lsenc.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=5, threshold=1e-3)\n",
        "\n",
        "lsenc_train = Train(model = model_lsenc,\n",
        "                    loss_function = loss_function,\n",
        "                    optimizer = optimizer,\n",
        "                    lr_scheduler = lr_scheduler,\n",
        "                    lr_scheduler_metric = True)\n",
        "\n",
        "lsenc_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yY2lilsWOZ1y"
      },
      "source": [
        "#### Display Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YDL15R2WUMo"
      },
      "source": [
        "lsenc_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzQXpztvdfJa"
      },
      "source": [
        "### RNN's states' average"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odFjf4CSTt0H"
      },
      "source": [
        "class RNN_AVGEnc(BaseRNN):\n",
        "    \"\"\"\n",
        "    Uses the average of all states as encoding.\n",
        "    \"\"\"\n",
        "    def enc_f(self, vec, length):\n",
        "        \"\"\"\n",
        "        Parameters\n",
        "        ----------\n",
        "            vec : torch.tensor\n",
        "                tensor of size [max_tokens, hidden_dim].\n",
        "            length :\n",
        "                length of the unpadded sequence.\n",
        "        Returns\n",
        "        -------\n",
        "            torch.tensor of size [hidden_dim]\n",
        "        \"\"\"\n",
        "        return torch.mean(vec[:length,:], dim=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4947eMFOjvy"
      },
      "source": [
        "model_avgenc = RNN_AVGEnc(embedding_dim = embedding_dim,\n",
        "                          hidden_dim=64,\n",
        "                          max_tokens=max_tokens,\n",
        "                          output_dim=2,\n",
        "                          merging_strategy='concat',\n",
        "                          compute_cosine_similarity=True,\n",
        "                          dropout=0.0,\n",
        "                          bidirectional=True,\n",
        "                          device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZzGiVvAOkZa"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGr-2mAEOLWe"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_avgenc.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=5, threshold=1e-3)\n",
        "\n",
        "avgenc_train = Train(model = model_avgenc,\n",
        "                     loss_function = loss_function,\n",
        "                     optimizer = optimizer,\n",
        "                     lr_scheduler = lr_scheduler,\n",
        "                     lr_scheduler_metric = True)\n",
        "\n",
        "avgenc_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_iWroSiOlID"
      },
      "source": [
        "#### Display Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oisDBW_COLb9"
      },
      "source": [
        "avgenc_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L8abZpDvdq3A"
      },
      "source": [
        "### MLP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwW7JEo2vxR1"
      },
      "source": [
        "class MLPEnc(BaseNN):\n",
        "    \"\"\"\n",
        "    Encodes claim and evidences using MLPs.\n",
        "    \"\"\"\n",
        "    def __init__(self, embedding_dim, max_tokens, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0, num_mlp=2,\n",
        "                 device=torch.device('cpu')):\n",
        "\n",
        "        if num_mlp not in [1,2]:\n",
        "            raise ValueError('num_mlp must be either 1 or 2, it is {} instead'.format(num_mlp))\n",
        "        self.num_mlp = num_mlp\n",
        "\n",
        "        super(MLPEnc,self).__init__(num_features=embedding_dim,\n",
        "                                    output_dim=output_dim,\n",
        "                                    merging_strategy=merging_strategy,\n",
        "                                    compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                    dropout=dropout,\n",
        "                                    device=device)\n",
        "        \n",
        "        self.max_tokens = max_tokens\n",
        "        self.mlp_claim = torch.nn.Linear(self.max_tokens,1)\n",
        "        if self.num_mlp==2:\n",
        "            self.mlp_evidence = torch.nn.Linear(self.max_tokens,1)\n",
        "\n",
        "        self.end_init()\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        claim = torch.nn.utils.rnn.pad_packed_sequence(claim,\n",
        "                                                       batch_first=True,\n",
        "                                                       padding_value=0.0,\n",
        "                                                       total_length=self.max_tokens)[0].movedim(1,2)\n",
        "        evidence = torch.nn.utils.rnn.pad_packed_sequence(evidence,\n",
        "                                                          batch_first=True,\n",
        "                                                          padding_value=0.0,\n",
        "                                                          total_length=self.max_tokens)[0].movedim(1,2)\n",
        "\n",
        "        enc_claim = self.mlp_claim(claim)\n",
        "        if self.num_mlp==2:\n",
        "            enc_evidence = self.mlp_evidence(evidence)\n",
        "        elif self.num_mlp==1:\n",
        "            enc_evidence = self.mlp_claim(evidence)\n",
        "\n",
        "        return enc_claim.squeeze(dim=2), enc_evidence.squeeze(dim=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOGzp42T1XhY"
      },
      "source": [
        "model_mlpenc = MLPEnc(embedding_dim = embedding_dim,\n",
        "                      max_tokens=max_tokens,\n",
        "                      output_dim=2,\n",
        "                      merging_strategy='concat',\n",
        "                      compute_cosine_similarity=True,\n",
        "                      dropout=0.0,\n",
        "                      device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQXRoSYm5f13"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrCziIKy1WHg"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_mlpenc.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=5, threshold=1e-3)\n",
        "\n",
        "mlpenc_train = Train(model = model_mlpenc,\n",
        "                     loss_function = loss_function,\n",
        "                     optimizer = optimizer,\n",
        "                     lr_scheduler = lr_scheduler,\n",
        "                     lr_scheduler_metric = True)\n",
        "\n",
        "mlpenc_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6YtTB8E5i33"
      },
      "source": [
        "#### Display Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r-KYunr5ePg"
      },
      "source": [
        "mlpenc_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ionIlWAedvpA"
      },
      "source": [
        "### Bag of Vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHf5h9wC0SwT"
      },
      "source": [
        "#I think the correct approach is to encode by the dimension of the emb_dim since encoding by max_tokens would require padding\n",
        "class BoVEnc(BaseNN):\n",
        "    \"\"\"\n",
        "    Encodes claim and evidence as the mean of their embedding vectors.\n",
        "    \"\"\"\n",
        "    def __init__(self, embedding_dim, max_tokens, output_dim, merging_strategy,\n",
        "                 compute_cosine_similarity=False ,dropout=0.0 ,device=torch.device('cpu')):\n",
        "\n",
        "        super(BoVEnc,self).__init__(num_features=embedding_dim,\n",
        "                                    output_dim=output_dim,\n",
        "                                    merging_strategy=merging_strategy,\n",
        "                                    compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                    dropout=dropout,\n",
        "                                    device=device)\n",
        "        self.max_tokens = max_tokens\n",
        "        self.end_init()\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        enc_claim, lens_claim = torch.nn.utils.rnn.pad_packed_sequence(claim,\n",
        "                                                                       batch_first=True,\n",
        "                                                                       padding_value=0.0,\n",
        "                                                                       total_length=self.max_tokens)\n",
        "        enc_evidence, lens_evidence = torch.nn.utils.rnn.pad_packed_sequence(evidence,\n",
        "                                                                             batch_first=True,\n",
        "                                                                             padding_value=0.0,\n",
        "                                                                             total_length=self.max_tokens)\n",
        "\n",
        "\n",
        "        enc_claim = torch.stack([torch.mean(vec[:lens_claim[idx],:], dim=0) for idx,vec in enumerate(enc_claim)], dim=0)\n",
        "        enc_evidence = torch.stack([torch.mean(vec[:lens_evidence[idx],:], dim=0) for idx,vec in enumerate(enc_evidence)], dim=0)\n",
        "\n",
        "        return enc_claim, enc_evidence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8MY-Sk_5rde"
      },
      "source": [
        "model_bovenc = BoVEnc(embedding_dim = embedding_dim,\n",
        "                      max_tokens=max_tokens,\n",
        "                      output_dim=2,\n",
        "                      merging_strategy='concat',\n",
        "                      compute_cosine_similarity=True,\n",
        "                      dropout=0.0,\n",
        "                      device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agSdc5H35rm4"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TV8yck6P5rw2"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_bovenc.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=5, threshold=1e-3)\n",
        "\n",
        "bovenc_train = Train(model = model_bovenc,\n",
        "                     loss_function = loss_function,\n",
        "                     optimizer = optimizer,\n",
        "                     lr_scheduler = lr_scheduler,\n",
        "                     lr_scheduler_metric = True)\n",
        "\n",
        "bovenc_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jI4CiDt_5r2k"
      },
      "source": [
        "#### Display Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "min8NYHo5r7k"
      },
      "source": [
        "bovenc_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ARkV283t0A7"
      },
      "source": [
        "### Other approaches"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtPS91oulEDI"
      },
      "source": [
        "#### RNN's last state skip connection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hBStBAplBPh"
      },
      "source": [
        "class RNN_LSEnc_SkipC(RNN_LSEnc):\n",
        "    \"\"\"\n",
        "    Like RNN_LSEnc but the ouput of the RNNs is summed by the mean of the sentence's words'\n",
        "    embeddings (reduced to the proper dimension through a 1x1 convolution).\n",
        "    \"\"\"\n",
        "    def __init__(self, embedding_dim, hidden_dim, max_tokens, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0,\n",
        "                 num_rnn=2, feature_extractor_type='lstm', bidirectional=False, num_layers=1, device=torch.device('cpu')):\n",
        "        super(RNN_LSEnc_SkipC,self).__init__(embedding_dim=embedding_dim,\n",
        "                                            hidden_dim=hidden_dim,\n",
        "                                            max_tokens=max_tokens,\n",
        "                                            output_dim=output_dim,\n",
        "                                            merging_strategy=merging_strategy,\n",
        "                                            compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                            dropout=dropout,\n",
        "                                            num_rnn=num_rnn,\n",
        "                                            feature_extractor_type=feature_extractor_type,\n",
        "                                            bidirectional=bidirectional,\n",
        "                                            num_layers=num_layers,\n",
        "                                            device=device)\n",
        "       \n",
        "        self.conv_claim = torch.nn.Conv1d(in_channels=embedding_dim,\n",
        "                                          out_channels=hidden_dim,\n",
        "                                          kernel_size=1)\n",
        "        self.batchn_claim = torch.nn.BatchNorm1d(hidden_dim)\n",
        "        if self.num_rnn==2:\n",
        "            self.conv_evidence = torch.nn.Conv1d(in_channels=embedding_dim,\n",
        "                                                 out_channels=hidden_dim,\n",
        "                                                 kernel_size=1)\n",
        "            self.batchn_evidence = torch.nn.BatchNorm1d(hidden_dim)\n",
        "\n",
        "        self.end_init()\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        enc_claim, enc_evidence = super(RNN_LSEnc_SkipC,self).get_encodings(claim, evidence)\n",
        "\n",
        "        claim, lens_claim = torch.nn.utils.rnn.pad_packed_sequence(claim,\n",
        "                                                                batch_first=True,\n",
        "                                                                padding_value=0.0,\n",
        "                                                                total_length=self.max_tokens)\n",
        "        evidence, lens_evidence = torch.nn.utils.rnn.pad_packed_sequence(evidence,\n",
        "                                                                        batch_first=True,\n",
        "                                                                        padding_value=0.0,\n",
        "                                                                        total_length=self.max_tokens)\n",
        "\n",
        "        claim = torch.stack([torch.mean(vec[:lens_claim[idx],:], dim=0) for idx,vec in enumerate(claim)], dim=0)\n",
        "        evidence = torch.stack([torch.mean(vec[:lens_evidence[idx],:], dim=0) for idx,vec in enumerate(evidence)], dim=0)\n",
        "\n",
        "        claim = self.conv_claim(claim.unsqueeze(dim=2)).squeeze()\n",
        "        claim = self.batchn_claim(claim)\n",
        "        if self.num_rnn==2:\n",
        "            evidence = self.conv_evidence(evidence.unsqueeze(dim=2)).squeeze()\n",
        "            evidence = self.batchn_evidence(evidence)\n",
        "        elif self.num_rnn==1:\n",
        "            evidence = self.conv_claim(evidence.unsqueeze(dim=2)).squeeze()\n",
        "            evidence = self.batchn_claim(evidence)\n",
        "\n",
        "        enc_claim = enc_claim + claim\n",
        "        enc_evidence = enc_evidence + evidence\n",
        "\n",
        "        return enc_claim, enc_evidence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrPsGMAKmdZw"
      },
      "source": [
        "model_lsenc_skipc = RNN_LSEnc_SkipC(embedding_dim = embedding_dim,\n",
        "                                    hidden_dim=128,\n",
        "                                    max_tokens=max_tokens,\n",
        "                                    output_dim=2,\n",
        "                                    merging_strategy='concat',\n",
        "                                    compute_cosine_similarity=True,\n",
        "                                    dropout=0.2,\n",
        "                                    device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tR5tNjYGmdia"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_lsenc_skipc.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=5, threshold=1e-3)\n",
        "\n",
        "lsenc_skipc_train = Train(model = model_lsenc_skipc,\n",
        "                          loss_function = loss_function,\n",
        "                          optimizer = optimizer,\n",
        "                          lr_scheduler = lr_scheduler,\n",
        "                          lr_scheduler_metric = True)\n",
        "\n",
        "lsenc_skipc_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqXiw8OPmdmd"
      },
      "source": [
        "lsenc_skipc_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdhzJ_h_k2Qc"
      },
      "source": [
        "#### RNN's states' average skip connection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2prMiIWJ8fzR"
      },
      "source": [
        "class RNN_AVGEnc_SkipC(BaseRNN):\n",
        "    \"\"\"\n",
        "    Like RNN_AVGEnc but before averaging the outputs they are summed to the\n",
        "    input embeddings (with dimension properly resized through a 1x1 convolution).\n",
        "    \"\"\"\n",
        "    def __init__(self, embedding_dim, hidden_dim, max_tokens, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0,\n",
        "                 num_rnn=2, feature_extractor_type='lstm', bidirectional=False, num_layers=1, device=torch.device('cpu')):\n",
        "        super(RNN_AVGEnc_SkipC,self).__init__(embedding_dim=embedding_dim,\n",
        "                                              hidden_dim=hidden_dim,\n",
        "                                              max_tokens=max_tokens,\n",
        "                                              output_dim=output_dim,\n",
        "                                              merging_strategy=merging_strategy,\n",
        "                                              compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                              dropout=dropout,\n",
        "                                              num_rnn=num_rnn,\n",
        "                                              feature_extractor_type=feature_extractor_type,\n",
        "                                              bidirectional=bidirectional,\n",
        "                                              num_layers=num_layers,\n",
        "                                              device=device)\n",
        "       \n",
        "        self.conv_claim = torch.nn.Conv1d(in_channels=embedding_dim,\n",
        "                                          out_channels=hidden_dim,\n",
        "                                          kernel_size=1)\n",
        "        self.batchn_claim = torch.nn.BatchNorm1d(hidden_dim)\n",
        "        if self.num_rnn==2:\n",
        "            self.conv_evidence = torch.nn.Conv1d(in_channels=embedding_dim,\n",
        "                                                 out_channels=hidden_dim,\n",
        "                                                 kernel_size=1)\n",
        "            self.batchn_evidence = torch.nn.BatchNorm1d(hidden_dim)\n",
        "\n",
        "        self.end_init()\n",
        "\n",
        "\n",
        "    def enc_f(self, vec, length):\n",
        "        return vec\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        enc_claim, enc_evidence = super(RNN_AVGEnc_SkipC,self).get_encodings(claim, evidence)\n",
        "\n",
        "        claim, lens_claim = torch.nn.utils.rnn.pad_packed_sequence(claim,\n",
        "                                                       batch_first=True,\n",
        "                                                       padding_value=0.0,\n",
        "                                                       total_length=self.max_tokens)\n",
        "        evidence, lens_evidence = torch.nn.utils.rnn.pad_packed_sequence(evidence,\n",
        "                                                          batch_first=True,\n",
        "                                                          padding_value=0.0,\n",
        "                                                          total_length=self.max_tokens)\n",
        "\n",
        "        claim = self.conv_claim(claim.movedim(1,2))\n",
        "        claim = self.batchn_claim(claim).movedim(1,2)\n",
        "        if self.num_rnn==2:\n",
        "            evidence = self.conv_evidence(evidence.movedim(1,2))\n",
        "            evidence = self.batchn_evidence(evidence).movedim(1,2)\n",
        "        elif self.num_rnn==1:\n",
        "            evidence = self.conv_claim(evidence.movedim(1,2))\n",
        "            evidence = self.batchn_claim(evidence).movedim(1,2)\n",
        "\n",
        "        enc_claim = enc_claim + claim\n",
        "        enc_evidence = enc_evidence + evidence\n",
        "\n",
        "        enc_claim = torch.stack([torch.mean(vec[:lens_claim[idx],:], dim=0) for idx,vec in enumerate(enc_claim)], dim=0)\n",
        "        enc_evidence = torch.stack([torch.mean(vec[:lens_evidence[idx],:], dim=0) for idx,vec in enumerate(enc_evidence)], dim=0)\n",
        "\n",
        "        return enc_claim, enc_evidence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2QtYmh6AimE"
      },
      "source": [
        "model_avgenc_skipc = RNN_AVGEnc_SkipC(embedding_dim = embedding_dim,\n",
        "                                      hidden_dim=128,\n",
        "                                      max_tokens=max_tokens,\n",
        "                                      output_dim=2,\n",
        "                                      merging_strategy='concat',\n",
        "                                      compute_cosine_similarity=True,\n",
        "                                      dropout=0.2,\n",
        "                                      bidirectional=True,\n",
        "                                      device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuMcPSmoA4vr"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_avgenc_skipc.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=5, threshold=1e-3)\n",
        "\n",
        "avgenc_skipc_train = Train(model = model_avgenc_skipc,\n",
        "                           loss_function = loss_function,\n",
        "                           optimizer = optimizer,\n",
        "                           lr_scheduler = lr_scheduler,\n",
        "                           lr_scheduler_metric = True)\n",
        "\n",
        "avgenc_skipc_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDNg6s_LBFvT"
      },
      "source": [
        "avgenc_skipc_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJ8N789O5Fgf"
      },
      "source": [
        "#### RNN's last state + Bag of Vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hl3vMvjq5F7y"
      },
      "source": [
        "class RNN_LSEnc_BoV(RNN_LSEnc):\n",
        "    \"\"\"\n",
        "    Like RNN_LSEnc but the Bag of Vectors gets concatenated to the RNNs output.\n",
        "    \"\"\"\n",
        "    def __init__(self, embedding_dim, hidden_dim, max_tokens, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0,\n",
        "                 num_rnn=2, feature_extractor_type='lstm', bidirectional=False, num_layers=1, device=torch.device('cpu')):\n",
        "        super(RNN_LSEnc_BoV,self).__init__(embedding_dim=embedding_dim,\n",
        "                                           hidden_dim=hidden_dim,\n",
        "                                           max_tokens=max_tokens,\n",
        "                                           output_dim=output_dim,\n",
        "                                           merging_strategy=merging_strategy,\n",
        "                                           compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                           num_features=hidden_dim+embedding_dim, #HIGHLIGHT\n",
        "                                           dropout=dropout,\n",
        "                                           num_rnn=num_rnn,\n",
        "                                           feature_extractor_type=feature_extractor_type,\n",
        "                                           bidirectional=bidirectional,\n",
        "                                           num_layers=num_layers,\n",
        "                                           device=device)\n",
        "\n",
        "        self.end_init()\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        enc_claim, enc_evidence = super(RNN_LSEnc_BoV,self).get_encodings(claim, evidence)\n",
        "\n",
        "        claim, lens_claim = torch.nn.utils.rnn.pad_packed_sequence(claim,\n",
        "                                                                batch_first=True,\n",
        "                                                                padding_value=0.0,\n",
        "                                                                total_length=self.max_tokens)\n",
        "        evidence, lens_evidence = torch.nn.utils.rnn.pad_packed_sequence(evidence,\n",
        "                                                                        batch_first=True,\n",
        "                                                                        padding_value=0.0,\n",
        "                                                                        total_length=self.max_tokens)\n",
        "\n",
        "        claim = torch.stack([torch.mean(vec[:lens_claim[idx],:], dim=0) for idx,vec in enumerate(claim)], dim=0)\n",
        "        evidence = torch.stack([torch.mean(vec[:lens_evidence[idx],:], dim=0) for idx,vec in enumerate(evidence)], dim=0)\n",
        "\n",
        "        enc_claim = torch.cat([enc_claim, claim], dim=1)\n",
        "        enc_evidence =  torch.cat([enc_evidence, evidence], dim=1)\n",
        "\n",
        "        return enc_claim, enc_evidence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38Dxbs_O6PGI"
      },
      "source": [
        "model_lsenc_bov = RNN_LSEnc_BoV(embedding_dim = embedding_dim,\n",
        "                                hidden_dim=64,\n",
        "                                max_tokens=max_tokens,\n",
        "                                output_dim=2,\n",
        "                                merging_strategy='concat',\n",
        "                                compute_cosine_similarity=True,\n",
        "                                dropout=0.0,\n",
        "                                device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8m_MPTqB6PSA"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_lsenc_bov.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=3, threshold=1e-3)\n",
        "\n",
        "lsenc_bov_train = Train(model = model_lsenc_bov,\n",
        "                        loss_function = loss_function,\n",
        "                        optimizer = optimizer,\n",
        "                        lr_scheduler = lr_scheduler,\n",
        "                        lr_scheduler_metric = True)\n",
        "\n",
        "lsenc_bov_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeK0OHb06Pbd"
      },
      "source": [
        "lsenc_bov_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9YXCBwjUo5A"
      },
      "source": [
        "#### RNN's last state + MLP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlqakbtNUpAD"
      },
      "source": [
        "class RNN_LSEnc_MLP(RNN_LSEnc):\n",
        "    \"\"\"\n",
        "    Like RNN_LSEnc but the RNNs' output is passed through a FC layer and its dimension\n",
        "    is brough back to be the same as the one of the input's embeddings dimension.\n",
        "    \"\"\"\n",
        "    def __init__(self, embedding_dim, hidden_dim, max_tokens, output_dim, merging_strategy, compute_cosine_similarity=False, dropout=0.0,\n",
        "                 num_rnn=2, num_mlp=2, feature_extractor_type='lstm', bidirectional=False, num_layers=1, device=torch.device('cpu')):\n",
        "        \n",
        "        if num_mlp not in [1,2]:\n",
        "            raise ValueError('num_mlp must be either 1 or 2, it is {} instead'.format(num_mlp))\n",
        "        self.num_mlp = num_mlp\n",
        "\n",
        "\n",
        "        super(RNN_LSEnc_MLP,self).__init__(embedding_dim=embedding_dim,\n",
        "                                           hidden_dim=hidden_dim,\n",
        "                                           max_tokens=max_tokens,\n",
        "                                           output_dim=output_dim,\n",
        "                                           merging_strategy=merging_strategy,\n",
        "                                           compute_cosine_similarity=compute_cosine_similarity,\n",
        "                                           num_features=embedding_dim, #HIGHLIGHT\n",
        "                                           dropout=dropout,\n",
        "                                           num_rnn=num_rnn,\n",
        "                                           feature_extractor_type=feature_extractor_type,\n",
        "                                           bidirectional=bidirectional,\n",
        "                                           num_layers=num_layers,\n",
        "                                           device=device)\n",
        "\n",
        "\n",
        "        self.batchn_claim = torch.nn.GroupNorm(1,hidden_dim)#torch.nn.BatchNorm1d(hidden_dim)\n",
        "        self.mlp_claim = torch.nn.Linear(hidden_dim, embedding_dim)\n",
        "        if self.num_mlp==2:\n",
        "            self.batchn_evidence = torch.nn.GroupNorm(1,hidden_dim)#torch.nn.BatchNorm1d(hidden_dim)\n",
        "            self.mlp_evidence = torch.nn.Linear(hidden_dim, embedding_dim)\n",
        "\n",
        "        self.end_init()\n",
        "\n",
        "    def get_encodings(self, claim, evidence):\n",
        "        enc_claim, enc_evidence = super(RNN_LSEnc_MLP,self).get_encodings(claim, evidence)\n",
        "\n",
        "        claim, lens_claim = torch.nn.utils.rnn.pad_packed_sequence(claim,\n",
        "                                                                batch_first=True,\n",
        "                                                                padding_value=0.0,\n",
        "                                                                total_length=self.max_tokens)\n",
        "        evidence, lens_evidence = torch.nn.utils.rnn.pad_packed_sequence(evidence,\n",
        "                                                                        batch_first=True,\n",
        "                                                                        padding_value=0.0,\n",
        "                                                                        total_length=self.max_tokens)\n",
        "\n",
        "        enc_claim = self.mlp_claim(self.batchn_claim(enc_claim))\n",
        "        if self.num_mlp==2:\n",
        "            enc_evidence = self.mlp_evidence(self.batchn_evidence(enc_evidence))\n",
        "        else:\n",
        "            enc_evidence = self.mlp_claim(self.batchn_claim(enc_evidence))\n",
        "\n",
        "        return enc_claim, enc_evidence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ml342DPqWUNw"
      },
      "source": [
        "model_lsenc_mlp = RNN_LSEnc_MLP(embedding_dim = embedding_dim,\n",
        "                                hidden_dim=64,\n",
        "                                max_tokens=max_tokens,\n",
        "                                output_dim=2,\n",
        "                                merging_strategy='concat',\n",
        "                                compute_cosine_similarity=True,\n",
        "                                dropout=0.1,\n",
        "                                device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALIjZIiqWUR2"
      },
      "source": [
        "optimizer = torch.optim.Adam(model_lsenc_mlp.parameters(), amsgrad=True, lr=0.001, weight_decay=1e-5)\n",
        "#optimizer = torch.optim.RMSprop(model_lsenc_mlp.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5, centered=False)\n",
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=3, threshold=1e-3)\n",
        "\n",
        "lsenc_mlp_train = Train(model = model_lsenc_mlp,\n",
        "                        loss_function = loss_function,\n",
        "                        optimizer = optimizer,\n",
        "                        lr_scheduler = lr_scheduler,\n",
        "                        lr_scheduler_metric = True)\n",
        "\n",
        "lsenc_mlp_train(20, train_batches, val_batches)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6pvlK_MWUVg"
      },
      "source": [
        "lsenc_mlp_train.display_results()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-eiij7XGfAS"
      },
      "source": [
        "# Test\n",
        "The best model found so far it has been observed to be the one which takes as encoding of the sentence the last output of the RNN with skip connections and with the addiction of the cosine similarity between the encodings of the claims and evidences."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159,
          "referenced_widgets": [
            "83b6cbdf9d054ba6b46419df0940296a",
            "913ad7960cae4682b79f763388c04012",
            "fd241428de2049d7915f4cf154272b7e",
            "b2414c8816144a7d8ed4f4968ce410e5",
            "bfb7ff24d11645ee8a5c63959eafeec1",
            "ae97efe95fc24bc0b45ae26c29df63ac",
            "b71d86ed7cc84709b5195240d569e66a",
            "10790c782af349cbb364cd260fb03d88"
          ]
        },
        "id": "GqEGngj2G1dU",
        "outputId": "6086db7a-a5b7-424b-bde0-586e5db6000e"
      },
      "source": [
        "test_evaluation = Train(model = model_lsenc_skipc,\n",
        "                        loss_function = loss_function,\n",
        "                        optimizer=None)\n",
        "\n",
        "loss_test, labels_test, predictions_test, id_claim_test = test_evaluation.evaluate(test_batches)\n",
        "\n",
        "labels_test = labels_test.cpu().detach().numpy()\n",
        "predictions_test = predictions_test.cpu().detach().numpy()\n",
        "id_claim_test = id_claim_test.cpu().detach().numpy()\n",
        "\n",
        "print('Normal measurements:')\n",
        "normal_measurements_dict = test_evaluation.get_measurements(labels_test, predictions_test, 'Test')\n",
        "\n",
        "print('\\nCompound measurements:') \n",
        "grouped_test_labels, test_voting = test_evaluation.get_compound_labels_and_predictions(id_claim_test, labels_test, predictions_test)\n",
        "comp_measurements_dict = test_evaluation.get_measurements(grouped_test_labels, test_voting, 'Test')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83b6cbdf9d054ba6b46419df0940296a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=57.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Normal measurements:\n",
            "\tTest Acc: 75.35% | Test F1: 0.748 | Test Rec: 0.753 | Test Prec: 0.775\n",
            "\n",
            "Compound measurements:\n",
            "\tTest Acc: 75.28% | Test F1: 0.747 | Test Rec: 0.753 | Test Prec: 0.776\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qekXMdLSXizM"
      },
      "source": [
        "# Summary\n",
        "\n",
        "The path followed in order to obtain better results can be divided in two directions: improvements on text preprocessing and construction of different NNs' architectures.<br><br>\n",
        "\n",
        "Preprocessing:\n",
        "\n",
        "DataFrame creation:\n",
        "> The first preprocessing step is in the process of the dataframe's creation. Here it has been performed some data cleaning since claim and evidence were not so easy to split from the other columns.<br>\n",
        "The preprocessing in the dataframe creation part is handled as follows:<br>\n",
        "- Deletion of all the quotation marks (\") in order to have an easier split of the data.<br>\n",
        "- Addition of a token identifying the beginning of the evidence<br>\n",
        "- Split of the columns<br>\n",
        "\n",
        "Note: After the dataframe creation, all of the training set's samples are checked in order to find errors in claim and evidence. The found incorrect instances are deleted and the dataframe's indexes are reset. The sentences that are found to be incorrect after the dataframe creation are very little (in the order of 30 instances).<br><br>\n",
        " \n",
        "Pipeline:<br>\n",
        "> Concerning the text preprocessing, it has been constructed a pipeline\n",
        "consisting of different preprocessing functions and then have been selected\n",
        "those which combinations brought the best results.<br>\n",
        "The preprocessing function composing the pipeline are (in order):<br>\n",
        "- Removal of the phonetic alphabet (enclosed between -LSB- and -RSB- tokens)\n",
        "- Convert text to lowercase characters.\n",
        "- Normalization of english contractions using a file<sup>[7]</sup> containing a list of contractions with their respective non-contracted form. An additional double check was done in order to identify the contractions that did not match with the ones in the file (such as: \"Mother's name\", \"It  's\", \"Come  'n\"). Whithout doing so these last words would've been classified as OOVs during the embedding procedure.\n",
        "- Compounded words by hyphen are splitted and the hyphen deleted.\n",
        "- Space added after the following symbols: \", ; .\" only when missing. After that, uncommon symbols are deleted.\n",
        "- Punctuation removal.\n",
        "- The white spaces were normalized and a function of strip was applied.\n",
        "- Tokenization\n",
        "\n",
        ">Other preprocessing steps have been tried but ended up worsening the results and so have been removed from the preprocessing pipeline:\n",
        "- Stemming has been done with porter, lancaster, snowball.<br>\n",
        "- Lemmatization using nltk and spacy, in this last part some tutorials were read in which recommended apply spacy in order to improve the results. In order to run spacy in a reasonable amount of time the lemmatization step has been parallelized.<br>\n",
        "- Spell correction. Unfortunately, the execution time is too long and so it has been discarded.<br>\n",
        "\n",
        "\n",
        "Resampling:\n",
        ">It has also been tried to perform undersampling in order to balance the numbers of SUPPORTED and REFUSED claims.<br>\n",
        "In order to perform undersampling the following methods have been used:\n",
        "- Random: randomly undersample the dataset so that the number of instances belonging to each class are the same (bringing the number of samples of the majority class equal to the minority one).\n",
        "- Proportional: allows to specify the proportion of samples that has to be kept between the classes.\n",
        "- imblearn library functions were tried but no one gave us better results.\n",
        "\n",
        "Encoding:\n",
        ">Labels:<br>\n",
        ">>Since the label only had 2 different values, a binary encoding has been applied.\n",
        "\n",
        ">Claims and evidences:<br>\n",
        ">>For the encoding of claim and evidence the following embedding models have been tried: GloVe, word2vec and Fasttext. With Fasttext there have been issues with Google Colab since it did not allow to download and process the data because of lack of allotted resources, as such the embeddings have been created locally and then uploaded on google drive. The links to dowload them are written in the function that download the embeddings.<br>\n",
        "The embedding model that provided with the best results is Fasttext. <br>\n",
        "\n",
        "<br><br>Architectures:<br>\n",
        "A different number of architectures has been constructed, starting from the ones indicated in the assignment's \"Handling multiple inputs\" section and ending with some additional architecture comprised of mergings of the previous approaches introducing, at times, some tweaks.<br><br>\n",
        "\n",
        "Assignment's architectures:<br>\n",
        "> For starters it has been defined some base architectures to facilitate the construction of the other neural networks (in the section \"Base Architectures\"): in BaseNN are defined the merging strategies of the encodings of claim and evidence and also the last fully connected layer for the classification. In base RNN are defined the forward pass for those architectures that relies on RNNs for feature extraction.<br>\n",
        "The first two models are those that relies on the RNNs for features extraction and are also those that have shown better results.\n",
        "Different kind of RNNs have been tried but through experimentation it has been observed that LSTM is the one that yields better results for this kind of task.<br>\n",
        "The first model uses as claim and evidence sentence's embedding the last output state of the output of the RNNs (also known as many to one approach) while the seconds uses the mean of the output states.<br>\n",
        "Between the two models it has been observed that the one that takes the last state as sentence embedding is the best.<br>\n",
        "For both of the previously mentioned models it has been tried to use just one RNN instead of two to encode both claim and evidence but it has been observed that doing so bring to a slight deterioration of the results<br>\n",
        "The last two model requested by the assignment relies on a fully connected layer and bag of vectors respectively as sentence's embeddings for claim and evidence.\n",
        "Both of the approaches have shown to be rather lacking compared with the first two models which were able to obtain results 10% higher, at least for what concern accuracy and F1-macro.<br><br>\n",
        "\n",
        "Additional architectures:<br>\n",
        ">Some other model that go out of the requirements' scope have been added. These architectures are comprised of tweaks done on the first two RNNs models since are those that showed the best results.<br>\n",
        "Firstly it has been implemented for both the last state and mean models a skip connection layer comprised of a convolutional layer with kernel size of 1x1 which role is to fit the dimension of the input of the RNN to the dimension of the sentence embedding (the output of the RNN). It is worth to say that in case of the last state it is not as easy as the mean case to sum input and output of the RNN since the input is of length max_tokens while the output is a single element. Thus, the input to be summed to the RNN's output has been taken to be the bag of vectors as defined in one of the previous models.<br>\n",
        "Then, in place of using the bag of vector as skip connection for the model that takes the last state as sentence encoding, it has been concatenated to the sentences encodings in the hope of providing more features for the classification phase.<br>\n",
        "Lastly it has been tried to add a fully connected layer right after the RNN bringing back the encoding dimension of the sentence to the encoding dimension of the words used to create such encoding.<br>\n",
        "The best of the additional architectures presented it has been observed to be the one that takes as sentences encoding the RNN's last state with the addition of the skip connection which was able to beat its vanilla counterpart (the one without the skip connection).\n",
        "\n",
        "\n",
        "<br>Some additional notes: it has not been tried to increase the number of layers of the RNNs because of time and computational constraint but, generally speaking, having a deeper network should bring also better results.<br>\n",
        "\n",
        "Regarding the merging strategy, it has been observed that the one that brings better results is the concatenation.<br>\n",
        "The cosine similarity was able to provide for better results.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rddGc451yXdH"
      },
      "source": [
        "# References\n",
        "\n",
        "Preprocessing<br>\n",
        "[1]: Internet, http://bit.ly/38nF7Ih <br>\n",
        "Resampling<br>\n",
        "[2]: Internet, http://bit.ly/2KHJ3vi <br>\n",
        "[3]: Internet, http://bit.ly/3rhII3i <br>\n",
        "[4]: Internet, http://bit.ly/3aE0lUS<br>\n",
        "Encoding<br>\n",
        "[5]: Internet, http://bit.ly/2KOf84v<br>\n",
        "[6]: Internet, http://bit.ly/3asKjNP<br>\n",
        "[7]: Internet, http://bit.ly/3atGZSB<br>"
      ]
    }
  ]
}