diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+---
+
+# Suryolo : Layout Model For Arabic Documents
+
+Suryolo is combination of Surya layout Model form SuryaOCR(based on Segformer) and YoloV10 objection detection. 
+
+## Setup Instructions
+
+### Clone the Surya OCR GitHub Repository
+
+```bash
+git clone https://github.com/vikp/surya.git
+cd surya
+```
+
+### Switch to v0.4.14
+
+```bash
+git checkout f7c6c04
+```
+
+### Install Dependencies
+
+You can install the required dependencies using the following command:
+
+```bash
+pip install -r requirements.txt
+```
+
+```bash
+pip install ultralytics
+```
+
+```bash
+pip install supervision
+```
+
+### Suryolo Pipeline 
+
+Download `surya_yolo_pipeline.py` file from the Repository.
+
+```python
+from surya_yolo_pipeline import suryolo
+from surya.postprocessing.heatmap import draw_bboxes_on_image
+
+image_path = "sample.jpg"
+image  = Image.open(image_path)
+bboxes = suryolo(image_path)
+plotted_image  = draw_bboxes_on_image(bboxes,image)
+```
+#### Refer to `benchmark.ipynb` for comparison between Traditional Surya Layout Model and Suryolo Layout Model.
\ No newline at end of file
diff --git a/benchmark.ipynb b/benchmark.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c9ced0d91644312b316129e888a6964",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/1.57k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "849da655f6a14abfa4fe8a1378fdd873",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/120M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded detection model vikp/surya_layout2 on device cuda with dtype torch.float16\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.environ['HF_HOME'] = '/share/data/drive_3/ketan/orc/HF_Cache_2'\n",
+    "from PIL import Image\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "from surya.postprocessing.heatmap import draw_bboxes_on_image\n",
+    "import torch\n",
+    "from surya_yolo_pipeline_copy import suryolo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def plot_images_fine_tune(image_path:str):\n",
+    "    image  = Image.open(image_path)\n",
+    "    bboxes = suryolo(image_path)\n",
+    "    return draw_bboxes_on_image(bboxes,image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded detection model vikp/surya_layout2 on device cuda with dtype torch.float16\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "800968fd489d4a3483935a2051d7d60b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "preprocessor_config.json:   0%|          | 0.00/430 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "from surya.layout import batch_layout_detection\n",
+    "from surya.model.detection.segformer import load_processor , load_model\n",
+    "from surya.settings import settings\n",
+    "\n",
+    "original_model = load_model(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)\n",
+    "original_processor = load_processor(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)\n",
+    "original_model.eval()\n",
+    "\".\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_images_original (image_path:str) :\n",
+    "\n",
+    "    layout_predictions = batch_layout_detection([Image.open(image_path)], original_model, original_processor,)\n",
+    "    bboxes = []\n",
+    "    for i in (layout_predictions)[0].bboxes :\n",
+    "        bboxes.append(i.bbox)\n",
+    "    return draw_bboxes_on_image(bboxes,Image.open(image_path))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_images_side_by_side(traditional, fine_tuned, save_path=None):\n",
+    "    \n",
+    "    # Create a figure with two subplots (1 row, 2 columns)\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "\n",
+    "    # Plot the first image\n",
+    "    axes[0].imshow(traditional)\n",
+    "    axes[0].axis('off')  # Turn off axis for a clean look\n",
+    "    axes[0].set_title(\"Traditional Model\")\n",
+    "\n",
+    "    # Plot the second image\n",
+    "    axes[1].imshow(fine_tuned)\n",
+    "    axes[1].axis('off')\n",
+    "    axes[1].set_title(\"Suryolo Layout Model\")\n",
+    "\n",
+    "    # Adjust layout to prevent overlap\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # Save the figure at a high resolution (e.g., 300 DPI)\n",
+    "    if save_path:\n",
+    "        plt.savefig(save_path, dpi=300, bbox_inches='tight')\n",
+    "\n",
+    "    # Display the images\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6626a78a4c3b41dabb99a475d3a8c6af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "preprocessor_config.json:   0%|          | 0.00/430 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-11.jpg: 640x480 1 Page-footer, 1 Picture, 11 Texts, 36.1ms\n",
+      "Speed: 2.2ms preprocess, 36.1ms inference, 3.4ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-11.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-1.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-10.jpg: 640x480 1 Page-footer, 1 Table, 1 Text, 19.0ms\n",
+      "Speed: 2.4ms preprocess, 19.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-10.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-2.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-12.jpg: 640x480 1 Page-footer, 9 Texts, 14.4ms\n",
+      "Speed: 2.2ms preprocess, 14.4ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-12.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-3.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-13.jpg: 640x480 1 Caption, 1 Page-footer, 7 Texts, 12.7ms\n",
+      "Speed: 2.4ms preprocess, 12.7ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-13.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-4.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/all_20_samples-images-0.jpg: 640x480 1 Page-footer, 1 Section-header, 11 Texts, 13.8ms\n",
+      "Speed: 2.2ms preprocess, 13.8ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/all_20_samples-images-0.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-5.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-7.jpg: 640x480 1 Caption, 1 Page-footer, 1 Picture, 1 Section-header, 9 Texts, 14.2ms\n",
+      "Speed: 2.5ms preprocess, 14.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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t27cHirq1wH+t0FlZWR77nUvt+d133827777LH3/8waJFi4iIiPDo+qLFvgMHDnjEPm2b9vfSxMbGoqoqBw8e9FgHKiUlhaysrNMeW16//PIL1113HV9//bXH9qysLMLDwz22tWrVirZt2zJz5kxiYmJISEgosWBgbGxsmTFK+zuc3WtwvgWqIUOG8Oqrr9K8eXOuuOKKUvdxf52K279/P+Hh4fj5+eHt7Y2Pj0+546sQggYNGnDZZZed1z1IZya7PFVzWqlbq80HsNvtfPLJJx77tW/fnoiICD777DPsdru+/bvvviuRoZRGm/P7TPsajUZ69uzJ77//7jEdXUpKCj/88AOdO3cmMDDwjNcrfk448z2ej9tuu42JEyfyySeflNqtTEtHee+tb9++bNiwgY0bN+r7paamevR/BejVqxeBgYG8/vrrOByOEtcsPn2uJEnSxWrlypUe+bRG6/+udUkJDAwkPDyc1atXe+x3Lnl669atad26NV999RW//vorgwcP9mjZbd++PZGRkXz22WceU70uWrSIffv20a9fvzLPrVUuFV8l+r333gM47bHlZTQaSzyz2bNnlzml7V133cWSJUv44IMPCAsLo0+fPiXSvHHjRtavX69vy8/P54svvqB+/fq0aNEC+K8yy/01cLlcfPHFFyWu6efnd15di0eNGsXEiRN59913y9yndu3aXHHFFUybNs3je8bu3btZsmSJ/loYjUZ69erF3LlzSUhI0Pfbt28ff/75p8c5Bw4ciNFo5OWXXy7xjIUQpKenn/M9SSXJFopq7tprryUkJIThw4fz2GOPoSgKM2bMKPHhMZvNvPrqq9x///1cf/313HHHHRw9epRvv/22XGMo2rVrB8Bjjz1Gr169MBqNDB48uNR9X331VZYuXUrnzp156KGHMJlMfP7559hsNt56661Ku8fzERQUVOoq4MWV997GjRvHjBkz6N27N48//rg+bWxsbCw7d+7U9wsMDOTTTz/lrrvu4sorr2Tw4MFERESQkJDAggUL6NSpEx999FGF3ackSVJlefTRRykoKGDAgAE0a9YMu93OunXrmDVrFvXr1/eYVGTUqFG88cYbjBo1ivbt27N69Wr+/fffc7ru3XffzdixY4GSC8WazWbefPNNRo4cSbdu3RgyZIg+bWz9+vUZM2ZMmedt06YNw4cP54svvtC73m7cuJFp06bRv39/rrvuunKl77333iuxCK3BYOC5557jpptu4pVXXmHkyJFce+217Nq1i5kzZ5YZl4cOHcq4ceOYM2cODz74YInpV8ePH8+PP/5Inz59eOyxxwgNDWXatGkcPXqUX3/9VR8A3rJlSzp27Mizzz5LRkYGoaGh/PTTTzidzhLXbNeuHbNmzeLJJ5+kQ4cO+Pv7c/PNN5fr3qGo9aE88fXtt9+mT58+XHPNNdx77736tLHF4/PLL7/M4sWL6dKlCw899BBOp5OpU6fSsmVLj/jaqFEjXn31VZ599lni4uLo378/AQEBHD16lDlz5jB69Gj9fSNVgCqaXUo6jbKmjdWmOS1u7dq1omPHjsLHx0dER0frU/VRbOo3IYT45JNPRIMGDYTFYhHt27cXq1evFt26dTvjtLFOp1M8+uijIiIiQiiK4pE+ik3zJ0TR1H29evUS/v7+wtfXV1x33XVi3bp1HvuUNm2cEKVPW1feezyXaWPLUtq0seW9NyGE2Llzp+jWrZvw9vYWderUEZMmTRJff/11mVP19erVSwQFBQlvb2/RqFEjMWLECLF582Z9HzltrCRJF7NFixaJe+65RzRr1kz4+/sLLy8v0bhxY/Hoo4+KlJQUj30LCgrEvffeK4KCgkRAQIAYNGiQOHnyZJnTxp5u6tXjx48Lo9EoLrvssjL3mTVrlmjbtq2wWCwiNDRU3HnnnSIpKcljn9LyWIfDIV5++WXRoEEDYTabRd26dcWzzz7rMV15WbTzlfZjNBqFEEXTxj711FOidu3awsfHR3Tq1EmsX7++RFx217dvXwGUGneEEOLw4cPitttuE8HBwcLb21tcddVVYv78+aXud8MNNwiLxSJq1aolnnvuObF06dIScTUvL08MHTpUBAcHC+CMMVabNvZ0yor/y5YtE506dRI+Pj4iMDBQ3HzzzWLv3r0ljv/rr79Eu3bthJeXl2jYsKH47LPPyoyRv/76q+jcubPw8/MTfn5+olmzZuLhhx8WBw4c0PeR08aeP0WICqzmlSRJkiRJuoDS0tKoXbs2L774Ii+88EJVJ6fSDRgwgF27dnHo0KGqTook6eQYCkmSJEmSqq3vvvsOl8vFXXfdVdVJqXTHjx9nwYIFl8S9StWLHEMhSZIkSVK1s2LFCvbu3ctrr71G//79qV+/flUnqdIcPXqUtWvX8tVXX2E2m7n//vurOkmS5EEWKCRJkiRJqnZeeeUV1q1bR6dOnUpMn1rT/PXXX4wcOZJ69eoxbdq0UtctkqSqJMdQSJIkSZIkSZJ0zuQYCkmSJEmSJEmSzpksUEiSJEmSJEmSdM5kgUKSJEmSJEmSpHMmB2VLkiRJ0tkYMQI6d67qVEiSJFW+Awfg7bfPuJssUEiSJEnS2ejcGUaNqupUSJIkVb5Vq8q1m+zyJEmSJEmSJEnSOZMFCkmSJEmSJEmSzpksUEiSJEmSJEmSdM5kgUKSJEmSJEmSpHMmCxSSJEmSJEmSJJ0zWaCQJEmSJEmSJOmcyQKFJEmSJEmSJEnnTBYoJEmSJEmSJEk6Z7JAIUmSJEmSJEnSOZMrZV9ihBD6/xVFqcKUSJIkSVLVkfFQkiqObKG4hLlnppIkSZJ0qZLxUJLOjyxQXGKEEDidTpl5SpIkSZc0GQ8lqeLIAsUlKD8/X2agkiRJ0iVPxkNJqhiyQHEJ8vf3l/1FJUmSpEuejIeSVDFkgeISoygKBoNBZqCSJEnSJU3GQ0mqOHKWp0uMoigy85QkSZIueTIeSlLFkS0UkiRJkiRJkiSdM1mgkCRJkiRJkiTpnF10XZ5UVUVVVQCMRqNsjqwiQogSM1+cVfOwqoLLVfEJMxrBIMvBkiTVfDIeXhzOOx5K0iXgoitQuFwu0tLSCAkJwWg0VnVyLmlaMFNVFYPBgNlsLv/B27fD2rUQFFRxCcrPh1atoEsXEAJ++w0KC6EmZOp2O3TsCM2bV3VKJEm6SMh4WIXcCxBCoKoq4lQ8VBQFs5eX5z7S+akJcfwSd9EVKBRFwWg0YjJddEkrwb3WotrUVpwhA9T+6nQ6mTdvHvv372f//v2MHTuWli1bYjAY9Az1tLNjFBZCz55w2WVlXkecyqS1c2jP02g0YijtvMeOwd69/91HVhbceeeZ7/k09ypUFRTFo/ZJey0v6KsZH190f7JAIUnSKTIeVqG//kIcPAgmEy5VZevWrZw8eZLCggKu7dSJ2rVr/xc3zjNmCCgqtAihn0OLSIYL9Cy1mMyptKAoIETRtSszHjqd0LAhXH99ZV1BukAuulzKZDIRGRlZ1ckoF5fLRUFBAUajEV9f36pOTvkcOQJ//gllpbegADFsGAmpqYwbN460tDSCg4N55JFHcLlcZGRkcOTIEdq0aYO3t/eZr1dWRigELpeL7OxssrKyqFu3Lnv27CEmJobQ0FAM5QmgRiNYLOdesyEEqsuF0+lEPVWQycvLw9/fHy8vrwtbY+LldeGuJUlStSDj4QUgBCQnF7WAa7+HhEBGBgwciAgKIv7oUYa+9preWvTThAmEX3EF2VlZHD16lNbljYen4XQ6S42HYaGhF6x1So+HqorJaCTXPR5WluxsWLmy8s4vXTAXXYGiOjEYDPj6+lavmpjMTLjmGmjduvS/z5uHYrcTGBhI3759adCgAQ0bNqR169YIIZg7dy5Tp05l6tSpdOvWrczLCCEQCQlgMKAYDKXWbiguF6vmzmXFihW8+OKLfPbCC9x7772EdehQ+jiJEyeKxmZUICEES5cuJSsri759+/LCCy8wfvx46tatW6HXudRoNV1a/28517sk1WzVMh5qfv0V2rYtqkRyOODvv4sKFQYDitFIYHAwffr1+y8eXnEFQlGY+8cfnvGwjHvXWuOhjLxQCDAY+G3uXObOncsHH3zAcxMmMG7cuKLzXogChRAIVWXp8uX/xcOJE/+Lh5X1ul4CYyIvlXgoCxTnwWAwYKhuHwaDAbZuhbS0Uv+sHDkC3boRHhLCO++8w5YtW5g+fTq9evVCCMGhQ4c4ePAgc+fOPW2BgmbNcG7dipKQgMloLDUzEk4n8WvXcnT1apZ/9x0nt23jxJYtULt26ZmMENCmzbneeamEEPzyyy8sWbKE7OxsfvvtNwYMGCALFBVACIHNZkNRlPOuvZMk6eJWLeOhJjAQOnUqijtWa1GLBRRVhCkK4eHh5xcPKWqB0LoKl0YIwa5du1i7di2//vorO3bs4OjRo2c8b0WS8bDyXArxsJp++qVz1rIlomdPCmNj2Wa1cjI0FNdll6E2bYpo1gwxeDAiMBBVVXE4HCxYsIBdu3bhcDgwmUwMGTKEwMBAevXqBRR9SJxOJ3a7HafTicPhwOVykevlxQfbt7PJxwfRtSuia1fULl1wde6Ms1MnXJ06Ibp0odVDD7E7LIzYu+/mUHQ0i/LzEV26QLduJX+6d0dERSFOdZfSSv1a7Y+WZi0d2v9dLpe+v/ajqqp+fNu2bTGZTERHRyOE4N9//z3nx6ulQXsOarEWFVVV9e3FZw2pabT+39X2S4YkSTWeAKxWK9u2b+fkyZO4TuXNAs+xfucVD3Nz+fDDD9m8ebNHDHKd6mLkOjUj4k033URgYCBdunQhLCyMTZs2nTZOaOdyjynVLh66pbsmuxTiYc29M6l0FgvExLAjI4Peo0bx7CefYI+MRNSpAzExEB2NqigUFhYyefJkPv/8c44cOcKJEycwGAxER0cTHh6OxWLRT5mfn8+mTZvIyMjA4XBgt9uZNWsWr7zyCj/++KOeWblcLqxWK//++y9Hjx7F6XTSqFEjwsLCCA0NJTo6msOHD+N0Ok97Cy6XC/upDAr+y/BzcnLYt28fubm5OBwOkpOT+e233zh06BAOh8MjM7PZbGRlZSGEoNOpAXatWrUiICCArKysc3682j3m5OSUyDyhKAN1DyA1lTYo02KxYLFYamTzriRJNYAQ7N23jz69e/Pss89it9s9vtyqqnrxx8NTBZii26l+8bC0v9Ukl0o8lAWKS1RUVBQ9e/akU6dOmEwmj5mWVFVl4cKFfPTRR1gsFmw2GydOnEBVVfz8/KhVqxYbN27UB1W/8sor3HLLLXz88ceoqkpaWhrffPMNqqqSmZmp1z44HA5mzJjBTTfdxPDhwykoKCA0NJSAgADS09OpVasWubm5Z8xAAXZs387+/ftxnqptsVqtvP322/Ts2ZPp06dz7Ngx7rrrLu655x5uvvlmZs6cSUFBgZ6Zp6en8/TTT5OZmUlsbCw+Pj64XC5iYmLIy8vzeBZaDY/VasVms2Gz2fT/a4UDLQAZDAZ27tzJ1KlTS7SiOJ1OZs2axYoVKyrpVZUkSZLOiqIQGRnJjTfeqMdDTbWJhzt2sHr1ar31obrFw5reOnGpkGMoLlF169Zl6tSpWCyWEgOEMjMzeeeddygsLMThcGA2m/ntt9+oX78+NpuNWrVqsWPHDlJTU/n555/5/PPPsdlsfPvtt3Tq1Illy5axZ88eoKjgYrVaAdi2bRuvv/466enpZGRksH79erp06UK9evWIj4/nmmuuYePGjWfMQFVVZcGCBdRdvJgGzZtjNpvJTEkh45NP6FVQQP7XX6NmZTHK35/EBg04dOgQG554gqhVq+hxww0YDQZISSFgwQIy6tQhtn59uh0/jm3aNO728sL5zz+In35CURSEy4W1sJCEhAQ9bdoUf35+frRp04amTZsWDZpTFBRVJfHnn+kRE4Ppl19QDAYwGIr6TxYUcODNNwkODqZHaqrnzE5ZWUVrbEiSJEkXVHTt2kx96CEs3t4YtKlSAYSoFvFw/vz5rFmzhrZt2+Lr68uhQ4f4+uuvyc3NZd68eVx33XWMGDGC8PBwlixZwpNPPklcXBzjx4/HZDKRm5vLqlWr2L17N+3bt8dut/Pvv//SunVrTp48iTj1TBwOB7m5ufz999988cUX2O12PR3+/v4MGjSI2267Te/WI4Rg4cKFXHHFFR7fM1RVJTs7mylTplDL25vr778fucpK9VdlBQr3/nv6vP81tBnoYnT06FGee+45brnlFu644w6P5+/v78+ECRNYsWIFDRo0YMWKFXzzzTcsWLCA/Px8MjMzsVgs7Ny5k8TEROx2O4qicOLECW699Va9FkMIwY8//siff/6JwWAgIyODtFODwQsKCnjooYcICQnh+PHjREVFceWVV54x3Vo6r77mGl754w9u69ULr4AADvz1F7/Y7Zi8vVmRlMS+HTuoVb8+zpgYlh8/TnZ2NgvmzaNHfj5h4eHYbDYWqyrHtmwh8OBB/srLIzQwkLx27YiLj2fUzTcXrU/hcmERgpC0NOpffTUpJ0/q/V5Vg4GCpk2hbVvEqczS6XDw47ff4nXkCIU33IDZbMZsMhESGopBUbD27MnCrVsZ1bMn5oAAz5tzazaXJOnSIeNh1YqLi2P8ffdxS//+3NG//39dNxSlWsTDjh07Mn/+fKBoquGTJ0+Sl5eHyWRi586dvPXWW9SqVYvatWvj7e1NTk4On3zyCUePHiU8PByr1Up+fj6ffPIJwcHBJCYmcvDgQYKDgz26PBkMBgIDA2nXrh2PPPIIKSkpejw0GAzExsZiOFWBpigKqqqybds29u3bR2BgIF5eXpjNZkJCQori+NVXs23VKpwuF3Li9OqvSlsoVFXFarXi7e0tVwG9wNLS0li/fj3+/v7cdttt+vPXZiDo27cvffr0QQhBvXr12LRpE8nJyXqzpdVqJTExkdDQUCIiIoiPjycvL08f+CWEwGAwkJaWRmZmJgaDAW9vby677DLS0tLIysoiOTmZY8eOYTKZiImJASAiIqLEe6F4c6iiKDRr1gzf0FAM/v4Y/P0JjIrCHBREeno6qqry/Zw5+owaWqDOyclh5ty5HtsS//xTP2cB4LRYitbo8PUFRdFrTaICA4lq2FBPg/v0b24Jw+B00uqqq3j//feZv3KlngaTyaQ3L19++eWo3t76NSRJkmQ8rCKnYsOGDRvwDwzktptvxkjRDE/VKR4GBwfrg36Dg4MJDAwkPT2dgoICZs6cWSIeZmRk8MMPP3hsmzdvnn5Ou92OwWDwWINC6w4WExOjpxHKiIenfm/bti3vv/8+ixYtKjUeto6NrfFjKC4VVVqgqHYL4NQgbdu2ZdmyZYSFhWE2m0v8XWueFELQu3dvli9fTkFBgcdsDEajkdDQUIKDgzl+/DgFBQUUFBQwevRo4uLi9EFrr7/+OrGxsXh7e1OnTh2ysrJITExk9+7dOJ1OjEYjffv2JT4+nscffxwfHx+PtGizYWi1MSkpKUyYMIGUlBTsdjuqqtKiRQumTJnCAw88gI+PD+3atQMotZbPZrNRUFBAcHCw/ndFUbj88svZu3cvXbp0KfNYQA8OGvf9FEVh6NCh7N+/n/Xr1+uD37RM22AwEBISUi1WvpUk6cKR8bCKKAot69Vj6cMPExYejnnRIhSHw2MXGQ8rLx4GBwcXdUOWqj1FVPFoGPfLyybeC+NsXnItEz3TNo3D4eCWW25h3bp1GI1G2rVrx++//46Pj4/H66tNa2cymUpMA2symTwyqJycHN59913atWuHwWDgo48+Yt26ddSvX59WrVrRu3fvolaJ779n3bp1jB8/nmeffbbUWj6n08n06dOZOnUq7733Ht27dy/qqnSqFiknJwdvb2/8/f1P+34s632rDVwrLCwkOTkZq9XK66+/TuPGjbn11luxWCz4+voSHR2N2WyW73lJqo6++gpGjarw08p4eOEJIaCgwKMQISwWWLgQrr4agoL07QpFU8m6K22bxuFwcPvtt7Np0yYMBgNXtGnDrJ9/LjseGo0IimZH4tTUtaZiU43m5uby0ccfc9VVV2E0GPjo449Zu3at3k2qX79+qKrKjBkzWL9+PY8//jjjxo0r+tJe7D3lcjqZ9fPPfPrpp7zxxht0uvZaUBRcTidq8Xh4umdY7Hm4b9fi4fHkZGw2G++9/z5NmjShX79+mEwm/JxOYhITMQwaJN/zF6tVq6B79zPuVuXVpPINdOGd7TMvbf+yzmE2mxk4cCCbNm2ioKCApk2bYjabPfoGu88AoZ1LK1ho+7nz8vKiadOmvPDCCyQmJmKz2WjdurXej/XFF18kNTUVIQQdOnTgrrvu0lsAin/ZdzgcLF68mKNHj5KQkKCnSds/LCysXAWu0z1DrTm7QYMG7NmzhzVr1rB8+XL69u1Lq1atZHcGSZJKJePhhacoCvj5eW4DaNgQ1qwpsUp1aa9QWa+aWQgmXHklX2zZQmFBAb0CA7EsX160gJ7BoBdGFCH07rUK/30xE9q53d4XFrudmwwG5kyYQEJ8PL4OB482bcqIESM4fPgwv0+YQGpqKkHAM23aMDI2FtOSJUWThrilTQiBareT/v33XHboEN4rV6Lk5ICiYDoVA8O0WHiGFoTTvWsNQuArBA1UlaSkJLyWLePIkiX4BwQQGxtb1LXssstOe36peqjyFgqpZhFCkJeXx/r169m2bRs33XQTl112GUajsUSBwl3x7e6BVZumLjk5mc2bN6OqKldffTW1a9cGICEhgb/++gs/Pz+6du1KVFSUx1S47mmzWq0cO3aMnTt30qlTJyIjI8u8l/MJ7lpLRVZWFjNmzCA5OZnx48cTEBBQamFHkqRqpJJaKKSaRcbD/64l42E1Vs4WClmgkCqU+6qb2o/76pDnkmlofUbdp6Jzp2W+qqrqg76KT4WrHee+n5a2ysjItDRqTdnatSrzmpIkXSCyQCGVg4yH/11LS7uMh9VQdenyJNU8ZS0tf66ZhnuXpNMpT1ciRVEuSJcjLYPWMnNJkiTp0iPjoYyHlwpZoJAqVGXUNFTUOS90LYisdZEkSbp0yXhYddeTLjxZVJQkSZIkSZIk6ZzV/BYKVQU5TESSLn6lTGtY0UrrbyxJkiRJl5qKjoc1v0CxcCGcOAFyITFJungVFkLr1tCpU6VfSlVVCgoK8Pf3r/RrSZIkSdLFqiLjYc3/ll1YCIMGQUBAuXY/XVtGVddllrV4jHRhlPXekK9FBTh2DPbsqbTTazUx2swmcqVySTqz000CWdWte3IRwKpV1ntDvhYXv8qKh5dOVC3vm/zUA4aiVS7z8/Px8fHB29u7aJGZqvywuC/8Jj+0F96p5+9yuQC3GTLka1EtaFMkGgwGvLy8qjo5knRxsFqLWvHLoMVDp9OJUFUMRmPRQqRUcWXKqZWk5RfYqiOEQHW59EXzDHLR1vILDYXAwCq7fGXEw0unQHEG7qVtl8tFSkoK69at01dirl+/fpVNd+Y+h7OWeZa1GE5pf6vMDPdcaynKu/xJWQv/uF/jfGrRzvZYVVVJS0tj3bp13HjjjWdsJizPs6/sWkBZk1fE5XJht9vx9vaWUxdKkmbXLti7F+rW1TfpOcapLx0pJ07wzz//sGPnTh584AEiIyOrrGJLS5uqqvoq0sI9HUJ4rAotytheaWnT8lu3NJ3pmqVGAPfzuKVdv0axZ1/q/ep/VM4vHWU8N+FykZGezrr167mhRw98/fxO+54oz7Mv/nqd7fHlPn8Fne+c5OWBwwG33lpVKaiUeCgLFG4cDgdWq5UFCxbw9ddf07JlS1599VVq165d5V9AHA4HX3/9NT179kRVVRo0aIDRaERVVdLT0wkODvYoZbpcLnJyclAUhZCQkEpNm81m4+TJk0RHR5/VnNZaa4vW7OZ0OlEUhby8PLy8vLBYLJjNZn0hH/fVRYufx+Vy4XQ6gaL5t90XDzrd9d3TAadq4YTAbDaXeozL5eLzzz9nxowZrFixgoBTXem0e9D2MRgMZ/0stPuvyIV+SrvHS5XJZJJdnSSpOCGgXTto1cpjm0c8nDaNli1bMnbmTMJr10bR8raqyFNOpa3c8fBUfLgg8VCI0uPhmZ6TW++DM8ZDl6v0eKhVsJ2Kl6XGw9Ok44zxsPixQuC02/lk8mRmzJ/PiiefxK9ePf0cpcbD8rxfit2/Rzw8z/dbiXusqpiYkQGrV1fNtU+pjHh4yVbTua9c6d4C8Mcff7By5Uo+/PBDXn31VSIiIjy+mBY/7nx+yntObZ+tW7dy7Ngxpk2bhqqqepp/+uknMjIySpx3//79bN68+Yz3Xla6ynpOxeXm5vL444+TlZV1xpaH4vek/Wu32/nss8/Iycnh9ddf5+DBgx4ZUnx8PPHx8foqm8XPd+TIEV555RWOHz/O0aNHT5sGd1phRTtnXl4eJ06cOO1zadasGU899RQhISEeaXQ6nTidTlJSUrBareVOg5YOm83Gvn37KCws1LtVVQSbzUZBQcEZX8fyvB+qKy3wFv+RJKmo1lbGQxkPZTz8j4yHZ++SLVBA0ZvW6XRit9v10nxycjJNmzbFYDBgNpsxGAwl3lBOpxOr1aqX3l0uFwUFBdjtdv3c2gfK4XDomZ223eFwlEiLzWbDbrd7ZBDauV1uNRKpqank5eV51H5nZWXpJU2n04nNZkMIQVZWVpn3rt2LqqoUFhbq1z7d/qd7jiEhIeWeJUBVVRwOB7NmzeLgwYP6c9y3b5+e/sDAQP2e0tLSGD9+PF988YW+b3F5eXkkJiayePFitm/fXu4WJZvNxp9//smxY8coLCxk9erVLFu2TH+dbTab/t6AolJ9q1atsNvt+Pr66hnfN998wzPPPENCQgLLli0jOTm5XNfXGAwG0tPTefLJJ1m1ahUHDx48q+NPZ/Xq1bz33nts2LCB48ePn/G1TExMJDU19bTvB0mSahYZD2U8lPHQk4yHZ+eSLlAIIcjNzeWbb77BZrOhqiq33nor+/fvp3///nz55Zd6ZudyucjIyMDlcqGqKr///js5OTkIIUhMTOTzzz8nPz9fP6+qqixbtoy4uDj9jShEUXNoUlKS/rv2s3HjRubPn+/x5na5XCxbtozFixejqirBwcGEh4frpWsoKmnWrl1bT2dWVhZ//PEHTqdTb2YsrZSdl5dHfn4+aWlpPPzww3z77bceGbf7j3vmWlapvXj3nuJ/V1XV4wOZlpbG5MmTefnllyksLERVVex2O4qiYDab9eZqIQSRkZFMmTKFW2+9tWgwYCm1Bb6+vvj6+updpEpLhxacih8bERHBunXrWLhwIenp6eTk5OB0OklOTubLL78sUQvkcrn01xCKMr+rrrqKAQMGEBkZiaqqxMfHn/Z5lVbj5uvrS2BgIDk5ORw7dqzcx5/p3Farlbp16/Lrr79y7NixUq/tfn/btm1j69atZb7mkiTVPDIeyngIMh7KeHjuLvkChdFopHbt2uTn53PkyBECAwN59tlnadCgAd9++y1z5swhLS2NX3/9lb59+/LTTz+RlZXFmjVrSE9PRwjBmjVrmDRpEtu2bdPfZLm5ufz6668EBgbqzXVCFDXT/vrrrxQWFuq1MFarldTUVNatWwcUlYrz8/PZu3cvI0eOZPjw4WzevJmoqCi9L6U7g8FAQUGBXjMwdepUrFYrLpeL7OxsbDYb//77Lxs3bsThcLBr1y569epFnz59OHz4MB07dqRDhw44HA4KCwvJz8+nsLCQgoICUlNTmTp1qkdtk9ZkWFBQQGFhIV5eXmRmZpKRkeGRLq35NCUlhbFjx3L//fdz+PBhPaO86667GDFihJ5Z+vn5YTAYCAoKIicnRz/earUya9Yspk+fzrFjxxgzZgxvvfUWiYmJ5OXl6QOLcnNzCQoK0mt8bDYbNpuNwsJC/vrrL5588kni4+MRQug1cUlJSbRu3RqbzcaaNWv0jFZRFHx9fWnXrh2KouByucjMzOTAgQM4HI4SzYShoaHMmDEDq9WK2WwmJiZGDwrutTpOp5O1a9dy8uRJPRi7B6GgoCAURSE3N7fM5mztR7sHq9VKfn4+NpvNo7laEx4ezuHDh2nVqpVeo+VwOPT9tFq5lStXkp6ejtVq1fsba9d1OBwV2uwsSdLFRcZDGQ9lPJTx8HxcciMU3d9cx48fJygoiF69erF9+3aGDx9OaGgohYWFdOnShVdeeYWnnnqKqVOn4u/vT3JyMuPGjePjjz+mTp06hIeHA0UfntDQUJKSkvTz5+fns23bNlauXMnAgQP1azocDn744QdSU1OJiooiNzeX7du3k5KSwqRJk/QP6/vvv8+ePXsIDAwkNzeXOXPmcPLkSXr16kWg21RjiqLQrFkz/vjjD4KDg/nmm29o2LAhO3fuZPXq1RQUFJCXl8enn37KyZMnefjhh/H39+fQoUM4HA727t2L3W7n0KFDrFu3Dj8/PxISEigsLCQjI4ObbrqJdevW8cADD+jX1GqxsrOzufPOOwkKCiIoKEivLdE+eKqqcvz4cQ4ePMhPP/1Efn4+N9xwAzabjdTUVK655hoOHjzIr7/+SteuXTl69CgJCQmEhIToNUxCCBYsWMCbb76p75+Xl8fUqVP5/vvvCQoKonfv3tx///1EREQQHBxMQkICNpuNlJQUQkND+emnn1i0aBEpKSlcd9111KtXDyEECQkJrFixghEjRtC5c2cWLVpEbGwse/fuxWAwEBgYiNVqxeFwYDKZmDdvHjNmzGDy5MmYTCZ9AJ3T6WTSpEmsWrWK7OxsvQbL4XCwdetWFEXh8ssvx2AwsG7dOkaPHs0HH3zADTfcwJEjR8jKyqJ169b666l1LyhNYWEhFosFRVFIT0/Xa3+mTJnCgAED6N69u14zpQUmo9GoBy1FUTh06BBOp5PmzZvr79f4+HiefPJJbrnlFpo1a+aRwQohKCws5NChQ7Rt29bjvSdJUvUlhCgalC2EjIcyHsp4KOPhebnkChRQ1ESXm5vL119/zeOPP47BYCAiIoJrrrmGhx56CB8fH+rXr4+fnx8zZ84kPT2d2NhYDhw4gKIorFmzhtWrV5OcnIyfnx/dunVj4MCBeukdIDIykvHjxxMYGKj3X1QUhauvvlrPHHNzczEYDIwePZqWLVsSHR2t9zHNysri2muv5ZVXXiEnJ4cjR45Qp04d6tSpw1133eXx5m3YsCELFixg7dq1pKenk52dzQ8//ECzZs3Iy8sjPj6eBx98kLlz53L8+HE9g05NTeW3336jRYsWzJs3j8aNGxMXF0fz5s0xGAw0atSI999/n8GDB+szaAD89NNPLFmyhG3btrFhwwbMZjM333wzYWFhJQbsTZs2jT/++IObbrqJ0NBQvvzyS+x2O7m5uURERJCenk6rVq349ttviYyM5NFHH6VZs2bceeedCCEwGAw0aNCAcePGsWfPHt5//30CAgIYO3Ys9erVY8KECXz33Xf8/fff9O7dmzp16vDWW2/x66+/kpKSQlhYGPv376dhw4a0a9eOq666Sq91OXDgAEuWLKFBgwZs2rSJ+vXrEx4eTkBAAAaDAUVRWLlyJc2bNyckJITExETi4uIoLCzEx8dHfx65ubmcPHmSqKgoJk+eTHJyMosWLaJOnTosXboUm81Gnz59iImJ0QPJpk2b2Lx5M3PnziUtLY2BAwfSqFEjvLy8CA0NxWq1lhgolZ2dzeTJk3nxxRcxGAw88cQTmEwm7rzzTsLCwhg3bhwtWrQgPz+fcePGUa9ePTZt2kSTJk0ICwvD19dXn/I2Pz+f5s2b669TrVq1ePfdd/nggw+oVasWUVFRKIqC1WolLS2NjIwMJk+ezPTp07FYLFU+65kkSRVDVVVys7P5+pdfZDyU8VDGQxkPz9klWaCAojfjypUrueKKKwgPD2fhwoVcd911XH755Xh5eelvkJiYGGJiYgC48sorgaJaFbvdjsViITk5mRUrViCEIDY2FijKKE0mE//73//0TEDbbrFYuOaaa7j22mtJS0tjwoQJPPLIIwQGBiKEID8/nxUrVnD06FE9AxZC0Lp1a72EXL9+ff0+FEUhPDycF198kaSkJO6++27MZjNvvPEGfn5+wH9NgwMGDMBisXj04bz33nvx8vLCbrdjNptxOp14eXmhKAqqqmK1Wlm2bBk33XQTtWvXRlEUsrOzefbZZ/nll1+YOHEiiqJ4BArtmloT7csvv0ynTp0AyMzMxMvLC1VVsVgsuFwuTCYT6enpREREkJaWRmBgoD6gTVEU2rZtS+vWrcnLyyM9PZ3AwEC9KbRt27Zs27aNsLAw2rRpg8ViYcKECWRnZ1OrVi3mz5/P3XffTfPmzWncuLF+XqPRSPv27ZkzZw5PPvkkV1xxBa+88gpBQUGEh4frzzokJETPTB988EHS0tKoU6cOffv21dMXHBzM9OnTSUhI4JtvvqFjx44cPHiQsLAwXnjhBX7++WcCAgLYs2ePPuPF3Llzufnmm3n88cf57rvvyMvL4++//2bEiBH6mifFazxMJhNZWVk4nU6MRiPp6els2bKFf//9V7+X/Px87r//fh555BE9I/b29uatt97Cy8uLMWPGoKoqb775pn5el8vFE088wcaNG2nZsiWRkZHUPTUnfX5+PsOGDePkyZP069dPr4kqTfGmZVljI0nVg4yHMh7KeFhExsNzd0kWKAwGA7Vq1eLqq69mwoQJmEwmevToQf/+/fH29i7zhffy8kIIQadOnbj22ms5duwYU6ZMAeDpp5/G19dXP1ab+7k49+0RERFMmjRJz3ysVitTpkxhw4YNjB8/Xv/wQtlvRi2zNhgMxMTEcNNNN+Ht7a038xVPv/t53N/wxfuhak2Xffv25c4776RWrVr6DB9Go5FatWrx4YcfltoUabPZiI+P588//+T48eN069ZNb5b09/cvNQ0hISF6ZqRt0/YzmUwYjUbCwsIICwvTm5GFENSvX5/69et7nLNbt276Pm3bttVfN/d9DAYDkZGRfPrpp2RlZREUFKQHEF9fXzIyMli0aBEJCQkEBATgcDjYvn07UVFRxMTE0LBhQ/18Xl5eeHl50apVK957770Sz0MbPJeZmcmwYcNQVZX77ruPgQMHoigKgwYN0gfPFV+DQgih9y3Nzc3Vg7uXlxfPPPMMv/zyCxMmTCAyMlJvYl+2bBk5OTlYrVaOHTtGy5YtqVOnDqqq8uOPP7JlyxYaN27s8frfeeedPPDAA7Rs2VLPJLVVNKOjo2nZsiWXX365HvDKUvy1kyTp4qblhTIeyngo42ERGQ/PjSIu4DD1si5VqQ979mzo1ctjiXOttsBut5OWlqYPfPLx8SnXYmTa8dogIIPBoDd7net8vu6D11wul97MWFrJvKz0AHpfP7PZfF6Llmi1KYBHKVxVVb799lsCAgK4/fbbS6RN61/4zz//kJaWRrNmzfQm44qa67iyaEEjIyOD33//nYyMDPr27Yuqqvz2229YrVYef/xxIiMjS1187nT3pk2nt2rVKtauXcsjjzxCrVq1TnuM9p6Ii4tjyZIl/PnnnwwfPpywsDDi4uJYsGAB48eP1/ujFs94ExMT2bZtG/369dNnN3E6naxbt47o6GgaNWqEwWDQp27UAjFARkYGhw4dYu7cudStWxeXy0Xt2rXp168fFoul1Ps/0wKEp5WUBHv2FH1WJeli99VXMGrUeZ2iSuJhaen45x9UHx/sTZrIeFgGGQ9lPKzQeKgtbNe//1m8IlVo1Sro3v2Mu13wAoU2gt7Ly0v/YFZFgUL7VyvZA+VOS/GZBrRjNeeTgTocDgwGg0fmV54MtDTn81zLaq5TVZWdO3eSmJjITTfdVGoGWvz38302F0ppr6vGZrN5rCypFUgBvbbpTJlh8fOW9xitj6nWLL1jxw52797NFVdcQZs2bfRaseIZaFm1I6VtLz548OjRo3z//ff4+voyatQogoODSxxzptdeFiikGquCChQXPB6Wlo6NG8HHB9GypYyH5TynjIcyHhY/5qzioSxQnD+tRJiXl4e3t3e53njnrZQCxcVIe8M7nc4qCyzlofUjNRqNJZqMLxXaa6VNSWc0GstVc3au13KfSk/jfq3SakfOh1azotXIabNsVOprLQsUUnVSQQWKCx4PS7NxI/j6QqtWF/a6pyHjYfUh4+E5qKEFigs+hsJkMpUYsCQVURSlzOnRLhaKopy2X+2lQuurW7zGqTKc7rNSGfUB2r1dqPuTpEuVjIdlk/Gw+pDxUIILXKBwH6Aleaoub9KLtabodM42kzlT06X7ttKexemuV55zu+93pmdd3tfiXLsBFG8GrojrVrf3jyRVBhkPy1Zd8ggZDz23yXhYzuu6/Vvd3j+nI6tFpEuC+0A9TWFhIXPnziU7O1sf/FeW+Ph48vPz9abWrKws8vLy9ObQ4v1NDx48SGJiIoWFhfpqnGWly+l06quyagsYVQZtcaHiK46eLl1Op9PjuWj3f7ZBqfizlyRJkqqGjIcXQTy8cKMNLhhZoJCqPfcPdVkfcKfTSWJiop4ZCCHIy8vjm2++YePGjWzdurXMmhdtnuqsrCx9++rVq9mzZ48+i0dBQYHHtefNm8e8efP4888/T5sxC1G0Qun+/fv57bff9GuUdU/nknlpXC4Xhw8fJicnx2NQXFn3bbVaOXToUJm1SGdKo7af0+kkLS1NFiokSZIqmYyH5VPV8bAyumdVNVmgkC5a55thuB9bWFjIkiVLsFqtqKqKoih4eXkRHR2N3W4nJSXltOfKzMzEZrPp56tfv76eMS5atIhly5bp19Pmqw4LC2PXrl2nrQGx2+088sgjvP766/z555/k5ubqf9Pm29amsdMWXyrPPZeWkSmKwpw5czh27JjHbC7Fj9PEx8czb948j22qqpKTk8OKFSuwWq0ex5c2WE6Ioun6Xn75ZZxO52nTLkmSJJVOxkMZDy92skAhXbTcP5TFtxf/wLtnXoCe2Wjzo+/YsQM/Pz8KCgr0jMnHxwen04mPjw+5ubklVjZ1P7eXlxeZmZn637XFgQoKCqhbty5t2rTRr60oCldccQUpKSn4+/t7NNuWlrm1atWKXr16ERgYqGfk2rldLhdxcXF89NFHpKenl8hAy3oW7vfvzsvLi4SEBL2Wxf25uf9o93zy5EmP568oChkZGfz555/YbDb9HFarlfT0dPLy8jxqoLRglZycfNqaKUmSJKlsMh7WnHh4/PjxGtlCcUmulC1VHzabDbPZXGJmB/cPaUZGBllZWWRlZaEoCg0aNCA+Pp7p06czdOhQYmJiePfdd9m2bRtTp07l2muvpW7dutxzzz1ER0cTHByM2WzG5XLpq5+612IYDAaaNGnCsWPHuOyyy7DZbBQWFnLixAlSUlJ47bXXCAwM5Prrr6dZs2Z07NiRsLAwPVNOS0vDz8+vxL0JUbTg0pgxY1i7di0BAQEkJCTQoUMHDhw4wNtvv80LL7xAXFwcP//8M/7+/owcObLEebKzszly5AitWrVCURQcDgc///wzoaGh9OrVS5/iTlEUQkNDOXbsGE6nkxMnTvDhhx9yzz33IIRg8eLFdOzYkQYNGuDv74+Xlxe5ubk4HA594GhhYSH79u2jQ4cOZGZm6lME7tixg0mTJjF06FBuu+02PRMuLCzEYrFgsVgoKCjAx8enIt8ekiRJlwwZD2tOPMzPz8erIt8cFwFZoJAuWi6Xi2nTpnH55ZfTuXNnoCjTsdvtnDx5koiICACef/55tm/fTnZ2NnXr1sVutxMQEMCmTZuYP38+DRo0wG63061bN/73v//xwQcf8O2337Ju3TosFguRkZElVunUMlEt4xBC8Morr/D555+TkZFBZGQkJ0+epF69ekRERODn58f27dt5++23GTx4MHXq1CEiIoKwsDB9VU53Qgiys7OZMmUKgYGBCCGIjIzUB6z9/PPP/PLLL1x55ZVce+21jBw5knXr1jF8+HD9eI2WoZ88eZK8vDzWr1/PM888Q/PmzenatavHQj9NmjRh2bJl5OTkMHXqVNavX8/IkSPJycnhn3/+4Y033iA6Opr27dszYcIE0tLSsNvtmM1m8vLyOHHiBGvWrGHx4sUUFBRQu3ZtWrZsycSJE3nqqado1qwZx44dIzg4mD/++IM9e/YwcuRIQkJCsFqtCFGzZrWQJEm6EGQ8rFnxUOsyVpPioSxQSBe1oKCgErUZmzdvJiUlhT59+mA0GqlXrx533303kZGRhIaGcvDgQZo1a8bmzZsxGAzk5OQQGRlJmzZt8Pb2pnHjxuzcuVNvXg0PD+eqq67yqPVxOp0sXbqUHj164OXlRVRUFFdeeSWxsbFkZ2dTu3ZtcnNzyc7OZvTo0Vx99dXk5OTw3nvvkZmZSU5ODiNGjMButxMeHl4ic9ZmmFi0aBFhYWFMmDCBrKws6tevj8FgYPDgwWRnZxMXF8eUKVOIiIjg3nvv9XgODoeDhIQE6tWrx1VXXcX06dOZPHkyV199NW+88QY+Pj4l5kiPiYlhyZIlzJs3j/j4eAwGA0899RRCCCwWC6+99hrt27cnICCAwMBAIiIiEKJo0aIZM2Ywd+5cfH19adOmDT169ODNN99k3rx5HD58mEOHDtGgQQMyMjKoVasWubm55OTkcN9999GwYUN94S5JkiTp7Ml4WIPiYQ1cv0QWKKSLlqIo3HrrrSXmaY+Pjyc1NZXs7GyOHz9OZmYmzZo1IzQ0FICOHTsC0KNHjxLzVwshaN26Na1bt/Y4p8Vi8fg9NTWVzZs3c8MNN+h9PgcPHszXX3+N1Wpl3LhxBAQEYDAY9GZhi8XC5MmTMRgMHn01i2caWuZ54MABrrnmGiZOnIi3t7eegQshqF27NiaTidtvv51Ro0YRFRWFn5+fx7NIT0/njz/+4JFHHsFgMNC2bVtuu+02HnroIWrVqlVitVJFUYiJieHzzz8nLi6Od999l+zsbLp3784111xDixYt+Oijj2jTpg2NGjXC4XDwwgsvYLFYUFWV6OhounbtSt++fWnRogVGo5HGjRuzb98+vLy8aNiwIUePHiU0NJTMzEy6du2K0+kkKiqKAQMGEBQUdJbvAEmSJAlkPKxp8TAgIeEs3wEXP1mgkC5aWgZQPAPq2LEjL774IsuXL6dOnToMGzaM4ODgEvuWVvovb42AwWDgr7/+4ujRo+Tn5xMZGUn//v154okn8Pb2JiAgQO+LqdGaL7V+pqU1Z6qqSkJCAl988QUJCQmMHz+egIAAfb+8vDy+//571q9fT7t27WjdurVeq1K8mfjEiRMsXryY0NBQjEYjGzZs4O6776Z27dqlLpalKApGo5GWLVvSvHlzduzYwfr167npppto2rQpQgiGDx9OaGiovkptWFgYhw8fZunSpaxevZr333+fOnXq6Ofv2LEj11xzjX6Na6+91uN5aNdt1KhRuZ67JEmSVJKMhzUnHjZs2BCOHSvXs69OFFETh5q7mz0bevWCwMCqTolUQVwuF/n5+dhsNnx9ffHy8sJkKiobV0QTojZLw5YtW1i+fDlNmjShR48ehISEYDQaPTLqs72eqqoUFhaSmZmJn58fgYGBeqDQ+sNu3rwZk8lEy5Yt8fLyKpFRa2nMyclhypQpbNu2jYCAAO644w66d+/uUbtT2r1pcnNzycrK8shw3e/L5XJhtVqZMWMGhw8f5rbbbuPKK6/Ua6EqtLk2KQn27Cn6rErSxe6rr2DUqKpORcXYuRNWrYJTNdpS9eJSVayFhVitVvz8/DCZTEX5uaJQETm0EAKHw8G2bdv4559/qFWrFj169CgqtBgMKJR/JeviVFXFbreTmZmJj49PUSuH0YgCCMBht7Nt2zb9i7/ZbNZjcPE05uXl8fvvv5ORkYEALm/Vik6dOpU6iF0/7r8TkJeXR3Z2NlFRUaW2ZmhpXbBgAcePH6dz5860atWqaN9Tz6HcrFaIjoa+fc/mqKqzahV0737G3WSBQqp23KfOc8/IKuoLrhACm82mn1OrYdGuU1bmVN5zu0+np/2rFSi0/pnu91Pal3chilbu1Kah0/YDSs1wS0uHlhb387sfp00xqO2v1ei4P5cKIwsUUnVSkwoUqgo1cE78S0WNjYeAOLXuxNnEQy09WqvI2cRD91YV9zRByXjoXrF21gUKAKOx6Kc6KGeBQnZ5kqqd88nAyqt4H1KomNYP7Ut5WX8D9NaWM11T2889mJQ3jcUDQ1n7aDNinMs1JEmqBgwG8KppE1heOio9GgqBpZT3R4XEQ6Csr9TKqWu7f0kt85pCYDqVxnOKh9r1TrePEJhPfS+Q8bB0skBRjPtiKNq/5e3eUfxYqLw3XPGGJfmmrjwX8tmeTYEA/ms5qEjF38cy05SkS5OMh1JxMh7KeFgWWaAohXvXk7OtDdeO05re3GubKzJ98N+qi/LNXbGq0/OsrLS6v4fLGgwoSVINIQTY7aX/6VSXGr175an++eU67akuK9oXPZPJVO5jy0sAuKVPuQAt2JeS6pTrV0paT32n097DHmMmzOYKfz9XZ7JAUQar1crnn3/OyJEjCQoKOqsvUwcOHGD//v1cccUV1KlTR59/v7RznK70W3yFSneqqpKRkcGRI0do3LgxwcHBZXalcb+WNsBKW0q+eLqEEOTn55Obm0tUVJR+rYKCAmw2m36d8/1yWdrQnfL2c9RWBdU/3OV4ttrftYzhbAZxa8HU/TxnU0tX/HU8ly/n7oVI9/O4q8g+s3Fxcfz666/cddddJRY5kiSphsnNhc8+gzp1Sv5NVXHY7SxcuJAbb7wRf3//8n2JEgKEIO7wYQ4fPsxll11G7dq1Tx8PTx13VvFQURCqSuapeNioUSMZD8/wbEuNh0r5BnFXeDw8h/EHZ4yH5byXcl1LVUmIi2PJkiUMHDiQsPDwokJrWhr06AGtWlXQlao/WaA4jZ07d2Ivo9bmdA4ePMiRI0fYvHkz999/P/Xq1Tvth00b9GQwGPAq1ldRG2ykLenufp7du3czY8YM2rRpw7333luU0Z+By+Xizz//xMvLixtvvLHUdG3ZsoVff/2V999/H0Upmu1n8eLFJCQkUL9+fW6++eYS6TxXWoZQnpYgLSP66aefiIqKon79+jRs2PCMz9Y948vNzeXff/+lQ4cO5Q4C2uuzceNGWrVqhaIohISElPt41a2W7nyem8vl0ucAt9ls+Pv7V8oAaVVV+fbbb/nqq69o3749ERERF2TciiRJVcTlgsaNYeBAz+2nCgWuwkLmrVlDpwED8IuIKF8rwKljd//+Owd9fVmVkcH9PXoUxUODofRCyan82iMeavsJgTg1MLZ4PBSqyq7Vq5mxYwdthODegQPPHA+FwOV08ueiRf/Fw+LpEoItq1cXxcMxY4riodPJ4t9/L4qH/v4VEw+LfUE2lPV83A859aX+px9+8IyHZ3i2Qgj99cvNyvKMh+UpFKgqNqu1ZDws53tCdbk842E5r1v8PGeMhxUUE1Wnk68nTuSrf/+lSevWdO3aFYPJVDQ7mtVaIdeoKeS3hDJoS9DbbDb9jat9CFRV1X9cpz4cWkYghCAwMBBfX1/MZjP5+fke3aDcS/fuDh48yB9//KHvq/3dZrPxww8/6AUb7XiXy4W/vz8NGzYkKytLr2XRZiJwX0imuCNHjhRNreY20497zYG3tzdRUVEeNQkGgwFfX19mzZpFYWGhnkb35+F+jxr39Lr/XQhBZmYmf//9N3v27Dltmt1ncHC5XKxZs4akpCR++uknj3SUxuVykZ2dze7du3E6nWRlZfHJJ5/gdDr1NNntdrKysjyeuztFUXA4HHz++eds376d+fPnezwvrcYqKSkJm83mUXOUkZHBsmXL2LJlC1u2bNGfQVnPy/2e3Z+dVlO2ZcsW0tLSeOSRR8jMzGT79u3k5OSUeZ7Tndc9DcXvt1evXrzzzju0bt36jDV9kiTVbDIeyniokfFQxsOyyAJFMVrp1mg00qZNG9avX8/TTz/N5MmTyc/Px+l06h8Op9OJ3W7Hbrd7ZABBQUHs27eP2rVr69OtZWdn888//2C320vNKDIyMvj7779xOBwcO3YMu92OEEXrEixevJjc3Fw9I9GmLvPz8yMpKYnAwEA9g7Xb7aSkpOjNoMUZDAaaNWvG8ePHUVWV/Pz8EhlHeHg4R44cIS8vj3///Zfs7GwCAwPJzc0FIDs72+MetABTUFCg1xi4c7lcFBYWkpeXpz8/IQQJCQl8+eWXrFy5Ug8ApdEyubi4OHbu3EmrVq0wGo2kpqbqz6ksQgj279/P4sWLKSws1DMDh8NBRkYG6enpxMfHs2jRolLTrrFYLNSqVYu8vDxSUlL0mjFtlc8vvviCcePGkXBq9Uvttfr999955pln2LhxI4cPH9Yzrd27d5OZmXnaQOdyuTh+/Dhr167Vn/H+/ftRFIW8vDwURWHhwoWnfb3LeiaqqrJz506ys7NL3ScqKoq0tDTCwsLk+AlJukTJeCjjYWlkPJTxsDSXbIFCexMVFhZ61JQ4nU4KCgrIzc1l8+bNfP/996Snp/P9998TFxenv2FXrVrFyZMnWbVqFYsXL2b16tVkZ2ezZcsWatWqhZ+fH40bN9ZLvKtXr2bo0KHs3bvXo/9fQUEBeXl51K5dm4MHD1JQUMDrr7/O8ePHycrK0hc1O3DgAA6Hg1mzZjF9+nQKCwv1BcksFgvWU01va9asYfTo0eTn55d570FBQRw7dozk5GRef/11PQAcPHiQrVu3YrFYOHToEM8//zz9+vXjtddeo3HjxhiNRmJiYigoKPAopTudTr755huGDBnC3LlzcTqdOBwO0tLSsFqtWK1WXn31VYYOHcq8efNITEwkMzOTyy67jJtvvpkBAwbo/VeLv0butRtTpkzh22+/pX79+qSlpZGYmEhhYaHH89QCjHsfz1atWpGens60adMIDg4mJCSEo0ePMnToUD755BPWr1+Pr68veXl5ei2N9rppNTcGg4HLL78cPz8/0tPT9Wtq/VZvvvlmOnfuTFhYmEctVlhYGPfddx8xMTHEx8ejKEXrTcyfP5/4+Hg9SLrXLGnPz+VysWjRIiZPnozVasVoNHLgwAH8/PwICQnBbrfTp08ftmzZ4hGYSqstLF7TJ4Rgw4YNZGZmYrPZPGq8tEw9MTFR3649V/fXRJKkmkMVQsZDGQ9lPCxvPHS5itbKkPFQd0mPoTh06BCTJk3i7bffplatWnoB48knn2THjh0kJCTg4+NDfn4+qqoycuRIMjMzCQoKIi8vj4CAAP3Nr61onJWVhb+/PwMGDCAiIgKTyYQQRasa5+fnc+TIEZo3b65vX7FiBe+//z6BgYH8+++/jB8/nhMnTpCWlsZDDz1EZGQkJ0+e5KOPPiImJobff/+dnJwcli9fTseOHfH19eXKK68EijKyhQsXsn79epKSkggODi5xz9obf8+ePWzbto1du3bhcDiw2+2MHz+epKQkXnjhBeLj42nRogVRUVE4HA5ycnKwWCyEhYWVGFdit9tZtGgRy5cvx9/fn+uuu45du3YxYcIE+vbty7BhwwgJCSE3N5dHHnmEgIAAYmNj+eabb/RjYmJiyhxYLUTRVIUPP/ywniFv27bNo4+slkEkJSWRmZlJmzZt9PMlJCSwe/ducnNzKSgoIDU1lZCQECZPnkxBQQGffvopa9asISYmhk6dOtG5c2f69OlDYmIi8+fPZ+jQoQQHBxMeHo6fnx8HDx7EZrPpa1Woqsr06dP57LPPCA4OZtCgQfq1u3TpwuTJk2nXrh1Lly7VM6dmzZqRnZ1NZmYmn3zyCePGjcPHxwchhF675eXlRWJiIgEBAXpGrV03LCyM3Nxctm/fzjvvvEPdunW59tpr9QzaZrOxZcsWEhMTad68OUFBQQQEBOiDCI1GI3fccQcLFizgl19+YfTo0Vx//fXYbDa2bt1K48aNOXz4MFarFYvFwv79+zGbzTRt2lSOp5CkGujIkSO8PHq0jIcyHsp4WI546HXwIE0bNqxWs2BVtku6QBEUFMRVV12lL94F4OPjw0svvcSJEycA9DdkSkoKhYWF+jLxHTp0ICsriyZNmugf6ri4OKCon+d1112HxWLRV2u8+eab2bt3L02aNPFYZfLKK69kyJAherPlzJkzad26NWFhYdxzzz04nU6uvfZaZs2aRUREBF26dGH+/PnUrVuX1NRUBg0aRJ06dfDx8UFRFIYMGcLRo0f1a2gZUEpKCiaTiaCgIHx9fYmPj+fFF19k0KBBeHl56TUbBw8e5OGHH8ZoNLJ06VLsdjs2m40dO3bw5JNP4u/vT1BQkF7bIYTg5MmT1KpVi6eeeooFCxYwePBgQkJCyMvL47vvvmP58uU4nU7atWvHxIkTmT17NkuWLOHee+9FVVVatmzpkXm6XC6Sk5P1zPvXX39ly5Yt7N+/n/z8fEJDQxkxYgSBpax+vn79erKzs4mOjsZoNJKTk8MPP/zAtm3byMjI4MMPP2Tbtm188sknbN26lRYtWhAeHk6LFi1o3rw5v/32Gxs2bGD//v2sXr2aDRs2sGzZMvr160dSUhJ33303fn5+Htd0uVykpaXRpk0b9u/frweplStXkpSUpGeCRqORDz/8kKioKNavX6/f8+HDh/X/a82p3333HXfeeScPPPAABoMBo9FIZmYmx44dIz4+nqCgIOx2Oz179kRRFNavX8+1114LoDfdz5gxgwULFujHP/fcc/Tu3ZuTJ09y2WWX8eWXX7Jr1y5UVWXNmjVcf/31rFmzhscff5wZM2ZgMpn0Zu+xY8cSEhLCjBkz9FpCSZJqjsDAQBkPZTyU8bCc8bDWiRN899VXGETFr31RXV3SBYrIyEgeeeQR/Xet2TI6Opro6OgS+y9ZsoS//vqLvn370q5dO483kaqqREZGMnv2bEaNGoWvr6/efGe329m8eTOAx6xEiqIQHR3NqFGjAPQmuUWLFpGdnc0tt9yiZ+5jx47Vr9W/f/8SaXM4HDgcDoKCgqhbty4xMTEAegn/ww8/ZNSoUfj7+2MwGBg9ejTr1q1j27ZtpKamEhYWxkMPPURMTAxDhw4lOjraYyVmo9FIUFAQDocDb29vj3v/+++/mTBhAuHh4YwePRqr1UpYWBh5eXlYrVZMJhNeXl7EnZp67dVXX+Wpp55CCIGfnx/h4eEe95KYmMjChQu5++672bZtG5MnT+a+++5j5MiR+gwfDRs2xOFw6LUYWno6derESy+9xI8//ojRaCQ4OJjWrVvz888/43Q6ycvLY+DAgcTHx9OzZ08yMjKIjo6mYcOG+Pv7683wJpOJN954g40bNxIWFkZqairdu3cnMjKSp59+2iPoGo1GrrzySlwuFyNGjMBoNOJwOFixYgX79u3j6aefJjo6mhEjRnDgwAGysrK44447WLFiBd999x2DBw/2uH8hBPHx8YwePZpGjRohhCA1NZWUlBTq1KnD6NGjueKKK9iwYQOffvopYWFhjBkzRj9eURR8fHxo0qQJzz//PL6+vrz77rtMnTqVb775Rs/gT548SZMmTbj22mv1dKuqio+PD8eOHaNLly6YzWYKCwsxmUxkZGRgt9s97l2SpJohIiKCRwYM0H+X8VDGQxkPy46Huae6hMn2+v8ooqZ3/po9G3r1glJK72dD68unNTdqP9rftCZGrQnPy8tLr+H48MMPOXnyJC+99BLNmjUD8Ji72r0PntZ8qk2LV575nYUQbN26ldmzZ7N7926effZZOnbsiNFo1Acv3XnnnXTu3Jno6Ghmz57N0KFDadKkCRaLhcOHD7Nnzx6WL1/OpEmTaNOmTanp0360QKMoCqqqMnHiRMaMGePR5KqlW3teAIWFhWRmZlKrVi29pko7j/s5ly5dypw5c3j++efJysriu+++Y8OGDfz222+EhISUuH+t+dN99hGbzQaAyWTC19e3xNziWs2HthiSe1qgKJhpGYqqqvrrofW71H7Xzuc+q4SiKBw8eJBXX32VN998k8jISI9naTQa9cC2YsUK9u/fz+OPP64HLK1G6t133+Xo0aMYjUY6duzIddddR7NmzbDb7fj4+JCbm8uxY8eIjY3F399f73frcrnYuXMna9eu5d577wWKpstNSEggMDAQi8XC7Nmzufbaa2natCl+fn7666ENnLvqqqsIDAwkODgYg8FAUlISX3/9Nc8//zze3t4ez6pCJCXBnj1Fn1VJuth99RWc+uJbI2RmwsqVJaeNLYOMhzIeXurxcPpTT/Hs009j7tjR41nVSKtWQffuZ9xNFijKqbTH5P6hKIvD4SA/Px+TyYS3t7fHytnlOd59v9OlLTs7m/Xr1+Pv70/79u315mVtANHKlSuZNGkSLpeLgQMHMnr0aPz9/VFVlaNHj7Jp0yaaN29Oq1atzqoGWlVVxowZQ926dRk8eDD+/v4IIbBYLPj6+pb7PO73euLECYYOHUpaWhoA3t7e3HvvvQwfPlzvp3kxUlWVnJwcvvnmG/744w+GDh3KsGHD8PHxKXP/tLQ0srOzadCggcd7QwvIWkavBaLyZFpOp5Np06YRFhbGTTfd5BHsNQ6HQ3+dVVXFarWSl5fHnj17+P3335k8eTJeXl7k5eWRk5PDtGnTaN68ObfeeitQCZmnLFBI1YksUJTYJuOhjIfuano8bOFwMPCWW6BY61yNJAsUp1RQgeJcuJfU3fuJVvSbT/uwaf/Xaii0DFT70Wa68PX11Qciude0uNc8lDeNQgiOHDnCa6+9xr59+wgMDOTKK6/kjjvuoHXr1uc0gNfhcJCUlMSIESMQQvDNN99Qr149AI9aqovpQ+xeK6dNQRgZGYmXl1epK6m618JptULutVjaPqXd4+nuW0vD4sWLeeuttxg/fjzXXHONfn4fHx+P2i9FUXA6nRw/fpyZM2eSnZ3N0KFD9ZrDLVu28MMPP3D11Vfzv//9Dz8/v8p57rJAIVUnl3iB4lzIeCjjYY2Khw0a4OflhdK+fYU8s4uaLFCcUsUFitJURgZa1nVO9/Ke7u9nk4FqtT7a9HEhISF6jdC53Ks2z/bx48cxm81ERUXpA6IOHz5M48aNMZlMF10GejqlZaBl7Xc+NXTa61FQUMBHH33EzJkzCQwMxGw206ZNG55++mnq1Knj0W/Z/TV0D77a+bRpAoFSg0GFkAUKqTqpiQWKBQugb99Ku4QAfUVoQF/JuKJzkxLXOXUtpay/6bso6H8pls7yptE9L9UWywsODj7veOhwOEhOTsbLy4tatWqVjIdm80U121Cpz1FTyvMUQhS9H0p57mf6gnq6+y4eD3/66SfCwsIwGo20aNGCsWPHFsVDrYALZcZDrUuYFg+VvXsx+PvLAoWbGlegcC/NulwuTHPmQO/eKFVQoLgUuD9v7f9a/8NzrTVxXw1T+0Br/V+dTicmk+mcM+eazr2GzeFwcOLECVJTU/H29qZu3br6IET3mjL319Bd8b605/OanpEsUEjVSTUpUJSIh6e6kZT4DNtssGjRhU5ejVO8wCJA/1KsnEXBxJ0+HuHUF24tJqpCoGpfbg2Gi6pAcbHQXg8BOE8t3udwOFAMhqIZvnx8SnyXKB4HtdewxO8uF3TujBIVVdm3UfXKWaCokbM8uZcwjULID1olcv8glvX/czmne/9JjcFgwMvL67zPX5O5tzxYLBZiY2OJjY0t17HurRLFz1f8/5IkVQ/u8bC0/uMAWCxQymxJ0tnRcki9IqbYds4hD1VK+w6jKChCYNR/lXlzabQWBwUwA7XK3NHt+RUbkF68lUU+6bLVyAKFNkvCxdavsCaryOdc1uA8+VqWz9k+p+L7y+csSTWHjIcXnoyHFw8ZDy+cGlmg0BgMhnOqEZAuHmUNxLrUlNVqIEmSVB5yhfvqT8bDIjIeXpxqXIFCvrmqh/IMSnafEaQ8g5XPRnV6n2j3rc0FLoOKJEnlIfOJ6kHGw/KT8fDiVeMKFNLFzz2D1AZfFxQU4Ovrqy+ApG3fvn07l112GcHBwfpAY22KP402DSD8t6hP8UFWWmZc2tiM6sDpdGK32/H29pY1jZIkSdVVbi7s36//qhULhBC4Ts0s5HQ6MZvNGLSpbE/FuBMnThAeHq6viO1yOjG4LSoHboO43SbR0GYx0gYoC1VFAEb3XhwGA7RqVTSe5iIn4+HFSb4SUpUQQpCYmMiOHTtwOp1MnjyZFStW6IWNrKwsfvjhB6ZMmcKqVasQQpCbm8sff/yhzzGusdlsbNmyhblz52K1Wj1qblRVJT09nby8PHbs2IHD4dCv7z4z1cXOZDJhsVhkTYwkSVJ1dugQJCSAt7f+I7y8SExNZee//+IwmXjzww9ZuX49wmIBi4XMwkJ++O03PvjsM/765x+ExUKew8G8JUtwmkwe57IrClv37GHu4sUUAsL9OhYL6Xl55Dqd7DxwAIfJBBYLwmJBbNuGyMio6qdTLjIeXpxkgUKqVO5f3N2/wAshWL16NdOmTQOgoKCAhIQEjzm8161bh5eXF8eOHePYsWPs2rWLL7/80mMqU4C8vDxefPFFvvrqK3Jzcz2ucejQIf73v//x2Wef8fbbb1NYWIjT6SQ1NRWn01kFT+TsafdZaWtASJIkSZVOaC0EDRsiWrVCtGoFp/79Kz2dbzdvRm3RgtSoKA77+qK2aIGzeXPSo6NZm5NDZkwMh319ORYayk4h+GL9ekTLltCqFcrll0OrVuTWr8+EWbP4csMGcmNj9b+Lli056O3N/yZM4LO//+bNhQspbNQIZ/PmpEZFoUZFlblGx8VExsOLlyxQSBeE1r1JI4SgVatWnDhxgsOHD9O6dWuEEOzfv5+5c+diNpvp1asXBw4c4PXXX2f48OFYrVYyMjKw2+0e5w4ODubGG28kMjKyRAZz6NAhFEUhKChIb704ePAgt99+O3v37r0g914R9KZrOVOLJElS9XWqC+7FFg/37dt3QW6/Ish4eHGqnh3KpWrFbrfz77//YjQaueyyy4CiFol33nmHjRs38ssvvxAbG8u2bdtYs2YNmzdvJjAwkPj4eO68805GjhxJdnY2tWvXprCwkBMnThAQEKCfPzc3lxkzZnD48GH69evH7bffro+r6NSpE926daNTp06sWLECq9VKbm4uJ06cYPLkyUyfPl1f20KSJEmSKpPT6eTAv/9i8PG5qOLhN99+y7t33llVj0WqAWSBQqp0hYWFvPXWWzz00EO4XC4URSEhIYGNGzcSGBhIWloacXFxJCQk8Mgjj9CuXTt27drFgQMH+Oqrr7BYLERERNCqVSuio6P1Adha16bU1FRatmxJXl4e2dnZQFEhZseOHXh5efHbb7/RqlUrGjduTEFBAf7+/kyaNInXX3+djIwMatWqJWs5JEmSpErncDr58MMPuWfKlIsqHv7z8sv6eWQ8lM6FLFBIlc5ms7Ft2zaGDRtG48aNMZvNnDx5koyMDFRVZcaMGTRo0IDc3FxGjx6t16b4+PgwYMAA+vTpQ506dWjcuDHt27cnPDzc4/zp6eksWbIEIQS1a9dGVVUSEhIYPHgwdrsdRVFo3LgxNpuNf//9l/vvv5/CwkKio6Nl64QkSZJ0wSjAgQMHLrp42N9i0VeVlqRzIQsUUqULCgri4Ycf5p133mHdunUEBQVx1VVX8dprrxEaGsqxY8f02pK0tDR8fHzIzc0lJiaGBg0a4OPjg9FoxOl0MmbMGIKCgjzOf/nll/PMM88QHR1N165dEUIQHR1NkyZNMJvN3H777bRo0YKGDRty5MgRWrduTVBQEMOGDStxLkmSJEmqLAajkaFDhvD20qWsW7v2v3j46qtnHw+feIKgwECPwdSXt2rFM+PGFcXDLl0Qqkp07dpcdqrwctvtt9OieXMaNmhQFA8vv5ygoCCG1KkjCxPSeVFEdZk381zNng29ekFgYFWn5JLkvuZEYmIix48fJzIykjp16pTZOqA1t2qzQrkPvHK5XCXWmdDO73A4cLlcuFwuli9fzptvvsn3339PvXr19HUrtDm+tXNoc1jLJt4qlpQEe/YUfVYl6WL31VcwalRVp0KqZoQQcOgQ6oIFJGVn6/HwdK3l7rFJFQKD+++q+t86E25f5YQQOJ1OfU2n3bt3s2rVKoYPH05YWJjH2g1atytDbi7K8OEQEiLjoeRp1Sro3v2Mu8kWCqlSaRmTwWCgbt261K1bt9QCQfFybWmzNwghPAoG7tsNBgMGg4EdO3bwzjvvsHnzZsaOHUu9evX0v2nMZnOJc0iSJElSZVIUBRo3xvDYY8SoKjEUxcbyRiKj2/8F/03TWdrxisPBtm3b9Hj49NNPEzJqFBgMKEajfozHVJ+K8t9Cd5J0lmSBQrpgTreiZXm+3Je1j6Io+irYLVq04MEHH8RqtdK9e3e98FDea0iSJElSpdEq2YzGEtvO6jSn+6MQGM1mWrRsyYMPPfRfPHRvBZHxUKpgskAhXRCV/WXevVBx5ZVXEhAQUGIBPEmSJEmqajIeSjWRLFBINYaiKFgsFiwWi8w0JUmSpEuWjIfShVbzCxQOB+zcCb6+VZ0SqZIpnKEZWLp4pabCqfnUJUmSpPMjV5GWLrSaX6C47jo4caKqUyFJ0ulERkK9elWdCkmSJEmSzkHNL1DUrl30I0mSJEmSJElShSt72h1JkiRJkiRJkqQzqPktFNIlq7S1LS418hlIkiRJMhbIZ1DZZAuFVOPV9MXgz8RqtVJYWIgqBz1LkiRd0mQ8lPGwssgWCqnmEQIOH4acnP9+B0Sx2ogz1k00aAAhIRWfvgvMbrejKApe7osaSZIkVbUjR4ryallTfGG4FSYEeDz3S+UVcObmFv3Hzw/cF9v18YFrrvHcJp0VWaCQah4hYNEixLXXYrXZ2L9/P19//TUxMTGMHj2a4OBgDAYDWtZaakZ64gRkZMANN1zAhFcObVGj061ULkmSdMFt2wZNmkBwcFWnpMYTqorVamXfvn18/c031C0tHipKjS9Y+J1qmVAUxbMgO38+dOgAsuLtnMkCRUUSouhLqM1W1SmpcmU1ql6QzEoIcLlQatUiPyOD5z74gLi4OIz79uHXsCH3jx6NyWwuWrfCrWDhweFAiY+H5OQLkeJKUyFrcxgMEBEBRmMFpEiSJOkUoxFiYiA0tKpTUqnK6mZ0QfvwqyoZycnc+/LLpKSkYNq+ncKICJ599lm99dpQVjy80GmtJGXGQyEgIOACp6bmkQWKiiQETJsGzZtXdUouCg6HA6fDgclsxqAoGE0X6O0mBCQkIHbs4K85cwg7dox3J04kKSmJn6dPp7B1a/z9/bE5HBgMBswm03+191qmefIkHD8O/v4XJs0Xs/37YfBgOf2yJEnSOXI4HDgcDsxmMwaDAWMVVNDMmTOH5ORkvvzyS06cOMH777/Po48+SnBwMHa7HaPRqKdPks6WLFBUJCGKalp6975k+4RqNTFCCI4nJvLUU09xzz330KNHDwxms/5cKrW2Q1Xh+HHULl1YOHMm+R06UOfOO9k4Zw47Q0JQu3Wj0GzmmWee4YYePbjp5pvBaEQB1FNdg5TEREhKgh49Ki+d1YXBIFexliRJOkse8fD4cc946Pal/ULU/quqyj///MNVV11Ft27d+Pnnn7FYLBgMBgoLC3nyySfp2bMn/fv390i/wWCoEa0TUuWTBQqpQpQ2HZu/vz/p6em88847tGvXjoiIiAuTGEWB8HDEnDk03rmT+vXrk/LppxydOZPxbdrgv2QJBQUFhKxcSf3QUAwuFwLYvWcPgUFB1KtbF8XlgquvvjDplSRJkmqMiyoenuJyuThx4gRt27YlOTmZmTNn0rt3b3x9fcnJyWHbtm307t0bAKfTyZ49ewgKCqJevXqYLlTvAqlak+1aUoURQqCqqv6vn58fjzzyCLt37+bNN9/EarVemCnrFAVxyy247riDZZGRfGuzMWD2bGYZjVz20kuogwezs1Ur/m3fnkYvvIAYMoSEzp25b+VKpqaloQ4eDMOHy65rkiRJ0jm5aOLhqbRo6di+fTu33XYbJ06cYNiwYSiKwoEDB2jYsCE33ngjiqJw7NgxHnjgAT7++OMLkj6pZpAFCqnCOBwO0tLS+P777/npp59IS0vjxhtv5NZbb2XatGksXboUl8t1wdJjMBiIjY1l3bp1ZGdn8+qrr9KoUSNyc3OZMmUKQ4cOxcfHB7vdzg8//MCBAwdo0KDBBUufJEmSVDPJeChdamQ7llRh8vPzGTFiBJs2bUIIwWWXXcbHH3/MmDFjWLduHdOnT+e6667D322gc2X2zTQajbz66qv07NmTkJAQunTpAsAPP/zAtm3beOONN1AUBYPBwNq1awkPD6dv376yv6gkSZJ0XmQ8lC41soVCqjBxcXFs2rSJJ598ko8//pjjx4/z6aefEhMTwzvvvMOOHTuYN2+e3gx8trRmW+3nTH8zGAxERUVxxx130LNnT7y9vVFVla1bt3LLLbdQu3ZtVFUlLi6Oo0eP8uCDD1KvXj05w4UkSZJ0XmQ8lC41soVCqjDR0dHExsayfft27rnnHm666SbWr1+P3W6nS5cu/O9//2PevHkMHDgQb2/vc7qGljmWVmtSPGM1GAweM1QIIfDy8uKdd97BbDZjsVhwOp3MmTOHoKAg7rrrLkwmk6yRkSRJks6LjIfSpUYWPaUKExISwqhRo1i+fDkffPABvr6+FBYWAmAymejRowf//vsveXl5+jFa7YzL5fL40QaQqaqK0+nE5XLhdDrJzs4mIyODjIwM0tPTPX4yMzNxOBwUFBTgdDpRT011qp1HVVUMBgMhISH4+fmhKAqKohASEsI999xDYGDgBRskJ0mSJNVcMh5KlxrZQiFVGKPRyLBhwzh8+DDfffcdBoOB6667Dm9vbxRFoWvXrnzwwQcEBwcD/2VsAHa7nby8PJKSksjKyiI/P5+0tDRSUlJITk4mNTWV/Px8jhw5Ql5enn6cOy8vL5o3b05ubi4hISE0a9aMOnXqEBUVhb+/P/7+/tSrV4+QkBDMZrOegY4cORJAzrctSZIkVQgZD6VLjSxQSBXGYDDg5+fHyy+/TJ8+fXA4HFxxxRWYTCaEEFgsFjp27IiiKNhsNhISEvj333/ZtWsXq1atIjExkeTkZOx2u15Lo1EUxWMaPm2bRlVVFEUhMTFR/33BggV6Jqn9hIeHU7duXZo2bUrv3r1p2bIl9evXx8vLy+M87ueWmaokSZJ0NmQ8lC41skAhVQj3TMbX15frr7/eY+5rl8tFQUEBBw4cYNeuXSxdupQNGzaQnp6OzWbTM67iA8C0WhKDwYDRaNT/7+3tTe3atVEUhdzcXFJTUxFCoCgKdrtdP869yRjg2LFjJCUl8c8///Djjz/i7+9P27ZtufXWW7nqqqto0KABPj4+eoYqM09JkiTpbMh4KF2KZIFCqhBaBuXe51JVVVJSUtiyZQt79+5lwYIF7N69W88wtdoVRVHw9vYmMDCQ8PBwYmJiCA0NJSAggIYNG1K3bl1CQ0MJCwsjODhY7/sZGRmJy+XCZrORm5sLFDUVp6en43K5yMjI4PDhwxw5coTU1FQyMjJISUkhNTWVvLw8nE4nWVlZrF69mrVr1+Lr60ujRo3o0aMH3bp1o0WLFnomLYTQM2WZqUqSJEllkfFQuhTJAoVUYRwOBwCFhYXExcUxa9Ys5syZQ3x8PDabDSiq4TCZTKiqSkhICHXq1KFbt2707duX2NhYQkND8fX1xWw26zUw7rTMrLjatWufNm12ux2n04nVaiUtLY3t27ezbt06Nm/ezNGjR8nIyCAnJ4dNmzaxZcsWpkyZQt26dRkwYAD9+/fnsssuw8fHR+9rKkmSJEllkfFQutQoQg7jrzguF8yYAcOHQw3/kBWfkk5VVfLz89m0aRMffvghGzduJCsry2M/Ly8vIiMjufrqq7nuuuvo3LmzPkBMy5Tcaz0qqvZDa2bW/q8oit6kXFBQQHJyMsuWLWP9+vVs2LCBpKQkfT9FUQgKCqJDhw4MGDCA2267TV+IyD19NTpT/fNPaNUK6tSp6pRI0sXhq69g1KiqTkX1N3cudO0KoaFVnZLzIuNhNY+HQsD338Mdd8Cp7l2Sm1WroHv3M+4mCxQV6RIrUDgcDlRVJTMzkzVr1vDll1+yY8cO0tLSgKIM09fXF39/f9q3b8+QIUPo2LEjkZGR5a7ZqKiM6Uxvc61fa3JyMmvXruWXX35hy5YtZGVlYbPZcLlcWCwWunXrxp133kmXLl2IiIjAy8ur5s+GIQsUkuRJFigqRg0qUMh4WI3joSxQnF45CxSyy5NULu4ZkDbjREJCAtOnT2fu3LnExcXhcDj0AWKtW7fm1ltvpWvXrtSqVUvPbCq6tqW8TnctrfbFZDJRp04dbrvtNgYOHEhaWhp79uxh2rRprF69mvT0dJYvX87ff/9NTEwMt9xyC/feey8NGjQ46/sq3se2+AJE1S5DliRJukTIeCjjoVSSLFBI5aY1lRYWFrJw4UJefvllDh8+rH/4zWYzDRo0YNy4cfTr14+QkJBqkxFo6TSZ/vtIxMTE6H1aDx06xOLFi/n6669JTEzk8OHDvPvuu8ydO5chQ4Zw3333ERYWdlZ9SrWBeHFxccTFxVGvXj1iY2OxWCyVco+SJElSxZDxUMZDyZMsUEin5d7f0ul0snPnTqZMmcIff/yB1WrVZ6S45ppruOuuu+jevTuRkZEAF7zWRUsv4LHQT/F+naUNbDvduSwWC02aNKFx48bcdtttzJo1i2nTpukZ37vvvsvatWt56qmn6NixIwEBAae9nsbhcDB37lwmTpxIeno6ERERfPPNN3Ts2FGvJZIkSZIuDjIeyngolU0WKKQzcjgcJCYm8vbbbzNv3jxOnjyJwWAgKCiInj178sADD9C6dWt9xc+qJoSgoKCAxYsXc/DgQfr27Uvz5s0xmUwl5vU+Ey0T02pJYmNjeeqppxg2bBjLly/nm2++Yfv27axevZpNmzbRsmVLnnnmGW644Qb9mNIyQpfLxaeffsp7773HzTffTLNmzZgwYQKbNm3iqquuOut0SpIkSZVPxkMZD6UyCKniOJ1CfPutEKpa1Sk5Z6qqCpfLpf8UFBSIH3/8UVx55ZXCy8tLmEwmYbFYxBVXXCHmz58vsrOzhcPhEE6nU6hVfN+qqgpVVYXdbhc///yzCAsLE35+fuLyyy8XR48eFXa7/bzSqD0bp9MpnE6nsNls4uTJk+Lbb78VTZs2FRaLRVgsFhEZGSkmTpwoUlJShN1uF06nU7hcLj19qqqKrKwsceWVV4revXuL+Ph48eijj4qIiAixevVqj+epHVelFi8WIimpatMgSReTL7+s6hTUDHPmCJGeXtWpKJOMh6c/f42Jh6oqxPTpQthsFX/ummDlynLtJot9kgdxaoCZw+EgNTWVZ555hgceeICdO3eiKAoRERE8//zz/P777/Tt25eAgACMRmOV1iCIYlP2GQwGnE4ndrsdl8uF0+nEZrNVSBq1lUkNBgNms5nQ0FCGDh3Kzz//zB133IHFYiEjI4O33nqLgQMH8tdff2G1WnG5XCXO4+Xlxd69e7nrrruYOXMmQ4cOpW3btgDYbDZSUlIoLCw844wckiRJUsWT8fD0ZDyU3MlpYytSFU0bW/wlPJe+hlomJE41j/7555988cUX/P3336iqip+fH8OGDePuu++mRYsWGI3Gi2JRG/c0e3t7A0X9RZOSkujVqxetW7fmlVdeoXHjxnoTb0VPvedyuVBVFZvNxsKFC/nhhx9YtmwZAJGRkYwePZq7776biIgIPdioqsrOnTtZtGgRqampNGvWjJtuuomwsDByc3N57733WL58OVOnTqV9+/Z65l8lz1tOGytJnuS0sRWjEqaNlfFQxsNzSLycNvZ0yjltrOzyVJGqqMuTqqrn3czqdDpFYWGhyMjIEOPGjROhoaHCZDIJs9ks2rZtK+bOnSvy8/P1a2g/5Unb2ex/tlRVFSdPnhT9+/cX7733nkhJSRF5eXni6aefFhEREWLFihVnneazuXbxH4fDIVJSUsQLL7wgIiIi9Gbf66+/XmzcuFFYrVaP/bUmY4fDIex2u8jPzxdjx44V4eHh4pVXXhE5OTnC4XBU2vMrF9nlSZI8yS5PFaMSujzJeCjj4TkkXnZ5Oh3Z5enScrYldeFWA6NN15adnc2bb77JZ599Rn5+PiaTiUGDBvHzzz/Tp08fj+nbis9Y4X4e93O7X6uyeHt7c/LkST788ENeffVVVq5cyfTp0xkwYADt27fXZ7io6Fk2is+1rf2EhITwzDPP8Pnnn9O8eXOEEKxfv5677rqLv/76C4fD4dHkqx3ncrlYtmwZP/30E48//jhjxozB19cXo9FYYWmWJEmq6WQ8lPFQuvDkLE81xLn2h9T6h27fvp3XX3+dv/76C6fTSUxMDPfccw/33nsvERERZ8x4hBAsX76clJQUateurW8PCQnh8ssvr9RMwNvbm6eeeopZs2bx999/s3z5cnJycti4cSMHDhzQ+2FeCFp/UpPJxE033USjRo146aWXWLJkCUePHuXee+/lkUce4d577yUkJEQPLA6Hg9mzZzNp0iT69evHo48+iq+vb5U3oUuSJFU3Mh7KeChVgYppD5GEENVqlietebGwsFCsXLlSNG7cWHh5eQlvb2/RtWtXsX79elFYWKjPBFFWE6N7U+XPP/8s2rVrJ5o3by7q1KkjmjZtKnr06CHi4uIqpYnXvVk1NzdXPPDAA8JisYiwsDBxww03iMDAQPHLL79UyYwb2jPRZr54/fXX9SZff39/MXz4cH3WC5vNJhYuXChq164t7rvvPnHixAmP517lZJcnSfIkuzxVjItklicZDyvXRR8PZZen0ytnlyfZQnGJEkLgdDpZt24dY8aMISkpCbPZzJVXXsmnn35Ko0aN8Crn4CSXy4XL5aJPnz706NEDVVXJzMwkMDAQb29vfHx8Kn1hmgMHDvDXX3/RsGFDrFYrmzdv5pprruHqq6+usloNRVEwm80EBwczZswYGjVqxOOPP056ejo///wzERERTJgwAV9fX5KTk+ncuTOvvPIKoaGh+gqlskZGkiSpcsl4WPlkPKz5ZIHiEiNONSna7Xbmzp3L+PHjOXnyJH5+fvTu3ZtJkyZRp06dM65qqXG5XOzcuZN58+bRvHlz+vbti8lkIigoqMJmYhBu/U0VRfHoi6pN5/fyyy8THh7Oxx9/jNPpZNeuXfTo0YNatWpVWiYkyugH6943VQihZ4b9+vXDaDTy9ttvs2PHDj7//HMcDgdjxoxh8ODBDBw4ED8/P9lHVJIk6QKQ8bDiyHgoyQLFJUYIgd1u55dffuGxxx4jOzubgIAAXnzxRUaMGHHWg55UVWXKlCnMmTMHi8XCRx99RP369alXrx5RUVF07dqVVq1aVdi83MJt4Ny6dev4+OOP2bt3L9nZ2fz44480b94cRVFo06YNUHk1Gu6Zp5Yep9OJyWTCaDSWuK62/X//+x9XX301jz/+OIsWLeKLL75g586dfPXVV8TGxlZqmiVJkqT/yHhYMWQ8lADkLE/ViJZ5lPZT3mOdTidLly7lueeeIzc3F7PZzMiRIxk5ciS+vr4etSjl+SCbTCZeeuklpk6dyvjx42nevDkWi4X9+/fzwgsv8OWXX+qzXZzPfRcWFurn0O53//79zJ8/n3///ZeWLVvSrl07fV5r9/RX9GwW2vWdTif5+fmoqorL5eLrr79m9+7dHq9JaemIiIjggw8+oFOnTqiqytq1a3nttdfIzs6ulov2nM37UJIkqSLIeCjj4cXoUo6HsoWimnE4HBw/fhwhBLVq1cLLy6tctR0ulwu73c769et54oknOH78ON7e3gwfPlzvt3gutSaKolCnTh2GDh2qL2gjhGDr1q2sWrWKBg0anHfm5XK5ePfdd7nyyivp1q2bns4TJ05gsVjw8fGhadOmF/QDLIRg7dq1zJo1i7FjxxIZGUlKSgpvvvkmn3zyCf7+/pjNZn1/7RkYDAa8vLyIjo7mgw8+4NFHH+Wff/5h9uzZeHl5MX78eOqcWkCuOjX1an2CK7tvsCRJkkbGQxkPL0aXbDws19BtqXwqeZYnVVVFXl6eGDZsmGjZsqWYOnWqsNlsp539QPubzWYTP/30k6hTp47w8vISoaGh4sMPPxQ5OTnC6XQKl8t1TjMoaOd3Op1i8+bN4uWXXxaTJk0SXbp0EXXq1BE7duwQdrvdYzGds2Wz2cTatWvFVVddJd544w2RlpYmZs+eLaKiosQLL7wg1q5dK5KSkvSZICqbdr9//PGHCAsLE/Xq1ROffPKJ2LVrl2jatKmYNm3aGV8Xp9Mp7Ha7SE5OFiNHjhS+vr7Cz89P3HHHHSIlJcVj8Z4qn+npDLM8afdyUaRVki4EOctTxTiPWZ5kPJTxsAJvosJmeaqR8VAubFczeXl58cwzz+Dv788HH3zAvn37sNlsZTahqqqKw+EgISGBd999l4yMDMxmMyNGjODee+/Va2LOtxRtMBiIiIggJyeHnTt3cvDgQQYPHkx+fj4jR45ky5YtHovXnA2z2czVV1/NO++8w+zZsxk2bBhjx45lwIABPP7447Rr145atWrpg70uBFVV6dChA3feeSfZ2dmoqkr9+vW5/fbbmTFjBvn5+aetIdLm5q5VqxaTJ0/m6quvxuVyMW/ePN59911cLle1aTLV7kWSJOlCkvFQxsOLzaUcD2WB4iImSukXqigKDRo04I033gBg9OjRJCQklNmXVFVV0tLSGDt2LHv37kVRFG644QaefvppLBbLeferdD8uKiqKSZMm8dxzz9G2bVuysrLYvHkzCxcuZMaMGeeVIRgMBq655hq++uorUlNTsdvtjBo1Cn9/f32Al5aeC8FgMBAWFsb48eNp0aIF3t7emM1m7r//foxGI4cPHz7t8e7PLSQkhJdffpl69eqhqiqzZ89m9+7d1SYTLb4yqiRJUkWT8fA/Mh5evC7leCgLFNWAODUdnDbgyWg00qFDBz799FNOnjzJqFGjOHHiBE6n02OglqqqZGVlMWbMGJYvX47L5aJfv3688847hIWFlTr7wvkwm82YzWZ+//131q5dq/clnTt3LhMmTDjnPpDaIC+A1q1bM3PmTPr27cu0adNwOp1VkslotVihoaG88cYbzJ49m8TEREJDQ3nsscf0YHUm2tzc11xzDZ9++ilNmjThxIkT3HfffWzfvh2n03kB7kaSJKl6kPFQxkPp4iQLFNWA0+lk2rRpjBgxgl9//ZWCggJMJhOdOnXinXfeIT4+nmeffZaMjIwSc1J/++23LF68GJfLRYcOHXjrrbeoW7euRw1GRc6Lraoq4eHhCCGoX78+QUFBdOrUifDw8HO+llar5HQ6URSFhg0b0qFDB5YvX47Vai33HOFncz+n+ym+b3R0NLm5uaxduxZFUejevTuDBg0qV5q0Z2IwGOjUqRPvvfcetWrVYv/+/Tz11FMkJydf0rNGSJIkuZPxUMZDGQ8vTrJAUQ3YbDY+++wz1qxZw8MPP8zw4cM5cOAAiqLQu3dvHnzwQX7//XcmT57s8SE7fPgwX375JVarFW9vb8aNG0d0dDRms7lSmuK0uaeXL1+O2WymTZs2GAwG/edcKYrC3LlzmT9/Pi6Xi0OHDvH+++8TEBBQYfN5F+dwOEr0xS0sLGTXrl3k5+dTWFhIYmIin3zyCbfffjvHjh2jUaNGmEwmLBZLuVdV1Wg1M127dmXkyJEAbNy4kVdffZX8/HxZMyNJkoSMhzIeynh4sbo0R45UE9q0Y2azmQYNGuDr60vfvn358ccfueuuu3jppZe48cYbueeee8jMzKRhw4b6sXFxcTz88MPEx8fj6+vL66+/TteuXT0GnFX0XNQul4u4uDgURSE8PFyf7u18r6WqKu3ateOll17i2LFjzJ07l9TUVF577TV8fHzOuabidMetX7+ehQsXMnHiRCwWCwBJSUnceuutNGjQgJCQEPbs2UNOTg533HEHt99+Oy1atNDn/T4XWs3M8OHDWbduHatWrWLu3Llcc801DBs2zCPNl1rfTEmSLm0yHhaR8VDGw4tWueaCksqnEqaNVVVVOBwOsXbtWlG/fn0xbNgwsWHDBtG1a1fRpk0bceLECeFwOPSp7mw2m8jOzhaDBg0SRqNReHt7i7Fjx4r8/Hx9n4qmTY9mt9vF2rVrRXR0tOjTp4/Iz88/5+n33LlcLmG328WsWbNEZGSkCAwMFM8884zIzc3Vz3+uU/y5XC6PH+1cmzdvFvXr1xe//vqrKCwsFHl5eWLmzJnCz89PeHl5iRYtWogPP/xQHDhwQNjtduFyufSpAM+Fdl3tXjdt2iRiY2OF2WwWDRo0ENu3bxd2u12fPu+COsO0sZJ0yZHTxlaMs5w2VsZDGQ8rJR5W4LSxNVI5p42VLRTVxBVXXMGoUaN4//33OX78OJGRkWzbto19+/bRrVs3fT8hBD/++CPz58/XazLGjBmDl5dXpS8MYzQaadWqFVFRUXTr1u2smznLojUR33zzzfj7+5OTk0OfPn3w8vI6q36ooowaGHFqwJ77VG9NmzalY8eOTJ48mYSEBPbu3cvSpUuJjo6moKCAKVOm0LlzZ8xmc4U2M2u1Mi1btmTs2LE899xzJCYm8uKLL/LVV18RHBzscS+yZkaSpEuNjIcyHsp4ePGRBYpqwGg0YrFYeOyxx4iJieHnn3/Wp75r2rQpgD7rw5EjR3jzzTdxOp0EBwfz3HPPERUVdUE+aKqqkpeXR35+Pm3btq2Q+bzdWSwWevXqhRDinM+dnZ3N1q1b6dixIxaLBVVVSUpK4quvvmL06NHk5eWRnZ1NcnIyO3fuxG6388UXXxAbG8tDDz1EmzZtMJlMdOnSpcJnBdEYDAYsFgt33303e/fu5ZtvvmH58uV88cUXPPbYY+e8iqskSVJ1J+NhERkPZTy82MgCxUXOfYl6X19fhgwZwoABA3A6nfj7++u1LOLULBbvvfceKSkpmEwm7r77bq677roL8mHTajUOHz6MqqpcdtllFT4Pc/Hzne25hRAcPXqURx99lBkzZtC6dWug6Nn+/vvvWCwWTCYTH3zwAUII+vfvz+OPP05ERIQ+r7b2vM+nb2hZ91acr68vY8aMYeXKlcTFxfHRRx/Ru3dv2rRpU2HXlSRJqi5kPPyPjIcyHl5sZLGuGjEYDJjNZvz9/QkODsZkMnl8iDdu3Mi8efMAaNy4MQ8//HCFNbOW1549e2jRogUREREVfu7zXTBGURQaNWpEq1ateOedd8jMzMRmsxEfH4+iKGRlZXHnnXfSv39/XnnlFd555x2aNWtGREQEAQEBWCwWfW7xC1HDZTAYaNCgAY8++iheXl7k5OTw9ddfY7Va5SwXkiRd0mQ8lPFQxsOLi2yhqCZKq4lw7wOZlZXFa6+9Rm5uLv7+/kyaNImoqChcLleF9hUtq98lFDUzb9iwgY4dO3rUFBVP/4XmnmaLxcLzzz/PgAEDeP755/H39+e3336jbdu23HPPPdSqVYspU6YghNBrXaqiOdV95pFBgwaxZs0a5syZwy+//ELPnj3p3bs3JpPponi+kiRJF5KMh+dOxkOpssgWimrO5XJhs9mYOXMma9euRQjBoEGD6NmzJz4+Ph4DqypKfn6+3kcV/lv4JjU1lW3btun9WB0Ox0VTc2C1WklKSuLAgQN8/vnn2O12NmzYwMaNG3n00Uf56quvaNy4sb6/l5fXBat5OR2DwUBISAgTJ06kUaNGZGdn89JLL5Geno6qqnJxH0mSpFNkPCwfGQ+lyiBbKKo5RVFIS0vj448/xuFwEBkZyYMPPujRv7Gimc3mEtscDgfz588nJSWFevXqoaoq27dvJzg4WO8/WpXS0tIYMmQIycnJ2O12XnrpJXr37q33E3U6nUyfPh2Anj17EhsbW+Vp1uZd15qmH3zwQcaOHcuhQ4eYOXMmY8aMqfSZSiRJkqoLGQ/LR8ZDqTLIFooaYPv27Rw/fhyDwUD37t2JjY2t1OsV76sqhCApKYlPPvmEpk2bUr9+fRYsWMCgQYOYM2dOpaalvHx9fWnYsCGdOnVi5syZDBs2jNq1a3PkyBGGDBnCF198waRJk5gwYQIrVqy4qGo6FEXBaDRy66230qhRI1wuF9988w3JyckeK5dKkiRd6mQ8PDMZD6XKIFsoqiH3D3dOTg7vvPMODoeD2NhYnn32WUwmU4XXJrhfs3gfSlVViYmJoVevXixatIiCggK2b99OgwYNGDhw4P/ZO+/wKor1j3/2tPRKOqF3kCZFERHsomK7cq1XRUGs2Hu71y7ei2KBn0hRsYCCoogCioqg0nuHAGkkkN5O3d35/QG7npMCAQIpzOd58hBOdqftzvs9887MO3VajmMlIiKCCRMmmCetGjG7rVYr0dHRNG/enIULF6IoCgkJCfVdXBP/5xgXF8eIESN49tlnSU9PZ/78+YwYMQKr1Wp6bg5n+OvbwySRSCR1jdTDo0fqodTDE4EcUDRSVFVFCMGSJUtYu3YtFouFq6++mo4dO54QAwp/x/beunUrQUFB5hpLp9NJeXk5mzZtIjExkY0bN/Lxxx9z9dVX07p16wbRca1WK6Ghoeb/jTKdfvrpfPLJJ2Ycb//NXw0Nq9XKddddx8cff8yOHTuYOnUql19+OUlJSVU8ZLqum4cC+X/eEOslkUgkx4PUw6ND6qHUwxOBXPLUSBFCUFZWxgcffIDb7aZ169bceuutVQyBsUGsLqYsN2/ezLhx47jhhht4/vnnycnJoaSkhJdffpnzzz+f5cuX07FjR7p06cKNN97Idddd1yAOnDE8L0aECv82slgsOBwObDZbwDUNzdAY5U1ISGDEiBHYbDY2b97M77//bl5jPGNVVSkpKSEzM5O0tDTy8/PlVLBEImmySD2sPVIPpR6eKOQMRSPmt99+Y/HixVitVu666y7atWtXxXAakQ+OdIqlv5GtKa51YmIiERER9O7dm6ioKB5++GFUVWX58uVYLBZUVeXCCy+kRYsWvPrqq0DV6eBThcrCdTzxwv2x2+3ceOONpldm3LhxDBkyxIxzrqoqv/zyCy+++KK54e70009n+vTpREdHy01rEomkSSL1sOEi9fDU4NR8u5sABw4cYOzYsfh8PpKSkrjyyiux2+2mwdI0DZ/Px7Jly3j11VfJzMw8omdG0zTcbjdQfXzthIQERo0axYcffsjYsWOZMmUKEydO5JlnnqG0tJRWrVqZMbdtNpu5Wa2heTdOBoZ4VQ4peDwYBjgmJobrrrsOIQRbtmzhhx9+QNM0VFVl2bJl3HfffURHR/Pf//6XkSNHsmXLFgoLC+ukDBKJRNLQkHrYsJF6eGogBxSNCKNT6rrOX3/9xcaNGwE466yzSElJCTBUXq+Xb7/9lhtuuIGxY8eydu1ac51pTdO+Vqv1sCN2Y6rU4XAQFBREaGgozZo1o3v37litVm666Sbi4uJOTOUbIbqu4/P5jugNO1osFgtXXHEF8fHxqKrKrFmzcLlcKIrCggULSEhI4JVXXkHTNL744gsuueQSkpOTT0khk0gkTROph40LqYdNHzmgaGQIIcwTOH0+H0FBQdx0001mJzU6SWlpKW+88QYVFRXous57773Hnj170DSt2gNgjHsdDof5/+r+bqyzNNZXKorCihUriIuLY/jw4afslG5ljHaKiYmp8mzqIu3WrVszaNAgAFauXMnq1asRQtC/f39KSkq48sorefnll7niiit47rnnzOcqkUgkTQWph40DqYenBnIPRSMkLy+PH374AUVR6Ny5M3379q3SOWNjY5k8eTI///wzBw4cYObMmUyfPp2nnnrK9Lz4h1eDY4vkoCgKZ511FmeffTYtWrSok/o1FgwR8v/XP5JEbdZn+q/t9b/+cM9CURSCgoI455xzmD17NmVlZWzatImzzz6boUOH0rlzZ7Kzs2nbti1JSUmm8RRCoKpqnXuIJBKJpL6QetgwaNR6eMy1lvgjBxSNkOXLl7N3716EEFx++eVERERUucZut9OjRw9OO+00NE2jWbNmTJs2jeLiYvbt28d9993H2WefbXba4/mC2b9//4DNVqcSxpT71q1bOXDgAIMHDz6qdbJCCHJzc1mxYgUXX3wxDofjiF4tI/JG586dCQsLo7y8nO3bt5ufd+rUiU6dOpmh8gwv3ObNm0lOTiYpKemUe04SiaRpIvWw4SD18NRGzsc1AvzXd/p8Pr7++mtUVSUqKoqLL74Yu91ebYfwDwf3r3/9i+joaD7++GN+++03pkyZUiebo/ynfZv6hjPDc6KqKpqmBYSle+ONN0hLSzuq8IRCCDweD//73/+YO3duwPPwXx/sn5b/VHHPnj3p06cPAL///jslJSUB7W8c7LNz507Gjh3LTTfdZK4zlkgkksaI1MOGgdRDSWXkDEUjQtd1cnNz+fPPPwHo06cPp512WrWGy+h4BQUFzJw5k3Xr1lFaWorP5wOgefPmdba+sykbTQiM8OHxePB6vfzyyy9ceOGFhISEIISgvLyc/v37B7RFbbxUbrebRYsWUVJSQl5eXsA0ubE+uCZxCgoK4vrrr2fJkiXs2bOHpUuXcumll5p5lpWV8eWXX/K///2PoKAgXn75ZQYMGFAnbSKRSCT1idTD+kHqoaQm5AxFI8EYoS9dupScnBxsNhvXXHPNYTcXOZ1Onn/+eZ566imys7NJSEjAbrfTrl077rrrriqnRtbFYT9NGV3X8Xg8vPfee/z6669YLBaEEHi9Xmw2GwkJCeZ1qqry559/UlZWZratMdXq/7N//37Kyso466yziIyMDPDoaJrGL7/8EnAQT+VY3gMGDCA2Nhav18uvv/5qvierVq1ixIgRvPLKKwwbNoyZM2dy+eWX43A40HXdLItEIpE0NqQe1j9NSg8P5S85PuQMRSNCCMGaNWvQdZ34+HhzfWJ1GNOFgwYN4vTTTycsLIzu3bsza9Yspk6dyo4dO0hNTTU7st1uP5lVaXQYxvP9999ny5YtvPXWWzgcDoQQbNu2jfT0dH744Qfat29PQkICmqYxcuRIpk6dSv/+/YGDoQuFENhsNtNQ/vTTTyQlJfHWW28RGxsbkOfu3bt5/vnneffdd4mNjTUNtn8Eknbt2tG/f39++OEHli5dSmFhITabjXHjxuHxeBg/fjyXXXYZNpsNr9dLWloaixYtYsiQIZx22mknvR0lEomkLpB6WH80ST2UA8jjRg4oGglCCIqKivjll19QFIWuXbuSmppa4/Sh1WolLCyM66+/3hzFWywWEhISmDt3LtOmTePss8/G4XBgs8nX4EgIIaioqOCTTz7hqquuwuPxmNPl8+bNo7CwkDfeeIPCwkKsVivx8fFERkaSkJCAoihkZmYyadIkcnJyeOONN4iKisLn8/HTTz8xbNgwoqOjAzwtxua0HTt28NJLL/HZZ59hs9nMaBVGpCar1crFF1/M/Pnz2bVrF1u3buWss85iypQpwMHNiFarFV3X+fPPPxkzZgxBQUFcfPHF9daWEolEcjxIPaxfpB5KqkP2nEbE3r17SU9PR9M0unbtisPhqNaA+odqq4xx8M66devYs2cPnTp1avJrPusCi8VCWFgYvXv3ZuLEifz888906NABgE2bNvHhhx/SsWNHNm7cyKZNm2jZsiX9+vUjJSWFzMxMbrvtNrZv305ycrK5/rOkpISKigp69+7NnDlz6NixIx07diQoKAg46AVq1qwZI0aMwOFw8NVXX1FQUMD9998P/H2wUv/+/YmMjKSkpIRffvmFIUOGEBQUhM/nQwhBWVkZc+bM4cUXX6RVq1a89957tGvXTsZIl0gkjRaph/VHk9TDFSvqrT2bCnJA0UgQQrB69WrzBEj/TWQ1GVHjPv/Y2nv37mXlypWkp6fz9NNPM2PGDNMjIw3p4XE4HLzxxhu0bt2apUuXsmXLFhISErj33nvp3Lkz0dHRDBgwAF3XadWqFc2aNcNisbB//37S0tIoLy+noKCAF154gYSEBFJSUjhw4AB33303paWlREdHM378eNNbkp+fz2mnncaFF16IruvMnTuXW265pYrnpkOHDnTq1IkVK1awZcsWVFXFbrejqippaWk8++yzLF++nM6dOzN+/Hg6duxYq5jgEolE0hCRelj/SD2UVEYOKBoRW7ZsQdd1QkJCOOuss2q1aczYEGWz2bBYLGiaRp8+fSgpKSE0NBSfz4fD4ZAd6jAYwmK1WmnRogUvvviiOcVbUVHBHXfcwe7du2nXrh2zZs1i5MiR3HnnneYhR4mJiZx99tksX76cIUOGsG3bNr7//nsKCwvRNI2goCBsNhuZmZmsXr2aQYMGYbVa2bNnD926dSMkJITS0lJ2795Ns2bNqjyr4OBgOnfuzLJly1i7di35+fkkJSUxbtw4pk2bRuvWrfnkk0/o1auXufZUiqVEImnMSD2sH5qsHkpNPG7kgKKRoKoq6enp6LpOdHQ0bdq0CdiUdDjcbjfh4eEAtG/fnkmTJvHxxx/z3HPP8ccffzBw4EBCQkLMdYjHc1JoU8XwfsDBdZh2ux1N0wgNDeWpp54iPj6eKVOmsG7dOpo3b87bb7/Nm2++ycyZM/F6vfTq1YtJkyYxcOBAfD4fpaWlZGRksHbtWtasWWPGzf7yyy9RVZUuXbrw559/csstt2CxWFBVFUVRiIyMrLZ8bdu2RVEU8vPzycrKIiEhgd27d9O3b1/+97//ERsbi91uD4jFLpFIJI0RqYf1S5PTw5PZeE0YOaBoJAghKCkpAaBLly7myPpIWCyWgE5ntVpxOBwMHjwYu93O448/Tnx8PK1ateKBBx6gc+fOBAUFmQZDGtFA/NvDarVitVoZMmQIQggef/xxzj77bHr37o3D4WDatGnk5OTg9Xpp1aqVKWLBwcFERETQvHlzzjzzTHw+H7///jsLFixg9+7dfPTRR8DBiCSDBg1CURRUVSU0NJT4+Pgqz8Rms5GYmIgQArfbzZo1a+jduzf//ve/CQkJoVmzZvI5SiSSJoPUw4aB1EOJP3JA0cAx1nwWFxeTk5MDHDzAp7brPCuvHdV1ncLCQr766isSExO55ZZbGDBgAM8++yy33XYbM2bMoEOHDnLKtxoO19ZCCOLi4hg2bBhWqxUhBCEhIbRt29aMKFJdGsbfhgwZwpAhQ1BVlcLCQgAiIiIIDQ0FICQkhPvvv5+YmJgq6ei6TteuXQkODsbpdLJv3z6EECQlJZkeNmlAJRJJY0fqYcOhSemhDBlbJ8gBRSNACEF2djY5OTlYrVY6dux41BF6jLWjv/zyC6+//jpRUVF89dVXtGrVCovFwhNPPMEtt9zCt99+y2OPPXaCatJ0URTlmMINVr7PZrORkpJS5bqYmBiuvfbaGtNJSUkhMjKS8vJyiouLj7k8EolE0pCRetjwkXp4aiJbuBEghKCgoABN03A4HHTu3Pmo0zBC58XFxfHEE09w+umnm6N7gE6dOhEeHk55eXldFv2U4VhnAOpi5sBisRAcHExISAiKopCVlRUQ+UIikUiaClIPGz5SD09N5ICiAeMftaKkpMSMaOFv+GqLMULv27evmbbX6+Xbb79l27ZtZGdnc8011zBixIg6K7/k5KAoClFRUXTt2pW9e/eyb98+3G53wAmkEolE0piReiipDVIP6w85oGgEKIpCaWkpuq4TGhpKVFTUMaXhj3Hk/bnnnkuvXr2Ijo4205VRLRoPxmZBm81GixYtUBSF/fv34/F4iIiIqO/iSSQSSZ0i9VBSE1IP6xc5oGgEKIrC6tWrURSF0NDQGk8EPdo0rVYr8fHxxMfH11FJTy6V446fygbfYrGQkpKCoij4fD7KyspkNAuJRNLkkHpYPVIP/0bqYf1wdDuZJCcEI+JE5R8DVVXNdYDt27cnJCQEOD6D4R/toKafhozRPrqu1+pAo6aM8azi4+MRQlBYWEh6eno9l0oikUiOHqmHR4/Uw7+Relh/yAFFPeJvKIUQaJqGz+fD6/WavxvXGL87HA4ZreAQPp+PAwcO4PF46rsoDYLo6GgzRrcR2UIikUgaA1IPjw+ph4FIPTz5yJ5YD1TndSktLWXjxo3k5uayZcsWPB4Pl156KWeccQYAmqah6zpxcXGyYxzCYrEQExMjY4QfIjo6GrvdjtfrldFJJBJJo0AIgajkWZd6ePRIPQxE6uHJRw4o6gmv10txcTEbN27k+++/Z9WqVWRlZREcHExycjIWi4XCwkL69u2LpmkcOHAAIQShoaHSgB7COJlTcpDWrVtjt9vxeDzyHZFIJI0GqYfHj9TDQKQennzkgKIeEEKwYsUKHnjgAXJzc2nRogWjRo2iX79+JCUlERkZaV4bFBSEy+VCVVXzXji1N1yBrH91WCyWKjNfEolE0pCRenj8nOr1rw6phycfOaCoBxRFwePx0Lt3b6677jp69uxJs2bNsFgs1Z74KY2FpLZYLBb5vkgkkkaD1EPJiULq4clFDijqicGDBzN48GAsFgu6rh/1VKUQQnYUiUQikTR6pB5KJI0fOaCoJ+x2O3DQEPp7YYzNZoZ3prKRPFaDK2n6yOldiUTSGJF6KKlrpB6efOSAop4xXnpd13G5XOzYsYOPP/4YIQQvv/wy0dHRwN8H71RUVNRvgSUNFrfbXd9FkEgkkmNG6qGkrpB6ePKRA4p6Qtd14GB0i/T0dD744AM2bNjA1q1bCQoK4oYbbgjwuhjeGemJkdTEzp07cblc1a47lkgkkoaK1ENJXSP18OQjBxT1hBCC3NxcXnzxRZYvX05ERASXXnopTzzxBF26dCE2NhabzWZOAcfHx7Nt2zYOHDhQ30WXNFCcTie6rmOz2cxTQuW6YolE0tCReiipa6QennzkgKKeUBSF4uJidF3nxRdfZNCgQURERJgG03/9n9VqJTo6GovFQmlpKV6vl+DgYNk5JAHk5OSY70xwcHA9l0YikUhqh9RDSV0j9fDkIwcU9YSiKHTu3Jnx48fjcDiw2WxomobP56O8vJyKigoSExOxWq0oikLLli3RdZ2dO3dSXl5OSEiIHHE3II5281ddPjcjb6fTCUBMTAypqal1lr5EIpGcSKQeNi2kHp6ayMVl9YSx/jM0NBSr1YrL5WLFihU89NBDXHLJJVx++eWMHTsWVVVRFIXevXsjhKCsrIyioiIZvaABIoRA0zRUVUVVVTRNM6OUOJ1O1q9fj8fjqfM84eAa5NzcXADi4uKIjY2V4iqRSBoFUg+bHlIPTz3kgKIeUBTF/DH45JNP+Mc//sGKFSsYNWoUN954I5999hkFBQUAxMfHY7PZ8Hg8lJWVSQPaADCm4f2NmMvloqSkhGXLlpGTk2Ma0g8++IAnn3wSr9d7Qsrh8XjYvHkzQgjCwsKw2+3yHZFIJA0eqYdNA6mHEjmgaACoqsqsWbPo2LEj77//Pk6nk88++4xevXoRGhoKQHR0NDabDa/Xy/bt22XnaGDouo7X6+Xrr79m1KhRzJgxA7vdjtVqpaysjK+++op27drhcDjqPG/DgObn56PrOq1atTLjukskEkljQuph40fq4amJHFA0AKxWK9dffz1er5fRo0czb948HnnkEd5//30iIiKAg9N2wcHBaJrG2rVrpQFtQOi6TmFhIa+88gqff/45l19+Oa+++ipxcXEAbNiwgbS0NC699FLTC+fvzTGeZeXPKv/9cJSWllJcXAxg5iuRSCSNDamHjRuph6cuclN2A8BisTBixAiuueYa0tLS6NSpE2FhYQHTwElJSbRq1Yri4mL27NlTzyWWVGbu3LlMnTqVH374ge7du6MoChaLBU3TWLhwIc2aNaNXr17AQQ+csYHQOAHWQAhBYWEhPp+PxMTEWq37FEKwb98+SktLURRFrheVSCSNFqmHjR+ph6cmcoainjGMpNVqJTY2ln79+hEZGYnVajUP7wGw2+2md2bXrl2Ul5cDRx9NQVJ3+Bup7du3Y7FYCA0NDTCKZWVlLFy4kPz8fKZMmUJBQQFut5uNGzfy6aefoqoq8PdzLC0t5b777uO1115D07Ral2Xr1q243W5sNhspKSl1WEuJRCI5OUg9bLxIPZTIAUUjQVEUmjdvDkB6ejppaWlH1cEkJ5YePXpQXl7Oa6+9xs8//8z+/fspKSnhzz//JCsri86dOzNp0iQuv/xyRo0axRVXXMEbb7zB2rVr8Xg8+Hw+SktLef755/nuu+8oLS2tdd6KopCVlYXFYiE8PJz+/ftjs9mkV0YikTRJpB42bKQenprIJU+NBEVR6NSpEwAul4tly5bRo0ePAK+NpP4YOnQojz76KFOnTmXOnDnExsYSHR1Nfn4+1113Hc8//zxZWVmkpaUxYcIEioqKKCoq4vbbb2fMmDEMGzaMpUuX8vXXX9O5c2cKCgrQdd1M/3CeN1VV2bZtG0II4uLiaNmyZcDa1OqQ74xEImmsSD1s2DQ6Pazb6p+yyAFFI8FisdCrVy9sNhs+n4+MjIz6LpLkEBaLhaioKJ588kmGDRvGwoUL2bRpE06nk5tuuok77riDiIgIYmJi6NKlCytWrGDNmjX4fD6ysrJ45JFHmDhxIuXl5XTu3Bm73W563wwjKoQw43cHBwdjs9mwWq0AeDwe0tLSsFgsdO/enZCQEDweD7t37+bAgQOkpKSQkJBgRkiR3hqJRNKYkXrYcJF6eOoiBxSNBEVR6NGjBwkJCWRnZ3PgwAHzc2PULTvFycdoc6vVitVqpXv37nTp0gVN08xNZg6HI8BD8sQTT3DNNdfgcrkICQlh5cqVTJ48mYyMDHJyclAUhQceeIC0tDSSkpKIiIiguLiYyZMnM3/+fB577DEuvvhiswxpaWns3bsXm81Gz549Afj888958cUXcTqdhISE0LJlS5544gnOP/98bDbZ7SUSSeNF6mHDROrhqY1syUZEYmIi/fv3Z/bs2axdu5aysjKio6MDoiJI6heLxYLdbsdms2GxWKpMsVqtVqKjo+nXr58pfn369KGoqIjt27cTHR2N0+lk3LhxTJ06lXbt2nHFFVewZs0avv/+e4QQLF++nKFDhwIHPTbLli2jpKSEiIgILr74YpxOJ5MnTyYsLIwpU6ZQUlLC999/z1133cXChQvp3LlzfTSNRCKR1BlSDxs+Ug9PLWTPawQYI3u73c6AAQNQFIWMjAx27dolo1o0MIxnZbVazd/9PWX+ofH8T4gdPnw4ffr0wefzoes6uq5TUVFBfn4+r732GosXL8ZqtaLrOsnJyabxVVWVRYsWoes6SUlJtG7dmrCwMB566CEqKip4/PHH2blzJ23atDFPLZWeO4lE0liReth4kHp4aiFnKBoZZ511FpGRkTidTn777TdOP/30+i6SpA7o2LEjs2fP5qOPPuLrr7/mwIED9OzZk7fffpuysjLsdjuzZ89m3rx5nHPOOSiKgqZpZGdns2rVKoQQXHDBBURFRWGz2bjyyisJDQ3lhRde4P3330fXdTp27EhiYiK6rpvrTSUSiaSxIvWwaXLS9VDOatUJipBD+rpD02D6dLj1VqjjUa+xCamiooIrrriCP//8k169erFgwQKio6PlKLuRI4RA0zRUVaWiogK3201wcDBhYWE4HA7z2Xs8HqKjo7Hb7Xg8Hj755BPuv/9+7HY7s2bNClgTqmkaubm5+Hw+hBCEh4cTERGBw+E4ugHFggVw2mlwaGOcRHLKM3kyjBxZ36Vo/MyZA+ecA7GxR3Wb1MOmzUnXQ4sFPv0UrrsOHI56rn0D5LffYMiQI14mZygaEYqiEBoaypVXXslff/3F1q1bWbNmDeeee67596aE/1i3qdWtOozDmxyHDJqu6wEHPYWFhREWFmYOBrxeL19//TVCCFq0aEHPnj3RdR2fz2dOISclJZlpGO0p1xhLJJLGjtTDpo3Uw8aHbMlGgtEJAIYMGUJsbCwej4fff/89ID5zU8LwUjTV+vljPF9jPanFYjFD4Rl/s9lsprdFCEF6ejrr1q0DoF+/fgQHB7N8+XLWrl1LZmYmPp8POGiINU0LWMsqkUgkjRWph00bqYeNEzlD0chQFIXOnTszbNgwpk2bxtdff81dd91FQkJCkzvUp7pNXKc6QgiEEPh8PubMmUNRURFCCHr37k1paSkvvPACmZmZWK1WUlNTiYiIIDIykqioKJ555hkSEhICxFgikUgaK1IPT23qTA/ruyJNBDlD0cgwolv06dMHm83Grl27WLhwYX0X64QgDWj1aJqG2+1mwYIF6LpObGws55xzDgkJCXz55ZcsXLiQ6dOnc8kll5CSkkJ2djaLFi0iPz+/vosukUgkdYbUQ4nUw4aDnKFoRBjr/iwWC3369CE8PJzS0lJmzpzJ8OHDCQkJQQghDU4TxVjzqes6e/bsYefOnVgsFgYOHGieKBobG0uzZs1o06YNffv2Rdd1XC4XZWVlcnZCIpE0GaQentrUqR7K2ER1gpyhaGQYHaBDhw4MHDgQgG3btpGXlydjcJ8CGNFNPvroI4qLi7Hb7dxyyy1m5CZjI5t//O+wsDASExPlelGJRNKkkHp4aiP1sGEhBxSNlODgYMaMGUNISAi5ubksXLgQTdOkEW3iCCHIysri66+/xmKxcPrppzNw4MAaI1X4b26TSCSSpojUw1MTqYcNC9mqjRSr1UqvXr3o0aMHqqoyadIkCgoKpAFt4ui6zldffUV+fj5Wq5XRo0cTGxsr19ZKJJJTFqmHpyZSDxsWckDRiAkPDzen97Zs2WLGYDZ+JI0f/+cphCAzM5Pp06fj8/lo1aoVZ5111hENp+GVkQZWIpE0VaQeNn2kHjZs5ICikWK1WrHb7QwfPpxzzz0XXdeZOnUq+fn50ng2MYx1oqqqMnnyZHbt2gXAyJEjSUlJkYZRIpGc0kg9PHWQethwkQOKRoj/6DokJITbbrsNq9XKtm3bmDVrllw72sRQFAVd1yksLGTevHlYLBaio6O54IILzLWg0ohKJJJTEamHpxZSDxsuckDRyFEUhb59+5KcnIzP5+P//u//pFemCSKE4Pvvv2fPnj1YrVYuu+wy2rdvb54UKpFIJKc6Ug9PDaQeNkzkgKIuqYdRsaIoJCYmcuONN6IoCunp6Xz33Xfouo6u69KQNmL842zn5OQwfvx4vF4viYmJPPjggwQFBQHSGyORSCQg9bApI/Ww4SMHFHVJPRgrq9VKUFAQd9xxB127dsXj8fDee++RkZGBz+dD07STXiZJ3SGEwOVy8e6777Jz506EEFx11VV06tRJGk6JRCLxQ+ph00bqYcNGDigaAJUjFxxLVIrExEQefvhhHA4HaWlp/O9//8PtdkuPTCPF3xuzZMkSPvnkE4QQdOrUiTFjxmC1WgHpjZFIJE0LqYeSykg9bBzIAUUDQdd1PB7PUU3LVg59dskllzB8+HA0TWPGjBnMnz9fdrBGjKqqHDhwgLFjx1JSUkJQUBAPPvggLVu2xG6313fxJBKJ5IQg9VBSGamHDR85oGggKIqCqqoIIY7J6NlsNqKiosyDXVwuFy+99BK7d+9u9HG468Jj1VioXL+5c+eyfPlyLBYLnTp1YujQoVIUJRJJk0bqYc1IPZR62FCRA4oGgqIohIWFmVN3R3uvxWLBZrPRvXt3brnlFgB27tzJxx9/jMvlatRrR4UQaJqGqqqoqoqu6/VdpBOKEWd7z549vPvuu/h8PkJDQ3n00UeJj483Dag0pBKJpCki9bBmpB5KPWyoyAFFXXKML7R/RzjWUxyN6x0OB/fccw/nnHMOiqIwZcoU5s2bB9DoPBn+5XU6nWzdujVgHWxjq8+R8F8nmp+fz9NPP01aWhp2u53777+foUOHmuIhjadEImmKSD2sHqmHUg8bOnJAUZccR2euq6PgbTYbLVu25N1336VTp06UlJTwzDPPsHHjRnw+X6PyZui6jqZplJaW8thjj3H55Zdzxx13sHnzZjweT6P2MtWErus4nU5eeuklvvvuOwCuuuoq7r33XkJCQo7JYyeRSCSNCamHVZF6KPWwoSMHFE2UVq1a8eijjxISEkJWVhaPP/44Bw4cQFGURrPu0lhHO2PGDGbOnMmtt97K0KFDefrpp8nIyKjv4tUZ/s9CCMGyZcuYNWsWuq4THx/P008/TWRkJBaLxTwJVCKRSCS1Q+ph40HqYeNFPo0mhL9Xx2azcdVVV3HJJZcA8McffzBp0iTKy8tRVbVReDOMmNNz5swhPj6eu+66i+uvv55//etfTJw4EZ/PV99FrDM0TcPlcrFhwwZefPFFSktLiYiIYNSoUXTo0AG73S6Np0QikdQSqYeNF6mHjRN5TnkTJigoiFdeeYV9+/axYsUK3n33XZKTk7n11ltxOBzA3+sUG+IaRCEEVqsVr9dLXFwcMTEx2O12rrrqKnr27BlwHTTMOhwJf69YRkYGd999N+vWrcNisXDzzTcHxNiGxllHiUQiqW+kHjZ8pB42buQQr4liRLlo3rw5zz77LImJiaiqyquvvsrSpUvRNK3Brx9VFIXg4GASEhKIjY01y2u32+nQoQMOh6PBT1MfCSEEXq+XzMxMRo0axfr161EUhWuuuYZnn32W8PBwrFarNJwSiURyjEg9bBxIPWzcyAFFXdLAXnJjyveMM85g/PjxREZGkpeXx3333ceaNWsa/LpRi8WC1Wrl0ksvJTMzk2+//Za//vqLjRs3kp6eTkZGBhUVFQ26DjXhv263sLCQl156ifXr1wPQrl07nn76aWJjY83r62qTokQikZyKSD1suEg9bBrIJU91SQPqyMa60JKSEkJCQjj33HN5+umnefrpp8nKyuKee+5h2rRpdOvWDZvN1mCnSS0WC8OHDyc0NJRJkyaxfPlyIiMjiYqKwuv10r9/fyZOnEh4eHh9F7VW+IfCU1WVgoICHn74YebMmQNASkoK7733Hh07dsRmk91TIpFIjhephw0TqYdNCzlD0YSofHLmqlWruP7669m2bRs2m40bbriBBx98kKCgIHbs2MHIkSPZtGkTmqYFRFVoKPjHEr/iiit4+OGHcTgcvPHGG3zzzTd88cUX3HjjjY3C0FSOXCGEoLS0lGeffZZvv/0WXddJTk7mnXfe4YwzzggQtIYmahKJRNLQkXrYcJF62DRp+G+epFYYHc6IjrBo0SKef/55unfvTps2bbDb7QQFBfHwww9TXFzMp59+ysaNGxk5ciQffPABPXv2NNcmCiEaTKf1P9goPT2dlJQULrnkEmJiYgAaVFlrgxACVVUpLy/n3//+NzNmzEAIQefOnXnjjTc477zzsNvt9V1MiUQiabRIPWwcSD1sWsgZiiaA/0i/oKCAe++9l8cff5y77rqLt99+m8jISPPvoaGhvPjii7z88svYbDY2bdrELbfcwtKlS9F1vUGeuqmqKsXFxfz1118oioLL5UJVVeDYT1I9WVT2xKiqyr59+3j88cf56KOP0DSNli1b8s4773D++edjsVgadH0kEomkISP1sOHqh9TDpo0cUDQRNE0jPT2dhx56iA0bNvD2229z++23ExUVhaqqFBUV4fV6URSFiIgIrrvuOm6++WZCQkLYu3cvt99+O5999hlOpxNd1xtUxAtFUfjxxx+ZPXs2aWlpPPjgg+Tk5DSquNuapuF0OtmxYwf33HMP06dPRwhBmzZteO+99xg4cCA2m80MiSeNqEQikRwbUg8bNlIPmyZyQFGXVHrhK6/hPJFs27aNm2++mV27dvHpp59ywQUXYLPZ2Lt3L6NGjeLSSy9l5cqV5pRoZGQkY8eONU8PzcnJ4bHHHmPq1Km4XC7TgDYEz4yiKOzbtw+fz0diYiIul4uCgoJ6LdORqG6N6M6dO7nllltYtGgRVquV1q1bM2HCBAYPHtzgPUsSiURyPEg9rBukHkoaKnJAcQIRQpw0z4bFYuG6667j008/pVOnTui6zq+//sott9yCy+UiLy+PlStXmqdLWq1WwsPDeeCBBxg7dixJSUmUl5fzwgsv8Nhjj5Geno7X6zWnUuvDiPobIZ/PR3JyMjNmzGDGjBmcdtppAQfcNFQ0TaO0tJRJkyZx0003sWXLFqxWK2eeeSYfffQRgwYNwuFwSE+MRCJp0kg9PD6kHkoaOnJAUZdUMjInszN06tSJe+65hzZt2gDw66+/cuedd9KjRw9effVVrFYrwcHBZrngoNENCQnh+uuvZ+LEiSQnJ+PxePjss8+48847Wb16NT6fr94iXhgCpGkaa9eupVWrVnTs2NE0OIYYNASq875pmkZeXh5PPvkkzzzzDGlpaVgsFq644gqmTJnC6aefHlAHaTglEklTRerh8SH1UNLQaThvYFOgmg5wsjqFceiNxWIhJyeHl156iXbt2vHUU0+xadMmysrKSE5OrhJ+zTCsQ4YMYfr06Zx99tlomsayZcu49tprmTNnDh6PJ8Azc6INqZGHpmmsW7eOcePGsXz5cuLj4wP+Xt9Tz9VhGHyfz8emTZsYM2YMH3/8MV6vl+DgYEaOHMn48eNJTU0NEAFpPCUSSVNH6uHRI/VQ0liQA4q6pIYZihPtmfFPXwjB+vXr2bZtG1dccQU///wzr732Gg8++CAXXHBBteWwWCw4HA5OP/10Jk+ezKhRowgKCqKgoIBHHnmEBx98kOXLl1fxzpwoA2Z4YgoKCrjvvvuYNm0aFRUVrF27lnfffZf9+/ebBr0+qWzIdV3H5/Nx4MAB3n77ba699lq+++47dF0nJSWFV199lZdeeolmzZoFGE5pPCUSSVNH6uGxIfVQ0mgQkrpDVYWYNk0IXa+X7HVdFz6fT2RkZIjzzjtPtGrVSvTo0UNMnTpVlJWVCa/XK/QayqbrulBVVXg8HlFeXi5mzJghOnXqJBwOh3A4HKJFixZi8uTJori4WPh8PuHz+YSu6+ZPXddDVVUxefJkERMTI+6++24RExMjLr74YnHJJZeI3r17i19//VV4PJ46z/toy2n8+Hw+UV5eLv78808xbNgwERoaKhwOhwgLCxPDhw8XGzZsEC6X64S22wlj/nwhsrLquxQSScPhww/ruwRNg2++EaKg4IQkLfXw5NKo9VDXhfjkEyE8nvorQ0Pm119rdZk82K4uaQAja4vFQnJyMl9++SUlJSWEhYURExNjHtJzpHuNad9rr72Wbt268cgjj7BkyRL279/P448/zi+//MKYMWPo1q0bISEh5n2imtmZ4yU/Px+3281nn32G2+1m27ZtOJ1OysvLefrpp/nxxx9P6qE3letofCYOxTv/6KOPmDBhAgcOHDAjh9xxxx088cQThIWFYbPZqrSL8Jtyl0gkEkndIfXwxCH1UFIZOaBoYhgGMDY2ltjY2Fp30OpOBO3cuTOffPIJ77//PjNmzCArK4tvvvmGX375hbvvvpurr76atm3b4nA4AAIiM9QF1157Lbt372bhwoWUlpYybtw4MjMzcblcdO3a1TTg9YEx3V1WVsaKFSsYP348S5YsQdM0wsLCGDRoECNGjOCiiy4iKCgIwIxwYrSPpmnmWl+JRCKR1C1SD08OUg8lAIqobpgpOTY0DaZPh1tvbRCzFQZHM+qv/DoIIXC73WRmZvLf//6XWbNmUV5ejqIoxMXFMWLECK655hq6du1qGtLjjdRglMEIMTds2DA0TWPhwoWEhoYihAg4QfNEeTOq6xrG6anl5eUsWLCAt99+m40bN+L1erFYLCQmJjJmzBhGjRpFSEiI6QnTdR2Xy8WSJUuYM2cO3bp1Y+jQobRs2dJsN2ignpkFC+C006B58/ouiUTSMJg8GUaOrO9SNH7mzIFzzoHY2JOWpdTDY6NJ66EQ8OmncN114Je/5BC//QZDhhzxMrkpuy5piF8GObqNTpU3zimKgsPhoHXr1owdO5aXX36ZDh06YLFYKCws5H//+x/Dhw9nxowZZGZmommaaWTg2Dar+ecthMDj8ZgH+Bh/NwzoiUYciq6haRqqqlJeXs7vv//O6NGjGTVqFGvXrjUjVlx99dXMnTuXe++9F4fDYd5rYGyqW7ZsGW+99RbDhw9n+fLlqKoacJ1EIpFITgxSD48dqYeSwyEHFHVJE53ssVqt2O12wsLCGDlyJN999x2PP/44HTp0wGq1kpmZyT333MOll17Kv//9b9asWUNpaSmaplUxpP4/R0LXdaxWK61atWLx4sU89thjZGVlndBoGpXLp2kabrebjIwMZs+ezU033cS1117LN998g8fjISYmhvPPP5+PP/6YiRMn0rlzZ+x2O7qus3nzZpxOJ/C3p0VRFG699VbmzJnDmWeeyeOPP86+ffsaRJQOiUQikRweqYdSDyU1UKut25LaUc9Rnk4kRqQJVVWFz+cTbrdbZGZminfffVf07t1bBAcHi6CgIBEUFCRiY2PFsGHDxOTJk8WePXuE1+sVPp/PvF/TtFpFdPD5fGLVqlUiISFBhIWFidDQUHH33XcLl8tVpxEh/KNMGGX0er2ioqJC/P7772LUqFGibdu2ZqQKu90uoqKixE033STWrl0rKioqzDpqmiZUVRXbt28Xbdu2Fc8884xwOp3C7XaL8vJy8eijj4rOnTuL5cuXi4yMDNG9e3cxderUw0YcqXdklCeJJBAZ5aluOIFRnk4kUg+bmB7KKE+HR0Z5qgca6JKnusJ/LajVaiU5OZm77rqLq6++mi+++ILPPvuMtLQ0SkpKmDdvHj///DMtW7Zk2LBhnHfeefTt25eoqChzHaWohWclMTGRAQMGsGLFCuLi4jjjjDOOuty1yUccmo71+XxkZmby008/sXDhQlauXElhYSFw0JvSrFkzzjnnHG688UbOO+88QkJCqpxQKoQgISGBXr16MWvWLG6//XZSUlIICgri4YcfZuXKlTzwwAO89tprptdK0zRsNtkdJRKJpDEg9VDqoSQQuSm7LtF1+OSTBrcp+0Qg/KZBxaGDd/Lz8/npp5946623yMzMNKc3hRDYbDb69OnDtddey+mnn0779u0JDQ0lKCio2k1rhoHVdZ2KigoOHDhAREQE0dHRWK3WakPOHa6sxr+qqprrTY21rU6nk/z8fJYsWcIvv/zC4sWLOXDgAFarFU3TCA4OJioqiosuuojRo0dz2mmnmeH5Kq9dNdpF13XWr1/PNddcw5AhQxg3bhwREREIIVizZg0PPPAAeXl5hIeHM3PmTNq1a4fD4ZCbsiWSxoDclF031MOm7BOB1MNGrodyU/bhqeWmbDmgqEsaaJSnE0F1r42u6+i6TmFhITk5Oaxfv56PPvqINWvW4HQ6TUMaFBREQkICiYmJdOrUibPOOoszzjiDli1bEhQUhNVqrVXouNp6dQBzI1lpaSl5eXns27ePRYsWsWbNGvbt20dRURFFRUW43W6EEAQHB5OamsqFF17IP/7xD1q1akV8fDxhYWFHjNphGFCPx8PUqVN59tln+ec//8lrr71GSEgINpuNvLw8MjIyiI+PJzU1FZvNVqvY6PWCHFBIJIHIAUXd0IQGFJWRevh32zR4PZQDisMjBxT1wCk0Q1EZfw+NEV9a13WcTicrV65k4cKFLFiwgN27d+Pz+QLus9lsRERE0L59e1JTU+nUqRPt2rWjW7duxMTEEBERgaIohIaGmh4QwyNiGFF/r4vb7cbtdlNRUUFRURHbt28nPT2d3bt3s3z5crKysnC5XObGL3Eo3nhwcDDR0dH069ePyy+/nPPOO4+EhIQAY15bT5Cu62iaRlZWFkOHDiU3N5f33nuPf/zjH1UOHxKHwv6drEgdR40cUEgkgcgBRd3QRAYUlZF6GEiD10M5oDg8ckBRD5xCMxSVOdxrZISYKy0tZf369WzcuJEFCxawefNmiouLzTBxxvpJYwo3ODgYh8NBSEgIQUFBJCUlmXG3W7dubRo2IzTf/v37zX8PHDiApmlUVFTgdrsD0oeDhtCI1NG8eXMuu+wyBg4cSLdu3YiOjiY8PLxGY3Y08ct9Ph/Tpk3jySefpFu3bsydO5fo6Oga75MDComkESAHFHVDEx5Q1ITUwwaoh3JAcXhqOaCQu14kdUJNHd8wiBaLhdjYWAYPHsx5553HXXfdRU5ODnv37iU3N5cVK1awc+dO9u/fT3Z2NhUVFTidTvPQIEVRSE9PBw6u01y8eLF5Oqd/HG7DcPnHGrdYLISGhhISEkL37t2Jj49n0KBBdOjQgdTUVBISEggLCwu453gPCDLutVqt/POf/+TPP//ku+++Y/r06dx9993mdG6DHEBIJBKJ5JiRelh9e0g9bNrIAUVdIjtDFQyjpihKwFrQkJAQ2rZtS+vWrdF1neHDh+P1elFVlQMHDpCRkcEvv/xCQUEBubm5aJpGXl4eHo+HkpIS3G43LpcLXdexWCxYrVYSEhIICQkhMTGRyMhI4uPjadOmDV26dCExMZFmzZoRHx9vxhGvHI3iRNU/PDyc119/naCgIBYsWMDtt99OcHBwrdbFSiQSiaRpIPVQ6mFTRg4oJCecw3lrDMMKBz0nuq7TsmVLWrRowTnnnGNGlgBQVRWPx0NpaSmqqpoRKgyio6MJCQnBbrebXiDDwPqvKzXyOlzZ6grDWxQfH89rr73G3r17q0TykEgkEsmpgdRDqYdNFTmgqEuMjVB+v0MDXRffQPBvG8Njox+KgGHRdSyaZh7nblcUQoKDiQ4Orj6xoKAqs0SGca7uGZyM52IYUIDY2Fhi/dYKy/dCIpE0dSrvJ5B2r2aq1cND4VyNwYahJ3a7nZCQkMPuQaiM1EPJiUQOKOoSRYHiYvjyy4NG9NCaQMOcyu4SSLXt4Xajz5xJVnY2sbGxREVGoqoqhYWFxMXFmcbIiEhhtVoPpqNpKJddBs2anazi15pG/9zT06FXr/ouhUQiaaQYkY4M7zjIL5CVqak93G43n376KRdddBFt2rTB5/ORn59PfHw8NpvNDMlqLHVq0BEDkc+9KSMHFHWJosDo0QifD03TcFZUEBYejtVikfsrjoDpwyopIX3vXi79v//jkX/9i9F33sn61asZPXo0cyZPJrVFC/Lz83nh+ecJDQvjoYceolmzZgStWYNISIB27Rr/F/iGhqJAaGh9l0IikTRC/A9kCw8Pb9BfdhsK/rM6+/bt4+WXX0bXde6++242bNjA6NGj+eabb2jRogWFhYU8//zzhIWF8eCDDxIXF0dQUJAcuElOOnJAUZcoCoSEoAQHY9F1wiIiDnrUjQ4tO3bNGGs6fT7WbN1KsarSd8gQRHg427KyKPB4ICICERbGkp9+4qv58xkwYAAjH3yQiRMn0jI4GCUkBMLDZTtLJBJJA8FY5iIHE0eHMSBYu3YtHo+H/v37I4Rg27ZtFBQUmBu8lyxZwqxZsxgwYACjRo1i4sSJtGjRQu5LkJx05Bt3gjCmH41QaNKI1g4hBBs3bKBZs2a0bNkSXdfJzs4mNDQUh8OBz+djxYoVxMTE8Nprr5GWlsbGjRsDlphJJBKJpOEg9fDY0HWdDceghwaynSUnEzmgOAH4G03ZoWuPEMI8fKdr165ERkai6zqFhYVmxApN08xDfOx2O4qikJWVJZc5SSQSSQNE6uGxIYRAVVX27dt31HookdQHckAhaTD4i02bNm3M/+fn5xMdHY3NZsNisRAcHIzX62XFihU4nU569+4tlzlJJBKJpMlgLGkqLS09aj2UAzdJfXDceyiEDI8qqUOsVistLBbifT6UVavQNY2o7dvpmpqKY/16LBYLPT0eOhQV8dMrr3BpXBxdystRMjIgObm+i9/gkSEcJZITh9RDSV1isVjweDykpKSYM/i5ubl07NgRh8OBxWKhU6dOVFRU8Prrr9OjRw+6dOki371aUkUP66kcTYXjmqEwDkep/FAkkmPF43CwNTWVsGbNEMHBuC0WsgoL0YOCsIaHo4SG0rVvX66+6SbsUVHc/8QTBMfEQP/+KC1a1HfxGw2yz0okdYvUQ0ldo2kaHo+H6OhohBB4PB7y8vLMMyoURaF79+7cdNNNREZG8sgjjxB86JwmOaioPUKIgLPDJMfG8c1QLF6M2LMHYbFgvekmsMmgUZLjo7SiglXl5fzzrLPQu3bFV17OdrudtsnJiG7dUBQFuxDc2LEj//B4iIiIOHhYj9Uqlz3VEsPTZZP9VSKpU4xDyGTfOgp++EGGpa4OIdCdTvqkp3N6ejrWb7+Figr6Z2fTe88e+PprFEUhVAhe7N0bT9euhOflYZs7F2SEp9ojBELXUXJz5XeI4+T4rF56Omrr1nh+/52IQwfXSCTHihCCyMhIxowZQ+fOnVEUheDgYG644QYGDRpkfglWFIWwsDDCwsIA6Yk5WhRFkV94JJITgKqqVFRUBJwALDkMQ4eC01nfpWiQCE3D5vPRz+slaeBAREwMdlWly759dB0wALVHj4N6CIQIQQiHluxIPTxqLHDQKSl18bhQxHHMz4qMDLTsbERwMLaePVHkqFhyjFReLuD/u7E5zTgBtDrkoKL2yHXeEslxMnkyjBwZ8JERlUcIYUbckUiOBamHJw+ph7Xgt99gyJAjXnZ8w7EWLbCkph78vaE8iO3bYedOOeXXCFEO/VS7lvHQ+9VA3rJGTYNvQ12HTp2gQ4f6LolEclRYrdb6LoKkiVCbMLvyC/DxI9uw7jju+Z0G9zA2boTu3SEqqr5LIjkKqnuLxBH+LmmiFBUd7MdyQCFpZDQ4PZQ0Svzfo+oWkcj3TNIQOa4BRYN8qa1WiI8HuYa10dMA3y7JycBuhx076rsUEslR0SD1UNLoke+VpLEgd6A0QWraFiMN06mLfCckEsmpiLR9ksrId+LEIAcUTRRd19F1HZ/Ph6IoOBwOub73FMcIaQkHDaoRx1wikZyiCAGaVt+lOOHouo6uaaiqimKxYLfbscp9lqc0uqYdXFYtBAJOnh5aLE12j+8pP6A42pODG9rI9nBBunRdZ8aMGRQXF3P//ffXyYDiSEHBatsOh1sXejTPpC7Wl9bn6dH+X/D98z1RZSgsLKSoqIhWrVpVeR/kKdoSySmG1wvvvw/NmgGB+9bgyMtOa1KD+rIcNZZHCISq8sfvv6NpGhdccEGdfKmrNj8hjjqISHXpKDX87XBpHi6d2mJ8ya6PQCj+X/AD9PAE5KXoOsXFxRQWFtK6VSsUmy0guNDR9oVaoaoQEwPXXFMXqTU4TvkBBRAQ6q826LqOpmlYLBYURakxdNvJQvc7A8QoixACn8/H559/TkhICKNGjap1/Y6E1+s9eMDcofQ8Hg9Wq/WozzZQVRVd17HZbAFtqGkaXq8Xu91eqzSNg9qOx8OgqirAST+fQQhBSUmJOYNkt9tP2Pvk8Xh4/PHHycvL47PPPqvyPggh0HW9QbzTEonkJKBpkJQEN9xgftQk9PDQF2KjLLqm4XW7eX3GDEJDQjjr2muJCA+vk/wanB4eeh7WQ2c2HQv1poe6Tklp6UnRQ7fLxSP33HNQDx955OAhuX55CSHQNQ3lMOF5jxqnE+bOrZu0GiCn3LcG//jOxhfxkpIS3G53td5u/+v9vclr166lvLwcj8djfnay8S/PX3/9RV5eXkCsaqvVyj333MOjjz5KcHBwjWkYP9qhKeHKdfavm6ZpfP/996xfvx4hBGVlZXz00UeUl5fXeG9N6fl8PoqKiqrE2j5w4AAbN27E4/GgaVqVvxtLd4zPdV2npKQE7dDUfeVnfLhyVFRUIIRg06ZNFBcX17r8ldvlcO16uPsyMjK4/fbbSUtLY+XKlaYhr206tS2P0U4hISF07ty5yoyIcf+WLVvIysrC4/HU2HYSiaTpIA796If6doAeKkrAj/C73rAE1eohVLn3RP/4l+evZctq1MNHDD2sIQ3jR9N11EPLYqrU+9D1R9TDau6tNj2q0cNDP1X08FD+/s/NPx1dCEpKS//Ww0rP+HDlqHA6q+phLetwuGde2/syMjOr18NaplPb8kANeuifjxBs2br1bz2soS2O6j1t4pxyAwo46EH49ttveeKJJ9i7dy8LFixg3bp1NX5ZUlWVNWvWcODAAfML7tSpU1m/fj0ffPABXq/3JNfgb4QQeL1e3nrrrQADarFYCAoK4oILLuDLL7+kvLz8sGn4fD5+/PFHVq1aZXqcvF6vaZQMjGVUDz30EHl5eeTm5rJixQoWLFjA2rVrA64vLy8nLS2NsrIyc7Ch6zoHDhzgo48+Ys2aNfz4449omoamabjdbrxeL8uXL+eDDz5g1qxZpqGGg4OZ/fv3s2XLloABhNfrZcqUKQHPz+12M3XqVCZMmIDL5QqYxTFwOp28/vrrVFRU8Nlnn7FmzRp27txJeXk5LpeL4uJi3G53QJ1UVcXn81WbXuV2KiwsZObMmfzwww+Ulpbidrur3FdQUMCaNWvIysrimWeeqfY5+Xw+MjMzycnJCWhHl8tFaWkpqqpWeU7VERwcTGJiItdeey0RERFVBhSapvHhhx+yZ88ePvzwQ/bt28eiRYuYMmUKRUVFtcpDIpE0LlRVlXpYKQ2ph1IPpR4ePafkgMLwJKSkpBAcHIzP5yM9Pd38W2VvrNfrZdasWWRlZZlphISE4Ha72bx5c7We8ZPxAwc7qt1up1WrVlWm5RRFQdd1MjMzDzvzYPy7evVqUwx0XWfp0qU4D3ksjB+r1cq///1v+vXrx4wZMygqKqJz584sXboUj8cTkOasWbO4/fbb2b59e0C55s2bxwsvvMD8+fNxOp0ohzw98+fPNz00LVu2ZMeOHabHy3gO//nPf3jhhRdIS0sz6+R2u9m9e3dAB9d1nbKyMlq1amUaLX8DaxiMbdu2UVxcTPPmzbHZbLz99tvs27cPgFdffZV169YFGBqPx8OWLVvMdvKvr/+PqqpMmzaNMWPGcMstt3DvvfeyefNm0+NiXNeyZUvi4uJQFAWn01nl78ZznD59OosXL/57Cl/XmTt3LiNGjGDp0qVVxL+6Z63rOunp6eyoFJLVP59u3bqhKAp79uyhuLiYXbt2UV5eLmcnJJImitRDqYdSDwOvNfKRenh0nJIDCoArrriCZs2aUVpaSlJSktn5dF3H7XaTk5NDXl4ebrcbVVW56KKLCA0NZcaMGfzvf//DarUSFBRkjoYrd87CwkJcLpf5/4qKCjIzM830jM9dLhf5+fmmV6KkpIRFixZRUlJijsINT4XX6zXvNZYnKYqCzWajR48e5OXlmZGdSkpKKCsrw+Fw4HA4cLlclJSU4PP5zM6gaRoej4fCwkI0TSM2Npa0tDTz759//jm5ubl88803vPzyy6Snp1NRUUGnTp148MEH+fHHH/n111+Jj4+nVatWAR4HRVG45JJLePzxx2nTpg1C/L3EplmzZowYMYKUlBScTicWiwVN01iwYAFpaWnExcXh8XiIiooK8DbY7Xbuu+8+7r77btq0aWNOEauqit1uD9jUrSgKQghKS0sD1gI7nU42btxoikNkZCQhISFYLBbCw8O5+eabyc3NRdd1/vWvf9GpU6cAA5qZmckzzzxjTgcbGEI1YcIESktLAfjHP/7BmDFj8Hq9/Pbbb1x33XUsX77cLKPH48FiseBwOEwDV1JSYqZpeMVUVaWwsJBNmzaZzz0jI4Nx48bhcrlYsGCBKXT+eL1edu/eTUZGBunp6UyfPp34+Hiys7OrGEQhBBaLhaSkJAoLC4mNjcXlctG/f3+CgoIICgqq97XREonkxCD1UOqh1EOph8fLKdsi7777Ls8//zyrV68GDka/EeLgVOcvv/zCueeey9ChQ8nOzub777/nscce4/fffyc2NpY+ffrQpk0bysvLzak2f1RV5aWXXjKnJ3VdZ968eVxxxRXs2bPHvE4IwZw5c7j//vspKyszP8/Ly2Pv3r3cc889XHzxxTz88MNMmjSpyr0ejwe32w1Aq1atKCkpQYiDU773338/b775prlZWdd1PvjgA7Zv3x5gQJcsWcKYMWMoLCykefPmZGdnmxtzQ0JC2LZtGxEREZx11lmsXLmSTz75BIDmzZvz1FNPMWXKFLp06UJSUhI7d+4MiBy0dOlSxowZw9y5c02DJoRgwIAB5OTk0KNHD4qLi/H5fNhsNk4//XR27NhB27ZtcbvdxMbGUlRUFOCFmDJlCnfccQdz5szhmWee4a233iI0NNSst4Hb7WbatGlMmDABp9Npfl5aWsqoUaO46qqr2LJlCx6Ph+LiYoKCghg/fjxvv/029913Hzt37mTatGmmoBi0adOGKVOmEBMTE/AsjPyLi4tZu3Yta9as4a+//uLdd9/Fbrfz6aefMn36dDp37gwc9OzMmDGDtLQ0fD4fUVFRtG3blqKiIjNdXdf55Zdf2LVrF0VFRezYsYP8/HyWLFnChAkTyMnJISIigvnz51fxHAEUFxdz2223MXPmTObOncsPP/xAWFgYkZGRAWLj9XopKytD0zQ6d+7Mrl27CA4OpqysjOnTp5seMBn1SSJpgggh9VDqodRDpB4eL6dklCdd11m5ciUul4uvv/6alJQUtm3bRmhoKOvWrcPr9ZKTk4PD4WD27NnMmTOHffv2sWbNGnbv3m12yD59+pCQkGB6AwwDDPDkk09it9t56623AJgzZw5lZWUBI+68vDymTp3KqFGjKC4uJjQ0lNDQUOx2O6tWraJv374UFxfj8XjYvn07/fv3N+8VQrB69WpiY2Pp2rUrYWFhpgjk5OTw22+/0bZtWx5++GFat27N4sWL2b59O2effbaZhs/n47333uMf//gHTqeTDh06sGLFCp566imaN29uTl936dKFVatWkZKSQnh4uGm8169fT2lpKT6fj2bNmlFRUYGu61itVoQ4uFEvNTWVtLQ0U2g++ugjLBYLeXl5hIWFmW1UVlbG+vXrcbvd5OfnEx0dTXJyMsHBwWbH9Xg87Nixg3bt2jFv3jwSExMZPHgwhYWFJCQkmFOrq1evZtGiRVgsFqKiogI8CVu3bqWoqIjzzz+f6OhoIiMj+eSTT1izZg3Z2dmkpqaiaRqLFy9m586dBAUFBRgOr9dLaWkpUVFR5rvk9XopKioiJSWFJ554gjfeeIN3333X9O61a9eOLl26EB8fb6ZTVFTECy+8gNfrJSwsjPj4eJKSkggLCwt4Pq+99hp79uxBVVVcLhfnnnsueXl55t/XrFnDNddcQ/v27at4ZPbs2cOWLVvYu3cvdrudoUOH0qxZM6KjowPeo+3bt5Obm8vgwYOJjo7GYrEQHBzM3Llz2bp1K7GxsURHRwd41SQSSdNAF0LqIVIPpR5KPTxeTskBhcPh4J133uHAgQOMGzeOgoICYmNjmTRpEsnJyTidTsaPH09xcTEzZszgtNNOo0+fPqxatYquXbuSn59P9+7d6dy5M4MHDyYoKAg4+DJmZGQQEhJCUlISLpeLhQsXkpKSwoQJE7Db7bRo0cIsR0REBKGhobz99ts89dRTJCcno+s6P/74I7m5ufTr148nn3yS5s2b06VLFzNShZHXggULGDp0KPv372f37t1mJKfExESeeOIJVq1ahaIo9OnTh//+97906NCBbt26AZhTw6mpqbzzzjvYbDbOOeccLr74YubPn8/27dsZM2YM06dPJz8/H6vVyl9//cWLL76IoigUFBQwZ84cLrvsMtOT0KJFC7ODKYpC9+7dWbFiBVdffbW5fnXhwoVYLBauuuoqkpKSuPDCC9m3bx9btmzh6aefZsmSJezevZu77rqL4OBg0xgbz61jx46sW7eO2267jWeffZasrCyeeuopzj//fBwOBwD79u1j9uzZnHXWWdx00004HA6zXLm5uYwdO5YLL7wQq9XKrbfeynvvvQdA9+7d0XWdPn36sHr1as455xw6duwY8O7s3buXZ599lsmTJxMcHGwKw5w5c/jXv/6FEILIyEg6derE4MGDadeuHcnJyURHRwd4QcLCwmjevDnJyclcd911NG/enDvvvJM2bdqYedntdv75z3+yZ88eLrvsMr788kv27dvHgAED8Pl89O/fn8svv5zk5GQsFkuAgRNCkJqayuDBg9m0aROnnXYaI0aMwOFwEBsbGyAqP/74I4mJiezfv5+dO3cihGDIkCGsX7+e1NRUHnroIVOgJBJJ08Jut0s9lHoo9fAQUg+PA9HU+PprIQoKavyzrutC13WhaZrQNE243W7hdruFy+US48ePF+eff75Ys2aN8Hq9wufzifLycqGqqlBVVZSXlwuv1yvcbrdQVTUgHV3XhaqqYubMmWLXrl3C7XaLvLw88cILL4iysjKhaZpQVdW8Vtd14fP5xPLly8W+ffuEz+cTLpdLrF+/Xtx8880iNzdXeDwesXHjRrF27Vrh8/nM+3RdFx6PR3z//fdi0KBB4owzzhBPPPGEyMnJEU6nUxQVFYnPP/9cTJgwQXg8HqGqqigsLBTPPvus2Lhxo/D5fELTNOHxeMTatWtFZmam8Pl8Zj2NOmmaJpxOp/B6vcLj8YhJkyaJb7/91qy30R7+1/vXz2gzt9stysvLxcKFC8V1110nioqKquRj5F25DEZaxrPzeDyitLRUFBUViU2bNomSkhLh8/mE1+sVLpdL7N+/Xzz22GNi4cKFZj3901m4cKH4/PPPzTx8Pp9wu92isLAwoA0qPyvjx3iuXq/XrONff/0l+vTpI/7v//5PzJgxQ4wcOVJs3bo14B1RVVW43W5RUVEhioqKxMcffywuu+wyUVBQEFB//zyN3/2fxdKlS8XFF18scnNzq7Sh/7tRVlYmNmzYIK644gqxYsUK8dJLL4n/+7//E06n0yyX8bNs2TJx3nnnib59+4o777xT7Ny500y7unKdcPLzhZgz58TnI5EcKx9+WN8lqBsqKoT+2WdSD6UeSj08GXpYXi7EF1+cgI58gvn111pddsrNUFSeojJG8QAXX3wxPXv2pEOHDuYhaaGhoebf/X+vnI7x/4qKCv71r3+RmpqK2+1m5MiR5hRw5ZOQrVYrvXv3Bg5OX3744Yf8/vvv3HzzzTRr1gyLxULHjh3NDULGfeLQyPi8887j9NNPp7S0lOTkZKxWK1999RULFizA5/Mxbtw4rFYrFouFsLAw7rjjDhISEswDiGw2G6eddppZtuqm7wwvjxCCa6+91jzopnLbVG4Lo8yhoaEUFRXx/vvvs2LFCu6//37CwsKqjcBRE/5/s9vtWK1WNE2jY8eOGAcp6brOggULmDVrFi1atKB///5mPf1p164dv//+u5mu5dChNcaUbU3P1WgDu91ObGxswGcJCQmoqspDDz2E3W7nX//6Fy1atKiSv67r/Pzzz3zwwQesW7eON99805zCrq7+xmeGt8V4Xi+99FKVqWv/dk9LS+ONN97gzz//pHfv3nTs2JH27dub75x/fkIIevXqxbRp03C5XKSmpuJwOALet9o+J4lE0vhQwIyRL/VQ6qHUQ6mHx4oiRBObt/nmGxg8GPxe8togxN+nBFc2dEeTRmFhIYsXL8br9dK8eXP69esXMMXon6bR9EbeTqcTXdeJiIgIOPHa/x7/zVzVlT8nJ4f169fTp08f4uPjTQPqf09tjVXl9I1/D9fZq7tP0zTzALnw8PAAw3IsHbK6uohD61hdLhehoaHYbDZzOtz/GpfLxcSJE7n33nvNqfmjqUt1qKrK+vXr2blzJ82aNePMM880RcI/b13X8Xg8rF69mtLSUgYPHmxG1DhSO4hD62GN3y0WS7Ung4tDm8p27NhBZmYmvXr1IjEx0XyvjedX3Tt4LO/FCaGgAJYuhSuvPHl5SiRHw+TJMHJkfZfi+HE6Yc4cuPHGgI+lHh6+vlIPpR4eExUVB0/Kvv76Y7u/vvjtNxgy5IiXyQHFIfyb4VhfFqOTGKHijNF+Ten651mb/Cu/9IfL399z4u/FqS692lKd0a6cTk11NIyef1n8O/LxlKU6UakuXcO4FBUVER8fXyvDVZsyGCIBmGlWFuLKYmkYwSN5pvzr5PP52LRpE7qu06tXrxoNqP+Pf5kq5+P/HPzzMd5f42/HI3bHhBxQSBo6p8CAwkDqYfVIPay+DFIPD0MTH1CcsmFjK3O4ac6jwWKxYLPZsNlsAdNkNXkx/F/O6rxBR/u51WrFZrPVqi5HO5asnGdhYSErV66scoJo5Q5s3Gt4htxuNz/99BN79uw55tMm/ctS2SAfrsPb7XZzmruuMNrd8H4d7ln7PyP/MhzpWei6zpo1a7jppptYt27dEctjeGz8p3Sra7Pq8tY0jXXr1vHjjz/i9XqP+j2RSCSNG6mHR0bqYc1lkXp4aiIHFHVIdS9qbQxybe+rPNr293JUNqT+BqyyN8D/sJ3apF9dB9J1nVdeeYU777yTgoKCgFG8f57++Rls2rTJjG3tf+3h6nu4v1dO43DtXptnc6S613R9Te1ZU/4GxtKCmu4xPisrK+OKK67gkksuOWJZqzOYla8zfjcOotJ1HU3TKCsr45FHHuGrr77C5/NV+2yPpn0kEsmph9TDwLSM66QeVp+/gdTDxoscUDQyjBe+uLi4Wm+GEAenMX/++WfzoCE4eCLm8uXLKS4uxul0VjnN1EDTNHJycswTRGtC13WaNWvGZZddBlCth8Pn8/HVV1+xb9++gE4YFhbGjTfeSLt27Q5ryDRNY9u2bfz888+HLYfX68Xj8RyxM9fWcBrpVjYcNeVfVlZm5u9/v6ZpR0wjPT2dnTt3mifDVlcHw8MyYMAAxowZQ2JiYkC4RONe4+dI5fX5fGiahs/nIzs7m7KyMlRVpaCggAULFvD+++/Tr18/hg4dGrD21h/j9FmJRCKpL6QeVi2H1EOph/WFHFA0MnRdp7y8nK+//rrKyNr4XVVVJk6cSGFhoXlfWVkZP/74IzNnzmTr1q1V7jX+7/V6mTx5chWDVHkUbrVaOffcc2nfvj2JiYnVGlBd18nNzSU0NDTg761atcLn85nrNmvKQ1EU9u3bx+bNmw/rLfrrr79YtmxZFeNxuHtq8igYhs8QmcOlAwc3oL311lusWrUqIA2Xy2UaqsN5L/bs2cPPP/+Mz+cLELzqPCxer5cZM2aYAmAYeWMdrHG6Z3X3G5+5XC5mz55NWVkZQgjeeustVq9ezezZs7n77rv58ccf6d+/P0lJSfTo0SNgo55/PTweD3/88ccxt7NEIpEcL1IPpR42Kj2Egz9NVA/lgKKRIcTB00fz8vIQQlTxHBij9xYtWhAcHIyqquamtObNm5Ofn096enq105GqqqKqKtnZ2Xi9XtOYqKpqTvX5GwRd1zlw4ECAl8O/oxjrFouKiqp4KIqKivB6vQH5+6drXGu1WgkJCTH/b/zdqBdA586dadOmTbXeCOO+yvcbv1dGVVV++uknFixYUMVD4n+vf5pXX301LVq0MMuj6zrLly/nrbfewul0HtZ4CCHYv38/mzZt4oEHHjCNaHVl1DSNHTt2mJ9t2rSJFStWsHnzZv75z38yc+ZM87rq2sIoc2FhIQcOHGDz5s1YrVZee+01Vq1axeuvv86bb77JWWedRUlJCS6X67DtuW3btoA6VzcV7P+cJBKJpC6Reij1sLHpoWjCeigHFA2I2ngOjBjaRkfzeDzmJjDjJTY2Q6WlpbF06VJ+/fVXrFYre/bsoVmzZuzfv7/KmkWn08n+/fuxWq14PB6Ki4vN8G8rV66ksLCQFStWkJubaxqviIgINm7cWKXjGP834lnv37/f7Ezbt2+noqKC6OhoM+ybf/1VVSUtLQ23240QB2NaZ2RkmHXz+Xz4fD7KyspYsWIF2dnZ5Obm8r///Y/8/HwzHf80MzIyKCwsNAWitLSUJUuWVDtFuWHDBubPn8/FF19sxks36mS0k6qq7N69m8WLF3PgwAH++OMPxo0bh9PpNOs9YMAACgoK+OCDDwIMWuXnqSgKubm5ZGRksHz5cubOnUtxcTFr1qzB5XIFGMKgoCCsVis+nw+ArKwsfvzxR9q0acNzzz3HlVdeidVqpby8nA0bNlRpX2M96B133EFRURGzZs0iLi6OvLw8SkpKcDgc2O12goODiYuLY9u2bQFlVlWV3Nxcdu/eTVBQEE6nE5fLFfBc/I2/qqrm+ymRSCRHg9RDqYdNUQ+PdeN9Y0AOKBoYxmjXmB6sjBEVoaioiPLycsaPH89ff/1lrv977733cLlcqKrKCy+8wPPPP88999xDYWEhERERtG3b1jRocLBzOJ1O3nzzTe677z4qKioICQlh79692Gw2QkJCCA8P59tvv+Wuu+5i2LBhjBkzhm+++Ybw8HA8Hg8+ny/AK5OVlWVOL6emplJcXAxATk4O119/PZMnT6ZDhw6mITDKYUxXfvrpp5SXl+NyuWjWrBkFBQVmZxw7dixLlixh0qRJXHbZZdxyyy189dVXfPLJJ/zyyy8BU6TGv19//bVp6FVVZfLkyfzzn/9k06ZNAW3r9Xr5z3/+Q//+/QkLCwtYK6koCvv37+eOO+5g48aN3H333Vx55ZX897//Zfbs2cyePZucnByEOBj+LigoiFGjRrFo0SLKysoC8vE3oK1atWLfvn2cfvrpTJ06lQsuuIBZs2Zx1VVXsWbNmoD7HA4Huq5TVFSEx+MhNjaWdevWYbfb+euvv0zx/O6777jhhhvIyMgIuN/pdLJhwwaEEHTr1g273U6bNm245557OO+885gyZQqapqEoCu3btycvL8+suyHWb731Fh9++CFCCEJCQsxY8b/88guzZs3C5/ORn59PWVkZs2fP5rnnngtYaiCRSCS1Reqh1MOmpIfPP/88RUVFR3rtGy2n3EnZDR1N09i/fz9lZWW0b98+oBMbL7eiKGRnZzN37lw2b95MmzZtEELwxRdfMG7cOBRFYf369VitVux2Ow6Hg19//RWn00lcXBxRUVEBHpmtW7eydu1ahg8fTn5+PqmpqeZhQlFRUXz++efExcXRsWNHnE4nwcHBbN26lfPPP5+2bdsGpCWEYO7cuQwdOpTw8PCA0HFZWVns37+fXbt20bdvXzM2uP+IX1EUHnvsMTweDw8//DAWi4WWLVtisVjYs2cPEyZM4LfffiMqKgqHw0F5eTnFxcU4HA7CwsKqTImqqsqdd94JwMsvv0xGRgYlJSWcdtpp5qmnBqqqcuDAAbp27Vrts8nIyGD+/PkUFxeTk5NDUlISGzduJC8vj9TUVCIjIwPaIiYmhry8PAoKCsyTRw2BVJSDJ7OGhYWxfft23nvvPZYsWcIFF1zA77//brZN5bW4zZs3Z86cOSxatMj0cE2cOJG0tDTzXSksLGT//v1kZmbSrl07896SkhIWL15MYmIiGRkZKIpCcnIyn332Gd27d8fr9ZrvV0JCApmZmQF5p6WlkZmZySWXXML27dsJCgoyyzl79myWLl3Kjh07WLJkCc2bN2fFihXEx8cHnL4rkUgktUXqodTDJqWHYWHYevSotj2bAnJA0QD58ccfycjI4JlnnsFutwPgdrvJz88nPj4eRVE47bTT+Oyzz3C73XTp0gU46P2IiYlh5syZKIpCYmIiLpeLsLAwvv/+e2677Tbi4+M5++yz0XXdjIMdGxvLvn37mDNnDsnJyfTt25e2bduaJ3rOmjWLLl26cMcdd1BYWMgll1xCaGgoAFdffXWVL4y7du1izpw5tG3blpUrV3LJJZeg6wcPn/nf//7H3r176dKlC9HR0eY9mqbx119/0bp1a1JSUtA0jezsbHr06ME///lPrFYr0dHRDBo0iG3bttGzZ0+efPJJfvjhB3bt2sVbb73FRRddZBocXdfZuHEjJSUlDBgwAF3XiYyMpGfPntxyyy2EhoaabWtgtVqJjY1l9+7d9OzZ00xHiIORI4yTXlNTU3nwwQdp3749S5cuxev1MmTIEOLj4820dF3H7XYTFhZGUFBQgIdo9+7dlJSU0LdvX3Rdp1u3bui6zvDhw9m+fTvjxo0jOjqaFi1aBAiCzWZj2LBh2O127HY7rVq1QlVVPv/8cwYNGkRMTAy6rnPttdeSnp5uGm2D2NhYiouLGT16NHFxcTzzzDN0796dc845h6ysLO68807TILZq1YrmzZsHCEJKSgqKorB27VoGDBjApZdeSlhYGJqmcfbZZ7N69Wry8/O55pprUBSFZ599luDgYGJiYo6yB0gkEslBpB5KPWwyeqjrRK1de5Q9oPEgBxQNDKvVyq233oqmaQHemC1btnDgwAGGDBmCpmmceeaZREREMH/+fHbs2EGHDh3o2bMn/fr145VXXiEsLIyIiIiAjmsYl+Tk5IBDhhITE5kxYwZJSUkEBQWZXgOn08ncuXP55z//yV133YWu6/z+++8BB8T07du3Sszvu+++mylTprBlyxauvvpq+vTpg8vloqSkhNWrV3P99deTmppqlsEwULNmzWLUqFHExsaSl5fHWWedxSOPPGIaupCQEJKSkrjttts477zzsFqt9O7d26yj/0mZmqaxevVqHA4HnTp1oqSkBJ/Px913301oaGi1od+CgoK4++67Wbp0KZdeeqmZr7HWtKCggNjYWF555RUSEhJQFIUOHTpUSceoz7x58zjjjDNM0TP48ssvGThwIE6nk+zsbHr16sVTTz1llsn/dNHK70aPHj1QFIWuXbse3OAlBBdeeKFZZ7fbze7du3G5XHTs2DHg/rCwMN5++22cTicOh8OMNvLAAw+Y75uRZ1xcXMC9iqIQERHBO++8Y3rDdF0317WWl5fz/PPPM3To0IDyGwZZIpFIjhaph1IPm5Qeer2wfn2VNmoqSLVvYFgsFhwOR8C6QiEOHuLy1VdfsXPnTvbs2UNYWBgPP/wwI0aMoLS0lNdff53ff/+d+++/n8TExGpPnvQf3RsdRVEUwsLCaNOmTUA59u7dy3/+8x/sdjuvvvqq2WEuuOACM+3KaRq0adOGl156yVzDqKoqU6ZM4YcffuDCCy+kV69eAdcb62CTk5O5+eabadasGZqm8cYbbxASEoLP52Pu3Ll8//33tGjRgsGDB1e7jMbf4FgsFjp27MiDDz7I+++/j81mY/To0YSEhASIR2Uuvvhi9u3bxx9//ME555yDxWIhLy+P8ePHs2XLFp588kni4uKqDQtoHJij6zpbt25l586dPProozgcDjMvi8VCeHg4Dz30EBEREbhcLsaNG2dGADlcnfz/7y+Ixmc7duxg7Nix5Ofn88orr1SZwrZYLAQHB5vvl3G/v+Gsrk2Max0OB7GxsQGnn86fP5+vvvqKpKQkbrnlFux2u/luHC5NiUQiORJSD6Ue1lQn//83Oj1sopqoiMPF8GqMfPMNDB4MsbH1XZJjpvIjEUJQXFzMCy+8wOrVq+nVqxdjxoyhQ4cOCHEwlvKmTZuIiIigY8eOpieh8lrOyhgdvjoMD0RYWBhhYWFH/FJYXZg8/7QqKiooKioyvT7VpVdSUsKWLVvwer2kpKTQunVrHA4HmqZRUFBAfn4+LVq0ICQkpFpj418nIQ6GE8zIyMDj8RAWFkZSUlKAUakuVKDhZSgrK6NZs2bmNWVlZXi9XuLi4gIMTmWMvAsKCrDb7ebaWwNd18nLy+Pjjz8mPz+foUOHcuaZZ+JwOGqsU3Xlra6djWUAAElJSeZ63Zqur+6ZHSmPynX1eDyUlJQQFhZW7XM5psFEQQEsXQpXXnn090okJ4PJk2HkyPouxfHjdMKcOXDjjfVdkhqReij18HBtbORTmQaph04nzJ0L119/2LQaHL/9BkOGHPEyOaBoBBhThj6fD6/XS3BwcMB0oLGuUVGUGqcHjxb/mM+VvS/+I/LalN04ZVSIgwcA2Wy2Go2XkbdRB6vVaoZdMz6rjefbPxyd0TZG/kcyfv6eMP/8jHW21XljqkvHKKN/fsZzrBwxw3h2BkbouYyMDFJTUwkKCjpsuxvviH+aR/OcqsM/lrZ/2/sbXuO5+Zf/uGck5IBC0tCRA4p6Q+qh1MNGq4cVFU16QCHDxjYSrFYrwcHBhIeHmwbN6CCGUfI3DpUNSHVensP93egQRl66fvDQntzc3BrLWFOaHo+HdevWHTFcmpGfURf/DukfHeNo62DceySPR+V6++dplMHf41PTFLd/GpXLa4TQs9ls5o9/XQ18Ph+vvvoqN954I4WFhUf0jgDmWt7q0quurWrzjrjdbubPn09aWlqVON7+7VJngwmJRCI5AlIPpR4eCamHJx85oGgEVPZCGC92dX83RuaGR6XySL0y/idQ+negyp4EVVV59tlnefvtt6vtzP55+f9diINh86699lrWr19/WKNT3Y9RRmPDlXGtkbaxYc74vbInpKY0D9fONXkf/E8ErY1Bq9w+lZ9LdXn657Vz506uu+46YmJiajXFfqS6Vj5x1KhLTadZK8rBcIyPPPJIlZB5NeUrkUgkJxKph1IPpR42TOSAohFhvKR2u53w8PAaO8nmzZvJzMw0DW1NHcTo1BUVFdX+3R+fz0doaCinnXZajcbD4/Hwww8/UFZWZl4jhMBut3PPPffQvn37Y+poRhkrC4GmaeTl5eFyufB4PEdt1I4Gl8tFdnY2Pp8Pt9sdYKxrg3EQztKlS/F4PEdsb7vdzjvvvMOtt95KcHDwEaeVa4PH46G0tNScPt62bRsTJkxgypQp5qme1ZX5zjvvpHXr1qekgZRIJA0TqYdSD48HqYd1T6MfUBgjS2PELoSgaW0K+ZvKHgjjM/820HWdL774gpUrV5qh57xeb7XpKYqCz+fjiy++CDhRs/KUn6IohISEcPbZZ3PmmWfW2JndbjfTp0+vsnbxzDPPpKKighYtWtR4r1EHI5ze0qVLycnJwefzkZWVxZdffhngEdE0jdLSUt599128Xi/vvPMO5eXlZlrH2r5G2j6fz1zrCrB9+3befvtt0tPTeeGFF3C5XOZ9qqri8/lQVdV8NpXLYEyVT5o0CafTidfrNY1WTVPT+/fvr9EDVtu6ZGRkkJeXh6ZpZGZmMn/+fPbu3csrr7zCY489RlZWFueee25AFBT/8jdv3hyXy0VKSkqdGHGJRHLiqFYPT+AXy/pE6qHUw6Oti9TDE0ujbxFj1FhcXHzYqcymgtvtZs2aNVXq6vP5WL16NW63m65duxIREYGiKKxbtw63211lNO0/nbhr1y7z8+qMJxxcj1hUVITT6ayxbEFBQWaIPv98fD6fmcfhRvUej4f//ve/DB8+nH/9618MGzaMZcuWUVFRwcaNG00D6nQ6zTpt3boVr9fL6aefjqIoR/R0HAmfz0dRURHTp09n+/btZjtERUWhqirR0dFcdNFF2Gw20+j7fD4mTpzIli1bAqaiK2OxWHA6nWiaxocffsgvv/xS7TpM41rjMKNjFQRVVVm5ciU7d+5k48aNeDwevvvuOx555BHi4+OZOHEiL730UpUTaCFwA15mZuZxt6tEIjnxSD08iNRDqYeVkXp44mn0Awpj01LlcGRNCf/1kBUVFbz77ruUl5cHeA00TePdd98lKyuL5ORkSkpKzLjXJSUlZic0PDdGhAyjzbxeL5qmsW7dOjIyMsjNzWXXrl3m1KphsDdt2lSlbIYXwuFwEBUVRX5+vplPUVERISEhptD53yfEwYgXhYWFqKqKxWLhvPPO49FHH+Xtt99m2LBhfPjhh/z666+sXr0ap9PJH3/8wcMPP8zEiRMBiI+Px+l08vPPPzNt2rQqnjmjfMXFxabHyvDm5ObmVvGeqKpqptOuXTvz88jISCoqKigvL2f69Ol888035n02m41u3brx9NNPs2fPnhq9grquU1hYSE5ODnv37uWdd96hqKiI/Px88vLyzPsMIxofH09ISIhZbqNNDc+P8bvP5zOnuP3z9fl8XHDBBXTp0oXnn3+e4uJiCgsLqaiooGXLliQkJJgRRvzX5+q6ztq1a/nkk08oKysz85NIJA2bynrYFGcnpB5KPZR62DBp9N/AFeXgabzmYSmKQlNc2Wa8xMHBweTl5ZGdnc3MmTNZsGABmqahKAopKSns3r2b5ORk1q9fj6ZphISEUFBQYKZjGLIvvviCsWPHoqoqVqvVnG7Mzs5m//79PPLII1x66aXcfvvtTJgwgUWLFhEWFsbevXurGAdjzSZAjx49qKioACA7O5vrrruO9PR0QkNDAwwoHDSiubm5PPPMM1RUVJjrS3Nychg2bBhPP/00I0eO5J133kHXdTIyMnjuuefo27cvLVq0YOPGjSiKQklJCbNnz+aTTz6hvLw8oHxCCIqKinjiiScoKChAVVU8Hg//+c9/GDNmTJXpb+MQn2uvvTYgnF9QUBBCCNauXct3333Hu+++a3qFbDYbAwcOpGvXrnz00UcBRrCyhysxMZGdO3fy8MMPM378eBRFYcSIETz33HNVvDPR0dFERkaaccuNH6/Xi8vlwu12s3fvXn788Ud++umnKl6TRYsWkZOTQ1BQEBdffDG5ubmceeaZPPXUU3z88cdkZGQAVb1kTqeTJ598kvvuu48JEyagKErAOySRSBomlfWwqa7zlnoo9VDqYcOjSZyU3VSMptHJKioqzHWaRt2Ki4v58ssvCQoKYvfu3SxatIj9+/fTtm1bvvnmGyIiIti8eTNut5trrrmG33//nZ9//pm4uDhzNG2kn52dzcKFC7niiivQdZ3o6GizDN26dePPP/9k0KBB5OfnU1hYyPr169F1nYEDB7Jv376A9hZCsGbNGnr06GGeeml05LVr1/LXX3+xcuVKmjVrVqWuqqoSHx/Pk08+SW5urmkEQkNDA6ZW+/TpQ5s2bZgwYQLp6en8+9//5owzzuDll18mPDycTz75BKfTSb9+/QLazPDGREZG8swzz1BaWsoXX3xBeXk5CxYsICYmBrfbHXDKqNfrJSEhgcjIyIAZL+Nk1N9++808xdT/7zabjdTUVBYsWGDG5zamnI241A6Hg+joaP7v//6PtLQ0Nm/eTFRUFJs3byYsLCxg3S4c9DZ6vV6+/fZb/vjjD5KSksjJyaFjx44UFxcTERHB8uXLWb58OS+99FKV9l28eLHpnSsrKyM1NZX09HTatm1repgq9x0hBJmZmezevZszzzyT1NRUc7pZIpE0fKQeSj2Ueij1sD5oEgOKpoSqqkydOpW4uDiuueYa09Ok6zq5ublomsbzzz/Pzz//jM1m45prruHXX39l69atXHLJJaxfv57Jkydz5plnsm7dOvr160dqamqVfHbu3Mn8+fPp3LkzZ555Jna7HSEOnmg5d+5cOnTowJgxY7BarZx33nnm2sGBAwcGpKPrOnPmzMFqtWK32/nzzz+54YYbEEJwxhlncPPNNxMTE0OHDh0IDQ017/F4PCxZsoSBAwfSvHlzVq5cyYcffsj555/PrbfeSnl5OYsWLeKvv/5i9OjRJCYm8vHHH3P77bczaNAgevXqRWhoKMOHD2fhwoU8+uijXHXVVX/PVB1qyw0bNtClSxdSUlLYsmULs2fPZtiwYXzzzTcoimKurTUICQnB5XKZ0+MGiqJw/fXX89NPP/Hkk09y9dVXm2tjDU+gy+UyQ9oZaXq9Xv744w8GDhxIUFAQdrud1q1bU1BQQFJSEikpKfz888/VxgW3WCz07t2buXPnkpSUxM6dO4mLi2PNmjXEx8eTnZ3NwIEDufDCCznnnHMC8lUUhQ4dOnD//fdjt9vp3r07t9xyC2VlZaxdu5ZOnTrRpk2bKvcAFBYW8tBDD3HzzTcTEhLCn3/+SVJS0rG/1BKJRHIMSD2Uemgg9bARII4DXdeFpmlC13Wh6/rxJFV3fP21EAUF9V2KY0LXdeHz+URpaakoKysTPp/PbFuPxyM8Ho9wuVzC4/GIP//8U1x44YXiySefFE6nU7jdbuH1eoXb7RYulyvgx+v1ms/J5/OJvLw8MXfuXFFUVCS8Xq/wer3C5XKJgoIC8dprr4klS5YIr9crCgoKxBdffCFUVRWapgmPxyPcbnfAs9Y0Tbz++uuiQ4cOomvXruI///mPKC4uFh6PRxQVFYnnnntO7N27t0oZMjIyxFNPPSVKS0uFx+MRy5cvF+PGjRNut1t4PB5RUVEhtm/fLkpLS80y+nw+oaqq8Pl8wuPxmJ8bf/NvL13XRUVFhZgyZYooLS0VLpdLrFq1SkydOtVsE+N6//Z3Op1izJgxYsWKFQHp+Xw+4fV6zXxVVTXbRVVVkZOTIy655BKxcOFCoaqqme7WrVvFo48+KioqKkRFRYW49957xYYNG4TH4zHro6qq8Hq9AWXXdd38vLy8XDz77LNi1apV4qmnnhLLli0zy+FfLk3TAuridrvF9u3bxebNm0VFRUVAWxn/Vn7/VFUVGzZsEOPHjxdOpzOg3U9aP8/PF2LOnBOfj0RyrHz4YZWPGqQeHomKCiE++6y+S1EtUg+lHjY5PSwvF+KLL46hN9Qzv/5aq8uOe4bCWK9Ym1MXJUfGarUSHh5e5XO73R7w/w4dOvDGG28QGxtb5VTQw2GxWIiJieGSSy4J2Hg0f/58Zs+ejRCCO+64A4vFQlRUFNdcc405lVm5DAb33HMPw4YNIzg4mISEBMrKypg8eTKrVq3itNNOIyUlJSDShbFx8NdffyUjI4Pk5GQyMjL4z3/+Y0632u12OnTocNh2qk1d//jjD+bNm0dERAQVFRW8+uqr2O32GjfwWywWRo0axccff0xKSgrJycmHfb+NqBazZs1i+PDhDBo0KCBtXdf55ZdfyMvLw+12ExoaSqtWraqkWV15jNNFAf7xj3+QmprK1VdfbXpS/Nu0OhwOx2HbsCaSkpLMuO2V36umspxCIjkRSD2sW6QeSj30/0zqYcNGEeLYw0Douk5FRQXBwcEBG3bqlW++gcGDITa2vktyTFR+HIqiVBshwdg8ZXRC49/K6zmPlJY4tK6yvLwcp9NJZGQkoaGh5tSf8AuXZtxbOY/KJ2Ua0THKysro168fkZGRAWkY9/zxxx8sXrwYq9XKpZdeSvfu3QOmTY+mjar7u6ZpbNmyhZUrV2KxWOjVqxennXZagFGori6KopCTk4PD4SAuLu6wZTEiTezfv5+EhATTOPtP8c6cOZPZs2fTtWtX7rnnnoD1prWppxHRxHgWxt/826rys/Fvo5reoerqLw6t5Z00aRLXX389sbGxJ39zZ0EBLF0KV1558vKUSI6GyZNh5MiAjxqkHh4JlwumToWLLqrvklRLZaulVPMZ+OnhIbtqtVqrBGipNq3Keghoqkp5RQXOigoio6IIPbQPoYoe+qVj3i8Emq4jDu0TMDRl3bp1lJeX069fv4PLiiwWsy7iUKSlP//8k98WL8ZqsXDpZZfRo3t3rIaNP8o2Cvi7oYe6zlY/PezZsyende+O1WJBMfSomrZQFIWc3Fwcdjtx8fGH10NNQ9U09ufmHtRDh+OgHh76u9fr5csvv2T27Nl06dIlUA+PEFBHHKwMuhCIQ5pYRQ8PpVH52fi3UU3vUHX1F4f0/eOPP+Yf115LdFSU+eyOCZcL1q+Hf/3rWFOoH377DYYMOeJlxzWgML6ACCECvkTVK418QFEbavPl8FjTqIma0j5cOsbmK/8v7/7XV47CYFx7vO9R5TL55+Pv+ahpIFKZY6m7v6EzQvcZHpaj8XAc6TnVZZ/zH8CUlZUREhJiesjkgEIi8aOaAUWD1MMjoWmwejVUijjUmKjNl8NjTaMmakr7cOkYm5P90/C/3nh3jL9V9yX/WKhcJiEECHHwy7e/Fh3hvpquO9L1/vcICNiYbT00kDhS2rXJozb3Hw1GXkIIc7O66bg9noSbN4e2bY+3eCeXWg4ojnvJk38nkJwcKn8xPxbhqs6TXdM1x8rhvrxX/ntd5Vk5DUPca5NPdZ78w5XncDMD/t6T6qZia1vP2jynusJ/4Oe/zKBRfDGSSBoAjU4PrVbo37++S3FcVJ5pOCY9PPTvcevhYe73VyEzLf9yU/WLap3ood/vQohqvwxXq4cceTVATddX0UPjMyGwUPW8glrr4aF/a3xOdahVZl66jsNwStZyKd2pynEPKFRVxel0EhUVJRv6JFJXbX286dTGe1+faw5rk191y8BqO1NS3Rd+4/7qjN6x1v9ktptcAy6RHBtSD+sHqYe1Q+rhseUl9bB2HPeAoqZNU6cah/OQHI3H6lg6yvF6Z2pKy///1e05OBx10eHrsl5HQlVVSkpKcLvd5sazo0GIg6e3lpaW4na7SUhIqPXGwCOlWx1Hu1TqaMpR034ZiURyeBqlHh44cPCnDqm8Zr2mvx2JY7E+h8v7eNKqjtqmXxdWtC7rdSQ0TTOXCht7E44WY/8I/L2h+kQ9j6NdKnU05ahuT8ZxERoKbdrU6WxKQ6FOBhSSg/gvczmZefrnXRdl8N84XVZWRmhoKI5Dm6tONtV5N05EG5eUlDB69GhSU1MZO3YsDofjqL7M67pOYWEho0aNonv37jz77LNH1TeOtIH+ZH3Bb1TLNSSSBkaj1MOFCyE1FUJC6jTZw2pRZTvjf53xt2P5ElspDXGEjb61SvOQHhp7y0IP7S1T6kMPjc3eYLbPiVCE0sJCnn76afP8DZvdflRf5nVdp7i4mCcef5y+/foxYsSIgAPzjsRhN+MLEfgFv7bvyTG8VydEDX/6CcaMObjcsInR4A+2Ox5P6/HkXOwCqQAARsNJREFU4//Zkab7/A1OTdF7VFXF5/OZUYAsFgtCCHPzq9VqNT+rbR39N9F6PJ6AcHlHu7nZf2BiDCS++eYblixZwtixY4mt403uh/uy7n9CpuHh0DStzgc1/uud8/PzSUtLo23btgGbuHW/aBKHa1NFUcjOziYtLY0LL7ywVnn7/1753THyzc/PN09V1XXdjB5zpC/+RppCiCpfcqqrg39krMr5yFkKiaRhcEL00G6Hvn3Bb2alTvWw8vWHAlXUSg+Poo4nWw+NSFJ1wXHpYR2WwSjHgR07WCYEEa1aofbpgy04GKhBD6vLX9PI2LCBZULQ6/TTEf36QVBQjWU9oh4eKluNeliLugXo4RGWndWohzVcX2uEgG3bDrvXpjFzXN/O/F+qE/ml42R5Tf2jdGiaxm+//UZJSUmt8vd6vXz++efmqZKVO4jH4+GFF14gPz/fNBC5ubncf//9bNy4kczMzGM6zl3XdVwuFy+88AJbtmxh/vz5+Hy+KhGUjoRRZ7fbzQcffMD9999Pbm4uDz/8MPHx8cfsefMXl5pEqvJGRlVV2bdvH1988QWLFy+muLiY//u//0NV1WMqQ015wsHn5nK5SElJ4b777uOhhx4iKCjIvHb79u28++67rF69+rD567pO69atGTFiBBfVIgSjqqqUlpbi8XgoLi5m4cKFZjo+n4/s7GwyMzOZMGGC+Ty/+uorPH7RWDRNw+fz4XQ6zUhSxud79+5l9erVLFmyhNLSUjPtw20aVVWVH3/8ke+///6I5ZdIJIFIPfwbqYc1pyv1sCpSD5sGJ3/O7igxQowdrUGoCf8OW7nzGh3d4/FQUlLCl19+aRrQ6q73x+128/333we84P7XWiwWKioqquTldrvJzMwkOzu7Sh1rKqt/usaGoYyMDDRNY8GCBYft6DWlZXijFyxYwJ49e3j++ed55JFH6NKlS433H6lD+ndo46yKytfruo7X6w2ou6IoFBUVsXbtWvP+P//806xXdeX3L8uRnpV/+69atYonnngCRVHYuHEjbrc74LqcnBzy8vKO+DwURSEoKIg9e/ZQUVFhHnpUU1nS09OZPn26+f8DfmuYdV1n3rx5/PDDD8TExODxeHC5XMyePZvi4mLzOp/Px6uvvsoNN9zAli1bzM8rKip48MEHefLJJ1mxYgUlJSXmM6jpHTNISkoyPUD+7+qR2lMikZx4TogeUn0fl3oo9VDqodTDo6HBL3k6ER4f/dBBMpmZmURGRhIWFsaOHTvMF3/hwoUIIYiMjAT+9qjA36da+pdHCIHdbic6OpqysjIiIiICpgbh4MamyMhI3G43qqpSVlZGWFgYYWFhKIpCRkYGffv2rba8Qgh8Pp+Zv3+bGHnHx8dTWlpKRUWFeTjbkdrAmFY0ppuFEGRmZhIdHU1QUJA55ej1es2/G58Z9bPb7Yc9oVLXdfLy8vjss88YOnQo7dq1q3Iy56RJk+jbty9nnHEGVqsVq9VKhw4dePLJJ8nOzsZqtZp7GgzPgxB/L+Xx+XxUVFQQHh6O1WpFPXQYj9VqDSibv0HweDzs2LGDFStW0K5dO/O5VVRUoOu6mfbAgQOJjIw0o7Z4PB5zGt2YCvU/OTUuLo6SkpKAPA1Pm39ZmjdvTs+ePSkrK8PhcOD1eikqKuKvv/5CURSuueYaVqxYwVtvvUVGRgZPPPEEQUFB5nsAB/vGwIEDzdNXDZxOJ0IIunXrhsPhoKCggNjYWPMZNG/evEq7KIqCqqqMHz8et9vNmWeeabal8Z4YSwgkEkn9cCL00LCrmbt3Sz2Ueij1UOrhMdPgZyj8jWddGVEhBKtWrWL06NH89ddf7N69m7feeosHHniA5557jj/++IOuXbvSsWNHc8q2oKCAsWPHUlZWVu2o1GazERoaSklJCYsXL2bLli0IcfBAlA0bNqCqKlarlS+//JLbb7+dESNG4HK5cDgcFBYWkpGRUWNZNU3j22+/5c477+Szzz5jz549FBcXmx1TURR69OjB/v37TeNwuNG0EILS0lKWL19uGsIDBw6wb98+rrjiCpxOJzfccANvvvkmRUVF5Obm8sILL/Dwww+zY8cOysvL+c9//sPSpUsD1nfWNHJftWoVr732GqNHjyYrK6tK+w0aNIgWLVqY6ei6TnZ2No899hi//fababBcLhcej4c//viDgoIC85TqmTNn8swzzyCEoLy8nMcee8wUwcoY06Lvv/8+I0eOJDg4mIsuuojg4GDi4+PZunVrgNdi/fr1PPHEE/z6668sWLCAyy+/nMcee4zdu3czceJE08BkZmaSkZFBXFwcW7duDWjr/Px8cnNzA9qpvLyc119/nffffx8hBFu2bKGgoIAVK1awceNGRo4cycsvv0yXLl2wWq2UlpbSvn17KioqAuqzfft23njjDXbs2GGmHRUVxUsvvUS3bt3o1asXhYWFqKpKeno6X375ZZXn5XK5cLvdpvAWFRWZ7/nmzZu5//77OXDggPTGSCT1zAnTw5UrpR5KPZR6KPXwuGgUMxR1jc/n46233mLZsmWcfvrpqKqKy+XiqaeeomvXrrRo0YKgoCAWLFhgdtKffvqJ8ePHc9FFF9G/f/8qBsqYwl26dCnZ2dm0bt2aTp06sXTpUkaPHs2ECRPIyMggMzOT3r17s2rVKgoLC3E4HCQlJZGWllblBTU62s6dO4mNjSUvL4+HHnqI+Ph4mjdvzuTJkxFCkJubS3BwMDabDYfDgcvlMtPQNA1VVU0vSGFhIXv37uXZZ58lODiY6dOno2kaDzzwAG63m7feeovbb7+dtLQ05s2bR2FhIeeccw7Tpk3D4/Fw9tlns2jRIiZNmkR+fj5nn322mZcxBWt4eDRNo7CwkHnz5mGz2cjKymLu3Lncd999phdAURS6dOlSJVTp999/z/z589E0jX79+rFu3ToWLVrE9u3beeeddxg0aBCPP/44BQUFvPPOO/Tt25ft27czb948vvjiCwAuvvjiKs8+Pz+fDRs2YLfbyc7O5rXXXmPevHncd999xMfHm4JpsGDBArZt28bChQtp1qwZq1atIiMjg969ewcI4+LFi9m+fTv9+/dn+/btpldHCMGsWbPo0qULiYmJ5vu3adMmtm3bhq7r/PHHH+zatYvy8nKSkpKw2+2UlpayefNm0tPTSUhIoHPnzrRo0cL0SCmKgtfr5ffffyciIoJly5bRv39/VFVl0aJFZGVlsWzZMnr37k1xcTEVFRVceOGFTJs2Da/XS8ihiC66rjNjxgwuvPBC4uLiOPPMM/nuu+/MiByLFi3iyy+/5OqrryY5Ofn4Op5EIjkuToQeapom9VDqodRDpB4eLw1+QHEisFqt3HjjjZxxxhl89dVXzJs3j9LSUvLz85k5cyYhISFYLBaSk5MJDQ1FURTatm1Lt27dqmz2KS8vJywsDICQkBD++9//EhkZyfjx4wHYu3cvZWVl3HfffXg8HoKCgti0aRN2u53rr7+es846i3bt2lFQUGCKhb8h3bx5MzfeeCMxMTEMGDCAe+65h+3bt/Paa69xzTXXoGkaLpeLxMREJk6cyM6dO83yGGmVlZURExMDwJw5cxg3bhxdu3blggsuwG63U15eTlpaGmlpaVx11VXous7gwYN58803eemll3jqqafo168f8fHxvPLKK3Tv3p0333yTDh06BLSrqqqsXr2avn374vF4eO2111i0aBGJiYk8++yzpKWlMWjQoCrT49OmTeOMM86gR48e5mfXXHMNPXv25Ntvv+XBBx9k4MCB/Oc//6F///60bduWrVu3Mnr0aIqKikyv0dKlSzn33HP5+OOPadWqVYDIGf9mZ2dz991307JlS/79739z3nnn4Xa7zSn30NBQ81pFUbjxxhs5++yzGT9+PDt37uSjjz5i9uzZvPnmmzz33HPmlKdhwJs3b05SUlJAnqqq8uSTT3LBBRcQGhrKzp072bx5M126dMHr9fLUU0/RsmVLbrnlFpo1a0ZhYSGpqam899579O3bl5iYGKKioli0aJG57MCgtLSUXbt20aNHD3O988SJE8nKymLs2LE0a9YMu93OzTffTFZWFqNHjzaNo1G23bt3ExERgdVqpVevXqxevRq3201wcDCXXnopf/75Z8CUv0QiaTpYLBZuuukmzjj/fKmHUg+lHko9PGZOyQGF3W7nyiuvRFVVzj//fDweD8nJyeYJp8aL0qlTJ7Oz9+7dmyuuuILWrVsDB1++wsJCPvvsM0aPHo3NZqNDhw489NBDbN682VyTedVVV7Fs2TIuueQSwsPDzWk6Y9NSq1atCAoKomXLltjtdrPjeb1erFYrPp+P888/n4ceeoj27dtjtVoZPHgwAwYMoLi4mLKyMioqKujbty+dOnUiMTGRiIgIs66lpaV89tln3HXXXQC0bNmSkSNHcscddxAcHIyu60RERPD888/z8ccf8/DDDxMdHU3Lli0JDQ1l3LhxpKen06JFCxwOB3v27CE5OblKRzbaY+HChfTp04c9e/Ywf/58rr/+ekaOHGkacCCgIyqKQseOHYmOjjbb2mKx0Lx5c1JSUujXrx9lZWVER0dTXFxMVFQUhYWFeL1e7HY727Zt46mnniI0NJRJkyaRmppKcHBwFU+ez+dDCEFOTg5XXHEFzzzzDNHR0QghTG9Y3759sdvtAQfStWvXjrZt29KvXz98Ph9RUVEMHTqUjRs3kp6ejhCCiooKfvrpJ9q3b0/Pnj2xWCymYbVYLNx4440IIdizZw/l5eX07NmT+++/n65du2Kz2SgpKSEkJITc3Fzi4uLwer2Eh4cTEhISIKrnn38+QUFB5mdhYWFMmTKF4uJi2rZta+4LOuuss7jtttvMw/VUVWXixIkEBQWRkpJSZd1nbm4ujz/+OO3bt2fv3r3079+f8PBwNE0jNjaW5s2b07Vr16PqYxKJpHFgtVq54vLLUYODpR5KPZR6KPXwmDkhA4qa1pY1hHj2/rH1bTYbPXr0CCivfxmDgoLIz883p/iSk5OJiYkx79+zZw+rV69m9+7dFBQUsG7dOu666y66dOlCUlIS69atY86cOcTFxXH55ZdXGdUqilLtKFfTNPbv38+vv/7KJ598wgsvvGAac4vFQlhYGGeccQZCCJYtW4bT6aRz585YrVbi4+OBv41UeXk5v/76qzmN+u2333LvvfeaU3y7d+9m165dfPjhhzz22GP069fPjAEOEBERQZcuXRDi4KavTp06mRvhKlNcXMyyZctYvXo1AAMGDGDlypWMGTOmykZC4zlYLBbOPvvsatcFK4pCaGio6SVJTExECGF6PIQQREdHc+mllxIfH0/btm1r3BCXlpbGRx99xNq1a3n77beJiYkxp6Lbt2+PfujAoOruVxQl4PRbh8NB586dSUlJYcGCBXz33XcAvPbaa8Dfm82MusTExHD//fdjtVrNgAD+bZyQkABgbkisrm2Ntqj8WVJSEgkJCezatYsvvviC33//nUcffdQMbagoCna7nY4dO1bZvAgHv0w8//zzLFy4kK+++oqWLVty5ZVXUlRUxIwZM1i7di2XX365GelCIpEcHQ1eDwHl0CZTqYdSD6UeSj08Vk7oDIXhVaipw9UX/oOK2pRr7dq1REZGcsMNN5ijZF3Xad68OQcOHOCqq64iLCyMBx98kN69e5ubvH777Tfi4+O5+eabCQsLMw1GdeUxMK4xRtJPP/20GWHA/3rjnrPOOqtKGv7ExsbSsWNHHn74YWJjY7ntttto1aqV+Uw8Hg/Lli3j3nvvZcCAAQHeCAP/vCs/S//OmJKSQpcuXRg+fDg+n4/+/fvz+OOPB3hIqru38gmaNdWlumdms9l47rnnEEJUMVz+9yUmJjJo0CBGjhxJ69atAw7NqfyOHu6dMMpgTAdfcMEF9O7dm9jYWNMA+mNEDfFvy5oOP6wsrofDvy0sFgstWrTgggsu4MYbb6Rdu3ZV8q2p/W02G6mpqQwfPpz169fTqlUrgoODsVgstGzZkh49ejBw4MCDJ8M2oD4skTQ2Gqwegnlgl9RDqYdSD6UeHiuKOAFb1Y0kjc08/iPBE84338DgwXCUJzv7b4Ay/m9EfFBVtUp4POPAlZKSEvbv3094eDhJSUnmtKn/RqajNRLw9wmNgGlQqzMMR0pTVVXcbjclJSUEBQURHh5upmexWMyy+nfGymkdycNmeEe8Xi/l5eU88cQTlJSU8Pbbb5OYmFglrODxcqRXtrp2Mtb6GobS+Lc2bXi0+R8unZryq/z+HQ2V3xkj5reRXk1pV17X6vV6zXYxNtAZ74W/p+qE9+OCAli6FK688sTmI5EcK5Mnw8iRtbq0XvWwNsycCZddVu1J2VIPpR4eb/6HS+eU00Mh4JNP4Kab4DDhhRscv/0GQ4Yc8bITWqPGtGmlupfCmAL0PynSeCEtFgsOh4P4+Hji4+MDXjYjdnRNL+7hXkAjff80arq+Ni+y1WoNmCatfG/lkfvRltf/GofDQUxMDBMmTKjS6eqSYzEyhlfpWOpWF/kf6d7jTdPfk1c5vdrkabPZqvVo+b9/DeYLkETSCJF6KPXwRCD1sOq9Ug/rhxPyhjelhq6uLkf6zHiha/MS1zb/E9Vpa1ove7Tp+wtIZe9TQ6AuDGdD51jflSMJusGRvEZH6ymTSE4FmtJ7L/WwdulLPax/GqweGvcdVakaB41ozuXkUlsvxPHcfyLuPdq06iqvhmyQGnLZ6orjraMhgj6fD6/XS2hoqLmMobCw0NzUdiQvq8/n48CBA0RGRhIcHGyGhvSfdpZIJI0LqYd1m0990pDLVlc0WD10uwnRNJrq+dqNZw5WIpEcM8a63so//ui6TmlpKV9//bX5WWFhIc888wxut7vGtCozY8YMDhw4gM/nY9asWeYJthKJRCKR1Df1poezZwfsJ2pqyAGFRHKKoGmaGafdOMXVH8Pbsn//fnPKPjIy0jzkxwjx5/P5UFW1WsNosVjo3r07wcHB+Hw+FixYgNPpPOF1k0gkEomkttSXHpaXl5/wutUXckAhkZxCvPfee7z33ns1elMsFgvl5eWmcbTZbISGhuLz+SguLqakpIStW7fy0UcfsWHDBoCAtBTlYAhBt9uNw+EwTyuVSCQSiaQhUR966D+70dSQeygkklMEIQSXX355lc1q/oZUiIMnnXo8HqxWK5qmUVRUxLhx49i8ebMZo9vpdJrG1vDWGEZXVVU2btxISkoKnTt3btIGVCKRSCSNj3rTwybsYJMDComkCVPZ69K6dWsU5e/46oY3xTCmNpuNrKwsVq5cyZIlS+jZsycbNmzA6/XSrFkzioqKEEKwe/duWrVqxfTp07nwwgv5+eef+f/27jw8qvJe4Pj3zJJ9sidAWAKEXXYLgoCKoEIvLlWrFn16r4VWa2tFUatUu7nbSy9Va2wL7bXq7b0i4oatCOKGCMiOhICsCYGQBLLPes57/5ic40wyCWESSIK/z/PMk2TmzDnvGZj3d37veZcTJ07Qu3dvsrKyeOWVV+jTpw89e/Y86+cshBBCNNbh8TAnB+3YsbN+3mfLGVnYrkNFubCdEOei0K+33+/nD3/4A36/n/nz51szL3k8Hk6ePEm3bt3weDzceOONlJeX069fP/bs2YPb7bZaZ+Li4oiPj6dHjx4cPXqUjIwMSktL6d+/P9nZ2Zw4cYLq6mqysrIYP348gwYNYsiQIXTv3r31M2/IwnaiszuNhe06vcYL2xkGVFVZ01sCwdc64fSnp0XXobo6/LyaExcHjdaqEF1fZ4iHwzZuJPuee9BkYTshRFdls9m44oorrN8hWMF++eWXlJSUMHPmTJRSTJ8+nczMTNauXcs999zDhRdeSGpqatg87Xa7Ha/Xi9PpxO/3ExcXh8PhsLo3xcXFWcfpSgt6CfGNV1EBS5ZA//7Bv6uqoF8/mD69Y8vVVkePwquvBs+lJYEA1NbCrbeenXKJDtEh8VDTcOzf3wFne3ZIQiHEN4TdbmfEiBFNnt+zZw9ffPEFycnJrF27FpfLxejRo+nevTtDhgwhJyenSVKg6zpJDS2aZsUauoqurCYqRBdhGPDOO2CugF1TAx9/DBs2BP/2++Fb3wo+35WdOBE8l9b46qtgbwdxzrIrhRUNv/oKNA1NKapWr8ZfWcmRffs4VlpK3vHj5OXl0cPhYMjJk/TYsiU8HiqFbhgkaRo0dJdSBJMTc015rWF7DaCyEs7RuCgJhRDnsNas7DlmzBheeOEF/vWvfzFx4kQeffRRunXrxrBhw6zVXkPfp5TCbg9fmqe1K+gKITqZa64Btzv8uVmzOqQonca0aR1dAnEGhEYk1cxzub168V/z5vHU+vVMnjyZX/ziF6SlpTEEwuNhyHsaL1QXKfJZz112GZyjd+0loRDiG6K5C/xBgwaxfPlyPB4PmZmZOJ3OFrspSaIgxDkkPj74EOIbJGIUU4p+Y8fy4ltvnToeNkoshCQUooNEmgsg0oVqc63q7XHMxvtsbn6C9ryAPhvHaOmYkY5jt9tJb5jEIPRuRHt+9kIIISKTeHjmjtHSMSUeti9JKESHMQwDpRQ2m63FyrM9mPsy54ZurgU+0uuh08i1B/MYzY0zOFVFfzqUUui6bnVTirSv0BU+I83FLYQQIgqnUY82iYeN3hv2VxvrZ/PdYfEuwj6bjYdtOnrkY4TFw9C1IBpt35ZjKwiPhy2UJ7RMYfHQ/F0SiyYkoRBnnfnlrK+vp7a2luzs7GYvmg3DCOu32JbjmatUmrcwI100ezwe7HY7TqezSetEcxf+oa0X5t8tlcXj8VBUVET//v1xNJo6ztxP43239fzLysqa/ZwNw8Dn8xHTaFrItn7uQgjxjbZrF6xb9/WA9wgUgFJ43W7cbjdpaWmR+9gr9XW//7bEA/OnYRDw+7E7HGCzoRrvUykCPl8wwQltjGoYdNy4BKFjEkIja0slVQ3HOFZaSs+ePYOJS+i4v5DtrDELmtampELTdU5UVJCRkYGyNx79AOg6gUAAu92OCvl3CPvMExPh2mslqWhEEgrRIZRS7Nmzh3fffZcFCxY0u8327dtJTU2lb9++QNsqUr/fz5/+9Cduu+02a87pUF6vl8cff5z77rsPm81GeXk5hmHQvXv3JoOQG5fT/Hmq8imlKCkp4aGHHuKvf/0riYmJTd6j6zpffvkllZWVTJo0qcVjt9bChQuZP38+PXr0CCuLeQfjgw8+4PLLL0fTNMrKytB1/ZTnLYQQogWlpcF1sXJzW9xMGQYFW7fyz3ff5cHbb7dmBQrbRim2b9tGamoquWY8jLZcSuHzenk+P5/bfvQjEhITm2zi9Xp55JFHuO+++0hISGh9PAwp7ynjoWFweP9+FixYwJLbbw/Gw0bnbug6O9sxHhqBAE898ADzr78+YjwM+P28v2oVl112GdjtTeOhUvDKK20qw7nq3BxqLjod88saevFtGAYulyviNubrmzdvZv/+/U1ea+7R0vGUUuzbty/sliYEW+jNVvqdO3fi8/lQSvHPf/6T9957r8k+Ih3HvF19qofZ6p+YmEhcXFyTfZu/l5SUsHPnTqusrTnPlj6PiooK3I1mcgkEAui6jmEYHDx4EK/XC8D69etZs2YNuq43e95CCCFawekMLgoY8lBOp/Wg4adht5OUnh7cPuR1FfL6ph072FdUhHI4go8I24Xtu4XjKaeTfYcPYzgc1jFpOI5ht+NTip2FhfiUQjkc/HPVKt774ANUw/ZNjuN0WuUy7PZWlVGLiUGLiSEhJYU4l6vJvnE6MRwOSsrK2FlYiGG3R/xsIp1nxM+j4VFRXY07EAg774Cmodts6HY7B4qL8RoGOJ2s37yZNZ9+im6zfX1sm61JVywhdyjEWRQIBFBK4XA4rItqj8djXTSHXuibtxzPP/98ysrK8Hq9xMTEWC0egUAg7ALZXAMhNjY2rN9jIBBA07Sw8QO6rqPrOpqmUV1dzcqVK+nZsydjx44lKyuL2tpaXC4XF110kdWCHwgEMAyD+Pj4sFaXuro63G63dRylFCkpKdZ25oW4z+ezzjstLQ2Xy2UtgGMmI4Zh4Pf70TSNESNGcPDgQWpra4O3wEPOx7zwD+0WFXqnI/T8zNac1NRU3G63lSS43W7y8/OJj4/n+9//Pm63m+rqamJjYxk7dqy1II/P5yMQCBAbG2v1o5VuUEII0TYSD7toPPT7iTeM4PotzYz//KaShEK0q5ZasSsrK/njH//I3XffTVJSEhkZGRQWFlrjFrZs2UK/fv149dVXWbFiBddeey2bN29mxYoVvPnmm4waNQqbzYZhGJSUlLBs2TL8DQsVKaWYMmUKF154YdiYiZdffplBgwYxYcIENE3D4XBQX19PXFwcfr+fBx54gIyMDNLT0ykuLiYtLY2amho8Hg/33HMPAwcO5LHHHuPxxx+npKSE/Px8EhISrHP65JNP2LRpE2632xrvceWVV/Ktb30Lh8OBYRhWV6pbbrmFvLw8kpKScDqduN1unE4n27ZtIzY2li1btvDiiy8yduxYAP785z8TExPDf/zHf6BpGrquU1xczOuvv25VugD9+vXjqquushaV03WdxYsXk52dzaxZs7Db7QwYMICKigrr7suf/vQn6urqGDRoEOvWrSMuLo4TJ06QmJjIgw8+SE1NDX//+99ZunQpn376Kc899xyJEW6LCyGEiMyKhhHiosTDLhoPP/6Y5ydORCZabkoSCtHuQgc2w9ezVyQkJDB16lRsNhubN28mKyuLnJwclFLs3r2b2bNn8+Mf/5jVq1ezdu1aq1vQyZMnKS8vDxsknZOTw89+9jOr1cOsvCC8i87kyZNJTU1l165dxMbGsmPHDrZt20ZCQgI1NTVW5ffxxx9z11134XK5WLFiBSdPnqSwsBCXy8WLL77I//zP/5CWlkZ9fX1YBXr55Zdz2WWXWa33ZvcpwGo5sdls3HzzzWRkZPDGG2+QnZ1NTEwMSilqa2u58847GTVqFEeOHGH9+vWcOHGCoUOHEggEKCwsRNd1HA4HNpuNXr16MW/evLBWEfPcQ/usXnrppcTHx1NQUEBZWRnvv/8+ZWVl7N69m6KiInbt2sWGDRvo2bMnd9xxB0lJSaxevRq73c6aNWsYMmQI77zzDs8++ywej4cTJ04QHx8vYyqEEOI0eNxu7H6/xMNzJR7W13Ny8GBiDQP7ObpAXbQkoRDtStd1Hn30UaZOncq0htVGdV2nsrISh8PB5MmTqaur4xe/+AXHjx/nBz/4ARC8VWoYBs8++yzDhg3j3nvvZdWqVVRWVnLTTTdZrTGA1bISKvRCVymFu2HGjP79+wPw1FNP8d5779GvXz8eeOABq4VDKcW0adOYO3cueXl5uFwuHn30UZKSkrjwwgspLi6moKCAN954g7q6OlJSUsIqL/O4ZgXWuCItLS0lPj6e/v3743a7eeKJJ6irq+OWW24hKSmJmpoa/H4/y5cvJzc3l1/+8pds2LCBdevWMXPmTGbPnm1NI6hpWsRuR+Zz5q3dkpIS8vLyAFi1ahVPPvkkl19+OevXr8dut+P3+wkEAjzwwAPMmDGDXr16sW7dOhYsWEB8fDxTp07l0KFDvPXWWzz//PMAZGdnt8d/DyGE+Maw4uGtt0o8PFfioVJk7drVTv9Dzi2SUIh2ZbPZuP766+nZs6f1nM/n49VXX+Waa64hOTkZh8PByJEjueWWW+jbty8Oh4Phw4dz0003MWDAAK6//noSExO58847cbvdZGRkhM3KdKppWTVN46OPPiI+Pp7JkyejaRo9e/bkqaeeYtKkSezcuZOHHnqICRMm8NOf/pT09HSrP+rUqVOZOHGiNWbA7/djs9ms44dW1I3LEfq3+fvmzZuZMmWK9fyYMWNYsGCBNUVeYmIiP/zhD6mqqmLOnDm4XC48Hg/V1dWkpaWFjV1o7txDp63dunUrGzduZO7cuQAMHjyYl156idGjR1NfX8+TTz5JWVkZjz32GJmZmVbFfOGFF7JixQpiYmJwOp3ouo7NZrMClUwhK4QQp8eKhyNHWs9JPOzi8dAw0PbsiTgT1zedJBSiXWmaxujRo8Oes9vtFBQU4Ha7mTRpEhs2bCArK4uBAwdaMy+9+eabHDlyhHvvvReXy4XT6bT6QDbe/6kopThw4AAbN27EMAx27dpFYWEhN910EwkJCQwbNow5c+YwfPhwcnJywvYZExMTth5DpOllW8tms7F//34+/PBDBg8ezP79+5k+fTp9+vQhEAjwyiuvsGbNGr766isWL15MRkaGVYbk5OSozv3EiRO8+uqr+P1+KisrOXz4MIsWLSI2NhalFLNmzSIxMZHExMSwhME8rhBCiPZhxcOsrOATSoXFw8mTJrF+wwayMjMjx8P581uOhw37bIkyjJbj4dChwXh43nnRxcNWzv5n0zT279tnxcMD+/czfdq0pvFw714WL1ly6njYimOHxcOTJ8PjoWEw69/+7fTjoc0WfEgDWxOaOtfmgly+PDjvc8PS6aJjKaXw+/1s2rSJRx55hKqqKsaOHcs999xDr169CAQCrFq1ij179nDdddeRk5OD3W5vdvXs1hxPKcWuXbt45JFHOHDgAH379uWee+5h7NixwcVqGvqYGoYRNgtGezIMA13X2b17N3/729+or68nLy+POXPmkJKSglKKQ4cOsXHjRkaNGkVeXl7YYnqny5x9o7q6mhdffJHCwkLS09O59tprGTNmDDabzbqlbW5vt9s755iIigr49FO4+uqOLokQkS1eDA2tnkI0a+NG2LAhuBAaDSs1BwLs27ePDz/6iEAgQGpqKlOnTqVbdja6YbBu3TqOHTvGpEmT6N69+9ddfKI4vLlo3sGDB1m1ejUet5uExETGfetbDB02LBgPQ6Y8dzid7boKtlUOpdANg8OHDrFh40aUUjidTi677DKSXS4Mw+BwURFfbNzI8BEjGJCX16ayKIKJVG1dHatXr8bj8aABQ4cOZcSIEWgNg9nNspnx0Nba+Kvr8IMffHOSig8/hEsuOeVmklCIMyr0Atbj8aDrepPBvaHTvZnaujJ0IBDA5/Oh6zp2u73J9HahzkRC0XitiNaueN2WhOJsHu+MkoRCdHaSUIjWUCo4vaj5p/W0wuv1Wo1a9pAWbysegvVcW2pppRQBXcfv92MYBjZNIy4uLlj3R+oy1IZjNVuGYEGs37WQny1dlLclobB+N+OgGSPb43jmZ9cZ4+eZ0MqEQro8iTMq9ILV7HYTOgd2423a87jmLWKzX+TZvHiOdH5n8vitPV5oghfpvUIIIdqJpkHoOAPzF6WIaVjYtEk8bO8yKIUGxIQMmNbO8voJWoTfz+TRIx0vkibxUMYKtokkFOKssZ3FQUydsitPJxHa5ctcXEgIIcTZI/Gwc5B42H4koRBnRUfdHWhJ6II/fr/fmgs7UqtRa/ZlrsbZ1hWlzTKYM0uY+2rLXZ3QfVZUVLB69Wr69+/PBRdc0KquUUIIIdrHGatvdR3+93/DBiu39khmtyRz3KOzIR5qELyjcRrFUEphmO9t6BrUpvEQSmGY8TB0X1F2CTPP1TAMTp48SVFxMT1zcsjMykKdqqweD3z3u5CScppHPfdJQiHOeY2HCYVW5uZiOm+//TY33HADR48epby83Fqds7V0XWf79u0MGzbMmnKvLWV1u938/ve/Z+7cuaSkpFBSUkJubm5UiY7J6/WyfPly/vznPzN06FBJJoQQ4lxiGOD3w/e+1+wmjQfNhtb+yjCoMePhNddw9NixYDwcM+a0xgvogQDbd+z4Oh5CVOMNzLK66+v5/e9/zw/nziW5LfEw5HePx8Mby5fz56VLGTFiBPO+/W0ycnNPPR3sqlVQWysJRQQyka7o0kJb3s3fQ18zDAO3243f70fXdWv2pUjbAWzcuJFNmzZFfD0QCFi3Rs2HuXhOdXU1f/jDH4KzSTRUcOaKqJEYhmENGg9dydQcUA6we/duDMOgoqKCl19+GYiuZcu8A7Ns2TLeffddHnvsMRYuXEhubq41eLvxI/Szbek8hBBCdA4KUDYbusOBiolBxcRAbCzExqJiYjCcTty6jl/T0O12DIfD2paGbZXTieFwQGwsG7dtY9OOHRH3E7DbMZzOsIfesL9qr5c/5OfjMQy0hvcZTmfYfkIfhtOJT9PCyq1iYlBOJwGbDWJi2L1/P7rDQUVNDS+/+irExAT33cw+Iz5iYjAcDvyaxrK332bFqlU8+vTTPPn739M7Lw+t4fzDymB+Lg2/G3Z7k6RMBElCIbq82tpa/u///o/a2tomrwUCAZYsWUJBQQFlZWVs2bKFiooKK4GAYP9Ss3uRy+Wirq6uyYV7IBDgxIkTuN1ufD4fBw8eZO/evRQWFvLaa6/x8ccfU1paitvtBsL7ZYYyL9LLysp44403rKTCvGg/efIkr7zyCrquk52djcPhQCnF4cOHT7mAUXOJgWEYHD58mKVLlzJ//nzGjRsXnFmkYQpdMykKTZQkmRBCiK6nTuKhxMMOIgmF6PL8fj8HDx60/g69o6DrOjU1NXzxxRd8/PHHPPDAA+zevRu/309dXR2GYaBpGvv378fr9ZKTk4PP57P2Yz7Ky8t56qmnKC0t5eTJk9x1113MmjWL2bNn8/Of/5zq6moyMjI4fvw4SikKCgpYtmyZdbchlFlJbdu2DV3X2bt3L6WlpdYdj88++wyAHj16UFRURHx8PJWVlREDRGgZzfe73W7r3HVdx+fzkZ6eztixY7n99tt5+eWXcbvd6LrOkSNHeP3119mzZw+6rnPw4EHWrl1r3TmpqKhg27ZtTSplIYQQnY/EwzMbD3ds326NNZF4GE4SCtHlpaSkcPfdd5OQkAAEKxWfz8fevXspKysjMTGRrVu3cuGFF/LQQw8xdOhQ3n77bb7//e/z6aef4vV6KS0tZdOmTSQnJ1v7gWBl5/V6eeKJJ3j55Zf54osvOHz4MDk5Odx///08/fTTrFy5kquuuoq+fftSWVmJz+fjmWee4f7776esrCysrFVVVRQXF1NfX8/evXupq6vj7bffZtOmTdTV1eF0OklMTMTv95OXl0dBQYE1Z7jP52vSHQmC4zcCgQBut5s1a9bwm9/8hsOHD6PrOsXFxdx2222Ul5dz55138r3vfY+HH36YBQsWUFFRwYIFC/jBD37A008/TVVVFXfffTe/+tWvrJai//7v/+a+++7D7XZL5SmEEJ1cssTDMxoPn3vuOXS5UxGRDMoWXZ7H42H16tVMmzaNhIQEa0akxYsXs2XLFuLi4igrK2Pu3LkcO3aMnJwcqqqq2LdvHzt37iQ3N5fy8nJ2797NCy+8QGZmpjVY2RxjkJ2dzeTJk8nPz8fhcPDVV1+RlJTE7NmziYmJASA3N5eUhoFaSUlJxMXF4ff7w8q6YcMGHn30UTIzMykrK+P222/H7/fTrVs35syZQ2ZmJps3b2bRokUUFxezd+9exo8fT1paWpOp/7xeLzabDZvNRllZGffddx979+4lISGB+fPno2kahw8fZsWKFSilSE5O5tChQ/zqV79i48aNzJkzh7KyMgYOHMiOHTuY27BQ19ChQ63Zqmpra6msrMTv9xMfH38W/jWFEEJEy+vxsOr99yUenqF4aHaNEk1JQiG6vEAgwL59+5gyZYrVmmK327n//vvxeDwkJydTW1uLx+MhJiYGv99PUlIShw8fJi0tDa1h5VDDMEhPTycnJydscbj4+Hjuu+8+vF4vO3fuxGaz0a1bN5xOJ06nE7vdjmEYXHPNNdZF94MPPohSisTExLCyTpkyhZdeegm73Y7T6eSDDz5gy5YtDBgwgNmzZ5OZmcm8efNYuXIll1xyCampqaSmpvLb3/4Wl8tlVeiGYfDaa68xbdo0MjIyMAwDp9PJP/7xD+Lj40lOTgZgyJAhXHnllQwePJgBAwYwePBgBg0axOzZs9mwYQNZWVnk5ORw4MABNE1j8ODB2Gw2KyjccccduN1uq++qzAglhBCdl1/i4RmNh69v2wYg8TACSShEl+dyufjpT3/a5MudkZFhreOQkpLSZOam7OzsJlPIRlpBWimFw+HA4XBwwQUXoOt62HR1ZstNamoq9fX1HD9+nGXLltGzZ09SU1PDyhQXF0evXr2sSlDTNIqLi9E0jauuugoIJkhz587F4XCElSX070AgwJYtW7joootwu90cO3aMcePG0bNnTzRNs/qO7tu3j9jYWG677TZSU1PDWpomT55s7XvUqFFAcLElcx5yj8fD2rVrGTx4MLGxsVJ5CiFEJ+dyufjp7NkSD89QPExMTGyyLpQIkoRCdHmNKxeIbmXQliqI0P013nfoYnbFxcU888wzZGdnc9dddzUpF3y9Qqrdbue6667jmmuuwel0hj3f2rJeddVVOJ1OkpOTyc/Px+l0WrNgPP744xw8eJCnnnqK5ORkbDZb2Dk2t1JrIBDgo48+Ij8/n7i4OBYtWhTxPIQQQnQuWlkZjmXLwtZ9sENwsbvQGGcmC83EPS3Se0L31/C69XvIInM2pdCUomz/flatWoVLKW688cZguRrFHVvIfq7Xdb4zYwbOQ4fQioqCx2que5F5PKWwBwKM2LWL/545k9jYWBwOB9/97ndxLl8OSlFRWspHH31EcXExd/3bv5G6ciX2SOUw9xty3nogQMGOHWzbto3aujpumjgRe0xMVOtqnOs0da51Blu+HC6+GNLTO7ok4iwx/ws3TghCnz/V4naR3t/ccSLtJ3RAmNvtthIEm80W8WK8rV87c8aJrVu34vP5GDJkCH369LFaVJRS1NTU4Ha7ycrKwm63N5tANP4szCnzKisrcTqduFyusCTnjLfMVFTAp5/C1Vef2eMIEa3Fi6Ghj7UQnYZSwZWcdb1ji9Hw0xwgrWkaNk1Ds9lwRGgwixQNT2eFb6UU1dXVfPnll/j9fgYNGvT1HRelUIDP58Pj8eByuazY3JpjBBpmeDLHaMTGxmJLSvo6ofkmJBYffgiXXHLKzaTZUXR5zX2hQ59v6Uvf2gqhNdvZ7XaSkpJatb9oygBf993MyMhg2rRpVgLR+M5GWloaaWlpp3UcTdOw2+3Y7XaysrKiLqMQQoizTNOgM0ye0dBgZifkboYpUhxpocGvNcfSgOTERCb26NFsPIxveJyyLI2Yd0gaXyxLPGxKEgrR4Rr302zpTkDj7SO93pbjh+7rVHcRIiUsLd0VaekY0dyx0E6zhaS5OziRynOq9wohhGh/Eg8lHnZVklCITsFsdW88n7TZ0hDa2mAuWGO2ppvva8uX3JzOzul0hj0fCARQSoWNP2hpQFZr74qEilTphr7X/BzMMpxuxdncMc0FhUIH1EH452veGpYZLYQQIkqn2RXJiocQOR6GdF8163Gzvjbf15bauqV4CF/HwFPFIq2Z31vS2nhot9kgtAyt3H/EYxL8HJVhYIsQD80Vvq14GBeHJuMKm5BPRHQoM4HQQypas6I6duwYLpeLpKSksJaC6upq6urqyMjIwO/3B/s0NlSwrWkFicTv97NhwwYmTZoUVokGAgF27tzJ8OHD0XUdh8OB0+ls88V1aEuMGQwguKaGOYVeaGVdXFxMVlaWVaHFxMS0+SLfXJ10xIgR1rR4EPwsSkpKyMnJwev1WoPcJKkQQogoGAa88ALk5LS4mYJgn3/DwAi9mNY0jh89SnJKCgnx8aiQhKKmqgqPx0N6ejp+XQ/OyGfGwxaO1WxNrhTK52PDhg1cMGFCMB42lMXw+/ly507OO+88AoEATqczOEawmfF5rWWdd+jMUpqG1+u1ZpSyyq0UR48cITMzM3jxTzDxUZrWtqQiEGDHjh2cd955YdcAut/P0WPH6NG9Oz5dx1lXh33MGNTEiRIPG5GEQnQYs+Lw+XwcPXqUwsJC9u3bx3e+8x0yMjJYtmwZ06ZNY8iQIdbALoD3338ft9tNRkYGmzdv5sEHHwwbcBx6t6O1X3jDMPjb3/7G0KFDycrKsiq2+vp6fv3rX5Ofn09+fj7XXnstY8aMaXbfkW6bRqLrOvX19Rw4cICSkhImTpxIbGwsS5Ys4YorrqBfv37WMXRd57HHHmPevHmsXr2ajIwMbrjhhjbPvOT3+1m8eDFPPvmktS/DMCgpKeGhhx7iueeeY9GiRdx6663k5uae8pyEEEI0Iy0Nbrih2X77Zuzwer0R4+HS/HymjR/PkCFDMPi6Ll75+uvBeJiWZsVDh8NhzVbUbDxsri5XCl9tLX9ZtYqB06eHxcPa6moe/t//Jf8nPwmLhzabzUpiIp0TnCIeBgIR4+HixYsjxsPf3nkn82bO/DoeXnfd1+ccDaXwud386ZNPePLKK3G5XEAwHhYXFYXHw4svJtfrDY7dkHgYRhIKcVY1voOglOLIkSMsXLiQmpoa1q9fz9q1a/n5z39OUVERFRUV6LrO0aNHKSoqYvTo0cTHx3PkyBGmTJkCBC+MzRZ0c5+6rmOz2cJuVbYkNjaW7OxsAg0V2yeffEJycjIjR44kLi4Or9fL1VdfTVxcnLXvxvN4h3bZanwruPF5FxcX88QTT1BRUcFnn33Gtddey7333suRI0c4fPgwubm5VFdXc+zYMfr37096ejq1tbWMHDkSh8Nh3S1pbV/TSH1YzTshPp8Pn89HYWEhSUlJxMfHU1NTQyAQYOrUqWHdzeQuhRBCtA+Jh0FdKR46bLbgHSckHjYmCYU463Rdp7y83Pqy9ujRg2uvvZbx48dTU1PDqlWrePjhh9m3bx9jx47lyJEj3HPPPQwaNIgePXqQnp7Ol19+SV5eHnfffTeLFi1i1qxZ1v5LS0v5y1/+Ql1dnVXJDBw4kJtuuom4uDirlWPLli1kZ2fTrVs37HY7NTU1VFdX884777B06VImT55MTk4OAwYMoKqqiqVLl7J9+3b+8Y9/kN4wLbGu62zatIlly5Zht9utCubWW29l4MCBYedcVFREZmYmCQkJdO/eneuuu46RI0eyc+dOPvvsM375y19SWlpK9+7dGTFiBA8++CClpaUsXLiQjIwMqqqqePfdd/n8889ZunSptWiP3++noKCADz74IKySHD9+POPHj7fuPgQCAY4ePUpKSorVjcxutxMIBHjvvff43e9+R15eHo888ghpaWl4PB6WLl2Kw+Hg6aefDusWJYQQou0kHnateOgsKuLpn/2M8NElAkLW8hCiPZktE40f5muffPIJX331ldUa8OKLL2IYBt26dePGG29k9OjRuFwuysvLeeuttyguLiYtLY2//OUvxMfHU19fz8aNGykpKWlScSQkJDBq1CjGjRvH+eefz/nnn8+gQYPCxmkYhsGaNWsoLCyktrYWr9eL2+2muLiY7du306dPHzweD8uWLSMlJYVjx45RUFDAjh07qKqqCjvXfv36MXPmTL797W8zY8YMLr/8cjIyMsI+B13Xeemllzh69CherxePx0N+fj66rnPRRRcxf/58unXrxtGjRykuLuajjz5i9+7dzJ07N1iJOZ3s2rWLHTt2sKNhkZ3QzzouLo7evXvTp08fcnNzyc3NJSMjo8kg99dee42tW7ei6zq6rlNZWUlRURErV65k+vTpjBo1ijVr1uByuSgrK+Ozzz7jgw8+wOv1nrH/K0IIcS5ToQ+Jh106Hq5evRqvx3Nm/qN0cXKHQpwxBQUFuN1uxowZg6ZpGIaBx+OhqqqKmTNnomka//mf/8nJkyetheB0XWffvn0cP36c+++/n9dee40TJ05QU1NDQUEBN998M3369MHtdrNlyxaSkpIYOHBg2K3H5ORkrrzySiD8FqtZkRmGQUVFBbfffjs2m40lS5awZs0aysvLefTRR3G73cTHx5OWlsaMGTMoKCjg2WefpaamhkmTJuFyuazj2e12MjIymDJlSsQZnnRdx+Px4Pf7uffeewF48sknqampwel0EhMTg2EYlJaWYrfbueOOO/joo4/417/+xcGDB1myZAnXX389OTk5LFy4kIqKCvr27UtOyOA+p9PJgAEDGDRoUNjnH3rOPp+P6upq5syZg8PhYNmyZXz++ecUFhaycOFCioqK0DSNcePGcemll1JUVMTzzz+PUoohQ4aEtTYJIYQ4DUpx8OBBTmzeLPFQ4uE5SxIKcUYopThw4ADl5eWMHj3a+uJt376dkydPWguyeTweBg8ezGWXXYbD4eDzzz/nzTff5JprruGiiy5iwIABlJeXM3DgQDIyMnA4HGiaxoMPPsiePXt4+OGHOe+886zjNjeNnfl3IBCgsLCQTZs2ceONN2Kz2ejevTtTp05l1qxZHDp0iKSkJIYOHWpVbmYLh1KKoUOHEh8fH7b/llag1jSNdevWkZCQwMSJE9F1nRMnTtC3b19+8pOfkJiYyJdffsnixYsZPXo0N9xwAyNHjuTAgQP87Gc/o1+/fsTExOD3+5k3bx4ej4fRo0fTrVu3JscJPU+zstM0jUAgwMGDB9mwYQM33ngjSgVX0U5JSeGvf/0re/fuJSEhgdGjR5OQkIDD4SAjI4P169dz9dVXc8EFFzQ5ZyGEEK1XXFzM/h07JB528Xg4ITubeLc7+gHg5zBJKMQZoWkaM2bMsNZPgOCXev369eTm5uLz+aisrMQwDK6++mqys7NRSjF+/HgmTJhgDQQeNmxYxHUfzFu35rFas/6D2ULx+eefc/jwYWpqavB4POzdu5frr7+evn37kpubG1ZeTdOIiYlh3LhxTebFbs0FtmEY/POf/+Tyyy/H6/VSVVVFYmIiN998s3XOvXv35oknnrBW2L7ggguYMGFC2H5iY2O54oorIg5wa005du/ebd3K9vl8lJSUMHv2bAYMGMDgwYPDzlfTNHr16kXPnj2t95ufiSQVQghxmjSNSZMmMfHmmyUedvV4uH8/2pEjYWtgiCBJKMQZ07ilQtM0evTowa9//Wv+/ve/c/LkSb7zne+QlpZmfXHj4uJate9IX+TWfLmVUowZM4b8/HzWrFmD3W5n+vTp9O/fP2J5G+83mgpk4MCB3H///eTl5VFbW8sNN9xAenq6dbysrKxW76u5lqZI24VW9qmpqfz2t79l9erV1NbWMmHCBHr37h2xNedUrVpCCCFOj1ZVhT2kr7/NMMgtL+eVRYvYPHQoJUePMm7cONL370crLkYDWhcNI68pccraWikIBPgW8M5f/8pD776L1+Nh+PDh9L/0UmybN4e1wmuNfrbqGI3LpOuM8PlYcscdrB08mNLSUiZNmkTGwYPYjhwBoPXRsOnxmyuPxtdrctgCAbIOHeKl//ovjq1YwYmKCnr16kWfadOwVVdb0982Pl9r3yUlkJws8TACTbV2nq2uYvlyuPhiaJh1QHQOZmtIIBCguLgYj8dDQkICPXr0sGYPOtNfULMMhmFQVlZGVVUVLpeLzMzMsBmM2rMcSil8Ph+HDx/G7/fjcrno3r27tTr12TpnXdc5duwYNTU1xMXFkZOTQ0xMzGm16pw1FRXw6adw9dUdXRIhIlu8GObO7ehSiK5CKdi3D+rrg382PGfGRJPdbg82NGltW6StVUUKKYM5QNuc6cgWMlV4e5bDjL+hA8LNblsdcc7m5W/o5w6nOGeloG9fSEk5w6XtRD78EC655JSbyR0KcdZomobT6aRfv34RXztbZbDb7XTv3p3u3buflTLExMQwYMCAM7Lv1tA0DYfDQa9evSK+JoQQ4gzSNAiNAebdY4g4/ehZqZdDyhDpQvCMlEEpbLQwveiZPu+OOOdvEEkoxFnRGb6oHVGGjj7vjj6+EEKIcJ2hXpZ4KNqbrEMhhBBCCCGEiNq5d4fCZoO33oL4+I4uiRAiGvX1kJnZ0aUQQgghRCudewnFt78NsqqvEF1bbGxHl0AIIYQQrXTuJRROZ/AhhBBCCCGEOONkDIUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiapJQCCGEEEIIIaImCYUQQgghhBAiao6OLoBoPaVUk+c0TeuAkgghhBAdR+KhEJ2L3KHoYgzDwO/3R6xMhRBCiG8KiYdCdB6SUHQxmqZJK4wQQohvPImHQnQe0uWpi9E0DYdD/tmEEEJ8s0k8FKLzkG9iFyItMUIIIYTEQyE6G+nyJIQQQgghhIia3KEQnVZzA+0at0y1djshhBCiK5J4KDo7SShEp6WUwjAMlFLW4DubLfJNNV3XrYpU0zTsdvvZLKoQQggRvePH4eOPoZkxIUqp4MMwrHioRYqHZtwEzBTC1lw8dLng0ktBkg3RDiShEB3iVNP8aZqGUgpd11myZAkzZ86kd+/eLb733XffpWfPnowZM6ZVxzCPI4QQQnQUpRQcOQKZmTByZOgLoGnBxMAwCAQCwXg4Ywa9e/fG7nCgQraz3mMYrHjnHXr26hWMhzbb19tBcFulYNkymDrVOpzEQ9EWklCIDmMYBtXV1Xi9Xmw2G7qu43A4SE9Pt+4wKKX47LPPqK6uZt68edjtdpRS+P1+bDYbDofDarlZsWIFPXr0YOTIkdhsNms7j8eD1+u1jmm320lPT2/2bocQQghxNimlqFYKj88XMR4qXUf5/XxWUEC13R6Mh7GxEeOhoeusWLcuGA8vvhib09k0HipFjNuNv7yc9IwMiYeizSShEB1GKcULL7zA+++/DwRbR/r27cszzzxDYmKiNSXgrbfeyiOPPMKPfvQjYmNj0XWdffv24fP5GD58OADl5eXs3r2bgoICfvzjH5OVlWVVkK+88gpLly4FwGaz0aNHD55//nlcLlfHnLgQQggRwoyHK8vKgDMfDzVgRmkpOzZu5I8SD0U70JQsMSk6gHlXob6+nkAgYHVx0jQNl8tl3WEAqKqq4t///d/54x//SM+ePdF1nTfeeINVq1bx3HPPoes6+fn5lJaWcvToUc4//3xuv/12q7XG4/Hg8/nCjp+cnCyLIgkhorN4Mcyd29GlEOcIpRRs3Up9XR3+4cPPTjxUCuc//oH/e98jOSVF4qFo3ocfwiWXnHIzuUMhOoymaSQmJrb4ulKKxMREhg8fbnVbUkpx8cUXM3DgQABqa2tZuXIlv/vd7zh+/DiLFi3ihz/8IQ6HA03TiI+PJz4+3nqvWWlK5SmEEKJT0DQSEhIgJaWZl9s5HioF8fHB40kyIdqBJBSiw7VUkZmtJtOnTyctLQ3A6leanp4OQGxsLA888AB5eXn07duX3/zmN9YYjNB9SzIhhBCi09q/H83pbPZlDbDpOpf16EHakSNQX49DKdINg3RNg507iXW7eXDWLPLcbvomJPDb734XR0EBOByERj2lFNTUBPcr8VC0A+nyJDo9XdfRdR2bzRZxOlhrOr2GhEEpFTGhEEKIdiFdnkR7q6mB3btPuZlhGMF4aLc3HUjdcDlnxkNzNidz2tgm0dDlgsGDZdpY0TLp8iTOFTabLax/p/kzdN2JSImDJBNCCCG6BJcLxo075WaaUtgj3G0PbRvWaJo8SDwUZ5okFKJLiNRVqXF3JiGEEOJcJ/FQdEaSUIhOrzUtK9L6IoQQ4lwn8VB0VrKSiRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqklAIIYQQQgghoiYJhRBCCCGEECJqjo4ugBBCCNGl7NkDH37Y0aUQQogzb+tWuOSSU26mKaXUGS+MEEIIIYQQ4pwkXZ6EEEIIIYQQUZOEQgghhBBCCBE1SSiEEEIIIYQQUZOEQgghhBBCCBE1SSiEEEIIIYQQUZOEQgghhBBCCBE1SSiEEEIIIYQQUZOEQgghhBBCCBE1SSiEEEIIIYQQUft/9C1V/juQjUAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-7.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-6.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/all_20_samples-images-2.jpg: 640x480 1 Page-footer, 2 Pictures, 19 Texts, 13.5ms\n",
+      "Speed: 2.2ms preprocess, 13.5ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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73rNXvQz25hnsDaeddhpz587lU5/6FGvWrOHqq68e1+PvCq01QgjuueeeEb8zxjoWxDI27NdKjfPoo4/S2dnJHXfcwTnnnJMsX7NmTcV2s2bNAkKPPR58DOB5HmvWrOH444/f5XnGalDb29vJZrOsWLFi2LrXX38dKeUuB4CPxFivcV+4+uqrue6662hububd7373iNvsybXNmjUraX0oZ+i+8UC1iRMnjjoJkMVisdQyCxcuBMJuLTDYCt3V1VWx3d5Ezz/0oQ/xr//6r/zud7/jnnvuob29vaLrS6x9K1asqNC+eFm8fiRmzZqF1ppVq1ZVzAO1detWurq6drnvWLnttts4//zz+elPf1qxvKuri7a2toplxxxzDCeeeCI33XQT06dPZ/369cMmDJw1a9aoGhWvhz17BvvqUF111VV84xvfYMGCBZxwwgkjblP+nIby+uuv09bWRi6XI5PJUFdXN2Z9NcYwZ84c5s+fv0/XYNk9tstTjRN73XE0H6BUKvHDH/6wYruFCxfS3t7Oj370I0qlUrL85z//+TCDMhJxzu/dbauU4qKLLuI3v/lNRTq6rVu38qtf/Yqzzz6bxsbG3Z5v6DFh99e4L1xxxRV8+ctf5oc//OGI3cricoz12t797nfzzDPP8NxzzyXbdXR0VPR/Bbj44otpbGzkW9/6Fp7nDTvn0PS5FovFUq088sgjFXY6Ju7/HndJaWxspK2tjccff7xiu72x6ccddxzHHXccP/nJT7j99tu58sorK1p2Fy5cyMSJE/nRj35Uker1nnvuYfny5Vx66aWjHjsOLg2dJfrf/u3fAHa571hRSg27Z7feeuuoKW0/+MEPcv/99/O9732PCRMmcMkllwwr83PPPcfTTz+dLOvv7+f6669n9uzZHHXUUcBgMKv8GQRBwPXXXz/snLlcbp+6Fl933XV8+ctf5l//9V9H3WbKlCmccMIJ3HDDDRXfGa+++ir3339/8iyUUlx88cXceeedrF+/Ptlu+fLl3HfffRXH/JM/+ROUUnz1q18ddo+NMXR2du71NVmGY1soapwzzzyTlpYWPvzhD/OJT3wCIQQ33njjsMrjui7f+MY3+NjHPsY73vEO/s//+T+sWbOGn/3sZ2MaQ3HyyScD8IlPfIKLL74YpRRXXnnliNt+4xvf4IEHHuDss8/mr//6r3Echx//+McUi0W+853v7Ldr3BeamppGnAV8KGO9ts9+9rPceOONvOtd7+KTn/xkkjZ21qxZvPLKK8l2jY2N/Nd//Rcf/OAHOemkk7jyyitpb29n/fr13HXXXZx11ln8f//f/zdu12mxWCz7i49//OMMDAxw+eWXc+SRR1IqlXjqqae45ZZbmD17dkVSkeuuu45/+Zd/4brrrmPhwoU8/vjjrFy5cq/O+6EPfYjPfOYzwPCJYl3X5dvf/jbXXnst5557LldddVWSNnb27Nn83d/93ajHPf744/nwhz/M9ddfn3S9fe6557jhhhu47LLLOP/888dUvn/7t38bNgmtlJIvfOELvOc97+FrX/sa1157LWeeeSZLly7lpptuGlWXr776aj772c/y61//mr/6q78aln71c5/7HDfffDOXXHIJn/jEJ2htbeWGG25gzZo13H777ckA8KOPPprTTz+dz3/+8+zYsYPW1lb+53/+B9/3h53z5JNP5pZbbuHTn/40p5xyCvX19fzRH/3RmK4dwtaHsejrd7/7XS655BLOOOMMPvrRjyZpY4fq81e/+lXuvfdeFi1axF//9V/j+z7f//73Ofrooyv0de7cuXzjG9/g85//PGvXruWyyy6joaGBNWvW8Otf/5q/+Iu/SN4byzhwgLJLWXbBaGlj4zSnQ1m8eLE5/fTTTV1dnZk6dWqSqo8hqd+MMeaHP/yhmTNnjkmn02bhwoXm8ccfN+eee+5u08b6vm8+/vGPm/b2diOEqCgfQ9L8GROm7rv44otNfX29yWaz5vzzzzdPPfVUxTYjpY0zZuS0dWO9xr1JGzsaI6WNHeu1GWPMK6+8Ys4991yTyWTMtGnTzNe//nXz05/+dNRUfRdffLFpamoymUzGzJ0713zkIx8xzz//fLKNTRtrsViqmXvuucf82Z/9mTnyyCNNfX29SaVSZt68eebjH/+42bp1a8W2AwMD5qMf/ahpamoyDQ0N5v3vf7/Ztm3bqGljd5V6dfPmzUYpZebPnz/qNrfccos58cQTTTqdNq2treaaa64xGzdurNhmJBvreZ756le/aubMmWNc1zUzZswwn//85yvSlY9GfLyRfpRSxpgwbezf//3fmylTppi6ujpz1llnmaeffnqYLpfz7ne/2wAj6o4xxqxevdpcccUVprm52WQyGXPqqaea3//+9yNud8EFF5h0Om0mTZpkvvCFL5gHHnhgmK729fWZq6++2jQ3Nxtgtxobp43dFaPp/4MPPmjOOussU1dXZxobG80f/dEfmWXLlg3b/7HHHjMnn3yySaVS5rDDDjM/+tGPRtXI22+/3Zx99tkml8uZXC5njjzySPM3f/M3ZsWKFck2Nm3sviOMGccwr8VisVgsFsvbyPbt25kyZQr/9E//xJe+9KUDXZz9zuWXX87SpUt54403DnRRLJYEO4bCYrFYLBZLzfLzn/+cIAj44Ac/eKCLst/ZvHkzd9111yFxrZbawo6hsFgsFovFUnM8/PDDLFu2jG9+85tcdtllzJ49+0AXab+xZs0aFi9ezE9+8hNc1+VjH/vYgS6SxVKBdSgsFovFYrHUHF/72td46qmnOOuss4alTz3YeOyxx7j22muZOXMmN9xww4jzFlksBxI7hsJisVgsFovFYrHsNXYMhcVisVgsFovFYtlrrENhsVgsFovFYrFY9hrrUFgsFovFYrFYLJa9xg7KtlgsFotlT/jIR+Dssw90KSwWi2X/s2IFfPe7u93MOhQWi8VisewJZ58N1113oEthsVgs+59HHx3TZrbLk8VisVgsFovFYtlrrENhsVgsFovFYrFY9hrrUFgsFovFYrFYLJa9xjoUFovFYrFYLBaLZa+xDoXFYrFYLBaLxWLZa6xDYbFYLBaLxWKxWPYa61BYLBaLxWKxWCyWvcY6FBaLxWKxWCwWi2WvsQ6FxWKxWCwWi8Vi2WusQ2GxWCwWi8VisVj2GudAF8Bisbx9GGMq/hZCHKCSWCwWi8Vy4LB6OL7YFgqL5RDDGEMQBAe6GBaLxWKxHFCsHo4f1qGwWA4xhBA2EmOxWCyWQx6rh+OHdSgslkMQKW3Vt1gsFovF6uH4UNtjKIb0f6sprEdsOQDYSIzFYrFYLFYPx5vadii0hl/8orY+zoMAZs2CCy440CWxjMLQgVqjUW6Myvc5WIyUHbBmsVgshzZWD0OsHu6e2nYojAmdiQ9+8ECXZOz09sL99x/oUlgsFovFYrFYLONCbTsUEDoUUu55K4WJ/xn/blMi+u+Ix1YqOnG5Bz9s0b6df5RIQXiikU6yJ/fOVO6RHE4M2WrPLkbsogzj9Yx2dY69Oc9okZuxRHRE8p94n11sV37sMZVs95S/CmM9ZnxdNjJjsRxkHCg9HGl7q4dWD60e1iS171DsJQYDASDARC+TEbqyEhkwu6h05e+RQSMRyHgfAwiDQVfuFHgI7WMCLzyyFBWVxxiG1BpRUY7QCJQbwopCgNAYNELExzUYoiwG0YWasqs0iGHXOLRClf8tAEfopDQAQsflMUMMganY2YiyEpvyv0V8mWUnEhVWZWiZYgNgytYKxODfRkf3I1ojZNlxK42lGDwExpjwp3wdAHJI+cygRAoQyQ6i8t+kTNFvYlCARPm9MdF/yh+tFIOXUH4PtC57fvElivBNK3+UuvLJGhFuJ6J7IEz8Phm00YnAxMfUCGQg8SUYCSmxOwmyWCy1yAHTw/Lve7B6GF+T1UOrhzXIIetQABQl8ZuCNCA1lFdVbQR6F961AhSDViEQUFJR5RChYQrK3jhhQAqBRKBFaAhNZDxiYyHjIkQVUEd2T0TWRkZGMfaKK4yviI9oylbIxFBhdFhH4wVRfZVDLjHuSRZvMzRcoFVkOKON8sJUVPTBC5ZD7OBQ9RiswOW21kSFiG2JMuCY+Hqj+yREVLTozBUWL7kLSZm00RhtyrI5mORaw7tUefyyuxH+V+iKcju+QQZEIhmtECCMwWiNKNc5E5raRNBMeCwpyzcSifEWQqC1TrYzxoTvTfJYDUYbhBedBwG+h/S8sPxRGYJIvl3HwWiNDoKwbLHwRc8/CAKEUmjfHzSwUalkIUCkXZwZreCIsDVwX1EK6uoqv0AsFssB5YDoIeXnsHpo9XCc9BCD0OEFGROuD4QO9VA5GKPRgYZAJ6eUCIQxBFojlCTw/ci5i/VQIAs+IuPizm4Hd5z08CDikHUoDOBjSOUDZL8HCHw3NFhJndYGtBmMDohomQgrS2BC4xOuDreVgRn0doVAIJOPPIxB9heQXR5sLgAQRIYiNtQiCP/VQVw5ApSUSeTGoMEYtImNgUBrQxIR8QUmGDQCRmhEJBRaBxgj8XVYNm10WOao8gohQBscHV6T7wckUZbyNkEhkFIipSTwA1RJR4a/7LxmeCzLBKFBEEom24Q/oTFRIrxXgQ7CexZFALQBHRmBwA8iQzgY8dBGJ88mMcjJP+HxNYSGLTIARkdliZVCm6RMQsik7ELGQhaEsS0pEFIQlAAdHUeAkCp63qC1RpgAGRk6o00klDIskwQpFV4iPOH915EwhkIo0DoI12mNlAolFVqHRlEbjXBDkZACUo/dj+8AbgoI0+AJDArQQiSOhIyFOY7+CBHFmgRyyEA6gUB39lDs7yd1/GHoxjRKjIMBXbsWPvlJSKf3/VgWi2WfOWB6WOaAgNVDq4fjo4fhs4uuWQiUUvhohBSUovdJCTl4j4MAIwVSlbXcBMGgyycEwlGUtnRSyueZ0NqAbsuihlakQ5yD16EYLZBSZr9SRuCv7aDntTfRrTnqAoXrh+sDP8CJaq9ywttkjB6s2EpiEPgYhJLhR5sE48ioEgIm/ChLjK8xiEKeVEcX3vqtUdQmrEJahzM1GqHCyqQ1Sim0MGUG1IASkTEMK5UQAh0EiVGLalfiuaMkKIWUAmFCo+zqwfugMQQqNCZKKpAgTVhm5brlt2zwlqZUYoiUMfhOVOnFYKQm/oCN75dUUezKEF5D+QMxBiFltL9B6cEIj5BhVMukVHg5UfNmeb9FCZhI5OIP5mR/QoM3zJrHZa18LYY1scfRfC3L/wqjbyYWzviaCQ14+Sd3mewkhYqPMcwYJdGkymVxU7BBRFHAkHS8XIDYvJTg3ZdArj6+pShDRTOyjl4PRHgMQ2yshxY0/FMD6o0OCk8uIf2O4+GwCaj47EPvZzmDIcCKbZPD3/QrxBj61VoslnGiSvUQKZFKgRRWD+OjWz0sK8Te6SFEznC0jzGQ2oUeOgzqoRxynrgoGsi+0UDxySV4vf3Qngn1MA7Ojcbu9DDe/SBosa9Zh2L3nyOm4p/BBy6SiIZvoLCzB7Jp2k86giDlJNu4hJ5zspcIm3uTw0gRBmuiQ0oDSgic+FTJeaMIS2xI+vpg5ypSJ88ZNKpl7aBm6MsWrYv/1pExSBoqjUESR3/CZsDy11ITO+qDd6y8SVdicKKIxqB5MGXlj2IcZe2+noyPExqsDJVllEKE2+q4d6pIjGM55bZKiEHDEUdzYkMpy/8euuPQ41U8bzH4sUxsWAcN3tCdRxs2KCLDBYP9e1Uc/kBGX/RxVCwpOKbClBoY0n9YlK8KLzwqyMiGxQy5Fi+yRAKBckA6IByTlNmr2Cd0MNSwKyz7oiizcHFvZ9OWI+24qLe6MDMmgKMjsaLsYZQXUFYuI+q7G91bQ9giMsojtFgse0HN6mH5B6PVQ6uHDO62L3oIoXMX38Xq1kOBQYz0CGuOmnMoyvv0Vb7uw1/9sndtcAsTvRzGoHyD011ATmpF1Kdxk9YuOe7OYuxFG0eEEQknLmNlTGDk0+6iMEkUZOQyx82oox9OMEJsYJclSMNwIzaCQSI26lH0przKDDVVyQCy0FpXHndco9mj3Mtyg7urvQcVdGzHrVg/vg2kqvycQoBU4U/EsA5FYyli2S+O1ngNKYJJjRTe6kQNzEI2OMk4IGMMwZDnKCPTGNrj8vXhC5OILJXRLIvFsudU6iG7looh66tCD+PuT0kZrR7G66we7hlq6DmHPINq1UND1GPgINDDmnMoIGzeRECAiaKdpiKIEBMP2EoGdEXLTeTJyu4C+Z4+sgumoVXodYshL9Gunu1o9Xq0fZLmYSGSJtK9YeSowdD/lq0oK9B4vKrDglxjYGglGclUDd76XRsGyyjvnhm8r6NJzp7cSS0ExpXkDptK15Ov0rxhJ/KwdoQwmGhEt4pVL44kirAJ3gRRHYuDVFGWEeVLKBJ1Q9CIsrCnnTjIYtlzEj2MIqVlcfUKqlIPoWxg8N5h9dAy6rsXrx9lv2rQQ52SmIwAal8Pa8qhGBy0FHoPTkDYghSM/LoICGfTHlYhAW0obtsZOiWNWQIMLgL2ZNDpCM93V49ciMEMOiOHT3Z9urJW191vVFaikS5/bxnasjdEbyxvFyO9e2LX68fQL6ICicEBdHsjfmOGwgtvUNy4jSAI+zdLKRE6zMARRtQMBD5SSrTR+H6ARoR9ngVIoXA9hVm9hYaSxmRGGKh4EERpLJa3g2F6GH/E7Orjqlr1cCSsHlrGyu70ror1sL8tzcTT5kJmuFtZa3pYEw5FfFONMZiij9/VDxqU5yG6Bgg276yo0LGRlVFGgUELGz0UKTCBpvjmVjJtzTi5TJg6TuxZP7Zxf8S7OeCYzjeOxnKXh6+N9/ugZdTbL3axfk+fmQlTR5r6NA1T2zCL34AdAxjfQxI2cgsTptpL6k7JQ0qB7wdI30e4Dsp1kI4bpu4raNSOPghMmNd7yCm11rv/0LBYDmFG1cPYwShrrrd6uBf77AFWD6uD3eldNeuhOHwC+OHMLbWuhzXhUEDYRKSNwWzvZ+cjLyOLhhQBasmbDKSfQwiB67pJyjgdBKgAiPqwxRklfN/HuAqlFG6gEIsOR0kZ9uarjWdmsbxNCIwSaKnJTZ9A14QNpHYEuCh8z8c3AY4K0zUaE/7ryYBACAIZYFxwlUEbH2MMnglQUpAyAeGUWcMrnFLj26/WYjkYGVEPpcHzvOQjxOqhxTKe7B89dIJS1HG/9vWwdhwKDFqA9APqekqoXg8RFHH7PZweH8dxQJTwPZ94AhTHcRPj6vs+gZQ4UuKg0CWNP6GeVHtz1P/NWk+LZSS0BDmpCTGrHbHtLQgMRmgCORgVDYIg/DEaKcIPlDDdo0FLgw4MwnFwdHk9GzqScZBaichYLAeCEfXQ+KQECClxlNVDi2V/MN566CVHrn09rBmHQiDQIvThgsDH6ABPBOSUwHOhaHwCP0DIwSwKReNjRDQ5ijI4HkglMSUdNj1Nb4OmLFqJ0Igy8uAoi+WQJGq2VUiEC9lZk+l6dQt1Az5KORhlkIEfRjmNwXGcZNbZ+MMFJQdjL7FB1TrqdhEngI8HfNq6Z7GMhRH1UAa4rosQwuqhxTLe7E895ODQw5qZNzxJvSUMJaHxHUOdcnEQKN+gfI1jBCmhcBE4RuD4hlQArm/IChfHURhpKJqAogOpaS1IV5ZNslV7D9Bi2d+4hPnc0xObUfMnUcwqAqHxAz+cpVUIlFRIIZLojCBc5jhO0gTs+36Ys5uoptWgwbRYqoHR9DBlBMrXVg8tlv3EuOthXN8OAj2smRYKhAi9PQngk0YjjETrcLZO5YSzbXq+H86pIgRIgSskxhA+PCUQBoKsizyiHWdaI3HGuoMqEpPPw333HehSWIYSBHDqqTBjxoEuydiIB5YZE0ZWci7Nx81kx85u5NY+siWF1h4IgeM46ECjg3BWWuFE9c7z8Y1GSEk4wa0Ks1yMay51i+UQYwQ9xISTzVk9tFj2A/tLD4UEfXDoYU04FCLy9DAgZfywCqHhhPBvMzjYLJk8SzkYJARhPzZPGDJKIdoaEMfNgjq3olmpFpuYRqSvL3QqLrpo3A89Hvmcx3qOvUjCMC7st7fgzTdhzZqacSji+5DcVylQbY3UHzWbvu7lZEsBWkqEEJS0xmiNFBIpwxlUg0AjlcR1FEIptICgFERNvBaLZW8YTQ8RQRIFtXr49jBskryI8bx3e5s6dLSy7Sn2PQixerh7asKhiJGAjifDURJpJFJICp4XzTgokxRbjuNQKoEfaBwpoxReAb2tDvULpmMm1GEO5nqSy8GECeN/3LKZRo0xgynNxtPo7G0T4AizoA6dPGmk9cNEdH8Z0B07oFDYP8fezwghwibclCB7+HSC7b3kX16H8AQympVWuikcAzoIKJVKSClRKkUgDL6n8XSAE30IGRPntDiYK6HFsv8YpofCQQiBZ/Vw/PF98LwRV5nog9AYg4g+KMcVs5eD5GNnsnw29ehYiR4O+ZhN9DDumprJHBRdccab8dRDycGjhzXlUCCioSvaoKSLCTTaaLTWSBWOpA+iZb7nYzQgFSgBgcERktJhk+GIqThC42AqZia0DGekKIcxBuOX8ItFnFw9Sqh9ntWx3EnxPA/XdYcdq/wcSZQuIXwPOjo6UEpRV5elp6eHrq4u5h0+DyHCVz2fz7N+/XomT55CLpdj27ZttLW14TiS3t4+Ghoak1RtNjITktwHbTBCEOQc6o+Zg9fRg97QGeaxB/zAAz/cXimFEILA9/GMDj9WBDhSIeXYMj3tCbU4q6jFsk8M1UMDQRBYPdwfPPoorFmDyeWGrTJag1/CL5VQ2RxKqmGt5Xvb2m6Mwfc8HNcd/Mgfsk28zJTvbQzaaLZ3bEcqRTabpae7m56eHubOm4eJHIpCPs+GjRuZNGkSuVw9W7dupb2tDeVI8m+uof5DH8bMnh2ew9pUYPz1kMjBj76AqGU9rCGHIszUK5WLFCl04IPxAUilUmFkxtcYEzY1GcB1nDCM44DnB+hJdbQcNZ1SWiGEoobGpFcNWhu8osdzt/8vb61YycWf+jsaW1tR4/DCaq3ZsmULN9xwA5dffjnz589PIilDGepM+EGRzZu38B//8T2klJx66mncf//9TJ48mS9+8Yts7+rk4Ycf5tlnn+WNN1bzmb//B4Ig4Kc//Sl/93efYs26lTz+2JN87h8+z5Qp063xHAETfcC4BmjJkj1hFvnuPkRfAaENvtYEWuAoB6UUnufhIHAdhVbhJHaOLovGjI/tTIibjkd7ZyyWg4cR9FCEM/ZaPdwPlErwnvfA5MkVi0M9LPHc7bfw1voNo+vhXrS2j6qH8bFMhQtRXqqR9fC5p0I9fP+f0NPTE+rhslcq9fC++xI93LDkRf6q9zLSQ1rxLSHjpYeFaKZtcRDoYU1YkIo+hCO0/iUj5iPjGWfA8HSAjCYYMfUZUicchpzUhBJh7m3LcOJZVeMfCKNe+Xye7u7u8GNQG7asWcfWFW8iCx79vb309vbieV4yDf3Q4+yqP2d564TjODiOQ3d397Bjaa2TCNzQZb7v09DQwDXXXMP73vc+jlpwFJ2dnQRBgDGGp59+mh//+Mc4jsP73/8+DjvsMJYsWcLWrVspFIo8+MADLF26lA0bNkSTQVlGQsT/cSV1syfjHDcLAzgFHyFD45nMzCsk0gm7YThInHhCrf3UrUxKaZ0Jy0GP1cO3j0S/4JDTwzfXvElHR4fVw10wHnoYdjEb/7IdCD2sGfUdWgkdx0makeIcv46QZNwUjgizV7iui5GCfEZSd9I8MvOn4bsCiUAZqPX+avuL8olZtNb4vs9NN93E5z73OVauWolxHKbNmklhYIDbb/lfPvV3f8cnP/lJnn766cSoaa3J5/O89dZbiSGMjz2aYTXGkMlkMMbwve99j5deeqnCaHZ2drJy5crEgMaUSj6/uOEmXlnyKhPbp3D/fQ/huA6pVKrielzX5YTjT+DSSy9l0qRJzJw5EyEE6XSKXH2OlpYWpk2bZqMxoxDbPQPh5Dx1itwxc8gcM5tifQpHSLQeFDjpSAJHoIFAB1DyB7Nk7LcyCvv8LAc9o+mhlNLq4ThjjMFEmnYo6aHjOLQ0N1t7OgrjpYdhS8L+ucdvtx7WjEMREkY2hZD4fjiBCIQVREqJAUq+jxFEE/yAX+eQnj8VecRURMbFFTJsorLGc1S01qxZs4a1a9cSBD5vbdzAc88+Qyad4td3/ppev4BOK7Z37eCV116lkM+zfPlybrv1VnZ07mB7RwerVq7k4Ycf4tv/8i9JJGcw8hJGznQQUCjkyQ/009/XS35ggPXr1/HM008xZfIkHn/ssTCHczRe4rHHHuXXv76Dzs7tdHd309PdTXd3N107u1j6yms8/dRzbFi/iReef4kdO3YSBAHFYpGOjo7QYM6YyY+vv57/+93/y6tLXyGTSRP4Pls2b6Y+V09dXYaGhoYRI37jlTGj5hGDs/QCiPoU6TOPIHPyPExKgZDhx43joFwXH4NxJCpKj5eI3zjfT+tIWA49huthnNnJ6uH4obVmZ1cXmzZvPqT0UClFpq7O6uGusHpYQQ2NoYgabqMBLFoblBFIJK5yCAJN3gQo1w1T6WnwHQ3zJ5I9YQ4i6yCkSCbUstVhdPr7+/mP//gPmpqa+Nzn/oGXX3yO9WvfYNKkSbz++jK2d3SiVr8GIkA4QFDCEZpVK5bx+X/4e/IDA/QPDOAoRcnz6Onuprm5OTFEb61fi4zc+x/+8If0dHeFPYKNYWAgT9/ObWyWhlxdGq01ynEx2mf58ld58KEHWLLkORzcSE8lSkk6OzpYuWwJLzz7BPm+AXQgwAhefPEF/vZv/xJXpCiVPLyBPI8+9DDPP/tcWL58H73dO6mrywx2x7HiOgoCQZiOUoZ/YoRBN6bJnnQYeVfhLX0LvbMfUypSRCMdF6FAuC4aQ7Hk4QAm0FEdtPfaYtlzRtBDEbY2WD0cX/r7+7nrrrvYvmQJf37kEYeMHvYlL4W10SMzPnqIMZhAJ8esZWrIoYgw4S0XUiBxkiwX0nFw3BTZfp8AzUBLBnH4BJqPmwvZ1OD4zyGZEiwh5REH13WZN28ejz32GJveeos333yT3t5e+vr6MAZeeOJJZhqPFhQrXnqBfidABwE9PQP09nVitIMg7IrW1NSE1oN9P3fu3MlPf/pTPvyhD3LPPffw5BNPhNkQCPv8Bb6PIwWbN2/mrHPOxXHCV1QIQS6Xw/M81q1fEzbRi7CpXwAmalosFLrJZBsQUWRgYGCAvr4unDiCIMP3pac7SNIpaq1JpzOkUumyPof2DRlKUnOSMYGDLoHJpcmcOBdaG+hasgqzbjsNJYHUArRB+x4ldDjh1hDGK0PYvhzDYqlJyvVQKowJU8laPdw3hurhtKlTeWTp0kNKD5WSZe+FfUOGMl566A85bi3rYY11eYoSw0SZDjwt8DV4RuJpifYMBSEotdZTf8qRtJyyANGcRTgKoWruUt924oiJ67pceumlSCnZ3tlJXV1dkkoVYwiCEkWp8IWDMSWMCVDR/ZVSkE6lkvEt4Qd6VPWE4A9/+AMvvPACO3bu5KijjuLII48Mp6mPBhCl0mkcpXBdh5NPPjmpCFJKLrroIubNm0s6lcFxMgjjgHEQxiEtM6RkGmUclAmnva+vr8d1XdLpNOlUCtd1cVwnaoJUuK6L67ooKWlubuL8888j5abe/htfw8RRTi0NpTpBav5kJl9wMk2nL6A0MUefCPA9HxGETbsizn8+JBXwvjajD+1HbLEcCgzVw6LVw3GjXA+PO/54isXiIaWHJ510sm252kP2Xg/D/WtdD6u2hWLkm2GiBDECPwjwdYASoCUoCcIYvPmTqD9xNqmprQh3MPpiJ+0ZnfjlLRQKlLwSGzds4Gc/+zn9/f2k02nee9llZLM57r33XorFAue/851MrGugWQs6ZD++qkNJBYT5lnPZHAjwSmH+7NYJrUm/z+3bw/6eP/zhD1m06GwOm3sYq99czaxZM3nf+95PoVjAlZJUOsMxxx4X5VQPK92RRy7gK1/5KmtWr8UrGLq7e8hm60g5LmmlMISTpjkpl3nzDueTn/oUW7duRklQqGTCnnD2yvCYjuMwffp0jNI0NDRHKRcr7091RLtN8k9F/vEDWDYT/UgNriBMlSgNekKG3OmHk547ib4X1+O9sRk54IMIs8+k4j6jBgI0woRGOOl+MYohrI7nYLG8/YxJDzGhHRZWD/eFkfRwyf33AxxSetg0bTaOcoY1TlSHHT549NBEWngw6GH1OhRAOPbdILTGoMOoptAUtQ9oHKURQhOkDEFLioYFs+CwiajWbDRYzTbYjRVjDC+88AI/+en1bNv2Ft1dfZx22pkUCiWu/8kNbNiwnq7eAf70T6/gQx/6IK7jIg0YCabs7kopk0FG8f1PvHBjOProo2ma0MKylct5/Y0VSClpb5vMBz78Uc4448ywyTZ6dkkGL0w0OUyKWTPnMWvmXDo7d/Av//Jtzjv3XN75rgsQUoR9EaNxEMYYmpoXcMT8BUgZJ04cND4iSs9gwoIlZazGSgpxuTXV1KiYGD0V/haXTAImZXCnttDcWI93WBv5N9/CbO5E9gUYIdDru5Amhd8YRsmEEAQCEOHMoXFO7tiY7i79XfzeGWNzplsOPsaih0qBxMdLKauH+8hQPTxq7Vscdf4Fh5Qeirr1YZ+oKuRg0kMpFXp9F6rUXPN6WFUORbknJiC6kVEThBYYoRE+SAROOkVJejgtdbgnzyU3dyqqOYuXVmgJbpSJy35cjJ0TTzyR91x6KY8/8Qjt7ZO48v9cw+TJ09i4cQOlUokLL7yI97znPbhuKskiAiBjYzSGl/3oo4/mi1/8Ao8++iieV2LSpEmkUlkmTGjjxRdf5OSTTyaVSiUVokIEoyZjYwy5XI4TTjiew+cfnkRsDMOzG4SViuRdEGXHKuv+WEHVvjMmTjdYHUZ0tPsUPzcjBF5jGnXkNBoOm4izvQu1rptgw8tse/l13NfWkmlowpvYjG5vQE7IkcopRMbBRKkuxzI5z/BZ0y2W2mdP9bDoBIiWetzZk6wejgPlenh8cxvvuPYjNM5fcAjp4WDXrKrkINHDns0dbHt5OSlUzeuhMFWkxOW5mAMN0kikFxAMFPG6+vG6eils2Y6zbjuiMQOTW2lc9gz85Z+jUwohwkqkMEgjqtOA9vbCPffA+9+//87R0QFPPw1//Mdj2rz8FdDGJ/ALSOkipYtAok2YR1tKuct0ZCN5w/Hf8XP1fR8ZWVxtNCteX8G3v/0d5sw5jJ6eHv7pn/6JlpaWiv3icw7tXzjSuvLzj1T5dvU+7Pd3ZdUq2LIFFi3a412N0WhTIPANrpshNqJJmW+5BS69FOrrx7HA+0aYvz3uYhFODqV9kL+4Ef/08/G6BvC25ukt5PEHimQ9A8dPZMIpR6CdcPIfWaZyu3o++zqQzWLZI37yE7juuv16ij3Ww2nt1M+fgWxvqB09rEJG0kN1/8OIkxYiJk89dPTwySdh0iSYP3+st+5tY7d6WIWMpof0Fwm29+Jv3VG9evjoo3DeebvdrGpaKIZWjvyG7ciNvRS7etE9AxAYdEMGt7We3HnT8CflSNdlMW8tQaeiXNpRZEAgMKNEnw91RjI05cZKCol0XcJXI1xe3sdy6D7lx02adEdYV76NECpKemiYN+8IvvKVr9LU1ESxWKSxsbFiQFKcU32kc492DeXrR9u2IvpXxUaoHEOAH3hRk6hT1qi6i31GeN5vH2ElNNF0vsIASkNK4sxqxTl8CmnPkPEC6Mnjv7qevkIJYeJuA2GlHkvZa+UZWixjYa/0sKGRwBEYKawejpEx62EqBffcCw0NSIbHxIfeW0M4hoWyVoDyddEJUFojom5FAsPhJY+vHnMsTY2NFFtbabrvPihr/YgKmCS1ID7P0NaLsnPEgwJl+bUO2bZiHEL8y8aNcPnlVCsHix7KeheZa8Wd2VLzelgVDkU4HsUgMOFgMV9QeG0zorMXd2YDuSmTke0tyOYMMpMCKRBO+G2CFDiizFqKsn/sR8aojBY9CXvspcO/AYwIK0DZNnt7PiDKjiHC6Jk2pFIuc+YcBlG/0KHOR3lfwNi5GMn4D7uOsmjNwYFAUkcmnaVsyFbVU3H7BTiIsE+AoyDlgDSojCCob0Cvy+L05cFE74gOSPIhWiyHCHuth0IS1hysHu4hu9XD8y9AFArhtuXb7O35tB7skhSeDKM1LjAn8QRDHYvHNcTjYPSQYJsQArRO1g+7LuJPUfbsHRACstm9vML9zUGkh6rMwatxPawKhyLBgBaEfcWMIbdgBukTpoJy8JWkjsibNoYUAhicwv7g+XDcv5R/mI9oRIe8rLEzMdK2o/WWG2l5ZYvAYDnCj/7QXobrooFnsTEd0pQbNzWPVp543/jfJL3fGMpd7QgxeC3D417Vx+DjGYz4EUfH4qiZjH4PQGmB9gKMNiOKo8VySLHHejiIrTtjY8x66LrgusMCXuVfiKPq4UjLou5HsS0Mu8Po5JiJ5kGlHpadO3YuKso+pDxj0sOhC2rk3Tlo9LCMWtfD6nIoIPI1BY5ywtzNTmhRBzvgDGmePBCFrGFG6wa0K8o/6ocazdGaEMu3L9+m3BhKKZN+nfG/cb7uYYYbCIJg2Pkq+rtqnRjO8mPG5yrfbui9qHZqpZy7Ytg7Qli3BQKERrgOQqqKqJ3Fciizx3po68weYfXQ6uGBYvg7Qs3rYdU5FCEG4QdILwATtvLE88EcDC/SgWQ87l+5AYqjJrJs3aABM4lRkzI0YkGgUcpUGMDy4wVBkEzKEhpYgzNkdmUd9TsND1Hetal84iCTHCdcFzYVjmTMD2Zq4fpM+X+jZ2ZsqMBiibB6uL+wemj1sNqoZT2sGofCxI1W2oAw+MLDNxopoiLGiXgtbzuVkQtDgA9aIz0oKYOQDrLMIMqoNTcaK4YQoSEM+3MKdGCSRxkEGmMGm2O1jjJMa0MQBJFRNcnxwvUGKQb7mFaWMeomhRetU2Gf00AjhIOSLsaI5JyWA4/BoKOhji5QkgalIRCC4Q30FsvBj9XD6mVEPezYjnrqWQJHIoRCl7cYxF1bwkeJIyIdkwJVNsjFmHhsRfj5KKKuTJJYS02YkjaeM8kYjA4dGCFFmDVID3Yjjh0JMGgThL9HA7x9NAKJnDodc8JJVg+riFrWw6pxKEIGp1tRjkIqGWYDEiDQNdHkc7AxvCk1GvOAYf3K1by0YhlelPfa9/1wez9A6yCKtMQOgkZHBlCI2GHw8TwPIRykVCil8H2fQPv4XimKyoRZoYLAJNGaMPiiwYDveziOk0R/QjSB9oFw5k/f9xDScPZZ53L2WecghJuUy/L2MayPcSyihD/aGIznEwhwDHY2X8shjtXDamNUPdy4nvXrNvKSQ6iHhiQgZvwAHQRoU6aHQRh51mZwjEPgB3h+mR5KiR8E6MDDDzy0LtNDX4d6aHTSMgFhGlrHcdCBDs9nog9U7YeDgJWDH3gIYTjr1DM4Z2sHnHAyVg/ffobroah5PawyhwI0BmVASTlkoE0N3M2DnCR3NhplYMkzz3Lrvb/DS6eTgWGu6yB0QBD4OI5KojnGhA4BUDGwOnQaXDCCdDpNsVjE90soZzAK5JUClHLL+pIalEOU4QKkVEl/UsdxknO4rhuVW9PZ2cGUydNZdPa50dXUTn/RMaE1dHeD7x/okuwaYyDKllKJRipRMUGUxXKoY/WweqnQQ+DlFSv53y0bakYPJx91LOdk4nmLDjI9rHlqUw+rzqGoeKXLMxZE/xn2znd1wZIl+71c40Y+D553oEsxjLFmPgozE0R/FEtMaGjCaZ9AoANSbgrHdQi8ATAax3EIgoAgCJBKkc/nk3RO8QA0bTQmECjlkslk8AMfIQyeVwwNcsqlkC+hlIsUodH1Aw/HEbhlWTc8z0NrjXJUmBUBFbVchP1V+/p6QiMLUfes0a93tHtR1Qb3iCPgmWeqOmqZlKy9HRyH8tucdCOw/cItloQ91kPLuLCneigMUPJqTg8htrcHmR7WACMOyi5bV4t6WDUORdhqZzBCYCRI5YQeODpcO9LMPErBRRfBwMDbX+C9JZWC888/0KUYkaGZJEbKHCEQKFyE8dBK4usSKVFCKQclICgFeKXIePoSbSAwgiAQGJECDH6pRJgdTWGQ0Ye+plgMuy8hA3DCqe8KXhEjIAh8jAwn2JNKUgoCSkERpRSu6+Jpg5QKjSLQPo5QeCUddsOSAinS+F7YnqiNv8up66EyagQ1UKlPOin8qSWiPsXhTCfh/wItUAIkGl9IHNtV3HIIsld6aBlX9kQPQaIdiV+sLT00gNEHoR7WKLWuh9XlUEQYDDrKdjDY4CMYdieFgAUL3qYSHhqUZ4Ioz1ldngFCIjEInHQKg6ZUKuCoNF7JxytpDJogMLiui9aaUuATROMbpJS4qQzCxFEUgxCKbF2OpqYm6urqUClJYHx6ensYGBjAK5TwiiUgHKfhuC5KSorFIgiJ8Xz8QONKBULg+RqNhw7Cc2bcDFKGiRZHSP08InG/1vh3y9uAEPhBQMpE42z2cPfKuU7sM7PULnulh5ZxZ4/0MJXC9NeWHo61P43VwwNADeph1TgU5ZhwRMpgu641nG8LcX7qoS9fRa5uKQiiRxJ4HoViAV1MYVyFQBHoANcNX6sgCEAIHNeFaMC2lJKUUpSK4aDrVCpFNpvFcZxogLZA+hItNPXZRupzTZjAp6+nh/7+/iQtX/xKaB1GXSCsQL7vh/1Wg7DPsVKDg73LJzGKtx+totnc7m8fQoQRV+W6OOnUbrukWSyHElYPDwx7pIcYglJt6SFDWhusHlYHtayHVelQQJgyLRyQUhs3stYZaXKfEftOCgYNEWHaVylCoysQpFIpfN8PZ3wkzLLkaZ1krBgYGEB7PkGUjcLzPHp7e5PDK6XwtSaIzuG6DilHka1LVQxmC6J8257nkY4GwRWLxdAAIxHGJOXyfb9iEqD42nZnPK3hfBtI0huGqTLj9MLsugV+GLU6+7nFMhasHr69HAp6qJPrMVHLvdXDA06N62HVOBSGKFVz/M6KsKl3D++jZT+itUYgMFFLqXJdpOMgSFHIeyhlSKfTCBE23xpjaGhoQARhpKSntwevVEIpiecVSZt02IwsBCrqn+lFxg4EQgq8oodXNJSKKnECDKAFZDKZcH9jUAgcIcJ5LYRBCINUAs8vopREOWCMjxBhukXB8MiT5QCQRF0BY5Daj8a57Fl/0fLZa+1ztdQ6Vg+rnwo9FKBStaeHoIlnZ7Z2swqocT2sGocCiKZzMdEH66Exc2MtETbFkTSxCilQjgNIgsBHay+JgEBocIvFIqVS2N8z7abQfoAxQTjbpzB4fol0Oh1O5aLCHsLFYilMcVeWy1vLcNZQKSW+DpCOQ7EYZr5IuS6BH1Y8AZSKRdLpNJ5XCvuslsJ5MYQk/BEGG+irHuK5ZcMMXOFAR2NEtGRs9d86EpaDDauH1U2FHkYf5DWlhyLUQ6weVhW1rIdV5VDECANClWVUsDa0Kqjoa4kIm+SCACdKAVosFsOBYQwO4vJ9n1KphFJhRCWTyRDoEkRNtEmTbRCQSqVwHIdUKpVklCgUCsl28fgHx3UxQiTrfM8j7boVedrj48bjJQYnxSsL+VmqAiFEmHZRyqgHQZzjwmKxWD2sTirHHgCC2tJDE2UMC0v49t04yy6pZT2sOodCEGXEE3KX/fos+5+R0uTFE+lEf2HKBoGl02lKpRLFYhEpJdlsNjF6sSFLpVK4bi7KfBEauNhgxv0+Gxoa8H2fQqGAlJJSqZQY0LgMjnIglaJYLBJog9BxH9Mwk0Y+n09yezuOU5kWrxZq5iFB/HEUNdsHQTKD7J4+I2snLAcjVg+rh93pYZiC1eqhZW+pfT2sOociRhuDjhp9LdVBkoMaEb74UuJ7PoEvMJTCfNilUhg1KTNaodF0aWhoYMqUKbiuwnVTaKNxHQfHdclms7iOS7FYYPv27Wzbto3+/n5SqTSFUpGB/ECYMs/z0dqn5HlJxgoRzSIqhSTAIB2Fchy8kgdSYIRAawFGYowdO1FdDDbjmpSkJCGLQCiBwlSst1gOVaweVh8Veghhms8a08NwkI4dmVM91LYeVqFDEY9wN2ijEfEEPtV7Dw8qhmYHGHkyHzBCoBwVOtVCoXVA4Aek0ymUCl8rrTWpVAqlFG1tbUydOhWtNdu3b6etrY3m5mYcx8FxHJqamnAchy1bttDT04fnBaRSGVzXpWXCBIKoyTafz9PdvZOdXZ2USiUcxwmzVhhDgMZ1XVKpFMYYNCWU46AcF0e5aE0U7rNORfVgQkcPA8qgHYnS4WPakz6jFsvBidXDA8lBr4dhZNDqYdVQ23pYdQ5F3FNMDpl0pfpv5aFB0swqDEJKlJRhf1Ak2gTUZXIYraP822GkZcaMGUydOhXf9+ns7KShoYH6+vpk4FAmk8FxHHp7e9m2bRupVIqmpqYkL3cqk0Y6DkEQ0NjYSFNzI+0TJ9Dd3U1HRwc9PT0EQUAmk6FQKFAoFEin0wRBEKbd87xhaWMt1UUc5QuCgNrJum2x7F+sHlY3iR6C1UPLuFGrelh1DkVMMkp9rFMbW8aFXeWiTvryRdoW9vNUFAoF3LREBAGlUomU6+K6LplMhnnz5jF9+nSklPT09CCEoK6uDsdxkgwYxWIRz/Po6OhAKUUmk0kymoSZK8IypFKpyOCmaGzM0dbWRltbGxs2bGDbtm1IKRPDWSwWE6NZV5cl0NaAVjMCkEpFaS2E/ViyWMqwenhgGKsegrF6aBk3alUPq8ahiOtllCgPZGw35QhbWQ4E8QA0gQwzzRnDQDFPJlfC0QopXRzpUCwVyeVyzJgxg2nTpiXZKvL5PEop0ul0lAkjHFjW39+fRE+y2Sxaa3TYHhs24QYBrlLU1dXhui6u61Ao5MnnC7Q2OaTdNE1NTWzevBk/moHU8zx838fzPIqFAsYESAUIXZZSzb5L1YIQoUg7qmpMksVywLB6WP1U6qGoPT2UhOUXxuphlVGrelg1pTVRI48k7I8oZGxQJXFGbsuBIYnGEDbrRvqWzEehRYAQoZEU2pByU8yYMYM5c+bgOE5yDMdx0FonUZc4chJntPB9P0mnVyqVkvWlUgmMoS6dQSLo7+2nu7ub3t5ejDG0tLbQ0tKC1podO3bgeR6e54W5u4FSqYTvlzAmIJzIRwDK9hutFpLMhSIZuGhjZ5ZDGauH1ctIeph0S6khPYRYDzVWD6uIGtbDqnEoymcHNEKEzT0mnvMgXm9f+AOJjIynwWAMyOiFz9bVkUnXRZP0+EyfMZ2jjjqKXC4XDgaLoiue59Hd3Z0cT2uNlBLXdTHGJCnuPM+jp6eHHTt20NvbSyqVAsIm3lQqhdZhKrW+vj7y+Tx1uTrq6+uTSM6OHTuiiXy8aDZTknPYZt7qxER1PYhSLtqabjm0sXpY7ZTrIYLEEagVPdTG2LeoSqlVPawihyIi6pYYBBpkUOaZ2Vf/QJJkt8BE8bFQ5NKZNG4qFU3E46Jclzlz5tDQ0JBEX5RSeJ7Hzp07yefzpFIpSqVw1s76+nrq6upQSiXeeG9vLx0dHRhjkmwXpVIJz/PI5/Ok02lSqRTpdJq+vj42rF/P1JlTk3zdXV1dieHWWiOVqnQo7KtUlQzONGuxWACrh1XKcD0k7L5k9dAyTtSiHlZNaSWDCfKkMUilEITT0ddGwqxDB4FBGoFWCilTOG4ahUAJyZRJk5K0diXPI5VyKQzkk+jKxIkTyeVySZ9OYwye5+E4Dp7n0d/fz7Zt2wBobm7G93201ripFJm6OgbyAxgDynFwMCAFm7dswfM9Zs+ZTX1dPbm6HIHnUfB9tB+Qy2UpBn7UOjGYdztptrZNvQeQuLOAACMwJvpbaNDCDkC1HJJYPawdBAaMIJCyBvUQrB5WE7Wth1XhUJTfIiNAaYNQEjyDRqOExJrP6kIYgXYcfF9Q8jXZdIb6XD1TpkxnoFBgR3cXnueRSqXY8tYmjNZJs+vWrVvp7+9P8m3ncjlc16VQCCfx6evro7GxMfm75Hso1yWdTpPL5Ui5LoExSMdBOg5aQ2fHDtJumEVjUtskvGKBwkCelOPgl0ogwmiM0WCMjLJmWON5YInvfQCEMwEHftgdwBD267VYDjWsHtYewoBWtaaHOvpotXpYHdS+HlaFQ5EgogCMAD8IEAHhZD4MDniyVA9xkyxAW3s706dMQQhBZ+d2itEMoQADAwO0NDeTTqfp6urCdV2klKExjPqDelFubN/3qasL+4DGqfS8IEBjkol74v2DIMBxHAId0NjYlPRBjY+byWSiiE4AEBpQsE28VUwQBEnu/RD7oCyHKFYPawopBx9ITeihjsfpYN+lKqXW9LC6HAoAITCIqGeiSJp9q/1GHlLELW9C4LouM2fOZNb0GWTTGYr5PBBmsIgzVLiuS319PdlsllwuhxCCiRMn4vs+6XQaIMmzLaXEcRyy2WxiBBECTwdIKRMjGQ9Wa2hooK2tjdamZpRS9Pf309BQT1NTUzLgTSkFgcboMFc41d9yeOgRfTmJcNpZW9stFrB6WAuIaCKyWtPD6BxWD6uQGtXDqnMo4tzOjlIYP5wzoHKSe8sBRRCnIMARgmyujsmTp+C4Lr7vRalZTWLkMIZcfQ43nUK5CuUoPM/HcV3cVArHdQkCHykkXuBTl8sSBJrAaJyUSzqTwRD2K5VSYTDJ5D5KStJuitaWVtAa13XxfZ9MJkMu14BSLjqIcoVLiUEiku4CNttTdRH1300ei0gmjLL13nKoYvWwyon1EIMja00P47knrB5WH7Wph3L3m7z9CARChP36TJgr70AXyVKGJkAQgAnI5DJks/XoQKONxnEgnU4lM4Cm0mnS2TTSlQhHYhTgSFAS6TogBRowUqBcFyeVwgiiZl1AhX0JlZBhBnY/QCEQxhCUPCSQdl3y+XySw9txXLJ19WTr6snlGpHSpb+/gNFhf1HL20OcRWRs6XoNUgok8bZxZNZiObSxeljdhHqoEega00M7Fuft5FDQwyr8uhKDTjNQO409Bz9DK4JSEkepcFZPp3xinHAm6iAIEEKQTmUAQRBofD9AEDa7xvmzgyAI+woak/QF7e7upq+vLzqnTlLexceM91UqHKgkhEiahNPpNNlslvr6ejKZDK7r4jgOUsrBdH9l1zH2Sm4ZK/G99H2fIAjGsEdoLMPnGT4j2wxvsVg9rFaGaoaUqrb0sGx2bKuH+5dDRQ+rzqEQ5YbTvtRVjZASXdbXE0Dr0CDGfT/DGUFTSOGiZIqUmyGTqUtmDC2fzEdF+bHr6uowxpDP56OBZm7S/xQG+5fG5xVC0NDQEJYpWl5fX09LS0tiUGNDb9+ntw8z5N3YNQKjDV7JgyHD0CyWQxWrh7WDkKK29FBrbFent49DQQ+rbgwFZjCzBUJgAzLVh4gntpMK13FxHRchQkPoC3DdsC9oXHEc5YbRGg2O65JKOQwM9EMUjcnn85SKRYwxdO3YyUB+AMdx0H6AVyrhZGTSBzURVCEQUiIxpNIpXMdN0vKFEwxlyERNzCqvwsFsQ6+jFlz+GkYIkUTMRiY2k+FwU6lEVOXLQrIWy6GM1cOqp1b1cOirZPVw/3Io6GHVORTxrYs9+5pxzQ4RBAKJgxEBGgW+wC+VUE6YPck4irRMY0zc7OriSoFXKobNtAT0FwYoFosopSgO5OnasYNt27bhum6YZ7tUoqmpibrJaYKSh44iMo7jhKn0dIAX/QRBgOs41NXV4bpumCIvnUa6DrnGBurqc/QN9CGjJuXkOqzxrBI0GBlVcx0aUFvnLRbA6mG1k+ghEm1kTelhoDWDvqrVw+qgtvWw+hwKIcK+g9G/9j2vHuJnE6OURACBH2DQKCfKxS3AUU7U3CqRgsQz11pTKBTwPI9CoUBnZycdHR309PTgui6lUgljDP39/axbt46Ojg4mT55Ma2sr6XQ6bPodyONFubSVUkn/1EwmQyaTwXEUQqmkz2g6kyHf71REB2IDag3p/mFP76sxtRKDsVjePqweVi+1r4cysblWD/cvh4oeVp1DYYxJkpgJhE1oUZUMPhQ/GoCG0Kh4QJpU+L5PKpXCUYp02oWCwfd9fN9n586dFIvFJCozdepUZs2ahdaa3t7eaPKd8Pfu7m601vi+z8yZM8Mm3EIhmoVeUCqV6Ni2jYkT2mhpaYmaeB2MEDQ1NTFhwgR6erqT5mRLdVJpb22lt1jA6mFtMNhVpab00Ld6WK3Uqh5WnUMRI0Q46DfJykNtemwHJfE0rSKccEkKhRHhNPGum8IEBiUkge+D6+I6DkE06CwIAjzfw3EdcvVZXDdFNpvDdR2CQNPR0UFvby/5/AB1dXW0T2wnm82Ry2bxgwDPK+EHfpSvO0BrTV9fH1MnTyKdTpPJZFBSYaRApiRNjU2kU2m0iQeg2ShftWEECA1+9C/RRF62xlssIVYPq5jBYQy1pYfRvAZWD6uLWtbDqnMokm9VFU7aIrQBWUs+2sGPMOHzcBwH3+gobZ0EqQEXRxgM4czUjgizW6RSqUQMW9taQJKkvkOFubdLgY9wFMJRSNelpb4+aaatq6ujWCzSl88TGEMqiv74vk8qrWhoTJGpUziOwhiB9gxKKtpbJvJWdiNuSoEI4hpqqRIMkgAQHhTTDqpkwnkT0aAVoury0Fksbx9WD6ufcNC8QNWaHuJj36Tqotb1sOocCgib7qRSFfMGWKoPA3i+jzYGKRVRZkNMlPrOSTs4yiGdThMEfvRcJRk3gxd4SZOw53sU/CK+72OMIZVK4bpuGF2JBp/FebHL827HfUyzdfU0NbZRn2tCSonneYAknUojpaS9vR25UlQMyiae1t5SNRg5GIMxUWtSLURlLJb9idXD2sHqoWW8qEU9rDqHYvClNmitrQGtYoQQlIpFgiAgnXZRykVKhRQeSgocx0kGiblR2jwhBEIJ0k4azwuNaKkUTtzT09OTGMempqZksh8Az/MYGBgIs1i4YUq8OHVeJpMjm2lByhS+H+D7RRxVR6lUQkpJS2sLuVxuiAG1VAVicABaGOiL8qvH9tNiOYSxelg71JoehjOvW6qKGtfDqnMoADBgtInyLGuqcP69Q5rYT5ZSoE1AEPgoJUODFwSE/TJlkm3CGEM6HUZHyidoEgh0oOnv7WP9+vV0dXXR1NhIa2srUgi8UgnP8yiVSriui9aauro6PM/DcRwGBgZwXZemxmYy6UaUcDGigA4MwhX4fhgFyqQz5HINCFHeV6D6vf1DBUHcb9TgpFykFARY589iAaweVjk1q4deMMJVWA40tayH1edQGEIXDRNVtANdIMtQjBBAgBQaggKlII+QOUzg40aDteMZOj3fw2iNclNJU60uBRSLJbTWpFIp0k6KtuYWMo5LY2Mj2WwWhcD3fLp37MQLfCa0t5PJZJIoXTzrZGNjI1OnTaW+IQsYHJHDUelwEGNKRE2+JZoa2+jt7cUazerCAC5QcgwZ39BdLJI1obgZO4+X5VDH6mHVY4RAoJHS1JYe7thRE1HvQ4la18Pqcyii9GdCyLAiHujyWIYQNsbFE7dqHSTp7oz2w8FpUT/PuEnX8zxkFLGRUmKMYWBggIGBASZOnAhANpulvr4ex3EoFArk83mKxSI9PT1ksnUASXQnCIKkqbetrY3W1haM8YhnKw2bfdNJiTOZDG1tbXR1dUVLbDSmqhCADCN1XuBFH1A1YD0tlv2N1cMqJ+qcEj2YWtLD7o6OaInVw6qihvWw+hwKwuYeoubBWriJhyJh/74wMpLP55FCIBwHaUAHGqMMrutSKOQpeR512ToymQx9fX309vZijGHHjh309fUlE/AA9PT00NHRQX9/P0EQUF9fT1NjU8UEQnFzb2trK21tbUyY0IJCkc/nQUAqpZL+pBBmo2poaLD9j6uQKHMhEjBSknZTEBgQqqZmCLVY9hdWD2sAU3t6aEdhVx+1rodV6VAACClQ0vYVrT4G32oR5TArloqUPI9cOhP2+3PD5VJKhJQUi8VkQFiY4SJgYCDMq71161aEENTV1SGlpK+vj4GBAaSUpFIpWlpaqMvWYRAYbSiWiiipmDZtGs0tzTiOEw5mcw3pdDgbqe8HKBVOJiSlTAbCxZkxrGNRXQjCIEz4rOTgsrL1FsuhjNXDaqWG9VDrJDBoqR5qWQ+rykIZCIeeSEEgQKqDI01e/CEb/9Q6YdYBg5Rhn8x8vof+/l60MYADShJICIRBpVyAZNKd2FgWCgWklLS2tmKMpqtrB9u3b6OnpwsIB621t7fT3NyMRBEUNV2d3eALZk2bTXtLOymRxiWF8QReCXxPEPgS16nDdV3S6TRKOQhR6VBYqg8jQGMomQBhAhyjo7E6FsuhidXD2iDOliSlrDk9tINyqpNa1cPqaaEQUbYDDFoblJL4nm8HDVUBoxl9Y8D3fUrFIj093Uxqm0RGpfFlgF8qohwnNKoi3C7Op53L5ZgxYwYdHR24rktrawu9vT3s2LEDgEymjubmMLVdX18ffX0DBL5h8uTJzJ49m3Q6Heb3NhrHcchkMlF/Up0MfgOBUg5Kha+WG80kGnMwCPNBiYlnMxe26lsOXaweVi0j62Fot2pKD8vSqFs9rFJqTA+rx6FIEGB0mNTCvuRVQ2xEy19qxwkn79HG0N/fj5IS13WTZtR40JlXGMD3fdLpNOl0Gt/3cRyHpqYmBgYG6Ovro6mpmfb2SbiuSz6fRwhBoVCgt7eXXK6BWTPn0NrailKKVCqVRHTiJtzyHN2OcjBGkXLDLBgGjRDhtpbqZTBqdqBLYrFUC1YPq5GR9FCpGtND+z5VNbWoh1XkUIR3TQAIgcEMf+FrLBnB0EhGNUYB4jKOVrbya9BJn8vww1wphZIKo0MDmi8UyGUaIABBOIgwnQrT46VSqcTQpVKpJEVea2sr/X15urp6MEaF5wkG6OregeM4LFiwgGnTZqCkmxhJz/NIpdOYKA83QiAjgx0abYXvh2VXSiGkCnOE24ntaoDqqyMWy9uP1cMDwSGlh7a7Uw1QfXVkV1SRQxEioyZBLQR4BqHBOFU22OMQQ2ud9PcM09yFywWClJuitakNx0lhBAQywJQCpAYZgCMUTmT4fN9P+s2GfU3DyEtz8wQmTJhId3c3a9eupa+3j0mTJjHn8HlMmjIZIR3yfXmUVPi+h45eBmMMwnXwjSbtppEGMAJhBEaG+b99DDJSXutQVCeJrIkoNV4VfmhYLAcCq4fVx6h6KKweWvadWtbDqnMoyhFRhTDx77VzX0dkd9GPA8mush+N1lUonkjnqAVHoU2Y31pKSTabpaRK+J6Pkop0Ok2hkMf3/QojKkRo1MKm3zqy2SwNDY0M9M+ivjGHk0mjjcFVYa7uQrGA67oox8H3fdxUCqkknu8hEOQyWQLfx2jNQKFIOp1GCEF/vp+Bgf6DYgDgwUjy1kXCGr6G1VdHLJYDidXDt49DQg+jFhZLdVHLelgVgQ5T9iOMQCCQSiGVrJX7WLMYY3YZqYiNamxEKz7KBRgMdXV1SZo7x3VwnQzZugbAwStpHMetmMTHGIPjhCnt8vkCxaKHDgSBD76naWxsxnXdskKSTA4Uzy4K4bJsNks6ncF1M/iexvM0xoR9V13XTdLtxanyLFWMEBDlba+pjqMWyzhi9fDAsS96KKKuaTWlhzbIVr3UoB5WZwuFEAgVDaKt4Rc+Nj6xkarVQcHlKVfL81YbbTA6MoYy7O/pui4ZlaW/fyCMnigHRBAZPzeaYCdMOauUwvM8urt7cFSYmSLQGiFccrksJe2jUi5KuTj1YXqKQqFAJp3BRBWsVCqRyWRBS3TJRwcaD590NozGBEFAti5LU1NTzd7/g52khhuDH/j2o8liKcfqYVUxmh5qrWtLD+OUT5aqopb1sGocChH9GBHldRbgEWCEqbWxZyMSG51qi5IPppXbPUqpMHJmiKJmUZQmMqLKUQS+JlWfBiWhXyAcRdrN4TipJG2rFC7FYpH6egcp+hEKZAq6u3pDw5qqx2hJSmbwiz5unUJH/VW1dAjwkK6LMYZisYijXJRxUEqRzmQwkGTRUHFkLxoUZ7s9VR9xN1FtTGiQhMRD4B4MFd9i2QusHh4Y9kkPZe3pYS07qAcrtayHVeFQRKYl8bLjhaZ8SQ3czF1RbYYzZqzlSrYz5U9J4AcBQaBJRc1zhUKBgXyBTCZDXTZLsVhECIWUDlpDEPgYI3DdFEppdKDJl4oUSgUMGoRKBrp5xRK+74M2YSYMYxCEhjyVTmNMtDwwONGr7LouQkp8o9FRHm7HcXEcxw5Cq2LimUHjWULD6l7jld5i2QusHh449loPRbjM6qFlPKhVPazeNkch0Jhqv39jIo56VKsR3VtcN+z3KVAI4SKEg5QuhUIBIQTZbDYycIIwGCIxWlb0P41bDEqlUpKPW0mHwA8jN+lUFtdJJ+cUQpDL5dBao5QKm5GlIp/PJwYyCAJUNKuslJIg8HHc0MBaI1rFHIR1xGIZF6weVj2O49acHtoW+yqmButIVbRQjIQhHoxiqVZk1E90IJ8nlckStuAKwIuacOtJpVLgh31LpZBIIQmG9BnVxXDCHmMMjQ2NZHM5hJY4joPneXh+McnBrrUm7TgUvBKOE76+qZSL1DKZvE5GaRaVkvi+j9ZhtKi8cu4qi4flwCCSFHmDIlf9MRmLZf9j9bD6iR0Dq4eW8aAW9bBqWyiENogAhBbUUNasQwIBaAQBkqKW5Av9BEERgxf+azQ9PT14nhf22XQdjAr7j0rXQUiQSuA4klTaJZ2S+MU8rpSkHRejBUZKPOOjpY+Px0CpyECpgGcCCsUixgtQRqAM6MBDU0BIH208fN8j0EVcVyJEgDE+pVLJtk5UKVE2TLQwKARCD+ZKt1gsVg+rmbBbiiBAWD207DO1rIdV5VBUNIMacIQM821TK0mzDg3ibHMGgecHlLwS+cIAYBACgkBTKpXo7+9PBoG5qRROysULfIpFH4zEGEk6VYejFEZrXOXiOhmMEZhoVKLBoI2m5JXwfD/sD+r7SCEwWuOVPALfQ2sPzy/heSWKpSLFYoG+/l4QhpIX/h0379pm3iphqH0U4BdL4PllH037bkSTjCz2uVtqCKuHtUE0XCIcPFtDeoi1i9XFQaCHVdvlibilp/qdskMWKSUymvVzYGAgyW8dBD6um6K/v5/6+npcd3AQWKFQoFAoIiDJjQ2KwDcI4YCReF5AypEopSiWwsl/YkqlEinHDRuSPS+cDMj4aO2joiZn3wuGpCU0uG4qOYZt2q1WJMOCZvZRWSxWD2sAKURN6WH8OWn1sFqpPT2sqhaKoVjPuXqJo2dGazCGQqEQDf5SyaQ9QRAaViBZnk6ncd00gQ+uk8FRKRyVIZ3OkXLrSKezCAS+7xMEAV7JI5/PJzOKQmg44zIYYwj8IMmwUSwW8YMgMZJKKRzlYKIMF5YqIqreg09FoBwF0j4ni2UoVg+rGyFlbenhgblNltE4CPSwah2KuHJYqhchwoFkQhm08TEEUXTE4JXCDBV9vf1oL8D4mqDo40oHRzoo5QKSTCZLKpVGa0MQeChlcJRCBOAXC5QKfbgSFAaCAAV4pQKlUh4hdXi+6EcpDcLDmCIEHiklSDkSYYJwsJvtM1p1GDSgQ0Mkk5jZgSuQxVKFWD2sbso/2GtGD+37VHXUuh5WbZcnE005bl/56iIWtvjfUqnEzp07QcBAvp+WlhaECTNMpFIuWms6OzaQSaejKE1AV3c3vu8hhKSpsZG+vn42bdrEtro6unu2I5WL0QGe30/fwM6wH6l2k4hPEPioaObYIAjw/RKOK/E8H98LAJHMQiqEwPM8tm/fjud5Sd9B21pRHcSZ9aWBIMpoYTDjGumwz9pS61g9rE6GOno1p4fYDE/VRK3rYdU6FADG2IhytSKEIJPJ0Nbexro1a8JZHR0Hx1EoFVaDuM9mKvo3btHQWiMECBH2C9WBwBgJ3bB9+1aM8VGOQAiD1j7g4Kg6jDG4Zfmzk25XJkAq0NqQTqVASHw/nBVbyvAcra2tzJ8/3xrOakVAlMsQYeygU4tlKFYPqxchBHXbtjG1t5fNDz+MMQalFG8piXLCLCaxHjpSIgj1SyoVpgMW4RiM0EGQYAR9wE6l0CZAKYGQBq09BA5KZjDGJPNJxDNeh3qoIz3UpFJpBIJAqEQPpVK0AkdMm4YqlQ7MDbPsmhrVw6p1KIQU4SyP1M7NPBCYMkNSsTxcOfK6UZaXr4sZmqu6nLa2Nr70j18KP+4BqcJ5KYwcnNVVAI6g4jkKASKa9xEGfzfGRAPJBJWnMggpkusxCGJtDZ0KkHJwHlkhJBhVcQ3lZY+vabRrHalrwUjrRjrurrolWFdmdEz0smijMdr277VYyrF6ODZG1cMR7P/u9hm639BtKtbNnUtbXx//eMQRiR6KeMI6WbatEChMlGp2tPMOao2UAowoe+bhGyCEqdBIY0J9FKG4IuJ0YJhQD1HJMSvO19Z24PTQBvdGpVb1sCodCmMiz0yF2XeNNaEjUl5Zh1Xc9evh+efDdaPtP5ZzjHBOI0AYkMaQEQIZf+jr8MNfR0YtsaGYiqOJYdUjdhQiYxcbUBNtLQyCQYcBIzA6cliECI0uZtCgEv9E5WXQeA01hGEmNjG4Xdnf5et1mfEfdq/jY4YHLnOUytYZA2+9hZgwYcT7fKgTvjI6SWVosVhCrB6OjV3q4SjbjWX5rrbRxqAFqFw9nHkmUoDUcXpyIj0EIgUL/z+yfpSdJdrWYEQcYIvVKQ6/6egE4bHDPw1CSqSMty93HuRg2nR2HSiMNW5P76EZopHladpHcx6sUzEytaqHVelQxC9Z0sBbO/fzbccYUzHgWAgBdXWYM8+EsvRyybiH8A/iySSGVvrylo3ybSo+lo3BNRKDoRB4SKUQQBBluPCNRutB6ZNSDhrS8goiBFrrJKITBEG4nyBZrrVGCQF6cNxGEGWtEEIQ6NCDL7+GeJkxJnQEhlwnUXaqIJzKdPB9Kxu0XVHGIEi2pWxbYwadmNiYE5VPKZWUJZ1Kcexxx5GbNw9mzRqHp37wEb9jxpiaymphsexvrB6OnRH1kOEfwOXjAMs/eHc3V9HQ9bHdSmmBxlDQfjhWgUgjMPhao8s0KO76FOpHpYMRax7R/uW/G2OiGbYFaD2iHpbvE1/X0OMk5S67Rj1GPRRCEJTpoRiqh2XbSymT8sWzeIfdsFIcd9xx5HK5Ee+xpXb1sKocimHevx8MftxaRkRrzWuvvUZHR0dZnumQ2JgoVdn9B0iWxfmplVKJAYqPE+fVjveJDYTUhqZUFmMMfUERnMHmVACfwfEN8TkHxzuYYX8nBt0MtlIkDkMQ4CoFetBAVebUpqJ8AG5ZK0K5WMTHjI8TL5NlYzyGHrO8zCOtKyce9BYb0/h3pRTp5mZwnBGiUZYEE75v9oPJYrF6uDfsVz2UMrHr8d/VrIfxNcXXEpepKvQwnR7D0zzEqUE9rCqHAsojw0T9Bw9seaodKSXHHnvsqJV6qCEZre/jrgxFxXGi35WvwYB2h+cf0GN4bCJqAh7e/YlkeZLxAJIZYiuOMUpZLTWKkFHjmX2WFgtYPdxTrB4O/91So9SgHladQzGIAc8P+8sbGfZTHOzlbilDlbUijMTog79GZqTtEgMN4RiJlIw6YxrkkGcy9ic02pai4l+xi03Lm25HExFLrWDsoFOLZUSsHo4Vq4dWDw8Oak8Pq9ehkAJpAkASaAHShH3pLRXsytjB8CbOPT3WUAzRbIhDjGcS6WEcJa5sHMOujmkN5sGAgWg2c6ODA10Yi6W6sHo4JqweWj08OKhNPTwwDoXW8OSTkM8PWyUiD1t1F8is2IxQnShHEGYTtRWlnLHcjfG+Y7s73nieryJb0j6cb6iHb9+i6iHMZEEoxL1F3FdWI8VbiPYcEA72J5WCRYvCMSgWyyHEYB90gaPiMVi1FLN8+xjLh/R4f2yLil9GcGbG8Vxj0sOxOEEjZHWyVBfxI/EDv6aGTR0YhQ4CeO01+KM/qlicmEqtMdv7CfozmKOOQjsSIamp0e6WfWc8n7bveWGmCWs8qwpjwuiLQWB25ilt8DHzj8BMbSDp1HHXXXD66dahsBxSxM4ERH31tQ5/M9aGHYqM54e/53m4rjtux7PsJ0xtBUAPnELncjBt2vAPPGNCh8LthXX9+FOnIRwZllQI+0E4BnYVgRhrTuNajFoMHVhXtgKpNaa8GXoPBrCNlnKwGu/Rnuas3pNrGO3Y5QMbR8o5Puo5TJxPPXomuQJyYidm6lSY1giosL43NIy5jBbLwYlBGvCJBuVWn+mpWqweDi//aJmfRtp2V8ct378a71FN6WG4NtoXpJK4rlNTgfSqDfkZCTII8AVkqM0KfaAZbUKZ0XJvV6tR2BNGu+Y42lcqldi0aRMtLS00NjZWpBDcFcl8GUPS4FUr5ekEy59znJM8Hri4p9dgjMH3fYQQIw5+NMbQ09PDmjVrOOaYY5L84yMiABNP1qURIppTpIrvq8VyILB6WEYUdNyjviDGDAskDa4qT9U6ONeSeDtsvBAg5Z7bvGeegVWrwn13RZgmaNhiacI5mgLfZ8fOneRyObLZ7LBUu6MfNrpnyQHHtt+BovwZV3z3xMv38lmXzxsy4jeBMeQLBXbu3MnkCROQV14J2ezIBzsI9LBqHYowkVpZJMFmtNglQyeqMcbQ29tLLpermFRGCIHv++zcuZPW1lYAVq1aheu6zJkzZ7cf2KNFe3bXL3M8+m2OdIzdTUBUTmdnJ11dXSxZsoTTTjuNhijyHU6opys+kLXW+L6P67oIEeYvX7t2LRMmTGDnzp3Mnj07uZ/x+Ua7pt2ti69lT9hd1M3zPJYsWcLRRx9NXV1dsr6zs5PFixdz0UUXkc1mK84fv0OxwzRS2UulEg8++CAnnXQSEydOTCJd5eXp6upi8eLFzJ8/H8dxdnn9lRcV1nMd1M4gNIvl7cHqYUKxCD/8IUyYMOomyaRgkDgJ/X191GWzOGVzTggpCXyf7u5umltaAFi7di2pVIpp06aFdmtXGaOi4wMVg6V3N24vWd/XB3/1V3uuhxs3Yi66CBobK85dcV4z+JYky2O90prOzk66u7p4ZelSTj3lFDJTpiCVIhgSdIJwRnDf83BTqXASWt9n3bp1tLa2JnoopKy4/tGuKFkXOXDD7svu9GKEj/bkOZRl3yo/ZoUeZjKJ87h9+3aefPLJRA/jXjDxPUv0cOgg++jvUrHIgw88wMknn0z7xIkoGTsEg2Xr2LCBu+++m4+4Lpl8HlOmx7ukBvWwah0KIcOPOAdTSw7afmEsH/Faa7Zu3UpfXx+9vb0cccQR/O53v+P444/n6KOPxvd97rnnHhYtWkR3dze33XYbf/u3f4sQgueff54ZM2YwZ86ciij83pZ1V03KY/64HOXYpVIJIQSpVCo5dvmMniPlD4/3XbduHY899hi5XI4JEyaglEJrzapVq3jssce45pprqKurA2D9+vXcfffdXHvttaRSKbTWPPjggxx11FGsX7+e6dOn09PTQ0NDA67rorWmr6+PUqlES0tL4sT5vs/SpUuZO3cuTU1NFc3MJjJo9fX1yXnLP+pj525oVpIgCBgYGOCZZ55h0aJFSV/YoRGSpUuXMmfOnIr71dXVxUMPPURTUxPnnnvusIhUsVjk3nvv5ayzzmLChAmJ0xRPfiSEYMuWLfT29jJx4sSkxcPzPJRSSClJpVJ0dnbS2dlZ4czsingLqRRSVHe0y2J5u7F6WIbWmClT4MorAUb8iB+mh/Pn85s77xxZD7u6uO322/nbK69ECMHTt97K9BkzmLpoESaekG1vyjlCq8jQD35uuGHPWlpihMCkUpSiiHsqlUqOPUwPh5QBQodi3ZYtiR62Tp2KqqtDGzM2PZSSB554YlAP582rXj00BqRk6cqVzDnySASQcl2MMezM53lo8WKaJk2q1MPo2MVCgXvvu2/XetjVRU+pRHs6jY6Cj+V66DY0sK2nh85Uimlj7WIX/Vtreli1DgVx05SB+PYeynZ0pP54YWU0GKMJAk2hkGcg309PTxda+/T0dtPX1wuElevUU08llUqRz+fZvn07Qggcx+FP/uRPhp2rvItP+SyiQRCEZZAy9MajZs94HYAbVdaYfL6frdu2UV9fT3NTc2Rcxvo0TWRvw/K88srLNDQ0cuSRCygWi2zatBmlJOvWreG4448nl81Gxw4rdmxctNZMmzaNOXPm0NHRQaFQIJ1OI6VkypQpnH766Ugp6ezspLW1lbb2CZx77iLe2rSRl158iUWLzuGII46kqamZ/v7X8bwSv//9b1m48BSmz5jO9o7t/OLGX6CDgA996MPMO/xwMOEH+uLFi8lms6RSKfr7+1FK0dzcjNaahx56iHnz5nHyySfT1b2TZa8tw3Ecnn/+D3zgAx+gLptFSUUQBBQKBXK5eiA87rp16zjqqKNoaWmho6ODiRMnkkqlkmfU2trCc889y9NPP8UHP/hBps+YQX19lssuey8zZ07H80q8vuJ1mpubmTljZtKdKQgC+vr6EELQ0dHB1KlTyeVySVenmTNnIqWkv7+fTCbF8uXLufXW2zjvvPOYM+cwnnnmGVavXs0LL7zA9OnTk3dquGNR9ikgBn83BpLG9L10bC2Wgwqrh5VEGlXegrBLPTTB6HpYKFTo4eX7oocwsh6WHS/Rw1w9rUYj98jGRXpoNMZoq4c1pIdL3nqLqf/n/4StH/uih1UcUahah0LIsJKavYsNHHRorXn99deZNGkSLS0tSSR++/atrFj5OqlUipNOOonpYgqFQgGlBEJotAnwPI9HHnmEFStWMHnyZI499tjEw96+fTtPP/00AJdeeikAhUKBl19+mdWrV3POOecwe/ZsIIwEPPzIw8w/8nCmTpsJWqCMoOgXefHFF1m1ahWLFi0Kmz/Loge3334r/3vr/zJr1mz+8YtfZOLEKXtYJzTGBGG/QmlY8srLHH74fJ555lluvfVWDps7h/vuu4t//Md/ZObMmTQ3t7Di9Tfo7u7mvPPOIwgCNmzYwJQpU/A8j+bmZvr6+mhubgYgl8uxfv16WlpaePTRR3nve99LLpdmy9ZNvP76Cn796zvI5XK0tbXT2xsKkueXmDZ9Cm9tWk93zw6eeeYZUinFu9/9R3Tu2MbM4gyUctm4cSMf/vCH2bZtG8888wzZbBbP8zjjjDMIgoAFCxawdu1aTjzpBJ55ZjG/+c1vWLBgAQ899BDnnreIjm0dHHPMMTz+xBP09ea58sor0VqzevVqrr76ah566CFmzpzJG2+8wQknnMCMGTNYtmwZc+bMoeQVeX3Fazz3h2c49bSFrN+wllmzZtG5YxsvL3mBd7zjHfziF7/g6KOP5tprr6VU9FmxYgWXXnopq1at4pVXXiGfzyOE4LDDDmPJkiXMnz+furo6Vq5cydNPP8XESW088sjDtLS08OJLz1MshmNUJk6cSH9//x526TL4fikUb8Su280tlkMIq4eVaK1Zvnx5jevhLL40YxbtRu+hmdMYNBBYPawhPSxs2zY4ZuMg1cOqdSggNKKH9OCzMnzf58knn+T000+nubkZIQT9/f386Ec/oruni3nz5nHiiSeitebOO+/krLPOAkID5nkezz33HG+99RYAxxxzDMYYisUiv/zlL1m1ahWTJk3iwgsvRCnFww8/zNNPP82bb77J8ccfDwx61AtPPpneru0seeRxTjjlNLxMipdfeonf/OY3DAwMcMopp1SUOwgCXnjxRXbu3Ek+n6djewcTJ07Zq3uwdOlSnn/+eRzHJQg83nxzFZ07tuK6Yd/+hx96mP6BflqaW1m58g3mzTucc889l02bNlEoFJBSUiwWaWpqGjbI2hjDz372M/L5PO9617vI5TK4rsvq1W/gui6PPvYwdXV1nH766aTSkt/99jesfnM1O3fuJJPJsGnTJrZu3crKlSs555xzOPmkU9ixYwebN29m7ty5pNNp7rzzTtLpNOeddx5aax544AGeeOIJ5s+fjw7CJuKOjg4g/AC//vrr6e7upq6ujq1bt3HE/KPQWrNx40aEELiuS0NDAz/84Q8BmDdvHsVikVWrVnHkkUfgex5Lly6lrq6OW2+9lSAIkFLS3d2NlJKXX34ZYwwbN27E8zzeeiu8T67r0tzczL333kt/fz9HHnlk2Dy8cyepVAohBDfeeCPpdIqu7rBb08aNG+nt7eO5Z18gCALq6+splUp7/IwH67uNwlos5Vg9HCQIgoNCDzuz9bTv5SN9ZekrPL98udXDGtFDz/f3+BnXmh5WrUMhIOqDWO238O1BSsmsWbNobGxMKn13dzcrV61k9uxZLF26lB/96EcEQcCrr77K6aefjtaa3/72dzz37PP09vayc+dOnnvuOTZt2pQ0GS5btgwhBMuXL+cb3/gGQgjWr1+PEIJ8NPFgbGC6u7tpaKhnydOPc9//u5Xnj3iMNRTZtmUr/b29SeUqFz3HcVi0aBEbN27gsMMOY/LkyXvVYmeMoVAosG7dOk488UQ8v8AJJx7LAw/eQyrtctVVV7Flyxbmzp3L1q3bOP30M2hpacEYw/r16/E8j2KxyLZt25g7dy4TogF9pVIJz/MSIxIvH8gPsGbNGqZOnco73/lOtm3bijGGmTOn09zcGDZni7CpdefOnSxYsICJEycyb948ZkyfgXIUO3bsYMOGDezYsYOdO3cyceJEJkyYwBFHHIGUkrlz56K1Zvr06fT29nLsscfx5JNP8s53vpMpU6bQ399PS0sL3d3d5LI50umwmbijo4O1a9cyadIkNm/ezMknn0wul2P69OkYY1iyZAl9fX0sf/1VrrnmGuYeNpeu7i5aWlro6elJmoGVUkyYMAHP80i5Kbq7u/jDH/5Ac3Mzb775JscccwyTJk1Kxta89NJLrFixgrfeeosPfvCDHH30UXT37Eied3//AEtfWcasWbOYNm0aEyZM2KOsIQKRvD+21lssg1g9rEQcDHo4Zw6TJk1ir6yd1cOa08OJTz+NGGNGr1rVw6p1KIaP/Re10OKz3xBCUCwW8TwvaZ7N5XKcf947KHlFZs2aHXa9k4Lzz38HbW3tnHjCSWTrGgDBhAkTOOqoo5BSopRi1qxZZDIZLrzwQlasWMG6des45phjcF2XE088EYBUKsXUqVOTPqSPPvoop512Cr07u5kxZQpnLTqbw7MCqSUyGhg2efLkinJLKbn03Zdy5plnkq3LUl9fz54/RYEQkoULT+HEE05ix45O7rrr96xbt46TTz6Zv/iLj5FO15VFWEBJN8lWNHHiRP793/8dpRRXXHEFJ510EplMhlKpxCOPPMKrr75KoVCgra0N3/dZtmwZzzyzmJJX4hOf+ERU5nBSKWMMRhukdDjjjLPYsGEjN930S/7k8j+lvr5+cMA8gpaWFpYsWcITTzxBJpPh8ssv54UXXkgiavPnz+fwww+nv7+fe+65m+f+8CyLzj6HS971bpQT9tOVQqKNjgZmhctmz57NXXfdxeOPP8706dM5/fTTWblyZXL+U089laeffpqLL34XZ599Nm40IC7J+RH3w4yzOQkw2jB79hwefPARfvrTn9Lc3MwFF1zA2rVryWQySCk588wz+cMf/sAll1zCGWecQSrlMlMMjpF4+eWX6erq4sILL2TGjBnDBs8Nwwx+HonoP1prTBAPKjyUa7zFMojVw0oEHBx6ePsde3X1iR6ee77VwxrRQ7lqVfj0DmI9FGZv0/nsC54HN98MH/zgMG8trqx+d56BR17DffdJZJXCuCKa0Ke6b+j+oDwTwu7SsZpolrDytJ+jHbNYLPKrX/2KxYsXc/rpp/ORj3ykIlVc+fmMMfzmN7/hySeeICh6XHPN1SxcuBAclTzDXUWjw9jaYNn35DkOvQatNW+99Ravvvpqkr50V8fTWrNjxw6klMncE/H2O3bs4M0332TmzJm89dZbTJ06lbq6OlavXs2MGTNobW0dFmUqp1QqsW3bNqZMmTIs5W4QBHR3d7Njx45wQHpzM729vTQ3hwPTy5uXPc9j+/bttLa2konT2o1CHE3L5/PU1dWhlKJYLNIYpRAsT/26p/fZ8zxKpRKO4+A4Dr7vJ5G2eJuR74ehWCwRBEFS/t05FCYcbYYRUb3vGWDb756n6YyjyBzWHs6TLUDc/D9w+eUQZf6wWA44P/kJXHfdfj+N1cMRGBjA3H475k//dAx6GFKe9nOkRA+xHt5888089dRTnHbaaXz4wx8eca6dePvf/Pa3LH7iCYJiiauvvoaFC08O9TDSwficwyLScfYnY8LvoI98BDHG+ZCSa7zjDnjnOyHqrmT1sAb08Le/hUWLEKOkOx6zHh6IrE+PPgrnnbfbzaq2hQLKUoYdikazjNFSocbrKv5GVDixu6pAqVSK9773vZx22mlMnz69olInTW1lFedd73oXJ554InWZOia0t4GQIEdujouPU1GuMZRpJIZdoxBMmzaNqVOn7tK4xUgpk6bbofexubmZk046CYC2trak3HFf2d112XFdl2nTpg0ra5zmrrm5ORnsBjBhwoRhH9tCCNLpNFOmTBnT9SilyGazFan14vkyysuwK8q3KR84nUqlkkwgEF5/+T0o36/8Wo0J36f477GURTAk8ho37RrbscNiGYrVwwjXBSkRd945bNXQO7O7v5PlxpDWmiuk5IJjjmFCJoP7+9/v0g5dWixyxty5pFIpmjdtRPx+06ANgwpHQlDm7JRv09S0b3oY6YXVwxrRw12VgdrXw6p1KIQQgx6lGNrke/AxUkvCSC/reKKUoq2tjba2tt1uK4Qgm80yZ86c3W5Xzuge/N4z0vF2dfxdfeCOZCDHeuyxZGsY6xiCodvuMqrPyPdgV/vtikpDWNkaNtbylx8j3mdvyiKlsplsLJYhWD0ssyeui7jmmnE/nwQao5/RylJu09LA7tKLVNhAM3y25vHA6mGN6OEel6T29LBqHQqEQO1F087uenCVP+ixbDce5xxrOTzPQwgxajPr6Ocr/3vQUIxWrt1dWzxZmRAimZCmnKHzNpY3bI6WJnRoWfbFoA5r/Rjlme5JhGLXxEZl8PfRzMNY34XdlWlfeyKOZf+h9y2eHVwIQalUIpsd7GIUbrNrITTGEARB8s6MJsSj7JxsX0sT+Vgsbwuj6WEQwLPPhrNHj8DurEBcK8e63VjYG8s1rBzGEOgwnarczUDWsZxvWPR3hHOPevxID6UQIw6q1UO2L39KQ9VCRP31k7KkUnD66XvU5WkoVg93T1Xo4eCKg1YPq9ahMBiUF6AxSAN6D8ajlL88sZc5kncZpw2LZ5bUWieDtPb0PFprisUiSqnkJz6H7/vJeVzXHVYOYwxvvPEG9957L8cddxxnn332bssQT5wjVXhtQeDT3dVNU1MLrptOylYqlaI83OHxstlshTdf7oXHfwdBQH9/fzJBXS6XqyizHNwYYww7urpIpVL09PTQ3t5e0UQYGzvf94c1Re4Lox0jdoTGv0VHA6GwhIO2BAMD4X11HCeZoKdQKABhLm/Hcejs7KS9vZ2Ojg5aW1tJp9NjLtumTZtoaWlBKcXSpUtpa2tj2rRpSYo/pRQbNmxg06ZNLFy4kHQ6Peyd1FozMDBAPp+ntbWVzZs3k06nmTRp0rDnvn37du6//37OPvts3njjDc47/1yUCo/z+OOP09DQxEknLhw1SrNlyxaWLVvGqaeeyqpVq2hubk5m6t4dYVc9YVNjWiwjMKoeeh68/DK8612j7zuknhN91A6txzoIEEP0UEmJHKselh9rHPTwvvvu4/jjj+fMM8/cAz0EKcAPArq7umhqasV1B2eR9jyPQj6PjIJ2dXV1oV6UXcPQ++X7PgMDA8kM0EP1UAxunOhhOp2mp6eHtra2Cj0sP6YxBvfhhxEnnAC53Jju8WhYPax+PZywbh2zzj57TNdaq3pYVQ7FMC8yruhjaOIt37dUKtHf309jYyNbt24llUrR2tpasZ0xhh07dtDc3MymTZsolUps3ryZY489NkmvVlmUkSP+8bJCocDPfvYzzjzzTI477rhkXVdXFw8++CAnnHAC/f39FXmsy/F9n6lTpzJz5szR70cZhUKB5cuX09vbxdmLzqK7u5v//P5/cs3VH2L+/COS/Tdt2sTtt9/Oueeei1KKE044YcRjl9+XdevW8b//+7+ce+657Ny5k3e/+92D25uyaJIgyfM9efJkurq6eO973ztspmxjDK+//jqbNm3ine98525bYPaF+Dntr+P39PbQ39/PpIlTeeSRR1BKceyxx/LjH/+YdDrN1q1bWbhwIfPnz2fSpEk8++yz/Omf/il33303559/fvKBvbuISRAE/OIXv+Css86ivb2d66+/nmOPPYaPfvSjLF68mFdeeYWrr76GX/7yl6xbt44jjzwyMaDFYpF0Ok2xWGTr1q10dXXxgx/8gKuuugqAhoYG2tvbR2zuXrNmDccddxwrVqzgnHMWoZSkVCrR1dWFUg6FQiER4fJrCIIgeXc8z2PZsmVcdNFFe3Rv46hP/EFjsRzKjFkPm5rgsMNG3bdULI6sh0olxzHG0Ll9e6UebtkyqIdDyrZbPcznK/VQqVAPd+wY1MOBgVAPy8oRHQS/WGTKmWcy/aSTYObM4dsMoTAwUKmHXV385y9vDPUwmojOGMNb69dz++9+N6iHRx0VThhYfi1Durqse/NN/vfuuyv1MP6IHKqHQcCdjz02qIfHH4+by1WU3WjN66+9FuphQwOKvesOMxasHlaPHr57D+dlqkU9rLq2lOFdZvas65HWmi1btvD1r3+dxx9/nGXLlrF58+ZhL0upVOLnP/85jz/+OKtXr+a2225j6dKl7Ny5s8KYDP09joTEvxcKBbq6ugBob29HRYYzXi+EoLe3lx07drBy5cpkne/7+NFEJ0KEMy82NjbS2dmJECJpbisUCkk0Iy6H7/ts27aNhx56CD8Ip4ZPpVJMnz4dIUVSRq01dXV1yWQ+mzdvrrjHcRnjf+MXt6mpiUwmQ319PT09PWit6ezspLe3F1/7BEaD5xPkw2b2iZMmkU6nk6hUfI3l96Gjo4O7776bgYGBPXkd9og4ErMn/TR3xeA1RL9jWLZsGd/79++xY0c4gc0jjzySRDM2b97M/PnzmTdvHi+88AIvv/wyM2fOZGBggK6uLp5//vnkfsctQfF7EN9/3/fp6ekhCAKmTZvG4sWL2bhxI1u2bGHbts309O7ghRef5c01q1i8eDFr166lq6uL/v5+tNb09PTw//7f/6O3t5fu7m5+/vOf09bWxhlnnMGjjz7Km2++SWdnJ0EQsGrVKkqlEh0dHTz77LPU1dUxf/58mpqaKBQKBEEYmbvhhhu49dZb+e///m/+8z//k4GBATZs2MCGDRuS69m4cSPpdJpMJsMZZ5zBFVdcMWJ3ud09PylFlAZyXB6hxVLTWD08uPUwn8/vV2fC6mH16KE6BPSw6hyKcgIdEMdikijAkMo5aOQMAwP9rF79BhMmTOC4447lvvvu4/XXX6erqwutNaVSmMarVCpRKpU46qijeOihh+jv7+ess86iqamJrq6u5OX2PC/Z1xhDX18fv/3tb9m2bSsQGoaHH36Yu+++i97eXh5++CEefPABfN9j27atPPnkE6TTaY499lgmT57E9s7tiXF7+OGHePTRR5PyP/vss9xzz91s3bqFQAcEQcADD9zPt771TV5Z+gpB4BPoAN/3uOuu37Ny5Qr+7M/+DEc5GGPYunUrDz/8MA8/9DA60Lz22mvce++9ZLNZJk2alFxbbCQ9zyMIAoSADRvW09HRQVfXTlatWgkYJkxojdK5KbZt6+A73/kO3//+99mxYyelUonizl5uv+GXrHpjFdOjZsfW1tZk8p+uri6WL1/OsmXLWLFiBfPnz6e5uZmenp6KZ1z5DPed2IiOV0Qmfl5B9O+KFSvZsHEDd999D6ecckqS1u7SSy/lve99L3/1V3/FwoULSaVSbNy4keOOO47HH3+cBQsWsGTJElauXInWmuXLl3PjjTfyi1/8gttvv53+/n6MMaxYsYKvfvWrPPfcc1xwwQUEQcAxxxzDJz7xcTJ1Kd54YyVr175JQ0OOWbNm8olPfIJ3vvOdyXHT6TQXX3wxjuPgui6TJk2ir6+PD3zgA1x33XUsX76cjo4O8vk8Tz31FJs2vcUdd9zOj3/8YzZu3IhSCs/zcByHYqkUiQdk67JMnDiJ448/Hs/zWLVqVTKzqNaaJ554gq6uLnzfp7+/n23btvH888/v8f020U9ZqpRxeY4WS60zoh7GP1YPa1IPe3t6KlsvrB4etHq4dOnSPb7ftaaHVdXlCQab6AxEzYoCHebOIr6Z8UutlIMQoHVAqZTnvvvv4f777+eLX/wiV19zJds7dvKDH/yAyZMn09HRwZIlS1i4cCEPPPAAAwMDvO9972P69On87Gc/49Of/jT5fJ6+vr6kQt93333ce++9fOYzn2HmzJmsWbOa227/H4TUvOMd76Cnp5tXlr7I9Okz6O7pBBGwctVy1m9YwyOPPMKTTz5JS2sjA/kepGqnWOzHDwoYz/DwIw8ihGDRokUEQcAdd9xBR8c23li9khM7jkNKSeuEJhAB//qv3+YfPvsPHHXUUfT0dfLsc0+glMO06VN46umnmDR5YvKh/sYbq3nhhRe549e/ZufOnUyZMjXsCxv1Z4zFYOnSpUycOJEpUybx8pIX6e3tYcuWrZRKJU488QRKXh6pND293WzYsIFzzzmPW2+7jf/7f/8vl7/3EnY8+QpvvLKU1Tve4pTTwy5XkydPplQqobWmq6uL1atXY0zYt/HUU09l4cKFIza/xsv2Z9PsvrBz507uuOMOent7eeWVV+jvK7By5Socx0lm+TzssMPYuXNn0od04cKFrF27llQqxfbt23nXu95FoVBgy5YtHHHEEUydOpXzzz8/iSBlMhkAVq5cSWdnJ/fffz+f+cxnOOyww2hqamLRonPCSXXSjZyy8Gyy2TqOPHIB2WwWYwyvvPIKEEYan3zySU477TR+/vOfs3PnTk4//XT6+vp46aWXAHjuuedYvHgxvb09PPbYI/i+T29vH9/5zrcBwYIFC2hqakIgkcIlk87xuc/9I08//TT/8z//w5133kk+n+fiiy9Oxh0ddthhyfP70pe+RBAEnH766WN+pkYAAoSjMBICYVA1EpWxWPYXY9FDYwy+51k9rEU9XLFi2Cei1cODUw8vjJzLsVCrelhVDsWwbAUQ9mekMrFCoVDglVde4dhjj2fFitf/f/beOzyO67r7/8xsxaIsegcJ9l5EUiQlVkmkJEpWi2VbsizXJLYTO/k9KXbsvE7ivHEc23H82oot2ypWNVVISaQkUuydYAcIgui997KL7Tszvz92Z7i7WBTKKgC13+cBCezeuXPbnO+cc889h+PHj9LW3kxraysul4uGhgbS09Pp6upCp9Nx4sQJDh8+jN1uZ8eOHfT29pKfn89nP/tZOjo6GB4exu12k5QUTCEfvEdDQwNdXV2cO3eO/Px8fH4fDoeDl156kXfeeZuhoSG6u7tJTk5m//59DA4O4nK5+MEPfoDZbGZwcJAf/ehHpKen83d/93ckJSUF+igE9E67fRhJ8mM0mvjWt76Fx+Nhx85X+c53vqMJOkmSGB4eZu97e5kzdw6CEHhIrl4toba2Aa/Xy+XLJYiiiMvlor6hkid//0vS09JwuQf4f//vv0lIsLJ27VpSU1O1euvq6rRU9zNmFPKzn/1M21ouK7tCcnIy999/PxaLmd8/9b/4/T58Ph8tbTaef/55VqVPJ60gh0uNjTQ2t7Fp4yasVquWnXL69OkUBn1XVSvQnDlziI+PHyEo1a3wD2pr9oOE2k7Vmrdx40bi4+O1Q+srVqwgPj6evLw8MjMzgYDv5YIFC7TcHvfccw/p6enccccd+Hw+ILCNbrVaw+7lcDioqalh8+bN5OXlYTKZ2Lhxo3Zw8aGH/oy4uDiWLl0WFg97/vz5TJs2DVEUMRgMLF68mIyMDLZu3YqiBA7Cv/rqq3i9Xr7xjW/gcrmorq6mt7eH5cuXM21aAQMDA/T3DyCKOnJycrjllluwWCzY7XZcLhd5eXls27aNuXPnMjg4SH19PbNmzaK/v5/e3l4OHDjA1772Ne677z6WLVuG1+sdN8FSNKiuCcHR/xNmLoYYpjYmxIeKgsftpuTixRgfTkE+tLS2jsh/HOPDAG40Psw9f/66x3uq8eGkUijCIAiBjJNRtLKqqioGBgaQJInXXnudrq527rp7C499/jH6B/pJT0/n5MlT1NU2cP/99+NyuXA6nSQkJNDd3U1qaio6nQ5RFOnp6WHx4sXEx8cTFxenZTXs6upCURS+/OUvk5GRQU1NDaIgMG3aNBYsWEBhYSEtzS0cO36MtLQ07rnnHu0wcmJiIoWFhfT19dHf34/BYCAtLY1NmzZRVVUFQG5uLgB6vUHzGQWFb3zj65pvp4CATn8tGsXVq1dxe2zk5uWQn1/AljvuCn5PSBZImYTEeJKtydjtNlwuP3qdCavVSlZWFgaDAa/XS01NDd3d3SQmJdDe3sJXv/pVbrrpJnw+H7IUiL6RmJTIpz/9abZtu5PqmmqqKqu4e9s2TMYEknQmFASGZR8EH1IIJHOJFIRqhI65c+cGp3bk4afJCkVRSElJ4Zvf/GbYZxBod2FhIYqiEBcXh6JcizltsViwWCwA5OQEopVbx0lipNPpyMzM5LOf/awW/UJNVgSQmpoaxacazGazZtExm81aYqLbbrtNa++cOXO0xIVut5uLFy+Sk5PLihWr0Ot1ZGfnIghovppq8iGTyUR/fz+vvfYa06dPx2KxMDw8DEBSUhJXr16lvLycNWvWkJ6ejtFoZPHixde9zS4ogfUuQIgAjSGGGIAx+bCuro6B3NwYH05BPhQuXRrxmhjjwwBuOD5saxsz/HEopiofTmKFIhAxQdQsNNe2d202Gzt37uTcuQsMDQ2xZctWHnroobD493Nmz2HzptsRxcDDG7rg1EVot9vR6XR8+ctf1iwJarnExETS09PZunUrPp+PCxcucPDgflatXMWDDz1EQnw8g0NDtLW3MWvmLG677Tb0usACVRdNRkYGIKAoMooC8ZZ42jvaOXToEKKo4zMPPzwijGpmRhYZGZnXhkFQ3bwkTCYzp05VkpWVxd133YM1KTUYVixk0BQBNTW72WwF5doWqro7YDAY+OxnP8u5c+cYHrazdu1aVq26ORhSNlCPGrdbrzdgMJiZViBjMiSSkpyFaAhkgBQUhQQhLmSOose6DhU4Uad6Em7rqhCDEUDGamO0Pk+kfCjUeUlJSdHCGUfWF9mO0VzHon0fGnbRbDbz8MMPYzbHIYq64LpXrwlvl9lsZtu2bWzfvp3jx4+zYcMG7r77bi1835w5c1i3bp32QjKRxEijIkhAOlGnvThdR7ToGGK4cTEKHwIMDw/H+HCq8mGUvBYxPozxYbADU44PBeWDOv1zPfD5YPt2ePzxETOmHmrxObx4911Bv20ZxjgjgnjtBLnT6aS4uJj9+/ezZs0aNm/erIXuCqmJ8Ybe6XTS3NzMrFmzNG1VbYPL5eLYsWPccccdGI1GFEXB7Xah1+vR6fTBONoSdvswoihisVjQ6cRx7hkYar8/sI2l0+sCiyUK1IgX1yLlqNEVAtrqNWH//peX6ht77QEaOReh7VHDvY54cCOunMwC8Xow3qPxQfZTXfdOpxOLxfInZZse7z4Tgea7HXQzUA9uxsXFodfrw9o3UYKJ0pjAmhYCv0vDbob3lqJfNg3TnBz0ggyCgLj9FXjoIYiLi15PDDF81Hj6afjzP//QbzMuH7rduF9+mYvz58f4cCry4fbt8MADELTeTxg7d8Idd0DQcv5RIMaHfyIf7toFGzYgpKWN1piJ8eHHkeju6FHYvHncYpN3hwJB04ZDPwMFs9nMqlWryMrKIjMzM4rwVMsGfxtFA1bDgkXTIk0mE5s3b9a2LgPbrJZwn1ZBJCkpacQ9QwVxtDaFJvoJ+1a45jMbrT8BY8/EM2pGs4pEfj7a7+oDHc3aEPq3oihRD5VF9mestk1WfNRtFAQhTHjCyDUUzQozWl0TKRdaPrRM6BwGXhp0xAVf6EdbC+O1YUzIgXWkKPKU2uKNIYaPBqPwoSBgMplifDgBTGo+nHAvPj7E+PBP5MMx7xiBKcqHk1ahUFCQZQWj6uoU8p0oiphMJmbNmoUgCNrBMdVaEM1qEA2h5SKFnhAU1JGLSp3cUAvOmP0YY+EqioLP59O2edWykdktQ9umlgm3plzTnl0uF2azeUQOAPX7sAyf47Q/Urj6/f4x+z2a8FfvrbZ3MvuIfpwInfPQ8VJ/Hy1bbGgsb3WrOBqircXRSFa1wKkRUSLLqe1Sy43WjwlDEFCCfdFNCXqNIYaPDmPxoRDjwynLh6IsI3wMTiJTATE+nHp8OCnf7LQJECInXYkqWBwOBxcuXKC4uFjbGg1dUJFCMNI6ESk8VUHp9/vx+XzaNldtbS3bt2+noaEhTFhE/kQKOjXGtcvlCot7feLECX73u9/R0dERlsjF5XKN0WYFr9eL2+3B5XJp/ZUkiUOHDvGrX/2KlpaWEf1XFAWHw6ElBRrtJxShPoDquRU1HF+08mps89C+A/T29uJyuejr69OiOsQwOlwuF3V1dYEDgbJMa2urdngx2ny1tbVx+vRpamtrqa+vH3VeXS4XXV1d+Hw++vv7tIOWar2B50DB6XTyxhtv0NTUxJUrV7Q1qz4Xathmp9PJ2bNnA7G2hx2UlJTQ0dExguTHswYFEPQr1ukQxKkjQGOI4cPGmHzINctnjA+nHh+q7Y1hdNwwfKj+3MB8OGkVChFw6xUEmTChGVpGFXytra0cO3YMh8MRVibUgjLei03oQmpsbOTo0aO0tLTQ1NSkXVtZWYnf7yc+Pn7CfZFlmfPnz1NRUcHRo0ex2+3a5729vRQWziA+PgGHw8XFi5e4fPky27dvp6WlJVorURSZkpLL7Hn3PYovldHa2q4taqPRqEUXiIQkSezatYv+/v6oYxEp9CK1ar1er0VOkBUZPwp+rmX+7OvrY/fu3Zw8eRK/JCEr18L8vfbaa5w+fZqXXnqJ9vb2CT5Qnzyo89jb28feve9pZNTb20t5ebkWQk79Ucd3cHCQo0eP0traqq1XNRGVJEl4PB6am5vp6Gzl3//vv3D02EEqq65SW1utzV/gfz+S7EOWvdQ3VNPd00ltbW3YfPX29nLs2DE8Hg+SJNPc3IrT6eLJ3/6OXbvexul0apaaCW3VCgHRKQsC6AIZQRU5tj5iiEFFjA9vXD5sa2+PFrgrBm5QPhzv3WeK8+GkVCgABCF4OEVFlAdendjZs2fz0EMPaSft1e9lWaa/v1+zqoReG7ktpn4OAQtPcXExHR0dVFZWAgE/zzvvvJPp06eP2IaL9iPLsnbf/v5+urq6aGhowGazoSiBbbi7774bvz+Qbv4Pf/gDr732Oj6fD5vNxvDwcFgb1SFQFAWj0Uh//wAOh5Pa2jrNr2/lypV0dnZq9w190CAQDWRoaCjsc7WcJAWykUbT5AVBwOv1cv78eSqrqpCUQJIVUQEJ8Et+Xn75ZcrLy5k+fTpup5OhgUGU4FZ1fn4+SUlJFBYWjrpN+UlDtDUjCIEQdllZGTz66GcpKiri2LFAGEbVMhbI4DoIBASu3W4nOzubadOmkZ6eTn9/v2YZe/vttxkeHsZut/PUU08hCgJr1qzm0KGD1NXV0tXdFcx2WoXb7cY+PExx8SVEnchNN91EeloafX192kuIer+6ujqGhoYAhSRrPIOD/dTXV3PvvXdTWDgdr9fLuXPn6OvrG/FshSHgBB34PvhyIMsS6kHLGGKIIYAYH96YfKiP8SHwyeDD/v7+sP6OwA3Ah5NSoQgsKAEREUWRR9XQ1Ie/t7eXd999l+rqavx+P93d3XR3dyNJErt376aurg6/34/NZuPEiRM0Nzfz4osv8tprr2Gz2bDb7VqK9MHBQbKyspg9ezbp6ena9qvP5+PMmTO88cYb9Pb2IkkSjY2N2Gw2ysrKtO1br9dLU1MTx48f54c//CFnz54lKysLs9lMcnKytvjdbjdvvPEGJ08eZ2Cgn5aWJpYtW8KSJUtYunSpFh/7yJEj1NXV4fG4OXPmDCWXL5Gfn01aupXUVCtdXZ3IsozD4eDFF1/kyJEj9PX14fV6tS3Vjo4OWltbSUlJ0batjx49yuDgoCZwOzo6qK+v17T+UO1fURQ6OztpbGzk7Nkzgc/8Ej3tHRw/cZyhoSEt8oclzsKrf9zOD//lXzl/7jx+r48ZM2YgyzLZ2dkxl6cQqJaTxsZGfD4vfsnDG2++Tk1tJW/tepN3332HP/7xjzidTjweD/39/Zw5c4aLFy9qQve9996jp6cHSZJISkrC4XAgyzJ6vZ7ly5cjCAIul4vMzEz6+wf5/KNf4Fvf+ltqaxtoaW6hs7OTN998k/Lycvbv28cvf/lLzVViYHAQSQoQvN/vp6amhri4OB5//HEqKiqoqqpEFBXcHgd33rWFXbvfpLKyiqKiIp5++mkOHTp0XbtRgiAg6vWBII2CQnRbbAwxfLIQ48Mblw/9kjS+1foTghudD0+dOoV8g/PhpDyUHfYSEmX81e8HBgbweDz88Y9/1ISYzWajoqKCvr4+br/9drKysqivr2f27NlUVFTw9NNPs2rVKi5evMjw8DAzZsygrKyM22+/ncOHD9Pd3c2DDz6Ix+PBYDDgdDo1If38888TyJxZRkpKCu+99x4LFy7kypUrJCYmkpeXh9/vZ/fu3UiSxNmzZ0lPT2fVqlUMDAyQnp5OX18fAI2Njezbt4/ERAulV4p58KH7eG/ve+zfb0GvN3D+/HmKi4s5fPgAaelprFq5igMHD4Dg4ytf/nN6ejqYO3cOHo8bgI6ODoqLizGZTJw4cYKUlBTq6upYsGABhw4dIisri5tuuonz58/T2dnJvn37aGhooKWlhWXLlnHs2DEKCgr49re/DUB3dzcWSxxxcRZqamqor68PJogRkCUZwatw+vAxXnh7J0sWLGTZ0mX09vayffsfmZ6Th0nU8/Of/JS77roL0WSksamR7Oxsbr311g917Uw1DA0N0draSm5uNorip7+/m3/+539ClhXS03JwOl3odDra29v5+7//e3w+H5/73Oe0Z0CWZX74wx9qmUCTkpK0F4uioiIWLlzIE088gdvtZvnym+js7OXEidPIfoH6+ia++93v4nK5uHz5MhkZaciyzBNPPIHJZOLLX/oKKSkpiKKIJElUVFSwZcsW9Ho9ubm5dHV3Iggy//3zn+ByufB6/PzkJz8hOTmZxMREBgYGkGV5xCH8MZUMzfcaJrvwjCGGjwLj8mHw/xgfTj0+TLTZNKt0DDc2Hzo6O6P2+Ubiw0mpUIiiiCQoSPLoB5ZkWebMmTPYbDbuvPNO1q1bx8GDB/nXf/1XZFnm5ptv5sknn6ShoYFt27Zp1oXBwUGOHTuG0WhEEAR++ctf4vf7OXr0KENDQyiKQnl5OVarlZtvvpnk5GQEQSAtLY3vfOc7eL1efv/737N7927NYmI0Grn77rtRlEDkgfj4ePbs2UNeXh5FRUWcOXOGe++9l9mzZ2M0BpLgzJ49mx//+Mf09HTzv//7vzgcDnw+H1eulGMymZBlmZycbFJS0qirbWCgf4jMzGxqa2v41S9/TW5uHps3byEzMwOAwsJCfvrTn2Kz2fjNb36D3W4nMTGRsrIyrFYrBoOB+fPnc+DAAd577z3NepSYmMiePXtIS0vDarUCCh6Pk9NFJ9i6dSunTp/gF//zS9LT00lKSmLJkiWIgHPQRnd5HbOnFTI8bKeo6BQAdp2e/r5eBLOeOQvm0tzegoyATqdjYGBglJCGn0woikJXVxdXr16loCAfr9eDzwtf++o3sFjiMRgM6HR6srKy+PznP8/ChQupra0lNzeXwcFBWlpaKCkp4Utf+hJZWVkkJyezfv16LQrFpk2bSExM5G/+5m/w+/2kpaVx8uRJFEXh69/4OrIsUVxcTGtrK5s2bWLatGk4HMMMDAxgMBhIT8/A7XZr1sGrV6+iKAqZmZlcvnyZlStXsHr1ncybtwRJ8iMKIoKgw2q1YjQao0a8iOy/5i4hCIgIKDoBFCWwZpQY18YQQ4wPb2A+tFimwGviR4MbnQ+VN99EHIPQbgQ+nJQKBRCMwTu65iYIAuvWrUNRFBITEwGYNm0aVVVVtLS0sHjxYoxGI06nE6vVSnd3N5mZmcyfP5+lS5eyfPlyjEYjDocDq9WK3W7HZDJpW6Amk4mMjIxAxs9g4pL58+cjSRLf/e53qayspKGhgczMTC0mcXd3N1euXKG6upp//dd/JSsrS9tyUxeVGhLPZDJRUFBATk4OP/7xf+HxeML6psZgNplMWtvMZrO2HW0wGLBYLOTl5QWyKep0mEwmrFYr3//+9zVhrvZFlmXi4uL4h3/4B/bu3YvT6eTRRx8lLi4Or9ertUuv19HbN8CVK6XMnTuH/v4+MjIy+P/+v/+PmTNnotfp0Ov1OCQfy5Yu47FPbUbUiwhcy6Kpzpua3VJRrkX8uJ6QsaHzfyMqIaIokpWVRWdnJz/+8X+hKApr1qzhrru2jQhHaLFYGBoawuFwEBcXx+HDhzl79iy33347W7duRa/XB7bYLRZtrPPy8gBYvnw5EBjPgoIC7fvBwUGqq6uZPn06c+bM0dZPbm6edl91DgwGA5s3b+bgwYN4PB5mz57NvHnzMZvimDljVlj5yLmKNneq1WhEiD1RDI2YH9UiG0MMnzTE+PDG5EPd669PeA2E8eH1LZ8pgRueD61WbcchEtfFh5N48ielQqEoSmDcBGFU90JBELQkOmpouszMTEpLS2lqamLNmjXMmTMHQRAYHh7WFtzMmTN58MEHtaQk0eoNPXyVmJgYFqZWp9ORnp5OVVUV8fHxJCYmIssyBoOBgwcPUlNTwyOPPMLChQsRRZHMzEytT6H3UKHT6cjKygrre2TYPjWKhqIoWmQJtVy0lze1zdFe7iK1dVXwqmUVRSYpKYnU1FR+/etfI8sK69bdyuzZs7XdBZ8iE5dqxYEfMUgewRGCYAbTyH5G9m2iiOYycyMhJSWF73znOzidTs2ap9PpRvRZr9dz3333odPpNOvatm3btEydEJFcKco4q4cV1TJWq5VvfetbGI3GUWNrq5/pdDpuueUWVq9eDaCR9kSUh2gYuXbVg5ZyYP3EFIkYYgBifHjD8+F1cGKMDwOI8eHkxKRVKFQNPxqiCQdVuN16662sW7cOi8WiaXsJCQnce++93H777RiNRkwm05gTPdriU+9pMpn42te+pi109dDPww8/jKIoWibFseoMFcrRPh/P0ht6faRwHq1ugNTUVB599FFt8Y8sK2CxxPOXf/mXnD59GpfTxe233xnmqiSjQJyRwsXz0WkJg65dP9rQjtbm0do62TGaxTBa/0aDIAjExcVFJfTIMQkNz6gK0vHqHq296vNisViitjna86UKzQ8K4RlQtTsF2iGEfRhDDJ9YjMuHoCkc2mcxPtT+ntR8KAhRd59ifHhj8uFYszpxPpy8a2NSKhQBn1E1S2Bg+GQELSTVaItTFMWg3+PIz0VRHKG5Xs9CD71OFEVt4YVab9QFMZYAC0W0e0/02mjXjAdFCWQGVSNQRLcSiegEA2aTnk0bbw/2S3ftgVLAoOjwGUQKFs1HJxCIQvA+2qi2YSyLy2S1xoy1btTvopHEWG5c10PqofWo4ehSU1NHrWei61yNtBG6rqM9L2O1Z6x2R36mBC6+9l3w5UlRQBYgFlQxhk86xuXDMa6L8eHY94vx4QeDGB+Oz4eCutM4Rl+mOh9OSoUCQgZZTcQT/Dx0cUb6Jo6FaBaOsRaVemgtWt3RLCORv0erf7yFHW3nJRrGErwTgRyMhx0Ngc8DNi9RDLcQadeJCjpAEhVQQJavEcv1tnk8v+DJhMi2SpIUtY2qla6vr4+GhgamT5+uJVdSlEBmV6PRqPl5qiQymnAaqw1+v593332XuXPnsnr16rB6IsuqwjFUKIbeT1EUhoaGuHjxIi6Xi61bt6IoSvAwXIBEHQ4Hqm9qtLaq4RUBzU/5ulzdgiQdQwwxXMOofKj+H+PDceseDTE+fH+I8eH18SFeLyau0/V7ivHhpFQoQgVjQGML/16SJDo7O6mpqWH58uUkJyePW2c04Ra6oBRF0bY9/X4/dXV1lJSUsH79enJycsIedjUWdaRvX+jC9fl86PV67WEZTbiUl5cHQ5gtn9D2mdpur9eLIAja4bGxNP1QSJKEw+HAbDZroftGu6/aZjXdvSiK5ObmIggyOr+ELPmxyT7wgSgErF7x8fEj+ipJXu2hEnUiOtGAqqdPVovLeHC5XFRWVmK325k3bx5dXV1kZWXhdrspKCjA7/dz6NAh2tvbSUtLY2BggPr6eubPn8+hQ4dYv3498+bNA6Cvrw+DwYDVah11LqMRjfpZdnZ2cF6EsDWobckH48Y7HA46OzsxGAzk5+fT0NDArFmzwtbQ4OAgtbW1zJ8/H78/kKBp06ZNzJ49G4CzZ89SVVXF8uXLycjIYMaMGWHrpLa2loaGBgoKCpAkSTsANx7UJSsw+Ygzhhg+TozHh7Is09HeHuPDKciHOsmHGNLWGB/euHxIVRVLtmyZ0HhOVT6clKs3TDONsnAkSeLSpUv09vZeu4bRs3RG+4HAhJ88eZKysjJOnz6N3W7Xvu/s7CQhIQGz2awJ2q6ubvbs2cOZM2d46aWXtFTwkdulXq+Xl19+mYqKCvbu3TsimVvoNR6Pm/7+/rA6xmu3oigUFRVx+fLlUR+s0a71+/089dRTXLx4kXfffVeLphGtbGh9Fy9e5OjRoyiKjGdokKKduznw0iuUXbrEH/7wB86cOaNlUY1oDfUNtTz52yd44cU/0N3dgaLImlUg0jrwcWGia0a1TNntds6ePYvX66W/v5+9e/fS1NTEpUuXNAvLpz71KW6++WaSkpJobW2lo6MDo9FIT08PDsewlo314MGDHDt2DL/fH3Xsx/pMEALRKbq6urTvQl8K1L/Lysp45513aGtro7q6muHhYc6dO4ff70eWZZxOJ8PDw+Tm5rJ582acTieiKGoh8tR+JycnI8uylmVWhZrsRxRF2traGBwcpK6ubkyLWzhUYT+yvzHE8EnGeHwox/hwyvJhT183CjE+/CTwYVNTU9TnNzqmJh9OSoVC0yQVmWgHUHQ6HVu3biEh0YIgyChIEExPrk506I/6uaJIKIofSfLi9jhRFBmXy0V/fz+1tbX09PSgKAFN9uabb8bpcAV3mAVaW9v5z//8MVVVNcRbEujq6kFR0B4C9b5+vx9BELS41o2NjbhcLq3tajl1kQwM9HHk6CEcTnugHyHmp9CHNjRTpyzLWCwWBgYGABmQkGUffsmD1+dGln3Iih9Z9iHJ/uD1MrKsoBN1TJs2DbPZzODgIA6HI2xs1aygajvVzKmFhYUYDAaaW1ro6BvAnJjM8pvXUlgwg2RrMmazmZaWFu2hVfsY6IKOY0dPUlZWgdkUjyBMymWHogQsXW63G0mSsdns9Pb04fP58fslJEmmp6eXvXv3YbcN43S6OXLkGElJycyaNZuUlBQ6OjqCcyVRWlrM22+/RW9fF5mZaRw+cpCa2ipmz55BS0sTzz77FLW1Vdx51xZmzJyOgkR7RytV1RX4/dfCJoYKrN7eXmpqavB6vZSUlNDW1kZ2djadnZ0oioLb7aaioiIw/kgMO2yUXimhYFou2dkZZGSm4vd7SE5JZOPGDdoaKy4u5sSJEzQ21vHCi09TXnEFp9PBhQtneXfPbjxeF2fPncFqteJwOLhy5QrvvPM2w8NDgMSFC2d59bXtZGZmkpmZSWpqKgMDAxMXhtpLTWDsYoghhgDG40NRFGN8OFX50Bg3oTMXHwdifPjB8qHD6byewQ/2dWrx4aR7swvV0kVBJHJ/V1EU/H4/RUVFHD58EPuwLSAYCTywTqeTmpoafD4fZWVlmsBxOp0MDPQhKxJnz53hP/7j3zlx4gTTp08nMTGRhIQEenp6APD5fLz33nucOn2S4WEbiiLT39+L2+3gnnvuYubsGWRmpgEK3d3dnDp1Cp/Px/DwMO+++y4Oh4Pp06dr8bidwYUUKlwgsFhKr1ymqamBgYE+hJC+qoJM/RkYGECSJHp7e2lsbCQ1NRWfzxfcGlNQkPF43Bw4sA+P140s++nq7sTtdge37Wqw2YZAgPz8fHw+HwkJCVryIrVdbreb8+fP4/P58Pv9vPTSSzz11FNYLBbOnTvHv/7wh/zy+T8w65ZVnK0sB4MJS3w8GRkZWl3Dw8NcvHiRoqIiTp48ickYx733PsCdW7dhsSSiRi6YLJp3qIXu0KFDPPnkk9hsQ1RVVXLw0AEuXDhPW1sLpaUl/OY3v+bY8SO88+5uhob6aW1twudzYzQaMJvNuFwuZFmmv7+PZ559mt6+HpqbmhgcGsBo1FNZWc6ww87BQwdoam7k6LEjOJ3DHD58kN7ebp5//g/89re/wekKFz5NTU0cOXKE5uZmDh8+TG1tLb///e955513SElJYXBwEICenh6qq6ux2WwM2+10dXVw5Mgh/D4fJZeL0elEhmyDDAz0U1lVycDAAIIgsHTpUm655Vbee28vtXXVdHV10NbWhiBCc3MT7W2tHD58iAsXLlBdXY3RaKS/v5+e3m78ko/jJ45x5kwRTU1NdHd3o9fro1qYRoMQ/EfQiZNW4Ywhho8a4/EhwZfrGB9OTT5MSLQS48NPBh/qrsOdbary4aQ8Q6FCENSYFuFoa2vjD889h8mko6+vj9TU1GDkBZGBgQH27dvHI488wsGDB5kxYwYWi4X29nbOnS/i3nvvob6+nrNnz5KWlsX9902noaEBq9XK8PAwiqLQ1tbGzp07iU+Io7q6nNQ0K/n52XzqU9vYsfNVvvCFx3F7HDQ113PkyFHOnTtHckoSly5e4tChI+j1ei05UFpamlavLMsMDAyQkpKC1+vFbreRnZ3D4sWLA1tkQUvI4MCgFlPcbrfT1dXFmTNnWLp0Ke+99x5Go5FPf/rTtLS0UFlVicGg10ji3LlzpCSnYI4zc+DAAZYuXcbVsqv09vay+bZNZGfl0tHRgdlsxmq14na7tXFVyeby5cssWLAAQRBobm6mtbUVr9eLw+Fg7eo1lJw9z9XDRbReuorH46Gzv5eVK1bgcrk0ga8mHFLJcPXq1dTX12uHmSbDtm4oVPKtqKigqKiIzs42RFGgq7sLn9dHSmoKbW1tpKSkkJKSwtlzRYiiQHt7O//7619h0JtYvHixFhc9Pj6ez33uc7hcLt58603MZjPt7e3s3bsXQQgk2RkYGKC2tpaqqira29u5cuUKDodD27ZNTEjW2paamsqBAwfwer0MDAxw7NgxJEmira0Ng8GgHX7W6/Xs2bOHvXv3giChKDJDQ0NUVVUhCIFY9TU1NRw+fBijwcysmbNRFIXS0lJ6enq4/4H7ue2ODVw4X8x///d/M+wYwu12828//DccDicXzl/CbDZz8eJFPB63ZnlLTEykqamJn/3sZ6SkpLBp0yZSUlKub54FAUERrh1iiyGGGDRE40OFIB8eORzjwynIh4IgTMpcZTE+/OD5MD4h4fomYQry4aRWKBRFQYlyxD01NZWvfPnLeLwunn/+ee0QjOQPDH5XVxc1NTXagTJ1go8ePcrx48dQlEASkzNFRZRfLWfVqlWawADIycnhn/7pn/B4HTzzzO/ZsfO1wM6I5Kenu4fq6kocTgfHTxzFaDBit9v56U9/hNkcj6IovPDCC1gsFh599FGSkpK0RDnt7e3U1dWxZs0adu7cyZEjR5CVQFZOn8+HJPkZHBjm8uXLrF+/nqNHj/LKK68gCAJms5njx4+j1+tJTk7WMkX+54/+E1EXaLea1bSurg5BEEhJSaG8/CoQODD3yit/RECHouh4+OGHSUtL0wS1JAW21Xw+n7YLZLFYyM/P59KlS1qoweJLlzBLcH7vIZyyj/L+42Tn52E2m5k7dy6CEEgktHnzZk14yrIcloBI/ZlsB9C6urpwOp1s3boVBB/DDjvTpuezYMF8env7cHvcoATGcsWKZSgK2kHExIRkkpOTueWWWzAYDJhMBrZu2YrX5yU3N5fOzk62bNmijYlOpwuLhgRgtVrJy8vDNmQj2WrF7/djs9kA2Lt3L3l5eeTm5hIfH8/cuXNJTk7G4/GQlpbGrbfeiizLZGZm8v3vf5+enh5qaiuZOXOGJlxNJhNZWVn81V/9Fd3d3ejEwGE0URRZsGABc+bMISMjBUXIoXD6LNauWY9CwBe0paWF7u5eVq1cjdFoDEY4AUFQaGlpYcaMGSxZvJgvfvGrJCYmkpaWRnJy8oQVCtU/XNSNH6Emhhg+iYjGhwKQlpYW48MpyocEz30okzBhXYwPP1g+TAsmwZsIpiofTl6FQgiEalMitDNVq9y4cRM+v5fk5BT6+/oRBBFZDmy5qodm8vPzkSSJ4eFhDhw4QOH0mdx66y3oDQays7IYGBjC5/ORlZWF0WjUktVYLBYWLVqEJHn5h3/4LgMDg4iB1YJOFAPp0IVAtsaszCzsw8P4fD5SU9KQZbRts/T0dNxuN8nJySiKwsmTJ8nOzmZgYIBLly4RHx/PX//1P2A0GsnMykIUDZSXlwcPKTm4evUqsizzgx/8gMzMTLxer3YoLnDtXzM8bEenF3E6XRQVFTFst3PvvfciiiJxFgt+vw+dTkBAQJIk/H4ZQQgkcPF6vcTFxSHLMu3t7bS2tnLs2DGMRqMWbWNgYID77ruPxx57DL1ej6jTIfn9mGURRAGXEXSSgtFgDAsFp86VitTUVC1qQ2iYvtEO0anff5QPk06nY9GiRdx7772cOHGU00Wn2bB+Ezctv0kLlxjwxVRdEYL/IyDLoCgCy5cv19osiiJGg46VK24O9DP0wF2UfqsHx2RFQRQEvF4/58+f5+LFi7jdblavXs2JEyd48MEHWbx4sRYST/1Rd3+mT59OWloaJ04cZ+OGTeTn5yOIYuCegkB2VhzZWbkoBNouCIIWs5vgpwkJZubNT0ZAob+/n33vHWD27DnMmTMHo9EYHAuJ7u4u3nnnHdrb23nsC18MexEJTf40FoJeCsiigKjTBfwwZZB0kz/udgwxfCQYhQ8RBBISE2N8OEX5kMuXkSUJIeR8SCRifHhj8KElNfVa+KYxMJX5cPIqFAAoUf3HVE1ep4tj7ZpbtQVUV1fH2bNnWbFiBW1tbaSlpdHc3MyBAwcYGhriG9/4Bvn5+doLrRoeLjJKgOazKpqZOWMuwszAIpAkSVu0kiRpiYGSk9PDrAuqIAmF3+/HbDbzwgsvkJ6ejs/nY8uWLcyePVe7Vt3Ke+mllzhx4gQ+n48NGzYwffp0LZtp6Iu4KIrExcUhiiI9cg/dXT2sWLGCjIwsrZ2RccNVy4saYk+9d1JSEl1dXWRnZ3P33XdjMpk4fvw4s2fP5q677tKUgUiFwBioTKvf6/WGz6ByLeRhtDlUy2hhZUOEwkdpsVGtdGpc6blzFzB37gIyMjIQRWPImrt2Tei4imI0/1cBQYi+hkfb4xaEawebjEaRO+64g1WrVmlWvbVr12qW/2gRQdS/ExMT+frXv4HJZEIU9eH3FML/DO+LAASFbXAzPjk5lW9+868xmUxh2Uh1Oj1ZWTl8+ctf1Z6H0DmbCAmq3yiKQjB8O5LfD1JAgE4+Z4AYYvi4EF2WqFwW48MpyIeCgBDc7QgtE+NDtc4bhw/5BPChoHwcJ4F8Pti+HR5/fITGph2I8kj0Hyghae18DMkWJL2AIcqCCW2+0+nEbreTkpKCx+PBYDCg1+sZGhrCaDQSHx8fdr2qAYcKpdDvIwWlKjjUNgaEbPjDEc2aoJb3eDyUlpbyzjvvsGnTJm655RZt+00t7/P5aG1t5ZVXXiEvL49Pf/rTmM1mTdiHtk/111MTx/h8Pk1bDm2L2mb1GrW8+n1om9UQa6GCLDQutyZAg4JZCFoQ1OtDxyj0vqEIFerq92okDb1er9UxkQRNHwRUAS5JEt3d3WRnZ2vtVBFNWH1Q9w5F5JqJLBNKLJGEGllvKHmNVi5an6LVFVk+9DlQy4wm1MeDWo/f58NxoBwhLwXzwukoRjADwvbt8NBDEBd3XfXGEMOHhqefhj//8w/9NuPyoccDO3fCY49p5VXE+HDy86Hw6quIDz4IEZnGx+XDnTvhjjtgAjlHrhcxPhy7Te+bD996CzZsgLS0sYZgfD78CN6JRuDoUdi8edxik3KHQtG0QcbdIgpdGBaLBYvFgiAIGI1GrUzaKBOoWieiCSf179EsrpFlQwVHtLoADAYDS5YsobCwkMTERAwGg/bwqvfS6/VMnz6dr3/965hMJi0DYzRhp0J9qFSrTWh59fdIga8KUJUc4Jo1JPRBCBW46nUQ0JOFQEdRQsL+Rbt36JhFc3dSy6tCTJblMM3/o4DqE6smbfqoHtrR7hNKfNEIUUXo3EazKoYiUiCP1cdQq9hoLwShCBXq7xuCgKgTUT4OgRlDDJMUMT68cfmQiHKh5WN8GP55jA8nPybXKaAgBEFAQUAng0wg9nboVpAqdPx+v6bJhy6aaFbxyPrVuqJBrT9U+43axhBNPjRE5lgLzWgM+FYGtt7CBVZoPO7k5GTNhzXag6DeW33w1c8iBZkaqSLy4VGzoIa+4Lvdbvr6+rTysiyHbYOHWnFCEbrtrSJ0/EZ7kCPHUxXmats+SiGm3itSYIwmjP4URM5LtLmLLO/3+4PxwMNjUvv9foaDPstOp3PMOhRF0epQ44tHtiG03urqagYHB8PWjtqWoaEhPB4Pdrv9Gqn+iWOk1j2VolrEEMOHjTH5kGvyJMaHU5QPRxnPGB/G+HCq8eGkUyg0IQiIEqjLRQyZG0mSaG5uZvfu3VpGRBXqZIdq96HJdkKh1+vDti+jtSP04Ym22GVZxuPxhFk63G63trBDfTRVYRdav3p/p9OpZVZUhXPofdTF7/F4tAWm1+ujPuzq34CWXl6SpLAMparAUqMyuN1unnzySZ5//nncbneY8Au15qjlowlR1WdQp9OF1e3z+WhpacFut2vJzkKtMKHzol7/ceDDEpjRIEkSDQ0NdHd309nZOa7QaG5uZteuXWHlZFmmoqKCAwcO0NHRwRtvvBE2lqqQVoWSoigcO3aMuro6jh07RnFxcRgZhwrToaEh3n33XWw224g6q6qq2LNnDx0dHRw4cGDcF5bxoK1ZBFDUBF4xxBDDuHwYfHZjfDg1+XBoaEjb0YjxYYwPYWrz4aRTKDQIAjIKiprxM+QrSZI4f/483d3dUa0RqsbY2NiIz+ejs7OTqqqqEVaJ0IckmvBRy4aWjxSoXq+XV155RbNk+P1+du3aRXl5Ofv27cPhcIzQWEOtRqol46WXXqKuri5suzf0cBpAUVERO3fuHKGVh7Y11JrS3d3N//zP//DCCy9QUVHBjh07tEygkVYag8HA5s2b2bp1a9iBN7V9qi9p6JhE/oxmvRoYGODJJ5+kpqaG6urqEQJTFciRBHMjIJpwgsAarampobOzU8vqCYSVC52fnp4eWlpawuZAfZHo7u6mv7+flpaWsHs3NTXR0tJCWVmZ9gKhJvqpqqrSQvCpCnpXV5e2VhMTE3n00Uc194jQNvX29tLb24vdbmdwcHBEm3w+XzDsozSmpSkaRFFEFy2BVwwxfJIxBh/KshzjwyiYCnxYVVUVNpcxPrxx+VBWFG1HcaKYanw4aRUKRRQQdCKiOFI71+v1fOpTn2LGjBmaAFUXqSzL9PT08NRTT3HkyBFOnjzJz372M8rLy8NecCJfdCIX7Gh/R27nulwuWltbeeuttzSLirpV2tjYqCVmiUSoEFWFh8fjwe/3a2H+QsvJsozVasXpdIZZMiLbJssyXq8XWZZJTExk9uzZ5OfnU15eTkJCgmbtkGUZl8uF3+8HAtuqFouFs2fPam1UXwrr6uo4d+5cWJ/Ve7hcLmpqaujv79cePrUdKqHEx8dTUFBAfHw8vb29YeOuCmpViH6UW7sfBdS5c7vdYVu0RqORTZs2kZeXp8U+V8dLXV+hcywIgQOKKjHW19fT3t6uHdpT7xV6T6fTidPpxGazadcKgqCRoVrO4XDw3HPPcenSJW3teb1e+vr6tC1cj8dDe3s7ZWVlWmz2aGTX39/PU089xY9//OOwl5aJQpblQBjFSR7NIoYYPkqMyodBq36MD6cmH9rs9hgf8sngw9qamrAzMxPBVOPDSadQaA+XunBkBSViLGVZpqysjL3v7cFuHyKgvcn09vZgsw2SkmLlc5/7DMuXL6W8vIwFC+Zxzz13IwiBTJctLS2aMI3cAlMUhc7OTq5cuaIt9v7+fk17dblcvPbaa3R2duL1elAUiU2bNrB27Rq8Pg86nY4lS5YQHx9PfHw89qDACBVsfr+frq4uJEnC4Rimv7+XxYsXoigBy8zPf/5zmpubg331c/lyMSUll8jLzyUxMZBtURAEPB4PV6+WMTQ0gMfr5tChA7jdTg4ePIDNNojeoKO2rpojRw6xYcM6BEEI+hXKlF29wi9+8XNs9iEABgcHeeWVV2htbdXibe/duxdFUXC5XOzbtw+fz4fH4+EPv/k9J/YdQPZLXLpUyj/+4z/ym9/8Bo/HE9TuG3E6A/0qKSlmYKCfnJwcLBYLHo/nhhOS48Hv97Njxw6OHj2q9dvr9fDH7S/zL//yA3btehNJCqzBK1eu0NXVhd/vp6G+EafDhSwrQTkk097eyve//0/88If/RkVFBXDtACRcI3uXy0VeXh6zZ89m9erVHD58GJvNpm3FA8iKhMfr4qWXX+D8+TO89NILFBWdwukc5ne/f5If/ef/pbq6Ekn2c+DAfv7zP/+Dp57+PZLkA2QURQ5znVAUhbNnz7Jjxw727dvHrl27JqRQjChznUI3hhhuVEyEDxVFifHhFOVD1S0qxoc3Ph8eOXp0QuMzlflwUkZ5kmUZQSGQ0ERSkAUBnaKgKmm9vb08++wzeH0ubPZB/JIHRVE4d74Ii8XCunXrSEm18upr21m/fj379u2j7GopebnTeOGFF3C73Xzve98jLi6O7u5u3G43mZmZ2kGf//f//h+KovD9738fq9VKWVkZgiCwbt06qqqqePfdd9Hr9SQmxpNfkEtvXzelV0rw+tzMmT0fURTp6OggNTUVt9sNBHxCy8rKWLFiBW63m+3bt7N582YuXDyDw+Fg2bJlNDU1MH36NEpKSvjJT37CN7/5TRT8/HH7S8TFxfHII48wODioxf+urq7mV0/8gpkzZ/Bnf/ZnnD1XRFp6ClfLS4lPMJOTk4MkuRkY7OFqeRmVlZXMnj2L5JQESkou0tTcQE93FynJqbS2ttLU1ER8fDxnz54lPj6e6upqFixYgN/vx2q14vF48LjdtFVUYO0doCYjna7ePgRBZGBggNLSUnw+L/v27WHlypWUXS2jo6ODm1etRZYVzeKkItQqdSNDFEWWLFmCxWLR+tve3s6ZM6dYsGABHR1tVNdUIqDnlVdeYdOmTZhMRnbs2MHcuXO54447aGioo72jjcqqcgaH+nnggQeJj7dQXl6Bz+cbMZaHDx+mu7ubpUuX4nA42LdvHzfddBMLFizAaDTS3NxMdVUlbvcwFy6cJS09lYSEeK6WlzJ33iyuXi1l2rQ8Sq+U4HQN8/Y7bzFjxgw6OjpQkPBLXmCkb6eaCVeSJFJTU6+bKPV6Pf4pYo2JIYaPAmPyoaLQ19cX48Mpyof6iBC0MT68cfkwMSHhusdqqvHhpFQogIAVRgCkQEpz7XMlkDJ9y5YtiDqF119/nbi4OM1PdHh4mEOHDiGKYjA9ejdDQ0M88cQT6EQTbrcbnU6H0+kkLi6O6upqPB4PAwMDWCwWWlpaiI+PZ/PmzcQF497n5+fT1NSk3d/lcvHyyy8THx+HqIPh4WFkWebEiVPEWxLx+Xxs3ryZadOmYTQaURSF+vp6LSa2Xq9nYGCAf//3H6I3BCwrp06dwu+XMJvicbvduFwu/vM//xOLxUiSNYmGhgb+4z/+gxmFszUBevXqVfx+P0VFRVy5cgW3201FRQVut5uLFy8iiiJeX2Bb8dlnn8GgN/OpT92Loiikp6cHhLEcsDotWLCAn/3sZ1RVVfHMM8/g8XiYO3cu58+f5/Dhw2RkZKAoCsY4E4nZSTRfvkJ9bxsNBpG58+bidrl47rnnEEWRjMxUDh46iMFgwGQyc/78eRITk7SkRKP5596IUAVbWVkZVquV2bNnBwVdIJ55eXk5Pp+P3/zmN6Do8Hq9vPPOOzgcw8ydV8iFS6dpaKwK+mLK7Nmzh/T0dM6cKeLsmQsAbNq0ibi4OHJycjQhajKZKC8vp7KyEkEQyMvLw2w2U11djV6vR5IkSktL6e7pIi0tHVEQEESBGTNmotfpmTZtGk1NzQwN2ejs7CQtLY3BwUG8Xi8ulwuDwUCSNYkZM2aEzeXKlSv53ve+h91uZ+3atdc9v6IgBA+hKTCFBGkMMXyYGI0PIeAuEuPDqcmH1mAeiRgf3vh8uK6nZ9ywz5GYanw4aRUKEQVBFJAjxlEQBJKTk3nwwQfx+d2kpaXR399PX18f7e3tbNy4UYuMoL7QCASiZFjiEsjPL6C3t5f4+HgURSE3N5e6ujqam5tZt26dZpUpKSkhPz8fgB07drBo0SKuXr2KwWCgsLCQFStWsGbNzQwO9RNnjsPn9+F0uhGFQKSJnJwc9Hq9lsWxr6+Ps2fP0tnZicPhIC4ujn/+538mPT0Fm92GLMuIog6X08OBAwe5/fbbSUlJIckaT7LVSm9fLx6PF5MxDpPJpB2u+/u/+3tMZiOnTp2iu7ubzMxM2traePCBB0lMSkQ9FyYKehRFh8fjZv/+/XR1dTFv3jwyM7OAACmlpaWxZs0a0tLS2Lt3L9OmTePOO+9k27ZtAFRUVNDQ3IhsjmPrV7/A4g234jFbMBuMyEE/0QBBgF/ya+HuJD9AIBNpfn7+DS80I6HX63nggQfConUUFBTw05/+FAiGh/NJ+Pwykl8iPj6erq5OiopOcMvaW7l59c3odHrUB0Hz1STwmXp4r6CgQLvH5s2b2bRpk3Y/9ZqsrCx2796N1+vlr/7qWyxZsiRse1gUdTidDqZPm4XD4eazn32YZcuWAQEf4p07d1JRUcGWLVvIy80jL3da2HyaTCbWrVun+bleD9QoL6IoEghwMb4QDfWtjiGGGxWj8SGCQJLVGuPDKcqHyvz5iJ8w2fVJ5UNx9+7rGqepyIeTUqEIDIagJj1HiKJUCIKI0Whm/boN+CWJsrIrdHf3snXrncRb4oPbweH1ynLAMj5z5kzts1mzZlFYWMgf/vAHfvSjH6EoAU35jjvuwOVy8d5775Gbm8vatWuprKzkwIED3HzzzTz00EOYTMZg44JQQBCuxam+1he46aabaGxs5PTp0yQmJnL//fczd+5cDAbxWjMV6O8f5MqVMubOnUtKSgpqCvtp8QnBHe5r/pY5OTnk5AT8SK+WlbNwwSLq6uu47bbbmTV7DnFmc8ggiCiKgMfjxj48hMPh5Gtf+wtSklPCNGqdTkdcXBzz5s1j+fLlxMXFkZCQgCzLzJ07F5/fz+zpM1m8eAFKnAmrYkAILnbtQUTCFKgwMG0mEVkOfG8wGD5xL3+CIJAQst0ZGAcjBoNei/pQV1tHQ0MTPT093HXXXcyaNZuZM2dFHMy7FnIxABG4trUbmpAqNJEVXPMlXbNmDUlJSRgMBubOnRecj4CbpjpHceZ41q3bwLJly1mwYC56vQFREDEYTDzyuUfx+f1Y4uIQBFFb7yqiRTaZ8HwH19Fo0VGi4eMWoDHE8GFjQnwoxvhwKvKhECGnJwy/H65cgffhRvNxQ1AUQlstCAIGFAzIgdlRFOobGmhuaqW3t5c77riDWVYrMxctD4xZTfPE+FCtHzCqJBe44BofmkwkLV4c4EMZDOWVGh8qsgKCQJzfx7r4JJbNX8QCn4T+ylVEUcSAwiPzF+KfNYc4nQGhtBQBXdguRBiTdXREtHe8gZp6fCgoH4fTns8H27fD44+P2AJSmyNLCvY9l7Asm4WYa0XQMaomryiKFprLbDYHLQHqd+M3R1EU+vv7qaqqwmw2U1BQEBReIl6vF4PBgE6n0w6S6fX6sIyaE4V6AE3d5o026YoSSGYy2vcqJEmipqaGrKwskpOTNWtIwLJzLdxcWAak68D7sTBfDz4pL4ATfbz8fr8WLUJN8vSnjFHktaO1Q40eoTAyw2hoPdeUjoCf6J+kOEQgNCqL69BVvOmJJCydgWyAOASE7dvhoYcg6HIRrV+flPUUwyTB00/Dn//5h36bcfnQ44GdO+Hznw+7JsaHU4QPX3kFHngAIZgBfMLo7IT29ut2ofm4MdFhl9VcKYBeTeynzqGiXHe/I0uHtSNE0dCWR4TyoZYRtN2QQB2KGgI5Gh9GfqDTwcKFMM6zMi4fjtH3D40Pjx6FzZvHLTbpdiiEEMknCGIwpf01k0y0FyXVIhsudK69CEWWD72Xen1ycjJr1qwJq18QBMxms/a3KIqYzeYJTVq0FzhRFDUf0mg+k+rnBoNhRBuj1TV79mzNeq3T6TRrUmTd70dn/FMX5PXcO1qZyHEZ6x4fVrnRyl/vvE+kvLa1OQ4io1eoa2Y8H9xo4xlWVhmvndfW5wdtgwitUxBElOAh1D8V0RSjGGKYShiXD68VDBaL8eENzYdKwPGFrCzIzAyb0KgvzVFevsd8uQ65ZrQeR5Yfa2Si9XYi5QVFQSfL19oe+X9EXepO/wg+HOU+oZ8rUT4Lb1RIL7RdJzRFQyG6QvF+MZX5cNIpFCoE7R8lTICOhet5IYvEeNkowyd5/LaMZm0J/W6setT7jfXyG2kVChVakQIs8kELbVPky2k063hoPWP1c7R+h14XbZ6iCQMVsixjs9kYHBwkPz9/TGuYGlN6vDGWJElL9jR//nws41iI1DCK462T0L7Ksjyu5U5RFM2aFjpnaj8kSdJCC0ZrU2VlJVlZWaSkpEw4o+p46zfy+9A48+r6UMdDLXs927JjQZaVgPBUJhZ5O6YoxPBJQIwPb0w+FBQFIUqfRuXD/ftRWlqwORw4nU4yMjLG5JjQJIKBNRR9jFWO7e/vJy8vD5PJNGqdoe0bb42F9nUi/BnaZtUjRVYUZEUJxiSQR9/NkiRa29qwJiWRlJQ04Zf88VZv5PeSJAV2JoRrOUNUBWPMtexwwJe+dF1ualONDyetQgGgE3UwzqKYiCX7T7n+2vdKxP9q+fGFYbR7jXa/UCtw5MMIaBaXaNqm+nKqRr1QrTQejwdAsyZFWrkdDoeWoCg+Pj7M6hQKNcayc9iBXq/HEGdGQEHyS+h0OvR6fdTr1EQyfr8fk8mkHcwbb3zUNg4NDfHrX/+azMxMHnvsMU2ARt5LLWu328nPz9eEVzTi8/l8bN++HUEI+BCHhrCLhr6+Pvr6+pg3b17U9qrX+v1+Lly4wKxZs2hqamLFihVjClFZVrh8uRSDwcCCBfPR6XRIksy5c2eZNm0alZWV3HbbbVEFt9fro6joDEuXLmHFihUjxuKDgCzL1NbWUlpaSmHhDBITE5gzew49vb2Ul5dz882rtPMh17vzEw16vT7gGSswwlc8hhg+yYjx4Y3Hh3E2G0nBl+XxxgdAGRhgcOtWfv3ccwE+/Mxn0MfHgzoGofMnywwODmK32cL5MFB52D29bjcvP/MMYkYGjz78MKbk5DHdk3q7uwN8OH++2tioVn+/z8eF8+eZNXs2TY2NrFi5cnQ+VECSJS6XXMZgNLBgwQJ0oojklzh37hzTpk+jsqKC226/PRBuN2J8PC4X+19+maVLl7JixYqwMlpfQsdI/T3aZ6OUlxWFmurqIB8WkpiQyJw5c+jp7Qny4c0kJCQgiOJIt6rdu8HjQbgOhWKq8eGkVihkRUYJhnGbCD6obckxWoRf8lJVVUVqaiq2oWFmzZoTdhj2eqEKPPV6daEmJSXR29uL1+vFbrcjCAIOh4OVK1dGzcioQlEUiouLqamp4eGHH0av13P58mW8Xi/r168fIXwVReH8+fOUlZWRnZ3NmjVrKCgoGEEsbrdb80W9fOY86ampZC+ey7kTp7hadpUvfumLpKWlIQiCZmFQ+1dcXExxcTHz589n+vTpJAdD5YVirLGLj4/nC1/4AjabDaPRqFnv4ZoVTu1Tb/BFNy8vTxtXGGlxMxqNPPLII5SWloZZY0azunV3d3P+/Hnmzp2rfT8ayRw7dgy9Xs+5c+dYunTpqBYuSQqEg2trbaOru4u5c+egKIExKy+vwGAwcunSpbDoFKHQ6/VYrVa6u7ujWu8i/1b9iSOJWZ3XyPMRoZl0W1tbyczMZGBgAFlWePbZZ5k7dw6rV9885rmK64UiCIh+GUGvoFMEJGGSC6kYYviIEOPDG48PC9vbSYoyDmPyYVISX/jiFwN8GBeHBNruQxgf6nT09vcH+HDaNEJXjk4Uw16UjWYzj3z+8wE+jIvTFNdR+bCvL8CHCxZolvpoCoikKBw7eRK9ycS5ixdZetNNiFF2GBRFQQpa/ds6OgN8OH9+gA8FgfKqKgxmM5cuX2bT7bcHziME+6z2QW80YU1Jpbu3F0UQUEL7qPYl5G9ZURCD7Q7jw5Bd+NAxVc90KIJAa3s7mdnZDAzZkAXhGh+uXatloxjBh++DH6caH066TNlhUIjuhPdR3Dpi61N90fP7/ezevZu///u/59ChQyNcXNS/Q38f7Ue9z7lz56irq8Pv9/Puu+9y9OhR7HY7r7zyCkNDQ5SUlPC9732PsrIyre5obYTAg5+amkp+fj5OpxOXy4XJZKKzsxNFUcKyoKp15ObmkpaWpqWxj3w5lSSJ3bt3c+zYMRRFITkpCVtPP8XnL7Dz9de55Za1JCQkaJlBVaFfU1ODzWajoKCAjIwMzGYzQ0NDI8Za3dYN/VHbpn7f1dXFkSNH8Hq9XL16laqqqrAXXq/Xy9DQEImJidoB54qKCpqamkaMlzpXjY2NHDx4ELfbjaIEMl3a7TaGh+1IwbB/an+sVqv22fDwMF6vV6tHLaceqr7ppptITk5meHgYn8+nlXO73fj9fiRJQpL81NZWUl9fw9x5M8nITOXIkcM899yzDAz2Mn/+HFJSktHpdFoyqMg5Hx4e4sKFM7z99m7sdhsIhK09l8uFy+XC7/fjcDg0y5zD4WD37t0MDAzQ2NhIT0+Pdk1HRwculwtZluno6ODgwYPaesrPz8Pnc9HV1Ybb42DbtrswGo1a/HdZlnG5XDgcDhwOBy6XK2wex3rWVMgoSD7/B/LYR66nGGKY0ojxYYwPCSgPMT4cuS4nOx+q97uR+XDSKjuCEPznYxKgQNiD53a76exsJzsnk40bNlJbW8vmzZu1xZeWlkZdXR2FhYU4HA5sNpsWjs/pdGoPgU6nIzExUdNefT4fFy9epKamhn/4h39gw4YNHD58mAULFlBWVkZhYSFr166lpKSErVu30tHRQW9vL4sWLcJgMAQiAbhcxMXF4XA46OzsJCcnh46ODn7605+SnJzMfffdh9/vx+/3c+jQIdauXUtSUhKSJFFSUqK5/CQmJtLV1aVZVQYGBkhKSmJgYIDly5ejKApXrlwhJc7CYFMHCalZJFit6IOhYPfv38/69es5cuQI/f397Nq1i7vuuotHH30URVFITU2lv79/wuOvCory8nKee+454uPjaWxs5Gc/+xler5ef/exnweynEvv27ePixYv8xV/8BYoSiFKyY8cOmpub+dKXvsTq1au18ero6MDr9fLMM8+g1+vp7+8jIdGMAJRcvoDT6cISl8jSpcuoqamhoaGBxYsXA+D1eikrKyMtLY2CggIaGhrQ6/WYTCYqKiqQZZnU1NRAFlS9HrfbTVxcHF1dXTz99NMsW7aMu+++G6/XzUsvv4DRaOTRRx+ltbWJpqZmLl++zIIFc9Dr9TgcDgwGA06nUwtV6HA4iI+PR5YlnK5h9AYRg1Fg2DGI1WrFZrPT19dHYWEhV65cwWazodfrqa2tZcmSJaxZs4arV6/yxhtvIAgCw8PDTJ8+naysLCRJ4sSJE6xatYrCwkLOnDnD3r17ycnJCSbAEunp7WLt2rUsXbqI13e8yr33PMD+/fvJy8vDarVy4sQJ2traEASB3Nxc7rjjjgn5zSqKgoCAjIJOb9CiaExui0cMMXw0iPHhjcmHQx7PhF/wFCVwCLi8vJznXnwxxodTjA8X1tSw+o47JjzXU5EPJ61CoSCg0+sQxIkeQftw0NjYiCAI1NfX8/LLL7L1zi3cf999rFixkqysLE6dKqK5uZkHH3yQX/3qV/zVX/0VnZ2dHD16lH/5l3/BZDLR2NjI1atXAbBarWzatAm/309PTw9DQ0OUlZUhyzIHDx7k4YcfprS0lJSUFDIzMwHIzc1lcTBWcllZGUePHg3G7DZgt9uprKxk5cqV9Pf38+KLL2IwGBgcHGTRokVcuHABQRBobW3l1KlTXLx4kTlz5pCUlISiKJSWltLQ0EBLSwtr1qyhv78fRVEYHBykqKiIdevWcfnyZTZu3IjH4+GZZ57hM3feS2tdAxbJxfTp03jqqae45ZZbqKmpob29naqqKgA8Hg/l5eXs2LGDzs5OVq9eHbb9O1HodDpWrlxJb28vL7zwAgAO5zDPP/8cRqOR4eFhqqurkWWZwcEB6uvrqKysBAJZW3/yk5/wrW99i61bt+JyuSgpKWHJkiXcfvvt+Hw+fvWrX5FkNYEAA/0D2Ow2JJ+O1NR0uru7MRqNLF68mIGBAf7t3/4Nt9uN1+vBEm+hr7cPo9EYtEINYzKZSE1N5fOf/7w2xrIsU1ZWRl1dHVVVVdx0000kJFqw2Qapr6+jqameoaEhzGYTJpPIc88/gyCKfPELf0FaWprm+9vW1kp7ezvLli3j1ddepajoOJLsD2zPKn5kRaK4+FKQuBVS01L5zZO/wTHswGg0kJOTjSRLiKKAy+Xi+eefx2Aw8LWvfU2zLKWkpOByuVAUBb1eT2dnJ//1X/9FdnY2W7bcTkdnO//1kx/j83pxuTxcOF9CfHw8Pp8PnU7H5s2bNQuL6rc8YQgEkm4pCkS4ssYQwycZMT68MflwCK5rPhVFifHhFOVD3TvvXN/DNgX5cPIqFAKgF5EFIeh7Jnwsjb169aomQP1+matlldz3qQdZsng5er2R9evXMzAwQHp6On/zN39Dbm4uhYWF5OXlYTAYEEWRxYsXs2jRIs3irvqGPvvss/h8PkwmE16vF7fbjdFoZP78wOHchIQETYAvWrQIs9nM2rVrmT17thZur7q6WovCk5KSgqIo1NfXYzAYKCoqIikpieTkZLKysnj11VcBNP9Ar9fL8PAwZ86cobCwEKvVislkQlECB9eOHTsWdlhZFEWSk5OJT7OSPbOAs+fPMSj6SUhIoKKiArvdzvDwsGah+NznPkd7ezuNjY0UFBSQkJCgZVudCNTtuYULFzJ//ny6u7t59913WbJ0ERaLiaamJgAKM/K559676OnpJi3NyrZ77qTo9HncbjcrV64kNTWVzMxM7QW3paWFdevW8eCDD+J2u0lNTWFwqAev103/wAA52TnMmjmfjo4uEhIS0Ol0ZGdn85nPfIaLFy/S2dlJfn4OJrOB9LR0nC4nfr9CYoIVu92OoijMnDmTlJQUEhIS8Pv9VFZW8vnPfx6r1Yrb7Uav17F27ToSEqzce++9GAx6dHoBa5KVIdsQAiLTCmaRm5sbFMQyp06f4Kbly+nsauPo0UOsWLFCO7CdkZ4NKDQ11dPX343LtZgrZWXcffcWbr755sDhLkmiuqock1nPjJkFLF++Asewi5ycHFpbW7Ut3fvvv5/a2lqsVitLlizhscceIzk5mbS0DL759b9lcHBQs6qdPXuWjIwMUlJSAEYk1LsuKKBDQApaYUXlYzXIxhDDpEGMD29MPhRzciasTAhCIBfDwoULmb9yZYwPpxof6vXXpxVMQT6cdIntIOj3JoHzyFX0c7LRF6SCKKAPamwfBVRh53a7GRwcZN++fWzatAmLxUJqaiqyLKPT6TRtWT3cNdGwcW63G4fDgSAEYoY7nU4URSEtLQ1JkjAYDJpgNBqNYWFLQw/Wnjx5kp07d7J69Wq6urpISkriM5/5TNhBNbPZjMfj4dChQ1RXV7Nt2zZ6e3u5dOkSw8PD/PVf/zUmk0mLmGE0GjWLkc1m44knnmD69Om43W5SUlP5yl98lThFRJYkfHoRv8fHe++9R2NjIxkZGaSmpnLXXXeh1+s1TR/QDiePFgY12jhF/h0YBwkEGRQFIfTAszoPCMiSwIULF9i/fz9/+7d/G4i8EJwjr9eLyWQK802VJA9vv/M2ZWVlPPLII8yaNU9ba2o5WZbZvXs3FRUVfOMbf0FKanKI9UCHooT7J6r9liSJl156iXvvvZfU1FRqa2vZs+dduro7+MpXvsy8efOuWSJC7qcogbUliiKS5OeFF//A6dOng1u5Nr7+9a9z0003hSWnKy8v5/XXX8dms5GWlsZXvvIVcnNzAXC5XBw/fpyioiLmzZtHWlomtTV13H333Vy5coXS0lLWrl3LihUrOH78OFevXuX+++9n0aJFYetc/ZFlmffee4/k5GTWrl37vn00Q/1cPcdq8Bsg7tZ56BVQ9AK6MRLbxRDDx4KPKLEdjMOH7mBiu8ce+9DbEOPDD54PxVdfRf/pTyPEx09oDnj1VbjvPggqNjE+nEJ8+NZbsGEDpKWNO89j8uHHsVUxwcR2k1eh8Cu4jlxFNycH/bSPR6FQ4Xa7ef3113nooYewWCzXHc1mouHxxrpmNNhsNo4dO0ZzczPJycls3rxZe2BC4Xa72bNnD3l5eSxYsIDS0lK6urpYt24dWVlZUV8E1YVdXl5OXV0dcXFx3LRiBcnp6egRUFCQBQW/y8OpU6eYNm0aEHBRmjFjxohxCiWY93MwSG3PtbaOvlmsKAo2m42enh6mT58elhwpGmTZT3t7O2azOZjTYWR5RVG0Ld/kZCuCMDGlSJZljh8/zty5c8nJyQHA6XQgKz7iLfFBtyABGCu8rIx9eJAjRw7z3nvv8fDDD7Nhw4aA1S/YDjXbtRqSUM1gqxOvhc9VCHyvE3VIMoiCTst6q+a8UOcmNEPtaPOlRtwKnes/RaHwHq/Fp4e4W+fGFIoYJi8+aoViND50e+C3v4X16z/cNoT87vF4OHjwILfffjtms/m6n/fI0tFeQN4vH9rt9hF8qMrcUHg8Hvbu2UNefj7z588P48PMzMwx+bCyspLa2tprfJiWhj74ci4JILmv8aHqKlNYWBidD0+dgr/8y4lnyn7llYBCER8f48OpxofvQ6GIyocxhSICE1AoJL+CWxWgoRaZj3Aw1aGRJInDhw9zyy23jKlQXE/bJjLs0aIdRbPaq9EW1IUeLdGNGpFD3SUIrXM8P3fVMiSKIqIQuF4OdlUMRvtQCFcW1PLj9e16oFqhIq+PHKfQ8uq4RHvAQ8tLUiCEiiiKWhKpyFCBEJ4oKLTO0QhQtUg5nU5t6zuQCCdgWRIEAQFVQAVcGaL2GwVJ8uJwOOjt6SEzK4t4iwUFJSggQ68LCYOIElTCo433tftFsyaOhtHGe7zyo0EToLKC62glkkEkbv38gEuHbnyFItqaiCGGDxUfsUIxKh8qCtTUQDCCzIfajuD/siRx8eJFFi1aRFxcXOC5i/LsvZ+ncSyJIkSUEYhSPoIP1dCuke1Tgi+MKmcqwc+ilQ+7T1BWqSG4A/JbDLikEUhCJivXzgmO4KnIcTIaYe7ciWdajlAoYnw4hfhw1y7YsAFhogrFaHw4AT4NbdcHggkqFJP2DAUCKDoRRRCCvmQf/VG00BfjjRs3hrnqhC6e9zNx13NNtAUbut2mJtAZa3tZFMXr8m8PvWc0BUUTHQKIupHC8E/ZiZhIu6LVHU0BC1WWxiqv040UMqH1TERJGu1aNZJJ2A4NYphF59rURhcGAgJ6nQlrkomkxJSQMoHnImSXO4RolMA9Qiwqo8m865mvaEQV6eJwvUJNEARkZGRRAVlBUgKu4tdxpDuGGG5cjMaHogghyTY/5CYEnnFFYemCBQFrbUTyUAh55t8PL15HmWh8CAGZIY7nbqUoE375UQjnCpGR0XZC7zQWQ8T48BPMh0r0BIaj3X8q8uHkVSgYW7h8WBsroy2gSL9/dWcg2o7Ah7npE7rTEHrfaO0erR1jWTDGE0qR9xrvHh8krtdi8H7qDbW8qD66o/Ux9PPRoleFbo9G7uCE1hNqwVF9gkOvV/9WrRcqrhFEuP1ucHCQzs5O5syZg8FgGCE4JzKHY/XX6/VqYxNKUur6FAQBSZJGZKONhtD+6XS6gA1JJYfYpkMMMQBjPAplZSh1de/rBf593VNRMCrBsDPqPWUZOeQZDiv+gbcqpO6QMwm6UD6MVlb7JTxkTuTOh1ZGKzB22ei28HCMOjN6Pdx5Z+D/60SMD6cWH8peLyaiv2dFtmWq8uGkVSiU4M94YUbDt+mkMC08NG42XFuM6mSqW6Oqph3NEg+BUGsVFRUsWbIEs9kMQH9/P4cPH8ZqtbJly5aRQjTCTUl9wfL7/UDg9H/oy7rallCXJDVRjSzL2tZybW0t7777LsuXL9cyKHu9Xk2xcTqdWtlArORAIpiEhIQwf0AVaoIZn8+HJEnExcURN46veuQDoT40MFLxmopQ/U3r6upYtmyZdpjO4/EgyzJms5m+vj4A0tPTUZRAWEGr1YrH49ESGnV1dZGRkUFGRsaItQeBcevt7cXtdmvRr5qbmxFFkfz8/BHjKEkSNTU1DA0NaZE6kpKSos5pe3s7586dY9asWWH9iibMQrfOx5s7r9dLV1cXJ0+eZOXKldTX17Nlyxbt2enp6eHw4cOsXr2ahoYG7rjjjusiNFmSmLgdJ4YYPhkYkw+vXoWbb4aEhLAX2qh8qCjXXIbG4kNBQD+Kn/2w3T6CD/v6+jhy5AhWqzVq7hmND0HzNZBU3lAUjCMOBY/Bh5JEnMWCIAjUVFezZ88eli1bxqZ160AJHDIWgnzudDqJM5sRRDE6H0b07X3xIeHvedfFh7t3g9f7vhSKjwoxPhwd18OH7WfPsmGCeSi0tk8xPpz0b36hRhAIn2x1UasZGcvKyqitrdXKDg0N0dfXpx0is9lsQYEcEM9NTU28++67lJWV0dTUFNViD4EDzaWlpVqmQwgs5qSkJGbMmBGmLau/S5LE2bNn6erqCvu8vLycF154QcsYqaKlpYVXXnmF06dP097ejs1m49lnn+XSpUucPXtWuz4hIYEVK1YED0AH6t23bx+HDx9maGiIX/ziF7S3tyNJEm+++SZHjx5l9+7d2Gy2qOOrjttvf/tb9u/fT2dnZ1hfIsc7WpbNxsZGnnnmGQ4fPjxiLEb7mYxQ2+X3+9mzZ8+Ivp4+fZqXX36Z7u5ufvrTn/LSSy/h9/ux2+28/PLL2O12Ll26xD/+4z/ywgsvYDabefvtt8PmOnI8a2trOXPmDD6fF0WR6erq4vLlyyjKyHFWFAW73U5JSQktLS1hSQIjxzUhIYG+vj6Gh4e1z7xeL5cuXdLaoyiylql0eHhY+z0yq61avywr1NbW8s4771BbW4vH46G3tyd4TeDHYDDQ0dHB8PAw9fX1YddHG+/Idos6HXLw+RRgQubNSGKKIYYbFVH5UKeDtDTktDQ8iYn4rFaUtDTKOjupHRyE9HRIT2fIYKBPFPEkJnKwpASb0YicmgrpaZCeRpPDwbtnz1LW2UmT04mSlgYZGdqPEqzHnZBAaXs7nsRErW4pJYXEGTMoXLUqcF16OkrwR/3+bF0dXbKMkpamfV7e3c0Le/fis1q1ukhPp8Xl4pWDBzldXU2714vNaOTZ3bu51NzM2fp67R4JhYXctHUr01asgPRAvfsuXeJwaSlDBgO/ePFF2n0+pJQU3jxxgqNlZew+fRqb0Ri4V0j/SE/Xxu23O3aw/9IlOv3+sL6E9klOTQ2OX/jnjcPDPLNrF4dLS0eMxYgfi2XShgKN8eEHy4fqu1Xo2EaO91Tnw0mtUKgHU6J97vf7qaur43e/+x2nTp1CkiRsNhtNTU1a2veGhgaefvppBgcHOXbsGH193QiChCR58fkDmRsHBwcZGhqitbV19DYoCo2NjdTV1Wl/p6SkkJGRQXt7u6bFqpEB1P8rKyvp7+8P+yw+GB5OlmVsNhs+nw9ZltHr9SQlJVFTU8MTTzxBb28vQ0NDyLJMY2OjtlNhtSbQ2dVKZ1cbELDYLFq4mBkzZmE2xZGeno7RaESn05GRkYHZbMbr9TIwMACMXMiCIJCfn6/FCe/s7EDBj4IfWfYiSV4kSaa/f4CWljb8fknbLpRlWVPmFixYwOLFi7WdmM7OThwOh9Zvn8+nWSomI0IFxfDwsJaVFQI7VJIkkZGRwZUrV+js7KSjo4POzk7cbjfNzc10dHRQWlpKXl4ePp+PxYsXk52dTXd3N21tbZpwUn+8Xg/DjiFW3XwTt9xyM9u3v8SevW+TkpqE1+vEL3kpKSnWwidKkkRLSwsFBQXk5uaSkpKiWYUCQkgGpMCPIFF29TJnz56hpqbmWgg6j4edO3fS3NyMLMu0tDTz8h+f59z503z/n7/L669vx+dzMTjUh1/yYLMP0trait/vx+v1Ul5eTnd3D4MDQ2Rl5RBviScrPQ0dEk0Ndbz37jsYDQYyMzNJSEjA6XSOUJxHG3cIWIM8evCJAcGkCALCZGXbGGL4iDEaHyoxPpyyfKjuGk02xPjwg+dDvcGAdB0eN1ORDyedQhE5oII4UtNShaNer2fFihXMmTOH2tpacnNztcVSXV1NWloaiYmJ7Nu3j1mzZpGbm4OCQmNTIzt2vI412UphYSGpqal0d3dr94/UEmtqamhsbOTYsWNa/RUVFezcuZPOzk5NuDkcDs6dO6fFzX7wwQe1g9B+v58jR46QnJxMeno6Fy5c4Ac/+AG7du3C4/GQlZWF1Wplw4YNrFy5kjfffJM5c+Ywc+ZMbWdFlmX2H9jPqVOn6OoKaLsut4Nnnv09TzzxCxxOG9nZWXg8HhRFYfr06eh0OrKysujr6xt1lyA5ORmTyURKSgpd3V0oiowsS1RXV/P666/T39/Prl27+b//9985efIUkuTXru/p6aG9vZ158+Zx/PjxQDg3u52f//znHDhwQBOgZWVl7N27l+7u7km3QxFqIVF3tfR6PXFxcTQ3N/Nf//VfHDp0iNmzZzN79mwsFgvf+ta3SE9Pp76+niNHjnD33Xezf/9+4uLi2LZtGwUFBRgMBkwmE11dXQiCoJG+3++nq6uLN97YyYUL5/nfX/8vPb09HDhwALfbhX3YjtPh4MiRI/T29gKBNb9nzx68Xi9OpxOz2Yzdbtfa7XS68Pl9yLKE2+0iPt7C7DmzGRoaQgm6AXg8HlauXKklQKpvqOf06dNcuHCB+vo6rpRdoa6+jjfe2ElTUyPbt2/n7bffxuv1MjQ0xNmzZxBFuOfeuzEaRTo6WrENDVJTXc0fX3qJt956i+qaGrxeL4oSOMw/nkIRCgGQBfDLMoI86d1FY4jhQ8dE+NDv98f4MMaHHxhifPjh8KHFYrkug+pU5MNJ6bgXWNAKok4XdYvHZrNx5swZ7rnnHpYtW8bw8LC2iPv6+rDZbFy4cIGlS5fyla98hd///vdkZ2djCGbTPHz4MJcvX2bRomV4vV4tXb36IKlacFxcHB5PwHKTn5+vWU8UReG9996jtbWVuLg46uvrMZvN2Gw2Tp48qfkZer1e3nvvPe6++25kWebSpUtYrVZ6e3tJT09nwYIFvPjiiwwNDbFu3ToqKiooKChg27ZtPPnkk8iyjMlk0iw6Xq9XE9BlZWVMnz4dWZKQFT+9fd1U11TS09NNcXExvb29mrUpLS1Ny1gpSRJDQ0MkJyfjcrlobm5mcHCQtrY2cnNz8fR4UJTAA3vy1En27NlLW1tnMGZ0Nr///W9xu51s2bJFOyx16tQpenp6aG5uxu/309/fr7m8qD6pV69exev10tDQQFZW1ke7oCYA1RLzy1/+kiVLlmiCv7Gxkd7eXl577TVWr17NggULSEhIYNasWbS1tVFfX4/FYuGmm26itLQUt9vNwoULtTjfoQfPOjs76e3tZcaMGVjiLRQXF/Pmm29iMpno7+/H7XZjNpmpr6/ne9//PrYhB5/61H2aL7TRaOQHP/gBGRkZ3HTTTVp2WJfLxYkTJ9iw4RYqKit48cUXg1akgLseBM78/OhHP6Kvr48vfOEL2ppqaWlhYGCAlJQU6urq+MlPfoIgCBw6dAhZhgXzFwGBw2E1NVUUnTmBoig4HA4O7Bfxe7zEx8eTmJiI0+ng5z//OSaTifXr15OWljauD2r4YThAVlA0t8SpIEJjiOHDxXh86HK5OHfuHHd/9rMxPpxifNjZ0UHmR7ucJoQYH37wfJiYmDiuK9JU58NJq1AoSvDwbxTtXafTUVxczMmTJ7WHOD4+HpPJRFlZGUePHsXj8TBv3jza2tooKysLLBhJQtQFDmu1trbyk//6L2bOnMUXv/hFEhISgMDZh9LSUqZPn87AwABPPfWUtrVrMBi0w89f/epXcTgc7Nmzh3/+53/WDo7l5eVpi1On01FRUcHJkyeRJAmPx0NpaSmKonDkyBEkSUIURd59912OHTuGyWTizjvv5OrVqxQVFZGcnIxeryc7OxtBEIiLi+N7//Q9fH4v27dv53//938BiYSEBAqm5fLss0+hKHpEITCtgiBw++23k5SUpB2S6+vro7W1lXnz5vHrX/+ac+fOkZOTQ1ZWFvHx8eTn5yMI0NvbS1VVNdMKCmhpaaClNbDNnJefQ09Pj6Zp22w2uru7sdlszJgxA0mSSE1NJSsrS3uZNJlMTJ8+ndraWnJyciatv3tcXByPP/44BoOBkpIShoeHuXLlCqtXr9asVsuXL9cEwx133KFZ3+Lj4/nCF76A2WwmPT0dURTx+XzYbDZNgero6KCyspLc3Fz6+/vIy8vnzjvvRK/TIysyFouF7JxsvvKVr1JSXEJjYws6nQ6Hw0FlZSXV1dU8+uijZGZmhh1ua2pqYnBwEIBjR4/R09PD1//y6yQkWplWMB1BEEhJSeGb3/wmPp8Pk8lEQ0MDFouFefPm8ZnPfIbCwkKcTieiqMNsNjE0NERCQhKiEIgVbjQa+fu//zsGbb2IQjDqhgJIApY4C9ZkK8MOB063D0EQyMjIwGq1jhnlaUQEMeFaNvWpIDxjiOGjwHh8KIoixcXFHC8pifHhFOPD1LS0GB9+QvgwY/589CEZtiNxI/DhpFQoRFFEJrBtqpcDEyUjaDF4rVYrf/u3f0tra6s2OfHx8aSlpfHNb36Tq1evUltbS15eHv39/UyfPp0VK1bQ1dWLwWBg7pyFFORf5uGHP0N2djaZmZmsW7cORVEYGhri/PnzzJkzh+LiYurq6vjSl75Ebm4uiYmJWqSktLQ0UlNTeeyxx7jrrruw2+2UlZUhiiIdHR2YTCZ27drF4sWLueOOO6iqqqKxsZE777xT06TPnz9PYmIiixcv1hZOSkoKXV1d3HvvvWzevJm4uDjuvvtu7b6JiVYUReHPv/Z1Hvu8Wzv4o9fr8HoD7iVqPGdFUTCZTJr7iaIoNDQ0kJubq2nq8+bN47vf/S56vZ7i4mKSk5NB0eFy+li54mbuv/9+BEHA6XSyc+dOtt55J5kZmbReraaxqYm9Rce466672LhxIx6PhyNHjmgC9b777kOn0yEIArfccgsrV67EFBLNYzJBJUaVBGbPnk13dzdpaWls27aN5ORkbZ2pSE5ODrs+JSUFWZa1bf2WlhbS0tLIzc1FEARycnJ4/fXXOX78OCaTiUce+RzrN2xAJ+oQhOCLA2A2xXP82EkMBgMul4vXX3+dkpISHnzwQTZu3KiRobpmZFlm//79XLlyBYdjmGXLVrJ69S2YzXFaOZPJxMKFC4FACL1Tp05x4cIF7vvUg9y8am1IdtKAMMvOyg/rm6IopKZmkJaeEfgs+J2iBGKCI4Al0RrmDmwJRmKZ6PijKCALxMkifhFMCijiVBGlMcTw4WA8PrRYLHz729+m1WaL8eEU40PzW2+9P/n2IbtJfeB8qCi0tLaSlpoazoevvRbkQyOPPPII69dvCI7RNUX6T+bDpSofmgEBURDeBx/moTKRIAgoskxqajppaUE+DBYN7CNE58P4lJRAMokJcOJU5cNJp1BcewFRgtYWOeBLFlJGFEWmTZtGQUFBmFYnCAJZWVkcOHAASZICGm52NvPnz0en09Hd3c3Bg4eoqKjg4Yc/w/r16zWrQWJiolZ3S0sL//Iv/4KiKEybNo2bb75Ze1giYygnJSVph8e6urrYsGEDiqLw1ltvIQgCDz/8MKIo0tjYyJw5c7S09z6fTwtJl52drYUZU7dDV69ezYwZM9DpdKSmpoaOEIIAZnOc9sKo9t1iiT6moT6iPT09vPXWW+h0Onp7e3nwwQdJSEjQtn7z8gIPTlpaGn6/hNFo0h6uRYsWk5qahsFkIndaAf2DA3z+0c+zdMkSzT/ylltuoby8nNWrVzNv3jztIR8rLO/HjdDQvhBYA1u3bqWsrIyMjAzcbneIpWD0OtRrVR/i7u5utmzZohFmbm4uP/jBD7Db7RiNRlJTU9Hr9CFrKliZDu67737MZjNGo5Fp06bxwAMPkJKSohFSKObMmcPf/d3fsWfPHubPn8+nPvUpTCZz1GyoEBD8d999N5s3b8ZsNketM1r/ZFlGIBgjO2LMRvQhytiMVfc1q4wQ2OYFpso2bwwxfFiYCB8KKh+GuFTE+HCK8OEEXzA1mEyBbNkfIpeGhvZFUdABn+rvp++NN1judqPr60OXnDxmu9VvxGAdiqKga2vjQcD0yisoQL4s868zZmAPHpxPaWhE19SsZQ1X6xBkmfsHB7HEx6M/d45Cr5c/mzWLpKYmdNu3BzN5X2vLPI+H7+bkcLWsjLS0NJbPsmB+7fVwPgxpa4qicLckcVtWFnFNzYgt28edE0FdQxHltL0E9bkI/bKnByYQRn0q86GgfBwngnw+2L4dHn981InzSTKeE1UIuSmYZ2Qi6wT0ExhL1ffPaDRquR5CX3z8fr+2zRXp360Kmv7+fvbv309LSwuPP/442dnZWtlwH7fwHBhqdArV31AVGuohIHWLWL1WDcMmiqJWvyRJ9PT0kJKSEparIvLekfeP9n1kGbVvBw8eRJZlli9fzqxZszT/xtC43z6fj6tXr7JkyZKwOObqeIqyGrJQiDqOke35U3YlJtLPD6LeUKiROIaHh4mPj8dsNo/o53h1+nw+7WU9kugjtz0jtzvVsVa/C603WpxtVYGJj48nISFBm8fx1sxodUbrl+pz7PV6MZvNI+r/U+dYlmVsZ+sRbE4MW5YQByg6Ad327fDQQzBOPPgYYvjI8PTT8Od//pHdbkw+3LEjkBwtKWnEdTE+HDkeob9/7Hy4fTs88ACjaj+RkOWgT31IPyd25bgYlQ0VJZAzxOdj2OHAYrEE+PA65L2iKPglCVEQEEQxLO2cpsCEjk9Em2RZRgxZu9r/RFkHwfXW09tLfHw88fHx1/hwtD6Hrp0o5Ub0J+Q+YXw4itKiQaebkAI5Jh9+HB4eR4/C5s3jFpucJmPQkvgIkQ/PBAZTta5EXqNu441mKVdfkFJTU9m2bRt+v5/U1NSw5DrXBFJ4O3Q6nSZoQn9X6zWZTCPuFe0FVY1CMZqgjuxP6PcTeZFPTU3l4Ycf1oRlqOU9tM0Gg4ElS5aEXR9KInLA9IAY5bGJvH/4uEVvVzSE9iuyb+ORxUTvEZkoSu2jOofqFnnkS/1o9YfOiUpMavtDX8BHm8PIdoTWq7Y1UkFR5zEnJ0fr02h9j9YPtZ6JKgSRysoHpeChgKgTA4dPr+02xxDDJx5j8mHoTxTE+HAK8OGIq0ZC2xkOJvjT6p7AC/BEMSofCgI6oxGjwYB2luc6bNECoA/WpciypggIId+jjEzhptKAGPp98H85eG4lmoKi1+nIzszU+iQIQtT2Rh23ifYrOAZisB9C4GZjz4UgBJSKCdU/9fhw0ioUAgKCMHGHsdGETKRFIvS70eoRRZGUlJQxLdiRsxt6v4m8/I7WfjVNe6gVxOv14vV6sVgsf5LbUGgbW1tbsVqtJCcnh7V3PAu2evgobENPCO9D6DWh4xH5Uj0RqJp6X18fzc3NLFq0iLi4uFFfgEOtXBN5SfZ4PNTX19Pd3c0tt9yiWRoi26v+rgqnifZBzZgaadUfq+2hljG1DtVKFpl5NbStXV1d1NTUcPPNN4/IVKsi8nmIvNdYUC2NqqL0YUAT/IHANjHEEAMxPrxh+VCJftB+NCjNzSivvkq/Xk9nZyezZ88OBL5g5NJQuDb+mvIzTv3+YCTG4eFhlixZEtgVCqlPe3EOqV/t21h1qz1UIzT5fL6wHafR2g7hfKjukquHvPV6fdgLemjb+vv6aGxsDOwqBc81jtauwB8RWeTH6A8Edyh8PowGQ9jOxKjw+SA1NbDbPkFMNT6ctAoFAMrYbikQ/n3ky2Tk56EvhypGs3SPV0aSFETxmsCNvJ/b7dYOpU5IKBF4ud21axcLFy5k8eLFQCBk3x//+EeMRiOPPvqoJkAj6/T5fGGCV60/2r1lWebkyZMsX74ci8WC3+/XXpjj4uKQZZnBwUEGBweZNm2aZqXXxkdghJBR26Qm9gkVFkNDQ9jtds2vNCUlJep4RIOiBMLAvfjii4iiyOzZs4mLu+YrGwmbzUZvby8zZ86cEHHZ7XZ27drF3Llzw+4ZCVWZuHLlCjk5OWRkZIxbt8fj4dChQyxZsoSKigo2bdqkHdge7R6dnZ0UFRVxzz33YDKZkGWZo0ePkpmZyeDgIMuWLQs7/BZaT19fH1evXuWmm24KqzfyXuo8qVnl582bhy4YfWIsdHR0UFZWxpYtW8a0GI6G8edDQZZkFL+fD09liSGGKYoYH95wfBg3NIQ16miMArcb15IlPH/1KmJ2Nvmf+QzmIB9EG9WhwcFwPhxHBg92d7Pz6aeZu3w5iz71KQgqK9Ewgg8nYLyLxoeCIES/h6JE5cMjhw+Pzochl3devcr5EyeY97nPYQxGLYssE7jNKHw4Zm+gvbVV40MxRLEd9brhYdi7V9sBuRH5cNIltgtFtCQ+0aBaWnt7e7l06ZKWyEaWZTweD1VVVZw/fx4puFUYbcsx9DOv10tJSQnt7e1UV1dr16nlPB4PJ08ep76+nsbGxrBtQkVRsNlsvPPOO5owUX3c1Zjdo/ZXCERNCH250+v1LF26lFtvvVUTjqFtkWWZtrY2/vjHP1JbWzvuPdTv1FjJ7e3tPPnkkxQVFWmp4QcGBnjiiSd45513tGyeanIWBQWfLOH3+5ElGUm5lpa+q6uLp59+mtOnT4f1u76+nueff56ysjLa2tom/AKqjonJZOLLX/4y69atIy4uTvPPjRxTSZIYHBzkzJkzWptkOZBFVi0bORbJycn8xV/8hXY4MLTdoWtF/byyslKLLhY6B5Hl1P+vXLlCc3MzxcXFuN3uMKtLtPp9Ph8lJSV4vV58Ph+SJNHf309zczO1tbUMDQ1FHadA1lgrNpuNwcFBra+R7VTv29XVxY4dO+jt7R3R39CxDW2HLMvaelfLqplOQ+8T+TNxCIEtXjGa40AMMXyyEePDG48PW1pbr8vwHOPDUfgwQlkalQ9D+vpx8KGqTExwtqccH05ihSK4xxMx/tFeVhRFwW6388ILL3DhwgXNzUSWZQ4dOsTvfve7sBcxdZGE1hf6udPp5A9/+AP/5//8HxoaGrTvQEbBj8Nh44UX/sA//5/v097ePkLT1Ov1DA0N4fV6tYd6z549HDt2DJfLNWKxqRAEgebmZg4fPqwJLIPBQH5+PnV1dWH3UZMKOZ1Ofvvb3yJJEpmZmciyFEYU6sJX/1av6+np4cknn8TlcqHT6bBYLAwNDSEIAomJidx///088sgjmM1mnE4nr732Gna7HVmScQ7asLV0ojjcCHK4AJg/fz5z5sxBUQIH71wuFzk5OeTm5hIfH6/dY6JQrWjNzc3s27dPC+/X3d3NkSNHaGlp0YhyYGCA+Pj4sPMGXV1dHD9+nL6+vjDBq467z+fj6NGjnDp1ShO0kiTR2dmJ3+8PzoOsHV7Mzc3F6/Vq89fX14fdbsfr9SLLkiYE/X4/RqORNWvWkJGRgV6vx+l0auvS5XJpRK8oCh0dHRw8eJCEhAQKCwu5fPkyP/7xj6murmbOnDmkpqaSmppKR0dH1HULcPnyZUpKSjhz5syIZ6ajowOXy0VTUxMdHR1kZWXxla98JRhKL0A+DocDSZJwu9309/dTVVXFj3/8Y4qKivD7/SQkJIzYmSgtLeXSpUtae5xOJzabjaGhIS051vUqFddXPoYYPgmI8eGNyId2m+36XhaDL84xPozx4WTEpHV5UgQBnV6PoB5ICYHP56OjoyOYdEZAkiS6urp4/PHHaWtr07ZcFUWhvr6exYsXs3HjRgQhkO792LFjpKens3TpUkRRxOv1IoqiNvlpaWls3LiRCxcusHr1aurr68nIyEAUBTo6Opg2bRq33XY7jY1NLFu2THsgu7u7URSFtLQ0UlJSNEF28uRJSktLycvLo7CwkIKCAm2rNnT7eWhoiOrqaoaHh7Hb7aSmptLb28sf/vAHkpOTue222xB1IEl+Dh8+xKpVqzCaTIg6SEyyEJ9g5tKlS8THJ7Bw4UIURaGsrJS8/FxSklPw+Xzs2PEGGRmZPPDAA5w6dYpXX32VVatWUVBQoJEFwMGDB/F4PHz7299Gr9fT0NBAbW0tCxct4uCut+k4cYZVn9rKom13YTHqEQWBzMw0XO58Llw8y913381bb+3C4Rjm4U9/Dr1er1mAFGXiB4AVRaGpqZGnn/kder2egcE+4iwmjh49xHPPP8dtt93Gt7/1NxQVnWX37t387d/+LaIo4PO7AQOXis/zxhtvkpuTzze/+U0yMzMRBAGbzYbD4eDKlSvs37+frOwMhh1DWK1W3B43O3a8yj333ENHRwdLly7ldNFpvF4v0wqmYbPb8ft9gMDFixcxGAwYDDpmzppBT3cPlZWV3HzzavLyCjCbzfT19WG1WrHb7WRmZuL1evn973+P0Wjky1/+MmazmdOnT7Nr1y4KCgqIi4ujtLSUY8eOMXfuXFavXk1XVxepqalawh5VCBsMBnQ6HT6fD4PBQFJSkpZoKdSl4eTJkxQWFtLd3Y3b7Wbz5s04nU5OnTrF9OnTg1bGk9x6660cPXoUu93OsmVLMZmM/OY3/4vP52HlylXIciCalMFgwGaz8cYbbyBJEgsXLsRsNlNUVMSVK1fw+/3k5uby0EMPaS5qE4IAsiSPXy6GGD5BGJMP/X46mpvJX7gwxodTjA973O7rCgaqKArNTY08ff5MjA+nGh8mJfGZ+Pjrc1+aYnw4aRUKCGqdUbblbDYbx48f56GHHtI03UOHDvHQQw9x5swZrFYrOTk5yLLMli1b2LVrF2VlZSxfvhy73c7evXvJyMhg0aJF+P1+SkpKWLRoEfv372fv3r389V//NRs3bqSjowO9Xs9LL73EmjVrmDZtGk888QQ/+MEP2LBhE3b7Hi1Rm9Pp5OWXX6a+vp7vfve76HQ6WltbSUlJoa2tDZ1OR2JiotbuxMREZFlmYGCA5ORkPB4PQ0NDTJs2LUxbr6yspLy8nNzcXEpLS0lNteLzeym5XIyoE8jNzeWee+5mz549ZGVl0tTYQnp6ppa0pbGxkbr6GqYVTKO2rpajx46QnpbJ+vXrue222zh37hwGg0GzvCiKot27o6ODhoYG7cE/f/48Q7YhMpLiicuw8tKzv2P5YD/TC3LRiSKyEvA1vXz5Mt3dXZw6dQpBEEhIsNLV1aVtoV4vfD4veXmBZDivv/4q+fn5wbnR0dHRzjPPPM25cxdxuVy0tLTQPzDA888/p8U3LyyczskTAavCd77zHZKSkigtLUWWZZKTk1mzZjU9vV386le/1IjvypUrVFZV4Ha7SUtLo66ujvnz57No0UJ27Hyds2fPocjQ399PX18fLtcwWdmZtLa24vf7uXDhAomJyQwNDbF161aSk5O1LfqBgQFaW1upqalh48aNLFy4EKfTyeDgIL/61a9wOBwYDAYsFguHDh3izJkzLFy4kBUrVmhb7h6Ph9OnT7NmzRoaGxvZuXMn3d3d2ktCpNUyJyeHJ598Eo/Hg8/nY8+ePQiCgN1up7y8HK/Xi9vt5s0339SsdoGt8sAW87PPPsusWbPR6/V873vfAwKuECaTCZPJhNPpmANEYgABAABJREFUxGw2s3HjRtavXw8EXgrezwHu69nBiiGGTwpG40OX08nJkyd5YMaMGB9OMT6cSCjyiAnHG+PDqcmHDgeGgwev+7mfSnw4iRWKYKjNiONOghAIL1dSUsLFixfR6XSav2hlZSWDg4OUlZVpB6cUJZDt02g0smzZsoAAdrkYGBjQtkiPHDlCYmIiNTU1DA4OcuLECc2PUK/Xc//992O1WsnMzOTzn/88SUlJiKLIrFmztHu0tbXR3NxMV1cXdXV1pKam8pOf/ARBEHC5XOj1evx+P7fffjsWiwVJkjThpNfr+eUvf0l1dTUGg4GEhASt3lWrVvHf//3fnD9/nl/96pcIgoJf8uF2uykuLtYO+Xo8noDPqARf+tJXtK3MhMQEnn7691qkjhmFM/D7A98dPXqU8vJyli5dil6vDybxCaCzs5OCggKSkpJ47bXX6O3tRRRFiosvkuR3M81gIrUgl8vnznDpfMC/VxUQgiDQ09PDli1bqKio4NTJk+Tl5WM2m8nLy7tul6c5c+bwj//4j/T19bFjxw4aGxpRUPjCY1+gvaMdl9vFI488Qn9/P6mpqaxds4bDRw4gy9dCAd5xxx1apBJRFNHpdJSWlvK1r32NVatW0dLawLFjx3C73fh8PubOncusWbNITEyku7ubjRs3YrFYyMnO4c4776SpqRXJL6PT6Zg/fz4zZk6nr69Hi+TkGHYCgXCJs2fPxuVykZGRgSzLFBcXM3fuXDZvDiSWGx4eZunSpZw4cYJ169aRlpaGTqcjIyODnp4eFEUhLy+PzMxMpk+fjiAI9PX14fP5EEWR3bt3U1ZWxhe+8AUSExPJzc1Fp9Ph8XhwuVwMDQ1x5MgR1q9fj8PhYMaMGVqmU/VHr9eTkpJCX18fiYmJ2vZ8eXkF6enpFBQUkJ6eycMPP8yKFSuQJAmj0UhOTg52ux2r1aqNK4RH/7geiKIIuknsiRlDDB8LRudDQRAoKSnhfFVVjA+nGB9mZmZel8uTxodffjzGh1OMDwWfb8RZj/Ew1fhw0ia288oK/tO1SGkWLHNykUUFvXAtjGZraytNTU1cvnyZmTNnauHeVIUjFIIgkJycrG39HjhwgLi4OL70pS8hSRIHDx7EbDbT3NzM8uXLSU9PJzc3F7fbTVxcHKIoIsvhsf1Vy4UapeDEiRM4nU7mzp1Leno6ZrOZrq4uLexdYmIiSUlJmsXW7/fzyiuvUFBQQHJyMr/85S+55ZZb2LZtG3q9noyMjLCkaG63m4GBATweJ8Ull2htbWXbtm0BPz4EFBSqqqoovlTKpk2bA1ujzc2cPXuax77weTIzMzGbzZhNFrxeP3FxcVRVVdHe3s7ixYu17KNqIiI1m7PJZMLlcmn9V1DQyT4MkoDfaABFRNDLKCiIQjDONKDXB4RH4LBaIOSherguMkHRWNB8dRUJJRj1QFYCbRGFgBUIBHSigWsrWUHhWrjW5557juXLVnHzzTdrW+uqP2hgfgUk2QeKgtPl5M0330QURR5++OFA8iWE8DjQAsiSwL59+7l69Sqf+9znyMvLQQyN1KqIqEeU1HWpuiOcP38eg8HA8uXL6ezs5Pjx41RVVbFw4UIeeOABzY8zfAzC/25ra+MXv/gFaWlp9PT0kJOTw7e//e2wCFjqTt6VK1coLCxEr9dr2WBzc3NHPCMqaV8LVyvx+us7yMnJYcOGDYiiMOJxvV7lMBrU/smSjONSM/7ufkx33RRI5KOPJbaLYRLiI05sNxYfyq+9RvP8+TQNDMT4cIrxIa+8guHTn0aIj5/QOlBqaqCjDWX9rTE+DPl7SvChGuXpM58Zs+y4fPhx7FhMMLHd5FYoTtXgT7MQPycXWYcmQEMPVL344ots3LiR2bNnaxMULTyc3++noaGBffv2YTAY+OxnP6tppZIk0d3dzblz57j33nvDBFfogorm+6/ep6ysjK6uroBfZ0SegNC2qHVIksSJEyd48803cTqdzJw5ky9+8Yvk5+dHHQ+1DpfLyf79+5heWMiiRYsw6A0IAvh8fs6cKcJgMDJ//gL6+/spKSlh+fJlFBZOR4thrkCox2aoJTm0v6GfR10isoBfCGxxCWKkj1/knIaHA1TvNTGEHmIKHcfA0lG/EoRILV5GDh6MGxocxGy2aGQYre8KgYQ0Pp9fy45tMOgJWAGvlQ+0W0BRAiFq9Xp9MIt2QMiGRtWOHDb1cFxPTw8tLS2sWLECCBwA83g8mM3mEZm1I9uq/q4q1bt27SI7O5t77713xCEx1WfZ7/ej1+u1+N2j3WNkWwPXiqKAKOrGNayM1eZo34eWUw8xuopb8Xb2Yrr7JiwIMYUihsmJj0OhGIUP2bEDecsWfHFxMT6canz4yisIDzwwYYWCmhqUzg5Yv54YH177fUrwod0+YYViTD6cxArFJHZ5upZIK4Brg6hOvsFg4PHHHw9bEKNNksFgYNasWXzjG99AluWwBGE6nY7U1FQKCgpGWHRChXLkZyoURdF8VKMlFIt2rSiKrF+/ntzcXI4fP859992n5TaI1gdVkJlMZu6774ER7dTp9Nxyyzrt86SkJAoLC0cK81ESEIX+He2z0P8VRQEdIYeLxk+Mdj0HscMRXZioH4V+FW4xExEFEHU60tLCc0ZEHd+g9cRgMJKSkjqifOg91ftardao5ULLRH4viiJpaWkkJiZqcyOK4oizBmPp+eranzZtGo888ghxcXFajPdQqGtRtUKNlQRK3Q4Pvw9BEpkYRmvzhOdeAUHNVnudgfLer4tVDDFMFYzGhwTlSowPpyAfqj/XgcAOQbgiEOPDKcKHEbseY1cw9fhwEisUiubvF4rQh1gURc1nMhLRhJxOpxtVwJlMJpYtWxbm9zhenaGfpaamagnbJiJM1MyPs2bN0rbfJnJAa7QMyGp94wn88bbZ1N8nUm4shAuS68uOPR4mUtcHVeb9lA29JnJcITCHkdu40TCe4NHpdGRlZYW173oVt9HcIkLrjGz/WHVFtkV1hZhQpCeBoK/z+980ff+KawwxTGaMw4cQ48MQxPjwwynzfsqGXvOJ50NZxhN0HRwXU5APJ7FCEdDcRxuM6x2kscqrg65qrO+n7tEE22jlQ+8ZqSlPxFUkmvvQn7pwxtrWDbd2jN0+9fvI7eIPoo2THYqihG2lApq/bWjfQ7PJqoSq/q7WoQo29Tu/348kSVokldDxjfT3VO/r9/s1313V8qNup/r9/oBPbKilLYjR5kmNqKHX6zVLqBrnXu1HaF8EQRj1JSfyXtdejZSg5fDGXisxxDBxTIAPP6AXwhgfjqzzQ+PDP6mFkx8xPgzhw2C/b2Q+nNTHx8fLcgnXBMifovV/EHW833uqCH0IJEnSQp2Ffqf+rmZjtNvtUcco1AcvtN6xoCiBZEh2u10rqyavCasLBb/sR/b78HndeKVrSW0i+9Df38/ly5cZHh4ekZXzRoPab6/Xy7lz5/B4PNp3apIhNfpKcXExHo8Hv99Pc3OzlkDI4/HQ3NyMLMs0NDSEZRVXlMChwnPnzoXdT1ECZ4lqamqora0LG+f+/n5eeOEFbDYb3d3dWpIr9V6HDh3S/ErVDK719fUj+nRtPQXu9frrOzh58pQ2z6Wlpezdu5dLly5pmXRbW1s5cuQITqfzOkIjCigCyLIScG+OeBQnspZvdIU1hk8uYnwY48OpghgfjuTDo0eO4AvmeJkYph4fTlqFQoDAwZ5PABRFYSAYocPpdNLW1kZDQ0PYQlGF6tDQEDt27KCtrY0dO3bg8/mi1qlq3G63O8TvdmxcunSJV155Rcug2dDQwLPPPovNZtPa6XI46W/toKO6nt0vbafuytWwdqoPp5pd9e2332Z4ePgT8aInSRI1NTWUl5eHWSHKysp45plncDgcvPXWWzzxxBP09vbi9XrZtWsXg4ODWgbZXbt24fP5SExM5ODBg/h8PiRJwuv1sm/fPnJycsLu53a7ef755/n1r38dTCQVEECKAm63m6qqKt58801SUlK4fPkyvb29QGAu1SSDqgC9dOkSV65cCRPaKnGq1iBFEcjLy8NoNAHXrEKdnZ3Y7XaampooLy/nf/7nf2hubtZcDyYKQRDRhfiMRvo4j37dR/fyE0MMHzVifBjjw6mGGB9G8GFLy3Xz1FTjw0mrUAQ3Gz/mVny4CBU8drudn/3sZzz33HO0tbVRX1+vHcKTZZnh4WEuXbqE1+tleHgYQIu9HGrFCbXC1NbW8swzz+B0OrU43H6/H6fTqT0QofdQw/spSsDv3WKxkJubi16v1zT/8+fPc/HyJYaGbWTl5pCRnYUsy/h8Pu1hv3TpEhUVFdx6663aFqMqWL1e77jWoamCaFaooqIi5s6diyAEQhv6/X5SUlKorq6mvb2dhoYG3G43w8PDtLe309PTw5UrV5Akifr6erq7u2lsbCQ5OZm2tjZN4A0MDNDc3ExOTo42P8ePH6e5uZnLly9z3333sWrVSgYG+unq6sLrDST8Wb16NcuXLwfAYrFQWloKoMXMliSJ4eFhmpqauOOOO5g9e7bWH7WdihKIVb9r11u0t7dx/PhxDh48gMPhoLGxkby8PHJyckhPT8fj8dDW1oYsy2zduhWz2Xxd1jghGB0EFCIjsMQQwycVMT68MfnQF7LzMtUR48Ox+XDLli0YDIYbmg8nrUIRGEBlkg/fnw5VsOXk5PDNb34TQRC4cuUKPT09yLLMyZMn6e/vx+kMxIOWZZmlS5fi8XhITk7WtmEPHjxIcXGxtlhdLhcWi4V58+bR2dlJR0cHkiRRXV3Nf//3f9PR0UF9fT0ul4u2tjZKS0uxWCyYTCY8Hg979+7Vwq6pAvjUqVO8tuN1yhtrmbV0AVkzCqhsqMXlcvHKK6/wzjvvaIL52LFjNDY2cvnyZSoqKhgeHub555/nwoULN9x2rxr6bmhoiLa2NnJzc+nt7eWJJ57gzTffJDs7m9WrV+PxePizP/sz1q9fT3V1NceOHWPTpk0cPnyYpqYmLly4wPr169m5cydutxuDwcDg4CAAvb29GI1Gzb94YGCAl19+mbNnz7JlyxaOHTtGbW0NpaUlvPfeu+zf/x4dHR2YTCaKiopoamoiKSmJpqYmIOCzajKZaG9vp76+nl27dtHR0UFRUZGWGbS7u5uysjJkWaampoa3397NocMH8Hpd1NfXUF9fw9GjR2ltbaWnJ5DEaHBwkGnTpjF79mzeeecdvF7v9Q2mIKDICooij7DFfNRuGDHEMHkQ48MbkQ+Li4uRbxCFQkWMD6Pz4bvvvosveL5iwphifDipD2XLAhPc4Jm6cLlc7Nu3j40bN7JmzRoWLlzIL37xCyRJ4vLlyxQXF2M2m5EkiaysLM6cOUNfXy/Tpk0jISGeU6dOapkfc3NzWbx4MTqdjrKyMmbMmMHmzZs5c+YMNpuNrVu3UlJSTHV1JS+//CJtbW3ctGIFxZeKcTqdrFq1ivr6Bvx+PxcuXMDhcFBbW8u8efNISUnB4XCQl52D1NLFsZd30G8f4kJdJfv3HqSqqoqUlBQMBgOtra3U1tYwODiA3W5jx44dvPPOO9TU1ODz+Vi9evXHPewfGFQ/0ZdffplZs2YhCIImcGpra7lw4f9n778D4zrOe2/8c85WYNE7CBIkQBLsReyiCklRsmirmZYly7YUO76Or+Nc37w3N29+uY7zxoljx05yY8dxkyVblmWZkqhmkRJFkWLvBDtBFIIoJHov2/ecM78/zp6D3cWiyZIMUPuVYQK7c2bmTHm+8zwz8zzlrFy5kjVr1uB0OlmwYAFZWVm89dZbuFwu1q5dS2dnJ42NjRQXF7N+/XpaWloitlT1GWBYxQzLj6Zp+Hw+Xn31VXJycujv7+cf//FbOJwOvF4vLpeLYCBEIBDE4XCwZs0aVFU13fHJskxqairf/va3UVUVr9dLRUUFxcXF5pEAp9Np7ijZbDaCoRBv7tyB3W7H5/fzb//2bwQCIQ4dOkRhYSGf+MQnaG5u5sCBA4RCITIyMhgYGCAnJ2fc7amFz0MnkEACkUjw4c3Ih2maxqo/dqO/j0jw4ch8mGW348/MJGUC7TnV+HDSKhQSIFnkcZ8Zm6oQQtDX14fX6zUDDWVkZKBpGr/4xS8QQrB48WLefPNNBgcHqayswGazMXfuHJYuXcIzzzyDLMssWDAfq9WKJOmXni5dusT8+fMB3YVfb28vAGVlc3ntdR8NjXWUlZVx8eJ50tJScTodVFRcRpIkjh07hqIonD17lqSkJOx2O7Isc++993LP5ruoOXaMS8dO0enpx5meTHZONp9d8Vm6u7vDF5CuU1CQjyzL3HrrOmTZQmHhNO66664JRcmeKrDZbDz++ONYrVa6u7vp7OyksrKSBx54gMzMTLKzsykoKEAI3etDUVERn/vc57BYLCQlJfHggw+aPtsdDgePPvoomqbh9/vJzs5GkiRycnKQZRmv10tPTw8Wi4V58+Yxc+ZMVq5caZ47rqyspKysjHnz5uHxeMz2Tk9Pp7y8nCVLlphWjaVLl/JXf/VXXLhwgezsbMrKynA6nXR0dKBpGm+99RalpaXU1NSgKArTi6azadMmFi1ahKqq1NbW0tHRwW233YbT6SQ7O5uvf/3rDAwMIITAarWariPHDQEhJf456AQS+KgiwYc3Jx/mvvvuTdejCT4cgQ/9flzl5RNrzCnGh5NWoRAYZypvrsVnLJKSknj88cfp7++nr6+PLVu2mILvzJkzXLhwgeXLl7Nu3TpkWUbTdG3VZrMhyzL/9E/fBnQfzPoizoaqaty4cYMf/ehH5ObmUldXx8aNG9m9ezfNzTeYOXMm/+t//S+ysrIQAmRJ19KFUHG7Pbz22ut4vV6+8IUvmNEqjTJVi4WyOzaycN2dSIBmkdBkXTAYrtg+8YmPQzgMqdGHsa5EbxYYruxycnLQNI3Vq1dz7tw5kpOTWb58OYWFhcPeV5ZlMjIyzL+Tk5PNvEAfE/X19cyYMcMUoOnp6SxYsID29nb6+/s5cOAABQUFfOpTnyI1NRUhBLt376a3t5dZs2ZRUFAQlafb7UZRFBYsWGB+5nA48Pv9tLW1sWzZMkpKShBCUFlZyYEDB5BlmdWrV1NbW8uhQ4dYs2YNGzduxG63EwwGeeedd5g1axZFRUWmuz2bzWbW571sx0qyMVZunjGSQAJ/KBJ8eHPyodTVleDDjwofejxIFguM28vT1ONDSfwxbgSFQrBtGzzxxIi+s0OaIHjyGqFUO6nzpqNZJKzSzeUWMrLpY7shGAyya9cuMjIyuPXWW3E4HHHTxcLYYmxubub8+fN4vV5mzpzJ4sWLqa+vp+ZqNbfcslSPGirJCCQQlvCdH8FA/wAnTuoXqWbOnBnXS48qBLIAyaiKJSJwixDokUIhdoM+Mp+bpR9j+zAYDHL69Gl8Ph9z5sxh1qxZE35XwztGcnIy06dPDxOnfoazqamJW265xfR9HRlR1O/3A/rWbKwLxvb2dnp7eykrK4vyEW9sJRteOIzxY2yzRl5ANHxtR7oENIh8PO84lu97TdXwXW7B19hK8sdX4JBksEpYt22DrVthPMGAEkjgw8DTT8OXv/yhFTcqH27bBnPmgMv1odXng4AAMDzqRPyOJBEKBtn19ttkZGSwbt06kw8R0Xs2Egz7WxMiig9nzZo1xIc1VdxyyzKT64SQgAg+HBjgxInjzCsro3jmTORII0n4X1UILCKiYDn2fcIfSENX603VcN8++G//DSm8iB4TDQ3wyisQ4d1oMiGy7YUQhIJBampq0ISgsKBAj34+QT4U4fWMxWqlID9fjzWBfgm/t7eX2aWlBINBLBaLHlcCvW0NPnQ4HEiyHDW2+vv66OntZebMmcP4UDOOQkm6byUhBEo4DobBf4qiYLVY9DEDUXxokeX47xgMQkoKPPww8Afw4R9j7XTgAGzcOGaySatQKJrAe6oW1WEhZcksJFnCMsUUitimHSngTTwYizojMMpI723kMZIP7+FpBZKMPuj1jfSwENXnm9AESJppaYhbpqaGpaERQGho90FVVSwWIyDS8GffS/+N1E7v11iIl/9E8450LRcMBgkEAthsNpxOZ1TQnrHKMISVYeE3guUA5ueRgZ/GU0/joiMQld9Y7zOWwIsMJjSR+kTmEVtPb0UL3mvNpN23CrskIxIKRQKTER+yQjEqH964AU1NE16ofdgYdql0jO+jvtM0VE0bFgE7OtHQglGnJ2nosxH4UNIpEFmShzgtkg+FACICsQ0rV4CmRX8uGRqFGKqz8bYxj0t2OyxbBuMNBKhpiIiYDlF5jS+HMWG2khDme73XvLWwMSoYDGK1WnE4HMhhhSBSYRwpfwGoioI2Ah9CdLT08fKhMR7G4kNDoRBj5C3CDhMix17c1DYbWOMfDBo3H05ihWLSHnmSJdDsFiwhgSqBbXLLyhEx2sIsXkRGA5ERFkdDvLwn7us44l85bKEZpa6SHF/4DU348dVzoohsp7Em90TLNgSM0Xaj9Vu8/CPTOhyOIQtanOfG6p/YSLEjfR5Z53gwlIjxjiWjbpF5Gn/HSxfv34nCeAezjhZdqZVjo/gkkMBHGKPy4YwZ+s9kR6y8ipEZkpksjrxh/IuVeJJDGuHzsfIZ6ZnRuDsS8gjy+T3zoSSBwzF8Z+Z9XmQKTTMXx1LYCj/aoj+qijG/251O7BCloJj1N9pnlPpb7HYscb63htshsh76+iQ+15l8OML4Gw1SxL/xelwa4d/hCUcvc6rz4aRVKDCs5ULE78FJjEhrdSgUMs/SjZRuIvlGCjLDmh0KhUhKShp5RyHOLkY8gThaupH+Hmnx/n7uHmiahtfrpa2tjcLCQvOM5UjpI61Rxu7ASLsBBnp7e6mqqmLevHlkZWWNWf/IiR8P8RbimqaZn5tnaMfa9oxQCmItM1rY+jWSAtLQ0ICiKMydO3fYDpZhpTGeHU15ilW2QD+WFbmLNZG7MZHtYryDAUmSkS3yRGR9Agnc/JiKfNjbC319ZnWNHWzD8BQ7yePyYcQCdDQYzxpybbw7sUYZoykz8dKZH41Qj9jvR6yL0wn5+eNe3IpQCPGzn+GzWunp6SE7J4ckp3PE541Feyy3x+XDcFqAwYEBmpubmT59uukud7Q6CiFGVQ6GLcSFQIR3dkw+HGWnAsJKjvEesmymNcaUfjrCoisUxu5UxPjpaGsjGAoxY/r06HqG+VATAmt4XEbVI0ZpMd43sg0NbjfXVfHexefTd6PWrx/edhH5TGU+nLwKBXpjSnKEVXqKXEwBfZB0dXXx9ttvk5uby5YtW0ZNawzIyAWaqqr09vaSkZGB3+/H4XAwODhIRkYGkiTR399vRvL84he/aJ6Ft1qt9PX1kZaWhtfrNS8bZWZmxrWOG1EpnU4nHo/H9KAwXhjHs0KhkHnE5/2E4Ybuxo0bfPWrXx1RoTAmpqqq1NfXM23atFEVLQOKorBjxw4aGxuZPn06GRkZUduo8RAKhaisrGTRokVRyuJIypjf7+fo0aMsW7aMxsZGli9fPmYZQuhnf69evcodd9xhlnP+/HkkSWJwcJBVq1bhcrmiFBhDuF6+fJmuri5KSkrCHk8kc6y1tLTQ3d3N0qVL47ZPpMLa2tpKMBikpKQESZLo6+ujrq6O0tJScyxGHut6z8fawqcHlJDCqCaxBBL4CGLK8eHOnZCTAw4HQgi6u7vZu3cvBQUFbBzl+IQhoyKNKAYf9vX1kZ6eTiAQwOFw4Ha7SU9PR5IkBgYGeO2117Db7Xzyk59EURSTD/v7+0lNTY3iQ0N2xbaipmkmH3q9XvMi8HhhHDE1+HDUZ8+fh7/8y/EfeVJVgqmpPBcIcMPn46v33UdRUVFcq7yhICiqSn1dXTQfjlSncPrXfvtbrvf18YWHHiJ52rS4CmAkQoFAfD6MSBP5u9/n0/lw+XIaGxqG+HA0pUVVaWpq0vnwzjuxhY1h586c0fnQ7db5MDlZ31WJUHJUTePUm2/S3dXF45//vM6H4TSaqtLS2jrEh7FKUYRRzeTDUIiS8P3Ivr4+6urrh/gQkC0WU/kwc2ppgUuXRny/qHedonw4aRUKAcYBtojPxOQXohFQVZXCwkJKS0uBka0fHo+H/fv3s2nTJlJSUsy0ra2t/OSnP+Zrf/413nzrTW5bfxunTp3miSeewGazYbPZWLlypelF4Oc//zkrVqzglltu4f/+3//gkUce4bXXXuOhhx6kvr6ehx9+eNh9DCEEbrebH/3oR9x7771cv36dRx99NOqM/Fjwer0cPHiQ69ev88UvfpGkpKQxn5uIgLbZbHz605/m8uXLpKWljVo3g3j27NnLvfd+LOypAUYy6xl5Pfjgg5w9e5bU1NQopW6kZwKBAHv37qWkpIT09PQx3zcQCLBv3z5yc3M5duyY6R893nNDgf8EXq+HI0cOh90N6sqg4afb6/Uyd+5cXOHLmIYiYSzwCwsLqampMS+sGQJRkiTTpd7ixYvj7jrpCi40Njawbds2Vq1exaxZM+no6OIHP/gBeXl5zJo1C7fbTWtrqxkNdbQxE2sdMyyJZltL+hwXmqYfY546Uz2BBD5QTEk+tNngzjshOVlfqLa3k5OZSXFpKZSWjrjR4nG7h/OhptHa3ByHD6/zxH336ZdhvV4WTptGamoq6vTp/OynPzX58N///f8O58MNG3Q+jFiMC01jcGCA//yP/9D5cHCQRzdu1K3WkjSuzSGv2z2cD0dKfP06MDFRZ7PZ+PSDD+p8mJ5urjXjlSHQ1yF79r47xIejlGbcFXjwoYeG+DC80B/pHYQQBIJB9r77LiWlpaTb7WPzYTDIvv37yc3L49jx4yxesgSL1Rq3DJMPJfD6vBw5eoRb1986xIddXSPzoabpC/xIPgyF9LLCkdKH8WGMcicgfIcDGq83xvBh59h8OLzBzPpF/nsz8OHkjZQtgRa2xujXh6fKPu8QcnNzycvLo66uzrRaGD+R1nRFUejq6oq6LKSqKmlpaRQVFaKJELIskC2C7u4uM/qww+EgKyuLs2fPYrVaueuuuygpKQn7QM4iLy+P9PR07HY77e3tUQvxyAnvcDjIycnBZrPR3t5uTmBFUXSvB5pGIBAIh7APRtVfCIHX68Xv97Ny5UrzCI3H48Htdpvva+xgRAanGS+EEPT09LBv3z48Hg9C6L7KDx06xLVr18z6CiHweDxommD69Bn4fH5ARlVVzp07z6VLlwgEAqYHI2PhbFjs9+/fH35e/6y/vx+32232RygUIhQK4ff7sVgsZGRkEAqFzHxaW1vN+oVCIbP9vF4vVquVpUuXkpubi9vtJhAIoIUF2uDgoNnOqqpSVVVFZeUVkDQKp+VRUlrM5csXePLJn3P58iUWL17CzJklZGdn09bWFtUXkfclzp07x8mTJ6mvr0cIgd/v5/nnn+fcuXNkZGSYUUeNd+3v7zePl73xxhu0tDQzq2QGX/3zP2PatHwkSfft3dvby4MPPkhmZiYdHR385je/YWBgAE3TCAaDeL1efD4ffr9/2FiLROxujiRJCAtYJBkNQJrMAiqBBD5EJPjwpuRDTdPGPM4ViwQfTl0+5Cbnw8lbP6H/mJraFJCfkefNhRBcvXqVN954g+bm5nC0yBpaW1sYGOgnFAri9/tMwbZ48WJTMALU1dVRWVnJyZMnOXz4MIWFhbqbULsdn88HwPXr1/ntb39LQ0M9bW2tPPvsr3nyyZ/j9XqYPXsWNptETk4mKSkppoeg2B/QvSTMnDmTlJQUgsGgGQ1ycHCQt956i/b2Nk6dOsk3v/l3vPzydrOeQgh8Ph9VVVVs2bKF2tpaU2j85je/4emnnzYFTHNzM0ePHqWtrW3C7drT08OTTz7JjRs36O7uBuD06dN85zvf4cknnzQFs9vt5uc//zlHjx4hOzs7vKDVwu/xJt/97nfZv39/lMCtra2lra2NH//4x7S1tdHa2moKxFOnTnHmzBlzm/PChQsMDAzwzjvvcObMGZxOJ36/33znAwcOmG1w8uRJ2trauHr1Kq+88gpXrlwxo6vKsmwK0IqKCv7xH/+R06dPo6oqfr+fF198kZ07d5qCWgjB8RPH2bFjB0ePHiMpKYnOzk7S0zPo7+8328loB0OQGcRovNPg4CD19fU8++yzpicqYww0NjZy6tQphBA0NjayY8cOjhw9SiioK4EHDhygr6+PgoICPvGJT7Bjxw7zWF1ubq65ADh48CC//vWv+dWvfsWuXbuixnQ8xB7Bs1otWIxLgFPl4GgCCXzQmIp8CAk+HIMPe3p6Jqwa9vb2JvhwCvLh22+/TSh8/3EkTHU+nLRHngCsshUhh9Ag7i3/yQhFUbh+/TpCCN5++22uXr2Ky+Wio6ODi5cuoqoBBgcHWbNmDTU1V7laU8eqVavp6OgwLSwAR44cweFwoGkaVVVVrF69mv7+ftLD9yI0TaO8vJy6+hrS0lKpqr5MSPHR3dxOU3MDXp+HiopLKIo+qYx4AobGbUT77O7upqenx7zDYbPZTGF76dIlXnhhG8dPHGLLli0sXbaQ5377a4LBEI899pgZu+D8+fNRZ+2FENy4cQO/32/+fe7cOTo6OvB4POTn50/onoXf7ycpKYk5c+bw5ptvUl1dTW9vLy6Xi56eHl577TXTknHp0iUcDgd3330Xr7zyKhUVlxBCkJmZidPp5Mc//jG5ubksX76cwcFBzpw5w6233sq8efNITk7mueee48iRI8iyTFtbGx6Ph9OnT9PQ0EAoFMLhcNDQ0MBnPvMZkpOT+clPfkJqaqoZ++PEiRNkZWVRW1trWj36+/spKCjA4XDwJ3/yJyQnJ6OqKqqqUl1dTVNTEy+88AJLlixBURT6+vqoqqrkm9/8OwCam5tJTk7CZrNw7NhxKiqqcLlcbNx4p3l3wefzcfLkSVauXEl9fT0vvvginZ2dBINBc6egt7eX5uZmCgoKSEpKijqalpaWRm1trWl98nq9bN++nQMH3gVgcHCQqqpqrBZ9TA4MDHDp0iVeeeUV3G43n/jEJ5AkifXr17Nu3Trz4uVEIqNrhmvIqbC3m0ACHzKmJB+qKtfDFuEEH8bwYXs72S0tZGrahCy7Xp8vwYdTkQ/b27HV1o77Av5U5MNJrVBIEf8/lXDs2DFsNhuf/exneeCBB9izZw9/+7d/i9/vRdH8ZGZmsG/fXiRJRmhWLl68hN1uZ/bs2aalJDU1le3bt6OJENXVNVxvvMGffeXPyMnJ1X05yzL33XcfGzfdxrlzZ3hp+4s47A7S01P5wQ/+L0IIbHYbSY4UNm7cTEFBAZIkEQwGOXPmDCtXruT8+fM8+eST2Gw2XC4XCxcuZNq0adhsNgKBABcuXCApOYn29laef/45NE2jsDCfhoYGgoEgNouVUCiEz+fj5ZdfZtq0aeY2Y3FxsXl52GKxMH36dDo6OigsLBzzMnIkJEli+vTpfOMb38Dr9fL888+b+W7ZsoWuri6qqqrQNI3MzEwef/xxbHYbhdMKmFZUQGNjHQKJUFAlJyeHuXPnRpV/9epV1q1bx9e+9jUCgQA7duzgxo0bgG6xyc/PRwjBmjVrTKvGunXrKCsro7i4mP7+fjweD4FAgIyMDFM43XbbbbS0tGCxWHC5XHR1dWGz2cjLy2PNmjWkpaXh8Xioq6vj3nvvJTs7m4GBAdLS0rjzzjs5cGA/K1euRpYkVq8SWKxWSmaVcP16M6oqyM7OoqhoGhkZGWiaRk1NDb29vQC8+OKLVFRU8NnPfpaUlBQWL14MQEFBAVu3bmXBggVkZmaybt06ent7TbJPS0vjxo0bpKWlsWDBAvLyclm8eCGyRd82FhrIsi4yLBYLxcXFJnkUFRUhy/KwC/MT9folhH5JLoEEEojGlORDIRJ8OAofJiUnD7kxHQckYHpREd94+OEEH041PnQ6h/rxJuXDSa1QqGJimvsfG8aZzEceeQQAu91OTk4Ojz76KAsXLuTcubOsW7eW0tJS+vr7SXImIUmyuWVrsVhobGzk2rVrnDx5km9+85ukp6cD+gDMzMwkGAySlpaGJEmkpKSQlORk08Z7WXHLOuwOO4ihCJG6swILqampZGZmYrFY6O7uZnBwEJvNRkNDAzabjf/5P/8nM2fOxOfz4XK5kGW9Tg6Hg+9/719xOGxoQrDrrV2UlJSwcN4C+to6qW5p4cCJY9gcNr73ve8hSRIXLlygp6eHzs5O1qxZg8PhwGKxsHz5cmbPnm1emJoIZFnG6XRit9v58z//c/Pca2zMBMMziPHZnzzxRUBPU1VVxfbt2/nKV75CRkYGsiyTlpbGX/3VX+F0Os2o08aF9J07d3L+/Hk+85nPMGvWrGF1Msp84oknUFWVV155hd7eXj796U+Tn58/6vtMnz7drO/06dPZunUrVquV8+fPc+zYMerq6vjc5z7P2rVrh73jypXxAzR5vV527dpFdXU1Ho+H1atX88ADD0R5GUlLSzMtJ5qmMXv2bI4fP86xY/q28ZYtW7hy5QonT54kJyeHxx77LGlpaVEenIy6GHnm5uYO26Z9r1uzsiyDCAeZmgJHOhJI4MPEVOND0N17JvhwZD5MfeWVCcVDgAQfTlk+jP13HP081fhwUisUQpta2plhfYj0pGSxWEhPT6epqYmUlFTmzJ5HZlYm2Vl5+rgKjy1j2+zo0aOcO3fOFLrxgpkZZen5W7FYrOTmOofGaYSLMSO9M6wd+3w+9uzZw7Vr17h+/TolJSWUlpaSnJwcdaEpKSmJtLQ0bDYbKSnpKIpKbm4+GRnZJKW4kDW43nSD2XPncMeGO03Bu2TJEi5cuMDatWtZu3at6a4UiCKD99q2E9ndiGyzadOm8alPfcp0sWr8pKamRqW1Wq0oisKCBQu45ZZbKCoqGjHOg5G3EIJVq1axYcMGcnJyxlVHIYQZAM+II7Jy5UqKi4tRFCXqWNhYi3VFUVi6dClPPPEEe/bsYfbs2Xz6058e5sI38llZlsPbxBtZvny5ueU7ffp01q1bh9PpjHp+pD6L/fwPPedps1n1C2gRJDFFPOYlkMAHiqnGh2gaFp8vfIkcCIWwCEG6xUJzVRWpyMzJLyLTkUx2btKQ7EhLM/nwmMGHn/gEC4uLh/Oh3Q5+P1L4HoMl/JPrTEaSJV14uMJuTKUwH4ZCOB0O8Hjwd3Vx8M03aampob6+ntK8PErz8nBJEm3NzYjBQXC7SQIybTbswSApDieKopCb5CLD6iAJgZzk5MZAP7MLC3Q+FAIZWDJrFhcGBli7cCFr163D6vcP8aHFgiTEhBUKw+NUgg8/onwoxh/z6cOGJEa6cv5BIhSCbdvgiSdGnEyqJnBfaEAOKNhWz8YmS7pgmqQNGYl4TWpcKIpcYMc+Y/yoqmqmG+19JdPXx0hdONyZnM/nY9euXTQ1NVFWVsbatWvNhX5kcBYhBFeuXGHWrFmmkFFVTc9SlrAoYc8Xsu6FINYVrVmDcU688WCkfGPbO9INmwHDnepIbRr7jNEXkenjuVcdrfx4iCxHURR6enrIyMjAarVGWT6MtCPlJWIEDOjxOhoaGkhPTyc3NzcquNNo03wsAfVhzDnzPPO1DvxnruHaeitOm4yQBfK2F5C2boWkpA+8HgkkMC48/TR8+csfWnFTkg9PnoSGBkScOgYDAWSLxQwkNsxqIPRIyiJ8jtxqyLJR+XDihtxQMMi1ujrcg4NkZGaSn5+vL7KJ4MOwXG5rayM7KwuH06nHCBDhQKWArIW9VEl6JGazX2Llbmz9k5LgvvtGjO48DD4fvPYafO5zCT6MU3bk75OOD5ub9TgUo8Qki6zDiHzI6OvCDwQHDsDGjWMmm9Q7FMC4tfex9KIPswPiDVqHwzHmII3c1Yj9Lp7wiLfVN3yyR6e12+3cd999CCHMSWvkb/xuPDt//vwojd5i0X9Xw1YVEVYuRnr/9xvjyXekNJFtOt58RhKY76Ve8fKwWCzk5OSMS0iPlpfxr81mY86cOVH5xZJAvPzGKzwn+ux7gSQZwR3DZU2Rrd4EEvhQMJX4cO1aWLs2bhRq2zisrMYGfuxS+w/jw+i6yKpKSfjSdSwfSgzxgATkhkK6bApziVEvTQjQBEICIUfz7geJBB/Gz+um58NJbEOYtAqFBKBJEdJk7JZ8Pwb4+wWjLoaVw/jdqE+kUBxp8kRiNM3c8FRhsVhISkoaNa3b7Wbfvn3cddddpmeLyO8jJ2XkbkrkBLRIElh0IRs7gOIJgFjrwfsdSfsPEazx0kXuFhnnUI06DwWdiyCbOO9s+PaOFV6R7z+aYItMZ1hvZFk2o7+C7tv87NmztLS08MADD5h9r6oqmqZhs9miztAaMPx9S5JEKBQyo8mOp51CYVKNIt4xhP54216vqxTl130yC88EEviwMCofVlVBff1wZSNs5Y+0ko9l5f+gYNRDE0Nh1SKlROyCD2n0kH0jfheW2YacMjksTtsAhPx+KisrKSsrw5GcHH9nISznbMZ3EXUTgMXgccJ8GFFWbD1j+0NYrUibNsEox4gmigQfTnI+HDXHIUxFPpy0CgWAbJGRhIQRJjSyLWMX4waMgWu4BjPOCI61jTUeZSSyzHi/x8II4hYKhbBYLIRCITPCtRDC9HFt3D94r6fPamtr2b59O5s2beLWW2+NW2djUvt8Ppqbm4eVFQgECAQC5qVpr9eLJOkX3UZ7x1jEa2cl7HvZ8MGdHCm4JxkiFcHW1lY6OztZtmyZ+X1PTw9Op5NgMEhGRsaI+bS0tDA4OMi8efOQJN2byLlz5+js7OSuu+4iOTl5mAVN0zT8fj+dnZ2kpKSQnZ1t1mXfvn2UlJTQ1tZmXu4bGBjg/PnzlJWVYbFYCAQC3Lhxg1AoREVFBZs3byYrK2tY/wkhOH78OA6HA4/Hw6pVq8yLjaP1s6ZpHD16lMWLF5OVlWUK+XjPjDRXxtP+qqoSd9WRQAIfYYzIh+fPI265BVJThz4Lzzk1vFDy+XwmH8qyjBhjwT7aIdrYNNIIv0dnKFDDC6NQMDicDyGaDy2Wsad+rNyRJISmURuOdbBx0ybW3nJLVORjYT4qUBWFgZ4eLly5wuxFixApKebRo0DYxavL5UIIYV4UTwmnGa1ukW0QzwwaCgZBklBCISxvv41t/Xqk91GheD+R4MP3kQ/DyvQfzIeTc+kETHKFAiHQou6gja75CSE4e/YsxcXFXLhwgStXrvDVr37VvNk/HqViLAv6SAMsNu9AIEB9fT1tbW0oioLD4aC/v5/Vq1eTl5dHZ2cnzz//POnp6Tz++OPYY8LVx2rqsZYA418hdJd6GzZsYN68eSPWS1EULl++TF5eHjabzbykbaCpqYlDhw7x8Y9/nI6ODgYHBwkEAmzatGlYXYZZk0Yo00h/9uxZ6uvr6erqYsaMGdx///3v+y7F+41QKMRbb73Fpk2bAMyonc8//zx9fX3cdtttbNy4cdgOjyEIMzIyeOONNygoKCAzMxO/3095eTlz585FkiS8Xi+Dg4Pk5OREWXv27NnDhQsXeOCBB0whpWka169fR1EUamtrmT17NgUFBeTm5vLYY49x/vx5hBBs27aNs2fPsnXrVq5evcqqVavIzMyMqp8Bv9/P9evXcbvdlJWVkZ6ePuLOgmEh0TSNtrY2CgsLcblc7N+/n9tuu22Y8JUkCY/HQ319PYsWLYprtYpF5PyUJH3RlNAlEkggAiPxodUKBQWI1NQo67imaZwtL9f58MqV4XzIKIuaCKt0lKyOTG/sPkd+Jknm5xPmw5deel/4MEWSuP3RR5m7YAFkZYEsm3UU4RMDSihk8mEgOxv7rFlgt+tpgabaWg4dOxafDyMs5bF1M9o1qikj6gZwNhytuaurixU1Ndz6Hg2JHyYSfPiH82HD5cssMhSLm5QPJ/WqThDdiILoqJZ+vz8qGqIx0BobG03vAvqWlxJlqY/3A/qOwtWrVxkcHIyKHhmZxoi6GPm98a+mGSHsNbp7unn77bfN0PX9/f309PTokTGFwGazsXjxYj72sY+Z0R8VRSEUCkbl2d7eziuvvMKpU6fMbbkrV65QXV1t1iEnJ4eBgYGoKJGRdQ6FQgSDQS5duoTf76evr890pWekSU1NJS0tjaamJhoaG0hKSgpH4YzQ5MP/aZowg65EtouxIxPbLr29vTz99NMcOXJkTIXt/cRIW7AjIbLOXV1dNDU1MW3aNNNSApCfn8+VK1eYOXOm6W9bURQGBgbMADg+nw+n04nD4eDq1asAuFwu5s6dS25uLjabjfb2dp555hm6urrYvn07J0+eRFEV1qxZw2OPfYacnBxCoSA1NTV4PG6WL19GUVER6enp9Pb2ml5QXn/9dcrLyxkcHOT48ePcc8893H77bSxatBCn04nP5+XUqVO43W5UVeXChQu0tLSwYMECcnNzSUtLM/11G5FJfT6f+V7GTpoR+XPatGnmGNq+fTs7duwwd6CE0C8uBoNBenp6eOGFF+jp6ZlQ20uyZM7bSWyISSCBDx0j8iEk+HCK8qHb7f4DRsTEkODDPy4fbt++HY/HM6G2n2p8OGkVCgmQkEHThm3xaJpGV1cX3/3ud9m5cyeapuHz+bhx4wbFxcWEQiGKioqQZI3Xf/8KO998g2DIZ56Vu3jxouki1RBcxjm7d955h6NHj5oaaG1tLV6v19x6On/+PN/61rc4duyYGfVSVVVOnTrFG2+8QSjkQwiF/LxsvvSlL2Cz2SgsLCQpKYnc3Fw6OjoA/ZL22bNnKS8vN8vft28fL7/yEooaABQUNcDvtv2G5uZGmlsaGXT3IYRGe3s7u3fvNp87duwY+/fvp7Gx0XwnQ6Bdv36dp59+mkAgwPTp0wkGg6SkpDA4OAjolqP+/n4cDgfBYJDS0hJUNYArxYmqBdC0EJoWRFUD+P0e2tpbUPw+Bq+3UVVZQ0jVTEFfWVmJEIL+/n4uX77MxYsXuXTpEvPnz2ft2rV8+9vfZsuWLZP2uBPoQsTtdutBh5KSsNlsVFZW8p3vfIeqqio2b97M0qVLKSgo4LXXXmPnzp20t7fz3e9+l4aGBk6ePMmvfvUrFEUhOzuburo6QCexs2fP8vOf/5ympibS09NwOCxcuXKR+oar/Oi/fsD+/XtITUumrb2Fo8cOUVVdwQ9++G+8s2cXmZlpdHV1kZqaSn9/P5IkcfjwYfbs2UNTUxM+n4/PP/4YJ04coaHxGnaHhYbGaxw7dpgf/+SHHDl6kCtXLvPkkz/n979/DU1T6O3tISUlBbfbjRCCyspKnnrqKX7xi1/w3HPPmRFjjx49yje+8Q3Ky8tJTk6mo6ODs2fPYrfbOXHiBC0tLeaY2717N83NzdhsNoqLi+ns7Bx322uaAJuEzWIBVQw/0pBAAh9RjMaHIsGHU5YPN9x5Z4IPPyJ8OLesjGDYvfF4MBX5cHIfeWLkI0aDg4PU19eTk5NDIBCgq6uLvXv3snbtWrq6uujs7Ay7PZ1Jc3MzK1euIDMjh5qaqzz77LMsWLCAL3/5ywghuHjxInPmzEFVVW699VZOnz6N2+1G0zTOnDmDEIK8vDzzYk9WVhb/9V//xZe+9CU2bNiApmm8++67dHV3smzZYlwpLhAQCAQpLy9n8+bNeDweCgoKaGlpAfQ7CsFgkD179rB+/XpSUlI4fvw4XV1tLF2yBFeKi1AoRGNjIwUFBfT393P48CGWL1tJVlYWoVDIvEBk1Pf06dPk5uZitVpNjX337t1UVlaSmZlpasfGlqMQgtraWqxWqxm589KlS1y6dInFixfT3t7OiZMnsMgWVE2lubmZ5hs3WF4wg/5TVXgyU5n7N/8DLSx8r1y5wvz5883jLqqqYrFYWLFiBStXriQ1NRWHw/GhjZ33gs7OTp599llWr16NxWJBVVUuX75MQ0MD27Zt4xvf+AZlZWVomkZeXh6hUAin00l+fj6SpAdbMoLfGEfNQO9vIzpoe3s7z//utzQ1NdLU3ITL5cLn8/Jf//Vf7Nu3j1AoRFtbG2+88Xv8fj+vv/46J06cYMH8JcyYMcO0WCxatIgvfOELXL9+ne9+97tYrdDe0c63v/1tbDabeVHf7Xbzy18+jd3mRFFU3n33XS5evEhJSSkrVqw0PX6UlZUxc+ZMcxfJ6XQSCASoqqqiv7+fvXv38vnPf55f//rXtLe3m+dFe3t7mTFjBqCPrXPnzrFr1y58Ph/r16+fQOvrlzYVRcUmNHSP8gkkkICBeHwohEjw4RTlQ3uCDz8yfGjt6ODjn/nMBFp/6vHhpFUo9HtnAkkARDemqqq0tbWxbt06zp8/z9///d9jtVrJyMggLS2Nffv20dHRQUjx09pqp6mpiX/5l3/BYXfR09ODoijmGbxQKMTevXtxOp28/vrrNDY20tvby6VLl1BVFY/Hw65du8w7B4ZVZ2BggOeff56FCxeSnZ2NxWKhqrKSv/vm35kTT0LG5wty8eJF1q9fT2lpqRnzwefzUV9fz6xZs8xLyikpLo4dv8o/f+efTS8DHo+HwcFBMjIyWLRoEXv27GHv3neZPn26KaC+/OUv43a7efrpp/ne974HYA7u0tJS0tLSeP3117FarZSWlpKdnU1KSgqapnH16lVWr16N1+vl6tWrlJefJjtHb8e0tDR++ctf6v0hBNnZ2dgdNt6svMLanFJEug23FCRVs5oXroUQFBYWcv/990edOVy8eLH57pMZqamp3HvvvSQnJ3Ps2DH6+vpobGzkzjvvpLCwEKvVyrp163A4HNx9992oqorNZuMv/uIvTI8iCxYsQJIkOjs7KSkpASA5OZn169eTk5PDnDlzaG9vY968OUhIyBaZZcuWRZ07tlqtFBcXI0kSra2thEIKs2bqkcaNdpwzZw5z5syhq6uLOXPmhC15Q+cuLRYLxcXF+Hw+urq6yM8vwpWcQnNzC4oSIjs7y+xnSZLMwELmWeDwVncwGOTrX/862dnZzJgxg69//ev09vZSUFBAY2Mj2dnZ9Pb20tDQwNmzZ3niiScoLi5G0zRTsI4XQgg0BLIk626JE0gggVH50DhakeDDqceHEvxRvG6NFwk+fP/4UG5rIyXi7s94MNX4cNIqFMCIt9k1TaO5uZnPfOYzrF+/3rS+3H777eTn5/OVr3yFnp5uBKo+V/VbUVgsdtLT0klKSjI9TEiSxNKlS7HZbDzyyCNRZxqbm5tpbm5m1apVUZeYDSuR1WolGAxSV1dHaWkpfr+PrZ96EIfDiT6QLSD0U2VpaWm4XC6Ki4sByMvL4+/+7u9IT0+ns7OT3t5e0tPTufPOO3n00Uew2x3Ikj65kpOTSU1NQ5IgJzufLVs+jtVqNd2iGcTxV3/1V3i93qHmkyTS09MJhUL4wxE6XS4XiqKYz4ZCIb7//e9jt9u54447uOuuTdjtFlwpLj7/+c+z9ZNbdWEM2G02hAxqSMEpO+kTIa5faaCzq51Dhw9x7733RkUJj6zH7Nmz31NUzw8bSUlJLFmyBJ/PR1ZWFm63m+nTp/Pwww+bbnanT59uprfZ9CiskUFzHA4HbrebgYEBFi1aZH62detWc+zc/8D9SAy5EYbo07n6/3RCWrJkaVigybrAjYnWmZ2dzebNm5FkbcjDiHFm2Dh7KQHCCkjMmjUL/avhbvsihTjoEdYzMjJYt26dSRBlZWVmuunTp9Pd3c2BAweorq7mrrvuYubMmZSWlkbVcSIQmhbe3h3RZ0wCCXz0MMJUUjWNlpaWBB9ORT6cxMoEJPjwfeXDlhaky5cn3AdTiQ8nrUKhAULWg6fJQibc44De0U1NTbzwwgusXr3avP1veDDSg5nMNfMyxoOeRbRHBlmWuffeexFCmJfBhBAEAgEuXLhAUVERS5YsGXZUxzgj5/F42L9/P93d3fzZn32FwsICJFmOkP3x/So7HA5mz56NpmkkJSVRVVWF3e7gv3/lz8nKygovvvUBNFRvQWamg4yMzCjPFqBP4PT09Li7AElJSaSlpUVNDuM977//ftasWYPdbtctLnZ7uL308hz2ZIaYTB/UwqGhCUgXDpJLS1G1EI8//jhz5swZNrkj2zny88kKo55JSUls2bKFrq4unE6nSTpjwXi/vr4+Nm3aFBWox2iLkdpAivlDF4BE9Ue8VcXQJXdL+NFIzysR4y6i3Mi5EFnv2Lq5XC5KSkpG7Eer1Upubi4PPvggmqaZQva9XLw35o2maWjo7z4VrDIJJPBBYzQ+lBN8OHX5cOyu/6MiwYfvIx8a3sbGuQaainwoiZEuKXyQCIVg2zZ44okRG1fRBJ6KG+AOkLRmDhZJIIfTKopCZWUlzz33HG63mwcffJDbb78dh8MRFYwtEpFuvMZC5OU0SZLMCJqR3xv/Rnq3iNTKjTIj04+GyMAvE1mMxb6XFDVJRNw0kYhMM1EIoV8QFBrDBEO8erzXcj5sRPa/pmkMDg6SmZlp7rCM9Q7Gs4C5VT8SRsorts3i9V+8vp/odB5pjBp5CaHHUTHm1Vj1HWkMjAXjeXdDJ4Hj1bgeWoPdaUdIAssLLyBt3QrjILAEEvhQ8PTT8OUvf2jFjcaH2ksvUVFUxHO//32CD6caH77wAjz0ECQnj68Qnw9eew0+97kJ1++9IsGH7xMftrTApUuwZcuY9RiVD0cp9wPDgQOwceOYySbtDoVkaP8MP3MmyzKLFy/mn/7pnwgEAqSkpAzTFEcSomMh0mphRGAcLZ94lvd4W5xjlTme+o+EyIkz0gR6rwv82O+jnw3XWR45/Uj1m+yKhUGcoG+hjiY84iFScL6Xdx1pDEW2f+x27EiIbPN46UfrG0mS4s6D2DH0XhWJ2DwlQJZk3SKLOm5rTgIJ3MwYjQ8lSdL5cOXKBB8yxfjQ+GzUUv/4SPDhULl/fD6cvKNl0ioUSNKIx8UMYeN0OocFaHv/ih/nttQEBs5o2vJogmq0tCNNKC0cwGekfI1/NU2LsjQYWvhYE80U9EQIxVGeifx9sisTBt5rPd+LZcTAeJ4LBoPDrITx8ggEAlitVv1CZIQgjFeG4U/eZrMNszzFphdCjxxrs9lQFAWbzRZFGPGsO+OBEHo0Xd1PvnHxNIJ4p9j4SSCB9w2j8KEUPk6R4MMpyIfhReNUQIIP3yc+HPONhp4bkQ8n6amPSRuHAiHiDkRDKIz1814xkfz+kDINQWUEIIr8W1VVFEUxvW4Y/rVHm1xGQCMjH6/Xa24zxkJVVTPyZ19fH6FQyKyT8V6R/0Z+p6oqwWDQ3JI2qjSa8AyFQnR1dREIBOjt7X3PwuXDwvsxpv6QsTg4OEhjYyN1dXUEAgHzcyF095CvvPIKPp/P/CwWxjjas2cP7e3tvP3221y/ft0cG7E/WvhSp+HDPpb0hBD09fVx4/p1RDjYz4vbX6Kjs5M9e/ZEBeuJfFaboEcLSZKQrRZkqxWLZEEWMpIYfrQigQQ+chiND9EXKQk+HMJU4cO+3t6hhyYpEnyY4MPxYvIqFOhnEieT9vV+QAg98I8ROMjtdlNdXW1GZbxy5Qrd3d2cPn2ayspKqqurOXPmDBcvXjQHauTgB8wARBcvXjTd+O3cudOMIBk7YVRV5eWXX6a8vJy9e/eagVzi1TXyXyOw0a9+9SvdbzeYl4TiTUwDbrebn/zkJ9TW1nLlypUPsHWnDkYSZqAL0Lq6OioqKqIC4RgRZi9cuDAsCmtsHkIIGhsb8fv9XLhwISofVVWprKykpaXFDGjV3d3NjRs3TKEfm9fAwABtrW0gIKQoXLt2jVAoZD5jQJIk07oT+65jwVwwmdbYiLk/CYVnAgl8mEjw4c3HhzU1NR9g604dfCT4MKZ+Y2FUPpykmLQKhcDQaCdtFceN2MHY09PDnj178Pl8NDQ08OSTTzIwMMCBAwe4ePEinZ2d/Pu//zvf//73qa+v5z/+4z946aWXorTlSKGmaRpZWVmkpKSYUUwlSaK/vx8hhi5TGc9Jku5az2q1MjAwQH9/vzkhYwd67CTNyclh1S0rUL0BBvv6CWgqwVDIJAXDgtTV1UV/f7/p6cDlcpGUlGRO2AR0GFFtI9skPz+fNWvWsGLFCtN/ejwhGWnN0zQNv99PR0cHLS0tBAIBs69j+1BVVVpbW+nv72dwcDCuhSZeeYFAgGAopAtGofvHVmIshYqicPbsWX74wx9y+PBh0x/7aBiqny5ElVCIYQ48/kBLawIJTGUk+PDm5EOPx5PgwwjczHx45OhRVEV5f/iQyXXUycCkvUNhNtUUnmuRAyfy9/r6en7zm98wMDDAvffeS15eHh0dHVy8eBGfz8fmzZtZuXIlubm53H777Zw9e5ZVq1aRlJRkTgBFUbDb7fT39+N2u5k3bx6HDx/mF7/4BXfffTf5+fkEAgEURWH//v1s2LABp9PJ4OAgDQ0NlJaWEgqFyM7Opru7mxkzZhAKhfB4PGZky8i6h0IhBgYGcCUn41Dg9V88S8dgH4//1de4fPES8+fPR9NU3nprFx/72Md48cUX6e7u5ktf+hKlpaXMmjULu93OwMAAiqJMiZgUHzQ0TaO9vZ1XXnmFP/3TP8XlcgF6dNLf/e53VFZW8o1vfIPi4mI8Hg8XL16kqKjIfN7r9dLR0cGMGTOQZZm33nqLt99+m+zsbP7yL/8yaswZwkdRFHp7e7n99tuRZZnGxkaqqqqAIQHb1tZGcnIyLpeLyspKXC49ANa2bdtITkqidO4cNC18HCDmnbxeL7/4xS+orKzkwoULlJSUjCu4XaSg1n23R099SX+JiTVwAgncJEjw4c3Jh5ZwFO+pryb+4bjZ+bDK5WLZl75E2jjaYkw+nKRcOGkVCjC0x/jnHqcKjPN4165dY9asWaiqwuHDB5FluHjxAmvXrmXz5s1YrVZmzpxJU1MTFouF++67j/r6etLS0njggQfw+XzIsoymadTX16OqKnPnzqWp+Tpvvfkmff19JCUlMaN4GmfPnuKuu+6ivPwkvX09lJeXc8stt+BwOAgGg+zatYs5c+bgcrlITU1lcHAQgGvXruF2u1m8eDGNjY2UlpbidrupqqoytwRlwH21gY+tvZ3q+ip2/f41GppuUH7mBJ2dXTTU19Pf30tBQT5XrlTwj//4D2zefDddXd3MnTvX1PgT0OFyuVi0aJF5iUsIwZkzZ2hvb0eS4OzZcpqabtDa2sKhwwfYsGEDnV2tHDz4LhcvXqShsZFb193K9BnTOXPmJMkuO4sXz+fU6WPcuHE9KmKrQKWjs4Uf/vCHrF69Boc9ibNnz7F06VLmzJkD6BfcnnzySXJycsjNzeHY8cNML5rOjBkzyMxO5VfPPsXXvvZ1JGQkTUaLsTZarVby8vJoaGggLy8v7iXReILd/Dv8mSYEkhD6EYJwGZNThCaQwIeBBB/ejHyY5HSiJXYoTNzMfJiTkxPXkDpRPjTST0alYvIqFJLeYBbL1NbdhRC0t7dz+PBhXnnlFdzuAVRVJSMjg+bmJvx+P7NmzUKWZRwOB83NzXg8HqZNm2Ze9CooKDCFnBCCq1evsnLlSiRJIi8vl6bmG/T39+NwOGhoqGfZsmUUTS/i9d+/zuEjR0hyusytPlVVaWtro6amhgceeIBZs2aZAWp6eno4fPiwOeh9Ph8/+tGPOH/+PKWlpaSlpXG9uYlcl5WK2isoTonzl87i9ftwJiWxZPEScnIyaG1tob29ndxcPYjN1au1WK1WHA4H8+fPH9MX9UcFxjb82bNnWb16tRksKikpCa/Xi9Pp4MiRI1gsFoKhIJoW4vCRAzidNva+u5uBAQ8zZszg6LEjegRYJYDQNE6XnwAJ0lNzSE9P14krKQkhVBwOKzk5mZw9dwqrxQnCwqxZs0hPT2f27Nn4fD4zUm1HRyvp6Sl4fYNUVl/G7rTgC7i5eOkCqiKQJSsLYvrTZrPxF3/xF9x7772UlpaSk5MT992N7eeYFgFJCu8gR1gzmQqnRxNI4ANEgg9vSj50ZWdjeQ9BQG9G3PR86HTi6uqK++4T4cPJjEkb2E4VGt6KVoTbg3P1XDOQz2TUykaCYY3XNM3cPlVVBavVit3upKOjnaKi6dhsVlO4hUIhfSs0HKXUsMKAHv1TURSefvppBgcHKSsro/ZaNdOnF3Hvvfea5wttNht2ux23282hg0doa+vgzjvvpKOjg3feeYeysjIeeughnE6n7klAlrFYLHi9Xrq7u3nnnXdISkpizpw5vPDCC6SkpPA3f/M32O12QqqCJAmcIcGgRWPvnj00XKvlicef0CO0IhCaDEjhrh06s2gNb+/abLb3FEn5ZkLkuU+fz0dSUpLZJsFgEEUJYTgh9PsDyLKEqikkOZ1IsoSmqZSXl1NVVWUeE4j03S4hoWkSVqstImKnhkD3miI0gUAGIYW3VCWCwSDPPvssJ06c4O/+7u/Iz8+jurqSxUsW60JSCE6Xl/Py9leZPbuML37xiziddmRZNss2rD+Rl9livaTEWmQi//bc6MZ98DKpD67FmWwHi4T8wgvwyU8ijTf4UwIJfND4kAPbjcqHL78MH/sYpI3nMMUfDwk+hFg+ZNs2rA8/jBQ+3jMmfD7Yvh0effQD6aM/FgwGMPnQ6US2WEAIgqGQfo9A0vnQ8MKlaSoOh93kwzPlZ6iqquRjsXwYvn+kaRI2qw11JD4UEiAP8WEgwHPPPceJEyf4P9/4BgUFeVy9WsPChQuwWC0goLy8nJdffpXZpWV8YSw+bG2F6mozsN175sM4Xsc+cEz1wHagb/OIKX48xhhcVqvVtHwY4yUlJSVKn9IFq+4bOXKwRC6+rVYrW7ZsYf/+/dTU1FBYmM+mTZtIT09HMuy40pDG29/fz/Lly8nKyuLixYusWrWKzZs3RwU/MgawcVFs+fLlPPvss+zcuZOioiIefvjhcF0lbDjQAKsDZDWAy5HCx++9j6ysXGw2K+FNuhEnSwI6jLaQZdk8K2rAbrdjt9vNs7rvvrubaYWFeLxuNm7ciM0mI4DbbruN2267zRSACEOhMPpVz2/IYiIjIZCtNqMW4XR6QqfTydatW9m8eTMlJSUoioLTmYLV4sBusyGAtavXs3jRcpxOe1gxjN7CjXUNOJ4+jxWikiSZhoZ4LhsTSOCjh1H4MBSC8+dhvIvSPyLk8I8VSIIhzzdIpABSf7+Z1ioEduN4RwQHysZzkoRVCD6el8f+K1e4dv48BQW53LVoEem114a5eZUHBxk4cJBb5s0np7GRywcOsNpuZ/PMmbhqa3U+jDiy4pIkkjSNW4Rg+7PPUh4IkJ6WxtatW0mpqUEC7BI6H2pgVYOkXKnhEzOmk9VwHVtzq173mBsSUUdWenomdjfMagVZhldeGf8zUwBGC8iAy+CCcLvYwRwHiqpSc+kK6elpBENB5syejWzR090uVG7PzEY6fSacpzyUe0SAEIuZdzhwXGRFIszrFiH4jM3GQ6tWkXPuHEIIZvT0YK+tNxWKtarGsmW3YLNZse16M4q7jPcy/wqFYMWKsdtiCvPhpN2h0DSB+0ozWs8gSbfNxyaJYYuVqYbhTW0MjpG+j5+H8aMoCrIsmUpLbNsYVp7IRV+k9XgkX9mqqtLU1MQrr7zCpk2bWL58eZRSowm95goCKaQiW+Vx980f0n+T+ezg+43IHQyP24vVpu/uOJ1O89iDJtTwGctoBQLinMUcpc1iPaUY6fVxZigJRmJD5moYiuP70R/GmPY19eA5XIHrgTU4khxIVgl52zakrVshrJAnkMAfHR/yDsWofNjWpv9MMYzEdtIY38ci0rOPJEvIkowsRy3lzBxVNTrAXbwd1Kgnwvl2dXWxZ88eVq9eTdncuVEKjmldFwJUDdkyPFr4SJAcDpg3T1cSJogEH35E+XCM+n8gmOo7FJIAyWbFKuvbXoLxhVWH+AvzsZ4dr141noE43jKHRNHIR0LilR9p3R6tLEmSsFonJqj0Z6wUFxfzta99LW4ESjlclA0J7PGH0Ps54GPbY6z2GU/aWOvVH5LX+43Y3anUtJS46WTJEq6TMW400z1eUlLSqO841i7CEMnGfmGU9v4fWRNC6OeJhdDLlT68Nk8ggcmMUfmwoED/GQGTlg9HKsOQPeOsiyGJ5OgVZNy0sVdix2IpKfxMrqbx8B136EdlbLZhaeLlHZUmwYfvGQk+jOHDSaxATkqFwtRGlRBSOLqlNObUH3o2VsMc73l9w1oRb7DFfjfS4Ix/uWaoXsYZUv0M3+jvpGkaHo8Hh8NhHoXSNC1CW9bLVVUVi8UStUsRWY9YTTu27vHqLMtylIee4WRgqENDbf1+a82R/aiqKt3d3SQnJ5OSkhL1vVG/eAIvMo9IS1RsfTVNY3BwkOTk5GF9E89iEQgECAaDcY+OGfkZ/THSsa+R2j4SI31nHIdqamqioKAAh8OB3+9nx44dLFq0iIULF5pnjQOBAA6HY9SdqbHKinyfDxKapukCU0DUPnUCCXxEMSYfqirU1UGcSNBCz0APuhaWB/J4XXaLIQkfd9ZLUoxJLKLMiOeNtPHqZewumN5vRpIvYihqtnHHAUky3ymKg2HM+5aR7xQlk2PrHa6TLARO43NZjtseIjJTY+E3ngVgQQFkZo6ehvA7XroE586hSBJutxuHw2G6z40sN+4aZFhlMesW28fGXQZ9ByB6vMTLJxgKEQqFSE5Oji47XB8hRLTr75jyzeRh+S9FtF1keSO1pABURaGnt5e0tDTsdjuqolBbVUV2djZJhYW6p6RwXW0225i7CSOWFebt93QPNBiEDRugpGRcyacaH05KhQIM7czCe3EW6ff7qa+vp62tjdtuuw2HwzHq9mDkdprxezAYxBa2REiShKIo2Gy2EeMnGMFyhNBdhRmXyCKhqiqnTp2iqqqKBx54gJycnLgKjFGnYDDIG2+8wcaNG8nNzUWWZfx+v6ltB4NBDh06RGVlJZ/5zGfIyMgAdM8Cfr8fu91uvkcgEDAX4rFtEKm4+P1+0tLS4ioeJiQpQnAOLTiNNjIuw0VitIX1WNA0jX379rFv3z4ee+wxli5diqIo+Hw+AoEAdrvdrHNsuT6fj+7ublJSUujv76e4uDiuouX1evnlL3/Jxz72MRYuXDgsL1VVaW5uJjc3F4fDwfnz5zl//jx/+qd/ao4vVVWpra0lNzeXzs5OZs+ebQZLamxsxOFwMHfuXFMQqarKxYsXWbBgAQ6Hw/STbtxXiaxjvPYaGBjgqaee4oknnqCsrAxZliksLCQtLY2BgQEOHjzI+vXr+c1vfsPtt99Oamoqs2fP1us7pBHGRWRxfX19HD16lLvuuovkOBej/1BrZtS4kCWsFgsgTXLRmUACHx5G5cPBQXjrLVizJu6zBh+2t7Wx/rbb9PtZ4e/iLozD/xrB3QyuMfkQ/Sy71WrVLahx5rWiKAhNQ4vkwzhpTp8+zbVr19iyZYvpES7uvBeCYCDA3j17WLN2LZkZGciyjC/Mh7IsEwwGOXz4MNeuXWPr1q2khS+pW61W/H4/DrudQDCI3W7H7/ebcnZEPrTb8UXyYZw2il9V/a6L0UayLA9Lby4N+/qgpgbpoYdGyTECN26g3nYb+yoq2H/2LI995jMsWbJkiA+DQew2G2np6XHL9Xq9dHd1kZKaSn9fH8XFxTofxSzcvYODPPXUU9wT5sPYvFRVpaW5mZzcXBx2O+dOn+bc+fP86SOP6PwSVv5qa2vJzcvT+bC0FKvVqsf+iOVDoR/hvnjpEgvmz8fhdBIM86HLpXvlGkmBNdDX1cV//vCHfP7zn6ds7lxURWGwqIjMmTPpd7k4eOgQ69ev57nf/IbbbruNtLQ0Zs+erc+HMdYjEphKUF9fH0ePHInPh9IYvFVdDa2tMGvWTcmHk1ahMM6jCdAXPxNQLLxeL5cvXzYDnIxmBY60Yjc0NNDV1cWCBQt45plnuOuuuwiFQkiSRFNTE3fccceIi+2TJ0/idrtxOp3MmTOHadOmheWswND/hdAF9JIlS0hJSYmql7HzELmQ1DQNu92Ow6G7SwsEAvT29nLfffeRmpqKLMsUFBSQlpZGamoqO3fuRFVV7rvvPn7yk59wzz33sGvXLj75yU9y8eJFHnnkkbguWxVF4ZlnnuGOO+6gurqaRx55BIvFMmy3Y5iSEP43EAhw7Ngxamtr+fznPx9lpYh9diI7GUYbCKGxYsUyHE4bubnZNDXfYPtLryLLErm5uVgsFh599JGo5yRJD3nf3d3Nj3/8Y7Zu3UptbS2f/exn45Zls9mYO3euGeHUWPQb/aJpGnv37mXDhg2UlJQwbdo06urqTC8kvb292Gw2Dh48SElJCY2NjUyfPh2LxUJNTQ2VlZWsWrUqqh0UReHw4cNMnz4dm83GyZMnqays5Etf+tKwy/nxFIukpCSmT59OT08PQgjsdjsej4f6+nqWL1+Oz+dBEwqKGqS7u4Py8pMUFuZjt+sCSg/yELlro4a3cGV0jxdDu0MXLlxg3bp1OJz6okJoGm1t7XR2djNr1iwywxa2yHpO5IyvuZuIjBZUkFUNOWxZTSCBBEbhQyFgxgy49dbhjwmBp7ubSx0dzNi4EWnVKkTkDuwou7oNdXXj58PIOwWaxsljx3CHLdxzZs2Kz4ehEJokMe/223EtXozmcAyTu5H8qPn9+Do7kdev5/DFi8P4kFCI3IwMXF4vyUuWsGPXruF8eOjQEB9u2qTzYaSlWQhCgQC/+vnPdT5saeGRzZtH50Nj5zucRcDvH5sPjeLa25HKy8c1Asy+cdhZsX4tjoxUcmYU0dTTFZ8Pw8bPKD7s7OTHv/zlEB+WlZnpImGTJOYsWYJqs6HabMhhZdLkQ0Vhz5EjJh8WlpZyraWFkMWC3eEY4sNTp4b4cM4cLE4n1RUVVFZV6XzocEDY+KoEAhw+fZrpc+Zgs9s5cerUyHwIUX8jBElZWUwrLaXH60U4ndhlmf5gkGvNzTofChXNYSVkk+n2DlJecZGCkpnYnK7wLsAofBjhwES12bhQU8O6TZtwOKzmmB8PH0p2O3i94+prmHp8OIl9d4bFpnG8ZwJNmZGRwaZNm+gPe4wwjhnB0KSMFJzG56FQiLq6Omw2Gzk5OabVo6enh6amJtxu96iLo+7ubnp6emhubta3ZzUVRQ2iagqgYbHKLFiwgJqaGrM+iqKgKArXr1+npqYmasEphKC8vJzXXnsNn89HW1sbvb299PX1AbrbvIKCAs6dO4eiKCxcuNCM81BYWEhqaioZGRk4HA66u7tNP96xsFgsFBUV4XA46Orqwu/3mwtJRVFM931tbW14vV59WzJmYex2u7nllluw2WwIIfD5fPT09KCqapSrQMPqNR4MEYogpAQ5ceIYfX09vL1rF263m0cf/QwLFy4iMzMTIQQej4f+/n5UVcXn89HX10dWVhbz588nIyODvr6+Ecv2eDwcOHCAV1991ayz1+ulvLycM2fOoCgK+fn5+vsLwcWLF9m1axeVlZWoqsrbb7/NxYsXWbZsGbm5uXi9XrxeL5IkUVZWRlJSkilkQFd6g2FrmTGuVq5cyaZNm8zdrZqaGsrLy0fst46ODsrLy9m5cyfBYBBJkpgxYwaBQID29nZ2vf0WN240Ulo6i/7+PhQ1RGVVBZqmoqkKJ0+dpLa21hxrbvcAJ04cxusdhHAgrVAoREpKCqWlpdhsVnw+L0ePHqap6Qa/eOpJdu/ezcWLF3WLZDi9z+fD5/Ph9/ujdv3GAyEEqAJJExHH6hJI4KOOBB/ejHw4kSCvCT6c+nw4kfWPMZ6mEh9OYoViaOtQQkIapSWNAWBM1P7+fl5++WXOnTuHqqp0dHTQ2tpqLmgVRcHj8eDxeExB0draSkZ4G9UIaLN3717y8vJITU0lLS3NFMhGmZGLpeLiYjIyMsjPz6ejowMhBH6fn2eeeYYrV64gEAwODvK73/2OS5cuMTAwQCAQ4Pjx4yiKQnNzM2+88YY5WdxuNzdu3OCJJ55AURQuXbrEmjVryAtvHwL09/fz61//murqalpbW3nmmWf4wQ9+QHNzMyUlJYRCIYqKirBYLLhcrgj/zVrUjxCCsrIynE4nsizj8XiQJIlAIMC7777LtWvXuHr1Kt/+9rd56hdP4ekd0F09CYGqqVy6dIk777yTnp4e8xLUO++8ww9/+EN8Ph8AnZ2dHDp0KIokxgNZlvEH/Dz99NPU1tbS0dnB/AXzGRwcoL+/l8zMDPr7+wgEAvz2t7/lmWeewe/38/bbb/Mf//EfBINBHA4HVquVUChkTnSDuIx+9Hq9DAwM0NfXZxLI0aNH2bVrF/X19QwODuJyufD7/SiKwsDAAADt7e2EQiGKi4uprq4mLS2Nrq4ubDabOb7a29vZt28fzz77rClQrl27xsmTJ0lJSTGDNDkcDk6ePGmSwOuvv862bdvw+XxmPQ2hZIxjh8NhCixN00hNTWVgYICuri4CgQCXL1+mvb2dd/e9y+DgIPv27UPVVLxeLy+99AJ79ryNxzPAwEAve/bu5uc//7lO8ELg9fqor69HlmVsNhvt7W0cPnyIZ599ln3799HV1cWKFStobGykvr4eIfQgU7/85S956qmn+N3vfkdHR8e4+tm83yIP+dkWkmHNTCCBBBJ8ePPxYXV19YRc40uShM+f4MOpzIfj2bGfqnw4iY88ARYJSQhdeI7RB5qm0dfXh8fj4dSpUxw5coSsrCy6urqoq6ujtbWVrKwsCgsLGRgY4NKlS8yaNYvNmzejqir79u3jzjvvpL+/n+7ubgCqq6vZvHkzvb29pKen43a7TWFtnN2XJD3Wg9/vZ3BwkJKSEpqampAkia7uLi5evIjT6WThwoVcvXqVY8eOkZyczJUrV5g+fTrHjh2jqKiItLQ0866DxWJhcHCQkydP8thjj5GWlsZPfvITMjMzyczMNOvR1NRERUUFSUlJXLx4EU3T6OjooK6uDp/PR1NTE1arFbfbTWpqqilA/H4/kiThcDjo6emhvb2d9vZ2iouLcTqdBINBNE2jrq6O7du3A/Dwww+zatUq3njtdcSAlz/7H1/DlZGGJgR1dXW0tbXR1dXF8uXLcTqdVFZW0t3dzcDAAA6Hg5qaGqqrq+nu7qa0tNSMyTEaDGtMMKCf351XNo8DBw6wYMFCsrLS+fmTP2X5smVcvHiJqqoqrly5gizLDA4OUlFRgdvt5pe//CW9vb2sXLkSp9NpElRDQwMWi4X8/HzOnDnDhQsXKCoqirJCVVdXI4SgtbWVc+fOkZeXx8svv8zBgwcJBoNMmzaN/v5+fvOb39DW1kZDQwPV1dUsXLjQvCgnhKC2tjaKrIQQpKamUl1dHXWp22KxkJubi8fjISUlhYGBAQYGBgiFQgghaGhooKenh/nz5/POO+9w4MABkxSNbe3U1FSOHDlCX18fPT09bN++HVVVyczMpKamhhkzZkSMYR/7Duzh0uXzBINBQqEAfn+QtrY2lizRaGttM61DPp+Pf/7nf0a26NaknTt34vcHeOGFF1iwYAHp6ekIIZg9ezYzZsww+3A8/RwJ2TxaoN/TmdziM4EEPkQk+PCm48NeoGzatHFZdo1FpnGfJcGHU48Pkxsbwe0e95Sfanw4iRUKXeOXpPB57zEgSRKnTp2ioqKCT33qUxQWFlJTU8O3vvUt04OP0+lE03RXYpqm8fjjj5vPZmRkmOn+9V//lZ6eHjRN47vf/S4bN26krKwMh8Nhau81NTUsXryYAwcOsH37djweD0uXLmX16tWkpqYihECWZAYHPHg8PiQsLFq0hH/5l3+hvb2dp59+Gq/XS0ZGBufOnePVV18lPz/fvNztcrlITU01z+5fu3aNgYEBM/icJEksWLCAf//3f6ejo4Of/vSnCCGYN28ev/vd70yPGElJScydO5fc3FxsNj1q8uXLl5k9ezaNjY384Ac/wOv1kpyczNe//nWKiopwuVwIIbhy5QoOhwO3283LL7+MLMvk5efR29uLe2CApPRU1JBCIBDg8OHD5qU6gAULFnD16lXTI8eMGTO4evUqhYWF5uW+8SIjI4v/9b/+N6qi8uJLL1JVVQ1AdlY212/cICMzk5SUFB555BEaGhqQJInPfe5zpiUlKyuL5ORkFi5ciN1ux+fzcf78ee655x7OnTvH9773PWbPnk1xcTHp6emmB4gZM2ZQVVXFkiVLWLVqFXa7nRUrVtDa2kpKSgpZWVmUlpbS1dWFqqrk5+cDMH/+fGRZJisrC0mSmDdvHqtWrWLDhg2mFejKlSsAlJSUkJ2dTSAQoLu7m/Pnz5tnMO+44w727duH3W5HURSOHj3K5s2baWxs5He/+x3Lly9n/vz5uFwu0yNXeno6X/nKV6ipqUG/azd0JlS2yGRlZtHS3EZqWiqrV6+mpeUGa9euxWK1MmvmLBRFkOJKBST6+vs4fPgQmZmZXLlyhc9+9vMsXrwQfyBAZ0cHLpeLoqIZpKammmdcjaB8E7l0H3UeWQhUVQJUJDSkUZ0xJpDARwUJPrwZ+TA/PX38XrfCyMjITPDhVOVDqx4FfrQZPJX5cPIqFGGlTKiRH4ySXJK4/fbbWbVqFZmZmcycOZMFCxYwffp0ampqKCgoYPHixfT29mKxWHA6nSQlJTEwMEB9fT1nzpzh1ltv5Qtf+AJ33nmnqbnLskxeXh5JSUmmt4GrV6+SkZGB2+1m165dFBcX88ADD5CRkUEoFCI7O5uqqiqcTiclJbOZWVyCLFtJclqZMWMGRUVF/O3f/i3l5eV4vV42btzIsmXLsNlsdHR00NvbyxtvvEFmZiayLDNz5kyeeOIJCgoKyM/PxxG+vGa328nJySEzM5N//ud/BnSPFsaWoFH/tLQ0gsEgSUlJuN1umpqauOWWW7h48SIAf/Inf8KaNWuQJImGhgasVqt5xvP//J//Q0ZGBkIIDh48iCzL3H777fT393P8xHEuXbpEa2sr3/rWt0hNTaW2tpaTJ09SV1fHvHnzSA97m5g+fTqPhD1ATMzdmh64z2FPQtgEf/LEF4ed9Y10h2qcSTUu4xvfCyHIz883t7CPHTuGz+ejv7+fzMxMHn30UdatW2eOpUAgQGdnJ9/4xjfIz8838/jkJz9ppomsQ7zPjDqVlJTwpS99CVVVOXv2LCdOnKCtrY2vfe1rFBYW0tXVxeuvv055eTlLlixBCMG2bdu4fPkyDz30EA6Hg2AwSGNjI08++SSpqank5uby4IMPsmjRIrNuRrvOnz+f+fPnx73QHQqFOHfuHC+//Cp9fd38+Z//9/CFyfDFMyHr99MEzJo1kyNHJDo7O3nkkUdYunSp6VJ3dumcqLkXKajfK/T30NA0GVAJx6B9z/klkMBNgwQf3px82N+PfP78hIaCLFsSfDhV+VCSxh3EcCry4eSuXdQllJH3eY0BlJKSMuRiDEhNTcVms9HV1cXdd9/NnDlzogbVwMAAu3bt4uzZs3ziE58wz4xmZWVFDcjIiQjQ2NhIMBgkIyMDi8XCunXrWLZsGZqm8frrr5OXl4fFYuH3v/89BQUF3HbbbWZdjajWDoeDxsZG1q5dS3p6OpmZmWiaRn19PTt27ECWZbZs2YLNZmPTpk1mPWIHqCRJ2Gw2srKyzM9SU1OHTR6Hw2H+XV5eTkdHB42NjWRlZbFkyRLS0tJobW01z4tarVYyMzMJBoO4XC40TSM3Nxer1UpKSgo2q5W2tjYyMjL4xCc+YV7amzNnjun+9Pbbb8fpdJr5RbpDnQginxnJbW9k+8aDEEO+ztPT09myZQtvvPEGHo+HRx99lBUrVpiePAxrVkdHB/39/SQnJ5t9ZrTje6m/LMusWLGCadOmYbfbycvLQ5Ik8vPz+cQnPsGtt95Kfn4+NpuNadOmcd9995Gfn28KrS984Qu88847XLp0iU9/+tOUlZXplpbYwIMxf0e2n81mY+XKlcybNw8hBOnpaWZ6aShKD5IEmZmZfPWrX0WSJLNtzLRx+vEPUSaG8tDroWliyPV2AgkkkODDm5EPfb64bndHg6S/KJDgwynLh+Ps86nGh5KYyA3Z9wuhEGzbBk88EbdhhRAIVcVb14m43o/tzjJsskC2jB5YK/ZVjOAsmqaRlJQ0zGWqYXUIBAK4XK4xJ6dRRmVlJdu2bUNVVRYuXMhDDz1kbsca8RwsFguhUAiLxRI36FwoHAjGCDgW6S5PURRz0I5mzY+nccdtywjBGwqFOHz4MBUVFUybNo1169ZRWFhophNC9xtunHXUt/GKzLoBIxJLZH0iCShqC2+Eeo4HE81jtKFt+EDv6upi8+bNwyJpBoNBtm3bxltvvUVqaiqLFi3i05/+dNR5yNHqElvXyL9jXSLGezbed5qm0dbWRktLC/PmzdNdJcZBbHkjfTbSs/HeYaQ07weMcoTQUNr76X+zkpRPLsSWkYyw2LFu24a0dStM8D5GAgl8YHj6afjylz+Uosbkw95e2L8fPvWpYc9FIsGHk5APOzqQTp+G++8fs60BPd7ILbdAuI6x7z4aEnw4+mcjPRvvHUZKMyYqK/XYI/FcPMeUMyIfTrTM9wMHDsDGjWMm+1B3KCLPhcHYypYg7C5rXKmHd74sy+b5x9g0xmc2m8087xabx0iYN28ef//3fw/owsQQzLIsR8WXMLT3eHnabLa40bINDwKRz41Vp7EGfeT3sixz5513cuedd5qTODYQnfF5aWnpsGdj840nPON9HjmBRxIQY2Giz4wloFavXj2iILPZbDzyyCOsXr2awcFBZs+ebVq9JqLUjvR9bBvEtt9InxcUFJCXlwcMReOOV6exLCax9YzcCo/8LFLYj9X+o737eJ4VApBkkDQQEhKGpSiBBG4+xFvkjJo+wYfjqtOU40Pem4xL8OEU5MPxrn1H48MPW5mYAD70I0+apoGmjenVQIQ7U9I0JAGaNDEft+Np9NG2wkbL17CyjJYm3iSIly7SmmL8PZ5nYxFv4BtWoMhJaHgpMNIbbRApRI16xAuCN9F6xdbvvSoT7zfGsr5JkmReXBsvIi1U8QRUZPsaXi7iCaZYy1dknUC3Fl24cAFFUVi/fv24LImj1VlVVbNOQghsNhuqqnLt2jUOHDjAgw8+aF6wG6vvDIuiLMumxXGs4JJRJCFbkSwCkJHE5L6AlkACfyhGmuuxSPDh+OsEU4wPJ/TkB4MEHw7V6wPlQyH0AHU3KR9+6AqFLMvjvpSCJCFMi8z49fjIyapnMzFrzkRgbNcaQX/sdvuwAR07qYTQ3exJksTg4CCBQIBp06ZNqB7xtF/jrKPX6zWjJqelpUVFmTSEa19fn3m5LNZqMlHL2WhpI/MyAuRECuY/hnIRT8DFq8dExo/RboZf7qSkJNrb21EUxXRN197eTm5urnmmNhQKkZ6ejt1uj6qP4cN6zpw5cfu5o6ODgwcPctdddw2zMBq/j7ddNU2jsrKSnp4eUlJSSEtLY86cOciyzI0bN0hOTjbPzMa2SzwYQY3mzJlDe3s7q1evjjrHPZ76KGqIUYpIIIGbBhNyUJHgw1HLjcWU4ENFwRazuP6wkeDDIXzgfJiaSsq4ajJUn6nEhx9qYDtT+xxn5xqpBIw7MqjRuZqm0drayuXLl6PLjqnLeLauhtUr4hnDjd3Vq1c5dOgQ7ggfw8akihRKhgA7fPgwN27c4MKFC/zsZz8zA96MtUUYm8b4XVVVTpw4QXl5OW+88QZHjhzh0KFDdHZ2DovGGQqF2Lt3L8Fg0PzM0MYNjdp45v0QcEa+N27c4Nq1axOOnvxBYDz9P95tzcj+vXDhAtu3b2dgYIBf/OIX/OY3vzGjZb744ou43W4uXrzI9773PX7729+iKIqZj4GmpiYuXbpEMBiktbXVDEJllFNUVMTnP/95XC4XMBSYSVEUGhsbzQjVBqEakV2Nfo30/w26H+1z587R3t5OS0uL+d1tt91mlhEZ4TXej6IoZhCn9vZ2Ojo6qK2tjQp+NRYMC6IsASJy9ieQwM2HiXJPgg9Hf8fI36cSH8ZTWj5sJPjwQ+LDUaKTx2Iq8uEfJVK2xDhtK7KMquph7c3mjBFK0T8aIBBCRdNUuru7aGioQ1MVNE0ZGjQRz2haxAQQGsL8URFaZN7hsrUhIaOXp9HS2kx7RxsNDfX09PSYA8aYAEawFSLeQVEUuru7mTZtGnm5eaiqii8cxj52kEf9hEOwG1aV/r5+vZ6abhkKBoIUFhZisVhwu91RkYqNPGVZpqWlxRT2oVCIwcFBNE2jpaWF/fv3RwndobYKTxghhn40EXdCxZLG4OAgzz77LD09PeZn8fpzKiAeKQaDQdMSc/r0aerr66mvr6ejowO/309nZyd9fX1cvnyZadOm0dbWxsyZM83zwUIIPB4PiqKQn5+P3+/n2LFjfP/73+enP/0ptbW1Znn9/f38/ve/5/z58wgh6O3tpbu7G1VVTfd6AG73IO+8s5vG6w389Kc/5qWXXsLv9xMMBk3B6vF4mDVrlhlMyohqq6oq5eXlHDp0yBwbFRUV+Hw+rl69itfrBfSx09rawmuvvcp3v/vPXLt2jRUrVpCRkYHL5aKzs3NC/SpJAowf/ZP3rZ8SSGAyYtyL+JH4kLCSkeDDKcmHsd9NNbmV4MPx82FXV9eE2naq8eEfRaEYD4QkISQJLEYVo60QhpZp+MfWv9cQKChqEEUNMG/eHBYumMfTTz/J/n3voikKaENCVFEU6urrCAQC4Rw0EApCCxEM+Dh+7Ajtra2I8L0PoWooSohTJ0/yL9/9F06eOIGiBlmyZCEZGamkpLro7+9HkiRzq+uv//qvOXz4sNmRfX19nDlzhoKCAgKBAAV5+fi8Xn7wf/+D//zP/2RwYJCOjg62b99Of3+/KWx9Ph/BYJD6ujquVFTQ3dXNP33rH/mH/+9btLa0I0kyixcuIRRSmD17NpqmkZ6ebuahqipVVVXmpDMC9BgT4/vf/z7d3d34/X7efvtt07JsDD6fz0d1dTWKqhDUAgS1ICFVwTM4yLVr1wgEAlRXV3P27Fkz+ujly5fNqJstLS0A9Pb2xiXPWKvRZIem6QGhPB4PnZ2d/OAHP2Dv3r2UlpaycOFCXC4Xn/3sZykoKKCxsZF9+/axfv163n77bZxOJ/fccw/Lli0zt5vr6ur4x3/8R44cOWJGFT137hxLlizhs5/9LM3NzXjDBPvmm29y4sQJrl+/jt/vp7KyksOHDxMKhVi5ciU1NTUIITh7tpyXtm/j3Xff4eixw+x+5216e3vZvXs3XV1dHDt2jNdeew2/38/AwABpaWn4/X4Aenp6eO655+jr66OhoQG3283u3btpbW3l4MGD3LhxA00TeDxeduz8PZVVFzl+4iC1tVdJS0ujs7OTlJQUBgcHx9WepoVTKIASnosfVO8lkMDUwlh8aHhoSvDh1OLDvr6+BB8m+HAYpiofTto4FBIgSTIWWSa2JTVNo7u7m/7+fmbOnImiKNjtdlRNIRgMcPXqVQYHBklPS2PHjh0kJSfx1q63WLJ0AS5Xsp65gJAS4ujRvdx55x3k5OQCFhC68Ku8coXfvfBbVqxYztatW5FlCSFA1TRkSxBnkuCHP/o3vvrVvyAtLZ3m5hZSUlLMQW4IHrfbjcfjoS+81dXe3s6ZM2e4++67qa6uBiGoqalh7bq1VFRUcPbcWS5dukR5eTlFRUXceuutpnCaPXs2L770EpVXrvDIo4+iKApNLY20tjegah56+/o4c/YUGZnpNDc3M2PGDHMb1+/3s2PHDs6fP8/f/M3fkJqaahJHRUUFHR0d1NXVYbFYyMnJYXBwkOzsbLO9FUXh7NmzeAb6SdVCWG123IpCRUM9NdW1LFq4iL179zJnzhyWLFli+q02tiYLCwvNc5ShUGjC0bInI/bt28e1a9dYs2YNjY2NXLhwgSVLlrBkyRJSUlLYvHkzqqpy+fJl/H4/K1eupKKigu7ububNm2de/lJVlevXr9Pe3s7zzz/PP/zDP3D16lWuXbtGb28vx48fZ8GCBaxfvx5Jkli+fDnp6emcOHGCb37zm6iqSklJCT/5yU9oampi3rx5aJqG2+Oht7eXgwcP6laxgQG+853v0N3dza5du8zdg1OnTpGamhq1pZuWlsaf/umf4vF4eO6557Db7bS3t1NTU0MwGGTNmjWAwOm009fbz7XaepKTU3n99d9jt+9i7dq1FBUVmS4Xx4KRRpIsCN0DeAIJJBDGmHzY1UV/Q0OCD6cgHyrp6ZM8/vH4kODD8fPheLw8wdTjw0mrUIDhEUoN37mIFqKVlZV4vV5UVUUIQXHxDN586w2OHz/OwMAAsizT19+P1WolNSWF9o4O/v4fvoUsy6Y3ASEEfb29HDlyHItFRhOgqvr2pH7LX2Hnrrc5eOQosiQhyVLUNu+A28OPfvJjUlJT2bhhAzNnzMLhcGD4+/b5fDzwwAO8/vrr7Ny5E1VVCQaDzJ07l4yMDC5dusTrv/89mqZx5NgxPF4PzzzzDBkZGTidTtNi4vP5eOedd7jzzjtpaWvBF/Tz4vaXcDod5BcW8vNf/CIc/EQjGFS4evUay5YtIzk5GcAU3E1NTdhsNq5fv05OTg7JyclomkZ+fj5ut5uGhgYz+qfRToalRFVV6uvrOX74ENmaQkpSKu1+L6HUZFwOF3v27MHlcpnBkJKTk7nrrrvMfgyFQjQ0NOD1es38IzHa5cShyTW5ptWyZcsoLi7m4MGDzJkzh/T0dBwOB/PmzSMtLc0M8uTxeEhKSiIzM5PHHnuMlJSUqMvpPp+PU6dOsWHDBrKyssjIyGDr1q1cvXqV/Px8Zs2aRVZWlukbfOnSpSxatIhFixZRXl5Oc3Mzq1atMrd7CwsL6ezsZN68MjNC7vLly1FVCPiD5OXl0dLSQnp6OoFAgEAgQGZmJtnZ2dxyyy1m/61bt878zhDmycnJWK1WMjIy6Ovr4fyFcjxeN1/5yp+Tl5dHT08vqqoybdo0023kRC7EaUJCkuxI4cVMAgkkoGNEPhSC6upq3MXFCT6canzY0DBxPgz3eYIPpyAfJiff1Hw4aRUKIXS5aZyRjNXpioqKuHjxIvv37+fuu++mtraWXbvepqxsHl/+8p+haYL+wQEkWQJheDKQEaALQ0mm6fp1Oto7WL16le5ZQBZoQsNi0aOD2m1Ourt7TIGrX5KRsVhkXXBLAsmia48pqalYkEhN0qNydnZ2YrVaeeSRR/j4xz9OMBgkEAjwzjvvsHHjRjIyMvjff/3XDLjduvDWdLd2VlkmxTU0wSRJIjU1lbvvvpuOjg4e2rqVBQsWIst6tE2r1UYgoFtdJEkiHDDe3AGwWq3IsszAwACf/vSnWbJkCTabDU3T6Onp4cSJE7S2tjJ//nw2bNjAli1bEELgdDrNi3y1tbWcPn2aBQsW8D/+x1/gsEjIioQqyfjsFpySpLsCNi4RxXHvZrfbue+++8w6Rff1kBu10bxLGHnF+zz2uw8akqT7wM7JyeHy5cts2LCBnJwcsw5GW2RlZUVFmi0oKIiqqyTpbhcLCwvZunWr6RFp8eLFLFmyxCwvtg0sFgulpaXcuHGDpqYm8vPzTS8YPp+PgwcPUlV1hcWLlvDYZx/DlewCSUZCd4e4aNGiuPlGvgOA3W5n9uzZnDt3joyMDFJTUwkGgwgh2Ld/Hw0NNWz95CdZtmwVAHPmDO+fiV08lZBkKyDpQgDpQ/PTnkACkxWj8aEkSUybNo0LPl+CD6caH3Z0YDl3Lqavx+BDTQvLxgQfRj4zFfhQcrv1wHbjbdd4fPgelckPYzxMWoVCkkESAlkTSAJExHUPi8XC7NmzKS4u5pe//CU/+MEPcDqdpKdl8eADD7FgfrSv5HiuxEKhEFerqpg3r4zly5dHBdWJbPiC3PxR62lYC0R42SOHLT0ul4t3332XS5cuUVJSwqpVq0ztuKCgAIvFQkZ6OlmZmVF1MyxFkXW22+0sX76c69evc+XKFfKys01XfJIkQZwAkZFCCSA7O5uuri7S0tLM55KTk1FVlUAgwD333ENeXt4wAZWXl0dvby+bNm1iyZIl2O32IQEpwCGNfRHHyDOeu9gh65c26oAfaRIZ7zkh94vvAwxhb7VaKSkpwePxxPVNPR7f7g6Hg9mzZxMIBEhJSYn7LrHPGUR16623sn79+qh+cblc3HvvvWzevBmr1YrFYhnm0WW0+sR+npqayhNPPGGOOaO9dfd/2ogRbN+LABOEUBX9WACShpgCVpkEEvigMRofyrJMaUkJM+6/P8GHU40Pw5yY4MMh3NR8OJGwCYzAh9Kkvfo8ORUKSZJMzxOaqmGThlsojcH7xBNPcPr0aY4cOcLDDz9MWVnZiJMpVig9+uijZlCe0SbgaFZz2bCgRphRDc32q1/9Kvv27QMgPz+fpKQk7r777mGWh3iTI/Z3WZbJy8vD6/WOaLmIV28jXVZWFrNmzYqymEiSRElJCbNmzTItCLHPOxwOli5dGt/aPIbwHM16EpnGmJCjTeZ4JBgPI+1mTAQT9YKwYMGCuFFe4yE2jfHuK1euHNYGY7WfJEkkJSXF/dxisYwYiCleW47W9mMFrhop/5HqPX4klIkEEhiTDyUJKRyhOsGHI2NS8mGcMsbkQ1lGCtc5wYfReU16Phz3U3Fz+oOe/jAwKRUKA5IEmtCGPGYR3fnGGe3169ezYsUKXC7XqNEsY/NwOp1xPx9f3aSh0SExTDBYLBaWLVtmbtPFE3rj0Yxh6F1dLhcLFiyIazmKTR+bd3JyMiUlJcPKH6lO73X7dDwa/nv5PvJiXyTZjZdMRspzPOWN1k8TvScwEmw225jjQVVV/H4/Pp+PjIyM9xy5dbTt9Hh5jJdQ4uU34XaxyFgNyw+S7t0igQQSGJ0PIcGHCT5M8OEImDR8OPTluPKYanw4efdOAMLWg3hNH9lZdrudtLS0YUdq4v2MlMd7q5/+M1o5xoWr2MkxnrIjJ7HuC3z8A1//wXxWkvQxLITQfZmPUF7kxbORoGoqqqaavrej6xntztD4ifXJHb++IxOC3++npqbGDDQzUjpMr+wjT/zY+kYGMTK+CwYDVFdXcezYURRjy5Gh6KujWTMi39fwbx1ZnohpM0VRqKmpMX1Zj1RXVVXZvXs3FRUVxrfD0se2oxCM0E/D6xK/jhqaphIKBeOWN1IeE0FsW6pq2Oe9xBTyb5FAAh8wRuPDiN8TfBhTrcnOhyPWN8GHo9U1wYeTD5NaoZAIWz1GaMNIy8R4rRuxk3WsyfteMZIAn3g5QQQBBAEU1UMoFJhQHgIVgYIQCpKsTwZV1VBVgaoKQiHF1PJ9Ph+BQIBgMIjP54ta/Bt11wBFUQn1uAn6AyhC1d31NTXFjXIJekCj6upqBgYGaGpqmvAkUzWNYyeO8+xvnuV60w00ESvgBaBC+F01ocQlgdgJLoQeYOjw4cP09fVx4cKF8HOC9o5WXn7lRTo6WxGoKEqItrY20++0EQUzsm0i+6WpqYnTp0+b7u7iCWzQBfLAwACvvvoqjY2Nw4RYZHtKksTMmTMjCFkFlIifaIQUlebWFk6cOEkwFELRNIQE4XBXUUI1tr10AlDRRIjBwT72vrubkBKMSiOEfuGtoqKCy5cvR/jAnzii+kXVIChQLERZYxNI4KOMBB/CzcaHzc3N+mp2AkjwYYIPJysmtUIhMKJ9jow/XEBNdkjh/2SsVjs2m33ElMO1Y/2zQCCAx+sJ+4C+xKlTJ9E0lbY2XRjU1NSwfft23nrrLd5++2127NjBzp07o4SEkacESL4gO375W25UXsWiSSiKwssvv0xvb685ESMnhKqq/Pa3v+X8+fOcOHFi3EF7jDItsszq1av51KceNr1CxEmt/4iRrUL630bkVz291+fltddeo6GhPqJuEgX5BXzxi18kKysLVVV47bVX+dd//T4VVyp49929eH1es0wjPyNPITQ6OzvYtWsX9fX1XLt2Lao+vb29ZoAlSZJIdiVTVjaXgYEBXWiZP9G7OpKkn681BK0AFFVBUZXwJciYrWlZo729mYorF1EUP0KoUVHqB/r7OXXqFA0NDcMi0ba1tfHGjjfw+XyomkptbS2KEop6D0VR2LlzJ7/61a8YGBgY0eo0XkuNJEloQuiWWAGaNBVOjSaQwIeDBB/CzcaHx48f12XeOGDI/AQfTnE+jBkPI2Eq8uGkVigAkKSJKvA3GSyABUmyIktWZDnCm0UcGNq1OfE0PerkU089hdfrpaGxgd///jUGB/t55plf0dHRjqIovPLKK1y6dAm3281TTz1FS0uLefEoMj9FVQg5rdz24L0k5WdxqbaaUChEcnIygUAARVHwer1D6RUFSZJYuXIlBQUFtLS0mAGExgtN0wh4/RzYv5/mG01hIalF/bg9bgbdg3i9Xnxenz4ZNQ2Px0MwGECIIJoI0N7RwquvvURdfTWKGiAzM4XVa26hsHAag4ODKIqiWxr8Pvbu3cuFCxfo7+tn9ztv8dAn7+O229Ywb/5sLBaBP+Cm9lo1gYAHVQugagH6+7upq7/KzFnFzJ07l+zsbHp6eqL65tSpU7z11ptoIogQQbq729mzdxc7dr5KIOhFUQNU11zhwoULhEIhampqzHplZGTQ1dWFEIJgMMjevXupvVqLqqioqhK1rayEgixZtIB779nMtud/y/YXXsTv9aGGFEKBIG+9+SZvvfUWu3fvpq+vzxSixpZ0d1cXXV1dyLJMRkYGSkgZdgTg+vXrLFq0iBUrVgCYUWwNK5/f7x+XAmkufsJCU0hMBbfbCSTw4SLBh9xMfNja2moG2xsvNFVN8OEU5cNIpXQ0TFU+nNSXsmHynxn7IKHLyHg63+htEggECIVCuFwuNE2jsqqS48ePU1JSwvr16zl58gRd3R0IFFpam7jvvvv42Mc+xu23305RURHV1dXcf//9pgD1+/2oqkZSkpNz585RmJMLbi/Pv/ICl69Ws/XBT5KekY7P76Op+QZnys/yya2fRJZk6urq6O7uorCwkMHBAVRVxefzmREoR4Mx8UKBIM8+8wxXa2qYMW0aoVUrkCzG3r9ueTp16hQej4eiaUUMDA6y4c67EEKwf/9+Zs0qpqS0CCEEh4/s52c/+xmffOghvvCFL+qX+1Kc+HxeFEUhGAxit9vZvfsd9u3bR/GMYgLBIJs2beDAwXcpLZ1JTk4m3d0dVFdX8eJLL/H//D9/ycyZs/D7/LzxxhucO3eO//2//wa3201ycrJu1VBVZFnG4/Ewffp0Kisv09vbgSTLuN19WKzg8Q7i8QyQnp7O/v3vUl/XxH//71/l9ddf58tf/jKZmZlkZmbqwlFR6Onp5NVXX2X+/AV87nOfwyLbEEInvN7eXlqa6klOcvDaa6+SmZnF9etnWbHsFrJzclAUhdqrtSxYsICMjAyOHTvGli1bAGhoaEAIwec+93ne3v0Ws2bNxOFw0NzcQmZGwOwbTWhs2LCBN954gwsXLrBixQpCoRA7duygL+xre/ny5axatWpcXmOEEMiyxbQmJpBAAtFI8OHNxYdCCEKhEMN9Ew2HEAJJCELBEM8+/7sEH05BPrzF6WT13LmjjtipzIeTWqGQhBy+MCUQqIDtj12lDxkTJw8h9NDxkiTR1dWFT/GxZ98eVKvEwZNHWX/Hndy67nasFjt+X5Ce7n4GBwf52Mc+Zmr8q1evBnTPHIqicPLkKRYsWEBSUhIdbe3sf+oZpLZ2PLdMJzs7jcbWFhYsmsevfvcUNptMkuxCU4NgseBKcfDLX21HkiXWrF6DM0lGE+M7W2hanWQLS5YtZ97CeRw5uo/TF4+gCDX8vUCoEt6BAAMDA2RmZnL35nvQFD2ibFZmFj/6zx8h21QIn3FMS0vn8OGjVFypRJZkQqEQM4pm43K5zK3UlStXM72omIorFXzrH74NUoienk6+ee2bqJqGLFlxOh24B938+7/9Gw5HMkpAYLVZ6e/r5zvf/hcyMrPYsGGD6etc0zR27tzJ/v37GXT3c6XygrltGwwqCGEnFJKQhA0ZOxUVV/iHf/gHUlJSTMuWw+EgLy9Pr6dmwSE7ObT/MJcvVyLLFkLBoHnx0Wa10N/Xh9BUens8DHq8/PsPf2AGefJ6vbR3drBo0SI2b95sRoMtLy/nnnvu0X2Kz5qj+xTPK+IHP/gRkpDMdKqmgqRbmerq6li1ahXJyck8/PDDpjIoy/KEXOwJTUJoAkkSMMbxjgQS+CghwYc3Ix9axjzGZr69cZNckhN8OEX50FJTgzw4OP7xO8X4cBIrFBKyxYI0iYN4TEYYF4MOHz5MRkYGKgrLly0nPT2d7p5u7HY7S5YsITMzk/vvf4C2tjbS09PJzMyM2o5NTk4GQFFCXLh4ijlzZxAI2rEnSaQUF1CydD7XnAEKVQfzFy5j6fIldHW20Hi9juKiYhRVAVXh3Llz2Gx2Zs6cSWnpbKZPn0Faatq43sVQKOx2G5vv3oymhZhVMo2a2ioUoYIEsiTj9fhpamghJyeHrq4uZs+Zjdvtxuv1cvjQIdauXce0ojz9/KnFwowZM0zvGMaCd0ZxMZs3O82dk1kzZzFr5iwWLVrE8mXL8Qc8yLKELOunLu02J9NnTMfrGaSzqw1ZsuNKSie/oID+vj76BwdwOh0UFBSQmpqKxWJB0zQ2b97MLbfcAmhI8tA5UJCQZAft7d0M9LrJSM9i/fr15OTkUFZWRlZWFpKkR1i96667qLhcgU2WKSoqYs3atdx51ybkcHvJYZ/0FtlCMBAEoUfDFTIQ9mxi+JpPSkrC4XAgSbpgVBSFxsZGtm3bRnFxMRUVFdx///3MmzePJYuXoKmaWWdNCDPGjtPpNC/HGQJ6ouOWcP/YbLZ4DjQSSOAjjAQfvhdMdj6c4XCSFJiAQgHYHfYEH05VPrRax+Uydqry4SRWKIxLSAINMflvo0wSyLLMxo0bWbVqlTlxATRUFE3BZrGROjMVSZJYu3YtMNyv9LRp08y/LRYZm83Kv/zLd3EmOQmpKv/7r/+G4twiFKuEjIYmLIDEbWtup7ayltSUVI4fP86lS5eor6/nq1/9KnPnlpldaLGOfJEu/jtJSJKMxWJn0cJbWLhoOZoeLhZVVdm1axdeTz1lty6gufldsrNyePfddzl+/DjTpk3j0w9/mtT0lKEMJYn58xYafgMBEEIiOyvbPLtoHNFJT09n1apVIOkWHSTj0IEc/slnVslMJCyAFQFkZWeaRQkhSE9PN/PMz88PX6QTEOFTWgCqJtHa3MK+vXu5fuMGn/vc5zh46KAZ3RP06Kr5+fkMDgzw5mu/Jzs7mwcefJC0zHTkmMWGIYMmMnWsViuf+tSn2Lt3LxUVFSxcuJDS0lIsFkv4vUSMp5n3J0I2DL8wmEACCQxhVD4MBMDt/qPUazLDIgQbV61i1fz5Q3wogYaGooawaYLUbF3ur128GBYt0ptW00CWkXw+poUt6rjdWEJBbMEA//YP/x9JyUkEgkH+n7/6fynOK0KxgizCfCgk7li8gvqzl0ibLnMiLE/r6ur4ylf+O2Vz5+r840rFWnl1Qu8kyxKSJcGHMIX5cJxKxVTjQ0n8MWobCsG2bfDEE3EbVgg9fIe/vpvg2UaS71sKDrBL44u++FFFrFcBiBjMkc0mxr/oE0Lg8bhpbm7G7/eTl59Hdl4uSFasAjRZw4IMAtyDg3R3d5OTl4UsS7S1tZGcnExuTgGSJIe7esj7SOTQi1ef2KEpDMcVkkCThiZbX08fVtlCSkoKXq+b5GQXSkhhoL+ftLQ07A4HsiVadzZKE0QOQfEex5fuS1yTZFNqyUxcB1YRSJpAVVSUUAi704GmaeaWrVlL3V0JakgBORz4xhJuV4bOX2rh9wsfrTXrFHlGMxKx40dVVSwWS4xbyVhxES1A/1BlQun3M/DaKZLvWg7FKTgFSC++BFu3IsWJgppAAn8UPP00fPnLH0pRY/Kh3w+vvz6uRcpHCZGSahgfvtc8hcDv99HX34/QBMnJSaSkpYEkY0FCSBoSMiAI+gO43W5cKcmAYGBwEIfdTmpqhmk9R1Vh6VKkJUvGx4e7dsEtt0BBQYIPjVpOJT6srIS+Prj11lHffVQ+HMMRwQeCAwdg48Yxk03aHQohAFlCsui+tyXx0duliB3g4x1AY7oLlIbSjVW+JEmkpKRSVjYPTRP6Ec7wWU7dCcGQ0HC5Ukh2ucyznMUzSpEkaUJn6I1y4/2taboElcIiG2QEguzMTGTZgiSBzZYB6JE2k5KThrxrmGdMdVgwBEKkKB0qz3j3sdpIkkCgB5uyQMwYHfkK5TA9XgKLkECWwAoWq2XE+wdGnWTZMvKckMKiTUS9Wtx8IsnW2PbW85eHpY1X4Aci2MJb0ULTLyJOmVtpCSTwAWBUPnQ64bHH/pjV+1AwYT6MXKCPI/9x8SGQBDiFGM6HeipT3to0QYa5KBdkafrv8vvBh+HyE3z4EeRDeWLj58PEpFUoMD0JT32MtQkUOwDjCZBYi/4fgsjyxpunrhjEmTxIZk9JshQhMiSs1vFPtthJbHxmuLuL/N3YLjWEuRRnoquqagpATQg0TfcqIYQe10LnZ13QG/lbLBZTeI7VFtFtMPw9wn+FLT7x+85wNyfLcpSQG5b/SH0f25QRW68S0c/Ea9/I7wzXenb70HE0I63R9mNhou0WD4ZrRZ00mTJnRxNI4IPFKHwYCkFTk35MZwpgrCkdT6y9l3zGi5jN+3E/E29ZJzEkB+WYBao1jvyTAHJzIS36XuFIfCiLMF+F+S3BhyMUrBc3eflwnHlMNT6ctAqFkCR9gGti0kcHHC8Mn8iDg4MkJyebl39GS3/jxg2Sk5PJyckxPzMu+2iaRigUore3l9TUVJKTk81BbwgFQ6uO/D2eAhMpOIYiT448iSK1+djJaXxnCAhDkI20jWv8GNYHSZJM38+RFonId4lsIyEEVqs1Kk/j36FyI7e89bEVKUziWSdilZnxWGiEEDQ1NSHLMoWFhcO2ZiOfj3Wlari/M8p1Op1x+8rn82Gz2aLaJrZuQ7s6GlVVVYRCIebPn4+iKNjtdnw+HykpKUiSxMDAADt27CAYDPL5z3/eHJexbTPS1nBs/VRVxe/3Y7VaCQaDuFyuce9SSZIcRYjhPenEcY4EPtIYlQ9bWvSjMIsW/VHqNmEYskkIVEVh0O0m2bgMO5J76TCn3bhxA5fLRXZ2tp5HJB8KgRIK0dPbS1pqKklJSVH8ZXBglFyLlekRaYfxYXgXQggxtFY1ZKP5+FDAOyFJ5sLROHJDLA/39el5PPhg1PMj8WGkFEzw4dTjw9DAAMmqGlcZjYe4fDiJMekUiigtEqG74poKqtk4oKoqu3fv5vjx4zz+xOPMK5s36s6Dqqrs37+f5cuXmy7cfD4fycnJJCUlmZEZz549yxe/+EXy8vIIhYJkZWXR3d2N0+mku7ub7Oxs+vv7zQkdW56qqrS2tuJyuRgYGKCoqAir1RpVN8NvdDwhZvzt8XhMpcZQnoBhecU+Z1gDRopVIMuyKVAN6HM5WnmJfD7S2tHT04MrNRW7w47f50e2yKQmJZvlBwIBfD4fGRkZ0fergNraWlRVZf78efq2I8bxm+HKlZFfZWUlkiSZUUwjBbphNokniNs7Ojh+/DgLFy6ko6OdDXduGNZWoVCIffv2cc899yDLMtevX8flcpGTkzNiGzc3N+N2u5k9ezbbtm1j48aNnD17lgcffBCn04mqqjgcDubNmzesfyOF/jALj2RY5KLLa21tZefOnaxbtxaPx8v69euH1SkWJhHL4fYM52lcrJvkcjSBBD4QjIsPhYD582HDhmHPT0oYnBIK6Xx4+vQQH4at5fGghkLsf/55ls+eTXJZ2XA+DPv8P3vhwjA+7OoahQ9jF7KKMjIfRqSJ4sPIDDQNTQi8Xi/JyclDbkUj+FA23rOzE06dMttkXHwoSaiQ4MMpyIfeixe5deHCYXWKxZh8OEkNbJPSB50QQrfESDIWq+4pQJoKYQLHhGDJ0oV87N67KCjIRZKGwrsrihIVQdoYuMaZx4qKCn72s59x4MABM9S8LMusXbuOO+64nYzMNKprKtix8/cEAn7eemsn+/e/ywsvbOPcuXMcOHCAYDAYtXthlCOE4ODBg1RUVLBv3z4GBwejLCWRP5qmoaGioQIKoKCqKm+88Qa//OUvzQiToLs8M6wGxrOGYhA70a1Wa1Q5kRYGI21zczODg4Po27IqqqqhKBqqKqIEtqIoHD58mO7ubgKBAD/76U85c+oUfo+XnW+8QV93jym0A4EAr7/+OlU1NYQ0FUVoKELTvaloGp2dnVRVVREMBaiuvsLRIwfpH+gl0nd47PukpaVx48YNMyqmLvgC7Hzz91yuuIAmQmhaaCh6q9AXCqrQqKqppm9wgGv19VH5G1BVlZqaGtxuN5qmcfHiRaqrq4cpXAYxCSHYsGEDmZmZeDweQA/01NDQYP6dlpbGwoULqampMa1hiqKY7djS0hKup4o/4CEY9NLV1UZXVzuKEkCgmD+g4Eyy4fEOIFBpbW0x+2U0mAJSktBkDdBAsiAmp4hKIIEPDQk+vIn5UFHMXZFIjMqH4TQJPpyKfNiKGMfRxKnKh5OydkPbhtLQGbhJqpFNBHpwFSsnT55kYKAfgW7VVxSFpqYmXnnlFUKhUNTi22az8dRTTzEwMEBBQQE2m40bN26Yk0UJKRw6dIi+3m7S0pPJz8/H6w0wZ858cnMKWLJkmbk97Pf7gfhHnJYsWRK26ITo7u4GdKuG3+8nGAwSCoU4duwo1dXVaCEFNA01/BMIBFi4cCH333+/6a97cHCQ06dPEwwGzaNZ/f39eDweU8BGbiEaJKLFmWyGJeLQoUPs3r2bX//6WZ5++in6+/sYGBjg+vVGU+gaVqvXX3+dJ598Ep/PR0lJCYcPH6a6qoqdO3bQ0txMV1cXbW1tdHR08OZbb1JZVaXb/YRAUVVaW9vw+/3k5OSgqirV1dX8/Oc/Y/+B/Zw4cdzsp8h2NITWmTNnOHDgAN3d3WZf+v0BHA4HQgja2lpNUjx48CChUIgzZ84gyTK5eXlkZmbi9/uHEV1raytutxu73Y7H48FisXDXXXeRmZlppjOEciAQYPfu3Vy6dAlVVSkvL6e6utq0urhcLgKBAJqmcfXqVZ5//nkaGhpQFIWuri7279+PqqrU1taav7s9Hv7zP3/I27vf5jvf/Q5/+7f/P5pbmmlrayUQ8NPX10vFlQoUJURBQT4ZGemEQsqE5645LhL7EgkkkODDm5gPvV5vjMU+wYc3Nx+GJjxPphIfTrojTxA+4hIePJqqofuNhjh3gic1YieW2z3IL576BV1dXXR1dTGtcBo7d+5g48ZN2Gw2qqqq6O3tJScnh0AgwMmTJ3nwwQcpKyvj1VdfNSMuXrt2DSEE/f39/OSnP8Xnc9PR0clbu3Zw8uRZ/t+/djJ9+nSuXr1KaWkJAwMDuFyucFTMNLNOkduj2dnZdHZ24nK56OnpobS0lEAgwLO/eRanw8odd9zB3nd3U3etjq/9t6+wat0aZKsFZJmTJ0+Sk5NDX18fQghKS0s5fvw4L7zwAn/zN39DWVkZbW1t7NixQ/cT/cAD5vlWIOpspvF35H0MIfTt40WLFvHaa69xra6Wzo4OVq9ehyTJnDt7nsef+ByhUAhVVWlqakJVVSorKzl+/Dh33303v/rVrygqKmLBggUkJydz4sQJDh8+zF//9V+zauUqioqmmW7h6urq+M//+AFr16xhy5YteL1eGurr0TSNtWvX0tnZSX9/Hzk5eaiqSn/YHZ8sy2ZbZ2dn4/F4TIF+7tw57rzzDgKBAL/+9a/ZsGEjr7+2g5YWPQDR6dPlzO7vR1EUrFYrNpstatvb6PdQKERSUpJJRJqm8c4771BcXIzFYqGnpweLxcKVK1c4ePAgt99+O1arlUWLFlFbW0tBQQGqqpKcnGwK6fLycq5fv05BQQFnzpwhGAxy9uxZZs6cSUlJCRUVFQgh6OzooK6ujo6ODlRVpa2tjcuXL3Px4kVuueUWLl26RHNzM+vXr8fj8SBJcjjw0YQmDZbwGd+bxy1DAgm8dyT48Oblw1wheKiwEFtE+Qk+vJn5UJ7YfcApxoeTUqGAIe9B5vaQcY9oCgrR3t5eXnzxRdauXQNAYWEhe/bsobHxOpcuX2b69Bl0dHRgt9sZHBw0L2Bfv36dFStWUFRURCgUwmazYbfbTatFKBQiPz8fp3M6+/bvQxMCQYDT5QeprkmjvqGBhoZSpk+fRVpammnx6Ovrw+/3M23aNCorKzl9+jQDAwPMmTOHlJQUVFVF0zTa29upvVpDReV5rt+ox2630t/XzZP/+SMyUv+OuUsWIiTdLexzzz1HUlISW7du1SdbZydut5vOzk7KysoYHBxEURT6+voIBoPmXQvAPIv6/2fvvaPjuK48/09VJzRyBgECIECCmSKYlSiJkixRkmVLtmxZssdjz4xndsbeM979/XY9O+P9zc6xvbZsj+0ZW7ZlWbJkSVaiqEAxSCIp5gQwgCRyzjl2TlX1+6O7itWNbgQqAXR/z8EBUPXqvVsv3G+9+967V6/UIdxDxSuvvEJnZ2fwgJch6D7uqaeewiAaWbSoFIDq6mqef/55xsfHGRgYwGq10t3dzW233cbixYvxeDw0NTVx7tw5brnlFs1DRkNDA83tbWzesoWkpCS6u7vx+3wcPXqUe+65h/b2durqqxkfH2fHjh1s2LBRiyA6NDREdXU1N910EwcOHGDXrl1hB8kEQaCtrY2kpCTNY4TNZuOnP/0p2Vl52Gw2HnvsMZwuJ+8dPEBGRgbbt28nIyMTQQw/+J6WlkZvby/JyclaXkajkeTkZM0Sc/78eTZs2EBDQwMGg4Hc3Fx+97vfMTQ0hM/nw2g08td//dekpaVhNpsRBIEHH3yQT33qU1y4cIEnn3wSn8/HmjVrOHbsGIcPH2bjxo0oiqKVW1tbS2FhIWVlZbzxxhtkZ2fz0ksvkZSUhM/n44033iAzM5O77rqLnJzcScv5U44XOeR1RFHm21CPI46PDHE+vDb5cMLhQMrLw6zb0hTnw2uZD3MmH7KYarzMMz6ckxMKRQl6spANAiZZQEHAOA+Vpwqz2UxhYSHFi4r553/+X7jdHl588U8cO3YUgeBhr/Pnz5OQkIDFYtFm+Q6HQ1ue6+zsxOPxkJGRwbJly7Qok//tv/0jXq+XnTt30D/Qxzf+5m9pa2/B7rCTk5OF2+MmJyeHrKwsLUJlW1sbhYWFtLS08L3vfY/09HRWr15NXl4eXq+XgoICBEHg8uXL5OUtYPGSzyEIAj6fj9tvv4sF6TkkJKXi88l4vC4uX75MdnY2RUVFpKSk4HQ62bJlC6dPn9Y8/BQWFrJy5UqSk5NJSUmZ5ClBv0c0cjnfYDBwww03sGjRIhRFISUlGZPJzODgIG1t7dx6660YjUYKCgr49Kc/jaIo5Ofn09vbGyKYBO68804yMzP5wQ9+QGJiIllZWZSWliJJEv/6r/+Kx+MhPSUFWVbwuT187WtfIy0tjezsbL7xjW/Q3d1NYmIiCxbkkZKSgtFkQpZlzpw5Q1paGmNjYxw9ehRRFPmHf/gHEhMTKSoqQhCCAf7Onz+PoiiMjIxgNFj45//1XYqKihkaGqKlpYWGhgZuuPFGMjIyyMvO4datWzGIBpxOJ4ODg7hcLl555RUefPBBysvL8fv9dHZ2sn//fsbHxzGZTPh8PgYGBhAEgRtuuIGmpiYMBgPf/e53tSVpQRBIT0/H4/GQlpamLfcmJSVx5513UlpaytGjRykoKODWW2/lvvvuIxAIcObMGYxGI+np2fzD39/D1q1bMZsteL0eEhIScLvdmlL3er0IokhKcjI52fmz8PAkYBBEfKJIgmIAQZ7xmNdPWq6F7SBxxKEizofXLh+muN1YonDflHwoinE+nKd8mLtlC+IMtz3F5sPp+fST4sM5PaGQBAXBH0ASwDJPtacgCCQnJ3PfffchCCCIEkajka9+9au8/fZuensGWbVqFRs3bgSgrq6OS5cuceTIkaDCSUnhxhtvZO3ateTn52uKQl36U8PQP/roV1AUEA0KihIg6DkBZFlA4Ir7Ob/fT11dHQsXLsTlcrFkyRLWr1/P5z73OTweD6+++irFxcUoioLJZOKee+5l5cpVyLLMO++8Q3tbOw9+6WFcLhdvvbWL6upqTGYz3/zWN8nIyKC3t5eXX36Z5uZmVqxYwcqVK4HgQadt27ahKLHd18aC0Whk3bp1rFu3Lmyg9PT0UFFRwcKFBZoXiQULFmgWDLVsQPMwUVxcHFa2IAjk5+dfOfiGTKLVypo1a8jMzNTyzc/PnySXrASXpZ9++mlSU1Pxer3cdtttbNq0KWyZev369dTV1fHSSy9jNpt54IEHWLky6OnBZDKxZ88e8vLyWL1qlbZykxT6bTAY6Orq4vjx42zcuJE1a9ZgMpkYHR3lzTffZHR0lC9/+cuYzWZt7/G//uu/4na7KS8vZ8WKFSQmJk6ydiUnJ4fVgyzLGI1GXC4XJpOJFStWkJKSQmpqqnZY79ChQ2zetJm77roLi8UCoO0RTkxMCstPbSeLJWFGbaw+q7pbFLVgTXHE8eeNOB9ew3w4MIBYVaXJMxPE+XAe82FyMkxMzKid5yMfCsps9iN8WPD74aWX4KtfnbSfTN0Hhwy2wQkMhxoQH9pIklkE0TAvrY9XqlgBgp4X3B43x44eo6xsBcXFxZrfaLfbTW1tLQ6Hg40brywlwmSFE90tmoSCrDvAI6I/e+/z+fjNb37D+fPnEQSB5cuX81d/9Vfk5ubi9Xqprq7W9lVevHgRm83G1q1bQ+7mxnC5XCwsKAAUJmw2BgYGKCgo0CwvsixrS7v5+flhW5siD77NtC1jdVG/34/dbic1NRWDIXrf0O9DnQkkSaKyspKioiLNMhVLJkVR8Hg8nDt3jl27dnHbbbdx5513hvnLVtNJkoTf78dgMGAymTQCVA/6qX60o7kKVIPbqPfVvH0+HxDulndsbIwTJ05w4sQJbr31VrZv3x7mkzwW1HGnLktH1qe+7fTWs5lgJmnV/OUJLwO7K8i8cS2GxSmYAF5+FeFznwOrdcrno5UX2Xfmo/6IYw7iqafgG9/4yIuZER+2t0NrK9xxx0cuz4eBOB/quGFgAKGyEuEzn5mZbtq7F2X9eghNCPSI8+E84MP6eoTxcbjxxmnLj8mHwvTfwR86Hx4+DNu2TZtsTq5QqBCEYJh68SOwxnwyHxoC6nJVgiWRO++8+8pBnRCsVisbNmyYsVx6JSoIoGBAUETt/2CZV9IYjUb+8i//kltvvZWUlBQKCwsxm82IoojVamXjxo3aAFmyZAlut1vb05mZmUFWViaq34nUjHRS09NDh4ZC7SWKpKWlkZaWFlXWq6nnWIrRaDSSkZERliZy8ETbgxoLah2pVo9Yz2nWG0HAarWyZcsWFi1aRGZmZpjyFCLqRVVk+vzUeo/2DvrJlz5ipwpVRv1zGRkZrF69WrNIqYopVvwQ/Tuq7Rytnq627WYLRQ65cdT2is+uzA+DROOIYy4izofXIB+KIsQK5jdFrUUzxMb5MM6Hkfi4+XAOTygUQEaW5Bmpzw+qED++jw61sxsQxckDXO/JQZUrmnIIy1GfR/CC9n8wn/DZanp6OuXl5VEtPPr97ikpKaSkpGgHxK4oegUhICMrMoLRAOrS3Me8by+a/FP9PxOoCm2qFRS9ghMEAYvFQlFRkXYvmtKOpcynUvJTyR/5nF7+0tJSSkpKosqjh2p9iSxT9UluMpkmPauPfh5NDj25RPblmbSHIBC0JooCs3G1H58sxHFtI86HqlzXFB+q1z+Eeo7z4TzgQ0K9L4a8kZhvfDiHt2VdWRadaleWfukp2vXI+9HSx8pjujxj5TPdvchOqE8T+b+6vBft/kzk1Zet5hUMhhN+P5q/a1WJ6F3YASDJDPb0ceLgYQI+/5VgNDECmE1Xbx8UH6a1QCURNc+Z5jtdev39SIU127KiPad/XhRFLYhSrGirihL0ZW6z2cI+HhRFYWBggOeee46JiL2esiwzPDyM1+tlYGBgko/48fFx3G532HLxbNtaEA2YLWYEUdD8oH8QfFyWpDji+GgR58Nrlg9n9RbTI86Hc5cPUZTgz0zfa57x4RyeUIQWJwU1tP30y3Ner1cLTiLLMna7Hbfbrd2PbIyRkRHGxsa0zjATqJ3D6XSGRX2eSolDMFplR0cHbreb1tbWqAorUraenh5GRkZ4+eWXw5TeTD7O1etqx3Y4HFRXV9Pb28sTTzzB0NAQdXV19PT0UFtbi81mmyS3+q6dnZ309PToFC7BCKBKcCmurbWVkZERzp49O0mJ6vPx+/3TvvdMcTVKZzZ5zsx6EF2BfZKYSrFGor+/n717907qS1arVfPjre9fgUCAl19+mdOnT2vBfQAOHz5MbW0tBw4c4ODBgyiKgsPh4I033mBoaGh2SlAAKSCBJIMifxiGu2C2c6Bt4ojjgyDOh3E+jIU4H85cpk+SDwcHB2c3gZxnfDinJxSKoiBLUnBCF6UC9BYFSZI4evQob7zxBoFAAEVRtGiI0RSNLMucPn2a9957j3379uFwOLS8JlsQwqM/jo+P85//+Z90dnZOsnrE+vH5fDz11FPU1NRw6tQpTZnof/TpOzo6qKiowOv1cuLEibBIkZEdfiql6vV6efLJJzl+/DgHDhxgZGSErq4u3n//fS5cuMDx48e5ePEiQ0NDYXXZ0tLC2NgYiqLQ2tpKTU1NsN4UGQUFr89LdU0NNoeDPXv2cOjQIU6fPq0NKr0csiwzODjI7373O+rr6z/wLDuO2SFaf5FlGbPZjM1m09rM7/cTCARISUmhqKiI+vp6zQe7miYrK4vk5GSGhoZwuVzIsozP50OSJPLz87V9rKrFR/17xghZDaWQFTKOOOIIIs6H1x4fNjQ0zMpiHccHxyfJh+qqx4wxz/hwTk8o9LNJQdcGkRYDRQnuR1uxchkbN61ncKgfu32clNRE7PZxZNmPx+MMG9CyLFNcXEx6eroW3GayEpIBCRQJkFCUAAgSyclWHnnki5gtBurra/B4Xbg9TvwBD5Lkw+m0EQh46e/voa6uGrfHiWiAxUtKyMrKYnx8HJfLBUBA8lFRcYojR97H63NrFpSsrCwA0tPTWbZsGcPDw1RVVeH1erVw8Kr1KbJO9P+bTCYefvhhVqxYQVJSEsnJyXz961/nxhtvZN26dRQWFmK1WsMsyX6/n3fffZfz589rfri9Xi+KouDxeujsaqEgP4eBiWFe2/Eq+Qvyue666xAEQSMvtY7V/Ydnzpyhq6uLt956S/PGEMdHC32fUK2HEOz/zc3NeL1ezc+7y+XipZdf5Pz5ShBkJiZGOXTooNaeNpsNWZY13+dGoxGHw4Eoitx8882aAlWRmprKxo0bmZiYmJUClQUwGA0YDUaChyf5wEdQ55LFLI44rhZT8iHE+XCe8mFgnnwszndcM3z4AXnso+TDOT2hUI9PBV/8imKAYCdQldDo6Cj19XWkpaVy+fJFvve9f+PJ3/+OhAQLAcmPP+DjrV1vaqHfvV4vR48eJTc3l5SUFLKyshgeHtYG/ujoaDBsvSwhyQFkRSIg+fH6PDQ3NyErEuMTY/z614/z2I9/RGNjPUePHKalpZnhkSH+/Wc/pfJsBfsPvMf//eEP2LdvD4oiUVRUqIWPdzqdgILP5+XNt97gmWf/gNNhx+fz8corr2gdXxAEnE4nTzzxBP/5n/9JY2Mjg4OD/PSnP+XMmTOazJ2dnbjdbk1h6WfRPp+PkydPkpiYiMViYdGiRRw9elQLQJOWlqYRiN/vp6Ojg7/8y79kw4YN7Nu3D5PJhNvtDtbb4cP8/j9+huzz8O3/+d/JSEmluamJgoICzUdzIBDgwIEDPP/887z00kvs3r2b8+fPI4oi6enpMfcxxvHRwOfzcfDgQTo6OrTx09fXR1tbGwBnz55l586dvPvOOxw6/D719bVIcgCzORgcyO12c+nSJRRFIT09neHhYRITE7UVCFmWOXLkCLW1tQwPDwPBcep0Omlubp6dRUYXDVUgdJgNtA+maNbVOOL4c8CUfBj6CII4H843PhTifPix4pPkw6bm5tmtSEXjwylW4D5pzGEvT+qeUXWJKPzk/MjICG1tbWzYsIGRkRFef/11FAI4HA62bt1KdXU1BoOB1tZWzGYzTU1NOJ0OkpKSUBSFixcvaktUGRkZuFwuFEXBbrdz+vRptm3bRkdnN7m52QiCQGNjI7W1tZw7d45FixZRVVWlhWA/ePAgbrebs+fOah9R4+PjbNiwgYULF/Lcc8/R1dUFikByUhqJiYmaVcLv85GSkoLP5+PFl14i0ZpMa2srgiDQ2dnJf/zHf1BVVcWGDRvwer28+OKLeDweenp6OHfuHDfddBOKolBVVcW2bdtobm4kNzeXhAQLx44fp7m5hYlxGxkZGRQXF/Pkk0+SmJhIa2srTU1NZGRkcOONN2oWl+7uboaGhigtLcVgMDA+Po7BYODixYucPXuWvu5uMrw+htp6KcjOprt/ALvTgaIopKSkoCgKBoOBdevWsXLlSs31Wnp6OpcvX56xH+g4PjwoikJfXx8LFizQLGUpKSk4HA7y8/N59tlnSUxMpKysDIPBwLlz53j//fdZWFAIwPDwMIODg3i9Xmw2G36/n6ysLC2Ij7qEf/HiRW655RZNyakH4WZ1uC7UXxRZRiDMDKu9S3ylIY4/R8TiQwhOIlprauJ8OM/48O7rr8cQ+uiM4+PBJ8qHojirCWRMPtS9y1ziw7n9ZaerKAVFC06jLlElJCSgKAoZGRmMjY1hdwQHe2VlJenp6WRmZmoh2k0mM4SUsdfrxe12s3//fm688UYWL15MQkIwoq/P5+Po0aMsXLiQjs42FizYxtNPP83Ro0cpKyujuLiYjo4O0tLS6OjoQBAEOjo68Hg8pKamsnTpUoqKiujo6KC6uhpRFFmxYgV9fX2YTGato6anpwMCtXW13H777SxZsoS6unpsE+3k5OSSnJzM9ddfz5EjR1i3bh2CIFBSUoKiBCNNpqamhqooWCfd3d3U19fT3dNO2dIS3nvvPX7968fZsvkm0tIyWblyJevXr8dutzM+Ps6aNWsQBIFly5aRlZVFbm4uAK2trQwNDbFixQp8Ph8Gg4Hk5GQ+//nP09DQwKOPPoJod5C3cBF2p4eu9m4WlRRqeSUkJCCKopYfBDt9Xl4et9xyy6w/MOP44LBYLDzyyCN4vV5qamoYGhpi3759/PVf/zVLlixh06ZNmEwmTGYDgUBwO9qnP/1pjAYTVqsVk8nE+++/z8GDBzGbzfzt3/4tixYtYnh4mIqKCg4fPszixYv5n//zfyKKIpcuXWJ0dJS3336br3zlK7OSVVG3bAT/I9KQMNcUaBxxfGyYig9bWrAsXRrnw/nGhyMjMDIy+74wh6zS8w2fKB9u3DirtptvfDiHI2UrOIZsyPvrMDy0kWSrEUEM+oRW9yC+++67LF26lP7+fpYsWcLdd9+FosjBLTWCgMloxG63c+DgQbweD1u33kpLSwvnz58nPT2dRx99VAuZrrpHU2eXg4ODvPnmG2zcuCHYcSwW/uWf/5mkpCTNX7GsKCiyzKFDh2hqauLhhx8OKg5BmBSIRFEUUIJeOtQ9dwBv797FxPg4t99+O4lJSex6622WLFnCzTffDFzxjax2HEUJD8qi/u1yuejv7+fXv/kl68qvY2h4mPb2dv7f/+c7FBYWa+8IhD2vd4MniiItLS08++yzBAIBTCYT69at47Of/axmWVFQUCQJQREIiAI+pweL1YzRaNTy0/us1u9hjXQPF8dHD339BwIBqqqqqKqqYsWKFVx//fVh7XZlU4XaPsEfv99PS0sLvb29LF68mIULF2IwGHA4HBw/fhyn08mnPvUp0tLSUJTgAcozZ86wcOFCrr/+ei3q6VRtripH2RlgaNcZ0tYvx7giAxMCvPwqPPggWK2arPH+E8cnio89UvYUfNjSQsXLL/OOzxfnw/nGh4ODUFmJcP/9M+sQhw5BQwOErOFxzA6a8+UQH9bX19PZ2Ul+fj5r164N8+QUbDL1I14JXlCCcSlaW1sZGBigpKSE/Px8bdvexYsXcbvdbN68mdTUVG2Vq76+nqysLNYWF2O64w6E9euvng8FQ1i6j4UPZxgpew5PKMAxbEfZX4v4+Q0kW0UE8cqJeZ/PR2NjI/39/WRnZ7N8+XJtyUmFJEm43W7q6uooLCwkIyODtrY23G43K1euDIviGCmD0+nk/fff58iRI+Tm5vKFL3yB0tLSsP3/6uGetrY2UlNTyc7ODguEMxM0Nzfzs5/9DJ/Px5o1a7j33ntZuHAhKSkpMZ+J5ilA9aldU1PDnj17cLvdfO1rX2PJkiWTDuFM5WlAURQ8Hg9utxuj0UhiYuKkLUrqwbepzkLo61Vf1mw6/tU+F8cVROsneiU0k3rVW0Ii00frC/p9nfrrM1GgktPPyNuVpK5bhnF5JiYRhJdeRfnc5yBkNZ0urzji+MjxsU8opuDDtjZ8DQ00LlwY58MQ5g0fDg1BRQXMYEKhKArIMkhSXP9dJRQIrhAIAijh8Uam5EP1uu7cQiw+nJYnjUaYhnun5EPBOKm/zpUJxZzc8hQ8iKku8wjBpV0h/L7ZbGbNmjXaUqX+HlyxViQmJrJp0ybN8rBixYqwtJENoXaUpKQk7r33XvLy8sjKymLRokVR0xsMhjAlpZdhOiiKQmlpKd///vcZGRkhKyuL9PR0TQlPlU+kghJFEZPJxNq15aSlpSMgUFxchCgatLEw0xUCq9WqLXlHptdbgWb6rjP9aI2WfjZLelORwgeZxX+Ug3c6mWMhGglGSx9pGYskvZm+y1RjLFq6q12NEgQBg9EY7LPClSVeIXhzxvnEEce1gpnwoSXOh2H5XLN8KAgIJtOMdGGcD6PwYUQ5kVPAGb2LtgUp/BlFUaLmF3n9Q+HDOcqFc25CoVVU6FdwuZArSlSJSBftWd3/+muz8S4kCAImk4nrr79+2nQfZCuPKIpkZmZqbvGuZl+cfglYFKGkJFzZzzavqa7NviNfCf4zXTnR3nu6WXwkIq1FM1mAm+ki3UexX1G16smyrFm+ZFmmsbGRvLw8MjMzJ00KYlm7Iu/N5PpMEFm+vszI1YgPo44URVF7DShzU3HGEcfHgRnxoSBEtXjG+XAe8KGq06OljMaHigJvvQWhAIWTc546D013T8WrMe98PJBlGVmSEEORrWVZZmRkhJTkZCwxVtGiYbLrgiv4IKyiTUoifoOOowEEYbIMkgTl5bB27YzLm8SHc5gS59yEAkIKQQjNAkUBQRGCR9Bm4YP3aj9qPqiy+aBlfhC5Iy0PH8ZHZCxM9RGu//AN/i1rFjEFBYMY9Kk8XZnRrGV6qNt3AoEARqMRURQZHBzE7/eTn5+v7Xv1eDwkJSVN+Q5qXj6fD4vFotWl1+vFaDTicrlISkqatNyt/8jW13+sZfBo7+hwOHjppZe45557KCkpwe/3097erpGqCr/fj9PpJCMjI4woBgYGsFqtJCcna+UHAgFt7+5MrVHRJgyRfUl9L7/fj6IojI2NkZCQgCRJZGZmfigugWVJ0u25/sDZxRHHvMaM+XCGFvzZlv1RpJ1pPtc6HyIHEGM8G5MPR0fh0UfDP/wVJXiGJYIP1ajnM+JD3d9XzYe6d54RH0Z5b9v4eBgfej0ezh89yoYNG7BkZ2v93OfzXeFDrsyvZ8SHUWs8en1E5UNdPcEs+HBwEKqrZzWhmE98OGcdIAsEXfD6jQKCpCDN9Zq8Sug/+Gbz8TeTvD5qqC7X1L+7urq0KJEDAwPYbLaQl4xBmpob8PrcDAz0oSjyNDlHh7qfWN0fe/78eVpbW3n//ffp7OzUvH+99tprtLe38+STT9LZ2cnhw4e1QDb6H/17dHd3Mzw8zK5du3C73Rw9epTq6mp27tzJ+fPn2bVrl+ZKMTIPRVEYHR3VoqMODw/z5ptvcuHChWlXPxQleCAxLy9PO8hnNpuxWq24Q1Yo9b3Hx8fZsWOH5mJRXd3Yt28fu3fvxm634/V6OXXqFL///e8ZHBycNOGJfPdo8ni9XlpaWqJGvg0EApw6dYonn3yS+vp6XnjhBWpqarh8+fJVtWk0CLpDjHHEEUecDz+MvD5qXC0fznpNwGAIOqhISEC2WJAtFgJmM+fr6mjt6+P9U6foHBpCtlho7unhtT17aB8Y4Mnnn6dzaIjDZ84gmc0oVmvYD6Ef2WKhe2SEYaeTXfv34waOVlZS3dLCzr17OV9Xx679+3EpCkpCwqQ8lIQERt1uDp0+zemLFxl2Onnz3Xe5UF+PkpCglYPVGjxcrvtREhIwpqSQV1KCMSUFJSEBc1oa5rQ0nLIc9t7jXi87du8mYDIFr1utSGYz+w4fZvfBg9gDAbyiyKmqKn7/wgsM2u1a+ZPePUIOEhO1sryiSEtvL51DQ+HPJyQQMJk4VVXFk88/T31HBy/s3ElNayuXm5ujv2NCwqy37s4nPpyzEwoEkAl+wCif+CLcR4NYyvPjUoCzQeQHqfoxe+bMGUZGRvB4PDzzzDP09vbS39/Pjh07sNls/O53v+N//a9/oqKigrNnz1JfX0+s1YlY7x1p7VDTNDQ00NrayujoKAMDAwB4PB68Xi9+v1/zEd3Y2BgMzBQKMqQGOILgQUU1sNP4+DhjY2O43W5GRkZwOp1aEKOhoSFtQqFuUVJlk2WZjo4OamtrsVgsPP/88zQ3N5Obm6tNftQyo33Y+3w+Tp8+TUVFhTZZUANK6T/ozWYzLpcLn88XFnnVZrNRU1PD8PCwFtRp+fLlmh90/SRMnVjJsqzVSeTkqL6+nqeeeorm5mZGRkbo6OjA7/dr+TidTtauXcvChQtZsmQJycnJ9PX1heUdrb/Mpq8Fh7wQXOWd8ZNxxHGNIs6Hn7R4Yfiw+LCuvj5qa07Jh6g73eJ8OO/4UG2/D8KHs3j248bcnVAARnWbzNytvz8rqANIHSBut5sXX3yRJ554ArfbTVFRERcuXODkyZNUVFRgt9tZt24dosHA5k2bOXz4MA0NDZqSmA0izw8YDAbuueceMjIyyM3NxePxoCgKN910E3fffTcJCQmsWLGCpKQknE6nFrSpsbGRsbExTfmo/5eWliJJErm5uUiSxK233oqiKCxatAiLxUJaWhojIyOaYhsYGNDqYnBwELfbzZe+9CX6+/uRJImFCxeSnZ2N2+3mtddeo6qqKixiq/q3LMsMDAzQ3d1NZWWltnSalJTE2NiYVu8XL17EbreTlpamKTO73c7g4CCf/exneeCBBzSF//nPfx6v10tvby+tra3aFqozZ87g9XqRJImJiQn+8Ic/0NraqilWNd/CwkKsVivj4+O8/vrrfPe73+Xdd9/F7/djMpm4/fbbsdlsDA8Pk56erkWWVQnq4sWLHD9+nBMnTtDc3Dy79lYJUgluL44P/TjiCCLOh3MLHwYfNjY0IEuzXLHXffDG+XD+8aGsm8BNi3nGh3PyDIU6I1MQEIXQmbNPWqg48Pv99PX2kpqUiAGB9s4O/AEvVVVVVFRUcNNNN1FZWcnGjRtpbm4mKSmJO++8k4GBPvIW5LNp4+bQrH32ZatWFqvVisPhwOl0Mjg4yJEjR7jlllvo6uqip6cHgJqaGi3ooc1mw2Qy4fMFA9ScOXOGNWvW4HA5kWWZcxfOY7UmgiIzPDJCUlIS9fX1WBOtHDp0iJtvvhmv10t2djYulwuAoaEhBgYGcDgcyLJMX18fZyrOIBhETp4+xT333MOe3XtIS0tjYmKC/fv309rayurVqzGZTLhcLrxeDykpyQwNDzMxMcbGjevx+yV8Ph8JCQkkJiYyOjqqWX6Ghobwer2YTCbOnj1Leno6VVVVuFwusrOzOXbsGAsWLNC2SdXX11NdXc3IyAibN2/m6NGjmjVFEAQuXbrEharzpKYms2hREYoCtXW1FC4sxOl00tLSTF9vH3fceQdFRUX8/ve/Z2hoiKKiIhRFoampia6uLgwGA4sXL9YOz6n7VT0ej+bTe1b7sEM/iqAgEuVQWxxx/JkhzodzEx8GH8r9A1dVttfjwR8IxPlwnvEhJtM1zYdzckIBoMgKARSMCBgEAXFOV+P8Q+Sy2Uw6+cTEBM8990cyjQKjbV3028fxJJtRUOjp6WHTpk0UFRXhcrno6uri0qVL3HnnnaxatQYBA3a7i+rqam65ZZsWQGkmkCSJCxcukJGRgcVi4Yc//CEul4uEhASWLFlCaWkp+/bt47333tMGsCRJJCQksHz5cvLz87FYLAiCwOLFi3nuuefwyxKB0JKkKIpYTCbuueceFpWW8PRTT+Nxu/H7/ezcuZO77rorzONSamoqL774IjabDVmWMRgMeAN+el59NfjMG28Q8Pk5cOAAOTk5bNq0Sfu4liSJo0ePsnbtdTQ01vKLX/yC3NxcMtIzyc4OnqMQhKCbxoKCAs1y43K5SEtLY/369ezcuRO/368d+mpoaGDx4sU4HA7eeecd7ZmEhAQWL17MpUuXgofbvF4qKipQFIXCwkI++9nPkJOThUJwmbitrYkVy8swGkVuvfU2Ll+uoaWlhbS0NG644QaGhoYYHBzULHJ+v19rkw0bNmA2mzEYDGzcuHHGfSoS6gREFEUECZQ5q6HiiOPjQ5wPP1p8UnxYd/Y8t2/bNqsPMVmWuVBVFefD+ciHAwNw7tys+uV84sM5F9gOQvvqAjITY3bkfZdIevB6TKkmRCHkgzuODwwlyrLpVEpUHfxerxfn+ARum4OALGHNzWRkYJDkpGQKCws1bwputxuz2YzZbMbv92M0GpGkoMUhOTl5Vt6A3G43v/jFL7jvvvuw2+08/vjjfOUrX2Hz5s2YzWaSk5Px+XyTrOKCIGCxWHC5XKSmpmIwGJAkCY/Hw7GTJxgbH2f79u0YDQZEQcBoNGIyhqw3ypVIrKrsJpMJo9Go7bdsb29n7969fPnLXyYpNSXUl4N1KKJgEK54lYCghwufz8dPfvITNm3awNj4CHv37uXb3/4269atR8CoRZSWZZmxsTEqKyvp7OykoaGB//E//gd5eXlae6jyKYoSFuFTD9WVq/qMHoKgIAgKCgr9/f386lf/wdatN5OSkkFnRy9ms4XPfe7BME8eqoUo0hOLXvFF60sz8ZKiKAoBl5+xt8+SWl6GqSwTTAKGl15F+Nznggfc4ohjLuBjCmwHM+DD9nZobYU77vhY5LkW8YnxYXc3KXV1CJ/5zMwElSQ8v/89Px8djfPhPORDQZ1QfPrTUzbzlHyI4eM/UzSfA9sB2sEzQRCRFSUY0wPm9nrPPIN6MGmmqwUGg4HExMRgBNb80AEsAfKzc7U06mAzm81hz6n3ZrMyoUIQBLKzs/nZz36GKIqsWLGCLVu2kJOTo91XFan+3VTlZdV9iKoBj/IWLMBkNpOSkoLBYMCAoPkEN0VEmI1WD6Iokp2dzapVq0hNTcVitWp+pyF4OCnWlGnLli28/fZbOJw2tm/fzqpVqzCbzYiCSVNEBoOBjIwMVq1ahcViYfv27SxYsEBTsB8OZCBoXcnMzGTr1q2cPn0Gt9vH4tKlPPjg5ye1lz7WhKqQY00Or05OATE05rWDaHHE8WeOKfnQaITz52F4+BOT71qAEtrPbzKZZpTeACSGfkInbZEEyFf1XmWl9oFljngOgh9fFqcTFiyYlZyCKJKdlRXnwzgfzjnM2QkFBKtOAsS5fhJlnkJVBDOBOhiCg0YJ9Ws52DbC5Eim+tn/Bx3wZrOZr3zlK5SVlVFVVcVXvvKVSTEaYskbC2uvuy4YTE40oK17xXhGryz017Kzs7n77rvVC4CAqkJjlW40GrnrrrtIT0+jq7uTT3/60yRarQjCZMu+0WikpKSERYsWafcirWexluojrW16hN8TAYWEhETuu+8zfOrOu5EVMJusGAxX1IO+DmbanrNNH0wbZXVjxk/HEce1i5h8WFAAf//3umhpcVwNREWZkgf0CE8hB+cTsoxBAQzG2emsiCjc08FkMvGVhx+mbOnSOB/ORz6cUWq1nNmv9n+SmLsTClHAgIBPDJ1wlxUEw9ytyPmK2XbOYHIBRQFBMGij46Ps9KpV5eabb+b666/HYrFEnQjJshxzy00kTKIBREPM+5IkxdzCE22ypC9NlmUEXaRuvdJTl4w3b97Cpk2btQBEkflFPhdZH9H+jpVm6nuh5WcBEIxYrWaiqbxI5am3yqjWL306Ne1s+0FwfioiCsKMiD2OOP4sMBUfiiLorNFxXAWifezNSP8o2q/ZP3t1EAQBa0JCnA8j0kz1nnOGD2NKER3zjQ/n7oRCCC2RKcHlXmXu1+WUmOqoylSzZvXAj7ptKFbes7MCC1EVQjQZJ+er/h+edmbPXj3UZVyTyRR10KrvNBOrQax0+npwOp04HA7y8/Njvtfkdw73Xx0pg/6eqjSjKc9Y5anPzqSuIxVZrP4XrDdNfEAI83OvXyqPJoPb7Q4e4gstBbvdblpbW8nMzCQ3N1drr6neL1Ie9RChac77tIgjjo8JsfjQ64WWFpCvLljoJ4Wp1lKijXh9+tny3XQQIspUIn5Hpp0JZvWswQBlZTDDrVYQ3PYU58N5yIdqtjPsw/OND+fshELgypaxubzEMxuoB4uqq6spLCyktLR0yi1HkiRx9uxZJEliy5YtYffUQ0eDg4NUVFSwbNkyysrKgCv7+iIVTbQtTmo6fRAWg8Ew7d5EvRKJnJBcjWV6KsSyAvh8PsxmMx0dHeTm5mK1WhFFEafTidVqnXJL11TyKYrCiRMn8Hq93HfffVp5DoeD7OzsqBYRNY16LsXj8Wj7USHY9sPDw6SmpjI0NKR9bMeSZSr5/H4/vb29ZGVlMT4+TmFh4ZT1p28fSZIYGhoiKysLU8iFXSzlKkkS9fX1lJaWkpCQgCRJjIyM4Pf78fl8ZGZmcuTIEbxeL5///Oc13+ednZ34/X4mJiZYvnx5mGVrKvl0V0BRkGVlbgfKiSOOjwkx+bC7G06ehOuu+4Qku3ooKh/W1LBw4UJKS0oQpzCcSZLE+XPnUICNGzaE3VP5cGhoiIrKSpYuXcqSJUuC98TgHnRFUVBUa3nISCdEWH6DRyEUpEAABAFZkjAYjRhEcUoLsRDxPMqVz9Bp+fDcOUhPhygf69OVFefDecaH6elXttZNI5/uyrzhwzk5oQg2qnBFiV5De0MvXbrE6dOnefDBB7VrsTqwoiiMjIyQm5uL0+mku7sbs9mMIAiasmxubqa9vZ1Vq1bR39/PhQsXuOOOO2htbcXn8zExMUFOTg5+v58VK1YED1RHQJZl6urqABgbG2PlypVkZ2dPKVuknHa7na6uLgoKCsjIyJj22dlMOPQKQPWa4XK52LNnD9u3b+d3v/sd27dv57bbbsPv9/Pyyy+zdetWli1bFlWBzuSdTCYT2dnZ+Hw+duzYwcqVK2lvb+eLX/xizBWlwcFBjh8/jiiKtLe38+Uvf5kFoQN3kiTx0ksvsWXLFi5dusQXv/hF0tPTp5En8nqwXL/fzzPPPMP9999PXV0dX/nKV2IQhUJACgT3xAqgKAJer5c33niDhx9+mKysLBTlSoRTfRkAIyMjVFVVsXjx4qCP8nPnqK+vZ3R0lLy8PNavX8/112/BZpvA5/Pyx+f+SPnacvr6+li7di0nT54kMTGRoqKiaetbm5gqCgbRgGAQCQUGjSOOP2tMyYeKAkuXwvXXf0LSXSVCH/cXjxzhtN/Pg+vWwfLlIIoxx7wSCDA0NkZubi6OpUsn8aGiKDSePElrdjYrbryR/oSEmfGhXp8rCrIkUVdbC4T4sKxM+3CeiT5SQkHWwvhwqmdHRxFm8Y2jraDE+RCYX3yYVFpK8bS1PX/5cM5OeASA0J4zWZ7r1Thz3Hjjjdx0001YLJawVQSn00l9fb0Wkl5dWXA6nRw8eJC+vj5efPFF2traqKmp0dyVbdmyhVWrVmnWe3UgjY6OUl1dzcTEBO3t7bS2tuJ0OmMO1kAgwNDQEG1tbZpvZTWKpfrb4/Hg8/qQJBlF1yayLHPhwgVqa2vD0vt8vrBQ9mpEzKuBogRduT355JM4HA5EUaS7uxuDwcCyZcu06JRerxez2azVn/49VFn0earyqhEzA4EAPp8Po9HIM888w+XLl5mYmEAQBHp6eiblob6/1+vl6aefZnR0lPXr15OcnKy55VMD/ixbtozExERcLpf2DhBUrjabjYGBAS1/SZYYGxvG63MTkLwEAl7N2mMwGLSop729vfh8PhRFYWxsDIfDock1MTGGy2nn7NkzVF08TyDgx2KxkJCQgNPpRJL9BCQvkuxjdGyI9/a/S0tLi1YvZ8+epbi4WJswHj9+HEmSyMzMDFmWBknPSOGdd/fS3d3BHXfcRl5eHmazGafTSV5eHudm4XNbIyZBCI75a2RlMo44PijifHgN8+FsDaahyVicD+N8ONcwZycUENrbqN/TNk+ht7BXV1eze/duent7kWWZ5uZmbDYbNpuNZ599VlNe6lLc+vXr8fl8nD59ms2bN5Ofn8/g4CAQHHgVFRW88847DA0NcfnyZfbu3cvZs2cpLCwkOTmZNWvWkJqaSlJSEuPj45oc+q1KgiBQXFxMamoqqampDAwMaOkGBgZoa2vD7Xbz+9//nv/4z18wPDqApPi1NH19fZSVlbF582bcbjeKojA8PMzPfvYzWltbNQXT1NREY2MjgUAg5t7HyHpTf7xeL4IgsGXLFnp6epiYmMBisWgDeufO1/jud/+FF154nsLChfh8PoaGhnA6ndhsNurr6+ns7AzL3+fzcfDgQVwuFzt37sThcFBdXc0zzzzD2rVr+frXv87Bgwcxm80sWrRIIwU98enrcOHChfT29pKUlERGRgYej4eenh4ee+wx6uvrycvL03yAq1FGIaiA9+7dy09+8hMmJiYIBAKMDA/zwx/9kDNnTtPa2kpHZydDQ4McOHCACxcuUFZWRmJiolY3AJWVlYyPj2tkcfjwYc6eO8u+d/bxk5/8hEOHDyFJEmlpabhcLjraOxgcHKS+vp7HH3+c13fu5LXXXtPy6+vrIzs7m/r6ek6ePMmCBQvYsGEDiqKQnZ3NyMgIkiSTnZ1Ne0c7fr+fc+fOkZ6ezujoKFlZWQwODs7IAqaHJElh+1bjiCOOOB9e03zI9CsFSmgblaIoeH2+OB/G+XDOYU5uedIQmlHP9x1PsizT1taG2Wzm+eefx26309bWxpo1azh16hTLly+nrKyMkpISenp6yMvL0wbVfffdx3/7b/+Nn//855SWlpKamorP50MOLau+9tprOBwO2traGBkZobq6mnXr1pGfn8/Y2BiSJNHf309eXh52u12bbavxJwKBAHa7nfHxcTo7O0lNTcXpdALg8Xh4+eWXuXjxIt/4xjcoKipix6uv8Oyvxvibv/sHsvILkEUjPT29NDc3kZubiyAIFBQU0NLSQlNTEzU1NSxevJjx8XEOHjyIwWAgMzOTnJycGbmsVeWtq6vDbDZTXl7Ovn37sFqtmM1mTp48ycmTxwlIXjZuWkd19WVS05Kpr2/AbrezatUqGhsb6e/v5+6779ZczqntUlVVRSAQ0H5fvnwZh8PBvffey8qVKzl9+jSCIJCYmIjVatUsZiMjIzgcDgoKCujo6KCjo4OsrCyampp4+umnEQSBvr4+urq66Orq4vXXX6egoIC8vDxSU1Px+/1hcnR2dtLf38/OnTtJTk7m0qVLtLe388orr2CxWEhNTcPrCdDZ2UlhYSHFxcXcddddJCcna4q8o6ODs2fPsmDBAiRJoqurHVmRyMnJwWw286tf/QqUYGTT5557Drt9ggSrmdbWVoqKiliwIJ/R0VEtP9Va1d3dzYEDB1i4cCGdnZ14PB42bNiA2+3mhReep6OjA5vNRmZmFnm5BVr5giDg9/tnfPgs+Fu8YuWY7wM/jjg+TMT58Jrkw+zERLJkeUbWXfXDva62FrPFEufDecaHapDA6TBf+XDuTii0wz3hl9WlzPkEn8/H888/z1133cUjjzyCoijs3LmTyspK7HY7aWlp7Nixg6GhIdatW6c9py4HTkxM0NnZyejoKMuXLyclJQVBCIaj/9u//VskSeK5556jt7eX3Nxc9u/fz9GjR7Hb7Rw8eJDy8nKKi4uxWq2alWV4eJilS5fy+uuv8/777+N0Orn++uvZsmWLdsCtp6eH3t5eJiYm+OUvfxkcEA47PWeGqSs/zdb7HyRgUkhNTeHAgQP4fD4effRRFEUhNTUVr9eLL2RJMRqN2kGm2QSjURVoa2srt99+O6IosnDhQkZGRigvL+fZZ5/F5XJgNCmcOnWcQCDAO+/sxSAmUFRUxO7du7WyzWZzmAXF7/djt9vZsWMHiqJw6tQpVq9erS2Ft7W1cfDgQW644QYAiouLNSvQmTNnKC8vp62tje9///sYDAZycnIQBIGxsTGMRiNOp5Ply5ezNLTft6uri6VLl2IymcjMzNQijAKsX7+es2fP0tbWhtVqpbS0hBUrl9LX10dOTg52ux1ZEli0aBEOh4NAIIDVamXNmjUkJiYiCAK33nor7733Hn19ffj9flJSU0GR8fn8lJevQxSNWK3W0HmbPkRRwOG0U1y8CJPRSCAgk5+/UGufjIwMnE4nmzZtwuFwYLPZEEWRBQsWkJ6eTnl5OS2tTRgMauRUMyuWryQnJ5fk5GSam5tJmKWPdQQwzNaPexxxXOuIxYfMdb8vkxHnw3A+FEVxdnyoKEE+vOOOOB/OMz60hgL+zRjzjA/n7oQCQDBgEowIiEx9Ln5uw2Kx8A//8A+kpqaSkJCA3+8nNzeXlpYWOjs7WbVqFQsXLkSWZfLy8hgfH6e9vZ3Lly9z++23MzAwQElJCUuWLCE5OZlbb70VURQxGAysWrUKgH/8x39kfHyc7OxsRkdHw84qpKamkpycrCmw9vZ2SktLGR0d5cCBA2RnZ/Ptb3+b9PR0zVuC6p1g69at/N3f/R2iKNLb20tDXT13bd1KYnoqHd09jLscvPynF/nc5z7HunXr8Pl8VFVV4fV6ycnJ0TxPpaam8pnPfAaAjIyMGStQ1WNVW1sbDQ0NLFq0iMuXL/OFL3yB8vLy0H5ZedIYFQQDFosFn88X5rVKkiRqampwOp0cPnwYg8HADTfcgNfr5dFHHyU9PR1FCbqxGxsb4zvf+Q7FxcUkJSVx1113YTabcTgcnDhxArPZjCRJJCYmcvfdd3PvvfciimLYyou+XDVfSZIwGo2aRai2tpbGxka2bdvG1772tWCU0BDJ6LcHXLFaXHnZ/Px8LTp5WVkZpaWlnDt3joMHD/L3/+WbJCZaQwc5w72NfOUrfxFz6VVNt3btWlpaWti0aRNf/vKXJ+23VRSFVatWh/6XAUEj30AgwIEDB7T+ORMoioIsgqzIiAEFcR6P+Tji+NAR58Nrkg/TKytnxYeB0ApPwxNPxPmQecaHK1fObvI4z/hwzk4oQt0Gv8+HMs98bEdCEISwLT5Go5GioiIOHz6sWWQWL16MIAgMDg6ye/duqqurufvuu8nKyiIjI4MVK1Zog1P1mqPvmIWFhZq7NNWTgh76PaLNzc10dnZisVhITk7mpptuYtWqVUiSxJEjR9gQcsmXnZ2N1+vVZHM6nXj9PnIWFWM0GGi7dIn333+fDRs28KlPfQqr1YrP52N0dJQTJ06wfft2rrvuOgRB0LxEzMalrKpATCYTX/ziF3nvvfcYGBjgjjvuYPXq1RgMhpDC0z0T8YfFYgkrT5IkSkpKOHv2LGVlZWzZsoVTp05x2223kZ6ejtFo1A6vZWZmcuONN2qKT7VoJSQkcP311/PGG29gs9nYuHEjt956a9D6QLgvbbXO9VYove/wdevWae2/fv16zboyU+jLUv9euXIlaWlpJCUlRZVFX7+RbvLU/9V9xE1NTXg8nklyTZYx3H+42+0mLS2NjRs3zuh99P3TYDQG/XQDMlc8W8x9dRpHHB8N4nx4DfPhFK5yI+tNIcgfX3z44TgfRsFc58MN5eVQUzPte0zJh7pVpbkGQZntCZEPA34/vPQSfPWrMZd/ZFnBPerE/tZ50j69EWOOFaP44cY3+LgQrYplWcZms2lLj2pHVz0XeL1eEhMTtRkufDgdSN0X+fbbb2MymVi7di233XabdqDJ5XKRkJCAwWBgdHSUlpYWNm/erFkSgntNzSiALMlaNE69Rw31EJ0gCDG3N12N21jVQ4U68K9MTmLlFT2Yjf4AmV5hAFMGENTLEwgEqK6u5uLFi9x///1kZGSEPavmF01pRV7XxwqJZnWZCfR1pD6vn7xNZX2JpkDVZ/T9QZ/PVBNDdb+p1+vFarVO+0562QN+GeeeKiylC7CsXoBsEjC8+DLi5z+PEMXlcRxxfCJ46in4xjc+tuJi8mFLC/T0wG23fWyyfFDE+TBC7nfeQSgvh4KC6QWWJJTnnoOvfQ1ZUeJ8OIVMal7q83OCDycmEC5cgPvuu2o+FCRFm+h9bDh8GLZtmzbZnF2hUAP4iIbwgDJTnaGI7AiR16IWM0WjRMvvg0AvkyAI2nKiPm9RFwHzagfTVBBFkeuuu47Vq1eHRadUB0RycrKWNiMjI2wPqyobBN0XqsvM0cpQtyp9mIidbyyCiV5vqmIPy2EGZ3P0ylatR9XiNJMD5mo50fKNLv/Mno+W30wU51TyqO+ZlJQUVYbpZDUYDGGTienk1StmRVG0plNQEAVhXrjMiyOOjwyx+JApPh/jfDgt5iUfhvShGOKdOB9OjTnFhzbbjOSdig+NM1zR+iQwZycUAiAICoqgIIsKQgzVOZMOFTlbnZiYwOfzkZeXN6PJyQdFZBnRlsr012bSQfVp1SVI1dKg+o1WI2PqB4teuc1kEOijbsda4otFNB+W4o8c7JH5zqa+1PTqM/rn9JYRfdmR76QoSpiFIlr96P/XW0ki84z2rF6WaO+lKAputxuv10tqauokWfSyxnrfyFUfuEJ8kWXp84r8W58mUn7VR/isCEbNL5SlOpmITyfi+HNGnA/jfKjlo5YTI984H85hPpTlaSNlh+XFZD6cy2w4p+NQACDGVj4q9J048poKt9tNdXU1Pp+PI0eOUBPaxzadolQ7V6zy9OVEux4rrdqZIzt5pDx6X8/6dIFAAEmScDqd2n31UNNvfvMbzp07py1jRst3plYAWZY112mx4PV6qa6uxuv1Tlpi1NdZtCA40TCVzPo8Vf/S09Wh/nogEACC0TVV7xWqQlJlV4MWxcon8n99wCJ1aTMyrepPO1pfcTgcNDc3R22vaPWlKEH/7Tt37ozaX9Ty9fB4PEGXdRGyd3d3c/nyZbq7u+nu7tYUql6x6tsuWp+NVjeCIFBbW0tvb++07aOH1rpaurmsPuOI42NGJB9GSRLnwzgfTvVcnA8/WT6M1g6xEJUPY0ys5gLm7oRCUbRAPqELEbfDG6+uro6GhgbNm4M6WMbGxmhtbWVsbIy33noLm83GggULyM/Pj9qZ9ZAkiRMnTuDxeKKIp2idbKqOFNkZI8vTPxeppBRFoauri1deeQWHw8H+/fsZHh5GlmVOnDjByZMnqayspLe3V8tHFEVWrFhBcXExdrudPXv2MDExEbVMFVN1To/Hw+7du8OUuCqb+nt4eJidO3dqgYKitc/Y2Bgvvvgie/fujVqfkYiss8h7LS0tvPDCCxw6dGhGikeSJLq7u9m5cycul4s333yTn/70p9hsNiRJYteuXQwODiJJEn19fezfvz9MgejzlSQJu92uRfw8efIkR48exe/3c/nyZU6ePBkmk9/vZ8eOHWHtoM93aGiId999VyNLm80WlTT1/xcUFJCZmRnmvUSWZY4fP87TTz+tkZm6f3f37t1aGeqP6lN7165dtLS0UFtbiyzLTExM4HQ6wwIVKYrCxMQEQ0NDGgmp171eL5cvX8blcmnpJUnSDjvORHHq8zMZjQjz9LxUHHF8JIjz4bXLh253zPIi6y3WvTgfzg8+7LrG+XDuTigI7RkVxKBXi1DdRzaCqnh6enpob29DENRrEq2trfz4xz/m7Nmz5OTk8Fd/9VcIgoDH49GiREabfaplyLJMe3u7Fu1Sf8/n89Ha2kpLS4sWWGdiYiJsBqrmcfLkSUZGRrQOqVe8Kux2Ow0NDciyWraEJAUQBEEro6GhQQvGY7fb6ezsZGRkhJ6eHm3WGnQbF7QwOJ1OGhsb8ft92tKvxxOuvKIpcT0JiaJIT0+PZqFQFYg+2IvP52PBggXU1dWFWV3U9Or71tXVcfDgQfx+/7QDKpa1Sn1mfHyc5ORkSktLNf/Vqo9wfVvq63p8fJw9e/bQ3NzM8ePHtQijTqeT+vp6mpqaAOjq6uLEiRPYbLZJZasiHz58mKeffhqHw8H4+Dj79u1jZGSE559/HrfbTSAQ0JSg3W6nu7ubxsbGsH6h1mNSUpLWZr///e/5t3/7Nw4cOBDbgiVAXV0tb775Jl1dXdplURRZtWoVW7Zs0dz8qX69b7jhBlauXBmSx8a+d/Zx/vx5Fi9ezMqVK8nMzKS/vx9Zljl85DCHDr2P3+9jeHgopAyb+MEPfsBPfvITzp49G9ZHHA4Hb775Jv39/bhcLjweD93d3aSkpDA6OjolsUVeUwgelFTHgXotjjj+3BGVDyPSxPlwHvLhDCJlh/Gh+hPnw1DlzC8+VNPpf0f2Qe1vovDhDCYinxTm7IRCEUBWFCSvDzkQQIkyOVOUoNVldHSUwsJCZNmPogRQFD8KEpcuXSQ3N5f7778fURTp7Oxk586dZGVlaVYMdclXtXSogy4QCCCKIgUFBdoHsAp15vv444/z6quv0t3dTSAQoKWlhT/84Q9aGHk1/+7ubsbHxxkbGyMQCNDU1MTly5e1TijLMkNDQzz//PM4HE4kKUBzSyNvvLmD5JQEFhbmUVt3id6+Tvr6epFlmRUrVpCVlUVKSgojIyOaMjtw4ADvvvsuHR0d5OTksHnzRgKSD7/fzfMvPMuPf/IYLS0t2uBUn/P7/Zocw8PD2iAYHR0lISEBt9uNIAQjPdbVVCN7A/gAl9vLc398DrfbzdDQEA6HQ2uXwcFBuru76enpwe/3853vfIfVq1fjcrmmXDKG6EvE+jZYu3YtW7ZsoaqqCkVRaG5u5nvf+x4//elPGR8f197L4/Gwf/9+zp49S2lpKevWrUOSJM3139jYGBcvXqS4uJj333+fwcFB+vv7WbhwIQcPHqSpqSlk/TrF+fMX8Ho8uN1eLl28xNGjR9m/fz833XQTJpMJm81Geno6DoeD3t5enn76acbHxzl58qQW0VS1WrlcLl588UXee++90KFDI52drbS1N/P1r/8FC/JzGBsb095DkgLIsh9Z8SNLPvr6e3A6nXR0dGj9VhAEMjMzaWxs1MhifHwcURTJzMykouIUXq+TxqY6fvObX/H883/EYrEgyzJWqxWn046s+CgpKaK+vobxiRHe3v0WPp+LmppLZGSk8rWvfQ2fz6d9EExMTDA2Nsa3v/1tfD4f7733HiMjI7zxxhukpqZq1kz9Qceo4z3UtgLBPbmqa0wlPp2II444H8b58IruDOnHOB/G+XCuYc5OKCA4OzOIYtAqo0yeoQlC0L/vm2++SUKCBafLic/vQ5IlZFli8+YtKIpCTU0Nfr9fi6qYkJCALXTavre3l5dffpkjR45oeVdWVvL4449jt9tJSkrCbrfjdrtxu924XC5tBl9aWsrdd99NTU0Ndrud/Px8DAaDpkTcbjdVVVU88MADGI1GbQb9yiuv8PTTT2sz/kAgQHJyMvn5BQwODgAKZ86c4p1399DV1YXRaERRgtE2H3/8cU6dOqVZSlJTU3GHlkzHx8fZuXMnExMTVFVVMTo6SnNzM01NjYyOjVJaWoLL6eTHP/4x9fX1mrVicHCQhoYGFCW4VP7uu++ya9cuXnnlFc6dOxc2ECSflwv73mG0tYXerna6Otvo7+/D6/UyNDTEiRMnCAQC+P1+zp07x7Fjxzh16hT19fVYrVbNQjWVAtXvp1WVvNPpZHx8HJ/Px+DgIFVVVVy8eJFTp05x6tQp+vr6uPHGG6mtreXXv/61ZhkfHh5m//79/PznP6enp4etW7eSmprKt771Lb7whS9w4sQJTp06xU033URCQoJGptu3b6eyspKJiQlsNhs2+wg9vW3s2fcmr+38E1UXz2MwGDh16hQWi4V169ZhMBiw2+20t7drym1sbIympia2bNlCUlIS7e3tWp1XVlbyhz/8gf7+fnw+H7v37Ka6+jJP/O4Jnn32WUZHRzQyP3fuHG6Pm4GBft548w36+/soKiqatLdVrbff/OY3VFRU4HQ6geCBws7OTv7zl//JgQMHSEy00tPTw9NPP011dXXQ+mkQ2b17N3v37qGmtoYnn3ySY8eO8ov/+AWHjxzmyNGj/PznP2ffvn0a4TY3N+Pz+UhKSiIpKYnDhw/z29/+lt7eXpKSkmbkdjC88UE0GBBEAYWZLQvHEcefA2LyYeh3nA/nJx+erayc0eqEIAja6oTT5Yrz4Tzkw1l7+5pnfDhnvTzBFQ8vcjCsoXZdv+RmMplITk4mKSmJy5cvc+HChVAaQBEZH5/A7/fT3t7OmTNnwpbVAoEALpcLl8ulzX79fj979uzB4/FQW1tLSkoKP/nJT8K8QUiSxOjoKC6Xi4sXL3LPPfdw4cIFdu7cqS33KkowsqbVasVoNJKens7Fixc5cuSIJrvL5SIjI4Oenh5+8YtfMDExwe2334EkB/f09fb08rOf/Tt+f4CUlBRkWcZut/Ob3/yGtLQ0CgsLWbdunebKLCMjg3/5l3/B5/Px1FNP8S//8i+43U4Ovh90bycIAk6nG5fTw549e1i6dClGo5Hq6moWLVqELMsUFhbyxz/+EZfLhdlsZunSpWRmZmpRMFHAPz7B7//9Z7QpTmRFxO0N7sdcv349GzduxGg0YjQauffee4Hwg3w333wzExMTs+oHsizT2NhIRkYGAwMD/OhHP8Lv92OxWAgEAjzzzDPaQEtJSaGrq4uJiQnS09O5dOkSExMTpKWlMT4+TnFxsebxo7y8XPNtXlJSwpe+9CXGxsYwm80sWrSIhx56iPLyckwmI5Lspbe3i1/+6ld4PB7Wb1iPIgv4/X4MBgNr1qwhLS2N22+/nbGxMUpLS/nOd76D2WzmvvvuY8GCBTz66KMkJCQA0NDQgMVi4brrrsNsNnPLLbewd+/b3HXXXaxYvoIVK1dSVFiiLfGrVqann36aY8eOccMNN5CVnUV+fj6AdsBNVeBer5e8vDxMJhM+n4+BgQFGR0dZkJ+HxWLhL/7iL3A63NjtTgoKCkhKSmLt2nJcLjvZ2dlkZGQwNjbGxo0bsVgsFBQUsP3ue1i0aDGFhYWkpqYC0N3dTX5+Pna7HbvdTnp6OhaLhU2bNpGenj7jSNnq5FFd1JekYKTR+bBvNI44Pg5MyYchzonz4fzjQ0dfH8zmQ1FRaGxoICMzM86H84wPV69ejehwTNvE85UP5/SEQt0nGM0tl9frBaCpqYn29naSkpN5+OGHqautQ1ZkBEHEIJoBKCgoID09HZPJREpKCpmZmWzcuJHBwUFycnJYunQp2dnZQHApasmSJSiKQllZGcnJyfzlX/6l5gFBdYNpNpvJzs4mLy+P5ORkhoeHMZlMmEwm0tLSgODew/b2dkwmE4ODgyxevJjbb7+dgoICbDYbmZmZQDCS5ze+8Q3NzWZ3VzclJaWsXLmKz3/+i6Snp2t1IAomFCXY4RITE0lMTKSgoABBEDAajSxevBhZlvnud7/L2NgYkuwPOgYRggfKTp06w5bNN5CXl4coisiyTF9fHy0tLaxdu5aamhq2b9/OrbfeitFoxGw24/V68fv91NfXc+b0aQJpafzD3/0DzkQDRtmAyWjAkmDBarVGDdyjt1KvWLECSZJm7EYUggeaOjo6WLZsGTU1Nfh8Pv76r/+azZs3a2n0KxqCIJCQkICiKNhsNv7xH/+R4uJiEhIStHIFIRitNTs7W9sbu2jRIoqLi7W8NmzYEHofEVEwU5Bfyvf+7bHQOxm1fZ8Wi4Vly5YhCAJ33303sixjMBi04EjLly8HoKioSFP0ZrOZb33rWxQUFATLEAVWr1kZtIyIYtA9nBCMINrV1cWBAwcIBIIHy5YvX8G3//HbJCena+/c39/PqVOnqKioICUlhcLCQu3D4ciRI5w+fZqbt97I5z73gPZOwX0TVywmubnZCKLCtm3b8Pv8/OGZP7Bt2zZWrFgZGo0iAoawgE0ZGRn84Q9/QBRFTCYT9913HxUVFSxbtkyzUE7nQUV9BxWKrC73EvY7jjj+nBGLD4nz4bzmQ3nDBoRZ8KE/EAjy4fLlcT6cb3xoMgUD2/EB+HAOTyrm7IRCABSDiGIQMUmTK3F8fJza2lrOnj3LzTffTIIlgVUrr2PVyutCaSdX+ooVKxCEYBj55ORkLly4QF1dHZmZmdx2222aErr77rvJysrCZDIBQSsCxJ7cAOTn52uzY/X6ypUraWxs5O233yYhIYG77rqLlStXIooiWVlZ2kC1Wq1cd911APT09HL69Ck6O9v54hceZcuWGxBFg84gFe43Wbsaykvdm5eVlUVWVhbqgrgky4yOjCJwlvz8fHJycrSOe9NNN3HgwAHOnDlDWloat912W5hPckVRGB0dpampCZPZzPavfJmUBQtIMxgQEAgazqZ276vKO93ewWhQlxLb2tpwOp2Ul5dTXl5OampqzImJavXKz8/H7XaTkpKiyaXfMqdXqJF1qo9GKYoGRNGA0WiKKWfkO0arB/V9MjMzGR0dpaioCKMxOAwNYoJGjnqUl5fT3d3Nq6++Tl5eLn/z139HSkq6JouiBL1c3HDDDRQWFrJs2TLa29tZuHAhmZmZFBYWsmbNGpYtX4bRaMag1ZkQ8d5GLT/RYuKhzz8c9OstXnlnQbiiPEVR5Oabb6asrEyzxmRlZbFu3TrS09M1Mp2tAjSZTSiCEPyJTyfiiGNKPlSI8+F85kPRYIBZTCgUWaa5pYW2J5+M8+E85MPZYj7xoaB8Epuy/H546SX46lfDlm71UBQZj9OH863zJN+yCkNhCkZd4wWz8eP3+7FarWGddiZQZ9OqtVxVlpEDTEW069NVnf5QmzbTnkZxXHkmqKwNhsmDMVa5kUpAL7/ee4cqh5pef4hJ/6NCVUbqewBT1vd0B42mShMNqtWotbWVlJQUlixZEtbm0ZS1+qN6dliwYEFUWa5mgEerZ7VuZ3pmQHXPp7e2xYJ6SPDSpUtkZWVRUlIStf7VcRHZV6PtKZ0O+ryAsP6h3o+8p08fienKVPuXFFBw7a2ChVkkrStEMQmYX3oVPvc5sFqjPjebcuKI40PBU0/BN77xsRUXkw9bWpC7uuDWW+N8GIF5w4fvvINQXg4FBVPWBQCShPzcc/TddRetbW1xPpxvfDgwAOfOIXz601OWNyUfCrHXAT4yPjx8GLZtmzbZnF2hUJSgV4uAJIEsBy+E6kZVQmazGYvFEvbcTD5c1Dz0ijNWHrH+nq4sddkQgkucM2no8GdCryzEliOaIp1KqRkMhkkDXL0edSldl0ZvvZjJB2SsfK4GgiCQn5/PggULosow1XMZGRlh11TFGinTVJOdaOXEuqa2oZ50Iq0+kiSRkJAQZvGJlFFfhmrZ2LRpk7bNIJr8enK+mn6rdykYjQj0z8UqayblTAdFVpAliasxdUxlNf0gMsURxyeJaflQFON8+OfCh0B+QQEL8vPjfBjnw6mf/Zj5cE57edK0xxXdGTYrjGZB0M/G9T9EeX764mNbFmLlH1lOpKyR+U6dR7gc0cpUB+kHRTRLTGT9Tif/bH6uRr6ZDNpoRKVaTNR7fr+f4eFhWltbw6J16n1h69PPRGZVPlVJKkpwr+fFixe1vBRFwePxsG/fPtrb26PKHqsslfAjFZuafqb9OpbCVvvR2bNnqamp0a6p9aF/fqqyIsfirCEIIAgI4swV3UzGwFXJEkcccwnR+JDJ3Bjnw3nGh7MXLs6HcT6MiU+SD+fsCoVAcLYjCqGlrGlmlWpj6we56ls60uqiVqZ6X501x5ohR4NalvozlYVgJlYidanZ4/FgNpunlcXtdmM2m+nv7ycxMXHapcJYnTpSOUfKNSlPIbgLVQB1O6rmP9xut2O1WnG73WH7OWVZZnR0lJSUFJxOJxkZGVdlydEri2hQ21J1UZeXl4fH4yE1NZXR0VEWLlyIwWCgp6eHJ598krKyMr70pS8xMjLC6OgoqampNDc3s23bNsxmM36/H5/PR2JiYpi7t6nqUq90GxsbtbYdGhrSDrwlJiaSlJQ0iYhUjyhtbW0UFxdreSUnJ4dZc9Q6dTqdJCcnYzKZwmRR68nn82nePVSvJtPV3+DgIIsWLcLtdtPT00MgECAzM5Pc3NyYz0ylwKcqLxoEFARBBmQEDFfldTuynSL/jiOO+YY4H17DfKjMbme8mjbmIX2dvHE+nGN8qGu/mWC+8eHcXqGA4J5JIFZN6gf++Pg4VVVVWij0gYEBLl++HJZO39G6u7s5duwY1dXVWkTPqSwt+plrZWUlDQ0Nk6I3ztY6AcHBUFFRwZkzZzh8+DCDg4NTyuL3+3nzzTex2Wy8/vrrvPXWWzMuT291iKy/aGVNkkEJtoWaRB3UFy5c4JlnnuH06dP88pe/ZHR0VLNE+P1+nnvuOU6ePMmxY8c0JTBTRKuHWDJKksSuXbt4/vnn6e/v54033uDo0aPs27cPu90OQG5uLl//+tfZsmULg4OD/PrXv+bixYv09fXxzjvvaP2nq6uLn/70pwwODkaNeKovP1IO1arjdDrxeDw888wz1NbWYjabGRsbo7OzM2xQS5LE8ePHqamp4dChQwwPD7N7925efvllrWxJkmhoaKC9vZ2hoSHNN7ze4qMve/fu3Tz55JNhaaLVmQpBEBgeHqa+vp6xsTGefvppTp48SVtbW8z2cLvdmo/zyLqJ1semb2sImmDV9DOzMs3Ea1h8UhHHfEecD+N8qKg/cT6cn3wY0V5TtvU848M5PaEQ1Om/ohuxxFZSqqswv9+PJEk4nU4tcEq0DuT1emlqaqK3t5fOzs6onSBaR5NlmZ6eHm1g6ZWR+ltfnqq09HlIkqS5n5PlYITFpqYm+vv7tfDsEFSW/f39uN3uMOU3PDyM2+2mpKSE7OxsfD4fPp8PSZIIBAKhcO3ypDoaGxujoqJCux8IBMKeGx0dxW63a9FQoyp9SUYI7esLSBLj4+Ps27eP5ORkcnJyyMnJwWAwIMsyfr8fQRAoKSkhMTGRzs5OfD7frD4y9XWq1qfb7daWZ9X2tdlsdHV1sWnTJiwWC2azmSVLlpCeno4kSYyMjCAIQc8lHR0dXLhwgQULFrB8+XIt0FNRURFDQ0M0NzeTlZXFHXfcQXJyMrIs43A4tL6lyq62oxSqh0AggNfrxel0kpuby5tvvsnRo0dJT08nOzsbQRAoLCwMC8DjcDhwuVwIgoDJZKKwsBCn08ntt9+uuQJUFIWBgQHefPNNuru7ycjI4DOf+QxWq1XrCyMjIwwODrJnzx6OHTvGqlWrePDBB7FarYyOjmpBfdRyVYuNWjd9fX2UlZVx9OhRzp07x5o1aygtLaW7u3vSeBgfH8fv99PW1qZFw1Xrwev1cvLkSd58800mJiZm1c6gKjqdtWqatLNZdo4jjvmKmHxIdE6M8+E84UOvd1Yfmeg+lNX6jPPhnxEfTvH8J82Hc3pCgUAw5LiiW+bTNeSVJVoJh8PB0qVL+eIXv8j+/ft5/fXXMZvN2h671tZWHA6HplRGR0dZsGABS5cuJScnh76+Pq1Y1dtFLKUrSRL33XcfycnJ/OIXv6C2tjYsfU9PT5jibm1t5dy5s2FLyk1NTfzkJz/h4MGDBAIBSktLSU1NZcGCBYyNjWnlnTt3jm9/+9scPXpUi8fQ1tZGWloasiyTm5tLXV0djz32GK+99hqBQICamhr27dunWRZUhSNJEhMTE7z++uvYbDZkWebcuXM88cQTGtn80z99hx//+Mc4nU5Nfr/fz+DgIH6/n6GBQbobW5C9fmRJ5tKlS3R3dyPLMosWLcJqtZKbm6sNyieeeIKenh7NH7iqXGY7mXC73ezdu5eGhgb8fj9vv/02g4OD1NXV4fV6qa2t5bHHHuN73/seZ86cobOzk9OnT1NcXIwsyxQUFGiWg9OnT2tRWp1Op+Zu0eVy0d3dzWOPPcYPf/hDLcBTb28vPT09/PCHP6S2tjasH3o8Hvbu3YvD4eDVV19ldHSUs2fP8vjjj1NUVMQ///M/U1tbSyAQ0JZ4MzMzw0irtraWU6dOkZOTo3mycrvdmEwmjh07pinlZ555hvr6egYHBxEEgUuXLtHT08O7775LU1MTJ06cYOfOnTz55JM8/fTTZGRkcPbsWc33dn19fVi9j42NsXfvXmw2G88//zy/+93vWLduHf/0T//EhQsXtAA+apArvdVr79699Pf3k5WVhdfr1coYHh7mj3/8I7t27eLdd9+lpqZmVnuaFa74NEcQZq18VcQnEHFcc4jGh+rvOB/OWz50uVyz1nNxPpy/fDirbx/mFx/O2TMUAELoO0RCCUYJDUFRFCRZor2thYKFBXi9Xnbt2kV5eTlvvfUWLqeL4eFhVqxYoQ3+o0ePsmnTJtasWYPf7+fAgQOsWbMGt9tNdna2NqgVRaGhoYHs7GzS0lLwB3xYE6y4XC7Gx21UVFRSVFREZ2cnJ08eJyU1mRMnj5KSYgVBQBSMXLhwgb6+Pu65Zzter5d9+/bR0NBAYqKV5OREAMbHh7EmmnnyySex2WyUlpYyOjrKmjVrNIuMLMvaQOvv76e9vZ36+nrsdjsZGRm0tLTQ2trEpUvnKV+3jorKkyxZspg333yLoaEhrr/+erKyshgeHuby5cusWbMGu91OYWEhExMTpKamcv78OS5dvkDl2RUosozb7aCzs5Wm5nqSk5KQFbBNODhy5Ag33ngjB995j4XWNL7+7W8im0T2HzzAimVL2Lr1BtweNxWVpxgeGuXkyRM0Nzdx6tRpRFFg8eJSfD4fFotJ2xs5U5dyiiLT1NTA66/vxGQy8qUvfYmammoGBoJ1snr1Ko4dO87GjRswmYx0dLRjt9upqKjQwt2npqbi9QYD4fT19ZGcnIzdbuedd95BkiQ6u9p49tmn6Opq4+atW7lYdYk9e96mt7ePy5cvYrPZ6ejopLq6mtWrV2sH20RRpK2tjR07dnDp0iX6+/vp7u5mfHycsbEx8vLyNAJTLQNpaWmaYjMajfT39zMwMEBRUREWi4WioiLeeustMjMzGRkZwe/3a4GGcnNzqayspLW1lc7OTmpra/H7/Vy8eBGfz4fD4cBisWCz2fjDH/5Ad3c3Fy9eRBAEMjMzw6yLZrOZmpoaTp06xdjYGCaTCZvNRiAQYGxsDJfLRWJiImazWXtO/UiwWCw0NjZy9OhROjs7+dWvfoUkSbz66qsMDg6ybNkyXC4XPp9vVmNeVK0rihL8mdXT8YlEHNcuYvEhcT6c33zoD/HhDPuBoig0Ncb5cL7yoWA2z3jMzzc+nNMTCtX+IiuTLZzDQ0N0dHawaFExggB9fb3s2vUWgUCA1NRUAoEAZouZnp4evvOd7xAIBFi5ciWKomgD6kc/+hFLlixhyZIlJCcnA8GQ7YcOHaK8vByjUaBgYTCM+q9//WsmJuwkJ6XidDrxer0YjdA/IFFbe4mjx97HZLQgCiZtufXUqZMYDAZyc3NxuVz85Kc/BgKIBjG0ZB1cbnzjjTewWq2sW7eOpKSksL2WnZ2d/P3f/z1vvfUWx44dQ5Zl7r33XlauXMlvf/tbRscGSLCaaGmpx+/388ILz1FcXIooirjdbiDo4/nIkSNaWapbPEVRKCwq4N33+njl1T9hNpspXVKMIiv86U/PBqN3KgJmczI52Tns2rWLFGsiCUmJKKKAaDKyctkyTpw8BCihg38yKEa6ujopKSnhpptupKurk/7+Pm655RaWLl1KSkrKrDq6JElUVlZSUJCPyWTm/fffx+PxkJhoZc2a1XR1dbF4cSk2mw2zOXjgcN26dQiCQH19PTfccAP5+fmkpKRgMBh46KGHePDBB2lqauLw4cMUFRVRWJyLIktsyd6IJHm5bu0anE4n6elpAGzevAWDwUhaWppmmRMEge7ubvr7+/H5fKSmppKXl8f27dsZGxsjJSWF3t5eGhoa2LRpk6ZAMzMzuf3222lubsbv94fy34zBYGDRokWsWLGC8fFxBgcHWblyJWazGZPJxKOPPgpATU0NjY2NLF68GJfLRVdXFwsWLGBkZIT8/HzKysq05fy1a9cyPj7O8PAwS5cuBYKWGIPBwIULFxAEgYcffpiSkhJN5vr6etLS0li0aBFpaWncfvvtGAwGbYm7v7+fqqoqHnnkEbZv346iXDlYNzIyoiloQRC0iKkzHvGKgoIS3N4RRxxx6BCdDxXifDiv+dDmmCUfBqisrIjz4Xzkw7Q0iOLRKuaIn2d8OCcD2ymhPaJ+pw/bG+dIvGkZ5tIMDGJwDi/LMnV1tbz+xg7uvfdebDYbVVVVbNu2TfNgIYoiJYuW4HZ7qKqqorq6mk996lNkZWXR0dHBm2++yf33309RURGZmZn4/X4yMjJQFIWamhokSWL//nd45NEvcf78eV599VW+8Y3/wvp1G3A6naHlWglBDJ3oDznw6+7u48KFC6xdu5aSkhLMZhMpKam4XE58Ph+i4Up1i4KIogTfSRAELBaL5g4tISFBW4L77Gc/iyzLdHV1cejQIb785S+TkZGBx+MhIHkRUIL1qIAomkhIsBIIBDAajYiiiN/vp7e3l4KCAm2vqLo/taWlmdGxIb75zW9q3hNEQUSSpZBHEREI/pZlWbNEGA1GDKIBRZKQhACyEjpYpoAgGFEUdG7jVAIMehqxWCxh3kemg8/n47XXdrBt2+0kJibyyiuvMjY2xn/5L39HcnJSKB9RV06Qn/RWE7Usvd9u9SfoNUUK30agqLKj9Sf9drvGxkZOnz7NuXPnKC8vZ2hoiMTERP7mb/6GpKQkXVf3a5aNaN5VGhsbeeWVV/jv//2/a/Uf6TUj0nOKeogvEAjw1FNP0dvbS3Z2NoODg3zjG9/QAv2oqKys5PDhw3zzm98kMTGRpqYmjhw5QmNjI1//+te1aLVq/upeXH1kT0EQGBkZ4f3336eiooLrr7+ez3zmM5gjrC16WdV3mG5Pp5pOlmUCfhnPOxfw56aQtnE5ihlML72MECOw3XTQq7f46kUcHxo+xsB2U/JhSwtKVxe1OdlxPpyvfHjoEMK6dZCfP72OkiT8f/gDOxIT2XZ7nA/nGx/S349w4QLcd9/V8yFXfwbiqvlw/ge2i3jxiGlPUVEx+fn5vPDCC1gsFu6//37Ky8uDgxa1Axowmcx0dXWRlpZGamoq586do6Kigvvuu4/rr79+kjs6QRBYtWoVfr+fd9/dy/e//wMEAZYvX8668nLS09NJT0/XS6r9JSsKFy/Wkp6eSVlZcC+qGPIfnJCQMCl9qMSY72+xWLh06RI1NTWUl5eTlZVFfn4+ycnJGAyG0EBNjJqfGr4ewGKxUFpaqv0tyzLLli3D4/GQm5vL5z//ebKychGFWEdqwmWUADHkeEAxGzFNWqyN3VH1EVhnCoPBSG5uARPjdtLTM7juurUUFRWRkpKKwaCXOfaisaYoVQknlR/57pPlU5+RZVlbabnllltYsGAB3d3d5OXlaUvKKiwWy6RgU3qZlixZwt///d+TlJQUNchStP/VdLIs8+CDD5KYmIjH48FisWhWJz3Ky8tZtmwZVqsVQRAoKysjPz8fv99Penq6ll7NX6/o9fWWlZXF/fffzx133EFKSkrM99LnNVvFpwCCKCLJcoi8Zvl8hH0kst3jiGM+Yio+VIjz4bzmQyHUQjPUU0E+zI/z4TzkQ2bgfSmsTphffDhnVygUWcbn9mN/4xxJNy3HXJKurVCoaWQ5gMfjCUb4NJsxGiLnR8G83W43RqMxzNOCav3QUupmkarlwel0cOrUSTo6Onnooc+TlpYREeI9vOpkGQIBf8iqYoipHyZfD7+gn/nX1NTw7rvvYjAYeOCBByguLtZkiN0vpp75Rv7WWwGmyzO4BIfmulAWgqpHfW6mvWk2nVqWFXp6ejGbTWRlZWlyCoI4pQ6OtGzELjty0MXOT7/ncqpyp3u/yMONs/0A11uTIv/W91G9vPo+rlqrosmrfzYQCIRZZvTlRMqsHlCLHFczeSfVIiNJ4H3vMu6MBDI3laGYRUwvvQwzXKGIVi96i1wccXwo+JhXKGLyYUsL9PSg3HJLnA/nKx++8w5CeTkUFEyfVpKQn3uOnjvvwmwxx/lQ98y84MPBQYRz5+DTn572fWLyYcyJ7szq5ar4cL6vUAAgCFcaT9dYagUZDCaSkkwRj0xWRlbdh4gasCdWejUNQFJSMrfcchs33hggISEhStrIGfPkoEFX8xGjf8eVK1eyfPlyFEXRlmxjlT/TvKP9P1M5g9YxRStelUa7pMvnw9puIghQULBgxh+nsd5xiicmlTeb/GeDyDqZrcUgWp2GE+Dk+o+lHCOVbORSMoRb9iLLjMw7Uk698poqbdhzgCAYgv1JkbmaPq4vLz6JiOOaQTQ+RB0zcT78s+FDQnwYNpmbRsYp/o9egj797PKfDf7s+FCVd4bvOd/4cM5OKBRAQUFGCS6TamaA2XXgq02rdqKEhIRJe/c+jLJmCoPBoB0a+zDL+WAf+NGVZLT/I69fbbniFMrzahbZpqvPWPc/zDae3lI0M5mipZmqXSKtNPprgiCEWTAirZTRPlD0+ah91el00trayrJly7SPj5n0AUVRkAIBpIB4Ve2qzz8+mYjjWsGUfAjTf/WFEOfD6Jh3fKizksfKezaI8+HHxIdpaVgjZLqW+HDOTigALdiLLMtXZnafAObCB8qc+jhSQouiIZHUNtIf+JIkKWxwqpawD6pIY4o0S8vG1T7zYSCWMpoKekUWPQpmuIJUl1oFQcBms6EoQc8TkiThdrsRRVFz6er3+8nMzASCHlDUQEyqUvV6vZw/f54NGzaEfVBMXoHQ/mJkZIR9+/aRn5+vHcKfSX0LgoDRZMRkMiKIAsqUYe2iPx9HHNci4nw4WYY5gQ+DD2dT3kws23E+DEs7Z/jwvvtIILSyeA3y4ZydUAihtVxZDu1RFK8MxujpZzZb188yp3ruk1ZYs5mdf5DnrkbpEPIe4Q0EkH0BTAkJdPf20tTUwC1btzI2MkZ7Wzvp6elUVVVhtpi4eetWFBQCUoCCBQUhjxlXv9I01Wx9ptarq2nj2VjGIq0j0dJHWkSmklEfWCqaxUUQgi7mFEVhbGyE0bFRShYt4tixI4yMDPOFLzzM+fMXGB4eJtyV9VYAAGaASURBVC0tjddff12L2HrdddeRmppKQ0MDDz30UFgUVp/PR0VFBUuXLsUX8DE4NERKchpZmRkIOnmCoiooikxaWgqZWenY7XbdPt8Z1LdqfZVkUAQ+yBJvtHr6pMd1HHFcDWLyoZpghv08zodTP/eJ8KEkISqKpktngkgp43w4T/jQ4SBbCcaTuBb5cO5GyhYEBFHEZDIG65HJHSlWp4uEajFQ/54v0L9jLLmDB3ekWUUjVt2sXZUsKEiKjBIIUN1Yx7//+DEGBgYZHRvn9TffYHB4iCd/9zuqL1/G6/HwxuuvU3muEo/XzW+f/A3/9r3/Q3Nz06Q21P+okVmna1/VCqTK5/f7w6xDYXLPor9Mh1lFfo6xtBrtmvruqpu6SOj3dKrvqj4nyzKBgA9ZDqAgUV1zif/7f79PQ2M9+QV5VFSeob2jnT/96U80NjZSUFCAIAiUl5ezbt069u/fT1VVFcuWLdPkcDqdDA8PYzKZyM7OZsI2we+eepIXX36JN956i97+AS1IodoHZSXoMrG65hIH9r/HkSNHJkXYnbJuldDmjoA86SPpajGb8uOIY05iCj5U4nyoYT7yYWtby6Q2nJIPp3j3OB/ObT48euSI1tYzqtt5xodzckJxZUkVBEFEkYOVGnl/pgNCvw9OUYIn9fUDNFannm2lz/a5WApEn4ckSdpgibynDpzZyBn5zEzeX/3t9/vx+nzISrCDXz53nkt1Nby6YweLFhWzauUqXG4nScnJSLLMkrIy7vzUp7jzzjuZsE3g8/rIy83TorCqPsAj5VD/VhVh5PVo8k4FVSHHItFYdT/VMuxUZU6nKPXP69tDryCj1YseerkkSaK7u5umpmZcLhcOh4Pa2loUReHtt99m8eLFlJaWYjKa2LhxI0uWLCEnJ4f77ruP/Px8ysvLKSwsxG63U1ZWpvng3rNnD//7f/9vurq6MJlMdHd3U1dXS+HCInJzczlx4qQWiMjj8eD1+pAlWfM0k19QgChObwGdVDeEDoIKzHqJNxbm04dTHHHoMR0fEudD7fn5yIdOpwt5Fnw4Xf3G+XDu8qEgimGMdq3x4Zzd8nQFCsgK0Q5lR1MmEH6AN/LAlKJccSOmdlr1nj5d5LNTShihaGLtF4/WiJFl6Z/T7y2MJpN6TQ3+MxMIgqD5WY6UV59vZL1IksThw4epvnyZNQuKEBweTp54H7ffS21dLcPDI2zatBmT0Yjb66F/cAC7y8Hm67cwPDaIxWrB6/OyaNEiiouLNVLTLyXq5dAHllGvK0rQUhDNK4man1756p9T2z3aXlZBCHcZF61OIutej1h9R1WMetdxqozq3k/VOqb3lT3VAXQV6vupZVZVVdHV3U5raws2mw2bzYbP56O7uxuA1atXI4oCvb29VFZWsnnzZkpKSrT+cNNNN9Hc3IzZbEZRFBwOB/X19fT09HD69GkKCgp49tlnGB4fZe/evaxZcx133P4pTCYTHo+HY8eOcf31W7h4qZ5XX31Fiz7qcrlntz9WCAbFkmSJD0l3znhsxBHH3EcMPtTpOojz4Xziw0VZeUF5Q2589e8ajQ9VOZSQHHE+nD986Ha5rmk+nNMTCrVfqvtFgUmdX6+81MEReT1SGUQOrMgJiiRJmnu+qWbDkRaEaDP5yDzUAaNPo9/+o7rZ08umH3Sq8ohmUYo12411T0WkVUdRlLADY6plpLi4GNeEnb5LTcguD2tXXsfmgmxa6ptJslpJLynBYjGzbds22trbSUiwUlyyiOycLLKys/j/vvuvmEwmzEaz9g6qItG3iSpDpFLVWy/0SijSC4Mqr9frRVGUsMA3+noVBEFr60hrzRXf5oL2v76OIuVS21UtT9+2et/VepLX9z31nt4Cpe/P+raJbHeAsrIyfH4vmzZtRhRELAkWDAYDIyMjJFgS2bL5BkSDkb/7u7/D5XKRmZlJeno6RqMRo9FIaWkpBQUFmkxqtNEf/OAH5ObmkpaWRsHCfAKyREpyKpmZWRgNJmRZpqOjg+7ubtatW0dFRSUd7V381//6X8kvyCctNWNSoKCYEAQERQYUJEVAIahMdd9Os8aHvUc0jjg+KUTlQ/Uek6MXx/lwfvBhwsFDwfeZCR8qCiiKtm0mzofzjA/dPsSenmn7YChBdD5UJk/OZ4qPmg/n9IQCpl7i0g8k9eNT71IuUuno81OvRw4o/SDTQ9/x9dDP8CMtKZGyTKXs1AlS5DtGs5bEqpNodRY5SPXX9O+uKmS9MlPTqliyZAmlJSXInw4tuYcGt9/v10LOK4rClk2b2bJps/Y+6SmpyLKMyXrFJ7mqcPRL72rdR1ov9O+lRnKNJD213iOtcaqC1be7vm711rPI8tX/IyNtRqt7vfKLjL2g9j/9+0SzskX2n8i+q5ZhMBgwGo1hSrusrIylS5dOsmqp+RoMJrxeL2lpaWRlZWmWLbWPms1mzUJkNBpJT08nOTmZlStXanW+bOnysDpS69RoNHLu3DnOnTtHIBBg/fqNbNiwSYtgOx20saAoBP1YKCCqbTPt43HE8WeDOB9ee3woMJl7YvKhJAUjBvp8Qd0righ+f/Bd5OBZDkXlL9WzkSyDJCEQ3OMuqnWma0dBCJ7REQBFkhAUJbiVS1EwqPmF5DKIIuj7kCSBqudVLvb7kSUpWCdGYzC9LCPIMkY5eB4gjA/Vd1PbJyQvkhScPBE8T2A0GLSy1QmYwWDAKMtIoe1GCrB00SKWhlYcwiZFi5cCYLBY8fp8pCUmkpWSEszHZMIgiih+P2bAYDQieTwYZZn0pCRSrVZWlpVhNBgQRJFlJYtRZBlZURAFAUWRUGQFo6Jw/swZqior8fv9rF+zlg3XlQf5cHgYentj9lV9v4zJh3PYRjbnJxQatDoOn53pP2wilUqkUtJbN9SPpMj8DAZD1CWhWDO7yNmymke09KpSjXyP6cqMtkQ2k2VAfb3oP+z0FuNoFgi9gtV/eENQ+ZlMIjJBSwmCgMlkCvuo1z8XTX69sta/u74svXVFfVZtM30dRNaX+qO2gZ4Y1Twi06ty6/OOVOTR2kUP9SM/krCm8puuHt7SyxRZH/rn1HuR/XY6y6H6t77PqBMtfV7q8q4gCGRlZVFYWKil1fdb/QQMYPHixXznO99hx44dZGZm8sUvfpGUlJRJ9Rbr4yFMZn3a+GQijjiiQ+VDwr8v4nwYG3OVDxVd3tPyoaIgJCUh7NiBGJGPGHxJEARQFAyhvw2KEozeHcxUc1kaq37VulLzE4TwVWJBCG6x08qVZdD3K0XBpE7A1OdEMejJCog0Mel1vmoUVOU06mTVZA61l/ZeoffVXCnrPsh1Lxc+TiA4sVHTK4o2oZJlGREwhOpcFARyAwE2t7RgfO01DEYjojoZ1dW5Enq/JbLM/1m+nDMVFaQkJ7MlP5+kffuC5QcCUF4e9j7XEh/O6QmFIBAK6ENYB75yP3wgBgIB7TpMthRH+3s6ZRlNyUZLO9VHXbTnI5+LdX+qMqe7r/5WD3tFW7bWK3FVDr1Cm1RmqLoMk6JpXglzH2uPnl6Zq/soo61GRFO80SxWkVCJIZpVfKaWclWhRUuvf0f9/3pijEwf7W8V0cg/kmxi9ZNY6aaCfmITjYD1eRqNRq6//npMJtOkciL7jCzLFBUV8bWvfQ2z2UxqauqkttLvf47EpA8fQJEVhBno0Jko5TjiuBYQ58PomO98KIqitmVtWj4URZSHH540KQorm8kTzci/9f8rEfeC+vfKWY6wbVSR76C7pj6rLyOyB0SWHSaDooAkIUfUufZRrRr9IiYH0d4x1j1NntDHfzTeVUIGT23FBhBkmUKPByEhITh5Ct2LLENRFJBlsmSZ60dGMJvNJKanI/yZ8OGcnlAoqB1K0HruVIpMrwTUa9HSaflHUSSR96ZqoNkqsmgKNpryjLV8O9N0+rQzPcg2Vb1O7uDBxhCiLL/FGiD6cmIpWb2s0SxDenmmkzuaIpsK+sNwM5l8qO8V63qkoorVdirxR0sTq6+qH/EqIi2AkXLp00bKoI6baIcAExMTJ8kRCVVug8FAbm5uVNKdiZK7UmciGAQEZBCDu0anwlRjOI44riVE5cMYaeN8GD3tXORDQRAwmkxhVv5IWSfxYejDVYiSX7QJp369QCGKXtWtKEBwOxGEPuJNpuA2K5g0+ZhcRsT/un6j9t9YEwIUBQMQ8PsR1N0Iej7RrSgo6vVQvuq2KAjxYYQcellk5cpqkjYJCOUpEFxBkRUFQc0ztBKSaLEEiw/VfbTeJoTaxgDkZmcH5VC3lanyTDEer1TF/OTDuT2h0CYRMWa1ypVDY16vVztQA5OVVjRFMV2FT7csGA36iIyx8tQrhljWC5/Px8jICImJiaSmpk6Z31RyTaU8I/PQf3jG9gwi45O8GDBhlEVko4AoXMkn0pqjX+6NdgBPj8itQbHSKorC+Pg4586dIzs7m1WrVoXtWVUAnxTQlIYoCIihbNX+4ff7w/a6qhMdVQb1Iz9y6VtNq19ZUD/w9XlfuHCB3t5e7rrrLpKTk6PWv7rlST27EG1ioNaZKqtadnV1NRcuXGD79u0UFBREbcto1j79u6kyR/bXSFnVZ9R0kX12Jl4jpkujyupTwC8qIMpgDC47z+S5OOK41hHnw2uUD9X7EbLE5EOHA37/ewgFDFUUBZfTSV19PSnJySwpK5t0TiYgX+FeIDj54crKvd/vRwoEMIU4RoTguYBQHlK0s4eqTFyZTOjjaYihvKVAgNq6OpwOBxs2bMCSkBD2bEjIII9KUvAcAwSt+vo6CfVtAfAHAphMJk3GpqYment7Wb9uHenp6dpzmlxqPSpK+HYlQPL7EURR40MhNLEQIp7Vt7skScEVjCjfJ1MynSRBVhbiAw9MlWre8uGcnFBoFSKEDqJKky2sappAIMDu3bupr6/nkUceYdGiReF5EH7wKPKgbSxFC2heETweD2lpaTE/hPUKqL+/n7y8vDCvEHr4/X7GxsZITU1lbGxM8yQQmd/Ro0d59913efjhh9mwYUNU5QIwNjaGLMtaNOJossmyTGdnJ4WFhdo+eT1kWWZwcBCz2cz4+Di5ubkkJydPqg/1f++YHcGrkJidicvpw5pgQRQFHA4HVqtV88yhz9/hcJCYmIjP58NgMIR9yOvz1tdxtMGhtp/FYmFiYoJFixZNapORkRHOXjiPx+Nh2bJlOBwOMlKC23DKyspQFIXGxkbefvttHnjgAQwGA4sXL9bKlGWZmpoaamtreeihh8LqTC+Tx+Oht7eXpKQkTp8+zZ133klSUhKBQIDLly+TmZkZ8yA2BPvCrl272Lx5Mw0NDdxxxx3hE6NQP0hPT2doaIhNmzaRlZWF3+9naGiI7OxsEhMTo7aR3+/H6XSSlpYWdsgc4NixY4iiSHp6OkuXLiUxMXHKdpBlWTtwfeONNzJTzPQDRJugEbQMiYiIYvAQYNzhaxx/7ojz4TXOh6FDwDBDPgwEYPFiePDB4MFpWUbweunYt4+1a9eilJRoB5cVYHhkmLPnz+Pxelm2dCkOh5OM0LbUsiVLUIDGmhre3r07jA+FkIFLkiQuX75MbU1NkA8tFs1yL6irBYqC2+2mt6/vCh/ecQdJSUn4vF4qXnyRrMxMpHvuQUlICK4O6CYlKAo+r/cKH9bXc8edd2I2mYKrB6F2O3L48BU+3LiRrKwsAoEAnUeO4PF44JZbUNLSwiYiSihvp9NJWnp68FC5IGjbuo4eOnSFD8vKND5UdOUKgGAwgBI8x1JZWUnA7+fGm27S8lPfI9RYYW2uTbpcLti168r1a4wP56x82vIY6kdY7FlXSUkJ27ZtIycnJ6aSOX78ODabjaamJurr68PcoukVYH19PfX19bjdbp555hnOnj3LpUuXouarfxaCyvGdd97B7XZHlVOWZYaHh3n88cdpaWnh0KFDYYpb/7N27VoeeughioqKJn2I6su9fPkyp0+fniSLHm63mxdeeIHh4eFJaTweDz6fj8rKSo4dO8bRo0cZGxuLKpOiBJcKe+uaaT51jl379/Gr//wVLU1N7N27l5/97GeMjo5OktHn8/Hiiy/S19fHwYMHaWtriypnNILS/+/3+2lpacHj8WAymRgYGCA9PT1si5KiKPT393P69GkSEhLo7u5m/3v7aWpqorGxMexw9sDAADU1Nbz33nsaservO53OsHy16JeyjM/n47XXXuOPf/wjAwMDVFZWMj4+DkBCQgIPPfQQEG6ZV59V85IkiebmZoaGhqirq9OibAYCAbxeL5IkMTIyQmtrK+3t7VrdGgwGbr75Zm17lt4yqebf3NzM4cOHtf3Cfr+frq4ufD4ffr+fgYEBent7mZiYmERi2sE4HfRyqZFL3W43Z8+e5dSpUzidzphbq6K1Zcw0iAhC0KoVdLk/vbXlk1jajSOOjxNxPrx2+bCjvT2qnDH5MJSPxode72Q+DK14BPlwgNNnzgT5sKeH/ft1fBjKyxh6fko+dLmuWP4VBSnk4UiWZXx+P6/t3BnOhxMTIAgaHyqoAdquGKo0Pgyt1mt8WF8f5ENFic2HY2MgCDPjw5YWDh85EuTDkMEtKh/abGErIxofqt8YOtkDoZXAQOjMhdvt5uy5c5w6fXoSH0bdHnUN8uGcnVCog8YYxeODXumIosiSJUtoaGjQFJcUkEIuxSQCAT8ej5tnn32GJ554gs7OTnp7e8MVUsjqo4QU3M9+9jOam5txOBx4PB56enpiKjp1UKhbTGw2Gw6HQ0urRiFVI3wmJydTUFCA1Wqlr68Pv9+PLMs4nU4tgrQgCFgsFs6ePUt/fz+yHIy2qA9Drw6YzMxM3G63tn1GTaemHRwcxGg0sm7dOhISErR0brcbSZK4ePEiR48eZc2aNZSWlmIwGBgcHNTKcDgcYe5JJ2w2MpJSkeweMnKz+fR99+Hz+Xj99dfZvn076enpYYpCHVR+v5/333+flpYWUlNTozT31O7/FEXB5/Pz1lu7GBoa0qJPjo2NaTN5m92OPxCgpKQEt8tJ9eVLrFyxnBUrlpGTk8PQ0JCWX37+AgoK8snISCc1NYWx8QlsdgdDw8PIikJzcwsnTpxkcHAIWZZpbm7hxRdfoq2tHVlWUGSFW2+9jS89/AgF+QtZv369Vr8+n4/33n2PysqzeL0+1I+A7u5eJiaCAXYuX76MosisWLGCrKxMMjIyg5E1ZYmLF6v4j//4OQ0N9axcuYLUtBQyMzOYmJjQ2v3MmTMcOXKUiQmb9k7t7R309PQiywo5ObkUFRUjCCKyrNDZ0cVPf/pThoeHWLq0jIyMNJKTkxgdHdX1bZmxsVFefPFPDA0NIssSHo8Ln8/Lxo0bSElJ5mf//nPeemsXgYBEdXUNO3bs4OzZs9oYCbaTD4fDgcPhwOVyTTvR0ENAQZIVkNUl56kVqJ50p+tDccQxbzEVH2pJ4nw4H/kwOSXlimVb15ZxPrw2+XA2/DTf+HDuTii4suyjWmb0kGUZt9uN0+nkT3/6E5cuXWJsZJSAz8+J48dxOp0MDfWz6+03yMhI5R+//S3y8nKpqKjQLBOSP8DI0DBejxebzUZzSwslJSV89rOfZc+ePSQmJrJ8+XKcTqfWMMPDw+zfv5/R0VHsdjsDAwN4PB6amppoaWnBarVit9uB4PLzmTNncLlcXLhwgaeeegpFUcjJydH2zasz5CeffJKXX35Z64CvvvoqNTU1DAwMIMsy77zzDv39/TQ3N+Pz+WhtbeXkyZOYzWZNYTU1NfHYY4/R19cHQGdnJ//2b/9GbW0t2dnZeDweFEWhr6+Pt99+m0AgQEJCAmfPniUhIQG3201aWho2m02bcZ85cya4vzKkdF997TV8RgMCAuvXljMyMkh/fx+rVq3i8uXLQLAjnzt3jrfeeos9e/Zw6tQp7r//fnp7e1m1ahU5OTlTHhSOHABBWTz09fXzV3/1V4yPT3Dq1CkyMzM1C7tflnhn/3v0DQ7g8rixT4zS2tyAUVRw2idISEjQPm4lWcLnd9DZ1cybb73KkWP7OXXmJHWN9bx38CBOj5uOzjZcLht1ddV4vW4GBvo4c+Y0P/3pT2lpacHt8RAIBLh4qYrL1ZcwmUyMj48jCAItLS28vfttxsZGGR0dweNx4Xa7qaw8Q1XVBaqqqvjtE7+m6mIlGRkp2Gw2rNYkxsfHcDht1DdUc+D9fdTVV2MyG7DZxsjOycRms2lWmueff57h4SGam5twOJw4nU5qa2s4ceI4TqcDo9HApUsXGRkZZnx8jEOH3ycQ8OJ02TCZRGz2cSwWs0b2we0PEucvVHLk6PucqTjJ6NgQB9/fz/kLlby163VeefUlMjLTOXPmNH19vfT0dGEwGCgpKeHw4cPYbDZkWaaqqornn3+eP/3pT+zevTu4FD3TUS+AYhAxKUYIHkWbMeITiTiuXcT58Frmw6kmDpP5ELweb5wP5ykfer3emY/6ecaHc/IMRRDhwXUioc5Ks7KyqKiowGwycfnSJQDOnD6N3+/jwqXz1NfXUb62nNWrV1NcVMrPfvZz6urqaGpqAlnh6JEjrFq1iouXLtHZ1clX//IvueuuuxgeHsZms5GUlKRZVgAqKip49tlnOX/+PAUFBdjtdlJTU7lw4QLr1q0jIyNDU1SKolBTU0NHRwcnT55keHiYgoICBgYGcLlcmM1mzXLS1tZGUlISfr8ft9tNe3s7qampnD59GqPRSHV1NZ2dnbS2trJ161aOHTuGwWDgW9/6Fr29vezevZvDhw/T09NDQ0MDCxYsoL29HYfDwb59+0hPT6epqYm8vDzsdjtdXV0cP36csbExJiYm2L9/P0ajkdzcXPx+P4qi4PV6OX78ON3d3VitVgKBAI0Njdx946009nZQvWMnvX29OCdsFBUXc/nyZUZHR8nJySEjI0NzOWo2m8nPz2fp0qVkZ2dPeUgv2nYbRYGm5ma8Hg+lpSUMDA7Q19fHggULtHYBsFqt/Pa3v8ViNjE4OIjdbueXv/wlPp+fzZtuwGg0atavH//436mvb2TlylV43H527HyRtPQ0BgcHOX/hBOOjIwwO9/D8C0+z/8AeFAVc7jHa29v43//6P1hYsJBAIIDD4cDv95OQkMzChQtRFIXc3Fy+/OWHGR0b4oc/+j9YrdaQNWsCn9eHwShit9v41eP/SVJiKl/9i78iIyONx378A0wmEYdzHJMZ3nzzDfaF9sUWFxfjsAc/zFNSUvjmN/+B0bEBnnn2CV540QSKgsPpxOvx8s57byEKIuPj4xw5+h6yotDd1QWIfP/738dkMrFs2TJKS5agKMKV5eGQhXNiYoJnn32WnTt3ata9iYkJkpIS6evrwmaz8b//9Tv4fQE87gBut5ubbroJq9WKKIqsX7+e6667TmtH9VzIzIa9gtFoClpIAWWWq7eqRTOOOK4txPnwWuVDQ1/fpD33KqLyIcEtPK4lS+J8yDzjQ5cLy7vvzmLYzy8+nLMTiuA+SZBlCSHkoUAPr9fLqVOneOSRR3jssccYHxvjd7/5LXv37MHpdHKxqoqFiwpITExkbHwMj9fD/vfex2q14vP5+NGPfoRREEhLTePc2bOIBgMmS9DDQXt7O++++y6LFi1ClmXy8/MxGAw4nU6qqqowm81cvnyZrq4uRFHE7XZjtVpJSEggLS2N5OTkMF/Xx44do7S0FKPRyL59+zCZTNxxxx0UFxdr+/8LCwsZGRlBEATS0tL4l3/5FwKBAM8//zw7duwgEAiQkpLCggULOHnyJOnp6VgsFtLT0yksLOTkyZOUlZWRn59PYmIisixTXFyM2WymsbGRrKwsDAYDly5dwuPxsH79ekZGRqisrEQQBOrq6tiyZQuZmZkkJCQAkJyczLp16zh37pzmNWRBbi4pmeksun4th86cxCdLmMxment7KSws1AK5LVmyRGsrlUxuuukmTalG2xuqdn79Up0oikhygJa2ZpJTUnB4HAQkP1arlfz8fDIyMvB6vdhdTmpqakhNTSUp0cq2bdu0fDMyMklLS2Xjxo0YDAZMJhMb1m9myZJlCAgYjAbcfhcDg/0sXryE9LR0BFnh1q3bMBrVj2EFUQw/IGcymbQgfUaDhcLCQkRRJCsri+3b78HpsrFgQT4ulwuHw0FvXx+lJSWsXr0aWZbo7OxEFE2sWLGC5ORUkpLMOJwOQGLBghzGxhz4fH4KCwtJTU3FbAoeFktKSmLjxo14vQ7y8/MZHR3F4XDQ3NxMcXExeXl5mozqkvOLL76I0+li+/btrF69mtTUVDLSszCZLCiKwtjYGCOjw2RnZ7NkyRK2bt1KaWkpWZlZuFwu3B43iqJgMFzxxmI0mEhKSiczMxOLxaJNFE0m06SDiLMZ92qdRnVvOM2zccRxLSLOh9cuH9LfP0s+lGjvbMPsmIjz4Xzjw1D08NmM+/nEh4LySayL+P3w0kvw1a/GnpkrCl5vAPeuCxjWFpK8bIEWGVKdQVZVVVFSUkJ6ejoCAiNDQ1RUVNDV1cW9995Ddl42ASmAy+ni0uVLmE1W1q5di8lkwuVyISiQmJiI1+sNehMwiJrCOXLkCDfffDOrV6/G5XKRmprK4OAge/bs4YEHHtA6ibq8qnYYv9/P6OgofX191NfX097ezje/+U0yMzO1faOyLGO1WnG73TQ1NTE4OEhFRQUrVqzg4Ycf1gK/qYeKjh49Sl1dHY888gjp6ekEAgHNa4b6OxAIaAeSxsbGOHXqFKOjo1y6dIlvfetbFBcXa3taf/Ob3/Dwww+zePFibTCoUN2XqhE71f2qL730EiUlJdyydWswJL0oYJAVJBRE5cpgVd2QRm3TUF1FRrxWoe+K6l5bs9lMQAlw7NQJnnn2GXKys/G5fXz54UfZtGkTIyMjHD5ymMpz58nMzORb3/oWidYEDKLunI0gIsvB8q5EZr1SpqLAgUMHOHLkKJ9/6POUr12rvROKEPJIEd1jUeTw0UefVu/5fD6ee+45Jmw2vvoXf0FOTg6CqCDL/tChKyPq7sPgIwqCIAOSdrAOQMCklRGSPpR/0FtUdXU1X/7ylykrKwuTQZIk/vCHP3Dy1HH+9V//P0pLShAEEUUREIRgXjabjaNHD3Om4jRr1qzhgQcewGK2hMpSgnWAAIiR233D2jKW27rplJuiBA/m+WUF99EGZL9E6p2rEAxgfPlVhM99DqzWKfOII46PDU89Bd/4xsdWXEw+bGlB6elBuvnmOB/OVz7cvx9h3TrQuf+G2HwojQ5z+t9/wu+GB+N8yDzjQ5cL3n4b4ZFHJtWfHlPyoRA98vxHisOHITQpnQpzeEIh4/VKeN6uwlhehLUsB4MghjWQ1rFCedjtdl555RXKysq4+eabNaUWmS4W1ENT+/fvJzU1lS1btugGnILL5WLPnj185jOfwWq1TspPLcfpdHLx4kVaW1u57bbbWLhwodbp1U6tKjubzcaFCxdwOp3cdtttYe7pVIVz7NgxMjIyWL16tWbhVw+9Rb6XOmB6enqoqalhyZIlLFmyRHsPr9fLpUuXWLp0aZjrP/2AV5Wqmq8kSTQ0NJCUlKRZHaary0iZprofrR3Uw3aq1SMgBejr7w8uqaekUJBfoAWFGx0dZXh4mOLiYs3lm/699IeU9O2gn5wODg4CkJObiym0FBxZL3q59fno2zVa3UiSxPDwMElJSdoyaBAycEWJXckreE+SvVpeBoMRgeA+ypAkam2hKAp2u12z1kTz0GSz2RgcHKSkpGRS9Gu9nG63G7PZfMUf9wwV1wdVcGp/lwIBbCeaMDkkTPeuxmIQEF9+JT6hiGNu4WOfUMTgw9CEgltvjfPhfOXDd95BKC+fNKHQpw/jw+FhpEOH6L1+S5wP5xsfOp3w9tswgwlFTD4Upu9vHzquhQmFxyvhfbsKY3kxiWXZYRUZTWx14KmRmK+m0hUlePhKjZWgzyMQCNDc3ExpaSmWUNREmOy7W93HKAjhIeT1Zaj/Rw5UVdHpPUOoeejfKdreSj3UZ/XKRC1blTOW96xIeVVZ9EHeor3TbDDdCoX+3WVJCl/qE4LRPtW607+bGkgpMkicej9anUWrk1ikG3ldfTZWvejdMUa7Hy3v4G8FhQA+nw9QsCZYgejbiGK1m/6+nnCn6jeR7RhNic6kvGiYbhIpyzKyP8DoyUYS7TLCp9eQKIDwygebUEwnbxxxzBqfwIQiKh+2tEBPD8qtt056Js6HVzCn+TDGhCImHw4PIx47hvLZB1Th4nwYo97mHB+GVij40peung+vcizPVN6omOGEYs6eoQCQCS6/yYpMKGq8hmiNKgjCpL3bM60w/cBITEyc9Lw6+MrKyiYFAYomh3pfnybWDD9aJ5UkSbO8qMut+vwj842UIdogiRzAsZS6vi7UvCLziywzmoLWW0RmYg2DK8o6LL0gIIWuqYHiIuXV31Pz+f/be7MguY7z3vP3ZZ6q6m7sjYUESXAnqIV2SLZIi2NZ5pVkjR0ajzZ77rU9Di8Rfpir+3Aj/HjlCNlhz9geh8KeG/K1QhG2HKIs61oSrY1UyBtNilq5iBQXABRE7PvSaPRSVedkfvOQeU6dqq5eADSAbiB/EQ10V52TJ9fvf/LLbZiI1uNTj+d86RlM27DrFhKPhcRmWJ7V7kTIaDWz6u9+A9u7z9fypl5OwzxRC6WzTN+gcM+XpkHqaViqF3RY2Jm1KH4lb0GXSFxxkh4mPYwBhQPt0KSHrDI9DH8MvX6+sFeTHq7IOKpqlenWhi3ZFiuCYRXjQl5mFru+rKD1+f9lBe41AJ3TOOakC6pFNtDzPMzHQusN6r39pfQ6B58zzJAvdH1p3Aav6c8DqjSW82OXSpmmwbDqnw+Lw7D4lt6ZulEYNMx18bqQz5cS9/nSUO6NPiwNqlo7iK4ALCIWYyzeCXme0+12+7ZhfeaZZ/jv//2/s3fvXs6ePcupUydxfYs2FecdMzPTfYZ3br3r/T41dZ5HHnmEyclzc/J1vrTX63a322V6erraf7u+b/twsRiIhQgmDSQkEkDSwzpJD4Gkh6taDwc7QNeSHq7cEQqNhkCg4VnUgMLyTGdYKIy5ngCH1wIjBkVxhQdsb1W+hjmP9XmnQOVlKT/rdDqoarW95unTp2k2m6xZswagGrYs7z116hQiYfX/hg0bqnmSC6WpbLynT59mfHy8MszDKnMZ93Jv8Ndee41t27YxMTHBTTfd1Of5qKcL4MiRI4gI09PTfPvb3+YDH/hAtdXgYvkrIn35Uhqs8nmDHpZhHpVBr1LJYJzne/5SKbfbm5qaYvPmzdWc03IHlDIO3W6XU6dOsXXrVg4fPszk5CRHjhzhp37qpxgfH+8Lc3Jykq985Su85S1v4dChQ7zrXe8CwDnl8OHDfOc732HnznuYmpriwQcfRFU5ffo0t956K+Pj43zr29/gqaee4sP/7b/FuceC4jl27BAvvfQSP/eu/xUw8wqBiKLq6HRmOHHyCJ1uuyqHxQTae8+xY8d4+umn2bx5M9/5znd497vfzYkTJ/jZn/3ZJQk8gGABjyvaNBRYhjadSKx6kh4mPSxf6kvPe9LDVaeHJ/ft4yGWPu1otenhihyhqBAJP7py8rHcF1s1VOp/+qd/oigKjh8/zne++1327t3LZz7zGb7whS/wyCOP8IlPfIInn3wS7321U0M/yve+910+//nP4Vz4/hvf+Aa7d++udpWozx+FsPf3P/7jP/LEE09Uh/bAfEOFvcp76tQp/vzP/5wTJ07MCbP0Jg0axjzP+fSnP80PfvADvv/97/fFvx5G+fPyyy/z3HPP0W63efbZZ+f0/ufzWJTUOxH130shqs8Hrg9rDv5dGrF6uPMNcw7m32I/JQcPHuT3f//3efzxx9mzZw/79u2rwinTPDU1xV/8xV9Uu608+uij7Nmzh7Nnz84pH+ccL774IufPn2f//v1R9Aq871K4Drt2vcKZM2fYt29f5V166KGHqudt3bqVzZvHsZml0+3g1aFeq4OmFvJkBSdoHEa3hmPHjrFn9+6+fKlfP1juJ06c4E//9E/ZtWsXo6Oj1X7kR44cmZPOwd8H4+K9jw1+eRr9MO9aIrHqSHp4beohoYN4IXpY33kq6eHq0cPDtVPpr0U9XNEdCg/oIkOgl5NhjebAgQN87GMf48yZM5w+fZrPfvazHDh4gE9+8pOcOH6c2dlZPve5z/HEE08wPj7O3r17ee211zDG0Gq1yLKMLCsHhhSvOfe+7m4efPB+jh47xMTEBOPj49XpnHme91UA5xx33nkn27ZtY2RkhP3791eVrj61ZDAN3ns2bdrEb/3Wb9HpdHjppZfiyarnabfPU7gO586dot2ZZt++fbz44ou0222MMdx7771s2bKFqampSjy63S5f/OIX+dznPke320VVOXPmDJs3b2Z2dpbt27ezfft29u/fzxNPPMHU1BTdbpfjx4/T6XTmNObBYdVBL8t8n5XhzHfNsPCXUubDxKH8mZycZPfu3ezYsYMHHniAf/mXf+GVV4Jx8z5sK1gURXUS6Zvf/Ga++tWvcvToUd7ylrcwPj7O6dOnUdVqx4twUM4adu7cydatW2k2G/xw7y4+8vu/xz987n+yadMGbtx+I1u3buXs2bPVUOpjjz3GU089xblz57jlllvI85xPfOIT/Nmf/RknTpzgm98Kp8d2Oh26eQ7A7Owshw4d6ktTPb0HDx5k9+7dPPHkk9WQ89mzZ6tTac+dO8d3v/vd6rsDBw5w9OhRzpw5wwMPPMDOnTu5/fbbq60hl2I46+VkbRBARZfmik0krgOSHl6betjtdi9MD8ufpIdJD1cYK7pD0cu9uQbhchvVei+59JSoen74w1d58sknefjhh7ntttvYuXNn8HA4z7Fjx7nrrrt417vexXvf+17e9a538Xu/93vViZ+lkTt//jyqyvT0NM8++12yDJ765hP84R9+hP/xP/6SsbExut0uzjm+/OUvV16XdrvNY489FvZtFmHbtm0cP368MmjHjh2rvD51T06322XPnt10u12OHj3CX/x/f8Gf/OmfsGvXKzz1zW/w7Pe/x8lTR/mT//f/5oUXnuPRR7/KH//x/8MXvvAF8rzLHXfcwfT0NHmeV0Zh4uxZvvTFL/L5L3yes2fPkOfBoJY7gXjv2L9/Px//+Mf567/+a5577jn27dvHH/3RH/Hyyy9f0FzSQeoGcqEdGhb7fj5mZmY4cuQIRRFOvSzzs9vtMjExwde+9jU+9rGP0W63+bVf+1X+y3/5EPv27ePAgQMcOnSIL3/5yxw8eJC/+7u/45FHHuEXf/EX+dVf/VWeeOIJ7r77bm644QYmJyeBsLXjRz/6UT760Y9y+vRpRkdHo7cF9rz6Ck8//R2+8Y1/5/z5czjnGBkZqQTr+PHjfOYzn+HkyZM888wzHDhwgBdffInz56c4ePAQzz//Ak899RSzs22MsRTRgJ4/f56vfe1rlZBNTk7SbrdxPtTNPM+59dZbWbNmTZX2r3/963z4wx/mX//1X9m/fz/f/va3qxNkX3nlFfbt28ev/Mqv8KUvfYldu3b1edLqZV1644DoBaqmh1dfCAbvfPDHXIAT5UrZhkTi6jBED8ufpIerVg9feeWVpIfXkR72dSi4tvRwxa6hUMCK4DKL+nBYDBrHemOmql7eY8W97/Kj117lxRdfZPPmzbTbbf7t8X9GTM6u3S8wce4s97/lQZqNMUZH15HnjqIoeOihhzh8+DDWWkZHRzl69CjHjx/nlltu4dixYxw5coSf/MmfDPt4P/ooX/7KVzh3boI3v/nN7NnzQ8bWZLz4+LN08yl27XqJe+/dyfbtN6Eq7NnzKlNTU5w+fZo777yz8sIcP36cJ554gve9773s3ftD7r77biYnJ/n+889y9OjhkIYtW3jm2Wd48KcfRG2XLz76eQRl+vw5ms0GBw8e4LOf/TQ77349m8fX8cm/+Tiv7d1Na3Qdr3/968myLOyH7Qtmzk/yxtfdy/O7X+az//AwoyNrOHL4GO94xzvZ9aNXePJ7T/Da0R/xwAMP0HE5//DIFxARzpybYPee3dx3332XVDblPMbF5qDW/18KzjlefvllHnvsMX7t136N48eP86Y3vYlvfetbPPnkk5w6dYp2e5aZ2fMcPvIas+1xnnvuOVqtjFdffZU//uM/pigKvvrVr9LpdHjDG96AMYann36aM2fOMDExwaZNm8jznKIoePXVV+l0Orz88st85CMfwVrL/fffT547Hv3KP7Ft681MnJ3ij/7wT2i1RviFn/8FNm/ejKqydetWPvzhD+Oc42//9m/58pe/jHOe1350gHPnJnj4U39HlmW8770Z27Zux9pGdVBTWOjWoXBtvvmtJ3jggQd4+ZV9fPKTf0O73SbLMmajWE5NTbF7926cc3zqU59idHSUHTt2VFtSrlu3jr//+7+n1WoxMzPDX/7lX7J+/Xre/va3s23btt7aF4nbGwJxVm2/8SyUAjA5FB7WFA5t2WUa6E0kVi/z6uGAtzPp4erTwx+2Z3njJZZN0sNVoodbtyIzM5eohytXEVdsh6JEAS/hp+xL1LNzsPe1rAZVYM2aNWzcuDEMeXba3HnnnfzET/wEu3btYmxsjB/7sR9DRHj729/OqVOnaLVa3HXXXWzZsoVyUdX73/9+Nm3aBMDLL79Mq9WKC89gfNNWxsZGsbdbrM1429vexo4dO7j//vt59dVXGRsbY8OG9eR5zqFDhzh58iSbN49z7733smnTJu6//36897RaLY4dO8bzzz/PxMRZ7r77bj75yU/yzW99g1/6pQ/w3ve+l5MnT7Lz3tehIuzYcQfdTpfjx4+x867Xs2PHDkTg+PETeDw/e+M7ePs7/gONrIGVFjt37gQI6VLYu/tV3v/+D/DO7s9z9PB+Duw/xH333cfWrVt53/vex779r/GOd7wjvEz+RIZg2LZtG61Wi7HWyEV5SuYUzxKM54VireXHf/zHueeee3j88cdZs2YNZ8+erQzi7/zO73DTTTfR7kwhonz/+9/HGMP/9Z//M6ih3W4zOjrKzMwMY2Nj1eK38+fP8/73v58dO3ZUa0IAnn/+eT74wQ+yffv2qr5s27aNX/7lX+bnfu7djI6Okuc5eZ7TaGRs2LCB97znPdVhPGXH7A/+4A+q4fcy7f/+7/+Oc45bb72VzZs3s3v3bvI852tf+xp33HEHzWaD1177EU8//TR33nknL730IidPnuR3f/d3ue2222hk4UCfU6dOcdNNN/Hbv/3bnD9/nkcffZRNmzbx9NNPc/bsWf7t3/6ND33oQ9x+++216Quh7WzatKlvqL7sHpQzj43zqDV0jJK3wOZQiDLbMqw1ALroMGoakUhcL8zRQ0l6uNr1cN0TTyxLOSU9XAV6+OCDyL/+aygTrj09XNEdCu8VVxS0nELh8ZlBUMpT3wcbyXJ7Z4xkbL/xFm684SZ+5md+tuoZighFUZBlTURC33Lbtm19965fvx4I29wF4xTme65du5a/+Zu/4Qtf+AJFUfD2t/8vfPCXfolGrHgKoMqDb/1pXn11L1PT55mamubTn/47nnvuOR566O28//3v71tkJSJs3bqVD33oQ3zzm9/kkUe+yMGDRzh79hyvu/cNvO99H2TdunUYETwGj6EoCh5++GEmzs7wf3zgP4SGLRL2OjcaB4MEVKtKX20zlztOnzzDvoOHePCdD/Hj9z3A/teOk2VNRkZavP6u1/H6u15HWVAqGapB+VQVy3ItL1p+RKQ6GXNkZISHH36YkZER8jzn3e9+N/fdd1/M+80gyi033xrvDKka9BKpKocOHaLRaPDud7+b9evXV94k5xw33XQT69ev5/bbb5/TyVq7dm01KFcPb2RkpJr/Wj5vw4YNbNiwoUqDqnLnnXfS6XRotVq0Wi2KouDxxx/nnnvu4T3veQ/WZoxvHqcoCj7ykY/QbDZ55zvfyRve8IawUwoGsGzdupUNGzZw4403Yozhjjvu4P7770dVeeqpp/iN3/gN3vSmNw1d+FffB997X7YgTFhbSm49We5oTnfJ2l3aZ6bQU+fRhsE4j2Rza0ppMAfzuf7cROJaY6geqiLew8AWoJD0cNXo4b79y+JguxwkPVxmPVy3Dmo7lF2UHg4065WkhyvypOxyUcxU4bBffAEdMZi7NpONr8OuGUGbFpcZGsYiClqOXAwJ6+Izs1yJf5G3Dwsxzu08evQox44dY8OGDdx2222MjLQwpl75wuKz3Xv2sHXLOBs3jnP69ASzszPccsvNNBqNeU+4nDo/zde//s889rWvccvNN/Mbv/nr7NhxcziqPg6xOTzOeY4ePcLasTVsXL8Baw3VkfcDNXYwB33Xs/vFl/nYX/0VZ9rTvHHnfXzwg7/IjltvZnR0NLaX3liS7wsz9LDnK6+rTdkcyl1Idu/ezZe+9CXe9ra38eCDD1ZbGV5IeOWczC1btvRtfVsUBQcOHKgWFg4aoEulHP6vb9tXtq0wpxNUHdMzMzz22KMUheO97/3fQxlK2e0zdDodXnnlFd74xjdWcR88QXWxuCshTzviaapgC8gnp3EnZimOTNA+dYZOu82MdlkrLbJ7bmb8gduxmUH+/n/2nZRdpqMeh0EDuhLrVuIa4gqelL2gHh4/AscOUfzM24IeEtpaGL0wNasbuNRWoQuuCr2w0DXOwz969CjHjh9nw/r13Hb7bYy0RjC2pm8KeZGzZ/dutmwdZ+PGzZw5fZaZ2dmgh1mGGWJ/6nr49a9/nZtvvolf//X/kx233lydHK2AU4/zjqNHj7J2bCzqYXmytMx5B+izLQqae3a9+DJ/+Vd/xZnODG+454188IP/Gzt23Mzo2Bg9qS71sP8cEfP1ryM/+ZPIwEnZ8zIxAR//ONx559KuvwR68/vDO8mhgwd58aWXuPOOO9h57700G434DjdYL+apC3F0YnZ2ls1btmCNiTuXabXVqvc+bMe7zHronQtTB+MzqzUG3sfTpxXVsIj8mWeeIc9zHnzrg6HDYiS8aBLOv3ht3z7uvPNOTKxD5f9o7eDCYRrU7YZn/8qvXLweip3TebjserjEk7JXbIdCFTrdHPf0XmaPnSSfnCHTBtmGNbBhDDO+jtHt42TrxtARg7ceIwajcQRDBOTSjihfTspsrr+wDi6SKuNarxCxrfUxbC5kPdx2u8Pzzz/P5s3j3H333b1nxMA0boknSMz+xfOoHjfvPa5wTE5Ocm5ykvHxLaxduwZre/Eqo6Y6POwVUixDqZdVnuecO3eO9evXVwvsLqROlWU57J4yL1W1ErDlqq8X0qxD5ykHhEYjq3l7+uM57KTVQRyK0XD0lhcQ7/FicUBWePLJNrOHTlCcnOD80VM0iwbNtWuwm9fSGl+H39hkdN0adMRiRhvB+H/mM30dinqe1eMHl3dLvESi4gp3KObTw4Z2sc99AxlfT2vTuqCHDYO3ihFBlMoLiqyc2dfly1f5e9CjmqbV4lq/djAMiddCv4pVL8LxULSjx44yOjrK1jj1ql+AanrLXA/wMH2sx0294p1janqa6ZkZ1q9bz9jYKGKkit9gx66Pdhv55V+G6FFfFFWYnoZLWMh9IdQ7FUVRMDszw+jYWHV2x4XUqaos53vv8z68oJdb31569HvPXeq13uNi3lpr4nuS9OVDqen9ncu56Qp6GOqLB0Q9vjWKa7UuXg/pf/e7Inq4xA7FipzyJHGoUVoNGg/czcjMrRQzHfLzOf7sFNOHTmCOnKHzvT20btqC3raR5u1bGGmtoWgYVMLhP2J6HpWr/YIxuGhqcIRhoeHqpUS9DN8Yw8hIiwceuH9uOGVgOhj+heWNMQYy2LhpIxs3bcSYuR2cSsauYLYv1/zh+n3NZrOa/3uxDGvQZV1Y7lGJ+jMXoj5MGuawNueUn8jweJZ1bfBZ1WcSvHAFHieeZgHmdJvuj44zs+sQ3ZMTSOFZO9Zi7S/8OI0dm/ENg1jB4Ks6M980gGH5eTELDhOJ1cBieji1cSNmusPMbIfWDQvpoVD6d652OxFqNgh6HYZh7Xge58jc9/5+rSs7KZn33FKGUXrE6bdbfZbvQvKm7Ah5z1pV1kJND2v2a0in56IRgbVrlxC1ZdLDXoA0gKy2Lq6Kz1IZMj0nfKxz1sdecNgLsFgofdOGVLGDTsDYuRkWz4X1MHR7PQQ9VEezMJjj09ekHq7IDgWEAmsoaCNDN1jM+lFa2wTjtzJy3w707DQz//wC+Yv70Fc8umMcvX0H9q4bkY1rUDF4dYi5uK3SLgd9BmyBF/pLrQQX0lkZZNh8vMF7B3vnl9I5uRyU8Rts5MMM7EJzDQc7gReTtoXy8WqyVAN0ofE3NVMrXrGzObN7jnH++X3Yw5No7mjZBs4YulawG0bRUYtKmHVrKD0wIYxBsV9qehKJa4mkhxdP0sOkh4uxqvRwCXl1tfJzRXcoguci/BVeLBSfCZpZvG/RzYSGV8bUMHPsHJMn27R2H2Xk9bciO7dj12VVV7L+YqK1h1yNQeDLVdiXEu7glKylhFdOg7nUZy8nw/ZdrhvKciiw3G1i8N7y+vq91zrLUW8gLiqLv2de4dQkM8/sob3nGDrZBhrQyFBjcHi63iHqyNTjRDBI5dURMb1h20XiW5ZvvWwTiWuJpIdXNtykh0kPL4YrooeLdHKuph6u2A4F0BuehaqLZoCmC3PzjResNJmyBXmhtDodsnNt2mcmkYNHWfOWu7A3biRvZWT1wCQMQZlqEmYCenPxllIBl3u6zuWYrlQ38PM9p/xs2JqWxML05WWc540I5I7u4QnOfe8HsO80zVxRL+SaI4Unl4KGGFwj7NrmJGyNp5R7g8gF1cU614PgJa5Pkh5eWZIeJj28EJIerugORc1X4grk/PneIi5VdCZHZqcops5hrLBODNaCKxzNzjTt48c4c/gArTfdyfp7dqCNjMIKxJ5faiZzEdXws0ilvVxVtDRilzLXs66JJu7eUP/Olh6bMo3lAiuSZ/tSCJ4wj5kpOPfMPqZfOUA2PUVTLBbBdXNGCiX3Qp4ZRmwTsRmcm0BGPFYgiwPEqmGRI9bCpk1Lrm8Lld9iUxcSiZVN/9hBfZ8gNYJaQY2QOwcY1vqoh7Ygm+3S+cF+Tp89Q+vNd7H+9bejrUZND0l6OIRSixazGZfLplyyHg6ZolT/vVzIO2xkInUmLg0PqHrkfM7Et3/I9MsHaM5M0ZCwm6b6sP1z7pRuw9IyNrRjwEhoj1ltHWP9JPWl1oeroYcrtkPRl8w9e+Bf/gVuuSW8MKpCx9HY/UPs8UnUSHgZEYfEF8Qx77HHGrRfeYlz42vYcM+tmPUjOANewk40pJeLPmSe368UZmBh3sVQdhyoG9KB7wZDN7XdGVKNuABCRuMhdMym20zuPkS27xw3dDy+4cILjhEy51ERmmowsVOfjWbIt3Jk4xgiUN/mMgN49VX4r/91+KMHDOJSDeNS56AmEiuJOTW2tv6gvELjS6AXATHgXRyBMDSzjDUTnqln93Nmos3mN98LG0cobOicNK/KZKeVzdWe6rMczyw7DnXv9mCnYtiUmau1qHfVo+H9UlXx020mnn4V/9whtpzvoiOWjisoxIWdpKxBsSCC857Ch23ey9eRMM0x/FI/IG/oY1eIHq7YDkWfdXMO3vpWuP/+kNuqMJPT8c+Q/egENDLwii86GBvmAiqQ2wbWQ57BuU03sfGtO5EtayiycEKhkDoVJVc9FwaHXi+lXBZaWDbn0qXPkU0MoNrrVJyd4uz3XqFzfITWToMqeG2jhYI1kBly7xCX0cWjWPLNY7iffyv2xg2oATBxD/1YDp/61FVLWiKxoljANFUOk+DODGcaeSUv8nCeg3owhtzB2Kku7YmDnJtybHzrToh6uOhDrjNWghYsZxyWusj4UhddJwAFnZhm4nuv0PnBIVpdQ9E0eNdGCkWsgWYj6KEKqh4lHAKrhNGJaotaVpcDbOV2KBZBDTTxeC2QTo4zphoe0jhtJxdH5qHVUbp7j3DGejY+8Hrs1jWoWT2FdD1wOYznlX7udYeGdxVmC6aeew154TDrZj2zxlEAjTxcZAh7tTfEIA3BI1hVEAfqK09MmsKdSFwcSQ+vLZIerkKSHq7eqZMKaJFjTBhtUHrz5b33eOcwhSO3Ci1Lljt09xHOf3cX2ZlZvNfqQJ1EInFhqIaD63zhaf9gP+0X92M6jnbexXQLWoXH1kYABcApzhWouuCVUV9NtEgTLhKJiyfpYSJx9Uh6GLh6IxTOQaeztGs7nXBkebsd/vYe6TqkyKHIMRp6c77I48mgirUZqgKFQ4scVFk3A7Mv72eqIYw+sBPGwrHxq7EnmEhcVaLns3P4LJ2nd7H29BRTODLvg9HUMC8UDVO5jbVxXQuhvboCcoFOG2l3wIZTfYlzRgHI89DmnZvz+GFD9QtdkzxviWuZcCpz3PnJhPUU3jvKQ0ytjYs+44YHqsraGZjdc4SpsRajb70X1jSBtM4okbgQqi2GvdI5fpb29/eyZqLDtDisMdXaTYcHAacei62kLizS1jhVv3fYo1zAi+lK0cOr06EwBkZH4fOfX9r1Bw+Gl4t9+8LfqkjhaD79KpyewngAHxZ7lh2E+H9ZJs57XGZZo0Jn13cpnt1OtmMLanrHqicTmkgsTuXJbOe4F/Zhj5zBqjBqIPNUhrFvfUpsXR7FGqEllmxtExk5C5vWxPnfA0tnnn4asizs9rTIojToGdH0MpS47ojLmYwY8ErhHTYeYldvF/XzBzrNjLEcpncfxt6wkex1N6NWaot1IaliIjEPtXd27z3MdJl59oe4kxNYL2hDsBgod2nSXme93AbWq+JFsLZBs9EAahvLcGmt72ro4dXpUFgL/+k/Lf36H/wgeCpri7K16yiyb+FeO0FDha4Gr8voyAhFUYT7arsVKErbwkiuFAa6t4zDO99Ma+t6MIJYRbn4LdoSiWsdVQVH2Cfbe3jpEO0zz6FbuuQa3C15UVRGs/7yUu4cYjAUmdCyTdrrGoy+763YmzbhjWC1ctkQb4T/+B+h2ZwbDxZfYJhIXDeIYK3BeR83HAHnPM2RJkVsk7jgdAMQa2kbxeQFcq6g/dxezPj6qIcEPVRJe5YkEvOg1PUQ2HsCffUYIobCCk0F74q+Eb+yM1HqYSZhl6emyeg4rXoSegEu7pWkh6t2DQXUzg+wlkazgc0szrnKgBoRNM4fdXnBaFcRBVsojcMTTH7vVXSyg69Ok0zzRxOJ+fCAQ1Gv5McnOPe93UjusDb6JeZs1NVv0IwJW8aKhvNifOHm7u51gdRPgk0disT1S9LDROJKkvRwLqu3Q6G93p7G4SM0eEOzLENEKmPqnMNai5ewZZcYQQtHc/cx2geO0xXwSLKficQQ6kbKG8F0HbMvvIY7OQkFuNzjvQCmN1+7dhiPtTZ8bgyCRdXgCsWYBiBx3dPysNTDqBKJa4qkh4nEFSHp4fys2g5FNUe7OrglfG6M6RteMsZU80i98+R5Tp7nADQLmNlziMbZdlwAk15CEolhlFMmRKF74hy69ziZCt4rhnD6p4ip2qO1Fmtt1f6MMWEnGRVELCIWEHx5rkz5nKuXxERi1ZL0MJG4ciQ9HM6q7VAYBLGWRpxqpgJWDOo8Ggu6NKC9+duCqNIwFgG6UlAcP0t7/zE017g8Y7UVYSJx+VFVnHp8O2d27zGYmqVbzEIGaj1qPbl28cahVimkwDQFFci9C1vqWcFJWJQtDYt6H7yjcVeopba8wZNc04hE4non6WEiceVIejicVduhQJUiC0bUNcOWeN4KzgjOCmakibYydKRBkRk64jGjLbKxUcxoi0Kg0AK6BWcOH8PnRbXyPhnRRKIfEQnbTp6cgpeP0/BNxqRJ0whGPeIcRhWjhB8PvuuQPCwSzYzBZhk+U0QdOTkGhxiJmycQPaKpY5BIXDBJDxOJK0bSw+Gs2pOyiQVqRMBDlnvUCJkYGo1GWOQiYfjIemiaBqbwcQ9uT4YgrYwxychfO8vsgROM7LwRfJjzlkgk+rEO2ofPUcx4aI0SttV2kMUzJBRwDZxzaDgOFNsskPhS44ziWgZ1imYG3zQ49TTC/mp4Lt189m1VW9tpqvw7kbgmSXqYSFxRkh7OZVV2KLScZ2YtU2OWXDzNZhYyX4SOgaIIC86InxUiiFe8D1vq+Tiv7WwDNkx2cbsOY++4AZpxD+6yKOt5frUcNZfxPehik7TUKF1LWbZYWharKlejKl1yPsSIigg622X68FHMjWtp3X0jvpEDgqrHINGgmnCunYabRcMKM7WCF1DvaIjBA75pMetG+04FvRznwdS3sU2disS1RtLD5SPp4dJJepj0cJBV2aEQEWhkjL3pDvQNO+hYT6aCqQ7UNYwawauvvCshA0MBlkUkCI0GsPcEM4dOMnZ2CrN9Q7WQBiGctl0+tzw96EpyuQ8X0rD92dyEzf9Ms9DXc/Knd/rj/AwGthyZvHzzCKuTMMu/KY9LkPpFA+mU/kWNqjGf+66orhl6smXt+yVEcmj4AzGay0Lh19Lt1OMmpsgnzrH2DXex5v5bcZnHxYN7TGxfiFbxFgWJZ0t4wIvS0FB/vFfUCFZ6sy4v1wBv2lY2cS2T9HAZSXq4KEkPkx7Ox6rrUJQLTlSUkds2owojte+gbvQGq1RJKDQnsD5X2o0m2YFT6OEz6A3rwunZPvQi6yiEdrHciVqA/qZ7eZHQAtAFE7jwYr3ht/rqS5kv9+oPleUxoMs597csh3qI6nXgigFzVR0MtcACq7JxMyRPe66NJSGD8Rv6vP4oLhx+WLRZiIbFnYfOkGFp3roZ3xAsgq3tBiNGws4W9fAHwis/s0NWb12u931jVu9SsURiIZIeXj6SHs5P0sOkh8NYdR2KOgKIAY0GUTQ01KogFikRAbQBzc3rGNm+menDJ1n/hpvQMdNrD/VONaEJyYAZGLz0wofyehW/DLmvKWj/5/MPHw4a/LJxDjat/mcJBI9CeZiRmAVjPhhWL+7x97JRytwc6Wu/8z1CJX4p846VVs+svu+3DG6Rxrhkh1LfN0rwe+iQvaIH/QkexSNqKA2oiqldoxitCbyGsPvLMYY5mJ91o9cXCa1drjjpr5cm7otdbi9Z1qaFfG+ZWgyC7xacP32O7MZxmhvXoSIYFTCmypl5IjWHYXZ1uVgJp4UmEleDpIf9aUl62Lsg6WHSw2F/LzertkMROrqm38aJWXJZlPcpoE2L3bqB/MWTyEyOjrYIjaRXeauTRuN81fpzhJ5DQRarR1L7RbXmBYnGLgZUd1CURhsA78PCOwYak4mpGTpUWHoEdOAzj0rYuqyWEkR9nyGM7bHWfsPz56ubpSGu4uzC80T67/HlI6JnQGvRGJQRKb0TZTLL+MWhRHwok3BZ3N+5TBK18u4FUc+gWuR7w/v1z0Ullk34MRrjHNMrYTyzSr8QrheppUJcTK8JJdK337SGZ5fpoSx3RSWccCsiVGavKo9ahkQPkYiACKYXUCg/db0kKWFHiRgW5XxKIXiaRFDvUF+gAn5yhtlzs4zvvA1GsvhMqcqq75WipxH9zPd5IpG4JJIeJj1MelhGPunh1WLVdiiqYaWLvZ2y4gfjkm1bhy8KZg+dInMO9bHRxZrqnCMTA17DSaTeI2JCFfceMSYsxpHQ6w2nIsaKqNEAG8F5Hypb2Zpd6BWXpyhipWqYELYcw8eecwzLe43DaYL3LnzufGiERhAjeOdj2qIpUnDOlR3xqmdujUGcq3rx4cHhHucdRky1YE/Lkx6LEIiviUtpYEVARVH1uMKFZ2DnDgvX4mFMBh6KIo/pCr8ba0ozj7EWH42IqqAu5pkIeMXExYXOOdAwiC8S5zNaU6WvjELPW9WLizE2HjajOC0wxgaDAlgNuzE47zAmeCNsLGfnPSIeY0K+ee8RTDXVQBDUgJqQXq/gnaMh8QTN6A1TIYij9gTH+1Cu6hVje2Xpwz+Y+lhpTLsYgzFh0aUoFFEQhRi+D+Xpi2BQrZgQZ9M7FKsUI2uErlG8c7RUsDdsABsluXxpmE9J52ucF9tol0Da1SlxXZL0MOlh0sOkhwNcaT1cvR2KZaAyIijd8Rb5Xdtxhyewx6fwzvVcEPQKQ72vjFs4Qt3ERhssSHmsOpQGOvzvY6XtLYgBVDDRw+OdDxWrafGqhJMWIZNwqmnplfGxJx08RAavXWw1J04piA2h7MFLE5EMVR8MsSqZDc9Q79HMxnwIhiA2XSA2MnXQsMEAaDgMyRT03DQI1SEsNs7PywxePWJKj4WvTocs79JorEN+hUaf0apcNJnv5YECYm3Pu+IJ3rjSixAztLd7gUcpsFnWM+4mDnMb01eudTeBMabykvi40q40iogPt5VzIqP98L50d4QtG0ujV3pFei7DkEfG2ujxCJ+ICBgbn6uIiSLNgAHQ0uMjVViVh0tj+stxZwnloBR45zBZKG9Egpj42lBy5YnpeXY0Pi/kv8XHoWgvilnTClnYc5utONJuTonEhZP0MOlh0sOkh5fCdd2hqFBojbTY+tNvRJ3WKmX8uhyyozc6FQrII+WwXl9w5RDcYCGWBqfXm1VTs0WA+KzXMKjV0erB4ZnlbMMwDF1eVGtEEIaJ64FDbxjQxHmMHozYylMhoqj46rnBBkhMk1YeBQgNCsJcxPC8+LePIqI1z4f0FoVp2d2PxrL07FRprWVb2VB7SSnnBVeulb68710YPUnRAJqYLSpVqGUGhmAq70mg0oj4QTnbM0oCZaSs0numlM/SyiDVirp6aq1qQa34pAq9l3daj21tGkDP7NfD6eVjKYWltNaXYZVVRKPh78u/GvX5yaKQqRKcQjFWK/SlvR6vwYV9KzXOicSKIelh0sOkh3NIerg4qUNBMCZGTZhjV55USC3z55uLqqYyhmUxzVcZ50OlvvNGb15lfQFZzyL2GlPV2a/PC5TYIOqRrVsBACP98w2NVB6VXqPuT21c4kfpjSjnmGb1HQzKawBjtP5nLyK9ZPRaMRqerzEtczr62h9QtUBuUGEGoyF9X1b51IvBPIQ4DJa4VIazdp3Wf+1VgpCswet7xrO8af5tFwc/1IGPh9ykQ4wFix1I1S++c8MdyFyZUzgrmkqwKy/oKop8InGVSHqY9HAwnkkPe3Hu/Zn0cJDUoYhIVTnC7gIeaouI/NAN94I3xNbCuMD6JaC1Sl61exbYaUAqcxY76HFuasRL//7LIqbWL689p3bXgKmBgThVNjr+n0n/33OiKJ75tygMz+3bFU8MpvT69AnHsFjH7xaygUO+Xmjvhv7g57tu0CAu9ODoE6kb7fi5UE/7hcrtIk+X/rJdbM+KfhaPS1lPVvKLeX16xXJuk5hIXE8kPax/m/RwnlgvHEbSw6vOldbD675DUc/wsopUBqXW6R9aZUJtYrDyzbu39ACVX6Gq9Vr7YKEwyogpDBhHM+eTZaB83JAO+nAWjsF83w4a+mFxAOY4aRa7fMmUCRra8BZO01z7a+a5oPy1X6iWjZoFXawWXeuktRSJxIWR9HAJJD0k6eHq40ro4XXfoSi5tIy+uHvn3HXBcei/ft7DdYaFu8ho3pzLl2nu3VJ7yQuGf7mtwjXwErr6U3DppM5EInFxJD1c+ElJD1cXqz8Fl07a5SlxQSzVE3RRYS9TZbyccUwk6qQORSJx/ZL0MJHokToUiQvjQurL1bJji3l+4j/DHTflfNJ5gkovkD2u86xInYlE4jon6eGlxOza4jrPirRtbOK6ZeHlXtGIam17N+n7L5FIJBKJa4Kkh4nVQupQJK46w+aRDjWGqohXVARnyv0a+jYSTFxHpO1gE4nEtUbSw8TFsBL0MHUoEleFYUaz/KxQjYf90BvzLf814aAcUx7GGvdJp3ZQUHrBvPYpt8FLZZ1IJFY7SQ8Tl8JK0cPUoZiH+feUvvB7LiSMC2Il7bNfbTc4+IXO2dWudv4NouEwG/VKkeeoc0i7QLoFXhX1HuccYkzcG10gs2ANYgymkSGtJto0tTgsfbfp/iQMv+taOs9gYYMzXzrnu2fh6y8m35ZSBuU13ntEBGsvw3aDiUSiIunhBZL0cFWQ9HB5SR2KBbjAneSG3jP4rSx6zQWyEtq2QN9hQvQfnFOo0vSA8zgLBYLJPdIucFOz+DPn0fNt8slpiulZ/Pk2vlNQFI6GGGxRkLucwgq22cA2GthGhrQa5E1Da8tG7Nb1tDasxawdRUcyrBXUGnzstWeLZJQgCzb4mk/oiiHaf2qsLqK8MhA9HfggpHGhEAZn6/a5xeahP46D1+sCBzoBOAyW2nbhF+BlSR2JROLKkfRwiSQ9vCwkPVyYlaCHq7ZDMdhDW6z3139gz/zf154wtOpcaue8QJe1HRqZu/HcMA/IsCp5qdEo80erNIWnGKBaIqbQdOAM+EyQyVnMgQna+4/TPj1B59x5zGxO5kE7OdYpNEKTGlVBnCczhhbg8oKs1Qg9ca901SNGcaMjFFaYbWU0N65jbPtWitu30Ni2EcYyioah0RfrubnQO5N0OL3zWIdTv3u+rQB1zm8y55t5biDUx2Cg+k9PrZXu4N7og8HNs/36QChDI6HxeTrku96ztO+3hUItKc+QbSx6ZT9LafOJxPVC0sNA0sOkh0kPrx6rrkOx0FzDhYxj/bthh9IMvVcZqDJSq6tDeo46WKHmBDdneLe8pff50obHqut1eMOfY/gXDG0xhjeEnhmIDTpGytXMiQo4o9B1yIEz6DM/YvLoCdrtNk0MjcIjqsFzooptNhAjSOGgm5Nngt80hhYec96R512chDM11QhiDDZ3mLaST3XonJ2mfeQU/HCM1u03sO51t5HduJF8NJj16vDPvkToEOPSf7Kn9N2gcx0V0m8gRaVnSKXu6wi/SS0AVU9ldqWKEkb64+AlypWG+40qRmoGVcLNvnxsqWE1+9pvkzXMuxXi/NsYgT4UVR/LVlAt41nWU6H2Z/WAnpGvGdWBOb3qPZlXNDMURmg4hXIO8BK42vNFE4mrTdLDgeuTHiY9THp41Vh1HQrozRVDwcf5hEBVeN73DysZaxDAO99vCsqK5cv+uPR9Z4bZ1MGaEuMRHqDgfG9uY/055eU+NpbBcOoRmvvU2nf9YiEDQcyJck1cxBhQRcTERTw+JmGIgAxa+8rSx2fH79U7jA/55mM6RRX1DhET5oS2Pew/zcwL++ienEC0YL0xoTycAxGsETAGBYwoTgtcU2nfuI7xu2/j/As/otkpMIDPDD4ziIINEcJHo9YUg+SKPTnNuckfMXviLGvv2M7orVvjgjWDMQbvy0VMMbGlFQHEBEMR8iyWrw8eOucKjDWg1AxLzBqNeek9iqBS5m14hPcu5newVn15LuCrfAV1Hq+hXnsf4im2V/eNMRSFwxVFmE+LYE0zGMlYRkZC+yjTZdRUZW1EcM6HNCAxT4pgRMusUI8xEubxWot3GuMUrg/3etR7jLUxC21V37xzWPG9v31Z3wyliHRdgdw8TnbTOCrCfN6spRrL5TrBNpFYLSQ9THqY9DDp4TCutB6uyg6FiOC90jl6Do5N4G3oleN9NIahPXgfFjGFnqRisBixePVYY9BY+JV5kmBU6otbvPeUK+i18CEMEw1QaMHhmar4wmG9wZjSQEYPjmpoBCjGO7zzVcP09Cq1eo+Rst8eDbqR6vnBYIExNizMColEYsVTwMa8CY9VChcaWNkoxDtMNBiFc1hro0HUytFAGV7VXQ+NwnuPAKbIQz4ZgiGQDPEhDOMVi+JcEQyOdzDbRc/PIp2CDHC5w2mBNQZDWDzmveK9w9igQ51MkKzBxvvuobtpDHlBaKhQZELLWNSDcy4IRpw7aBGMCiKGwnhaTvCHz9I5O0P3BwdCXnrFZhnge3kIoFLleaORodHz4bzHiOALh0QvXGmwisIFAzPgaRAxFEUOaE9MtSy3orpWkFAXrcV5j4/XCwKiONftLbhSRUZGwCmaF2QIklkK5zDxBSJzhDYQXTrSyMKMTQFXOGws37K++cKFPIv3F+px2qvvRoSG2FCGsV6Q2XA/QSgpXMhTazAmfFe4IqRDBCdFaIMC3mllYEWERpZhPPg33cambZtxTY0G6dKM3krY7SKRuFIkPUx6mPQw6eF8XEk9XJUdCgCjQv7DE7SffhW1hqYKrUxw3uGKUODqbVXhS0MZ+8SoEZzLq/BcUWAzE21euKftXdWrBkG7BQ2xFNGwGGvwBAPmigIrBnHBa+NVMcZUhgwIFVVDQ3TeY0yYGynGkOddvIsGVMMCGxGhKBciaWj4RgzeKcSKbkRiDxfUOwox2NiwymFTIyaKjsN7j7dSiUzuHFmjAVFsFEHVVD1jYwxKCMMKFHmOEx+MBIJYg7cCThHnaRgb0++Dx0UVJwU4h/EaGj5grA27VtQ8IirheUYkDG8WhnxiFrt1A84KzgCZRQtH7n3IXxF87ffS01SYEJYvcnSqg/q8VwbW4jUH9bEcDNZnIR+jd0cygiQVRfAUxf+zRoNGo0GRh+HJUqR7hlKjkCnGRGGNbhLV4KEy5eIpVYJvIgfvaTYbZFkWhK/IEe1UQqsCHRdV0CsOoZV7NC9iuj3kGoa+NXghncmr+mfisHDDWoqiCHWycBibBW+PGIx3/U44I3RtFBogE0OzE1qQc6FtFI2Q70XuUO1W6cqyrKqHzjvKnUZKrc6spdPNMV4xzjHEl3hJpE5F4noi6WHSw6SHSQ/n40rp4artUARvg6dRCOoUEaWIw1neOTKbBQ+EChKvzX1eeUtcEbwj6uOuB0YwTuPQWUHhPV7AZrZqBOochXHRMAreOYwGY0lRIFlGWdvVOXycvGeswRUOL+CiYUUJHiEjOPEUuDimHHrIjmCAymHD8nmqRRjWix4jjMUT4uQ0hOFFURuG1IxCt8iDYTKCF6XQ4InKGg0ks2FI0ApONQ77EfNPsQJeHWgRjJqBUd9ExYDLgwF0ORlCURSI8SHdIpWXptEKYtDtdgnTAqXqsZdl6b2rPvPOgwftdvDnpuhOTSNrRvB2Ci2CIQ7pMZX3zEXvUjWE6cHgsSphUVwsC3Ue33WICd6UDIMWveFuJYTtO100Nnw1gkEwApo7CudRH4UMyEwsXwVjQrlYA14LnPPVAjb1cR6oC4JUbgMIMU9c8GT5+FxjMgxBIFBlbZ6DEURBrSHXbrjfmODdikKZd7uI07gaUKuydM5RxP+zLEOzUGaIBGMfvY8lojASvWXWWhTFa88TaI2hlYd5uHle4L0ny+JystxhMzCeMDatUdB8rPvdIJpuTTOIIpfqh+mROhKJ642kh0kPkx4mPRzGldTDVduhQEAySwOhyAto2tDwopcCglejyIvQ11NFjafAh6FV72moYquevMepxflgrLLMBoNTDt8BWMGLw+OCE8ELFou1FtNogCiFL4IRl9AQrbVxu7JwoqV3wYsRKqqiEnq4thWbmTNxr+kwDFwNKZZeIZ+HRm4NYgWnBXlR0MyakIGIYq3S7cZhWDGYlomLlUKcm5JV3hKbZcFLExtPMO6m6pkXLsdmQZSCmyJ4k1SCV0UyixeP5jnGKmIUFYMYW4mZK3KsBKNsfK+hqIa5ukIcArXBIHaLHMksVmBkZAS7dRy2zzD1oxNYYxEjlRCWnhincRhRPUYMxmmYn+sdGIs2bDS2Ie9NFnwhzkUvlLGIGLx6MmvxRTCQ1gQPl2SWbl568OJYqY3D0wjaCGko1JOrJ7MGEUMuQaysNUhsbr25phbVKALGoJkN5RSHQYvoMdRoGPE5tpGRSzDqufOgIT+8MWS08GIojI0C40AdBWBMg8KFtiCtZhgWV6XoBtNlM4tRiUPEcZqBMag1OO8oRCi8w5sseKQKR2YMhYtlODoShLA2LUFE6PqsegEo57kaa+KwsMU3DdK0lSf0YhkcZk8kriuSHiY9THqY9LBWl+DK6+Hq7VAoMJLhN4zQdND2OY2sURmBMHcN7JpWb5jSh6Et7xwWiG0IJHRePYJXaGZhqM35MCexmneKr3qkRkKYYbivwJcLugg98CwO3YoNPfEmYd6fIXorpKxkITnlUCXRk9K0oZdaehzKCpmpYOMwoYigBppxrqEVwfgwRNyIPX2NXiDJsjBPNg43Sm2hnkXI4rAsImEv5GjkRWOvGqmMsJpGr8JmgjHtsIDJh+dlNBExOOfCULRV3HQbnZjBTOfRwxDTTZjHiZZDk5ZW0+ANFMZz7pW9mINHGMtBGhnERqiuwGQZGFDjAYexQpHniFqszyjWNNB167BbNmKzJlmWhRJSj2uE4fxycZlI3IujHL6PcUOD0fVGaZrgtVIUKzbUgTDaXqmCCMHASwP1vYbtcUgjBg94H35Kz5ICthW8Gd4FI1N6fGKUcDobhuMlGG51RawXYb6mMUEkxnwp+g4kDilrXOSGxikBBlUXPWHR4Ja7aMShaq8+GFE0eqeCF8baDFcEwXKq2DhMTNVWyiAURwZioreMMHRtBPGhfdAA3TQWNrMo5x1cAmmqU+K6JOlh0sOkh0kPB7jSergqOxQigjfK2OtvwmzfhFUhM2F+JBB6t6pxr2UBwo4ONu4SUA1jSdzNQsPQHhKHmmxc1ANhCDguXkJA1VHN0dSw+KYp5UKk2DuVcOiKETBIrEgajUacDxmn/4Vwws4CCGhsyMFoxnZZ/iO9oUqiMWtK//fV1DsNyiASY6VhWLGMQzl/r7qhzBJrggepNOgKFlPlKQiSxd/LvJHQuMoEqS93SYjG3ylMdXBnzzO19zD5sTP4c9M0vdAowIlCIxgnEbBOsUXYcWFNrlC0oxMkLp4ShWZYYGUQGmoRn6Fi8FmD9kgG29azZscNjOzYhhlfi2+a3uJBVYwNmeW1t8DPRC9Fmb9lGUdfWMgz70srGRqq9sqcqr7Ru1ektEdhOLwqn3pRxfK20nd//yE5hAWHRvCx/kllrMKUA5WwADCWbjCYlUKD9VRDwiomLEYsn6ix/tXSbqq8CM9WwhxfJdYJEbIyjfTyK1wR5s6Ww84ljViny09MuWDSxjbDpZE6E4nrjaSHJD1Mepj0cAhphGKJGCO01o7AmpFQzQzYssJFL4vGKlLV2/hHPYtD5dGqKmnPssQ6FXY7qHZ5iMGEYWTF079nsomhl8/sGc7SoEnVcMqHlA0slH1ZAcuw+itE/e7et72WGQ5GqV8jzHd+Yj1kHfJZ78TGkH6pPV1q8RQafba7FLKyMeQodlOL1s3rady+GXf8PN1Dp8mPnaF7ehIz45CuDwulciX3DiSISpPgPSgyIc9zrAlD6g2bheHj3OGblnztCGbtKNnmjYzu2Iq5YxPZurHwXRbS4iGWbSiZIGbBInix0UTORaHaw1pq/5bf9v6qfS4+eESolaaavssk1jtVDXNBB5+qfTUFaICE8gypsPWrh/xe7tNR+7zc5q5maAaf0otkPW3xpSEM4va3C6oWUDfJCGCHpqNn6KQKd3lIIxSJ65Gkh0kPkx4mPRwkjVAsQn24M9ouDEpTQeIh9xD+U9G+gjGwYEmVTatXAP33S+UeKT/vGZHwTeyZ9tVYau0qXl/7Xms95qpnPKdS1W4Qz0KJyOY0w/44zkf9rv79xgUV23ddMCZCdUJM3yOVwXxphpaEGkU2NDHrbmB0x1ZGpzt0z0ziJtp0T03izk2Tt7uYvIt2Ovi8wHVzunmOzRqQZZhWC21kdFrhxE+zbozRbRtpbBpjZHwjsm4N0sqQpokNXGkgtYN4QoTL+ZtlNGVOFs1nJGs3zaH2uQr9zasWQmXE47aDwpAgBZ3TPAdM9TDVG7xe6xeVJm/ghqFvF0OMntZFvW6aex/VxTp8O+BVGgxeQhpXmycmkVgJJD1Mepj0MOnhnLhdJT1cdR0KKI0oYHuVI2Rfv+9hjj9jkUwO1/dXjH7/yXyfD4Yy99eFnnlht9vBLvZATKQ3f49er3+pBI0Y9ALNE8ehEZQ5cbSVlQo/XkDXWMzYGka3jIEX1Hm0k1N0uvhuF98J2wa6PA87cNgsztUVpNmAkQzbamJHW5gsQw2okepUTFM9cDCuw3N18ZqxnJQ1c9BszCnJxYNZ9FH1i+YJsXoxWCys+nVDrh8azqCk97OoBixCWoyduN5Jehh/TXqY9HDRRyU9vJysyg4FUHlHyjl94bNL7deFMBf+e7HPL+FRF3pDzIMFYyiXslfAMlBrVGVlz+ptUAQMSCbQatDQBuiasJ81WsVfondBlDAcWi6OUgXT8xMM1oDV9aK5muK6PFx/KU4klp+khyQ9THq46lntKV49HYpOB2Zm5ny82gvgekKYO3BY/6xelqY2z7C+F3RtG5BgNGsjk8PCTqxiiuJqxyCRWFWsrhfG65tq/UifvGmclVObLiZUJ4ZDTQ9FanpY3iGUd8/Rw1Q1EpeZ1dGh2LIFnnkGDhy42jFJXCLDbNrgZzrPZ/N9t1DYiVXM2FjYH3wB0gtUIpFYrcydFSNzXvznLKwVqRaaBz2sjbrURjuSZbz+uNp6uDo6FNu3w2/+5tWOReIKUW8SwWAO32/iKg9eJxKJRCJxWam/JJZ6aIdoX9LDxNVmdXQoEtctUvs3kUgkEonrlaSHiZXMcqzaSiQSiUQikUgkEtcpaYQikUisWOoL8q/2/NBEIpFIJK4WK10P0whFIpFY0eiQk0UTiUQikbjeWMl6mDoUiURiRaOqK9qIJhKJRCJxJVjJepg6FIlEYkUjIityeDeRSCQSiSvJStbD1KFIJBIrmpVqPBOJRCKRuJKsZD1Mi7ITicSKZSUbz0QikUgkrhQrXQ/TCEUikUgkEolEIpG4aNIIRSKRSCQSF8KePfD441c7FolEInH5+f734aGHFr1MdKUuF08kEolEIpFIJBIrnjTlKZFIJBKJRCKRSFw0qUORSCQSiUQikUgkLprUoUgkEolEIpFIJBIXTepQJBKJRCKRSCQSiYsmdSgSiUQikUgkEonERZM6FIlEIpFIJBKJROKiSR2KRCKRSCQSiUQicdGkDkUikUgkEolEIpG4aFKHIpFIJBKJRCKRSFw0/z+7MPmq6d0olwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/all_20_samples-images-2.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-7.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/all_20_samples-images-18.jpg: 640x480 2 Captions, 1 Page-footer, 2 Page-headers, 1 Picture, 1 Table, 6 Texts, 15.1ms\n",
+      "Speed: 2.3ms preprocess, 15.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/all_20_samples-images-18.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-8.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-19.jpg: 640x480 1 Picture, 1 Section-header, 17 Texts, 13.7ms\n",
+      "Speed: 2.2ms preprocess, 13.7ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-19.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-9.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detecting bboxes: 100%|██████████| 1/1 [00:00<00:00,  1.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "image 1/1 /share/data/drive_3/ketan/orc/test-assests/0058_0-images-16.jpg: 640x480 10 Texts, 13.9ms\n",
+      "Speed: 2.4ms preprocess, 13.9ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 480)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image_path = \"/share/data/drive_3/ketan/orc/test-assests/0058_0-images-16.jpg\"\n",
+    "save_dir = \"/share/data/drive_3/ketan/orc/suryolo-arabic-layout/results/layout-benchmark-results-images-10.jpg\"\n",
+    "# save_dir = None\n",
+    "original = plot_images_original(image_path)\n",
+    "fine_tuned = plot_images_fine_tune(image_path)\n",
+    "plot_images_side_by_side(original, fine_tuned ,save_dir)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95c996f3c95b6b26f1da9ddefb0510c5a87dc611
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,299 @@
+absl-py==2.1.0
+accelerate==0.34.2
+addict==2.4.0
+aiofiles==23.2.1
+aiohappyeyeballs==2.4.0
+aiohttp==3.10.5
+aiosignal==1.3.1
+albucore==0.0.17
+albumentations==1.4.18
+altair==5.4.1
+annotated-types==0.7.0
+antlr4-python3-runtime==4.8
+anyio==4.6.0
+appdirs==1.4.4
+astor==0.8.1
+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1698341106958/work
+async-timeout==4.0.3
+attrs==24.2.0
+av==13.1.0
+babel==2.16.0
+bce-python-sdk==0.9.23
+bcrypt==4.2.0
+beartype==0.19.0
+beautifulsoup4==4.12.3
+bitsandbytes==0.44.1
+blinker==1.8.2
+boto3==1.35.34
+botocore==1.35.34
+braceexpand==0.1.7
+Brotli @ file:///croot/brotli-split_1714483155106/work
+cachetools==5.5.0
+certifi @ file:///croot/certifi_1725551672989/work/certifi
+cffi==1.17.1
+cfgv==3.4.0
+charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work
+click==8.1.7
+colossalai==0.4.0
+comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1710320294760/work
+contexttimer==0.3.3
+contourpy==1.3.0
+cpm-kernels==1.0.11
+cryptography==43.0.1
+cycler==0.12.1
+Cython==3.0.11
+datasets==3.0.0
+debugpy @ file:///croot/debugpy_1690905042057/work
+decorator==4.4.2
+decord==0.6.0
+deepspeed==0.15.1
+defusedxml==0.7.1
+Deprecated==1.2.14
+diffusers==0.30.3
+dill==0.3.8
+distlib==0.3.8
+distro==1.9.0
+docker-pycreds==0.4.0
+doclayout_yolo==0.0.2
+easydict==1.13
+einops==0.7.0
+entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work
+eval_type_backport==0.2.0
+exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1720869315914/work
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1725214404607/work
+fabric==3.2.2
+faiss-cpu==1.8.0.post1
+fastapi==0.110.0
+ffmpy==0.4.0
+filelock @ file:///croot/filelock_1700591183607/work
+fire==0.6.0
+flash-attn==2.6.3
+Flask==3.0.3
+flask-babel==4.0.0
+fonttools==4.54.1
+frozenlist==1.4.1
+fsspec==2024.6.1
+ftfy==6.2.3
+future==1.0.0
+fvcore==0.1.5.post20221221
+galore-torch==1.0
+gast==0.3.3
+gdown==5.1.0
+gitdb==4.0.11
+GitPython==3.1.43
+gmpy2 @ file:///tmp/build/80754af9/gmpy2_1645455533097/work
+google==3.0.0
+google-auth==2.35.0
+google-auth-oauthlib==1.0.0
+gradio==4.44.1
+gradio_client==1.3.0
+grpcio==1.66.1
+h11==0.14.0
+h5py==3.10.0
+hjson==3.1.0
+httpcore==1.0.5
+httpx==0.27.2
+huggingface-hub==0.25.0
+identify==2.6.1
+idna==3.6
+imageio==2.35.1
+imageio-ffmpeg==0.5.1
+imgaug==0.4.0
+importlib_metadata==8.5.0
+importlib_resources==6.4.5
+invoke==2.2.0
+iopath==0.1.10
+ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1719845459717/work
+ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1725050136642/work
+ipywidgets==8.1.5
+itsdangerous==2.2.0
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
+Jinja2 @ file:///croot/jinja2_1716993405101/work
+jiter==0.5.0
+jmespath==1.0.1
+joblib==1.4.2
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter-client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1654730843242/work
+jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1727163409502/work
+jupyterlab_widgets==3.0.13
+kiwisolver==1.4.7
+lazy_loader==0.4
+lightning-utilities==0.11.7
+lmdb==1.5.1
+lxml==5.3.0
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe @ file:///croot/markupsafe_1704205993651/work
+matplotlib==3.7.5
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1713250518406/work
+mdurl==0.1.2
+mkl-service==2.4.0
+mkl_fft @ file:///croot/mkl_fft_1725370245198/work
+mkl_random @ file:///croot/mkl_random_1725370241878/work
+mmengine==0.10.5
+moviepy==1.0.3
+mpmath @ file:///croot/mpmath_1690848262763/work
+msgpack==1.1.0
+multidict==6.1.0
+multiprocess==0.70.16
+narwhals==1.9.1
+nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1705850609492/work
+networkx @ file:///croot/networkx_1717597493534/work
+ninja==1.11.1.1
+nodeenv==1.9.1
+numpy==1.26.0
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-ml-py==12.560.30
+nvidia-nccl-cu12==2.20.5
+nvidia-nvjitlink-cu12==12.6.77
+nvidia-nvtx-cu12==12.1.105
+oauthlib==3.2.2
+omegaconf==2.1.1
+openai==1.51.0
+opencv-contrib-python==4.10.0.84
+opencv-python==4.9.0.80
+opencv-python-headless==4.9.0.80
+opensora @ file:///share/data/drive_3/ketan/t2v/Open-Sora
+opt-einsum==3.3.0
+orjson==3.10.7
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1718189413536/work
+paddleclas==2.5.2
+paddleocr==2.8.1
+paddlepaddle==2.6.2
+pandarallel==1.6.5
+pandas==2.0.3
+parameterized==0.9.0
+paramiko==3.5.0
+parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1712320355065/work
+peft==0.13.0
+pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1706113125309/work
+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
+Pillow==9.5.0
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1726613481435/work
+plumbum==1.9.0
+portalocker==2.10.1
+pre_commit==4.0.0
+prettytable==3.11.0
+proglog==0.1.10
+prompt_toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1718047967974/work
+protobuf==4.25.5
+psutil @ file:///opt/conda/conda-bld/psutil_1656431268089/work
+ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1721585709575/work
+py-cpuinfo==9.0.0
+pyarrow==17.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.1
+pyclipper==1.3.0.post5
+pycparser==2.22
+pycryptodome==3.20.0
+pydantic==2.9.2
+pydantic-settings==2.5.2
+pydantic_core==2.23.4
+pydub==0.25.1
+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1714846767233/work
+PyNaCl==1.5.0
+pyparsing==3.1.4
+pypdfium2==4.30.0
+PySocks @ file:///home/builder/ci_310/pysocks_1640793678128/work
+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
+python-docx==1.1.2
+python-dotenv==1.0.1
+python-multipart==0.0.12
+pytorch-lightning==2.2.1
+pytorchvideo==0.1.5
+pytz==2024.2
+PyYAML @ file:///croot/pyyaml_1698096049011/work
+pyzmq @ file:///croot/pyzmq_1705605076900/work
+qudida==0.0.4
+RapidFuzz==3.10.0
+rarfile==4.2
+ray==2.37.0
+referencing==0.35.1
+regex==2023.12.25
+requests==2.32.3
+requests-oauthlib==2.0.0
+rich==13.9.2
+rotary-embedding-torch==0.5.3
+rpds-py==0.20.0
+rpyc==6.0.0
+rsa==4.9
+ruff==0.6.9
+s3transfer==0.10.2
+safetensors==0.4.5
+scikit-image==0.24.0
+scikit-learn==1.3.2
+scikit-video==1.1.11
+scipy==1.10.1
+seaborn==0.13.2
+semantic-version==2.10.0
+sentencepiece==0.2.0
+sentry-sdk==2.15.0
+setproctitle==1.3.3
+shapely==2.0.6
+shellingham==1.5.4
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
+smmap==5.0.1
+sniffio==1.3.1
+soupsieve==2.6
+spaces==0.30.3
+stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
+starlette==0.36.3
+supervision==0.23.0
+SwissArmyTransformer==0.4.12
+sympy @ file:///croot/sympy_1724938189289/work
+tabulate==0.9.0
+tensorboard==2.14.0
+tensorboard-data-server==0.7.2
+tensorboardX==2.6.2.2
+termcolor==2.4.0
+test_tube==0.7.5
+thop==0.1.1.post2209072238
+threadpoolctl==3.5.0
+tifffile==2024.9.20
+timm==0.9.16
+tokenizers==0.20.0
+tomli==2.0.2
+tomlkit==0.12.0
+torch==2.4.1
+torch-lr-finder==0.2.2
+torchaudio==2.4.1
+torchdiffeq==0.2.3
+torchmetrics==1.3.2
+torchvision==0.19.1
+tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1648827254365/work
+tqdm==4.66.5
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1713535121073/work
+transformers==4.45.1
+triton==3.0.0
+typer==0.12.5
+typing_extensions @ file:///croot/typing_extensions_1715268824938/work
+tzdata==2024.1
+ujson==5.10.0
+ultralytics==8.3.1
+ultralytics-thop==2.0.8
+urllib3==2.2.1
+uvicorn==0.29.0
+virtualenv==20.26.6
+visualdl==2.5.3
+wandb==0.18.3
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1704731205417/work
+webdataset==0.2.100
+websockets==11.0.3
+Werkzeug==3.0.4
+widgetsnbextension==4.0.13
+wrapt==1.16.0
+xxhash==3.5.0
+yacs==0.1.8
+yapf==0.40.2
+yarl==1.11.1
+zipp==3.20.2
diff --git a/results/layout-benchmark-results-images-1.jpg b/results/layout-benchmark-results-images-1.jpg
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diff --git a/surya/__pycache__/detection.cpython-310.pyc b/surya/__pycache__/detection.cpython-310.pyc
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diff --git a/surya/__pycache__/ocr.cpython-310.pyc b/surya/__pycache__/ocr.cpython-310.pyc
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index 0000000000000000000000000000000000000000..f5717cd629c84302e4edc66d3b8b55cb150950c2
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diff --git a/surya/__pycache__/recognition.cpython-310.pyc b/surya/__pycache__/recognition.cpython-310.pyc
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index 0000000000000000000000000000000000000000..27313a57201ef48b1b56b06bda1deb32ada4c113
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diff --git a/surya/__pycache__/schema.cpython-310.pyc b/surya/__pycache__/schema.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..202322565dddc59bc1212af7f1d58fc18576537d
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diff --git a/surya/__pycache__/settings.cpython-310.pyc b/surya/__pycache__/settings.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..af59e27e2e5c5beca2e838d64b544b36514de3b1
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diff --git a/surya/benchmark/bbox.py b/surya/benchmark/bbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7593e836dd4cc3402557d51e1ac55773da06849
--- /dev/null
+++ b/surya/benchmark/bbox.py
@@ -0,0 +1,22 @@
+import fitz as pymupdf
+from surya.postprocessing.util import rescale_bbox
+
+
+def get_pdf_lines(pdf_path, img_sizes):
+    doc = pymupdf.open(pdf_path)
+    page_lines = []
+    for idx, img_size in enumerate(img_sizes):
+        page = doc[idx]
+        blocks = page.get_text("dict", sort=True, flags=pymupdf.TEXTFLAGS_DICT & ~pymupdf.TEXT_PRESERVE_LIGATURES & ~pymupdf.TEXT_PRESERVE_IMAGES)["blocks"]
+
+        line_boxes = []
+        for block_idx, block in enumerate(blocks):
+            for l in block["lines"]:
+                line_boxes.append(list(l["bbox"]))
+
+        page_box = page.bound()
+        pwidth, pheight = page_box[2] - page_box[0], page_box[3] - page_box[1]
+        line_boxes = [rescale_bbox(bbox, (pwidth, pheight), img_size) for bbox in line_boxes]
+        page_lines.append(line_boxes)
+
+    return page_lines
\ No newline at end of file
diff --git a/surya/benchmark/metrics.py b/surya/benchmark/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..afcb41734ba244917db2317e32b54f02117b00ae
--- /dev/null
+++ b/surya/benchmark/metrics.py
@@ -0,0 +1,139 @@
+from functools import partial
+from itertools import repeat
+
+import numpy as np
+from concurrent.futures import ProcessPoolExecutor
+
+def intersection_area(box1, box2):
+    x_left = max(box1[0], box2[0])
+    y_top = max(box1[1], box2[1])
+    x_right = min(box1[2], box2[2])
+    y_bottom = min(box1[3], box2[3])
+
+    if x_right < x_left or y_bottom < y_top:
+        return 0.0
+
+    return (x_right - x_left) * (y_bottom - y_top)
+
+
+def intersection_pixels(box1, box2):
+    x_left = max(box1[0], box2[0])
+    y_top = max(box1[1], box2[1])
+    x_right = min(box1[2], box2[2])
+    y_bottom = min(box1[3], box2[3])
+
+    if x_right < x_left or y_bottom < y_top:
+        return set()
+
+    x_left, x_right = int(x_left), int(x_right)
+    y_top, y_bottom = int(y_top), int(y_bottom)
+
+    coords = np.meshgrid(np.arange(x_left, x_right), np.arange(y_top, y_bottom))
+    pixels = set(zip(coords[0].flat, coords[1].flat))
+
+    return pixels
+
+
+def calculate_coverage(box, other_boxes, penalize_double=False):
+    box_area = (box[2] - box[0]) * (box[3] - box[1])
+    if box_area == 0:
+        return 0
+
+    # find total coverage of the box
+    covered_pixels = set()
+    double_coverage = list()
+    for other_box in other_boxes:
+        ia = intersection_pixels(box, other_box)
+        double_coverage.append(list(covered_pixels.intersection(ia)))
+        covered_pixels = covered_pixels.union(ia)
+
+    # Penalize double coverage - having multiple bboxes overlapping the same pixels
+    double_coverage_penalty = len(double_coverage)
+    if not penalize_double:
+        double_coverage_penalty = 0
+    covered_pixels_count = max(0, len(covered_pixels) - double_coverage_penalty)
+    return covered_pixels_count / box_area
+
+
+def calculate_coverage_fast(box, other_boxes, penalize_double=False):
+    box_area = (box[2] - box[0]) * (box[3] - box[1])
+    if box_area == 0:
+        return 0
+
+    total_intersect = 0
+    for other_box in other_boxes:
+        total_intersect += intersection_area(box, other_box)
+
+    return min(1, total_intersect / box_area)
+
+
+def precision_recall(preds, references, threshold=.5, workers=8, penalize_double=True):
+    if len(references) == 0:
+        return {
+            "precision": 1,
+            "recall": 1,
+        }
+
+    if len(preds) == 0:
+        return {
+            "precision": 0,
+            "recall": 0,
+        }
+
+    # If we're not penalizing double coverage, we can use a faster calculation
+    coverage_func = calculate_coverage_fast
+    if penalize_double:
+        coverage_func = calculate_coverage
+
+    with ProcessPoolExecutor(max_workers=workers) as executor:
+        precision_func = partial(coverage_func, penalize_double=penalize_double)
+        precision_iou = executor.map(precision_func, preds, repeat(references))
+        reference_iou = executor.map(coverage_func, references, repeat(preds))
+
+    precision_classes = [1 if i > threshold else 0 for i in precision_iou]
+    precision = sum(precision_classes) / len(precision_classes)
+
+    recall_classes = [1 if i > threshold else 0 for i in reference_iou]
+    recall = sum(recall_classes) / len(recall_classes)
+
+    return {
+        "precision": precision,
+        "recall": recall,
+    }
+
+
+def mean_coverage(preds, references):
+    coverages = []
+
+    for box1 in references:
+        coverage = calculate_coverage(box1, preds)
+        coverages.append(coverage)
+
+    for box2 in preds:
+        coverage = calculate_coverage(box2, references)
+        coverages.append(coverage)
+
+    # Calculate the average coverage over all comparisons
+    if len(coverages) == 0:
+        return 0
+    coverage = sum(coverages) / len(coverages)
+    return {"coverage": coverage}
+
+
+def rank_accuracy(preds, references):
+    # Preds and references need to be aligned so each position refers to the same bbox
+    pairs = []
+    for i, pred in enumerate(preds):
+        for j, pred2 in enumerate(preds):
+            if i == j:
+                continue
+            pairs.append((i, j, pred > pred2))
+
+    # Find how many of the prediction rankings are correct
+    correct = 0
+    for i, ref in enumerate(references):
+        for j, ref2 in enumerate(references):
+            if (i, j, ref > ref2) in pairs:
+                correct += 1
+
+    return correct / len(pairs)
\ No newline at end of file
diff --git a/surya/benchmark/tesseract.py b/surya/benchmark/tesseract.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2d025e0f01fc9e1a3907817f1fcc70461fa42e2
--- /dev/null
+++ b/surya/benchmark/tesseract.py
@@ -0,0 +1,179 @@
+from typing import List, Optional
+
+import numpy as np
+import pytesseract
+from pytesseract import Output
+from tqdm import tqdm
+
+from surya.input.processing import slice_bboxes_from_image
+from surya.settings import settings
+import os
+from concurrent.futures import ProcessPoolExecutor
+from surya.detection import get_batch_size as get_det_batch_size
+from surya.recognition import get_batch_size as get_rec_batch_size
+from surya.languages import CODE_TO_LANGUAGE
+
+
+def surya_lang_to_tesseract(code: str) -> Optional[str]:
+    lang_str = CODE_TO_LANGUAGE[code]
+    try:
+        tess_lang = TESS_LANGUAGE_TO_CODE[lang_str]
+    except KeyError:
+        return None
+    return tess_lang
+
+
+def tesseract_ocr(img, bboxes, lang: str):
+    line_imgs = slice_bboxes_from_image(img, bboxes)
+    config = f'--tessdata-dir "{settings.TESSDATA_PREFIX}"'
+    lines = []
+    for line_img in line_imgs:
+        line = pytesseract.image_to_string(line_img, lang=lang, config=config)
+        lines.append(line)
+    return lines
+
+
+def tesseract_ocr_parallel(imgs, bboxes, langs: List[str], cpus=None):
+    tess_parallel_cores = min(len(imgs), get_rec_batch_size())
+    if not cpus:
+        cpus = os.cpu_count()
+    tess_parallel_cores = min(tess_parallel_cores, cpus)
+
+    # Tesseract uses up to 4 processes per instance
+    # Divide by 2 because tesseract doesn't seem to saturate all 4 cores with these small images
+    tess_parallel = max(tess_parallel_cores // 2, 1)
+
+    with ProcessPoolExecutor(max_workers=tess_parallel) as executor:
+        tess_text = tqdm(executor.map(tesseract_ocr, imgs, bboxes, langs), total=len(imgs), desc="Running tesseract OCR")
+        tess_text = list(tess_text)
+    return tess_text
+
+
+def tesseract_bboxes(img):
+    arr_img = np.asarray(img, dtype=np.uint8)
+    ocr = pytesseract.image_to_data(arr_img, output_type=Output.DICT)
+
+    bboxes = []
+    n_boxes = len(ocr['level'])
+    for i in range(n_boxes):
+        # It is possible to merge by line here with line number, but it gives bad results.
+        _, x, y, w, h = ocr['text'][i], ocr['left'][i], ocr['top'][i], ocr['width'][i], ocr['height'][i]
+        bbox = (x, y, x + w, y + h)
+        bboxes.append(bbox)
+
+    return bboxes
+
+
+def tesseract_parallel(imgs):
+    # Tesseract uses 4 threads per instance
+    tess_parallel_cores = min(len(imgs), get_det_batch_size())
+    cpus = os.cpu_count()
+    tess_parallel_cores = min(tess_parallel_cores, cpus)
+
+    # Tesseract uses 4 threads per instance
+    tess_parallel = max(tess_parallel_cores // 4, 1)
+
+    with ProcessPoolExecutor(max_workers=tess_parallel) as executor:
+        tess_bboxes = tqdm(executor.map(tesseract_bboxes, imgs), total=len(imgs), desc="Running tesseract bbox detection")
+        tess_bboxes = list(tess_bboxes)
+    return tess_bboxes
+
+
+TESS_CODE_TO_LANGUAGE = {
+    "afr": "Afrikaans",
+    "amh": "Amharic",
+    "ara": "Arabic",
+    "asm": "Assamese",
+    "aze": "Azerbaijani",
+    "bel": "Belarusian",
+    "ben": "Bengali",
+    "bod": "Tibetan",
+    "bos": "Bosnian",
+    "bre": "Breton",
+    "bul": "Bulgarian",
+    "cat": "Catalan",
+    "ceb": "Cebuano",
+    "ces": "Czech",
+    "chi_sim": "Chinese",
+    "chr": "Cherokee",
+    "cym": "Welsh",
+    "dan": "Danish",
+    "deu": "German",
+    "dzo": "Dzongkha",
+    "ell": "Greek",
+    "eng": "English",
+    "epo": "Esperanto",
+    "est": "Estonian",
+    "eus": "Basque",
+    "fas": "Persian",
+    "fin": "Finnish",
+    "fra": "French",
+    "fry": "Western Frisian",
+    "guj": "Gujarati",
+    "gla": "Scottish Gaelic",
+    "gle": "Irish",
+    "glg": "Galician",
+    "heb": "Hebrew",
+    "hin": "Hindi",
+    "hrv": "Croatian",
+    "hun": "Hungarian",
+    "hye": "Armenian",
+    "iku": "Inuktitut",
+    "ind": "Indonesian",
+    "isl": "Icelandic",
+    "ita": "Italian",
+    "jav": "Javanese",
+    "jpn": "Japanese",
+    "kan": "Kannada",
+    "kat": "Georgian",
+    "kaz": "Kazakh",
+    "khm": "Khmer",
+    "kir": "Kyrgyz",
+    "kor": "Korean",
+    "lao": "Lao",
+    "lat": "Latin",
+    "lav": "Latvian",
+    "lit": "Lithuanian",
+    "mal": "Malayalam",
+    "mar": "Marathi",
+    "mkd": "Macedonian",
+    "mlt": "Maltese",
+    "mon": "Mongolian",
+    "msa": "Malay",
+    "mya": "Burmese",
+    "nep": "Nepali",
+    "nld": "Dutch",
+    "nor": "Norwegian",
+    "ori": "Oriya",
+    "pan": "Punjabi",
+    "pol": "Polish",
+    "por": "Portuguese",
+    "pus": "Pashto",
+    "ron": "Romanian",
+    "rus": "Russian",
+    "san": "Sanskrit",
+    "sin": "Sinhala",
+    "slk": "Slovak",
+    "slv": "Slovenian",
+    "snd": "Sindhi",
+    "spa": "Spanish",
+    "sqi": "Albanian",
+    "srp": "Serbian",
+    "swa": "Swahili",
+    "swe": "Swedish",
+    "syr": "Syriac",
+    "tam": "Tamil",
+    "tel": "Telugu",
+    "tgk": "Tajik",
+    "tha": "Thai",
+    "tir": "Tigrinya",
+    "tur": "Turkish",
+    "uig": "Uyghur",
+    "ukr": "Ukrainian",
+    "urd": "Urdu",
+    "uzb": "Uzbek",
+    "vie": "Vietnamese",
+    "yid": "Yiddish"
+}
+
+TESS_LANGUAGE_TO_CODE = {v:k for k,v in TESS_CODE_TO_LANGUAGE.items()}
diff --git a/surya/benchmark/util.py b/surya/benchmark/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..a32f470390845e4feffce6adcc58aa763749c29d
--- /dev/null
+++ b/surya/benchmark/util.py
@@ -0,0 +1,31 @@
+def merge_boxes(box1, box2):
+    return (min(box1[0], box2[0]), min(box1[1], box2[1]), max(box1[2], box2[2]), max(box1[3], box2[3]))
+
+
+def join_lines(bboxes, max_gap=5):
+    to_merge = {}
+    for i, box1 in bboxes:
+        for z, box2 in bboxes[i + 1:]:
+            j = i + z + 1
+            if box1 == box2:
+                continue
+
+            if box1[0] <= box2[0] and box1[2] >= box2[2]:
+                if abs(box1[1] - box2[3]) <= max_gap:
+                    if i not in to_merge:
+                        to_merge[i] = []
+                    to_merge[i].append(j)
+
+    merged_boxes = set()
+    merged = []
+    for i, box in bboxes:
+        if i in merged_boxes:
+            continue
+
+        if i in to_merge:
+            for j in to_merge[i]:
+                box = merge_boxes(box, bboxes[j][1])
+                merged_boxes.add(j)
+
+        merged.append(box)
+    return merged
diff --git a/surya/detection.py b/surya/detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf439acf05e7638cff74c4661a9bcec0219fac31
--- /dev/null
+++ b/surya/detection.py
@@ -0,0 +1,139 @@
+from typing import List, Tuple
+
+import torch
+import numpy as np
+from PIL import Image
+
+from surya.model.detection.segformer import SegformerForRegressionMask
+from surya.postprocessing.heatmap import get_and_clean_boxes
+from surya.postprocessing.affinity import get_vertical_lines
+from surya.input.processing import prepare_image_detection, split_image, get_total_splits, convert_if_not_rgb
+from surya.schema import TextDetectionResult
+from surya.settings import settings
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor
+import torch.nn.functional as F
+
+
+def get_batch_size():
+    batch_size = settings.DETECTOR_BATCH_SIZE
+    if batch_size is None:
+        batch_size = 6
+        if settings.TORCH_DEVICE_MODEL == "cuda":
+            batch_size = 24
+    return batch_size
+
+
+def batch_detection(images: List, model: SegformerForRegressionMask, processor, batch_size=None) -> Tuple[List[List[np.ndarray]], List[Tuple[int, int]]]:
+    assert all([isinstance(image, Image.Image) for image in images])
+    if batch_size is None:
+        batch_size = get_batch_size()
+    heatmap_count = model.config.num_labels
+
+    images = [image.convert("RGB") for image in images]  # also copies the images
+
+    orig_sizes = [image.size for image in images]
+    splits_per_image = [get_total_splits(size, processor) for size in orig_sizes]
+
+    batches = []
+    current_batch_size = 0
+    current_batch = []
+    for i in range(len(images)):
+        if current_batch_size + splits_per_image[i] > batch_size:
+            if len(current_batch) > 0:
+                batches.append(current_batch)
+            current_batch = []
+            current_batch_size = 0
+        current_batch.append(i)
+        current_batch_size += splits_per_image[i]
+
+    if len(current_batch) > 0:
+        batches.append(current_batch)
+
+    all_preds = []
+    for batch_idx in tqdm(range(len(batches)), desc="Detecting bboxes"):
+        batch_image_idxs = batches[batch_idx]
+        batch_images = convert_if_not_rgb([images[j] for j in batch_image_idxs])
+
+        split_index = []
+        split_heights = []
+        image_splits = []
+        for image_idx, image in enumerate(batch_images):
+            image_parts, split_height = split_image(image, processor)
+            image_splits.extend(image_parts)
+            split_index.extend([image_idx] * len(image_parts))
+            split_heights.extend(split_height)
+
+        image_splits = [prepare_image_detection(image, processor) for image in image_splits]
+        # Batch images in dim 0
+        batch = torch.stack(image_splits, dim=0).to(model.dtype).to(model.device)
+
+        with torch.inference_mode():
+            pred = model(pixel_values=batch)
+
+        logits = pred.logits
+        correct_shape = [processor.size["height"], processor.size["width"]]
+        current_shape = list(logits.shape[2:])
+        if current_shape != correct_shape:
+            logits = F.interpolate(logits, size=correct_shape, mode='bilinear', align_corners=False)
+
+        logits = logits.cpu().detach().numpy().astype(np.float32)
+        preds = []
+        for i, (idx, height) in enumerate(zip(split_index, split_heights)):
+            # If our current prediction length is below the image idx, that means we have a new image
+            # Otherwise, we need to add to the current image
+            if len(preds) <= idx:
+                preds.append([logits[i][k] for k in range(heatmap_count)])
+            else:
+                heatmaps = preds[idx]
+                pred_heatmaps = [logits[i][k] for k in range(heatmap_count)]
+
+                if height < processor.size["height"]:
+                    # Cut off padding to get original height
+                    pred_heatmaps = [pred_heatmap[:height, :] for pred_heatmap in pred_heatmaps]
+
+                for k in range(heatmap_count):
+                    heatmaps[k] = np.vstack([heatmaps[k], pred_heatmaps[k]])
+                preds[idx] = heatmaps
+
+        all_preds.extend(preds)
+
+    assert len(all_preds) == len(images)
+    assert all([len(pred) == heatmap_count for pred in all_preds])
+    return all_preds, orig_sizes
+
+
+def parallel_get_lines(preds, orig_sizes):
+    heatmap, affinity_map = preds
+    heat_img = Image.fromarray((heatmap * 255).astype(np.uint8))
+    aff_img = Image.fromarray((affinity_map * 255).astype(np.uint8))
+    affinity_size = list(reversed(affinity_map.shape))
+    heatmap_size = list(reversed(heatmap.shape))
+    bboxes = get_and_clean_boxes(heatmap, heatmap_size, orig_sizes)
+    vertical_lines = get_vertical_lines(affinity_map, affinity_size, orig_sizes)
+
+    result = TextDetectionResult(
+        bboxes=bboxes,
+        vertical_lines=vertical_lines,
+        heatmap=heat_img,
+        affinity_map=aff_img,
+        image_bbox=[0, 0, orig_sizes[0], orig_sizes[1]]
+    )
+    return result
+
+
+def batch_text_detection(images: List, model, processor, batch_size=None) -> List[TextDetectionResult]:
+    preds, orig_sizes = batch_detection(images, model, processor, batch_size=batch_size)
+    results = []
+    if settings.IN_STREAMLIT or len(images) < settings.DETECTOR_MIN_PARALLEL_THRESH: # Ensures we don't parallelize with streamlit, or with very few images
+        for i in range(len(images)):
+            result = parallel_get_lines(preds[i], orig_sizes[i])
+            results.append(result)
+    else:
+        max_workers = min(settings.DETECTOR_POSTPROCESSING_CPU_WORKERS, len(images))
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            results = list(executor.map(parallel_get_lines, preds, orig_sizes))
+
+    return results
+
+
diff --git a/surya/input/__pycache__/processing.cpython-310.pyc b/surya/input/__pycache__/processing.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..79a54db8c43452b620e7d92b6969808270019a14
Binary files /dev/null and b/surya/input/__pycache__/processing.cpython-310.pyc differ
diff --git a/surya/input/langs.py b/surya/input/langs.py
new file mode 100644
index 0000000000000000000000000000000000000000..e347408ff7c6adc9ca62a48ba10c60057b70b9b7
--- /dev/null
+++ b/surya/input/langs.py
@@ -0,0 +1,19 @@
+from typing import List
+from surya.languages import LANGUAGE_TO_CODE, CODE_TO_LANGUAGE
+
+
+def replace_lang_with_code(langs: List[str]):
+    for i in range(len(langs)):
+        if langs[i].title() in LANGUAGE_TO_CODE:
+            langs[i] = LANGUAGE_TO_CODE[langs[i].title()]
+        if langs[i] not in CODE_TO_LANGUAGE:
+            raise ValueError(f"Language code {langs[i]} not found.")
+
+
+def get_unique_langs(langs: List[List[str]]):
+    uniques = []
+    for lang_list in langs:
+        for lang in lang_list:
+            if lang not in uniques:
+                uniques.append(lang)
+    return uniques
\ No newline at end of file
diff --git a/surya/input/load.py b/surya/input/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa8f1a1a5d06f7da95769a597d02cd0480bd3a43
--- /dev/null
+++ b/surya/input/load.py
@@ -0,0 +1,74 @@
+import PIL
+
+from surya.input.processing import open_pdf, get_page_images
+import os
+import filetype
+from PIL import Image
+import json
+
+
+def get_name_from_path(path):
+    return os.path.basename(path).split(".")[0]
+
+
+def load_pdf(pdf_path, max_pages=None, start_page=None):
+    doc = open_pdf(pdf_path)
+    last_page = len(doc)
+
+    if start_page:
+        assert start_page < last_page and start_page >= 0, f"Start page must be between 0 and {last_page}"
+    else:
+        start_page = 0
+
+    if max_pages:
+        assert max_pages >= 0, f"Max pages must be greater than 0"
+        last_page = min(start_page + max_pages, last_page)
+
+    page_indices = list(range(start_page, last_page))
+    images = get_page_images(doc, page_indices)
+    doc.close()
+    names = [get_name_from_path(pdf_path) for _ in page_indices]
+    return images, names
+
+
+def load_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    name = get_name_from_path(image_path)
+    return [image], [name]
+
+
+def load_from_file(input_path, max_pages=None, start_page=None):
+    input_type = filetype.guess(input_path)
+    if input_type.extension == "pdf":
+        return load_pdf(input_path, max_pages, start_page)
+    else:
+        return load_image(input_path)
+
+
+def load_from_folder(folder_path, max_pages=None, start_page=None):
+    image_paths = [os.path.join(folder_path, image_name) for image_name in os.listdir(folder_path) if not image_name.startswith(".")]
+    image_paths = [ip for ip in image_paths if not os.path.isdir(ip)]
+
+    images = []
+    names = []
+    for path in image_paths:
+        extension = filetype.guess(path)
+        if extension and extension.extension == "pdf":
+            image, name = load_pdf(path, max_pages, start_page)
+            images.extend(image)
+            names.extend(name)
+        else:
+            try:
+                image, name = load_image(path)
+                images.extend(image)
+                names.extend(name)
+            except PIL.UnidentifiedImageError:
+                print(f"Could not load image {path}")
+                continue
+    return images, names
+
+
+def load_lang_file(lang_path, names):
+    with open(lang_path, "r") as f:
+        lang_dict = json.load(f)
+    return [lang_dict[name].copy() for name in names]
diff --git a/surya/input/processing.py b/surya/input/processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..99332798924d8f54041153521db8812bd68138a2
--- /dev/null
+++ b/surya/input/processing.py
@@ -0,0 +1,116 @@
+from typing import List
+
+import cv2
+import numpy as np
+import math
+import pypdfium2
+from PIL import Image, ImageOps, ImageDraw
+import torch
+from surya.settings import settings
+
+
+def convert_if_not_rgb(images: List[Image.Image]) -> List[Image.Image]:
+    new_images = []
+    for image in images:
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+        new_images.append(image)
+    return new_images
+
+
+def get_total_splits(image_size, processor):
+    img_height = list(image_size)[1]
+    max_height = settings.DETECTOR_IMAGE_CHUNK_HEIGHT
+    processor_height = processor.size["height"]
+    if img_height > max_height:
+        num_splits = math.ceil(img_height / processor_height)
+        return num_splits
+    return 1
+
+
+def split_image(img, processor):
+    # This will not modify/return the original image - it will either crop, or copy the image
+    img_height = list(img.size)[1]
+    max_height = settings.DETECTOR_IMAGE_CHUNK_HEIGHT
+    processor_height = processor.size["height"]
+    if img_height > max_height:
+        num_splits = math.ceil(img_height / processor_height)
+        splits = []
+        split_heights = []
+        for i in range(num_splits):
+            top = i * processor_height
+            bottom = (i + 1) * processor_height
+            if bottom > img_height:
+                bottom = img_height
+            cropped = img.crop((0, top, img.size[0], bottom))
+            height = bottom - top
+            if height < processor_height:
+                cropped = ImageOps.pad(cropped, (img.size[0], processor_height), color=255, centering=(0, 0))
+            splits.append(cropped)
+            split_heights.append(height)
+        return splits, split_heights
+    return [img.copy()], [img_height]
+
+
+def prepare_image_detection(img, processor):
+    new_size = (processor.size["width"], processor.size["height"])
+
+    # This double resize actually necessary for downstream accuracy
+    img.thumbnail(new_size, Image.Resampling.LANCZOS)
+    img = img.resize(new_size, Image.Resampling.LANCZOS) # Stretch smaller dimension to fit new size
+
+    img = np.asarray(img, dtype=np.uint8)
+    img = processor(img)["pixel_values"][0]
+    img = torch.from_numpy(img)
+    return img
+
+
+def open_pdf(pdf_filepath):
+    return pypdfium2.PdfDocument(pdf_filepath)
+
+
+def get_page_images(doc, indices: List, dpi=settings.IMAGE_DPI):
+    renderer = doc.render(
+        pypdfium2.PdfBitmap.to_pil,
+        page_indices=indices,
+        scale=dpi / 72,
+    )
+    images = list(renderer)
+    images = [image.convert("RGB") for image in images]
+    return images
+
+
+def slice_bboxes_from_image(image: Image.Image, bboxes):
+    lines = []
+    for bbox in bboxes:
+        line = image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))
+        lines.append(line)
+    return lines
+
+
+def slice_polys_from_image(image: Image.Image, polys):
+    image_array = np.array(image, dtype=np.uint8)
+    lines = []
+    for idx, poly in enumerate(polys):
+        lines.append(slice_and_pad_poly(image_array, poly))
+    return lines
+
+
+def slice_and_pad_poly(image_array: np.array, coordinates):
+    # Draw polygon onto mask
+    coordinates = [(corner[0], corner[1]) for corner in coordinates]
+    bbox = [min([x[0] for x in coordinates]), min([x[1] for x in coordinates]), max([x[0] for x in coordinates]), max([x[1] for x in coordinates])]
+
+    # We mask out anything not in the polygon
+    cropped_polygon = image_array[bbox[1]:bbox[3], bbox[0]:bbox[2]].copy()
+    coordinates = [(x - bbox[0], y - bbox[1]) for x, y in coordinates]
+
+    # Pad the area outside the polygon with the pad value
+    mask = np.zeros(cropped_polygon.shape[:2], dtype=np.uint8)
+    cv2.fillPoly(mask, [np.int32(coordinates)], 1)
+    mask = np.stack([mask] * 3, axis=-1)
+
+    cropped_polygon[mask == 0] = settings.RECOGNITION_PAD_VALUE
+    rectangle_image = Image.fromarray(cropped_polygon)
+
+    return rectangle_image
\ No newline at end of file
diff --git a/surya/languages.py b/surya/languages.py
new file mode 100644
index 0000000000000000000000000000000000000000..83667cf832d3d0a1414d4b42107d802531c01425
--- /dev/null
+++ b/surya/languages.py
@@ -0,0 +1,101 @@
+CODE_TO_LANGUAGE = {
+    'af': 'Afrikaans',
+    'am': 'Amharic',
+    'ar': 'Arabic',
+    'as': 'Assamese',
+    'az': 'Azerbaijani',
+    'be': 'Belarusian',
+    'bg': 'Bulgarian',
+    'bn': 'Bengali',
+    'br': 'Breton',
+    'bs': 'Bosnian',
+    'ca': 'Catalan',
+    'cs': 'Czech',
+    'cy': 'Welsh',
+    'da': 'Danish',
+    'de': 'German',
+    'el': 'Greek',
+    'en': 'English',
+    'eo': 'Esperanto',
+    'es': 'Spanish',
+    'et': 'Estonian',
+    'eu': 'Basque',
+    'fa': 'Persian',
+    'fi': 'Finnish',
+    'fr': 'French',
+    'fy': 'Western Frisian',
+    'ga': 'Irish',
+    'gd': 'Scottish Gaelic',
+    'gl': 'Galician',
+    'gu': 'Gujarati',
+    'ha': 'Hausa',
+    'he': 'Hebrew',
+    'hi': 'Hindi',
+    'hr': 'Croatian',
+    'hu': 'Hungarian',
+    'hy': 'Armenian',
+    'id': 'Indonesian',
+    'is': 'Icelandic',
+    'it': 'Italian',
+    'ja': 'Japanese',
+    'jv': 'Javanese',
+    'ka': 'Georgian',
+    'kk': 'Kazakh',
+    'km': 'Khmer',
+    'kn': 'Kannada',
+    'ko': 'Korean',
+    'ku': 'Kurdish',
+    'ky': 'Kyrgyz',
+    'la': 'Latin',
+    'lo': 'Lao',
+    'lt': 'Lithuanian',
+    'lv': 'Latvian',
+    'mg': 'Malagasy',
+    'mk': 'Macedonian',
+    'ml': 'Malayalam',
+    'mn': 'Mongolian',
+    'mr': 'Marathi',
+    'ms': 'Malay',
+    'my': 'Burmese',
+    'ne': 'Nepali',
+    'nl': 'Dutch',
+    'no': 'Norwegian',
+    'om': 'Oromo',
+    'or': 'Oriya',
+    'pa': 'Punjabi',
+    'pl': 'Polish',
+    'ps': 'Pashto',
+    'pt': 'Portuguese',
+    'ro': 'Romanian',
+    'ru': 'Russian',
+    'sa': 'Sanskrit',
+    'sd': 'Sindhi',
+    'si': 'Sinhala',
+    'sk': 'Slovak',
+    'sl': 'Slovenian',
+    'so': 'Somali',
+    'sq': 'Albanian',
+    'sr': 'Serbian',
+    'su': 'Sundanese',
+    'sv': 'Swedish',
+    'sw': 'Swahili',
+    'ta': 'Tamil',
+    'te': 'Telugu',
+    'th': 'Thai',
+    'tl': 'Tagalog',
+    'tr': 'Turkish',
+    'ug': 'Uyghur',
+    'uk': 'Ukrainian',
+    'ur': 'Urdu',
+    'uz': 'Uzbek',
+    'vi': 'Vietnamese',
+    'xh': 'Xhosa',
+    'yi': 'Yiddish',
+    'zh': 'Chinese',
+}
+
+LANGUAGE_TO_CODE = {v: k for k, v in CODE_TO_LANGUAGE.items()}
+
+
+def is_arabic(lang_code):
+    return lang_code in ["ar", "fa", "ps", "ug", "ur"]
diff --git a/surya/layout.py b/surya/layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..89f2a65981a7bca1bc7abe1a214e9cceb4ac338b
--- /dev/null
+++ b/surya/layout.py
@@ -0,0 +1,204 @@
+from collections import defaultdict
+from concurrent.futures import ProcessPoolExecutor
+from typing import List, Optional
+from PIL import Image
+import numpy as np
+
+from surya.detection import batch_detection
+from surya.postprocessing.heatmap import keep_largest_boxes, get_and_clean_boxes, get_detected_boxes
+from surya.schema import LayoutResult, LayoutBox, TextDetectionResult
+from surya.settings import settings
+
+
+def get_regions_from_detection_result(detection_result: TextDetectionResult, heatmaps: List[np.ndarray], orig_size, id2label, segment_assignment, vertical_line_width=20) -> List[LayoutBox]:
+    logits = np.stack(heatmaps, axis=0)
+    vertical_line_bboxes = [line for line in detection_result.vertical_lines]
+    line_bboxes = detection_result.bboxes
+
+    # Scale back to processor size
+    for line in vertical_line_bboxes:
+        line.rescale_bbox(orig_size, list(reversed(heatmaps[0].shape)))
+
+    for line in line_bboxes:
+        line.rescale(orig_size, list(reversed(heatmaps[0].shape)))
+
+    for bbox in vertical_line_bboxes:
+        # Give some width to the vertical lines
+        vert_bbox = list(bbox.bbox)
+        vert_bbox[2] = min(heatmaps[0].shape[0], vert_bbox[2] + vertical_line_width)
+
+        logits[:, vert_bbox[1]:vert_bbox[3], vert_bbox[0]:vert_bbox[2]] = 0  # zero out where the column lines are
+
+    logits[:, logits[0] >= .5] = 0 # zero out where blanks are
+
+    # Zero out where other segments are
+    for i in range(logits.shape[0]):
+        logits[i, segment_assignment != i] = 0
+
+    detected_boxes = []
+    for heatmap_idx in range(1, len(id2label)):  # Skip the blank class
+        heatmap = logits[heatmap_idx]
+        bboxes = get_detected_boxes(heatmap)
+        bboxes = [bbox for bbox in bboxes if bbox.area > 25]
+        for bb in bboxes:
+            bb.fit_to_bounds([0, 0, heatmap.shape[1] - 1, heatmap.shape[0] - 1])
+
+        for bbox in bboxes:
+            detected_boxes.append(LayoutBox(polygon=bbox.polygon, label=id2label[heatmap_idx], confidence=1))
+
+    detected_boxes = sorted(detected_boxes, key=lambda x: x.confidence, reverse=True)
+    # Expand bbox to cover intersecting lines
+    box_lines = defaultdict(list)
+    used_lines = set()
+
+    # We try 2 rounds of identifying the correct lines to snap to
+    # First round is majority intersection, second lowers the threshold
+    for thresh in [.5, .4]:
+        for bbox_idx, bbox in enumerate(detected_boxes):
+            for line_idx, line_bbox in enumerate(line_bboxes):
+                if line_bbox.intersection_pct(bbox) > thresh and line_idx not in used_lines:
+                    box_lines[bbox_idx].append(line_bbox.bbox)
+                    used_lines.add(line_idx)
+
+    new_boxes = []
+    for bbox_idx, bbox in enumerate(detected_boxes):
+        if bbox.label == "Picture" and bbox.area < 200: # Remove very small figures
+            continue
+
+        # Skip if we didn't find any lines to snap to, except for Pictures and Formulas
+        if bbox_idx not in box_lines and bbox.label not in ["Picture", "Formula"]:
+            continue
+
+        covered_lines = box_lines[bbox_idx]
+        # Snap non-picture layout boxes to correct text boundaries
+        if len(covered_lines) > 0 and bbox.label not in ["Picture"]:
+            min_x = min([line[0] for line in covered_lines])
+            min_y = min([line[1] for line in covered_lines])
+            max_x = max([line[2] for line in covered_lines])
+            max_y = max([line[3] for line in covered_lines])
+
+            # Tables and formulas can contain text, but text isn't the whole area
+            if bbox.label in ["Table", "Formula"]:
+                min_x_box = min([b[0] for b in bbox.polygon])
+                min_y_box = min([b[1] for b in bbox.polygon])
+                max_x_box = max([b[0] for b in bbox.polygon])
+                max_y_box = max([b[1] for b in bbox.polygon])
+
+                min_x = min(min_x, min_x_box)
+                min_y = min(min_y, min_y_box)
+                max_x = max(max_x, max_x_box)
+                max_y = max(max_y, max_y_box)
+
+            bbox.polygon[0][0] = min_x
+            bbox.polygon[0][1] = min_y
+            bbox.polygon[1][0] = max_x
+            bbox.polygon[1][1] = min_y
+            bbox.polygon[2][0] = max_x
+            bbox.polygon[2][1] = max_y
+            bbox.polygon[3][0] = min_x
+            bbox.polygon[3][1] = max_y
+
+        if bbox_idx in box_lines and bbox.label in ["Picture"]:
+            bbox.label = "Figure"
+
+        new_boxes.append(bbox)
+
+    # Merge tables together (sometimes one column is detected as a separate table)
+    for i in range(5): # Up to 5 rounds of merging
+        to_remove = set()
+        for bbox_idx, bbox in enumerate(new_boxes):
+            if bbox.label != "Table" or bbox_idx in to_remove:
+                continue
+
+            for bbox_idx2, bbox2 in enumerate(new_boxes):
+                if bbox2.label != "Table" or bbox_idx2 in to_remove or bbox_idx == bbox_idx2:
+                    continue
+
+                if bbox.intersection_pct(bbox2) > 0:
+                    bbox.merge(bbox2)
+                    to_remove.add(bbox_idx2)
+
+        new_boxes = [bbox for idx, bbox in enumerate(new_boxes) if idx not in to_remove]
+
+    # Ensure we account for all text lines in the layout
+    unused_lines = [line for idx, line in enumerate(line_bboxes) if idx not in used_lines]
+    for bbox in unused_lines:
+        new_boxes.append(LayoutBox(polygon=bbox.polygon, label="Text", confidence=.5))
+
+    for bbox in new_boxes:
+        bbox.rescale(list(reversed(heatmaps[0].shape)), orig_size)
+
+    detected_boxes = [bbox for bbox in new_boxes if bbox.area > 16]
+
+    # Remove bboxes contained inside others, unless they're captions
+    contained_bbox = []
+    for i, bbox in enumerate(detected_boxes):
+        for j, bbox2 in enumerate(detected_boxes):
+            if i == j:
+                continue
+
+            if bbox2.intersection_pct(bbox) >= .95 and bbox2.label not in ["Caption"]:
+                contained_bbox.append(j)
+
+    detected_boxes = [bbox for idx, bbox in enumerate(detected_boxes) if idx not in contained_bbox]
+
+    return detected_boxes
+
+
+def get_regions(heatmaps: List[np.ndarray], orig_size, id2label, segment_assignment) -> List[LayoutBox]:
+    bboxes = []
+    for i in range(1, len(id2label)):  # Skip the blank class
+        heatmap = heatmaps[i]
+        assert heatmap.shape == segment_assignment.shape
+        heatmap[segment_assignment != i] = 0  # zero out where another segment is
+        bbox = get_and_clean_boxes(heatmap, list(reversed(heatmap.shape)), orig_size)
+        for bb in bbox:
+            bboxes.append(LayoutBox(polygon=bb.polygon, label=id2label[i]))
+        heatmaps.append(heatmap)
+
+    bboxes = keep_largest_boxes(bboxes)
+    return bboxes
+
+
+def parallel_get_regions(heatmaps: List[np.ndarray], orig_size, id2label, detection_results=None) -> LayoutResult:
+    logits = np.stack(heatmaps, axis=0)
+    segment_assignment = logits.argmax(axis=0)
+    if detection_results is not None:
+        bboxes = get_regions_from_detection_result(detection_results, heatmaps, orig_size, id2label,
+                                                   segment_assignment)
+    else:
+        bboxes = get_regions(heatmaps, orig_size, id2label, segment_assignment)
+
+    segmentation_img = Image.fromarray(segment_assignment.astype(np.uint8))
+
+    result = LayoutResult(
+        bboxes=bboxes,
+        segmentation_map=segmentation_img,
+        heatmaps=heatmaps,
+        image_bbox=[0, 0, orig_size[0], orig_size[1]]
+    )
+
+    return result
+
+
+def batch_layout_detection(images: List, model, processor, detection_results: Optional[List[TextDetectionResult]] = None, batch_size=None) -> List[LayoutResult]:
+    preds, orig_sizes = batch_detection(images, model, processor, batch_size=batch_size)
+    id2label = model.config.id2label
+
+    results = []
+    if settings.IN_STREAMLIT or len(images) < settings.DETECTOR_MIN_PARALLEL_THRESH: # Ensures we don't parallelize with streamlit or too few images
+        for i in range(len(images)):
+            result = parallel_get_regions(preds[i], orig_sizes[i], id2label, detection_results[i] if detection_results else None)
+            results.append(result)
+    else:
+        futures = []
+        max_workers = min(settings.DETECTOR_POSTPROCESSING_CPU_WORKERS, len(images))
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            for i in range(len(images)):
+                future = executor.submit(parallel_get_regions, preds[i], orig_sizes[i], id2label, detection_results[i] if detection_results else None)
+                futures.append(future)
+
+            for future in futures:
+                results.append(future.result())
+
+    return results
\ No newline at end of file
diff --git a/surya/model/detection/__pycache__/processor.cpython-310.pyc b/surya/model/detection/__pycache__/processor.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..44404383579e9b1ee144c396f181890b5a1be72c
Binary files /dev/null and b/surya/model/detection/__pycache__/processor.cpython-310.pyc differ
diff --git a/surya/model/detection/__pycache__/segformer.cpython-310.pyc b/surya/model/detection/__pycache__/segformer.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bbb7ea0ca30484ea02d3673d98b9f36cd00f3f53
Binary files /dev/null and b/surya/model/detection/__pycache__/segformer.cpython-310.pyc differ
diff --git a/surya/model/detection/processor.py b/surya/model/detection/processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..822d7d152b8032bca0c9e1642e8c017ca8067dc0
--- /dev/null
+++ b/surya/model/detection/processor.py
@@ -0,0 +1,284 @@
+import warnings
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from transformers.image_transforms import to_channel_dimension_format
+from transformers.image_utils import (
+    IMAGENET_DEFAULT_MEAN,
+    IMAGENET_DEFAULT_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    infer_channel_dimension_format,
+    make_list_of_images,
+)
+from transformers.utils import TensorType
+
+
+import PIL.Image
+import torch
+
+
+class SegformerImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Segformer image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
+            size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
+        size (`Dict[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`):
+            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
+            method.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+            `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in the `preprocess` method.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+        do_reduce_labels (`bool`, *optional*, defaults to `False`):
+            Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
+            used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
+            background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
+            `preprocess` method.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_reduce_labels: bool = False,
+        **kwargs,
+    ) -> None:
+        if "reduce_labels" in kwargs:
+            warnings.warn(
+                "The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use "
+                "`do_reduce_labels` instead.",
+                FutureWarning,
+            )
+            do_reduce_labels = kwargs.pop("reduce_labels")
+
+        super().__init__(**kwargs)
+        size = size if size is not None else {"height": 512, "width": 512}
+        size = get_size_dict(size)
+        self.do_resize = do_resize
+        self.size = size
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+        self.do_reduce_labels = do_reduce_labels
+        self._valid_processor_keys = [
+            "images",
+            "segmentation_maps",
+            "do_resize",
+            "size",
+            "resample",
+            "do_rescale",
+            "rescale_factor",
+            "do_normalize",
+            "image_mean",
+            "image_std",
+            "do_reduce_labels",
+            "return_tensors",
+            "data_format",
+            "input_data_format",
+        ]
+
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure `do_reduce_labels` is updated if image
+        processor is created using from_dict and kwargs e.g. `SegformerImageProcessor.from_pretrained(checkpoint,
+        reduce_labels=True)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "reduce_labels" in kwargs:
+            image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels")
+        return super().from_dict(image_processor_dict, **kwargs)
+
+    def _preprocess(
+        self,
+        image: ImageInput,
+        do_resize: bool,
+        do_rescale: bool,
+        do_normalize: bool,
+        size: Optional[Dict[str, int]] = None,
+        resample: PILImageResampling = None,
+        rescale_factor: Optional[float] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+
+        if do_rescale:
+            image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+        if do_normalize:
+            image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+
+        return image
+
+    def _preprocess_image(
+        self,
+        image: ImageInput,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ) -> np.ndarray:
+        """Preprocesses a single image."""
+        # All transformations expect numpy arrays.
+        if input_data_format is None:
+            input_data_format = infer_channel_dimension_format(image)
+
+        image = self._preprocess(
+            image=image,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            input_data_format=input_data_format,
+        )
+        if data_format is not None:
+            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+        return image
+
+    def __call__(self, images, segmentation_maps=None, **kwargs):
+        """
+        Preprocesses a batch of images and optionally segmentation maps.
+
+        Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be
+        passed in as positional arguments.
+        """
+        return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        segmentation_maps: Optional[ImageInput] = None,
+        do_resize: Optional[bool] = None,
+        size: Optional[Dict[str, int]] = None,
+        resample: PILImageResampling = None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_reduce_labels: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: ChannelDimension = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            segmentation_maps (`ImageInput`, *optional*):
+                Segmentation map to preprocess.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after `resize` is applied.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image values between [0 - 1].
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation.
+            do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
+                Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
+                is used for background, and background itself is not included in all classes of a dataset (e.g.
+                ADE20k). The background label will be replaced by 255.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                    - Unset: Return a list of `np.ndarray`.
+                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        resample = resample if resample is not None else self.resample
+        size = size if size is not None else self.size
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+
+        images = make_list_of_images(images)
+        images = [
+            self._preprocess_image(
+                image=img,
+                do_resize=do_resize,
+                resample=resample,
+                size=size,
+                do_rescale=do_rescale,
+                rescale_factor=rescale_factor,
+                do_normalize=do_normalize,
+                image_mean=image_mean,
+                image_std=image_std,
+                data_format=data_format,
+                input_data_format=input_data_format,
+            )
+            for img in images
+        ]
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
\ No newline at end of file
diff --git a/surya/model/detection/segformer.py b/surya/model/detection/segformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b23d8328b1cb5b180c9dc58d03766f7932e5839d
--- /dev/null
+++ b/surya/model/detection/segformer.py
@@ -0,0 +1,468 @@
+import gc
+import warnings
+
+from transformers.activations import ACT2FN
+from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+
+warnings.filterwarnings("ignore", message="torch.utils._pytree._register_pytree_node is deprecated")
+
+import math
+from typing import Optional, Tuple, Union
+
+from transformers import SegformerConfig, SegformerForSemanticSegmentation, SegformerDecodeHead, \
+    SegformerPreTrainedModel
+from surya.model.detection.processor import SegformerImageProcessor
+import torch
+from torch import nn
+
+from transformers.modeling_outputs import SemanticSegmenterOutput, BaseModelOutput
+from surya.settings import settings
+
+
+def load_model(checkpoint=settings.DETECTOR_MODEL_CHECKPOINT, device=settings.TORCH_DEVICE_DETECTION, dtype=settings.MODEL_DTYPE_DETECTION):
+    config = SegformerConfig.from_pretrained(checkpoint)
+    model = SegformerForRegressionMask.from_pretrained(checkpoint, torch_dtype=dtype, config=config)
+    if "mps" in device:
+        print("Warning: MPS may have poor results. This is a bug with MPS, see here - https://github.com/pytorch/pytorch/issues/84936")
+    model = model.to(device)
+    model = model.eval()
+    print(f"Loaded detection model {checkpoint} on device {device} with dtype {dtype}")
+    return model
+
+
+def load_processor(checkpoint=settings.DETECTOR_MODEL_CHECKPOINT):
+    processor = SegformerImageProcessor.from_pretrained(checkpoint)
+    return processor
+
+
+class SegformerForMaskMLP(nn.Module):
+    def __init__(self, config: SegformerConfig, input_dim, output_dim):
+        super().__init__()
+        self.proj = nn.Linear(input_dim, output_dim)
+
+    def forward(self, hidden_states: torch.Tensor):
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        hidden_states = self.proj(hidden_states)
+        return hidden_states
+
+
+class SegformerForMaskDecodeHead(SegformerDecodeHead):
+    def __init__(self, config):
+        super().__init__(config)
+        decoder_layer_hidden_size = getattr(config, "decoder_layer_hidden_size", config.decoder_hidden_size)
+
+        # linear layers which will unify the channel dimension of each of the encoder blocks to the same config.decoder_hidden_size
+        mlps = []
+        for i in range(config.num_encoder_blocks):
+            mlp = SegformerForMaskMLP(config, input_dim=config.hidden_sizes[i], output_dim=decoder_layer_hidden_size)
+            mlps.append(mlp)
+        self.linear_c = nn.ModuleList(mlps)
+
+        # the following 3 layers implement the ConvModule of the original implementation
+        self.linear_fuse = nn.Conv2d(
+            in_channels=decoder_layer_hidden_size * config.num_encoder_blocks,
+            out_channels=config.decoder_hidden_size,
+            kernel_size=1,
+            bias=False,
+        )
+        self.batch_norm = nn.BatchNorm2d(config.decoder_hidden_size)
+        self.activation = nn.ReLU()
+
+        self.classifier = nn.Conv2d(config.decoder_hidden_size, config.num_labels, kernel_size=1)
+
+        self.config = config
+
+    def forward(self, encoder_hidden_states: torch.FloatTensor) -> torch.Tensor:
+        batch_size = encoder_hidden_states[-1].shape[0]
+
+        all_hidden_states = ()
+        for encoder_hidden_state, mlp in zip(encoder_hidden_states, self.linear_c):
+            if self.config.reshape_last_stage is False and encoder_hidden_state.ndim == 3:
+                height = width = int(math.sqrt(encoder_hidden_state.shape[-1]))
+                encoder_hidden_state = (
+                    encoder_hidden_state.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
+                )
+
+            # unify channel dimension
+            height, width = encoder_hidden_state.shape[2], encoder_hidden_state.shape[3]
+            encoder_hidden_state = mlp(encoder_hidden_state)
+            encoder_hidden_state = encoder_hidden_state.permute(0, 2, 1)
+            encoder_hidden_state = encoder_hidden_state.reshape(batch_size, -1, height, width)
+            # upsample
+            encoder_hidden_state = encoder_hidden_state.contiguous()
+            encoder_hidden_state = nn.functional.interpolate(
+                encoder_hidden_state, size=encoder_hidden_states[0].size()[2:], mode="bilinear", align_corners=False
+            )
+            all_hidden_states += (encoder_hidden_state,)
+
+        hidden_states = self.linear_fuse(torch.cat(all_hidden_states[::-1], dim=1))
+        hidden_states = self.batch_norm(hidden_states)
+        hidden_states = self.activation(hidden_states)
+
+        # logits are of shape (batch_size, num_labels, height/4, width/4)
+        logits = self.classifier(hidden_states)
+
+        return logits
+
+
+class SegformerOverlapPatchEmbeddings(nn.Module):
+    """Construct the overlapping patch embeddings."""
+
+    def __init__(self, patch_size, stride, num_channels, hidden_size):
+        super().__init__()
+        self.proj = nn.Conv2d(
+            num_channels,
+            hidden_size,
+            kernel_size=patch_size,
+            stride=stride,
+            padding=patch_size // 2,
+        )
+
+        self.layer_norm = nn.LayerNorm(hidden_size)
+
+    def forward(self, pixel_values):
+        embeddings = self.proj(pixel_values)
+        _, _, height, width = embeddings.shape
+        # (batch_size, num_channels, height, width) -> (batch_size, num_channels, height*width) -> (batch_size, height*width, num_channels)
+        # this can be fed to a Transformer layer
+        embeddings = embeddings.flatten(2).transpose(1, 2)
+        embeddings = self.layer_norm(embeddings)
+        return embeddings, height, width
+
+
+class SegformerEfficientSelfAttention(nn.Module):
+    """SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT
+    paper](https://arxiv.org/abs/2102.12122)."""
+
+    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({self.num_attention_heads})"
+            )
+
+        self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(self.hidden_size, self.all_head_size)
+        self.key = nn.Linear(self.hidden_size, self.all_head_size)
+        self.value = nn.Linear(self.hidden_size, self.all_head_size)
+
+        self.sr_ratio = sequence_reduction_ratio
+        if sequence_reduction_ratio > 1:
+            self.sr = nn.Conv2d(
+                hidden_size, hidden_size, kernel_size=sequence_reduction_ratio, stride=sequence_reduction_ratio
+            )
+            self.layer_norm = nn.LayerNorm(hidden_size)
+
+    def transpose_for_scores(self, hidden_states):
+        new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        hidden_states = hidden_states.view(new_shape)
+        return hidden_states.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        height,
+        width,
+        output_attentions=False,
+    ):
+        query_layer = self.transpose_for_scores(self.query(hidden_states))
+
+        if self.sr_ratio > 1:
+            batch_size, seq_len, num_channels = hidden_states.shape
+            # Reshape to (batch_size, num_channels, height, width)
+            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
+            # Apply sequence reduction
+            hidden_states = self.sr(hidden_states)
+            # Reshape back to (batch_size, seq_len, num_channels)
+            hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1)
+            hidden_states = self.layer_norm(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+        return outputs
+
+class SegformerEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        # patch embeddings
+        embeddings = []
+        for i in range(config.num_encoder_blocks):
+            embeddings.append(
+                SegformerOverlapPatchEmbeddings(
+                    patch_size=config.patch_sizes[i],
+                    stride=config.strides[i],
+                    num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],
+                    hidden_size=config.hidden_sizes[i],
+                )
+            )
+        self.patch_embeddings = nn.ModuleList(embeddings)
+
+        # Transformer blocks
+        blocks = []
+        cur = 0
+        for i in range(config.num_encoder_blocks):
+            # each block consists of layers
+            layers = []
+            if i != 0:
+                cur += config.depths[i - 1]
+            for j in range(config.depths[i]):
+                layers.append(
+                    SegformerLayer(
+                        config,
+                        hidden_size=config.hidden_sizes[i],
+                        num_attention_heads=config.num_attention_heads[i],
+                        sequence_reduction_ratio=config.sr_ratios[i],
+                        mlp_ratio=config.mlp_ratios[i],
+                    )
+                )
+            blocks.append(nn.ModuleList(layers))
+
+        self.block = nn.ModuleList(blocks)
+
+        # Layer norms
+        self.layer_norm = nn.ModuleList(
+            [nn.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)]
+        )
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple, BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+
+        batch_size = pixel_values.shape[0]
+
+        hidden_states = pixel_values
+        for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)):
+            embedding_layer, block_layer, norm_layer = x
+            # first, obtain patch embeddings
+            hidden_states, height, width = embedding_layer(hidden_states)
+            # second, send embeddings through blocks
+            for i, blk in enumerate(block_layer):
+                layer_outputs = blk(hidden_states, height, width, output_attentions)
+                hidden_states = layer_outputs[0]
+            # third, apply layer norm
+            hidden_states = norm_layer(hidden_states)
+            # fourth, optionally reshape back to (batch_size, num_channels, height, width)
+            if idx != len(self.patch_embeddings) - 1 or (
+                idx == len(self.patch_embeddings) - 1 and self.config.reshape_last_stage
+            ):
+                hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        return all_hidden_states
+
+class SegformerSelfOutput(nn.Module):
+    def __init__(self, config, hidden_size):
+        super().__init__()
+        self.dense = nn.Linear(hidden_size, hidden_size)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        return hidden_states
+
+
+class SegformerAttention(nn.Module):
+    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
+        super().__init__()
+        self.self = SegformerEfficientSelfAttention(
+            config=config,
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            sequence_reduction_ratio=sequence_reduction_ratio,
+        )
+        self.output = SegformerSelfOutput(config, hidden_size=hidden_size)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(self, hidden_states, height, width, output_attentions=False):
+        self_outputs = self.self(hidden_states, height, width, output_attentions)
+
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+class SegformerDWConv(nn.Module):
+    def __init__(self, dim=768):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
+
+    def forward(self, hidden_states, height, width):
+        batch_size, seq_len, num_channels = hidden_states.shape
+        hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width)
+        hidden_states = self.dwconv(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        return hidden_states
+
+
+class SegformerMixFFN(nn.Module):
+    def __init__(self, config, in_features, hidden_features=None, out_features=None):
+        super().__init__()
+        out_features = out_features or in_features
+        self.dense1 = nn.Linear(in_features, hidden_features)
+        self.dwconv = SegformerDWConv(hidden_features)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+        self.dense2 = nn.Linear(hidden_features, out_features)
+
+    def forward(self, hidden_states, height, width):
+        hidden_states = self.dense1(hidden_states)
+        hidden_states = self.dwconv(hidden_states, height, width)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        hidden_states = self.dense2(hidden_states)
+        return hidden_states
+
+
+class SegformerLayer(nn.Module):
+    """This corresponds to the Block class in the original implementation."""
+
+    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio, mlp_ratio):
+        super().__init__()
+        self.layer_norm_1 = nn.LayerNorm(hidden_size)
+        self.attention = SegformerAttention(
+            config,
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            sequence_reduction_ratio=sequence_reduction_ratio,
+        )
+        self.layer_norm_2 = nn.LayerNorm(hidden_size)
+        mlp_hidden_size = int(hidden_size * mlp_ratio)
+        self.mlp = SegformerMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size)
+
+    def forward(self, hidden_states, height, width, output_attentions=False):
+        self_attention_outputs = self.attention(
+            self.layer_norm_1(hidden_states),  # in Segformer, layernorm is applied before self-attention
+            height,
+            width,
+            output_attentions=output_attentions,
+        )
+
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        # first residual connection (with stochastic depth)
+        hidden_states = attention_output + hidden_states
+
+        mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width)
+
+        # second residual connection (with stochastic depth)
+        layer_output = mlp_output + hidden_states
+
+        outputs = (layer_output,) + outputs
+
+        return outputs
+
+class SegformerModel(SegformerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+
+        # hierarchical Transformer encoder
+        self.encoder = SegformerEncoder(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        encoder_outputs = self.encoder(
+            pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        return encoder_outputs
+
+class SegformerForRegressionMask(SegformerForSemanticSegmentation):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        self.segformer = SegformerModel(config)
+        self.decode_head = SegformerForMaskDecodeHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        **kwargs
+    ) -> Union[Tuple, SemanticSegmenterOutput]:
+
+        encoder_hidden_states = self.segformer(
+            pixel_values,
+            output_attentions=False,
+            output_hidden_states=True,  # we need the intermediate hidden states
+            return_dict=False,
+        )
+
+        logits = self.decode_head(encoder_hidden_states)
+        # Apply sigmoid to get 0-1 output
+        sigmoid_logits = torch.special.expit(logits)
+
+        return SemanticSegmenterOutput(
+            loss=None,
+            logits=sigmoid_logits,
+            hidden_states=None,
+            attentions=None,
+        )
\ No newline at end of file
diff --git a/surya/model/ordering/config.py b/surya/model/ordering/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcf20f71e7119022d95021e280b4db0e10bf60a5
--- /dev/null
+++ b/surya/model/ordering/config.py
@@ -0,0 +1,8 @@
+from transformers import MBartConfig, DonutSwinConfig
+
+
+class MBartOrderConfig(MBartConfig):
+    pass
+
+class VariableDonutSwinConfig(DonutSwinConfig):
+    pass
\ No newline at end of file
diff --git a/surya/model/ordering/decoder.py b/surya/model/ordering/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..89fc3ebce073ba5c4bef5cd3fd049f657324c3b3
--- /dev/null
+++ b/surya/model/ordering/decoder.py
@@ -0,0 +1,557 @@
+import copy
+from typing import Optional, List, Union, Tuple
+
+from transformers import MBartForCausalLM, MBartConfig
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_attention_mask
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions, BaseModelOutputWithPastAndCrossAttentions
+from transformers.models.mbart.modeling_mbart import MBartPreTrainedModel, MBartDecoder, MBartLearnedPositionalEmbedding, MBartDecoderLayer
+from surya.model.ordering.config import MBartOrderConfig
+import torch
+import math
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    From llama
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class MBartGQAttention(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+        is_causal: bool = False,
+        config: Optional[MBartConfig] = None,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.num_kv_groups = self.num_heads // self.num_kv_heads
+
+        assert self.num_heads % self.num_kv_heads == 0, f"num_heads ({self.num_heads}) must be divisible by num_kv_heads ({self.num_kv_heads})"
+        assert embed_dim % self.num_kv_heads == 0, f"embed_dim ({self.embed_dim}) must be divisible by num_kv_heads ({self.num_kv_heads})"
+
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        self.config = config
+
+        if (self.head_dim * num_heads) != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+                f" and `num_heads`: {num_heads})."
+            )
+        self.scaling = self.head_dim**-0.5
+        self.is_decoder = is_decoder
+        self.is_causal = is_causal
+
+        self.k_proj = nn.Linear(embed_dim, self.num_kv_heads * self.head_dim, bias=bias)
+        self.v_proj = nn.Linear(embed_dim, self.num_kv_heads * self.head_dim, bias=bias)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _shape_key_value(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        key_value_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        layer_head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+
+        bsz, tgt_len, _ = hidden_states.size()
+
+        # get query proj
+        query_states = self.q_proj(hidden_states) * self.scaling
+        # get key, value proj
+        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
+        # is checking that the `sequence_length` of the `past_key_value` is the same as
+        # the provided `key_value_states` to support prefix tuning
+        if (
+            is_cross_attention
+            and past_key_value is not None
+            and past_key_value[0].shape[2] == key_value_states.shape[1]
+        ):
+            # reuse k,v, cross_attentions
+            key_states = past_key_value[0]
+            value_states = past_key_value[1]
+        elif is_cross_attention:
+            # cross_attentions
+            key_states = self._shape_key_value(self.k_proj(key_value_states), -1, bsz)
+            value_states = self._shape_key_value(self.v_proj(key_value_states), -1, bsz)
+        elif past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = self._shape_key_value(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape_key_value(self.v_proj(hidden_states), -1, bsz)
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+        else:
+            # self_attention
+            key_states = self._shape_key_value(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape_key_value(self.v_proj(hidden_states), -1, bsz)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_states, value_states)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+
+        # Expand kv heads, then match query shape
+        key_states = repeat_kv(key_states, self.num_kv_groups)
+        value_states = repeat_kv(value_states, self.num_kv_groups)
+        key_states = key_states.reshape(*proj_shape)
+        value_states = value_states.reshape(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        if layer_head_mask is not None:
+            if layer_head_mask.size() != (self.num_heads,):
+                raise ValueError(
+                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
+                    f" {layer_head_mask.size()}"
+                )
+            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if output_attentions:
+            # this operation is a bit awkward, but it's required to
+            # make sure that attn_weights keeps its gradient.
+            # In order to do so, attn_weights have to be reshaped
+            # twice and have to be reused in the following
+            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
+        else:
+            attn_weights_reshaped = None
+
+        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+        attn_output = torch.bmm(attn_probs, value_states)
+
+        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output = attn_output.transpose(1, 2)
+
+        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
+        # partitioned across GPUs when using tensor-parallelism.
+        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights_reshaped, past_key_value
+
+
+MBART_ATTENTION_CLASSES = {
+    "eager": MBartGQAttention,
+    "flash_attention_2": None
+}
+
+
+class MBartOrderDecoderLayer(MBartDecoderLayer):
+    def __init__(self, config: MBartConfig):
+        nn.Module.__init__(self)
+        self.embed_dim = config.d_model
+
+        self.self_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
+            embed_dim=self.embed_dim,
+            num_heads=config.decoder_attention_heads,
+            num_kv_heads=config.kv_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+            is_causal=True,
+            config=config,
+        )
+        self.dropout = config.dropout
+        self.activation_fn = ACT2FN[config.activation_function]
+        self.activation_dropout = config.activation_dropout
+
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.encoder_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
+            self.embed_dim,
+            config.decoder_attention_heads,
+            num_kv_heads=config.kv_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+            config=config,
+        )
+        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+
+class BboxEmbedding(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.x1_embed = nn.Embedding(config.max_width, config.d_model)
+        self.y1_embed = nn.Embedding(config.max_height, config.d_model)
+        self.x2_embed = nn.Embedding(config.max_width, config.d_model)
+        self.y2_embed = nn.Embedding(config.max_height, config.d_model)
+        self.w_embed = nn.Embedding(config.max_width, config.d_model)
+        self.h_embed = nn.Embedding(config.max_height, config.d_model)
+        self.cx_embed = nn.Embedding(config.max_width, config.d_model)
+        self.cy_embed = nn.Embedding(config.max_height, config.d_model)
+        self.box_pos_embed = nn.Embedding(config.max_position_embeddings, config.d_model)
+
+    def forward(self, boxes: torch.LongTensor, input_box_counts: torch.LongTensor, past_key_values_length: int):
+        x1, y1, x2, y2 = boxes.unbind(dim=-1)
+        # Shape is (batch_size, num_boxes/seq len, d_model)
+        w = x2 - x1
+        h = y2 - y1
+        # Center x and y in torch long tensors
+        cx = (x1 + x2) / 2
+        cy = (y1 + y2) / 2
+        cx = cx.long()
+        cy = cy.long()
+
+        coord_embeds = self.x1_embed(x1) + self.y1_embed(y1) + self.x2_embed(x2) + self.y2_embed(y2)
+        embedded = coord_embeds + self.w_embed(w) + self.h_embed(h) + self.cx_embed(cx) + self.cy_embed(cy)
+
+        # Add in positional embeddings for the boxes
+        if past_key_values_length == 0:
+            for j in range(embedded.shape[0]):
+                box_start = input_box_counts[j, 0]
+                box_end = input_box_counts[j, 1] - 1 # Skip the sep token
+                box_count = box_end - box_start
+                embedded[j, box_start:box_end] = embedded[j, box_start:box_end] + self.box_pos_embed.weight[:box_count]
+
+        return embedded
+
+
+class MBartOrderDecoder(MBartDecoder):
+    def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
+        MBartPreTrainedModel.__init__(self, config)
+        self.dropout = config.dropout
+        self.layerdrop = config.decoder_layerdrop
+        self.padding_idx = config.pad_token_id
+        self.max_target_positions = config.max_position_embeddings
+        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+        self.embed_tokens = BboxEmbedding(config) if embed_tokens is None else embed_tokens
+
+        if embed_tokens is not None:
+            self.embed_tokens.weight = embed_tokens.weight
+
+        self.embed_positions = MBartLearnedPositionalEmbedding(
+            config.max_position_embeddings,
+            config.d_model,
+        )
+        # Language-specific MoE goes at second and second-to-last layer
+        self.layers = nn.ModuleList([MBartOrderDecoderLayer(config) for _ in range(config.decoder_layers)])
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.layernorm_embedding = nn.LayerNorm(config.d_model)
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_boxes: torch.LongTensor = None,
+        input_boxes_mask: Optional[torch.Tensor] = None,
+        input_boxes_counts: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_boxes is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_boxes is not None:
+            input = input_boxes
+            input_shape = input_boxes.size()[:-1] # Shape (batch_size, num_boxes)
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            input = inputs_embeds[:, :, -1]
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_boxes, input_boxes_counts, past_key_values_length) * self.embed_scale
+
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = input_boxes_mask if (input_boxes_mask is not None and 0 in input_boxes_mask) else None
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                input_boxes_mask, input_shape, inputs_embeds, past_key_values_length
+            )
+
+            if past_key_values_length == 0:
+                box_ends = input_boxes_counts[:, 1]
+                box_starts = input_boxes_counts[:, 0]
+                input_shape_arranged = torch.arange(input_shape[1], device=attention_mask.device)[None, :]
+                # Enable all boxes to attend to each other (before the sep token)
+                # Ensure that the boxes are not attending to the padding tokens
+                boxes_end_mask = input_shape_arranged < box_ends[:, None]
+                boxes_start_mask = input_shape_arranged >= box_starts[:, None]
+                boxes_mask = boxes_end_mask & boxes_start_mask
+                boxes_mask = boxes_mask.unsqueeze(1).unsqueeze(1) # Enable proper broadcasting
+                attention_mask = attention_mask.masked_fill(boxes_mask, 0)
+
+        # expand encoder attention mask
+        if encoder_hidden_states is not None and encoder_attention_mask is not None:
+            if self._use_flash_attention_2:
+                encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+            else:
+                # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+                encoder_attention_mask = _prepare_4d_attention_mask(
+                    encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+                )
+
+        # embed positions
+        positions = self.embed_positions(input, past_key_values_length)
+
+        hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
+        hidden_states = self.layernorm_embedding(hidden_states)
+
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+        next_decoder_cache = () if use_cache else None
+
+        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+            if attn_mask is not None:
+                if attn_mask.size()[0] != len(self.layers):
+                    raise ValueError(
+                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+                        f" {attn_mask.size()[0]}."
+                    )
+        for idx, decoder_layer in enumerate(self.layers):
+            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            if self.training:
+                dropout_probability = torch.rand([])
+                if dropout_probability < self.layerdrop:
+                    continue
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    head_mask[idx] if head_mask is not None else None,
+                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
+                    None,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+                    cross_attn_layer_head_mask=(
+                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
+                    ),
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+                if encoder_hidden_states is not None:
+                    all_cross_attentions += (layer_outputs[2],)
+
+        hidden_states = self.layer_norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class MBartOrderDecoderWrapper(MBartPreTrainedModel):
+    """
+    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
+    used in combination with the [`EncoderDecoderModel`] framework.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.decoder = MBartOrderDecoder(config)
+
+    def forward(self, *args, **kwargs):
+        return self.decoder(*args, **kwargs)
+
+
+class MBartOrder(MBartForCausalLM):
+    config_class = MBartOrderConfig
+    _tied_weights_keys = []
+
+    def __init__(self, config, **kwargs):
+        config = copy.deepcopy(config)
+        config.is_decoder = True
+        config.is_encoder_decoder = False
+        MBartPreTrainedModel.__init__(self, config)
+        self.model = MBartOrderDecoderWrapper(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_boxes: torch.LongTensor = None,
+        input_boxes_mask: Optional[torch.Tensor] = None,
+        input_boxes_counts: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.decoder(
+            input_boxes=input_boxes,
+            input_boxes_mask=input_boxes_mask,
+            input_boxes_counts=input_boxes_counts,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            head_mask=head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        logits = self.lm_head(outputs[0])
+
+        loss = None
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
\ No newline at end of file
diff --git a/surya/model/ordering/encoder.py b/surya/model/ordering/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff001b135a558bd3e810a4ffb45ab6de765bfc3a
--- /dev/null
+++ b/surya/model/ordering/encoder.py
@@ -0,0 +1,83 @@
+from torch import nn
+import torch
+from typing import Optional, Tuple, Union
+import collections
+import math
+
+from transformers import DonutSwinPreTrainedModel
+from transformers.models.donut.modeling_donut_swin import DonutSwinPatchEmbeddings, DonutSwinEmbeddings, DonutSwinModel, \
+    DonutSwinEncoder
+
+from surya.model.ordering.config import VariableDonutSwinConfig
+
+class VariableDonutSwinEmbeddings(DonutSwinEmbeddings):
+    """
+    Construct the patch and position embeddings. Optionally, also the mask token.
+    """
+
+    def __init__(self, config, use_mask_token=False, **kwargs):
+        super().__init__(config, use_mask_token)
+
+        self.patch_embeddings = DonutSwinPatchEmbeddings(config)
+        num_patches = self.patch_embeddings.num_patches
+        self.patch_grid = self.patch_embeddings.grid_size
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.embed_dim)) if use_mask_token else None
+        self.position_embeddings = None
+
+        if config.use_absolute_embeddings:
+            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim))
+
+        self.row_embeddings = None
+        self.column_embeddings = None
+        if config.use_2d_embeddings:
+            self.row_embeddings = nn.Parameter(torch.zeros(1, self.patch_grid[0] + 1, config.embed_dim))
+            self.column_embeddings = nn.Parameter(torch.zeros(1, self.patch_grid[1] + 1, config.embed_dim))
+
+        self.norm = nn.LayerNorm(config.embed_dim)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(
+        self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None, **kwargs
+    ) -> Tuple[torch.Tensor]:
+
+        embeddings, output_dimensions = self.patch_embeddings(pixel_values)
+        # Layernorm across the last dimension (each patch is a single row)
+        embeddings = self.norm(embeddings)
+        batch_size, seq_len, embed_dim = embeddings.size()
+
+        if bool_masked_pos is not None:
+            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
+            # replace the masked visual tokens by mask_tokens
+            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
+            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
+
+        if self.position_embeddings is not None:
+            embeddings = embeddings + self.position_embeddings[:, :seq_len, :]
+
+        if self.row_embeddings is not None and self.column_embeddings is not None:
+            # Repeat the x position embeddings across the y axis like 0, 1, 2, 3, 0, 1, 2, 3, ...
+            row_embeddings = self.row_embeddings[:, :output_dimensions[0], :].repeat_interleave(output_dimensions[1], dim=1)
+            column_embeddings = self.column_embeddings[:, :output_dimensions[1], :].repeat(1, output_dimensions[0], 1)
+
+            embeddings = embeddings + row_embeddings + column_embeddings
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings, output_dimensions
+
+
+class VariableDonutSwinModel(DonutSwinModel):
+    config_class = VariableDonutSwinConfig
+    def __init__(self, config, add_pooling_layer=True, use_mask_token=False, **kwargs):
+        super().__init__(config)
+        self.config = config
+        self.num_layers = len(config.depths)
+        self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
+
+        self.embeddings = VariableDonutSwinEmbeddings(config, use_mask_token=use_mask_token)
+        self.encoder = DonutSwinEncoder(config, self.embeddings.patch_grid)
+
+        self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
\ No newline at end of file
diff --git a/surya/model/ordering/encoderdecoder.py b/surya/model/ordering/encoderdecoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7351f11f533f01bad9a74cc5ebec7ca272ba8dd
--- /dev/null
+++ b/surya/model/ordering/encoderdecoder.py
@@ -0,0 +1,90 @@
+from typing import Optional, Union, Tuple, List
+
+import torch
+from transformers import VisionEncoderDecoderModel
+from transformers.modeling_outputs import Seq2SeqLMOutput, BaseModelOutput
+
+
+class OrderVisionEncoderDecoderModel(VisionEncoderDecoderModel):
+    def forward(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        decoder_input_boxes: torch.LongTensor = None,
+        # Shape (batch_size, num_boxes, 4), all coords scaled 0 - 1000, with 1001 as padding
+        decoder_input_boxes_mask: torch.LongTensor = None,  # Shape (batch_size, num_boxes), 0 if padding, 1 otherwise
+        decoder_input_boxes_counts: torch.LongTensor = None,  # Shape (batch_size), number of boxes in each image
+        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[List[List[int]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
+
+        kwargs_decoder = {
+            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+        }
+
+        if encoder_outputs is None:
+            if pixel_values is None:
+                raise ValueError("You have to specify pixel_values")
+
+            encoder_outputs = self.encoder(
+                pixel_values=pixel_values,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs_encoder,
+            )
+        elif isinstance(encoder_outputs, tuple):
+            encoder_outputs = BaseModelOutput(*encoder_outputs)
+
+        encoder_hidden_states = encoder_outputs[0]
+
+        # optionally project encoder_hidden_states
+        if (
+            self.encoder.config.hidden_size != self.decoder.config.hidden_size
+            and self.decoder.config.cross_attention_hidden_size is None
+        ):
+            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
+
+        # else:
+        encoder_attention_mask = None
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_boxes=decoder_input_boxes,
+            input_boxes_mask=decoder_input_boxes_mask,
+            input_boxes_counts=decoder_input_boxes_counts,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            use_cache=use_cache,
+            past_key_values=past_key_values,
+            return_dict=return_dict,
+            labels=labels,
+            **kwargs_decoder,
+        )
+
+        if not return_dict:
+            return decoder_outputs + encoder_outputs
+
+        return Seq2SeqLMOutput(
+            loss=decoder_outputs.loss,
+            logits=decoder_outputs.logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
diff --git a/surya/model/ordering/model.py b/surya/model/ordering/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c92fee9784e330c39433414168a0eb1d697913f
--- /dev/null
+++ b/surya/model/ordering/model.py
@@ -0,0 +1,34 @@
+from transformers import DetrConfig, BeitConfig, DetrImageProcessor, VisionEncoderDecoderConfig, AutoModelForCausalLM, \
+    AutoModel
+from surya.model.ordering.config import MBartOrderConfig, VariableDonutSwinConfig
+from surya.model.ordering.decoder import MBartOrder
+from surya.model.ordering.encoder import VariableDonutSwinModel
+from surya.model.ordering.encoderdecoder import OrderVisionEncoderDecoderModel
+from surya.model.ordering.processor import OrderImageProcessor
+from surya.settings import settings
+
+
+def load_model(checkpoint=settings.ORDER_MODEL_CHECKPOINT, device=settings.TORCH_DEVICE_MODEL, dtype=settings.MODEL_DTYPE):
+    config = VisionEncoderDecoderConfig.from_pretrained(checkpoint)
+
+    decoder_config = vars(config.decoder)
+    decoder = MBartOrderConfig(**decoder_config)
+    config.decoder = decoder
+
+    encoder_config = vars(config.encoder)
+    encoder = VariableDonutSwinConfig(**encoder_config)
+    config.encoder = encoder
+
+    # Get transformers to load custom model
+    AutoModel.register(MBartOrderConfig, MBartOrder)
+    AutoModelForCausalLM.register(MBartOrderConfig, MBartOrder)
+    AutoModel.register(VariableDonutSwinConfig, VariableDonutSwinModel)
+
+    model = OrderVisionEncoderDecoderModel.from_pretrained(checkpoint, config=config, torch_dtype=dtype)
+    assert isinstance(model.decoder, MBartOrder)
+    assert isinstance(model.encoder, VariableDonutSwinModel)
+
+    model = model.to(device)
+    model = model.eval()
+    print(f"Loaded reading order model {checkpoint} on device {device} with dtype {dtype}")
+    return model
\ No newline at end of file
diff --git a/surya/model/ordering/processor.py b/surya/model/ordering/processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6f463be058f554e2aeee7c65e51d1fd9f5bbac6
--- /dev/null
+++ b/surya/model/ordering/processor.py
@@ -0,0 +1,156 @@
+from copy import deepcopy
+from typing import Dict, Union, Optional, List, Tuple
+
+import torch
+from torch import TensorType
+from transformers import DonutImageProcessor, DonutProcessor
+from transformers.image_processing_utils import BatchFeature
+from transformers.image_utils import PILImageResampling, ImageInput, ChannelDimension, make_list_of_images, \
+    valid_images, to_numpy_array
+import numpy as np
+from PIL import Image
+import PIL
+from surya.settings import settings
+
+
+def load_processor(checkpoint=settings.ORDER_MODEL_CHECKPOINT):
+    processor = OrderImageProcessor.from_pretrained(checkpoint)
+    processor.size = settings.ORDER_IMAGE_SIZE
+    box_size = 1024
+    max_tokens = 256
+    processor.token_sep_id = max_tokens + box_size + 1
+    processor.token_pad_id = max_tokens + box_size + 2
+    processor.max_boxes = settings.ORDER_MAX_BOXES - 1
+    processor.box_size = {"height": box_size, "width": box_size}
+    return processor
+
+
+class OrderImageProcessor(DonutImageProcessor):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.patch_size = kwargs.get("patch_size", (4, 4))
+
+    def process_inner(self, images: List[np.ndarray]):
+        images = [img.transpose(2, 0, 1) for img in images] # convert to CHW format
+
+        assert images[0].shape[0] == 3 # RGB input images, channel dim last
+
+        # Convert to float32 for rescale/normalize
+        images = [img.astype(np.float32) for img in images]
+
+        # Rescale and normalize
+        images = [
+            self.rescale(img, scale=self.rescale_factor, input_data_format=ChannelDimension.FIRST)
+            for img in images
+        ]
+        images = [
+            self.normalize(img, mean=self.image_mean, std=self.image_std, input_data_format=ChannelDimension.FIRST)
+            for img in images
+        ]
+
+        return images
+
+    def process_boxes(self, boxes):
+        padded_boxes = []
+        box_masks = []
+        box_counts = []
+        for b in boxes:
+            # Left pad for generation
+            padded_b = deepcopy(b)
+            padded_b.append([self.token_sep_id] * 4) # Sep token to indicate start of label predictions
+            padded_boxes.append(padded_b)
+
+        max_boxes = max(len(b) for b in padded_boxes)
+        for i in range(len(padded_boxes)):
+            pad_len = max_boxes - len(padded_boxes[i])
+            box_len = len(padded_boxes[i])
+            box_mask = [0] * pad_len + [1] * box_len
+            padded_box = [[self.token_pad_id] * 4] * pad_len + padded_boxes[i]
+            padded_boxes[i] = padded_box
+            box_masks.append(box_mask)
+            box_counts.append([pad_len, max_boxes])
+
+        return padded_boxes, box_masks, box_counts
+
+    def resize_img_and_boxes(self, img, boxes):
+        orig_dim = img.size
+        new_size = (self.size["width"], self.size["height"])
+        img.thumbnail(new_size, Image.Resampling.LANCZOS)  # Shrink largest dimension to fit new size
+        img = img.resize(new_size, Image.Resampling.LANCZOS)  # Stretch smaller dimension to fit new size
+
+        img = np.asarray(img, dtype=np.uint8)
+
+        width, height = orig_dim
+        box_width, box_height = self.box_size["width"], self.box_size["height"]
+        for box in boxes:
+            # Rescale to 0-1024
+            box[0] = box[0] / width * box_width
+            box[1] = box[1] / height * box_height
+            box[2] = box[2] / width * box_width
+            box[3] = box[3] / height * box_height
+
+            if box[0] < 0:
+                box[0] = 0
+            if box[1] < 0:
+                box[1] = 0
+            if box[2] > box_width:
+                box[2] = box_width
+            if box[3] > box_height:
+                box[3] = box_height
+
+        return img, boxes
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        boxes: List[List[int]],
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_thumbnail: bool = None,
+        do_align_long_axis: bool = None,
+        do_pad: bool = None,
+        random_padding: bool = False,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        new_images = []
+        new_boxes = []
+        for img, box in zip(images, boxes):
+            if len(box) > self.max_boxes:
+                raise ValueError(f"Too many boxes, max is {self.max_boxes}")
+            img, box = self.resize_img_and_boxes(img, box)
+            new_images.append(img)
+            new_boxes.append(box)
+
+        images = new_images
+        boxes = new_boxes
+
+        # Convert to numpy for later processing steps
+        images = [np.array(image) for image in images]
+
+        images = self.process_inner(images)
+        boxes, box_mask, box_counts = self.process_boxes(boxes)
+        data = {
+            "pixel_values": images,
+            "input_boxes": boxes,
+            "input_boxes_mask": box_mask,
+            "input_boxes_counts": box_counts,
+        }
+        return BatchFeature(data=data, tensor_type=return_tensors)
\ No newline at end of file
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diff --git a/surya/model/recognition/__pycache__/tokenizer.cpython-310.pyc b/surya/model/recognition/__pycache__/tokenizer.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..40f51f84da76274945d016f20f8fed0adb3eb9b4
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diff --git a/surya/model/recognition/config.py b/surya/model/recognition/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..23d9bbf5bbf3f0328a5ac3df691ab254b82da932
--- /dev/null
+++ b/surya/model/recognition/config.py
@@ -0,0 +1,111 @@
+from transformers import T5Config, MBartConfig, DonutSwinConfig
+
+
+class MBartMoEConfig(MBartConfig):
+    pass
+
+
+class VariableDonutSwinConfig(DonutSwinConfig):
+    pass
+
+
+# Config specific to the model, needed for the tokenizer
+TOTAL_TOKENS = 65536
+TOKEN_OFFSET = 3 # Pad, eos, bos
+SPECIAL_TOKENS = 253
+TOTAL_VOCAB_SIZE = TOTAL_TOKENS + TOKEN_OFFSET + SPECIAL_TOKENS
+LANGUAGE_MAP = {
+    'af': 0,
+    'am': 1,
+    'ar': 2,
+    'as': 3,
+    'az': 4,
+    'be': 5,
+    'bg': 6,
+    'bn': 7,
+    'br': 8,
+    'bs': 9,
+    'ca': 10,
+    'cs': 11,
+    'cy': 12,
+    'da': 13,
+    'de': 14,
+    'el': 15,
+    'en': 16,
+    'eo': 17,
+    'es': 18,
+    'et': 19,
+    'eu': 20,
+    'fa': 21,
+    'fi': 22,
+    'fr': 23,
+    'fy': 24,
+    'ga': 25,
+    'gd': 26,
+    'gl': 27,
+    'gu': 28,
+    'ha': 29,
+    'he': 30,
+    'hi': 31,
+    'hr': 32,
+    'hu': 33,
+    'hy': 34,
+    'id': 35,
+    'is': 36,
+    'it': 37,
+    'ja': 38,
+    'jv': 39,
+    'ka': 40,
+    'kk': 41,
+    'km': 42,
+    'kn': 43,
+    'ko': 44,
+    'ku': 45,
+    'ky': 46,
+    'la': 47,
+    'lo': 48,
+    'lt': 49,
+    'lv': 50,
+    'mg': 51,
+    'mk': 52,
+    'ml': 53,
+    'mn': 54,
+    'mr': 55,
+    'ms': 56,
+    'my': 57,
+    'ne': 58,
+    'nl': 59,
+    'no': 60,
+    'om': 61,
+    'or': 62,
+    'pa': 63,
+    'pl': 64,
+    'ps': 65,
+    'pt': 66,
+    'ro': 67,
+    'ru': 68,
+    'sa': 69,
+    'sd': 70,
+    'si': 71,
+    'sk': 72,
+    'sl': 73,
+    'so': 74,
+    'sq': 75,
+    'sr': 76,
+    'su': 77,
+    'sv': 78,
+    'sw': 79,
+    'ta': 80,
+    'te': 81,
+    'th': 82,
+    'tl': 83,
+    'tr': 84,
+    'ug': 85,
+    'uk': 86,
+    'ur': 87,
+    'uz': 88,
+    'vi': 89,
+    'xh': 90,
+    'yi': 91,
+    'zh': 92
+}
\ No newline at end of file
diff --git a/surya/model/recognition/decoder.py b/surya/model/recognition/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd13421c87526fcce0f001c4da7508e45d990176
--- /dev/null
+++ b/surya/model/recognition/decoder.py
@@ -0,0 +1,511 @@
+import copy
+from typing import Optional, List, Union, Tuple
+
+from transformers import MBartForCausalLM, MBartConfig
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions, BaseModelOutputWithPastAndCrossAttentions
+from transformers.models.mbart.modeling_mbart import MBartPreTrainedModel, MBartDecoder
+from .config import MBartMoEConfig
+import torch
+import math
+
+
+class MBartLearnedPositionalEmbedding(nn.Embedding):
+    """
+    This module learns positional embeddings up to a fixed maximum size.
+    """
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
+        # and adjust num_embeddings appropriately. Other models don't have this hack
+        self.offset = 2
+        super().__init__(num_embeddings + self.offset, embedding_dim)
+
+    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        bsz, seq_len = input_ids.shape[:2]
+        positions = torch.arange(
+            past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
+        ).expand(bsz, -1)
+
+        return super().forward(positions + self.offset)
+
+
+class MBartExpertMLP(nn.Module):
+    def __init__(self, config: MBartConfig, is_lg=False, is_xl=False):
+        super().__init__()
+        self.ffn_dim = config.d_expert
+        if is_lg:
+            self.ffn_dim = config.d_expert_lg
+        if is_xl:
+            self.ffn_dim = config.d_expert_xl
+        self.hidden_dim = config.d_model
+
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.dropout = nn.Dropout(config.activation_dropout)
+
+        self.act_fn = ACT2FN[config.activation_function]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class MBartExpertLayer(nn.Module):
+    # From mixtral, with modifications
+    def __init__(self, config):
+        super().__init__()
+        self.dropout = nn.Dropout(config.activation_dropout)
+
+        self.hidden_dim = config.d_model
+
+        self.lg_lang_codes = sorted(config.lg_langs.values()) if hasattr(config, "lg_langs") else []
+        self.xl_lang_codes = sorted(config.xl_langs.values()) if hasattr(config, "xl_langs") else []
+
+        self.lang_codes = sorted(config.langs.values())
+        self.num_experts = len(self.lang_codes)
+
+        self.experts = nn.ModuleDict({str(lang): MBartExpertMLP(config, is_lg=(lang in self.lg_lang_codes), is_xl=(lang in self.xl_lang_codes)) for lang in self.lang_codes})
+
+    def forward(self, hidden_states: torch.Tensor, langs: torch.LongTensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+
+        final_hidden_states = torch.zeros(
+            (batch_size, sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # Weight experts based on how many languages in the input
+        routing_weights = 1 / ((langs > 3).sum(axis=-1))
+        # Set weights to 1 if zero experts activated
+        routing_weights[torch.isinf(routing_weights)] = 1
+
+        unique_langs = langs.unique(dim=None, sorted=True)
+        unique_langs = unique_langs[unique_langs > 3] # Remove start token
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_lang in unique_langs:
+            # Check which samples match with this expert
+            lang_match = (langs == expert_lang).any(dim=-1)
+            idx = torch.nonzero(lang_match, as_tuple=True)[0]
+
+            if idx.shape[0] == 0:
+                continue
+
+            expert_layer = self.experts[str(expert_lang.item())]
+
+            current_state = hidden_states[idx]
+            current_hidden_states = expert_layer(current_state.view(-1, hidden_dim))
+            current_hidden_states = current_hidden_states.view(-1, sequence_length, hidden_dim)
+
+            # Weight by number of languages in the input
+            selected_routing_weights = routing_weights[idx].view(-1, 1, 1)
+            current_hidden_states *= selected_routing_weights
+
+            final_hidden_states.index_add_(0, idx, current_hidden_states)
+
+        return final_hidden_states
+
+
+class MBartGQAttention(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+        is_causal: bool = False,
+        config: Optional[MBartConfig] = None,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.num_kv_groups = self.num_heads // self.num_kv_heads
+
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        self.config = config
+        self.scaling = self.head_dim**-0.5
+        self.is_decoder = is_decoder
+        self.is_causal = is_causal
+
+        self.k_proj = nn.Linear(embed_dim, self.num_kv_heads * self.head_dim, bias=bias)
+        self.v_proj = nn.Linear(embed_dim, self.num_kv_heads * self.head_dim, bias=bias)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _shape_key_value(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        key_value_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        is_prefill: Optional[bool] = False,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+
+        bsz, tgt_len, _ = hidden_states.size()
+
+        # get query proj
+        query_states = self.q_proj(hidden_states) * self.scaling
+        # get key, value proj
+        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
+        # is checking that the `sequence_length` of the `past_key_value` is the same as
+        # the provided `key_value_states` to support prefix tuning
+        if is_cross_attention:
+            if is_prefill:
+                # cross_attentions
+                key_states = self._shape_key_value(self.k_proj(key_value_states), -1, bsz)
+                value_states = self._shape_key_value(self.v_proj(key_value_states), -1, bsz)
+                past_key_value = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
+            else:
+                # reuse k,v, cross_attentions
+                key_states = past_key_value[0]
+                value_states = past_key_value[1]
+                past_key_value = None
+        # Self-attention
+        else:
+            if is_prefill:
+                # initial prompt
+                key_states = self._shape_key_value(self.k_proj(hidden_states), -1, bsz)
+                value_states = self._shape_key_value(self.v_proj(hidden_states), -1, bsz)
+                past_key_value = torch.cat([key_states[:, :, -tgt_len:].unsqueeze(0), value_states[:, :, -tgt_len:].unsqueeze(0)], dim=0)
+            else:
+                # reuse k, v, self_attention
+                key_states = self._shape_key_value(self.k_proj(hidden_states), -1, bsz)
+                value_states = self._shape_key_value(self.v_proj(hidden_states), -1, bsz)
+                key_states = torch.cat([past_key_value[0], key_states], dim=2)
+                value_states = torch.cat([past_key_value[1], value_states], dim=2)
+                past_key_value = torch.cat([key_states[:, :, -tgt_len:].unsqueeze(0), value_states[:, :, -tgt_len:].unsqueeze(0)], dim=0)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+
+        # Expand kv heads, then match query shape
+        key_states = key_states.repeat_interleave(self.num_kv_groups, dim=1).reshape(*proj_shape)
+        value_states = value_states.repeat_interleave(self.num_kv_groups, dim=1).reshape(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+        if not is_cross_attention:
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+        attn_output = torch.bmm(attn_weights, value_states).view(bsz, self.num_heads, tgt_len, self.head_dim).transpose(1,2)
+
+        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
+        # partitioned across GPUs when using tensor-parallelism.
+        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, past_key_value
+
+
+class MBartMoEDecoderLayer(nn.Module):
+    def __init__(self, config: MBartConfig, has_moe=False):
+        super().__init__()
+        self.embed_dim = config.d_model
+
+        self.self_attn = MBartGQAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.decoder_attention_heads,
+            num_kv_heads=config.kv_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+            is_causal=True,
+            config=config,
+        )
+        self.dropout = config.dropout
+        self.activation_fn = ACT2FN[config.activation_function]
+        self.activation_dropout = config.activation_dropout
+
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.encoder_attn = MBartGQAttention(
+            self.embed_dim,
+            config.decoder_attention_heads,
+            num_kv_heads=config.kv_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+            config=config,
+        )
+        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.has_moe = has_moe
+        if has_moe:
+            self.moe = MBartExpertLayer(config)
+        else:
+            self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+            self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        langs: Optional[torch.LongTensor] = None,
+        self_kv_cache: Optional[torch.Tensor] = None,
+        cross_kv_cache: Optional[torch.Tensor] = None,
+        is_prefill: Optional[bool] = False,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = True,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Self Attention
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        # add present self-attn cache to positions 1,2 of present_key_value tuple
+        hidden_states, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            past_key_value=self_kv_cache,
+            is_prefill=is_prefill,
+            attention_mask=attention_mask,
+        )
+        hidden_states = residual + hidden_states
+
+        # Cross-Attention Block
+        if encoder_hidden_states is not None:
+            residual = hidden_states
+            hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
+            hidden_states, cross_attn_present_key_value = self.encoder_attn(
+                hidden_states=hidden_states,
+                key_value_states=encoder_hidden_states,
+                is_prefill=is_prefill,
+                attention_mask=encoder_attention_mask,
+                past_key_value=cross_kv_cache,
+            )
+            hidden_states = residual + hidden_states
+
+            # add cross-attn to positions 3,4 of present_key_value tuple
+            present_key_value = (present_key_value, cross_attn_present_key_value)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        if self.has_moe:
+            hidden_states = self.moe(hidden_states, langs)
+        else:
+            hidden_states = self.activation_fn(self.fc1(hidden_states))
+            hidden_states = self.fc2(hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class MBartMoEDecoder(MBartDecoder):
+    def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
+        MBartPreTrainedModel.__init__(self, config)
+        self.dropout = config.dropout
+        self.layerdrop = config.decoder_layerdrop
+        self.padding_idx = config.pad_token_id
+        self.max_target_positions = config.max_position_embeddings
+        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
+
+        if embed_tokens is not None:
+            self.embed_tokens.weight = embed_tokens.weight
+
+        self.embed_positions = MBartLearnedPositionalEmbedding(
+            config.max_position_embeddings,
+            config.d_model,
+        )
+        # Language-specific MoE goes at second and second-to-last layer
+        self.layers = nn.ModuleList([MBartMoEDecoderLayer(config, has_moe=(i in config.moe_layers) and config.use_moe) for i in range(config.decoder_layers)])
+        self.layernorm_embedding = nn.LayerNorm(config.d_model)
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        self_kv_cache: Optional[torch.Tensor] = None,
+        cross_kv_cache: Optional[torch.Tensor] = None,
+        past_token_count: Optional[int] = None,
+        langs: Optional[torch.LongTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
+        use_cache = True
+        return_dict = True
+
+        input = input_ids
+        input_shape = input.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        # past_key_values_length
+        past_key_values_length = past_token_count if self_kv_cache is not None else 0
+        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+        # embed positions
+        positions = self.embed_positions(input, past_key_values_length)
+
+        hidden_states = inputs_embeds + positions
+        hidden_states = self.layernorm_embedding(hidden_states)
+
+        # decoder layers
+        all_hidden_states = None
+        all_self_attns = None
+        all_cross_attentions = None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            is_prefill = past_token_count == 0
+            layer_self_kv_cache = self_kv_cache[idx] if self_kv_cache is not None else None
+            layer_cross_kv_cache = cross_kv_cache[idx] if cross_kv_cache is not None else None
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                langs=langs,
+                self_kv_cache=layer_self_kv_cache,
+                cross_kv_cache=layer_cross_kv_cache,
+                is_prefill=is_prefill,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=None,
+                use_cache=use_cache,
+            )
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[1],)
+
+        hidden_states = self.layer_norm(hidden_states)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class MBartMoEDecoderWrapper(MBartPreTrainedModel):
+    """
+    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
+    used in combination with the [`EncoderDecoderModel`] framework.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.decoder = MBartMoEDecoder(config)
+
+    def forward(self, *args, **kwargs):
+        return self.decoder(*args, **kwargs)
+
+
+class MBartMoE(MBartForCausalLM):
+    config_class = MBartMoEConfig
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config, **kwargs):
+        config = copy.deepcopy(config)
+        config.is_decoder = True
+        config.is_encoder_decoder = False
+        MBartPreTrainedModel.__init__(self, config)
+        self.model = MBartMoEDecoderWrapper(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        self_kv_cache: Optional[torch.FloatTensor] = None,
+        cross_kv_cache: Optional[torch.FloatTensor] = None,
+        past_token_count: Optional[int] = None,
+        langs: Optional[torch.LongTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            self_kv_cache=self_kv_cache,
+            cross_kv_cache=cross_kv_cache,
+            past_token_count=past_token_count,
+            langs=langs,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+
+        logits = self.lm_head(outputs[0])
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=None,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prune_moe_experts(self, keep_keys: List[int]):
+        # Remove experts not specified in keep_keys
+        str_keep_keys = [str(key) for key in keep_keys]
+        for layer in self.model.decoder.layers:
+            if not layer.has_moe:
+                continue
+
+            lang_keys = list(layer.moe.experts.keys())
+            for lang in lang_keys:
+                if lang not in str_keep_keys:
+                    layer.moe.experts.pop(lang)
+            layer.lang_codes = keep_keys
diff --git a/surya/model/recognition/encoder.py b/surya/model/recognition/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..184a543e785d14341be7a60c48d9a3ee39251555
--- /dev/null
+++ b/surya/model/recognition/encoder.py
@@ -0,0 +1,469 @@
+from torch import nn
+import torch
+from typing import Optional, Tuple, Union
+
+from transformers.models.donut.modeling_donut_swin import DonutSwinPatchEmbeddings, DonutSwinEmbeddings, DonutSwinModel, \
+    DonutSwinEncoder, DonutSwinModelOutput, DonutSwinEncoderOutput, DonutSwinAttention, DonutSwinDropPath, \
+    DonutSwinIntermediate, DonutSwinOutput, window_partition, window_reverse
+
+# from config import VariableDonutSwinConfig
+
+from .config import VariableDonutSwinConfig
+
+
+class VariableDonutSwinEmbeddings(DonutSwinEmbeddings):
+    """
+    Construct the patch and position embeddings. Optionally, also the mask token.
+    """
+
+    def __init__(self, config, use_mask_token=False):
+        super().__init__(config, use_mask_token)
+
+        self.patch_embeddings = DonutSwinPatchEmbeddings(config)
+        num_patches = self.patch_embeddings.num_patches
+        self.patch_grid = self.patch_embeddings.grid_size
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.embed_dim)) if use_mask_token else None
+        self.position_embeddings = None
+
+        if config.use_absolute_embeddings:
+            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim))
+
+        self.norm = nn.LayerNorm(config.embed_dim)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(
+        self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None
+    ) -> Tuple[torch.Tensor]:
+
+        embeddings, output_dimensions = self.patch_embeddings(pixel_values)
+        # Layernorm across the last dimension (each patch is a single row)
+        embeddings = self.norm(embeddings)
+        batch_size, seq_len, embed_dim = embeddings.size()
+
+        if bool_masked_pos is not None:
+            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
+            # replace the masked visual tokens by mask_tokens
+            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
+            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
+
+        if self.position_embeddings is not None:
+            embeddings = embeddings + self.position_embeddings[:, :seq_len, :]
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings, output_dimensions
+
+
+class VariableDonutSwinPatchMerging(nn.Module):
+    """
+    Patch Merging Layer.
+
+    Args:
+        input_resolution (`Tuple[int]`):
+            Resolution of input feature.
+        dim (`int`):
+            Number of input channels.
+        norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
+            Normalization layer class.
+    """
+
+    def __init__(self, input_resolution: Tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def maybe_pad(self, input_feature, height, width):
+        should_pad = (height % 2 == 1) or (width % 2 == 1)
+        if should_pad:
+            pad_values = (0, 0, 0, width % 2, 0, height % 2)
+            input_feature = nn.functional.pad(input_feature, pad_values)
+
+        return input_feature
+
+    def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor:
+        height, width = input_dimensions
+        # `dim` is height * width
+        batch_size, dim, num_channels = input_feature.shape
+
+        input_feature = input_feature.view(batch_size, height, width, num_channels)
+        # pad input to be disible by width and height, if needed
+        input_feature = self.maybe_pad(input_feature, height, width)
+        # [batch_size, height/2, width/2, num_channels]
+        input_feature_0 = input_feature[:, 0::2, 0::2, :]
+        # [batch_size, height/2, width/2, num_channels]
+        input_feature_1 = input_feature[:, 1::2, 0::2, :]
+        # [batch_size, height/2, width/2, num_channels]
+        input_feature_2 = input_feature[:, 0::2, 1::2, :]
+        # [batch_size, height/2, width/2, num_channels]
+        input_feature_3 = input_feature[:, 1::2, 1::2, :]
+        # batch_size height/2 width/2 4*num_channels
+        input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
+        input_feature = input_feature.view(batch_size, -1, 4 * num_channels)  # batch_size height/2*width/2 4*C
+
+        input_feature = self.norm(input_feature)
+        input_feature = self.reduction(input_feature)
+
+        return input_feature
+
+
+class VariableDonutSwinLayer(nn.Module):
+    def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.shift_size = shift_size
+        self.window_size = config.window_size
+        self.input_resolution = input_resolution
+        self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
+        self.attention = DonutSwinAttention(config, dim, num_heads, window_size=self.window_size)
+        self.drop_path = DonutSwinDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
+        self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
+        self.intermediate = DonutSwinIntermediate(config, dim)
+        self.output = DonutSwinOutput(config, dim)
+
+    def set_shift_and_window_size(self, input_resolution):
+        if min(input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(input_resolution)
+
+    def get_attn_mask(self, height, width, dtype):
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
+            height_slices = (
+                slice(0, -self.window_size),
+                slice(-self.window_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            width_slices = (
+                slice(0, -self.window_size),
+                slice(-self.window_size, -self.shift_size),
+                slice(-self.shift_size, None),
+            )
+            count = 0
+            for height_slice in height_slices:
+                for width_slice in width_slices:
+                    img_mask[:, height_slice, width_slice, :] = count
+                    count += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+        return attn_mask
+
+    def maybe_pad(self, hidden_states, height, width):
+        pad_right = (self.window_size - width % self.window_size) % self.window_size
+        pad_bottom = (self.window_size - height % self.window_size) % self.window_size
+        pad_values = (0, 0, 0, pad_right, 0, pad_bottom)
+        hidden_states = nn.functional.pad(hidden_states, pad_values)
+        return hidden_states, pad_values
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        input_dimensions: Tuple[int, int],
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        always_partition: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if not always_partition:
+            self.set_shift_and_window_size(input_dimensions)
+        else:
+            pass
+        height, width = input_dimensions
+        batch_size, _, channels = hidden_states.size()
+        shortcut = hidden_states
+
+        hidden_states = self.layernorm_before(hidden_states)
+
+        hidden_states = hidden_states.view(batch_size, height, width, channels)
+
+        # pad hidden_states to multiples of window size
+        hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
+
+        _, height_pad, width_pad, _ = hidden_states.shape
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_hidden_states = hidden_states
+
+        # partition windows
+        hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
+        hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
+        attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype)
+        if attn_mask is not None:
+            attn_mask = attn_mask.to(hidden_states_windows.device)
+
+        attention_outputs = self.attention(
+            hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions
+        )
+
+        attention_output = attention_outputs[0]
+
+        attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
+        shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad)
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            attention_windows = shifted_windows
+
+        was_padded = pad_values[3] > 0 or pad_values[5] > 0
+        if was_padded:
+            attention_windows = attention_windows[:, :height, :width, :].contiguous()
+
+        attention_windows = attention_windows.view(batch_size, height * width, channels)
+
+        hidden_states = shortcut + self.drop_path(attention_windows)
+
+        layer_output = self.layernorm_after(hidden_states)
+        layer_output = self.intermediate(layer_output)
+        layer_output = hidden_states + self.output(layer_output)
+
+        layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
+        return layer_outputs
+
+
+class VariableDonutSwinStage(nn.Module):
+    def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
+        super().__init__()
+        self.config = config
+        self.dim = dim
+        self.blocks = nn.ModuleList(
+            [
+                VariableDonutSwinLayer(
+                    config=config,
+                    dim=dim,
+                    input_resolution=input_resolution,
+                    num_heads=num_heads,
+                    shift_size=0 if (i % 2 == 0) else int(config.window_size // 2),
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=nn.LayerNorm)
+        else:
+            self.downsample = None
+
+        self.pointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        input_dimensions: Tuple[int, int],
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        always_partition: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        height, width = input_dimensions
+        for i, layer_module in enumerate(self.blocks):
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            layer_outputs = layer_module(
+                hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
+            )
+
+            hidden_states = layer_outputs[0]
+
+        hidden_states_before_downsampling = hidden_states
+        if self.downsample is not None:
+            height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
+            output_dimensions = (height, width, height_downsampled, width_downsampled)
+            hidden_states = self.downsample(hidden_states_before_downsampling, input_dimensions)
+        else:
+            output_dimensions = (height, width, height, width)
+
+        stage_outputs = (hidden_states, hidden_states_before_downsampling, output_dimensions)
+
+        if output_attentions:
+            stage_outputs += layer_outputs[1:]
+        return stage_outputs
+
+
+class VariableDonutSwinEncoder(nn.Module):
+    def __init__(self, config, grid_size):
+        super().__init__()
+        self.num_layers = len(config.depths)
+        self.config = config
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
+        self.layers = nn.ModuleList(
+            [
+                VariableDonutSwinStage(
+                    config=config,
+                    dim=int(config.embed_dim * 2**i_layer),
+                    input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)),
+                    depth=config.depths[i_layer],
+                    num_heads=config.num_heads[i_layer],
+                    drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
+                    downsample=VariableDonutSwinPatchMerging if (i_layer < self.num_layers - 1) else None,
+                )
+                for i_layer in range(self.num_layers)
+            ]
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        input_dimensions: Tuple[int, int],
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        output_hidden_states_before_downsampling: Optional[bool] = False,
+        always_partition: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple, DonutSwinEncoderOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_reshaped_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        if output_hidden_states:
+            batch_size, _, hidden_size = hidden_states.shape
+            # rearrange b (h w) c -> b c h w
+            reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
+            reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
+            all_hidden_states += (hidden_states,)
+            all_reshaped_hidden_states += (reshaped_hidden_state,)
+
+        for i, layer_module in enumerate(self.layers):
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    input_dimensions,
+                    layer_head_mask,
+                    output_attentions,
+                    always_partition,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
+                )
+
+            hidden_states = layer_outputs[0]
+            hidden_states_before_downsampling = layer_outputs[1]
+            output_dimensions = layer_outputs[2]
+
+            input_dimensions = (output_dimensions[-2], output_dimensions[-1])
+
+            if output_hidden_states and output_hidden_states_before_downsampling:
+                batch_size, _, hidden_size = hidden_states_before_downsampling.shape
+                # rearrange b (h w) c -> b c h w
+                # here we use the original (not downsampled) height and width
+                reshaped_hidden_state = hidden_states_before_downsampling.view(
+                    batch_size, *(output_dimensions[0], output_dimensions[1]), hidden_size
+                )
+                reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
+                all_hidden_states += (hidden_states_before_downsampling,)
+                all_reshaped_hidden_states += (reshaped_hidden_state,)
+            elif output_hidden_states and not output_hidden_states_before_downsampling:
+                batch_size, _, hidden_size = hidden_states.shape
+                # rearrange b (h w) c -> b c h w
+                reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
+                reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
+                all_hidden_states += (hidden_states,)
+                all_reshaped_hidden_states += (reshaped_hidden_state,)
+
+            if output_attentions:
+                all_self_attentions += layer_outputs[3:]
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+
+        return DonutSwinEncoderOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            reshaped_hidden_states=all_reshaped_hidden_states,
+        )
+
+
+class VariableDonutSwinModel(DonutSwinModel):
+    config_class = VariableDonutSwinConfig
+    def __init__(self, config, add_pooling_layer=True, use_mask_token=False, **kwargs):
+        super().__init__(config)
+        self.config = config
+        self.num_layers = len(config.depths)
+        self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
+
+        self.embeddings = VariableDonutSwinEmbeddings(config, use_mask_token=use_mask_token)
+        self.encoder = VariableDonutSwinEncoder(config, self.embeddings.patch_grid)
+
+        self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, DonutSwinModelOutput]:
+        r"""
+        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, len(self.config.depths))
+
+        embedding_output, input_dimensions = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            input_dimensions,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = encoder_outputs[0]
+
+        pooled_output = None
+        if self.pooler is not None:
+            pooled_output = self.pooler(sequence_output.transpose(1, 2))
+            pooled_output = torch.flatten(pooled_output, 1)
+
+        if not return_dict:
+            output = (sequence_output, pooled_output) + encoder_outputs[1:]
+
+            return output
+
+        return DonutSwinModelOutput(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
+        )
diff --git a/surya/model/recognition/model.py b/surya/model/recognition/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ee25632d03d081215854796a6fcbb13e67fe376
--- /dev/null
+++ b/surya/model/recognition/model.py
@@ -0,0 +1,64 @@
+import warnings
+
+import torch
+
+warnings.filterwarnings("ignore", message="torch.utils._pytree._register_pytree_node is deprecated")
+
+import logging
+logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
+
+from typing import List, Optional, Tuple
+from transformers import VisionEncoderDecoderModel, VisionEncoderDecoderConfig, AutoModel, AutoModelForCausalLM
+from surya.model.recognition.config import MBartMoEConfig, VariableDonutSwinConfig
+from surya.model.recognition.encoder import VariableDonutSwinModel
+from surya.model.recognition.decoder import MBartMoE
+from surya.settings import settings
+
+
+def load_model(checkpoint=settings.RECOGNITION_MODEL_CHECKPOINT, device=settings.TORCH_DEVICE_MODEL, dtype=settings.MODEL_DTYPE, langs: Optional[List[int]] = None):
+    config = VisionEncoderDecoderConfig.from_pretrained(checkpoint)
+
+    # Prune moe experts that are not needed before loading the model
+    if langs is not None:
+        config.decoder.langs = {lang_iso : lang_int for lang_iso, lang_int in config.decoder.langs.items() if lang_int in langs}
+
+    decoder_config = vars(config.decoder)
+    decoder = MBartMoEConfig(**decoder_config)
+    config.decoder = decoder
+
+    encoder_config = vars(config.encoder)
+    encoder = VariableDonutSwinConfig(**encoder_config)
+    config.encoder = encoder
+
+    # Get transformers to load custom encoder/decoder
+    AutoModel.register(MBartMoEConfig, MBartMoE)
+    AutoModelForCausalLM.register(MBartMoEConfig, MBartMoE)
+    AutoModel.register(VariableDonutSwinConfig, VariableDonutSwinModel)
+
+    model = LangVisionEncoderDecoderModel.from_pretrained(checkpoint, config=config, torch_dtype=dtype)
+    assert isinstance(model.decoder, MBartMoE)
+    assert isinstance(model.encoder, VariableDonutSwinModel)
+
+    model = model.to(device)
+    model = model.eval()
+    print(f"Loaded recognition model {checkpoint} on device {device} with dtype {dtype}")
+    return model
+
+
+class LangVisionEncoderDecoderModel(VisionEncoderDecoderModel):
+    def prepare_inputs_for_generation(
+            self, input_ids, decoder_langs=None, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
+    ):
+        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, langs=decoder_langs, past_key_values=past_key_values)
+        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
+        input_dict = {
+            "attention_mask": attention_mask,
+            "decoder_attention_mask": decoder_attention_mask,
+            "decoder_input_ids": decoder_inputs["input_ids"],
+            "encoder_outputs": encoder_outputs,
+            "past_key_values": decoder_inputs["past_key_values"],
+            "use_cache": use_cache,
+            "decoder_langs": decoder_inputs["langs"],
+        }
+        return input_dict
+
diff --git a/surya/model/recognition/processor.py b/surya/model/recognition/processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a62c27c547f6699d9c737752e76f2d3552786db5
--- /dev/null
+++ b/surya/model/recognition/processor.py
@@ -0,0 +1,216 @@
+from typing import Dict, Union, Optional, List, Iterable
+
+import cv2
+from torch import TensorType
+from transformers import DonutImageProcessor, DonutProcessor
+from transformers.image_processing_utils import BatchFeature
+from transformers.image_transforms import pad, normalize
+from transformers.image_utils import PILImageResampling, ImageInput, ChannelDimension, make_list_of_images, get_image_size
+import numpy as np
+from PIL import Image
+import PIL
+from test_surya.surya.model.recognition.tokenizer import Byt5LangTokenizer
+from surya.settings import settings
+
+
+def load_processor():
+    processor = SuryaProcessor()
+    processor.image_processor.train = False
+    processor.image_processor.max_size = settings.RECOGNITION_IMAGE_SIZE
+    processor.tokenizer.model_max_length = settings.RECOGNITION_MAX_TOKENS
+    return processor
+
+
+class SuryaImageProcessor(DonutImageProcessor):
+    def __init__(self, *args, max_size=None, train=False, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.patch_size = kwargs.get("patch_size", (4, 4))
+        self.max_size = max_size
+        self.train = train
+
+    @classmethod
+    def numpy_resize(cls, image: np.ndarray, size, interpolation=cv2.INTER_LANCZOS4):
+        height, width = image.shape[:2]
+        max_width, max_height = size["width"], size["height"]
+
+        if (height == max_height and width <= max_width) or (width == max_width and height <= max_height):
+            image = image.transpose(2, 0, 1)
+            return image
+
+        scale = min(max_width / width, max_height / height)
+
+        new_width = int(width * scale)
+        new_height = int(height * scale)
+
+        resized_image = cv2.resize(image, (new_width, new_height), interpolation=interpolation)
+        resized_image = resized_image.transpose(2, 0, 1)
+
+        return resized_image
+
+    def process_inner(self, images: List[np.ndarray]):
+        assert images[0].shape[2] == 3 # RGB input images, channel dim last
+
+        # Rotate if the bbox is wider than it is tall
+        images = [SuryaImageProcessor.align_long_axis(image, size=self.max_size, input_data_format=ChannelDimension.LAST) for image in images]
+
+        # Verify that the image is wider than it is tall
+        for img in images:
+            assert img.shape[1] >= img.shape[0]
+
+        # This also applies the right channel dim format, to channel x height x width
+        images = [SuryaImageProcessor.numpy_resize(img, self.max_size, self.resample) for img in images]
+        assert images[0].shape[0] == 3 # RGB input images, channel dim first
+
+        # Convert to float32 for rescale/normalize
+        images = [img.astype(np.float32) for img in images]
+
+        # Pads with 255 (whitespace)
+        # Pad to max size to improve performance
+        max_size = self.max_size
+        images = [
+            SuryaImageProcessor.pad_image(
+                image=image,
+                size=max_size,
+                input_data_format=ChannelDimension.FIRST,
+                pad_value=settings.RECOGNITION_PAD_VALUE
+            )
+            for image in images
+        ]
+        # Rescale and normalize
+        for idx in range(len(images)):
+            images[idx] = images[idx] * self.rescale_factor
+        images = [
+            SuryaImageProcessor.normalize(img, mean=self.image_mean, std=self.image_std, input_data_format=ChannelDimension.FIRST)
+            for img in images
+        ]
+
+        return images
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_thumbnail: bool = None,
+        do_align_long_axis: bool = None,
+        do_pad: bool = None,
+        random_padding: bool = False,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        images = make_list_of_images(images)
+
+        # Convert to numpy for later processing steps
+        images = [np.array(img) for img in images]
+        images = self.process_inner(images)
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    @classmethod
+    def pad_image(
+        cls,
+        image: np.ndarray,
+        size: Dict[str, int],
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        pad_value: float = 0.0,
+    ) -> np.ndarray:
+        output_height, output_width = size["height"], size["width"]
+        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+
+        delta_width = output_width - input_width
+        delta_height = output_height - input_height
+
+        assert delta_width >= 0 and delta_height >= 0
+
+        pad_top = delta_height // 2
+        pad_left = delta_width // 2
+
+        pad_bottom = delta_height - pad_top
+        pad_right = delta_width - pad_left
+
+        padding = ((pad_top, pad_bottom), (pad_left, pad_right))
+        return pad(image, padding, data_format=data_format, input_data_format=input_data_format, constant_values=pad_value)
+
+    @classmethod
+    def align_long_axis(
+        cls,
+        image: np.ndarray,
+        size: Dict[str, int],
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ) -> np.ndarray:
+        input_height, input_width = image.shape[:2]
+        output_height, output_width = size["height"], size["width"]
+
+        if (output_width < output_height and input_width > input_height) or (
+            output_width > output_height and input_width < input_height
+        ):
+            image = np.rot90(image, 3)
+
+        return image
+
+    @classmethod
+    def normalize(
+        cls,
+        image: np.ndarray,
+        mean: Union[float, Iterable[float]],
+        std: Union[float, Iterable[float]],
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        return normalize(
+            image, mean=mean, std=std, data_format=data_format, input_data_format=input_data_format, **kwargs
+        )
+
+
+class SuryaProcessor(DonutProcessor):
+    def __init__(self, image_processor=None, tokenizer=None, train=False, **kwargs):
+        image_processor = SuryaImageProcessor.from_pretrained(settings.RECOGNITION_MODEL_CHECKPOINT)
+        tokenizer = Byt5LangTokenizer()
+        if image_processor is None:
+            raise ValueError("You need to specify an `image_processor`.")
+        if tokenizer is None:
+            raise ValueError("You need to specify a `tokenizer`.")
+
+        super().__init__(image_processor, tokenizer)
+        self.current_processor = self.image_processor
+        self._in_target_context_manager = False
+
+    def __call__(self, *args, **kwargs):
+        images = kwargs.pop("images", None)
+        text = kwargs.pop("text", None)
+        lang = kwargs.pop("lang", None)
+
+        if len(args) > 0:
+            images = args[0]
+            args = args[1:]
+
+        if images is None and text is None:
+            raise ValueError("You need to specify either an `images` or `text` input to process.")
+
+        if images is not None:
+            inputs = self.image_processor(images, *args, **kwargs)
+
+        if text is not None:
+            encodings = self.tokenizer(text, lang, **kwargs)
+
+        if text is None:
+            return inputs
+        elif images is None:
+            return encodings
+        else:
+            inputs["labels"] = encodings["input_ids"]
+            inputs["langs"] = encodings["langs"]
+            return inputs
\ No newline at end of file
diff --git a/surya/model/recognition/tokenizer.py b/surya/model/recognition/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c062c95de868ec4d06568ffb393bba9ee23155
--- /dev/null
+++ b/surya/model/recognition/tokenizer.py
@@ -0,0 +1,117 @@
+from itertools import chain
+from typing import List, Union
+from transformers import ByT5Tokenizer
+import numpy as np
+import torch
+from surya.model.recognition.config import LANGUAGE_MAP, TOTAL_TOKENS, TOKEN_OFFSET
+
+
+def text_to_utf16_numbers(text):
+    utf16_bytes = text.encode('utf-16le')  # Little-endian to simplify byte order handling
+
+    numbers = []
+
+    # Iterate through each pair of bytes and combine them into a single number
+    for i in range(0, len(utf16_bytes), 2):
+        # Combine two adjacent bytes into a single number
+        number = utf16_bytes[i] + (utf16_bytes[i + 1] << 8)
+        numbers.append(number)
+
+    return numbers
+
+
+def utf16_numbers_to_text(numbers):
+    byte_array = bytearray()
+    for number in numbers:
+        # Extract the two bytes from the number and add them to the byte array
+        byte_array.append(number & 0xFF)         # Lower byte
+        byte_array.append((number >> 8) & 0xFF)  # Upper byte
+
+    text = byte_array.decode('utf-16le', errors="ignore")
+    return text
+
+
+def _tokenize(text: str, langs: List[str], eos_token_id: int = 1, add_eos: bool = True, add_bos: bool = True):
+    tokens = text_to_utf16_numbers(text)
+    tokens = [t + TOKEN_OFFSET for t in tokens] # Account for special pad, etc, tokens
+
+    lang_list = []
+    for lang in langs:
+        code = LANGUAGE_MAP[lang]
+        lang_list.append(code + TOKEN_OFFSET + TOTAL_TOKENS)
+
+    tokens = lang_list + tokens
+
+    if add_eos:
+        tokens.append(eos_token_id)
+    if add_bos:
+        tokens.insert(0, eos_token_id)
+
+    return tokens, lang_list
+
+
+class Byt5LangTokenizer(ByT5Tokenizer):
+    def __init__(self,
+        eos_token="</s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        model_max_length=None,
+        **kwargs,
+    ):
+        self.pad_token = pad_token
+        self.eos_token = eos_token
+        self.unk_token = unk_token
+        self.bos_token = eos_token
+        self.offset = TOKEN_OFFSET
+
+        self.pad_id = 0
+        self.eos_id = 1
+        self.unk_id = 2
+
+        self.model_max_length = model_max_length
+        self.special_token_start = TOKEN_OFFSET + TOTAL_TOKENS
+
+        super().__init__()
+
+    def __call__(self, texts: Union[List[str], str], langs: Union[List[List[str]], List[str]], pad_token_id: int = 0, **kwargs):
+        tokenized = []
+        all_langs = []
+
+        is_list = True
+        # Convert to list of lists format
+        if isinstance(texts, str):
+            texts = [texts]
+            is_list = False
+
+        if isinstance(langs[0], str):
+            langs = [langs]
+
+        # One language input per text input
+        assert len(langs) == len(texts)
+
+        for text, lang in zip(texts, langs):
+            tokens, lang_list = _tokenize(text, lang)
+            tokenized.append(tokens)
+            all_langs.append(lang_list)
+
+        # Convert back to flat format
+        if not is_list:
+            tokenized = tokenized[0]
+            all_langs = all_langs[0]
+
+        return {"input_ids": tokenized, "langs": all_langs}
+
+    def decode(
+        self,
+        token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, (np.ndarray, torch.Tensor)):
+            token_ids = token_ids.tolist()
+
+        token_ids = [t for t in token_ids if TOKEN_OFFSET <= t < self.special_token_start]
+        token_ids = [t - TOKEN_OFFSET for t in token_ids]
+        text = utf16_numbers_to_text(token_ids)
+        return text
diff --git a/surya/ocr.py b/surya/ocr.py
new file mode 100644
index 0000000000000000000000000000000000000000..1744098b1bcc0282f2809c58a66398ae60ae209e
--- /dev/null
+++ b/surya/ocr.py
@@ -0,0 +1,106 @@
+from typing import List
+from PIL import Image
+
+from surya.detection import batch_text_detection
+from surya.input.processing import slice_polys_from_image, slice_bboxes_from_image, convert_if_not_rgb
+from surya.postprocessing.text import sort_text_lines
+from surya.recognition import batch_recognition
+from surya.schema import TextLine, OCRResult
+
+
+def run_recognition(images: List[Image.Image], langs: List[List[str]], rec_model, rec_processor, bboxes: List[List[List[int]]] = None, polygons: List[List[List[List[int]]]] = None, batch_size=None) -> List[OCRResult]:
+    # Polygons need to be in corner format - [[x1, y1], [x2, y2], [x3, y3], [x4, y4]], bboxes in [x1, y1, x2, y2] format
+    assert bboxes is not None or polygons is not None
+    assert len(images) == len(langs), "You need to pass in one list of languages for each image"
+
+    images = convert_if_not_rgb(images)
+
+    slice_map = []
+    all_slices = []
+    all_langs = []
+    for idx, (image, lang) in enumerate(zip(images, langs)):
+        if polygons is not None:
+            slices = slice_polys_from_image(image, polygons[idx])
+        else:
+            slices = slice_bboxes_from_image(image, bboxes[idx])
+        slice_map.append(len(slices))
+        all_slices.extend(slices)
+        all_langs.extend([lang] * len(slices))
+
+    rec_predictions, _ = batch_recognition(all_slices, all_langs, rec_model, rec_processor, batch_size=batch_size)
+
+    predictions_by_image = []
+    slice_start = 0
+    for idx, (image, lang) in enumerate(zip(images, langs)):
+        slice_end = slice_start + slice_map[idx]
+        image_lines = rec_predictions[slice_start:slice_end]
+        slice_start = slice_end
+
+        text_lines = []
+        for i in range(len(image_lines)):
+            if polygons is not None:
+                poly = polygons[idx][i]
+            else:
+                bbox = bboxes[idx][i]
+                poly = [[bbox[0], bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]]
+
+            text_lines.append(TextLine(
+                text=image_lines[i],
+                polygon=poly
+            ))
+
+        pred = OCRResult(
+            text_lines=text_lines,
+            languages=lang,
+            image_bbox=[0, 0, image.size[0], image.size[1]]
+        )
+        predictions_by_image.append(pred)
+
+    return predictions_by_image
+
+
+def run_ocr(images: List[Image.Image], langs: List[List[str]], det_model, det_processor, rec_model, rec_processor, batch_size=None) -> List[OCRResult]:
+    images = convert_if_not_rgb(images)
+    det_predictions = batch_text_detection(images, det_model, det_processor)
+
+    all_slices = []
+    slice_map = []
+    all_langs = []
+
+    for idx, (det_pred, image, lang) in enumerate(zip(det_predictions, images, langs)):
+        polygons = [p.polygon for p in det_pred.bboxes]
+        slices = slice_polys_from_image(image, polygons)
+        slice_map.append(len(slices))
+        all_langs.extend([lang] * len(slices))
+        all_slices.extend(slices)
+
+    rec_predictions, confidence_scores = batch_recognition(all_slices, all_langs, rec_model, rec_processor, batch_size=batch_size)
+
+    predictions_by_image = []
+    slice_start = 0
+    for idx, (image, det_pred, lang) in enumerate(zip(images, det_predictions, langs)):
+        slice_end = slice_start + slice_map[idx]
+        image_lines = rec_predictions[slice_start:slice_end]
+        line_confidences = confidence_scores[slice_start:slice_end]
+        slice_start = slice_end
+
+        assert len(image_lines) == len(det_pred.bboxes)
+
+        lines = []
+        for text_line, confidence, bbox in zip(image_lines, line_confidences, det_pred.bboxes):
+            lines.append(TextLine(
+                text=text_line,
+                polygon=bbox.polygon,
+                bbox=bbox.bbox,
+                confidence=confidence
+            ))
+
+        lines = sort_text_lines(lines)
+
+        predictions_by_image.append(OCRResult(
+            text_lines=lines,
+            languages=lang,
+            image_bbox=det_pred.image_bbox
+        ))
+
+    return predictions_by_image
diff --git a/surya/ordering.py b/surya/ordering.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b87ba17efc98f6d89047c085866a962052bd6bf
--- /dev/null
+++ b/surya/ordering.py
@@ -0,0 +1,140 @@
+from copy import deepcopy
+from typing import List
+import torch
+from PIL import Image
+
+from surya.input.processing import convert_if_not_rgb
+from surya.model.ordering.encoderdecoder import OrderVisionEncoderDecoderModel
+from surya.schema import OrderBox, OrderResult
+from surya.settings import settings
+from tqdm import tqdm
+import numpy as np
+
+
+def get_batch_size():
+    batch_size = settings.ORDER_BATCH_SIZE
+    if batch_size is None:
+        batch_size = 8
+        if settings.TORCH_DEVICE_MODEL == "mps":
+            batch_size = 8
+        if settings.TORCH_DEVICE_MODEL == "cuda":
+            batch_size = 32
+    return batch_size
+
+
+def rank_elements(arr):
+    enumerated_and_sorted = sorted(enumerate(arr), key=lambda x: x[1])
+    rank = [0] * len(arr)
+
+    for rank_value, (original_index, value) in enumerate(enumerated_and_sorted):
+        rank[original_index] = rank_value
+
+    return rank
+
+
+def batch_ordering(images: List, bboxes: List[List[List[float]]], model: OrderVisionEncoderDecoderModel, processor, batch_size=None) -> List[OrderResult]:
+    assert all([isinstance(image, Image.Image) for image in images])
+    assert len(images) == len(bboxes)
+    if batch_size is None:
+        batch_size = get_batch_size()
+
+    images = [image.convert("RGB") for image in images] # also copies the images
+
+    output_order = []
+    for i in tqdm(range(0, len(images), batch_size), desc="Finding reading order"):
+        batch_bboxes = deepcopy(bboxes[i:i+batch_size])
+        batch_images = images[i:i+batch_size]
+        orig_sizes = [image.size for image in batch_images]
+        model_inputs = processor(images=batch_images, boxes=batch_bboxes)
+
+        batch_pixel_values = model_inputs["pixel_values"]
+        batch_bboxes = model_inputs["input_boxes"]
+        batch_bbox_mask = model_inputs["input_boxes_mask"]
+        batch_bbox_counts = model_inputs["input_boxes_counts"]
+
+        batch_bboxes = torch.from_numpy(np.array(batch_bboxes, dtype=np.int32)).to(model.device)
+        batch_bbox_mask = torch.from_numpy(np.array(batch_bbox_mask, dtype=np.int32)).to(model.device)
+        batch_pixel_values = torch.tensor(np.array(batch_pixel_values), dtype=model.dtype).to(model.device)
+        batch_bbox_counts = torch.tensor(np.array(batch_bbox_counts), dtype=torch.long).to(model.device)
+
+        token_count = 0
+        past_key_values = None
+        encoder_outputs = None
+        batch_predictions = [[] for _ in range(len(batch_images))]
+        done = torch.zeros(len(batch_images), dtype=torch.bool, device=model.device)
+
+        with torch.inference_mode():
+            while token_count < settings.ORDER_MAX_BOXES:
+                return_dict = model(
+                    pixel_values=batch_pixel_values,
+                    decoder_input_boxes=batch_bboxes,
+                    decoder_input_boxes_mask=batch_bbox_mask,
+                    decoder_input_boxes_counts=batch_bbox_counts,
+                    encoder_outputs=encoder_outputs,
+                    past_key_values=past_key_values,
+                )
+                logits = return_dict["logits"].detach()
+
+                last_tokens = []
+                last_token_mask = []
+                min_val = torch.finfo(model.dtype).min
+                for j in range(logits.shape[0]):
+                    label_count = batch_bbox_counts[j, 1] - batch_bbox_counts[j, 0] - 1  # Subtract 1 for the sep token
+                    new_logits = logits[j, -1]
+                    new_logits[batch_predictions[j]] = min_val  # Mask out already predicted tokens, we can only predict each token once
+                    new_logits[label_count:] = min_val  # Mask out all logit positions above the number of bboxes
+                    pred = int(torch.argmax(new_logits, dim=-1).item())
+
+                    # Add one to avoid colliding with the 1000 height/width token for bboxes
+                    last_tokens.append([[pred + processor.box_size["height"] + 1] * 4])
+                    if len(batch_predictions[j]) == label_count - 1:  # Minus one since we're appending the final label
+                        last_token_mask.append([0])
+                        batch_predictions[j].append(pred)
+                        done[j] = True
+                    elif len(batch_predictions[j]) < label_count - 1:
+                        last_token_mask.append([1])
+                        batch_predictions[j].append(pred)  # Get rank prediction for given position
+                    else:
+                        last_token_mask.append([0])
+
+                if done.all():
+                    break
+
+                past_key_values = return_dict["past_key_values"]
+                encoder_outputs = (return_dict["encoder_last_hidden_state"],)
+
+                batch_bboxes = torch.tensor(last_tokens, dtype=torch.long).to(model.device)
+                token_bbox_mask = torch.tensor(last_token_mask, dtype=torch.long).to(model.device)
+                batch_bbox_mask = torch.cat([batch_bbox_mask, token_bbox_mask], dim=1)
+                token_count += 1
+
+        for j, row_pred in enumerate(batch_predictions):
+            row_bboxes = bboxes[i+j]
+            assert len(row_pred) == len(row_bboxes), f"Mismatch between logits and bboxes. Logits: {len(row_pred)}, Bboxes: {len(row_bboxes)}"
+
+            orig_size = orig_sizes[j]
+            ranks = [0] * len(row_bboxes)
+
+            for box_idx in range(len(row_bboxes)):
+                ranks[row_pred[box_idx]] = box_idx
+
+            order_boxes = []
+            for row_bbox, rank in zip(row_bboxes, ranks):
+                order_box = OrderBox(
+                    bbox=row_bbox,
+                    position=rank,
+                )
+                order_boxes.append(order_box)
+
+            result = OrderResult(
+                bboxes=order_boxes,
+                image_bbox=[0, 0, orig_size[0], orig_size[1]],
+            )
+            output_order.append(result)
+    return output_order
+
+
+
+
+
+
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diff --git a/surya/postprocessing/affinity.py b/surya/postprocessing/affinity.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cb538cbe6fed233e03c03e4bd198a7e896d6165
--- /dev/null
+++ b/surya/postprocessing/affinity.py
@@ -0,0 +1,165 @@
+from typing import List
+
+import cv2
+import numpy as np
+
+from PIL import Image, ImageDraw
+
+from surya.postprocessing.util import get_line_angle, rescale_bbox
+from surya.schema import ColumnLine
+
+
+def get_detected_lines_sobel(image, vertical=True):
+    # Apply Sobel operator with a kernel size of 3 to detect vertical edges
+    if vertical:
+        dx = 1
+        dy = 0
+    else:
+        dx = 0
+        dy = 1
+
+    sobelx = cv2.Sobel(image, cv2.CV_32F, dx, dy, ksize=3)
+
+
+    # Absolute Sobel (to capture both edges)
+    abs_sobelx = np.absolute(sobelx)
+
+    # Convert to 8-bit image
+    scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
+
+    kernel = np.ones((20, 1), np.uint8)
+    eroded = cv2.erode(scaled_sobel, kernel, iterations=1)
+    scaled_sobel = cv2.dilate(eroded, kernel, iterations=3)
+
+    return scaled_sobel
+
+
+def get_detected_lines(image, slope_tol_deg=2, vertical=False, horizontal=False) -> List[ColumnLine]:
+    assert not (vertical and horizontal)
+    new_image = image.astype(np.float32) * 255  # Convert to 0-255 range
+    if vertical or horizontal:
+        new_image = get_detected_lines_sobel(new_image, vertical)
+    new_image = new_image.astype(np.uint8)
+
+    edges = cv2.Canny(new_image, 150, 200, apertureSize=3)
+    if vertical:
+        max_gap = 100
+        min_length = 10
+    else:
+        max_gap = 10
+        min_length = 4
+
+    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=150, minLineLength=min_length, maxLineGap=max_gap)
+
+    line_info = []
+    if lines is not None:
+        for line in lines:
+            vertical_line = False
+            horizontal_line = False
+            x1, y1, x2, y2 = line[0]
+            bbox = [x1, y1, x2, y2]
+
+            if x2 == x1:
+                vertical_line = True
+            else:
+                line_angle = get_line_angle(x1, y1, x2, y2)
+                if 90 - slope_tol_deg < line_angle < 90 + slope_tol_deg:
+                    vertical_line = True
+                elif -90 - slope_tol_deg < line_angle < -90 + slope_tol_deg:
+                    vertical_line = True
+                elif -slope_tol_deg < line_angle < slope_tol_deg:
+                    horizontal_line = True
+
+            if bbox[3] < bbox[1]:
+                bbox[1], bbox[3] = bbox[3], bbox[1]
+            if bbox[2] < bbox[0]:
+                bbox[0], bbox[2] = bbox[2], bbox[0]
+            row = ColumnLine(bbox=bbox, vertical=vertical_line, horizontal=horizontal_line)
+            line_info.append(row)
+
+    if vertical:
+        line_info = [line for line in line_info if line.vertical]
+
+    if horizontal:
+        line_info = [line for line in line_info if line.horizontal]
+
+    return line_info
+
+
+def draw_lines_on_image(line_info: List[ColumnLine], img):
+    draw = ImageDraw.Draw(img)
+
+    for line in line_info:
+        divisor = 20
+        if line.horizontal:
+            divisor = 200
+        x1, y1, x2, y2 = [x // divisor * divisor for x in line.bbox]
+        if line.vertical:
+            draw.line((x1, y1, x2, y2), fill="red", width=3)
+
+    return img
+
+
+def get_vertical_lines(image, processor_size, image_size, divisor=20, x_tolerance=40, y_tolerance=20) -> List[ColumnLine]:
+    vertical_lines = get_detected_lines(image, vertical=True)
+    for line in vertical_lines:
+        line.rescale_bbox(processor_size, image_size)
+    vertical_lines = sorted(vertical_lines, key=lambda x: x.bbox[0])
+    for line in vertical_lines:
+        line.round_bbox(divisor)
+
+    # Merge adjacent line segments together
+    to_remove = []
+    for i, line in enumerate(vertical_lines):
+        for j, line2 in enumerate(vertical_lines):
+            if j <= i:
+                continue
+            if line.bbox[0] != line2.bbox[0]:
+                continue
+
+            expanded_line1 = [line.bbox[0], line.bbox[1] - y_tolerance, line.bbox[2],
+                              line.bbox[3] + y_tolerance]
+
+            line1_points = set(range(int(expanded_line1[1]), int(expanded_line1[3])))
+            line2_points = set(range(int(line2.bbox[1]), int(line2.bbox[3])))
+            intersect_y = len(line1_points.intersection(line2_points)) > 0
+
+            if intersect_y:
+                vertical_lines[j].bbox[1] = min(line.bbox[1], line2.bbox[1])
+                vertical_lines[j].bbox[3] = max(line.bbox[3], line2.bbox[3])
+                to_remove.append(i)
+
+    vertical_lines = [line for i, line in enumerate(vertical_lines) if i not in to_remove]
+
+    # Remove redundant segments
+    to_remove = []
+    for i, line in enumerate(vertical_lines):
+        if i in to_remove:
+            continue
+        for j, line2 in enumerate(vertical_lines):
+            if j <= i or j in to_remove:
+                continue
+            close_in_x = abs(line.bbox[0] - line2.bbox[0]) < x_tolerance
+            line1_points = set(range(int(line.bbox[1]), int(line.bbox[3])))
+            line2_points = set(range(int(line2.bbox[1]), int(line2.bbox[3])))
+
+            intersect_y = len(line1_points.intersection(line2_points)) > 0
+
+            if close_in_x and intersect_y:
+                # Keep the longer line and extend it
+                if len(line2_points) > len(line1_points):
+                    vertical_lines[j].bbox[1] = min(line.bbox[1], line2.bbox[1])
+                    vertical_lines[j].bbox[3] = max(line.bbox[3], line2.bbox[3])
+                    to_remove.append(i)
+                else:
+                    vertical_lines[i].bbox[1] = min(line.bbox[1], line2.bbox[1])
+                    vertical_lines[i].bbox[3] = max(line.bbox[3], line2.bbox[3])
+                    to_remove.append(j)
+
+    vertical_lines = [line for i, line in enumerate(vertical_lines) if i not in to_remove]
+
+    if len(vertical_lines) > 0:
+        # Always start with top left of page
+        vertical_lines[0].bbox[1] = 0
+
+    return vertical_lines
\ No newline at end of file
diff --git a/surya/postprocessing/fonts.py b/surya/postprocessing/fonts.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9e18789c356413ac544da345a158f253fa7365b
--- /dev/null
+++ b/surya/postprocessing/fonts.py
@@ -0,0 +1,24 @@
+from typing import List, Optional
+import os
+import requests
+
+from surya.settings import settings
+
+
+def get_font_path(langs: Optional[List[str]] = None) -> str:
+    font_path = settings.RECOGNITION_RENDER_FONTS["all"]
+    if langs is not None:
+        for k in settings.RECOGNITION_RENDER_FONTS:
+            if k in langs and len(langs) == 1:
+                font_path = settings.RECOGNITION_RENDER_FONTS[k]
+                break
+
+    if not os.path.exists(font_path):
+        os.makedirs(os.path.dirname(font_path), exist_ok=True)
+        font_dl_path = f"{settings.RECOGNITION_FONT_DL_BASE}/{os.path.basename(font_path)}"
+        with requests.get(font_dl_path, stream=True) as r, open(font_path, 'wb') as f:
+            r.raise_for_status()
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+    return font_path
\ No newline at end of file
diff --git a/surya/postprocessing/heatmap.py b/surya/postprocessing/heatmap.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8c3951a49d87faae4ba1d7cca3336ef8a867630
--- /dev/null
+++ b/surya/postprocessing/heatmap.py
@@ -0,0 +1,233 @@
+from typing import List, Tuple
+
+import numpy as np
+import cv2
+import math
+from PIL import ImageDraw, ImageFont
+
+from surya.postprocessing.fonts import get_font_path
+from surya.postprocessing.util import rescale_bbox
+from surya.schema import PolygonBox
+from surya.settings import settings
+from surya.postprocessing.text import get_text_size
+
+
+def keep_largest_boxes(boxes: List[PolygonBox]) -> List[PolygonBox]:
+    new_boxes = []
+    for box_obj in boxes:
+        box = box_obj.bbox
+        box_area = (box[2] - box[0]) * (box[3] - box[1])
+        contained = False
+        for other_box_obj in boxes:
+            if other_box_obj.polygon == box_obj.polygon:
+                continue
+
+            other_box = other_box_obj.bbox
+            other_box_area = (other_box[2] - other_box[0]) * (other_box[3] - other_box[1])
+            if box == other_box:
+                continue
+            # find overlap percentage
+            overlap = box_obj.intersection_pct(other_box_obj)
+            if overlap > .9 and box_area < other_box_area:
+                contained = True
+                break
+        if not contained:
+            new_boxes.append(box_obj)
+    return new_boxes
+
+
+def clean_contained_boxes(boxes: List[PolygonBox]) -> List[PolygonBox]:
+    new_boxes = []
+    for box_obj in boxes:
+        box = box_obj.bbox
+        contained = False
+        for other_box_obj in boxes:
+            if other_box_obj.polygon == box_obj.polygon:
+                continue
+
+            other_box = other_box_obj.bbox
+            if box == other_box:
+                continue
+            if box[0] >= other_box[0] and box[1] >= other_box[1] and box[2] <= other_box[2] and box[3] <= other_box[3]:
+                contained = True
+                break
+        if not contained:
+            new_boxes.append(box_obj)
+    return new_boxes
+
+
+def get_dynamic_thresholds(linemap, text_threshold, low_text, typical_top10_avg=.7):
+    # Find average intensity of top 10% pixels
+    # Do top 10% to account for pdfs that are mostly whitespace, etc.
+    flat_map = linemap.flatten()
+    sorted_map = np.sort(flat_map)[::-1]
+    top_10_count = int(np.ceil(len(flat_map) * 0.1))
+    top_10 = sorted_map[:top_10_count]
+    avg_intensity = np.mean(top_10)
+
+    # Adjust thresholds based on normalized intensityy
+    scaling_factor = min(1, avg_intensity / typical_top10_avg) ** (1 / 2)
+
+    low_text = max(low_text * scaling_factor, 0.1)
+    text_threshold = max(text_threshold * scaling_factor, 0.15)
+
+    low_text = min(low_text, 0.6)
+    text_threshold = min(text_threshold, 0.8)
+    return text_threshold, low_text
+
+
+def detect_boxes(linemap, text_threshold, low_text):
+    # From CRAFT - https://github.com/clovaai/CRAFT-pytorch
+    # prepare data
+    img_h, img_w = linemap.shape
+
+    text_threshold, low_text = get_dynamic_thresholds(linemap, text_threshold, low_text)
+
+    ret, text_score = cv2.threshold(linemap, low_text, 1, cv2.THRESH_BINARY)
+
+    text_score_comb = np.clip(text_score, 0, 1).astype(np.uint8)
+    label_count, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb, connectivity=4)
+
+    det = []
+    confidences = []
+    max_confidence = 0
+    mask = np.zeros_like(linemap, dtype=np.uint8)
+
+    for k in range(1, label_count):
+        # size filtering
+        size = stats[k, cv2.CC_STAT_AREA]
+        if size < 10:
+            continue
+
+        # thresholding
+        if np.max(linemap[labels == k]) < text_threshold:
+            continue
+
+        # make segmentation map
+        segmap = np.zeros(linemap.shape, dtype=np.uint8)
+        segmap[labels == k] = 255
+        x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]
+        w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]
+        try:
+            niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
+        except ValueError:
+            # Overflow when size is too large
+            niter = 0
+        sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
+
+        # boundary checks
+        if sx < 0:
+            sx = 0
+        if sy < 0:
+            sy = 0
+        if ex >= img_w:
+            ex = img_w
+        if ey >= img_h:
+            ey = img_h
+
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1 + niter, 1 + niter))
+        segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
+
+        # make box
+        np_contours = np.roll(np.array(np.where(segmap != 0)),1, axis=0).transpose().reshape(-1,2)
+        rectangle = cv2.minAreaRect(np_contours)
+        box = cv2.boxPoints(rectangle)
+
+        # align diamond-shape
+        w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
+        box_ratio = max(w, h) / (min(w, h) + 1e-5)
+        if abs(1 - box_ratio) <= 0.1:
+            l, r = min(np_contours[:, 0]), max(np_contours[:, 0])
+            t, b = min(np_contours[:, 1]), max(np_contours[:, 1])
+            box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
+
+        # make clock-wise order
+        startidx = box.sum(axis=1).argmin()
+        box = np.roll(box, 4-startidx, 0)
+        box = np.array(box)
+
+        mask.fill(0)
+        cv2.fillPoly(mask, [np.int32(box)], 1)
+
+        roi = np.where(mask == 1, linemap, 0)
+        confidence = np.mean(roi[roi != 0])
+
+        if confidence > max_confidence:
+            max_confidence = confidence
+
+        confidences.append(confidence)
+        det.append(box)
+
+    if max_confidence > 0:
+        confidences = [c / max_confidence for c in confidences]
+    return det, labels, confidences
+
+
+def get_detected_boxes(textmap, text_threshold=None,  low_text=None) -> List[PolygonBox]:
+    if text_threshold is None:
+        text_threshold = settings.DETECTOR_TEXT_THRESHOLD
+
+    if low_text is None:
+        low_text = settings.DETECTOR_BLANK_THRESHOLD
+
+    textmap = textmap.copy()
+    textmap = textmap.astype(np.float32)
+    boxes, labels, confidences = detect_boxes(textmap, text_threshold, low_text)
+    # From point form to box form
+    boxes = [PolygonBox(polygon=box, confidence=confidence) for box, confidence in zip(boxes, confidences)]
+    return boxes
+
+
+def get_and_clean_boxes(textmap, processor_size, image_size, text_threshold=None, low_text=None) -> List[PolygonBox]:
+    bboxes = get_detected_boxes(textmap, text_threshold, low_text)
+    for bbox in bboxes:
+        bbox.rescale(processor_size, image_size)
+        bbox.fit_to_bounds([0, 0, image_size[0], image_size[1]])
+
+    bboxes = clean_contained_boxes(bboxes)
+    return bboxes
+
+
+def draw_bboxes_on_image(bboxes, image, labels=None):
+    draw = ImageDraw.Draw(image)
+
+    for bbox in bboxes:
+        draw.rectangle(bbox, outline="red", width=3)
+
+    return image
+
+
+def draw_polys_on_image(corners, image, labels=None, box_padding=-1, label_offset=1, label_font_size=10):
+    draw = ImageDraw.Draw(image)
+    font_path = get_font_path()
+    label_font = ImageFont.truetype(font_path, label_font_size)
+
+    for i in range(len(corners)):
+        poly = corners[i]
+        poly = [(int(p[0]), int(p[1])) for p in poly]
+        draw.polygon(poly, outline='red', width=1)
+
+        if labels is not None:
+            label = labels[i]
+            text_position = (
+                min([p[0] for p in poly]) + label_offset,
+                min([p[1] for p in poly]) + label_offset
+            )
+            text_size = get_text_size(label, label_font)
+            box_position = (
+                text_position[0] - box_padding + label_offset,
+                text_position[1] - box_padding + label_offset,
+                text_position[0] + text_size[0] + box_padding + label_offset,
+                text_position[1] + text_size[1] + box_padding + label_offset
+            )
+            draw.rectangle(box_position, fill="white")
+            draw.text(
+                text_position,
+                label,
+                fill="red",
+                font=label_font
+            )
+
+    return image
+
+
diff --git a/surya/postprocessing/math/__pycache__/latex.cpython-310.pyc b/surya/postprocessing/math/__pycache__/latex.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..616fe89e8c2e0646868f2dd07a46c4e0efb416b2
Binary files /dev/null and b/surya/postprocessing/math/__pycache__/latex.cpython-310.pyc differ
diff --git a/surya/postprocessing/math/latex.py b/surya/postprocessing/math/latex.py
new file mode 100644
index 0000000000000000000000000000000000000000..b07e5fb8e51200dbb32e0055e8ea1de04b008caf
--- /dev/null
+++ b/surya/postprocessing/math/latex.py
@@ -0,0 +1,125 @@
+import re
+from ftfy import fix_text
+
+
+def contains_math(text):
+    return text.startswith("$") or text.endswith("$")
+
+
+def fix_math(text):
+    # Fix any issues with the text
+    text = fix_text(text)
+
+    # Remove LaTeX labels and references
+    text = remove_labels(text)
+    text = replace_katex_invalid(text)
+    text = fix_fences(text)
+    return text
+
+
+def remove_labels(text):
+    pattern = r'\\label\{[^}]*\}'
+    text = re.sub(pattern, '', text)
+
+    ref_pattern = r'\\ref\{[^}]*\}'
+    text = re.sub(ref_pattern, '', text)
+
+    pageref_pattern = r'\\pageref\{[^}]*\}'
+    text = re.sub(pageref_pattern, '', text)
+    return text
+
+
+def replace_katex_invalid(string):
+    # KaTeX cannot render all LaTeX, so we need to replace some things
+    string = re.sub(r'\\tag\{.*?\}', '', string)
+    string = re.sub(r'\\(?:Bigg?|bigg?)\{(.*?)\}', r'\1', string)
+    string = re.sub(r'\\quad\\mbox\{(.*?)\}', r'\1', string)
+    string = re.sub(r'\\mbox\{(.*?)\}', r'\1', string)
+    string = remove_inner_dollars(string)
+    return string
+
+
+def remove_inner_dollars(text):
+    def replace_dollar(match):
+        # Replace single $ with nothing, keep $$ intact
+        math_block = match.group(1)
+        return '$$' + math_block.replace('$', '') + '$$'
+
+    pattern = r'\$\$(.*?)\$\$'
+    return re.sub(pattern, replace_dollar, text, flags=re.DOTALL)
+
+
+def extract_latex_with_positions(text):
+    pattern = r'(\$\$.*?\$\$|\$.*?\$)'
+    matches = []
+    for match in re.finditer(pattern, text, re.DOTALL):
+        matches.append((match.group(), match.start(), match.end()))
+    return matches
+
+
+def slice_latex(text):
+    # Extract LaTeX blocks along with their positions
+    latex_blocks_with_positions = extract_latex_with_positions(text)
+
+    chunks = []
+    last_position = 0
+    for block, start, end in latex_blocks_with_positions:
+        # Add text before the current LaTeX block, if any
+        if start > last_position:
+            chunks.append({"text": text[last_position:start], "type": "text"})
+        # Add the LaTeX block
+        chunks.append({"text": block, "type": "latex"})
+        last_position = end
+    # Add remaining text after the last LaTeX block, if any
+    if last_position < len(text):
+        chunks.append({"text": text[last_position:], "type": "text"})
+
+    return chunks
+
+
+def is_latex(text):
+    latex_patterns = [
+        r'\\(?:begin|end)\{[a-zA-Z]*\}',
+        r'\$.*?\$',
+        r'\$\$.*?\$\$',
+        r'\\[a-zA-Z]+',
+        r'\\[^a-zA-Z]',
+    ]
+
+    combined_pattern = '|'.join(latex_patterns)
+    if re.search(combined_pattern, text, re.DOTALL):
+        return True
+
+    return False
+
+
+def fix_fences(text):
+    if text.startswith("$$") and not text.endswith("$$"):
+        if text[-1] == "$":
+            text += "$"
+        else:
+            text += "$$"
+
+    if text.endswith("$$") and not text.startswith("$$"):
+        if text[0] == "$":
+            text = "$" + text
+        else:
+            text = "$$" + text
+
+    if text.startswith("$") and not text.endswith("$"):
+        text = "$" + text + "$$"
+
+    if text.endswith("$") and not text.startswith("$"):
+        text = "$$" + text + "$"
+
+    return text
+
+
+def strip_fences(text):
+    while text.startswith("$"):
+        text = text[1:]
+    while text.endswith("$"):
+        text = text[:-1]
+    return text
+
+
diff --git a/surya/postprocessing/math/render.py b/surya/postprocessing/math/render.py
new file mode 100644
index 0000000000000000000000000000000000000000..761334a0bd923e48478075949885ed1a829ac2d9
--- /dev/null
+++ b/surya/postprocessing/math/render.py
@@ -0,0 +1,88 @@
+from playwright.sync_api import sync_playwright
+from PIL import Image
+import io
+
+
+def latex_to_pil(latex_code, target_width, target_height, fontsize=18):
+    html_template = """
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.13.0/dist/katex.min.css">
+        <script src="https://cdn.jsdelivr.net/npm/katex@0.12.0/dist/katex.min.js"></script>
+        <style>
+            body {
+                margin: 0;
+                padding: 0;
+                display: flex;
+            }
+            #content {
+                font-size: {fontsize}px;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="content">{content}</div>
+        <script>
+            function renderMath() {
+                let content = document.getElementById('content');
+                let html = content.innerHTML;
+
+                // Replace display equations
+                html = html.replace(/\\$\\$(.*?)\\$\\$/gs, (match, equation) => {
+                    let span = document.createElement('span');
+                    katex.render(equation, span, { displayMode: true, throwOnError: false, errorColor: '#000' });
+                    return span.outerHTML;
+                });
+
+                // Replace inline equations
+                html = html.replace(/\\$(.*?)\\$/g, (match, equation) => {
+                    if(match.startsWith('\\\\$')) return match; // Ignore escaped dollars
+                    let span = document.createElement('span');
+                    katex.render(equation, span, { displayMode: false, throwOnError: false, errorColor: '#000' });
+                    return span.outerHTML;
+                });
+
+                content.innerHTML = html;
+            }
+
+            renderMath();
+        </script>
+    </body>
+    </html>
+    """
+
+    formatted_latex = latex_code.replace('\n', '\\n').replace('"', '\\"')
+    with sync_playwright() as p:
+        browser = p.chromium.launch()
+        page = browser.new_page()
+        page.set_viewport_size({'width': target_width, 'height': target_height})
+
+        while fontsize <= 30:
+            html_content = html_template.replace("{content}", formatted_latex).replace("{fontsize}", str(fontsize))
+            page.set_content(html_content)
+
+            dimensions = page.evaluate("""() => {
+                const render = document.getElementById('content');
+                return {
+                    width: render.offsetWidth,
+                    height: render.offsetHeight
+                };
+            }""")
+
+            if dimensions['width'] >= target_width or dimensions['height'] >= target_height:
+                fontsize -= 1
+                break
+            else:
+                fontsize += 1
+
+        html_content = html_template.replace("{content}", formatted_latex).replace("{fontsize}", str(fontsize))
+        page.set_content(html_content)
+
+        screenshot_bytes = page.screenshot()
+        browser.close()
+
+        image_stream = io.BytesIO(screenshot_bytes)
+        pil_image = Image.open(image_stream)
+        pil_image.load()
+        return pil_image
\ No newline at end of file
diff --git a/surya/postprocessing/text.py b/surya/postprocessing/text.py
new file mode 100644
index 0000000000000000000000000000000000000000..fea9c3ef69a1b7dd600ec45184d5b12ca4f8bb53
--- /dev/null
+++ b/surya/postprocessing/text.py
@@ -0,0 +1,118 @@
+import os
+from typing import List, Tuple
+
+import requests
+from PIL import Image, ImageDraw, ImageFont
+
+from surya.postprocessing.fonts import get_font_path
+from surya.schema import TextLine
+from surya.settings import settings
+from surya.postprocessing.math.latex import is_latex
+
+
+def sort_text_lines(lines: List[TextLine], tolerance=1.25):
+    # Sorts in reading order.  Not 100% accurate, this should only
+    # be used as a starting point for more advanced sorting.
+    vertical_groups = {}
+    for line in lines:
+        group_key = round(line.bbox[1] / tolerance) * tolerance
+        if group_key not in vertical_groups:
+            vertical_groups[group_key] = []
+        vertical_groups[group_key].append(line)
+
+    # Sort each group horizontally and flatten the groups into a single list
+    sorted_lines = []
+    for _, group in sorted(vertical_groups.items()):
+        sorted_group = sorted(group, key=lambda x: x.bbox[0])
+        sorted_lines.extend(sorted_group)
+
+    return sorted_lines
+
+
+def truncate_repetitions(text: str, min_len=15):
+    # From nougat, with some cleanup
+    if len(text) < 2 * min_len:
+        return text
+
+    # try to find a length at which the tail is repeating
+    max_rep_len = None
+    for rep_len in range(min_len, int(len(text) / 2)):
+        # check if there is a repetition at the end
+        same = True
+        for i in range(0, rep_len):
+            if text[len(text) - rep_len - i - 1] != text[len(text) - i - 1]:
+                same = False
+                break
+
+        if same:
+            max_rep_len = rep_len
+
+    if max_rep_len is None:
+        return text
+
+    lcs = text[-max_rep_len:]
+
+    # remove all but the last repetition
+    text_to_truncate = text
+    while text_to_truncate.endswith(lcs):
+        text_to_truncate = text_to_truncate[:-max_rep_len]
+
+    return text[:len(text_to_truncate)]
+
+
+def get_text_size(text, font):
+    im = Image.new(mode="P", size=(0, 0))
+    draw = ImageDraw.Draw(im)
+    _, _, width, height = draw.textbbox((0, 0), text=text, font=font)
+    return width, height
+
+
+def render_text(draw, text, s_bbox, bbox_width, bbox_height, font_path, box_font_size):
+    font = ImageFont.truetype(font_path, box_font_size)
+    text_width, text_height = get_text_size(text, font)
+    while (text_width > bbox_width or text_height > bbox_height) and box_font_size > 6:
+        box_font_size = box_font_size - 1
+        font = ImageFont.truetype(font_path, box_font_size)
+        text_width, text_height = get_text_size(text, font)
+
+    # Calculate text position (centered in bbox)
+    text_width, text_height = get_text_size(text, font)
+    x = s_bbox[0]
+    y = s_bbox[1] + (bbox_height - text_height) / 2
+
+    draw.text((x, y), text, fill="black", font=font)
+
+
+def render_math(image, draw, text, s_bbox, bbox_width, bbox_height, font_path):
+        try:
+            from surya.postprocessing.math.render import latex_to_pil
+            box_font_size = max(10, min(int(.2 * bbox_height), 24))
+            img = latex_to_pil(text, bbox_width, bbox_height, fontsize=box_font_size)
+            img.thumbnail((bbox_width, bbox_height))
+            image.paste(img, (s_bbox[0], s_bbox[1]))
+        except Exception as e:
+            print(f"Failed to render math: {e}")
+            box_font_size = max(10, min(int(.75 * bbox_height), 24))
+            render_text(draw, text, s_bbox, bbox_width, bbox_height, font_path, box_font_size)
+
+
+def draw_text_on_image(bboxes, texts, image_size: Tuple[int, int], langs: List[str], font_path=None, max_font_size=60, res_upscale=2, has_math=False):
+    if font_path is None:
+        font_path = get_font_path(langs)
+    new_image_size = (image_size[0] * res_upscale, image_size[1] * res_upscale)
+    image = Image.new('RGB', new_image_size, color='white')
+    draw = ImageDraw.Draw(image)
+
+    for bbox, text in zip(bboxes, texts):
+        s_bbox = [int(coord * res_upscale) for coord in bbox]
+        bbox_width = s_bbox[2] - s_bbox[0]
+        bbox_height = s_bbox[3] - s_bbox[1]
+
+        # Shrink the text to fit in the bbox if needed
+        if has_math and is_latex(text):
+            render_math(image, draw, text, s_bbox, bbox_width, bbox_height, font_path)
+        else:
+            box_font_size = max(6, min(int(.75 * bbox_height), max_font_size))
+            render_text(draw, text, s_bbox, bbox_width, bbox_height, font_path, box_font_size)
+
+    return image
diff --git a/surya/postprocessing/util.py b/surya/postprocessing/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3da0e9bcb7ec73be304170f70bf99c28da3d37ef
--- /dev/null
+++ b/surya/postprocessing/util.py
@@ -0,0 +1,44 @@
+import math
+import copy
+
+
+def get_line_angle(x1, y1, x2, y2):
+    slope = (y2 - y1) / (x2 - x1)
+
+    angle_radians = math.atan(slope)
+    angle_degrees = math.degrees(angle_radians)
+
+    return angle_degrees
+
+
+def rescale_bbox(bbox, processor_size, image_size):
+    page_width, page_height = processor_size
+
+    img_width, img_height = image_size
+    width_scaler = img_width / page_width
+    height_scaler = img_height / page_height
+
+    new_bbox = copy.deepcopy(bbox)
+    new_bbox[0] = int(new_bbox[0] * width_scaler)
+    new_bbox[1] = int(new_bbox[1] * height_scaler)
+    new_bbox[2] = int(new_bbox[2] * width_scaler)
+    new_bbox[3] = int(new_bbox[3] * height_scaler)
+    return new_bbox
+
+
+def rescale_point(point, processor_size, image_size):
+    # Point is in x, y format
+    page_width, page_height = processor_size
+
+    img_width, img_height = image_size
+    width_scaler = img_width / page_width
+    height_scaler = img_height / page_height
+
+    new_point = copy.deepcopy(point)
+    new_point[0] = int(new_point[0] * width_scaler)
+    new_point[1] = int(new_point[1] * height_scaler)
+    return new_point
+
+
+def rescale_points(points, processor_size, image_size):
+    return [rescale_point(point, processor_size, image_size) for point in points]
\ No newline at end of file
diff --git a/surya/recognition.py b/surya/recognition.py
new file mode 100644
index 0000000000000000000000000000000000000000..210ac0d08277380c44de620fcf925ee6bd75cc6c
--- /dev/null
+++ b/surya/recognition.py
@@ -0,0 +1,219 @@
+from typing import List
+import torch
+from PIL import Image
+
+from surya.input.processing import convert_if_not_rgb
+from surya.postprocessing.math.latex import fix_math, contains_math
+from surya.postprocessing.text import truncate_repetitions
+from surya.settings import settings
+from tqdm import tqdm
+import numpy as np
+import torch.nn.functional as F
+
+
+def get_batch_size():
+    batch_size = settings.RECOGNITION_BATCH_SIZE
+    if batch_size is None:
+        batch_size = 32
+        if settings.TORCH_DEVICE_MODEL == "mps":
+            batch_size = 64 # 12GB RAM max
+        if settings.TORCH_DEVICE_MODEL == "cuda":
+            batch_size = 256
+    return batch_size
+
+
+def batch_recognition(images: List, languages: List[List[str]], model, processor, batch_size=None):
+    import inspect
+    print("&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&",inspect.getargspec(processor).args)
+    assert all([isinstance(image, Image.Image) for image in images])
+    assert len(images) == len(languages)
+
+    for l in languages:
+        assert len(l) <= settings.RECOGNITION_MAX_LANGS, f"OCR only supports up to {settings.RECOGNITION_MAX_LANGS} languages per image, you passed {l}."
+
+    images = [image.convert("RGB") for image in images] # also copies the images
+    if batch_size is None:
+        batch_size = get_batch_size()
+
+    output_text = []
+    confidences = []
+
+    dec_config = model.config.decoder
+    layer_count = dec_config.decoder_layers
+    kv_heads = dec_config.kv_heads
+    head_dim = int(dec_config.d_model / dec_config.decoder_attention_heads)
+    min_val = torch.finfo(model.dtype).min
+
+    if settings.RECOGNITION_STATIC_CACHE:
+        # We'll re-use these for all batches to avoid recopying
+        kv_mask = torch.full((batch_size, 1, 1, settings.RECOGNITION_MAX_TOKENS + 1), min_val, dtype=model.dtype, device=model.device)
+        # The +1 accounts for start token
+        initial_attn_mask = torch.full((batch_size, 1, settings.RECOGNITION_MAX_LANGS + 1, settings.RECOGNITION_MAX_LANGS + 1), min_val, dtype=model.dtype, device=model.device)
+
+        # Decoder kv cache
+        # 7 (layers) x 2 (kv) x bs x 4 (heads) x max tokens x 64 (head dim)
+        decoder_cache = [torch.zeros((2, batch_size, kv_heads, settings.RECOGNITION_MAX_TOKENS, head_dim), dtype=model.dtype, device=model.device) for _ in range(layer_count)]
+
+        # Prefill
+        decoder_input = torch.zeros((batch_size, settings.RECOGNITION_MAX_LANGS + 1), dtype=torch.long, device=model.device)
+    else:
+        initial_kv_mask = torch.zeros((batch_size, 1, 1, 1), dtype=model.dtype, device=model.device)
+        initial_attn_mask = torch.zeros((batch_size, 1, settings.RECOGNITION_MAX_LANGS + 1, settings.RECOGNITION_MAX_LANGS + 1), dtype=model.dtype, device=model.device)
+
+    processed_batches = processor(text=[""] * len(images), images=images, lang=languages)
+
+    for i in tqdm(range(0, len(images), batch_size), desc="Recognizing Text"):
+        batch_langs = languages[i:i+batch_size]
+        has_math = ["_math" in lang for lang in batch_langs]
+
+        batch_pixel_values = processed_batches["pixel_values"][i:i+batch_size]
+        batch_langs = processed_batches["langs"][i:i+batch_size]
+        max_lang_len = max([len(lang) for lang in batch_langs])
+
+        # Pad languages to max length if needed, to ensure we can convert to a tensor
+        for lang_idx in range(len(batch_langs)):
+            lang_len = len(batch_langs[lang_idx])
+            if lang_len < max_lang_len:
+                batch_langs[lang_idx] = [processor.tokenizer.pad_id] * (max_lang_len - lang_len) + batch_langs[lang_idx]
+
+        batch_decoder_input = [[model.config.decoder_start_token_id] + lang for lang in batch_langs]
+        current_batch_size = len(batch_pixel_values)
+
+        batch_langs = torch.tensor(np.stack(batch_langs, axis=0), dtype=torch.long, device=model.device)
+        batch_pixel_values = torch.tensor(np.stack(batch_pixel_values, axis=0), dtype=model.dtype, device=model.device)
+        batch_decoder_input = torch.tensor(np.stack(batch_decoder_input, axis=0), dtype=torch.long, device=model.device)
+
+        token_count = 0
+        inference_token_count = batch_decoder_input.shape[-1]
+        batch_predictions = [[] for _ in range(current_batch_size)]
+
+        decoder_input_pad = torch.zeros((batch_size - current_batch_size, 1), dtype=torch.long, device=model.device)
+
+        if settings.RECOGNITION_STATIC_CACHE:
+            # Reset shared tensors
+            if i > 0:
+                # Decoder cache
+                for layer_cache in decoder_cache:
+                    layer_cache.fill_(0)
+
+                # KV mask
+                kv_mask.fill_(min_val)
+                kv_mask[:, :, :, -1] = 0
+                kv_mask[:, :, :, :inference_token_count] = 0
+
+                # Attention mask
+                initial_attn_mask.fill_(min_val)
+
+                # Prefill
+                decoder_input.fill_(0)
+
+            # Prefill attention mask
+            attention_mask = initial_attn_mask
+            attention_mask[:, :, -inference_token_count:, -inference_token_count:] = 0
+
+            # Prefill input
+            decoder_input[:current_batch_size, -inference_token_count:] = batch_decoder_input
+            batch_decoder_input = decoder_input
+
+            # Pad to max batch size
+            batch_langs = torch.cat([batch_langs, torch.zeros((batch_size - current_batch_size, batch_langs.shape[-1]), dtype=torch.long, device=model.device)], dim=0)
+            batch_pixel_values = torch.cat([batch_pixel_values, torch.zeros((batch_size - current_batch_size,) + batch_pixel_values.shape[1:], dtype=model.dtype, device=model.device)], dim=0)
+        else:
+            # Select seed attention mask
+            kv_mask = initial_kv_mask[:current_batch_size]
+            kv_mask.fill_(0)
+
+            # Select prefill attention mask
+            attention_mask = initial_attn_mask[:current_batch_size, :, :inference_token_count, :inference_token_count]
+
+            decoder_cache = [None] * layer_count
+
+        encoder_outputs = None
+        sequence_scores = None
+        encoder_cache = [None] * layer_count
+        all_done = torch.zeros(current_batch_size, dtype=torch.bool, device=model.device)
+
+        with torch.no_grad(): # inference_mode doesn't work with torch.compile
+            # Run post-prefill tokens
+            while token_count < settings.RECOGNITION_MAX_TOKENS:
+                is_prefill = token_count == 0
+                return_dict = model(
+                    decoder_input_ids=batch_decoder_input,
+                    decoder_attention_mask=attention_mask,
+                    decoder_self_kv_cache=None if is_prefill else decoder_cache,
+                    decoder_cross_kv_cache=None if is_prefill else encoder_cache,
+                    decoder_past_token_count=token_count,
+                    decoder_langs=batch_langs,
+                    pixel_values=batch_pixel_values,
+                    encoder_outputs=encoder_outputs,
+                    return_dict=True,
+                )
+
+                logits = return_dict["logits"][:current_batch_size] # Ignore batch padding
+                preds = torch.argmax(logits[:, -1], dim=-1)
+                scores = torch.max(F.softmax(logits, dim=-1), dim=-1).values
+                done = (preds == processor.tokenizer.eos_id) | (preds == processor.tokenizer.pad_id)
+                done = done
+                all_done = all_done | done
+
+                scores[all_done == 1] = 0
+
+                if is_prefill:
+                    sequence_scores = scores
+                    encoder_outputs = (return_dict["encoder_last_hidden_state"],)
+                else:
+                    sequence_scores = torch.cat([sequence_scores, scores], dim=1)
+
+                if all_done.all():
+                    break
+
+                past_key_values = return_dict["past_key_values"]
+                token_range = torch.arange(token_count, token_count + inference_token_count, device=model.device)
+
+                for layer_idx, layer in enumerate(past_key_values):
+                    if is_prefill:
+                        encoder_cache[layer_idx] = layer[1]
+
+                    if settings.RECOGNITION_STATIC_CACHE:
+                        # Fill in entries in static kv cache
+                        decoder_cache[layer_idx][:, :, :, token_range, :] = layer[0][:, :, :, -inference_token_count:, :]
+                    else:
+                        # Cat to generate new kv cache including current tokens
+                        if is_prefill:
+                            decoder_cache[layer_idx] = layer[0]
+                        else:
+                            decoder_cache[layer_idx] = torch.cat([decoder_cache[layer_idx], layer[0]], dim=3)
+
+                batch_decoder_input = preds.unsqueeze(1)
+                if settings.RECOGNITION_STATIC_CACHE:
+                    # Setup new attention mask and input token
+                    kv_mask[:, :, :, token_count:(token_count + inference_token_count)] = 0
+                    batch_decoder_input = torch.cat([batch_decoder_input, decoder_input_pad], dim=0) # Pad to full batch
+                else:
+                    kv_mask = torch.cat([kv_mask, torch.zeros((current_batch_size, 1, 1, inference_token_count), dtype=model.dtype, device=model.device)], dim=-1)
+
+                attention_mask = kv_mask
+
+                for j, (pred, status) in enumerate(zip(preds, all_done)):
+                    if not status:
+                        batch_predictions[j].append(int(pred))
+
+                token_count += inference_token_count
+                inference_token_count = batch_decoder_input.shape[-1]
+
+        sequence_scores = torch.sum(sequence_scores, dim=-1) / torch.sum(sequence_scores != 0, dim=-1)
+        detected_text = processor.tokenizer.batch_decode(batch_predictions)
+        detected_text = [truncate_repetitions(dt) for dt in detected_text]
+
+        # Postprocess to fix LaTeX output (add $$ signs, etc)
+        detected_text = [fix_math(text) if math and contains_math(text) else text for text, math in zip(detected_text, has_math)]
+        output_text.extend(detected_text)
+        confidences.extend(sequence_scores.tolist())
+
+    return output_text, confidences
+
+
+
+
+
+
diff --git a/surya/schema.py b/surya/schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..129f991e7977d91927dad825d4e05ca5134e74e9
--- /dev/null
+++ b/surya/schema.py
@@ -0,0 +1,163 @@
+import copy
+from typing import List, Tuple, Any, Optional
+
+from pydantic import BaseModel, field_validator, computed_field
+
+from surya.postprocessing.util import rescale_bbox
+
+
+class PolygonBox(BaseModel):
+    polygon: List[List[float]]
+    confidence: Optional[float] = None
+
+    @field_validator('polygon')
+    @classmethod
+    def check_elements(cls, v: List[List[float]]) -> List[List[float]]:
+        if len(v) != 4:
+            raise ValueError('corner must have 4 elements')
+
+        for corner in v:
+            if len(corner) != 2:
+                raise ValueError('corner must have 2 elements')
+        return v
+
+    @property
+    def height(self):
+        return self.bbox[3] - self.bbox[1]
+
+    @property
+    def width(self):
+        return self.bbox[2] - self.bbox[0]
+
+    @property
+    def area(self):
+        return self.width * self.height
+
+    @computed_field
+    @property
+    def bbox(self) -> List[float]:
+        box = [self.polygon[0][0], self.polygon[0][1], self.polygon[1][0], self.polygon[2][1]]
+        if box[0] > box[2]:
+            box[0], box[2] = box[2], box[0]
+        if box[1] > box[3]:
+            box[1], box[3] = box[3], box[1]
+        return box
+
+    def rescale(self, processor_size, image_size):
+        # Point is in x, y format
+        page_width, page_height = processor_size
+
+        img_width, img_height = image_size
+        width_scaler = img_width / page_width
+        height_scaler = img_height / page_height
+
+        new_corners = copy.deepcopy(self.polygon)
+        for corner in new_corners:
+            corner[0] = int(corner[0] * width_scaler)
+            corner[1] = int(corner[1] * height_scaler)
+        self.polygon = new_corners
+
+    def fit_to_bounds(self, bounds):
+        new_corners = copy.deepcopy(self.polygon)
+        for corner in new_corners:
+            corner[0] = max(min(corner[0], bounds[2]), bounds[0])
+            corner[1] = max(min(corner[1], bounds[3]), bounds[1])
+        self.polygon = new_corners
+
+    def merge(self, other):
+        x1 = min(self.bbox[0], other.bbox[0])
+        y1 = min(self.bbox[1], other.bbox[1])
+        x2 = max(self.bbox[2], other.bbox[2])
+        y2 = max(self.bbox[3], other.bbox[3])
+        self.polygon = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+
+    def intersection_area(self, other, margin=0):
+        x_overlap = max(0, min(self.bbox[2], other.bbox[2] - margin) - max(self.bbox[0], other.bbox[0] + margin))
+        y_overlap = max(0, min(self.bbox[3], other.bbox[3] - margin) - max(self.bbox[1], other.bbox[1] + margin))
+        return x_overlap * y_overlap
+
+    def intersection_pct(self, other, margin=0):
+        assert 0 <= margin <= 1
+        if self.area == 0:
+            return 0
+
+        if margin:
+            margin = int(min(self.width, other.width) * margin)
+        intersection = self.intersection_area(other, margin)
+        return intersection / self.area
+
+
+class Bbox(BaseModel):
+    bbox: List[float]
+
+    @field_validator('bbox')
+    @classmethod
+    def check_4_elements(cls, v: List[float]) -> List[float]:
+        if len(v) != 4:
+            raise ValueError('bbox must have 4 elements')
+        return v
+
+    def rescale_bbox(self, orig_size, new_size):
+        self.bbox = rescale_bbox(self.bbox, orig_size, new_size)
+
+    def round_bbox(self, divisor):
+        self.bbox = [x // divisor * divisor for x in self.bbox]
+
+    @property
+    def height(self):
+        return self.bbox[3] - self.bbox[1]
+
+    @property
+    def width(self):
+        return self.bbox[2] - self.bbox[0]
+
+    @property
+    def area(self):
+        return self.width * self.height
+
+    @property
+    def polygon(self):
+        return [[self.bbox[0], self.bbox[1]], [self.bbox[2], self.bbox[1]], [self.bbox[2], self.bbox[3]], [self.bbox[0], self.bbox[3]]]
+
+
+class LayoutBox(PolygonBox):
+    label: str
+
+
+class OrderBox(Bbox):
+    position: int
+
+
+class ColumnLine(Bbox):
+    vertical: bool
+    horizontal: bool
+
+
+class TextLine(PolygonBox):
+    text: str
+    confidence: Optional[float] = None
+
+
+class OCRResult(BaseModel):
+    text_lines: List[TextLine]
+    languages: List[str]
+    image_bbox: List[float]
+
+
+class TextDetectionResult(BaseModel):
+    bboxes: List[PolygonBox]
+    vertical_lines: List[ColumnLine]
+    heatmap: Any
+    affinity_map: Any
+    image_bbox: List[float]
+
+
+class LayoutResult(BaseModel):
+    bboxes: List[LayoutBox]
+    segmentation_map: Any
+    image_bbox: List[float]
+
+
+class OrderResult(BaseModel):
+    bboxes: List[OrderBox]
+    image_bbox: List[float]
diff --git a/surya/settings.py b/surya/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..2deb8fa30048e0e1cdcba69592fc6df8c0f839dc
--- /dev/null
+++ b/surya/settings.py
@@ -0,0 +1,107 @@
+from typing import Dict, Optional
+
+from dotenv import find_dotenv
+from pydantic import computed_field
+from pydantic_settings import BaseSettings
+import torch
+import os
+
+
+class Settings(BaseSettings):
+    # General
+    TORCH_DEVICE: Optional[str] = None
+    IMAGE_DPI: int = 96
+    IN_STREAMLIT: bool = False # Whether we're running in streamlit
+
+    # Paths
+    DATA_DIR: str = "data"
+    RESULT_DIR: str = "results"
+    BASE_DIR: str = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    FONT_DIR: str = os.path.join(BASE_DIR, "static", "fonts")
+
+    @computed_field
+    def TORCH_DEVICE_MODEL(self) -> str:
+        if self.TORCH_DEVICE is not None:
+            return self.TORCH_DEVICE
+
+        if torch.cuda.is_available():
+            return "cuda"
+
+        if torch.backends.mps.is_available():
+            return "mps"
+
+        return "cpu"
+
+    @computed_field
+    def TORCH_DEVICE_DETECTION(self) -> str:
+        if self.TORCH_DEVICE is not None:
+            # Does not work with mps
+            if "mps" in self.TORCH_DEVICE:
+                return "cpu"
+
+            return self.TORCH_DEVICE
+
+        if torch.cuda.is_available():
+            return "cuda"
+
+        # Does not work with mps
+        return "cpu"
+
+    # Text detection
+    DETECTOR_BATCH_SIZE: Optional[int] = None # Defaults to 2 for CPU, 32 otherwise
+    DETECTOR_MODEL_CHECKPOINT: str = "vikp/surya_det2"
+    DETECTOR_MATH_MODEL_CHECKPOINT: str = "vikp/surya_det_math"
+    DETECTOR_BENCH_DATASET_NAME: str = "vikp/doclaynet_bench"
+    DETECTOR_IMAGE_CHUNK_HEIGHT: int = 1400 # Height at which to slice images vertically
+    DETECTOR_TEXT_THRESHOLD: float = 0.6 # Threshold for text detection (above this is considered text)
+    DETECTOR_BLANK_THRESHOLD: float = 0.35 # Threshold for blank space (below this is considered blank)
+    DETECTOR_POSTPROCESSING_CPU_WORKERS: int = min(8, os.cpu_count()) # Number of workers for postprocessing
+    DETECTOR_MIN_PARALLEL_THRESH: int = 3 # Minimum number of images before we parallelize
+
+    # Text recognition
+    RECOGNITION_MODEL_CHECKPOINT: str = "vikp/surya_rec"
+    RECOGNITION_MAX_TOKENS: int = 175
+    RECOGNITION_BATCH_SIZE: Optional[int] = None # Defaults to 8 for CPU/MPS, 256 otherwise
+    RECOGNITION_IMAGE_SIZE: Dict = {"height": 196, "width": 896}
+    RECOGNITION_RENDER_FONTS: Dict[str, str] = {
+        "all": os.path.join(FONT_DIR, "GoNotoCurrent-Regular.ttf"),
+        "zh": os.path.join(FONT_DIR, "GoNotoCJKCore.ttf"),
+        "ja": os.path.join(FONT_DIR, "GoNotoCJKCore.ttf"),
+        "ko": os.path.join(FONT_DIR, "GoNotoCJKCore.ttf"),
+    }
+    RECOGNITION_FONT_DL_BASE: str = "https://github.com/satbyy/go-noto-universal/releases/download/v7.0"
+    RECOGNITION_BENCH_DATASET_NAME: str = "vikp/rec_bench"
+    RECOGNITION_PAD_VALUE: int = 255 # Should be 0 or 255
+    RECOGNITION_STATIC_CACHE: bool = False # Static cache for torch compile
+    RECOGNITION_MAX_LANGS: int = 4
+
+    # Layout
+    LAYOUT_MODEL_CHECKPOINT: str = "vikp/surya_layout2"
+    LAYOUT_BENCH_DATASET_NAME: str = "vikp/publaynet_bench"
+
+    # Ordering
+    ORDER_MODEL_CHECKPOINT: str = "vikp/surya_order"
+    ORDER_IMAGE_SIZE: Dict = {"height": 1024, "width": 1024}
+    ORDER_MAX_BOXES: int = 256
+    ORDER_BATCH_SIZE: Optional[int] = None  # Defaults to 4 for CPU/MPS, 32 otherwise
+    ORDER_BENCH_DATASET_NAME: str = "vikp/order_bench"
+
+    # Tesseract (for benchmarks only)
+    TESSDATA_PREFIX: Optional[str] = None
+
+    @computed_field
+    @property
+    def MODEL_DTYPE(self) -> torch.dtype:
+        return torch.float32 if self.TORCH_DEVICE_MODEL == "cpu" else torch.float16
+
+    @computed_field
+    @property
+    def MODEL_DTYPE_DETECTION(self) -> torch.dtype:
+        return torch.float32 if self.TORCH_DEVICE_DETECTION == "cpu" else torch.float16
+
+    class Config:
+        env_file = find_dotenv("local.env")
+        extra = "ignore"
+
+
+settings = Settings()
\ No newline at end of file
diff --git a/surya_yolo_pipeline.py b/surya_yolo_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac48848d8c415680e4b065b23cfb3b0e7554755a
--- /dev/null
+++ b/surya_yolo_pipeline.py
@@ -0,0 +1,169 @@
+import cv2
+import supervision as sv # pip install supervision
+from ultralytics import YOLO
+import numpy as np
+import matplotlib.pyplot as plt
+
+yolo_model = YOLO('yolov10x_best.pt')
+
+
+from surya.model.detection.segformer import load_processor , load_model
+import torch
+import os
+
+
+from surya.model.detection.segformer import load_processor , load_model
+import torch
+import os
+# os.environ['HF_HOME'] = '/share/data/drive_3/ketan/orc/HF_Cache'
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = load_model("vikp/surya_layout2").to(device)
+
+
+from PIL import Image
+from surya.input.processing import prepare_image_detection
+
+
+def predicted_mask_function(image_path) :
+    
+    img = Image.open(image_path)
+    img = [prepare_image_detection(img=img, processor=load_processor())]
+    img = torch.stack(img, dim=0).to(model.dtype).to(model.device)
+    logits  = model(img).logits
+
+    predicted_mask = torch.argmax(logits[0], dim=0).cpu().numpy() 
+    
+    return predicted_mask
+
+
+
+def predict_boxes_labels(image_path):
+    results = yolo_model(source=image_path, conf=0.2, iou=0.8)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    labels = detections.data["class_name"].tolist()
+    bboxes = detections.xyxy.tolist()
+    return bboxes,labels
+    
+    
+
+def resize_segment(mask, class_id, target_size, method=cv2.INTER_AREA):
+    # Create a binary mask for the current class
+    class_mask = np.where(mask == class_id, 1, 0).astype(np.uint8)
+    
+    # Resize the class mask to the target size
+    resized_class_mask = cv2.resize(class_mask, (target_size[1], target_size[0]), interpolation=method)
+    
+    return resized_class_mask
+
+def resize_and_combine_classes(mask, target_size, method=cv2.INTER_AREA):
+    unique_classes = np.unique(mask)
+    
+    # Initialize a zero-filled mask for the combined result with the correct target size
+    resized_masks = np.zeros((target_size[0], target_size[1]), dtype=np.uint8)
+
+    # Process each class found in the mask
+    for class_id in unique_classes:
+        resized_class_mask = resize_segment(mask, class_id, target_size, method)
+        
+        # Assign the class ID to the resized output mask where the resized class mask is 1
+        resized_masks[resized_class_mask == 1] = class_id
+
+    return resized_masks
+
+
+class_labels = {
+    0: 'Blank',
+    1: 'Caption',
+    2: 'Footnote',
+    3: 'Formula',
+    4: 'List-item',
+    5: 'Page-footer',
+    6: 'Page-header',
+    7: 'Picture',
+    8: 'Section-header',
+    9: 'Table',
+    10: 'Text',
+    11: 'Title'
+}
+
+colors = plt.cm.get_cmap('tab20', len(class_labels))
+
+def colormap_to_rgb(cmap, index):
+    color = cmap(index)[:3]  # Extract RGB, ignore alpha
+    return tuple(int(c * 255) for c in color)
+    
+def mask_to_bboxes(colored_mask, class_labels):
+    bboxes = []
+
+    # Loop through each class in the class_labels
+    for label, class_name in class_labels.items():
+        # Get the RGB color for the current label
+        color = colormap_to_rgb(colors, label)
+        
+        # Create a binary mask for the current label by checking where the colored mask matches the class color
+        class_mask = np.all(colored_mask == color, axis=-1).astype(np.uint8)
+
+        # Find contours of the class region in the binary mask
+        contours, _ = cv2.findContours(class_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        # Loop through all contours and extract bounding boxes
+        for contour in contours:
+            # Get the bounding box for the contour (in xywh format)
+            x, y, w, h = cv2.boundingRect(contour)
+            
+            # Convert to xyxy format: (xmin, ymin, xmax, ymax)
+            xmin, ymin, xmax, ymax = x, y, x + w, y + h
+            
+            # Append the bounding box with the corresponding class label
+            bboxes.append((xmin, ymin, xmax, ymax))
+            # bboxes.append((xmin, ymin, xmax, ymax, class_name))
+
+    return bboxes
+
+
+
+import matplotlib.pyplot as plt
+# from matplotlib import colors
+
+def suryolo(image_path) :
+    
+    image = Image.open(image_path)
+    L, W = image.size
+    
+    
+    predicted_mask = predicted_mask_function(image_path)
+    
+    colored_mask = np.zeros((W, L, 3), dtype=np.uint8)  # 3 channels for RGB
+    
+    label_name_to_int = {v: k for k, v in class_labels.items()}
+    
+    colors = plt.cm.get_cmap('tab20', len(class_labels))
+    
+    bboxes,labels = predict_boxes_labels(image_path)
+    
+    for box, label in zip(bboxes, labels):  # Assuming labels list corresponds to bboxes
+        xmin, ymin, xmax, ymax = box
+        xmin, ymin, xmax, ymax = int(xmin), int(ymin), int(xmax), int(ymax)
+        
+        # Resize predicted mask to match the image dimensions (W = width, L = height)
+        predicted_mask = resize_and_combine_classes(predicted_mask, (W, L))
+        
+        # Extract the mask region within the bounding box
+        mask_region = predicted_mask[ymin:ymax, xmin:xmax]
+        
+        # Get the corresponding integer index for the label
+        label_index = label_name_to_int[label]
+        
+        # Get the corresponding color for the label using the colormap
+        color = colormap_to_rgb(colors, label_index)
+        
+        # Apply the color to the regions where mask_region > 0.5
+        colored_mask[ymin:ymax, xmin:xmax][mask_region > 0.5] = color
+        
+    blank_color = colormap_to_rgb(colors, 0)
+    colored_mask[(colored_mask == 0).all(axis=-1)] = blank_color
+    
+    return mask_to_bboxes(colored_mask,class_labels)
+    
+    
\ No newline at end of file
diff --git a/surya_yolo_pipeline_copy.cpython-310-x86_64-linux-gnu.so b/surya_yolo_pipeline_copy.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..944d7a2c8586153008fa48ddee34c299f2e3e391
Binary files /dev/null and b/surya_yolo_pipeline_copy.cpython-310-x86_64-linux-gnu.so differ
diff --git a/yolov10x_best.pt b/yolov10x_best.pt
new file mode 100644
index 0000000000000000000000000000000000000000..d5d011c8bca9aee5d522a50fdddcb4c74394ce70
--- /dev/null
+++ b/yolov10x_best.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7355a56b9ce4dc842fb2214dc416768476379ba9e60159e0ab4b8ddf51b5e24d
+size 64133947