{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 🚀 Barrier Reef YOLOv5 [Training]","metadata":{"papermill":{"duration":0.079471,"end_time":"2022-01-18T15:42:04.963271","exception":false,"start_time":"2022-01-18T15:42:04.8838","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### Hi kagglers, This is `Training` notebook using `YOLOv5`.\n\n\n### Other notebooks in the competition\n- [Barrier Reef YOLOv5 [Inference]](https://www.kaggle.com/ammarnassanalhajali/barrier-reef-yolov5-inference/edit)\n\n\n\n### Please if this kernel is useful, <font color='red'>please upvote !!</font>","metadata":{"papermill":{"duration":0.151263,"end_time":"2022-01-18T15:42:05.196702","exception":false,"start_time":"2022-01-18T15:42:05.045439","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# 📚 YOLOv5\nYOLO, \"You Only Look Once\", has a long and succesful history with real time object detection.","metadata":{"papermill":{"duration":0.087451,"end_time":"2022-01-18T15:42:05.393362","exception":false,"start_time":"2022-01-18T15:42:05.305911","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"#  ⬇️ Download YOLOv5\nClone this repo and install requirements.txt dependencies, including Python>=3.8 and PyTorch>=1.7.","metadata":{"papermill":{"duration":0.098576,"end_time":"2022-01-18T15:42:05.591763","exception":false,"start_time":"2022-01-18T15:42:05.493187","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#!git clone https://github.com/meituan/YOLOv6\n#%cd ..\n!git clone https://github.com/WongKinYiu/yolov7    ","metadata":{"papermill":{"duration":0.077425,"end_time":"2022-01-18T15:42:05.746073","exception":false,"start_time":"2022-01-18T15:42:05.668648","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install -qU wandb\n!pip install -qU bbox-utility","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"#!git clone https://github.com/meituan/YOLOv6# Download YOLOv5\n#!git clone https://github.com/ultralytics/yolov5  # clone repo\n#%cd ./YOLOv7\n'''\n%cd ./yolov7\n# Install dependencies\n%pip install -qr requirements.txt  \n\n# change directory\n%cd ../\n'''\nimport torch\nprint(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")","metadata":{"_kg_hide-output":true,"papermill":{"duration":13.347451,"end_time":"2022-01-18T15:42:19.170758","exception":false,"start_time":"2022-01-18T15:42:05.823307","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!add-apt-repository ppa:ubuntu-toolchain-r/test -y\n!apt-get update\n!apt-get upgrade libstdc++6 -y","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"papermill":{"duration":258.112749,"end_time":"2022-01-18T15:46:37.368243","exception":false,"start_time":"2022-01-18T15:42:19.255494","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🔨 Weights & Biases\n* Weights & Biases is a set of tools that tracks machine learning experiments, visualizes metrics, and shares results.\n* Weights & Biases is directly integrated into YOLOv5, providing experiment metric tracking, model and dataset versioning, rich model prediction visualization, and more.","metadata":{"papermill":{"duration":0.163006,"end_time":"2022-01-18T15:46:37.690756","exception":false,"start_time":"2022-01-18T15:46:37.52775","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Install W&B \n!pip install -q --upgrade wandb\n\n# Login \nimport wandb\n\nfrom kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient() \n\npersonal_key_for_api = user_secrets.get_secret(\"API_key\")\n! wandb login $personal_key_for_api","metadata":{"papermill":{"duration":15.431042,"end_time":"2022-01-18T15:46:53.282268","exception":false,"start_time":"2022-01-18T15:46:37.851226","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ☀️ Importing Libraries","metadata":{"papermill":{"duration":0.180989,"end_time":"2022-01-18T15:46:53.724515","exception":false,"start_time":"2022-01-18T15:46:53.543526","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom itertools import groupby\nimport numpy as np\nfrom tqdm.notebook import tqdm\ntqdm.pandas()\nimport pandas as pd\nimport os\nimport pickle\nimport cv2\nfrom multiprocessing import Pool\nimport matplotlib.pyplot as plt\n# import cupy as cp\nimport ast\nimport glob\nfrom os import listdir\nfrom os.path import isfile, join\nfrom glob import glob\nimport yaml\n\nimport shutil\nfrom shutil import copyfile\nimport sys\n\nfrom joblib import Parallel, delayed\n\n# --- Read data ---\nTRAIN_PATH = '/kaggle/input/tensorflow-great-barrier-reef'","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":0.386218,"end_time":"2022-01-18T15:46:54.271421","exception":false,"start_time":"2022-01-18T15:46:53.885203","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:18:49.144369Z","iopub.execute_input":"2022-09-09T14:18:49.144940Z","iopub.status.idle":"2022-09-09T14:18:49.285554Z","shell.execute_reply.started":"2022-09-09T14:18:49.144901Z","shell.execute_reply":"2022-09-09T14:18:49.284812Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🔨 Functions","metadata":{"papermill":{"duration":0.170144,"end_time":"2022-01-18T15:46:54.625845","exception":false,"start_time":"2022-01-18T15:46:54.455701","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def get_bbox(annots):\n    #print(annots)\n    bboxes = [list(annot.values()) for annot in annots]\n    return bboxes\n\ndef get_bbox_wh(annots):\n    #print(annots)\n    bboxes = [list(annot.values())[2]* list(annot.values())[3] for annot in annots]\n    return np.median(bboxes)\n\ndef get_bbox_w(annots):\n    #print(annots)\n    bboxes = [list(annot.values())[2]  for annot in annots]\n    return np.median(bboxes)\n\ndef get_bbox_x2(annots):\n    #print(annots)\n    bboxes = [list(annot.values())[2]+list(annot.values())[0]  for annot in annots]\n    return np.median(bboxes)\n\ndef get_bbox_y2(annots):\n    #print(annots)\n    bboxes = [list(annot.values())[1]+list(annot.values())[3]  for annot in annots]\n    return np.median(bboxes)\n\ndef get_bbox_x1(annots):\n    #print(annots)\n    bboxes = [ list(annot.values())[0]  for annot in annots]\n    return np.median(bboxes)\n\ndef get_bbox_y1(annots):\n    #print(annots)\n    bboxes = [list(annot.values())[1]  for annot in annots]\n    return np.median(bboxes)\n\ndef get_bbox_h(annots):\n    #print(annots)\n    bboxes = [list(annot.values())[3]  for annot in annots]\n    return np.median(bboxes)\ndef get_path(row):\n    row['image_path'] = f'{TRAIN_PATH}/train_images/video_{row.video_id}/{row.video_frame}.jpg'\n    return row\ndef load_image(image_path):\n    return cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)\n\ndef coco2yolo(image_height, image_width, bboxes):\n    \"\"\"\n    coco => [xmin, ymin, w, h]\n    yolo => [xmid, ymid, w, h] (normalized)\n    \"\"\"\n    \n    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n    \n    # normolizinig\n    bboxes[..., [0, 2]]= bboxes[..., [0, 2]]/ image_width\n    bboxes[..., [1, 3]]= bboxes[..., [1, 3]]/ image_height\n    \n    # converstion (xmin, ymin) => (xmid, ymid)\n    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]/2\n    \n    return bboxes\n\ndef yolo2coco(image_height, image_width, bboxes):\n    \"\"\"\n    yolo => [xmid, ymid, w, h] (normalized)\n    coco => [xmin, ymin, w, h]\n    \n    \"\"\" \n    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n    \n    # denormalizing\n    bboxes[..., [0, 2]]= bboxes[..., [0, 2]]* image_width\n    bboxes[..., [1, 3]]= bboxes[..., [1, 3]]* image_height\n    \n    # converstion (xmid, ymid) => (xmin, ymin) \n    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2\n    \n    return bboxes\ndef plot_one_box(x, img, color=None, label=None, line_thickness=None):\n    # Plots one bounding box on image img\n    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness\n    color = color or [random.randint(0, 255) for _ in range(3)]\n    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)\n    if label:\n        tf = max(tl - 1, 1)  # font thickness\n        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled\n        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [0, 0, 255], thickness=tf, lineType=cv2.LINE_AA)\n\n\n\ndef draw_bboxes(img, bboxes, classes, class_ids, colors = None, show_classes = None, bbox_format = 'yolo', class_name = False, line_thickness = 2):  \n    image = img.copy()\n    show_classes = classes if show_classes is None else show_classes\n    colors = (0, 255 ,0) if colors is None else colors\n    \n    if bbox_format == 'yolo':\n        \n        for idx in range(len(bboxes)):  \n            \n            bbox  = bboxes[idx]\n            cls   = classes[idx]\n            cls_id = class_ids[idx]\n            color = colors[cls_id] if type(colors) is list else colors\n            \n            if cls in show_classes:\n            \n                x1 = round(float(bbox[0])*image.shape[1])\n                y1 = round(float(bbox[1])*image.shape[0])\n                w  = round(float(bbox[2])*image.shape[1]/2) #w/2 \n                h  = round(float(bbox[3])*image.shape[0]/2)\n\n                voc_bbox = (x1-w, y1-h, x1+w, y1+h)\n                plot_one_box(voc_bbox, \n                             image,\n                             color = color,\n                             label = cls if class_name else str(get_label(cls)),\n                             line_thickness = line_thickness)\n            \n    elif bbox_format == 'coco':\n        \n        for idx in range(len(bboxes)):  \n            \n            bbox  = bboxes[idx]\n            cls   = classes[idx]\n            cls_id = class_ids[idx]\n            color = colors[cls_id] if type(colors) is list else colors\n            \n            if cls in show_classes:            \n                x1 = int(round(bbox[0]))\n                y1 = int(round(bbox[1]))\n                w  = int(round(bbox[2]))\n                h  = int(round(bbox[3]))\n\n                voc_bbox = (x1, y1, x1+w, y1+h)\n                plot_one_box(voc_bbox, \n                             image,\n                             color = color,\n                             label = cls if class_name else str(cls_id),\n                             line_thickness = line_thickness)\n\n    elif bbox_format == 'voc_pascal':\n        \n        for idx in range(len(bboxes)):  \n            \n            bbox  = bboxes[idx]\n            cls   = classes[idx]\n            cls_id = class_ids[idx]\n            color = colors[cls_id] if type(colors) is list else colors\n            \n            if cls in show_classes: \n                x1 = int(round(bbox[0]))\n                y1 = int(round(bbox[1]))\n                x2 = int(round(bbox[2]))\n                y2 = int(round(bbox[3]))\n                voc_bbox = (x1, y1, x2, y2)\n                plot_one_box(voc_bbox, \n                             image,\n                             color = color,\n                             label = cls if class_name else str(cls_id),\n                             line_thickness = line_thickness)\n    else:\n        raise ValueError('wrong bbox format')\n\n    return image\n\nnp.random.seed(8)\ncolors = (np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255))\ncolors=(255,0,0)","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.194465,"end_time":"2022-01-18T15:46:54.982334","exception":false,"start_time":"2022-01-18T15:46:54.787869","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:11.520990Z","iopub.execute_input":"2022-09-09T14:19:11.521449Z","iopub.status.idle":"2022-09-09T14:19:11.552825Z","shell.execute_reply.started":"2022-09-09T14:19:11.521412Z","shell.execute_reply":"2022-09-09T14:19:11.552051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍘 Hyperparameters","metadata":{"papermill":{"duration":0.15926,"end_time":"2022-01-18T15:46:55.338391","exception":false,"start_time":"2022-01-18T15:46:55.179131","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#BATCH_SIZE = 8\n#EPOCHS = 20\n#IMG_SIZE=2560\nSelected_Fold=1  #0..4","metadata":{"papermill":{"duration":0.164957,"end_time":"2022-01-18T15:46:55.662055","exception":false,"start_time":"2022-01-18T15:46:55.497098","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍮 Loading Data","metadata":{"papermill":{"duration":0.159362,"end_time":"2022-01-18T15:46:55.982563","exception":false,"start_time":"2022-01-18T15:46:55.823201","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Read in the data CSV files\ndf = pd.read_csv(\"/kaggle/input/tensorflow-great-barrier-reef/train.csv\")\n#df=pd.read_csv('../input/more-annotations/more_annotations_train.csv')\n#df.head(5),df.shape","metadata":{"papermill":{"duration":0.225688,"end_time":"2022-01-18T15:46:56.367594","exception":false,"start_time":"2022-01-18T15:46:56.141906","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:16.443570Z","iopub.execute_input":"2022-09-09T14:19:16.444379Z","iopub.status.idle":"2022-09-09T14:19:16.497270Z","shell.execute_reply.started":"2022-09-09T14:19:16.444341Z","shell.execute_reply":"2022-09-09T14:19:16.496540Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_subset=df[df.sequence==40258]\n\ndf_subset.video_id.unique()","metadata":{"papermill":{"duration":0.17583,"end_time":"2022-01-18T15:46:56.702971","exception":false,"start_time":"2022-01-18T15:46:56.527141","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:19.013798Z","iopub.execute_input":"2022-09-09T14:19:19.014342Z","iopub.status.idle":"2022-09-09T14:19:19.031345Z","shell.execute_reply.started":"2022-09-09T14:19:19.014304Z","shell.execute_reply":"2022-09-09T14:19:19.030627Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.shape","metadata":{"papermill":{"duration":0.16978,"end_time":"2022-01-18T15:46:57.033052","exception":false,"start_time":"2022-01-18T15:46:56.863272","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.groupby(['video_id','sequence']).image_id.count()","metadata":{"papermill":{"duration":0.2048,"end_time":"2022-01-18T15:46:57.396714","exception":false,"start_time":"2022-01-18T15:46:57.191914","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:22.433915Z","iopub.execute_input":"2022-09-09T14:19:22.434211Z","iopub.status.idle":"2022-09-09T14:19:22.450732Z","shell.execute_reply.started":"2022-09-09T14:19:22.434178Z","shell.execute_reply":"2022-09-09T14:19:22.449966Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_subset[df_subset.sequence_frame!=df_subset.video_frame]\n#df_seq_group=df.groupby(['video_id','sequence'])\ndf['New_number_bbox'] = df['annotations'].apply(lambda x:len(eval(x)))\n\n ","metadata":{"papermill":{"duration":0.310308,"end_time":"2022-01-18T15:46:57.986203","exception":false,"start_time":"2022-01-18T15:46:57.675895","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:26.393658Z","iopub.execute_input":"2022-09-09T14:19:26.394550Z","iopub.status.idle":"2022-09-09T14:19:26.589957Z","shell.execute_reply.started":"2022-09-09T14:19:26.394506Z","shell.execute_reply":"2022-09-09T14:19:26.589247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#groups[(0, 996)][0:500] \ndf[df['New_number_bbox']>0].shape\n#df[df.New_number_bbox ==0] \n ","metadata":{"papermill":{"duration":0.166599,"end_time":"2022-01-18T15:46:58.31337","exception":false,"start_time":"2022-01-18T15:46:58.146771","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:27.973405Z","iopub.execute_input":"2022-09-09T14:19:27.973992Z","iopub.status.idle":"2022-09-09T14:19:27.982033Z","shell.execute_reply.started":"2022-09-09T14:19:27.973954Z","shell.execute_reply":"2022-09-09T14:19:27.981294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n# BBoxes\n##### 📌 Note \n> We can see there are many images without any BBox. ","metadata":{"papermill":{"duration":0.161627,"end_time":"2022-01-18T15:46:58.635604","exception":false,"start_time":"2022-01-18T15:46:58.473977","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df[\"NumBBox\"]=df['annotations'].apply(lambda x: str.count(x, 'x'))\ndf.head(5)","metadata":{"papermill":{"duration":0.19371,"end_time":"2022-01-18T15:46:58.995611","exception":false,"start_time":"2022-01-18T15:46:58.801901","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:32.768522Z","iopub.execute_input":"2022-09-09T14:19:32.769128Z","iopub.status.idle":"2022-09-09T14:19:32.800006Z","shell.execute_reply.started":"2022-09-09T14:19:32.769082Z","shell.execute_reply":"2022-09-09T14:19:32.799309Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd.set_option(\"display.max_rows\", 500)","metadata":{"papermill":{"duration":0.167556,"end_time":"2022-01-18T15:46:59.324348","exception":false,"start_time":"2022-01-18T15:46:59.156792","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:35.373740Z","iopub.execute_input":"2022-09-09T14:19:35.375045Z","iopub.status.idle":"2022-09-09T14:19:35.381322Z","shell.execute_reply.started":"2022-09-09T14:19:35.375004Z","shell.execute_reply":"2022-09-09T14:19:35.380190Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"neg_img_count=25\ngroups=dict(list(df.groupby(['video_id','sequence'])))\ngroups_neg_ids={}\nfor i in groups:\n    t=groups[i]\n    t['shifted']= (t.NumBBox.shift(1)) \n\n    t.fillna(0,inplace=True)\n    t['shifted']= t.shifted.astype('int').clip(0,1)\n    #for i in tmp.NumBBox.values:\n    # check for neg frames before or after the bbox\n    t1=t[(t.shifted!=t.NumBBox) & ((t.shifted==0) & (t.NumBBox!=0 )) |((t.shifted!=0) & (t.NumBBox==0 ))]\n    neg_seq_frame=t1[t1.shifted==0].sequence_frame.values # this is to take frames prior to object frame\n    frame_indices1=[]\n    for f in neg_seq_frame:\n        frame_indices1=frame_indices1+(np.arange(f-neg_img_count,f)).tolist() #generate the frame no prior to object frame\n        \n    pos_seq_frame=t1[t1.shifted==1].sequence_frame.values # this is to take frames after   object frame\n    frame_indices2=[]\n    for f in pos_seq_frame:\n        frame_indices2= frame_indices2+(np.arange(f ,f+neg_img_count)).tolist()    #generate the frame no after object frame \n     \n    frame_indices=np.concatenate([frame_indices1,frame_indices2])\n    neg_img_ids=t[(t.sequence_frame.isin(frame_indices)) & (t.NumBBox==0)].image_id.values #gather image ids\n    groups_neg_ids[i]=neg_img_ids\n     \n    ","metadata":{"papermill":{"duration":0.345004,"end_time":"2022-01-18T15:46:59.830476","exception":false,"start_time":"2022-01-18T15:46:59.485472","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#frame_indices2\n#groups_neg_ids[(0, 996)]\n#frame_indices\n \n ","metadata":{"papermill":{"duration":0.167934,"end_time":"2022-01-18T15:47:00.162397","exception":false,"start_time":"2022-01-18T15:46:59.994463","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#groups[(0, 996)].iloc[440:450]\n#np.arange??\n#(t[t.shifted==0].sequence_frame.values[0]\n#groups[(0, 996)][0:500]\n ","metadata":{"papermill":{"duration":0.169759,"end_time":"2022-01-18T15:47:00.491896","exception":false,"start_time":"2022-01-18T15:47:00.322137","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#np.where(groups[(0, 996)]['NumBBox'].values>0)[0] \n#groups[(0, 996)][groups[(0, 996)]['NumBBox']>0].shape\n#t=groups[((0, 996))]\ntotal_neg_list=[a for a in groups_neg_ids.values() ]\ntotal_neg_list=np.concatenate(total_neg_list)","metadata":{"papermill":{"duration":0.166718,"end_time":"2022-01-18T15:47:00.819269","exception":false,"start_time":"2022-01-18T15:47:00.652551","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:42.014263Z","iopub.execute_input":"2022-09-09T14:19:42.015099Z","iopub.status.idle":"2022-09-09T14:19:42.039359Z","shell.execute_reply.started":"2022-09-09T14:19:42.015050Z","shell.execute_reply":"2022-09-09T14:19:42.038509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#groups[(1, 17665)]\n#df_train","metadata":{"papermill":{"duration":0.171035,"end_time":"2022-01-18T15:47:01.154191","exception":false,"start_time":"2022-01-18T15:47:00.983156","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(df[\"NumBBox\"].sum())","metadata":{"papermill":{"duration":0.169099,"end_time":"2022-01-18T15:47:01.484557","exception":false,"start_time":"2022-01-18T15:47:01.315458","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_train=df[(df[\"NumBBox\"]>0 )| (df.image_id.isin(total_neg_list)) ]\ndf_train=df[(df[\"NumBBox\"]>0 ) ]\n\ndf_train.sample(2)\ndf_train.shape,df[df[\"NumBBox\"]==0].shape\n ","metadata":{"papermill":{"duration":0.17782,"end_time":"2022-01-18T15:47:01.823856","exception":false,"start_time":"2022-01-18T15:47:01.646036","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:19:45.253688Z","iopub.execute_input":"2022-09-09T14:19:45.254216Z","iopub.status.idle":"2022-09-09T14:19:45.268450Z","shell.execute_reply.started":"2022-09-09T14:19:45.254176Z","shell.execute_reply":"2022-09-09T14:19:45.267466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_train=df_train.sort_values(by=['video_id','sequence','sequence_frame']).reset_index(drop=True)","metadata":{"papermill":{"duration":0.175506,"end_time":"2022-01-18T15:47:02.161699","exception":false,"start_time":"2022-01-18T15:47:01.986193","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"original=pd.read_csv('../input/tensorflow-great-barrier-reef/train.csv')\noriginal.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:20:22.713990Z","iopub.execute_input":"2022-09-09T14:20:22.714846Z","iopub.status.idle":"2022-09-09T14:20:22.744489Z","shell.execute_reply.started":"2022-09-09T14:20:22.714805Z","shell.execute_reply":"2022-09-09T14:20:22.743524Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_train[(df_train.video_id==0 )& (df_train.sequence==996)][0:500]\ndf_train.shape","metadata":{"papermill":{"duration":0.166024,"end_time":"2022-01-18T15:47:02.488877","exception":false,"start_time":"2022-01-18T15:47:02.322853","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(df_train['NumBBox'].sum())","metadata":{"papermill":{"duration":0.167534,"end_time":"2022-01-18T15:47:02.817489","exception":false,"start_time":"2022-01-18T15:47:02.649955","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:20:36.254147Z","iopub.execute_input":"2022-09-09T14:20:36.254431Z","iopub.status.idle":"2022-09-09T14:20:36.261005Z","shell.execute_reply.started":"2022-09-09T14:20:36.254400Z","shell.execute_reply":"2022-09-09T14:20:36.260245Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> We have just 4919 images with 11898 BBox, we will use them in training.","metadata":{"papermill":{"duration":0.165169,"end_time":"2022-01-18T15:47:03.1463","exception":false,"start_time":"2022-01-18T15:47:02.981131","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df_train['annotations'] = df_train['annotations'].progress_apply(lambda x: ast.literal_eval(x)) #gives string list of dicts\ndf_train['bboxes'] = df_train.annotations.progress_apply(get_bbox)\n\ndf_train.sample(2)","metadata":{"papermill":{"duration":0.706108,"end_time":"2022-01-18T15:47:04.013797","exception":false,"start_time":"2022-01-18T15:47:03.307689","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-09-09T14:20:38.404094Z","iopub.execute_input":"2022-09-09T14:20:38.404429Z","iopub.status.idle":"2022-09-09T14:20:38.861348Z","shell.execute_reply.started":"2022-09-09T14:20:38.404391Z","shell.execute_reply":"2022-09-09T14:20:38.860430Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train['mean_area'] = df_train.annotations.progress_apply(get_bbox_wh)\n ","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:20:42.333637Z","iopub.execute_input":"2022-09-09T14:20:42.334379Z","iopub.status.idle":"2022-09-09T14:20:42.524230Z","shell.execute_reply.started":"2022-09-09T14:20:42.334344Z","shell.execute_reply":"2022-09-09T14:20:42.523469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train['mean_w'] = df_train.annotations.progress_apply(get_bbox_w )\ndf_train['mean_h'] = df_train.annotations.progress_apply(get_bbox_h )","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:20:44.633574Z","iopub.execute_input":"2022-09-09T14:20:44.634196Z","iopub.status.idle":"2022-09-09T14:20:44.996022Z","shell.execute_reply.started":"2022-09-09T14:20:44.634159Z","shell.execute_reply":"2022-09-09T14:20:44.995169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train['mean_x2'] = df_train.annotations.progress_apply(get_bbox_x2 )\ndf_train['mean_y2'] = df_train.annotations.progress_apply(get_bbox_y2 )","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:20:48.353856Z","iopub.execute_input":"2022-09-09T14:20:48.354431Z","iopub.status.idle":"2022-09-09T14:20:48.730494Z","shell.execute_reply.started":"2022-09-09T14:20:48.354393Z","shell.execute_reply":"2022-09-09T14:20:48.729663Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train['mean_x1'] = df_train.annotations.progress_apply(get_bbox_x1 )\ndf_train['mean_y1'] = df_train.annotations.progress_apply(get_bbox_y1 )","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:20:51.093633Z","iopub.execute_input":"2022-09-09T14:20:51.094215Z","iopub.status.idle":"2022-09-09T14:20:51.578639Z","shell.execute_reply.started":"2022-09-09T14:20:51.094177Z","shell.execute_reply":"2022-09-09T14:20:51.577737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train[df_train.NumBBox>0]\n\nplt.hist(df_train[df_train.NumBBox>0].mean_area)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train[(df_train.NumBBox>0) & (df_train.mean_x2>1216) & (df_train.mean_y2<500)] \n\ndf_train[df_train.image_id=='1-855'].bboxes.values","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import random\nimg=cv2.imread('../input/tensorflow-great-barrier-reef/train_images/video_1/855.jpg')\nx=[1142, 283, 1142+82, 283+63]\nplot_one_box(x, img, color=None, label=None, line_thickness=4) \nplt.imshow(img)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" \nplt.hist(df_train[df_train.NumBBox>0].mean_x2,bins=100)","metadata":{"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(df_train[df_train.NumBBox>0].mean_w)","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:21:08.704174Z","iopub.execute_input":"2022-09-09T14:21:08.704700Z","iopub.status.idle":"2022-09-09T14:21:08.942836Z","shell.execute_reply.started":"2022-09-09T14:21:08.704659Z","shell.execute_reply":"2022-09-09T14:21:08.942128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.hist(df_train[df_train.NumBBox>0].mean_h)","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:21:31.234769Z","iopub.execute_input":"2022-09-09T14:21:31.235430Z","iopub.status.idle":"2022-09-09T14:21:31.451196Z","shell.execute_reply.started":"2022-09-09T14:21:31.235389Z","shell.execute_reply":"2022-09-09T14:21:31.450505Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_train[df_train.image_id=='1-5907']['annotations']\n\n#ast.literal_eval(df_train['annotations'].values[0])\n#df[df.sequence==53708]\n\n#df_train['annotations'].values[0]\n ","metadata":{"papermill":{"duration":0.285546,"end_time":"2022-01-18T15:47:04.589305","exception":false,"start_time":"2022-01-18T15:47:04.303759","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ast.literal_eval(df_train[df_train.image_id=='1-5907']['annotations'] )","metadata":{"papermill":{"duration":0.171168,"end_time":"2022-01-18T15:47:04.953068","exception":false,"start_time":"2022-01-18T15:47:04.7819","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Size of Images\n##### 📌 Note \n> All images have Width=1280 & Height=720 ","metadata":{"papermill":{"duration":0.162908,"end_time":"2022-01-18T15:47:05.294461","exception":false,"start_time":"2022-01-18T15:47:05.131553","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#img=cv2.imread('../input/tensorflow-great-barrier-reef/train_images/video_0/0.jpg')\n#img.shape\ndf_train.shape","metadata":{"papermill":{"duration":0.169302,"end_time":"2022-01-18T15:47:05.626254","exception":false,"start_time":"2022-01-18T15:47:05.456952","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train[\"Width\"]=1280\ndf_train[\"Height\"]=720\ndf_train.sample(2)","metadata":{"papermill":{"duration":0.184289,"end_time":"2022-01-18T15:47:05.973242","exception":false,"start_time":"2022-01-18T15:47:05.788953","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Path of Images","metadata":{"papermill":{"duration":0.16298,"end_time":"2022-01-18T15:47:06.30182","exception":false,"start_time":"2022-01-18T15:47:06.13884","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df_train = df_train.progress_apply(get_path, axis=1)\ndf_train.sample(2)","metadata":{"papermill":{"duration":4.452945,"end_time":"2022-01-18T15:47:10.917795","exception":false,"start_time":"2022-01-18T15:47:06.46485","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🌈 Visualizing BBoxes","metadata":{"papermill":{"duration":0.163544,"end_time":"2022-01-18T15:47:11.259247","exception":false,"start_time":"2022-01-18T15:47:11.095703","status":"completed"},"tags":[]}},{"cell_type":"code","source":"'''\ndf_v = df_train[(df_train.NumBBox==13)].sample(2) \nfig,ax = plt.subplots(1,2,figsize=(30,20))\ni=0;\nfor index, row in df_v.iterrows():\n    img           = load_image(row.image_path)\n    image_height  = row.Height\n    image_width   = row.Width\n    bboxes_coco   = np.array(row.bboxes)\n    bboxes_yolo   = coco2yolo(image_height, image_width, bboxes_coco)\n    names         = ['COTS']*len(bboxes_coco)\n    labels        = [0]*len(bboxes_coco)\n    im=draw_bboxes(img = img,\n                           bboxes = bboxes_yolo, \n                           classes = names,\n                           class_ids = labels,\n                           class_name = True, \n                           colors = colors, \n                           bbox_format = 'yolo',\n                           line_thickness = 2)\n    ax[i].imshow(im)\n    ax[i].axis('OFF')\n    i=i+1\n'''","metadata":{"papermill":{"duration":0.172672,"end_time":"2022-01-18T15:47:11.595862","exception":false,"start_time":"2022-01-18T15:47:11.42319","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍚 Splitting Dataset","metadata":{"papermill":{"duration":0.165767,"end_time":"2022-01-18T15:47:11.925437","exception":false,"start_time":"2022-01-18T15:47:11.75967","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#df_train","metadata":{"papermill":{"duration":0.170521,"end_time":"2022-01-18T15:47:12.263436","exception":false,"start_time":"2022-01-18T15:47:12.092915","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip uninstall -q -y scikit-learn\n!pip install -q scikit-learn","metadata":{"papermill":{"duration":0.163998,"end_time":"2022-01-18T15:47:12.591498","exception":false,"start_time":"2022-01-18T15:47:12.4275","status":"completed"},"tags":[],"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import StratifiedGroupKFold","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gkf_video=False\ndf_train['fold'] = -1\nCV_split=False\nif CV_split and gkf_video:\n    \n    from sklearn.model_selection import GroupKFold\n    kf = GroupKFold(n_splits = 5) \n    df_train = df_train.reset_index(drop=True)\n    df_train['fold'] = -1\n    for fold, (train_idx, val_idx) in enumerate(kf.split(df_train, y = df_train.video_id.tolist(), groups=df_train.sequence)):\n        df_train.loc[val_idx, 'fold'] = fold\n    display(df_train.fold.value_counts())\n    \n    \nelif CV_split:\n    df_train.loc[df_train.video_id==0,'fold']=0\n    df_train.loc[df_train.video_id==1,'fold']=1\n    df_train.loc[df_train.video_id==2,'fold']=2\nelse:\n    test_train_split=pd.read_csv('../input/reef-a-cv-strategy-subsequences/train-validation-split/train-0.1.csv')\n    '''\n    from sklearn.model_selection import StratifiedGroupKFold\n    sgkf = StratifiedGroupKFold(n_splits=8)\n    for fold, (t_idx, v_idx) in enumerate( sgkf.split(test_train_split, test_train_split.has_annotations, test_train_split.subsequence_id) ):\n        test_train_split.loc[v_idx,'fold'] = fold\n    \n    \n    fold=1\n    \n    test_train_split['is_train']=True\n    test_train_split.loc[test_train_split.fold==1,'is_train']=False\n    '''\n    df_train =pd.merge(df_train,test_train_split[['image_id','is_train']],on='image_id') \n    #df_train=df_train[df_train.fold==1]\n    ","metadata":{"papermill":{"duration":0.854561,"end_time":"2022-01-18T15:47:13.60934","exception":false,"start_time":"2022-01-18T15:47:12.754779","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" \n ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"array([35305, 53708, 17665, 60510])2\narray([  996, 45015, 59337, 22643]) 3\n\narray([ 8399, 40258, 15827, 26651])4\n\narray([45518, 18048, 60754, 29859]) 1\n8513-0","metadata":{"papermill":{"duration":0.165337,"end_time":"2022-01-18T15:47:13.941038","exception":false,"start_time":"2022-01-18T15:47:13.775701","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#df_train[df_train.fold==4].sequence.unique()","metadata":{"papermill":{"duration":0.171185,"end_time":"2022-01-18T15:47:14.284002","exception":false,"start_time":"2022-01-18T15:47:14.112817","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 🍚 Organize Directories\n\nI organized train and val images and labels according to the example below.\n\n```\n/Kaggle/working\n    /COTS\n         /images\n             /train/img0.jpg\n             /val\n         /labels\n             /train/img0.txt\n             /val\n    /yolov5\n```","metadata":{"papermill":{"duration":0.166354,"end_time":"2022-01-18T15:47:14.615255","exception":false,"start_time":"2022-01-18T15:47:14.448901","status":"completed"},"tags":[]}},{"cell_type":"code","source":"os.makedirs('COTS/images/train', exist_ok=True)\nos.makedirs('COTS/images/valid', exist_ok=True)\nos.makedirs('COTS/labels/train', exist_ok=True)\nos.makedirs('COTS/labels/valid', exist_ok=True)","metadata":{"papermill":{"duration":0.173364,"end_time":"2022-01-18T15:47:14.95439","exception":false,"start_time":"2022-01-18T15:47:14.781026","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_train\nlen(df_train)","metadata":{"papermill":{"duration":0.288037,"end_time":"2022-01-18T15:47:15.483287","exception":false,"start_time":"2022-01-18T15:47:15.19525","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in tqdm(range(len(df_train))):\n    row = df_train.loc[i]\n    if (CV_split and row.fold != Selected_Fold) or row.is_train:\n        copyfile(f'{row.image_path}', f'COTS/images/train/{row.image_id}.jpg')\n    else:\n        copyfile(f'{row.image_path}', f'COTS/images/valid/{row.image_id}.jpg') ","metadata":{"papermill":{"duration":82.819994,"end_time":"2022-01-18T15:48:38.539448","exception":false,"start_time":"2022-01-18T15:47:15.719454","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#!mv   COTS /kaggle/working/\n \n ","metadata":{"papermill":{"duration":0.832081,"end_time":"2022-01-18T15:48:39.538373","exception":false,"start_time":"2022-01-18T15:48:38.706292","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"list1 = os.listdir('/kaggle/working/COTS/images/train') # dir is your directory path\nnumber_files1 = len(list1)\nprint(\"Number of images in ./COTS/images/train folder\",number_files1)\nlist2 = os.listdir('/kaggle/working/COTS/images/valid') # dir is your directory path\nnumber_files2 = len(list2)\nprint(\"Number of images in ./COTS/images/valid folder\",number_files2)","metadata":{"papermill":{"duration":0.177878,"end_time":"2022-01-18T15:48:39.883603","exception":false,"start_time":"2022-01-18T15:48:39.705725","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍜 Create `Data.YAML` file\n\nThe `data.yaml`, is the dataset configuration file that defines:\n\n1. the dataset root directory and relative paths to train/val/test image directories (or paths to *.txt files with image paths).\n1. the number of classes.\n1. a list of class names.\n\n> 📍 Note: The `data.yaml` is created in the `yolov5/data` directory as required. ","metadata":{"papermill":{"duration":0.167467,"end_time":"2022-01-18T15:48:40.220529","exception":false,"start_time":"2022-01-18T15:48:40.053062","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import yaml\nwith open('/kaggle/working/train.txt', 'w') as f:\n    for path in glob('/kaggle/working/train/*'):\n        f.write(path+'\\n')\n            \nwith open('/kaggle/working/val.txt', 'w') as f:\n    for path in glob('/kaggle/working/val/*'):\n        f.write(path+'\\n')\n\ndata = dict(\n    train = '/kaggle/working/COTS/images/train',\n    val = '/kaggle/working/COTS/images/valid',\n    \n    nc    = 1, # number of classes\n    names =  ['cots'] # classes\n    )\n\nwith open('/kaggle/working/yolov7/data/data.yaml', 'w') as outfile:\n    yaml.dump(data, outfile, default_flow_style=False)\n\n#%cat /kaggle/working/YOLOv6/data/data.yaml\n%cat /kaggle/working/yolov7/data/data.yaml","metadata":{"papermill":{"duration":0.844341,"end_time":"2022-01-18T15:48:41.229793","exception":false,"start_time":"2022-01-18T15:48:40.385452","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls '/kaggle/working/yolov7/data/data.yaml'\n!ls -l /kaggle/working ","metadata":{"papermill":{"duration":0.901552,"end_time":"2022-01-18T15:48:42.299842","exception":false,"start_time":"2022-01-18T15:48:41.39829","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#!rm -rf /kaggle/working/YOLOv6/YOLOv6/","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 🍮 Create Labels for YOLOv5\n\nTo label your images,a `.txt` file with the same name of the image,will be created (if no objects in image, no *.txt file is required)\nThe *.txt file specifications are:\n\n* One row per object\n* Each row is class x_center y_center width height format.\n* Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide x_center and width by image width, and y_center and height by image height.\n* Class numbers are zero-indexed (start from 0).\n\n> 📍 Note: We don't have to remove the images without bounding boxes from the training or validation sets. ","metadata":{"papermill":{"duration":0.165123,"end_time":"2022-01-18T15:48:42.632346","exception":false,"start_time":"2022-01-18T15:48:42.467223","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg](attachment:812ff98c-03ef-48f5-b171-0c8b3b0fab54.jpg)","metadata":{"papermill":{"duration":0.167145,"end_time":"2022-01-18T15:48:42.964624","exception":false,"start_time":"2022-01-18T15:48:42.797479","status":"completed"},"tags":[]},"attachments":{"812ff98c-03ef-48f5-b171-0c8b3b0fab54.jpg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEASABIAAD/4QC8RXhpZgAATU0AKgAAAAgABwESAAMAAAABAAEAAAEaAAUAAAABAAAAYgEbAAUAAAABAAAAagEoAAMAAAABAAIAAAExAAIAAAANAAAAcgEyAAIAAAAUAAAAgIdpAAQAAAABAAAAlAAAAAAAAABIAAAAAQAAAEgAAAABR0lNUCAyLjEwLjE0AAAyMDIwOjA4OjI3IDE0OjMzOjI3AAACoAIABAAAAAEAAAXIoAMABAAAAAEAAAOYAAAAAAAA/+ICsElDQ19QUk9GSUxFAAEBAAACoGxjbXMEMAAAbW50clJHQiBYWVogB+QACAAbABUAHwAGYWNzcEFQUEwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPbWAAEAAAAA0y1sY21zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANZGVzYwAAASAAAABAY3BydAAAAWAAAAA2d3RwdAAAAZgAAAAUY2hhZAAAAawAAAAsclhZWgAAAdgAAAAUYlhZWgAAAewAAAAUZ1hZWgAAAgAAAAAUclRSQwAAAhQAAAAgZ1RSQwAAAhQAAAAgYlRSQwAAAhQAAAAgY2hybQAAAjQAAAAkZG1uZAAAAlgAAAAkZG1kZAAAAnwAAAAkbWx1YwAAAAAAAAABAAAADGVuVVMAAAAkAAAAHABHAEkATQBQACAAYgB1AGkAbAB0AC0AaQBuACAAcwBSAEcAQm1sdWMAAAAAAAAAAQAAAAxlblVTAAAAGgAAABwAUAB1AGIAbABpAGMAIABEAG8AbQBhAGkAbgAAWFlaIAAAAAAAAPbWAAEAAAAA0y1zZjMyAAAAAAABDEIAAAXe///zJQAAB5MAAP2Q///7of///aIAAAPcAADAblhZWiAAAAAAAABvoAAAOPUAAAOQWFlaIAAAAAAAACSfAAAPhAAAtsRYWVogAAAAAAAAYpcAALeHAAAY2XBhcmEAAAAAAAMAAAACZmYAAPKnAAANWQAAE9AAAApbY2hybQAAAAAAAwAAAACj1wAAVHwAAEzNAACZmgAAJmcAAA9cbWx1YwAAAAAAAAABAAAADGVuVVMAAAAIAAAAHABHAEkATQBQbWx1YwAAAAAAAAABAAAADGVuVVMAAAAIAAAAHABzAFIARwBC/9sAQwACAQECAQECAgICAgICAgMFAwMDAwMGBAQDBQcGBwcHBgcHCAkLCQgICggHBwoNCgoLDAwMDAcJDg8NDA4LDAwM/9sAQwECAgIDAwMGAwMGDAgHCAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM/8AAEQgBNgH0AwEiAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A/fyiiigAooooAKK8F/aw/ag8VeBPib4P+F3wv0PRtf8Aid44t7rUo5Namki0jw7plqY1nv7vyv3sgMksUUcSbS7ufnUKc8on7Snxg/Zq+NXgXw/8aLX4e674R+JWqDw7pXifwla3entpOrPG8kFteWlxLOWjuNjpHLHLw6gOgDBqAPqWivnv4zf8FAfAumeHfEul+C/iN8Nv+E60CCS5mt9dup/sWnxW+qxabdNdeQpkjxcO0KA4LSYxlQxHZfFP9tn4S/BH4j2vhHxb8QPDXh/xFeRpMLK7uwjQRyNtjeZvuwq7AhDKVDkYXJoA9Sorxs/tX6LJ8RbO8j8X+A2+Hdx8P5/HBm8+c6o9sk0RF+gA8o2Ahc7m+/vZMDGatfDf9vD4O/F/4px+CfDPxD8N614muIZJ7eztrjd9sWMBpRDJjy5njBy6RszJhtwXacAHrVFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUE4rl9D+IVl8W/h5qOp+B9a0q8bzL/TbS/eJri0gvraaW1lWRFZGcRXMTo6h1J8tgGHWgDqKK+JLT/gqD4s1X4Q23guHwvocP7Vk3iM+CJvB0rSnTbS/RBNJq7gN539kCzP2sSAksrLEHLndX2npMd1Dpdsl7NDc3ixKJ5oYTDHLIANzKhZiqk5IUsxAOMnrQBYooooAKKKKACiiigAr46/4LQ/8FEfF/8AwTd+APhnxZ4N0fw3rV9rWunS5otZjmeFIxaXE+5fKkjO7dEo5JGCeK4j9sr/AIODvhv+xX+054r+FuveDfF2qav4Se1S4urK4slgm+0WcF2u0STK/CXCqcgcqccYNfn7/wAFgP8AgsR4H/4KcfAnw74Q8KeGfEmg32h62dVlm1Ka1eN4zazwbQIZHbdumU8gDAPegD9TP2sBqv7NX7c3gf48HQNf8R+CJPCV74F8Vtouny6jeeHonuob22v/ALNCrTSw+ZHJHL5asyKytggVzfxY+L1l/wAFJfi78H/DfwysPEmoeD/BPjSy8deKPFV7oV5penWi6dvkt7GFrqKIz3E1w0WREGEaIxYjIFeGH/g6r+EX/RO/H2f+vnT/AP4/R/xFV/CQ/wDNOvH3/gVp/wD8foA63x18G9Wtf+CWH7QlpZeE9WXxD4k+K+uai9tDpcv2zU0/4S1fKnEYXfIpto4yrgEGNQQdozXZP40t/wBm7xL+1F4d8deBfGniLWvidrt3q+hvYeF7vU7Pxdpk2j21tDZ/a4onhi8gwyxOly8axKd5+Ri1eQf8RVXwmP8AzTnx7/4Faf8A/H68I+Lf/BbH4I/FXxf4o1CCH9pDwnpvjpEXxNoeheI9Kh07WtsKwFiJfMmtmeFEjdrOWAuFBJLZYgHvHgvwLr2tfADwjc2Gi6zqENx+xbe6bBcW1jLNHPdyRWZjt0ZVIaZwMrGPnYcgGvoDxL8MLrQ/Cf7Ddvpfh29t18K+ILKO6S309x/Y9v8A8IrqcTiXC/uU8wxo2/aNxUHkgV83+E/+Dnz4M+BvC2m6Jo/wq8b6fpOj2sVjZWsNzYLHbQRIEjjUedwqqoAHoKv/APEVL8Kz/wA018ff+BVh/wDHqAP1JByKK/Lb/iKj+FmP+SZ+Pv8AwKsP/j1H/EVH8Lv+iZ+Pv/Aqw/8AjtAH6k0V+W//ABFQfC/t8MfH3/gVY/8Ax2j/AIin/hj/ANEx8ff+BVj/APHaAP1Ior8t/wDiKe+GZ/5pf4+/8CrL/wCO0f8AEU58Nf8Aolvj7/wKsv8A45QB+pFFflv/AMRTnw1z/wAkt8ff+BVl/wDHKP8AiKa+G/8A0Svx9/4FWX/xygD9SKK/Lf8A4imfhyf+aV+Pv/Auz/8AjlH/ABFLfDvt8KfHx/7erP8A+LoA/Uiivy3P/B0r8Pf+iT+P/wDwKs//AIuj/iKU+Hx/5pP4+/8AAuz/APi6AP1Ior8t/wDiKT+H/wD0SXx9/wCBdp/8XQf+DpHwD/0SXx9/4F2n/wAVQB+pFFflv/xFIeA8/wDJI/H3/gXaf/FUf8RR3gT/AKJF4+/8C7X/AOKoA/Uiivy3/wCIo7wL/wBEh8ff+Bdr/jQP+DozwORx8IPH3/gZa/40AfqRRX5b/wDEUV4JP/NHvH3/AIGWv+NJ/wARRXgv/ojvj7/wMtv8aAP1Jor8t/8AiKI8GEf8kd8ff+BltSf8RQ/g3/ojfj7/AMDLegD9SaK/Lf8A4ihfCH/RGfH3/gZb0f8AEUJ4Rx/yRnx9/wCBlvQB+pFFflsP+DoTwkf+aMePv/A23/wpf+IoHwqenwW8ff8Agbb/AOFAH6kUV+W//EUB4V/6Ir4+/wDA2D/Ck/4igPC5/wCaKePv/A2D/CgD9SaK/LX/AIif/DOf+SJ+Pv8AwNg/+Jpx/wCDnzw3/wBES8ff+BsH/wATQB+pFFflqP8Ag598Nn/miPj3/wADYf8A4mnf8RPXh0/80Q8e/wDgbD/8RQB+pFFflt/xE8+H+3wO8en/ALfof/iKP+InfQcf8kN8e/8AgdF/8RQB+pNFflqP+DnfQj0+Bnj7/wADYv8A43Qf+DnXRO3wL8en/t+i/wDjdAH6lUV+Wv8AxE6aL/0Qrx5/4HR//G6D/wAHOej4/wCSE+PP/A6P/wCN0AfqVRX5a/8AETlpPb4D+PP/AAPj/wDjVH/ETjpf/RBvHn/gfH/8aoA/Uqivy1/4ib9N/wCiC+PP/A9P/jVJ/wARN+nZ/wCSB+PP/A9P/jVAH6l0V+Wo/wCDm3Tz/wA0D8ef+B6f/GaB/wAHNliR/wAkB8ef+B6//GaAP1Kor8tf+ImuzP8AzQHx5/4MF/8AjNH/ABE02f8A0QDx5/4MF/8AjNAH6LfHLwD4o+IvhS3sfCfjzUvh3qUd2k0mpWWmWeoSTRBXBgMd1HJGAzMrbgNwMYAOCQfl3/gnp8JfiR+z78EtS8U+L/Hvjy68P6Hr/jC9uvBjeDbVbjUR/bWpOt1EsVuLx3uci6jjjO1zOqxgxlBXhf8AxEz2xH/Jvvjz/wAGC/8Axik/4iZ7f/o33x5/4MB/8YoA7VPg18ZPCmqRftmSeH9YuPijfTn+1/htBbiW6j8ESbFTSY4wA76pCqJek5UvN5kO0jatfcHxM/aE8KfBv4IXXxE8TX11o/hKwtIb66urjT7hZrWGUoFaS32echHmLuQoHXnKggivzp/4iZof+je/HX/gwH/xivKP26P+C8Mf7VX7Jfjj4ej4L+MPDf8AwlFitr/ad3eh4bPEqPuYeSuR8uOo60AfsJ8Mvip4Z+NHg208ReEde0jxNoN+u631DTLtLq3l9QHQkZHQjqDwcGt+v5T/ANnj9qL4h/smeMjr3w58Xax4T1CQqbgWkga2vQOgnt3DRTAZOBIjYzkYPNfsh/wSn/4Lh+Jv20fGdr4E8XfC/WbrXkKxz+JfClm9xo9rnOHvo3JNmCBgN5kgds4VOlAH6QUUA5FFABRRRQAV+X3/AAdVMR+xd4BXJwfGBJH/AHDb2v1Br8vf+Dqv/kzDwD/2N7f+m29oA8Q/bs/bf/aUg/4KwfFD4b+B/jBq3g3wT4dKX8hOm2dxa6Bp0GjW97e3DboGkcKvmuEL5d2VAQWUVw3gX9qb9szxv8PdF1JP2iobHxR4q0W78R+HvB91Yw/21rmnWwnMk8RTT2tkZvstwYo5JkeUQsVByubn7Z2uw3n/AAVq/bC8LxkHWvF3gK40/SYxy09xBpujai8KDu8ltY3CKByxYKPvYPHfCr9oPwLZ+KPg78Y7rxp4ds7z4S+BU8P3nhKVbo6xqOo2UF7BZrbKsJge3uBPbO8jSqI/3wbkKG8PEVnFu8ur620v+iPosPTg4q0Vey6Xu7afe9CD4H/8FEf2sfjbda1Kv7Qtz4a0HwvpZ1jXNe1iwtPsOl2vmxQoSsFlJNJJJLNFHHHGjM7OOMBiD4p/tpftp/D6z8XTQ/tEXmtDw1oOmeMbV9M02ykt9d8P3siwjU7Z5LNH2wzS26SRSRq6+ZKSAIXrjvgv4b0f4b/B/wAU/D/XfGXhvSf+F2+BtG1vSdVuxdQ2elXdpq3mrZXpEJeMyJbT/vVVosmBgzI+4dVd+OvDv9reKBperWviDwr8Jf2eL/wFd61bxSpaazqF758MawCVUkMf2/VAsZdVZktGkCgDjzMPiak6keaT+/10t2trc+gjh6UL2ppr/CttOtt27xt87HjVv/wW5/a0Ztp+N3iRm99N0z/5Gqy3/BbH9rBcbvjd4iXdnH/Eu03/AORq+YG05op9q87RjB60y8H2OQA49s8/54r7/DRh7NXWpwZhGhCXLGC+5H1bZ/8ABaz9q2Rxv+NfiLkjH/Eu03n/AMl66PSv+Cy37UU23f8AGTxAeMkf2fp3T/wHr4u0rUN9ztzzjrkV2Wl6p5Fr23ZyMnH+e9fO5vieS6g7DweAp1NeRfcj6yl/4LF/tNwWpLfGLXuT94afp/5f8e/05qOP/gsJ+1BIwH/C5PEAHQY0/Tj+Z+z18vwXZlkPO1sgAnPT/JrbtofMVONw2gkHnj359/evk/rle3xP73/melLB4dO3IvuR9Byf8Fjv2oI0P/F4Nf67edP0/J+n+j1ynir/AILaftUWi/uPjR4ij5OMabppz+dt7GvJn0dZmx/C3A4wB+Iri/G2lJuZVzvz8y8/L0Hp/wDq5rowuIrSqfE/vZ5OMo0qcbKK+5Hpmsf8Fzf2wvMP2f47eJl56DS9M/8AkWtDw9/wW/8A2vpgv2n46+JJA3/UM0wH/wBJa+eIfDse7aydzg+g+v8A+uluNAYTx7eBnpznH+f51731mSVjw6eGUpXPt3wX/wAFnP2mrmJGuvjJrk/AY50/T+Ovpb13+l/8Fifj/LH+8+KmsNu6ZsrLH6QD/Ir4E0OdtM2MxHpx0+tao8RNGMJNt2843cNx/wDWruw+IUlqZ4iioPY+6L7/AILF/H6EHb8UtYXtn7HZcH8YP84rgfF3/BZT9p55pPsPxg8QRr0wun6fx7j/AEfp/jXzDY61LclY9x+bjAPc/wCf1rrNF8GSXkPmKisG4JPc/T9a6KkXL4TGFWCeqPSpP+Czf7WED7W+NniNvcadp3H/AJLVXb/gtN+1fDMu743eIiCfu/2bp3P/AJLV57rnwqeKLcqAqxx9373PH9a4nxD4Ong3BI2JxzjsP85rz8TRqW92T+87KeKo6XivuR9Q+G/+C0H7Tcqf6R8ZNel29f8AiX6f/wDI9X7z/gs/+0oFXy/i9rw6Z/0Cw/8Akevknw74eunnVTn5iV56D8a6aHwW84VWzleMDOc+/P8AnivzjFU8fHENKrO3+KX+Z9tl9TAzp3lTj/4Cv8j3p/8Agst+1Nck+V8ZPEGOPu6dp/8AW2pyf8FbP2tr0r5Xxo8RAN0P9nadx+dtXmfhL4Xx3rDuqgfkM/4V6FonwsVG2+TtXscflX3ORYWvy3qyb9W3+p4Oc4rDX5aUIr0ikbkf/BUn9riZfl+NXiToDn+zdO/l9m+lV7j/AIKpftc2UilvjZ4mZe6nTdNz3/6dv5V0ukfB8tB/qflY5JxyMY9KwvGnwvFjE37nG1ufVv8AOMV6eNwtTlfK397PEw+Kp86Uor7kZd//AMFfP2rrFcH40eIt2Acf2dp3Gff7PWQv/BZz9q5i3/F6vEeMc/8AEu04YP421cB470T7OJP3a/KeCoGFHevLNWaOyuJFXPckDjbzXzi+sxlyuT+9/wCZ9Avqso3UV9y/yPoXVv8Agtt+1XbFlX41eIlb207Tf/kasZv+C4P7WjNtHxu8Sf8Agt03/wCRq+bNWbzd/wA2G5+bmsu1DGRV2jhua9jDqailJv72eTWlTctIr7kfVUP/AAW1/a6Yj/i+HiRu3/IL03/5GrTT/gs9+1xKnHxs8S+xGm6bz/5LV87eE9B/tOAFV3DOMjt2rvdK8BZtVypZt2AAMnJ/nz6V3UalpEVeVR+Ffcjs/EP/AAWk/bAtI2Mfx28UJgdRpemcH3za/SvNfFf/AAXg/bW0STcnx98UbOhB0rSuP/JWr+v/AAovBDg6beNzg4gbjtnpXmnj74KX1xbtjSdQLdcfZmOP0616Ma+Hcb80fvR4daFXnsov7mdlo/8AwXx/bT1Bxu+P3ij2A0nS/wD5ErqdN/4LoftkS7d3x68UN6n+y9Lx/wCktfMFr8I9Z0692jR9U2+1o/H6V3GhfCbUp4tzaTqS7Rj/AI9nGf0rwMZmlKH2196/zPawOW1Ku8X9x74v/Bcf9sEBW/4Xt4mZcZ/5Bemc9P8Ap1/zmqt7/wAF2f2vrbJ/4Xt4l/8ABZpn/wAi15RL8K9SSFtulage3/Hs3H049/pXF+LfhjrEIby9J1LjuLV/8K82lnUHK3Ovv/4J9D/YvLG7h+H/AAD6Dg/4L2ftdCbDfHbxK3/cM0z/AORa0f8Ah/d+1kIf+S4eIiw6n+zdM/8AkavjObwN4gilbOiaxjPH+hyYP6VW1DwX4oFoZ18P64Id/liX7DLtL4ztztxnBzj05r3qOMoPeovvR4uKp8nw0/8AyX/gH15qf/Bf/wDa4iLeX8dfES89tN0w/wDtrXNat/wcLfthwOyx/HvxMD6f2XpfH/krXyPfeCPE1y+1dD1tuMDFlJ/hWPefDfxMrj/in9c3ZwB9glyfX+GuqVehaymvvR40XUcr8n4H2JH/AMHDv7ZRfH/C+vEZ9M6Xpmf/AElrSsP+Dgn9sq4Vd3x18RbfX+zNM/8Akaviqw+GXiQXI8zw/rS9CQbGQf8Asvv+tdn4f+GWvbQv9h6uvTrZydfyrx8Ti6a2qL7/APgn2uTxpSS9pSXzj/wD6xT/AIL8/tjEf8l08Reozpmm8/8AktT/APh//wDthYP/ABfLxF/4LdN/+Rq+aV+F+sbeNF1UDHQ2j8n6YrP1T4aa9CGP9i6t6cWknp/u15316LelX/yb/gn1nssHFXdCP/gC/wAj6fuf+Dgb9sOHH/F9PEW3p/yDdN5/8lqozf8ABwn+2IE+X48eIs/9gvTf/kavkfVPBfiSKQ50PWjnt9hlP/stcTqOrMgZcbdvBz1+lephZe0fuzv6O54GY4rBwX8GK/7dS/Q+4/8AiIV/bJkbj4+eJh6Y0vS+R/4C1t6J/wAF7v2yb5lz8e/Ezev/ABK9L/8AkSvgHQLhrm5Via9c+H+jtciM4+/jnFeny2R8JicRCTfLFL5H6Kfsv/8ABVX9sb9oDx2mkr8fNesrO0sbvVtV1G40rTmg0uxtIWnuLhwtrubbGhCqOXkdEHLCvavD37ZX7bGqfsw6j8Wrj4r65ZeDrG6tbOGe4tNOE2oyzyyRYhRbYkiN4m3M20dlLHIHyr/wTw09ZNI+NmgwjGseKfhjqEGloD81w9peWOozwqO7Na2VycdTs2969C+DHxC0HRP2N/jFoNxfW1vrviO/8NS6ba7W8y8S2nvGmYEDGEWVCdxB+bjPNaRt1POctT1n4K/8FBv2sfjTea9Mvx6m8NaD4T08arret6zZ2qWOmQGVIIw3k2UsrySzSpGkccbszN2AJFP45/trftofDTSvGk0Px+vL+Twfpml+Iw2mWNjNBrGh37KkWq2sj2iloFlmtEdWVXU3Iyo8uQLznw38P6V4Q+Efjb4c614y8J6LdfGLwnoHibRNTvpbi30+0ntr+SUafeSGEmGR4/OIcqYiyxEOVdWq/wCK/iV4dkvviF/Z2rWfiHwr8L/2eH+H11rNuki2et6lcOkMQgMiq7J9uuyYyyqWSyaQDA4mpJI+nyilGrq4p/L0693ex+sX/BCD9oDxv+0//wAEyfBHjT4ieIrvxV4s1K/1iK61K5iiiknSHVLqGIFYkRBtjjReFHSvR/8Agp58IPEnx+/YM+JHg3wfprax4l8Q6dHaWFms0cPnObiI4LyMqKAoJJZgMA14f/wbcAL/AMEhvh4F6DU9eA/8HN7X3VRTd4pnkZhTVPFVIR2UmvxPyk/Yr/4NoND8PfY9c+OviD/hJLwYk/4RnQJ5LfT4/wDZnuvlnm7cRCEAgjc4r9O/hf8ACfwz8E/Blp4d8H+H9H8M6DYjEGn6ZaJa28WepCIANxPJY8k8kk10FFUcYUUUUAFFFFABX5e/8HVf/JmHgH/sb2/9Nt7X6hV+Xv8AwdV/8mYeAf8Asb2/9Nt7QB+X/wDwXE+IWoeAP+Czvxk1LS7+803UtL1fSrqzurSdoLi1mTSNPdJI5FIZHVgCGBBBHFfMem/EC3eJfm5IyPm6fSva/wDg4KuNv/BYz47jPTUdM7/9QXT6+PNKuJndcMwGQSDzXzeYYd1bpO2rPosHzpRl5I9+0jxV9rGd27phs9AOAMZ6YAH6VvHxJqE3h6fS4b66XTbyaK5ntkmIguJolkWKRkB2s6LLMFY8qJHAxuOfHvC1/OpXcW/Pr/n8a9L8MyebCrO3HfIxnvj/AD6V52X5fyVNXc+ilj70+VIptockdxu+Zjnk+o9qydbtxPKwVT7e+c8Zro/EuoNasyr8xxjIGOMVzUt1n/ZbjJ9R6V93GTjT0PAqVeareRl2Vu0c+7AXmugsZnkUK3zcAHHHX/PSs9l8yYlc5k5HP51taZYsqLuB3Lzxx1/yK+LzKTc3c+qy9w5LmpolyzSfNnLY5xx7fT/61dpparJArbW9j3H+f84rj9Ptfr1O38uf8+9dd4fP7vJB4HY/p/n/AOvXjzV/hNKktdS00MUUTM20DHPHX8q4vxXF5s7H73Jy3p9a6zUZsFkDFVYkDGMf5xisTULePy925W4BJx04/wDr/wCeterlcXe7PFzKnzJHEtbmO5wuNvct296s3Y3OFXBPG7A5PPNSalcBLj+6cZ/L/P61NYyecfMOGDHjAya92pTXJoeFThKE7GHfu0SvtUZ9c5J5qjpt3c3FwyfNhfuE5/Ous/sL7ZO3yqVPfHStTSfDkNnP8yqGzjoOa5cPK0rIrGwbhcm+HHhdrq+Tdk7SMknrX0l4C+HgmtI+oOOcjnH5f5/KvM/hppULTpt/vDnOMZ//AFe9fS3wutIYbVR0xjof88//AK6+loxvE+RxGIcHZHP6z8KmNguY25Jb7uF/z0rg9f8AhOsYY+SrbeT8vK19K6hYxtEqk8quMenr/ntiuK8W20aocDdk5x7e3+fyq6kYpEU605s+b9V8GJpx2hNoHBJHQfX39PSs2ONYJ1zIc8Dnn/J6V6j4o0xZPMU9VxnHbv8A56V55rYj0d3kkcDaecYr5vE4Wm6nM0fS4LFTjHlR1HgmcRyqnXbwe34/zr3T4W+HF1Xy3bBHAH5/rXy/4T8fxNqqor4288f5ye1fS3wj8ZwxW8OSvT9OP898/nX0eXxSWh5uPqPVnvWmeBLeHT+Y1DbecjGf6+nNeZ/FvwxEYphGnsPw9816Fo3j9bqxXau5iCRgjgn/AD1rj/iRctOjSM3QZHJ/pXZiKXMrnj0cRJSsz5P+LegKnmj+8OT1x/j/APXr5n8ZN9n1JuflJx6V9ZfFw+ck2OCvOM5yPb/Pavln4raO/wBukZUON3zHGPp6/wCcV4GIw6TufRYbEPY42/n3Oc4wwyevFR6fOGl3Nj5Rjr6jsKZPaySFQzcqent/hVnSrcAbjn06HmufmXKdEZNzPQvhrrlvZzorBevrgdK9m8O61a3Mln5fljMqf+hDtnvivn/SoPsxDBSGUdBxXbeC9fkt9XslZ2ZTNHuAPA+cc/yrn5rQlfsd0k20j37Xv4tv8J456/5/ziuJ8Qv8n04P6/5/Gup1XUPNX72eMHjjFcP4luR84zjrkDofSvyP6yrWPunRfNc564xHNu6c5xmtLSNZ8g49MDFc/e3bRuTx1NRWmrhZcZDemTXmV4qZ34eo4M7h9W8yPhvXGexrO1mMzxen9aq6fqO8dMVeuvmTB9MdK4PZpS0PVVRuOpyuoWG52wC30PWvtv4ffsS6X8Sv2WdM8ExTrNfS3Mer3Wp2Ef24Wt2WCyv+7J3KkbGHg8jB64rw79lP9m3Uv2jPibb6Ta2t5cRxjzJjDHuVEB5LN/COnP4dxX6jeE/Clv8As1arcaTDdWelpaeHDGJrL960BN2m0HceXAAUYUkDbwSCx8fiTL6+IjhZUqzp8tWL0V7vpfVaLW62d9distzGjRlXhOmp3py3drLrbR6vv0seB2n7Afwq/Zktp9POjf8ACSeLIlSW2truD7dd3W5k2YQERD35GCSVMhGBt+INeTVby3t7/wCDvh3TddhutyNc3ErXG51X9+kjwiNcYQbPMz8nPavY/C/jDwj8AfBsviHXbq/1DxXqxEQmdTd6lcHKqkXmFT++IZSI1+Yh1OMHcPm/4yfFi+1zxK1k0Nroa30f2lZLu6MFxYRDliDGyjeMEFWLgZIKgggfbVK3VHxPIm9Sex+FXw6+N0l9ofiaHwxdagolkuI722kiu7QjltlzG7smwd9pB3DIx08V+Pv/AASeuvAHgq+8VeEdat9R0fd51ta3csazrCckfv4yYZcDkE+USADtycD0zTPG9tpHiiFpL271S4gBinuxbmSCa1IKFWaBUZkJz+8jDo2eHIOa7b4im58EWtnrNjdajp9tqzJHcWWjaks+m2ytld4jeNGfnG+KRSymRP3rYYLyzqRcff1OijKrSlemfmYdMeGRo3VlZDtZSOhrP1S18tD/AC/ya+0/id+wXD8RvDGveLPAt815qdm/2rUNJcKiqG5ZojgYHJJB4A79BXxv4kha1nmhkRo5oWKSI33lYHBB/GuH2fK79Ge9Txkasbp69UcTqXFyp/2hXwjra7tRueD/AK1/5mvvLVFUzjt81fCWppjU5+Pm81/5mv0zgFJOt/27/wC3HwvF1R2p/wDb36Gh4Nh23K57nFe8fDVF2xZVcqRyO9eIeGB5U6/5/wA9K9f8DX5hWP8AvYHXiv0aVz4m9z6D+Gmt3nhPV7LVNLvLzTNS0+YXFrd2spimt5FOQ6MuCrD1HNbFzdCz0/am3aBkY7Zzz/kV534c8UYi+8GByD6+laOp+MF8hlWRcdOOB+PpWa0VzLluaGr+I3SfMkjMSoHLZ4AAA69lAA9sdqydZ8b3zeFrzTRfXUelX1xHd3Fms5WCeaJZFikZM7WZFmlCseVErgdTXG634reSXhmHcAY61h6n4n3xtlsd+T0rmrXZ9fw/ilTfKf0xf8G3Th/+CRHw9Zeh1PXv/TzeV91V8E/8Gzdwbr/gjl8OHPfU9f8A/Tze197V1U/gXoeDmMubFVJd5P8AMKKKKs4wooooAKKKKACvy9/4Oq/+TMPAP/Y3t/6bb2v1Cr8vf+Dqv/kzDwD/ANje3/ptvaAPyN/4OCrbd/wWQ+Ozf3tR0zP/AIJdPr5G0i2/e/7vPrmvsn/g4Bsy/wDwWF+OT4+9qGmfXjRrCvkHT43t2BKjAx+FfM4ut77Xmz9iybJ4vCQnJbxT+9I6rw8VVMMBzwOetdl4e1NlVMKWX72c8D1/z/8AWrz7T9RYH931x9K6rw/qGI0zn73XPFGHlZqTPl8yo+zquCOh1JWnj3Kvbg/jVGGxVpGMi7W7DH+fen3Gukp/Eo4Ge5qvba6qTcc7eTz/AJ/zivWlirQPFdFtmzp2h77nbt3L6A/1/wD111FvooSEYjx82Cdv3qxfD2tQhN21T0wxOa6izvVu5o847jgYxXzFaM61U+gwtaNGmO0/QGLNlRzge/8AnrW5o3hWaWVlVWOflB546cH/AD39q6nwr4Y/tGJdkfpjPOO/+f8A61epeBPhOrurPH8rdh346/y/pXt4XKVyrmPFxWbPmvE8T1f4bXT2w+XaQM9wR/8AWrlNR8HT2kMh+YbV4AXr+FfZ2sfCZE0smSMDahPTvXmPj/4bW9srsvynHOD9c569c/5616VPAqnsFHMYz0kfIXiO0axkZm7NjIPP5VXtdXxDuDYGcHI6deldH8abKPTLiVVOPmOf8/5614zeeOBbXW3f82SCd3+f0rOpTk/dKrVofGj1i08QbYlJ27hwCMnIrR0rUvtjnc2VPo2MdvUV48nj5cHayn3z1rV8PePCZo9zHAPNYYbCtSuebjMYnGyPo7wLqJtCr7mUZ3A5xj0r3H4e+OY7eJWaRV+XI9Rz+f8Ak18g6P8AEpYVX94OMHJPau28JfFVyka+YfxPf3r6P3YwsfJzpynVPsCTx8j2jDdtCjBw3+fpXI+KvH8SRN+8ztB5Y/e/L3rxib4rMLcKJDsPCqfy6VzmtfEGS+jbbOxU/d9D/nivAxeOinZM+iweWycb2Ok+JXxS8gS7GG0cdRtB6jivnr4kfF2+nnZVk6c8HB7/AOJra8eazJe+YrMyhflGO36/5xXAnRzfXjeYoHXGa5PbKWrO6ODcXZHRfCTxTd32qLLJu3Mc45xX178Itek+zQ5OHYDdnjIzzXyv8M9HhsLxSMf3uBwxr6M+H2spp9ouP94H/PP+fxr2MDWuro83HYXl0Z9SeAtXU24DN94E7j6+3vUfxI8RRW9kVU87evpwfT8Oa808N/EhbS2IaTj3Pp/npVbxn8SIbqyb95/9b/Oa9/m9zU+blC0zifH1817JJn6YJ6ZP8+grxXxzZx3Cy7scE56cHNeg+N/GVtHC7M+G9+ABzXi/ijx1HdPJ8y7WY4GfmrxMZL3WezhdzIuNFVoZAq7fT2qtZ6asM6r9xVbrzzV6PW4VtvmLbvu8fQ//AFqn094boZ3dwRngV8/ztaM+hpU76mhZaejRLtX7o6Y6fX8K1NDshBrdi38PnxkH33j8+P0qGwjU/Nn8zj1GP8+ta+mQf8Tmz+9/r0xznJ3A/wCTWVaolTlbszqhTfMvVHo+vTSQI21sbuw9PpXKan5ly5yd397Irt9btPMiOR04xjpXKawEtkI6cZ+lfhzre9ofqUcO5Ruchq67d3+yfz/GsEXDLPxzzW54imEu4f5NYdlD597j37V6VL3jgqU3GVjp/DYkl29hx3rs9K0drrbnvjp2rK8HaT5ir8vcZBr1HwT4fin1S0ST93G8ihjjOwZ647/1olRV9T0aNN8t2fcf7APw/X4JfC2w8QzNNZLeMfLzAZJHL43sEAPSPKg4IGXPJwKwv2wvjtdeD7uTxNpOh2tpcNdRogvYVuXYmKRvP2MPLEhK4AIK9yCSc/RXgrxFp3h74fWsuqWtvI+mWSRWceP9HVDkI7Dn5mVUcjsQB16/Lf7VXjHQviMt89ha+dZx6lHbvKylftkywszSlRwqk8BR0AHevB4inyU6Ou1SP6nn5DRlXr1b9Yy/Q8p079pvxtrurx6os1lb+KLoeQuoToZDpluQWfDtlg7ZLSS8bV+VNgzjxn4i/FjUPFWpah9lkufE+rXWVutfvk3NOA6tst4s+XDCpXChVBwF4VcIPQviD4Nm1nTplt45o7HYFuZhGFjfLEkMzMq/dAAGTnB61R+Gvw2srWeOaE4VvlcSwFQD2HmRs4GffFEsU1C7O7D5Spy5TQ/Zd+GnjzxxY3CxXjQ2ajdN9rO6dD0DRPw6N6MpUg+or7Jl/ZhuvFnhrzNQ1ZrzUpP3k9zPzJPI2DubgZJ2jJ56V49+x9cSfD7xks12JX064URkNIsqRtz347A8npivtj4EeI7S8sLmO4iW6jlkLebNIHY9MAf571jRkqsuWUrfed1fAU8PS9pGF7Hyj+zl8KNQ8GftNx6XdC6tZr6F0QRzBMnaTjkFZFwPunr718V/8FCfAi+D/wBozVP9Bj0u51BftF1bRjEYmBKMyrk7QxQkqDjJJHBr9el+Etr4k+Kum69YztbXGjs5ChQVlAxgEH8RjPf1FfnL/wAFl9Fgn/aVs7iFts9xaPJcwk5aNiwAbPdWCccdVPfr6eE0p6vr+R83iIqGM5Y7NHw3qOnFplbrlq+Ftb01otSuN33hK/Hp8xr9CLnSGMig+or4w8ZeGTDc3Emf+WjdOO5r9J4DqpTrf9u/+3HyfFtFuNL5/ocZo8nktjOG/lXf+Db/AOVev+6COPWuBuLf7PNjofeuh8JX/kSrhuFOeehHNfpe6PieVnsuhX3mIv0+XPJFXtRbfD8pbdj9K5Xw1qQxGrMemetdEW83rz2NVG3U56kWmc7q+9d3zfL0/TpWDq1yRFuU/Ma7TWrT91kdPUd64fxLa+WkjKT0447UqkEzpw1aUHdH9PH/AAbCSeb/AMEY/hq3/UT1/wD9PV7X6AV+fP8Awa6/8oWPhj/2E/EH/p7vq/QaktEKpJyk5PqFFFFMkKKKKACiiigAr8vf+Dqv/kzDwD/2N7f+m29r9Qq/L3/g6r/5Mw8A/wDY3t/6bb2gD8u/+C8umfaf+Cu3xvf+H+0NN69/+JPYf5xXx7eaP9mQEKRxnj0r7m/4Lh6Sbn/grN8bJNvytf6d07/8SixHSvkXXNC2w87cg9CRzXxOKl/tEl5v8z9pynMUsDSj2jFfgjgZbn7FKB365BrotA1GMlWk4XGDnPOe1Yur6Uzycr07gcVqeHtF8+3XavoeQen+f6V6FOKVO58dmVZ1K7sb1xPHcBV3fN6YH8qz5mZZMx7uuMFutbll4eztBUnODhic479KhutHZG27SWwenv8Ajih1Ohx+zluWvC+pbAyn7w/z/jXoXhqT7Q8Zx+bHnp2ry+wja2uv3g4Xr9PU16D4G16JUVWPf5sEcc9h61vhcOufmDESfJys+g/hjeIYIlbjzOeOS3+eP0r6W+GdjBPBG/8AdHOOw9K+PfAfipLG62hvun8uvT/AV9BfC34owRQopl4j9+D+fH9a+ljUhGJ8vKnJysez+IrNDZMi/wAQOMt/k14h8Wkjt7WTaArDOMnr6/yr0K++JlrPaeXuVnC45NeQfGLxhHNBIgZTtJ79MdTj3zXJVxkV1Oyhhp9j4+/aKlbzbgHI3ZOBjjJr5R8W6pJbas2d23P519Q/Hi4W5vbg/K2DtA29Tx/n2zXzN4901pbhnZfmboOlKjWjOVicVTlFXZDZ6w0iLhuccjNdB4f1JndfmJZfevPtPZorhUK45ziu08KQmTbgM3auiTUXc4403PRHo+hzvNjDH6jnFd34amFtGpO3OMLx/WuM8H6e0qqcFuBjj9P8+tehaTojCFXxy3DY4zXl4zMko2PVwWUynLmaNpdSEiLuDbQMjrj/AD0qjqOr+QWVeW7A+vf/AD/kTQ6LIYup7ZIHb61T1Hw3cSux6rnOc5xn8ce35183zupUufY08G4U7IwdZu/PkXarbsE8HHuKxY45Um74blcjnNduvhVliVpF3Ln9KiuPDqh/MZRt6/NjivShRly6nn1qNnco+G9TNlJH8xVc9z0Jr0Lw58Q4bGEK8gU8HJPPPUf1zXnur2QsEEka9AM8dK52/wDEz2qbvMPHHp/n6f412YWpySszixeF5oXR73L8V0iVtsyqzYbhq5zxL8Z5II2/f7uMdfavFx4ulSX5nxjnArN1bW5dRLKCc55GeTzXvfXlGNrnkR4dq1ffSOr8XfF2a+VlWRtxJ5z1rh5/FzM+ZH79COT7VUn0Kaf5l3t3z6f5zVObRZoicjdzgZrzK2MjJ2YVslrUFdo3IvF7SN97j+YzWxo3i6WN1XzDsb9a4J7OSMfKrDbxV7SrqRGXIbd1Gea86rZ7bmuFqNPlZ7j4Z177ZEp3h2JBAzwPT8T/AE/Cuw8P3huNZs+jfvkPPQfOK8j8D6nI0sKj7qnp04/z/WvYvBNk02o2Tbfm86MZOP7wNeLjqjjBryZ9PluHVSa9UevahbFmY/iMjmuP8T2+zdgBex4r0ibTS0Z+XIYnH+fzrj/E2lF2P454r8Up3vc/acLg4yVkeX6vbkluMc44qvo+n+XcAnse9dFqembC2716dMVRSHy3DKCvNe3h5OxwY3Awg7s7rwLZrIqr931xXrHhHSf3kJ2/LuU4FeO+DtSMG3tivWvAGv7L23G5c+YuM8AfMP8AP+RXTOoktTGhGGzPsj4eeOYf2iteuPDfh/UBYRWtjJCItRiKyrdRgqVCjOYj5ZIftyCM15B4u0Nfhh4ZvIdaZJvJ1oPK6NlWH2dsEH3AHXueldX+xRreqp8dfEms6kjrovhzQnt9HQWqxrfPMqpIzTAbmAKuSpJwWxxk5xdf1a213RdTj1aXybO41b7OJMhiqlCVJznPT2/CvzvOMbOrRoQqb88WzswOV08Pi686K93laS/P9D5f/aG/a+1bW78W+hxx2VrDHsRcbtijgADoGI5d+WJIUEKoFeY+Ev2ktc03VRJdTt5hGwTL1xngHvxXdftC/syadoKS3ljeOY3kO2dmbySmfl+ZQwH4nrwK8f0X4a3fjLxClvZRqsKYV5FP7vjvn1+lfQ0aNKVPl3Pn69XEwq8yuj6W8CftkyaU9r/aKW81qt7EXuAvzSR+W+4Ed8Hbz719MfDP/gqj4K069tNG07TfEGppdSv5r28C7bcE8EgrlgBzx0B9sH578MfsjP4//ZK8R6foVjFN4w0m6t9XhlSIyTyWwEkc0KrnOSSrAAEnyyB1NTfsC/svfEmHXodS0/WoLO0bEwF3a+fYXUeMjEi8o+ejKcj0PIrjVFRTknZ/15M9CpWrSahJaOzb3/VH6Y/Az4k2fiXxFYXVjMr6fqCMyMo4wen/AOqvy5/bJ+I//C9v2k/FGsQyCbTheNbWBB3AQx/ICPZiC3/Aq+0/jZrc3wN+Deu3ltI9vql5o9wIntW2mC4uFESyLx2eUN68HuK+DdH8Ktb267V+UDoO2K7Moc5UZ83836I5cbhVPFKol9lfmzi7vws0TKzbcZ5r5B+IfhFUlnVc53MeO4r701XRW+7tDHpkA18i+PfD7SXVwGXb87Dp15r9K4HjJ1Kv/bv6nx3FmHtCC9f0PlvxVpZsLlu4z6VTsLswyLlgc4+lej+PfCYYsAG289R0615TeQPY3PlknI6elfqUU0rn5vom0z0Hw1r+HU7lH9K7Kx8RRtFlm+6McmvFbHV5IeA/etGHxZcI42s3THHStIyszmrxTPW77X43X7yt7VyPiTVo5Ukxz6DNY9lql1dhd25Scdv84q42lNOmW+vFTOskTTpPof1Cf8Gubbv+CK3wxPrqfiA/+Vu9r9Bq+AP+DYW3+y/8EY/hpH6an4g/9PV7X3/Vxd1cUtHYKKKKYgooooAKKKKACvy9/wCDqv8A5Mw8A/8AY3t/6bb2v1Cr8vf+Dqv/AJMw8A/9je3/AKbb2gD4J/4LSaK0v/BU74ySn7sl9p+Mjj/kE2I/pXyPrOjbEZmXAxwMf59/1r7g/wCCx2nLN/wUv+LUjDI+3WBP/grsxXyL4p0bZCzc7WUknAyR1r5HEYWTqyku7P0rBrlwkG/5V+SPJtS0pftTcc9QPbnvXU+DtCiuU2nCpxwB8x7dPWuZ1m5K6kY8r8pPoM8/5/Wui8J6g0CbiVXb27HNKfMo2W5w+653Z2TaBDbW+4r90dAP8mqGlfDiTxZYyXUNxbwqJWiw4JYkAHPH16fyqpqfidoVIdjtAzkjJPH4VtfC3xEsnhS4fPW9cZ45+SOvGzPEV8Nhva0nZ3S2uejhadGrWVOa0sZt18DLxo939pWe4Meqv0pmifCDUrG4P/Eys9vb5X69q6c+JAJWVm696VNa3HORu+vWvFo8SZhH7a+5f5HXWy3BvTlf3ss6F4N1C2Ks2o2e5QP4WA+vSu88M2WoWm3Ze2u7qRhuT/nNcLY6wzFfTv710mj62Ym+97fhU4jirMXG3OvuRGHyHBc/Ny/iz0K3uNSRBuvrfdjqAw6Vzfizw/qOswspvrf1OQ3HX9aig8StIvJ3d+varsF80x2k9BzgV4suJMxe8/wR7X9k4NaKP4s8g8ZfA26vwzfb7PBJK7kbj9P8815H4z/Zevr9nP8AaVj7fI/H6V9Ua3Y70b8+fWuH8Q22zcu0nvXXh+KsfB6TV/Rf5HJX4dwtXSUfxZ8uv+ydqUNxldV04c9o3/wrp/C37MmoQSLu1Kxb2CP/AIV6tc2m+fpx+tbvh3TvLxkfr1rqrcYZjbWa+5HPT4ZwcHpB/e/8znvBHwCu9NC7r+xYg5yEbpXfaZ8H7hI1/wBMtf8AvhuPwx/n+etocJO0YrqLG081V/r6/wCc141biPHSesl9yPbw2VYaC91fizkbH4L3E8i/6Vb+/wAhHf8A+tW1Z/ASWeRQ1xbsM/3ST/Ku88M6Z5j+pJ6etdlpOhCNg7encV24PPsarPmX3I7JZfh9rfieNzfs9zJHsSa3XnpsPX8q5zXPgFdBW23Vv6jMZ4/SvqKDQYmONudvpWNr3gl7q5jt7WJ5ri4kEccca5aRjwFA65PH9K9mXE2PUL86+5HBWynCtrT8WfJviP4A3d1Yuv261VgMBijc/pXQ+Df+CQvxk+JNhHeWPhmaGzkAeKTUWTT/ADVPdVuHRmHuoxX7C/sSfsLeG/gH4Yg8Sa/b2OreNJIvOaaQLJHpI67IQeN47ydc8LgZzgfFD9qDSbLxdeWv2qWOS3kZWcjcAQfc/wA68DM+MMww8I1YyT5vJGGX5Xha9d0oqyXVtn5HeO/+COfxq8GW7zT+F7q4jjGWbT3j1BgOf4IHdz+X868jj/ZQ1TTtYltbq8ht7qFtskM0MkcsR6YZWAIPHev30179qDwLpN1a2tv4Qlkultle/hudUuoGs7nc26L5hn7uxx7PjqDXl/xb8c+D/jFdywa14d0XUNPY/uIboC4ltQc/KkzASr16qwPNcmI45x1L3ZVoTe3up6f+BRivJ2d79D6nLcPTso1KDS82k/wlL8Ul5n4+WX7MdxZxBvt1nxnPyt09/wDPWk8c/sfa34L1SK21Ca1tZri1hvI0ZH+aORdyt0+oI6gqw7V+h3jT9i/Q7bxFaeIPDUl5eaBZv9p1DQv+Pq8IXLBLYj5plY4Uxt+8xnaZCQBwf7YXinwz44tNFaCS5XXYbeK8hLW5VZrSdQ4Ut2YcMAenzDqa+dxPiHmkMyoYaknKFRS5pKOiatbp0+12TTO7MMjy/FYSpU2cbaN2f9drb2sfBc/7LF5In/H9Z49kbkflWZH+zDfWt2x/tOyGeSdjc19KzWC+S33SPWuc1HT2En/1q+qhxZmPWa+5f5HwkuG8GteX8X/meeeBP2eL5J1LahaYznCo1eyeCPg3cWd3at9rt2VZUZvkYdxU3gDTvNTay/d56df8/wCNek6DpXk3FvjnEi9+nNayz7H1Yu8lt2Qo5bhqM1yr8X/mbQ8DSNDt86Nuw4P5Vyfiz4dzAM32iFcZGCpr1ZYwsbHqOwHeuU8brmJvf071+eRzDEpbr7l/kfoFCtThovzPHtS+GNxOv/H1b/irVjS/Cu5EmPtVr1/utzXoFzcFQe3NZs02Zevetf7YxUVo19y/yMMVKnU3Rn6B8MZoQpN1btjnhWrs/DHguaCVP9Lh9AQG49/0rM0y8IFb2maoEb/e/WspZ5i76yX3Iwp4elul+J9mR6y3hTwvZ6Hp7+Zpcuihm+zP5lhFcHbK7q3eWRpGBTOFWE8EkEeb+I/hvZ+Lvhrc2N9Iqi6fzYHDFAsmCn48FuKt/BfWri6/Zv1qOPyZG0vUUldR/roklj27v93KqufVhXOfE34hf2N8JbK+RWiZbqOJc/LklJD/AEr5POcdiI1ad/5otaLufRZbTpVKE31s0/X/ADPCfir8L7PwVK9rY6nPNcMMTLKzeWPbjrx7Vi/CfwzG+syvqWvaXpNlb7RGCjZllJ+UDjoMEknjoO9N8YeLJPEOrtcXEjMJmyxH61raJHp/iazjs/sMMm7Cpuwrhif72c16yzTEWu3b5I8f2VFytFX+bPp39lX4w+H9G8Z6sumtDNMunIlmAPIF3cowfLezcqCeOhxXS/BH+yfHPju/1rwvv0Wxvne51PToZGSKeQnJO0HarE53YGCRnHJz51+zn8NvBMfhuRta8P3UV9Y6gyieSR2XaANvP3cA5PI9KrfCvWbX4OftF6h4fsbgHT72JZ7fa+5QrjOz8CGH5Vz1M1xDd3JNeiOyjGi/dcbPZanon7VurT+I/Bl5ayNHHJqDwW8YIyFRJBJx/wB+/wBa8F0rwJJAP9dG3rgHmvVv2gvES6j4s0+1Vv8AU2xkYDHG9sD8fkP51zej2nm9vvV7WVZhiYUN1q29kZYnD0XWd+mn3HJ6j8NZLglleMHjHynj/P8AhXzz8Q/2cri7eR0ubVcE9Yjzz7V9oW2k70I2/hXH698PBfROwTuSeK/QeFc2xkHUcGvs9F5+R8txBgsNUhFTXfq/I/Prxr+yvfXWdt/Zq3T7j9O35V8k/EzwvJZ374Vvvdh71+u/if4Tsc/u8bDkEj/P0r4Z+M/7PUkk0zJbncx3DC8Hn/69frvDeOxOL9osQ78vLbRLe/b0R+R8QYWhh3B0Va976t7W/wAz5V0nwrPe/dVvmPfj9a7Dw98M5iVLL93GRjvXrXw++CUxmWNo/mzjp2r2bw58B1vIlX7Ku3gZx359K+i5e585KR88eHfh00kiqbdmA56EGujvPhI8Nqsiw9sccH8q+qPCf7MPlIj+VgseoHb2/MV2S/s4QvYN5ka/dxjAzjnj/wDVXHXoux1YfERR+sv/AAbb6c2lf8EhPh3A2d0epa7nP/YYvK+66+T/APgiT4WXwZ/wTm8H6fGoVYb/AFc4Ax97Urlv619YV1U9IpHNVleba7hRRRVGYUUUUAFFFFABX5e/8HVf/JmHgH/sb2/9Nt7X6hV+Xv8AwdV/8mYeAf8Asb2/9Nt7QB8Q/wDBZXXVT/gqF8W7f5lEd9YZIODn+y7I/wBa+YdRvBeQkMzY29+Bzx/nnvXun/BaW+b/AIex/GaP5Tt1DT+c8j/iUWP+fxr5p1PU1sbDfu3Bh6HGf8a8upFczP0Cni4RwsIrflj+R5n45m+za9Iqnuc8Yz0rW8OebLFH833xjORzx/8ArrPh0g634gMjfMu7j07cf/WrubDSYrGzVf4uA3P+cVw8qbsefr8RhawGEDKzbseh9xW18M3aDwTcYYYF/J9R8kdZ/iZQkbsyjbgk4/8Ar8/lmrfwtIufBNz3/wBPkOfX5I68TiSnbL2/NHdlcv8Aavky7LPJJKPvda1NLV9iqeKLOy8yThcL0roNF0USuBj689Olfm0qiij6ynhpSIrK0Y49uMCtOEyINoztFWodIMLbT90+3+f84rSstNU/Nt4HXArjqYroelRwTINPimVlzn8a6XSYGwv6+v8AhVe00xBt/ut1H/1zXR6ZYKo525x1rllWR2fU2UL7TPOg6du/Y1yfiLw95+5vWvSZ7MNH/d461zmt6ZukyN2G55rONR7m3sbI84m8OFZF2/X+dXbW3W1ZV2jP866ObStuWx2B4zxVGXTcy7uSM+vUVopN7mNWSSsi/oPz9wcdx/Sus0ldijr68DnP+f51iaDp4iC57H1rr9M0pZR+GD2zWMm2yqUdDe8GQea2MfdPc8n/ACP5V6BaaUXtgFVT3Fct4Q0g27o3GcjqK9V8NaGlxErH0x1716+CpuSOetW5GZljZ/YQu5OuOx61Y8NeL7PwZfvr7+WzWrNFavkEBhkO/wCH3R9Dxgg11t34I+1acyxuEMvyg7c7O5b8ACfqB1ryH4jw3F1q8VrHHstrVVjQsuNgxyOv1Ht7Vlm1VwiqKe+/octTEcysja8V/ts6zHeM8epXETEY8vzD849CDx26GvD/ABr8abzWr2/uI5gJrhX2LnBy2T+HPqfTms/xb4aFo7XNxJ9nT+AY3EA55/TvXnvii5h02MyxrI3lx7hkckAE4+teTGEHT5Tl5pQnzI+5PjZ/wUJ8N/E/9ofxlDreiQ+PPBdzq07aFq0EkmlaxaWrN8ixXBjJeEZOIrmKQL0XyxXH213b6v4rvZ9BbVv7AabdYrqUqPeRRkj5ZWjCruGT0AHTgdK5X4h+EvgP+y/8SNQ+HvjBfjNqHi7w/ObLUdT0yWws7WWYAZe0tJonllgbIMbtMhlQqw2BhXSfGDxrrHwq/ac8T6Tr3iCbxHq1telbzULqBoZr0SRxyK80ZJMUgR0Dxk/I4ZSSVq+IKVeXPWr2b5tbJXTd9HLd2tZfElbdWSffw7UpNqlBtLl0u3ZpWV0tuuuz12d9PRPCD3nmRrHNNumPUEAZ4wOfw9q8k/bX+H97Br1l4qZZJI7sLY3knLBJhu2sT23cj6gdzXsPwe+KGnx+ObFttt9lvmSNi23pnORk43enPSvZvGXw80Px78O/EehapbtLY6tfXdoGUDzIGEuImU/3gxBHuK+Vg4LF0ZrtJP8A8lPocRSq0qU2tVeP6n5ow263MQHT8RxVa70RZDjjpnpWzfaFP4Z1i80+7XF1YXEltMMnAdHKN+GVNVZJPNm+XntnOa+ppwuedUkkix4MsFtbrLenYf1/z0r0DSI1+0w47SL16dRXJ+GoPKRWb+Igg/5/lXYaWii5t2XpvXjHTkV7kI8tO3keBXi3P5naEKkZ+X61yfjBDKnH5A11k7ZXaP8AIrG1DSftj7F6t3r8+5nY+qjT1PPhoMk5O5Tz0qtceE3B3Be3HFey6F8OVMClkHT8qfrng200qFvPkjh4yATz+X9a1VF2uypQtueHQ6NLHJs2sX6Bcck+1dH8P9BXW9Z2zOsaW6mVg52k7eox1rq5LOHQN11HHH52MwySH5Yj/ex3b07fXt5r4ouPLvHmV5ZJGJJcHHXrWM6KT5mY+2bXLH7zrtD/AGh7jw34rvrzTYfIt5oZLG6tHGI7u2YFdjDsw4YN2YA9q1vij4lh8ffs+QzWu1RHrMS9eh8mU4Pvya8Ja+a0vpHXlZD8wr1D4b6TF4q+CF/p5vFsZ77xHBHZySHERuDbSbEc/wAKuRt3diyk8A14+bU3V9k+qnH8Wj0Mtq+yVSD2cX+CPPxYSvKsbKflOTWppenXmp3cNrDpsOobnwiMuGP/AAIEGqc11eaJqU1reQy293as0MsUo2ujA4KkeoOa0PDPiWWz1a3kUlPJcMCOoNaS547oKUoX3Por4P8Aw4uPDkBa88O3GjXlwCVxfSOki9uNxBH1zXm/wr8MTaF42uNY1aOa2ktJ5ZJRIDuQAkAe/oK93+HvxQm8X+G7eLcrfZ4/LE2DxwOM15x8YtYkk19dPkmjaZcTXQjHy7iPkBzzwvOP9oV5sW6tb2a2e56Fe0VGSeqKt14hm8VeJrjUJ12tcP8AKuc+WgGFH4AD6muw8MWvmMu4Z74rh9HZInXPrnpjP612ehausbL1Hbr/AJ/ya+xwsVGNjw6lR81zs7HR125/pitS08IJd27ZQ9OaydK1hXjXkeldd4V1uFhy3zV9zwvZyqfL9T5vPaztH5/ocP4q+Hgbcuzr7V8++N/2fo72Ry1uNuegU19kanLDdueA3fI7/wCf6Vy/irw/aoV3qm3rj1/ya/auDoxXtn/h/wDbj8p4mqNumv8AF/7afFY+AEOmEbYMck8KOPr+Nek/Db4TxGCMFccDAP8An2rv/GFhaWs3y7SMdCOv4/h+lbHw9+zPFuYLgE4IPXv/AI19HLc+d6DdP+FKxWOQoXjpjbn/AD/Ws7VfBpgLLFG3yHPHOOf8a7O68VRaZuR5PlBHt6/l3/T3qnpepW+t3eNw24xgHoK56j1Jptn6I/8ABKuxOnfsSeGYiMYvNSOM56305r6JrxH/AIJ2W6Wv7J3h9I9u0XN9jaOP+PuavbqUdjYKKKKoAooooAKKKKACvy9/4Oq/+TMPAP8A2N7f+m29r9Qq/L3/AIOq/wDkzDwD/wBje3/ptvaAPzp/4LU7of8AgrZ8bGwedR07GM8/8Siw/wDr9K+XdY82ZNrK3TqRkH/P9a+yP+C0Hhj7Z/wVL+MExUbZL7Tz83GT/ZVkP6Yr5R8SaStum7PuMD8+a+RrYz99KHm/zP0DA4GU6EHFdF+Rxzyf2Sys3y9846mtZfEomt87lVsAcdq5jxdeMF+6RtB/CsG11qaSMxhs7ucCvQw8bwucmMpTpys0dB418TYhYLubjr2/z/8AWrW+Bl8974KuFUNgX8ijJ6fJHXn18s2ozeWo3NJ0Jr239nHwQ0XgqQMjZN7IT+KJXh8WVIrL7Luh5HzSxqv2f6GtpkbBst9a67w6q7l+X8amfwWsaBlHy561PaaV9gTuW+nWvyPEVE1ofqGFgo6mncRoYxjHfPqf8/0p1rFvHyqAPTtVFJiXAPr1rSsj8g4zn2/OvPlc9CnJX0LdnFIXB/8A1V0FhceUGVhu9umazLAMThenTmtbTLKW6eNVDMXOAB8xNaU482x2x5eXUsGOSf8AE8Z//XTpNG8xfmHXinaveQ+FFjOoeZas2PLWSMhpMDsMVR/4Wp4feYJ/aUKsOGBRgR+ldP1WotonmVcXh9lNfeinqHh5UdlHy+wrNn0Bi2R37Vs3PjnQ7gcahBtbg9ev5UkHiPRW+9qEBXjnn29qcaNRdGeXUxFF/aX3lfRdMaN/4cDtXYeHrIKVUjtgDPB9M1i2fiLRLP5vt0Z3cZ2M364rpfDeqWWrJus5ftCjGWAO1c571McLVlL3YnTTxmHhG85r7ztfC1soiXJHy9vXpXovhJGG3y1OSegGa8q0XxHZ2N7Db+dG08i79mRuCf3yM/KvoT17A8180/tFftJeMvj98QJPA/gmbUBpO6SBorKQxHUimWleRxgCFArE7iECoXbAHHvYem6cdTyMTjqMpWp6+Z+h7+LdOvrG/tbPULO5m0+Pzb6O2uFkktVBHyuFJKM27gHGQD6GvnrU/iTH4x8YjRtJtHvtQv7iOCNV7yEhVX6nJz9TX0B+zh+zxcfsk/sP+H/Ds2nxTateRvrGtrt2v9puVXKAMAcpEIY8HDZj4+9x8q/EvxlYvFr9p4f0ZtGurq3NoLqYYljWWWKOZx/2zeRM9cPXyeaY1VsQ2ltp8j0stwMq1RKWidmY/wC0j4O1bwbJdeZ9hu4rU7pJbK/t75IM46vC7rjkDIOM8HnivA5NXluZWfzGWRuDnnPtXWeJ9b/4Vv41ez0f7VbrGhguoWkDQzBhgrt/iyCMnuc9uvKWlzb3GFaNUUFuVHXHv/npXPh27Xa0e3oGZUacanLS6aO/c+jP2ef2i/2mPGng6x8MfD/x/wCMv7LttU03wpZQJrSRNaXF8twtnbQvMwkhRltJwpRlSMRgErlc+AReJdWm1u4urq7uLy+vZWuJri6kMkk0jks8juxJZixJLE5JOc8179+xX+0J4Z/Z/vrOS+tNWvGh8d+GPFRSzWN822ljUxMmXdf3rfbY9g+6dr7mXAz5T/YEwlklitWZlZV80rwhPTnsa7cwxsJ0KSlUcnrdN35bWta+1zz8LhZUqs5qKitLNK1+9+9j6e/Yy8Gah8TdHkk+wWesajagPDHb3PkXSIucsjFGUkDJwTz7Gvsz4W3KS6QbryZ5GmvpGJKqu45VgD0XcCc8cAn0r8nfEnjPXPhZoun30P8Awl2jtfOJtNv1Jht5sYO9CWVzx0ZevbPOPsD4Q/toX/h7xR4c0/xNf7rXVPD+neIby8EXmXNxcSQl5mY4y28pjBbq/Az1+QxWDlHE0XJb836H12Bxqq05wl05U/xPI/2k/Bc/h34+eNLV14/tSW4xjp52JgPfHmAZ74z3wOL03RHnnGWPJ6EdK6z4ifEPUPid471bxDqbbrzVrqS4aMkHyELHy4R2xHHtQHuEz3qv4ctVlmwMEDvX22Fp8tovseLWnze8S2GmC1T7vOck+vX/AOtVuG6+zX0I3fxr/MVrvYiK2De+ax5iv22L/rov8696NO8Gl2PNl8VzsZNQ8uDdnHNaXhGKO8vBJJ0U557CuR1bUvKbjgZ71k+LPiJJ4b0XZAwWSYdf7or83lHl1Z9fGtGCuz1zxT8W9J8JwtbRTxrcqvDMfu57n279Ca8t1X4sBpmP2iKSZ+dxcls/j0ryO51qS8laRmZ5JG3MTzVGe9aPnncTkmplWbOKpeo7y+47rXvGE99PmSSTce27NY0981wDn7vesfSdQaWXy5Dn0raFgyD7rDPTjrU8zZnaxi6la5O7HXriur0+KRf2adWwN3/FSWzKR/17S81Ui0hTZM7dVrd0WNW+AeuJt27fENtwOh/0eXBrzcxVoQ/xw/8ASkdmDlrL/DL8je0+ey/aA8J2txeFY/FWnqLW7kPH21VGI5W/2iPlJ9Vz3r1L4Tf8E2PFPiu0ttWaaxsIciSNJ5FbzR7bc4+jCvmzw5rc3hXVobyHJMLZZB/Gvcf57ivtL4A/GbVoNFhGm32Gki861bOUlGAdjD9Pavap0aVXWojyalSdKXuvQuaL8M5PCMVxHr1vPay6XlpYGQR+aRjaQQSCpwMFTzmuS/betYb3xJ4Q8SR2q2Nx4g0horiEDAL2zqgf8UkRR7Ril/aF/bcjvtZbbbxarqmlTLZSaM5kitYJo8O88r4xIof5BGjAkoSduAa89/bW8Wa1qPxe0ea91CO+hfw9ZX9gYYxFCkE4ZmVEHCgSKy/RATzmuOjgY0XU5HdO1v8AgnqVMdGpCn7uqvd/18jEtdS3Jt+6a0rLXvKk5c9eua4my8RLfR7cbZMZx6j2qGbxA6nv8oxmuunUSWphUV3dHr2leN1UKu7FWvDXxbQSY83kHPXr+teKw+LJIXXk4yBwa4vw38Rrg35PmELv9vXFfVcOY6MXU+X6nhZ1RlJQt5/ofadh8Rlnj5f5emTWV4h+JIu4+ZN23Pf1/wA/nXifh74jmWBW3E9M5P8An3rjZfi3cXD/ACs0a5JJ384r9e4RzKKVbX+X/wBuPz3iHL5y9np3/Q9L+InjIT7jv29WIHc9/wDPtWb4P+KjWELKZWXjOV/wrzTW/Fcmqx7h8xxn1/z/APWrg9e8WXej3TNGT6Y/u4xX18MbGo9D5qrgJwWp7x4v+NwAkBmLN2PGSf8A9QH5Vh/D79oaSy1eSNmZvMOOD0OPT/P6186658SLi8Un5uhH5CofBvie4h1DzZCF5OPr/n+VZ4io1qRRw6k7M/o6/wCCTGvt4n/YX8LXr8tNealn8L+cf0r6Qr5K/wCCHt9/aP8AwTa8EzcZa81bp/2ErmvrWuyi7wT8kc1SPLNx7BRRRWhAUUUUAFFFFABX5e/8HVf/ACZh4B/7G9v/AE23tfqFX5e/8HVf/JmHgH/sb2/9Nt7QB8h/8FibFh/wUf8AixII2cyXljjn/qGWdfFPjZGjJ+XcM8YPTFfeX/BWiw+3f8FGfiou3d/pdkNvr/xLLOvkDxl4Se45SPKtnbx3/wA+1fneIjP6xUf95/mz994VwtOrh6V/5Y/kj568VWkimQ8AN05zj6VhaTYySXv8PXhjXovjvw7Jp8g/dgxkn5h3/wA+lcrFD9huuinnOB0Fehg8VL4GenxdkdGnQVSJqaF4PEk6sRGcntX0d8APCYh8ISLsXAun4OOPlTr+VeGeC9SW4miU8dB169v8/wBK+ofgeYU8IqNwK+e3zf8AAVrxuLZuODs+6PzPIqUqmNulpZl6XQFLbdm7NUNS0DdCNq/pXoEmno9qxVW6daw7+0WFmzt479q/LpXkrn6BTXI7M4BvDxif7pGfXnrWpbaeEhwRjuT+Nal9GjKu1gfTio9KZbiXBz15zXPUizqoyXQ0vDOg/aE3cKo746D1ri/iH4vj8Q+Drz+ybi5tZLW9kHDbDOIwMg45AIYkD0UE+3eX+tR6LDa21vMsN7dyokJwD/EM57YOMfjXnXxnSG7uptctJUGoeasd7EiqEeSPIjkAAxypZG9QwP8ADXs5bh/3ftpddjxc7zCXN9Wg/X/L0OJvfG15qfhO70+6upJoZF82MM5bZIAcFeeDz2rzq78QXEqq2VVhwQFHJ9fxrpvE8kcMMktvG0dvcDzIlPPlg9Vz7HNcTKPMfPau+TPDpost4ruE/i289uKefG9yRtDN71jXykf41Wjkwjfpk0bmmhvN41ulX5ZWT3FWPDHja+0u/a8jup0mb5F2OV3Z6k4PIHp71y4HmDnlugAq7ZNtulXP3SByO1HKLQ9ntfiNd6B8O9U1Dz5RfX0flqxPzDIwOev/AOqvpr9nf4Gwfs1f8E2bX4k3liLjxP8AGfxdpfha1Yj54NFF35tzEh7fahaTI/8AejKjoTn4h8Q6x5+jWtmv8bAEFsmv2E/4KMeDE8A/8ExPhGtjH5dv8PdX8NX8qgfdjSE2zt/33cAn8TXTCLdOc47pO33M5uaMa0Iy6tfmjB/bE/aEvE8tryz1TRZLvdJGk8RjE565U9JBxwVP1r438XeK9Y8T6rNJJb/Z7WSIxLI6kRynIYY7/eA68DmvdviT+1beazpUjSrDdQwR7IVuIxIqYx93PI6V8v8AxP8AjldeJNQCv5MNuhBaOFfLWXHsPpX55TpqXTc/QvbKi007WON+IaM3i+TWrmWG3kkl3vbsrPGowVJjPXAxjB5HHJxmsfRmxppO07pssMj7uTxn9K0PFOtv4+E01x5McmxUjWNFQbRwOB1wB1OT0FZ+naitgsQkTzEiYbkDbDIoPIz2zyM9q7oRfJyvdaHj4utGVRyjsfbPwz+Cfwv8ffBjwh4i0f4S+LNPh/0uzn1TV/i5oXh9NWuI58s0P223U3Cxq6KWjjRUP7s+Yylyuox6D4h+Mt3b6Np+h+G4bG+wdIttUj1a2g2qAoF3GxSbHUuh2lmbGBxV/wAIQ/DL46fs8+CbrVvh38PPC/hq11DUdO8N/wDCUfGm50vUADMstzEu6w2vbrNL5m85CPM43rkqPJPjN+zh4k/Zo+NbNb6Zo9r4f1DWLrTLSDR/EK67FptzbeX59jNNtSQTxrLGxEsaFllVl3LzXs8R5XJYSOIp2aSi21FLpa+kItJt6Xk76aX28zJcfTWI9lWvrdJNt9f8ck7LfRW2v3+7Nd/Zi8B6x8JLa81LSdFlsdHVL5kW5M0eVYSNEBJlIlfBVsdmOBXjfgvStJ8X21x4KW1t4tQ8beHbW200JGqDT7mJN9oA56RtINjKD90Y715b+0V+1vq3hn4DR6Do8dxdafdXMcGuTQMx2wBW3IGQgqC4QFs4bkHgjNP4XfGrR/hj+0z8LdSvL6RNDfwlpN5It6dqBUEjo8j9ASqkMfb6V8N7/wBaw1Rppe959uh9zGFKFKrTTTb5fLe9vxORtLJp23NGycYKuuCvse+R0/A10nhvTlRyVG4+v0rL8FeMYfija3WvWrbob67uJBlcMMyueR2JUgkds4rqdOs9i4XPTPC9sV+iYfCuT5mfJSxF1yoffjMDfe27eBjrXL3jhLuLb/z1X8eRXV6nZySW33cqPTsP8/59OUurXfewg5P7wfhyDXp8vLBp9i/q8pJNMm1mdUM00ziOKPu3QnsK82vjdeM/EAt4m83ceoGAPf6V3uqfDrXPi14p/snSYJWtbPBnnI2xoxAPLdOB269ax/GFtYfCC3udLsZkutUkLRXFz0CAHBVfy5r8nliKc6jpqSutWrq+vl59D3qlOSXNZ2bsn089TnfEFnaaGVtoHE0i8Ow9awZQHamyXPnylmbLHnOaC3H3h+NT5krRDoVKSBl6qeldnp2oi5sIWb723Bzz9a5CDbIvXn1zWpBefZtP/hG1hyD1zTi2mTLVHUSAPZv/AHWB6H2pthqEi/s36xOnzNb+IbQsPVfJlH9aq6DffbtLVVHmOW2KqjJYnoAPU16roH7LPiTT/gpqulzNp7ahqV9Dfi1WU/IqRupjLY278uP9nj71eNnuPw+HjSVeajecbX7KSv8AJLdnoZXhK1ZzdKLdk9vNO33njSbby2juI+VkGR7V9If8E+4dO8beJLjwpqV19lUD7dZEkjz0UjzYVOMBhkMM9mPpXzL4QVrG+vtJuMo8LFlVuCCOCP8A63tXuH7EmiXmu/HXT7TTdWt9N1W1Ju7WOZNyagq8TW3UY3wmTGepGOOo+jwjaqJo8XExTi0zmv2tPgBq37NnxXklurh9S0TxLPcXthfMuHm/enzEkHaVCy7gOCGBHUgc18QviX/wldp4PeZWdtB006QzE8TQefLKg9fl81xz/s4r7X/b3+HMvxu/ZGfV9Ob7VqngrUJLy4jUbnkii3QXQPcMq7JG7/uT3r88LArd6dNCxG6E71z1x3/z7VeMpOnUaWzDD1OeCb3R6R8SrHQ9IGi6p4dvHeG+h866sZMtJpsoba0ZbA3owwyt1wSDyKlm0MXAEir94Bh7A1ynhe7XW9Ce3k2tJa8YJ6rXr3gjSI/Eum2Kw7GaXbDz2bO3n9K4pWjDmlsjuw65pciOFTw2wlXjPv6V5vY6C1tcucMvzHj05r6a1HwBPpUwhubdoHzxnow9j0P4V5lqXgYWhkfaw55r6Dh2MKsJ1KTTTSs1qnvszlzSm4SjGas9d/kY3h6zcMoZtvT8Oa53w14d85yZC3XH3Rn867Cyj+y3C7Rt5Hbkf55rjtA8RlSsTY+UkZXp+H/16+z4e54Sqpf3f1PLzGnBxhdd/wBC/eeHxH91SV/T/J9vesHWvDCajuVkXn5gG75/z2rso72K7j7bsAkg49Kr3FpGrttYl24z/Ca+/wADXlFHzOMwMZs8o13wNCkbMF2tz8wXHp/n/JrFsPDEqtmNVxn16dR/TOa9T1zSvOPYfNnhehOf/r1m6do6R3KsyqzLjI68+h9e1etUxEnHUxp5PT0aR+43/BCmyk0//gmb4Hil/wBYt7qxJ9c6ncmvryvlf/gi4yv/AME7vBpX7v2vVOcdf+Jjc19UV72H/hR9F+R+fY+PLiakV0k/zCiiitjkCiiigAooooAK/L3/AIOq/wDkzDwD/wBje3/ptva/UKvy9/4Oq/8AkzDwD/2N7f8AptvaAPmn/gqIyy/8FKfiwr7dq3liMf8AcMtP/r14TrGjRXqbQNztwvHX9PavRv8Agq541XT/APgqX8ZLXzNrR6hYL19dKsj/AFrzHw5rC3YXc27jnvn0z+deNiMEruSP0PhziX2EY0pdEked/EP4e/2iJl2AY+YALj3/AM/06V4j4k8D3FnI3ytxnJHJ+tfWviFI7tNvyMzIAc9/zrz7X/Bcc1u7NH94nqen1NfNVZ+xq6H2+Mzb69Q9l3PnfS7eTR5V+91P4e1fSf7POtyt4MaRm2/6U5x3+6teba14ITzt3l4xkn6V6h8BdBEHhCRP+npyc9/lT/CvneKsY54TXuj6Dhnh7Dwjz9T1jT9aMsC859B61R1ecyBj+WBRp1p5ELNn5cc+wxXYz/s9+M7zx5feGLfw7fza9p9gdVubKNkeSO1ECXHnZDbSpidWGCSd4ABJAr4bDRnP4U3026vZeujMc6hSpStdL/JfpqeYXcjyIw9gadoKNJLtBySTnPAx1P4YrstA+CviXxT4Hm8RWOkyXGhwm5WS782JVzbxxyzYVmDNsSaNjtB++MZORXK6h4Z1K+8JeKLzTLZpk8M6dFqWpOGVfsto9wsBlOSOA7opxk/OOMZIuOFlVqKDTs9flvoeHUxio03UTV1p8ziPF3i9pruS6yu+ORHj5+6qsMfyryzxF4+urvRpNkrRyXAxKQ3XHX8/5V2fxB8B+LPDuiapqV/oGpabZ+G72103VDeRfZ5LOe7ilmtY3iciTMsMMjqduNqgkjcufGr66xNMmTtLllHbmvcnFwtBKx87GXO3OTu2bNx4vW90JreVcycsp9CetY1u/mKvQjkVmvPulIY7RivQfg5+zJ8RvjZ4V1bXvCPgXxZ4m0bQcrf32m6bLcwWzBfMKFlHzOE+couWC/MQBzSjTnN2im35GkpRirt2OQvofl6YPWsyZCo7+nvXR6Dod/411yw0nSLG81bVdUnjtbGzsoGuLi8mkYKkcUags7MxACqCSSMA113ij9jH4seG/iro/gm9+G/jKHxh4gRptM0hdMke51BE3b3iCgh1TY+9gcJsbcVwcOnSnLWKbXp16BKpCLtJo8tgT/a254xmrVpIsUrMw5UbVyOPc1qfE/4TeKPgh43ufDnjDw/q3hnXLVFley1G1aCYI4ykgBA3Iw5V1ypHQmsFJG3DOdvXOaKkZRfLLccZKWqNfQgdV8V6fGzbg88aAe24f41+8v7TPhYfFL9ku68I3DJ9m1bRxZyFvvQnywA6+6sAw9xX4L+CJGi8WafMAWMMyycDqAc1+5ep+K4vHPwf024RmNvcWkcgKNnIKg/1/WvTy23JOLPGzS6nCS6f8A/KO/8AGWoJ4Xk0+/aaHUNOmexvo/O+7NGdj4H1U/n6VwF3Ms1zuYybc5xu5r2j9tvwI3gX4wz6taxsuneJl3yAfdW5QBW9vmXa3uQxrxO4Tc3y96+MxGFVGs4I+nji3Xoxl5Gvaz28djuhSSRxj2/Os4SNeXirIywrI4Uu2dqAnqcAnA68An60adctp7srbWjk9eNpqS5jcsqqpZ8gAAZyfQeprHls+4ua6sfXnwA0rwR4q+FEXhvxtqnwX8dad4F1G+i0aa9ufFun3ljbzXR3bpNOsikllcTZmjWbbKpncbkLlF439pf9rfVNSi1HSVn8G3O7Xr7XLvWPDsFzDbapNcJFDGIhcYeOCC3higiTYhCIxbezsx9v/Y2m1WL9mC68D6pq3xM+EN1pthqEcsEfg/WJrXWNQn1Gyu7bV91nEX+120dr9nVZANipE0cg3SAeQftTfDSy/ao1P4j+ONLW80O+m8aQafp2iXtmLXU9ZunSCN3ktgP3Lybrm6cD5Vc7ecg19FmsX9UpwckoyspJLXZ6PVvSySulvppdHJlGHnVxUvZxbavbtuldaJa3vpfz1sec/s7/ALUOlfCu8N94g0ZfFGjyt5VxYCURvIhyCBuBU8HPPHAqD9u3WdJ8QfEvwfqHh+xm03Q77wZp09hZyY3W0DS3RjjIBIyqFV4J5B5ryv4oXtrJ4tm03T/JOl6GTp9o0S4W48slXnP94yPubJ52lB0UV3P7TieYfhfj/onmkn/x64/wr5mph/ZY/DNf39P+3Ue/UxEpYavSdnyuKv3s3+B6l+xvYST+AdQbaTbte70PoSgD8/7wz+Ne3aVpW8D73HAHp3718V/Cv9oTWvgbBm1S3vrC6dhNZ3JfaGXB3IQRsYgkFsEEYyDgV9QfAz9qXwv8VdV+yQySaXfuyrDbXpVGucgEhGztYgkgLwxwCAelfbYWrBaPc8CnfRHpl34bU2TL0wv+cVx9xoSPdHOMxkH7vTkV6TKVeJlbaCq4I9/8/wCe9c1b6O1/eXHy4Vh8vetMVJKDXkfV5bFTXvHCfDP9sm48LSXFrrGnwXWjwykQvZxrFcRgt0xwj4HrtJxyTXDeJ/2lNW1PxLfyWun+GZbWS5laBp9DgeRo9x2liwJLYxnPeuB1G1kgvJLMKS0MrqwHruI/pWtZeHl0O1W4vV2lhlIzwzfhX4LDh/ARrSrqkry37ettk/Q9ypm2KlTVKU3aO3f+vU6iH46a55e6TS/CIHb/AIkFt/8AE1C37QWshv8AkG+ESuf+gDbf/E1yOpao12PlG1euB2qn1Nb/ANk4L/n1H7jH69X/AJ3956HD8edZki3DTfCfB/6AVt/8TVlvjvqv9lzP9g8KrIi5CnQ7fa3/AI7XA2kW0c/darM1kyWM0incm0hh6Uv7JwX/AD6j9xP16v8Azv7zvPCH7RerRxfam03wzm1kWULHpEERODn7yrkHjqORX0HpH7TvhTW/Ac2v21xdPDbyJby2xgYTRzOCViPG3J2n5gdvHWvinSbr7PpF4ufvKMfjXeeApWsf2dfEcy/e/tuyK57fu5q8PPuGcBXjTk48rUor3dLqUknfT7j18rzrFUHOMXe6b111SbOg8RfHXV08QNdx6b4b2tKWmLaPA8gUk5+cruOPU9a2IPjnrfh7Vbe9s7bQYJ7dxJHLFpkUciEd1dQCp9wQa8lOs3V2hWST/WDGF4zWnpOtfadNjik/1lufKf8ADofyr34ZTgVoqUfuPGljsTu5v7z79+HXiqz8ZfDW48V6XHfXWk6oga7tIbp1fTr5SPtcU2DmRZUJkVzyckHqoPyd8T/iS3hT40+I9K0ez8MzaHY6rdWdozaNblxCkrKgZtvzYXAz3xmt/wDY1+OM/wAIfi0ui3EkjaD4y8uxuUVGkEE+cQ3AQcttbAYDqjN6Cur8cf8ABOjxZe/FXVru2m0TQ/D+pXPn2z6heNvWV8l4Y41UySFTwDtAPbjmvRqZTgalFSVKN09dEcdPHYuNVxc5NNabnhv/AAvfVtH15Y5NN8LrC0hikK6Jbq3scha9J+E3xguLzxZHY3lvpMcMxEkZtrCOI5Xk/dA5KgkH/Zr0rXv+CTrSad9ovvGlyskijzDbaIskcJHdt9wrfjishv2efhz8LtWuob/xN401jUvD8oQtaWUFikkiqHyPM8xtmCvzYwd3ylucebVynAKLU6cUn5Hp4XFYyU06cm2jb1jxXJrULQxwrHbt1LgMx/TA/D868r8X2vk2EjY9eQK9qvviD8IfCfh/7cbfxbqXA2JLcRxq+cH7yqDjmvH/ABv+3H8CdQnSGH4f+Jvs/KOyeJCuT2+8jYGcfme9fU8HUMuy+jUoYVWWmybvvq3a5hnlTF4ipGpXWuu7iu2m6PJb3U/IuNvQM2AD/nFeJr4tjtZt26PCseT6H/I/SvoyTxT8K/jfbauvhebXPDWuafYXGpwW19fx39rdiCNpDACsMbpI6qQrbnXdgEAHI+GPG/iO6tpn8p2bDdh61+l8L041p1VF/wAv6nyufVHRhCT8+z7dVdHtkHxQht5P3jj8+B/T/Petex+JVvKfmkVVbkDNfKsfijUjn94wUnAwOCO/+f8AGtey8R6gkY2sy8dMEV9p9RcFdM+TpZwpztY+sbXxFa38WfMXbjPPT6fpWB4j8ZWunJIyyozYPH+fp+v1rw7QfGd/jazMVbHBPQevuP8ACna/e3E8SyM75xxk9DXHKpN6M+sozhKi5n9HP/BCTVxrn/BMrwPcjaQ19q4GPbU7of0r6/r4h/4N1Zmn/wCCTHw+Zs5/tHXOvf8A4m95X29X3GF/gw9F+R+OZk74uq/7z/MKKKK6DiCiiigAooooAK/L3/g6r/5Mw8A/9je3/ptva/UKvy9/4Oq/+TMPAP8A2N7f+m29oA/NP/gtp47bw5/wWG+N0bORGNR00YJ450bT/wDOPeuA+GnxWjvLNVLKxf09Pf8Az612/wDwXx+H9xff8FXPjNfQj5bi+04sAOTjSbFev5V8w/D+zu9NeNnZvlP8Rryq+KSTUd7s9CjhZqafdJn1hpF8uqsu1t3GMD/P+fzFaur6VCumn+8T3x+vP0ryLwd46bSEVuWU8fMOv+fwrf1r4sQmPbuB8wDIzzn6d6+Wr0Z1Jn3OBxVOlFXZF4jt44A+5MdcE8YNdJ8H7jy9DlXkZuX49flT/GvM9c8arcfc3Hdzj6/rXuX7KD+Abjw/av4gbxB9s+0yeeDs+wk/Lt5T97jbtz757Yr4zjScsJlrryhKeq0guZ7N7adj7rh3PPa4hYenNR03k7Lp1NywQSp29M+vtX2dofxUX4W/A/wT8ZLOSOTX9TtdD8G3EKP+9lj0q+knvN467ZbSz02M+onYdzXhfxZu/BlkdFLG8FmbdzanRxAYNu4Zzn+LPp+PNc/Y694NhJZT4mDfxFlt+a/L8h4zqKh9Yp0J3kmlpezTaT82ldW6N76a+5nGQxr1VTqVY2i03rurJ29G7eqXmfTHxZ+EugfC1NZ+HMMOm6to/gH4eeJvEVtcPCs3lT3l2Jre4GQdsqWMVlhhyuSAeTnzb9pqz8T+AvBH7R7aT4D0HT/hLp3gKzfwh4ig8M23lapD/aulvbst+Ys6itxEfPmSR5VV1AKx5KN498V/HfgTSfAlxHeHxWtvqH+jH7Gtt5w3Ak43HaOARk+tfOviTUvgvqNqlvcSfGBl09mMSq2mfu9x3HaCMDJ64xnFfZ4HiyLjOosJUV9FZbJJxSvbVJW9bI+MzLIGpQpfWIO2ru922m3bpfX0ufXX/BQD45eJvhT4U+PniePQPC7Xmoa58Pjod3qfg/T7izktpND1V/t0MMtuba6JZbiETyJLtXcisDCnl/Kf7Z/w88J+Ff8AgplqmgLo2h6H4VvtT0i5udN+3f2Hplql5YWd1cIJwkn2ODfPJhljYRKRhCFAriblfgLcnzNvxr3Ku3aG0roPqKoiH4AiP5V+OG3PKh9I/X5a7cTxYq/xYapvf4b9ZNr53/BHFh8h9ltWhtbfyil+X4npX7VXwh+E/hD4PahqHhOz+HMOuQzW6wPpPxkfxJeBWlUPtsjp0Acbc5bzRsHzYOMV6d+yt4A8VfFDwP8AsY6x4B03Ub7Rvhr401O48Y6hZwO1p4WuhrNvdyXuoSKNlvE2mi3xLKQrJA6gnaVr5kZP2fx8uPjkf+2mkf8AxNS2Vz8BYkkWJvjosdxhJlWbSVWYA5AYBfmAPPPerhxVFVHU+qVErJWUUtmn+nYc8jbhy+3he99ZN7pr9e571+w7pFvbft9R+MrXQdQXwj44l8a2PgJobtdL/tW9On3iW1na3B/1NwftVvDG2MrLPEFBbAr07wR8OdS0LwR4a8Nt8JPHPgDVfH3gvxno2qfDPS9UvH1XTtL8zTZjrOj6bqszXIuLhoJIpLQyf6ZFYzlNoDEfIE6fAMht8fxqO4YP7zSeR/3zT5b74FNefanb45Nchlk85rjSvMDLja27bnIwMHtgUsNxaqUeX6rV3v8ACutr9+2nk+pNXIeaXN7eG1t30vb8/wAPM77/AIKQ6DofgHw98A/AOnaf4o0vWPBfge4i1Gz8TxRW+uWaXOq3l1axXkEZYW0hjkMy2xZmiiuYlLE5J+XZrdoX/hH9a9atrr4CTat5u344STTuWZ5J9JbezcksdmSTnqa0L61+BO75ofjN6/LNpX/xFcGM4gdWq5rD1EtNOVdFbudVHKvZwUfbQe/Xu79jgfgf4ck8TePYbePZlIZH+fO3AXHbPrX6l/sj+Phd/Amz07UJlWTRoDBM+7CoqdGJOOAozk9BXxd+zVoHwgv/ABdeXGhw/FRZ7W22yHUJdOMeGPQeWgOfl+leseI9R8DW9rd6e7eOha3BWS4ggltBG5XpkbecdcHvg9QMeDieK8ThcRelRnZrZxX+Z+c8WyxdLGKlQxVGMeVO0nre78ij+2J8Q/DfxP0e80vS45NStbO6iWXVIxi3s5TuKbGwfMJCuOMIR/EcivmlvAVxIh8vUdKPOAXd0/HG019a+H4fhvD8I9V02PVLj+xb2RGv5bhw11BNLgx5ZY9m5Sg24UqCnOeSee+FPwM8A+Pr+aXT7X4gS29mGeSe4msvs6IpwZCQmduenr6da+do8XYjGVajlRneLsvd6WW97a+XazPyjgfxQzTOcTmGDUo0VhavJF1IuKqRtpOLlHW7Tdt1CVNte8eR/AL9nhfHvjESaxqEE+h6TBLqWqGAOvl2sCmSUjcAT8oI471xuseH7+3vRdR2y2TXU+5YYX2rZFnyoDE/KqZADE8AAk96+pLay8FeDtL1CRV8c2/9uWbaY8Qls/MktzLFM+AFwoJhRTnqrsMc5GP4gs/hz4GSG6urfx39ru1LRQ+bZmSIdA+CuB7Zz9K+owmaKeFjKrQqdbtRXfS2p+/ZHKvXwkK9bEU5Sd9npo2u33nvfhfwb+0J4q/Yx8QaNZ/HNbjx9L4x06/hnHxntriaPTo7O/jmUXKXzbFaaW3JjLDcVU4O3I+XPHn7Q3jz9h7xl4n8M3WraV428ba9bLe6trd3fNqraPqMySqXhud7C5k+zvC+9yV3leGCYOCfF/w0+Gd/dTaXcfGKwmukIlFncaVtcnn7rwkZ6HOPSuJ/4Sb4JeI5ZJbmP40TTzMXkkmvNLaR2PJJPlckmu6WeSrJKVCq1Ha/vdW9dtdd9dND6PB4WVC9SjWgpPR200as110Z5arxxQo33VUBf6CvcfjhpD3WtfC2G4jkh2/DvTS6yRlWBWS5yMHnI44PSu9/Ya0n4P6r8fof7Ft/Gza5a2cs9gviOexktzIpXJjWGNWMyruIycBQ5xkAj6V/axsvC83wY1K68WR3EllYlJIXtGjW8WUsABC0gKhmGQQQQV3Z6ZHwec8crD5/hcE8NP56SfP7qtHW9vVX1XQ+qy7heVbKa+JVaP5r3ddX0v6H5m/ES1jiezjPyqrSyEDv9wCsjT9RaC43K3Hpjg16v491z4H3OrRrc2nxi3qhP+j3mlqpB9d0R9O1YcWo/AeFjts/jYcdjf6Tz/5Br7+Wayf/AC4qf+Ar/wCSPko5fp/Eh9//AADvPhl+2f4o8F2sKXmpf2vZQqIxbXy+Y5UcAJIP3gPpklRjkGvQ/DH7fdxfeKLVV8Iqun3TiNh9vL3HPBYYjA4z0I/Eda8Lg1r4Ew/vGs/jMewD32l5/Lyf58Vr6L42+C8Wq2fl23xgjmaVUR2vtMXIJA2nEX3eeQOuT1pSzmai17Cpt/Kv/kjro06tNpQrR+//AIB7Td+K9F8OWlxdabocl5rV1NJI811iRU3MWyEHAPPTmvPry6vvEN+9xdNIzs3OR09sdq7uPX/htCV/0X4gNJnobqzzj3+T+XNa2k+LPBes3Xk6dpPxAkkPRIXsnKge5jJ/E5r87/tGT0dKf3L/ADPqPqlnfnj9/wDwDyyS0YSCNVZnx0A6/hU8PhrUJGDCxugP7zRlR+Zr3eD4feEZtJk1DWtZ8YaDaxru23OqWu5vYJHEST7Cqngv4pfDnw95n9m6DrhaIkfb7kwvNL7jcTt/4CAaFjNfepT+5f5k+w00nH73/kebWHww17yVjbSboM6BwCB909CeeM9s9ar6p4J1iBZrZdPujPgqYguTn8K9ptfjn4X1eVltdJ8QQqDl5E+z5kPqSwY89e1OtfHnh/xHrDW9nY+JmmUbZPJ+yKv4/J/9atPrkelKp9y/zM/q8utSP3v/ACPn/T/hV4gu7Vkj0q7JZxu3AKoA9zgfrXsfwi/Zw8XfE74Zap4X0fT4/wC0r7VLSWLzZ0WNY445fMdmBIVRke5yMZOK9Dks/D2iWD3mpXGtx29uhkcSzxlQF5IwiD8hXpf7DOt/8Jf4hv8AXPD/AJ1jNfBrfydYIkigSJFIVFixtB8wnkklixJ6Vy47EqShF0p/HDov54+Z1YWm+aT9pH4ZdX/K/I4Hwv8A8EiPFzRL/ani7wzYTsuQlvFPdEDoeSE/TNer+A/+Cbnwv+DlrJqHia41TxZecb/tUhtbPd/sxRnd/wB9O30rsvij+0TrXww+IMmn3C6Hd3DafBcPJGkixRoZJwMAvndlWz7bfQ1438QP2tZfHOs+TeXWp/Y4WLmKFIIbeHHO7oXbp/ET1roxmaKnLlp0ZJ+i/WRnh8vc/elVi16v/I9kn1TwD8CPD+qeI/CnhHRbO9s4jD9qtoN0gYg5RZD24+YK3XANfIPxd/bM8U/EbW4bv7cY7i1yLYRLt8jJGdo/vHjnrxXQ6j4t0XxV8OtS23uuWulzXQSUpeRQm8ZATtx5ZG0bsjKjkjkVymmfE74b+BNZsr2x8H6nDe6eVksr83L7xNGfluV3SFS+4buVKBhwo6V51TNKlVWnCdvRflc9bD4GnRfNGUfW7/yNKb4zfE/SfCLx6xbeKILWYrMbu6tZ44o1GflbegU565BIGB0616/4Bk8O/tAWdrq/iSazhvtHsSglVfMjggC7xIyk7Jdhz8rLhdzAZ6D5y1DXfBXjjWLq9mtvFFvcTkyS4vraIOSck4EZ5P611mg6L4C0D4TXXiKC41mzhW4NuI47hJLuWQLuUD5MKrNhecDI545rk+vNO8aUvTT/ADOrmWq51bvt+hp+NdMtPDnxIv8Awzfyw6pPpSpJC8qB45reQB43QY2bSrDoB9BmsjRprZCY7e3tbcDoIYlT+QqOw8ceFvE2uS6nJb+IvtdykUB33EGFSKNY0UYToFUfXGan8PeJfCSD9zb68Oc4e4ibn8Fr7zh3MpU4zth5302UfP8AvHymaU4ylG9WO73b8vIxvG3hoX8byRwR+a3DkJhj9T3x/SviHxH8OpINamEqs7M7NtYdAT3r9OPiTrvhe4kkjm23WoKcE2jAOh/22+77c5I9BXzDrPw7tPEN+ZFRQGJIJXOP5d/8+n2nh9xHPMcLUxk6E6Pw6SW/xaxeja82keNxTlsMNVjho1I1N9um2/Z+V2fOWi/CmHUbAbYctk8Y68DpTU+GEtnIV8oqo9R1569K+rPBHwehRWDKq7u+M7qteKPg7ZpD5i7V79M++a+6nm32Wz5unlKVpWPky+8B/wBmQLJt8ts556nj/H19ap3dvDJFtK5xkcH8P6CvXPidoq2NrJHs5XOOe3I64/WvFb6TbezKvzHccGtKM3NXPqsDThyOJ/Q9/wAG99utr/wSo8AouNo1DWunf/ibXdfalfFv/BvoMf8ABKrwD/1/612/6i13X2lX32D/AIEPRfkfiObR5cdWj/el+bCiiiug88KKKKACiiigAr8vf+Dqv/kzDwD/ANje3/ptva/UKvy9/wCDqv8A5Mw8A/8AY3t/6bb2gD4r/wCC02kR3v8AwUh+LTunP2yx5B7f2XZ9vwr5DsfC5S6kWOP7w4YD29ce1fdX/BXDS4dV/wCCjXxUjfGWvLEH/wAFlp/hXgfhv4b26gyFVAYjjPb/ACTXw+IqWrTXm/zPv8LRjOhB/wB1fkjxPXtKuNOhZ9zYb1BFcTq/ixoXVWb5h3Y8/wCcV9E/E/wzZw6e0Q2lipx+WK+ZPG3hiQ3xkA285GB29f1ruwPv7nj5hem9GXrTxC1xIrHLY6EDqK94+Bl9I/hAleCLp8Y6fdQ/0/zivBfA+kfarhA275WAP+H1r6M+EFnHY+GGC8gXLnB652qK+Z41pSeDtFdUejwu39au30Z1CX0iNnPU9u1WrXVWdyOg61G0SRwZ43d+e1Y8+rmJpGx8qfMSB+NflMcNNvY/TIYpRVmznfjn4p33ENqz5S2XJXtubk/oAPzrx3Wla5naWPdleGHrXQeM/E39tXdyz7lbdnnvyTXJS6u1ukgX723ivpKVNQgoLofM1qrq1HUfUzby38+TzYWZWXgqaqNukf7uyTHzJ/fprJNFNuVjluevWpHl+3ttkZYplwVY8BvrWuxJTdccjOOwPY062KqY9vHzdKffB1udsqbZFHzDH3veoy3K9smqA0bg7o19ufrVeeTMHt2IFOFxmNQ341VmfOF7f5NRFE7jrH5b2Fv9tev1rortMKfz+tc2v+uj/wB5f5111/ab1VsdRngUSGz179kiNbHS9WumO37RKqZx2jQnr/20r2ZPhZe6h8KPEniyZjHp+j24becjzZZHWONfqXcV5X+ydpDeINW0PQI/lbVLkoTju77c/wDfIUCvsj9vvXdA+H3wC0X4f+HLi1dpb+I6kYJA5leIO7KxH92RFyD3x6V83mlGMqzqS6RVvXU/DuPrvNlb+Vfmz57/AGbPhFefG3S9Q8M2ccjTalqthvKnlYkWd5Gz2wisea+lfjBo+m/Bnwbb+C9EEflNtk1K4jGDdOowsY9I1wcDvjPfngv+CbuqxeF/FniC6f5mEKhMDJ3GOYfyJr0r4snT7WY6tq255JQfs1oCFkuj/eP91B3P4DJNeHlcYqFeUv5/w5IH5Nk/++Y//r8v/TFA8D8Uy/2asOo3UO7ywxgh9Txz/wDXrxH4geKp77VJLq4ZpbqT7q/3B2H09q9U+N/j1tynakl5MGVI1HyxAAADHoM9/wDGvnzxnrsOg2UlxdOs0x5xnqf619BhKrq0IRjsr/m/xP6N4NTeV0v+3v8A0pnKeLdeXT5fOmfdNIcgY5Jrhn1IPqLTKvlqxzik1rVJdcv2nlY9eFz0HaqqkFDiveo0VCOu593CHKjqNFvJ7W8t7m1lmguIXEkM0TmOSJhyGVhypB6EdK+p9c+MU3g/4j/BvxF4m0VfiVo8Pg60fVvD+rE3UesrcSXMM+3dkpdOnEc6jzEkWJhnbg/KngS8SS78qTv0zjivp7xD8Z9W/Zy8ZfCnxloFjo99rmjeAoX0qTU7Y3UNhdh7sRXaR7lVpoWbzIt4ZFkVWKNtAry8TTgs0wdSVrqUtbXa91/1bqd9OU3gsRTWzUdL2vqL/wAFNvgD4b/Yg0XSfhromkrruoeLLyfxPceMdUso2vYLOG4uLS20SCQFljktykhvtm1muGRDhIlB8I+F/wCzavxO8IRas3xM+DfhdpJXi+weI/Fkem36bTjcYWQkK3VTnkV0Xw08V+Lvjj+ztr3w8uJtJ1bT7PxAnieyvNXilm1HTr2dHS6Nvch8qlyFQzRyLIrvHG42OCx3PAPxk/aO/Zk8Gx+G/CfiTxJomgwTSXEdrpggnhDudzt91m5PrX1lTFYKeIvPSNtEt1+Kv3v56nk08PiY0fd1d9+/4O3oZf7G/wCz/ofxG/bm8P8AgXxNf6d4i8M2dxqF9qsug6gZLXWrbTrC5v5Ibe5jwxSdbUxCRMMBISMEA1ufEuLw38Zv2VPBvxe0/wAE+EfAPiCP4gXPg7UNP8NQ3EGnX1v9itb62m8qaaUrNEXniZlPzq0RbLAsfMtF+Pvj74c/tG2fxQk1C6Pjyz1T+13vb+3DG6nORIJY8AOkilkdcAMrsO9dZe/tiN4g8ReBdPsfhr8M9C8F+ENRu7+18I21pfTaRe3t6kcM95cme6kuJZQkUAjHnBIhbxBVA3BiNfDfVp09r36a9LO/lr9/mL2dX20Zry69db/fp93Q+y/2f/2ULW2/Z98aa14k8L6xrHizxF4EvfEPhmJbOVoNHtI5oEhumIUh7m6Z3MKA5SGFpCCJ4yO8Pwu8M2f7MlxpVvYeCbf/AItYni06bBfyp49fWPKWU3UkYk8o2IBafyjk/Yyr+WH5r5Z+Gf7W3xC+Emi6tpek+Ktfax1TRJdAWCfVLox6fA5jPmW6rKqxSr5YCsBhQzDHPGzL+2Zq0vhpo/8AhE/BY8WyeG/+ESbxeLa5/tdtN+zCzMe3z/svnfZP9H+0eR5vlcbt3z18fh8yyuFKMVDVRad1f1aet291ey6XSSPo62BxsqjfNpzXVnb0uuiXW131s7s8wnvpNScebKxVem5s1taDd2caKtxIY4MguwUkj6YrlkOWx91QMfSn3M3RV+6vFfG210PoGj0PUPiPpNppv2XTIJ0XOPNkX5m96zYPiXcaZbeXYsbdWzuZeGf6muN83cNuKXzVDbRztod2TypHVTeP9U16RLe6vJpIJWAZC/ykZ/8A1V9QfsaeIZND0O+8qQxhbiNQQem9XB/PaK+NxdbJVZcgqc/Q17x8F/iZa+GPhH4g1KWby1tb+yD4UsVLeaBwOeoNedmEnBU5/wB+n/6XE1owvKSX8s//AElnp37asl5BrXh3UYZCy3lrcWk3lvu/1UiuhP186T8q+e9T1uSGBjJcGKJ/vBnwD/jXbeIv2ptA8WCO1v8ASdc1a1jO8bLtLFo2HdW2ydfukMpBDHoQCPI/iV4mn8a+I7rVbiG3tmvJdy28C7YrZAoVI0H91UVRk5JxkkkknpxVCNer7Xa/QwwspwpqEjUtvFxaymjhmlktYW3bT9wseOAfoKBrEeoQ7WTcyfMDnkfhXN2M5stIc43eZLj8AKtaZfxu/BCn61P1WC2Oj200rX0PTvAWpXV1o3nR6L4d1ZLOUbnvFt98Qx3EhGQcfTINeiftQ/tew/Fb4fWHg/R7FbTTbWSOe7YQCFd8akLFGg6LuYsTx0AGRmvK/hhaeHpP7cbWtUn0q+TTZH0VzHutJrwcpFcEAlUPZugPUgVvftDTaLb+HPCcOh3mi68um6Q09/rWmW3kNcSTSALBcKDgSwlCoPGVkQ966o4ejGmpRb5u19P8zm+tVnJ05W5e9tTh7XWX02a3w33uuPwrB8LfERzIPnyFJGKuSo1xbWrLyVGT+leR6JqclncMrbuGOPzr6bhqN5VPl+p4udaRg35/ofR2jeMPMC4bH17Vzvhbx4d+6RlXc3foPX+X51xnh7xa1u8beYw6V5TF8fbW3AVZFV19P4uef51+mcPYF1VW0/l/9uPjs1zBUXTv5/ofYWgfE5VZUjbnjOOMnmo/E/xBa5smbzMjB4BIJ/z9K+YPD37RsMYDOyqwPykdcf07VJrX7SCXkf7uZduOBn19/wAaurldWM7JHZTzem4c7Z3nxF8SQTWkvnSKjEYJx1HTH6/zrxqa4hlvG2soByB6kmub+IPxik1EsI5NrZJ69PrWD4U8TSXs2WPU4yT2/wAgV9Dgcrmqd5HJU4mhTn+7P6Yf+CA+3/h1t4D2/d+3azj/AMGl1X2VXxP/AMG807XH/BKL4fs3fUNa/wDTtd19sV9dh48tKK8kfB4yt7WvOr/M2/vYUUUVscwUUUUAFFFFABX5e/8AB1X/AMmYeAf+xvb/ANNt7X6hV+Xv/B1X/wAmYeAf+xvb/wBNt7QB8X/8Fg/F8mgf8FPvi0hYeX9usMDP/ULs/wDPNeS+FvHf2mLfuHA24J/p+tdX/wAFxlmh/wCCn/xcmXcAt/Yduv8AxKrL/P4V8t6J44urCMKrbueT79v8/Wh8Pur+8S31+82pZ+6f7tvRafcey+LtS/tbzFd1kVflOT05xx+deX+J/DC6g5VYyd/JIGcVY0rxlc6syx7tqgjtiuy8F+H21a/j3YYsN2Tznr/hXXR4fnT3RyYjPI1N2cL4T8BzWE6hom+bnp079P8A6/8A9b2j4Z6G1n4TkWRfmFy+SPov/wBaum0r4bRoqt5YU7ST8uBW5YeGv7P8PSLsK7ZGPTHYf/XrweLsn/2G9vtI9ThvNYvGWT6M4nVZ1hTb7dfTv/WuT+IfiSPwt4TmuCpMkzLEnHXufwwDzXXeKtPZT8y9q8q+OLfZvD9sGDMvmHAPTPT+tflGIyt0aTqH6HTxvPLlPPtd1iPVLhp4V2rJzj0zWFeBlG5T8tWvK+zruH3G6iqFzcrEfm3Fefwryt2dC00HLCL6zLKTvi5bHPBpiWv2yCRXA3JyOefrS2Fw1neLcQhZU6OnXcvcH8KtXcSW+2WE5hYZQ9wP7p9x0p7DMe4maVo1OS0a7Tk+9NZc0PIsk5Zdu1uR7049KsYeblQo+tNHJ59fWkHyeppc457UAH8Y+tdpqV2fsq4/ujaB3ri2GHX3NdnpVm2r+IbG1XPzMHPsF+b+lRImWiue2/s/XreCvGGg36ySRSafLGyuhwybMHcOozkZ5r1b9qvxde+LviXayX1jY2slx5+qeZa79t2s7Kqy5ZmzkRdsYJbPOa8x+F3g3UfE/ie1sNPtLi6uJEf/AFaFlVQOWJ7DJHNdd8YtKuPD/wAR/wCyrucXEmj2Fta8NuCZUzFc+xlPTPXvXy+b3VTysvzZ+H8cSTzNWf2F+bPRv2PfE/8AwhzeIL9Y7dmjCAed/q0JimAY+uDzjvVzxr4sufFGpz3U00k0snLyycFsdgOgA7AdB2Fcv8MNLm0Xw34ijuVWJlezfI+YDPm8cdx0x61X8W6h5OnyFvkVvlwT8x+v+FfK0qtpVo9Of/2yB+T5Pb67mH/X5f8AqPQPNvjJrq2McchkVVHmF3B4Y/LwD3+tfNHjPxXJ4r1VpAxW3jJEa5/X6mvSP2pPFLXRsLOFtsZMm/n02cf4/lXj6fKcY68+1fc5DRthYzlvrb72f07wPTUcnoye/vf+lSB+McVLBZNNDuTlmOMetMoEhXoWHoVbFe2fWkmn3TWF6rnKlSOCK+u734m6D8ONW+Cev+JvDOn+NPD8HhS2/tPRbtfl1C1eS5jmVGyDHMEZmikBBSVY26Ag/H7SsU2/e9M9q9t/aI1P7P8ADz4QxsfmbwTbEf8AgTcg/wBK8jHTcMfhJrfml/6RI7aMVLC1o+Uf/SkfcHw4/Y18L/sn/s66jY2F5pHjq++Jmopruga+EJe18MgBrGQqVHlXd18xkXGY1QpxvJrPtf2cZvF+h/bofEnw+0jc7J9n1jxPZ6dcjacZMUrhwp7E8Gu4/Z68dXPxd/Y2+FV/eW9rG+k+F4dBiEAYB4rKWa3RmySS52fNjAyTgCpLz45+NPhfo/8AZuha0lhYxs0gj/s+1m+ZuSd0kTNz9cV6eO+r1MTzTTUOnKtfxa633f4bcuBrVoUuSDXPfW97fgvQ8p+CXwH8P+KfjxJovi+30zxN4f8ADtnqer6jb2N8txbarHYWU935MVxHkFJWhVC6HO1mKkHBrk/2jv2d/AHjP4G+D/iRongfRvBGsXWtaho2p2OgzXK6fM9qllPDPFFPLMUJW6KOA5UmNWAUsc9X4b+MGveEvjTF44jex1DXPtEs9wLu1Q218s0bxTxSxIFHlSxyOjKm3hzgqcEdLq/xctfFU/huzg8E+EbHwr4Y+1NZ+HS15PZTzXIHmzzyPObiWQlYiCZQFFvEoGAwbDD1qaw86Sdrt7rW142d9bWSldJ9dL3du7EUZOtGo1fRbPS9pXVtN21Z+XSyvueDv2R/Deh/CHwNpb+HPh/4o8TfEDwhceIWsdQ1u+svFN/PKt01uulmMNaQiBIomC3Ck3MizoARtxxPwm8C+APjT+z34zGleD/CscPhHwW99FqDavL/AMJpqGuQ28Ek9zHaC5aJ9NWV5gw8kBLeMtuEqkVh6R+11rnhOy0cnw/4YvvE/guwm0Xw94quxdf2ho1s/nDCRpOttI8ZnlMTyxO0RbIztXbk337ZV5/YVx5Hg/wmvjS68PjwrP4vxeNqUmn/AGNbEgRGc2qztaKIDOId+wnGGO6vOjmeXcsbJK0bNcienKlvy35ua77PT3k732+q4xOWrvfR83W7e1/htZd1r7rVreIy2MkbkYpyaXPK3yxyN9B1q9aWcjpv27V9SKv2lzHZLkyLux+VfF8zPoXcyH0O7hXLxkcFuSOlQQ2TSxbs4x1PeupuNXgGiXHlgvNIhBYnoK5y5k8i0VB1brVcz6ApMqHhvl/Ou+8GHd+zt44DfMBqWl9T6mevPwcseue1d/4Jfd+z146/2dR0o8/709cOafwY/wCOn/6cideD+N/4Zf8ApLOQgZSwCqqr7dTUmtuFhhX+Lkn8qish5kuKTUz9q1WOIc7SEH9a7IrU5Cx9pfTLSBTHujZMtx0J/wDrU6G0h1D5oTtkHpSNdlLyTcMpnABHUU9tNVx51sxSQdU9aAL1hK8qNbzfexge9e4fBez8I+Pv2c9S0HVdMt7XXLa5e3j1CNZNzrGr3ERkKsFDFg0e5lYbM9CqmvCrG7+1ERyDbMvANdz8I/Nll8UWi3EkS3GktcpCrHE8sZ2j8QsjkZ/rWlL4jnrR93Q6L4xfC4+AfiPdWdvBIuktDDNYOVyDE8aNsL7V3uh3IzFVJZGOBkV83ap4a8m7dlXgscfn2r6l+IHjC58WeJNLsZE/0ax09LqNpHEsjySxwLKd+0MEzAMISQp3EfewPEdX0qMSspwDuNfWcMr36i9P1PBzyTVKF/P9DhfsLROo5HzDHua+Qr95Le5f5sHcx4NfccukLI+1FU5OfavlnXvhvM7SPheScgDrya/aOCYXdf8A7d/9uPyfiqvy+y/7e/8AbTz861cKmAzfL+dWU1qdEwWfdjn/APXWjP4Okt5drI3B6c8fpT4PDO5lGx/m6HH+fyr66rRinseVh6jlHczba0utfulRNzFjnPWvQfCPgS+tIlZVbkA+n8/wrr/gl8G2169UtDlc/M23r6fhX074X+BFlHYqsiAtJxwPl/Sn7ijZmFRT5tD9gf8Ag3ojaH/glH8P0b7yX+tA/wDg2u6+16+V/wDgi74aXwj/AME8vCFgmAsV9qrcf7Wo3Lf1r6orSOxrHbUKKKKYwooooAKKKKACvy9/4Oq/+TMPAP8A2N7f+m29r9Qq/L3/AIOq/wDkzDwD/wBje3/ptvaAPhf/AILhwRyf8FJvixj7zXdiDz1/4ldnXxva6fJt3bZG74API9/8K+vP+Cy8zar/AMFXfjBasG8uK/0/PoP+JVZH/P0rzHwb8OrW+gVWjXcw6197hcZQpYeDlvZfkfJ1sLWlUm493+bPJvD8MlteYYNz/n869i+GOpQwXEbNxz1NVvEHw2htPmihXoDnHzHn/wCtXKXWrN4fbcu3cudwA9KdTNsPU0jsYxwdZH0vpfie1kjXEi5xwOnP1/z2q3LrFrPoz7WXb5pwOBjgZr5Uufjm2lxf65RtOBg13Hwj+Kx8VeF5JtxZftToPcBUNfH8XYqksvvf7SPpeGcNP67e3Rnb+JYll3YXb3PrXlnxv063l8P2yzyrAs0xi8xh8sZIJBPtlcH/AHs16Ut59tTJ6d683/aNt9/he1PGxbk5+nlt/hX5DmGOpzw06a6/5o/TsLhpRqKTPHtX0a70qLbNCyhejL8ySD+8rDgj3FYxVbh8MflPBxVjSfEd/p07R2dxMsLcMm7Kt9R0NaS3MWonEtpbM3dkjEZ/NcV8jsezqtzBiRLC8WaKRZNrAsjfKSKdqNx9nuJI4WKxSc7SeOehFdFqejQ6jpvmeWTIp5EeFbGOvvXL3ioiKqzeYqkgBkwy1S1Y1qVY1V8sc5U1KTuWmyYBBLY/GlDAP71T11GIo7dqXBBpWGaUGkAInmTxrzywHH1r7G/4Jg/syaT8efGPjLWvEiX39g+G9JYRtChCTXBZTsZwDtAAAJA4DgnAr48s/wDj9j9M5r9Uf+CKmvXHg7QvE/gGaT7PealbQeKVUHDF1CfL/vGOeHI7eWR61vhqalVSkcOYVHGlZHonguDw/wCEPhyq+G1sPJ1Zxc29vaqWSJUXyzvmYYmfcrHcBtww2YBwfE/jX4T0K6sb7Ury4h028vL+P7XfiBrqUuiFRFgHK/Ko4GANoyMmvT/2gvjRoPgPWU0nw/Z6fbaxdCS6uoIIwIdKd1jO0Io2+YcEso6EjPPFfH2qarcajrmrTTXFxI15eySTGRzmVgxALDpkfpXwnG2DnicfTdGvKKgk2la2jflv63Xkj+X/ABT4VxuZ53hsVh8fUoRpqLcI8vLK0n3i/e7c6nHRe6tz6ssUhFjCtsI2tWjBj2/MrqRwffPXPfNcHY+G/DPiNvEcVxdWt1Y2sqlyym3/ALM+/wDdmJ5zzyMD5RnNee+DfGF9ovwn8UN9smWG0a1SINISsCu0gfaM8ZwOBXjera5PrEkgaSUW8jhvJ3nYSM4JHQkZPPua/LsDwxXU68FXa95K66/DK76316Pfq1ofzjwt4P5lRnmVClmc6cvaQjzRWsrezrc8rvm5rTsuWUXzXvKUW4vl/wBrrQvD+geJdNh8O6zNrVn++y8kO3yj8nAfgSf7wUD615ErbhXd/G4Hdpfp+9/9krhScV+4ZJTcMDThKTk0nq7Xer3skvwP9CfD3Czw3D2GoVKkqjipJylbml78tXyqKv6Jed3dgelNiQ9PWhvmH4d6t+HLOS71q3jhaKNmP3pTiNBg5LHsoGST2FesvM+zexPYeFL690q4uoo0kjt1LsokXzNoBZmCZ3MqqCWYAhR1xXpn7SeT4I+Dp+9/xRMQ/K8uRVG5+JttoXwmj0i3s7bU7y6sZLaTUBK8M2lJJMX8qIBsFX5Em5ASCqhghYPc/aPDN8P/AIN9efBq5P0vbmvFzL/fML/il/6bkd2Du8NWb7R/9KR93fsD6ru/Ya8EqzYMJ1CMfhqF0f8A2b9Km+Id6hMmOv8APrWH+xXaSaV+x14IhkUq0kV3Pg9w97cMPzBB/Gr/AI7tjIjbd2eR/WvWrRUonj0ZSVRvzPPnm/0rkAkHp0xV28ujY6JcSocNEhKke3NZUwZJ2yOhOP51HqF1JNYSQqR+9XZ9c8V5skop3PX9o3Y87/sufxDd7EdVXJyScAe5q1qelaT4Rt13Sfb75uiJ91PqawXnktizbmU5wcHFRrdyB8lQ3ua+DjLQ+ptqWLrUZbr5pPX5Y14Aq3pzKUx9nhbA+ZnXgVneeZB91V47VZ095AX/AHy/NgFCOtLqGhLqVzaGBo41Tzs4OwEBfzrCmkaeXdxjNdD4vsVXT4bmOPy5NirNjpk9DXOx5C81olYcWDLjkda7rwOoX9nnx72xfaSf/H7gVwzHAru/Aq7/ANn/AOIHr9s0g4/7aTiuDNP4Mf8AHT/9OROvB/xH/hl/6SzkdO226tIfuxgmodLZpb5pT95QWPsakvG8myVB96Q/oKNKXejN3bgfhXb0bOUsFFuo/wDaXmpbSRoz33LUePLYGpyAx3A1AFlrZb6ISJ8syjJ98V0fwn1M2nxC02NpPJGoCTT2bspnjaEE/QuD+Fc1AxifcPx4pNTuZLB0uImZZI2EsbD+EjBFVF2dzNq6sewNpk0moS6hMyyCG3SFnX7pDEFSP1/HNeG6/wCIg+oyKpO1WIr6vuvhrdeJfhpaeK9NlTUNH8RWz/KkREmmz7gVhYd1LBEDjvwe2fhm/wDEPmXbtu+85Ir7nIqfs51H0ai/zPls6l7SEI+v6HfaVeLJt69R0NeJQ+VNnKr15Oeg/wA+vtXoWieJ1LqCe4H1rxay8WRs24N8uSM+nNfsPA8let/27/7cflHGFOVqP/b3/tpq+JNIt87l+XcOcc8/y71zgto4pRx8obPP9fT/AOvTfFHjhRIsat82fz/z61lx68Zj/Cc9x2/zzX2le19DycsbUNT6j/ZYntfMhVvlYjB569P1r6Z0myRoFkB2qV5JA+X6+9fnx8LPiq/hK9X94cbvXHp/n6/jX0d4X/aejGmZM+11XnJ5rzpU3e51VpK5+9P/AASmx/ww94Vxji61Ece19PX0XXyX/wAEQvFy+Of+CcPg3UlfzBNqGrru/wB3U7lf6V9aV20/hVzP0CiiiqAKKKKACiiigAr8vf8Ag6r/AOTMPAP/AGN7f+m29r9Qq/L3/g6r/wCTMPAP/Y3t/wCm29oA/PD/AILCeIorT/gsR8boWbldR00Yz1/4k9genTvXOfDTWlkKqrAjsck/Q/8A665H/gun4zk8O/8ABaf49hTtC6lpfGcZ/wCJJp9eb/Cr9oK2sJ0WRju4zz09f896yxGImo6PY0p04yaR9V63ax3OnFmXezcA9Tk9uffn/GvE/inoBt7SeTauTn2rqdJ+MkPiOJVi53DAUkdT/j6VP4ktv7Y05t0e4tnr/j/nrXylTN6kanK2fUUeH4zpczPjzxzrdxpSyhvu4OM84r2b9jzXW1P4eSZ3f8fzj/xyOuX+M3wwa4gkkSNhxz8vWu2/Y18KNpfgqaNlb/j/AJCpx/spXTxHWlVyvmfdHl5PRVHMOXyZ7hpkbFF/iLAcY6V5z+1NcSQ+FrO1jU+beXQQAc4AQ54/EfnXsEGk/ZoFZsKNuWJPC9s15D+0Zra2Wt+dFtVdPne3hZhu2HGN/wCJGfy9K/LJxlfV6H3sJI8dv9Nh8GWKpN89/IOUB+57Gsu11u6gPmQ7Qv8Au9fzrfvtC0kRC6TVJPEGpTfMYI42jVT1+dmwTj0UH6isOeWQvtmt3jZT2Xbt9vapdtzVSui+mqy3KCYTX8LE5KgqU/X/AAqj4i0uVfLvNyyLJyw27WXJ7j39RWhoNvDNexeYFMYYFgT2HWrHi5ZGtbhoYZDasfLVscynP3sddox9M4oiCepyoTcWJ78fhQRtI/IU1JAv90c9B3pWHzf1qupQ5l3GhRtFDIH69uaNny+xPrSA7T9nz4Qah8dvixpnhnTl/fXxd5XZGZYIY0aSV22gnARWPHU8d6+v/Ct1rvwE8X3Wq6T4ksZNSuNOe1a8glPmosqqDtLLgMoAAODggHsK5L/gjtJpvhf4leONe1a5trG1j8NyafDcXBVVWWW4t2OCejBU6+hIrvfjX8SbXxF401KS3MF1C8ihZUXJdtqgt+Jyc961koxpc99Tx8ZUbrcnRI4vw1NNdeIDIZJJGkLuzPOzs5OSWYnqTycnnqa5WbV1TfJ1MkjOce7E12Oja2tlcPJ5e1lhkcADnha80eQpCv8AsgCvg80f+0Xv0X5s/I+NP+Rgn/dX5yOvsdQa++CvjTj7tzpvX3llrz4DArtvCpZvgn459PtGl4/7/S1xOea8TB/xK3+Nf+m4H5lkP++Zj/1/j/6jYc4P43HnS/8Atr/7JXBMcV3nxubnS/8Atr/7JXBq28V+g5X/ALrD5/mz+muDf+RPR/7e/wDS5Cfeb2+tOjla3lVkdlboCDSMcCkzuOOQa9E+nJmvZnXazsyZ5HavWP2gV3fDT4Ms2cf8Iecn6X1zXkI/nX0Nqvgk/Et/2dtDwPL1bRRbTHH3Yv7Sn8w/gm4/hXiZk7YvC/45f+m5nfhP4FZLtH/0uJ9r/Crw83g34K+D9HkTZNp+jWkcyMPuSGFWkH/fbNSa7bC6DYT86ueIPERvrxm27dzFsenPSqou18j5tuW69q9RVFJaHnU6LjLU4HXNBZfMYRjP61zNxZvHdrgdHXtnv6V6frcUcsOfl46Y/LFcVqiIbqNct8rqOOe/rWWJp/u2zqpTXtFE8l+Kfh3+wdfDRjEN3mRVH8J7gVzeSVXr61618cdBW68PR3C/etXyfdT1/pXkKyMsny9elfm1KV4n2dSNpaFgDy0IH3v5Ve8OQb75d4+VTk1nxzbvvfKcVpaFemwvFk27/Y1p6mTOu1+yg1Xwpcxw/Mwj3Ekc7h0/livMV4w3tjFehW2v299N5ctwsKHOF2cZNcDdWv2C8eJxteNiuT3Fa8yewqd1oIw6/Su/+HcXmfAT4gD/AKetHP8A5FnrgHPy16H8P5PI+AHxEf8Auz6SR9fOmrzM1/gR/wAdP/05A7sH/Ef+Gf8A6RI4C+cz3pVfuj5Fq7EixRLt/gqnp0OJNzfxdKuQvslZW4rvl2OQn/1y570QvsOP4ehqLBt5MjpUykS/MuKj0DUsKyqP51DqzZssegOKfH8v/wCuq+qSYg/PNVck+wP+CY/xATxF4cl8J6hiS0V541Lj5V3p5i/TLKR9a+AP2kfAVz8Gfj34v8LXC+XJouqzQqoPSMtvj/8AHGX3r6E/Yq+IVx4J+La2kc3k/wBsReQrejg7k/kR+Ncp/wAFQbCXxF8bovGTWkdpNr0K294qHKm4iRVL/Vxk/wDAa+y4fxEZU503urW9Nf8AgHyOeVFDFwpv7SbX4Hzxp+oSR3SMrdWBr5nsfF14Y2jEjKMkHAx3r6U02PM6H3FfLcNmwZsAcEnkV+wcB3/f/wDbv/tx+d8YWtR/7e/9tNmz1N5nH3stySTk963dOvWf8c57+3+frXM6bD5Mihu56V0mnNuX/e9+g+lfeVr7nzeCRsIfNUf3fXqavW+rXEFs375x2OD19qr2cAkCk8/lxippIMQnBz75oo22M8wTWqP6UP8Ag2zmaf8A4JBfDtmO5v7S17nOf+YxeV92V8I/8G2CbP8AgkB8Ox/1E9ez/wCDm8r7upS3NKfwL0CiiikaBRRRQAUUVk+O/Fa+BPBWra29jqWqJpFnLeNaadb/AGi8uVjQuUhjyDJIQCFUcscAcmgDWr8vf+Dqv/kzDwD/ANje3/ptva+7rv8AbP8Ah3Zfsit8cX8QW5+HP9iDX11FVyzwFQRGI/veeXIi8r7/AJp2Y3cV+f8A/wAHPXiP/hL/ANgP4Xat9h1HTP7T8Spdiz1CDyLu18zSrtvLmjydki5wy54II7UAflz/AMFlfgxq37QH/Bff43+GdLuLPT991a6hfajells9HsLTw5ZXV5ezsoZhFDbxSyMFVmYIFUMzKp8h+DX/AAT78e+PvhVoviq21LwTbah4i0a+8SaD4Ru9a8rxN4h0uy8/7Te2tmEKtEv2a6KrJIkkotpTEkgXJ+xP2/Io7/8A4LOft56JBzr3ij4V3dpo6j/WTS2+laFqNzEnfc1hY3oAHLDK4O7Fc98AfiR4NX4i/s+fH+bx14JsdJ+C3wyTw3rfhi41VI/EU+q6db6jb29pa2OPMuIr03FtIJ0zFGJrjzWQxkHjrabnZhKbcrxPIP2NfhL4q/aD8e6b4a8G6HqHiLWr4Bhb2kLP5cZZUM0hAxHEpdd0jYVdwyea+krr9njUobTx5aGewnv/AIc3TWmsW9u7O7RpdNaS3ERKjfEk5iQ5w2LiMhcByvzn8E/h7L8B/iT8HdQ1nVtFuNP1+LSPE8VxaXDSJZWp1CSF1uNyrsmje1m3qNwAUEMc8fZXws+Jem63+0p+0v4jt7y2vvCraT4vuBdxtut7tL27a3sSp6N5lxcWrL/30OnHx9TDxdRN73/C17/LqffUcRKFJpbW/G9rfM+U/HfgsXckirs4DZHPPXoK1Pgt4bXw5oMi4X5rtzkHplU/z+NXvG+qQyOrcNu5x6+maq6P4sh0fwVKse3zpLlo12gYyVXn8B/SujOqnLlln3X6nz+Hp3zG67M2PE3iwX2rx2ELH7LajzLp16SEAgIPYck/QeleR/GvXVvdRull2zNO3zg9Ay43Mf8AePzf8C+tbWu6p/YWlldzecx3ykdST2/Hj9a8z8Vak+pSbn+8w45zwOn5frX53WZ9RThrcwp9LtJyGhkkhkHRSf5GmXGp3NlFt+2TSZPyq3zD9adBB9ujMLH98pyrDjj0zVV1a3mKzR7m9SKxudXkPttduI3Bb5vbGBXU22qprKecsM0jKP4ZOYvYDt+tcstzGvRfvY5xV3R9QNqzBcruz0ouS4mfr+nyWV+XaPy/OBbA6KapqOc12WvRw3mmRxyLukk5GOqntXHshjkZWGGQ4NUNB1NSWtk11cRwxq0kkjBVUdWJOAB+NRx8Seuf0r1n9mnwTZyeLbTUdWVtgbNrH/eboG/Xj8T6U9UtCKs1CPMzufht8O7nwd4YtreaF1kbEsqk4DN+Pp0rpyfKclvvNksc9fxrZ1XTbd5fllmhLZIyx6/TJ9azrixMO7dcRbVPLNxivMqwmnc8W7buyvNcyRWF9t+VRbS7u2crjH15/WuQlBKbVHPvV7WfE0dzqi6faszRsSJZCfvn0A/OpvsKqPu7vWvmszX775L9T8r40jbMFf8AkX5s0fCqMnwU8dq3/PbSyMf9dpa4ivoLwN8B4/Efwh1UQ67bxw+IVtZWke2OLI28js6t8/PJIzkcDPevAbtYYrqZYJjcW6uwjlKbDKoPDbcnbkYOMnGcV85leMo1q2IjTd2pq+jVvcjHql1i1by7H4twdxBgMfmWa0cJNylCsnJcs1b9zSp680Ur89Ocbb+63a1m/PfjgM/2X9Zf/ZK4RV2iu7+Nxz/Zn/bX/wBkrgh1+v6V+l5V/usfn+bP604N/wCRPR/7e/8AS5CvytDDH+HSgpkUNzn6/lXoo+nDdtr7s/ZX+H/9s6P8LvFEqbofD/g24t4SR1uJ7+5QflEJc+hK18R+ELfStQ8VabDrV1cWOjz3Mcd5dW6B5LeJiA0iqeG2g7sd8Yr9V/A/wYsvhH8INC8P2uoz6jDotu1vbzsig3IeRpNxxkY+Y9Owr4nirOKGExeDo1L80pu2jd/clHe1tHJX7J3eh9Rw7ltTE069RW5YpX1X8ya0+T+6xlXVwbifHPzHqT0qnrLNajd6962rzwpMhVlB24zyOBXL+MZ3soGVmPy84yRXo4PHe8ovYxxmW6OSKOo6/wCSnzt04GT1rk9R10TalD83ymVQBnryKpa/rqxlvMbnGR7GuUTXlfVoNrbl85P/AEIV9DKSqUnbsfMq8Ki9TuPijex/8IpceYcLkZI714s0exztA9q9D+Lmp/aNDW3DHlgzDPUV57FL5lr/ALS/Ka/MYxtoj7zm5hsbAt6n+dXU822kVlXdg5+tZhU5+Xd7EVatr6SJhkbqGuoHQxatb6iirNaRoehbGP1FVPHOjpFY2txGjKM7GLNuyO2P1qO0dZyG/wAir3jHTZ5LQeSzTR4UKFU4AH6f/rqo3J2ZyZTCba9E8FxA/s9fEFO/n6Qx+n2iWvPYUaWdVx35FfSnwg/Z+03xd8B9Ymh8TeXa+JVtZLiR7If8SxrSR2kRv3nzDJPJ28YbBzivD4gx1HC0ITruy54dG9pxk9k+ib87WWp6mVYWpWquNNXfLLqlvFpb+bR8626eWfUA8VJKpeQMvSpL4Qi7lW1maa3WVlilkj2NIgJ2sy5O0kYOMnGcZPWkRi04XHHevYvfU819iRZBImG647UyPMbf0qSSIhzgKfSl4UbW69CAKNUIlWXd26d6o6ud6cfSrGd3rj0qGaxkuW+p9aqIEfhjWZ/D3iSwv7VttxZzpNE3oVII/lXsn7TAb9p/4a61d6TatefZLU+J7WVUCtbqqj7TCMY3KuXAx90cHpx5Hp+iLHG0xkXdHOkYjwct1JP4f1HvVD4FftBat8BPGBlhkkuNJuGljubVgGBR1KOVB4yUZgR0YHB7EfacI1qalVjVV4tL5PXU+S4pw7qeyqQdpRba/DQ8o0mAGZPTINfNaWa8/N8u7I/M19X69YWOneMb2HSZvtGmrcMbSQDBMROUyOxAIBHqDXyxFBvduOd3av2LgXR11/h/9uPzbi6V1Rb/AL3/ALadP8BvghqXx/8AihZ+HNMuLOx823utQvtSvWZbPSLC1t5Lq7vJyqs3lQ28MkjbVLNtCqrMyqe+8HfsdfETVv2Vdc+NTeHbzTfhtodxaWv9r30bwR6pNcXJtRHZkri4McqsJCpCx4wW3YU7/wCwC6XMXx60ODH9veJ/g3r9royr9+eW3lstRuY07lnsLG+GByRled1Z/wCz18S9B8M/sLftJaDf6tYWeueKW8InRrGWXbPqQttUmluPKX+Ly42DtjoCDX3VWN0fM4OXLsZn7PHwG8ZftO/Eyx8H/D/w7qnirxFqLDZa2UTSeQm9EaeZhxFCpkXfIxCqGGSK39d/Zk8QaNL8VbM3Gl3WqfBu/Npr9pbSu7vAl82nzXkBKDzIIrr7OjE7XAu4m2FRIUx/BfgaX4GfGf4J6zrmseH5tJ8Tf2P4wjubO6aVLCzOpyQulyWRfLmiazn3oNygAEMc8fRngHxppOvftkftk+NLW9ttR8FzeGvH8322Nt9tfR6leNaaaVbo3m3l3ZMnfgMPu5qaWheMtNH7Jf8ABtrH5X/BIT4dr/1Etd/9PF5X3ZXwt/wbef8AKIj4d/8AYS13/wBPF5X3TRLcqj8C9EFFFFSaBRRRQBxnxw+IPif4ceE4b7wn4D1T4ialJdpC+mWGpWenyxRFXLTmS6kjjKqVVdobcS4IGASPnD/gmL+0V8TviN4Kew8TfDfxc2kzeLfFgm8V6l4m0++jsjHrmohLExC4a4YQFRaKVQxjyQUPlbTX2FWH8P8A4b6H8K/D8mleHdMttJ06a9u9SeCAEI1xdXElzcS8/wAUk8skh93NAHw3ZfstQRf8FOv+FOnVC3wZsYB8dIfCnkkQprT3jWott27/AI8luw+oCEAKLhhwVAFee/8AB1Vx+xf4B/7G9v8A023tfol4k8IeCfBXjy4+Jerw6NpOuQ6SmhT69eTLAY7E3HmpbtIxChDM+RnqzAd8V+Zv/Bzx8WPCvxH/AGPfAtr4e8TeH9euofFhlkh07UYbqSNP7OvBuKoxIGSBnpkgdxQBL+2//wAG6Pjn9p//AIKEeK/jz4V+N+m+BdQ1rUbLU9NiTw/NNd6VLbWdtbqwmW5QFt1vuztHDYOec+MWH/Box4u09QF+Onhn5RgAeEpgAPT/AI+/5Yr9lNK/aG8A69qlvY2Pjjwfe3t5IsUFvBrNtJLO7HAVVVyWYnoBya7AHIqakVNWkaU606bvBn4v2X/Bq342t5Fab47aHcvHGkKNJ4ZnZkjRQqICbo/KqgKB0AAA4Fdp4a/4Nv8Ax/4a8P3mlQ/HHRU07UZ4Lm6to/Dkyx3EkIkETOPtXzFPOlxngbzX62UVwyyvDS1cfxZ6Ec4xcVZS/Bf5H5I6t/wbXeL9Uh2/8Lk0JMAgH/hG5j9M/wCk81T0X/g2Z8XaWqpJ8YvD1wqSNKM+HJl3M2OSPtB9B+Vfr1RRWyrC1qfsqkbx7Xf+ZjHMcQqntFLX0X+R+PPiL/g2M8Z65IzL8ZfDiBuSG8OTnJ/8CKwbn/g1W8ZXCkH40+Gfm6n/AIRqfP8A6UV+0lFcH+q+Wf8APv8AGX+Z0LPMatp/hH/I/E9f+DUTxokwdfjZ4ZXac8eGp+v/AIEVqal/waueKNW09Vk+MHhtLpRjzU8OTYb3I+0V+zlFH+q+Wf8APv8AGX+Yf27jt+f8F/kfiX/xCe+Msf8AJbPDH1/4Rmf/AOSali/4NRfGcT5/4XZ4Y/8ACZn/APkmv2vop/6s5Z/z6/F/5lf29jv5/wAF/kfi2P8Ag1Y8ZMy7vjR4axGPlH/CNz//ACRVO9/4NQPF92xZfjR4YSTPUeGp8H/yZr9sKKX+q+WL/l1+Mv8AMX9vY7+f8F/kfinoX/Bp/wCKLLUFkvPjN4cuIV5KL4bmXd9T9o6V31t/wbU+JrO6hki+LXh6PyXDLt8PTdu3/HxX63UUpcK5Y3d0/wDyaX+ZEs6xkt5/gv8AI/L/AFD/AIN9fEV8ArfE7Q2VST82hS9/+29UB/wbs+IEk3D4n6Ezdi+hzHH4efX6n0Vm+Esqbu6X/k0v8zP+1cV/N+C/yPyRuv8Ag2p8TSeKodRj+LHh9VXe0sX9gTDezAjP+v461txf8G7HiWMY/wCFoeH/APwRS/8Ax6v1TornrcD5LVlzTo6/4p//ACR5WPowxtT2uJ96VrdtPlbufl/D/wAED/HWm+B9a0Cz+K3h2O01hUBY6HNug5Ilx++58xNqnngA+tcP/wAQ0fi7/orXhv8A8EE3/wAfr9eKK5qPh3w/ScpQw9nJ3fvT1dkv5vI8TB8LZXhalWth6SjKrLmm0370uVRu9e0V87vdtv8AGfx1/wAGtnjDxl9l2/GPwzb/AGbf18Oztu3bf+ngen61z/8AxCaeMj/zWzwz/wCEzP8A/JNftxRXp0eFcqpQVOFLRecv8z63BZjiMJRWHw8rRjeysnu77tN7s/Ef/iE18Z/9Fu8M/wDhMz//ACTQP+DTXxkD/wAls8Mfj4Zn/wDkmv24orX/AFZy3/n1+Mv8zr/t7Hfz/gv8j8U9J/4NPfE1trdrNdfGbw7LZxyq80KeHJgzqCNyg/aOM8819iaV/wAEjfEFpZWdvN480do7KPykVdHkAx2/5bdhgfhX3RRXnY3gXJMXKE69G7hdxfNJWurPZ9U7HbhOLs2wykqNW3Nv7sdba9j4tm/4JR6nNB5f/Caab9f7Kf8A+O1xPin/AIIoa54ifK/ELSYfro8h5/7/AFfoRRUR4ByOLuqL/wDAp/8AyRvPjbOJK0qq/wDAY/8AyJ+WviD/AIN6fFOrO3k/FTQYQex0CU/+16ybH/g3I8WWl1HK3xa8PyGNw+P+EfmGcHOP+Piv1gor0I8K5XGPKqen+KX+Z5c8+x0nzOf4R/yPyn8V/wDBvH428Q3junxS8MRxlQFDaJOT1Oc/vqx7f/g268axwsrfFbwqWb00K4/+PV+uFFef/qBkH/QP/wCTT/8Akjs/1uzb/n7/AOSx/wDkT8j1/wCDbvxov/NVPCv/AII5/wD49Tj/AMG4Hjg/81W8K/8AgiuP/j1frdRR/qBkP/QP/wCTT/8Akh/64Zv/AM/f/JY//In5M2H/AAbleNLWUM3xU8LMB6aHP/8AHq6LTf8Ag358Vaam1fiZ4dYN9/dosxH4Dzq/USil/qBkP/Pj/wAmn/8AJCfF2bPer/5LH/5E/JvU/wDg3K8X3WrTXUPxQ8Lx+cclToU/B/7/AFb3hX/ggt8SPCHw88QeG7X4r+F/sPiBo2kP9i3GYMcSbR53/LRQqt7L71+o1FZ1vDnhytHkq4ZNXT+Ke6aa+13RpT40zmm7wr20a+GOzVn9k/JMf8G4HjRenxV8Lj/uB3H/AMep0f8AwbieNY3LD4q+F/8AwRz/APx6v1qorT/iH+Qf9A//AJNP/wCSI/1vzf8A5+/+Sx/+RPyZ/wCIcrxqzZPxU8L+/wDxI5//AI9Tv+Ic3xn/ANFS8K/+COf/AOPV+slFH/EP8g/6B/8Ayaf/AMkL/W/N/wDn9/5LH/5E/Jv/AIhzvGYH/JVPC/8A4I5//j1C/wDBud40V93/AAtTwv1z/wAgOf8A+PV+slFH/EP8g/6B/wDyaf8A8kH+t2bf8/f/ACWP/wAiflHb/wDBuz4yigdW+KXhht0hf/kBz+3/AE29q5G8/wCDYHxdcysw+MXhxdxJx/wjk3/yRX7FUV1YbgvJsO26NG19/en/APJHPiOJMxrW9pUvb+7H/I/HGL/g158XROrf8Lj8N8HOP+Ebm5/8mK8vi/4M7fGkWf8Ai/3hnk5/5FCf/wCTK/d6ivawGW4bBc31aPLzWvq3te27fc8vGYqriuX27vy7bLf0t2Pwx8If8GjXxK+H3i7Tdf0L9pDRdI1vR7qO9sb618Jzxz2syMGR0YXnBBH49DkcVi/8Qb3jMH5fj94WVRwAPB04wPb/AEyv3oor0HJvc44U4x+E/Cdv+DP7x1dSpJdftCeH7qSONIUaXwncOUjRQqICb0/KqgKo6KAAOOK3tH/4NO/iJofhW/0G3/aG0OLRdVuLe6vbNPCk6xXctuJRA8g+1/MYxPNtzwDIT1r9uqKSdtUOdOMtJHgH/BMP9i++/wCCfn7GXhr4V6j4gtPFF3oFzqFw2pW1m1nHMLm9nuQBGzuRtEoX7xyVzxnFe/0UUioqysgooooGFFFFABRRRQBzPxn+Eeh/Hz4TeIvBPiazW/8AD/irT5tMv4TgM0UqFSVODtdc7lYcqwBHIFfy1/tF/s9ax+yn8e/Fnw78QRquseEdQewlmWPy1vI+HhuFHZJoWjlUHkLIM85r+rqvyv8A+DiX/gnfrHxm1HwL8UvAejyal4mnvrTwbrFrCuDcrdTiPT52PRQlxKYWc5OLiLOFj4APGf8Ag2t/Yfh+Ifxm1r42a1p8Z0rwNu0nw+XjG2bU5Yv38w/6428gUcYLXWRzHX7bDgV5j+xr+zFpP7HH7M3hD4c6OVmg8N2KxXF0E2m/unJkuLgjnBkmaR8Z4DADgCvTqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACjHNFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH//2Q=="}}},{"cell_type":"markdown","source":"The label file corresponding to the above image contains 2 persons (class 0) and a tie (class 27):","metadata":{"papermill":{"duration":0.275476,"end_time":"2022-01-18T15:48:43.484208","exception":false,"start_time":"2022-01-18T15:48:43.208732","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![10.png](attachment:caf5c201-af01-4c90-b306-3e6e43787992.png)","metadata":{"papermill":{"duration":0.214991,"end_time":"2022-01-18T15:48:43.931178","exception":false,"start_time":"2022-01-18T15:48:43.716187","status":"completed"},"tags":[]},"attachments":{"caf5c201-af01-4c90-b306-3e6e43787992.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAi4AAACHCAIAAAC+pEWHAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAEjfSURBVHhe7Z15YBVFtsaDCKKiI4swPtkcUdQZwAVkUFyQ5+ByIcxDxREEFDDIIHsIIpsKCCKLg4AgCiowGFlEloEg+5YQEhAIkS2BIBgCIWExCCHhffQ5t16l+3bf6psbbvKs3x/JrdOnu6tPnTqnqtewHTt27Nmz57JGo9FoNCEiLC4uDv/y8/OprNFoNBrNVSYsOztb5yGNRqPRhJAwnYc0Go1GE1rC8vLy+KdGo9FoNKEgjP9rNMUeDJvOnz9/5syZ7OzsLINTBpkGJyVOnDiRYXDp0iVeWaPRFGN0KtKUGH799dezZ8+eNvCbjdLT05OTk7FIZyONpvijU5GmxIBkgykRUMlGv/zyS2JiInR0NtJoij86FWlKDJjoYFZE2QhZZ8OGDRs3bkxLS9u7d++2bdt+/PHHdevWbdmyBcK4uDgIo6OjMZFC0ro62Qi7iI2NxU5PnDjBIo1Go4ZORZoSAyY6SEWUjTDdSUhI2LRp0759+3bs2IG/u3btggQ5ae3atZCAr7/++sKFC1ctG3366aceg1atWqF6LLVn9+7d472sWrWKpRrN7xKdijQlBqSic+fOUTbCrCg+Ph75BtOgzZs3Iw9hVoRiYmLiDz/8sH379piYmKlTpyL9XLVs9OKLL1IqAkuWLGGpPcuWLWNtj+df//oXSzWa3yU6FWlKDJSKKBshtZw4cQIJ6ejRo/iBv+kGx44dO3ToEIpJSUmLFy/Oz8+/atlo6NChnFg8nv3797PUnpKeio4cOTLBS3R0NEsDZd68ebytCRPS0tJYqvndoFORpsQgUhHIzs5OTU1FzMrIyPj555/xF8WDBw8iFSFE7tu3D8lg0aJFOTk5cjbCWsheRZSNUI2xY8f27dt36dKlLHKkpKciTD259h4PjpqlgRIZGcnb8ngSEhJYqvndoFORpsSAVIR0IlLRxo0b4+PjkXISExM3bNiwzWDnzp0IZFsMkIrmzJnz+eefr169OjY2Fkt37Nixd+/e48ePX7x4kTcaOnQqktGp6HeOTkWaEgOlIspGZ86cQfHEiRNZWVmYEh06dAgJBjMe/E1PT4cccyMIf/rpp+TkZPzGbAnQSTzoYHXeqCPYBcKiM+fPnydlTMVYlJCASpJQsHv37iVLlsydO3f9+vWoJyR+UxGqvXnzZqwyffr0FStWIImKfQkg5F0mJGAKCAkmZ5iWffbZZ/iLaSKp+SQpKWnhwoXQnDlzJpRxsLzAHydPnsTu/v3vf3PtPZ433niD6gCbQwFmpyLAb1qLQCvwgoQE2AQSGj1ERETwtjwebJkUMJeltXyi+NIy/W6zEoFORZoSA2Iloi2lotOnTyOoIewi9qWlpSHeIccgLh8+fBhyzH527dqFRUgP+EuTIXreCCBRIXvxRh35+uuvOTraI8J9v379WOTxUJAlMIHr2bMnLzBo3br1ggULHFIRjvSdd97hZRIvv/wyjoWVDOQtI+m+9957XPBC926wthck4+7du7OGxJgxY1TOXn7//fe8goXRo0dDAbmTyx5PeHg4WoRWzMvL69atGy/weCZMmAAhDorLFhwuGv3nP/9p1arV4MGD4QkssgDLQwFqUGaRpriiU5GmxECpiLIRosw241miPXv2YOqAVLR9+/YtW7Zs3boVEvzF5AM/IEHsphN3nIgMFGcAhU9FiOwDBw5kaUHefvtt/lUwFSG/IlfxAgstW7Zct24dqxZMRVFRUfyrIKZrVzBd165deZkFZCO/0wi/qQizt9dee41FXiFYtWoVizyedu3anT17FsLAUtGAAQNI5/XXX/c5+YMQi0gHyizVFFd0KtKUGJA/EOMoG505cwZ5KDEx8cCBA0g8mPokJSUh8WBmgPkQfiMD4feGDRsSEhKgefTo0cyC72LgjTqCII7Zgwn5pm38PnXqFCn7TEUzZsxgkRfkEv4lIaeiyZMns9TjadOmzdChQzFDwtCeRR5PZGQkqxZMRQRWadGiBRcMXn31VdY2kkTv3r15gTHNGjVqFOZScvKbNm0aa9uANA87yMkG1SPjzJw5k3RiY2N5mQGa6eLFi/IqIqEiJWNF+QChRlvD7I10rKDR/+d//of0UXk5PQMUxRFBDcq8QFNc0alIU2KgVETZCANqTHQQ4AByT2pqKvINIg6K+Lt3716kori4OOQqpCJMm+jOb5GNFFORFYzT5VSELfMCm1T0yiuvsMjj6dixI+SYJ6G2YlBPyKkoIiICMwbwxhtviHoePnyYVY1kJq5FyakI0Tw+Ph7C9PT0cePGsdRAnMWKiYlhkcfTvn17mILksJgc2VVO0/m9bWHkyJG82OMZPHjwd999xwWP591332UlLwHctrB//36YlNfxeJD1MZ8DcvqHgsqN9ZqQo1ORpsSAuPzbb79RNkIqQlhHNEQMBdu2bTt48CAyTVZWFobSmKkg9xw7diwjI4N+YBGERjK6ko0CS0Xnzp1DeuAg5/F88cUXvMDAmoqwLy57PJipyBfwUU8kG15mc9uCiV69erG2x7Nv3z4Syqnoyy+/JCHAAbLUACYiufzw09q1a0lIyBd4kNFZao/fVAQ7y2lbTFNeeOEF67W6wO6gy87Olk9L4ujkA8QiKLCqpnijU5GmxECpiLIRpgUIZ5Ag3lGywQ+6neGM8b5U+guQga68M7XgW1MDSEV5eXlymBs4cKBp6mBNRQipXPZ4MNchNYE8cbGmIhwmJnbz58+fOXPmpEmTPvzwQ1Y1EKlFTkWm01CdO3fmBdIsTRZ26tTpHQmWGpguL/lE5WbuxYsXs4bE999/z4slAr6ZOzc3F/bhNSUmT56MRaykKfboVKQpMSB/XLhwgbIRnaBLTU09dOgQYiJmQhjI79mzB383btyIH1BOTExEyEaA3r9/PxKVnI0CSEVICRzkPJ4OHTpYh9vWVCSfkho+fDipCebNm8fLCqYiHOPUqVPDw8N5mS98piIhJHyeMJSvCTmA4E76DqikIuRv09Wsbt26+fxcZyGfK1q+fLm42oQfKPICTQlBpyJNiYFSEWUjpKK4uDikHATZ2NhYRGGkJfzYt2/f5s2bEcvS0tK2bNkCBbpodPDgQXlu5DYVrV+/nsIcQKQzPStDWEM/AiKXjYslpCaYPXs2L5NSUX5+vil2E/KZLhBwKmrTpg2LPJ62bdu2lyAh/Z4xYwbpO6CSitBYXbp0YSWDF154AbNYXixRyFQE0NaY8wH8YJGm5KBTkabEgPxx8eJFykY5OTmY6yDBICsgCR0+fBjTI0yD8JfkKSkpKGIp0g9mMEg/+CuykatUhE3Jkwm7k1fW0I+9c9kI8aQmGDFiBC+TUhEqzyLjXjh68pTOBMrbDzgVybdx47hIGBgqqejLL79kDYn333+fF0sUPhVpSjQ6FWlKDJSKKBudP38+MzOTsgv+njlzhpINRtz4CyjlZGRk4Dd+0HUjkgP1VIS1OnXqxDHS4xk3bhwvsGAN/aikfF+1nMOSk5PlU3AiFUVHR7PI45kyZQoJATYl3+4ccCoaM2YMizyeb7/9loQEjLPTy0nvnXUAFli3bh2MxmUvcipq164d5nO8wAuGCOIYkYnlCZl85yEhpyKVK1Wa/2foVKQpMSB/5ObmUjY6d+4cxs5JSUlpaWl79uzBZAJTkCNHjiAc4zckKGJuhBCMoA9NUzZSTEWmB1Rff/11rIhNyaAypOwz9Mt33CGXzJw5MzY2dt68ea+++ipLDUQqkq/zY12kWwhRYdNtBQGnoq1bt7LIuCl848aNNOWCGbtL71+AnPRRW0qBSCqmZ3dgWFImZs+ejSkpzEtL8/LyMFXiZR7PqlWr5CtnHTp0ML0bafDgwbzM40FN4uPjjx075vOqkub/JToVaUoMlIooG2GWQFknJSUFP3788Uc6WYdouMMAixKNB2Dj4uJ27dqFaZO4pw7hUjEVya9Zs2PNmjWk7Df0OyBSEVICi7xgPsG/JAJOReCTTz5hqcGLL74oP50DunbtKm4OlDMxNkhC4uzZs7xAQrxYQc6pqCTmTGg4OTFPnTqVNAkUeYEErMGLNf/f0alIU2IQqQjk5ORgVH748GFKSPRhCPzGPIkuFFFaQooCP//8M+KmnI0UU9GECRM4KNrjnIqAfHuCAFONiRMnckFKRcB03zbRrl07+fp/YVIRpho+d0FgR7Jxhg0bxgt8vT7Hah9KRaYnijAUIH35FQwtWrQQT0cBZB3r3X06Ff1+0KlIU2JAiMRoXU5FSDwIspAjFe3cuRPpZ8+ePRkZGUhFmBghAm7evBmZCRKkIjkbKaYiZAgOivaI01b9+/dnkccj38SFCcGcOXPkN60hf6Dm8v11mKmwtqE/c+ZMeTI0ZMgQBGX5HB2yLynLb/ERQsKuPgAGxC7atm3Liw2QPL744gtYiZUMtm/fTtd4sBSJhKVefvvtt6VLl+JwkFnpqhi94XTs2LHGJq9guovdYZqFKSwM/o9//AOLsDVs8+jRo7xM8/8dnYo0JQZKRZSNkIqQhJB7MBXAD0T2+Ph4FDFPQh5KSEigN9GhmJqaCvk575fIKRsppqIggmpjp6ibKdY7gAyKiZ3pmkoQgRkphcOA8n0KJpBvoHPe8n0KExcvXsQxcqFwYDviCpzmd4JORZoSA0J5Xl7elVx06RLiIyYKp06dom9DIN8gyh8zvkiEyQHlJKQrxHEkIYHIRlc/FWk0Ggd0KtKUGJBmkIRENrpyns4AI2hAzxshRQEM4QFSkc9shFSETfFGNRpNMUCnIk2JITMzE1kEuSeAbCQSEraAuRTdJK3RaIoJOhVpSgxIJ8eOHdtjvMtne0BgRayOjWBTvFGNRlMM0KlIU2LAZAgpBBOa9PT0XwICK2J1bASb4o1qNJpigE5FGo1GowkxOhVpNBqNJsToVKTRaDSaEKNTkUaj0WhCjE5FGo1GowkxOhVpNBqNJsToVKTRaDSaEKNTkUaj0WhCjE5FGo1GowkxOhVpNBqNJsToVKTRaDSaEKNTkUaj0WhCjE5FGo1GowkxOhVpNBqNJsSEHdBoNBqNJqSEZWg0Go1GE1LCzmg0Go1GE1LCfvPHsWPH5s2bN2TIkC+++GLv3r0sLWJycnK2bt06bty4kSNHrly5Mjs7mxcUmk2bNs03yMzMZJHEf/7znw8++ODNN9/E8c6ePRsG4gW+2LlzJ+r2ww8/nD9/nkUGqC3twg4MAVhVAmthU9ignZHPnTu3cOHC999//5///OfUqVO3b9/OCwIl6EZGDTdv3vyvf/0LBhw4cOCsWbNOnDjBywxgqB07dqDyWBoRETFs2DAY+fTp07zYBmyE7LZlyxYWFUTFRVGxjz/+uHfv3r169Zo0adKPP/7ICyykpaWhhpGRkW+99daECRMSExN5QaAEvQcdPnwYGxwwYABqOHr0aDQiL5BISkr69NNPe/bsif0uXrzY1BAyitXz27gAjo0Gfffdd7t27free+9FR0efPXuWl3k5cOAAtaaJRYsWsUZABMXIRdFzscqSJUsQVXr06IHmgAG3bdvGywri13oBVM9VQAstYZccQeuWLl06TCIqKoqXFRmITY8//jjvz+D222//6aefeHEh2L9//3XXXUfbRIhhqQEC0COPPEKLBDVq1Fi7di1rSKxfv/6ZZ55hpbAwuAgvMNi3bx8vsAHpkFUNTp06NWLEiMqVK9NSxEpeILFnz54HHniAFATQzM3NZQ2XBN3IP//8c4MGDXhbXmBSXmzw0ksv8QKJ//qv/0IYYg1fdOnShTRbtmzJIgm/Lgpr16lTh5dJYLO//vorK3lBpLjhhhtYw8vrr79+8eJF1nBJ0HvQ5MmTr7nmGt6Wl5iYGF5sMHz4cNNOYWSMnHixhGL1VBr3jTfeKFu2LC/z8qc//QkxmjUMMA7gZQW59tprWcM9wTJy0Hvu7t27aZGJl19+GVmBlQxUrOeqeq4CWnEgLM+eZcuWkdPfeeed3bp1e/TRR+l4kGBZowhAnZo0aYK9wLf+/ve/IwrccsstKFatWhX9gZUC5b//+7+NI7gCZhUsNfjrX/9K8oYNG2K07vF40DdQvPXWW3/55RdWysvbtWvXY489RpoChHVebED91kr58uWhjL8Y7LBqXh6GPzfffDNth8DInZd5QRz885//jEWwCUZMY8aMadGiBSn37duXldwQdCMfOnQImQxbKFWqFDoAvKVdu3bVq1dv1KgRaxhgX9CBKV599VXM7erWrYsiKFeuHGZLrFQQZH3SAeHh4Sz1ouKiM2fOJCFyefv27UXLAowWWckAWyP5H/7wB9gEE6NatWqRZOjQoazkhqD3oMGDB9MWbrvtthdffBE2hEvD5hj8skZe3ty5c0mnfv36yElvv/12lSpVUMRBYTrFSgaK1VNsXLJVxYoVMUpDmr/jjjuMjV1xeMRQVsrLQyqFsEyZMtQpBNg7a7gkiEYOes/F5Bty2ASjKNStdevWN910Eym/8sorrGSgYj1X1VMMaMWHsHx7nnzySVQdmTkrKwtFaMOgkMCamK2TTtBB0r5iv7CwiRMnkgTzWZK8//77JAmMr7/+GhupVKkSbQ2xjxfk5yclJZEQTsCi/HzMZ0k4ffp0FuXnT5s2jYRNmzbt1KkT/cYAhxc7cu+990K5c+fOXDbAcBVCZAL0HBpe9enTh5d5+fLLL6/sJiwMc3AW5ecjDEEC/wugLYJu5B49emBdzCeWLl3Kovx8JDxkbi4YYC/p6elcMBCZRra8ANN2GA0RkDJlq1ateIEXFRfFkLlt27aYE1ARYDD74IMPXtlrWBgGjyzNz0dwJ+GRI0dYlJ9/1113QVKtWjUuuyG4PQgRhAbOCCvy6jiEzMxMLuTnUxRDhDp//jxJcDi0IqInSQjF6ik2Lmo1ZcoUsVMg/BZ5nUX5+dCBBEMrLheaqxCmAu65iAwbN26Erbicn3/hwoXmzZtDGRw9epSlytbzibV66gGt+GCbilJSUqjesAhJEBfuuecekzDodOzYEdtHd+Jyfv5XX31FO4W3scg9J06cgLtg9E0JCcipKDo6moTLly9nUX4+hhjUgTHYYVF+/hdffPHcc89hIozfs2bNorVUUhE8kpRpXQFCxqhRozCvwu/atWtDwerQ3bt3hxxLuWwgNgj7sEiZ4BoZIYDGZRMmTGCRGx566CGsCztwWQKjOSyKiIho1qwZfphSUWFclMbm4LvvvmORt0tjsM9lg4EDB5ImtZE6Qe9BgwYNwoqY4mA7LLJw8uRJ2j78nEUGdD4A8wYuK1evMI2bm5uL2RvWvf/++1kU7FR0FcJUYXquTxYvXkwbXLJkCYt84dN6VnxWTz2gFR9sH3GdOnUqHQzGYiTBkIokAMNMEgYdsn7Xrl2piOEeZpS0U7B//36Su6VDhw5Y/d133xUth7kzL7t8+dChQyTEtINFly9j/E4noBGmWVQQMcpAG7PIHor+derU4bIvaADet29fLnt59tlnIce4icsGGGpVqFAB8jfeeINFygTXyHRG6MYbb8zOzmaRG+joMCPhspfk5OTrrrsOs1hUjyLp3//+d15mUBgXTUhIIDW5xV977TVI6taty2UDmn0iQ3NZmaD3IIqw77zzDpd9ERcXR9vHBJRFBuPGjSP5sWPHSKJYvUI2Lp1Jlk366aefQvKXv/yFy4XjKoSpwvRcn2zdutWoXRjmLiyywWo9Kz6rF1hACy22qQhRG5VGzKLivn37EBcwq6CL5xilkjy4YHJNxoKHkQSTUxQf8V5/27BhA8ld8cMPP2BdRBPMf32mIvDwww9DiLCIST2KCPRDhw6FBOMIu06onoowdKKL4RhGscgXdg6NeRjkdqkIcwUWqRF0I48ZMwZrPfroo+gAcJsmTZpgYtGpU6c1a9awhg0Ywk+cOLFUqVJY/dtvv2WpAUZJdFfFtGnTUPSZigrjojQ2ByKEAXGtaPXq1SRB4KYLLVFRUSRRJ+g9CPkAK65cuRJj3vbt22NigVCFyEseS4gwZ5eKduzYQRLF6gXcuACeRoMezGtZ5E1F6A5PPPEEkuvf/va3Xr16/fTTT7zYJUE3solC9lwT8Or4+Hi6ioPmY6kNPq1nwqF6AQS00GKbirp164Z633fffVR86qmnUBw5ciTsgh9+7RgYGRkZ2DhAZ0Nx7dq1iFNVq1bds2cPyU0BS4WcnJw777wT62JejKJdKkpMTKQzRWjaZ555BjvFb4Ts8ePHs4YF9VREgQ9bE2NSn9g59FtvvQX53XffzWWDzZs3X9m3kUJYpEbQjdyzZ0+shciCZE9bILBZeD8rSSCYNm/evEGDBnS1GbRu3fq3337jxQZ0Ta5hw4bokCj6TEUBu2hWVhZdh2/atCmLDHJzc4cMGYJYRptF2KUaYgjv3HA+CW4PQp2xFqCpm0zt2rUPHDhAaphBkhDOSRICEZ/kMTExJFGsntvGlfnwww9JEz7GIm8qMgGbT5gwgTXcUNRhqpA9V9ChQ4enn35anHu45ZZb0At4mQ0+rWfCoXoBBLTQYpuKEB1QdQpzXxoX0OrXr4++OmDAAPyGTUktuOzatQsbB+gw4pwvIiZ+k3zSpEmsqgxV+Pnnn6eiXSoCOEw0PC0lxLzBJ+qpiHwCkxsu22Dn0GR/sGjRIhZdvkwXkAAiEYvUCLqRX3jhBVoRgXvw4MEJCQmYVdCNTLAniqzn5fPPPyd9Ak2D2SovM8BMBX0V62KMTxKfqShgF23bti0Uypcvn5qayiIvWJ0Sv6BOnTonTpzgxW4Ibg+SbwtGhMUEDvObjz76iBJneHg4612+TLctYO5y4cIFkvz888+kBmbNmkVCxeq5bVwB3Ix2+s9//pNFBtOnT2/cuDECIoaGCxYswO5oXI/tb9myhZWUKeowVcieK6hevToUBGLabYed9Uw4Vw8GcRXQQottKnr55ZdRdQxLT548WblyZaRTiguRkZGQixlxcME83bBY2NKlS+mSdcuWLSH/9ddfSe7WlMg31157LRpVDBvtUtGrr74KIfpD+/btx4wZ07t3b7pT0+PxYKTJSgVRTEUYnpCa39mGnUNjck1X1DG5hmuieq1atTI2eQW3p92DbmRyFdCpUycWXb587tw5ulmxXbt2LPKSlpaGGISYiCkInYLAtFVkHUCPH3Xu3JnLNqkoMBdF+MZSMHPmTBZ5QUvBmFiECSiG/CNGjMBsAEUcyJw5c1hJmeD2INFqAAZk6eXLo0aNIqHwcOGWiFM4hEGDBv3xj38U96/PmzeP1BSr57ZxiePHj9OpCMxX4FcstQFZtly5clA2TVJVCK6RTRS+5wpWrFgxf/78Tz75BD4MTcQZDCV5mQVF6zlXL4CAFlpsUxFNzGERuiyGIyF5F+N5w/vvv5+KwUWchUD/Qf646aabjhw5AvnRo0dJ/t1335GmCvn5+XTCFKM5FtmkInFPy8SJE1lknHem8ZqdhymmIjqHgK4rRql2ODg0uqt4CoeAm9KWMftmJTWCa2RAN/sC+RopQC6BEGGCy744c+YMDgRq9erVI8mSJUtQrFChgjwX8ZmKAnDRr776isaJPk8u0Rjz9ttvl88W0tbgCenp6SxSI7g96NSpU1gLmC5iY8ZDcgwsSAK3xwhD5B4CQZB+xMbGkppi9QJo3NOnT9O98tWqVZOzpgM0E0Wjc1mZIg1TQem5VsRZAZNJCXXrOVQvsIAWWmxT0ciRI+lgQK1atcSlUcrqbsOfOjQtJcSZIqQNkoiOpML58+dprZo1a97jRcyUSbhq1Spovvnmm5Cg92JsResSGFBAjhkJlwuikoowqPnDH/4AHXRpFtnj7NCIj4sWLRo+fPiAAQNiYmJwdFQ9efagSBCNDFAlWhG9iEUGNOXCWJXLNggzIheiGBUVhd9oC2ovgkbN6Ej4LRKSWxf9/vvvKUBH+LoOjKGosaX/C2eEuF34m2++YZEaQe9BZcqUwYq9evXiskFeXh5GvpAj2bDIIDk5edq0aX369Jk8efLBgwfFUSB1kYJi9dw2LtySHvSpWLEixk8s9Yd4XC8jI4NFahRdmApiz7VC+siXXPaibj3n6gUW0EKLbSr62vv8DVi+fDlLL1/GZB8SqxGDBQY4tNNHHnkE4zsSivudREcikEgQVoi5c+ey1ItIRQ4sWLAAmnTTJAYjtKKAWrR8+fJcLohKKqLz12D79u0ssseVQ+Po6CTJ6NGjWaSMKyMDRGe2ckSE6O0CMcozHSMOBEIcFJdtQFql1ePj41GkVOSAmD+5ctHVq1dTPnv55ZfpVggT4iFfZCwWGYgU9dFHH7FIDbc9yNnIgEZR8mUhAN8z9mC+T8EE3QgHT7506RJJFKvnqnEvXrzo8Xggx47i4uJYqgCdZixdurTfs3km3BrZOWLIFF3PBeh30BcXsAlX1nOuXmABLbTYpqJTp07R2Ll58+Yskrqr3OrBpX///rQL+eIBtdBf//pXLnsZO3YsKQNYmaUSGFnsKsiMGTNIPzo6GkXKIvTsCCK7fGYGAYseqLZ7pkQlFdH7dRRPFLhyaMocZcuWlW9HVsSVkcFN3reVAIRmlno5duwYgggWjRgxgkUGdHHb7yVf8Qwp3QWEcTG1lAydaMWAEb8xxqcV1V0UHRvdD0IcIzo8Swty4sQJWhFRm0UG4o4vt09juO1BzkYGdEIGByKfkFm5ciWtIjelCcR3uv9NHkErVk+9cdFf6MoNUr7fy/IyWLFhw4ZYMYAb3twa2W/EEBRdz8VMhS7byE8EurWec/UCC2ihxTYVgTZt2qDeN9xww86dO1GEQ+PIIalWrRqOinSCjriluEOHDrQXEfGtl9PVHUvg81qReNdA9+7dc3NzSSjGHaYzYBixZhqIacThw4dJYgpzycnJpPDxxx+zyAKOkdYFtYz3UOFAqCifElm0aBFqTr8RiRAf6VxTx44dSegKV0YGfqMkxndYhA5Jl52AMKk4AZiamtqsWbM5c+bk5OSQBKaeNm0aHcgDDzxAQp/4vFYEVFwUw5GKFStCWLdu3bS0NKQcGUwuSQ1QvK5cuTISHknQCnQBqVSpUoqXPWRc9SC/RsaskZaKeTCqR0EcEyZxIPBP+FtWVhYVYXZK5Ndcc43I4oRi9VQaFyCwkhCjPTauF3GaCJmjVatWS5YsEZMzHAI9fg7ef/99ErrClZEVI0awem5kZGS/fv2wNSqCffv20Sk4IN8Qq2I9gd/quQpoxQSnVJSUlHSrcSM8nBitS10FgYOeRyk6KKUDzLLreN+p3LhxY+tZi2ClIngV3SsFatasib5H3gxgAbqGIRCPaFiBG7GSAZ3EwMTF6kwC8XiQFdSElS5f7tq1KyQIqffddx8NA0H9+vWtJ9MUUTcy8BslMe2gM9fQRL4Rb3jDOFf0BHEPGNwJoROLxGYxEqSzc3bYpSIVF6WnIO2Q71/ADINuaihTpgxGnWhoeogYYOrGSm5w1YP8Ghn84x//IAW4AapHZ2jBv//9b9YwHqqHBHu588476fEpgApYw5Zi9VQaF5juGzZBOlQ3AAujerVr18buSIKcKm9NHVdGVowYweq54v5DzGVhrho1aqCSJDG9XkTFegK/1XMV0IoJTqkIYPgMC9IxAIQtn3d9BJ2oqCg6owLgtS1atPB5Emz8+PGkAxBbWeqIcCAx8iUwXuvduzdal5YS4eHhGMWwhpfm3rcZWpHvD87Pz6fHylq3bs0iX2zZsoXWtYKhFisZT8tff/31vMDw7FdeecVn2lBH0ciAIhGBYRpLC4IxqewqAP0QI0RebJx5gz3pxaYy6Cc0nnWA0r9PS/p1UYy1eZkvkKhYz2DFihWmOxWrVKmC6a8YxbtFvQepGBlOhTAkWg3AxxYuXMiLDTAkv/vuu3mxASK+uL/OhGL1/DYuoPN4PkGcJZ3s7OxGjRqZNOHYQ4YMkaenblE3skrECGLPxaS/Xr16pjSDaTeqYbrtTcV6hGL11ANaMcFPKiIQRFatWmWa3Rc16PwJCQmbNm3CjJtFRU9OTg5SFOIRBuniFEcxAXZArVA3DAOtZx4CI+hGRgdYt25dbGzsmTNnWFQQ7HH//v2Ymy5fvjwxMdF0X1bABNFF0dUxbMRRrF69GhsMOAnJBLcHofV3796NORw2iNqytCApKSmo//r169EiLLJHsXp+G1cR9Cx48rJly3AIcIagWBiEJEypgJy9fft2HOyaNWtSU1ODdbwqFOeAZkIpFWk0Go1GU3ToVKTRaDSaEKNTkUaj0WhCjE5FGo1GowkxOhVpNBqNJsToVKTRaDSaEKNTkUaj0WhCjE5FGo1Gowkx/lNRZmbm4sWLR4wYMXv27EOHDrG0iMnLy/vxxx8nTZo0duzY9evXB/wk9s6dO5fYs23bNtazgEWk4/BGg71796JuGzZssHvMUPEocnNzly1b9uGHH/bp02fmzJl79uzhBfaoVM8vwTIyWL16NdXHREpKCmt4OX369Lx580aNGtWzZ8/Ro0cvWrTI7v2kgjNnztDWEhMTWeQFW1u5cuW4ceMiIyP79+8/depU00s0ZNQ9OT09HZpDhw7t16/fhAkT5HdEBUCwehDcLDk5GR7y7rvv9ujR44MPPoAl5fddWvHrojI+nUq9Bx05coSlBbG+kxQE0feI4IapAwcOzJgxAx6FDcbExPh8pFfR9xTVXDXu8ePHoTlo0CD455QpU5KSknhBQQIILCHETypauHCh6dURgb2MyxXoCU2bNuX9GdSoUWP//v282A30KUM77D5nggB6g/GNKeAzDG3atIneEUn4fF+O4lEgWDRo0IA1vPTt29chdvitngpBNDL4o/FOfivoUaxh8Oabb8qvLyLuuusu51cR0/v3gOkddOi64o1eglKlSrVt29b68gh1T0awoC8DyawyvmsVAEHsQeIFdDLVq1dHuGcNCRUXlbFzKvUeJF5hbuK6665jDS/B9T0Q3DAFpzVtDUY2hXtF31N3UfXGRf6WX1cIsIsuXbogu7OGQQCBJbQ4pSIMZ+hlhQgW3bt3f+yxx+h4hg0bxhpFACz1+OOPYy8IB61bt+7cuTO9Vvm2226jjwi44r333nvYQv369Y3jCBs+fDjrFUR+4akp1sMjxXsGBdZ+rngUly5dojeeQa1bt24YIYaHh1/ZYlgYxlCsZMGheooE18iAUtGf/vQnNrEX04cVxKuvn3vuuYiICPHZpJtvvll8D9vExo0bxSu8TKkIo0sIsbVWrVrBP1988UXxGjfTh67VPRkzIVp0++23t2nT5q233oK1S5cuvWLFCtZwQ3B7EFoKQQdW7dChAyom3Fi8lJpQdFETdk6l3oMoFSHxsJIXHDVrGATd94Jr5OjoaFr9gQceGDly5DvvvEO+XaFCBfFicqDoe+ouqti4OFjqDqgPTBcVFUV9CshvUwwssIQWp1REIxfEi+zsbBThQ3Q8CBzBemWZlXXr1hkW+7+vUiYkJJDELnO4hbwNDe/ztf+zZs3CUniPsU9zrP/ss89I/tRTT8EV6Le1nysehXiXO33Bj4DLQoKBj08jO1dPkaAbmbrrvHnzuGxDixYtELDk0w7CAp06dWKRxIULF+677z60FL0h25SKYHaM/eXB4MWLFzFOpw3KcU3Rk9PT0+mt56inLEcMUnmTm5Xg9iC0kemN3Rs2bMDWgPzlG0UXlXHrVD57EKWiv/zlL1y2Iei+F1wjU2Rv2LCh8NKff/6ZvEL+hoWi76m7qGLjvvTSS5AgG8mv5Ke332IKxeWAAkvIsU1FqamplH7FwBZx4d577zUO0PVnxNR57bXXsH04BJelDzXC21hUOJ5++mlsDcNALkucPHny1ltvvf7666lzAlO3nDFjxvPPP79582b8Ft/4sfZzxaOgT/pjNMdlA/guaWIVFnnxWz1Fgm5kxVRkBcM3+pyXz+8V0fcdunbtaveRCCtLliyBJhDvolb35MGDB0NStWpV01uTA+Pq9CA6CYPQyWVlFxUE4FQ+e5BiKgqu7wXXyJmZmbQiTMEiAzre2rVrc9kGq+/5RFENWBsXIzNITN+3xNTN2F6YuKblNrAUB2xT0dSpU6ne4iOhmKqTBLRt25aEQQfzdGwf0YeKcA76GAlRmBPKREpKCvmu/IkXAX3FCxHQ52eNTDj0c8WjePbZZ1H0eDxUJBCdaRIgj4YIV9VzIOhGDjgVAfr4cd26dbnsJTk5GaPRSpUqoXrqqWjr1q1XjiEsTJzcV/fke+65BxJ0bC4XjqvTg8iFTB+/EaikIrdOZdeDFFNRcH0vuEaOi4ujFU03NI0bN47kzqcQrb7nE0U1YG1cSuSmzkLfHpOzu9vAUhywTUU0IIXfUHHfvn2IC+XKlcPoFfJmzZqRPLhgJkvf7YCHkeT1119H8RHjU/AAk1aSB8ygQYOwnVtuucV6084PP/yARWhRLCpMKlI/iueeew5FO49p1aoViwzcVs+OojAypaL69evXq1cPHoIQgI3jQHixPagMxaaIiAgWGYgrCtOmTUNRJRVhlfj4eAwYofln6dPU6p584403QrJy5cro6Oj27dtjI0iTCG3yHWXqFHUPwmxm4sSJlBW+/fZblhbEbyoKwKnsehClohtuuOGJJ55AUsecqVevXj/99BMvNgi67wXXyCJJ2KWiHTt2sKggdr5nQlEN2DXusmXLrtQjLEzc6YPsWKVKFUiioqJIAlwFlmKCbSqiD+ljPkjFp556CsWRI0ciZOCHsx0DJiMjAxsHiAUorl27Fo1RtWrVPd4PYNt1OUXQGPRdS+sHHHNycu40rqIvXrwYxcKkIvWjoHn03XffTUVCfNwP/ZNFAVXPjqIwMqUiEw0bNpSv9Prkww8/hCYqgGqwyAAZiLaA4IWicyrCuP7pp58Wg2tESaQTXqbsyVlZWVdWDgujgadM7dq17e6qcKCIehAOrXnz5g0aNLjGe2tW69at7e76dU5FATiVQw+iVGQCiWHChAmsUQS+F1wjY4p2pRJhYbAbiwzEPR0xMTEs8uLsewJFNb+Nm5ubO2TIEFgVi3DUTZo0IU1MRuUZm3pgKT7YpiKYQFSaPoqOMS8MMWDAAPyGTUktuNANJwBNjgagEybwWvwm+STpW/oBgC5H28Hwh0Ve6Lief/55KhYmFakfhfjavPyVe7q6CORz0wFUz46iMDJ6AoLCrFmzVqxY8dlnnzVu3Jg2hdEZa/gCNaFOZfqe5i+//IK+iiAlmsk5FVWvXt3YG2O6NVzRk3fv3n1lZQOEMAw/MQT+6KOPqIbh4eGkpk4R9aDPP/8cqwvgEtb5vcA5FQXgVA49aPr06Wj38ePHQ2fBggXYON0djli5ZcsW0gm67wXdyHfccQdWbNSokbheKG5bAKZrSMDZ9wSKaiqNi6OjTCOoU6eO6bO/6oGl+GCbiuir7BiWYqpYuXJlTKvJ+SIjIyEXM+Lggum8YasrF/SGDRuGHy1btoT8119/JbmY1wcGtoaNWE9no+9de+21cDgx+C1MKlI/Cowx6RJr2bJlEY7HjBmDuTPpAFHPwKpnR1EbGeTn57dv3562tm7dOpYW5Pjx4zQkx+DOdEsP3SbUuXNnLvtLRch/8+fP/+STT6AANcQ+9DpepuzJwixAvjFs1KhRJHQ7MSqiHoS6IdAjJmJ0jI1gUzCjNTEQDqkoMKey60E+QXYvV64c9Js2bUqSoPte0I0sLPbQQw+NGDFi0KBBmPHTzeLAejXU2fcEimp+GzcxMRHGhxwznqFDh6KGdON+pUqV5syZw0rKgaVYYZuKevbsiUrDEB07dsSP3r17k7xLly4o3n///VQMLuIkCTwAneSmm26iMzxHjx4l+XfffUeaAYCNkEthqMsiA8TNhx9+GPLBgwezqHCpyNVRoLvSEwAChGY67UDPDwZcPTuK1MgChAba2scff8wiidOnTz/44INYWq1aNdMt9XR/UYUKFeSBnvptC2JciZ5PEkVPPnXq1JXVLNeEMSgmud/7nUxchR505swZeAu2Vq9ePRYVxM5FA3Mqux7kAI3f0ZpUDLrvBd3IsAxypMg9BFII/YiNjWU9X1h9zyeKaj4bFwkSkttvvx3zSBZ5DxZz0PT0dBYpBJbihm0qGjlyJB0AqFWrlrhyS1m96A4GDko7BWK2jr5BEmdXcAYjCGwBTma6fx9TYNp4zZo17/EiJtQk9PmwvcOQ09VRwKswjx4+fPiAAQNiYmJQH5pS0LQg4Oo5UHRGlqG7tOH9XPaCI3ryySexqGLFiugwLPUSFRWFRWgmOlKCBtfobPjtNyHdddddUEb/pKK6J5cxXrLQq1cvLhvk5eXR6XjxHIwiV6cHCSdENGeRhJ2LBuZUdj3IAbrmBzIyMkgSXN8rIiMnJyej5n369Jk8efLBgwdTUlJoF/LTPD4x+Z4dimqmxoXZqSgyLiGq980337DIwDmwFDdsU9HX3pv9wXLpLVJ0gdqvEQMGAxza6SOPPIIRCgmnTJlCQpMroLdEeJk7dy5LfYFN/cl4eM160l90SwfkJ8UEDqnI1VGYQH0w3Yba6NGjqUhrOeCzeg64rR5cn60cEaF4Oxk2S5cK3n77bRYZXLx40ePxQF6+fPm4uDiWSlAqcsBuBiDAQUFNXAJR92SKxSYPQeMaq5ovZftFfb9EAEYGCDG0i/j4eBZJ+E1FDpicyqEHOUCnN0uXLi3OwRama1hxa2T1iCEzZswYbA0ee8nfTaEm37NDUc3UuNu2baPi999/TwqESFHOs1VTYClu2KaiU6dO0filefPmLJJsIbd6cOnfvz/tQj5DSsHL9GAXGDt2LCkD6y09MnTTKvB5BgBj810FmTFjBulHR0ejaE02wCEVuToKE9Qty5YtKx6VCKx6Drit3k3SO68UR8ToLaQv3xOFGQad3Mcsx+7KLYbPdIwydDYJcyn8xiiVVX1x8uTJm2++Gcri4Ql1T6bTF4g48iOuK1euJE3ZViq47UEBGBkMHDiQVvH5yIuDi7p1Kuce5BM0d8OGDbGKfCdbYbqGFbdGVo8YAiRRysE9evRgkQ1W3/OJohowNe6JEyeoiNRICoS4d9H5kV5rYClW2KYi0KZNG1QdY1t6CRKa5P7774ekWrVqdIttUSBu6+zQoQPtRXQn6yVNdceiY6lSpUpubi6LHHE4b44Ra6aBGModPnyYJOIl0+pHgRk09kW/EQHhVXSeumPHjiT0SWGuFQFXRgbOURIdY9iwYfJ920uXLqVhKQwOs7D08mX0PdoIoh76lQz6Jyv5wue1osjIyH79+iUnJ3PZeKyETv0B+d4hRU/G2NNY9f+Gjag8BVNMmDCoJKE6rnqQs5FTU1ObNWs2Z86cnJwcksCTp02bRt7yQMF3Vai4qBVnp3LuQUgJrVq1WrJkiZg3YF/wLtqg/Moct77nF1dG9hsxYLqPP/44KyuLijA7DYOuueYaeQyk6HuKauqNS0mxcuXKGCuQBHamC0ilSpWSL7sGFlhCiFMqSkpKohvh0QxoXeoqOB4MmlijaKCHhwHCWZ06deh348aNrWctFFMRwhwNnfr06cMifzh0S/nFkSYQYVlJ+Si6Gq+drlix4n333UeVBPXr13c+U1HIVATUjQycoyQdAsD0v27duvR2SwC3WbhwISsZoLfQIp+wki98piKaYAHMYzDurlGjBl3UAaa3D6h7snhBMpoDDU0nNIDPd3P4xVUPcjayuPcMm0JexPEKfUwxTWfnFF3UhINT+e1B6enptG6ZMmXuvPPO2rVrU+ADyOWm7OXK9/ziysh+IwYdCFbHUdATVABbNt19o+h7imrqjYs5OnUi2LlJkyZoaHpqFZheRh5YYAkhTv0fYAgDu9BhALiO810fwSIqKgqNRzuF0Vu0aOHzHNT48eNJB8C/WWph8uTJpCOGEn4Rj4NZV2nevDktsjJz5kxWMlA5inHjxsnfTYD+K6+84rdPOlRPHUUjA/E6YYAZDEu9TJo0iWKBDAKQ9YxWaeNJe5+gg7GSLyi2tm7dmssGGDbWq1fPlN4wYIRXyGfYCEVPzs/P79u3rzALqFq1qimhukK9BzkbOSMjIzw8/JZbbmENL88//zzNBmTUXVTGwan89qDs7OxGjRqZ2heOPWTIEJ+zSXXfU0HdyH4jxunTp+ntogKkVevNk4q+p6jmqnFXrFhhujUOU1VMf03XsQILLCHETyoiYKlVq1Y5n6MPOrBsQkLCpk2bMONmUQlE5SiwCAMfeBjGd9ZTCkVKsIyMCL537961a9ei08bGxorzG1eBzMzM7du3Y7S4Zs2a1NRUU4c0oejJaIXdu3djm9AUl9YLQ7B6EI5u//79mL4sX748MTERcZMXFA/Q7vDkZcuWwXSop3NbBMv3BEEMUykpKatXr16/fv0pxzeyK/qeopp648Injx49um7dOlQSx2u3wRAGlgBQSkUajUaj0RQdOhVpNBqNJsToVKTRaDSaEKNTkUaj0WhCjE5FGo1GowkxOhVpNBqNJsToVKTRaDSaEKNTkUaj0WhCjJ9UlJCQ8Omnnw4YMCAqKmr69Ok/FfxSPdi5c+cSe7YV/ES8Onl5eT/++OOkSZPGjh27fv36AF7/JTh9+vTKlSvHjRsXGRnZv3//qVOnOryhIDc3F4cMnV69er377rvffvvtmTNneJkvsJSONDExkUUSx48fnzlz5qBBg/r16zdlypSkpCReYA8sRht0fi5aUc2ZYBnZrQ8cOHBgxowZaIsRI0bExMRYLXzkyBFeuSDWt1sSq1evRvuiybDBefPmWV+1oOgD+fn5ycnJaDI0fY8ePT744ANsTf4wTGBkZmYuXrwYdZs9e/ahQ4dYGijOrYYiG8sG64OTfq0nwMY3bNiAndodBTaOLYwaNapnz56jR49etGiR9ZV3bhtXkeAaWeDcwYHifhXVFLukes8tIrMUBbapKDY2lj7cJFO6dOmuXbvKnfPVV1/lZb4I7HshCK9NmzblTRjUqFFj//79vNgNiCzipU+CUqVKtW3b1vqM9y+//NKoUSNW8vLYY4/xYl+I16+ZXo8G4CjyW8UAatKlSxe4EWtYSElJoQ8rALgaSy0oqjkTRCO78gHE97Jly/Iyg+rVq5uStHjTsInrrruONbwcPXoUDcSLvdxxxx2ImKzhxgfEC+hkUD1EItZwz8KFC03Ha3pXmCv8ttrBgwd5gQ3yB4FUrEcgxyDBVK1alXT69u3LCyTefPNN+U0zxF133WV6Bbt646oTXCPLOHRwoLhfRTXFLqnec4vOLEWBbSr66quvUHX02Iceeqhjx44tW7YURyW/u+m999572EL9+vVJc/jw4aynDEamjz/+ONYtU6ZM69atO3fuTK/XvO2223y+Bt8ZDH6xbuXKlVu1atW9e/cXX3xRvOmrXbt2rGSQlpaGoAM50m2TJk2g3L59+5o1azZu3Jg1LGzcuFG8XcrkqRji0aIKFSrgEDCnpFfqAoy4WcmC/BZLhxyjqOZAcI2s7gPR0dEkfOCBB0aOHPnOO+/80XiBN6wkv9ibohViE2/Li3VYQN99ARhDoBrCSxE0xRtF1X0ApkDSwo46dOjw1ltviUMQb312C9yA3gqKiIxdi7g/bNgw1nCDSqvhB9nKBH2VAH/l7KtiPYAWNL0ezWcqEi+Nfu655yIiIsR3ibBT+Vvs6o2rSHCNLOPQwYHifhXVFLukes8tOrMUEbapaP78+eio8nmMrKwsehs5kAOHFYo46NWmb0WrsG7dOtqF+GhmQkICSQJIbGfPnt20aZM8Ebl48SLG6bRBufHoy8Q33njjsmXLWGRMhK2fGSUuXLiAWSOOkd6Ma/LUl156CUL4sfweXHrNIhIelwsya9YsLEVPxl9gl2MU1ZwJrpF94tMHKFo1bNhQTKxhH3ptsPwdAYpWfr/ALz43IH/3Zc6cOST8/PPPSaLuAzCC6ZXYmB+Qmt9Py/iERq8IytnZ2SgijoSHh0NiSgmKFKbV6AyH/Ck5ResBekE1/Hzo0KH03lufqahFixZoOPmUCQ1nQadOnVik3LjqBNfIAucODhT3q6im2LjqPlBEZik63N22ID5/In8MxsrTTz8NHQzeueyG1157DesiZnFZ+lAjzMqiwrFkyRLaoHjhLlqLTqaZXgXvACY30Mf83ef3C6jnmz4FhkkAhMB6deTkyZPo5Ndffz1lGuAzxyiq+eUqGNnqA5mZmbQLVJ5FBqRZu3ZtLitHq2+//dbYXtiKFStYZJy+oNzm/EEQqw/Y0aBBA6ghfXJZmdTUVBpTiw+aIbrde++9xm79fOXMJwG3GjIxqW3evJlFbqyHYx89ejQ5LYbYWOozFVm5dOkSfVRe/uJOcFNR0I0scO7givtVr55i4yqqFZ1Zig53qUhkYIdPRaSkpJAVAvvEC2aaWBceQEXEL/kDBIFdzDCxdetW2pq4RDF37lwUMSWiEYRfkpOT0WMrVaqE6vn0VPKYunXrctmAPtMiu5GAPjIG73f4ZgxQVPNLURvZpw/ExcXR9k03MowbN47kYoKiGK0OHTpEK8remJ6eTp8qcO5vVh+w49lnn4Wa6QNIKkydOpV2IT6a+d5775EEtG3bloTqBNxqHTt2hEKdOnW4bBCY9VylIoCpEvTljhDcVBR0IxN+O7jiftWrp9i4impFZJYiJcBZkThCK4MGDYLCLbfc4nBfhx15eXnUE2BKkrz++usoPuI9qW29oOoKzFLj4+MxWcGm/ix95Ji+Xf/oo4+ifyLQN2nSpFGjRp06dVqzZg1rSGAjdLp22rRpKPr01GXLlkEIxDVbxNkqVapAEhUVRRIBfaoZKQoWc8gximp+KWojA58+IKK/XSrasWMHSSha3XDDDU888cQ999yDqVWvXr2sd2+Ch40vbCJbYDiPIobhQ4cOhaRs2bJ2owo7H7CCOejEiRMpp8qfRVeEhtWIHVTct28folu5cuUwRYC8WbNmJFck4FY7ffo03eQyatQoFnkJwHquUhHqTKEzIiKCRW4aV4XgGplQ6eCK+1VUU2xcdR8oCrMUNS5SUVZWFp01btq0KYsswJtJx/nz3nZkZGRgXUBfYFy7di0CQdWqVcV57QAiAoH5xNNPPy1GEIiSK1eu5GXeC0XoGwj0pEBg7+icrOQFDopFDRs2hGeg6NNTc3NzhwwZgubHovvuuw+57RrjJi4MBk1XF3NycjCzxqLFixejaJdjFNVUKDojE3Y+gBEcbX/27NksMhB3YcTExJCEopUJGHPChAmkIEhMTKTrlwhtzzzzDI4Cv9Fdx48fzxoSzj4ggLB58+YNGjSgJgOtW7cO4Jbubt26YV20PhWfeuopFEeOHIm4jB/OWdBKwK1Gw0fYxOR4wJX1CFep6MMPP4Qy6onasshN46oQXCMTKh1ccb+KaoqNq+4DRWGWosZFKsK0DodRvnz51NRUFllAoIQOsH7BUwW62QkgKqHzY9CE37A7fpN80qRJrOoSujtOYLrB9IUXXiA5os/gwYMTEhKggEkSJGhsFFnPuOEbIQxCcYA+PRUgG7311lvGVpk6depYv845YMAALHr++eepaJdjFNVUKDojEw4+cMcdd0COGecF78Mr4rYFIK4hTZ8+vXHjxgiI2NSCBQtw7DSuR+ts2bKFdARffvklmoO2QIgxowlnHxB8/vnnrGEAmwcwvwdIYFgdI1b8RiXxu379+vAKakpkRFJTJOBWo2Tz3HPPcbkg6tYj1FMRKkwta/pYqqvG9UtwjQwUO7jifhXVFBtX3QeCbpargGoq+uijj64cq+MHiUHLli2hE/CJYPGN96VLlw4bNgw/sEHIf/31V5I79xMHVqxYMX/+/E8++QQuhe3A77t3787LpE/Qy7f6nDt3rlKlShDKt/zSrXGdO3fmso2nYrwJI0B+9913Y141YsQITLlQxAbnzJnDSpcvI5Fce+216LHiblefOUZRTZGiMzLh4AOYD9EuEB9hk0GDBv3xj3+kW07BvHnzWM/C7t27y5UrBx3TjJweaUJrtm/ffsyYMb17977ZuGvZ4/FgEsZKXpx9QJCWloYQibyIeS2dX8J8NIChFTkVBtcnT56sXLkyZhu0kcjISMjFyRNFAms1+CEt9TlncmU9QjEVHT9+nCbxGJj7vV/LrnFVCK6RgWIHV9yvoppi46r7QNDNchVQSkVfffUVDZ2sZ6tkjh49SmEFeYtFLsnKysLqAEEKkfemm26iu8axZZJ/9913pFkYxLBXXLDt0aOHSULAIyFEi1KRbruqUKGCPLnx6ak0FL399tsxYGHR5ctdunSBEGPA9PR0FPPz8+lkPeZhpACsOUZRTZ0iNbKzD+BY0IVE7iGQG+iH/PSlFZpiwvhclqZfEydOZJFxWpxG2c7h0uoDPjlz5gyCKdTq1avHImXorC8iMt01gEBPcnKD+++/n4qKBNZqdKIGAyAxDRUEZj2VVHT69OkHH3wQatWqVVN8nMPauIoE18jqHVxxv4pqio2r7gPBNcvVwX8q+v777yl2REjXHn2CcS7UoGx6OMMVMDE2Qoj5JgIuSZyjlTrUo9AwVBw+fDht3/RaFBp6YFhBxaioKBRxgJgaC2hAhw6M3+SvOPwr25I8gEhJSSH5N998g+L58+epWLNmTdoUECeRSLhq1SpFNdqFIkVnZBUfSE5OnjZtWp8+fSZPnnzw4EFhFvkZLCt0Bh9kZGSQ5M0330QR+8LQjyQExviQ33vvvVy2weQDdoiZHDo8i9QYOXIkrQhq1apFtwYAmpOZXkKhgttWw2CZHubFSItFEoFZz28qgrs++eST0KlYsaLdM3lWrI2rSHCNrNjBgeJ+1aun2LiKakH3vauAn1S0evVqagnM+Oginh0Y8NI1//DwcBYFBDL5FfsZJzqxTRKKO/dM0QohGAmSmDt3LksVwMaxNXHpRYyRt2/fThICXQ5CdD8qkqc6QGPnbdu2URFZnFYkRIqiGYPIMQ4sWLBAUY12oYgrIwPkVLZyRIRwayuB+QDdvli+fPlLly6xyBejRo2CWunSpcUJH7pRGANwKgooyGKDXLbB5AN2xMTEQA3Ex8ezSI2vvQ98gOXSC9boBRPWFOjXyG5bjS4SAJNXE4FZzzkVXbx40ePx0OpxcXEsVcDauIq4NbJzxFDs4EBxv+rVU2xcRTW3ZikOOKUiOBNcClWHe1lfa2iCbjUGhTyH1r9/f9oOndwkyL9NT4yCsWPHkjIw3a/lAIaBdE5cPGd+7NgxdANIMKgnCUF3LohLvhiy7bJAZ88wEsRvjPGhhtk9JABBllYkxL1D4qENDBtpI4IZM2aQTnR0NIpnz55VV1PHlZHBTdLL9BymOwH4AEIPZS+fI3cBhkENGzaEmnzzDz2nValSJfksKDTpsUpslkW+sPqAHQMHDoQasN6B5sypU6doDNu8eXMWScMUOUAQfo3sttWaNGmCRXZnYwKznkMqwrp0iQKDV7tbQnzis3EVcWtk54ih2MGB4n7Vq6fYuIpqbs1SHLBNRQh/9HajunXrpqWlIbzKYKjOel7atGkD5SpVquTm5rIoIMSNiR06dICPQiLOkFgvzPpNRZGRkf369UtOTuaycTacTiAA+Z0RGB1Dgp5Gp18BEgapiYmwT3yeSqbwWrlyZbgvSTIzM+kCUqlSpRxOoCteBCrMtSLgyshAMRX59QEM9j/++OOsrCwqpqamUj+/5ppr5E7eqlWrJUuWiEkSTEfP9gL5/UCigbp37y72KKYC4sqzog+gMs2aNZszZ05OTg5JsM1p06Zda5ydfkB6X4A6ZJAbvK+wQ95FYoCkWrVqZHYZv0Z21Wo4Xlpk9wIRResB7AtNQNSqVQtL0deoKJ/QRlKndTFO4jDhRZwDVG9cdVwZOYDBq88ODhT3q6im2LjqPuDKLMUB21RED0nZYbp/Aa5GSdj5bSuK0HgNYDpZp04d+t24cWPrWQu/jkXDNIDpHcZcNWrUQNQjien5eUwB6cQ6IgJCEl13BVjLObn69NSVK1fSjR5lypTB4PRvf/sbvckKOL8c9+qkIqBuZKCSilR8ID09HQoI7nfeeSc9ewTQInK4JB0A00Gtdu3alAwAxs5yW6BH0X2JoGbNmhhMUGcDt956q7i0o+gD4vYkLK1evTo0xVFjmO/27ByRlJSEmtA2UTfaIA4Hc1nWkFAxsnqr0bnlsmXLijRgQtF6YPPmzSS3ghVZ6fJl003hJkhHvXHVcWXkIKYixf2qV0+xcRXVXJmlOGCbijBCMY7RN0hUrGcwefJkkotJQCGJioqic4MAXtuiRQuf56DGjx9POgAtxFIJjGrr1atn6iSYrGBF6z1FGD4gALGSAaIYRm282AZ6QrN169Zc9rJixQpMKGk7BKYLU6ZMcb4iIrq9syUV1ZxRNDIQr7IGGOSytCAqPoBBNL0TVoBgtLTgW+Cys7MbNWpE50sF119//ZAhQ6xzcYyye/fuTe+TFoSHh2PewxrKPpCRkYEVbyn4CmqAGE3jysDAMFZ2KoQPu3v2VIwMVFotPz+fnle1uqWMivXAli1beJkFTJJY6fJlU5PJwPik46px1VE3st+IYcWugwPF/apXT7FLKqqp77c44HStKLQgZCckJGzatAlTSxYFCtLJ9u3bMVNZs2ZNamqqczJA/1y3bl1sbKz1paVuQUTA6BJbW7169cGDB533GxKCaGR1UlJSYJD169fD1CyykJWVhYnIsmXL0Gr79+93Nl1OTg7yH3I/VhFn/0wo+gDk2B0mncuXL09MTDTdURkwyHOrVq0SJyELSXBbTcV6wcVV46oTXCOro7hfRTXFxlX3gVCZxS3FNxVpNBqN5neCTkUajUajCTE6FWk0Go0mxOhUpNFoNJoQo1ORRqPRaEKMTkUajUajCTE6FWk0Go0mxOhUpNFoNJoQo1ORRqPRaEKMTkUajUajCTE6FWk0Go0mxOhUpNFoNJoQo1ORRqPRaELK5cv/C6ywlRr4hWQtAAAAAElFTkSuQmCC"}}},{"cell_type":"code","source":"#df_train.fold\n288/32","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"all_bboxes = []\nfor row_idx in tqdm(range(df_train.shape[0])):\n    row = df_train.iloc[row_idx]\n    # Get image\n    image_name = row.image_id\n    image_height = row.Height\n    image_width  = row.Width\n    bboxes_coco  = np.array(row.bboxes).astype(np.float32).copy()\n    num_bbox     = len(bboxes_coco)\n    names        = ['cots']*num_bbox\n    labels       = [0]*num_bbox\n    #if row.fold != Selected_Fold:\n    if (CV_split and row.fold != Selected_Fold) or row.is_train:\n        file_name = f'/kaggle/working/COTS/labels/train/{image_name}.txt'\n    else:\n        file_name = f'/kaggle/working/COTS/labels/valid/{image_name}.txt'\n    with open(file_name, 'w') as f:\n        if bboxes_coco.shape[0]!=0:\n            \n            bboxes_yolo  = coco2yolo(image_height, image_width, bboxes_coco)\n            bboxes_yolo  = np.clip(bboxes_yolo, 0, 1)\n            all_bboxes.extend(bboxes_yolo)\n            for bbox_idx in range(len(bboxes_yolo)):\n                bb=str(bboxes_yolo[bbox_idx])\n                bb=bb[1:-1]\n                #annot = [str(labels[bbox_idx])]+ list(bboxes_yolo[bbox_idx].astype(str))+(['\\n'] if num_bbox!=(bbox_idx+1) else [''])\n                annot = str(str(labels[bbox_idx])) + ' ' + bb + '\\n'\n                annot = ''.join(annot)\n                annot = annot.strip('')\n                f.write(annot)\n        else:\n            f.write( '')","metadata":{"papermill":{"duration":4.71688,"end_time":"2022-01-18T15:48:49.064441","exception":false,"start_time":"2022-01-18T15:48:44.347561","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"list1 = os.listdir('/kaggle/working/COTS/labels/train') # dir is your directory path\nnumber_files1 = len(list1)\nprint(\"Number of txt file in ./COTS/labels/train folder\",number_files1)\nlist2 = os.listdir('/kaggle/working/COTS/labels/valid') # dir is your directory path\nnumber_files2 = len(list2)\nprint(\"Number of txt file in ./COTS/labels/valid folder\",number_files2)\n","metadata":{"papermill":{"duration":0.180146,"end_time":"2022-01-18T15:48:49.419144","exception":false,"start_time":"2022-01-18T15:48:49.238998","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%cat '/kaggle/working/COTS/labels/train/{list1[12]}'","metadata":{"papermill":{"duration":0.833442,"end_time":"2022-01-18T15:48:50.419212","exception":false,"start_time":"2022-01-18T15:48:49.58577","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📦 Select a Model\nSelect a pretrained model to start training from. \n* Here we select YOLOv5s, the smallest and fastest model available.\n* I will try YOLO5s","metadata":{"papermill":{"duration":0.167886,"end_time":"2022-01-18T15:48:50.755743","exception":false,"start_time":"2022-01-18T15:48:50.587857","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![model_comparison.png](attachment:6f64ed0a-fd0e-43de-9d26-77412d6e87cc.png)","metadata":{"papermill":{"duration":0.171111,"end_time":"2022-01-18T15:48:51.092597","exception":false,"start_time":"2022-01-18T15:48:50.921486","status":"completed"},"tags":[]},"attachments":{"6f64ed0a-fd0e-43de-9d26-77412d6e87cc.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# 🚅 Train with W&B","metadata":{"papermill":{"duration":0.167811,"end_time":"2022-01-18T15:48:51.426994","exception":false,"start_time":"2022-01-18T15:48:51.259183","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#!rm -rf yolov5\n#!cp -r /kaggle/input/yolosupsize/yolov5 /kaggle/working/","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#%cd ./YOLOv6","metadata":{"papermill":{"duration":0.176514,"end_time":"2022-01-18T15:48:51.77262","exception":false,"start_time":"2022-01-18T15:48:51.596106","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All training results are saved to runs/train/ with incrementing run directories, i.e. runs/train/exp2, runs/train/exp3 etc. ","metadata":{"papermill":{"duration":0.166758,"end_time":"2022-01-18T15:48:52.105893","exception":false,"start_time":"2022-01-18T15:48:51.939135","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"'''\nlr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937  # SGD momentum/Adam beta1\nweight_decay: 0.0005  # optimizer weight decay 5e-4\nwarmup_epochs: 3.0  # warmup epochs (fractions ok)\nwarmup_momentum: 0.8  # warmup initial momentum\nwarmup_bias_lr: 0.1  # warmup initial bias lr\nbox: 0.05  # box loss gain\ncls: 0.5  # cls loss gain\ncls_pw: 1.0  # cls BCELoss positive_weight\nobj: 1.0  # obj loss gain (scale with pixels)\nobj_pw: 1.0  # obj BCELoss positive_weight\n'''","metadata":{}},{"cell_type":"code","source":"'''\n%%writefile /kaggle/working/yolov5/data/hyps/hyp.scratch.yaml\n \n# YOLOv5 by Ultralytics, GPL-3.0 license\n# Hyperparameters for COCO training from scratch\n# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300\n# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials\n\nlr0: 0.0032\nlrf: 0.12\nmomentum: 0.843\nweight_decay: 0.00036\nwarmup_epochs: 2.0\nwarmup_momentum: 0.5\nwarmup_bias_lr: 0.05\nbox: 0.0296\ncls: 0.243\ncls_pw: 0.631\nobj: 0.301\nobj_pw: 0.911\niou_t: 0.2\nanchor_t: 2.91\n#iou_t: 0.20  # IoU training threshold\n#anchor_t: 4.0  # anchor-multiple threshold\n# anchors: 3  # anchors per output layer (0 to ignore)\nfl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015  # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7  # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4  # image HSV-Value augmentation (fraction)\ndegrees: 10.0  # image rotation (+/- deg)\ntranslate: 0.1  # image translation (+/- fraction)\nscale: 0.5  # image scale (+/- gain) degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0\nshear: 10.0  # image shear (+/- deg)\nperspective: 0.0  # image perspective (+/- fraction), range 0-0.001\nflipud: 0.5  # image flip up-down (probability)\nfliplr: 0.5  # image flip left-right (probability)\nmosaic: 1.0  # image mosaic (probability)\nmixup: 0.6   # image mixup (probability)\ncopy_paste: 0.25 # segment copy-paste (probability)\n''' ","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.176397,"end_time":"2022-01-18T15:48:52.449215","exception":false,"start_time":"2022-01-18T15:48:52.272818","status":"completed"},"tags":[],"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# def __init__(self,crop_height=480,width=736):\n#  A.OneOf([ A.RandomCrop(width=width, height=crop_height,p=0.5),\n#                           A.CenterCrop(width=width, height=crop_height,p=0.0),\n#                           #A.RandomSizedBBoxSafeCrop(width=width, height=crop_height,p=0.0)\n                          \n#                           A.RandomResizedCrop (width=3200,height=1792, scale=(0.15, 1.0), \n#                                                ratio=(0.75, 1.3333333333333333), \n#                                                interpolation=1, always_apply=False, p=0.0)\n            \n#                          ], p=1),\n# A.Resize(width=3200, height=1824,p=1),\n#!ls /kaggle/working/yolov5/data/hyps/hyp.scratch.yaml","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.mod(1800,64)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\n%%writefile /kaggle/working/yolov5/utils/augmentations.py\n\n\"\"\"\nImage augmentation functions\n\"\"\"\n\nimport math\nimport random\n\nimport cv2\nimport numpy as np\n\nfrom utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box\nfrom utils.metrics import bbox_ioa\n\n\nclass Albumentations:\n    # YOLOv5 Albumentations class (optional, only used if package is installed)\n    def __init__(self,crop_height=1856,width=1856):\n        self.transform = None\n        try:\n            import albumentations as A\n            check_version(A.__version__, '1.0.3', hard=True)  # version requirement\n\n            self.transform = A.Compose([\n                 \n#                 A.Resize(width=3200, height=1824,p=1),\n                #A.Resize(width=3200, height=1792,p=1),\n                #A.RandomResizedCrop(width=320, height=320,p=1)\n                #A.RandomSizedCrop( (320,640), 640, 640,p=1) ,\n                #A.UnsharpMask(p=0.25),\n                A.RandomCrop(width=width, height=crop_height,p=0),\n                A.Blur(p=0.01),\n                A.MedianBlur(p=0.01),\n                A.ToGray(p=0.01),\n                #A.OneOf([A.CLAHE(p=0.1,clip_limit=4),A.Sharpen(p=0.1)],p=0.2),\n                A.CLAHE(p=0.1,clip_limit=4),\n                A.RandomBrightnessContrast(p=0.0),\n                A.RandomGamma(p=0.0),\n                A.ImageCompression(quality_lower=75, p=0.0)],\n                bbox_params=A.BboxParams(format='yolo',min_area=512, min_visibility=0.1, \n                                         label_fields=['class_labels']))\n\n            LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))\n        except ImportError:  # package not installed, skip\n            pass\n        except Exception as e:\n            LOGGER.info(colorstr('albumentations: ') + f'{e}')\n\n    def __call__(self, im, labels, p=1.0):\n        if self.transform and random.random() < p:\n            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed\n            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])\n        return im, labels\n\n\ndef augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):\n    # HSV color-space augmentation\n    if hgain or sgain or vgain:\n        r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains\n        hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))\n        dtype = im.dtype  # uint8\n\n        x = np.arange(0, 256, dtype=r.dtype)\n        lut_hue = ((x * r[0]) % 180).astype(dtype)\n        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)\n        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)\n\n        im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))\n        cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed\n\n\ndef hist_equalize(im, clahe=True, bgr=False):\n    # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255\n    yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)\n    if clahe:\n        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n        yuv[:, :, 0] = c.apply(yuv[:, :, 0])\n    else:\n        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram\n    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB\n\n\ndef replicate(im, labels):\n    # Replicate labels\n    h, w = im.shape[:2]\n    boxes = labels[:, 1:].astype(int)\n    x1, y1, x2, y2 = boxes.T\n    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)\n    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices\n        x1b, y1b, x2b, y2b = boxes[i]\n        bh, bw = y2b - y1b, x2b - x1b\n        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y\n        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]\n        im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b]  # im4[ymin:ymax, xmin:xmax]\n        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)\n\n    return im, labels\n\n\ndef letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):\n    # Resize and pad image while meeting stride-multiple constraints\n    shape = im.shape[:2]  # current shape [height, width]\n    if isinstance(new_shape, int):\n        new_shape = (new_shape, new_shape)\n\n    # Scale ratio (new / old)\n    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])\n    if not scaleup:  # only scale down, do not scale up (for better val mAP)\n        r = min(r, 1.0)\n\n    # Compute padding\n    ratio = r, r  # width, height ratios\n    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))\n    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding\n    if auto:  # minimum rectangle\n        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding\n    elif scaleFill:  # stretch\n        dw, dh = 0.0, 0.0\n        new_unpad = (new_shape[1], new_shape[0])\n        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios\n\n    dw /= 2  # divide padding into 2 sides\n    dh /= 2\n\n    if shape[::-1] != new_unpad:  # resize\n        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)\n    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))\n    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))\n    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border\n    return im, ratio, (dw, dh)\n\n\ndef random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,\n                       border=(0, 0)):\n    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))\n    # targets = [cls, xyxy]\n\n    height = im.shape[0] + border[0] * 2  # shape(h,w,c)\n    width = im.shape[1] + border[1] * 2\n\n    # Center\n    C = np.eye(3)\n    C[0, 2] = -im.shape[1] / 2  # x translation (pixels)\n    C[1, 2] = -im.shape[0] / 2  # y translation (pixels)\n\n    # Perspective\n    P = np.eye(3)\n    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)\n    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)\n\n    # Rotation and Scale\n    R = np.eye(3)\n    a = random.uniform(-degrees, degrees)\n    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations\n    s = random.uniform(1 - scale, 1 + scale)\n    # s = 2 ** random.uniform(-scale, scale)\n    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)\n\n    # Shear\n    S = np.eye(3)\n    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)\n    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)\n\n    # Translation\n    T = np.eye(3)\n    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)\n    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)\n\n    # Combined rotation matrix\n    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT\n    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed\n        if perspective:\n            im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))\n        else:  # affine\n            im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))\n\n    # Visualize\n    # import matplotlib.pyplot as plt\n    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()\n    # ax[0].imshow(im[:, :, ::-1])  # base\n    # ax[1].imshow(im2[:, :, ::-1])  # warped\n\n    # Transform label coordinates\n    n = len(targets)\n    if n:\n        use_segments = any(x.any() for x in segments)\n        new = np.zeros((n, 4))\n        if use_segments:  # warp segments\n            segments = resample_segments(segments)  # upsample\n            for i, segment in enumerate(segments):\n                xy = np.ones((len(segment), 3))\n                xy[:, :2] = segment\n                xy = xy @ M.T  # transform\n                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine\n\n                # clip\n                new[i] = segment2box(xy, width, height)\n\n        else:  # warp boxes\n            xy = np.ones((n * 4, 3))\n            xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1\n            xy = xy @ M.T  # transform\n            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine\n\n            # create new boxes\n            x = xy[:, [0, 2, 4, 6]]\n            y = xy[:, [1, 3, 5, 7]]\n            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T\n\n            # clip\n            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)\n            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)\n\n        # filter candidates\n        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)\n        targets = targets[i]\n        targets[:, 1:5] = new[i]\n\n    return im, targets\n\n\ndef copy_paste(im, labels, segments, p=0.5):\n    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)\n    n = len(segments)\n    if p and n:\n        h, w, c = im.shape  # height, width, channels\n        im_new = np.zeros(im.shape, np.uint8)\n        for j in random.sample(range(n), k=round(p * n)):\n            l, s = labels[j], segments[j]\n            box = w - l[3], l[2], w - l[1], l[4]\n            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area\n            if (ioa < 0.30).all():  # allow 30% obscuration of existing labels\n                labels = np.concatenate((labels, [[l[0], *box]]), 0)\n                segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))\n                cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)\n\n        result = cv2.bitwise_and(src1=im, src2=im_new)\n        result = cv2.flip(result, 1)  # augment segments (flip left-right)\n        i = result > 0  # pixels to replace\n        # i[:, :] = result.max(2).reshape(h, w, 1)  # act over ch\n        im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug\n\n    return im, labels, segments\n\n\ndef cutout(im, labels, p=0.5):\n    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552\n    if random.random() < p:\n        h, w = im.shape[:2]\n        scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction\n        for s in scales:\n            mask_h = random.randint(1, int(h * s))  # create random masks\n            mask_w = random.randint(1, int(w * s))\n\n            # box\n            xmin = max(0, random.randint(0, w) - mask_w // 2)\n            ymin = max(0, random.randint(0, h) - mask_h // 2)\n            xmax = min(w, xmin + mask_w)\n            ymax = min(h, ymin + mask_h)\n\n            # apply random color mask\n            im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]\n\n            # return unobscured labels\n            if len(labels) and s > 0.03:\n                box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)\n                ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area\n                labels = labels[ioa < 0.60]  # remove >60% obscured labels\n\n    return labels\n\n\ndef mixup(im, labels, im2, labels2):\n    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf\n    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0\n    im = (im * r + im2 * (1 - r)).astype(np.uint8)\n    labels = np.concatenate((labels, labels2), 0)\n    return im, labels\n\n\ndef box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)\n    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio\n    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]\n    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]\n    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio\n    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates\n    '''","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.181134,"end_time":"2022-01-18T15:48:52.796919","exception":false,"start_time":"2022-01-18T15:48:52.615785","status":"completed"},"tags":[],"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"BATCH_SIZE=4\nIMG_SIZE=2560\nEPOCHS=11\n\nprint('crop height',(704/IMG_SIZE )*704,'crop_width',(1280/IMG_SIZE)*(1280))","metadata":{"papermill":{"duration":0.29807,"end_time":"2022-01-18T15:48:53.285612","exception":false,"start_time":"2022-01-18T15:48:52.987542","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"torch.cuda.empty_cache()\nimport gc\ngc.collect()","metadata":{"papermill":{"duration":0.464556,"end_time":"2022-01-18T15:48:54.021126","exception":false,"start_time":"2022-01-18T15:48:53.55657","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ls -l /kaggle/input/barrier-reef-yolov5-training/yolov5/kaggle-Reef/exp \n#[0, 1, 2, 3,4][:4]\n!ls -l","metadata":{"papermill":{"duration":1.031639,"end_time":"2022-01-18T15:48:55.349309","exception":false,"start_time":"2022-01-18T15:48:54.31767","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\n%%writefile /kaggle/working/yolov5/val1.py\n\n# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nValidate a trained YOLOv5 model accuracy on a custom dataset\nUsage:\n    $ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640\nUsage - formats:\n    $ python path/to/val.py --weights yolov5s.pt                 # PyTorch\n                                      yolov5s.torchscript        # TorchScript\n                                      yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn\n                                      yolov5s.xml                # OpenVINO\n                                      yolov5s.engine             # TensorRT\n                                      yolov5s.mlmodel            # CoreML (MacOS-only)\n                                      yolov5s_saved_model        # TensorFlow SavedModel\n                                      yolov5s.pb                 # TensorFlow GraphDef\n                                      yolov5s.tflite             # TensorFlow Lite\n                                      yolov5s_edgetpu.tflite     # TensorFlow Edge TPU\n\"\"\"\n\nimport argparse\nimport json\nimport os\nimport sys\nfrom pathlib import Path\nfrom threading import Thread\n\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0]  # YOLOv5 root directory\nif str(ROOT) not in sys.path:\n    sys.path.append(str(ROOT))  # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative\n\nfrom models.common import DetectMultiBackend\nfrom utils.callbacks import Callbacks\nfrom utils.datasets import create_dataloader\nfrom utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,\n                           coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,\n                           scale_coords, xywh2xyxy, xyxy2xywh)\nfrom utils.metrics import ConfusionMatrix, ap_per_class\nfrom utils.plots import output_to_target, plot_images, plot_val_study\nfrom utils.torch_utils import select_device, time_sync\n\n\ndef save_one_txt(predn, save_conf, shape, file):\n    # Save one txt result\n    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh\n    for *xyxy, conf, cls in predn.tolist():\n        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh\n        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format\n        with open(file, 'a') as f:\n            f.write(('%g ' * len(line)).rstrip() % line + '\\n')\n\n\ndef save_one_json(predn, jdict, path, class_map):\n    # Save one JSON result {\"image_id\": 42, \"category_id\": 18, \"bbox\": [258.15, 41.29, 348.26, 243.78], \"score\": 0.236}\n    image_id = int(path.stem) if path.stem.isnumeric() else path.stem\n    box = xyxy2xywh(predn[:, :4])  # xywh\n    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner\n    for p, b in zip(predn.tolist(), box.tolist()):\n        jdict.append({'image_id': image_id,\n                      'category_id': class_map[int(p[5])],\n                      'bbox': [round(x, 3) for x in b],\n                      'score': round(p[4], 5)})\n\n\ndef process_batch(detections, labels, iouv):\n    \"\"\"\n    Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.\n    Arguments:\n        detections (Array[N, 6]), x1, y1, x2, y2, conf, class\n        labels (Array[M, 5]), class, x1, y1, x2, y2\n    Returns:\n        correct (Array[N, 10]), for 10 IoU levels\n    \"\"\"\n    correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)\n    iou = box_iou(labels[:, 1:], detections[:, :4])\n    x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes match\n    if x[0].shape[0]:\n        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]\n        if x[0].shape[0] > 1:\n            matches = matches[matches[:, 2].argsort()[::-1]]\n            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n            # matches = matches[matches[:, 2].argsort()[::-1]]\n            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n        matches = torch.Tensor(matches).to(iouv.device)\n        correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv\n    return correct\n\n\n@torch.no_grad()\ndef run(data,\n        weights=None,  # model.pt path(s)\n        batch_size=32,  # batch size\n        imgsz=640,  # inference size (pixels)\n        conf_thres=0.001,  # confidence threshold\n        iou_thres=0.6,  # NMS IoU threshold\n        task='val',  # train, val, test, speed or study\n        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu\n        workers=8,  # max dataloader workers (per RANK in DDP mode)\n        single_cls=False,  # treat as single-class dataset\n        augment=False,  # augmented inference\n        verbose=False,  # verbose output\n        save_txt=False,  # save results to *.txt\n        save_hybrid=False,  # save label+prediction hybrid results to *.txt\n        save_conf=False,  # save confidences in --save-txt labels\n        save_json=False,  # save a COCO-JSON results file\n        project=ROOT / 'runs/val',  # save to project/name\n        name='exp',  # save to project/name\n        exist_ok=False,  # existing project/name ok, do not increment\n        half=True,  # use FP16 half-precision inference\n        dnn=False,  # use OpenCV DNN for ONNX inference\n        model=None,\n        dataloader=None,\n        save_dir=Path(''),\n        plots=True,\n        callbacks=Callbacks(),\n        compute_loss=None,\n        ):\n    # Initialize/load model and set device\n    training = model is not None\n    if training:  # called by train.py\n        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model\n\n        half &= device.type != 'cpu'  # half precision only supported on CUDA\n        model.half() if half else model.float()\n    else:  # called directly\n        device = select_device(device, batch_size=batch_size)\n\n        # Directories\n        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run\n        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n\n        # Load model\n        model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)\n        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine\n        imgsz = check_img_size(imgsz, s=stride)  # check image size\n        half &= (pt or jit or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA\n        if pt or jit:\n            model.model.half() if half else model.model.float()\n        elif engine:\n            batch_size = model.batch_size\n        else:\n            half = False\n            batch_size = 1  # export.py models default to batch-size 1\n            device = torch.device('cpu')\n            LOGGER.info(f'Forcing --batch-size 1 square inference shape(1,3,{imgsz},{imgsz}) for non-PyTorch backends')\n\n        # Data\n        data = check_dataset(data)  # check\n\n    # Configure\n    model.eval()\n    is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt')  # COCO dataset\n    nc = 1 if single_cls else int(data['nc'])  # number of classes\n    #iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95\n    iouv=torch.tensor([0.05,0.3,0.85]).to(device) \n    niou = iouv.numel()\n    print('no of niou',niou)\n\n    # Dataloader\n    if not training:\n        model.warmup(imgsz=(1, 3, imgsz, imgsz), half=half)  # warmup\n        pad = 0.0 if task == 'speed' else 0.5\n        task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images\n        dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=pt,\n                                       workers=workers, prefix=colorstr(f'{task}: '))[0]\n\n    seen = 0\n    confusion_matrix = ConfusionMatrix(nc=nc)\n    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}\n    class_map = coco80_to_coco91_class() if is_coco else list(range(1000))\n    s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')\n    dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\n    loss = torch.zeros(3, device=device)\n    jdict, stats, ap, ap_class = [], [], [], []\n    pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar\n    for batch_i, (im, targets, paths, shapes) in enumerate(pbar):\n        t1 = time_sync()\n        if pt or jit or engine:\n            im = im.to(device, non_blocking=True)\n            targets = targets.to(device)\n        im = im.half() if half else im.float()  # uint8 to fp16/32\n        im /= 255  # 0 - 255 to 0.0 - 1.0\n        nb, _, height, width = im.shape  # batch size, channels, height, width\n        t2 = time_sync()\n        dt[0] += t2 - t1\n\n        # Inference\n        out, train_out = model(im) if training else model(im, augment=augment, val=True)  # inference, loss outputs\n        dt[1] += time_sync() - t2\n\n        # Loss\n        if compute_loss:\n            loss += compute_loss([x.float() for x in train_out], targets)[1]  # box, obj, cls\n\n        # NMS\n        targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels\n        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling\n        t3 = time_sync()\n        out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)\n        dt[2] += time_sync() - t3\n\n        # Metrics\n        for si, pred in enumerate(out):\n            labels = targets[targets[:, 0] == si, 1:]\n            nl = len(labels)\n            tcls = labels[:, 0].tolist() if nl else []  # target class\n            path, shape = Path(paths[si]), shapes[si][0]\n            seen += 1\n\n            if len(pred) == 0:\n                if nl:\n                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))\n                continue\n\n            # Predictions\n            if single_cls:\n                pred[:, 5] = 0\n            predn = pred.clone()\n            scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred\n\n            # Evaluate\n            if nl:\n                tbox = xywh2xyxy(labels[:, 1:5])  # target boxes\n                scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels\n                labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels\n                correct = process_batch(predn, labelsn, iouv)\n                if plots:\n                    confusion_matrix.process_batch(predn, labelsn)\n            else:\n                correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)\n            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))  # (correct, conf, pcls, tcls)\n\n            # Save/log\n            if save_txt:\n                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))\n            if save_json:\n                save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary\n            callbacks.run('on_val_image_end', pred, predn, path, names, im[si])\n\n        # Plot images\n        if plots and batch_i < 3:\n            f = save_dir / f'val_batch{batch_i}_labels.jpg'  # labels\n            Thread(target=plot_images, args=(im, targets, paths, f, names), daemon=True).start()\n            f = save_dir / f'val_batch{batch_i}_pred.jpg'  # predictions\n            Thread(target=plot_images, args=(im, output_to_target(out), paths, f, names), daemon=True).start()\n\n    # Compute metrics\n    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy\n    if len(stats) and stats[0].any():\n        tp, fp, p, r, f1, ap, ap_class,f2 = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)\n        #print('ap shape',ap.shape,ap)\n        #print('f1 shape return',f1.shape,f1,'p',p.shape)\n        print('f2 shape',f2.shape,f2,'meanf2 score',f2.mean() )\n        ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95\n        #f2=f2.mean()\n        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()\n        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class\n    else:\n        nt = torch.zeros(1)\n        f2=torch.zeros(1)\n\n    # Print results\n    pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format\n    LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map ))\n\n    # Print results per class\n    if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):\n        for i, c in enumerate(ap_class):\n            LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))\n\n    # Print speeds\n    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image\n    if not training:\n        shape = (batch_size, 3, imgsz, imgsz)\n        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)\n\n    # Plots\n    if plots:\n        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))\n        callbacks.run('on_val_end')\n\n    # Save JSON\n    if save_json and len(jdict):\n        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights\n        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json\n        pred_json = str(save_dir / f\"{w}_predictions.json\")  # predictions json\n        LOGGER.info(f'\\nEvaluating pycocotools mAP... saving {pred_json}...')\n        with open(pred_json, 'w') as f:\n            json.dump(jdict, f)\n\n        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb\n            check_requirements(['pycocotools'])\n            from pycocotools.coco import COCO\n            from pycocotools.cocoeval import COCOeval\n\n            anno = COCO(anno_json)  # init annotations api\n            pred = anno.loadRes(pred_json)  # init predictions api\n            eval = COCOeval(anno, pred, 'bbox')\n            if is_coco:\n                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate\n            eval.evaluate()\n            eval.accumulate()\n            eval.summarize()\n            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)\n        except Exception as e:\n            LOGGER.info(f'pycocotools unable to run: {e}')\n\n    # Return results\n    model.float()  # for training\n    if not training:\n        s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else ''\n        LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n    maps = np.zeros(nc) + map\n    for i, c in enumerate(ap_class):\n        maps[c] = ap[i]\n    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t,f2.mean() \n\n\ndef parse_opt():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')\n    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')\n    parser.add_argument('--batch-size', type=int, default=32, help='batch size')\n    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')\n    parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')\n    parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')\n    parser.add_argument('--task', default='val', help='train, val, test, speed or study')\n    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')\n    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')\n    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')\n    parser.add_argument('--augment', action='store_true', help='augmented inference')\n    parser.add_argument('--verbose', action='store_true', help='report mAP by class')\n    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')\n    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')\n    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')\n    parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')\n    parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')\n    parser.add_argument('--name', default='exp', help='save to project/name')\n    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')\n    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')\n    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')\n    opt = parser.parse_args()\n    opt.data = check_yaml(opt.data)  # check YAML\n    opt.save_json |= opt.data.endswith('coco.yaml')\n    opt.save_txt |= opt.save_hybrid\n    print_args(FILE.stem, opt)\n    return opt\n\n\ndef main(opt):\n    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))\n\n    if opt.task in ('train', 'val', 'test'):  # run normally\n        if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466\n            LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} >> 0.001 will produce invalid mAP values.')\n        run(**vars(opt))\n\n    else:\n        weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]\n        opt.half = True  # FP16 for fastest results\n        if opt.task == 'speed':  # speed benchmarks\n            # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...\n            opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False\n            for opt.weights in weights:\n                run(**vars(opt), plots=False)\n\n        elif opt.task == 'study':  # speed vs mAP benchmarks\n            # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...\n            for opt.weights in weights:\n                f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt'  # filename to save to\n                x, y = list(range(256, 1536 + 128, 128)), []  # x axis (image sizes), y axis\n                for opt.imgsz in x:  # img-size\n                    LOGGER.info(f'\\nRunning {f} --imgsz {opt.imgsz}...')\n                    r, _, t = run(**vars(opt), plots=False)\n                    y.append(r + t)  # results and times\n                np.savetxt(f, y, fmt='%10.4g')  # save\n            os.system('zip -r study.zip study_*.txt')\n            plot_val_study(x=x)  # plot\n\n\nif __name__ == \"__main__\":\n    opt = parse_opt()\n    main(opt)\n'''","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.190451,"end_time":"2022-01-18T15:48:55.710221","exception":false,"start_time":"2022-01-18T15:48:55.51977","status":"completed"},"tags":[],"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f2= np.zeros((1, 3))\nf2[0,0]=2\na=[1,2,3,4]\na[:4]","metadata":{"papermill":{"duration":0.179291,"end_time":"2022-01-18T15:48:56.059389","exception":false,"start_time":"2022-01-18T15:48:55.880098","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\n%%writefile  /kaggle/working/yolov5/utils/metrics1.py\n\n# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nModel validation metrics\n\"\"\"\n\nimport math\nimport warnings\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\n\ndef fitness(x):\n    # Model fitness as a weighted combination of metrics\n    w = [0.0, 0.0, 0.1, 0.35,0.55]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]\n    return (x[:, :5] * w).sum(1)\n\n\ndef ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):\n    \"\"\" Compute the average precision, given the recall and precision curves.\n    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.\n    # Arguments\n        tp:  True positives (nparray, nx1 or nx10).\n        conf:  Objectness value from 0-1 (nparray).\n        pred_cls:  Predicted object classes (nparray).\n        target_cls:  True object classes (nparray).\n        plot:  Plot precision-recall curve at mAP@0.5\n        save_dir:  Plot save directory\n    # Returns\n        The average precision as computed in py-faster-rcnn.\n    \"\"\"\n\n    # Sort by objectness\n    i = np.argsort(-conf)\n    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]\n\n    # Find unique classes\n    unique_classes, nt = np.unique(target_cls, return_counts=True)\n    nc = unique_classes.shape[0]  # number of classes, number of detections\n\n    # Create Precision-Recall curve and compute AP for each class\n    px, py = np.linspace(0, 1, 1000), []  # for plotting\n    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((tp.shape[1], 1000)), np.zeros((tp.shape[1], 1000))\n    f2= np.zeros((nc, tp.shape[1]))\n    #print('tpshape',tp.shape[1])\n    for ci, c in enumerate(unique_classes):\n        i = pred_cls == c\n        n_l = nt[ci]  # number of labels\n        n_p = i.sum()  # number of predictions\n\n        if n_p == 0 or n_l == 0:\n            continue\n        else:\n            # Accumulate FPs and TPs\n            fpc = (1 - tp[i]).cumsum(0)\n            tpc = tp[i].cumsum(0)\n\n            # Recall\n            recall = tpc / (n_l + eps)  # recall curve\n            for th in range(0,tp.shape[1]):\n                \n                r[th] = np.interp(-px, -conf[i], recall[:,th], left=0)  # negative x, xp because xp decreases\n            #print('recall shape',recall.shape,'r',r.shape)# 3,1000\n            # Precision\n            precision = tpc / (tpc + fpc)  # precision curve\n            for th in range(0,tp.shape[1]):\n                p[th] = np.interp(-px, -conf[i], precision[:,th], left=1)  # p at pr_score\n            #print('precision shape',precision.shape,'r',p.shape) # 3 1000\n            # AP from recall-precision curve\n            beta2 = 2 ** 2\n            for j in range(tp.shape[1]):\n                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])\n               \n                if plot and j == 0:\n                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5\n\n    # Compute F1 (harmonic mean of precision and recall)\n    f1 = 2 * p * r / (p + r + eps)\n    #print('f1 shape',f1.shape) 3,1000\n    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data\n    names = {i: v for i, v in enumerate(names)}  # to dict\n    if plot:\n        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)\n        plot_mc_curve(px, f1[1][None,:], Path(save_dir) / 'F1_curve.png', names, ylabel='F1')\n        plot_mc_curve(px, p[1][None,:], Path(save_dir) / 'P_curve.png', names, ylabel='Precision')\n        plot_mc_curve(px, r[1][None,:], Path(save_dir) / 'R_curve.png', names, ylabel='Recall')\n\n    #i = f1.mean(0).argmax(-1)  # max F1 index\n    i = f1.argmax(-1) # get max across 1k detections\n    p, r, f1 = p[np.arange(len(p)), i], r[np.arange(len(r)), i], f1[np.arange(len(f1)), i]\n    #p=3,r  get max across all detections for three thresholds\n    denom = beta2 * p + r\n                 \n    denom[denom == 0.0] = 1  # avoid division by 0\n    f2  = ((1 + beta2) * p* r )/ denom\n    f2=f2[None,:]\n    f1=f1[1] #get 0.3 tresholds\n    \n    tp = (r * nt).round()  # true positives\n    fp = (tp / (p + eps) - tp).round()  # false positives\n    return tp[1], fp[1], p[1], r[1], f1, ap, unique_classes.astype('int32'),f2\n\n\ndef compute_ap(recall, precision):\n    \"\"\" Compute the average precision, given the recall and precision curves\n    # Arguments\n        recall:    The recall curve (list)\n        precision: The precision curve (list)\n    # Returns\n        Average precision, precision curve, recall curve\n    \"\"\"\n\n    # Append sentinel values to beginning and end\n    mrec = np.concatenate(([0.0], recall, [1.0]))\n    mpre = np.concatenate(([1.0], precision, [0.0]))\n\n    # Compute the precision envelope\n    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))\n\n    # Integrate area under curve\n    method = 'interp'  # methods: 'continuous', 'interp'\n    if method == 'interp':\n        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)\n        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate\n    else:  # 'continuous'\n        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes\n        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve\n\n    return ap, mpre, mrec\n\n\nclass ConfusionMatrix:\n    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix\n    def __init__(self, nc, conf=0.25, iou_thres=0.45):\n        self.matrix = np.zeros((nc + 1, nc + 1))\n        self.nc = nc  # number of classes\n        self.conf = conf\n        self.iou_thres = iou_thres\n\n    def process_batch(self, detections, labels):\n        \"\"\"\n        Return intersection-over-union (Jaccard index) of boxes.\n        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n        Arguments:\n            detections (Array[N, 6]), x1, y1, x2, y2, conf, class\n            labels (Array[M, 5]), class, x1, y1, x2, y2\n        Returns:\n            None, updates confusion matrix accordingly\n        \"\"\"\n        detections = detections[detections[:, 4] > self.conf]\n        gt_classes = labels[:, 0].int()\n        detection_classes = detections[:, 5].int()\n        iou = box_iou(labels[:, 1:], detections[:, :4])\n\n        x = torch.where(iou > self.iou_thres)\n        if x[0].shape[0]:\n            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\n            if x[0].shape[0] > 1:\n                matches = matches[matches[:, 2].argsort()[::-1]]\n                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n                matches = matches[matches[:, 2].argsort()[::-1]]\n                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n        else:\n            matches = np.zeros((0, 3))\n\n        n = matches.shape[0] > 0\n        m0, m1, _ = matches.transpose().astype(np.int16)\n        for i, gc in enumerate(gt_classes):\n            j = m0 == i\n            if n and sum(j) == 1:\n                self.matrix[detection_classes[m1[j]], gc] += 1  # correct\n            else:\n                self.matrix[self.nc, gc] += 1  # background FP\n\n        if n:\n            for i, dc in enumerate(detection_classes):\n                if not any(m1 == i):\n                    self.matrix[dc, self.nc] += 1  # background FN\n\n    def matrix(self):\n        return self.matrix\n\n    def tp_fp(self):\n        tp = self.matrix.diagonal()  # true positives\n        fp = self.matrix.sum(1) - tp  # false positives\n        # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)\n        return tp[:-1], fp[:-1]  # remove background class\n\n    def plot(self, normalize=True, save_dir='', names=()):\n        try:\n            import seaborn as sn\n\n            array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-6) if normalize else 1)  # normalize columns\n            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)\n\n            fig = plt.figure(figsize=(12, 9), tight_layout=True)\n            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size\n            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels\n            with warnings.catch_warnings():\n                warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered\n                sn.heatmap(array, annot=self.nc < 30, annot_kws={\"size\": 8}, cmap='Blues', fmt='.2f', square=True,\n                           xticklabels=names + ['background FP'] if labels else \"auto\",\n                           yticklabels=names + ['background FN'] if labels else \"auto\").set_facecolor((1, 1, 1))\n            fig.axes[0].set_xlabel('True')\n            fig.axes[0].set_ylabel('Predicted')\n            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)\n            plt.close()\n        except Exception as e:\n            print(f'WARNING: ConfusionMatrix plot failure: {e}')\n\n    def print(self):\n        for i in range(self.nc + 1):\n            print(' '.join(map(str, self.matrix[i])))\n\n\ndef bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):\n    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4\n    box2 = box2.T\n\n    # Get the coordinates of bounding boxes\n    if x1y1x2y2:  # x1, y1, x2, y2 = box1\n        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]\n        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]\n    else:  # transform from xywh to xyxy\n        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2\n        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2\n        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2\n        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2\n\n    # Intersection area\n    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \\\n            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)\n\n    # Union Area\n    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps\n    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps\n    union = w1 * h1 + w2 * h2 - inter + eps\n\n    iou = inter / union\n    if CIoU or DIoU or GIoU:\n        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width\n        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height\n        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1\n            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared\n            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +\n                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared\n            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47\n                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)\n                with torch.no_grad():\n                    alpha = v / (v - iou + (1 + eps))\n                return iou - (rho2 / c2 + v * alpha)  # CIoU\n            return iou - rho2 / c2  # DIoU\n        c_area = cw * ch + eps  # convex area\n        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf\n    return iou  # IoU\n\ndef box_iou(box1, box2):\n    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n    \"\"\"\n    Return intersection-over-union (Jaccard index) of boxes.\n    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n    Arguments:\n        box1 (Tensor[N, 4])\n        box2 (Tensor[M, 4])\n    Returns:\n        iou (Tensor[N, M]): the NxM matrix containing the pairwise\n            IoU values for every element in boxes1 and boxes2\n    \"\"\"\n\n    def box_area(box):\n        # box = 4xn\n        return (box[2] - box[0]) * (box[3] - box[1])\n\n    area1 = box_area(box1.T)\n    area2 = box_area(box2.T)\n\n    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)\n    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)\n\n\ndef bbox_ioa(box1, box2, eps=1E-7):\n    \"\"\" Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2\n    box1:       np.array of shape(4)\n    box2:       np.array of shape(nx4)\n    returns:    np.array of shape(n)\n    \"\"\"\n\n    box2 = box2.transpose()\n\n    # Get the coordinates of bounding boxes\n    b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]\n    b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]\n\n    # Intersection area\n    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \\\n                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)\n\n    # box2 area\n    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps\n\n    # Intersection over box2 area\n    return inter_area / box2_area\n\n\ndef wh_iou(wh1, wh2):\n    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2\n    wh1 = wh1[:, None]  # [N,1,2]\n    wh2 = wh2[None]  # [1,M,2]\n    inter = torch.min(wh1, wh2).prod(2)  # [N,M]\n    return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)\n\n\n# Plots ----------------------------------------------------------------------------------------------------------------\n\ndef plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):\n    # Precision-recall curve\n    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n    py = np.stack(py, axis=1)\n\n    if 0 < len(names) < 21:  # display per-class legend if < 21 classes\n        for i, y in enumerate(py.T):\n            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)\n    else:\n        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)\n\n    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())\n    ax.set_xlabel('Recall')\n    ax.set_ylabel('Precision')\n    ax.set_xlim(0, 1)\n    ax.set_ylim(0, 1)\n    plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n    fig.savefig(Path(save_dir), dpi=250)\n    plt.close()\n\n\ndef plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):\n    # Metric-confidence curve\n    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)\n\n    if 0 < len(names) < 21:  # display per-class legend if < 21 classes\n        for i, y in enumerate(py):\n            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)\n    else:\n        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)\n\n    y = py.mean(0)\n    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    ax.set_xlim(0, 1)\n    ax.set_ylim(0, 1)\n    plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n    fig.savefig(Path(save_dir), dpi=250)\n    plt.close()\n'''","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.195978,"end_time":"2022-01-18T15:48:56.425755","exception":false,"start_time":"2022-01-18T15:48:56.229777","status":"completed"},"tags":[],"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results= (1,2,3,4,5)\nf2=6\n#np.concatenate([a,[5]]).reshape(1,-1)\nnp.concatenate([np.array(results[:4]),[f2]]).reshape(1,-1)\nimport random\nrandom.random()","metadata":{"papermill":{"duration":0.184485,"end_time":"2022-01-18T15:48:56.784862","exception":false,"start_time":"2022-01-18T15:48:56.600377","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport psutil\n \n# inner psutil function\ndef process_memory():\n    process = psutil.Process(os.getpid())\n    mem_info = process.memory_info()\n    return mem_info.rss \nprocess_memory()/(1024*1024)\n ","metadata":{"papermill":{"duration":0.200502,"end_time":"2022-01-18T15:48:57.164716","exception":false,"start_time":"2022-01-18T15:48:56.964214","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\n%%writefile /kaggle/working/yolov5/train.py\n\nimport argparse\nimport math\nimport os\nimport random\nimport sys\nimport time\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom pathlib import Path\n\n\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport yaml\nfrom torch.cuda import amp\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom torch.optim import SGD, Adam, AdamW, lr_scheduler\nfrom tqdm import tqdm\nimport os\nimport psutil\n \n# inner psutil function\n\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0]  # YOLOv5 root directory\nif str(ROOT) not in sys.path:\n    sys.path.append(str(ROOT))  # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative\n\nimport val  # for end-of-epoch mAP\nfrom models.experimental import attempt_load\nfrom models.yolo import Model\nfrom utils.autoanchor import check_anchors\nfrom utils.autobatch import check_train_batch_size\nfrom utils.callbacks import Callbacks\nfrom utils.datasets import create_dataloader\nfrom utils.downloads import attempt_download\nfrom utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,\n                           check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,\n                           intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,\n                           print_args, print_mutation, strip_optimizer)\nfrom utils.loggers import Loggers\nfrom utils.loggers.wandb.wandb_utils import check_wandb_resume\nfrom utils.loss import ComputeLoss\nfrom utils.metrics import fitness\nfrom utils.plots import plot_evolve, plot_labels\nfrom utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first\nimport os\nLOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv('RANK', -1))\nWORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = True\nseed_everything(42)\ndef process_memory():\n    process = psutil.Process(os.getpid())\n    mem_info = process.memory_info()\n    return mem_info.rss \n\ndef train(hyp,  # path/to/hyp.yaml or hyp dictionary\n          opt,\n          device,\n          callbacks\n          ):\n    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \\\n        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \\\n        opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze\n\n    # Directories\n    w = save_dir / 'weights'  # weights dir\n    (w.parent if evolve else w).mkdir(parents=True, exist_ok=True)  # make dir\n    last, best = w / 'last.pt', w / 'best.pt'\n\n    # Hyperparameters\n    if isinstance(hyp, str):\n        with open(hyp, errors='ignore') as f:\n            hyp = yaml.safe_load(f)  # load hyps dict\n    LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))\n\n    # Save run settings\n    if not evolve:\n        with open(save_dir / 'hyp.yaml', 'w') as f:\n            yaml.safe_dump(hyp, f, sort_keys=False)\n        with open(save_dir / 'opt.yaml', 'w') as f:\n            yaml.safe_dump(vars(opt), f, sort_keys=False)\n\n    # Loggers\n    data_dict = None\n    if RANK in [-1, 0]:\n        loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance\n        if loggers.wandb:\n            data_dict = loggers.wandb.data_dict\n            if resume:\n                weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp\n\n        # Register actions\n        for k in methods(loggers):\n            callbacks.register_action(k, callback=getattr(loggers, k))\n\n    # Config\n    plots = not evolve  # create plots\n    cuda = device.type != 'cpu'\n    init_seeds(1 + RANK)\n    with torch_distributed_zero_first(LOCAL_RANK):\n        data_dict = data_dict or check_dataset(data)  # check if None\n    train_path, val_path = data_dict['train'], data_dict['val']\n    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes\n    names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names\n    assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check\n    is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt')  # COCO dataset\n\n    # Model\n    check_suffix(weights, '.pt')  # check weights\n    pretrained = weights.endswith('.pt')\n    if pretrained:\n        with torch_distributed_zero_first(LOCAL_RANK):\n            weights = attempt_download(weights)  # download if not found locally\n        ckpt = torch.load(weights, map_location=device)  # load checkpoint\n        model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create\n        exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys\n        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32\n        csd = intersect_dicts(csd, model.state_dict(), exclude=exclude)  # intersect\n        model.load_state_dict(csd, strict=False)  # load\n        LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report\n    else:\n        model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create\n\n    # Freeze\n    freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))]  # layers to freeze\n    for k, v in model.named_parameters():\n        v.requires_grad = True  # train all layers\n        if any(x in k for x in freeze):\n            LOGGER.info(f'freezing {k}')\n            v.requires_grad = False\n\n    # Image size\n    gs = max(int(model.stride.max()), 32)  # grid size (max stride)\n    imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple\n\n    # Batch size\n    if RANK == -1 and batch_size == -1:  # single-GPU only, estimate best batch size\n        batch_size = check_train_batch_size(model, imgsz)\n        loggers.on_params_update({\"batch_size\": batch_size})\n\n    # Optimizer\n    nbs = 64  # nominal batch size\n    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing\n    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay\n    LOGGER.info(f\"Scaled weight_decay = {hyp['weight_decay']}\")\n\n    g0, g1, g2 = [], [], []  # optimizer parameter groups\n    for v in model.modules():\n        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias\n            g2.append(v.bias)\n        if isinstance(v, nn.BatchNorm2d):  # weight (no decay)\n            g0.append(v.weight)\n        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)\n            g1.append(v.weight)\n\n    if opt.optimizer == 'Adam':\n        optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum\n    elif opt.optimizer == 'AdamW':\n        optimizer = AdamW(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum\n    else:\n        optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)\n\n    optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']})  # add g1 with weight_decay\n    optimizer.add_param_group({'params': g2})  # add g2 (biases)\n    LOGGER.info(f\"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups \"\n                f\"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias\")\n    del g0, g1, g2\n\n    # Scheduler\n    if opt.linear_lr:\n        lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear\n    else:\n        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']\n    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)  # plot_lr_scheduler(optimizer, scheduler, epochs)\n\n    # EMA\n    ema = ModelEMA(model) if RANK in [-1, 0] else None\n\n    # Resume\n    start_epoch, best_fitness = 0, 0.0\n    if pretrained:\n        # Optimizer\n        if ckpt['optimizer'] is not None:\n            optimizer.load_state_dict(ckpt['optimizer'])\n            best_fitness = ckpt['best_fitness']\n\n        # EMA\n        if ema and ckpt.get('ema'):\n            ema.ema.load_state_dict(ckpt['ema'].float().state_dict())\n            ema.updates = ckpt['updates']\n\n        # Epochs\n        start_epoch = ckpt['epoch'] + 1\n        if resume:\n            assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'\n        if epochs < start_epoch:\n            LOGGER.info(f\"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.\")\n            epochs += ckpt['epoch']  # finetune additional epochs\n\n        del ckpt, csd\n\n    # DP mode\n    if cuda and RANK == -1 and torch.cuda.device_count() > 1:\n        LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\\n'\n                       'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')\n        model = torch.nn.DataParallel(model)\n\n    # SyncBatchNorm\n    if opt.sync_bn and cuda and RANK != -1:\n        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)\n        LOGGER.info('Using SyncBatchNorm()')\n\n    # Trainloader\n    train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,\n                                              hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,\n                                              workers=workers, image_weights=opt.image_weights, quad=opt.quad,\n                                              prefix=colorstr('train: '), shuffle=True)\n    mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max())  # max label class\n    nb = len(train_loader)  # number of batches\n    assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'\n\n    # Process 0\n    if RANK in [-1, 0]:\n        #print('val loader')\n        val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,\n                                       hyp=hyp,augment=True, cache=None if noval else opt.cache, rect=True, rank=-1,\n                                       workers=workers, pad=0.5,\n                                       prefix=colorstr('val: ') )[0]\n\n        if not resume:\n            labels = np.concatenate(dataset.labels, 0)\n            # c = torch.tensor(labels[:, 0])  # classes\n            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency\n            # model._initialize_biases(cf.to(device))\n            if plots:\n                plot_labels(labels, names, save_dir)\n\n            # Anchors\n            if not opt.noautoanchor:\n                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)\n            model.half().float()  # pre-reduce anchor precision\n\n        callbacks.run('on_pretrain_routine_end')\n\n    # DDP mode\n    if cuda and RANK != -1:\n        model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)\n\n    # Model attributes\n    nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)\n    hyp['box'] *= 3 / nl  # scale to layers\n    hyp['cls'] *= nc / 80 * 3 / nl  # scale to classes and layers\n    hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers\n    hyp['label_smoothing'] = opt.label_smoothing\n    model.nc = nc  # attach number of classes to model\n    model.hyp = hyp  # attach hyperparameters to model\n    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights\n    model.names = names\n\n    # Start training\n    t0 = time.time()\n    nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)\n    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training\n    last_opt_step = -1\n    maps = np.zeros(nc)  # mAP per class\n    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95,  val_loss(box, obj, cls)\n    scheduler.last_epoch = start_epoch - 1  # do not move\n    scaler = amp.GradScaler(enabled=cuda)\n    stopper = EarlyStopping(patience=opt.patience)\n    compute_loss = ComputeLoss(model)  # init loss class\n    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\\n'\n                f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\\n'\n                f\"Logging results to {colorstr('bold', save_dir)}\\n\"\n                f'Starting training for {epochs} epochs...')\n    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------\n        model.train()\n\n        # Update image weights (optional, single-GPU only)\n        if opt.image_weights:\n            cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights\n            iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights\n            dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx\n\n        # Update mosaic border (optional)\n        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)\n        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders\n\n        mloss = torch.zeros(3, device=device)  # mean losses\n        if RANK != -1:\n            train_loader.sampler.set_epoch(epoch)\n        pbar = enumerate(train_loader)\n        LOGGER.info(('\\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))\n        if RANK in [-1, 0]:\n            pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar\n        optimizer.zero_grad()\n        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------\n            ni = i + nb * epoch  # number integrated batches (since train start)\n            imgs = imgs.to(device, non_blocking=True).float() / 255  # uint8 to float32, 0-255 to 0.0-1.0\n\n            # Warmup\n            if ni <= nw:\n                xi = [0, nw]  # x interp\n                # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)\n                accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())\n                for j, x in enumerate(optimizer.param_groups):\n                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0\n                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])\n                    if 'momentum' in x:\n                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])\n\n            # Multi-scale\n            if opt.multi_scale:\n                #sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size\n                sz = random.randrange(imgsz * 0.6, imgsz * 1.55 + gs) // gs * gs  # size\n\n                sf = sz / max(imgs.shape[2:])  # scale factor\n                if sf != 1:\n                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)\n                    imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', \n                                                     align_corners=False)\n\n            # Forward\n            with amp.autocast(enabled=cuda):\n                pred = model(imgs)  # forward\n                loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size\n                if RANK != -1:\n                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode\n                if opt.quad:\n                    loss *= 4.\n\n            # Backward\n            scaler.scale(loss).backward()\n\n            # Optimize\n            if ni - last_opt_step >= accumulate:\n                scaler.step(optimizer)  # optimizer.step\n                scaler.update()\n                optimizer.zero_grad()\n                if ema:\n                    ema.update(model)\n                last_opt_step = ni\n\n            # Log\n            if RANK in [-1, 0]:\n                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses\n                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)\n                pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (\n                    f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))\n                callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)\n            # end batch ------------------------------------------------------------------------------------------------\n\n        # Scheduler\n        lr = [x['lr'] for x in optimizer.param_groups]  # for loggers\n        scheduler.step()\n\n        if RANK in [-1, 0]:\n            # mAP\n            callbacks.run('on_train_epoch_end', epoch=epoch)\n            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])\n            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop\n            if not noval or final_epoch:  # Calculate mAP\n                results, maps, _,f2 = val.run(data_dict,\n                                           batch_size= batch_size // WORLD_SIZE * 2,\n                                           imgsz=imgsz,\n                                           model=ema.ema,\n                                           single_cls=single_cls,\n                                           dataloader=val_loader,\n                                           save_dir=save_dir,\n                                           plots=False,\n                                           callbacks=callbacks,\n                                           compute_loss=compute_loss)\n\n            # Update best mAP\n            #fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]\n            fi = fitness(np.concatenate([np.array(results[:4]),[f2]]).reshape(1, -1))\n            if fi > best_fitness:\n                print(f'saving best weights at {epoch} {fi}')\n                best_fitness = fi\n            log_vals = list(mloss) + list(results) + lr\n            callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)\n\n            # Save model\n            if (not nosave) or (final_epoch and not evolve):  # if save\n                ckpt = {'epoch': epoch,\n                        'best_fitness': best_fitness,\n                        'model': deepcopy(de_parallel(model)).half(),\n                        'ema': deepcopy(ema.ema).half(),\n                        'updates': ema.updates,\n                        'optimizer': optimizer.state_dict(),\n                        'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,\n                        'date': datetime.now().isoformat()}\n\n                # Save last, best and delete\n                torch.save(ckpt, last)\n                if best_fitness == fi:\n                    torch.save(ckpt, best)\n                if (epoch > 0) and (opt.save_period > 0) and (epoch % opt.save_period == 0):\n                    torch.save(ckpt, w / f'epoch{epoch}.pt')\n                del ckpt\n                callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)\n\n            # Stop Single-GPU\n            if RANK == -1 and stopper(epoch=epoch, fitness=fi):\n                break\n\n            # Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576\n            # stop = stopper(epoch=epoch, fitness=fi)\n            # if RANK == 0:\n            #    dist.broadcast_object_list([stop], 0)  # broadcast 'stop' to all ranks\n\n        # Stop DPP\n        # with torch_distributed_zero_first(RANK):\n        # if stop:\n        #    break  # must break all DDP ranks\n\n        # end epoch ----------------------------------------------------------------------------------------------------\n    # end training -----------------------------------------------------------------------------------------------------\n    if RANK in [-1, 0]:\n        LOGGER.info(f'\\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')\n        for f in last, best:\n            if f.exists():\n                strip_optimizer(f)  # strip optimizers\n                print('memory bf',process_memory()/(1024*1024))\n                if f is best:\n                    LOGGER.info(f'\\nValidating {f}...')\n                    results, _, _,f2 = val.run(data_dict,\n                                            batch_size=batch_size // WORLD_SIZE * 2,\n                                            #batch_size=4,\n                                            imgsz=imgsz,\n                                            model=attempt_load(f, device).half(),\n                                            iou_thres=0.65 if is_coco else 0.60,  # best pycocotools results at 0.65\n                                            single_cls=single_cls,\n                                            dataloader=val_loader,\n                                            save_dir=save_dir,\n                                            save_json=is_coco,\n                                            verbose=True,\n                                            plots=False,\n                                            callbacks=callbacks,\n                                            compute_loss=compute_loss)  # val best model with plots\n                    if is_coco:\n                        callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)\n        print('calling train end')\n        callbacks.run('on_train_end', last, best, plots, epoch, results)\n        LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}\")\n\n    torch.cuda.empty_cache()\n    return results\n\n\ndef parse_opt(known=False):\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')\n    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')\n    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')\n    parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')\n    parser.add_argument('--epochs', type=int, default=300)\n    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')\n    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')\n    parser.add_argument('--rect', action='store_true', help='rectangular training')\n    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')\n    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')\n    parser.add_argument('--noval', action='store_true', help='only validate final epoch')\n    parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')\n    parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')\n    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')\n    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in \"ram\" (default) or \"disk\"')\n    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')\n    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')\n    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')\n    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')\n    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')\n    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')\n    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')\n    parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')\n    parser.add_argument('--name', default='exp', help='save to project/name')\n    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')\n    parser.add_argument('--quad', action='store_true', help='quad dataloader')\n    parser.add_argument('--linear-lr', action='store_true', help='linear LR')\n    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')\n    parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')\n    parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')\n    parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')\n    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')\n\n    # Weights & Biases arguments\n    parser.add_argument('--entity', default=None, help='W&B: Entity')\n    parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, \"val\" option')\n    parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')\n    parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')\n\n    opt = parser.parse_known_args()[0] if known else parser.parse_args()\n    return opt\n\n\ndef main(opt, callbacks=Callbacks()):\n    # Checks\n    if RANK in [-1, 0]:\n        print_args(FILE.stem, opt)\n        check_git_status()\n        check_requirements(exclude=['thop'])\n\n    # Resume\n    if opt.resume and not check_wandb_resume(opt) and not opt.evolve:  # resume an interrupted run\n        ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path\n        assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'\n        with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:\n            opt = argparse.Namespace(**yaml.safe_load(f))  # replace\n        opt.cfg, opt.weights, opt.resume = '', ckpt, True  # reinstate\n        LOGGER.info(f'Resuming training from {ckpt}')\n    else:\n        opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \\\n            check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks\n        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'\n        if opt.evolve:\n            opt.project = str(ROOT / 'runs/evolve')\n            opt.exist_ok, opt.resume = opt.resume, False  # pass resume to exist_ok and disable resume\n        opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))\n\n    # DDP mode\n    device = select_device(opt.device, batch_size=opt.batch_size)\n    if LOCAL_RANK != -1:\n        assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'\n        assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'\n        assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'\n        assert not opt.evolve, '--evolve argument is not compatible with DDP training'\n        torch.cuda.set_device(LOCAL_RANK)\n        device = torch.device('cuda', LOCAL_RANK)\n        dist.init_process_group(backend=\"nccl\" if dist.is_nccl_available() else \"gloo\")\n\n    # Train\n    if not opt.evolve:\n        train(opt.hyp, opt, device, callbacks)\n        if WORLD_SIZE > 1 and RANK == 0:\n            LOGGER.info('Destroying process group... ')\n            dist.destroy_process_group()\n\n    # Evolve hyperparameters (optional)\n    else:\n        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)\n        meta = {'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)\n                'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)\n                'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1\n                'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay\n                'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)\n                'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum\n                'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr\n                'box': (1, 0.02, 0.2),  # box loss gain\n                'cls': (1, 0.2, 4.0),  # cls loss gain\n                'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight\n                'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)\n                'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight\n                'iou_t': (0, 0.1, 0.7),  # IoU training threshold\n                'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold\n                'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)\n                'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)\n                'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)\n                'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)\n                'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)\n                'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)\n                'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)\n                'scale': (1, 0.0, 0.9),  # image scale (+/- gain)\n                'shear': (1, 0.0, 10.0),  # image shear (+/- deg)\n                'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001\n                'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)\n                'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)\n                'mosaic': (1, 0.0, 1.0),  # image mixup (probability)\n                'mixup': (1, 0.0, 1.0),  # image mixup (probability)\n                'copy_paste': (1, 0.0, 1.0)}  # segment copy-paste (probability)\n\n        with open(opt.hyp, errors='ignore') as f:\n            hyp = yaml.safe_load(f)  # load hyps dict\n            if 'anchors' not in hyp:  # anchors commented in hyp.yaml\n                hyp['anchors'] = 3\n        opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir)  # only val/save final epoch\n        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices\n        evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'\n        if opt.bucket:\n            os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}')  # download evolve.csv if exists\n\n        for _ in range(opt.evolve):  # generations to evolve\n            if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate\n                # Select parent(s)\n                parent = 'single'  # parent selection method: 'single' or 'weighted'\n                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)\n                n = min(5, len(x))  # number of previous results to consider\n                x = x[np.argsort(-fitness(x))][:n]  # top n mutations\n                w = fitness(x) - fitness(x).min() + 1E-6  # weights (sum > 0)\n                if parent == 'single' or len(x) == 1:\n                    # x = x[random.randint(0, n - 1)]  # random selection\n                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection\n                elif parent == 'weighted':\n                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination\n\n                # Mutate\n                mp, s = 0.8, 0.2  # mutation probability, sigma\n                npr = np.random\n                npr.seed(int(time.time()))\n                g = np.array([meta[k][0] for k in hyp.keys()])  # gains 0-1\n                ng = len(meta)\n                v = np.ones(ng)\n                while all(v == 1):  # mutate until a change occurs (prevent duplicates)\n                    v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)\n                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)\n                    hyp[k] = float(x[i + 7] * v[i])  # mutate\n\n            # Constrain to limits\n            for k, v in meta.items():\n                hyp[k] = max(hyp[k], v[1])  # lower limit\n                hyp[k] = min(hyp[k], v[2])  # upper limit\n                hyp[k] = round(hyp[k], 5)  # significant digits\n\n            # Train mutation\n            results = train(hyp.copy(), opt, device, callbacks)\n\n            # Write mutation results\n            print_mutation(results, hyp.copy(), save_dir, opt.bucket)\n\n        # Plot results\n        plot_evolve(evolve_csv)\n        LOGGER.info(f'Hyperparameter evolution finished\\n'\n                    f\"Results saved to {colorstr('bold', save_dir)}\\n\"\n                    f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')\n\n\ndef run(**kwargs):\n    # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')\n    opt = parse_opt(True)\n    for k, v in kwargs.items():\n        setattr(opt, k, v)\n    main(opt)\n\n\nif __name__ == \"__main__\":\n    opt = parse_opt()\n    main(opt)\n'''","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.214248,"end_time":"2022-01-18T15:48:57.572526","exception":false,"start_time":"2022-01-18T15:48:57.358278","status":"completed"},"tags":[],"_kg_hide-output":true,"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install pycocotools\n!pip install addict","metadata":{"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#%cd YOLOv6\n%cd yolov7","metadata":{"execution":{"iopub.status.busy":"2022-09-09T12:47:25.667533Z","iopub.execute_input":"2022-09-09T12:47:25.668219Z","iopub.status.idle":"2022-09-09T12:47:25.681792Z","shell.execute_reply.started":"2022-09-09T12:47:25.668125Z","shell.execute_reply":"2022-09-09T12:47:25.680671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#!wget https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n.pt","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile /kaggle/working/hyp.yaml\nlr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937  # SGD momentum/Adam beta1\nweight_decay: 0.0005  # optimizer weight decay 5e-4\nwarmup_epochs: 3.0  # warmup epochs (fractions ok)\nwarmup_momentum: 0.8  # warmup initial momentum\nwarmup_bias_lr: 0.1  # warmup initial bias lr\nbox: 0.05  # box loss gain\ncls: 0.3  # cls loss gain\ncls_pw: 1.0  # cls BCELoss positive_weight\nobj: 0.7  # obj loss gain (scale with pixels)\nobj_pw: 1.0  # obj BCELoss positive_weight\niou_t: 0.20  # IoU training threshold\nanchor_t: 4.0  # anchor-multiple threshold\n# anchors: 3  # anchors per output layer (0 to ignore)\nfl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015  # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7  # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4  # image HSV-Value augmentation (fraction)\ndegrees: 0.0  # image rotation (+/- deg)\ntranslate: 0.2  # image translation (+/- fraction)\nscale: 0.9  # image scale (+/- gain)\nshear: 0.0  # image shear (+/- deg)\nperspective: 0.0  # image perspective (+/- fraction), range 0-0.001\nflipud: 0.0  # image flip up-down (probability)\nfliplr: 0.5  # image flip left-right (probability)\nmosaic: 1.0  # image mosaic (probability)\nmixup: 0.15  # image mixup (probability)\ncopy_paste: 0.0  # image copy paste (probability)\npaste_in: 0.15  # image copy paste (probability)","metadata":{"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile /kaggle/working/yolov7.yaml\n# parameters\nnc: 1  # number of classes\ndepth_multiple: 1.0  # model depth multiple\nwidth_multiple: 1.0  # layer channel multiple\n\ndepth_multiple: 1.0  # model depth multiple\nwidth_multiple: 1.0  # layer channel multiple\n\n# anchors\nanchors:\n  - [12,16, 19,36, 40,28]  # P3/8\n  - [36,75, 76,55, 72,146]  # P4/16\n  - [142,110, 192,243, 459,401]  # P5/32\n\n# yolov7 backbone\nbackbone:\n  # [from, number, module, args]\n  [[-1, 1, Conv, [32, 3, 1]],  # 0\n  \n   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      \n   [-1, 1, Conv, [64, 3, 1]],\n   \n   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  \n   [-1, 1, Conv, [64, 1, 1]],\n   [-2, 1, Conv, [64, 1, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [[-1, -3, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [256, 1, 1]],  # 11\n         \n   [-1, 1, MP, []],\n   [-1, 1, Conv, [128, 1, 1]],\n   [-3, 1, Conv, [128, 1, 1]],\n   [-1, 1, Conv, [128, 3, 2]],\n   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  \n   [-1, 1, Conv, [128, 1, 1]],\n   [-2, 1, Conv, [128, 1, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [[-1, -3, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [512, 1, 1]],  # 24\n         \n   [-1, 1, MP, []],\n   [-1, 1, Conv, [256, 1, 1]],\n   [-3, 1, Conv, [256, 1, 1]],\n   [-1, 1, Conv, [256, 3, 2]],\n   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  \n   [-1, 1, Conv, [256, 1, 1]],\n   [-2, 1, Conv, [256, 1, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [[-1, -3, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [1024, 1, 1]],  # 37\n         \n   [-1, 1, MP, []],\n   [-1, 1, Conv, [512, 1, 1]],\n   [-3, 1, Conv, [512, 1, 1]],\n   [-1, 1, Conv, [512, 3, 2]],\n   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  \n   [-1, 1, Conv, [256, 1, 1]],\n   [-2, 1, Conv, [256, 1, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [[-1, -3, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [1024, 1, 1]],  # 50\n  ]\n\n# yolov7 head\nhead:\n  [[-1, 1, SPPCSPC, [512]], # 51\n  \n   [-1, 1, Conv, [256, 1, 1]],\n   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n   [37, 1, Conv, [256, 1, 1]], # route backbone P4\n   [[-1, -2], 1, Concat, [1]],\n   \n   [-1, 1, Conv, [256, 1, 1]],\n   [-2, 1, Conv, [256, 1, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [256, 1, 1]], # 63\n   \n   [-1, 1, Conv, [128, 1, 1]],\n   [-1, 1, nn.Upsample, [None, 2, 'nearest']],\n   [24, 1, Conv, [128, 1, 1]], # route backbone P3\n   [[-1, -2], 1, Concat, [1]],\n   \n   [-1, 1, Conv, [128, 1, 1]],\n   [-2, 1, Conv, [128, 1, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [-1, 1, Conv, [64, 3, 1]],\n   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [128, 1, 1]], # 75\n      \n   [-1, 1, MP, []],\n   [-1, 1, Conv, [128, 1, 1]],\n   [-3, 1, Conv, [128, 1, 1]],\n   [-1, 1, Conv, [128, 3, 2]],\n   [[-1, -3, 63], 1, Concat, [1]],\n   \n   [-1, 1, Conv, [256, 1, 1]],\n   [-2, 1, Conv, [256, 1, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [-1, 1, Conv, [128, 3, 1]],\n   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [256, 1, 1]], # 88\n      \n   [-1, 1, MP, []],\n   [-1, 1, Conv, [256, 1, 1]],\n   [-3, 1, Conv, [256, 1, 1]],\n   [-1, 1, Conv, [256, 3, 2]],\n   [[-1, -3, 51], 1, Concat, [1]],\n   \n   [-1, 1, Conv, [512, 1, 1]],\n   [-2, 1, Conv, [512, 1, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [-1, 1, Conv, [256, 3, 1]],\n   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],\n   [-1, 1, Conv, [512, 1, 1]], # 101\n   \n   [75, 1, RepConv, [256, 3, 1]],\n   [88, 1, RepConv, [512, 3, 1]],\n   [101, 1, RepConv, [1024, 3, 1]],\n\n   [[102,103,104], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)\n  ]","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:23:09.065443Z","iopub.execute_input":"2022-09-09T14:23:09.065918Z","iopub.status.idle":"2022-09-09T14:23:09.073031Z","shell.execute_reply.started":"2022-09-09T14:23:09.065879Z","shell.execute_reply":"2022-09-09T14:23:09.072220Z"},"jupyter":{"source_hidden":true},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile /kaggle/working/hyp.yaml\nlr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937  # SGD momentum/Adam beta1\nweight_decay: 0.0005  # optimizer weight decay 5e-4\nwarmup_epochs: 3.0  # warmup epochs (fractions ok)\nwarmup_momentum: 0.8  # warmup initial momentum\nwarmup_bias_lr: 0.1  # warmup initial bias lr\nbox: 0.05  # box loss gain\ncls: 0.3  # cls loss gain\ncls_pw: 1.0  # cls BCELoss positive_weight\nobj: 0.7  # obj loss gain (scale with pixels)\nobj_pw: 1.0  # obj BCELoss positive_weight\niou_t: 0.20  # IoU training threshold\nanchor_t: 4.0  # anchor-multiple threshold\n# anchors: 3  # anchors per output layer (0 to ignore)\nfl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015  # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7  # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4  # image HSV-Value augmentation (fraction)\ndegrees: 0.0  # image rotation (+/- deg)\ntranslate: 0.2  # image translation (+/- fraction)\nscale: 0.9  # image scale (+/- gain)\nshear: 0.0  # image shear (+/- deg)\nperspective: 0.0  # image perspective (+/- fraction), range 0-0.001\nflipud: 0.0  # image flip up-down (probability)\nfliplr: 0.5  # image flip left-right (probability)\nmosaic: 1.0  # image mosaic (probability)\nmixup: 0.3  # image mixup (probability)\ncopy_paste: 0.0  # image copy paste (probability)\npaste_in: 0.15  # image copy paste (probability)","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:24:42.935756Z","iopub.execute_input":"2022-09-09T14:24:42.936638Z","iopub.status.idle":"2022-09-09T14:24:42.945473Z","shell.execute_reply.started":"2022-09-09T14:24:42.936565Z","shell.execute_reply":"2022-09-09T14:24:42.944597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#!python  ./tools/train.py --batch 4 --img {IMG_SIZE} --epochs 5 --conf configs/yolov6n.py --data ./data/data.yaml --device 0","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install scipy=='1.3.1'\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python train.py --epochs 12 --device 0 --batch-size 6 --data data/data.yaml --img 1280 1280 --cfg /kaggle/working/yolov7.yaml --weights yolov7.pt --name yolov7 --hyp /kaggle/working/hyp.yaml\n","metadata":{"execution":{"iopub.status.busy":"2022-09-09T14:24:59.904523Z","iopub.execute_input":"2022-09-09T14:24:59.904820Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\nbest_weights='/kaggle/input/barrier-reef-yolov5-training/yolov5/kaggle-Reef-more-neg/exp/weights/best.pt'\nbest_weights='/kaggle/input/yolomcrops/yolov5/kaggle-Reef-more-neg/exp/weights/best.pt' #more neg 448\nbest_weights='/kaggle/input/pretrain-1856-scrop/yolov5/kaggle-Reef/exp/weights/last.pt' #old 448\n#yolov5m.pt\npath='kaggle-Reef'\n#hyp='/kaggle/working/yolov5/data/hyps/hyp.scratch.yaml'\nhyp='/kaggle/working/yolov6/data/hyps/hyp.scratch.yaml'\n!python /kaggle/working/YOLOv6/yolov6/train.py --img {IMG_SIZE} \\\n                 --batch {BATCH_SIZE} \\\n                 --epochs {EPOCHS} \\\n                 --data data.yaml \\\n                 --hyp {hyp}\\\n                 --weights {best_weights}\\\n                 --project kaggle-Reef\\\n                 \n                 \n \n#v1-448 crops -54.1 LB ,CV 52.81\n#v2 704 crops reduced LR,added fitness changes ,dint work well got 55 map,53 f2\n#v3 IOU loss 0.1,640 crops\n#v4 more clahe 6,448,more neg images,mosaic 0.65 poor results 52.4,47.4\n'''","metadata":{"_kg_hide-output":true,"papermill":{"duration":27270.350086,"end_time":"2022-01-18T23:23:28.102784","exception":false,"start_time":"2022-01-18T15:48:57.752698","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Removing Files","metadata":{"papermill":{"duration":1.916297,"end_time":"2022-01-18T23:23:31.937501","exception":false,"start_time":"2022-01-18T23:23:30.021204","status":"completed"},"tags":[]}},{"cell_type":"code","source":"'''  \nval_img='720,1280'\nval_wts='/kaggle/input/barrier-reef-yolov5-training/yolov5/kaggle-Reef/exp/weights/last.pt'\n!python val.py --img 1280 \\\n                 --batch {BATCH_SIZE} \\\n                 --data data.yaml \\\n                 --weights {val_wts} \\\n                 --project kaggle-Reef \n''' \n ","metadata":{"papermill":{"duration":2.303861,"end_time":"2022-01-18T23:23:36.189763","exception":false,"start_time":"2022-01-18T23:23:33.885902","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#!ls -l /kaggle/working/yolov5/kaggle-Reef/exp/weights/\n#!rm -rf /kaggle/working/yolov5\n#!cp /kaggle/working/yolov5/kaggle-Reef/exp/weights/*.pt  /kaggle/working/\n#!cp -r  /kaggle/input/barrier-reef-yolov5-training-upsize/yolov5 /kaggle/working","metadata":{"papermill":{"duration":1.930217,"end_time":"2022-01-18T23:23:40.034711","exception":false,"start_time":"2022-01-18T23:23:38.104494","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#!ls -l /kaggle/working/yolov5/kaggle-Reef/exp/weights/*.pt ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n%cd \"../\"\npath1 = \"COTS\"\nshutil.rmtree(path1)","metadata":{"papermill":{"duration":3.103898,"end_time":"2022-01-18T23:23:45.350799","exception":false,"start_time":"2022-01-18T23:23:42.246901","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🖼️ Visualizing and Results\n\n* Weights & Biases (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration among team members.\n\n* During training you will see live updates at https://wandb.ai, and you can create Detailed Reports of your results using the W&B Reports tool.\n* To see my project on Weights & Biases (W&B) [here](https://wandb.ai/ammaralhajali/kaggle-Reef)","metadata":{"papermill":{"duration":1.925663,"end_time":"2022-01-18T23:23:49.430713","exception":false,"start_time":"2022-01-18T23:23:47.50505","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![1.JPG](attachment:48f84d42-a426-4503-84fb-e53a8e1693c0.JPG)","metadata":{"papermill":{"duration":1.938722,"end_time":"2022-01-18T23:23:53.386453","exception":false,"start_time":"2022-01-18T23:23:51.447731","status":"completed"},"tags":[]},"attachments":{"48f84d42-a426-4503-84fb-e53a8e1693c0.JPG":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RDiRXhpZgAATU0AKgAAAAgABAE7AAIAAAAIAAAISodpAAQAAAABAAAIUpydAAEAAAAQAAAQyuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRPU0hJQkEAAAWQAwACAAAAFAAAEKCQBAACAAAAFAAAELSSkQACAAAAAzk3AACSkgACAAAAAzk3AADqHAAHAAAIDAAACJQAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjExOjI5IDEyOjA2OjE1ADIwMjE6MTE6MjkgMTI6MDY6MTUAAABUAE8AUwBIAEkAQgBBAAAA/+ELGmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8APD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4NCjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iPjxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iLz48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyI+PHhtcDpDcmVhdGVEYXRlPjIwMjEtMTEtMjlUMTI6MDY6MTUuOTcwPC94bXA6Q3JlYXRlRGF0ZT48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyI+PGRjOmNyZWF0b3I+PHJkZjpTZXEgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOmxpPlRPU0hJQkE8L3JkZjpsaT48L3JkZjpTZXE+DQoJCQk8L2RjOmNyZWF0b3I+PC9yZGY6RGVzY3JpcHRpb24+PC9yZGY6UkRGPjwveDp4bXBtZXRhPg0KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICA8P3hwYWNrZXQgZW5kPSd3Jz8+/9sAQwAHBQUGBQQHBgUGCAcHCAoRCwoJCQoVDxAMERgVGhkYFRgXGx4nIRsdJR0XGCIuIiUoKSssKxogLzMvKjInKisq/9sAQwEHCAgKCQoUCwsUKhwYHCoqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioq/8AAEQgCGgRZAwEiAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A+iqKi+zJ/el/7+t/jR9mT+9L/wB/W/xoAloqL7Mn96X/AL+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v8A39b/ABoAloqL7Mn96X/v63+NH2ZP70v/AH9b/GgCWiovsyf3pf8Av63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/wDf1v8AGgCWiovsyf3pf+/rf40fZk/vS/8Af1v8aAJaKi+zJ/el/wC/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/AN/W/wAaAJaKi+zJ/el/7+t/jR9mT+9L/wB/W/xoAloqL7Mn96X/AL+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v8A39b/ABoAloqL7Mn96X/v63+NH2ZP70v/AH9b/GgCWiovsyf3pf8Av63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/wDf1v8AGgCWiovsyf3pf+/rf40fZk/vS/8Af1v8aAJaKi+zJ/el/wC/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/AN/W/wAaAJaKi+zJ/el/7+t/jR9mT+9L/wB/W/xoAloqL7Mn96X/AL+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v8A39b/ABoAloqL7Mn96X/v63+NH2ZP70v/AH9b/GgCWiovsyf3pf8Av63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/wDf1v8AGgCWiovsyf3pf+/rf40fZk/vS/8Af1v8aAJaKi+zJ/el/wC/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/AN/W/wAaAJaKi+zJ/el/7+t/jR9mT+9L/wB/W/xoAloqL7Mn96X/AL+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v8A39b/ABoAloqL7Mn96X/v63+NH2ZP70v/AH9b/GgCWiovsyf3pf8Av63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/wDf1v8AGgCWiovsyf3pf+/rf40fZk/vS/8Af1v8aAJaKi+zJ/el/wC/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/AN/W/wAaAJaKi+zJ/el/7+t/jR9mT+9L/wB/W/xoAloqL7Mn96X/AL+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v8A39b/ABoAloqL7Mn96X/v63+NH2ZP70v/AH9b/GgCWiovsyf3pf8Av63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/wDf1v8AGgCWiovsyf3pf+/rf40fZk/vS/8Af1v8aAJaKi+zJ/el/wC/rf40fZk/vS/9/W/xoAloqL7Mn96X/v63+NH2ZP70v/f1v8aAJaKi+zJ/el/7+t/jR9mT+9L/AN/W/wAaAJaKi+zJ/el/7+t/jR9mT+9L/wB/W/xoAloqL7Mn96X/AL+t/jR9mT+9L/39b/GgCWiovsyf3pf+/rf40fZk/vS/9/W/xoAlqmtg8ahIb65ijUYVAIyFHpkqT+tT/Zk/vS/9/W/xo+zJ/el/7+t/jQBD9jn/AOgldf8AfMX/AMRR9jn/AOgldf8AfMX/AMRU32ZP70v/AH9b/Gj7Mn96X/v63+NAEP2Of/oJXX/fMX/xFH2Of/oJXX/fMX/xFTfZk/vS/wDf1v8AGj7Mn96X/v63+NAEP2Of/oJXX/fMX/xFH2Of/oJXX/fMX/xFTfZk/vS/9/W/xo+zJ/el/wC/rf40AQ/Y5/8AoJXX/fMX/wARR9jn/wCgldf98xf/ABFTfZk/vS/9/W/xo+zJ/el/7+t/jQBCLFiy+feTzKrBtjhACRyPuqD15q3UX2ZP70v/AH9b/Gj7Mn96X/v63+NAEtFRfZk/vS/9/W/xo+zJ/el/7+t/jQBLRRVGTUjBK0E0DeeT+5RORKPY9sd89KAL1FZmu3V1aaBLNbOsNyDGA23eFJdQeD16msO3uNYtNV2zazPeRQ6kLPypYIV81GhD5Yqg+YFsArtGByCeaV9bf1vYOlzr6K8zi1jVtY0HVv7Q1q1hYW/mzW1vdxPPZMHG5TGYFMagbgd5c8DB6k7V3cz3dlqci6r9vsraGERBooZI7jcqnzGITDeo24HJ4PGGJ72OyorjW1uY3GsCbxDBY38HnpDp1x5apEi/6uY/KXIIwxbJXDYxxWx4U1E6noYmN297tleP7Q5ibzMHqHiAR19GAHHBAYGhaob0ZtE460Ag9DVTUJriCxuJbO2N1cRxs0UAcJ5jAcLuPAye5rE8JXN/NHqKalDeRzR3XW6eMnJjQkKEdgoBOQM8Ajqc0LUHodNRQOlFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWZp+n2dzplrNcWkEsskKu7vGGLEjJJJ9zWnVTSf+QLZf8AXvH/AOgigCOew0u3jDyWFtgsqcQL1JAHb1NSf2Tp3/Pha/8Aflf8Kp6tb3ZgDC9whuIsL5Q4/eLjmtG3inj3faLjzs9PkC4/KgCL+ydO/wCfC1/78r/hTf7N0vzDH9is94G4r5S5A9cY9qyLw6l/brajb2W+G2cW6kswkdSPmITbyu9lJOekfSqcFxqe6a636g0jQwJM72O1ofnPmCNdg34B4+//AMCoA6NNN0uQEx2VmwBKnbEpwR1HSnf2Tp3/AD4Wv/flf8KwxZz3fhWWArJM8t8GBvLbl1M4O548LxjkjA49Kr3kWpaBDFb6UJJGZ2maO0tSkPUfIAEk2jAzjcuSSd3OAIDpP7J07/nwtf8Avyv+FNOnaUJREbOzEjKWCeUuSBjJxjpyPzrGebXo4ZZ4XmmkcXAWCSFVWPbJhCCFzkrk85z6UuiyX8+qW01+sz4hnjEjxOCOYiAxMUfPDY+UDA7kGhauwPQ07uytbSKOW1toYZBPEA0aBTgyKD09ia0qqan/AMeif9fEP/o1at0AFFFFABRRRQAUU132Y+UsWOAB3pu+X/n1l/NP/iqAJKKj3y/8+sv5p/8AFUb5f+fWX80/+KoAkoqPfL/z6y/mn/xVG+X/AJ9ZfzT/AOKoAcyZ5FIE9aTfL/z6y/mn/wAVRvl/59ZfzT/4qgCSio98v/PrL+af/FUb5f8An1l/NP8A4qgCSio98v8Az6y/mn/xVG+X/n1l/NP/AIqgCSio98v/AD6y/mn/AMVRvl/59ZfzT/4qgCSio98v/PrL+af/ABVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8VRvl/wCfWX80/wDiqAJKKj3y/wDPrL+af/FUb5f+fWX80/8AiqAJKKj3y/8APrL+af8AxVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8AFUb5f+fWX80/+KoAkoqPfL/z6y/mn/xVG+X/AJ9ZfzT/AOKoAkoqPfL/AM+sv5p/8VRvl/59ZfzT/wCKoAkoqPfL/wA+sv5p/wDFUb5f+fWX80/+KoAkoqPfL/z6y/mn/wAVRvl/59ZfzT/4qgCSio98v/PrL+af/FUb5f8An1l/NP8A4qgCSio98v8Az6y/mn/xVG+X/n1l/NP/AIqgCSio98v/AD6y/mn/AMVRvl/59ZfzT/4qgCSio98v/PrL+af/ABVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8VRvl/wCfWX80/wDiqAJKKj3y/wDPrL+af/FUb5f+fWX80/8AiqAJKKaj788FSpwQe1K7BFLHoKAFoqPfL/z6y/mv/wAVRvl/59ZfzT/4qgCSio98v/PrL+af/FUb5f8An1l/NP8A4qgCSio98v8Az6y/mn/xVG+X/n1l/NP/AIqgCSio98v/AD6y/mn/AMVRvl/59ZfzT/4qgCSio98v/PrL+af/ABVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8VRvl/wCfWX80/wDiqAJKKj3y/wDPrL+af/FUb5f+fWX80/8AiqAJKKj3y/8APrL+af8AxVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8AFUb5f+fWX80/+KoAkoqPfL/z6y/mn/xVG+X/AJ9ZfzT/AOKoAkoqPfL/AM+sv5p/8VRvl/59ZfzT/wCKoAkoqPfL/wA+sv5p/wDFUb5f+fWX80/+KoAkoqPfL/z6y/mn/wAVRvl/59ZfzT/4qgCSio98v/PrL+af/FUb5f8An1l/NP8A4qgCSio98v8Az6y/mn/xVG+X/n1l/NP/AIqgCSio98v/AD6y/mn/AMVRvl/59ZfzT/4qgCSio98v/PrL+af/ABVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8VRvl/wCfWX80/wDiqAJKKj3y/wDPrL+af/FUb5f+fWX80/8AiqAJKKj3y/8APrL+af8AxVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8AFUb5f+fWX80/+KoAkoqPfL/z6y/mn/xVKshLFWRkbGcNj+lAD6KKjErsoaO3kZTyCCoz+ZoAkoqPfL/z6y/mn/xVG+X/AJ9ZfzT/AOKoAkoqPfL/AM+sv5p/8VRvl/59ZfzT/wCKoAkoqPfL/wA+sv5p/wDFUb5f+fWX80/+KoAkoqPfL/z6y/mn/wAVRvl/59ZfzT/4qgCSio98v/PrL+af/FUb5f8An1l/NP8A4qgCSio98v8Az6y/mn/xVG+X/n1l/NP/AIqgCSio98v/AD6y/mn/AMVRvl/59ZfzT/4qgCSio98v/PrL+af/ABVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8VRvl/wCfWX80/wDiqAJKKj3y/wDPrL+af/FUb5f+fWX80/8AiqAJKKj3y/8APrL+af8AxVG+X/n1l/NP/iqAJKKj3y/8+sv5p/8AFUb5f+fWX80/+KoAkoqPfL/z6y/mn/xVG+X/AJ9ZfzT/AOKoAkoqPfL/AM+sv5p/8VRvl/59ZfzT/wCKoAkoqPfL/wA+sv5p/wDFUb5f+fWX80/+KoAkoqPfL/z6y/mn/wAVRvl/59ZfzT/4qgCSszT9Qs7bTLWG4u4IpY4VR0eQKVIGCCD7ir++X/n1l/NP/iqN8v8Az6y/mn/xVAFc6pprDDX1oRnPMy/40v8Aa2nf8/8Aa/8Af5f8an3y/wDPrL+af/FUb5f+fWX80/8AiqAIP7W07/n/ALX/AL/L/jR/a2nf8/8Aa/8Af5f8an3y/wDPrL+af/FUb5f+fWX80/8AiqAIP7W07/n/ALX/AL/L/jR/a2nf8/8Aa/8Af5f8an3y/wDPrL+af/FUb5f+fWX80/8AiqAIP7W07/n/ALX/AL/L/jR/a2nf8/8Aa/8Af5f8an3y/wDPrL+af/FUb5f+fWX80/8AiqAKN3e2t3FHFa3MM0hniIWNwxwJFJ6ewNaVMEjblEkTx7uAWwc/kTT6ACiiigAooooAjf8A4+Lf/rof/QGq3VR/+Pi3/wCuh/8AQGq3QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBUT/j4uP+ug/wDQFpLn/j3b8P50qf8AHxcf9dB/6AtJc/8AHu34fzoAuUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVWk/wCP4f8AXP8ArVmq0n/H8P8Arn/WgB1LZ/8AHhb/APXJf5UlLZ/8eFv/ANcl/lQBNRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFe6+/b/9dT/6A1LSXX37f/rqf/QGpaACiiigAooooAjf/j4t/wDrof8A0Bqt1Uf/AI+Lf/rof/QGq3QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGPr9/fWklhb6Y1vHLeTNGZLiJpFQCNnztDLn7uOo61yp8ba5Dof26aGylM9lBdR+VEVFuHfa5ffKAwHXO5AO5716FRQB5qPGOrC4j1Ga905EbSZpYbXIZbmRHPKlJmXOACQCxUEjPete/13UdJurw3M1oJBHah7pxILe3WR5BvaMyYwoABIKknqQMAdnRQBxkXjNl8Q21m+qaVfWz2xlaSyAYuQpYtgTFkXA4+Vx6uCcUi6rq+l2l1HZ29vJ9isFvZYfKLSTSymRmUHcAMMM55zyOOtdZfJby2MsV7zBINjgEjIJxjI570C9g85IgzZZiinY23IzkbsYzwaLoai3qjhrHx5e3Vram6v9H0+OaWRTqM5jeBtoQiPbHcMqudx6yE4QnHPFTV/GVxqFxrmkMbeWzjtLgiWOMIYjGVGGPmsTwx+8idMjcOa9MooEYPhSaOWHUxFIjldRmztYHGSCPzBB+hreoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCon/AB8XH/XQf+gLSXP/AB7t+H86VP8Aj4uP+ug/9AWkuf8Aj3b8P50AXKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigClrN5Lp+j3N1bhGkiTKhwSCffFcqvibxBBdMLj+z7lIp1iaOC2kR5Ny7hgmRsEdOhz7V29FAHmP8AwmesalbRyveWGnpDexrNIdh8tST8jhLliP8AgW3PoK3Jdd1KRUunFvLCt6YYorfzEZ9ozksHwc+hBH1rsqKAOAuPHU8OkxXaazoUsrzBZIEC+ZAP7hVp13N+IPop6VpT6neLIbzTjbrNeXCW6vPEzKoC5J2hlPXtmutqGGCGytykKlI1yx5Le5680AcDN4t1ybT1hmFvJNfxbbdLFDFKH37Thnkx0BI5GPWqqz6xrM+n6bE1yl3bxSxzxz6pLbMrA/K7mEtvOOxJB9a9FbUbZF3F2I4yVjY4z0zgcVZznpRdDcWtzMulvbPwxIqzNNeRW+PNA5ZgOorntA1uzghniOryS2s21be4mmef97sy43kk8HtnjpxXRnQrQ6l9uM1/527dtGo3HlZ/6579mPbGK0aBHIeB5w09/DFqi6xChVhfR3EsiEn+DDyyAEd9pA9hXX0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVaT/AI/h/wBc/wCtWarSf8fw/wCuf9aAHUtn/wAeFv8A9cl/lSUtn/x4W/8A1yX+VAE1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGVr19eWUNomnGBZrq6W3Dzxl1TcGOdoZScY6ZH9a5N/Gmv22mfapobG4MlvK6CGBk8po5liZ23S4K4YtglcYwW/ir0GigDzdPF2qzXOmahLqGnwQSWd5iMFWjupEK7QDHOy7iP4dzEYb1407vxBqenB7u5Ns8rWEU29RIsNury7SzqXIIQHJb5SQOqjp2tFAHFDxm8eu6XZjWNGv4LpCXksgpaQ5bkL55ZVAXqBIMhslKiutb1XSdPL6eLNcWn9pTLcQs7SGSU5QFXXbwR82DjHQ9u2uLeO6tZbecFopUKOAxGQRg8jkU9VCqFUYAGAKAPPdZ1vXrxJdGtg8+pwXLsW0siBjEkaupIklGBvkQEbjkA8c4pLKTU/FPiOW90u5kit1W0k8w6lMggBXc6i3UeXITgg7iMH6V6JVZ7y3KspZz94EIjFvl4OABnuKLjUW9jN8VzNBoys1xJa2xuIlup45DGY4Sw3neOVGOrAggZOR1rDTWLW48Iz2Fz4gGny4Z4b2eR8i384rG5kDKSGAA3bwSDnPOa6TSdJsrL/SbGe+lEqDH2nULicY68LI7AH8M1p0CseeW+r3X/COhLXVmDi2nKJGJJTcxrMqm5SWRnYAKTgbm65BOBV6z1m3XRNRs7XXFjSUz/2XfXE7TAxpGpZ/NJO4KzN8xJ6d8V2tFAHljXzzaBKbjxJb6daxXRa0kOp3Fwl2BF80aXAkikcbzkcnkEAHFek6XLJNpNpLNC8EjwIzRSMWZCVHBJ5JHqatUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFe6+/b/9dT/6A1LSXX37f/rqf/QGpaACiiigAooooAjf/j4t/wDrof8A0Bqt1Uf/AI+Lf/rof/QGq3QAUVSl1NItUjsjDKxcDMw27EJDEKec5O09ARTLHWrXUNQu7O3yXtG2uSynJ6dASw5H8QGe2aANCiik3D1oAWik3D1o3D1oAWik3D1o3D1oAWik3D1o3D1oAWik3D1o3D1oAWik3D1o3D1oAWik3D1o3D1oAWik3D1o3D1oAWsq+8M6TqN21zeW7vK2AWE8i9OOgYCtTcPWjcPWk0nuXCpODvB29CsNPgj0s2EAKQ+WY1BYsQPqearR2Fyv2BWMbeR88sm8jcxB3YTGDknqTkVpbh60bh60cqGqkkLRSbh60bh60zMWik3D1o3D1oAWik3D1qNbmB5XjSZGkT76BgSv1HagLMlopNw9aNw9aAFopNw9aNw9aAFopNw9aQyKoJLAAckntQA6isp/ENo7mPTll1GUHBFqu5QfdzhB+eaV9R1KCJZZ9LRkyd6W9x5kijsdpUA+4B+manmRr7GfVW9dPzNSiq1lqNpqEJls51kUHDDoyH0YHkH2NWNw9arczacXZi0Um4etKCD0oEFFFFAFRP8Aj4uP+ug/9AWkuf8Aj3b8P50qf8fFx/10H/oC0lz/AMe7fh/OgC5RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBn6loen6u8bahC0hjBC7ZXTGf90ipdO0u00q3aCxjaONm3ENIz8/ViT2q3RSsr3NPaTceS7t26GT/AGXcR2UkCFJDJLu3GRk2rnjoDn6dK1VGFAPUClooSsKU3LcKKKKZAUUUUAFFZba/bGd47aC6u1jOHltoS6KfTPc/TNH/AAkNiv8Arlu4f+utlKo/Mrip5l3NfY1P5WalFZyeItGkbaNUtA391plVvyJzV2K4hnGYJY5B6owP8qaaexMoTj8SsSUUVVutUsLL/j8vbeD2llVf5mnexKi5OyRaorL/AOEhsX/49hc3R7fZ7aRwf+BAbf1o/tPUJf8Aj20WcDs1zNHGD+RY/pU8yNPYz6q3rp+ZqUVQM+rLAW+wWjSD+BbxufxMYplvrls8y292sljctwIrkbdx/wBlvut+BNPmQvZTtda+mppUUUUzMKKKKACq0n/H8P8Arn/WrNVpP+P4f9c/60AOpbP/AI8Lf/rkv8qSls/+PC3/AOuS/wAqAJqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKq6hptrqtsIL6NpIw24BZGTn6qQatUUb7jjJxd4uzM/TdC07SJHfT4WjaQYYtM75H/AAImg2Msd5d3ERVxMgCRlym09GO4AkZAXp6VoUUuVdC/azbbbu2QWMD21jDDIQWjQKdvSp6KKZDd3dhRRRQIKKKKACiiigAooooAKKKKACiobu8trC3ae9njgiXq8jYFZ8GrXt85bTtMb7Ng7Z7yQwbz22rtZse5A/Gk2kaRpykrrY1qKybfX4hcJa6tA+m3THCrMQY5D/sSDhvpwfatahNPYU4Sg/eQUUUUyAooooAr3X37f/rqf/QGpaS6+/b/APXU/wDoDUtABRRRQAUUUUARv/x8W/8A10P/AKA1W6qP/wAfFv8A9dD/AOgNVugCpNplrcXgupFk84JsDLK6jHIzgHGRuOD1GeDS22m2tpdSXECuJJRhi0rMPU4BJAyeTjGTyatUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAdKp2Gr6fqm8afdxTmM4YI3I98enoehrNu5H8QXkmm2rsunwttvZ1OPNPeFT/wChH8OucX7vRbC8jiV4BG0C7YZYT5bxD/ZYcge3Spu3sdHJCKtPd/h6l+isffq+lf61Tq1qP40AW4Qe68K/4YPsavWOpWmoxs9nMJNpw6EEMh9GU8qfYimpGcqbS5lqu6/rT5lqiiimZhTZJEhjaSV1RFGWZjgAepNVr/UoNORPN3PLIcRQRDdJKfRR/XoO5FU49NuNSkW413bsU7orFDmND2Ln+Nv0HYd6lvojWNPTmlovz9Bn2m81z5dPZ7PTz1uyMSTD/pmD0H+2fwHerH/CO6R5CRHT4CE+65XLg9zv+9k+uc1pUUcq6jdWS0hojKOiywf8g7Vby39EkcTp/wCP5b8mFJ5uv2v37ez1BR3hdoH/AO+W3D/x4VrUUcvYPat/Ek/677/iY58SW8HGp2l5p57tPAWQf8DTcv60+TxNoyRo0eoQ3DSfcjtj5zv9FXJNatRx28EUjyRQxo7/AH2VQC31Pei0u4c1J7xf3/8AAMsXms3/APx42KWER/5bXx3P+ESn+bD6Uq+HoZyH1e4m1N+u2c4iH0jGF/ME+9a9FHL3D2zXwK3p/nuNRFjQJGqoqjAVRgAU6iiqMShe6Pb3cwuY2e1vFGFuYDtfHoezD2YEVW/tO80v5dbhEkA6X1shKj/fTkp9Rke4rYoqeXqjVVNOWauv62f9LyGQzR3EKywSJLG4yrowIYeoIp9ZM2ieTM1zos/2CdjudAu6GU/7SevuuD9aq2XiaaXxL/YV9pskFysXmNLG2+I+4OAcH19eKOa25fsedOVPW2r7r+vI6CiiiqOcqJ/x8XH/AF0H/oC0lz/x7t+H86VP+Pi4/wCug/8AQFpLn/j3b8P50AXKKKydb1ObT2thDJbx+a+P36k7/wDZXDDk/j9KANaisL+3nbxNDYR+UYJEyCCrMx74O/OB/un6it2gAopMn+7+tGT/AHf1oAWikyf7v60ZP939aAFopMn+7+tGT/d/WgBaKTJ/u/rRk/3f1oAWikyf7v60ZP8Ad/WgBaKTJ/u/rRk/3f1oAWikyf7v60ZP939aAFqrqF/Hp1qZZAXYnbHGv3pGPRRUlxcx2lu89wdkcYyzE9KzdPgmvrsapfRlTjFrCf8Alkp/iP8AtH9KlvojWEVbmlsvx8hsOnaqqm8+3kXkh3PbyfNAB2QDqMf3h+tWbXV0e4FrfRNZ3Z6RyH5ZPdG6N/P2rQyf7v61DdWsN9bmG7t1ljbqrfz9jRy22K9op/Gvu/r+u5PRWL5ep6Rzbb9Rsx/yxdh50Y/2WP3x7Hn3NXrHU7bUoi9o+4qcOh+V0PoynkGmn0JlTaXMtV/X3Fyikyf7v61nXureTP8AZLKE3V6RnylbAQern+Efqe1DdiIxcnZFu8vbewtzPdyiNBxk9SfQDufaszyLzXObwPZWB6W4OJZh/tkfdH+yOfX0qaz0thcC81N/tV4PunGEi9kXt9eprTyf7v60rN7mvNGn8Gr7/wCX+Y2GGO3hWKCNY40GFVRgAU+kyf7v60ZP939aow3EeNJF2yIrj0YZqlLoOkTtmbS7N2/vGBc/nir2T/d/WjJ/u/rSsnuVGco/C7GZ/wAI3pPT7INv9zzG2/8AfOcfpVq10yxsv+POyt7f/rlEq/yFWcn+7+tGT/d/WjlS6FOrUkrOTFopMn+7+tGT/d/WmZi1HcW8N1C0NzEk0TfeSRQwP4Gn5P8Ad/WjJ/u/rQNNp3Rk/wBkXNj82i3rRKP+Xa5zJF9B/Ev4HHtSjXfshCa3avYnp52d8J/4GOn/AAICtXJ/u/rSH5gQUyD1BqeW2xr7Tm+NX/P+vW4JIksYeJldGGQynINOrnL3wjFPqlrd6fcy6csMm+SGA4SQ/QHAPviujoTfUVSMEk4Sv8tgqtJ/x/D/AK5/1qzVaT/j+H/XP+tUZDqWz/48Lf8A65L/ACpKWz/48Lf/AK5L/KgCaiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACk3KHClhuIJAzyQOv8x+dKTjrXO2+np4gkl1aZ5Iix26fLG2GijXPzj/AHzk88FdoNS3bY1hBSTcnZI6Kisi21SezuUsdcCpK52w3SDEVwew/wBl/wDZPXsT0GvTTuTODg9QooopkBRRRQAUUVkya6ZZmi0ezk1IxnEkkbqkan03ngn2Gcd8Um0i4wlPY1qKx/7R1o9NCUf716v9AacL3XT00a2H+9f/AOEZpcyL9jLuvvX+ZrUVlrc68eul6ev11B//AIzTmfXJIyqW+n27H/lobh5dvvt2Ln8xRzIXspdWvvRdubqCzt2nu5o4Yk+88jBQPxNZf9pahqfGjW3kwn/l8vEKgj1SPhm+p2j61NbaHAlwt1fSPf3a8rNcYIQ/7Cj5V/AZ9Sa06NWO9OG2r/D7v8/uMy00K3huFu7x5L+8XpPckEp/uL91PwH51p0UU0ktiJTlN3kyO4t4buBoLqJJonGGSRQyn6g1k/2VfaX82h3O+Ef8uN2xZMeiPyyfQ5HsK2qKGkxxqSirdO3QzLTXYJrhbS8jksL09Le4wN/+43Rx9D+ArTqG7s7a/t2gvYI54m6pIuR9fr71z+qw67oNiZPDjNqMe4L9kugZGjHqr5DEexz9eKltxWppGEKrtF2fnt9/+f3nTUVHbtI9tE08YjlZAXQHO1scjNSVZg9GV7r79v8A9dT/AOgNS0l19+3/AOup/wDQGpaBBRRRQAUUUUARv/x8W/8A10P/AKA1W6qP/wAfFv8A9dD/AOgNVugClLqaRapHZGGVi4GZht2ISGIU85ydp6Aim2erLd6pd2P2S6ge1x+8mj2pKD3Q5+Ye9STaZa3F4LqRZPOCbAyyuoxyM4Bxkbjg9Rng0ttpttaXEk8QkMknDNLM8mB1wNxO0Z7DFMC1Sbh60tFIBNw9aNw9aWigBNw9aNw9ar6jeppum3F5IpcQoWCDq57KPcnA/GqC6vqUQBvNAugO5tpo5QPzZT+lJySNI0pSV1+aNfcPWjcPWsr/AISXT0/4+hdWh/6eLWRB/wB9bdv61btdW06+x9jv7acntHMrH9DRzJ9QlSqRV3FlrcPWjcPWlopmYm4etG4etLRQAm4etG4etLRQAm4etY2pXk1/eNpGlymNgAbu6X/lgh/hX/bI6eg59MzarqE4nTTdL2m/mXcXIytunQyMP0A7n2Bq1p2nw6ZZrb2+4jJZ3c5aRj1Zj3JNS9XZG8Uqa55b9F+v+X+W77S2t7CzjtbRFihiXaijt/n1qbcPWloqjFtt3Ym4etUb7SrO+kWZ90VygwlzC2yRfbI6j2OR7VfopNJ7jjKUXeLMgXWpabxeJ/aNuP8AlvbpiVR/tR/xfVef9mmtrn29vI0AJcyfxzuCIoP97uW/2Bz64rZopWfc09pHdx1/D7v8rFCw02Gyd55JGubyQYluZfvN7Dsq/wCyOPx5q9uHrS0U0rGcpOTuxNw9aNw9aWimSJuHrRuHrS0UAJuHrRuHrS0UAJuHrRuHrS0UAJuHrRuHrS0UAJuHrRuHrS0UAJuHrSgg9KKKACiiigCon/Hxcf8AXQf+gLSXP/Hu34fzpU/4+Lj/AK6D/wBAWkuf+Pdvw/nQBcooooAKKKKACiiigAooooAKKY80Uf8ArJET/eYCq76tp0X+s1C1T/emUf1pXRSjJ7It0VmN4k0NTg6xY59BcoT+WaQ+JdIxlb1HH+wrN/IUuaPcv2NX+V/calFZ1tr+l3coihvY/MPSOTKMfoGwTWjTTT2IlCUHaSsFFFFMkKCQqkscAckntRWLdyPrV62n27FbOE4upVP3z/zzH9TSbsaQhzPyEiB8QXi3Dj/iW27ZhU/8t3H8Z/2R29a26aiLFGqRqFRRhVA4Ap1CVgnPmdlstgooopmYVn3+j297KLhGe2u0GEuYThx7Hsw9jkVoUUmk9yoylB3iYqxa/N/olxLbxRj717Dne49Ah4U++T7VpWVjb6fB5VrHtBOWYnLOfUnqT71YooUbFSqOStsvIKKKKZmFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVaT/j+H/XP+tWarSf8AH8P+uf8AWgB1LZ/8eFv/ANcl/lSUtn/x4W//AFyX+VAE1FUdYs3v9NeBIoZyWVjDOcRyAMCVY4PBx6Gsy50W8mt9Nt/Is5FsnjIkklPy4xkhdh54IGCp4HIyRTA6GiikIz3NIBaKTb7mjb7mgBaKTb7mjb7mgBaKTb7mjb7mgBaKTb7mjb7mgBaKTb7mjb7mgBaKTb7mjb7mgBaKTb7mmyMsUbSSPtRQWZicAAd6AMzW3a6MOkQMRJe581lPMcA++fYnIUe7Z7VqIixxqkahUUAKoGAB6VlaJE9z52rXAZZL3HlK3WOEfcX2JyWPu2O1a233NTHXU2qe7amun59f8iO5toby2e3uolmhkGGRxkEVkeZd+Hf9eZb3Sh0k5aa2H+13dPf7w756jb2+5o2+5ptXJhU5VyvVdv66jYZo7iFJoJFkjcbldDkMPUGn1iTaXc6XM93oIBRiWmsGbakh7sh/gb9D3x1q5ZatZ31nJcJMYhDxOk3yNCR1Dg9KSfRlSp6c0NV/W5fqlqGq2umhFmZnmk4it4l3SSH2X+vQdzVD+0b3WPk0IeTbHrqE68H/AK5ofvf7xwv1q9p+j22nF5I98txJ/rbmZt0kn1Pp7DAHYUXb2H7OMP4m/b/Pt+ZT/s++1j5tZb7PanpYQv8AeH/TRx1/3Rx6lq2Ioo4IlihRY40GFRBgKPQCl2+5o2+5ppJETqOWnTsLRSbfc0bfc0zMWik2+5o2+5oAWik2+5o2+5oAWik2+5o2+5oAWik2+5o2+5oAWik2+5ox7mgBaKKKAK919+3/AOup/wDQGpaS6+/b/wDXU/8AoDUtABRRRQAUUUUARv8A8fFv/wBdD/6A1W6qP/x8W/8A10P/AKA1W6ACiqUuppFqkdkYZWLgZmG3YhIYhTznJ2noCKkgvorm8nt4g5MGNzkfKScjAPfBBB/L1oAs0UUm5fUUALRSbl9RRuX1FAGTqn+m6vp+nDlFb7XOP9mMjYPxcqf+AGtesfRWF3dX+qMci4l8mE/9MoyVH5tvb6EVr7l9RUx7m1X3bQ7fn1/y+QtVbrTLC9/4/LK3uP8ArrErfzFWdy+oo3L6iq3MlJxd0Zf/AAjmnJ/x7LPaHt9muZIwPwVsfpR9g1W15sdUE6j/AJZX0Qb8A6bSPqQ1am5fUUbl9RU8qNfbT6u/rr+Zlf2tfWv/ACEtImCjrLZt56/lw/8A46asWetadfyeXa3kTyjrETtkH1Q4I/Kru5fUVWvLCw1GMJf20Fwo6eagbH0z0os0HNTluren+T/zLVZ+q6k1mscFpGJ764ysEJPB9Wb0UdSfw6kVW/sM2/Ok6pd2eOkbP58f/fL5I/AirGm6d9kklury4F1fT8ST7doCjoirk7VHpnk5Jo956DUacfevfy/z8vmP0vTV06By8hnupm33Fww+aVv6AdAOwq9Sbl9RRuX1FNK2hlKTk7sWik3L6ijcvqKZItFJuX1FG5fUUALRSbl9RRuX1FAC0Um5fUUbl9RQAtFJuX1FG5fUUALRSbl9RRuX1FAC0Um5fUUbl9RQAtFJuX1FG5fUUALRSbl9RRuX1FAC0Um5fUUbl9RQAtFJuX1FKCD0OaACiiigCon/AB8XH/XQf+gLSXP/AB7t+H86VP8Aj4uP+ug/9AWkuf8Aj3b8P50AXKydbgvJ2tvsa3DAPz5E/l7T2LfMMj25+la1FAGGbe9m8SQXQTUIokQq6vcILdh67FYnd+GK3KKKAEwfX9KMH+9+lLRQAmD/AHv0qvfXH2PT57hm/wBWhYcdT2qzWRr3+kNZ6cv/AC9TAuP9heT/AEpSdkaUoqU0nsRaT4esF06GS7sLWW5kXzJJJIFZix55JGe9aSabZxf6u1gT/diUf0q1RQopDnVnNttjVTaMLhR6AUuD/e/SlopmRDcWkN3CYruKOeM9UkQMPyNZ3/CPxwc6XeXVgeyRPuj/AO+GyB+GK16KTSZpGpOKsmY5fX7P70drqUY7xkwSfkcqfzFA8R2cbBdRE+mueP8AS4ti/wDfYyh/OtikZQylWAIPUEdaVmtmV7SD+KP3af8AA/Ax77UHvJE0/SJ1eWVd0k6EMIY/XPqe1aNnZRWFolvbfLGg/EnuSe5pbaxtbIOLO2htxIdziJAu4+pxU9CXVinNW5Y7CYP979KMH+9+lLRVGQmD/e/SjB/vfpS0UAJg/wB79KMH+9+lLRQAmD/e/SjB/vfpS0UAJg/3v0owf736UtFACYP979KMH+9+lLRQAmD/AHv0owf736UtFACYP979KMH+9+lLRQAmD/e/SjB/vfpS0UAJg/3v0owf736UtFACYP8Ae/SjB/vfpS0UAIAe5zS0UUAFVpP+P4f9c/61ZqtJ/wAfw/65/wBaAHUtn/x4W/8A1yX+VJS2f/Hhb/8AXJf5UATUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWRq5/tC8g0ZOUlHnXZ9IQfu/8AAzx9A1adzcRWlrLcXDhIokLux7ADJNUdFt5RBJfXiFLq9bzZFPWNcYRP+Ar19yx71L10Nqfup1O23r/wN/uNLpRRRVGIUUUUAFULzQtMv72O6vLOOWaMYDMOG9Nw6NjtnOKv0Umk9yoylB3i7B0ooopkhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAV7r79v/11P/oDUtJdfft/+up/9AaloAKKKKACiiigCN/+Pi3/AOuh/wDQGq3VR/8Aj4t/+uh/9AardAFSbTLW4vBdSLJ5wTYGWV1GORnAOMjccHqM8GktdJ0+yupLq0s4YZ5RiSVEAZ+c8nuc9z1q5RQAUUUUAFZ2u3UlppEv2Y4uZiIIP+ujnap/DOfoDWjWRP8A6f4pgh6xadEZ3/66vlU/Jd5/EVMtjWilzXey1/r12NGztY7GxgtIBiOCNY1+gGKmooqjNtt3YUUUUCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCon/Hxcf9dB/6AtJc/wDHu34fzpU/4+Lj/roP/QFpLn/j3b8P50AXKrXmo21h5f2p2XzG2rtjZ/z2g4HueKs1Tv8ATUv2iLzSxeWefL2/OP7pyDx9MGgCZruBLqO2aQedIpZUAzkDvU1Zy6JbDUUvmluWuE7+ewUjoAUGF4+laNABRSbV9BRtX0H5UALWRbf6Z4ouZuqWcYhT/ePLf4VpTvHb28k0gAWNSx+gFZ/h63KaSs0q/vbpmnfI/vHj9MVL1aRtD3YSl8jVopNq+g/KjavoPyqjEWik2r6D8qNq+g/KgBaKTavoPyo2r6D8qAFopNq+g/KjavoPyoAWik2r6D8qNq+g/KgBaKTavoPyo2r6D8qAFopNq+g/KjavoPyoAWik2r6D8qNq+g/KgBaKTavoPyo2r6D8qAFopNq+g/KjavoPyoAWik2r6D8qNq+g/KgBaKTavoPyo2r6D8qAFopNq+g/KjavoPyoAWik2r6D8qNq+g/KgBaKTavoPyo2r6D8qAFopNq+g/KjavoPyoAWikCgdAKWgAqtJ/x/D/rn/WrNVpP+P4f9c/60AOpbP/jwt/8Arkv8qSls/wDjwt/+uS/yoAmoqO4uILS3ee6mjghQZaSRgqr9SeBUcl/ZwrA013BGtwwWEtIAJSegX1J9qALFFFFABRRRQAUUVz+m2EOtxzane+c63MrG3UTuqrEPlXABA5xu/wCBUm+iNIQTTlJ2SOgorL/4RzSu9sT/AL0rn+tH/CN6P3sIj/vZNL3h2pd393/BNSiso+F9Db72lWp+sYpo8L6TFlrK1FjL/wA9bRjE35r1+hyKPe7D5aX8z+7/AIJr0VleXrNj/q5YtTiH8MoEUv8A30Btb8l+tPi1208xYrwSWEzHAju12bj6Bvut+BNHMuovZS3jr6f5bmlRRUF9eRafYzXU+dkS7iAMk+gHqSeB9aozSbdkZ+of8TPV4dMXmCDbc3focH92n4sNx9l9616oaPZy2tmZLvH2y5czXBBzhj/CPZQAo9hV+pj3NKjV+VbL+m/66WCiiiqMgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAK919+3/66n/0BqWkuvv2//XU/+gNS0AFFFFABRRRQBG//AB8W/wD10P8A6A1W6qP/AMfFv/10P/oDVboApS6mkWqR2RhlYuBmYbdiEhiFPOcnaegIpbXUku7+6tVhlRrYgM77cNn0wSR/wIDIwRkc0s2mWtxeC6kWTzgmwMsrqMcjOAcZG44PUZ4NFrpttZ3Ms8IlMsoAZpJ3k4znA3E7Rk9BigC3Sb19RS0UAMeWONGd3VVUZJJ6Csvw7+8sJNQl+WXUJTckHqEOBGPwQL+OaPETGaxi02MkPqMotzg8iPkyH/vgMPqRWsqhFCqAFAwAO1TvI2+Gl6/kv83+Qb19RRvX1FLRVGIm9fUUb19RS0UAJvX1FG9fUUtFACb19RRvX1FLRQAm9fUUb19RS0UAJvX1FG9fUUtFACb19RRvX1FLRQAm9fUUb19RS0UAJvX1FG9fUUtFACb19RRvX1FLRQAm9fUUb19RS0UAJvX1FG9fUUtFACb19RRvX1FLRQAm9fUUb19RS0UAJvX1FG9fUUtFACb19RRvX1FLRQAm9fUUAg9DmlooAKKKKAKif8fFx/10H/oC0lz/AMe7fh/OlT/j4uP+ug/9AWkuf+Pdvw/nQBcoorP1TUpLAxCGBZt5JbdJs2qOpHByfbj60AaFFZ8moyprENp9nXyZVJ80yEHPpjbj/wAez7VoUAFFJu+v5Ubh6H8qAMrxE5exjs4z895KsQ9hnJP5CtVEWONUQYVRgD0FY5P2zxWo5MdjDnp/G/8A9atjcPQ/lUrVtm1T3Yxj8/v/AOBYWik3D0P5Ubh6H8qoxFopNw9D+VG4eh/KgBaKTcPQ/lRuHofyoAWik3D0P5Ubh6H8qAFopNw9D+VG4eh/KgBaKTcPQ/lRuHofyoAWik3D0P5Ubh6H8qAFopNw9D+VG4eh/KgBaKTcPQ/lRuHofyoAWik3D0P5Ubh6H8qAFopNw9D+VG4eh/KgBaKTcPQ/lRuHofyoAWik3D0P5Ubh6H8qAFopNw9D+VG4eh/KgBaKTcPQ/lRuHofyoAWik3D0P5Ubh6H8qAFopAc+v5UtABVaT/j+H/XP+tWarSf8fw/65/1oAdS2f/Hhb/8AXJf5UlLZ/wDHhb/9cl/lQAzULWS7tdkEqxSq6ujum9QVYEZXIyOPUVny6NemKzht763SK2K7lktN5lUAfKTvGBuGePQDnHOzRQAUhUHrS0UAJtH+TRtH+TS0UAZPiB2XTBaW5K3F9ILaMg8ru+834KGb8K0obeK3gjhhTbHGoRFHYAYArLj/ANP8VSSdYdNj8pfQzSAFvyTaP+BmtipWrubVPdjGHz+//gCbR/k0bR/k0tFUYibR/k0bR/k0tFACbR/k0ySCKaNo5o1kRhhlYZB/CpKKAMr+wo7fnSriaw/6Zod8R/4AeAP93bUZsdRvL+2XUxbfZbZvO3Qs376Qfcyp+6By3U8hfStmip5UbKtLrq/xE2j/ACaNo/yaWiqMRNo/yaNo/wAmlooATaP8mjaP8mlooATaP8mjaP8AJpaKAE2j/Jo2j/JpaKAE2j/Jo2j/ACaWigBNo/yaNo/yaWigBNo/yaNo/wAmlooATaP8mjaP8mlooATaP8mjaP8AJpaKAE2j/Jo2j/JpaKAE2j/Jo2ilooAKKKKAK919+3/66n/0BqWkuvv2/wD11P8A6A1LQAUUUUAFFFFAEb/8fFv/ANdD/wCgNVuqj/8AHxb/APXQ/wDoDVboAKKpS6mkWqR2RhlYuBmYbdiEhiFPOcnaegIp9vffaL2e3NtPF5IBEkgXbICSMjBJ6g9QPbIoAtUUVWvr6HT9Pnu5m/dwRtI2D1wM4oGk27IoW/8Ap/ii5uOsWnxi2j/66Ph5D+A8sfnWxWboVs1no8K3JH2mTM1wf+mjnc35E4+gFaO5fUfnUx2NKzTlZbLT+vXcWik3L6j86Ny+o/OqMhaKTcvqPzo3L6j86AFopNy+o/OjcvqPzoAWik3L6j86Ny+o/OgBaKTcvqPzo3L6j86AFopNy+o/OjcvqPzoAWik3L6j86Ny+o/OgBaKTcvqPzo3L6j86AFopNy+o/OjcvqPzoAWik3L6j86Ny+o/OgBaKTcvqPzo3L6j86AFopNy+o/OjcvqPzoAWik3L6j86Ny+o/OgBaKTcvqPzo3L6j86AFopNy+o/OjcvqPzoAWik3L6j86Ny+o/OgBaKTcvqPzpQQehzQAUUUUAVE/4+Lj/roP/QFpLn/j3b8P50qf8fFx/wBdB/6AtJc/8e7fh/OgC5UM9pbXRQ3NvFMY23J5iBtp9RnoamooAqx6ZYRXQuYrG2ScAgSrCobHpnGatUUUAFIzBFLMcBRkn0pazPEM7RaPJHF/rbgiFPqxx/LNJuyuXCPPJR7kfh5TLaTXzjD3kzSD/d6KPyFa9R20C21rFBH92NAo/AVJQlZDqS5ptoKKKKZmFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVaT/j+H/XP+tWarSf8fw/65/1oAdS2f/Hhb/8AXJf5UlLZ/wDHhb/9cl/lQBNRRRQAUUUUAFQXt3FYWE93cHEUEbSN9AM1PWPrP+m39hpQ5WWT7ROP+mUZBx+LlB9M0pOyNKcVKaT26+hPoVpLaaTH9qGLqcme4/66OdxH4Zx9AK0aKKErKxM5OcnJ9QooopkhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXuvv2//AF1P/oDUtJdfft/+up/9AaloAKKKKACiiigCN/8Aj4t/+uh/9AardVH/AOPi3/66H/0Bqt0AVJtMtbi8F1IsnnBNgZZXUY5GcA4yNxweozwabZ6TaWF1cXFqsokuW3Sl55HDHpnDMQDgAcVdooAKx9a/0y+0/Shys0v2icf9MoyDg/Vyg+ma2Kx9I/03VdR1M8oX+yQH/YjJ3EfVy3/fIqZa6G1L3bz7fn0/z+RsUUUVRiFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVE/wCPi4/66D/0BaS5/wCPdvw/nSp/x8XH/XQf+gLSXP8Ax7t+H86ALlZOtzXkTWws3uEDPz5EPmbj2DfKdo9+PrWtRQBi/b5pPEUMKNfLCYyWU2TCEn/fK5z+IH1raoooAT5vase7zeeJrO34KWqG4f8A3jwv+NbNY+hf6TNfaieftExSM/7CcD+tTLojalopT7L8/wCma/ze1Hze1LRVGInze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAnze1Hze1LRQAgz3xS0UUAFVpP+P4f9c/61ZqtJ/wAfw/65/wBaAHUtn/x4W/8A1yX+VJS2f/Hhb/8AXJf5UAF3dxWVs09wWCKQPkRnYknAAVQSTk9AKjOp2apaM06gXjBYMg5kJBbGOvQHrTr20F7bGLzZIW3KyyxY3IwOQRuBHbuDVV9GzFbxR391FHburoqCM52jGCShOD1PfmgDSoopCAeoFAC1j6N/pt/f6q3KySfZ7c/9M4yQT+Llz9MVNrt09npEhtQPtUxEFvx/y0c7VP4ZyfYGrVlZQ2FhBaQLiOCNY1z6AYqd5Gy92m330/V/p+JYopNo9B+VG0eg/KqMRaKTaPQflRtHoPyoAWik2j0H5UbR6D8qAFopNo9B+VG0eg/KgBaKTaPQflRtHoPyoAWik2j0H5UbR6D8qAFopNo9B+VG0eg/KgBaKTaPQflRtHoPyoAWik2j0H5UbR6D8qAFopNo9B+VG0eg/KgBaKTaPQflRtHoPyoAWik2j0H5UbR6D8qAFopNo9B+VG0eg/KgBaKTaPQflRtHoPyoAWik2j0H5UbR6D8qAFopNo9B+VG0eg/KgBaKTaPQflRtHoPyoAWiiigCvdfft/8Arqf/AEBqWkuvv2//AF1P/oDUtABRRRQAUUUUARv/AMfFv/10P/oDVbqo/wDx8W//AF0P/oDVboApS6mkWqR2RhlYuBmYbdiEhiFPOcnaegIqW3vY7m6uYESVWtnCsXTaGyM5X1HPWmTaZa3F4LqRZPOCbAyyuoxyM4Bxkbjg9Rng0ttp1va3U1xD53mT43l53cHHTAYkD8KALVVNNtItN0y3s43DLDGE3E8se5PuTzVuigd3awm9f7w/Ojev94fnS0UCE3r/AHh+dG9f7w/OlooATev94fnRvX+8PzpaKAE3r/eH50b1/vD86WigBN6/3h+dG9f7w/OlooATev8AeH50b1/vD86WigBN6/3h+dG9f7w/OlooATev94fnRvX+8PzpaKAE3r/eH50b1/vD86WigBN6/wB4fnRvX+8PzpaKAE3r/eH50b1/vD86WigBN6/3h+dG9f7w/OlooATev94fnRvX+8PzpaKAE3r/AHh+dG9f7w/OlooATev94fnRvX+8PzpaKAE3r/eH50b1/vD86WigBN6/3h+dG9f7w/OlooATev8AeH50oIPQ5oooAKKKKAKif8fFx/10H/oC0lz/AMe7fh/OlT/j4uP+ug/9AWkuf+Pdvw/nQBcqG4vLa02fariKDzG2p5jhdx9BnqamqhqemyX7RGOdYthIfdHv3Kew5GD78/SgC6ZYxKIi6iQjcEzyR64p1Zq6bcDWIr1p7YhIzGwW2Idh2+ff/StKgChrd0bPR7iVfvldie7HgfzqbTbUWOm29sP+WaAH69/1rP1Ufa9Z0+xBJUMbiQZ7L0/Wtjb7n86lau5tL3aaj31/RfqLRSbfc/nRt9z+dUYi0Um33P50bfc/nQAtFJt9z+dG33P50ALRSbfc/nRt9z+dAC0Um33P50bfc/nQAtFJt9z+dG33P50ALRSbfc/nRt9z+dAC0Um33P50bfc/nQAtFJt9z+dG33P50ALRSbfc/nRt9z+dAC0Um33P50bfc/nQAtFJt9z+dG33P50ALRSbfc/nRt9z+dAC0Um33P50bfc/nQAtFJt9z+dG33P50ALRSbfc/nRt9z+dAC0Um33P50bfc/nQAtFIBjufzpaACq0n/H8P+uf9as1Wk/4/h/1z/rQA6ls/+PC3/wCuS/ypKWz/AOPC3/65L/KgCaiiigAoopk0yW8Ek0zBI41Lux7ADJNAbmVL/wATDxVFF1h02PzW9DM4Kr+Sbj/wIVsVleHYpBpZvLhStxfyG6kB6ru+6v4IFH4Vq1Mdrm1bSXKumn+f4hRRRVGIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFe6+/b/wDXU/8AoDUtJdfft/8Arqf/AEBqWgAooooAKKKKAI3/AOPi3/66H/0Bqt1Uf/j4t/8Arof/AEBqt0AFFFUrXU0u72a3WGVPLztkfbtlwdrFcEng8cgUAXaKa7bY2YdgTTftEP8Az1X86AJKKj+0Q/8APVfzo+0Q/wDPVfzoAkoqP7RD/wA9V/Oj7RD/AM9V/OgCSio/tEP/AD1X86PtEP8Az1X86AJKKj+0Q/8APVfzpq3MRQFpFBI5GaAJqKj+0Q/89V/Oj7RD/wA9V/OgCSio/tEP/PVfzo+0Q/8APVfzoAkoqP7RD/z1X86PtEP/AD1X86AJKKj+0Q/89V/OmrcxEfNIoOT396AJqKj+0Q/89V/Oj7RD/wA9V/OgCSio/tEP/PVfzo+0Q/8APVfzoAkoqP7RD/z1X86PtEP/AD1X86AJKKj+0Q/89V/Omi5iJbMi4B459qAJqKj+0Q/89V/Oj7RD/wA9V/OgCSio/tEP/PVfzo+0Q/8APVfzoAkoqP7RD/z1X86PtEP/AD1X86AJKKj+0Q/89V/Om/aYt5HmLjAwc0ATVmx+INLkmSL7UI5JHKRrMjRmQgZyu4DcOOo4PrV77RD/AM9V/OsNfD1iDLu1G4bzpC8g/dKHypVgQqDqDy33jgc8UwNOPWdLmaFYtStHNwSIQs6nzCODt55/Crtc5aeHrWzuIVhvitrEATEiRR+awcsNwRAMA46YJ75roUkSTOxg2OuDQA6iiikBUT/j4uP+ug/9AWkuf+Pdvw/nSp/x8XH/AF0H/oC0lz/x7t+H86ALlFFFABRVJ9TjTU1sjDKScZmG3YpPQHnOfwxV2gDI0xGuNb1G9cEBWFvHn0Xk/qa16gjlhRT8yKWJLDPen/aIf+eq/nSSsXOXM7klFR/aIf8Anqv50faIf+eq/nTIJKKj+0Q/89V/Oj7RD/z1X86AJKKj+0Q/89V/Oj7RD/z1X86AJKKhW5iOcyKOeOad9oh/56r+dAElFR/aIf8Anqv50faIf+eq/nQBJRUf2iH/AJ6r+dH2iH/nqv50ASUVH9oh/wCeq/nR9oh/56r+dAElFQi5i3EGRcDpzTvtEP8Az1X86AJKKj+0Q/8APVfzo+0Q/wDPVfzoAkoqP7RD/wA9V/Oj7RD/AM9V/OgCSio/tEP/AD1X86PtEP8Az1X86AJKKh+0xb8eYuMdc077RD/z1X86AJKKj+0Q/wDPVfzo+0Q/89V/OgCSio/tEP8Az1X86PtEP/PVfzoAkoqP7RD/AM9V/Oj7RD/z1X86AJKKhNzFuAEi4PXmnfaIf+eq/nQBSuNe020mkiurnyTGQGaRGVMnsGI2k+wNSPrWlIGL6lZqEYIxM6jax6A89ao3OjWlzfy3bajMkkigfIIhtx77Mn6MSKqf8I1aQy5s75oBJIWldUhDkEcjPl5OffJ9CKYHSghlBUggjII70tRQNCsaRROCFUADOTgVLSAKrSf8fw/65/1qzVaT/j+H/XP+tADqWz/48Lf/AK5L/KkpbP8A48Lf/rkv8qAJqKKpX+ppp7xK8MsvmEkmPb+7UEAs2SOBkdMn2oAu1j+IP9KW10let/LiX2hX5pPzGF/4HWxWeIEXXpb2aVcrbrBGpP3eSzH8fk/75pSV1Y1pSUZcz6fn0NCio/tEP/PVfzo+0Q/89V/OmZElFR/aIf8Anqv50faIf+eq/nQBJRUf2iH/AJ6r+dH2iH/nqv50ASUVH9oh/wCeq/nTftMW8jzFxgYOaAJqKj+0Q/8APVfzo+0Q/wDPVfzoAkoqP7RD/wA9V/Oj7RD/AM9V/OgCSio/tEP/AD1X86PtEP8Az1X86AJKKj+0Q/8APVfzpv2mLeB5i4wcnNAE1FR/aIf+eq/nR9oh/wCeq/nQBJRUf2iH/nqv50faIf8Anqv50ASUVH9oh/56r+dH2iH/AJ6r+dAElFR/aIf+eq/nTTcxAriRcE88+1AE1FR/aIf+eq/nR9oh/wCeq/nQBJRUf2iH/nqv50faIf8Anqv50ASUVH9oh/56r+dH2iH/AJ6r+dAElFR/aIf+eq/nTWuYgPlkUnI7+9AE1U7vVrOwnWK7laMspct5bFFA7swGF/EjNWPtEP8Az1X86zdQ0611G9t7mW9kQwfdRPLx7/MVLDPQ4IyODmgCwmuaVJGrrqVptaLzhmZR+7/vdenvVqC4huoEntpUmicZSSNgysPUEda5yXwzZgtNb37G7MSRedIkJbCkYO7y8ggADA+X1HU1uWKQWdslulwZTkkvIw3OxOSTgAck9gBTAt0UUUgK919+3/66n/0BqWkuvv2//XU/+gNS0AFFFFABRRRQBG//AB8W/wD10P8A6A1W6qP/AMfFv/10P/oDVboAKrW+n21rdTXEKMJJjlyZGYevAJwuTycYyeas0UAI67kZemRijaPQflVLWLx7DTXnSWGAhlUzTjMcYLAFmGRwM+oqbT53utOt55QFeSNWYKMDJHb2oAn2j0H5UbR6D8qWigBNo9B+VG0eg/KlooATaPQflRtHoPypaKAE2j0H5UioFQLwcDGcVFfXAtLCe4MkcQijLGSXO1cDqcdqr6Jf/wBp6PBd+fBceYCfMtxhGGTjgkkHHUEnBoAvbR6D8qNo9B+VLRQAm0eg/KjaPQflS0UAJtHoPyo2j0H5UtFACbR6D8qRUCjHB5J6e9KzbVLEE4GcAZNZuiao+q280ksMkDJKVCSQPGVGAQDvAyeeSOKANLaPQflRtHoPypaKAE2j0H5UbR6D8qWigBNo9B+VG0eg/KlooATaPQflSBACx45OentTqxtF1mbUdR1G3nVE+zSbVVQMqMkfMQ55OM8hevQjmgDY2j0H5UbR6D8qWigBNo9B+VG0eg/KlooATaPQflRtHoPypaKAE2j0H5Umwby3HIAxinVh2ms3U3jG80uRbcW0UQaPB/eZwvJG4nHzHkqBxwTQBt7R6D8qNo9B+VLRQAm0eg/KlAA6CiigAooooAqJ/wAfFx/10H/oC0lz/wAe7fh/OlT/AI+Lj/roP/QFpLn/AI92/D+dAFyiiigCs2n2zX4vGRvPUYB8xtv/AHznGffGas0VFcytDayyoMsiFgD9KAHqgVQOD+FLtHoPyrP0S/k1Gw86WSCY7iBLbqQjD2yT/OtGgBNo9B+VG0eg/KlooATaPQflRtHoPypaKAE2j0H5UbR6D8qWgnAJ/nQA1UC56HJz0pdo9B+VZWh6sNUN1i7tLkQy7B9nBBX2YEn8+M+la1ACbR6D8qNo9B+VLRQAm0eg/KjaPQflS0UAJtHoPyo2j0H5UtFADQgDE8c+1LtHoPyrMstWe61q5s2gliSIZUvBIu7nruI2kfStSgBNo9B+VG0eg/KlooATaPQflRtHoPypaKAE2j0H5UbR6D8qWigBuwb93HTGMUu0eg/Ksb+2Zv8AhK/7MZY1i8vcvA3Nx1+/nH/Ace9bVACbR6D8qNo9B+VLRQAm0eg/KjaPQflS0UAJtHoPyo2j0H5UtFADSgLA8ce1LtHoPyrD1TWbqy8R2FjEtv5Fx/rDIcP/AMB+YE/gD+FbtACbR6D8qNo9B+VLRQAYA6CiiigAqtJ/x/D/AK5/1qzVaT/j+H/XP+tADqWz/wCPC3/65L/KkpbP/jwt/wDrkv8AKgCaq13p9tfPE1yjMYW3JtkZfwOCNw4HByOBVmigApoQBmJ53HPT2p1ZOkanNfX17FK8DrC2AsSkNEdzDY/zHLYAPQdenQ0Aau0eg/KjaPQflS0UAJtHoPyo2j0H5UtFACbR6D8qNo9B+VLRQAm0eg/Kk2DeW45AGMU6sm31cTeJ7nTReWb+TEGMCgiVDxgkk4YdeAOOOeaANXaPQflRtHoPypaKAE2j0H5UbR6D8qWigBNo9B+VG0eg/KlooATaPQflSbBvDccAjGKdWXcas8PiC208QS+XKCWl8iQqTgkAOBtGMc5PcUAae0eg/KjaPQflS0UAJtHoPyo2j0H5UtFACbR6D8qNo9B+VLRQAm0eg/KkKAlTxwc9PanVja1rM2m6lp0Eap5VzJtdnAJPIG1cuvPOeAx4+71IANjaPQflRtHoPypaKAE2j0H5UbR6D8qWigBNo9B+VG0eg/KlooATaPQflSMgYY4HIPT3p1YnirWLvRdNinsVty7TBG+0EBQuCepZeeB3z6A9KANraPQflRtHoPyoU7lB9RmloATaPQflRgegpaKACiiigCvdfft/+up/9AalpLr79v8A9dT/AOgNS0AFFFFABRRRQBG//Hxb/wDXQ/8AoDVbqrIDvjdV3bGyQD14I/rTvtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQAxP+Pi4/66D/0BaS5/492/D+dOjB3yOy7d7ZAJ6cAf0omQvCyjrQBaoqv9qb/n2l/Nf/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYoqv9qb/n2m/NP/iqPtTf8+035p/8VQBYqtJ/x/D/AK5/1pftTf8APtN+af8AxVMBaS48wxsgC7cMRnr7UASUtn/x4W//AFyX+VJUcMrwwRxG3kYooXKlcHHHc0AW6Kr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAWKKr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAWKKr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAWKKr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAWKKr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAWKKr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAWKKr/AGpv+fab80/+Ko+1N/z7Tfmn/wAVQBYoqv8Aam/59pvzT/4qj7U3/PtN+af/ABVAFiiq/wBqb/n2m/NP/iqPtTf8+035p/8AFUAF19+3/wCup/8AQGpajd2nki/dPGEYsSxHoR2J9akoAKKKKACiiigAopkhO+NFYrvfBIHTgn+lO+yt/wA/M35J/wDE0ALRSfZW/wCfmb8k/wDiaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8AE0ALRSfZW/5+ZvyT/wCJo+yt/wA/M35J/wDE0ALRSfZW/wCfmb8k/wDiaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8AE0ALRSfZW/5+ZvyT/wCJo+yt/wA/M35J/wDE0ALRSfZW/wCfmb8k/wDiaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8AE0ALRSfZW/5+ZvyT/wCJo+yt/wA/M35J/wDE0ALRSfZW/wCfmb8k/wDiaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8AE0ALRSfZW/5+ZvyT/wCJo+yt/wA/M35J/wDE0ALRSfZW/wCfmb8k/wDiaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8TQAtFJ9lb/n5m/JP/iaPsrf8/M35J/8AE0ALRSfZW/5+ZvyT/wCJo+yt/wA/M35J/wDE0ALRSfZW/wCfmb8k/wDiaPsrf8/M35J/8TQAtFMjJ3yIzbtjYBI68A/1p9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARv/wAfFv8A9dD/AOgNVuqj/wDHxb/9dD/6A1W6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKgW8ge/ks1fNxHGsrpg8KxIBz06qfyoAnoqNZ0e4khAk3xgFiY2CnPTDEYPToCcd6kJwMmgAoqCyu0vrKK6iV1SVdyhwM4PQ8fnU9ABRUdxOttbvNIJCsaliI42dj9FUEk+wFJNcJbwea6yFcgYjiZ25OPuqCe/px3oAlooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCon/Hxcf8AXQf+gLUlRp/x8XH/AF0H/oC1JQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEb/APHxb/8AXQ/+gNVuqj/8fFv/ANdD/wCgNVugAooooAKKKKACiiigAooooAKKKKACiiigAooooAKwfEnhxPEEtsLiG2nt4oplaK4XcCzKAhxgg4Iz7cEVvUUAefXPw8ujpjW9m1pHvitjPENuy6ljDhy++J1OdyncUYnaMgcEKngG6iWfy7ew8260tbNrh5v3sLqW6MsS7lIKjICYCjjgV6BRQBx+peDpHmmOnW2ntZkwH+z5SY4ZwnmblfCMACXDdGyRyKqT+HNStdYfU7bTdPtAbQWwS1k85lJUIAn7hWRVJ7PtwM7ATkd3WVfa+tjdtAdN1Ocrj95b2jOh+hFJtLcuFOU3aKOZ1Hwvc3/i6T7Pp1otvDDZiLUJmZZbfY7FhDhCCSAAfmXGRnPSotW+Hk93p8AtxaNcCeaSdWKATB3JRi8kMvKA4Hycbjgjv2jXzNpJvYoZIyE8wxzoVYAdQR2OM1Ct/M0lqwMflXEjKBsP3RnB3ZwOAOMc54o5kNU5M5ufwhfSPqata6dcy3luY4tSuJibmEGML5f+q5TIJyGH3j8uaSbwfqM2u392kVhBFctG2/zd8khSRGyT5QZRhT8pdxnGNtdvRTMzg7rwRqVxd37p9hjecXOL4SN59yJR8kco2cKmRjDN91cAdK6TQdCTQ7jURbRQQW1zMsscUA2hT5aqxxgAEkE8detbFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVE/wCPi4/66D/0BakqNP8Aj4uP+ug/9AWpKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAjf/AI+Lf/rof/QGq3VR/wDj4t/+uh/9AardABRWfNqUkesR2YgVom2h5TJhlZgxAC45HyHJyPoah0zWpL/V7+zezaBLVsRzFiRMM4JGVHQ+hPvigDWoopN3sfyoAWik3ex/Kjd7H8qAFopN3sfyo3ex/KgBaKTd7H8qN3sfyoAWik3ex/Kjd7H8qAFopN3sfyo3ex/KgBaKTd7H8qN3sfyoAWik3ex/Ko5rmK2haW4kWKNRlnkYKo+pNAb6IlorEOvy33y6BYSXgPS5lzFAPfcRlv8AgIP1pVTxMoy11pbk87fs0i49s7z/ACqeZdDf2LXxNL1/rT5myyhlKsMgjBHrVdbC2QwbUYC3ULGgdtqgDA+XOD9cVQFx4hj+/YafOP8AYunQ/kYz/OnDU9UT/W6DM3/XG5jb/wBCK0XQKnNbNfev8zWorK/txl/12k6nF/2wD/8AoDGj+34m/wBVYalIfT7FIn6sAKOZE+xqdjVorJOrX7/6jQbz6zSxIP0cn9KQ3XiCT/V6bYwD1lu2Y/kI/wCtHMh+xl3X3r/M16KxvJ8RS/evtPtx/wBM7R5D+Zcfyo/szV2+/wCIJx/1ztIh/MGjmfYPZx6zX4/5GzRWP/Y963+t8Qakf91IF/lHSjRHP39Y1Rv+2qj+Sii77B7OH86/H/I16Kyf7CiP377U2/7fHH8iKcNBtB/y21In31K4/wDi6NewuWn/ADP7v+CalFZw0e1XpJf/AI385/8AZ6kGmwL0e8/G7lP/ALNT1FaHd/d/wS7RVT7BD/euv/AmT/4qo59KhmTHnXsZByGju5AQf++sH6HNGokod/w/4JforH+z63Z/8et7FfoP+Wd5Hsf/AL+IMf8Ajho/t9rbjVtNu7MDrKqedH/30mSB9QKXMupfsW/gd/T/AC3Niiq1nqVnqMXmWFzFcJ3MThsfXHSrIOarcyacXZhRRRQIqJ/x8XH/AF0H/oC1JUaf8fFx/wBdB/6AtSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBG/wDx8W//AF0P/oDVbqo//Hxb/wDXQ/8AoDVboAglsbSefz57WGSXYY/MeMFth6rk9j6UQWNpbTyTW1rDDLLjzHjjCs+OmSOuKnooAKKKKACiiigAooooAKKhuru3sbV7i8mSGGMZZ3OAKydt74g/1ol0/TD/AAcpPcj37xqfT7x9ulJu2hpCm5Lmei7ks+qzXtw9noSpLIjbZrtxmKA9x/tt/sjp3IpwtNagUeTqkNzjqLq2wT/wJCAPyNaNvbw2tukFtEkUUY2oiLgKPYVJSt3KdRLSC089X/XoZX2/V7f/AI+tHEwHVrO5Vv0fZ/M0n/CS2EX/AB/C5sT3+1W7ov8A31jb+ta1FFn3Dng94/c/87kFrf2l8m6yuobhfWKQMP0pl7qdnpyqby4WMvwidWc+iqOWP0FQ3egaTevvudOt3k7SCMBx/wACHNSWOkWOnEtZ2yo7DDSMS7sPQs2SR+NHvB+5319NPz/4BTN3q+ocWFothCf+W96MufpED/6ER9KfD4etfOW41B5NSuVORJdEMEP+ygwq/gM+9atFHKuoe2a0hp6f57/oFFFFUYhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGfe6Dpl/L51xaJ5/aePMcg/wCBrg/rWfZaDqdl4lF3/bdxcaaIiotZ2LnP17+uevaugoqXFN3No16kYuN9Ntdfu7BRRRVGJUT/AI+Lj/roP/QFqSo0/wCPi4/66D/0BakoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCN/8Aj4t/+uh/9AardVH/AOPi3/66H/0Bqt0AFFQteWyXiWj3ES3MilkhLgOwHUhepFEd3bTXEsENxFJNDjzY1cFo89MjqM+9AE1FFFABRRRQAVnalrEWnulvHG91fSjMVrF95vc9lX1Y8U7W7ySw0a4mt8faCBHAD3kYhU/8eIo0vSLfS438stLcSkGe5lO6SVvUn+Q6DtUtu9kbQjFR55/cVrTR5Z7pL/XZEuLlDuhgT/U2/wDug/eb/aPPpitiiimkkROcpvUKKKKZAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBUT/AI+Lj/roP/QFqSo0/wCPi4/66D/0BakoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCN/8Aj4t/+uh/9AardVH/AOPi3/66H/0Bqt0AZtzpk0uqLdxXMaKFB2NEWO9QwU53Dj5zkY59RTNN0Z9P1K7uWujKlwSwiw2FJOSeWI6/3Qvvk81q0UAFJtHqfzpaKAE2j1P50bR6n86WigDH1FRd6/ptkMlId15Lz/d+VB/302f+AVr7R6n86ydH/wBK1LU9RPIeb7NEf9iLIP8A4+ZK16mPc2q6NQ7L/gv/ACE2j1P50bR6n86WiqMRNo9T+dG0ep/OlooATaPU/nRtHqfzpaKAE2j1P50bR6n86WigBNo9T+dG0ep/OlooATaPU/nRtHqfzpaKAE2j1P50bR6n86WigBNo9T+dG0ep/OlooATaPU/nRtHqfzpaKAE2j1P50bR6n86WigBNo9T+dG0ep/OlooATaPU/nRtHqfzpaKAE2j1P50bR6n86WigBNo9T+dG0ep/OlooATaPU/nRtHqfzpaKAE2j1P50oGPX86KKACiiigCon/Hxcf9dB/wCgLUlRp/x8XH/XQf8AoC1JQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEb/8fFv/ANdD/wCgNVuqj/8AHxb/APXQ/wDoDVboAKKKKACiiigAqpq17/Z2kXN2BuaKMlF/vN/CPxOB+NW6yNX/ANK1PTNOHIaU3Uo/2IsEf+PmP8jSk7I1pRUpq+36LUuaVZf2dpNtaZ3NFGFZv7zfxH8Tk1booprRWM5Scm5PqFFFFAgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAqJ/x8XH/AF0H/oC1JUaf8fFx/wBdB/6AtSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBG/wDx8W//AF0P/oDVbqo//Hxb/wDXQ/8AoDVboAx7vV3tvEEVo01tHD5e90cfvCuHJcHdgKu0Z4PXqOMv07WGv9WvLbyZI44VBjZoJF3jJBbcQFIOOMZ459hq0UAFJk/3TS0UAJk/3TWRpxN3r2pXxBKxFbOL/gPzOf8Avpsf8ArQv7tNP064vJvuQRtI3uAM4qDQ7R7HRbaGf/XlfMmPrIx3Of8AvompersbR92nKXfT9X+n3l7J/umjJ/umloqjETJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umjJ/umlooATJ/umlBz1GKKKACiiigCon/Hxcf8AXQf+gLUlRp/x8XH/AF0H/oC1JQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEb/APHxb/8AXQ/+gNVuqj/8fFv/ANdD/wCgNVugAooooAKKKKAMjXf9JksNNHP2q4DSD/pnH87fgSFX/gVa9ZFp/pnie9ueqWca2sf+82Hk/Tyx+Fa9THqzapoow+f3/wDAsFFFFUYhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVE/wCPi4/66D/0BakqNP8Aj4uP+ug/9AWpKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAjf/AI+Lf/rof/QGq3VR/wDj4t/+uh/9AardAELXlsl4lo9xEtzIpZIS4DsB1IXqRRFeW09xLBBcRSTQECWNHBaPPTcByPxqnc6ZNLqi3cVzGihQdjRFjvUMFOdw4+c5GOfUUthYXdtqF1PdXcE8c2PLSO28sxDOcZ3HIySemcnr2oA0ajuJ47W2lnmbbHEhdz6ADJqSsfxContbfTlHN/OsLD/pmPmk/wDHVI/EUm7IunHmmkyXw9BJFosMk67Z7ktczA9mkO4j8M4/CtOk2L6UbR6UJWVhTlzycn1FopNo9KNo9KZItFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpRtHpQAtFJtHpSgAdKACiiigCon/Hxcf9dB/wCgLUlRp/x8XH/XQf8AoC1JQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEb/8fFv/ANdD/wCgNVuqj/8AHxb/APXQ/wDoDVboAKKKKACshP8ATPFkr9Y9PtxGP+ukh3N+Sqv/AH1WvWV4cVm0n7ZICJL+RrpgeoDfcH4IFH4VL1aRtT92MpfL7/8AgXNWiiiqMQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCon/Hxcf9dB/wCgLUlRp/x8XH/XQf8AoC1JQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEb/8fFv/ANdD/wCgNVuqj/8AHxb/APXQ/wDoDVboAKKKydNmvZNWvFuGnMSkgLJDsRPmO3Y20bwV5JycH06UAa1IiqiKiAKqjAA7Cmy/6l/90/ypu6f/AJ5p/wB9f/WoAloqLdP/AM80/wC+v/rUbp/+eaf99f8A1qAJaKi3T/8APNP++v8A61G6f/nmn/fX/wBagCWiot0//PNP++v/AK1G6f8A55p/31/9agCWiot0/wDzzT/vr/61NjabykwiY2jHzf8A1qAJ6Ki3T/8APNP++v8A61G6f/nmn/fX/wBagCWiot0//PNP++v/AK1G6f8A55p/31/9agCWiot0/wDzzT/vr/61G6f/AJ5p/wB9f/WoAloqLdP/AM80/wC+v/rU2NptpwifeP8AF7/SgCeiot0//PNP++v/AK1G6f8A55p/31/9agCWiot0/wDzzT/vr/61G6f/AJ5p/wB9f/WoAloqLdP/AM80/wC+v/rUbp/+eaf99f8A1qAJaKi3T/8APNP++v8A61NVptz4RPvc/N7D2oAnoqLdP/zzT/vr/wCtRun/AOeaf99f/WoAloqLdP8A880/76/+tRun/wCeaf8AfX/1qAJaKi3T/wDPNP8Avr/61G6f/nmn/fX/ANagCWiot0//ADzT/vr/AOtTQ03mt8iZ2j+L6+1AE9cjD4uuPPIdrC4jWZ0YwyYP3SVVcM4Zjt6EqfVRxXU7p/8Anmn/AH1/9ajdP/zzT/vr/wCtTA5yw8YveNZLJaWkT3TY8tdQSVj8+35NgIfHU8jAzXUVW8txcGfyU8wqELbz0znH61OhkOfMVR6YOaAHUUUUgKif8fFx/wBdB/6AtSVGn/Hxcf8AXQf+gLUlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARv8A8fFv/wBdD/6A1W6qP/x8W/8A10P/AKA1W6ACiiigAIyMHkUVR1ize/014EihnJZWMM5xHIAwJVjg8HHoam0+3a0063t3ILRRqhK9OB29qALFFFFABRRRQAUUUUAFAGBgcCoL6J57CeKKOOR3jKqkrEIxx0YjnHrVfRLSWw0eC2uIbeCRAd0dsxMYJJPy5AwOemOOlAF+iiigAooooAKKKKACgDHSkYEqQpw2OCRnBrN0TT7vTbeaO9uYbl3lMgkjiZM5AyTlm5JB70AadFFFABRRRQAUUUUAFGMfjRWNoukXOm6jqM07xtHcyb1ZSNzck5bCKehA5LdOoHFAGzRRRQAUUUUAFFFFABRjnPeisO00Oa38Y3mrny/LuIgmQ/zHheNu3/Z6lj7Ac0AblFFFABRRRQAUUUUAVE/4+Lj/AK6D/wBAWpKjT/j4uP8AroP/AEBakoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCN/+Pi3/wCuh/8AQGq3VZ0345KlTkEdqbsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaALdFVNkv/P1L+Sf/E0bJf8An6l/JP8A4mgC3RVTZL/z9S/kn/xNGyX/AJ+pfyT/AOJoAt0VU2S/8/Uv5J/8TRsl/wCfqX8k/wDiaABP+Pi4/wCug/8AQFqSmomzPJYscknvTqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAloqLzn/59pfzX/4qjzn/AOfaX81/+KoAlooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKiujizmI4Plt/KgAt7mG6jL27h1BwSPWpazdJAWScAYGyLgf7laVACb137Nw3Yztzzimxy+Y8i7HXY2MsMBuOo9RVX/mPf8Abv8A+zVdoAKKKKAE3rv2bhuxnbnnFLVL/mPf9u//ALNV2gBGYKpZugGTxTY545YBNG2Y2XcG9qfWRAceF5ccYSTGO3JoA04riKe3E8Tho2GQ3tTY7pJjH5SuySKWWTaQuPes21JHh67xxjzcY7da0rQAWUAAwPLX+VAE1FFFAES3CNdPbgNvRQxPbBz/AIVLVGL/AJDlx/1xT+Zq9QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//9k="}}},{"cell_type":"markdown","source":"![2.JPG](attachment:71cbe291-66c2-4a16-9b1d-47c6d2b9b9ba.JPG)","metadata":{"papermill":{"duration":2.206906,"end_time":"2022-01-18T23:23:57.524245","exception":false,"start_time":"2022-01-18T23:23:55.317339","status":"completed"},"tags":[]},"attachments":{"71cbe291-66c2-4a16-9b1d-47c6d2b9b9ba.JPG":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RDiRXhpZgAATU0AKgAAAAgABAE7AAIAAAAIAAAISodpAAQAAAABAAAIUpydAAEAAAAQAAAQyuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRPU0hJQkEAAAWQAwACAAAAFAAAEKCQBAACAAAAFAAAELSSkQACAAAAAzU0AACSkgACAAAAAzU0AADqHAAHAAAIDAAACJQAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjExOjI5IDEyOjEwOjQxADIwMjE6MTE6MjkgMTI6MTA6NDEAAABUAE8AUwBIAEkAQgBBAAAA/+ELGmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8APD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4NCjx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iPjxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iLz48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyI+PHhtcDpDcmVhdGVEYXRlPjIwMjEtMTEtMjlUMTI6MTA6NDEuNTM3PC94bXA6Q3JlYXRlRGF0ZT48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyI+PGRjOmNyZWF0b3I+PHJkZjpTZXEgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOmxpPlRPU0hJQkE8L3JkZjpsaT48L3JkZjpTZXE+DQoJCQk8L2RjOmNyZWF0b3I+PC9yZGY6RGVzY3JpcHRpb24+PC9yZGY6UkRGPjwveDp4bXBtZXRhPg0KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICA8P3hwYWNrZXQgZW5kPSd3Jz8+/9sAQwAHBQUGBQQHBgUGCAcHCAoRCwoJCQoVDxAMERgVGhkYFRgXGx4nIRsdJR0XGCIuIiUoKSssKxogLzMvKjInKisq/9sAQwEHCAgKCQoUCwsUKhwYHCoqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioq/8AAEQgCagUfAwEiAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A8P8A+E98X/8AQ1a3/wCDGb/4qj/hPfF//Q1a3/4MZv8A4qu78O/s4+MvEnh2x1m3u9ItYL6FZ4Y7m4kD7GGVJCxsBkEHr3rS/wCGVvG//QU8P/8AgRN/8ZpgcN4b1vx/4s8RWmi6P4n1l7u7fam/U5VUcZJJ3dAAT/LJr1gfBf4wY58dXGf+wrcf41Y+GnwJ8TeBfiJputavfaTNbQllZLWaVnJYEDAaMDv619G0AfNX/Cl/i/8A9D3cf+DW4/xo/wCFL/F//oe7j/wa3H+NfStFID5q/wCFL/F//oe7j/wa3H+NH/Cl/i//AND3cf8Ag1uP8a+laKAPmr/hS/xf/wCh7uP/AAa3H+NH/Cl/i/8A9D3cf+DW4/xr6VooA+av+FL/ABf/AOh7uP8Awa3H+NH/AApf4v8A/Q93H/g1uP8AGvpWigD5q/4Uv8X/APoe7j/wa3H+NH/Cl/i//wBD3cf+DW4/xr6VooA+av8AhS/xf/6Hu4/8Gtx/jR/wpf4v/wDQ93H/AINbj/GvpWigD5q/4Uv8X/8Aoe7j/wAGtx/jR/wpf4v/APQ93H/g1uP8a+laKAPmr/hS/wAX/wDoe7j/AMGtx/jR/wAKX+L/AP0Pdx/4Nbj/ABr6VooA+av+FL/F/wD6Hu4/8Gtx/jR/wpf4v/8AQ93H/g1uP8a+laKAPmr/AIUv8X/+h7uP/Brcf40f8KX+L/8A0Pdx/wCDW4/xr6VooA+av+FL/F//AKHu4/8ABrcf40f8KX+L/wD0Pdx/4Nbj/GvpWigD5q/4Uv8AF/8A6Hu4/wDBrcf40f8ACl/i/wD9D3cf+DW4/wAa+laKAPmr/hS/xf8A+h7uP/Brcf40f8KX+L//AEPdx/4Nbj/GvpWigD5q/wCFL/F//oe7j/wa3H+NH/Cl/i//AND3cf8Ag1uP8a+laKAPmr/hS/xf/wCh7uP/AAa3H+NH/Cl/i/8A9D3cf+DW4/xr6VooA+av+FL/ABf/AOh7uP8Awa3H+NH/AApf4v8A/Q93H/g1uP8AGvpWigD5q/4Uv8X/APoe7j/wa3H+NR3Hwb+MUVtJJF43uJWRSwT+1rgbsdvrX0zSP9xvpQB8Bz+NvGlrcy29x4n1yOWJyjodRlyrA4I+961H/wAJ54v/AOhq1v8A8GM3/wAVXq+sfs2eMNb8QajqdpqWhpDeXUs8ayTzBgrOWAOIiM4PrVX/AIZW8b/9BTw//wCBE3/xmmB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVem/8MreN/8AoKeH/wDwIm/+M0f8MreN/wDoKeH/APwIm/8AjNAHmX/CeeL/APoatb/8GM3/AMVR/wAJ54v/AOhq1v8A8GM3/wAVXpv/AAyt43/6Cnh//wACJv8A4zR/wyt43/6Cnh//AMCJv/jNAHmX/CeeL/8Aoatb/wDBjN/8VR/wnni//oatb/8ABjN/8VXpv/DK3jf/AKCnh/8A8CJv/jNH/DK3jf8A6Cnh/wD8CJv/AIzQB5l/wnni/wD6GrW//BjN/wDFUf8ACeeL/wDoatb/APBjN/8AFV6b/wAMreN/+gp4f/8AAib/AOM0f8MreN/+gp4f/wDAib/4zQB5l/wnni//AKGrW/8AwYzf/FUf8J54v/6GrW//AAYzf/FV6b/wyt43/wCgp4f/APAib/4zR/wyt43/AOgp4f8A/Aib/wCM0AeZf8J54v8A+hq1v/wYzf8AxVH/AAnni/8A6GrW/wDwYzf/ABVem/8ADK3jf/oKeH//AAIm/wDjNH/DK3jf/oKeH/8AwIm/+M0AeZf8J54v/wChq1v/AMGM3/xVH/CeeL/+hq1v/wAGM3/xVenD9lbxtuG7VNAxnnFxN/8AGaf/AMMteNP+f/w7/wCBNx/8apXA8u/4Tzxf/wBDVrf/AIMZv/iqP+E88X/9DVrf/gxm/wDiq9R/4Za8a/8AP/4c/wDAm4/+NUf8MteNf+f/AMOf+BNx/wDGqLgeXf8ACeeL/wDoatb/APBjN/8AFUf8J54v/wChq1v/AMGM3/xVeo/8MteNf+f/AMOf+BNx/wDGqP8Ahlrxr/z/APhz/wACbj/41RcDy7/hPPF//Q1a3/4MZv8A4qj/AITzxf8A9DVrf/gxm/8Aiq9R/wCGWvGv/P8A+HP/AAJuP/jVH/DLXjX/AJ//AA5/4E3H/wAaouB5d/wnni//AKGrW/8AwYzf/FUf8J74v/6GrW//AAYzf/FV6g37LHjRl41Lw+pz2uZ8frFTP+GVvG//AEFPD/8A4ETf/GaYHmf/AAnvi/8A6GrW/wDwYzf/ABVH/CeeL/8Aoatb/wDBjN/8VU3jbwHrHgDxINF1027TtCs6SW7l43jJIyCQD1VhyB0rLtbaMqSVGPcZzQB91fDn/klvhX/sDWf/AKJSukrm/hz/AMkt8K/9gaz/APRKV0lIChd/8fMH/XVf51fqhd/8fMH/AF1X+dX6ACioJbuKGXy28xn2hiEjZsA9M4HHQ0w6jAuN4mQZxloHAH44oAtU1nVPvGnVBc9VprView/z09f0o89PX9Kq0VfKieZlrz09f0o89PX9Kq0UcqDmZa89PX9KPPT1/SqtFHKg5mWvPT1/Sjz09f0qrRRyoOZlrz09f0o89PX9Kq0UcqDmZa89PX9KPPT1/SqtFHKg5mWvPT1/SnK6v905qnVi26NScUkNMe0qKcE8/Sjzo/736VXl/wBa1MoUULmZb86P+9+lHnR/3v0qpRT5UHMy350f979Kdkbc9utUqtn/AI9/+A1LVhp3E89PX9KPPT1/SqtFVyoXMy156ev6Ueenr+lVaKOVBzMteenr+lHnp6/pVWijlQczLXnp6/pR56ev6VVoo5UHMy6rhx8pof7jfSorf7rfWpX+430qHoykU9O/49U+lXapad/x6p9Ku0hhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8o/tRNt+Ken5/6A0X/o+evJefLXYQK9W/ambb8VNN/7A0X/AKPmryCKTIw3bp70mVE+7fhz/wAkt8K/9gaz/wDRKV0lc/4B/wCSb+Gv+wTa/wDola6CmSULv/j5g/66r/Or9ULv/j5g/wCuq/zq/QByfjSbxRa2k154LS2uL22EMstpOmTdRAvujU5+Vj2Nc54f0DX9Zubjxt43MlpeNbvHp+kJIQllEw539Nznvn/9Xo81mJZvNSaWFyoUmMjkDOOCD6mo20/eNs11cSJ3RiuG+uBQBcqC56rU9QXPVaqO4nscXr+tO0+qaa95Y2wW1dY7SUH7RcgxFt6HcPlHIxtP3TyKgPjGSPXjYW01lJbpbE5YqPKcR7wXYSFtpx/zzA9GPSu0oqyDibTxrLN/ZW++01xczGKXywm6TnA2ATsMdsoZeeoHON/xRq0mi6I93DPawOGAD3RUL9BudAT6Auo9+x12G5SDnBGODiqGn6La6ZK0ltLfOzDaRc3884/ASOwH1FAHPSeL5hNL5d3pyskKtFZSIwmnBQN5y/NnYMnICn7h+YGpbPxDqN75ENnc6deeZctEL+CJjDMqx7yUXeeQcofnIzz7V1dFAHMeEfEt3r814l5FDEYdp8tGj3xE5+RwsrtkY6sqf7tdPRRTAKKKKBBRRRQAVYtujVXqxbdGqZbFLcil/wBa1UdVUtpsoSC6uG4Ijs5hFITnsxZR+ZwenPSr0v8ArWqtd2dtf2r219bxXMD/AH4pkDq31B4NNbCKGhwatDC/9r3JmDHMay7GlUf7TRqidMfKFODn5mzxLrF5qFjZiXTNNGovuAaLz/LIB7jIOf0p2naJpWkNI2k6ZZ2JkADm2t0j3Y6Z2gZ61eqotJ3auS02rIZC0rQI1wixylQXRW3BT6A4Gfyq8f8Aj3/4DVSrZ/49/wDgNRIuJUoooqiSC+aZNPuHtRunWJjGMZy2Dj9a5zQdRshBcpDrDy2jRrsuJrnzGE2xjKFZyeVABK9F54HSuqooGee/2y9z4dvpPC1zd6jaOzMzwagk09qgTqTJIGXcRnGcqOgB4Fm21K7m8d2wkuPJVkRTZPeObgfuiSTAG8sx5I/ecnIIzjp3NFIBk7mO3kcMqlVJ3OOB7n2qKxlM1ormeO4OSPNiGFbB+p/nVigAKAFAAHQCmIsW/wB1vrUr/cb6VFb/AHW+tSv9xvpWUtzRbFPTv+PVPpV2qWnf8eqfSrtIYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGL4s1e50TQGvLLy/O86KMGSB5gA8iqTsQhmOD0ByaytO8aubNlvLOe9vlllHlWlt5DmKPaTI0Uzgx8MvykljkEDmuk1PS7XWLFrS/R3hZlf5JWjYMpBBDKQQQQDwazn8G6JJbrE8FwcMzGX7bN5r7gAweXfvcEADDEjAHHAoAanjDTZVLwrNJH9oit1dVGGMkYkVhz0wR757VXi8cWktu0n9nagjMkMlvEyx7rkSsVTZh8DkH7xXH0q/J4V0eW/W8a1YSrsIVZ5FjBQYU+WG25A4zjOOKWXwxpE1qLd7VhGsMcK7JnVkWM7kwwbIIPO4HPvQBiWHi+eOGafVo5o1j+2StbmFGlVYpQoXcr7cgNjGGz/e45sX3j2z06wluL3Tr6Fobj7PJDI0CFW2BwSzShMEEYG7JJxjNaS+FtHS3MItWKMkkbbppGLCRgz5JbJJIBz1qnr3hP+03SbTboWFx5xmkkPnHedgTgxyxsvAA4bB7g9QAY8njbUjr3lrbiC0EtsrW89hLvVZQpJe4VzFGV3fdIOcY71rt43s1LN9gvjBtd4pwse2dEYLI6/PnC5yQQCQDgGpbbwbpsbRS3bXd3cqsfmyS3k2ydkAAZ49+xjwPvA/jUn/CH6J50khtZCZGyUNzKUX5txCpu2qpYAlQADjkGgDLvPEuq3V3nRBa29slg19i/gZmnUOQMMkg2BgMgkEjPI7VHJ8RdMuTNY2bNFfG0aSMtJEdknk+YFKb9/A77NvbNa0vgvQpmQvbTBUVk8tbyZUZC24oyB9rJn+Agr2xirUnh3TZLuW48uZDMhSSOO6lSJwV2kmNWCE44zjPA54FAGbpvjCKY2tpf2V7bXkxhQCVI8P5isVkBViNp2Nxww7qKiuviBYWzWapYX073rMkSIYVZiJDHgB5FLHIzhd2BycVf/4Q/RRayQCCfEjpJv8Atk3mIU+4Fk37kAycKpAGTxyazdQ8DGS7t20bUX0uCKPyykTT78by5IZJlBOWPDhwOw6ggDZviPpcFzfxTWd2i2APnSNJAFU79iqR5uULE8bwoxzwOavaZ4zstYa0XTba5uGuQ7fumidYgjhGLOHKnBI+6WyOmaqWHghoL+4e/wBTkubOXzCLVGnjGWfcGIMzIGB5DRohB5res9Gs7GSKSETvLFG0ayXFzJM+1mDEFnYk8gdenagC9RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8l/tU/8lS03/sDRf8Ao6avFAxHQkfQ19AftKf8lLsP+wRF/wCjpq8jHSgD7b8A/wDJN/DX/YJtf/RK10Fc/wCAf+Sb+Gv+wTa/+iVroKAKF3/x8wf9dV/nV+qF3/x8wf8AXVf51foAKKKKACmvGHxuzxTqKAIvs6e/50fZ09/zqWindisiL7Onv+dH2dPf86loouwsiL7Onv8AnR9nT3/OpaKLsLIi+zp7/nR9nT3/ADqWii7CyIvs6e/50fZ09/zqWii7CyIvs6e/50fZ09/zqWii7CyIvs6e/wCdPSMJnb3p1FF2FiNoVZsnOaT7Onv+dS0UXYWRF9nT3/Oj7Onv+dS0UXYWRF9nT3/OpCoK7e2MUtFF2Mi+zp7/AJ0fZ09/zqWii7FZEX2dPf8AOj7Onv8AnUtFF2FkRfZ09/zo+zp7/nUtFF2FkRfZ09/zo+zp7/nUtFF2FkNRAg+Wlf7jfSlpH+430pDPHZvifrun3dxbw2diYoJWjVnifJAJAyd/tQfi54hC7jZaeFzjPlSdf++656e+uBql9bxQszC5kAIz8q7ienvz+dPZrq/l8m5QGNT8yKSpbrj8c/yr6CGUxcU3Jnw1biatSqSi6asn3Nz/AIW9r5/5dNN/79Sf/F0v/C3tf/589O/79Sf/ABdY6aXBaEzRxmTsh39TjoMj/PNNnszIJGbdKcnCdCv157Z61aymi/tsx/1tm3pTVvU2T8X9eHW003/v3J/8XQPi/r5BItNNIAyT5UnH/j9cpdWNqlp5wkyWcqAuTgc9xwentVJvsix7Tw+3BGS2e+fbqOKf9j0v52dMeJqkldU/zO2Pxi1wdbbTB/2zf/4uj/hcet/8++l/98P/APF158Y0MmNjFc8diee3WophCbh/KJ2knaB2p/2NT/mZuuIKj+wj0b/hcet/8++l/wDfD/8AxdH/AAuPW/8An30v/vh//i685iiVrlElkEanq+M4FWJLONpUjtrlZ3YgHamAvuTk0/7GpfzsT4hmn8C/E77/AIXHrf8Az76X/wB8P/8AF0f8Lj1v/n30v/vh/wD4uuBvLFYkYo+4qcMrjBrOTBbDEin/AGLT/nZUeIJyV1Bfien/APC49b/599L/AO+H/wDi6cPjBrxxi10056Yjk5/8frzcBNm0DJ9c1LGsscalUP1x0zTWSU39tkviCqvsL7z0X/hbfiH/AJ8dP/78yf8AxdIfi74gHWy07/v1J/8AF15+WnC7nzgcHJpZZWVVAfzBjg4xVrIqX87I/wBYq38i+9nf/wDC3fEH/Pnp3/fqT/4uj/hbviD/AJ8tO/79Sf8AxdcEs6iMFvxp6TLITs7Vosgov/l4yXxHXX/Ltfid1/wt3xB/z5ad/wB+pP8A4ul/4W54g/58tO/79Sf/ABdcODTs1X+r1L+dk/6y1v8An2vvZ23/AAtzxD/z5af/AN+pP/i6P+Ft+If+fLT/APv1J/8AF1xWacDS/wBX6X87F/rNW/59r72dn/wtvxD/AM+On/8AfmT/AOLo/wCFt+If+fHT/wDvzJ/8XXHA0opf6v0v52T/AKz1v+fa+9nYf8Lb8Rf8+On/APfmT/4ul/4W14i/58dP/wC/Mn/xdceKXNL+wKX87F/rRW/59r72df8A8La8Rf8APhp//fmT/wCLpP8AhbfiL/nx0/8A78yf/F1yWc000f2BS/nY/wDWit/z7X3s6/8A4W34h/58dP8A+/Mn/wAXR/wtzxD/AM+Wn/8AfqT/AOLrjjTCaf8Aq/S/nY/9Zq3/AD7X3s7T/hbniD/ny07/AL9Sf/F0n/C3fEH/AD5ad/36k/8Ai64omkqv9XqX87K/1lrf8+197O2/4W74g/589O/79Sf/ABdH/C3df/589O/79Sf/ABdcQTzTSaP9XaX87H/rLW/59r72dz/wt3X/APnz07/v1J/8XR/wt3X/APnz07/v1J/8XXC0tP8A1dpfzsP9ZK3/AD7X3s7n/hb2v/8APnp3/fqT/wCLo/4W7r//AD56d/36k/8Ai64Wlo/1dpfzsP8AWSt/z7X3s7n/AIW74g/589O/79Sf/F0f8Ld8Qf8APnp3/fqT/wCLrh6KP9XaX87D/WWt/wA+197O4/4W74g/589O/wC/Un/xdH/C3tf/AOfPTv8Av1J/8XXDU09Kxr5DTpUZ1FN6Jv7kb4fiCrWrQpuC1aX3s9U8JfEfV9e8U2mm3ltZJDPv3NFG4YYRmGMsR1HpXS+O/Ed34X0KG9sI4JJJLlYiJ1JGCrHsRz8oryz4b/8AJQdN/wC2v/op6734vf8AIo2v/X8n/ouSvkz685V/jFrka5kttMUdMtG4/wDZ6Z/wufWP+eWk/wDfL/8AxdecawcWaf8AXQfyNZG7Nd9DCxqw5mzzMTjJUanIkev/APC5da27vI0vHrsf/wCLo/4XNrP/ADx0r/vl/wD4uvIg5xt3HGaC3btW/wBQj/Mc/wDaU/5Ueuf8Lm1j/njpX/fL/wDxdKPjLrR6QaWf+AP/APF15IGBAHT3AqWOYxqyAAqT1K80/qEP5hf2nP8AlR6uPjHrbdLfSz9Ef/4upo/ix4kl2+Vp9g+84XbBIdx9vnrgPD2nRX5muJiDFDgMg45P8uAfyrsdMjgjGCnkxnkgsFAH4ZwemB16+hpvL6cVeUjkqZ3Ui+WMEzWHxJ8YEsBolvlfvf6JNx/49UR+KXigMqnS7PcwJUfZ5cnHX+OsTVPFdrqljcWFnG1u6ABZgQoc9zjqOQR15B/Itr5To9jdX135slw5t3WQbgoz8zE55IAz+OelcywsbXbt+Zs80rJK8Ff5m2/xR8UxqWfSrNVB2km3lAz6ffqFvi9r6OFez05WOMAxSA89P464jV9VmUCG3ZoraTLeXHIWDHJBPrz6HtWTcEyyPJGZNuQuZB044BP4fpWkcCnuy/7Vn1ij01Pi9r8pxHZ6c59FikP/ALPQfi74gVQWs9OAPQmKTn/x+vObCWNJFZZGSQcN6Efh3rY+222ohUuRO7KP3ccRJEjntk9PwHB9eMX9Rh3ZDzWqteRWOwj+LXiKXHlWFg+TgbYZDk/990j/ABc8QxybHstPV/7pikB6Z/v1yUF0bqSC2eyFusLnCqTGwBAwWyMHpycc85Her15p1zcfvZvLKFdsbxzlgDuHHAwR1x+PfFH1GC3l+Ri86qJ25F+J0Q+KviYwiUabYmMnAfyJMH8d9Sf8LN8WYJ/si1+UZP8Ao0vA/wC+6w9Jv4LawfTbwmSN33BYwQY/dWBO7kDgj39q1L/UId8V6u64gAASXcVwOdxGOv4gZp/UIc1k2YSz6vH/AJdokf4reJYwpk06xQMMgtBIM/8Aj9R/8Lc8Qf8APlp3/fqT/wCLqK8ijntZ/MgYwffWbd8x/wBrgcD8/wAq5xrK3ErZvFHodnBOOnX/AOtW0cspveTMqfEdWS/hr7zrH+K3iWNiJNOsVIGSGgkHH/fdMHxc8QHpZacf+2Un/wAXXJXk0qbrbzRIq8FlYkMMDHB6YGO2e3biumVIwOpGBitI5TTf2mX/AKwVrX5F+J3a/E/xW67k0m0ZfUW0p/8AZ6d/ws3xb/0B7X/wFl/+LrnJdSnspoxFOs8TIMFRjHtjsf8APtSr4glaRAI1znB3HIz+Q/rUf2SntIy/1jr2uqa+9m+/xR8UxsFfSrNWPQG3lB/9DpD8U/FA3Z0yz+X73+jy8f8Aj9ZX9sJMdl1ECAwOd7DbyeRj61dCpdW/+gzM4UEsrjn1IJ/LA/Wj+y4LeTMZcT4iO9Nfeyb/AIWx4k/6B9h/34k/+LpR8VfExXcNNsSucZ8iT/4usN7SSF1eVSsbHhtuQfxq6i/aFyJYxGo6DqvatXlFK11NkS4qrL/l2vvZe/4Wv4kxn+zrHH/XCT/4ulX4reJXYKmnWLMeABBIT/6HVOKxAydwYNnqpU/rUSW9utwVWRVZs42ktj/Pp70v7Ko9JshcWVntSX4ml/wtTxOSB/Zlllug+zy8/wDj9Nb4r+JEYq+nWKkHBBgkGP8Ax+q32hQ7B90ZUbUkKZFVLu2E0TEPxnKs64z7Z/Efl70o5TTb1kxriuvezpr72af/AAtnxH/z4WH/AH5k/wDi6cPit4lI406xP/bCT/4uuehhX54XQCToCzdKRwbdjGz5xwRn7p5/wrX+xqWymy/9aq17ezX3s6L/AIWt4l/6B1j/AN+JP/i6P+Fq+Jv+gbZf9+Jf/i65vdIrld6rjncRVlJ9vPmo7Yx19KHksP52J8VYjpTX3s2v+FseI/8AoH2H/fmT/wCLpR8VvErfd06xP0gk/wDi6wn8toA7Kq7s4Yg9fWqYbAIU4+jcGqWSU39pguKq7X8Nfezp/wDhbPiP/nwsP+/Mn/xdH/C2fEf/AD4WH/fmT/4uuZZl6uhX6Gk+QR72YEf3O9V/YdP+dj/1prf8+197Om/4W14i/wCfHT/+/Mn/AMXR/wALc8Q/8+Wn/wDfqT/4uuRkdD0J/GoSatZBS/nZouJ6z/5dr72dp/wtvxD/AM+Wn/8AfqT/AOLo/wCFt+If+fHT/wDvzJ/8XXFFqTfVf6v0v52V/rLW/wCfa+9nbf8AC2/EJ/5cdP8A+/Mn/wAXR/wtzxD/AM+Wn/8AfqT/AOLri92elGcUf6v0v52L/Wat/wA+197O0/4W34h/58dP/wC/Mn/xdH/C2/EX/Pjp/wD35k/+LrjAc09c7c449aHw/S/nYv8AWat/z7X3s7H/AIW14i/58dP/AO/Mn/xdH/C2vEX/AD4af/35k/8Ai648MKdnNT/YFP8AnZP+tFb/AJ9r72dd/wALa8Rf8+Gn/wDfmT/4uj/hbPiP/nwsP+/Mn/xdcnnFGaP7Bp/zsX+tFb/n2vvZ1n/C2fEf/PhYf9+ZP/i6P+Fs+I/+fCw/78yf/F1yoNLupf2DT/nYv9aa/wDz7X3s6n/hbPiP/nwsP+/Mn/xdH/C2fEf/AD4WH/fmT/4uuVzSdaP7Bp/zsf8ArTW/59r72dX/AMLZ8R/8+Fh/35k/+Lo/4Wz4j/58LD/vzJ/8XXK5paP7Bp/zsX+tNb/n2vvZ1P8AwtrxF/z4af8A9+ZP/i6T/hbXiL/nx0//AL8yf/F1ypplP+wKX87K/wBaK3/Ptfezrv8AhbXiL/nx0/8A78yf/F0n/C2/EX/Pjp//AH5k/wDi65E0maf9gUv52P8A1orf8+197Ov/AOFt+Iv+fHT/APvzJ/8AF0f8Lb8Q/wDPjp//AH5k/wDi65CjNH9gUv52H+s9b/n2vvZ1/wDwtvxF/wA+On/9+ZP/AIuj/hbfiL/nx0//AL8yf/F1yGaKP7ApfzsP9aK3/PtfezsD8WvEQxmw08Z6fuZP/i6T/hbfiH/nx0//AL8yf/F1x+aDR/q/S/nYf6z1v+fa+9nYf8Lb8Q/8+On/APfmT/4uj/hbfiH/AJ8dP/78yf8AxdccTSGn/q/S/nY/9Z63/Ptfezsv+FueIf8Any0//v1J/wDF0f8AC2/EP/Pjp/8A35k/+LrjKWj/AFfpfzsP9Zq3/Ptfezsv+Ft+If8Any0//v1J/wDF0f8AC3PEP/Plp/8A36k/+LrjaKP9X6X87D/Wet/z7X3s7L/hbfiH/nx0/wD78yf/ABdH/C3PEP8Az5af/wB+pP8A4uuNpM0f6v0v52H+s1b/AJ9r72dkfi7r462enD/tlJ/8XXfeBPEd34o0Ka9v44I5I7logIFIGAqnuTz8xrwib74+lew/CH/kUbr/AK/n/wDRcdfM4qiqFeVJO9j67B13iMPCq1a6O8ooormOs+Wf2m7jyfidp425/wCJPEev/TaavJLe5ExK7dpAz1r1X9qH/kqGnf8AYHi/9HTV5FY/68/7v9RWnKuW5F3zWPunwD/yTfw1/wBgm1/9ErXQVz/gE5+G3hkjkf2Ta/8Aola6Csyyhd/8fMH/AF1X+dX6oXf/AB8wf9dV/nV+gApAQRkEH6VR1e0lvLMJDhgDl4mOBIMEY9DjOcHg4wfWua8B+HNZ0WzC6zcyzTCV2aaYqHkU/dTCswIHXJOR0AxQB02r6omkWS3ElvNcl5UiSKALuZnYKB8xA6nuazIvGNrKyJ9gvklxK08bogNuI2CuXO/HGQflLZHTNX9e0WPXtPSzmlkiRZ45i0bMrHYwbAZSCpOOoPFLZ6BplgE+zW3KRvHmSRpCyu259xYksSRkk5PvQBhWPxF0zVYsaXa3N5ctIEjtoJbd3cFS27Il2KMKeGYMMdORTdI8XS/2XBJqIlubiS1tmWCKFFkkllLgKG3hM/L6KBjqc8a3/CJaSYViK3hVJBJHnULjMRAIwh35QYJG1cDHGKefCuj/AGQ262jJH5ccY2TOrIIySm1g2VILHkEHnrQBk3Pi6+i1W8tZNLns44LWCbzJkikKNI5XDBZuemBjoQTyMZuSeM7GKaYSW12tvH5wjutqeXO8QJkRfm3ZG1vvAA7TgmrLeFtKkkV5Y7iR1iWEs95MxdVbcu/L/OQeQWyRSnwro5uri4Nq5a4WRXU3EhQbxhyqbtqFu5UAnn1oAz7jx1aWlnPPdadfQGGWOMxzNAmQ65V97SBFU4x8zA54xk4rpYpBLEkgBAdQwB6jNZl54a0y+DiZLhPMChzb3k0JYKpUAlGGRgnjvWlFEkEKRQqEjjUKqjoAOAKAOauvEniKC7lig8FXlxEjlUmW9twJADwwBbIz71v6dc3F3p8U97ZPYzuMvbu6uY+ehK5B/CrNFAEdx5v2WX7P/rdh2Z/vY4/Wsy3liivrdYpLsGUESfaDJtJxwPm4DZ7Dtn2rXpCAwwwB780ARXjSJYztA8aSiNijSnCKccEn0rnj4hudOjW1kgvL683MZI5YwXQAKesCMpzuBHAHPJBrpnRZEZJFDKwwVYZBHpVL+xNK+yrbf2ZZ+Qr+YsX2ddob+8BjGfegDMbxRIb+W1hsAzhlWFmkZVfLhDltmOCf4S3vg8U6bWNRktlltrSFQbpYU/0jO8hyrBvk+UZB5GTjsDxWrHplhFM80VjbJLI293WFQzNnOSccnIzSjTbFbp7pbK3FxIQXmES72I6ZOMmgA0+7N7YR3DRiNmyGQNuAIJBwcDIyPQVZpscaRIEiRUUdFUYFOoAKKKKACiiigAooooAKKKKACiiigAooooAKR/uN9KWkf7jfSgD54uruaHWL/wAvq07gcA/xHHX8cVPFqSSOVk3YbPIbJAIHrx39RWffSW/9t6gbYOHiuJWkaQ8Ahz0x1HXtTLW/s4YWLXCsSysY1BwcZOM4/wA8V9rTkuReh+Q4qipVZvl1uzW1EAac0qhnX5fKKvgp2Df565z6Vn6fJcQXAjmV3eUEIgIUjp1B6Z6c+uaiTUI7++EqWfnFQN259rMcYHfnGKvrqNg0s0F7bLArAtuiYsXPGBuB6fT298vmstjnUHCPI1f+vUqcyqICkyx53ryJADjjgADvk81nzzwpMYXWOS33E5RNpb3zjP4fWtTU761gVJrdtxD4CLuUsBxknGD06/8A6q5+S4bUbsGaRY+MKXJwAOg/z+lWpdTooQctWrI1JHt7WNltmUqz4+Yfw46A5z/kVFeXFvxFBIrx9lRQR09Tk5x+tVp7eGFfJW6BfkgY+Vh2+Yd/zHTnHNVUQBllUlGU5wOx7VomaxpRfvXFKXEkRliGY8kZZhn8jz3qGO8eBWCcFsZ/z+NaDuJpNy3EMSqPnUtj9O/0qlNDAJGLzLx128huOCMfqKZvBqWkkRG58xizFjk8r6/jUPOTjNPQbJGYKSgHBIxmpIXQsXZSqKckqOnpTTN9FsRB3I61KLyQLtJzznmpL68gnt40RMOhIyvC469PUnNUA1WmKK5ldovPdvKMNgknOcVbtDFImJ/4eh9qzIEMsoVSAT6nFW2dIpCFk3AAcqODWidjKcFblRblESpgZIHTHf8AWmpNFH/q8nPUntVdZ4x/e/EZpmct8ucVrGXYzUNLM1FbIz0zTgaZbL5sSqPvZxknindDg9q64tM5GtbDxSg0wGnA0EtDwacKZThUkNDs06mA0tSTYcaaaWmtQCGmmmnGmE1aLQlJRmkJqiwJpppSabVFC5opKKAFpQabRQA6ikozQAZpD0oNIa5Mb/utX/C/yZ2YD/e6X+KP5o6f4b/8lB03/tr/AOinrvfi9/yKNr/1/J/6Lkrgvhv/AMlB03/tr/6Keu9+L3/Io2v/AF/J/wCi5K/MT9TPCtZOLNP+ug/kaxxjitfWv+PNP+ug/kaxMV7WD/hHz+P/AIxOoLdOtWl064kt3njAeNMbiCMj8KgjlCRAqg3Hqc5/StyznlfRzZ28ZTzWzLK7DB9AM12ybUdDy5SaavsYO3FP+6uCOvrVm8LWsjW6SB1U9du0n6jtVRXLHtSvfYtX6m3oUF95VxPaXBhiUfvNuGzjkZXP157c/ilxearc4sLidj5ZxsZgmfx4z+NNtZbYaaEYxRTrJliU+aRe3zHIH5D/AIFTriRIbHMfkStJ9/YRkfgSe/bHbjjFTJrmvYzine9tTS0Twnd38jC832sUkHmxyqokyAR/CDn/APX9Kv3Glaa2k5k1G3SeMFRcKWG/rgMOTnGOeo6Y7VyMmrzmHy1bZGQAVTocZA/maotPIVI3vt9N1RL2kpdkUqemstSeV1DYBLEE5OePwpXuAYQik7mOWY85P161TJNGcAVoXZbluCQq7EjdgZI9qvpqbuyLMu5BjGDhlx0wfas2CVgy78lQe/augmXRzbvNBct9oZMBBAFAbscbj9D/AF6luSWrM3du1jX06y0mRJpbrVwiAb9rSgvIOQOxAOeoIJHHTNUrm1tgskNlqouGDbyVUqq/XPboMj246VHpp0co39vOYp2JKFTuRlA4ztywOfpxUFzd2018ItMjVowu35iPpnIVSfy78g85hX5rX/DQjlvsS2WI4hLfzMIySqqq7mbgdD0AOTznsa0oPEBgkgfbvMMZSKIj5R78HnjqPxrBurOay2yvh4icKQQecdMA+/8Anir+l2sF9PCJt0Ks4BA6kH+6Txn25PfBwa6E1LVs46sEk2zXGtTM3miDBlG5DE5UKc4PGCD0P4Hkmq98AzfakjaESfdTG3BHUrjjHf2/nvafoFslx/o91HMg58qZMkHseOv1FT6npp1bdBBuMtoNux0CM3Xp044B6fnWkakItJHnSspcyRyJLvIk0rLNuODuY5+h/wA9qvmGwjsF3SNLOSMkDG0EcfXn/PaobrTbm0umhkhbPpnp3zVhYJooNlxCqq2V+bnkd+D1GT1rfpoyJST1Rn7TNJHHCpLt8u0dz7VcGlXEDqWCsScEKQ2w8dcdOtaFnFbPI52wS5TlgMBB1JPv+tZ1xZTQNI9q3moqEs8bZCA9QTxzjr7fjV82tkZuV3bYZMggwSu09B83I68+/WrtneC1YJHISc5+XtWfa/arlvIhO8/fI4/P9au3FnfMqNLaMX/vIucj3xn86p66MzkktGzVmWKZlkaT92qZCZABxztz26d/WoZpUlsvND+XIT8gH8PbGffFQRIzW7LciTzBghG4PQgDHX/9ZqxZWV0r/u4ZfLPUOh4/xqUlHd7HHPl3uMtVlmiY3ErsqtxHvxnjv7c09pYY4zLBbeYqnDkMTtH5e3erbabeE4ePy16hgQO//wBc1C+yOFY9+7OflA/gxjHT8aXNFvQjRszjqUvmAgLgZOMflU1zJPFbK8vAfICKx6Ac5P8AT36ircVlamMTqkjbeV29CfXJ7cc+lVZ7OS6Bm81QpPDOflAz04+v86vmg3oUtXsUllhljEZldT6MMj6f59KjdI1kJBLnHGOf89qnh01gjSSnaqDc3GQR7H1/+v8ASqxby2IeMxt0AI6VupLoy1a+hXkkYAgjAIyM01SxPGc5HFWJRJ5vlXWMoBnP+IqZikkY2wqdoxkHHb2/WtFIu9iBbpgiqOinnPOfw/OojknK56+lPZV2KynIAwMZyec9ce+KcLWRzkEA+lUmkTohFlKrtddy/WlEkSrhkz9acLdlyHZV9NwNRTQbZOHVx3K9qq6J91siYKeUP/1qQx4Hc010IypHIPXNMbeq+w54q7m6VxP0pOlMLZp8Tr91+h7+lUpGjWgv40bu/XNOIAyEcMDUZGKtMkeGyadnnNRcj1p27NVuDRKDShqjBp2cUWIaJN1PDVBmnhqViXEmBozUYanZqbEWHGkzSZpKQWHZpc0wGlosFhTTSfSlJ4phNUhoCaM03NFMqwuaM03NLmmOwtLnim0tIVgzRSGkzTGKTTc0E0hNAxTRmm5pRTHYdmim/SlzQIWikzRu4oCxDN98fSvYfhD/AMijdf8AX8//AKLjrx2U5YfSvYvhD/yKN1/1/P8A+i46/Osy/wB8qep+o5T/ALjS9DvKKKK889M+U/2of+Soad/2B4v/AEdNXkVj/rz/ALv9RXrv7UP/ACVDTv8AsDxf+jpq8isf9ef93+orX7Jn9o+5/h5/yTDwv/2B7T/0SldFXO/Dz/kmHhf/ALA9p/6JSuirI0KF3/x8wf8AXVf51fqhd/8AHzB/11X+dX6ACiiigAorz34o6ne2UmnRWd1Nbo4kZxE5XcRtxnFef/21qv8A0E7z/wACG/xr1KGWyrU1U5rXOKri1Tm42PoKivn3+2tV/wCgnef+BDf40DXNWVgV1O8BByD9ob/Gt/7In/OZ/Xo/yn0FRVXS5nuNIs5pTl5IEdj6kqCatV4rVnY9BO6uFFFFIYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSP9xvpS0j/cb6UAfLur2dy+vX37p/nupdgxy3zHoKhttKubjiOM/KcNuIH+etbuoWKWesalOQ6MxllSRXGN25sjI6cZ4PXgd8VYiuim15LqK5feMsCACwGGySecA8evXDCvsqUkoK5+V4mtP2kuTuylZ2McdxbxtAxRpNskjjaB0JIbJB4zxnqKtR3ml6bcPEbgSOfnMoIddwU45BwDz2Ht2zVGC4jbSrvyfMmupFKlyM/IAc478jrx2rmpvlwecEZq3qYwo+1bU2dVdnTdStSC4S5iGI/LztbnGOmMCsuLR5ZLhAqGZC+AY2XDgdcEkVkW101vMrj5tpzjNbh8WTRQ7YoYmdeI5GHIHfPqegz6Cq5i/YVaS5aWq8xskiWN6HtUZp0B3wYJMS4OSG9h3/GqtvcrHCS/mxO7Y89BlRweCO5Jx3HrUdx4guJbd0dYzJJnzJggDuOmCR2xkYrKW5kVjtdlz1waOY6KdCTj7xbu2dJCsrIxXjch69v855qsHO0kYx0pksnmSMwAXcc4UYA/CmA1V7nXGFkWEkd+ATt6VY8rNv8AMu1Qc78iqaTPG2EbANEk8kpBkbOOgqkyXBt6CuAGIU5qYKGRWZsDGOlV1APcD61OjBGG8bkPXFWmEhWQqAQcg96EbGc1YcwyIwt0ZVJztJyR+NQCPj1rS5mndakoKkDkg96tW8EpUtH+WetQ26rv5VnwDwoz/nvVu0ljF2rbtgPGW6CtIswqN20JP3kDKyHnOeO1Wllh2NvVnkYdd3Cn196juZ4/MP2Xa0YGMHjmqYmIbahOO1bRkcvK5q7LoNOBqMZz83Xvin9+OldadzNocDTwajBpwpMhjwafUYp4NSQxaSikJoENNMNONNNUi0ITTTSmkNUWhDSUUhqigpaSigBaKSlpAFFGaSgBTTaDSd65cd/ulX/C/wAmduB/3ql/ij+aOp+G/wDyUHTf+2v/AKKeu9+L3/Io2v8A1/J/6Lkrgvhv/wAlB03/ALa/+inrvfi9/wAija/9fyf+i5K/MD9RPCtZGbNP+ug/kaxTW3q4Js1x/fH8jWKQSa9nB/wjwMf/ABhoNbMGtNBpJskVGDPubegYcdMZ6Hrz71kYOelKMj3rseqszznGLepPeXZurhpGVF4AwihQPoBUCEBuTxSqjSMAqliewGaVkKMQylSOzDFC00Q9Nh5b5PlLfl1pYZmiY88Ec+1RozAcf/qqQYJ5x9T2oYFq81SS+KrcEMqjareWu7HYbsZIHQZPHbrVFgo6Hip7e+ls5meDAJVk5APBGCKgaQyybpD6ZwAKSstgDGR8vNSSWV1AQJbeRMjK7kPIPp611WjWekpZwXEdx50rZ3wb1RgemOQT+WODnPate78T6HC014+y8lljVEtfJV1hwOVyedp9fpx2Ezqwhpq/QxUqknaK+884YurbWypHYjpT0lOQjuyp34zTr67N7dNOUjj3dEjXCqOwFVg2enXPerW2ptYvRwNOpMTq8hJHl87vrzx+tRAspHJBJwOcVBHKYnVlOGU5B9KduyaZNjUsLhfM23MhaPr5ZJAk9jW9zoV9Fd2MkL+YhxHICy4zyAwOD0GGB5xmuRjLbhj07Ct7RprKK7T7YI7kbTiORiiKRyDkEZ7jB9R16VV7HPUhfV6nRjxtrEESyrDaiFiV+4GzkYwcHI6EjOCeeoAxXi8RXd5In224kbYwMeDt2HPX06Z569O2ais9U0aW889NLt4pI0O+KSQmKbpwFIbBwOnTnOQQMyR2K6nqBubLyrSOX5ooTtAJG35Pl6csOoBI6ZPFKEYJ3sctRWTi1ZHYQXNvqtvk+Wkm4BnJGSCOOwG44+nT6VBc6DMP3ccTSb14GCM9+W6du+P5moYLIaXqq3sMUn2d02v5TK67No6Edexz7H143W1GO7Iks52lRVAASTBznjg9Dwevp+U+1cX7mqPOdFLXY5mO31C381LazQMOAysBgdwT3P4+lQ3FpqSgyT2rK5IyBIAGwe4Xn8q6e1kW6uGWVkjDBTHC5xn6evY88jjPoMi42M0iW92uWJ/dxPvBHUbec8dfw+ldEKzb1RjKLWplQQtYzLJ5EQMeWLeb1BBPfn6cdquQ+I4kUb1bOPm4yCaVlWAR2hhEiOcANjr0GVPU5+v51HdsnliSSOAjaSMoGJPXg/XP1rqajLc5ee7s0ObxORgQxZPbJx+n9P51esvFwyqXcQYZ+8h7Vx8oPnHepjb723BGAeR19j+VCvjOeh6e9T7GnJanQ6cVqj0tdbtLyNVgZTyMhuCo/wA+lYGo/v8AUFlZVKKMF4ieOvGT9ar6bEg0p5FO0SEgynHyADp14/wIqRtYuNJhaB4I5o8bdzJj8D68evNYxgqbbhqcsrzlZkFy0UEIZ51abzBtbbkBRn29algu9KMJ+03bseMgrgE+vT2+tVV1yzOWubOIyMMfJEuAPTB/nUcNzBNvmtrTaUK7MRbtvPU4P1/yK1s5LW6+4r2bW6NN47dkCrYOhZiVdcIWPXOCcj8R6cVRudKC7Ql0k7ZIwThj9OeemMVAt1JcbWvZUVASFSU/f7E9QR14/HpVm4mViVt0EvUCaJMgfKCFJ6Drg/TnHWhXg9w5Gtim9hIQ26MKE7lsVUe1CNgFmz0G0/4V0VrqYhh3vCslwR+8Dy9BnGTu4HJAI4/EVWuoB9o2xRZXJIZmAI7ZxxkHPH6da1jXd7NC5ZLcydpidGkTajY69PWrEVxE8uGOW7YHHFWJbF4VaMlmHXbng+/p+PsaYummUARoHCj5hEwb6A4PH481t7SPVmUuV7jjKj+YseGLcLkj5Pf+VVhMtmpBHz56Ac49+1SRWwkPmwyBQi5Mcuee2M/561TuZlKlZYPLcHcCFx/Pmri03ZExhd2CQxXEbGL5HPWIE81nliuRnHUYqXzQ0wYg4AAHQHio5J3WYllDnrll5z9e9bJ20OqEeXQr5w+SMj0pVcnkD8RUjPJc/dUn2AqEb484BFVc33J1m+Qjbz600Pn71NWVj1H4ipU8naGZmUgjcMdfpzVpkWsNxnOMD2zSjk0MU3/u8kEfxDFN5FaJiJAakG0jrUINKp61ZLRKetA4qPd704N607E2JQ3HWlBzUWacGpWJaJM+lJmm5zRSFYfmimA07NKwrBmmk0pNNzTGgzRmm0ZqrFWFzRmm5ozQOw/NFNzRmgVhxOetITn2pKCeKAAmkJpO1JTKHUZptKKAHZoptGaBDs0mc0hooGRyfe/CvYvhD/yKN1/1/P8A+i468ck+9+Fex/CH/kUbr/r+f/0XHX5zmf8AvlT1P1DKv9xp+h3lFFFeeekfLf7TNus3xN08sWH/ABKIhx/12mrySGBIclcknua9g/aU/wCSl2H/AGCIv/R01eRjpTu9hWR9s/Dz/kmHhf8A7A9p/wCiUroq534ef8kw8L/9ge0/9EpXRUhlC7/4+YP+uq/zq/VC7/4+YP8Arqv86v0AFVdPtntbdkkKjc5YRp92MH+Fc84/yABxVqigDzP4tf8AH1pn+5J/NaxtF8L2eo2WnyS/a3+1u4lngYeXbbSMKwKnlu2SOo610HxTsbu5m02S2tpZkVZAxjQttOV646V5/wD2XqH/AD43P/flv8K+nwnvYWKUrb/qeNX0rybVztU8LadHpdxElneS3Eot3aEsjT2wZiD/AAfiemR6da5fxLpEWj6jHDbiVY5IhIombLckjkFFI4A4I/OqX9l6h/z43P8A35b/AAoGlagTgWF0T/1xb/CumnBwldzv/XqYzkpKyjY940X/AJAGn/8AXrH/AOgirtVNKieDRrKKVdrx28asD2IUA1br5GfxM92PwoKKKKkoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApH+430paR/uN9KAPnu5KpqeoyTygQtLMhDuSFOScBcjkjIABwcjuKsx6fZ2lg8YeK3E3yyBmLH6denv8ATvSX+lwmS4W9t4g09zOYXThzwxDEk84IHH86wL64udN+zrcxEZjyrEZSVMDBB9weR1598V9ZTfupJn5LXh7StJRfVk2q6GtvZi5sZXRuQUzxgg/dPXoDwc1yrs+ArMcDoCela02tGaVnkyVYHjrz/n+VZl1tknLxA4Yk4Pat7tnXh41Iq1QhHXinBOefyqWJZIlLhMgHB3LkZIOM/wCe1SqGt43E0JRposo0qcFT0I9c+v8AhVHS32KDYD5PIpi5L8DJJ4FSvGd2xhtYHGG4xSxEQsW6sB8pBxtPrSNL6DVAQ8tyOQQabip2CyxlyG8z1HQ1AAasSdxUieV8IMnBP4AZNN+tS8kDp6dKZsJqh3EyNuKljJfC1FsO7FWkyigY5XvjrTRMtiyBsK+X8gGe+c0NHsAOcZ6YpkW6Rh8oPqM4JpysWONpyOMVqmczvcDG6x7wWVScZqMu+ACTgdBVuOR16Kg9Nwz/AD4qCZVCKVUhj+tWmKMtbMYrkmpUJHOarjg1YVhtHGTmtIsci/bTKVPmcke/Wp1cNnPHpVFWVsbVwcc1OhziumEjinFXuWRzThTEb14FOz6Vte5g0OBp9Rg5p4oZDHdqQ0tIaQhhpppTTTVItCU00pNJVljaDRQaYwozSUUDFopKKAFoozSE0AIaBSGgda5cd/ulX/C/yZ2YH/eqX+KP5o6r4b/8lB03/tr/AOinrvfi9/yKNr/1/J/6Lkrgvhv/AMlB03/tr/6Keu9+L3/Io2v/AF/J/wCi5K/Lz9RPILXRW12RrZJhCY1MpYrnOOMfrU83gExmMfbDuLAORDlV+h3fN9AM/SnaPNLb3jywLuKRksu7aSvGcH9fwrXGuXk8wa3t5rYBCCDGTH+LYzyMDHf2617GDlNU/dVz5TNG1iG+aysjm9Y8HapYwLJHsvIMnBgGWwOM7cZx+dcwR6c56V6jpdhqup2t29vBDaiQ7SWnkCMxxlsAN2GM57VhTeCtXvneS5geKfHJlCjzG5JywPJ9wDnNdHNKMmp6WOKlVi1vfWxxY3KwKkhh0I7Uc9+a63S/BNzeyH7bOllFjhivmFvoAfbvWpJ8N0Fqzx6qruRlD5BCMfTOc+nY49K1k4x3kgeIgnb9Dz8Ng/yxS4UgjOTXQ3vg6+hJFrNa3W04YLMEZOcZIbHGf61nX2g3+mp5lzGojyAJEcEE/nn9KnS17msakW7JmY0TKu48cd6I4fNUleSvb+tObd/Eaj5U5Q4I9KDQOVbBNMLENQSScnk+9IaCgcjsD+dM4PTrTuT16U0+1MYpGMEHNOX1zSemacoUMPTvincllyyt/tVykW9Y1ZgCzHGM/qfwrW1Tw/cadcKIf9IikIWORBwzHsPXn0/HB4GTaw/a7tY1kig9ZJG2quB3P4V3a+JND02OGDynupo5cyXJjVWGPutH1HBORnqBzwaUp8tjnlzX905Geyu9MucXEUic8F0xu9xnqKuteXLOL61LQLDsTfDiP5sdcA9TjJx0yM9Rl13c33ibU5HhiVyzbpJfLCrGD03tgBR79/c1et9RtodPuLWWVXMbYjRkDpuC7Q4PB7tjkjJ+73Jze7eS1IcXewzSreSWJrpbpYpI9xjDnaGA5K7icdCfXv0rovD0trqVxPJNAIZMKqm23Iq5z12HqSB14rk7O5kuZfKV5Elmf78Q254x90YznJHbqc5rq9H1Wy8PbIGini3k/aJTEG3DnAOeRjPIHp6k1buoto4qsbu3UuzWZlvluXdiXyALr92vY7lYKRjJxjB+vHNSysrEXkkt1PLbuJVEcYfJRv7wJB3DI4/XOM10UOs2mpJELPbdqjqGMOUKdi2w84+oI9zUWo+HIL2MKjuJVy3zAFzwBxg4GT17fTjERqtLXQ5pQs7MqS3sdza7bW9ZZQxXLqh3ce/BHP046965a80uS3ZFWRmY8KGjKkn2656Vr/2DLol1FJqkiSFmzFHbtuLEdyCAevp+fSroa2naMXEJj83O1ZBlVxgk5J4OOevXOa2hUUdnchU+V+6c3PNJcWsMX2dWkhHMyZLEeh57f56mordsKUJX5iOSua6u90/TrYuC1tAyPhHkYhWIA6KrZAOR0z+B5pLXTNOMXmzqANvmyIZDJs4P8RHsT7844rSNeFtCHCSVrGInl20iOLhwjSZeMAqDjlc+vY9O/FTXwmuE2razswH3whCHPfpWuXghvBHYQLC4i3NmIR5XOAc59ccjj68VFPPqSIXHlvBuAMhGWXkEcEAE9MY5PHfmrVS6ukczjLmMmPSB5YElpdNI20cRsNp789CPf6e+JdNur6Mrb2FkG3HCuY8buCee3QetWbaWSWYxzyPIuECSW33X4+YZyTntn659KrvPNHelw0rmQB4/JcEbcH5hnJTgYHGAMn+HBOZt2Zfs+ZWZa1GO2neC1vrmKE4L7IssEwDgFi2M88kD0rNvZILC3iFnteFtwG4ZOTjnt9Oc9KinuLmxZN3nHYo3kJsAY847jpxk46dMg06G3t7xVjjhVS5BQK/DE+n0I6ZHB9am7W+xcaagrofZO2ppLJMSrwp+78tRu3E+ncdfxNaWnXUdvcwC6nf92AWRYyhT1Y+3Jz61WWEC5W2/0a3cFipeRC2QCA3HQYHA655zxTnt/Nu5xZSRSySr8ipMWJHdSTg9znnoO1NtNWMZXbLF94nnmuDDBIsMJZVUyD5ienPXjr0H86x72XyJ38kqjuoDoqkAnuR6cj9fqKoXAnsZkEwJDAP97cH/AJjg5H4c1a0tbjULpBBCzeV8ypgbQpIGDnt6deSTzk1pCEYLTYtxSXMWTbTCFSolaMDnZzj3yOoA79sVXliCy7pDmIn5lXG7n0z+laNtq8smV8qJFZtixEkY4A/Aevpn8obi6smjmiBRdvzBkTGOOO+MDr+lbKo7nMlO9rDm0e1eHfFMYyykruYZ646f4etYM0LwXTRNlWB2ncf6+lW47yPy3jt3JBU/M+cE49MYz/LimTzGeZEiQyKSFIzgFuwH4d+/89oyl1LhGcXqVUKxEHeN2e3aluTzuGGwcFh0P6VHCBJvOBx2z+FSRhQhCOxyRiPHX3zWvMbWs7keCUDc/ShGAOdyj0DKD/Ola5/fbYyQOgP4YpHkjRhhTHxxg9apSHZlqGbyJDIhy2OCDjFRuVeQkfKvXAHvUIkQnrn05pBLI5Ij+Y+y1akiOTW5PFE8hwqsfwpzxtHwwIb3GKksbpgzCT5ccEVanuI54Spxx0Poa0jJmEpSU7WM00oakYc8UfhWyZqPBpQTTM4pwNWTYdmnZpmaXNKxNh+aXNRg07NTYVhc00mgmmk00NIXNJmkzRTHYWkzQTRmmMU0optGaBC5ozSZooAM0ZpD60lModmiiIosgMql07gHGfxpCQSccDtSCwuaWm0GgQtJRmkzTGNf734V7H8If+RRuv8Ar+f/ANFx1423WvZPhD/yKN1/1/P/AOi46/N8z/3yp6n6dlX+5U/Q7yiiivPPSPmD9pT/AJKXYf8AYIi/9HTV5GOleuftKf8AJS7D/sERf+jpq8jHSgD7Z+HoK/DLwuD1Gj2gP/fla6Kuf8A/8k38Nf8AYJtf/RK10FAFC7/4+YP+uq/zq/VC7H+kQH/pqv8AOr9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUj/cb6UtI/EbZ9DQB4FrWr2wvLprWG4knglKszuBEcFuMZ5OTjH5VxF1d3GozlruZndiSMnIyevHvWn4mjmtNeuio2R3DyEMjHEilz/wDqI9qdDMJtQhhso44GaNF+WNSdwXOQSM5yP/r19TS92CPzKUYwqSkut9SkNGnhjzK8KNnJQnJQYzz6dumT+PFTafDMjiWEJgDhz8gI2ld2fvYzwcDBOc9MVpQzwW8DrNBBdXSxmQNKoJf0yckcHcxPOQMe9OuDax6WMRRSzQIQ6ZCk7hjdyM9e56AdOhq+czdST0ZTtrmzjR47m6EEWPmUBSzYHA7HqPTqATUcuq6ddKomeSKSND5UkaAMG28D2GegGME9hzWbfy/bbdFjkhWMMSEXG48ZyxHXAOAT06CqttamK5WUMSqEOCOvHI+nai+ptGjFq7vc6HSpPs6bLmYGMlUQxkbV9eQRng++PQ9o7uCWezka94dASGkhIbgD5RjJxzjnpwfaqP2m8mspGwQkkq/vYm24KjHzKD3yDuI5I69avtpF+YE8+Ty9w2qkjENwOmD9fwGfQ40i+5jOKjLmvqZ1s1vJCzmJi6ddrYUjI/XnH1I4pUlllu02KY0xuwxyOBnOcUvkMzRJyPNHBwSXOf15GKtPoeopCuy3kkSRyqqFJIYe30/Dg+lWpFSlC+r3J4b+yCuWgkugg+RCoxuyBznt7YPX8aybiKK4m3Wo2CRj8jHAX2+lbyaZd6axF5ZgSyIWZcoQAT1Cg+x4AHQ1FIbQI3kKiOw4bOdvPvx07j1rWJzRnGErw1+ZgJYzMHZUOI/vseAv40jI0bDPzD1rWaO5e1kC+Y0SAscA7Rz1P44qkrtEDFNH15JPGAehPb0p3SOuNSUyEpuUuuAOlOSWdSQHZSRtPPapMLJGW2BAfukdKikSSFsuDg9M1Seg730ZZhQ7Csr7R2IUEk/WnPJGNo2kOPvHOQ1JaztwgiD7uMYyas3UQij81FVd2MoeCOx7eorSL0OeTtKzM+SLafY8inRRErntU0ciysEcYB4yBkirEsawuoVXGRyMjn8a0iypTa0IIRhquKRnITBqFYTgPGpKk8A8mpJVddoZSp6c8VvGRhKzZKu5uFGT6VMj44PB6ECqsayAjaSDU65X74yTWylcwkkSkDt1pBSg96dgevNXcxEpDUgXK9KYy0XBMiNNp7CmkVaNENxTSacaYapFoQ0lKaaaooM0ZpKKYxc0UlGaAFoNJmkoAWkH3qKFPzCuXHf7pV/wv8mduB/3ql/iX5o6v4b/APJQdN/7a/8Aop6734vf8ija/wDX8n/ouSuC+G//ACUHTf8Atr/6Keu9+L3/ACKNr/1/J/6Lkr8uP08838I3clprZMNubhpIjHtBAxyDnkj0rvrMa7cO5uNNtrSMfcLy7z/471/P8K8tsIXlnLR6h9g8obzLtLE8gAAZGckint4g1F9XZbjUrjyVYgG3Bj8zB4BCnIB9skds16OGs42S1+f6Hy2Z0uau5eS6f5npnid5o9HZLS5iS4PIRpBGXwOVAIO7P1B9D2PnGj6e2rM9zc6hMscHLCLcXHcHOCByPUZI68Vt/wBo+G9YiQ3TNHOvVLi5dgPbJx+p49K1LW78K6Wo8ua3ErEFkjm4/HBweK0pKFLmb+J+X4nDebhGCWiKJ1HSVt0ubie5mJyoeT5UbBwflUgY9c579aqnXtDl/wBcLcEjbsMTdj+IH1//AFVf12Hw5rSww6RJYxb8OTbxCSY9yCgAP5Hjng1Zbwn4WktVdLK8gdAGMgJUtgcnD8Y/AGsvdWs27X7f0i7QjaKXT/hlqYE9p4emIuIpI0Yjeu26Jy2PdsjGegI9xUVlo+mM4udT1BbmxZm/cvLtdn9eGzn25P8AKmeLNDhW1F/p8MKwRHbKY7hdwB4GUBwOc/dz61jaVdRaNdme409Ls7SoSdFcYPQjIOD710Uryi2pf5hJXSOiPw+m1iOGfSbFtOR1JZLqUlBxwR1b65HpXGanot3pepS2NzF+/Rtm1Pm3Httx1zXrFhrt5ceE45tASF3jYAxD5QDwSADxxzwT0zjsKzVfT9UvHfXCtpqyld8JVWZifukFgT+APFXzTjNqS0X3kxqRUL9fyPN5PDeqpGHewnUEEgFfmwP9nrWebdgoZlYBuhPevXLu70m3uYRPDDPAWdJj5e0h1Iwc/iRz+HetHQ7m1lRv3D+TOdqzxoS75PXBAPfnjGM8041r30uU5ySuzw8xkdB0qNlxXuFz4Xt7OYrb6NpX2Jg2ZpYmd149M8c9wcDrmuY1TwJooglntbue1J+dQwEqIpB7AbsEjAJPp172qsGroPbWdmjzPGDzU0YiORJuHHBXpn3rVt/D8929wokhSSB9hVmJ3tzkLgHOMHn6VYm8MNpjPJq8q/Z4wNwtZQzFj0UEjGfX0qnOK6mjaZjGZVDrFkKwwc96jXk81cuLCNTvtJhMmMkHCsmc8Y79OoH5VrWGhtaxrdXKLOrL9xei5BPzHqOAcEdwcZwcVsuZkuSSsUI1vbLdBtkjEqhynI3gA8+4wWH4mtHw9BbvNJLfWzyWgyuUAJTv36np6d+RS3t15/2aK2hcR42RKFHzMW9Fz7AZ5P41ctbS0s7GaO8nMNzJhXingbdH0b7pB4GO/PTFQ5NRb2IaT0Y2HUbdL2e4jT7MHQxtFFhSRnkD06D69PetbTIZLqx/s7ULZtse54gz7d+c7SGxyOGwe+OvBrn5bmKGUR3CytGmVSCQYMY3evXjGM59uldPbavL+4m2KsV1vkmZt2VVRnPHOOh46dR97NF7LQxqR6ItWcMWiWs0drqskSF8mMNEW3Y652kjOMDH69aiF9Z3SBLm8luI3ww33T5J5BTJ7ccdx1wOlZs+tWk6Ok0nyoDsCKSFyMA9umc4PH9aVxdRmCG006UvNLLkFF656DOcg9MDLYyRkVKTk79SOS25sGxtkUSaaZJbkquCC5Ee4EKeAMduo6AngYNVLPXnjnAcEpu3ZzuKDByDuGWwPpyM5xViSG30RBaa40IkWEuoC7zIWzu+YLkHjHJx+Wapk6LG0B+wzI7HMhefdhcY4A6dc857YzmtU1LzI5Svd3tvfyuIkmUktIfmypHzMTt/h6/l+unfXSafplvDbvcQXRRTJ5i9SAVIyQCCuSB9Ox5q3o3hkXUkd19l8xXw8bSy7NwzkNtAOQffg+mOhcaBcXd0Rdz2+yRzmcScwqowSQwUnBxwMjg5wcGrfLpF9DLd3ItH1YyrbQpp/wBpuLdDiUL8ycHgnI+X5u59AO1NuPFNzDMI5kWVCxJDrnHpwR2+pH5VqWa5v7SwhtGj05Mh2lADylcnAc8FclhgHP3846DN1bRrrVL2SGxSFp4cyNFCcBwzEkjPQgFARnv+aTi56mbjF7obqGof6VE0lv8AZ45U3ANjvyCpx2BHPJ4xx0qpouoPBM4I2iZfLJTbnkY53cEfyyT61q6Pbajb6YIrxIponYboLhDuiA6DaRyTjgY9PXiNNf0QSZ/s6KKRXDfuxtIK8e3pnbyPXmt04pctjms3flRk3s585nSGaJzjaxmz0yNxHrgYznHWr8ct1BZi4WG3nlI/egp8wXBxyDzkA98j0rRuRoayRqJkhmkVijNKXWMFf4gWA5B47fhiq2oWF3ptmZZ5w9qrhv3ce4g8gMRnpj36/gaakrWM5NtpWKDeI2utlspijhl2oxdQTHk/MTwFPftjHX0G1AYrS6FlPF95l2so5kIJzuOcfKwGAc8dq4s6XcNcMsG2aME/OrDGPU88fjV+5WbylikuJZpJI8ybQzheWG3Pf1z6k8+o4p7FzpRlojQSw07UmDW4khCttbkc89uuPpjj8RUsOmtBv/s1nVJFZWmlbARcEEeh4wehqloMk9lq0CzSMYpSVXa2VfIIBIz0yQT9Km13VpbK/ubZDtG8sAp65OQfxzn3zmqvL4UZezfNZPQf/Z1gYw73UKsW6QMSoyOBypx9SST785zL202MdzI+OWwfwHPceh/lV3TpPLty01150MqjfEYwcNzjB5OMk42456kVYg8OJcnek7BE7zDAPIHJA478+3p11UnHWTJuoy1ZyipmfABYAgnae317VqWaMJbcyOxWPO/ZjK8cEc8jJx/jSavpclkzq0LKAzEEsATggEYBP1696o2GoeRdbmjBVuMZ6cj1+mPpn61pzXV0bSXPG6Nu6ubi4t2xLD5UmQgkI3OMEcDHHXuc9PXBzlsJAoJkiZD1RJVJ6Zz1xVkSQssKRRm6lZSU3uNqYHPycgdDz07/AEzmtpDEbgOPKBBdUyWQHPbpxj1/rjSMjCMbKy0IZ1KTMjLhgccioGyegrWaa0vPLt7UKrsQCzQqpP0x3z69faqs0X2WVopFyOoB4zkcVsnc1jLo9ykM5461ajEnl5XJx19h708eTI+IkAxnBIOT74GfSq8z7jtAxjgAcd6tFX5tBVk2vkvgY54p6ueDuBLcY9KqOxdueMccVJAPmBY9+O9VFjlFWuXSSrcin7gw6YqN2ZjuYDPsMU0Mc10pnPa5OTke1IDmowc81JjFaJktDs4pd3500DNBqyR2acDTAaUGgVhxpvSlzTTQCAnikzSGjNMYv0opM0ZpjHY4z2ozTc0tKwhc+tJRSGgBSabmgmkpjHcUUmaXNAC5opKM0AGaKKSgBD1r2T4Q/wDIo3X/AF/P/wCi468bNeyfCH/kUbr/AK/n/wDRcdfm+Z/75U9T9Nyv/cqfod5RRRXnHpHzB+0p/wAlLsP+wRF/6OmryMdK9c/aU/5KXYf9giL/ANHTV5GOlAH234B/5Jv4a/7BNr/6JWugrn/AP/JN/DX/AGCbX/0StdBQBXubcToVNeGa74u8SWfiLUba31aZIobqWNFwvChyAOnoK97r5y8Tj/irtY/6/p//AEYaAKVl408c6jq0lrBr8yBXPJjBwM+iqSf85xW8dS8ceU7r4jucI3zFoUAC/wB7OOnsAelcnaafJPLcPGdsiNuGAwbGeTu2kDqOSRii/wBR8QaZCbNtQlEFwu4qXBOPqOcH8M17TpppRjFXffr+B8nLEVXVkvaOyOgPibxpFsaXW7nY/QlEGfplffrVmLxJ4uKBptZukU5AOxf8OfwpNE8Oa3DBERewTWgG6KGZ8MR6YG7aPpmtW/077FYPO37lUwZTtysYzjIZkUZzmoqqlDTT8Dnhiq83aMm/vCx1PxPdIZDrtyYk6upQ7jnpjaMfT9au/bvFRVki1WQOuctIhIH/AHyP8+/NZekBrhTJZXJnG7epQZZY8Y5UAHr34/Kn33iXTNLcx310obHMcavu5Hoc4/HHWsaqina2vaxUK+Ik3yyb+ZLNrHi5GZRq6biOMIePw/LrXKT+KPiPFO8f27UDt7rbgjH1CkfrVif4jWEcJFpFcu7N0ZFjUL9Qzbj+AqlN8R9s2+Cxjc8cyMTjrn8f881UIJP4LmyrYq3xfidDo2r+N5oxLqGtTpk7REY1JPvwK121rxCoCJq8jyAZIIB/PA4rzi8+IV7cECKCAYBxlMjcf4uf5ZI+tZV94v1a/iMc84Csu1tqAFh/n0olScndRSGp176zf3nqEvi/VohiXV5o9xCxyMBsY+gxye3p/hyWvfELxRZakV07xLLNAw3AAIdnJ+XO3n/69cPJfTz482aR8cDcxOPzq0tykv8Ax/RRv8uFKqEIPrwOfTmrjQS1Zftqq+0/vNn/AIWd40Df8h+c+2xP/iasQfEHx7d7/surXc3ljL+XCrbR74WuYSEXF3Fb2673dgqjHc8Y/OvUorHSo7630oJDDBaqjSM5IaQjHOQOpx1yOvet/Y0+W9jmrY6rT0uzIsvGPjtEE9zqN7cJjmBEUSAdc425HAPOCOfpVW8+I/ijzWNvr13Fyf3Uka5HOMdPxz+GOOdJtctbXULuwKJcWscbfvZYwJGbPXI5HBxxjp71npI2s3YEdrDNbsGYo7DKZONqk5bjIxjJAx1qFTg7S5dCfrlZN3k/vZsW3jHxKt9Batr09xJJw2FQKB/eBx6DP6e1a/8AwkevwQZfXriXGCZWgVFznBHXHtk4/DrWZealBY6fFiJYdQQkrI4Usy5yQSxyBgY4/D0rMl1GX7Hb3moNJ5EjK4ljjVWdc4cbiN3fGQcfMD6isHFS+FfgJYmvZNyevmaWrfEHxVZlcv5P2hGMWJlfPTBA/Hv1/Csq4+I/iySWO1t725t59wUiTBck4xnIA7+g4NSXM+gXfhdA19du1sGCRYRnxv8AlZh1UgEDqBgY71zN44hvILqx1Ka6lKBzJLGUaM46ZJOT79PTNbU4Ur2sae3xDV+Z/ez0tvEPifIjm1X7O5gRt8cqupJHUZUlsnOAuehGc81yGpfEjxRbztHb63OTnBbC/mBt4z6ZP4VVvtW1c6QmoG4hi+0OV+Q4eQ5+ZvQHIAOOvHvnl5ppribfPI0j8Almz0pQpRvqkOFaslrN/ez1vwZ4p8R6sl5c6lqU7wwAbR9wHqW+bGCQAOCRU/iXxnqVpBJ/Z+ryecuQhjdCrdzuznGOmR1bI7VyPh3VpNP0WeLYzRbiriNAwVmAUFm44wWwATnacjkEZutam97d4k+cx7kMjzea7Z65YALjJOCAPrjFU6MJT2MFiMRe/O/vZr2Xj7xfdxys2viNIVLMZGiUt7IDgsfb6Z60lr8QPFjO5m1u4CNGxjbEajI7nK8jg8DkkYrlbWyur268qzhkmZ2+VEUsfWte58DeJLe2MraVNt9IisjH/gKkn9K19hTtsinjKidud/edR4e8d+I9RuHthqNxdyxoXGEUbvmUDpjux/Tp1GgnifxFdXDqus7fIiMsjWuJYwBz16+3p15715lYXt1o91K0PyOyNDKjKOVPVSD9PwIq/pdzc3t+LaCZbRJ8mRxhVIAOA2AB3xwO4wCcVEqEL3SRMsRX/nf3s9W1HWdcjUzQ6hcxrI4SFPKOWDAYwCAd2G74xjnvjzub4k+LrLUGjm1aV/KkZXTau0447AHGfcVe1Tw9aaVp15KdYF5PDFmKMkKQdwG4cnI5+nXHIyORkkt4nD+R82BuWUndkDnA7A+/4dDUU6dPtcIYmvvzv7zudJ+IurXelvHd6reNdgFvNtogdiD+IgnGRg57AYPrTo/iFqrXLxrrdxu3ERq4XYeeAWAJz+Y7Z7nkrN57WZ73SGSNyNoWNQ2Nw5A3Z75H6dxTvDdsjasiXEZcbSREyrhzxhTuIA5x/k10KjStsjnnia6u+d/ezqbbxv4nN0yTa0kkcf8ArCHVQeR0OM498H1wRVqTxbqskaRXfiIjJMciIwHzA8jdwR256c9qzIPDn2jIn06SOXkCN7g/uxv3E8DkYIHGTyeDwTooLSzRo/Iis4SSJfLwd+MHO4c4J6D86Tp0uiX3HNUxtd7Tl95h3U1hfRQRWnmXT5JCFWBjJGep6j2z2JyKmeG3HlW0ilHkwsSD5cgkeuRxjv39xVaW5tXeR9NURtGgSWcRkAgHknjjOPbjjvTpp57ryw94kpRiA+AoGM42sCRjCnv64z1rbocjg9BJbuKGJIpHjEshKynI3bM5G1+V/XPA68ViXJa7uVtZLtTEpwJSMAdMsQOp45PJ471oi8jtbW4dpUuftRKpGM4Qc5O3sen4dKq6bbzNc/6FCr8Fh8m85Xk8fjx2OB1qkbQjypsyrnT5LaWRGcN5bEErz0OPwquQ4OCxIHvmujTRplgke7mihZiB5bPtLk4/LAOTnH6UNoyRQncdk5kOEYYBAx+Hc8VZssRHZsNGnto44TNuSXBHmEAKmMnI+YDPTqM+meMdLF4i0xFtGjLSSRnewYjcNuRjnAIIYj1546HPFtBDLZvsaRp2kCRr5ZGSCcqCDgk5HGM5x61fPhjXngjkuIC6xqNsbzqGC44HJ44A469sUrcxjUp0m7zdhyzWV/qkJkcJCECvtyOQMZHX68+/HQVv6XrFykRdRG8MZ5W5faWAxgoB06j6nH0HIXEZ+YW9k9nsO2UMzZyeQDnpkdvrXQabZTXGlrbTyqiq+4Fo/lQerN+GOmOM9zT9TGvThynT3Y/tW0RyU2lSRMzFT3HoeMZzjH4VUSxtI3LTkXEg2yM0iKWOBjGSPp19AM8mrqzrshDzIsgHymEllbHAB2+wB6/0qBL1MJLdA7clVyxKtIG/vAAE/KePfrzWnNZWR5cIyei2J2E93Cy6dKsNwGCKZ8sq4Pzbc5GQAOgxzXHaxC0SlL2KRpZ+RPKnIbgffwSQOp7HIwFrrY53k5V1XzlMatIGZcZyR9c9O3PVh15rVNYi1NvIFvuuTJzGg+XcEA4Hru7D369Soydzqw8XTei/r1M/T7mS0tZoLiJI9wC7ydrcZ46HJ5z26fSrt4bSOZUdPtBZxsWMMFIB7epxkc89+tZq2UsJZbuF12qeCuNvPXI56nHI7/SuggWbULNxDK1wPu+S2QUwAMEY2r6ZJB756g3exvUtzcyMSWzeZ2ns18sfwxj5SvHTH0Ge/uSeta3Seb5lV5EY4JwcHn/Gr1zcp/Zs00JhSVJl3I7LzkDJWPkHnuDjA6VnRanPbXamN9qlcEdAMjB6f54rSNSxpGM3Fl1rQLINu1D1xmkmknjYIX8zd0A5IFaen3nnyRraW5lnCHekm0gqF4wSOucHtgDHToy+neANHcwgygbiy4ZRnnt3rojUTOTmlz8skZouTn96pGR8pqwJA8Q6qe5POaryQPvQMrZI+VTngdf61PHbeSRJLuKbfvKw/qP0rWLZcuW2hZaZTGgEXIHJHoP89aiWcFtu4AHualhtn1B9hdkjjO3eQCO/Ax/+qrEujQEBraXgfeDsD+WB9KtVOhzOVOLtJ6lcKxXKMpB96eokIzwf6U0Wrx/LGxLKcjAJZuOmP/rVNG8qZD2zoGHZTitvaEy8hRuxQQduaRnDLvRgw+vNKZT5YGPxrVO+xnZkDCo2qR2GaiJHatkbIQ0wmnGmmrRohDTTSmmk1RSCikoplC0ZpKKYC0maKQmgBTQv3hSE0J98VyY7/dKv+GX5M7MD/vVL/EvzOt+G/wDyUHTf+2v/AKKeu9+L3/Io2v8A1/J/6Lkrgvhv/wAlB03/ALa/+inrvfi9/wAija/9fyf+i5K/LT9OPLtA8s6gwmjaVPL5RYzIT8y9FCtn8uOvbBv3VrpUMfntYPFcTHasAZtyP3DFcjPPChOpxtzWLaXq2EN3IxiG6HaPMzgncpwAMZ6dCQOv0OZe6vdT2xiGxVkJJMbBQQeoKr8uPoK78O3yWR87mFNyxF35G7fC3OkSC2SOQ7ePtihZ4DwCoIIPb0/rXJ3CTLAu+NepIkH8VJDNcW0RATMcwwR1z/PB/wDr1JPbXtvbJJcW7IkgyvbjPXA6V2RVndnGk4+6jb0nWbTTNLQM13PN8xhG5o48nG4Z3HvwdoGcDpTb3Wbu6eGa9nifzE5nWViAeSmVQ/IQQeMA5JJBrmnupFg8lG2x5BIXjcRnrjqeep5qFZjuzuPtilKPNLmYRpqKO38O6gkl9GZbgxm7Z4rgSTZM6HblSQcg5JwSoz0zXZaz4J0vVbe5e3h+xXSAskqMQGxnO4E45J5OM/18ehkQZZpJFYgnI9a0LbxPqlsoWO9lwrK2GO7oMDr2APTpSqRk0lF2sRGnafMdxYXzaNFEn9pafHECiOkEqgq2D97IO7JABOeDjtTrljDZzR3pmigSJl8x5pZsbhhVcoNgyuc4z1HoRWDpGqz6xdRNqtwwhij2B0UoqYUjJAB3HGRgLk5HIHFY18t0AbW2+0yWXmZWNomGCMjOCzY4PY/yBpunJvbVjjTuvJfmLf2Yt4Umt9Rju7ctt2rkOMcnggcdsnBPoK2tD8RmKL+zHmu4tOcfKbeQJJE3XcpAxz3HPU96zNPtZUkaIl1hkG7bIh25wQCeuCOex6/nPovh+61FZH+22dgR90XbbNxz9Dit1CSVmrombTWp1H9iR3jI1r4ikRXcrF5h3SSKCOS6kY5GQCoxxz3q7q2l2ENrbWsmpzsZm8xpJLnJ6EHGPlB+bHQfePXOKzotGh0mxVWuk1a9kJ2JayPIsTHHzBVOSeBzjPA4rjb3xBeNqE0lw/70na/7pV6cfdwB0AHTtXPZqXKunl/XzIUXLU9Q0n/hG4Hgt4U3Fk3K7xKPM2gDJIUZ4OQcckEjJFczfS2qSyp/aNh+/lYRyRMEKrjguq/KCMY7E8HPauHXUriR+MblBCFVAIz9Km1HSnt7eCdbiC5hmyVljJ+U/wB18gYbvjkdcE4qZU25XkzWEVHQ29Kur+K/mh0n/TPOtnG63DHYBjJOR2+ncc10FlqdvquiTz6pDCLuxgdYlkg2ujqobtwvzcgdv0rj9I1iHS9Kv0jjkW8uY/J85W42EjI56dMdDkHt1qJtf1I6aLaF/KtfnVgnVtwAYsTyc88+5A9KuV3deREoOUr+Y06nsKi1M0cgPyneNq+m1QOK7zSfDusaxILvWLprSNX/AHcbgCXY2DjcCCp6AAj8K5bwVp9pea0s17jybdd5MjhELcYHPXrnt27V6FqmpzWdozrA1zuB2vtJGO5Y/wB0ZBznjBBxWjtFJLc5q8rysjl73SrHTdQI1HVWkeOSR5FuYOTnAzk7uSCeTnlenBrp5rDwqLeF9SZQl8C0IV8IMbfuleBn5fu8849hzmv39lqVm0ckkpRWd1ZVDZwOFJUnHIAzjpjgnO3j7i+jNvBbQTlYTISM7tsJJGSMnPYE9eg9xXPLnlZKRvTipe9JG7faXpl/dyro0VzA6x7zDLggjttI7gAnn7w9x80B8JarDH5ggDFiNqq6nk8jPIxx7f41Q0vVn07VIGkmeWKN97K4D54OQATjBOOfoR0rq7bU9R1TTZZ9MtI/MEi5Z7lD5Q68KxAxx1Oe+B3HXC6RjU54u0SO00fUdW02VNZV/tQbFtcXDFsjOCpOScdCML+OM1a0vwvDauW1LyciVVjJBaNjn/EEYOOh4wajtL3WNS1KPTEAsJol27ZIzIzDOS+cBdoAOOgIIAzkkXNc028hki/sm+nu7qZcSs8qLuAxkLjGBxyBnI7+tKbjKy6nP7z+Jkmq3luira2czyh42HkRBWRCeFLKmSoGAPxX3rnIvEO+KU3MVw0rxhA0UijKLzzxk9PpjgAVV1RL3T5p5G1KC5nb5JJInLtx23EZGMf5xWFI7swYKdzHr60lFM6FFWOyHiTT7jT5Jd0tvfrEkfnEktL03HA+XbjI2n0Xvja3yNb0HUn/ALNZZJZxz5K7lBJ44PA6jk8DcRXKQ2ziRPOWSJSR82zp/nNdxaJfPoI/sWV7pB/rZmK4XCgdGGdvXHHRSc8gBO0PmYzjroVXS/vvOk+3293CCgaVW+cMq/eAGWJG8jIzn6YxkT6JcSyH7CjSqoGFVfmwR3HqeOK1daSXStrLGZLgIZJ7hm379xGdw545UYGAcEHPeHw/dyXLtcG5ma+3sQ2AwAIHzuxGep9RnAHUjOlObtczceVNxDStMKbn1eOQDA2Mx+ZOcDC4Ocnjoeh4ovrfUbG3LNeQvb3DOoVsqCSNpYpjAxgdM4OK07m2stS1O2tTdJcy71dwrYLAD5yNqE5IBIG4c9hgZzrrVXsZ5dPubZHskfAVCJNoIyMN68k5J3cYzwa0jUvuc/LJvQW0vtFhtwEkkinKlSzxDHUc9+eM96r3OpWVvIYoIvMUD5juypOQSMdlz2z7HIqlKqaiiLp9vGNzDdvZVcNkjqT05HU9a1ri/wBH0m2traTRra4uhGGmAkb5W9MnOOme/wBKel7sbglsrkEVxdmJ5oZIZizZ8sEDaMFcknHOG988k9Mms8TJIov4pgvlblCgMTn7uD2GT1/Q1MurtPIjwacLdGcECBWPGehzx0BPA6jPqKDeNG07ajkKeYgiOgJz90Hj+9nJB6H8bTtrYh3vsY32hkIALAY45x39Pz/OtBNe1GxkXy51dCwl2sNwJIHBJ56YHXI9eKn1D7NJMxhnC2cyKY9u0shxhlPA53A9wMEetRPos6aSf3GHWXiUKf3gIPyrxzjYT+PPFae7Jag5R6mbeXFxdzPPdSGRn6sT1I//AF1WYbWyCGGeorVVba3Zo2mmUscSeZF8uPdd2c8561Lc2WnRhjEHliZ8JIGPpklcgZHv2xzjIxqpLYOZLoVbXWLiGE2/mMI24YsxbKjouCcY69u5qSa9ktwrrGobrG787OcggZzn3Oc1YNpBcKNkkZRc5AjCnaB1zjJOcD35+tVHsZV04T7kkg37Dt+8DgHP0qrJGF4NkEcQlnSMvHACOXkBA9u3pV6PZcKlvcDygEOGcn06jA5yfTrtqjDa4my02AvIwAexIxzj/P4VIdRYfurlUlRWGdjFQ4Hbjgj8K0THOLk9C3amK3SVQqFSmDJJg89RgcdxVB/LY78bu20ngUTOs+Hhi8lSeFDEj8M81HHblnXdIF3EgBsj6/4VomKMbXbGCPccbucdCaXa8M21gcjHFOMSuMhlBXjk/rTorWaRQYlMnPIUZ2/X0rRF8ytqWkfzMbsnaKNvU8YpqXE6/uRg9iABzTyWKfvFwc55GK3jI5mrMaPSlyRRkY5H40owetbRYhwpaTHOAcigj1rVMkdmjNMzTlBPApiFNIaU9KbTBCUUlFMoOtLmm0ZpjsOzRSUuaQgopM0UwFpKKSgYpx2ozSZpaAFzRSUUCCikpM0DFNey/CH/AJFG6/6/n/8ARcdeNV7L8If+RRuv+v5//Rcdfm2af77U9T9Myv8A3Kn6HeUUUV5x6J8v/tK5/wCFl2GP+gRH/wCjpq8jGfUflXrv7Sn/ACUuw/7BEX/o6avIx0oA+2/AP/JN/DX/AGCbX/0StdBXP+Af+Sb+Gv8AsE2v/ola6CgAr5y8T/8AI3ax/wBf0/8A6MNfRtfOXif/AJG7WP8Ar+n/APRhoA4+91ryJWjhVxLHIdrEKAuG9gC2cfxf/XqrDr9zFftdSRw3DsckTKWH55z+tV9UUfbJSp/5aNn86zwctzXu0/ds1ufKTpQk2mtzePibUjcGeKbySQVKQ5jXB7YBrTtvEukDTo01Wwvr+6iDFPMvmEQYnOQo6Aeg69zXJkgLwQfxphfvxRZLbT8/vFyJ6HT6/wCONU1xRDIy29qoAWCIcAD1PUmudMufc1CWPWkL55qYwjD4UaW0S6EhY9zTd5pu/wBakiQM3Tk9AK0E7IaCRyatJAGA4JOMnjpRDDEcmbcAFJAHPParSKcDzT5aY+UjuaasjKTb2KrRbV3KpYfnS+YcgrgEe3SpJJgoVkYkk8kYz+XaqrTnnDHBNJu40m9zb8OyibXbZbieIRoCsf2iRgEJ5+XHQ5ORnjP1rrvEGjyapPNd2l9HGI2WNkORls8knoAPXuc9Mc+ao3XHAPWtqy1j7OsUc01wsKsCwjIbdjpwcU97X6HPVpy5uaLLet2H9n3DRx3sF4c4MkJPP1B5B/Me55pdCilf7bJGGZ4LcyxpuZQSGGeQQfuluh71Q1S+tri886zmdg6Bn8xAh3d++PQ8etL/AG7cQ6c9oscSq/JkK/PgjGM+lF/cdxcsmkka8mr2k0MV5fOJLuBdggMR2zbfus5zzgk/lznPGVeeI7u+cm5fI5+UdBn09MYGKxDIWzik3bSGPrWSgrnQoJHQWF7p0LfaPsu6cjKpK+6JeckFcZbjI5P1zViylspdd/e8w+cXMUcG6OXAJCBB0ycADoAe1ZWkXdutw0d3FbmKRSu6VMiMnowx6cfyrV+0aL/aGxY1EGU2lNyKg/iznczen4cdRitmZSjvoXbzwzBbRCKe9jjuQitKrDYYmIJAKYJGeDzjqMYHSnaWGjzTRQg3HmK22RmmUB+TnYoQ9gOrDk10lrqek29jJa2mn5tlfINuzNHIWwOWwW5x0wM8isa+uxDqMlzp8EdqDIJUEYwiBfujbjb2yc5z6nJzEZO+pmlJqzep2aaNoek2rRwW9qsjwm4eS5j87ManaHUGRlAO7rnJ3dB284ezLXDmLJhWby1YKzbj7cAnjnoDjHHNWpfEuo30U1rnImI3Fc5Cj+DPUrnnacjPNXxo62enrFKoafaZ3IB3KoyuM+gwenXOeRirXMnzSI5VHS+p3fw702wtNIXUVfzbm4DKXJ+4oYjaPTOAT+Fbeo63Yq2x5ArMMoxJx35A9evcEjoa8ysdTvNNtDb2dwFiTLKMk8NwSATxzg44qtcR6lJdwzQ3qpcSNlZSTFkn/a6n8qpuMneRyPDzcnqbXifS4ddh/tKNfst7DL5V0jfMZEzw4/vYB65OVHX5cU3ZpegWT2yr5N8y4eTcm5VJZTu3E7s8ZUcYGSR2ZoesWNppc9tf6i0d1JIqgrOwiWP+IKcE5PfpwBgjrXF3eoiaWbyo5FjZmEaNIW2LkkAk9Tz19c+vESTnotjeFOaVpGlaeJZdAa+h05o5kuTtzKG3JjOCCCOfmPqPaqWvNaPcxNa3sl9KyAzO6FQG9Fzg4+vse+BTvIbVbW3kt53kndSZlYDCnsQQT69/TPfAqKhPfp1rVRV7o2UVuadldvFbmJI1yCXLb2GR02nB6d66XRorgxbrWySQTuFeRbYkRjCnYGfKg5ODkE8emK5iC2lt5l86J14BwwI4IBH5gj869W8P6ja6lYrb2kdtbGQsQm/Gc8bQBjL7c8DGMZzhuHJpI5a8mtEYurJeWFkmoQajIHjOPImRVWZOMlQvGAGXg9snPrSheGWzN3aTWwkV3Di4jAEjKoIKggjHI+9tPJHNdFq+j6Xqd3sN06TwwiJmKExoOCCuMDJ3cAEjgjrWZdafaLq7w3WlwwmI4aZFKxRjbkHZk7sAbjn1OeADUxatocnMraoyhr2n3Nq0LB7Z2U/IYlaNG3ZyMc4PpjHb3NVmtXhjlS4JZXLSIJlTdlRhghAGQTjjHA7AZqafw0I4tkcbrdiQBFaTImHfau3nt0JzkVIfDk66eDdQ3SzRsFCMu0AEnIPHBBxye34Z1Sv1K5qcdUVrexhl2TIFe337nLOhbjqFUHJPynjGQCCRg1Yh+wWumq0cgacFnMIkwgdSOGBHIweP68gSaZotxfwPLcmZILNWjDYyFIyxUqe2Tz09fWpL3RriO9F1eaeJomJZnaRigyO4XDYGCc4xx+FKV2Q3FuzZUe7hv0i3C6ncqWZYV3Mg3EBj13HJPB29Ryais7m8Fncy2djMZfLVnmWTOwFs/dAAIIxkHPBrpfD9u8MjhljJjnPEaqgTIwdxBwOV6HPT7p6VgRwywyqs9+tqDIZkkP8Aqz0bJYHIbI44Jz6HFUnoKDi249jN0mcw6sNQdFkMGWO5gSx24B9yCQf/ANVdZJr32i1aSJUkBAPzAcd+D+PWqejrFqV9umgtfKtgZJbgSEq5zjlTjbnBOAPQ9DVm006xj1K4uxI4USN5dvIRtOc5YHn5Tzgc9OSeM6Rkr2Mq/LKXvLVE8F4k+JAAxR8LIsZKgcZAY9OcH/ODS1ye8tLcGw8uaGbiTy/3qqRgDGAAGOWGCTnJAzTdY1CV1WC32xkIVZMdT0x7fr161QvZZm0aCe9u4ZC8WIovMJKMP7ygEEkY64A6Ec5qppaMilC8k3t2My5EHLXkmJlwGeEoSCOudp+bJJ+bkkKDnBqYG8Up5evIEkXCPNcHAGMYB5xwMfhj2p8sCrdM97KnlIN5ZQd0kjHqzMp/vHpkdOhOahfw5++hDSm2FxJsiMyEqM/d3MBxnHGAfwqeZHe3F9Rl1qN5b7Ik1SOfaQ26JfmB9d+P6/yqfTEiMsb20ZR7fDsztneSwAAwMDqP19hWdcaa9jMBMu/HJ2EnA6Z7e3+TXUaXrA0fS455rF5muRvDybgF2khVBIwTgA7h6/lVzOrZQ9zW/wAjo7q+0qaG3ed5oBdqCqNLtQD3z24OCe3TsK5LXLvzNSBMiTR9YpVRVZgCRgY6A8nH8smrM3ipbq63XWnWtwhzuGzc4X0JIIOMZP8ATtPe21jbP9sAIhmKmKLylTyjt75+XrnjH0zQux59On7KS5kYGm6fFe6hGIFMvOfKWNmJ9Tgdh9avXfhXy7lYYJ8z5wQykovcfMM9sntjb9DTVul0e4lMckLRSYGxVw8Z3Z4Bzj7v/j1aFh4ihubjdPb74LaPO5pSFCgf3ec4OD8q5AHoKHJpnTOVW/NHYhEdvoGnb7ubzLiba8YhdScj+Pdycc8Y68c1RkvrS5sZ1gDwS53GJiHDjK8A469T+fris3Wda/tbU3uXHljoigdBVa3vVilzHEu7Bx85ABwef8+laRk0aRw75eaXxG7BdedaotzHKoXhSp4Pp/X8jVkWyyLHtaR4DjC/3m/CqsdzJc2TO0wPzbgrtv4x6ZzkEgdOfwNVY725h3s0rfdKrhuOvbFdanocrpSbfLoXbI6w90ltFbPapnlmRwBjvk1fEF1Hv3xGQ8j5ATtJ5z6ClttWUQ23mTBSOJHK5wdvr+P/AOqn2t9NcNPJCGcdQVHzAAZ9R+YpqSOSo6jbfKkQxXLNIqFFQoAAFyMn368/lTLiaa4vljmw4U8gdqtyPNeuqKpW5Xqzpjb6/wCfpVKC1e3kZ/OQEZB3Z5H5VonfUUeXfZl8wMoETRBRt+WUdW9unNUmGGILgD3pH1LC+SU3Kpzz6/5NNZvNk3JhhnhSK6KcraCjCS+IZIvzHZ8w9aQRsFyRipVRxGTtPFNJBFdMZGlyJqjNTlQe1RSYB4rVSLTIzTCac1NNaXNUGaM0lJVDHCjOKSigBc0lFFMANCH94KQ0R/6wVyY7/dKv+GX5M7MD/vVP/EvzOv8Ahv8A8lB03/tr/wCinrvfi9/yKNr/ANfyf+i5K4L4b/8AJQdN/wC2v/op6734vf8AIo2v/X8n/ouSvy0/TTxO78s2rCWJpM/d2ttKn16H/JrFmChgFRoxgZDHk109vbW915i3d0bWNU3bwm7JyOMfjSXejRT2oFpcQXAjI3ShJN654A246fhXp4b+HfzPCx11WJvAlxp1vcXL6iqERpv3uudijrj3PFOunHiTWonvJpjCMAqjEhU9ixP64osPCF1GziKI3hmjK/KmwJ75Y5X8QO9GoeGdWstDM87Wflo2Mq+HYfio/n+Fa1JRc1Ub2SR5UNHLl+0/0Rz+uabZ2WoPHp8ss0CgZaQAFW7g/jmsvydv0q2pcOxwDkEEEAj8jXTQeDmv9PiuYdSjd2VR5XlYAJ7FieOPxPpjmtYxajdlTrRhZS6nNfY4iyC3laRXHI2cj/P4VFc2bQyYVXI6biODXYP4Bv7aEyyXtopVc7R5hP0GEP51k2eiXV7fCCONSc9ZWxkevqR9BVRcW7ISlZXvoX/B+mtfRPbjzN+WdUjj3u+AOFXIyf6A11sPhVHTm4nYyRh4yluWyCYhuwCSwzIRx3U/SsvTfDEccE0U940LZV1kUq64JIxgZz93seM9DUL+HbtbsRjU1Kk9ohnHtnG4/l/SulV0rQUrfI3WJpQXvI0YdCQ/2kknnNJZsRlQBtwjkFhz1ZVXGeCcdcU/TtFtLlrWC5W6S6uA7BVOQArbeQEJXpJ642jOMkizb+HLRbd1kW4kZidskjgMoxxgAY/PNcTf6d4j09z5ke+MH/WIoI+tH1lSbV393/BHDE0pbI6jV4IvDZhuLW5ISYFEnZsiUFVyQu37uWIzk9CMjFcNJZNqr3N3aWqxRWyAyMD8q9ACR3P0+uOubLR6vI1tBJAzvPIUQeWvzkHHGf5+x9K7PRrXTX0O5BigCwyEzF41dsgZDKGwAQOgIOT/AAnJrCpyv97fVaf1/W5nWqJu8Ued3+iz6fBHc+YlzDJgl4t2BnOM5AIzg445xXT+FIrTU4Jv7T8tI2cKkKxsC+ACeQ2SvyjPXsSR1q34rsI7pba41GZmuVi+ZVAHyk5Vc8cgZ69ccdQKpY0y8nhsrUSkoSyo1uCoXoOGYls5yAcdfeudVPdae5zzfOk0W72Dw3cafPbadEkV3cXHlxojtzjowLOV25yMnHU9OcY83gfUD8+lv9tt3VGjkYeSXVh1KseOo75596vaZpdgJ0eWUTXLyhlWNAixZweUxwvXocA9sCuim8WafaX4sJyZLuM4EhMawEn/AGwQAMHPIHTuSCOWtVqU0nHV/wCVi4RbbUdjEtdLuYreSLUNNuY5OZFFsVG1wMKNsZ5HQl8e3vWRfeJ57ObNrIspdxK5kj2SE4GMsuGYcA5zznJyTXWy+ONMSMqWDbVww8zljggYK5B5zyM9emMsOK8RNbX8cN3BG6IibGln2rndyAFU9PvEHGT35qKNepUl70bI0VJL4jCl1NpMmRULEYDAbdn0A4qB7wydlH8z/jVeVQr4B7dzTP4c16SSNLIvQSFnBDYPqa7jwvd3VvpywwRBWMm8zBznJAHHI5wFGO+MgggGuCtiqn5iQ3YjtXRW+vR2luYSC+PlV1zg8nnB9RgdAcZ+lOTaVkYzi2elaXMLm+kJuIG+zgMFAI2O+cjHOBg4JHykgD1rXnv/AOz7C4vNWk3W68orpuCvn5Tjnnpj068c15NpniC4F/PPGIxAkQ8xOE3BW+VuScMN3bP9ah1bxPe3sa2qs0VouD5W8t5h/vMT97J5/wA5rJxbZzezfP5Hbxz6J4ms7tbe1NsCPnZFCYOAQTg45weu4DbnHXNeX4czRRrLBqcBJUHE2Y9ueoJUtzxVPwFbWMi3Nzd3EYXGzy5Djkj3yBnnnr19K7m2uLy6b7VZz24hJZdkqk7yGwcENkYwRj1zx3Osvc0izCUpKTR5Vex6jouoSWt0xDxFQ8TMHQjbxkcg/Kx+maprqVzFc+dbOYH3ZAiO3H0xXpviT+wr6ZxrEUh8qPy0u1bDcAEgdAT82QCp74HWsq18L6Dfw74ZJWiCh98eWkADY7HABB5yOCDyOgXtI295Gqlc5+11jW9Xc2xmd49pZgdudg5IBI6cn24yeBVK2LRXBVJvIikUHaI/NHBB6HqMjPpjHWuun8M+HI/3IvnklAI2pKNxcAEjaAxHOeOSB68ZXSrdbWOKK11GK5KNvVGjZW2n0Jx8vH598A4cakLXiZzuuhi2a2sTRy2U0n2zJVpIY2ZYyfl43EZBHHI6nORwKv3rfaJktZmU3DSsHjckRTBs4LAcg+45HHI4xQeCa91Jn0lvNlcM7RLOMhipBcE4AAzjB559/l19KjS1uLeJkha4MZRgoViWGD8wDHGOBnPO4Y6Vrfqc8klqUbLwRqsVzI81ymnxRj/XM+dwz/snp65IrCv9Nksb0wXDhpCAd53AY4xgsBz7H0xXVeJBqQWCCKEfZpHVE2tja2cCM5woGQDyO/Ymq+saZklrDyp7dULylzCXZgxOdwBwpG772AcY6kZr2uq5upMHK95GNpb6nDcyS2cqvIp3k7twkIxgZzjjOffvniuhvdNtpyG1XWHmmkHlllQFSQQSEJPXoMdeuB2qjpV18tvaJPawxyu2YlUsqgAkBkY8g5ABOWIwMnpUmqaRB/abSS3uxmjAEUoKxFiDxuU/KvpyckNycGrVm7mU782ug+30a/0i488COeMgbooyQZkJHG3rjJAI9T0xW3cQulosZInuY382JhKc4BJySeSuG5xnGCAT249dd1aw1u5MsMMtyuUdSnBB5P3cE5AHPXgVduZcRJfRXKS3BO5p4EyBlfmJY8gjKk5UEbuBnOLablqYTpSa1ZR8RRzyXayOuU2hBIjbkJA5CtzxnseeeeTVWzaR/OidZZpJotiEP8wO4ADnPHt6Vrf2zaWnmJDHM8CsWGVCq4+QemcZQ8HqMH71Pt9R02+heWEjT7vaSyowTeO+CRgADP449q0UtLWKipqKVjDWa5siYJIgHByG7j6MK6E2byafAbdTOoXcuXIJOOvr1JxxxjtVazu5b26WK6tm3yZWORFZgxz6DOQOTwccVosJLSRluYZI4jjcXU4OegPUe+PzFac2pz1m7rozmEgVGJkDSMmQV6Y9jnH6eo+lVp4SJyJlCB+VCDIAIyOhro9V0lSBdzzmFHG4tGrOWz0P0x3qjd6Xp9oqE3snmlQ6oWAwMDg8ZBz2/rVqRpCqnqZEECurKzLlTjGeT9Pyq1DZlGLYDBRnDex6fXpwPWoYo0SYPBK2dxAwOT05HT1/StSKFmDW4aFg2fmlTcVyMcDGBnjnrwD2rZSCrJrYWC1t7pECxbJcklk7/Ttis/7MDevFvwQcH5xz7f8A1qZfwz2hEN1uXafuHt/Q9feqzBlUMDj6cVpzEwg7XUtGXUVE8pkDRvkh0dfu4xg/rVwXC3QaOfovzBzksfqc1nW6HALjO4ZGe9akCL5yuFUFiRljkH/CrT6mdWyIWtWyoVevTHOaZJAY8HHBrRuWMYCps4H1xmo4ik0bLJGpkY4Ug4x9f89q3U7GCqStcor05OMdPejrU0kYXOA3Bwcg8/pUOPSt4s1TuJTgCOabjmlBrRMBSaQ0tMJq0CENFFJVFhmjNJRQA7NGaTNJQA6jNJmkzQAuaTNFFMYopQabRmkIdmjNJSZoAUmkzQaTNADhXs3wh/5FG6/6/n/9Fx14yvSvZvhD/wAijdf9fz/+i46/Nc0/32p6n6Xlf+5U/Q7yiiivOPRPmD9pT/kpdh/2CIv/AEdNXkY6V65+0p/yUuw/7BEX/o6avIx0oA+2/AP/ACTfw1/2CbX/ANErXQVz/gH/AJJv4a/7BNr/AOiVroKACvnLxP8A8jdrH/X9P/6MNfRtfOXif/kbtY/6/p//AEYaAPM79yNSueT/AK5/5mqx5NWr5GbUrkAf8tnP6motnHTFe5H4UfMzfvsjGO/FBIPSl2kDAzzTlgZs4wPcnFUTdEfG3qc0CnBPXoKcE9OaBj4LaSc4ijZ/XHQfjWja2REsYONzngVPo1i16VjW4it1zgtKxAH4AGuz8P8AhW/uoWuHW3JwFRZYiJEwc7gCMdsZzxzjFNyUdzknU6HOa14fu4I47qCxuIVcBGRoyMsRxgdeQCcY4qlDoWpTW5le2/co+xmM8a7G9CpOf5V6NrXiKBRKt7bWVw5j2runKkduInGVI4JJCgn1xWZZwPb3jXV0kVpJNEssmJVZSigASFEByWYfw49hxmuanUbv/X9dS3J2Xc5BfCt/PBvt4juLbYwSAZT7DOfXnp09ao6j4e1LSmQahbGHfnaSww2Md8+4rsrzX01Ce5+1y+SsfywizL/6MMncGLgElsL27fw1k6vrFhem3S3tZJkg2h5pWA3D6AbQSOMkN0704zm2lYpcyvc5Mo2eOMdqtWdm8zEvHIY8ckLgfnW9Pod1FIJ10e4WK6IMLTrh+nIAwARwTkL0q/c+Gb4aNJdrbxDy5FDRRNuKZ78ZJHIHXIPQYNbKXu3ZlKotF3OLmiKsRUTR4bG7I+lbc+lXE1rFdNsxM2yKJchmA43AYxjI9az7rTLq1hE0sLojkhSwxnBwcevPFF0aRkmUjhTgHj+dSpHvVsMMLjgnrSRQ+bKqsQoJxk9B7mpHgEcm0Muc8lTnvVIbfQh2nd8vSpI1kY4UE571oxaXcSyrFaQyTy9CsXzEf5wc10NpbWenabNNcWSfa1YIfMfKsTj5AvPzZHt94/7tKT5VdmftL6Ih0Kxvtakdb26mWFgz+bIcmRvTLdydo3c9RmnaxDZmY2NnLODlVJmAZi390naDx06n6DoK+r6pIzW8ZtohHCGEO9SQoyCeRwTnOcfL6Ac1oaFcW3h6GbVL+wmurmYhbaRkDRxsG+YnJwTgj3BGMjOayu373QUvdfmdV4X8PfY9DmtVhkW6u48y3DRZCqehTIwTyBjI9c5HGtqH2S6v4otRjjke2/enz2Hz8Njd22ffOM5yv58Nf+PJZr4T6TZpZbERYoQqyAlSTknAPcjjpgEc81X0nWoE+2Nel7lXl+UXCrg5DMS6nJ5Kr3OOe5BEv2kryZySpPfqT6lZWloLqyjcxXUIDoQ6GNwQT5YRmzknbyM5GeM4B5cSSTLO0kxWXadkajg8jOcdBjP4j3zXRaolxKNO1pWka9khXyoWiSRZc9AB+Lc8nPQA9OVuRMlwwmTY5OWXIyD3+lawTsb072Rf0LR31eb97PHa20bfvLiUqAvp1I4zgZ7V1OoeBrGCYW8eqp56qhdkTKIDkbmJOOWx3z7E4B4pb0xoqoqxjaQTtDZJHv6/413Nl48vv7NCLYRzRRxlLh7mMyJMxAxuLNkHKnnJ69DgYcua2hM1K97mTdxyaBawQ3qJIsqOWiC4KNwo4bocDrg9e4NZqTWjZnt7JwU+ZoIiSNowWLNkso4BzjucEVvreSeMfItmWNJobdsSvJnLgAuzc/xHHzNtGeMnocdrK1tRCs1st1IjssvOEOGIyrBgT6dOevTFJO2jFGzVnuU0ukmiCXLyb0j2oAnC8/cA3YAOS2cdc8cmr+kJKJTNLE3kkM4Xjb6cA8HGSPWr+i+F7bxBNey29y9t5LqBbJA00gLeoByFzxuyehJxxXTW2reDdJt7dLENcmWUI1xJkPEufmb1HBOMDnHWiVRQ01MKt9kYF3qFxYNbTvB9sRIiRO0Rj+8BjcMYzyRuOQwIzkjja0TUNQ8R3LMpFtZQsXdETd83uSpy3O4E5OQeOtWrq2tdVjZtJaE7pBApkUupRXJGdxx0G7bzkAHOOKedPhsH8mxtNjud0rxgBSSccBvvAZB7gAjHGanmjKN0czcWrdS9B9t0a2N7Dex6l5h2mNIhGsO8dS275VyMnj/aI71LHrVtq2ipJ9k+0yyRfvixO1XIwV284U4HoO+Aea5zWtemg1KG3nu5re1ff5jqgIQlAAdm0Z5HUMep57VBGJZJnGkXsl1MYW6uGAbng87FjG7uxK5HNGu4cnu2Ng2iW0Mk9q88DbgYIAy+UDxg428DOD3Hqa3Y/s+tW3IAmQHa7ReYEIAyMkYP3sZHHt2rLtbfUzHKlwsNq8iZURSq2VJ57AcDBJyeMetbOiT6fEphlgijcvksFzk5yDuI6D3/ADpts5/Zpy1M+z0iDSNHlFy8cTTKxZMspK/d2lhntgfKM5PfIrD1jTbOWIi82QPKxIaSX5l5wM8+ijk9T24JPbXlvp08ckkdzGAndHB45OCvOcn261yl3DbTi4yreW2FHmjeeMDvx2Hv096fM3qmS6fJIw/CKQpezwIDMjMpBZchcZ/h6Eng/wDAc8VNr8kMkbxWUCh458m73n5yxLeWVPpu4Jz0IHU1ojTbOG1k+x3SQBgC2wgbSu4cqAcHB75we/JpuIYw91JbyGRywSVJnwRk4J53YC49Mg9801UfNe5as3zGEug3V7bJLeagyYPzR7doQbR3bHIzjABPHGaqpPbXNuNOu4hcMm1PNUDds3jJTg7eMnn3zwNta9w8pRf7SWVIVBSEHAJbBIVUyDyfw4qulxBbWyz+Q0crsieaXKtJxjl8fLzyRj2xySNvaPqaOOhV/sWY3Y82+uA8rGNUcYMqnIAyW5Yj5cbR09K0jfyzQZTTUtoY1yhWQO6DYNrZJzgcHP8AdIxxjOSdTtHBFpY/a52k+UAHLDI4OQSWPOCCPbk8T3K6i9tKYrP7O8ihWR2EjoSigDnO1cEgZ59uMiebUr2blbmFNtJBJcIwls1DqHDkKzrnDnHJOWPGTgDvzTTe+fHDbLew3QVkcxTAvEi8nAyRk7TyOOQwHSqtyiWluF1K0lhEZ2zKy4Q7vmAUgg5x1U5PIPQYpYruy1O9jjtvM05JHBEjAhXTOXOFyQ3Hb1PIp89jRUk1exp3l3DPGjW9vDbMibjJGwEcgBVWUEbRu3EdO209OaqaldxajosUEIKR7QsRYHOQOB0xz/WtDWPDbWtvJa3etmOKV0YRtF8keWbAJ3MwA55P5k5rMa/GlOljO1qquMhLaUyBGBYAEsxI65wTgE5PtrCty67nM6EHbk3TuZFlbrLGYrxHDMA0ZP8AECeoP4Hnke1JFIp+1WTqCFBMDkncBken3sjt79q6e10211q+jkW5e3aWQ74ZYxmQg8qrYGDz1x3HHFN1fQrWDWH+yNHayPACsNzISpIOOON/Yc/UA9qI1E0acyT1OBljYOwIIKk5GOlJG7JnbxuGDx1rqpfDZvLXzItSglcZ+5Edsjf3VbGWOMEnHGR1zxybh0kIIwQeQexrZNNXR1wmp6G1pWobB9naMkEZBUZOe3H5+/NTag0LiGWLOXJ42kZHYjP5f5zXPh2PU981uabdmZxDKIvLVBhGQYOPX1PJ561rGT2OarTUX7RFRzIkjBi3oeau2erPbFDtBKkEE1ZmtrPz99yuw7hkI+FY/wA6gn0oEmS3ZVR+UQnr7Ak81qjFzpzVpI1IfE8kPQBlPJVxlQfX+dV7vVvOk3W6sjHB3B+nH0H9aw1NWoU3H0rRaGbw9KD5kjTsr20hk3TWpY47N3qU3qu+1Yyu4YGW+56YPoKpC3RWKypJuxxjp9a0bazkNqJjjYvzHjke/HOK1TOap7OL5iWJJgq7D5rLxvH19acytt2v36igXGUQhHcEkBlxk+30oeQksjKQ3QcdDXTGRye9fUovlJPvY56ntSEgr1BPqKtS2UroeQO/NZrbkbFaKR0wtPYlYDbkHNRmnKw4HrQ64PByPpW8ZdzRaDKKQ9aK1uWLS5puaWqTEGaCaM0hNMYlOj/1gppNLH/rRXHjv91q/wCF/kdeC/3qn/iX5nYfDf8A5KDpv/bX/wBFPXe/F7/kUbX/AK/k/wDRclcF8N/+Sg6b/wBtf/RT13vxe/5FG1/6/k/9FyV+Xn6Yea+FtFtde1Ka0vp/IjEBcN7hl4/U1val4E0uOaOW38RRWqRgbcQlh9c7/wD61cZbQXM8jCzALqu4qc8gdquLrF7aQEXUQFu44iViM+//ANevRw6lyXTt8j5zMH+/aSvp3Nk+E5La+j1PS9QXUjtZcqoiGMdm3Ed/ukfnWnqFpbL4YM2peW+xdytOdrv/ALOQcknp1+tYNrqljDpqT3klwmT8pSQ4PPTHOefbt9K1NG8VWjWDNqUNuhhb5Q8reZgHqQFJ79yK2nTsmr7W9f07nmJtyTsJ4e8HaXqsf9o3UchgkJ8tFYxqfoOuPqa6W18L6bZyK2mWflSDJAaXJx6gndj8PxqWw8UrqUappiwS+hQ5JH0JBH41KxvRMPLtgi/9MlYFc9eQOaVStO9krL+tyVTi9ZO7/rYt21mjW4jlt1uXztfhGPuD0H5U2bw1YXET20cK26kkvHCRDlT1BA6g9fqPam2KZQyy5Zky0azOyEt2HzdPyqS81WYP9njXy7l0DYmkUKo+uCOcY5rGrNxN6UYWu/6/q5yt6lhpmrzWxeZEWKNEBkLsTluPlyT9Af8A61MXBs7yRlKyPuxswQwHOM4BwOD1544yauXNx9nv7k5EKFIyQkXmb+XHLD7vUH3GKzha5VwDLPI2HjmNkwWNgPlZQBnjjr6dq2jKTSunsvnohTormbitOxek1ZWv/wCzxbsLwKHMZfPHU/p/OqE+uLe2MstpErJCSkyI26RD24+vc8H61DHZ6XpVy129/Mt1g7FmidVJJyTkjJPXJPXvmkhexg08PeW9xbsrkxzclCCPw/p070Ju2z+5kewaeiJTFFqsK288TXsYAKtGGye2CMbl9fpz0NJFp2rafZ22o6TmKz3NGWQswVt5UFlyOeAM4PGOO1MTVLKJTLHA6SJjddxSSKG5znG7A6Cs288fzaZai0hlneEhiuJAY2yxPTn15/8Ar5rV1JRg0o9evXc0hRbTUtivd67Lp+pMfEGnwXLFvMicsWV8c9G5C+2B7jnIxNTu7bV7gi0hYSALh/ODAnGMfMFwPruPAHvV6LWd7STJq88ThtwjefCDPP3QRnnjjt29c2a/uI2lIe3cDMhWJMp83ykcAbW4z2NYapbFxp63RKb26tNNgjnWSWG3LbHjudhUt1GATkcEdM9MnGBWIn2i9uRHHGWdiBhQTVpdQknkjjuyDb7h5igHJ6/MSOSQDxn0FbY1q20C6QWWkQtA3KzTAO7euHBwfcevHFXdrVrUq3LojnL1Z/NG9SWVQpO0rux04PfHFZ7Me9dRe+JRPpv2M2du7szbZFydqnt0HPfnP4Vlx6TcTRGbymKn5mYDPeqg3bVFc1lqZGGxnnB/KmgYbnpnmtG4tp0lFtLC0Ui8eWybWz6EfjU0Xh/UJdJm1CO3LwQyrG+ASee/A6A7QTnqy9c8a3E5JbmZGQG471a8ggHcD0yM9x2ps1lJbFA+35lDDYwbAPTOOh9jz7UqbyuFBwoyxHP+etO4n3NTSLC5E0VxJDOtsSR5iR5LDBDbc9TjI74z9Ki1CK1N47abLcSQnkG4A359yOD+nX8Ts6BdNJB5Wu3E76YisiwA7tzYGFH90c57DI+talp4atr24klit7iIRgtbvu2rIRkqSGGeeOcgcgcHkvrc53PllqcZDcy20u6M7WHYjgfUH6D8q6aw8YarpOmtBFGpZ3aRpJkJOSAOOfTuf6VC/hu/sJnlkOLmPDIYVyqN8p5I6EZ7Dr3q5PZvrE8mmpbeRfJEAzlDskk3At0yF9ARgH/ZBNLmTQqln0L9vpVn4iiivLvV47eadZHeN2Ejgg8BUGMfLz1OMe2a0dFXRNGhhCX0Yu5W+e4DZKjBIMZwcg4YfKG54OCOOHtLLU0nt7dbeSQvIyJFHICd2OcgE7ehPOMgE9Oa6aW21qzuoruKS2vZbX5jHG/OP4QV4J4HGefTGOcpys7N6Ml07l+XS4pLG61OyW7vXupjIgghPyqeQpVQQSRn5x0ypHXm1oN9pNrGqW0q/a5IAfLLH1yFJOFGc47Djk5ORzuieItRm1JLJN7zSSSGQnDh2JXcSAjFhhMHqcHgjirWuaiLi8uLaSz+aMtJESWQtn723oRlSSc4zt5GeK01skYuDu09TT1Rp9TFxf6PAwljO5rooBkDtvJ+YZUcAYB6ZGMSXzSOIdTsrlbe+kgRktwzeYybQfL3Zwx3cbdvXHc4MFmz2GlI+lQpdSBNyW8krOsJYgqEX5edzMOrEYxu7Vi2Piy7+2W8d7cXUg4iZN33F3feB4IftnPAPBBwRW+yMFTk9hYPGkkVobe5Vrrdu3OZe5UAYxnGMHkHPPaufvdZursSIxZo2G1cnkLxjOMAnCJk45256812NtLotnYtNZiGWAKWbz2UBgDnam9cDO19qsMnYTxzXKa/E1xrt2Y7VISkhjMcSBVJBIGFBOOnYmqioN7FwST2IbC6NpaSiSLMchUq2OhHb3BBP6dK2LbWAIYoYrl4XOFYqASrAn5lYgY4OOPzxwKWsXGkLo8Frp6Ti5V8zNMMAcYbA7c9vasa01KeyOISpTO7Y4yufXH61sh+zU1ex2dwlwYpILm4mZnABaPcC4U/Jz/ERgdvfrzWW2nXZIdpSIVlAMed2ZCB/CcDJBXr16egquvitmtVja1Uyqwy5cspGTwFPI6+tay+XNCJI4mntZV8suy5RSU2hjg/MR82MjgZxjGRV2jn5JQ3Jo7ex/suOS9aKNDFvRty5J2j5ghkyc/hnaeFIGefMn9j32YyxnjYNG7qAU6MrLgkZ6eo+ta2py6WluAkEaLbssY2h95HJ5BOGyfp04OMioLnUrfWbhBdTm6lbqRGF2Dj1wF4649PpTTYRj5aGvp+rJc2JvLqPzbiabbkTBC7AMwBVcFc4YKfmyVxjpUh8TQQXzQXemGzYkCYTkk4xxuyu45B6dPzOeQuEe3WSa0EkMRJjwTgsPfB6ZA4JIzW14WuL1w9tFNFHEyM8jSNtIAHOD+Oeo6E/W7W1OerQhZysW/EWsx+dsSN5reQncrAYLdMg8kkdPTr71zcSy3lzsTzDMciOIDdgDnaP/rVqajo7yXTx2FxHMecxlQhGCAc4JAOT3I9PQVU0e3e4vHIuFtJ1+ZJpJvKVOcEHjPOe3vWql7tyacYU6fukdsX88ROrb48qBjkHv8AlitKW1WIBrt2ZsZ2xqAQBnJ68cjHTsfSrl7aQLMZJNQtrq5hACAqF88HkNuHB5bHJ7HOOQMm61K9tp2ikIJU4IIPBBI9iCOnbge9aRlczd5v3SK5BaH55GWX+JSDh/cnPWq3lvGwEqkpnnaQcfjT57qGe5eVYRCrHPlqAQvAz6dTk/jSpKmxgHZCwKnHBOevWtOY1SkkXISt1YMgWRRDwjE5AHcZ/Kkt5tq+XN/DwGz29Kr7zGqgMSi8tgnk54pLmVJJy8IYIQOuOT36VsnoYcl210NJ5UjA3MFX060sLiVWcIQq8bjwT/kVjLlnCjkk4AFaVs4dfKQ7HA6joa0UjOdJRRY8rMO5ZFC55BycflUcYEwPI3+/ersMaCMKThj7Hr61E9sTIWJ+77VtFnMprVFJuCcUDmnSFA/yj5c4NLtBPynP0reMrm/QZTSakYY61HWqYITfwRxz7UmaQ9eKTOKpF2FzRmm5pc1Q7C5ozSfypaYgoopKBi0UmaM0wFooFFIQtJmikoAWikJxSUxki9K9m+EP/Io3X/X8/wD6LjrxhPu/jXs/wh/5FG6/6/n/APRcdfmmaf77U9T9Jyz/AHOn6HeUUUV5p6J8v/tKkD4l2GT/AMwiP/0dNXkYYeor139pT/kpdh/2CIv/AEdNXkY6UAfbfgH/AJJv4a/7BNr/AOiVroK5/wAA/wDJN/DX/YJtf/RK10FABXzl4n/5G7WP+v6f/wBGGvo2vnLxP/yN2sf9f0//AKMNAHDSws95cLhBukYjcACeT+NVVRI7wLL0Xt7+nerF+M3cytOgXex2hTuPPTpUQW2t9jKHkyedxUEfhk4/GvYg2kmfLTXvNEM8O+Y7B07Y+7UTQS5VcZJ7A5q9HewvJ/pOwKo4G04x+HenrNaTZMMnlYOf3jbensAc1VyVdOxnpGY5FZsgZ5561ZmtfMmQpz5h+U5wD7fWlu3jWNZIZQZM/wAPVan0fWjY36z3LSSY44boPY9un5ZqlJXuyZKVuaK1Nmyhj8PQCe7gkW7diixyRh4iPX5uD6d+/wBDp3Xj2FLSLaoa62ESLB8iA5+nB91Ncvr3iOTWBGmHCqOTI24k/X05PHvWaJImgiRYCkigl3LE7+fTtxisXF1F74o07JOS16l+41y9ktTAt9cMsjFpU8w7G9Mr3PvVWy1W70268+ylMM2GHmJww3DB5659+1U5FkyGJ+9+FLGjNIBjOTxWiiloavlsXba4uppDFCXmkuGA2bN7Ox6Y6nPPbnmt+y0tLOCXU9Wha1ktHAS1lDo8j4+UA9uQT7AVn2+n3VgIZ3HkEfOs6bmKnPGcEgfkKmbWroX7lJHjjkwZERwfl9BkEDjjGPbFTJyUv3e5i/eV+n5mzN42ur7VLZLOOSxtRMZJI4my0rk4GSFGcAADgkcn0x38mrae2lfaEu9hQiJxcxmMlyMnBx3GMjjjGQR1459J0+8tbCS4u1hkVFiwI0VVAQkEtuG7JU89R0I6CsLUIL9LBbfyLV1QJI80EoJbcGIHXnGHyV4zwScDGbd/dj3OVxU5J7K39fidx4o02yntYNWsWiiaEKDbTKyjIOcgI2VOAeemATkYBrj7jw7d6xrCpJL5Uk6AxKCsi9ORlWOAAOp5OMc1Tsp9UaNLYyOsMjArE0h8sv0BIzxxkbvTPNdZY2Uqut54in8u/mjjW1jMjsyx4PJwcjJySD2BHGcUlB0k23/XkPma92L6GAvhZNNvoYru4t724dxHHaW7k7ieMktsyM9h1PfrTLzw5ZwsgsUkv5JX3PJbxsYrdccKQN3JJzyRgAepqn4glEepSC3aFgpKgwxbFTrwoPTj1yc981u+F9cu4tCOn+WkW3L20su9Vk+blc5wOp+bHTg9OdI80lzGtS8FuOt/C17pWlS39+6RyMGPlKA3owJ7EEhegbgnoRXPavr8uo3nmTs4ZGBXyyVKEDA53Z+pz1yeTyegv7rV7XT449T0yWVZHJZjO0kTN1AJBb0PyggnBOfSnqPgTXPtkEk9rAsl4C6QQyKBGMZ2nJ4AHGST7kmqd27y0M6bjH3m7v8AQ5BHKtmJih/vA4x34r1DRtXi1svaaxpAfUzHvlM8RWEIATGxX2DAKSCfm4PIIzfDmmWWl6tBbzQQ3F1NKYzI2G8oLknAPHOAORnn8D2N9fafrTTWUgd1kdYQphdAoLjJ3thM4YMAORjuTtolNWtEU5c71Rx2r680d4p0lbTZDiEhbcrHhSRwMk7Tu6AnpjpjPITWt3LdNcvHJEkxZ1kkGAef72ADjocflXo0MXhfwu8s6rcSvCP3Tl1dpVIAIZWAUHkjbjOBnuM2deRPFGiWIWGZIN++RmAhZgFJyqltpOFcnsAynoGIiMko3SK5lF26HKW1pBqzWqqsD+XGNiRxbduMDluNy555ORnoc86Woan4d0u3kguGS4mCMVto1XYGOMZCrj1zk9jxyKraV4V11NVm22621vcq0fmwsk4ijLAjGW9QMHOQOTxmqXiew0PT4jYaFc21xJytw8igyE7xt2ue/GGCkDnOMZw93uTaDlYzXs4NWv0TQYBISC7QNIATgc7NxyeOcDJ69qvqzzQT2kdveLZmUb5hKqJIocBV4G1cfM338E89s1W8PaVPYatBdakzWVpG25pHJRmAGcDHIyAQDx354rRvtVUX4ikn+zQtNuEfmBFijVSoJxE2WIZhlckEYPQU5S6IppuVl0HaX4aS1ia8JmleRX8i3ii3BsfdbcG5XIxgMDnaPmyN1e3jR75YbcrcpFBEXa4XZGEcBweSGDDsB1JAHqzn1mTy7bz75kiaREaO0ugrBDuZ9o3cjBRjvHJ+Xg5Aw9U1yOWztktbm4e4R/NmZgFVpGAZmXuDvLc9xj0qfekyowlrc7vw9Kt3p1va2dxOrQhjMttCICSG3FWfcFwMnggbtwII5Cxap4XjgvGP2WGIO4kdoJzIYUb7pYMVIyQ56AYHB644jQ9Tv7e+tjZyPEUbaCG2/LnJHUAjvzx68V1F740nFzbzw2HkW6gNGhUDe4AXecY9HHGD1G7qK0cWnoc7pyc7Gjcy2GiLZ3Ng26eKTzPO8rKTHB3JhV2gYx8wAwpG3jpNc67MdPS5v7nzpZCxSNgUb92uMlckISQeBgdTnnFXPB9imuWtxd3EEIhaTy4YNgeNcg7vcZ3H5cKACCAcitXUU0m+uIrJdLZBbIyTTxRNH9nG3jAAYFSRjnJUjI5BImyWjMnBdTzfUdQXWr5reeZLe3iLGKTy8kc9CBjrzzjPAzTrDxNcaRozabZpFGHlExudgLng8EcjrjHpz1zTtQ8MTfarltJnS9hgcK5H7sj73r8pHy9Q3ORxyBWz4U8M2d9ZxahfxJH9mlIZJeVlbOBuGc4B7fL05BzxpePL5Fz5UtTIuNf1uWwaw+0rIPvbYdpMZDZBVl6Hkj5T0OPWorLWdXmdrY310JJDwMhgzdAGLEbQPmy2TjjgYzXfWNnpkFw1rY6bFPKtuDK0iAmTeCSrNnuMfKQBS2+kRaW8T6XZNHI7Z/fFWaLAYZGZM84wSO2fXFONrbHO5rZI5y40XxDJqT20N9I65Z8LOflH93JCgtg8YwO/AqMya/p2YRaPcxxMQ32q32tL85B2ydW/hGN3V+Aetd7Jp0V1cs0t3OruQRtAQAegON3OehI6jtirmoWttdQSMYAJeG3hCJVXnDKV+bcMnAHP0zRzdzNS01R5enieBZ5xeWskBTOI2AODkAjGAAw+nPfFNfxpaWiq+nQz+crb8MiKucj+LksMAdcHjqK7s6Dp1zaTw3SQrb7AfMUbCwC/L820HAAz948DHA4qhD4C8PXchaOzzG6gg/aX2gEE5XB5/Eke1O8Rp0+qPJ7K/wDK1eG7vMuscgdgAMtg9PStbVNS0R76CSye4kh2L5kUinjgAqCWPHGR6dM8VUvtDlg1SeFba5jijmYL5y4fZnjOOM4HOOOvakTwzfXMg/s6F7pT/cHKn0P+fy6DRpHZeF7tnQeH5LbVreSALIVgAZ3kVAuMDnDMBnhz1AAJ5NbUMFvNcx3qz+asbecXZQZVIZePunHJGASP94Vk6QlrZ2f2RLe9tbqONJLpi2FBPAyMZ/5aDAwT17Zq3HcXCGW3isbq83gIqykMAuclVLcL0P168Zrne5hNXehcnl06NBHcTiGa2WRYDLOsO/OSG6k8gAAEAhuRzy1Gz8PWtnd2+pxXLXIabfwgQFMjPyN04YY+bP8AtA9HHSn1NXdy0It2zAwk2hWHRVOMHpkbcAHvycU7eK5kSKTVNSaZCpFvFE+FQH+J8jATjHGe/TFDvbQcLJbnZaxd2t5axZilkkC745ljhYBgRzgseecjIBIyMZyDzsdva6pZ+fBab5oYsslzEXQLt+9g5xk5IyTz1zk1RM4tr77LbysFXDCVRuXnKnpnGMnhuv4itZtT1DSNOkMCrIbjMIvJ7cuZCN2cnsMDGMZyPQVKTTQmkloUf7UWGzhe0jiUZ2nZjClMENgKwwDzyoxjgDjFeS2vNQjM9w0cN5chx9kjVgJAvJY8HcTxjdnJAIYYIHNNJqNjJJ9muIpgz5YQ+uD2IBxgnPaugW6u7eOK70WCG3kaAAqHj4+Ysfl4KjK8enPfONrNDdO2wmk2lxcXr3+knyreEgOu5huODzySN3U8nAxmk1/Q7PYs1sWM2wPI7Y+bJxyM8Ek8nkdOec0w67rsKx6bqVqDaySbZII4QPNy2SAR1J555NaV7Lp1okyx3EiwwLwtwx80lgcLg9Rg9OhBz6100uZSOWs5RkpL8DlU0OSeMyRAg4/unA+vpUkGk6lA5eCNSQDhhIBn9QfwrWs4laxiDRrtkJ2u7EnjHG3JAwOhIzjpWpbwb5NgXL7QSpK9M49eOtb3W5z1MVOLa3OQmsLrIkuEaNjknzPlqa3lvLVtjRbwwxtZQwIxXYSxsMFFAUglgB05x9O/6VjxSx2lytvFIm+Q4EQOQTngMAMfTp1rVNERxMqiacSCCzsryVHvJXG9juWIAN68k5xzU8OlRwiILcB5S3KEccc9/pVhI5Y7Zkjt2doyx8tARIRnsPx/IVVm1BIdxeCaJjlf3ke38K0UjJupN2jsTPIZJJopVCHkZ2Zx9PT8KrusiSL9nYrtbuPun0zT0jkntxMmHTA+UdB2Gen+e9UTO6ynzCSM+ucVsmEY9EWrRZBMcfMB97Bxmrzv+7Cwfu2BOc8k/jWWJlDfulYL1POas282+NshOvy5HbvWkZEVINvmL0dwsMW2RWbj5uMiqM+JA00aDYzEY29Kc0zQXGSxCH7pPzA+vIqSeRAu53ADck7eCTW8WjOMeV3XUoTkEAqpU+mKYnzVNJdQ7gocFSOeDxUYU43RfOBycA8VopHUr21QxlpuMU7zM9aCwC1vGZWoylzRikrVMYppKKQ1VwEp0X+tFNp0X+uFcmOf+y1f8L/I7cF/vVP/ABL8zsfhv/yUHTf+2v8A6Keu9+L3/Io2v/X8n/ouSuC+G/8AyUHTf+2v/op6734vf8ija/8AX8n/AKLkr8wP0o8O1K6e0t1dDglwp468H/CobPVPtjLbXNylvGTjcYsge+FUk/QdaTXQfsKEdpAefoap2Xh/VdRtWurGzaWMAnKkAtgZO1c5b8Aa9LDO1O72PDx0Iurq7M7C00rw6t9GX13UXkA+Z47OUMSfu/w5API/qK6rQj4UtdQFvbT7rtTgYjcgHHbdnn8Sa8aS/ng3Kh2HgHaMEY//AFVGL6cOjCV1aPGwhiCuOmPSumM2uuh508Nz3uz6ZNvZ3LpK0UMjKMRzFF+X6MeR+FVrgQrauLy6by92NrbVA9gSP614PbeNdftIwIdUm4yAzkO3/fTAmsu61O4vpvNu5nmk/vOxJxXO4yb0CNBrc+i4vEunJaLM91Db2zNsSZ5lw7A4IBPBPHrWJ4l8e2OmSQPKlveBh8iI6GaM4PLYyAp454/HFeEeeck9z1NIZd1Dp3e5pGkoqz2PVf8AhPNM1q08q5cabOrkqzZkUr79BnBOOtSxfEDR7K1eygZI448hZIdw3j/ZG07c/TrXkYbOTSEE/Nxj61MqMWmujNVFJpnXX/jKe+/dSQQyxZyokdmI9s5H8s1lXOu3M22G8DSWytvW3E8mxSe4BYgHt0rGimaJ8xuw7HB60qo0zqkas7McKqjJJ9hVwpxj8I35m63iQoxktlullb75muhKrn1KlMH8c881mXE82o3Us9zI0ksh3MzH7xpklhdxTPBLazJNGhd42jIZFAySR1AxzmkhGPvBi2OADjn8jWlkiNFsNKMMAn9av6XNqEE7DS/O86RNpEK5fGR0xyDkDkV6P4Wht7/SVWO1s7hwgiuI5BtRGA6NHswTkdRnOCc8YrTku4NAaG2jtdssn3YLKzI8wjGWAHy7enNcrxK5uTluFm1c4VfDl7ZWYlayaWdyFyylBGx46uACc8EHI5PHpVh8O7ooEvbyGFnmC9XLIpx/AQMnkHA9e/b0/wC16xfN5kXkWiY4M0gZw2OpKZABBxnk9/Ss+DS76y02/uraGCa5wI4JLdGmKqWPyrnDfeLZO0g8HB24pwqTteWhjOStpuYQ8L+FrbENzczRgqw+1PKCgPXORgdMcc9e9TTT2ukwy6deQEW1puVGmtt/mZ/iXccAgyKcDg5HI4JXS9G0/wAR3KTz3UwkQgPbsWwHU/cYYPuMjHToSahvND1uZruJ9Rj+z3EiuIC0jiTsgBkHJwNoB+Y7Rxg5rVX2kzm06mFGt/qupKuiw+fLGq7kbYq7sEcDIDfrnGT3FdFtfQvDklvfWskV9PvaREXeyHBAOQPl6AYBI56jkDGbwvcS2/m6W6sNxREHySnr1XsOD3PHtWzpPgC6jttupXawLIRstmJYBueThgAfQ89SODitUk1ZEzqRWrexwN3MLuSIAKiRosQCrgYAA6Z6k5J9SSavNZRwq/2dyyghSx6EnP8AQGvRIfh1aWd8ZXupDDLlfLYBWA5OA2cE4HTHrXQ6dNp3h+1MGnW6lZG/dRRwu7zSAH5jjcTna3IGAEPJolUjHQ1jVUrW2OW8NeFlvNBNzqdmtyZijQxrIUIi3YZjtK54BPJPTpya3ZPDVva6W1pYhbUNlY71j+8aRiQF3EA9sEccHHNSz+N7ORmt7OWaS5kbCoIy2BnnIYDPHReSxO3qRWpqs66ZawJfPcSRMzvdTRQM6IuNx3D5tqk8fT0IzUucm9jNxvd3OHfStW0/UJW0yO6vbyZGzduVVNwODwwwPlDcFjnPHpSX6eJYbOSQ2cflwxHfPBKpIO35nwHwp56KAcHj36DXr7UZLBT4fKSedGJIWiVkdYyvBHzAk98FcY9cV5tqVtremxyX8okt4b5j5kkT4WTPO07D0PJA6HBx04pRbWqCDT2ZHpGp3P8AbTlbtYLi4AjWaRcBjkHaSOV3EfeHc546jduotSsYkudTspESJwr38AVf9YuBuIOHxk4DYAzgkjg8dbQ/bJXiiVyXHHzLtABB5LYx065HOKn15Lazkt7XTLua4hEIeRZOPLkJORjGBwFPfrjPFXOmnY03lY6/Stb06O1utl55cjyCWWZFjRzGAFC4YBXOXPHXAY+gq5FrOlapdNbXkkBVPmC3Lr5jyfL824Dy8YXBHboM9BxOo2Olx2tqNJvpLq5YiO4gKn7+PvIdoBXPGDk5x1zxu6P4Ju7fbdapvtBn90qgMWYZJ6cDp+PPpy1FWvcxqcsY6nWRmzlD/ZoZpHkQkMshRxlhztI4wRkHoCMfexnA8Q6fdSaY06izupkmeSa4RFRwuDksB8p554yfbg1vwXJVfs8MUsnlhjtVMjdwVJPzEbug6dMjimy6lYQbodQu4I0VQjAXSpuBJHyqccjHKkZDEcAENWcZSizii25XOIgvJbCSOR7WCTcEl8wRDaVH3TgADrkEkbuvNdzcpbtDumhs7hQPLxIySMhHRcsMDCK7devr1POWviDS3upULtcRRrDHBGxJknG8eoHIOBgDOXZugY1rNeacZxdzGBX2PJI+VKuGJJXKsQWwQW25Ld+ozXM3LY2q03bmM7UPCmm6tJcTaVduVjkAKQwqyIMgDaFIBBz1zkdG55riNV0ufSrpYZ9p3oJEZc4ZT3556gj8PTBr1Pw1HJ/Ztzc6azsJMqRjc2ScZBPB5XIOTz1OOnA+JtHaz1AzIlyqThXb7SPnDsMkFujc5568HPIrri9eUypTfNZs5zkVcsL57GYSrDHLtOQJEDYPqM967Hw94Ptli+06/CXRlBhhWX73GSSE5HbuD1yOlVPEuh6dFGsukDyX53W7OTkDqQW5yPQ9e3I5PaLm5TR1oN8phaprc2qpDCUEaIc7VPBOMfTH+J9TWegLMFBAJPBJxTkjIuFRvkzxljgDPr7VpTWljbzWrSXUd1vdfMigcklcgH5gMAkZ9/Y1stEV7sdEaGn6d5lpGs4DfaJCy5kC4ByBjOfvHHUY+Xr0qzHaXeizpcWsy4ZQQfN4l443cjIJ/H9KfbXTWeqiRnKW8JcRtyqI3P3fXBztx3AOBUN9r8sc7xSonlonkyxtGq5JB5BUk9QenHT6lXZyvmcrIcmpGK7ubh42E8wPlhzuCNgk+nO48EdPTpjQWystSgld5kMu7yo2EW0uQM5HPPVQcgdMnHUYE95DcoWhudqhgqRMrlhkdjgjAxx3OO1aunPqeiwNNZTGWymTzBKqrhc9Mhs7emD68cng1ab6GFak7XWjL+jadZxGSO8VZZVA+8vOCeCM8Z7dT+dWdY0CzmhLP+5lC/63GcAHqf73Hrz78VpacVg0WC91C4XlB8yDOc4HrweBnHHfAxUxeMmMKP3bHJLLnHUg+2c+x4qlPW6PKnOanzHAP4fvImTzIg4boqSKxPcDAOR9OvBqpeW6JJiOOSP0DjHGeOwz6Z9q7rU7CC43NslEbbQ6ooAfaTgZyOzHuPuj2rGuYtPsLqS5KiW5k/5dpRxG55JODnjPoB6E9tYyudMMS2YEUUscUit9w43ZXt6gnpUjgR2whZWJ++CSO/4en+eK6GRLaQwqodpFYl0BBGOwyec5Ax+HtVdrdLgmMwrhcAScgKD9PpxwfpWsJon6xd3aOfVWB3cD6irFvIATn5ZGIw3pWhPpMlo/zIzjHD7flP09qWDSFZ8kOwJ+UdPwziuhDdeDje5JAZhGftG0nPBI/oKgubzC/KRuz2qzMxtmaIrjaSMFgxU+xqtJF57K5Gdw4NaxZzxs3zPYqg7l5zknJ5qTO1QSQc9RjpUwhQEeYu7H8OetMk8tgqqGLDg54x+VaxdjXmTY9HTafMGfQf1qKWM/eUcH3pgDbsOcgd89KmYvFbbmBA6A1rGQWs9CqRxTTjtT2cMSRxmoyDznrWyZqhaKb+tLnFVcdh1FN3djS+9UmFhT70hopKYBRSZpaYxaWmg807NAgpCaKSgAopKM0xksf3fxr2f4Q/8AIo3X/X8//ouOvF4vu/jXtHwh/wCRRuv+v5//AEXHX5nmn++1PU/SMs/3On6HeUUUV5p6B8wftKf8lLsP+wRF/wCjpq8jHSvXP2lP+Sl2H/YIi/8AR01eRjpQB9t+Af8Akm/hr/sE2v8A6JWugrn/AAD/AMk38Nf9gm1/9ErXQUAFfOXif/kbtY/6/p//AEYa+ja+cvE//I3ax/1/T/8Aow0AcJPZ3F5qVz9mheQRuxbA6c0v9h6jPdLHFZXBLAEnyWOM9zgdPeuxWwtkhS6vNUXyIxvEIkELBj6cEnGeSAfrWqbu0uUVJLmZYlXcoG8b/ffxuxx/hXr+0hGK/r+vQ+RqVJc7t5nn114XvrLUktLiPzH43CLLYz+taL6JaWTMs9m5c42rK5GPoBg/nXX28m67W6RSYoVyu+5LkA8YOSefbr7isaaBI5JJYIyyfxM0I5z2X5cr9etZ+0loieZyl9xRtYIYskW1uGlwuJF/Q4BHP50y78O2F1O4t90UwP3IVJwO7svOB9CBTp54pssrMY+FOJAzE4z/ABYP9Kntw0Vs4mC2Vuh3ENIDkj1Gc0OUnqF7bHPahoP2RFkgm85C23JjKZPtyc1klDG2GB47V2mp+KrU2iwRQebIqbUdHZFU+pHf1x+vasCGzjvQZ7i7hiDNgIMs5P0HOPerjJ632No35feKZllvmRZXdvLQKpdicKOgH+FW4I4AyebJtVmIYdxV+Pwxf3CIdKhlufmUbguz7wJB+YjggdelV7l7+08ywuPlaIlHTapKnOSN3XqPXFXGpFuy3M5Xa9007+8F5ZQW2ntE/lJ86lt2PTAP1PQZ9+cVQjsGuHUWFjcs6qBIxcMu44Ax8oxn0JPWr+laTK1q97KgkVsosCxsZHkPA42nuckDkgHFd5Z+CYZ7fy7WZ45GBA8+InzAMZLMB8v4DuOoOaiX7v3jF1Yq0UcA+k61ckwyWdwPLG518k7k9M9xx2PQdqsz6VHBNbyLfx6gVRFlW3IZYXIyI1YZDH6DsfQ11eqWV3ZQPp50S5vpLkjzZULMkOPlGW2sD8uepyAa5qGxTS5p7XxCt7boxUJDbXShWAOTkkMGA5OF79SKmNS9v6uPWR0uhadptzfbwY55IYRI1s+J5NnAwy7M7hhsgDIyo5zTL3TNT1LVraa6S8s8xl0MsJAVc8KNgAGAc4Y+wAOabZ+IPC0BX7LYzQE2+2SZNnmOMg7WWPA75JGDwMjimr4nYeeLFodjOzRuqBSmdoy2OR+PPOO1S25My9m4bDNZ0TSNPsBpn2NklBVnu5FKfe43ZbGQMDhe2ehzVHRtPaazC294n2jT5EmjheZSiqXUMQQTgAjJ4AAPOc1p6zpeoW0ouLcTahbTRBrqYQEIU+8cEcBSCACrZwOtdN4Jnto7GDTtUjntZY2kmCyfJEQ5wyA8k467T/EfYGim2lJ3u/6/r0BRvyxuYw1LZqyRzBLeOJxse5bbycZPJ6nJI45z2+bJ4p177JdfaLTZLbFdkkaOPMJ5+Y5BIGCwHpnoO93xHZWF3cSi5eMm23RqwLIFGVChlGAQcjHPIwckVy17a6fJDHLb2tzGY4XkkktDscDaOTkEsGDIflH8ZIA5FVJ86T7GUIKE/wADnNR1j+0rxQihQSBvIXe3GOSAP5V6JpHh+SbRZLW48QXAlaAMzWbNkDrtLHORg42rgH6g15r4bW2fV4nvw7Qq2THHEZDIQMhdo65x07+o616tpF21zc/bYvOmmi2LGjPhOSQQy5LcDd1JYkHnaVFbSjywsjSo/fS2Rw/jO4ktz9ksb0XFgsERYSGMiRh/Eo/ngcZYfdrnW8S6i9rcWtw6zrcIiNI4O8BST1HBJzyWBPTngV0134Rn1rXrkaeFaBHKySiF41RxnerbgFU8ZxzjIGTitfQvCWnx2zwXmnGW8WMv58rALvG0eWoViG5J68n5ePmGFaKjr6lupBHmDX12Igoll8tBsAJJUDJbHoOecevNWdF1aTSdQS8QFpoz8gDlcH8CMj26eua3dSngmsrglrDMe1lhSJVbqAoAIHAB5AOSOT93K1bDT7LW9RSLyfIhiTMkqS4yAMsx3DgdPoB75q4Lm0KlNKLckQJdXV3qjm8Ml5bQlixJLZjUE7U3HOMNkD3BNUtauYZp/KtMrChZo4xhggJzt3D7x6ZPTOQOBk3ZLN70SWunOJY9yhQvyhtqnHHA6bjn3J71Qu9G1Cw/4/bR4cOV+cfxDnH6VPKky4Ti2Q7jaaY0c8LCS6AIkYnhFONoH1HfPReB1NBmTtzirE9qyo8k/wArcYAweff04z9f1qo0RB+o4qkbKxeTU5I7fy4mdTx0bjr1x61b0+S61Nksl8pmBLK8jIh98u2CRznGTz0GTWOgUDBBz65rV0C9l07Wra4t42meNuYwM70IIdehxlSwzjiqZnJaaHt/g3Qbmx8HJBPeMGYiUPbujiNWwSOQyn1zzwas6Ta2qxyWscc91a7VSRpTuXBBP3yMMGBXIHc8H05XRtZ8P319cW+p3EkdvDMHig1GQhUJ42eWWKjb83TAHHPTFvxD8SND0+1SLSES/Z49qJF+7jiXAAyAB6AY64XqARWEuZ7HCoX6akHjPw1Zac1vfI0ChVYGFlwJAc8kk5PUDk55Hpir/hjxFDfWDpdzxRRQSeRHaL2VdvzHPbnHJ5JbrgAef6142vNa1VpozLDbtA0BhaTeArfePIwM4B9sDngGpdDV0tS1/bSS6feOI5JtzKq/NuwCOmTtyOfwp8vu67lSp6Xkd7DDBJ4ZiGj/AOjwz3MjSK7o+1ArkkAnkDCjrn8AMUWv7u3s0XT4LiVIw0UqlSkqBc7irjaGbABI9cEgnOLmirY6NCI45zDGA77XXmUbFUuBnJHGfQk54ArjZhZR+IL2TSpoodMjG1pfmaI5GREWABxlSQf4dvGTjLjO5nGmjoYZCCLea9maS8mLlssyquMsGiVssT9zYDlcDGa37h0Nv9kmS2uW5djbBo90aj7uPMJ6EkEkL34IGeEkvEVri8nimcSOq20sMWwuEJQgAAbQeFCgEdSc4UFNX8UyXOoWiQTrItvNHiYNjfj5t2CckfOcMcZGffGl+rE6LlodyVtWjCXUzRWKRuJleUyA49csGBGUOQOpHJ3CqN7rl5HLLbafZskcLH7yeWSBnIIBwR3JPG3nHORhW+rS3qtbtIjM6qsxm3hlYjbhQoJPBUjgg8Z+bFQ+IJr7T4rZ9JJy0bPhSCzqwzuC/wBzrwQBwTt4JrG9zONK0jufD+s3N5qRW/tF3IgH2zytuQxGD5nRQBu+U4J+XGDxVmz8DaW11dtY3C3Ec5wrIwxE65yh2nodxxj7pWvHR4v1iOyWCW6dmWUzKXAOGIxn3P1z68Gu6tviXp2ieGxFpNzLf6kzDMlzAUUcAFyM9cADGT6mpcZr4TtjCCspq5meJJZk8USLb2My74lid1iL5OW3nPAyFfB7AjHTmti4nsdvlz3CQRKM/Zi+6ddo+/tPDKAOvIwDxgE1s6LJFD4btLsfM7xiR5WYkFyd53YwAR057cdDXAePdLu9SRtbgiiFrHGglYMoJZj1GOo5GOpGfSmpc87HLyRdkaTXmmaPau9/cLPbO7tGVGGPHK4LBuu3GP73YZJzbjxdoqXRnt/JeKNP3Vswl+dxgAnsMgYz6cc9a84uZppH/fSOxHHzEnFV+TnAJ+lbcup0Rwsbas7iykkvZHkmUrOrEqka4ZTgk8DHA966u61O1vVjspGGS5kzATGG9Tt3DLehO3OeODivNtP8ST29xGbkybV2qWjOGwowPYkDOPfmup1DxhpVxZxbZ5t8pYuI49rIcDDMv3cllydp5B9eAWdzKtRnzKy0LIt9JO5PsqzRgh4ZPNXD9mzkcYPHXvnjFOhtJ49VJlUbUUEIJCyqODnhQR05GAw9iK4ybxHdJcRy2J+z7QvyryGx0JB4P0NV/wC3NRP/AC9OPYdO3b8Bx7VXLcr2E+56iyTQXj30t7KzRgEbTvIzwAN2TkKxHIPDHBJ4O9YPb3D/AGITpcSW4Vrh3iZTuGMMCO5J4GenfFeM2eoT3t0i3l6saryDISFH5Diuh8RavpEEVrHov2eW53f6U7IJcEYIw7Da2STk4/h9OukW1ocdTBOUldnVeM5LS1sYGMrC63IMr8rOmCScbuRnIz26dyTy1rqAeaN/s8Pmlh+8B+bOc5BGT2xz71j/AGlr+Saa6vEMkUYKAqq+Zt7Z4AOMnvk+pNV3v41kzCGVwc8Y2MR0JXHP4571qpNaAsKrWNXU9ee5jkh8sRPuB3R5AI+mTn6/41r+FrdL9GN7bSXLR4aIjcAuOpPRSOfXPsc8cS9w73HmSksT1yetbuh641pfx4crE7BHRiAjKRg5/Ord+gVsO1S5YI79r6MSMJFdH25aURttxn1Pb9KpX9vDqjB72aPcoCKsQwBzxnJOSfw/rXL6r4ha5WeCCJIYZGySEG5vcnH41jx3MxwPNfHb5jjt/gPyrrUrHmU8vklzJ2Z2dxb2UatbRXCo67UKSHIyR1AP/wBbH60200uKWNpp8eUp+VgMZHfr7j+dZBu5JbWPJbCgLvZV6+gPXHt61ri5ns0SOYskgBDK4+6PoRnkf5NUpdiZwqQjZPUsnTrAsSrLwvyg5Off2qhJZxwthZBLgYbA2lfz7VXlO7duZ0GOSGPB7Ejn6dBTTNJbqixllLKRuJOcEfmKtT1IjTmvtDi0XmojActknbnP1q00VmsLpiLB65J3f/W+lJ/ZsYijkhWVG4BBKuWPrjjH0qubOdpCpChu6s4Uj861jUS1Y7xlsygkALfMSfTFSsz2q7Ul3BhyB2p+xoZXRx8y8HJz+R/qKjdoZuASr9uK1TOm7k9diPcX6mlCk9Saa0TRsACM/WpIwQcuDitYsp6bBgjjmlIxTz6gg/WlJ3gbsCuqMjO5FTTTjxTTWlykIadD/rlplOh/1wrlxr/2Wr/hf5Hbgv8Aeaf+Jfmdn8N/+Sg6b/21/wDRT13vxe/5FG1/6/k/9FyVwXw3/wCSg6b/ANtf/RT13vxe/wCRRtf+v5P/AEXJX5kfpJ5DaWVjqEjW+pzNDEy/K6MFIbIxyRj1rq9P1Pw7Z6fDptxHNLHbOIlnS4RWU9jwR09Rn8a49bOG+DRXM3kLjIk4+U57cim6zpamwj/s54ZZVGHbBSRvcgkr+X45reMvc5W9L/0zxsXFSrp9l/X3mb4yazk1pmsrz7aQNrz5LZI4xnaM49ec+tcwVJarLgng5B/nW7ofhoarZTXDmYmLB8mFTucex2kfnj8a7acVCnq9Ec9SVmc3ikC10s/hhSP3cslrk4RLiMtuPplRnPttq0ngHWBaicxpnd/qw2ZCvqF9P19qpTTM3OK3OVVM4ypP0qWK2knnWG3geSVzhI0BYsfQCt+48L6nYfLcWFw7MuVWOPzO/cjpVrwhFGNSupJrWN/IhYPvuBGIgSBuKAbm9OOhx1yKmc1GLktbE891oYll4d1G7uUhjtyjyNtzIdoX69/y9D6GtSLwBq0ltcMVTfCcBVIO/nHHI446ntXoQuNLgtvOt7eW6eUDLRw4kA+jDjk55Az15qK78RWWnz+XPayCUgEgncyjsADx6V5jxVeS92JpZdWedP4J1OO2M5EOF5bEy4Uc8kk4/hP5Guu0Dw3LotucoFvJDkyKdwC7gBwGH4DnnByOK0dL8TWl9KbVIZLdhlYVzuBXA4yO+AeCD6ZJIpl/4ha2vGs1twoH3d7FA4x2AOMfj09M8axxVdy5HFJmNSLa3NGPUEkgNtHPGofKBJflLZHA5YsSenoaZqNlosBiur+ztFlUbobjzWhwy/N1TBwPXrnjGSM8rLq9pG4uHnh80kh5HYyF+c4KknPP6gcmsLW7m0ubky3N5JcTbRgCQyEDqFycgY9O3pXXzOSsZQou52V342l0hriGOy05vObzPPs7kujvtBySfvN93PTp3q34KsLjUoZdXv5nkmuCwiYu2VXk4HcAk5wOw61w2meHrzXIlk0m0eQBiBhgTkYyTyOPfv8ApXp9rrVjZ2ZsIJZGufMaF3ugHnLA5IcqM56cHpjBpeyjGLtux1J2Vl8ya71JNAtt17tkLHoVZA+SegPU/KT39x0qKS+uIZIrlLmxhj2fvraWY42n7uMqOevXA4AHGTXJeLNegutPFtJcRyXcLKw2gOPdQcEDoPT7ozXMX3iCS5RmmkcyuRvVjuBwMDH+fw4rSFK3xGPK5q66nqNjDp2svdXM0sMl9EzRxyw7WxmMAP1wwwcDJwMEVVufC93b6a1vpFxMzArvlJkTcTgY2rxtOFB6gcnsQOK8C63FZa0zXcgSJ8K65wCuefyxnPWu00rUtf8A+EoSK7spRYF2dJ41IUAn5W+U4BPHy++eOMOpGSkuXZr8gUbJp9B+kaRIl1cvqFyjXDYWNo5gGjYjbuAxwcAdMjHUGtO4W9jkjkgjj8yMDc2WZT/7N17YH4knFbVNY0+w1Ixy3UUTyMWjeb7pHqGGVA5OQSD3xVy6Z5reK6a5IMfzN5YBVwe3sOhyOw964lWqX12CdGDOc1XXtRkUxqohdZPKFs7mJpSQQpAySBjB528924w1nW0gnh1u5k1GS9fzZbKy7/JtwcsGZdqqcYG3C9O/TXGjadqflLf27XRVflYbgCD7rj3P4ms7xBq93pEEzWUMbRQNsdQQ7LnjnBOxS20EcHrwCc1caq2tqaRpt69DH0rw75OtW98bK/Fs0jOz3V3GSvyE4ZccFiR1YHPHbNWr3xHqunh4Le0mVC7RecLcxp8xwCpUkA5zjj0OCScvn8QXNh4Vi1qKOAltglhgjX92/wB/5scBSQBg8jdnn7tZV38RIZrKSD7PdHznwZRc8qmcltrAhiSWONqDp9BvGU3LVbBy+7p1PRtFFjrOm2l7diOC9WIIZlCiQOByuCDjac8Yrzj4lpcQX22LVrm8tnco8UnSPAVwGxgZ+YkAgEBR17XrLQdWv4N1n4lSK3ul3Dy+XYEtxhSM4YsDjjr6AVyE1s8Gotp0Fx9uiZfkbyypcuozxk9O3PUA+orVJKehPN7q8hugaRZ6ulx9q1qGwkjjCxRykKHY5I5J5AwCcDPP52n8CXMVutxdXkIRg2RGdzIR/eHGO39cVXHhPUH1T7HC9sSkZmM4mARYgcbyfTPHH8ua2l8PanFGklqBOke2JWG3y09SykkHg44OeeOc40c7vRmcn2Zy9/pLWFwBFcq7oqycRlCuRzk9OGyOCc4GOTiom8S6oEMZvXA5GVI6bduPpjjH9a0/GliljcWyZhNyI/36pEUwxJI4IG7jPzYHuBxXLNE4GWBx1yauLbV2apJx1Nj/AITDXQfl1GQLs2FcKVYf7SkYJxxkjOMDPArNvLy5vZ3u7qZ5pXPzyOxJ6cfTgcD0FMhtJpbgRiKRmzyioSwx1468V1Gl+HzKwkgvUCxsMyxjAXjknJUjHHPYYJHIp3SF7sNjmYLae5YLbqZWxlgv8HOBk9u3512PhnWrjSbGTz7VXQujtlCrNsGFw3C8Y4JPUng0uoR2cMPkaejSrDg9MgNyCRkkbSQCc8rxg9TUMMnlw/6dA0ceSCZ125UAHYOOQcAcHOG54PMSlzIl+8jsF1+RrfT1triAPJEAzTK0joC23OSRgAHO7ODtPfip7m8s7lJfMmtorQhSzTPGFkc4bDg8lh+ByOvXGPYWWlXmnLp8k72ku4tLZoyq/mc9VIJ4wpyTjjtgAYWsXuj6bb3tmkk13OR+4ZncbWzgls8nGMjGAccgg8xGTcrIwdGL6G5rGvCeCC8sI2S1jjIPlAAKdwUHPcEEcHkHGQOBXLSavALhHZQ6xHcoKhgxA6EHtn68U3T9XlaxhtPtcahpRlJFyFyu35hgrswzDgBvXhVrLa3urKSJriB4xIgeLzVIBU9CPXiuyMUY+xjd3N7xRczazqQ8m1tmeIgNJbQlNxPGDn72dvHXHQdya1l4Z1E2P9oGGMQYVlYzopKk9Qc4BGR97HX61FBPLDbIkE32ZvLy+7Cso3cFeRuHOP4mABPQAV2piW20sf2jGuZAsmy5YBRIQc5JYjn0PPBJG0EKczVkjKblSSUdjgjcZ+Uu5ypHD9Tk445PT+eOnFXoLFLkg3MqGMp8zomdhx02jvx3IycYznnVufCljPrDx2+pQ2kalt0bncyEc4xnsCAcnqGGeMVVf7XDqEqae0d2qqAbhozuYbc/N3IweQcjHBx0Gl+wOopaRKUen2lvqk9qXW4jX5VlTlMkZHIzz26kZ6EjmreiakLV54HRp0MZRbckkP8AODj05GRnn15OKvRvI/h+7ExknuZCZGZSSshPUvnPoAAMDO3v0z9K0HU5LgNFEFBQMXcjy8HoMjIycj8/StOe8LM55WfNzMmtNX8nTxamMfaFk+R5IwAcEfKSSMDqec4PpwRqyatcLJ9qvFFs8iBYQwbPljp95SDxzwQM8+lVjps1jcRPqVsA0p+fzVUoTkjjHTGDyTyOeMVX1awe3P2+BhOkx5JADRk5AJA4wcZB/wAMlxV3dHPNU5P1N2O+uzbqbf5HZdwkkG4Drg+2SOMntUljptvdWkX9oxx3FyOQ6MSznOeTn5uvuKw9GvLyWMQy7TBxhpwSo+8cZ6DPPAxUOo6NcQ3LyQqfKZt2OOMjP06E1dnsjkdNKTjexcmS+FysbFN8JKSYAHRj0HQ/h0qaNZIbuRljJjlC4IHQjsG9fr+tWtNtVv8ATG3TlZQAHQplQvr/ALI4OTx9eavrYQWkJAZzIyb3UcgevfgfWtOeKOSpUtdNGLe3a/a4Y1BVsckLhskY7H2xV6KJIrcAM3I4YsPl9uKy9TCrJHLtYBemzkcY/wA/jU898i2BaVdrTghVHBOf4q3iKVNyhFRG3Yg1NGaGYKYhwxHB9zx3/pVdLV45FFwGMgx7YyM/1rO1CFluk2j5F+VXbqwwDn9ajSee3ZlVvv8A3s85raLOuNFqFovQ2LpEH3lyc8FeT9PWqIdHkJXAZR3/AJVWW5kSLy25/usTyKltljlaQMG5OVbvWyY1TcI6k5gjCF9/HcEiqUi/P8uSPetCGwkOSWyq45AOCD/+uqr7Fd0JOATgjnNaKVxwkr2TuQcDrwB3xSZ6EUhfdRnj1/CtoyNxCcmjNJ60A4rRMYtOplKKtAONJSZpc4piCik60UwF+lFAopgLSZopDTAKDSUhoGTQ/dP1r2n4Q/8AIo3X/X8//ouOvFofuH617T8If+RRuv8Ar+f/ANFx1+Z5p/vtT1P0fLf9zp+h3lFFFecegfMH7Sn/ACUuw/7BEX/o6avIx0r1v9pXP/Cy7DH/AECI/wD0dNXkYz6D86APtzwD/wAk38Nf9gm1/wDRK10Fc/4B/wCSb+Gv+wTa/wDola6CgAr5y8T/API3ax/1/T/+jDX0bXzl4n/5G7WP+v6f/wBGGgB/2OC+t0We32HylKv5O12IGM9csvvgCp/7M+y6Rc/axcSRyrmVooQu0DkZx2HvxU1vcSx6RHM1ndMVAVBKyJ5uB/Bubkfh6Vzeqtrmv3KFtMlXywfLURFTj8ev4eldsXKXuI+QcLTcpPTf8Q8y1i8uOGc21psz5iO02Dn+PaSAfw781uTQXFtbw4lYh+Wcof3mfUYG3P1rCj0rUVtBbRpIExiTzFAC55APqO44FKfCXiB9PMFtd2txayuZNqydXAxjcyjB7Yzj1rZpuL8jJcrteQ+S2MNw8p+wQMDhAWAKjrxkdsetFxrNlY2JS3uIry9mcAKn+rB9WPHqePfP14mWGSJ2WRSrA4KnqKjHB681cY6JPY6fZq9zvdVsIWKHX4vtVycRNLC4jdTgEALwpyD8p5zxnqBXQ+FPDEejw3N1ahpLuT90om2t5Sk452kEHp0bP6Z830nUPsTBxPKrZyqIoxn1znj8jXRvPqKabiNbq3Z5R5aiAbegyxm4HJxxn64oqJcjj3+XyOVxqXSv/S/r0Oi0a41jxHqk0RuPIisAyPJKSjDORggYw3BxgA+ua3pdEtLOGJ30/wC3tI+HlulSTaOeQhBdj/wH64rzS003VZ5kdkfE7BAXYkSgHsAcuB1wueldpYeAns5vtK3ovYTHuXaoDseAAAQwAHXkH04rJ0FFJxdu/m+t/UU5e9b7vLoW73w6Ulie2h+wwtguju0YUNjgD7je4z7Adqi0u81GG+uTe/Z7ZFm2+UoVdhQfOw44Bxjrj271qp4ctDa2J8qJJbaQP1cHevJ+8RnPHVfyqPW7UQ2d5cwRCW4B3hXXLb+N3PU8AcdSMY6c5TlGGl/60swhap7ve36kOp3FhP8AZpY5Jbe6kIYSRXC7gpJGfmyCM44Byc8eo4vxPb20OoR2Wm2c0dzbn5pDJJI8z5HOOQOckbcH26AOvfGklpNLJbQN57DAeYAbPTp1A9DjmuMm1G4mlkZ5XPmP5kgLH52yeT6nk8+5raMVoo7f1p8jWlCom5SLrxNDdT28223dCVdpldc5Hpt3DI7ECrWlx3N7PDDZGBZZCUWEsBliDyd3Bz06+gwOlTS6i+p6Jbwmxik+yr5W9cl9ucjgDjqRnn8O8tp4anutSA0qRJLdl8xDK6qwA5II7kdyMj+VaRv9oqcly36nrGktc+C9BltbqzlmuFZpWS3SSVEB6DcV6e/1rE1i48S318psLC7VFQxyxBlbcrcnKg5GRjHzE/TOKy9Je+1QrBeLdLHaPlJLbYI9wYYRgyndyOgJrXvobl7SEW12wtoYsHeADI+0gYbJH3ipI24x/F0rhlHlqc8tW/yM1OT9yO363ucz9oV5JY9QmmJYqfIki3Ip2hucNzj5hgFeMcjJxnx6ZdX9nvmia42x4BjKjCruVdoyPmBUjGMndzzmtp9Atp47tCBLOsxeQW86swG4BV5xg5JBLDv1woyw2d1o1vJaxwzXEcmNwRCQ0YBDbiCSAQfukDk54wa64yViLW+EsaDaQ28iaXcG1ubm0uP9EaNxu4cs0hAYEYO3A788NgkWILHwtpV5JYRvNctfRqTNIxCuzMu1U2KBjBzuAPUYPasjTdfl/tBYjLavGQpE0sTGSIja3zcjcu4ngkfMx5x10hqwtVn1HU70yySGSEKyOqMCzHK9QTggBSTgA8ntrtLUxd0dBONN0nRxb6J5cjSYRjHe7XGRhclWLE5I4AwN3A9auoPqE2mXESsrzT2qReXbAlAccnbuZgvUj5RwD1HNc3b+G31q3k1KK5mgstqvFmPcXc8Nyi8nOBuxz3PHO/No9rbX0cC6d5iqqSNcbWOR0VDmQ7unoAAMYw1EoJ6S3MnLl1icWfA2oyvAy25IlwT5bByMjP3QMjj9Ofp0GjWlho3hrVZVT7FqlsxiMty+JURyqltm3IyDwMH2Iya6dIo7Kx26SzIIny2y6ZSOPu4JOO/XIHb2yL3WreaS3UXiyyLk7UuSGdDwcOSADgsM9ePvdcVz390XPKVrmFplvqkfh+71DSLpfLlIkFt1eU8ZOMcZKjg5yMc+vIv4o1BTtlkLoFZVjJwvzAgkgYJ6+vavU9VinvtNmj0aeaBEjBaEhZVZcnbgg/KB1OM8Y75rifFnhplktZLTeyIiwF3GQduFBBH8IGDnk/N17CW481mdFKcWtTlHlu9d1ABl2qzc7AdsY7kZPQAE4z2qW5lQQJFEHMcZ4SQcDOTj3+vU13//AAjjJdukKL9jdjsSKIQllBO4tgjleQAc+nHIFfWPBZu/tN3YjaFJkZly27gk5JJIICk+h6dSAUpq9jdVIs43S7CwuY7mTVbh7dYkAURqDhsdWz9DxjJPp3ttJDosc8T3E+7YQse0KVJHG4g5GCBkcj5foauXnh6fT9PVrjJ+bzHXyg4LgEgbxxjH67vTnnRDJqM8r4dpW+7FGpYtjt3OAP5U9JdS1Z63HteeZHKZHLXHmb1nUYLgrt2n0wAMY9T14xR+dyXZc7vUdc1vaLp90ILnMURhaJlclgD2OOvt3wM9afNHE1uJhH86IoIboxwfmGTnGB36HjuKqMlewnvdGCsbjjb1PI9K9O8Fhrvw6yXNwqyW6NJAXjB8hUXhyOmA5P3h1JOR35/RkS60+K3MdojQsS8jGWOUPggc4KHABO3AzjOetWbfXI21SwtpZliFqzmQSJvRolG/7uB8zDzOOdxKgkHOcqkr6EyTlpY2/EsWm6ncPbXH2aykiRHDW0CyTyEjCqNuAQRjHzYy464qO28LQi3zrMkMUduzrHdsibnUBVZWwGBAGQpOcbgR6CpoPiGzMunaVaWsvmSo0e5JGLAsSUIYAfKoZtw6k9fuiuotNSiure8s11uOVLrdbra2yIgVsnLRoeTkEdOOWJ4xU3kttjPllFWZjP4L0mCSS41vWZriJSQyu4QsA2MHBZm5HQYJJHTnHPagumtavFojxRBZCxsZX+0KQG8vzfNBwh4zjoVxyQRm+msyWnm3UKRrcRn91NbwHy3ywAYuyghWG9cZH3WPBAFXfC19qm2Yz6dCgZHle/EiR54LOzMyOXPIJGMjJyOBt22FHmtdmCkttootVvpZE8x5IxIF2uIuUV2XG5eCeCu7HKj16WO90/WZbZoG8668uURmJt3ljf5Zwi7yowRjg8AMD96sXUJ4vEeqvZanp9pZRwyJFJeQo3mNjP3WYZYsMlcgZCjjOBWG/gPxJbSTNFbKPIjZpJUuEAVcYbJ3cZB79Qe9S0u5bjF7nb3WkaVKgnnFvLEyiYStIRGEBycFQTgkAZ2jgHPrXL6jotvrPiS4uLGeGzsid3OC3c5VQTnI5ycdcnHNcXcefG3kzmQGMkbHz8h7jB6Vo3ouvD7Wpguj5kkEUxwuNm9A4XnrgMPxFUk11Gqdup3d1NbxeDpdOtbeeONHBM4kWQq4POTtBzyEAB43d657xdr+s3F5caXd3IW0jKqIIk2oFGCvGMkYwao6Zqn9rXEdtqF15ESliJM4ZieADyAevc57A9q7HSrSKS8hSWRJLcMXjtXI5A77ckZHynbwAQeOTkTUdzCSVPV6nn9zpLJpaXQZGjYbwDtDsM4JAPzEAjHcDBrPh2KhHua9E8TLawzC4uVC2l1CuwjCugwNwQAgOV+YDIx82M4AI4I2jz2TzrgeXwQo6YHOf8a1gzSE+ZalC5OHxjtkH1qEtT5opIz+8GKiPNO52RtYmjcAjPSpyI/4ScHn5hzUNtt3ENnpxgd60obGMR77tZIkY4EoHC/zyKadiJblJl+Y469qfCUjbMwOO2O9dDZ6I1zHdCytjcmOIS+YZVRgoXJKoeoJIPQnAHrVLU7cRWzwSQmK5gkKyI6kNjjHJPJGCCMDrnnOBcddTDnTfKUJLlC29AQc4OOmPT+VSNdRtn93t5yFHrRa20NwQJpGi55ZUDcY9MilGmzNOYoFM7DnEY3cetab6kvkvYgLl33GrKh42V0JVlwwbOMVMLDyMJdAxyk/dIOQPbsf/wBXvVyW0uY7aOGLbKQ3CBMtuOMLkdc+n1q0ZTqR0SMvzGJ5Ofqat2zRbT5jFW7cdatN4avhapMgRiwyYzIqsh7ggnOfas4JtUbu9aIlyhNWizasdQMZ8lV+U4wQecj0J6fy9q37mZJnXZLmLaN28hmZu4JAA/zzg1x0TYxjiu5jji+yxRviRFQGMMMg+4JHT/61aR1Z5WLtTs7FK5tI7h0MJNuWGG4LKR/WqN1pj2cSTCcTs2coiE7QO+e3UdcVvrGgQPGsahV78D8cU6C6IOxIJCiqWYRqG+X1OCSPxq20tzhjXmvh2RgWd15RE8e0iPBwSOfwrTudchtrgm33NKR87qcZJ/hOOvSrrW2lagWLwoHK43DKnp7/AM6x7nSbax+Z8yKzfIAwBYfn1xRbsVGdKpL3k0+wyO5SR1tpC0yvllZMlg3pz1+lRKd7+W3yt0Cvwf1qJLKSOYXNpHIVXLcDO0d6vo8XJvHCr3R3wwz6jrVQk4m0uWPw6lYWzqTkYI6g8EUiptIDoQD6d61rm0KyRT26IkFygZCnIAx06/19aoSfMzqW5B4IOc+/Nd1OXNFMzVTmGukSqGiB6YIPNQk7TyuaVYnkYr5u0j+8TTArKp3jmuhSNEvMQ80w0/gmmGtoyuWhpp0P+uWmmnQ/65awxv8AutT/AAv8jtwX+80/8S/M7P4b/wDJQdN/7a/+inrvfi9/yKNr/wBfyf8AouSuC+G//JQdN/7a/wDop6734vf8ija/9fyf+i5K/Mz9JPILdYGZ/tEwhAXKsQCM/iR/MVD5dpd3IjtdSzJKct5qCNGHoMMSD+BrL1+VodPRkIB8wDp7Gua+1yFslip9RxXVSg3G55WKjeoej6bY2UcV5PfwSW9xattkBlLcdR0B/P8AlW/a3FhfWSffFu4wDL5Z3e23JJP4V4z55ypzznuK1l8y4t/PuCrrj5SJRuJAwABzitJ029Wzi9murPTWewjkkja9SyMceCske1j7coB+ZHsKZa2cd3GJbW8hki3EAgKxHPXOcjoev5CvM2ntTCv7qRZNvJ3E8/mMfr9KNPvfKvI9sgt9rZDszbV9yFBP5VCpPoyJU1ynrtzE8O3agmIYFliPIH49foMYwPww5/HSWMMtrDbXAkU/M3mtIU69SxGCOB/hTdM8Q2lvpb395rDXClvL+ysVyRjp5f4ZzwPxqZr7wtq+iLHLcpBI37xoi5T5v9rAC5HQEiuXlcG76lwjHls0Q6Z4pt78EzSpbsrBR5+Av1B+Yjvx79aS+1F5bMnT7drwu5Us1q7Q54yd4CkAA/54otrjw1Yw29lp11HPdzEJGDEZWVj0zgAHrjGcjPHNcrr2vX+Gi2qroTG8iR7cYOMDk4A9eDV8t5Wii1TVrst6frVjZ2MsiXiRXUmcqyHZwcgDGTjgHHQ8DPWsXUdRguI3825NzMzs5bY3BJHRicngdwMccnrWZYJZyXaf2m8yW54Z4QCw9wD1rU0uz+z3TC3ZrgyrsVY4+SD2ZTxjpkAke5rthSttqzOVou5iA5Y4zivR/DXhDTrE2Umvx/abi6KtFECxiCnBB6AMeDkEkc9CACc2Czg0u1kjljaPzx80EM580FWLAnj5sBe2QOpxxna0++Gn6XELaDM00udlyXbeuMls4Cnqo24zj7py/PTKnU5fdRjUmnodNa61bLcxpoVjDb2dyGWIRwiBbgoxDBMEbjyB8xQcnjOK5LxFql5qkkiXnnFrQurrG4a2SRQw4G0AE89Mk5PSujiurfUbxnvY8PCBPveJViVI5WAIdOV2jnDHIyRzjibV3WIpd3dnYteReUzXLLGXMRGMq55BB78YA6jcDXFKm6aUmEai53Gx4zNdSvwyBc9gDUBU9z+ZrrNT0FriB760SGKFQJFlLswbIUkElcN97ryB0yTxW1eeMF0LT/7OsLS3uI3hXzZoE+zZfByQFyCAT7EEN0IGOpTvHXfsVd39xXRwy21zaRGTzEjYc+WxIYgjOenp78+9dp4O8W3M9x9luZI5WSELEJSPNYA8pGxKqDgnAJ/hX3rAJmu9HEKafHNLcSD51j3uAMY2nqBlumTnHPaqt/4Y1GwvGgS3e45XY0Klt4b7pA684+nvU3T0kNxUke0B4Z5rj7HNFdK8pUeb80JYdV5PTucA5zyc15146VtH8TQroiPpbSQBWW3cxM3zEbvl4APTj+70FWLIy6fpdpumt7e5sOJIVlUMNyk7yQcsGz0DH6fNWZriQwWouL6RpbqTBWGRxvzkgsGGSFBQgYPcDsSedJxnbojOnBW5urNez17UNNsbKS88R3UtvceYLiG3jjM6IARuDkliMjOTtIAJHbO3pHi2LxFpmo6ZftapZW1sGgAwhRkAIYYwNuRyOO3SvHZC6NnselPF9NHctNEREzghhGMAg9Rj09q19krOxooWO41m/Gm3Npf6fNHbz7Tm4tgFFwpLdFGOBjb0PUhmyuBFrt8niaGK+Dqt/bQEXKBdisinhgvXIB56ABTjtXFzXslwY1fGIxtUAnGM5x+tT2kimQYUZz68dfT6cVvGOljKVPqeq+C4NI8UabDbXd5eW9zbbkRYb1AcY3HapGQCFHr93k8ZHa6RottpcE0VlqU7qy7o45JUZVAJIZc9Mknkce3SvJNH0Wxa6ik0zXxbxyxN5wZD5kWAG2kcAjOBuJAyR16V0sHhSyjt/tcHiyBVtyplICfeU5XgPxjHck8E/SakbvexzRaR3EelaFO6W+qWlpKl1iNZ8L5kjZH8aY5JweBxznrXF+K/Ap0/WDGuq+TAPnjnvdygbmOArrnLbsluBjOeecamu/EiLSre2s9LWW6kIHmGXMTxheAGGMhiRnHGO45xUGneKbPxks41dY08lCbdbqaJlBwoywIBzljtOOjN3AFZQ50+bobtw5OW2v8AWhymg+HbbUWmu737ROsPymN48+a5zkdf5/7JNT39nbaVcCSxgjkllXeFMYXy1IbdwrbcgAD5f9rkmt3TtTistFjt3YDyx+6mRlPl/Ng/Ked2Djk4I5zgbjg+I9Zmv/E0URtJYrZmCO7R7JZVPbOT0yMZxjgkDNbK8peRz2cm7mlb+HbrxAsF4NJg0fyVLwm3GWbDfKGRh8zHHU9c89sYfiaztvCl1b6dY6jNNNHEGliYK2GyCFJ4wCGYheex53A1v6bfaRqIv3sD5ZVVRWmjDqc8gFlGSTtzk5O7PLDANHUruabW7Xw7FbCbT7edPMk+aWR0DHfjk4zzwoBJHpxWl7ysjOCcXd7HGeakOQLkovDDYNpY4JDAkZAOf5Z6Zra0u80aRb86yIiIUAUvKSSx4/dqnXoMjOB7DpkeINK1bSdQOlX7SzFXHl7SzJISAAU9fT8MdqqWvh/VLmR1+yTRRxllkkmjZUQr94E44I7jt3o0a3Or3WtyK5he91B/sMMkmQDhF3ZwgLEAKOOCenAHPQ1TC5bjv39K6zTPDNzPdJNp87DcQInidQVbp19Rzkj86o6t4cvtHnMNzasAHYBlYOcKSDnb0rX4dGYwrQm+VMzLcvZ3EUwm2YYMrqOVIrq9NluPFVtPaTKZ5Utx5WZMlWEmS+OpxvxgZJAwMkcZej6cLq4IkJSMAbi65U88Zz/LnODwcV2DWdsl9b6g5YOi/OwcR7yvG4v/ABHk89ePQ4A6kUZVdXY4e60+40u+ktrpNrrjII7H+hFdhp+sjUdGjt9RjF6wkACzPtJ5xndkkkZxxjg963bbS5L6SJdTsHljcZBMeA3BOQW6E8cHHTPQFauav4Ts7e0e50+yhLh9yRYJ3E/KODxgZJwTyRzjtmq0ZSSZyVbuOqKun2fm2c88FjDbxoAbYXEe5RwCWQgZ5JPUHgKOelUNXbS0mxq8SXCIoEfl7lyOTjygV5Zt3XgZ/GtC00u6ht769leKEySIz28HlpgKCA2SvcjPqPm6HpwVzN9u1OZLeOa6d5WI8vO6Tn72MH8scV0rXqcsIXkdFpa2E/mS3FzESWIMhkUkRlTlXz3YsDknnHQAMarTwJ5ZWz16dFuvmKkqVY46uVbjJzk4OB16VY02W6UBbx2WcrhxjLLtOMsBjAwcEkgd9wxUN1fSRW6wzSTh5lyVUI6oFAyQGyfXnAI46lTmluLW5Dpc9zqMBSSMysE8qEM/3Wzu3DrjkAk9xnt0vWWn3l5bTLIsIJXa8YZeDnuoIII+n4dKhk8x0s7qziRFYmC4eFTvkBAO44UDI9eDkgYIFdNASd1zCplORuRiPMQkAdvY/XHbqTpzPdHFXkzlbbTr1JlhEUyBmBZH+VWxnAIP3vWtnUNIvYIzcsxMYO8goCB2JI9PwpL6ZnuZAJY1dUBjDLyQBkk98Hd7f49A97/xL40uZfLfywBkcSHHqTj88VpKo7qyOSpOS95s5S1MsqpNBbzud/J2HbIAcnscfmas3GpfalMd3AoQgqSrMHUZxnOTzxn045zUsuqbE2/IXI284AY4/L2/+tVB2bW7N3tJFhmUDCb9rM3cEenBx+vvo463kRH3veasjL+aOWQ25klhKdHHp2I9eKx1kUzyYyFJJA9B+NaWnTS+bJBKCECkEPk7T6fz49zTLmz86YMQFcgAsRgH6+/09K3iehBqEnGRCVW6mcLKx2qAm7HPHufpV2G2WOBVmUtIuRuTlQP0OfpVCG8S3Zkij83a3BOSp6jOOM1q2UytuZ2WFm4G9c47egAz9a1i+pNXmS02KLSI+EkbeA3GG6fhVmGaONcLMVXPyqo5zSXsSTTB5JE2gYygHP0PGayZwYZGjzkAkA+ta3CMFUVjoGvEUvLEEBbghn++euQM8f571WkvkuIAiRReZnJkI5FYYcg8HFWrcPInlxgsTyQq5OB3qla4fV4x1LMkKRqxDZbGSpxzVTeD2Of5VLcyBHAAYFT1fr/Kq5IHJ/irZSNYJ21HfSgHIoQZB2fN60bT6VrFligevBoApO2aUVqhC0c9qMYPP6Uh4qgDNHXpSUA0AOpc88U3JA60UxCk5pKKSmMCaSgnNIaBliD7h+te0/CH/kUbr/r+f/0XHXitv/qz9a9q+EP/ACKN1/1/P/6Ljr81zT/fanqfouW/7nT9DvKKKK849A+X/wBpXP8AwsywwQP+JRF1H/TaavI1JBwcHjsK9d/aU/5KXYf9giL/ANHTV5F/GPof6UAfbngH/km/hr/sE2v/AKJWugrn/AP/ACTfw1/2CbX/ANErXQUAFfOXif8A5G7WP+v6f/0Ya+ja+cvE/wDyN2sf9f0//ow0AW9P12G6sypaS2a1QRs6OvyheM4LKTnHQdK7KLSj4ghtpINRuBZxR7G3RKZpD25KkbfwryTRrNdT16aMxqWVixUSmPcAe7dvzFdc/iPxgSLPRIoltVGzNgvmn0+ZiWKkevFehKCjTUYr3nqu3a/+R8rU96s+Z6K6b8tHb8PLQ7XRzZxi8h06FI5YHCPNOoVenQOBwfX0rHntNY1e5mluoPsjoNp2HIUHkYZRz07de9WdCXxDp+kNBqFpuZz5hK3A8xcYyT5QJP0qFdR0+91BomvI4ZyfMNspyrgjuzDGee7A89K5qkJyrPkd9P0Wv5+gR5VS1XX9W1+n3ryOQ8T6RqM0DWkk0E22QsEd0QhsAcFiCTj1/LpXM6L4MvdW1ARyK9pEBl5JV284yAM+uPw616tJ4Zs9SIfUo47yEZ2eSAi4x93K5djxnOccVcmt59Mt4PsEFnNDAudirja3A55GcBjnAyfSuyinGNp7/wBf1+pE5O3LDQ8svPCLaLrUSi6a3eErKwnwkmAckxEcSY6joenFNudUT7XDcXkt3dzJKHMko5cjGFC8cD17+g5NeiXlrpxt38+yg2uC1wPtLJuJP+0OOfQkj0rmdT0jws2IbjzrWTy9yvBIX8s9iSxwwHcAKeaa501foHuyTv1/Iji1Swit5jo3l2V9Jk/6Yzo8ecAKrA8nqfmJA75FdboYvdOjljuLmSU4wejBDnkD5sH8APp0FZmh6DothFJI15barfOcLJdKV6f3VJPT1yela8s092w82AMEGVkZsKOe4PX6Dt+VZVKnLeMPn/wPQ5HDndpLT8dy2n2czDMse84LSbfMZQT253ckfTNVr+40aw0q5YOqLAWRpFQbDJgk5G44cnPB5PoRzWTqt7dWWmPHYyQtcbvmNxIq7Bz8x3rtXDEYBP0x1HP+E7G28SLeXGuXz3UpY+XZebtYuQMPknjhSOnQVhCm6nNbobpRgk3/AF/X5HP6vb2up6pbjRxJNLOADCF5B7D0+uOlURoF6Lp4CnltkhlbGV9jnHNeoJ4dtfBv2rUtN1G4huGQ+XFJAJPkODhjtyPX+H9M1z0/iWFbopc2mn6hKxyGMA3Qg8FSwJLc87+Tk55xiuunKC92Opb9o1dbHOaZ4cmEzSXNwkFrGqtJdFWMaAkdwDk+w/Oux0rwkv8AZ6ahOuZRkoolZVnjK/e6Bjzk4B69hVLVrjWdXhEOnWbW9lI/kPZ27iVFfAJLFAOowfmHf1HG9FpWpweBbKyN89q/Waac71jTJ4Jb5QpGAFJGc7T3pVJNwbWnT+v62Mmpc0eZ7/1/XmT6Pa3FnZpFZWV4ts8hZPtYZxt25+QAYXcSOeTjcAAc4ra3peo6i0NrZXQieQIzQ2ythQSQXL7QSo47AHBGMjnJi0SK1bzrzU7h7S0Km3RYlcyfxSNsBwF5IPykAEDLDGbuo6/pOjaev9mXY1TVZG3i9U7CAT0yMgjI+6eOvBOamylK78iUpL4fMZf6Avh2wFrdXCmxR/khKGJp5gGUljlTg9hkgqFwe9UbrxXLPp0FpbzQW58v7OSXbCArtB3ElsYOeh4HJJPDru9uvEeqMq211ewSASLBdQqswCrtLMUX5eSQCegYHrisC9uILec2lhZPBeLNh552C7Bhg0ZU8DqAW4+7yBk1cE78r1bCWu3Q6Hwl4ZtFhGt6u0NzAqOBbPzhx3PZhtye/Xua66ztrDWbWQWttGsMUgZAsaqLcgAgqSv8gOcg9MCtpehR3Ol/6XEpjd9ypbTqiuuOoEeMDgZyc5XuMZs2ljBp8K2Nl5tvCp3ECQsjZGCMtnr32+tdPK5S9DzalWyu3uWB50ztFCwWMYVSTy3sPToKzLu/e5t7+OytpAbWVUkmiXlhwXyQRgjnrwferV4rTKCVmXDnlZSrrz2YHqfqOD+FOt3jTTn09LOWSOUsf9cyNI3VmYt82Cep9/z2rQco2sclGajK7Zzmk2MjDbfNfzbWZjEDhXUjbkgcDG1juzyACM8ir1h4f023kd5YvM24ScTRl4on284DHLdQMt9Oo4sXdxfGRk054lml+/y2E3c/fJG44GMgcDkjptsQR3VjaQxXF+ZWkUKZHU/fAJ+UncCQAfvD5jj6Vz1IcqukdManNLV7mfHIttrFzDcQrDIUdoUHIZv4toBIUnnIPPIzVixeCaWa1k2SzozgJGj/AL1QwOcHOBnpg4I+mBnXGjXd3IlzcXAjNgFZtPtyDKI1YMAfmxnG3BAwT1x1rptChidbh4YGtpJJArCSKLLsF3CT5fv8E8gnv6VzyskdEY3ZSurW1kUNqMgjtlUpcNK6+WACpUew+UDI74HUjKmPSdTvdlvfmbydxuBv3RvuQjy2Ib7o3c8EgkYIwRUWoavptkwtdbL2rInzRlFeN8DgZALHALdCFyTz1xRHie2W6t7TSVWS3G3ylZmAIJ+YqDnIUYIB24xkD0jXobK8UV7G1N01xp0tuwRozKjEiRJUBKhiSdqptAAJJJBHPIqlr+iadbaSWRvLnZ0VoYrcRoWbbwDwAQrbup9+nHTRyzQQGSbY106ebcOwYmJ2YDG3H7sbV+8flPXJC84fivU7Sawns7u4iRhOZoot3msWOWXaARkPuKZA4DMSDkNVWd9S4Sd7bHALf3ViWiguZN5JXy2f5W5HHB45644OBnpVIXCQRsjwqsi8bgd2CBjGeuD/AE75OdSeyl1QwXGmC7mkbbHI4iOS5JYkNyT069uvQV21n4DGpaNbW+orBbSxMQktrJ8skeRhypHO4E/3f4Rxk7blNR1Z0KSscTaeI/8AQZPOm3uJI0MKoSXhByeegxwoHA+Y1D4k1i2/tbZpk90bWGIRoXZlAIA+6hxtUYGAeTgZ56dFdfDO5sNUZmuVNuiF1YJ9/r8pDMMZAyeeB3zXBavZXGnalNaXkZjmibDKSDj05HHSqjyvYqCi3dElpdRlp5JbryZNj7DtOGyrcDHTJwP+Bc8ZqWbXLqaApKUZmIPmhRuAAxgEduB78daztPsn1HUoLOOWGEzPt82eQRxoO7Mx6ADJ/lk8V6Npmh2Gj2kRNmJvtXlI9xcNE4Vir/cBQsI5AMAFQxGevGackhzlGO5R8IXVo+m6ib95EiMcQY4cfICyyHcAV4DqAr/KSV57VeuPEMOnW11ZadAZ4zAizBJAykhtypkcmLYHDso+YnBJUg1d07QdLtoxFL58VmsLC4iilAebKKxMm3LFSql8AkDccYKHPH67aX+gzo0enPbCCUSR3xUMxZlXq6krnK7gM/KScAZp3UjnjaUmdfbNot3Pc2Wl2bSusMhiO0SxxSJuEbFfmMm4kH5t3DBVyCRXQWGsSW/h22fX7tpkVc3IYYI5MgVlJHzEjqQ25VwBlsnx2DVNRmuICLuRzEysGZyGJxtHz/ewADgZ4ycYyc9cPCl1qVwL+fU4Io5gs3zO8sgJXhQjEsx7AsRn1qZRtrcmSUfdbLWp6TbyaPJdXNlFcWpAnGoRWyWkr7pCuQvIwcjl8KADgZINY2o6Jat9pmd7zUfJtd8cMUgm+zoGXBaReqhA3YAEAc4NehJpg0ixhtbm5kmtUlK23mHYxUkffHAbncQQQcdDzivJ/F9t/Y/iGS1tkWFQgDCMsA+eckED6cccD3pRnzsmlrJq5jQI0d8DEWiQkgN6CvStE8i50Vjcv+8tELJOWUeXgDptwVye/fb1wTnzqKVYrcyuCXBwI+evbPt/+rvmtXw3q0s+oJDNOlvs3PGzsyIpweDswx9M8n8zXTKMOW73HWp1J7dDcvoYTA39p6tNJI6rlPP/AHZ4yrgbcMOwHGCV56hWmK2KpPaZRBOrM7EEvjGVI6Nzg5244JIPRX2QSSaM6ZIlowkAdFAZlRuM5A25BOASVIJByBzRFDFdRTGWW0eV2JMcW1imzJbO0YXK4wy5HUjNczdhapakGoaXBFKslxY2ctxF5kc0aHaFYHghVYA49eRk45wTWLqfhyM5Okl5ZFXc9sVy/wDwEDOeCO57+hrpnELqWgmkd/uRKCHG35RtZWwNuOgA4x045dLDKskYe9kknIIeV5m3KzHOd2eQQQT0647Yo5pII1LbHnHltHMUkUq6thlIwQfStx7+S1sbdRcq0kUmQIzh8c9TjHHbOTz6Yq5Npb6leBnmjwF2hi2TgZ4x17fQDGTjmrQ8Owsn2ci5ebPzRvBIGi9VPBUc85BPBz7VpzJm0qkdLkFh4tkt1vGgkmiu7oKGOdyy4GCTnOX64PPLdql1OVNb086hdXMIvFjC4Qjc4UBMMpbduzzuO4kHOABWDb20EepJmJrmNXC+U3yl/Y4Jxz6Hn1Fams/Ybu6U6THNGPKXdDJciTcVGOuT2zgdse9bQ0kROMU0195mQqyHc4ymMZx3rp7e4EtjHbvtjSEIYgRnH97Hpng9+n4Vz8NvMnmF3MTRcOpBBX61rWyrc2k8aGSRo08wPEpPT147gfoK6YWucWI1LTyTI5lt72RGiUqZX+YHIxgjHA56c1mPqTy3CpOu91n38HaGHoQAB9CAPfPGBLPUb5diyK+4AAZO4jsB2/XJz78wmDUNNCGaIKqsQAdrbWx3HJBqnrsKEYLS6uauo62jQx28wEoX5sLu3eg+Y5+vA+uazLlG8tHSIiJgCG2n8sn+lVcmWQySlnY8kk11Wj/Z7my8ua5jY4wLcKQxHUjPf6Yx9aab2M52oRvFepV8KRRS3VzHJapcEw8B1zjkZI9/fj6jvqywQWhYWdnLEjANvJO3HbJZsDnjg81JZ6WItQXyZFkjYEcKVK+4IGB/jx0PHTSR2VxamKN1SY9A7KMjpjjr/wDqqdpanDVqKUuZbM5CW6u7iRRbq3l4GTtPJ/z6U4TtbRmAeW0Tgb0j4DYPGe/f/PWtu80l7ddkiI3IY+XwV5z2BOPrWTPGs0gBy7KOfmA/Xv8AnV3urmalFuyQ4XFmiRrCrRZXDklQRznkj7wGP/rCr+meXeWs257WRIcv5VwNzHjjaxOAPwrOuNNltYFeOPdFjL70Kk9yAx6/hSKT5EbW/wAspYli5Ck59vT6jFJS0VmTKEZp2JpPEbQebbvZQ24JCv5bAnA6jI4P61JJNZ3Fk9zJp8bRFDsmMWwHtjP3Qcj3/oaj39ytwZZreC54Acm2UgZ6duDWy0S3dmv2GBGYLlIXt0xx7EgEduMnn8K2jUTdjGpCFKzirfMoIh/sp4Iz+7O1whGQqnphsc/pVGSKS2ACKN/+0vX39xWnp1tbuZJXuooJ1z5kCxBRtxyCOw9R+dV4Ea/uEeybMOSpjcllzjP3gvy/y9Ca2p1eVWZN7Slb5mUyyOCzj5s8YAA+lRSEsAD1Hb0ro7i2sfL2tZnOM5jcsM/X/ECqL6KXhzahy391hg/mcfyrtjURcMRB/FoZABHXikYVPcW09tIUnQqw7YqOSOROJUZDjOGXFbRkdSknqiE06H/XL/ntTc80+IHzl4qMXK+Fqf4X+R3YL/eaf+Jfmdl8N/8AkoOm/wDbX/0U9d78Xv8AkUbX/r+T/wBFyVwXw3/5KDpv/bX/ANFPXe/F7/kUbX/r+T/0XJX5ufpJ4B4pz/ZkWP8AnuP/AEFq5YBzjINdb4ibbpycZzKBwPY1Q0TSotSlLXVzDaRxnJEpKmT2BOB+td1FpU7s8rFStUMq2iMkoTbuJ6DFek6L4UuL/R2j1qSCEBQYAyNJL7DIICj2z+AxVrTpE0hjb2lgIpmAICrmQr6DaSxB/EGrx16cQq6IjSY3FTj5cH+LIB/lXLWxTlHlgiIYdyknIzV8FKLQ/Z1s4ZDkP5xLrk9Dkrke2M84rW1nwVo+keFgbHT2u75IvMa4VN5J4BYoxxt+nPt1NczqHjqQSFba1RpCRueRlkGQOCoK8EeoP0Pr6Zpd02oeGLebUtPE7zwbvKdDGCgP8RLYIGc8kdelTKpUhQ55aar8n/X5k1KadeMI+f5o84tPCdlrVjb3epaxa2l1cx7lgitwrEDIGEUjcTgYAA/HrXG6hpk1nfzWp3ExSGM5BByD6V63Jq2jtfrFp95JDb+V5ko6q5HBVGboRjnacex7Sad4W8Oa7apqFlaTFZGPzeY53MCQQBnjp15rZVdee1l27a6GSTh7jd3/AMC54s9nMvLg/QinR3lxbMPJbyyP7gAz9fWvW9Y0rTNJhuZ5Y4zFEMLbtbvLtP8AtFmPTOeNv6EVzDeHIdZV7toYkR0zAmn4DP33FNo4/AZPfvVwxEZK/Qp6bmbZ6dqGus1xpNk1wzRgTNNbRlN2OTvbgHv2Na0OmahaaXdS6mj2UMaqZGMDMJpPMxgEDYRnA7jB6N1Gl4T8Jaz9mltr0yWdvvLIBJsLNjqR6DAznk9qydS8QXQjntrOaePJIcRY+bjB3Mp+bnPTA6c10ut7N8tN9jljF1JWlsijNPBa2gB1JnkkJBXyGfy8DHRyo7nHyjA6ehqm+s44Wnj1FYyxGbbyD5hJKgsD8ydFJ5IIJOBjrp+HLJ7wy3sVrCfIbLXd8HdFwOgIQru6H+lW9afwm8y3BtoLy4ZSHjgZog7Z/wBjKk57/LkY471rLFtPbUSpps5f7SdW1a2EayQxwBRCkJ+ZMdMHHXPf6c8VLdXGoWlywmuJ5YZG2zCOYb37kMcHBzzyPwqOG3a1828sreaS3U+W4G1mjYqTjlTkEA9hjBB9682s+ariaadnkK7sqCfl7bi3TpxjAwMdMVjOpKUjVRS0Lt14x1Bp9i7TbqPngkKsrtgjOVC+ucZ6gcnAxnvMl5Zo08rLL5gQIqk7lxySSeo+UAfmeBTrKx1DUtRji060815RhN5XgepbgD9OMYrdn8OaklzJaXheRbfcyG0jZ0SQ4TC/L82SqrgH+HvtxSbS1e5S5VoiYWulSf2fbwwXMsMQWOaW8mMTTZc5ARZDtQlsAAZBY53dD1T6fDqSNpmixpdrGqEJNcsFiBXI42gtnjJJbIAAPYee2lrqkmoQWUTOv73KRnACvtAyw7HavJPYV6BfeKbPw4s2n3EUM89ioiDS2+1p1AG1Sxy3DFyQRg4+9ySSV7J7mUk+ayMnUfDWorfSX0sv2q4iYCGGyDyxxqnG3OSRjAGDx154NYE2h3txeRjUQtvGkO875CPKiXOeuc9xhcncG713Go6vd6voD67BY/6GGWWGOcxiF8KVkw21WY5XbsY5O3Kt0A4weIry/s7fTdQZfKhZiu/II3EEjJzxlV4PGRkgkKQo35n5ELmsjnb5AQNrZKAL1z3P9MdqqxW0s86RQRtJI5CoiKWZiTgAAdT7V2Wm+Epry5hmURSJKw8yGFsvHGwyWIAO1cEjJ749jXTQ+E7jwXq1rrOmWKan5qeXbxiUswkbowbZj7me/QsegNaOaih+0WyOE1HwPruk2UVzfWZiEyllQOrNgYzwDxwQf/1GspNOuh8yxkYOM++K7HxL461fUnmsrqwWyBGwrJGTIisQSCSO5AJwB0rpdLtdO1OHVrDTp47meC3KQGZNikMANy55bknt1x6gnWm1yc0tzKdSatZaHmdre6ppju0EtxGcDJjlZcehypHvj60+/wBbu9RuTNcuC5JJO0AgZJwOOnPTpXS6VYacL6cN5N3DGE8tZGBkuN4yq7dwGRt5G4DqM8g10F54bsJ1Etlb2lvcvjzoRMxCKeCUG0dcNgkgHGenQ5oJq7Byd2rHnfm3NzM9zJJKXJ+aRny7N9ep6/qK14NMeaxSXQ766nlnbyrmCKJsQBuAJGUn7xzxjn17Vlaq0JupVNu8EkbbCN2VOOuOPbGapi+fdFHJ88cDFo1kGSM4yPp/ia0aWokm9T2bT47C68L2MGoW9umpWJDs6RFZCB8q/MmAW3L33ZwMjJFee+I9WuhrN5b6vazwyxEiOESFDA38LY78Yx2III465A1yZImjjmlkQnAjmPAXBGMA4x87fL055zViaSLWbBGdol1LzWkkbdtDKTnGAAo+Yk+gBGOnGMY8suZj5e4k2vaky2zwl1hs1IjIYsY9zf3uoJwO/b3rpPAlyYXvNXKmSRWUPJK28JlslsHkkgkZyOC3PGKNH8JvNo6f2kJdjSBmt1lQA8kDLDJBwGyOwXJ9hrXTtAspJ7K9iuYLhCDC0mdjdDgAhuh9iMcnB5TmndLcmSTVkaniKSDxJ5IhZLecSr5U4JRYyeBvIXGCcAcDluMndWFrw1HSrMQ6qw1MywKFdpWxFJ15z8xwPouc55Uiq2ieMhpusC7lhDx7SGRBGhbkkZOwkrz0JyTj5sDFZviTXjrV5LLFGsY3lQ4ZizxjAQHJxgAex55pxptaAou9mtC3oKXjanEkeoNCiJ8kiTqu0HOMANjOTnAyc+/FdOTIjCN7t707iGupV3Kxb/ZJyCAAeT1ycgE15zaSyrOhRnDKRtKMQynPBGOc5x/kV1DXV9pmh2l1Jbb1uoS0c0ow5+6cgD+HLAZP3jlvrU4Sk9DOcVFmjeXKRmIRs0bohSVmfcWHB3Z56hRkgc4ycZwWaDf3SeIYp2Rrm1EmFlMg/dMeBk5ChuuAcZB45wRz8uu395siWTyzu4G3cR8vJx0xkDtxx7k6mlLfR36f2oFSO2XzVV1+Vuc5yvQ4DfNkY2474o5bLUhxXKenaZrkWoWq3lv5jRmRBumJjy2QMD+8c59uncGuf1rVrsXt4LuVHtGjK21xbgsBiUYOeCeCeN2NwXkcEMgupL6HTZ7FoVT7U6JtiCpEN4BKbyCyhjGQBkbgTngbW3dvpr2VokrSm+vPmCogaUKxUSFwmQ+SSw/iGB1PFTTjGLOKUGtEQWljeXVrJLbSXZkmVGEsrHYfLYg7xzxgkhTknjb/ABLWHfa9f2OvPdqqrqMeUuTvMiydAVIzgDIxtHTnnoB19lrECzJaXOxZ5UDo+0w7iGKngldxYg44J3cbSBmsPXIYDraXDyI6eRJbXMS28gJKc8E8u2cHJxjZyeCRvTm3NqWwnFLWxjp4pm/tF7qezjlMsiSSRu2UJUYwOMrkcZye+c54rapqU+p6tLcxebE03RfOJHTt6cdunpxxV4eHp5FM8UbGIthSU2sSSoHfH8Xrgc8nFbmj+F4Lm1ZbyFhJIAVYEFV44YY4b17/AK13xjFLmOadWnT1MGwN5dyJDf3koG9VZ5ZS3lqQcsVJ9B7D16iuw063luLibT4by2vIR87SvxvJ6MD/ABHI65PQc9axYNN0q0kmlsriSTyMfu9wDhiOvQZUHHPofeus057XT4IzbyIR8p3LH8rd/ujIBznOAOh6YyJlKy0OKu1J7aEF7oc8QUXSyBnySBKTk4AOfU8AdcY7elV2hhVUmiUFQM7mLKBnHfjsePrj339R1dZ2jWeGRYpOki8DsfvHH+PFctcyvLE/2cOspc/vtpfaCPl+7yDlvQ9eueKdKUnH3zirUk5tQd4lbUntLeMSSOxUEMYk+XcCckrnr7jP5YpLfV9OVy1syopTLKNxI746YwBnqMDrzVXUDHcQyedKXVSSigDC9ySf8/zrmIt7SSLBn94cYxk4zn+ldU1obUKEZwtI25Ljy72V5QrpI24kYOCecDqOvp9adafZbgiIu0Z3H5s5GOMZ+XGOtVba5SG1FvcMJGQnaMD5M88EkHPXpVV4mimJDlRL91duSeaFLqb+zT02Lkq3NpqLJbw+U6vllRN2Rn6ZbI/n2pI0b7RIL9FKnJVSThORz14HYZqOaNhbm4jlc3CnJbdyB2B/LpUSPcamsaXj4UDAlZew9T3rS5Sjdc339zUjMkUIihVPs0jE7JT8jY69yQfx9Kz75FkbMIwB1AYEE56gD+YqN5bjTnRZJUniPC4IPSrEJW7iaQRKu1xlt2Bz6/lWkH0ZmouD5+hQSLdwWC896njBhYKBuDcexqaS0YEE7MH+LP6+tMRjE5WU7kOQDtyGrdaGjlzLQYxjkVRs5I5POT71VkRo2wwq1NKLa+c4znB6eoz0pt1Ks0h2D5OuBVlxbT8mMiKleB+dPcgrgHn6VXIaMkjODT45Mt8ybquLsNx6oeOvvSn60jZJ6Y/Cm555roTESZGME/pRnc3+FIAD6/gKbj8Ku4rCk4NGMUm00lMY6lzTQaUGmAE0maTNFMAzSE0lFBRZt/8AVn617V8If+RRuv8Ar+f/ANFx14pbf6s/Wva/hD/yKN1/1/P/AOi46/Nsz/3yp6n6Jlv+6U/Q7yiiivOO8+YP2lP+SmWH/YIi/wDR01eRfxj6H+leuftKgn4mWHJH/Eoi6f8AXaavI1XHJJJ96APtzwD/AMk38Nf9gm1/9ErXQVz/AIB/5Jv4a/7BNr/6JWugoAK+cvE//I3ax/1/T/8Aow19G185eJ/+Ru1j/r+n/wDRhoAk8Oazb6ZGbSxtwlzLIxubwgZCkk4yWGB26j2rp7fTre80l5NJmgt5TuVcXRZt/UL3x7gH8a4WF9KjVolvFilB8y4S4jIZ26gR7QeMd2ZRWh4eu0EM+sXOp+RcyOY7W2k3kJFnGQAw9MZB+ldE4qom3e+mv5L0/JK58xUp8krx7/8ABbf9dkjf0W6vbW9Fr4ikT7RLGVjtzAC1yo6gnaQR2wSAa2RfWGmX99fJDHZyXEaxsxvEzIw4VNo3qvHT1/CspNMa7jjvbfxk8kUS5mT7GHIPqpPIPYEgkeuTUbadHp13nTReXskkfneZdbcLg8MTtB454z+BpxlF1EpadH/Xpr6+RN2k5R12fzX9Jd162LR1q+i1hkAuJAsaRStChlJyM8sVQL+ue1c9N4j1D7BeQXuozW8r3S7YJLZ/PkUMMAPnCnHONuPeuk8Q+ILOLSobqK48qXzViluI4w4G4EttDE8gdcAjJ7GvPpdfgstat7m1v7m8kVHZmvDkKv8ACq8k5YDHHY49SOmnLXXpp93vfcRZyjdddfwa+/v6I2obvWdUs7iEGHyruRmifUhvk2r12KinawwedoPTANQ6b/Z99NY2WpW+nzmEN5klosqTyOSPl2hRvOO2DjHUVi2S3eu6hLLPdz6Y2DIRH5gSONgS7E/MQv165q9pllLa2KyBbiODY8rXTwmQSpvGNgbYAu0ZZiTgHJ9B1RWquu35NL8PxREkrOz6/n/X4nWW9qy39udI0mWUoWiiWTMSKhztYhm3cDj7uPYGrKabdW/npGivNdbs4beI8gAM2d3bsB9T3rk7nxU88YnW3SxhC8RQZj8xuzkKc8dgS2P0HN3niZ5bmVnBdpPvFWwWI+7k98ccfyq5YV8vvu35nP8AWU52pxvb7v6/An8RRail75L3qXDKhVzayOQoJ5V/QnuOnStHw54H1p5YrxppdMCnKtGT5+PVFH9SOveuh8JeJUvltRqNwpumOyCOQO+75iMAknPTocY/luR6zDc3cqJB5h8wyExuhI5XOGPBwGHAP4GvPk50/dW+p1351psVDpCR6BcafdNeODJwkk3zyqCNz/eO0knoAAcE8444y08PahM1zJDa2tgj4AtpiX8zqMjJL8EZyOTzjjIr0MCMWe+4L4XIjmMYDyNuJHPAxyTyoz6njMWpQPc223y8xlWT5HU4buCuFC5GOx7+gI5vaSjdp3/r+vQuytZI4yXXtW8NeHoFttQinMrOI4Y7ceXbLu3Ehmw7En1HGTkk4Iyr34ja9eyNmWGMOgjZViBBGMHlskE9yCK7O80GLULdbeQee8BMqRwqwhY/xKzckE8dME+g4NRWPg7wvc20wuraSK4GI3MM7BEbbkugbnseDuxwDzxW8KkXdsHCy2OOh8bSrZ3NvPZeebmNkkke7m3Nk55+bGO2ABkfjnn3kKsfLUKXJOxcnHtzXf3/AIB0dH22U1yq7Swa5uUQrgjLOCm0Lj/a/HOAbcXgnTNP0Ga8iSPV3zsjFtOWbceBjbw3I+6Mk+2DWka0LcxHJy6RRm+GNQ1zT/DdzNZOpe4jMdvGqguAGJLZHI/iAHqc8Y5oaeh1G8aKVI4riSQB/MQr6cY7EegGeMZ6Cup0QH+x7FcTRp87SI/y7CSchSB65PJ69607bQtKhunvGtmkZTgO6tuZs4DYJx1+n4du9UlCTnbVnkVK28fP+vyIdJshaxxo6RSywkorFQWUZweeR29ea2ojbk+VGvlkNgNH8qqemPTPbgHoehoESzy/vUCICNpwpbAzngjqRx37461XgtHXUGuLnULifapVYQEjVMnP8IBPQDn+tYf2hRa996+RDy+tJ80C+YQJ1Me3LHAjOQSf94AkYGePp07wpbrHcFXfyVx8w5II9cjg9OnX9BUn2W1PlsqufLRlWMBSoBxgAbcDGBj+tSKsUURiLzXDu3DyJlsk9MKBxz6f0pQxVOcvdl/XzJngatOPvIz50s4sXdxuCwY78ZPy9M47/majNm1zbzhm2zSRsISMEW5YY3ggZz78+wAJFZfiT+0b2eLSba28tZJFYs0gZphk44UnCjGTnn7vA79PAtraxvPcXHlpEhZtvdR1J9eldcpRdNvc5FCcZR7nmujx65Z6tcQeaEM0irPcMN0agq2H3jIyMk4PORnscbFvfeIIdPWDT0hvjGzQEQkuECgBTlW4ztO08DrxnrZurtft97JLd28lvbnz502pMFXb90qzqu5ipUBdxBY5IzWE1lp+v6g8Krp5vGDx2hsXKqmArDEQkwpG5uVJXKtw/LDi5pWuz1UlJ6orapJq2tx6db3Wnyp5rruvPLBLkk42ufuqFOQgwBnOO9Taz8P20yWKO61aJzJGGH7tyRk4GVxn2wMnjpUxl1q0mX7ZpdxN9jgXymLmSPfgZZwGAPG4jHI/3RxXuvGCX7Qz6nbTG6aUO80FwCqpgbQqEYwOTtz1+vEpy+ydcVpqbE/hrWF0yxvLTWo2KxffnneGSN9uSofJ3AZYZBx8zYIDVzIS48B+KI77VtOfUHCkiRy0atK3JYMQdxAz1Gec4yAa2tc8dSzeGtJInhS68srLCw3bChwMjHIbAPPBI9q5Pxj4yufE95C8iGGNIY08oZCllDfMBn1dsexxVR59VIpRTacT1+28Z6XeaNJqcMczxojyyxsrBkOe+MjHU5HQDPaubm+LunR+VDY2c5yw8yVsNsHT5QfvHHrjHv0rzSC+u4dP8ku5tZMgJnKuTwcD6N1rNCSx/fizvAxz68gj61EaML6j5bdT1xtYPiFMTalayIVZolt8Aptb5MJJ3ZkU88ncu328+8YeHb/QrwreWskUZICu86yFyR1BHOODjgYrR8Kpcfal+xRQ+fEvnPIxZTsDDuSNuCOox1z1wa6LxIlpPDYWmoW8ksscPmtcW7B9uAwUbGUfxcHPOPvHiqdoSSMoz5Z2PLrORo7yJ4pGhlVgUkD7djDoc9vrXaWkl1Nd7mgsoooDsLxgWgZsq2/CFXlYddiHhmQ8Z5u2ngUWdyt68zyCB1lj8l9jSYO4bcA4zjgg9x9KdJqD6hfbNPtL25uI8ypMELyyAsVd2kQrIFAMYCqV67SD1o5lLYuclJlC0nso/L/0+W2Jj3u0BMUEqlY8QgKQSxON5z91AxyfmqzJepqlpd211NJdvM87xNZy/a0CrGVGFndjH8xZtxAY5IQgYJoDxpbSB/tdxfvv8yI5EDN5Ih2qm4xZJZiQewBJ2kkmubuddub6SWBJbo28krTGHzSVJ2j5iv8AewuSc/kKfLdhGnJlexka2uY5hGJEVgXjJwJAOo6emea9v0HVrLUdNWbSYg0EDYAuCm4D5TyATsO44yey9OAa858NafZaza3FtOYbURHzDMzMHCbWOMbWDdA2B1Ck8YwczxDJb6XqLxaNeSxsAgmMeUjZuG4wxztPHoSuR6kmuZpDcVUfL1R63r3izStOjaR5FjutmTbyMVPQHBIB+ZguOTnBxn5q8k1rULfxJrEl3ti063RNqAruwAOM7RliT/Pr1NYV7JvcytNM7SSFmEpyeQCDu7nk84HQHvwlteBMoRmMsCVJ7j/J/M1UYpbBGjyK5rabp0Q1edLm5eW22Pl7YkF12gg9OOD0OOeCRVTU7CK21SVNPkmntQ+I5ZEwW4BIOOCRuAP54GcV1PhWztPEVxfmWGNduG8uP5dudo4GDwSMHptznI6Hql8FfY7cG1+yFlwGWYs6oSCM5Gckg8fe/h98Q5tSJnWjB6v5HL20Mc2mWk+uRvJaQ/urVASokPR9zgbjjAwMgehAG09GYNGttChuLCKHTZZoArpHE5nEJlyfn8zkkqCM84IAYDFO1yxu7fSW0uKUTvAy+XGq4aTA6Y68KSevRMHNYto2pSxi2vrdLCxtf3Ls52+WC4JXJPzMD8wUnop98tX3OVz51e9tSq0sllfIofGSrNFO3QEDBABPXHv2B9+ssdBktrEyT3sku1htDKy+QiseN6cDkZDfdPBA9c200/TNSkaO0ZpZWdpDdT7VWXGcYUYIyWJOV6ALXaWFo8Hlxy3NxOEDSF4cblIwNhQDIB54weh6Gq5VLRnPVqbKJiWulww3c91HaSQWhIADrsduobDqTlOOmSB1yBUuvabCLFDO0dva28gaSS4BdNucuo28kMdvB/uiukltB54LJuLffG0gnjPJAPoR82OcewqnPp32hbmG6iikgkZsTGISZXC8FSTyCCOMYIBxyacaaTOaVSV7voeWS2mh3Ul1DYsREsT5uZsR5KDOQCCep6fKetU7G9n0LTXu7eFVS6zHHcTxZaTHDlM8YB4PJ5/Ts9T0jw9deVNNIIJ0QrsMoQhgoAVkHJxg8BvUZwBXJeIRpttaQW8F5NKiq7RxPFgRkkDGf4vuk5zjjp67qLTujtp1I1Pcd2aGjX39s2Zt9QZALch1SRdke3PCjaB1J5yRj5ce12waO01Ge007S1CrI3mZRfMcEHBiLjIX5WxnOcAdTWLa+JrEW0NrNaKzRRiISrCsm7/aBODknPB/vnuOZH8QQx27W6D92uDuTJBJxn5sBucDgEDgZzjNTHmHOm72UdPwJtc01tIyYJmdJTvEQTGAeoABJUDIBGeo+lYR1B7mN0lIzI6nZGgA4+nTqe1astw1/FG3nxpbgOfKSJd3YFuCMZ7gsOhweabbaYbqeORVicnBClMAgdsg9Rz3+vpXTFu1zNWive3Ipf7Pi0ncEKXJKhdj53deoJ9O/rjiqs17PcTR72yVAwV6j0rQubm0s7orNbFbq3OECbVCn6qSDn+uRjNMs9JuNRn+1IjCN2Lbkj+Uc/l+VXey1IjZay/H9DT07UWWaH97veJMBdzKTkdepz9RUiaw0n2YyNvZZN5KuCT2PGPU55rO1Gw1B7wQTJ+8HC54yD3yfX8qqz2l1aSrE22ElBtKvwwPfOf/ANVBPs4S1O/tLtWsw9y0KSP8u2SQISO3JwO9YOp3MbS+ZCI8tnLb927nPU1zHnSWkbxGNhIr5Egbpxxjt75+lRPf3lwsMUkskyxjbGpJO0egHaiOiMlhFzXTO10/V573T3s1jjjVyT8kY2/TGMY/zmp7nTnREETJJdrgKuwkZ5yckjg9+w9653RNTFs0jOigqpyDKU3egGMnP4EUieNQJZo7qwU28nC+U+2RBkfxY+bjPUZ9xWSTXTQiWHlze4jpYrkvdR2/iFIjKoJXymaJUz2IwP6j3qtbakHuLhbEAIDkxxSAkkccbcHGM4Az9PTJPirQC/y2N4hI5fKnJ+mRXPzXcR1D7Tag7c7gHUDn6CtIaLQX1Vyb5lb8jvrrTrO/s8SJNDMTuDsoXgf3gG5H1OR61nWljLp15JcJFshAyLhVZVQd+hJ/MmsaHxTOvyyuCr8OF4OPbg4/D6Va0y9t9VmjOoQxq8YIRkA59Ad24YHrjP41tzaWZi6FWnB32OghgtQJJGcxqVJZ1k3bweM/T8DWXeXurafceXbzSyWv8LPGpI9c9eKu7otAhVJ0bzJOfNjAYFfQZJ/Os+bU1ljK20ckrKwzK5ChfbGK1i9Xc46cG5XtzR8x2qStcafDK7ESKxRhuyD+HQVktcysoV3Z1HQE5xV+SVJrdozGFfOSQCCPrnj8qz5I9rYruo3cEdlJJLlaG71z8vX0NWI2bcoYdar7DnOPxNSxFjOu73/lSxN1hql/5X+R34NJ4ql/iX5nZfDf/koOm/8AbX/0U9d78Xv+RRtf+v5P/RclcF8N/wDkoOm/9tf/AEU9d78Xv+RRtf8Ar+T/ANFyV+fH6SeB+JJfJ01DjOZQP0NZVjrxtYdtsimdvl3SIDt9x7++K0/FAzpaf9dh/I1yQTvz+FdtGzhZnmYlL2lzWvb29mbdeX0l0c7iXkLY/OnXWrahcQJbzssafeASFIyeO5ABP410XhbUX1DR5LO8s7C4ihwiGS3wwB/2lKnPueadJ4TiumPlO6hQSVjQnA7nkniuuGDqSV1FNdNjmVV9dzA0WaE6lCl/KyW7P+8ZERmA9txA/Emuzmubi6upbK78hZmiyLmQxPHa25/55xbipJ/2csOcVjw+B/tG8W8txJ5aGR/LjztUdScdAPWug0bTNasdGay02a7lsriUoFFokqs5XBUblbnHYVOIwNacdLXXn/X/AARQqWldf1/X/BMOG/1IabatbWhSEO9uL2CDLSBtoK9OD6dCcn3r1Hw5FqDXU1tJI6aVaqqW7XsTIScAhgHw+ByDz9K4vw5oIstesTLdXNvaxTksJB8oZRyOnXp2zzXfeLLiWx0mHKC7jupseZO8jBAFJG4DnHHYAZPJFRWpRoJRn1/pfNa29Tnd5VLbdf8AP9E+5X8R24Z7B9Una0WORd1yoUrLgE45OVzwCc+g5ODU6Iw0rN15szbiWlgt/LLjOANsm4AEccnnjkcVkppq+LmS31SyuLW2jQM08MkSszr2Klm2gZ7j8fXTn0+2n003FlrNxci2hMQSRxnOcD5lwo44zt75B71zex9nB273+X/BB1FKSv6f8Dz/AOAXfLv57B4xAtpcrxFL5gdVBOeegPHYg496o6h4VtJru2Gqwi6MZ3fbZEDKqqCNswlZsqcnpgdCKreDruOJrmXUZEF/PtiTLhmkVR2GMNgn7xBPqTWtdaNqcuqx/Y7yPTbLYY2Ag3s5yfTDBSO+4frSlDlqaev4ERndW+X9f8E5zxVc6eukrpukPbyC5JhKWsvkwkAZLBiSmOQDx1I5yK831SG/0lYp57aeAum0vJwr4PXGPryc5ya90vDDplpHa3FtHchRkKQpEpC8kKxAXvySR3xzmvJJvF2o2mpKLmws5PKwdkiK65HzFlKYXOec4JGBz1qaLTk0nfzOhqSSdvkW/DPhye707UNZ8Q6Y8pURi2Wbcu8fNu+VTnHA4YYrhTp2oX+qSWsEEl1c5bcAm5sAkkk/1P0r2izFv4m0JbhjdwTOrMGsmCStyflUEkY+mO3als9O0rSdPmtXvAsbRm5MskqPIVJxvOFBwcYyvp161u5KFRnOqkuW55wPDmv6T4c1B7qSG3jeKMNEZcvIm7J27SRx3J4xnB4NVG8YXTa3YtGCba0CQxwjdgRL/CBnI498nAyTivQtOvYdZttRm0S3s0uvLEbBkJD/AHjl225ZWHPPJ5DcGvOH0K8sUubm/WNYwzDMEqTYwechT0Bx1AHI9hWq1laYoVLxfNv/AMMb/hjxnoekXk95qWiLcT3YMc1xHcykhHYEqUkJzj1zngck8nD8Zajpt3qqTaX/AGiAw/eR383mlFwCpVtxbByThievWsBUmv7xktBvGSQQu0AeuB0q1fWGqLDBby2kb7Q0iPA4kypx1IJ2jjIBxyTxyappKSOiyvc7TwNc2mr6Zd6Fqlu97sG60jgdI2UbgWHmbhkFinr0PtjmNW0v7NJFIHVi+SyRqqkDtwO3XHHYnkEE39B8L6xYT2uqRvLA4EgPkSRrJA218hi5wo2gnJ7EDqeO4j0i41e6+06bdRz38MgJunljLyO6/MGMag4GDjOOCeSAAsSmua8fmZ/DfseaW+q3GmalL5sQ849HKhWXvleMcg46dDketW9R8QXl3YyF5pJHnlVpLmY/vSVVgsfBI24ckHAPJHIFV9f0i7stZeC4miafujSjcBuK4Oeh9j2wemCaul6Jd6ndeVjykU4aVslAewyM5J6ADOc004NXY5RS1Z0Phfwhqmr28V5btaCO6lMJ86VweFZjlU52sFZeuevA6jqtH0PVPDy6lfao1hBYy2U1qjRsXXcThWCvwwBXABxkEYqnp+hv4etJLvTLmC5mKhlR51QliDGSOm3AfruBJAxjBxNrE91Np87rqKWl+0IAt/tJfcAxjO1duAGwxwCTwDk5qXNSbszBOTOBN+Ydba6064mto/OYxSNneqsSG5yTnDHuevrWuPGRFk6wbIbhsKpjjIKgYZmzkYLMBwOOM4yATiRwxx214dQ82G6UoEjJC8k8l0ZeRjsDnJzgjNZrxvNM2CvJPUhR+vAq+WMtzR26m7rPiGXWYYIpgrtFlWlAO51DZRQSThQCcDjGTVKCw+0CObG3eSqhTkf5z+da2g+BNS1bY7Ktpbl8efOwA4OG2r1bHPt8p5GOO/0Xw7oVlbNBLbzzuxDEXUTxs2OMgEKQo5PY8N1HNW5qKsc8pqOkTyzUrW0tUgjgeR5BkzMY9g6DAAJzn7xzxwV461ShceeAyMY88qDzj0r2TXPCWmatMsmoXEkZX5IrO1dABlT8qllGW+XJGOOfauG1/wAG3ehSDy3N1DkgSpEy4YZBVhj5ScEjqCMntRGqpDjP3dSW78WLp1qbfQy8SSBSpL5Ma4/1bKwIIB78Dv3IHN6rqjalDZoIBEtrD5Qw7NvOeXOehPTA4GAOmMdTZeHftuh389tb2kk+RKpt5N8gIK/KqYAGF8xiPcHgBawNf0sadNbiK4Wb7RAJCquG2jJxkjHOAOOnoSMVUVFPQcZJvQwfmUEe+cVIUkG0DksucDt3/wDr1pWWkT300MYCp52SskrhQ3sOpY8dACeRxzXbeHvAelX+mma9ku4bmIkOqygrwB8wxGSOj4XrgZ+qnWjTV2XzdjzdVJBZucCvX/EWkf2vZPaWSr5lhEoOPkUDocADA+5gDPTjgYrk9T8GTWOs/YLaRbpmOA6soC9SSfYYOT2x2rspPDslhoFnZ6tqs8kAi2pHYLs3tknHmNkYAcgbto2oDxg1pGvDluedi4OpKLTs0ebaXcGWYQtNGqFgyLJCXLNyBgKDkgEkAnHrnpXoPiaCNPBCvc747fcrFyDuZmYeZ3DffGSrd+f4RXM6hpsV5eNb+E9LuIpLGXMs0u1GRwAuA2Rx8pPOD1/DndZ1fVr+OGDVLuaZY+UV1CgjoD0G76n3ospyTRty81raF6bUdMigD6XGVn8/J83LrtXofLYFc84GWbjcMdGJpetajZR20wbzIrQ7YvMI+Tcc4HcjK8jkYOOMiuejdwDsI98gf5FbFrfNPAtou63tiQXQOSGb5fmP4rnHbJx150cdLClGyILu5nuJiZ5DIVwoyByBx2/DPqck810kd2ieH7VZHiMyghkkI2MhYMFIC885PXg57k4qx6Q6XAZ8JDHgS3EkL7SCc5CgZ4GB9e+MUjR2VxqCySweYHjG+MblVCDglSD146EEAnuBWl1ZHLJqehsW8dw0C3OkNZpLI5VHEKDzG67MkZQneADnnpkYFTWuqX2sXN5bGSWCKT919nYL5it02nIz3wW65A54xWRb6LqOn6la3UdvIYvMEsW3EwRAerbTx+gPODxWhrB0u4uvtmkpJFqBcyM0mWGMcllIIHXvhRgknG3dcZo5pwXNa3zN+CytZmjSBIYHkiR2EwbdsXaOWPThgCox1xu4qDQtQkTUJR5BJyy+UhOQdwHViSAOcYAOSBzmuQj8RO+nTCaL9+o8oPHsSPYxyQVAx2PAwCGPTHMuk6hcQastxYTRxSRru3TPtXaqkcnPfPbn+dWr2MpYeXK0z1G8iSGyE1vexSKhzKrlTjOOOSMdenXOOnNYU09i8SRtPDLctnYByoBPUt/FwxHUZHXrmrcF2JrXyNUnid59oUGHIfcRwoJJOM4GP/1YlzHYRXEl7dzhBbvtgtrcggjk4Bwc9cZ4xg+mSoJrc4rRbtsReKdLgGix3cIjieNgGRWLZzxt+oIPOB0PsK42JxHJl1yBzj1/Ou28RzwpafZEnEyyoWcGNdzHJKsuMEYIOSc5yMcZxxe1VkK/e9AeM1vF6bnRQuo2ZeN3I8imT51b7hJz36H0GPQDOPwE8KMscjycrk4/d4x0OQeuD0/AmmgSPACRIqrzvwAccY+Yj6H0p63NzFDtWTzoX+8qrjcPY/5796q4parQckBWNmdtzklV/u8+ox7g1PFtafyduVfGXjB9M4IxwRycf4VnXtyBP5WxoSFX5XB3EgdcHp1x1qNZpHdIxlx0UMSQpP8ALNWmHs3JamxNpdvkhgQ27Kxvxu9ccg/hj+VNs9Kkt7rc9m7RN9xipJHTJOOcc+2apm8ltmAldTIFwuBuYHqB6Yxke3Wuih/tCO0WWRirAfOjISAQemc4H5e3Xg3zpM5KvtIRtfcw78+WDHHGPLOSDg5H1pHlSKGL94rfKOgwwOB1OPw/CthVivpXN424t8oKYGOe+f55rDvLUQyEQvuTJAOR0ziuqM77E0pKVoy3M+4YtOxcliTnOc/rRGrMpPQdqmMI/jViT37U6ExJJho9655rRHbze7oRqxI2t0qKRSnK8D1qW7EUcgELMy4z8w5BqEtlMFjj0rS5UddSSP5lyTzQeDzUYxj5c5qVWOPX61rFg0KDjlTg0ZyeaRCQxwAeOhFN5zWtyRxooHH3uvvSZPSqAWlptFUA402jPrSE0DsBppNKfem0DLdr/qj9a9r+EP8AyKN1/wBfz/8AouOvE7X/AFR/3q9s+EP/ACKN1/1/P/6Ljr83zP8A3yp6n6Hl3+6U/Q7yiiivPO8+YP2lBn4l2H/YIj7/APTaavIxwRXrn7Sn/JS7D/sER/8Ao6avI+4+lAH234B/5Jv4a/7BNr/6JWugrn/AP/JN/DX/AGCbX/0StdBQAV85eJ/+Ru1j/r+n/wDRhr6Nr5y8T/8AI3ax/wBf0/8A6MNAFzQPC58SxKmpSWqQK3yFVzMVBPG7H6ZrpZtA8KeH7kRW1naRSxoGJumDlzz/AH2x+Q9ea4/TPixFo1u9gdP2mH90GVgA2OCTgZGeT3/rXQzX1lrGjteXd1JZWfLvL55uVA6bcqWw3TjqO/rXVUdSy5Y2j38/66HzDtzNVN23ZfP9RF8RW0K3jXt3BblkHkJFCdhHP3ArneO2RtHcZFJ4RtNGnguGkS3v/NmDM7x4jXHYjaVU5OMHGfWub8UeKvD50CPTtGtBIzAr5hQL5a56gnOCfrVbwdrEemafdNeyS6nDCF+ywMxVUY5yRk57jopH86qlyuM5SVtEl5pW/H/ginCTjFR6u/6HdR+DfDssty9tp7YkbDh5jtTHPCjnn2PH04rn9S+Hul2iPqUMpwAWS3nJiVcDqWzk/hUyatqVxcQgWcUcMiElZRM7hOAXLMvB9CFxyBjpWPP4e8ZeKbG6kJEdpCd6JJ8jTYBCqqqoJOMY4Uc59aztyvmjK1v+G/T1Lg3zWlqn+u3/AAfkZg13TNLuZ0v2nvL6RxvuvM82Db/BhW+YgAjKsD0I9RVV7y+W1Ol3zbY93mzJ5KxnAwUTgAgcBsdORxla5+XTbuy1AR3VrIsoO5o5kKnHuCM4rY16/mvdau7u5jWKSRVGxBgBQoAx+AFeth5e7zPX/gbHHiVy+6t/6X6/kZ+paiZflXOPaqVsqzbgfvKQc1E+Wauv8DaNJ9s/tSXylit/3ifaAwjdl5ALAeuOOtVzc87y2JhTVKnaK1PSvAXhxNNs4ZLrSfJu1jKy3ImUk5w4X73y4HB46D3Nbus2kVzY319AkJmiQB1L7kbaMjPGQee3Tr9aVjfLa2sdxcGSdsALL5LTNIxJ3EAIMHPAUFiQBwMEVJoeoWWrwXWowC4tTNIYVcJtYj+J+pA5OM+1eZiVKV5dV+d/6/yOulHlik+tjEvNR0y1t7a41GGW3Z02QxTpgpg9cqPm46Hp0ye9YmoeN7rUroWGgWzXKyxlI4W+5EoGPugDGB15IznqMCt7xTpU2o6baWckQ86B9sBjZfm3YGeOT07CuZ0nwj4ig1F7awZoJLghJZHtVYxqc5bewO3HPQgnj2rnpuE2/VlOHIk3v/X9eZrW/wAthB/ascUNw4XMcieSA3QqN5yWGTwBjgEgYzWDd+LW0om4094k+0AvtkmSeQkP8mQvC44IUgYx3q9f6RdaTIbTxPc3N4Cxjt3glKIXyOWb2APHJ+lc/HoMF94hkj1Keef5ggMZALNyOu04HQ5YAgfTlxhd8wudRWpo6H4qe+vY0mvEs/3nmsHjXyieSckneepI+YnPQc4rs11WyurA6pezKDGBJJ5jkrIFbGF5LFc+5BI7dKwo/Cdno6w3dta3s13HiSMySr5cZ4xyAu4g4+7nPXgYFbGrXdtbeGLu7vorW5SIBDl43YNuIZVMiZPpyDwOMEYqKko2tFDjdu99zL0jUH1KRLWMLHP5Xnu4iKx43bfk5Bx6E+4/hrofOjiIABdv77ckVT0thFotmUgaD/RYhsbqigZ2/XLMT3556cI8iLkyuFJPJPPrxSxWLqSXsYv3V+IqOGp87rNasveaMLzy3r3pMtgbm5yTt6A88Zql9tRFZhIDtGMZ5bjOPrSxXUbsMpzxh1HA7devWvPUWd90W5r8RwBmEeEQsfOOMYHQ4BA7n8KxbnxGqQSNGmFQgsc5BBPAHPHHt3B4Bq9cJbtJ8pIeQfMDGWLnPfHt0z1561RFrodvu3WkHneWokikYsmPTy2OPXHHp9K6aXLFao56qcnoVFvv7dFtc/2nHpjlQXe4bIOO+cgL2HU5Hpg5W51E2uuQ6QNVhmS5dBHeujo0JDAqw3oV5YAZG7OAMrwabfwaQyq9u4sp4fnhktyPkK5YYQHBGSWwMd+RnnK1TRrwQXlxoSxzwzxBZ7bhf3m/kLGvf0HHG5RnO2vTVVSjo7Hlzw657yKmqXUEtltsVhktIp/nmm8v7QfnY7g64yrZIGf7nYZzXt7+Ca8On6Zpi2Ut9IpO47nVWb5URyPlTYw+bnccHoeOfn1uW71B59RRboSPG0gkLEuEGAM5yAR1x+GKJpre/mhjtoAm0sDvfCv8xI4JJXjAxux6c5J6Uu5Tp2PVPD3jCBtFvnaWWSexURo8pQnaWIUswAGexxnOOPSsHWAPFuoyfZSLWWJWkluXDKXG7AGFUk46Z49xkVzcQjsJ5oJEXd5W9FiRmAw2Wz1wBtPJ4yB6cSw+KIbKOQQ+WfNUrIVJVmBYN94EHqAfwx04qWkm+U5OZqekWYdzHJF5gZldYmMZdW3DvwGGQfzqirmSffjOBnkZB4rtpfE9lqixNd6ZmSOKTyzJkRlABsVT1bBJPUdDk5YkZUunW9xNLJpCTLBHGruZAoKnaNxGM8ZPvj9aOZpe8dsZ9GR3Mt/rZR7q4jfYSxUuwzjtsz0HP3Rx8xPrT5re4tpINQkt42j3h2hknAlk4xyFO4DHQ4FWDq2s6Np62K+aqSI4RnTDsjjBUH+732g4yTnNauh+GXOiLrusyKLKxWSeS3dissjIxwmWXABIPQnkAYznao3uE5RjG7N3wz4bZngvH8qGfYrzWcmQHQtk4YZ4247HPOcA8z6b4gurPWNQs9V094FWRWCzyxiNBtZgnmEDAO3IyTyDyTnPnlh4khGkGyurm8t2j3lJIDu3A7QI9pYfKNu7r1J78l2hamkM37+6ZAsgkR3diqsRgsVBAII4PB4HIIzVOF3dnM4SV20d5/bCX2rRWyWaW1s0qlZElARdu0OXQcMvzgKRgnGOCa4/XdGsbe0j1Sz1iUoV/dQNEFaH5d6qDv4GCemTnJwc1a1HT9ROixQx2E32OZgI5p7JA0akb/vIWckDqcc4IPIxUtwmh2cdle3emyIba58iRQuCTEq5E6nchL9wo6EnLEFaUUlsOL5WYni3w/LBeQvHCkA+yLLdNI4XbJnawPPzMMpnABJbJGTzz9ndRad5ssUx+0FMREIPlO5eefbPp1/A6Go3K23m2WoP58cipLGLaQqq7lDK3zAnkEfKfbpgYzX0qd5BDBGZJfKExUkBtpUPwO/ykHv+hq+mp2wXu2kb3hzxKLKHUiYYWnuLOSNnkVTvyQT1UgDapGDkZC8da5SWRpJCW2jJycDH/wCqpY4JE2A4TzSV+ZsYx1z6V22h2EOgSNctbtNcBQfMVxvT7wBXGCgJxyCWIHG0MMl7Ib5YNvucs1rssQl66QPnIH3mx0wR0Uj0ODzz1GKAij3fI7MvfI2kfhk11vi6ax8qKGysDbhTlThVPCqu0KAMLwW78tkk5wM2PxNqd7pLaVfyia1kKqqfZkZgQQdwbg7sADnOQSOOKa2FzNrRGn4PsszNNau0c6qT5qdUQ8M2c/Lhd3Pfkd69f0+SCdUlsmIhmA2Bfuk/7Q78cYGPT0x5R4dklt4pLaWFLuGRljIDFN7PgLGCOCxAztyDhW5612s0usRaLHa2cuJFyHuHJJxuOVGeQPTB6dDzmsGm3qefivekrDvFMGo22pK9hcw3czwKr2zqikBBuwOQc5LNxg1wWoXt3d3nl61dXsZVvuyx58tfzH6DsOtdTba1fSQ/ZLG3iaSAtEbhldvnCnc4VQ7E/OOoAGe4Y4l1GwvtUfTJNTMEdpb5a6ZWG1cNzgNkklMYzxnPbGeunbka6nNG8JLmS9epoTXlv4Z8I2t1AwbdIoaWOENvO04bbkeikE9gAc1j3fxMjnlijtfMhhVQH3RjCNnOQN3zfmp+lUrrVrq1sYbfR5JDEqlppPL3BQMBQTyeuTnp8w59OR1Gxkhkby2WR2JMgi+6BxjsPeogrGtOjF/Hud/aeL1uNWS5m1S2jjbAW1CZVWCrn5mAC8jjnHJ5yDmp4+8WJILdNJvN0qyEyPG2VPA44G0kHrjI9zXnxgl7rj6CrWn2dvJJ5dy6xljjMhIA98jpgc5PHFaxlZ3L+r001Lt0HPrF3d3wmvJDK+7d8yA5PrgjB/EVqHzBrI0+ITxQQjy5luhuCpwSWwMYyMgY445J5roU8JWk2n+XZxs15szg4wCAMnJOAMHrnvx6VxZ1S4mEWntNKBDKXBMpypxhsYznovOCeKbi27vYqnUU7qCtY7ZW0swIsdrD8owshyoznnG0qwHoMZ59zm6ILUK0n2cCNysV3C8R3HPoXz83y99zc54Jrl7ObZCFlIcodhZXY4HTOVzgZ6gnnnpV6O4MlorLjcm2XyvO2qVDA9DnbwSeR03ccnHdFR5djzZRlGe7HXltb28jXUbW9lPBwiodpcE4U4GR0OQR1GDzg1b03Tri5v1F9OwZYtwDDLKBg5ODkgcn9O9VdMmja4gjkQEJJxFaxqCSc9WLcqCe/PvWslzZwyMLV22yMcuZRjA5GGyMdPUHPXjFc1R9EVVm4LzNFfD1rcvi5tUZk5MrQOAfxHToeDk89AKjbyLO4RbAxrLOrK4gkDgjI28nOD/jTbe8t3u0ezuIZZQoWQIHbI/2/l+f9O2emarX139h1LFxp+6MsFS6t5mK7u3LLndjsT+YqE9bnNTbn7si40luLiNb+DgkqQvy+wOP51avLXzbZPsime2KNlZguVzxgZ5HGeOn61At3GZknZ4pJGIDx7ssBz1Axz2yPc1pQ3qbWHDxqCxaNNwA9G7HoeeP8RzaldEWaSMNNICCMLa5tQuFM2HOT9M4+hPHWlfSs2L2ELNYsQSyhcs6kY53dPwPT06VrPdmRVxboYw3DKShUHHGWI5+lNnucJiQmI7sYJ+8PzzRzO5TnM8/udHuNOnMsE0M8Sn5JMKwJHYjkfhzVKfSGlsRdRj5t+1h0APXGO1dl4gWC38pbmdVXjYpO4ufQ4OQBnvTHtp44n+RxFt+TapIYkcc5AI+mauU9U+50QrS5ebqcCLLbC7zblIOFwMgn69qrs3lnGOK6x9Curm8W1Hlebu3NbeYAzDr0JznHb8qt3PhT7VJPE2lzaYww8ZZWbccD5ASSG5PY5/2RSVSPNynV7ZJc0tjj7SL7RMAqu5xnavU1pxf6LMGnk2q+cMseQ30HY1etrez0jUGheNmnRAOWWRM/h2x7/gKszwXGoK5sbSOeLP7owTqu0+pXJPPpmumNvtHNUq80tvd7l+01DTHsI1utkkqLwzdWGPu5BLD1HTtxVuKxjv1+0S2htEKDYomDYUfxYPb86wbBp7GbfqNtHEc9ZbZZG3dOSRkf412FoIra0meJi0Mx3sZoWyjYHG1hgZ9ef6U1Jp6I8yulB3TOWv1ksb6NZ51eKVch4x8v5DqcY5qAJHPkq30PtW7qGjXN/CzRwBIYyX+ZFRt3uB7ew96x3AZGM0c0QiIUuFBCk/59a76VSPR6BCakrLdbkZt8KQTyO9RpHtkB3c+mKmMZC5E28duDzSAKRli28dBxitMU08NU/wv8jvwMn9ap/4l+aOp+G//ACUHTf8Atr/6Keu9+L3/ACKNr/1/J/6Lkrgvhv8A8lB03/tr/wCinrvfi9/yKNr/ANfyf+i5K/Pj9PPDtU046nZGFH2up3oNudxxjH6/pXK3MFxA0cF3btDJjC7k2gj19/rXS63KkVijSSbF8wAjnLcHgf57GtZF0y3t4pXuLKAtGHZPOkaQjHTaF4B9jxnmumDcKfN0PKxUv3vLboZ/g24Xw9I93eDaqyKw3Hb2OCD2PcHscV29l44intZEszLMiriWQ3O772/JJK4yxYZznOwcV5xqN/C2nzS6c0nzXAXZyAo2t0IOTketdR4U083WnRS2oe3MMzedAyBFOPfqSP8AaIHtxmvThVfs4pxv/wAOclSbp6pnTjUrtLw6o2m3LR3DQMjvIzbpFIKksRyCR047YxiqyayLW1SwktMSwxuobcgdVbdnqhPO8j0x0GeanksRPqMTBfMhCLiSPa7qAPuhxnbz6Yz69qlxaDU5pHsXlujyqSkKODwSWOB06/7NUsT0cV+PTTuc31me8VfT8zM1fXZbuKOS4ijCx3bTxC5cmNd+0hMccAqTxjOe1Y/i/wARzajqEEtlds8AG/a7MFOOBwGIABz0/XvB4quUvGj+26vYySKRmCBvMCgnkbx8mRwOSOPxNc1fzqLW22/LEyEHg5HzN2z/ADrCpNVlrG3Lt/XzOimuZ88t3ozROrv5cj2qTKmQJ5gRIo5yB0wPXkfyrv8Aw9oOpeJ7S1luRNbWsWZYojCqqHzgEE/Pgj5uOGJ7dB5FaatLZX0d1GWBiBCsjFCAQQQD2616Pb/FC+tPCsK2qJDJG+EkRU+bIPVe5z1OetQvdjdb3M6sPfSPU5NNs4MK1szMemxW+ZurMVKnHPPGck1W+0rFD+6tr5wycedEwD8dDuUduTiuS0v4k6fHCG1aSSWYuqmcW+x+2SyhmGAWJ3cZx0JzXWW9zp19Y29/FEI7eUed9vfMZA6Aff3jcP0PPXFcc4OCbKioytoVpH/tCa1lvLcfaN6j9zIyleTjA35HykHoM+9UfHOvw6Ha263wRxckB4JAd7xdCMqecHnGQDjrXMeJvGdrpGoJc6UdNumS4DQg3kknLRDEnlhQgUDjuSe+a4DXdU8Ra7MWvJTetbRZeWC3C7FzzkqoPUnk+9NQ5knsaxi4y17G1deLUXRk0/SraWOFbjzPKBO0jGMruLHJOeOg6YPOelF9f+IdNudOsrCOyMcKokVtcEMJSoGZn2AkKp6AfMVIB4OPJ12s+1o5PMUZKHqx68YH+RWvoWslYtShks7i8M9usSrE2NihscrjkksBk9K3nFOLtv8A0iFFXXl+rOhTxXZeFtMOi2MKX0c25b64J2F9y7SFwMgDJwSSfwrH1iGKTw2+oIswlMnlmWd1GY8fIAMcnAGW4zzx3rHk0/U47WWaeyke3QtH5rxsURu4B6ZHpzUNxrkzaZDYAD7PFKJTE7Eh3Axk9D04rSKTjq9dBSilJcq0O40zQdQs/DdvNe+HjY29vHIZZpztluZJHVAjKCrqAMlScL35zznaf4oXTHeCHT1kdiS0lxLvZBnnZkDBOeS27PcHOKfr3iu08Q6SBNemG4ZRPme3MfzqCoWPaG+QjgbmPzcnAFYmm3q6VFJJHYx3UEqRNKjrvLYwxw4GYwfmBxjr14FZRcpNuSsOMfd1Na48TSvoQSzhis5ZJfMmeHBa4+VVBypxkMsnylRgFdu4Emp/DnjL/hHPPhFj9vM7AsPNDb2VmAZcrzkNjpzgciuImvkaSURqsMTHcqt85HoN2Mng+1NS7coVWRtrD5lBC8qvp04BODx1NapFSpxasz2C2u4td129ttRht0kijZt1k3+rBRVIDAszZ3EbCmAA3GS22bQ7xNN8Lzy6fNaz2IaXzEmm3GEFVKRK/rhxkYwGLAZPNeUp4lZGZZIVuVePbM8jt5krbNuS4O7AOCFzjgZGc1D/AGjNfuFkDyKu1VVnJ2KOABnPA/x9azdLSxkoPqeo63qUsukQ3V3dW7eZILeOFRu3HADIBuOArde/3eT8pqtYw3tzeQXmryA2unv5iMAI3QqxPLOMjHzdSuDyCW68BEdPdmhnZbUq+4S+VkhSOQccs3yrjtyfu8mu21PxqIPBsdjZW9wgu7EQrOcETFQI3YnJHKjkYzkj5vUVNpaEyTWiRH4wtdFnktAZna6nZjPqK4bzBjcvyg4YfN97OeMZ+Uga+g+HvDdrpkOo6ZM+reYpif7TahlVx1ZEK5POAoPUkDdnivOV1JjcvPqFpBNHOSWiOQB7jByp963fCHi200iO+s7wtHbytvjCIG3BQSYyx5w21VGRxvZuDzWrg1HQxnGXLoesSSw21sAqqscse2JbYrhwQ2CMcLntk4JK8ZJFSLcW7RoZgR8xhBmwvmZXgBsdNpPII9RkEZ5qC/2yw/ZZZR9oQNI6BnALsoXKsHyXVG8sktuLLkEFjXP+JvExtrGNIYpPIuFkgDKzchXUyJuZQ4B7q3zg4PAxv51C7scyg27I7+81a0sJIjMbUKwco+0RoM4w2d21hukzwc4Lf3WzzninxVoDCzspY1v4mKl5BcEiJeQGzGTuJySQSD09scBHd3HiW5RNQ1Y+aCBGbu4ZkXJy3XJHr9QBySK3rmxTSRDFqcaTwIN8l0il41yx2nJBGGwR052gY4IOvJGGrLVO0rMs6Prugy6rHAtpNHZPGIzGYfOM8m1vvA7yoLNnaAwzg4zuJXUfCa2H76104TKR55RrnIiG3JhJOFPr8rbsEH/eyDqvhptPWIaY4vJWlTzICejMdnLEAAbhwB0ABOMrVq7ht9U8Oyr4fneSwsyr3dvcktPGzKMurZCyA7ZOAFwFzjkGreuysaKNh+hNdfZYb26jVra4kcMUfMjFVyDx0UDI644IOScHZHjC00q0P2SL5YcBSCI1zuTIVQvoFBBzxngDIOTpPmJavHaNLD8+2SS4T549yngKM/eGckEcDHOTQfCcV7C0yWm3a3lu0LlOd2M7SMDPpyecnNcslFy98TsndmzeajfazqGm6jC9tDp9tG379JGlIbqVfJRjxjIBOAWO5ialude1XT1vLe6t0NjuMsksMjMbZScHaH2gkkjC8csR93gcnbzRaFb/AGWSKSZ5J28uVtqIRsC8knj5t3cfdPTJrsNOtkvNMjllkZRclpFeJWff83CZU4zk4yM4znPauiPLFabGE1d3Y86hd+LtLv4tKaWzjWAKJboYZ26bAVbA4zkqOdwGADg+YXWhXtmx+0WzxPx8rIQeSw/DlD1r2+2sHurCeKCRYbwKyW0xYlSB8yBT1woI7cc4rktXv4jfT2OuxrBeqAkziMy70UMfOjyuc/MehyMcbdu4bwdnoZRlNPRaHKQ+GnSFmvFS2m5IUyIse0A4PmkkFiwzt4yvzbgvI6tfDVpe6TFfzxm2byQYYbVQPNwWyArYJ3KByOvB5A3FJrprVtNt9C1nTY7WUkL9ogwFaLG1WLfMzI2wr3JBJGBisPxFq2saZZi3Or2JzGluYLYfOQFUl+hA3HBDZBPXap3KNNWx+9NnUeEvJ1rXJYmu3S2tApgWHEZuEUl1LbcKCCQcAZ5wTwa0tX0ixOsWt7O0cErykBiux5SMgADnJzwDgtyvPSvKPD2q32nXhfT7pLZmxuL52nB4yMHP/wCuthfF19qTxwXkkJmy/MqqixfKRtGTtAxx0/M4NNwd9DGpSknod22mwW+1xNKmSY1DudzEFgm0HHPJygx3GOTnzLVd1zrkrWpdogyiMyHO3Kg/wlsHJJwDwePatk6uHWK3uSfKLK3yxBE2g8PgoQGOcDaQp5GATVgLZ6hPLJZTQwqJPuSqUR4y3y7VI5OQAwKkEnPOdtXBJMxTdO7kcu0D7ws1szsBuUFtoYdeD3B7Y79KiSBUMnIjaIZZJGwSM9uOeCPwBPtXXzaQ6RSEeXL5asqRFW+YAEkqDgnHGTjBPsOMPVrFvJRYNhjhxhwrDCHjJz77fTrwMZJ6Ula6Y4VlPQ0fD+rTvdQpqNsl5aBfLdXj3Z53cDpnoeck4I7YHS3epz6jbz2EVrCVkjKFPNH7s4xwpHOMZzgAADHauYt44tOjhJ8q5EseSVUssZG4dWAAVgRnPPQ8cVAmqRtbMFzC+c7TgKVJxnBHzYGD09etJSVzkq0VVndLY6a705dO0OFb57mSWOEshiHELgj5eG2qMkZO0E+uTisqadAELrEJMZCyEfMGGRg+mSBWfc65NfWsQlunEi/uyhzjZnI5x69u2ByQcC9cOLa1giuDvZRwAAd6nBOcdf5EVaujGVOSa5tylb3RJBCMF3YXZkk+uO/H17+vTTjs2eJ/NIt3ZX2RswJb0OePX8ahttNa/ZFa8gaRlJVShIIVc/lx0HNaYN7ma0FlcysxZA0zjy/rkcfhxxx3rWNjKrJXtHc5q6tHkjaORcunRyo/I+nSswyS2a7Q+A2QVHT/APXXbXGhYeEQOECDDeYxB4OMAgEYxz71yGu2ktpcL5gjCyDK7DnbjqPXuOe+epqtbHThq0aj5bkui3bLq0UrYbY2QW7ccduK7C5vpbXYuxLmaUsNiJ/CRyOOoye+K4HTZ3t7rzIxlwpC5UNknjGDwcjIrdTVLuxka4WKO3lKAbShyPwPT8Klq5GKoc1TmR07eH5DIlyJG2k4KbVIA9tv5cDvVPULJ2aMXEDQ4ICttAMgxj1xnjvjitrRbqe606A34hlWRdybTtYgcnAHU/lTL3U7HVWeztSqyHOS/AbB6E9Qe/TtSo1pN2PLqKcJehgyaXujZRcyIw+ZI5kBDEe4JH6VkFYn6ghhzkADNdVNFi0hE8W6Jm5kVvmT/aBxjryPbrWBqsbteSypHIY8/fdcc+/avQpyLw9VydmzLuISRuUH34qKJFZtsh2g/wAXpV6GVQ6iYcj9aZdwBmLocDoa3Vtz0IzfwsrCPy3w2cHp6MKXY3Uc1NFbc5DZdedh/iHtT32ou5ePQg8VtEHPUrsn7veueOvtTOpqWQh1A2gEfxDvURUjrWiZSFLE9efrSA47UdOTTQSSciquUPzntikopQPWqEIaSg0GmMQ03NLSZoGW7T/VH/er2z4Q/wDIo3X/AF/P/wCi468TtP8AVH/er2z4Q/8AIo3X/X8//ouOvzjM/wDfKnqfoWXf7pT9DvKKKK887z5g/aU/5KXYf9giP/0dNXkfcfSvXP2lP+Sl2H/YIj/9HTV5H3H0oA+2/AP/ACTfw1/2CbX/ANErXQVz/gH/AJJv4a/7BNr/AOiVroKACvnLxP8A8jdrH/X9P/6MNfRtfOXif/kbtY/6/p//AEYaAPMLpo11yczqzRC4YuF6kbjXeab4kvrmaIaJNFZafbqSqOF3L6kAZOfcVz09nYxSzzT7nmd2ZUJ+U/Me+P0zXS6J4Zj17TGub2VbOONtuAgBLAZwijrgfTrXovWFnt/nofOVOWc7/wBd/wAiRfC+nhbi91i+t7l2UNGISzLz3ZVG4n2HTvjIrWk0jTLrT3k0PULYLCgMjxR+Rs7kMdu5uM+/FJqlt4esrK20+w16MW6RkvbvmQyN1y+Bxn0BGK0NMfS4/B0LrPZSkFnie5iZIXYckcqB6c8lsewxy6crlqkmrfl+gndcre7/AMr/AC/R9y7oVs8OtQ6g93DfW5hMceSxiiORjblR83XnOevJ612V9otxq1i8IvJ7UMu0MpZCgPPTOPz5964G18SPbXiXdhpdtbQGQQeaAoXzj0IRVZjwDgLjrXZp4k0641K2sLrUALvymZlZcN05+XoD1ODzxWkovkX5emvz/wAmZRaUuZ/8Dov680V9Xs49I8NzW50qDUbaOBmWOd/MLMB94hsn3yOntXk3inS2k0621OFSglhQMpXHBHB/p+VereIJ57ixntdOgupN52PcxRrJuycbWycoPU9h6duZ1q2W40safPAsE0cIGAoKkgcksPlJz1x+GetdOBmpKalu9u+l/wDP+tDz8xjKnGnOntB6+jsv+D9551pvhHULqRN0DoWQPGrxsDKP9k4x+ZAr1Xwx4al07S4VUxtv5lXB5fPQMQdpAA6DOe/FGizaHp8NvHJayaS90Nn2W4tHUy49WIIbI9Ae3NUPHAJ0mOERNboJVkEsUBG0D5cZBGAM8/hxzUvEyi+S1rv9bemm52cl7Nvb/Ltv5Gvf24hupbjXL6OWKNTGLRN2Yg38Pm5HUAE8Z5xnnl+ieLoX057WwheVYsIht4CyoPQ+wA47YHbFeczeIVSJrNrncQThlUvkE9SSfm9c8V6D8PtNs49PUXawtJMVmZIN5KL/AAh2H8XXjgdeOCTrKMY03fX+v+HOfmqzqK3unVabOztNdT2hmmiXYoEfKnn5RgHv74FU9V1zUNNtbO/tdGlk80g3cJlRDCGIC56nP6euKxvEPjjS/DVzb2tjaRtIjF0jtmYIrHK/MowGPsec/XNX7tU194kv4XsrzTWEiJviMbu4B+ZWzySQckdfU1zRXNaaWn9X/P8AI79IQ5G9V18+mpzHiDVLLU4TJp631xcyyZMNwCYoQGwW55x75IyRwM8UtK1DTxqlvKLbY6k/aJ8DyowOMk9ARu6dAMHjHJ8Tr2XS9Wt47AvbPPCFuIiM43Hruz16g44GepzWFN4ntdI8IS6PCwuruYSRTFX/AHcSODuAbu/OOAQDnJyMVnDWLl/XYiVN83J37Hfa0lvNCZZZViInXZG0ATzOflVt4AJJxwOvuOAzTNGgs9QMhk/fCMshlmBbA7pjABycbgAOfQivCpdWvre6/wBFv549vCGKZxgZ7EnOKl0jUr2bXrJJruZllu4vN3SE78sBlvXj1rP6vfS5vZwXoevXV1GzlpXVixLYY9PqPrVeS4fk/vHBJ+7nIyPvE9uuPxz6VXuJmjmGSU643kY6Eg5/A5/zmpLdlXBIdSGCgKxGOOpGfTt9K4owuXzW2NCO8P2UrGpbGGIKtkHrxjGeD7U17hlwzSxhcHcD8xbB+9jIzz6YxuPXkjGW+iaKTMaqu9iWG47xnqOOp5/Ljk024f7NG229t7xDhpIowzDgHP3gF5PGAMADjPbZU9CZSNmTV4YIB/o23zGBfLFtueBg7evTOMjJIqs+qCaX91cbVX+HPDDrk5IHBwMY7g89Rjf2qstwySNtbG5WaU5DkANj2Jwc/L0IJHAqW2COTHNeSByAURiVZQM7QMkZPPAOMZOPfZU7IzlIfcPLeWqBZfMcEMN8mRHyBtPX5iWzyQefbFRvqqWEjbZt7KGz5IHI4wD689OmMA4wMU6/gEEaRSyJcO0YAjWMMpBYYAxgbgWByPTocnGNd3Cj55p8iMtjYMqeWAHzdwc44Oc/jXRTiZPUkNnYa1qk7lpFuvKG6BI95nkAUHZt6seTjocHnnhlv4I1GeXCmOFN5BMjAMuMZJ56DcM4zWNpurSWOt214jFmglDhSSM85Iz1weR+NewWlnFf6499BcPegxfK4YqM5YogJODkHPTseBnnqm+VI5KjnGVkZMVzrGjlN6QXltEkivP5BiIcjONoI4IC/wAIyW6jrWVqWuwXeq28LWoibTn/ANMkaEOWbzMMiggttBwAMr1IwBiuqe4muNNhksSj5iWO1leNlBYoSX6BRkcAnHOTnAri9Cu9K/4TG/uLvUDBCt5JcQMsJHm/OSDjGVxwQPw9jhTu03IxjGN3KxjXmpS6mGN9FKs8W1Yo4o8oD/cwW+XJySBnnOFHWu1t7Y2IumMkDw28ccJntJEKxHALuXJz3ABAJCkdGBzyP2h5bByiSNbLdOsE4QCPoMfIQc8KDjPHueTr/wBp6bpWofZ7S1upYT88zyblknZgNu8c+obAwDyMc81PVbHRKN1ZFqDVtBs4ZrdY2kuN5je4L5Zk4BCkgcf3eB0zla5Txb4iS+VLXTmeOFlU3KLlVkceozggEcDGB1HJJqvrupWly05SBo0f5oo1f/VknoSeWAH06/nzp6CrhTSfMawppasb1rZ8KXclh4is7mKWCFonJV7gZRW2kAnAPfpx1xyOtZkcDSfLxyRycfzrto/h9ef2DZXi/M9zgbBHwpJIGWJC+nUjr0OCK0k0tyqjVrPqdNfeIBbTH+15IZPIJwlsyssRLKpQFmO/C7sgc/7vUee6rrNxqsy29l5s1vGCiLKoYuSxwcDJH3jhSzbSzYb5jUOo+H9Q0yKN7u3eFJlyhbgNjqAehPt1qrpGpy6LqKXKLIU6OsczRFx6bl7exyD3BqIxT1MqVKMdVqaXifw7FoGsRWryNNbzQx3COcK+xs8Fc4BHI69s9+C0sNLlvpGsIJJXibctvJvUTBgcFeQQg+U8sS2V45IqJPENrZyl9O09Vdgrl7thMRIDncOBjjjH55rEZppmdyck/Mx6Z96dm9zoiny2Zr3qLqs6sZIohHII2jVj8iAAZztJJ4xnBJ9DV7Uvtmjwslm4SwaZlELgMwznjJUZ4A5+h4rGsdLvJrczwQGWNTyB+X9f1p3kXH2yNZonSMS7VickBcnO3J6d6WgmjajdZ4rdZNkiNFgbsbyARhc/wgEnvzyPSte208RSvuUfZJjjyVDqpUDOQA2epHGcHnBPWtixsrWWCRtIfyZ3K+X5EZCu5DAKQWJ6gc5zyTgdKgv7K7m0+5S1uNk4DbThs4XBLZOdox1YYHzL2Gam9zjdS7sjmZ7qxvrGGyAuEtIWbCQQI7Ssz5yu45XhAu4buABjk7qNhrmr6fIqaVqLxJIcGN0VkTJ7KQQD0JIANM1exfSbo22IwqhHcBiwXjiMtwcgZyBjnOAQAaqWbNHMiqCXGCo384wTxjofoev4itE+p1KnDlt0Ok0S2W1ubu5uZIZWthIs9zLIrIJsnbs5y4ITG5eRvzzgZ07MSarfSPqWsWdtAVkEc0Jj8snPKlTg5O4kFsHAYjPONfw/J4ZntVu73TLcXECGQwxR7kVdo+ZkBOflOcnPqfmK1oatNFpGqweTJbQC7H+xbOqklmOcFlHzIeWB+VjgMQapN2OGck3otTmbHw5O+sKF1G1nYqQ2y4PmKHQhflOCeoY4BA9e1Sz6CfC1xeXl5IJtybI/n2EFlYn3yAAccE+oreW709ppdQuZY2s5JFRruSKWRSzZbBxKQCqt83Xr0+VqksL9dRWO40pvK0+3faEigaOSSXGzKkgAknPAPAxkA8URTlcwnKbWqOWsvDLXUOfOVW+Voi6jdtIBGV3cnBHGfzripNSmWYyl1kbOACPlA9VwRj8MV6D4w8T20WntYxh3nkXc6TpknI656A9DkZyGBGOK81SIOvqfpWnLaOu5vhlJ3lNF6HWL0TCS3nkhdcAMjnjknHPOOemcVlzH96WQY7/KMYq5axb3KAHJPatKfRTPD5kKAO3KAnCkD7w9ucc9OfyV7I6OeEJ+pX0zWLkzCOdfOjX5srHyh4G87cbiBnr3Y9yTWuNVyq708tyxYoXkbfwTuJZiOp7DggHPeuXe3ubeZm8lkMTENxnaRxg1PFqbKrhlY7hjO7kdeB2xz/8AqraNR2sxToxk+aJ1iXVtBtkgiSOXh8lPLwCBkAfxZz1wOmcY5rTe6tZbpZCyxr8izfvFw+MY24OCwwfvEDJGT0rl7GC81gynTxNLDCoLqzdB16ngDjpn1xWxoemQWs8hmETtJlVQ4ZsEEd/fjgHkGonKLeh59ahF6t6mlceJbOAiGIZWIfI6kDaTg7/lGCenXJB65yaP+EgbWNtpbWwdQxdnlOSx7ntj/OPSmWuj6Y8zLDEsjOSxQxj5Of4emQOOP06VrWsWl6fEGkjt8ScLIhJ2HHTnB7+xFLmtpEwVKlDVJtjIreKW4CvIiMQAxcng+hHfntx/OrsdhPbFrgbUCnd5ixh8jPOB9PQg9q0rCx2XGI4BIFbK7fvf99E/+O1bvIpWjTzJVRwzAMpI69QQevTkZ696y9peViJJpporQTRSyibeoEuA6rEzMx9R3zwOoP8ASlv7qGOBN8SvI5zyFDRjHGcYPPPU5H4VWuYtsMyKitLkAlUBPzDODk45P+T0rmtQ8T6tZ3AS8tsLyhPTzFxgjJzyf69K2inL3ieTmdkZvjGZ5LqLzGiiaMbokQSZIPux45H61T0I6veXL3FvPcsFYLJtkZd+ecFsEc4zz1xUeoRX2pGOf7MqxMSEKYC5PqegNU/sVzHD5qhdqk5w4PI68VrPVWOyEYqCiztINPvUJv7aVri5Tcu0bo4oiP8AbxtPT1H1qaLVr2505RqkEUCM20yAybuevXPXP8PPeuU0O/vm1IGKSVUPysEJCY7ggdB1/wDrV3MHhpL1pPs3l2jSJsby53kYqemQ45BxnIOOKyuoy8zkq07aSSKF5ohu7RDdgIo5j2K5YgnGRvxj1I/lWfbW8ukXX2VsBwQzKCHwMcE4OP8APWtj/hCdetZzNY6tE7Qqcb5G3AHt8wIp2n/2jeW9xDqCK6QruMsgKMxHQB1+8B9R1xW7mra/1c538LSd12KEt5JGjr9nTy2O+QNuOPTg4Cg/ifxqUajpd3utblLmIkDGFPX/AGRxz9eB+tV55LsSNayIpsp1wBI5UA8A8gjgfUjryaoJZRhBFLIhC5wdjKCM9j3/AFq5SsZqnFx1N63NpaQt9k+1+V0MRLFifoCQePSn3ZvI7xI4rOS5sZCGb9wHEZPZhznpnmsK4uVs1iQOVBTGUPQE9jjp+tatle6hIyiwuFkhjOZo3UnbjuPm5/OqTsv6+XzMZU2veevqS3GkwO0mZhGseAXhTepY9toOV+mKxLi3eGQbiCOxBPP51uXOp30eGthFJJcAAo5Pyc8fJ+J7kDvWNfefHeNHdZD9TjAB/AcV0Tm/q9SLf2X+RvlnP9bpXf2l+aOh+G//ACUHTf8Atr/6Keu9+L3/ACKNr/1/J/6Lkrgvhv8A8lB03/tr/wCinrvfi9/yKNr/ANfyf+i5K+LP1k8L1aSGGO2luI/MVJwyruAy21sdQapQ6ve3+qhZIUkjJ6bACwHJLFeT9eak8TSiHTonIz++HfH8LVD4bvVtgZZmtFjOeZoixVscHp+g/Hiu2lFOF30PJxbam7dbHTz2E0irHaWxjhjwzFrdIiQM7mCkZAyRhjgn1rqdA0A6bAL7U5WnkY4Rj8z89s54H4VxVtqWltJvv9bl8oTANc+TjY2CQML16Hmussde0rUfDyz396sbhSpCcMF+gzk45yK2fPGknH+ldnmV0m4xlt/Vjo7afeJXkjSG1Q4URjr7k/5xXGa3dx6nqZFgunyRoDH5V04i3HPOGDYJ6dCG56VU1XxPZQ2/2TS7i4Co2fNWZlJz3PRj3OMjp1Ga5y+i0u5eO4bUWikEZdrhomXBGMDAJZic4yQMDBPeso025KbWg6VoX7steINP0uS2aWwiaGSLCPBksFPcK2ScL/tKhOScmsSbTbu4tLNVRUEVuZJHkkVFUGQ4yx4Gc8evOKdHqMtwy20am9mChIiY/mUY7A5A/DnP69Np/gu51zTIbWzeFtQ4DRySI/kxZZmcsBnqVX5R6gmurl5Yya/rUqM3BKMmef29pc3cwWGN3zyQF6CtePSbqUwRwxzq0pzBC0Z3tjqcenuPxr0XSNP0PwcFbUtQ3ysxjjlFkybjnDbTISCMnrt6D1IqhrHxAtLNjJoc6xoXQvZC2GUwvzN5g4A6DG38s1hOa0UNTWLnNttWOKvNP1W2khtbyGRDMcxQ4yXySMgDryKbfeJtZk00aPcahcLYx5AticDrnkd+fXpVyPxFBJfS6j58lveea0sZiLLhm6k4x2OPcZzWLax/2xrCxXO4NNKAWhiLsMnsq8seT65opqU3yNGsrRXN2KjEhhvPHUcVpad4quNP06SxGGhkcMeSCD7EH/JxU3jHQ20XVDFAkgtWUeSZYyjkAc5VvmHO4cgdDXKO/PHSneMkOK5kmz0PQfHstsuoxXpWaC4iKo7xAuTuzgksDtbnOdw6cGtfw742sdI1FLtbWxF68XltOiPGUUYI4UhccAdMnFeSiTgDPSrNu648x5dp6Yz1o5Fe4OK5bHpfiLx9aXVvPHHAsr3DYmleIEyqBx94ttAOMKOmBjFebXUiG4YxksHORkAfyps9550YUAYBznFVGfLdc9qcYqJSVizBMyTI0bFWVgQfQjvWodW8hMWy4lkz5srArn2AB6e/X6d8HdjBFWftAZRke1N6kuJLe3U91eTXF3IZZ5WLSOzElmPJJJ5J9zUTcLkYz7GopGAc7eR603d3ppDsSZz0qxb3clpIs0DlJV5VgSMVXSfYfugj0prPvPPFUKxet7tRcK115jozAyiNgrFc84JBGfqMZrV1jxPPq1rZ2cUK2NnZxlI7eF2Kklsljk4LHjOMZOTiudHPABNPR9pGcnnp6imrEyim7mnbw3V2yCKMs7NhdpySf51BMphk2E5Pcg5BrQ0/xFc6ZGI47e3kQEsFmi3cnocnoRxgjGMe5zmTTNdXLyFQC7Z2qMAew9qfUzV+pesdZv7Ly0trqSNELERk7o/mwGBQ5UghQCCMMBg5FMvNQvNQMbXtxLOYY1ij8xy2xFGAo9APb+tV1VVjBI3Hrg9/amE5fKDb7UdQ5VuSxSNvBU4II5ziux0jxFt0/wAnVw95CoeNFaZ1RVchiWCldx3LnOcnOD0BHFR4LgFsAn8qstII5QEYMFHXBptXViJRudQvhvzdNe9hmikjczPHJv3OqoPl3Ip+TLYUscjLJ0HJkOoqt9k272yRvzE2NoAwoXBAxgYySecDJzycGxvCJjvjV0lYGQFiu5Qd23IIOMgdOeOta0Nnfap5s2lCUSl2ZFW4wsCquW5Zt33SACSScEcnmoluTb+YtRa7JHN5wkG5i7gEsfvAY+7jHPcH0xwK6S01aDUbFpNOgeGXcTMzNuZSVxxkHg5IxjnJ79fNLv7dZP5FwZIzyQM9eq8EdehH4EVLp2ofZXG8kgHcpHBBwQcHtkHB9cDtUypJoicFJHoNxc/bWRLu1eCG6XD4LDYrAjcGbC5DcbuAe+VJNY0XiWTw1rUn9jObqyjjEbRzPuRzgBnHYZwenY4rCF/biPf5xmnifzFZl2MT6qQfvZAJznI9COa8mpSyaRFYyF5AsjSoWb7hbAbGfXaO/wDWqpQ5VqZ8nQ9O0f4p6NHpoh1WxvXkSQtGFKSBBnhSxKlsAnkjvXn2v61c69q8l1dSGUFisQKhQkeSVXaCcAZPc/U9aycEJnGOfWnqnmqdqMSxABAzj2966FFR1RKpxi7iLPJEW8tmXcCpIOMj0+lHLt1yTxnNRgtkeZyK0LK1+16jDbWCtJJKyoiyAdTjr2xn9Kq9hvQZaytbXALDDKckdQ2BwOCDz061NBALiSSe5LFnJKxR8MT1z0I2j8/510mleBdUuL5o38uBRgH5w3fBxt4yMZI68jANV/FcMFmbWGKNQYY9rgReWS4JBBBAzgjtU86bsjLmXNYg0vUIoZY5haW9ytrvkYSnZ5mQAAxzyvQgHryMfN81s6/byglLb7Okoy4gbaEG5TglQvGOBkn7w75rmPtK7sqMljksc8d8defxzUZnQnkZ9NxJ/wA9auxMqMZO52NhrEdy+VV2CkmRQCMj13KRuwCcBh8vsBiobj+yw8iSyNh8gxsEBR++GUgEe4wMFeDtNc/YyyurgbtmBlix6gcZPtzxTrofaCrPK/yDaFPIUc9+3FXF2MHh0pXR2OhrDrUsVsbW2lggkMkpZhuOcKAMfeydue2SOelLrUmnw6fdWeYIJrUmRLd4Wjk3nC5bsxKnIwTjjPHTE0OCztLgPeuNjcbypKAg54ZTgj7uf94ZFdB4qFlqUIuraPfdOvzSJE+2WMIDvHygZGORkkYPXAIa3OOa5aiWtji4ZAkiMBjB5A4zzWjrOtQ3kqLZW5iSNQu91Ad8DGSBxVSTTZl3eSDKV5wo+bHrj+dSpYRXDKJJfLmY8oMccdMEj/Dmtrq5bUHLmZWjnubm5D+YwYNuBB2hOeoxwPwr0GLWLGPT4jGzThY90rZYkHGCTuY/1HIxwTXKaXpAnvo7Tzo40kHzSMy57cc98kDHHccU2702Dc3lMF2sUZo33oxH8Q6cHsP51cZK5y4inCs1Fu1i1rniNJ7Y2dg7tCwwXZQvHpgdB2wOPTFc9JLPcbBJLJLsGFDsTtHoM1uar4YnsrGK8gcXFvIqlnVduwkcZGT1/LPHcVnWNhcyMJBbymPON2w7c/WtHK5dH2UYXpiwP9khcLkl0+Xjv1/pUz6mb+eBdQAWOJdmUQAhf8n/ADzVieye0iJuU8roSd3zYORjA6Z9D6VUt2t3lxcwF1UcOpIyPfjp+X9KNhLln71vmXINVuVt1sbaXYu/1yAfUH0rUs9O1C1L3cQVZgWHmtkZ9wT1znriucu3gg1A/wBnTSSQIQYncYYcA4PuDxx6V0SeILmKESi8aESBQ2MSlSuRgK579c89qpLsYVoyaTgt+50ujazaX2mrHJueaMbZN3Vj6j/9eeKr3UIgbb5OxX52bQflPt/nrWbp9/a21nC2nW6FiRu84YIf+LDYzn8RxgVYjae7kSSOILCkm2UFsrjIO5VPPfqR6nrmqgnF36HkVKKhUbjojnryILfSIqHAOAv93261WyUfBzjuDXZsYoG86aNZIY/49pDIO/Y5HfjH41maxBplynm2koikAyB5bBXHbnGPxrqjUuzopYq7UXF27mA8GHEkcn7thwCM49qci5O1wQPb/ChoJol3kcdsVINxgDZPy9TiuiJ2OWm4xvLQMpGT/CajJx0YHIyRS53NjHNMYbSR1+lbqVxpCEZXNJTskcdM9ab0q0WFFGaOCOM5p3AQ0h4opDVFBnFJmikoGXLT/VH/AHq9s+EP/Io3X/X8/wD6LjrxO0/1J/3q9s+EP/Io3X/X8/8A6Ljr85zP/fKnqfoOXf7pT9DvKKKK887z5g/aU/5KXYf9giL/ANHTV5H3H0r1z9pP/kplh/2CIv8A0dNXkfcfSgD7b8A/8k38Nf8AYJtf/RK10Fc/4B/5Jv4a/wCwTa/+iVroKACvnLxP/wAjdrH/AF/T/wDow19G185eJ/8AkbtY/wCv6f8A9GGgClaaBLr9x9uvfOiiV1giKzLHlVG0tlwd3IxhemMc11Jxotj9l0NVvVtyHWFI3lMjn7zyNGfkAAIz04PpgT2j+VpNhbC1vbwNB5iKVjSJSeSVZ9qlgSRyQcDHOcmLTXtU0lZGgiuBEgX7MZS+wlieVBIHXmiVa0Wvsrp3/q33v0PBkm5Jta7f18n93qNh02x0xnuPEc2lvNdKLgSmBP3UfZEXbj6lcHjqSKo6hqs+oXDW2iS38xjlEZvIbqWZFUkgAIAqnOOOfXr1p2jXNzfLcXUsdravatJGsNum1mZjgMzEEkE8ZwT15HNaWmWzeGtLnCQTRz4SNY7Ri7CR/wCIl/u54BIXuMcCui176dtF66fhb79et8eZxdpO7u9/Ja/jr200toZ+k+E9QtdXTUtRZ54wDsS5jcSFgcLkHpz0Gc9K6HwvpmuXbSTz24hWOTasIUIJgBnkklsZA9PrWvaNZWkaz61cW6fYwCysSJFY/wATsW6Z7Z56YqPxD4906DTntbvyV3Q58gTYaUHG0FcYCnI4Y9M5GM1zu6TV76WVvv8Am2jaKjP3p6X/AOG+Vm1r3/HFhh1uHxTbRX1lG8EoKxLZKpRTkbnc5JO0dhkZwOCaXXdP1lLiJ9PmleK4n2LbNKxMqdyAQAuOv4dfXF8My+LJfE1trEsElxZsNmAxkTYTn5Mk4GQO+BXZeIrzUYruO20MW/8AaE+S3yhjDHnjJJOOh4A+nTnV1PYOlUg9Vdfi3Z/Lr6+VsHTjVU4yWj/ytp8/0fcS+1WzVoIbuxtbpcq7ROYlWEcDdkkgAEj39q4A+N7H7RNZatdTS21zKy/6N8ws+cDl02kEZIAB2jj3qr8Q7nU7CRbOaMqZQvmSDJ3EZOFB6YJJ/Eeozxmi6jcw30KabYW91c+ZuU3FqJzuHTAwSMcHjuKeFl7T3pWs/wCv66hWpe5y/wBL+vuOp1Sw0ePUJ2i1OzjtfLEnnNIs0hPQIFyHxz2B7cDGK9D8B6lYT+H40tr208xw0s0cQCujHgkqo9MDJ9B+Hlfi/SdSuruDULtR9quog0kZiMTE+yk844HGOAMjOScbQfEF94Zv7h7aNW8xfLkjlXpg9R6MD6gj2rppuE4OO3/AOf2UklKLu+x7XrHha1uL1tW1SS5ubiCXzVTgKY9xIG0bW/H19ec8zp3jzShNNfavatDe2cokhGSxkBGCDuGM5C5wBnggDBxx2pfEDU5tQMmnXt5HBsCkO4DEYGc4GM5zg44GK5e7ne5uGkJYhjnn1qKacVbp+T/4JvycyvPc6rxl4wfxTIrvK3HEcSn5Yx36k9e56mucETywqFjYyZ42jtTbK2zLkhjn7oAzuPpXrHwv8Oabdajd3d1BHcC3iwsM6B+TnqDxnjAznvitVDR29RyqcjR5pp+nSX1wtvJCS7uELuMFMnHXtzXf+EPh5p8DXWqanN/aYtCvl20KMcNwSWA5JA/h6cg5PQd/q8Wi32vttgWGMxb7ueSfylGCo2hfUDPUD9K5Hxnf+VYXMXhm9jszatteK1vPNa4I2fMRj5SAxycjpj0rgqVKko2WjZ0Qivaau6/Mr63ILVmZ3X5l2l/Vfbjpkj8zjlq50XQKM0YIbbvOGHyr3GSB7+p6GtDSIjceCNOlJ3Hy3R4RjJCuQuPQ42/genSqEv7yYFDn5/lYneAcnJ/AfT8T0tw5ZOJyxlcgL+cxSMldg2ALv2oOARkjPPQHOfbpTfbyyjM653FSFAyeTzxgH175HNB3Rx7LcEkODGWGdzHq3GM4BJz1yTjjpTdpCqFI1xnKuU4J2jPTg9SfxHfmtIxuaXLaSIqyOQ6Qx5LSbs9TjOSOeATz0wcVLaMTNMFaWaRdsg3kcdGAOcDHI9OnHTiltaEtNMuchSrMCDxgZI6Zx25xn05pl1Mq2qKRvGdwBXGG+6MYwOmenoTkZ41UDNu4+5miEEbKy3MjlmYMSc4OB04YccEeoz61i3cotlMBQGY53uw+7z0A7cf1/GCW+d02eYVwRhIzhR7+5AAH4VRllH35GKrk8t1J/wAa6FFIIxuy0pCOXJ+bGTzXqnhzWLPVdHisHuLdDa2EUZV2dS2fvKCB975eoUnDAckEnyWy33d0I7ZVCv8AdMjBc+pJOAv5/ietXoNTvdFnmEJaMXEHl57MpYElSPpjjsSKmpaasTOk36nres6d/aF5BdJLFLFFukcSyAoIWVehIbaDliGI4+UinLfWGkwNHpT21nqWoxxMbiJSIcnKhyMEYO4HPRifqa8y+2al4g8iy0xbmfy03FActI+OuMkkj69sgDpS6et4t2kl9dkTW+YFglTcVRcjYQ3AA54wf5CojHl3Zyyw/MrXOsvNEutJstQ06ORLhcp5xVlCMVOSAGA5+ZhheTnHXg4eq6hLF4ctrWYxoLfDBFi2kbiSAGPI4Knjnk5PNbk+q6k2nx2+ltHayQlWIjOd3yhsbWJG3o2B8ozkY6ng9WW+nkElw2+NhuXyzlAOgP15PXnn3px957mkaUlqzPmcTznyEMaEjam7cfT8SakFuMBWRi7Yzjtx0q3YadK6ebGm854wM+4/lWjp+j3F3DefZ43MtqqyL8vQg87u2Mbj+FdduVajlUXQzIYmhdJAjHBwdvr/AEr1fRlfWtLgurWSI2zQKksSqzFHGMjrngqDz0yCNwODwOmQlLqebU4wkaqylGLRqWGfkJ7E4PBrR8N2Or6RZXWq2LJLD9mEkiSxyBJFz/wHcR8wO0kcNngjMVLNHNUu/kdB4i8MXc19aSWZnu1SGVSnyjCgFfkAIB3B0BwDghQSc153PpUjSTJFbsTGCzKvzbV4547c9a9V8PQarI27xDCljFuYrGAysflXttLA8gFmYk4IxklqglvLePVWgfSUgJTDTzEPGpViqnsCp24Izkg46gmsvhRMKji7djxpYmVlZkymeMjritK20id2dGikjMa733gqFXIHOenJAHvxXU6tc+HNMjbT1tWaQEkPPGH2KM/KpHckAZ6dc+1WPxLqNxJcaja6YtvE7KlzLDD8hXIOGIHfHPQe2CBVOTsdfPKSukMt7m90i0JtLlZkbjyUQh0PcEYxkcdiDkdcYqJrS7Ghwaj9uRpJjuntCMbFVjyxDZxwvVRy4HcZ2XuishnvIY4mDl2jYYJbnIIGf7xHPoOgGKbb31u+oSyw2cUZkRJGggLbXMa59f7u7pk8EjknOPN1Er32K667aJb+W1z5MoO5kmBOwg9jjnoOmDgEc5xWdNqOmaw8Flc7iQxxMiqoTjIIPrnORxwBz6VfEX2afUPlkRdibWyQCSucnk55/meKwI50ik2B38pyPMVTjcP8561vFLccKa3RdlXyGuLdypuBL5W8OuBtwB68Ebh1x93njmqk++ZWlbK55BY8Z7/59K6HxG1s3hy1vLBGuUuJ2WS7mTc7SABiGfAIbk8EkHOeoNctNM0jDdj5QFwqhRwAOgA54GT1J5OTVG0XzI6/w9FdhrfUI7fzIzdxxMHQ+W7ZAw20EKAWj4xkkrgHHHY3Omf2jY+ddQT21xeXjf6bcyoyJkYYMrhduUywUgEfIV7geZXetQ3FpDbW9kII4o9vzSByzn7z5wMZPYdOmSAAPVPDHjPTdT8FtZ6nHYwzRv5Rtd5CzqFJ3lMHg5AOAxJBPpT5b2OSrGSfNYjtdPkuNQktJLVLSyhORLLA9usk7IFV1jR/nPy5ypI+9jGVJr3urxLdyWxjltYYkRpWZIvu7GywQAk5JHI4wWyRxi9PeXGpW8s1/Ylo0mUusTI/khgM+bGz8FQGyCMMU5C7ayL8z3F6scV7ObKcYW+RJV8wgFNu4E5jUEcAgHe2AeBXRTSjqjnavuQ6zLFqlq9rK+7cvyZWNVVhuIcYO/AxyMDGZAVU8Vzh046bbNZXiMbqRVlt5YwSkqkZPYHI6fX863Va6ub+20u31lVtlc4khnRWVs43EryRyMYwCNvHBqzY6FbeIbZ457OWFDErmazSPaWUFWjzsUKSVPDZA28Fh81XNcyDmUFZvQ5W1tngmZ5LZ3VW+fYuWUnOARn1H8+vbSivTJKdsQCREoVkjAOTwRjOeCMYHPX8a2oR/ZNSeCysY5I7e3+z/cJJwPnY7huU5J7KRx6VZgu7eO3W3mkubWaaNl3xxqUTOSFUcnkbfz6jOa5pJuNglFStIpvcNteG7iCSyHLkqTnk8bcZ5Pr/APXGUdHZ18yCRZYyM/IeR7Edvxrr9MSGHDT3Kx7SGDSKMjP+0xyM9e/sTVz7WfJuHhgjcs+7dHGT5hbBIyoOTw2Sev4VUYt7szddwk1FHMeF9NuJNX+zwP5MsylUdxjAxk/jxV3XrLVE1JVm3ecgxEF6lVHr1P1PXtUoke4P2gSBWHIZSG2HPHyjJHp0963bbUrm+hZL+R7mYAnYJCAvHRR0HHf3/OdnoOdSXNzM5RPEOpW0ZjlDB+NzOMliM43A9uelT2d5Jf6uLu9gNwCWbyhwM47fzrdk0+yumzkySlchfLBGNvHP4dOnTkVsDS7G20+LyWVWbC4ZQyhu7HGceo5Iz04o5uliHWglotRIfGK6Pbq0FtfL+72MJSwXdx69aktfE+n6nbp58vlyruJEhXjJPfv2qle2zS6bPbXEMu8vgeUxMgPXBGMlQOecg5GDVjw74RsotMM2oKtzNuG9FG4oDwB6Z74//VSlyRhZIxXK03IlvNXtbpGNqn2h0XARBvAOOuK5V5dXuJJLye1lkijA3rJFlVHXIBHy/UYPPXmvQF0KLSrPyrRA6sQQpc4YtjnnOO3/AOoU37BcRWTsLiMSxp5kccxVlbjPUHp2zgdfenCrZWiiU4JM4y1tBco7Wt20bFMSQJICPVduew5Bz09ecViZu9L1RBcQ+aquf3cifLIOh7V6Bpdw1kJ/ttvaOHO4yQRKinIJGCByevJ/pitG40e7nuVuYoraa3KBlYkiUAYxzj/PqKuU7tpi5nB67HEaNZImrJ51hdRGRmXbEGx9MBSeOeRXTx6ReG8EonZY4SUcKVdlx2yM/rWp/Y13aXMF55sjIUzJBuEgzjH3mPHX15xS/wBqILVdrLv3FVdmBUnPKgnqe2BxTUefroziqV5xem6G6kJlha5gmmdlGFSMbeeuNp6npxUbwXH9mpf75UJ/11vHG0rD/gOeoPPPSmXMymQRWkM8ErHIkMbgK3XnHy8nt0/HFLDqAZIcRmzlVwY1YbdwHUFVAyOv860aeltv6sc0Jx5dVr/V/wCtzLvLSVbf7RbXLz+YclUjZ1Y56soGBjr05NYiPHczsg+eOIY3xKAFJP8Asg9Se/euzutU024Z4LpFlZmKgq2WGOuVGW4wfY+tY82hSOpvNDunCZDJIhJIIGO4Jx22n8aOa8bdSoVFH41YoPpimUSzM4JUIonUn8AOuP8Avnr1qsLa/wBFu/tUCQyBWyUQBXZDnBOQQO/vTLnVLrToZv7VspvPmACNtaHcR/FnAz9ORVnQpf7YtZEacQ3EmUR1G1kbt0xn8Ov5VolFL3vQ3caiXM9UXtIWymtpGeBRNM5ZlfBOT3UqvA/w61Q187r+Fmyr7MMpxx+VT6MF0vULtZZZXmDYImY4zzk+hyMep9xVLUJc6hOyy71mUDG7ODnOPpxVy5o0at+z/EnAxX9pU2u8fzRu/Df/AJKDpv8A21/9FPXe/F7/AJFG1/6/k/8ARclcF8N/+Sg6b/21/wDRT13vxe/5FG1/6/k/9FyV8mfrJ89+MCRpERH/AD3H/oLVyjXwZFUxKxVcZ5HPrx3+tdV4y/5A8Wf+e4/9BauJyD0ruo6wPPxCTqGvZy20ukS2092lvIZ0kXejEEBWH8IPrU9sYYFKprMG1uuIpRn8dtYAOTzUiOoOO1danpaxyyimbLQwO2TrEJJ/6Zy//E0+Gygljd/7Ytiq9QVcenqvP4VmR4YAq2CKrzNhuCeeTScl2/P/ADEonXeF7zRdJ1yK41W6try2GRJGIZGJ44IBUDOa3dc+JMR1C8fS1SaG7h2NuDKUYcKwPqMZx05+leYbjigEnqcUpSUla3SxPsY812XPtcoPyNxnvzTJLlrid5LuSSRnyWdmyxPqSetVt2G65ppOahG1jXtooZIciaMu2QFZ9pHHqRiremX2m6fqAmlDTpGuDGJGj3nHPzLzjPTGOMZrmwxH0oJ9ORVRbi7ozdO6s2bWteIJ9Zv57u6Cb5udqg7YxjAC5JIAHHf6msYtk8cD0pB3o+lSkkrGo7IC5X+VIGxTc+lL/KmAMxxgUmaWkzigBc8YpwHHpTVP5+9OzTJCl+92pAO/Wng4HynnPSgQnTrSr0z79KUAck9aQHmmIehw1PUfNzUfVhipR8vOefpmmiWP74B/+tT4o97BQcknGM4zUKITwOvWtCCCOSZXMyRLk/M2489QSACefb07UyJOyNO28NTXLtBF5pnSNpHAiLKiDBJJXOByOSABkZ9apalpwsZggnjmOM5jVsY/4EAf0/OvS/C+sNpOl3kx0ecxH5AFiDOzsM5ZjyVBAAwdpBGcH73OXNxY2qzSXlobi7WJCxbBUFsnBxkAA7VHse2OcuZpnLTqyk2rHEqMHPNTxbN3zHaD0J5pfs7AKCR05x+ffvQ0O1SPvcZANbG/Mi1E1tLiKKUIWdVzKCAAScsSB0Hf/OOhkvJ226NdTfZLO1ZisU0W0qSBncp78ZwOQzE81zumWkst7E0dtJOqyLvSMcn26HBOD2NdrZWFjdsZbOwjjikgZsjezQqQFYkbzjDbiOnYgbTis5uxlORyup2/C4kUqoyFDFioJ4B7HjB4yOc+oGc0ZBw34V2NzZxateQafFc7p5GVEDQlChGF3MSSSCWOQCeRwMBRSXHhuKNALnzWOwkEIEJbkrnJ4yuCAeW7ZyDVRmloyObucdt+bOOp7Vo2UUUM0c92xRFOQwXdlhyBj39elbEXhVruQfYp4/mGVjkBD5IyF4GMk/Lzjn9dS00q502zME06xzqqXAgSAuyswPJYdDjPr3yRyBfPpcznNLZnLW0sGwNM4QjcN24/McdMAE4II9AeRS6hLDDeyCxdV8uQbTCeOO4YE9CBznv9a6q98Nw3oiSaSVpI9ysscHlHBY4ckKQAM5OQM9iSeOMERhlk+UtsJ+ZSADz17gjg8U4NMSqRlqixczS3Yt1e1hiPKq+0IGBJOT0GPm69sV3ej3kqz/Z9evoZrmIiWa2nby3G2NwpDY2yMVbcWJHQZJzWLoVxbPPbx67czi3tlea1a3Qb0bd1wVIILL78+mWNOtrbWdXMk6h4bkr5kJ3rCX35UlicbhtMhzjnJ7Gh2MpO6s9DttIaHTpUM+tyT27lZVuJX8q3XPRBndk/f/i64yCcVx+otca1Zvd3WmSxzzOxiJhYLIvlkhvXcOpOcH5eozjHvheFgusBvNjUKJ3JkY9dq5BIHfA4wAfpW7pl8Y9N0y2hmmsmlHlm/IymC5ynP8I+UnGDnH92p1Wxk4OHvI4iaBwpIQjb7df8/wBaZHAMEu2FBr1SXw7oml6xc/2k813I0cYk+UoDJgFjhdzZcgEBcAZYelc5qmkweXcrEstoYm2p5sew3AyikYJGCC249x0xwSN+Y0hWi2c4pjgUosjDBUnBAJ9e3657dOeH2jxyPIsshTcoQEkhV5GCcA8Dr7V0974U02DT7W8ga62uF8xHkVyS3TaQo4AKknnO7A6cujfTLVocaZZlZoSyiYMvGCRls/MWxwR8vI6Yp6MidTsjM8PWUWr6zBZtuSKUneA+B0JHQeuPpxXQeINFm8NXUX9l3MnkzDaySS/cXB4JPYhjg8cg98Yq+FkRfFNp5qAuNwkBB+X5TyWz3J9OenTGdr4g3x0/T7dtPllik+0DLGQsD8rHoevPpxVwfvHnV5SdZRj1DT9H1SLS7cS/ZY497TSXn2gCTngjeMkk4xwB+OMmrf8AhS0hklZGe2TbiBhkhWPQAZOeoBxk965S48UahcsJGuAjBiRKkaq+eDkEdOnb1PrXUWnjlNS04/brpIdQjBI3xARybQSvzc8g4PIHIAyc1pyu5jUp14rmX4GUtssMkFoZ2WaJjvPk7FwATnOM8D/Z7deRjZtbTTZYUcW21YAN7JuAY5IGQDk9evPHHtWfq+sWt1q1qLeOKWYYaaSDDA4YMNpwATwQQcjkd+mraqFYSrcRqsqDY+9SWxnLA8g45/D8qexNRycU3oFxBHbWbWclzM0EhDIsqnKHGQCAOOTzjn2qEXKafbKbkFLOZf3MsOQPmI5IyckAcgknj061/FFrcMYUgczMHyqIpLoDz2ySOM5/LqaNFhS6hEd1brImw7ZPKYbTkBW54O0BuBxyfrWl9DFU04czJrnXbPUIhbunnKwH3c7sD7xJ7dP8jGeZvrXKr9nG1AfuFh8v4/8A6665rCE6m9+qHa0Oxojg7MqV5PHHHpwCCehFZkmizwtInmxCDC7PPlG7bjuwXkZ4x7dMCtVK6ClKFN+7oc6LNosGcFSx+UY6jnJrWXQpUs4plCzeYwAVcsFB/iOB/nBqK8sLjzJJJLSSDaQWXHCk/wAvpWhpTTS7hqEsqRRoUiVMKcnk4B69OnvVbF1qkrXTM24s5LWeQxzq6hdxAI59sdOOtaWnWkzW4lLMJYzvjj3dXGMZXsCOM0+HQhLcpIl28qyOBjySMf72DwP58fhPqklpYCGHTrV7SfOJWAO1scEg55GaqMruxzVKjklGLuxLS6vpZ1XUbeSKFCFfOVY56e/X0/wpLqyk+ym1keMKpJjkHOR2x0H64otyTG0808EdyFxAy4PXqCc8en41ZewllYSTyMHQbXeVtyn0x2//AFVrF2OSUlCd9v8AP+v8jEa0bTAvKyROch1G7n0IpwRXjODg98d6vXYlELQh1MMjfex8uR6fl/8AqrObKg7VAxydvpXVC6OmMnNXe5HIFCh1K8dRnn8qZJDnDxA4IyaawXduUEKT3p8hZIACPlzlW9PatoyOhXVrEJHHNIXY49uPrUqKZV3ZxxzULDFaJloCc9qbn0px7cYpnfmrKDOOaOKCM8DmkplCGkz70E0houMu2f8AqT/vV7b8If8AkUbr/r+f/wBFx14lZ/6k/wC9Xtvwh/5FG6/6/n/9Fx1+d5l/vlT1P0DLv90p+h3lFFFeed58wftJ/wDJTLD/ALBEX/o6avI+4+leuftJ/wDJTLD/ALBEX/o6avI+4+lAH234B/5Jv4a/7BNr/wCiVroK5/wD/wAk38Nf9gm1/wDRK10FABXzl4n/AORu1j/r+n/9GGvo2vnLxP8A8jdrH/X9P/6MNAFrxTJfW/h2yFq9nDDJYxmUyhWlkHTCggkL78Cm6Jq41Lw80dnBJcyxK4WK2bb/AAjk7mBC5OScFc9hiuJGtyafPe/ZraXzlc+XIB8sJz9/OOp7dMds1peEvEdvJqzf2y5ZzmQyEbnlk6fM7HOMZ79a0p0pKEorXr+vz7Hh4h2alb4X9/S/lc7e+u7hEM8LTRTNAiwxxt5nmTA4IcZxgAgH5sc98Yq5ZxSWeoLf37StLLGXFpa2bE+ai43ZOT0JGSq8HHFUtLa2OsSi3uUaOaMzSlmcsUz6sgQLnsev4CtE6RpFzPcXo0tr2aL93E80qumcE/KMlcngY4x7dyM3CN1orP5La332+7Q5XFOXI3e1l67b/Jfi+5Bq41PUPDtxeXTurqS1narAjB8Af8smLHdnIB6gE+teb+J9Eu9K0mwv7lJbdp1CsjwSoUbryzgZPXgDA6e9eiXF5rLw3VxLZyQXMziGLClhBDwCNoBZRnr6nnHavK/Hlrcabr81lcEtsIbczBmbI4Jx0JGDisqc17Vcumz+5ar8VfzOuEZcvvdL/i/81p5bnd+E9eXXdHuG1G+gXUlgFhY2sC4l6Z3jLY5LYJwOnUVnanpXjbTpYZZYdQG1j5Pl3JkAYjBbIY4PvxXmunalLp2oQXdvhpIJFkCt91sHOD6g9DXrF54n0vU9EsTPLq9qJvnnuIljJZycEM2ewyBgDAxwcCuudJ+0VWD3aT/zW2+t/M5l+7Xs2tOn5W+SsvS5y2tx+Ib/AFL/AE6yvrbcdyRxW7PjPHHt9M569easeEtYtfD+uXTS6aZ72NDGougQ4kz85K9j29RzyMkV6DP4zsbqeGx0hri+1K8RQ/2VxsTqQmTx0IDMvPGOvStZeHrDTfEP2vUordDF88d07ANIx4x97AH+1wOR06DHmlTfI10f9f1v0Jl78b+hxmra3d6tdNdaxZeTpvnFookOyNDgAhc53dAcep4wDVTRNOh19poLK3itTETM0szsw2/3cAHn06d66D4j2OoXcdv5OmNBbeU03lw/OVfqzPjqcAcnt3rlPAaz/wDCYW8AmiSEhmmEr7AygHjPY/p68Vpgmqq5JdLrTy/4YvExcabnD1/X9fvE1nw4+nXLq8ZCkny5EIKOB39c+1dv8N/hfZeJ9JTVNYnmSIyMscEKgbwpwSzc8E5GBjp1rZ1f+zrW8X7aLVibNhbxSx7zuxweAeAAR+fHQ1Q8I+OJPCeiz2V7cR3EcL/6NDEQCdxJYliMEZI6c1pbljKN9en3tfeYUa3Pyykuuv3G5e/Dnwxb6xDDZy3FrPHhhGkgc5HIYhs4/wDrVQ1fRdQ8BWT6jY+Tcozbp8rwuThWHuPXpyAR0qj/AMJRqEl89/rd68Ucvy+WmUBzwQBjk4wPYZ56112leO7C5jisFhiF5t/dxzzDgAgc+px8xBwcDPYUo0aigpQeoe2pSm1UXu9zmfCnii41PxLHZw20E9xcRM1zM6BRu9MBemQO5xkc8HOvq/h211P+0P7X02wt4Gj2xyWqr5oPG0o+OAT2I65zxxXQW2j2lrOZYbaJVZhJvihKbnHXdgDt1yDjJHJyK5PxlBo6Kl3q2oTSGbdIwWQFVG7BhjHBG4HacfNhQeMEHOolO2luhrB8rsn5jPs9lDp8NjCEDxRhQIgwBRQBluoU4AGcnO3PtXM3dtJ/aDWgc5GCSykbh3IUjJx8vbv6jFeevq+qwKfs11LbjaFcQtt3AE4BxjOPeunt9Xj1K0W4LgkKFdVJPltnO0jIJXcMjGeM8jtfsm/euTOLi9tCWYqrHHHbYcZz6ZPQdOQfXP3ahiu7eCQyzbXcEsFH6j3PP6Dris68u4o5N6kJIpONpzzn19M4/I+uDiy3crucNx6sM4z1HOeK1jC240uZG5cXEHlvKskvlocBWfG45BIAPUZHv79Kwri8nvZtznaAAFUdhwAP5D8KrG8DMVkkcqpxk5NSRXcO/ZHF5oYEHcduTjGB7evr06ddkkVyuPQrm6iTO1TI3QHOB/8AX/T61Tnme4kLP+AHRR6Crd5bS+cXY7mP3gFxtI4IpV0+bOdmR/Kk2zaPKldDLOVoWyhxkEH3BrX09UuZGgmdUDDcCy5GQD37Hk1s+F/AV14kjMlnsURkB1wdx4ySM4UkgHA3Zz2Ayw7bRvhpaWyouqTXbXhjLFbZsQ7skIvmYxzgYPqp9RWTmk7ES95OxymgyHS555LRbePjygbpcswbJBwRhQy4G4jHze9XYhHeToVSOFN+xYlyodicbSMkkDLfMTkcYIyM+h2nhOO4uJreztfIaKRkkaNmjLKdytsPTHzAEjdznOQTnirmd/CL/wBlIsdvdOv7uaKAxyFt4XKPwdr4bJYdAVHUEYOrzSsONFrcztUgNvCRdsLV1Ch4WiZWUtyCBjJPO4AZ65x1Jx7yxnbTbW/gSSeO6aRZQZMsGUjHy4BwQQd3OSe2OfQ9euYbzT9HsLSL/T728Z7qOebFyChZNzP/AAkLk4x1AI5BovfCMOlabPeNY3drP9p/0OK3YyTNvkCxsFX5Nyq3CcEkE5HalOUUmEo30uee6bpry6mlrIk1sUk2yv5R3IM85Hr/ALPGeB1NdbP4oTS9LmsdFtbmWV7cLJdlsKijAOwgZ9Rk8Ag4Haq+maIb3xRLDf3AmljnESW8qPHLIpyd5UYYDBz2OTz3z1954StdBs5odOs2ubGeIyTxyTFNroVIXkcZUkYPXHJGM11QqqbXMeXiKavprY8ikunOhiKQghZvlXGf4eo9Ov459q63wh4iup7eDRbeygdUDK5LEl05bG3vg7mx7nHJFZOheGF1+KU/boYGhT5EkPb+8x42rnAzz16etO+0ptF1NrW7xvQKwaM7lOVDDB+hFVzJuwSUZKyPTdSlTSJ98pikiiIVVTcpYDnafl54z83PTvng1LWdJs44pl22yygtmMhGcKGG7GByc8Fj3IHUZ87vNZvI4o3aU3UhU7LwyEvkgdc9xzzwfX0rEubm7v1iS6u5plhBWISyFhGPRQfujgcD0ppJoxWHb1bGXEk3iDXvNlwst3MFOOcZIH4/Xv1NdhY3EUnh23guFe4uYY1aIGNeF6rtJGRySSfcZyMg8zpVhPJq1rHbwvMyyq5VI/MyAcnjvwK7iXRLrxHZyW2nLbxxFSj3EkquT5RwFTb90HcfqMk44BUmtjepJKyPP7x7u2uJjbSKI2bf5UZDKoz6egPtjpVD+0b2J1PmAMpBGUB5H869AvPAEFppq/ZJTcXHzJJLtO0sCoIUemGJ6ZOBisG68I3MqE2zRsV+YtIdu4E8EDr+B/U0lOLNFWh1Zxzh5HLOSSepJqIqRXSy6LqOm27vJYFxLAsiSqdyorZweOOQrcHHSq1voF5dws8UW8jb8q/eO7OMD8DV3RuqiMlbuf7GbbfmLcH2kDgjPf8AE03b/E3QmrNxZPb7yMOi43FTkDP+cVE8pMCIQFX2HX8aejKv2I9mSMHNTxM8TLInDKcg46EUyErE+S4PPatKXVbOLT2gWJpXePGeAqk9/U/T/wDVVIibleyVy0fGGpy3gmvJRIMOG+XhwzF8MAQHAYk4bI9cjippvFUXlyrArqrKVEK5VAPugKVKtwo7k5J5BrmomD/K5H+NRdWJHAqudh7GPY6ufWbD7ClpbXshtoSx2SQ/fLZPygAFePlY7jnOMEDJzm8TXsNyJNPuZoUEXkhTIWyh6pz/AA9sfnk5NYfUVPaWxubhY1IXJ5LHGKPaNIPZQS1N3Trn7fcBt4to4UyTuOWbj2PfHGP8a0J7N7y1aOO5g81fvFpQmMgbl/yenUCsFIoLW4ZZ0eSMjgJJsbnuDg/qKdLc2SDbbfamQjJWSRTg/gOaEznlTbleJt+HNPgv4bt7m/lglj2oUiI+aMkAk5+8M44BH1HGdZYrDQ9PLtetfTxkm3s5YXhZWJ+ZhsO4HAAzuHT1AxyoitxbxslzkzITKgT5ogDwPx454+mOa15GVo3ae9LKsKeTIyELkYwAdoJ4VlyB94ZzjJoi0mY1Kcm730LKahfy3wuT5hbYWCvwIxgdCOdvbBzyOea09M1C1nuB9nWK3l3KWhlywJAAYk4PXnjPQ49K424mQk7Ru4z8uDUBkkeMlSE2Y4zhj2p2T3B0bno9vOktwIST5S8BEIHzduAf5dPTsa2r3/8AZ8a3EMqAtgK0UhOSOvYEHPHPXHU1wAvbuNhiZj6Z5wKln1O7vFUXExdU+6D2q0le7Mvqz5k76HceH9RFzfRO7hCjllXkF8jGBg55+h/DrXo1lCVmeMMxlkIVQ7nCjtkYwT0HBz9O/lOneK9Ot7SET6Sj3MaqnmL8vC9G9C2fVT756Vd1DxvNcSbrSJIySNuFAAA6cY54x/QVl7OU3d6I56tKTloj0S+t3to5F/drM7bgy5J347Dk9+fx4rFvLrU7IMumWTXDOP3phjCNHkfxDaSPzx0zXPaf49vIpY2njMgUjIRivH1H4+tdf4c1KLW5Z7k/ud037wqgAHGR6+4pT/dq+9jJU5LRnI3uoawl0g1WFYlcCTa0S5C9c5xn8euOueK9Ae8a10i2uYICwVEDIxOMcdTg4A65HbrUhZdOssMgmjWR5PNQEIdzHqOueRkfl0qlL4mtbLyjk/ZGcAiWFtqA5wV+Xd0HB5A6dMGmrykpW2v8/wCtzKTU48q7r+v0uRwani4VEVhASAURRtBJxgehHX3rMns/Lv5J7CWQSy5VUaDaN3cM3U56gfTrTyLa9hl11rLYLcna0UpJkA77SPl49+vr1rYsbyy1SJ5bCWC8HRxnDKPQqw4H+FdKvr1X6o82SlB3XT9SjaX+o2kYe/hR0ThljTcyj169u4IB6+nOJrOrXt7NIujWU6LglpduGI74A/mefpXawIUQiN0yg4xgLH+A6D8aQFoLpZ5QHbG1pIyTnPYqP5nP4Uc3InZak07c3O0cTFFrSWiMTbzQSjBnt51jKjHc5AyP7xB9M1rw6lcaWYzdwefOEy0oIdiufvEIS20DHPPTp66epW9nCrzw3P2VpMthAAJm9d3XP0Nefa/qeN8tq9wkrOrhpJcsjYwQD1/yacaLnq1ojWEvbStE6m8mt9QlSJ4BJb3abpXjuiUBPZSevrwB9K5P+zNR0bVpotPjkmiYboXExjyOvJBH0IOPwrT8LSyNDztRW+aWUkncffIPOOc+9VNZ1G90zVGsrWZlEpDiV3zuyeueDntzk8dcYrSneUHfp/V/8zamnCq6cPu/r+rEN7PfahLE3lPC8YKyv5y4Oeg47D/9dX3sVj0JJJH3yqcggYxk8g/4Y9KTTLOfW1kuHmktZojtZkjaTew9SHOe3aoZpGkWdLtkNzGduMYOMjPHHt1HeprS5qUnfRJ/kb4a/wBbpQWlpRenqbvw3/5KDpv/AG1/9FPXe/F7/kUbX/r+T/0XJXBfDf8A5KDpv/bX/wBFPXe/F7/kUbX/AK/k/wDRclfLn6gfPPjQ/wDElix/z8L/AOgtXD55ruPGn/IFi/6+B/6C1cLjNd1D4Dgr/GP4oAA6mgLgUNjitznAHnqQKVfmODTSKTvQBNIE2AKct3qPOOKCMCm9qAQBsdvwpWI6jHQdM0hpMcUhiUUc0uTtx2pgAxmlY7nJ6Z7DtTffFO7UANPFKKaTTx0zQIKMjaAPXJoPAGPxzSe9MBwwevFOYYY9uaYDTgM5zQIVT2J+lKvBppwFGDnmigRJgnGAOeKfjgZ4FRdQBS5z9BTETBSeBnHarFvDHIRvOMHnI4/xqor8e1aelgSO4Zd+1Qw46HIq4q7M5u0bluHTwJgYPldgMBj0/wA8U+zEZSZHkJGxvmVcbeAQTnAwTx378UThbdSokaF3B/hO0dwPUHHccYIHc4pwzzWzjyGIfGNyEgjPXke3FOSRzq8kepWN0Lq0hu9S1W3Rlmaa3g+xBwkcbDMjIASG3cZODgkhsZxjXvj2+gFzDbW0UNtdo6y+cpkeQnPzFujEn047dqz/AA8s8dqwjeON8bt/O8gZPGO/HXrxWJrFulrqAihiaMqqkh8Z+pA6A9QPTB71grSkZ06cYtna6ZoFtq2h20Zt7FJyCHnaQRlTweNgJbCj+I/eLAgd6mqeD4dIimvIRKy2mwyLcspDbuBtwOxZDzj/ABZoHjiPSNLjtRpbTSLvCSeeEKAjoGC7hkk8hgfrXaaek3iWx+y+IJ4oTdKI5be3k2+QqncpKsSTkjqOARjHBBupJQVzKKnzW6HOaBoa24XVpvNFo+UJaTYp4JySxBxgbuMjGOeTt3NT0WSeG4bRbUtcyRh/tDSblmB6iMgfMMtz0AJGeuaxrrw5c6h4mtLOwvbiOKZUW8nBJjQoX4yvythVI9Nyv0wcdZcWfhrQry0lur+68y1t8xxxuNnzLt34wfmYDb8uc4ycjmsbqbuE0463PPP7Nv8ARof9NtHSSYFFSVjGZMEDC9iNxyR6DjFaza8BDthKyw29sEdGcxgEhlyEwQCeDxnG7rktXbTeMbe/UjTdNE0FrcAKrx5YEIx3AEfKc9O/PvVHxN8O7J7ebUNKlWzdS07wv90HGWwQMjoMDoOccHjWUGo+8cscTGc7S0aODufEttbrFNpkfk3K3DOQgYBE5AXdnJOMZI2hucjPJq6n4nutVCuZcXUKqsc20E7VCAAE/Mp+UscEglzwOtb+t6FHd2lnaOYo9SgCxh0jCIy4ztOAOgKnpxyB145650ma2ZIJ7FWdSI0lEqhXyxIOf4gcjnPA7gdKjKKVkdScXq0Zc+qarfyKstzK+TjhsA9PT/dB+ozWxNo8sNnb3cgVll8wSF3G1iuOnTHDZ/D2q1BaxQaK/l2yvNcLtZJrcM6EEkMjdgehI65HykYFUtOfTWeMT3KJMDkmUN8hwMYbcBkY6nHYc/w6xkmrIiV90rJHSWVikT/brTzDa4ljVnTcsSsgRXJJAHA6ttOemcAVIJY98Sw3JS7eR4syNskC7sAhiDzlhz6g4I+bMOk61pMdlBbaja/axDNJHbRG3O4wSBWJI5BY5yOR0GODxhaqNJTUnbQ1EcTyAJHNKZIzGRnHK7g24dASOoyRScdTHlctGmaN9JHBaK6XcWBmRYyu7ezKV3YO4DACcAAjIwQPmqay0650WSCXXDbXmk3CCSJ8EKhYgrgYUq2GJxkcBsZKgVipBd6myzyW/DtuY26Bd55ILAc5HzDOOh68Vo3iypDIbmC4MSKqFLtwHiB+YhUJJxkEj3I9RU2TG42XKXZLlbiMTwQOBhQLqSQvjB67/mYr8wyOT0HQAm+1ot54XMSJYwfulLrNJsCuG3NljxyNhHAHGBjBxkalq41XT0GmWsUE24AR28O0Z/h4UdT8qgHIyT9BhxazrlrdCbYxuWkDxmWAMyMuDuUEYBwB27A9gau3dkxpya00NS48RLCLwanp8LfbMFZrZVwSpbruHzcMFJ46Z5JzXOJqWLxrh4V3t2DEL05OPUnk/j07OXStRuivl2c0jOwACRliCSBjA6E56flVW8s3tJmhcHcvUEYI71suV7Gqgo7mzY60lpcJIXYAFd4zuLYz6+hx37fWk1/xM+sRRWsfmC3iYt85G526ZIHAwCR+PWufHWpNmMYqlFJ3MnShzcz3EORxnilGcZq79gDovlt8+MkHvVdo2SQxsMHPNaBzJoktZ5LeZZYjtdTwf0rv9Mu4pJGt2SFpLdskREbe3zrjtwOfp1rk7bwvq1xD5sNuGTYsgxIuSpGR0PXHOOvtUd7azWtyIbqExSKNuV6NjjI/l+FNWZx1uSr7qep21/8AYBeRqZY1uckLIZWDIRjKk/d9OGyOvesVfFVxblEJUSQrt8yFfvEZGDkkEdO3b2zWZZXJskYWse5pcfNKMBTngjn/AD/K7b6Ldx3izNbqq43Oij/Vn0II+XOM+nPWr0sc6pxirSZp2mrRX1wz3F1GkroI/OJIxjJGFxwuepwOOmO2zZ6b/a2mSQpIjPEQUcNjg9Rlcjt23Vk2wit2Fu6faYXO85jzuGP7ozgjkHHbnnAFbdrcf2Xp3mWcEb2ZPmKI5Q7qpwC2D1GeD0x+FGt9Dz8RJW91ehQuHtLO+Fvf27RNFwwA3qw6g5znB/L2Bzm7b6rFdyxRyW3kQ8qV2hg4J6f7Pbmk1iOXVoYZ9Jw5gXHlkZZwx5Jz/Xrk8+uFfedp0wiuUa1kADYyR+R7/hWujVmYxiqqV9zpzZwW8amK0d2V/lZNzFeeOPbJ/PPfNMu7KSKz8p3jlgyMryD68nqPm54/+vVC1167uYUJcSgjlCgLEDr6dhmrNtqC3bFSg8zbyCeCPfv/AD6fhVRT6nHONSDu+hVlnCM8KeWsL4LBWOXHUA5/P8amGA29/MEcQ2lTjgfQjjt0rM1S1eaRZ7G3Ea5w21hgt/IH1zj6dahuJdVnQGa3aOPy8ZTnA9x2raNr2Zr7LnSaf46ks91E9y/lozs2WKMevvx/Oqk8SybZkk/dsP7pBB/HP51mfbJYpcxu6yqeD6Vq2l4xXklQxy3y9T1zxXRGWp1ulKkk0U8lWeLAZSOVB6H2pEBkt5EBxxkBu5FP1eRhOsbKm5QMOuenp2/lVWJ/Nh8sPh/rwa2UtTeK5oqQ2Jvm64I96VmyDg5z1pH/ANcwYZY9OeKUhoyrEYyMj3q0a+Y3krgD8aaQR1p/mMW3d/akPOTjA9q1QyOkNKaaaZYZ9aSlptMZesv9Sf8Aer234Q/8ijdf9fz/APouOvErI5hP+9Xtvwh/5FG6/wCv5/8A0XHX55mX+91PU/QMu/3SHod5RRRXAdx8wftJ/wDJTLD/ALBEX/o6avI+4+leuftJ/wDJTLD/ALBEX/o6avI+4+lAH234B/5Jv4a/7BNr/wCiVroK5/wD/wAk38Nf9gm1/wDRK10FABXzl4n/AORu1j/r+n/9GGvo2vnLxP8A8jdrH/X9P/6MNAHG6t4glaOXTreJNmSkkrgMzc5wCRkYP/68VP4X8NX+pRyS2OyNC3lvcyKCEHcLnvg889D277Oh6poOirc3EsX2i+llcOBb7imGPRmOB0zlRnnv1rpNGNxqsEOqX7eQhc/ZzdytLLI/QbY1KqFxnnOeCTxg1tZwk1FadX/wOp4NWt7jm9LN2+XX+vU0NJ0Ky06BhFBDHA7bXN8d7SEAclOwzjH8gDSa1q5srA2WkSWwuw3lxQpsbZ7AdBnPTH0zyTFq/i7T9J1CGHVJJYiqBhK0B+cjgdB90Y428/WuWj+KOnaRDPFZaPa3xcFo5LiAKInLEnA3MSMbQTlScegGOTklO8t12767Xtv69DSHuu1tfPzX47r+kR+Idem024t9H1SSWW4t2D3vlShVGcHardF646cdqw/Fmu6bq2kw/YdIs9P25AYXDSTyjOASSB6dyT+ueOurp55mkkbLsxYnGOTUG0suR1rrjSTiubda/Mv4ZXi9NhwPzcV0eht/ad3a2V1cyBSwSNQMgEnjIHPU+h+lc0oO6rtjdS2N9Dc27bJoXDo2AcMOhweK7Kai5JT2MasW4vl3Ppnwv4e07wXpk8AD3MpfJcQnfKccYwCcckZ7c+tZPizxRp+hX0UUtkjNhUWGIDZCOfmJ4wxHGOw+tW/C/jG58S+HZJba0W4urSHJg3GN2fPBJzkrjn3rzjXNV0PTdckuL/brFzHJxYQAxQQuCcqxKYYDGBgYPcGuapRf1j95Z2t+X6/qc8ZL2T5U9br5vX8Dsh8Q57Xwy+ox2nk27TP5bPGNs2WJAQ7hk8ks23AA7nFeL2uuXthrL6jpsv2SdmJHk8KoJztAOeOnBz0Fdklt4n+Jsz3NzdrZaRE45ml2wxcYyqkjc3J598ZAwBS8QfDu68PadPdPc/ajbyhJBHbuECkcNv6de309RmXONKpzT0b0Wn9d/wATSnDmhy79yjqPjC51QxtIsiTCIRSTea7PIO+SWxg+mOw65NdX4UbQH0CQX1k13PcKw82RA5D4+VU6FefTBOTk4xjE8GaNBe3SxXMMEu8Fj5gHHb8P65r2DS/Ben2UKvFZQoyjKjy+n1J61vKcVdSW/wCB585ctlTvocbf+E3bQYbvR1muptqh43UMI2xltrbexwOOcn2NP8K+G4ZBMzII70KUaB42EqyAgoVIxsGQuTyeCfp381xHdWJitSzuCVAjwDkZXuOB156/zrSuYUlt1hWKIgYLySqCqn2Hc89en16URi4tvo9vIiNTnSXbc8/12wn0KaX7VqF1NYRWZkRJWeWIS72/d7if7u0c5Jzn6cMdMvtTae9ksLiRPJcxBAVRT1AXPYbuMe2Ote2R21lcy3JeBVhljET3BXLy8HoTycDp2Fcre+LtBt/7SsY7E3FyjeSlqsbO00x+TYBgbRkDJHJ4x0xWU1Ule2rsdNKUU72sr/1/XqeCC3lnbZbq8kjZ2qgJJ9eB7UQ2t5bXkls6SwzhzFJEQVbOcFCvXOeMGvoXRfDFh4V0eJIILVr+NH8+/Yjcr4+bDAbgowQFGB65JJrF1a70mCOe9mW3a9S1eZLiaz3zeZwkS5PzZBIHzEgYHbmpdfllZHbH3lqedXGg3cIVdSWC3kKDam7DBm6BgBgHnvj8TxXMXVrOxYgHZGfm29B9a9BbWYNRvLGONYPMMMcUUF2okZJSSFbLBQxYMDnsevTdV7Uvh+buFl0S/Z08vfGlw4Pmc87TwCMKTwOvHXON4ylduexDcINWPJxbSMchfrg1Pb6ZMxDmNiACeBnpyfyrvE8K3UdvM8Nh9o5HktI6ADaSGLkkcbj15HXng10ui+CblbVhdvbySNGHWJH80svAdAfuluGwNx5K5JB405oJXTFKpO2x53p+j+ZBBlSDOvmSP5ZZI1xyzYzgAEn8K9C8MeAoEuYjrFxNBbzMdytH/rGXkZLDAUkZA5PBHHNdXp1jpenWhS0WNblYg5aNmPmxsHVGBDbRnD/NggYJ4qhdaoIbWQm2WS3jjLxxXUi/v4kTCSKWcAAy7cORlgQecEiJScpWRl9m5sCZbG1mjFjHG1sJJnhjTygrN2LgYOVc49wD1UgU725kjnsYUlS4g0u4dr64nnXMZBwrsu4kjAYgHONozyK1Ndv7Ky0J/tcCzZwghkQH7Q/UJkggBjxz0z0HFeaeNbm0u7m70xLmxtJkWW6vLmIOPMfkrbbskyYJQbiBjGdoIK1mqbi/I0jUVRXW5pax8RJv+EpstKtbSeyjgO27kv4xFcSBmR29Nu5V9shzwOKxtW8SeHPEHiy3hvodtrEGaa8Bfzd+3AXJbkKwzxgcnGcbm4jxjeX7+JJxeWzWM/loJIRc+cWBUMpZ8ncSCOpz+NR6HBcZxHass0g8qG5lwsSM/ALM3yr8odgc5yuR0NXyQupNFrnd2mepXmm+HdP8XaVBFpt1d3EkDSXE6iV3uJCr7GXLfedkfp6HHZgniL4jpc3tpZyTXFheQ3MTXDouY4CkjMF25UsCGG7JH+rHrmsa01bW9PuZI5rVBf6hLm6nkkjlmRd+5RbKXAG0OSD/AHsnPynHI+ILS70/xDcWtzcRXMrv5hlj+YSK4DAhsDIKlSO2Dx1qYpXsgd93ud3dyR6v4ojnsrq7uoILmV7+WaMpEpds7Qh6EHC88naB0XJ73SryKe7lgZVSB4IdiL8oDrlix25APKdz938a4rRNYgvPBtrp9gYdLS4Bjv2+zF2YlgC6N0BIIGT0wOQADXU+FbWAWwhF8920MLLIXJidyzgqSyk5IGRnJI+pwMpScempxzjGU+YuXuifZdHSaNrWBoyWmljtd6tu4Ztqn5jgKeR296peVaXVpZ6GI7e9jcyPumjB+YnIcBVzg4K5GM549a6Qp9ogWO0ucNIfmXllwf4RyMDrz9a8lubvWE8QX0K31rLcyAQQLbDeZSckGI8bSuQDjaD8uA1XB31aMuVdDttV8B6Je3FxdR6ckUdp/rLe1c7Z1C5wFXB3cnpjOMZya8tn0CUakINGilvtyh/KELM8fAO1go+8oIzjj6dB6QYb5tO2BBc3smnrYXU7TRhRIGJKEZx8gbGSefxFZ+dW+H2luGFjKLi5RTNktJCCC2PpkFweR2xk8bR0ejFzdA0/w/HbCyUW3l3EPzybDkzHgqGUnI54IwPXtisDxetpoUCDTFaIXUzSqYztaN1I3rlcYQfJtGODuPGBXcmcWemQwXN7ax3U5iZvNI+VCvyxhgwwpIxx0z3HJrN4L0qV1lu1Mk0dz5iFyCCAxHllSCNme3GTngDiqi3uzlclCXvPQ4i28ZNHHafbDLcTyFimZAqxruO1SWGTzjJJ9SdxOaktPEFu+sNDPOq/OMlpCMtj06dRjt1FdXrXhrR57mMXGkQzRoVVntSIlEhyfLbYQQMKFBPZxjnmuRn8HXX2g2elLLdWGFZocpkuAFZlLEruLA52njIXOTtq04voRejKTWxbfxRo4iex1O3FwFaQNmM/ucZAwDjoOy46ngk15mb+5Kt5cjBAACpbIGDkcfWt/WNeK6PHodravarA0iT+dGBIx3Zwe6kHII9h+HLFGxgZ561qzvw9NRV2PuNQmuHdpnZ2YbWz3Gc/0H6UWqrdXKxuoCdeXwAPr2qP7OzJnac554rTi0XU7PbcLbsoHIDkAkYPbOcYU8+1ZtpaHTL4XYqvp+0cHPU5wduOO/44qq9udrHHStOfXJ2txGsSA8hnJJyCTxg/X68Uo17ETedZQSS54JXCqPQL279PWhcxgnWS2/EwsYpualK5WmFcVZ2Jir0FSK7Rvg44PpUltIixusgHqpx3qxIkTwsxctJu+UdgMcn36UzOUtdRYw19GQw/1SfKVGO5PPr1NSQaU8sTyLtIRWYqG+bCgEnHsDn6AnsakhuWhTZ9mwMAFlyOSOD+Pp3rpLGWI6HJaXNs32p5RLbzWsx+dguFQx4O7G48/wC0QOcgzrc551HBaIwtOtf3Mso2YXA+f8+PxGM+/vV2OC1/s1jcATXLqSi7PmQkZ3Fg2Sfu4HTg5HYu0e3uJ9bisJQzQMzCWNZDFlcHcM8EcA8H0p+sWdt4f1hpNMkkkSNgUWUZ3DHzHPHAPtz+ByR3ZnKonPlvqxv9niGyjcmS3adWYI6hlfbkHgdh644Oe4pj6JPJIiyQm1eaMSxGX5VkU4wVbow+Yc1f1CSW40tEEM32hA87XSZUxBmUHzEK7iQAMkE8dMgGqiuLa1Rb9lmaQhnSdDDLG75PJJyyEc7uVB9OrWr2JTe5iXO2ORo9mCpwDnOfx7/WmJW5DcRtFJJaNDGGzuRtgfAPQZ+o5A/kayJY1a4ymBnsp4zWl7lJ9CRULYKqcAVatreWSQLEu4k8CtTTNHWRQJ3MZK5PGQ3GQMgnqCPz5rufD/gOTy47+a5SMPh0i2kN6gkjofzqZVEl5mE6kYvUzvDvh+Cdza6rp91bXBAwcKAeDyCxH5ex54Nbd8Y/DNoRCXMYcsWKq6FsYAdfvYwSufce1atz5Kusd0yG4hf91NPgLIABkNgfKfTjgjPoDnajqkc2m3P2qCzuJCuXEAEgUAjqw4z/AIfSnSg3O8zyp1ucoaL4shYZvZVTyjxE+5hLnP1AIJ9BnPPSupa3XU7WETW0L22zItm5Cg9T2wfTB4rnNK1HQ7qImSwsonh+/vjUFxjjAx6nknsK35JNsKQpCoTG1VX5VXP+yBn9BnIwOtJUnKV1p/XQivVVN2S/yLKafZ2KSxQSy/ZrhNvkOdwQjj5ScnGOMGvM9a8OLpGpRxm4cwynKuqZYDPIIzzjPXv+ld9JMVjzuUlum8EZ7A9RgE469eg9KdbRxXiT2bRRyYAwsgDAE84HyjBHU8Z5rphFUoct9P6RyxrTdTm+8ztB0u/0XUreK1kdrGVS0iT43A4+8oHI7cZIrptReOGzZfN8tj9wk45+tZeqfaIYolimW0MQwpkbh8dicgHuMdelY+oeIbuO1MUjWrvuwjiQfKMc/McbW7dKmlRlJqUvmE5ufu/8Oc34pOt6fdtJIZI4JtuDFMxQnHTrx6gH8M1gWcNzqTOWDTEenO33PtXY3OtSa3az6XbxnasRP76TJ4/hyOCe+ec+3QcYLy/02VrPIaJWOEYZXJ7g/gO9dLnJxV36nZRj7rhFJPp6E8u+xh2QyglTuwR0P0qC91CbULhHvFG5U2oIxtA71sTRx3Gkwys6RlRteSbAyD1UNnt1xgfrTI9Q0fTbcmS1juZFXAG47T9cDrz6r9e1KLuk4LcuL12uxmkx6kLGVtPhMkTYEioolwevMeC3pyAO/OM1uDV9N1Tw/KQI479SodC+wsQcZVcndx26DHSs+PUfD123m2sl1aXO3BHnmLf7liTyPQEfjVSWW0u78GK0e3niGWkkkZml4wSck88+v49qxrQfsZO+ln+RthUp4qneNnzR6ef5eh1vw3/5KDpv/bX/ANFPXe/F7/kUbX/r+T/0XJXBfDf/AJKDpv8A21/9FPXe/F7/AJFG1/6/k/8ARclfMn6MfPHjX/kCw/8AXwv/AKC1cXEgYjiu08af8gWL/r4H/oLVxkTbGBI4r0cNbl1PNxN+fQm8pBk0piTbuTBPc0hmXaduAD1pC++PAJP14rp0SOTUiIy2O9N8s59h1oJI70byy46AVmageTSHA4zmkNKiFs8gDvk0hiYOOOlIfSlwcUbTQA1jjg0lO2880YoGH8NIBnvinAevSnKmcnOMcigVyLBpQMDrS4+bmg9/emIAD94du/vSHtTwM8ClwMdKAuMUVJ29zSbcY496kdNuCMEUEtjNmenWk7c08jnjvQFphcbgjn1p6JnHvU0MBkwFU59PWiRSjjIoJ5r6EgSJV+Yc98Gp7OaezbzoCqD0cA7u3H5nkUxIi+zjHmcDHb3q3pelS6pqAtot2xQZJJAoJjiHLNgkZwOcZ9qalbUylZqzJZ7i61BgZmBC42xKCF44H1xnqfWpra3nGxo5DECCN+doHB4yTjHUe+K7DRvB9pAjzzzT3T2wIktYkwZJAzYXruHKlSpGe+RnFdBBp2kn5Y4LeURuF+eKNhysQ6ZK5Lyq2SMLyAF3GolNtnL7WMdjzVpdTlJxNcvkFSxJIxgKRn05A9s9s8v/AOEd1IzIJIiWlQShycgqcEHP0IP0Ir021i82zt0nSO4gLhViLBYnIt1dQ4P8ORuPvJnjbzswadBqFtDGIJJXeBEZgmxoVADbSFI2jkDaD2wOAcZylKIvb9kcLoHhWDTp4tR1C7AhMIkt1Zdu6Q8jkE8DHsc9QBye0sdQ8P27i1vdPga+jCmKGVRJKpUDO7eMqvIA74zjoRUd/pD2mivDcJMLvaixSSRlvKXzBnaF4wARjkdT06VwniiS1sGmt4PKk2oCCIyFfdhvlbPVSzd++Oe3PKKrOzNISnc2NNj1jS9QtIbefyUnkbZMUdQGZdvzZXlQWUkEEDrgEnOna+HNTsjqE2twpqr+UzBJZXAUgDJLcgHAHIyflxkckcZpF7fXGktGxkkjMnzhz9/5C4IY8jhTnHZF68Y66HxZrmnaVPLdX+nzi2kVF3k+fKpP8JIGQfUjJG7BBArVKSfLcxqQ6nSaDDo7anHBplhMC8SXcszFkXJ5RSo4xgnA6DHrzXRygSQXQmjUwEMv38EjHPPbvXEeHPiDDeXTpqxggLqzSSABDnIVVPdvlyd3vjHFZ/iL4sWQaay0oGRJVK/aSGUxk8dCuRgc557cV2RXMjy50KiqOye5JLo17FcvFZTSTtqUHmsz4VdoZev8WRuUbQF2g9c81lapHp011Zx39/JOLW3Uf6PGuMKPLLF2BzjYOPUdQSMcfruuNf60Zre8uJrdFVIy7E4AA4GRwMjOP61Npem/btJeeS6ggbIALygNzn+H04Pv0rDkasz1fZ2V5M2fEXmyaWbgXim1t14UOWYszYXO4A5ICnjOAD05rk9Nv/stwzxlUkdSC7Ir9wf4unTqCDjI6E10N34QkUWqTX+2K4YnerCVGf5c55ChsFj945GMZzuNeDwj5WtTWgmivpYQWEChgZRtDJgD5txyQV/hI5IzmtYWUboI8qW5mLNJcXkk0s4ld2Z3YLjOevAI4/IVt2tzHPGqzTW6+W+No35IBzwDwBgY7n8sFfEFpoOnqtrptrcW2owStHJuDMroCcNknIbBXJAwSDgY5OSt1eRzrLbBozuXDxoFCntg9u9WOymro7OSyjhCSwXUYJkO9Q4wVJyGAKgnvxk5z9RVe5WxVX+0RNKkLgoTEArnAAGR06E4/wAjEsNUj2nzWY8hPlkGSGJDMCen19/rWzFPG9u8dpHGz7AhMgIDqAOeueoHGRz65pWMOVxZh3mrw2jS29rb/IJjLGxVR8pHHGCcc+v681TuNeeS2MUKyxfMcP5mOD14A79ycngc10dxZ3F9ceZcW8HmkrvC26rn5eTkg85+gzUHkJDhWtoAVb7pgQgkjjPB7c4OevSmpWNFylaw8SamLyG3srS3E7oIgWBLSKVHBJPHHpgeoNS3giWORbvTbGe9mmIVrWUr5TBvmUord88fKPxxgOktbprR1hghiYt1MSxmQYyp3YG3G0kY9OetTaDZXEUE9lNb27F9oR9ql42bgdeRyB0xn3yK0Vmc9XTY5hrIrKyyAqVOM49KZJbyQYc8qTgEc13N1o9mdNlvbi4USkqZikvmrEMEDqRliVxtzxyec/LNa+F7LUbBpYnLqyYjZ1KZI4LbcjjsK6VaWxw1cUqOs9jm2VpLbcTvZkOCpA4I69eT7cEZPBBqrFAzahEz7wxOQzrgAjnBJxxWjLoU9nJGU+RWcqx4fkdlyOec8cdB7Gp9Egls9fWa9gkeGUFUmVQVRmGAxPOO+eeOTyAQVJ9AU0k2mbnh7VonneCSaGOT5UiQuB5nTgZ4zxxj246Y6SaCInbcwxzI7BSHTO/I6njAPQZ4rzfXrNbecT2WQqhcsrZ5OTkH6gjj061PpXjTUtOtJIJM3ZdtyvNIxKZGPyzg9u/rxpbSx59TDOb9pT+46W/k+xS3UFtbhraNceWjj953cbegwO+MZxznArPjuoViJ08PIqR7gr7QQA3JwMnjA6Eck461XGpTeKLtWuIzHBCGVcAPKuRn5DgDPynqMAZORgESf2NLp8s6G6+1F4As0XllQecbSPUYB9e+PWilDljae4QahFGfNcZVUO6NQPm4xz+BJ5GeK0tLhEkKl9s1my4EzkpJG/3SvykbgfQnHPPoeLa7KynzCQ46hh1z1zirFvqjqwiSQhN+dhQFT74/P86F5lVMPePunpNg8iA4u1uXuH3oVQDK4xjPQgY7evSoL9raXNvqUbtK/QMOF7FlJ4Hr1H+OY18raLDcxTCIoRIN2ATzjkYx1xyMc88dDsRzrfWLI8u0FAC2SwHHPH49avltqeBUi1K/nb7jjNQsmtpiIuCDlWHcfWrGn3flKtvdyMgc4VsFiMdBjoQT9etP16OaFvLAErpjLKhA2kdsk/5zU+kSWkunyStEqSRuXBmwSDgHgntx9a2tqrHXKd6ClLUtOkM7eXIWb+LKnbk9yB/TpWPDb3lleK895N9jL7N7gjvjGP07iqN94gae2EMNpHF82RJ94qPQccVmSajczmNbyeR0izsB5xVtpaG1HCVUnfZ/P7jevbJV1WSaX/SIXGUJwNw6enX3FVbiAxxCOEkoWyp5yvsfep9EvLa5ma2uY2QuPkOSwyeh9vwxUcm4yOkZ2guQxYDkDj8P881rCS3KjzxlyS6WHpsuIQjxM67SQw5wf6fpWetn85Y7tqnOOhxVuS4+xyYciSPJK7ON3r9PpU1vNFI6y7VJPOC3UfjWqd2PmlBNrYyfL3SfNlcnvU2+QIbeXBAbIJHI+lX9QgiSQSQcxvyFI5U+lVxbO1u8pbcI8fXFbRZoqqkk2VcbOB+dNbcGIb5WBwRjFTb0dsspxjoD0+lPluVkgRZI9zoceZ/eX0PuK1TLu77FTOFIIBz37imHrVp4UaIOmcE/gD6VAY8Ptf5D7iqNFJMjJIPHFNY57U51KNg8Gg5YdB+FMtFux/1B/wB7+le3fCH/AJFG6/6/n/8ARcdeI2P+pb/e/oK9u+EP/Io3X/X8/wD6Ljr89zL/AHup6n6Bl/8AukPQ7yiiiuA7j5g/aT/5KZYf9giL/wBHTV5H3H0r1z9pP/kplh/2CIv/AEdNXkfcfSgD7b8A/wDJN/DX/YJtf/RK10Fc/wCAf+Sb+Gv+wTa/+iVroKACvnLxP/yN2sf9f0//AKMNfRtfOXif/kbtY/6/p/8A0YaAPLNTmf8AtO7RCVXznBAPX5jXofgmXToNIjv7vTDHfWSMy3krPM0jclQkIwD1xksoGeprznUA51e7YKSFncn0+8a6Cb4g3z26QRw2yIsYjCeX8igHPTOG99wOfwrtbl7FqD1f3rTf8Tw6sFOdpLT8/L5lLxX4kvvEup/ab+KOKQLtKxlucZ5O4nn6YFc67k9SSO2avajqzXshd0hDMSx2A8k9Sck8nr/hWd8zmsqUOWKVrG8ndjo08x8fzrTGmSIIiRgTHahOQHPp78/rVe0gYSDOR711qeHtRjkhOoW01qzfJmSIgqW7BcbicHp/IZxs3FJLqznm5XutjmZNLkijMgeNgBuIDcqM4ya1LDQ7jMcs1sWyRkyZVF54DNwF+pIrqovCdxZXMMMwa68t1meKNCFKrgtuJ5IAIyRwCQDyQKZcyQzSSvGjTbXOww/KsXy4B3udiknnhc8ZB61zurraI43nG7NT+1n0yMx6Sqabc3io8k6SMHlXDHOHJCjbggqMYZcEdK5AW8bSQmJFLSnas2TNJcSZxlR1PPsMn3q/d21/YMYFZpm3+Xu+zFY5JCfm3M4BfHOSRkkYxVMTmC8SO7JeSCVUMcsZPmHBVgSArKmeAg7E+9TGTl717lKLgjRsNRlkt2+Wdo1ttygIsryBGOflJxGq/MT0Pf3r0yLxubLQZk1e/tRLHbq6DzfMYyP8oGxcBtvUqQOx4ySPIrEu0n9nwXkNul9MsUjIWXjOMtghdgz09vapdX1nS3iFjoyvcOW/e6jMu1pBjG1R1AOcknB7YHfqqR9pSaetzmleNVW/r/h7bHQeDN+oeIWa0AjiWQuMKECgnjgE469M/jxmvoSztFS0Ck5JHJr5y0DV4NEsmubolIo/m2qOXPoPU0/XPivrutwLaQy/2fZqchLZiHfpjc+c8Y7Y685qNJQUOxzUX+/lUlG/Y97vImg3CFVJxxilghdbcec6yykcsR0/CvnDRPEeswamFtdTuIPOyHZ33g8ehyM8da9Fi1jxVpzxR39wlysikutxEMKMDg4246jg1rGMYq3N96/4cwqPlqN8v4/8A7i81SAXMUXnpHCql5WPDNxwAPfOfyryLxGsVp49vLqMGS0Wb7WFjb5mbaJDg9uWK/XP4799FPrWuXU1yjW8ixRu8SIXbJCIAF78sPwz9KmHhUJbyNPdOjI+ziLjIUsx5I+XAGGOB1zgDNdrwVNcsnOz9DqpUpTjzdHb+vzOUg8fT3OnzXWp2039orcRNbW1pbCGKNFYE4flgxXcuecZ6EHAzTPq8832uOGaJfJZJpfMfczMS+AXJyA3p9ScnjsBo2y2ujMXEsMksZwBtjMYB+b/AHs4HuO9XI/DsDR2+XlZ5o1wOi728rALBTtGJc55H3ecnAp4Givhlb5HVFT6nKeCvDMupzPd6kHEEZbe0kw4jYFCMj5sYJycgAc4JIBfd+NF0nVpodAmUCP93bwmFndxgAAyBjyASB/wInnG6XxPaf2dZad5MyXBuipeNU3+Y29h5aepyo5+pHauaintDpc6ypM126H7JsXbEzN5IYBVUEFgjhiTjAIySC1clePspWi7k8l5e8aGl69r+rXCWMN1NLCjI8yoy2+7djgN/eLFiCQWYjkEZrootT1K40uK2sJnkaRQjv5jEyFWIYI+AHBHOcYUdeTg8vpGozw350zSWihja6jnijDkF8uNp3Z+XCMTgngdeeneaL4c1COxjjlgDQRotvLcfaVEUoaQK/AGTgvJtwBwck5ABy97qZ1LXsjXtHhttFvYNDhZdRikmuIrcfOz4V40YNhj0APHc4/vAcYjNZxSyXctnJd3J8pnieS5N2ZCdsBb7qABQmAWB2nkEBq6Hxt4suPCmgz6To++K4d0s4JZXxKB5e55BgAhsOoz1y5PGBnj/EWtQ6Lodjper3kesTbVXUNOQIvkeTgJCJFB2jKjdjJbbwVyTSV73XkJx1S/r+rmtqeq38VxHJrBe/utKuWnbULeUmCFRCZIkVWj8rexIAYqWDEd9tef3+p3Wh+K7i41C3tru+81rgl5Fl8uR1YjO35GKswYgj7yY4GRW/ea/p2iWsM9vqFtd3Lyf2hHZ6bC0dslwXUgPvG4rgDgFf8AVrlc/M3GW1jd+I9UlcPCtxOzON42LI5YfKuBgH5s44GB9BTS11RrFKO+wukaemozO01ykb4JRWUtvbjA4IIzk8+oA716Y1jPe29jp1ti7sSNjy6dLudQki/K7beRswR1AMhI4AWue03wza21lAV868vXjaWVYm+WPC7sLt53DaRk55/h4OOg/wCE3+02yaRc3LrZr56z3cEfmOkbRnarHbndngleMN1B5EVWuRvsKMpSnaJn6n4Xg/stbqwuo7W00jZHePxI6StgybCApO0uFCtg7lYZHJrTufD2i6vDpkulQz2EcyzIsaRtI7MrDy225yS4CkkcDaSeMmvMTqF5JpsVtDPK6o8rtCQCqggEtnrkhWz6AejEV0Wna1qF4umW0bzR3umEpZSRA5VS7M5fLcYBUDapGMhuBSjFLqXNSS1Nyayu/D1rfoiw3USPEWlgjDKu4uCFUj5nzGpXsPmz1xXXWV1JcWcc8d1bJNLIBB5qhw7lzlVQ4YKQ7oOBwMjAIzzHiLxRpGqX2m6ikE6Xlu5F3GmFETA7d6MMMZOFbjggAZGM1wV/rF3HrEksbjoqqwRUbaMYBKY54wSOTV+zd/e/Q5VFt+6e4eGpJJLOFrRYYreFQYky3KNlt2WAPAeIDAx945ORXC3thqGn+L7WXT0Zt581poQU86QbmkOD908EAYA+XIGDXM6df67d2Mzw3lyLeGNYfluJAI124woDYAwACOevvXayWvifxHpcF9q96otSd8cdvGGLbjtyFGASBzyeAD6VLXI73FZK6Z0Oi3LavJdlSiXF5aM5WUBVicMVeQ8E55AHbC1Jq/8AZGrXlqmr6czXbAQpIhwy7jnrkbsEnqOmeMkViWvgyXRtXgmhvGlVEDcwYzjOVAzgDHB59fx6xbcTzTy2zozhFCxByN+QOSQMnnHXvk4p88Zao4ppxlocjcfDuO3ktZbPVAsc/wDrN65aNhkdARkHkdsZ6962L2aV5tiyFZLaMlrsszgSfdDLCrnLcnGc8cZPQ2Zru/ivGtbW1hnORl5GIJJzgsozjoOvqOc02bw3c3zyyfuXcRBjFDEoYEE4VpABknB6AHoAQOaFVcSJx9o7MzV1BYrdbOS5mvZ5FaR0i3JIeRhmVvmU8v1yCMHGCKqXFwg+R4HgQM5jumU2+xScB8/KCRngFic4yB20Y7XVrSG8WPTocm4d4trhRkFFB5wMZBDD5S3HIPXmNT+2MY1vmS18xMmSaZ413/KGwqqF4Z8kYz8o+Y9+iMk3dHE8O1Ip6/4XfW9Sn1PRnjnSZY5JUD5ZZCo3ru6HsSTg5cDA71bDwDfBVutTtSLd03hVuFQryMbic4yDnH3iOccGun8Kaxo0VvJd38WzfKkRVkDhQOirkE9CxAGCSOnGK6G4UXCBbOS3yswXBk3jy8SBCeWOfmyDwCGHJHNZydRuy2PSp1HCCjLc5i10+0W61CM6dLYpiVWjZSEDuu3Bx83XOOwGfc1Qa3iFqJE3S+YU2Sll25IRipB+798kktwBgjvXUavZSR2Vytqj2yuwa4IOTHHg4RgzFiQTu4AHDA4HFc+0UOnkPdWbyebEZliuJI2zwOi4JHyr+ajpkKIV0XzJ6laODT7WF7hFeBok3xfZ0AZGCYOV25JOxiew2kkgMGrH1HQoPEF01xZG8N9gvdAwKfMLPgEAEHg8MeeeeeSdq/R5NKHlSW7NGpG1AcFh5ijb2O3fu3kjHAxghi62uWuFW2n1NY0nMkc0e7LyNgv97lcvjbvwG+QcnobjoK7XvI5mz8D3Ushkv38ixWMsbiIB/Xb8pIODjH88drU/gjTormFEmuWZtg8tsDe3VkVgD8zDbtGCRuyeBmtyOSaxWJLORplgJcrMPvMjsxYeigr6g5x16B9yZ9TUQraK0ofy2git2WMZHPzA/KwwvJ4IA67cHshyNasxnVrKW5y114NtxaXU9pdtvjRZI0cLsIP8O8HknIAyATgjGemSNGvVhmdoz5UZwWBBG7ggHB46gfiK7FNMudPkAW3eWV4Q7xR+XKI41Az5jdB8zKSTnHLY6VqPpc1nb3V3PdQTRRuiCPDKAQuNuSuSMNkMOoxgAVpywk9BfWKkPidzzm3v1ghK3EIkbPBI3Yx0H0PeludY328UNvG0JiG1SpxkdyeMk59+3TpjsNS02K6HnXUdu1wZCZpYZgH5JzuVuvb8uCOcZI8MwXocRM8c7tmIEExkYY7dwU/NwfTgVEoOLNYVqctWaOmaKPEWixXNvIzyqirNITuYSAnkD/ZGwevQjrWP4hubuPFprEEczLK7RyJ8hBOA3A4HReP8TUcUWo6NNHNpUl3ZySghlyRuIJGCMYOORz0OararZ6tdSG51VpJCG2hyQVBOTtGOB1JwPes4RfcaXv3bVvxLEmqRXVi01/m8CokEEjllaPB3fMqkbgRlck56YPHFJLmC4h+zmyDzMqqriR87snPGcc56Y6ge+cp1AcheQOM+tPgmkt5Q8LlXHRh1B9qtaaG3s0lodNFJc6NFFY3kF5cxpKW8iUgw9CCAjIQGBOdwz1xgHmse6E9veSQzxNCVbOx1+ZQQCOoz0xz361X+23ZmEhuJS6gAEuTwOg+lTW0U99ehfnmmmbHqzEmnpFEqLTuz0DwLqumz20lvqbQwTw4ZXkYKbgHOQTjtxxnJ+nT0+bULUWcWUyky/KA4UH05JwPavGfDtg2n+IoftVtJJsDebEYdzKpGN209cZH4c16Ppd3aR6pIqzTSgLtOXBSMZ7YPTkdM9+ayUUpOVv6Z5WLV52TN7z4Qhla12yRYPBywHY8fjjNZ66i2t3DQ3Ns/2bbtkEg2lTnsep47GiWC8nmBZjEY3wjCTjHGRgrnPHXkEED6XYLk26uW3OqkD5cEEev1/UZ5qXPk+Dfuc6hePv7HG+JPsdrcQQW9oCLcMMbXHJwRkjGenQ/4Gn6VFezQfZvt8kKxgBYVjUgnPKkqvAPTpxnOTV7xdFbPbDUhbBgrgSkHoO3A53deTz16VzkN7Z2bDMsHmAkGSAsdnPfaRkevJzwfp13dlyFQivZ2erC6sUgvJPOa4ctloTO6rMgyRyoPzD3Ud+grc0rXU0oKIY2SNjibMRyDjCnBB4yOT+HWsXUNQN5ZySSC5u0iX/VPtKRt3IBG8DOQDzwev8BqTSxCby4rae3hb5QxUD1BBIJzlgeOcf3eK30npP8AElwbS/Q9Vt72G9sg1wBkj51YDBB+nH4VzNnoWk3msXE0F3FPtXZLbtGCcno2fw7CsnTWu/LkZLxbqI/K0K2wGV75KkAEds84H4Vd8PaDbNq4u4HukOPnWN8ruJ6Z6gdODnn0xUTio0pW/rUwUZKo2nbT+v67nOa7aHQL+WMFNjIWWJY2+Ze3IPbvkj8a5q41ckDykwQOQ/zDPrzXreoWn27XI1ktBOkaZWVlB2nP8PQHp35Fcd4o8EGNTdaXEjKzH5EyD9MYwMHPpWjnze7YrDVKfuqe7/zscLJez3coE8nToOABSvL5kIj6YPYdaWa0kgkMdxE8Tr1R1KkfgamtbFpo2dP4fWs+Zo9SThFX2IrWAPIFYhfqa0dPklW8WFHLQgnPHsaqCM+Zt6H0Naunyk7omXaV7f8A1/6VNW6pS9GXQlfEU/8AEvzO2+G//JQdN/7a/wDop6734vf8ija/9fyf+i5K4L4b/wDJQdN/7a/+inrvfi9/yKNr/wBfyf8AouSvmz7s+efGeP7Hhz0+0L/6C1cYShAznHoK7Txlj+x4s/8APwP/AEFq4kDe2PzrvofAediLc4K6hjhM/U1J9qcchVH4UjBFHynJqM88YGa2Oaye4juXbccZ9qQE9DT2QrwwpVTOSKZV7IYB/wDWoxnpxTiOQKXbQK4iAGQBjgeoGaeR/wDWpVUbcjk0BmGdv0pWEREYpAM9uKsLEHA9e+KjcAHCHIz1pgpDDj15pyZGNwyD+tDjLZHAp6HcQrYA9TTQN6EYQsSQAOfWliWNnPm8D2qZPKjz5hZsnnaaiQB5PQE0WFe9xUXcSVQkD9KcF8sgsAfQGrbCOK3AVSw7kntVeWRRjHzbuR/s1VrEKXMBPm4C8Y47CmvG4XrkZ9KaMtirJR1h2vyTgj6VndhezKo/h7N3qaKEnqOPWhYvmBfjPYdauqghb5GDjGck9aoUpWREMxsrEZXqABSSxOQrOeWGcY6VNLIY22hRyPSp7a1eV8YJ4BB7Y4/+tj/69KUrK5KfUXRrI6hqMVju2NPlY225+Yg4GOpyePx79K9Mt2j0xo7axsitvGzqIVAeKQu0Q4eQfeAmJD89V4xnPG6Gq2HiCzla6aJgxQuqbsKVKlce4JGe3ocV1LxGUzC2t3uZBs3vGokxk/dbaTlB5QUKrHBLckqSM73VznrXvYneW51KNvsoKLOzQQ7bbBQSBWV2zkAOXcZzxlyozVuxDXEaTW6xgvJIEKMN0MLSLLvQDhSC6gHb/CCQPmUZscN3bqkq3EyG1UsfMVxcOgRxu2PhSFAcDBQgR5681P5p08yaVbW3zK4naOKNkEjiXCY2HGJAwAJUEZjAYnDi1Y5mrmimt2FjDHeXM7p5dwHjUYYD/R1yFyegV1UHJJOCQcjGj4W8aaBJrENjEtyHZNiTzNhCeSIwuThRwATkk5ySeT5LdatJqMjm6lmC79yqGJ8s4A+XPIA4446D0FM0yZ1v4PKLLIrgrgYIbPB/Cq0bN/q1oNvc9y8Xz6sgij0ecLIoacusayBimBxnhcckDvt57ivOdU8N31xZKotNpiWNZmZwqiQqMjAA78E46g+5HRr4jvPEOkx3un2hmtrdmiazjgZuWixuG3rlvN4yBgAnnmuE1rxfd4ktiuZN+WMq5ZSOMHPce/QiuZQfM0nqVS5oxVkdX4a0JbLw9DdW1vBqNxLeLBidgViG3GVAOc7mPXBwAwwFyeT12wurXUIhJcC4UhWiZY1CupGVYY4IOeD3wai8N+LnsDPBe7WsplYsjZYxuEbYycgg5wM56H8Rm6jrzazq8CF/stomyCEHnyowcAtgZYgck9fyArX2clK9zWKfM7hLFK6mNiqEksXIyXzjj6d/z9qyXG1ipPSupvRFAgNxOjMyD5Bwo6DGevQcnHrXLXUySXDeXzGpwCO49etaQbe5Ss9iSNVyOwrVs7547xHTZJHE3EcoLIflC8rnGOB9cDNYW9lbOeR2p4nfzGfIyc5yM5zn1rQiUbqx12qajqVlcJHPMsKyxBjbW05ZU4KqpBJK9ztz34x0HS+GJtSvb64v7+28jfi3W5lLxfP/AKssMYZiNpGNygc8ggEeZpLI8nzksxbcSe5969G8NQanqGiQwoXkWOYELuVecjAzjJGVI6dSBknAESlyo56kUo2NHSNNe71jUbS0v7ZA9sy2vl2sUBZldTtfy1HKnBypySMjj5a5HXPDniDTppJby2aMFiR5e0bgOrBVPA55x6+xx3dvEdPtZ7hY4ZJo2C2373DxKrnLMuMAFjnnGdpCkFlzV/ta31Wxe3gNzcNbiMqsMEkjyEjDf3lyWJUEj0BLZJExvuKLe6OZ03wfruoWcYeFYlJyjuh3N7DscYz16Z96dfQz+HLs217IC6ruBjAw/oT6j09m/Cui03xsiCGwhtnuIERRHtkCHA2hgecM2A23BJOVGTya2LvxU2oKtrBpztBMrERXtuHXdyVbaOvIVucHk46ii84vWxDnO+q0ONs/EdpIghlhTLAhmyVY5+9k/QcfgcZJraEttpgW9sncwgs7KVXGeO/RvvA8DptB5xWs8ei6rapYata6fBdzRgxT2wVHwDksW25XCqQOoJyD2z5tLqFxYJ9hllW5gDCWLy2Ow56/L2PA64II9+SL59bErlk7I7vzNPu7Oe40xJJZYztktjwWViQrEgkdCc444HTgHHkubm2i8zUbG4gjlBjV3BEbllxgnBGcAnBwcjJ6GoPB813PqctzplmJkRAj+a4AQsc/KT91tqEA5JHNbvxJvzd6dZWcM8put7PLbo3G0DILKO/Ix6gH2q0/f5SJJLTuc1FFdXgijkkyc4jndmRbcEjgRqMZJA98EZXoajsry9uCtskrAmQMspJ3ocYwCOecD8hV3wYLTU79bG+iRvNjaMhn2AjGRjg4ORngdQORyD6nbWiWkkAtGjTyWWNlaNeVGM4x0wvNbqcl0OOvKEZcrMIeHdObTIoXgkV87yblSHbjB4yCMgdvxrIayktLdVJm6EgSy7xjByeASfw649BXdajD5zHzZSvlkneOOPTI7dMj2qrFBb3MWd5ljPI3dMdOn+ea0i9LniTqShJrdHBX8Nzfy/2fs8yNP3jOzbgRxypHHTByCeO/FUrTwilxcSjbIyR/ISDtKtjoc9wePTpya7DWbe1023EajymuMRROFT5TksOWwOpOTnsOD2j0e5mu7oKIpCjAsLg4IZATtAO48deMnHqQeCTko3R106vu3jockNFutMuNi/ejkDo+0F1IOR8pYL0+vX3zW/ZWV3DJ85heCM5VYcITzxgAYGPbFb17awy4klPOMA4GR3wff2HvXOnXDafu7yzZoM5MgjJV0xjKnI5znIPTBzyCKcJuS1Jc3UWhX10aSBG9zblzvO7adzHr7gj3+g9eYLe30Fyk1pJHkL/qywDdcd+nXv8ArjiUa7DqOqSTR2aNDGrCMOBk5IyenHT6cmsW/wDDvkw+ZbzB3AyU4XgAdj75xjPb1rosthRdlyybRtxalpT3P2VGWWNs/O6/KxPY5HPfGeP51btjDpsobbNGHIVduWXtjkjPr6dT+FLQ4tPXS4i1ohmwd8rRbhkdeTkd+2Oo4zitRbiGRwEXMezAG3KEfTjHHbOK2jdx2PNrWU3FXt1LCOke+y1ESSST5YEA4Mecqc+2QMjnP51zhkLSyW9vIJYFJKhuhPbg9R9PwrZT9xj5dhYZVy4bAB6AHGO5zj/Cobb7NLeNJc+XG4PSM48z3YDryf8AODVU7xTIVo3ZWsNPt5oWXULWMMwO1yAv4DofzrJvNCdLYm1jExZ8ERAsRz0/SuieJo9Q86P/AFbgEKh2qePbv0+vHFKNzK0gRhIq5HIHzdxxgfh/+ur0FHEThLmT0OEmS90fUVf/AFE8fIZSDj+n4Vbhu5LuQtcsvnP8ysoGD7HtXT3tpFd26QTGKEGMEmSAfJnsCSCPTj0I6VgfY47BthBKswCtwULeoPv+nSqjoz0Y141Y6r3iIOXOyRh5bHaSRgH09Ksw2jwLsibzNndh/LNJHEtxcKJGVHVuF/8A1/5+ta5ih2qylkZicq2Bk+g4raPxWZjVq8uiMpPPUlZID83XHG78DSGYx7x5ZG4FSD3HvUtxg3HluV3AdTxvFQyTSQ3IWRTtIyjEfeXHFbp8oL3uhDbKDIcqHU9UPf8AHsajbMFw4UMYj1Vx296scCUkqpQ9dh6D/PaklCtIsMhJRRlJM/MM9q0TNlL3iix2FlHQ9MHrTGnYx+W4BA6HuKsXlu1uVVgPVXXow9arM4P3lGfX1q7nRGzV0NXDnBIH1pskbRtz06jHenMndeR39qYGx1GRTNF5F2xOYD/vf0Fe3fCH/kUbr/r+f/0XHXiVljyWwMfN/QV7b8If+RRuv+v5/wD0XHX5/mP+91PU/QMv/wB0h6HeUUUVwHcfMH7Sf/JTLD/sERf+jpq8j7j6V65+0n/yUyw/7BEX/o6avI+4+lAH234B/wCSb+Gv+wTa/wDola6Cuf8AAP8AyTfw1/2CbX/0StdBQAV85eJ/+Ru1j/r+n/8ARhr6Nr5y8T/8jdrH/X9P/wCjDQB5VrF239oXUMY2oJ3yB3OTyazCMjJ616FrXh46rFHNaw2kV3IQhjRCuxBx5jt0G7G7J5OfoKr2nw9txePDqOqrAsabsrEd0px0RWwdv+0QBWsKya1Wqv8AgebNRWqen+bt/X3nNeHdAl8RasllFPDb5G5pJTwAOuB1J9B+ZAya7bSfhe1pb3Goa5PC0EanyYVfmQ88nByPXAyfXpg3f7F0fw5YqqWzTXz8BPM8xpQ3QPjAQcZ4G70x26rSr2WPRzcaube2gTdGkUYG2M/wrtySWPYEnGMkYzmKk5zhKVK+m/8AwPN7Iw5uScebVPb+r7dfQpaf4V0+y07TbvT4V+1JMXHl/M8r8kDc23gY9vbnky3euX+qteJbXlsl9GpQR2sDSS46ZLDGznvg5I9iKy9Nl8TNfRadGGNzHxJJgOIFbuUAwDjHBJbPBx0rWh01NLuBFdaos10rOUcxADkHcdgOfTkHJ6e4mUl3utWu6/Aj2ck7vfS/pq9SlZW32HT7i4aO9j+1RfNdcyzyrzvKgtgsxOVJJ74GCWOfb6Fpd5dQRWEExNpMss5Vx5EeSCAzs2XOB/CQSeBjg1qQfbW1KOfUpZtTt4kaWJQQmQMlWK/M7kjkAYHu2ajv9RaxvbS1j0uy00XJ812kiysAX/lo4UqCy54BOMn1Na07SqxT3fy/D0u/UJaQkou2nr0Oe8d2N5axtfah9tiH2jyoRNIrDaQWz+7AVSOgGTn5ugXJ5H+2UFqNscTMp/dq0QwmTkscY3noOeB6Vs+J9TvvG3i429m1xqaI5jtI0Ukle5CjpnGe3A5q/e+AYbLw/wDarueR9Ud1VbO1h3pGCdoBIHOT/EOCem7lqVNctNXNJTTkk9Gco+szzWtxAqRkTMGeZ41MmAMYBx8qn0H0yajsrWWSYFI2bbyVA616D4a+FE1zHdPqUrLIkBaK2jPl5kP3d0hBAHcgDP5YOFZXieGL25guLdWbcYWfA3ZVucMQQBkdRz0reMo83Kt7XOapzODcFfUytde4e4jgZQhRcNGD0b396zrfvnqOtPu9VkuL6a4faWYnBAwPrTo7G9Gmre/ZXFrKxEcjfKJD0OzP3sHg4zgkA4yMuOi1GoWSRo+HdWk0rXra+jhExgYnYxIzkEHB7HB6173p+mnXfDLXlvbbJL5AwMk2Qg4P+sGGxxjA9ccDp4l4G0lNW8SRQ6hHKLeJTLIinaZAMfLk9MkgfTPTqPZ73xQvhzSNxdDdzELa2iwMIlUH+HHQDPfB4Axk8uooyik9+hg0va3OV1Hwxe6fZarFaSrPdaeI3dpf49wLO3Xt15yf51m+D57LXLW5hvJt99EzNiIFFVAQBuLYGSemCevIwDUuv6bq3ifUium+bHBPPHDcFBkKjEBXfZwB8uSM/wAq27DwNp3hfZGHS6vG3oLsJtLKx9Cx9MfT6nPX9YqxfLGTd0tNrOyZNVqMLy09O39foZetrpunMqWtws0jxhwofdye2RwAAQd3TGD3Cnl/7TvL+S4nsAlvZLKVjeVSxVQMkt9AQT0AGTXbyWUGk6htkslkjnX5138KuRk85IGAc/QVifZWsdMGnCGOEmYP8gOd25S2PUjAGDjlT6AHN4itb43/AF0MoVvn/kcu+ukxJ9uClRG0jAxK24gttwpPfMfGeBvOTxWO3iBYkRobKwXchwfKUsRypyF6dD8pwcEdjXSa54dFxayMGWHJLZ6nncduO5+XGc9xXGXekvEALVvPTdtVlHBztGc9MfMvPuOBkUvb1Hq2dsJxkaFv4weIvGbKEQt1VFC5wpAB4x1x+AIrrbD4kpoEUSyaWZ0uLe3lRUmCeUUZiVUlTgEntyPfNeb3FhLbxLJKGUNg/MuOCOP68e1b93o8d1pdhcNeRQusMcbRSEA7S2Nw5ycbjwB0BJxxWvtW6bUvIbhBu5W8U+KbjxPeJNMkkapuOx5jJ8zHkgnnoEH/AAEc9AMMLuYA8f0r0HRvB2j2Epl8TO1wlwky2dsrGKWUghI2GD8pZi3D4A287ucX/Beh2+k6rfSyfYL1ltTJG00QPlbWUjCyAAE5GCQCMdskHmVRMrSKOV8M+GE1u3luZLlR5MiolrGQZJz94qBnK5A4OOTn0NdrLp8EVi9vFZ27J9okaG1l3BNoYM6xuW6LsySMj5zxgmu6gnmi06aaRdkiQJ9oaABnOB5jDdt+YjdgYH8WeBiuOghN0jXawXnlTyqotreQSqhlA3htyjJAiaQgEf62MdGJM3u/6/U55uT16GKsstrHHIYbdPMZpX8lNqeVgRfM8YyUR1yV5yWOc555zUY7u0PlSQxyC5hCAqDl24O4+jHjPqrDgGt/VLu9vYILZ9upMB5gkPLyiXMpUxlshd20lgAduM4HFYOoXiX11cQRpbyMsktw7RxFEbEeAEAwVA2gbeOnPThM1o66lODz47OdoWjiFuEBBRd03zknhuuDgY9B0IBNWdF8R3OhW19Pp4VGlPlghA68nvu5HDDBHoR61Y0iSw02R114MsC22+WGNY3lnV5F27N5wGGevUDJwRmrGvzXGqTZbTV0u2wsaxbQHIXP+s4GDkZAxwoUDIAJdrb7f10LclJNGG165czRhY0kcbk6rnr3znknrXV6d4fm8RwTTpp8UcQYmRkZF8s4zhdzDjnpzgfhXOz6NevHb2luv2jz3BVkGAPlAGfT736V9FaBp9lpHh+20pZIs28AFxKRw525ZgOpz1x7+tOrVtG0TmaTadzyzw98OtWh1c2z35hsJmCyyRceau7hRzkMcd8Y568Z9NsdMtrGZdLsJIo/KlzIhK7gOP4Rycrxzk8knNN8q3trf7LYXPnyIuCsoOd4BJY84J4Bx7Ht0LZbZr5bi7jjmu1U/Nw2eSoB56ndg5/kK53Jy0kYymk9Sp4pivYtFgttIaRp53EZkKBmkTaztjJ/2Tz9B71xEWsQRzTXlvp1tDcXQLTtJNIzNg56npk5OBjOB16V6nFcS3N9IrNHDa20YYxx8kE4OWPHQhuO5zz1rjWsRdatfPa6Xt8mZi7xNuD8iRCVxgZ68Y7D3q6UuRO5E0papk+m6fAt00guZvK2oqW8sRD7sE8NwDg5OegGadb63eeH7z7JqY+0Q3rG8gWNNrjo21gW4OQ3cjpjrUolfTpjabHi80FoUkQnnIHAz6n1AAArMuNJv/EN1aPBvtY7bAJwjbeOBjIySflx0we/Nac0UtyFB31Oz1mGG9sZra4MSK6EHdMFw/G3B56Ee3UnnpXn+l6eoCWl3FDcRAqswnKtjJI3hsHHBx1HrnBFdpN4Zkn023tTceTsb5zHHtZ+cnLZ7k5+pz1p0Ph3TdOVN7s7FQiM8jHoCcZJOCeTxjue9RKovhSCMJNXZl6L4P0j7HLHF+8QyGRQ0IyrduTnpyOMdwapz+CtZ/t/+09Iuh5sAULDIwXeoXjDEtkZJHzAHg/Wt7StHv49Rnma5htoY96RRwx/f3lWLMM8EYC9Tnk59di2ubWyUJuWSRcYJbJYsGP1557fTvi91eP9feKKSfvvQ5Z9Fv7HTxLPN88yxQtJcgBoVWdmb5gvzZLL1xknP0528sby1aykub+K3V45la0v2lPkna7gFecHbtLE9D03Dg+ha9qcEVt5Es3lSKpmICF2K8g8DnkZGeMZHfiuN121K3lxqlzcWsMoML26XUZcCMMVR33HI5bliflBrSPM3/X/AASJuMXoYl5p8k3hu2ZY1uLxXBbOSqliwL5XhiwAcgkMmOeNxHPSeHJdMiLTE/bo0eThEPAIQ/eI6YOCBu9AOot6R4pvbrVEtY4jcFphLNcTxhHfKgKuNxwdzOQc8lzxzXViOHVo3uLqL7NdlJpIHx5csSgBCNw9CHzuG5Sw4wKTi07GjqSg/I4557m6kttojxLcIsSwRKwEu0KABtx/CoJyRz3yahuGkGoNa2Fu0zCDy5HWRGUPIMKWc5U5JwRkAnPTjGlq1rBb288sVy+n8vJBGzhUV+F+RQG7uDxjHUHpjH0GW2tftdpdTrLHegB3UKYQVyArDaCMbidwIXsfUXCOmgNpq5uwOdLuFGt3BgeeNHRJCXDZZzuGRlRlvmXAOQ396pG1WO50V7bTbD7PfyTAKTIG3HPQsMfMPmwuOMN9adozahqWjXNvFKt5aOWK5Kbt4ZcqmSQnG1hgbQDnPUVSttEfTlf+zspcSxtbtbXS78B8FZA6gYbI4DZPBwPTSM1F3TOeUVK6luc1rX9oaZMbKaILxvMiEneozg59Adx6DknIyOK5vE+0W0cnKwurLuAAlYgFmJZsLkhRnptye2K6+DR7OWG2uZ717iC1dQbIxlVJ6c5b+Lk57ltoPIY814i8KHSbVNQgxJFJM2FiBkSAAjasj4A3fNgjHJX3xVc7bOinKm2o9QhvUkhs4UuJjbqFV2YZAPX5QT1AJ+vXgk1HrIaHRxHDHL5byfvHkOckY+6wJyOnNUrGUHejSC0BVclQfnXdyp6gg9eQfuj6V1FpdQPaC3SaG9t4kUKZ1CkMewzkn5iecfdzkFeBSn1CpFRaaOMstEvtTJ+wW0kuApJA45baOenJBAHfn0qy/hXWIImkm0+dAgJcPGVKYxnIPThlPPUHjNeoaYIodUW3tbeRY03hAZAAFY4x6sDtCn3UcnPM0ttLczAW18FhnVi0ancUPzMm0YI29W+gwAcmo5mt/wAjP603KyWh44kahc/e966DSsR7ZFdSEIff5ZGw5x2HIPHPTnH10/GXhtNP1dLmDywl7I2IYUIEZGAR9SSSRxjPTGKyEsZDKBauzsV+ZSu0A45GCeRjIrR2sEpqaTR2d7NE/wBl1HToWM9sFBlXJTByGQ84yBjuBywNR2GoRtaSzkW5VcSm3QLtVmIAHyn5Rz324PGeAByk02oR2/kDIWUbXcEnIwPlPbgAc9fU1RWN4ZAynBH+cVas9zmVFWtc7/TvEcpdLSO2e4VpvlmHEiq3CgDAwN2TgHA6fXsIraedgjXEUckgBkQLnB59wB+Azz04ryzRNY+yTxI8cYbzCfOkYkLkAZx0Ge5zyMeleiRXc1pbzv5mZFX50iTbjgjJbJHUY4HTHBJ5mScpb2OevHkS5UaVz4ZsrtoXlmlLxfcCygb8Y4IIOen/ANesPUdBd7pJrSXyosbWy24lOpODxgdlGRjGD3M39oLLII45ZHYL5jq025tpIO9AqsGHp6Z4ORgPs9dW4RvtCS48392xKnIztyccr+OAOD1FFuVaN/P9NTJOrdOyZn2ukvp999rSeDKxu0bjcqjK45HOB0Jx7ehrMgtJZ5XvleRDgmY7VPmNzu+QEA+m4YyM8Z5ruTaQ/Z0KyOg9GO7n2J5B6cgj161mavaJ9nk8mHN2V4lRiufqeuOemPypxnKW3UXOrvTXYw7DSRfMzQSPBMUDEo5Ulc8KQrZbI/vHPT2qGKbVNLnVLOWUTKNitOvReu3lRyPQZ7etT6VPJDJFFO7XN1sZQm7BxwcZ9O+f1xxVm8Qzma4+zpJnG/eACVA57j5uTzjn8quNaWt3vt/wxpNLm12/rqW7fVri4lie/Utc5wUDom4gcgjvyO+MEcVPbPc37C4Hl25c7oLa8Xbs9cHG78qzm8i3iR/tTRo2EYEllkXjsAPwyw6e1UdS1COxdHJt5xGwEYDBvLbH8Snn9aE5Tjppf8l+X3HM6a5tF/XX9Opf1mzub+1e31CwllRCWje1kVlB7ldwyPTGOa4e60/VNEjSfyg1tIcCSPDKT/dbHRvb69ea7CPxCGUSWclrcb2wMTSQrnrt2MpH/j36Uye+j1ZXtb7fatINpjkAkBznGHLDK56Hp6VpCSbtLb0/r8hQc6S5XHQ8+uLvLRsqYx94E9a17NVYq6qDxnOcEf40XPh64WV0SNp0Vsbkx8v54P44p1taS6ayQTISsoLIxGCpHVSO3+fWjEJ+zkl2Z6eFnTlXpcj15l+Z2fw3/wCSg6b/ANtf/RT13vxe/wCRRtf+v5P/AEXJXBfDf/koOm/9tf8A0U9d78Xv+RRtf+v5P/RclfNH6CfPXjIZ0eL/AK+B/wCgtXHRIM9c12XjE40eLjP78f8AoLVxibh908mvQw/wanm4n4xTGFPXrThblVLHtQQQ2TyaMF255PvWzsc12SRQecpZm4zxxk0sqRwxJtQs2edxPNSRJ5S73PHoKhu38xgFbjHSm7JaEK7ZXdwx4UL7Up+bJAx9KQoAOKcDwBUmoCPbjNC5zR1NLjmmSP42defSolXPtjvVjh1VY1C4GCSeppGiCfdO76U2SiMruXqBmoihD+uPSpmPOB/Km4PXPWkUgjhVs73wcZxTWQqw29aU8NTowHbHemA475AqHHXJpPLDuFjXPON2evv7U9VYKRuIz2FTW8cKtmR3VcHoevb8qL3M27K5AUeHHHU/nU4Zimc89zSuyBzt27QMDbxmkyeAowM5zSJ3Gr13Fhnjj1qaJmY7SOM5PbFKsLTsDHyS3XAFPWBlmkVsFl5Y547cUCumPe3JYk5BIyD/AHhW5oloERndJ5V2nIRgAenBz2zjv2FZ8WxUHylgOATzmtuzmuSZrSIyQs8ahgowwTB3Dsec5688cemM72sZSbtY0Y/CV7fxsz+XC0LgiLaucZOQWHPAGOnOeKLPVryxt7WLU7RJYrGYTBJlOSQhCDIIwB8p9zyc9+vCalbGS7e3DlyCZViUEOzFFwDnJ+bGOeOgPArA1Gys9txEsji5dJBICrN5nyRsGZznqJFbJ2qCMHGd5zUpKVnsZwmp6SKq+JtJsAtmymykAjBuJFNyRxGFZum/bvdtjAqBGFUZbIwJvF9rFbR21tbG/EbE5vFCxO24Dd5KYzlUjyGLcjv1MHibRksrO2uhMZmnQM2F+VcgH73Q9R6da5gRSMd6K3+ycdcda6Y2Nowg1dEzyru/elixGTnqc+v86s2+rPbqRGo8wA7ZM/cJ/iAx1rPaJ2JJ6HqxOajyUcFlztOcEdfrVrQ15Uzr7HXrzwxIi2N0UWS2XzY05AOCR0YZYcHOQQcj1BwtX1KPUNQmuoomQSOW2uwJ/QDv+XqetZjSs7szHLMck00n1oBU0nckMxPT0xUsCluWxtAzk1X3YHFNz2poprQsTPG8ClW+YHGz2qIGo+9TbBtHOD3yab1DbQkgQzSLGON3etFtOSCFXuWIyeM96y1yrBhxg5FdBPrsF5aJB9jG5iCRnhT04/Orja2py1efmXLsQ6dFEZ/OP3EJbYehA9T+X516bp19G2m2loUax+0bFt5ZZHZjKMl5F3fKnzsMnoTx1BxwWmRJa2skew/aJNpRmYkNyrbdoH48nvV6ES3MAz8iqxdEBJIJwOg55IUcD098YN3dyasFJWZ2Gr+I5dNMU8luzyuRLEXgCsoHzYI453BBvA4CgAdhi2XiHVbu1iX7Gu+ErI1wQArwRoVKOSDw3zZ6KORjHStDcx2EslzNCbloV2xnCgIy7AATg56HjqepyN2eostdms/IeV4/9VEvluxQcIo3bioJAAI6HcG4yBktStsYyfLG1rlLV4F8L2aQ209kbuGJYfKtXHnrM+SzkEAhBkgE7ieOFVuKd9qesLaafC1uYLi3JP8AaDudrBkA28jBAHI+hxx1m13Vom165kfTIXlKrJMzxmZHOUfIxtxxgZPIUkdTW+IrfWFij8x7m3a1jmvLgTqPK5Z/LbjBfHynADEYPALZWsdULTRyRzOnX2nyQWdhf20ty9y6m7fzWO9R1AVR/dwc56jODxWz4u8M32p3CTSwRW4EKFREy527mwuD0PzE4Bx8vfGBuT6TZ+Hiz3V2sDgyGGeKPaRnACgKcEY44Vfu+jAC5BJa6yv2u4vY1tISMyTRbeVAILDP3e3Uc57ZFZSUrqSRDlFbHGr9l0TS77RbO7t7S4eKEtesZUec5Mgwp+6QCuD9OmeMvSo7ex1lYb9/tMjXAjacsTgDIxg9ewz7cV1MMuneJNU1K005zNO6CSOeVcqCrEKQM5yE4H1ORzVnTvDWlLciWSVBbbPs7YwHLgh2D9sAgDPB5A9DVOUopwsxe69WWrbwbpaXn2mKBBy3+jscLuAyOv68jr0HfU0m3N5M908D20gZkVC5CzIuVy2OCQc89eAMkCuNn8WTLqTfZhavarGY18zc28AHng7snkAdt2f9qus0zWpptHgNlbCSBo2jdXm/ejaBlRkYJIIxzz7dtI80Iq7OStTT1ki3qaOn7xLgxNGpYpj5W+p7fXOO5FY+qSSaPdF4YI2tWU+fJGcvCf7zDaeB64Pf2piRxeIJbiwtxcWttZyMGxgFW9FPPA544wCOnfXi04YlF3JbzMzFTGVG1U54/FdpIxgke9dkZWVmePOnFNs54a9YSTw2dx9nnWcZkbIZc8Ffl569OcHjoO+tLcx2So0+2GCRtpIAI3Hpz65IPtyT0GeAlsG0PxJGNUgTyGYspRzsKnOGU8nAyCO/FdPY6nZa6/lzRTXEsLMGVpDt29NwA2ggg9Oo+lU43mpIdSnGnTt0OhWVW3rINyqMeWUwxP0/CoZ3RYd5UK7HBCjcWGeeAai+3xwTfZn2uVHJeTkce/65Pf3GbEMsKw/KXKoSQu7JJ7jnrk55q3C/Q8uMnB3OYttF+yeYLFwNjFX3HPy5I/hPBxg7h6jOM4rOnuILZ4o764W6ByUZ1yjZJByefTHHt15rrLgWH25ZJYRJ5oEcsZyCyk5BzwQVYdfqPTHPah4MNtq0Q3S3FpPnbMFAK89G7dO/c54oVRRvzHpQaqa3JdJv4JrRxbptjjkCqrLt2A85JGe5YDJ59cmia5gQqlsBOHOEMbKACTwMcf4fyGtcWZtoYFFq0Lbdg+xPnd0zkYwBnofT06VUj8H/AGRP7Qgu5Ip0YyRiQbMnsCQc4PQ/jWkaqS8zllCLk29EUYryWO6S1kjdww+8WJIOO/UEE4GckVFd3S2t9HKqK8qY3xqc8nn29CM+2cVKmvaLc2r3V7AYrsYEsTHKvyBlR645wAOR9DXLajrLTX8k8TPsccJx8vt09hW8ZSabkaQw7lKyR1UGo/O7zr5EkgARSx3MOcdsg/hznnsakNyZLT960y7eHXABI/LjHTPHT8aw9EmTVAGvWjQxgCMseA2evPA4/wA9atyvPpWotaLE0iynKlpM5z9evTp7cc1UZJs5alFRm4rdG3dwRXSr5qkiM8gMR/kjsfc9alXYLUpncpyG3/MG9c561k2srw3iwyf6vkAFc8DnH8q0Jj5f3SIx3O3iteVbHn1ItWjf0OfmjRGZ1jG4ZBDjkY98c/kaZd3joyWl0odyokWZGDDJ568DH0zWz5qyI1tcIskinG3JAbjqDUV34etpis8GICo6BcqfqK1lJp3O2NeCaVQylEb3ELnKMn3iBuDfhTri8iC7WtxMATs3k459D/QU2SwaG83DdgKA4ycHtkU57YSKwSXy2xxkZUn3rWKurnR7l07kczAxRsiqg6Mgz+vf86zml3/KoI28dc8VaEgkbcF56H2Pf/8AVUEgAn3J1b7wzVrRHRTVtGQzXDNCIyQUByB6Gooo1lfYziMnoW6VK7orEOmAaj+SRccjbzkelaJnTHRaCmOS2Y5HI4ZTVdiC2QMe1WLjzYigc7lxweoI9jRHFFckKknlSEcBhwT9e1WUnZczJbL/AFJ/3v6V7b8If+RRuv8Ar+f/ANFx14pbRPCrpIMMG5H4Cva/hD/yKN1/1/P/AOi46+AzH/e6nqfoWXa4Snbsd5RRRXAd58wftJ/8lMsP+wRF/wCjpq8j7j6V65+0n/yUyw/7BEX/AKOmryPuPpQB9t+Af+Sb+Gv+wTa/+iVroK5/wD/yTfw1/wBgm1/9ErXQUAFfOXif/kbtY/6/p/8A0Ya+ja+cvE//ACN2sf8AX9P/AOjDQBXuLzUYraKPRr1YxIoUK0IVo2wAxVuMZOfmx65OaUm9m09kl2XNxc5gR1VtpP8Ae4Usw4J4HOM1rr4Wht9Hhvru8Zmu0BXCjEeVyBzx0xyPX3rJ1PxFo+ixSxrO1zdIm0LAeAf7rPnAHTIGe2RxWSkp6Lv2PJekk47/APB3+/8AIrvoWpR2UVjGkUGY1WNSPu4++24McZOOTzwANuSK7Dw3bw2cKR3Fv5ptUXJtyPLjYZ+f5iCzH2Xj8RXml38RrmeONba3iidfvseRjsAOw+ldR4e1G+1exlmk/dyKBmJY/LQ+nPUnrW1pwpy5nv8A1/XmZu8uVWt/X9as6TUrmd7qdrSCTYy7EBYorE9Tnue3Az198cpqCmLUxZTSxz3syK0ixFuAOSPUZx6+nTgDcC3YRLLzUSZk3SSofmYE849v58enFSx0OC5mea1HlZyHuCdx9OCRg9+gx9TyeSHuNLojbmUrye42KaVLxIYZkeRSsgW1G7C/7Tcj8QcDHB64i1WPTtSu/wDibyiRjkRgMVWFASWJI+8T1Pbp06Hp10Gf+zXs7GERrI3+sLBS3A6nBJPAGMAe56Vgp4One1NtqsTCWdtqNHKHYZ9QBgDv1+taOpG9n0M4x05r2Od8PS6TbRaiLBLuD7arQJIHUMsOQW7/AHiBjrj6451jOlxHCzK6iOQSxW8RVpCFXanfGBkegyOSc4EOt+B7LwppyXN1d3lwz5CpCgAU44BPP5/XFctZyX9w7pHBO8agbiFPyr25xxzXQqyqL3W9P0JlSd773O9sr2eGBL3W7hGt3uHmKS3ReQYG0AhRtYAAnrghuASQKwdR8KXes6leT61qI+3SxboCkbeTH6KzH7q/whRyNyn2pukaZqWoamtwLRnijDGNJNxSIBcDBXJzngADg/TjtdO0XV7y3mc6ZHEEkEZlmkGccZcH1J6c8bFHAHKk7tNy1/r8TOKlF3iv6/yOEt/AWoaRpMlyNQhgv2kxEiwBtyDB3eYeV6HAUZJxnjkU00CXVLxZrzVnvZ2IBmuHyAoB4J3EnHbkegByAO08SWOoDWubOO4CxqFTzAeWyFBAyWxz8ozk4znvxlrp2qxyHWDKyjzCtuI/l2853AHkeo79+6mpjWb3loa+zbV1a/8ATOijsY9MuhNYstqhhe1kVM5ctt56A8kc9PoB0vafpUvie5A0nbaKIBG9yhK565Gc8bsHAGRjjAxmqGn/AGeTw7Eb2JvPM77Ll5ljWQsxLfM3Q8BdxIPy/wCycSeG9ahlvL6x1WBzZoAY4bR2jLSseCGUjaAB64x1rSlHnlyN7f1+vUznFU4OaWv9K/4Hd+HbD+whrGnJOzsI7fDtyWYozY/M4HHpVO/aeyE0d5avOyMsYeHGBu6A5PPQg46Ec44NQWviGRJtUuFsmDSJHtTzGl/dopAbeATlhtPIzzg81iX/AI6063W5N03meWzIIywPmMrANhR7bsEkD1xlN3pVYy53ZrZafJHmVk5OK5W/NE4tRFbpvdFwDuMjBhgDk9eQAD6deoIzUVs8E0Bnv5hIcuFeNizODkDbnk8L3Gc49DVDTNSk+ywS6qqxNMAyxEYwuMhyT95jgEk5AyoA3Nhbk9vYJEZLhihfcP3i7c44wex5B+uc9qy1UtVcwd7WvYr3Ms88B8iDfHGW2Ox5lIIUADacBi3GQ3OOMcHBMl7bwytbxwzI6ecHkkMmS5LcJjPJ3HqVDA5ZtvOvrGtfaJJ7Gzz5iKzMI1OVAwxyccHEfB42lcnAyRmWdxaC4ltr5ns9RVzuDgwpLuxuKnb+73dCSu7oMYJRXCLb8zZ6Ru1oc7LoGqXbm7ljZohIxEssi5DH5gzHHznknPJOO1ac9oDqdnpdu8Mt1JAkMU29ShYtgdTwBzyuSeABzWqLpNJaWbV7i3nYuCtoyvtUYGMAZO0c4J+bLZ960dOsbfXtdSRIkeC1b5YJLcASPMHHJA+UqY87SMdcAc5zi3yyv5fmelF3jcYQ11eK51C41W+ijeYzJFsEYIIwNqlfvJt4Odx+UEkmktdN1HTmvdVngWyVUaOYSAzPGhAZ9y7vmQIQSSCcjAHQjoNZbT/DunyTX0q+RNaw26RhwGlKszDD5BVcDtjA6AY+bDk8RaPN4cj1K7iW8jUvIySWjvHCzKxVWz90F/kyowSnUcsYUftIjm6Iq32qXQsEsbSY2ttqSpE/7hszSFfIKiMklAAScZLEohGOAb1zdW+pTLfeIvDWpSiDYLezaQhNzrGQpRer7n4HVgy5UfKK53wfqGnRafql7OscU0axmAhpGGT80abfTzYlzg9+wGa6I+IbHRpRqurOI4b4wtYSg+c6IQsczZw20qqK2NvOQvPbVc39f8OQ2r+ZwEviqW4Vrhoo7aT7OiLLbKDLIwG1iz8dVLZzk8gc9ayr7VJr7UpLu3DW0ZY7MvuZV/hDNjLHCjJ7kH1rR0E28imX+zLe/wDMvECW8k5i3qWACYBwBlhy2R0HTIOg3hqHS9WOk3qumpM8VvDIs6qsdyy5G5SMmPLKd+Dxx1Jo+F7GyUFsRwRyeJfEkms6opnO2O4uFmTZG8SoAxZk5UfLgEBicZPRq1/FpFhY3ME05l86/Ro2ldmeQLEd7EHuA0YycnlhzyahutV0bSPCMukWsbTam8RhS7tXaMFiWWQNuAO3kcEYIY8jGapeNLWWTT7bUNX1b7Vq8jANZ+Xs+yxPvfZtPzDa+5QOyheg25TRPLf5Gn4GubaO+a+mt5LuaI7BGrg7UCZzjIJwcA/hj0Pcx6vc3NuWuDGsDJHtKYcqmD8xPcZCrwT3OO1eG2c7WsLiKRonkQLkDhvmHGR0Hf8ACuosLi6RQs1xdyMGMbSIQ5SMA4RdwOz5tx/D2zWc4x6kypnoAim/tC5VbmPd5G4R28p+8oVcMcYyvpyeH68iqXhaS4k8avfW8zzW1tEPNswzHYMY2KTkHDc5JAOAeOgwLnWoNO062S2u/td0BtlHmfdB5Yg/3uZBn1YHttEfg7xJbaf4mW6lVwqQiNTGxGcDG5lydxPJ5zyxIqadOeqjpfuc9RLlu1c9fs2vEa/kkWVLdd0hhRSxO4ElQRglgVz65cDPFY/hrVfJ1K41LUrZ7K3uo1EK8newxksFJGeAFzzwfU1uy6hZazpsgs7nyZpV3MCduCQM5Hrg/qK4qx1B7vUP7Lu7iCS3t/MjQMOBn5VHUBjnptJGGOc5qffUtURGnC10z0hZYL61hmhdbiGXBVlG4Nxj8O9c5qSnR9Fu7jTCPtEMfmpazpln/iCYHO44IHv+OdW81uK08NSX9zGv2eJRIAOfkDEL1HXhePXivNr7xXa3l5evpm+5j27pLibarKSGUdMcDPBAOAQMZ5PTGH2jOylodAnjC5k8t3hw0m0lkkzENyKQSRyPT/eJHGKz4vF0vnwTJc2+zzWjeSZi4X92BkFcrnnJGcjPHpXDTaul7eWK2N1PFMESDMj7hLKVC7wDwvOMDgYAPByTV8Uw2uiao+nWDXDIgDOszfMjkAHkHB3bQ2RxhgO1NavVGvsktD1K18RT2Oo+dFHp50wuGae1ulEaq2Bja2MMAu4gcHjvyTXXvNQmhNrcXEUYXZMtqmRKkm0gbiNvAYH/AIEex58n03WdLi067jv4JGvTG4t5goYA+W2Ac/7RHOM85yCAa3/DHiK4t9Ic2paW78xI2jeTYOVZUYN0+VxHwQScDnA5uUXJ+7oZcqWrX9fidzBeW+6L7LJI8axhd94+6dnlkIUHJGceWDtB4B49mapYQ66tzG11bu8sLDzFgDSRurhGGWwRuLgY9AVyO8F9o1xrOoQ25uUj0/a008xVC4+UKN4BAY8Zzz6+lVtWsotD8L3Wo6RNMkohVI3lJ3L++AaT/geQeR26DkDG7T5f6/HcmMYu0rnNXJ0jRNQNlbSWrXTttlumLFIjs9CSOGGc9RkcccW9O1KVLWWyc2+o3d4ZUtRDMrIGJH3VXpn5/mwB78GvM5DLPcPlt5ySzURme2mWWJnikUgq6nBU+oNa8sdkdfJdanqGu6jLpjbtTSaK9keK4ljRozGxU5RQN7cDoTgD5QcHJNVfDehajqSi6sJbeBccG4yyNxgjAz2OORnAwCOo87mmnmZpJpHkdjuZ2bJYk8knvXp/gA3KaU08805tYImkG9sqiDBbapznBweP/rG4NRer1Zy4iMowui1D4duYNOt7EsxL2xFw0Ui+WTkEdRkH5V7dOBjmti1u2stOjS7sY3uLdNxYXCOuVB+du4GWbnqC34Cjqeo2VlZLeTvKqrtDImXZombpySQenJx6EDcRXKv4ysXhi8jQmdxCLYs0u1XC8cgc5IwT2yeQSM1Xsra9PM44N1lt+H/BF8Sahqen61PI62MVy7Ru8lqx3bsBhlCfmDfLliu1ivrkVRn1TUdbmlhuprW3M+xWRpDEs4wAM4OCQRnJ/vcnAAGiYl8WWGqalqUDCSGVdiJlniBXqPUcA+h5ridQ0m8scfaEYLkhSeMe2Ox46U+dJ2R2UoxlutUdPYW9jDe3Kb7JIYY3i81pMiQnaCc5ORjdg98k4BBxtk2ryLZW1o86qdsn2Zh8r5Cn5XAIYlhxjkoMg4zXnFtfXlpcSSw3EqPKCshVyC4PUGtTSLqzVWjkma2O/KSMCdoHI+6M8Efrntgq0eiLnSk9WzvYhHKwtLZVnkicIXRcyE/dKMNh2sMH5hu6Dpk0+71K0ghe7t7+0muBGXRZvlzhN6gA5PbC8nJIGQcg8zqXiwzSTpBMWic8iPdyNpHDEBiMcfNngAcgYOdaQS6vqM0l26xySLlht5PTcQO7dT2ySfWrSjDVnJ7KUleWht2+pwavZPcXrztNAwLGSXJAJ525PTPYDueOtbM2l6PLZiaCVUui3ywSyEYGD8uQwy2RgZbnOM85HM6XpNzJc3KtEZZbcjLbS2Ap5G0jleR2z06DNdvoyRw3U120JhW3ODHkKImPAA5OOpAB46ZPTMyneV3qTVioKydjLs/Bmo3EfmxzQJDMoZV3NuxjIPTg4PPP59a2bT4bacWQ3lzckH5XRVUFm9m7D6g1tG6RFDsWdmPEannrjA9cA89+nBOKvQXKnLSYjMZA552nHT64479qhxnbVnMq8k9Dl774Uxu8c2lXuIiR5kdx8rAcZ2sAR69R+dXodBvbQRQ/a2nMWERhldpwRglTu4VuSoUngFiDXV28hlUsjKuQAWVgNo6j17HIz1z71kzQ3LXk5DmRi2d2cA4GQOpGOTjHXnpQm9bvYqdRtLQwYrWG8Mkeq2kkDwTkwyxzeZ5gOcsGILqDhckEn19Kla5tYrpriG4EDOwiZZlJ3gccg/MD23A4G0ZHPF37Jbm4AaXY6sGdXwvzEZ7nHGecZ4J68AYYW5tJlkv4vMKFnVZHKhOPl5z0P+Ax603vK25CjFuzei/p/edCkqbPKkQI7D7p+UjnBPoee/8AiKa0G+XJZ9w+VVbIBH/6+9cLrGvfY7eKaItHOHyW3b1cHqBz1Ax26cHtXY6T4is9asYRZTRm524ELONwPfOeg98duAeKUk4tJdUL2bUOYydXhdbx7lDtZk2v/tD09uvvnPINZpSaSBpYJsBfv+YN6kn2PIPXBz26iui1HRbi9aObypt2SZIBjDA8HAB7Y7Z9fc5cuny242qVkC9GY4PXpVOeq5WOCtDUyEu55mSyvbaCe2PLvllZu/VSAenfP19GXel2Z1eV7dhGlvEdyyTYJOMqR1IP169ceuvAkNy0v2ebZOThAf4PVcjt261nzaLfPNlbeN5f4lWTBPPQFh0x+NL2rV4y/rubKOt07f1uRSare2TPJBOt1Dw027DuDgdSdpI9/bms7U9SFxG1wLUo7Ng52n6gjqDx1/Lqc7kn2KcrZ2saJNt2OtwArducL9M9Oe/HSJbWO0kNnqsCzFsSRxldrIO4ByDzgDCk9wMd9oVLWtGzfQxlCEfe7fj/AF+Ro+H74X+mMtyNojAVlYc/kB09+3fOc1DrdlDCqkKwZB8hEjPx0x8x4XnoO9QSTE3Nv/YSzoVB81N+R26jrn/PenXxvRYgj95avjeAf9UeoJA4weldDbjQlCW9mzjw1Jxx9KrHROS0+Zp/Df8A5KDpv/bX/wBFPXe/F7/kUbX/AK/k/wDRclcF8N/+Sg6b/wBtf/RT13vxe/5FG1/6/k/9FyV80fqZ8++Ll3aTEP8ApuP/AEFq5IxBQBgg967PxI/l6dG2ASJhjP8AumuRlZpGyx5POK7qPwHlYm/tSEKu8ZycnoKsRxeUS0qNlugFRKCCCvXrmpsTzON7HHv2rf0OaX4DbkSM3zDC444qttzxV7y3MyrM7FSQDjmr5gijYtEdpK4A29PxNRKVhKVlYyFsZWxlGVicYYY5pk1q8KjcOTXSWsR3AJAZCqnawGTnnk+vfAqLU9Onib95tQdViLAMOvJXOfz/ADrPndxRqa2ZzXkncOlSOo3EKCAPXrVo2zBixBHrmosDcc8n2ra9zTciAI2gcH3qbaiRbiwLnsKZkbx7HNOIy2SMZ5HvTvoFrsgKHdxRsIGTxzirsMGcksFx3JqGYKJSAdw789+9K/QLkITdjByaQxsnbB960YLVSfvMOMhV5IHr1qC7/eFfKHBA4AGF+lLm1EncrI3JyMGpN4kPzhicYAH0wKakYONueuKkCFW+ZTn/AD/hVWuJj7aQW8gkYAj+6cc1MtyJFIKDfk7RjjmoUjLPhkLE+/tU0UascLHuHQA+p/8A1ihWM5W3FiWSPaFLLkZ+Qjj2/WtOK2SeJflEfY7WySR3I5PemYS4lRItxkUsZCFC4yeFBzggc84HXHoBqRiGORIFJLTKMswxtB44AGT+vftQ5X0MHLqkatnpGmrZp9tjmypGCWwAc4OeOOR0I4HPsNDTxBbXcbeTGHVAvmSYBIx3JOBxx3yOntTtHitNK8yH94Uz87L90bu56ZO48cng+nC6S6TX6tdyQkYyYpcDcxbhMcY6Ec8Zx2OTKUU9DlfO029i/e+Jp1tJbF5WvmkgYxRoCFBxgEnAOQc9O4Hsa5yLSCN63QCebIxRPLO5mUhXPPBUN0QnJyucZ56SdIodjWMMcEysY1k2sW5wGUck/wASEfVvQ5Zf3EAurmK+jd7eT5ZFXBUsGMm0twAQZHYjPRAMggqW5ORcOWK91HN+Ill1q8u7yZ47WONF2xHO1DgfKCOAP06DJ4J5u1kXd5DyBiDiMs3yj6Voa9bRx3Ef2C8a9tpUAjcEqSRwfkPzAcYyRg44qpfaNLpzW8V1HieeMSeXuBIU5546dO9VCUYs6rc0bXKk1ttuFiH+rkG4PjoMVBc27RsiMwZiKu3jQebCkBBAhQMcEEOOtR3DGWFN7r8uSDjHX/8AVWkrDjKWhQlgeNlViuScYB6fWoyCexqeTc6jAJIyCcf596a0eMgH9ak2uyEg5xQEJNTLBIBuAzkcYNRrxyRTHe43aeadg9M04YYj1qZ7d1jVypw33T7UBcj2475FSQSCOZXYZA7UigKhDAlufoKWOMuwVVLE8DFMk37O9ZI5BGI181NhUxgqVPPQjr8oIPsK1knju7uW4nATYV/1EQKqM44CqqjAX1HPQYzjnhYXSW5bggDJA64/yP0qzFdW8kOApE+Mln+6McgZHJJ6c+3NQ12Mmk9Tdt7yCzt55LiH/SEVVWRw2wEEErjPoAOxOT9BTuWeCFheCQMxLoGQg45ByTz1XHQc7vTnNFzI00UauXLIc54LEgjGT1xk/wBPWiafM374MxHDBQEb6dPwHHFFncnlSZqSy3kRieaLO4l9rsR5gVsN6NyykfgfSvTfh87W+kwxz2rQLtMyzvkKpJJAYg+ozg9mHfgef+DLZ9T8U2p1OZRA7srq/T5lboOg+Yj/AL6r1jxTAJbJIbG7e3WJSy7VZg7cdccgYHXpyameiOac/e5TC1mygv8ASbgpGyyXDjZbypkxBkjAfjJDDbjrnJ5yFwfNtZ0q706eOK4YyKUUhgxIzjJ9xjPf9a7RXkVh5zICrgvL5pIbcSd348/4etOxnjvtZ+yanukUwFY0TLDPUcYycE/l046iqOK0JTsczZ3Y060nEkMyzTJsUj7rrkEhvyB/DHSug8La9dPbWujWFik0pufO8x1B2jgsq9MbtoySeBnHtvav4Q0/UdTj0+3f7E6I5QHnBGPXrz2z09M87mm+Ao9K+zzwXRgu7dNzhYy3mcEeoyMdhyeeh6Htk4rT9TO6ldrc8xlMlv8Au0Z9/wB1wVKlCOMEEcd/85rutF1ea38NrLHZhg7GGMhc7WZuWYenykjb6Y4pkfhGXUdUkmLxNFLmTcrb1znPOQCM5Bx7fjXQQeGmj8MyWcdx5MxmV1cLjcMgqowQRyB15GaqpOKaRnJupv08zLtdXg095fLuLPz2LLNDuAy68EBSfmJOccjt8vannxBLp8Zl1O3cCQALcW8a4jU8jdkkjk5Hr3GcisjxLo93f6vHds0TzkLgAEIykZGOuASSRye47Gtb7PMILaQvFPD5RIKTBG3qoDYPTIycduD0AArWFRLTc46uHUlfqSxavpetzLb3/wBnLKBJFJMAVKkc464+XBPp+GTJP4Whsb1brRSIY3z58SvlHXBII9CGA6Y4JwRWLe6Fc+GNQbUYmjvYI5N8i8iXB5YgZ5IAJz3xnGAcYtx40vprotayyQOwKiMfMrDORx6+4rojyyfNF2RyToz5eSOp3jzQyWoWVFdcYIkXcCSPpnoSPWqzQCSTdDCsUcX3WUlQ3sQOvrn/AOvnJ0jVbueyButPCA/xeYoUjHGQTlf8mtOS+jtR5MClnVMpCTh25966Ur6xPGlGcHysdMUClJQghYK7MrEcqQT0+gGe3HStC01Zbu1hkjSZUGRvxgEAYyfb/Cufl1exurZ5P3jFSN5ibayfw568jn36ZqPTvENtaWclk0yNPncnyMuQVyVIxxznnkc56ZrGrB3VjooKfI99Dpo57a2usywuIWBKtt4Qk5bke/rjv7ZTVtPW7MLR3DNbEb2A+ZGWsm01hGvi9hLlZVEYVh8uST8x7g/d5x0+ldBHLJ5i7lVoBHwy4wScdPasruEtC2rxtJHmvi2ytotTWW3iwHA3R9B0659a5u/g2bNxwHJwQOD6HP8AOvTfEFkk7b5IFMgyd6HnGOK83mhlurvZG4Do+FVz3z0z0/lXfZcnMjbC1W1ZvYXS5o7clWk2SNgZB7fT16VrW96dQk23qiZonIQgckH0/wDrdO1Zv9nCUyRzL5d2pOCPukDqeD7dgah09J4SyurKGyAzKSu7/IqqcrOzNKlONROSev8AWx20DLcEJNkSI2QuRlSB6j0+g49qndS6lSAyZOcdR7Y71x8/iSVFEYj2XUZ2+YRk47+xHX86oS67qJkBScx4/wCeYx+fvW7cY7s85ZdVm76I7K8jQTKcgSBRk9M9hUD6lLp0gid0mRhuycqyKexxwevXiuTj1u8nmQXVyxVe5UVb1CcXKwXSsCrDaCvb1HP+fzrRSjKNl0NVgXFqNTVHZLZJdQiaJ96uAV3fw/596x7yGW2kdiAY/uyKw6f59aTS/EX2K3SC7Ge4lDDnJ6mtW9mWaZDGN+9OcHOR/k06cpXszg5atGpyyV10ORlItbgPCP3THLKRyp9qeyC6geRAw2nqo5Hvj0rTntLaaBxGjJngMATz+NZsQktlkikBVgcHrzWuqZ6kKimrrdFMF45hFdjfG38Q/mKiZTCzKCfxGMirV15WzgbXHOc9TUTNHKP3nynsRVp2djqi76kQnbyfKf5lzkf7JpNuRlG7cg0i7Vkw43L3xRIFV/3b7l7HGD9K0TNLa6F+1laWHLnJU4zXtfwh/wCRRuv+v5//AEXHXiNh/qG/3v6CvbvhD/yKN1/1/P8A+i46+CzD/e5+p9/lythIeh3lFFFcJ3nzB+0n/wAlMsP+wRF/6OmryPuPpXrn7Sf/ACUyw/7BEX/o6avI+4+lAH234B/5Jv4a/wCwTa/+iVroK5/wD/yTfw1/2CbX/wBErXQUAFfOXif/AJG7WP8Ar+n/APRhr6NrwTxF4d1ufxRqssOj38kcl5MyOlq5DAuSCDjkUAcXf+H/ABBc7ZEMl1E2WiVnJSNTyB8xwOD9Kz5/Cet3AEU9hDGqDHmtOmAOuRhuc9eM9q9FhtPE0NqLceHrtogmwK1lLjHf8TUE2leI5YgieH7yEYwTHZSZbnOSSDmo5qrl7zOP2TWysZXhnwfZ2Ee6WKZ7xgJIpAAoAH8QJ6AnOMfexkcAmuinvbnQ9FjgmeMzOrF5SM7BnA4Ge2fy5qsbDxOYyo0G+VmKlnFpLuO3oDmnRWfieNy7eH7yViQd0lnK3SlUjzL+v6RkqFW6ciT7PHd6BDFHl2luBvlkQoSvP1JrorPw8TYxyYM9mmJIIx94t2PGO/PUjnrXIzaX4unvjdS6fq7HnEf2R9g+i7f161a2+OGASa01eWHBDQ/Y2RW4wOUUMMdeCOa19nBwb5rPe3Qz+r11JJJW9f69DpI9H1fXhEzXUemWtvKFRbZVLOoOGGQ2Ae2ff8+ptNH03TgmyGWa528TuC747/N0Ge+MZ64rjLbX/Gdpbx29t4WMcMYwqLp8wAHpwelVrjVfG9ykvmaLeb5ARvFlLlAf7o6D8qUkvh6XNIYaa33PQPsUkkTO8MBAPy/xE+/p+pqSKAzWu/7NFgfdUoDXBDX/AB15AibQ7lhgAk2M2TUv/CS+N/LEY8NOEAwALCfj9azlTjd6L7kXClU0vp8ztoJJJphDJF5bJ3RgAfr3/Kua8Y60qoul6fHvvCSYok4UDOC5OQOzDqDlh9Ryhbxsdd/tRbDVUfjMC2snldMdCM+/Wkij8Vx60NTfQbyWYDAWSyl2Ac9hj1/zk5c6aajy/P1FGlUTd16amReWuoafKsl35MBkiIiTO8uVY8YIGQqjPIx8xwORjlNR1pr21EayySJ9o/eSLLs8wdOMjIHOMnnHavQdUg8UavqNreXXh26DWxfYi2MpU7scEHOQMdOnPsMc7ceA9Wubz7TJoeqb87sLauFznJONvc9aIcyfvlSou3uo5S91y+/s9bLc0YR23AEgqDj5B3A4Oc88nNO0NppNWt5dkbE3CDEq7lJDDqCeR/nNdWfAeotcmWTw3qDrjiJraXaPyGf1q1P4T1SWBYofCt1bBTlTDaTAj885/HNbQqW3RnPDTkrFCfU72OW3ga5a8N3I/moE2iZVB4wBwgUkdsDtjpW+16Zp91cPfwW2pXM9obiORmjMMXzfIqK2DjPBwC2AdowSa2pvDGv3FqkU2l6uWEflNJ9jYsyYA25K9MADj0qreeBNSvWDSaHqqNwGMdpINwAAUEYxgAdsZ6nJ5reolOSe6sluu3+YKnLXo/8AgkWnSLqWrBo3WO78jbGxXZsO7BbdnIJAByFyQTkjGys5Z544dFc+XAXdERPLLGAFihdgDy2Y8KmX+7ICA3Fa8fgXU4L5LqDQ9UiZM7UWzfaM59u2f8c5OYrfwDrNqxaCw11CXjcEWhypjRlQg7cgqG4I5BAIORmtYyS1/Vdzh+pTu+xBEV0nxxFPBcyqkxS2dFk3GJyV/dkKcswXYOcDevcgCsrX9QW1uoLtnCR3EbMsBI2R7kOCigjGBJnJxk85J5rcPgDVBDsj0nWImJ3F47Iq2dmzj5cDjsABnpjAxBd/DTUbyCGGbTNc2xZxi0OST1J+XrxTck1a+22qCngZxmpPtZ6HIt4ykjvop0M06wSCRI7iQuN64IOST1bOR6cA85rq9C8WfY7a6jmDm91SxUpLkKhkIlAL9gql92AOuORjmuPhHdL00zXv/AU//E1rxeB9ThUJ/wAI/qUyeQICstpJyobPYA9cflTdSMYSvZ3OhYVppRRxWt65qGv6j9mvr9Y42lLMNxEERYk5AGTxu6nJ5PvUNrpgmv8A7FDKssaLudolMgc4OOFzkjd+AzyOcd1b+BdQt5riQ+GL2YTKFCy2krCMDGNpxkdAOSeOOmak0/whrml3f2iy0XU4zkEx/ZH2NjI5G3uGYZ64Y4xmuZ1eYt4SaXukHhDw9DHr4sPFkUMNpDaYhESjFwUdXOXXIkKZOcE9wcgEVynjO0Nt4kuNPtGiFmszeWqLhzhclmGSc5B64zycDt3d5ofi2/sDZXtlrE9uIniRZLIsYw5BYqSmQflx14BOKwG+FuospB0nWue/2Zs/+gVcakFrfUhYWtfy/U4C2dkuHit3ZFyG4IDfLz19sZyPSussNc0y2ujBPqGJLi8VpLny8+REn3WzxvOWZiMHd5a9zg3f+FSXx/5hWuf+Azf/ABFB+El8Tk6Vrn/gO3/xFN1YNWZp9VncmufEWjx3FlqV4sct4kNqI2nYFYY1GMJHHyMZZju5G0YJ3DGH4+8Tab4g8UTzaVHm2DnbdSFjJOdqjJ3cqo28KMAZJxzgax+Et+xy2l64x9Tbt/8AEUo+E18DxpOt/wDgM3/xFCq00rIn6pUvdnGWskIkUylSMgFelWtWmtrK5lttOlSWNwhLxuTg4+Zc8AjJPPtwccnq2+FN+zlv7I1oZOcC2bH/AKBTv+FV6hx/xKda4/6dm/8AiKn2kL3D6pUODDE53LxjAI7f5Ga1NIhP2lJFYqUYEsRwQO1dUPhhqQUr/ZOtYPX/AEZv/iKv2PgbWtPbdBo+ql+m57Rjx6fdq1VgTPC1WrI2zCZSUtJFSTMbRwhC7xMMc7uhJORg4yee2T0UnhK1j1IajdvJcG4LCRXIPlKIydwOMk7go45ySeeowrK38T2JQx+Hbp2QqVL2UvG0kjgYHUk4q9/aPjTcxGhXYDMWKizmxz/T29q5ZNOTa2OengK0YpaG34h0x9a8K3Flp+I7iRVhAlJCqSy5z14yMA46kelcFrHgX/iT3kPh92lmNw7+UUVVdGbCR7mYYwqM3Oei8Ddk9NLqvjCaCeF/DMnl3CMkgFhNyDwe/pWbZweKrOaZxoN7MsrbjHNaSso+9+J++epPQDoMVUJpLUf1GtH4bHD2vgvVPEDRGw0yaJIvknZ2WPLBFOFXrwDncfvbgeKraz4e1tbWHWtUiFwLlE5EgJUYCoGxx028+4zya9Otb3xfZQyRweHLgGSQyM5spi2Sxb1x3x06VnXtj4jvLNLb/hG7m3jjJKeRYyqQS4cn8wRjp8x74I0jVXUJYPENqyR5npuhXOoYWzgaWbLMI1xkKCAWySB1YCtO905/CVtDNcxK9xLKxtZ4mYNHInludysBkfNjBHPJHGDXT2XhXW7L7ug6jJ9771rIpGeeCoBHPPBHb0FQaj4L1rU9SkvJtF1VC5B8pLaTYMDGBkE4wMdaI1VzXkyvqVZ6NK3qM0XUria8+3XFo91GqNKFYhfmP3tvRQoJ3H0CjOKq+Jtfebw/Mk1+8kk8rMIlGAwLB8n5cgAswwT2HpXRnTPEIEAh8N3MAgTy18mxkU7cEHnryGbOOu7PUDGZd+D9ZvrdIbnQdRdYwApFm4IwMZ4HpjPrgVXt1uyP7PqX2OQ8J29xJrcdvZRLNcXKsiDjBJX5uvoM89q769+HcMs4trq4iFwyPtS3Q7WZM4HPTOcfl7VDo3hzW9CvEurLw3eGRFwpks5SR7g9R6cHGCR3rYuZ/GNxkjRLyJuqtHYyZU/NyMg/3u+egrGU+Z9hzweITvBfiY114U0G0ms7O5smaRw8Zc+YpZRuPmjGRkDGRzkY6d6s+g2Wl3hsrTX5tOxh0+0FD5Rzj73A529Rg8EcgZPSwXPjOOPE+iXd1LnInm09944xgbQB+neuSvPAWt38xlu9M1qRz1ZrdyT9SVzWtKcYPVszlgcVU3t+Zyt7NqNzceRPetc28Q2xyI37tlzwx/IdeRjHaumsLmwS1SW7twszOIy+cqF6ZK8np9P0OLr+DtaaONI9B1GJY4xGAlk/Yk7uVPPPX2+tTSeFtclt3iOg6gA+0ki0k6gYGOMDj0q514zd5agsvrJJJJIfYx2tvpd/qjyrEYGbbBhlLruyB17bwNuMZxu6iuV1O6e9Miou6KRwdkZIjQjptGMDgD8+/FdXP4d1+4n8x9Bvx+7Ee0WT4wPqKpw+C9aguPNXRNTLckD7I4Az6YUUlXikEctqqXM7HGQ6HLeXJjt43Lc/K2F/A56H+uB1NTx+FbmeTy7fbJJtZymcbVAySScDGMc9K9AGl+J1Ee3RNQVoySrfYnJ5xnqOegqC60DxHd3cV1Jo2prcx5zMltKGcHseP5Y96n27ua/U676ficjpfhxpL9bO6Uw5YhncHaoAJPI+ldi/hG7srETOkdwqjzEdJCQu3+E99pHQjO3HPGTVtLTxOvlA+HrxxEAEDWcpwAoUD379f7ze2Jrc+LrWPy49CvSmc4azlP8Ant+Q9KtVoP4mctXL8W37tvvK1joCXuoHUXdXdW2y9P3qkEZB7EEgg+/tzfhju9DjgkmRPLV9vnbQQhY8cj7qnOCT3xxgnMcreKpM7PDU8Oe0VjKv4/5/rSP/AMJVLYSWc3h26khkG1g1lN0qlWpLfU5p5VjJ22VvMmW8XzBHbPGZHOFWFF2wEkAcBjgdFIXg7cDDAirguY0lRIcNtbaxRiu4lsYJz8uDgYbaCQAM8rXOx6Lr0MLRQ+G7xI2OSn2SYr1zjacj2z1x0NXYofEcUKRDwrMUjJKA2c/y5JPGD7kZ64OM4q3iqb6E/wBi4ldjq45svJEwErJEGICkhg2eigEkYzgqDk5FQpe/arV3jCtExG4ow2sep5OCc8Y79++DzSJ4sS3WFdDvlCjAZLSZCBx/dwDwMZPJHXPWqw0/xWrytHo+op5oAYCzc9Oh5Gc55z1Hakq1JNCllGLaskvvOh1D+zbloZC8clxEOHdgNuTwGJ75HHvwMZFYGs3wvnFrbrm7j+eMqQQoxhuScDg9DyO2D0qXugeJdQmEt1pGpswXA22kijnqcAYye574Gami0bXYbMWy+GLor/ExtJtz8Y5Oaf1ilF+6nc0WT4m2tjkrPw3qmuTSFLMrCgLNJgKFxn1xnr061JqOixadMdrGK6jjSVIWXDZx6HqMjqMj1wcivQIb7xlb26wR+H7gRomxVNhKcD69azb7TvEF/sMvhaZCisq+XYSjAbr+vOOmeazjiLy55/cdUsvxHwxWnyMXRPGd7dTR2d3dxWU0e1I0wEBHQYZsqOnOfwBzitBfFNq1yTdajCyLGVAmAC+g4GSDg4x1xyM4OaF94I1e/XbNoOpLzkFLR8r7DIqh/wAKx1L/AKBGs/8AgM3/AMRWqxUHHlkZvKKjldJI34YdkdxdRJNbW+1Z4zICVKsM5U9xznqewqW38SwWDqb5dqFdySPyG9sDvVKy8MeJLGExRabrLRbNgRoJML9AB/8AW9qryeCtbmUxy6Rq7w5JELW8hRT7cZFZ+0ovWW/p/wAEiWU4mejtb1/4BsPqeiazfbnKLfMpSMzJkD0GevesybTb12bz5DdEfMgEZK446E8jp3OOvrmoofBmv26bItL1YLt24Nmx4znHKdK2bey8UWzSMmhXzNIAHLWcpyR3+vPXvWjxkW9Fa/8AX3GH9iYinH93r2u/6sUtQi8jTbe8I+zLv2+YkoY59sDKnnt154rKkScSGVZmaOTIcMc89cZH4cH/APVen8Ja7dF/P0XVHDknb9lkAUnuOO3vn3zUyeHPEUduYRo+plDjO60cnA6DOOlXUxkFS9nB33V9vQ6MNlFanVjUlbRp/kaXw3/5KDpv/bX/ANFPXe/F7/kUbX/r+T/0XJXI+ANC1ey8c6fPeaVe28KeZuklt3VVzGwGSRjqa7X4p2F3qHhe2isLWe6kW8VikEZcgbHGcDtyPzryT6s+fPEiGTTowP8AnsP5GsWw0HUNSdhY2VxclMFhBEzlc/QcV6BceENcuYwkmiangHPFo/8AhXR6JL4o8P6Wlhp3haRYlO5maxmLO3dmOev+RXRCoowstzz69GpOpeOx5ZqHhTUtHmWLU7NoSQG+8GGPcgnn261DLZPD+7mhdHxkKRg4r2VNa8ZJKZD4ZkdzgZewmOB6AZwB9K57WdF8Ra1qn2+40C+hnPUxWcnJ9TuB+n4VlGvVckppW8v8v+CZVMHJq8dzhLWyeJVXYySueDgghe5+nTrVuPTR5KySMiKwGJJScbepPI5B7EccHk4NdKPCviA3q3U2j6nPIuMebZswH4bf85PrT5fDGuXCuJ9A1CTduOTaSDBOPmwBjPUe4JByMAKU2zGODrdTjYr63+yTBMtM3CEEqAM5JI/AcA/Wtbw/p9vqG79xiVwxdS244HTjjH88ngCtD/hBtX8wMdC1MgKV2fZXCn0PC9uvpxyDV/SPD3iDRrnzoNC1CVtpUCazkIGe/ABrb2q5HG25E8DX+z37mHqHgi5isw1p+93sFAbCsc4we6gZPrXFXWi31hd+RewNA+4qc4wDgEjI7gMMjqMivYpYPFEhDDw7dJIpBWT7FKzD2y2ff8z6msm58L6/eXsFxd6Lqcot1KxQ/ZHVEz1IAA5J5LdSep4FYUqlSL943WGrJWscFa+FLuSF5ZsW8aOqZmUqSS2OmMkDqcA9D1wayZY0jlIEiuB/EucV69Jo3iGYRJL4dvHgiAUQfYpApHPXABPUnk9ST3NZV/4F1PUb6a6m8O36STEEiKydFXAxwoXA7fl7nO0a8ub3ioYet9o84M4VFFrEfMz989vpRYadd396kcMLTSM+0BR1Nd+nw41JCCND1U49bZ//AImrtt4P160ZWt9H1RGQfIRaN8vuPl9efqAa1VaKYnhqttDB1DRptNgZL3yLUxE5D4wxGOM8ZPzZAx3z2zXL3NxDPDPK1zmYhiFEbAsT27DHr9TgcCvSpfCmvTqouNF1ObaSR5lrIe+fT1NULv4d6hdyF28P6nGx7x2sg/mKyjVS+IccJUW55irEJ8zYx0HrUtvIEJEiKBjAY/w/h344r0OH4ZX8Tqx0PVpApyFe1fH6LTX+GGov10jWepP/AB7N/wDEVv7eA3haj0OPgs3mmaMIzvyNoQnLEdOOcgAnBHY/Qw6jJGtyYLBj5YRfMZcgM2NxGDzlSSvf7vfqe7k+HOryqAdK1hcLt+S0K/LgDHCf7P4kknJJqH/hV+o5J/sjWf8AwGb/AOIpe2iZrB1b3Zh6PfWVjarJOkZbaQUbOQeeuB7549etaLapFeLPPA8kqyBkSOWMbV5GW44AG44XsRx2rQm+G+qTBQ2j6su0Y+SzK5+uE5qf/hANUFukI0HVAqDjFrJkn16df8+lQ6kOhlHL6nNzP8zm47qWN5DK7iSRsKhYAEAAKOwXBJJPfPbqdzSWQWrTCOF3Rw5ZowxIHfdn5T7Zx/XQt/BurW6Y/wCEcvZWx9+Wydm657jirlvoGvWzgr4dvHABG2SxkYc/h71PtUuhpLB1GrIjFybbMkECywxsIpMKFdGAHzAY67iCp5OM4xnFLb2s8iTRB1Ep3FUhUyB48gNhFyfuiMAg87c+rGWfRPEc0PlDRNRiXay/urWVTg88kDJwcnnPJOc1C3h/xTtCxWOtQrg5VLdyGz3OVPIycHqM8Y4raNaDVmcssur/AGbGRe6JpukeIra/uQ2n6fmMh0bzlRsnrtYsQQhIYdTnHSue/tmC+1S51PUQokuFZWjCcZ2jBGBxyPw967PWfCmv65EqXmjaqu1ww8u2kGAF2hcYxgc9s8nmsf8A4VdqPfSda/8AAZv/AIiiVWlJWNaWBrpXnucLMy3F47RLhCeuadJZTIwUj92W+91/Cu9h+GupQH5dG1duc4Ns/X8Fq7F4J1lLrzn0TUpckEo1mwU4Of4VBH4U/bwWxv8AVap5wYkafYrAKB8x/wDrUyWxdHBT5lYZBz24P9RXfR/DbUo5WkXRtX3E5BNs52n2+Wpf+FfaqFULomqqF6AWrn09V9qf1iBLwlZbHmr7s4+7tGOKZHbF9x6hRk47V6I3wx1NtxbSdZJbqfszc/8AjlTJ8OtUSHyho2r7fm/5dnzkgA/w+w9qPrEB/VKqWiPNzC8fziMquM5rRgBurVIsEum5t2ONgUs35AV23/CudU8kxnRtXOSPmNq5PGf9n/OBTj8PNUIUDRdWAUYwLV+f/Haf1mCE8JVe5zM/hK8Not3a4lgkyEd3VS5DbW+UnI+Y4rofCvh/SbYLd31vPc3CLt8v7yGTgkAAejKPmOPvH0xvjRPEK2ttbroF8sVsjIqizl+bJLZPHXJJqS30jX7W0MEHhu7TP8Ys5d2SRk+mSAB04HTFYfWNdVdGVbA16kOVO3oZL2dzbXTyW1rGWV2IaGM4VTkcKOoBVhxjkdeRXnVxC9tIY1BCqSq5HXBr1q10nxFawxRp4fvXEXCl7OUnGcgf/q9T61X1fw1rOtBPtnhi6VkJw0VlIh5+gxTjiHF+QUsDWirS/M8shkzJ++PB7+lX1MBiPl5dzx06V1X/AArTU85/sfWP/AZv/iakj+HOqRsCuj6vnOebV/8A4mun61Cw5YGq3dFvUNI0ubwNba3aXEkciwrbyW5Tq4YrkkfXHuAOlaeg69Z6jpf2iSaNNRhZVeCVhhlOF2xg4wOpwvABxg4rLj8EaqiuH8P6lLuHG62lG0+owB+uajHgTWVkZotH1aPcCpC2r4wRjH3fQ1DrUWranPHLsRazItX1qPzBHC0qllYKImKqGHbg5wc5P14I7Lolsba+ivrxyJg/mBY0CqhyT0GAD3AHrmrNv4L1q3vEuf7E1OWSMgr5to7AY6cbfar95oXiC7YEaBfwAEFRFaygLgY4zntUe2gtFsa/2fV5bHR3015YXi6tbWqXTXNoi3UUjqrQNk4KuTghix4HdevQVqSazqFiyQvtv2lZfKAG1xkEkHIAHIUDOMlh06Dl7SPxVaae1n/wj11PGQR+/sZXxnBzg/L1XPTqT60+ceKZxF/xTdxH5IAj2WUvy4xjGemCM/iaj26vojL+y6tv+CdQ17cIoEoEUzHzJFLBieNuDjIz0OAe/wCdGTX762kzIBLCSoxgK2ApBPOBycNg49O/GX9o8WmZZH8OTuwGCXsZW3DOcHP5VVuYvFd3Y/ZJdCvPLBOCLBw2P7u7Gcd/Wp+sS5tNjP8AsmrbVK5oTG5Wxa/IkZRNncVYMckkYGOeMNxgHJOT0qQvLK1vp/mW958ircwOmzyl8sbSuQc5BVSw+bnjrhKZuPGH2cwDQbpYvlwgsZcKR0I9+n5CsC88K69fTeZNouqDgYVbaTavrgEcZPOOnJxitqdeP22J5XiG7JK3qa819qivF52nLJYyxKlsu9ZTbSMgJQuckgsehweBgjBrnte8PSadcQz5Tyrgbx5QOF6EgA44Gf5VtWWn+KbHTvsMejag9uXZ2WSzkO4t1ycc+tWrpfFN5aiCfw5csFBw5spSwyMZyc/561v9bgmYPKcXfRL7ynp2rm/uGtIJMCFS5ZwTvA4OOOByOuD098Pa9lfC7FfYd0DNGAyZ9cn0znnsOnNU4fDmvwGFo9C1DfA4dHNm+7I7HjkVPPpHiW437tE1BSzmTctk+QSc9cVsswgckuHq17pL7zI1mO/uNQjKWexZRxsO0yHgkHnkq3t6Gn31qzSQyiw+yXTfu5Ld2Lhsjhww5U5BOD7HkZq8fD3iExhP7E1IKOmLWTj9Kdc6H4nulUS6XqvypsBFrJ93jjp7Ck8bSk7t/gbLJsVFKMUvvL+ltDqT+RJJF5hfe8g4wep69uT/AJBrqbPS7yx3Itzvt2P+qxgISeSCeR+HvxXADwtrok3nQtRZsEZNpJ0Ix6c/jXQW114ztrVIRot7JsXG+SylLH0JPc/5NZyxVJu6b+455ZDi+iT+Zb1P9zqU8Ny5cmMMoDY2jpz9Se3tXP30Gn/aJneJW3KoO1ivPqCP68dz7y3lj4qvr0XU2i6gJAoUbbOQAAZ9ven/AGTxPtQHw/dkpjBNlLmuqOPopWbuc/8Aq9jk7xSXzOfvNHxqCBYZRHx80b53HnLegI9PY9KsWsb2F9Pb3bq8UnAKtx0yP8fzrQi0fxFExP8AYF82SDzZy8EemBxUF94b16/mSWTQL+ORV27orSQZHbPFDx9C97s2/sPHyXLNK3qYms6PMPMnh2OgX5iM7hjv/SoLGS1uII7eSI7+7IMH8TXQr4c8SLEyJpOqLuXaWFo+SPTO2ol8KeIkxjS9WzxybR+f/Hap5hQb5m/wNY5JjuTllbTbUwhpQkuFESFV5JdmBBHY08aFK00klsd9uDzhtrJ9c/z/ABrdHhfXgH/4kepEsME/ZJOPpxU8WheIYoTF/YF+yEYIazk5/IVSzDDX3f3A8ozFfDb7zKsrEmBre52jnAKjOB3GPX2rSt7SGyKpEu+MHcGY85x1GPTP5fWnLoHiBZA66DfggYH+hyf4VIdH8SEg/wBhX4x2+ySY/LHNbf2phrbv7jlnkGYTfSz8yT7R5bMGKlW5ZSSOM49O3fj+lY94olZ2CgoZPvZ5I6Y9e/6VqS6R4mlVV/sbUlVey2suD9QRzVf/AIRrXgeNB1Ae32ST/CrWa4VLr9xNPhzGwd7L7zAuYwEx1UdGP9f8/lVAoRzyAe9dVJ4U16RSDompgHji0k/wpn/CIa5t2/2JqePT7I//AMTR/a2G7v7jvhk2MitUvvOdZPlye/eosc10/wDwiGubSv8AYmp4/wCvR/8A4mmHwXrR/wCYLqn/AICP/wDE1azfDd39xaybF9UvvMuyGIDju2a9t+EP/Io3X/X8/wD6LjryuLwnrsSbV0TUyPe0f/CvW/hZYXen+F7mK/tZ7WRrxmCTxlCRsQZwe3B/Kvl8XUjVrynHZs+rwlKVKhGnLdI7WiiiuY6j5g/aT/5KZYf9giL/ANHTV5H3H0r1z9pP/kplh/2CIv8A0dNXkfcfSgD7b8A/8k38Nf8AYJtf/RK10Fc/4B/5Jv4a/wCwTa/+iVroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACorq6isrV7i4YrHGMkhSx+gAySe2ByalqtqNmb6xaFZBG+5XRyu4KysGUkZGRkDIyPqKAGNq1jHpDanLcCOzVC7SSKV2gdcgjIOeMYznjrUcuu6dBcvBLOyukXmsfKfaBjdjdjG7HO3O7HOKqvoMv9lTxRXpF7cRNHJM4kaH5mJYiHeFB+Y4Oc9Mk1C/hq4nytzfxlHUM4jtypMwj2bwS5wuB93k5/i7UAa1vqNvdWaXUHmtGzbMGFw6nO3BQjcuD1yBjvVqsKfTtQjtYImdLxpLuOWZoohGFIkDFvmc4XAACjcc965u/8JX0Ph6cxabb3t1cSJutbaKGEsAzHdI0pdJG56lT9M8gA9BoqnpEJt9Gs4WtVszHCqm3VwwiwPu5AAOPWrlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFSy1S01F5ltJGYwtht0bJn3G4DcpwcMMg4PNVrrxHptpHM8kk7pBIYpWgtZZQjAAnJRTjgjnp27VDaeHvJJ+0XkrrHtW38hnhKIu4gNhvnPzHOflPHy0zTfDR06CSE6lcXUb3Ec376OMEBAo2/Iqg5KjnH9SQC42vWCC4JeYi3ID7baRtxJxhML85zwQuSDwatx3cUsUEke9ln+4fLbjjPPHy9O+OeOtYDeEB5moNFNaqLtt21rPIky+4ib5h5o6gD5cA45pmpaPqP9l22nwAXQiikCyKojCfuWQKcuScswxjoBye5AOoorz7VPCl3bR6dFbaLb6oouWlmitkhtYIc7Bwsm8j7uSV+Y8/MM4PoI6DtQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7Sf8AyUyw/wCwRF/6OmryPuPpXrn7Sf8AyUuw/wCwRF/6OmryPuPpQB9t+Af+Sb+Gv+wTa/8Aola6Cuf8A/8AJN/DX/YJtf8A0StdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzB+0p/yUuw/wCwRH/6OmryPuPpXrn7Sn/JS7D/ALBEf/o6avI+4+lAH234B/5Jv4a/7BNr/wCiVroK5/wD/wAk38Nf9gm1/wDRK10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7Sn/JS7D/ALBEf/o6avI+4+leuftKf8lLsP8AsER/+jpq8j7j6UAfbfgH/km/hr/sE2v/AKJWugrn/AP/ACTfw1/2CbX/ANErXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wftKf8lLsP8AsER/+jpq8j7j6V65+0p/yUuw/wCwRH/6OmryPuPpQB9t+Af+Sb+Gv+wTa/8Aola6Cuf8A/8AJN/DX/YJtf8A0StdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHy/+0qP+Ll2H/YIj7/9Npq8kAr1v9pU/wDFy7D/ALBEfb/ptNXkgNAH234B/wCSb+Gv+wTa/wDola6Cuf8AAP8AyTfw1/2CbX/0StdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzB+0p/yUuw/7BEf/o6avI+4+leuftKf8lLsP+wRH/6OmryPuPpQB9t+Af8Akm/hr/sE2v8A6JWugrn/AAD/AMk38Nf9gm1/9ErXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wftKf8lLsP+wRH/6OmryPuPpXrn7Sn/JS7D/sER/+jpq8j7j6UAfbfgH/AJJv4a/7BNr/AOiVroK5/wAA/wDJN/DX/YJtf/RK10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUyWaOCF5Z5FjjQbmd2ACj1JPSjcB9FZum+ItH1iZ4tM1K2upEGWSOQE49cenvWlVzhKD5ZqzJjKM1eLugoqBBJLubzWQbiAFA7HHce1O8p/+fiT8l/wqCiWiovKf/n4k/Jf8KPKf/n4k/Jf8KAJaKi8p/wDn4k/Jf8KPKf8A5+JPyX/CgCWioHEkW1vNZxuAIYDucdh71PQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7Sf/ACUuw/7BEX/o6avI+4+leuftJ/8AJTLD/sERf+jpq8j7j6UAfbfgH/km/hr/ALBNr/6JWugrn/AP/JN/DX/YJtf/AEStdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU2TiJ+GPynhep+nvTqKAPN7exa2aP+zbe+tLaG8iknubbSGhmk4YESIyN5zZIJlRccn0zVga54kgXVJNSae2EboFCW7yeWDMq/uv9HVW+UngPKc4xXoFMlijnTZNGsi5B2uoIyDkH8CM0AZnhya7m0+Rrua6uE80iCa8t/ImkTA5dNq4Odw+6vAHHc+Pah4l+IiX8yynUIWDkbEtcKvPb5envXu9Fd2DxccM25U1O/focmJw8q6SjNxt2PAP+En+IP8Az21P/wABv/saP+En+IP/AD21P/wG/wDsa9/or0f7Xpf9A8PuOH+zan/P+R4B/wAJP8Qf+e2p/wDgN/8AY0f8JP8AEH/ntqf/AIDf/Y17/RR/a9L/AKB4fcH9m1P+f8jwD/hJ/iD/AM9tT/8AAb/7Gj/hJ/iD/wA9tT/8Bv8A7Gvf6KP7Xpf9A8PuD+zan/P+R4B/wk/xB/57an/4Df8A2NH/AAk/xB/57an/AOA3/wBjXv8ARR/a9L/oHh9wf2bU/wCf8jwD/hJ/iD/z21P/AMBv/saz9a1vxdf6a0Oty35s9wLiWIqpOeMnA719H0yWKOeF4p41kjcbWR1BDD0IPWtKedUoTUlh46dtyZ5XUlFx9tI+Z/CX2z/hMNL/ALN3faftKbdvpn5s+23OfbNfTdZum+HdH0eZ5dM022tZHGGeOMA49M+ntWlXLm2Yxx9SMoRskvmdGXYKWDg4yldsit/9Wf8Aff8A9CNS1BHJ5QZHV872IwhIIJJ7fWnfaE9JP+/Tf4V4x6ZLXHapYzS+PLWaxQNOskbSSy6bKTFEB8wS63BApHWPBJJP1HWfaE9JP+/Tf4UfaE9JP+/Tf4UAS1XvV3WpBjkkGRlIm2sR+n86f9oT0k/79N/hR9oT0k/79N/hQA1gRaRhgQQyZDHJ+8O/ep6gkk80KiK+d6kkoQAAQe/0qegAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2k/+SmWH/YIi/wDR01eR9x9K9c/aT/5KZYf9giL/ANHTV5H3H0oA+1vAc7D4c+Gx5EhxpNryCvP7lfet77Q3/PvL+a/41g+A/wDknHhv/sE2v/ola36YhPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv8AjR9ob/n3l/Nf8aWigBPtDf8APvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/40faG/wCfeX81/wAaWigBPtDf8+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv8AjR9ob/n3l/Nf8aWigBPtDf8APvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/40faG/wCfeX81/wAaWigBPtDf8+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv8AjR9ob/n3l/Nf8aWigBPtDf8APvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/40faG/wCfeX81/wAaWigBPtDf8+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv8AjR9ob/n3l/Nf8aWigBPtDf8APvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/40faG/wCfeX81/wAaWigBPtDf8+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv8AjR9ob/n3l/Nf8aWigBPtDf8APvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/40faG/wCfeX81/wAaWigBPtDf8+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv8AjR9ob/n3l/Nf8aWigBPtDf8APvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/40faG/wCfeX81/wAaWigBPtDf8+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/AI0faG/595fzX/GlooAT7Q3/AD7y/mv+NH2hv+feX81/xpaKAE+0N/z7y/mv+NH2hv8An3l/Nf8AGlooAT7Q3/PvL+a/40faG/595fzX/GlooAT7Q3/PvL+a/wCNH2hv+feX81/xpaKAE+0N/wA+8v5r/jR9ob/n3l/Nf8aWigBPtDf8+8v5r/jR9ob/AJ95fzX/ABpaKAE+0N/z7y/mv+NH2hv+feX81/xpaKAPmT9pBy/xKsCUZP8AiUxcNj/ntN6V5L3H0r1r9pD/AJKVYf8AYJi/9HTV5L3H0pDPtbwH/wAk48N/9gm1/wDRK1v1geA/+SceG/8AsE2v/ola36YgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCK4dlCKh2l225x04J/pUW2X/n5k/Jf8Kfc/fg/wCun/srUtICPbL/AM/Mn5L/AIUbZf8An5k/Jf8ACpKzE1oS6kbaCwvJoUfynvEVfKV+6/e3nB4JClQep4OAZf2y/wDPzJ+S/wCFG2X/AJ+ZPyX/AArOtvEFg9jYzX1zb2Ul8P3MM06gu391c43Hp09atjU7Brya0W9tjcwJvmhEq7419WXOQPc0ATbZf+fmT8l/wo2y/wDPzJ+S/wCFR2V/Z6nai5027gu4GJAlgkEikjqMjirFAEZaWIqxlZwWCkMB3OOwHrVqqs33F/66J/6EKtUxHzJ+0h/yUqw/7BMX/o6avJe4+letftIf8lKsP+wTF/6OmryXuPpSGfa3gP8A5Jx4b/7BNr/6JWt+sDwH/wAk48N/9gm1/wDRK1v0xBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVQ1AzTSJbWsjRybWkJU46dB+JP6VYivI3sFupDsTZubP8PrQBPRVRb8uCVtLnG0lSUA3frx+OKrQXv2nR5ZL+GQoEYucABxk8DB/woA1KKgiuY2nNuqsrKgcZHBB9KQ30YExCSN5LBDtXO4+g/OgCxRVWO/Enmr5EyyxqGMTAbiD3HOP1qGw1BpNPM92rIFBLSMAFPJ6YNAGhRVWG/SWZY3hmhZwSnmrjf8ATn9DTNSd4UhuEdlWOUbwDwVPByPxoAu0Vl6rdSxXEfkMwWAedKAeq5AwfwyfwqeeRpNTtYY3YKA0r7TjI6AH8TQBdoqhHeWttZGVEdIjMUI6ncWwT16ZqaG/SW58hopYnK7l8xcbh7UAWaKoy6lGUl2RTPGoIaVUyoPf3P4CmWV1HbaNaGTcWZAERRlmOOgFAGjRVWO/V5GjeGWKQKWCSKAWHtg4NI2pQJawTtu2zsFUY5GfWgC3RVaa/hgklWTd+6QMxAyOTgD60+3uDPu3QSwlf+egAz9ME0ATUUUUAQXP34P+un/srUtJdceU5+6r5Y+g2kf1qP7TB/z3j/77FIZLWLZ2mrafePb262cmnyTtN50krCVNzFmTYFw3JOG3Dr0OOdX7TB/z3j/77FH2mD/nvH/32KAOU/4RrVYrF7aMWUn2qzFlM0kzjyVDPh0Gw7iQ+Sp28j7x60t74Y1G9e+t1khtrSeJhvW4d2kkO3DFdoMfC4JR+cnjPI6r7TB/z3j/AO+xR9pg/wCe8f8A32KAMzw7ps2nWswurdYJpZNzY1Ka93cAA75QGHTGOlbFRfaYP+e8f/fYo+0wf894/wDvsUALN9xf+uif+hCrVUmljm2JE6u29ThTngMCf5VdpiPmT9pD/kpVh/2CYv8A0dNXkvcfSvWv2kP+SlWH/YJi/wDR01eS9x9KQz7W8B/8k48N/wDYJtf/AEStb9fN/hz9omfQPDWnaTP4ZS8axt0t1mS+8reqLtX5fLbnAGea0v8Ahp9v+hP/APKn/wDaqYj36ivAf+Gn2/6E/wD8qf8A9qo/4afb/oT/APyp/wD2qgD36ivAf+Gn2/6E/wD8qf8A9qo/4afb/oT/APyp/wD2qgD36ivAf+Gn2/6E/wD8qf8A9qo/4afb/oT/APyp/wD2qgD36ivAf+GoD/0J/wD5U/8A7VR/w1Af+hP/APKn/wDaqAPfqK8B/wCGoD/0J/8A5U//ALVR/wANQH/oT/8Ayp//AGqgD36ivAf+GoD/ANCf/wCVP/7VR/w1Af8AoT//ACp//aqAPfqK8B/4agP/AEJ//lT/APtVH/DUB/6E/wD8qf8A9qoA9+orwH/hqA/9Cf8A+VP/AO1Uf8NQH/oT/wDyp/8A2qgD36ivAf8AhqA/9Cf/AOVP/wC1Uf8ADUB/6E//AMqf/wBqoA9+orwH/hqA/wDQn/8AlT/+1Uf8NQH/AKE//wAqf/2qgD36ivAf+Gn2/wChP/8AKn/9qo/4afb/AKE//wAqf/2qgD36ivAf+GoD/wBCf/5U/wD7VR/w1Af+hP8A/Kn/APaqAPfqK8B/4agP/Qn/APlT/wDtVA/agY/8yf8A+VP/AO1UAe/UV4D/AMNPt/0J/wD5U/8A7VR/w0+3/Qn/APlT/wDtVAHv1FeA/wDDT7f9Cf8A+VP/AO1Uf8NPt/0J/wD5U/8A7VQB79RXgP8Aw0+3/Qn/APlT/wDtVH/DT7f9Cf8A+VP/AO1UAe/UV4D/AMNPt/0J/wD5U/8A7VR/w0+3/Qn/APlT/wDtVAHv1FeA/wDDT7f9Cf8A+VP/AO1Uf8NPt/0J/wD5U/8A7VQB79RXgP8Aw0+3/Qn/APlT/wDtVH/DT7f9Cf8A+VP/AO1UAe/UV4D/AMNPt/0J/wD5U/8A7VR/w0+3/Qn/APlT/wDtVAHv1FeA/wDDT7f9Cf8A+VP/AO1Uf8NPt/0J/wD5U/8A7VQB79RXgP8Aw0+3/Qn/APlT/wDtVH/DT7f9Cf8A+VP/AO1UAe/UV4D/AMNPt/0J/wD5U/8A7VR/w0+3/Qn/APlT/wDtVAHv1FeA/wDDT7f9Cf8A+VP/AO1Uf8NPt/0J/wD5U/8A7VQB79RXgP8Aw0+3/Qn/APlT/wDtVH/DT7f9Cf8A+VP/AO1UAe/UV4D/AMNPt/0J/wD5U/8A7VQf2oGCk/8ACH9B/wBBP/7VQB79RXz4f2piBn/hDvx/tP8A+00wftVErn/hDe+P+Qp/9pp2Yro+hqK+ej+1SRj/AIo3r/1FP/tNKv7U5Y4Hg3P/AHFP/tNHKwuj6Eor58X9qYsAR4N/D+1P/tNL/wANSnbz4Nwc4x/af/2mizC6PoKivn5P2oy5x/wh3/lU/wDtVP8A+Gn2/wChP/8AKn/9qpDPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hqA5x/wh/P/AGE//tVAHv1FeA/8NPt/0J//AJU//tVH/DT7f9Cf/wCVP/7VQB79RXgP/DT7f9Cf/wCVP/7VR/w0+f8AoT//ACp//aqAPfqK8B/4afb/AKE//wAqf/2qj/hp8/8AQn/+VP8A+1UAe/UV4D/w0+3/AEJ//lT/APtVH/DT7f8AQn/+VP8A+1UAe/UV4D/w0+3/AEJ//lT/APtVH/DT7f8AQn/+VP8A+1UAe/UV8/yftRmML/xR2dzBf+Qp6/8AbKnD9qAnp4P/APKn/wDaqAPbIba4nuZ7rz5bYu2xV2L91eB94fU1CbO4WC7s13SfMJo3YYD85K56DkfrXjP/AA0+3/Qn/wDlT/8AtVH/AA0+3/Qn/wDlT/8AtVAHusF557BPs88bY58yMgD8e/4VQVZP7BuLQwyiVEYY2HDZJxg968Z/4afb/oT/APyp/wD2qj/hp9v+hP8A/Kn/APaqAPbb1ZYUt7uCJpJIhtZFGSykf44NNkiuLPTYUi8wsXBnaJctzySB9a8U/wCGn2/6E/8A8qf/ANqo/wCGn2/6E/8A8qf/ANqoA9qso2/tWSQJcCNoAA8+ck59+n0qJYJZdFksvKkWaM55GA2GzwfevGv+Gn2/6E//AMqf/wBqo/4afb/oT/8Ayp//AGqgD2u1jhluI28u+3p8w88vtU4x3OD+FXrmEXFrLCf41K/SvBv+Gn2/6E//AMqf/wBqo/4afb/oT/8Ayp//AGqgD2rTYJbi2uHvo2R5VERVhjgLj+ZJpdHinzJLdoyOFWEbh1C9/wASa8U/4afb/oT/APyp/wD2qj/hp9v+hP8A/Kn/APaqAPZhBL/ZqKYn3fbNxG09N+c/SrlxG7araMqsVVZAzAcDIFeG/wDDT7f9Cf8A+VP/AO1Uf8NPt/0J/wD5U/8A7VQB7TayS2tibNraZpkDKrBPkbrg7v8AJpsUM0Nrp0/ku3kIVkjA+YZHXHtXjH/DT7f9Cf8A+VP/AO1Uf8NPt/0J/wD5U/8A7VQB7eC95qEEqRSRxQBiWkXaWJGMAHmq8dlLJJcWzoyxRK4iYjqX54+nSvGP+Gn2/wChP/8AKn/9qo/4afb/AKE//wAqf/2qgD2uBDJp8819byMZiN8QU7sDA6frUunGbzJVJmNuAPLM64bPcc846da8P/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8B/4afb/oT/8Ayp//AGqj/hp9v+hP/wDKn/8AaqAPfqK8Ab9qFl/5k7P/AHE//tVA/ahb/oTv/Kp/9qoAw/2kP+SlWH/YJj/9HTV5L/EPpXTfELx1P8QfFC6vPYpYrHbrbxwrIZCFBZuWwMnLnsO1cxnkUhikE44NH/ATTqKAG4/2TRj/AGTTqKAG4/2TRj/ZNOooAbj/AGTRj/ZNOooAZj/ZNGP9k0+igBmP9k0Y/wBk0+igBmP9k0Y/2TT6KAGY/wBk0Y/2TT6KAGY/2TRj/ZNPooAZj/ZNGP8AZNPooAZj/ZNGP9k0+igBuP8AZNGP9k06igBgU+lGD6Gn0UAMwfQ0AEZ4NPooAbj/AGTRj/ZNOooAbj/ZNGP9k06igBuP9k0Y/wBk06igBuP9k0Y/2TTqKAG4/wBk0Y/2TTqKAG4/2TRj/ZNOooAbj/ZNGP8AZNOooAbj/ZNGP9k06igBuP8AZNGP9k06igBuP9k0Y/2TTqKAG4/2TRj/AGTTqKAG4/2TRj/ZNOooAbj/AGTTWVjGRjnGM1JRQBUNvIRjt6ZqIWUoHGD9TWhRVczJ5UUDaS9tv505LaVWB4/A1doo5mHKioLdwq44IHPNP8l9uCM9e9WKKOZj5UQJEVJO3HToalx/smnUUtxkPlyFn+dgGxtAA+X/ABp4B2jcMnuRT6KQDcf7Jox/smnUUANx/smjH+yadRQA3H+yaTB9DT6KAG49j+lGP9k06igBuP8AZNGP9k06igBuP9k0Y/2TTqKAG4/2TRj/AGTTqKAG4/2TRj/ZNOooAbj/AGTRj/ZNOooAbj/ZNGP9k06igBuP9k0Y/wBk06igBuP9k0Y/2TTqKAG4/wBk0Y/2TTqKAG4/2TRj/ZNOooAbj/ZNGP8AZNOooAbj/ZNGP9k06igBuP8AZNGP9k06igBuP9k0Y/2TTqKAG4/2TRj/AGTTqKAG4/2TRj/ZNOooAbj/AGTRj/ZNOooAbj/ZNGP9k06igBuP9k0Y/wBk06igBuP9k0Y/2TTqKAG4/wBk0Y/2TTqKAG4/2TRj/ZNOooAbj/ZNRSRF5Fb5l2hhlTzz/n9BU9FAECQFHLb5GyAMMwI471Lj/ZNOooAbj/ZNGP8AZNOooAbj/ZNGP9k06igBuP8AZNGP9k06igBuP9k0Y/2TTqKAK9xE8saiMYIYNk+1SIpWNQRyBipKKANh0eyuIrGxgtTL5SSSS3KxHezKGxmTgAZxgYz71XfT1TzZ9TZrQGdo1jgiDncPvYG4AAZA61JqnNxp7Hq1rESfXt/SrXiD/j3b21C5/wDZaAMe7tPsd75LtvT5WEiD7ykAg4PselaN5pdn/aEi20ssdvDCsszPF93KrjA3HJJPTjr6c1X1n/j/AF/694f/AEWtaM/39VHb7FD/AO06AMi7s44beK4tZWmglLKC6BGVhjIIyR3B61ftVZ9Li/suC2mmUMbmKSJHkbk8ruGcbf7vI5z61Wn/AORfs/8Ar4l/klJon/IesP8Ar4T/ANCFAFmx8OT3ltDKUuR9oJ8ox2xkQc4y7Z+UZ9M8c037Dp62Nk0r3CyyTMkm2IEHBXj7/bPXvmjW/wDU2H/XFh/5Fem/8wzTf+viT+aUALe2cMMmpiyY+TDIqbZIVzyx4BySMY69T3pLvRooBdJBdNNPa4Lp5W1SpIHBz1BIyMfianuumt/9fK/+htVl+NT1zH90f+jEoAz20e3VrmEXjG5tY2eRPJ+Uleqq27nn1AqC906Oyt4i8kpmkRZAvk4QqRn5XzzjoeOua1JAP7f1zj/llP8AzqlbOzeHL6NmJRJYmVSeFJ3AkD1oAjhtLR9BknYzfahMqKFjBHIPGd3fHXHFOk0mFZJrZLpmvIUZnj8rCEqMsobdkkAHsBx1pbfjQ2x/z+R/+gtVyTjxVqf+7cn/AMcagDGsrdbm6WJ/NG7oIovMdj6BcjP51fm0JoLtlmkkigSATu8kW11UnGCmfvZ4xnHvUemsyWOplCVP2cDIOODIuRWk/wDyCpB2/stD/wCRhQBRt7PTHtL5zPcP5UasjeQARlgOm/3x3459qjmsC0G4yKXitUmVFiVdyk85I6kZ6nJNJY/8gzUv+uSf+jFrStxnVrRTyDpxBHr+6agDPj0lDeC2drh5fKVylvb+YwJGcY3DoCMn9Kn/ALJtbUalFfPN5tuqlGSLoCRg4LDnnBB6VNqbssOsBWIDagqnB6j95x+lO1DiTVcf8+8H/slAFdfDVwbQSMlyshh84f6MTEBjdgyZ4OPbHQZqppkSXEd5btEGkaAvESBkMnzHH1ANSa5/yEEPc28P/otaNAGdetAeQXwR68GgC5LpsU+nWNtBEFukkRZmwAT5vIyfbH61DPbJezn7P5cUE195KBYVyowADnrjB6Vs6f8A8h/Wv9lPl9sMMY+lZOl/8etj/wBhJf5LQBXbRlkCixuDO4uBbuGj2AMc4IOTkcHqAfanizsxpl8bWZrmRCijfCFIJbqvJ4/I+1aGiHEjY/6CcP8A7PVCx40/UiOCHix7fPQBFJpMKyTWyXTNeQozPH5WEJUZZQ27JIAPYDjrS/2Gxa52TZWGBZUYr/rMru29eDgMf+A1ck48Van/ALtyf/HGqza82ek5/icK3uPnGPyJoAy00ZFhkluJpVSJI2k8qEOyFxkZG4cAd89TjFZTDBOASOx9a3WkeHxqDE7IftSrlTjjIGPpjism9AW8nCjAEjAAduaAKpGTTHkSP7x/Cn1Qn/1zfWgCytzGTjkfUVOOeazBV61/1P40Af/Z"}}},{"cell_type":"markdown","source":"### Class Distribution","metadata":{"papermill":{"duration":1.928901,"end_time":"2022-01-18T23:24:01.377251","exception":false,"start_time":"2022-01-18T23:23:59.44835","status":"completed"},"tags":[]}},{"cell_type":"code","source":"\npath='kaggle-Reef'\ntry:\n\n    plt.figure(figsize = (20,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/labels.jpg'));\nexcept:\n    print('x')\n","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.569021,"end_time":"2022-01-18T23:24:05.905224","exception":false,"start_time":"2022-01-18T23:24:03.336203","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Batch Image","metadata":{"papermill":{"duration":1.922168,"end_time":"2022-01-18T23:24:10.001294","exception":false,"start_time":"2022-01-18T23:24:08.079126","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\ntry:\n    \n    plt.figure(figsize = (15, 15))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/YOLOv6/{path}/exp/train_batch0.jpg'))\nexcept:\n    print('x')","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.636094,"end_time":"2022-01-18T23:24:14.659379","exception":false,"start_time":"2022-01-18T23:24:12.023285","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## P Curve","metadata":{"papermill":{"duration":2.191868,"end_time":"2022-01-18T23:24:19.253162","exception":false,"start_time":"2022-01-18T23:24:17.061294","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#!ls /kaggle/working/yolov5/kaggle-NFL/exp\ntry:\n    \n    plt.figure(figsize=(10,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/YOLOv6/{path}/exp/P_curve.png'));\nexcept:\n    None","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.066463,"end_time":"2022-01-18T23:24:23.265391","exception":false,"start_time":"2022-01-18T23:24:21.198928","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## PR Curve","metadata":{"papermill":{"duration":2.074712,"end_time":"2022-01-18T23:24:27.365405","exception":false,"start_time":"2022-01-18T23:24:25.290693","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#plt.figure(figsize=(10,10))\n#plt.axis('off')\n#plt.imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/PR_curve.png'));\n","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.17719,"end_time":"2022-01-18T23:24:31.585337","exception":false,"start_time":"2022-01-18T23:24:29.408147","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## F1 Curve","metadata":{"papermill":{"duration":1.965614,"end_time":"2022-01-18T23:24:35.533648","exception":false,"start_time":"2022-01-18T23:24:33.568034","status":"completed"},"tags":[]}},{"cell_type":"code","source":"try:\n    \n    plt.figure(figsize=(10,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/F1_curve.png'));\nexcept:\n    None","metadata":{"papermill":{"duration":2.043872,"end_time":"2022-01-18T23:24:39.526042","exception":false,"start_time":"2022-01-18T23:24:37.48217","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## R Curve","metadata":{"papermill":{"duration":1.97192,"end_time":"2022-01-18T23:24:43.845292","exception":false,"start_time":"2022-01-18T23:24:41.873372","status":"completed"},"tags":[]}},{"cell_type":"code","source":"try:\n\n    plt.figure(figsize=(10,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/R_curve.png'));\nexcept:\n    print('x')","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.270691,"end_time":"2022-01-18T23:24:48.071621","exception":false,"start_time":"2022-01-18T23:24:45.80093","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### GT Vs Pred","metadata":{"papermill":{"duration":2.217173,"end_time":"2022-01-18T23:24:52.746176","exception":false,"start_time":"2022-01-18T23:24:50.529003","status":"completed"},"tags":[]}},{"cell_type":"code","source":"try:\n    \n    ig, ax = plt.subplots(3, 2, figsize = (2*5,3*5), constrained_layout = True)\n    for row in range(3):\n        ax[row][0].imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/val_batch{row}_labels.jpg'))\n        ax[row][0].set_xticks([])\n        ax[row][0].set_yticks([])\n        ax[row][0].set_title(f'/kaggle/working/yolov5/{path}/exp/val_batch{row}_labels.jpg', fontsize = 12)\n\n        ax[row][1].imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/val_batch{row}_pred.jpg'))\n        ax[row][1].set_xticks([])\n        ax[row][1].set_yticks([])\n        ax[row][1].set_title(f'/kaggle/working/yolov5/runs/{path}/val_batch{row}_pred.jpg', fontsize = 12)\nexcept:\n    print('x')","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.985759,"end_time":"2022-01-18T23:24:57.690759","exception":false,"start_time":"2022-01-18T23:24:54.705","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### (Loss, Map) Vs Epoch\n","metadata":{"papermill":{"duration":2.049587,"end_time":"2022-01-18T23:25:01.700461","exception":false,"start_time":"2022-01-18T23:24:59.650874","status":"completed"},"tags":[]}},{"cell_type":"code","source":"try:\n    \n    plt.figure(figsize=(10,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/results.png'));\nexcept:\n    print('x')\n","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.770226,"end_time":"2022-01-18T23:25:06.768229","exception":false,"start_time":"2022-01-18T23:25:03.998003","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Confusion Matrix","metadata":{"papermill":{"duration":1.961975,"end_time":"2022-01-18T23:25:10.729911","exception":false,"start_time":"2022-01-18T23:25:08.767936","status":"completed"},"tags":[]}},{"cell_type":"code","source":"try:\n\n    plt.figure(figsize=(10,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'/kaggle/working/yolov5/{path}/exp/confusion_matrix.png'));\nexcept:\n    print('x')","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.328063,"end_time":"2022-01-18T23:25:15.029787","exception":false,"start_time":"2022-01-18T23:25:12.701724","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Please if this kernel is useful, <font color='red'>please upvote !!</font>","metadata":{"papermill":{"duration":1.958325,"end_time":"2022-01-18T23:25:19.061469","exception":false,"start_time":"2022-01-18T23:25:17.103144","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![download.jpg](attachment:07de9c65-7c16-40e7-a821-d5354296394c.jpg)","metadata":{"papermill":{"duration":2.167072,"end_time":"2022-01-18T23:25:23.488077","exception":false,"start_time":"2022-01-18T23:25:21.321005","status":"completed"},"tags":[]},"attachments":{"07de9c65-7c16-40e7-a821-d5354296394c.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"# References","metadata":{"papermill":{"duration":1.968723,"end_time":"2022-01-18T23:25:27.687619","exception":false,"start_time":"2022-01-18T23:25:25.718896","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"1. https://www.kaggle.com/awsaf49/great-barrier-reef-yolov5-train\n","metadata":{"papermill":{"duration":1.972301,"end_time":"2022-01-18T23:25:31.633494","exception":false,"start_time":"2022-01-18T23:25:29.661193","status":"completed"},"tags":[]}}]}