{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "61736bca",
   "metadata": {
    "id": "uAS16NdUceq9"
   },
   "source": [
    "# Personal Ideas\n",
    "\n",
    "*We're encouraging you to follow your own AI idea, however this won't be a good fit for most students.*\n",
    "\n",
    "If you have a personal idea, you may go through your own project flow, but you should include/utilize the full DS-stack of what we learned during the semester:\n",
    "\n",
    "* **Create/collect your own dataset** — text, image, audio\n",
    "* **Apply EDA** to understand the data and the story it tells\n",
    "* **Use/Train models** that classifies, predicts, or generates something valuable\n",
    "* **Create workflows** that can answer the problem\n",
    "* **Create UI** that enable to use your AI\n",
    "* **Publish your Dataset** using Hugging Face Dataset repo\n",
    "* **Publish your Model** using Hugging Face Model repo\n",
    "* **Publish your App** using Hugging Face Space repo\n",
    "* **Present your work live to the faculty**\n",
    "* ...\n",
    "\n",
    "For any student who pursue this route, we request you contact the faculty before you start, to make sure you're on the right path - hitting all marks to complete the course, and not diving too deep into the rabbit hole.\n",
    "\n",
    "<br>\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbb14ec",
   "metadata": {
    "id": "Fgh3opcbigm4"
   },
   "source": [
    "# Submission:\n",
    "- Work in solo or in pairs.  \n",
    "- Upload your app to HF Space.\n",
    "- Submit your all of your code on Moodle.\n",
    "- Scudeule a Live presentation with the faculty.\n",
    "\n",
    "  TIP:\n",
    "  - Work on something you care about.  \n",
    "  - Make it useful. Make it meaningful. Make it yours.  \n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce363aa9",
   "metadata": {
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   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5cb0e94",
   "metadata": {
    "id": "_Hl7BA43CZ1n"
   },
   "outputs": [],
   "source": [
    "# Install libraries\n",
    "!pip -q install -U \"pillow<11\" diffusers transformers accelerate safetensors tqdm pillow pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c606d86f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ofCgyr1qCZfa",
    "outputId": "9f0c5709-8016-4163-faf2-2a925fc7318a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA available: True\n",
      "GPU: Tesla T4\n"
     ]
    }
   ],
   "source": [
    "# Check GPU\n",
    "import torch\n",
    "\n",
    "print(\"CUDA available:\", torch.cuda.is_available())\n",
    "if torch.cuda.is_available():\n",
    "    print(\"GPU:\", torch.cuda.get_device_name(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec319628",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6g-wKNabDgpM",
    "outputId": "f6416813-b220-4fa0-aa41-eb51f22435d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA available: True\n",
      "GPU: Tesla T4\n"
     ]
    }
   ],
   "source": [
    "# Check GPU\n",
    "import torch\n",
    "\n",
    "print(\"CUDA available:\", torch.cuda.is_available())\n",
    "if torch.cuda.is_available():\n",
    "    print(\"GPU:\", torch.cuda.get_device_name(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3391e72",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121,
     "referenced_widgets": [
      "e54a3f5bf82d4853b2489164e7196461",
      "75c998316df246a9a4a308942801176a",
      "264fbff9e6fe46df8bcdb0434eefcb49",
      "15c50fcca2e54b2cad408b6db52f5086",
      "d25f9857c28d4a68a6362c7dd34a57d4",
      "452edd0448d5445dbae0e9a95ba169be",
      "7d6fa8d1c8ca4309a9ff84440ccdfcb4",
      "b3c8c6a40b8f4747a3bc59619d3fdac1",
      "18bb89324cdd4d39be252410c6341dbb",
      "2fd1bc7f44de4f56b128a72d1ffa2d34",
      "86ccde68022a40f181d33e50ba8513e1"
     ]
    },
    "id": "pxIghYAXCZa1",
    "outputId": "13411b97-9568-473a-8fd4-f7a894ec6e78"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e54a3f5bf82d4853b2489164e7196461",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image model loaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/dist-packages/diffusers/pipelines/pipeline_utils.py:2186: FutureWarning: `enable_vae_slicing` is deprecated and will be removed in version 0.40.0. Calling `enable_vae_slicing()` on a `StableDiffusionPipeline` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`.\n",
      "  deprecate(\n"
     ]
    }
   ],
   "source": [
    "# Load image model\n",
    "from diffusers import StableDiffusionPipeline\n",
    "import torch\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "IMAGE_MODEL_ID = \"stable-diffusion-v1-5/stable-diffusion-v1-5\"\n",
    "\n",
    "pipe_img = StableDiffusionPipeline.from_pretrained(\n",
    "    IMAGE_MODEL_ID,\n",
    "    torch_dtype=torch.float16 if device == \"cuda\" else torch.float32,\n",
    "    safety_checker=None,\n",
    "    requires_safety_checker=False\n",
    ").to(device)\n",
    "\n",
    "pipe_img.enable_attention_slicing()\n",
    "if device == \"cuda\":\n",
    "    pipe_img.enable_vae_slicing()\n",
    "\n",
    "print(\"Image model loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e3e852d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84,
     "referenced_widgets": [
      "99e6f77a2079401faf0bb80648db655d",
      "ff12d2a9ed6a4783bfcdd3a1f228b619",
      "0af8a78300ba424986c257ffe3ea1ea2",
      "93cc0a5a193842efadfba90c9ba65dc4",
      "47af28ad988e443485f020eda438594f",
      "c07dfcf11c3249bc92680f8960aae961",
      "ffd3ff972db043cda6af0e2f30cb4794",
      "52361f00ff994ee59e0f17c9b6bb6fb9",
      "bb08d8d1b8074be98eca118d9bb0900a",
      "b0e29ec98a1e4e838e6303c9f7c605dd",
      "28fa4622318c4bb18fd67bb608be800e"
     ]
    },
    "id": "hp1yioDsCZRz",
    "outputId": "4b682ee9-18f2-4260-9e6c-3b745adeec36"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99e6f77a2079401faf0bb80648db655d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use cuda:0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text model loaded\n"
     ]
    }
   ],
   "source": [
    "# Load text model\n",
    "from transformers import pipeline\n",
    "\n",
    "TEXT_MODEL_ID = \"microsoft/phi-2\"\n",
    "\n",
    "pipe_txt = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=TEXT_MODEL_ID,\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "\n",
    "print(\"Text model loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a1c9ca",
   "metadata": {
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     "height": 1000,
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    {
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     "text": [
      "Synthetic multimodal dataset saved: multimodal_dataset_clean/dataset.jsonl\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "\n"
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   ],
   "source": [
    "# Generate multimodal synthetic dataset (images + ad text)\n",
    "\n",
    "import os, json, random, re\n",
    "from tqdm import tqdm\n",
    "\n",
    "# 1. Dataset size and folders\n",
    "N_SAMPLES = 120\n",
    "OUT_DIR = \"multimodal_dataset_clean\"\n",
    "IMG_DIR = os.path.join(OUT_DIR, \"images\")\n",
    "os.makedirs(IMG_DIR, exist_ok=True)\n",
    "OUT_JSONL = os.path.join(OUT_DIR, \"dataset.jsonl\")\n",
    "\n",
    "# 2. Product pools by category\n",
    "categories = {\n",
    "    \"Food\": [\"protein bar\", \"iced coffee can\", \"matcha drink\", \"granola snack\", \"sparkling water bottle\"],\n",
    "    \"Beauty\": [\"face cream jar\", \"lip balm stick\", \"hair serum bottle\", \"sunscreen tube\", \"cleanser bottle\"],\n",
    "    \"Fitness\": [\"yoga mat\", \"water bottle\", \"resistance bands\", \"gym gloves\", \"running shoes\"],\n",
    "    \"Tech\": [\"wireless earbuds\", \"smart watch\", \"portable charger\", \"bluetooth speaker\", \"mini projector\"],\n",
    "    \"Home\": [\"scented candle\", \"desk lamp\", \"travel mug\", \"storage box\", \"plant pot\"]\n",
    "}\n",
    "\n",
    "styles = [\n",
    "    \"studio product photo, clean background, realistic\",\n",
    "    \"commercial product photography, high detail, realistic\",\n",
    "    \"minimalist product shot, premium lighting, realistic\"\n",
    "]\n",
    "\n",
    "angles = [\"front view\", \"top-down view\", \"45-degree angle\", \"close-up shot\"]\n",
    "colors = [\"black\", \"white\", \"blue\", \"green\", \"pink\", \"silver\", \"beige\"]\n",
    "audiences = [\"students\", \"young professionals\", \"fitness lovers\", \"busy parents\", \"remote workers\"]\n",
    "brands = [\"Nova\", \"Bloom\", \"Pulse\", \"Lumen\", \"Aero\"]\n",
    "features = [\"eco-friendly\", \"compact\", \"lightweight\", \"premium\", \"long-lasting\"]\n",
    "\n",
    "# 3. Fixed schema\n",
    "ALLOWED_KEYS = [\"product_name\", \"category\", \"tagline\", \"ad_copy\", \"target_audience\"]\n",
    "\n",
    "# 4. Prompt builders\n",
    "def build_image_prompt(category, product, brand, feature, color, style, angle):\n",
    "    return f\"{brand} {product}, {category} product, {feature}, color {color}, {style}, {angle}\"\n",
    "\n",
    "def build_text_prompt(category, product, brand, feature, audience):\n",
    "    return f\"\"\"\n",
    "Return ONLY valid JSON with exactly these keys:\n",
    "product_name, category, tagline, ad_copy, target_audience\n",
    "\n",
    "Brand: {brand}\n",
    "Product: {product}\n",
    "Category: {category}\n",
    "Feature: {feature}\n",
    "Audience: {audience}\n",
    "\"\"\"\n",
    "\n",
    "# 5. JSON extraction\n",
    "def extract_first_json(text):\n",
    "    m = re.search(r\"\\{.*\\}\", text, flags=re.DOTALL)\n",
    "    if not m:\n",
    "        return None\n",
    "    try:\n",
    "        return json.loads(m.group(0))\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# 6. Schema cleaning\n",
    "def clean_ad(ad, fallback):\n",
    "    clean = {}\n",
    "    for k in ALLOWED_KEYS:\n",
    "        v = ad.get(k, fallback[k]) if isinstance(ad, dict) else fallback[k]\n",
    "        clean[k] = str(v).strip()\n",
    "    clean[\"tagline\"] = \" \".join(clean[\"tagline\"].split()[:10])\n",
    "    clean[\"ad_copy\"] = \" \".join(clean[\"ad_copy\"].split()[:45])\n",
    "    return clean\n",
    "\n",
    "# 7. Build base combination pool\n",
    "pool = []\n",
    "for cat, prods in categories.items():\n",
    "    for prod in prods:\n",
    "        for aud in audiences:\n",
    "            pool.append((cat, prod, aud))\n",
    "random.shuffle(pool)\n",
    "\n",
    "base_choices = pool[:N_SAMPLES]\n",
    "\n",
    "# 8. Dataset generation loop\n",
    "with open(OUT_JSONL, \"w\", encoding=\"utf-8\") as f:\n",
    "    for i in tqdm(range(N_SAMPLES)):\n",
    "        category, product, audience = base_choices[i]\n",
    "\n",
    "        brand = random.choice(brands)\n",
    "        feature = random.choice(features)\n",
    "        color = random.choice(colors)\n",
    "        style = random.choice(styles)\n",
    "        angle = random.choice(angles)\n",
    "\n",
    "        # Image generation\n",
    "        img_prompt = build_image_prompt(category, product, brand, feature, color, style, angle)\n",
    "        image = pipe_img(img_prompt, num_inference_steps=18).images[0]\n",
    "        img_path = os.path.join(IMG_DIR, f\"img_{i:04d}.png\")\n",
    "        image.save(img_path)\n",
    "\n",
    "        # Fallback text\n",
    "        fallback = {\n",
    "            \"product_name\": f\"{brand} {product.title()}\",\n",
    "            \"category\": category,\n",
    "            \"tagline\": f\"{feature.title()} {product.title()}\",\n",
    "            \"ad_copy\": f\"Meet {brand} {product}, designed for {audience}.\",\n",
    "            \"target_audience\": audience\n",
    "        }\n",
    "\n",
    "        # Text generation\n",
    "        ad_raw = None\n",
    "        for _ in range(3):\n",
    "            gen = pipe_txt(\n",
    "                build_text_prompt(category, product, brand, feature, audience),\n",
    "                max_new_tokens=140,\n",
    "                temperature=0.3,\n",
    "                do_sample=True\n",
    "            )[0][\"generated_text\"]\n",
    "\n",
    "            ad_try = extract_first_json(gen)\n",
    "            if isinstance(ad_try, dict):\n",
    "                ad_raw = ad_try\n",
    "                break\n",
    "\n",
    "        ad = clean_ad(ad_raw, fallback)\n",
    "\n",
    "        # Final row\n",
    "        row = {\n",
    "            \"id\": i,\n",
    "            \"image_path\": img_path,\n",
    "            \"product_name\": ad[\"product_name\"],\n",
    "            \"category\": ad[\"category\"],\n",
    "            \"tagline\": ad[\"tagline\"],\n",
    "            \"ad_copy\": ad[\"ad_copy\"],\n",
    "            \"target_audience\": ad[\"target_audience\"]\n",
    "        }\n",
    "\n",
    "        f.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n",
    "\n",
    "print(\"Synthetic multimodal dataset saved:\", OUT_JSONL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0e15211",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 247
    },
    "id": "HlzTYjY-CZCe",
    "outputId": "ae493c78-23ad-4540-fbc2-91d03683fd8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (120, 7)\n",
      "Columns: ['id', 'image_path', 'product_name', 'category', 'tagline', 'ad_copy', 'target_audience']\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>image_path</th>\n",
       "      <th>product_name</th>\n",
       "      <th>category</th>\n",
       "      <th>tagline</th>\n",
       "      <th>ad_copy</th>\n",
       "      <th>target_audience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>multimodal_dataset_clean/images/img_0000.png</td>\n",
       "      <td>Nova Mini Projector</td>\n",
       "      <td>Tech</td>\n",
       "      <td>Compact Mini Projector</td>\n",
       "      <td>Meet Nova mini projector, designed for remote ...</td>\n",
       "      <td>remote workers</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>multimodal_dataset_clean/images/img_0001.png</td>\n",
       "      <td>Desk Lamp</td>\n",
       "      <td>Home</td>\n",
       "      <td>Eco-friendly</td>\n",
       "      <td>Save energy and the environment with our eco-f...</td>\n",
       "      <td>Remote workers</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>multimodal_dataset_clean/images/img_0002.png</td>\n",
       "      <td>Pulse</td>\n",
       "      <td>Home</td>\n",
       "      <td>Light up your life</td>\n",
       "      <td>Experience the magic of Pulse scented candles</td>\n",
       "      <td>18-35</td>\n",
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      ],
      "text/plain": [
       "   id                                    image_path         product_name  \\\n",
       "0   0  multimodal_dataset_clean/images/img_0000.png  Nova Mini Projector   \n",
       "1   1  multimodal_dataset_clean/images/img_0001.png            Desk Lamp   \n",
       "2   2  multimodal_dataset_clean/images/img_0002.png                Pulse   \n",
       "\n",
       "  category                 tagline  \\\n",
       "0     Tech  Compact Mini Projector   \n",
       "1     Home            Eco-friendly   \n",
       "2     Home      Light up your life   \n",
       "\n",
       "                                             ad_copy target_audience  \n",
       "0  Meet Nova mini projector, designed for remote ...  remote workers  \n",
       "1  Save energy and the environment with our eco-f...  Remote workers  \n",
       "2      Experience the magic of Pulse scented candles           18-35  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json(OUT_JSONL, lines=True)\n",
    "print(\"Dataset shape:\", df.shape)\n",
    "print(\"Columns:\", list(df.columns))\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c3bc0e",
   "metadata": {
    "id": "NWIXmKcx1dgB"
   },
   "source": [
    "During the EDA stage, the multimodal dataset was analyzed to validate its structure, quality, and key distributions. The dataset contains 120 samples with no missing values and a fairly balanced distribution across product categories and target audiences. Advertising texts are short and mostly consistent in length, with a few outliers including very short or empty entries. All samples include images (no missing values in the image column), and the visuals appear clear and consistent. Overall, the dataset is clean and well-prepared for the next stages of embedding generation, pipeline construction, and application development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42a5dcb4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4_2YK2ez3BTq",
    "outputId": "d32399b7-1c8f-4d97-cc6c-bdb7c0729e96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HF dataset folder created: hf_dataset\n",
      "Files in train folder: 121\n"
     ]
    }
   ],
   "source": [
    "import os, shutil, pandas as pd, json\n",
    "\n",
    "src_dir = \"multimodal_dataset_clean\"\n",
    "src_images = os.path.join(src_dir, \"images\")\n",
    "src_jsonl = os.path.join(src_dir, \"dataset.jsonl\")\n",
    "\n",
    "out_dir = \"hf_dataset\"\n",
    "train_dir = os.path.join(out_dir, \"train\")\n",
    "os.makedirs(train_dir, exist_ok=True)\n",
    "\n",
    "# Load your dataset\n",
    "df = pd.read_json(src_jsonl, lines=True)\n",
    "\n",
    "# Copy images into hf_dataset/train\n",
    "for p in df[\"image_path\"]:\n",
    "    fname = os.path.basename(p)\n",
    "    shutil.copy(p, os.path.join(train_dir, fname))\n",
    "\n",
    "# Build metadata.jsonl with relative paths\n",
    "meta_path = os.path.join(train_dir, \"metadata.jsonl\")\n",
    "with open(meta_path, \"w\", encoding=\"utf-8\") as f:\n",
    "    for _, r in df.iterrows():\n",
    "        fname = os.path.basename(r[\"image_path\"])\n",
    "        row = {\n",
    "            \"file_path\": fname,\n",
    "            \"id\": int(r[\"id\"]),\n",
    "            \"product_name\": r[\"product_name\"],\n",
    "            \"category\": r[\"category\"],\n",
    "            \"tagline\": r[\"tagline\"],\n",
    "            \"ad_copy\": r[\"ad_copy\"],\n",
    "            \"target_audience\": r[\"target_audience\"],\n",
    "        }\n",
    "        f.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n",
    "\n",
    "print(\"HF dataset folder created:\", out_dir)\n",
    "print(\"Files in train folder:\", len(os.listdir(train_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b784b2b8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CSw4x9Wi3_91",
    "outputId": "96cb01c6-0a03-41af-c224-ec0d86632b9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "updating: hf_dataset/train/img_0107.png (deflated 1%)\n",
      "updating: hf_dataset/train/img_0063.png (deflated 0%)\n",
      "updating: hf_dataset/train/img_0118.png (deflated 0%)\n",
      "updating: hf_dataset/train/img_0075.png (deflated 0%)\n",
      "updating: hf_dataset/train/img_0078.png (deflated 0%)\n",
      "updating: hf_dataset/train/img_0025.png (deflated 0%)\n",
      "updating: hf_dataset/train/img_0013.png (deflated 0%)\n"
     ]
    }
   ],
   "source": [
    "!zip -r hf_dataset_train.zip hf_dataset/train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95bd2e1d",
   "metadata": {
    "id": "H6A_dpJP3PBa"
   },
   "source": [
    "These cells were used to package the dataset for Hugging Face upload. After publishing the dataset, all subsequent project stages load the dataset directly from HF."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fec099c",
   "metadata": {
    "id": "mmucBzLt6Qk9"
   },
   "source": [
    "<br><br>\n",
    "\n",
    "---\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64624918",
   "metadata": {
    "id": "M_HCdRVptJPI"
   },
   "source": [
    "# Part 3: Embeddings\n",
    "\n",
    "Use a 3 different HF models to embed your dataset, evaluate each model, and choose the best one. Consider time, effort, size, and more.\n",
    "\n",
    "> text2Vec  \n",
    "> image2Vec  \n",
    "> audio2vec  \n",
    "> tabular2vec  \n",
    "> text+image2vec  \n",
    "> multimodal2vec  \n",
    "etc.\n",
    "\n",
    "Save the Embeddings in a `pickle/parquet/csv/json/numpy` file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0e4a42",
   "metadata": {
    "id": "ilZnDQUL09oB"
   },
   "source": [
    "Examples of tasks and high-level clusters of models on the HF platform:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e82efc",
   "metadata": {
    "id": "UADS5TSPD4qW"
   },
   "source": [
    "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbIAAALuCAIAAABEtQRaAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAABsqADAAQAAAABAAAC7gAAAAAh2OHxAABAAElEQVR4AexdB4BV1dHepbNLVRQQG8XeMKKJJdYYASv2XrFh7wWNDSIae0GjAvbeFcGWGBsJojSjUYqKhcWGwNLL/t/MnDLn3nPfu29ZjPndy+O8uXOmnZk5c8899+175S1adyjLd9SUlZUXoUS/UOm2CI/pFlY5iYohkeVlNTXUBloC+Yk+bUcaZs7ymrKachqZ7g9kJjs1ocBCrmEvICE5TuTJFSScggiklJfX1JDNZLnAvvXuMa5SAnODgTbrmXzcUVYatI+dsZzsXz7XBhZFFS+nggyZSq9QCCJKnRyjuMEwWJ9IqEyrbFaKcoNRK7K5MwbgwsVGcb4hx3im2NbHTqSXkNlJc5TNophkKz8knZgnJb3MkvJNVJXnL4s8FjZXj0q0C8ZbYmaBeM67ODnShKOlPJGsunQxqRfFXFK1i/VA8sHiYhFXkrtJvPJPDLbdpp4tn6HBcKxk6wg2JaTgGZqMlzgsw20qRsXlB8rynPikKWhEMVExyyJZZy+NJE6NS0kXQYKICWU2W+ay/awk5gS9siDfRJVYa9UG1VW5raAmL18laEGOaGfgn6hBgfiA3PpcZ3wKNlELaaOmpJDRIXoFke6GTZq1SImJI9IXMy/ZcgiGzyLKLFUp75n+i7k+W3CmmMDNkQlj+2Oio0Ms5JQoQ0xybpwt08SQWjnKWlIv1Cg1TdXNq6KObI6IsabELgMRchsJ7WCVb7qcRbnDAUdJtOiQXM3pQkRJJj4vqookxu4AgupaslrojmqO2phCCquglRixU1LK55vRJHcwy6M12+YMe/IpUwPwDMGQExS1WC2m4yOYQI3RnnKrtyoUo66E9vYw+yZLpMa0hjYEZ2rgfiWyXJmTxVxSBGkUtj7INFBVItoRjKr0E+UHH4xsMYrc2WkukKlVJEckemnJlp+rJ2pElvtLkCiktcg9r0OZ5t2pg6qd5dlqB2Uqc+JMjOQSYtNHm6Bh4VLzKCrfya4VIAaJa3wC5YqdWCNaQ8siOaZv7QsZmi1UXYTKS1kt8lgKqQzzwl7dC3JEO0MfaHPtUodDKREOJeQatbVMT+MAp1SS+JKEhvZY5sSQkkRFzxP83nK/q+j3GYPyWlR0hCCmLUIWQ0VZyVyZo+Rmv+LI2Kvi0QUxEIzo07CyIKrYul/ooiRamRLHYAGGJCnOC1BrJR62sTNZbSqWEhRRUhQVtaIoFxMo1lg1ixaj5KjzaUpQKcVenvKDd1k4TUWM9CZEejmaOY7VFALXerVorZARyVl0dHaPgyxKjrSIo0VqfNTSV7j1VydrBGGUEbVR4AepVhl53e3H4sUoe6jbXuIjhhYebYHeTGWOJzYNYrM1tUJkCSaOsZx1GkoE4gYlfGUVZsuWkUt/6IVcuecFZwtSgVpxsUuOIMg9q1avBDUszD73/KgAhV4xgwkoSj5xOayNyJ53UQs0OcOReFnDMkZmu+ND1AqI0jqRPVDKajE+JbIcGR2sszQ3EBFjBxA4nY3LlhoRY+eU8k8wvfU2vBcsguS8qFDPVoAhSVT0PDRBpYtdfaQdIxiRrOGiupZr2mT6JxbBDE0qOqr+hLFLjSKqOKSKkmhlIXmGdUkidx7GKNNyVsgRtLGLXYtYuRNdAhAdZD7+KCtNMnoqTRfvHCt90SROzaE1qtLyRTvT8YorizKbaikKEhS1Xi3G9dtBZGRROIzQFDW95aMnxfYWva5MSGlIrj4KWpgpMNHh5cvVW/LXYwsq8R60VYJnj5zIvA86bClI2FDqadS6UIgiSbpNXxo1TCMNImit9aMMdZR0ljKIzMpwbj7BCWv1zlQI0+SRIz4SZZqZZwbz88VOFIqREq9CrXIbswTcYdTiIzbuyPkWNyVphBNW1Bq2Kcg0vXwpbrGJTnqgQbxKWS3yWNwAYkBUZ4wwjcvlj8B0O7JAVtQCcVa6ZU7jYist5tiiQsWEGKuKv9Yf2Jx1kukSWVlIGbKrDPokI8FyISe1Ja4KtRXRAWuCbDjKSkN3BhV/pJb2VNy1gRVRxRnuz6sgQ6bSKxSCiFJrVQSLGwyD9YmEyrTKZqUoNxi1Ips7YwAuXGxU/ecWsx3IPfBieBR0q1w/vYtVxDldlqPqh1aoM5UWScXJ5Y3iyguqVWHp01uZFq4spCbbblPP9ITKa14GnZUcLLECWnFVMl7isAy3iX0spbj8QFmeEx+7gkYUExWzLJJ1xVccIkjUxYSqlX6iu5iJRfq9NLXC9eXVl9pojWXnFdTg5avZWZAj2ilipCtH7ALycEmi817BOZYvUcOAjA7Ri450l7JaTE0PL9kaJBg+iyizVKW8Z/ovlgXZgjPFBCGJTBjbHxMdHWIhp0QZYpJz42yZJgb/OTKzcvTrR11pDZxXRR3ZHBETRjAs2RFyGwntYJVvhp+HFeUOBxwl0aJD8oy5lSSKnhdVRWpjdwCBSwrZFlULZFRzFnGIF1bBKTF+b1HnmNFU/7lFnYQBbJ2b4dasSKkrYfFVmOjQU8JqLfAexNZubC9X5mQxlzR0nhL2hpeHpEqYLRzi4RLrWYYvlB98MDJogVbkRRcBbH700pItP1dP1IjANm9nLoGGXGhrkXteiTLN26CDKlFbgbFjr1uDTIzkEmLTR5ugYWFS/NHBWNG1exeDxDU+gXLFTqwRtaFlkRzTO8KFLM0Wqi5C9Z9btA8KCq1FcrnSCkjGJAxo9BKSZImcx6RwciT3FmmFuNxVNKYtYlQMFWX1ZYK85FccOZ5myqRVUzfLg1HFdvqJpVGStAI/rAIMnshBBai1Eg/b2NmLdOp2zIkuAYhakY9fscaqWbQYhdeCfHpSVEqxl2djF+0snhBRNi/dR8FOXI2p9ZNoOzTRLmdRSyTWdhghSRFHi1TLajXmf/dXJ2uELRnB3CpNgR+BWmXkdbdX5cUkwm8v8cZEz5F/3CnKTGWOMjYNYrM1PnNNHMMEc7JrBcQNSvjKKszWICOX/tALuXLPC84WpAK14mKXHEGQe1atXglqWJgzMin0SjAx/OhLg0qcd1ELxFrVRuJlrcoYme0uPjmJ0jqRPVCbvcXOnbvssPMua6y+xk8/zZw/f16GUdHBOktzAxExdgA68gLHpLZrt8qWW/12RtX0xUuWiLAO7TtsvkWPqunf/OmKAcB89tlUq8RP75NPOb375r/5YMyYpEghFaxlUxPDTtS4U6IMSQ1Fz0MTVLrY1UfaMdo9Gi6qq+Q6pCVGh0ueiUUwQ5M4snCrdTIcVRxSRUm0mpA8w7okkTsPY5SZIKyQI2hjF7sWsXInugQgOsh8/FFWuij+Kj632Cifk4gKydygQcML+1/abpVV5s2d27hxk1577HnHbbdMnjQpIoTciktaolXZpUCfHPaWVu8UODEtKitRyAZeedns2bONQWKWa5N2kPoG5eV799l36dKl77z1Jg+iZo+99u7Stduod9+eO7d6wcIFZCVffLllm8vKq+fOWbp0GdmfPjwyYKtBeosoN+jkGDE94MWgJfG2SvDskRMi8uWDTwQl5ngTRKVrSTzriLXMS4lN5CmZIpla1ckVjH0id+f+Ht3OX0+uCrQTYX1ixIpwankEuiUKYou1ZAXzmHkJdiM4cyhpSSzaNhQ5/Eu2bLfg/bgMj8QutFERJS2h+sHDtJabMUfHSNZiTKQpaFm1DIX7wqiJSuNrJiXu8KJIUvnpiG2VyVY65BjuQH5G7IydPA7OjOiInEjrh8CspBGemiwwscNb4FSThuiOxE4kSK2w0vS7kUmyTUEhG2SIklwSL8m0EsoiTDym7/GoibfddMPUqVPgnd577PXltC/YeLFADQS6oY1a2y/OV60CQQhi/kdmM2RaK6amrEXLVo0aNypv0IC6eXzc6sF7LFMgFGXfffft3OpqLP3efutNzpmybuusO3HC+KVLllx79UBIgDSaCMZaOoPEB+6918vlzoRoNkEMoXEQRCoFJlaCqCXmVOt6DRFRGgZ+44avy6ZDLDctZRqsZMsF9i1PK7AzqUwx1uEao8YrNrY4Amj03iA9IGCfiIWmFX5vLBEBp2PHBoqD0TqDrOXsrEzXklbyndMDfoIjRgiJs1P4LHeGAm8nD4+sD2JHunTU2BbothaxTjQUAyZ1sGAkdqZlo23sTDaQAOcTEmz9E5pPZP4gVeyYoBUsd0E4DTjVsgXUkeS3A2BuazqNnE23LecbTigPXMuZ7WNEfkh6h8wUlemWNTKa+YQbhhs/ESvHLtaS0228SIQEgSWSGXwwM9sY2MykaqDGAYl4lVAWGzZosNHGm7z1jzemTp2KkWC8w194nj1R3rp1m+NPPLljp9UWL1r0z1HvPvv0UxjdjjvvCvrZs2dtuln3JYsXP/rwg2uu1fn32++waNGiF5575p//fBfm9j3xpKVLlnbo2BHVdvGixU8+/uiY90Y3bNjwyoGDHnnogQ8nTsAITz/rnK++nDZu3FiowOmFF1+6YMH8K/50SevWbcHecbXVIOGl4S/84+9/Q+8VA65+/dWXd92tV5MmTS4472yysrx8wvjxv916aywb4bJOHVdr0rTJP0eNAv6Kq/48csTwUe++06PHlrvvuVer1q2r51Q/+shDH3/0774nnLRwwYIH7r8XMtffYIMDDjykTds2c2bPeeG5Z99//z0Mv++JJ/40c+bqONZcE8CjDz88efIkHwodakp5HBguhzqDiKk4OdhmijWhGEPs7GnXskiWCCISb1tPKlrtucjRLffYhjTZToZJHiWpa1m1t0bsM+zhkMjNYpozV6wgOwVlLHfnCUKioinJDqDWwkynjWAJtvHyrTzlcu/+cK0hU51XiCInFG9l0hAFVjbIQIWBWmOnRM22JNVKYdiK8SfwMhFJS6BhMJBRgBFQNaDWOsZliXdVGDWOIEvMaow8E08Vu8AoPmEXmtDqEYmZxCoHiFXsLMyDkqFY0JCTblJgWko6k/c0WsDUmqjJ6E1rFWa8WxuZmUWQYjNEtkRcaSNl/AqiBhkSI+g111wb2PHjxsJOrrnc1tQ0aNjggosvabvSSo8/+vDYD97ffsed9tx7H/S1bNmyS9euK7Vd6YH7hi1cuPDIY47bZNPNAM+ZM3vvPvth3LiotWnTduNNN8Xa7d4h91RXzznokMOAbNCgQbPmzZo3bw4YVG3atkXBwlbgW2/9AwY8/+zTDz/4ANaM5114cZMmTe+6Y/A7b7+11z59GjdpgmFVtqjce9/9Ro16587BtzE7aUHhg8yu3dYB3GOr3y5ZvGTKlEnoAHFlRSW6Djr0MNT6QQMH/Otfo6rnzAFZW1TBtm1hZKfVOqFELpg///5hw374/vtDjzhi3XXXAy+6t95226qqqgfuu7d584o99tyTNMmoXAuUhXm49iQgFSJCETU1ChYMYeOHSJQ+OAdAqvUIEDsSAPrFjKSMCXRrWUiH6QTAMLX8oi7qD1pGuCbdn9BEkng01HJfpGUFTKWVaSMYJnYeH1EB1qKZkUSk/zGhUUFv+sXkxkKBqXV6BLbaiJP1qDeNisAyYmZ04yahbKW0rFbOWbpTbtWyDD9gQpOmhDZmI6S80gTKaAa1AJEoOpyd1kx5F53JFuekT1qrIWYBM4oC3/KonR1gA0xtanCCEbmuNcTC4lruFm1eEfGjo4SyiHUZHPH9d9+hmnIdRov38q5du6GKPfbIQ/8aNeqxRx7+5quvt/rt76inrBzruJtuuA6VdNQ77+B80MArJ0wY9+orLzdt1rRVq9ZMU/b5Z5+98PyzqIz33zsUFbZzly58tWAlBLGg8vK58+Z98dlnEIKl3KRPP+nWtVvziuYPP3jftC8+f2XkS6h0m2/+G7aq7MMJE1564fnPcJsPGfz6+qsvFy5Y2GPLrSBtk002/eST/4hQSCMKOKKsbNVVV126bOlLL7zw5ZfTjAHcu9XvtsbpLTffMH782NtuvWnZsmVbb7udWP7lF9OwtBw/DscHHVfrJJbCN2QzeYheAlPrnUZycW5bf6KJGGYpEEjyHEygO1iVOWOJJBjnqgVoVJAVtpOwfM4taYu/mEUzsmwjkG1i2I6YVLO1uiX7SG+iZRS0Ep7JfRsYRySMYFsYZlHEqfgMzDjiIKG6JenuxaJIKAmI/guZiZ4wRKraJBF1GZ1MxkMDitjRUl9Gy/0s3dCyQmqAtC2LVueqjzSzDG4NTMxWt4OBYnk8EoItEJjGjIwJjBYposlJZHkkhjAkW7WiXxBOMU6dYg2TKjJZWiYRmBCekKCsf8ScenlWls2CvR70snDflrJa/PxzqkqdVl/dFBIqJ/Rae+3OwH/00YdocfrJJx9XtmhBZqPqUu0lqpk/zcQ7agrBP/wAGJWUuOXgyv/Vl1/iDBt/VNzpYCzDttwLljrW6kxKTzvz7AGDrsWrYaOGKEzCNm3aNABWhGH56N8TN9xoIyhdqd3K/xr1LvVa21AN777zDpTp/pdedtY557Zq2cp0EU3ZWmutNXvWbFRV0rqs5tuqGWussQYJLSsDI6up+enHmTBAYJYr+qllM9It05J4Oly3ZmCYesiJ8sakdCIofhdcohW5IhZd4QFZOBItKVAvRUCE0MQ80ZbMYBJpmZgY1D83AqJlswyG5RIh685uZShGPVOLMt1aO0FLaGnJbD5NKmAhppdI5R9RSY+IIGY6Vy0IqM+9mEG0aWZQEWOiJToeurS2n0XSCauihq1gIGiYwPQlYJzSy2hlEcoCK5uZjB4eCcEWYOP4jMhIFGNELKNIBSFVy5qoYSwDQSPKpNMpJhkk1rbMTQiWbVrmIVVJlUQk/6RHwywCCC1dwVYUvVuY2BNwTQl7i9O/+Rqu+v32O3780UdkK5Vb3JvXTJ/+DeCuXddBQcQ2wFprd14wfwHroiqMg4qzHFwsuThzeXYdhC/fYMMNQYXiuAyPgMvKWlS2lCsNqPCPDhaCLUKcTv+GlN58w3VfTpvGaGqEAK1RCEr4h9tRo97Fh3J23uUPKM0ff/wRi2Q+hj799JPLLr0Yd8fH9j3h0MOPuPOO243M8rLpVdOxe4iqh2fZQGIPFHuIooG0iCAxjS2Ei4EPW+rWuyckPEVkMXZfJpBhOIRPtWwCa2flNFZHKiCdWtFJuwhvDhBTKrFVDJOkyN4iD8MSOn5KA/KE2MwbNqZPNJiW36QxxtEJM6bIoZ8Non0fgckgptbqBbaD4HejTHA4gWUaFgyUYs8+2SpPGR5nlxfkBxqEiCyx+cYdYq4oz2zFD0HLmoWBQFKsjDGDJ8vZ56yWYkfuMW0ydsIunFaUexf5tlWxE6PEAcZAPuHxAyIT6X/Y8rk3RZllhyI8oLMGsW7SwOa7loSbrCJlkXiRLhxis8BBK5rY0nR6svZguOw+iVoJN9HgGjH8xfU33PCgQw7tsFrH9dZf/7IrB/TqtTvuSXGzvP+BB2MZtf0OO+Iu+GOsHKGV/6MFo4ERScB0Sq10dOy4Gj4Iufoaa/TZ7wDImTJ50pIlS/DoY8vf/nbllVfGRiE2Fpm25quvqAJusll3bCmikIH48COPXnPNtRo2bvzbrbdRgkkDvViBtFMmTcKN9s5/2PWzKVOWLV0KgWwK0WFX9Igjj8ZO6Beffz537txmzbCMFROp9/3R7yHLjjr6uFXarXLgQYfgUfiY0aOFgIhYkBUFNhLrW+pgQ0xLDHReiIgJ2GhqmJQB3xC3PQJZJJ2NooY1mdagCWtJBMapvJiClDGBbi0LaXSwEFLLL7wBIALVhme6J6VDjCDp1iwiJ5hbgVk841mNGjlRBS/mI6HMSV0kmmS5lqD0PyZlRUyohDKxESAwtazWt6zBKmVmPqGG5GW01MNDpZatdC2dkxpzCMTEjDUmWLXEFsDE7jRLJ5+SEvvyBMRszLTvzGNwwu90eAOMgco4sBMdWU0iBWaVoGXZ3Gtgq9WJZh404HRMBCX/MQmRMRXL5TN/KsqEINmycDaNFVFD1pLSUlaLqOevvfIyqsZ2v98eZQhCZlRV4VMveIJ8y03X9zv1jLPOvQBIfIzxoQfuR3lnDUDo6wqdkXmC5mqO585gRemBHDwnWbR4EeDXX3sFz20u/tPlP37/AxaGfLUon/XTrM+nTsWHEPG69KILbrnx+pNOOe2Mc86FMKxPP/3kPzN//BFwTc0yuUyoyxNdhz75z8cbbbLJ6H/9k6/q3In7+ppleOqCR0OXX4UP65TBhvuGDaFLHOTglr+8bPKUSc889dTeffpstMnGGNGrL788duz7wDMBxOKdLjl0MIwW7+riRKMljGmTV0VhRDCkg97oRBgEVtzUIRzyLjAhjZfZcm5kEEHLPPCutcWq8bIEUgrZHDMkgamVsbDNShbk8vpLj15djo3tMoK0iWJcvDU6STOZLy2PWak3ZpnBGDVKnumQNwwpslo0A5VRJL3DwwW3liyOiLbGThdIEGnOEKYzjIxsC1pCGIUGigwYbMDKQZ6SuJiWpEZW/V5qwO3zFFozYmfsZLPYYYFHeAzaZOuHwCxWL0OxlvM7DR6GK5hOzJhNdICJxE7EyLwLRJoTaxeJE0Fkg5oKNq9YWwl//KdHsdJKK+Oz0Hi+rC1o23YlIBctWuwG4p2v6RSMp8kQcvstN6208srfffutH0JNWaMmjSuaVcyeM8t6BcIowljc4X6WP9FNFrVq1Qpc5gPerM84MQfM6sxkrqjAw++KH3/4nvmUiQA5Miuv0m7mDz+ijCp77ECFXDvIC/BhVRb57gKQcApBIIUiaAuQwL7lacWWSB4WkJ/dFWgLB5nNFDHUspI9dr6q0pknUiI17trAlgybo2ituJCCKHNEq6Ci1FoVweIGw2B9kqtkBHqzT6JWFCM39vh4uXCxaZxvyLGM2Al3jhhlmaFsFsU05dR0STrR2pklL2Tm6pe81GWJFHwJe4vWUtL5I6/LyHAZEdtHz1WMNnONkl7v4uRIWSQ/u6AH3CTMYDCIpYuXzFk82+Okp7wcHxKENj6jZg7/xYthIzNIm2lhhYkqMYSwEcGiiHse/oxx/nwuKiQleZSX4dM5QJIYcxVlR+PMXXLIVusAFm9dD5OEFd2ZRKyaOUw9s6OQsVp7ZaRyxWZZ4COcbUmTcIhWe25t8TZyj23Ia5aEYZJXcJVhRhqRb/xje/BujXYoY7k7dyQOA/3iBxNBWfwwndjJlGFj1DgZAIAyLhfHUxuuNWSqcxxFWijeyhRBQkGwt0SGK0lnckxOTEs8VgrDcmJE8YmEyrTc4WkAGWVkuSlMlCzkGOkT2LfJ2ImqjJalWkkml9NGsW3kPBqLtCSPCVmwiGHQGucNYoz3mDBiVIacRkEKTMup7McMPKk0UbMWSr8VEX8X6wwzi2DrmdVOYmDIl/Sm2hLKIucnGvKFb/mW2HhHYPSBAEMBFbcCc0uM1GvaGhS1BQsWECHjpOVOQ2OUCSpQnHC6uFTJJtCYApASymIENi0bYwUTjuPrW9sPZjqshSyMRVCVZLZIy8wGT8zeJwoWIrTWK0SpYPBnH2Kt9HMloc/v0lrStwg47WegJUkODmVqhQLblmw2EeRgQJJjtZalrbU9jtS6mG3gIVMectSkVR1gYjOjrQwipGYtNDijzitja0kBeqyV9G6jJrHzLQ+Rg2NE2TeJne+WkHuhRGddxjxWG1slthVqJV6cpy52aqjeQr4qW1WiiVVp5RYmP0jslBXM4xtvZzAYcaGQeXdSDZFkgmBnJ41LDKT3aNTERDGXWtEq4gW2LdvMfg5iJwqEz2Yy+4dlKQs51CLYtjnmHeciWMlzaEu4iWYlUf0sytpg3oVQThJMQq5bMoXGmmppjJpQa9JwQnnsVEnyE5IUZyqISQlxcWbBCmWUIjkiWx/EEkQGBjIc7QhNKPksalG2FEXu3cYWip3JscTimC09Z0/UiOWKnUgU9amskzzMlxrKtJDBxs64SYKac7xZZJnKHIOJUZg+2gQNC5eaR1H5TnatADFIXOMTKFfswhjpaRqtFT5qhQzNFqoU1H/fop3GsepoEyaXK62AZEzqJtViUjg5ZJURtkF5TdqT5zymLQ8faKKs5EgpE+Qlv+IILoTZrKFrbVQSBkUVW3uENkoi4uJCCzAktNNpAWqtxMM2arRGiZaMiJKiqKgVRbmYQLHGqlm0GCVHnU9Tgkop9vJyOFTElBA7L91HwWaXxtR6tWgHJiOSs+jobMTJouRIizhapMZHLX2FW59q1gjCKCNqo8APUq0y8rrbj8WLUfZQt11lRAwtPNoCvZnKHE9sGsRmqzhSmcwSTBzDBHOyawXEDVKK03GM6ZGRS0/ohVy550VmC1KBWnGxS44gyD2rNqiuKuWF2eeeHxWg0CtmMAFFyScuhwODlCqRaA2KWqDDy3AkXtYwK8ieJ9+LKiAG60T2QG2+b9FpEfUZRjkqPcCkwcXPI2LsAAKncxZki4uICSexdr30yE19UqQIEmxRoXHmBFuSqOh5aIJKF7v6SDtGMCJZw0V1qVwuPY6JgXoBsQhmaPJMkXhlTeKo4nCsURKtLCTPsC5J5M7DGKnKGRkFR9DGLnYtYuVOdAlAdJD5+KOsNMns3mKOlb5oEqfm0BpVafminel4xZVFmc0FQRQkKGq9Wozrt4PIyKJwGKEpanrbG1tNrmGvpQikNCRXHwUtLCLWdXv5cvWW/PXYgkq8B22V4NkjJzJ7gg47oZz22gFR60JRiiTpNr1a1DCNNIigtdaPMtRR0lnKIDIrw7n5BCestfvaMlX8LpW2XsNeiTLNzDOD+fliJwrFpPhCTSKVjJfh4EhpODEkP9baQHGDVOxCocFYwhxSAQ8yTS9f4jHSKhKD80KDeJWyWuSxaBUpOKozRRVF5PJHYLp1WiAuaoEfu+VR7jMujvQ4wUWFCqUS6lhV/LUVqj8bzHSJrCykDNlVBj1xJJjuwqVklLgq1HZYEUHt0QTZcJSVhu5jp0pSWEnCGGh3ajhDd1RxhvvToYgryJCpLBAKQUSptSqCxQ2GwfpEQmVaZbNSlBuMWpHNnTEAFy42ilaIlF0ZK0SRHndhtmbVo2wWxZQXyg9JJ9pEUSJSoJdZUr6JqlqsFkMDCrpVst67ODnShKPpKi3S69LFpF4Uxy+X4XiKnImLRVxJ7ia5PlJR2HabeqZzoYhVxbqtZOsIVh8w+WzUKwsNK5OFU8WouPxAWZ4TnzQFjSgmKmZZJOuKrzhEkKiLCWXX2jKX7edi9qb7vbIg32x5pShYtUF1VW5LC1UYLz8VYUVVFAz8EzUoEB+Q2yqnMz4F51i+ZBkZHaJXEOkuZbUojlaj85KtQYLhs4gyS1XKe6b/Yq7PFpwpJghJZMLY/pjo6BALOSXKEJOcG2fLNDHIvk+q9dNGZqupunlV1JHNETFhBMNSEiG3kdAOVvlm+HlYUe5wwFESLTokL5j1SdLEeVFVpDZ2BxC4pJBtCYXuNKrZ9RYEhFVIlBixU1LKZ5rxTnLzoTYmx9cO1v0pe7LIw7GpAXiGgCRBUYvVYnqwggnUGO25h6GuhOLc4i6OaQ1tCM7UwP3l1bpbdZLleY8Em7FIsCIjSqE9SLCtD0HZyuzIa1wGXdSiDFqgFbmzM1iVBCQgj15asuXn6okakVSs62JRqSJRyKI2J2Nki3JSsjJN+8oHVTsryVzqeaYyJ8jESKy1eaVN0LBwyUgZjsp3smsFiEHiGp9AuWIXxkhfIyI5Zm/wSVOhI1uoUlD/uUU7jW3ap6eDDaE4O5o5mikZkyhDkqjoeUwKJ0dyb5H2gILyWlR0hCCmLUIWQ0VZyT8yR8nNfsWRsVel3alh0SeYlO6o4hURuwz9VlXUDj0KD9vY/QKfPseqWbQYhdeCVFTyIaI+y+HQQgkRlxnF+ojYKlDr1aIdr+iRswyd3sXJkRZxdKFRW/2F3s1UTFw0lRG1UeAHqVYZyeRIO1pjSK0Xk4CtteriVWiMufoylTluHyO1J6VXFsb+1EYKSzBxtEkltE52rYC4QQlfWYXZGmTk0h96IVfuecHZglSgVlzskiMIcs+qDeKl4ijMGVEJvWIG48ddG6jEeRe1QM8XhiPxsrZljMx2Z881nT/WieyB5dpbFL0ZRkUH6yzNDUTE2AGksyBbakSM9YkKQDC99Ta8FyyC5LyoUM9WgCFJVPQ8NEGli119pB0jGJGs4aK6Sq5DWmKmf2IRzNCkoqPqTxg7rZPhqOKQKkqilYXkGdYlidx5GKNMy1khR9DG7he4ctRlpaSVvnhDnOo8kw1EQ2LJo53peMWVRZnNsERBgqLWq8W4fjuIjCwKhxGaoqa3vbHV5Br2WopASkNy9VHQwiJiXbeXL1dvFqqzqDhMsmyV4NkjJzLvgw5bCpz22gHeZuWCUJQiSbpNrxY1TLKCCFpri+VJqDnjLGUQmaXM13CGiAQ6Ya3emQphiqAc8ZEo08Jg/3yxExPEyPhCTa0cU64KuMOoxUds/ZHvPW6Qil0opqg1bFOQaXr5UtzifPEqZbXIYwlHkTiL6kzQZJzm8kfxVItaoFNBw2yKcXGYEKGRRYUKeTwmUeZQfsZZpktkZSFlyK4y6j+3GEbQBiPq/lQSmCKbDESUOSDKjFHMGlIrKSwySiwZgeLMk+I2B6wZAwhN+9V9brGELxaDA11hbNSocZNmzdE0aFiChCAe9Scr2AP4bYYl+H3uRQvw6w6xSRqol2nQsHGjxo2b4n+DBg2D7vqT/1kP4MeLli1FHixGPsQGwetmt9DFIoHW/USIlFAtnUiHbbmXGj5EjIHt8iXYzeHiEV6tLCuuSqTNtPTQ0N4MsDL+PPPP+n2LJdxEmzHSbzG3btKsAmtX/BIeD0FGV8t2yeKFi8Jv+a6loHq20AMN8btcdPlqumjhgvlzq12uZe0tNqto0aRpMwkrzwCeFqHM/Gf1Yc3vqxVK6dJg8SIkwoK8uqQyCbWvUupTBInvW6RCVvxDdTm0K2VUnjPrqO8UEt0m1Tg5mUQJihLKIusqb9GqbYNGjZe/GmrLcTHDzNWYergOPYBih3jNq57NMhMJYNKuokWrBg0bNmjQgL5ktI6O+rDWkSPrRgzSADcB8+fNwwVSXxpNBpCSeG4sl3pRJpKdYl2dNBxqEmsEF1oW2VvURTkUE55lC7ULVFonl7a3iHVieYNG5Q1K+L3A0Kj4GQRi3mYs8uMs9dj8HsDNNNaMWDUsWbwoyoV1Ynl5A0ShDmsiFNWHNert/xYSadCoSRP8ohx+WTNug6o9sWoWLUbJUhqXXASrFHt5GVU6XUZVXVd64jKjWC1S4BIKXGM4tVlFg4YlsCgri4C0TVnX1baIyl9T9+KFC7FYwD015xpGjvSQtrwh3Wc3w1KRMXXc1Ie1jh26fOKQBo1RGRs1RNWzL7rxw39q6V1apIfBEYphaKbOZEsGuWSiExzEkm5JPHMzoGCvgXQSo+kkIU4lQYl/OKUXYw0sGGqJ2LRCQK0WqGBRyjYzTymrxabNW7BgcONAVa3ztqx+wcherfsG28CoiVgSYsGob6CgqSm2iQ1qRcSUrr71Ya37iNZKok+DrAWjE4tKIQunsEURctmCOqNvXUNCXyoE7wRnA1GVljzaqVUKYVxZlJksBLkcCYoSniPjOrOs7jaerD3+fQUtWLyCXzckC0YUKSQ2XSht1uOpDJ5Vrjjf1Id1xfm2FpIpDZo1R/yRAXxkFwfJE6FTLa/lwEoouwIjSYrECQeW6iit24KWqSMljNZqoCZxvNYLMpWV6afS+LUvKs3cMgs/9BEo2VrrSIEIIhvUVDB2wlB0lFAW8VmcZUsW0+hoiJAtE6vO4ORA6s/r1APYWuIKhajj8C2Q/OPXdRbHVG5IRtbpYOqF1dYDlAbYrUKQRAIt+Kwsgbm1i0JZGi7H5xaRVjgiLSu1RphaAjopoFwZpcTEWrLZ/AIiW6/G4EZj6xNXQSKXGsikaqC84kUh5G1105bVlFAWMQ5fZnlQUlkZSbWS+pcbLxJ0a9btGiVaJHS1xTdr2rRz57UnT5mKHWj2SVLQCtLr1PyX5Lt0cYbYsDHCx5HyyMe0RYctKHk4vSSJNVxd9R4lh6LXcrwmBf2Xhi/LFzMdlTmEX221jlg4T59elcDXOj1at2698047jH5vzNdff/OLGi+tysxBYeOZy63A3Cs0tiWUnekCE39SjMiU0pZsvRJhdDGAEPOJRVkhwlOuTLJKKmmwUlaFxkKxWfRltWKdYWYRPAY9UILJUHpTbWllEXsTYgLXXMiig6eHgATziOh01e4nrbrZSQC+HX/njHF3OHwWPfMaOfpNkvKA/fu0adMGWTtl6mdjx46fNWtWNFnBmBPfvftm1wy66oSTTvvkk0+1Ogdvu+3WG6y/njudN2/eQw8/Vlh+ZUXFE0889MILL91x592OMac9UfozTj9l1z/svO/+hyxahD1B43BHKUCJeCLHBUULEQk6dgRbdYAqO2yxyqYnzZ0xRnM5uLJ9D6w351WZ3iw5jl4AUbrddtu0btVq+EsjXW+Jw8kb7rT8rl0677FH74qK5q+99vf3xrwPvTdcP2jhgoVHHn28IwZQkj06XnvvtftZZ54Gb7z22hvbbvu7RFY4FXnka7F56J3wLPttbKmfiVOtVCYR5KsU1RAygEuewNSiqFDU3YqMxJuVF5vL18tAlegT8aFuUkZ3pEEL0axArGVYVDIhlLFw14pc18YWnNRpFNMbaiLeXFtaWbSKSI89IvCq3U9GQUQ1/PC+zUCG002OGo/Tb7k4Fua1YpPvffse07hRI1SH5s2bw9XPv/DSDTfekiQqdn7NoAH4cN65511cjJD6Dzv0IJTF+fPNByqrq+dKWUzw7r/fPocddnCffQ8GfvGSJXOr5+JI0JR0+vRTjz7++JOPPvYkuObMmTN33tysmVCSWEts0ljlkfRE4sgdBo+ayOEzYio79KjssKVgOu82pHYxhSw4uVOn1XRZNApW8NufB16+7TZb40YBGdVzt12ffe7FG2+6dfl16njttdces2fP3mufA5s0adJ9s6GlZsUKTgOqbHJwORAQKDnjlsudIRJibpFAhORWYNtJ7xZmEiaz9xCmmHJJZTqlyqglJhxGBt64PhoUEpExaM3KUdaPYWuYWY5urF0kQuCkDTJcbkvZW4QSqqjacSkYs6V995PnVr038V4URDPuGWPvmDF2cPvu/TZGcRyHlePgpEWWUo8jDY95f+z5F/RHkt1y83W4FH/00ccjX341TVYA07Fjh4Wl/EXN7Dlz9tr7gAIC0dWpU6cWlZVCgzl2wEGHF6Yv2tumdatKK3DosPvxKspSCgGFkEPjWoQ1naEBhvpBRKwGj1KImljRvgdiTVhcy1VvKr4g+QUdRxx+CGqiK4UozR99/J86sU/Ha+WVV/qG78drlxUrOA0kXmbQFDocahVmNqgYw0tDVDM6oZIUtK5UmcwQSb7ltDD5JrBpmUQUU51iqTa7OH+4fJl0M7AlBDEwknAhbDTRYBhMtHaI0kesFhPAJe4teqU0KBkTD8/CXXoOmVs1hm+ZpSR7Gmw/VVZhidGDGTw+lCPSCrVIsn79zhgx4rm+fY+Wsnj6af323KMXyuW333532hnnVFXNOPmk43fYfrvq6upu3bpivO+++6/+l1x+9123r7nG6hD96ssvPPnUs+PHTwR80IH74Wa5SZPG778/Ns8qcpNNNr6k//kd2rfHwnD48BGQsOcevfERWcicNGlKv1PPfGn4M4888vgDDz5yx+Cbf/jhx25du6AWwxIIP/vs09fp1vXHH2cOvPpaqMPO93nnnrnjDr+vqKgA5R1/vefVV18fMfwZSMNEPfig/c8+58Lttttmt93+sE+fg6BotY4dcZfXoQOpfvPNt68aMAhIjHSrLbf47vsfemyx+dKlSx97/Kl7htwLfPaRTJSQMk9ciAaXN6wWAUhZZCFZvBofagvP4LqRI1/t2XNX7EV8+dVXAwdeO3DgFSu1bYP93/PO7z9z5szum216zjlndFqtI/jeG/PBlVf+ee68eYj7rTdft9566wKJv/xFO2TY/Y8++kQ6K5y2Pn32/u77793yMH0T0K5du0v6X7DRRhvgBgWbNlcPum7y5Cm4TYGirt26QM7HH39y+hnnNm7cOIE5vu8xEi9Eqi02fVq3RmL02r3P8889IVkB3hOOP7bPPnsi6MiKAQOvmTR5alpXgTTYZZedzji9H3YewD7s3geQyZBZahq4pWKyfFCt4VUZ3mQGcyuwdIQtOVUR0imvB/XaUGCmY/FMJJWJyQkkDWHLhY2VUTFG+eK9Rd0aOWKzOdFv1i4SLYLIBjNEGageLuo1rX4JVdpns2nFQAc+qOOOAJ4wbBOUv02OnoCNRUcBetxHt+9+EtaMU0Yco/EJWI+pAAyVH374EbIONMccfcR+++790oiXUSYqW1RiFwbItm3boBihfFw18JrJk6dut+3WmFFDhty3YMGC73/44YYbb32RKxooURPvumso6uaWPbbYZOONEkqRuxAoLwhEb/+Lz2/atOlJJ58+dOh93333/YvDR37xxTTUI8i88657QFDRvDm0A1hppba/324bTGnUu4rKijvvuAUF/dbb7mzZquWJJxxHimpq1l9v3aeefg4VE7fqZ595KnC33IpN2LIPxo6DQPBCVMsWLYDBDBw27K9t2ra56+6h7733/h922emiC88FHgRdunTGfej1N9yCoR16yIFAFjzgcvTTf275hMIQxDEZF2YCDbYRceXDWdXY2xHKqrGDpQdjsUdEDusq3sB1+/bZ6+mnn8M99Rqrr47ryrvvjsIFBtcS1BHwd+3aZc7sOVdc+Wds3f7ut1vuuWdvIOEH1MRLLr3iuhtuRol84x9vvf7636NZ4SxAqR03boI7TQPNmzVbZZV2t91+5yV/uhKX0jNO6wcaFCPUxAsuvPTyywfiEQpuwNMYFy+YjZgiQ/5y/U2gdFlx9FGH45o3dtyE8y7oP2Hiv3GNjOrKSgNclS/tfwEkYwcJ4T7t1JN33HF72FZqGiAH7Atxk+jpNobjfuiiQFPqSBpJSy4kSFomAB2f85vhAMYrdrC1AAgRrVsWQ8oi/5iciPHPvPjMnwpeCJIt6yedooJMBERvNSWvFmmkhY+509+b26FHC9xNHzORl41lcluNxUVlxzH0yLIujh9//LER/2HG7rv3/Oqrr4cMpTtN7J3/cdedRTxy8eR+ZwB+/fW///1vI3r33u3PV/9lwcKFM2f+NGLkK8Bj4qE999yLJn747+dfGP7aKy/utNMOgIVdWqjYZecdBB71z3/RM8qaGqwaVm638sOPPC746VUzVl+9k8jUvICx5MF0BbDH7j2xdjjl1LMAo1ZusOH6AFA/jjmOHkkh17+ZPh1CsBKEnAsvOAeLkYTAbbb+LR6dX3f9zS+8+BJYHn34vu1/vy1WMYBRlA87/BgAixYvvuTi81Ejsh4igYYu5ebqivjTZZSRRRpETQKHtrJqy669hk4ZcWwLCuiWuNQVYS6l+7nnh8tqF/t948ZPwHjBvd9++2y1VQ/cnz719LN4YS2JFRkGu8MOv8cO7LrrdENxefudUaBEmUB0UIyysgI0WAnCBciZAnYhcHAplvNYy/84c+a6660DYswYLDVwJXvwoUf/8ebbUYyTiaeCSDZUrldeed0hAeyxR6+qGTMu7n8Z4NGjx0hXWldWGmAFAOP7Ht9vzpxqPMN5/fWXcMF44403Iae0NODFE2vXOSCwbdXKkTIFB7e0orInIsafGxJDzlR0O55aP9rUE04WJzKBALWC6cRkKisrvHKURaGX5yEjk8WJILJBTQVjp6wWS33kQtWUFpoZbZdew6AfiwjcW4Gmw2/6YUwThm0s9Ojd9JiJ44dunCXBj6IY1Gm11RYuor/wXaltW9x1Pv+sKVJ0SUgdc6vnra+eKet+zC6c4uYLWYWHkroLMO0t7hOsv3A3d+21AwZedZncAY365+gEiz5dttQ8uP/pp1n4rIZ04fZtE/uXdmedeWrv3j3xrU94noPeVdq1ww6AluDgzTbbFDDusgUzfsJEFA78JTlO+QJH6K95qrdu1VJoMlrxj255wmfHVGKHq5ok6Iyxt5d174fKCBGIMq55UMRuz8yNDEsiaHyqTrBYWSMiAsPVzZs3A4xFMW5O4aVvv/seY2/ZktbR738wDhvNePDVqlUrXDne4fpYICu+//57SF5nnW4iPNo2atTo5pv+ggdus2bPboKFOv3RZNmNN93Wvv2qxx5z5FFHHoaL4pCh96UxUWkaCcNw+68xUV2aQMPdunVBQcQLSFxWsWu01lprCkFpaeDmCQqDmzECc0tFgua4tPTGl1MqO2AI6om1D2Kk9lgEv0MKjkgrvdyq0vcL+dxiA7Erfysjz2qxVMT8wToCAkGDpYSsJgC337wf2qkjjsniFXzaEnt18j3Ys9lgw/W+nPYVUJgzX339zY4795TXTjv3StBj+YD5M23al4JPa0nQezUxCOuIQw875uhjT1y4aPGVV/7JkHB5ypKThcdiZ5+993z++Rex94RbYK0tzTJp0mQQbLPN74Rs3XXXwWIEE0NzJeC0ECZw7gcgL5PNrgNkDPOoGEZA8UIEu/Ya1qLDVrIZMvWlYyS4UjFBmJJg5CQMk9MM8yBFxCSZgEepQlnZc+8DDj7kSCzEhOIf/3gLk/bYY4/q02evx594WlbZyArc5+qscOIgB1emLX7THTXUIemrSpRebJVsvNGG55x3ET5g8O6ofwkZKjV2e3GZnDjx30cecWjntddKYNa2FcqJDQEaFz6nsO66QUXWut5595+aRZsk+C+//LpFi0rsFchpu5VXzrqOOjlpIejCWM2L/U2DB0ZgamGqbrmDMoGGYFvS4EMlWMIRa6wl8cxNAKmwL0YTgvDMznCgjMTzP3Thn2mdiAjAZKzPsuJdzhOtVS0GcFtaWTQbUJiOOGxL7xau+uD28UNoh27TYz5cdfN+gscz6E2P/RAXpcnDj57zzWhNn5BD3kwdoBEcqhvuN/EY8bFHH4BrrhxwNfDI2tU7rXZKvxPx9HbTTTdG4RB6XIQPPvgArC9uvOEakI0c+QrwuLriXhULN/eoF11OvmjRLfJv8803kxdUQ+ZNN1zbvfum2E+c9sU0fA8XiCdPnoIFxTrrdEO+al4HZ8nHfhNocLeFbawTTjjW0WOy4Z4Rt4ou+9H15ptvY41z2mknw5i+xx2NOYndVccSBTL0ChoudS/yACKVigsHlfGIWvX00Sh/1dNxE+DxABFxRgId4EUmt2bJnDASXQmMnBbAw88//DgTX67bZ5+9Vl11FaE/+OD9sag/6ujj99xr/9sH/1WQyAqEXmeF0wX5N954W7NmzYYMuRMbEciZe+66/YH7acMUw+Fv0yhDL7z9+efTEHTsPgvvBeefg61PFNx3RlHxatGyZQLTslUrpyUG0HjHjPkAlR2Mbdu2Pe7Yo7bbbhutC6eOMZoG2HXFJEZK41kiPmOEhHzlldccSxSI+hO+ty/qp7EHrT3jDt0DFUxLrcDU8n+QWVj3GZioCOSWxdIpv0iUxTAJyyaMU0ZszEpIpvZnTEgN47l1oo0CzeJg7hMNXhFJQQfdHZR8oKCCWQ7UJwULHlMFW/IdNj9ls+Noqw5rCqmVxBGjT+LpPHJsuMH6t91yPR6kVFVVXXLJFVgAgmjQNddjhuDD3gcesC9OX//bG+/aSy7S7uQT+2Kg2JCSvafHH3/qwgvPxR33hAkfPvDQI6B3fw7M/kgqxXY46qDDHnjQEXiQIhjQ33f/Q+gaPnwktGNq4fPeWPcBs9TeOy9Vf2vs5DsA6xo8uBw44HKw6A+IvPra33r32g2PZfFgwRHjkSsWL9cOGiDa+eEsffrSEQDW6nCacWBaIWASQhXIgnFp0XHLrr3vnTLi6PbdTxGxXXe/1+UAemeMtdqy5Nj+wu/OdSCjBOXDjRGPjE86qe/LI5+nPeIff5LeN998Z6stezz5OMUCB7YCsZwvkBWg+ee/RuP5TL+TT7iK1/vzFyzAUxTg8aRr9949cT9+zz3DevT4zbNPP4qLAorjWmuugV5c9vB0W57pTZj44cSJHyLlEhhsIoMyesjQrrzqatyJ9+q5a+9ef4Twv/51SFQXJETT4O2330XWHXHEIUPupodd2Gh++pnnQexcBDhfGtD6Sg7JBoZ1VkggLZG8c8uLK2QQnQhsOxljBVGvUFAgAep9N4MI0lCkELtApAA8hBByWEQYtJGn0vYJtWUQNt8amSzCyHTirFARjba8hK+hbdOuI77lWUSsoNZtLfnh5INw2ezceW0s4nCNBcfFF523y8477rLr7ljEffbZ53j84sRgH329dddBWcF+okOWBODma62118JdrSupYMeDDjyQwcd3SxIFYnzsZuZPP+HZombEUgJPS/HREI0UGE+K8LgJVTLdVRRT2bL17JnfJ8hatW23YN7cRIok4tu970dY6VdXjcYdNFr0tlhtK01T/c3oAhKwvksorfUpVtDtV10VWxlOwtNPPoIQ33zrYHRttOH6Z591Om6lZdmYyArH4oB27dphyT/j228dBjfCP/z4o2zeAcalF2tg14t77a7dun7z9TfO/2mMIy4AwFTsCU6ZPMUJT+sCe4E06MzZrjOwgLpEF9KgOpqotuogsqhJqWrmdhXjO4xS/BK68p0qxT6NpBYWbEV6XHFcZhQrAnRbq9VivrH+nFQofLL7llCaRiKTPv7PJwmykk6xVEk/6k1jcsrEM+g0JT6jhyONB0ZXhChBMWQiMYqRc/+4IRtKEcStNOUt9sgA6DzKJaYOiHDZS3gAH35q1rwZrklYMeGmGDr+/dHHoikrK5wdePziYAE+/2Kaw2hYkKhiiYxKYxx7AQCjSMhJ6wJ7gTTAlaCA/KJdbqmYrDpyJ8EhNjQK5uUa4k8o25IqJjEtn0ef3ylVRGQWfngHO9fhRMt4VkNlOLZCZCngj9dFbxcpEEGEM0NkpmC4uA7Q6pc6SiiLtJST23oZUt23MKhujlmzZs8L1191I/d/WQq+nZu/95Dut/g+GoMhhzsknwmuTts6i2rc+zfffPsZZ5yCT02je8nSpY88+oR8YCVO/avHUhrQLrBzhECplmuVVCzdom5IAkmlQT2wdYbLCScOU6D6sEy+vbcwK/WqTZpZ3aQSuRm2NitRwEhZsrW2WaF4h4DEARuZOWypPhIbs5rFsVgLPS1a06eU8xwV+MUCfPH9okXpBbYYt5x43rKPb8/nMa+eprAH8BXcCDrdL1MicKJwS79Y0KDBUv562jqJI00HFi/5gNzD05jCti1/L+43ISRrib388v/fSEAaoLTh7xps9ZBMkPH5rFAZsnxDlyRItpnii1rDyUUFjcuxb8VKSb1CFkeHSGx2hcuzo4TfckHZal7ZEr9GBa1afZ3B9ut5Co2qvq+2HmhWUblgfnW6QqFoNWteiZ+F41Qz0usspkiVZRC2wg/McxwrXM3/vgKkAX7/zwclow65KkHrN14hUhsUI39pFa/onCnRT6pWiWKSrUqniE63hdR4maqAepvTwjSmhLJYs2wpfjkMv86B2y622hhVJzAWMvB7dJj1eHHL8vgBawT60eiFKBziZLTmhRmC1SKeTuifFtCRWB4YK8WMqPI2TizeyzNMLa9eTjpt6I5hGb5LejGCYl4cB/iKTgWmlnJDtdxBGUO5YFsS73NDsFYl93EniSJhzGdbkk3a5MWdVhsTG3KCnUqCiEG3TkQEYGIRYFidKC0WsFVN1hm4lLJYU4aJ1bhpc7DqKSQ35zR4HCi57sgN8y6EpnYi6oHl9QD2kpryByTnz50ji3y+/gdi8QMv+JkrJA9+aN106GgsB8x7NYGu+pP/igckDfBjPgty7rmjRri4C8wti9mwJQAAQABJREFU5j7FFMVJ/u7F/PULVRy7lhQ42eYetVLmZVAhi9rjSWCtY00qcx2ZRAmKEvYWWRexY5MRxXHhgnlBcUyakvsc7vQjzs1VT1jMA5gJjZvS76DSr6bPoz8X4yORAJIo1IPbKxRHDiu2At2cEK6S2/qCWLLLVgyDSwPskyATbE2jyiaHzwBfVzLLR8k2cgE1FUtgalUJ03AoXTJVcGHWJm7n/Q6jGksoy51lC1VWlfK5Ra2yEf04anP6ucuGJTzLdrbVAz+DB3DRwn3z4kUL8n9sEH/j0bhJMzT1v0v1MwTo51GB585IACQC/m6niEZVe2LVLFqMkqW0iIp4t1Ls5WWUznQZ1YXJy4/LjGK1SIFrs1pke61+0SNnGTq9i5MjLeJokRoftdVf6N1fncyynxf/yojaKPCDxF1n4RuHtLv9WLwYZQ91B0/EeNUG0uU8MpU5uT5G2lUaFjMFo0xmCcYP1lo/Sie/ZCBukFKsnZstXUYu/aEXXOySa4249dmC9CqDP2ES6sm2LV9PXHGQezZl9EpQw6IoPi5fhvI5NI/RLoe1EWnx1qCEvzShgiPxsrZYQfY8+V5UATFYJ3I4S9tbpPGqzBT1GUZFbUkaXPw8IsYOIHA6z9dscREx4STmUQTTW0peUqQIEmxRoXHmBFuSqOh5aIJKF/fEUJczBYtkcVtRLZYgYW1GtC118B5lJQGxCKq8Eh3520ApnUQVh1RREq0yJM8lU7GIeEEUU8URtLEzhdXktlKrhOcGo5rzcUdZKZfs3mLGU+m0CwWTQ2tUpeWLduZVFmU2WSIKEhS1Xi0WG2xCjx6AZQ1J1PQutgqzrir6rjQkVx8q4WK2FRXNBF6+XL1ZaHxSppVYNxCTKVWEkhOp2UGHLeP5TMuk8jYrF4TUiiTpNimywqlhwgQRtNb6UYY6SjpLGURmKfM1nE9wwtpw1S/rRx9HERkfiTLNMxDpzxc7MUGMdGrTa4aMShtwh1GLjzifgy1V3CAVO0to7GcLNBxzcJBpevlS3OKYOM6lIF6lrBZ5LOEoEmdRnQmajNOi0cmXalELxFnplk0xLg4TIjSyqFAhj8ckyhzKzzjLdImsLKQM2VWGfMJJlVdbajOEF0TX3uawNGin+rRTJSmb3LKKnXHXBmPIsDmK1qlQSEGUOaJVUFFqrYpgcYNhsD4JalhmyQgUZ55ErcikNgEw9linqzsMNo1WiLROzFghCneOGGWZoWwWB5FZyg9JJ1o7s+SFzCXlm6iqxWoxtEVGJLjo6LSLkyNNOJqu0iKpLl1MjhbFyeVNOJJcZ+JiEVeSu0m68k8Mtt26thnTc9mWTWQlW0ewKQG5z0YdLw0r84VTxai4/EBZnhNTPxKFI2VEMVExyyJZV3zFIYJEXUxoesWRZ+4WMx/9XlmQb7a8UhQSTtJRY7igFi9f6SrIEe0M/BM1KBAfkFtPmWmqCBUmx/IlahiQ0SF60ZHuUlaL4u4MLWKS6GI4oizL7EL4TP/FXJ8tKFNMEJLIhLH9MdHRIXp3WxbvlCiDJavVuy3TxCz7PqnWTxupiqbq5lVXRzZHxIQRDEt2hNxGQjvYu9b28rCi3OGAoyRadEheMOuTpInzoqpIbewOIHBJIdsSCt1pVLPrLQgIq5AoMWKnpJTPNOOd5OZDbUyO1zDr/pQ9WeTh2NQAPENAkqAo4eM1uChR+DhWjemDHPUf0Ak8+ws5SXwuh6NGYaNDIoji75ODYP5cTtP6z+X8QiJY52bgMzr4kM7iRYvxpu/XRZFkA8NhcTB5YnvwzlmEakgobgXmc3TSu4WZhMn4eyKA1otYpkumIbHwYWQIj0PJvSQz/ZK+b9G5Dx/nbtKsAvcd9HdjZsKZAdXiDX85s6iU326uhYpfFQs+vitlLvwUd6YP8FUR9Ddh9dHM9ND/fIdLCXyiGx/tzxwP1yEqbLaCaZjWhsHeYnxtmCm8UIdSLCqlukbRupiKTFeYAhVRZi9di0nDJe8ttmjVtkGjxvgrWtrSr6MDV7KFC4LvYa0jwb9qMfJdKfOq8c240RShZKho0Qqf3K6P5q8kUZAS+FqDBfP1dxhHc2P5/IGlkl6UGliloYiXaqTQ6fKkMJGibK20gux58j06RCWa6a3NfDdc2t4ivte3vEGjup1FsAkCcS2qmz8lTLrk13uOP3LBH/NhpYDbJ8lM8YWD6SvFyhvUR/PXkyJICfx5Giqj/r76+PAzSgmtGU0CyWoxvmbUhUrDcV0GG1VpOaKdydIWbDBbTrxHmQ1WyBIUJfzEVePG+IO/igYNhQUWuaMOYPo7Qv7FKCe0Hlh+DyxeuBALBNxTq60O2fag/URaO5hoiqo6iCMLqqmP5vLHbgVJQEpgHmOjBSnBL8oH+xKYW+BosxCtogKIGkMY6iLYtGQsnYctnxsegW1LgpmbAcD0AkJEa9gSsmirkggS/3DKGCNLTm1LxEafI7ICRTOpJowxoITVYtMK+lleO3AG67qpXzDWrUex+8s1sQEWjAnJTXl3uD6aCbf8vz/1KSE/sINi4K6GAnOLImHufWmf8Vf3ucXSVouSNFRW7Xxi2Ph1OfH1X08g7q3bFqsDPGJmmUF8LJIu0jjqMI4yz+qjyX79JTZICbmB4IUSrY9okeSXUXziFk7oQHpIy2lChMH05zEKVobLxOChM9+SGuJDSxoMwDAroE6m5k4iJHbXEoR/xOdaAJkvJhMBzMLsWqCDWatRauASPqCDL8vBN1jiagN59OASenh7EjB7Ay3hKzpsCTwsYTzRCDy36v0ovZPDQuqbOvYAtpNQofjKLwGRraEaIJctwW964Luz43FcbnwdD6ReXF15gFKiQQOkBB9u8uJMwZIUQmIShPpRNszeYuL7Fim57D4jUWD+By1JUuIFFPEKTRpAl2ohmhWICFNaJJ9ZrLKQBCQO2CXWBa2zh/Tz4ti3JZRFZha7SK1YIgCPhJAbHzlu7owxBKWOyj/2+OyVvvOq3mezbVCSclJsIJBIpHocHj+Ujg9m4bffhMThExy/ZjxfhdnX7E4D8aXLxRHuQnJIF8N0UtmhB6e7ICStyqur3sM5ujr/8Z5/3785YBxZcqTXtb/mKDgnkLuKZbUmXhH0JiVsVWF1EkPbovpQVG0P3vkMlhOKW4GFiNZklsFiiAz1kdFUU+2duUGYpAG1UuVkMFokMSErYMIV/rnF0soiFzOpaEEro6zo0GNu1ZjPXu5rR0lDqOywhawTV+1+MpzClxDjJrzZemqkESJ1MAthcYnbcYffb7/9dp9+Omn4Sy/PmjVLaG+4ftDCBQuPPPp4ORX6ysrKJx5/8NlnX7jr7qEaL7BunXyNBOzw2223zQbrrwfM9KqqsWPHf/31NwlKOXX0id5S8RXNmz/xxEMvvPDSHXferUWVKieDnr1OaSgDDOLIV2m6G5C4IHarbHpi+jondwyoid+Ov1PgRBytHG2+gWEVfmj7iCMOxU+/V1XNcBQZ1vooOEoBakGPjzvsu+/em2226TffTH/xxRHyw6q1kJOwpNb2lCTn7rtuxy+bn9zvjARXre2XwBvj5U2twkzRZoytZnQid36qzahszkqjJmOum2TkAihrOi+PlJlyKVdm3wLi9SP1hzBzsHLFrGSaIYpgYrV3tQFcU1pZdIPNCaza/aTK9j3KNiv77OXjcrJkkVVWVDz5xEMVFRWz58zBzwGfcPyx5194yejR8ZUphOCrN+fNmzd3Ln7oroTj6aceffzxJx997EnNc9ihB6Es4hdWmzdvhs+zfPTxf8499yL3A+qaUuCokDSZxuy/3z6HHXZwn30PBnLxkiVzq2F4aZZraRlwIlEyqEI0auK34+6QQulalMvOu92DSyBeIXmus9atWx9+2MG4xqA25WKoI6KHH76vY4f21dXVW2/924MO3G/QNdePfPnVOpJdshgd8Sjzyiuv9PijDxx2xLFy8ZgzZ07x75GNCspAyprPxdRUIJQNFBFkCt641bB0hC1TsgohN5yohraa4srGsFLF9FyTDDtTAEE6bcvFjJVRMUb5wr+wFTFU2MRKc+7e7ABIqAjiwckQdctW0KqWVr/UUVpZ5KUBZIAz0sqFS9NQTSwrm1v1HrpmjB3MXNLEJSiCJDh48E34lfRTTzt74of/bt68+UMPDvvzwCsOOPDwrN/AxI/c73/AYUkpxc7btG6FZWaaCrV4r70PAP6UficeeMC+/fuff3H/y9NkgskSkkUPfKdOnVpYvfhu+QMOOrwAcS26OOLG5w7WkYrGFJS0WuM3VkoS6N55tyFTRx7XvvvJ3ElErjeEa2HpimLZZJONUROHDL3v/gcexrLx/PPPfm8MNrv/a4eOeNSI9quuit8da9iwofSedfYFUbJaIymqdGTEjusTVyZVzrhuUD5w+aIiZdaPISFnDN87c/5AgCjTqpRyiyaVEB221j5e5v1svxNdWlmUsRRuKzts2aXnEKwjpo48Fi+coiwWZinai9uutddaa8LED1ETQTx//vxhw+4/95wzevf640MPPwZMm7ZtsJZcpV07LLX+eteQJ554GsgXnn/yqaeevfe+BwGfflq/Pffo1aRJk2+//e60M86RKzCWnH322RMrUKwgBgy85k+XXoQsxNrw4IP2P/ucC0UXePVx++C/brzxhtts/TtMLTyw2HmnHc4667RWLVvCJEh4+51RI4Y/o4V8/J9Pbrz+mo032RBCPpz40RlnnYdt0FVWaXfjDddgSxTI8eMmfjFt2p579AYXfgN+0qQp/U4986XhzzzyyOMPPPgICGD57rv3bNqkyfc//ND/kis++eRTIEGApVbPnrtC9bRpX15w0aVuaxW96YPS2By02cOZbhE53rv0HIogzhh3hwQXNZFjenIO1iIkJ590/FZb9Zj106zNum+yZMnS226/c6MNN/jDLjstWrz4/vsfeviRx7Fzct65Z2LzBGH64Ycf7/jrPa+++jqErr3Wmjdcfw2WVMtq8CBwCTBHH3siHiBhR6VDh/ZYWD3+xNPIBKd+lXYrA541G3/zU4bAYakoXSg96QDtuusu55x9evNmzfCxZ8RrTvXcffc7GD4fOfJV+Bw3LrgBHzjw2oEDr1ipbZvJU6aed35/uTyn84EGuOUW333/Q48tNodVjz3+1D1D7j3rzFN1xP90+YBL+l+w0UYbNG7UaMrUz64edN2qq65yxeWXwMJ7h92FDaI99trvjsE3I8eQlkDusstOZ5zer3WrVsjbYfc+8ORTzwIJgu+++75Ll7VX79Tpp1mzrrv+5rffflfGGG1tSkj9ExKBbSt5IpmjWlpR0UEtr8EYsinl+5iI1l9SRoOWqSNpyOXWyoMoITGtLPiozVw5yqJQNIettYuEiiC23iswdspqsYQP6ECNrA4gygB8WXAw21GDmjh1xLHYlQeArurpo0EPGC9sL2bxCj4ciT/rseUWOHnjjbccSuZG9+6bCgYp8v77Y5EKuG3ud7LZZGzRolJ+Vf2Yo4/Yb9+9Xxrx8lUDBlW2qDzj9FPAdfRRh6MCjh034bwL+k+Y+O8ff5x5y624Wyz7YOy4G268FbnudCWAV155HbHu0rUL8hiV9IsvpvW/5PIZ3357/nlngTIh5LZbb9hgw/UH33H3kCH3oTju9sc/YB4+cP+QVVZZ5fobbrnp5tu/+/77F4ePhBDMGei98657IATbi23btgHQ97ijYfnHH/0H5bhZ02aDb7tRRgQCLFoxSzEr1lxzjYMPopVsvgOexkFt4VhQbDm+CByiWT39PVMTEVyOKQmQ2BfKB9aW3WCYXTqvvXK7lW+66Tb8afzZZ5625ZZb3HjTbZjwiBrx1dSsv966Tz393LnnXUx14cxTRdh1113dtGmTk04+/c0338HV7uZbB8+c+dPQoXc2adr0yquu/tvf/3HoIQeifjnNb739LvY9IP/2225E4Bw+HaDGjRv3v+i8Lz7/4rjj+6HQYGIPGDAI9PD5vn32evrp54a/NHKN1VdHGXr33VG4dK3TrSsuriCI5gMNsEtnXAIRblzYYBUoExFH/cWVEpeES/505ZprrH7Gaf1w8Xvttb+D8oEHHr7uhpsBrLRS23YrU2XHsvfS/hfAFTfcSAJPO/XkHXfcXgh22H67r7765sabb8Pt1EknFtm24vBKCOFjflE4DYZDCqkUYHmjFv98C5DP6Z0O6ksfzE7ScfiW9ZE8eXEXSKwykhUqI36PIcifcQ9OGWMkioCUGC0kAbNxMtyamtLKohk1S4jCmDlSE3HLXF01pmsvetyBuy3AU0YcC7hrT38BN24SQVqmEe3fOq+9Fk5GjfqXQ+EeGVuHuKgKBmUF11js4l814Bps/239u60cJQCstr766ushQ+8f/d77yLbNuZjusUevqhkzLu5/2ejRYy66+E+ffDppxMhXQPzxx58AQNppCRquqqJH3qt17LD/fn1Qyy659ApUVSQ9tsyQ3FrIwoULMaVffvnVl19+7fkXXvryy697997td7/bCtPgsssGYLn33HMvDvzztZMmTZ5eNQOiwDthwodaV69ef8T9+5lnn//Ek89cdsUAlNQ/7rqzEDz3/PDBd9x13/0PzfzpJxmRZsyG7WUTFNrnGbDETjZAUB+xSESJbIHPYGXQB/hsI3QPBn7EkcdhOK//7R/AH3TwkYjjY489iWKHdR9WdsccdxJWWIj4N9OnY80IJMiwTnxvzAdYjA8efBdOF8xfgOUYHDto0F/GvD/21tvuxEJvz712d4rwvOLgQ47Ccn5DXKVuu2nY0L+idmApmg7QBhush8veI489OXnyFESkSePG48aNFzkwEpZc+5cbYTMun7gM464c1RYLXhBE8wF4EB92+DEopkOHPYB7gvXWWzcRcaw9QYCHbND148yZ6663DtbF48dPAO/rf3vjjTfeFO3S4jIJ8/oe3w/GHHPMiVgsS1FGL+RceNGlSKoxYz7o2KGD5krDWJjRCx0CEAzBpgWWVm7UmjchVC1AZqd3OogufTC76RSYWlbpW7CRYjKIQIEJwf9Np5Bwy1aafqFyLTHziwiBJVLfWojeLRxSML7EvUUkPQowtOEN/AkYCF5W0PIQNFUf3N6y45bgwNQaP3Rj0APTrfcwYNK8IhOmRo9x4yhFsOzCxHAESOupUz93pwKMnzARAErPqH+Odl0rtW2LdHz+2ccFI9cZIDGvHE1+oGvXLiD+dNLkE044FnXq2afpLh4HxrXJxhthnSKnaDfaaENk1e69e+IlyKomTbbakqbQmPdzqW7bpo0rlFgOQ8Vmm22CGzFIwKfPROacOdVYIgmco0VwcJg2GkcV35oZ4wZXfTAYcezSa+jUEcesunk/XOrA3KUDXfDKygZTNDPzgUmKNZAgJHK9wW/U4RSXMbS4CcB2B245e/fuCXx19VwgsVUCJD4PgChjobTP3ntCAsJ93LFHoffaawailQPrOAvS++zZs/tfcjmq7ZlnnIKIDBxw2bB7H0wH6MMPP0Ih63vcUSise++9Jz7wgNIscpzPsf8LGkFiYYtncYDXWadrOh+AdwP82gyqpTC6Flw33/QXPNbDPT4qI26lXVca6NatCyKOF7pgGDaF1lprTSFbtnSZAEAi4dO8GmPHRDgzPN6/4w1DzF+2mjEmvrT5QvefYWvrAAvBvE4eRg1rENi0TCiKpR7YquAkaYQm8TuMRMr/YJTAzMyiTY2x0kBA1vGArM3EajEBXOqTaPYKayUtbIxoMzB1KTyqJGwxe4uMx3KSrYvTG8nhGxL33//+CFHar8/er7/+d+nce+89gMdVMaQt22XnHYGZOJG2IN2BxMX20OFHHOsSFF142Lvuut0AQI7G41QYE3gnbb9998HOF2YmVn+rdez4x557yR/ea3oRMunTSeDCZ4n+ct2Njp1L5O477bTDa6/9TeulKwkfzgCcwXLcIwse1qLr008ny6lrDZs7t4C2h3EglOQwKWIIbbzoNAEjdtPfa0E1cRhqIqI556VjUCJxQy1I5jBikrxQEjv06HR/Fn6HHX6PwvfkU89gAfi73251zaCrhOuDseN377XbRReeCxfhrhkP7rEEQxd237CO05IFRrVBxcHKDhUNyz2IWnvttW2ARv7lups0C66+7dq1u/SSC2fM+BZ3Eror5VXTCXwiH6QDT701u4Mp2jbi/S8+f+ONNsQ9AT7+BXiXnXd0ZLj1sbCJM7Rg9xDFHQNBF+6sJ02eYmmKvzv7rfIwK2hKIwlIjiFQsHSELVOyWmOfcKL2gT9oSbBJQKZ3MKNJm3mUw5pNprIy1ET8+zmfRDuni6VFWmxpy0FXQAsTRmCMqwa3zP3ab94PLX0SmPEohZsd+2GXnsMwu3A75unhNcdL2PhMEpKnn3keezeXX9Z/ww3WP/KIQ3HBxy3wiBEvi8UdO3bAjcxvNu9+8kl9cRnXt9sgeHfUv1bvtFq/k0/AU+ZNN914m21+ByRKKhaMF5x/Tps2bbDQ2G67bYBEqkEO9qSQdtoeXMBx77P3Xrs/+sj92OW5i/fycVuEa/L1112NnSY8N8TsFWOcEIwed8e77fYH7ElhRYCCiDL65ltv48bntFNPwoTp2qUz5h64Jk+egkm7zjrdWrZsofViEYS7RWyGYtRXXn4purCzKVpcG/cakkz5k0EhFDTBBOG/jWManvMNFv41XXsNm/LS0QQzvQBzpo9GcQRGjjQv8M5CDZSKx44e2FEvsEGB5bkTtdOO20/88KN99z14v/0PlRX6m2++jdDj2cWWPbbAMzp8BsgRA/jLNQOffeYxPEzDs5ojDj8EXv3Pfz7BjTkHaFcdIIQeAX3wwUd69d7nyKP6Jj6mWsD+RD702Wcv6M2ix7M1F/FmzZrB8s8/n4Z9w2233VrMlid+eMKDlGCM8Se0oLThkV23bl3/PPBy5NUrr7wmLHlaZw8Hk8yTLAjb8ExRQQVz8FQHjH/U0uFbEkl0jHUwJRtzMKBgawGxMFq3lolVkTr1j8mJGP/Mi8/8qeCFINmyLWymUco2C1xouU6jKuXAPCmrud1wlJfRKTYTe9+LUojbZ6wvcIoVRykiPe0ttw5u06Y1PsuN+YCxT/vyq1NOPRMzWiiwFXjtoAFIFzyJxr514kOFeOyIXcgD9u+DxxSgx37Nu+/+E0uM9u1X7dVzVzzORp3661+HoOvV1/7Wu9dueOaIj1LrTy9iS+uuO28F2axZs68cMOj11/8OYjzpGzrs/qOOPOzBB4gXz4Kfefb5hJATTjx1yN2DUdfwAvsNN9yKjbMLLrx04FWXXT3wChCjuKMKDx8+Eubdc9ft2DDttXsf4JfyDRE2TFFwsXmEeYuhwWa3jSAEoOTEofcCh7nyEwUtF/jCXYDcdCFkXXvdi5P2m5/SfnN7tZfO8jLsMM4Yayhr/UZpHDscGrt7x/c9ZuCAy0GFT4w6WjyUQPlDpICBEGyenHHmeVgqDhp01XV/+TOQKHlvvvUOHtMLy6Brrwcez4XxAmbq1M+uvJLIogHCfi6U4gUCFKxHHn3i7nuGMWzuUgE7C90Qovngeol9mWfXEcfHznr0+M2zTz+KDEFxXIvvD1COP//iCxR3vPbhD7QKO7RgQ/mIIw5BXkE40gkrBgjX8rVS6Uq3NiV4lWa6BbatWjnK+tGuIkGAg1q7crTnNrGEgrF0L1Z85UikOMyaUcGkRvKV14zyGDlz5Sg0zJ5orM0kTpagbL0RTZrFTixx6aRF6w4JAVmnbdp1XDB/rrWRhBaFO2xxCmiqxt5elFKkub2bLBuAx9X+q6+/kftWTYa7JDzFw+cbHPKNv4185tkXbr7FVGpcVzt3XhsPZ+TuQ8iwNMDWzJTJU1yFxaNefOpCy3ECswCIxR45tq4cQUIILvi4KcPDTacFlHg6OW/efP25SyxIUVu1HBEII/Gcwc1wpyUngG/JnD3z+wRxq7btikaz+3EfTX7p6AKxq64aXaA3TzQTVmWdYpWNJ0vuORhW/cNfeApP5/F0BQvD/fbbZ9c/7IwCh+dmkABfIcnhybS0aA6ATAdol112uqT/+ShVeIzTsmXLc845A4/XdtnVP71Ji01gOqfyIUHgTnXEkdgIsc4QkGHdumTpkuhYoAXJjM8POWn5AaREtaSrzGLhVDMa9UlVM9pl4sspVRSsurj1BUC4pcLkt0FRphST7BwVRolIgV5m1GZfD2NlbPlWi2x8yiAeD2OxSDTTRoiK0UdEpVCffzEthSMEHjW6WoYbLiwlEEj3DBEEqKSy/aTZUSITSNQpXao0cRb82WefJ7oSQtweuSbDckCfApbPJCaQOIWRta6JSppPFB8h6c6Iy7gh9HHLSOETrvQ8yJAj5MvTujWyCMHuB4KLZ27wfKtWrbDqx7xFjZBe+VBqVF00B0CpA9Sh/ao0kZbVIJ1QlVq3boVbhKi0LGQ6H7IodcSjiY2Hy1m8+bVEJfjVorl74HCqFaIQ2JZCC7eolqT6gEulJBywQU219ZWpbc6AEaAcDJMes7eI0LqkY5W8vDMrRH4oIcpAVfgQAhIBZm55DHqgBJO5YrRv868WW7ZdBV9JROtzbzWpiUwbVlw6vqZ2l760b/Bcr/tmm+Ie5Li+/dLryjT9/2MMvp0bXzc7Z9ZMl2uyCsCPT+B3PVZkNMvwg0orzrHYFMZnlbC3i6zGzgN2G3DLXFfqhg25s3PntWVZVDXj2/POu7hAeaorpT+bHEqJZs3nVdOz7MxD5rV0qzkOb3POUOETmFpTBpKrSIunIpH7UMp8cbF1JmWPJymkLEOmsilBUcJNNH7ZCt/pvATPv5QMqrTaIAvXAr8M9w/0rWV1c+B26VdeEMWPqIlw64J5bg6Y4DWrqMTPFbho1iJehePOT3LqLJpZOYGNiB++/wHbiFkEtcbjr5jwGYD0XW2tBf5yGJESKDO4KIpJMmUZVhPbT/LlM1wnlsDU2jKXUBiqkk7BhYTuRl4KsW7J7kJHtlBlVXkJ386NstW8siV+jQpalSvrDq67mggL62rhWcjF/wt9KH8L5ldjHZ4wFlegZs0rJZrSVbcxrcMrXMJyfYovUFhiPz+o8csPwxv4Ezq0//8OpMSiBQswnZNDU7UnVs2ixciXT50/SclFzpViLy+jdIoarUzDXlFcZhSrRQpcQlmsWbYUP46Dn+nATwtAujvqBMaKBqtxJ1MD9XjxRi38gHUBvkkIWx8sQdxrWhRKrBYbNWrsfihCe3854fpougSuRdQcrwbqSg7dPSzDX5EvxmzjFwnGf2p5m03OUJMERx0MwxgiSbZko2SLzxliIaawJcHMzYCCGVR6WHFKGSlP/DOCGGtg0iEvIjb6hIDapPUGQ3RETC3DpZTFmjLMsMZNm4O1jreN+CNF5MT6o448IPtHEDaveg4iLfuJItvB+IEX/DQgsqVuo0k313QLUX/8sjyAlGjaHJO3wYLsP2z1FqM6pBdR9EzE7i0Gf/FCFSdNLtIE7yVnQgVkUDmLKNAqCymLMhuRwpegKGFvkfmJHZuMKI4LF8xzC43MkebpqK+JebyUmwbZ37gp/Q4q/VK631J0/IkEqMHPoqI41lk0kR9cF52+euC/7gGXErgQLliwwGUAGyZnYqPrSdeb2g7CXZPlamxaVeZCwUWtYcsit/MipngJjg6R2NzagU/yP4nWKvFrmniYRb972XD5PuITOqX+bPk9gGsV7puR//i6LbojyHHgB48aN2mGpv53qXJ463+PBPvsyAkkBT6anrQ+ow65KsF17Ff3ucVarBZDxxZ0q6xTvYvVRYKL7HJU/dAKdaYuB0nF8ml7ZYRiywnilpNWtzwwgbnNt8ZXpsUYbLdcUa0W1pXTuiwyKzlbprgqGS9xWIbb1GWyuPwsyzLxPnYFjcjkNx0xyyJZJ5/FER41LiVdBAkiJpRdG6w47F2fElIb0CsL8k1UiQ6rNliQKbcVVOvlL+fEIA/IkSN2udxpPcgRMVELcFZhkffoECXM1Ea6S9tbpPEq52lYLBMMwxFlRayPdmf6L5YLUQnKGumPWsaWRyaMDUNMdLYg4yZh8k6JMsQk58bZMk0Msu+TahP3B8E+Yw49dWRzREwYwSCzYlcNGwmddd61tpdHFFGWHGmURIvOxZAkip4XVUVqec/OFD6Bi062qLIAGdUcUGSeCKt0KzF+b7H+c4vKd8pDlLpy6OS0uAy3xvOdSm2kJOks1XC2Vqs98q4s91ezgjU+IiSBUjL9wAwkpFEKPRaCbX2QyqAWi9GOhA2lnkYtyhaiyJ2dwaok6cJIHLOl5+yJGpFUHFyKigkWiUJVi9zz4pVpPgV0ULWzPFvtoExlTpyJkVwkbPpoEzQsXJKNDEflO9m1AsQgcY1PoFyxC2OkL5uRHJP1vhpLhrXZQpWCUj63WFxlmBfB9TvDyDg6Gh2dakH9CGTkGrW1jEdkXBzglIdIeklCA3Mcc2JISaKi5wl+b7ldWchnYswnY7hbJkBRyTGCmLYYXQQXZfWxIzf7FUeOp5k8FBMPUSeYlOqo4hURuwz9VlXUDj0KD9vYmQW8qVhKUGqQORBR/Tn4QKJYY9UsWow0E8G1OpRiLy+wx6O9+6yqeEDiMqNYLVLgWuwthlaIHrEwQ6d3cXKkRRwtUkN91hd53v3VSW20aB/URoEfpFplxKOmVWmY1HoxCdhe4hOlOc94M2kylTkOHyPtKg2LmYJRJrOE6KXFya4VEDdIKU46NK5FRi59oRdy5Z4Xmi1IBWrFxS45giD3rFq9EtSwMIvD/IgMFHoluAClaHMiSpx3UQt0eBmOxMuakzEy25091+xaiCitE9kDy7W3KHozjIoO1lmaG4iIsQPQkRc4W2pEjPWJCkAwvfU2vBcsguS8qFDPVoAhSVT0PDRBpYtdfaQdo92j4aK6Sq5DWmKmf2IRzNCkoqPqTxg7rZPhqOKQKkqilYXkGdYlidx5GKNMy1khR9DG7he4ctRlpaSVvnhDnOo8kw1EQ2LJo53peMWVRZnNsERBgqKEj9fgGoDkQEajbUyf6Kj/gI6N2Yp554/aLFq8aOHSJfgefxc4UkahsAcWDgClbURfQl3/URvrmhX2bj7ygp8uWLYko+ZRnaPpwt2YNfjwICYNGvyAzAqzq15wLg/g73zwdaX4yBL9zIMNkcRL6lsJN9GuEOPj3E2aVWAxha8DwNUjlyHZRPjLGfzkW3b/r7cHE4mnUtNFC+cvmFvtL42yspCHVHaVgV1F/MU6/WlXfVxWfMq40CxetGjB/Hmi0MyvcCUrUWvavKIxvu8dJRLfh0JXsOWaNfVTZjkj7MKHz/ai+PiZZWNXQllkU8rxhVQNGjXGn9PSar+ODqyGFi7I/KW9OlLyPywGxQ6Xn3nVhb71r6JFK3y0vj4uP3OYERp8UcB8/DURzS27ORAsIMua85cVYZFYh3//Uz9l6iTQFL6GjfDnYOpujAJZ2t4ivtS3vIHMPf2NLLo+1gbGZMa0r5s/JawTb/3ChODvVvD3efhuQaxNEqZh3YE1SPPKFuXlDbkm1scl4aEVe4rQ4A++sPrAHVlUU7Pm9FvV5Q3oFs0eHuLViUVTMXVHEbh+yjhPLQ9A4WvcuGGDRvgrIHjcrfdL2ObArhXunRs0zGLRgdSm5sLT3xHW77lot4UwvgWnSVNs5jbG9UMfiCSQ6KqPS+iwn+8MocHMwH4HFouYV7al3SV86Se6MkKTa16oYSTpEff6KaP8U0sQ4WvUuBFiRPsasruBJUaTZvK7YsWFNq0QSmJfQUf9gjHLsdjHxT4jCiIWjAgApoisE4Fp2qw5z5j6uGQ5b8XiTWgaNFi6eDHHxd9C01YvfYtofWhWbAiWR7qEr0F5Q3xxp51ZZVlLv4giLEoEy0E21646hOu/pyDidIWiy1qjJqiDWJBwS+/oB7I+LspP/wUQocFFy8SDg8MxKiOkKYp1P19wcayfMnUSbAoffv2CQsVTqry8hA/oYG8SX18pVz/sZ0EKPVkzX50AkQZugZ+Htn+9wClBT97wO6hZ9A5fJyP8fywE+yCYBvR3LOxrGSl8C+SyJXjASauS+rj8VxKAQuOeqPAmIj98KQMSHwVByArEBdGs6LCFrP3dfAH9vKr30abnl5sv0vtfGe//M6UIH23+kq8peIhdCWURvrDBC9yCOOGAOBwbHzlu7owxDAaNhFlQml4TCV5jDD1/BqUeL2tD04buKBoXkK+y2YkhE52t/ce7P3/lePnx7vq4iH/gYZ4dSW8VxdPanWcVtyQlKcKeC6GcbXTk2OiUmdf+/Rnj7xAaTW9l0LuETGMELmpqgqWe3jgTjuDglVYWOV0k2kHLXq6p6NBjbtWYz17uq7MDcGWHLSvRNR3l0nAJPWdNWk4iZOCp6dXzj6uv3inR8cSTT//006wEEqfRnK4T/HbbbdO6VavhL41MK60T+SK2sP3olSSWUkhxNEMOPCm+ZYGEr+ywBULz7ThMM5piiMjcqvcAr9r9pIr2W1RPH52gd5FSeLHOt2Jn1y6dd9jh90OH3e86CtvvyBzwM9DjR7r77LOn0ygAfoD01VdfTyBxWnt71OrOV1YjLhIdF5fPXj5O4pJuxf8F2/QIaAj4+ewjjjj0hRdf0j8PW/uhhUrqVs4vJIXg5FquFkPnZJ6hCGLiSTdPxTKehJn0RTt6996tW9cuIKuoqMBXaS7kj3+/9977436aUJR3//32Oeywg/vse3Ca8umnHn388ScffezJdFcUc9ihB+Gn5rLKYpSlzpFSB02buVzIVIs6iL7K9j3m8jUsky53By4VRx15mC6LuVmXlzB/+FZbrcO+ffaCviZ4ZNyoEX5AFTB+BjpaFtNmXTNoQMOGDc497+J0V4Dh6xXvMNIU43VH0F/gBHFZdTMKTfT48L7uUXwBZOvWrQ8/7ODpVVUvvjiiANmK6MrrLqv7v5hC1gR65wVGLVeL8rE4vnqZ+2YPyzUEm1zV09/DxENlnDHuDiCxGIFWnGLbERg2xXPh8qwwDKaa004/R3B//9uIl195/Zprr0+RZCI6derUorIy2t2mdavKjK4o/S8BmbhQY80Iq3hjkd7ZwkhLa5Wamor2PdDKUhEtwoRTZqfuLF7G/xKb/OGbMOHDXrv3wRguOP/snj13FTj/kDp27CBX4v9j7zoAqyiaf+hpNCkJYiFU6SBFxUpv0kGw06RJRzpYEKSDSO9YPlFAikhHUf6KSi82pCgiELpAQof8f7Nzt7d3t/fyLgmK35fjsZmbnZ2dndmdnd3b9y5wEcM0HBca+oSCA+mWaUEDn7hnXikt//y15iRmX225fwwZpLr+Mfk8KoYteEfY996iB0MNOi52Kw8/zosq0xG3gKPKhJieUVPKL6prl071nqyNEODEiZNduvW6cuXqhx/MWbV67TsTpxYsWGDypPG//Ly3ZMnieMy0bs3yffsOdOrcXVaxasUS4BEAtmjetGevfj//snf82JHFixfF5uuB/Qe7de8dL2IKSe8AVq5Ysnr1OoyxiPBwvFt92LBRw4a9cUf2bPsPHOzdZ+DZs2fLlC7Vq1e3vHfmQcEtW7cPGfIWGELUiRPGFClSGMhr1+gA8Oy573300UJHQ9SFj6NejhMdyOBvc5fpeHB1GwTvJVt2xCwVfMFgKBPVCRazgwb2hZIzpE+PYA3vvN+//wA4936le82a1YG8Cp0kJOzctRs6DKyTZJqPm4PIEUYvUbIYbn/Y81O3Hr0H9O/9wAMVWjz9Ynx8/LixI+7Mk+dCXNw9d98FAnShRZ8snT5jNpd1pxRuwAcaKQ8xN1VQGLhCLLnYUkEVCEg0dcqE06fPYL0FhxUXF4ewt2fProUKFjhz5uyw4aO2bduBR0MwwROPP4rVGCinTp/FcXT16lV79ewaFhqKVx7ga+AX4uIbN2lRpfLjPXp0yZI586VLl4YOG/n1N9+qlc+cMdmhLtixbt1amTJmPHX69MBBb+zd+6tK74ATFdVXFwosqqNqii/EzhTiRh8HdFAK05q4JIA7CxYTpKSwyDEU8Tm+Y8qBVa0AGBT0xyrLsEPQwLetWj7fpHGDlavWvDl0RERkRLeuL8MZfb95a+NGDbDUff21AfhOz6Qp0w8d+gPr7nHjJ06bMUtlCNeJ2+07diILvgz2wPAAt4ULl8TE5JszZ5pK7IbDw8KwLlu8eBnW1HffdReKb9r07fsfzEdv422sAgXyXzh/4Y0hb02dNvPBByrUq1cHTPr3ewU+cdDgN8aMmwAX+eVX//f55xvcDXFXJzFSa9A2weICIC6nPiWeSRG2Y07KX2t2yZa74Rljd0yN3TEFn8BlkStrDwwkqhMMsFy5ck6aPG3Qq0MweLp16QSGjz/2yJN1a0OTmIpwe/zEiekz5iSqk2SajxsyaeK4osXumzJ15uzZ78L6NWtUg7Ewzw15Y/D9ZcuUu7/se+9/iCy8FgpDGv3ksxUBF6Rib5HsAu6m1gLrFmQ8qriEIKaStBcfuwWp0L2Jdo0XIg3iuuOO7I8+Uok6+fRZ4RHh06a+c/Xq1YmTpmXOkrl9O2xrkgj3FSn8yeJl8Jhwdj27dwYOvzoysH/vQ78favNSp5MnT8FZDB06AlPaq4P7Y0wNHPQ6LNWndw9H/Q51tW3TEoP0559+mTxlemim0CmTxmfPnt1RRL1NVNTgu1Cioqr1AhZWM2zn75ELM1Kf1KkwnQ+RV5qE4zvI70SV7YSo5OCq1vlrz4GleasxET6SSUAAU9Cffx6ZPYc2+9ev31CjehUAmL4eqFh++tSJmTNHdu7aa9++/cdij+NxDUJI5KLH81EyRGrA9Ovb6+ef9wLAbAl3tmXrtjFjJ4AMZevUrpktW9aLFy+FhoYCA8eK8AGAei37dMWs2fOAqVWzOgIcLtukScOKFctjr+2TxUvxQY01a1ZDEITnEtjELFyoIKZonmC7dO4YFhaGDqdtiFqRCpu7ioTjFbTMVW3hgCUNTHBw1ZaIPBUwS9E+SoJhILhLWlOLy1E2uKFn1BBYJwirn32uFbSNwOTM2bOFixRCsfLl78fwnjZtJg4ZIXiMjo5COmL4ELdxU9Z8EAO+ALPamjXrIUaNGtWwhY3OMHvOu+1eal2saJGDv/2OaRJZl69cOXv2L+5CRju1fzz2Fr30CbzKJjK6QlTZjlhmndg5ZffckrilY20UwxiXFx8zP9BfaB6TMSierFsre7ZsL3cmdwZfiVkBADTfqg3tbJYsWeLosWMYL7BC7ty50KD5Hy+COdD21q1e2Llz1+DB/TEWwOrmzYSx496ZOGEs5rlz587LYfLd95tVddWuXeP8hQvde/YBc+hz3JgRGKcLFy2JjIwEBlfchQsMyDSwqMF3oddeHeAWlfy7xyWixSTtLRpzoJgPwUX6VxOmEQQneOHYlrij9HAzumwnzHgHVrYCjAGJgYfRCNikJw4q7CGwHn1H9uxYBX+6dAFn8ySNaH/V6nWYoA79cXjPnh8cJf/zn7noE0Bi5mzTtqPMLV68GHrApk3fM2bDlxvhFuHdKj/xeKWHHgDy2vXr1arXlfQM4LgTA5h7YQCGsUgJCyNPiogVq7BcOXOeOHkKX/mGqwVy2/adDerXxVOgLFmy4FnhN2IBom0Ic3On8CCGxkSe4Rk5bPC0C/SMf3SxXYxHz8InYn8DUXyB2nN3zSmh2kKF3WJ4YQLrBIvWCW+PLnpfkXPnz2dENCJOOyNkrl+v7qBB/TZv3oppY/OWbWCu1UnKmo+NXrdOLXy4ObEZ6WD8/PkL4AKwnBw1apxXM7V4NgJNNdSrjdmEkIHtIsJFFEHccHBVK+w15S7dESG8tBGxY9Y6PlpJ3Ej6BS1x4fAGnsYwfPLUqZLp0jHco3vnOnVq4e2AcXE0/aPf/vDDT+jVbdu8iDHVoEG9c+fOwXsWKlQARly6+GMuBblKliiOtbbXMMFww8YuE2O1DvrSpUteuBCHTV5GIhRlQKaBRQ2+C2lF/WLDV7IiB0C+SOg5ZfYWMX74oict0fhUQAXxx7bAJ0bmqbB/ZSsQFKgzF7kwNmBJb5Yz/nri1VlSKQMHhM0ORB8KLgSPUOrXr3v6zNl777kbUfSPP/5MuWLOheNr2Ki5SixygE6z79d9gMuUKbVk6acAKpQvhxTdYu1azQEO0HMvBY16OfAY/zjNU69BMzz3/Gj+u0z55Zcb69er07r1i+hwOGDEAYijIeCjspUw441UYr0BPRdF/wjkETZiugJAwaMHK098cHqQXCH5wAF9ShQvhthhx45d2MWrVrUycvfu3YdZp2KF8pUqPbhr955XX3sTSOgE4/MZu3HZfOAj9c/aYPOVtZtvz54f2XwqvRQGwP59+5EiWhw95m0V371HFxTBVIdttbbtXuYsKMGLj8ST3WhscUpSqmxtMNMIFDTPWZicYAuEFBg4+MAowMNRUq5Cz8RGCrzugkg6NDVBi8dSpmGDeos+WYKV9YMPVBw5gkyAzcSjx2Kxlzd4UL/jx0/0H/AqkIcPH8F+a83aDbDekqykr5H8ZTWw4j333M0qKlSoIIBff92PGJzDcMY/XOlByYoBxjuQuAU++C7EotaoVR9u3cFKy59kRoYwoc+9RQ467Cn6qJgQKY3dPjl2x2SEJDTkouETW2Kiy19nLrwk8GLSI3JJzxiJd0jPt0SuuzZ9+/1dee98uVN7uMJSpUpgUIFq2Juvgb55i+exRhv+1hCEafv3H0BgApNERjqfR6PrIyTEjhimwdjjxx9++CGEctimxT4gJrSjR4/pqhXC6jIccqJSeGf8Mgc6HNYjXOLpp5thTfFiy5fq1W86afJ0Rjoa8pCIT901MH8rhRJNzXjpU8UTQyqA/5RSIJ+nwoEVLTFdsXUYzzRqSuS6Kwl4LLUwH/z++x9YrD3ySCXmWvmJx9KnS9frlX61ajfAsy8eb9AJwm2HcZle1svmw8qazVfJbr5jx2Id9I5GXLp8GRsseNSDPWLEIIgZMeDz3XtP/SfrYJU3euzb6DP1nqyDUnigh3UlAnztuQUpDw0LUBupqTXKxj97Snf0HyPlAs5pAMRMgAWWwNNIwfmhVS0PrGyZv/ZcrKYZr+cDUtdF9LrLC48hAHLMVVgRt2vXmoti7/vuu/J+8MH82nUavvBi2yNHjgKPWQRLtDGj38J+OpbZjRrSsSd5MX9WF2JSqOvb7zbnyHEHnroUK3rfm28MBoEaanjJEwAffBdiUceOGe4WVctf2E7kJPj8lotsf6IA7V5tJ+sWqDOPRh1gOYMkWjgRAsPkI0aOhbtp1rTRU80ao8TnX3yJJUDZsqWxGY+hNXDg63gM0q1b5w8//Bg0s2ZMRtTmOJmxbv0XWCzj+Sk22tu17zJn9tSePbqCFTxXu/a06xz4wg/8SgLZD6XG//Phxx06tF2z+lPaajnzF1Nu3PgNwqJFC/7Dt/Ddzzzbyt2QTZu+k5wdAM1nPKtRDJF0nWLe2jWnePT9FA2loHUC62TWrLnYSVy6+KObCTfhHBHRo3aMHKQzpk9CigvqGjTojWB0kgTzSetwXbDy7JlT8LAOH4g0btzEFi2aYRN5+vRZcLVPNW3crdvL69Z9vmDBJ/36vYLtGqwHceCBy2pSEYTIiEPEHRqqYFCGRRLIORaoPS+YIoFpbtxU+6oxgqQ2sGp5qW2rYUNfB5Offv6FWWHWwUAAHh9gMJ/N/2jhzFlzsW/+4gvPfvA+PZHH3MMLLC7CqUNdwnvWw9yDNcGQN4dj71IldsOBRQ2+C3399aZERVVrNwYWTW1pfPwMbbaceS5fjCdGGIxUVkmZvYIpWPddxmGLRMSJidAzN7kzxWWDSTHPx8Tkw6MxWDEAPZ7/woTnz5930OC5GE7V4LAI4/GYJX36DKdOee7LOooHvsV8iz6BTWJJtnjR/N9++33CxCnIKl7sPnjhBQsX4yEdCIJpCH7v8tyZk5IbAHjGLHfkTNQukdEVC9adh8AQRTDkYJQybX/CrdM6zFqxY5LtogrpgBGR/fHHYfgdxr/x+iCsfzu+3B2brVHRUdgpR5zeqnV75CaqkxQxH7Z9sVTEI1cpkkNg3OL5TJHChbAlrS4eVTKY5sI5Y/KT+MxZs5FpXPo0CBS8HC+yrASwE7VzVjG6VegNmLyVc4UoC/oFECyf/esvPInmglWrVh40sE/nLj0vX7qcOXNmnDa7M090VXOHPSYmH47yuAcUl3WoC70dz3BgdL8iedEH34XAISagqFwFzBcfFy8VnJQn0RpZmZ+SsX/lizbXqWQR6KJ35Ad/i40DPG5OlN7rtBTO9OCSxbXfJpS5fgF4atUnojiOR4SGhcJBY6J+7NGHgfnxJ7H7GRISZEM4WgxWElPPcH87ZxdTLUIjTfYCyc6kl4hbAeBbdyrbnDlz4KcDsZOIZRoWXDjghkmOCRLVSYqYDxsm+KgiuWFstOFkqxuvYijcsMIF+dBFJTFhl56d48Xio/A0S1t/wSflLkcch2fRmHTxYhIEDViHZs2aBU+cZW2Y3SXsBhzqwkBIQZ+I6oLvQiAOLKoUnnRJDUbqJ1rMnD0XfoGHAm9pfmbpNmFS8fi5ESnlfyWAL3djXYawCK3D77stWrRk2vRZQbYUPwGN3++7cO6MqX5YkL4fjddIXL1y+d9rF8QRWEHj8RSWsVDFrp17+vUfjKV0kGq5HcjINKFhFy+cdwyFiMxZ8KoXsScoQgGW1UEkXVsS8PRg5BYOmbmzp8XE5KNuFpIm9viJ3r0HOKb520H5kCGZXQjmw4+W4jSeObJCfCyi8WarNPhx9mviZ1DV9wWnEIyTUOaXnG4Tbd8qMfhEqxqlBlOT+E3ThMt4bYj9Cg2PxE/Y/9vtgqkiR84cvK9vb9+/4A6mgduj9xFR0CAHV0Km0HDEEDeuXWW04fdSaLyITQi8w87aNLwVmsJDSzxKVjc9bkUtKcIzyV0I5sPP8129fEXazsevc8Nt4d1yeFMW2iBMkuLprTVwiqg+RZjgWxO4/LIKDY/Aq5QQHfCw4+KYxhFk4SVK/3a7IHa+4DrZ61dF/xQ9THPl0iXVQ7GNMM+HhoXhlba3ZrxgAN7yIQNX/te5c0hv/yvJXQjmw/v/EJXJCc2HW0y4eQMvOcPLKfBqAZSndTj9oSV18mF0IK9ds1Q8FIwJDS97wwAjbeC/eUHzGBuIFvGkKNUu1B1dF1TlwhEipfAwDU4gY1oiyxBbHhFUq9jlwJhJT2/jMMeLqFozdvzjU4cM2RFXckwJ82FyuX6dfZrg5OtdLpgxYHu8TQlFU3xHg7bGUi+dBmjjQxwruxh3QYw06gQ0i9DusPHbi/CYeM0VMKl20anwVuEM06RJyycBDOsoz0iAwRsBM2akreRU09wqMySVL5sPy+crl684bOdjb1HUjuIheCVxhkxhVy5fFF/TSQF3luoTtZaF2TJkwntQ0+OJintL0V0Em4xwjql2cWsmxTHSNNeuXr1y6SLFhxgHCBAxu3OYaKK4auzo42k7TJNw4yY9gUnmRSySzSSZMvybi0vzXb92XRzsI+Op05kPt8gWZwbioWhYugz0htx/s35ua9mx8roOq125gmDQS1COGTkXUSQWbFgUYEGd+vIjL42lCB6h343reJSC78InfmyQB1x6rKXxRun06XGmL0VkSGWSZA1gFxgh3bXr1+T3r21OET4yMmu0H+6O4lyUXaWdDRMyzlHI6ZqJyFgS8sLQSu0+3PTnzFVXK+doU0UIOau7JfHHVOFpSWpALISWwlmtPchQAg5thrZ1wSO1EnkXV8gttfGzVPWJqjXbiqW9ZUFa7HtzDzJHK4RVpYjU7J0jMcbMkamS0Pcs9opoVhcgA5u2M6IJjiKtckmCPCuT3Ixq7cGrKoIKcymly2v5S95JAlggVo2lk0e85mkAAEAASURBVKBsZ7eRamRNH6MNJep1ifU2b6ZKBX7eE514lfZ+4a+fqjrXWkftatwCw3+oJQ0JGOXJxpRMtMhQsQ2naIhYBaVKk4FNHFnYIYuTKNF7R3lLct5ntKfe6km0HibQ1ZacopbtSEu8N8qLTcUl6XuPaKlhDxaBMS5xPGRmdIBmuSuwWGt5etRvdpMAZdSqCDatZiy9DY+lMLIkCR7S1h9ccaWozptpnZHTasHV5KBSKrb4KXpwKs4+1vQG0fPUYt3skxwtmg3jevjOo05Lxc6WJqJo5qpvtVl/oL/W7OQR3iSlAquRvoa0qnqq1mLjgM0ow+GaA7Uz0TzPymRJy0aqqlSYxWSMIrLgoJ1aJO8kAXqBlIqdCtXXwi3nPLsWgup7FlNvRoqhbp3tnC2w9T2zWjUSVGEu7DGO7FqxTUBW6/1BPsedVgLVvALW2MuUyqNlZrb3WFPdq6lEoQE/B3T0Q8JLkdrGSkmDBjRszAaolmfYm6uGjakTxQC24c1huZMlM2Jsokz1hR3FnESJ3ttFULqLGX24FaOqR4UTrcu3H1I5OhpqKVpnQY+arEIaeyWj73nKZlajNoRgbQEnkby328iQ06MtwoKm7W7DyFFtuq9In7XBrZaa8QYCalibqSo0UGXawkazuJyDIsnRYmKNddSjNsAsaidRhre1M6VaxIK9NevIUWpwRh8e/dyUzcHI49biz7O3YGrJ6W60G0OcTS8hRg/f8OC0ZXiNVw/ZvNCWzIoK7MQKiVNt6tSowsTLZkFTWn8Ktcsh71wCkViK+CosCwUEHNKqO1N2mKzJl74limh2w/99tmMRWEh9oMaWctrLKCEspcKOJnFWUlO9QIrt7IxtbbH3IcXItp6mhi96G6lVOBpnMbXZy0+0KNqiVuGCtXW6qLSIoPRhE91Umo2dVgKr7WYZRX2GijU5knGiTJlSYSqLKvZXpVDyvUFPlXBkwW7IjDL4PLwRCYqqfEaFqhzaBqsE3rC2KMlj2U5xSXZPYreBqk4V9qhbW7GH+pmdylSFrQo8eFoEhvyM0FKrVRHMajAKmDphUxmpIrNSUdCgVgrv0kxuyGMZQJpLCCXOyaKnGY81rIcbqtpU2Ls+bY4iM1fMk5GC1ncULTMDaRX21d+4FT6O16AeYTH6gyMg+G48EpwIofvU62/XgDi+g++9XMYpHlSO1Y1IhRxsKpGa92TudOlxpCr1+I5QSWpy22sA3/fGjzziV5SQkmvDP3hHOGeZJtIEHgBcyFlUYUOemAaQkvpwamLeQBISEZk1Y2g4pg786FDyT2Jfv3YFR/MSaWBqtksDODGKQ3ARmbPhPPGlePr9CPlk04wc+TswwJOVwiIi6HtOqVZzaTIVcXtqgHs4vlSOb73Se7rRd8lzoaOLv0ZqRbhWROloD9wpFXWkoiAoyaWhKP2Rqe+9RfyMVdr0GTg2cdSe5FscV6ZfH0m9kqQBODuY42Kc9Vt4bjb4YhLierwCm/uVmyAJmFSrJUFpqUWSoAH0cPTey5fkD3+RRxRxozsl3xjoEr7RIGBYk/o8t4g4MU1ajK4UPqYPhhjYiJgDtSc1z0MDWGfgO394wwYmVY4TyfeJiU8srEPol8fgEWE1Mdd6sPGNTrWab5WlFkiSBtDD8dVJ6uFwEaJvGylzY4yTs8bbgUSLVVky7MPBZcB3l0LD06bzUcQpqvc9bVOmtLf1ru2/LQdrDPyEB9bURhQP46MHiJS+ckaTbarV/tuM/j/VHvRw9oxwbPIfAPqIewNmjOj7AkPEBgGRkV/UpEDxyEEqYB9PojOFRQqWbA54VVwpm4akBoysXL8pdnmFT0zLvzyGbRJYl1P8EqqxI5Py9mLrp1rNr7lS6X1rgHs4ljz4Kjp5IQ7q1JRZMsbJXl+AsUzroPARRGQQLxd31phy96m/bpAcXdJ0au750oxnRotAJodtomVTrZaoilIJUkQD6OFYR4twj0I+xz/cCgylrg8RY0xQiv9GakSOAuOMFn08icau583r2L0CXyT8XCcl4RTR3f8sE+y/wENxhMhzKcOEvI4tx5S3F/eB/1mFpzb8b9YAejjvj+OkDno4pRQ1WinLQ/d0WfEf/KDoq1RAFNWmGC7AizTE53uiydXymDNqJlYCSf5RiBISEV0+Iqocw4LKEAZkJ3ZNddOLFlh8RBFbAg8vpLUhqdS/H483suP9ant/3ae2LTntojmRVCMShgWCE2kjk8TIAz4C72gXT/cYZcKG7S7GblOZ2fmoOQZD4v9PWydDhgwFC+THW9/4bVl/gzx33pkHP1iF1zpaWrgN9KAKczvYJdnyUCPIsXEvd7Cz3XLPJP/kPvLIDESKQcP+x0p9LKJRo3FSkYIPXOLUIv0BYMDA5irVPi52Cz4XYjfH0YdgfMKjyuEjitnoZVl+8ZutXeIGRfC3WdNG95cto+YyXsUw/A/iH3mk0jNPP6WKVKXy40/Wrf3Iww+tXrWsQnnMFtYFOV94/mn52niZkRz5uayagi1uxWXZSOhc3t6EXWC1iKjybCOkJkyY3KU6hEfdL82kAOBKppSSqwDy8G7u1q1eUJHPPtO86H1FVIyEQS9hFUgyPn9MvmlT33nggQrgNmvmlCmT31bZMtyt68vLly3CW4zdWUmod9zYESOHv6myKpA/plXL51WMhJPAX5ZVAS8+eLNzs2aN3xzyaosWzSIiImQRL/pbgVe7fcrxhw8jZiKlv3Q5UtyqH9wQgSMVFMbo4D9W6s8tmsqlFZl5aeD42K3xsVsuxm4FTe7SHRE/Ao4/vhUhCWoOXNZk6/zbtm0rGNiJvfX3TZs0XLL4oyDrKVmiePt2bfAacknfp0/POnVq4l2LuP6Gl3wiJkLVaiokSVznsM6JnVPlB+ZDQb6VbfG2nUKigFWrPPHiC88qiJA2rV+sWbO6ivl7YLw8Kz4+nuta/MlHLZo3ZZjwF+NpxNyaC9OkQwO3ph4n12pVKy9ftvDlju3KlCnVvl3rpYs/4pdNOuluwb06Xm5JtxfBHwWA+n+0eHV9iBQDg1L8N2BBR4MFGJFnpWn8uUWXW3Y7auphwEpKDLDcpTuIe8MZq7kO+BaYKbks8+bNG6lMtoHZLVy0BAQNG9Zjsnz33hMWGrpy5ZrtO3Y2atxiz54fHMXxMkUHJpm3pGdcnJi8pS0c2lbwVETNjak5G5jwqPIGP3uuSmnUmEy5b0FxxLGSa4+efV/pPYBvs2XNIqOnOXPfa97ihWvXPH/8XHL4FwFFChcaOLAP1vK1ajeoV79p4ybPrFv/hd+37ya5vep48er2SWZOBUXQZ6TqHcMiBKTeyj3amRpEgo0JE0MH7HNvkUpblxccElWmY+4yHQ+ubgOfGBldHoAo5EWv4i3uXlCZ0qVGjHhz6dLlTZs2Spcu7YYNG3ft2t2xY7tMmTJu3PjNa68PRcEmjRs+91yL7NmyIUD79NMVU6bOABJOatzYkTly3IHVOt7hAEzL1u2PHz8xfuzIEiWL4faHPT9169Fbfa1lj+6d6z1ZB8+/1q1Zvm/fgU6du1etWrlb105413tcXNzcee8v+mQpCsrr1KlTp8+cffjhh7jGRo0awGusWr0WMo8eNaxdhy7Y6sKe16R3xhYuUggL0PPKK0ARY2IVhheB4xugCxYunj5jNthK5LXr1zdu/PrNoSNkXVqA40Qx/Yk50Uak6lkPI66XcSKH9gj8bTzoRl/WRRYI0bHDSxUrlDt56nT5cmXR3o8XfDJr9jwUwLH+0aPeKl2mVPp06WJjj3fp1uvs2b9goOLFi+JA+oH9B7t17x1/8SIoV65Y8tlnq2rVqp4lc2a8xbhv/8G8qde3T89q1apkSJ/++IkTUoKpUyYgWu/Zq9+qFUtgTazlETDiFtFczZrVGjZqDkqtqr3k1HYwWZ0WgAynT5/BdmeePNHoPHDTPXt2LVSwwJkzZ4cNH7Vt2w6c2+39SvcnHn80PDwclFOnz1q37nOwql69aq+eXTG/4tvB6JwX4uIbN2mBzZkePbqg7WjX0GEjv/7mW1np02Ibp0Onrrw6gUMcNXo856ZPn97d2yHYyZOn8ufPd1fevHj36ZixE77+ehPo3VXw0Pv440XPP/f0n38eWfbpCscoc4yXGTPnyG7PVQdvR9kcJyDCPeP70Zznuc9IUaARB2K9hDv0XPx1p4IMTzAwZnif0W+0KAIFZSfRtdOEzfvy+OyeWzKqTAdsUcXumMJxCIShT8CyohmJJKGhoegiDRs8OXny9IMHf69a5YnOnTvOm/f+9m070aXy5r0T5UuVKrF9+84+fQd+993m5k81gSsEcsyY4XCdHTp2hffEdtKEiVPQGyZNHFe02H1Tps6cPftdOMeaNaqp1X+2YvWhQ39g3I4bP3HajFklS5YYPLAvOuK48e+cOn26S+eOTzzxmEoP+JtvNuW9Mw98H+CHHqp46I8/0JshM2rEBeTUyW8XKVJ43rwP0JvlCXZ0mjlzpmXMlGnIm8O/2PAVNigjwsPBZO7c6dmyZ0P32rJlG1ZG/fu94qjOccuxmwzxODewzqmI+I+ZDCbLX2s26GG1A6taIQVMdmOze9jOIUMwt9mzZ8ufPwbGGjuONCk3ZCdPfrtcubIffbRw0OA3jh47du7ceQxa2GXlqjULFy6JickHLTH/8LCwp5o1Xr16HSYnvN+9RXPaYHmpbas6tWvC6P0HvAp/KiW5447sOXPkwO07E/HQLwRRDAy6/8BBiJE5MhIYL1V7yantYLI6LQAZHn2kEiqFvwuPCMe+J16uNHHStMxZMmPjhYokJNxXpPAni5fBY5IT794ZOAg2sH/vQ78favNSJ3RXjNuhQ0fAubw6uD965sBBr8P79+ndQ63xvvuKwKv+9dc5FcmwtrdDsMcfe+TPP4+OnzApLCysQ3sSRlsFD73nnm0BnY8YOdatBMd4sXV7P3Z0S25hKLgz/lGv5L6LlD+MkbcGknuwIxX9mrROH/xHQqmA/blFS7iA0IFVrZGPNCIPNhON6/iOKfHHaMcqRa7efQYuWfrp2xMmg9vUqTM++njRG28OB1y9WhWkiBkRWB3+88jvvx/Cbd06tZDCOW7Zuv3nX/ZOmULB4+VLl+Gw0BHXrFm3Zs36T5evPHz4CPYB4arwgJivA/sPHIs9DreIiG/37h+aNG6A6aTtS50wT7Zq1R5RZyOxXjbJs6ATL1iwGDQQIzRTpty5cq1dSxO+vMC8YMEC6z/f8O57/4H743AAuZUeegC+fsSI0Vu37cBQgWD16tcFEkzg/T+cv2DAwNcQDT326MOSlRbgaJH2UcSmiZZGi0SAj7geJouL3Vqg9hzQREZXiCrbSUucIkho9dnnWq1YuXrO3PcRwWGqgHLwQGbtus8ROSL8QTQHPSCe2rp1O0IYBODIio6KypYtKwsAKwAJTZ7966+yZUoBidDvxImTg18d8u13m8ePn+iWE3YE8uef9wKA65EEAVTtlhOltB1McvMCDv/5J9w9nP6RI0cRML7cuccni5f++MNP9957D4pg1d+qTQe0HVEepgTEjFg6FC1aBDad//Gi/fsPQOaMGTLs3LmraZNGkAqsdu/5EfNK1qxZc+XKKSuFfs6fv8C3ny5dgIUOPphToV53b2cyCNav/+Blyz6DqvNE08udAlQxc/a89z+Y/9PPv7iVsG/ffnW8SJFQtS87yoIaQESLFPTZ/+FWYCh1fYgUY4JS/DdSI44UGBotNGiQEl0aH+cWhYjkTVEyQIpVGIiY5viOyflrz8UAk807uKrVhWNbvDhIskSBq2I/6PDhw6C8JlbE58+fR72ZM9Pkj07QvVtnRJVHjxzFbQ4RKaAvPvhgRcR3DRvUAyVGTvHixaAGOE32m6CMzZixVs3qWIgBxoUpmgFOCxbMf+FCHD64RSfGCESHxroP29tMAO88ddpMEGB8hoZmAnMs9lUOhQoVBHLXrj0qEnCZMqWRjho5TOLRjeBVcStd567deyAbqkPVkswBsF0wLcL4BkwUiVktJAFW27+yFewSu31y9P2d8teeg+4Rd2xLVNmOojJwMGzqtp1DBnkLiwBG2MvLXkiO4YGnHExAMonryJ9H8DdrlszFixV1KIcNtGnT90y54cuNCAYrVizPkw0OsjEeCkegDRjbJogEGekrLV2avKpb1UC65QRS28ESrVG+Zw6hHHwZ0588daokTimLC4vQOnVq4bvtcXHxQOTKmfOHH36CB2zb5kXMEA0a1Dt37hysX6hQASwvli7+mEtBQjzrwyzLtwgqET4zjOg4IjKiZ/cuCHu1vZ3JpGDo0piigNRWgUcoyNqxYxeXCl4Jfu3I/PUpgjssgzmFdaivGynTG70KA4CyKIUfJHJePYsCgoHAgsLAUHdM4rlFVIx6fKXwibvnlmAJo8p0QgASVRaBZCsvPoK9LcFQkV1TzQBevZUw1qqDBvb9DeuONh3Qh778YjVnbd+xq27tmliHYqLGWhWPJvf9SgcGV6xcM3qMsfnClFiyAbDqNStCOIn9l0yZMtHPHIWEYF22b/8BVPF45Zoq/febtzz22COIHDFzwiOocmI6RYyJ54PLP1vpwINDj1590edkvbVr1QCyUqUHucdjKx1xhNsnSnohAxLDSKbUEhHIdjhBJS2CuN7sUSFR93eCBbFLJ3NN9jYM1WG/IBX2y7CqxaJ44/99g0zEg0Bic8NOaN6lSYMFJm7QXoSQjGUDQV1YGQDDJ5z27PnRLOP8i83KfPfe68S67iGGAwe7ACNVXdhD1VQqTRpY1t3B3DwdVSR6i5UsJmzsVk+cNPXBByqOHPEmimAzEZFjzpw5Bw/qh31wbA4AiX6IndAaterDV6Jex+j48cef7rn7rnL3l922fcf6zzeAvkc3Wo979XZk4XLwkVXAKUv+kIqJkXqNMlKtW71ioAVvR7c8sl7B23BxEukBmH2WyPGfPKAuBUvKUFN/i2hjcwlDE5eZ0l8TBogQAxFi/lpzkcIPQmKZS3ACzntXkBgHH23zQOMLj14L+oMHf8sUGoptF9lfKz/x2J4ffmrcuEWTps+wo4GXgedCZNe4UX1Mv4gZ0dtkXVwvFi9YuSDKQxyK4QpueDCChfBbw15HkbVr1zvocbtgwScoUqzofV9+uRG3qvzo5Xg+gLVwjRpVsWCsWaM6F9+48Wv0v8ED+2HkZ8qYEds3wDOyS5eOZcuWbtumZb589yJ2YHo1VfkzrKagdGjbYS/ORcfQ4mO3TYZBwZAuxcqAJb0qjIRBjv0KPCnCPIQDp9iHGj58CIbxzl27JY0NSEjAqhah04MPVMBWIw6UYPssLDw89vhxPMJqUL8ungDUq1cHgSE/WrGVNW+wsYvlZIf2bfFYo1evbiba9hc7eog3EcNiYMsMh6pjPFRN9AkJXPCgvYOxfvDNdMnTLxAeHo4iO3bsRBPataNtKFyo6+677vrgg/m16zR84cW2WPEAiX6ImG7smOHIwpKiUcP6gtZIpk2fBU2+9dYbmFaxDEff5iEQTG+XfGQViAOicud2VAEyr1GGJ5NyvEhuqNqXHVEQ+pTFVQBo/DNT+kuXI8Wt+sENEThSQSEqQhblK2mSrAgvLGVmH8yCCzxWYXRn0iA8jLyzgqTHYo2fdao0DJMvD+LCeASV+ryY2qtcCAOxQEaEjw+GGS+3kb93769wOniCCRg6wJoUjzXbte88e+YUHOvFB5zHjZt49LNjCrOQFStW4yT5rBmTL168WLtuI+xkPf/80ygCDoj4Fi+hKMZx4Vsr6JfYvV64aDFnqTJjXTNyxNCB/fsgC3s6kZF01BZBJbbS8IR9zOi3cItuhAgLDrRX7/6jRgx9e9woIBFM9e4zAECAiweAmhKxaQuGLT0reJhJPHoWvBU8yiJaPL5Dg7f4iExt0qVLz7ffHj1+3Ejkwh8hHGbDiT5olLhxkwzK10vtOs+eNRWPIPC5dPnykqXL27XvMmf21J49uoIAD+5hL5M25MYNsyD1aLqw51WlyhNPt2iGD3QLjGQuAZxWwUoc3QDbHVIM6F+rakmgsvLqYFi/Y2bFwT3H+QSU5UvKgFvJWQLYOkRwPWzo68jFzh1SXFAaWg08PrjF3Dn/o4UzZ83F0aIXX3j2g/fpuALmCY6mqUBICFbo2PyBzrEXxD3h4MHf1n/+JbK8ersUTAqDh9HuKtRu7KUEdby89sYwVMoW92VHlPK8OOwT4R8/j7an1Nl1F6OpsHVymmBzdNhiyTQ+foY2W848ly/Fq4yCgaPLvmwMSyFE7I7JAUrJ3SJdw/zhsHeDCQ0nZrgYjqqtWP7J5CnT8UwDzzGaNGmIpyLoJfzFO0SCWKcc+v0QIiBtNUWKFEbn480yEMTE5MNzQNU1a0sFQCLexMyv7vozMaZ3dGVHQISg4MyZMxi6ARgiKyJz1nNnTtJIEMZGUwBmyZ4zGKtF3lkxgF3wbaUAuYlaDeEGjtfgwE1g+WUuHhpkjszMfo2RwKRPb1lTUmoBhPyYC6Xp3TQIRe/Inu3Awd/cWUGqGgUdHYxZ4RDY6TNnENK6OQeJgfB4giQ7RtWqlQcN7NO5S088IcQXVxAC35knumr1uswtJiYfHjrLbumoAv0/f0w+NBNBupqVaG9XiQNXoVUCijvGi2Toy46ylATQw+OhW+nOOINdmyQyAEnE2QaRFqtSMJykaNEpQaB7Ch4dsgQiT7E8bE6rvO7Inh3+AhEcTg7iwXFUVG5MjHBtTIN+HLgrI9JUuYGJepsEGGtzbSmt71AdhLaURJJPxMWJSGVWYCDuWCDHxwwDcwiQqx4hDEAmsxDvOM6XOG4lpRbAZpwWL5E4yodL3qpA8Kp2dDBm8rvZo1SevmCH8PjKPMUxNxPg3eCys2bNgkNLkmHgfohj6jzrS3oGEu3tKn3gKrRKQHHHeJEMfdlRlrIBFPAhVuSDiCKHtwxtRHzDA4AKiCcuRmzI5LaUyGmPFomxwxiZNVrDUIfKnD0XftuHwmyqxgo+Uw7GO23MZZFOgGTi+vbpVaN6FWzKoO1YEQ8fMYYfBSST7W1SHC++wM/NXjh3Bq3jOJFTvGTi6pXLt9JqWCXduE2U8F8pxtzZ02Ji8mGnCOM79viJ3r0HBO+7/5sUQj08NOxS/EW9/+GmGkGho91ah2WwYVIHhY9FdDjeWJAu/fWrV8mjmm4xBWF8E04cHnY0KYVvcYT49KnT2LxLYb7/NDv4RKjv8kXn8o3eWJA2LazGvSAF7cV9gJ+8/NOt/y+vH2ebcOYGe81emzz/5e0XzUMPxxEvHAKhaNE4cMPHbqzIUe8VVe04/J/px2i/ieMIEeX5+HVuuK2wiMx4GxVqUatPMdjjt1jURiUfxrk5eiPEf90VGh5x+VIcDqDBvmwetjJmmtCwCLYaNzrF7MULhr/Fav915vLXIJgMX8tD+r98oYdfuXoV07AVLQZShzkMbA7UKsqjwCv14RYTbt5Ii5+izZARrxZAnfJKERiRjrEvJvmaQCqeNRFAD5hI8T5dvD6RfCIukTKMjQlEi3heIV8IkSL24kpSrSbULVRuqF4iDCCA1Zyk4j6VntXi0AMthm4m3LiGc5ro3ZRJaaAPE1Cq/KOhQf3floqqkCDDTP24xYQQjLwMmcJQVo4xaoM6iyUJpuWYjQtxTb2C0QBvuIASL0SFXdhP2dOQG9ev4tWA6AnGJmCSbGQIo5RNtVowBkqlSaYG0MMzhYWlSZvuyqXLBiv4L/TDAKmzSi2pjchB4WNvUbCh4thkhHO8cvmizTnaavFzI45Z+imQSksaQHfJkAnvQU2PJyruLUW3jrDJCOeYYlZDPxB+0V1RKiZVAymiAdnD8W3Iq3jYa3lCzd6iudsogsEA1bP/YwKHL7Qq8HNukUsxQ7xlE0+F6C2m6W75EZ8AbfxfzsKcdP36VfQXdBpHnKjuLaoqwncwMmQMRZL6XipVLanw7akBnEtBJ8cXpW6m9Nv+PP2hGYYmJVoUXtvUZBDeF4t2CixAafl7Hrlar29Fx1yHWdSsMfi/XLEzVYRISgWWSrEsVecoa7IxleuFoWotNg7Y9kTM5BR8m7WUnpVJastG4hA47KVXG+MVkQUHQw+mtEm3mJTHqJ7VZImiVKwq1yrmhLjljLVrQdrOepoZiKU3I2VBd+ts52yBre+Z1WqsJuzFhT2sYteK0RinGv3dW/ZSu5FiO7tAWglUWwhYYy9TKo+WmdneY83ssERpKlFowN/eIrXXs3VSCga0jXXQBHGrYWM2wN0LvPlp2Jg6UVpkG97s8pwsmRFjE2WqL+wo5iRK9N4ugtJdrL1Fl1NTh4cCJ1qXYm1FT0EUIxJHQy0GOgsGINdYStMPFaG0FSv5gWQzK7OTe0jnJJL3dhvZpLXUYFQlLGjazjgmYvRtpVrJ2gcQhB68uGmLUs8Rv0wBRxDEE17mze31qkfBa6s087WZqioDVaYtbHRPLuegSHK0mFhjHfWoDTCL2kmU4Z1YFGaqKtG/Sg08g7EqHN5dJ1uirAWBxZ9nb9GLvb2BOeLUComP6SXE6OEbJrVlmKWDE82TypLZc8wpJE618fTPJVWYMDYLmtKaxvaUJ5gMl0ABLBgMP5e09qif40fLjsxS3xJFNKsAkf59tmMRWEhZrTtm8PC0ttJ2q+lbHJyCTSq9QNyBRGoSGvILCVRYp2BbT1PDl8Ql1rFTxp2R7Sda1LTC3iZ7vzBV7KDxuE3UOsF1Nc9Wm9Kw4hT1GSq259uFTJQpkytMlfLawkq+N+ipEo4s2A2ZUYb59JnGIwcnPAy82QfIMVnYopwA9EqWtqjddooD1fcYl40MOZRq3KC2YmX4qUxVmDkxxsnVg6dCxhSM0FKrVRFsznpUxKfLUKoNAGql8Kb3aIBdNPOEg0eEyNz1KvSuWclRZOaKqV8EbTuFkQJaPH31N25FEqJFpWopOuMsSQyLc6+3VOxsaTK8vl0K5c4lBFXPFTvDG6VUsKASFer3pIyqnC0V/BXRdP7AzDb8mcopWPE86EzOpiKEdDZaqzeyktyp0iIuyfIJOHH+tsqCubE6jSqKS4jEWOkk0/S6xCMOZsTV6ZgK1d7aCNE2vE33SlYwq/WKEAMqSduYgCW0mTb9aAWy2c5Gbg9J1H6vwEGEL1rBgNQ20WKtyfYTLbq8isXZFIgx4k5TmUnl56+n/nSq92bsycZmEs2AMfN1rLVNDKQUbQEd56BxppumAo7n0Wr8qHpan1FkCsmsYWO3oN1la8hNS6gKVvqbUV5oTlvarlMticraTu4xtpxE2vtEq6JqdSsAm0oCyaatFkhtzV7EdjwXZZzCxtpbFN+/MPqYUZNz8yEpInvJ7CGPF7muMUozqJjtcuT5OF6DSYnMJ2yVgU56pB7QsWn2X3ojDvrQwTCc99E0gfoLfC+lIiqhFKey8JKAdOlSD/poFJZMFB1KwQ+BXbuKn1Y0Bi+NO+F4cI+JWxjDlRrVChuZFDTvWFYzYSu65DLMUcBkZouzUZeZg79CEuJIMKUMi3sSziRhQr4nf2/wNIRjF2KrSp3TjGpEBYjEqC4uz92QYOMXdOy/tCicMgvBZWypIiMI+cJfUzTBlFkjTdK5RRznzhgajnUHvo5j6Mgmgb8bfHMGZzX9lUmlTjkN4NCsOM+YSTkWbu8v9j6Lr6bSN7FSrZ9yJlA5SXNcuwqDWC/hUmkCwoFsF7Ag+x4jZQ9GTsnaw9CspRSvYu8lidTkyPaQWYtWq2Q2jHGwZNGdqfNeZabCvvcW8UNVadNnwNds6SRBCl036Ou8SegBKVR9KhuhAfqFkrRpL17AlwjJts5UjJewiCw4Cp5q/b+hy8Ac+AmCS/Tbw3KgWX6CIibjAYjLUi7bsbQ87F2SWzwVv+ii8oVwdh0WyMXeFEgrAWcqqWwvL9V5CROwZVLmRCsgSrPTC/fub28Rv46bJm36lB0VJFFa/HKS/XvWskmpwN+lAXxrJkPGjIhWsIJT6rT6FP1GGX5lO0VnRFSUan1F2xYIc+CrZPh5UHzPg4KcpFyW7Vw+KTF22qLk3/4nzi36eMVVBgya0PC06bgI/Li8UgCm7xFivKVe/6gG8DPDCFLS0TvCEI5gZPAeCW3Y0E8nIX4xrM9SpoDdBaOEVOtrzQ5z0ESV3nhdqnBtIGQfKVPYiSzFH2EwJHRLiTs1DEvmFR8iMz8Mi1QUpBwFR6DgKCoTsBCGIFG9IxVYowzDZuoUQkggWSNXwpSDW7UyunP9A0LgKHV9iJzZWERCVlEPMycCUAm6ND6ixUzh9ApmkvGWXSnz2xO3TLz/esbYLcY+I0JCETDC0NYiJlNoGFZtqdb/O/uANIf+aZheFJvVTAvqSQnL5JyvFIV3UDZS/ufOLfoI0BAtSu2xPs3UiBqgVRPDvtMfPvX3C4T+/uEEEUp6ES2asyeZFLBAkmyKlf3ZV+6RKRysfpJqfa3hYQ7xklXSGUVDRmpGNYQxYY5zBInDdrj1/AjjEgviLlMHW5FhE0CIQhjzYuH4TlRPVeKyUiGEqITYUX3Gh0gEglJRRGAEc2ZLqfGPCgk6SgN9BBnLLIoIBnzvSEWtUgAhjI8DOjiWcfP6Ncxg4EoPIlGPCB9EEIHWUCiRHDwpMfX6pzWALS3s9MOa/EQNpzoEjA3AdDg7QocPUtruZv/5p1t+W9YvzIHYhZSE8yhGSiYhcXleMi0lMIw3UsN2gtAYra5WGjwEXoGZnKllUXVv0XFukUUjD4D/JJEjFcJZlXBNzF6pFS0SMjtTs+3cYqEH4WxQjb1xVDFzVVLRh+kBlVCfmYqCoBLqE8ExigrJQxJ8RIvgoNYoYQBgJ6VIDl4yUQGeQlQMwwHweN1ojhx3OIoEoHdQavnjNap44T1eMplMPrIu8HnowYp4KzyeN0kkgBTkr7KVcHD8hVVhWBKNU3kjOQFjPRBU25AcvMVdQMFJaxW6HejxmtAihQuxTCklj2II1rQjxS19RHUiojL6EdevphRi4ZKxFtnV+BAZ/lNKfxk2/4o7URAYkS1T0VQqBIwB0x9xT8QG2gCBINaaFEhCG6kgYZhZmYWouNc/Kuz6ELFRnygoYFkPcoUsVupnbzE0PDNeFSJ9LDXW8IbsoskzRkSXy1awfkR0efcnPnarm17BEDftHkrGjBlbt3rh0sVLp06dpjpDQvCiW7w7HC9THj9+VO3aNfDGbsbLdOHHH0RHR3/51f9JjF8AldaqWf2F55+5K++deCck3mJevny5CW+P/vqbb6UYfnlGhIcvW7oAr2PdunU7yn6y6MP69eo+/PBDB3/7fcb0SXv37jt6NJE3eWprdLDV0vhC4tEKfq2WegrZ10gzhYWLnV+e/2TKuWDvaX3kXYs7IgSQ3Ax6xfrER2t9kLZ88bkKFcqVu7+s/BQvVnTX7j2C59+RPPJIpUcefmjPDz/KyjCZlShRPHeunDNmTHYbrt1LrfCayXnvfiDpkwPAHH4O9tqsZlowuPqVovAiHDjBsAzDTVLAKoI0JTX7h7+aHPIoFVv8ZNczWcvuI8mZDeMdLC0+ajE9VqVg2F+06KzadR9Tc7YLR4iIqPKMh7vUEgRAwiU1b960W7eXJU2D+k8+92wL3OJ9VfHx8RKfUgDesrZqxZI+vXuULVO6desXFi+aj/k/+czxg5rxcZCXBI6JyZczR44RI8dWq14XSLwu3dfLCJs2abhk8Ucskso2+UKaHChYQMdUUjPH4y8sm7t0R0x+8bFbZAra3KU7eJQIFt24cYPGjeo/1awxjP7UU00AN2vWOMjCI0cMHTP6LTexqkB3rgNTskTx9u3a4MX2Et+nT886dWpevAi7+TOc5JAkgEwivAWGtzAPpbCRmhKBYjWC+UNlNB8mEKkoSCRUnlIVFhXLSqUApkAsFt1RUV0q6laFMGGzNq5TpsgmpmZKkOMfbukjsAbMGCGBwFARg4DIVIYKjAyiEqmAfewtopTYWKS/+O9OMTAwHo7vmCJybUn+WnNQFilo9swr5S5rYmyl5M2PP/5UvHgxefvEE4+dv3ABLxrv0bOvRKYUgDhx+tR3sI/WuOnTp0+fyZ49e9s2L3q9JtxXpfDvzZo/x0V4kbVj5y7cbt+xs1Fj8vLBX3nz5o2MiGB6lW3wHBKjdO4tgj6w9blA3LHNEdEVIqPLgT4udmvcsS0oJrL0fca0O+fqharfoBkyypUrO27MiAEDXtuydZueTofNkycar9B056gKdOc6MAsXLWnRvGnDhvWmTJ2BrHz33hMWGrpy5Rovw2EP1sEhhW4tHdJWHulOpAIttshEiEeWogrlvrBQMoY70DT27ZcgdY9oIifH4UjhPcia5EPUyNFOKOwNOqrIBouauUIWgkkMNPF07S2aksl9VQpZqd2cmhWbTPEXbByXc1eRCoOEtyi5AiM4Zmn97i06qvN1W7Ll7uM7p4qltK9yRPzp8pUZ0qcvW7Y0l8wfk+/777cAnjplwrixIxhZt06tFcs/+WrDGoRROATLSKRdu3Rat2Y58GJlHQVM+vTpJ04Y+8X6lRu+WDVrxmQsQiUxgEcffTg8PPzNoSPgE3F79uzZ0WPeVgkAN2lMwdqXX6xevWpZp47tOLdkyRIff/QeKlq/bkWP7p2BRFCz/NNFwKxcsaR69arAAHj+uafhE195pTtu339v9oD+vcuULgUJY2LyAZMrV84P3p8NwfB5e9woYNx1gXmD+nXhvlFqyiSSjdkC8GoaCCDnp8sWQub35s2EswBxwMuYa3kO1XS1gIXhGZGPsBEpnCNuGROwkL9MbikrCtbEoddBA/tC2xFitkCv+OjDd2fOmHzP3XcVKlgAikK4JytwK7Bq1cpQDiyFLoRAUlIycAr7JmfOYruDbxs1aoDhs2r1WtVw2HTGbAp50K+qV68iOSDGhCRQ++frVkgZJHLd2s8GD+oniRMDeMQ7UhEMkYXoI4IemYKfQKipFUSRcyNSgVFggRFFrHymApGsQ/QJEBBGyO1OCU0FKWXYTEWVKCdqNVPJWnBFQRSShMREVESp5h9QAmvjyEiVjUVkMhQYUTVhuNI0/hbR6A10iTmAQRWmNtPsIC4LIvq42C0HV7VGKKFQaPgIDppkwxdf4pvz2IlDHh5TwOstXLgY8B13ZMdSFABcUu9Xuv/117mBg9/47rvNonHEp1XL55s0brBy1Rq4uYjIiG5dXwYSzrREyWJALly4JCYm35w504jUvCpWKIcGbPy/b0yE5m+pUiW2b9/Zp+9A1NX8qSb8eGfggD6ZMmXq0LHrnDnvnjx5Cm6rU8eXDuw/2Lbdy1999TWGFhiFh4Vlz54Nce66dV/gdt689z/+eFFoaCiIcWGow1HmypVr7Lh33p4w+aQo4q7rsxWrDx36AwoZN37itBmzJFsAXk1DvViErl69bu6897FF0KI5xV8BL5qQhQ0p5RlVtbUbpple/BdWTkCcmLtMx6gyHcxPR9xiL4U6h71viO7C6IAS2TMnTRxXtNh9U6bOnD37XVizZo1qU6fNxAw35I3B95ctg13I997/EFmXL18+dfo0FPXZilWSgUOB6DyDB/bFcnjc+HdA3KVzRyxHJDED33yzKe+deeD7cPvQQxUP/fEHfs9BGg7IqZPfLlKk8Lx5HwwdNlJ+MQEGRe/Cz2oMeXP4Fxu+eubppyAhmMydOz1b9mwzZs7ZsmVbtaqV+/d7xVGdxy00h8uRQp+kUf6QcmkMOlJhGMogKpkCoDuBsWCBUZgovKgWKuIQArcSw7CVUmVcsZqKKkXlJA1IzFSwZoxRDVdmT4lAEFkpbgWGUvGhLGKMf1ZqQSpWwESMEqJMgr9FNLWQvDZVpaaMR3RwYNXm6LKdOBcDA96Q6eOPYb+JYPVSOTCs5qowDobs27e/3P1lgHzyyTqXLl/e++s+lQD7TWh/m7YdsUP39debaojQDAR169b6888js+e8B3j9+g01qldBl0X4gIXYmLETgMTTmzq1a2bLlhUulRneffddeEs3w17pa68PRRZirt9/PwQAgSoGIXQaFhaWI2eOD+cvAJLHRnR0FL7zP3LUWJXVhQtxu3btrlO7BkQ6fuIE/CDnPvhgRazO+vYb/N33NH8sW/YZUm1dx2KP33VXXgQsKtvATVv26QpeA2IxWLYM9jECXzSzmL+5YswyXvZSbafCkdEVDqxqZZRKkwbLhZAyHTE7xh/fqu0/gQVSc9HS+4oUXrFy9Zo164GvUaMadvqgjdlz3m33UutiRYvgKRamPWShP5w9+xeyUAQPu5jJgf0HVAW+/tpAtLDtS51gl+XLV37++cpGDet9+eVGSQ+PuWDBYszK1atV+eKLL3PnyjVz1lyHPDj8sP7zDe++9x/gixcv2rRJIwCVHnoABn311SG/7N23ddsOPKipV7/usaPHcKoB3W/5ZytBg1jysUcfHj5ijMrQBZMWEfugk4uUQiMMYFO3AmaM3WpsQRc3FWHwM8Y0ahCxGChQhZLSDUiVVORSIi5mY8CWKKagwCguwwTNosik2szFOcOidaJKajPajn/irxCE2TEDr1Q0gIpQYUdRo6HEhnRJf5TUn1vkaQhGABt86DJQFsyI3HCOCQkXRHgImtxlO0ZGzxHbjkAbRT35CGaOZOXqtT27d0FcVqZ0yR07aEtOvQrkj4mLi8cYUJGA78ieHaHlp0vJT+FCr8IeJfSwadP3jNnw5Ua4xYoVy69d+zlj9u07gAedmOcRDjDGnWKG796tc2hY6NEjR5GbQ0SsvfsMHDVq6LA3X4uLi0PI8O13m98YMhzPbd6dNxOuuVfv/rGxx92sVEzFCvQ8auu27SpSW5dKIOHATcMJOKbE4Ef8Ikt5AIaVlP2pkASxZ+Zpd5gTGhY0sTumYFKMKtvRMnZCAlxkFE2Z2KKkvTcNHw9R3GhuKWYjfDg3NiN912D+/AU4tIA9kFGjxjlK4VxB3z49GdnmpU5qbsGC+aETfIDEBHzixMl7770Hp6aWL1vIZB99vAihKAhq1qwWGpoJ/Wfp0uUqh0KFCgK5a5fz4XiZMrTtM2rkMEmMKRleFbfr1hn9DY/UIRuqQ9WSzAVAbcZuGu2p0bAjEi5gwIyxpeRq2IKmI2JPobI3eAiUAgsvZdAxLFI0k3oG+RD69SziTk+oeZ/OdNlEAYwtJVYKewaZv4IGCVXjSs22MwtUKCoQqWCrSEgMHBfkAqkzlfJQ/dQeJfW3iFZr9ILh+yLyVOAHLyrN7jklUD3vMal4FXY0h2+hdwDY4Yae27ZpFRkZuWTJMmAYzzQ//fxL5shIDtBUPDzUn0eOPlGlFj6Vq9auXKX2PhFmljHDpQrl6eHAnj0/Mh+k335LHhMPPSUfx7lCrHaxjYUVbo0aT77Q8iVhfSr955EjTz/TsmXr9leuXhsy5FVgEHHUqdvo9TeGRUVHDRUYovO+9u79FZlVqjwuSbDgknW92KqdrIsIhFrEX0OFjqZhNwC5atOoFNlef8n2imxQiQ9R00wrLwVkCiMHeISHBerMxQeAJJNAgdp6vKhArUFWRYBdKiOLW4pokS2LtMXTLyCve/cuoMczqF49uxqkZnsRMD5euSZ/9u8/QLlCgaA/fPhIZGQEzMpFsC2DCQyDRNLDJyLr+81bihUrWr1aVUSa8fTDNtaFpczNhJuyU8kM4AH36NVXyonNHEZWMncqCxcuhOlc+kRte4kh6ZENwRp1pLiljyiOhjFMRcxWSpjbTSl/iKvxIWJigZT+Mmz+FXfMxckdWC/hiI3IpIRBKi1gV0pVCTKRErmBIbRViCCvf1TA9bGKCn5IgDF5EyvB3Er97i2S04VbDZTmrzP3+PbJcbG0DAQd1tSIETBOAMdun0zu0psDirgv9gXXrl3Dhk7tWtVxHmXzFnoWqfqIJUuXQ/+jR72Fzewe3bsg1mM+m779HgcPX+7UHjvxJUsWr1TpQXTB2OPHsX2Opxa0qKlXB1EAjkDKerGA/f3QIZyVwwZ5TEy+Z59pvnbtZw8+UPHa9WugCQ8P4w2mgwd/yxQaimCQ7Y4ax48diYdC2PX749Af6dKmxfPK8eNGYhGN6O/ixYsILWUVXsDG//sao6vzyx0gJ+JfbMbzWOW6sHlq9LGQEAzsjBkyIEjBJoDUg6Np2G1wNI3rpclRd0k+IpOpyNTCjISjGyKypUAwBiuDXbOLx+6YzNYX+BBYPyJP+UicY81THqbH58LRzQ4OzJPY6C4tHi2Fb6pZszo2T6B5xIywOxRev14dPDUePfZtaKbek3XAD6EfdhtwvgohpMpeKhAOEe4VioWxsBB+a9jrYLh27Xp3vQsWfAKdFy1aBLOdygowzi388cdhrIVr1KharOh9NWtUZ4KNG7/GFvDggf0w+2LhzKfKGNmlcwf0lrZtWsbku/eHH36SDN31yiyyAF3aFEj6CC2yKk1YkJsoUd7EAEl4kRKsWJdZCQyzpTJE4kwFQ0pEngBsCVfMmQzLKokfVy9Ki3qIEEgFTdycFdO9hVNhURgIZuBKTVYsrcXCxBuYJO0t2pptv4mkOHEyHctIExJ3VKRYRwNeSV4SF4YNA0lIsZEEV6X2ITC5cfMmUsRZO3ftLl+u7PwP58EH0fQr8DgYmDt3rmZNG+GBA8g+/+LLTZu+a9e+y5zZU3v2oIACB33ataenxurV9qWXsYPevHmTp1s0Q+fauXM3XBtGDro4gr669ZpggYy1LT6IGa9eI3eJ0YXnP/zsGEWwx5QtWzYMs4/n07YmXPkbQ97iKm7QkXjjghMEZKQ3b8KLYWMRy/Dhw94AHu4b9nXXhSycYEej8Bgdja1dl7axmG2Apln1Up9J9CIPLFZIRppoASYg04M9zb8h6AmR0RXxMbM2814z3/pN6WuHpq4AwGqzZ07BMzR8oMBx4ya2aNEMZ0KnT5+FyOuppo27dXsZC1X4sn79XsEuyu7dP3Tp1ktW6lAg7PX880+DIWyHLb/FSz6VlBLAdjY2GbF9vHDRYkZKw+H2nYlTcUZyYP8+gA//+Sf6AwAElT179Rsx4k0+O4luiUd5cKDYURk1Yij3lv0HDvbuM4AZJpZCraxcXq6ai1axAhT2oghItRrDzNY0i6MSi6cImQSVMB9Y0aXAmrjKImFCvncKJMQSrFQhTFhIQLUZe4tErbQTMFWs+UVuURnYmoyMe/lHiE5FDRbMlMltKZWgHQEkEJ3gyKyJntUwqsmWM8/lS/GmvEojWaqUSOX+l2yZLwBBQZbMmdEpHaUw/8fE5EMch+WVzMJjlvTpM/ADYolUAaydCxQscODAQfawyMKQyJv3TgQagFEXwkZHcUQE9+a7l5ZUwimDDMceIdPvh/5QOScKoxYcFcbZIKbU1oUsPPpEnHv+/HkHw0Sb5qB33OJXNc+fpefm4jK6aJbsOW5D6yNYzpkz56HfD8lFqCm29RdbKzgRBe+DBYeFFZBDgTGik0jbOYiDucVEeOTIUXhPBzEWDRhx6qIEBHffddeZM2cc63FHQdzCHHHnz7nxQWAs96IM1yDKgURbVPgNikKF4yJXJVyW19Dnmjg3iFq1VZrltJlqxYEq0xY2msjlHBQ+3eLFeOLi4KEKlzw4mW6RW5iaJlMDwi2eZivT7ClsmiXb7egWk9nSf0VxxS1qB559eGsDNeHH2JupA1Q0n3myJvT8k6Ul7kDOVPEhdu6JSiPkl+7YipqZDbfOztJ+p20iFTMVJ+I+H0+i2WfR/ID/YCSuWwHb25F697dqAL/ObXz3GV0OdhYpbE5Isrth+JS3u9mj/tbW3vaVkTnMlYdYWUJi1pSVqppje7lSY8Cy9aySYEaGNLXAsEjNB84cGsonzpYbQiHpYbg8Y0xe5l+jSlGHDRYERtWSEy36hazAUCBKXU6TkszGE3khvdIG2RqLpy2qFaRKQw0F2J6b+/qWy41rV8XvNov2iAW70fQUguWQM9im/vknNIBf97txA4tN2mVR0+s3rpH1U8jWbj5qz/4n2n2b1knmoINiwhhWCusAg/8iZdhIbVZjCxKd10cYmtgRL5k62IoMEkIVgzRG93xxngELNMkjZDRSYi94iHoUgQSaqjarYdhWGbEX/6iclJM4en0EmajPLIq/fO9IzapZAJH6+HVunDYLi8h8/SqdDbT5ffdMkESM9SwCVaRe/4gG8GK/yxfjcLTQrB3dBObEbHozNCziFlrfqtGsOfVvSAjMcfXSJfo2OhmBTRG8XmQB/4W5KFelsIEL4QASLoBhSg3ROJa0xDTxwh8FK7VSmcXJbLtLHoskUGUePBWRHBQ+zi3euH716uWLeKOH5EYO3rwUkBy4vBQwEB66Fk5blrOAVDzr4m/Qg/gNq8s3rt8Q3RCmo6mZUyCvXb3M1rfZVzFwkvGp1pfdXbUyzIGvSN24CXMIg0hzmPeMFakwFkYY2QD/RUqmYX5qagxDIhIf4mp8iAz/KaW/DJt/xR1zdHIneTyEIzYikxIGqbSAXSlVJchESuQGhtBWIYK8/lEB18cqKvghAcbkTawEcyv183uLcMcIFjJmCkNL8Y5vMEmxi1ZQYJ96/WMawB5WxtAwVH8p/oLoM+rky3DI9WvXMmQMRR9Ktf6tthPMkSkMAy3t5Uu2c+NB1Av7aGwXREGb2eE5aEUomDGMUY9BCh9iT5nESoOqyEnkIbMWbUolnJlgZHRPB1NtYUtOlY0b9vEkWlRLlYVHZs2QKQy/VKr8Kq1DJn+3qbuK/vSVotQYgRkyZUKKt7XzERxzGNDAsGAeIwkhYRERKCCsnzJTY6r1VXtKcyBOFC9P5+HNJNZQV9yTYiPVXgrMhfXeQ+8oVIn8w1anUYRQfY9dIKtVNs/skMzhjslBm5J5tMzMdjDymDVsT6J9uEVjkAjREVvgVXD0Hst0Pp5lSzlTgdtHA3jEfB1x4FWsnbXfAdd0WjwHwGoaSep7qVLcjnjuDENgv4rWzm5X4q8+je2CZaAtSl7O3Fu0RY56z8N1Jea0pETaKs1cbaZbP/rKtIUNmbkCB4UPtyjKO4qbQrv/OgjVBpii20k0s4G7kFnUXZ8Wo9TAMxirguBAE5OWlw5p8efZWzDVdxGvComtfaISQRqvhGwZ5uJIJ4gPnCWzogJ7eYXEqTae/rmkChPGZkFTWp8Wswti3rkECmBBs0zgvw5pzYUh28756IBZ6VuiiGY3/N9nOxaBhWR7BUqdVreVtltN3+LAinXm6kVxCiFLJSqNkMnW09h2zCFxiYOzl48n0YlXae8XpoplkwMCQekj8a7m2WpTGm6F0hZDxfZ8u6iJMg1kE21hO3+PO0+ViBnbGNgMWyktqdjpc3TvwTsw2mRhmz0CFzFztUVJ3ZbtFJek7zEuGxlymHVo/2orVoafylSFmRljnIw9eCpkTMEILbVaFcGsBqOAqRPNboWQXKkoaFArhXdpjwZIcwnRKEJ07S1atmPuehV616zkKDJzxcQ7aNspjBTQ4umrv3ErkhAtKlVL0RlnSWKYnzVnqdjZ0mR4fbsUyp1LCKqeK3aGN0qpYEFWMbPzpW6qQBFNB5vZhj8zhBbFghXPg87kbCrCzdPqjawkd6qIz7UowyBx/h6CeaOtTqOK4hLCmwHn6CTT9LrEIw5m5M1UqNZ0c956Tkxed77VAlt/M90rWcGs1uZdFbW5mSoYi79v5SpcjP7MGL1ANvZBqdMWqgQRvqjyqLC2idbw0mT7iRZZ0UrrLM6mECk/VDz1p+sLphjuv55sdKo3l1S8rau0ycFXo08bO4PcYqAt4GDq79Z001SK931cqTVseLQaXjfYelJIZg0buwXtrkRDbldtcvteohU49aMt4CTS3mtLqvITrFsB2FSiFtBWo0Fqa9bQaVBclDMUNiwndymrpxk+wbn5kBSRdfECSeEhjxe5vUXAntroAABAAElEQVRKA6wCNhIHRRKiRXdjGWOrxm8zlJmQlZu4inW12mWw3SkNt2YzU91KJkke7OUoZkjEWOahpVA1SLDpH2xuyzMjWOE86LQSedACrZBLOW1RiY0E5JpYzJt7kDlaIZwVG04kOJbMkWm1MjttZDplJ3tFNFVXllFVZTkL+733rEwyMmzE0pr9ShVBhbkUt1TAWv6Sd5IAFohVY3WgoGxnt5E6R2j6mBnKUE2BLm+mSgU+zy0mUqW9X3h1pEBCcx6LrklNO9v8h41fUK02JRMdQhucq4PCNGEisplMbeLgRtMMJ0kQ9zouQnJrV9H61oG3eoKoiUh0tSWnKKmTbUdasiKOIJ5m2i1BQjDGJY6HzIxmai2JuwKLdYACFpGEAlCrlViwaTvdSShhBMnaB6CVIrjySlGdN9M6I2dfCa4mB5VSscXPoxta6jOZMMa8M//qeWqxKkuGfRyvAUcpKZ3QSD2gY1og9e8/pQGcZqHjRTjOQq9kED3UlopBxmjuu2lC0uN4UcaMOKzJv+X+T0meWq9bAzicBHvix0kTbtwwXBUieUyElN6uv7dohN/4AbjIrBlDwxG14pdBMfO7m+cLc/3alauud7D44pBK/D+rARx+5kOU13D2mX/1jnRBQ8mVUkYoXryXMRN3XRpqRJn0K7XrJl13upLSmjhHi+8WmPOcINWaVDUyM2SMk7m2sNFLmNZB4XtvMTJL9rTpM6RJizWRs/Ik399A/7rs/OXOJHNLLfg/qAF8uyBN2rSX4uk1VV5XWATe9oPXSaRNwe/VpHZdL20nBw9r4nsiV/AbGa7JTWA0y3muTu8VVVEc/s+qwLZB5+dJtIgT06RNj/6Xgj4RMoMhok76Rb/UK1UDSdIAFtEZsDROlx4ramUoWWBoWDhiQ/S0FPSJkDS16ybJXIkUgjXTZ8iAd3bSr5Z4OrLATKxitAA3drEDMbM8JN5mHJi3mpsBWzKh4ZhuVWRKwfQ9Qnjb1CtVA0nVwLUrV7BAxqYh9X3s7VBKCf5jpY2sW/RVxdSum1SLBSoHa8Izpk2fji1IlhT/hGFp6w7/An6YwCxm/LW6hMmWugoYOVIf0WKmsEgwNy/4Vlwpm4akBoymelP/+tYAtrqxz4jJFTtTjr1FPB6kN2RR90/ZHiu5pa51fNsrcAHDmmnSXqefuSN3Zq1QGQ6QOllrSW1EDgofARoWKTZOKX2DfZ+UZpnK739LAwgx4BlpCIlwQqaEvJXXLYpDb6XI/wLesCYplqxJCaUGTPce/8jwro9VVPQLZiN7B3KVKghO46O7YBP05vVr9O1Ictwpnwrp/gXWShXxttUA9qRocqUYDv+pt1OSEAIkvRGFthUD9dvw6HIYUeY+FB0LYRjvcU2sz4u6blu9/DsFE9YUcZvTbLiHIdnMdtiKKYXF2PxWKixqdAoTJgYO2Od7otFRrHqNfgcU9QnuFxHR5SOjKlidkuxhVHpy13SmUenZXiqeMWqKpb92m/xW4/HadbwY2vESSwh2q+u9RfzxatYcOe7A21xTkP+DD1TAS2LxOnn1faQpyD+Jdrf6o+ivontxH1P7G8HC8zEyd+kOcIsMO9Jcpdv/vuYlQir04s4kVLmbOCJIua6L147fkT3bgYO/KexTkn8SVa1Kk6LtlfJAtTRBwXHhnz01Kif/qDUAI6kwsxCuiDygYUdiJ2BiRKYihQoiH9EiitI7JcQlPCwhxH+agfmKiCqPrPjYLagGgIlPgz4XH7VV4FFG4gkWBQ0+JhvbXygID6rbtGmpYuGw5sx9T8VIWCpUYhgIgAf/xo0blC5d6ujRY599torfND12zPArl6+80FKMB4VXAD4KFYF4bfTzzz+D17HHxh53ZOE2eD5c1hc9nGC9J2sXKJB/y9ZtaBE4dOzQtmqVJ6pWr+uLjypnRHj4woX/Wb585dRpM4FftPA/OXPkwOHbUqVKVKtapXHTp/k13H75P1zpwUGD+g0ePASicks59cvHoufeJEYDerrRS9HpCbL3N4FkfHhUuRM7p8bHbuP+iQkefVjEiWlias4Ccyqr0AshTZ4mniWXKYtUIH9MlSpPrFv3uXxdeJYsWZ5q1njXrt2btwTbZJgPTKpVryuZA7CaLLB48XSdOjXz5In+7rvNq9esUykZdtBLghTEd+v6cvVqVmdIfr2sWvG6UTgOYQQlNcxL7k1c+Gs6HQODP+Rv2KpqKjwU5RKhOJ4DDNUG2J9bJAbiElUbojjEgO+Lj90aEV0BeAAgBxx/HBj0M3jMbSp9AD5GTeIPthgaN6ovMeHh4TcTbnq5RUkWPPDhh+/miY6Ki4t76KEHmj/VZMTIsdpeFYBh0yYNn322RaPGLVQavPb+uWdbHIuNZcekZt1SuN6TdV7p1Q0mvhAX98Tjjz7VrMkLL7ZNfo3wgPFx8bjAKiYmH3wiFLVq9drWrV6IvxjvNbS09Y4cMTRdurSv9B6A3IsX8br5S5dT8Eg/9SruZWpKgnj1N6YjCkGDvhpTczZ6r5jINf3ciw9zcKSPPFIJ3aBEiWLduvfmrBeef7ppk0YFCxZwuEVHQV+3gwb2hT/CuIiPu1j5iceee67Fc8+38cUhycSqNS9cuOC3MwSu14jfhHOD2uHiKMjziBxFrpYfW5gKMwvyhsRI2JvSZEWLqJJ7ieTHGKqNxMHgoDk1ARMs5t64Y+QNEULiw7m5S3c8uLq1KYu9rMGZCB0Xfq64dt1GjEQnG/bmax99tMhBk+TbkiVLwCfOnvPue+9/iLCxT5+ejrAlGM558+aNjIgIhvJW0xS9r0ivnl0PHfqjzUudrl27VrZs6WJF70uRShEMNmv+HLMqUrgQgB07dyHF/OR3ikJEc8X0g9t37HRMJ8mVlnoi90+ro5le26Pfir6HrouYEa4Q/ZZndFGKRpQAApTlURdI8JIliqN38W5D5cpPBCL1n9eiRTP4xHXrv3jrrVGoAkYvXqyofzZJLKFaMwmdIXCtbEx7wGfGjGRmI18yoXu62CNRKrwfp0aEKMJNByziRMPeSY0WuWavlMLD2K3Hd06FcBw8RpXpECfm3vy1sPOY9CtDhgyYFU+fPjN9xmxwSZ8+/fixI0uULAb4hz0/devRGzvrU6dMOHz4zzJlSkXlzt2lW69TJ0+PGzsiOjoKwc7GjV+/OXSEo/pcOXMAc+78eaToUoiAJEFoWOj7782++668f507N2bshK+/3oSsqlUrd+vaKWuWLIgu5857f9EnS3t074wADadP161Zvm/fgU6du0sOEoBUELtggfzoQyiIQKlnz66FChY4c+bssOGjtm3bgZMlvV/pjuAOsTAop06fhWUXilevXhVuLiw0FHMDWnchLr5xkxZ35snDjcJmwoKFi1kbXNczzzRHX+nctRd8IjA7duzCR4rBQJPGDRFNZM+WDTHap5+umDJ1BvDNmjV+4flnsmTOHH/x4vi3J6H2Vwf3f/SRStD5iZMn+/QZiDXgyhVL5s9fsHnz1ldeoTZCORs2fHX27F81a1Zr2Kg5MLly5Rw/bmTevHcC3rVzT/eefdx1zZwx+Z677wIB1AXtff/9ltGjhrXr0OW3335ngxYvXhRfozqw/yDCKwhTpnSpESPeXLDgk+bNm2bMmAG64jATHPQX+SjDT/Gw0JO5sFFlOgIXET374Oo25uTtIkoSAn0P0XGdurWwboByctyR/eJF491VOXPmRJdGkzOkT4+tw+EjxmD/Fzu2EyeMKVAwP2r7+ee9Xbu9olYLBWbLmvWZ51qxiZH1VNPGp8+cHTpsJJOpRtd2FdgRktSqVR3m/uOPw337D+Y99K5dOmHvJWPGjCdOnMTYweZPxw4vlbu/zLnzF5AuWbo8NDSTo4s6rIluLDuDrFoden6taTg2M0I0AjwjzFO14oa5D1CEKIJLW1FmIFIjHuVdRU7Fgx43Qw8MfQuaLu8UBRMS4o5tPr5jikoZu2MKkMCIedeTg0e1FnrwoH7YsOvTbxCjJk0cV7TYfVOmzpw9+104x5o1qgF/xx3ZAZw/dx4Obu/efXPnTs+WPduMmXO2bNlWrWrl/v1sPQz0//f1Joy9nt27TJ40Hr3TqiwkBI71yJGj4ydMQjft0J6WJAgtBw/si0XfuPHvnDp9ukvnjk888dhnK1YjOoOHGjd+4rQZs1QOEoZUcDH7DxyEvwuPCJ829R0EXxMnTcucJXP7dmKxk5BwX5HCnyxehjEP/j27d0ZZuKSB/Xsf+v0QQr+TJ08h1B86dAR8x5w50zJmyjTkzeFfbPjqmaefwq6frKhw4YKYCc4LLy+RDgC7gdu37+zTdyA2obBpgEcxGAmdOr4ET9S23ctfffX1qVOnKlYsX7XKE4igX+7SAwP19JkzYBIeFpY9e7ajx46tW/cFbufNe//jjxcBkzkyErcQDI4yV65cY8e98/aEySdPnQLSXReMdfnyZWgP6vpsxarQ0FDUjgvEmDxgx5Wr1ixcuCQmJh+aCSQIMCs89VSTGTPmbNr0fYXy5RB5Ae95iV5GYQR1QUpBSWCAfivosJN4YFWrg6ta5681G+ub3GU6IEUp4AOXRa6nMCIDAxS6bdSQNoKee/bpc+fOSY+GpmEumTR52qBXh2C26NalE2gw78In9u03+PXXh6EHYkaU/N94fRBm0yFDR0gOyELv2ikid0nGgFdXgR2xubl69TrM6/fcc3eL5s1A36rl800aN4DyETpEREZglxBIGLdQoYLoVLDpwkWL3V3UYU3ZGdB1tUPPrzWFZURUKJahuBV/OaU7uhwpmVz54IYIHKmgMDqG1VVAhgx/btGh9OBvI6OTFSRyRXBJjz/2CDazMEqBwbwEI61Zs27NmvWfLl95+PAR7DczJaZiRB+gxKNSuNHJk6d/OH/BgIGvYUp87NGHQYM9b75gPHSvFk+/+PU33xaDh5309tw50+EEmQ+evfTrP3jZss+2bt2eJzoaSPQb7EK0fanTsk9XtGrVHls5jRrW27dv/7HY43CLqHH37h+4rDsFt0GD3/joo4Xo6AgYX+7c45PFS3/84ad7770HxJhqWrXpMGv2PERw8DuIGRHhFi1aBNXN/3gRmgzmGTNkQO+v9NADGEsjRozeum0HHCvGTL361k48NjTZhbkFkJjXXh+Krn/4zyO//34IyLp1avHAo5j66tWRo8Yi3EBzkHXffUWg2AEDX79wIU4WB4zHBbhdv36D+mz0wQcrQrDXXhuKSARKG/bWKNC46/ru+81oI2JMtAiqkGxhUAx4qBqBOQLYtes+j46KypYtKxP07j0A6nr9jWG4rVz5cVlKA8AJIQJQUw2RBhV/bAuwcIK755ZECjgiD/Vb4KPKkrdK8gUjfvTxIjx7QW/E7IjVLn2DVlzoFc8+1woPsmDcM2fPFi5CuxMYxGgAujfmbFYjE2OxjGBt1Ojxe/ZY3Sxnzpzgj/gONHBhiMH5g5kmQFdBB4aS333vP2f/+qtsmVIoW7durT//PDJ7znvY8YRlGcnCoK/iySEeSLq7qJc1UbV26HFDfFpTBP/QCNk08D8yvOtDRbgo5RmwoDN7imDKzAnl85EL+Vxihz+oxg1TRkgC9hOjyna8sHKzpOHnLRdEtxMm15Slot4X9mWGDX0NA3L06PFMVbx4MfQGDGl8GBNrHjhHYEXn1EJCSpcme/NqFMCu3Xtq1ayOXwtYvmwhF0FnxXNVxFYDB72ObtS928vghop69uoHAvHGVyJEn8MaGUDBgvkhAz6Um5AAPDs1Ikrsktz++uscnBeTI6QqKTjjFovxOvBQ167FxcXjNlfOnD/88BPcU9s2L8JtNWhQD1EGKi1TpjRyR40kB8EXvIkJhiDQi4rKLW+1AKLm7t06IwY7KrxSjhw5oK43hgzv07vHu/NmYmz06t0fa1UoB4+SPl26AJut/Qe8psYsWrYVK5QHfuu27Wquuy41V4XZoIgHGbnhy411atdE0Hr+HF5dHXJV7AlgDoNCwsONeUstbsHUkahvEoa7IDsa737LfY/iCfJIVEj4Qax7tggE4bHEpjWQR/+3aveA1n++oW/fXt27d46MjHjv/fk1xMoGtAjoJrw9GjvC2MaBZ8RSGkhsYsCIeJb14gvPYkbHxjdRpkvXQSwsrtOvqFkXLA6dPFCx/LTpsw7/cXjSlOngVrtWDbAK0FVwKpBZoDNj5QH4juzZ0clhbsYLbRAI5lhoM9LdRbWnLECsHXoYxczHhzVRAKIIO5qpCPxoAUw5IteECcOXKGB6KYGmqlGILMj3DGtSv3uL8LRmteDNsKjNgDHHRuapwLFhdNlOYveaZlqx20hnYkky8V/PRxA4Evg+dNXu3btgOw+bTfALTLB/334AK1auHj3mbXsRrodwiOOQVqr0IBab4FO4cCHEKTdu3ny8shFXIhddEd0R62isajEPP/hAxXz58gHPkgrAShA63ZU3LxwoiIHF09h9InQlCtPkFrWAaH7SXdwumYNAuGGDeos+WTJp8vQHKlYYOeJNZMFbIXLMmTMndg9OHD/Rb8CrQHKjevTqy5uGDj4//fRLjepVixQp/Ouv+8QsFiJ3+rkuCI/NrN+wMG/TAcr88ovVjP/qq//DCUTsCSB36JBXsZrGhIFdy1atnn/huWewwpo5ay5TeqV79/6KmKNKlcfXrqVdUVzQrawLZtvwOR0V4ksqRapt36/7kIVN4SVLPwVQsQKdItyz58d776FoOtHL0gOxNtjjD40leS8gd98jipAQ9Fv0Xh43RuR4bAvEAwwe6MOyzxO12f8ZptR1WSKJrM2bt8JbYb9F3eIYOKBPieLFsAkLawKuWuUJ0KJ3YWLGgmbI64Ow4fvFF18CCW7ffb8la9YsvV/p8X//twk7LZL/iZOn8sXcC0Ojey9b9hk2rFERigTuKiDgS2goBCsY7FwjdDXRVKOEAWi7qMpBpdcOPTl4jVJ2/rIu2S5gIAEsSMr2ePosXI9NTpMPI8X4EyzYsmR9tp1MqYDtSbS/RbSxOQPHhAGHJsrLhOOObqbPsS2oGpuJF47SfiJtNW6fzDDwVMikd/Ax22P7C/J8995Tv14d9CfsxOMpG3/wKBNL15o1q+PsDqZcRHnY4hUlUYlxbdz4Nea6Ll06okib1i/G5LsX8ZeZafwdPXLY0iUft2jeFLU8/9zT2Gj75Zf/Z+87AKwokr/JG0mKBOEUkGAWFDD+TxElmlDvUNFTTAgSVCRIMKMYwEMEFAWzImYRCcbjjASJBo5glkVRVHZhCcr3q6runu6ZnnnvLet9p+yw9Kuprq6qrq6uqe6Z92YF1wV8TBNEYYwZ7io0abLPLSOvh9w5c15D7apVqxFbmzZtUrUq7bLZB1+NbISC0S8bi1UzTjE3atXa/dJLL5QqhDA8jPbYY0926nzaeedfLEtO6dSIYUOwy4Z1Svdz6F6HOSbe+8CWrVuRgJze9VSs2oZeM3D2rJfy8vJgB/mZD4QqEK9Z81lWdjbSQ3Fl9F3u4Sxc+CG2IJBInnpKF9RmZWW9/tqbULVq1apGRBww999vY1ehz+WX4ToE0Qjl0N/Iwg0lM22QZTdoUB8pMxQzZsCULli37uijj4To49see9JJnZHIYN8jTlwIH9iTnYzmAFACC+j6re17gFfNuACOqv6+JdfduHaeoQFc8OF4r98KTUgZOQ1U4nNs5OHz6Weet/HYa8PQfP75l9gjQt+l4eBBA+DViFPvvPc+MPlsfJANHjIcy5pKlSpiQQO84TNmzFjM8ScefwjXEswC3JUWPsmuIjTEh6F33/ugQf09L+/dE4OCHWEMouEvlF4XRZUZTSEQYhGdPPVC/KUhShvPIwgNPf9AxpGI6tSfYMypANyUKJixLqnb4h0sggqqZ1SGi2ijeDxA6w69KUPXXn3YsMal+/mPf3THjMJy9Z9jaLtKjs5dul7as8/k+ydgbxh/mJBjxoz79uW1qEU+KDTIAbEevH3UzdIQdzwGDhqq2uuPUbePvvOOW3DHDX/ArVnz2Y033iKVhg9bi3Bvv/0utmPOO+9syAUSGy7PPU+pzYwZs/52ZtcHJo1HTDHPEgkTUxpuwBiGBsBG2yUX9xh58/Wo/fiTT1HiQNbwy8aNwOMPp5gYT059GlkbUgncnIXaQCKazP33O2aZs2HDhst69Rt9x639+vZCLczy2GNT8bDhO++8hzUpAvqVVw1+7/15WNviD0t4Wc7gSxQI9E89+Qia4L7hDTfegoV5+/YnyAYF9rymPPgwqnD8+quyLWAwR2m6gCiGuwR4fOrWkTcAjxiHuqgsVOG28pAhV2O9tnTp8kcff5JY8ZBd2rPvlMkTr7qyHzDoOMaXqliKEODUiAPsP8oDTf/dkhGJBbku2nGQqHvo5YUFuMaTM+PgNVAbREY5LVm5atXqM//e/Yf1P6A5eiEdeeCBB1u1OvSF56aimwiOe+/1FxKXn4ebwnLTY+my5dhJPKlLR+QSqMJTCk88MQ0PQuKiaJ4kw27gyFvvwIjfcTu5BDhjeYR0Ek3iXCUYR2aLVrhFWbv2HnBj3I3B6etvvPXuu++LkjjF4XVR4O3RxOKGacvFTb1MRxMJJbpNwxKkfbGZo9CIAm6p/SFgREwlc2QBTraYwc/Q1qhVr3hzkdZRNI0t6x52uZcSq2ykk8r5xAWt0ux3uF1KcYYErVatWrhdK37jpUbO9eOPP2KovLVAVqpUqVGjhkhIZXUcR2bwQmzmquCbN2+G7MZeIhn6NAFc6rEFDocW+nbt2g4fNqhP36uKNxcjXxswoP+e9erimypSizskuFrE5VO4cQQCPPViRMNK2LsSemRqSBuxLWVqAeC7MdWrVcWDOAYJuxVvKcZercGkA+AZFDykjQAtxF5ZuMGChx9xocJ2YYhnjRrVK1UK6xaiiTvNq1p940+QK1NJ+WfVGjXpt7stT0sJ59dtk78nLagLFo0HTFESCeOi8V6vBje8OyFOpXTwyNZxYbMdGCvifZrsg83fBKeNcsa6G98RtEdQaJJdxeaTciKEXFTaJoxmyqlnSw/BGM2iwsI4m4eI3dNgsDn6UcBU4TU2aAVOsXNhURSJSispvmRh0TXHn+oMC2Tkib0vvwL5I9xr/Pi7tm/bjq/Z/ak6WaqdobD4swrHYCyTo2p1NyyKxFR+m79nG0xI5Iz59dqghedybvHZybAonMrKkAUwmpsKC3mgENSsDUIVKUPkMadBhNQO4WCcE7mgZhAWq9bcAz/1Q0m1h4++PEddLQPMjlDyFdPLXQv94OR7GzVqKD5RsO47PNkgX9netayQXm/x69xVsrOL6JlNx+3yqlXfWky/gO+ivZOkRDS0CUDPM5UdpWgBHs2czUW8wpOYI9xTxp+wEt4GDlGIIoOwmJtfvTx+FB53YC0etPJ3PFC5Wgnwv2EJkeqxWKcru8wJ1lN45ja0yNplep9BR/EOEFAXbw5vleCNBbjTSO8z0L5aAv9M8HPaxdfb2RmoW0aaaAF6P0/5CvIlUSvkIPyozFFyBbek4JR0CCOhcJka34CrZPDr3AhbOXlV8TYq8BTvEualBpfFxJjxhIXx7UOUZUeyBbJz87Zs3oT7xYGHsuvjeovIuH3rllLzVb76izIkrCwmJg9MiWppNLdspeWpDFu0NAPgiYXemGdndElwBmFxx2+/4uc88eYKvFrAVqNUYHTePL0RsmEZXgxSZodkOyC5oLcMy2oG2SD8kguUyOVwTwC/0W2/FaNU/JZUKnNdGRjYG2b3HSXAYzRxecM7wIkjjaPnH1D0xzUKFgyV1ECVQkBl4BIOTHRETCXDmYTFHeWQKlbOykHTUt5JoesBrgVlR5kFSmIBbEJl4fuaeJhZvRAVrh1OLfDdocpZWagoZdelqFjmuiUZtbg2sqVIy+diWpiqwzOk7iALnQy7bqQ/vY0pSpqRC1FksLfIMqg5NhkRHPG6cvvaqzXI/LMsJmZus7IWYgFMIQQ7lNu2wh83cT6QZBsspbHcYdfFE5dmUiQ1Saorc90k62RcZ0YT1zBsKYZClUQ8Lj17iyJMaJIExzLlmySUKxKPDMKiLbISvRw1h14FWbH0HwhP6lVZXZkFLAsg9cOTMYiJ9ELhSIZIhJFpgKU0vVGa3hFY0eJUBv7/twAeRKFtkG3b1K8HRMYuPJpJKgeNsXrG9YvLMAOJadEyg7DIOkCYe4h0wQWaIN5icaGcUmAqw15q1BWl5bkk4iWUrqQ0zyJKBILxaJGrRJosLTIxsfQjI3MTD0s1H6yrYTjo6VjL0qAkYIibx7pkGD08AodLS31RweKSmn/GWofFl3DsfJp5vE6mTaRfltbCSBA+pjxcbERo/vuMneNvIkokabHiONHS6kYUDGlrjWqUNgHj2MerkONADrlrL+2GoSmiRs2lTVDIqvJ2MRDjqc5sb5F81eqdDYsWgmHYI8zSNG0w1n4+08dzjWXjmNkzYXS9j7W3i0lG8TbwcU4bp8M0NcCer9y5ckt1iTKzVUXddEWUks4eNu4IOp4VmhJOnW1gy98UDXfLIyzcXS+JzTqtBmEi73lKUSRWxk7yGj2Osd32ivEgvZI9dB6UNJUKi43oKS4V+JuKCUkZmUeEH2UJC7xAB52IPgHJzo1dSGoJssWofMG4vYwxa1w3rCthkDnakmxYJPmkujo4Z1bHg0xEm9uqJA3TPULNlEaCFR5eCrsvBOv4INNAha3YinSVi6HzahRDC7RFbvR08hGHBOTeS0s8/7RqvEqEBdtxMSVX4ShkXp3DYxR3ebRUs20VDKptrJRqpSCIFWbaqTESbbVf2SrYsLSSnjLs5W94lwgQhcQ0gQOlNXbuGNnXCI+P2cvkJEXjmVoCMnlu0TJfnNxSMquXTXphIq1eaxfnHnmTc+mr7nFGTMO28XYmTJTy3MeFNdeZhZ0hOuE1JWsPgU+ah8yH8jYNxo5MH2QcascnKctwR4Ik6lEJSfcK1tNPaL0kUQEB44QGAZGBEqhtIQGsx472L7whw7DOAPBqkV57q6kvmnmDkXstSE9OhMoSHPDTY+ettJ3AhgPW3mYB92AUtEfZmBJni1q+SJczryZ6xEmjcE9TGFq4+nstdcll4GpaCcJYSpREQNBJK8tI19xBXwI2lj5UrS/xHkWTe5tQGyvMtPFNA99sFUNaKjMH76XF8C4R4FfIEhwdR58c6bnUuFZIy/cClvGMrIH6/cYu3APH97RYJ7paLi+NA98LegXItUrcRcdpkurE+LCjkCVKGGiFvBrYw8uwZ7y0HpqRPg9/phRADbQR2QI7tbco4mOU8uoSVjj1uYeN7oBjdPaCeHYeNp6rhDO97W34gLEwkvOUTINmCQ3CRCnPXRUsd9HZR9QwghHONpxSluXLtqOm0Q4ksfbxjWACuWekrFjk0cUr2KXzkiR10dvA5WmdCbUgvC0tUTyCeux81yI2jcU8bdArOb3W3qY0yfhdygh8aWT6IikmPkTV8IrUZN5Ky4iKzi/M21i5pzQMUWTweA2ZgpMZlJXxkEPZAzp6yMo+d9IC1nM29PNc5GhOSemH8j2el3DDSvR9q7LnbNhSiQU99ILXTWzfhmeYlB31ZYbbSUAQFm5wQLjGUHgGI0BJdXDujJqwpzgarIEUzHSeEMbhVot0tdG/n0Mi/5d+b9H0Ao9zV8nORTKFb+fASxMHJXUlvjmzVb8yODV1GcWf0QJ4jpcfJ8zaClfYVCSeZvwNPbZhnOLbspWrZJV5YDq+YGyLr0VuKS4ON5HYI1grDlEADaKZfsIhJkOU1qExCgtKOo8IlshooQlhu0VqYUEDCqNKcz+bKOOM9xbzq9WsUKkyvbcMZiul41eExmL1w6ulxLKMzR/SAvSjKRUqbC7cGMkvZI5SlpGTl48nsfEd51L81t2u4IGwLewW+XmhIHzEpXBpeZKwEVI3pur46rB3yH0RLxKqVGhzadNSLVUs9Fggs71F/Cpk+QqVOCYGv1zPihr97FiZLgyGyDpL56uERpEy4A9oAXxnBUvjCpUq4ScfIhOD0oWc3Dxc/OEwOiam62PaGH56vCPoT++BsC2+m4bImPINjmSrmLgFK5HlOfAJTKUiT3qiQNs/5acnSCXo4w95YSExPC2yEEUFqyoFiBfkYe0sr0nykdoOZ9enhafvEeo359qNy+BdzQL4NmyVKllYU8NTcXApOzXl8Xv6WDtrD0zLryzrpaDfFTwQtuUNWfrWI2wqfzCxhhHiAHNJnwLrTz7jhsBwtSnZyiCnWgXTB58TsUIrEAgWGS1JFCpVSeQKFlZ0Ro2S/lGDyB81UFK5McOaN2GErSkzeW4xK1feaUdMfqejLGH8nQz7B2KLHWv+wnIFfDcWroZgZsqs7BxOEss8sITjKbZFsu1/xYIxtN5VFNMjonCCaHbokr5wUSLNLMHBaFsDbzuBDYswwYQF+3l6sTZLgTPLFkU0WJuFcynCFfRb5MMdLDvfxSyApEZni3SRx2VdSiDZEnBdOkrR98SfdwUPVLYNEiqTFcKeJk+EaQHD7FyokgjY7HZpBkKGg05B7itZJAshAqLh0pbgwCFhJDz0T7fXjBRfmztXcTPCkm6+knQhhahkOIMHdPBjOb9t34YLDlrznR1cySm2cgmWCs6v20rf95HIq64tmwoWeumhhuDJlGVHmQXonavbsQXG3kP39Xg3i0og8biJtauY5IfGr4x/5tVtjRdPRvHGn3cF27NtsTNr+ipQpGQzUQihAQhKhA21txhzV5rIJaUXGQ7MQkWUyGdYEMhQISZSknwcOzjMREutm2aKT7AJHYhAFGNCpY5c3JQTYq15uR0ZhEXIQqjVThZIFjVEr9qHXJZXt1VQZ0OHlPts9sXMhLC6HwFFtDtSp0YiIFTQ741v0KA+3sscfd1oacnF21MLCtb9/PPPoZ6VFv8/PB+2C3sFLuHK38UD2cEDs0kGgPNkfF4dumDj8pySPmAtOkgsCGEh7o+LNyZwbCb20yXmOs14MUFQotfmRKqDcx4C1AqGyNhGKPX9aFmfM4UaUmamCmLNkZFbcpRgDGvJwijJQhTCP7cUBlxrMzSw1pHYCSPSQbrIzkVjSdrySWZhkUO/vgqQRAWzcILhed8tvreoAC8dJ/m4PqMsKliIslGHySAHDWBDD1gwFp4rrQKNcJ/woosusHD0IvkpD9K73qOHEhKpSMCD/+mnn3rIIQd/++3al1+eKa/WG33nrfh94H9ccEmIUwKfEKWc4lXoHTu2P/CA/d9597033viXuQ9438S7n3v+xbvHTbRb/fX/jh46dNA1Q69dtGiJjQecqdwzzzitRo0aSK9Wr/kM3Ez8zZRPmvTHHH3k8OFDRoy40bzTfSf1h0uIL4uTsL+KERyf0Z5D/mb7Ughfu8VluXUOQ1hkF4XN4+hDWtMpLIBXbHc97eRQHV7H/Oqrr4eQQh9FlhYevtry0BabN23GO3JFSnZW1nnnnQMYP936yScrlixdFnrXuWcETXZj5zkCc6mTQmoq0Z/KmAxR1LDGSxBcesZEjwzqGZSxUCMiAZRHHuELBL6StKfMketNyfI0Sx5f4cnRj2IhaUhNvSUpqvLcTLNFEZxmibSxUYcHigoWfFZASeLOHNj0Ob3rKYZDbm7ubzt+iwuLhix94IknHq5Xt05hYeGRRx7e7e9njLpt9KzZr6bfHJSIQd27n9X19LNCrQ466MBxY+/EWGzcWHjsX48ZOOCKv3U796efwhmiabV5c/HmzZtDbm1qvUCc6Isv7lG5UiWwysmhOxUvTX9lzF13ezkAGcckjl7wt426uWLFClcPHIrTTZug+Obi0nsyn2KizjLkSp6sTKgW7oc/XKSBNysYxMSidQtClOmc7rlnXfHAKlWq4Ib4JvwSeLlyuN54w2KU4XPPTp027ZmpTz0TqsrU7Cee2G7IoKugAPjA1D179kForl69+rndzxKfgXrbtm+//Y4xc+Z44rWRTlmZOpyUSUKHXI6ERpc8GlyBBmgqDMJshKceNbpOBTC3odhDRCgAysEwyWFqxDuBmYaFcXqnMkStoWakmfg+WRLYUBelJMEcHe2SVESFXWaaLcrjitIjf4kZmAvnK5hfuHY+YiLU5e1IUpuvWv5WlpU8HUSG1alLV6k45pijRt503dSpYQ/zNEsPhciFmDh5ysOPPPoELsWDBl0Vl+8k8Ktfv35+Xl6IIC8vb8ydt24sLDyn+wUIi02bNrl3wth7J4476+x/hCjNKURHY6up9QJe0UK5YOGiQYOHYarcPfbOU0/p8vHHn8SF+wQmXqGCrFevrryvEqcfLlqcqeYJnFFFrsoeY0pCqtdDJnvRjsYdp4AYTnjg+Yu/Wzxx3eKJhWvn1WnRq7BgAS9lhL23BNJzLF26XDxw8KCrOnY80Xijh9SHqlG9GpwhWpOp2cFn+UcfDxw0bPfdd3v80Sm9el06eMhwYTt23AQsdOrX3/PeiXcPHTJw4cJFP/zwY1SiYCSHYzjGkpG4xVFKZY4S7KwskqIMJXASbdxRk3EkWZYoAZUyjh4kGPHRLTk3ZBFE66R8zFa0NaXwNSX0Eu2ckhuChm2BmEh66jKzsGgEJQBwPtTm1Z28ZtZFa2ZdmEBZgqrKlSsPHzYY433fpMlojsvmXaNvO/Cg/QEvX/Zx/ysHYs04ccLYr776ukWLg+vUrt23/4D13/8wZvSounXr4Co6d+7bN908KiR3j1q7A/MzvXO9HPbzkSoaguyc7EcfmfyXBvXxPtI7R499++13UdWuXdv+/XpXr1YN2eWDDz36zLMvXHlFn5NP6lyxYsVXZ09fuXJ17z5XCIe2bf+KkDT5wUcQE4FZuXIVYkeb1q3g0+KyrQ47dNYrLyCbAwEWzsuWf9S8ebN77h7du8+VIPb2bo89at015jZ4PxguWbzsiy+/9IoWBaREHtG7d/+ZM1+8+OILJCz269v75JM6QbfvvvseJjr7rL+FmBzf9tgrr+xbrWpVJIA3j7zt7XfeAysskwcM6L9bzZpbt2176qlnDj+89V5/aQA8eg0jfPDB/DtuH3npZX0/++xz0fyAA/bDY9KrV63pf8XAok2bWhxy8KhRN02b9my3bmdWqVIZ81bSTFtVG4Yv4+BSXcnt2jgY7le7RS/4noQ/BERgJCACjmtVYvye9eqJd2FXZ9rTz8Et4Z+wzFlnn19UVIQqEFSvXg2+0f2cbmd1O/OqAUMwyiIu6jZR1wop9vQzz+MPSGx5r//hh4YN9woRfPPNt/36X/3QlPsu793zxptuDdXap5SS8cHhQEAOHxL/UEpaZWoAqMHQYwIEcxFOlI8pEiGUc96zIzAmcxTxlhKGh7Qx8jnho9BKmkX2FjVGiZZWVmnpSFGSDxYgqjFTYY2yfAYP6IATLgN8GABnDgwS3OxbPbPHmpkXNe44GWsW+GhendagAz65LfFKdYwYPgQ7KYP0FfKecWP223/fCRPvnzz5YQTHDu1PAIPddqsJ4Jeff0GAW7Fi5YMP3lejZo1J90+ZP3/hCe3aXjPk6pCQf7/9LibtVVf0HX/PXZjJdi0CK/zsrrH3IHJd1vMiVCG1HDFsMIIFFqTwy759eh133F9fnjHriy++xMQYc9e4eyc9YDgc2rIF4Jkz5xjMnFffAHzUkUcIBtHtscenIktF/B3CiiESIVqhjyCI9g7hBmF6jz32GD3m7n+OHf/9+vVxoo1EARDuly//GFuNOO1xwXlnnH7qKzNn4wqRl5/Xv9/lISYwwrUjrkGPhg2/ft133w0aeCVaAXnzTdcVFxcPG3HD9OkzcBWBzXEKI6DXL8+YmZ2dDc1xgBhXJgwHRDz99PONGjWcMoVWsiDIyc7++9/PmDRpyrvvftC61WEHHXgA8HGHeAOX4hkEhvyNPUoK8kPxN8kNdRVNJHJCPCDh+qomwGfgw3HKePEYDnStSlYWAtAbb/7rnLP/npebO/He+1HeeMMIjP5hh7bE4Mr2Ma6IMNSq1WsMq5DZva5liA2AzKBTx/ajbr2p9h574Gpk8AbAZQlX2UYN9zYYL8C9JntSCiV/ZAY+E0srmCioggkjJfGmOl0yaBXcUFUKTCXLC0puzXKIFwiExDBlBJErQoLCZ8xUGqNKGERKRaQYByw0XmEyvBMNdjhkO9YLA1m0FvdbyhWtm7f0wYPy6ZZLufx6rfLqtQK+Tsvesrj2thWjSZW3hN9gew6TbdWq1SDAt2L2bd5sxiuzZs9+Daft25/QuXOHmbMoBmHrB2kLMkfQI8Qg0Zv+8ivAT33iYdzTuHXUndWqVcMpDgQ4PDaMazsWR0cddfiEe/655rPPe19+BfCoxb2XIdeMAIAU74jDqS8IKLhCXnxJb3je9OmvvP76K9iJRza0tmAd7lyLdJDJgRQVmZq51wHk++9/gNJc5F986WWERWBq1arVqeOJ9vd8vL3DehyRZfCQEe9/MA+tXnzxZZRe0cCHjh9//LESPxnapUvHr7/+ZvIUumH12mtvtj/xeGSmNpPrrh2KED98xA14GAbxd9zY0UhRzzj9NPhMjx49saUlWTOaA96w4Sfp9V8aUOaIA5o3bbIPdgNgdpxWrZrfuVOHGjWqc2W5gQOHIl16afqM1+a83LbtsSZ1klq75Gs654mwOJxDEhNZUTOd1w/hYGh4cI9lCI7scrRwXjPzwrx6rb304GTjbQVSwkcdeTiG49prb/x0xUrsVyDFPvmULlOnPo0NmUsvuXD//ZrDl+Cu4DNk8ADcDIGhYBzje6tXrbbNfv11w6KuNXfu2/n5+aJJ4caNuLzVrVMHvgpKtH3zzbleJTdt3lS1WlVvlSDZlgqkkBBOmahK0ehhIAyRhUqmpILrGKCWMl5OyRQiSsg0DKZqV5FlapgYqgyRBHsyRGHjZLUKpfTgD9baSTW5Dyxd7TMSnfIx2WHMcBFtXTW4MyrK2jBQRMVxl7a6OU+s3bI3nmfEbiOu5xu/nWfT27D0xlti12/kzdchGN1xx11CcMAB+8M5unTuiD/BFHCqAvj779cjJgLAzWWUZl8cN+k6djgRPzMw/cWnpQl2wXF5/+WXX5AZIdO5ov/l4AZBVw0YAgL1BrJy5bDYxDoImCZNGkMH/FHtjh3A7713eCEjnFFiQX3A/vthJogywOy3374olyxZZmgE+HTFis6d2u+3X3OD9/YO0RkECxZ+aMjSB+rvuecWvFoe2XTNmujLSy9Mk7ZygbT5NG26D/KgF557ShHs2IG0rlmzJug14qBN6YVFc+SDUvvmW3MRFtu0afXLzxuBwQIcJS5FiLy5uTleDoJkv4Unwe1VCTx+tElmE0rxMdd/qLbgw/FrPxxft2Xvxp2mrJnZY+Pa+VXrtSavTGyr+CQoFKlq0eIQ4G6/baSpwfUA8JNPTruwxz9wY/D228eYKgHgfghqAl90SW+71utaIXokBLhUH3d8x+bNm42+49Z7J44982/dbSYCI/J+/vkXUbzBkJnUIVCkFHNESr5CqcBFocrsJ9JocCBzx4vMzhiSZgsR6YzRaBIG1m6pG/F+It19JpFBaUQGvQGD0EGrZtbUKVl7ULJ4DuTa00rlTnRIDTyUw8/lKNWwdsZFu3ZLOgWM/DFEr+jYpga2ARmJK67oi+085GXs21S/auUqlMgW77jznza9zQl5EKqOOuoIrHHAp1mzppjYvyKLbNvBNMGqpErlylhHI7O7/Y67jji8TcOGDbnWo+lXX33ToH59BFAQg6bW7ruv5NSV6K1LMDcvN3/+gtNOPQmbSo8+9qRguv3tDADz5tGdKPuAUJxiyd+SZxrglf9ZidLuHfRHfClXrguSLBPoQUNiI6KBtw8ka/vt3xzbfEBiS3RjYVH3c3vYBARrJugjdsTadzxFniUS+3focCI6js0EyaOZnOwTtZGMC/Z2n3/hJRBgsYxy2bKPGsZcP4Q/aEKHcOYSmsF5o6JUC28F0kZ7aWIz99KDIBYfk0+uXEXedeWAwaGnqa64si86BQ8ZcFW/iy+9XEQDgwPJo+SPgT6MR/TwupaHngaq/IoV/5k959Uzz+iKETGsgAefdu3aIoddsCC4dgrekBHAKRRj0GmJS4xVdmZrI/oYOjENl+CmG6rq4FzbUDBExrLJtSjimZLpPENKrDkyckvWjDE0NOlkjkLD6oUK0cifOXKnnWwxw71FMKDw75RAGMzqGRcUFszDXT9TIg5i7bzuw/FIFQEDb9Pb3IiN7wAeM+qUkztjtwtb+C1bHiJ/uAeKdQSmK56cQHaDLA+TmRkEfLAGQVbSt28vNLnowvOx4YIttpCQO24b+cLzT2E7HFLOO/ds3A/59NMVIT6mCeIUBho3PZo02eeWkddD7pw5r6EWl3HE1qZNm2DNaIhxpwI6Qy5yh/32bY5tzVatDp095zWTcx111JFYe2J92qb1YeiLeaQRHEAT6l29unXn/vttPJnUt89lCPT7NG6EnVZQIieNihYdoAx2HtCpp6Y+Co+4kW83vfveBw3q74ktedwbPfjgA8EKxLb+6CPSSTy2Cd2wu3raqfS83uzZr6LjeNgIVsJ26sUXXYBxQbKMrQM8IGLfZt1cXFywbt3RRx+Je9+0rjy5M9JM3CKIGV52AVHXLYNRBAlVUWE8LeQ5Bi/+JrXil1imYLFity2BB5L8yDH3X+RdI4YNQejHXg2ekgEJ+epJnXFj5I7R/4Q/4F4WkAiRyJdzc3Jk79VwErPDlzBSca5liAHgQZ8brh8OT8Cwdmh/IpzEXKWAbN36MARi7H3D4FOmPGwaemYWzWD+U5ayYMbA0mx0NpU5MUMgg6FLEWSNl5FMLekkXLJsDiNUBRJd8hgLxhHGeIMhAiYKSpwyhkr+oypijH9BGUA2lmEiRgtusyOD31usUaveluJNJCUI4CnguoeqSyWZhlc3yW23b6cVVvTAHYB2xx8Xwnfu0hVfkp18/4TatfdAFeLFmDHjsIf41NRH8Axdjwt7Cj0C4u2jbhZ3xIb3JZdebpa0QoAdwDvvuMVsja1Z81mv3v3hcDYf3LpF8MXiBU0Q48477+wK5em3rXCrQXbQED4eevA+rJuwrWk/vYHZMmHC2MaNGiKmIOp9MG/B0GHXidy33piFxb4ojzsYUBi3pzHBoEyfvldh0w1LoWjvMLvwfJJ0B9Hnwgsvy8/P94qePesluXWDW/AFBQXDh9+AZ9xENO6Q4oYAVMLp62+8hTsGIf3P/0d3/Mm+ASLaWeecD8rLel7crdsZ6Djgd959Hx3BLSzcKcLTkUuXLn/08SdxgUFyhAwdgXLK5IlIpUH5y8aNl/bsAybQ3BCQ3FdnvPraG/Z9fyDNgZ+w2/jTj1Z+QblG1eo10/FA8TpsJsJXkTBi7bJu8fjCb+cn+57UxnmgUQw3oDp1at/2+E6CkdvryM5wCp+Bd916y414jObkU87EsuaBSeMbNmp40kmn9+vXG5k+DI4dG/vpxZDZva5lRAPoetopvXtdIqOP7Qg8PI9dZjCZ9tSjQgYdPvn4U+wIYfVjN7Rh2LZoIz13wYd3MuvK9D+FjWJpssLYDNEhp2CVcnBo5SzJnkWbnn7eLgYiPdWZhcXiTUWkSJSPMke4f3DQ/Hq0PJRj1YzzVVs5j/DBFzY1bQafuNLWqlXri8+/MOvraGNEPdxzSPAV5H2NGjVEcier4yiHEEaIQxG2efNmmP/YqQwRY3sRW5C4RRjC4xQRBFtsaCVVWE3fNuomxGXzHQZv73ALG6F/w4YNhmGcaEMQArz9DTFp1KghInWoO8iAvvryK0w/YYiuNW/WFNcbbBeGRNSoUb1Spcrr168P4dM5xdT95acfQw5StcZuxZuL+PLPPELVcHQ5yper2/JyeBrKgkXjCxaON/jw/NP0Bl8yD8SVFSHPDKIS537UrFlzt5o18AS4i6azqNnhhyHXslvhDhjeGvLt2rU2Mn0Yti2MuGi4udhWsJad0U1KqHg5LDCVKiRQ2NJwOEiE+ceeW8ICHjrmRPQJSGzBYeYxPC2yEEUphcUQV1vFTOCSOaXVuz82iNh96aUX/t8xR53Q/iR7Qf3H7lWJtFfZomqr3KtqjZoqLJaSv0Un1a7ggRIWealA9pUJypb2mlWNQQk/OICqqCYwlTrMhQS6MqRScC6hyhwlf3RLGtKkI56ppVX5DO5Ei8fw4pttKcLRQ/zhsEs5tTECp4MnXrvoMem+cXi077XX39zFYyJeP4L3MsFl9GRAfgJ4B5BwM8cD0/croUymN7V/Xgdk29JDGmRTfSiQog0MTaUkhRomYs4KJR+0SzNG3FQzDD6VGJYgsCqZRASDH42rHm3NyUbYJMGdaG7GTYkRa0+N+QhxAxoY6ZDWOeiuxitMJnuLeLNV+YqVtm/bSgIlj2ZRpQXzT0bRgO3KB9a2u3hMxOjjrSMoeceGnVtnGdk5uZid27duDRKOUvVDbMIg9P653Y/ellO+vPWuK3/42Ckj6PHSYcIeJIlOzF5FKh2kKK4lwJ4MUSupGenz8Ke3i7YwaqD3Q1mLDN7lgrCVk1d1+1baVHLiPgSUDuZP7pFkplRHwo5SqqZ/nnq82G/L5k0wRcifEbYQGfE+NHT1d/HAP3tMhN3ItsXFmMsp3CVkeh1EZD+R9hb5BgjneAhY/nAmIqQ2hTiq9orU7byVtmAh9AvzNlbypF2IIoMHdH7FZbp4E16mIT2gEsy4NzsP4zoNixO7yFGGF5PsInZAOrNt6xb8qr54g7iYlL/hPvFWvI2EPVBQbBrbcaI+qa1Hn4qnfFhtgdgVPJBsu20r7UXg5oj6I7fSfwJzCRzwVHKlLsmGBFMV21NKMiVBbsnnREwCnJJFMnuqUn9ERoR0amA6I0JqbkqCQv9wyhgqI39ELGwCIs2QMSyONWS6TN7lgkCMVLFKVg50J8uW6qE2jEqVZxmzP5YFsO2VhYeTy5ffXFQIJ7Yv/Abevm1b5aws1JayB9KmEoT8aQ+xLX4DAb9b53RSDC0oY3RZ9QYl3X3GrNe5ITb6nAxRWpsxcvindRIRzAGQCmEaVyYxDxqztmGd41gKPoM70awDhJXLza9WOSsHT5Dxd+PAZ2ePspi4sxb8g7fHpEWwQ4lkUD8Epvd61Na1sz+VnZuLF/WVeWA6w25sS5l2cXGkSRA+KB9zLkYR2gSENBUCZFzOqDljZ0c8Ra6lJoYqFZRd2gSFrCrTLb8AT3UGYVH1lI1XsRLy8ZyKlavA6Jb8MrDMAiWxAFI/rI+3bd2K19izfwUT1O/J+EGKSnjpMb03FS94KYnIXaYNtmhhXFiXnyRBCEjvcMJcEIsoZ9QhT2DOImW8kjKy9KSCyhOkqG2MPnHkrrgYnhZRiCKDsMhMQs2FsbiuJQRght2wEl0xbmoT+6S6OjhnlubB1UzraVWS5ukeoWZKI8EKDy9FeKrri6tcqsXrGPZWpKtcDJ1XoxhaoC3ywGzB8iq6ygkttWgc47mnWeNVwtEt0DM9lsJRaEvge4EQS7VABxpgPXY6iPDuVtCuRFCsMMNNiZU45lMhqo41j7z8De8SAaKQmCawSVpj546R7WkeH9ML/FTeFs/UEpDh3iJ4Jh6lZFYvG9vVpAcqfjgapdXr4OIHI3qTcztw6SEUObG6aaaOOjjxNggTpTz3cWHN+eodXLflzhWr7zNPSjlC4JO2M02DsSMrBRlHGncz3ZEgJQQTUSdGZ0ELtZckKiBgndAgIDJQArUtJID12LlLTstlDOsMAK8W6bW3mvqimTcYhf07PUkhKktwwM+yQ2AyPfyCETY2HDD28/Rio+xLnC1q+SJHzmJkBiYO9zSFoZN6reUnfQZXp5j0piQCgk6akBo1azKGxAZsQrC+xFsXr6Q+plUXK8y0DsbINpUNi5qCsVRmDt5Li+FdIsCvkCXYvYcZsgAAQABJREFUNnG8BOm51LtWSMv3AsbxjKyB+v3GLtwDx/e0WDsTtGFpLAYLeqQg1ypxF51IsyREhvPOq4E9vAx7xkvrENMzXR0/13SQJUptRLZABs8tKkNbnilyY5TydtZomjbgYaM7YI+8wPFcPWy0TawBcKa3pOVhlsJIsCmZ+huHmoWJUp67KljuorOPqGFs89hwSlnWaFt2SqMZkYQ6GjDwjWACuWekrFjkUcYr2KXzkgQausR05m0QJVMYoZYTb0tLFI+gHrv/wczR7npGmb50X3oaa6mgwmsnXe2ttIyo6PzCvI1Vt6RhiKLE2aJfvu5EjBe53XBVsaa3Xtja5DYcSEkBWRLC2UeihinYmuqAv1y9mantRalh4qWjBM91OZFI4FTo4GCklwwIdLZM4LKySMJms7NFGyZezghqbVP5iSs55iyiEKllqW/DMSxC6JC29s6UC9MIyuHviaWaO9j/vbETFURJf6IWzfrZeNzEae2Omr/HyhxpfvgVssbO5ZNSG9bJ8TQ7fUmtcXrjlUm2GJjS7Upw5pUZVCdBadkjtat5NRBjRUvWR5nYdQhX05RMhdw/Jt7GLv+Ys1iTSGYhYUhnGerJMp4AEjIyzAptJUqusxsabKMGY2eFpHhy3TTJtLbKfk7W9IsOvz1eNhywTW2H2DHS+ofFihlERIYhI9ArCUqts9M6pgOuarvcc4sZPF4DA5rAiB+MqpKdg6JC2QM6jpeV5OQ3PDqxfSt/tYMfT1HxjCYUJpGxOdufZj+OSvST4tkYBbxBuyQiy9rsGhbA0zl4PAe/+QYfi+kxX6TMRQVJAmCZ6k5JJ1KhS+In3igQNZXDjal6W4CptTujYUBOMNroEgt1c1FhYfw0g3qXCyUDpIhmpGX6PkU7YsEvCnSaWmzULJO5psr86nV9HD04ma1QJy+/epXsXOSueDkGd8FDnD4KX3Hdqn+8L/1WfyZKPPuJ5+/wnbat+K3lInpLTPKRk5eP7xqV2T/ZSmW1sIBxLTwTuqUY32+RSJGGbSQyCWEQpaynCOR7L+rbL8Q3uvkgoSudAGYpZAkTrqJzjD4BSZKwGJ7xUjPeW8yvVrNCpco7Hw0tlcrhesZjZuN2RVh+3aRo48/6cikrLj2ofF3CV4yQoZfZf1f0j53oM1wLz70Xb96kkhtJz5ihFba0p1EmZ+CdkIqmlIppbgKr5MwkhLGiRAMRb7RhXkj17KebbViCaLzO8UytTmf43CLyxPIVKpWvkMEPTMQrGNSAIeZ5fJ4fUP65ISymkTMic8S1PdRTGU3kieXLVyyzf8g4ZacpLQDXwreCEBmxXxNLbMUeXzTzBqMgfFrhNVZCTIUlOOBnhUsdV4NKW5gNBwL8PL3YKPsMAlxl2DU7t0LFDJoESqaCaJuytKNtKpn/i/XbttCPcWDrEH6JS4Uu6QRIVJXZ/39x2P4IOsG1Klepgg0bdipK47R3aU+jT0QedjuNI5TCUBU66pbUc8LqkiFGUENqqktizK0ZsGAGLTlKfkgYiQ39U4wYq2CSIX9ErOQJAZUsP1oSHRFTyXAGd6KzcvKZMXWTM+BSL/Fe5riNYZa5CxTYr0W2iMGJJoxZOdjPNYeApVuW2d+Y908IaNeqkNbvHCM6RJMoWnrr70Q7y1gKDFFyMaLg0zBoAg8KZx4BtsgkYd7GiqW0C1FkcCcal5qEd0il0e0UJGU3VcVAnDBmY5zoaidbQewR+G0EvN0whRF3orrM/jthvD9GU3KtbPwwoIowrLQEBNHfDQ7qZq2mwic5pcqr5ERnYIynQkhUyecURwNPVjDTeeIl5WqgFk6uNhyGUcupH95bgE+75CaKRmC3VDyJN7MQE0gXGVZ6im0yCIvY6f9t+zbqF01W8BbODpxb9zBSl3tdWDDf0MThbT5uN3bdM2wDIUKRDeUH12BgsucOIH/bThemBPvb9swU3nUtvsv0nFyrAl7kSx3mgpxMQxpmjE4K5edyduK5RSVMRNolG10roWIJAoukAhS9VIiRQOOWpDO90YWaBSVzNL3RsYejIJFLXOIG0mm3JEVlxmF+ZRAWIZUiNcklpfmUYEYCtQMv5G3Y/v6idQtQtalgYVEBAcCjyKvTKq9uK5wC+OiRlgYPwMDCRzB2qfJ2G8XwnxiP/tF1ky+yZEJ9JNtfKNnOaowwBDIuexzSEyPy3ZKJzJNZx4yjFhV8/ontLNedoKsM/en7y65lOg2fEm/hUmD2OCHTpfiMXRIHJhSrMRvhijZ09Q6VTK0ckxoCVOQEkxxVYgB0lKFgBpjK/+pzi5mFRWxPSE845qp+cSQmNJwM8/Cz2RcxDcXixh0mI0p+t/jedYsnons4GnWYjJWgwDgN8WGScCG++7czu65e/dmHixabaq9PE0+5QBk6DfzX8Lit3vLQFps3bZZ3PUMuXmN/3nnnQJEteLv5JyuWLF2Gt1En68OdEAsb/0HXkuxPfS+3A1cm6THGonDtvNoteuFSBLj2IZehFmOBsgT236dxo2OP/b8pDz6izfnfsPMxxxy1377NjcRNmzY9/sRTyXYzxAYoLfrGjRoef/xxhi2Ap6Y9i5dolxb/OD5HH3XE8OFDRoy4cf6Chbb0OPqUeHd+2D5mwRSNtDSBuVTXDA55AlOJiEcz2WRkJEFlXqwNYOIlDC1QBFhokJCYSAnWLEBYMCwiVXy1NCRM6CB/F6+3S6MPyecQHpSZhUUtLphX4G2pIR0kqtotLsM8RIhECnng+Yu/W3Lvd4uRqtBh08fAQuiUF1/cY8mSZXZYdKp/t5Mzzzite/ezup5+VpoSTjyx3ZBBV+EFfqDHO+Z79uzz+RdfVq9e/dzuZyEUAlmlSpVt27fffseYOXNeT+BJljHXW/IGy1Suza2KHfmcki9/+BDDGaMgp0UF82sf0is0XnZbCzatAwAR6vx/dLfDYlD3u0Hdz+mGsLhZ/85+YWERwmL60p57duq0ac9MfeqZ9JskUMICGESEZkMza9arCIvmVIBMHSbUXE5vG3VzxYoVrh44FKebNsEAm+FLXsoSIAO34saWb8EF5IxLDneKvzgHl5zMsXNSQShdacPUjs95z45AzgUp4iEEcV20dIVJG8ERLJkjNXV2Fe0dRqFRbJwP0oWbWjOABYhqzFRYo8zkPdHgykE/2huFQS3mXm6dVnVa9EJ6suyhgyGhcO38dYsm1mlx2YHnL1kz6yLg5fpBZjIaBTAr/79U1K9fPz8vL32NalSvtvyjjwcOGrb77rs9/uiUXr0uHTxkuDQfO27Cyy/PrF9/z3sn3j10yMCFCxf98MOPcZxhHTG3lOxKKewPe/IAgcy2bTmMCMYlr05rrK+TRzBOmf+P+F82bjzl1L+VTAGMRV4mY5eOlE5duiaTZeowXm716tXFwkKqkAqkf1X2cgshtZMEaHI2HFYWJnuLgtFhlKopJDlleA4Lp6AUYTTBHX8WhC45PlF+5fitjRBYl1ABShBPhiyYWbD0EDfhrLsokqixxjhwhnuLgVDuJYuXglREyMP0Q0wEjGwRxFg7A8ZsRM4odIKJ5yNUSWWLQw4eNeqmF16YfuaZXXFFffPNuUuWLEX0ycqqMnfuO9ddfzMan3H6aeeee1bNGjVwjX3ppRkTJk4CsuHee40ZfRuiFVbx27fRQ60XXNhz3brv7hp924EH7Y/T5cs+7n/lQHyLFLAcV17R5+STOlesWPHV2dNXrlzdu88V7dq17d+vd/Vq1QoLCx986NFnnn1B06rPp595Hn84Wbu2YP0PPzRsuFeI4Jtvvu3X/+qHptx3ee+eN950a6jWnOrLsBpOjSc76yMO1vX0iVXzxMYdJ2NoMAoodV06bTVt5POVGc8jUerY8cS83Nyvvv565MjbR468YbeaNVatXoPrwYYNG2rVqjV82OADDtivcqVKq9d8duuoO1etWg02A6++okOHE4Hcum0bgvfiJUtBf3zbY6+8sm+1qlWRE9088ra333kvItBB7Fmv3pjRo+rWrfPrr79Oe/q5+yZN9oqbOeN5DBzyzbO6nXnVgCEnn9z5oIMOOPucC8ALEq8ZcvXpZ569cWMh+vLccy8ixcO3zNud2CUjZWrWrPnEY1Nmzppz97iJTZrsM/6euz79ZAWk2A5j8x9w9TUDBvSvv2c96DB/wYc33nhLEeeexxx9JPC71awJszz11DOHH956r780AA28Dg72wQfz77h95KWX9f3ss89N37HgmDv37ZtuHgUymRHTpj3brduZVapUxuVW0kxUeQ92LVPjhg+KNZyV4QM1OLgUWCrc0pAIoTQJ7SpGMkSmUjGQJTAFECRHl8QQcVCXngxR2FBgs4QrJH2w6ig1C5GouigdtbuLCE7ZL6EyezYbaQgf9J5xfUTgcuVwD3rpgweBPTa2MCeRKq5bNIECIrWJ0CtGhLe6FAtmZ2fnZGefdupJ48fft2bN5+2OP65Pn14PPfTohwsXH3fs/yEXQ8uDDz7www8XDxo87P3353X7+xkIhUDeeeetCJ2X9eqH6ImVLHK3779ff8+4Mfvtv++EifdPnvwwgmOH9ifYgl+eMeuLL77E9Btz17h7Jz1w0EEHjhg2GLN3zF13I+T17dPruOP+atMLjAnWqWP7UbfeVHuPPaJxEzTwb0zIRg33jrY1GBiDDSMlgahiDIpYG4JCkymagkUTMBaw/5qZF2JcGnd8QDPx8DHSk4HcnJzTu56CaDLjlVl/adBg4oSx77773qOPPdm0yT5dTzsZbTFAe+xR657x9w6/9kbM8P59ewN57F+POalLJ7Tqf8VAnK777rv7Jk1B6Lx2xDUw8rDh1wMzaOCVIdEYqcMOayl/OTk52J2YMuXeKllZuKK88ea/zjn77wjNXnEIVWCFVAtjh3iNsahRvbowr1o1H2zlJTDoy3nnnv3mW/8ePuKGlMqgef9+l8tfm9aH4QLwwbwFp3c9FV53/XVD8QKpeybcZzsM6G3+++zTeOMvG2+48ZaJ995/xOGtEalBAKE333RdcXHxsBE3TJ8+4+dffoEr4hQOBs1fnjETDg9tccCvHnzwvho1a0y6f8r8+QtPaNcWwR0cZEb8/e9nTJo05d13P2jd6rCDDjwA+LiDXYs8hf/gBuJXdunDcT14kv9Q8GJPUyWJonMpmQB0fM4fqoW0U4KZhy0bCGFtl8yGhHn+MTkR45/647PgVPBCEC5ZPskUEaQiIPrYkXG2SD1N88BU3KfTlMKCBQDQJK+eShjTbJ5Mhixj2fKP4O733D164sRJzz3/0isz50x/8ekTTzj+oYcfk5wRK5HPP/8CfLp07vjIo08gOP7rX29/8umKCRMmIYAWby7Gc637Nm+GuT179msga9/+hM6dO8ye81p+fr5IX71q9dqCdQ0a1EdGAMz11w3D5eTiS3ojqE2f/srrr7+CKPDWW3OrVasm9IiY+LWSunXqDB50FSjRFsmsVIXKTZs3Va1WNYS0T+lKR5cu+9pp12cAYxRWz7wQDRAWy7WkCLXzx4svzXhg8kPg07HDiUj67hw9FvAZZ5zWpk0r7D8ihex+bg88CwI7/7hhQ7PmTVHbqtWhcLp7770fARvJI9I9lNddOxRXHYQkvNB99Ji7x40djXiKyxXo5UDIG32Hyqlvu31MUVERMNdee+OnK1YuWLgIyd3Jp3SZOvXpqDgM2ZDBA3CDS8ZO8/N8Ll22/LbbR6MipTKgadfuOJ5G5TDQ8+YvRHp7eJtW900ch1Dbp9+AlStX2Q4jwgx/nD773AuI4x06nIDEELewsO+JlQ0mZo8ePbGyefvtd6UJ4A0bfhLNceER5FFHHo57dzD19JdfAWbqEw//9f+ORiYutQMHDsWMeGn6jNfmvNy27bGABR8tVUakKuBoHL/oVGBdWpmjSbyICO0VsWRidqUDMxXvLRpPFn+GQC2EWZmCWMPj+VzggDCdzFFoDDsL0DqTAMkfSQeVaJI+Sk/JFjO95ULRNNxHF0P9IjsTJc1DDROGTpI4WN1IAdIqrFy5r776CuU2XhHLPUF4JzC4kF7Rvw+yym+/+Ranu+++O0qsXo84og3yu9NOPRlKvPf+vAMO2B/WQNDEHwhwFFSpgnmOoCanF13iBJEmTRojIOIPtZjb3333/d5774X7zgjHQg8vRyKAoHDc8R2bN2+G+XzvxLFn/q271NolIqmEbBtpwzRSZCo2G40ggcnWo3HR5q57aG9sXKyZ2aNxpwcBYGdj3eIJdVr0XrdofMIIsoi0CjwBJ3S4j4S4JjA2FnJysgEjpxv7zztwtwS5DyU5fAPqrX/9+5STu+Cm6rx5C5o1bYKYAsqmTfcB8QvPqRsp6DEyHaSBwhAlONh7i/048bz9tpGGACmqV5whSAkglglNSmVAZiuDU1xZZ8569YzTT/3iy6+WLVvulWX4I6nE8n+PWrW++3493EZ8tVmzJvAoxEFvWxt5yCHYrC/36qvqTh2eZ4Cvgo/QyIxAsMZwIIG2G4Zg9irlTxQYxLNAJDCXsreoS/JEihscUXQZBCzhTx4bkoRTJYxlODCTKtEUq7j1H/O5RXRFeu4t8+tiU7910dr5mIqgW/chJYly1G3ZG7XA1Dm0t+C9HDS588lxXNnPrgDePjUwlhvY2Prs8y8uuugyBK+33pglVR8uWtKlUwesOzB7sQRD3rHyPytRNeOV2XfceZdpDuCVmbNRBnK1oK+++qZB/fpZWVmyHV5r991XrloNEce27eDQM68VK/4ze86rZ57RNTc3lxFB0a5dW6Q8CxZ8GKAsSPrFMlGIuwTVcXaz8VXrIQ5SQD+4h5qodVr2pm1fLnETDFU2vQ0HkjQU2EFj5DMBP2zooAMP2P+KqwYtWrRk6DUDcZVCkxUrVmJHrE3rVkcddQSm9LXX3QQkTIr9svYdT4l+I03s4MosJyHmygGDwdlUIcuDuCuvGowlsxEntWCCA7MaUQNf0xKkLJ/1qBo2SpkOnU5FcAmwGgIfDQafuKVzyildfvhxw957/QXL4Y8++oTqmFLkBqTlyuFSgV3pU077OzzwqamPSNU336yFU2F/AAtnuQ4K3ggzYqXvsJ5cNpo3a4pgCvezRSTARh/DkImdlEmlTyxbyHRJKM6yTEmtjZKENYqgDbQKl0yt3RkNA3KCSY4q2dBCGM0QtYaaEfchphDtSGtsTCoNRU+nJEV5zIKyQgxHP1ptamEocOiSPgXeQU90b/x23pLJB0DQwRcu57ufrWVyrppxAQiBCeg1B+GGM69U1GaER3YC+jVrPsvKzsZelfHmtsf9ddnyj08//awzzjxHHAtehSUPVjTYKUPGgZwRU9TIErlY6CHfadq0Ca7tWG6DGy742F+/ZeT1aDJnDq2+5RB6PBRyw/XDwQcbkR3anwgR5pGOenXrYo054Kp+2KBEgjBlysO6qfMpfNBpALpkCH7jWoywuH/EpcAowUuGYPHkAzAQ5g9IVCnaGD6OHvpEeOqz4DMBj90u5Cyff/4ljIDnWqQN7F+pYsUBVw/p2OlU3AOR0AOT4gbF6DtvxVKxTu3aXU87xQjw8p87921wHjFsCHbQsKLEEzOgF3G4ENrigEcmC4Njdw9XyvnzFyBpxQ00bFNedtnFqI26lShz5x23RJVherJty5aHmD/EspE3XQc9u511HvYKbr3lRuRuxmHy88MPMMCREEDxq7DoZu3ae4AbjtmzX4VTjRt7JwIrljIXX3QBkFiINGhQH891IewaPaXvffv2ggIga9hw7+XLPwZxmoexJzsVdZ//CI3/bqnPuMKuhyympVJgKvk/yDRs1ymYqAjkktnSKf8RK41hEuZNGCOMmnFTQjJ1cMaEVDCeS8NaCbCbGJjrREIgiLigItNFNDquoz6D7lVCUFQWLBpf8OH4uodejkC5ZApFSXVQ1NYHXyACvKHR9d5P+V6wfb+YDGUdSAOxQEaGgr/v16+XxQXqkb5hLuHOIGD0HgkL9v4v7dln8v0TZBMdnMeMGffty2stZuVmzJiFJ8kfmDQe0Q0PZzz8yOPnnXc2moADtniwp2kTA3700Sd697oEe2qAIRpP4RoCzGH8IVAuXrwUdxjiLgNCT1Yy11vqYGA2Ak2P5boXtCHokIti95WwrREwiuMj3BLLX38N7teTJ/HBPkXQAw88iJ3EF56bCpMiOGLCA4lBQTnpvnuIgh/qHD78BuymYS/y/H90f+zRyUDi9v3zL4RNyuSqwK1bhFQ8ioDgJUzm/vsdrzjUvvraG507dcCIY2cDW89nn93t6gH9oSSW84jRhq3pSzrK/HPM7abhiy++jAiFezsI8cOGXY9bT/3793niiadshwGx4Y+HLhGRZ896CT6w4cefhA+u0M2aNe3W7YyHH7ofmHfefR8lbisPGXL1Sy9MW7p0+aOPPwkMHB59HzDwmttH3Sw68H3/oVTFD/mbGWFGAVXeI3ArrrZ8SxxCl5T+aWeRTy5VngEu5KFcMh+Z2RYhNwYF+QfQdv6oEMqTudKI0kKJNdpIFwhmAczLc1daP72oG0izoBQ+wkLxDEmX7nJZPoOfoa1Rq17x5iI1Q3VPbdb5e7Zp0vkhWaMVFswrWDje1NZteXl+vTZQMr9e68UP7G/wIW5mxyroTkkhXGmRNq5frzbvcdWdMf3Z8RPuwz49sgzcHMDNGcTEFbyORiZYq1atLz7/Ii5UYaMQM9Y8u9uoUUPccDSOGNUR9w0qV6r87VonwkbJ4jB5Vav//OP3di1GstpueyTb31g1v24bA4csjHEJYWzKUrQ/lMcTUV9++ZUxKZLoli0O7nX5FbB/nbp1sPL99tu1PS7sKd1s1KghnuI0Frb77oVxxwYzDoNiakPiBI9naPDkEB4SklOkgWsLCqILdsNEgEYZKhNqjtOQwxgC5K1IirH7bDAGwIrkqy+/QsQUDG5YYZmM2CdptSEDgF78+OOP8nCPjU8HhmsVRh5BVw0p9jgRTHxDh1FEDApNbmm7D8ElOizBAb+Y0Kk1DIQJJizYz9OLtVkKXKJsMayBOsdXzZyQZ5EhcyxXfrwyuoX//cCff/7ZZo6HwjCLsOrBwzG43VGnTm1cVBHahAZLWvzZ9CEYmaaNARP7NAp/b91Ljdamg9GXYf+YJ3PAQATeFR3z5MalV4uv99jMatXaHT8ZiW+q4N4XngrIqlLF2B9kKU1qswJcULAuhAmJk1o8Q4PDUHrjkak1QKbKmIYGCDmMwWNdH6eDuTMjxLjo4qkJ09AG4jjYNAkwu5apdwOFSpmoVpGhXsM6HySUhrmWiiBMBasc5Ik6ppqAG9Bp12YNSJraW2TJym11ekfhmDcI7ZLFQrBmpM7NB6sumjqpJmvPjZzusrZqnzH9d7lUrbkHfpiIUnTuwe9Q7kjIv0xfSwwMHjSg/YnHYycL8RErYjzZgPVXibn9fg3x/g38xPzGn38MiahafTe8j+OPa3/kd1hB456DLPqWLF425JoRJjkKdbbs9PewALlWds6mwqQMQMmNmeO8Jlbhi0KVyh/9wUBYxQWtSAe9IjWVt1LFTisk+4V5GyudRUCIIoNFdC7eWFCx0vatW+UCIFxLEcaTa7iDoM3we33iIYkf1v/wvzwbERNhh2K868pcaflSm52bV75CxT+6/bGC3r3W7kgYf68BLuMbbwG4FpxqK255B5FEAoK0CQUHiyqeZ4oa48PJmaPmklIb1smE42BRLwxSa+ztIjXTd895rmXw69wIWzl5VfE2Kmhgiy81+PePidB848aN2/Vzdnos/rc+Ef42bypC4hxyEVw1cnLz/uj2h/ExBP9bFt9ltIFrISZiIgc9DjmZTGZ77UvwTjy3GEhKgKxYFeRZHLm1PknpqJ9xwDNlVhsVkkFY3PHbr3jgCy9dwasFINMcpQIjUYT1DU8b2KXwuJ7Te323biFjoOcYUj7wgRQSL7eqVKlymf1t9zAwGct3lOHFKrQK+Q2/BrCNnQoOxX/kX/SfSoH5TDmfgrmCwhRZWJfENbC4YEUSeFFdqCT23JoAlscYBhlDTaiNwjjCiD3/AwX+qRJA7B+TsTzdFJ9yHipZqhKq4EzC4o5ySFUqZ+FHz9239FnXHkojzZE2jPyaE1DTclcEsO+TlUNfTthUGJtMbd+2Fc4NV3BeepO2nZVZI/Rl9v9zO5y4VvnyFYr1T7Sl7i9CiPETgbnE3OfdbVpyCkwlxRmQy5JW4HCZWqKisIQFPJSAJBKtBDUKHzE8LbIQRQZ7i8yEmmOTEcFxS/EmZ3JaMjIDYc5gBDJr+ueghtdWzspCuXXL5uJNhbIJQybBtdA60XC57Nx8vAOwzP5/jtH/XXthXAsXVPwIgHEo5VYsW+IJg6HgYNWUWEsRKQEuEK/DXEigK0UqBecSevYW7aDssnHP4pnyjOOLASJ9+neibSPhdUu4pUVvMa1Ymo/4uD3YVc5wddmOOylbtmD5HNdn1y3KVYDps7KxoC57L1WcxcrwsAB2qOFbWDjjq0GpDWI5mS+aeYNRkNHZ8SG1LIfCEhzwiwmdIsYWZsMBWz9PL9ZmKXAGYZElGrZaviDkzFS6cgITh3uawtDC1d9rLT/pM7g62XvIlhIlERB0EotZ+xrldjoY3iiexAZsQrCbIapLWFIv06iLFWbaBmNkm8qGRU3BWCozB2UHudTq0vAuEeBXyBJsmzVegvRc6l0rmLEL7mYmsYxnZGcZOqNP4hSvrLfGL9jxPe0ykgNGS+ErOkVkuFYpFX/LcN55NbAtyLBnvHRfYnqmq+Pnmu2q2ohsgcz2Fqm/lmf+N8ztsZnuQNz4G3NYgIeNtonVI2d6S8izeDAojASbkqm/cahZmCjluauC5S7mjqEdzixYONsLqJSyrNG27JRGMyIJdTRg4BvBBHLPSHn80FLKK9iqT9JNC3PJY7QLE5lzd4wcbQMzKFE8gnrsVGBVvm2JNawzANKwQxw3b1PyJb236HzjxT/Uwlv6GyfHwntF6npvpW3KJGHexkpnaReiKHG2mKqzITl2B3RTl8Sa3qmyMG2qlJ+WhHD2YTmcT7eUrJkg4C9Xb2bqd5GoEG0GaqRCFaHkRGaMUxE3X9NT1VAFOlsmMLWhbil1pEtBEiAtw5mjM4Ja26CXroyMziydw0pYnchElJVtRbPF8K0D0dXP3lLNHfj/3tiJCqJkYB7rcujkD5bBuInT2h01f48zGjntz9oaqcJ9Sm1YJ8fT7PQltcbpjVcm2SIbNNEoXpmJLUxlWvbQxnWuvoYFAV4NxFjRklsqE7sO4fBMzVTI/WPi1cjlH3MWaxLJLOTiobMMecLJCq861MYwT0SXXOd4WwVjZ4WkeHI9HqKn37ROH2J09qJtV0gS4G3skSooL7UtimAxg2qgbZIYtxx5qU+8WsQ3E3KljzZ6OKTucs8tliBbdE2caFbx+uAKFg5bOxH1XS2sM8stwoLD6Y3VKl3QygqjWUbSJCcBlmo+WFfbsU1dANJVL4ZOc064nPAMDY+XPT0s9UWKFapS849RLB4djF2iEvEMpManmcfrUmccwiieKZtWh7l4O6fSN1of9MC5nOjwGoRaLdyJsWy8KFMLE/CPjLBFlRJ07ONVxWHvkAfR2DctVG0a6Uuckt4uBtcrT3Um2WIkqgSctUKlP1Vi7eczvVYj+hnLxhkSz4TR9VGW1jDbhrBhaRQYxTMAPr4Z4HSYpibBc2T2M2VWVJHZqqJuukJKSWcPG3cE3VDiIdcjYRs4MK2u5W55W7sd9pLYrF3ymMEOE3nPU4oisb4VgGOSJN28YoH0So4jdvHSVHAWm2Bv0fYxJSm8+VASleN0jtEnjtzXGasb1Mw5QnUlyBaps3g0pOwBHceuf8ATfjCIvlGDZzhi1bf8JWUCx9PAe2mJZZ9ehVeJUpz1Hp3tKW3DYYUt1dwp6gb9DK9FYSnqPFaYoVdjJBcJnwp2LimtpHcMe/kb3iUCRCExTeBAaY2daCNSXc284xUE5SRF45laF6ESPbeIx7mrZOdi3YEfhsbVI0mJNOrwzRk8spcGYRlJKVsAz/pWpOtbFn6YB4+RW9xdJ1T+YtUngN6mNPNkjtJ8DTKONO5mRkOSNY1tPbyC9fQTQi9JVEDANaFBQGSgBGpbSADrfeH/wbvPvmjmDUbutcDYIjPAazk9dt5K2wlsOJDrbRbWNhgLvdoQTMbZYn61mniWGF/OpbEspQOPMW8p3lxKzMrYZGwB+kJhhQqbNv4SvqhbC3DlQJGNFBamJozrWhmr4TTwzUvrep6uKJkcwtmdKCkmuaMMTuIZWVrp9MxChflkfO4X7Nln1MLtfNBOUmUEI/JdqyjNI1QZIdQlMKSQtiEEyqEV8mqg/C1o5BmvMCN9Hv5MKYAaaG3ZApntLeKnfctXqFS6MZE0qoB3YLjf8w33rOz8d7QAvmWD1z8hecRXxCJivD4VofIivE3J37UL2jM4mABOUIlMD09tRLhXsEvlJbGFueQx2oWJzLmwl9NUonjC/w9njnaKlVGmL90XoxrLxANeO2lyb2V0vPzCvI2TrnQZfHWvMqZOdi6+TcSa2vJLAcZaDjnj7/oztNrAZZ8eC+AHhvGrU2qf0fIijmBITZCd0X6J/M6R8sZw5uhcz1U6BxwaozkOp+QTcCS8VERICCGNSDLTqRU4r8OpTtg7kZJbqXaOSFUhH9Q08mvPtDtFHQ12qVzPVuoyB60zNWBp4ZLiHHdNa677KS3dks7IvvxhlQSqTigoLEbqgZWDLCXjokpr1GQELWW5CYQGrZ3OxIyd0pPVkqF1+0JcBcOQtoOjVlgJ1oTasTaKH8M8IkKuRgcYz9gJBxlBzc3+VDyZnTAippb/KD3F0zLIFrNy6RXMrKEtrzTh0vntidLUaFfhhX1i7DPiR1Y4YQw7ZAaD7m2qIoMKE+yN4dATne6CkQGw4Zgh8QqW+ZRYJgmI4WlpIBSC8FKH+yVhSzUIxzAKC3YDS1DaoFeL+NYxHXDD6y733GJm2aK8rihieOW0O4kv+9WDeOf9b9QgYaySnY0QwlMiUlqxRbSxQpU78jFZBqcCaApiuk4rSM6jJeRb+YWGmc4SLJrokni6nEiMzgEN7OYactuHM0Th47LXPIWRUBAcaBKXcUiE0+0tzQSlWPEJJUlgKCVXBDSAlDDncqLDK9WFo2t47ERUTOmkTCp9stVRMH3oUROY+HElMxY2DEYVYgxTa+OiIUBFTjB1Xl82BTbjJSOoRk1rqBlpJr5P0Y60luFVjslNLZjsJ1Y0ZQbZYk5+dV7kUnfoNjQpomDWaWfxYJLwEzIsoqz4HS2AhDErm34vzpUhDhwqXZJ0zkIM4Cyh2cyuyR5F3uwh13XU1IVFvuAZDtVbNUZVL4nN2lAqwNsgTOQ997a0RRFMVwHJoIPfMXRMYjfwivEgvZI9dB6UNJUKi43oKWOndIbmakSSVgAeEX6UJSwYZz3kEX0CEq0EYcJHDE+LLERRwapKDdoSDQyAw61qbuPz6x5mmNr4OHpDbAN07fMdZXixSmnbgceTnIv/yPYqS+BxUNJkTKhEpVPyxRkowqsSAJ+FSiKgplI6bKhnJFmVREaEXOgyEEBUxIRK/mNa0DFOl0Rl/pgJMaVG3n+6GdUSTKTMwC7DRELKJZNx10BEzVHiM67kelJE07JAKki+KlkX69yqI5nMg0sFUzOLn8BAMT+SxbAGHNW4IWOYiprSmXAUSQF34geRXKtgwWj5XMXCCMO0vpKEMAGXTCgYxVo1xVnsP2IR+SNyJY8bMsx0WhozZL2oJ+UzyBazc6v+Ri9NR1jGESohKIQhmhpNTs2r26qoYEGk1k+f8FwxXkmenZWNl/yCL+5b/+Mf3WvUrPH551+QLqV95OXmvvjCtGpVqy5Y8GFGvPdp3OjMM7oedmjLffZpjB+IX7/+h4yal4C4f7/Lhw8b/MyzL6T1g3qpBOBJHc4WZXRCZarGUh9qFFzDZaVHjoDZpX7hWRawqoRnkg8llCJBaCLaeNtpdil004LDTL08Y+RrUQltwr2TDBET1toucEwQ1iedc6/8dBo6HRClZDC0gsgKdT5LS1l/bpiepBBVjM5etG1EYeMfEG/jJO+yGWewtxjqyn/59OKLeyxZsuzDRYsh9+abrz/6qCMGDxnxO+mwbfv2osIiHAn8bxt1c8WKFa4eONSmOeaYo87tflZhYWF2Nn4jttKPGzZcffU15t3tNqXAXiZRshDmuWenTpv2zNSnngEer4sq2lREUabUDsoJ1B3n1HefRaqaJCq6yKIKbonDKflECsjQ1XT9DhHSOcUM+tBxVEDQCntf+Ah4an48DQTNKJrK0JJTBs+daKExjbWGhJBmgiHYIg1g1pnth2rSnEqbSwSWeqe0Gihy8JEjEMxm0IEpFFftUWNYGouampX5DHhyN5mKlbZVVwqSFcQWpiQ+TK5KPg8rpI2h7UZEAcwakARnh5HZJo+XsNEOoc6sD60XsZYRtLooHbW7yxcnGbXMwiK2n1gqmOFIo+TpmkkrZpxYdD+nG2Lio489+f4H8xIJS16Jd5z/rdu5ye3r1au7JebLOV1OPgNtj/3rMdddO3T06FGnde0WxyqBSVwT4GtUr5aXlycEUx58BH8JxJlXqRgrkZb2uxAkpSTPF9gZe/EEpA8syyqJXLw7VPqmTYiEtWA6uD0HSFUySxZGwRitRKDTz0AwZppWwtWPGyMyAuuWRE7dDR1sAaWjwEJkdZeSPhxciuYsOtCAtNXaWDApIRm0XJE4iHCIICLSkJoxa1MyljQFxhIbgkmajJ1IZmswj6AgDqKnU/oUhTTRk6M9+s+a8wg45Hq8mLEau7AQOlcCtQasLVtIRk20VbWekdKWAQVq5SBYgcFHMF5owpry2Ilp2YRopPRkBjsyC4uBpBgor27r/LqtCgsWFBXMD5Ggqk6LXusWT4xWhSgTTlu3OuySi3ssXbr8gckPCdme9eqNGT0Kr2bHKnLa08/dN2ny4489+PXX3wweMhwElStXfumFaQ8/8njNmjURp5DHNWmyD2zz7rsfDBt+PQiQ0901+rYDDtivfIXyq1et6X/FwKJNdM/hlRnPP/nkNATfiRPGfv/9+saNGzaoX/+nn3++c/TYt99+9/5J4/f6SwOQvTp7OhawEAo4dPxr7tuQe2GPfyCFRBMRdOBB+4Ns+bKP+1858L57x4WY9Ovb++STOlWpUuW7777v239AQcE6EF96yYVdTzs5NzcXyt888rZrR1xTsWJFXBvO6nbmVQOGgHmHDidI5G3Xrm3/fr3xfnpQPvjQo1AMzXtddkmb1od9v/6HVoe1hImemvasMV1IYX0aXFd5GsBfBCNzlkszyeBNAlNpJolqoHBUw7ydkk/sLClKEmBAR3Ko5HnJZcDeViLoBUG2SF3Dn6Qt3550S2qgMNzYnmUkO8qTGlikAawyR6nW+ofba/3okyzLH1ZJoOqEggIBWjDrzIREC0msqC6tUZMs0lKWG0kHpL3AuqRY4xk7pSerJdWst1LThompHi9XrbASIp6oqVeKB8N0ovrMwti3PGMnHMTzNDf7U/FkdsKIOxeY08n0M7vlgssAHVDUHC5cuHYearGfeNAFS2u36CVw445TGnecXKfFZesWTSgsmG+aRvnY/YjCVavm33rrjbDzjTffKrWINVOm3FslK+vGm259481/nXP237Et+NFHH7dpcxiCC2gQZRBQXnv9rZo1ayA1w+r4ppG3rVq15pijj2xxyMEgQNRDqHpl5uynn36+UaOG4Cacc3Ny0ATwbrtRPP3662/vGntPTk7OZT0vAnLy5IeLi4vX//DDmLvGvTxjpjSJlk/xOvfggw5E1T3jxuy3/74TJt6PtpDYof0JISY9LjjvjNNPhSY33TwqLz8Pm4ZodcH55yICLlq8dODgYUuXffTjjxvuHjcReGwmQPSq1WugZNV8ep70oIMOHDFs8ObNm8fcdTcU69un13HH/RV4EDRu3Kh+/T1HjyE8TARk4qFSEHJGGmcqGSbHZhQjQjBIUG39kzNdUlMad81LODIP4R1bkkgt2QKFk10SGXFhkbqkFl7WgabSTM5VM8OIGhsZGmYhzBVVhtTAgkE7xkifTUnM6ET+FHthbEpgia3vEGyoJF7cgEotlVhYMAlkFBESrFWgzxDsNEQttVQlqwVywhA/VTLowIJhCiYMxBMvamdKAIRQJVErmBlqYYw3GMKGanDKGCr5jwhEErHEPyoDKAITMVpwmxJli0H81kEfDHEI/rtFEyDgu8UTERaROSJJXDPrIoRL1QpBWag1vc1N1/g/999vX8S133b8hsVpn75XgeioIw/Pyc6+9tobP12xcsHCRce3PfbkU7pMuv9BBB0kU488+kSXLp0K1q1bv349iPGqn169+wN4/fU333xjZufOHZYuW960yT7zFyxEDgg8wm7nTh1q1Kj+008/49QcX3399ZBraB+zTetWRxzeGgDW78VbtmzY8NPMWXMMWRQADcy8xx61KlSosG/zZjNemTV79msga9/+BEjv22+AzaRLl45IcidPoRXxa6+92f7E4wGcdBLpP3TYdYDnzVuAcsV/Vg4ZPOCTT1aERCOk4oJx8SW9N24snD79lddffwU55ltvzUUTJIndz+0BYOu2bcOHDmrevNmKFf/BacxB2QAyxHBpjZc0xDhi7PiwxxCXYq6ROqcMTpKzDKKDfHioTn40HFFCKWDUoJZ8IqWVHMTlGrSsUtmHcLH6ZXEDPxDKQXCgiXSXMU7GEZshajbEjNKjUEkIY9lAjOipSm0YUSRVhsgc4wo9XtIla+xc1UhPPWoCE8OgK8JGhOhRo7gUwFZXNKjIqRckTZXkeqIN9xYwCdZjpDR0R0kYhUvRTjVmFtwHcc+gJBVRYZeZLaI5lkJNDqmihELxiYsHWeHa+RCYV+ewjd9+AICOeHpFIGS+EgGxX78BJ5xwPELAYYe1XLhwUYsWh4Dw9ttGGnKEualTn17z2ecnn9T5iSenYfH78MOPm1oDFBVu2nff5gccsD+GAAtqwb/51lyExTZtWs2Z87qhBMD33wmBtS0WsHZVMrz77ruB/xdffCmCunTuiD9pUsDJrN18t5o1wRxLfkHK9Q3I+endDW/SpDECIv7QHE/dQ9W9995LsaLhouObr79BWb1aVT6LK9QISSO1q0gRgXxcl6qtHjL5jJaWiMDT9ZwgP7SmjZ4IXI1JQPJ9JdOxdQxLEUP0SqCpcXTW+tGnu59I7ATDXaR5GD7YAkp1gYVIMyV6UlmXpCad4H+gTQJMERUcMentfUY0kK4GGpKeWoiSwR+2cA2TZBlBSzKpZR2KB2MsWJQWOqsDfLVSoyYwlUpN0d87aqJiIEQkCXtLKuzFOodLPV5iVcsmfuMKX1MG40XWpUGhUgZHmZJ9EczIcigzC4tiHxGXDOfXa12nZe+lUw7csajcIRcux9q5iENkclupDZVqJMqVQxz8+JNPP/10RaeOJ1434ppTu3ZbuXIViK8cMHjRoiWmFeixqXfDdcNwUxh+9vTTz5kqAbAWRmK4eMnSlf9ZCUzLFgc//8JLALBxiXLZso+EjD1Jg9an0YdNG1QAH5wwJAvhd959/6svvwIC2eIdd/7T0Ai9aYMNwY2FRUjrDH9Q4n54s2ZNpImNl7aGFYCvvvoGu5/YOsD9IpzW2n33latW2wQBHNFTqjRPoxGjcUbeTiXHMSn9SQC7FPmsmt4yyamxZoVPOVMlfyh97Aqit85JMhBUQj5cl0qikJIrNcyinNZGAVWlmMvUICZoin+hklmwEGHPslVTfIh2HiKqUcqq8BHozApY/VJcFIbtQOqAq9gkTEo6xohk1tSQrzWBCjJedqlphLlQ6tb6zDZuWGmtW6CnVpMxIXJQS6gR+/HYGYNqW2mxgRqAyAQcJQlLMJtGaRYdL4VRJlWcgg9LRzXwYUNaHlU+w71F6EmX8dRl7Za9sZMolKtnXlCnRe90WgW9sCDOG+j8t99gE0qFEFyqV6+Ozbi5c9/GCnHEsCGIaNlZWYiDIAA9Fo/YZTv//O6fff6F3EIBHhuRZ531N+yy3TXmNpzOmjUHa1gsUY86+shTT+lCC/CTOyPbWruWHo3kg8RFD9EH6ViDBvWhhrkpbPRs2rQJuI2+cxQ2JRd+uAixG4LWFqzr0OHE07ueAjWQM+JOEehtJu++90GD+nte3rsnNkMPPvjAo446AqLx4CQSxsGDBuCWkdy9ARKBDyktdlFl/1Q0RMyFB6JruKd0y8jrIWXOHFqwew5yU8+h9ZdaXdInDR19eEvyBSaRkol5rAMInKmSSgHVh/DVJYugQqTZpZLMdQwzv5BgVoLEUEtVanasm80aCCOAGsg/xYLPmDszIlKi5xKEwkyV1JBQROCU3IQ7jmqu5b5TQTyopIZUUvugJJDZMeAUTKbqQjBO6U9xZRaxErgpauWP1JY/bs7KkQaEpFLYUhtChEuWRAXXMeAU1BwHlwIzQxJJ/KRkAgJZgiqVMGlN7QPxBIXPVD015j8isGUwrJnQp4YDRgazI7OwSCqmcdQ99HLkhgUfjhdaLKVx97luS7qHUCoH7q5gZYogiBCJu7G5ebl33nHL7FkvnXfeOXvt9RcRMefVNyqUr/DCi9NtiRddeP4Tjz2Ibb5nn3vh7XfeQ9WlPfv+/PPPV13ZD/uV27Ztu6RnoOSv9Ow6Hb/+pgBxOkFOm/ZsJb7NPeqWGwVjygcmjcf94v32aw4pUE/wl/bss+HHDcgfX391xtVX98cmAPA2k1G3jUYMxVPrr7z83Lixo09o1xYEuJW0/KOPkR2/8NzUc889C3ETyFdfe6NZ0ya4V44ga1TCzW7kyPvvv+/k+yccdeQR019+5bnnKQU2BIBNRwDHH5INREpcq/mKS6XAXOKUSLnUn8EZqnQNkQkM0QwTGcGqJI1YgpyDmBBMoUqmJSaMtkrGERLUTimsmC9XUlMWQ5/Rf1ZjVBIzUzKxwhBskWoYKGbOH6qH/EEFcQqX1JBaEK+gJJAEqCOANJYwrIKUzJa5EyOuDJU4JQyXAlulpRQ3F0WZFVNxS8WX1bJE8zkxpkNKhhikhsRGlxEl0CIkwekKMwyEERT6h1P6Y6yCBUOlcOdSCKhkbLQkampCpcD51euS3mkcNWrVK95cRGEWLTMv8/dsU/jtvOS2+OG/NBTxkOABHXTISvTKDRs6qN3xx53Q/iT5sbKh1wzEabsTuyCV++yzz3H7xeaC2yz4mWq5M2Pjk2HcSGnerCluByOeJlOaWizea9Wq9cXnXyCgCzLEBFleo0YNEfRlLSw0yAqxUbh61WrTCsnjbjVreB8Ul+Yl+4k2/J7mLxvo9pR1eAfbqk8AvU3Jc2Sl55YldCv29bAOXsEukZfE9mqXPEa7MJE5F/ZymkoUVm/2fmKw9HZmmWGdAeCVnF57b1PZvYDrUvDgbQdV+qe1SBKjpiHVK1K381ZGx8svzNtY6SwCQhSZ7S1y6qkVzfATN6MzbJEBuTziJw2wijzxhOPxN+fV16PRQbYjQ6xDt55DtXGnYP7Jpyviar14c1fE1IaYIF5HNUSIDCE38GGY2ACCvn2aOcxXVe1wHMEEQxdSdYdagoTM3yBg8PSm3S9nF4jq4XQ4nJJPaHYBLxUREkJII5LMdGpe0lWdNZTSFz4cYczJKaCTZz9RdVQmPIm2Z5kEA7cbfKZtFTRgZWUPVHho/U2PXGPwGScq3EvqtliGdXbMExWmxEr3yFIyLqq0Rk12hy2VNXfwVK11j1mKMrGmwqcopcaLTmRoRcFoSS1ppByFqGt2J4jIHMQa1HwusOod4dXokNDo2AkLoTHsLEBrFzBi7QNNlJ6iXAZhUVI5cnr815b8PWCrMyUBsQeHrTc82Tdq1J2m/c8//7Jpc9l7EYw9PAB+nZt/75LDGpwTLkZLcCoZVoMuQy/j78Dk7OIcquQICedDc5oear4SL0JxaTk9eSkTuiUrSnLYX1HHkl3BRBPoybSKUwxTEk9aUUnibJiZUXP8ZwtYeukZSzRySOeYXHeUmAFmnaVkpbn/2gyqsbYJaI19XPW1HPkkrUgvt5RzRkJSMGoWHGs2pbQ0ls5wSUGClJKSPvhK5B87JZqNLXBQqpFiHR2YSZTqwchDjLKWO6T28IbHTkZQi9TWCHg6/qZGR8bILkk5NWrwi/QX0XizVXn+XftAYKzXWa6UNg3uqOivCeouln3+Fy2A34mAzxQXFQUzNHG2khsp9bwesXOqp6lEKiE+zWjeW1MlgIWZ1S+LuzAShI8pzzAd5viE4xdId/IIhFk66ygqMrRYDmQUVULGS9Qg4G/N1MQW3kphI1Uh8Uohh71Dri0llo8p1ai5tF5VIkhvFwMxnuoMfkEHYSsnr+r2rfSWPgqtpV+qOxuRbpUh/hsWwEsL8P4//PCPK8zjNC5BemceNu5sdkOJh9ydEIFXa/mC4TNva00nn16SKNOgkbdBUJ0AeVvaogjmxEwFPoEpuFihxIYThDlVXskOReyJNJVqi43oKXFX6QxtlZpygfGnRLGCwhWWsICTtkNEn4BEK2Gu1RbjGJ7xFBlki2CChLFCxUp444dcAIStglmtEuMpWac4S2XoUCMRwmpPiqCVh5XhM7IbpYo7dhRvivxokOVRKZMAdgFPLhYdiwwxXiX0VLEqfVPCL0oaSZ2VhQWZI/fFE5fC7ELSg2Zu0LcnRphF+uexwgwLNUZyCfGpIIrY6ojKzMHL3/AuESAKSfQKHCitsXPHyB4Lj4/pFUAqF4hnagnI5PcWYT6kilWycjDf9IuuSmSpaCNa8IN92fH/wQLYUszKyYHgzUUbtfidmB7epjTzZI7SfFXxGhhnMStTJ6kU9axprPWlT69gjRZCL4mw8zNNaGCLVnACtS0kgIOs0F73ap3BriSHV4v0GFlNfdHMG4zCdk9PUojKEhzws+wQmEzivS6FTQZjF3CPYyn4zLJF0RQ5Y+Us+nV761dpQ/3M7NSbJGbGoow6cwsgIFbOykK5dUvxls340UbliMFFPTpbBWN5LItVE0Z7q99PM1PQNy+t63m6omTCiWh38qWY5GFt4xlZWun0zEKF+WR87hdsZbj6iqOF2/lgTFZoK+FaRWluE2QOJzkQu46w1G7i1UA7owlknvHSmmlG+jz8mVIANdDmYwtkEBaVublfFSth1ZWDt5hiTR1Wouz8D2IB3Hfevn3btq3FCT+KbgVA21HT62GsO2oXtGdwjCRbahwc0cYr2KXyktgCXPIY7cJE5lzYy2kqUTzh/4czRxOZEDEyyvSl+2JUY5l4wGsnTe6tjI6XX5i3seqWCAhRZBAWuX2ouVY6+hkitDugVXdJPFeDaCPdNCrPi7EkhLOPGD/PTEDAX67ezNT2otQw6a2jhFyo+FaiZENOhU6QvD1NHxnobJnAbW6RhM1mZ4s2TLycEdTaZmZQVw9zFlGI1LLUt2HTKBEIaWvvTLkwjaAc/p5YqrmD/d8bO1FBlPQnajJS4fFSLXikbDjUJakqaelXyBo7l7HTF9eHrEF2PE3GS9j4x8gWEepcwNQZrwzuRKcW6fqF7pStVDyclj0c1b38Y3utqQM7KF2Uid16V8+UTIXcbyBvY5d/zFmsSSSzkDCkswxsRPCVnJZUEjLsBVSMhDi0ZuHEnjhiF+9tSpYJxs4KSX6PiYyR0sOVFDrzCramn83UhoWNYEIs/do5RCJVUF4NbFEEixlUA20TO2u2Gzii0jzxahHfNqYDZrhYtZ14bjFeslVj6SyCyfJpj53FyAIDnhn5m5i/BNmiJdqoLrhAEzX84leBicM93Ymo72phnUWUIPEi2H+5tNqmBsXEwi4jcxNrSzUfrKtVPNvZ+WF1RnPWhmBVrHqc8/w4mLkAAEAASURBVAwNj1dSlmGHqtT8HWHpnAROk6hEKlY+zTxelzrjEEYizseUzaHDXLydU+kbrQ+EOf4mouQKqMU60dUyW5SphQn4Ww5q1acJOvbxKuSwd8jdlMT2ewtOI32JU9XbxYC1pzqTbDESVQLOWiHB8JlHmKbK5DPWfj7TxzOOZeMMiWfC6Hofa28Xk4zibeDjnDZOh2lqIPs+kVJnKFq4irrpitDNdnKqe9i4I+jy95DrkbANbPmbas/d8rZ2O+wlsVm75NacTiIKN4rXxuZCsG8F4JjEbuAV40F6O+mh86D+H3vXAZhFlfwTID10EJUioNjrqYj9BFFEEQV7PxVQz3Z6ejZUsPeKiCi2s9cTURAR2+mdig3UU4rSlN4JISHk/5uZ93bn7b79ShLur2eW8L7Z96a9mdl5s283+YRUBhQb0VNCKow0Y53f63uLseAQs4nHXNsmmNVXJRGhWgkzfXfMJ9XVwTlzfGt/FSA2oeyYKp7hxAwkwr0YOsIJtvlBLgNVLHoHnFllf+LVKJmNQg/0dKqSqAm9S0sy/4xGvEpEBeu8mJarcBS0GsReyF+pFoaAdqo2VkhWMyhRWMDO+EiWEBs+WgUNC5UKeS//gHeNAFFITBMGUEa+c32k1whPjOkd4VSKJjNVArJ8bxE8Ux51ZFYvGx1qTv5wNMpo1k7d4S3OJVRswGTF1FEHJ97JRJHSnvu4sObRvcXw7wHIBZCWsw/BJ82H5+nzkoa+I9OHFUf9e4u8I2wX6djtmMe+abu8DkhLxQiK1JfNvMkoGt+ZSYpgKcEhv4RLx700iZG9TF2mfp7eXs1S4BrsLbpaiBzRKEFmaOLoTNMYWri68ty5pz4LVye10aJtUBMB4SRVlRE6U7NPgklsyCYC2yVeLV6pZ5nBaKKwgDb0kTaVhkVN/5XrXVoC3jUC/ApFbJV0SYQSZeZy7loho9jLhJFy1IbzXXQGTuxZsboS1LAQJ1xHrlUSckxoh0ygLK87rwaxa8fjL6tLwszscPK1puPHGpEtUKu9RZGboJR3soGmGQMeNnYC2vPpSiMPG2sT5QDn8tbb8KG6wkjO0zINyVIQRJHSnrsqqHAJK8fwxlwbSTinM1VEvneSEZyEUy8pmdvnwYSMp7yj8o/ru5h4r2AXy4uihbnoCdpFkYJzYS+n6USxB63vfoWVo04rWVX6Mn0xamCZZMBrJ4vuHYz7yy/MS2ymJQIiGDWuFv3y7SQSosidhquKurzDN+C0R0I4lJIGUhKi1UdKDdOwDYZD/rJ6M9NQTz3dJJh42SzB172cyHXvDNhUEEivGRDqrEzgslIoUbPpalHDxMvxoNU2XZy4khPOYgqRWkp9DSewiHRHtNU7Uy5M3pTDPxOlmuv4/57vRAVR0rvi6KUxZiqH2vWaf8bWHpl9+hVSvnPZpNWGdXIiTZcv6TXOzF/ZVIs8F3cWkTOvzAhOwmlG9kgfal4NdChomFUxJnYDwlUyLVNB9/vES+zyTzhLNInvqWX9e4uuB60zvOaPBYFJslFHeIkdpEQf+bQhsRLCwiPLlOEITjxJr7NDmjABV7X69xYdm8VPYEX3SGlWWT9DE6tFgiOzFlnf1UKdqbCICo6WN4oqU1BVhemfmEevPqWaW1nINWSHzV1ulDpTFT14lrNTYjl4Yqqov8RgCWYT/ZhLev6OsExOQt+lVCIdK59mnqhLX3EIIxHnY6oq/chwOhXTjIfcVIUbplfygq0WnKpQmS2lhJC/ujpTUngHhY0M+RVy2DvoSUuI059B+eJVDJ3eKYaXl2c4m2oxdnmEnK1CdX+pJNrPFwtWjfhnIhuf6e1tFCU+Ox5nmdbcliQ0SsAtbjmLnOWnTdNEFr5HZn7jJfy9F51pDZypoDrS2cPG9aCbsj3o1hPadqFp7ShPy0vtTtiLolm76AnOjiJ5z9OKIrG+OwDHJKl084pFp1dyErLbL6TSp9iEe4s6xoyk+vcWjcfEbDo4rXETzJrkKbUSpq/CkqVa6Z5Px7d2Y7tWkZNEnNXU+ZIw6ohRVQqzicMZ8Mwtmy5lh9AZyQwUetoigOPAU4slc89wxKtEkvkz4un6yKOzzkIajnJXqoXm1E7VJVyUONvzRGEBI+MjWSRs+GgVNCxUMjuGvfwD3jUCRCExTRhAGfnO9ZFeI7z+CpNyKkWTmSoB9e8t2gcFttrQl4ANmIxMaRlEfVI3oebjwsFhn2DavcX69xa1B+3lJ07xGVFdDFHPWWIvWRzZzYo2HLQ2Udj6zi7Ssdsxj5C0XZlq62GkSH3ZzJuMorP2sE3fpQSH/FKa316axFvDoSw/T29v1C+QnPl3ubDEgK2VLx1yFgy6ckITR2eaxtDC1T9rKz/VZ7g6qY0WrVtNBISTVBVu6EzNPgkmsSGbCGyX+JTXa6pZe8YShQW4oY+0qTQsavqvXONHG6E191igj9kwEzOFfozYygoMyaKQzFx6XStkFHshu2RGylEbznfRGTixZ8XqSlDDQpzgFdcqCTkmtEMmUOiveACxB12FvBrErh2Pv6wuCTOzw8nXmo4fa0S2QK32Ft3ZBVoI4J1sBCeDUw8bOwHteYGT+XnYWJsoBziXt96GDxkLIzlPyzQkS0EQRUp77qqgwsVWH3HDaPNoOK2srPOQ5phoH58HEyQp76j84/pOy2TYK9jF8qJoYS56gnZRpODc9VGi5iyQPWh99yusHHVaCfcWM/gNJbGGGDWwTDLgdYlF9w7G/eUX5iU20xIBEYwaV4t++XYSCVHkTsNVRV3e9sZWo2s4lJIGUhKi5VBKDdOwDYZD/vbRB3p0FKWHiZfNEnz1yIlc986ATQWB9JoBoc5KU5eVQomaTVeLGiZejgettunixJWccBZTiNRS6ms4gUWkO6Ktfcgm/gp3qbT2Gg65KdVcZ//3fCcqiEr+Qi1V0eZQu17zzziceyaQXyHlO5dJWm1YJyfSdPmSXuPM/JVNtchzcWcROfPKjOAknGZkj/Sh5tVAjBVvWRVjYjcgXCXTMhV0v0+8xC7/hLNEk0hlIWnIVhl2b5HuPiVlZFkVaiUsCyf3aIRk2EtKlgl9p1KSm0lcH2hzajhBtlewuvyERbwVdn4BCTyVBoIhHV7sqEAxgyGwNnHKe6WzEpQx6NUimTphAoG7WLX69xaTDcgjsKJ7pDSrRH1oYuVxDpdaZH1XC3WmwiIqOFreKKpMQVUVpn9iri8JEqBU88F22OSzKHWmKnrwLOfkNCemivorVZVheLG09Pw9SqXuCn2XUonUTPwm90Rd+opDpijivNMld9k0l2zndPrGx0NhznJi06sW62RXZbY4U9UT8lfWUuMZgo59rB0chRz2DrpvOdTRz3AG5UuSqt4phgI8w9lUi7GsEnK2CkkPn3mEWaxsPhPt5zN9MuNENo5LPBeMHfex9k4xlVG8BD7OGffZNE0E9e8twr58ZGBnL0rcd4YhPrwE4XAKyEupRRHsuwNwsqsmSCHMGfJKdjAST4RUhhWbcG+x/r1FZTtlIQoUOcRj9kw+E8yaFF1qJUxfhSVLdXVwzpTmwaquQ03DDmGKE8UznJiBhMyLoUURbDO8XAaqWPQOpNAnkyGvRsmECj3QM6kI4Hl5l5Zk/hmNeJWoTaaK+Mijc9RHScujUi0MAe1UbayMJpsCKVFYQGN8JNra8NEqaFioZKYMe/kHvGsEiEJimjCAMvKdaCNSXc28/gp3hFMpmsxULUL17y3aBwU27OOXg3Whzz/KlJZB1CeuQw1BFCntuY8LB0d0b7H+vUXtwQ3hO5VGXLf5fOQJkFA/67tf4dNnXzbzJiN3LXANkvGZ13LWd95B7QQNhyK9ZFFtQ1/Yy1d6avwk2soX6XLm1cTucZBG0ZmmMbRwbZiX17plq4ULF1RVVUlPpm24OlklqEcpIYz8Zk0SEk5SVbiZmjsUFbJR+tCwXeI9iiaplLY/UVhA6bsMfFdrbCOFORg/uqEV8K4R4FcoYisrMFmCzFzGXStkFHsh42RGylEbznfRGTixZ8XqSlDDQhzGXjgrQK5VzGQcjKxPghjWSoh4rYSFvRpodIY9/rKKWUb2PPqZVgARWCOyBbLbW9yta9emzZotWrhAFNl4k0222Wa7uXPnQG7s8OoSw0rbkZuz2Wab/eWSSz//7LNVq1exzjSBLbps2bZtu/kL5jvXbjI3rzZJpperTW/Dh4yFkZynZRqSEbTrbrt36dJls46dWrVuta6qajWmI2uFi5b2zFVBhYutPpxoVCsCOOfn5w++ZgjE/vTjj2kFMYJ3krUgJaPbENSKJlygMR+pLJSYFTPQ2YuihUWn6CWIIgXngi2nXkolij1ofefEs0IKOGcDeCVnxsBLSrFET6XJg//T7y02ysxIhAVT9D/q2ILCgqFXD16+fCk6dtxplwN7HvzpJ//2MGG7HX/SyQ1yGzz198fZhjaKxdnRy0Bd3vbGViGSeFoh8Z9VOaDHgZtssumUyV+zh1i5BCWMHPYn44lvqRsiY60RQEJYlJ+r6Q1ZEGTYEZnoqaMacL/+R+fl5a1evbpxk8ZQ5z/ffffwiAeJk80SQdJnFuh3Bqz5hMJowNRsEtaWjMR03hadVVXrocDa8rWASbBJMnwWNEG3SalsCbk7D+/R7fUbcnE8aDhbmxhpIpNa0tNp6Zzn4GnJEIxtrksQat85mZL5eHiY/mCY/CVeUy1bRPqZhXhQaCQZuJwVUsTZEm80Tau5ma13jmQN8Z1jEzaSISAlDDfrNaOd9IqSZCnjNeJkvxOBeBtYcWESh9rhnOA7oyerxZHhnZGow7NyFWK1okqE2KSBmXZ0tiYMMezxnXAw+cGyU5+GJ/FWF6dMUVyEfM8+IKtmkRaBjgNGQbJ7cNi9bEGRKyM0GJoVsnOqN2q90dq1a0kqjljrdEBB+QdEgU1rKWmRwhg51/IyLIm5PZQG0IZITMvkDmzEkxi6EEh80Fp28ukyJammhzTm5ErECiYyw55R2dPU8cUXk5556inYsM/hffc/4IB27TvMnj1LUHl6NMGAmNdl0+PMRSoLEkxZik0StBR1rAlxItg9qqrW3XbLTbbP6mjPzWdoB+JN7Ki1qoUK2h4m4/mTKRmdzxhmNWElngErR2FNHNFaQxqVRXGnFd6MKYJ1awTTR6in5pooINSTp2d0ZjWIGamA/zR367sAJmnBIVNkdDtRM3VypHjQ9Z3hSBwCm4irTKt0DsQYgOcuwlQrvYIi/oq11lOMqrkYpYVY+csWhXTZhDFmvBb1nRHNihtNgw8yA1sx2jKGUZ1jTKwtNiHYcal2b9R35CnDiKiYsTKiaGt1Nt4hNDVd6ymbJbJIi6RpTs5PM2ZsudVWHTtt/tOPM4wCObkNGjQ45NDDuu25V0lp6aKFC1956cX/fPfNoLPP69CxI3BuuvWOcW+OmT9/3imnnTHk6ivK165t2KDB9Tfd9vTfn5g8+asBg86ZP2/eNtts22aTjYcPu2/vvffdeptt8/Lzpk+b9uJzzyxcuJAXCfINKcBNYH9RSdSADr16Hxro8OrLL3333beIyyHX3/TuxHf22Xe/ps2azvzpp8dHPbJi5QpYBArv/8cDIKhibQWsgcLz888/O+XU04dccxVSObhdf+MtTz/1JPrbtWvf/+hjcM8OQV98PumlF5+vqKhs2KjBGWcO3HLrrdEJDmhffeWlSZ9+evJpf9pu++1x+s2UKU889mj1+vU2O7H6NA+KkonvvI20uOVWW86ZPeva626Y8PZbPQ/qhTvcyy7969Zbb330scdhs2LlipWjX3v180mTwA1kJ5540vY77Ngor9EvP//80IPDV61a1bPnwQd074H6ff68+SOGD1uxYsVuu+9+6GGHNWnadNXKVc898zQsgDt3t+e7a4deN/bNN/718cd/7N592223W7pkyc677LKuat3YN9744IP3ES8tW7YYMOhs3OmvX7++srISPbfdevOyZctI8cD0DhyM8DiMi7Cm6VpsA4ddxqdxlKAHE1bJwsLMUCvB+LYJ+VvBpAVdEE7r1hpyqXOFKHxc9pansBAMgkNNZLrcY3RGHsSwbYnGcmFYTgwrPpGoNi0PhDiAjDDMAxc1t8w9SNh2xbEGoxij8sG2IiqhNfyMFOU7Ryk+YUNiLqScKKjVJFI5WLBVMVAutJiQh+gkmwSYlhRXcwZMIo3XyAZ8ZlWwQj2fVkcmDog0A4ZJXXDTbQMPt4QuyfszZkxH4jvplNNYc0KFq6qr13fq1Hn8uLF333HbmjVrTjjpZCCPG/vGmrKyhQsWPPXkY19+Mam4pBQXME2GCHIAFxUVAW7WrBmuz+Urlo8aOWLJokUtWrZ85uknh99/b/v2HQ7t05dxyYCQwYJMS4JZA+5nDaqrO3XefPxb4+658w7ocPyJJ8lQSWnJoX36fPjBey+/+MJmHTt222svYG/RpcuBBx0EDe+67VbwWTB/3lvj3iwpJg3JhiSENCwuKgbyRhu1WbVy5cgHhyPRYHd1p513QWefw49AThx+/32PPzoKmP/6+KNvp3xz+oCBW2+9zXNPP/XS888jOW6z7bbESDQHYGxFXbt37YbTWTNnoRca9j2yHzg8OHzYpptuesbAQWvK1iClLl68CJbs0mVLMDj9zAE7/+EPb4wZPXzY/Qvmzy8rK9tr73169e6NjP/wiBElJSVYEho2bHjs8cf/+OOPN99wwyef/GvlypWq50b0YBZQAeJwAGhcWtp5885tNt74ySceW7Rw0WF9Doeq6D9jwKCCgoLbb7n5Xx99BODZZ55CgqZZ0CC18sMzE1g6Iy2hhgdTsi2YGgOGmbAUZkHLY2QvQlOtMWaEGU6DHoaJBLy4M2ipjwXQkPcfaWsxADg/MqAYWAkgiEkjSualPnSXB2Y1jXCBOVxYIEOsgNGb6eNimU5P29jO1QLE+ifQUyvFKExGjRoRjiLDcCcVRU35ZC3CMZBTD2PYljkHSkQlsEiWyayESGbOQ6yPmMrRLdQzYC0ABohdpOUxkUAiCCFos6kWOfk2atQIieDiS/+2x557Y744KLVX59x/792AUVIhxbTv0AEFy48zZmAPa9nSpVOmTIZG5rAZnE4FzslZvWrVyAcfqFpfhZR05+23NGzQcIstt1y6ZHGXLbdCj8GjNcSu6ZaQPmlp4ba6eliow3zo0LRJExRQGHxr7JsTJ0yAmgcc0GOrrbd5a9zYLbboAjO8+847sNX06dPatGmDvICHIcwKLFkssSXOqCLxg1Juu+13QGGItIjtVCwDixctAi0wyteUI32UrVm91dZbTxg/HnUiptu1W7euXff4FjAzEwPsuOPO7dq2b9W6NYq+r7/8atrUH0hiTs6Ur79GyoOVjujfH9Lvu/cumG7y11/fcvsde+299/TpU7feZpv33333g/ffB/KM6dPRov6dM3v2B++/B/jjj/659z77vvj8c4Bbt26NKu+N118HjJrX9lRJD08JfWy0nNyqdVV333kHzqvWrT9j4IA2bTaeP38+6sSP//nRvHnzx7/11l777FNZuQ6uIUPQwi8tWR1Op5Z5qRYLsFQ0YQv+dBiZGuYucIkOR86NHJIG+SxYr/mihPQwKzR+nnaUhqEfHeQdt37kKFMzCuYoiloy4UWMoibgnsBASmcjUMTGWmYvkUdWxqFFEUyaeoURMh9kKQgUpQjWXov6TkiEo1A7k9EGVZpY3VghozNRG80tI+5BL/tLNDLKkRutio5AJjV0REmDIpjRWQCT+vwlXhMlLCP1aSdATA3PiA4qorLfW4SkuXNmff3ll3iA8PZbY63g6oN69e7e40Dcc+HmDp2lJaUrli8zo5hKcAis25wc3CnLyze5DXJPP2MgbqJxy5afj+cTeZTi6QcN3M2tmIsZChsGqTmo1yHdu0OHCqNDaePlnBbxnIEoc3JQQCF/Afzm2yk9e/U6/Mgjp/3wA8qxzyd9RlKEHVrghnBOs+bNBgw8u/VGGy1YMB+JBsUWZH377TcH9er1h113Ky4uKSwqxOkmG2+KGOjRsyd+RKV5v/zCbIJAzVmxYjlEz/tl3jdTJtOuqz1mz57N08vZrEPHFctXlPMjEdyAL5g3v1379shW4DxjxgwxAnk2B7e6LRs2anjdjWajEOZBNhz54IMnnnzKFYMHI2M+MnLkypUrRo4YceJJJ0vPqIdHyjrBDCAbRDRbQEuXLkZbWFiI07lz5vxh112xmO25115A+HHGdLGGaOhpeRY8U4QcmS7ecgY11ytfKZQsIIsvdGkjUUoyZaZ8pQNmCawxZUYcRqSIZ1yKeEWHEelglIAdUDRiqC0RE1PbErEorVtmKhxZGhOg4akHLanI9x1uyxRGS8oYKdcZK8R+OhPmTt1DHeAX6iwwtTQNb0u9rBJ9qAmwaqbHZjMaVv4SWBuV4OghgtmqrhKMaNRne7C2EMnqsM4BaP0lKFF/0Sgxsl4TFRSx4mmmKDKYQE06MEB1NtViOOPc55556robb+nabS/uy+2y5Za4iXvj9dfefuutTp07n3fhRWwhTt24CDiuka2A3Lhx4/K15Q0a0LRpgFEEhpJ9j+y/3Q474E581qxZffseuc9++wsLQRAawGw79cFdyG69Dun9xuujJ4x/C6XcuRf+JWROFFJlMGpuLtJNZUXlzjv/Yffd98Du4YsvPAeEynWVGC5lDZGgCRVNbs6gs//ctGmzwVdehkR2yaWXC4tvv5nS8+CD+x7Rb/36qjGjR6MqxPuVGEK1OPaNMfASSG3LCYA6cn766ac3uY7DGZ0LknBk+Jd5v7Tr0B4lOZ6NYBh15bRpUxctWgSUbbfdFsmUqJgOCQ7PlO++83buMM3UqT9ce/VVqLJPP+PME046acTwB6b+8P21Vw+Gg9CDjYURw4czasBGmLEvCEQ/PSI/oPump572J6wiDz80Yu3aCvIgC6UkEcDcI9woMs1k8Mm4UQnC3NCQ6+mgNrCEOTe9psogacZ3RrCwtwoZhsSMubkt6ySieMBoSSJ9FYfgWBYOTzChi5KlEKxQQ1hizFhI1LU0QhlrxQ5OSzhmKgaCADlCwWxlmg0JhyTxi2nJujbfWktblS2r4DPkSVKV70Qp0cQoyCdAArFtiY+eI59HFfIGjbEhk7Pi7GnSk6QZo7IY9prHXyQLhwkIOdGt1YvYCSPWm1mbiQrMXMxdCEvOIi2avE8sqsvWlOGm77DD+66rXIdLgkqwnBzcNZc2Lu19aB9GIbQFCxa0b9++oLCoen3V1O+/R1f3Aw/C44WTTj6VhsGR/tHBRUBOQUE+buuwd9l207a77t6V+nOqOUHk4NmIwWafYwhC23XoIBFRvmZNvujw44yS0sZ4nMI8LfNAgBWGbUHcxj5w+70L589nyxCmaNjjwJ5IrCeeciqIqLc6Jy8vf8mSxSg5kUY32rjN3NmzIXTf/fZfvmz5Xbfftmo19t3I3OsqKvAwBI9x5s375asvvth2++1nz5q1nEtmjNtDrIipsOupNYcM4KFN1z32OOW0P43+x6t4nAIlP/v0k4qKigXzF+DhycyZP+HO+qCDe2Ef4IsvPu/eo0f37j0+/PCD1q03yi/IX75sWe/D+rz26it4slRWtrqwoLBFixZODxeDLE80CluxPxkzp3qPbt1Q/D779FOQK0Ebq0Foxjiklc/Ak+EIT5EuGuA5re+yiaCwQoyHsCe1bMvMWBgim/tZoNOEwnCNWfFGW9E5rDjIFcTItoSu3GL5ir+Em/adYSeT5hN2pGgulgk0IG2tNgom8Zz5vZUj6UZkdDB/brmXNKVe7va1JE18J5LZGswpbEKemr9XUUgTPSVxIdOQ5tS66NZfJMSBWaoIFAVEc9PN2jIn8Zpoa/RzfETzFn9ZwZYpPo2tRICwJs7sVN2y9hhnUifScrKqFkN5lJkmTngbKaCoqBh2mTJ5MnLZn8+/EFK+++Yb0oWQc99/d+Jpp5954y23Tf7qq0dHjcSt9x577okfFGjIp7QocBLH3R+ZO6capdbOu/zh+ptvRc/MH3/ssFlHjM+dOxe58syBZ187+ApyAjGmpqi46C8XXwIAx4rly4dcfRXpcN4FOP3u22/RMnsaxf2l9afYIPdH3p677IqraDgnB5uDDw1/YOasn77+6ivaE+zW7ZvJk6GhsMCTmWOOOx7bfEBbsphuNtGPe8xdd999yA030GlODvpvvfnG+++95/wLLzrplFPxgyk8+fhjmLh4igUDkVSn6LItU+ewBagLm5WvvvQS7u7xxAYR9fa4cV9+8QWwsXV7wV8ughr4wQOZTz/9BHUxst4hhx3Wuw+tQ9h2nPTpJ507d756yFCcohZ+/NFHi4uLIz1sFYlVtCKcjKng3GnTpuEmesedduL+6ilfT3788Uf5ygUaVR9mRjyNYHbwDAedaWkyMBPOqGVBTssnZAYxCSOIcp7WyCTJfF1yy4yVeKNWIiczIB+kmdmN4gvbwKSp6Wc1hL2QiNdc7RRSVBNTOYodxGaOAZip7aFPsix/qJZAYz0DRcXIOHrlIEuJX0zL/jL5Vvku5AqhIbXxWkrfGT1ZLXGtnQWpoGE+t3Zw1GI8mYrobVoyARTnM4HpxMyZhXEG8PhOGJj84PCUE6sXsRNGpIOEJwtwK/3Mv7TAMwtXfNOmzcvKVmGHPpgIJGMzbqM2bfA8WnYP8QwUQHl5uUtqzcS9LVq1XLp4Ca4xaw8qFZs1bb5w0ULj82DAWDBk1qxps9Vlq9dVVhpTWsahcbmn31HHbLvd9vffc2ejRvlNmjbBs9dffp6LpAbNS0pKoxrm5uApENCWLlkaeOnyKwejGBzz+uhGjRpu2rYd6rsxo197Z8LbUAVlLO7EkShlpdTWsHCocBLUslWrZUuWVK3Hrigdon9xUVFBYSE2XsGZfVqNZ80tWrRcunTJunVYZug+oKSkuLCwCOWtxCFokRylhzmlaaD89TfdjFLxp59m5jVq2LXbnvvtv/+tN92MfdU0lDIcMXToBnu9clTaBBqY0/FoSGRFpo09a6GI+ITuTAVEuHm0EIw0U7deJ3oxgyGwNskoZVhbpPlMr7PDIGECbnqlSp2iK8F3wtBjHUdSihOlswiW61p1+wMlBUtFIFeKkwO1R3xwFjfRUJKjzNVFmXX5imU2unld4qyMOmj+vF+M33NycHNngoN4uYbmFRvcOSfSKE0NB+5PK9ctWoQXGDmspCtsCQqO5cuXyzQRcbHKQnqIYbNmzfE8p3xtRfny5SWNS/CGzc8/z2VxuciqxE1EW754FLt0KeVE0pmXGGRPZBA8QoGcrbbeFohzZuOXIGkMT6sXr13MDGTFo5Y44kOtVNZWLEuMIl1srCWLsZ8oC7NRBR9rylGw0opiB3JhXlgm7MmFhengHjR0lGHLg3pIvGpl0LRBNOIJEpjn5xdAgQK+DcclgbRr1Gd6oRFeDLuc2T4kDYfThidsC3cwgg4lVLKwcJL6TOzhR13G5KH53VpDLnUbe2Ck5qUmIIwCOcqQMl0mcysO6WKScOaYgZwYVnxiAkRgRUAgOo0wji5ZDokJmUfGotmVosPU9VwniqiElrlaTqZ8iivFGlqv0TCjmJY4CxuRYZSLqKWmYkGDTrMgAabl4A7njH4SbLxmNXS9JIyirdWRiY2GoqfTkrrQyGmzqBZZa6OunRhPJrSOUjYY0RRRxdOfCxvBc1hGjC4mTeQXYdO4SZMLL7oEj5iRVkCDW92nnnqyivdJ2WISfOaC4rlGOeNlHTzBwDNoDCBr4wXpd9+ZQIaITzc0SmQ4HIhyz/hctBV0ziS8qsvablp7CVnhkm1SSOh92GH77fdHPOYGw9WrVuOtxmlTp/kmloJHbMhKV/ZxPagGQOxBdzVIZWY/tatSWgEueoJGUSTveVpRNBfKqDbqBDbXqW/aXjGeTq9kD56nS0hlQLERPSWkwngz1hH9M7G+R6DtUsJCTtb8MX1ClHhAWI6WOMI5HAYUGavxX9DRl7SGrTCRI2cRmXoCljSSjGy5HlXXohNfDVupKT6VEhRwlEYbNcorLS1ZtnRZVEwKNs5QbnFxEUyKElgZ1jATRCU1KiQ0g80PMiWTtmjYO+BokP2JVyMPG/yaDXLiuir6LRdRPVBHNDStmjkjoniylzet7LK0ePhn06V0DpWICs4qIISj6FCD2AuVV6q5Dra+08YKyWoGJQoL2BnzyNXhUyGujriXOXj5B7xrBIhC0QDKyHeuj4IwBC9PjNmoI0mpjmSmSkBuFmlRmS9Jbh2Z1cuGxFs/ywzEw64uGc3aplR9GTt9ykLEPiumrj6WODKlKFLa8wh9qLmtLMzuj7l3IrE+86SVIwg+abUhDX1HZg4rDieBGjN7ZfN8jQYaVkp56az5U0xL2PmZenn6USEhBbYWEsLWd8ZVJmMpRmp6GYNeLTKjVqS+bOZNRtFZZyYpgqUEh/yUHUKTuZepsPE7xM/T2xtnn0VaZB0CtnZi0iFnwaArJzRxdKZpDJ1q1lZ+qs8gjeolUutWEwHhJNWqFTpTs0+CSWzIJgIH+UxTp5plBmOJwgLa0EeyVRJvRU3pVyozh6AqrDud/QopwZmJkpnLNF0rZBR7gX2Mv3yM1Bq64XwXFezEnhXrhLnyoBCLwcIZGci1iplMDCurjiyvO68G2r0Me/xllUqYmR1OvtZskiVMa0S2QHZ/b5HmqyJT5CYo5Z1soGnGgIeNnUA8CpK5ethYm6gZOZe3/wZQGImgtEyj+ngJokhpz10VVLjY6iNuGOkRzhpOK0t5W9kpAzJC8U6X2Pg8mALd4ylPHCqlvILVeCrdrDAXPUG7KFJw7vrI0VbbkWH2oPXdr7By1H7MqtIXa8h8A8skAym95h3UpkwlzEtspiV0EYwaV4vpJhuRoydgSV0UdXmHewfaIyGcbNnIiJIQrT4S4tzqFmGUcBryl9WbmYZ6xicd7yHONkvw1SMncnE6A0nXa4JuSd2hzsoELrJCiZpNV4saJl6OB6222RnU1SM4iykUWZ+1WQOilEBEW70z5cLkTTn8M1GquY7/7/lOVBAlvSuOs0xGve5Qu17zz9jaI7NPv0JRJQJeabVhnZxI0+VLeo0z81c21SLPJZiAD/DK9CHG+zKyR/pQ82ogxoq3rIUxsRsQrn5pmQq63ydeYpd/wlmiSaSykDRkqwy7t0ilmBQnWVaFWgnLwqlyNEIy7CUly4S+UynJzSSuD7Q5NZwg2ytYXX7CIt4KO7+ABJ5KA8GQDi92VKCYwRBYm6TMW0paJqBXi2TChAkE7mLVfnfvLdagWnRNnNKsEvWhiaNRWous72qhzlRYRAVHyxtFlSmoqkL9vDWzy1up5iOwwyaf6QsqU/US8Czn5DQnpor6S+1PxbVRmSQ9/wTFkrtD36VUIpmBjPg080Rd+opDGCUzVZV+RGY6FdOMh9yc5cSmV/KCN7sqs6WUEPJXV2dKCu+gYx+vQg57B923HOp4YziD8sWrGDq9UwwFeIazqRZjWSXkbBWSHj7zCLNY2Xwm2s9n+mTGiWwcl3guGDvuY+2dYiqjeAl8nDPus2maCML3yGr33qIrvI509rBxPeimbA+69YQ2sIo3Q8/Ke6kzmJZm7aInXFtRJO+5VxstimDfHYBjEk3gFePp9Er24Hm6hFQGFJtwb1HHmLFO/XuLOggd2Fo4waz+zE1m9aQkHQkaFhnSY+Wl/XR8aze2axPtkKh4hrCBRCEvhp4LwTY/iCFVsegdSDvT1AhejZJJFHqgZ9I9H8/L48dk7hmOeJVIMn9GPIWjoNYg9kIZSrUwBLRTtbFCsppBicICdsZHsoTY8NEqaFioJBoZ9vIPeNcIEIXENGEAZeQ710d6jfDEmN4RTqVoMlMloP57om06TlWLZGRKyyDqk7oJNR8XDo7o3qL8VQAKO7kAoupkdO6TlhFhQriHaYKsFFYc9e8tmvdM1R2vZCmVqzI0vEWrG9/5spk3GblrgVUiy88Enb3dcfv4beUljmqrmWm4BnuLrhYiXczg1cR6nDSKXjVpDC1cXXnSl1kbrk5WCepRStREQDhJVWVkau5wLiEbpQ8NB/ksrmhms/ZgJQoLcH2XgS2pxXiipoa5hzkYP9qVIZxlwD9rwK9QxFZWYDJ3mbmMu1bIKPZCxsmMVERtON9FZ+DEnhWrK0ENC3GCV1yrmMmE864JFMSwViIezlYhrwYanWGPv6xulpE9j36mFUAE1ohsgVrtLYr4BKW8ukQVTn/uYWMn4Bidr9dkdh429ppSDnAub70NHzIWRnKelmlIloIgipT23FVBhUtYOYbZVRtJOEtPWikWwTtJO5j600tK5vZ5MCHjKe+o/OP6LqaFV7CL5UXRwlz0BO2iSMG5sJfTdKLYg9Z3vrWIhQesswC8kjOj95LSRUZPpcmDGVT6IkmMmoFUr0hL5x2M+8svzEtsShkREMGocbXol28nkRBF7jRcVdTlbW9sNbqGQylpICUhWn2k1DAN22A45C+rt8Rv2JtSSGhBmyX4upcTue6dAZsKAuk1A7zauawUStRsulrUMM3U8aDVNpylKyOrs5hCpFaCcTNjHNFW70y5MF08cvhnolQz15np+e/5TgSKkuKvVK0yG5M41K7X/DM25sjww69KVImAWVptWCcn0nT5kl5j4534RB1/ZVMt8lyCCfgAr0wfYrwvI3s4qtuZOby8Goix4i1TGhNbbj7DpmUqKvhIlf+1fEfnpJNEk0hlIWnIVhn17y26HrTO2MC+S/SRTxvSSUJYXJ5lykiKE7ffO2EXRZ8lTMBV7Xf33mIN/94i/vZMfmERmgYNs+Cg3VEP/zctsB7f/IC/zVtRjr9zbi9YR75cBlLvMJyL70tolFfQKC+vQYOGDmr9yW/TAvgbeuurEASVCAY1A66bg0IXRQJg5ErO4KqlLhmwLeNQw4ewMbAtX/QOjlsVxLIxgg43ANziRt3GqPz9JW5/9X9vsaS0aX5hMWrX6vXp/8qlsVryx7rKtRXqO/CSEetHam6BhvjribSUFVSsLV+zehXFXcqjsLg0v6BQXMxXQzqClNzqXZzSPP+lwSAGKisQBbE/jx/RQjKTdIZZSu0t1r+3qCyWW9qkeYNGeZzQVXftQKxhuFprx6OeOiMLINnBd2WrVgbrMRUB7lFc2qRBw4b4tgnaXK+jo97FdWTIOmCDGMAdQPka+hPuOKSMY1DlP4oKNcLDNWmwAlMUMTeBqTUdoRCfKNFAhAbaMKJnb1HvCKfSM5mp0irL9xZRJ+Y2aITvZ0klOPsxMMS16tb22XOpp8jAAriZRs2IwmEdfz9tnAJ1Yi6+DbZOcyKk1Ls4bur/rx7EQKP8fGRGfP+Po4PKPb5s5k1GqTObwz75RAkO+SWkTp1ShaMvpSYQh9w1mzicRYLLgy0Lixs0zIIk2RDREdqmrOtsG5VRf84WqFy7FvUC7qk5dBCR9MPlfy6+6ppKiYYbZDOx3sW/ngBEDOTl58v3UiADkvtpyzBoTUjIiA0PQsAU3JbmRL22ZYg7wJF6g5aYMTUDCmaQRLMeQRsRRmIj/wwj7jUwyZAfQjbyBIFalh9vRTRrK2pk8SS6oKiUGYMaBzJsnbc59QUjW3XDNtgSRk5ESRgvGAuwZWzfSmMl6tzL9fcEG9a5GXIPYyBSMAb0yBTxIoqeifwu3lvM4jkylpf1dbfZFNg/ADZQkRLwrwcCC0jBSEuniX0zgqcy8p1fAWbdAvUurlt71oYbxUBhERdpsviBWRAPnBHVU2lTD2GcizmWSye2AuP+oJcZ8Rljy0LrtIwhadfgyQfVasi9wolhpRML00+l9Td9C708uXZYmhPDk9kJI9Y+TP2U7ynr8xWRxR2xvItDiREVBT64yRZu0YBXGx+tbzL1fRvEAthd4gwFX+Igj8oPdVIwsnt8PkJfzfwuPOUq2iBTqmeapQUoBrCDTI7Bf/4RmFsOAooNGg5OKE7wX1qSR5A9NGz7iJLgaMvyiJMRzAChMWuWwE0gjPuNYBqRf6SAGQHAsOGIXu4JEUgL/p/QGtH0UV2dRVoET4Q1Z1OAdAgsse7tf6xd08fbNQXmrkV5aM9tWTy+U4uWvHXlxSemsYMXtFgvpNPS4jlq019YULDN1lvhK6TBtzZ8tFpbbN65z2G9S0pKdGcd8q+FnjCg+WEmZE9t09BH5GMKJxyDWhSPbNv0obZNd4NPVT96pH9QixLdD5KAFpD3yHYKW221ZdOmFFeRI1s+Sfh1zn/zzp10DCTJ/e/30xXN/6kVmFqKCtXyAPlRIkRasn3oTxkTf8iFGW2JPfNgOYDtD3dbacRUYEcYsed/RMTqUJvqh9FEZyZhcjmPtFY0aWfgLG6ioTC2JGTikpgDOLBOpH/i6rVV1Tm7FTYa0bbZPYtWfVteOWzx6kXr1iXhC8NIiyXkkF4HtWvXNtL/wosvL1u2PNKJU1lyMunPz8/veWD3bt26/vDD1Ff/MXrlylU777zTLTdfN/Cs877//ofM+YisAP/hkQ/gpdmzz7kA/dcNvXq/ffdeX72+VauWxx571ODBQz/9bFIEP6JqwEf3lxQXv/DCU6NHvzH8wZG6H7AXP7N+k+kY2XAVbhE/QoYMox8r3IA5Swe1LAXwWVnF1gWN/rO2alDL4s/WVIxYTF8M+1C7ZtWL/XES8DHC7AeEnnbqSbIa2b6c8vLyJ//+THCqgRHD733+hZcfGP6Q7gRcM1N07brbwQcduGzZMpj3p5mzwAf8X37lH/feN7zG/DOMgWz5a7Y1nq8Wai0m/lUtLtEgOgTmFmmDSJCRIu8tUsTQLSyIGCPaklDFPuDtdgOFxMRasGYBwoJh/iUvSnDUF2n1DEkAD0fbQB/Sheaj2uzSohVHEuyRCn5siXk3CjnxnVVrWzdqWAFrWkrR157pfttnP3v3Phg1F86Ki4urqqrW8uvfn3466ctlX1uUxM+j+h9x4onHHdnvuAhGhw7tH33kwUaNGiEb7rPPnqecfMJRx5wUwanZ6cqVK6Gk0O655x4fffzvy6+4+g+77Hz44YeWZ/Piuta8ct06fGszjpqplEAVRFNkXPvCA+9WlC8x1Sg359TmxYPnr1D0Hnwe1f0K3YL9+vXFr9XgwFpVUVlJv42xbl1SWrREtf2EuOef+3vLFs3XrFmD3+fp3++Iq64e+uGHH9WWb05OXcVAy5Ytnn/2yRNPPn3evPnQSrOtvZLgQPmNswpzC+KB+2RvUcTIRcstF1RMSQ112UENExmf856d5C1OpqFIEaxbkWX5gZzzo2GFAOIetOY3XvTeooUNseEUfpAupBGxEJj1U9JlutzmZpcWuWhQnDyJ2jN6TsvS/6ytnFVRdVLzoqObFr+9svznSlQTHsxwFi503vkXS8fEd94c99aEW269wx1Pdda2bdvS2A0srj1UBHi80O+o4xcvXtK8efMzzzh1+XJP7ZmKdcLYXy76WzCCSx2FJ04//+LLeGoO0LyA1ryiouLoY+smaytZcAEOaU03lw82/Hw+QoVIWyLVOQ8uXoXIuuyX5bsVF/AmSe6gFsJOShCvf21EGmnhx+F9j8bJrrvucuftN19xxTVBTR1ibADo3rtvR0687PLBH//rE7w6e8klF33xxVd1IqeuYqDNRhshdze0r0xptnWiJ1/RNgJUFSZFoVRkNpvRMNeDuuU8ozJMVCsRIBKMMI43HXqUp5g3oQWxZ4o86dAopnKkBEZkTGrhMJoDPg5PM0VGU9O1pScxA1ydXVoMhdLsSRV7OPAfSwv2KylAhbi8an2rRg0GtCx5ZMlqVIu3LFj5xoq1P1fqX8kEA01r+WX2ef555/Q57BDkuAULFp53wcVr11Y8/fdRb459C7c/W2yx+bD77/rPd9/vsMN2iKrx40ZPnTr9nHMvFMb77rs3Cs/BVw9FTkTP0qVLb7v97ojMnXfa8eKLL2i76Sbo//Szz4cOvXF1WdkOO2x/1ZWXbtymDWqZMWPevOvu+48+uh8qzSaNG2MUp+PHTxj+wD2oPi66+LJXXn4WtBjt0eOAO+6457Zbb8Dt+Y8//oQr8LZbb9xp5x0R76gCoHnbTTeNyBo48HTsRmnN3xjzyjPPPC8FFCZ+6KG9CvLzFy1efOVVQyTzAuH119/s1asnlJk1a/bfLh/8yy/zIpNyT8MkJaHvjmq/hDBnQyAKLfWPbNcMd9CBH/lWOsQP+pm57nel+c6QEe6645btttsGL5hPnzbjggsvgZE1Yrc9ug4ZctXDjzz2wgsvR4IBhj37rAFdd9914aLFu+26C+r3555/CZgBOZiD8zsT30NORCfesogvtxnGQDwq6iQGECFDrr0Kuj326ENry9cednj/gC06vTEAhIULF3Xu3LFd27bLli+//Y57Uhe/XO7BKTaJUK7hkgof4mFuBZYBtyVzKkQ65XrQ3GgrmPFEFGNxBmJ0ChGS4LasEwujNOyrEIUNpTSjgumwH1YvYi2MCNFMUSaqp4usTeagruweuaAM4AMhFBwO/Hj7Fic3K964UYMjmxa1bkibDAsrq3pMX3DfwpWA162v/qJsbUBJv1MdHgTb+WT0+afTTu7fr+8bb4677vqbS0pLLjj/z8hu//7ks35H9m3bdtNrr7kCv/h5/wMjZs6chUvizrvue/ChhwO+uFog7v0P/hn0xIHNN++8csXKIUNvxHZetz1279OnN3CuvOLSgoKCs84+f9SoxxF/yMjnnD0AV+yZA//83nsfLlq0CDgtWjRv1bIlgPuHjUCLlHrvvQ8UFhYCGQd6hg27G2XRs8++cNXgIT//8svy5Svisl4fMzaieXFRUfPmzUB+5hmnYeLfffufYQ+MKCwofOD+u1Dtoh8Ixxzdb+zY8Y8+9iS2CI47luqvlAcMbn7Y+GR/dkjUL9yJhvoHtSwZ2a45UiEA6ScR1TmoIocvWomfr8qwUyKHh09KfaKDuMi332FbuPiFF17p1KnjqFEPagx4+Ybrr5kx40fkxHgwABPm6ty5E9DuuPNerB8nHH+MJv/DH3bG6fi339GdETjuFyBEYsDbUycxgNXu7bcngv+TTz59+533AAjYJsUAEPbfb585c36+6577i4qKzhp0BqhSHOwncbpucSn6R8CKBsjhHC2mJQl0Li0jAI/P+cNQCB3x5lEDELphKqx1axFZIPD0PyYiZKKXHz4LT00nIwha2Jo5WF3wiR6iR5tttUgzTXEsXFfVMDfniSVlH6yumFOxTjAXr1vfNq/h3GiRmIJNRkMol+bMmfvIqCeAjeg5qGd3ANffcMseXXcbMfy+xo1Lzz3/4qlTp/0yb367dm1RQmqm7du3W1uBAifV8dLLr+IHzzoOPvhA7Hbtv/++zz73IkyGaGvZquXTzzwPYrzigHbjjdvg1+/jtcaECROvvuqyb7/9DreEqGtEGEjwsPut8ROkcvnwnx+j3yvLqzmQDznkoBUrV1540aWAZ/z4E+46MXeUQjj9x2tj5EHEEUf02WXnHdGT8uAV2r/SJtJhY/HM2VRin9WqdLfifGTDnb+fh13jkga0ODdp2GBY22YnzyKEWh4wVJctNofpUPKAFRza+5CDmzVrKs/ZCgqwDXIfVhTZYPEGA6iwIp540p8AwINXXXHpVlttKZU1erbasgvaGTN+RJt0eP0SiQGidaNCc6tlDHz11de9Dzlowjvvzp37s2abIgZmz5mDbQEgd919NyznmioO22pRRoJ4QG4IyioeIt+amoxJzAnXYCaAFIoKKWAj01ARpltmxUKYb9CYmpHPBSZWgmgKPhLp2VsUFoITsFOA1Y7YsdZqiizA6FmTapH8L+k0ob1o7tJHl6w6pXnx651a7VSI11wIf6eivDGdW5/UvBgpBHBKDmoi6cAWzZsj37326vP4ObxP74LCAlDg1zzfHDsel9DMWbMnT56SxAM31HgXB7dRSQjoR5Xx3LNPjH7txWOOOQq3veCJzksuvXLJkiU3XHfNmNEv7dmtK3Ynhwy9qUnTJo8/NvKpJ0chP6ZgKEPbbbsNQuWrryZrTK8sjaDh5s2aoT6VnkmTviAL77SDnOJlNAHwHCm/gAyS8kBo4EAb/JDD0nnZ4BCRjYdejQsObFwA2hVVVadTTgTo58MSM2q2225bGOqjj/4t2BPffR8AnhrLKXYY4BHcXMsv9nqDAZisBlHMnTMXbdMmjemEjy+/pOd1uP81574Pr18iMQC6eI+PWdi3QWNgfZV5DQA7S8GmZCjbhaiwCoorgY3NxHJojZtBR5VW2BIjOreHhm0fOYDgaMsiiZ/8MAIQrTDi6wojLmEPQeEZj+CUewxHYRBjo5lEYFZRppvte4vQzWZdmmwc7t2kaNO8hlPKK/++dDVawQGAXPnt2srzWpc+3qElKkcvrXAjvu5hVye3Nydn1apVc+b+/MfuveTngO6HAAOvB+KB7+IlSzfr0B47R4aGFh7eN7A8Pv6YLraTTqTH08Ific8Oms977r4NF1ufvkcfd/wpuAWTXizFJ5z4p9NOH7S2onLo0KvR+e677/c+9MhrhtzQZuM213NPhE9E/2nTKaPttVc33e/KUqVWTCvQYuK4RxYpXbpsAT4//DAtIhSn0flYDCUXKOaHO4lCqGKt0UP6z27V+KxWjVEq4mELYFA9uWT1w4tX7V6cj/5KCjAHX3ET0KpiP5VKtisnZ+oPU3Gys615d99tV5xOnvxNgHH9jbfCQeedexZ6JBgO6HGIDoYA08t/8pRvcBVgOyJAyyAGSH/EwPEnnKZjYM7cuZEe4emVi6HpM35EixgQNGndGDDxJkP4c0YaE7DEgPCPx0CS3Hg/Cie4Cv+pFZjPyIPkQ2l5gGKDpm9b0ih0p/RSHxH5WmLP1AQQc/vD3dRB/UzOsCOM2PM/DOGfaQMWHoDRWJ4lxaecR1orWhTgNmpumk/yYTYDZcvItvTJcOe8hjdt2uyopsWTVq+9bd7yyvV4XY/S7/FNiz5YuXbSqrVob5+/HDfXSXy8ksHB248XX9q13fTP5wxCKtxxx+0lyFDHAf/Y405esnTpTTcORaBPmzY9Py8PoVNaGr5Q/a9/f/LTzJl4V+6sQWd27LjZiScc+9Zbr+M+Fy+GQFZxcRFaUCG94s+3HnnE4Rtt1Bo9qC7vvvNWXKjY9Zs1cxaCteNmHe668xYUiZMmfV5WVlZYVBhXNaI/nsYsXLQIdzfHH3c09gSvHnw5WldWK2ESaC6FasAZjwjw3gb2UrfdZuvrhgwG/7femhCMBoDfalgjQ3sCxfxwJ1EAiHiH/cuO5OazsrW8zhKyvKnzcPuWIDqrZSlyIugf7tCyGq5P4BOopwHg6lOB8TLTvPnz9957z76HH9r9gP2xt4sSOHiIhO0CPN1CCYm3atq3ayfBcM7ZA3UwBDy9/DGKlxOxoXHjDUOwgkLEa/948a8X09umqDuwgwzA9QtioFpiYJdddgpiAD14LqR70spFqEgMYLszIQYo3nAgd6M9+OCe3hjAUxdvDCTNN96PHtg+1to+HtDjUIYpqBWYWv7PxZrAeszAbFQgErNAnoaJYcCaEFmCaYmMSamT8cIzRqGG+7kVvkHrkATkPCxyouJqtreIhEqW4AOp28LTK9ZdNnfpV2sqdi0u2LdxwX0LVlZVV6M2/Gubpo/Syxw5+5YW3LdoJeVsORL42OGkTyPv5lvuQLY6+qgj8ZwBqNh/wctuiE48hsZLb1deeS027C+44Nynn34OOA8/NAyxeMihRwZMzxzw5+HD7j722P5ITzApbqk+m/Q5Fg7sRqHoO7RP/6eefu6ss84cN/Y1XJ9LlywDIRIrtrSRGQGD5PEnnmrWrBkeeT/3zBPowbNpPJ8R/lXrzY2MYKLFEkEt9w8YeO4jDw8fNPBKwESRAABAAElEQVQM/KwpL3/l1dFxWUAeM2ZsRPMqvj+66ebb8d7GkUf06Xfk4RA69Lqb8NwG+DgEgSCER/pDHBA6xFAk+YX7hy9cSWi5OWe3yvl0dQWy5CObmbt1DH22Zu0jHezNexKfZMWQkTEotgIwcNB5ox4ZftFfzgeM7dSBg84NSOXd1RtuvBVvht5801C82RcJho8++hddKfbQHrF9OQgVvJDQo8cBe3PhhrvOJ598BqM//zzvsEMPeXDEw3G/xGMg3iP8tUTRpAYxgC1FrN+4rcHPEfzurbBNFQM29vT0gylHAJRdsJGNAHGYbfUTXsHglosr0NCJwHaQe1iA7WEM8gI6EvYW7WCgBCMzb6YRhYme6kTR1rO3WLfvLWbxFVfNWm2Cv+xsrKgnE4NPblF6SZump/y08Eu8t1Gds3Nx/uQ1lSe0KDGdZRXB/CLcgq0xsUUmLdbqTp06YunGm30p8LHXjkJjxQr94jGho5zcfIvNp0+fIQkLPXiigh0lVGqA8eAYCQg3TYRqD2xKbtZxMzzMCUiw4GPTSn5BwmKl/8TTg8aljQPmXlngkqQ58FGl4kWc9JJ8GCWNm65YSs/N9dGkeavystVJ3on074476NZ0B/0pnkEvWLF7iT1djUfSKyKeDWhRfWuJmcAwFP6GhTzlT42fYTDEmaDqx3s8+m15VJHf/zAVLvb6JR4D8Z64lHhPhjGAcnhd1bqgUg741D4GVukrwmYd+I7Spb2uBUYGRNXFVZBuA8cahwe6ZQMowSE/k0vDDqtP2CMypD8qz8/T2xtnvEHSIiy0aX7Dnyuqwgmw5E0bNfwZ3yUS18L21CAtRq1Rf56ZBTgthhtYEvpNmrfMPC2m8GPE7xqzBmkxswnVY2VtAcTA6pWo/e3lF7ota1YOgc2jlFlDWKU5wRaxqjuuh+rxJGUr1DKy59HPtLmQCOyzcrZGqkexEe6Us+S2HgPQJGU7F2/nxHCoU1T00woBc65vNqQF8Ne5+U9bhgaX203pJDjmu7rpCQVuyOnV887AAhQDvAXMuDF/J6QSqhkl2TmVY5hQwYgxqCXOfCdvYSWKQQkqK5tEIo+6rY07yooQGW2tYOHBcQsGkQPVLRO7Le3+ERmTmuJYtMUOcoRDitOqygr89eZ1FRUsxCDWIczbSink1w/VmQXwZ2jxRYBqnTac0dkwLx+OtlEWrmLAqL2vJQ7rbBr1jGphAcQAvgiQNwcj3hY/y0MD3s8T39tW9hM5HBASQOb/vpa04zyqakapHxkbYolaH6gtkaakV2A6EUSzt0giPXuLwkVwNEcLG57MjrVmpsiMVoDz3mIWf50bS0tRSWN8GxUk6RnVGWz/PI+dSf3nhrJAYXFJ+ZpVeF4cEYCH0IVFJXg1nRZmO1Zn/kXY1C991qr/75+IAfwaGD0FgoPlCDNQJI9R/Uf5LaFCFGodJ5Zjhp8xwZSrgoRl040I0G0q9iFPSqNGc5sDk1kK+yzSYvX6KnwtDr6RA7darLVRqk5gsbt3mnZ1ig7W94tFsrUDvq2FvjSa/pYPXGd+mIlEfwM8uNDfHlEn/iVV6dKKOrFmU8h2yvX4ETsjBrAo4s0NpAw4Bf+pFZjPEBjUZVoeoFgh/9mWWIb+lF4rhsd4UFzOrMCFqVkOYPvD3VYaMRXYEUbs+R8RsTrUpvphNNGWSZhcziOtFU3aGTibtFidg4spr4D+0Lm+bMLVBlNCsg2OjGHehdDYAYt6oC4tgO2kgiJ6JXPNan7JxscbX/CCr7lCIOG71s249kwtYN638Yms7/svWkBiAN/kU74m5VcQI0cEvhaYW1z75EdkpMjfWyR0uoUFkUIP4YynmEAt3cLFi6IFR4V5CRykCEYWT6KZDZEXlzZFclxbXuYkR0dKNicwZ+iBbAjrcTO2AC6GvAL6HlT64vQyvEMaCYMoI9xhITmyi3GjHVwfUbQMz+sTYoaG2qBoQQxg5StfU041Gh+STxiMRIUaMbjZf3ACNfEmMLWxAPSJEm1EpKtZcFMsiVi3FNmpjmSmSqvcLNKi1pz+WmhhEX3FZcMsHtqkUrd+bENaAAsY7ptxOWT2fgzFDhIpMiNeGKz/XqoN6Zn/Hm88d4b3EQX4hQW/VJV7fNnMm4zSLbB+SZFeJTjkl5A6dUoVNjoxhYz9PL29mqXAWaRFlhiwtfKlQ86CQVdOaOLoTNMYWrj6Z23lp/oMVydT9nPxr5SoiYBwknzLkOrGwTWDdrjSIYrkvD/FlRok1vIIdY4JNpxDH2lTaVgopUdxYXrjR6ttzT0WztOvkBKs7RaSRSGZufS6Vsgo9kJ2yYx0lcFvmLhyQhY1g/yCndizIWPvbuUeN2xFcIJXItomYGWle5bXnVcD7V6GPf6ySqXTOa0AYmSNyO7Mbm+R5qsiU9RKUMqri51I5p8eNnYC8ShIZuth417E2vQyInslUZbCSHrTMvUTR8iiSGnPXRVUuPC+D+/+hJeENpJwlp60UixCRNsEb1ts59NLSgx8HlRxJTIybx2hdOIV7GJ5UbRIFz0jnopE2EtHOlHsQes7k1iN25RYxTxj0Cs5M2ovKS2Kdm8x4al03ITSk4FUr0hL5x3MVJiX2ESJCIhg1LhaTDfZiBw9AUvqoqjLO90DdWuqtJ9KQrT6UAHn0y0ta0YI+cvqzUz9F2VciDUDEZlURV1yIpnZGbBpPDPVErFCnZUJXGyFEjWbrhY1TLwcD1ptw1m6MrI6iylEain1NZwZ44i27uOC6B2AsPTPRKnmOv6/5ztRQZQ04WOF60UxIdM61K7X/DPOzMAWy6+Q8p1FNPqzBhr2GdiJNF2+pNfYx45jyZos62qR5+LOInLmlRnBSThN6x2asaO6daHD0KuBGCveMqUxseXmM2xapqKCj1T5X8t3dE46STSJVBaShmyVIW84qfRqU20S91T93gmnIgjHvKQ09dB3KiW5mcT1gTanhkNZDuQVnGD+uCv8AhJ4KrmCIR1ebC2KYDGDIbA2cbKX0lkJyhj0apFMnTCBwF2s2u/uvcUaVIuuiVOaVaI+NLHyOIdLLbK+q4U6U2ERFRwtbxRVpqCqCqPPv6IXgHuRkwClmg+2wzq3maIoU/US8Cxnp8RycMVUUX+JwRLMJvNlLun5O8IyOQl9l1KJdKx8mnmiLn3FIYxEnI9pvOKw/k+nYprxUJiznNj0GqZab45l46WUEPJXAZqSwjvo2MerisPeQbeWSnkFZVC+eBVDp3eKoTDPcK32FkPOViHp4TOPMIuVzWei/XymT2acyMZxieeCseM+1t4ppjKKl8DHOeM+m6aJQPZ9Yq2tUKxwk3UzFWHJklNqRpw8bFwPuvw96NYT2sAq3gw9K+OldtX0omjWLnrCtRVF8p6nFUVifXcAjklS6eYVi06v5CRkt19IpU+xCfcW/6ffW8zi9Rqs3+Q+9lUe3s2pf0HHDaT6s0wsgLdE6F2hirX0qhCHlGnpGsYhbQjhnF8Vysfv8OKXrBinvvnNWwBvCyEQ8NdC1+NPasHHen2zkcDdJh4IRglgED2/E123f28xi7TImqPJKS5tkl9YjPsO+TPOtXQRfnOmIpsvla+luHry/18L4KViJDiEUAXeLC9b5dwY2qtDF0YFxSX5+QX1wfb/67U6ly5hgL9tivco8V2v5HmkPac8djpk0LZAxijlIoYUzCxY2wg3iSnejw+qaCK2PQ5cnfXeYmmT5g0a5eXiyyVwM1ZHRxVSY3nKX0WqI0H1bH49FsCv5SKK1qxK+DVE3lssKm2MChFfAViHvydTH2y/nhiAJggD/EoIfhPRm8YkmXHr3eCSqQhW8rTSsWZKu6XDqTK7vUX83crcBo3qNidCJzDEnkXd/CphsnHqR35VFsC9dF5+Pv4uBf5UndxHR6IXdSJqScRGHebE+mD7VcUAlEEY4Nep8A2F63A3zSnJba2+keBQ+dIQCaI/Q3qJzS250EUwsviKK/wuGO6dsXgzI8gPjjqA6fcIY19vFgioB/4nLYC/4oMbZFwVCEr84Aha3GhjyAYbRuogxlgC8akPNjbFr6VBGCAGGjRqiEWQdpvxn1uGecXkDnRSeFDr+Ycu7qU29kMExBIt/ptWWDE2sSUEall0FtViQTF9UTIx22BHfcG4wUz7a2SMzWmkP9SDVZWVSFcIrKAtKCziIrE+2H6NjqtbnSQMGuQ2ME/haHeO/a4Dwgun0iMkQB6krUhqnRgLMWL92VWLogbY4VAtxEV6ZDS7/vo/SSBm/F21VCk0oud+KpwIRroM7BAZCirH2vTXB1tg3l8DgDDAfbRbsnFIUHFHwRH8U+Uegck/RALagJAhOY+0pkJkbAOHwZfWOtgZxR+uRGoHV2RfvCgqqzs/wwE15eLa9KdVoB7hf88C2FrCQxVePyl+bL2Yg077TSOJcbVrUb7Fr560Zl1WcUhXS/3xq7EAwkCeWEhWibX8rJkczJFCaUaSjQoZJ3zcidkH2BQtIcyxA0R+dMwPXBATUqlm810u4CBhKzIDmOOLefOA9O9alLdbUR4JtVTSj3bEktVGF4UPkPlwl9sggXs33b39+G7SVq1a4ttKXR45+I5TMPn5Z/N9ynrUywcI9f1ipQ1tBzI1SRL/O1EgYWrUsGHMqNUIsIfaNp20Bt9IQ8fAucvRAh/9SJEMyzVEo0l8aMw9NvRk6/mLvf12oIINh2nxwfnRdNFtsMknv6b3FqEdJz/JgE7LM3F6BrUoHrEY6S96DGpZYveMHHxeBbiJUkBo9SG9DmrXrm1k5IUXX162jC4GfZx91pk9uv+xR89DdSfgO++4Ga9HnXLagEg/TuM5d9NNNjn88ENbtGg+duz4z7/4MkISxxeEzPv32XvPq666bPDgoZ9+NinC3KtPEv8+h/UuKSl+9rkX40yy4pPEP0X/wyMfqKysPPucC+KiM7eDpo0snzREjOhCkNhwWyS6HOTEAXOWIvZGtmvGQpET8x9q2+wPUxfGqLx8tHwDa+V32GH73occvGrVqtGvv5H0fdwaX7PLpP+C8//c88Du/Y46Hl9xngl+Ev8zTj91+owf3333fSDUhk8S/6T+kuLiF154avToN4Y/OLJu5JKrberDXAJQF3kEU7W4Qd9bzOImWlsnBYzEhxkBYddiVItqZgZOQZo41Lv3wVts3hnDxcXF+COaa/n1708/nfTlsq8TaWo6cPqfTjn1lBMrcNFXVh580IEzZ87yJtNs2d9y8/UNGzb46yVXgLCsbA2O8tq9xI5XYS++6Hysuh99/O+kizZbJTPHX7lyZeJfM82ci8JEoODQrRr0gygMP++yEcakZtwNObFdM74X8eNn1fvg8Hu32XqrsrIybH0ec3S/l1957Z57h2XFwYv88kvPPv/8i7KSwYary1YnJRQvebyz+wH7n3LyCUiskhbjCBuuB7+jsnrVahx1JgLRTLlDWs4XUiHyBqH9PRaqGgU2cpElTeBEFLHRREx1qUnoQmRaoqN7UrpDpNyVm11a5I1FsKCs523PalmKBeuzNRUI2c/KKhgtB4kyqBzRz3GQxEEoou15518sXRPfeXPcWxNuufWOKEYdne+66y7IiV9/PeW8C0hivyMPx5J+2d8uvvmW2krcZJONJZuDLSrQI/sdV0uVoRv/AlXVCccfU3v1slXmLxf9LVuSFPgSDXxJ8MXAqCnjpPrTsrW7/DBf8xzYshinCLCH2jWV/klllQ8uxjc04Mgy3s49CzlxxEOPPP3M8yC+9ZYbYO0vvvjy/Q/+ydxq3jRr2qSkBHUDHaMefQI/Ate47d//iCVLl7Zo3rzr7rt+8qnn5qPGnNMSIhcffexJadGyQCCXUxSo1nouqBB1tQivmqAR/7Kf0RM5pOyMtvwkBJhMKtssslsHOIsn0RFZKU4lIaKVH2BqOAVhVkM777Tjk0888s7bb+AHtRhKeiHHy24jht/77jtjkUNvuP7aOE8ssKNfe/G9iePGvvEq7mc1wrHHHIXTIdfdJJ2oEVasXPnHP+6H0/z8/HFjX8NdlQw98fjD115zpcDnn3fO+HGjwfCF5/6+8cZt0In7r+eefQI9b48f85cLzx350LAO7dt12WJzoA0aeAY0B9CpU0dgohi57547MAVo+/BDw2QWQBj75j9Qt0Ii+m+/7UZgRo5DDjlozpy5P0ydtredQhJVRJmtttoS0nffbVcwRAu47+G054BtWcB7dusKOD6j4Q/cc8Xllzz/3JOY1I47bo9T7EsAM0lox806vPziM0CG/mCLH2zvAt97IJIlmHXrxQw6dyvOH9m+ufzQYpyTM2D2UqzHSIUA5IfvVwKKLICePXugnJecCLIhQ8n+xx5LsdGjxwGYi6Q2vGkL+NDevdAvfsRk8QOHyku4Rx/dTyLtjTGvgOebY17B89YTTzgWVDtsv93ZZw149ZXnQIsD+zbPPv04gnb8W68Pvuoy6QTCo488iKSM8ADJmWecJv1Bi9+CQPqeOPG9NeXlxxzTX/qTPBJRBtEFfcABVOefdzb4IwAA9+17GK4L6O+dESYCNYAwYfwYIOP05JOOB5CkKmaNSEYYAB8iUCwDOfFAsUahwJWdgbl05A6ECOlKrecfuriX2tgPERBjtPhvWmHF2MSWEKhlBbJLi8jmdFA+t0cEzqlGbTiyXXPc0aCVHw1DtKX08MFo5sfmm3deuWIlQhZbG9322L1Pn95Cixeg1ldXX3fDLdOmzUDWQ5Rontttt83Vgy/HrfGVV107f8GCSy/5ix7t3Lkj9pIWLVoUdKJyLCosxB1rXqNGhQUFTZo0lqHmzZu1bNkC8J9OO7l/v75vvDnuuutvLiktQXWJziuvuLSgoOCss88fNerxhQsXPfLI4+Xl5YsWL77zrvteH/NmYWEhkiwOYCK/bL/DtiB/4YVXOnXqOGrUg+gEAoQi0B96aNRHH/0byQtXEfqDA9mzXdu2b0+YOGbM2CaNG3fq1BFDSVQRZab+MLVRXqNevXqCBLsTUOPggwk+pFfPvLy8SZ9/6Z0RdlqxpbBi+QpUplOmfIvTVi1bphB6++03FRTkwwLvv/9PiLjnvgdgB+AnHbLcO20QKAkxA1ZnzlqCH7oFYRwNgxqFQIpYTdIEmQIm/e4/3wcIuE9ERdahQ3v0tGjeDNOht0n4ANyYQ+L+++7cZtutHxg+Er6GQ2ErDJ1z9oDp02acOfDP7733IYLq3vuGgwj3CgiDadNnIIQal1JCh9kffXREs+bNHho5CltDB/Y44PLL/op+IHTu3AnLyR133ovgwW0BywybA3t2hyYvvPjKl19+vZONc28YxJUBCbak9tqrG9jtt98+QDjsUMrv2O5EksWNSHxGGC0uKkIenPjuB1cNHiKnUBKAV1XM68rLL5n508wzBpxD3s/Nvf56WkoTD/IZOZLdxmUcg+JH1YpnGddioUvIBCC/U48iIjh6rnqMaKapUbVIaZVSbdgyaHpGLF515uwlWLfRyo/AA/hUMDV+nI/GSQG/9PKr55x74WeffY5NLmwF7r//voKMr/HBo4AJEyYOGPhnfDv4oezsgM9R/Y8EPpz69eRvEG1NmzZt3bpVMNq4ceO57tPqL7+i7Uu5JAI0DYA/qrZHRj2BW5i33564y86chaurkUlbtmqJiuPvTz37r39/gp3EpUuXvTn2rblzfw7IsSajhMQUbr/jngeGP/TW+Akbt2nTrJm5B7zkkiswx2uH3AD8Aw7YP6ACcNRRR2Jde+XV18aOG4856gvGQ+UqgzVj9qw5O7OeaLFCdOmyBXjus/dei5cswZ2Rf0a0JVo28KzzMAVcM1oZwHGhWDM+/exzJJcHHngICPiqOXCOUOlTHQYCB6Mphs5qVXp2K8osAQlCLgk/qT8QJAC8BttOnvyN7p85czYygu7RMPy49VZbjhs3fty4t18b/cbs2XOx3iAOgYO7h8qKCmz7fPHFVzAder777nsAqEYDDnvtuQdW3GHDRiBarrjyml9+mbffvnvLKGL1xJP+NOaNsaMefRIZcKuttgyoABx5xOHLV6wA/suv/CM/L08qfUGIeCSuDLIzOg88sDsSIlY4hMH+++0D2i222PyrryZ7ZyScv548BdP5+F+fyGnQxlXdZputYMlnnntx2rTpmDI0/PLLrwJ8D8C1mi3Z2KdU3JF3g3+q3CMw+YdIQBsQMiTnkZaloiFs02a7t0jpleo9Sb0Wpk4c6DYZmwHOv9xPFEzJ2dqCiXyYWdoGqyju41q3arVg4SKs8I0b0+UROVavKtt66610Z5cum+Pu4NWXzc0L9EIh9s7E9wRnyZIlrVqFWRKdm3fuhBZv/KB803wCGNs6iNfXXqVNKBxsgJxLLr3y1luvv+G6a1B7Xn/DLfEYEuTtttsWfkA9KKcT330fN+ldu+62YvlK9CDXo8WTHwRccbFzTR500IEYwl0qWlTHuK4AyBGniivzwYcf4VYO+RfKY4V44P67kRk336Lze+99ACbeGaEfC348ISYJRfbv1q0r9h+O6NsHRk6ygJBLVFG00oqOlo4gZLxx8unqtQHGJ6vX4svgTYBZSjlN6gctC/E0NM3q9R07bqbHkN2wnaJ7NCx+xN00fqR/Xn4+bDVk6E24HXn8sZFYOC++5PJ58+ZrqgCWQm/8+AnS89XXk3sd3FNubwMjzJ0zF6NN7c0KYMpcW2+Jyx43p0KI24vAzpEw8Crz448/7bjjDigPEWOjx7x51sAzME3EOV7A8M5IpMTffpP+uKqfTfoCoXvmGaci//bt22f58uVYkgXZ3xILioFYa58+c8FnNgV5tzEMGTB2wkdJiO4q4hrlGBNdWCWzt8hhATi7tChyRWASjE2fRzq0lFYwNZyaVk0lBJE4AouHvTk599x9W9MmTfr0PRr3ONjI00MCo15DrpRyLxjFSo59nIN6HS7rJ/p5gTDjP/wwDWsm9sV+mjkL/Zgjrm2kNkSVxBn2X0QfWoX4wOjKVauxpEu/dM6eM+f4E07r1KnjHbffPHTo1T0POgz9hkAwWO7UH6biDCUb6j4Ast+HW9QO7el+LXIE/DGpdm03/edHH+P+FDi77LITriKp+CIkchpR5qCD++CNk5NOPO6Sv/4Ft0vffPPdypWrkCVRsEg5E8xIyCHXyza5k/A///yrQ3sfjJtBcBt63U3wUaB/nJAFiBRHFk6Cy8jA/CHwbsUFAatJayqAGsGnUYUvyAYHH75DlFy6ZNmuf9gZeUeWASSLNm1a464TFOX0V7ByWrdquWLFCiyHwkP8iJruttvvlh4x2rvvvo8frA1XXfm364dejbtpjMbtKYkG97OyPG+5ZRfcW6TOIGCCt9awImIHSV5Tw9bnDjtsH2ce6BNX5r33P8TLPQceeMDUadNR5557zqCBA07HkoCbGyRHEOoZCZ+k1isX1vv5l3moM7BbOn/+gsuvuDogB77nojaRJr6hE3YpERHMlxxnvw3+3mKWe4vQk2aT3OZUf1a29oyZi/CMBe0ZsxZFYJpiMgcajR0e8zEOavLFS5bi9yhxK7HRRq0DOhSDxx17FGrJu+68BZ1j+c4FfxpSfp8MnkY033H7Te3btWuz0Uag1fxxW43XDu6778599tkL9cI999zevFmzO+++H3ywoq4uK8PjGvRjdzIoTvF+DJLUn88ZhJ0aPItAcEOBu++8FdkKNyazZs7CH8YC+YIFC9u1a4t79uBBJOQi+ufNn49nJnjoAc7YHkWG8r5zDg6Bnscc3R+nd951/9hx4/Fz9z3DMKTvozEaHHFlgIyyBXPZe69uckFO+ebb/fffBws7bvRAGMwIqmJGe6pSNGCbEqBUdsAB+02e8m2/fsf1P+oEudoD/b20ZuXmMZMKiSAWadRH/7FhjTtounAAF+dzXxQ/qZ84ANd3SP/Nt95RWlo64sH7sNh026Mr9n8RPzfceBsoPviQlqIzzjgNtxGjHqGNYBzw4y/z5h98cE88sIbBUTNusvHGWFwRgUipkyZ9jv0H7BYDEzsJuBvA1jBuXZmUmvff/xDGP++8sxEzeKDRqeNmWBqDUS8APY84og/Y4l0fCYOnnnoW29/77rOXF3+zDu3jymB1RHraZeed8IQdWR7VHLyGugEcsF5GZoRiwstZOr32xBzbt2v7978/c0jvI0459Uy9feTFh1/Yn9ISyP+pFdi2dB72aZhZoAOYvtayEsYhC9tverKsFlPYRYY+XV1xduvGuxXTLhIt5pzwI3BaJukQyDg4nnr6ubPOOhPPamnbbsky6YS5EXlYA/FoDDD25j7858cYwk4KgvWo/ke8+NKreCvi1FNO/PuTj6Af+zJSqQk5guPSv12JZxS4/0UPgvXFl16ZMGGijL766ujjjz/6sVEjsAGPdCadeASBpHz0UUfi7Tb0THjn3W+//Q5PJJAZcQodHn/iKQDPP//SZZf9FffaX3895cmn6OZXKpGBg84b9cjwi/5yPnpwmzZw0Lk0RL9haRAAgAna4MBdDxQIngthl+qXefP22GN3qfWELZCFqrS0JK4MRrF3hudUuCABj3/7HWxLzZj5o4iIz+ijj/6FoSp3S1FOk1T9/vsfUPziSSUIoQluDC+48BLh720RKTh060XTnVh6EW+gkbIRLWJPIzy4KPG2V6PF4U8++QxV2GmnnoR3AzCKHIEne2JwlGa4/8CKgp/JU75Zt8nGQg7HPTLyATxwww9scued982ePQf7dM89Q/cxWGvlcTZMjX0SmEW/Ao0lCrfYt958vcQMnsZccukVoNJ+jxgft9hbbNH5n/8kv8jxzjvvXnnlpf2O7Pv0s7SfEwmDZs2axZXBXLA1iVsuKR2++noK9jT/ZTcN4zP6+fVfwLmqytlZllOvqrgSEdIDzvwTfpiw6plnXxj58KOisKflmhMVoqQNaqVCpNZTIRoO8vKhjx33hSyQKZlRrCU8573FLP4MbbNWm5SvWS2sk9rdiwt2KylIGkU/aknaFQrnzZcCq4tR/GqkZ3bJXViOUPHhJjGOgsV85qzZwZ0yELB645ECyjFB7tSp4+LFS5AH47TowR0KXldE3XTNtdfDuwEO7jSbt2iOZBr0CIAaoVOnjigPA2RgbtZxM5RjQYDipmyrLbsg6FF4RsixzdeoUV6Q6SKjtT+NK5OWZ3xGaUkCBJSZY0a/NOyBEdhdguj+/Y9AKsdl9j1vGgRoAuCPeK5YtgSwDoomzVqUl612uvRwbg4iLUiCSI7DF61IEXVePvT3WtIdQ4cMxqbK/Q+MeOnFV/RdLR4oVVauiwcP7iFwz4hnrwEydl2wIYg9mUAUevA4G7+XEvQEAO5gsLuNLBn01C0QVyYt//iM0pIECD16HHDVlZeee95FeOCGh5kXX3zBpptsHP8NNMFHGKym3VvXzQGvSLfNGCG6YEp/QGUAL7EhFZQIRpZpEZGKI8IjrmJNe7JNi2bWG+YD98VHHXUE/b2jqio8KcY7NBtGzv8gV1zeKMYfGfX4E08+3aRJkxuuvwbPtXod0hd1fXy2uB5WclrUQ43TpcVsk2AcP5O0CJVwB413AwFgeTu0T3/9BFkrXA/HLYANa9SJ5/z5wm+/+w9CYtiwu9ZVrsMvO8Yx0UNpcRW+xIIcJUdymqGnLVQ/qlZI/FnRMqTPRKb80MXWqVmkxcbNW+Pv/7AqKO9ZgEipOxhfe6On8P8O41al2557oCB9970Pli7Fr9/WH5la4G+XXnwQv1WHGyNsgd108+3eXxHBd3rkFxauXrFcgihoS5s0rShfg1McFMl1F2PEDkc10lymwYaCt3v3PyItvj1hor7/YEb1TSoL4HX0Tp06Sv6aN38BXhvy3ttxGBStwe8RIjE5zk6RyFLJ1WQqgSZnRU7HQc7MIi0WlzbNbdgIX9oWEAdBXCc9eMXC/nJh6gnXj/5mLIAHX4sXLfYWiTIHfJUHALpfdo/C4hJcIVWVFXUbYwE3XHvB5oYruf6sji2A2gKv/eLX9oONhbgA+mKf3Ab47djkTGJqQzd9xTn5epKZ8j2EZziLv86NtFVU0nhdBd0HUUKv+9bZyvXNr77vN2YB/DWEdVWpijKkP+REvmCClIUwpdgqLCrG96ZumEijrPgbM+VvVl34FY92OGEkzgFhgJyIlUpXeQq2oDDw5LHU2dJL4CgTwcjiBZ0qfBdReVlefkHAj+7E7aFAfpKUZT8KRX4MZcnUZ32/GON/zw6oEZD48E0VCB7c6ag2Bxt/GEKw1TKugjjSfHAN/e8Z87cbJAgDfC0qFe8IAQoC03JACMw7H0GIULAk/SPi2A8hm+hiQoYDORjl+Avb3CyqReR7lIr5BUUIqcz3ZYK4TAVg74HK2frj92IB7CUV4HfpcnPLV6+SjSe4H9GpWzyvzyugtFjHwUaVaH2w/SoiTcIgN7chfx9yxP+xgNDBIbBMQsPhtCLcDJK3N844i71FlkhsscmYV1CELz9fT28wgWdtj/owra0Ffzv0uBKQ7NCiGIxvKYbzwOrOW++FxcX4or46C7b6BTg08f8nFIQB6sQKepAbXRRjPc5zZ1lK7QQE157FP9PmQiKp6ZNoRCnFKk8Azw/x3Wz0xZINs/v1wbjO9T2/Kwvglnkdf4M9XnuixCfpj25xEi8MvEGJcEODL3j5Xdnqf3iyuGVGJOCGQH3Zpzd7WRt4B3XuFER/hvQSm3gTughGTapFFcBW6fhnRI6egFXdRfGsBnEiSxqX5+1REmz1QeVtkN1rKyDkjy0L3gdAT+LlHWIrHNLbXag4WXAVHqYMrah3ppl3JmoRslAoUbNp42mYJuF4UCZg25B3jaCYQik8mJmAiLbujby8Exf6UVj6Y0+pFhIQquPUurdDKMxAomQgVq84DhyJPZfa6ll38eZXKKpE4DQxp5mLTxvWzIk0XTn6fRRwB5CZv7LbWwTPlIdXZkqKYDAje6QPNa8G2skaZuHGxK4LArUExR3ULLQfNBwy8GoUDqeAEk3Cv2ZvLmyBw5auRynp5WJIwT95yLIw9wb+ifnJvaTEIPSdSknJUWpNLlIy0MArOMVlkImABJ5q5oIhHV5s0TxsxQyGwNokZd5S0jIBvVokEyZMIHAXq0Z/1gFPFOzikc2ykSxZjSidRTDFRca+U4wUGPLMKt7EUzWoFpXoQHXpCzUxV4FEfWji6EwjhpbfgCReGVwGIjLexpQg8SI4Wt7EidP2iImFXVbmJs5KNR9sh00+Cy+ltFqlQ7CcrSFYFYcojEYxUrxV6gul8lF6/o6wTE7CoNGqxJRIx8qnmSfq0lccwkjE+ZiyaW2aS7ZzOn3j46EwJ95seiUvWLFOdlVmizNVPSH/rI2ruJh4lh6/Qg77jMxp8wBHWgbli9ZHw94phpeXZzibalEMrWYXcrZKSA+feYRZrGw+E+3ni4VkxolsfKbXq6Id97H2TjGVUbwEPs4Z99k0TQS0nsuq7rThZSNXq8m6mYqoI509bFwPuqnEg249oQ2s4s3Q87S81O6EvSiatYueMuqjqJHztKJIrPhONiKsHx2TpNItIjA49UoORlMCQiooio3oKSEVxpuxTrSKrInKvnqBtEjQJwndnZuaQEjgoEQwalAtxicrPY6YbKehVkIxbnoT+6S6OjhnauLhambNrQZJ80yPCJnRSHqFhxdDW5Bgmx+ctJU4kKlyCXhejRJw0a3QAz2dqsRBAbqnFkvmnuGIV4moYJ0X0/IVjoLm1TnqI5uUo5yVatpWoVO1saLE2Z4nCgsYGR+JtjautAoaFiqZKcNe/gHvGgGikJgmDKCMfOf6SK8RnhizpQxJSnUkM1UCsnxvMY1INy6SAimV0jImqnta62cnfzj8Mpq11YwDwluc64vCujCNbpapow5OPNOIomRw7uPCmtvKQteJyebJQBKh+KTVhpTMKb4jK4UVh/pVf23xJFhUkNGYOgk6S7dge1G0sCjTFARRVJynwNZCQtj6znkiHw57RGTQ5dUiAzp3Ar5s5k1G0VlnJimClaCztztuH+mJsIzqZZDSshS8LF6vAcfA9fgrWPn1L+hEPfF7P6d3Lui3U8rxDg6FJQ6ntRFESdIM4G8D41dZ0Na/fJNV9ND7LfirFfyFFmERZvYTybryW2PSCmd/9oikD6Q+9IijlO/EXTLgtlEn87ktX3RpKuK1EhaGHFY80rJmLIyfOfxa/96imSP+BFBp0/zCYlSt+JPFNr6z8qmDvK5yLd7ndLrqT36bFsA7upLm8KZ2yr+WaC4b/NYzXn+VQOKLUS7EGk7+dxVIganx/Vn8KyJeo3HC4fQSFDRePE+nlzTLSl/Y2uznEeJ2eUVaFO9gcqq1ZPLpJTZ28WJkvbdY2qR5g0Z5uQ0Q2a7oWpzx673hF6HVglM96a/CAkh2uQ0arFmFvyqaeFEUlTZGhYg/zVuHv+P0OwwkmBpmXGP/cm2scrS7F0EBF02QkjIkbPzpo1Yh5VcoqkQgIq02HE+e23nhkBhtEQGeiVoDccRm8ySa68TcBo0Q8XWYE6EwGKLqVO+7B5OoB36TFsC9dF5+foNGDddVVNpiJQw73BgVFBfn0lcW1mVO/H0GEkzdiL+3GksCx4rnik8VQwl5yM1m9g2HhL1g4Z8+JSXqoXQWwRQ0KnUK63ibyNAhRrzpBzJxNvGeLP6CTh7MX1iMBT6VMjUdo98j5K+CqimDerpflwXwF4sRLo3y8hCUdCDIpc3BH+2kSGpgvzavbvX+HQYSTI1FCFZlG8OcbGjVwvqJP4xGrgGOwNSG7mKYByhNUbayLfmNsxcBppdBYHNPpCX2jMdySJ754W4rk1gJ7AgDqvwjIsLgNmDhAQRBiILW6knkAWxFk3YGzqJaLCgqZWYydWRYHHXb5tQXjGLc/4EWG8+4UBFleARD00HM2UU5v6gIu9IUyXUcP0E0/r7uPKypG9iCMQgfZfTQAcFoSiBwjus7OJQ2PeA7/W6scW/6l+pSipTBBJ0T9AmnZaNL5ahAWgLPYNwJT2KQxZNorEgp/r6uElFDsP5ZZA0N92slo4KxEH91GWHGh3zm5uDhM//tpQ2l9waqQzeUunXBl01dxNUOv+0ja5DJXc5TaZvTeFVi0ZJPGHTThzyVFvWs73BmHMpuFdgO0qeFiYzPKY8yaJIpp1TTQRh20FBaOmDwoPBjRC7xGN3zVLoafbhZljKQmEQO4SMsDM+IdPMQngTkZpEW8cdy1q+rpPfjaC513yq7RKZUf/qbtAC2vbDUIVQ49GVvkUIHnfQ3R6nqqPsoYp427n+TZquJ0mxq7G6ZqxMZKITJxpQrva0I40GkA/EUtVIUSg+XhugJdujAMYAjuUX4qVYEUwRElGAcEayjIORngkM6NAoyIPhR+3/sXQeAFUXSXsLmJYOKgBFMGI9gvDMLgiBBBBXv9MgKAiKICgYExQCYlyAYuDOAIjmY8DALKMEMokQDOe0uS/q/quru6Znpefve7uKvuMPSr6a6uqq6qru6pmfeG7CUfwYmjBwcpTxuQJMduKnGUmON8cH7E9sohDmItz4ElgBt47tWzuxSGXvqpeqlJ9dPT9bkSYAFD4xNb/MxxAbw0g2DYuBA4/G2++r6LcC25KLLxSuT8DI5+wXqReePFz1fdOH5uHVlWBVdT2FVRD40ZpgFlwIqHT1d1Zh14DGWzFiJh55obDrFkj5YC+tcg8WIxxtH8TpmzVh9FiP/AGc5tfgLGCwxKGhccCmwVRIx/lPJFpIztpaqERiyiCRYkgpib8/qaEfYQEmMuTUDFswgKcB6mDIgjIQH/ilGjFUwyZA/IlbyhIBKlh8uRTTrLGokkC2ilXkDBsdWQvB/Xo8AcfDuUiUTtV0qZ2Zv3IEG9TKSF+TsFnpESfzh5eKkHx8BPhrt+8QKiNneocMNNha/1jfuuRdtjIEpC3EdMfDg36rVlaedduq6dT9Pnz5LXk427NEHd+Xt+ucNnQLMYvAJUMppcnIy3pJ81lkNv/9+2eQp0/Ci6tNPP+2hofd37toD75gPN4mHf2ZGxsSJ/502baa8hf31116qWqUK3tF+6qknX3LxRXjnJF5XHQ8fWzrozzv37AED+g8cOGj+goWmqhB8TFsA5GJZqk1JA0l8RCVX86kiJRxWUIwcQJ0r4wlZpkoqNQojKoI+gKcWoaNKlSotWzQLoPEG57feeieAlFOtZLAyBr5b144XX3RB4FXIUfTnnnNW2NoQFkUfB16R6NwQ8wwYKikeAObI4O8PW5cISLJXeo2kqSoRvUgGBQ/wpdyLS+ZuCFkLvnYmmSRbKeSxZ0gJ1FKNhqKnlFonTvO8PJEE88BRegoPZghM4BALBEuVJ4oAvpYhPUnbpP2JhUUjjkUrVWw1AC/MyUdkHLUpx8Z3rZK5MGf3qI07RyX58FF8jCABsFvUqmVzg8zIyEBsjQqLhix+4KWXXqh+2KE7duw4++wz217deuhDw2bPeSv+5qC8qnWL665r17JVu0ArvPMM74TET6giGp533tn/vP7aq65uH6ApxCki4M4dO3Gg7dFHH4WYCJ1nzX7z3zf+c2fOTnZuvFwfGjq4TJnSt/W9Ew1ycvAa5NwYL+qLl6lFR4u0DAWrjPK7wdfLSJFJM2rTzvrpgJMW5PKtG557hr2hB0bYm6owcPjhh8koQp4Oj+A1raDBa+yjwmKYQ9ExB9jalHNROPFKwVglG4onv/QGZtMxhUwoNVwi7jh9RzERB5XiWu9csLpkKt5btBRi5ZhCRBGROYi1jt0CWzqxMArDCF9qJ5GDsrWjyLWGmw1oHUmAMGLtve4qPWE5VCQWFimWKiMafoLh7iQlIfzhvOPqTV2rZCFhXJC7G+S4cOY8MWX+avk2i7ut5mx3RsH4mtPlTVvKyXnnnTPk/nteeeU1B12hUKeccjJiorzrHWljv3632rlSnCxr1KiRlUl9tw9Mv1HZT2AfDenbxo2bcIXVscO/tm7datMUDkYy2KatCq/HH1cHTL5YtBgllopEVwtsFOCla6LG518sCkf2wmloWqkU0EwMHj9qVY4eSyM37KifgR2YlJEbd2AsodQzCYxjjB81Do10G1iy5EsZRbf3u7Vx40vNiLJpDjR8gK0Nu5IFQiXFShyq5E4yAkblnEu6rfIvwuikkBwlGSKVERmitBavCOyVPsGRShif4mtzrCV6QcKgiaskDb29RaYyElW//OFUa666a3XUYEjRomaLRol4AARHyh83Bt8FHE/bAA0uSAfcdTtCzKjRY1GFNX/EsIdOPuUkwF8u/bpn776IQdnPPL569ZrTTz8Vr73v0bPPhvUbhw8bethhhyLDmjfvg/sHDw3wrFa1CjBbt21DiVvtSLsMQVp62vgXx9aqWQNvdHx02OMffPARqi6++MKet9xUoXx5ZJfPPT/+tdcn9+7VvdkVTcqUKfPWnGnLlv1wU/dewuHvfz8Xie3AuwdBYWA2b978yKOPGeYCtG7Von37dpUqVkSONnXqjGeyRwPfpk0r5JXly5XbmZMz4rGnkM6EMTNnvPHyyxM++2zBbbeROOg5d+7/Nm/e0qjRJS1atgWmWrWqI4Y/hDc1A168aGmvW/uFZY0Z/fQRtWqCAJqjI59+Ov+Rh4fg6v7HH38S29ateyK+0fTD8hU9e/WFMqefdurQofdPmPB627ZXpaQkL1z4haSZ4BB18OrOlbT845CSwZjFArrsyBqTUQnXGTEJi1rZrWunen87feu27SjfmDwtLS31gvP/DsfBa9mjnpVcEtbG7gqCKZyCVx7ffsfAn3/+JT09/cnHHz229jHQ4Jtvvrul521GFTyE2/e2XgE+Max9ePXq4VGauLWtrJC/l4v4qKxuNAsClHvpJUfD7CJpqEtCcZZlSmLk+VKaCnO04WjqLz0h0lDHL2LCCa4uWWWlE4vk9E5liEpbpbPIiypFO2IhCaU0CpWkLpSzy8RuudC3oOmILOfn7MIfckMNlAUMeuSPgq+XXjYGh6j+GfzAAf1xv6Jf/wGCeerJ4SeedMIz2WPGjn0BwbHRZZcAX7lyJQDbtm5DgPvuu2XPPTeqYqWKo8eMmz9/4SUXX3hHf2/sCpP3P/gIE/7WXj2efmoEooCRBQCBde3adSMefwoToGuXDsAgtRx41+240hw+4okNGzf26N7tggv+MX3G7JUrV2G7c/iIJ0eOftZwaNigHjo77/0PDSYMYDfw888X9bv9rk8++QzX71WqVEaOeVO3TohEHTvf/L//fbBhw4YwBnwy0tMrVaq47uef33rrXZw+//z4V199DZhyWVk4RVBDoKxWrdqw4U889vjT6zdsADIsC3bLy8tDR6D59Bmz0tLSIAsHiLG6wKQzZ82ZOPGNo48+aty4kUCCID0t7eqrW48ePe6jjz5tUL/eKSfXBT7Gwe7GLOFPLkEcYwzYo2tBzi4kjNkbtht6jB/avI4egUIZQ59wFYxWp07t446rDVtNfG3SCccf9/qkKQj38PKtvboLPax9dZtWs2e/hYUQGyPt2rYBHqsjYuLt/Qfee+8QjBNc03jM9+8P84myNhZ75yhN3NrUe7Eul1CHEGwTUwKw/8IEFjmDNgPhKDIUd8ggiP8pmJQgeVbJFNROZItUGxYMtSGhgVKxpxoWpEv6FJyvFL6m9DVhDQjD1SInKK6we4vor/Oon5EiO0F2LS6C7NOOOZQ6FeJASDr/H+dhoi5f/gOaY0HGyJsxc/acOW/j9LLLLmnSpBH21wBj2wgpDzJH0COMItGbNn0m8K+89MI//n7ug0MfLV++PE5xYOjjPTvtrvkXLqzOOefMZ556bMWPP910cy/gUYt7L/3vGAigYYP6Z53ZAEDrVldiMevY6SbsFeKOxzvvzMQuPjKpn3/5FXeuRTrI5KhVq+aufLUjpnHBz3vuHQwUrq1++mklgKZNGv/nv68AoPQ2P/+hhyl1le//2Bgg5YAaixcvaXL5ZW+/PffX337T6CTc4UH8woz95NPPgJwyZTrKsKwXx7+ELBU5pmheq2ZN4QCJdWofi80EmA6YcuWymlzeqGLFClLbt++dS7/8auq0GW+/Of3CC88HLHhn6aUrevF2koWRXatmyUX0ouMPw0U0LqtBExhL4VaFw2BS3dy9N9JANL+xQ1eUGGxYcuBTmP2XX34FZorO5Vu0aHbG6acCg1ZIRDAI4bL/zfsAGHMgcQjzgS+c1j7n7DOdo1S4JWJtMjZCBedfnP9YOaNkjlSrkznhj5ggnrHSOcbZGZnlO+VQcKEUi0tmxImdYuWRgwIiRSMjWCO0ojClp4SGiDV3hbgTzAKUZqG9Rb3bqBRgjexCaySMpIYFGB2sLDLBvUUaCFAxaHrNuVRS9vrtIqtBZoq4p35G6oJcrPmpI7Hmx2xLrKMP7PoNGXwPosAjj4wQqrp1T4LNEUfwJ5hfOM0BvH79BnoyLikJN5dRmj31xUuWNm50KX6fYNqUidLklVdfw83cbdu23TXgXmRJvXreDG4QdGuf/iAwTx3/9tt6XCMDU7v2MdABf1S7fz/wRx55hLAKl7igrnvSiUjcfHmEnw4JbK+e3ZGDrVu7DjW4WwrN7xv0YL++vV94fsyaNWv79L0D0zKM8bMJniGOA7Vg4ed2RViWXWvDYlvkg4Kc+948hMWGDetv24pff0jK301XtVhOkCBnZOAp4lgHL9V+x6uAwnMhYixh/KC646qNGBQYOV2rlMPYw30Y3HiZvxPDCZcj+THGYSyFXHXoiMREVGJLpEmTxvjNrh07aNunWtWqEhbxbKA0hfdTUlMBY3/j0EMPwW2uf/3zupdenoDtaSGQMoqPTSOwc5RiwEttItbmQK0Do7XDSCgc4VJEqJmHwACIS9lb1DBzDO4tUpgicgpeXikMvVLNapbgE88kjPZtH1qcIMCwFliXPKZIOOr9MLdg1lZji6fqojD2uisdNWWCzy2Cv/IVJGuYMBomhRjGOG6QkSrUiIxY+Q2Nk57bUevwIStSr149sJ03YOB9CEZCs3zZcgDIFi+4qLH8tbvmn1zl6biMac455yzgwee44+pgud6LLPLCRvKHmIhLGDzvAgLcx3j4kRHYUTrqqKMCfPiUitWr12ZlZcplJk5xC1jmDNXpcUwwHx9/TGGlfftr1DnyPosG+shWKa5wL7vsCjwJxIsO6fkewlDTlvfcN+TQww4dPOhuNAem6RWtbIzhGZaLKnn0B6mcITOyGjVqZmRJrdhL7CyYZd8vA4D9WZTA42IZwNKlvqzQppdWUgbwGA50MB+BFXFozICEqhiPjUVcO+sxU0pgGlGI9Tn5XauWU6SanhriUDylQ4LyyoBipsLG4/KixZXNpk6djhsy2CcxNDZguGPAYPls3uJqWAZ7wUcfdaQhi8HHNDdjwR6l4IB7aBilZpwbnlGApb+AwRKC2AFUhv6IGP+ppE8poaPCEYphSKfKYElKSY+k5HMGqSE11SWx59YMWDCDlhySHBTDerECFqQYMUbBJEP+iFjJEwIqw2yVRqDlWqVGgnuL8BUfWD7U9o6cW/h6acgT93dYtSF7/bb9lLHtB3DaN+tAj+C46Pjq1MKiNzDQZMHQAfxRRx7RvFkT7N9h+/+MM06TP9w/xaVro0aX4qkLZGTI8rBvza09PvPmfYBEoEePbmjS4d//wsD98suvAxIeeWjI5Ddebdf2Kki5vv012N379tvvAnxME0RhWA63MmrXPvaBIfdC7ptv0iU8rutTkpPr1KmN601DjIumn1auvOGf13Xt0vHoo4+67tq2b745/awzG+7mHztJT6eNPBCvWPFjaloa0kNxzZFH1AJ/XLstXPg5dgOQSEIxYJCYGIwRQYDLbvPe/wDPMPXo3hVLwrHHHI09WSMLmY6RhdZIeHGpWKFCBdxkMGwxLX/59ddzzz37yuZNL7zgH82aNUGKhJsMhoDFenaOgceSTIdVEjEwjjHAX32Jxstl9dgjqmCvpl46hlk0va2QhkkL12HjxQhffLEYN6w6dfq3i5wyGDlu79cHYw933j78+BNgssqVw2CT31KJ4mOsnZmJHVLFxx6lHTvccJRrlCpS14elP0AwNSWoCUHnGs2nwJg/j0DIuDSNLByzEY7EjM2gS1JLekMlsaSSsQZmkdRO/2mYyEkONwyWzAb1rn/ciFsbpnymBWhJ3JhOSIwpSWOWJiJIPtUzKrEHdKifMY9u1cqhHiXCok1Il9I5u3CJjWsfGx8n/M9/XoeQgcvVx4Y/bJogn+rcpfvYMc/0vOVm/CEKDB/+5LrpP4MA+aCQ4V4KLkIfHjpYGi7/YUXffncaDgIMfXjYo488gNuR+ANmxYofBw16QKoMH7YW4XAz+oUX/3v99ddALpDYspz0xlTgZ8yY3eaqls+OfhqBzH7yo2Onm7Offqxt29bXtGsD+kWLluDCFn3B/EEa2LRZ648/+QzXtvhDziiXSxUrVkTMffXlF8EWd8/vG/RAGEOqoJt7VTcBo/sojZ6IYthYxJNMDw65D3jEONSFZaEKt5X7979t6uQJS5Z8Of6/LxMrtl7nLj3Gjc2+tfctwGzbvh2mpiqWIgQ4NeIARx28VHMlLce0OBfuwKDC1TSljfuTMJx4jKnnihJlGFDbPsUea6eONw4ZfC94fv3NtzZnz9o8g1CF64ZbetyEsQd4ydIvly79skL5cthtwBrW+9bbnXyc1o4apYlbmxZWaGeVgrFK9gDmvvYDPikg8CGwLuUSWeisknjRQaXOwPS5ZisUjOW9RUshVo7pLCWIkg5irfcTBSYxQkgX7YB5POmdRDoTWNoLjcD+UjTyWAhTaxdA6Qn7kRpZFQ7zt488q1i1el7uTq2j1TGlNWG6VS2PXaH5O3EFtA2UDTJTJSCOPbIqBnT2b9usXjo4mO2bSCVcFUjQqlatuvKnlcgzZHuyvAAAQABJREFUXPWEw82ETZs2YfxFESDvO/roo5CQ4sooisbGC7EJEFJ1/PHHIaXCTqVNCRjXzsfWPvaHH1YYetzaxqMzyDFRi0wNV7i442y3wkOOmGP4DoZBhjGmKgYAKXhIG88GCY1TFm6w4MINawa2CwOscJsFv+wQ0C1AE+M0s1yFbVs2ykhT8zVpf/mKVWL+dreZB74R0u2Q8vb4wUiTMeYcUfhB/xhaFViFy47NW7bIbbfYxOJZ7AuboYWhmJxcVjJrJ58Y1i5wlMZQBqbesQ2PxHphxIKj2wm51FtN/XGMnudDNER2RWFIlZ4kaS1hIFpSjJqQYOJtqW9FGF/8icHS086tcxRLwRctLIpafgkIhXgWh2oYj/0gOhUaF71YQOgLFxaFa0n5B7QA5ur2LZsCipWrWFmFRanwjx97PFhjmydJ3PRFDIsBhf8UpxwWt/nDmV6JYnXACkmeuWM1cNcJG6lzKmF7WUc8Re5eBz1tuKkKx35aty5BrLOLnj6O6gR+bzE5NX3fnr3qigNMcbjKtbv32Pi1+bxuuyhDHMBb6Jh5SfEntwDeN4Ify8GrRqgf1thLTk3Fy5kIGdeoIMLEKJHP/MUGEpm6TNnd+YFdBcvongPYngUW0lTILDaUM+qrdIE5ixTukpd5kuBe07RAgZrAtPC3jtDHE+An19xCHfAa+EgCUhPYW9y7O79McvIe/zWmra0tpxB4/DJpyXEwWQA/Q7tn725MG9Up+SyVhFQOv6EtP09biHHiHCY2H95JP5gMWXBfYGpOkMnYFLUkpyJYMHbp7TwKX4knDKt20poul4mTrsEnnymHslsFFiLet1MNNAZNeM9ONPKySCteihBLlBZKAvQ+I7eXjUGqVvuJ4R3GePcWTacs6dJdLhN5T/S+ffvTM8vh/Wrgafei2GDezheFS8qDwAJpGZm4WKb7BjRErPmatB/v/NujU5tiGz96PuEe90FgvYS6AFPn5+XSrXkJQMEyPmYch6SpuMuGKb759hbduWF8kgJUlmBP84iuyHCJGjQeYzdPJ9ZmKXACF9H79+3FNRHWebxaANzNUSwwMnO1Chm+GijBiyX+XHbAG+kwTnBZh7WdDgqMKKnYv28fbjsgwZF3VBTL+IGJhA8PJD10/J8k33X82fFs6n28LyFdCZZsdzKQAP6SiPGfSvqUUrlLapTrmAHsRyReSQYVs3rGBRfCBkpixu0YsGAGSTTrYUqfGBIa+qcYMV7BJEP+iFzJEwIqWX64FNGss6iRSFjcn4RUETuM6AFe3g0mxXbQZQ/CdMlxMFgA+1yp6fjqS6ncHHqYhgZjsCyFrZhkvDSVHgYq1oEEUX+lXUUxdalSpfNynY9YOEwf7whzNqVHf/Teoi9zdDlZSxLn67MYn06Rmt5ZaY8rIXQLczZWOku7AEUCd6K5PTXPyKqA4LgrL6d43khVEhO16//sn5iluJ2CEkliXg6+H2kGm7tnuPTDxQcPJFz9YUQX6firBURt6vz8vDxjOyQ7+o4IJUsOOOgV8ZFY3vgrHG8K65r4lDDcC9SGNXNczgsH0dtwcwDOLlIzbSxewxMIi7ZIfqVlOsY0roUcsktQf0kLYJnEPRa8boketJLh4pXesMPVjN6lol/6KZuSigJX1X9JmxWy03gAFvdYcNsK3wvws3DOez+JfRYRh0yU4MBa8tyibTIHDCv6j5hmlXTBM7G1UvF8MdPDe+RSuNsh2C+vwDNrWAQF0zWAb7oWyCxEIFNa+mFPb0uqlyN5MUH4FECkq2WJL6KiPs0152ieYqqgv8RgEWazfFQwf5868Zx4voupREGsXJo5Rp2EaWFm9cviLowE4WLKpvVCvzXSLSaFAT1hvvEmomR/Qot15IZsvJhiPf5F0tlnH6dCPvY+culFAaXymp8qZs9MpbOL3uR0VCe2txiIKh5nrYE1pBzCNFUin5H2c5k+mnEkG5+ZHRNG17tYO7sYyyjOBi7OceN0mKYGsu8TKgPXB+rCKgEJvmBqeThuFqycNqRnH78HfVLcS4vX1M9MFLE0i8POThJbQLB3zgZBIue5s6UtimDes1OBT+ACJ5tTmA/plOyjiDyRplJtsfH2FuV7L+rbL+Iv971pyy+R0qwKS5g3CnRIDenjkdgGtdgxGMHTIgtQJHAR7RIgjF0dFzlSH5Bpd0A3tVZCL3MME2py4mvDIidmaSnhZSLa3FYlGTreI9BMaSRY4eGkCPZLxwfpkpUsOiviVS6CzqlRBC3QFrlntogEjvvlXFqi+cdV41TCp5unZ1wMFbnQFmLseUIs1TwdyBDad3YK5zUrHBQpzLBTYmV2uFQIq6NGLbFw8je8CwWIQsLZs0lcokQbkerXzDHGzLYMKGMd0UytdTmR5xYt80XJ9SufaOAyXJ1s7KHmix+mGQFx9VqHVO6RMzmXvuoeJ8TUp47RKNClIFGB54H2nuY6s5AnnKSMNk+BcoTAJa0oTUldmaNkei/jiONupt8TpIT2SkChCJ0FLbROkrAAj3GMBh6RgWJQ20I8WPvOf2dED2KwK8zh1CI+RlZTVzRzBqNgKI1PUoDKEuzxs+zgmUy7XzDCxoY9xm6eTmyYfaGzRS1f5MhZhEzPxMGeFmDoWL3W8mN9eqtTRHpTGAFeJ60sw3Nm2MRhDIn12ARgvcSr6e/2eaxeO+oihRlaz0e2qWxY1BSMpTJzcC4thnehALdClmDbrNESpOdS77dCXGPPYxzNyHLUgfNdsAe+safF2pmgDUvjiJHkt0rUouMZIg4owXnn1MB2L8MOf2lVInqmq6Pnmg6yRKmNyBYo0t6iyI1QytlZo2ncgION7oDteYGjuTrYaJtYDvBNb3sb3mMsjOS8QKZesxgNgkQFnvtVsIaLzj7ChrHNY8MFyko4DtkcI+3j8mCEJMs7Vvzx+86WybBTsJ/KSWIL85NHaBckMud+H0VqzgLZg9p3f8DM0Q4rCWX6Yg0xqrFMNOB0iSZ3Vob95RbmbKy6JQICFAk8XoM1AIMDIxplMp7NSSt5QEf77P/jk56GwS/r5+fxl2HhY3GPcpK4KlDiiyVwGwq8d/v/Q+USmQlbwP8gTmDyqhggWGEdStQQxxBprdIK8NzE19q33CBcq2HFbUBNg4v2QHRDPerMuY9QsBTv9RaKWQ6ZzhHCiDWoVUufNrzhAgwNdMd3ormJohHYX2qdSQDPFemc6ECl0lPiWwIX0aYXeJw7JS0DyRS+fKpt5NchkTN8cyZfv6c4kXZ/dVo8Nc1hLjV/Vy6+eqz3FmkESnISyArTMrLwLbESr/25xo3xMt53tiuPXrtW0KHd78tQoxsJudRbTTmYmmhW8txitAG5plRW+UqlyyaXKl2asv1iOvYiNMbl8mKSd3CxQbDD4pSzI/jbt1YvS2VklcOD9/gmcjF+FaTEa5aFDzgIL+OJ99ycnZQrUXLDV9uyApokLJyWMSamclYspDTNJD8xG4UrfeHVqZAvQ/SRa6kiO6JUm0V+2rAeLozplpu1ozqxvUX81GWp0mU5Jno/l8+KGm0g2RzxwmCIiV08XyU0wv8yAC6mk1PwlbsyuKTWOSNl8RpOSs/MwtdmYWQdE+P1izahmx4sS7ymTXTAP+HlsikpSB7VT1XGJdAx4eNqB6KIuCXjSqKyN8YU+cHz3GICr7hKTk7BtbO8wcdlXHvy2PVx4el7hMhAS45CWQDft0tJTcc1NUYqGNhl2WR8uS7N5bW4/GKp46Av8ZplnwMOwsuYgoiMLIkczeFISpzSH7seg0BgqgqNCIppXEEAwVwyTMTEgtob2MMRypOhYMIQP2piwYJhag2yMMIwratkwURAlSxNwYq1asqiWMcwRA1Cf0Sm5HEzho0czdzrQyLPLaZmyDvtwPNAHSUJY+Esi11ejoml6Ye8MAAQwfR1TCrvAtP5ATtKvHbATOtjbLwsN9l8dd4JHM3u95VedSzIakqDSLMRGONKPzId6wsXsfhH1lmCaaCKYB6zWgcPbWOEoSb3szd8fA2cWJtC4AQSNGSLIhiszYVzMcIlt0f9fk3sDKkEbjHzCqtiIK2PSfgthmRmBHfTUYz+kjFQ4jUx7O9TwstY/9iP5ElKfUxaRf4W/wdL1HAl50cMC4ZLIsZ/Krm5nAlbG9biRKgpqd88qFTJ54wAR6ozJTEjWpLkgxlNCgBvlcwQhCyAPkP/gKA/xitYsWY2BFMjRUBkfB4uRTRry21KJfCADvbs9+3B7hX9AjCvG4jqFFu5BEsFQ0Th8GBRchTaAth7QoQy+4nwrtyJBnLfHnr1cqH9UpB/C61yScOELUBe5r0mcYoqecmTO6D+knyuPE8xgUZESKSsl6GShws3olDCk5tKxBjaoabgEZ0zEgXiglLLglm4iBI9hEShiSdraJckGQfeT0BhJVRq3TRTfIJN4FBjn7vhwTpycVOVHIvmSfsTCIuQhYArnrDlihpar6R66cn4M7qJniM37TRNAvQBvDk1gPKEOddACV4sYdsBMJBcqJJOQ15jFxgXJdVPT+5SOVPb1fE5alPOwtzgu1KZsy5CjWyt7MoSvFijyHaA+1QMEEizVcshhRaa6zQifCXHDdWS2vhaa55cj7lOY4cZWyVxVA1VtXfO7IQp0+BcPQ/oV0uLlZaKlNoBwZHRwEo/wrMuXB7w5xYTC4scTPUqQJ1RMIEaRkwEvCDHP4u4paGx6WF7P54rrUI1TUo677xzLjj/73iP89tvz/38i0UGb9ES+Hvinx3zDN6t3O2mnoWQm5GePnHif6dNm5k9cozdhaLoL5NB5ouClUGcXqPBBvsjJi7I3b0wx/2C7HoZCJoZndbgTdOK3vhLfGcrb2D04thjjr7oogsMBsCrE14Pv0RbCIrSa1tEFJ/UlJTrr7/Wpvzww4+//ubbKPriwp97zlkDBvQfOHDQ/AULbelF5s+2J0/zf81acsNQqSen5TBp71sxJefhUieFpKZEcCojMkQRDoYYH8HDMe4cSnAneHRxFBdOXmLHgtFPjaHcjDJHauaVIpq5AyRuUkpawKXqrtVRgyFFORNGywSzxWCfI84X5uzGNEMOElFfGPSdd/RtdNklebt24U0gTZs0nvPm2w88+EhhGBVrm+3bt4d+BzSWgKtat7juunYtW7UD0e49e3bu2IkjVoME66yFnFfeuJsjJi7IRVj0BpMFI51U28px8yNCLGPtr2uXk+P9nv7s2W9FhcUw54eGDi5TpvRtfe8MVxUCU6FCBSiTv3s3HmOS5tAEYTHAqkqVyhNeGX/d9f/+5ZdfA1Xxn9qa5+Tk4sC4jb95HJSUsVHAkqkvsFdKrVfGZKjChQoiFGs4K8MHuHslnUiFLrmWCj6EjYI9VYgLR1Ydo4gLDtFckZNskqZKUlyTszA7Q6QwRopIyBMGUSVLYq2RXppGNgOGSUXwtMtEs0V5XBFccESUtMDg/77OVTIQH0HXpQqSkfxOq5FuRLdS3JjEVVxy8YUYxJKXtb269dp1P7uofm9c71tvT0hkjRo1sjLV5SrS3jZt2yfUvEBi2B2Dyi7RhLeD6ZObO0r4C87qnJQh/BEEOUTKGZXsUDR0tPWIIqDLm7aMqCkAXb36YbuKOZokPf7E09Onz4oh+NBDDsFPheMh0Bg0BVbZmuOyRlbBAlslQkBzDIfyh8COksKLZI46EEmksEUpHoyyYIlMQigwlzK6JNh5Iw3cKW2j2AMWKudiHQ1MnCz2Agp7Cw0SEhMqOTdkEdSEhemS2VoaEoPAoZNMIvVgow/J5+DtlYmFxYC4GKf1M1IQE0duxAs9knAVBnhMrUo6MsZo567iH7UvnbNT5R24FhM6pJCnnFL3mmtvwOlFF55/R//bWl11zfbtO2bOeAOJSePGl2ZmZKxes2bIkIeHDLmvcqWKy39Y0bffXZs3bz79tFOHDr1/8uRpV13VEinJ3LnzFi9e0q1b59TUlHnzPrzn3sFg2LpVi/bt21WqWBFL/dSpM57JHg1k9jOPr1695vTTT8X86dGzT7eunZAL3Nqnf9/bel126cUgkOPxJ59JTUkNNO/dq3uzK5pgyr01Z9qyZT/c1L0X9Hz55Qnj//MyOjhi2EN1656IZ6R/WL6iZ6++OznJAgGmMTpSvly5VatW337HwJ9//kULcXxipAJrlw4iF2rUxp0L9EV01yr0JJb4DgBciYXN1agwOOnpyaechMZfLv26Z+++AGA9bI9kZGRs3Lgpe9Szb731zpjRTx9RqyaqYKvXXp8Mx/Xvd2uLVu2QXOOew5xZUx57/OkZM2cH3LFh/cbhw4YedtihSOEnTJw0avTY2Cr+Z/xYZHOdu3QHz9cmvrRu3brjjz8OTZ5/bvSuvF1XNG9t8x9496BWrVrAR8lly/6w4scHhz66fPkPIK5WreqI4Q/VqHE44MWLlmZmZdqaf/rp/EceHtK5a48ff/ypuLzMnXJHAsp5JKjQMABMY8FfCp5KORAVNOjnySFOEQkFlzK6pL0aaUohqrYIGQYFhXCgORck5aCQQgjaK/3CpI3gCJbMkZo69hb1O6OJmesQtLDQJMTU00S6y2WpBB7QgTAsAHwYAGchWCEoMmvqpOwN2zHrxtSs5KA3VGQ+94G3Vnz51df16//tlZdeaNzoUkN0SLVqFStUkNNy5bJSUvBMOK3z2LZr1bL5pElTMHNq1ayJwf3RRx8j+tSpfWzLFs1AkJaWlp6W1uLKK55+etSKFT9dfNEF3bt3e/758Z8vXIT5KUP81FNP/vzzRf1uv+uTTz5DforLKzSsXLkSruW3bd029KFhX375NU6rVqkCPOLm8BFP4g85IL5Pgtgabj59xuyVK1dhxoJs5OhnRc9KlSoCgIaIFDNnzZk48Y2jjz5q3LiRQOJAR65u0woh/rnnxx9xRK12bdsIPqokf+CQQpszts2pCblKe4tZWGeE1760aGyKKG0Y36B+vXr1zsAfFhIgnnpy+IknnfBM9pixY19Al2FMMD3h+ONenzQF18u0xvTqDjLU5uXlbdi4EbaaPmMWljQ41yRxgMuVLwcy2x3ffvs97JaSmjro/gffnfu/a6+5Gosiq+AVGDw9b7kZfxgewI4b9+Lxx9UB3Ktn90qVKjz11EhsWwM/fvxLjw5/HIDN/6eVqxABn3p65IC7ByHw9exxEwgQ6ca/OLZatWrDhj+BSL1+w4aA5hhp0BYHiIvLy2ClfCyeplL9wVc0TZlAwepEUL6SHMvtBGCXiquZiXY7MRReLI7QJE9KZkgFnzPgK7ihqhSYShJlldyaEMxVlT4BIkyXRMak6tODmQVObe4WzDJZP81KYywWaF2YbFG2Y6XzYViSDuCNdRbm5j9bi2IK8o4wfZiPaWgDt9zSp1Onf7dqdSVSwu43d+3T947vvvveJgjAU6bOeHbs80BiJixavOTRYTTKW7du0bBh/XHPvSjEyByXfvkVMpGnnhiWnT160htTZ856c9qUiZdectHzL/xHckZcEP3000rQY0PzxfEvAcBmGRZ//LSJMJHyu++X4Q9TMSsra9Dgodi3cjb/+Zdfa9asMWv2m3ZbpCqI19iSFyUR35tc3qhixQpbtmwFGToiiWqLFs3OOP1Uu2EY1is51Zh0IB6bN8igG2XCEKt5vfQUOEuYAFZ4uSLiE5un1EaVyJWk6sOPPkHChQiItWrOnLeBvOyyS5o0aQRr3NihK05POeXkdT//DPsg3fvk08+QpG/evCVgq7AU445//P1cLHV33z3o2++WLVj4Ba4emjVv+sorE+0mtWsfK6ncqtVr4G5ETwyJm27qgiuG116b/O133x+1eEmTyy9759331q5dJw0Nf5xe1/5GOAsL56bNm487vg4wZ53VEEJv7z8QCuN0ypTpKG3NsSoDg6MYvQxufLmqL1opH+QkTJU0CuwMkeWTd3WaJAgpnSkTVanxw20E5lyLRFOtKpmSCj5nwFbFgpnCVkLDrAFJYPVNSQxVhkjCHBmiSKOraUu4QtIH6Umlx4jPmNzKDYWORjS0lVQ2wbBorRrcDRVrbXjBzl2Q2wCTCh9Mn71+uyj+7BFV8KM7AqMEQQDmbrgLtMM1Ef6wtt98U5fhjz7YtFlrNylj8YSX1CJ9M3dFduzYkZ6eZlphAx7w6tWrUWIvHiXCGVRGYAKM3UwkEcgq1/EMqcJZIfDr128IxEQgcSDH7NTpRkS3d96Zi9Oo5kzrK+rWPQnu+OijTwU79715CIsI32+++Q4wpiPYHEAq5GsZOoHyYGVPEpDEtrmMHcQ+E/4QELG2yaW0kUCeDPlL+d0QuYALLmps0Ah8UA8LDP4E+QunUdheaNKkMW6G7NhBN6CqVa0a/x0P447TTz8NbR9+SEVhwFhsRIopkesF9hafeDJ79MgncTny9DOjDJkNGP5IDB9/7JETTzh+67ZtKcnJyfxYdcMG9UG8YOHndhMnXIxeBn9/ckMCaV4FS/IPRgSRc2zAvCci38HNiACHVXqNpKkqeXSp8EWhigOX4m4Iaaygjrn5YEsIgyJQS2VtPT3RnrRVtSQKwoIlk5Mw4UfUji5yZ8QOXsnaawG8spDOzKBQ2aLSQH+E1dA17s8o+kg8Zybly5dHzAJHLPKnnnrKhRf8Ax5CXMO3REWMeaMm8E7BCeEx7AfcdfuPP63s0KErIvJ778528rSRjw1/OC8374477wHSbg5Dz33H2ubX6hl9ln2/DE2wX/nG5KkAGjaoh3Lp0q9Q2oe7VxgGVuYmPEWClmPz8OAAt5Ebts/PyRdk16p0fQqMjLF6GSndqpYL0BtGkXiXeOkpssVHHn3McDj/H+e1uLIZNhCffCr7rDMbPjT0flMF5tK7vDy6k1utahWMAbmUDstdtmw5aLDPi7schoMBjLUNRoCBA/pv274dW7edOt445tnnBInf1bCtKsi77ux3ct2TcJMN/LGpjWUPeL5kaXrhhee//fa7PKeE1pubxgzF6GWWAQOIf1RkEMEQR+HEK6nfduYobVRLaiN8VGvNk+sp1nCqJVS61JYkD0g1QcwoUDLWv0or5bRYaUl0cpC1dOwWWOlHeC/ti8wchUZzsz+1jh4j1t4zh9IT1kJFgnuLaEPO95VABDBMA51CeOozkbvoGW13RMOoaHNVy6mTJ/S59ZY6dWrjohjPgm3esgX4+fMXYNHGfQxsXXXt2lFaEHvXkRBeNoNWrPgxNS2tX9/eeii4+DKud68ehxxS7ens0TUOr37UkUdUqFAeaGmO+wmmOTbpkWigF0hIjT645vrl11/PPffsK5s3xaXfFVc0QWIYvrXi7hVZ2atRICMMOsraHp78IozADCD9ebWKm43xYFu6bR0nHj3FNkKjRpci5UfyhZzx8OrVcacFDb/4YhF27jp3/rdh8ttv63FBjeUwMzPz/Q8+BL5DhxvwLOS4sbTxykoZWgLmzfsAlwVYzLCbmZaaisdx7GrR59hjjjnjjNPkD+LatGlVq2aN/ncMfP+Dj6699moog00VtMI9rqys4F0m7BKCP1ZK5Lx49kiYz3v/g3379/Xo3vXss8+EbgiywIvmeCQImpM5+ShGLzM/4YuSfcEuI5jQ4ZKIuIIMRzCX3JKI1R83pDMfE+uErQ4CYkX8VMmgDxYMUzAhc2TBaAMMi5SSZDOSS6JWGGaohTHeYAgbqMEpY6jkPyIglvjnlR5kYxkmYrTgNsWRLVLnXUf9jNT6GfmkFS8BVBb2mDxler16f7viisubN2sKHogavXr1BTDnzXeuuabtbX16ojfv/e99pJBGwt693t4f95hqxOEAMJSptPYHxXxExAfueH78yWfICPCHffR8/bAbKvdarczpFU3pqrBvn17cOgk3T5zNZ8yYjRD/7OinsWMlT66Inp279Bg3NvvW3regOZIX3BsVPii9jphumLoQQCssDi4VHKJxItSmh3iKdoE931FV8R3o2tgxz8h9D3hh+PAnp02fiUxtyOB7IcR+kHDChNf7978Ny+GSJV/ipj82iLEc4g+Ra0/1w4xGxh24d49UEQ8YPPrIA6hFGJr3/oe4fW8oASAc408wYIjsD6nfV199g7s006a+9vDDg9tf3+GnlSsRUvGHG9+gNPyfffa5+vX/NnnSK1D7p59WHXlELdRiKGJjccj99zw45D6cYnnDfR5b8/H/fRl4GWnF5WU4GM7FcBB3KTg6Q4QC0QdfCmtOfDWqMkQZP7qEKBDZJbGkczmEjYI9VazElaklGmhQN0WQIDmq5CRChQ0WyTmgyhC1hpqREu/8EO2IBRpzyX3gpioVJpjsRx92mVXBG2FO3gZZsWp1/Ao0nXIPfKUQ+fHdqlLGRIfGY9txfs4uki6HxrM+ZBWzj6Ypgp9IxDZv2YrDrsCu9s+//ILtIRtZLDAWfFwOb9iwoXDcopoff/xxSAZlT8DmjNss+GWHQovDr2Fu3bQeXkb0N2X5SlXzcncaCytfiFRt/wYZqd2q0YVz1IHd4fk7dxk/ety4QYFeC7NFsly1atWVP63krWaqR6aGKwDcibaJcZsCd4pxTwxfJQIeDwNgCzhsN7sJYNyxQffD6XaALMYpRtSevXucHDACEWqN2oYJdpbxrA+e/RJMQHNDBqDoXt6xzTf+beZ+WDvYcxgw8R3SVGgtNjKuJNiZMcZxBjIo9mg4OFjikwoqS5gHa3RIH4/EFhwUFsHTIgtQJPDSglhhMcDVVjERuBATzOraXx1EWNy2ORjB3WGxmPwlg7LEa7/nyIOXOSwGXKhUoJwnMouUaOaV0kYmKMMBnlZNoXsoCslAUcpJWkZpUDCo+aWINoLza4bgKyE4XBLLWEc0U0ufRH5BR0a/ug5Fl+QAILBdoqrQeM245DMhC+DXSfG7h5InioNpepRKoh9DxCAyF+CF9kts/yakawlxYS1AXlZ7OLY/BCamct9ZAqOCEToIL7XBks71DKZUD2Rcqtt4CuZQC5QjGMWObFow8dWwUoUQgiauNEYlYpnSh7BJvPvR3IybEn/WXrEMRVzqGGhUF1mygr1O69r9+xO45bJ3d36Z5GQwJ/YixC6LjPemLvMvKRKyAH6GDz9nj+saaiUFl0CS12xPCVxkfwlPGYsktOQ48BaAl30veiRPw+d2CSVkFHgl6pmEytAfkeE/lfQppWIoNVoEVRF3X0l9JqwuGWIEeBHWlMSMWzNgwQySaNbDlD4xJDT0TzFivIJJhvwRuZInBFSy/HApollbUSOBd7ns27c/PbPcnnx6VCJq8Ska3rtJAhElR0IWSMvIzMvdgdwQ4wtegHelRHKRnpGJd4iBW9G8Q+o4OPhvQCWkcwlxohaAl/PzcuNOIDDdJUuyy/hkOpvSuOJn+yh4IIEz+SNFHFuGys1YlA3HlB2DB4UzhwBbpLB2C3M2ViylXYAigbC4f99ePBuI13fQ3AMzicascNFhdfXnMhuvI46KErzyaKlSeFsLvTR6V57YhAYt/MIlficCX0bEnRyOmMqMqFI0RKdgaZIQnuaGNFCMvY8SvNiiGO0AL+/bu0/uPsm045KcKQBKFmdKdinPVfaSVLK72enUkmkZMDCRcY2UFhVAI4OEEgGXKBjyl4xVbQTWJbHn1gywBGrPylCVB2tCFiDCqDr0DwjGKV5yqksiV/IMkWYo0lDPzPmjVCLvckEgRqqIVymh6b59e6mHxXfEvQAWn8iDghM2m1LS0tEVvBAVfpEMUdZVBcNru/PxakCMh2L2Gl0/Y1CUHAfcAuJlvL4xL9f7lbYIqZjddhLlTp+8tkIu51ZTBAkZP1zqZxsiMkRpXZAkT2YICgmmuMghNtwVGxNiZCE8nhj5GKhcEl+bQRScwJ1oFknqZmSVT05N35WXg7WLpVjKFAosiYmFMBumSnIq3oNaFq9Vz8uhXyqKPmgwpGVkITiy14pnSSvxWrTBi63GeHk3vsSal+dN78TvOEfo5AwUEbQx0MJGCPwxVcdXK8xpUJHHFapUaPPTxlDIqnJ20YuIjuoEwqJaPbhLyFFS09LLJJvXM1pKlIC/iwVwUbxnTz7eeYSrZxHI2aL3xKLsS1hrfhLPsTRcUJe8l+p3cVExCMHWMNyMfH/vnr1eqhMvY8eET6ypUFtsZIzRJan1bKyOMLEysnjlep3UXO3MMaRPFLlfnNUBr4GPJECRQFhkNoHmwlo64BOjpAsu0Mjur25qJbrFvpUbUsJbzfSyFdDQ35Xos0Az1RnBhqR6DgkaQF+wSH4vgYxhZ0W0OnHVuHWObGqRe2azbukE++JdsHh+jGQeb4VTiSL5zu8jc5Hl6Rzql7r4CqpsqeZ3sPadvS4FGyd6HinMMFI+kpzKpUJYHTVqiYWTv+FdKEAUEs7eAIpLlN9H9sWv019eUI6laDRTS0CCe4vgGfMoJrM62ZD3tJ+lByp++DSKq9e+RNyZnNuTQrtQ5ETqppn61MGJs0GQqMBzFxfWnO8MBvLEaPMUKEcIXNKK0tTzHVnJyzgi9qps69uwqCCYkDoROgs6RrfCAjzWTp4R8iM8bbMPwtp3+jJTVhuLkadJ/JBT5/iaW01d0cwZjILjOz5JASpLsMfPskPQcP655naIm6cTG2Zf6GxRd0zkyFmETM/EwZ4WYGjh6u61lh/r04RRe4m0bVAYAV4nrQzXc6bNPgomsR6bAOwP/WoJi9XLOOoihZm2no/sTNCGRU33zHUuLYZ3oQC3QgFb+aeHS470XGr8Vohr7HksoxkpF5GzD5zvgj3wjT0t1jfMLd9JYxmNXo8U5LdKsYw3YwefQpbv/Ao5NQjNHYe/dF8ieqaro+eaPX60EdkCiTyg454SUYZ0dtZoGjfgYKM74DM6KxfN1cFG28RygG96y/Z2kKUwEmyBTN2NA82CRAWe+1WwhovOPsKGEYxwtuECZVlj2bJTHM2IJNBRj4HLgzHIHZ6yYpFDGadgP52TxNPQT0xnzgZhMoURajlxtrREsQe17/6AmaPd9YQyfem+9DTSUl6F00662llpGVHRuYU5G6tuScMARaGzRbd83YmIUeTvhl8Va3p7+1O2RzzYk1IAZEkIZh8xNSyAran2+MvqzUw9Pe3uRsHES0cJnutyIpHAV6GDg5FeOMDT2TKBn5VFEjSbvTTaMPHyeVBrW9A48UuOOAspRGpZ6ttwBIsAOqCtd4eX9PZ2qWztbdjjZqnmd/zv5ztRQVRyJ2riqaC/VAv2lA0HuuT1tTCQWyHLd36mvr74x5DlZN9Is9MXt49sEYHOeUx9/kokW+S+2CJCsFNmiMqJiMsePtW10XzsnBp4fddtLPMpEztqDOMCmQqlxdQ0tfxva2HVR4ORJpHMQsKQzjLs+84SMhLMCm09nB22CaJhZ1Pquuc7KyT5I4nfB7Y5bThCtlNwhPnDrnALiOBpaSAUgnBS26IIFjOoBtomvvTe0tkSFDfo1CK6dUQHjLtYtb/cc4sJvLQABmSP0Qce8sBTxCjwtRc6Lzn+nyzAj+ns3p2fp74qy06iYMMzUCslY1+VZcriwaqSx3S0bQ7STzzZg0d7dufvLugZfl6kzPKEJAGwNX4QH3Hg2plKrtAl46ngQ9gouOBwL2NRN8U6TgJUycLUcsLCKHtHEhD+0URpH1my6qx1uKnqKK9UUJeni1XG/3uLaklLSsrMqpCSloHcFS8JEXtFKhZHBb6zll/crwOOQ+xBQoJHEfHzAXhOO39XLn4NU+eM9tOLOkPhcZeWmYUnTkt8d5C4P7obZmDgOfBdefgVSwlEEimim5kaIZdT05Tilv5OdMlzi8ZWMG5W+UqlyyYXPRpaPJOwqrHnbFwJnJgFEOzgFHwFMEYzfD0J2X2J72KY6OCrwsDATxngW4O+K3VJzLi3kpcxaMW/RMNolOHkaly4CUylFaVt2M8kIjJT+qf3f+3nTL0dYT8b/1k0U0urBJ9bRJ5YqnRZ/PSAX1RRz8AQcxXXg0Vl9Bduj++8IGfE65/w/jxxsEwDYxJ8+Q9fqi3xnTHIXwTAwCjL70+XbZZYvbaioiuaOYORFz6t8BpLiKvOEuzxiwid4TDqFuzm6cTaLAVOIMAlw7ppGaXLJNDEZQI3jrYpizvauiUdvFh8ERA/5IFrahpaGFRccndLAUlZQ4nvDl7vx+gZBgZekIntFgyJ0B9dPOA/lfQpJYaOwhGKYRpRPLD8JYmVgWYNNwapITXVJbFnHgxYMIOWHJIcFkZiA/8UI8YqmGTIHxEreUJAZZit0gi0XKvUSOBOdGp6FjOmbvI9w2Iv+aekmXtJUQgLYK8X4Q8pIbaT4GT7oimV94I1T6yIOIq3LPGdtu4f79MMjATed4QgEU6i7L3FOL6hJJYQNnFYxSlSt3NW2hrGEuZsTMFM5gCaBigSuI+MBSf8Zh+tdTF8lvx+QdGNyAkjNhmJk13S7y3yyw6LLsLJocR3TrP8cZA0MNLwk4C+WMfqRQcH6660yofUuOIPRukMjDh5WA0zlu7R0Fj0lVYcYiV0QWMW1HwqMJ2o6MfC7LvScm9a3aHmNlyrmfk+tXbEThiRDtJFFqD0FAslEBaxW79vz27qHXURvEXdYoN9vSg5KZQFsJFEEYrHkYxDYQPkvj20qJX4rlB2/dM3ooFRujQGAA5JkaTkuxe6d7R5iGBBpb7hTL8ehwtL9YRDRIYo7amp5uR9KpEszQcziVbChD48QkHRhTlJiHGVpCG90YWaeaUIVSyJg/ChKEjkEgO5gdVRZQC+vw5YZCf4nmgWJcKMDhTUtalLdamcWT+d3hxiHwtzdy/IzV+YixeWUlubXpMZvEZYn+IVC6HAErwYwmEHNrEY2thNBoceujA4DkNifBrEw6H1tEPhx1Gb+I24asCBQ4CeuVqFQzeuLcGLkX5nO8DfmPemFB0oYCiXqdAhAQTBkEaIGkv0IYS6pDa6YRCmczlEmAQpI1iGm6+0uIpIZk4xEQdXip5cqmcYdbAjnIZtlXRjT3OGeMyq/rAO3FHptOp6Atki1ONwz4WAupQuQARi4qiNMnO4P1x0qZIJpRfk4GcBVVtDbzCsKhdeOwUhgl/e+LKaNWsEaia+NmnLFscLczniB2jpNIwvW7bs+ef//e/nnYM3As+cOWf1mjV2szC91BY7/tkxz+CX6Lvd1LPY+LOzZQKYHrHaxncYFcYXIPHwgVUNnLCkjdqIH4XeP6ZmJd13j960NYJsAPQ3/Ks93rVtI/Py8sb/h14qHz40/2CNwR999FGVK1dauPALoWjc6NL4BwaaGD4BAQF87IERPx8jJcA/HnxmRsbEif+dNm1m9sgx8dAbGhsIyDVuA43AQqwmnpVESbbI0caEURN6gmGIh5u3VNoKaDEswSeeqUQw+KG1lBJAObjZCJuE8kQip8YMWTBh5GCeHjegORJSA40V2FEmmC0qiTE/FuTutqcWJlXSxqR6Gb6JEZOBo7JJk0a1jz0GFRkZGXv37t3Fj3/Pn79w0ZYlDmo/6qrWLa67rl3LVu386KTq1Q8b/+LY5LJld+bkpKSkXNOuzT33Dv7fvA8CZAfiFK+Bn/DK+Ouu//cvv/wK/tu3b0enilMQDQAvFZQBEQ9/OA5rGDzIo6vUwpz8eFrFpmnV6koYGQeMnL97N54f2r1nT1RYDLCyfXd49eojRz5RoXx50OTt2nX/4KEffPDRQTYw7P7CSjt37MQRsElRTnUeZvPwhw+KNZK46fFjjSWd0XF8kSjDnJiEz4WxlxVaqakXjbR0FakkSJFqTG1Kxnt5os4Q+Qsr9n6iSvM0V+tT60WshRF3Trpol9SGFgIUyKcBJ5otysv50BKHo0Tkpe++JO3H7KqfnoKYOH/1rvk59Fc/I2UBTTNHK4sbg6Gixy19BDf33Vlz3nznoYeHhUgiETVq1MjKzAxUI395dvTTQF5z7Q3rfv4Zj0327Xvr518sCpAdoNNDDzkEYQLPGAr/3rfeXryC2L+0TuvxSTaHX1hKLPvDcfDRyI3eKxDElV0qZxAHci6aR3Fwd6L5lW1QUa/eGcMfHXrnnffMX7DQTefC2r5r1qwJ5F/d9nrYbfSopzrc+E+ExYNsYNj9xUsK2rRt77JK4XHkveAR4U2OUhKx7FJd9XMQQ6TBkODSJgGW5FCUweGDWbatg5AoNInEmPWXerSZDJFE4r8qtWDNFJ9gEDj8qSYNYMKw9vjgpmpHU7RNOgDZIgRx+EtKqpLUafVm0bBrlSwkjIiVBiP4Ipa39Lip2RWXIw357bf1PXr22bUr/6X/jJs1+80nnsyuXfvYp58a8e03351ySl1MpLfmTFu27IebuvcSieeee3ZWVtagwUMRE4HBzQg71AbYIqfr1rVTwwb11m/YWL/eGcjsXp3w+rNjn0dDpDDDhw097LBDgZwwcdKo0WOBnDnjjUmTpmDlR/Dtc9sdffr0rHF4deDnL/h80KAHTjv91PvuHYDT558bvStv1xXNW2c/83hubu6tffoDCdFNmzZOTUnZsHHjXQPu++6774EEwfr1G4455qiaNWps2br10WGPIyIAH3XQoIXXrTKKskC8ciXT2eGywIYFEgSMfMopJ/e7rdedd92LuHl9+2uuu7bt+x98dPFFF9i+G/Psc/imLzh//Mmnl1xyYQwRhWAu3BIdGFGuKcTA6Nz5382uaGL3FwPp5ZcnSGbtHBggmD59VuPGl5YvV27VqtW33zEQ20ExzELLJMcNK3pglHBgoGYC69LKHFWsQQ2NK/5gFOdgdqUPVtQU6/ybmiEliJIOYs2R0cDEUJFLwkdlZOYoSSGzChRaZ2InjKi70kUW4MsWE3s2G8GUDnA2hx+GAFUDrTTcpUoWAmLHVZswx8bUov0pQ6MhxTPQldinN95wfetWV86cNQfXU5lZmT1vuXnz5s2ffragVcsra9Q4/N577sSrn556ZtTKlasQs4aPeHLk6GcNw7PObADRc999z2AMEGaLqkqVKh5zzNFgO2z4EwhY115zNZDI+MaNG5mSmjro/gffnfs/ILEfBHxGejom9tz33h8w8L5jjz1m+7bt9w16ADtEEIqUB5Hu7bfngmz8+JceHf44AGyWVa1SBUDHDjegR998/e3Tz4xKS0175qkRlSpVEoLz/3HemjXrRjz+VHp6etcuHYCMdcBD5BcfSZTNA3jcYBF/wWX8lwUM/ubv3BXD72DiE1bQSdjIb731zrbt2++5+07Y8F//av/FoiVYZgK+k5gI3scfV2fzZse2sogtHHNpm+jAgO/CrincwJg+Y3agvxhIGHhQLGpggODqNq1mz37ruefHH3FErXZtKTGPcfA044FBc5P/UARgxvC0BicaHfJBJcUPUwLkc/qkwz0CuDlJIgpTskjiJH9chUotjHj5hVFLD0OQd8Y1OGWM4igMQmxsJgGYlZPu7i9UtujFb72+UJ8ZlhLagIZ2FZOSulbNqp+RjESjW1VMsJSFdONFHVF8dH0Bn8iq1qxZO3bci6BDoLns0osADB7y0JkN64/KfrJcuazut/RZtmz5z7/8il15pJA2O4yhPXv3ymOYj414pO5JJ6B27nvzHnjwESdb1CK2Xtf+RgDYIBtwZ7/jjz/u0EOqpael3X33oG+/W7Zg4RcXXXh+s+ZNX3llImiWLP3SpJ+vT5qMqd6o0SVoiDs8r7z62uLFS5pcftk77763du06EJvj8ssvQ2jodWs/YFb8+BOuOtEpZKY4xe2g/ncMBNCwQX1MXdPEDcCycAGXTEAQ/otfUMphYwSmK+ikHXKhpGp57x3JPvZAcCfacJBam4/mGten08j9+t019tnsV155EYPzvvuGYAPR6TuEoSOPPOKFF/8bJakozAsxMMKuOefsMws3MJz9RTdjDIwpU2c8kz0aNC1aNDvj9FOjbCJ4a0gYQl/KpNIneFePH2kiGSJnWZRjUS0zkJKpuSkjQ7mhZIvWENSgIqeRqfJEMJSfn1EjjYVxeqcyRK2h1AuDqFLrSF0kRir95aYWDNGEsstE9xbRHopTZGVIwUovDrgSa+bvyPtsR163quWww9hx5UYQLz6h+sgNO0Zu2K4axuCj2BXwUblSJVxxTJ08Qehk9cBz/LNmv4Wca+Wq1UuXfhnFAuGy7kkn1qlTG8ALL/4Hk6Fr5w6HVKsGeidb4GUlA7B2zVqUFcqXO/300wA8/NAQlHLUqX2sAGArABJMXGVXq1r1t/UbsIOJYK1IXR+VKlZcskTpjJutkHjaaadIWOR3z1IbbBeg167WHg4NVWhTVwZkcvGL1w2gQn5E7ANuASeGwo6GKda2KlnA79+HXWO33z2felrEgpxG/mHFj7gSRMib9MZUxERne9gTGSWyqnHP0XLoPArNHNwKMTDCrvk9BwYeSBQjbN++AxcuToMYJE9QcyaAuC5UiuOFRGAuZVxJ4PPGGIIKTT+KPWBk9hZtmDhZQgR0aUBi+DraLsGaBQgLhkUkhWcS4y+Frymhl2jnK7kJaUW68EW+VyYWFkW6iHPCSDfGHlGFko4N2ykmZqR0WLURlEAiJmZv2A6YlZD+BWGuDBbKE0F00o4dO7bv2CkZnKnMzMxs3rzpxk2bjzyiVt26J3711TdUxUuezefDDz9ucWWzli2aPfzIiEWLlnzxxWJc9goTw9amN/wNNwAS+3r3uR3Nceqkf/yxR3DztNmVbXBL8dVXfDO5TOg74BCNAC18ELIBfP+9Cq9R/J14NBQ8lyjocPrLxou/4LsGGaniKXvsMo9YfAyBDThtAgJjZJu4Qf16iImbt2zB/WXsOeCeA9XqviAsYw933NiROTm5N/foLQ2d/MF8x46d17a/MU7mLETmcpI9MOBWjI3wwLDpbREGDgwMoTe1AsjAaN7iaujqGxhWf00T0GBgCB9smgcGhuANsQ0E7APeFGx0KZQSVRiWMaJLDnGKG3A4uARPhqkUWFcyhuqEkEvWgBGW4HAgs5QwrcGO46NiyAkfxQ1O/jgEW3el5d600LAKgcLSEURysADSUjEV1ihLlQ60jn3KERuGxT93mb1+W4dVG1DytTNi4gZQPkuBclf2hm1RrQzeKZ1EuY6PPv60Zo3Db76pC0LhqaeefM45Z4FqyP33gL5tu+s3bd784AODkKAtX/5DSnIyokxWlnc/+rP5C5f/sKLJ5Y06dbwR9zHOOrOhPPkBDoYtHgYybH3ytT7z5n2AK+uBd/XHfE5LTcVdAh8Zn0A0YjR+vKRli+aHHELZKI6lX36FslGjSwPJ48effIZnd7C5ftKJJ9x/30B05M033+EWVETZwYFng2k1FYMYXhP7w2XiOyxmyPHhNSxmUgLDKWSk38HB6GkDDt242hjZ+A7fwcB28IoVP3bsdHNKSvLg++8Boe073L7HTSo864MNWYSGM844rWrVqk7+YI6k0h4YsZnDC4aPPTBq1ax5ZsMG4YEBnXEfT8ab3VkDBwZG++vaGf6GRgYGHleyB4bdX0MJQAYGds9PPOH48MAAQZi/NA/gxUumBEAwlwTLOFDMqCljiEL4EILJrZLkEFaXDFoFCcDBpcBUEmOrZAJCsARVKmHSmtp74gkKnql6asx/RGDLYFgzoU8Ne4wMplB7i6RoHEf2+u2gGntkVYqJgHWMjqNpbBLqEY6hDw1DoGlzVUvsOuMUW3UYZJgtuA2NZ+Puuute3CXs2bP7Sy+9Cho8jpOTk3N505bclIouXXs88dijeFwRoxanW7dtG8vXZWG2H330iT289vLNUDTBA4+4fTx06P2PPvIATnHdN+/9D3EZCBj7lihx/PelV7t27ThnNl0Vbt60RZDYUvxp5UrIxV8LfqBSeD449FFMfuSwrVo2x2NruJMjN8rRygi1NRFujpJNLYs618ZleuSJSBjhKQqCtIzx2OG1dEGu+5LWIToCRU9t0YW8MkvYyNiPQ7gZcHePDRs2vPTSBFgGz/TMmDHb+G7pl19jww5M7rvnLhEybfpM3JS3BEYOjAKZF3pgQHrYNYUeGHZ/RSUZSLEGhh5pHAQsY7hAa0iYanZwMGWiWkVsjSXan+OMyiqZkgprfqMlIlKwZAq4iBlSAw2zBiSN25iSdWKRnANG3n2WC3diGDxEksdCJEoS7Cupoe9OdAKvuKpYtXpe7k7VGelSdNntkPINMlIgbf7OfM4TLaNEtzIbJcEORp+XLVv26KOPwmaTuuaKoMQdEjy7sG2b41dakXqgKvDcbJxsRRou7nCtEPVsREpKCoJd4Cs0aIhkZM/ePeFWoAdDCa8RvYmFzixXYeumDYExWb5S1QJ9h2vn+pmp8BoiI+4786iMy2tC+f/iuxiGiNODf8CBEaVS0QfGDtf4d9uQo1R4DPA1sQpfdDFr9hN1lLMnt3AWjFuKD+sUqSmclfEKczZWnRMBAYoEw2LOTuIS4GErZ8GYZqBNaIIVYmpJr0pKYwGExW2bken5DgqLifguzsXPHgklvvNZ/I93goGxc/s2a4KKijKZbTgwvYvWk8D6rLJIK4b42ReoDetvwjHvMHJoFjbSOz9L/1mgc545dIbLQz+BWy4y7ml9kKt2FhcDxp1omjawQHz0tBNQchTNAniDB/3IOTtbRqDwk18+j993xhclviuaQ/4orWlg4EECnmIyz9RsIwdrJQXmkp/L4gtLSg3pfhSVKjeUYOStidIebHi6a24Ky9yVYBvmaiXai1V/vl/Q2bs7v0xyMl4mRh2SC3bpeTHBce2aicSSMsIC+BnaPXt2q9XIG6SlgCzxXYTN/hJoDAy8CJA28IIHXwqbEIfQJ3MbH0ysSzrhiW5KYuTxEzbCPI4MEQ1VSGQmvl1FBGBzSS6xhcrf9bnFBH6dG4tNema5Pfm0++6I/vZKUEhY7ceLbUvKQlggLSMzL3cHckNvhecRh0whPSOzxHeFMOnB0QQDA2+R4wdP4+tQKG5RCNSZI4VM+9lYFdqCWSSHWLl0iU8oUXkh0YI1KGycJLGEORv4VApQJPCAzt49+fl5OXiPkuEnK4lRtSh4ydINBxvgxcNGKLgEL4YwdsDbWvDuSzwMJBhdEhUyBVT5fMeNMRpwhP2YEL7Ed2xFsaRYziAUYHwUqPh98BgYuMjDTW1xtCkBEMwlw6QO/lNJnwLrTz6D/lzP1QwThntFjTyYUHyO5hqtQCCItaMkUUCrkkkEVqxVI2oe9Y8ah/6IWMnjhgwbOahlXbwywTf/YW7hJUqwS0Hv5CZLJHDQ/gZnmAm0KSH1LICdo9T0dJzjhag0nHnlREZPY0EfmBgIixgVJb7TJjn4P2Vg4PHkvFy8KrqgA6NF51w0iPwwxpV/b9GdGxYkw1lvCaYYJYIpVBl93LAw0+R+1s7Gbja6o15tAneiWSoJy8iqkJyavisvh7/2BJ5FPUp2FQttQYz75FS8B7UsksG8HO8HwfwMvSGC16IiOJb4zm+fg/DMDAwshxga6KE7enihwA4ORTOIxNRgaYU5Ya8V8kanRWJrw7AjKGstNSN9HvwsUAA18N2JTiAsSvYhKpSh92um01tMyyRwLzuobsl5kS2AbcQ9e/LxAiPcVKH1XO34UJ6o/MU5o505wmVwHl56VfJeqiKb/4/LALvJGBly7axDYkHhw+5NRCiRMcaXId7DMcI3XAq/uKU6RWqdnJW2yFjCnI3VgiDtAhQJhEVuH2iulQ5/BgjtDmg7+Ukcq0G4kW4alufEWBKCVwWxFiYnLxfS44+LU94HAMa9/sbqiw5hvKDLiYxkX4Ue3C5FEsB5Olua+ttbJEGzSZCVljZMGJ8HtbYJesyviD4LKURqWerbsG4T+zOgrb48FN8FLw+Flbsnlmp+x/9+vhMVREnncuhbIC2zcRNfa7/X3D2ObdhgrVuhoBKmVYHasE6+kSa+Ew4FaxyfvxK4E12wSP+40CY2XY4JxGWPgodaZK+1NtILqy/KxCc3JvUAAEAASURBVP56v6oFMo3lE2djP/+Is0iTSFYoYcjLEOVXc2Qhp2FnZ4gREqLQhdfZPQTI3EahgrOMkI+iLgBt/SN0dqLjFeBsbAtV3RWUk9oW5ZlBNdA2iRm3fPIKPnFqEd1MyJU+3iQw7mLV9FVIIZ5bjJZs1Vg6i2CyqxU6g0bUelosQqDH01oCo4enZimiCpEt+hWIaVbRwjNxsKdFiPp+LawzzxzeVFRmDaY3Vqt4QSsrTHx6W6q5/KOrVTyzx0K86kXQac6+FMtH641G66I79mwV/ZhLwfx9wuI58QaNrZA1gOJhYpFb1nSMuoIzDumiCHV2lwToMBdt5/i0tqk8Yb7pLaJkMmuxPn9ZZrPZhWCPv2WtEFWBCJ99nAr52PvI/SHJ8pRtxzjSlyglnV30xDiqE8kWQ1HF46wVEgyfOYRpqkQ+I+3nMn0040g2Ppc4Joyud7F2djGWUZwNXJzjxukwTQ2svcXA7y3qzJF1U1E3XhHFpLODjd+D9hSwZpBtzihYuiK1DDuEBbvrJLEFxNUgSOQ8L1AUiXVdAfhMEks3p1ggnZKjiP14aSo4i423t3hQP7dYiGwx7B/BxGXWKE9ZK2HBWZhIckn16+A78/lW348o0siJahwxotxdp17o+CDTwEoWnRW+XiV+YtnB0yiajUVu9PRlJUErOJeWaP5x1TiVCApWQSQuhqrnQluIsecJsVTzzGk71TaW16xwUKQww075KJEs0ppHTv6Gd6EAUUhM4w2guHwn2ohUv2aOMWbvDsdSNJqptQgl+NwieMY8/MonNE5tvk429lDzxQ+7ZWC4Wz3VWZ8MAqt0JudWPdjHZUotwKeOaRzoUpCowPNAe9aPNQ/uLVKeGG2eAuUIgUtaUZqSuhLiyUpexhGxV2Vb34ZFBcGE1InQWdAxuhUW4LF28oyQr4dJjDa2KIK17/QiHboc8zSJH3LKj6+51dQVzZzByL8WxCcnRGUJ9vjFYVBh5HaIm6cTG/QLXtMUUjESAY5GUzzekVLygI5lKt+DMjpCanNjjHE00KUYsgy+pZyaivcnlzwoYxnyzweqR2Hy8/fu24t7ExTteF3SpXjbV+K1E5hA+J4yfh/3z9fhP6rGeKkGvsmDh5L0ayQ4YiHO0vSTkgIYonuwZOf4fJdV4bA4u6muBpKSMrMqpKRlIGvFb4vSGCjasWf3rvyIF3cUjfHv2hrjHKMcT0rn4/sEOdslXwtpwOGRvYRvl+P7QiU2DJnoz4cwrt+NL8fm5kgHxNM2bHyfmp6RnJIirqf5SeOh8MfBMX0K33+rpXEEfo56ly+kGNvrRIVtHsNHCe8tZpWvVLpscqnSvBZaOhUF3Avf5sXx5aSiyPi92uJJaSwVO7dvdbpC3JJRrgKeqS6x4e/lk99JDlxfunSZXHzXiNwsmwaBUZCEH+wohRSxdOli/GbXwTR9isVV5IgyZXfl0nd7gkfAIV6c9PkrkTvRnCeWKo35XJocX3wHGCKUIAMuPpb/b5zwrRPkjMgcsWKxEkE/IE8sVapMiQ3/3zx0wATD9WVTUpCz0DeOghsplJ8gT4RwuL4YY6IwPGimT7E4hxyRnIydCrz0WK6f7RsyXiR0+UhqE9jaSIbP0zJKl0mgSfydpO8RHiz7LMjgcYGMTUPOGWjcYkrgWgkfQKKqxIbxD4w/FyVcjwtkvC+BvE1e5w8ueY/lQO0jH0zTp1g8DkdwZMRVrf2P5yHNSFqldGk8Jf6iMoFsMTU9i5mJ2pIuFm+J3786GBJG7LliDuAaGXsDZCxZgAgqhXxBTEZnKuUusSEb46AolOtLlcb2lukQZiB8jCs73BPAlDswfqdBdnBMH2O3ogDiiNJwBL1HW2weKH3sA3UJpH5YBn2civsE+zLFzfL/jR8tVipblDQRZqcJwcgDqFXJTe0DaNz4WMP1tChaKQjljElJQMbHoJBUJa4PGI4cUaYMTT+Ki7qkPFFg+pBpiZKwXlkqAVdhF3Pfnt30Gy203hV/yXoFuvZnPcXuBg9TunsPa5kSyH178EqNBKxXPz15QW4+uNRLh7OI28LcPVH2/7Pa6yDSm1xPm+9wOp4F8Uog6ZcuCRPpffiac8kCvBzBAa1KDs8CcARt46qLNfIG6ujZS5lFBhaMr0zwPdEcUsFWHeIHHWcJCb/izeu63vtEm9GbcsL0QmHjvTYawlrLo0uf608bj9el4/Exebko3if5yy+/bt26VROqT5verkoIf/xxdX797bctW4LMwdDFh9YlfkhXlUTGoc0oYPdd4C6VM7tUpr15WGzUphw2aTIA4KVVvfTkesvp3X5CL0ibj2Ds0qUb1f9F8PDaL7/+hiHx+/UXkngCar+wtS2XeL7DgOApBUePrlFhYa66+u68bqvBYyEM0wszG2+x98Dfr8ueTIIKlFupUqXKlSr+sOJHaVcgvZ99wfwVWzXh9LQTawdKIqUoQ0zZawlki2iKK3YRxrGVEPyfVj85MGOBWphDr8ECYPCdq2TWSyuLrIfxHn2Aj+Li/5CYiNeoV6xY0dSs+PGnTz7+dOLE/06bNjN75Jjhw4buytv1zxs6gWBU9hOT3pjyxJPZhhhAWmrq9ddf+803337w4ccGf921bXfs2DFl6gyDsQFnLB418skJEyc9kz3aphTYRU/JAg5dkj2YjBDcd6khWPstCTHxb8t+A8GYmpX+llaWKfaL9UZtpBfSAo/30Bv6AB8QhA+j23nnnXPB+X/HO7Xffnvu518sMvhAkz8CPqAqNOx5y82XXnJRq6uuMe8Ej6GnTQyvyZCIQW8sYDeMh940BODRK4i8B6+LmwUX9BfV8f/9SYiJndZsRovRNSvioWDgOVZWlPHAdNxa6KmkhhZeznwlhF7e+LKaNWv4sElJE1+b5FzdvS74GwTwZj7iBetff/3tosVL/OSWKfwVhk+3rh0vuuiCSy5tKvWCt41v4/086MzwCVQF8GQvd3auMkcxIT+eo7P7pASzRaMBxzvlEhP7pBYxEeEPOSPwEgcBL8zNr5eBCwRg6NkFc8TgY2gE6NjxxpSU5Dz9LNKixUvff//DnTvgFH51dYA6dFqhQoX217XLycm5vGlLqURS2bnTv7du2xYVFkM8CoGQbFFNDN1xh91sO9hi4sFrttTOhm0+At95R99Gl12St2sX9v6bNmk85823H3jwkTBZDMxDQweXKVP6tr53xqApliqnqtu3b9+ZszMw7qPEJURsM7EbTnr9lQkTXnvl1ddsgrhgTn6QfXDkkhxEtbN9xLA3HpBVfF7nENBxzoiYmIL4OGqTN8ID9EYTG2+QBmjSpFHtY4/BaUZGxt69e+VR5/nzFy7aEoxlpokBqlSpPOGV8ddd/29cgRkkAMzH5LJld+bkZGZkYAtvzdp1N9zYebd1o8kmjh+2jR9/qxiUnD2gXqwuuYTOKPAJ26OGyiJlixAgXjT8BCO8qRIVGLjkzo075+fk189Ipos+2jSh/BRpY6fVm7Qu/raKM5DuY/78z/vdfpdd16Zte/u0QBjD4pRTTl669EtQIkoWSF9kAsrLyR5eSSexbQi7YW5gRVmQkz8/ZxebjkyDi+h6tEGxHxgODdrmVGnDkVpfcvGFX3/zbbebeoKi7dWt1677OZI0oqJ69cP83x+IoCsy2qnquOdexF+cvBMitnnaDStWKJ+ZqfYubJqCYfGQ8pO+aNCffn8p38HXZ3yPqwTjzVKdq9BeCtw9umYlwSPnGElXDDSGLEoDMzpU9Lilj+DmvjtrzpvvPPTwsBBJJOLQQw7B80YIfGGKBQu/kPmI9AIXXn8/75x35/4vTJYQxjZ+Qg2jiDlXpB1FFQM5QNE5fOAryTdqL7jQ2WKUEoKvn5GCrHDkRrxapBTm9oKc3V2TMoFZkLNrTEbl2G0Tqp05442XX54w/j8vx9kKl8zXt79GfNmwYX2sddIQ2+F9b+uFq0vEzY0bN2WPevatt95B1d0D74Czk5OTf1u/vl+/u35aucoIOuvMhvfdN+DZsc9PnDjJIEOAZAtU0kzgjxBNEAG7YehjOYHdTB3MKJdXmAyjkpTapjYeAIMb3czZqdq+OuF1aQX8iGEPnXzKSTj9cunXPXv3xS5t9jOPr1+/4ZhjjqpZo8aWrVsfHfb4Bx98NGb000fUqgmyt+ZMe+31yaNGj72lx03Nrrg8JSXlt9/W9+jZBwlFt66dGjaot37Dxvr1zkBiAikwEZpUq1Z1xPCHsAUMePGipb1u7eeUi1ocUaqCeaNGl7Ro2fb0004dOvT+yZOnXXVVS2Svc+fOW7x4SbdunVNTU+bN+/CeeweDiSFmlqpAwz59etY4vDrO5y/4fNCgBzAGQFnvb6dv3bYd5aQ3piLlESmzZryBcIAJ367tVRMmvH711a3/dUPndT/TWtK7Vw8MDFzO28x9sN6lEp/H43nMmi5Vsuzw12n15jG1Ki/MwVSSUFhqTK2KSRt9copyctGF5/fu3aN8uXK5ubmDhzz01dffvvSfcbNmv4kNqNq1j336qRGzZs1p2vRyiHj+udHYpLqieWunOHgZVjr55JPe+9/7znmEeTp9+qzGjS+FrFWrVt9+x0C5DWC4YWhVrFDh2vY3duxwgxgfVVGtIKJRo0uRq+YjOd2/H9fvffv5siXDFgDNOoqIUvI5rzuSJ3IpubyvTOABHcjA3iLWv1glSPbvm79z18gN223K7A3b5+/MA4bq6XDzsfsThitXrlSv3hn4w+CW2oz09EqVvA3HcJMA5vMvFv/tb6cD2aB+vdSUlO+/W6YI9u8/4fjjXp80BZeHGCK39uoOPOLmxRdd8OL4l27u0Xv58h82bkKSqw5M7yGD71mx4seYMRHE1GHqMgO0HsVhwy5VMrtUyUCOgBIwAIEZKCslmTBBG+LFHl9+9XX9+n975aUXGje6VPUkKempJ4efeNIJz2SPGTv2BQRHXGWjCqY+/x/nrVmzbsTjT6Wnp3ft0gFIEOTl5W3YuHH4iCenz5h14w3Xt2515cxZc+4fPDQzKxO7QqCBO4455mjYZ9jwJ0B57TVXA4kwN/7FsdWqVQPyscefXr+B7hc55QKPI0pVMC+XhcCRlJaWlp6W1uLKK55+etSKFT/BTd27d3v++fGfL1yEtU2CryFmlqo49thjtm/bft+gB7AffdaZDZo1a4IKUNapU/u442pDPey4mYayPY0dWPR34uuTyyaX7dDhX8LokosvwPS2OQdgdpAa7PIBghheE2+CouOqTR1XbYSXBWPDwPBocs8dqk3kqFv3RKz6K1euumvAvbiL2K9v782bN3/62YJWLa+EAe+95068GAsJB/agwXX8+JceHf64k/1hhx1694D+qJo2fRb0D88jVGGeXt2m1ezZbz33/PgjjqjVrm0bm9V99w6oU/vYQYOHYkEyxo9qhWF5RdPLJ02a0rNXX9BA81Gjx9ncArCafmR7PRUVjA/KGcVT/CEnVCYWFgMi4z/FShg/cRQlbDfskQfx99iIh6NobHx5fSDdE/y06TNxPXDBBf9o167NylWrdu9R6RgG2o0duiKvwb4b0gHkjHA2kh20OuGE41evXnvnXfdu367eq4eUZFT2k1u3bjPXJrZQP4wlCP+45H0mf63jrFgM5eDLqFtu6fPSyxNw/++O/rdNn/o6dleRP2Icz5nz1pw5b0+dNhM9xT6UNF+9Zk3/OwZOmTJ9wYLPqx92GJCffPoZ7LN58xYkFGvXrmvatPGaNWvHjnvxs/kLMXnOOF2tVbDbde1vnDFz9rjnxiPbgpSzzmqIKHbPPYORMoDhkAcejiFXpIdVFbxdIkd4Y/JUxFkgs7NHYwfwvvsfBIzbMjaZDb8+afJN3XuhR1ASucb55/9dajE3bu7eG8PDzmLQTdR+8813ALZs2bJo0RLcBQLm2GOOzsrKir3hCJ8jQ5SSB4CtRSy4a9Us/NkUHWnTqfiPq1q3hBEGDLxvydKvsB5g8x0ZPXJGpAUY3rVq1rxr4CBcOSENh+x33n3vvffmBZQ4s2F9XJW/+vKLyFTe/+CjH3/8yTmPpBV28HGj8oUX/7t5yxYzVFCFmYiV7OFHRsjWVkBEuBXWdThr5MgxSBKXL/8BWxwoA63sU5p+nCOqUiYkYajClDRN0UyViTy3SMIo5hI7fIBLGJbwi7Wua5WsDjs3Ghp6amf/fmw1Cg+DD/AhEdEHlrLA3mI0bRK+ZT1tykQhwAjG8gI4NycXDwRg4TruuDqPPf4UVh7DoXev7k2aNMaXE3bswAVLUrWqVRcu/AINr2rdYurkCfMXLLzjznuQxaCq2RVNMNs3bd4sp4aDC1D9s/cWY9sQ+wzItW37dKtaDpxHbkT27bZ5AO9SQ+EwanHli79WLZvffFOX4Y8+2P/OezB1cfsFf0L0i35oX/86UxIukNHfMNvKlSoBD+NIFS+9BPIYIWDtmrUoK5Qv17BBfQALFn5OWD7q1j0pSq4QhFVt2ix4BUfXUElJq1dT1rZ7N7lm27ZtkF6unC+sCEMpkQfhoQU497f1GzBCDCUCROzsD81Hjho7euST5517NoIplgcsEjbnICwzxSpBgLMYcwd+N2PDhm3/khTh6ZqDQR1intepcyyy+MmTXhUqcD3l5LrYHJw1+y1cBKxctdoZp2yWy5Ytf3bsC8grc3LzEF6lKjyP5F4NniIUAqQXKampAiNH6dqZLkT4uyiC85XhVrhOb96s6YAB/T/7bMFxdWpjSfY1CJ3wsFTzCZVqP1Fil9Q5ykSzRYmmIlvDkKmiLPZrcnbVz0yVmYyyfibdj+4KANmii5442Xjh7C953fWj+CwKL6SYV+df2Ej+cMVk2iPS1T3pRGg1c8ZsQYIP4mOLK5tNnTod96mxcho8Gl7aqBkuJTCxcc1omAx+4GEEhR7duxoMAJc+tFJRjVd6/WUcRTocVGo7AOxardzYI6oYPAA5gBfbBuhNW+Gmyb1P0Q3Zs6Cwg4blHSnP8mXLgUFmd8FFjeWv3TX/ZJooTkph0GCjFvcfpdWFF19+4UW0CeU8vvvue+AvvPB8Uxsl19gwrKppawOG3kYCjsI//tgjcFyzK9ugm7jGD7RynhpW6AWyp2uvaXvmmQ0WRMxGQ0wawIRUwq9cipdFhuVr4zvUYO40yEitn0GljY8Tjq1/oHbN6rVYDC657ArjesREJF/NmzfduGnzkUfUwlW2aYJv0VFH/MemzVuwNox47KkqlSvJDUx7Hg0foeaRv5E3foAHT+Q633z7Xd/beuMSLUAppwG53377/e49ezAfe/XqvnjJ0rvvuT/QKkDP1if7kR9IpKsUT1Etty5VKrGLaLWdhfULiwsCjzk0vGDnrvk7diHlgQiseIBBAiB7/XaBgadGmj7AB7XhA+RhJDA2HgzNl6ugXKpejsINcS8FZsWGEVSQWvARl3zxxWJcR3Tu/G/BN2/WBBsuYPXOO3NBU64cZW04kNiDydz35rVu1QLXGoJECRoDa4AsRTWqJHRU3208cm1Q4r4z8SS2+NzfDddW+/dnr6eEiI4EbYgHzZDZ9bn1FuyjYW/x3HPOwuVMbl7ez7/8ig1s5I/IHZAzHl6dbkdAIpfBApljzZo1cMGF+fPRx5/WrHE4sk7Ap5xS95xzzgpS6/N573+ABy2xioAGV6ADB/SPkotuoZFTVc3M9yn0PhSfBPBmSKQkJ2PO4wXKLVs0P+SQauGGAQwekMQW8/+x9x2AdhXF3y+9vTRICAgCoSQhEIoUaYL0Jk1AQPRTpPcOKiK9ShHpHaSI1EAoCVX5g6CISCcQmrSEloT0/v1mZsvsnt1z77nvhfpOXvbNmZ2dmZ2ZnTNnz3n34h2UzlxE33b7nUOHDundqxd2nCNKOfVyAZHr/C8Q+NOU73BHxX6nYSgjkv7N4Yk1veeYONCTwDY13XPfSBT755x9OmIYj5thEJCdevLxoN95l5/jZuj0005CLnzhxZeAx9OS5ub0E3m85oW9FBQNsJJeR3vtZdZRJF1rg7x8zK9/h81NPOs+5eTfR5RyGumPx0SoMY848tebb7Ht4Uf8GtuR0aiInp2AaCZ02FKMQxl2E/sGjEDFqGppMdIgecq3gXSzjAPXPfwUYcG0SovP4wUfpDksUdzwAv7gg7HYlHX3R0UpO+z40+OOOwn4uYgkDhpsHqEQOPWUE2675Ub3AgoeNWy66cYj7xt+3bVXIINcfc11wkoIsEE2Y+bMM04nPvlDLlGuzROqnv3698QDK1xIpDBUPWRPfVoJHn7XPU/98+kf/WiLKy+/CHuLM2bMPJQ3rffe58Dxn43HA5OHH7z3yCMPwT6RsJ2DzzXgQ8cZHsjig0mQXs847aQzzjznmf88ixR23z13XHD+OXilBuSa2HHAfdMxvz4O24unn3ri1VddikeWWEI5uWCSU9UxR5IFGZ6Ys4LUcIS7M8awZwXlQuLGm/46YED/USPv3nffPcd/NkF6Hdvi6YMPPYI7NTwSxWUDvXiTH0IxndGvvS7E2daUHe5XllB30CtZU/BWll8+Vy3e76ol7M/i/RAYmr4h2OQlvFqAt2Fw43zD9Vfd8tfr8cIWXiFYZZWVLr30SuSaY489Adn/kEMOxCby2++8g2IQm9H4u5SkRJRsuKYee+zRyXUkQ2SdEmz9ArNLaYKld9NNt6z6vVXwIDTyRXHUk0/R3sXll13490dH4QeuxCgRkWypTuQa0bRULZJTcCqVo4WJzFWLFT6Gtk+/RaZPm0IJFgxqtfst1CtJI3tnOQ5uKyE5w3LkkkssjofFCFmQLTdkMAJXL5vysdKLQokKqGnBB+LiWjp9xvSPP/6kHg6apkfP3hM/+ygyVu8F+te04fPLLSrlNpLgHu98ghsrWPKSTz5HK1YVuGEbwlDj8dJN+MeRuIr069fvnbffcUW0nouG8bQEf0s35o035UKN9TBw4JJ4oOn+8kQTRzC29qZOnYYnng5fLjepqhtbCXAhgboPxREeKNU5XP+NGurie0bchgdHbqclyQSunzxxQrREevbuM33qlJzXgMeNM66IwhDJsdzLST6ogpP6lCMHDlwSiQnbsiVkWAXY/tPPo0qIk+uohL5SFx5b44nNfgccir9bG7DwgON//9sPPvhw91/tk2QCR0yZPBk5EFdNbpNmSySqlqVF0SXyP2VdPqrjW5IWrdSvym9Oix+7Kwip1a6pV19Oi6JjdfvEXk3x+SbZUOb3FWmxw/DTn+68yMIDtt5mR/fGa1I3SoufT7S1gyFp7tU7mxaFpMXx0FhaTE7hK4vE25QDBy65609/ies6ytuzzjwVD2FOOPHUpMJwxFQ8QXWLsGhhwiS6K6TFnn3747N6qMpN8LExkBZcZy/+Js3fFiXn+XVB4iOa8fl6kybypr7YhFRv17P3AjNn8PubbTb8uviS9cR26p23/wV/a3r66X+Q+7ic+uT6rt2mTPLFl7i6R89eM6dPRSwkipMWrRp7Q0K7CvRK2Tf7wJtzuIPGDb5spOBPA/AaGV4MKM5aHDGN/mQjudiCERFFhbTYvbl3O3wg+8yZWgpSLe0VRFyROavjsdOH3YZA2a/tCX3m6Ly506aggA9WQdceze3ad4ANG7BPPXbmffpviA2/ps6H67Ee6LuJqICQ92iopW8saNc0Z9bMevzogqb+OKFt8m9KVVHT9biDXrDfgtj3LKGEI/Ax2/HX58mClGHR4vR5rMqncyNt4XtI5BOnhYMwbzX4m5ITYZau3Xvgq47m0dU7sD0it1v35jYblkTz170Lrp8xbRoylF9lfGnkzNht1swZrbZeOLbcGsw9if662zOpP76kBR8qkexySHIErE1Zxa1BD0XecRSCr/ClBVjk+ABtfGsEPhsdXNzRKjAKRX5o57h64GuHx2UKX8aGLzOEP9g4vsUuAR5Z4JtT22zoHaygr52vle4EwvV4DZ4ue5gJfE8tP/nkzwzF+3B4jUx/tUCrrB0SzC+KE1A4WI8CllVLYL8peDgC+XDOLHxOJXvAtJgeZca4FU8Rnq1V6btcUPzD3526dMPQVt7FoNcrkKa/9ge2M7p064ZpTMXWkg55MzOg5iFhUnnf6t+88U2x4dc0CIzr27Wn5yq07nxAC4wW3wiIbzhDbysvH8qK34Tl0yquF0fgAoS/6bYMnQd0jZj2kVBU2FtkGSQAm4xIjjOmT9XXPatB9d/fiPUMZ3Tq0gXtTHw97ZTJ2vxJuFuP5k6du7bZsHq4fOVGONfPmgnnTzWrDUWi7C26bUJ7nezStRve/WTX4/4O0dGig/NhS5m0SIOvzGDniNmzZsdbik7JZIakJRr4q0JalOUt/OmLPcm7+ILHap/v7dT7hgG4QszGspg5Hb/CqSX9QCS0H9GlCyzZ9uVEocW+ZmfYGMGbMXA+fskaKWt59XXEvTS+UZo/6u1rNtuvsLpwBJYh/oaNyzW/7nBnhtKLW3vN4suRp7DVvcNUSItsEAwMD+EkOMfVPoITLVwiLoSLU1eU5g9FY1Y6BYfyap4VlCDxMm9RS9mgJrMCgZhYWFQyN3FSqqVg252pMgq61I+wnK0hWJVguPgo9pf1ozehGql8VJt/IKyeEx80pUrUYpXSLBF1smyEmZqX4i6MBJFiyqYNKg679BSTRkAvLIg3ESUyrFgJnGJbKtbzVwFaOiLZGdgnqVDAPiC3ljLLVBEqjPFaSJtUpYBMTtGzTnRXeeRSyCqes9VEMHyWEGapqvzO2i9l+jzjLJvAzIkFY/tTrJNTLDNKckCKc904m6ZpAPZ85clV2Eb3B/berl4RraRzgk3owTBlJ8itJ7SBVbyZ8Tyt5OhwwkkSzTokTy/VmCZ9XlMUiRXfSV1j/RiYpEy3tNyW6Gwu28JYTUD0lJDy8WYklVVkORULeCXM1w52KgV9PEmZfTI8leyIooFqsShfMEoIQJFT9zTUldBXjlqShoVrSmqoQ3CmJu4rEaun6iTN6z2iYUYjwQqPJIWeC8E2P8gyUMVisqNe5TJ0SY0ytEArcqdnUI8EJCBPXlry/OvqSSoRC9Z5sSZX4ShkSZ1jH9mkHHNWqmlbeadqY8WDq55nhTlGxkeirQ0frYKGZZSJWjpJ8ne8GwJEIeHsA6guUaGP9DUiEWP6NrlM0TxTJaDKe4vKfDm5rWTWJJts/giUqWvWNsR5RsniXOZqZ1yJaaAOTpKTiYlqnqe4sOa2stAVYpBea7JOEKSkJchSqORQ7zsyva84zI5PWZUReoIkWq9E0pOCrfmFNklSFOAZlwzwRA4qodZCPGx9Zwp4k7GszmDXyJHUoj5GamgqmyWTURzf9UmKqJRgz0/ZwZvMul8wwkbDnnGaZxJbZN9wtWjlixw5y8j0Jo5nWsPQZbO28st++6tT6f5U2qw5xn6SqsrwziyauIgh1p5NBNtLvLp45VSpG58V5jh4H2lTaVjUFIxSmTkkLy2Od0NAWiElWJs1L0FmLv2hFeqKPc84z0g5av75Lp5BEHtWrK4ENSyDM2EeWiV30fGGqAOquO6SGmj3Mpzwl1UlMzPbnV9rNskSpTUiW6BFe4siN6NUcrJO07qBBBs7Ae15gfNcE2ysTZQDguWtt+E9Y2Ek5zWZ+mElA2KimuehCipcbPVRNIw2j4ZryqqchzTHrH1SHsxIUt5R+Sf0nZbJcFJwSJUk0cJC8ox2MZE7D32U1ZwFsget776ClaNOK5UqfbGGGNVZJg8kXWLJk51Ff6WFJQebaYmAiKLhajEt304iE0XhNEJV1PL2+1PaIx72UmpASkJcfZRqWIOt6/b85erNTL2eero5mHjZLMGrR05k3QcdNhU46Y0BXmdlgpCVIonNpqtFDROvwINW21pxEkrOnBUUIrWU+hrOsIjQkbZ6ZyqEyZtypGeiVAsd/8X5TlQQJdOFWrHqZ+PxkGB06LX0jI056vyVVkj5LuRTUxvWKYg0Xb7U1rg+f1WpFr0pw6n4s6RM310G1WWP2qGW1ECMVWxZH2PiMCBCTWsyFfK0T5KDQ/6Zs6xJpLIIn1ravUXa65eUUbEq1EpYFkHu0QR5ODmULON9p1JSmElCH2hzajgjOylYLT9hUWyFXVpAhqfSQCgEkaSOBYoZzABrk6C8VzorQXWDSS3yozMTcO5i1ewbDpm9YOGeNmFesupROotguRgpdDpQFIsC6AdXijeZRQPVYqhAqVllMt7EyuMsvgVZP9RCnXlz+KVoYjMub9SoekExscyjkrlJgFItBdtuk8+M0jysXvUydJZzPs35aNSVhYaV+iJF9GO4Nv+MYnm0D5pSJfIMpCelWSLqalccwijPlE1r01zezrX0Lfb7GQTxZtMrecGKDbKrMluRqcJ4/gUPK6qaYGCfpEIB+4A8dTnU0c9wHeVLTsnkFL2ARHeValEMrWbnOVuFBMNnCWGWqsrvrP1Sps8zzrIJXJJYMLY/xTo5xTKjJAekONeNs2maBvj3yPhzN3T9qDOtgesV0Uo6J9iEHgxTSYLcekIbWMWbGc/TSo4OJ5wk0axD8tKoj0mj85qiSGzqDiAwSZlukUB3mpTseksBGSokio3fW9QxZqxT9kZBqTDdqYT52sGav6CPJymzT4anEhtRNFAtFuULRgkBKHLqnoa6Era9t0jXf14SNnHI+qiYz0J/uLMoAFK+c7QAFHm6KglIQJ68tGiODcBJJWLBOi/WlCEchSyps45yDceclWraVukSLh5c9TwrzDEyPgrDp7yKVBGQ5O94NwSIQmIaH0B1+S70kb5GJGJM7wiXKZpnqgS0vbdol3FZLVKXKS2D2CetE2opLhwc9gmmrQ3589k40luQRVPS4nllzpNDSR9J8WQlX3Fk9qp0GtKwiBRMQXxSsF1+Qp0kKQrwrEsGeCIHlVBrIR62vvsKPn1OZbNkMgqvBc4W1YCk5azvkp06CDTs5SaHxdp6X9jlK5iGq0UrX6TLWVITu8dBGsUzrWFo4ZqetfSVt/7qZJUgjFKiEQF+kqrKqNfcfi6ejdKHusMK0ahbPs+avVlhbmRqGaRWa2EjhTkYP4ah5Xg3BKQVimxlBeYlyMylP7RCXbHnGecZqYiaf76LZxDEnhVbd1Xop1XZoHpoDnYxHCikRMlAuxhCvyhzWvcyYcJfVr5lZM/j3zUF0ABrRJbfor1FEZ9RKqlLrHDt8wQbO4HA6Lxe8+wSbAKjizOC5a234T1jYSTnNZn6YSUDYqKa56EKKlxs9VE0jGCEs4ZrylKxLH7OeDvJKGuflAczkrTUHFwQnhQcUiVJtICQPKNdTOTOQx/llrqKOuu71LWIhTvWFYDkJOsbnxxKi4yeStPFu45KXyTVHTFJkVbbZGfRX2lhycGmlBEBEUXD1WJavp1EJorCaYSqqOVtb2w1uYa9lBqQkhBXH6Ua1mDruj1/uXpL/HpsqRBvQZsleJXIieTsoMOmcSe9MSCpXchKkcRm09WihmmmgQettn6WoYxKZwWFSK2McetjHGmrd6ZCmBaPHOmZKNXMOjOYL853IlCUFH+VtcpsPCQYHXotPWNjjjp/pVWJlXDMamrDOgWRpsuX2hob7xQnGvirSrXIc3ETSAFJmSnCIq4uewSq25kFvJIaiLGKLY80JrbcUoatyVRUSA1V/tfyA51zJ1mTSGUhachWGXZvUS7kJLZiVai1SE5YE+Th5FCaulOo9iO1oqXSpg20SArOmL9eARmeSq5QCCJJrUURLGYwA6xNxFWmVTorQXWDSS3yozMTcO5ipdreW8wbkHtgxfAoNatcP72Jlcc5XFqQ9UMt1JkKi1hwXN6oUfWCqiqsvryVamFlITnZdpt8phdUvepl6CznoMQKaMVUsb/EYBmziX7MpTb/QFg9J953pUrUYpXSLBF1tSsOYSTiUkxVpR9111KxRr/npipcn159qk3mWDZeqQTPX63O0hHJTmEjXXX4LiAPSxId9wquo3xJKgZkcoqedaK7SrVYWB6es1VIMHyWEGapqvzO2i8VBXnGWTaBSxILxvanWCenWGaU5IAU57pxNk3TAPPuW6p+1JnWwPWKaCWdE2xCD4YpO0FuPaENrOLNjOdpJUeHE06SaNYheWZtxUTJ85qiSGzqDiAwSZluSbFAJiXniEO8DBWcYuP3FtveW1QGUxYio8uhg9PiMmbNeUpdCWtXYXmpVnrit9LcX81aFDm5wZWmzkvC3vCyIVUKs4lD1kfFfJawAaGUHTycoQ3JvdkyBRyrn7y05PnX1aN09kpUnooWFfooobPEdLHVTAhWqoWw9Z14bT76TnQ0ehnzyCUkpUJRHTU+OZl4xtXORSExTUXfhT7S14ikvyRvkKSyI89UCWh7b9E+KCirReoypWUQ+0QGR21MVPM8Gs+hzMER14Zt7y3qVGbTltg3ZUS1GIpOKBlQJA6zog0HrU0MW999BZ8+p7JZMhnFs07YpTYqaWfru2SnmFI4a9jLSg6LtY09Yr3W8JNoK1+ky1lSE1tZSBoPSWoYumzWVn7Zb391skoQRpm7EQF+BqrCrdfc3oOejdKHuu0lPqFo2VxL+7LC3KjUMkit1sJGCnMwfrRB5Wfp+FcG0gpFtrIC89xl5tIfWqGu2POM84yUo+af7+IZBLFnxepKUMMyOOOV0CpmMn7ejUAuhrUSIl4rYeGkBpqc4YS/rG6WkT2Pf9cUQAOsEdkCLdpbFPEZpZK6xArXPk+wsRMIjM7rNc8uwcauKeWAYHnrbXjPWBjJeU2mfljJgJio5nmoggoXW30UDSMY4azhmrIq5yHNMWuflAczkpR3VP4JfadlMpwUHFIlSbSwkDyjXUzkzkMfZTVngexB67vUtYiFO9YVgOQk6xufHEqLrO29xYQBI2slKAwqJOzatVuv3r0+GjvOl1VxoKnlbW9sdZRqOC816lFKxNVHLD6I3IhN9tTzt48+gPFT1DrnYOJtswTrICey7oMOmwqy2tTX4XVWmoZDFUlsNl0taph4BR602sq8Q/6VzwoKkVpKfQ3XxzzSNnxX0e9Sae017IUo1ULHf3G+ExVEpXShJp6K/WVGsKc0HE3Jz7URKK2Q8l3INJhLGEPKyUGk6fIl7SMtIpqcZxr4q0q1yHOBiB+s/8OuXbt++uknWhzDSZnU88MNNtx9z71H3X9f586djz/pVGDefvNNHmKbuuwRqG6NZjnw76QGfu52jDKfMXGixzGuyVQoFVM3VPlfa6H682DWJFJZtL23GJgu6aSM+YuuaNR3WR+F8eQFSgiL5hVTRjDd7EnGDjn6zARC1b517y12zJmriIcBEWWDhyy3/Q47zZg+4zdHH8EI9r9Qk4lxGaaWzSowbZeZo10TvuB6ypTJM6ZPp+oirC/mtZPBRGsgGRi0wYnlq3+TZOhJLVf83NLygG8VzAuGx7GWgDRnzdAQ0S/RGYmU2RFL1tkEPTPyC4ANA6Z8kTaDrX1YWkzKtOBs7nKpW+bCGgT6kX1FHSJi2LRMSwgabA9AsTDbZX6DGGJJhtQU1oOMZ2+aTqMGhgk5jw/5G/vYHvwWTYxCdCI2N+giIVFQ7mcDiNcELijBY21T5McTEr/41nrN+I5rQ4aFj5oX6cFIO1gogLO2opl42OgsXrMtjbFcGJYTw4pPxDKm5Q5PA8gIYFGsLZmHw0T6BPYt+0t5UERl2mACZjJFpVg3tgLNGKxEQWmJs7ARGUY5rxCry2OscTEQoCEnmASYlgPazxl4Emm8ZjW0jCyT1G+rIw82GoqeQUvqikVdWyEtcpTOW/cH6336yacL9ltwiSWXfPvtt6jW4vmSWgLjHCCmQufUAp5La44Qs2fPOfO0Uwjm7mTLncSWDmYjoIEFkzQ90ck4owQjCBY9eekb2GmObsOSe9hiEnzsDmKIfnPwFAETT+RE/LYtz4cZ+akbOjYDD8gToYd4yogANjPizkIj2gqaMwmtBvsXLwIj4uTPWFl3B4esisKNx6wHWT+aHmLXDRWNjc5GTzULRweALRO24bKh4Y4IniD5qZbpZCos2Gkj41mm4yPaGp2tZvTbek170DOlSUeHOFU0jB3sDMASSHEcvvXaqBmiO8CL1yQCrQfVVEM9wV1EiSSBEy1JMCvRS+MxvuFhSlujuSgnZEpRiS7Jyj7SyFNgAbvgd9JropyoS61IFfahBqwza8uRZn0nAmQcwyLST4sEM1+00eH9xTqKpqAxgukXx6JvK6RFJKJ27doPWW7oqJH3bbDhxuv+YP133n4bHLfdbvtlBw0+56wzAA8Zstwvf7XnCccdO2Pm9A7tO+6x976DBg2GZz6fOBG9dLRrOumUM+6/b8STTzzRvkP7X/xyj6HLr9Cufbs3Xn/9qisvmzVzljWuNTGUh1w7X4HpnPB0heHLjG0JnTvMNYE4sT+5JX3Yk64lYSzQtESRPeLBzI4GkCP4EJhaZmSuh6y0hemEnG+JFJzsiLURbY04YxISnzCPQVtdWNPSCYrSLEFVH6Khac28HC+KWbaLb43GJN1KMzD/Yp3DjoiQLAQCalO+E+OKH1lGLMYi/W8INj6iyZlKBLnSw/G8WDYx0NNIEFlDWN+xmZDfjT/C8ZqX8RfzFzgQRaRsVyvAx5ShY+VErPQRTJzClu0o8UYjxHgMRZy1QZWiVjdWiH1nO0VFYmUxgCjHM4JFZoJGK+FGgwnGeM3IU0KY8pfxnR0gw3xrNSIWhqdjZ5mSZJ50u/Z+YC0Ik1tu6NAOHTv866knX3zhuWErrshKzuvTt2+fPn0YburarWuXrl2I67ymvfbdf/CQIXfefuuVl1/arr0VNK+pR3OPHj2aIW3vffZffoVhjzz04H0jRgxcaulDDjuSSoTgn5y7FmwBEwlaC9Ok3A+h7Y/AtuUhaiAP98JoEDPl8QyzWEZajkTAsGnVEDecRDgVlFARbVrPlTgxWzMsgKkHMza/mJROBMW/BRe1wp1GJQ7BRi3JUT/Ej4fqCXo9qZv7WTGGSRkalPznZkBUPNJgWAoN8fJIrJXtYCONz814FiYijWART3oQQloawafcCswtiaD/1Pp/cmZaI5SpPAxiYu9+RA63zMix4CFutiSIJ61bxrEOWgghSEDiEGyyBZJ+jFQaa2Em9xOgDukmgGBuCeYh0spwhoUtjSGSuGVW1HAfA0EjwqRTYCeS+Il4Hs1yiBBIhSZusWA69zgN82AghEGhtaxEW8/C4g1mXpVqsalpnXXXmzB+/Oeff/7kP/6x+vfXXGqpZd586w1rBU7m5qQdKsFlBw167O9/e+Lx/8MV5R+PP7bl1ttQMmYCtEQwePB//v30yPvvhTLdunfbaJNNu3fvPnXaNEnoRMnUdAW3MF3p6ODWwIxQjVAwFV+p5HrlW3dNsMqEApiTkiKcFVNdWNr6glgJG3iC4KAlReXy7C/SBSI7zFYZAQ+eTUEzYxi2g1zDpZWJKfNkhcnk0IKYQskqGtYXxNV0yiVfzU6UggdZGaaSKgOsiK9u+UQaoxydBO515OIv1stUHFYJU8RYbY0IGhgIY06EgmYaFgyEWt+R5gaO56V4ekZ+otYobpiNN+4Qm4nwbCt2CFpWVgYQSIIZ5WDCkM6mNb4j84h3ChWiDBeDWVbut/C3rfKdKCVGNQryCc8fEIZIZ9ASX6WKgu1UaBwfViGWTRL0GjHuFWNTa31kqkKJN+ZjAsJwVb9Ekmch/jJTZAHBdNl84rUqabFdezxvwQ3v6WedI7LXXW+9N98cY/WQTCtn8/r3WwiB/dYbSJq4allLWBKotMjC3wHByy+9JBe1l158EWlxyYFLvfzSi+jFIS2ZnP0vU+LW+18Z3Scc0YBZMA9a7jAqr3rTMhtWhtlziJHXYCUxN4Y42PMjiND4xf8ZxorCGU2x2NpZEDXBRCStGhARMS3rHHYwC25EZzl3OpNwuydFMgIj0bTRyUMC2DHVAgW2LevM0zP6szftjIQBnZEV6FCtKJpoA+XMaib702DTiqeoDybXLdOwMHGFmRbLto3nZKxNXLVmMBaxSLU8EyIPD3GwaOedHTHlubPheIbgQBivTRomVZzvjDXAhFxIk+dWlGH+zJOxgAjDApMtSebVR4yYMDExZu05i845pUVP0g062zViuLvZWX8R5wBWohhk9aVB6zSkyTttjWYZf1k9hYp1T0yRuJG+QauXMtiQFU2kNVWpFlcYNgwl3s03Xj9lylQI2GCjTZZfAffR7WbPmt25cxe2SVN7c1fe7uNPPpo7Z+5SSy/9/HP/JdXdYeFxH48DDgT/ffYZKLT0Msvg9P333gVxbG7CsBNsy16BQOalWyfFADSOFaMgkz0OijWCCZ1qWRgJpP6Yn5x7tGdBkGFHwyhkvGwNc0Cx6qSHZSirgJVlB5nBAkecaIwZyMNFT9MyS20SDQu5mbyzTMDMcHSdGE7rilSA1QimVmzKE7D2BQlFrmvN7K1NiK8obVr+VVTOyg/JjUySLH4MPWgVsiIKwphr0JCequ6wsExUZsGwYmn9pachhki2HG80QbKQ2EyPDGE6I8vyL9USaOxmIGVvM22jJ5OyJPGLadlfRd95rhAKnma08ZpwzvjO6MlqcWQYBcMZOZbWDhLipmXxMhVLyL/FXoaTaOZbFsbBmPCdsJFQDViaE6sdsRNGpINMUVxE+YGthY4K1SLuoKdMnvyvfz4lgubOnYvdQ9wpjx79ymprrLHiSitPmDB+p5135d55yIn/e+ft76+59mujR3/22afrrre+Uc+6YPbMWR9+8MEa319rzJjXp0+bvuFGm+CxDA6Qhddw8hQmwi2tS7iYTkwwyGwNb/wK+zBRYLiVq5aGRRMWRgtEifHsBAqZKuF8tSbT0mDGC0zDDHvWCPYGQrWulwUwKWlJw+SEQLl2CcbMQvSnSCPfkmEE9i0tDNaE5QGOD6sX4TWs6JQ1SA7IqLWqeQUthofy/APfsYI0DdaHZ8DKGc2pI/KXKB60wpspRbBujWD65fXUXLMCfIzx9HiKxJm50xnDNHfxmjBimKS5QyYnw8xEZbq2SmJtyKnGg2IGGQ9rEPuoVTo7MQaw2tGphwUSEvFXobWeYlI/EmPMBHi0mgAHFFSjafgYIysTUZBPRDArlAo3EVlseZiooqKAVrdYCwxd6LFI8orBkInJgyTUt6KIYal4BjrzADVRG56hv+pNizDNMssO+udTT7LTSIFXX3kFuW+tdda95S83ffLxJ7/cY09wfuL//g931qDB7IbfefsBBx265z77gvh/77zTs1cvqCqGmzdvLqAL//THQw8/8he77wGCzydMPPecs2gYH2R4AHIWtMGJEIct2ZUUQMv+5JZYmasBdZItDHM+Yw6ac8gSZ7aThspgXWtwv+gsjMNWZsVTyhORyqy5WSpuFlZRqwLpJoZijhZmISJJRhiYyW3Qar2owx0gNtFo1ilJwOryrczRWgIDAxOaM8YZ+zDvUGmHMpq7c8vWk1vfiTXEd1oID40aP9ryI0sYk3vzh7UG98OnohI4qnnhzPIURiKQYO5R02UMpRH2B3XYOKQxlgvDcmJY8QloiUha7vA0gIwwaEjZQIKX/WX6BPat8hp7UERlWsPPSFG+C5TiEzGRbYmfVpOGygFitkPYMrVMxYKGnGSTANOS+mrOgEmk8ZrM3rRWYOa3aGcGMwsSbKbImjBM6qIjaJt7L5xhGqPNTO2UvOqM6dt3gYkTJ8yZO1d0cVT9+/efMGHCrFkzGRPzxHm3bt06dOgwefLkRB9QIkb6ApEp06dZEDbLxjmAaGAlCTvf2v4U60AfSyiOt+6nYR5ODkhxrhsnOgs5Z5J0/Si+E00MXK+IVtI5wSb0oF0IEicJcmtIbWBvWtvL00qODiecJNGsQ3IbQdGwmCh5Ho3RQjws9b5EoMBmndqJedKkkCQyKTlJWUDKUEErNhJjnENUpBnr2JTdEpXNSlUiCWPNX9AnRy6Eto24iSltJ/+OKBr+BB3NWsNWmMiRs0imdrAdmkxJRUJLTnw1bKWW/FZKUMBJUWTNrTrFCyWMVFc0zGgkWCFLUsTzsuoEaYuIkh1KfiNgUqM8I0Xu1DHGS5sweWnJ86+rJ6lEi3wnHEV4A7HntVaqhUvU+k4byw9rDMoKc+yMj2R1pFQoqqPWUZK/490QIAqJaXwA1eW70Ef6spmIMVvQ1Fq+eaZKQNvnLdoK0abZOGVRLNRlSssgDp7WCbUUFw4OW1lgB8NVi0F6jfWp5zwlrZ5xoEkO9SmerOQrjqA2zw8NTauWsdYpKdjqI4RJEu1wzY/gkgExaTm1FuJh6zt7kS5cZxJCaqKSOtccxQRqaCqbJZNRbKP6JEVUSrDnV2p+HQQa9ozTPJNY7xEbaQ1Xi1a+yJGzjExv4nimNQwtXNOztvLLfvurkwRcJuyqCfCTVFWGd2bRxEUM6ezZRLC9xKuLV9kc6+rLCnOjvY+0qTQsaqZNaPxog6qaQZ0OAZBWKLKVFRiMDE5k5oIKrVBX7HleeUbKUfPPd/EMgtizYnUlqGEZnPFKaBUzGT/vRqCK6y6pQWHVJPxldcvMzHbn15qOH2tEtkCVT9BJL4mcIZOTdZrWDSTY2Alozwuc55pgY22iHBAsbynLY5bCSLA1maYHR8NioprnoQoqXGz1UTSMNo+Ga8qqnIc0x2ii3tApD2Yk+UEJf7Ug9rK6WTF6IgQnB8RE7jz0kdEzMxf2oPXdV7By1FOvVOmLNWTWzjJ5oNTCyU5t0DJhycFmWjIuomi4Wqw12UiOnoAdGpKo5W1vbIuD7NC8bYMeJSGuPjJxXk2A5y9Xb2aqo6g2TPraLMGrR05kcQYdufUazLj2iddZmSAcpkhis+lLo4aJV+BBq201g4Z6uLOCQqSWUl/DblApEGmrd6ZCmDwoR3omSrXQ2V+c70QFUTJdqImnYn+ZEewpDUdTkq5G27RCynch42AuYQwpJweRpsuXtI+0iGhynmngryrVIs9FiyjASZkFqiSiLnsEqlujBeySGvi52zHKfMbEiR7HuCZToVRM3VDlf62F6s+DWZNIZRE+tbR7i/ScRlJGxapQ62FZBLlHE+Th5FCauvedSklhJgl9oM2p4YzspOCM+YuuSAvI8FQaCIUgktRaFMFiBjPA2iQo75XOSlDdYFKL/OjMBJy7WDV6fZHqxMxesHBPmzAvWfUonUWwXIwUOh0oikUB9IMrxZvMooFqMVSg1KwyGW9i5XEWHxma309i9q1pYhIvgtOXy3A+Nc7ExMKukrmJr/dUErbdJp8ZpZm0hla1ui1na4giTx+NurLQsFJZxCkf1eZfS8NCvw+aUiUK4yJESrNE1NWuOISRcE8xZdPaNJe3c6ReHadeWBBvNr2SF6zYILsqs5VK8fwLHi4dF3UG9kkqFLAPyFOXQx39DNdRvkQqudPkFL2ARHeValEMrWbnOVsNBMNnCWGWqsrvrP1Sps8zzrIJXJJYMLY/xTo5xTKjJAekONeNs2maBrgn0bZylGfTftnIajVZt14RraRzgk3owTCVJMitJ7SBVbyZ8Tyt5OhwwkkSzTokL436mDQ6rymKxKbuAAKTlOkWCXSnScmutxSQoUKi2Pi9RfXmg1Wt7b1FHYQBbG2dMWu6YqKgS6Qka+54kMiQXiuv5u/At3ZjuyXRDomKp4cNJAolKeJ52fwghlTFYrKj5kzLCZIa5Yco8nRVElsh4cc89zp7kkrEgpNhmBMgHKW3gdjzbJVqPgTIwdZ3uoTzwxqDssIcOyNWLiEpFYrqqHWU5O94NwSIQmIab5O6fBf6yLiXtU3EmL3BJ0llR56pEtD23qJNx2W1SF2mtAxin8jgqI2Jap5H431w2CeYukIM0mtN1gmClLQEWQqVHOrTBFnJVxyZvSpZqMVW5Am+IDsp2C4/oU6SaDEx05IBMSnOS6i1EA94eO+CAABAAElEQVRb39mLdOF2LCGkJiqpRc1RTKCGprJZMhnFs65PUkSlBHt+dRhU2KQDIs0zifUesYu4gb3FUAuRI/plZHoTxzOtYeiyWUeGTZ76q5PaaNE2aESAn6SqMrwzNfscTGI9mwi2l3h18UpOrgoyK8wx8T7SptKwqJleucaPNqjCCHEyKgFphSJbWYF5zjJz6Q+tUFfsecZ5RspR88938QyC2LNidSWoYRmc8UpoFTMZP+9GoIrrLqlBYe0k/GV1y8zMdufXmo4fa0S2QIv2FkVuRqnkZJ2mdQMJNnYC2vMC57km2FibKAcEy1tvw3vGwkjOazL1w0oGxEQ1z0MVVLjY6qNoGG0eDdeUVTkPaY5Z+6Q8mJGkvKPyT+g7LZPhpOCQKkmihYXkGe1iInce+iirOQtkD1rffQUrR51WKlX6Yg0xqrNMHki6xJInO4v+SgtLDjbTEgERRcPVYlq+nUQmisJphKqo5W1vbDW5hr2UGpCSEFcfpRrWYOu6PX+5ejNTHUW1YeJlswSvHjmRdR902FTgpDcGeJ2VCUJWiiQ2m64WNUy8Ag9abWvFSSg5c1ZQiNRS6ms4wyJCR9rqnakQJg/KkZ6JUi109hfnO1FBlEwXasWqn43HQ4LRodfSMzbmqPNXWiHlu5BPTW1YpyDSdPlSW+P6/FWlWvSmDKfiz5IyfXcZVJc9aodaUgMxVrFlfYyJw4AINa3JVMjTPkkODvlnzrImkcpC0pCtMuzeIu31S8qoWBVqJSyLIPdogjycHEqW8b5TKSnMJKEPtDk1nJGdFKyWn7AotsIuLSDDU2kgFIJIUscCxQxmgLVJUN4rnZWgusGkFvnRmQk4d7Fqbe8t5g3IPbBieJSaVaLem1h5nMOlBVk/1EKdqbCIBcfljRpVL6iqQv/hY3Ho55a3Ui2VD2y3yWeaa73qZegs53yaE1PF/iqrMgwvllibf0axPNr7rlSJPAPpSWmWiLraFYcwyjNlc9g0l7dzLX2L/X4GweXEpleKEis2yK7KbEWmCuP5q9Wp+usEA/skFQrYB+Sp9aKjn+E6ypecqskpegGJ7irVYiGreM5WIcHwWUKYparyO2u/lOnzjLNsApckFoztT7FOTrHMKMkBKc5142yapgHm3bdU/agzrYHrFdFKOifYhB4MU0mC3HpCG1jFW8U0XVNAbJ/kgJgoeZ4cGc8idQcQmEQPSIpJIJOSE3QJlAyVDsXG7y22vbeorGYs1LFjp85du6Fp36Hez/dWTNrAxi0wd86c2bPxqb7T8atOLh3YW2jad+hQ55A2svotgG/vgC9mz5w5Z+5slcjCpF/xWpSRrvJTfLthRpjKUS4hKRV0RSlj1KUlyT+jS53o9G2Iqhzz6V60ETmhZonyRe8Il6mWZ6p9V+GRizNf9+benbt2hx7z5tLfUZQpUUff7FkzZs6YUQdhGwlZoAO+qJvSXJeZM6ZPmzIpWSGKg2UBdO3e3LlL1zZnzb/ocR6ZNXPm9GlTwwWs1lqx2rV1/Vfw6XMqmyWTUSY9VzN30maZ1FlMoy4xBULTPJNYzVLgCmmRhbZr7tW3fcdO7dq3J1+20jEHqXH6tFZi9m1hg2SHnDh18uclE+7e3AvlfPv27fFApoSsUlebs3Lmgkfat+8wbeqUMAHmyOvEy0IWYr+oE/uM+dpQBsuCL0j1PFUeKlBVQpiSNVKowN4qlNRAOlWbSMpWKcvInse/awqgAVZbvpBV21vs0bN3u/YdWzcnkkbt22OF4/Ywnk/bed4CuJlGzYhSZdbMGWaR2OrD1Ynt8K3erZoT25yVd0gTPNKxc2d4BJscshKFuNaq5AVvffcVrBx1QSh3J/K6Q5Caw0uBTlQaLrGeypoqGboBtYxoCNPCkoPNtJI+au/k1gQ6deqMe2d8VTRTQr47WgGmbUpUoG1HFQvMmjEDFUrHTp3gYcQrhtq2CTfaVLwYZ6GnFXzEqhGfNmexKRINPIJVgm0O8QTcQW7xLfsJKOkwrfMaeRAnqmURjFCQnLuW2HFOoRaHapkdETJaWhJO5DSMfqjL/gjMLfVIWCkqgI47CSUCbtEwFLaMNWMEti2LpNH6x7FmrpBv9XRirEgiiP7hlDEBR0FqNp6INaYuGsaipWVUhWqxS/dmmhUzYaD1m7aCsZJNsbmL9IfwQXkSvbfYpRtt/rY5q5I9W05sPNK+/Rx4hK0ft3KvJpKwIqWwD1o1rBGFsKxjmWVshNzo44eGqn3r3lusUKChWnTWA0Ap1rRwg4Mbx7c9JxUzVmpRnqAuxBC+ttKlj452TXgqw5CcRW0r+KvNWc68EQCP4FpF/oAvqKWSxbfSwUj2CjtM3GZa5id+ZAp3bsoaWmHMEK2GaTjJKbZEl/vhIcSOeLlWidAcSRyRcUt6ESSHYA3MaB5IgnFQS+xNa+QxhkWTZOpkagMbMTSemJt/oMA/0xLH3A+TBUOBkfOotaJFAW4rvF6Dzfu5s1GVzAVXerJJUui6xFUJBBq4YTxYtB1VLYD9LGQoeaIC99qacR6Qc2fPZXxL/ZL0LwVn25GyAHnE7+fSFcisE1+KpTDiu/Avl+ySMotsHi81asGKGuqnVuBES5LxfiETupbH+MbwYISChVzI3FB6c5lijHJR9N4iRYn9vEUOO6bDfEg7tMRJsRdQ2Cs0SEhYoQVrFiAsJOWYNpwceIFBdIgF4tbpQ/K5bvdthbTIg83UNMxqyLxJnebB/bsP6s/LhuTh/8cjXi6hF5sLAdriYTxR6PgG4/v06bPggguMGfOGnnRuvqBBl1AK4E/NkqFOZ2eGjb96DF6oedBCNqApcMaNeMmwsmGj6SM+QqnbnJJfI3zHjh2XWXqpt956e7p6b6wB/Xl1m9tkygvWK+KqRMtOlOuNoY2J0CnrvtgaJ3C+Mr62uUsymGslp1Erh/BiGCjF2ciyPfjNQ6xudCKwcDKaR+SgoFhjzkY52MSGI6OtujySGsNPxgiWYJtTyAr4l27NYMfLAoYnyxI41kGmy227ammRF5OsqKAVCZjsQtss32PQQlNe+4hyOx9TRn+ELNljcH+cfXT3i4w1VhFYt3YOwW9cb7bYfNPFFls0wDY13XrbHRMmTIyQOOXrUxHdOvh11137h+v/YObMmQ899Oh/nv2vFtOKcvfbd8+NNvzhRptsVQ9/oYF0jkB+k5RtyhOmcNIWtjDh+5Oz+k957WNwEH9NHv0xMMiVwHx0N/KjG5vko7UzMNRYeaUVV1991ajvqaf+9cKLJuHqrlY0mmbr4NVW/d7KK694019umTJlikMCKJG71MAlL73kT78//uS/P/b4lVdcPGvWrP32P6SEXrP1MA9wtRLMbmAytc0DtronMydThmcnEHvPrKwiTDRSG5pqi+sjxnhHMo1QUiuH8KKcS8pRK0WhhUVB6qBkFLR2LnZelqX9LREkOrtoQmegPkUXcSWsawOEdNqWNDRVM0EKtowVn4CnmaJIoqF60g6eVy0t2rmW/UZORPrTaq1wxc5vnf0oFht+poymRVj12HLLzXABx6ju3bvPmTNnBl/Gn376mf9OeL4mqx132G633XbZ/se7FCnvuP3mW2657ea/3lbsSmJ++5ujNtt0YxQR8+bO3WrLzUc98NBpp/8hSfnFI811W2oAhFZ9B3KipD/nrxWu+MlbZ/8NlzF21rj62ARUa6yx2vbbbQ1Ut27d0E6bRm+k4r4ymRaDkU1NqJFvufn63X7+q7FjY9FVnSWcf/OboxZcoC9i5upr/hzJqud00qRJGFsPZUxD/qBFJ67ItsZf3K995z0ICHzkEJhalZ5URjXZlWXL9VH9vYWkB8vK/fY8KQaQNqilXq26VY06pNu2TEmNDBLI1Mg10r1ViDUgCay+a4khzdO0iQqRhUGwZWTO3S8MxeFZsEPsFGWierp8cRKvVUuLvLEISWCWafmCsNDWyy+0zQqoE/EzeTRlycmvjsPPUkdt+Oarj2THckeyOejgIwT/6CP3j3rg4TPPOidJlkQuuuiizT16JLv69O7VI9OVpN94ow1efuVV1A7o3fknO7z/wYdJsi8BKZdMW33YqzLclPcUtDTOGlpw1tjJr45lZ42twSE11cuvuBo/6Ln5puvQ7vLTX6So0rgBCy2Ee9gOqT9SrOosCFh44QHIiZ+NH7/pphvXnxaxI+uUO+zwYxxcDTDlJbGihV3Wcv1lfVdIJVLSiHBRjFrG2pbR1ulEaWGSLPWjyOcqUli51vNkNWk09WWUljsSTplhauZhZpCtlIlPABNC2DNkYKsBa8uCWU9MAhhDbvYWudjTsNVTeDA1DQoPW2RSt4dNbWinSzazFf18qBah0TiqFpuQEJEW5Y4sVLPVzg4+aP+tf7RF586dP/ro44MOOWLGjJk33XD1/SMf+NMFlyyzzNIXXXjeq6+MHjZseSyzB0eNeP31N/Y/8FAn+/577wR+t5/uvMvOOx5+xK9feXX0eeecufzyy+EN6DfGvHnIoUdNmTrVEQPAckXJM3WKQf71ltuld79991p/vXUnT54MibD6P/7xz2N/d4LQg+EKw4YCfvGFlw857Cj88SyYFJF4l/0PZ5220sorIh+gSsJEhPNZZ5662qqroFqBrCuvulaQ6ZauiBQOtmZMUyWxX5izNtxg/cMOO6hXz56oH0859cyXXn41ctb994/aaqstoOS111w+Y/qMH22zg1M4chYKT7h+q60279K58yeffnrs704cPfo1R+yAn+66M/5Y+bbb7tx7r1/17t174sSJCJURd992/vkX3Xf/KJD9+bor33zzrRNOPBXwMUcfvvHGG3bq2HHcRx85DpdcfD60RXgAs9FGGxxy8P69e/WCr6+59vrbbh/uyBIA5w9JH+QWWny6/GJvAcUjte8MlS3PqN+vc0DEJ2zNuehAkrjosi1FBO+v2FaNN8IlMRhJir+qHJ2ipI6ah9HcMqJeBRPo7GAVogkqIUzuGmJtc7fAarYsjOvHbOXIvY6bBqxeJEAYka6m0GTn0B4oWwsdFV7QgRCkfjocUIRpVkxmqenE0hPOwkk+eh414d1/+fMdfrwt4vvkU87o0dzjkIMPGD9+/D//9e8fb7/toot+54Tjfztj+vQLL77snXf+h8xy7nkXXHr5lZonUidOsT+IrjFvvIkFgBQGbrfeeufAgUteffWlmhgwFtiLL7282mrfQxG0+WabuN6+ffssssjCs2bPPvnUM8eMeXPdddbC5hp6L7zg3OWGDrn4kiuuuuo6cMbddw550UV/XHXVVW6++dbfHXfiBx9+OHEi/T0fEihmcc65f8Ky/+muP3HicgCbGcbmfm7JwnKU2Fy6ZBAGg976CDganR+b0ySJx/Xm98f9Br7ANQN55+ijDis66/ob/oIdWwy//vqbzj73fM0nctaee/wSrn/l5Vcvuviyrl26XnzheX379tX0Aq+33jqvvT5m+F334BTVPVpkva5duvTq1VMI4DvctgPea8/dt9xiM2yA/ua3vx8/foL0ol1ggb79FlwQwLBhKxx37DFIkeeeRx456MD9fvjD9RxZAmDTKQsaOxqjsqlpFBk4aglB6yRxCDZqiRcPoJb5FVslh5xM5DSMRhJxADNGsSJxhiOrRYOIAR3SMhjAgmEKJoEAHL5lkRhhBTPgxLAEK4ZGxjCdRzicMsZwxCjGRGRFVg7DymGeNHJetbRIcwsvCJKEi3jUiUAuffRGEf3C26zQzDv6EV7zEW41WxQL7733/lVX//lfTz+D5bTKypSMUIYgdi+75ILvLrbYsced9PrrYz4cOw5pESXk88+/2KN79158YNsLGNC/8spoANisXHaZpf/97/+cfc75F19y+QMPPrzwgAF9+vRGcSH0cq998MFHYPO+7wJ9f/PrI++5+/bBgweJksiYuLN++OFH99r7gLnz5mInFHXlkMGDRo16cNSoh+4ecd+7775fglxuyGBIRD34+BNPojDB8xywhc67/Wz3e+8befU116OqdbJEYrGVa7i6kHsSe6EkjLazw095lTbyxFmaZsC2w5yzHN6NAqb+Y8cdtseMkPeff+El5HrUbv3794uc9emnnz33HG0WP/zI3/72t8dyzoJ/t9hi088nTTr08KNvve3O4088BZeQTTfZMHIWLip9+/S555778bBl7LhxG2+8QYm2m222MW44jvv9SU8+9a/zzrugSIksDAvvudf+d9197+677wMvy/5pkdJgQA1LUYuaRLV8goZ+cCRaZiBWZgp3zkUOlTjClwcrmFiRtGILZPaHhxgFBaY2Yssd4MvhY1uvnEAkWQ5WAmPozLesBPGgHxJhf4iEEdTyEMYEwkAt/2gQ01Fb9sNkojMP4eHxHJQ4lm3VqLq3aLI+J2O2gVxHGJTcz9l23uRXxgJYaNth8qQFVFhj2F4E4SRuaYSkZjuWWctJXe0CffsiX9w9/BbDABcGrunuH/kggvid/737wgt0L6+PG2+8BksFGJSHe+y5n+tafvmhMAjufwXz6N8eQ+2ARwcb/HD9tdf6PpAoBjfeZCvsOl12+VX4+fH22xyw/z7nnn36Vlv7Gz0ZO2Xy1CFDBgtDPJbBj+DHdu6cRg5dDqKfe+4FIXOts+v7770PZG9b4DgCDQgx+ACQFr2OQ87OQoBJwSOID+wwGmfNneucJX40sniAcRNfXLUO5fCyyy6N5DX8jr8KGTgNW2H5Rx79ewPOAgc4ERc5YfXMM8+C20orDVt55ZW0s6TEPvTQAw895IBOnTrBLKgoc5/VBIbRewXRdJZZZqlJkybjB3hYDDl0iSUWj2j0qdiWShFaifSrRiu+4/0u50cexjVQtKdGziVp4gsDCyZoSabeW2QttJqeB2MNP4JFXcZq1U10ISPZSCMMyElNujtlmDQy+3RsCLo/xcGNtAIKe4VGJ+sct2DNAmQwwyJS6+Zh4eta6IWBcev0Ifm89+DbammxaCsR7fDYT1x422FLDxkADJLgR3e9gMwIYOljNgZmzJkPoZeVkPnRaDdWWBVb44lCB7Z4Jk2egpJK96Cs22abrT79bPwSi38XN24vvfQK9dLVhy4x222/syYWJPCvv/Y6YLzJcefwuwGsvtqqaF988eUHHngYgDtQOX7++eegv+POu1dccdgG4W0U8F27du3Zs/m/zz0vDFHr/eHsPwIviwS3b2AlSACCl8e1a6+9JvAiCHgnUQOOj0ZGQ2Ss46Bta2D+JTAqenhnmSEDEBdTRo8bd9cL8I44C5g3z3wIyVHck+ZT1MNOqtjz3rvvf2eRRTbdfBtU1q437Sw8tsb+bt5ZMCZcv/ji3xU+yy67DIhfe/2Na6+93nEG8IMfrIMbhWuvuwELuEuXzocfdvCuu+x0xZXXoAv5EUM4YWBmdHz8yadLLrEEAGc6RvsG+i+26KIoSKWWx5316/xWqfDxdBYiPsgFtqW8YFmLyEQrUUoWt7QxkUk7vKDRBy+51gg2AqXP5C7JYK51SpkhwoVPHDfGcYqzfG2/142UkzkRRKowxsCCYQrKiczZWiNQ3HbyaB5sIDIHZ0nDipKumXNibxF5k6wjSgiboCXdjI4gksOyE31kutxW3VsEA1ri2RbFxZgzH3zjjAfRjh3+/KRXx8oN2rjhz4+963ladaPHlXAw6oa/JKeEODr7x5P/XGzR76Bqw+paccUVkFmAPPXk40G/8y4/x/PH0087CctrzJg3OnfqhMXT3Bw/j0aIoyTs3q0brv+4z1pnnbW23WYrPBnYeustURd8ED5o3mnH7VGZHnH4wXi0gr3FddZec/wEswmFOmiXXXb6zncWOe/cM6HDyJEP4CUerMnNNtsEdSVKWtSMSAoaiSEoSIHELeHHn3yy5vdXR3WD5YoNOLzLTdMrHDk7CKH0GhqEGg46KXiKcAYP74in0MJZ8J2U8/AU/CUFI9Nm+IjgsDUKhEic3XPfSNjhnLNPx+YGHjdvv902QBadJS/xbL75JiXOQm7CrS72BLGbPHS5ISefeByEPjDqIS0TSRPPRu688y74YuSoB3Hni3jYcMP18RIinqTBxUsuuQRMjWuYjHriiX/gpn7fffbEw+sjjqA3DaID+iMFwL/w/mmnngD3PfAASczN13RIt2vF+K51w4FhXtxSQws1cRi/ck8E45R+mJNlRyRGQYuioQ4GQDC3BDOxtDKQYWHLvFgtIuRB3DJDahjLQNCIMOkU2IkE0sA8muUQIZDUUsP/PSwY6pF/0qNhHgyEMCi0lpUwpoEWE8AN7S0G0651gvUGEqyx5sEDaO25O+haA/P9NBccZ5x5zjP/eRbZ6r577rjg/HPw9swaq6+6yiorXXrplYj+Y489AQvjkEMOvPfekVOnTr3y8ovwQpwMdO2DDz0yaNll7rv3TiSvvfc5CE8qUVMc//vfYvhe+xzgyATAzv1T/3z6Rz/aAqywt4in3oceepSj2eNXv7jphmuwn3j7HcOxRQj83vscOP6z8Vi6Dz9475FHHoKHKjnkXnsfOPHzSfvsvcfwO25Gcl9ssUVNLDP3OXPnOiklgLluc8WhLrwlIxJdSIjAkrOGDADcQmdJpImYxx//B16RwY3zDddfdctfr8cDkKSz3n//g7ffeednu+2CrdvoKYp21ulnnP3ss89hdw8Pyvr173fSyafjUZWeDwpD2BC7ug755JP/7N+vH/aLhw8f0a//gtdefRlCBddCIcDTHlzkMArP07p37wakM7sA0P+6P984dOiQq664eO211hxxz324Y3DMEwD8gWKEWhQwmZb7aazxGlcxUspIy32Kue+TqohbZs4yLD+SqWFWAQiSU/ghYmJBvFyL8QZHKIaJIwdW2HoVRTk+Z5AG0lDbEnvmwYCCGVRySHJRGImN/hlGjDUwyZAfIjbyhIDaIlujEWi516rR3Hth0ruOo0+/RfB50JRgwaBKu/D2Ky687Yq05OY1SZvjgL8nrUORgATX7YEDl8QjTrm7CfrUCR5ZfPjhWNwCKxyBWHsL9O3zxptvCR7LBh+y8Mknn0Rk+nTJJRYfPwEp1PyBDd7x3oj/ImXZZZfBX4zpm0SMQj3Sr1+/d95+BwWpY5JEQnTP5p7vvveeI6sTwIdgfv7ZJ8hBcClyAbdNvRfoNx2fh1rRU6CHpxbebkW6mLVrGnsntTlf1/+tCXoiAwcuiUcrRUdoGlSUs+fMhr80EnDkLJSNKO7+9793I7Kap9jNgNeL/FG8z5w1q9z7AznY8K5ViRR4ZPLECc7feROKaWmrzPmOCh2sUNOqtVYiL9uVlJylDjqSQ3kngK7ZlEP4ptW06TARhpIqAubpk6RIS5rsFNZagIbtUGXEYICwFKqIfYVP56a0SJ88nJGiJYZw83ID3NLim+gshwbSop/7lwS5tPglyW+itDg+zuO9+jaYFlHUuxif/Mo4BzsPOkxjafHLstIXKZfS4ucTTWoLDNdCLaLFa3JAsLxdRtXZVcNq8bE2wejUNbBlOvsUX6aEk1FTG54zX0j4mQtKQLqoUFDSYSwiJ8lWBCRaazjmUeGRi+QsUgL/IZ+PemB6mmn1JX1qjU1O5yuLxGuGU/nv274UDfFZ0PIhla5O5At5EyPpuo6jHh85Gmw4Gh/hV35sU1m1RAO/tQd5BLUk216etfGCw8LlRSCrT6xTMWXwqsdIWT++teuJmMpz50JrFp3Egx+JAeR4GkiHwNzKY0Lb2ruQTIUoo8GGQ0bObGtEsowAZgIjmiKOR9PNjtgJ3fRohHNeoSWdKQ1xv2uZIzHiw/N0qZNk8HRTLXtHvAbe9d9E45ut2tEHss/0AmUqrdTOxX0mfWpZ21HBAvgIblht+lR6cUQf+GYrfF3BnFkzTbi1ko8cN8Ru+Y2kVuZbBcMjMDZ/N5FYq4Wz96stWN42vZIMgfNtqQaev4qR0hHJTmEjXWmFAvYBuUuKisRFmtWPUhlXhbonqUoBaVlQykzACVSFT+dG2urWo+fsmfQtfUHeh7DWwbTlxIJHayG6du8xbepkfCUnCN3+FF/z5nXt1gNf8zJ/PEVZsZZq39J+eGTGtGn6mpFYdsVEYF8YosVv94gzy7h+wyYl1zdchgqtYiO6SSZ2etpsI7uNqcxTn0ymUsI8J5vOCvp4EqsEYeIjw1ORRRQV/splDr4Kd/pUfK2S44argjsUyBuytqNOPFYyrGwHBb/b8GKOoh1QmOBrUfEtxeiSXtdi4w9dcJa2qTZwS/BY1CIo8BOffMvx8Ai+FtV4hC9UZGfnFTgAcLIVKu53I8wKF1cxI8b4c84WRO5+WBQJEH6p1ixP1oJhEio/SjUgWBn8Mr8Jw0rwLweLeG65FxBrY1qhk5nwcJKE02QLJCtu1Pcwj3eDaHjuH7Eo/BCxkcoDGWY6MhB6WSPftqtQLSIdY6l17tINM5XyBHxa56Abfq45W4fdN58LNrC6yMd2TZkkVxTdSpWB9dm5S5fWdxbdGbQ5K44x45F27WdMnaILlzLYV4V1PY6IRabPscaLMtOkMVYNRbLgzTZiJjACSW5jw5ZyipYX86zrPOIh/Di5atZFWJhb8lBUmmcSW2RcYW+RpRJbbDJ26tJtxvSpc+fgTgo8W3q0LbP6LYjl16kLfQ8qisHilmKRDzYZUTO2mrPaLmAFEzuP4Do0Y9pUrkv0QisMqIaQhSxj/KJGiWPTk0tblMhsCgtgGZzOHnFay1BV0jmpRNEkVpSfVVkmdPOVW3XZZyydmdO5pgCitFuznOQrpEVjdFa9A33dZjf6Ykx892Pb8QVaAI+YcX+Mv+3F19izM1W42OojWh4dO8FRXfE+Ztv3Us0PR2EbES5BTtSvl6WTWXrZswet74J8Vkwl1SaQzAf1sUgOpbTLT9eR+PgBiE/NYc1oMx7J0nCp7KRIOyLZWbRPWlhysLkgiICIokJa5PHRcKt08XdEqCdgVQ9J1PLWV0JrVs2gKC2DURLkCiamIDgdofX7kCV6/nL1Zqbp629OIPEJL1S8MGTaQYe1RGau9aK9zsoE4WBFEptN6hEZqWHCBB602lpnhxIqnhUUKvFgfawjbcPlHT86EJbpmSjVQsd/cb4TFURJ8VdZG3s9GB16LT3j+gxsqdKqxEpYamNCM5eUNqxTEGniOxlSW+P6/FVtbxE8S4+kzNIRrrOmd2jGtUMtqYEYq9iycGPi0AVOLSEJOzOMMik1qVHIP3OWNYlUFpKGbJVh9xbJSKKJ1IwZ3uVoyyK4epQPsb3JoaHvVEoKM0nKzMJXTG5lJH8nBavlV/SaZqphzz7D0xPUuYyVHSWEhYOL56C8VzorQXWDtXUOWAm50cc7IFStBe8tBsJyJ0pnEUx2VXYo912aq+dZKd5EVAPVYqhFqVklaryJ45m2IOuHWqgzbw6fRo1Z4/JGjaoXFBPLPCqZmwQo1VKw7Tb5TMdCvepl6CxntTwjSh+NYqRiq9SXsaIfw7X5R+Jqn/qg0aoUlKjFKKVZIupqVxzCSMSlmLJpa1+2a+lb7PfCgniz6ZW8YMUG2VWZrchUYTz/ysZVXEw8CyatUMC+LnP6LA32dZQvWh8NJ6fol1eiu0q1KIZWs/OcrRKtv1Sy9kvFglWj+DvLJmV6e0sl27pqThHfhD0DdobcM0gOiJhWO7VpmkbJvk+h9ctGsmLFKrKVdE6wCT0YpuwEeWjalsZeTQGxH5IDYqLkeXKk1p/g1B1AYBI9ICkmgUxKTtAlUDJUOhQbv7fIb9SZexSTE+LNh0ZUTtULpEVGnxx5OCM1AT8gIIkoGqgWi5MVTCCm6jTc1UCnJC1JwyIpJTXUIThTE/dXM2tu1Uma13tEw4xGghUeSQo9F4JtfgjSVrajXuUydEmNMrRAK3KnZ1CVBCQgT9Riee519iSViAWbJFIfS+EotEmdYx/ZpByzV6ppW3mnamPFg6ueZ4U5RsZHoq2NK62ChmWUzJThJH/HuyFAFBLT+ACqy3ehj/Q1IhFjNm+QpLIjz1QJqPjeYg2RYVzkAqlMaekT1ROt9XOQPwJ+dc3aasYBkSzO9aKwLqyhm2UaqIOTxDRikjrOU1xY83hvkV+0ZvVlAdTBu0iSklakSmKSQ0kf8R1ZyVccdTzNDD1BIgVTEJ4UbM0v1EmSogDPumSAJ3JQCbUW4mHru6/g0+dUNksmozi+nTWqAEnLWd8lO3UQaNhLTQ6LtfW+sHElmAqv10CO0xRve3Rue0HH+6AN+jItgDdk1CsyHKcU/3TQ4jCggQXTsUOnjp074R1QfOuOULa1X6QF8FYTnIbvAkFLHqHLZtQG/qKK3nz+Nt+q68/hRrq27g6nII7HUA4CFgBYyIOWhtE1iq7YfNmtcBNtyu+mph7NvTt37Y6qdd7c7B+BhfqVnc2eNSP3DRtlw9r62ixgLYAXqjvQhboL/gacP/vOrgBTWcbLpmv37iCWAKZ1Quum8aMtgBuwnXPZ7Fmz8HcJzKHUa8lOXeyJErGrBZscTF4HeZKiQlrk8e2ae/Vt37ETvmzDs7SsG/6NN5P5E0caZtA2sM0CZAH8jQE+N2jalPjzhLR1uvVobt++A4rEVvzbqrYA1hauBMNl+JOQ6faj+UpSlcqBidt5EZrOilqhZIakYcEGXZUn0VwntmvfEZHXijkROoMhqleU01r/NrjNAlUtgHvpTp07oxLBH52YG6twGXTt1h21IeKtFXMilGwL4KqecvRwWcdOnfA9P3NmY/nLyy42uYW+o5wTYRyXBOBJ+b657Cl5kXGFjRVsxuDeGRfahA4tRtHfEbbt8rTYjG0MZs2YgRtkbBrSEpLbY9viThtd8+kvINsCuOHYg8soM3bswFt78Brt8EW+Ix/yP3Thn2kBZH+YTNjwEB5eZOvEiVDTVqgWu3RrBmt7IMPiaN1WPlPaSmj73WaB6hbAhjf2GXGJxaYVx6erGpo6d+2K3XBaR60ct24VtN3xVHcYnCEua9cez80obyXLQuc0SEiS6JIv1iI5ICCKKCqUfrg9CTi19gl2fFqbZRu/b6MFUH0gM9Li4aLCtk2EnJ/HfKpD56fKXxXecBlZL/CXKdzYj9TgP13RfOmXhIhF4ccPZQnCJpDGbIU5hU2FQMHO6NzZs+i9dsrnrd9Cs7ajzQIttwC2q+gSKzUcB6vwBJI+NJu2FVs/eplnWwQ36D12GZ5YBCWfOInSQuwwnMOF5GCGFMylJishjo9a/mMDyn4UHfTgJt1W/J5oTsN+5uCOg5I2S+IzwTT1GNy/efBCRTxjEvQOT+PDg68aIYrP2vBiFG0HfGUovt+9iNfm0/TfZDyizIUmw26yFH/20LFYxPdQMexhS2d/Ey/Nx/I2v1to8N69ew8etCyJoConcXwV8Ii6BRdcQJRrXB+MxBRNa0wqhiVPFv4BQT+MN7BgqCVy0woBtYwttkRHxNQyXOEmGqPoTcXCMZeQAb7/1sv3/9Hy3Qf177/1ULTRiCK9EAAPEcUDvQ6Jb6n/4Q/Xk1ONdwQAvkj8IQcfgC96xxcW15S77rpr/+7YY44+6rDvrbLy/NN/v333vOySP5Xw32nH7ffc45d77bn7T3f9ycorrSiUJfToal17XnnFxfjO+6LcHF5TVtMTUQPVuaWqgIOIEMHBcRsE7zyJW6KaO68Hx7CMANx9UD/BC0a1hk9RW2BA5vANBPD/+/mul192ITh079btvnvv3G/fvRw3ATR/3dUYHl+WjfCQRFw/f0TdQQfuB3qsiBF33SYrQisDuLY+oGB/qRajyHfJf0xOQ/DP/PCZPxW8EMStsDUj8YucxBzRVriJ1pOkzCqBRvnXH4JHAL119qNCsNA2K0wZ/XE5vYzXfDxHBW24wfr/7+c/nTlz5t/+9phCJ8Add9hut9122f7HuyT6Woy64/abb7nltpv/ehs4TZo0aQo+pJ5NWsIY3yW92aYbT58xA1+Xt9WWm4964KHTTv9DCf3869pzz907dew4ZerUHt27462I997/4Je77z2Lnk58QQcsNse+iRVZ0uFbRxWKJ4kpH1kMUfy7PgXH+IW2WR6RjCJxyuiPEMNCWUKvCZJw/QGcHI6/CZkyeQqOZG8ReeYZp3To0P7Io35b7CrB7Lvvnuuvt+7qq6+69z4HlpDluupcEenhXKvhoRj3Ut0Gl/iWyz3cWeNf7i9euDfJ2/BkdsKIWMvfurCY4K9cqqVFyags1imMswDuge9fB4pICY/AcjBjYno7ccGjzR477LDdZ+PHL9C37xqrr/qvp5/J0jU1Lbroos09epQQtKSrT+9ePSzzq6/5M35qctt4ow1efuXV/fY/BJQ7/2SH9z/4sOaQ+Ufw72eePfqYY8F/771+tdtPd/7Bums/8ujf55+4iPNhhx/jMNqSGu8IWgQg+nD4VUVnHIqCojPbHcBEQ/UGtZIQEcOTX/2IUIJnTsmx1JM/6g9gzQPfuCmnKAh22vlnuqscXmSRhWfMoO/prHSsscZqWGXLLrt0x44dZ9Oj4WpHnSsizRQGRjKk1EFZLL8BzA5ye4vkSGMiGmVYe8dz9qOESXyZNNWyVPIwZFfcW0xPJsRyJH0kGzFypQ37GzzDC9/LDRn86KN/nzZ9+k9+soNwQRX2l5uuFRiX4gdHjejZs/mwQw/cdputUMbj9OIL/4jejTba4O67bv37o6PuHXE7Ckmhx63cKScff/NN1wkezK+4/KK/PTLyjtv+suqqq4AGL3kcc/Th9997JwiA3GSTjYDEKYosZBMwH7bC8rijGX7nX4Vh//79brj+qkcfuR8/fzz3LEGiRXiB1dQpUwXz11tuf/zxfwDGWEi/koViyKmnnCAEoL/g/HOEDwB5nTOJhE3O+cPpDz14L9QGKyggHM4689RHHroPGuJ+WTDJFpoAv8IKQ9HCeiPuvg0zHXnf8HXXWUvoAdx5x81APvjAPcIKd1gQBHHAHPe7XwsZTI17KCAJP2oEfnbZZSfM7pqrLi1qArOfe84ZGBhZ0uHRlfRXjqHokGixLrgscG2CphQ1eTSFMXbJAZQS1tVZfwCDXadOnWBSxAD8uMkmGzoBuIn++c92ldODD9p/1Mi7YfPbbr1x8OBBjkYABPPi311s2WWWhjv22XsPIMvpZdSwYSt069r1vPMuaN+u/VZbbibI6/981QnH03UUx29+fSQCQGDc92BBITwQJFgUgtQrIhktQpZuuVq0G3ycGr0HCcI/pE3f4jT7w2QSAjyEh8chgRix4lg2Ggqbdo3tLcpmTLad/Oo4BNNSR204YJvlx971AjZpcCs97Mqd0dpdyPTYtLEYu/EmG8L0t95253//+/xKdlNsof79+/TuLaOQELE+8bTxnntHvvPO/3BHdu55F1x6+ZXw9HHHHjNt2rRzz/vTJ59+ih0Q2Z1cYIG+KJTGvPHmJZdd2b1H90sv+ROuxhdceGnPXj0ljHDVGjJ40O133IXbEAw//FC6p/jTBZeg/c+z/wVzjO3bt0/P5mZgkLYQPf379z/n3D/98fyLPv7kE9EKLS65L7708mqrfQ/xtPlmmzg8xuJ6jjujk089c8yYN5GDZLPvwgvOXW7okIsvueKqq65bYdhQ3H1jSBJ50UV/RAa/+eZbf3fciR98+OHEiZ+DEposuuh3oAYmiw1EJy4CFl54wO85r4245/7ll1/u98f9BkY79ncnjPvoI2yAghhIXDamT59+7HEnjhhx78TPP8daveaay/r07XP5FVc//fQzKIGxSECJFssSOpx97vlwwd/+/n8PP/woZrfUUgOLmsDs/RZcEKMiSzp8zl85htG8/Cmu+7j4+5Z6CGH2wdMRiF4iYsLJr45FQqQfxPOgfpIi+e4nP9aLj6H6AxgjL7nojzDptdfecMqpZ8p1UdhhexF2AIyr1A4/3vaVl1+96OLLunbpevGF5+FpmxaJ4IHvEAMI1Hvuvb8mvYzddZcdsdXz2P89MXHixK233kqQeJay4ALmcQrgPn1oxcFNRx156IQJExEeTz31L04nRA71ZEXkokV4plvyTuQ1wXA9KF6hco89JM5MtkDqn2CIG84UJiRiodXSYnoyBSwiacqr48bd9QJ+pBOnAOTZdIG8LsT2222Dlfnhh2PvuPOuzp06rbXmGrlhr78+5sOx45AW7x/5wPPPv4jogc/23Gv/u+6+d/fd98GDne2321rGvvvee1jMSCvvv//B5MmTDzjwsNvvGP7Siy8vscTiIEDs777HvldedS0CBUmne/fuSCXgia5XXhkNALnS6bDmmmvgMnv88afcc8/9d911z6mn+WoRNAcffMRNf7ml7wJ9kUHwiAYRLwORMXFn/fDDj+619wFQbMstN8MaQC4eNerBUaMeunvEfe+++34JEhXuAw8+DA0ff+LJw4/4NdI62GLiu/1s93vvG3n1NdfjQuJkOVW/v8ZqKEP++pc/I6X+3+P/eOutt3fcYXuMgimef+El5FM8+kThucOPt0M4wWKobZHCbr31jrXX+n7XLl0uuugyzOW3xx4PX6z3g3XAdtCyy3z22XjogLnDVt26dfv4Y7oqlGuSs2SJv8oZugkagC/7qBG4nqAyoSXHgG2HSTD3GEJ7RA0c9QcwYgAPdh96+NHr/nwj9jcefPDhorgtttj080mTDj38aBQKx594Cq6Fm26yIa5JvfjAJs9T//wXfDF+/ATYGeGdpIcgoUeLYhZSVl31eyg7ADz51L+WXnogUltRtGB+vP02CI899twP4XHmWecWb7dz0ZJjSHguBV0rhRt7kL3HlSGg0n/EovBDQygQ0OK/gZnOymSmTMFqVNtbpNTMjPALYpLwwtsO4xlyd1PTUkdvNO7uF8YOf+HNsx7G9RaZcdKr43JjaWDqoGQxZBDmgzsC6cd9NDyXoo1xyyyz1KRJk/GDDmS6jz76WLIendIXutKBix5ygcAo9IbZOwLcj2+55eb4e4nJk2mfu3+/fmPHUn4vHmusvhqQ/37mP8UuYCD3ssuvwg+C6YD99zn37NO32trsAzj6KZOnDhkyePnlh8J1uD3Bj3SN7dw5jRy6HCife85ceBwfdhGdvf/e+2h79+rpugTAZePKq6474fjfTp02HakQSNlIGn6H2Q0AB+wPDBq0DIyGpeWGr8RFululzz3/AopfLKdn/vNf7FpgdwKrC3nziSeelCE1NXGcNVDir2oMEZ04JNSsAHDIxZ7DS5SO5W0uqii4kkBORCRjg2jMmQ9haeXi38qJf1cK4GWXXSbpWc20b58+zz//omCeeeZZzGullYatvPJKSEZA4hZk401MrSc0SXr4F9tEQrDHXvv36tkT7lt9te9hleGCivvobbbeCoWCEETt0ksNxKLQ4RER5KIFayGi9KfUZfwApHUVZZJwn5HLP7e3SL3sJkpI/J84Oj4BT7OryGSpHUbOaZWfRCMzkWg+LAz50EHjxw1/wWmFeBqwHSXKyaMpofQYshBuSSJ6GUt8UgdCZIvNN4WTLrn0CuQvkOy8846o4YGfOWuW+9sb90cywBMbaZuaUHAttuiiuJDieSuiB3dwr495Q8sx9BrF8Prr/2C7bbe+7fY7cWe95vfXOPOMkx1Jccjo0a81NW21AfY3C9d2EPfs2fPzz+kO9447715xxWEb8DtGmgkqLGwC/Pe5519/7XWQodb7w9m0KyoHghXAvfeN+sPZ51lcE4YAXnvtNe+7f5TLF66XAGuBANnUhAoCpcS5f7zg2N8c/bPddrnhxpthImwDbbbFtvqR9GabbQK7QQruxYQ/8ilYQaI8osE7HFgViHK8GLDN1lv+6le/QDV3y613jBz1YCTRnKb0gRFwaP0b9lfEhyJQFkcUWGGscuyBgq7zgFEV4pkhYnWZYzZ+4ywu04Dn6EUkIy06rqkYjiS5eVcL4DFj3sCtw8orrzjinvsMC/6FCbpT3Nwsvvh35RQXMHS99tqYa6+7wREI4AYk6RE5+HFDzjrzFOTTs23gHXrogT/aanOkRVSCzc3mAabbdsMjRLootm9Pb8injihaBtloKdJ6x/EM3Z9nQnl6UGLa7NNnTj1uopq9ID0LznmGnPzOHqeWBgVPoqvdRLvXE6lsxIJwh4WpVETydgfjgUGRKPSm19JHfPScHAxm22239dSpU/FODJYcfm688Wa8ZYKdwaef/jeArX+0Je4H8WKBDAE9Ags32rjqItcgxcDa55175tJLL3XaqSfgduOBBx5yzAGAXp86GPs4gJ999jncUe69968cHveqeFqHF1yQah3ysf97HKF80IH7ImvgQuoeR4AAZdTdw2854vCDoQ8iaZ211xw/YQLwkAtl8HQCG3BQD5iRIx9AosEOAFIS6kr0omZEwrLIjTUSt/AobNf8/uq77rIT9pWwORjtLkGAU08Dgn3ggYffe+/93X/5c0wEJkJ1cPYfTvvuYosNWGgh3O6BHjfysNsF55+9xOLfxW4sNqcee+xxJL6DDtpvlVVWwumSSy7x4osvg3LXXXfCDd0vfrnX1tvsiK2unD2L+oglYWdtyYb9FcvFPIHiltaDTBuIROzxfiLj6Zo9D68r0sWb9hlx2Hgm0ByengR4nukEgf5KAQwj/+9/72KDYtNNNxq63JDNNjX70STLHrhVwjYfXhIEwUknHIcuONR2mt+4MVpssUVxG4R76nroV1l5pZdeelmWGNp///s/AwcuCde8+cZb3138uyuuuMJOO/141e+tLNzvHD4C4fGHs05DfB5+2MGI1Uh6FC0DbbREZDj18wLE/lItetkF5Ib4H5PTEPKB/PCZPzVIJhAy3wpbMxK/yLjMEW21tFicUhLTPGSA/gGNO03SlyNxm4YbK9ypObJHHvkbctCPt9921AMPf/rZ+COPOAQPZJ988p+O4N57RyKN4iHvLTdfj70P7NEMHTrkqisuXnutNXEFRskmlHPUhU7MArwDsCnz6aefnXrKCbfdcqN+0eHBhx7BbhqeCdL2ChsTo+h+5NfHYXvx9FNPvPqqS/F4F+lGpAy/656n/vn0j360BfTB3uKMGTMPPfQo6UK7x69+cdMN1/CzneHYngMG74uN/2w8Iv7hB+898shDkPFzyL32PnDi55PwgGj4HTcjHWMNOH0wRM8Op8Xj98efjGg+9tijYSK8V4EbZzxJv+Wv1+MVIhCjJPzLzbcuvcxS1117xYnHH4vnJ3jb8YijfoM3n/Cc/ec/2/WNN9866mh6J+6xx57o3asXrISHkvJcEjVmiSZOMW1J8BF8zl8lDItTIwxqAJRXvk1TRVhkQ6kKxw6nLbZWORoIYGzm4sPKUM7jAf2EiROCufPOz+lnnI0LNnbJQdCvf7+TTj4d29+Rtrfccjs+mQaX5DNOO6kmPcIMGXDECF+f3nHHXUh8uA9AOYJMjZciEGnYOREpuD3Czc1qq66CV0E22XgDum/g1eRUzUVLpGRwCnneXwLzPY8UdL4lKPqHU8ZQW/ghYmKMFv9Ny+c2RhhHJ0zXrsLH0Pbpt8j0aVOQeME41zYPHtC83IBcL/C4PZn8Ct1EJ2nwp5GBmeo7QY3z4dixxU3fwYMH4bGA3L2C08CBS+Jha67mz4nCxRDFnX66AkrUZQv07YO8UByF0m/q1Gnjx48vdi25xOLjJ+ARH+0D4MDbRRtt+MONNtlq2WWXwXOPSH/Uuf369Xvn7XdQxAg92iQSTwZ7NvfE4yNH1jAwcOCSuBI4iwkfqPfu/95F3Du2MPhnn32GuBcM3l6C/udfcDHW1fJDh6B2wK00ykZHXwKUWHJgQ/4SWT169p40gV2gIq1nn7702d0Kk4QRw8BLlEYwbQSVxj9/BkzJdNNduQDGg5f33/8gij3NAgbHY0CUlhqpYdzkYq8Dr0zI9khNej02ghG9b7/zvwiJUhQ7kuWxF0VLxMGdwmVTJk1CYkJhwimmLM8YL7jBCcC7GXkQNSK3SYcnXNqytCjaFAOlUXxjaTFhk68DyqXFr4OyZTqOvP+uN95489DDjkalgD9D+sX/2+34E0/FhmPZmPncR2lxIqVFvzhwUelt06JIb724dWIaS4vz2RhfD/aUFidP1v4KYZPawnqqvqmFjApZMNEd7wiUyKGcRXftiCbWTVrhqTGN45kzhn87DrxmOFW94vP1nfT55190yCEHyEsCs+fMwa33l5sT8enc+P4kin4qPChAuW0CkneW1MoiEja8a1sSz9+u+G3NgGWXyRujzl+B7+hZs3EernUE2VZ5U3yHNjrCx9g0gDDO9eQ2W6NSdgNcoVrs3ty7HT4OHi/HOfH80Eg4saJeRV8LsxzXW4LHHzmZTe5oVm2nXwcLyAOf5O7BF6w+vh4EEqdPM/f4Tjq+sQD3aPR9Bq0aty7+sdrwN+9OXBtQvwXoS3jatdc7+BgraUaYqJQDvLsplltj3dKosiPP1OUoBEmFT+dG2urWoye+jQpSJdeJ+FaDM5+gUzbJtr6vjAXwHg+Or4I6Xbv3mDFtKp44kzJqGeCqi8w4e6bZJG21uGUhEGUkfhVM8HXTgVw2YyZfVFwCtL5zCHGYbmWa2pF+4slhJhySfZpxhbQ4b+4cvBuI76yQr3Z1CkCGOxqGEbL8EMhx8kAbXmzRZod67IC6g76AWO5pUFdgDP7Lz7y5eLyLz+iWL1NrOFaFpSjjYA5ghwuANseJOXJ2gMtwRcG3J6JMMw+C2V/kIOtB+l34BwT9MN7AgqGWyE0rBNRqhgomOiKmluEqaXFeE0rFTl26YSjt3bTi4XYNWpFnG6tvmQWwP9UFr5q2azedPnpLX/t9OYE/WOrUpQsiv5UDGPLoXrrtqGYBcRndPtN9BtyS9hoxTXZqcpHsXa01SQ42LIUuoqiwt8jjaTg2GZEc8UXlrfMVpm05UXuwDa5uAawuJDu0s2bi+8bjLcUiP9xK46aHAxhbgS1NZ20JsWjhmhjnMlyo9JaipCcZHqUqlQMTe4syJJ0VtTZZpvIXnaa7QlrUIjvSl6N2oy+B7FDhWbZWrw1us0BrWQClH96MQU403zXsS4tslYFbafpGafrMN/OJWK2lTBufeiyA14dpr2PWLC6tVK7CTax5hpX1nXZvXpbnibtnfvaM1g8thyukRdYAwsJDpAvOa0K39e4JncDUxmo5df2zJOGkU3Aor+ZZQQkvWB5AKiVqMisQiImFRSVzEyelWgq23TAcdA6sVdCjGiLilrCu+Ei0EjhulfoiXHGpzb+avqCOxTfou5RmiaiTZVOYl9JaGAkixZTdFVQc1oOKSSOgFxbEm4gSGVasBE6xLRXr+Rc8XDou6gzsk1QoYB+QW0sV8oNeA8ZrIW2kROY0OUUvLNFdbW+RYlXNTsOikWAYTgjLaF2KztovZfo8pyybwMyJBWP7U6yTUywzSnJAinPdOJumaQD2fOXJVdj6DCPOM1m3XhGtpHOCTejBILJSVw3rCW1gFW9mPE8rISyebpJEs65rQEyUPK8pisSK76SusX4MTFKmW1IskEnJOeIQL0MFp9iInhJSPt6MJCluWiI1r3NGn/qEqQn4AcF8I4oGqsWifwQTiDHSC2b1WoVs1JXQV46aRMPCNSU11CE4UxP3lUiLIic3uJIHaRY2PwRpK9sRzKr6ibKDd0aejSJ3egb1SGyF5KUlz7+unqQSsWCdF2tyDX2U0FnHm4Zjzko1b07tO22seHDV86wwx8j4SC4hNq60ChqWUTI7hpP8He+GAFFITOMDqC7fhT7S14ikv3xSLlM0z1QJqPLeojJfTm4rmTXJRodakD8CZeqadVB3GBMHOGUh4l6JaaCOGxxNKSaqeR6NZ1+w5ray0BVi3jw15QhBSlpLhnrfkZl9xWH+UtUHtMRYrhUVpLegTkZnQZdMSwuLmSZ5ZuTbMCkZo0URbH1H+03JlBHrU895Un49A4MJpLJZMhmF14L65BSoS9CU6AAAJqBJREFUMjon0dqIwijtkOTgWFvNTMMNV4t2aiJdzpKaWI+TRoHlgahhaOGanrX0lbc+1KwShFFKNCLAT1LpX6+5/Vw8G6UPddtLfELR8tmW9GaFuTGpZZBarWJIpTJzSF5aHO+GgLRCSnDRjyk5MnPpCa1QV+x5lnlGylHzz3fxDILYs2KD7KpCXgb72POzAhRaxUwmoKh84mI4UEiJEo5WoaQG2r0MJ/xlFbOM7Hn8u6YAGmCNyBZo0d6iiM8oldQlVrj2eYKNnUBgdI6CPLsEm0R9GCxvvQ3vGQsjOa/J1A8rGRAT1TwPVVDhYquPomEEI5w1XFOWimUdqHWMA0nWPikPlpAnPKVyUUKXpOCQLklSNsXkgJCnOhNqQSRHKlHsQeu71LWITaOY1w0mJdc3OjmUFhl/ZjASXx2VvkjK5IeiGkmRlizZqYxo6NLCkoNNeMrAiKLC6zVkCi5m0HbC6w1tL+hYl33df8v7LfyCC3+wm4oRvvCjNKFVIDC1/EVaeFcQH+fX9oLL1937Lddfv21DSUKncc6j/h7IXLGZIpHCKMcg9woPhtW11YQhMNlP6qbtGB5caAxPZieMSAdckRiDlvI9tXzBrXAT7WaB17k7d+2OYgp/skN8WnbgL2dmqs/yaxmzttGNWADv1vJ7fF3wNvT0qfjSmzggGeM54y9Y6Q+22gLAm+RbDbn4oXezG/m7eBVvlJdsplHoWiFZtL8fzNmv3v1rkV0hLbLkds29+rbv2Al/W8raF7VpBIM/h8RfJzQysm1Mq1pAPshk2pTPzUWddyaiC3z35l6oEPH5pq341x1tAdCqbvzSmCF+EBv0iSElAYR8ZS+8oqhPYBafwgQbXHZbpb6ZptipXZhEd7W9RXxUZLv2HTkn6g9QQoZ1RyMwGKLq5PfdHZ824EuwAP5YBH/7gb9cok/fMkcQNKgT8Res8BfnxEZ8bdkGY9u179AWANYyX+PfiB/8+Ru+FwgfNR/ETYU5ReMQJ8DYfCl8kiRCaMlDgckBAUlEUeG7XDphxXTtjkIh4OdPdKB7LOd1ferggJ7+jhAVaNvxZVsAf6GKaz7+yJgjUbZqTNuxU0cqB+IACPyo1K+G523KtgBQ9vt6gogfuBKZEepTPjO7bATSDqNpEVGACy2QhDYtkwgsrOwgGpj7R4MLP0Rs5PFAhp0c9LIuvq3y3mKX7s00K2bCQOs3bQVj69u0IkdsGGOfESUh9onsrQoSHEUOHrJRkUgBNr+OtgCYX5b9ovhK/KDEqfL9DYgoiTHdctBpRBGWSQk+nmCaZxJbZFzh+oxqUUTzsgArOloRbm+/tF44t7VflgXogo/tY3KtXOu5bdeEKsBdE1vR7+5+oi0AviyPt65cxA+5Ui6fvvUVG9dpiCBT1REJQixuOdaouGNCIo7/2fGEL/wIklseSBSGlTBUrYimXqNGhRd0sOU0dza+gR67irL9iQxPeZZbsDQwRDSGB4u246tgAewQIazliQo+HRj+lBa76fS9idTRmn538SNx+1WwQJsOLbEAxY97JMthRO++4ODGtCIgQHOkUe6iWxLb2kGcZsw3uiiYCTkJOYYYGh0cw/SeJZj51mYu1sE8/xZtm+ZVSIuQhcC1QewlixoyZYftMbi/wMCzOtROHv0RM6GeiN7hqS88kMBZ2xALtvXh8Z2NCw9YaPRrr9dJ78QU6bt26TJw4JL4HlR877sjc0CRXrrK8WutuQa+VRnfCeW++LSc3olzwPyix+XVPInmfEXxFB/i2RiLrwUf3B/+bR600LgRL+reHH0OL2Pn1wStZm38W93O1rRmlVIxJ9svplVJwZMCAh0FHeME9tkDAUi91M739xYrPInu2r0nb/1IQitrF9pm+T5rDcTcOi3Yo/OCPVy70NbLj//H2zYlJjjU3I/Al80jzb399jtsuLqavffa/Zijj7j2uhvwffZ3Db+lV69e//73f+oZiS/V3XyzTfANn4st+h18DzJS4WqrrXr+H/+AL7n/9NNP6+FQpIl0uP22m7bZeqt11lnrzbfevvyyC0ePfv2DD+IvQS8yKWIitkWCqhg8WplB30oIH1EoctvUpVt3u/eX8B2LILx4f8rojzr16zHwyA2mjP541v9v7zrgrCiS/nqCLBJcJB8GlrBkw3nnnYqKhCVLzhhQgllUgojhMHyAsggGDEgWJbPkKMksItEMnqCcgAhKEkHg/t01PV090xPeW0B/32+HR29NdXVVdVVNdU3P7L7deBcSR9go6hV/ud531Kx5Zc2rrti46VO3p/Z111avXg1LnYvhwGsjRzRsmDlv3kKOjA//4++XNW7UAL6wLn5+Pq7xV3+8xtqbnUjUEYfy5dJbt2px2d8uLV++HL7hZPfuJOPNr08Q5t577ny4f99p07OPHTsJf3gf8SO+NyLu4caYsw5TvOnQowD0t2H8NU+RRmU69TMIwiRWLYapYfbtmvPpQXzXuDmz9F61TKrEznAxIEkhWJP7vs2jv/9+8MBBHHGkXnDB+WNGvZwnT579+w/UrHkF5LZu2znOwHAarkN6etliRYsOGpy1YOHiv116Cb4lnX9FfTgf9LZu1bxTp/YtWrYHzNlGDoxJIBd4FFJY5mUbcxjSYtNq/xmyokClEhJYjsy4qdvk2KMthDWqV2vfrvWKFW//9wdnzejT534sVHPmzrdQp6Ts378/J9d2v369i55bBBxGjxlv5Q9kfONz1xQteu6USRM63XDLjh24NMIOrASdO7U/cOBAamoqgnDP3r29evXDbUrQmMGDnjzzzL/06v1QEIEVP2P6pClTpk2aPA29MNrBQwett2XWsdFIGTqsTmTJQIHERCYwqg1lK8JNLci+ClGkOFkvSh7hWoANDqe8dAdxBhIWioKMt4mlRbmxCEngEtqeOFGiadUUfHxHFAffAIZo1ao54uPcIkUu/8dlH622rMyMVoP4wkI6QT5t0y5WakOd+MpLz2EfrWXrDj/9tAdf9dn11pt++eUXzTRZiOtQKaMi2Kxdtx7tJ2vXUYKLz7hMmTIFCxQges42PodwSrpC+N4i6CUyzPslr6+2a/YmrH8bu07aOWtjud61UTaenVEcrRQXNlbFlVevqdNmIi02b950xEuvoq/shRfkT02dP3+Rl06d33d/XwUm/LNUqZLIiQizzMy6IWkxvvG5a0qWKIEcRy+vxNGscdNWILv2mpqPPfpQVtag5i3aBY0qXboU/9P/QWQefNo5hQuoEMJkQ+brGRjrVMYK7X/59xada1IyMmNCpES2t0gwij2xe+fs4EnY2c/TGVTmSfCiZMhV1PuJsmoUbCSRI1j8kDf5uk3gSTQXFAnjMtg5e5PnEzkqhAAvfFepXGn58pW/Hj7ctq0IFxwP9ev95htjJZiCWhLf4F6okHiLKG/evMhry5ctWLZ0fr16tYkA7fx5M2/o3AEAovP54VnoBc1rr76I+yCXBsDVV1919tlnP/HkIOREnOK7j58ZMowTAG7VsvnMGZNWLFu4cMGsO27vTr1t2rScM3vayuWLIKhevTpA+jGkA3Jir149QTBh/CjM4pKLL4Ly6ellgSlevNjrE0ZBMXyGDX0aGL+s+3re1ez6xkjfGDXiBaFb5NRAAD1nz5oKncePHYkLCaNCD4SOXKzdNpSaOsnj8D6qRWC+eWaZSogxBgeQ7MY95J692Gqg/hYtmuGKQ4mN03vuvmPRwtmY0bSpEytVyiCCl0YMH5o1iGDcfcNN8MiSxXO73nozkIgT8tHC+dnoJTK37dihHV5FnjZtZulSJbFdQ3iPR4KMj1tvuAO5j0b17tVzxrQ3AZNrrrzyX8OHD8Hp2DGvzp09/dFH+mELhSjR4sb/maefck85sHLVO+PGTyySllaz5pXAU+hSeCCG8XBj5KsvXnD+eRUrlIf0Ht1vBQ3MAhiznjr5dSR64ta92y0L5s0Ect6c6djRBowE3aljO1CiHr/9tm7ZM52i/q+lS096YxysCqM98vCDNBwEuH96evBTuGowhIxJXUGtyD2y9HNylagCBUbGla1Fl0A7rSQkWKBFGNIgGZCiw/JPDPZ99FDR57Bx5QAjmev2jMTSIsJRHqJqUIcNxtOVL3Yd+GKn+jgw5CL5q4H46YVBEHTUrVcbXkThsG7dhosvvojIShQvnqZiFwkRaYL+csFLLw7DRTJ27OtPPjVYPBRTx9n58xcpkoYzXDnVa1Sdv2DR1Kkz09PLjh79siIRP1GNQrlVb7/LkR74oouqf/LJuj59+3/wwUft2rbC/RGk33F7ty2bv+na/c6VK9/BxezHgAnpgPvBJUuW4XTs2AmTJ0/DvRKIcSDokSiLFy+eNfS5YcNf/HH3btD4Zc2dt3Dr1m240Rv67PMvv/qayxZA0NQgt22blgsXLhkzdgK2CNq3awPiqAMeAgm1gjbEd6pL0KBOLHl9dfi3QEZx5EeEgd/XnN6Fg/R59933yvy1NFY7EFxxxeVbt21D8sKV2apls88/++LFEa+k5ksd8cKzqOtBcO65RbA7AaBatSpPPvEYfhet/yMD5syZ98u+fcAgH8F0/R/+985du/r0vs8j8Zprrvrq683Zs+YCD7ei9XskyPgrVr4NDTt2dGq6OrVrQU9wII9/+eVXS5cux+mECW8MGTp8yZK3oCTl5bS0c5DUPvroY/RaD0QI8BfVqI72heeHVqlaecRLI0eNGocYrp9ZFwDmuPunnxAMc+ct6HLzDTALYhvreoGCBbBpiFE339QZGXDtug29+/bfsPHTPXv2Pvf8S8DjNgWjNm/5BtdFoYIFgcEUxox5Ja1I2qsjR69evaZunev6PdgLeBCUK5depsxfEZmQ1bFDWyCjDzheHLIlWLTiymetJBAIGWlO64QejRbjdTAKyHvm9IvB8iMIuAwJKybip4I1IxeT4JNooaLI5ghjAgNhuQUQQRPExxlm/mjR/HqE9Q8/7JgxcxbWOnze/+Ajk8Q5Qx6sUKH80reWY40FClcCdq85JQgQhdggH5I1HHjk00YN6yM0f/75FyI7//zzfovaMH7s30+CGDUXPf9BpfD6xEnAYHHGZvPgp7MAU0bmGOKPFluW69dvaNQwE5cKrk/kQer6Fx5Mp6b2ffCRDz4Us5slr0+/rPET3vhhx87zzitDRZPLNnxqs2bPo/tQ3JBeeomztLhjPQDWVWdX0ba3GOQ7wuOVgwJf7ipYuXi53nU23PpmQVSOWJFPiFcRAB/8aldQ/Hh0cE+nTJmBZ1P16tZetmwF1sKRr41BF56r7Nu/v+f9fQDjmdXQIYMy69WePGW6OwpVNmK+S5ce2LR95533gMfdKNaShx8ZgK0VXN6otlAJ/vjjbhqCax5F2ciRY7ABvWPnzrp1r0NqsHrEanyMwo7ntdfWfHbY86i/8IIBhQQxx50HefytZSu2b/8vkNg6xO4wnuC1a9v6+InjlIuJ2NNCf1xyUBX+rVwpY978hYsWLQVNZmbdRo3q333PAyDYu/dnCobGjRt8//32UaPHgwDRBZsAaNKkIWb0UP/HAFP+xQOrB/s+8PnnX3pC6Mor/ok3LnBp0NYtysZrrr5q4KAhGAjTdercBcCRo0cffqhPpUoZyPU4tR8ygERJBmeLVlRkTksDCOOgQYFO3kpyUdxFPH12dhstSmAoDs2CpPv2FoUIymqQT4rqSkp0RR60rONdEkw1ABbXgOgV/V4azDB0bJB8EQ2VMwoXKoTq/aknhGvd+2j/kIoVK2B269dv9HcRplq1qiB4770P6XT5ilUALr/87y79119vQWSgTHAxfgCrKG6FUNnhpgy9RYsWxV7kgMcHFj6n8LixIydOGI1s6Mf4+Xgwl/9DqPHxGuNZuV+WZ5R7Gj41vFBGlEjKZ+XL546yAsJ90odOK05tPgWd6VPUiYiBHdkbDny+U7yShY3mZjUQDygbRQkZGgNWTYD87vvvoXP9+iILwHfZ2XOARApDbU5D1qxZC0UuvrgG55CRUQGjkDJcZMWK5eHW7BmTZ2dPeW7YEAxB/nJ7qQLq2fMuhBnuhfFB+Wn1iDvEA+BJLrRCeu3QoQ12e6CVh4Cfzl+wGFGNSKtTpxYefONr8Hgvh3EvglmjyCX/Yg2G/vjg3tldUF16bL5jvSSC65s2ypcqHA3kt99uc2lCALoVQzFLNOs3bMSGErawcCpDQqC3f78d7TmFC4mToEMGjBtBGA3YaQEwWLDFfwo3b6sIxXDvPzlIjhZUDhcHcDEA5EBfK3URkxIfSSVbCSeYFm0moJxMPaWa1SgpPzh18cCU71sXH6Jx8R5mgfgzzmjYIBN/n+DlV17DCo8PSoMaNaojULBq4Re1iQ/dPgPevHkL1t5LfNUQ6Inya/lih0uAW2bgN27U73+8/77ImJ3lLiQNobAgGHxwo4G3GXCHm5nZ5Mabu0mXipVmxYpVjRq3eGzAUyVLlXzy8UdBD0zjJi05hpiIVumjMSkptPxeJ1Mt4V1Z9es3dWU5QyQHd15A8qkBj3dNgORTwymnd/jIH168YE44ud5qf/JBNNLAiFKxUomClUvC4we/EI9ccU8NzA7cWTergRvqQE4BEUBKfPjR6qpVq9StWxuV2sFDh8AW1RZ2A0g2rYVffbVZnjqMtm//AUspqjaiAZ/vvtuOkqduZpNatRvQZ9nylcQfNFdffRWYZ2UNR4yh4gOmQ/s2fo849D73Ab9gwSLc3WMUXi149933Sa6nRSQTZty412HZG27oiPyL3UyXzNXHxdCN8LvvfUD+RbXo6t++w41E7xoPZvl++39BcF2dhqKt3RB88AoGFgliyPlzmHq//lrYEDuhdJqRURHrChY+OjVanwWo1+DpKCe1I3oZVzL4MXv+keEGQoF0YUUI1hItWwF5/uFUYgyOhBTMFBtNpBiSNPQLFdFK0YmlRWvapZxNSRnRv3nwErGXJM6dBA1xO7M3AE80Lt7DTZDbDuBx03fo0CG8SbBw0RJ8Jk6clDdPnqtrXrl69ccAmjZpdNlll952W1cajbjftu07VP6ZmXWqVqlcP7Me4V3+cDNuKLCFj6cWqPWaNGmEmgK3565w3MB+u3XrzTd2uq1H1/T0stiUWbx47r/+eflR+VZd/vxiHxDE33zzn3ypqdifIpNeeMH5zw4djCJxzZpPoG1q/lQ8MwWmZMkSLsYVIQDbfFe9/Q5y+t133Ya4xMtr2PB2ZaG+c2VhNLL/WXnzIh0ULOg8jwaST+26Wtc0beqdmhQbaGf06kNQkc2Eo5xDIKRveStwGn/g8x0HvtwpCsZZG1AzEj0w5fvUQX4UlCa9Hgu87RD0KSlTpkzHfOFQrDREhV0UlFFIGUA+MeARkC1eTDWOw2fRoiVwzfPDh8ARtWpdc+stNyGhYIc6a8jA8887D8kIOzNgRfyRYc8pXHjmzFkUY9hwwPPo2rWv9XsE9K7x6REf6QM80gf2vrHfh1xMezjURS29etmgQT0ahVS16dPPOnZsi5d4lr613KUkfXAK5yI+s4YMwsPoNZ+sRcKCf5G469ev17LF9Xny5EHZiMcjoN+160dUiHhGhCfL773/Id60vfOOHqjysCtNCQ7v6qJgxAu8qH9v6XIjPb3BU3LcJOF5I8UYKbBq1Tu4gu6++/ZLL70Yu7fpZS/ctOkzVzcDCPWXQ0k03lYEDAwvWvGR14Js0SiMQAsSSyuwnh6cSozDUTATGA+Z95wxF8QYIcecSCwtYmD4gceOVBWKokAd4pKQ1WJyDyVRqVWoUG7NJ+sUvxRsMCF3tGzRbNHit/CMstcD92Y9M5BKPKLBdjL++FX/fn3w/OHnX3524wy9x47hlxdTuve4Gy/c3H/fPdhsOnr0aLceYluaH1273YnQb9eu1djRr3Tr2mXTxk9xY7thwyZEDMpA7CLhmsS9LZ5mIrCOyNuftLQ07GlOfnM8bq4Rkc8Oe8GPIRGkA8GYCACnPX4cCRobi9heHPjUgNGjXq5evSpc5ZeFIXhdGckXj9HxKhxnGzI1LVfGAI0KatXqiX5aQoMILXh4X9xEqxigGwjkRCRKC3U8FPbC8GonVJk6bQaNwIbX2rXrWzRvCi8XK17s8ScGui82HsMvKaakoBJ8c9LU8hXKYVtjwGP98cQAO4x4DQU3znjWP2XyBHqoQtxQ4iFOZs+Z76qDiCperBjSqMcjyCNBxsfYcRMmIssgpWJtdlmR5bGliOUWLyQiQujpEPYuUTyuX2c3C5yLB0RVqlSaPiP7/gecJ8Lde9y1d89eLAZvLZnXq9e9KAggBWsGfmMdN86D/u9xvAmLHNqmdYv5c2dg8xRRCgIYBym4YYN62TMmde7cHnkTyCVLl2VUrIAH5Uiy7jWCSvyB3v3w7hfegrihcwe8LNm7z0MgdgkAk3kBhB0ygBA7gka0umITAQWM+shOgRB4SSxhDJCDnBb0kgVvXRYWQMujgbLlDF1YiSYFqC14TinIjXOkFSt9+JB8FxojkVp5S+MlpmCVkthUcnoZPoie493Nrzj6uDRY+X/YsQM3Ly6GACQpBCKuJQ/ePcVjFvxNBDwydjEeABm5fIXyW7Z8g11C6kIVgJ0jZEycYnHGHa5nOMIdey7fbt3msvJj3K4QAFIOHfoV7wYRjVUWuipVykCdu2/fPg+ryKl56D2n+MOa+/aSWbSbCxcpJgJAI9QgjiGYehLHR/6akxKpfyIHoULnOUj3KQhl13fbvkOppRAp6ell8QzEbzeXwA94PAKCIOP7x3IMwvX3Y7/T3QmeHA4a+MS9PXuvW2/PjHwgh1FvFitWbOu3W93bW+y/460vPFPGMg9K1JLp6WW3bt2GktAdCFtdeOEFWzZjl0nURjgQnOcWSbO+KA499+zZQ/sVRBy/Rfwc3L+fJ4nYY/1BA4zKNsTFShImzDrA0MhDkcBf5w5Lix6uXMVE4OTSojG/3JOTYQGZFn8SnJhnCxcp6k2LrJdTJg0nkRZPxnT/AB5Ytxo0yMRGDd6vaNf+xj9Ag1MpktKikIDCT6RgGSgEi9aIKx0tpkoUXIQzA028z42q0dYKZmFHMFOm1RlhD1s93ClnObU0JkYHAIJ5i66k8Ypx7s8/ygL4ag79u8/6NwRS6DfinTojaf+GxAl1/VHTPr1ysV3YvWsXbBTec+8Dp1fyKZcm4kfdXcmcCInStRQ6TivVcIJB5iqKNJYjOUKHociF+C9bwdeEVTYKyrtyqJIhhqpfljHgEwlUi/hmqzPkn7MXk+CvIp0kGK+SqV8NPOWeyxUQYgH8nj888duv+A1ZZ7GnBT7/2QVSzvjLsaNHRDCdghhAVOnLKUS/3K4/twUQP/gzxuKXET0BRJUaWjoojAISmBNjutdSG3oZqXPvT1euj6nMi0Quq1oEukQl8Bd0kLbyFyj0+xGxQeNkfIg5mbCzf0dq5rZ/lAXwhS2Hfz2gMpSOKeTK1PwF8L2pp8b7Iiv+UVPOlXsSLYD4QU7EEkc8VfaLlKAjTSdDd5C1k6e5MGHWwU7NSOM8FAmkxRPHj+HdQHzpCm6mwAVcRW6VM8g5jEuOHkORlrzNxZM1To8dsNRjtwR/YFl6l2SKh3/CzyeOowrAQ6rjx48plSwxIEhtsRGOx/J6eiZImvM2Vy5Z46TYQdxqHD8hv/FCcnUiiDKF21J8yJZiRbQibkRKMWAZYLJTJBmwFK3lH1ASK1rfRwwQjNHiv9MSK0kt2AoC0UoFEkmLKSkoFc/Klx9D3QtDTv0kNEiLJ4FLLoscWABbQvnkm8+/HjxAtxJmK1gj3POelYrIyQ2AHFj6/+dQih9x+5zwV0UjoFThh7QkUoHIUaJRaDscZkg9GHkQO4eytbPxC0lgb1HqINTFNwXnzZf/t8OHjot3AMEzp0duTsypBXM2HgGdN18+tEeOHD588CBFprdlUYq7JAxAAJw4hr/aluMAECxyzCRnFsgdnRMLuPGDVVPkRG/o8IcPRsYjoTqBsRjzpSontZmZMp7WEQIs3QmkRSRxMV+p+pniqzHzn5kX3wmbwLPseJPIpTrdFsCuyO+I6COHbe/HWIIG+uHbAfOehV8bz5v7vVSn21t/PnnYhkYIIYBQJ/nSmZMxYmttjzenzCMuVhIu2CvMOsAg8lAkkBYlG89wYk0aGWISnQYrdKncDSt6g6WaOhhnTHO9mgUsT8bAkBPGU5fniU1d2M54COY8vpPRZO0I0SdOl13nwJGM3NXTWSDZMsli0r1h0X4MZB63w6pEjnxHHEl+ErGnFWeq6RDgTuXG0sOSgwKFuewcH1FNpcKHq8BhGsWuXit/l3dSAClEptEBFMt3po+c9Cq1tcQYv00OUzSYKROQyPdEM/MFyT1JZrWy4aFGMyAPm7rEmrVRiFuLc5qrmnFCTE19YvnfO8RybjOJ1Fz+JS/s9tIzK+fJlVTcZh4LZxvKJs1GZ8FZh2rfCdOTtmI1cHZ8wpZA0xNCoPKKR7hVsDI/0VpJ/AI045ABmsiFQqi5EA0rrzkroZOxlM5gl8xh1SIeIzbUls2sychcC+LJ8VExwZofs4M2mXI/YYgRhzVrO08r1s8+6WpRySc5dBYgU5vYO9MIQ4fNWskP+6lXJypsfOVNMgL0JFmVoZ3pN7EfI8RqNh5YLfFs8QqbY6y+QGHuaO0jbioOk5p2E1qXFpd3UoBdIY+t1EUSLIFmTv2mFWLFnmYczIg56tT5zjsDI/aUWF4JcpgG27OHPRD1vJOBErzuTL8wcyr3Ss0t/lK6BcxMddunyC9LQamMKOUn8iTafkk403B1UIB1sqoz/k8LGzUB7nmCg9la2BhGp1kYlzeV5V6WxIiwkUztgz3DvESR56YKLFxU9eE3DDcPhyNlJZyHOEfPRHUg2jwYIEkPsvgrB7EXqJsSwyciYOsAL5F7bvrI0TNgLtKDynd/wsqRTz2hSp+sQbN2LRMMhFrY2skNGibMOtiZFo3zUCRdLUZN1iOHT0ANNUnY5R31QD3Ysp4eJsFbfQTEudLNwyjgVPOn1Vsy5VEUDQvOKkvIq4dO6OI0OoKu1wDdgtBaZ2YCk5iReM3Gl0YOC16GB5W2iRnU1MM98ykk1GLqc9gdFAp4tOU7UyYsPEiHfSZMNdPZp893pAIpaS/UyFNefzkjpKc47JkSdSXb2hVivjMZG3MxY4g52Yg0Xr7YfcRFeCanmRr+SqRalHPhInywVaaPyoqIZQ9DdWU0g51VAz13NYaZzzGxpcdlHMmUKBlTdyjzP9eC9QeDgSahyoLSkKoy1N4ibdkJsQlWhVwP64Q5QTBsHSqm7iqkH8VwiwTBJIl6g6Wixyo4AM2FhQkI4Mn0IApCWKm5KAGTGZwByiZGec90ZoJig1YtgkcHTMB1l1RN7VwH7AUT9xg+ClKD6UyCaTFi6CD3BnFkRkws3mgWSVSLpiahZqXJaBNzZQWcg6xvasHOmC29gu3LJRsbDbKqMDFzC9ZMNRusup18xi+oaMVCKRRno8QyRuho5JUFh5n6NJJdBtH8DWFxTrTvQpWIYmXTzBJ10RUHMSJxNqas0vd0R6kY0a+5setFp1fhBWt2ZWYLlaD5+zwcOs7TSWwIaVfIYG+QmyUJj3sGxyhfPCq5p9YpataW7kSqRTI0m53mrDQgjDyzCFNUifwMtJ8tFoIZB7IxXGK5YFS/jbV1imFGsQ6wcY6NU2laDPA8j+b1I8+0DhxXxEnS2cLG9KCZsi3kyhPcwCzenPFyWtbR5oStJJy1SR4a9V5Sz3mkKCHWdgdgmCRMN49A99Qq2e0NBWgokTA2em9R/rauE2OOdcLeKAgVxjuZMF07KPP79NEkYfYJ4MnEeiiSqBb98gnDhAAkObGnwVbC6CqMuNqkmjoYZ2ziejVTerJOoXncwzPM0YiwxMNKwS0oYJUf6DJgKczaEVe5ADqrRgG0QDNyV0/S0GkNEpBbl5Zg/rF6rEp4BfO8GMmVOBKZVWevj1RS9nJmqnFbaadyY3kHJ3oeKMxl5PiItFXhw1XgMI1yolacWPm7vJMCSCHirAMolijTR3yNsMQY3xEOUzSYKROQ+96iuoxV2PsvB+VCMrY1cvggr0+sA7xEkec2LjI4vHuL6ttL1SURydlGYJNmo7PgrEOFfUghYWZdcQTsVXFzcpjEEcYn2ir4VPguQL4SZdWDz0LDynd/wqfPtmxmTUbeVOrzSxyE1WYxDBoWEHaeVqz2iMoCSVeLarYkh84CZGoTe2caYeiwWSv5YT/16sQ2WrgNkhGgJ8mqDG9wcCF+WIjVbDywm8/4uLBZxugLFOaO1T7ipuIwqUkYprLk4PhRBRVp7vJOCrArxATHMw/NnDQwrRAr9rTqwYx4laGec8VTTnMPgeyCjdhTIcMrQQ4T9wCvmFZxJhOiT4yuBK87qwbcghK2+EupEjAz1R18ramAFZTKiNICOdpbJLkBSlkn62oaG7CwURPgnic4mKuFjbIJc4BxefNteM2YGNF5JFM9LGSAlyjy3FSBhYuqPvyG4ebhcKSshPMQ5xhoH5sHAyQx77D8Y/qOy5SwVbBJZSXhwkzyAO28RO656aNAzaVA6UHluz9h5cjTSkKVPlmDjOpaJhiwukSRWzv9/rILsw52pkUCPBRJV4t2+WoSAVFkTsNUhV3e6saWk3NYS4mAmARv9RGqYQRbt1vzp9VbMuVRFA0LXipLyKuHTui6NzpUKnClJwdonZkJTFaMxGs2Xi1yWPAyPKi0jYoTU3LAmU8hoRZTn8MBLDxoj7Z8Z8qEhQfpsM+EqWY6+/T5jlQgJe2Fmr/ql8aTQ4zRptfsM3bMEfOHXSHmO5NPpDZSJyPSePkSrXE8fyVSLWpTmlPRZ1aZujsMimWP6FCzakDG8rdSH8fEZkCYmkYyJXK7T6yDTf4BZ4EmocqC0pCqMvhzZ0oZCVaFXInkdTZTAzeq9h1LScHkamiYabnKdk7s8vO7n/uLw5pttB0CfaT094olM5CIBFOG1isMitbZGB0wAVO13PcWDZv5T2BF8wg1K8WqNrE3SnOQ9U0t2BkLC69gb3nDRsUFWVUY/cScXxJCAFPNBqtuJ595R8dV0UKnOBsllkFHpvL6K6zKcHhJLtH8DWFxTrTvQpWIYmXTzBJ10RUHMSJxNqbSHDr1M2dHqRjRr4UZy4lKryJKlFgKHH8bKkHzz5HOhn2sChnsDfKgJcTAxyhfguZpnaK+vCzd/wMLt8E4PjuslgAAAABJRU5ErkJggg==)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e42981fe",
   "metadata": {
    "id": "h_5mEE7u2Ih8"
   },
   "outputs": [],
   "source": [
    "# input (text) --> model (text2vec) -->  vec\n",
    "# vec --> 1000K vecs --> 3 vecotrs similar --> article\n",
    "\n",
    "# Evaluation\n",
    "# model1 (text2vec)\n",
    "# model2 (text2vec)\n",
    "# model3 (text2vec)\n",
    "\n",
    "# --> Optimal Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "412426fc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D3mksPCc6S9G",
    "outputId": "39b3dd3e-8d64-41d9-cd90-0173cb1666af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.0/44.0 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m512.3/512.3 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.0/12.0 MB\u001b[0m \u001b[31m55.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m94.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.4/16.4 MB\u001b[0m \u001b[31m57.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m118.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 MB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\n",
      "tensorflow 2.19.0 requires numpy<2.2.0,>=1.26.0, but you have numpy 2.4.1 which is incompatible.\n",
      "opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.4.1 which is incompatible.\n",
      "gradio 5.50.0 requires pillow<12.0,>=8.0, but you have pillow 12.1.0 which is incompatible.\n",
      "opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.4.1 which is incompatible.\n",
      "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.4.1 which is incompatible.\n",
      "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.4.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip -q install -U datasets transformers sentence-transformers pillow numpy pandas scikit-learn tqdm pyarrow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39dae63d",
   "metadata": {
    "id": "Cbzxuw_rU44g"
   },
   "source": [
    "Installed all libraries needed for loading the HF dataset, generating embeddings, evaluating similarity, and saving files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50430f60",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361,
     "referenced_widgets": [
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      "text/plain": [
       "README.md:   0%|          | 0.00/902 [00:00<?, ?B/s]"
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     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
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       "Resolving data files:   0%|          | 0/121 [00:00<?, ?it/s]"
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       "Downloading data:   0%|          | 0/121 [00:00<?, ?files/s]"
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      "text/plain": [
       "Generating train split:   0%|          | 0/120 [00:00<?, ? examples/s]"
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     "text": [
      "HF dataset: Barvero/adautogen-multimodal\n",
      "Shape: (120, 7)\n",
      "Columns: ['image', 'id', 'product_name', 'category', 'tagline', 'ad_copy', 'target_audience']\n"
     ]
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
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       "\n",
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       "\n",
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      ],
      "text/plain": [
       "                                               image  id        product_name  \\\n",
       "0  {'bytes': None, 'path': '/root/.cache/huggingf...   0  Bloom Matcha Drink   \n",
       "1  {'bytes': None, 'path': '/root/.cache/huggingf...   1    resistance bands   \n",
       "\n",
       "  category                   tagline  \\\n",
       "0     Food  Lightweight Matcha Drink   \n",
       "1  Fitness              Long-lasting   \n",
       "\n",
       "                                             ad_copy target_audience  \n",
       "0  Meet Bloom matcha drink, designed for remote w...  remote workers  \n",
       "1    Get fit with our long-lasting resistance bands!        students  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "\n",
    "DS_NAME = \"Barvero/adautogen-multimodal\"\n",
    "ds = load_dataset(DS_NAME, split=\"train\")  # HF only\n",
    "df = ds.to_pandas()\n",
    "\n",
    "print(\"HF dataset:\", DS_NAME)\n",
    "print(\"Shape:\", df.shape)\n",
    "print(\"Columns:\", list(df.columns))\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e144cdbf",
   "metadata": {
    "id": "vOllBXPBVDic"
   },
   "source": [
    "Loaded the latest dataset directly from Hugging Face and converted it into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b653c7ad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "id": "HpH2S1SQU6qg",
    "outputId": "be2a2d78-09af-4ae8-fd5d-99e1074a23b1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
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       "      <td>Product: Bloom Matcha Drink | Category: Food |...</td>\n",
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       "      <td>Product: resistance bands | Category: Fitness ...</td>\n",
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       "      <td>Product: Bloom Bluetooth Speaker | Category: T...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> object</label>"
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       "0    Product: Bloom Matcha Drink | Category: Food |...\n",
       "1    Product: resistance bands | Category: Fitness ...\n",
       "2    Product: Bloom Bluetooth Speaker | Category: T...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build text input for text2vec\n",
    "df[\"text\"] = (\n",
    "    \"Product: \" + df[\"product_name\"].astype(str)\n",
    "    + \" | Category: \" + df[\"category\"].astype(str)\n",
    "    + \" | Tagline: \" + df[\"tagline\"].astype(str)\n",
    "    + \" | Ad: \" + df[\"ad_copy\"].astype(str)\n",
    "    + \" | Audience: \" + df[\"target_audience\"].astype(str)\n",
    ")\n",
    "\n",
    "df[\"text\"].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e465a017",
   "metadata": {
    "id": "_r3_AtBbU74K"
   },
   "source": [
    "Created one combined text string per item to represent the marketing content for text embedding models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae051b50",
   "metadata": {
    "id": "qWMxS8NCU6hr"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def match_at_k(emb, labels, k=5):\n",
    "    # Similarity matrix\n",
    "    sim = cosine_similarity(emb)\n",
    "    np.fill_diagonal(sim, -1)\n",
    "\n",
    "    # Top-k neighbors\n",
    "    topk = np.argsort(-sim, axis=1)[:, :k]\n",
    "\n",
    "    labels = np.array(labels)\n",
    "    hits = [(labels[i] == labels[topk[i]]).mean() for i in range(len(labels))]\n",
    "    return float(np.mean(hits))\n",
    "\n",
    "def evaluate_embeddings(emb, df, k=5):\n",
    "    # Evaluate category + audience retrieval\n",
    "    cat = match_at_k(emb, df[\"category\"].tolist(), k=k)\n",
    "    aud = match_at_k(emb, df[\"target_audience\"].tolist(), k=k)\n",
    "    return cat, aud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a84ead",
   "metadata": {
    "id": "DaKJ7hn2U8cf"
   },
   "source": [
    "Defined a simple retrieval-based evaluation: how often the Top-K similar items share the same category or audience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "text": [
      "Model: sentence-transformers/all-MiniLM-L6-v2\n",
      "Shape: (120, 384) Time(s): 0.44\n",
      "Category match@5: 0.838 Audience match@5: 0.128\n"
     ]
    }
   ],
   "source": [
    "import time, os\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "def embed_text_model(model_id, texts, batch_size=32):\n",
    "    model = SentenceTransformer(model_id)\n",
    "    t0 = time.time()\n",
    "    emb = model.encode(texts, batch_size=batch_size, show_progress_bar=True, normalize_embeddings=True)\n",
    "    return np.array(emb), time.time() - t0\n",
    "\n",
    "texts = df[\"text\"].tolist()\n",
    "\n",
    "MODEL_TEXT_1 = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
    "emb_text_1, t_text_1 = embed_text_model(MODEL_TEXT_1, texts)\n",
    "\n",
    "cat1, aud1 = evaluate_embeddings(emb_text_1, df, k=5)\n",
    "print(\"Model:\", MODEL_TEXT_1)\n",
    "print(\"Shape:\", emb_text_1.shape, \"Time(s):\", round(t_text_1, 2))\n",
    "print(\"Category match@5:\", round(cat1, 3), \"Audience match@5:\", round(aud1, 3))"
   ]
  },
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   "source": [
    "Generated normalized text embeddings using a fast baseline model (MiniLM) and evaluated Top-5 retrieval quality.\n"
   ]
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     "text": [
      "Model: intfloat/e5-small-v2\n",
      "Shape: (120, 384) Time(s): 0.27\n",
      "Category match@5: 0.705 Audience match@5: 0.34\n"
     ]
    }
   ],
   "source": [
    "MODEL_TEXT_2 = \"intfloat/e5-small-v2\"\n",
    "emb_text_2, t_text_2 = embed_text_model(MODEL_TEXT_2, texts)\n",
    "\n",
    "cat2, aud2 = evaluate_embeddings(emb_text_2, df, k=5)\n",
    "print(\"Model:\", MODEL_TEXT_2)\n",
    "print(\"Shape:\", emb_text_2.shape, \"Time(s):\", round(t_text_2, 2))\n",
    "print(\"Category match@5:\", round(cat2, 3), \"Audience match@5:\", round(aud2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3fa785",
   "metadata": {
    "id": "rkA_6BwGV2pc"
   },
   "source": [
    "Generated text embeddings with a retrieval-focused model (E5) and evaluated it using the same Top-5 metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebe69bfc",
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     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
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      "text/plain": [
       "Resolving data files:   0%|          | 0/121 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
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     "text": [
      "HF dataset: Barvero/adautogen-multimodal\n",
      "CLIP model: openai/clip-vit-base-patch32\n",
      "Embedding shape: (120, 512) | Time(s): 1.95\n",
      "Category match@5: 0.678 | Audience match@5: 0.137\n"
     ]
    }
   ],
   "source": [
    "# CLIP image embeddings (HF dataset only)\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "from datasets import load_dataset, Image as HFImage\n",
    "from transformers import CLIPProcessor, CLIPModel\n",
    "\n",
    "# Load from HF only + decode images to PIL\n",
    "DS_NAME = \"Barvero/adautogen-multimodal\"\n",
    "ds = load_dataset(DS_NAME, split=\"train\")                 # HF only\n",
    "ds = ds.cast_column(\"image\", HFImage(decode=True))         # PIL decode\n",
    "\n",
    "df = ds.to_pandas()\n",
    "images = list(ds[\"image\"])                                 # list of PIL images\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "MODEL_CLIP = \"openai/clip-vit-base-patch32\"\n",
    "clip_model = CLIPModel.from_pretrained(MODEL_CLIP).to(device)\n",
    "clip_proc = CLIPProcessor.from_pretrained(MODEL_CLIP)\n",
    "\n",
    "def embed_clip_images(images, batch_size=16):\n",
    "    all_emb = []\n",
    "    t0 = time.time()\n",
    "\n",
    "    for i in range(0, len(images), batch_size):\n",
    "        batch = images[i:i+batch_size]\n",
    "        inputs = clip_proc(images=batch, return_tensors=\"pt\").to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            feats = clip_model.get_image_features(**inputs)\n",
    "\n",
    "        feats = feats / feats.norm(dim=-1, keepdim=True)   # normalize\n",
    "        all_emb.append(feats.cpu().numpy())\n",
    "\n",
    "    return np.vstack(all_emb), time.time() - t0\n",
    "\n",
    "emb_img_clip, t_clip = embed_clip_images(images, batch_size=16)\n",
    "\n",
    "# Simple retrieval eval (Top-5)\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def match_at_k(emb, labels, k=5):\n",
    "    sim = cosine_similarity(emb)\n",
    "    np.fill_diagonal(sim, -1)\n",
    "    topk = np.argsort(-sim, axis=1)[:, :k]\n",
    "    labels = np.array(labels)\n",
    "    hits = [(labels[i] == labels[topk[i]]).mean() for i in range(len(labels))]\n",
    "    return float(np.mean(hits))\n",
    "\n",
    "cat3 = match_at_k(emb_img_clip, df[\"category\"].tolist(), k=5)\n",
    "aud3 = match_at_k(emb_img_clip, df[\"target_audience\"].tolist(), k=5)\n",
    "\n",
    "print(\"HF dataset:\", DS_NAME)\n",
    "print(\"CLIP model:\", MODEL_CLIP)\n",
    "print(\"Embedding shape:\", emb_img_clip.shape, \"| Time(s):\", round(t_clip, 2))\n",
    "print(\"Category match@5:\", round(cat3, 3), \"| Audience match@5:\", round(aud3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d70c26e",
   "metadata": {
    "id": "8VdiNaR3V9gJ"
   },
   "source": [
    "Loaded the dataset directly from Hugging Face, decoded images into PIL format, generated CLIP image embeddings, and evaluated Top-5 similarity matches."
   ]
  },
  {
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   "execution_count": null,
   "id": "17191889",
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    },
    "id": "viL2xs3zWGa4",
    "outputId": "8ac33400-026a-43de-923e-343728c0436f"
   },
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      ],
      "text/plain": [
       "                                    model       type  dim  time_s  \\\n",
       "0  sentence-transformers/all-MiniLM-L6-v2   text2vec  384    0.44   \n",
       "1                    intfloat/e5-small-v2   text2vec  384    0.27   \n",
       "2            openai/clip-vit-base-patch32  image2vec  512    1.95   \n",
       "\n",
       "   cat_match@5  aud_match@5  \n",
       "0        0.838        0.128  \n",
       "1        0.705        0.340  \n",
       "2        0.678        0.137  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "rows = [\n",
    "    {\"model\": MODEL_TEXT_1, \"type\": \"text2vec\", \"dim\": emb_text_1.shape[1], \"time_s\": round(t_text_1,2),\n",
    "     \"cat_match@5\": round(cat1,3), \"aud_match@5\": round(aud1,3)},\n",
    "    {\"model\": MODEL_TEXT_2, \"type\": \"text2vec\", \"dim\": emb_text_2.shape[1], \"time_s\": round(t_text_2,2),\n",
    "     \"cat_match@5\": round(cat2,3), \"aud_match@5\": round(aud2,3)},\n",
    "    {\"model\": MODEL_CLIP, \"type\": \"image2vec\", \"dim\": emb_img_clip.shape[1], \"time_s\": round(t_clip,2),\n",
    "     \"cat_match@5\": round(cat3,3), \"aud_match@5\": round(aud3,3)},\n",
    "]\n",
    "\n",
    "eval_df = pd.DataFrame(rows).sort_values(by=[\"cat_match@5\", \"aud_match@5\", \"time_s\"], ascending=[False, False, True])\n",
    "eval_df"
   ]
  },
  {
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   "execution_count": null,
   "id": "fc14ab37",
   "metadata": {
    "colab": {
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    },
    "id": "6od8MqjhWJFr",
    "outputId": "513c58d6-e8cf-47d5-e527-9f9f3f0d3918"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winner: sentence-transformers/all-MiniLM-L6-v2 | Type: text2vec\n"
     ]
    }
   ],
   "source": [
    "winner = eval_df.iloc[0][\"model\"]\n",
    "winner_type = eval_df.iloc[0][\"type\"]\n",
    "print(\"Winner:\", winner, \"| Type:\", winner_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6392efbc",
   "metadata": {
    "id": "8lRuNQOMWK0H"
   },
   "source": [
    "Compared the 3 models on quality (match@5), speed, and embedding size, then selected the best one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7a5bd3a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fS3m8WrgWND5",
    "outputId": "bd559420-2956-4430-f550-85ea820e3ca0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved files: ['metadata.parquet', 'emb_text_minilm.npy', 'emb_img_clip.npy', 'embedding_eval.csv', 'emb_text_e5.npy']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "out_dir = \"embeddings_out\"\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "# Save all embeddings (recommended)\n",
    "np.save(os.path.join(out_dir, \"emb_text_minilm.npy\"), emb_text_1)\n",
    "np.save(os.path.join(out_dir, \"emb_text_e5.npy\"), emb_text_2)\n",
    "np.save(os.path.join(out_dir, \"emb_img_clip.npy\"), emb_img_clip)\n",
    "\n",
    "# Save metadata needed for retrieval UI\n",
    "meta_cols = [\"id\",\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\",\"image\"]\n",
    "df[meta_cols].to_parquet(os.path.join(out_dir, \"metadata.parquet\"), index=False)\n",
    "\n",
    "# Save evaluation summary\n",
    "eval_df.to_csv(os.path.join(out_dir, \"embedding_eval.csv\"), index=False)\n",
    "\n",
    "print(\"Saved files:\", os.listdir(out_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb21586",
   "metadata": {
    "id": "UtrV_HDVWSuh"
   },
   "source": [
    "Saved all embeddings, metadata, and evaluation results into files that can be uploaded to your HF Space repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6b74b58",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "2ZLfpLheZCFE",
    "outputId": "8e544749-e360-4bf6-e518-91363e66d6e4"
   },
   "outputs": [
    {
     "data": {
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       "summary": "{\n  \"name\": \"eval_df[[\\\"model\\\",\\\"type\\\",\\\"dim\\\",\\\"time_s\\\",\\\"cat_match@5\\\",\\\"aud_match@5\\\",\\\"score\\\"]]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"sentence-transformers/all-MiniLM-L6-v2\",\n          \"intfloat/e5-small-v2\",\n          \"openai/clip-vit-base-patch32\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"image2vec\",\n          \"text2vec\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dim\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73,\n        \"min\": 384,\n        \"max\": 512,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          512,\n          384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.924788264054715,\n        \"min\": 0.27,\n        \"max\": 1.95,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.44,\n          0.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cat_match@5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08565239829294524,\n        \"min\": 0.678,\n        \"max\": 0.838,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.838,\n          0.705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aud_match@5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11988466679827466,\n        \"min\": 0.128,\n        \"max\": 0.34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.128,\n          0.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05654611687227801,\n        \"min\": 0.5157,\n        \"max\": 0.6249999999999999,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6249999999999999,\n          0.5954999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>type</th>\n",
       "      <th>dim</th>\n",
       "      <th>time_s</th>\n",
       "      <th>cat_match@5</th>\n",
       "      <th>aud_match@5</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
       "      <td>text2vec</td>\n",
       "      <td>384</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>intfloat/e5-small-v2</td>\n",
       "      <td>text2vec</td>\n",
       "      <td>384</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.5955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>openai/clip-vit-base-patch32</td>\n",
       "      <td>image2vec</td>\n",
       "      <td>512</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.5157</td>\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
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       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                    model       type  dim  time_s  \\\n",
       "0  sentence-transformers/all-MiniLM-L6-v2   text2vec  384    0.44   \n",
       "1                    intfloat/e5-small-v2   text2vec  384    0.27   \n",
       "2            openai/clip-vit-base-patch32  image2vec  512    1.95   \n",
       "\n",
       "   cat_match@5  aud_match@5   score  \n",
       "0        0.838        0.128  0.6250  \n",
       "1        0.705        0.340  0.5955  \n",
       "2        0.678        0.137  0.5157  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Weighted winner (better decision)\n",
    "eval_df[\"score\"] = 0.7*eval_df[\"cat_match@5\"] + 0.3*eval_df[\"aud_match@5\"]\n",
    "eval_df = eval_df.sort_values(by=[\"score\",\"time_s\"], ascending=[False, True])\n",
    "eval_df[[\"model\",\"type\",\"dim\",\"time_s\",\"cat_match@5\",\"aud_match@5\",\"score\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10d15766",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4yC6hOMDZEDs",
    "outputId": "c2af3be8-07e2-4ecd-ee45-932ab0b63643"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted Winner: sentence-transformers/all-MiniLM-L6-v2 | Type: text2vec\n"
     ]
    }
   ],
   "source": [
    "winner = eval_df.iloc[0][\"model\"]\n",
    "winner_type = eval_df.iloc[0][\"type\"]\n",
    "print(\"Weighted Winner:\", winner, \"| Type:\", winner_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b29fe2",
   "metadata": {
    "id": "l5AfpZXCZWgy"
   },
   "source": [
    "Based on the weighted evaluation score, the MiniLM text embedding model was selected as the optimal model, providing the best balance between retrieval quality, efficiency, and practical usability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "154234a2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "-HC8gHZ2ZaA_",
    "outputId": "ee745adc-4680-4a1d-c521-36e0587d8dce"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"get_top_k_similar(0, k=3)\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Bloom Iced Coffee Can\",\n          \"Lumen Matcha Drink\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Food\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Long-Lasting Matcha Drink\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ad_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Meet Lumen matcha drink, designed for young professionals.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_audience\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"young professionals\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
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       "      <th>tagline</th>\n",
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       "      <th>target_audience</th>\n",
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       "  <tbody>\n",
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       "      <th>110</th>\n",
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       "      <td>Long-Lasting Matcha Drink</td>\n",
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       "      <th>90</th>\n",
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       "      <td>Lightweight Iced Coffee Can</td>\n",
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       "      <td>Eco-Friendly Iced Coffee Can</td>\n",
       "      <td>Meet Bloom iced coffee can, designed for stude...</td>\n",
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       "                                                    [key], {});\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
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      ],
      "text/plain": [
       "              product_name category                       tagline  \\\n",
       "110     Lumen Matcha Drink     Food     Long-Lasting Matcha Drink   \n",
       "90   Bloom Iced Coffee Can     Food   Lightweight Iced Coffee Can   \n",
       "41   Bloom Iced Coffee Can     Food  Eco-Friendly Iced Coffee Can   \n",
       "\n",
       "                                               ad_copy      target_audience  \n",
       "110  Meet Lumen matcha drink, designed for young pr...  young professionals  \n",
       "90   Meet Bloom iced coffee can, designed for remot...       remote workers  \n",
       "41   Meet Bloom iced coffee can, designed for stude...             students  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Top-3 similar items using the winner embedding model\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# Load saved embeddings and metadata\n",
    "emb = np.load(\"embeddings_out/emb_text_minilm.npy\")\n",
    "meta = pd.read_parquet(\"embeddings_out/metadata.parquet\")\n",
    "\n",
    "def get_top_k_similar(index, k=3):\n",
    "    query_vec = emb[index].reshape(1, -1)\n",
    "    sims = cosine_similarity(query_vec, emb)[0]\n",
    "\n",
    "    sims[index] = -1  # exclude itself\n",
    "    top_idx = np.argsort(-sims)[:k]\n",
    "\n",
    "    return meta.iloc[top_idx][\n",
    "        [\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\"]\n",
    "    ]\n",
    "\n",
    "# Example: get similar items to item 0\n",
    "get_top_k_similar(0, k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677ab3e6",
   "metadata": {
    "id": "lkRgmFsiZaw7"
   },
   "source": [
    "Retrieved the top-3 most similar products based on cosine similarity between embedding vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f24dd1e0",
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     "height": 143
    },
    "id": "fCPzDn44ZnLU",
    "outputId": "3e646b89-4916-4376-d7fb-0b4260990534"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>product_name</th>\n",
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      "text/plain": [
       "              product_name category                        tagline  \\\n",
       "10  Pulse Portable Charger     Tech       Compact Portable Charger   \n",
       "77  Pulse Portable Charger     Tech  Eco-Friendly Portable Charger   \n",
       "75  Bloom Portable Charger     Tech  Long-Lasting Portable Charger   \n",
       "\n",
       "                                              ad_copy      target_audience  \n",
       "10  Meet Pulse portable charger, designed for stud...             students  \n",
       "77  Meet Pulse portable charger, designed for youn...  young professionals  \n",
       "75  Meet Bloom portable charger, designed for busy...         busy parents  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "model = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
    "\n",
    "def search_by_text(query, k=3):\n",
    "    q_emb = model.encode([query], normalize_embeddings=True)\n",
    "    sims = cosine_similarity(q_emb, emb)[0]\n",
    "    top_idx = np.argsort(-sims)[:k]\n",
    "\n",
    "    return meta.iloc[top_idx][\n",
    "        [\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\"]\n",
    "    ]\n",
    "\n",
    "search_by_text(\"portable charger for students\", k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cfc154d",
   "metadata": {
    "id": "ewLbkKWLZynO"
   },
   "source": [
    "The query-based retrieval returned relevant recommendations: the top results are portable chargers in the Tech category, and the first match also targets students, showing that the embedding-based similarity search works as intended."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0a6939",
   "metadata": {
    "id": "ZGxkOOXKW-lw"
   },
   "source": [
    "In this stage, we generated embeddings using three Hugging Face models: two text models (MiniLM, E5) and one image model (CLIP), all based on our Hugging Face dataset.\n",
    "\n",
    "The models were evaluated using a retrieval-based metric measuring Top-5 similarity by product category and target audience, along with runtime and embedding size.\n",
    "\n",
    "Based on a weighted comparison, MiniLM was selected as the final embedding model for the recommendation system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d611cdc",
   "metadata": {
    "id": "xoGKMtcO6TdN"
   },
   "source": [
    "<br><br>\n",
    "\n",
    "---\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a969ca0",
   "metadata": {
    "id": "5JKjxJ7duKzN"
   },
   "source": [
    "# Part 4: Input-Output Pipeline\n",
    "\n",
    "**USER INPUT --> PIPELINE --> AI OUTPUT**\n",
    "\n",
    "Build an IO pipeline - from user input to AI output:\n",
    "\n",
    "- Classification\n",
    "- Prediction\n",
    "- Recommendion\n",
    "- Style transfer  \n",
    "etc.\n",
    "\n",
    "\n",
    "For example, in the healthcare domain:\n",
    "- User uploads a skin mole image, and gets the top 10 most similar skin moles in the dataset.\n",
    "- User writes symptoms/disease description and gets the probability of the posibble disease.\n",
    "- User uploads a sound of a Coughs and gets the probability of the conditions.\n",
    "- User uploads a CT image and gets an MRI image in return.\n",
    "- Doctor A writes a medical report and choose doctor B from a list, and gets the report in doctor B's style.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab93925",
   "metadata": {
    "id": "20mKc0zE1H5a"
   },
   "source": [
    "Example of how an IO Recommendation Pipeline would work:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d847faee",
   "metadata": {
    "id": "GEn2a0o9DeZT"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67ddd653",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248,
     "referenced_widgets": [
      "7e2250272800498fb4209796e509536b",
      "21be431abbf5472193c3921be9992140",
      "f5d312dc45b045f18a3e637dcac0c0ad",
      "b94380b702e14ed2b4ed37328a062d1b",
      "edc346f73661489ea4ef81fddd9c0b02",
      "d884adb4b3ca45e5ae3297ff84bb11b7",
      "3f4d12b241174214b3bb62b69faac7d7",
      "f46539d6146e46ad9b74e9c3096261b4",
      "907d5e49b93f4e4aaba99a03a02fef9d",
      "7c1791394e21483fad8679123ffb002b",
      "59c60f9d435540aaabb902a08eae99cc"
     ]
    },
    "id": "ecnHQRQZ2UgX",
    "outputId": "6836b1d4-b2c6-482e-d4ed-f92f5625771f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e2250272800498fb4209796e509536b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/121 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HF dataset: Barvero/adautogen-multimodal\n",
      "Rows: 120 | Emb shape: (120, 384)\n"
     ]
    },
    {
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       "summary": "{\n  \"name\": \"meta\",\n  \"rows\": 120,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 0,\n        \"max\": 119,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          44,\n          47,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 84,\n        \"samples\": [\n          \"Lumen Portable Charger\",\n          \"Bloom Matcha Drink\",\n          \"Pulse Face Cream Jar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Food\",\n          \"Fitness\",\n          \"category: Home\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 86,\n        \"samples\": [\n          \"Long-Lasting Hair Serum Bottle\",\n          \"Lightweight Matcha Drink\",\n          \"Compact Gym Gloves\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ad_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 118,\n        \"samples\": [\n          \"Meet Aero gym gloves, designed for fitness lovers.\",\n          \"Meet Pulse smart watch, designed for young professionals.\",\n          \"Meet Pulse cleanser bottle, designed for busy parents.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_audience\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"remote workers\",\n          \"students\",\n          \"Busy parents\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"image\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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       "\n",
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      ],
      "text/plain": [
       "   id        product_name category                   tagline  \\\n",
       "0   0  Bloom Matcha Drink     Food  Lightweight Matcha Drink   \n",
       "1   1    resistance bands  Fitness              Long-lasting   \n",
       "\n",
       "                                             ad_copy target_audience  \\\n",
       "0  Meet Bloom matcha drink, designed for remote w...  remote workers   \n",
       "1    Get fit with our long-lasting resistance bands!        students   \n",
       "\n",
       "                                               image  \n",
       "0  {'bytes': None, 'path': '/root/.cache/huggingf...  \n",
       "1  {'bytes': None, 'path': '/root/.cache/huggingf...  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load HF dataset + saved embeddings\n",
    "\n",
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DS_NAME = \"Barvero/adautogen-multimodal\"\n",
    "ds = load_dataset(DS_NAME, split=\"train\")  # HF only\n",
    "df = ds.to_pandas()\n",
    "\n",
    "meta = pd.read_parquet(\"embeddings_out/metadata.parquet\")\n",
    "emb_text = np.load(\"embeddings_out/emb_text_minilm.npy\")  # winner embeddings\n",
    "\n",
    "print(\"HF dataset:\", DS_NAME)\n",
    "print(\"Rows:\", len(meta), \"| Emb shape:\", emb_text.shape)\n",
    "meta.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7662a8d8",
   "metadata": {
    "id": "Bao_1a0KblH-"
   },
   "source": [
    "Loaded the dataset directly from Hugging Face and loaded the saved winner embeddings + metadata files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "236f25e8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PrpoeseZ6UhL",
    "outputId": "cfe3cf5b-b9b9-4a50-d983-c2bdf2e6f37b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winner model loaded: sentence-transformers/all-MiniLM-L6-v2\n"
     ]
    }
   ],
   "source": [
    "# Load winner text embedding model\n",
    "\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "MODEL_WINNER = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
    "embedder = SentenceTransformer(MODEL_WINNER)\n",
    "\n",
    "print(\"Winner model loaded:\", MODEL_WINNER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37e5556",
   "metadata": {
    "id": "OltbpZxJblk0"
   },
   "source": [
    "Loaded the selected embedding model to embed new user text queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2342943",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "dRQ5aWKx6Ujn",
    "outputId": "53ddde57-d278-456b-8588-c910e536ac81"
   },
   "outputs": [
    {
     "data": {
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       "      <td>Pulse Portable Charger</td>\n",
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       "      <th>75</th>\n",
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       "      <td>Long-Lasting Portable Charger</td>\n",
       "      <td>Meet Bloom portable charger, designed for busy...</td>\n",
       "      <td>busy parents</td>\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "              product_name category                        tagline  \\\n",
       "10  Pulse Portable Charger     Tech       Compact Portable Charger   \n",
       "77  Pulse Portable Charger     Tech  Eco-Friendly Portable Charger   \n",
       "75  Bloom Portable Charger     Tech  Long-Lasting Portable Charger   \n",
       "\n",
       "                                              ad_copy      target_audience  \\\n",
       "10  Meet Pulse portable charger, designed for stud...             students   \n",
       "77  Meet Pulse portable charger, designed for youn...  young professionals   \n",
       "75  Meet Bloom portable charger, designed for busy...         busy parents   \n",
       "\n",
       "    similarity  \n",
       "10       0.691  \n",
       "77       0.677  \n",
       "75       0.611  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Text query -> embedding -> similarity -> Top-K\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np\n",
    "\n",
    "def recommend_by_text(query, k=3):\n",
    "    # Embed query\n",
    "    q_emb = embedder.encode([query], normalize_embeddings=True)\n",
    "    # Similarity vs dataset\n",
    "    sims = cosine_similarity(q_emb, emb_text)[0]\n",
    "    # Top-K\n",
    "    top_idx = np.argsort(-sims)[:k]\n",
    "    # Results\n",
    "    out = meta.iloc[top_idx][[\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\"]].copy()\n",
    "    out[\"similarity\"] = sims[top_idx].round(3)\n",
    "    return out\n",
    "\n",
    "recommend_by_text(\"portable charger for students\", k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6fa96cc",
   "metadata": {
    "id": "QdrqAiptbmEm"
   },
   "source": [
    "Built the full IO pipeline for text input: embed the query, compute cosine similarity, and return Top-K recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9147ced",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "id": "HTiezYQIbkzH",
    "outputId": "a707fdf3-1531-41f6-d5ce-32a3a616cf0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Query: portable charger for students\n"
     ]
    },
    {
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       "\n",
       "    .colab-df-buttons div {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
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       "  </style>\n",
       "\n",
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       "        document.querySelector('#df-6910919b-e32a-43df-96d9-3b2cb680ddb7 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-6910919b-e32a-43df-96d9-3b2cb680ddb7');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        element.appendChild(docLink);\n",
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       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "             product_name category                      tagline  \\\n",
       "114  Pulse Face Cream Jar   Beauty  Long-Lasting Face Cream Jar   \n",
       "72   Pulse Face Cream Jar   Beauty       Premium Face Cream Jar   \n",
       "109  Lumen Face Cream Jar   Beauty       Premium Face Cream Jar   \n",
       "\n",
       "                                               ad_copy      target_audience  \\\n",
       "114  Meet Pulse face cream jar, designed for young ...  young professionals   \n",
       "72   Meet Pulse face cream jar, designed for fitnes...       fitness lovers   \n",
       "109  Meet Lumen face cream jar, designed for remote...       remote workers   \n",
       "\n",
       "     similarity  \n",
       "114       0.617  \n",
       "72        0.572  \n",
       "109       0.564  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick starters\n",
    "\n",
    "quick_starters = [\n",
    "    \"portable charger for students\",\n",
    "    \"minimalist scented candle for home\",\n",
    "    \"premium face cream for young professionals\"\n",
    "]\n",
    "\n",
    "for q in quick_starters:\n",
    "    print(\"\\nQuery:\", q)\n",
    "    display(recommend_by_text(q, k=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12bc18bc",
   "metadata": {
    "id": "BqOjdY3-bmd1"
   },
   "source": [
    "Added predefined example queries to demonstrate the pipeline with one click (useful for the app demo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11bc832f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101,
     "referenced_widgets": [
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      "4c65e1331bae4237a4813eef49216cdc"
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    },
    "id": "hfrHA8edbkp-",
    "outputId": "b276d3bb-c3d1-4717-b984-63d4ad7a1a76"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a5d857204f24f279d4565ace705185f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/121 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLIP loaded: openai/clip-vit-base-patch32 | Image emb: (120, 512)\n"
     ]
    }
   ],
   "source": [
    "# Image query -> CLIP embedding -> similarity -> Top-K\n",
    "\n",
    "import torch\n",
    "from transformers import CLIPModel, CLIPProcessor\n",
    "from datasets import Image as HFImage\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "# HF only + decode images to PIL (for example inputs)\n",
    "ds_img = load_dataset(DS_NAME, split=\"train\")\n",
    "ds_img = ds_img.cast_column(\"image\", HFImage(decode=True))\n",
    "images_pil = list(ds_img[\"image\"])\n",
    "\n",
    "emb_img = np.load(\"embeddings_out/emb_img_clip.npy\")\n",
    "\n",
    "MODEL_CLIP = \"openai/clip-vit-base-patch32\"\n",
    "clip_model = CLIPModel.from_pretrained(MODEL_CLIP).to(device)\n",
    "clip_proc = CLIPProcessor.from_pretrained(MODEL_CLIP)\n",
    "\n",
    "print(\"CLIP loaded:\", MODEL_CLIP, \"| Image emb:\", emb_img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb0f837",
   "metadata": {
    "id": "AaxQKeeWbm4C"
   },
   "source": [
    "Loaded CLIP and the saved image embeddings, and decoded HF images into PIL format for image-based querying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c90fb7bd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "R1tdHbC2bkfh",
    "outputId": "4886e5fc-2fd4-4d11-aa8b-1c4381d3b3c9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"recommend_by_image(images_pil[0], k=3)\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Bloom Matcha Drink\",\n          \"Lumen Matcha Drink\",\n          \"Aero Scented Candle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Home\",\n          \"Food\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Lightweight Matcha Drink\",\n          \"Long-Lasting Matcha Drink\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ad_copy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Meet Bloom matcha drink, designed for remote workers.\",\n          \"Meet Lumen matcha drink, designed for young professionals.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_audience\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"remote workers\",\n          \"young professionals\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"similarity\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.8489999771118164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
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       "\n",
       "  <div id=\"df-d19c51b3-f432-4e95-9da9-cdae10e0c828\" class=\"colab-df-container\">\n",
       "    <div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>category</th>\n",
       "      <th>tagline</th>\n",
       "      <th>ad_copy</th>\n",
       "      <th>target_audience</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bloom Matcha Drink</td>\n",
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       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Lumen Matcha Drink</td>\n",
       "      <td>Food</td>\n",
       "      <td>Long-Lasting Matcha Drink</td>\n",
       "      <td>Meet Lumen matcha drink, designed for young pr...</td>\n",
       "      <td>young professionals</td>\n",
       "      <td>0.849</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Aero Scented Candle</td>\n",
       "      <td>Home</td>\n",
       "      <td>Premium Scented Candle</td>\n",
       "      <td>Meet Aero scented candle, designed for fitness...</td>\n",
       "      <td>fitness lovers</td>\n",
       "      <td>0.741</td>\n",
       "    </tr>\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "\n",
       "\n",
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      ],
      "text/plain": [
       "            product_name category                    tagline  \\\n",
       "0     Bloom Matcha Drink     Food   Lightweight Matcha Drink   \n",
       "110   Lumen Matcha Drink     Food  Long-Lasting Matcha Drink   \n",
       "117  Aero Scented Candle     Home     Premium Scented Candle   \n",
       "\n",
       "                                               ad_copy      target_audience  \\\n",
       "0    Meet Bloom matcha drink, designed for remote w...       remote workers   \n",
       "110  Meet Lumen matcha drink, designed for young pr...  young professionals   \n",
       "117  Meet Aero scented candle, designed for fitness...       fitness lovers   \n",
       "\n",
       "     similarity  \n",
       "0         1.000  \n",
       "110       0.849  \n",
       "117       0.741  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recommend by image\n",
    "\n",
    "def embed_one_image(pil_img):\n",
    "    inputs = clip_proc(images=[pil_img], return_tensors=\"pt\").to(device)\n",
    "    with torch.no_grad():\n",
    "        feats = clip_model.get_image_features(**inputs)\n",
    "    feats = feats / feats.norm(dim=-1, keepdim=True)\n",
    "    return feats.cpu().numpy()\n",
    "\n",
    "def recommend_by_image(pil_img, k=3):\n",
    "    q = embed_one_image(pil_img)\n",
    "    sims = cosine_similarity(q, emb_img)[0]\n",
    "    top_idx = np.argsort(-sims)[:k]\n",
    "    out = meta.iloc[top_idx][[\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\"]].copy()\n",
    "    out[\"similarity\"] = sims[top_idx].round(3)\n",
    "    return out\n",
    "\n",
    "# Demo: use an image from the HF dataset as a query\n",
    "recommend_by_image(images_pil[0], k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a03b6d7",
   "metadata": {
    "id": "ALawV4bBbnQJ"
   },
   "source": [
    "Created an image-based IO pipeline: embed the uploaded image with CLIP, compare to dataset embeddings, and return Top-K similar products."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498ba61f",
   "metadata": {
    "id": "8P5hGWLadO-G"
   },
   "source": [
    "In this stage, we implemented a complete input-output recommendation pipeline for both text and image queries. User input is converted into an embedding using the selected model, compared against the dataset embeddings with cosine similarity, and the top-K most similar products are returned.\n",
    "\n",
    "The text-based pipeline demonstrated strong semantic matching across product categories and target audiences, while the image-based CLIP pipeline successfully retrieved visually similar products. Together, these pipelines form a practical and scalable multimodal recommendation system that directly connects user input to meaningful AI-driven outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdec990f",
   "metadata": {
    "id": "kSMI7wcK6UuR"
   },
   "source": [
    "<br><br>\n",
    "\n",
    "---\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c35d125",
   "metadata": {
    "id": "paQD8bFXzzGn"
   },
   "source": [
    "# Part 5: Create an Application\n",
    "\n",
    "Use Gradio to create a UI for your application.\n",
    "\n",
    "1. Open a new `HF` Space repo.\n",
    "2. Upload the Embeddings file to the Space repo.\n",
    "3. Upload the IO Pipeline code to the repo.\n",
    "4. Read the dataset directly from your HF dataset repo.   \n",
    "5. Read the winning Embedding model directly from HF model repo.\n",
    "6. Create your application on the `HF` Space, and make sure it works well.\n",
    "7. Include 3 \"Quick Starters\", which any user can 1-click and see an example of how the application works.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2967d805",
   "metadata": {
    "id": "2BdF-o2B2EQK"
   },
   "outputs": [],
   "source": [
    "# Examples\n",
    "# https://huggingface.co/spaces/Tesneem/Netflix_Recommendation\n",
    "# https://huggingface.co/spaces/librarian-bots/recommend_similar_papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7ee0ff0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yLJFanDG8ljm",
    "outputId": "17e3187c-96ee-4b50-c29c-28d47a79314a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy: 1.26.4\n",
      "pandas: 2.2.2\n",
      "Versions OK. No restart needed.\n"
     ]
    }
   ],
   "source": [
    "# Stable installs for Colab (avoid binary incompatibility)\n",
    "\n",
    "import os, sys, subprocess\n",
    "\n",
    "def run(cmd):\n",
    "    return subprocess.run(cmd, shell=True, check=False, capture_output=True, text=True)\n",
    "\n",
    "# 1) Force compatible core versions\n",
    "run(\"pip -q uninstall -y numpy pandas\")\n",
    "run(\"pip -q install -U numpy==1.26.4 pandas==2.2.2 pyarrow\")\n",
    "run(\"pip -q install -U gradio datasets sentence-transformers transformers torch scikit-learn pillow<12\")\n",
    "\n",
    "# 2) Quick sanity check\n",
    "try:\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    print(\"numpy:\", np.__version__)\n",
    "    print(\"pandas:\", pd.__version__)\n",
    "    print(\"Versions OK. No restart needed.\")\n",
    "except Exception as e:\n",
    "    print(\"Detected environment mismatch. Restarting runtime to apply versions...\")\n",
    "    os.kill(os.getpid(), 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4036f3",
   "metadata": {
    "id": "4ebZhYxEjPe5"
   },
   "source": [
    "Installed a stable, compatible set of packages for Colab to prevent numpy/pandas binary conflicts and ensure the Gradio app builds reliably."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f1d800f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sJGwq7XO6VlG",
    "outputId": "93c72ebf-a847-4e2e-ff9f-dc8a45b4b1e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created folder: space_app\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.makedirs(\"space_app\", exist_ok=True)\n",
    "print(\"Created folder:\", \"space_app\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8902b271",
   "metadata": {
    "id": "bMyTYCHEjktV"
   },
   "source": [
    "Created a dedicated folder that will become the Hugging Face Space repository content."
   ]
  },
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   "execution_count": 4,
   "id": "def8d2eb",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work\n",
      "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
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     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: ['metadata.parquet', 'emb_text_minilm.npy']\n",
      "Text emb shape: (120, 384)\n"
     ]
    }
   ],
   "source": [
    "# Generate TEXT embeddings from HF dataset and save into space_app\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "DS_NAME = \"Barvero/adautogen-multimodal\"\n",
    "\n",
    "# Load from HF only\n",
    "ds = load_dataset(DS_NAME, split=\"train\")\n",
    "df = ds.to_pandas()\n",
    "\n",
    "# Build text field\n",
    "df[\"text\"] = (\n",
    "    \"Product: \" + df[\"product_name\"].astype(str)\n",
    "    + \" | Category: \" + df[\"category\"].astype(str)\n",
    "    + \" | Tagline: \" + df[\"tagline\"].astype(str)\n",
    "    + \" | Ad: \" + df[\"ad_copy\"].astype(str)\n",
    "    + \" | Audience: \" + df[\"target_audience\"].astype(str)\n",
    ")\n",
    "\n",
    "# Winner model (HF model repo)\n",
    "WIN_TEXT_MODEL = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
    "embedder = SentenceTransformer(WIN_TEXT_MODEL)\n",
    "\n",
    "# Embed + save\n",
    "emb_text = embedder.encode(df[\"text\"].tolist(), normalize_embeddings=True)\n",
    "np.save(\"space_app/emb_text_minilm.npy\", emb_text)\n",
    "\n",
    "# Metadata for UI\n",
    "meta_cols = [\"id\",\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\"]\n",
    "df[meta_cols].to_parquet(\"space_app/metadata.parquet\", index=False)\n",
    "\n",
    "print(\"Saved:\", os.listdir(\"space_app\"))\n",
    "print(\"Text emb shape:\", emb_text.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf1519b",
   "metadata": {
    "id": "xNfwc_L6j1IB"
   },
   "source": [
    "Loaded the dataset directly from Hugging Face, generated MiniLM text embeddings, and saved embeddings + metadata for the Space."
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n",
      "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    },
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      "text/plain": [
       "Resolving data files:   0%|          | 0/121 [00:00<?, ?it/s]"
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     "name": "stderr",
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     "text": [
      "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: ['metadata.parquet', 'emb_img_clip.npy', 'emb_text_minilm.npy']\n",
      "Image emb shape: (120, 512) | Time(s): 37.64\n"
     ]
    }
   ],
   "source": [
    "# Generate IMAGE embeddings from HF dataset using CLIP and save into space_app\n",
    "\n",
    "import time\n",
    "import torch\n",
    "from datasets import Image as HFImage\n",
    "from transformers import CLIPModel, CLIPProcessor\n",
    "\n",
    "# Load from HF only + decode images to PIL\n",
    "ds_img = load_dataset(DS_NAME, split=\"train\")\n",
    "ds_img = ds_img.cast_column(\"image\", HFImage(decode=True))\n",
    "images = list(ds_img[\"image\"])\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "CLIP_MODEL = \"openai/clip-vit-base-patch32\"\n",
    "clip_model = CLIPModel.from_pretrained(CLIP_MODEL).to(device)\n",
    "clip_proc = CLIPProcessor.from_pretrained(CLIP_MODEL)\n",
    "\n",
    "def embed_clip_images(images, batch_size=16):\n",
    "    all_emb = []\n",
    "    t0 = time.time()\n",
    "    for i in range(0, len(images), batch_size):\n",
    "        batch = images[i:i+batch_size]\n",
    "        inputs = clip_proc(images=batch, return_tensors=\"pt\").to(device)\n",
    "        with torch.no_grad():\n",
    "            feats = clip_model.get_image_features(**inputs)\n",
    "        feats = feats / feats.norm(dim=-1, keepdim=True)\n",
    "        all_emb.append(feats.cpu().numpy())\n",
    "    return np.vstack(all_emb), time.time() - t0\n",
    "\n",
    "emb_img, sec = embed_clip_images(images, batch_size=16)\n",
    "np.save(\"space_app/emb_img_clip.npy\", emb_img)\n",
    "\n",
    "print(\"Saved:\", os.listdir(\"space_app\"))\n",
    "print(\"Image emb shape:\", emb_img.shape, \"| Time(s):\", round(sec, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e83c08",
   "metadata": {
    "id": "2bZk-OFNkF8E"
   },
   "source": [
    "Loaded images from the HF dataset, generated CLIP image embeddings, and saved them for fast image-based retrieval in the app."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64466ef8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6yxp2oOfmMbz",
    "outputId": "6b852e62-b8a3-434d-eba3-cd6cb630179a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gradio>=5.0\n",
      "datasets\n",
      "sentence-transformers\n",
      "transformers\n",
      "torch\n",
      "pillow<12\n",
      "numpy==1.26.4\n",
      "pandas==2.2.2\n",
      "scikit-learn\n",
      "pyarrow\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Clean requirements.txt (no duplicates)\n",
    "\n",
    "req = \"\"\"gradio>=5.0\n",
    "datasets\n",
    "sentence-transformers\n",
    "transformers\n",
    "torch\n",
    "pillow<12\n",
    "numpy==1.26.4\n",
    "pandas==2.2.2\n",
    "scikit-learn\n",
    "pyarrow\n",
    "\"\"\"\n",
    "\n",
    "with open(\"space_app/requirements.txt\", \"w\") as f:\n",
    "    f.write(req)\n",
    "\n",
    "print(open(\"space_app/requirements.txt\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4633304c",
   "metadata": {
    "id": "nfKeg1QvmPae"
   },
   "source": [
    "Cleaned the Space dependencies file to remove duplicates and ensure a stable build on Hugging Face Spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "863b0bb0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B5c2kTxJmXBA",
    "outputId": "dafef38d-efef-4211-a4ac-8f8ce29ead72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created: space_app/app.py\n"
     ]
    }
   ],
   "source": [
    "# Write app.py\n",
    "\n",
    "app_code = r'''\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import gradio as gr\n",
    "\n",
    "from datasets import load_dataset, Image as HFImage\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import torch\n",
    "from transformers import CLIPModel, CLIPProcessor\n",
    "\n",
    "\n",
    "# ---- Config ----\n",
    "DS_NAME = \"Barvero/adautogen-multimodal\"\n",
    "WIN_TEXT_MODEL = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
    "CLIP_MODEL = \"openai/clip-vit-base-patch32\"\n",
    "TOP_K_DEFAULT = 3\n",
    "\n",
    "# ---- Local assets (uploaded to Space repo) ----\n",
    "meta = pd.read_parquet(\"metadata.parquet\")\n",
    "emb_text = np.load(\"emb_text_minilm.npy\")\n",
    "emb_img = np.load(\"emb_img_clip.npy\")\n",
    "\n",
    "# ---- HF dataset (HF-only) ----\n",
    "ds = load_dataset(DS_NAME, split=\"train\")\n",
    "ds = ds.cast_column(\"image\", HFImage(decode=True))\n",
    "\n",
    "# ---- HF models (HF-only) ----\n",
    "embedder = SentenceTransformer(WIN_TEXT_MODEL)\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "clip_model = CLIPModel.from_pretrained(CLIP_MODEL).to(device)\n",
    "clip_proc = CLIPProcessor.from_pretrained(CLIP_MODEL)\n",
    "\n",
    "# ---- Helpers ----\n",
    "def fetch_images_by_ids(ids):\n",
    "    return [ds[int(i)][\"image\"] for i in ids]\n",
    "\n",
    "def format_results(top_idx, sims):\n",
    "    out = meta.iloc[top_idx][[\"id\",\"product_name\",\"category\",\"tagline\",\"ad_copy\",\"target_audience\"]].copy()\n",
    "    out[\"similarity\"] = sims[top_idx].round(3)\n",
    "    imgs = fetch_images_by_ids(out[\"id\"].tolist())\n",
    "    return out, imgs\n",
    "\n",
    "# ---- Text pipeline ----\n",
    "def recommend_by_text(query, k=TOP_K_DEFAULT):\n",
    "    if query is None or str(query).strip() == \"\":\n",
    "        return pd.DataFrame(), []\n",
    "    q_emb = embedder.encode([query], normalize_embeddings=True)\n",
    "    sims = cosine_similarity(q_emb, emb_text)[0]\n",
    "    top_idx = np.argsort(-sims)[:int(k)]\n",
    "    return format_results(top_idx, sims)\n",
    "\n",
    "# ---- Image pipeline ----\n",
    "def embed_one_image(pil_img):\n",
    "    inputs = clip_proc(images=[pil_img], return_tensors=\"pt\").to(device)\n",
    "    with torch.no_grad():\n",
    "        feats = clip_model.get_image_features(**inputs)\n",
    "    feats = feats / feats.norm(dim=-1, keepdim=True)\n",
    "    return feats.cpu().numpy()\n",
    "\n",
    "def recommend_by_image(pil_img, k=TOP_K_DEFAULT):\n",
    "    if pil_img is None:\n",
    "        return pd.DataFrame(), []\n",
    "    q = embed_one_image(pil_img)\n",
    "    sims = cosine_similarity(q, emb_img)[0]\n",
    "    top_idx = np.argsort(-sims)[:int(k)]\n",
    "    return format_results(top_idx, sims)\n",
    "\n",
    "# ---- Quick starters ----\n",
    "QS = [\n",
    "    \"portable charger for students\",\n",
    "    \"minimalist scented candle for home\",\n",
    "    \"premium face cream for young professionals\",\n",
    "]\n",
    "\n",
    "def qs_click(q):\n",
    "    return q, *recommend_by_text(q, TOP_K_DEFAULT)\n",
    "\n",
    "# ---- UI ----\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"# AdAutoGen – Multimodal Recommendation App\")\n",
    "    gr.Markdown(\"Dataset and models are loaded directly from Hugging Face. Embeddings are precomputed for fast retrieval.\")\n",
    "\n",
    "    with gr.Tab(\"Text Search\"):\n",
    "        query = gr.Textbox(label=\"Enter a marketing query\")\n",
    "        k_text = gr.Slider(1, 10, value=TOP_K_DEFAULT, step=1, label=\"Top-K\")\n",
    "        btn = gr.Button(\"Search\")\n",
    "        out_table = gr.Dataframe(label=\"Top-K Results\", wrap=True)\n",
    "        out_gallery = gr.Gallery(label=\"Matched Images\", columns=3, height=240)\n",
    "\n",
    "        btn.click(fn=recommend_by_text, inputs=[query, k_text], outputs=[out_table, out_gallery])\n",
    "\n",
    "        gr.Markdown(\"### Quick Starters\")\n",
    "        for i, q in enumerate(QS):\n",
    "            b = gr.Button(f\"Example {i+1}: {q}\")\n",
    "            b.click(fn=qs_click, inputs=[gr.State(q)], outputs=[query, out_table, out_gallery])\n",
    "\n",
    "    with gr.Tab(\"Image Search\"):\n",
    "        img_in = gr.Image(type=\"pil\", label=\"Upload an image\")\n",
    "        k_img = gr.Slider(1, 10, value=TOP_K_DEFAULT, step=1, label=\"Top-K\")\n",
    "        btn2 = gr.Button(\"Search by Image\")\n",
    "        out_table2 = gr.Dataframe(label=\"Top-K Results\", wrap=True)\n",
    "        out_gallery2 = gr.Gallery(label=\"Matched Images\", columns=3, height=240)\n",
    "\n",
    "        btn2.click(fn=recommend_by_image, inputs=[img_in, k_img], outputs=[out_table2, out_gallery2])\n",
    "\n",
    "demo.launch()\n",
    "'''\n",
    "with open(\"space_app/app.py\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(app_code)\n",
    "\n",
    "print(\"Created:\", \"space_app/app.py\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a08622",
   "metadata": {
    "id": "7AbLeXwBmaGp"
   },
   "source": [
    "Wrote the full Gradio app code (text + image search + quick starters) that loads dataset/models from HF and uses saved embeddings for fast recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9466c8a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1OqOJC4Rmic1",
    "outputId": "2e07fa74-0796-4163-d975-8b41f73bad49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: space_app/ (stored 0%)\n",
      "  adding: space_app/requirements.txt (deflated 20%)\n",
      "  adding: space_app/metadata.parquet (deflated 39%)\n",
      "  adding: space_app/emb_img_clip.npy (deflated 7%)\n",
      "  adding: space_app/app.py (deflated 59%)\n",
      "  adding: space_app/emb_text_minilm.npy (deflated 7%)\n"
     ]
    }
   ],
   "source": [
    "!zip -r space_app.zip space_app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23d945c4",
   "metadata": {
    "id": "TONrzgpJmlQE"
   },
   "source": [
    "Packaged the Space files into a zip for easy upload to Hugging Face Spaces."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb4c7092",
   "metadata": {
    "id": "PeTcM844nKec"
   },
   "source": [
    "In this stage, we deployed a working Gradio application on Hugging Face Spaces. The app loads the dataset and embedding models directly from Hugging Face, and we generated and saved the final text (MiniLM) and image (CLIP) embeddings as local files for fast cosine-similarity retrieval. The interface supports both text and image queries and includes three one-click quick starters to demonstrate the full pipeline end-to-end."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29856662",
   "metadata": {
    "id": "1X7lRSjr6Vqi"
   },
   "source": [
    "<br><br>\n",
    "\n",
    "---\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7bc3bf",
   "metadata": {
    "id": "p4Si5I7B8c_p"
   },
   "source": [
    "\n",
    "# Part 6: **Live Demo Presentation**\n",
    "\n",
    "* **Each student/pair will present their project live**\n",
    "\n",
    "  * **20-minute session** per team\n",
    "\n",
    "    * \\~10–12 minutes: Demo walkthrough\n",
    "    * \\~8–10 minutes: Q&A with the faculty\n",
    "* Be prepared to present:\n",
    "\n",
    "  * Your **modality and use case**\n",
    "  * The **dataset** and **models** you tested\n",
    "  * A live demo of your Hugging Face Space\n",
    "  * Clear explanation of how your AI pipeline/engine works\n",
    "\n",
    "* Expect technical and conceptual questions from the faculty about your work\n",
    "* Expect Knowledge questions from the faculty about the main topics we covered during the course\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2d4b82",
   "metadata": {
    "id": "f8xJVIis6Xt_"
   },
   "source": [
    "<br><br>\n",
    "\n",
    "---\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8dff34",
   "metadata": {
    "id": "DhfOAwt_2WWN"
   },
   "source": [
    "Good Luck & Have Fun."
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}