ocr_engine_comparison.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "source": [
                "Baseline text:\n",
                "* latin letters + numbers\n",
                "* random inclination between -1/+1 degrees\n",
                "* up to 5 separated \"words\"\n",
                "* random gaussian blur between -1/+1 radius\n",
                "\n",
                "Tests performed: \n",
                "1. `trdg -c 500 -na 2 -rs -num -let -t 4 -f 64 -w 5 -r -b 1 -tc '#000000' -bl 1 -rbl -k 1 -rk -m 10,10,10,10 --output_dir 1` \n",
                "2. `trdg -c 500 -na 2 -rs -num -let -t 4 -f 64 -w 5 -r -b 1 -tc '#000000' -bl 1 -rbl -k 1 -rk --output_dir 2`\n",
                "3. `trdg -c 500 -na 2 -rs -num -let -t 4 -f 32 -w 5 -r -b 1 -tc '#000000' -bl 1 -rbl -k 1 -rk --output_dir 3`\n",
                "4. `trdg -c 500 -na 2 -rs -num -let -t 4 -f 32 -w 5 -r -b 0 -tc '#000000' -bl 1 -rbl -k 1 -rk --output_dir 4`\n",
                "5. `trdg -c 500 -na 2 -rs -num -let -t 4 -f 32 -w 5 -r -b 0 -tc '#000000,#2B21C8' -bl 1 -rbl -k 1 -rk --output_dir 5`\n",
                "6. `trdg -c 500 -na 2 -rs -num -let -t 4 -f 32 -w 5 -r -b 0 -tc '#000000,#2B21C8' -bl 1 -rbl -k 1 -rk -d 3 --output_dir 6`"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "source": [
                "tests = {\n",
                "    \"1\": \"1. white back + black text + 10px margin (big) + 64px res\",\n",
                "    \"2\": \"2. white back + black text + small margin + 64px res\",\n",
                "    \"3\": \"3. white back + black text + small margin + 32px res\",\n",
                "    \"4\": \"4. noise back + black text + small margin + 32px res\",\n",
                "    \"5\": \"5. noise back + black/blue text + small margin + 32px res\",\n",
                "    \"6\": \"6. noise back + black/blue text + small margin + 32px res + distorted text\",\n",
                "}\n",
                "direc = \"6\"\n",
                "what = tests[direc]"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "source": [
                "import pytesseract\n",
                "pytesseract.pytesseract.tesseract_cmd = r'/usr/local/bin/tesseract'\n",
                "import easyocr\n",
                "from PIL import Image\n",
                "import boto3\n",
                "import io\n",
                "import pandas as pd\n",
                "from Levenshtein import distance\n",
                "from tqdm.notebook import tqdm\n",
                "tqdm.pandas()\n",
                "\n",
                "import seaborn as sns\n",
                "sns.set_theme(style=\"whitegrid\")\n",
                "import numpy as np\n",
                "\n",
                "reader = easyocr.Reader(['en'], gpu = False)\n",
                "textract = boto3.client('textract')\n",
                "\n",
                "def image_to_byte_array(image: Image):\n",
                "    imgByteArr = io.BytesIO()\n",
                "    image.save(imgByteArr, format=image.format)\n",
                "    imgByteArr = imgByteArr.getvalue()\n",
                "    return imgByteArr\n",
                "\n",
                "def aws_textract_read(path):\n",
                "    img = Image.open(path)\n",
                "    response = textract.detect_document_text(Document={'Bytes': image_to_byte_array(img)})\n",
                "\n",
                "    text = \" \"\n",
                "    for block in response[\"Blocks\"]:\n",
                "        if block[\"BlockType\"] == \"LINE\":\n",
                "            text+=(block[\"Text\"]+\" \")\n",
                "    return text.strip()\n",
                "\n",
                "def easy_ocr_read(path):\n",
                "    text = reader.readtext(path, detail = 0)\n",
                "    if len(text) > 0:\n",
                "        return text[0]\n",
                "    else:\n",
                "        return \"\"\n",
                "\n",
                "def pytesseract_read(path):\n",
                "    text = pytesseract.image_to_string(Image.open(path))\n",
                "    text = text.replace(\"\\n\\x0c\", \"\")\n",
                "    text = text.replace(\"\\n\", \"\")\n",
                "    text = text.replace(\"\\\\x\", \"\")\n",
                "    return text"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "/Users/francescopochetti/miniconda3/envs/ocr/lib/python3.8/site-packages/skimage/io/manage_plugins.py:23: UserWarning: Your installed pillow version is < 7.1.0. Several security issues (CVE-2020-11538, CVE-2020-10379, CVE-2020-10994, CVE-2020-10177) have been fixed in pillow 7.1.0 or higher. We recommend to upgrade this library.\n",
                        "  from .collection import imread_collection_wrapper\n",
                        "Using CPU. Note: This module is much faster with a GPU.\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "source": [
                "df = pd.read_csv(\"./\"+direc+\"/labels.txt\", sep=\".jpg \", names=[\"filename\", \"sentence\"])\n",
                "df.filename = \"./\"+direc+\"/\" + df.filename.astype(str) + \".jpg\"\n",
                "df.sentence = df.sentence.str.strip()\n",
                "print(df.shape)\n",
                "df.head()"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "(500, 2)\n"
                    ]
                },
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "/Users/francescopochetti/miniconda3/envs/ocr/lib/python3.8/site-packages/pandas/util/_decorators.py:311: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
                        "  return func(*args, **kwargs)\n"
                    ]
                },
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "    filename                       sentence\n",
                            "0  ./6/0.jpg                            VLa\n",
                            "1  ./6/1.jpg                           3QYw\n",
                            "2  ./6/2.jpg       S0 zv TZCnBt0W NzNnoxXT3\n",
                            "3  ./6/3.jpg  J1KF7U HNuR ek0KBDqP 0oKw0dkJ\n",
                            "4  ./6/4.jpg            itN IzBsI CYHFpdtVR"
                        ],
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                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>filename</th>\n",
                            "      <th>sentence</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>./6/0.jpg</td>\n",
                            "      <td>VLa</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>./6/1.jpg</td>\n",
                            "      <td>3QYw</td>\n",
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                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>./6/2.jpg</td>\n",
                            "      <td>S0 zv TZCnBt0W NzNnoxXT3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>./6/3.jpg</td>\n",
                            "      <td>J1KF7U HNuR ek0KBDqP 0oKw0dkJ</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>./6/4.jpg</td>\n",
                            "      <td>itN IzBsI CYHFpdtVR</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 5
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "source": [
                "df[\"easyocr\"] = df.filename.progress_apply(lambda x: easy_ocr_read(x))\n",
                "df[\"tesseract\"] = df.filename.progress_apply(lambda x: pytesseract_read(x))\n",
                "df[\"textract\"] = df.filename.progress_apply(lambda x: aws_textract_read(x))"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
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                    },
                    "metadata": {}
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                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "/home/ubuntu/anaconda3/envs/ocr/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
                        "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
                    ]
                },
                {
                    "output_type": "display_data",
                    "data": {
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                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "source": [
                "df[\"easyocr_score\"] = df.apply(lambda row: distance(row[\"sentence\"], row[\"easyocr\"]), axis=1)\n",
                "df[\"tesseract_score\"] = df.apply(lambda row: distance(row[\"sentence\"], row[\"tesseract\"]), axis=1)\n",
                "df[\"textract_score\"] = df.apply(lambda row: distance(row[\"sentence\"], row[\"textract\"]), axis=1)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "source": [
                "df[\"what\"] = what\n",
                "df.to_csv(\"./\"+direc+\"/df.csv\", index=False)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "source": [
                "dfs = [\"./\"+str(i)+\"/df.csv\" for i in range(1, 7)]\n",
                "dfs = [pd.read_csv(i) for i in dfs]\n",
                "\n",
                "big = pd.concat(dfs)\n",
                "big.head()"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "    filename                                  sentence  \\\n",
                            "0  ./1/0.jpg                     Aw vmP 2zQ5O G3iO4VAi   \n",
                            "1  ./1/1.jpg  QGwmKGmEkU wvuJkiLN m0V g1iT8jt0H 3rvsNa   \n",
                            "2  ./1/2.jpg                                 yFHgJVKc8   \n",
                            "3  ./1/3.jpg        6PfdhEtBFc aaTj l1kfRA sasHKkm 74t   \n",
                            "4  ./1/4.jpg           MG siF8VgUjij jgfKWc wwMo77CUSr   \n",
                            "\n",
                            "                                     easyocr  \\\n",
                            "0                               @u unG g2Qs0   \n",
                            "1  QGwmK GmEkU wvuJkiLN mOV gliT8jtOH 3rvsNa   \n",
                            "2                                  YFHgJVKc8   \n",
                            "3       6Pf dhEtBFc aaTj I1kf RA sasHKkm 74t   \n",
                            "4          MG SIF8 VGUJIJ JGFK WC WWMOTTCUSR   \n",
                            "\n",
                            "                                   tesseract  \\\n",
                            "0                      Q. um@® 22Qs0 G3iO Qi   \n",
                            "1  QGwmKGmEkKU wvuJkiLN mOV g1iT8jtOH 3rvsNa   \n",
                            "2                                  yFHgJVKc8   \n",
                            "3       6Pf dhEtBFc aaTj I1kf RA sasHKkm 74t   \n",
                            "4            MG SIESVGUI JGEK WC WWMOT77CUSR   \n",
                            "\n",
                            "                                    textract  easyocr_score  tesseract_score  \\\n",
                            "0                      am ump 2ZQ50 GsiOnkai             18               11   \n",
                            "1  QGwmK GmEkU wvulkiLN mOV g1iT8jtOH 3rvsNa              4                3   \n",
                            "2                                  yFHgJVKc8              1                0   \n",
                            "3       6Pf dhEtBFc aaTj l1kf RA sasHKkm 74t              3                3   \n",
                            "4             MG SIF8VGUJU IGFKWC WWMOT7CUSR             18               18   \n",
                            "\n",
                            "   textract_score                                               what  \n",
                            "0              10  1. white back + black text + 10px margin (big)...  \n",
                            "1               4  1. white back + black text + 10px margin (big)...  \n",
                            "2               0  1. white back + black text + 10px margin (big)...  \n",
                            "3               2  1. white back + black text + 10px margin (big)...  \n",
                            "4              15  1. white back + black text + 10px margin (big)...  "
                        ],
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                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>filename</th>\n",
                            "      <th>sentence</th>\n",
                            "      <th>easyocr</th>\n",
                            "      <th>tesseract</th>\n",
                            "      <th>textract</th>\n",
                            "      <th>easyocr_score</th>\n",
                            "      <th>tesseract_score</th>\n",
                            "      <th>textract_score</th>\n",
                            "      <th>what</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>./1/0.jpg</td>\n",
                            "      <td>Aw vmP 2zQ5O G3iO4VAi</td>\n",
                            "      <td>@u unG g2Qs0</td>\n",
                            "      <td>Q. um@® 22Qs0 G3iO Qi</td>\n",
                            "      <td>am ump 2ZQ50 GsiOnkai</td>\n",
                            "      <td>18</td>\n",
                            "      <td>11</td>\n",
                            "      <td>10</td>\n",
                            "      <td>1. white back + black text + 10px margin (big)...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>./1/1.jpg</td>\n",
                            "      <td>QGwmKGmEkU wvuJkiLN m0V g1iT8jt0H 3rvsNa</td>\n",
                            "      <td>QGwmK GmEkU wvuJkiLN mOV gliT8jtOH 3rvsNa</td>\n",
                            "      <td>QGwmKGmEkKU wvuJkiLN mOV g1iT8jtOH 3rvsNa</td>\n",
                            "      <td>QGwmK GmEkU wvulkiLN mOV g1iT8jtOH 3rvsNa</td>\n",
                            "      <td>4</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4</td>\n",
                            "      <td>1. white back + black text + 10px margin (big)...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>./1/2.jpg</td>\n",
                            "      <td>yFHgJVKc8</td>\n",
                            "      <td>YFHgJVKc8</td>\n",
                            "      <td>yFHgJVKc8</td>\n",
                            "      <td>yFHgJVKc8</td>\n",
                            "      <td>1</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1. white back + black text + 10px margin (big)...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>./1/3.jpg</td>\n",
                            "      <td>6PfdhEtBFc aaTj l1kfRA sasHKkm 74t</td>\n",
                            "      <td>6Pf dhEtBFc aaTj I1kf RA sasHKkm 74t</td>\n",
                            "      <td>6Pf dhEtBFc aaTj I1kf RA sasHKkm 74t</td>\n",
                            "      <td>6Pf dhEtBFc aaTj l1kf RA sasHKkm 74t</td>\n",
                            "      <td>3</td>\n",
                            "      <td>3</td>\n",
                            "      <td>2</td>\n",
                            "      <td>1. white back + black text + 10px margin (big)...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>./1/4.jpg</td>\n",
                            "      <td>MG siF8VgUjij jgfKWc wwMo77CUSr</td>\n",
                            "      <td>MG SIF8 VGUJIJ JGFK WC WWMOTTCUSR</td>\n",
                            "      <td>MG SIESVGUI JGEK WC WWMOT77CUSR</td>\n",
                            "      <td>MG SIF8VGUJU IGFKWC WWMOT7CUSR</td>\n",
                            "      <td>18</td>\n",
                            "      <td>18</td>\n",
                            "      <td>15</td>\n",
                            "      <td>1. white back + black text + 10px margin (big)...</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 11
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "source": [
                "big.iloc[0]"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "filename                                                   ./1/0.jpg\n",
                            "sentence                                       Aw vmP 2zQ5O G3iO4VAi\n",
                            "easyocr                                                 @u unG g2Qs0\n",
                            "tesseract                                      Q. um@® 22Qs0 G3iO Qi\n",
                            "textract                                       am ump 2ZQ50 GsiOnkai\n",
                            "easyocr_score                                                     18\n",
                            "tesseract_score                                                   11\n",
                            "textract_score                                                    10\n",
                            "what               1. white back + black text + 10px margin (big)...\n",
                            "Name: 0, dtype: object"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 12
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 84,
            "source": [
                "d = big[[\"easyocr_score\", \"tesseract_score\", \"textract_score\"]].stack().reset_index()\n",
                "d[\"experiment\"] = sorted(np.arange(1, 7).tolist() * 1500)\n",
                "d.drop(\"level_0\", axis=1, inplace=True)\n",
                "d.columns = [\"OCR Engine\", \"Levenshtein Distance\", \"Experiment\"]\n",
                "d['OCR Engine'] = d['OCR Engine'].map({\"easyocr_score\": \"EasyOCR\", \"tesseract_score\": \"Tesseract\", \"textract_score\": \"Amazon Textract\"})\n",
                "\n",
                "fig, ax = plt.subplots(figsize=(10, 6))\n",
                "ax = sns.barplot(x=\"Experiment\", y=\"Levenshtein Distance\", hue=\"OCR Engine\", data=d, ax=ax)\n",
                "plt.figtext(0.2, -.17, \"\\n\".join(big.what.unique().tolist()), ha=\"left\", fontsize=13, bbox={\"facecolor\":\"orange\", \"alpha\":0.5, \"pad\":5});"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 720x432 with 1 Axes>"
                        ],
                        "image/png": 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"
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 82,
            "source": [
                "import matplotlib.pyplot as plt\n",
                "\n",
                "def plot_df(df, experiment=None):\n",
                "    if experiment is None:\n",
                "        scores = df.easyocr_score.append(df.tesseract_score).append(df.textract_score)\n",
                "        n = 3000\n",
                "        title = \"All experiments combined\"\n",
                "    else:\n",
                "        df = df.loc[df.what.str.startswith(experiment)]\n",
                "        scores = df.easyocr_score.append(df.tesseract_score).append(df.textract_score)\n",
                "        n = 500\n",
                "        assert len(df) == n\n",
                "        title = df.what.unique()[0].split(\"text\")\n",
                "        title = title[0] + \"text\\n\" + title[1]\n",
                "        \n",
                "    labels = ([\"EasyOCR\"] * n) + ([\"Tesseract\"] * n) + ([\"Amazon Textract\"] * n)\n",
                "    temp = pd.DataFrame({\"Levenshtein Distance\": scores, \"OCR Engine\": labels})\n",
                "    table = df[[\"easyocr_score\", \"tesseract_score\", \"textract_score\"]].describe()\n",
                "\n",
                "    ax = sns.boxplot(x=\"OCR Engine\", y=\"Levenshtein Distance\", data=temp, \n",
                "                     showfliers=False, showmeans=True,\n",
                "                     meanprops={\"marker\":\"o\",\n",
                "                                \"markerfacecolor\":\"white\", \n",
                "                                \"markeredgecolor\":\"black\",\n",
                "                                \"markersize\":\"10\"})\n",
                "    ax.set_title(title)\n",
                "    ax.table(cellText=np.round(table.values, 2), rowLabels=table.index, loc='bottom', bbox=[0, -0.8, 1, 0.6])\n",
                "    plt.figtext(0.5, -.6, \"1. *white dots*: distribution mean\\n2. outliers (outside whiskers) not shown\", ha=\"left\", bbox={\"facecolor\":\"orange\", \"alpha\":0.5, \"pad\":5})\n",
                "    return ax\n",
                "\n",
                "\n",
                "_ = plot_df(big, \"6.\")"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": [
                            "<Figure size 720x432 with 1 Axes>"
                        ],
                        "image/png": 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4uLjg6+sLwJAhQ2jTpg0//fQTXbt25dq1a099rSIvIvWAiaSiNm3aEB4ezvXr19m3bx8//PADVapUifU/8oYNG/LRRx/x119/UbNmTXx9fa2TNteqVcs6QKqzszN169YFoGjRoty/fx+If87CpCpcuDBly5a1Hjdr1qw4ODiQK1cuXnrpJQIDA6lcuTLZs2dn2bJlXL58mf379/PSSy9Zj1G1alXrv3fu3MmaNWusNT8Zdp70+uuvkz9/fgDKli3Lli1bYm3z448/smDBAiIiIrh58yaenp4ATJkyhTJlysTaPk+ePPz+++/8/fffdO/enRIlSlCsWDHg8ej+AwcOxMnJiUGDBln3admypfVaPD092bZtG507d6Zt27b8/vvvjBs3Dn9//xgj2T/pyWvfsWMHQUFB7NmzB3g8hVDu3LnjnVvzv7799ls2b97Mo0ePuH37tvV658+fb+3Rg8ezF9SuXds6+Gu7du3YtWtXjGMl9Zy//fYbgwYNsoa4Ll260K9fP+v6N954I87rjm9Oynv37nHy5ElatGhhfX9021IyGgUwkVS0cuVKIPFbkPXr17c+q7R3715mzpzJsmXLAGJNtv3fOQMh/jkLkyop59ixYwfjx4+nR48euLm5Ubx4cdatW2ddnyVLlhj7P3n+f8Pkf8U1T+V/tWrVilatWllvQcbXsxcUFMS+ffto2LAh8Djcvfrqq5w6dYpixYpx4sQJ+vbtS4MGDfjoo49izMX433kZ/603PDycS5cukTVrVk6cOMErr7wS57mfvPbo6GhGjBiBi4sLAA8fPiQsLAyIe27NJ0f4h8fzSP47l2RCtyD/+349eQ1PSso5/+1tffL1k3NPPnl9/90urjkp/50j88n64mpTIumZbkGKvAA+/PBDNm7cSLNmzRg9ejROTk5cv349yfvHN2fh87R7927q169Px44dKV++PFu3bo33HC4uLqxatQqAe/fusXXr1hgTV6cEGxsbRowYYb2td/r0ac6dO0fFihW5ePEi3bp1o2/fvowYMSJWWNm0aRPh4eGEhYWxZs0a6tevDzzuZStVqhRz587l008/5erVq4nWUadOHRYvXmydy3LkyJF8/vnn8c6t+bRq1arF7t27uXHjBoC1x/FJCZ3zyTke69aty7Jly4iIiCA6OprFixdTu3btOM/75H7xzUmZI0cOXn/9desfIH///TenTp166msVeRHpTw6RNCCuefqe1LdvXz7++GOWL1+Ora0tDRo0oFq1avzxxx9JOn5CcxY+L+3bt2fIkCF4eHhga2vLG2+8YX34+r+GDx+Or68vHh4e5MiRI9ZE1E+jcOHCCc7v+NJLLzFz5kwmTJhAZGQkDg4OTJs2jfz58+Pr60tISAgLFy609ig5ODhYA0KmTJno2LEjDx48oHHjxrRq1Ypff/2VrVu3sm7dOrJly0a3bt348MMPWbRoUYK9OX379mXy5Mm0bNmSqKgoXnvtNYYNG4aTk1O8c2vGpXr16lSvXj3e9WXKlMHHx4du3brx0ksvUaFChVjbJDSfZ7169fj000+Bx71ukydPpkWLFkRGRlKhQgVGjhwZ53mf3K93797xzkn5+eefM3z4cJYtW0bRokUpXrx4vNcikh5pLkgRMd3ixYspW7YslStXJjw8nI4dOzJgwADrbbm0ZNiwYZQqVYqePXumdikiko6oB0xETFeyZEk+/fRT67NETZo0SZPhS0QkpagHTERERMRkeghfRERExGQvzC3I6OhoHj58iL29fYp/W0pERETkWRiGQUREBC+99FKcQ+28MAHs4cOH+pqyiIiIvFBKly5tHSz7SS9MAPt3BObSpUvHGhRSREREJC0JDw/n1KlTcU4DBi9QAPv3tqODgwOOjo6pXI2IiIhI4uJ7bCpFA9hXX33Fzz//jMVioXXr1vTo0YPhw4dz6NAhMmfODED//v2tU4OIiIiIZAQpFsD++OMP9u3bx7p164iMjKRp06a4uLhw7NgxFi1ahLOzc0qdWkRERCRNS7FhKKpVq8aCBQuws7Pjzp07REVF4ejoyLVr1xg5ciQeHh5Mnz49zmlKRERERNKzFB0HzN7enunTp9OsWTNq1qxJVFQUNWrUYMKECaxYsYKDBw9aJ+QVERERyShMGQk/JCSEd999l6ZNm9KuXTvr8i1btrB27VpmzpyZ6DHCwsI4duxYSpYpIiIi8lyVK1cuzi8PptgzYGfPniU8PJzXXnuNzJkz06hRIzZu3EiOHDlo3Lgx8HiQMju75JUQ34WIiIiIpBWJdRyl2C3IK1eu4OvrS3h4OOHh4Wzbto0333yTCRMmEBgYSEREBMuXL9c3IEVERCTDSbEeMBcXF44cOUKLFi2wtbWlUaNG9O/fn5w5c9KhQwciIyNp1KgR7u7uKVWCiIiISJpkyjNgz8O/XXm6BSkiIiJpXWK5JUW/BSkiIiIisSmAiYiIiJjshZkLUkRE5EWwfft2tmzZktplxHD//n0AcuTIkap1/FfDhg1xdXVN7TJShQKYiIhIOnf37l0g7QWwjEwBTERE5DlydXVNc706w4cPB2DixImpXIn8S8+AiYiIiJhMAUxERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyu9QuQB7bvn07W7ZsSe0yYrh//z4AOXLkSNU6/qthw4a4urqmdhkiIiJPTQFM4nX37l0g7QUwERGRF50CWBrh6uqa5np1hg8fDsDEiRNTuRIREZH0Rc+AiYiIiJhMAUxERETEZApgIiIiIiZTABMRERExWYoGsK+++oqmTZvSrFkz5s2bB8CePXvw8PCgUaNGfPHFFyl5ehEREZE0KcW+BfnHH3+wb98+1q1bR2RkJE2bNqVmzZqMGDGChQsXUqBAAfr06cPOnTtxcXFJqTJERERE0pwU6wGrVq0aCxYswM7Ojjt37hAVFcWDBw94+eWXKVKkCHZ2dnh4eLB58+aUKkFEREQkTUrRccDs7e2ZPn0633//PU2aNOHmzZvkzZvXut7Z2ZmAgIBkHfPYsWPPu0yJR1BQEACHDh1K5UpERORZ6PM87UnxgVgHDhxI7969effdd7lw4UKs9RaLJVnHK1euHI6Ojs+pOknIqlWrAKhatWoqVyIiIs9Cn+fmCwsLS7DTKMVuQZ49e5Z//vkHgMyZM9OoUSP279/P7du3rdvcvHkTZ2fnlCpBREREJE1KsQB25coVfH19CQ8PJzw8nG3bttG+fXvOnz/PxYsXiYqKYv369dSrVy+lShARERFJk1LsFqSLiwtHjhyhRYsW2Nra0qhRI5o1a0auXLkYMGAAYWFhuLi40KRJk5QqQURERCRNStFnwAYOHMjAgQNjLKtZsybr1q1LydOKiIiIpGkaCV9ERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiYiIiJhMAUxERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJrNLyYN//fXXbNq0CQAXFxeGDh3K8OHDOXToEJkzZwagf//+NGzYMCXLEBEREUlTUiyA7dmzh127drFmzRosFgu9evViy5YtHDt2jEWLFuHs7JxSpxYRERFJ01LsFmTevHkZNmwYDg4O2NvbU6JECa5du8a1a9cYOXIkHh4eTJ8+nejo6JQqQURERCRNSrEAVqpUKSpVqgTAhQsX2LhxI3Xr1qVGjRpMmDCBFStWcPDgQVatWpVSJYiIiIikSSn6DBjA6dOn6dOnDx999BHFixdn5syZ1nVdunRh7dq1tG3bNsnHO3bsWEqUKXEICgoC4NChQ6lciYiIPAt9nqc9KRrADh06xMCBAxkxYgTNmjXj5MmTXLhwgcaNGwNgGAZ2dskroVy5cjg6OqZEufIf//ZOVq1aNZUrERGRZ6HPc/OFhYUl2GmUYrcgr1+/Tr9+/Zg2bRrNmjUDHgeuCRMmEBgYSEREBMuXL9c3IEVERCTDSbEesLlz5xIWFsakSZOsy9q3b4+3tzcdOnQgMjKSRo0a4e7unlIliIiIiKRJKRbAfH198fX1jXNdp06dUuq0IiIiImmeRsIXERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiYiIiJhMAUxERETEZApgIiIiIiZLUgALDQ3l5MmTGIZBaGhoStckIiIikq4lGsD+97//0aBBA/r06UNAQAAuLi4cPnzYjNpERERE0qVEA9iUKVOYP38+OXLkIH/+/EyZMoXx48ebUZuIiIhIupToXJChoaGULFnS+trFxYUvvvgiRYsSkfRp+/btbNmyJbXLiOH+/fsA5MiRI1Xr+K+GDRvi6uqa2mWISApJNIDZ2dkRGBiIxWIB4Ny5cylelIiIWe7evQukvQAmIulbogHsvffeo3Pnzty+fZvBgweze/duxo4da0ZtIpLOuLq6prleneHDhwMwceLEVK5ERDKSRANY/fr1KV68OLt37yY6Opp+/fpRokQJM2oTERERSZcSfQj/xo0bzJs3j44dO1KrVi0+++wzbt26ZUZtIiIiIulSogFs2LBhFC9eHIBChQpRrVo1RowYkeKFiYiIiKRXiQawe/fu0bVrVwAcHR3p3r27esBEREREnkGiASwqKoqAgADr69u3b2MYRooWJSIiIpKeJfoQfvfu3WnRogV169bFYrGwZ88ehg4dakZtIiIiIulSogGsdevWlCtXjn379mFra0vPnj0pXbq0GbWJiIiIpEuJBjCArFmzUq1aNQzDICIigr///pvXX389pWsTERERSZcSDWBTp05l0aJF5M6d27rMYrGwbdu2FC1MREREJL1KNIBt2rSJX375hXz58plRj4iIiEi6l+i3IAsUKKDwJSIiIvIcJdoDVrNmTaZMmYKbmxuZMmWyLtczYCIiIiJPJ9EAtnr1agA2b95sXaZnwERERESeXqIBbPv27WbUISIiIpJhJBrA7t69y7p163j48CGGYRAdHc3Fixf57LPPzKhPREREJN1JNIB98MEHZMqUiTNnzlCrVi327NlD1apVzahNREREJF1K9FuQ165dw8/Pj3r16tG5c2eWLl3KpUuXzKhNREREJF1KNIDlyZMHgFdeeYVTp06RL18+IiMjU7wwERERkfQq0VuQuXPnZs6cOVSqVIkZM2bg5OREcHCwGbWJiIiIpEuJ9oCNHTsWBwcH3njjDcqVK8f06dPx8fExozYRERGRdCnRAPbzzz/TtWtXAHx8fFi7di3nzp1L8cJERERE0qt4b0EuXbqU0NBQ5s+fT1hYmHV5REQECxcuxNvbO9GDf/3112zatAkAFxcXhg4dyp49e5g4cSJhYWG8/fbbDBo06DlchoiIiMiLI94AZmdnx6lTpwgNDeXUqVPW5ba2towcOTLRA+/Zs4ddu3axZs0aLBYLvXr1Yv369UybNo2FCxdSoEAB+vTpw86dO3FxcXk+VyMiIiLyAog3gLVp04Y2bdqwdetWGjRokOwD582bl2HDhuHg4ABAiRIluHDhAi+//DJFihQBwMPDg82bNyuAiYiISIaS6LcgixUrxsqVK2ndujX9+vXj5MmTjB8/nho1aiS4X6lSpaz/vnDhAhs3bqRLly7kzZvXutzZ2ZmAgIBkFXzs2LFkbS9PLygoCIBDhw6lciUiKUftXDICtfO0J9EANnr0aNq2bcuvv/7KvXv3mDBhAp9//jnLly9P0glOnz5Nnz59+Oijj7Czs+P8+fMx1lsslmQVXK5cORwdHZO1jzydVatWAWjmA0nX1M4lI1A7N19YWFiCnUaJfgsyLCyM5s2bs3v3bt5++22qV69OREREkk5+6NAhunfvzocffkjLli3Jly8ft2/ftq6/efMmzs7OSTqWiIiISHqRaAALDw/n9u3b7Nixg1q1anH79u0Y34qMz/Xr1+nXrx/Tpk2jWbNmAFSsWJHz589z8eJFoqKiWL9+PfXq1Xv2qxARERF5gSR6C7Jdu3bUr1+ft99+m5IlS/LWW2/Rt2/fRA88d+5cwsLCmDRpknVZ+/btmTRpEgMGDCAsLAwXFxeaNGnybFcgIiIi8oJJNIB17NiR9u3bY2PzuLNszZo15MyZM9ED+/r64uvrG+e6devWJbNMERERkfQj3gA2e/Zsevfuzbhx4+JcH1+4EhEREZGExRvAsmbNCkCOHDnMqkVEREQkQ4g3gLVv3x6A/v37c+/ePTJlykTmzJlNK0xEREQkvUrwW5Dz58+nTp061KpViypVqtCwYUOWLVtmVm0iIiIi6VK8PWDff/89P//8MzNmzODVV1/FYrHw119/8dlnnwH/v4dMRERERJIn3h6wdevW8d1331G5cmUyZ85MpkyZqFatGtOnT7eOqCsiIiIiyRdvADMMI84H8PPly0d0dHRK1iQiIiKSrsUbwP4d9ysuhmGkSDEiIiIiGUG8z4BFRUURGBgYZ9iKiopK0aJERERE0rN4A9ipU6eoUaNGnAHMYrGkaFEiIiIi6Vm8AezEiRNm1iEiIiKSYSQ4DpiIiIiIPH8KYCIiIiImUwATERERMZkCmIiIiIjJ4n0I/1+bNm1i6tSp1iEpDMPAYrFw+PBhM+oTERERSXcSDWBffvklw4YNo2zZshp+QkREROQ5SDSAZcuWjUaNGplRi4iIiEiGkOgzYBUrVmTnzp1m1CIiIiKSISTaA7Zz504WLVqEvb099vb2egZMRERE5BklGsDmz59vQhkiIiIiGUe8AWzv3r3UrFmTv//+O871hQoVSrGiRERERNKzeAPYhg0bqFmzJgsXLoy1zmKx6MF8ERERkacUbwAbN24cQJwBTERERESeXqLfgrx16xbe3t40btyYO3fu0LNnT27dumVGbSIiIiLpUqIBbMyYMTRo0ABHR0eyZcvGq6++yscff2xGbSIiIiLpUqIB7OrVq7Rt2xYbGxvs7e3x8fHh+vXrZtQmIiIiki4lGsAsFgvR0dHW18HBwTFei4iIiEjyJDoOWKNGjRgyZAhBQUEsW7aMlStX0qRJEzNqExEREUmXEg1g7777LmvXriU6Opo9e/bQrl072rZta0ZtIiIiIulSogFsyZIldOzYkRYtWliX+fn54e3tnZJ1iYiIiKRb8QawpUuXEhoayvz58wkLC7Muj4iIYOHChQpgIiIiIk8p3gBmZ2fHqVOnCA0N5dSpU9bltra2jBw50pTiRERERNKjeANYmzZtaNOmDVu3bqVBgwZm1iQiIiKSriX6DFilSpX49ttvuXPnTozlvr6+STpBcHAw7du3Z9asWRQuXJjhw4dz6NAhMmfODED//v1p2LDhU5QuIiIi8mJKNIANGjTIOgK+xWJJ1sGPHDmCr68vFy5csC47duwYixYtwtnZOdnFioiIiKQHiQaw27dvP/WE3CtWrGD06NEMHToUgEePHnHt2jVGjhzJtWvXaNiwIf3798fGJtHxYEVERETSjUQDWN68ebl//z45cuRI9sHHjx8f4/WdO3eoUaMGY8eOJUuWLPTp04dVq1aZPq7Y7NmzOXfunKnnfBH9+x4NHz48lSt5MRQvXpzevXundhkiGYo+z5NGn+fJY8bnebwBbNy4ccDjbz22adOGWrVqYW9vb12f1GfAnlSkSBFmzpxpfd2lSxfWrl2brAB27NixZJ/3v44cOcLFy9ewzZTjmY+VnkVH2gLwz7mAVK4k7YsKvU9QUBCHDh1K7VIkmYKCggD0s3tBHTlyhEvXLmOb3SG1S0nTom2iADhx40wqV5L2RQWGm/J5Hm8A+7fHq2rVqlStWvW5nOzkyZNcuHCBxo0bA2AYBnZ2iXbCxVCuXDkcHR2fqY5Vq1ZhmykHWV52e6bjiPzr0cVtZM2a9bn9roh5Vq1aBaCf3Qtq1apV2GZ3IHu9gqldiqQTgb9dey6f52FhYQl2GsWbfvr37x9r2eXLl7lx4wZvvvnmUxVjGAYTJkygRo0aZMmSheXLl9OyZcunOpaIiIjIiyrR7qelS5dy8OBBPv74Y9q3b4+TkxONGjXiww8/TPbJXn31Vby9venQoQORkZE0atQId3f3pypcRERE5EWVaABbuXIlfn5+bN68GVdXV0aPHk3btm2TFcC2b99u/XenTp3o1KnT01UrIiIikg4kOv6DxWIhT5487N27l5o1a2JnZ0d0dLQZtYmIiIikS4kGMAcHB2bPns0ff/xB7dq1WbJkiXUUexERERFJvkQD2Pjx47lw4QKTJ08me/bsHDp0yDpEhYiIiIgkX6LPgBUvXjzGgKqfffZZihYkIiIikt4lGsA2bdrE1KlTCQwMxDAM6/LDhw+naGEiIiIi6VWiAezLL79k2LBhlC1bNtmTcYuIiIhIbIkGsGzZstGoUSMzahERERHJEBJ9CL9ixYrs3LnTjFpEREREMoREe8B27tzJokWLsLe3x97eHsMwsFgsegZMRERE5CklGsDmz59vQhkiIiIiGUeityALFSrE0aNHWbFiBbly5eLPP/+kUKFCZtQmIiIiki4lGsD8/PxYunQpmzdvJjQ0lK+//pqZM2eaUZuIiIhIupRoANuwYQOzZ88mc+bM5MyZkxUrVrB+/XozahMRERFJlxINYHZ2djg4OFhfZ8uWDTu7RB8dExEREZF4JJqkChQowI4dO7BYLISHhzN37lw9AyYiIiLyDBINYCNHjmTo0KGcPHmSSpUqUbFiRc0HKSIiIvIMEg1gWbJk4YcffiAkJISoqCicnJzMqEtEREQk3Ur0GTA3NzeGDh3K33//rfAlIiIi8hwkGsC2bdtG5cqVmTx5Mk2aNGHu3LncvXvXjNpERERE0qVEA1jWrFnp0KEDK1eu5Msvv+Tnn3/GxcXFjNpERERE0qUkjSfx999/s2bNGjZt2kT58uX56quvUrouERERkXQr0QDm4eFBSEgIrVq1YvXq1eTLl8+MukRERETSrUQD2LBhw6hdu7YZtYiIiIhkCIk+A1axYkXGjh1Lt27duH//PqNGjeLhw4dm1CYiIiKSLiUawMaPH0/WrFm5c+cOjo6OBAcHM2rUKDNqExEREUmXEg1g//zzD4MGDcLOzo7MmTMzbdo0/vnnHzNqExEREUmXEg1gNjYxN4mKioq1TERERESSLtGH8N98802mTp1KaGgov//+O4sXL6ZatWpm1CYiIiKSLiXalTVkyBCyZMlC1qxZ+eKLLyhTpgwfffSRGbWJiIiIpEuJ9oDZ29vTr18/+vXrZ112+vRpSpUqlaKFicizmT17NufOnUvtMtK8f9+j4cOHp3IlL4bixYvTu3fv1C5D5IWXpJHw/6tdu3YcPnz4edciIs/RuXPnOP3P3+R3eqpf8wwjsxENQNDlk6lcSdp3IzgytUsQSTee6pPZMIznXYeIpID8Tnb0qJArtcuQdGLeX3dTuwSRdOOpApjFYnnedUgaEnQvgPNHt3HpxF6CgwJxypqdoq/WpFh5N7Lm1FRUIiIiz0rjSUgM184dYeeKT2larxx/HjpAeFgYfx46QNN65di54lOunTuS2iWKiIi88OLtAatcuXKcPV2GYRAaGpqiRUnqCLoXwOEts/l580Zq1qxpXV6iRAmmTJlMy5YtaNykKVnbjlRPmIiIyDOIN4CtX7/+uZwgODiY9u3bM2vWLAoXLsyePXuYOHEiYWFhvP322wwaNOi5nEee3fmj23i3j3eM8PWkmjVr0se7N5t+30aFeh1Nrk5ERCT9iDeAFSpU6JkPfuTIEXx9fblw4QIAoaGhjBgxgoULF1KgQAH69OnDzp07cXFxeeZzybO7dGIvfRZPT3Cbd9/tg9/sagpgIiIvgAc373Pm17+5+MdpggODccruxMvVSlGy/utkc86R2uVlaCn6DNiKFSsYPXo0zs7OAPz111+8/PLLFClSBDs7Ozw8PNi8eXNKliDJEBwUyMsvv5zgNkWLFiU4KNCkikRE5Gld+esC2yatpUlFVw4fOExYWBiHDxymSUVXtk1ay5W/LqR2iRlaig4QNH78+Bivb968Sd68ea2vnZ2dCQgISMkSJBmcsmbn4sWLlChRIt5tLl26hFPW7CZWJSIiyfXg5n0OzN/B5o2bYz/TO3kKLVu0pEnTJmQb1kI9YanE1BEa4xo/LLlDWhw7duyZ6wgKCnrmY6RHRV+tyXff+TFlyuR4t5k16zuKvFrDxKpeHEFBQRw6dCi1y7BSO5eUoHb+Yjjz69/08e6T4DO93r29+XnHr1RpW9vk6tI+M9q5qQEsX7583L592/r65s2b1tuTSVWuXDkcHR2fqY5Vq1bBrUfPdIz0qFh5N2Z99yktW7aI85d27969fOc3G5e2I1OhurQva9asVK1aNbXLsFq1ahVB91O7Cklv0mI756HupPzXxT9O02fm8gS3ebfPu/hVm60AFofn0c7DwsIS7DQydRywihUrcv78eS5evEhUVBTr16+nXr16ZpYgCciaMx9VGvamcZOm+PgM5ezZs0RERHD27Fl8fIbSuElTqjTsrSEoRETSuODA4KQ90xuoHsTUYmoPmKOjI5MmTWLAgAGEhYXh4uJCkyZNzCxBElGweEWyth3Jpt+34Te7mnUk/CKv1sBF43+JiLwQnLI7Je2Z3uxZTaxKnmRKANu+fbv13zVr1mTdunVmnFaeUtac+ahQr6OGmhAReUG9XK0U3/l9x5TJU+LdZtZ3s3i5WkkTq5InaSoiERGRdKZk/df5zu879u7dG+f6vXv34jfbj5JvvW5yZfIvBTAREZF0JptzDt7s/hZNmjbBZ6hPzGd6h/rQpGkT3uz+loagSEWmPgMmIiIi5ihc4RWyDWvBzzt+xa/abIIDg3DKnpWXq5XETeN/pToFMBHJkK7cecCKfaf4+a/z3HsQTM5sTjSuUIy2NUpTOHe21C5P5LnI5pyDKm1ra6iJNEi3IEUkw9l94jLvfPczRWo0Yv/Bx1O07D94mCI1GvHOdz+z+8Tl1C5RRNI59YCJSIZy5c4Dxqzex/pNsadomTR5Cp4tWuL+dhO+75NdPWEikmLUAyYiGcqKfafo3SfhKVp6eXuzcv8pkysTkYxEAUxEMpSf/zpPb+8+CW7j3eddfv7rvEkViUhGpAAmIhnKvQdJm6Ll3oNgkyoSkYxIAUxEMpSc2R5P0ZKQS5cukTObk0kViUhGpAAmIhlK4wrFmO33XYLb+H03i8YViplUkYhkRApgIpKhtK1RmtnfJTxFyxw/P9pUL21yZSKSkSiAiUiGUjh3NkZ71cD97SZ89J8pWj4a6oP7200Y7VVDQ1CISIrSOGAikuHUfrUI3/fJzsr9W6gxxy/GSPjf92ms8CUiKU4BTEQypMK5szGo6RsMavpGapciIhmQbkGKiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyTLktyDv3btHVOh9Hl3cltqlSDoRFXqfe/ccUrsMERF5QagHTERERMRkGbIHLGfOnNy4F06Wl91SuxRJJx5d3EbOnDlTuwwREXlBqAdMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETJYhvwUpkhHcu3eP28GRzPvrbmqXIunEjeBIIu/dS+0yRNIF9YCJiIiImEw9YCLpVM6cObELvkmPCrlSuxRJJ+b9dZesGu9O5LlQD5iIiIiIyRTAREREREymACYiIiJiMgUwEREREZOlykP4Xbt25c6dO9jZPT792LFjqVixYmqUIiIiImI60wOYYRicO3eOHTt2WAOYiIiISEZiegI6d+4cFouF3r17c+fOHdq2bUvnzp3NLkNERNKBe/fuEXk/jMDfrqV2KZJORN4P455jyg84bHoAe/DgATVr1uSTTz4hNDSUrl27UqxYMWrXrp2k/Y8dO/bMNQQFBT3zMUT+KygoiEOHDqV2GVZq55IS0lo7Dw0NTe0SJB0KDQ1N8XZuegCrXLkylStXBiBLliy0bt2anTt3JjmAlStXDkdHx2eqYdWqVXDr0TMdQ+S/smbNStWqVVO7DKtVq1YRdD+1q5D0Jq218wIFChBoeUj2egVTuxRJJwJ/u0aB/AWeuZ2HhYUl2Glk+rcgDx48yN69e62vDcPQs2AiIiKSoZgewIKCgpgyZQphYWEEBwezZs0aGjZsaHYZIiIiIqnG9K6n+vXrc+TIEVq0aEF0dDQdO3a03pIUERERyQhS5d7fBx98wAcffJAapxYRERFJdRoJX0RERMRkCmAiIiIiJsuwXz+MCr3Po4vbUruMNC068vH4OjZ2mVK5krQvKvQ+kC+1yxARkRdEhgxgxYsXT+0SXgjnzp0DoHhxBYvE5VO7EhGRJMuQAax3796pXcILYfjw4QBMnDgxlSsRERFJX/QMmIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiYiIiJhMAUxERETEZBlyGAqRjOJGcCTz/rqb2mWkacHh0QA4Oejv0cTcCI4ka2oXIZJOKICJpFMaGDZpbv3fgMMFiuj9SkxW1K5EnhcFMJF0SgMOJ40GHBaR1KA+dxERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZvgUpIiIvtKjAcAJ/u5baZaRp0aFRANhksk3lStK+qMBwyJ/y51EAExGRF5bGJUuac/833l3x/Hq/EpXfnHalACYiIi8sjXeXNBrvLu3RM2AiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETJYqAeynn36iadOmNGzYkMWLF6dGCSIiIiKpxs7sEwYEBPDFF1+wevVqHBwcaN++PdWrV6dkyZJmlyIiIiKSKkzvAduzZw81atQgR44cZMmShcaNG7N582azyxARERFJNab3gN28eZO8efNaXzs7O/PXX3+ZXUaas337drZs2ZLaZcRw7tw5AIYPH57KlcTUsGFDXF1dU7sMeQpq50mndv7iUjtPuozczk0PYIZhxFpmsViSvP+xY8eeZzlpxvnz5wkKCkrtMmLInDkzQJqr6/z58xw6dCi1y5CnoHaedGrnLy6186TLyO3c9ACWL18+Dh48aH198+ZNnJ2dk7x/uXLlcHR0TInSUlXVqlVTuwSRFKd2LhmB2rkAhIWFJdhpZPozYLVq1WLv3r3cvXuXkJAQfvnlF+rVq2d2GSIiIiKpJlV6wAYNGkTXrl2JiIigdevWVKhQwewyRERERFKN6QEMwMPDAw8Pj9Q4tYiIiEiq00j4IiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJkuVkfCfhmEYAISHh6dyJSIiIiIJ+zev/Jtf/uuFCWAREREAnDp1KpUrEREREUmaiIgIMmXKFGu5xYgvmqUx0dHRPHz4EHt7eywWS2qXIyIiIhIvwzCIiIjgpZdewsYm9hNfL0wAExEREUkv9BC+iIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJnthRsKX+F25coUmTZpQokSJGMvbtm1Lp06dnss55s+fz/Lly7G1tcXW1jbWsc+dO8eUKVO4evUqAKVLl+bjjz8mV65crF69mkmTJlGgQAEAoqKiCA8PZ+jQoTRo0OC51Cfp35gxYzh8+DARERFcunTJ2t67du1Kq1atUrm6mC5fvsy3337LhAkTUrsUSQWnTp3Cw8OD6dOn07hx49QuJ5Yff/yRBQsWAHD27FmKFi2Kvb09VapUYfTo0Uk+zvTp06lVqxZvvPHGM9WTUX9fFMDSCWdnZ/z9/VPk2DNmzODAgQMsXLiQPHnycPfuXfr27cv9+/fp168fAQEBdO3albFjx+Lq6ophGHz33Xf079+fJUuWAODq6sqkSZOsx9y6dSujRo1SAJMk+/d/DFeuXKFr164p1t6fh2vXrnH58uXULkNSyerVq2ncuDHLli1LkwGsVatW1j9aXF1d8fPzo3Dhwsk+zoEDB6hevfoz15NRf190CzKdW7RoEW3atMHd3R0PDw/Onj0LwOTJk2nevDktW7bk66+/Jjo6GldXV86fPw/Ao0ePcHFxISQkhLlz5zJhwgTy5MkDQK5cuRg3bhxz5swhJCSEpUuXUqdOHVxdXQGwWCz07t2bjh07EhkZGWddV69eJXv27Ca8A5KeXbx4kR49etCyZUs6dOjA8ePHAfjpp5/w9PTEy8uLgQMHEhYWxo0bN+jcuTNeXl60bt2a//3vfwD89ddfdOjQgZYtW/LOO+9Y/0fQpUsX+vfvT+PGjfnnn3/i/V3as2cPzZs3x8PDgz59+hAcHMy4ceM4duwYY8aMSZX3RVJPZGQk69atY9CgQRw/fpxLly4Bj4PO1KlTadasGc2bN2fHjh107doVFxcXNm7cCDzuOevSpQutWrWifv361l6qoUOH4unpiaenJ3Xr1sXd3R2AX3/9FU9PTzw8POjbty+3b9+2nuvLL7+kdevWNGvWjGPHjiWp9ocPH/LRRx/h5eWFp6cn69evB2DixIn4+PgAj3+32rVrx5o1azh27Bi+vr6cPHlSvy9Pw5AX3uXLl43XX3/daN68eYz//vnnH6Nbt25GSEiIYRiG8eWXXxpjx441rly5YjRt2tQwDMMIDQ01PvzwQyM0NNT46quvjC+//NIwDMNYs2aNMWrUKOPIkSNGjRo14jxvtWrVjGPHjhne3t7G4sWL463vxx9/NN58802jefPmhqurq1GrVi3Dx8fHOHfu3HN+JyQjuHz5slG/fn3DMAyjXbt2xt9//20YhmGcPn3aaNSokWEYhuHq6mrcvn3bMAzD+Pzzz43jx48bM2bMMGbPnm0YhmHs27fPmDNnjhEWFmZ4eHgYV69eNQzDMH777TejW7duhmEYRufOnY3p06cbhmEYQUFBcf4uhYWFGTVr1jSOHz9uGIZhfPbZZ8aCBQuMffv2GZ07dzbh3ZC0ZsuWLUarVq0MwzCMESNGGJMnTzYMwzDq169vzJ8/3zAMwxg2bJjRoUMHIyIiwti/f7/h6elpGIZhjBs3ztizZ49hGIZx6dIlo1KlSjGOfffuXaNJkybGwYMHjdu3bxt16tQxLl++bBiGYcyePdsYMGCA9Vzz5s0zDMMwFixYYPTv3z/eeuvXr289xtSpU40ffvjBMIzHbb5Zs2bGpUuXjJCQEKNJkybGTz/9ZLz11lvGxYsXDcN4/Duyb98+67/1+5I8ugWZTsR3C/Kzzz5jw4YNXLhwgd9//53XXnuNfPny4ejoSPv27alfvz4ffPABjo6OeHl50aNHD95//33WrFnD4MGDsVgsREVFxXnOf3u3LBYLRiJTiv57CzI4OBhvb28KFixIsWLFnv3CJcN6+PAhx44dY/jw4dZljx494t69e9SvX58OHTrg5uZG48aNee2113j06BEDBgzgn3/+wcXFhc6dO3PhwgUuX77Me++9Zz1GcHCw9d8VKlQAwMnJKc7fpZMnT5IvXz5ee+01AAYPHgzA/v37zXgLJA1avXq1tYeqadOmDBkyhA8++ACAevXqAVCwYEGcnZ2xs7OjYMGCPHjwAIBhw4bx+++/891333Hy5EkePXpkPW5kZCTvv/8+Xbt2pWrVqvz6669UqFDBeuuwXbt2+Pn5WbevW7cuAKVKleKXX35JUu179uwhNDSUH3/8EXj8+3T69GmKFCnCxIkTad++PSNHjqRo0aJx7q/fl+RRAEvHrl+/TpcuXejcuTP16tUjT548/PPPP9jZ2bFy5Ur++OMPfvvtN9q3b8/ChQspVqwYBQsW5JdffuHOnTtUrFiRkJAQIiIiOHfuHMWLF7ce+/Tp00RHR1O8eHHKlSsXq4s7OjqagQMH8sknn8RY7uTkxOTJk3F3d6du3bpUrVrVjLdC0qHo6GgcHBxi/OFx48YNcuTIga+vLydOnGDnzp34+PjQv39/PD092bBhAzt27GDjxo2sWbOGjz76iMKFC1uPERUVZb2NA5ApUyYg/t8le3v7GDUFBQXx8OFDE65e0qI7d+7w22+/cezYMRYsWIBhGDx48MAagJ5sL3Z2sf/3+8EHH5AtWzbq169P06ZN2bBhg3XdhAkTKFq0KB06dAAet/8nGYYR45EPR0dH4PEfyEkVHR3N1KlTef311wG4ffu29VGR8+fPkytXrgRvZ+r3JXn0DFg6dvToUV5++WW6d+9OxYoV+e2334iKiuL48eN07tyZN998k48++ogSJUpYn/1q1aoV48aNo3nz5gBkzpyZ9957j48//pg7d+4Ajz9kRo4cSa9evcicOTPt2rVj586d7Ny5E3j8QfDNN99w584d63NjTypSpAhdunRh4sSJifacicQna9asvPLKK9bwtHv3bjp16kRkZCSNGjUiZ86c9OnTB09PT/755x+mTJmCv78/LVu2ZNSoURw/fpzixYsTGBjIwYMHgcffDhsyZEisc8X3u1SsWDHu3r3LmTNnAJgzZw5Lly7F1tY23ucfJf1at24dNWrU4LfffmP79u38+uuvvPvuuyxfvjxJ++/evZuBAwfSoEEDDhw4ADz+o2DFihUcP36cUaNGWbetWLEiR44c4cqVKwAsX778mR+Ir1GjBkuXLgXg5s2bNG/enOvXrxMQEMCXX37J8uXL+eeff6yf9ba2tnHeIdHvS9KoByyduHnzJp6enjGWlS1blujoaJo2bYqDgwMVKlTg9OnTlC1blkqVKuHu7k7mzJl57bXXrF3jjRo1YuTIkTGO5e3tTdasWenevTuGYWCxWGjfvr11GIq8efMye/ZspkyZwrRp04iKiqJs2bLMnDkz3nr79OnDqlWrWLduXay6RZJq6tSpfPLJJ8yZMwd7e3u++OIL7O3tGThwID169CBTpkxky5aNyZMnEx0dzYcffsiaNWuwtbVl9OjRODg48NVXXzF+/HjCwsKsPbT/Vbt2bZYuXRrrd8nR0ZGpU6cydOhQIiIiKFq0KFOmTCE8PJygoCB8fHyYOnVqKrwzkhpWr17NoEGDYizr2LEjc+bMwcnJKdH9BwwYQMeOHcmWLRvFihWjUKFCXLlyhbFjx1K4cGHatm1r/aN1+fLljB07lv79+xMREUHBggUZP378M9Xfv39/PvnkE9zd3YmKisLHx4eiRYvi7e1Njx49KFKkCGPHjmXgwIGsW7eOunXrMnr06Fi/M/p9SRqLoS4I+T+GYfDbb7+xdOlSZs2aldrliIiIpFvqAROrCRMm8OuvvzJ79uzULkVERCRdUw+YiIiIiMn0EL6IiIiIyRTAREREREymACYiIiJiMj2ELyJpwtKlS1m6dCmRkZFYLBbKli3LoEGDKFiwoHWbw4cPM3PmTG7fvk1UVBQFCxZkyJAhlC5dGoAyZcpQunRpbGxssFgshISE4OTkxCeffEL58uVjndPV1RV7e3vrAJL/Gj16NFWqVHmq6+jduzcfffQRJUuWfKr9RSRj0EP4IpLqJk+ezIkTJ5gwYQIFChQgOjqadevW8dlnn7Fy5Ury58/PgQMH8PHx4euvv6ZcuXLA44Evx48fz6ZNm8iVKxdlypRh79695MqVy3rsuXPn8ssvv8Q5GKarqytfffVVnOFMRCQlqQdMRFLVjRs3WLZsGTt27LBOe2JjY0OLFi04duwY3333HaNHj2b69On07dvXGr4AmjdvjqOjY4LzlV6/ft163OS4cuUK3bt3x8XFhSNHjhAYGMigQYNo2rQpISEhjB49miNHjpA1a1Zrb9ekSZOsoe7Ro0d88cUXFClShNOnTxMeHs6oUaOoUaMG4eHhTJs2jQMHDlgHLvb19U3SYJ0ikj7oGTARSVVHjhyhePHicYakWrVqcejQIQCOHTsW523Bxo0bkzdvXuvrbt260bx5c+rUqUPjxo0BmDhxYrznHzJkCJ6entb/2rRpY113+fJl6tSpw6pVqxgyZIh1lO5vvvmGqKgoNm3axPz58zl+/Hicx/7rr7945513WLt2La1bt+brr78GwM/PD1tbW1avXs26detwdnZm2rRpib1VIpKOqAdMRFJdfPPAhYeHWycTtrGxiTUBcVx++OEHcuXKxfHjx+nduzeVK1cmd+7c8W4/bdq0eG9B2tvb4+LiAjye2uv+/fsA7Ny5k+HDh2NjY4OTkxMtW7bk5MmTsfYvWLAgr732mnX/NWvWALBjxw6CgoLYs2cPABEREQnWKCLpjwKYiKSqSpUqcfHiRW7duhWjJwtg//79VK5c2brdkSNHrA/c/2vMmDE0bNiQWrVqxVhetmxZhg8fjq+vLxUrVqRw4cLJrs3e3h4bm8c3Cv4NggB2dnYxJpL/d5v/evLhfovFYt0nOjqaESNGWMPdw4cPCQsLS3Z9IvLi0i1IEUlV+fLlo0uXLgwePJiAgADr8h9//JFffvmF3r17A/Dee+/x9ddfc+zYMes2q1ev5ueff44Vyv7l7u5OpUqVmDBhwnOt2cXFhR9//JHo6GhCQkJYv359jICWmDp16rB48WLCw8OJjo5m5MiRfP7558+1RhFJ29QDJiKp7sMPP2TlypW89957hIeHEx4eTvny5Vm2bBmFChUC4I033mDcuHGMHz+eR48eERERQdGiRVmwYAF58uSJ99gjR46kefPm/P7779StWzfW+iFDhsQahqJz587UrFkz3mP26dOHsWPH4uHhQdasWcmdO3esYySkb9++TJ48mZYtWxIVFcVrr73GsGHDkry/iLz4NAyFiEgybdiwAScnJ1xcXIiOjmbAgAHUrl2bjh07pnZpIvKCUAATEUmmU6dOMWrUKEJCQoiIiKB69eqMGDECe3v71C5NRF4QCmAiIiIiJtND+CIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiYiIiJhMAUxERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTARERERk9kltLJ69epcuXLFrFoSlTlzZkJCQlK7DJEUpXYuGYHauWQEhQsXZv/+/XGuSzCAXblyhatXr6ZIUU+jUKFCaaoekZSgdi4Zgdq5ZASFChWKd51uQYqIiIiYTAFMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJEvwWZGp55513mDZtGrly5UrtUuQFNWnSJDZv3kz27NkBKFasGF9++SWzZs1i7dq1REVF0bx5c/r374/FYuHu3bsMHTqUa9euYWNjw9ixY6lSpUqs4yZ1OxEzqJ1LRpBu27mRgIIFCya0OsWULl3auHPnTqzlqVWPvHjatm1rHDp0KMayHTt2GJ6ensbDhw+N0NBQo1OnTsaGDRsMwzCMgQMHGt9++61hGIZx/Phxo06dOsajR49iHTep2z0LtXNJKrVzyQjSaztP9i3IVatW0axZMzw8POjatSvXr19n+fLluLu707x5c9555x3Onz8PwLBhw5g7d6513ydfu7q6MmPGDDp27Ej9+vWZMmUKAMOHDwegW7duXL9+/ZkDpmQ84eHhHD9+nO+//57mzZszYMAArl27xpYtW3B3dydLliw4Ojri5eXFunXriIyMZMeOHbRt2xaA1157jVdeeYXff/89xnGTup2IGdTOJSNIz+08WQHsxIkTTJs2jTlz5vDTTz/h6upK9+7dmTNnDgsWLGDdunW4u7vTr18/DMNI9HiPHj1iyZIlLFu2jEWLFnH58mUmTpwIwA8//ECBAgWe7qokQwsICKBGjRoMHjwYf39/KlasSN++fbl+/XqMNpU/f34CAgK4d+8e0dHRMW5558uXjxs3bsQ4blK3EzGD2rlkBOm5nScrgO3du5c6depYL7p79+64ubnRtGlT60V4eXkREBCQpCmM3NzcgMcXnTt3bgIDA5Nbv0gsRYoUYfbs2RQvXhyLxULPnj25dOkS0dHRsba1sbGJczmAra1tjNdJ3U7EDGrnkhGk53aerABma2uLxWKxvg4NDY0zaBmGQWRkJBaLJUZPWERERIztHB0drf/+77YiT+vEiROsXbs2xjLDMChYsCC3bt2yLgsICCB//vzkzp0bIMYfAAEBAeTLly/GMZK6nYgZ1M4lI0jP7TxZAax69ers3buXmzdvArBs2TJ27NjBxo0buXv3LgA//vgjOXLk4OWXXyZnzpwcO3YMePxtg4MHDybpPLa2tkRGRianNBErGxsbxo8fz+XLlwFYsmQJZcqUwc3NjXXr1vHo0SPCw8NZvXo1DRo0wM7Ojrfeeovly5cDj3/hz549S/Xq1WMcN6nbiZhB7VwygvTczpM1DEWZMmXw8fGhV69eAOTNm5ctW7awdetWunXrZr2f+t1332FjY0OXLl0YMmQIjRs3pnDhwlSrVi1J52nYsCEdO3bkm2++oXTp0sm/KsnQSpcuja+vL++99x5RUVHkz5+fzz//nIIFC3Lq1CnatGlDREQEbm5utGjRAoDRo0fj6+uLu7s7FouFKVOmkDVrVgB69+5N+/btcXNzS3A7ETOpnUtGkJ7bucVI4L5fWputPq3VI5IS1M4lI1A7l4wgoXaukfBFRERETKYAJiIiImIyBTARERERkymAiYiIiJhMAUxERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkSHIi1ZMmShISEmFlPghwdHQkLC0vtMkRSlNq5ZARq55IRZM6cmTNnzsS5LsGpiEJCQtLUSMUaOVkyArVzyQjUziUjKFSoULzrdAtSRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImKyBIehEHmRGYbB8OHDKVWqFD179iQqKoqJEyeya9cuoqKieOedd+jQoUOs/UJDQxkzZgzHjh0jOjqaChUqMHr0aDJlysSJEyf45JNPCAkJwWKxMGjQIFxcXFLh6kQStmXLFqZPn46NjQ3ZsmVj/PjxFC1aNM5tt27dytChQzl8+DAA4eHhjBs3joMHDwJQr149fHx8sLW1Na1+kaRYuHAhixYtIlOmTJQoUYJRo0aRI0cO6/q1a9cyb9486+ugoCACAgLYuXMnjo6OfPzxx5w7d47o6GhatGiBt7e3ecUbCShYsGBCq02X1uqRtOvMmTNGly5djAoVKhhz5swxDMMwFi1aZPTq1cuIiIgw7t+/bzRu3Ng4cuRIrH0///xzw8fHx4iKijIiIyONQYMGGV9++aVhGIbh7u5ubNmyxTAMwzh58qRRqVIlIyws7LnWrnYuzyokJMSoWLGiceHCBcMwDGPevHlG796949z2/PnzRoMGDYxKlSpZl33//fdGv379jKioKCMiIsJo27at8dNPPz3XGtXO5Vnt3bvXqFu3rnH9+nXDMAxjzZo1xoABA+LdPjw83Gjbtq2xdOlSwzAM49NPPzXGjRtnGIZhPHz40Khfv75x+PDh51pjQu082T1g+/fv5/PPP8fZ2ZnTp0+TOXNmBgwYwMKFCzl//jyNGjVixIgRbN++nW+//ZaIiAgyZcrERx99ROXKlbl9+zajRo3izp073Lp1i0KFCvHll1+SO3duXF1dadmyJXv37uX69eu8/fbbDB06NCVyp6RzixcvxsvLi4IFC1qXbd26lbZt22JnZ0f27Nlp1qwZ69ato0KFCjH2ffPNNylUqBA2No/v0L/22mvWkYzXrFlj7QW4dOkS2bJlU6+ApDlRUVEYhkFQUBAADx8+xNHRMdZ2ISEh+Pj4MGzYMIYMGWJd3qNHDzp37oyNjQ13797lwYMHZM+e3bT6RZLi77//platWuTPnx+ARo0a4evrS3h4OA4ODrG2nz17Nrly5aJ9+/YAfPzxx0RFRQFw69YtwsPDyZo1q2n1P9UtyKNHj7Jq1SrKli1Lr1698PPzY8GCBQQHB1OvXj0aNGjAF198wYIFC8iZMyenT5+mR48e/PLLL2zYsIFKlSrh7e2NYRh4e3vj7+/PO++8A8CjR49YsmQJAQEBNGzYkA4dOlCkSJHnetGS/o0aNQqAffv2WZddv36dAgUKWF/nz5+fkydPxtq3Tp061n9fvXqVH374gU8//RQAOzs7DMOgQYMGXL16lY8//lgBTNKcl156iTFjxtC+fXty5MhBdHQ0S5cujbXdqFGjaNeuHWXKlIm1zt7enmnTprF48WLKlSvHG2+8YUbpIklWoUIFFi5cyNWrVylUqBCrV68mIiKC+/fv4+zsHGPbu3fvMm/ePFavXm1dZrFYsLOzY8iQIfz88880bNiQYsWKmVb/Uz2EX7hwYcqWLQtA0aJFqV69Og4ODuTKlYuXXnqJEydOcPPmTbp3746npydDhgzBYrFw6dIlunXrRpUqVZg3bx6ffPIJp0+f5tGjR9Zju7m5AZAvXz5y585NYGDgc7hMkcfPhP3Xv71ccTl27BidOnWic+fO1K9f37rcYrGwdetWfvnlF/z8/Ni7d2+K1CvytE6ePMnMmTPZuHEju3bt4t1332XAgAExfgcWL16MnZ0drVu3jvc4Q4YM4Y8//qBQoUJ88sknJlQuknRvvvkm/fr1o3///nh5eWGxWMiRIwf29vaxtl2xYgVubm5xduhMmzaNffv2ERgYyMyZM80oHXjKAPbfrj07u5gdaRaLhZo1a+Lv72/9b8WKFZQqVYqpU6fy1VdfkTNnTtq1a0ft2rVjfCg82U1usVji/J+myNMoUKAAt27dsr4OCAiwdl3/14YNG3jnnXf48MMPeffdd4HHDyZv2LCB6OhoAIoUKUKtWrX4559/Ur54kWTYtWsXVapUsT5036lTJ06fPs29e/es26xZs4ajR4/i6emJt7c3oaGheHp6EhAQwKFDhzh//jzwuCesZcuWHD9+PFWuRSQ+wcHBVKtWjTVr1rB69WoaN24MEOMh/H9t3LgRLy+vGMt+//13AgICgMe9xs2aNTO1nafIMBRvvvkmu3fv5uzZswDs3LmT5s2bExYWxq5du+jWrRstWrQgd+7c7Nmzx3oPViQlubm58eOPPxIZGcmDBw/YsGEDDRo0iLXd5s2bGTduHHPnzsXDw8O63MHBgS+//JINGzYAjwPc/v37efPNN027BpGkKFu2LAcOHOD27dvA4+cfCxcuTK5cuazbrFq1ivXr1+Pv74+fnx+ZMmXC39+ffPnysW/fPiZOnEhkZCTR0dH89NNPVK9ePbUuRyRON2/epEuXLgQHBwPwzTff0KxZMywWS4ztAgMDuXTpEpUrV46xfNOmTcycORPDMAgPD2fTpk3UqFHDtPpTZBgKGxsbxo4dy+DBgzEMAzs7O7799luyZMlCv379mDJlCt988w22trZUqVKFS5cupUQZIjF06NCBS5cu4enpSUREBO3ataNatWoAfPXVVwC8//77fP755xiGga+vr3XfKlWqMHr0aL7++mvGjh3LnDlzsLGxwcfHh/Lly6fK9YjEp2bNmvTs2ZMuXbpgb29P9uzZ+eabbzh69Ci+vr74+/snuH/v3r2ZMGECnp6e2NjYUKVKFT788EOTqhdJmuLFi+Pt7U2bNm2Ijo6matWqjBo1KlY7v3jxInnz5o11a3LYsGGMHj0aDw8PLBYLbm5udO3a1bT6LUYC9/gKFSrE1atXTSsmMWmtHpGUoHYuGYHauWQECbVzjYQvIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRTAREREREyW4ECsJUuWJCQkxMx6EuTo6EhYWFhqlyGSotTOJSNQO5eMIHPmzJw5cybOdQlORRQSEpKmRirWyMmSEaidS0agdi4ZQaFCheJdp1uQIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYrIEvwWZVO+88w7Tpk0jV65csdZVrlyZn376icKFCz+PU4k8sy1btjB9+nRsbGzIli0b48ePp2jRoknaZuDAgVy8eNG63ZUrV3jzzTeZNWuW2ZchEq+1a9cyb9486+ugoCACAgLYuXMnefLksS739/dn7ty5WCwWMmfOzMcff0z58uUZN24cBw4csG4XEBBA3rx5+emnn0y9DpF/GYbB8OHDKVWqFD179iQqKoqJEyeya9cuoqKieOedd+jQoUOs/e7fv88nn3zCP//8Q5YsWfDy8qJLly4A/PXXX0yYMIGQkBCio6Pp1asXnp6epl5UvAoWLJjQaqvSpUsbd+7ciXNdpUqVjMuXLyfpOIlJaj0i8QkJCTEqVqxoXLhwwTAMw5g3b57Ru3fvZG9jGIZx5MgR46233jKuXbv2XGtUO5fnKTw83Gjbtq2xdOnSGMvPnj1r1K5d2wgICDAMwzB27NhhuLi4xNr/8uXLRr169Yzjx48/17rUziWpzpw5Y3Tp0sWoUKGCMWfOHMMwDGPRokVGr169jIiICOP+/ftG48aNjSNHjsTad+jQocbw4cONyMhIIywszOjVq5exfft2Izo62nBxcTF2795tGIZhXL9+3ahRo4Zx/vz551p7Qu08WT1gDx8+ZPjw4Vy8eBEbGxtef/11oqKiAOjWrRt+fn5cvXqVTz/9FIvFQvny5YmOjk6R4CjyNKKiojAMg6CgIOBxm3Z0dEz2NuHh4QwbNowRI0ZQoEABc4oXeQqzZ88mV65ctG/fPsZyBwcHxo0bh7OzMwDlypXj9u3bhIeH4+DgYN1u5MiR9OjRg9dee83UukX+tXjxYry8vChYsKB12datW2nbti12dnZkz56dZs2asW7dOipUqBBj37///puRI0dia2uLra0tb731Fj///DO1atWiX79+1KpVC4D8+fOTM2dObty4wSuvvGLKdSUrgG3ZsoWHDx/i7+9PVFQUo0eP5t1332X16tX88MMPODk50bp1a6ZNm0bNmjVZv349K1asSKnaRZLtpZdeYsyYMbRv354cOXIQHR3N0qVLk73NqlWrcHZ2pmHDhmaWL5Isd+/eZd68eaxevTrWusKFC1sfDTEMg4kTJ+Lq6hojfO3cuZPr169bb9mIpIZRo0YBsG/fPuuy69evx/jjN3/+/Jw8eTLWvhUqVMDf358qVaoQHh7Ozz//jL29PY6OjrRp08a63fLly3n06BGVKlVKuQv5j2Q9hF+1alXOnDlDly5d8PPzo1u3brz88svW9adOncLOzo6aNWsC4O7uzksvvfR8KxZ5BidPnmTmzJls3LiRXbt28e677zJgwACMJ2bkSso2P/zwA++9915qXIJIkq1YsQI3NzeKFCkS7zaPHj3i/fff59KlS4wbNy7Guh9++AFvb29sbW1TulSRZDHimEXRxiZ2pBk2bBgWi4WWLVvSv39/ateujb29fYxt/Pz8mDFjBrNmzSJTpkwpVvN/JSuAFSlShC1btuDt7U1wcDA9evRg8+bN1vUWiyXWm2Jn91ye8xd5Lnbt2kWVKlWsD9136tSJ06dPc+/evSRvc/z4cSIjI6lWrZr5FyCSDBs3bsTLyyve9deuXaN9+/bY2tqyYMECsmXLZl139+5djhw5QpMmTcwoVSRZChQowK1bt6yvAwICyJ8/f6ztgoOD8fHxYf369cybNw+LxWL9bA8PD2fw4MGsX7+eZcuW8eqrr5pWPyQzgC1ZsoThw4dTp04dfHx8qFOnDqdPn8bW1pbIyEhKly6NYRjs3LkTgG3bthEYGJgihYs8jbJly3LgwAFu374NPH6OoHDhwjG+wZvYNn/88Qc1atTAYrGYfwEiSRQYGMilS5eoXLlynOvv379P586dadSoEV988UWsv/wPHz5M+fLlyZIlixnliiSLm5sbP/74I5GRkTx48IANGzbQoEGDWNstW7aM6dOnA3D79m1WrlyJu7s7AAMHDiQ4OJhly5alykgNyeqeatGiBX/88QdNmzYlc+bMFCxYkK5du3LmzBk6duzIN998w8yZM/nkk0/4/PPPee2118idO3dK1S6SbDVr1qRnz5506dIFe3t7smfPzjfffMPRo0fx9fXF398/3m3+dfHixQQnWBVJCy5evEjevHlj3G55sp0vXbqU69evs2XLFrZs2WLdZv78+eTMmZMLFy6onUua1aFDBy5duoSnpycRERG0a9fOelfiq6++AuD999/H29uboUOH4u7ujmEY9O/fnwoVKnDo0CF+/fVXXnnllRjDVwwZMoS6deuacg0WI64bqf8nrc1Wn9bqEUkJaueSEaidS0aQUDvXSPgiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQJDsRasmRJQkJCzKwnQY6OjoSFhaV2GSIpSu1cMgK1c8kIMmfOzJkzZ+Jcl+BURCEhIWlqpGKNnCwZgdq5ZARq55IRJDSdl25BioiIiJhMAUxERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMliIB7OjRowwcODAlDi2SYnbs2IGHhweNGzdm4MCBBAcHP9U2ImmZ2rlkBC9CO0+RAFa+fHmmT5+eEocWSRF3795l+PDhzJgxg59//pkiRYowbdq0ZG8jkpapnUtG8KK082QHsP3799OuXTsGDBhAkyZNaNmyJdu3b6dHjx689dZbTJgwgf379+Pu7g7AsGHDGDduHF26dKFhw4b06dOHhw8fPvcLEXkWu3btonz58rzyyisAdOjQgZ9++oknJ4pIyjYiaZnauWQEL0o7f6oesKNHj/Lee++xefNmcufOjZ+fH9999x2rV69myZIl3Lx5M8b2x44dY+7cuWzcuJGbN2+yefPm51K8yPNy48YN8ufPb32dP39+goODY/yxkJRtRNIytXPJCF6Udp7gVETxKVy4MGXLlgWgaNGiZM2aFQcHB3LlysVLL71EYGBgjO3r1q2Lg4MDAKVLl461XiS1RUdHx7ncxsYmWduIpGVq55IRvCjt/KnO9G+Y+pedXcI5LlOmTNZ/WywWdWVLmlOgQAFu3bplfR0QEED27NnJkiVLsrYRScvUziUjeFHauf6kEQHq1KnDkSNHuHDhAgDLli3Dzc0t2duIpGVq55IRvCjt/KluQYqkN7lz52bixIkMHDiQiIgIihYtyuTJkzl69Ci+vr74+/vHu43Ii0LtXDKCF6WdW4wE7gcWKlSIq1evmllPgtJaPSIpQe1cMgK1c8kIEmrnugUpIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRTAREREREyW4ECsJUuWJCQkxMx6EuTo6EhYWFhqlyGSotTOJSNQO5eMIHPmzJw5cybOdQlORRQSEpKmRirWyMmSEaidS0agdi4ZQaFCheJdp1uQIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYrJkBzB/f3+aN2+Op6cn7du35+jRowB4eXnRtGlTPD098fT0ZM6cOQAcOXIEDw8PPDw82Llzp/U433zzDStXrnxOlyHy/GzdupUqVarEue7IkSN4eXnx9ttv061bN27evGlydSLPZtGiRTRr1gx3d3fee+897ty5E2ubHTt24OHhQePGjRk4cCDBwcGpUKnI04svqzwp1T/PjQQULFgwxuuzZ88atWvXNgICAgzDMIwdO3YYLi4uxsOHD42qVasa4eHhsY7Rr18/488//zRu3rxptGzZ0jAMw7h69arRuXNnIzo6OqHTJ1qPyPN2/vx5o0GDBkalSpVirQsLCzPq1atnHDx40DAMw1i8eLHRq1ev516D2rmklKNHjxr169c3Hjx4YBiGYUyaNMkYOXJkjG3u3Llj1KhRwzh//rxhGIYxZcoUY/To0c+9FrVzSSnxZZUnpYXP82T1gDk4ODBu3DicnZ0BKFeuHLdv3+bQoUNkyZKFPn364OHhwYQJEwgNDbXuExISQkhICA4ODgBMnjwZHx8fLBbLc46TIk8vJCQEHx8fhg0bFuf6o0eP4uTkRNWqVQFo3bo1e/fu5d69e2aWKfLUypUrx88//0zWrFkJCwsjICCAHDlyxNhm165dlC9fnldeeQWADh068NNPP2HEP2a3SJoSX1YJDw+3bpMWPs+TFcAKFy7MW2+9BYBhGEycOBFXV1fCw8OpXr0606dPZ9WqVVy/fp3PPvsMgL59+zJjxgyGDBnC0KFD2bNnDy+99BIVKlR47hcj8ixGjRpFu3btKFOmTJzrb9y4Qf78+a2vHRwcyJUrFwEBAWaVKPLM7O3t2bp1K/Xq1ePAgQN4eXnFWP/fdp4/f36Cg4N5+PCh2aWKPJX4ssq/nUCQNj7PExwJPz6PHj1i2LBh3Lhxgzlz5pAtWzbc3Nys6/v06cOAAQP4+OOPKVmyJEuWLAEgIiKCLl26MHPmTFauXMm2bdtwdnbG19c3xhsjYrbFixdjZ2dH69atuXLlSpzbREdHx7nc1tY2JUsTee4aNGhAgwYNWLFiBT179mTLli3Y2Dz+ezy+dv7vepEXxX+zypPSwud5sn+jrl27Rvv27bG1tWXBggVky5aN7du3c+DAAes2hmFgZxc72y1YsICmTZuSKVMm5s+fzzfffEOBAgXw9/d/tqsQeUZr1qzh6NGjeHp64u3tTWhoKJ6enjH+GipQoAC3bt2yvo6IiODevXvky5cvNUoWSbaLFy9y8OBB6+tWrVpx7do1AgMDrcv+284DAgLInj07WbJkMbVWkWcRV1Z5Ulr4PE9WALt//z6dO3emUaNGfPHFF2TKlAl43JU3efJkQkNDiYqKYv78+TRt2jTGvjdv3mTLli107NgRwzAwDAOLxYKNjY31eTGR1LJq1SrWr1+Pv78/fn5+ZMqUCX9//xi/jBUrVuT+/fscPnwYgB9//JFKlSrF+sUWSatu3brF4MGDuXv3LgA//fQTpUqVImfOnNZt6tSpw5EjR7hw4QIAy5Yti3GHQyStiy+rPCktfJ4n6xbk0qVLuX79Olu2bGHLli3W5fPnz+fy5cu0bNmSqKgoqlevTr9+/WLsO3XqVAYNGoSdnR1OTk40aNAADw8PcuXKxYwZM57P1Yg8ZwEBAXh7e+Pn50e+fPn4+uuvGTt2LCEhIeTIkYPJkyendokiSfbGG2/w7rvv0rVrV2xtbXF2dmbmzJkcPXoUX19f/P39yZ07NxMnTmTgwIFERERQtGhRtXN5ocSXVfz8/NLU57nFSOCrLWlttvq0Vo9ISlA7l4xA7VwygoTauZ6qFBERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQKYCIiIiImS3Ag1pIlSxISEmJmPQlydHQkLCwstcsQSVFq55IRqJ1LRpA5c2bOnDkT57oEpyIKCQlJUyMVa+RkyQjUziUjUDuXjKBQoULxrtMtSBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRIchiIukyZNYvPmzWTPnh2AYsWK8eWXXzJr1izWrl1LVFQUzZs3p3///lgsFrZv385nn32Gg4MDY8eOpXz58gD4+vrStGlTatWq9XyvSOQprV27lnnz5llfBwUFERAQwM6dO8mTJ491+Y4dO/jss88IDw+nTJkyTJgwAScnp9QoWSTZTp48ybhx4wgKCsLGxoaxY8dSrly5GNscOXKEMWPGEBISgrOzM1OnTsXZ2TmVKhZJvviyypNSvZ0bCShYsGCsZW3btjUOHToUY9mOHTsMT09P4+HDh0ZoaKjRqVMnY8OGDYZhGEaLFi2MmzdvGn/++acxYMAAwzAM48iRI8bAgQMTOnWS6xFJCeHh4Ubbtm2NpUuXxlh+584do0aNGsb58+cNwzCMKVOmGKNHj36u51Y7l5Ty6NEjo3bt2saOHTsMwzCMLVu2GI0bN46xTVhYmFGvXj3j4MGDhmEYxuLFi41evXo991rUziUlxZVVnpQW2nmybkGGh4dz/Phxvv/+e5o3b86AAQO4du0aW7Zswd3dnSxZsuDo6IiXlxfr1q0DwMHBgdDQUEJCQrC3t8cwDKZOncrQoUNTJFCKPA+zZ88mV65ctG/fPsbyXbt2Ub58eV555RUAOnTowE8//YQR/4QSImnG7t27KVKkCC4uLgC4ubnF6hU4evQoTk5OVK1aFYDWrVuzd+9e7t27Z3a5Ik8lvqzypLTQzpMVwAICAqhRowaDBw/G39+fihUr0rdvX65fv06BAgWs2+XPn5+AgAAAfHx8GDRoEDNnzqRv376sWrWKGjVqJDg6rEhqunv3LvPmzWPEiBGx1t24cYP8+fNbX+fPn5/g4GAePnxoZokiT+X8+fPkzZuXESNG4OXlRY8ePYiKioqxzX/buIODA7ly5bJ+poukdfFllSf/UE4L7TxZAaxIkSLMnj2b4sWLY7FY6NmzJ5cuXSI6Ojr2gW0eH/qNN95g1apVLFq0iDx58rBy5Up69erFrFmzePfdd5k2bdrzuRKR52TFihW4ublRpEiRWOviauvw/9u7SFoWGRnJzp07adeuHatXr6Zz5854e3sTHh5u3Sa+Nm5ra2tWmSLPJL6scuXKFes2aaGdJ+v/GidOnGDt2rUxlhmGQcGCBbl165Z1WUBAQIxk+a+vvvqKPn36cO3aNfbu3cusWbO4f/8+e/fufbrqRVLAxo0b8fLyinNdgQIFYrX17NmzkyVLFrPKE3lqzs7OFC9enIoVKwLQoEEDoqKiuHz5snWb/7bxiIgI7t27R758+UyvV+RpxJdV7O3tra/TQjtPVgCzsbFh/Pjx1l/WJUuWUKZMGdzc3Fi3bh2PHj0iPDyc1atX06BBgxj7njhxgmvXruHm5kZ4eDh2dnbWY4aEhDynyxF5NoGBgVy6dInKlSvHub5OnTocOXKECxcuALBs2TLc3NxMrFDk6dWrV4+rV69y7NgxAA4cOIDFYqFw4cLWbSpWrMj9+/c5fPgwAD/++COVKlUiW7ZsqVKzSHLFl1We7BhKC+08WcNQlC5dGl9fX9577z2ioqLInz8/n3/+OQULFuTUqVO0adOGiIgI3NzcaNGiRYx9J02axOjRowEoU6YMuXPnxsPDgyJFilC3bt3ndkEiz+LixYvkzZs3xl9KR48exdfXF39/f3Lnzs3EiRMZOHAgERERFC1alMmTJ6dixSJJlzdvXmbOnGn96r2DgwMzZszg/v37eHt74+fnR758+fj6668ZO3YsISEh5MiRQ21cXijxZZWAgIA01c4tRgJf3ypUqBBXr141s54EpbV6RFKC2rlkBGrnkhEk1M715LCIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYTAFMRERExGQKYCIiIiImUwATERERMVmCA7GWLFkyTU0T5OjoSFhYWGqXIZKi1M4lI1A7l4wgc+bMnDlzJs51CU5FFBISkqZGKtbIyZIRqJ1LRqB2LhlBoUKF4l2nW5AiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJisgS/Bflfa9euZd68edbXQUFBBAQEsHPnTtzd3cmXL591Xc+ePWnevDnbt2/ns88+w8HBgbFjx1K+fHkAfH19adq0KbVq1XpOlyKSNIZhMHz4cEqVKkXPnj0BqFGjRpzt979mzZrF2rVriYqKonnz5vTv3x+LxWJa7SLJEVdbf/DgAZ06dWLChAnWz+P/UjuXF8WL/HmerADWokULWrRoAUBERASdO3fG29ubBw8ekD17dvz9/WPtM2PGDObPn8/Vq1eZPXs206dP56+//iIoKEjhS0x39uxZxowZw5EjRyhVqhQA586di7f9Pmnnzp1s3ryZ1atXY2trS8+ePSlRogRNmzY1o3SRZImrre/cuZMJEyYkOPyD2rm8KF70z/OnvgU5e/ZscuXKRfv27fnzzz+xsbGhS5cueHh48PXXXxMVFQWAg4MDoaGhhISEYG9vj2EYTJ06laFDhz63ixBJqsWLF+Pl5cXbb79tXZZQ+33Sli1bcHd3J0uWLDg6OuLl5cW6devMLF8kyeJq6wsWLGDSpEk4OzvHu5/aubwoXvTP82T1gP3r7t27zJs3j9WrVwMQFRVF7dq1GTp0KKGhoXh7e+Pk5ET37t3x8fFh0KBBZMqUiTFjxrBq1Spq1KiR4OBkIill1KhRAOzbt8+6LKH2+6Tr169Ts2ZN6+v8+fMTEBBgSt0iyRVXW587d26i+6mdy4viRf88f6oAtmLFCtzc3ChSpAgAbdu2ta5zcHCgR48eLFy4kO7du/PGG2+watUqAAIDA1m5ciULFy5k1qxZ/O9//6NkyZIMGTLkOVyKyNNJqP0+Ka5Zu2xs9D0WSV/UzuVF9iJ9nj/V2TZu3IiXl5f19dq1azlx4oT1tWEY2NnFznZfffUVffr04dq1a+zdu5dZs2Zx//599u7d+zRliDwXSW2/BQoU4NatW9bXAQEB5M+f35QaRcyidi4vshfp8zzZASwwMJBLly5RuXJl67LTp08zffp0oqKiCA0NZfHixbEeZDtx4gTXrl3Dzc2N8PBw6xtiY2OTpib8lownKe0XwM3NjXXr1vHo0SPCw8NZvXo1DRo0SIWKRVKO2rm8yF6kz/NkB7CLFy+SN29e7O3trcv69+9P9uzZ8fDwoHnz5lSuXJk2bdrE2G/SpEl89NFHAJQpU4bcuXPj4eHB7du3qVu37jNehsjTS6j9Ll26lI8//hgAV1dXGjVqRJs2bXB3d6dcuXLWbwWLvMjUziW9eJE+zy1GXDdC/09am60+rdUjkhLUziUjUDuXjCChdq4nK0VERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRIciLVkyZJpapogR0dHwsLCUrsMkRSldi4Zgdq5ZASZM2fmzJkzca6LPUPlE0JCQtLUSMUaOVkyArVzyQjUziUjKFSoULzrdAtSRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTDJkLZu3UqVKlUACA8PZ9SoUTRt2pSmTZsyadIkoqKi4txv1qxZNGnShIYNGzJjxgwSGEZPJNWpnUtG8KK2cwUwyXAuXLjA5MmTrb9sixcv5u7du6xfv55169bx559/smnTplj77dy5k82bN7N69WrWr1/P/v3749xOJC1QO5eM4EVu58kOYPv376ddu3YMGDCAJk2a0LJlS7Zv306PHj146623mDBhAtHR0YwbN442bdrQtGlT3n77bQ4dOkR0dDTdunVjypQpAOzZs4d69epx+/bt535hInEJCQnBx8eHYcOGWZf16NGDL774AhsbG+7fv8+DBw/Inj17rH23bNmCu7s7WbJkwdHRES8vL9atW2dm+SJJonYuGcGL3s6fqgfs6NGjvPfee2zevJncuXPj5+fHd999x+rVq1myZAl//vknN2/eZPny5WzcuJGWLVsye/ZsbGxsmDp1Kv7+/mzdupXhw4fz2WefkSdPnud9XSJxGjVqFO3ataNMmTIxltvb2zNt2jQaNmxInjx5eOONN2Lte/36dQoUKGB9nT9/fgICAlK8ZpHkUjuXjOBFb+dPFcAKFy5M2bJlAShatCjVq1fHwcGBXLly8dJLL5E1a1Y++OADli1bxuTJk9m8eTMPHz4EwNnZmU8//ZT+/fvTtm1b3nzzzed3NSIJWLx4MXZ2drRu3TrO9UOGDOGPP/6gUKFCfPLJJ7HWx/V8gI2N7uJL2qJ2LhlBemjnT3U2BweHGK/t7GJOKbl371769OkDgJubGx06dIix/syZM+TJk4ejR48+zelFnsqaNWs4evQonp6eeHt7ExoaiqenJ4cOHeL8+fPA47+cWrZsyfHjx2PtX6BAAW7dumV9HRAQQP78+U2rXyQp1M4lI0gP7TxF4t6vv/5K/fr16dixI+XLl2fr1q3WbyH89ddfLFiwgB9//JEHDx7www8/pEQJIrGsWrWK9evX4+/vj5+fH5kyZcLf3599+/YxceJEIiMjiY6O5qeffqJ69eqx9ndzc2PdunU8evSI8PBwVq9eTYMGDVLhSkTip3YuGUF6aOcpEsBGjBjBgQMH8PDwoF27dhQpUoQrV64QFBTE4MGD8fX1JV++fEyaNIlvvvkmznQqYpbevXtTsGBBPD098fT0xNbWlg8//BCApUuX8vHHHwPg6upKo0aNaNOmDe7u7pQrV44WLVqkYuUiSad2LhnBi9TOLUYCA18UKlSIq1evmllPgtJaPSIpQe1cMgK1c8kIEmrnerJSRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiYiIiJjMLqGVhQsXplChQmbVkqjMmTOnqXpEUoLauWQEaueSERQuXDjedQmOhC8iIiIiz59uQYqIiIiYTAFMRERExGQKYCIiIiImUwATERERMZkCmIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiYiIiJhMAUxERETEZApgIiIiIiZTABMRERExmQKYiIiIiMkUwERERERMpgAmIiIiYjIFMBERERGTKYCJiIiImEwBTERERMRkCmAiIiIiJlMAExERETGZApiIiIiIyRTAREREREymACYiIiJiMgUwEREREZMpgImIiIiYzC61CxAReW7C7kLEg9SuQiRjsM8GjrlSu4oXlgKYiKQfEQ/g3PzUrkIkYyjeXQHsGegWpIiIiIjJFMBERERETKYAJiIiImIyBTARERERkymAiUiGYBgGw77ex9x1/yRp+9CwSHpP2MH9oDDCwqOStE9Cx/ccsokHD8MJehhO10+2Jbnu+FTuvJIrN4MT3OavM3cY5XcgwW1CwyK5ExjKu5N+S/J1xlXH0bN3GDht11PX8+T+yfk5PemdT3/l7oMwAHpP2MGZy4HJPoaIWRTARCTdO3slkG5jtrNp76UkbX/rXgjvTf6Ns1ce8NHX+5K8X0L8p71NtpccCHwYztEzd5/5eElx5nIgAXceJbjN5n2X+ejrfZy6dJ++U37j1r2QpzpX+RK5mT6kzlPXk5T9E7P7rxvWf88e8RYli2R/puOJpCQNQyEi6d7izafxql+cgnleStL2eXNmxqt+cbbsv0JkdDTudV6mhc8mhnapTK0K+dmw+yLDZu7jwLxWZHK0w3fWH7xWLCcAf568TfuPt3A7MJRSRbLz2fu1yJLJjjJtlrJ3rhfDv9lPaHgUnkM2sXpyYy5cD2L8vMPcDwojKtqgS9PStHYtEaumg//c5NO5h7BYHoeVaMOwrlu+5QwLN53CxsZCnuyZGNmzKpkcbJm+/ChBjyIYPnMfvu9UZfg3+7l4PQgbGwuvF8/FWO83ca/zMr/sv0y54rloUL0weXNm5ujZO/h++wf+095Och37/w7g07mHWP95Uw7+c4tJPxwmOtoAi4U+LctSoWSuGPW0eKsY4+cdJoujHY/CIvHpXInJC/5k/edNATh04hY/77tM8KMIalfMz0ddK2Nna2N9H3NlcwSwvp668E8Aun2yDb8RLnQatY2vPqxN+RK543x/ihXMxrCv9+GUxZ6Tl+5z4/YjihfKxucf1OKlzPYxrnnY1/twdLDl6Nm73L4fwts1i5IruyO/HrzGrfshjHu3GjXL5yc8Ioppi49w4PhNoqINyr6SE993quKUxZ5fD13lu9XHCY+M5m5gKC3eKsYH7Suw/+8Avlj6F0WcnTh9+T7hEdGM6vUGNcrlS1JblReXesBEJN0b1esNWrgUS9Y+JQtnZ8qAGni3KIutjYUG1Yrw+/+uA/D7n9fJ/pIDB0/cIjraYMfhazSqVhiAgLshzBtVn5+/akbAnUf8sv9yjONO7FudTA62+E97GwMY+NkuPuxUkdVTmrBojBvfrzvB/07djrFPeEQU73+2m2HdKrN26ttUL+dM6P/dLtx79AZz/P9hwWhX1k17G/c6L9Nvyu/kz52Fge3K88ZreZnYrwZb/rjCw5AI/Ke9zaqJjQC4HBCMrc3jgDRlQA1KFX7cY1S+RO44w1dCdTxpxoqj9PB4ldVTmjChbzX2HQugQJ6XYtQDcPpSIJ99UIt1097GwT7m/45u3Alh/ihX1k5twomL91mx9WyCP69/j/nDJ24UeCJox/f+GP8XHI+du8vcj99i45fNuHkvhM37Lsd5/H8u3GP5+Ib8OKkx8zecJEsme5aNb0jXpmWYvfbx7VK/tf9ga2Nh9eTGrJv2Ns65MjNt8f8wDIPvfzrBpP41WD25McsnNMRvzXHr7dK/Tt/hHY9XWTv1bVq7FefrFUcTvFZJHxTARETi8FqxnGRytKNS6TxYLBYaVivMb39eA+DgiVt0d3+V3UducOT0HYrmcyJvzswANKhWiMyOdtja2lCqaHbuPgiN9xwXrgVxKSCYEd/sx3PIJjqP3kZoeBTHz9+Lsd2pS4HY2dlQs3x+ANzrvMJLmR/fwPj9f9dpWqsoubJnAsCrfnEC7oZw5ebDGMeo+mpezlx5QJfR2/Bb+w/dmpXh5QJZsVgsVCyVh0yOdtZevPgkVMeT3q5VlLFzDvLhV3v4+9w9BneoEOfxCuTJQqG8cfdKetZ7hSyZ7HCwt6V53VfY88TtxeRI7P2pW6kADva22NvZULpodgKDw+I8Tv2qhbC3syFvzsxkcbSjbqUCABTN78T94HAAdhy6yvaDV2nhsxnPIZvY+scVzl55gMViYdZH9fj73F2+XnmUST/8iWFASFgkAAXzvmR978sWy0Xg/x1P0jfdghQRSYIyL+cgIjKabQeu8HJ+J+q/UYhBX+zGztZCoxqFrdvZ2f7/v2stWHjiTmEsUdEG2bI4xOhtun0/hKxZHGJsZ7Fg7bGxnsfm8XniOr6BQWRUdIxlRfI5sWWGO/v/vsm+YwH0GLsd33eq0qRm0USvPSl1PKl9w5LUr1qQ3Udu8Pv/rvP1iqOsi6NHLUum+P8XZGtjiXmeJ97Xfy86PCLxLw0k9v5kcrC1Lk/o5/XfHjo7W0usbaKjDUb0qIJL5YIAPAyJICwimkehkbQcupkG1QrzxmvOtKpfnK0Hrljfyxg1WCCBJiPpiHrARESSqEG1wkxbfITaFfNTolA2gh9F8NPvF2lcvUiSj2Fna0NUtIFhGBQrmBVHB1v8fzsPwPXbD3EfvIlj52I+pF+6aA4MA3YeftwDt+3AFQIfPu4lqVMxPxv3XOJu4OOeth9/PUcOJ0dezp8VW1uLNWgs+fk0w2fup07F/Ph0rkSdigU4ncxvCSZUx5Paf7yFf87fw6t+cT7tU40HDyMIfBgeo57EbNh9kfCIKMLCo1j96znqVX7c45QrmyNH/+/92fLHlRj72NrEPn5C78/zVqdSARZvOk14RBTR0QYjv/uDz5cc4eL1IIJDIvigfQVc3yjEH8dvEh4R/fgZOcmw1AMmIhlWQg+bx6VhtSLMXXeCWhUe34KrVSE/Jy/ej/HMUWLy5shE2eI5efuDDSwd15BvhtZl/LzDzPH/h8gog/fbl6fqq3lj7GNvZ8PMoXX5xO8Any85wmuv5CR39scPodeuWIDu7mXoNmY70YZBrmyZ+G64CzY2FiqXzsOXS/+i35TfmTqwJn/8fZOmgzaS2dGOgnmy0LVpmWS9LwnV8aQhnSsxYd4hvlz2FzYWC/3blKOwsxPR0Ya1nq7NSif4PhV2dqLDyK08Co2kYbXCtHzr8TN8vu9UZeycg2R7yYFaFfJbb/0CNKxemI4jt/LN0LrWZQm9P89b31avM3nh/2g5dDNR0QavvZKTYV0rkyWTHW9VKcTbH2wg20sOFM3vRMnC2bh4IzhWz5pkHBbjv/3JIiIvquALmoxbxCzFu4PTK6ldxQtL0VtERETEZApgIiIiIiZTABORdM//t/M0H7IJzyGbaP/xFo6evZMi5/nvNENl2izl7oMwth24wrjvD6XIOQGCQyLoOe5XQv9vWIOn8eQ0PvGJb3qfzXsv0WX0s0+v9F+ufdfF+bNKyrRHTzudUVJM+uEw+/8OeK7HTMr7/6QZK44yds7B51qDmEsBTETStXNXHzB14f+Y8/Fb+E97m/davc6AqQn/z/tpxTfNkNubhfF9p2qKnBNg2qL/0catBJkcn/57VbuTMM5WWpne53lMW/Qs+rUux7i5h54p8P5XUt5/SV/0LUgRSdcc7G0Y9241nP/v23LlSuTi9v1QwiOicLC3jXe/G3ce8cnsA1y9+RADaOFSjF6er3HlZjAegzfx56I2ADFe/3eaoX+t/vUcP++7zHfDXQh6GM74eYc5dek+EVHR1Cyfn6FdKmFna0O5Dstxe7MQJ/5fe3ce1PSZx3H8nYSEcAtaDjmEeiyFDoioKIgolUPFaT1mx7a66zitq3a2u9JV61Wra1GstbpW2/HE3bpe1fG+wKO0RVHArXQrKLgIciiCAQTlSvaPQEyEQFocncLz+g/y5Pc8eTIDzzy/5/f95KlY85ehnE8rIvFyAXIzGfY2Cla+N0T3OZoV36/mQnqRboFXVV3Hsm1pZOWpkAChAS7EvuX/i2J8zqcXsScxB7mZFHO5jOUzBtHH3Y7w2Ud08T7r91zj6Pe36WatoJfLk5IObcXxNHtQVcvIWYdJ2ToBS6UZH22+Qu6dCnYtHwVA5J+PsmnecAD2JuaydHMa5RWPeX24J3Pe8m839ihqiGFZkJUJGWTdVrFpXihyM6nR8YXPPoJfn+5k56uIfdOPUtXjVufBxkpBgPdL7E3K5Y9jDZ8k3bAvk8J71ZSqHlFYWo2DrZLP5wTj5GDJzYIKlm9LQ1VVi0QiYfo4b94I82LBxksG86//VG1uYSWLvkylrq4RDTDptd68HdUXgFtFlUz9+CylDx7Rw07J2jkhONpbGO3HlDitzJtlJsUzCR0ndsAEQejU3BytGRHoCmiLiK7cmUH4QNc2F18Af1ufQpCvE0fXjmH330dx5Ls8jv9wu8336McMyWSt/3mN25mBb28HDq6O5tDqaB5U1rLjWBYA9Q1qRga6cvofMfSwU7LzeDYHVkVxMD6KEH9nrt1seTvu7JVChrzqpCtUumJHOt2szTn62WgOxEeRfVvF9iNZbY9bL8bH0d6CuIQMti4awYFVUfx+VG/Ss0oN2idducOZ1AIOfRrNnk8ieFhTr3vNWByPPnsbc/z6dNfdxkv96S55RVVUP6onp6ACM5lUt9NmLpdyMD6K/Ssj2X4si+L7hhX+W4s9aqbRwLKtaRSWVrNlYRhWFvJ2x9fXw46T68YSPtC1zXkID+xJYmrrsUVpWaWsjx3GqfUx2Fop2JuYS0OjmlnxyUwd3Y+jn41hy8Iw1v77R65m3zcaowSw7fB1wgNdObg6ms0Lwkj7+Z6ufljB3YesnxOi7cdawf6zbfdjapyWqfFMQseIHTBBELqEmscNfLjxEiVlNWxdNKLdthnZ99m+ZCQANlYKJozwIvlqEf59u3doHBfSi8jMKeebs9psw6ezFAe+oq0B5uRgibdnN8bPO8XwgJ4MD3DRRQDpu1VYiYeLte7n5KvF7F4RgUQiQSGXMTmiDzuPZzNjvI9J45PJpEQPdWfyokRGDOhJiL8z44b1Mmhz8VoJEUHuWDftikwMf5l/nbjR9PkKqaqp10UH1Teo6d4UA6QvIsid5KvFeDjb4ORgQT8PO678fI/sfBWRejtYMcM8AW1Aeg87JWUVhuekmmOPzqUVEuznbBB7lHAsi7KKWg59Gq1bcLc3vub5b28e3J1t+F9RVatzONjHUbfj5+NlT8XDWvKKqqitaySyqWivk4MlkUHaBVHA73q0eh3tPLkxf8MlruWUMdTPmcXTA3U1zEL8nHURS9697CmveNxmP1FD3Ild9wPz/xBgEKdlpZQbxGk1xzMBbcYzCR0jFmCCIHR6RaXVzIxPprerLf9cGt7uWSm1RtMikkat0dDQoEEikaDRC4upbzCtsrvuOmoN62ND6N0UfF1ZXYd+SVBLpfYft1Qq4etlr5GZW87FzBLiEjII8nVqcZZMKpUYVFRXPzVwtQYaGvV+Z0KMz5r3g7mRryIls4Qth6/zzblbfDl/uO51icQwskc/NshYHM/TIga78fZHSXi62BDi74ytlYLvfywhM6eMj98dpGtnZvbk2k/PPbQdezTIx5EB3i+xYOMl9sZFIjeTtjs+/XiktuZBrdYYLebaIlpI0/J7Ae2ObHvJACMDXTm9IYaUayVczLzLxv2Z7FkRoZ0b/dgrE/oxNU7L1HgmoWPELUhBEDo1VVUtU5aeJTLIjc/nhJh0UN3aQo5/v+7sOnUT0J6rOvRtHsH+zthayqlvUOueBtSPw9GPGTJmmL8LCcey0Wg01NU3Mis+ma+b+tGXlfeAmNgT9Ha15U/jfZkW4032bVWLdp4uNhTcfWhw/V2nbuiuvy8pR1e535QYn/LKWsJmHqabjTnTxnrz18l+LfoN7e/CqYv5VFbXoVZrOJyc96R/I3E8T3Pubom9jTl7EnMI8XNhmL8LZ1ILUD2sazcUXJ+x2CPQnvebEt0XGysFX+zL/EXja28eCu4+5GVXW5PH6dXTFrmZlDNNty3vltdwOvWO7rtpLUYJ4IN1KZxIyWdsSC+WvjMQaws5xWU1v7qfZxGnJTwbYgdMEIRObfeZHIrv15CYeofE1CeLjoSl4dTVNzIj7ls2LwzDycHS4H1r3h/K8q3pHDx/i7oGNeNCPZkwwguJRMLcKf15N+4CDnZKooc++cf1dMxQaxZND+STHemM++Ak9Q1qgv2ceef1V1q08/a0Z3SwBxM/PI2lUo5SIWPx9AEt2o0a7MbWw9dpbFQjk0lZPD2QFdueXD+0vwszJ2hvP5oa4zNroi/Tlp1DqZAhk2kfYtAXNqAn2fkqJs4/ja21Au9e3XjQVELBWBxPayKC3Nh+NAsfL3ukUglKhYxRg91abWuMsdijZhKJhLjZQbwx9yRhgT1NHp+DrXmb8/Ddf4oNvvv2yM2kbJoXyort6WzYl0ljo4b3Jvky5FUn3Vw0z38/j266982e5Muiry6zNzEHmVTCqMFuDPZx5PJ/7/26fp5BnJbwbIgoIkEQOo8uGkW05KvLDPVzZkywx4seSpdQVV3Hm0uSOLAqCnNF2w9zdGoiiqhDxC1IQRCE37i5U/uzLynnmdalEoz7Yv9PLJw2oGsvvoQOEztggiB0Hl10B0wQXgixA9YhYgdMEARBEAThORMLMEEQBEEQhOdMLMAEQRAEQRCeM7EAEwRBEARBeM7EIXxBEDqP2nKor3zRoxCErkFuC+YOL3oUv1liASYIgiAIgvCc/R938v6nkXNZEwAAAABJRU5ErkJggg=="
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 60,
            "source": [
                "import time\n",
                "\n",
                "cpu_reader = easyocr.Reader(['en'], gpu = False)\n",
                "gpu_reader = easyocr.Reader(['en'], gpu = True)\n",
                "\n",
                "df = pd.read_csv(\"./1/labels.txt\", sep=\".jpg \", names=[\"filename\", \"sentence\"])\n",
                "df.filename = \"./1/\" + df.filename.astype(str) + \".jpg\"\n",
                "df.sentence = df.sentence.str.strip()\n",
                "\n",
                "def easyocr_time(path, reader): reader.readtext(path, detail = 0)\n",
                "def tesseract_time(path): pytesseract.image_to_string(Image.open(path))\n",
                "\n",
                "def crono(what, df):\n",
                "    start = time.time()\n",
                "\n",
                "    if what == \"Tesseract\":\n",
                "        _ = [tesseract_time(filename) for filename in df.filename]\n",
                "    elif what == \"EasyOCR CPU\":\n",
                "        _ = [easyocr_time(filename, cpu_reader) for filename in df.filename]\n",
                "    elif what == \"EasyOCR GPU\":\n",
                "        _ = [easyocr_time(filename, gpu_reader) for filename in df.filename]\n",
                "    \n",
                "    ocr_time = time.time() - start\n",
                "    print(f\"{what}: processed {len(df)} images in {ocr_time:.4f}s, at ({len(df) / ocr_time:.2f} FPS).\")\n",
                "\n",
                "crono(\"Tesseract\", df)\n",
                "crono(\"EasyOCR CPU\", df)\n",
                "crono(\"EasyOCR GPU\", df)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "Using CPU. Note: This module is much faster with a GPU.\n"
                    ]
                },
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "Tesseract: processed 500 images in 131.6549s, at (3.80 FPS).\n"
                    ]
                },
                {
                    "output_type": "stream",
                    "name": "stderr",
                    "text": [
                        "/home/ubuntu/anaconda3/envs/ocr/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
                        "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
                    ]
                },
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "EasyOCR CPU: processed 500 images in 71.4887s, at (6.99 FPS).\n",
                        "EasyOCR GPU: processed 500 images in 17.3699s, at (28.79 FPS).\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "source": [
                "from Levenshtein import distance\n",
                "a, b = \"aaa\", \"aaa\"\n",
                "print(f\"Levenshtein distance between '{a}' and '{b}': {distance(a, b)}\")\n",
                "\n",
                "a, b = \"aaa\", \"aaaa\"\n",
                "print(f\"Levenshtein distance between '{a}' and '{b}': {distance(a, b)}\")\n",
                "\n",
                "a, b = \"aaa\", \"aab\"\n",
                "print(f\"Levenshtein distance between '{a}' and '{b}': {distance(a, b)}\")\n",
                "\n",
                "a, b = \"bba\", \"aaa\"\n",
                "print(f\"Levenshtein distance between '{a}' and '{b}': {distance(a, b)}\")\n",
                "\n",
                "a, b = \"bbb\", \"aaa\"\n",
                "print(f\"Levenshtein distance between '{a}' and '{b}': {distance(a, b)}\")\n",
                "\n",
                "a, b = \"bbbb\", \"aaa\"\n",
                "print(f\"Levenshtein distance between '{a}' and '{b}': {distance(a, b)}\")"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "Levenshtein distance between 'aaa' and 'aaa': 0\n",
                        "Levenshtein distance between 'aaa' and 'aaaa': 1\n",
                        "Levenshtein distance between 'aaa' and 'aab': 1\n",
                        "Levenshtein distance between 'bba' and 'aaa': 2\n",
                        "Levenshtein distance between 'bbb' and 'aaa': 3\n",
                        "Levenshtein distance between 'bbbb' and 'aaa': 4\n"
                    ]
                }
            ],
            "metadata": {}
        }
    ],
    "metadata": {
        "orig_nbformat": 4,
        "language_info": {
            "name": "python",
            "version": "3.8.10",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
            "file_extension": ".py"
        },
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.8.10 64-bit ('ocr': conda)"
        },
        "interpreter": {
            "hash": "c780b730d5359305d65c050e037acb69fbe32ace1951d559bd5124189df47c7f"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}