From 428322b2e6e5175fc08a3828c93183cba09411ee Mon Sep 17 00:00:00 2001
From: lisekarimi <lisaka18121986@gmail.com>
Date: Thu, 5 Jun 2025 16:41:38 +0200
Subject: [PATCH] Add week7 contributions

---
 .../09_part5_llama31_8b_quant.ipynb           | 6467 +++++++++++
 .../09_part6_ft_llama_qlora.ipynb             | 9447 +++++++++++++++++
 .../09_part7_eval_llama_qlora.ipynb           | 7931 ++++++++++++++
 .../09_part8_summary.ipynb                    | 7260 +++++++++++++
 4 files changed, 31105 insertions(+)
 create mode 100644 week7/community-contributions/09_part5_llama31_8b_quant.ipynb
 create mode 100644 week7/community-contributions/09_part6_ft_llama_qlora.ipynb
 create mode 100644 week7/community-contributions/09_part7_eval_llama_qlora.ipynb
 create mode 100644 week7/community-contributions/09_part8_summary.ipynb

diff --git a/week7/community-contributions/09_part5_llama31_8b_quant.ipynb b/week7/community-contributions/09_part5_llama31_8b_quant.ipynb
new file mode 100644
index 0000000..a8c61c1
--- /dev/null
+++ b/week7/community-contributions/09_part5_llama31_8b_quant.ipynb
@@ -0,0 +1,6467 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4WDyBU0Vm0Zl"
+   },
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 5)\n",
+    "---\n",
+    "-  Data Curation & Preprocessing\n",
+    "- Model Benchmarking – Traditional ML vs LLMs\n",
+    "- E5 Embeddings & RAG\n",
+    "- Fine-Tuning GPT-4o Mini\n",
+    "- ➡️ Evaluating LLaMA 3.1 8B Quantized\n",
+    "- Fine-Tuning LLaMA 3.1 with QLoRA\n",
+    "- Evaluating Fine-Tuned LLaMA\n",
+    "- Summary & Leaderboard\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# 🦙 Part 5: Evaluating LLaMA 3.1 8B Quantized\n",
+    "\n",
+    "- 🧑‍💻 Skill Level: Advanced\n",
+    "- ⚙️ Hardware: ⚠️ GPU required - use Google Colab\n",
+    "- 🛠️ Requirements: 🔑 HF Token\n",
+    "- Tasks:\n",
+    "    - Quantize LLaMA 3.1 8B to 4-bit\n",
+    "    - Define prediction function\n",
+    "    - Evaluate with Tester\n",
+    "\n",
+    "We know LLaMA 3.1 won’t beat frontier models — but how far behind is it without any tuning?\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MDyR63OTNUJ6",
+    "outputId": "7e9e5b6b-d11c-45df-d774-2da5f6455d51"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required packages in Google Colab\n",
+    "%pip install -q datasets torch transformers bitsandbytes accelerate matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-yikV8pRBer9"
+   },
+   "outputs": [],
+   "source": [
+    "#  imports\n",
+    "\n",
+    "import re\n",
+    "import math\n",
+    "import torch\n",
+    "from huggingface_hub import login\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
+    "from google.colab import userdata\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uuTX-xonNeOK"
+   },
+   "outputs": [],
+   "source": [
+    "# Google Colab User Data\n",
+    "# Ensure you have set the following in your Google Colab environment:\n",
+    "hf_token = userdata.get('HF_TOKEN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Constants\n",
+    "\n",
+    "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
+    "HF_USER = \"lisekarimi\"\n",
+    "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
+    "\n",
+    "login(hf_token, add_to_git_credential=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DTMo_1msQb9X"
+   },
+   "source": [
+    "## 📥 Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #If you face NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported run:\n",
+    "# %pip install -U datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
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+     ]
+    },
+    "id": "5PVXACKHQhh4",
+    "outputId": "80dc4772-ea31-4752-8f97-573efaa43917"
+   },
+   "outputs": [],
+   "source": [
+    "dataset = load_dataset(DATASET_NAME)\n",
+    "train = dataset['train']\n",
+    "test = dataset['test']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGJR24lbQlRS",
+    "outputId": "a1bb5e66-1aa9-40b7-c361-562eafae5d8c"
+   },
+   "outputs": [],
+   "source": [
+    "test[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vrxH6h00P9qc"
+   },
+   "source": [
+    "## 🦙 Load Tokenizer and Quantized LLaMA Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "G4veOcy5dNmG"
+   },
+   "source": [
+    "A quick reminder of how padding for batch processing works:\n",
+    "\n",
+    "`texts = [\"I love AI\", \"AI is\"]`\n",
+    "\n",
+    "If you tokenize them without padding, you get:\n",
+    "```\n",
+    "['I', 'love', 'AI'] → length = 3\n",
+    "['AI', 'is'] → length = 2\n",
+    "```\n",
+    "❗ Models require equal-length inputs for batching.\n",
+    "\n",
+    "Set padding like this:\n",
+    "```\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"right\"\n",
+    "tokens = tokenizer(texts, padding=True, return_tensors=\"pt\")\n",
+    "```\n",
+    "\n",
+    "Result:\n",
+    "```\n",
+    "['I', 'love', 'AI'] → [Token1, Token2, Token3]\n",
+    "['AI', 'is', '<eos>'] → [Token1, Token2, PAD]\n",
+    "```\n",
+    "Now both sequences are the same length (3) → batch-ready.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
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+     "height": 418,
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+     ]
+    },
+    "id": "TAit9IzsQLcc",
+    "outputId": "176a77ad-0245-4a3d-b9f3-e139de359da7"
+   },
+   "outputs": [],
+   "source": [
+    "quant_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
+    "    bnb_4bit_quant_type=\"nf4\"\n",
+    ")\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"right\"\n",
+    "\n",
+    "base_model = AutoModelForCausalLM.from_pretrained(\n",
+    "    BASE_MODEL,\n",
+    "    quantization_config=quant_config,\n",
+    "    device_map=\"auto\",\n",
+    ")\n",
+    "base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n",
+    "\n",
+    "print(f\"Memory footprint: {base_model.get_memory_footprint() / 1e9:.1f} GB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🤖 Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "1FfMJ2JbzEr3"
+   },
+   "outputs": [],
+   "source": [
+    "def extract_price(s):\n",
+    "    if \"Price is $\" in s:\n",
+    "      contents = s.split(\"Price is $\")[1]\n",
+    "      contents = contents.replace(',','').replace('$','')\n",
+    "      match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", contents)\n",
+    "      return float(match.group()) if match else 0\n",
+    "    return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CgN8eRttRAZx"
+   },
+   "outputs": [],
+   "source": [
+    "def model_predict(prompt):\n",
+    "    set_seed(42)\n",
+    "    inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
+    "    attention_mask = torch.ones(inputs.shape, device=\"cuda\")\n",
+    "    outputs = base_model.generate(inputs, max_new_tokens=4, attention_mask=attention_mask, num_return_sequences=1)\n",
+    "    response = tokenizer.decode(outputs[0])\n",
+    "    return extract_price(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hpdEk2-FW6aT",
+    "outputId": "f8913c56-1a8f-4a13-9084-21acfdb64ceb"
+   },
+   "outputs": [],
+   "source": [
+    "model_predict(test[0]['text']), test[0]['price']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "auFzPUJKTLln"
+   },
+   "source": [
+    "## 🧪 Run Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jnoI1EWGTUau"
+   },
+   "outputs": [],
+   "source": [
+    "# Helper class for evaluating model predictions\n",
+    "\n",
+    "GREEN = \"\\033[92m\"\n",
+    "YELLOW = \"\\033[93m\"\n",
+    "RED = \"\\033[91m\"\n",
+    "RESET = \"\\033[0m\"\n",
+    "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}\n",
+    "\n",
+    "class Tester:\n",
+    "\n",
+    "    def __init__(self, predictor, data, title=None, size=100):\n",
+    "        self.predictor = predictor\n",
+    "        self.data = data\n",
+    "        self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
+    "        self.size = size\n",
+    "        self.guesses = []\n",
+    "        self.truths = []\n",
+    "        self.errors = []\n",
+    "        self.sles = []\n",
+    "        self.colors = []\n",
+    "\n",
+    "    def color_for(self, error, truth):\n",
+    "        if error<40 or error/truth < 0.2:\n",
+    "            return \"green\"\n",
+    "        elif error<80 or error/truth < 0.4:\n",
+    "            return \"orange\"\n",
+    "        else:\n",
+    "            return \"red\"\n",
+    "\n",
+    "    def run_datapoint(self, i):\n",
+    "        datapoint = self.data[i]\n",
+    "        guess = self.predictor(datapoint[\"text\"])\n",
+    "        truth = datapoint[\"price\"]\n",
+    "        error = abs(guess - truth)\n",
+    "        log_error = math.log(truth+1) - math.log(guess+1)\n",
+    "        sle = log_error ** 2\n",
+    "        color = self.color_for(error, truth)\n",
+    "        # title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n",
+    "        self.guesses.append(guess)\n",
+    "        self.truths.append(truth)\n",
+    "        self.errors.append(error)\n",
+    "        self.sles.append(sle)\n",
+    "        self.colors.append(color)\n",
+    "        # print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
+    "\n",
+    "    def chart(self, title):\n",
+    "        # max_error = max(self.errors)\n",
+    "        plt.figure(figsize=(12, 8))\n",
+    "        max_val = max(max(self.truths), max(self.guesses))\n",
+    "        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
+    "        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
+    "        plt.xlabel('Ground Truth')\n",
+    "        plt.ylabel('Model Estimate')\n",
+    "        plt.xlim(0, max_val)\n",
+    "        plt.ylim(0, max_val)\n",
+    "        plt.title(title)\n",
+    "\n",
+    "        # Add color legend\n",
+    "        from matplotlib.lines import Line2D\n",
+    "        legend_elements = [\n",
+    "            Line2D([0], [0], marker='o', color='w', label='Accurate (green)', markerfacecolor='green', markersize=8),\n",
+    "            Line2D([0], [0], marker='o', color='w', label='Medium error (orange)', markerfacecolor='orange', markersize=8),\n",
+    "            Line2D([0], [0], marker='o', color='w', label='High error (red)', markerfacecolor='red', markersize=8)\n",
+    "        ]\n",
+    "        plt.legend(handles=legend_elements, loc='upper right')\n",
+    "\n",
+    "        plt.show()\n",
+    "\n",
+    "\n",
+    "    def report(self):\n",
+    "        average_error = sum(self.errors) / self.size\n",
+    "        rmsle = math.sqrt(sum(self.sles) / self.size)\n",
+    "        hits = sum(1 for color in self.colors if color==\"green\")\n",
+    "        title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
+    "        self.chart(title)\n",
+    "\n",
+    "    def run(self):\n",
+    "        self.error = 0\n",
+    "        for i in range(self.size):\n",
+    "            self.run_datapoint(i)\n",
+    "        self.report()\n",
+    "\n",
+    "    @classmethod\n",
+    "    def test(cls, function, data):\n",
+    "        cls(function, data).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 692
+    },
+    "id": "1wA5uVgpTWLC",
+    "outputId": "5a597437-50c8-419c-c1da-af0166dabe0f"
+   },
+   "outputs": [],
+   "source": [
+    "Tester.test(model_predict, test)"
+   ]
+  },
+  {
+   "attachments": {
+    "99d1317e-d872-46f7-bf2c-cc73c825993a.png": {
+     "image/png": 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ly9zVZh3sH8eB1+CiNrlg/9KXvhSW424u+88FbHKx3R7u6nFBT8k/F9Lf+973QvkvATUXvLw+x4P23iRRCLJp483x50J9WXg/OObsR/rEnVEu2NPx/pAs4OKchEF6B38E2rwfbBP7R1BOcML7Q4KE48J2U9bMe8rxIKhJD34IxtkWLvg5ztxh5KKfgCRz+9qbCMh4XZCEIIDjXCUBwR12Amn6BDjhhBPCNraFhA9JIBJPHEcCG/aL5ybYJhJrvCfclSbQyESQQuBBgJ55jibnHUEL20ng1RaCG44L73NmEJ9UXyRBE1guM4gDQS6BF3fFk+TWsrA+tpF1sf8kIilzJ2FAkqOt5ACVF3x2CKY5H3guSUWOAVUNmc8hoUJgTQDL8eZzw3cNiafP+0wsC9tNlVXmucGdfJIiCZJrJBkzl2tr4n3m/eoofPdwrEg+cD4lOGYcU95P7rCnS84pjtnybBufZ5JymftGldDnnRME0XxH831C5QVN2Phs8L3Da9N8gYQaf5OQ5LNOxQuf3wTnOt/lma/f1sTneFkJVN5jPjesk+88jhE4RpzjVJNkVjpQ0QAezzyWCT5vnBck+dI/7yDhQDUPnwU+H3wW+C6hI2USvyROOcYkP/jMM4/vMpJzSVLJzOzzOAlhZr1eEliRXOBij58ECFxkJXeIEly8cVeeCzwuQgki6SmfhAGBJ5UABNMkIEhkcIecC1uSCkcffXQonSZQJNGQjos6yoKpzKBkmzvY9BrPXXlKXLkbT3CcHswuL/aFAIi78gRB3PXjJ0E2SYJ0HAsCb5IIlN6yvwTd7APBMu3uaefN/nBhSkBBUMXFMkEZASuP0y6d7SaA53kEa8tC8EGgxIXv8ccfHxIMlH2zHRyDY489Nlwwc9wIUrlQplSYzv8InLlQXhbWT3DGiBHpExfUlC6n43VYJ+8pATzHIcH58c1vfjPsF/tJQMqx5Zzh2JA0IYhhGdpzE2AQ7PO8dJQ9Uy3C/nEcCWIIcjK3r72J5ElyTDkWJNP4m+CTYJ5AvL27oAmCB+7oUv5OlQFVDARU6YEE5zkJGT4HPNZWkxeCb7af5BXHIqmq4NwhMcPf7Bt3v9sLIkkeEGySXEiqIBIEOuwfx5KAn4CV85RzhfWzXl6PAJcEGIEmCZr2ArB0fDYoS0+CRipv2AeSSe3d1SVxx91izncSI1QK8VknmCPZmImAjs8+OJdJRvIZp0kVnxe24Yt8rjkWfNdknhskxHjPEiQJ6VMjc7m2Jipelue4fVEE8BwrAmW+fxJU1xBQc75x/Hn/OB94j/n88Pnl/P68cxokUngfM/eNiikC+mWhEoNt4zzkM8r3AN8xfO9wXvF8tpvmHHzv8x3H99spp5ySWkOc9KHKKfP125pI0Kb3LZKOBBXndLL/fFcnCS6OA8eIJHEmEiict6yX57WFY8tnhgQf3x/pSHTweefzxueVZeg/hkQgSWc+K3z30VyJzzbvFyNH8b3BZ4J/D/h3YXmSgGbWezkJYWa9HoE/F5ncaeMuKBdSXIil36lLcGFG4MNFM3fK0hF8cvHGOgjACAgJMBhiLr3EmOAmaaMPAhAqMbg45wKSu2MEEUwEh1xocsFICTrB/oriQp6L8p/+9Kf6/e9/Hy5qCeDpEogL/3QE3ekXpQR1BLXMI1GSbBeBO4Ejx4P1c8HJXVkCeC5Ok5J8kgNJ2+Rl4fkcf9qgc2wyL4xXFttz+umnL2lGkUwEgwQZ6ai4YF5bd8HZLu6CEqQkOBZc9BMYJ50S8lzaZxOsUgXCMUrQPIJqjswEF8cqc/vam0444YQldzCp2OEuJMeQShySRkm1C+9Ze0EllRw0HeE5dExH0MO2cq6BIJd+MAi0qUxpK8BO0H6cQJtlaaZEwoukGneROWc5PgRO7d35T5Im3NXNPO7M4/3juQRVrIu+BAh6qEgiQcfr0RyGcyhZ1/JUGfC+/PKXvwwJJ0bCITFABQTb3d45yPaQfOJzQRBGWTwBMscn864yOPfpk4WED58v9o/ncO4RzFJ9REC5PNubjuCQ9yXz3KCaiGY5CT7jHJvM5dqaSKSt6s9egu8LglPOLwL59O8E3kuSXHzv8n6QEOI9JaGSVFG1F1BnIoHE91zmvvH9x3v7RfGdT5KC7zkS0SQ7+JxlvncknWiWlfn6bU0kXDM7Mk1wftEXCokhEhDpydD0z0Km5LuXx9s7p/g3h88I5+LyfN5IzFAxdfXVV4dqEBKofNfTGS3fcXxnk4Dnu4TKNB6niUZmZZSZWcJJCDPr9biYouqBII6LPgIZLtwzm2KAgIo7TASSPC8dF7hcRHMnjOVIQBDIcbGdviwXs+nNB7jbxGtTUk+FAk0ekok7UFz8se5lXVQuC+X5XIBzF4u7gQSHBFsEMJkIotIvbLnzR/DA87i7n75trIf95e44gS7HheA886KaZaheyCwbTsexIjjhIj+zz4FVgQtt7mzSkWf6REIos0M5kgGZ8xLsW2Y1AAEo28x7nL6PvMdMXIgTqCQ4Rm3108Bx52J+eSbuSiaJEIJRqkYIyHmvSTwQABDg0N6dph8EHJkIHkickEwjIOZcJVlFVQdIepFwosKBJAfnNFMS4HM+MoHqBapDSI4QYFPJQiDCuU8SiuND0oVzoS0cU44768usliBg4vwiMGKfeS9JorG/rI8+RDi/ucvO3WmSMgTSbVVtZOJ4kYihOogAiju6fBY575f1WSOJxGtQiUAlBe8BSYj2znGW5Thwh5xmAVdddVWoiGBf2HaCc47tiuB84XzMPKdJfqUH2xx3gv62zqPMife6rcB2VaDvABK8vD+8Vjo+OyQPOF85DzkfCWZZnuoR3kvOocyAuS18dvk+zzwuvD8rk2DhM8f5xTpIKFHVRiKX70ESJUk1C+cor5V5bNuaWGdbVVxUTpGIoQkEn1E+W+kdbXIcOOf4bs7EPM5djml7x4vjyZR8ptPx+U4+b+nbxvL0ucK5xTnCdwMdB5NY4/XoK4dkEuczTUfoJyOzyszMLOEkhJlZhFJ0AhruInMBSHCaBHnpCDK4AONCLbOEOkkSJBfx/GT5JGhL8DdTgmW4WOTimWYHBPxtTZQGL09glYkAkWoPOnhjP7nwbS/Q4KIzPZBiu/ibpEOSxEif6DSSJgXJcUnusKVj35NgtT08l9fKPDarA9vR1nFmHzOPD5L9zjwfkn1hfUyJ9tbPslSeLM/EHdj0CgfeY4JcmlYQGBFMMxQi1TgEdknTjfYQWBE4kYAjGQaCZSoFSJhQ0k9lBUEFVTtU+nAnOElYcExIuFAFQR8Y9CNCkxH6xOBzxTEiEdXWZwoEygSZBFAkpNKRlOCuME0+0pMYVB5Rzk7ijonmPPTJwLZwNzpzdIS2sAwVT1QrsT4qBphH4EsyIv1zmy7pL4ZKJRI+VFK1lbTMxLaxrwTFVF+QiCAIJFGUVKCsalQr8V61dR5lTgTTn/dZ/SI4jpw7VBFwnDjXMlFNQ4UL5x3vJwlhSv/5XuS4kURsL4m1KmV+vhPMp+8fElY0pSAxx/lP0E1CLGlyxTlMQqyt45s58Tkm6ZKO84DEBokq9pnkb2bfPRwHPg90hpqJzy/fJW0lTBN8X3CekyDNbA7C9vAZzPy8pWNfSVhS9ca/LZxfSaKNKjKqWvieS74fzMwyOQlhZhbhopi7PFywE3Sl9ziejiCUagECsczAjgsuAibuJhJssRx3qwjgCGgSBFT075DgQpE7xlzEEaxxl5WgKH3izi8XlenBbGdgX9iuJEjN3C7u0nFnjMfZPi7AKclPEHxQBUC7+fYCOvBcglCO1bLuQhMIJMdgWevrTATXXMgzpSdQeI/ZF/arrcqHTCQVuHhfnonRNwhgwHFIPxbcwaT5D+3UTz311BCUEBQlMpMlYF7STjzZB4IRnkt/ArR9T/r4ILnAHWqqdOj7Ix3vD/tKYM82sE8EW1QW8R5zfiNJViXbzfnD3VM+P1RyJNvI4wQ8zCPYycTni/nc6eezSVMotpvP8vIc83RsOwEviQGqQLgTvazqBD6nfHbZBl6/veoZ9qG99RBkckz4fkg/d1YlqixontHWeZQ5EVjyHfhFZL6n6XhPCFRZhvcrs9+PBO8B5wJJYI4pSEpwjPgObC+JtSqxDWA/0vcl+ZvPO8kIElZUqfE75x3NEUDijv6B2jq+mROfY77zEpz/JMLpq4bPAwkIEmuZOOdITPBvTno1BMeX6hzOy/Rqqcz3hkQY6+e5fI8knzd+8j1GMopKqbawDvabc5Z9Zz18RnnNJOnBe8hybo5hZu1xEsLMLMLFE+3iuZNLh4IEMW3h4pmKBC6uaA/LxTUBBsEW5ahc1HGxTOBJWTQXaFxEEwRygcny3FFmSnDRy4Utdwhpa0yAx7Ksl5+smw7bkgvFzsS+kIjgwphgMtlftot95eKTJAN3MbloZT5luNwpZzkuVGnz/nmBDceVi20ufumsj0RNcgzYf14XBCNcgHO3lrt1/FzeO7dchCfLp08rG/wRjBL403abYJntJpinU1LKlQnA0/sEaQ8BA3d/l2ciOElK7nkt3geOEfvDecLE37xHHLOkORCBAQkh5ifHl58ETpyT3P1M2urTxIN26+mVL9wBpsqCZgy0u6fZBVhv8l7xM/mdYIp189lK7y+B5AYBVHrVA8eJQJ6qAO7wsg7OgyeffDLsHx21Jni9zH0gEKRpBAlEzsUvctecc4tOYamkIPmyrOoE7oTTQevPf/7z8Ht72Ef2KX17k+PD9nL+cX6kl74nx5Pl06fk/U2wXPq5nD4l+F7hrnpb51HmRPUMAWqC10peOwliWXcyLx3BLN9z7GcmPgckdjhvkuRCJl4r/RjxOwkU+iHgjjv9EnRGJQQBdJIY4j1if9k2vsM4D9O3kWWTjlyT94UqGSqA2jq+mROfY777wfP5t4Lzjn+P6MOGJG/yfqYnEfj3hX4i+Jzw3ZxsD58rkoQ8j89yklDh3w8+b+nJcP6N43uX5AkJU57Pz2SYaY55W/hMkqwi0UIfPiSF+c7gPGdiW9gOvn/aSzaZmfX7BXWbZma9DGXBXPBxx40mCgRpBEmUSRMQcBEKOouk7SsXbFz0ESgQKBEYEVxzEcgFHm2EuaikpJvRLwjKKU/ngo2ScS70WI4LRDpc44KdcliCF9bN8lzIUcbOqAXcQSd4YxtpR07pNne2CZII8OnTgYtytre9snOSAwSW3GmjM8X0duLpuPhlf5hodsHFa1JtwEUk+0ywRLk77brZLkruKf0n4UCiJSmV5jXpKJDONdl3OjFjLH2OLdvJsWF97ANBLfvOBTOvw8U7F8rsGwkPLojpX4BAmGYFHH9wzCnX5nEudAn8eW5yFy4TyQA6E+XYUb1CYJM+EWhyl55t4njxPnGHL/1OOhfWvC8sS1vw9NfijiR3H9lXAi0SKdwpZHmOC8tzTrEO7gazT5QsZ97Z55hT1r88E9uWbAProyM8StcJlOgske3hvaJ0nICcHvx5fwg0CAIJ2FmO7SWIoaSc36kCoKSazwOvQWKAwDGZCH5YjveZZZOKIZJy7PO5554b3heCTjpIpLkOwQzBOudJgqEPmZhH23hw3rH9nD+8DwQ0lKXz/nOeE+zzuSQQI5FCkoSf7Acjn/A4j9GXAOdUcg63hc8X5zvvOZ1Gpt9h53kEfGwH3w28dySZ2D/mM5oJ280xojqI75CkjxfO+SSI4043n2mONYkN3h/OLT4/nJO8Z7wG5wEVJUnQSLMnziGOMecL60sm5vM5ot8ZmoFwPrK9mec0733S5IF94654W+dR5sT2ph833msSSbw2iRSqnAiISTbwGSchm6BKhhGD6GMkPeEEvjv4HuQzzLHgeGbinOW9SIZLphKFITA550hEcMyToLotHFu+l1h+6tSpn/le5HuD718SB3QICj6TfL/y/cGID3ymeF/5buc48nnh+5PX5b2g2QXbxXnHMeAzftNNN4VED59zEjisg+/ato5v5sRnLEmscJ7QISrrpcqMx9LfU44L5wjfY3yHcjxIWrMNbCffnXQ2SwKAc4ttSo4zIx6RYOdcTr53+M7j+5BzkO9bzl32hWPN69OMK/l3MB0duPK54b1JPtN8f/MZ5LuIf1v53HIc+B4liW1mvRf9LbUp+gfbzKzXueeee1qjYKw1+nJsjS7oU3M/K7qwCstFQUJqTmtrdBHeGl2Mt0YX3a3RBVZrdFHbGgWbYZnoQrE1CupTS7a2RsFZ68UXX9waBUWt0QVkaxRwtf79739vjS7wWqPAqjW6WEst2doaBS6t0YV+61e/+tXW6CK2NbqgbY2Ch9YoiGuNAv7wOKKL7dboQrL1Jz/5Sfi9PVHA0hpdfLbutNNOrc8880xq7mdFF7Ctv/jFL1qji8bWd955J+xfOv5+4oknWqOL7NboIrZ10KBBYbv23nvv1ugCvjW66A3Lsd8clygQCY9HAU3rbrvt1nrXXXe1TpkypTUKDFqjwCosy3ZHF8lhH6KL/DAvef5vfvOb1ii4bY0Cu9Y11lijdb/99muNAvwly3z44YetX//618NjUYDVOmLEiNZZs2aFx9vy29/+tnXNNddsjS7Iwz5mTuwX5wDrjQLr8F5FF/SpZ8eii+6w/VFgF37PxPvO67DdHJ8oqAnnx/PPP98aBa5hGZ534YUXto4bN641uvAP81aFKHhojQKR1uiCP7w/nDdRoNI6fvz41jPOOKM1CkhTS8bvNefp9ttv3xoFiuHc5ThGAXNrFFy1RoH/UudvJtbFsY+Cz9YoQE3NbW2tq6trjQKicK5FwU1rFAC2RsF5axRUtc6bN+8z6zzzzDPDMbriiitSc+L3dsGCBa2XX35561ZbbRXefz5XnMPJOQKWYzsPP/zw1rFjx4bX4ueJJ57YGgVTrfX19akl2xcFeq1RIBrOy7KystTcGOvn/YyCwNadd945HF9wvu+yyy6tUbAc/m5LFCy2HnvsseGcjYKxMI/95/MbBeet6623XmsUJIeJc4l9iwLapT5zbFPfvn3D+RoFqEtNURDf+sADD4TPFO9vFCS2eU7zmVgVOCeS1+a1omB8yXbx/qTj/YgC+PAepKupqQmfBb77ks9xW/jM7b///uG7g/eUY3X66ae3zpw58zPfSW3hu5Pz+NRTTw3ffZnYrii4bj344INTc+LPJOfOMccc01pVVRXm8f7feOON4VzmsxwF/K0///nPw/rPPffcsA6OL9/nnON8l7Ptmef4iuLfAv5NSI5x5nvK98/rr7+eWjr+LE+fPj38e8H2sK1sG/+28e9OupNOOil83tK/d9jeuXPnhu8k3hveT76b+E5mflvefvvt8H1x5513hvc1wbo4LznH+Tdxr732ar377rvDNppZ78Z3S1tTHx5M5SPMzHoN7oBxF5E7StwpbO8OW7JcdBG4pKQd0UVXmM/j/M4dI9bFXarMdVHSy8RdVJbj9XgOz2WdrDsRXWyHu6jc2eLrmXUl62bib55LWXCy7X3buePLcqyLdXL3L7kr1hb2hWWpyOD1MrEO9oHtYr1sB+tj25mSbeCxZH/ZfpZhH1k/uDu5rH1gPtUNTByv5HVYJjlOyX6xTLIt0T9o7R4HlmVi2bawbraR7U32kTt76ceBx7gzzTo4RrxmuuS56dvN9rLu5LizDI+zLbwe+74qsF7OJdbNNnKHnLvU3LVlGzgn049N+nnLc9OPcRRcfmbf0rFvbD8/ueubHCPWwznCuvmJ5LxlvZnrpHKC7eU48Hgi2ZfkNdjutvaB5aiaSP+cJMu1df5mYt1sK8cgOSfTsU7axrMuzgVem2PL/GV9lpL1JsvxfF6DfWLi8eQ8bG/f+FywX23h/UnOVY4h62sL+8Nd85XFe8Rd77ZkvgbLsY9UIqQfn2RbOS/Ydva5LexLshzPae/4tCc5/1iW18l8Do+zfrab9xy8Dk0U2F7O5+Q8SM7BZFuS7eZ9Sd5H5vP+Mp/zfHnOu2Vhvemfn0y895nnHucS+5R8DniMbeVn+jnNZ4X18/z0753k3OS7i33imPE4xzz9+QmW4fzkWGV+V7ANyTHj9VkHy5hZ70aFXVuchDAzM+shCEjuv/9+PfPMM6H/BjMzM7PVpb0kxOenls3MzKxb4A4k7b5p021mZmbWFbkSwszMrIfgn3RK6CmxTkrOzczMzFYHN8cwMzMzMzMzs07h5hhmZmZmZmZmtlo5CWFmZmZmZmZmncLNMdrBYWE4Ljr5Wp6hoczMzMzMzMx6MobiTYZDbms433TuE2IFMQ7yb37zG5WWljoJYWZmZmZmZr1ec3OzttpqKx177LHKy8tLzW2bkxArqKSkRNttt52mTJmiMWPGOBHRXZSWSu+9J9XVSRtsIK25ZuqBbqCsTHr/famqSlpvPUUnXuoB+1xNTdLcudI770hFRdKWWyr60MbHc/p0aexYaeONGb8w9QQzMzMzM2tPQ4s0MwqpZkVTbfQ7+jQ3aeHrz2nymKG66KKLPnckLichVlBlZWUUx2wZDu7222+vfv36pR6xLo1A/oMP4iQEgfyoUakHuoHFi+Ntr66W1llHGj069YB9LpIQ8+dL774bJyE23zxOQnz0kTRjRpzQGTdO0Qc59QQzMzMzM2vLwkbpnSgkeaPqf0mIgugyemy/er367wvVMmuaLrnkEg0YMCD1jLY5CbGCqqqqNGnSJF1xxRWaPHmykxBmZmZmZmbWY1H98GGt9EYqAVHSFI9kMSpH2jRPGp9Tr1svuUDvvPXmSiUh3MbAzMzMzMzMrBdb1Ci9UCHdWyo9vTiuhsjvFycf9hwi7TpYGttf6rfsviiXi5MQZmZmZmZmZr0Q1Q8zaqRHy6Q7S6Tp0e9NrdJaudLkQmn/YmnLAqlwFXat5uYY7XBzDDMzMzOzrq2lpSUMrW9mK668SfqoVnqqIu77obFFysvqqzED+mjbQmn8wOjvtDC4vr5eF1xwgd58c+WaYzgJ0Q4nIczMzMzMuiaSDw0NDSEo4nczW35kAKh2mFErvV4p1aYyAjSTWHNAX203dIDWzMtWv75Lt71wEqKDOQlhZmZmZtY11dTUqKysTLW1tb5ON1tOBP4t0X9oglEXTU3x7JB8yO4j5Ua/9G1pUmFBgQYPHqzc3Nx4gRQnITqYkxBmZmZmZl0PlQ+LFi0K1+uDBg1SXl6e+vRZBb3lmfVQBPxE/TS3YLjN6uZ4HrKij86AvnGzi359WlVRUaHFixdr6NChKioqWuqz5SREB3MSwszMzMys62lqatLChQtDXxDDhg1TdnZ26hEzy0Sw3xz9h8qHiiapMRX9J9UPRVlS/yjUTVINJBrmzp2rgoKCkORLj4NXVRKC1zYzMzMzMzOzHoRyA5peLG6SShvjBATJBobZzM+ShudIA9ISEKDyoaMri5yEMDMzMzMzM+tBqH6g2UVJo1SVan5BaiG7rzQ0SxocTRn9TnYaJyHMzMzMzKzXollHU3OTmluao0AtVatuq0VjY6Oam6OIuRvh/GGklq4ySktS/VBG9UNa8wuqHwqzpBHZcfOL1clJCDMzMzMz63WSpMO0RdN0z/v36NGZj2pB9QK1tLaEyToXHSIee+yxuvLKK0P/fN1FeXm5dtttNz344IOhz4TViZEvqHpY1Pi/zicpdugfRf3DsqRBq7H6IZ2TEGZmZmZm1quQZHj202f1pRu+pJ3/tbOOuvkoHXTDQZp8xWT97NGf6cOyDzu8KmL27Nn63ve+p80220z/+Mc/UnO7h9LSUn3zm9/Ud7/7Xc2bNy81d+X85z//CaMxbLrppmHEk+6Czhv3228/3XHHHZo1a1Zqbuei+qG+JW56UZ5R/UDHk0OzpdwuNM6CkxBmZmZmZtZrUAHxzCfP6Nv3fVv3fnCvFtUsUk1jTZhmls/U+c+fr/OeO08zSmZ0aEXE66+/rpqamjAU4quvvhqGRVzdaA6xPGiCUFtbq7q6uvD7yqKC4N5779X666+vddZZZ6U6RqQ5R2c2jWBbDzjgAL3yyiv64IMPlvsYrir0/VCR6vuB4TfZc44ew27S9wNNMEhGdCVOQpiZmZmZWa/Rt29f/f2Fv+vdRe+qvql+qYoHkg5VDVW66727QvOMvn06JlyiDwGSEGzLoYceGoZBfO6551KPxmiScOutt+rAAw/UJptsom222UZf+9rXQvAPgl2aLvD45ptvrh122EG/+93v9NFHH4V1f+Mb31iqwoJhF88991yddNJJ4e8FCxaE5b/85S/rqquu0tSpU7XxxhuH9T/77LM6+eSTtd1222mrrbbSUUcdpUceeSQkTfCXv/xFN910k6699lpNnjxZO+64oy677LLwGBUe5513nnbZZRdtscUWOuyww8J+LMvLL7+sRYsWady4cSEpk3j//fd1zjnnaPvttw/b99Of/jT8fcIJJ4RtKSsrC0NF7r777qESYa+99grHaubMmSFBcuGFF4Z5bMe+++6rG264IVRxJD755BP99a9/Dc0pttxyy7Cf//3vf8P7A4aiPPjgg8OwlN/+9rfDetiv66+/PgwVm9hwww3DcLEkIhg+tjOQ+2HYTUa9SIbe5EzOSlU/DElVP3Sx/EPgJISZmZmZmfUa0xZO0ytzX1FNUxxQt2VO5Ry9OvdVza6cnZqzak2fPl1z5szR2LFjtfPOO2vw4MF64oknllQV0D8CQfXPf/7zkBggWfCjH/1Iw4cPX1Il8Oc//1mXX355SECwHM0jxowZE6oACMBpJkGQniBoLikpCcmI5G9+f/LJJ0OC4ayzztLvf/975eTkhOeRgPjJT36is88+W/n5+SFYp2KDbSQwJ/HAtv/xj3/UH/7whxDss08E+qzvmGOO0S9/+UtNmjQpJD+odGgPSROaNQwZMiQkZEBfC1dffbWefvrp8HokYEg8MI/XYT+pemCfSGJcd911IcHC9rAetpfXPOigg/TrX/86JA8uvvhi3X///aqurg77eOmll+r555/XkUceGfaTSgwSDCRYwHFk20i2UKHB/kycODG8F++9996STjSzs7NDIoIEUHqSo6Mk1Q9ladUPBPYDo0NH8qEg+kkyoismIOAkhJmZmZmZ9QpUOsxaPEvVjdXLbEbQ1BIF7DUlYVoVzQ0yETRzt53+INZdd90wcdf/ww8/DI+TQLj55pvDnXeSC3vssUcI8k877bSQJCAIpw8F5tOZ49577x36Jdhnn3205pprhnUsDwL5rKysUDXBung+SQAqD770pS+FCoM999xTZ555piorK/XWW2+FCo21115bI0aM0KhRo0KFBtPo0aM1bdo0vfDCCzriiCPC81nn4YcfHhIlJAnaQ/VEQUGBBgwYkJqjkPB49913Q7LjxBNP1P777x+2j2OVjvcnNzc3HAceZ5up5qC6g4oJXp9Kh+OOO04bbbSRXnrppVABQfJl/vz54biyDD/ZbvbtgQceWNKson///powYUKokmB/OFYkQ0hepDe9GDlyZEiIJNUiHYFTkaQD1Q+Vzf+rfshJVT/Q8SSdUHaFzieXxUkIMzMzMzPrFagiKMwtVL8+n99LX25WrgZkDVip/gnawh34N954IwTdJCEIvNdbbz0NHDgwBMagEoJqCZogkFRgGTpr5HeacBCcU/bP41RT8FzWRwUAQfPyIgFBIoFkB+tnPaAC4JprrtG3vvUtffWrX9X3v//9kIAgOUIVAYkKtoOJKgAmkgE8/thjj+miiy4KlRAE9SRRmMc+t4d1sg62J/Hxxx+HY0/igKYO7B/Hafz48aklYixDpcaUKVOWJDJI6FDlcf7554dEBNtBlQQVGm+//XZ4D2bMmBESCVRHkMCgGoKONu++++6QnEgqGlgf7xNVKPzOe8Dvn3766VLDiXLs6NsivZnGqtTUGje7KE9VP1ANwZmZF53Kg7Ljn9nRjC6efwichDAzMzMzs16hT/S/TYZtorWK1lJ23yhyaweJig2KN9CahctfVbC8CMbpa+DOO+/UqaeeGioGKPOnOQZJiKSzR6oUaKLQluQOfGFh4VKBe6KtxAnrY0pHEoF1kABI95vf/CYE7FQFUAlBvxNUPrBt7XW8yHyqAFgfz6EZBBN9QhDc//jHP04t+VkE9wTv6UE9lSIkO6hySFAFwrLp2FcSLyQiwLGjIgFUhyTbwUQyhWYdJG6o6OD4kshJHidZwbZS7ZCM0ME2sE/p2Kak34gEx4btS5qTrCpUP9Skml5URm9fQ0b1Q1H0clQ/dLXOJ5fFSQgzMzMzM+s1SDActelRGpY3LDVnaSQqtllzG+04ZsdQCbEqkQTg7jtBLCMq7LTTTqHpA8EyfRZw954kBY8XFxeHfgfaQtMHgl2abyQdVaYjKCfBQGCc3Jkn6KYzynQE8JlBM8tRDUCfEHRaSTMEtpNAPEliJFUQBPxMIJFBJQLbTT8WVBYkE+thf9tDXxaMDpLelIHXI9BP79eCJiFUKaTL3Af+plqBn3RSSZIn2Q72hQQJlQz0w0ESgqYi6dvKMjTfSKpCwLoype87aFJCp5pJMmRVoPphcXNcAZFUPxDA50e7S9OLUP0QzehG+YfASQgzMzMzM+s1SDIcMu4QfW2rr2njYRsrp19O6pE4QbHnenvq5Ekna9LISas8uqMvAqogaFLAHXlGoEgm7sITeD/88MOh+QHNC6iWoHNFkhOU/9OpIhUH3MmnHwY6UHz00UfDY8moGIwyQdUCATF9NDDqBkNHUmlBXxSfh8oKgms6huQn67vrrrtCPxQJqhEI0nmMZhMkLqhioGNH+mxgu2m+QTMLqhLYrhdffDH17M8iEUATFNaXJE023XTTENCz3ck+UCmyPPtAE46tt946dJL52muvheQFyQz6mWC7+JvjR7UDTUWSZXh9Rrig3wiSLMuL94SEEZ1XktxYWeQ2apul8uhQVEU/66O/SXfkpqofCql+iKbuVP2Qrt8vIqnfLQ1ZN4Z7oSxnrbXWWqGT0MzMzMzMOgZ345M75gSRK1r+zl3tgtwCrTtoXY0bNk7rDV5PGxZvqO3W3E6HjD9EX970y9pu9HYaMmBISFisSnR4SCUElQVUPhDIE9AzEfyTSCDIphNGYhAqHQjAGSHiqaeeCoEunSPyPO7mv/POO2F5HmNYTQJpmlCsscYaIZ4hCcF8Eh8kA6ia4HjRzwPL8hh9Sxx99NGpLYybH5CAIGlAQM5EMwP6oWAITYJ3+p6gzwRen+VIMhAv0XcCiQP6s6CDyscffzxsO9tN1QEdPLaF5AvNP3g/SWSwDuaRkCBpwDpIIFDJwT5wrL7yla+EfWT7WOb0009PrS1uLkFfF/T/wOMkMjhGLFdUVBSOEdUSrCc5xizD69BXRNJfB4kXqkLYZ/rNAMmWK664QhtssEHoNJNjw3G47bbbwigeJE/aaiKzvOhsksQDE9UPJB+IRKl6oAKCETCyOrD6gf0jqcR+JRU1CR7j/OV9oLIlsxlPJkZNaYuTEO1wEsLMzMzMrOtZ2SQESC6QiKASYsKICZo0apJ2GrOTpo6dGpISA7IHrPIEBKgMIAAmqKXaIR2BM4E6yQUCWYJkAnISFATljL5AIEwVBftMjEKgzWMEzSQeCPK5G89xYT08zu88RmXA5MmTQyKB9ZOMIZnB0JL8nWA+HUCSBGDdNJUg8UHQveWWW4b1E5xSacFrsH0kJZjP9rLdbFvSTwOvTfOMrbbaKizXFpalOQZNUajyoLkJgTz7TFUH28m+sF6OIRUaNJ0gRuO5vDZDgaZjPbw2z+UYsC62g2OYrJP1sQz7wP7yGjThYBn2j/VT2cB+8xxwfHiv2B9el/eCpATbQfIoWW5FtbRKdS3xqBfV0RTXg8TVDwVZcQIiN5o6euSLzkhC9IneQJIrloEDz4nMCcWH9Yt8uZmZmZmZ2arF3XHu3hPGEMh/XiC0PEJIFAV3HZF4sOVDU5V//vOfobqAShGCYPpZIOlAIoCRJ2iqQhMUEgBd5V46/W6cc845oYNLtp3Ey4ogGKfvB5pf0AElTS9A6D8gCkHzol9yo6mzht3kZjzNf9gPkmLpcTDvwQUXXBAqa7hhT/JmWdpLOvn2vpmZmZmZ9Wrc3XYCYvWiKuHQQw8NVRgJmnHQr8Pf//73MD300EOhSmLfffdNLbH6UZnD6CF0MLqiCQg6miT5QMeTi6MpSUBQ/VCYFXc+SSKisxIQncVJCDMzMzMzM1vtaBZCMwqqIMBIGzQ1oakGd+GpgDjllFPCz66CZh0kIOhrYnmRa2hsUej3gQQEPxl3hI4m6fOBBASdT2b10LyYkxBmZmZmZmbW5dA55FlnnaW//vWvOu+883TmmWeG/hm6c399VD/UkXxIJSCofiDXQJOLgmganBUnIvr00AQEnIQwMzMzMzMz60BUPzS0xJ1OJkNvJtUPA6KoPAy9GU09tfohnZMQZmZmZmZmZh0kVD+0xNUP9P3QkKp+yIn+E6ofsuNERE+ufkjnJISZmZmZmZnZKkb1A30/UP1Q1hj/ZF5S/TAou/dUP6RzEsLMzMzMzMxsFWpplepTTS+ofmhMVT9kU/3QTxrSy6of0jkJYWZmZmZmZrYKUOnQFP2HPh9Km6SalrjvBwJvkg40vWDkC6oheisnIczMzMzMrPdqjSLFphqpuS76nXCxZ2hubg5DW1ZUVKi1tTX8ze9lZWWpJSwTx6m2tjYco5aWFT8XSEBQ/VDaGFdApFc/0PEk1Q/9V1P1A/vW0NCghQsXht9XJychzMzMzMys92mul5qqo4jxVWn2HdLc+6XqmXFCoiWKIDtIY2OjqqqqVF1draampV+HwLempkaVlZWqr4+2byXMmjVL3/nOd/STn/wkBNb8/dvf/lbf+ta3UktYJo7T9ddfr69//etatGhRau7yofNJml0siqbaljghQbA9MPpPcXbcBGN1Vj9w3j3//PPafffdVV5enpq7ejgJYWZmZmZmvUtrs7TwaemJQ6SHdpaePir6/WDpge2lN/5Pqnw/WqZjqiIIcrfbbjtNnTpVd9xxR2pu7O2339aXv/xlrb/++vr1r3+dmrtqrL322vrpT3+qv//976k5lun111/Xq6++qilTpmj48OGpuZ+vjtOpUapoipMRoPphUKr6IXc1VT+ky8nJ0fjx47XxxhvroosuWq3VEE5CmJmZmZlZ79ESRYwLnpBeOkOa/3AUNdakHojULZTeu0Cadm4UUU7vsEREUVGRPv300xDwcocaNJeYOXOmnnvuuRUKgJdXv379VFBQEF67u6AyhCkzYGZeZhXJ8uJ5bTW1YP5bb72l2bNna5dddknNXTY6n6TZBdUP9anqB3INua1NGtyvRfn9ooB7NScf0uXl5enAAw/Uv/71ry98/FYFJyHMzMzMzKz36BtFhtPPl6pntZFkiMJImmLMvS9OUPTpmHBpjTXWCNUO8+fPD4Ev5syZozfffDNULPBYurq6Ov3lL3/RlltuqdGjR2vy5Mm66qqrQp8Piddee00nn3yyNthgg1BpccUVVyz1+EcffRQqIU444YTw98cff6zTTz9d3/72t8PfiZ///Oc65JBDtGDBgvA3TTq++c1v6sc//nGoEGD9PIeqgVtvvVV77bWXNtpoo7CuZF/aQ3OHX/ziF9piiy00ZsyYUA1yyy23hOYneOKJJ3TQQQfpZz/7WWg2Mm7cOP3f//2fLrjgAh1xxBFLtm3bbbcNFR00K7jyyiu1zTbbaM0119RWW22lf/zjH0sF2AcffHBoksJ+8PiRRx6pl156KfXo/3A83n33XQ0bNizsY4JmM1SssK28b5tuuqnOOecclVbWaH5DXP3w029/Qz//zrf0x5/9WEfuPkVH7rO7nnn8UT355BM65ZRTwnN4T/fbbz9de+21S5ra8P7/4Q9/0I477qj//ve/YT/Gjh0btpFkSJIsIUHFMdh666213nrrhfeZ/TnxxBN1//33h2VI1Lz88svhfaDiYcMNN9Sxxx6rp59+OjyO/v37h9fivX3mmWfCelcHJyHMzMzMzKz3KHtDKo+CZTqibE/d/Gi516WaT1IzVq2+fftqs802U3Z2tl588cUwjyqI6dOna9dddw1VCwmC4AsvvDA04yDwvOGGG3Tcccfpj3/8o+69994QwJeUlOjcc88NnQ4ScH/3u98NQfXjjz+eWktcPZD0NwEC0KRvinQkClgmqT7g8bvvvjus+6yzztKZZ54Zgt1vfOMbYdsJiJPXu+yyy0Lnh21hvSQXSDSQ0CAYP+CAA0JAzzwepyqEfbn99ts1atSosN/sK8eDChECagL5v/3tb9p555314IMP6ve//732339/XXPNNaEpC81Y/vOf/yzZDvaFx0hMkADhtTOTPCAJRIeUJIGS48862Ncf/ehHIXHy73//W9/81pn611VX6U8XXKDF1TWh+qE2OkaP3HOHyufO1ne+/S397re/De8vx3iHHXbQeeedF5pAkIzgfSTxguQ9Iflx11136Ve/+pX++te/atq0aWE/SD7huuuu09VXXx32nWOy7rrr6sknnwzPSyppSKxw/ElwcC6wnuLi4rDPJLfQp08f5efnhwQFSYi2KkI6g5MQZmZmZmbWOxB01X4aReC10R9xkN0mKiQayqT60jaqJVaNCRMmhCDxjTfeCAHwe++9F+7sk4RIkAhg3qWXXhqSC5TSc7f8qKOOCkH4I488EpIDDz/8cPjJHfTDDz9c++67bwjw11lnndSaVg7NQw477LBQ9XD00UeHCgUCdPaB16LagOqL999/PzQzyUQwTpKCQJrOMQmmqWYggUFgTkCdVF6QLKDzRCofqLwg4KY/gyFDhoRmEsynIiQ3NzckYSZNmhQSIgT73Pln3ZdccsmSAB6sgwTFbrvtps0331yDBg1KPfI/VI2QCOF1EhzTe+65RyNGjNBPfvpTbbndDpp60OE69JgTdN0/L1dNdZVy+kjZUVQ9JFrnscccrX332SdUXPDeso9f+tKXtNNOO4V94f0hyUECIEEigO1hH6i24P1nHx966KElCQaSELzvPJ/3/dRTTw1JDpJZ4PiSoCGhQSUEx4/18J7x3pFESpBgIcFDZYyTEGZmZmZmZh2JBvrZg6U+/6s0aFe/AVJWXoc1ySDwTIJ57ozPmDEjBPXp/UEQkBO8EzD+8pe/1D5RgEsgTaBNEwGaRBB48lyCXgJc7nTT9p+73fy9KhDE09xh4MCBYbt5rbXWWivM47WGDh0atpuqibaGAGU/uGvPiBNUUuy9995hP0iUkEihQ86kIoMgmeYdbDuJhqysrDCf9XO8CgsLQwUJSQaaLBDwkzhgWX4SyNMsJKmEAJ0x0pRiwIAB4blJ8J6OgJ9gnvUkqBRhVJFNNttcOYOHq6pfrvoXFGr7nXfTogXzVbtovor6NIdOKGnCQVMZjgdJE16DxApVGCeccEJI1px22mmhyoP5yfaxHP10kFSguQTbSJMLElMkCTguVMlssskmGjlyZFiG483rDR4cncsRElWffPKJHnjgAZ1xxhnac889tccee4QmLc8++6w++OCDsByohuB9TCpiVgcnIczMzMzMrJeIosXBE6S8sVEklJ2a14bsIqlwI2ng6NSMVY/gkyoAkgaU+RNEcpc7vSkGQSh36AnE6VuAoJL+GZhojkGpPYEvAS3BdRKwg0CYqT0Eo20F4+n9KSQIjNPXzTYyLwnYWQ/z2N62ns98AmXWQRIifT9oNkAzExIaYD8yXw+8FgF+goQBr8Wy6TieNElI78ySIJ/1LgvHin1IHxqV7a5rbFLf3AGqaomODauMjtuggny1trQqp6VR2X1bQ2eUma/B+/bPf/4zJAboo4FExPHHHx+adbDdyXHifUh/r5K/kyqI5JiSfEg/N9ITNFRwMJGo+NrXvrbk2P7whz8M58lJJ50UlgPHhcQGyZzVxUkIMzMzMzPrPahwWOdYqf+I1IxMUUg5bLI0fEoULbUfxK8K3PGmI0Lu6BNkclc/HUEmpfMEpTQJ4G46/R8wUUVA0oJgkiqB0tLSpe5u05SAqT0EtNwRJzmQ7sMPPwwBfjoC40xtzWsP+0FHlGB/qehI3w862mTkDrDe9l4vPWnC8WLf0+/yE7C/88474ZilJzHaSrZkoqqAJAdVCuQaGlqk+n65GlA4KDomH6i5pTUEz/l9WvTp9HeUm5uj4UOLlZVKDGS+Bs1SqMigeoMEBB1u8v7yGiQCkiRJe/ubIMmSjKZCogE8d968eaqoqAh/8zgVKhxDEhHJsWXiWE+cODEsB95bKmvYruU5Lh1h9byqmZmZmZnZahGFQKMPkDY6K4o8N4/+/F/5vbIHRY8dKK3/teixLYgQUw90DAJSEgt0XEg7f4LJdASJVAhQWk/Hhs8//3xIGjCqAv0KUGpP4oERDwgu6dyQziLpZ4JRE+jgsD0kIEiC0GnhU089FYLcG2+8MfzdVjXDyiAhQDUA/UawrwxNyn4QSD/22GN65ZVXljTHWF4kXrbffvvQnOPOO+8MlSSPPvpo6Ljz0EMPXapZxfLgODMyxkczZ6m0ui4MvZk7aKi2mryj3n3rTd11w7WqmvuJ3nn5eV152cWhSQmBf3sJBBJHvH80QWHi+NJRKKOYrAiOHe8/nXfyfJJE7C8dZtIUB5xH9I3BOcAoG3RwSrMYEjT0t5GMWsLjJFlIetGHRnplRWdyEsLMzMzMzHoPgsb+w6WxR0kT/yht/itpo29L438gTYr+3uTsKMKdKmVTrt6xSQjQ30JylzwzoOVvEhPf+973Qh8JF198cWjOQCeVdFZIEM/df/pQoBNCAl1GWPjTn/4UglCaarSHKgI6L+SOOCNMMAIEyQCSBelVBKsC+0E/BgyxyZ19RrdgmM+zzz479IdBxQb7sSKoXOAuP4kNOqJkfQzPSceTNHtY0STE8OEjtP4GG4YRL155e5rqos3pn5+v7Xacov33P0B33XS9fvq9s3Ten/4Y9oUmJenNQzLRBwXJAxIQNJthKE4SJVSCrCg63CRhxLCsjO5BEw9em745SHQwkYiiE0zOCTr/ZCQTXpMkTdJJJ01N6MCS0UHoJ2N1VUL0aU3qQGwpdEJCNonxdSkPWl1ZIjMzMzMz+x/u0hO0EsZw5/rz2vq3izCIoJ9RMBrKpb5R4J07VOrXP7VAxyQgGAWDzg4ZuaKtoSIZopLhFgmyGREB3MGm80aaGlCCzz4TgFJ6TweR3HXnDjcdVZKIoKkCzR+Iaah4oGKAu+ZUOVBxQMeFoLyfqgQ6tkySGfwkgUHHkTyXu+8ca4L7pCNEKjJYD31aJB1pUnXBHXhei6YjbWE/ktdj29hu9oNOGWlCQYUHiRASI3SsmWC/OWa8HvubIKhmRA4qP6gIoT8IjgkdfCYBNh14sj08t72kQXN0KpB0eO6lV3TDdf/W8FFr6LQzv6vcfn2U3dyoRbM/1uuvvhKOC8eWbSNplCRrqMDg9ejvIb2ahYoDjjmdTJJ8Yds5BiQFSADR7wP7RnLikEMOCc/h+NMRJcOPJskUjj8VFBxjnktyif2iCQ6dXdIZJ/hcJMeKfkJ4/6jw4JhwnDm+LM8oJ1//+tfDczLxvLlz54ZjSaVHehzM8b7gggvCPpH4yeyPI1P6SCPpnIRoh5MQZmZmZmZdzypLQqRLEhLWqxAI0/dDLVOzVFZRoWcef0yffvSBTjz5FA0vzFdu39V/apB4ohkGiQTOdxINVLuQrGE0DKokPg+fG9ZBEoFKGio12tIZSQg3xzAzMzMzs97NCYheh+qH6uYowG+SqqKfDdHfBQWF2nGHyTpg7700KLuv+kfxd1c4NbhBztCef/nLX0LTmcsvvzw0CaG5R3vJhEw0iSGpwCgry/ucjuIkhJmZmZmZmfUKVD/Ut0iVqQQEVRAkJLL7SHn9pLWHD9WkCRurMH9g/IQugFEvaCpDJQTNPbbeeuvQrIJ+ID6vGiFBRQNNZ2hWs7o5CWFmZmZmZmY9HsmGmlTyoTKaGqO/KXToH0XFBf2kwixpANUP8eJdBkmIL3/5y6Ejz3POOSd0OsnoFsmwpt2NkxBmZmZmZmbWY6VXPyxukmqi36P/h+qHgf2koiwpP5qyulr2oYdyEsLMzMzMzMx6JKof6HSS5ENS/UAQnFQ/kIDgd+cfOo+TEGZmZmZmZtajMOAJI19Q/VDWFPf9QPUD1Q5UPwxy9cNq4ySEmZmZmZmZ9Rih+qElTj7Q/0NTqvoht49UmEpAhKE348WtkzkJYWZmZmZmZt3ekuqHJqm0UaqLfo9mLal+GJIdVz/0c/ZhtXISwszMzMzMeh8iVtTXS4sXS1VVUlMUvXYRzc3NWrRokd5///3UnOVTXl6uTz/9VBUVFak5PUdNTY3mzp3b5r5R/UDSoTR6Cxc3R39H8wh2c/rE/T4Mjqacz6l+WBydB7NmzVJlZWX4m9ebOXNmdIpE54itMk5CmJmZmZlZ7xMFmJo3T3rqKek//5Fuu016802ppERqaEgttOpVVVVp4cKFqq6uTs2JkXQgCJ4XbVNLS0tY7uabb9bJJ5+cWmL53HLLLfrud7+rBx98MDWnZ+CYPPPMM/rlL3+pxx9/PDU3rnRoTFU/LGyMR8FA31T1w9BsKS/6yd+f55577tFpp52mxx57TK2trXr77bd1+umn66WXXkotYauCkxBmZmZmZta7cGf70Uelo46SDjhAOvVU6ZhjpL32kn72M+mddzqsKoIkwTe+8Q3dddddqTmxkpIS/eMf/9CXv/zlcAe+sLAw2qRjwvKmkLh58cUX1dDQoD322CPMa6H6oVlalKp+SNW2hKE3i7Pi5hfZn1P90J4+ffporbXW0i677BLeF5JEtmo4CWFmZmZmZr0HweRjj0lnnhn/rK1NPRCJAl1ddJH0pz9J06Zx+z31wOqRnZ2tgQMHpv6KNTY2RptcGyaaCfA3PzODZP5mPsvV1dWpaTmSKjyHZUmC8DwCfioCEqyPeUzJcvxM5jFR4ZFUeVC9wHOS9fF7+vrYJp6f7APNLPi9LVQlfPjhh9p+++2V279/qH4oa2jRp5XRdtQ3Rq9dr7ramuj9rNLwbKl/nxY1pPY/2Re2J8F2JK/f1uMYOnSoJk+erJdfflnvvfdeaq6tLCchzMzMzMys9+jXTzrvPGn27NSMDATJ998vPfxwFC2tvnCJvh3OP/98bbHFFqk5MeZtsskmGjlypL761a/qxz/+sfbbbz89+eSTqSViNF04+uijNXbsWO28887697//vcy7+TxGdQbLDh8+XOPHj9ePfvSj0L9Ekjjg9Wiu8J3vfEeTJk3Sl770Jf3+978PTUZOOOGEsPx6660X5pNUeP7553XAAQdoxIgRoaqAZWhukqyPCoO99tpL5557rvbZZx+tscYabVZ+sPwHH3yg+fPna4uJk1Qb7QbVDy++/qYO3nl7XfD7X+u7Jx2ryeuvpXO++y21NDWEqonDDjtMa665ZtgmXvuNN95YkoyhuctFF12kLbfcUmPGjAmP85x0WVlZ4Tjz/Lvvvjs111aWkxCdiQ89ZVc77igdfHCcXe2gMq82lZVJv/iFom8W6Te/kWbOTD2wnBoapPvuk7bfXtp/f+ndd+N9+qLo/OeOO6RddpEOPzwue1sZixbFmWvW9+1vSx9+mHogA196LBt9iWq77aSrrpJSnc/0OK++Kn3ta9Kee8b7mbQ9XLCAb/34XOBYRf+4dGmcK7y3lN6ddVbcXtOso3En5rnnFF3pKbp6ikt26+pSD5qZWbfF9dH06XGTjPbQL0QUsGrWrNSM1ScJ2Pl500036U9/+lN0efc1PfDAA1FYsWN0eX5fSBSkozPL119/PQT4N954o7beeuuQhHjiiSdSSyyN6gP6WTjllFN03HHH6amnntLFF18cgvK//vWvS63/9ttvD4E8yRCmiRMnhr4sWJbKjdtuuy08590oVqBviry8PN17773h9dmu448/PlRGJD755JOwL7z2ww8/rN133z31yP/QQScdRGb3H6gha62jkuifaEbB4MiURe/VnTf8W7vsuEPYhx/84Adhe7/yla+ERAnJgxtuuCFUOvzhD3+ILiPj60i257///W8Ulh0cEh8ke+hHI7PTSypRSEK4X4hVx0mIzsKXB4HuSSdJL7yg6JMWZ2AJhjvL9dcr+haSnn1W+uMf44TCiiQR+EBGXw56+eU4M/zrX5OiTT34BfDlf8458fZwPH7609QDX9Ajj0h/+1u8vuiLRhdemHogA4EF+3/nnfG+ENSSsFjN5XarHPsZ/eMRfcMq+hdHuvJKRd/M8WP8fe658bGKvnzbPVZdBXcjrrkm3u7LLpOuu27ZFw5mqwIXXGefHX9XUZ5L0jS6+AufLTMz65649qUCgptry8K1OyNmcBOvA64RKe3/5je/qXXWWWfJtNVWW0WXqNE1ajtoKnBZdB1ExcCxxx4b7uBTlbDtttt+pslG//79tf/++4dAfIcddgjJiMGDBy8JwDORFPhbdB3N+qiemDBhgnbdddfQd8Vzzz0XRqRIkHQ44ogjQsXE+uuvH9ZLFQXbf1Z0Xc3PYcOGhUoMqg0I/Lfbbruw/F/+8peQCHn11VdD4gMkB84888xQMcE+DRkyJMxPV764QiWlZerXf4D65heJd4R+HvpFU/7AAZo6ZYq+duopoXqDbbowurbdaaeddOqpp4YEDE04SIh8/PHHoUkH+8P2bbbZZlF4dlLYPqo8SEiwPelIrLCPs9urnLEV5iREZ+JLjy8yPnCc3ATwK1NJsKJIgtTWxq/PnWXu6KUyq8uFL2Cywsn2r+yXMuthHayLgJLfVwb7RqKE9ZFdbWPonoB95tizHJUo/APDtqzIsegOeG94n9Pfc34HP5P9Tn7vyng/qeJge5PfO+CCwGwpyXc2P/l+4DuD75We9l1hZtab0BQjCpDDz8+TlycVFHRIk4zRo0eHURf+9a9/LZlIAtB8oD1UQrzzzjvh7n5BtF00FRgwYIA23njj0IllOppT0AyB5ARB9KBBg0JigiYebaGy4YUXXtDll18eAnKCc5INVBW88soroTog6S9h3XXXDc0mWG+/6DjSgSPVDjS34HXZLvpZIGgfNWpUqCJgHq/POtkmkjBJswies/baa4d1sBzrS0fFQ2ldo6oampUVvWaf6P3oFy1SEL2FdDw5MDoGbC/Hgu3pGz1OkoORLqiq2HzzzUOVA518vvbaa6E5CE07SktLw+vSFIN9odkF+8Z60rE9ubm5IVFjq4aTEJ2FDxNfYvS2S1nvhhtKxx8ffXI+m+nrMAceKO2wg6Jvg7j5A78vzxdwIj9f+vnP6aGFbx9F35zxPn1R66wjnXaaom8xady4uFnAyqCZCF/crI99o7lFW6IvmVDRQYl1cXHcKVH05bM62/x1iJyc+H3eaSdF/ypI++wjbbtt/NjWW8fHh3OBeUcfHc/vqqZMkXbbLd4PmpawL9E/BmYdiu9qvh/4nosuirTppvH3KN8hZmbWfUUBa7iW5VqpPVHQrvHj42uPDkCgu2EUD3CHPpmoAiCQXxaqB3Ki7U4P1AmgCbzTMY+APsHyTEngn4kEB51JUg1ANcaf//znMF1yySVLmiokr0kSgaA8HdvEPiXbQcKC12IbSAwkkm1N3w6SD8xP3ycw8gXDbpY2Ss1ZueoXvUZjQ4OyWps1JNq1wmjKil6O9WUmYWgeslt07fib3/xmyb6Q5KH5Bf1n8Hrsc/r2sZ62kiDsCx1XZr6GfXFOQnSm6GQPQfe998ZjERNY9e+ferATrL9+3ISCTlV+97v4gjrjQ7ZMbCvNSSiNv/nmuD+BlQkESWaceGLcT8a110pttP9aIWPHSt//fry+Cy6Qttkm9UAGvhwnTIjL+2kGwnNIRqzIsegO2B8C9+gfD91+u/T1r0sjR8aP8Q/vj34Unwvnny9NmhTP76rWXFP67nfjcnia3JBYSf0jZ9ZhuAN20EHxEG5PPx1/T5Hk7GnfFWZmvQ3Jh5NPjpPNbeF7nuv0qVPjJHQHINAl4CV4T5/SEweZeA537WfMmLFUEE9fCTR7SMeymcH0shCIU7FAsE0zBppOJBNDVNJEIllfW+vNfD0SEowsQUeSDD0Kgnm2tbKyMuxHEvxnro+Cw/oWqSzaxYpmqSH6u3BIsYZF71dDVYUaS+ZrQPRUqiF4ZuZr8zv7wjGhqUrmvpDooeqBZApVEcn2sV38TRVHOjrYnDNnjjbYYIPUHFtZvorvTHw4KP+aODFOAFBZkPGh61AkQag+oIddAvaMUqPPRdBH4oCAlQzyym4/XzwcD7aHqgTWtzL4B4VglfXxJZHRNm4pJE823jjeF6oB0jK0PQp3cDfaSNp883g/k3/YOFajR//vWHVmMuyLYLt5b9kPtndlzxWz5cF3XlGRtOWWcVKT74ye+l1hZtab8P2+777ST34ibbXV0tdBgwdLRxwhnXFGfL3bmdfqn4Pg+sgjjwzNDOiMkr4N6ASSPhuSITG/KJIGdBh5xx136D//+U/oLJIEAp0x3nzzzVpAp+YrgOYf9A1B4oGONEmcMDLFr371K40bNy40kaAaIVNzq1TVLJU3STXRz6ZUC8ihhfnaaOzaysnqp48/fD8kH5aFfZk2bZquvPJKTZ8+XQsXLgxDfNJBJYkQmomwDWzTrbfeGpZhZBA60MysKuHY0qEmzVRs1XASwszMzMzMeg8SCyQbDj00rrCkKvT//k9RhBxXkPI7zXw7+4bh5yA4pqNJKhUYueLrX/96SEhQVVBcXPyZ4HlFUIVxaHQ86JiS0STokJI+Ky644ALNmjUrdDy5Ikgw0Lkl66AfhjPOOCP0L0EzCZpI0HFlZgUE1Q8kH6h+4Hd6oMiOFqHZxZCcvtp03Aah/4jMYTQzsV6O0fe///2QeGDYUDqo/O1vfxs65qSygf1lGFH6kiAxQYeadJhJXxf5aTe7qAwhgUIioq1RO+yL6dNKYxj7DMp36PTliiuu0OTJk5eUC5mZmZmZ2epDUwTubBPGEMy2dUd9uRAGEQjT6TATlZf0BZE0N+6gBAR34hmdgY4jSSAkCI6pbqBJAEE0VQT8zbQvlRspDH3JnXlGcRgxYkQYgpPKhR//+MfaZptt9NFHH4V10OyADjDBEJd0xkj/C5tSkd0GjifVD1QQlKU6jGd5tpHRO+hY8umnnw7NGOjPgsfAe8E2ErxvQnVzCttP548kAnh9kiQ0g6B/iaTzRwL8mR9/onGbb6mcwqLQCSXJB9Ip/aP/DIxCMH7S9IJtuu6660JHmb/85S/D8aOjTTrUpKkE25iO5dkXqjhIovCavD7L0rEn/WtwrDguNMHgMfaRTjhZhoQECZh//OMf4dj87ne/6xUxIecV5yfvJxUt6fvMOUpiimQO/YUk72N72hrpBE5CtMNJCDMzMzOzrmeVJSHSJQmJLo4A8amnngp9HnA3/6233goBOfEKVQdjaXLdTXDI66Opulmqa4mbYhCYUv1A8mFAXykn+j15W3i/qapgaE32l5E2OhKvR4KCSgmG7mwvedPTdEYS4ovX7JiZmZmZmfUE3SABAe7o04fBr3/9a5199tm67LLLQkKCSgmqIroLEg6VzdLipjgJQd8PvAMkH4qy4uE3c6NINf1toZnFRhttpAMPPDBUKXQGmn8ccsghGs9IKbbKOAlhZmZmZmbWDVD1wSgP9GVA04c999wz9N+w9dZbf+5d6a6A6geqHuj7gSQEvyfVDwVZUmG/OBFB84u20BSEZiadkXAh6cGwnBxnV8WvWk5CmJmZmZmZdQMM4UkHkt/85jf1ne98RyeffHIYhYLgvKuj2oGRL6h+4Cd/E4wOjP6zVPVDvLj1YE5CmJmZmZmZWYdIqh8qmqTFqeoH0N9Dfr84AbGs6gfrfB3dbaSTEGZmZmZm1q1QKk+gxAgM1jURxqZXP9D8gr4gCEDpdJKhN0lA5Lj6oUtJhmPNHEJ1Veq00TEYE5beRefMmaPKysrQloexY4uKisLjDI9CT6+Z6PV1880317hx48Lfd955Z1hX+mYzhAptdRhqBTU1NWHYGoaKoQdPes2lExOGcVleHh3DzMzMzKzrSYZ+rK6uDr3304u/dS0tUajWGE21LXHHk/xNpiEr+sGQm/nRL/QDYV0PsTYTI1vQJ0Z6MqLbDdHJ8DH//e9/9frrr4ehVQjsf/rTn2rjjTcOjz/22GP64x//GH4HQ+/Mnj07DA/CDh522GFhPkOj8MVDEoM2Udhrr710+OGHa9SoUSFzQzLjpptuCokIxqPl4DDW7le+8pXQw+nycBLCzMzMzKxr4lq9rKws3JjMy8tLzbWugNqUhug/tc3xEJwgjM2K/kMFBE0vrGvi80TBAIUAxcXF6t+/f+qRWLdLQpAQIBGRm5urq666KgT16UkIxiMlm5lYtGhRSABQ+fDII48s6QGVJATDpJx00klLqihYJweIhMO8efP0i1/8Ijz/+OOP15gxY8IwNowny99HHXVUeE4mEhvp5Vx8sdHL7D//+U8nIczMzMzMuhCu2wmWuGObfg1vqw99PZQ1xSNf1Ee/U/1A4mFwdhSMZsUVEO73oesjpiYBQWenmU0yul0SIt13v/vdkCxIT0KkY5PefffdMNwMY7L+/e9/Tz0SJyEYloZExODBg8OOjRw5ckmW5p577tFFF10Uxso97bTTwkF85ZVX9Le//S0sw7oyEwpUT9B0gyYhCZp0nHHGGbrhhhuchDAzMzMz62KIGUhAJG3YbfWg3wc6nXy/VnqlUvq4LpoZxa6MerFWrrRloTQ+ilX7RX9b10fc217s26OTELW1tbrvvvvCsDNUQ+y6666pRxQSEHV1nNnxOLH09XD00UeHphMcBA4GlRM0vTjooIPCcjTpIPlAJcbVV1+9pIIiwetddtll4bUSfKHNmDFDDz74oJMQZmZmZmZmaQgiqXiYUy+9WBlNFXHnk/T1MDRb2jRP2mWwVBz9bj3DqkpCdMl81IIFC0Lwv/baa2vHHXdMzY2dcsop+stf/qIrr7xS3/rWt/Tee+/pz3/+s6ZPnx4epyyL6of09is016CtGE0+KiqiT0cGlv3a176mp59+esnE648ePTq1hJmZmZmZmYFRLqh+eLlS+s8C6dGyuANK+nvYYKB08FDpkGFOQFjbulwSggoEKhdeeOEF7b///qFTjHRf/epXQ2UCzTQOPvhgff/7318yEgadWZKAQHqBB78z0aYleTwd83kdetZNn9pa1szMzMzMrLei08lP66U7Fkk3L5Q+qov7eqD6YUqRdOwIaYsC9/9g7etyUXZ5eXlIQNDkImlOsSzrr79+KAOhI0qaVTBMD8mI9E4u6d+BTmtYjsfNzMzMzMxs+dH9Z02z9EKFdNU86anF8d85UUS54UDpyyOkg4bGHVGaLUunJiGSioSkSiH998ScOXP05JNPauLEidpggw1Sc2MsS8czyfOomqA5RpJ8oNnFuuuuG5IOs2bNUmNjY1juk08+CcN90rzDQ/iYmZmZmZktPxIQ9P1w3XzppoXS7Oh3Ch1IOOw9RDphpLRJFGb1dfWDLYdOS0JQ2cDoE2+88UaoWqDigX4cSCKUlpaGZahgmDlzpqZNm6YDDjggzEuQcKBC4uKLL9bLL78cml8wfOcvf/nLMAwnCQuaVGyzzTYaN26cbr/9dv3rX/8KCY1rrrkmJDf22Wef1NrMzMzMzMzs8zS2SI+XSZfMll6qjIfiJNkwIS9OPpCEKMxKLWy2HDotCUECgtEudtppJ914442h48djjz02jGzBsJpIqiDoRXOPPfYI8xL025Cdna3bbrtNhx56aOgX4uyzz9a2226r3/3ud2HoTtDkgqE1p0yZovPOOy+MkvHxxx/rm9/8pnbZZZewjJmZmZmZmS3bJ3XS5XOlO0qkhY0KI2LQ2eSXhkpfGSGtz9Cbrn6wFdRpQ3RS5UC/DAzrkS4rKyt0AslwmzS1oC8HRrEoLi4OiYd0NK9gdIukmQXDZjKyBc9lPQnWQ5MMmmlQQUHygmYY6SNmfJ6qqqow7CfDdnqITjMzMzMz6y3ofPLxcunJxVJJYzwaBtUPE/OlqYOktaOwir4gnH/oXbrdEJ0kCUgsrLHGGktNw4cPD0kEEOgXFhZq6NChn0lAgGQC6xg5cqRGjRoVnsvy6QkIsJ6CgoLwOMvynBVJQJiZmZmZmfVGs+qkS+dI95dKCxukplZpWI50+LB42M11o7gz1wkIWwmdloQwMzMzMzOzrqm2RbqvRPrnXOndGqmymSbx0naF0jEjpG2jnzTFyHL2wVaSkxBmZmZmZma9FJUOM2qkK+dKj5RL8xukxmjeyBzpyOHSvsVx9cPAfq5+sFXDSQgzMzMzM7Neho4ByxulB8ukGxdI71RLi5vivh8mF0lHDZe2LpCG57j6wVYtJyHMzMzMzMx6kfoWaVq1dNPCePjNT+rjiog1cqUjhkl7DZE2GOjqB+sYTkKYmZmZmZn1Ai2tcWeTj5RJdyySXq+SypukAVFUuENR3PnkttHPETkeetM6jpMQZmZmZmZmPVxNi/R2tXRnifTEYumjurhJxpj+0gFDpT0GSxvnxQkJ5x+sIzkJYWZmZmZm1kM1t0pzG6QnyqV7S6VXKqWyRqmgXzzixf7FcRXEyNzUE8w6mJMQZmZmZmZmPVB1s/RWtXR/qfR4ufRBbVzlsE5/ac8hcfXDZvlSf0eF1ol8upmZmZmZmfUgVD/MrpeeXhwnIF6qiKsfBmdJWxVIew+RpgySRrn6wVYDJyHMzMzMzMx6iKrmuO+Hh8qkh6OJ6gc6mVx3gLTzoLgCYvMCVz/Y6uNTz8zMzMzMrJuj+mFOvfRshXRPqfRC9LOiSRqSLW2RHw+7uctgVz/Y6uckhJmZmZmZWTdW2SxNq5EeKI2nj2qlrD7S2P7S1CJp32Jp83wp19GfdQE+Dc3MzMzMzLqhZOQLqh5uWxj9rIybY1D9QNJhv2Jp18HSiJzUE8y6ACchzMzMzMzMuhlGvphRI92zSLqrRPqkXsruI62VG/f9cGCxtGm+lOOIz7oYn5JmZmZmZmbdBNUPixql5yuk6xZIL1VJdc3SoCxpQp70pWFx9cNQVz9YF+UkhJmZmZmZWTdQ2yLNqpP+u1C6dZE0vyGufqCzyT2GSIcNk8YNjOeZdVVOQpiZmZmZmXVhVD+UN0rPLZYumiO9Vik1tkh5/eLqh2NGxE0wBmWnnmDWhTkJYWZmZmZm1gW1RlN9i/RpvXT1fOnGBfGwm/36SEOzpYOHSl8ZLq0zIB4Nw6w7cBLCzMzMzMysi2lplSqbpCcXS+d9Kr1dHc+nqQUjX5yxprRjkZSXFc836y6chDAzMzMzM+tCmlrjvh8unyvdtECqbZYodCjoJx0zUjphVNwPRF9XP1g35CSEmZmZmZlZF7G4UXqgVPr7bOm9mngezS+2LJB+NEbaKvrpjietO3MSwszMzMzMbDWjo8lp1dKV86R7S6Vqqh/6SMNypONHxhUQQ6LfSUiYdWdOQpiZmZmZma1Gixqke0rjzidn1EoNLVL/KFLbvlD6xprSFgXx384/WE/gJISZmZmZmdlqwMgXr1VJ1y2QHiuXyhrjETHG9JeOGhGPfjE8R8px9sF6ECchzMzMzMzMOtnceumeEumWhXHfDzXNcceTOxVJx4yIR8AozHLAZj2Pz2kzMzMzM7NOUtcivVgh3bxQenqxtKAhnr/+AOlLw6S9hkijc+PmF2Y9kU9tMzMzMzOzTvBpvXR3Sdz/w/QaqbJZGpwtTRkUJyDo+2FI9LeH3rSezEkIMzMzMzOzDkRTi+cqpDsWxT9pioFxA6X9i6VdBkvr9JcGODqzXsCnuZmZmZmZWQeZVSfdVyo9GE3vVEsVTdLQVPUDCYhJBdIwVz9YL+IkhJmZmZmZ2SpWTfXDYunekrjvh9n1caJhk7y434edB0nrDXD1g/U+PuXNzMzMzMxWoZl10gOl0VQmvVktVTXHQ23uWCTtPUTaulAaFv3t6gfrjZyEMDMzMzMzWwWofni+Qrq/VHq8XJpTL/VLVT/sPljaLZoYBcMjX1hv5tPfzMzMzMxsJbS0xn0/PFwm3VMivVYl1bbE1Q87FEn7DJG2LZSK3feDmZMQZmZmZmZmX0RrNFU2Sa9USfeVxE0w5jVIOX2k8QPjygf6f1g/+j3XkZdZ4I+CmZmZmZnZCmpMVT88US7dtjBORDBvRI60TaF04FBpcpE0KEty8YPZ/zgJYWZmZmZmtpxoerG4SXqT6odS6a5oWtgYVzpsOCCufjhoqLRO9Hu2sw9mn+EkhJmZmZmZ2XJoaJE+qZeeWiz9d6H0amU0s1Uali1tWSAdMVzaqUjK7+fqB7P2OAlhZmZmZma2DEn1wzvV0t0l0l3RVNooDYiiqbH9434fvjJcWiv63R1Pmi2bkxBmZmZmZmbtoPphToP05GLpxoXS61VxlQMjXWxRIB07UtppkJTtyMpsufijYmZmZmZmloHqh6pm6e1q6ZaFcQVEWaPUP4qgxuRK+xZLR4+QRkW/m9nycxLCzMzMzMwsDaNczG+QHiqVrl8QJyKofmCki60KpBNGSTsUSVluemG2wpyEMDMzMzMzi7RGU12z9FaVdO0C6b4yqaIpHuWC6odDhkpHjYiH4TSzL8ZJCDMzMzMz6/Vaomlhg3RHiXTtfGlGTTSjVSrIiqseTl5D2tbVD2YrzUkIMzMzMzPr1ZpbpVcqpMvnSo+US5XNcfOLdQbEo14w9ObQ7HhZM1s5TkKYmZmZmVmvRfUD/T4w8sUndVJrq1TYT9pjiHTiSGnzfKmfqx/MVhknIczMzMzMrNdpapWeXSxdNld6vkKqbJJa+0jjBkpfHSHtO0QqzokCJicgzFYpJyHMzMzMzKxXmVcvXTtPun2R9Gn0e0OLVJQVJx6+PFzaJF8a0M/BkllH8OfKzMzMzMx6haYW6cly6cp50itVUjnVD9H8CXnSV0ZIuwyOR75gNAwz6xhOQpiZmZmZWY83p166Zr50b6n0SfR7XYs0OFvav1g6eJg0fqBUQPWDExBmHcpJCDMzMzMz67HqW6THy6Wr50mvVUmljfH8iflx04udiqQ1cqQcR0ZmncIfNTMzMzMz63EYdnNmrXTDAumB0uj3urj6oThbOqBY2jeaqH4ozHL1g1lnchLCzMzMzMx6DPp4qGiSnlws3bxQerlSWtQYJxq2LJAOGybtUCSN7u/qB7PVwR87MzMzMzPrERjl4oNa6c4S6eEy6b3od+YNy5H2GyLtFU0T8lPVD6nnmFnn8mfPzMzMzMy6Naof6OvhmQrprkXS89HPBQ1SbhTtTCqQDh4qTR0sjekvZbnphdlq5SSEmZmZmZl1W/Tz8F5N3O/DI2XStOj3pta4s8ndB8fVDzTDyO8nOf9gtvo5CWFmZmZmZt1OS6u0sCGuerivJK6CoPohr5+0Rb60T7G022Bp7f5OPph1JU5CmJmZmZlZt1LbIs2olR4plx4qk96piZMSa+ZKU4ri6odtCuOEhJl1LU5CmJmZmZlZt5BUP7xE9UOp9NTi+O/CftKmedIeg6U9h8TVD2bWNTkJYWZmZmZmXR7VD+/XSo+WxwmId6qjmanqh8lF0r7F0nbRz4GufjDr0pyEMDMzMzOzLitUPzRKr1TGQ28+US6VRH8zzObGeXHTi72jiZEvzKzrcxLCzMzMzMy6JKofPqqTHi6T7lgUj4JBJ5OjqH4olA4cGvf9MMDVD2bdhpMQZmZmZmbWpVD9UNoovVop3bRAerJcWtwkFfSTxg2U9i+OR7+gKYaZdS9OQpiZmZmZWZdR1yJ9Wi/dUyLdvDCuhKD6YXiOtGORdNhwacsCqb8jGbNuyR9dMzMzMzNb7VqiqbJZeq1K+tc86ZkKqTr6m44mNxwoHT4s7nxyRE68vJl1T05CmJmZmZnZatWYGnrz5gXSdfOl2fVx9cOgLGm3QdJXR0gT8qUcRy9m3Z4/xmZmZmZmtlq0RlN9SzzyxV8/lZ6rkBqiv7P7SBsMkE4cJe1VLBVnx8ubWffnJISZmZmZmXU6Op9c1CD9a650ZTTRESUY6YJRL04aFTfDICFhZj2HkxBmZmZmZtapqH6g6uGPn0ivVsUVERiXJ317TWnXwVJBVmqmmfUonZaEmD59us455xztuOOOGj16tE499dQwL/Hiiy/qoIMO0pAhQ5ZMLHfaaaelloiVl5fru9/9rsaNG6exY8fq6KOP1htvvKHm5ubUEtLcuXN17rnnauLEiVpnnXV05JFH6sEHH0w9amZmZmZmq0Nzq/RpnXTlPOn6BVJFU5yAoPPJI4dLJ42U1uovZbn6wazH6rQkRE1NjQoLC3XooYdqk002UXV19VKJA37Pzc3V4YcfrocffjhMd999t37yk5+kloh973vf09NPP62f/vSnOv/881VSUqI//OEPmjZtWni8rq5Ol1xyiR544AF95Stf0Z/+9Cfl5+frggsu0FNPPRWWMTMzMzOzzlXZJD1RLl08V3qrKu77oW8faUKe9J3R0g5FUn5WPM/Meq5OS0KMHz9exx9/vE488URtuOGGqblLy87O1ogRIzRhwoQwkaxYa621wmOtra368MMPdeedd+ob3/iG9ttvP+25554644wz9N577+ntt99WfX29Xn31Vb322muaOnWqTjrpJO2zzz4hsTFgwADdd999YV2ZWHdDQ4OqqqqWmlpaGCjIzMzMzMy+qKZW6f2auPLhrpK4HwjmDcmOh908eoS0Zq6UG0Umzj+Y9XydloTo37+/Bg8erKKiIuXktD24L00tbrzxxpBgIGFx6aWXhmQASBS89NJL4SdNOlgXiYUpU6YoLy9PM2bMUFlZWfiZlZUVEh0sM3DgQG200UahaQePUZGRieqJiy++OKw3mUhwfPrpp6klzMzMzMxsRdHZ5CNlcQLirWqpqjlONkwqkE4eJW1bKA3KdvWDWW/SaUmIz1NcXKy99947VDnQjGK99dYLCQn6kaAigeQDSQH6iiDp0KdP/E1VUFAQEhskIEgwlJaWhiQETT/SlyEhQUJj8eLFYV46kiK8Ns06konXZZvMzMzMzGzFNLZK71RLtyyUHi6T5tTH1Q9r5EgHDZUOGSaN7R/3BWFmvUuXSUJQqUCzCRIQBx98sI477rjw9x133KG33norLEOTCRIMSXIB/N6vX7+QqGBqamoK8/r2/d+u8XuyDI9n4rH1119fu+2225Jpl112CVUUZmZmZma2/BY2SA+XSrcvkt6slsqjy2+SDVsXSEcMl7YplIZlR9fgrn4w65W6TBKCphUjR44M1QdUNjDyBf06kHhIRtEYNGiQKisrl+qrgd9ra2tDcw8qGqiSYB79QyRYB1US9DlBJ5VtIVFBgiN9Sk92mJmZmZlZ+xh2840q6Y5F0uPl0sfR5TgVEWv3l/YtlvaJpg0HSvmufjDr1bpMEiITzS9IJDQ2Ni5JCNBRZUVFhWbNmrWkooHOKmmKseaaa4YmGPwkKTF//vwlyYoFCxaEiU4vaZphZmZmZmarzryGuO+He0ukN6ql0uhSvaCftF2hdODQuO+HkTmufjCzTkxCkExYuHChPvnkk1DNQGXCvHnzwkRigWQCw3LyOPOef/55/etf/wqVEVtssUWoVGDEjA022EBXXnllGA3jgw8+0FVXXRWaTTD6BlUOm222mYYNGxaG42SiM8qHHnpIs2fP1vbbbx8SGmZmZmZmtvKofnitMk4+MPzmzLq474d1B0h7DpF2HyyNd/WDmaXp00rJQScgCXDttdeG/h0Y5YIRKUgqkDxgJAqSDCQXcnNzw+80oaCiYffddw8jZdBvA+is8oYbbggVDcxbtGiR9tprLx122GGhOQcVEvfcc49uv/12VVdXh2YarGfjjTfWCSecoDFjxoT1fB46sZw0aZKuuOIKTZ48ecnrm5mZmZlZ3NkkzS9er46u9aPfSUgMzpI2zpO2yI8TEU4+mPUctFS44IIL9Oabb+qSSy4JXSosC4NKtKXTkhBUQTzwwAOheiEdVQvbbbddaErx2GOPhdEtmpubQ/8PDLPJY+kdRJK8oGKCfiKorkj6jhg+fHhIXoBKi1dffVWvv/56SEDQ6eXWW28dqiiWl5MQZmZmZmafVdcivVstvVYlTauRFjdJWX3ivh82z5c2zZNGuOmFWY/T7ZIQ3Y2TEGZmZmZmS6P64a1q6ZVK6dNUx5PF2dJGA6WJ+dJ6UUyS58tmsx5pVSUhumzHlGZmZmZm1jVQ/UDTiwfLpPtL474fKHRYP4pBpg6S9o5iDSognIAws8/jJISZmZmZmbWJmum59dLTi+OhN5+vkKqapSHZ0qSCeNjNKYPi5hd93fzCzJaDkxBmZmZmZrYU2mvXNMd9PjxYKt2+SPqkPg4e1ukvTSmKh97ceKA0wBGFma0Af2WYmZmZmdkSDLE5ryGuerhpofR09DMZ+WKzfOngYdKug+O+IFz9YGYrykkIMzMzMzML1Q/VzdL7NdL9JXECgo4os/tIo3OlnQdLRw2PO6HMcRRhZl+Qvz7MzMzMzHo5qh8WNEgvVkjXL5CejX62RPOKsqTxedJXR0h7DpYKor9d/GBmK8NJCDMzMzOzXqolmuj74YNa6a5F0n8WSvMb4uoHOpvcbbB0yihp3QFuemFmq4aTEGZmZmZmvRDVDyUN0rOLpWvmSy9UxqNh5PeTNs6TThol7TXETS/MbNXyV4qZmZmZWS9C3w90NEn1w80Lpf8ukhY1SFl9pOE50r7F0smjpDH94+XNzFYlJyHMzMzMzHoJml+UN0qPlkn/mie9WhVXPwzoF498ceqoeOQLVz+YWUfx14uZmZmZWS9AsuG96rjpxe0lUmljPJ/qh0OGSieOlNZ09YOZdTAnIczMzMzMerjFTdKdJdLV86Vp1fHIFwOiSGC7QunUNaQdiqRsRwZm1gn8VWNmZmZm1kORbHizSrpynvRQWVz9QJOMtXKlw4dLR0TTGjlSP498YWadxEkIMzMzM7MeiITDrQul/yyQZtTEnVHm9ZOmDpKOGSltXRD/7aE3zawzOQlhZmZmZtaDNLdKr1RKV82TnqqQFjXG89YbIB02LB79YnSuO580s9XDXz1mZmZmZj0ECYdbFkp3LIqH4KxplvKy4hEvSEBMLJCKor/d/MLMVhcnIczMzMzMujkqHV6skK6dLz0X/ZzfIDVF8zcaKB06VNp9sLR2f6l/dPXv/IOZrU5OQpiZmZmZdWMLG6SbF0r3lErv1UhVzVJBVpx4OHCotEWBNCTb1Q9m1jU4CWFmZmZm1g01tEgvVEg3LIirH+bWS83R/PEDpYOHSrsMktbpHw/FaWbWVfgryczMzMysG6HpxZz6uN+H+0qlt2uk6ua4r4fdB8UdT04qkIpd/WBmXZCTEGZmZmZm3UBrNJFseKlSum2R9PRiaXZ93MfDJnlx0ws6oFx/QNz3g5lZV+SvJzMzMzOzLq6xVfqkTnqgNJ5er5JqW+K+HnYdJO0zRNqmMP67r6sfzKwLcxLCzMzMzKyLovphcZP0SqV0b6n0RLn0ab2U00faOC9OPuwZTesPlLKdfDCzbsBJCDMzMzOzLoiOJ2fVSY+Xx9UPJCLqW6Vh2dIORdLeQ6Sdop/0BeH8g5l1F05CmJmZmZl1IVQ/lDVJr1VJ95dIj5TF1Q+McrHRwHjoTTqf3DD6vY+zD2bWzTgJYWZmZmbWRdSnqh+eLI+bX7xSFfcHMTwn7vOB5MPUQVJhVuoJZmbdjJMQZmZmZmarWVL98GaVdG+J9HCZNLdeyusnbTBA2m2wdEBx/LurH8ysO3MSwszMzMxsNaL6gZEvqH64oyRuhtGU6vthy4J46M2pRVKBqx/MrAdwEsLMzMzMbDVobY1Hvni7Wrp9kfRQmbSgQRrYT1pvgLTHEOngodK6rn4wsx7ESQgzMzMzs07GyBdzG6THyqT/LpTeqpaaW6Uh2dJWBdKhw6SdBsXNMczMehInIczMzMzMOgl9P1Q3S9NqpBsWSA+WSaWNUv/oqnxsf2n/YumQodLa0e8ufjCznshJCDMzMzOzTkA/D6UN0n0l0jXzpOk1Uks0n74etiuUvjpCmlwUN8cwM+upnIQwMzMzM+tAVD/Q/GJatXTxHOnhcqmyWcruE1c8fHm4dMgwaY3ceHkzs57MSQgzMzMzsw5CAqKqSbp5oXTZXOnjeqklmknzi50HSSeNjEfA4G8zs97AX3dmZmZmZh2AphZvV0nnfiw9sTgeihNUP5yyhnTAUGloTjzPzKy3cBLCzMzMzGwVovqhvEm6dp506VxpYWNc/ZAbXXnvO0Q6ZZQ0fmD8t5lZb+OvPjMzMzOzVYRqh5crpfM/lV6oiP8mKbHeAOn0NaQ9hkjFOVI/D31hZr2UkxBmZmZmZiuJSod5DdJNC6Qb58e/N6SqH2h2ccJIaYOB8cgXzj+YWW/mJISZmZmZ2Uqobpaeq5CunCu9WClVRH+TaNhoYFz9QAeU9P2Q5eyDmZmTEGZmZmZmX0RzqzSrVrp1oXRXifRJfdz8YlCWtH+xdNTwuPoh39UPZmZLOAlhZmZmZraCFjdJTy+WbloovVwllTTGlQ6b5klfHSHtWCSNynX1g5lZJichzMzMzMyWU1Or9H5tXPnwYJn0YZ1U1yINz5H2GiIdNFTaOE8qzEo9wczMluIkhJmZmZnZcihtlJ4ql+5cFI+AsaAh7nhyszzpkKHSDkXSWv1d/WBmtixOQpiZmZmZLUNjqzS9Rrq/VHqkXJpRG1c/rJEr7TpI2meItFl+3BeEmZktm5MQZmZmZmbtWNQY9/1wb4n0QoU0v0EaGF1Bb54v7TtE2mmQNHaAqx/MzJaXkxBmZmZmZhkaWqRp1dJDpdLDZdL02mheq7RWrjRlUNz/w8QCVz+Yma0oJyHMzMzMzNIsbJCerYibXzwX/aTvB4bZpMnFnkOkqVQ/uO8HM7MvxEkIMzMzM7MI1Q/vVkuPlscJCKof6A9iTG485ObeQ6RJBVKRqx/MzL4wJyHMzMzMrNej7wf6fLinRHpysVQS/V3QT5qQF1c/7DpYWtvVD2ZmK81JCDMzMzPrtah+mFEjPVom3b5Ieq9WammVRudK2xdJBwyNqx9ISJiZ2cpzEsLMzMzMep3W1rja4dUq6ZZFcROMiuY42TA+T9q3WNprsLRmrtTP1Q9mZquMkxBmZmZm1mu0RlNdizSzLh714rr50oe1EnmGUTnS5CLp8GHSxHxpoKsfzMxWOSchzMzMzKxXaG6VyhqlN6ri5ANJCBISjHyxwQDpS8PiCogROdFFsqsfzMw6hJMQZmZmZtajUf1Q3yJ9Wic9UCZdPU/6uD5uZlGcLe1QJB0/SpqQL+X66tjMrEP5a9bMzMzMeiyqHxY3Sa9VSlfPjzugbIrmDYyugtfpLx03Ujp4qDQoK26SYWZmHctJCDMzMzPrcah+aGyRZtdLdy2SrpkvfZqqfijKkqYOkr65prTRQDe9MDPrTE5CmJmZmVmPQgKiull6sVL65zzpycVx9QNNLdYdIJ2yhnTwMKm/O540M+t0TkKYmZmZWY/B0Juf1Ek3LZCunS/NrY+bWRRmSXsOkb65hrT+gHhZMzPrfE5CmJmZmVmPQPXD4+XSFXOlFyri6oecPtL4POm0NaR9h0i5rn4wM1utnIQwMzMzs26N6ocPauNhN29bJC1okFqi+UOzpQOGSieOijuhdN8PZmarn5MQZmZmZtZtVTZLD5bFTS9er5JqW6Ts6Ap3Yn488sWUQVJ+PycgzMy6CichzMzMzKzbofrh3Zq4+uGBUmlug9QYzVsjVzpwqHT48LgTyv7R1a7zD2ZmXYeTEGZmZmbWrSxuku4tlW5eKL1dHVdDkGzYtlA6Ypi0ffRzSLaU5eyDmVmX4ySEmZmZmXULVD+8XSXdsCDugHJ2vVTfIq3dXzqgWNovmtYfKOX1c/WDmVlX5SSEmZmZmXV55U3S3aXSHSVx9QN/U/2wQ5F00FBpm0JpeI6U7eyDmVmX5iSEmZmZmXVZLa3Sm1XSTQukJ8ulj+ulhhZpnQHS/sXSnkOkDQfGnU86/2Bm1vU5CWFmZmZmXU5rNJU1SveUSHdHEyNfUP0wMLp63bEoTkBQ/TDC1Q9mZt2KkxBmZmZm1qU0tsTVDzcvivt+mFknNbVK6w2Q9hki7T5YGufqBzOzbqlPayT1e4eaP3++Xn75Zb377rsqLS3VJptsol133VUjRowQmzB79mw9/fTT+uijj1RdXa2CggKNHz9eO++8c/g98ec//1kLFiwIz0lss8022mWXXVRcXBz+rqio0PPPPx9er7a2VmPGjNEOO+ygcePGhceXR1VVlSZNmqQrrrhCkydPVr9+0b9yZmZmZtZhaHqxqFF6uVJ6o0r6qD7ujLIgugzbLF/aIprWHyANcPLBzKzT1dfX64ILLtCbb76pSy65RAMGRF/IyzBkyJDUb0vrtEoIkhCvvvqqpk2bpjvuuEN33XWXSkpKwmPNzc16//33df/992vOnDmqqanRzJkzddVVV+m6664LyySuvPJKPffcc6qrqwuJiMwcSlNTkx544AH9+9//1jvvvKOFCxfqwQcf1DXXXKMPP/wwtZSZmZmZdSW1zdKb1XHzi4fLpA/ropnRZR7VD/T7sHc0bZInDXQCwsysW+u0JARVClOnTtXJJ5+siRMnpubG+vTpEyoi9t13X33961/XD37wg7DcqFGjdNlll6mysjK1ZIzqiB/+8If62c9+Fqb9999fgwcPDo/NmjVLd999t7KysnT66afre9/7XqiCeP3110MywszMzMy6DppZzK2Xnlws3VcqPVchVTVLhVnSdkXSPsXRtd+geOSLfs4+mJl1e52WhFhzzTW14447atttt9XQoUNTc2M0daDpxWGHHRaaTJCQ2GijjULyYO7cuaFJRbrp06fr1ltvDdUUb731lhoaGtS3b7wrlIbQ3GP77bcPr7XuuuuGphprrLGGXnjhBTU2NoblMlGNwXrSp05qqWJmZmbW63CVRbKB4TYfLJMeKJU+jC75SDSsO0DadZB0YLG0aZ6U417MzMx6jC75lU7wT78Ob7zxhsaOHavCwsLUIwrJiZaWFj3zzDO6/fbbdeGFF4YKh7Ky6F+vCM05+vfvHxIZSWKCpAd/0zQjs6oCJCZeeukl/fOf/1wyXX311WEbzMzMzGzVakxVPzy3WLq3RHom+lndIg3KkiYVxJ1P7hVNQ7Ld9MLMrKfpkkmIxYsX65FHHgmJhuOOOy4kFRKnnHKKzj77bP32t78NzS1IWNB55HvvvRd+pz8Jkg85OTmpZyj8TqcZJBvo9DITVRAzZswIfVIk08MPP9zmsmZmZmb2xVD9UNksvUv1Q6l0TzR9VBdXOoyNLvemDpK+NFSakB9dpDr7YGbWI3W5JER5eXmobLj++uu100476Zhjjkk9Ett777212WabhQoJ+pggKUH1A31BkGTIzs4OyQgSCwkqJ+iwkuQEj2ciyXHkkUfq2muvXTKR2Bg5cmRqCTMzMzNbGVQ/zKuXnl8s3Un1Q4VU1yINzopHvti/OO58cvBnL9XMzKwH6VJJCCogHnroodAUgkQDnU9+3rAfw4YNC4kFmlmQhKADTH6yrqRPB5pV0FyDZh1FRUVhXibWMXDgwKWmpDmHmZmZmX0xSd8P79VI95ZKd0fTrDqpf3SZtVZuXP1w2LB45AtXP5iZ9XydFmVTjcC4ojSXIElApQLDbDJRpUDSgGYQl19+uTbYYIMwqkVubm5YNkkmVFVVhaE+aSbBukgsPP/882Hd9PtAs4sNN9wwLPvuu+9qwYIFITnBUJ2zZ88O/Ul8XlLDzMzMzFYNqh8WNUjPLpZuXiA9XyHVp/p+mJAnHTI07vuBv83MrHfotCQECYQXX3xR9913n2bOnBmaUDz55JNhoikF/T/88Y9/DMkFRrNg1Itnn302dBhJIoFEw9tvvx2SFKzj8ccf14033qirrroqDPlJgoFqBioottxyyzASBk0q6LzylltuCc/fa6+9UltjZmZmZh2F20c1zdIHtdKti6S7SqTZDVJudOW5Zq6022DpiOHSOFc/mJn1On1akzKDDkbHkb/61a9C0iHd+uuvr6OPPjpUO5xzzjmpuf8zZMgQXXLJJdpqq61CRcMvf/nLMEQnFRSMeLHHHnuE548ZM2ZJ8wmqJW644QbdfPPNoY+JSZMmhQ4uSW4sL5ImPI9ExuTJk8MwomZmZma2bM3RlWVFk/RCpfRUubSgMU40DIwu08bnSTsPktbrH12EOvlgZtatUDBwwQUX6M033wwx+ue1MiCWb0unJSG6GychzMzMzFZMQ3RVOatWuq807gOCv7P6SCNypJ2KpK0LpXxfUpmZdUurKgnRac0xzMzMzKxn4o4WzS8eLJEumyu9VR33B5HTR9qqQDp+pLTzYCcgzMzMSQgzMzMzW0kf1UoXzo5Hv1jcFM+j+uGoEdKRw6UxNL+IZ5uZWS/nJISZmZmZfSH1zdJtC+MExIe1cfUDzS9oenHKGtK2hdJAVz+YmVkaJyHMzMzMbIW0RBN9Ppz3qfRIuVTdHM9j5AuaXhw4VFojR+rn8gczM8vgJISZmZmZLRf6fqhskm5dKF0+V/q4XqpvkbKiK8pdBkknjpQ2z5cKsqKLTCcgzMysDU5CmJmZmdnnamiR3qySLpkjPbU47vuB4TjX6i+dMELau1haI1fKia4unX8wM7P2OAlhZmZmZu1qaZUWNkh3lkj/WSB9WBePhEGyYffB0rEjpE3zpSJXP5iZ2XJwEsLMzMzM2kSy4dUq6boF0rOLpYWN8fx1+kvHjZR2GyKtlSvluvrBzMyWk5MQZmZmZrYUmlnMro+H3LxrUdwJZVWzVNBP2nVQPOzmZvnSkCypj7MPZma2ApyEMDMzM7MlKpullyul2xZJzy2W5jTEzSw2HCgdNkzaZbC0zgApx8kHMzP7ApyEMDMzM7NQ/TCrTnqoVLo/mt6pjhMSQ7KlqUXSAcXSpAKpOPrbzMzsi3ISwszMzKyXY6SLlyqle0qkZyqkT+ulrD7SuIHSfkOknQdLG0S/0xmlmZnZyvA/JWZmZma9VFOr9FGt9GiZ9ECp9Fa1VNUkDc+WdiiS9imWtimUhrr6wczMVhEnIczMzMx6ofKmuO8Hml48tTiufqDSYVyetMcQadfB0kaufjAzs1XM/6yYmZmZ9SJJ9cMT5XEC4o1qqbpFGpkjbVco7T0k/unqBzMz6whOQpiZmZn1ElQ/vFop3VcqPVoeD8PZn+qHgdJug6U9XP1gZmYdzP/EmJmZmfVwVD/MrJWeLJfuKImrH+pT1Q/bFEj7DpG2L4pHwjAzM+tITkKYmZmZ9WCMfPFmlXRXifRgmbSgQRoQXQGuP0DafbC0d3E88kV2n9QTzMzMOtAykxD19fX69NNP9dprr2n69OlqbGxUQ0OD5s2bp4qKitRSZmZmZtbVUP3waV1c/XDjgnjkC+Yx8sVWBdKXhsXVD4OyUk8wMzPrBO0mIWpra/XKK6/o8ssv19lnn62//vWvWrx4sUpLS3X//ffrqaeeSi1pZmZmZl1FazRVNEnv1kg3LZTuLZXKor+pfhjbPx5288Ch8e9Zrn4wM7NO1m4S4u2339YVV1yhl156SQMGDNCcOXPU1BT9CxZZuHCh/vOf/4TfzczMzKxraGyV5tXHI19cPjdORLRE8wdnSVsVSkePkLaNfub3i5c3MzPrbO0mIV588UX16dNHv/zlL3XIIYeoX7/4X6v8/HyNGTNG77//fvjbzMzMzFavllapsll6r1r693zpzhKpNvqbkS9G50iHDounNXKlfq5+MDOz1ajdJARNL7Kzs7XWWmul5sT69u0bEhJ1dXWpOWZmZma2utDPw8JG6ZEy6ZK50vu18QVeXj9pm0Lp9DXjKggSEmZmZqtbu/8cFRYWqrm5OTTDaGlpUWtra/h70aJFmjZtmtZee+3UkmZmZmbW2ej7gWqHadXSlXOle0riYTcZ5WJ4rnTCSOmo4fGwmy5+MDOzrqLdJMQWW2wRRse49NJL9cEHH4SOKt944w3985//1C233KLDDjsstaSZmZmZdabWVmlRg3R3iXTpXOmjujjRQLXD5CLpB2tJE/KjCz1nH8zMrItpNwmx3Xbb6dRTT9X8+fN17rnn6qGHHgp9Q9xwww06+eSTdeSRR6aWNDMzM7POQrXDq1XSZXOlh8ukhuhv+nkYnSudsob05eFxUwwzM7OuqN0kBH0/bLPNNqHygQqIxx57LEyPP/64TjrppPC4mZmZmXWeOfXSLQvjzic/qYtHvmCki10HS98YLY0f6OoHMzPr2trNJDzwwAO6/fbblZubq3XWWUfbbrutttpqKw0dOlQff/xxqI4wMzMzs45H3w/PLpaumSc9UyFVR3+TbNhggHT8SGn/YqkoyyNfmJlZ19duEuK1117TM888o8bGxlD1wEgZWVlZoZPK2bNn69Zbb00taWZmZmYdgb4fZtVJNy+UblsU/V4fN78YkiXtOVg6dqQ0Li/uC8L5BzMz6w4+k4SgM8qqqipVVlaGid+rq6vDxO9z584NCQqG6TQzMzOzjkG1w5OLpf8skF6qlBY3SVx9bZInfXmEtOsQaVhOPBqGmZlZd/GZJMSFF16ovfbaS1dffbXuuusuHXzwwdpzzz3DxHxGxbj++uu19957p55hZmZmZqsK1Q8f1sZ9P9xfKs2sk2pb4oTDXkOkQ4fFfT8U9HP1g5mZdT+fSUJsvvnmYRSM8ePHa+zYsTrggAPC30yHHnqojj/+eJ199tk67rjjUs8wMzMzs1Whqll6rDxuevFKlVTSKGX1kbbIlw4ZKu00SFojR8ppt0GtmZlZ19anNZL6PaDJRV1dnaZNmxZ+TpgwIfQFkeD3AQMGhA4rezKOw6RJk3TFFVdo8uTJbn5iZmZmHYarsfdrpecqpOk1UlmT1BTNI+GwZYG0Wb40Ivo918kHMzNbTei64YILLtCbb76pSy65JOQFlmXIkCGp35b2mX/K8vPzwwgYjIYxZcoUDRs2LCQceMHa2trQT8SCBQs0b9681DPMzMzM7IuqbJYeKZfuKpFerpQWNcb9PJB82G+otOMgaa1cJyDMzKxn+EwlRKKhoUGvvvqq7rvvPn300UehKoKRMRKjR4/WX/7yl9RfPY8rIczMzKwjcQX2Xm3c6eQ71XH1Q3M0b3SuNLFAmpAnjXLTCzMz6yI6rBIi8dZbb+mmm27SO++8o4ULF4YRMYqKisIoGW+88YYKCqJ/Hc3MzMxshXD3p6Ip7vvh3pK4CQbVD1Q6bBVdXu0dXbPtVCSN6e8EhJmZ9Tzt/tNG0oHhOI844gjtt99+Wm+99fS1r31NZ555ZhgZg4SEmZmZmS0/+nl4r0a6p0R6oFR6N/q9oSVubrHroHj0iy0KpKIsj3xhZmY9U7tJiNLS0pBo2H777VVcXKz+/ftr8ODB2m677bT11lvrwQcfTC1pZmZmZsvS0hpdWzVKzyyOqx8ej36WNkkD+kmTCuLkwy6DpbX6x/1BmJmZ9VTtJiGys7PDSBjNzc3Ky8tT3759Q9OMiooKlZSUhJ9mZmZmtmz1LdKMWumhMunuEmlaTZyUoPphSpG0f3GciMjv5+oHMzPr+dpNQowdOzaMjEHCgU4ohw8frn//+9+69NJL9fDDD4fRM8zMzMysbXQySV8PL1bE1Q+PlkuLm6S8ftJmeXH1w77F0pq5Uj9nH8zMrJdoNwkxceJETZ06NVRBrL/++qFfCEaIeOWVV7T22mvr2GOPTS1pZmZmZgk6nqxplt6vlR4pk+5IVT+QZyDhMLlQOny4tFX008NumplZb9PuEJ2ZGJ6zsrJSixcvDn1EkJzoyTxEp5mZma0oOp4sb4w7nHyhMm6GQVaC6oe1+0vbF8bDb7rywczMupsOH6ITNTU1+uSTTzRt2jS9/fbb+vjjj0MS4sMPP9R7772XWsrMzMysd0uqHz6qkx4ul25fJE2vkbiFMSpX2q5I+nKq+sEJCDMz683arYQoLy/Xc889p8cff1yzZ89WXV2d0heln4jzzjsv9VfP40oIMzMzWx5UP1Q0xU0uGP3ig9q46cXAVPXDjkXS5vlOPpiZWffW4ZUQJB/+/Oc/h0QET95www210UYbLZnWWWed1JJmZmZmvVNdi/RxnXRfqXTLwrgfiKw+0ogcaYci6asj4pEvnIAwMzOLtVsJ8ac//SlkOH70ox9p/Pjxqbm9hyshzMzMrD2MfFHdLL1RJT2xOE5EkGfIjS4X1u0v7TxI2jRP6uvkg5mZ9RAdXgmRn58fhugcOHDgUs0wzMzMzHqzxuiyaHZ9XPlwczTNqosrHYqzpV0HSceMkDbLdwLCzMysLe0mIXbYYQcVFRXprrvuCp1R0kll+kQfEWZmZma9Bbdk6pqlZxdLV8yNflZItS1Sdh9p3MC46cX+xdLg7Hh5MzMz+6x2kxDjxo3TBhtsoMsuuyw0x9h+++214447LpmOPvro1JJmZmZmPd+ceulf86T/LpTmNcTzSDiQeDhupDTezS/MzMw+V7tJCCogLrzwQuXm5urwww/XgQceqP3333/JNHXq1NSSZmZmZj1XU4v0SJl00Wzp9SqpPvqb5hcT86UTR0q7D5EKslILm5mZ2TK12zHlueeeq9dff13f+973QkVE375L5yv69OnzuR1RdGfumNLMzMw+qZfuWBQPu1nTHDfJGErfD4PjJERRlke+MDOz3qHDO6akQ8oRI0aEzinppJK/06eenIAwMzOz3q2xRbq/VLp0tjStOk5A0NRim0Lp+JHx8Js0xXACwszMbMV8bseUd9xxh8rKylJzzczMzHqulmiaWStdMld6sFRa2BiPhjEsWzpimHTgUGmdAVL/6ArK+QczM7MV124S4qWXXtK///3v0Cxjr7320i677KLddtttyXTiiSemljQzMzPr3mhmUdUs3V8iXTlPerdaqoz+ptJh+0LpmJFxFQRNMbKcfTAzM/vC2k1C0A/EkUceqa985SvafffdQ2UEfSMk08SJE1NLmpmZmXVfVDq8VyNdM096tDwe+YJ5o3KkI4dL+xRL6w6QBvZz9YOZmdnKardjyoqKijC1Jzs7O/QZ0VO5Y0ozM7OejQugskbp+ehy55UqaW59nHzI7hNXPTCNyZUGOPlgZmbWMR1TNjQ0qKmpKfzOCouLi9ud6C/CzMzMrDuqa5HerpZuWSg9US59XCc1R/NH50qHDZP2iq6bNnT1g5mZ2Sq3VCXE5ZdfHqob6APinnvu0c033xyG4kyXLL7mmmvq97//ffi9J3IlhJmZWc/TEl3G0NnkK5XS61XS7FT1A8kGhtzcsiBuekHHk2ZmZvY/HVIJwVCcrIjEAz+HDh36mQoI5jENGjQo9SwzMzOzro9hNt+qlu4pkZ5YLH1UFzfJWLu/tF+xtHt0rbRxnhMQZmZmHWmpSojS0tLQ1wPJiIULF6q8vLzN7AZNNhobG7Xhhhum5vQ8roQwMzPrGah+oLPJN6qiqTpuetEUzSvMkjbNk7bIlzYc6OSDmZnZsnRIJQQLFRQUhEqI119/Pax8+PDhWmuttZZMNMMgCXHbbbelnmVmZmbWNTHs5ptV0gOl0mPl0ge10cVPn7jJxe6DpT2iabN8JyDMzMw6S7v/5D733HN65JFHQmeV6UhAfPjhh7ruuutSc8zMzMy6lubWuL+HpxdL95VKL1VK5U3SkGxpqwJp7yHSlEHSqNzUE8zMzKxTfCYJ8dFHH+mZZ57RJ598onnz5un5558PfzM9/fTTeuyxx8I8+oUwMzMz62oqm+ORLx4qkx6Jpg/rpH6p6oepg6Q9h7j6wczMbHX5zD+/JBouvfRSvfTSS3rnnXd05ZVXhr+ZLrvsMl111VV67bXXtPfee6eeYWZmZrb6Uf0wp156brF0V4n0QoVU0SQVZ8f9PlD9sMsgaWRO6glmZmbW6T6ThKBfiLXXXjtMY8aMWfI709ixYzVhwgQdeeSROu6441LPMDMzM1u96Pvh3Rrp/tJ4mlUnZfWRxvaPqx8Y/YLqh1xXP5iZma1WS42OkY4+Iaqrq7XTTjspJydHLFZXVxfm5eXlfW5PmN2dR8cwMzPr+qh+WNgovVUlPV0hzW9QdM0iDcqWNoguVbYrlNaPfuY4+WBmZrZSOmR0jHTrr7++1llnHdXW1oYEBEN23n///br66qv18MMPq6ysLLWkmZmZWeerbpber5HuXBRNJXFTjOw+0ujcuNnFQUOljfOcgDAzM+tK2v1n+YEHHtDf/va30C8EFRCMlPGTn/xE11xzjX7729/q3nvvTS1pZmZm1nmofihplJ6vkK6eL71SKTW0SEVZ0oR86Yjh0i6D474gzMzMrGtpNwkxd+7cMDwnfUEwUgadUe6yyy664447dPDBB+uGG25ILbl8KN2gmoLRN9577z3NmTMnzEvH6/G6M2bMCBPLNDZGVxlpmpubw6gd77//fljP7NmzQ5IkvVUJw4guWrRIH3zwQVjPp59+GppXmJmZWffFv/S1LdLHddKNC6SbF0qLosuErOhqZkRu3PHkEcOkDQbGFRFmZmbW9bSbhCD4J7DPzs4OyQCCevpGGDZsmDbeeOOQLFgRJA3OO+88HX300WE93//+90OSIMHrkej44Q9/qAMOOED777+/vvOd7+j1119fKsEwc+ZM/fSnP9WBBx6offfdV2eccUbov4JEBFiW9f7lL3/RoYceGpb5+te/rrvvvnvJMmZmZta9UP2wuFF6tlz622zp9ap43sB+0oQ86cSRcQeUhVmpJ5iZmVmX1G4SYvDgwaqpqQmJgenTp4ckxOabbx6SBZWVlerfv39qyeVDBQNVFaeddlro7DLTu+++q3/84x+hsoGfF110UaiMIMmQVDGwjrPOOitsDwmNa6+9Vn369NG5556rt99+OyQgWPbiiy8OiYkzzzxT//rXv8Lr0nHGo48+GtZjZmZm3QO3IepT1Q80vbhxYdwXRL8+0pBs6dCh0jEjpDHRZQnzzMzMrGtrNwmx1VZbheYShx9+uP7whz9o3Lhx2mSTTVRRUREqDTbaaKPUkstns802CwmIY489Vuuuu25qbozkAU0rWC9VC7vuuqumTp2q//u//wtNKegQk2VIPpBI+NnPfqadd95Z2267rX70ox+F5hkkIah0ePHFF8NyVErwWjvssIO++tWvatSoUWE9yyu9+sLMzMw6H/8UVzRJj5dL58+W3q6O55Ns2DxPOmu0NLlIGuABrMzMzLqNdpMQDE9Jk4Zbb701VBwQ+FN1QAUECYrTTz89teTKo3qBZANNP6i2AENiDh06VOPHj9fLL7+slpYWvfDCC8rPzw/L5ObmhuW23HLLMPQHzT1KS0v14YcfhmXGjBmzZFjN0aNHh2nWrFlt9g1BwoGECwmWZKLag9c0MzOzztfUKn1QJ10xV7ploVTbLFHoQOeTx4+UThwlDcuJLmRc/WBmZtattJuEIOEwcuRITZkyRdtss40GDhwY5hPw77777tpiiy3C36sCw4AuXrxYOTk5oRlIIisrK/y9YMGC8Pf8+fPD6yfJBbBMYWFhSC5QCcF6eDwvLy+1hMK2swyPl5eXp+b+D81Ofv/73y9JVjBR+UEnmmZmZta5Shul+0qkS+ZIM2rjJhk5faRtCqUfriVtWSBlt3sFY2ZmZl3ZZ/4JP//883XFFVeEgJ1EBM0brr/++iUVBIw88cwzz2ifffYJf68KVCIw8XpM6fg7aRqRLNOeZD1tSZ7X1uMDBgwIfU+89NJLS6bHH39ca60VXemYmZlZp2CYzbeiyw36fnigTKpsiuePzJGOGSkdNUIaEv3uvh/MzMy6r88kIRh9giE56QQS9NXw2GOPhWoBEMSToFjR0TGWhSYeBQUFoSNKKhkSbAN/FxcXh79pnlFWVrZUMwmWIUFCtQNNNFgP86iuSLC9LMPjRUVFqbn/07dv3/Aa66+//pJpvfXWC81DzMzMrOMtaJDuKZWuXyDNiC456IySkS92KJROW0PaIl8aEF21OP9gZmbWvX0mCUEAnyQgQGKAgD69gmBZFQdfBH04rLnmmmHkjXfeeSfMI9FAwoEkCH1AkCiYOHFiSCbQ8STbBZZnubFjx4amG4yEQcKEUTaSZAUJE4YZ5TV4rbZQKcFrpE/LqrowMzOzlUey4ZVK6YYF0hPlUkljPH9sf+mIYdIBQ+NKiBw3vzAzM+sRvvA/6SsaoJM0IDEwY8aMkDSg40c6imSqrq4O1Qc0f7jqqqtCB5Q0A7nwwgtDYoE+KHi9jTfeOHSKyfCcPP7mm2/qb3/7W1iGx6iGoENNOqV88MEHdccdd+jVV1/VbbfdFhIRjKhBcsHMzMxWvzn10t0l0u2L4uoHht4szJJ2GhQ3vdiiIO6I0p1PmpmZ9RyficgJ9ttKMKxsVQCjXzDU5zHHHKN777039CvB8Jo//OEP9cQTT4SOIHmMJMEPfvCDMJ/RLn7xi19o+PDhYR303fCrX/0qNN84++yz9Y1vfCMMz3nqqaeG4UN5Lk02WA+VERdccIHOOussvfXWWzr44IO12267hfWYmZnZ6lPbIj1fId2ySHpmsTS/QaHzyQ0HSocMlfYYLI3Jlfr7voGZmVmP06c1o13Ft771rVBlQMUBfSIQwNNHxNSpU0OlAU01SCjQTIJKhOVF5QPrSka6SNCHA1UQVC+wDENtklgAyQeG6ExG5kDSZIPKBppbkHTYaKONwugXSaKEPiCosGC7qcBgRI111llHI0aMCI8vD5p9UFVBJ52TJ09eakQOMzMzW3FccXxSL71YKb1VLS1qkBqiecXZ0sR8aVKBtEZu3PeDmZmZdS319fXhRj95gEsuuSQUCSwLcXhbPpOEIOimI0qqDUAQT+BPIiAJ8hkhY9CgQaFZRE/lJISZmdmqQ/XDa5XSq1XSh7XRv7PNUnZ0WbF+dP2yVaG00cDoYsVNL8zMzLqsDktCUD2wcOHC1F/tI0lBPww9lZMQZmZmK4+rjI/rpZcqpber41EwGqN5w7OlzfLjUS9G93f1g5mZWVfXYUkIizkJYWZmtnJqmqXXq6TXomlGbfw3o1xQ/UDzi/F5rn4wMzPrLlZVEsL3HczMzGyV4vbGrDrpgdJoKpPerI5HvhieI+1YJO0dXZNsXSgNzXYCwszMrLdxEsLMzMxWCUorSTa8UCHdWyI9Wh4Pw5nVR9o0T9p9cDxtMMAjX5iZmfVWvgQwMzOzldaUqn54uEy6qyRuglHXIo3MkSYXSvsUS9tFP4dkM+x36klmZmbW6zgJYWZmZl9YSzRVNEmvVkr3lURTqbSgMe77YfxAabfB0r7F0noD4nlmZmbWuy11OTBv3jxNnz59uaaZM2emnmVmZma9UUOL9HGd9ORi6fZF8fCbzamRL7YpkA4eJk0ukgqzJBc/mJmZGZYaHeP666/Xc889l/pr2UaOHKkf//jHqb96Ho+OYWZm1raW6Mqhsln6oFZ6viLueJJ5uX2l0bnStoVxx5P0++Dkg5mZWc/QIaNjVFdXq6SkZLmmxYsXp55lZmZmvUV9i/RpvfRkuXTzwrjvBzDSxaR86SsjpJ0GSQOcgDAzM7M2LFUJ0RYebm5uVktLi7Kzs9Wnl/Qm5UoIMzOz/0mqHz6slZ6pkN6qjofipNphjVxp+8JoKopHwjAzM7Oep0MqIdKRfKioqNCMGTP07LPP6pVXXlFDQ0N44Tlz5qi0tDS1pJmZmfVkVD/MaZAeL5duXCC9URVXORRT/VAgHTMyrn5wAsLMzMw+T7tJiMrKSt199906/fTT9aUvfUm//vWvVVZWptmzZ4fsxzXXXJNa0szMzHoiqh+qmqW3q6X/LpDuLZXKm+Lqh7Vzpf2L4+YXo3JSTzAzMzP7HO0mIZ588knddttt2nbbbfWtb30rNMXAoEGDtM466+jRRx8Nf5uZmVnP09QqzW+Q7i+VblggTauJqx8GZUnbFErHj3LzCzMzM1tx7SYh3n//fQ0dOlSnnnqq1ltvvSV9QdDug5ExaJJhZmZmPQsdRdH84vUq6Zr50oOlUkVTnGwYkysdOkz68nBphKsfzMzM7AtoNwlRU1MTOqPMy8tLzYnRSWVtbW2v6aDSzMystyABsahBumWhdN38eAhO5uX3k6YUSSePiofe7OdLADMzM/uC2k1CjBo1So2NjXrnnXdScxQSD/Pnzw9NMTbbbLPUXDMzM+vumlulFyukS+dKTy6O+4LgfsP6A6SjR0hfGiYVu/rBzMzMVlK7SYgpU6aooKBAZ599tq677rrQPIPOKWme8dprr4UOK83MzKz7W9AgXTtfummh9GldnJAozJL2GiIdO1KakC9lR1cMLoAwMzOzldVuEmLttdfWWWedpRNOOEE5OTnq169fSD5svvnmYXSMCRMmpJY0MzOz7qi5RXpmsXTZXOnFSqmySWrtI22SJx0zQtprsDQsx80vzMzMbNXp0xpJ/f4Z9AlRXV2tioqK0DSD5hgDBw5UUVFRSEz0ZFVVVZo0aZKuuOIKTZ48OSRhzMzMeop59fGQm9NrpIrmuPphcJa0U5E0sSBOPmQ7+WBmZmYp9fX1oSDhzTff1CWXXBIGrViWIUOGpH5b2lKVEKyUpEMy0QFl3759w7Ccw4YNC6NlkIQgIcFjZmZm1r00tUiPl0v/nCu9ViWVN0nRLG2WJx01XJoySBrpBISZmZl1kKUqIX7/+9/r7rvvTv21bGPHjtU111yT+qvncSWEmZn1JCQa5tRLD5RK79dKZY3xvOLsuPph07x42M2cdhtqmpmZWW/WIZUQW2yxhQ488MAw7bbbbmF4TppijB8/Pvy97bbbhkqIBQsWeHQMMzOzboA7DTXN0pPl0rXz4uqHksZoZh9pYr50xHBphyJpzVwnIMzMzKzjLVUJUVlZGbIbeOihh/TUU0+FaoCddtpJ+fn5amho0AcffBCqJXJzc/Xb3/42LNsTuRLCzMy6u8boX/hP6qSnFkvv1UgLST5EhqaqHzbOk0bluumFmZmZfb4OqYRgSE76fWCaP3++yE/ssssu2mCDDTRq1KgwYsZWW22liRMn6vnnn089y8zMzLoS7i4sbpKeLpduWyS9XBknILL6SFsWSIcNk3YYJK3V3wkIMzMz61ztFl4yMsaiRYs0b9681JxYTU2NZs6cGR43MzOzrqU++ud5Ro10d4n0SHk8+kVDa9zZ5L7F0t5DpM3zpYJ+oUWGmZmZWadqNwkxYcIEZWVl6cILL9TFF1+sm266SVdffbXOP/98Pfnkk9pvv/1SS5qZmdnqxq2BRY3Ss4ule0qk5yuk+Q3SgOhfevp+OKBY2nmQNKZ/9I+/sw9mZma2mrSbhKA/hEMPPTQ00XjkkUd022236d5779Wnn36qPfbYQ4cffnhqSTMzM1ud6lLVDw+VSg+XSdOi35ta484mdx0s7TVE2rJQynP3RmZmZraaLdUxZSY6nqDpxbRp08IoGXRGudZaa4WRMeiosidzx5RmZtbVUf1Q0iC9UxOPevFBrdQQzSTZsNHAuNkFQ286+WBmZmYrq0M6psxEc4yioqLQKeXo0aO1xhprhE4rSUaYmZnZ6lPbHFc/0O/Dg6XSO9VSS6s0OvonmpEvqH7YxtUPZmZm1sW0m4SgA8oXX3xRl19+uS666CJdeeWV4Xf6h7jzzjtVXh5d9ZiZmVmnItGwoEF6qTLu+4HhN+kLojBLmpAn7Tkkntbu744nzczMrOtpNwkxffr00BHl448/rmHDhmnzzTfXeuutF0bLIClBPxFmZmbWeWqapfdrpUfLpXtKpXdr4vn0/TC5UNqvOK5+GOjqBzMzM+ui2k1CvPDCC1rw/+3dB5hV1b338d8wvVc6UgUFOyoq9t7FXmKLmpiYmMQk5t4kmuYbb2Lu1dwYYy4mEo3G3kVFxS72CqgggtJhmN77vPu39zlmRBDQGZjy/cyzn3Nmn33q2m3993+tVVysH/7wh7rqqqt06aWX6he/+IWuvPJKbb/99rrllltiSwIAgK7k7Ic1zdLbNdKDJdILFVJ5LPthQoZ0ZEE09OawtNgTAAAAuqn1BiHKy8uVn58fdkLZr1+0WEJCQpgVMWnSpHCUDAAA0LXq26RPGqJ+Hx4oiTIh3MxicKq0d450fJG0W3CbTvYDAADoAdYbhMjJyVF9fb3mzZsXjozR1NQU9hOxfPlyLViwQIMGDYotCQAAOpuzH8pbpHeqpTuKpVmVUnXwf3Zs5IvjCqUjgsnBCAAAgJ5ivUGIHXbYIbx1/w/33XefXnzxRT3xxBNh55QzZ87UscceGz4OAAA6V2ObtKJRml4i3VUsLW6Ish+KkqX98qTTBki7ZEtp6z2KAwAAdE/rPX2ZPHmyvv3tbystLU1XXHGFzj33XF1yySV65ZVXdM455+i8886LLQkAADpDezDVtEZ9P9ywUnqpKmqOkR4crcdlSGcMjPp+6J8SLQ8AANDTrDcIkZiYqH322Uc333yzXn311TAL4tlnnw2zIL7xjW8oOTk5tiQAAPiqmt35ZJN0Z7F0yyppdXDf2Q+5SdGQm+cMlLbLlFLIfgAAAD3Y505lWlpawv4f4lNbW5tyc3PD4TmHDBkSdk7p+c3NzbFnAACAL8vZDw1t0lvV0h+WSq9VBcfiYGZigjQ2XfrWkCgIkUfsHwAA9AIJ7YHY/dBtt92mp556Kvbf+g0ePFi//e1vY//1PjU1NZo4caJuvPHGsGmKM0MAAOhM4dCbTdL9JVETjDj39XBsoTQ5V8rg8AMAALqBxsZGXXvttZozZ46mTp2q9PT02CPrVlBQELv3WZ/LhHj99df18MMPh6NieJjO9U0eMQMAAHw5da3RiBd/XCa9WxubGRifIV26lbR/PgEIAADQ+3wuCLHbbrtp9OjRWrVqlTIyMnTWWWfpuuuu07Rp0z4zXXnllbFnAACAjdXaLn1SL92yWrpnjVTRIjknMTtJOn2AdMFgaWiqlOwOIQAAAHqZzwUhjj/+eN19993h0JxDhw7Vn/70J5155pn6v//7Py1btizsHyIvL0/Z2dmxZwAAgI1R2SI9UyFNWyXNqYn6gkhKkHbKkr4/NGp+4WBEPwIQAACgl/pcECIzM1PDhg3Tfvvtpx/96Edhm4/zzz9fixYt0ne/+91wXkNDQ2xpAACwIR75Yn5dNPLFjNKoH4jWYL6H2jy5v3TaAGlYqpT6uaMyAABA77LO0x2PgJGWlqaBAweGo2I4KOFRM5YsWaLFixeHI2YAAIANK22WniqX7iqW5tZK1a1Rx5O7ZUlfHyhNypEKksl+AAAAfcM6gxCtra1auHBhODLExRdfrGuuuUY5OTn65S9/qV//+tdhgAIAAKxfU5s0t0a6f430TLm0ojEaetMZD8cVSccE06h0Op8EAAB9y+eCEC+++KJ++tOfhtMrr7yiHXbYQd/+9rd14YUX6oQTTtCOO+6ofv3IFwUAYH2Km6SZ5dLDpdKc2qjzycxEaY/sqPnF7jnSwBQpkewHAADQx3wumvDMM8+EHVMuWLBAFRUVWrlypebOnasZM2aEo2I4K8LTzTffHHsGAACwxjbpnZoo+PB8pbQklv0wKk06slA6LJjGZUhZZD8AAIA+6nNBiKysrHCIzlGjRiklJSUMQsyePVtvvvnmZ6b33nsv9gwAALCyMer7YUaZ9G6NVN4s5SRKe+ZIxxRKewS3g8l+AAAAfVxCeyB2PzR//nwtXbo09t/6eRSNvfbaK/Zf71NTU6OJEyeG/WJMnjxZiYlctgIAfJ6H2fygNsqA8AgYHobTgYYRadIuWdKETJpeAACAnq+xsTEcPXPOnDmaOnWq0tPTY4+sW0FBQezeZ30uCIEIQQgAwIa4s0lnPXha3hR1RumRLiZkSDtnSaODY7P7ggAAAOjpOisIQQ+TAABsImc/vFMtPVEuPV0hfdIgOaQ/NjgWH5gnHRIcc50BQQACAADgswhCAACwCZz98HyF9GiZ9HqVVNUSZT94xIvDC6R9cun7AQAAYH0IQgAAsBHq26Q5NdKT5dJjpdKSBslxhq3TpQPypKMKpO0ypQyyHwAAANaLIAQAAF+gtV1a2SS9XCndXyK9EtzWtUn5ydLEbOnoQmn/PKl/SnBQJfsBAADgCxGEAABgHdxrc22rNK9OeqJMum+NtLwxamYxMi3KfjihSBqfKaVyNAUAANgonDYBALCWlnZpVaP0WpV0d7H0UqXUHMzLT5J2yJRO6i8dnB9lQ5D8AAAAsPEIQgAAENMWTM5+WFAnzSiT7lkTNcVICY6Ww1Kj7IezB0ljM6Qkog8AAACbjCAEAAABZzqsaZJeqZJuL45u24J5eUnShAzp7IHS4YVRx5PEHwAAAL4cghAAgD7NfT/UtUoL66SHSqR710jFzn5IkAalSIfkSxcMlkamE3wAAAD4qghCAAD6LPf9UNoszaqUblktvVEttQfzshKj4TbPGywdWhA1xwAAAMBXx2kVAKDPcfZDU5v0UZ10V3E09GZJczTyxYCUaNhNZz8MT4uWBwAAQOcgCAEA6FMcgKhskWaWSzevlt6tifp+SA+OiBOzpAuHSAfmS8kcIQEAADodp1gAgD7DTS0+qJVuWilNL5XKmqP5g1OkkwdI5wyShqRG8wAAAND5CEIAAPqEihbpgRLp1tXS/HqptT0a6WLvXOmCIdKeOVHfD3Q+CQAA0HUIQgAAejVnP7jJxT9WSs9WRNkPbn4xMk06tb90YjANSZGSiD4AAAB0OYIQAIBeywGHe0uke9ZIH9VLDW1SZqJ0YJ50xkBpYnY0EkY/AhAAAACbBUEIAECv40yHN6ulm1ZJL1VKJU1R84ux6dIp/aUjCqWtUqVUjoIAAACbFadfAIBexQGHu4ulh0qkhfVSbWuU7XBIftT0YudsKTcpGo4TAAAAm1dCeyB2f4t788039Yc//EEVFRWxOZHk5GQdffTR+ta3vqVf/vKXevvtt9XS0hJ7VNpjjz103nnnadSoUeH/1dXVmjlzph599FGVlJRo9OjROvbYY7XffvupX7+Ni7vU1NRo4sSJuvHGGzV58mQlJgZnsACAbsuZDs5+mFUpLW2U6lqDmQnSNunSXjnSuAyCDwAAAF9WY2Ojrr32Ws2ZM0dTp05VenpwkvUFCgoKYvc+q1tlQgwaNEgnnHCCTj/99HA66aSTtP322+vZZ59Vfn6+EhIS9NZbb6mpqUnHHHPMp8s5uJCXlxe+hmMqDz/8sG677TalpqaGAYTy8vIwmPDiiy+GywAAeo+2YFrdJN1TLD1WKi2IZT/kJEmH5UvHFkXZDwXJBCAAAAC2tG4VhBg8eLCOP/74T4MLzl7IysoK5x944IFhEMLGjBnzmWBFxyDE0qVLwywIBy3OP/98ff3rXw9f01EbZ0a0tvrSGACgN2gIdumvV0l3FUuvVEsrmqKgxIRM6fgiaf/g0DAqTUqj8SEAAEC30K1Oy9xUIi0tLUzr8G1DQ4OefvppHXLIIRo4cGBsKWn27Nn63e9+p9///ve67777VFpaGmZA2IIFC1RWVqYddtghnPr376/ddttN48aN07x588Jl18XNO/x+8am+vl5tbT6VBQB0Ny3BLn9Zo/RImfREML1XGzW/yEuSDs2XjiqQdsuWCsl+AAAA6Fa67bUhBwEcbFi8eLFOO+202Fxp55131qRJk8JMB/f9cNddd2natGlatmxZ+HhxcXHYf0NhYWHYl4S5LYqbelRVVYWPry2eJXHppZd+Ol1++eVhfxIAgO7D4ebqFumNaunhUunFyigY4SE2t8uUji2UDsqTxmRIKWQ/AAAAdDvd9hTNnVM+8cQTYaeSzmSIc0Di4osvDqcLL7wwDEg4gPDuu++GfUU4oOBmG/EAhCUlJSklJeXTbIe1eXlnXziwEZ/cvIPOKAGg+2hqk5YEu/CnyqUny6R3q6PmGM52OCBPOrJA2itXyg/+J/4AAADQPXXL8zT327B8+XK9+uqr4agY7hcibqeddtLWW28dZjZsu+22Ouuss8LlFy5cGI5o4c4o3TSjubk59gyF9x2ccDDCzTzW5gCF+5xw9kN8+slPfrLe3jwBAJuPsx8qWqS3a6QZpdJzFVH2gzMdxmdKhwe76iOCaWwGTS8AAAC6u24ZhHCziTfeeEN1dXVhEOKLOIDgjAVnQbgPB/cd4aCEm1LEAxEeHWPVqlXKycnRgAEDwnlrc4DCAYz45GBFvCNMAMCW0dgmfVIvPV8hPV4mvVUTzGuXBqRIk3Oj7Id9g1uPhAEAAIDur1sGIdxvgzukdP8PznaIcx8Q7ifCj/u+syU8HKcDEMOHD1dGRobGjh2roqKicOxSN9Hwsq+99po+/PBDTZgwIewrAgDQvTn7obxZercmCj48XS6taJQygqPWNhnSIfnS0bHsB/cHAQAAgJ6h2wUh3GzCTSvmz5+vKVOmhCNmmJtYeGSLm266KZz++c9/hrfTp0/XnnvuqV122SUMQgwbNkyHHnpoGKRwh5U33nijHnroIWVmZoZZFfTzAADdm7MfFjdIL1RKj5ZGzTCaY9kPu+dEwYf9cqVssh8AAAB6nG4XhHBWg5tR7LvvvjrggANicyPuGyI7OzvMcnjmmWe0aNGiMODw4x//OOwnwtyE4qijjtIZZ5wR3nc2hIfpPP/887XXXnuFywAAup8w+6FFmlMbBR/cAeWqJikzURqbLh2cH41+sXWG9/XRcwAAANCzJLQ7xQCf404uJ06cGGZSTJ48mQwKAOhCzn4obor6fHijKrjfLCUlSPlJ0oRMaa8caWQawQcAAIAtxa0Wrr322jApYOrUqeEIk19kfQM9dMs+IQAAfYOj4JUt0nu10n1rpJnl0ppmKSNRGhMc1zzqxZQiaVRwnwAEAABAz0cQAgCwRTS1Saubok4n7yyW3q+T2tqj7IdJ2dIp/aW986LmGAAAAOgdCEIAADYrZz/UtUof1Er/XBVlP1S0SGnBEclNLo7vH01buflF9BQAAAD0EgQhAACbTWt7NPTmI6XSzaulhfVSWzA/K1GanCudPVDaI0dK5+gEAADQK3GaBwDYLFrapXm10nXLpWcqpNpWKTFBGpEWBR+OL5IGpcYWBgAAQK9EEAIA0KXc/KK2RbpjtXT9Cml5Y5QRkRocgQ7Jly4cLO2cHf0PAACA3o1TPgBAl3H2w5wa6XdLpJeqov/NfT9cNEQ6ulDKT47mAQAAoPcjCAEA6HTu56GkWbp9tXTjyui+sx9SgqPOMYXShUOkcRlR9gOdTwIAAPQdBCEAAJ2qvk16o0r663LptWqpwRGJwJh06btDpYPzpYLkqD8IAAAA9C0EIQAAncKZDssapXuKg2mNtKJJam6TMhOl4wql8wZJW6dLGcH/xB8AAAD6JoIQAICvrLpVerlKunW19Ga1VNkiJQbzt82UvjFY2i9f6p8iJRF9AAAA6NMIQgAAvjR3NLmoXnqwRHq0VFrSEDW/KEiSjiqUTu0f9f2Q5YgEAAAA+jyCEACAL6WiRZpVId23Jsp+KG2WkhOkHbOkMwZKe+dKQ1LJfgAAAMC/EYQAAGyS5nbpwzrpkVJpZrm0KJb9MChFOiw/Gv1iQqaUmxQtDwAAAMQRhAAAbDRnO7xYEQUg3qiWioP/04Ijyc5Z0pQiae88aXga2Q8AAABYN4IQAIAN8igX8+qkJ8qkpyukBfVSYzBvWKp0UJ50eEHUDCOP7AcAAAB8AYIQAIAvVOLsh0ppRqn0WpVU3CRlxLIfjiiQ9smTRpL9AAAAgI1AEAIAsE7OfvigVppZFvX9MN/ZD+3S8FRpvzzpsAJpl2yyHwAAALDxCEIAAD5nTbP0UpU0o0x6Obh1NkR2YpT94ODDAWQ/AAAA4EsgCAEA+FST+36olZ4ulx4rlebXSS3OfkiLhtw8skCamC3lkP0AAACAL4EgBAAg5GyH16uk6aXSCxVSeUsUbNghM+p48uB8aas0KZHsBwAAAHxJBCEAoI/zKBcf1UXZD/eVBPfrpfZgvke+mJwTDb25S5aUlRgtDwAAAHxZBCEAoI9qa4+yH96ulu5eIz1TLtW0Rn0/jM+UjimUDg+mwalkPwAAAKBzEIQAgD7GWQ4NbdInDdJT5dK/Vkf3ExKkQSnS5Fzp9AHSTtlSOkcJAAAAdCJOLwGgD2ltl8qapXdqpNuLoyYYTcG8zERp63TplAHSMUVSUTIHCAAAAHQ+zjEBoA9w9oP7fljaID1eJv1rlbQkuO8hNguTpX1zpQuHSNtlSsk0vQAAAEAXIQgBAL2csx8qW6S3qqVbVkvPVUjNwbyM4AgwKk36+iDphP5Rx5PEHwAAANCVCEIAQC/l7IfmNmlZo/RQSdT3g++7k8m8JOnAfOm7Q6VxGdHyAAAAQFcjCAEAvZADELWt0qtV0rSV0qxKqSWYmRLs9d33wzeHSMcVSekMuwkAAIDNiCAEAPQy7e3SknrpjuJoWtUUzXf2w1EF0neGSGPSo3kAAADA5kQQAgB6kZrWaMSLaauiPiDc94OzH3bIirIfDi2Q0sh+AAAAwBZCEAIAegFnPyyoi/p9mF4qFTdFHVIOTJGOK5TOHiiNTItGwwAAAAC2FIIQANDDVbVIM8qk24uluTVSXVuU/bBbtnTWQGnfPCknKeqQEgAAANiSCEIAQA/l7IcPaqXbVkdNMNz3Q1Mwb1iqNKUoGnZzdLqUFuzpiT8AAACgOyAIAQA9UGWL9EiZdF+J9H6dVNUqpSZIe+VIJ/eX9ghuC5NpfgEAAIDuhSAEAPQgzn6YWyvdsVp6oUJa3ig1tUX9PRxbJB1ZKG2dIWUmkv0AAACA7ocgBAD0EBUtUaeTD5dIc+ui/93UYu9c6bgiaVKONCBFSib6AAAAgG6KIAQAdHNt7dLsGumuYumFSmlJg9TcJo1Jk44pjIbd3CZDyiL7AQAAAN0cQQgA6Kbag6m0WXqsTHq0VHq3JuoLIiNR2idXOqoo6vvBw3DS9wMAAAB6AoIQANANNbZF2Q8PlEjPlEsfN0it7dLW6dJRseyH8fT9AAAAgB6GIAQAdCMONHioTQ+5OaNMer1Kqm6VshOjvh+OKIhunf2QSPQBAAAAPQxBCADoBtz0orZVmlMrzSiVniqXFtUHO+kEaUy6dFhBFIDYLjPqjBIAAADoiTiVBYAtrKVdWtkozaqUHi+TXqmKAhK5SdGIF0cWSAflS/1ToqAEAAAA0FMRhACALcTZD25q8V6t9GS59ERZlP2QHOyZR6dLB+ZJxxVKO2TR8SQAAAB6B4IQALAFNMeyH16pjEa+eDm4rWuT8pOkXbKkIwulwwukguTYEwAAAIBegCAEAGxGYfZDizSvVnqiPBp+85MGKSXYG49Mk/bPk04okrbPpOkFAAAAeh+CEACwmTj7YZWzH6qkh0qiLIiGWPbDjlnSMbHsh3yyHwAAANBLEYQAgC7m7IeaVunDOumR0qj5xZJGKTXYA2+VGvX9cEp/aQLZDwAAAOjlCEIAQBfyyBclTdJLldLdxdLr1VJjMM8jX7jDyRP7S4cWRP8DAAAAvR1BCADoAs5+qG+TFtZL96yJMiBWNkXZD0NTo2YXp/aXtskg+wEAAAB9B0EIAOhkre1SVYv0bLl08yrpnRqpqU3KTJR2zpK+NkA6KF/KIfsBAAAAfQxBCADoRG5+8XG9NG1llP1Q2iwlJkiDUqTji6TTBkij0iWSHwAAANAXEYQAgE7g5hce6WJGqfTXFdK8umg0jKQEaY8c6YIh0r55UTYEAAAA0FcRhACATrCoXvrjUmlGWTQShg1Okc4ZKJ3cP+oHguwHAAAA9HUEIQDgK3Dnk/etkf6yPBp2M579sH+u9M0h0q45UkYiAQgAAADACEIAwJfgvh8+qJX+tFR6rkKqbZXagnnDUqXzBkvHFEWZEA5IEIAAAAAAIgQhAGATuO+H8mbpwRLpplVR9oP7gnCw4eB86fzB0o6ZUnZSsIMl+gAAAAB8BkEIANhIDjbMrpGmrZJmVUoVLVH2w1apUfDhsIIo+yEl2LMSfwAAAAA+jyAEAGyAAw2rGqVHSqR71kSdUNa1SumJ0iH50rmDpO0zpVyyHwAAAIAvRBACAL6A+3p4s1q6a430cpVU3BTNH50unTlAOihfGpYmpZL9AAAAAGwQQQgAWIfWdmlpQzTk5qPBNL8uGnrT2Q4H5kfDbu6UJRUkE3wAAAAANhZBCABYS3WL9Ea19FCp9EqVtKJRSkyQtsmQji+SDsyLMiHc9wMAAACAjccpNADEOPvh43rpyXLpiTLp/VqppkXqnyztlysdUyjtli0VBv8DAAAA2HQEIQAgUNkivV4dNb94qVJa1iglJ0jjM6UjCqQD8qWxGWQ/AAAAAF8Fp9MA+rSW9mi0i2cqouyHubVRZ5SDUqS9c6XDC6RJOVIR2Q8AAADAV0YQAkCfVdESjXzh4MMLFdLyxmiUC2c/eOhNj3zhfiDIfgAAAAA6B6fWAPqcePbDcxXSY6XS7Fqpvk0anCLtkR01v9gzh74fAAAAgM5GEAJAn+Lsh7djfT88Uy6tbJLSgj2hMx6c+XBYgTQuuJ/M3hEAAADodAntgdj9LaqyslLvv/++lixZEpsj9evXT0VFRTrwwAPD/1tbWzVnzpxwmebm5vCx7bffXvn5+eGy1tTUpEWLFunjjz9WfX19+NjYsWM1dOhQJSRs/Gj+NTU1mjhxom688UZNnjxZiYmJsUcA9ETOfnBzi9k10bCbZc3BDjDYJXjki20zon4fhqdFnVECAAAA+KzGxkZde+21YZ186tSpSk9Pjz2ybgUFBbF7n9VtghD+IldddZWee+45jR8/PpyXlJQU3r/66qvlj/n222+H90tKStTW1qbk5GSdfPLJmjJligoLC8Nl3nzzTd1+++2aO3duuIx/mP3331+nn356GIjYWAQhgN7DI1946M3nK6UFdVJTsNfLDDbpYalR8GHHTCknKbYw+h4fBtsag6k5OPAEB9MEVgYAAIC1dVYQolslHGdkZOiQQw7RHXfcEU7/+te/9Otf/zp8rKGhQVdeeaVWrVqlyy67TH/+85+1ww476IYbbtDs2bPV0tKi6upq/fOf/9SCBQt0wQUX6JprrtGee+6pJ554Qo8++mgYpADQdzQ7+6Eh6nTy1mLp/VqpJZjv7IfdsqWT+0t75RCA6NPa26T6ZdKKGdLSe6Wyt6S2ptiDAAAA6GzdKgjhIIEzENzcYvny5aqqqlJqamqY0bB69Wo98sgj+t73vqc99thD2267rX7wgx+Ez3nrrbdUUVERNudwAMLNN4477rgwSHHCCSdoxIgRevnll8PmGevj93Bzj44TQQugZ2oLNt2qFml+rXRnsfRIqVQd/O++H0amSccUSlOKouYXiTS/6Nta66Ql90mvXiC9dqE0+1dS7SexBwEAANDZuk0Qws0d3LzCgYSLLrooDDb89re/DQMM7v/hvffeCwMDkyZNUkpKSvicIUOGaNiwYVq6dGnYp8Qnn3wSvoabXaSlBbWLgB8fPny41qxZo5UrV4bz1uYAhIMebsoRn/y+TjcB0HM4bOjsh9VN0cgXfw82+fmx2GNekrRbjvT1QVETDDfHANRYKtUslJrKg4NBi1S/Qqr8IPYgAAAAOlu3CUK4k8mTTjpJ1113XdjE4uKLL9ayZcv03e9+NwwglJaWhn1EZGVlfaaDydzcXNXV1YUdUjpzwkGIeADCfN/PcXMON9dYFz9222236ZRTTvl0Ovfcc9cbtADQ/Tj7obZVml8n3bpamh7ULT3sZmqwlxuSKp06QDotmAamBDs+sh8Ql1IgZY2R0gdH9zNHSNljYw8CAACgs3WbIMSAAQN08MEHh00p4s0ofve736msrEyPPfbYp6NfrN1Ewv87KOHJy/j/tZdxpkN8mXVxhxqXXHJJ2JllfHr11VfDZhwAuj+PfLGmSZpZJv1thfRRfbRzc7bDHtnS94dJE4NbRr7A5yQHK8aoM6VJN0g7/5e042+l3AmxBwEAANDZuk0QYm1unuHhNd3kwhkRAwcODJtllJeXh0EFc7DBQYrs7Oyw74i8vLxwGWdGxNXW1oYZEA40+PF1cXDCz/frdJzigQ8A3ZPDjfWt0ns10rRV0mNlUkOwe0hKkAalSOcNks4MptykYDuPngJ8XmqRNPRoaesLpfwdYzMBAADQFbptLduBBvfz4L4aBg8erJ133jnsC+LFF18Mm17Y4sWLw2nUqFFhgGHrrbcOH3PHlvFAhB//+OOPNWjQoDCgAaD3cPbDw6VR3w+fNETz0oO92n550k+GSztkRfMAAAAAdA/dJgjx7rvv6k9/+pPeeOMNffTRR3r44Yf1ox/9KOzPwU0zHGQ4++yzw2E6n3zyybDzyJ///OfhsJ7urNKPb7/99tp1113D4ThvuukmvfLKK+GQnQ5EHHTQQWF/EQB6vsY26fUq6caV0rMVUlN7NMqFR774xhDppP5SBh1PAgAAAN1OtwlCuNnEE088odNPPz0MGFxxxRVhZsO//vUv9e/fP+yU0gEI9xtx6aWXasqUKWFnlb/5zW+03XbbhU0qvIxH1jjkkEP0t7/9TaeddloY3Dj//PPDITsB9HzLGqR71kRDby5plFrbo+YWh+RL3xoijc+ImmMAAAAA6H4S2tfuxXEL8QgVFRUVYXMKfyT3CeF+HDz6hYML5vnuE6Kmpia8734c3G+Em2nEO510Mw73AeHJQ3r6sZycHGVmZoaPbyy/x8SJE3XjjTdq8uTJ4ecBsOV4pIs3q6RZldLyJqkp1vfDmHTp4Hxp6+A2PdhMiT8AAAAAna+xsVHXXnut5syZo6lTp4b19S9SUFAQu/dZ3SYTwkNput+G4cOHh6NSDBs2TIWFhZ8GIMyBBn+R+DJe3oGIjqNeuDNJBy78fC/j/iQ2NQABoPtwmNT9PdxTHA276ewHN8coTJYOC/ZrZwyUtg02cTe/IAABAAAAdG/dtmNKAKhtlZ6rkO4ult6slipaor4fdsySTh0gHZgvDUyRUog+AAAAAD0CQQgA3Y6zHxbWSfeukZ4sjzIh3BzDAYfD86UpRVHfD9lkPwAAAAA9CkEIAN1KTav0TIX0QKn0VrVU2hz1/TAxWzq+SNonTxqaKqWw9wIAAAB6HE7jAXQLzn74sE66b430dLm0sD7KfhiSGvX9cGQwTciMRsIg+wEAAADomQhCANjiqlulpyukR0qjvh/WNEf9POyWLR1VKO2TK22VJqWyxwIAAAB6NE7pAWwxbbHsh4di2Q8f1ksNbdKwVOmQAunwYNqR7AcAAACg1yAIAWCzaw8mj3TxbCz74eUqqaRZSo1lPxxRIO0fy36g7wcAAACg9+D0HsBm1dQmza+TZpRKT5RJ84L7ze3S8DTpoPwoALFLtpRN9gMAAADQ6xCEALBZtLZHI10468EBiOcqpfIWKSNRmpglHZYvHRJMw9Ki0TAAAAAA9D4EIQB0OffzsKBeeqpcerRU+qAumNkubZUq7ZcrHVck7ZodBSSIPwAAAAC9F0EIAF3G2Q/u6+GNKumxUumZCqmyRcpKlHbIijqePKZQGpwa7IyIPgAAAAC9HkEIAF2irlVaWB+NevFwadT3g3c4Q1OlybnSyf2l3XOkZPZCAAAAQJ/B6T+ATtUSy354qyYKPjgIUdUiZSdK4zOkIwul44ukASmxJwAAAADoMwhCAOgU7cHk7IdP6qUny6QH10gf1kWdTLq5hbMfThsQDcGZSNMLAAAAoE8iCAHgK3PfD+XOfqiW7l0jPVch1bRFfT9skyEdWyhNKZL6k/0AAAAA9GkEIQB8JR75YklDNOrF/SXSouC+sx8GJEv75klnDpR2IfsBAAAAQIAgBIAvpa1dqm6RXq+Sbl0tvVgZNcdID/Yqzn5wx5PHFUoFybEnAAAAAOjzCEIA2GTufHJ5o3TXmqj5xbLgvofYLEyWDi2Qzh4UDcHJsJsAAAAAOiIIAWCjufPJpjZpVoV0w8ooC6I++N9NLSZkSGcPlI4skPKSouUBAAAAoCOCEAA22spG6e8rpPtKpOKmKChRkCSdUBRlP4zLJPsBAAAAwPoRhACwQS1t0swy6brl0ty6qDNKBxt2zZYuGCIdkC/lJEnEHwAAAAB8EYIQAL7Q0gbp+hXSo2VSWXM0HGf/ZOm0AVHnk6PSpOQEAhAAAAAANowgBIB1crbDjFJp6grpwzqptjXKftgjR/r6YGnP4DY/maE3AQAAAGw8ghAAPsOZDgvrpL+tlGaWSyXNUnMwb2CKdPoA6dgiaWSalBbsPYg/AAAAANgUBCEAhNzJZGWL9ESZdOtqaX6tVB3LfpicG418sVu2VJQsJRF9AAAAAPAlEIQAoMY26YNa6fbV0rMV0oomqaVdGpwqnTEgGnZzdLqUkUj2AwAAAIAvjyAE0Ie1BZObWzxVLt1fIr1XK1W0RE0t9s6NAhDOfuifQt8PAAAAAL46ghBAH1XfKs2tkR5YI71QIS1piIISw9OkE4qkwwqkcRlSOtkPAAAAADoJQQigj2lrl1Y2Ss9USI+WSu/WSOUtUnZiNOLFlKLgNjfqiJLgAwAAAIDORBAC6EM8zOacWmlGWZT98HFDNH9UmnREgXRIMG2XGTXHAAAAAIDORlUD6AOc/bC8MQo8OADxVnWU/ZCfJE3KkY4slPbOkwaT/QAAAACgCxGEAHq5mlZpdo30ZFnUBGNRvZSQII1Jlw7Klw4Jph2zpHT2BgAAAAC6GNUOoJdqbZeWNUqzKqXHyqQ3qqXKFqkwORrx4vACaT9nP6TGngAAAAAAXYwgBNALOfvBw206+2FmufRJQzTEprMf9s+LAhDOfqDvBwAAAACbE1UQoBdx9sOKRunlSumhkij7oSaW/bBLlnRkgXRAXjTyBQAAAABsbgQhgF7C2Q/z66KmF56WNkpJCdKItCjwcHShtH2WlMpWDwAAAGALoToC9HDOfljdJL1aJd1ZLL1ZLdW3SgVJ0k5Z0vH9oyBEf7IfAAAAAGxhBCGAHqyuNRrt4sES6YFgWtUUZT8MTZUOLpBOKJK2zZBS2NIBAAAAdANUTYAeyNkPpc3SK1XSjaukt6qlljYpJ0naMVM6Y2CU/ZCfHHsCAAAAAHQDBCGAHqQ9mOrbpMUN0u2rpbuKpfJmKTlBGpAiHVMgnT4gGgXDGREAAAAA0J0QhAB6CGc/VLZIL1VK1y2X5tRG89ODrXiHLOnCwdI+eVJWUjQfAAAAALobghBAN+fsh6ZY9sPNq6Lsh9pWKTFByk2WTh0gnTtIGpoWzQMAAACA7oogBNCNOQBR1Sw9XS79eZn0fiz7wcGGnTKln20l7ZXLsJsAAAAAegaqLkA31dwufVgnTVslPVQq1bVJTnQoTJa+Pkg6f3CUCUHyAwAAAICegiAE0A2VNEuPlUo3rpQW1Ef9QaQFW+ueudIlw6SJ2VJy8D8BCAAAAAA9CUEIoBtx3w/v1ki3rpKeKpeqWqL5w1KlMwdKp/SX+qcw8gUAAACAnokgBNBNrGqUppdKd6+RPqqXGtqkrERpn1zpvEHSTllSZvA/8QcAAAAAPRVBCGALa2yTXq+S7lojzaqUSpqiDinHpEunDJCOKpSGpNL5JAAAAICej2oNsIW0t0vLG6WHS6RHSqUFdVJNq5SXJO2fFw296ewH/9+P9AcAAAAAvQBBCGALaGiVXq2S7l8jvRLcroplP2ybIU0pkg7Ol4anRp1RAgAAAEBvQRUH2Iyc/bCkIer74fFyaV6dVN0aDbt5QJ50bJG0c3b0P9kPAAAAAHobghDAZlLXKr1cFQUgfLuyUUpIkLbPlI4ulA7Il0anSelslQAAAAB6Kao7QBdz9sMnDdKjZdKT5dJ7tVHfDwNSpP1zpSMKpF2ypSKyHwAAAAD0cgQhepP6emnmTOmhh6Q1a6S2ttgDm6CpSfroI+mRR6TXX5daWmIPrGXlSmn6dOn556XaoFYd5/dcsUJatiz6PGsrC2ris2YFtfEng5r5J1JpqbRw4eeXDdstLJEWL5aam2MzN4Kf11QR1PKD1/btF2hubdZHZR9pTe0atba1xuZ2ntqmWs1e85GeLq3To6Xt4cgXzn5ITJB2yJQOK5AOzI9Gwfgy2Q+t7a16YfELenDeg1pauXSd36G5rVmzV8/Wowse1XvF76mxJfgAG6s9KMuK96Sl9we3c4Ky3YRy2Bhtwbq1JlgXlj4Q/FhBOQffBwAAAEDvlvjrQOw+OmgKKuNTp07VlClTtNVWW6lfv24er2loiAIHf/yj9OyzUkmJtPvuUmpqbIGN9P770vXXS/fcI82fL/XvL40cGXswpjGoyF55pXTHHdKrr0oDBkhDh0opKdJTT0m33BIFGRw8GDRIysiInldVJT3+uHTdddIzz0jvBRVcBzq8rAMXO+wgJSdHgQw/Hvz+4XdJSvr3629I1Tzpk+D9lwUV27qlwXsPC56XG3vw31yB//tbf9ftc2/Xq8te1eDswSrMKFRiv8TYEl9ee/BXUV+p/3n7Ht23fLWeKmtQhQrUnpCsQSkJ2j1b2if4SBMypezgq7lJxqZywOGFJS/oD7P+oCc/flKLKxZr4pCJykzJVELwF/fastd049s36v4P7tei8kXqn9Vfw3OHxx79Ag5AODAw51fS8ulSxWwpd/vgtywIPnAnbAsOaJS8Eqxvv5NWBeVfs0gq3CMoa68rpIMAAAAA3U1ra2tQ/XtVxcXFOvbYY4OqW1B3+wJXXXVV7N5nkQnRWzgIcdddUaXdgYG//lWqro49uAnmBZX4W2+NshWeeEKaMSP2QIwzDSoqpBtvlF5++d/LVFZGWRS33x4FD/72t+h1FiyIPTEQrKzh53vsseh22jTp2mulm26KAh/OwDAHIf785+g9PP3lL1HGxIbEK7bz/yQt+oe0MHh9X2lfS3vwHUrqSvSHl/6gf777T/3l9b9o+ofTVdVYFVviywt+HVU1t+mhFSv1P/Pm6u6VpXp62fsqqV2pbdNbwlEvDg2mrYO6dupX2Pqc4fDwhw/ryUVP6rXlr4UBlVXVq9Tm4EEHDlQ88uEjenHpi7r3g3vDoMRGaW+Ryt+VFt8plb0uLbk7+P+t4DcO1rPO4NdZGaw7K4J1wa+/MCjnuuXB+5INAQAAAPRmBCF6C2dqOCPBl9VdiXfmQOKXuKrvrIXCwui+Mw+KiqL7Hfm9Bg+O3svRr4KCKFvB7+sAhYMR5iBIx6YaXjY7O8rO8PJpaVFzDwc26uqkmppoOf/v5h6+NTctWV+zkI68fGvwfs2x12lrDO6XR/fX4mYJlQ2VYUDCf2X1ZWpx84CvoDl4+8VB3fqZ4Cd4pCxBjWnjg98oRW1NKzUuuUxH5Ldqr1wpL/gZvuq1/n4J/TQgY4CS+iWFgYdB2YOUlBj8pmvJT8tXRnJG+D1z03LDaeMEnzApKyizYHn/rsk5wf+ZwfzO2mUEr5MarGeJwTrmwEnaoGCWI6lkQQAAAAC9Gc0x1qPHNcfw5xs4MKrMjxsnXXCBtPPOUcV/UzhAkBNUOD3tt5902mmfDUQ48OBAhQMcvt11V+nss6URI6KghZtcOAjhZhhHHSXtu29Q686Lnuvl/fr+rNtsI+25Z1BLzo+CJ/vsI51ySrSMOejgIIaDKaeeKu21l5SeHj22Pm4m0Ba8d0vwvGSPc7mHNOz44HmDYwtEEoLvkJaUpmVVy8JK/LjCcTpzhzO1TdE2SnGleBO1BXX0qtaow8kXKqSXqhLUnJCmhqYqDU6q1u5ZzfrBtjtrQm6hkjuhuYf5OwzKGqTqxmqNLhit07Y7TfuN2E/pyZ/9jfLS8sLv2D+jv44ee7QO2/owFWWsI7C0Nv+WacFyDuj49xt4UFDGQfmkBGXpdeCrSgh+h7Sg3JuD9SVrlDT85GCdOSRYr9JiCwAAAADoTjqrOUZCuy+R4nNqamo0ceJE3XjjjZo8eXJQ5+6cymOXclE6m8BNMxw4+LKVRQcR/DrOVIgHBdbm93KGQmZmtEz8vRwEmTs3+gwONDgw0pGzJbyM38MBiUWLpFWrpO22k4YMiS0UcH8SbhISrOiaNCmoqGZt3PdxEMJ9GXjK2ErKHrfe55XWler1Fa+HFfTtBmwXBiY2VWPwdVYGbzk3+Lk87GZJ8LH9bv2T2zUwsVK5De9qUtFQjckfGQYDOltdc51qmmpUmL7+/izqW+rV0NIQfr/0pA0Ectbm7JDGkihrIcxU6EzBOtQarCdNlVFAojP6mgAAAADQJRobG3Xttddqzpw54QX79A1cJC5wxvw6EIRYjx4ZhMBmE89+WFgvvVolza2N5qUF9ehhqdIeOdGUQr0aAAAAQC/QWUEIqkjAJnL2w7LGqOnFvWukd2NdUPRPkSZmS2cMlPbNIwABAAAAAGujmgRspDD7oSXq++HBEunRMqmsWUoPtqJRadLhBVEAYmhq7AkAAAAAgM8gCAFshKZY3w9Pl0t3FUfNL7zxFCRLu+VIZw2U9smVkr9kNxwAAAAA0BcQhAC+gLMfalujoMOdxdLjZVJFi5QabDkj0qTjCqXTB0iDyX4AAAAAgA0iCAGsR0u7VNwsPVoq3V4sza8LZiZIuUnSXjnS1wdJe+ZKSWQ/AAAAAMBGIQgBrKU9mJrbpHdqpH+slJ4qj/qCSEyI+n44bYB0ajANSImWBwAAAABsHIIQwFpKm6Q7iqXbVkufNERBiexE6eA86YLB0QgYDkgAAAAAADYNQQggprVNeqVS+uuK4LZKqmsNW19omwzp7EHSMUVRR5QAAAAAgC+n2wQh5s+fr8suu0z77befxo0bp0mTJunHP/6xFi9eHFsictZZZ2n48OEaPHjwp9M555yjDz74ILaEVFFRob/97W/af//9w9c6/vjj9cADD6itLahlAutQ3CTdslq6r0RaEdx3fxA5SdJRhdKZA6XtMqPOKEmAAAAAAIAvr9sEIVavXq3i4mKde+65uu666/S9731P77//vi6++GLV19fHlpLKysq055576uabbw4DC54uv/xyjRgxInzcgYZbbrlFd911VxjQ+N3vfqdhw4Zp6tSpmj59ergMEOfshxcrpBtWSm/VRH0/uPnF9pnS2QOlg/Ol/sl0PgkAAAAAnaHbBCF23XVX/epXv9Ipp5wSBg+OPfZYffvb39bbb7/9mSwHKyoq0o477hg+x9OYMWOUnp4ePvbRRx9p1qxZ4ePf/OY3deSRR+q0004Ln/P444+rpSWoZQKBlU3STaul6aXS8kapsU3KS4qG3Typv7RtppSZGGwkBCAAAAAAoFN0myBEZmZmmLGQk5OjtLQ0ZWRkKDs7OwwaJCd/tiH+c889pzPPPDOcrr76ai1ZskTt7b5+LS1cuFANDQ1hM4yhQ4eGrzN+/Pjw/08++USrVq0Kl1tbU1OTamtrPzPRfKN3agqK9blyadpKaXaNVBHLftg5S/raQGnfXGlQipRM8AEAAAAAOlW3CUKszU0z7rzzTk2YMEGjR4+OzVXYz8P5558fBiCcBfHUU0/pv//7v7VgwYLw8dLSUiUmJio3Nze8NQczCgsLw8CCH1+bm3u4ecfRRx/96XTyySdrxYoVsSXQG7S2S0sapH+tlh4vl5YG9xvaos4mjy+Uji2Sts0I1pcksh8AAAAAoCt0yyCEsxWmTZumDz/8UP/5n/8ZZknEfe1rXws7pzzppJN09tlnh/+/+eabmj17dpgB4cyJhISETwMQ5vtJSUlhZkNzc3Ns7r8502KPPfbQRRdd9Ol0wQUXhIEM9HzOcqhpkZ6viIbefLtGKg1Wg2A1CYfbPG2ANDko6iGpUkq3DcsBAAAAQM/X7apczj74xz/+oVdffTUMBrh/iI622morDRw4MAwQeGSMI444IgwwuKlFTU1N2PzCwYbGxsbYM6KmFs52cLChY0Ajzs/fbrvtdOKJJ346TZkyRVlZWbEl0FM1tUsL66X7S6Sny6P7bo5RlCxNKYpGv/DIFx4Jg/gDAAAAAHStblXvWrp0aTiyxcsvv6wzzjgjbBYR73ByfRxsaG1tVb9+/cIMCPcD4SCEsynigYg1a9Zo2bJlys/P16BBg8J5a3O2hIMUHSe/HnomZz+UN0uzKqSHSqQ3qqXi4H9nOjj7wR1P7pMrDUtl5AsAAAAA2Fy6TRDCnUs6APHss8/qkEMO0YEHHhgGF6qrq8MmFO54sqSkRDNnztTHH38c3p83b16YNeGgw9Zbbx1mLrgDyuHDh+uNN94IX8vLut+I+fPnh31I0MSi9/MoFx/WSY+VRdkP84P7ze3S4BTpiIJo2iVbykqUiD8AAAAAwOaTEFTufdF4i5sxY4a+973vhU0nHIDwKBnm5hUetnPixIlhoOLKK68Mm094cnDCWQ6777572DeEm2r46zjocM8996iioiJ8ncrKyvCx8847L2x2sTHctMPveeONN2ry5Mmf6WMC3ZNX5JJm6b0a6Z1g+rghCkhkBEU3PkPaJSu4jQ27CQAAAADYeG5pcO2112rOnDmaOnXqBlstFBQUxO59VrfJhBgyZIhOP/10nXPOOWHAwBkLnjyyRbxpRF5envbee+8w08GjXYwZMybspPLcc88Nm2GYl/Myfh0HD7ysMyv8v0faQO9U3ybNr5WeKgumCumDumg0jKGp0gF50mHB+r9rDgEIAAAAANiSuk0mRHezWTIh2oKa80svSfPmSXlBTXmffaSBA6NhG/qotvY2La5crJmLZob/HzTqII3IHaGkfknh/2trC9ZeZz846PB2tbSoIep4MjsorrEZ0s5ZUceTBB8AAAAA4MvrdZkQfdLy5dL//q/0H/8hXX659Oyzjn7EHuybmlqb9Pc3/67/fPI/w2naW9NU3Vgde/Sz6tqkBfVRvw9PlEWBCIfU3NnkPnlR3w+7k/0AAAAAAN0GQYgtaeFC6fXXpXL3njhfeucdqaIi9mDf5CDE3e/frcrGSlU2VOrB+Q+qpumzgRlnPxQ3SW9WSY+USrMqpdJmKTdJ2i4ranpxaL40PI2OJwEAAACgOyEIsSV5uNBttpGKiqSRI6VRo6TMzNiDfZObXUzearKKMorUP7O/Jg2dpLSktNijUl2rtLBeeqpcml4ajXzhSIP7fpicIx1bGGU/uDNKAAAAAED3QhBiS3IA4vvfl849V7rkEunII91wJvZg3+SAw+X7Xa5zdjonnP5j8n8oPz3/074f3q6R7iuJsh8qW6LshwkZ0lGF0uHB5GAE2Q8AAAAA0D3RMeV6MERn9+GRL1Y1Si9VSm/VBGXTKqX0kwqTo2E398iRBqXEFgYAAAAAdDo6pkSv5+yHihbpnWrpltXSrCqptlXKSpS2Cdb3E4uizicJQAAAAABAz0AQAt2Sh9lc1SQ9WCLdXiwtb4yaWTj74cB86YyB0o5ZUiprMAAAAAD0GFTh0K20BZOzHZz9cN1y6eXKKCCRFqyp4zKkcwZJhxdEwQgAAAAAQM9CEAJdyl2OtLS1hNOGuh9pCR5e0yTdtlqatioadjMhQcpOko4skM73YCIZUjI9TwKA1NbmxplSXZ3UHOwwAQAAegCCEOhSq2tX64mFT4RTcW1xbO5nOTThzidfq5L+Z6n0RnU0zyvnuHTpe0OlQwuiYAQAIGZpsMO87jrpu9+V7rtPamiIPQAAANB9EYRAl3HQ4YrnrtBJd52kk+86Wb9+9tcqqSuJPRpx55MrGqWbV0WdT1a1RPPd/OKUAdK3h0hbpUmJZD8AwL+1tko33CD9+tfSrbdK11wjTZ8eexAAAKD7IgiBLrOofJE+qfhEDS0Nqm+p1yeVn4Tz4jzU5vMV0l+WS7NrooCEgw3bZUr/sZW0b56Unhh1SAkA6KClRarykEG1UUDC98vLYw8CAAB0XwQh0GVG5o3UsJxhSuqXpOTEZA3LHqYRuSPCvh8W1kn/WiU9UCKVNUcBiLwk6dT+0nmDpMGp9P0AAOuVkiIdfLA0aZKUGuwwd99d2m+/2IMAAADdF0EIdJn+Gf116eRLdfPxN+umKTfpJ3v/REkpRXq6PGp6Mbc26gsiJVgLd8mOml7smRv1/dCPAAQArJ977T30UOlf/5Jef136/e+l0aNjDwIAAHRfBCHQZRL7JWpM/hgdt81xOnLscWpMHaN7SxL1ZLm0ukly9w+DUqQT+0snB9PwtKgvCADARsjMjAIPEyZIQ4ZIyYxdDAAAuj+qfOhSDkTUK0uv1GbpwZLEMPvBnU+6r4dJ2dKZA6Xdg9vC4NyZzicBYBM5I6Ifh3IAANBzcOaCLtPUFnU46X4fnq2Qljcq7A/CGQ/HFkpHBdOYdCkzMfYEAAAAAECvRhACXcLNLdzs4pHSqO+Hipaor4c9c6QTiqTdg9uBKWQ/AAAAAEBfQhACnaqxTXq7WppeKr1QIS1piLIfRqdLRxRIhwbTNhlSFtkPAAAAANDnEIRAp1nZKM0slx4vk96tkcpbpNxY9sPRBdKk4HYw2Q8AAAAA0GcRhMBX5uyHt6qlGWXS8xXSJw1SW7s0Nl06JD+axmeS/QAAAAAAfR1BCHwlKxqlp2PZD29WR30/FCRLe+RIhxdIe+VKg1PJfgAAAAAAEITAl9TQJr1TLT1RFjXBWNwQzR+bIR2YJx1WIE3IZOQLAAAAAMC/EYTAJmlvj7If3OzCnU++Vi3VtEbZDx7x4sgCaZ88Rr4AAAAAAHweQQhslPZgqmuV5tRG2Q8OQCxtjFagMenSAXnS0YXS+AwpnbUKAAAAALAOVBexQR5i0yNfvFwl3b8munVnlM5+2CVbOrYwCkIUBf/3I/sBAAAAALAeBCGwXs5+qG2V5seyHxyAWNEkJSdII9KiwMMp/aVtM6UU1iQAAAAAwAZQdcQ6NbdLqxqlV6uku2PZD86IyE+Sts+UTu0fDb2ZE/xP8gMAAAAAYGMQhMBntAWTsx8W1EmPlkr3rpFWNUkpCdLQVOmgfOm8wdLWGXQ8CQAAAADYNAQh8ClnP6xpkl6ulG4vjka+cFDC2Q7bZUpnD4qG3kxlrQEAAAAAfAlUJxH2/VDfKi2skx4oke4PJgcj3PfD4BTp8ALp/MHSyLRoeQAAAAAAvgyCEH1ca7tU1iw9XyHdslp6y9kPwbzMRGnHLOm8QdLB+XQ8CQAAAAD46qha9lHOfnBHkx/WSbetlh4slUqagxUiQRqYIh1XKH19kLQV2Q8AAAAAgE5CEKKPqm6RHiuVblolza2NMiLSgrVh92zpwiHS/mQ/AAAAAAA6GdXMPqa9XZpbI924Unq8TKpsieYPS5VOGyB9baA0OLgPAAAAAEBnIwjRh5Q3S/etkW4rlhbUR6NhZCRK++VFTS8m5UTZEIy8CQAAAADoCgQh+gBnP7xTI/1jlfRCZdQRpTufHJ0mndo/6v9hSKqURPQBAAAAANCFCEL0cqXN0t1rpHuDaWG9VN8WjXxxUL502kBpl2wpK0lKJAABAAAAAOhiBCF6KWc6vFEl3bxKeiW4LWmKOp8clyGdOkA6tEDaKlVKpfkFAAAAAGAzIQjRCzngcNca6eHSKPuhtlXKSowCDycUSTtlSflkPwAAAAAANjOCEL1Ic5v0apV0y2rpteB2dSz7YXyGdHJ/6cA8aURa1PkkAAAAAACbG9XRXsCBhhWN0n0l0mOl0od1UfZDTpJ0eIF0TJG0c7ZUkNzDsx/cw+bMmdLPfiZdeaU0d67U1hZ7EAhUVUkPPCB9//vSNddI1dWxBwAAAAB0BwQherCgSh4GG14P6lkeevOVSmllUzA/QdouM2p6sX9eNAqG+37o8ZYskaZNk/7+d+n666WHHpJWrYo9iD6vNdgYFiyIAlQ33yzdcIP08MOxBwEAAAB0BwQheqjmdmlpgzSjTHoymObWRiNf5CVJh+ZLRxZKu8WyH/r1lr4fyoIvOn++VFIirVghLVoUXfkGzFkxXkfefDNaL5Yvj7JlAAAAAHQbBCF6GGc/VLVEI188Uiq9WCkta5SSYtkPxxZKB+dLW6dLyb2tdIcMkfbaSxo+XNpmG2niRKmoKPYg+rzExGgdOewwqaBAGjdO2nff2IMAAAAAugOCED1IU5u0uF56ulyaGUzv1kgNwbyiZGm/POmIoN41OTfKhuiVA18MHCidf750+eXRdNRRUmFh7EH0ef2C3dnIkdLPfy794hfST34i7b9/7EEAAAAA3QFBiB7A2Q/lzdJb1VHzi+di2Q8e5WJCRtT5pAMQY4P7vabpxfo4++Gb35TOOiuqcCb09i+MTZKZKe23n3TJJdLpp0sZwUYBAAAAoNsgCNHNNbZJn9RLz1dKj5dLb9dEGREDU6Q9c6Lgwz650UgYAAAAAAB0ZwQhuilnP5Q1R00unP3wTLm0slHKTJS2yYj6fTiqsI9kPwAAAAAAegWCEN2Qsx+WNESdTj5SKr1TE42GMSBF2j1bOrpQ2pfsBwAAAABAD0MQohsJ+35okebUSg+XRJ1Prm6Ksh/GpkdDbx5bJI0J7m8o+6GhpUHzS+ZrXsk81TbVqr3dr74Z1ddLH38cDanpYRM39v2bgi/s57z3nlRTs/HP+zIqKqL3Wbgwel8AAAAAQJciCNFNOPtheaP0XLl0/5ooENEa1L898oWzH47vL+2TJ2Ulxp6wAS8sfkFXPHeFfvXMr/Tc4udU2xy84ObSFnyZ116TrrlG+s1vpEcfjSr8G9LcLL39dvQcj3AwY0bwwwQ/Slfwe917r3TZZdJVV0nvv08gAgAAAAC6GEGILczX+atagjpwrXR3sfRUUFcvDerHGYnS6PSo34fjiqSRaZs27OZlT1+mez64J5yufulqLa9aHntkM2hokKZNk6ZOle68U7rpJumtt2IPfgFnPlx9dfSc6dOlH/0o+HGqYg92ImdXODvjxz+O3ufvf5duu00qL48tAAAAAADoCgQhtiD381DcJD0Z1IdvWy3Nq5Pagnl5SdIe2dJpA6S9cqPmGJuqubU5eK22sBlGQ2uDWttbY49sBvHsBQ+f6amlJco82BAHB9yMw5xN4ed0RXMMv2ZtrZSSEr1PUvCD+319HwAAAADQZQhCbAGuVtcH9d0PgnrwtJXS0xVSVauUGpTGiDTp5AFR84thqZuW/dDRD/f6ocYWjNWI3BE6f+fzNSR7SOyRzSAvTzr8cGnPPaWRI6VDDpEmTow9+AUyM6WLLoqeM3So9JOfSNnZsQc7Ub/gh/Z7nHlm8IOPkLbZJvjRT5YKCmILAAAAAAC6QkL7Zu+xsGeoqakJ6s0TdeONN2ry5MlKTPwS6Qjr4H4ealqjYTdfqpQa2qJIUHrw8nvlSPsH9ff+KV8++NBRRUOFEhMSlZmSqX4JWyDeVF0dZR1kZETZBhvDy9fVRdkU+flRJkVX8XutWSPlBD98qiM+XfheAAAAANCDNQZ1tGuvvVZz5szR1KlTlZ6eHntk3QrWc5GXTIjNqCWo886rla5eKj1bHgtABPXe4WnS+YOkKUXRMJydVRXOS8tTdmr2lglAmLMYXMHf2ACEORDgjAivsF0dFPDrDxggpbnDjS5+LwAAAAAAQYjNoT2Y3PnkHcXSdSuiYTfd+0By8Osfni9dNETaLktKoTQAAAAAAL0Y1d4u1tQmvV0t/c9S6eXKqONJG5UufW+IdEShlJvcedkPAAAAAAB0VwQhuoiDDc54cPbDLaukNcF9N8dwtsOxhdKFg6WtM6S04H8CEAAAAACAvoAgRBeoa5VerZL+vkJ6ozr4vy3qcmCssx+GSgflS/nJUiLRBwAAAABAH0IQohN55IslDdK9a6QHSqTlTVFzjJwk6ZhC6dxB0uh0KSPx89kPZfVlKqkrUVNr8KTuqqVFWrw4Gr0CAAAAAIBNxBCd69FxiM6qAVV65KNHlJ6UrrN2PEs7DdopHHGipqlGj3/0uB796FENyNla40eeoZX9Rmplo1TWWK8FpXO1dNXTGtj8nn6794U6ctQ+sVf36JDt+sc7/9Cznzyr1rZWLa1aqvqWehWkF6itrS0cVvP8bY7QUenNSip/U8rfRUofIq2eKTVXBSWXJDWVRrep/aWWGr+q1NYs5UyQxl8azM8L5sXCHStWSP/4h/Thh9Jhh0kNDdIrr0grV0r19dFwmKecIp16avCU4DlNTdLcudLf/x68Zpt05JHSI49IbwafpahI+stfpNGjpX7riWO9/LJ0333Ra3t0DAcv/Jo77BB8j3TpmWei9/H9+LT11tK8edLIkdJPfxqNrHH77dKDD0rl5dHyWVnS8cdLhx8uvfCCdPfdUkWFdNxx0umnB79N8P3vv1+6997oc550krTrrtL//V/w262OlvNn+MMfos/zne9IhYXS3/4mLV0aDduZkhI978ADo8+wsRycefJJ6Y47pKFDpZ/9rPNG+Vi1Srr5Zmn2bGnffaVzz5X++teoTDx87IUXRmXkkUU6i3+f66+XXnpJ2mMP6RvfkHJzYw/GeJkHHoi+9/Dh0v77Sx9/LD37bPQ7n3Za9Fv0dMuXR+vUG2/8+7fwsK4AAABAH9FZQ3QShFiPeBDiN3/8jW6ruE0vLntRSf2SdOHEC/XNXb+pkXkj9fTHT+ual6/Vi8VLlVpwgMaPOFHjB+2l1uAnXV3xvp565wrVVbys1LYqTR62u2aePTOoj0YV0teWv6bvPPIdzS+dHwYkGloague1Krlfstra28L3+s1Ww/WDvHalNRVLaYOk5Ozggy2S2oKKn4MLbY3Bbb+gEhpUhtpagvsuymBKDiqKO/5WGvW14LGMYF7gqquiIISDDiNGRJX1eACitTWqeB97rHT55VHlcVHwPn/8o3TbbVEQYtgwadmyqMLvytfvfy9961tR8GBtxcHn/fWvowCBX9uBitra6HVciXWl2cuY73tyoMIBhqoqKS9PuuwyafLkqLL3/vtRZde/nZcbNUr65jejQIcr4X5s++2lSy+NPo8DGP78/py77BJV2qdNi76rK8qeN3169Nn23FMaPFh6+uko0OHNwZ/HgYv/9/+kff4dOPpC/m4LFkgnnxxVwj086Q9/KF1ySfTbfhXV1dHn/clPot/f3+H735f+67+i4JJ/l7Fjo6DPuHHrDwxtCq8fDvKcd55UUhIFahyQOPjgz5a5l3FAx7cZwbrmz+FgjH8LBx8cHPnBD2IL91D+Pg8/LP3Hf0hlZdI220i/+Y109NGxBQAAAIDer7OCEDTH2IDaplotrlysioYKldaVann18nCeLaur1cK2warMPkDFCcP0SW2NWlrrNSG9RbsnL1Zt6ZNqb1yuxuYavVf8ntrDIEHETS+c/eBsitrm2jAAYc1tzeH9xtZGNdYuVnvtkmCmO5ZYHlRGFwb3K6WW4P2d+eCsBwcinBnRGlSUgvdWa4PUsDpY9qPgseg1Q64Yu+LvCu3C4HX8f2XwWq7AuzLuCrozBVwRN1e8PvkkqvR6uQ8+iAIJruD6Oa6or+8Kv1/LAQtXXv16paVR5oWft2ZNdFXflXZPruz6sZrg+3i+39fPcxDBFT5/hnigxM1BvOxHwXfzfH8fL+/5vlLtCrmf66wLLx//Dv6+nu/vPn9+lOHhYIff0/c9+b38/fwcz/f7+7tvCj83/jv59fw5/R2/Kv9uLht/R7/2kmCd8Gs7cyP+uziDxJ+7s2KKfh2/p9/D38u3/r39Xh25PB3M8u/pWweM/Pn8W/t/3+/p/J29/vi7xNdTr2sAAAAANhlBiA0YnDVYh44+VKPyRmnbom219/C9VZQ5WPOCuuDChG3VnLOflDYm+CXT1K9ppYa3zdeU/kk6dNBg7T90ZyUmJCovLU/n7HSO4lkQtuPAHbX/iP01MHOgxhWOC5tfdJSdkq3sIYeq38ADpPTBUv99pKHHSRnDpdSi4D0HKGyKsT4ZQ4LHE2P/BI46KnjTHaVBg6KMBzdn2Gqr6Op1cnLUHGPnnaMr6TZwYLSMm0Y4c8LNFty8wE05nIVwxBHR89bFzSCclr/ddlGTDV85dgaE32tA8LmdJRDnYIYf699f2m23qPnC+PHSMcdEt/4Mnuf3cmaDm0e4SYWbHjijwe/l+c6a8OSMiEMPjZb3a/r5Xn7ChOi7u8nGBRdEV+n9WXyl//zzoyv4fi1f8fd391VuNw/ZWC5bv95ZZ0Wv4d/xhBOizI2vyhkizszYe+/oO7mZiF97v2Dd8/d0poW/l79fZ2RBWDwb5IBg/fO64fL0752WFlsgZqedouYJzpTx7de/Lh10UPRZdt89ut/TOcLr9cpZIF43vJ56XQMAAACwyWiOsR4d+4QoGFegD8s/DJtKDC/cRWsSBmt2bT/Nr6nTyyve1dyVr6it9gPtkZusK/Y6V4eNmKTqpmq9vfJtLShboLSkNO01bC+Nzg8q5DH+2d9Y8YYWVSxSRlKGrnv9Or245EXVNddp18G76oTxJ+iEMQdr2+RW9atfEQUikoIKeM1HkjuvbGuQip+XFt0UvWDw2cLMCGdb9Asqigc9EVSq94zmm7MZ3J+Dr+i6cu2ru7667Su7vpruyqWDBg5UuELteb7i+/rr0VVxz3cQwRkUrmS70v5FFV5nILz3XpTp4Aqtr5T7NX01/aGHovR9V+7i7eu9jF/XV9Zd6XXl15/p7bejq/yNjdHncqXewQlX8v1ZfOXd2REOdPgz+j08zxkJfn0v6wryq69G33XbbaMKst/fv4GbY/h7+Xv6M/q7+nu50jlmzOcr3V/EGQvuc2POnChY4u/g115fxsimcGaB+yNwdoKDQg4QuDmKszz8+sG6Gn7Xr9r0oyP/Pn4PZwA4YDVp0ud/Dy/jcnZmhpvRuKmMs1+cSeIKu4MUa/cj0RN5+3G5Ohsl/lt0RoAJAAAA6CHoE6KLdQxCTJ48WW0JifqoPqjHV7fqlbIqrWxoVmZKhjLaylVbPksJte9p74EjddL4KSrKKIq9SsR9PKypW6NZS2aFAYkDRx6otOQ0JXQYI2PGRzM0/cPpqmqs0mFjDgunAZkDYo+ug/uFqJgbVEL/EawNQcU9J6iEu6lG3dKgMhhUxsddLKUGhV5bF1USXcl3xb1jFkJXcIXVFTVXQF35X7tS7CCDOzGcMSO6wu+sDGc1dDY3g3AzEPdN4O/sTAj/BgAAAACATUYQYiMsW7ZMr776qubPn69+/fpp/Pjx2nvvvVXktPsN6BiEGLvbZH3QkKh3aqQXipfo/dKPVd+wWjvlpOv0kdtoh+w09Wut1uDsweHoFmtzYOGqF6/SzEUzlZqUqm/v9m2dOP7EMCAR19zarE8qPlFTW5OGZQ9TbtpGXD12IKJ+pdRUrrCZhvuFqF8VVO5HS6n5YVJEOIKCK/0u5jPOiJpTONOgK/iqfPB76d13owCEU/Od6bB2xoQzDpyF4aYEvnK+vmYdX4X7hfDIGs8/HzWTOPPMqJkGAAAAAGCT0THlBhQXF+uBBx7QnXfeqY8++kgffPBBeP+ee+5Rg9P3N9KHQb3+qaCO/2Qwza9r1/yyRVq48mktWXKrViy5SekN87VD4XBtN2C7dQYgrLqxWn976296Y+UbmrV0lu58785wNIyOkhOTNTYjS9slNSu3NaikhyNgbEC/FClzhJS/c5T1kBFU/At3i+47y8IdOnpEDI+s4GEuPXSlOwvsKm7y4Pd47DHpn/+M/l/Xb+2mCm764cyMrghAmDMgPCKGP4uHkPRIHT2RO6J00xI3BXAHkQAAAADQg/XaIMQbb7yh5557TqNGjdKll16qH/7wh9pqq62COvL9muc+BjbSy1XRVNosZSVKA9tXKb3mBaniWaW2rFLKRnQE6OE23bGlm2W4CcZWOVspsd9aTQPqlkkf3yK9/3vpw2uD1w8qncHyX4mzH9yG330GeHKFfyM+75fmNvLxJg9uhuH7WyrRxu8dbwri7+7OK3sa90PwzDPREJj/8z/SvfdGzUwAAAAAoIfqlc0x/JWuuuqqMBDxne98RwfFeuh/8skng7rc/2jKlCnh/I5aW1vDKc7NMfbYYw9tc/lUDd1lLw1JT9R2mVJV6bN6aeG9WlW9XAeMPCDsQNJBhS/irIdHFjyiB+Y9oJTEFH1n9+9op4E7hcGJTy19QJr7m6Di+UFQgU6TdrxC2vqbwf0vTnHZIDePmDkz6rDxa1+LRitwJkJXcBOLv/9dmj076rzvnHOikQS2BA8Res890rPPRv1TeNSKffeNPdhDOFj23/8t3XprFODxb+mslq7u1wMAAAAA1tLU1KTrrrtO77//Pn1CrM3NLX7+859rzZo1uvzyy7WNR04IvPnmm/rTn/6kQYMG6Q++uhzT0tKid955R++6L4MYv8YVV1yhwcd9XUUjttaErH4amyGl9WvX6ppVamhtCDug9FCaG6M9+KtqqAqbXWQkBy+0tpUzpdXB1FgS/T/kGGnwoUHl87NDd24yBx/cP4KzAtw3QmeOnrAuvnofH+HCU1dmXmyImzJ4hA9vHEOGxGb2IB794777ouYY5lE9fvzjruvTAwAAAADWw/Xmp556KqhSpuivf/2rsjdwcbRPBSEqg4qwgxAOJPzyl7/UCA9pGJg9e3bYkYZ/tOuvvz6cZ17O/UXcddddsTnRDzxr1iztsMMOQZ0vXwmdMcwiuh2X8/PPP69dd91Vub1hKEl8Tnxnud9++20wWoueqb6+PtyODz74YCUxdGqv5QzFl19+WQceeCDl3Av5dLS8vDzs7GyfffZRIiNa9TouY/fZtnDhwjDbmDLunVzOK1eu1NKlS7XbbrtRzr1QW1tbeF598cUXE4ToyCcqP/vZz8LbX/ziFxo9enQ439kOzoTIyckJbztqbm4Op7iqqqrwIOhgxZ577skJTy9VXV0dBiDuuOMO7bTTTuwoexnv3rwtjxkzJmyeNXz48HCkHPQePhAuWbIkPNFZtGhReDAkaNz7uJw90tWhhx4apoD6OI7exQFjj2h20UUXhReBNnRii57H59kzZszQ1VdfrenTpyvLQ7Wj13E5e3CAadOmhQMCZGZ+xaxudCs+t/b+2ufT3oY3dM7Vp4IQ7tvhV7/6VTgqhjul9Mmp+aB2zTXXhENvXnbZZeG89ek4ROfkyZOpnPZSDkJMmDAh7LB0l112oZx7mXgQYvDgwXrvvffCrCiCEL2LK6eLFy/Wdtttp1WrVhGE6KVczu5Uet9999XHH39MEKIX8rnbSy+9pAsuuEBvvfUWFdReyBUXBx/cb9vMmTOpnPZSDkLce++9uuGGG/Twww9Tzn1cnxqi0xVJX/n0RuC0Plc0Kyoq9OGHH6qkpEQ77rhjbEkAAAAAALC59NpLgnvttZcGDBigBx98UPfdd1+Ybu+r3WPHjg3bsGwM9xFAM4zezVdM8/LyKOdezGXsfl3IgOi9XLb03dP7+QKD99eUc+/lYzH9M/Vu7peNpja9X7yc2V9jfRJ/HYjd71WKioo0bNiwsI3wv/71rzC1z307XHLJJWFq9sbwBuQmGT4gshH1Xi7n3XfffaPaNaFniZdnampquP2npaVRxr2Qy9TpnvTf07u5nH1SO2nSJJrO9UIuX08OKPrcizLunRw07t+/f5iVTBn3Tt6OXc4DBw4Mm0pSzn2bm1+tS6/sEwIAAAAAAGw5fapPCAAAAAAA0P0QhAAAAAAAAJsFQQgAAAAAALBZEIQAAAAAAACbBR1TrscDDzygf/7zn1q8eLGGDBmib3zjGzrwwAOVk5MTWwLd0fvvv6+77rpLr732mtasWRN2hrLvvvvqvPPO09ChQ8NlGhoadOGFF+rNN99UW1tbOM8OPfRQ/fjHP9aIESPC/ysqKnT33XeHQ7yWlpaGPfyeddZZ4XrAcI9bzuuvv66pU6dq1qxZsTlSenp6WH7ugde7NJf9tddeq5kzZ6qpqUmTJ0/Wf/7nf4brQLzsXKbTpk3TY489ptraWu266676+te/rt12243y3cL+8pe/6KabblJNTU1sTmSrrbYKy8hl9fOf/zzc3uPc+/bJJ58czveIN+b14Prrr9dTTz0VrgcePeOCCy7Q9ttvH/bejc1r3rx5uueee/Tyyy9r+fLlOvHEE8PyHD58eGwJac6cObr55pvD7Ts5OVkHHXSQvvWtb306qpW3b2+73taff/75cB/uYbe///3vh+tHfNtdsWKFbr311nD7rq+v1x577BHuvz0SErrO3LlzwyHR33jjDa1cuVLnnnuuvva1r2nQoEFhWb344ovhOuBy9n7Xo5gdddRR4bbr4Vdt6dKlOvvss7V69erw/7hvf/vbOv/88z8d3tHL3XjjjXr66afV0tKiffbZJyxjj7qArvPOO++Eo875dtWqVbr44ovD8issLAwf93D4PrZ+9NFH4f/mURLOPPNMffOb3wz/d3ktXLgwPE6/+uqr4T7bx/Cf/OQn4UhH8f3zJ598Eu7Dvd54nvcHXjfGjRsXPo6u41EFb7nlFs2ePTvcFn1+fMIJJ3y6nf70pz/Vgw8+GN7vaIcddtB3vvMdHXDAAeF5tkclLCkpiT0ajVb2ve99Lzwvj++vP/74Y/35z38Ojw0+lntdcDmPHj06fBw922QPS3YAABm5SURBVPo6puy1Q3R+Fc8884x+8YtfhJWR448/XnV1dWFQwhWYkSNHMtRMN+adpnd6DjwcccQRYQDJJyiuuB5++OHh8H3Nzc3hQc0nPz7gHXvssTryyCPD5/gk1ie+Pln6v//7Pz366KNhheewww7TkiVL9Morr4RDS40aNSp6Q2x2PrHxCYkPTi4/l53Ldq+99gpPdFxB+eMf/6gnn3wyPOHZf//99fjjj4cnOj4oZmRkhOX7X//1X3rppZd0yCGHhGXvA+17770Xbudeb7Dl+GTWlUWXq8t37733DvfDrliedtpp4YnLQw89FJ7sXHrppeEy3t4dbBowYEB4suoy/tnPfqZ333033MYdgHB5+8TXgUYvh83LQ2b7ZNPHUQckfDLr4ED8BMVBpb/97W/hNu5y3nrrrfXCCy+EZeh9sI+9Xg988ut9uoMTHq7TgQaX60477RReKHCg+b//+7/DZRw09vrj7dsVX5e9K8ToGh9++GFYcXQl0cdiB5h8DPVQ5w4EPvfcc2GFxtu2K5Telr1/dsDQ+2HzPtyBZi/j4JK3b09eV3z89XMcWPrVr36l+fPnh4/5fM3H5/j65eXQNT744IPwAp2DuQ4E+gLNzjvvHA5zbj7W+ljq8nXgyOXjSuUuu+wSDr9qLqcrr7wyDCT5OD1hwoQweOXgpIOKPlfztv6jH/0oDHS48uvgks/niouLw3Ow+Guha7gMfd7rcvMFHZex97E+hzLvRx34i2+fPh478Ojjr/ffLh/v8x2oOProo8PAhJfzdu3t1ft/L+tydlDC272DWV6fZsyYofLy8vA8Lx70QM+1viE6CUKsg38Sb2TeKHzi6o3OV9Ictd9mm23Y8XVjPpn1jtIVmG233VZjx44ND2bOaHBl0xUPR+AdxfcJriOxXsYnLa7AOhrvneKCBQv097//PTzI+sqpT6J89cWR/6qqqrBiG4/gYvPyyY8rJS4/X/Vy2XWsVLoC4wCDr5idccYZ4XrgoIKvrruS6vuuADnqftxxx+n0008PD7IOPrmS6qwKHyC9HmDLcIXFlReXrSdXOOIBQG+zPjlxsNgnpb66Fl/Oj8eDxN5Wr7nmmnD79RV3L+sydSXIJ09kQ2x+3od6e/T+1Fe8fMXTwcN4EMKBQ5ePg/8+GXXFxOXpbDTv150N4UruZZddpl/+8pdhcMknrL6ydtttt4XHawcRfRX+9ttv18EHH6xzzjknPIabg9R+PQcu0DW87bpMXMYOLjjY7+Ox5/uY6TL0YxMnTgzPpxz4d8XFZeaMCO9/vX3feeedYWDxpJNO+nT79nri47k5EO3juCs7p5xySrh9NzY2hq/jSku8zNH5fA7sCqfL0VktLm+XZzwI4X2vj9MOJrhMXXYuZ5eL1wEHCb39ext1hqIrpg5a+TEfp31MdjDRAQdnM333u98Nt3W/Z3V1dZjp6vXK+xJ0HZeztyufD3n/6jL2uVI8COHt0ede8e3T27HPzbyvPuaYY8J9rYMYPla7jF2GXs7Pia8L9sQTT4QZF/ELCi5nZyI7mOUghM/10LOtLwhBLWotrqD64ObKijcUnzR5o/FGtWzZss+lB6J78U7RQQVfSfUJrk98ioqKwiswa3M5u5mNUwmd0ukrMfHWSb664uf4JNjrgV/HO19XjBzZ9bLYcpym7+wkl5+zIXwi5HmurLqS4hMVBxh8oPM27KuhPkHyAdLL+KqoD5A+wDoo4fJ1BSYeufdJMLYcl42DQg4KutLhMvEJjq9ox090Xd6+wuJ0b6eJutLi/Xecr8K6QuPt1gEqrwu+0pqWlha+XmVlZWxJbC6uWLjy4ICvAwcduTx9ddRl7sCEt0WXm7dRP8/l6X2yKzguZ1duXKZ+zCe8znxxcNEXC5zx4HJ25cjv5eV8Au1bB5hdCULX8L7UFc54UL+jeBAifnXTx2iXs8vQAQRv93HeT7uS6pRsXw13ir+XiXNAya/hMnbw0a/j4JLf09t3x2XRufy7+1zI22c8KLQ2Zy/89a9/DbMcnLHigEL8PMwXcrytev/sY7OP0T5nc7aDr4Z7+/U27mCD1xeXsR/3+Z2PAc5mdeV2Xed16Dzepnz+2zG435HL3tubp/j+12Xjcykfv+Ncpv/4xz/CpnfOTnSWQ0cOSPm44HJ2GXtyhoX3Ac6U8WuidyIIsRafmLoCEk/Lj/OO0Cc3a7dRRvfm9G1fjXGUPd7u2DtTV0Z8hcUZDa6E+krbn/70pzAV0Jzu5x2rD7bxna93yD4Q+gBaVlYWzsPm5wOUm1X4hMUVC5ePo/TXXXddGHxwZdVlF+8DxHyy65Nirw8+oPnWJ74++YlH431C5XmOwFNB7T5cnq54+uqLr6yZKzo+efU64Aqry9dp/G6GEz9hcdDY64ofi5exMyD8Ot5+2Zd3Ly4Pl4vLJ57V5EwVl5/3u943t7a2huXqwLLXgXgmix/3tuuLBA4w+NbBKm/f8WW8LsS3b8q+e3DFxUEhB4c7pnm7zB1kciDZlU7Pd+aa+4nxPt68D/cx2cvGy9iVJS/rMvb5GrYMnz87i8Up+L6q7f4AbrjhhvBigS/0uHLped5uvY2a99He7v2/K57xbd1l6mBFXDy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+   "source": [
+    "![image.png](attachment:99d1317e-d872-46f7-bf2c-cc73c825993a.png)"
+   ]
+  },
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+   "source": [
+    "Alright — now that we know where things stand, it’s time to shake things up.\n",
+    "\n",
+    "Can QLoRA fine-tuning unlock the true power of LLaMA 3.1?\n",
+    "\n",
+    "👀 Let’s find out... in the [next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part6_ft_llama_qlora.ipynb)"
+   ]
+  }
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diff --git a/week7/community-contributions/09_part6_ft_llama_qlora.ipynb b/week7/community-contributions/09_part6_ft_llama_qlora.ipynb
new file mode 100644
index 0000000..a8a4468
--- /dev/null
+++ b/week7/community-contributions/09_part6_ft_llama_qlora.ipynb
@@ -0,0 +1,9447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 6)\n",
+    "---\n",
+    "- Data Curation & Preprocessing\n",
+    "- Model Benchmarking – Traditional ML vs LLMs\n",
+    "- E5 Embeddings & RAG\n",
+    "- Fine-Tuning GPT-4o Mini\n",
+    "- Evaluating LLaMA 3.1 8B Quantized\n",
+    "- ➡️ Fine-Tuning LLaMA 3.1 with QLoRA\n",
+    "- Evaluating Fine-Tuned LLaMA\n",
+    "- Summary & Leaderboard\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# ⚙️ Part 6: Fine-Tuning LLaMA 3.1 with QLoRA\n",
+    "\n",
+    "- 🧑‍💻 Skill Level: Advanced\n",
+    "- ⚙️ Hardware: ⚠️ GPU required - use Google Colab (A100)\n",
+    "- 🛠️ Requirements: 🔑 HF Token, wandb API Key ([Weights & Biases](https://wandb.ai))\n",
+    "- Tasks:\n",
+    "    - Load and split dataset (Train/validation); set up [Weights & Biases](https://wandb.ai) logging\n",
+    "    - Load quantized LLaMA 3.1 8B and tokenizer\n",
+    "    - Prepare data with a collator for fine-tuning\n",
+    "    - Configure QLoRA (LoRAConfig), training settings (SFTConfig), and tune key hyperparameters\n",
+    "    - Fine-tune and push best model to Hugging Face Hub\n",
+    "\n",
+    "⚠️ I attempted to fine-tune the model on the full 400K dataset using an A100 on Google Colab, but it consistently crashed. So for now, I’m training on a 20K subset to understand the process, play with hyperparameters, track progress in Weights & Biases, and push the best checkpoint to the Hub.\n",
+    "\n",
+    "⏱️ Training on 20,000 examples took over 2 hours.\n",
+    "\n",
+    "The full model fine-tuned on the complete 400K dataset is available thanks to our instructor, [Ed](https://www.linkedin.com/in/eddonner) — much appreciated!  \n",
+    "We’ll dive into that model in the next notebook — **stay tuned** 😉\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MDyR63OTNUJ6",
+    "outputId": "525372ce-f614-44f1-b894-80e289958197"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required packages in Google Colab\n",
+    "%pip install -q datasets transformers torch peft bitsandbytes trl accelerate wandb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-yikV8pRBer9"
+   },
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import torch\n",
+    "import wandb\n",
+    "from google.colab import userdata\n",
+    "from datetime import datetime\n",
+    "from datasets import load_dataset\n",
+    "from huggingface_hub import login\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, EarlyStoppingCallback\n",
+    "from peft import LoraConfig\n",
+    "from trl import SFTTrainer, SFTConfig, DataCollatorForCompletionOnlyLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Google Colab User Data\n",
+    "# Ensure you have set the following in your Google Colab environment:\n",
+    "hf_token = userdata.get('HF_TOKEN')\n",
+    "login(hf_token, add_to_git_credential=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B48QsPsvUs_x"
+   },
+   "source": [
+    "## 🔀 Load Dataset from HF and Split into Train/Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #If you face NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported run:\n",
+    "# %pip install -U datasets (for Google Colab)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
+     "referenced_widgets": [
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+     ]
+    },
+    "id": "XEE1FrSIh-EF",
+    "outputId": "8cd19745-2f6f-41e0-96dd-5a2f72ac3a63"
+   },
+   "outputs": [],
+   "source": [
+    "HF_USER = \"lisekarimi\" # your HF name here!\n",
+    "\n",
+    "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
+    "dataset = load_dataset(DATASET_NAME)\n",
+    "train = dataset['train']\n",
+    "test = dataset['test']\n",
+    "split_ratio = 0.1  # 10% for validation\n",
+    "\n",
+    "##############################################################################\n",
+    "# Optional: limit training dataset to TRAIN_SIZE for testing/debugging\n",
+    "# Comment the two lines below to use the full dataset\n",
+    "TRAIN_SIZE = 20000\n",
+    "train = train.select(range(TRAIN_SIZE))\n",
+    "##############################################################################\n",
+    "\n",
+    "total_size = len(train)\n",
+    "val_size = int(total_size * split_ratio)\n",
+    "\n",
+    "val_data = train.select(range(val_size))\n",
+    "train_data = train.select(range(val_size, total_size))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lUPNqb2Bse21",
+    "outputId": "a3d09c8f-ce5a-46b0-e1b0-b4471a659f69"
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"Train data size     : {len(train_data)}\")\n",
+    "print(f\"Validation data size: {len(val_data)}\")\n",
+    "print(f\"Test data size      : {len(test)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wixbM-VeVfsR"
+   },
+   "source": [
+    "## 🛠️ Hugging Face Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "OixVUG06VmZk",
+    "outputId": "3cb523e0-fd03-4a18-913b-c22fa90e3bdd"
+   },
+   "outputs": [],
+   "source": [
+    "PROJECT_NAME = \"llama3-pricer\"\n",
+    "\n",
+    "# Run name for saving the model in the hub\n",
+    "\n",
+    "RUN_NAME =  f\"{datetime.now():%Y-%m-%d_%H.%M.%S}-size{total_size}\"\n",
+    "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n",
+    "HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
+    "HUB_MODEL_NAME"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1-t1nGgnVTU4"
+   },
+   "source": [
+    "## 🛠️ wandb Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load from Colab's secure storage\n",
+    "wandb_api_key = userdata.get('WANDB_API_KEY')\n",
+    "\n",
+    "# Load from environment variables (.env file) if running Locally (GPU setup)\n",
+    "# wandb_api_key = os.getenv('WANDB_API_KEY')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"WANDB_API_KEY\"] = wandb_api_key\n",
+    "wandb.login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 156
+    },
+    "id": "yJNOv3cVvJ68",
+    "outputId": "0c03623e-6887-49e3-8989-bbe45dfc5d35"
+   },
+   "outputs": [],
+   "source": [
+    "# Configure Weights & Biases to record against our project\n",
+    "\n",
+    "LOG_TO_WANDB = True\n",
+    "\n",
+    "os.environ[\"WANDB_PROJECT\"] = PROJECT_NAME\n",
+    "os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\" if LOG_TO_WANDB else \"end\"\n",
+    "os.environ[\"WANDB_WATCH\"] = \"gradients\"\n",
+    "\n",
+    "if LOG_TO_WANDB:\n",
+    "  wandb.init(project=PROJECT_NAME, name=RUN_NAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qJWQ0a3wZ0Bw"
+   },
+   "source": [
+    "## 📥 Load the Tokenizer and Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
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+     ]
+    },
+    "id": "R_O04fKxMMT-",
+    "outputId": "29aa1cf7-2a2e-492e-adc9-cd0a5bfb123e"
+   },
+   "outputs": [],
+   "source": [
+    "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
+    "\n",
+    "quant_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,  # Reduce the precision to 4 bits\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
+    "    bnb_4bit_quant_type=\"nf4\"\n",
+    ")\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"right\"\n",
+    "\n",
+    "base_model = AutoModelForCausalLM.from_pretrained(\n",
+    "    BASE_MODEL,\n",
+    "    quantization_config=quant_config,\n",
+    "    device_map=\"auto\",\n",
+    ")\n",
+    "base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n",
+    "\n",
+    "print(f\"Memory footprint: {base_model.get_memory_footprint() / 1e6:.1f} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SrCE2Le7RBRj"
+   },
+   "source": [
+    "## ⚙️ Fine-tune our LLaMA 3 8B (4-bit quantized) model with QLoRA\n",
+    "- 1. Prepare the Data with a Data Collator\n",
+    "- 2. Define the QLoRA Configuration (LoraConfig)\n",
+    "- 3. Set the Training Parameters (SFTConfig)\n",
+    "- 4. Initialize the Fine-Tuning Trainer (SFTTrainer)\n",
+    "- 5. Run Fine-Tuning and Push to Hub"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9BYO0If4uWys"
+   },
+   "source": [
+    "### 🔄 1. Prepare the Data with a Data Collator\n",
+    "\n",
+    "We only want the model to learn the price, not the product description. Everything before \"Price is $\" is context, not training target. HuggingFace’s DataCollatorForCompletionOnlyLM handles this masking automatically:\n",
+    "\n",
+    "1. Tokenizes the response_template (\"Price is $\")\n",
+    "2. Finds its token position in each input\n",
+    "3. Masks all tokens before it (context)\n",
+    "4. Trains the model only on tokens after it (the price)\n",
+    "\n",
+    "\n",
+    "Example:\n",
+    "\n",
+    "Input: \"Product: Red T-shirt. Price is $12.99\"\n",
+    "\n",
+    "Masked: \"Product: Red T-shirt. Price is $\" → masked (no loss)\n",
+    "\n",
+    "\"12.99\" → not masked (model is trained to predict this)\n",
+    "\n",
+    "So the model learns to generate 12.99 given the context, but isn’t trained to repeat or memorize the description."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2omVEaPIVJZa"
+   },
+   "outputs": [],
+   "source": [
+    "response_template = \"Price is $\"\n",
+    "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4DaOeBhyy9eS"
+   },
+   "source": [
+    "### 🧠 2. Define the QLoRA Configuration (LoraConfig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0HKuVS_XR3cw"
+   },
+   "outputs": [],
+   "source": [
+    "LORA_R = 32\n",
+    "LORA_ALPHA = 64\n",
+    "TARGET_MODULES = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"]\n",
+    "LORA_DROPOUT = 0.1\n",
+    "\n",
+    "lora_parameters = LoraConfig(\n",
+    "    r=LORA_R,\n",
+    "    lora_alpha=LORA_ALPHA,\n",
+    "    target_modules=TARGET_MODULES,\n",
+    "    lora_dropout=LORA_DROPOUT,\n",
+    "    bias=\"none\",\n",
+    "    task_type=\"CAUSAL_LM\", # Specifies we're doing causal language modeling\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uLfFsfNQSBAm"
+   },
+   "source": [
+    "### ⚙️ 3. Set the Training Parameters (SFTConfig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7PKXdhPXSJot"
+   },
+   "outputs": [],
+   "source": [
+    "# 📦 Training Setup:\n",
+    "EPOCHS = 1\n",
+    "BATCH_SIZE = 16                     # A100 GPU can go up to 16\n",
+    "GRADIENT_ACCUMULATION_STEPS = 2\n",
+    "MAX_SEQUENCE_LENGTH = 182          # Max token length per input\n",
+    "\n",
+    "# ⚙️ Optimization:\n",
+    "LEARNING_RATE = 1e-4\n",
+    "LR_SCHEDULER_TYPE = 'cosine'\n",
+    "WARMUP_RATIO = 0.03\n",
+    "OPTIMIZER = \"paged_adamw_32bit\"\n",
+    "\n",
+    "# 💾 Checkpointing & Logging:\n",
+    "SAVE_STEPS = 200        # Checkpoint\n",
+    "STEPS = 20              # Log every 20 steps\n",
+    "save_total_limit = 10   # Keep latest 10 only\n",
+    "\n",
+    "\n",
+    "LOG_TO_WANDB = True\n",
+    "\n",
+    "HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
+    "\n",
+    "train_parameters = SFTConfig(\n",
+    "    # Output & Run\n",
+    "    output_dir=PROJECT_RUN_NAME,\n",
+    "    run_name=RUN_NAME,\n",
+    "    dataset_text_field=\"text\",\n",
+    "    max_seq_length=MAX_SEQUENCE_LENGTH,\n",
+    "\n",
+    "    # Training\n",
+    "    num_train_epochs=EPOCHS,\n",
+    "    per_device_train_batch_size=BATCH_SIZE,\n",
+    "    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
+    "    max_steps=-1,\n",
+    "    group_by_length=True,\n",
+    "\n",
+    "    # Evaluation\n",
+    "    eval_strategy=\"steps\",\n",
+    "    eval_steps=STEPS,\n",
+    "    per_device_eval_batch_size=1,\n",
+    "\n",
+    "    # Optimization\n",
+    "    learning_rate=LEARNING_RATE,\n",
+    "    lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
+    "    warmup_ratio=WARMUP_RATIO,\n",
+    "    optim=OPTIMIZER,\n",
+    "    weight_decay=0.001,\n",
+    "    max_grad_norm=0.3,\n",
+    "\n",
+    "    # Precision\n",
+    "    fp16=False,\n",
+    "    bf16=True,\n",
+    "\n",
+    "    # Logging & Saving\n",
+    "    logging_steps=STEPS,            # See loss after each {STEP} batches\n",
+    "    save_strategy=\"steps\",\n",
+    "    save_steps=SAVE_STEPS,          # Model Checkpointed locally\n",
+    "    save_total_limit=save_total_limit,\n",
+    "    report_to=\"wandb\" if LOG_TO_WANDB else None,\n",
+    "\n",
+    "    # Hub\n",
+    "    push_to_hub=True,\n",
+    "    hub_strategy=\"end\",  # Only push once, at the end\n",
+    "    load_best_model_at_end=True, # Loads the best eval_loss checkpoint\n",
+    "    metric_for_best_model=\"eval_loss\", # Monitors eval_loss\n",
+    "    greater_is_better=False, # Lower eval_loss = better model\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1q-a3LHDSoxQ"
+   },
+   "source": [
+    "### 🧩 4. Initialize the Fine-Tuning Trainer (SFTTrainer)\n",
+    "Combining everything"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
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+     ]
+    },
+    "id": "fCwmDmkSATvj",
+    "outputId": "2b4adc75-e0db-4e0b-c90b-9f9ff2dfd3c6"
+   },
+   "outputs": [],
+   "source": [
+    "# The latest version of trl is showing a warning about labels - please ignore this warning\n",
+    "fine_tuning = SFTTrainer(\n",
+    "    model=base_model,\n",
+    "    train_dataset=train_data,\n",
+    "    eval_dataset=val_data,\n",
+    "    peft_config=lora_parameters,    # QLoRA config\n",
+    "    args=train_parameters,          # SFTConfig\n",
+    "    data_collator=collator,\n",
+    "    callbacks=[EarlyStoppingCallback(early_stopping_patience=5)] # Early stop if no val improvement for 5 steps\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vHz6JA5_XJ07"
+   },
+   "source": [
+    "### 🚀 5. Run Fine-Tuning and Push to Hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GfvAxnXPvB7w",
+    "outputId": "d351d89a-b3d7-4e2b-fee2-5ba2e929837e"
+   },
+   "outputs": [],
+   "source": [
+    "fine_tuning.train()\n",
+    "print(f\"✅ Best model pushed to HF Hub: {HUB_MODEL_NAME}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://github.com/lisek75/nlp_llms_notebook/blob/main/assets/09_train_eval_loss_steps.png?raw=true)\n",
+    "\n",
+    "![](https://github.com/lisek75/nlp_llms_notebook/blob/main/assets/09_train_eval_loss_wandb.png?raw=true)\n",
+    "\n",
+    "This chart shows training loss vs evaluation loss over steps during fine-tuning of Llama 31 8B 4-Bit FT (20K Samples).\n",
+    "\n",
+    "- Blue line (train/loss): Decreasing overall, with some noise. Final value: 1.8596.\n",
+    "- Orange line (eval/loss): Smoother and consistently lower than training loss. Final value: 1.8103.\n",
+    "\n",
+    "- No overfitting: Eval loss < train loss throughout — a good sign.\n",
+    "- Stable convergence: Both curves flatten around step 500, suggesting the model is reaching training stability.\n",
+    "- Final eval loss is low, indicating decent generalization to unseen data.\n",
+    "\n",
+    "This fine-tuning run looks healthy. We can likely push further with more data - 400K run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 938
+    },
+    "id": "32vvrYRVAUNg",
+    "outputId": "bb4ab0f6-c390-48f3-a71c-2d259bb0ec0b"
+   },
+   "outputs": [],
+   "source": [
+    "if LOG_TO_WANDB:\n",
+    "  wandb.finish()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://github.com/lisek75/nlp_llms_notebook/blob/main/assets/09_run_summary_qlora_llama.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IyKZ0r38IfT3"
+   },
+   "source": [
+    "Now that our best model is pushed to Hugging Face, let’s put it to the test.\n",
+    "\n",
+    "🔜 See you in the [next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part7_eval_llama_qlora.ipynb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "A100",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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+    "name": "ipython",
+    "version": 3
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diff --git a/week7/community-contributions/09_part7_eval_llama_qlora.ipynb b/week7/community-contributions/09_part7_eval_llama_qlora.ipynb
new file mode 100644
index 0000000..62445aa
--- /dev/null
+++ b/week7/community-contributions/09_part7_eval_llama_qlora.ipynb
@@ -0,0 +1,7931 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GHsssBgWM_l0"
+   },
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 7)\n",
+    "---\n",
+    "- Data Curation & Preprocessing\n",
+    "- Model Benchmarking – Traditional ML vs LLMs\n",
+    "- E5 Embeddings & RAG\n",
+    "- Fine-Tuning GPT-4o Mini\n",
+    "- Evaluating LLaMA 3.1 8B Quantized\n",
+    "- Fine-Tuning LLaMA 3.1 with QLoRA\n",
+    "- ➡️ Evaluating Fine-Tuned LLaMA\n",
+    "- Summary & Leaderboard\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# 🧪 Part 7: Evaluating the Fine-Tuned LLaMA 3.1 8B (Quantized)\n",
+    "\n",
+    "- 🧑‍💻 Skill Level: Advanced\n",
+    "- ⚙️ Hardware: ⚠️ GPU required - use Google Colab\n",
+    "- 🛠️ Requirements: 🔑 HF Token\n",
+    "- Tasks:\n",
+    "    - Load the tokenizer and fine-tuned base model\n",
+    "    - Load the PEFT adapter for the fine-tuned weights\n",
+    "    - Run evaluation — the moment of truth!\n",
+    "\n",
+    "🔔 **Reminder:**  \n",
+    "As mentioned in Part 6, I fine-tuned the model on only 20K samples.  \n",
+    "In this notebook, we’ll evaluate both this model and the full 400K-sample version fine-tuned by our instructor.\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "MDyR63OTNUJ6"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required packages in Google Colab\n",
+    "%pip install -q datasets transformers torch peft bitsandbytes matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-yikV8pRBer9"
+   },
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import math\n",
+    "import torch\n",
+    "from huggingface_hub import login\n",
+    "import torch.nn.functional as F\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
+    "from datasets import load_dataset\n",
+    "from peft import PeftModel\n",
+    "import matplotlib.pyplot as plt\n",
+    "from google.colab import userdata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "WyFPZeMcM88v"
+   },
+   "outputs": [],
+   "source": [
+    "# Google Colab User Data\n",
+    "# Ensure you have set the following in your Google Colab environment:\n",
+    "hf_token = userdata.get('HF_TOKEN')\n",
+    "login(hf_token, add_to_git_credential=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "30lzJXBH7BcK"
+   },
+   "outputs": [],
+   "source": [
+    "# Helper class for evaluating model predictions\n",
+    "\n",
+    "GREEN = \"\\033[92m\"\n",
+    "YELLOW = \"\\033[93m\"\n",
+    "RED = \"\\033[91m\"\n",
+    "RESET = \"\\033[0m\"\n",
+    "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}\n",
+    "\n",
+    "class Tester:\n",
+    "\n",
+    "    def __init__(self, predictor, data, title=None, size=250):\n",
+    "        self.predictor = predictor\n",
+    "        self.data = data\n",
+    "        self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
+    "        self.size = size\n",
+    "        self.guesses = []\n",
+    "        self.truths = []\n",
+    "        self.errors = []\n",
+    "        self.sles = []\n",
+    "        self.colors = []\n",
+    "\n",
+    "    def color_for(self, error, truth):\n",
+    "        if error<40 or error/truth < 0.2:\n",
+    "            return \"green\"\n",
+    "        elif error<80 or error/truth < 0.4:\n",
+    "            return \"orange\"\n",
+    "        else:\n",
+    "            return \"red\"\n",
+    "\n",
+    "    def run_datapoint(self, i):\n",
+    "        datapoint = self.data[i]\n",
+    "        guess = self.predictor(datapoint[\"text\"])\n",
+    "        truth = datapoint[\"price\"]\n",
+    "        error = abs(guess - truth)\n",
+    "        log_error = math.log(truth+1) - math.log(guess+1)\n",
+    "        sle = log_error ** 2\n",
+    "        color = self.color_for(error, truth)\n",
+    "        # title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n",
+    "        self.guesses.append(guess)\n",
+    "        self.truths.append(truth)\n",
+    "        self.errors.append(error)\n",
+    "        self.sles.append(sle)\n",
+    "        self.colors.append(color)\n",
+    "        # print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
+    "\n",
+    "    def chart(self, title):\n",
+    "        # max_error = max(self.errors)\n",
+    "        plt.figure(figsize=(12, 8))\n",
+    "        max_val = max(max(self.truths), max(self.guesses))\n",
+    "        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
+    "        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
+    "        plt.xlabel('Ground Truth')\n",
+    "        plt.ylabel('Model Estimate')\n",
+    "        plt.xlim(0, max_val)\n",
+    "        plt.ylim(0, max_val)\n",
+    "        plt.title(title)\n",
+    "\n",
+    "        # Add color legend\n",
+    "        from matplotlib.lines import Line2D\n",
+    "        legend_elements = [\n",
+    "            Line2D([0], [0], marker='o', color='w', label='Accurate (green)', markerfacecolor='green', markersize=8),\n",
+    "            Line2D([0], [0], marker='o', color='w', label='Medium error (orange)', markerfacecolor='orange', markersize=8),\n",
+    "            Line2D([0], [0], marker='o', color='w', label='High error (red)', markerfacecolor='red', markersize=8)\n",
+    "        ]\n",
+    "        plt.legend(handles=legend_elements, loc='upper right')\n",
+    "\n",
+    "        plt.show()\n",
+    "\n",
+    "\n",
+    "    def report(self):\n",
+    "        average_error = sum(self.errors) / self.size\n",
+    "        rmsle = math.sqrt(sum(self.sles) / self.size)\n",
+    "        hits = sum(1 for color in self.colors if color==\"green\")\n",
+    "        title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
+    "        self.chart(title)\n",
+    "\n",
+    "    def run(self):\n",
+    "        self.error = 0\n",
+    "        for i in range(self.size):\n",
+    "            self.run_datapoint(i)\n",
+    "        self.report()\n",
+    "\n",
+    "    @classmethod\n",
+    "    def test(cls, function, data):\n",
+    "        cls(function, data).run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 📥 Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #If you face NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported run:\n",
+    "# %pip install -U datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
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+      "abdd2ff8028b432091434805f81c455c"
+     ]
+    },
+    "id": "cvXVoJH8LS6u",
+    "outputId": "6308b124-a922-4e82-fb6a-5933d3c324e0"
+   },
+   "outputs": [],
+   "source": [
+    "DATASET_NAME = \"lisekarimi/pricer-data\"\n",
+    "dataset = load_dataset(DATASET_NAME)\n",
+    "train = dataset['train']\n",
+    "test = dataset['test']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xb86e__Wc7j_",
+    "outputId": "8b699099-7414-4663-fab1-d069d3ec3d35"
+   },
+   "outputs": [],
+   "source": [
+    "test[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qJWQ0a3wZ0Bw"
+   },
+   "source": [
+    "## 📥 Load Tokenizer and Model\n",
+    "The fine-tuned model (PeftModel) only holds the LoRA adapters, so it requires the base model to apply them correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 401,
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+      "3e8d7274ee3a4dfbbdd44ea0b2cd61b6",
+      "fa768ce193b94a4882a1e796e69cffea",
+      "c37a4882e4474f8690c4b479baf2d785",
+      "68a033bbcb4d4774bdb115e09d78365b",
+      "10b5e7970aa04bd6b3384aa645c48d92",
+      "f838b073dd254bb091a7db7175cd2ce8"
+     ]
+    },
+    "id": "lAUAAcEC6ido",
+    "outputId": "b2983922-5036-4083-8cba-0cb3f51fbc51"
+   },
+   "outputs": [],
+   "source": [
+    "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
+    "\n",
+    "quant_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,  # Reduce the precision to 4 bits\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
+    "    bnb_4bit_quant_type=\"nf4\"\n",
+    ")\n",
+    "\n",
+    "# Load the Tokenizer and the Model\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"right\"\n",
+    "\n",
+    "base_model = AutoModelForCausalLM.from_pretrained(\n",
+    "    BASE_MODEL,\n",
+    "    quantization_config=quant_config,\n",
+    "    device_map=\"auto\",\n",
+    ")\n",
+    "base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2RJ0G-WRJGMK"
+   },
+   "source": [
+    "## 🧪 Load and Evaluate the Fine-Tuned Model with PEFT Adapters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 20K Sample Fine-Tuned Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
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+      "00dbc32bdb0440c0bc3ba2cc6677b04c",
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+      "10b7df1ecfab4e5cb146932fc4fb2c17",
+      "07c6fd1fe1ac442dbeb7037161841b78",
+      "88adf6ab3f3e476fa66ad22e9ff49aa8",
+      "fe522e9cee55448a9c13a5daaad5e7e7",
+      "4b1b9e5a67e54a3b90f2c113355e735a",
+      "5cdbdf93af9344ccabd7c3f236446541",
+      "c4af3ca6696d4fcd9b831d825456c7fa",
+      "525b1673c902412db32691056d49fd35",
+      "42de37b9a74143b4a851a178c484a706",
+      "f5f42d9201dc4fbaaa9c684fdb748d4a",
+      "10a0e99256a149a0a94ff652a4fd259a"
+     ]
+    },
+    "id": "R_O04fKxMMT-",
+    "outputId": "06fc64f8-3407-460b-e093-0293e958915e",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load lisekarimi model (trained on 20K datapoints)\n",
+    "\n",
+    "FINETUNED_MODEL = \"lisekarimi/llama3-pricer-2025-04-08_18.44.04-size20000\"\n",
+    "fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL)\n",
+    "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")\n",
+    "fine_tuned_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Je5dR8QEAI1d"
+   },
+   "outputs": [],
+   "source": [
+    "# Gets top 3 predicted tokens from the model\n",
+    "# Filters valid numeric outputs (prices)\n",
+    "# Returns a weighted average based on token probabilities\n",
+    "\n",
+    "# This code would be more complex if we couldn't take advantage of the fact\n",
+    "# That Llama generates 1 token for any 3 digit number\n",
+    "\n",
+    "top_K = 3\n",
+    "\n",
+    "def improved_model_predict(prompt, device=\"cuda\"):\n",
+    "    set_seed(42) # Reproducibility : same prompt = same o/p every time\n",
+    "    inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n",
+    "    attention_mask = torch.ones(inputs.shape, device=device)\n",
+    "\n",
+    "    with torch.no_grad(): # Do not track gradients during inference\n",
+    "        outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n",
+    "        next_token_logits = outputs.logits[:, -1, :].to('cpu')\n",
+    "\n",
+    "    next_token_probs = F.softmax(next_token_logits, dim=-1)\n",
+    "    top_prob, top_token_id = next_token_probs.topk(top_K)\n",
+    "\n",
+    "    prices, weights = [], [] # weights = corresponding probabilities\n",
+    "\n",
+    "    for i in range(top_K):\n",
+    "      predicted_token = tokenizer.decode(top_token_id[0][i])\n",
+    "      probability = top_prob[0][i]\n",
+    "\n",
+    "      try:\n",
+    "        result = float(predicted_token)\n",
+    "      except ValueError as e:\n",
+    "        result = 0.0\n",
+    "\n",
+    "      if result > 0:\n",
+    "        prices.append(result)\n",
+    "        weights.append(probability)\n",
+    "\n",
+    "    if not prices:\n",
+    "      return 0.0, 0.0\n",
+    "\n",
+    "    total = sum(weights)\n",
+    "\n",
+    "    weighted_prices = [price * weight / total for price, weight in zip(prices, weights)]\n",
+    "\n",
+    "    return sum(weighted_prices).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t_GHfTwHXD5f",
+    "outputId": "056b0fc2-5632-4be8-ee24-b6bcefe14ab9"
+   },
+   "outputs": [],
+   "source": [
+    "improved_model_predict(test[0][\"text\"], device=\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "W_KcLvyt6kbb",
+    "outputId": "fba4200d-b911-467b-ab3c-17b78aa3b408"
+   },
+   "outputs": [],
+   "source": [
+    "Tester.test(improved_model_predict, test)"
+   ]
+  },
+  {
+   "attachments": {
+    "0dcb25a7-83fa-4313-a94f-d3a56a0f07bc.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:0dcb25a7-83fa-4313-a94f-d3a56a0f07bc.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 400K Sample Fine-Tuned Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "dd1b57e03f2641d3b702f2cc66942b8f",
+      "e1d477dccbfc44a8a6da301486180e82",
+      "c312a5111a284c3db88f22290869c023",
+      "ce118d8b8146497f9c7fdd3b38188e72",
+      "bc46c271637341bb82d6b87df22ab2af",
+      "602adf3242f54731938b68d3cf68465e",
+      "39fae5e74834421795729a259a046fb8",
+      "0618d8626e2e46cb9a17f86444de3c48",
+      "1cd43b5b2fe445088c84e19773ad861e",
+      "f70a29870ab34f34a1900b2df2bf177e",
+      "41a96c5e35a44b898b872c189f531d3a",
+      "0a524a73d5d6478db81256371bf2bc9b",
+      "275f6179dc624bceaa5d0639fe0b1b00",
+      "79c41b26746344bc9a220f2376360110",
+      "287a6430766c44e5a71dda1048fa2a2c",
+      "3bbe1a454a854747a96fe83e91d6cb3c",
+      "8a93759afe21414fb0d6684f0a591d60",
+      "a3d76b3ce67a495db861bac80cfc0864",
+      "8fc794262ed14fc785c8f06e734c57d4",
+      "7dc967baa0e7427bb66cf3e26849d508",
+      "2d7a6dbd15304347a37dbfb6e5ec7203",
+      "288393e05947444bad11034071015baf"
+     ]
+    },
+    "id": "Kl6n_0sAbU0g",
+    "outputId": "2fb53efb-da22-4c29-a594-c2cf5a079388",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "FINETUNED_MODEL = \"ed-donner/pricer-2024-09-13_13.04.39\"\n",
+    "REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\"\n",
+    "fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)\n",
+    "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")\n",
+    "fine_tuned_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "R0YlorBhbeSE",
+    "outputId": "f42de9bf-d45a-4d2d-c218-fe000d716e54"
+   },
+   "outputs": [],
+   "source": [
+    "Tester.test(improved_model_predict, test)"
+   ]
+  },
+  {
+   "attachments": {
+    "470345f8-39db-4beb-a5ee-5a8f71f8ee60.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:470345f8-39db-4beb-a5ee-5a8f71f8ee60.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🎉  And there it is — the open-source, quantized, and fine-tuned model outperforms the rest. 🙌 \n",
+    "\n",
+    "📘 We'll continue in [the next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part8_summary.ipynb) with a final wrap-up and summary of key insights.\n"
+   ]
+  }
+ ],
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+   "gpuType": "T4",
+   "provenance": []
+  },
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+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
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+   },
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+   "mimetype": "text/x-python",
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diff --git a/week7/community-contributions/09_part8_summary.ipynb b/week7/community-contributions/09_part8_summary.ipynb
new file mode 100644
index 0000000..2a697d5
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+++ b/week7/community-contributions/09_part8_summary.ipynb
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+   "attachments": {},
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+   },
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 8)\n",
+    "---\n",
+    "- Data Curation & Preprocessing\n",
+    "- Model Benchmarking – Traditional ML vs LLMs\n",
+    "- E5 Embeddings & RAG\n",
+    "- Fine-Tuning GPT-4o Mini\n",
+    "- Evaluating LLaMA 3.1 8B Quantized\n",
+    "- Fine-Tuning LLaMA 3.1 with QLoRA\n",
+    "- Evaluating Fine-Tuned LLaMA\n",
+    "- ➡️ Summary & Leaderboard\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# 🧪 Part 8: Summary & Leaderboard\n",
+    "\n",
+    "![](https://github.com/lisekarimi/lexo/blob/main/assets/09_ft_leaderboard.png?raw=true)\n",
+    "\n",
+    "# 🥇 The winner is the LLaMA 3.1 8B (4-bit) fine-tuned on 400K samples \n",
+    "\n",
+    "LLaMA 3.1 8B (4-bit) fine-tuned on 400K samples is outperforming even the big guy GPT-4o  — with the lowest error and highest accuracy (75.6%).\n",
+    "\n",
+    "RAG + GPT-4o Mini also did well, proving that retrieval adds real value.\n",
+    "\n",
+    "On the other hand, traditional ML models and even human guesses, gave weaker results and fell behind the top models.\n",
+    "\n",
+    "💡 As we’ve seen, a **well-tuned open-source small model** can do amazing things on a focused task — sometimes even better than large, closed models.\n",
+    "It’s not about size — it’s about fit, focus, and fine-tuning.\n",
+    "\n",
+    "# ✨ Conclusion\n",
+    "What a journey! From classic ML to state-of-the-art LLMs, from embeddings to retrieval and fine-tuning — we explored it all to answer: who predicts prices best?\n",
+    "\n",
+    "Thanks for following along — see you in the next challenge! 🚀\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ]
+  }
+ ],
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