From 6c87f44e9124066be200db79269b66f0f7482beb Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 12 Apr 2021 14:47:21 +0200 Subject: [PATCH 1/6] YOLOv5 v5.0 release compatibility update --- .gitattributes | 2 +- .github/workflows/ci-testing.yml | 9 +- .github/workflows/greetings.yml | 9 +- Dockerfile | 19 +- data/argoverse_hd.yaml | 21 ++ data/coco.yaml | 18 +- data/coco128.yaml | 18 +- data/scripts/get_argoverse_hd.sh | 62 ++++ data/scripts/get_coco.sh | 9 +- data/scripts/get_voc.sh | 6 +- data/voc.yaml | 4 +- detect.py | 43 +-- hubconf.py | 101 +++---- models/common.py | 165 ++++++++--- models/experimental.py | 9 +- models/export.py | 19 +- models/yolo.py | 57 ++-- requirements.txt | 9 +- test.py | 111 +++---- train.py | 213 ++++++++------ tutorial.ipynb | 450 ++++++++++++++++------------- utils/autoanchor.py | 19 +- utils/aws/__init__.py | 0 utils/aws/mime.sh | 26 ++ utils/aws/resume.py | 37 +++ utils/aws/userdata.sh | 27 ++ utils/datasets.py | 258 +++++++++-------- utils/general.py | 163 +++++++++-- utils/google_utils.py | 14 +- utils/loss.py | 241 +++++++-------- utils/metrics.py | 61 ++-- utils/plots.py | 58 ++-- utils/torch_utils.py | 31 +- utils/wandb_logging/__init__.py | 0 utils/wandb_logging/log_dataset.py | 24 ++ utils/wandb_logging/wandb_utils.py | 306 ++++++++++++++++++++ 36 files changed, 1744 insertions(+), 875 deletions(-) create mode 100644 data/argoverse_hd.yaml create mode 100644 data/scripts/get_argoverse_hd.sh create mode 100644 utils/aws/__init__.py create mode 100644 utils/aws/mime.sh create mode 100644 utils/aws/resume.py create mode 100644 utils/aws/userdata.sh create mode 100644 utils/wandb_logging/__init__.py create mode 100644 utils/wandb_logging/log_dataset.py create mode 100644 utils/wandb_logging/wandb_utils.py diff --git a/.gitattributes b/.gitattributes index 6c8722f6e0..dad4239eba 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,2 @@ -# remove notebooks from GitHub language stats +# this drop notebooks from GitHub language stats *.ipynb linguist-vendored diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 1ae0bbb573..77ac2c3f35 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -66,14 +66,15 @@ jobs: di=cpu # inference devices # define device # train - python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di + python train.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di # detect python detect.py --weights weights/${{ matrix.model }}.pt --device $di python detect.py --weights runs/train/exp/weights/last.pt --device $di # test - python test.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --device $di - python test.py --img 256 --batch 8 --weights runs/train/exp/weights/last.pt --device $di + python test.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --device $di + python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di + python hubconf.py # hub python models/yolo.py --cfg models/${{ matrix.model }}.yaml # inspect - python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt # export + python models/export.py --img 128 --batch 1 --weights weights/${{ matrix.model }}.pt # export shell: bash diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 1d6ec61d2d..3c3b9fc1ef 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -23,7 +23,7 @@ jobs: - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ -Bruce Lee issue-message: | - 👋 Hello @${{ github.actor }}, thank you for your interest in 🚀 YOLOv3! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov3/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607). + 👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv3 🚀! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov3/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607). If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you. @@ -42,10 +42,11 @@ jobs: YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - - **Google Colab Notebook** with free GPU: Open In Colab - - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3) + - **Google Colab and Kaggle** notebooks with free GPU: Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) - - **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker) + - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart) + - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) Docker Pulls + ## Status diff --git a/Dockerfile b/Dockerfile index d0a797fdf3..ca07c4d7ef 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,14 +1,14 @@ # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch -FROM nvcr.io/nvidia/pytorch:20.12-py3 +FROM nvcr.io/nvidia/pytorch:21.03-py3 # Install linux packages -RUN apt update && apt install -y screen libgl1-mesa-glx +RUN apt update && apt install -y zip htop screen libgl1-mesa-glx # Install python dependencies -RUN pip install --upgrade pip COPY requirements.txt . -RUN pip install -r requirements.txt -RUN pip install gsutil +RUN python -m pip install --upgrade pip +RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof +RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook # Create working directory RUN mkdir -p /usr/src/app @@ -17,6 +17,9 @@ WORKDIR /usr/src/app # Copy contents COPY . /usr/src/app +# Set environment variables +ENV HOME=/usr/src/app + # --------------------------------------------------- Extras Below --------------------------------------------------- @@ -34,13 +37,13 @@ COPY . /usr/src/app # sudo docker kill $(sudo docker ps -q) # Kill all image-based -# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov3:latest) +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) # Bash into running container -# sudo docker container exec -it ba65811811ab bash +# sudo docker exec -it 5a9b5863d93d bash # Bash into stopped container -# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume +# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash # Send weights to GCP # python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt diff --git a/data/argoverse_hd.yaml b/data/argoverse_hd.yaml new file mode 100644 index 0000000000..df7a9361e7 --- /dev/null +++ b/data/argoverse_hd.yaml @@ -0,0 +1,21 @@ +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /argoverse +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_argoverse_hd.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images +val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges +test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview + +# number of classes +nc: 8 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] diff --git a/data/coco.yaml b/data/coco.yaml index 09e0a4f463..1bc888e619 100644 --- a/data/coco.yaml +++ b/data/coco.yaml @@ -18,15 +18,15 @@ test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://compe nc: 80 # class names -names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', - 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', - 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', - 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', - 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', - 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush'] +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] # Print classes # with open('data/coco.yaml') as f: diff --git a/data/coco128.yaml b/data/coco128.yaml index abd129d962..f9d4c96053 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -17,12 +17,12 @@ val: ../coco128/images/train2017/ # 128 images nc: 80 # class names -names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', - 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', - 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', - 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', - 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', - 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush'] +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] diff --git a/data/scripts/get_argoverse_hd.sh b/data/scripts/get_argoverse_hd.sh new file mode 100644 index 0000000000..caec61efed --- /dev/null +++ b/data/scripts/get_argoverse_hd.sh @@ -0,0 +1,62 @@ +#!/bin/bash +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Download command: bash data/scripts/get_argoverse_hd.sh +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /argoverse +# /yolov5 + +# Download/unzip images +d='../argoverse/' # unzip directory +mkdir $d +url=https://argoverse-hd.s3.us-east-2.amazonaws.com/ +f=Argoverse-HD-Full.zip +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background +wait # finish background tasks + +cd ../argoverse/Argoverse-1.1/ +ln -s tracking images + +cd ../Argoverse-HD/annotations/ + +python3 - "$@" < 1)}, " # add to string # Write results for *xyxy, conf, cls in reversed(det): @@ -117,22 +118,25 @@ def detect(save_img=False): # Stream results if view_img: cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) - else: # 'video' + else: # 'video' or 'stream' if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer - - fourcc = 'mp4v' # output video codec - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path += '.mp4' + vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: @@ -153,6 +157,7 @@ def detect(save_img=False): parser.add_argument('--view-img', action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') @@ -162,7 +167,7 @@ def detect(save_img=False): parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') opt = parser.parse_args() print(opt) - check_requirements() + check_requirements(exclude=('pycocotools', 'thop')) with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) diff --git a/hubconf.py b/hubconf.py index 7b0f20115e..7d969b5ad6 100644 --- a/hubconf.py +++ b/hubconf.py @@ -1,8 +1,8 @@ -"""File for accessing YOLOv3 via PyTorch Hub https://pytorch.org/hub/ +"""YOLOv3 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov3/ Usage: import torch - model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True, channels=3, classes=80) + model = torch.hub.load('ultralytics/yolov3', 'yolov3tiny') """ from pathlib import Path @@ -10,10 +10,12 @@ import torch from models.yolo import Model -from utils.general import set_logging +from utils.general import check_requirements, set_logging from utils.google_utils import attempt_download +from utils.torch_utils import select_device dependencies = ['torch', 'yaml'] +check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop')) set_logging() @@ -21,7 +23,7 @@ def create(name, pretrained, channels, classes, autoshape): """Creates a specified YOLOv3 model Arguments: - name (str): name of model, i.e. 'yolov3_spp' + name (str): name of model, i.e. 'yolov3' pretrained (bool): load pretrained weights into the model channels (int): number of input channels classes (int): number of model classes @@ -29,21 +31,23 @@ def create(name, pretrained, channels, classes, autoshape): Returns: pytorch model """ - config = Path(__file__).parent / 'models' / f'{name}.yaml' # model.yaml path try: - model = Model(config, channels, classes) + cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path + model = Model(cfg, channels, classes) if pretrained: fname = f'{name}.pt' # checkpoint filename attempt_download(fname) # download if not found locally ckpt = torch.load(fname, map_location=torch.device('cpu')) # load - state_dict = ckpt['model'].float().state_dict() # to FP32 - state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter - model.load_state_dict(state_dict, strict=False) # load + msd = model.state_dict() # model state_dict + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter + model.load_state_dict(csd, strict=False) # load if len(ckpt['model'].names) == classes: model.names = ckpt['model'].names # set class names attribute if autoshape: model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS - return model + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return model.to(device) except Exception as e: help_url = 'https://github.com/ultralytics/yolov5/issues/36' @@ -51,50 +55,8 @@ def create(name, pretrained, channels, classes, autoshape): raise Exception(s) from e -def yolov3(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv3 model from https://github.com/ultralytics/yolov3 - - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - - Returns: - pytorch model - """ - return create('yolov3', pretrained, channels, classes, autoshape) - - -def yolov3_spp(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv3-SPP model from https://github.com/ultralytics/yolov3 - - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - - Returns: - pytorch model - """ - return create('yolov3-spp', pretrained, channels, classes, autoshape) - - -def yolov3_tiny(pretrained=False, channels=3, classes=80, autoshape=True): - """YOLOv3-tiny model from https://github.com/ultralytics/yolov3 - - Arguments: - pretrained (bool): load pretrained weights into the model, default=False - channels (int): number of input channels, default=3 - classes (int): number of model classes, default=80 - - Returns: - pytorch model - """ - return create('yolov3-tiny', pretrained, channels, classes, autoshape) - - def custom(path_or_model='path/to/model.pt', autoshape=True): - """YOLOv3-custom model from https://github.com/ultralytics/yolov3 + """YOLOv3-custom model https://github.com/ultralytics/yolov3 Arguments (3 options): path_or_model (str): 'path/to/model.pt' @@ -106,12 +68,30 @@ def custom(path_or_model='path/to/model.pt', autoshape=True): """ model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint if isinstance(model, dict): - model = model['model'] # load model + model = model['ema' if model.get('ema') else 'model'] # load model hub_model = Model(model.yaml).to(next(model.parameters()).device) # create hub_model.load_state_dict(model.float().state_dict()) # load state_dict hub_model.names = model.names # class names - return hub_model.autoshape() if autoshape else hub_model + if autoshape: + hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS + device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available + return hub_model.to(device) + + +def yolov3(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv3 model https://github.com/ultralytics/yolov3 + return create('yolov3', pretrained, channels, classes, autoshape) + + +def yolov3_spp(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv3-SPP model https://github.com/ultralytics/yolov3 + return create('yolov3-spp', pretrained, channels, classes, autoshape) + + +def yolov3_tiny(pretrained=True, channels=3, classes=80, autoshape=True): + # YOLOv3-tiny model https://github.com/ultralytics/yolov3 + return create('yolov3-tiny', pretrained, channels, classes, autoshape) if __name__ == '__main__': @@ -119,9 +99,14 @@ def custom(path_or_model='path/to/model.pt', autoshape=True): # model = custom(path_or_model='path/to/model.pt') # custom example # Verify inference + import numpy as np from PIL import Image - imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')] - results = model(imgs) - results.show() + imgs = [Image.open('data/images/bus.jpg'), # PIL + 'data/images/zidane.jpg', # filename + 'https://github.com/ultralytics/yolov3/raw/master/data/images/bus.jpg', # URI + np.zeros((640, 480, 3))] # numpy + + results = model(imgs) # batched inference results.print() + results.save() diff --git a/models/common.py b/models/common.py index fd9d9fcdd7..db62e1dfa1 100644 --- a/models/common.py +++ b/models/common.py @@ -1,16 +1,21 @@ -# This file contains modules common to various models +# YOLOv3 common modules import math +from copy import copy +from pathlib import Path import numpy as np +import pandas as pd import requests import torch import torch.nn as nn -from PIL import Image, ImageDraw +from PIL import Image +from torch.cuda import amp from utils.datasets import letterbox -from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh -from utils.plots import color_list +from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh +from utils.plots import color_list, plot_one_box +from utils.torch_utils import time_synchronized def autopad(k, p=None): # kernel, padding @@ -31,7 +36,7 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, k super(Conv, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) - self.act = nn.LeakyReLU(0.1) if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) @@ -40,6 +45,52 @@ def fuseforward(self, x): return self.act(self.conv(x)) +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2) + p = p.unsqueeze(0) + p = p.transpose(0, 3) + p = p.squeeze(3) + e = self.linear(p) + x = p + e + + x = self.tr(x) + x = x.unsqueeze(3) + x = x.transpose(0, 3) + x = x.reshape(b, self.c2, w, h) + return x + + class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion @@ -87,6 +138,14 @@ def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) +class C3TR(C3): + # C3 module with TransformerBlock() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + class SPP(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13)): @@ -166,7 +225,6 @@ def forward(self, x): class autoShape(nn.Module): # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - img_size = 640 # inference size (pixels) conf = 0.25 # NMS confidence threshold iou = 0.45 # NMS IoU threshold classes = None # (optional list) filter by class @@ -179,27 +237,33 @@ def autoshape(self): print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() return self + @torch.no_grad() def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=720, width=1280, RGB images example inputs are: + # Inference from various sources. For height=640, width=1280, RGB images example inputs are: # filename: imgs = 'data/samples/zidane.jpg' # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) - # PIL: = Image.open('image.jpg') # HWC x(720,1280,3) - # numpy: = np.zeros((720,1280,3)) # HWC - # torch: = torch.zeros(16,3,720,1280) # BCHW + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + t = [time_synchronized()] p = next(self.model.parameters()) # for device and type if isinstance(imgs, torch.Tensor): # torch - return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference + with amp.autocast(enabled=p.device.type != 'cpu'): + return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference # Pre-process n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images - shape0, shape1 = [], [] # image and inference shapes + shape0, shape1, files = [], [], [] # image and inference shapes, filenames for i, im in enumerate(imgs): + f = f'image{i}' # filename if isinstance(im, str): # filename or uri - im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im) # open - im = np.array(im) # to numpy + im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(im), getattr(im, 'filename', f) or f + files.append(Path(f).with_suffix('.jpg').name) if im.shape[0] < 5: # image in CHW im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input @@ -213,82 +277,101 @@ def forward(self, imgs, size=640, augment=False, profile=False): x = np.stack(x, 0) if n > 1 else x[0][None] # stack x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 + t.append(time_synchronized()) - # Inference - with torch.no_grad(): + with amp.autocast(enabled=p.device.type != 'cpu'): + # Inference y = self.model(x, augment, profile)[0] # forward - y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS + t.append(time_synchronized()) - # Post-process - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) + # Post-process + y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) - return Detections(imgs, y, self.names) + t.append(time_synchronized()) + return Detections(imgs, y, files, t, self.names, x.shape) class Detections: - # detections class for YOLOv5 inference results - def __init__(self, imgs, pred, names=None): + # detections class for YOLOv3 inference results + def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super(Detections, self).__init__() d = pred[0].device # device gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations self.imgs = imgs # list of images as numpy arrays self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) self.names = names # class names + self.files = files # image filenames self.xyxy = pred # xyxy pixels self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) + self.n = len(self.pred) # number of images (batch size) + self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) + self.s = shape # inference BCHW shape - def display(self, pprint=False, show=False, save=False, render=False): + def display(self, pprint=False, show=False, save=False, render=False, save_dir=''): colors = color_list() for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): - str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' + str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' if pred is not None: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class - str += f'{n} {self.names[int(c)]}s, ' # add to string + str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string if show or save or render: - img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np for *box, conf, cls in pred: # xyxy, confidence, class - # str += '%s %.2f, ' % (names[int(cls)], conf) # label - ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot + label = f'{self.names[int(cls)]} {conf:.2f}' + plot_one_box(box, img, label=label, color=colors[int(cls) % 10]) + img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np if pprint: - print(str) + print(str.rstrip(', ')) if show: - img.show(f'Image {i}') # show + img.show(self.files[i]) # show if save: - f = f'results{i}.jpg' - str += f"saved to '{f}'" - img.save(f) # save + f = self.files[i] + img.save(Path(save_dir) / f) # save + print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') if render: self.imgs[i] = np.asarray(img) def print(self): self.display(pprint=True) # print results + print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) def show(self): self.display(show=True) # show results - def save(self): - self.display(save=True) # save results + def save(self, save_dir='runs/hub/exp'): + save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir + Path(save_dir).mkdir(parents=True, exist_ok=True) + self.display(save=True, save_dir=save_dir) # save results def render(self): self.display(render=True) # render results return self.imgs - def __len__(self): - return self.n + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns + cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns + for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new def tolist(self): # return a list of Detections objects, i.e. 'for result in results.tolist():' - x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)] + x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] for d in x: for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: setattr(d, k, getattr(d, k)[0]) # pop out of list return x + def __len__(self): + return self.n + class Classify(nn.Module): # Classification head, i.e. x(b,c1,20,20) to x(b,c2) diff --git a/models/experimental.py b/models/experimental.py index 2dbbf7fa32..622791540c 100644 --- a/models/experimental.py +++ b/models/experimental.py @@ -1,4 +1,4 @@ -# This file contains experimental modules +# YOLOv3 experimental modules import numpy as np import torch @@ -58,7 +58,7 @@ def forward(self, x): class GhostBottleneck(nn.Module): # Ghost Bottleneck https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k, s): + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride super(GhostBottleneck, self).__init__() c_ = c2 // 2 self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw @@ -115,11 +115,12 @@ def attempt_load(weights, map_location=None): model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: attempt_download(w) - model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model + ckpt = torch.load(w, map_location=map_location) # load + model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model # Compatibility updates for m in model.modules(): - if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: m.inplace = True # pytorch 1.7.0 compatibility elif type(m) is Conv: m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility diff --git a/models/export.py b/models/export.py index 49df43e9fe..99189601a0 100644 --- a/models/export.py +++ b/models/export.py @@ -1,4 +1,4 @@ -"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats +"""Exports a YOLOv3 *.pt model to ONNX and TorchScript formats Usage: $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov3.pt --img 640 --batch 1 @@ -17,12 +17,16 @@ from models.experimental import attempt_load from utils.activations import Hardswish, SiLU from utils.general import set_logging, check_img_size +from utils.torch_utils import select_device if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./yolov3.pt', help='weights path') # from yolov3/models/ parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') + parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') + parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) @@ -30,7 +34,8 @@ t = time.time() # Load PyTorch model - model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model + device = select_device(opt.device) + model = attempt_load(opt.weights, map_location=device) # load FP32 model labels = model.names # Checks @@ -38,7 +43,7 @@ opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples # Input - img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection + img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection # Update model for k, m in model.named_modules(): @@ -50,14 +55,14 @@ m.act = SiLU() # elif isinstance(m, models.yolo.Detect): # m.forward = m.forward_export # assign forward (optional) - model.model[-1].export = True # set Detect() layer export=True + model.model[-1].export = not opt.grid # set Detect() layer grid export y = model(img) # dry run # TorchScript export try: print('\nStarting TorchScript export with torch %s...' % torch.__version__) f = opt.weights.replace('.pt', '.torchscript.pt') # filename - ts = torch.jit.trace(model, img) + ts = torch.jit.trace(model, img, strict=False) ts.save(f) print('TorchScript export success, saved as %s' % f) except Exception as e: @@ -70,7 +75,9 @@ print('\nStarting ONNX export with onnx %s...' % onnx.__version__) f = opt.weights.replace('.pt', '.onnx') # filename torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], - output_names=['classes', 'boxes'] if y is None else ['output']) + output_names=['classes', 'boxes'] if y is None else ['output'], + dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) + 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None) # Checks onnx_model = onnx.load(f) # load onnx model diff --git a/models/yolo.py b/models/yolo.py index 9f47100999..706ea20e12 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -1,14 +1,15 @@ +# YOLOv3 YOLO-specific modules + import argparse import logging import sys from copy import deepcopy -from pathlib import Path sys.path.append('./') # to run '$ python *.py' files in subdirectories logger = logging.getLogger(__name__) from models.common import * -from models.experimental import MixConv2d, CrossConv +from models.experimental import * from utils.autoanchor import check_anchor_order from utils.general import make_divisible, check_file, set_logging from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ @@ -50,7 +51,7 @@ def forward(self, x): self.grid[i] = self._make_grid(nx, ny).to(x[i].device) y = x[i].sigmoid() - y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z.append(y.view(bs, -1, self.no)) @@ -63,7 +64,7 @@ def _make_grid(nx=20, ny=20): class Model(nn.Module): - def __init__(self, cfg='yolov3.yaml', ch=3, nc=None): # model, input channels, number of classes + def __init__(self, cfg='yolov3.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict @@ -71,13 +72,16 @@ def __init__(self, cfg='yolov3.yaml', ch=3, nc=None): # model, input channels, import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: - self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict # Define model ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels if nc and nc != self.yaml['nc']: - logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) + logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value + if anchors: + logger.info(f'Overriding model.yaml anchors with anchors={anchors}') + self.yaml['anchors'] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) @@ -107,7 +111,7 @@ def forward(self, x, augment=False, profile=False): for si, fi in zip(s, f): xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) yi = self.forward_once(xi)[0] # forward - # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi[..., :4] /= si # de-scale if fi == 2: yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud @@ -210,45 +214,30 @@ def parse_model(d, ch): # model_dict, input_channels(3) pass n = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: + if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, + C3, C3TR]: c1, c2 = ch[f], args[0] - - # Normal - # if i > 0 and args[0] != no: # channel expansion factor - # ex = 1.75 # exponential (default 2.0) - # e = math.log(c2 / ch[1]) / math.log(2) - # c2 = int(ch[1] * ex ** e) - # if m != Focus: - - c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 - - # Experimental - # if i > 0 and args[0] != no: # channel expansion factor - # ex = 1 + gw # exponential (default 2.0) - # ch1 = 32 # ch[1] - # e = math.log(c2 / ch1) / math.log(2) # level 1-n - # c2 = int(ch1 * ex ** e) - # if m != Focus: - # c2 = make_divisible(c2, 8) if c2 != no else c2 + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] - if m in [BottleneckCSP, C3]: - args.insert(2, n) + if m in [BottleneckCSP, C3, C3TR]: + args.insert(2, n) # number of repeats n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: - c2 = sum([ch[x if x < 0 else x + 1] for x in f]) + c2 = sum([ch[x] for x in f]) elif m is Detect: - args.append([ch[x + 1] for x in f]) + args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is Contract: - c2 = ch[f if f < 0 else f + 1] * args[0] ** 2 + c2 = ch[f] * args[0] ** 2 elif m is Expand: - c2 = ch[f if f < 0 else f + 1] // args[0] ** 2 + c2 = ch[f] // args[0] ** 2 else: - c2 = ch[f if f < 0 else f + 1] + c2 = ch[f] m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type @@ -257,6 +246,8 @@ def parse_model(d, ch): # model_dict, input_channels(3) logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) + if i == 0: + ch = [] ch.append(c2) return nn.Sequential(*layers), sorted(save) diff --git a/requirements.txt b/requirements.txt index 3c23f2b750..fd187eb56c 100755 --- a/requirements.txt +++ b/requirements.txt @@ -1,19 +1,18 @@ # pip install -r requirements.txt # base ---------------------------------------- -Cython matplotlib>=3.2.2 numpy>=1.18.5 opencv-python>=4.1.2 Pillow -PyYAML>=5.3 +PyYAML>=5.3.1 scipy>=1.4.1 -tensorboard>=2.2 torch>=1.7.0 torchvision>=0.8.1 tqdm>=4.41.0 # logging ------------------------------------- +tensorboard>=2.4.1 # wandb # plotting ------------------------------------ @@ -21,8 +20,8 @@ seaborn>=0.11.0 pandas # export -------------------------------------- -# coremltools==4.0 -# onnx>=1.8.0 +# coremltools>=4.1 +# onnx>=1.8.1 # scikit-learn==0.19.2 # for coreml quantization # extras -------------------------------------- diff --git a/test.py b/test.py index c570a7889a..0b7f61c106 100644 --- a/test.py +++ b/test.py @@ -13,7 +13,6 @@ from utils.datasets import create_dataloader from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \ box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr -from utils.loss import compute_loss from utils.metrics import ap_per_class, ConfusionMatrix from utils.plots import plot_images, output_to_target, plot_study_txt from utils.torch_utils import select_device, time_synchronized @@ -36,8 +35,10 @@ def test(data, save_hybrid=False, # for hybrid auto-labelling save_conf=False, # save auto-label confidences plots=True, - log_imgs=0): # number of logged images - + wandb_logger=None, + compute_loss=None, + half_precision=True, + is_coco=False): # Initialize/load model and set device training = model is not None if training: # called by train.py @@ -53,47 +54,46 @@ def test(data, # Load model model = attempt_load(weights, map_location=device) # load FP32 model - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(imgsz, s=gs) # check img_size # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 # if device.type != 'cpu' and torch.cuda.device_count() > 1: # model = nn.DataParallel(model) # Half - half = device.type != 'cpu' # half precision only supported on CUDA + half = device.type != 'cpu' and half_precision # half precision only supported on CUDA if half: model.half() # Configure model.eval() - is_coco = data.endswith('coco.yaml') # is COCO dataset - with open(data) as f: - data = yaml.load(f, Loader=yaml.FullLoader) # model dict + if isinstance(data, str): + is_coco = data.endswith('coco.yaml') + with open(data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) check_dataset(data) # check nc = 1 if single_cls else int(data['nc']) # number of classes iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() # Logging - log_imgs, wandb = min(log_imgs, 100), None # ceil - try: - import wandb # Weights & Biases - except ImportError: - log_imgs = 0 - + log_imgs = 0 + if wandb_logger and wandb_logger.wandb: + log_imgs = min(wandb_logger.log_imgs, 100) # Dataloader if not training: - img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once - path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images - dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True, - prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0] + if device.type != 'cpu': + model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once + task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, + prefix=colorstr(f'{task}: '))[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} coco91class = coco80_to_coco91_class() - s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') + s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] @@ -107,22 +107,22 @@ def test(data, with torch.no_grad(): # Run model t = time_synchronized() - inf_out, train_out = model(img, augment=augment) # inference and training outputs + out, train_out = model(img, augment=augment) # inference and training outputs t0 += time_synchronized() - t # Compute loss - if training: - loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls + if compute_loss: + loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls # Run NMS targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling t = time_synchronized() - output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb) + out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True) t1 += time_synchronized() - t # Statistics per image - for si, pred in enumerate(output): + for si, pred in enumerate(out): labels = targets[targets[:, 0] == si, 1:] nl = len(labels) tcls = labels[:, 0].tolist() if nl else [] # target class @@ -147,15 +147,17 @@ def test(data, with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') - # W&B logging - if plots and len(wandb_images) < log_imgs: - box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, - "class_id": int(cls), - "box_caption": "%s %.3f" % (names[cls], conf), - "scores": {"class_score": conf}, - "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] - boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space - wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name)) + # W&B logging - Media Panel Plots + if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation + if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: + box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name)) + wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None # Append to pycocotools JSON dictionary if save_json: @@ -179,7 +181,7 @@ def test(data, tbox = xywh2xyxy(labels[:, 1:5]) scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels if plots: - confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1)) + confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) # Per target class for cls in torch.unique(tcls_tensor): @@ -210,24 +212,24 @@ def test(data, f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions - Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start() + Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start() # Compute statistics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy if len(stats) and stats[0].any(): p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) - p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95] + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class else: nt = torch.zeros(1) # Print results - pf = '%20s' + '%12.3g' * 6 # print format + pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) # Print results per class - if (verbose or (nc <= 20 and not training)) and nc > 1 and len(stats): + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(ap_class): print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) @@ -239,9 +241,11 @@ def test(data, # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) - if wandb and wandb.run: - wandb.log({"Images": wandb_images}) - wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]}) + if wandb_logger and wandb_logger.wandb: + val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))] + wandb_logger.log({"Validation": val_batches}) + if wandb_images: + wandb_logger.log({"Bounding Box Debugger/Images": wandb_images}) # Save JSON if save_json and len(jdict): @@ -269,10 +273,10 @@ def test(data, print(f'pycocotools unable to run: {e}') # Return results + model.float() # for training if not training: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f"Results saved to {save_dir}{s}") - model.float() # for training maps = np.zeros(nc) + map for i, c in enumerate(ap_class): maps[c] = ap[i] @@ -287,7 +291,7 @@ def test(data, parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') - parser.add_argument('--task', default='val', help="'val', 'test', 'study'") + parser.add_argument('--task', default='val', help='train, val, test, speed or study') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') parser.add_argument('--augment', action='store_true', help='augmented inference') @@ -305,7 +309,7 @@ def test(data, print(opt) check_requirements() - if opt.task in ['val', 'test']: # run normally + if opt.task in ('train', 'val', 'test'): # run normally test(opt.data, opt.weights, opt.batch_size, @@ -321,16 +325,21 @@ def test(data, save_conf=opt.save_conf, ) + elif opt.task == 'speed': # speed benchmarks + for w in opt.weights: + test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False) + elif opt.task == 'study': # run over a range of settings and save/plot - for weights in ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt']: - f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to - x = list(range(320, 800, 64)) # x axis + # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov3.pt yolov3-spp.pt yolov3-tiny.pt + x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) + for w in opt.weights: + f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to y = [] # y axis for i in x: # img-size - print('\nRunning %s point %s...' % (f, i)) - r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, + print(f'\nRunning {f} point {i}...') + r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, plots=False) y.append(r + t) # results and times np.savetxt(f, y, fmt='%10.4g') # save os.system('zip -r study.zip study_*.txt') - plot_study_txt(f, x) # plot + plot_study_txt(x=x) # plot diff --git a/train.py b/train.py index 91d8dfe033..ff7d96b772 100644 --- a/train.py +++ b/train.py @@ -4,6 +4,7 @@ import os import random import time +from copy import deepcopy from pathlib import Path from threading import Thread @@ -29,14 +30,15 @@ fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ check_requirements, print_mutation, set_logging, one_cycle, colorstr from utils.google_utils import attempt_download -from utils.loss import compute_loss +from utils.loss import ComputeLoss from utils.plots import plot_images, plot_labels, plot_results, plot_evolution -from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first +from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel +from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume logger = logging.getLogger(__name__) -def train(hyp, opt, device, tb_writer=None, wandb=None): +def train(hyp, opt, device, tb_writer=None): logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) save_dir, epochs, batch_size, total_batch_size, weights, rank = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank @@ -60,10 +62,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): init_seeds(2 + rank) with open(opt.data) as f: data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict - with torch_distributed_zero_first(rank): - check_dataset(data_dict) # check - train_path = data_dict['train'] - test_path = data_dict['val'] + is_coco = opt.data.endswith('coco.yaml') + + # Logging- Doing this before checking the dataset. Might update data_dict + loggers = {'wandb': None} # loggers dict + if rank in [-1, 0]: + opt.hyp = hyp # add hyperparameters + run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None + wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict) + loggers['wandb'] = wandb_logger.wandb + data_dict = wandb_logger.data_dict + if wandb_logger.wandb: + weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming + nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check @@ -74,16 +85,18 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): with torch_distributed_zero_first(rank): attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location=device) # load checkpoint - if hyp.get('anchors'): - ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor - model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create - exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys state_dict = ckpt['model'].float().state_dict() # to FP32 state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(state_dict, strict=False) # load logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report else: - model = Model(opt.cfg, ch=3, nc=nc).to(device) # create + model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + train_path = data_dict['train'] + test_path = data_dict['val'] # Freeze freeze = [] # parameter names to freeze (full or partial) @@ -120,18 +133,15 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): # Scheduler https://arxiv.org/pdf/1812.01187.pdf # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR - lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + if opt.linear_lr: + lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + else: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) - # Logging - if rank in [-1, 0] and wandb and wandb.run is None: - opt.hyp = hyp # add hyperparameters - wandb_run = wandb.init(config=opt, resume="allow", - project='YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem, - name=save_dir.stem, - id=ckpt.get('wandb_id') if 'ckpt' in locals() else None) - loggers = {'wandb': wandb} # loggers dict + # EMA + ema = ModelEMA(model) if rank in [-1, 0] else None # Resume start_epoch, best_fitness = 0, 0.0 @@ -141,10 +151,14 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): optimizer.load_state_dict(ckpt['optimizer']) best_fitness = ckpt['best_fitness'] + # EMA + if ema and ckpt.get('ema'): + ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) + ema.updates = ckpt['updates'] + # Results if ckpt.get('training_results') is not None: - with open(results_file, 'w') as file: - file.write(ckpt['training_results']) # write results.txt + results_file.write_text(ckpt['training_results']) # write results.txt # Epochs start_epoch = ckpt['epoch'] + 1 @@ -158,7 +172,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): del ckpt, state_dict # Image sizes - gs = int(model.stride.max()) # grid size (max stride) + gs = max(int(model.stride.max()), 32) # grid size (max stride) nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples @@ -171,13 +185,6 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) logger.info('Using SyncBatchNorm()') - # EMA - ema = ModelEMA(model) if rank in [-1, 0] else None - - # DDP mode - if cuda and rank != -1: - model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank) - # Trainloader dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, @@ -189,8 +196,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): # Process 0 if rank in [-1, 0]: - ema.updates = start_epoch * nb // accumulate # set EMA updates - testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader + testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5, prefix=colorstr('val: '))[0] @@ -201,18 +207,26 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency # model._initialize_biases(cf.to(device)) if plots: - plot_labels(labels, save_dir, loggers) + plot_labels(labels, names, save_dir, loggers) if tb_writer: tb_writer.add_histogram('classes', c, 0) # Anchors if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + model.half().float() # pre-reduce anchor precision + + # DDP mode + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, + # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 + find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) # Model parameters hyp['box'] *= 3. / nl # scale to layers hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) @@ -227,6 +241,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = amp.GradScaler(enabled=cuda) + compute_loss = ComputeLoss(model) # init loss class logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' f'Using {dataloader.num_workers} dataloader workers\n' f'Logging results to {save_dir}\n' @@ -256,7 +271,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): if rank != -1: dataloader.sampler.set_epoch(epoch) pbar = enumerate(dataloader) - logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size')) + logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) if rank in [-1, 0]: pbar = tqdm(pbar, total=nb) # progress bar optimizer.zero_grad() @@ -286,7 +301,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): # Forward with amp.autocast(enabled=cuda): pred = model(imgs) # forward - loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size + loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if rank != -1: loss *= opt.world_size # gradient averaged between devices in DDP mode if opt.quad: @@ -317,9 +332,10 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() # if tb_writer: # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) - # tb_writer.add_graph(model, imgs) # add model to tensorboard - elif plots and ni == 3 and wandb: - wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]}) + # tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph + elif plots and ni == 10 and wandb_logger.wandb: + wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in + save_dir.glob('train*.jpg') if x.exists()]}) # end batch ------------------------------------------------------------------------------------------------ # end epoch ---------------------------------------------------------------------------------------------------- @@ -331,23 +347,26 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): # DDP process 0 or single-GPU if rank in [-1, 0]: # mAP - if ema: - ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) final_epoch = epoch + 1 == epochs if not opt.notest or final_epoch: # Calculate mAP - results, maps, times = test.test(opt.data, - batch_size=total_batch_size, + wandb_logger.current_epoch = epoch + 1 + results, maps, times = test.test(data_dict, + batch_size=batch_size * 2, imgsz=imgsz_test, model=ema.ema, single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, + verbose=nc < 50 and final_epoch, plots=plots and final_epoch, - log_imgs=opt.log_imgs if wandb else 0) + wandb_logger=wandb_logger, + compute_loss=compute_loss, + is_coco=is_coco) # Write with open(results_file, 'a') as f: - f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss if len(opt.name) and opt.bucket: os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) @@ -359,72 +378,77 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): if tb_writer: tb_writer.add_scalar(tag, x, epoch) # tensorboard - if wandb: - wandb.log({tag: x}) # W&B + if wandb_logger.wandb: + wandb_logger.log({tag: x}) # W&B # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] if fi > best_fitness: best_fitness = fi + wandb_logger.end_epoch(best_result=best_fitness == fi) # Save model - save = (not opt.nosave) or (final_epoch and not opt.evolve) - if save: - with open(results_file, 'r') as f: # create checkpoint - ckpt = {'epoch': epoch, - 'best_fitness': best_fitness, - 'training_results': f.read(), - 'model': ema.ema, - 'optimizer': None if final_epoch else optimizer.state_dict(), - 'wandb_id': wandb_run.id if wandb else None} + if (not opt.nosave) or (final_epoch and not opt.evolve): # if save + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': results_file.read_text(), + 'model': deepcopy(model.module if is_parallel(model) else model).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) + if wandb_logger.wandb: + if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: + wandb_logger.log_model( + last.parent, opt, epoch, fi, best_model=best_fitness == fi) del ckpt + # end epoch ---------------------------------------------------------------------------------------------------- # end training - if rank in [-1, 0]: - # Strip optimizers - final = best if best.exists() else last # final model - for f in [last, best]: - if f.exists(): - strip_optimizer(f) # strip optimizers - if opt.bucket: - os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload - # Plots if plots: plot_results(save_dir=save_dir) # save as results.png - if wandb: - files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png'] - wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files - if (save_dir / f).exists()]}) - if opt.log_artifacts: - wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem) - + if wandb_logger.wandb: + files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] + wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files + if (save_dir / f).exists()]}) # Test best.pt logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) if opt.data.endswith('coco.yaml') and nc == 80: # if COCO - for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]): # speed, mAP tests + for m in (last, best) if best.exists() else (last): # speed, mAP tests results, _, _ = test.test(opt.data, - batch_size=total_batch_size, + batch_size=batch_size * 2, imgsz=imgsz_test, - conf_thres=conf, - iou_thres=iou, - model=attempt_load(final, device).half(), + conf_thres=0.001, + iou_thres=0.7, + model=attempt_load(m, device).half(), single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, - save_json=save_json, - plots=False) + save_json=True, + plots=False, + is_coco=is_coco) + # Strip optimizers + final = best if best.exists() else last # final model + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if opt.bucket: + os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + if wandb_logger.wandb and not opt.evolve: # Log the stripped model + wandb_logger.wandb.log_artifact(str(final), type='model', + name='run_' + wandb_logger.wandb_run.id + '_model', + aliases=['last', 'best', 'stripped']) + wandb_logger.finish_run() else: dist.destroy_process_group() - - wandb.run.finish() if wandb and wandb.run else None torch.cuda.empty_cache() return results @@ -453,13 +477,18 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') - parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100') - parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model') parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') parser.add_argument('--project', default='runs/train', help='save to project/name') + parser.add_argument('--entity', default=None, help='W&B entity') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--linear-lr', action='store_true', help='linear LR') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') + parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') + parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') + parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') opt = parser.parse_args() # Set DDP variables @@ -471,13 +500,14 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): check_requirements() # Resume - if opt.resume: # resume an interrupted run + wandb_run = check_wandb_resume(opt) + if opt.resume and not wandb_run: # resume an interrupted run ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' apriori = opt.global_rank, opt.local_rank with open(Path(ckpt).parent.parent / 'opt.yaml') as f: opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace - opt.cfg, opt.weights, opt.resume, opt.global_rank, opt.local_rank = '', ckpt, True, *apriori # reinstate + opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate logger.info('Resuming training from %s' % ckpt) else: # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') @@ -504,18 +534,13 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): # Train logger.info(opt) - try: - import wandb - except ImportError: - wandb = None - prefix = colorstr('wandb: ') - logger.info(f"{prefix}Install Weights & Biases for YOLOv3 logging with 'pip install wandb' (recommended)") if not opt.evolve: tb_writer = None # init loggers if opt.global_rank in [-1, 0]: - logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/') + prefix = colorstr('tensorboard: ') + logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") tb_writer = SummaryWriter(opt.save_dir) # Tensorboard - train(hyp, opt, device, tb_writer, wandb) + train(hyp, opt, device, tb_writer) # Evolve hyperparameters (optional) else: @@ -589,7 +614,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): hyp[k] = round(hyp[k], 5) # significant digits # Train mutation - results = train(hyp.copy(), opt, device, wandb=wandb) + results = train(hyp.copy(), opt, device) # Write mutation results print_mutation(hyp.copy(), results, yaml_file, opt.bucket) diff --git a/tutorial.ipynb b/tutorial.ipynb index 5e190e212a..b8969bc45f 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -16,7 +16,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "b257add75888401ebf17767cdc9ed439": { + "8815626359d84416a2f44a95500580a4": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { @@ -28,15 +28,15 @@ "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", - "layout": "IPY_MODEL_4b685e8b26f3496db73186063e19f785", + "layout": "IPY_MODEL_3b85609c4ce94a74823f2cfe141ce68e", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_0980232d74a14bdfa353a3f248bbe8ff", - "IPY_MODEL_e981f3dfbf374643b58cba7dfbef3bca" + "IPY_MODEL_876609753c2946248890344722963d44", + "IPY_MODEL_8abfdd8778e44b7ca0d29881cb1ada05" ] } }, - "4b685e8b26f3496db73186063e19f785": { + "3b85609c4ce94a74823f2cfe141ce68e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -87,12 +87,12 @@ "left": null } }, - "0980232d74a14bdfa353a3f248bbe8ff": { + "876609753c2946248890344722963d44": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_07bb32c950654e9fa401e35a0030eadc", + "style": "IPY_MODEL_78c6c3d97c484916b8ee167c63556800", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", @@ -107,30 +107,30 @@ "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_ec3fce2f475b4f31b8caf1a0ca912af1" + "layout": "IPY_MODEL_9dd0f182db5d45378ceafb855e486eb8" } }, - "e981f3dfbf374643b58cba7dfbef3bca": { + "8abfdd8778e44b7ca0d29881cb1ada05": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", - "style": "IPY_MODEL_9a1c27af326e43ca8a8b6b90cf0075db", + "style": "IPY_MODEL_a3dab28b45c247089a3d1b8b09f327de", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "value": " 781M/781M [00:49<00:00, 16.7MB/s]", + "value": " 781M/781M [08:43<00:00, 1.56MB/s]", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_7cf92d6d6c704a8d8e7834783813228d" + "layout": "IPY_MODEL_32451332b7a94ba9aacddeaa6ac94d50" } }, - "07bb32c950654e9fa401e35a0030eadc": { + "78c6c3d97c484916b8ee167c63556800": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "state": { @@ -145,7 +145,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "ec3fce2f475b4f31b8caf1a0ca912af1": { + "9dd0f182db5d45378ceafb855e486eb8": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -196,7 +196,7 @@ "left": null } }, - "9a1c27af326e43ca8a8b6b90cf0075db": { + "a3dab28b45c247089a3d1b8b09f327de": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -210,7 +210,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "7cf92d6d6c704a8d8e7834783813228d": { + "32451332b7a94ba9aacddeaa6ac94d50": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -261,7 +261,7 @@ "left": null } }, - "c1928794b5bd400da6e7817883a0ee9c": { + "0fffa335322b41658508e06aed0acbf0": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "state": { @@ -273,15 +273,15 @@ "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", - "layout": "IPY_MODEL_804fae06a69f4e11b919d8ab80822186", + "layout": "IPY_MODEL_a354c6f80ce347e5a3ef64af87c0eccb", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_138cbb92b4fd4eaa9b7fdcbed1f57a4d", - "IPY_MODEL_28bb2eea5b114f82b201e5fa39fdfc58" + "IPY_MODEL_85823e71fea54c39bd11e2e972348836", + "IPY_MODEL_fb11acd663fa4e71b041d67310d045fd" ] } }, - "804fae06a69f4e11b919d8ab80822186": { + "a354c6f80ce347e5a3ef64af87c0eccb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -332,50 +332,50 @@ "left": null } }, - "138cbb92b4fd4eaa9b7fdcbed1f57a4d": { + "85823e71fea54c39bd11e2e972348836": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_aea8bd6f395845f696e3abedbff59423", + "style": "IPY_MODEL_8a919053b780449aae5523658ad611fa", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", "bar_style": "success", - "max": 22090455, + "max": 22091032, "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "value": 22090455, + "value": 22091032, "_view_count": null, "_view_module_version": "1.5.0", "orientation": "horizontal", "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_0514774dafdf4e39bdd5a8833d1cbcb0" + "layout": "IPY_MODEL_5bae9393a58b44f7b69fb04816f94f6f" } }, - "28bb2eea5b114f82b201e5fa39fdfc58": { + "fb11acd663fa4e71b041d67310d045fd": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", - "style": "IPY_MODEL_7dabd1f8236045729c90ae78a0d9af24", + "style": "IPY_MODEL_d26c6d16c7f24030ab2da5285bf198ee", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "value": " 21.1M/21.1M [00:02<00:00, 9.27MB/s]", + "value": " 21.1M/21.1M [00:02<00:00, 9.36MB/s]", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_227e357d925345f995aeea7b72750cf1" + "layout": "IPY_MODEL_f7767886b2364c8d9efdc79e175ad8eb" } }, - "aea8bd6f395845f696e3abedbff59423": { + "8a919053b780449aae5523658ad611fa": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "state": { @@ -390,7 +390,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "0514774dafdf4e39bdd5a8833d1cbcb0": { + "5bae9393a58b44f7b69fb04816f94f6f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -441,7 +441,7 @@ "left": null } }, - "7dabd1f8236045729c90ae78a0d9af24": { + "d26c6d16c7f24030ab2da5285bf198ee": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -455,7 +455,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "227e357d925345f995aeea7b72750cf1": { + "f7767886b2364c8d9efdc79e175ad8eb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -517,7 +517,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -528,8 +528,8 @@ "source": [ "\n", "\n", - "This notebook was written by Ultralytics LLC, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", - "For more information please visit https://github.com/ultralytics/yolov3 and https://www.ultralytics.com." + "This is the **official YOLOv3 🚀 notebook** authored by **Ultralytics**, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", + "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com. Thank you!" ] }, { @@ -550,25 +550,25 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "edc03bfa-6dd3-49ae-9095-405ba8cbe87d" + "outputId": "9b022435-4197-41fc-abea-81f86ce857d0" }, "source": [ - "!git clone https://github.com/ultralytics/yolov3 # clone repo\n", - "%cd yolov3\n", + "!git clone https://github.com/ultralytics/yolov5 # clone repo\n", + "%cd yolov5\n", "%pip install -qr requirements.txt # install dependencies\n", "\n", "import torch\n", "from IPython.display import Image, clear_output # to display images\n", "\n", "clear_output()\n", - "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))" + "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")" ], - "execution_count": 1, + "execution_count": 31, "outputs": [ { "output_type": "stream", "text": [ - "Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)\n" + "Setup complete. Using torch 1.8.1+cu101 (Tesla V100-SXM2-16GB)\n" ], "name": "stdout" } @@ -582,7 +582,9 @@ "source": [ "# 1. Inference\n", "\n", - "`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases)." + "`detect.py` runs YOLOv3 inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases), and saving results to `runs/detect`. Example inference sources are:\n", + "\n", + " " ] }, { @@ -591,38 +593,35 @@ "id": "zR9ZbuQCH7FX", "colab": { "base_uri": "https://localhost:8080/", - "height": 587 + "height": 534 }, - "outputId": "44211008-da79-4175-f719-ac265dad26eb" + "outputId": "c9a308f7-2216-4805-8003-eca8dd0dc30d" }, "source": [ "!python detect.py --weights yolov3.pt --img 640 --conf 0.25 --source data/images/\n", "Image(filename='runs/detect/exp/zidane.jpg', width=600)" ], - "execution_count": 2, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov3.pt'])\n", - "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", - "\n", - "Downloading https://github.com/ultralytics/yolov3/releases/download/v1.0/yolov3.pt to yolov3.pt...\n", - "100% 118M/118M [00:08<00:00, 14.6MB/s]\n", + "YOLOv3 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", "\n", "Fusing layers... \n", - "Model Summary: 261 layers, 61922845 parameters, 0 gradients\n", - "image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 buss, Done. (0.046s)\n", - "image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.013s)\n", + "Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.008s)\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n", "Results saved to runs/detect/exp\n", - "Done. (0.293s)\n" + "Done. (0.087)\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { - "image/jpeg": 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Lf4VUuEuI7X+rS/D/ADM/7IzK1vZP8DjY4fLO/e3+7Vy1tppLjY8zMrf3q6mP4RaspBfUbY477m/wq7bfDbUIvnkntC2MZAb/AArkq8I8TyjphZfh/mbxynHr/l2/wMC3s/JZX/8AHVatO1t3mVUf5Wb7nzba2YfBF5EykXMQA7Ln/CrkXhd4wxEse4/d46Vyy4N4of8AzCy/D/M64ZXjOsGZccMkbbEfdWhaxO3753Zd38O77tW49CYt+/lDALgHPJ+tTQaY0WCWXIBCkdga55cF8V/Zwkvw/wAzpp5biY/ZIVhfa02G/wBrdR5m6RH3so+XZ5lXvs6LGfLyGKnknjce9BtlcKJGztGPu1kuCOLHLXCy/D/M7Y4OutokUK3M0hEO1tv8MlQyLMq7BDt2p97+JWru/gj+zx8av2lvHcXwy/Z/+Fet+LNeeEzf2dodm88kcIdUM0hUYiiDOgMjkIpYZIzX0R8WP+CEP/BVD4ReDB438R/soanfWKKGuLfw1qlpqt1GMdWt7SWSUgdyqkDBziuPFZHmmBrxo4iChOW0ZTgm/ROVw9mqdS02k33aPjWaOFvk+YsvzIzfe3f3qbNJNGyvsV3VNv36tzaQMN5EhBZskE9KS28OX99epa2ERlmncJFDEpZnYnAVQBkkngCvRXBnFfLrhJfh/mbPC1+xTaZ2IRp9ir/CtT28yNhPuv8AxfNX2x4f/wCDcb/grb4m0Gx8R2v7OllZpfWiTx22peLdNt7iJXUMFkiecNG+DyjAMpyCAQRV1f8Ag2z/AOCusG64H7P+ju6jKhfHel5b25uAK8T2CWkqlO//AF8p/wDyRj7akpfEvvX+Z8Uw6hNZzFHdXZkx/e+WtK31B/LDu7FvvLW1/wAMv/tFW/x2uP2b0+EviC4+IFvqUlhL4OtNKlmv/PjUs6LDGpZsIpfcuV2DdnbzX1Ro/wDwbvf8FcdR8PLrq/s128KywedFZXPjLS47heMhShuMq3+ycHscV6mIynHZbyyxSjDmV4804K67q8tV5rQzq89OScmlfu0fH0mpJD/qXVWZtzNUDas8yt5z5O/5ljeui+OP7P8A8d/2bfHcnwv+Pvw01jwjrsEIlOn63p7wSyQlmVZIywxLEWRwJEJVtpwTg1wF3eCCcgTK7A4K/wB2rxmTY/AUIYivTtCfwu6afo02YV1VpxUpLRl3UNQSZW2fKu3+J6oXGsFbYJ1H96s+a9TzBxt/4HVW61RMum/5dnzbUrhjHl3PLrYqXQtSag7Rq4RmDVm3WpO0hzwNm5d38TVXkvPlM0L7fl+Vqy7rWAsbfPlv7tORzxrcuhbur6bdvEyorL8+16z5rpJG+d2A/gaqdxqVxJ1Rdv8Adqq15t+dD/3zXPU5onoUcV7xoSXzwvvjRmC/xUPfbmWd5vvVmmZ/M+ebj+7UsdwjTfuf4a4q0T6XC4jm5UpGg9x5o2I/zN97dUlrO8bbHfIX7rVn+ckkjOn3mT5FarVrb3MkyJ/d/irjqR5Ynv4fEcpfjd5Dsmbf/vfw1a8lGj+R8bapQ/K29P73zVcjVFjZkdmZv4f7tcso8p71HEcw9V8nbsT/AH938VSSSTecm/8A4DuqJo3EeyZ9277tTKrxxBJn+63zbqwlE741o7AvzM8D9Ff/AMeqV5n8kTO+Xb+7/DUG14+UPDNQzbVbu27alKXMbfWi2rbcBJvl/wDZqjEyfxp80b1C0k7RsnnZZfm2r96mtIi5+RV/hZv71ONHmD61zaEk9w6qzu6urf8AjtQeY8g8xX/i+7SbXdmh+X/4qq7N5Mm9E2lflb5639nyxOSrjOXVlmaZFw6Q7v4WpiybVbY7B/7u+qTXSKx8rd/trUf25CvO5N38TVvGMjzsRjI2Lk2oTfK/kqrN8zVVuJvMU75uKjaZNzbPmX+CqjXz+Zs2Kob7611U4yPn8ViuaBbWfzo2QOwVv4abHIGbyJnVW+aqUcxgTejrsVqlW6e4bej/ADL/AOPV1xifN4qtGRo28jqyu82K0Ld0VVfzvl/j/wBqsfTz5jOj/wB/b9+tWyjhkXGzKr/DVx974jyZVjTs5Hb+8qtt+Za1tPV7iRt77v4X3N95aztNhj8zfDDw235pK2tLs/45o1wv3dtETzZVOY17Kzmkib51CSfwr96un0u12xxv+8lZay9DsX+R0RTtXb838NdJ4ds0t12XMK7Y1+b5/u1RjKRv6XZoqpvTG5NyL/drXsrR5F2PDlmbdujpmh2cnlxpDbbdybl/2q6SxsUZdkO7eqtv3JXR8Rz+0MSaxSaFt5bG1fl/utUz2MLRrDv2/LudW/hataPR5ptsM0O3d/F/DUU2nusjO+5P4Plf5ttLl+0dNGXvmLFZzNILn5nLfLu3/d209LH/AEgzTTbBJt3/AN7bV9bNIW8m28z95/DJ95aiurHbCvlvkqn3WWplGPLzHvYWXumfqFnbSMsyIzr9o2ozfw/7Vc3rWnzLI2yb+L7ytXYXEcP8aSbf4GX+9XMapF9ly+xfmZvlVt3zU4xhL3j3sNH4VI8916xeaF5oX85Gb5Gb/wBBrifEmnvuZN7Hd8vy/er1DxBDuVvkZfn/AHTf3a4zXLPzo3RI2+X5kauStL+Y9WOF5vePatPhtoVd5k3jzfvL95WqW3ie4Z08mPyldmiZflZWqtBcQKzGzdSkbfeZfvN/8TU9vff6R5P2Zn2qu6vGjHlgfPe05S/Y2rxt/pKK/wAu5P4q0YWhkU+S/wDBtRWX5l/4FWfHceS2x5P3TPuWNU+7/wACq9C3a5fCKu5G/wBqvPryf8p3UqnNK4l1HCshR9rHylV1jb5vm/iqKO1eORXd2DLU9xIn2h0kRS+zf8qfLI1Ma2RVc+Sr/wAfyt8q1xVKko7noU483wjbVYZmm3wyNul2+S3/AKFQ9rtcQvc+Ujf61Wb7tJHHu/fOm5FRmdV+XbU0UMEkZ3wyO/31+X/Zrz6lTlmehRj7nMYUlml5NN5Lr8r7YpG+9WdcW8k1wbnZkqu3d8v3q6K+hmVTcpNCu2Vfl2VmTW8Maunkr81VR5vanRzROavFeGRndVx/A2z7tZHkpcSbHmk2w/c+euq1K1/d703fd+ZWX+GsG401Plfy1T5fk/vNX2GW80ZamNTl+yUVt0n3P527y3ZH/h3VZhjezhD7G27/ALyruqKZXmjKb/nXavy/3v8AaqSNUj8mBE2/99Nur66jI8fFVIxiyS1m8xvn3L/dZl+9Uc7PdQuiPt3P977tWJFfdG43f3trVXZhCux3Xdv+df7tdXtD56pWlzipavtX58t/Au+pIZn2l/4f9moi7x4TZhWT5Nv8S1HG0083nPNt2/Ki1lUkRGUvhL8LPJMXS8V9v3W2bf8AgNW4ZIIYf9J6tu3x/wB7bWTCsLf3nZX+b5q1IWm+RPl3fe+/t2rXj4yUeb4juoxl8Rs2q7WSZE3pJtb93977v8VaVvb2c0bu6eadvysvy1iQslozvs3bl2qY627e8W3hR9ioqps2r81fPYip73MehRp/zF2FpJo23lmdYv3rbNq/8Bp0Nxt3fZnbDJt2t/D/AL1VWunVfJR9z/wxt/dqK41Bwzvcwqu5l/1fyqteBiJSR6VOnH7Rc/tHd/oz+XtX7rf7NIq2dwy3M0LP5fy/u0+bbUMEiLJKks0Mn8X7xfur/dohkeKQQwhvK3fN/C3/ANlWNP8AdmdT3i7Z26XUabHZYv4W+61Tx+TIsqPt2xt/F8zUluqcfaTGg++iyVJJHMsK/wCjZO3czM27b833aqpUv0MoqXukZjmjBhSaFd38W/7q1G0dzJI1zN8yN/Du3VoW/wBpjwk0MaLt+RVX71V5rWZZGSG5yzfdVtq7amnps7k1PhM+SNLiYwpuy0W7d/Cv+zVa6aa3X502ysm3aq1qrC7KH2MhZ/mj2/e/2qJrOzVBDsbc3yp/epSqcsjmlzRicvqVikKmRNzt/EqtWFqVuFtRbTJl5P4l+81dhqVnDcRZhvGJb5XXZ/drB1a18iRnm+VlVV8xV+8telh+afK38J5NZzlzHGX0KRp5Oxvl+X5mrMhsYbjPyKHVtq/7VdJqEMMxkhfau75/L/hqjNawzzKkO1FV/nWvXhKThI8qpGRi/YHmuC6bk/h8tqdb6XJn592zbuX/AGa6Ly/Lk3pbbgzU6OxRo28mZU/2ZE+7TdZxjboJU4xlzGLDpaRw+YkO9t3y7vlpZo0hm8mYZMifN8/zVr6ha7ZNnzbvlZfL/iWqV5beTNvmT7yfI1VGXveR1RpjtPuIfMSDYvm/d+V/urXQabcNDH/Djdu3L/drBs7d/LZ3hjZvvIytWxp95J9nWaZMtv8AvLRGXNL3TXl/mOm0u+mXcn3tybkZvvba1Y7pJLdH3yF1Tckkb7d1c3ZQ+RcfaYUb7nzsr1qrNA1v++g3Bt33n2/w/wANd1Op/McdSHc2LHXHhkVEjwjbVZmX+L/Zq4viCGFlh+079r/N81c19sTMVtvm3rF91vu/71RTzO0hd3Ybfm+WuyNQ45e6dWvihJJGKbgY/l3bfl+akt9aS6+TerMrt+8jb5a5iPVEWNRMm1pPmaN/4amt9ShjjUlP4vvbvl21vze4RGUInRtqibQj9F/h/vVRn2eYUhhykz/Ou7dtrNtbqGb5N7Hd9yP+GrMYeRWkttyq391qip8JPNzTGTXE0in7SmzyW2J/tL/DU0NrwZPJXP8ADGv3auw2bzQq7yKzMn3mq39lX5C6bG+VF/3v71Yez5jaOInExbrS3jfzHtlb+FN1Zlxo9ysLOkOH3fekNdtJpourgTJbfw/JJ/D8tVpNBmaY/aUZk37tqvVRjy+6VHEe+cZJo6LbyOiYC/NL8v8AFVP7Dc/IJnj3N837uuqvNNuV822hh5Vtz7vl3L/dqpcaP9ntzvhXfs+Ty1rCVM3jiOWRzVxDDHbxfJ/HuRlqncWafK7ozbm3f71dDNYQxq2yHlfm3f3azdQjSOZo0mZPM+bds+9XFUplxrFCG8dZG+997b838NXLHYZP3LqFX+GR6z2jmjkZHRfm+VVZqlt7Py1KbGMrfcb7y189mGF7ns4fFcupv2V06yrvRSyvu/4DU0t5DHL++48yf5V/2ap6TCi5V3Ztvy/M9aMNjM0n7tOF2sjSfer4upg/3up9Hh8VzQvIRrdFk+d/KDbtnz1Xmm3bt9tk7Nqbv/QmrUW1eb9yLPf5ab/m+7upV0ie+WLZZK0mz593yr/31UUcHONXnkepGt7hlQRm4mSH5l3f3vu1oR6XtbzoUXbuX5v71amn6D5kjP8ALK2/+F/l/wB2tG10GGGQQ/Y2Qxy7fvfdr38HhfayuonHiMZCnuY8elptl875933l/u1p2Phu8uI44ZrDcYdr7lT/AMeroLPwujSLsh2/vfvN/FW/p/heGGP59ysr/Jtb/wAdr63A4dxjFcp8xjsUqnMzmrDw/wCfHveFSu7bEzfMq1ft/CszfOnzorfej+7XU6fpaRwwxojRS7925fmX5f71XrPQdy/PMsS/Mdv+1ur6TDx90+eqVOXQ52z8O+Z86WDbf+en95qv2fhuaVXh+zeUY3rp9L8MoxkhFvs2s3leW3y/8CrTsfDsNrCiPuVd29f/ALKumVM5JVpSlochJ4XhWFPsyMyxy7pf3VNuNBtof9MhRvK/h+T+Ku8j0ea6VfJRV+f96y/xL/DTbzwzMsjp8rxfd/u7VrhqUQ9oeY3HheaRW+TAX5ty/wAS/wC1WZdeH3tZN7+ZI+z5Gb+7XqOqaLDGpRIW2Rp/yx+63+9XNalpIkYQ7/nk+4si/Kv+9XmVqPMd+FrHCXmjzO4mS2ZF/gWTb8zVialo/kyM9z5m5k27W+Xy2rudZj8u6eHevy/cm/grm7y3ea4R3m2wszNumfdubb/erwZYWUXI9+jionHXmn3LSB3hVvLTa7L822uf1qF7fe8MLOnysku2uz12HdcBNjLu+bcr/wANYOsQuy/J8yr9ysXDqdSl3MBZUhZ5h12bdrfxf7tSyf6RcH5F27d25qfcWbwzOwSNg38TfwtUc0jyR749o8xNu1v4a1p04OXuGXtPds5Gfc3ULW/7m2bazt83/s1ZN3vm+5Cw+X5avagzx7UV2Ksm7d91lWqlwHf7jKX/AIfk+9Xp4enA8vES965nxzW0ke9NwdV3bv71U5ZvMXY/yKv/AI9VvUNjKzojIF+/tqi03lxhBCswb7m1/mWu+MeX3jklU5vdLMdu8yqfvlU3bdu1aIZA21HTy3/vLUexNxkR/wB1/dVvmqeG6/eed5bfM+3avzbquPvGXoWbeNGZg8MbLt+Vl/5af71X4bF5I2/c/LJ9+oLWHaxdE2N/drZsbfzYUR/l3fwt/FVuUoxLjKXxFSz0+CH7+11Z/mjrRs9Pj3qnnb93/LNfu1bhsbaRUSYLmT+LbWhYabDEwhhRmWNfvf7X96rL9pzR90x/FVgo8M3r5HyW77VX6V9Cfslf8EDf27/2xPhL4P8Ajt8Ox4OsPCnjGa6+zajq/iLa9nBCxT7RNFFG7bZJFkjRU3yBoz5ixqVY+LeK9MlTwZqbtHkiwlJP935TX6Q/HD44/Fn4T/8ABr18INU+GPjq/wDD13rmtpomp3mizfZZpbA3mqF4N8e1lD+RGHIILruDEh2B/RuF8yzbL8ohSy+UYzr4iNO8k2knTbvZNaq1/wADsw1etCklSsnKSWvofKn7bH/Bvx+3Z+xX8Lrv416kvhvxt4Y0m0e58Q33g+/leXSYVKgyywXEcUjx/NktEH2KrM+xV3V8f/B74P8AxL+P3xO0X4NfB3whda94m8Q3q2mj6TZ7Q88pBP3mIVFVQzM7EKiqzMQoJH6R/wDBsj+1l+0BD+2lc/szah4z1jW/BPibw1fXl9pGo3r3EGn3Nuqul3Gsjfui3+pfaPn8xNwOxSv1H/wRC/Z++BvwC/bZ/bF8aaL4hhkTwP4tl0WwEdtAwsNKNxdXMhUQu78NAsZUKo/0cDBbKR/Y4zi/OOG6ONw+YKNarRhCcJRTipqcuRKUdbOMuz1XZ6nZPG1sLGpGraTik01pe7tqvU+Qf+IWP/goX/wrf/hL/wDhOfhx/bX9nfaf+ET/ALbuPtPm7c/ZvO+z/Z/N/hz5nl7v49vzV8QP+x1+0XZftTWv7F/iH4cT6P8AEa78Qw6LHoGr3EVvi6mZRH+9ZvLMbhlZZFYo6srIWDAn9fdL+J//AAQ60z9paH9rf/h7j8XrjxxFq41BtSutdvXjkO7Jt2i/soJ9mK/u/IAEfl/IFC4FeSftV/tdfsi/tm/8F7P2cviL+zH4hfWLPTte0LTtd8R22kywJf3aX7SQhROUZ1QOkbOY1IGcGQKoHNk3FPFUq9WOLpSlD2U580qE6ShOKuo6u0ovZbSfkTQxeLcmpptWbvytWa6ea/E8u8Af8Gu//BR3xT4p13QfF2seAPDNlpNyIbDWr7xBJcQayP8AnrbJbwvKseP+e6QvyPl648m+HX/BDH9uz4s/tNeNf2Z/h9pPhbUpfh9qkVj4p8YQeJYjo9q8qs8Y8wAytIVUloREZYzgSImRXvv/AAc0ftlfGvW/22v+GXdB+IGsaV4Q8GaDYTPo1hevBBeahcxfaGuZQjDzWWOWONd33Nr7QN7FqP8AwRV/4Kk/syfAz9nzx7+wp+2Rr2u+FPD3jm/uLnT/AB/4YjkiuLNrm28mcTz23+kI48qIxSqkuC7K+1FFb0M048lwys3ThUlUjFxpxptuKbXv/Feb5deTu9HZWbjWzB4X22jbS0S2899dOhmfF7/g11/4KI/DvwlJ4l8DeIvAXji6hBL6JoWtTW9049UN5DDE30Lg+gJ4r4I+F/wO+Lfxn+L2mfAT4Z+AtQ1Txjq+pnT7HQI4hHO1yCd6P5hURBNrF2cqqKjMxUKSP2v/AGSP2FfCngLx/efHr/gjN/wVa0rxr4hs7CRrn4dfEXVf7R0/U7ZiFK3otJIriFVLqVk8nIfaNy7jXF/8ED/+E7tviJ+1/wDtf/FrQbab4vaEk8Wq6DHp1rbNHelry6uoxHEV8vzLm2RCAEQmPO5jkrzYXjbM8JluMrVqkK7pKHKnCVGanOXKlODv7qbT5k+6vd6TDH1YUpyk1K1raOLu3bVdvM8GP/BrH/wULHw4/wCEv/4Tr4cHWv7O+0/8ImNbuftPm7d32bzvs/keb/DnzPL3fx7fmr8/vib8Dfiv8GvjDqXwC+KXgy40TxdpGqDTtQ0a+kjVobgkbRv3eWUYMrLIGKMrKwYqQa7T/hvz9sj/AIaI/wCGqf8AhonxV/wnH9q/b/7X/tiXG7dnyPL3bPs+3935GPK8v93t28V+lv8AwcYaRoniqb9lX9qzXNHtrTxT4k06OHWdCmRBuiH2O7Eb7ZPMKxyXEqcFgPMPzKSN3vUMz4kyfOMPg80qU6scQp8rhFxcJwjzNbvmi1ono77269EauKo14wqtPmvaytZrX5o8G8G/8Gw3/BS3xD46PhXxLF4G0DTV0mG8bxJeeJvOtTNIoLWYSCN5zNGSVcmMRZUlJHBUt4//AMFC/wDgjJ+2D/wTj8P2fxB+K1voev8AhC9uorRfFPha+eWC3upA5WCaOZI5oyRGxD7DGcgb9x219zf8HT37XXx/8C/EnwN+y34C+Iuo6F4R1Xwodb1yz0i5a3fVLg3csKJO6EM8UYgDLHnaXcswYqhTnfgR4s1f4s/8Gt/xYHxNnfXW8LeL5LbQ5dSnkle1UX2mzIysWyCj3UpAzjBwQQSD4GWcR8XSy/A5ti6lN0q9SFN04waaU5OPPzOXxXV7bW+456WKxvsqdabVpNKyXfS977n5D0UUV+unsn7Cf8E7fFel/wDBM3/ghL46/wCCgngbwxbR/E3x/rEml6DrN+BKVQXX2O2CIw2hImF1cbMESMg3kqFCfP3/AATj/wCC3X7dPg39tLwb/wAL0/aL17xh4P8AFHiW30zxRo/iK5WaCOC6l8rz4QV/0domkEgEe1SE2EbTge4fFm/Pxk/4NWvBt/4VkvHHgPxbBFrqG63FfL1O5hw/y8p/pcDKvG0bOTt5/MT9mXwbrPxF/aP8AeAfD3n/AG7WvGml2VobZ9sgklu40Uq2DggnIODjFfl+T5VlmcYbNq+YUoynKtVg3JJuMYJKKTeq5VqrWt9x5VCjSrxrSqK7cmteiWx9Tf8ABwd+zN4K/Zn/AOCkfiG0+HHhddI0bxjpNt4lhsoXzCtxcNItyYlx+7Vp4pX2DIUuQuF2qvxCCQcg1+nf/B1V8QtI8S/t3+FPA2nyStceGvh1bpfgz5jWSe6uJVATHytsKEnJyCvAxz+YlfUcFV8RieE8HUr35nBb6t20T+aSZ1YGUp4ODlvY9z8O/wDBTj/gof4T0Gy8L+G/21viZZ6dp1qltY2kPjC7CQQooVI1G/hVUAAdAAAOBX6lf8E0v2rP2nv2dv8AgmF8Uf8Agp3+2V+0H4v8W/bom074X6D4r8QXFzDPKjmFJEjaTGZrtvLLY3JHbO4+Vjn8hf2T/wBnLxp+1v8AtHeD/wBnLwDEx1HxXrUVmJwm4WsH3p7hh/diiWSQ+yGvvn/g4u/aN8GeE9a+H/8AwS++AkotfBnwd0S1bV7SB8h9QNuEt4nP8TRWzby3drt88rXg8T5bl2Y5jhsloUYKVV+0qtRSapQd3qldOcrRT9TnxdKlVqxoRirvV6dF/m9Bv/BtL8XPBfiT/gpx4p8UfHDW0vvHHjTwtqUugarqeXlutSkuI7m72uX4leFZm+6SVVwGXkP6Z8b/ANnL/g55uv2oNbbwZ8UPF91plx4inm0bVdF8c2Npov2UTHymFs0yiKPYF/dPGWxkEMSc+Z/8G4f7MXwD8Sa/8UP26vj7pMWsW3wQ0qHUtF0wpI5tbnyri5e+8vhJXjjtmESsWAdi+1WSNhi+Nv8Ag6C/4KJ6t8YpfHHge08GaR4Wju3Nl4KuPD4uIXt9x2LPcFxcPJtxueOSJSwyEUcV4eZ4PNMbxji3lFClV9nTpwn7eN4xlZuMadtdYtXVkr7vY56sKs8bP2MU7JJ8y0Xax6z/AMHS/iDwnDpfwG+HfjfV9M1P4taX4cuZ/Fl/pttsDW7pAm/G4bI5LqK4aNCpICvgryH/ABV1/UvJv7mLGcyFc+nNfs//AMFxvh78BP2wP2BfhZ/wWF+Gvg6Lwx4p8X3Vppfiu0LyFtQ3RTx7GwNjyW81pIizFUMkRG4nZGg/EnxRdSLrd2hPC3TYX8a+exKhDgTB0FdSp1KkZppLlmnJyirXVk3pbp2eh5eOmoZbTXVSafrrcJLxFt9jyMXX+Kqk2oTq38OzZ8v+9VOa8mZn+fcn8FULqZ5I1w9fKcp8xKsXJtW/dtsdj/wKs241B5I23pnb91lpJpHXci8hl+b+GqzTR/7Q8tf4qzluXGXMDSPHJ/EtI0jq52P97+FahmO3aX3Eqn8P3ajM2VDhG3VhLY6qfuyJmmmRmcuo/wBrZUsMjlldPvVWXe8gR+f92p4V3Zd/lP8ABtrjqHsYWtKJowxuw+eHYq/drRsW8tUh2Zbdu8xapWfywhMMfmrYtY0XD/xfx1xyj/MfT4XEc3KWoYU8vfD/ABffqxHbbVMybiip92ktUeSMPJu+/wD7tX7G16TxzfL/AHa4pe7K57tHEFWGNvvzfL8ud1SJb7W+4z/P95q0XsUaNXfy2/v/AOzSf2fCu6Yncuzd9+sebmO2nWnH4jPktX3EPDu3fN8tV5okVfkTB/u1pzW/yrlPlj+aq81rN9oabft+TbRKJcq5TaQbmmd9n+yv3qiZtjby/Ky0+4VJJtk3G1f++qqXBEKN5MnzL83zfdrSMZcxhUx0YizXgVntkRg7fxNVO4nhEwS5Rjt+XdvqGaZ/OR4Xy38VQ/aHXdv6ru/4FXV7Pm9482pmHQfcXDrMon+Rfu7qr3Ujrl/OqO4vPOXyXRqga9EcLbHyd3ybq6KceU8+tjuaRLNeeVCu9Nq7qryX26Rnfb/s/PVS61Dzj/eDfeVqrXE3lt9zcrfxV1xo+6eLisd9lGl9qLnZ2Wp7Kbfj+6392smOb5mRHb5vutWnpq+Y339rfw1v8J4VbEe0+E3LKN929OFrZ0uF1uESR9wZaxdOSZtvHy/3a6XTLd2kT512q3/AqfLA5uY2NLhcxj5Ffa//AI7WzYwwttPZvl+b5dtUdNjSFi7phW/irpNLtYVVXeH/AHKy+GdznlLmjYv6D+5mSOH7mz7zV1+gw2zLvS23y/8APTduX/vmue0m1RbjfMi/L9z/AGa63Qo/Lk+5l9m5tqVoYnU+H9PvLhijzblaJWXau3btrpNLhSRlkdFZGXdL8+2ud0WZETY7zLMzL5S7vl2sv3f9mujsJoY2Xzo1+78m1PutVR934Q5eUBst4/4Rt+ZIVqpeQ+dcHZtb+6zVekupvLWaSZd7bt6slU47lNy3Nm7MrN8kjJ8tX7kjSnU5SvJDHbsqb/vL/rGqncb2bydmUVd3mfwtVv7SX/0ZIcf7TfNuqnIyyTFJTtjX5t33fmrGUvsxPcwMpylEz739+vkpDl/+em77tYmqW6TbEhdQPm+6lbd5CkcaTbPm37dtZWqtNGvk2yfK27Z/FWUfaR2PscHT5o6nIassMbNMnyuv8P8ADXIawu2Yl0z977v8Ndlq1m+7986r/eVV/hrmfEFqFVvJC7Gf52rlqS/mPdo05S1O3t9QSSH9y/zRruSNqvWuoCaYyIjIPl+69cbHqEcaKnnbR912jrRs9UtodqNNxv3fe+7XnR54nwEpQO4hm3Rl9/G3a1PtrpI93DKzP8zfw7a5uHWP3Pzcbvusv3m21bbVIZLd/J/eMyfdV682s6q0R006lOMjfh1C2j3PC7JLJ8qMq/L/AN9UyOea3t12Op2/LuZ/vVjW944jaDYu1X3fNVyK6eSMpMi7GT/x7+GvJruSqcqkevg6vPG8jZh2TRrNcphlfbtb7rU+6vBbwsLl1+Zdqru27azY7pDCqO3zx/Mn91f4aktbyC+sd/7uXzH3Iv8Ad21x1JR5z2acuaA66k8yNZNm9o/7yfw1FeWsNuy+d95vmTb81WZP9IZYflZ9m3cvy7v96kkW28vfvUK332/u11UNZjqfCYmqTXPluls67l+ZFaL7v+ytY15C8mXf5jHtXds2qrV0OoNC1uyfwL99V+VqxNQukUL87b9v+98tfYZd8BxVJe7zGbIv2iZ7W2TLfw/J8zU+GHzFVHmw2z5WVN3zVG0XzO6TN8v3NtXYdkLDznZH/uqlfS0/gPAxVacfiK627rbiZ3Ulf/Qap6hsVfOebzgyfdVfm3VqSL5Sp5MOxdrfNv8Avf71ZmpRyeY0MjrtX76r95a6uc8aX7yZSmmSNUR0ZW27d2+mMyLI8PnK25N25qc2yGFk3xqu/wCVm/h/2aoSQTbd6I33awrVOWB10qPL7qLVnMJJAjv8rNt2rW1ZqkbfvH3DZ861z8MbqsSP/frWtZEWYI6Mi/3m+b5a+dxlRfFE9TD03/26bunt5apvuOF/5Z7a04ZH8z/XKu3+Jm+7/u1j28w8lT5yq27+L+7Vr7Rsb55spJz/ALW3/ZrwMRU5j2qNOlGNjQb99GyO6/e/3mp819NJM8KJGqfK37z5vl21lecPsY8mZkfzdqsy/wAP+7Wgt4625RHV3ZFXdJXnc3unR7P3S5byblEPyu33V+SrFtcLNIr3L58yLYv95dtUbdUkuPubdr/Lt+Vt2371aMNr+8ed7n5PlV2X7tP4jjlGcfeNLTY3a3WDep2/N81WNs0dyJEdl3N91f4t1V7fyYl8nyVR9ytuZ/8A2WrK2r+Z+5kbeyN8tEpS+RjGHtJyuPit/LZ4d+7a+3dI25lahVgkjWaabLfe+VPlaplKSqrzJs+Vdyr/ABVG1veKzO9mqN9/5n+9WcoRjsO0uXlIGuYZrV7l4WVVTd8v3qs/ZYWU/uZA+z7v8VOjkmO9HTHyr8qp8zUy4S5t5Em2bd2394r7mrSMeeXKctaPu6mJqElnpcO9EbLJ8+5N21q57UB9skb/AEZty/xN91lrpdfje4uDeQ7XVX2v89c1qEcMe6FflfZ91q9PD0+aOkTwsRLml7pgyRo0z+Wn3fuNsqvNZ/vmmh2v/fj/AIv96ta62SZdHj+58zKtUlV4Wf5/m+7j+Ja9Tl+yefUkQeXNJ8kP9/bL5iVN5YmtQ7ws6bv4Vp6xu2PLG5l+/wD7VWbWL7RP5M14wC/3fm2/8Bo9n7pdGXNAqeWNv76FTu+Vlb5dv+7VdrFI423oz7m/3t1bUipMweFN6q23a38TVXjsf3peS2VD955I3qJe6dcfiKOn2f3kMPO35Fb+GrsEflRuksa4ZqLeZPuZx/tL/vfdqNZnt28n5n8tt3zf3awjKUfhOr7KTLLec26ZPmRfv7flqzp91Dbx7EfCr/yzk/hrNhkmuZGd0y7N8nz/AC/981XmvfJbzp5sL935q66NSRyVI/ym3JfQxqHS5/1i/MrL83/fVL9uhZB8kZ2v8+59vy1j294nkqnmMU/2aSe9jjk3o+P76tXZGXvHmVvdNe9nhnuEQIv3f4X/APQqsed9n2w9v4GVd1Y63ULK7p8kit93Z93/AHam+0/aFiaG5VX/ANqumnLm+I4pe7I2ftS7k2Pu2p87bPu/7NbeixbZd6TLiRflVovmb+9XPWKvMqQzTbEkf/LV2Hh/TfObyURV+dWZpK6OX2hPOaOn6U91t39d/wAkbJ8q/wC9W7D4d3MzI6vNIit5kf3fl/u1Lpen7QXs0UPs/if+KujtdG+0RhEG1lbZtb+KrjHlj7wuaJhafof2dWm8mMqybX2vu20+Tw5CsRf7Suxfm/d/wtXV2ug7pV8mzXzfvbWT5asWei+SrwpZs+59vyp/FUcsPiHznnGreHZreT98jOu3c0flfeb/AHqxL/Rba3A+XnYzKzfdX/Zr1HWPD6SzOj+dv/gZU/irF1LRZhZ/JCrIr7tu2jl5gjI8w1DSXh/0l0jZNn/LP+Hd/erDuvD94yywwOzqybk3V6fe+GftEbQTI37x977k+Vazrjwvfqr7EUn7z/7K1yyo8upXtTzCbQUVZfORn2/MjLF81TWOmorf6lsR/Nt2/NXcXnhfy5BMiSOu/wDh/ioh8M7VbyYZFEm7ezfeVa8zGYeFTRnbRxEjnNP01Gutjwrs27XjkT71b8fh11hCfZmlfY25f/ZatW+k/ZFKXNsu77jrs+Zf9qrVmzrM0MjyYZtvzRfNtr5bFYGMat4xPosHivd5WUodLSSEeTuTd/y7t95amt9L3LJYfvFTau1t38VWp47ZGWb+7827+61WLMv5WzyWZ1X5P7v/AAKs44eMqfwnqSxkoy5IkNjpL+TJC6Lv2Lsjjfb81b9jYu0ccybss6rLGqfLHVWFXkt4oUt2/d7f3lbml2sMkaRTJgb1b/gVe7gsNbU83FYjoaek6GGYQzQ7Wh+b5f4q1rPSYbiMwwuyOy/8CqPS5k3n983lbtvmKnzVv6arxz7E25b5XaRfvL/er6TD0fePArVuYzDoP+jv9mf+Otaz0d3VY5tzbV3O396tS1s/l8xNv7z7nmfdrUsdJS8mSeaPYPvN5f8AFXrRo9jy6lQz7HQYZVWZ5m+ZtzKvy7f9mtiz8Kw3SOZod0uzdt+9/wB81v6P4ZRoxCV+Rn/1i/NXS6X4Vv7fHlzL/sSRp/DXRKj7phKscPD4dSePe9uz7V+X5flWi88PzXFqETdtaJU27fmkr0OHwyiRqmxmO9vvfw1n3WiotnDDdbmjVf8AdrklTHGoeXXPhl4W3+T5TSfKit97bXLa5ovk3UqTW23alera9o376VPlb5/l8z5dtcb4ktfJkdPJkVZH27t+7ctcNaiddOtynlniDTIW+R3ZNz7kVvutXK6pbzR79m1tr7vlX5V/h+WvR9e0+G4beLbe0b/xf8s1/wBmuR1ixjhIud67fm3r/drzalGly2PQpVr/AGjz7VrN5pxJM/8Aq12xN/ernNUZI4/km3fw+YyfM3+7Xb+IhMYd/wDGy/d3rt21xOqRzRwzeS+1Vfd8v8NeXUoy+zE9KjiNDntR1COaTYjsybNrSN92qM0kPmNDvkdc/dX5al1KSOS4EPks6L823Z8rf7VU7jUI5pNiSYTb8391WraFLoEq0ZCXVxCyI8ySJ8uzcr/Mq1RuJnVdhRvlf7rf+O1PcO8cY3opP3tq/wB2qvnbpltkj2/LnzK64x5TkqS5viI5Niw79mP+erVnzQ20LZSHazfxVaumeb5EmZE/hZaaqpIqJNwq/KsmzdXRy80eY5+b+YrR28KyI6Pu21PY2b7iiJJvZtq/wtTdyQtvdGZm+X99V6zhm8ltjsdz7t1L3aZdOPMWLPyVmSGfb8q7fm/vV0+j2Mcs7eSjN5aqqs33awLGxGPPmttyK33d/wA1dl4fO2NHRMn7u1fvLWfNaHKdX2C3Y6K8ckSwzLKq/eWT+JWrb0/S4I1HnIrtJ9/b8u2rOi6fbOqfJG0rfL8qNuX/AHq3tN0Xew3pvaNf7vy7aun70jCpT/lOJ+K+raJoPw1vFu5cPfRmG0jjB3F2B4b04BP4V+uX7P8Arn7DN1/wb7/Bnw3/AMFEbY2/w+8Q3EmlDU7G0mDaZfi91F7a7BtgZI3HkMDIqvuaQiRWR5K/Ib9rOC1g8GWAhtVSRtWDSNH90Zif5RX1bpP7aHwx+Nn/AAR6+Gf7C918ML7+1PC+vzXlzql5cxSWbrHcXbIyrt3FmN1IChACBFId9xA+/wDqlKhwbhMVeaviVKTg0pRtGaTi+jVr9dTocY0cvhN3+Ppvs9j2LR/29v8AgjX/AMEgvhj4k1T/AIJlHU/if8VfFWnNbWniDX4LmWCyQOpSO6llS22QBsyeVbJvlaNRI6gRunxZ/wAEvv8Agqx46/YF/a01j46eLNBXxJoPj2do/iLYxQgXU0bzmZrm1+ZEWdHZmCudjqzodpZZE525+GXw9V3iXwhprO2SqxwL8u38Kzr/AOHHgWPJTw1YfKu4KLPr+levhM94XhhMRRxFGrWddJVJ1JJzkl8KurWUfs2SsyVmmBhCUZwlLm3bav8A0j9Bnuf+DW2Tx6f2yH8V62btn/to/CQ2N/8AZPtv+s8j7J5GzPm8+T9p+y5+X/U/LXy58bP+Cqvw4/aM/wCCuHw+/bY134WweFPAvgbxXpbWkGkaLD/a11plnOrLNeFXAmuCqgBQ+2KNUjXds3P4PP4K8Fhh5vhi0RCvykQDNZV14R8JiSULpdoFT5QUgGGangM4yDDVZTqvEVpOEqcXUmpckJaNR2V31bu9EYQznB05PmjOWltWtE+x6T/wWY/ak+Df7Y//AAUG8Y/Hj4Caze6j4Y1C20+1sb+9097U3JtrSKBpEjkw4jYx5XeqPg8opr2X/gkp+3v+wN8OP2a/H/7CX/BQv4Xl/CPjzVobyPxZpOhLLNEVTAW6khxcjyXVZIJEEjI0sowq9fjzUPDGj2xdYtEtmZTjJUbVPpWDqWk6fBtQafHE5bLJtyMf71duK4o4dq5HTyqVKqoU1BRkpJTThblkmuqt2t5Gv9uYKpQVFwkkrW1V9Nj9ef2fvjr/AMG7v/BLLxhc/tJ/s5fGnx38QvF7abLY6fpkMdzPLFHLjzAolt7SBcgAFpXYgA7Rnr8ffsUf8FpfiN+zX/wUK8a/th+OfBtvquhfFXV5W8faDZRL58No85kjNmxZFM0AIVfM+WRdysVZxInxRf25iaRIAhH0+ZapLGQwaeYhSuSyn7prxcPxBwvCFdYunWxEq0VCUqkot8qd0lblsk9dNbpMqnmWCk3GcZSclZttbH7IfbP+DW//AIWB/wANl/8ACWa59t8z+2v+FTfYdQ+y/bv9Z5H2TyNmfN58n7T9lz8v+p+Wvir/AIKh/wDBUHU/+Cjf7WWkfFW68LtoXgjwhMtn4Q0sQKb1bHzxI89x+8KNcSYBKIwRQqICxVpH+SbSBXOXdmdU3Ov8NbOlafbsFa6t4v8Aayu6s8v4w4dyrGLFVI4ivOMXGDqTjLki91HbVrRt3bXU7sPWw8J875m1oru9vQ+0P+C+P7ef7On7f/7Sng34k/s3a1ql/pmjeAoNP1CbUtJa02XLTzXDRKHO5inneW5xs3IdjSKQxv8A7Pv/AAUD/Zo+Hn/BDL4r/sL+J9b1mP4h+KfFwu9H0+HRi8E0MkllJ5nn7tion2Jg+4q+ZU2JIAxX460vTdEn+/o0RwcNvjArQg8N+HnX7TJZ2zLjKiOAfMd33aiPH/CtLK8Nlqw1bkoSjOL5oXvCXMrvZq++i+R6FKjQqUI01e0WmvlqedUV6aPDfh6MZbQbVvk3/dFR3Wh+G1cJDpNoMfeLRCvoV42ZPe31Wp98f8zubVrn1v8A8Ef/APgq18I/2Xvhx41/Yu/bh8L6h4l+Cvjq2kza2dmLh9MuZgsVxld6P5EkfzMY2LxyQq0a7nc19KfCD4r/APBth/wTw8fQftWfA7x54x+IPiyz3yeG9CFrd3T6W8ishaJLmG2iVgrlQ08jsvVfmANflXJouhLGT/ZdoGPAUxis+70TThHI62UAU9CsY+Wvjcw444WzHGVasaeJpKt/EhTnGMJ6W95a6taNpq/U4qmApVW5qTSluk9H6nqPxI/bK0b9rv8A4KBQftX/ALbXhy+1vwvqniu2n8ReGdFuvLeHRYnVVsLdsqdqQqFyCjSEMxdXcuPa/wDgtl8c/wDglP8AGnxH4In/AOCb/wAM7DSryx0+VfFWq+HvDD6Jp08R2+TAbV4ojJcI3mFpwgyrqC8uB5fxdqGl2topdFBCpu5T71c9qEd1BGJo+cjJAbpX0EfEThz65hsTSoVoKjFxjCM0qdmre9HrZbfK97KymqVOpGWq5VZJPT7j7x/4Igftj/sXfsI+L/iR+0H+0RPq0njiz8Itb/Diwt9GNxb3UrEtLCJF3GGeRlhVXcJGsay5fLBT8ZfFP4leL/jL8Stf+LXj/VGvdb8S6vcalqt0wx5k80jSOQOgGWOAOAMAcCuI1F7yGYol5Mm7tu+7WadR1WOVo5b2YBf4vMPzV34XxEyChmdbHxw9R1KqindxaSitFHst297vU53i6NGrKpytt2/DsfoJ/wAEXv8Agqj4Z/4JyfETxT4a+NXg3UvEfw18e6clv4h03TFjlmtp4w4jnSCZljmVkkkikQsuVYHJ8sI30vd/Az/g1b8Xaq3xNg/aY8YaHaXEn2tvB8UuprFCBybcK9jJMAeRgTE8/Kw4x+L0+qavGGLalMpX+HzDVe41vXRj/TpwPaQ152P4nyLHZhPG4d4jD1KiSm6c4pTsrJtNSV0tE1Y86vj8M6rnHmi3vZrU/UP/AIK+/wDBV74BftNfBTwT+w/+wx4A1Xw18JvA0qSM+oR/ZzqLwI0VtHHDvd/JRWeQvM3mSSSBmRWTc/5oan8OYdR1Ca/TVmjM8hZl8nPX3zXP3niDXEiYR6zcBv4R5pqpN4j8UmFT/bFyGCfNtmP3q7aGfcIQy2GBlhJzhFuXvS95ylvKTTTbZzVcxyx0VSnSbS13699zoJPhNBI2TrbY9Ps//wBlUTfBu3ONviCQbRhcW44/8eqtpFp4+16VFs9RvMPt2gTGvY/hZ+xr+0T8Rrj7Npek61PMy+ZZwxo7fah/s/L92lLNuB4rXAS/8Df/AMkedUzDIKc9cO7+r/zPID8FIdxb/hJJMnr/AKMP/iqYfgZalNh8ROec5NqP/iq/Rn9kX/gi38RfF2vQSftR+KrfwNplyvB1SWR509P3Knd/31Xp2q/8EI/hBD8RbDTvCv7Suo6tpM2pbL64GlSLGkO7+H591YrO+Aub/cZf+Bv/AOSF/aWSQ1WFl9//AAT8lpPgTbSnc3iV8+v2Uf8AxVNPwFgOP+KocYOeLQf/ABVfo/8AFr/ggZ8Z/DPijUl0P40afDZC4b+zYb698qdod25WaPf/AHa7Hwh/wRc+Anh7wit38V/2nNWu9TdVaS18M+G7qdI++3zmO3dUVM58P4/FgZf+By/+TNIZrkctsPL7/wDgn5YL8B4FHHiZ/wDwEGP/AEKpI/gbaoQT4jkOOn+jD/4qv0X8T/8ABKX4U+KEjsvht8SfFlo0LsI31OwAa4+b5Q21/lNeL/HH/gkH+2L8JreXXtBa51bSo87ZRcsssncfKx+Wpjm/h/P4cBL/AMDl/wDJnTTzfJYS1pOPz/4J8tw/CGziZW/thiV6HyB/jVtfhxCoXbqrDaMZ8n/69M13wd8TvCepyaTr9xeQzRf6yMzEsG/u1V+0eJxs3ajOv97LGuWrnHh5T0lls/8AwOX/AMme1hsfgZ/w1+JrweC1hTZ/abHByCYun61Zt/Dn2ZPkvSXzneYx/jVC3uNROXF9Mc/dUuat2lxdyDaLhy3+01cc888N1/zLJ/8Agcv/AJM9SniIu1kSnw/vwJL0sAc4MY5+vPNSLoNqi4Q4I4U7eAPpTI55/lK3DMxbDLmpFWaSNniupSxX5VI6Vk898NFvlc//AAOX/wAmdDxcovVkcnh6GWQu8x57Bajm8LxSnIuyB6bM/wBaf593FEVkdsA4aXNV7y+mTKpeOrFsD5qv+3PDb/oVz/8AA5f/ACZH1pb3GP4HhcFTqL4JyR5f/wBeqtz8Nbe4k3jV5FH90R//AF6dfX+qRSMi3sgRf4gTWZc61rST7U1Kdfvc7jitqeeeHK0WWT/8Dl/8mYVMVB/EW3+FUDNvGtMD6iAf/FVWf4M2zrsPiCTBOT/o45/8erI1DxP4kgQk6zOOM4WUjiseXxf4qBZl8R3hA+6FuGrohnHh5LRZbP8A8Dl/8mefUx2EhLWLOsf4KQuMHxJJ9TbAn/0KoJPgPbyOXbxPLz/06j/4quQu/GnisLuj8U36nGcfaGqk3xA8a7W/4qi8+b7mLhv8a1Wb+H1v+RdL/wADl/8AJnO8fgXvTf3/APBO5/4UDalSh8TPg/8AToP/AIqo5P2eoG4TxY6jGMCyH/xVcM3xC8cF/L/4Si84X7wuW/xqJPiD43I3DxhfZ97pv8a2jnHAXTL5f+Bv/wCTOSWMyp70n9//AAT0FPgBboVZfFLgr0xZj/4qjVPhHLoumve2Wp/ajCNzxPCE+UdSDuPT0ridO8eeM5MM/ii+I/27hq9Z8N3V3qHw5W5vrl5ZZLObfJI2SeWFetlmG4N4ijWoYfCSpyjCUubmelrLT3nrr1TRWHhleO5oQpOLSbvd/wCZxWjqjMPvfN/31XW6Xbusib4fmb5m2/drn9Bs0uWR8shV/wDvquy0O3n3L/dr8nlH3T5v3jW0e38xlhSH7zV0tjZpJMYY0ysfzfd+9/s1l6PbuPkf7yv8u2umt9PkWPe6f7SKzbd1TzcpEpchd0nTfLk8maHczJt3L8u2t3S4nZkhd22/3v71QWMMMkLWz2zFP4V+9WxZ2/8AG9ttH3V/utT5uYiUeb4TRshJCsUOzeP4ZG/iat7S9Q8tNs02fk3Kqpu+asO3WbH2mDdEd25G+6sf+ytalhbW1vbo00Pk7n2/Mn3f9qrgTItXM0KrFsRU+XdKq/3qp6hcJJvh2MP7irSz3U1uzWc3kzLG25fl+WqV5dTMq/6verbttHNylxJFk2zbERt6ptZaz5JpLqb5Jmx/d2f7VMkuEjzM7sEZ/vf+zUz7VDdKJHmaJPmWKRU+XdUylynt5dHuF1JDJI9zs3vH8qKybttY+sybZk+RlZnbasbfLWjcSJJbojvsk2/I275W/wBmse6kSSZYbl/l+9u/iVq5JS5vtH3eBfKopow9QVIy0HnKzN821f4a5HWY/wBykcLqy72+X+9XXa03lwvMnKsv/Aq5TXIdzJ5aNt27kZflrnlLm9496lGMdeYry3CKyIiL+8+V6t2t9tkXznVF2fdb+KsRrzayPvzt/vJVeTUt0iyPwuz/AFbfxVzR5z8wlU5jutF1iFl/fzcbm+X+Ja1tNura3be7s275vmrzvSdWSFUTezN975q11151z/pK7fu+X/erxsT7X3uxtTly+9I7Zb5Lq1Lu7Yb+Jf4l/wDZad/aj+Wgs4WdPmZmWX5mrkB4kdIWRJN235X3Vfs9Y8yM2vnbFV1bdXgVKLlPmR7WFlzfaOtsdYktnSF92d+xfl3Ntb5quNceZGfu7/vbvu1zdvfSXRQedtZfv/P/AA1oWciKxR/MVl+aLcn3qihRnznuU6ns48sja8x5Jnm8ljJu2rtb5f8Aep91cQx7pprlVbZ8u37u6qlvcQXCp/yzDfK83m/dokt45lRN/wDB96RPvf7te3g6fN8RliKn8siO42SbUTh5F+8vy1m3FveSXHzvwvyNt+XbW4sH2hf9S26Oo7q1jvIdjj7qfe/vLX0+DXs/dPMrYiMfdMKTT/MjV042/M7M/wDDS2+xrx/nZmk+433v++avSW6GQIkLCJvl+b7v3afHablS6hTZui+Vd/8AFXv05R6ni1qntJycivqFvCsYeGHemzdu37ay7qQzTP5iMrf3VTbV+8t33eXcjftTcm5/u1l3TS+dve52nZt3N92t5S5feOen/dKF1CkanzpmUK25F27laqkzbcuH+ZvlZf7tas1v5kcfnTN5n3VaqElu/wBsbemx/wDnotcGIqe6ejTjIgjV5tj/ALtP7lW7fzlUPvZ5mbb96o1g8yZPJf8AdKu5/k+81W4YMZTzmb5vkZa+axlT3rnsYWPuk9nL9ouJXfh97fL/AA1bhvHkZ0uU+VfuNs/hqqrIVKJ9+rS/eCBGI2fL/FXmVHze8enRo8vvFmNraaTf520qm19yVYgm85l+2feX5V3P8u2q0dvNCybPnHlbnX+LdUluvmK/7lh/Du+98tc/uSNKnuwNe1+03lwk0D43fK/+1V+PyVmhtprlQVdml3J96sSz3syJ+8zH8z/L8v8Au1s6XI80x/0be0f3amVP3tDjlU5o6mxbh2bZ9pVCv/LPZ95a0rVU/wBdCiurfNuZvmWsuxW8YI7vx833fvVpx7LiHzoUjjPlL/Dt+7XNUjOUeUI8kpFhreby2ms0VnX725PlVaS3tPMkbzn+78rfxbv9qmwu8kPkO8jj7rtsqWOPdGuyZdu7aiqm3atTGE4mcpR5vd2EWNFkREhy0j7Hbzd3+7TZoXVf9TtLfLu/u1IsaW+XQ/LI/wB1arXl9DMzoiNG8f8AwL5a66VSXPojire9EwNQj3SfPudvmVF37aw76bdl4YFz/GrJ8y10OoSJCyzeczPH827+L5qw9Qhm+0P5zsUZNzsvy17+F908HERMW8VJleESful+bzG/hqC+s4Y1R38xx95F2/xVoTQ+TG0r7WTf91v4qgvFdpC7uwVk+aNW3V3Rj7Q4akYxj7xnJNDbtJ87I7fxM+6rNjcXN4qb0VWhTnb/ABf71MaNPMPycfxqybt1WbVUXYlt5bs393+KtJR+yc0eaPvRLiy3MEZTZv3IzKzfLtqjdXCXOz5GX+9/9lV24kntWa2RFO5f+A/7tVWbcp+fa33kX+9/s1zypxO6nUnHUjs03QO7ou7dtRv71RW8kNunnfKPvKis9Tx2bq331XzPmTa/3aq3Fq7Qt523fv8A9Xt27q4pHdGRG15tUzJbSD+8v3dv+1urOuZvOZtkzbmfc67d1Xmt08n99D95Plj3/erPuI3Vt6PtXft21dP3feRnW94qSag6rJ5Pmf3U+bbVyxvPtiK+/wD1a/3N1Zl1au0i+dHvVv4m+6rVoaRbvax+T5LfL8u7furvpyjI8vEe6XVkubi4/wBJRdvysjR/e3Vr6fYvJ5WdpX+6y/Nuqvpdj5jfOjFl+VFX+Fq3be1druL7x/6Zqv8AF/tV2U9zy6kjR0nTXVVmR1kb+7J/7LXf+F9BkmhSF4WRbj/Vbn+7/tNXP+FtDhWRTvx8m5ZGZfvV3PhvTUbydl1HL5abkVvur/s13Rj7hlKXKbvhvQ0jX7i5X5XZfm3V0VjoqRyedZvnc+5Gb5W/4D/epfD9snyzJCy+Sm3a33m3fe211+l6fZria2h+ZV3Rfxf71ax/vGEqhj2ehvGyTfvGZfv7m2/LVqPw88du23d8rs3mRvXT2OlpMo3w71k+Xbv+arlroX2e3fenz7/4k+Wp9nGQvaHBah4X2zB4bxvm3OzSN/Ey1g3Ghvaqvlp/q/uLu3bq9L1Lw/NJN88PyttV6ytQ8P8Al/vnto3WP7jKnzbafLKKI+sc3uo83utB8uFv3O+L5v3cn3l3f3aoXGhwzbvkyq/wyJ97/er0W80d5IQkyKrKjfeT/vlayL7w68cYm3rvVFbatTKnAftPe5UefXWh+cqvDIyIq/3aqX2ipNH+5fcy/fXdXa3Wlv5Mlr+8VvvfN91ayLiz8uGSGz3M/wDeZf4f71ediKZ3UZHKXUPkhofJbdJ/rW/5aN/u1BPZvHIpmdkkjT5P4W21tTaajYvH+V9+0LJ8u6ql8jtumf5Tv2LtfcteNiKMuX3T28PU5Sh/ZsMkbI824SJ/C3ysy0iL9laK2hjYeZ827zdq1JqH2do/kh2NI251X7tCXyNJslH/AFyVv7q1jSw/Nudntv5S7b2b7kR/lVflX5/mrbs/OW42Q8GNfvNWLpc0Eiqk53D73mbt33f71aGm3jqxSaZmVnXymb+LdXqYWn7tjixFT2h1mjtDAq7HVnk+Z1X/ANCrodP/AHjefdT+af4F2/dWuV0yH98Hh3I6/cX+8tdPo4Ro4przdvZvur93/dr2aUbHm1JS5jpNLt4/LSbZJhfmiXbuX/vmu20vSUhUPNB8/wB5F/8AZawPDtv5ePveUzfdrvdFsZvJSZ7bc/3tsn3lWvWjLlgedUlyy+I0dB0aH7OgSHbuTczf3a6jS/Du6GN9m1G+VGqHR7PzrdZprVtkbbEWP+Kujhh+VExho33bmX5v+BU5e8cHtvf94zTpP2fe9m8OPKZdzfxVmaho9tGPtSIu1f4fvV1lxFDHjft/eJuST+GsHXLdJIW8lNyxy7mWFtv3v4q5qkf5TWMjgvElj51zs+zM4VF+9/erhPEFq8jfcw8jsjbvlaNq9K8RWs1rJL+5+dXVnbd/DXE+KbV5pHh3szSJu27K5KkeY7KdTmPMtYsZpVljdFRo/k/2f++q4nxBpPyskz+ai/fb+GvTNbs4bebZv+dkZmVv7tcR4mtftDP50zbFTYy/8s2X+8rVxVKZ2U582p5v4i0+2mt3RLbDr9z+Fq4DxNZ3yyJ/q8/xrv8A/Hq9P1i1eSdHh+YL8kUjV534ou386a5ddhk+VFjX/wAerklT5Trp1u5wOqNMpZPJbfGjfNWbHLDNJsT7jPtfcvzM1aurRzLfTHf8uzcjVhySJu2ONh+8y7/u1hyxOzm5tIkrXf2VsoMov8OyqVxMbdv3yblk/hqdpkZT5P8AF9zd96q9xcbW+R1I27WZV+b/AHa1jHm94mUo/aIjJDJuROv3VVYqj84s0qTOo2/dWP7q/wD2VK0jxqzvDtl/vb9vy1XaSHc3z79v+xV8vKc3NGXulkSQts86ZX/hbzFrRtWDQu+xQWVdm2sqxXy9ieT8n3trfNuq5Gybi6bdjJ91f4aipHqbUeeO5uae3lzFJk2tt/iTdtrrNDk8mOLf5Zfb97/7GuK0+6/0jZI6t8vz7v4q2NN1KRV8ybanzf8Ajtc8o8p2xl9k9T8N3kMcbPNMuPu/L8rf7NdRot5MtqLb5VO355F+7Xmvh/VkCo+/fHH/AKrzH+9XT6XrzyOiJ5abX+fd/FVUfdCpI5r9r3yj4E0oRhCy6oA7KMEN5T5Fan7N0txH8JtN8vIUTTnIX/pq1c9+1ReLceBtNiaYtIupgkBvlA8t+3atr9nW7WL4S2AJYAXUokI9PNav1Cv73hpQ/wCvz/KZtWcXlSf97/M6q6WFZD9jRSu7du+61YmrSbmeFJty/wB5v4q19WmhZUf7SybflVttZF4okj3oV/d/Kqs33q+IjKx81Uic/fSfZ7xPJ+Rm/vfdrKmkhuJHR4VUM+5l/vVsa4qXDuicoq7opI03f8BrCvLdPMDpbbP4d27+9XbGUZHHL3fiMnV4k85n8z+9urntUb7/AJnA8r7u3+Kuh1CFIWmT5X+f71c3fLNJHM/y72b5lWspS9wqnzc5g6iqNGsyDe38O3+7VBEVo/kT5W/iartxHCytDNCy7f8Aa2qtUr1oY1CRuqovy7f4a4K38qO6nyc4+1/cybn/AL+1f9qtO3vXhj/vfN83+zWQ0iR26SdAv8P92pI7ry5Dvfn/AGUrzalOGx6uHqHUabqULJv85tv91q1rfUIZodjTZTf+6WP5WauSsbxNqpN8h/3/AL1XLPUfOm86b5P4Yq82ph49D2MPiJfCdSsh8l5EmXKpsfzKVbry1CXKKWZP9IVawpNWm+d/Oxt+5/FU6TO3zp8u5PvN97dXNKnynTGtzS5S1c3SYXzYcLv+Tb/DUU0t5JLshhXCoy/M6ru/4DTFm+ZE+1Kz/e2/w1H5N1Izb3X5vmRV+61Ryx+I6I1JfDEztUhDKszoyln/AN7b/s1h6hp8Kt874WukuLd4/wDl53f3qyp7dGmaHyV2yfLuauqn7sDnrcn2jm76yeNTv2rt+7WfcWqSf3WP95q3byO2ZWdHZh93/gVUGsTJM/8Af2/xJ8telh+aWp5OIMaaxhcNudm2/LWddWrjdv8Aur93dXRTWflx/cZv93+KtLQ/hf4h8XahbW2m6bM/2j/VKsW6vQp+R4uI92JwkOh3moXwtrOGRnkfaixpur6t/YF/4Jb/ABa/a+8bQ6DpthJDZQuralqTWrN5Mf3v3a/xSf7NfTf/AATc/wCCPN58WPFWlTeM7C6kdp/39v5DQRQr97dJI395f7tftv8ADn9nnwZ8J9BT4Y/BCw0vwpp0Ngtkl1ptv/pLN/y0m3f3m/vV01MVGnDQ8StKdSX90/N74H/8EnfgP8HdUtk1jw1fajrEM6ppui3Vh9puZmX7zSRx/LH/AMCr64tfAPiT4Z61Jfv42sfB93DpaxWWi6LpcMl0saruVVjjVmVmavVPip4Xh/Zu8FpbeANYt9Ctr68/4qjx3rlx5lzHH/EsG75mkavHIv2//hXoPhPxJD+zZ4QnfxBZRMsHi7xLpm77Rt+9Nt+8y1xVMVzS5WXTw/L7yOX8K/FBPg/dal42+P3w8vNVl1L5tN1LxxerbPI277qw/eb/AL5ru/Bf/BTT/gnnovgtbbWjpqazMzLcaboejSSLbyL/AAtJXxF4t+GHxW/af8bRePvit441DxDqN7bs0+qTW8m2ONm+VYY1+VV/3aqa5+xK/wAFvGWka3bfBzxV4q0uO3Wee3kv/sP2q43fd3f3f/Qq53CpKXuPlNE6VPc+wPFH/BQ34FXes2vie5+IXgl9NUtDB4dvtJVGjbd8rS3Mq/NXC/FL47eJNcmmh8K+OfCOo6Rq22WLTdDZZFt938LMteM/EDwWnxc8Nv4V1j9jzQfDdtJLHturrxD9p8v/AGfu/erX8K/sY/Ejwr8PLPXdBh8F21nYzt5tro8reb5f8O5qy96UV7xMow+Jm58MdF/aO0fWP+EttvBOn30Cy/6LcWsqsrf3dyt/FXeaD8SPidpr3Oq/Ff8AZj1TxHaXXmNPqEflvJ5f95VX5dq14/pf7Q2sfD9pfCvjPVY8W8u6KO1l3Ku2vdfgD+258HNeuLawh1W8QL/x+QyReWtZ+2UQlRlKMXE8y+K3/BPX9gD9u6G/mS0uPCXiS8s2SzvltWgnt5tvy+Z/wKvzI+P3/BK/9rf9mfxlc+FdV+G8nirSmlb+zvEGnwSNHcQr96Rm2/LX7r+LPiZ+yv8AELWk8K6P4w0XTNcjfzbpm/cNH/Cu6T7rMtdn8O/hj8SPCvh+5vNA+P1n4gh8rbYW94iy+Yv935vl211RxMakbTd0OjWr4efun8ynjj4HPoumpqtnbXFvcxytFe6beOqvHtX7yr97bXAR2cMbfI6vX9Gf7c37HvwW+PHg26vPiL8KNJ0PxDNAyweJtBSOLcyr91lX7zV+Mf7Y37HOm/BHXnv/AAf4kj1K0js/Nlt2XbPG38W5VrKtTpSjzQZ9Bl+cc0+SZ85NZ9Ajrhfv7V+aljtZFWQOjfM23/eq15aSMuy22/xPuqaGzePbN8pK/wANeb8Punv83tDPkjdYwnk/M395artZ/udnkLlX+9trXaOZFE0f3d9UL5XjmebZv3f53VtGM5GftIR2MS8jdt7vD93+Ld96sfUF+UTeSy/Jt21vahGki+XHwu/bu3Vh6hHNGyk8j/erppx984q1ae5zl9+8fzHfG35dv8VYl+szSIhRcNu+7/DW3fRvu3p/31WFfed5bJs3L/dr0acTy61aX2ijdeSFPH/AqpSSfMRs+b/Zq3dKm0b3+8n3aqyb45PkSuuMTjlL3iuy7U3fNTHjjaPZ/Fu+7T5PMb5H3N/Ft/u0+GOT/lp1rSMSOYt6evmyK7zfd/u17P4RTZ8NI1Uf8uc3H4tXjtjGke3Yn/fVexeEhIvwziEg+b7FLkA+7V974eK2YYr/AK8y/OJ7OS/x6n+F/oYeit5bI2zG3+7XaaDJDNCJgjb93yN/s/7Vcho7Iyqn975n/vV1mgx7Yx5PB3LtZvmr88+I8T4Tq9BRFuPJTa25l+9/DXTWMcefJSbhpfk+SuY0dnmZnMyn5vu10+m/6QphSFt2/wCSH+7U83LqRKJ0Om28Mio6Pu/3a1rKGZ5h90JGzNFGv8X+9WPpcOJBvmZdvzba17GT7029n+Xcny/NUc3MZc38pft7VI28+d/mZv4fm+Wrq3Ajbf1+Rdke35aq291C6mPYx/2Vp8sn2VjGqKEb5nbfWg+vvDdQvP8AXJMipui+8v8A6DtrIvBDHbp5M0h2/L538TNVi8TY4m2btyfOtZzNc7vOj8tW+75av81Ev5R04jbi5eRnmnTLfef+7Uc108Sq/n7vn2ov3trVS1ZpPuQ3qsmxWlVf4f8AZqlJMnk797EfdX56mUoSPbwcYR/xGpdagjMPO2zLHL/c+81ZvmJGuLmZdzP8jbP4arSXENxi2D7Qsu7a1Sfbna2Z7lPkZ9qLsrhl/dPs8vqdGU9Q2SSecj4G755P9n+61c3rkcciqiI2xW/4FWtql0jWpdJmRG+9urA1i+eMMvVf42rGMZnvU5fZkcxJebXZPJYj+Fd9VJblI2++uf4qdJsWQpDNnb9/b/eqtfSYwn8O3722p5o8x+aezl0HrqCRss3zMVq7b6kkiqjzLvrGa6h8wSO+xV/hWhV3TLJC8g2/N8r/AHqyqU6UmbxjzHRrqjthCrZb+781a9nqCbU3vsZtquv/ANjXIW8rrdK7uyfxLtatm3uJopF+2Jvb7vmVxVsDCUuaJ14ep7OZ19vqyQyK8z+Vt+X5fm3VuafqHmKs0d583yr/AMBritPuEkmb5Gdf+WTVu2cx3I/95/8Ad2rXP9ThHb4jvp4n+Y6qxkRVM00LEyS7UZvu/LWpZ3Dqyfw7vm3fe2r/ABVzenLNJG6Q3P3pflkX7tb+myTbET+HbtdVWuzD049TT2kuX3S/HIkLK8ULHc23zI//AGarMlrI0bP5Kh1+ZpF/u1FYsFUzfLt/us9TzLNHI7pu+7/E/wAu2vcw8eU4J1Jbsr3SvHINjr8vzPuqpcXCW8L3M37pP4pG+7RdXGYWuZnjV42ZEWP71ZN1dYyk021Pu7Wr1Inm1Je97xHJcCTe/nM/mf8Aj1UtszM/MeW+VKG3ov7l4/8AeX+7TjLbNjY+1v71ay5eUUZAFh8tNj7P4dzfdZqr6hDDGyb3VWZNz7XqPbBJIyeT8zP86s//AI9VmS3trj5xtfy/l3f3a8mt8R6VCpKUdSnG6K+zf/ubfu09Y7aORPn2My/6tnpsKp5ypsZh/B8v3qWbY0hTZlP7q14OIj7/ALp7eHqe7flLVuz/AGXYgx97zdz/ADNVvyfLWJ4fM+7tZf4f++qqW8GNiPbM3y7dy1qxwpJcLbfMm2Jvvf8AoNeZWlHlPTpS/mEt9isr72+98rK9Ti3RZP310xVfvSN/FTrdfLj/ANJ8z5f/AB6nCH7Mod3U/wAP3P4a5+aP2RylEnaORbdUttyP8rPJ/C22rcMiMQk6KG37naP7rLUFjDNNEmP9T95d38NWo40Me93YN935V+Vqly5jjk5RjsamnqGZ5ndtm9XX+7u/vVr2NzNNIzudvl/Lu2VjW7JG0MUCYf8Au7/vLWxDceZI8KfMzfdbd8y1MnPm2FH3Y3LscyTR/ang2iP5f97/AHagkj+wWZhvH3D7yeWm35f7tXY1SNSiTSJtlVvLk/hqpcLunMM0kjD73zPURlze6Ty+01CaRFjDpNsCrvdf4t1Z+qTQyMiQJsTb8zQr826pN0LebNs3Ju2rtqpcWciqfJ3NIy7V8v7rV04WPLLmMa/vU+WJRvtlyv2lnUqu3ezVUuoZnWSZNv8Adf8Ai+WtNo5lVraFFDrFu/efdbd/FUU1vNbqT5ynzPl+Wvfw/NLWR4eIjyysYzW8MiiR0Xb/AHd9U5LWHyX2Js+fdu/hate4hSdfO/i/vf3dtZ95dfaIVhtvn8v5mVlr0InDUo80TOW18tok8n5pP+BLS2qwySb4XVhu2eY3y+XSsqeYzo+Fb5f+BVFBM+4QuVVt252at/sHNKny6lqa3gVfJR2O7/x6k+xp5aOk27/Zao4ZJo7jydmdvzKzJ8rf7NWLFXZGhk25b7lY1Iy5CqPuysxzWKXEJTydq/eVaoXcP/Lbeu/+8zVqws8MZePlGfb81UbqNFYQfNmvN5pRm7ndy/CZkmn3KIj71/iXa3zMv+1UNxp6bvJ+6rfwtWxb27+YvnJt+b7qru3U8WbyO/7lTHH8q/J8u2p+1Y1lH3eYxI9D3KEtvL2bmbbJ/D/u1paboMKyqgfJXa0XyfM1ai2KK290/wBcvybl3eXWja2Hl7NkLfwrtrqjzR2PLxEeYraXob2uLqZGdvvLt+Xa1bGj6Sizfvty+Z9/5Pu1LpunpuKQwfOu5vmb5dtbGm2aSGLfCzIvy7d6/N/vV6VH3Tx60eXcvaRZWzTJMiRukfy7v4dtdv4ftkWNXt/lVvm+X5ttY+m2MMe1Lb54ViX5tm75v7tdh4fsbbzYXg+UMvzf7VenH4DzK0v7x1GhrN5imZGdfN+TdXa6LbwxRmFIWV9n+98tc34dVI5IYXm2szfIv3v++v7tdzp9ukE3/HzJumi2+Yq7q15TjlKWxcs9Lgjj+zQpG0m1d8n8W2tLb5luk3kqzeVsfcvy/wC9/vUy3tbbcPvff27l+XctXriFI7dnR8hfl8ur92RMako6uRz0kE0yO6RYfd8m7+7/ABM1Z91paLJK6QsRvXdt+6tdHLY+cuxEb7m5vmqnNbwL++N4ybk3P/wKp+EdOXNPU5TVNMhmf/j2yypt3M/ytWZe6akcOy3dUSRdv3/u/wC9XUSWPnMsPRPm+Zv4m/vVi6pDNbnzoX2DY33f4f8AaaspbnXTp+9zHI6lZ/vvn8tmX77bNq/7O2sK+08Nu2IrTN/d+XdXW6zsmt08yGNPM3bJNlYOoWm5WSaHZ8v3o5f/AB6uKodtPc5XVLMNl0TL7W+X/arCvLWGORUTa3l/Nt/2tv8A47XXalb2DRvD58yFU3K0fy/N/drC1iz25d9qSfefdXnVNzvoylE5uaFI5vO+VGb5trS7qVrBHUpv81YUVt38S1eutPS4vI9/Kr9zau2oGi+xvsfzNzfLtX+7/vVivdj7x3RlIns2SMtbQ2ysrfwr8qr/AHmq3YmFpmd5G3+VugZW+bbWW159hkV9isGfam7d92mLrCsyO9s2PuL/ALtduG+EipUOz0m4SOZIXeTe0vyqv92us0OOG4uFhRP3e35P9lq8+0W5hZtkL73aL727b8tdn4V1J5V/fJtG3btX71erRlyxPPqe8eo6HH5K7PtP/LLbuWu50Vt0Mc06Kdybk8z+Fv8AarzfwjqFt5amaFmbytm1m27q7fR9UeaH7S7tvZ13bk+Zq9Knyyjynl1z0rQbqdrWJ5v3Use50kVvlrorW6e8jM0nz/umZ5FrgrDWLOOzjtUfY0jM27f/AA1vaXrkMduAjrjytqbmrSMuh50v7p0MMjr++WaNl+/tb+7/ALtY2sfZrjehhZ/4n2/w7ql/tRPs6TW0yo3lMrr/AHl/2qyNSvEMbQpNt3fcVajl/lK9pyxMLxI0Nqz7PMVd33fN+b7tcXqkqfbE3+Z91vmVPu11WrNctvWHy9/8fnf3f7y1zGtN8rgOoRV+dW/i2/xVzVInTRkcVr2zezvDIib9qyfxNuriNcs1kt5bZ3yG/hb+9Xd+JHhmhk2P8yxblkZq4nWmhuJPO2YLbf8AV/drjlE9GNTlPP8AxEnlwmF3bf8AfXy/mVa878YWaMx3IpVvlSvR/Elvf/aGhhRY90u/zGf+Hb92uG8VQzSQs6W0afN/f/irlqU/tHTTkeZeIPPSZYd7M/8AGq1yuoNM1wz7Njt/eeu18QQJFG8yWzI8f/LRX+7XF3ypNJiab5m2/NsrilGX8p2U5fCNW6RpGe6ufm27dv8An+KorxkkYSI7BF+bd/eprY+bYmdvy7tn3qjZXkh2R8Kv3VVKiPu7FS96ZHdTeYyeYm5Wfbu/u09tkcjBE+T5vNVv7v8As1EsO750+f8A3f4qnVbnc0yfKrPuXc9dHOYR/lEt5H+R0hbcyfdVa0ls0bdD9mX5ov8Ae3VDD+8k3zblbfuWRavWqpFbibZub723dtrCpzHXRjEfDazyQ7HdU8z5av2K/YQ0Gzeypt3Mv8NRWfyt+7RlC8uy/NWiqotwE877yKyK33WauSUj0I0+aPMXtJvvLKQpbN91tm7+Gui0vUoYcfvtxaJWRl/vVzcMn2Vk3vtkb5tqt93+9VvT7lPtC7327vmRq2oyjzaGNTm6lP4/XsNx4OsY4wHb+0AZJR/EfLeug+A8rr8NrTy1O1JJTLg5/wCWrdq4v4zO8ugW88jks16MgNx9xu3aum+CNxGvgG3klwRbtKQBLt5Ln71fp+Il/wAazoP/AKfP8pnXViv7LSf83+Z6HM0M0hd5sx/egk/i/wB1lrM1SZFmZEXft/550sN5NJv3oz/3Pl21VvPJtVZ3WTzmbcvzbVX/AGWr8/jW5T5+UeYz9Um3W+yzdkGzG37u3/ZrmtWe5jkeB5PvIu9l+Zd3+9WzfSXNxvnd4w7Jt+ZPutWFqnzMbOZ1X733vlrf2vKYez5jIvrpIZnT5drJtT5657Vrx0kf7M+1dv3l/vVq60sMbI6HYrN95fmrn76Nzbt94bX+fb/DU+0jIUaZR1C83LsRGz/Hu+9WbNJ5myE7cbv4v71SXTTSN87/AMHzbkrNk1Dy/kfao/vf3mrKUjaJdaS28nfnezfLtb+8tE037nZNMo27dm16zPt0kymGZ9nzfL/wKk+0+W38IEf8LVxyp+/7p3Rqe7ym/HdbpmdEw7fe/wB2rVm37svvXG/+L+GsG11D5g7z7vm+ar1jqTx7kSRUXduT5fvVhWozOyjW93U3FkdcRu/Kp/D/ABVowyfaI2d3+Xbt2rWLa6i8lu375Wb7tX47qFoRsuflj+Z1b+9XLUhc76cv5S/DDDtVHTe0j7UVfvf8Cqwtwkmz5Nj/AMSt/wAs6p2uoTXCsiOyI38S1Z/0mRVQbXKttrmlCXNqdManLH3SGaPzrg/6Tx/BWRqao0nlzIxC/c21pXXm7gn2bO1vnVW+7VXVbd1jXyXxt+4qvWtOPLyozl72pl/Z98mJEZFb+GR/4qgjsvtDNDNwP9+rUkPmfuXm5X5v9qug8C/DnWPFV5DbaPZtI8kqrt8rd97/AGa76MffPNxEuWIz4d/Dn/hJtUt7Ob5GklXytyM26v1X/YB/Yr8N6Ppmm3Phvwfa3+syXqtLNfQeay/L/DH/AA15Z+wd+y3onhbxxpupX+lTX91Y7vtUi2qtBHJ/d/2mr9Iv2ZdXv/hjdXegfCv4dXl34h1S68+61TVHVbazh3f3v+Wkm37qrW1Sty7HzmIrc0uU+n/gb4RvPDHgu0Txtptra6lt8qLbbrHub/ZVaZ8YPj18FfgHoH9u/FTxZbrLC/7qxt4t8s0n8KrGv8VXfBS/ESz0e5vby1tbm9dPMW+1GXarSN/D/sqtfLH7S/7Pt54oj1Kf4o+P7O8e+bzLWx0O3k3Ltb5m3fwqv96uadaWltjDlgcH+17+2T4s/aD8PWmmfCT4bxu+oXXkRXWpL9subFdv3ooF/dxyf7TfdrR/ZM/4J16zqnh99e+KnxomsZbhVW90+SyWWVl/66N8vzf7NdB+yX8FfCXhfxNB4d8GaVNDYWq+f9quLhpLm4mb723+GvtHw54Gg0zTWxD5Esi5+0OFZl/76ralLmjdA3zHn2ueD9F+C/wxTwf4M8MRu2n26susahax+WvzfxV84/FLUvFvjApfwwx6s8ibtq3m1V2/3a98+M9x4D0XTbhvGXxDk12Wadk/s1rzy49yr8qsq/e/3a+Hf2jvFFnY31lrE3iTw7C902yK3sbzY8a/7Xzf3ayqVpSkR7Pm+I5r4qXH/CD2Z1vxD4Svrb70v2O3XzW/3lVayvDv7Tnwx8VXD+HtE8STaU7Oqz2NxEySM38S1zV9+3dr3w1kn0H4V+D9J23EW1LrxFbtdyNtX5m3V5d4d+Hvij48ahc6xeeP/wCxVuL1p5bePSVggaRvvMsn3ttYc0560zaPLy2Z6J8df2W/hjcyS+NvD/iG8m8SXm1pdPjb900f91m/vU/9ku3T4X/FKFPEP7PM0+nSSq11qWoX+9vl/iWNV/8AHawvh/8ACvwv4Z8cW/h6/wDi7Nc3lvFu3Lu8hV/2t38X+1Wx8TJvHng/XI9d8E+PPFFzbQsq/aNJ8P8A7tV/2W/5af71XKVRxszLl968T1zx/qHir4u+NbjUtN/Z40e3sVuluItQ8SWG2OPb/CsS/wB7/arpV/bI+IfwFsf7S8YX/gPWIo7rb/Y+ny7WjVv+Waxr92s/4cf8FKNH+GOk6VpvxC+G/ibXLVpV+1alqlrDErfLt+6y7qZ8Qvgj+wr+3B4o/wCEw/Zsv7jSvFNr+91bSdJnZYtSb+KNlb5d3+1/DWUnGcOSXus05ZU5cyOzvv8AgpB+zx8bIbbwH8Qvh7JoL3UW+K8huvk3f7K18x/tu/su/D34jaTe/FH4Y69b6kbPTpl22/yvMu37sn975v4q1/FH7PPwf8H6hN4e+KnxR8H+E9ct5dkXh1de+13Ua/w7tv3W/wBmotF+F/irw3Dcak+vSavo99dMqXUKbVVV+6u3/dqIylR93muyZS5vfSsfkfren3NrqUttqUP2d1fa8Kr/AKtv7tRtbp8sKXO4L/DX6afFr/gkXZ/GbVpfGfw68W2drPef8fFiz7WVvvbtu3+7Xx3+0F+xH4/+A9xNZXiR3IVmbzIZdzfL/wChVv8AV5Shzo+hwWbUJxjCR4VJbusO/oyu1Zd9DIyffwflb+9W5cWqeZ5MzsP93+9WfqUaLDstnUj+Jm/vVjTly+5I9aUYS96JzuqKnlumf9r5flrA1SN5Nz718r+Ba3r797vRztrG1CN41d3KszbvlX+7XoU+aXKeZWly/CcxqCsyfcZf4lZaxb5YYso+7c38X92uj1CP7VKUHyv/AHf7tYF7Gm1t75P96u+np7p5NSUuYxplSGTYPnaT5f8AZqnIhaRk875tm6rV1+5Zn+8KhSP5d6Pkfx7q6eb3jAgh3rjed277zVNCgDLsT71J5O5tiOv+zUlurq2eu1/vVXwkczLmnr8zI/8Ac+WvX/C3zfDZOcZs5vw5avI7SOFV2Puyv8VeueE2ZvhxGxGD9jl/m1ffeHn/ACMMV/15l+cT3ck/jVP8L/NGLoc22RU+993ezV1GlybbhRbQ7v8Aab+GuS0tpmmbZNuLJXT6TJtjXDsD/Btr88lueL751+lyPu+4yhvl3fwtXUaTIjQLDcvvH8HzbdtcRpd6LZlczfKybWX71dDpd4k0KtDCrSq/3qUo+7qRLc7TT7gsvL7FX7m162dPvOvkovm/eX+61cfZ6i6srzbU3f8APP7tbGm6lCJgiXS/7zfdZaCOU3LWZ1mind2mOzZ5e3bVjzpriQvhd0fzP5ifK3/AaxV1aaOFIURjt/29vl0rXyXMi+Rt2t8u5W3bacpdi+Vl3VtQRV2eYwOzdu/hrEvdQmjQvFwsjbd0f8NR3l86x5mfcuzd8r1kXuqPI2903bfuLvrP2nN8I4k91eNCNnXzH+fd8u1azW1BBIqQpz95Nv3az7y6SRTsO0fx7m/9BrPluPIhD/aW2qv3vvVMvhPRwsve5jbbUEhf5IWG7du3Lu+WoLjWHjXZH86qnz/3WrLfUkZVhSZsKm1WqOS4dVkhRshf7r1zS96Vj6XD1pS5bE2oXz3EbB441ib+GsfUrtFZ02ZSRF+XfS3V4kkLI/3f++dtZOo6ltLP8uF+X5aPtHuRrS+KRQZndt6J91NrLUEzTMux9uV/u/3aWRn3EoF+9/FUNxv3Y/8AHqxlHofM0cPIrxx+dO//ALLVm1V5lV0Taf4/9qo9PaFV3ojDa+3dVuzjeNtibss/3qwlLlO+OBLFmqSRo6Jl/u/7NalvboyedDu3/wB6R6p26SGRd6Zb/ZWta1XbvT5nZf7tc8pC+qyLelfaW/fImwbvk3LWzY25tzvR2+VPk3N96qFnbfuzM+1gy7f92tS1t/mX7Gnmqq/N5jVj8RHLGMbcpq6RdOrfuY2Ib5X3fdX/AGa6TR445lYP99f4q5vS7ya3Tf8AZlkRk3Ozf+O1vaXdRLvR1ZW2K3yv96uqjH3r8plzRjA3YLqH/XXMLfwqrK/zM396pJpvLs3ITlXbay/3ayLGQS73dG2NL/rPu/N/s1LNduzfZoXyF/h2V61HT3jmqVOpR1Ca8aP5/m3J/q2+X5qx51nZj8nmL/eWtXUo5pJXeN2Rl+bb/eqhdRp9xEyrfxV6UalveZzylGWhQWHzEdPmcb/vfd3VGvnJj7MWXanzL96rTWrwsHc/Ky/eqr5flyJ9m3fN9z/apylCWhzRjyiqyNlIdqt2206OSFsQ/eVvv7f4qctq6yMXhx/Fu+8tOisXhjX59rM3ybYvlrgrShI7aeI5SGaHb/qX2HZt3M9TQwu0e+F9j/8AoX96pY7O1kxseSQr9zany1ZtbVI1aFUZnZvk/wDidteDiJcp7mFqc0boXT7GGNftKWyru+Z5t33qu29q83+u8zPy7F/h21aXTJo4Whhhjb5Pur91atR6SjKqXO4O3zMq/wANeNUlzTuevH4LFX7O6/c3Ju/1qtT44fMbe/8AvD+6tXvsSLG8yTea6/wt/DTvsMLZ+Rn3Rbm2/LtqJcktC+YghhdVSa54aT/Vbv4aswxpJidNy7X27m+7T7WxmkjKPDvjX7m1Pu1baPyYzDDbM6qv3tv3v+BVVOMpS5UclSpGMfeEs2medUm8vPmt+8ZP4a1bXzpIfJfbn/noqbaz42hMPyQ7lZN3zIystXtMmkab77GJfuKzfNWvs/d5TnVb+8abC5fENzcqBvVtzLuZtq1BqUh++/71l+60bU5V/ef6HCzHdUSW6K3kujfLuqI0fhjE09t7tylczQxzIiP8235/k/8AHajVnjXYl1s2o37v+9U99G726p8zRN/Cr/NWbJsbeE3fvF27ZK7qNH+6ZVKkuhZDW0dnFN99o23bpH/8dWq0k32xY3hG3722ooWdtiTusQj+5H/dam/aFbCJNI+59u6RNtetTj9k8+Ufae8QX29VeF7Zd0f8W+si4WEp5PzB2++1aWoRp5m9/l2ru2q1ZGpTCRXdPkVX2uyvXZGJlKmUppJlmSM+Wf3rfM38W3+7TXuPMZX2bA3/AI7TbhUO933KG+VZGb73+7TYVhuGW2m2sFXdtrTlicso8vulnS9jBkeRfNkl3bVf+GtBlhWTZCGyv32ZapQqkOx3RmZfueXV+3y0bu6bW3bkVqxqSMvZ+8Ps2f8A2i21lZWT/wAep8cbzSec7/I3y7f9qo8/ZWcb5P323+OrkKpMpeMbN3y7lT7teXiJHZTjLoRWNj5Mjzf3vmRfvbav2Vu9xN5MMzFtm/aqVDDC6qIUdsKvzySJ97/aq5DMsOIfOVm27W2rWfv/ABDlL+UhW3R12b+Vfd8tWY0ht2+/J8u35v4mqOS4hVgnmMTs+8qUjXFttT9zIXX5ty7q66ceY8yv+85mXrVoY4zNv3R/x/3q2dPms2jbzrPP/TRk+6v8NYNnI5vPs3zQ7k3btnytW5pcyKyvIdu19v7z+Jv7tetQjc8StzROx0VUWGB3mbYyfd27dy13Hh2OHyYpk4+6yMvy7a4HRLj7Kse+HasfzJ83zbq7Hw3eJJCgSz3Mz/djavUpx5o3PGxHLzHfaJdJGUR5lldnb93H/F/vV2GmybrFX+UDarfe2/8AfNcFod19oxMgZm+6q/d211mn33lt88yuI1XcrLu+atDm+I7K1urmZV3uvl7N+3+JavTXQWYujqVX/wAerDsdSeHcltc7PtH+vaSL5W/3atw3kDBkSZREqs26T5anmKjR6FmRvJk+SH+Dc676zb5oZG8504/ut/DU0l1bNvuYdwWNd3mN/Ev96s241S2mvo0dF2MjOjL826ueVY66eH7kGpN5kfnQw+aNjKqs+2ud1CzSOHyfJhfam5vm+Zf93/ZrWurjzoXx/C7bFasO6mgWMonKL8qLv+7WNSodUaMomVqEHnR/aJpuN67VZfu/8BrG1T5bjfM+WVWfatbd9I/lt5NysRX7/wAn3a5rWrqwt1ffy2/7qvXPKpzHTGnLqV7repXzoWZG+9Gyfdb+9WPeQw3Sko7OrPt2zfw1Y1jXnkheZJvmX5d0lc1qXiHyo3huplcM25F/hWuSpLm+E6IR6SC+Z12JG67o137l/wDiqyNa1C2tzL5MzI393bVHWvFE1vas9sdz7v4X+WuS8QeMHjulSF49qo3y7/vNXPzfZO2nKXKbd14gWOQPNu2L9z/ab+9VBfECNJsXc7s3yM1cfdeKIWbzk+R1+b7/AMu6qlv4onkuAfOZWX+9/FXdh4mFaXMevaNrn2VVf7TGyqvyL96u/wDCGsTTRiJHVZv71eIeE/EQ3DyZvm3/AD/JXo/hXUE2hJrna7Judv7telSlzfEcNT+6e0aLq1tDsR/LhZkVkk+9XZ6DrjyNFdPcsNvybVf73+1Xkeg3ztNDNbTKdybf96u40XUkmj3/AGaNC3y7mfbXqQkjzqz6cp6NpviDbcLcjaBu27W+aultdSeONfnjRG+7XnukahD9lTNzI7Kyq/yfeWun0GT5tk3zhkb5vvbf4q3jKPN5HDUjE6iO8vGhaF5s7vvsqU+OG9jiXe8L/LtZf9qqtrHuhQ/adqKqs+1lb5qsw7Jrj5NqM3y/vKfLEiUZe6ZOtRw+ciTWykL/ABK/ytXM+IpP3a2ybXbazPI3+fu11uqeYZmd027U3bf4q5DWo4Y4du9lT7vmfdZa5qnum0NzifEUyXCu7lgGiZVj2f8AoNcRqioqrbPctsbdsXZ92u38SNB5h+x7VZX2vuri/EkltGy232fc+/crfxMv96uSUrSPRhLQ43xVa21xbslsWdFi2pJv+Zq4fXrSZreOaymz5O5fm/hrvNUWFZNiI0Qbcu1f4v8AdrlNYtUWHekzIZH+6yfLtrmlE6Kex5p4gtXvIXRHZoWbc0dcfqWlbd/ybB93a1eo6pYwwh97rmT5UZf4a5LxBoyNdeXN8+37sn96vPqe8d1P+Y4ma1RYzMv99f8AZ+WkaF4/9GdI8r8yturYmsXkm3p91fvLt3Ky1FHpcMsjzPDt/v8A/wATXHKf2TfllLYyI7OZma5m8xF/g2/xUxbfdNuR2ba3/fNa91p/BdIZF+f73+zVFrTy5hvRstLuVv71bfFDQUocvKOtW8mcbId6q+7y2q1DcP532NE+XZuSOn2tuix73k3t/sr/AOO1YsXhkPnIm5WZlbzErGp/KdNPmjEnt47ltnkpuVk+81XIV8uQQonzKvzsv8LU6ztZjGYX3KF/1S76tyaakKq7uxhZf4vvK1RH4dDqUp8xXZksVZ53bOz59y7qu2q20U0cEyQ7V2/N/wDE06GGdZC7uys38S/w01bd1X7NNwW+bzNv3f7tHxfCEo8pzPxZeJtEt0jBG29OAW7bTzW18Jnlj8HQYC4LyZDdhvPNZPxgt2t9Es13BgZly47nY1aPwpRX8Hx/vGVt8mGzgHDE7f1r9MxUpf8AEMKDX/P9/lM66i/4Top/zf5ndR6pC1v9pLtvV9z+X8zbahvdRRp5Eh8x1+6qyf8AoTVR3T27eT/q9ybty/dZaryal5kLeV5m/wDu/dr82lWOCOH7C3135PzpDHub79YGvSO2+ZJvk27kVv71XbzVvJVnhfHzrurF1jUPtELom5t33FX7tRGtLmuZywsTKvrh2k+fhf42/u1zuqTec/z3Klf7v96tK+utirv+dtn9/wCVWrIvo5mVd+3d952WtI1uaXumX1flmZ2pR7rht6Y/hVmf71ZV5sZvuKo/gZkrQul8zbs2v/d+SqF8r/K8z8L/AA1rGXtCJUyncO6/Ojqyf7S1BJqCbd+9d27/AL5qO8ZNron8Pzbaz5pvLYQvHubq1ax7mMvdmadvqG2TZs2/7Va1jNC0yTbPmXdtWuUjuE/g+Y/x/P8AerR028dWaabhm/i30qkZ/ZNqMpc51kd4jRD73zL8/wAladldvIu/5v8AYrlrW8dlx9p5Vv4q37G+eRdgTKbP93bXBOPNI9ej8Bu2Nx+5SF9ys331X+KtNV3fvN+xv46wtPkSOH/XMzKn8Va6s7bZim9pPv8Az/w1xVI8tU76ceaER+o7/McI/wAzf8tKozQpJh5nUt/Gy1oyR+XGHROVX5/7u2mW9ik2fu/3vm+WrjGPLzGVaPNLlKNjpdzcSeXs2v8Ad3L/AA19HfsX/BPxz428UQab4VsNQlu9Qf7PYWtv96Zv4mZv4Vrz34DfD3SvEXii2tvEnFvJdKl15MW6SNd38P8Aeav1t/Y9+GmlfBfxF4a03wToOzxTq37+1s1iVm0+zZvlaRv4Wb722uylpHmkfOZpWlH3EfQf7Fv7HE3hpbLRfiEjJLawxyfZbG12bW/i3O1fXGpeAdH0icaroWg6VHKu1P342Ksa1bub6Hw9o1os2u6bazKkf2yW7dV3f3q+SP2wNO8YweNTrnhv4reIdYhvnaKLSLCz3QW7MvzL95d1TVqez+D3jx404QXvnu3jJ9burpNP8N+PNLheblo7V/P+X+L5f/Ha+Y/iV+0J4t1rXLzwB4WezsVt7hotSvLho5ZY4938Kru27q8Z1j/htXRdaSGb4PyWMMcSwJqGpaktqs0e75VWOP5q9c+FPwtT4aabL8Zf2h5vD/hXTdPeS6XT4Z1iW8Zf4mZv30zVyzS1lNGnxQjyHrXwQ1fwH8APCr/ET4l6xpelK0TLbzanKz31x/d8iD+L/gK1l/Ff9tr4kaxo93beHvhvNoOix2Uk/wDb3jLUo7F75f4fJi+9t/8AHq8W8eftKab4km1T45eAPAOjldPX7Ra+KvGTyeRGv3VWDzP/AB1Y1r5r8J/C/wCJ37fnx5uvFXxC+Md5r0G7zdUuriLyoreFV/1ca/dgWoVaOIjy/ZNYw5IEXxE/au/aN/aS8ZWHw6+CejR3UC3TJOuk7vIt933ppp/vN/31U3xA+CPhf4a6Dcw2dnpOseIbW183xDrl5KzQW67fmt4F3fNJu/iavqjRdS/Za+Gvhu1/Za+APifw7pVu2lyXvjXxFNdKktjCvzNuk/hXbu+9XwB+1x/wUy+BviLWdc+Fv7LXgxb/AMOaDLJbp4qvIt0epTN8rNHH96Tc3/LRqvD1MNT92GpnKnX+KZm+H7fw34s1h7nWPEP2izsUjW1tbN1T7VNI3lxwx/xN81fQ2h2fwf0HXNU8B+Lfijo+gQ+GdNZ/GV5ay+e1j8u77LD/AAtcMvy/7NfEX7JXhP4nax4/tvjB45SbTfDug+dq95faha+XH5yxt5Kq33du7+Fa89t/Elh4H+0eJ/H/AIwW+k1jV5tRvJrjd5V5IzM3/AqVatyRLp04y1Pr7xN+0xc3nhhrn4G/C6Pwx4Gs5WR/EmtQLJqWqNu+983yqu2qtr8cPj9ps1t42sPiLrmrWkL/APINt5Y/L27flVo1+6teReHf20dB+I11Z6N4nnsYNKhg2263Vv8AuF/7Z1778MdN8T3Ghr4q+A8PhXUWk2xXWnwxL/pTbd23b977tc8cVRnK8jX2M/hien/AT9szxD4+1XT/AAl8WvBmkvZ3Hy+TfWCu8m5tv3tvy19Q+KvhP8OvAfwz1HR/gb9l8I61rE6y+I9U8P8Altc2q/eW1X/nnu/5aba+SP2Vf2tvgtpPxkvfDf7VPwht/DF7oqSOt1b7mit1X7vyt975v/Qa7rwj4hf4f/EzxJ4q+EvxLutc8PeKriS9lutUVd7eZ95W3fd/2a562MdKTUZfeaRwrqbxPN/26Pgbf6potr8QhbWaap4fZU1TyUXzLxZF/dyM22sz4O/EDXtL0uz0TU55prZv3kULP8sfy/xVteMNU8SX2g6rba3qTP8AatyfvpdytGrfKv8AwGvM7q617SfD8iTSQxMq/wCsX+7XFWzLnnHQ7KeWyjTlc+mfhT8dLDTPESWdzZ7HmuN3nLKqqsf8W3+9/wACr3b9oz9iXwL+0F8C7q/1XwBDcOsTXFlq1rdbZV3L/s1+YeqePPFWh61bX9hfw3Lx2qxRRtF8u3duavtX9hX9uLVbJYfDfjy/aIr8zTMjLEqt/Dt/u16VHHOjGM90eVWwvf3T8yv23P2Fde+BMlxrdhqtrcvCypFHbuy7l/4FXyheXyfMiQ43P/49/tV/Qt+2Z+yzoP7SWgzeIfCv2W+tNY01kuI7ODzPssi/N53+zX8/nxu8G6l8OfilrngO8hk83Tb9leSbcrMtemksRHnid2V4ypH91Pc5TUZE3M8219vy/L/FWLqChV8x/My33v8AarTvbibkbNyfd3L/AA1lyrhSmGf+63+zXRT5uU6qkjn9QYKxm8liW+X5W2/LWLfKjN8/Cr81bl4vms6Qp83+9WPfRPJuf73y130pRPNqGPfTfP8Acb+6q1U8vbtd/lXf92rtwvyrlmX/AGarTJD8rp83+9W0eaRzczKskO5h5gbDVZtU8tvk+6v3KZt2bn2Z/wBn+7U0LOXZN9WLl6Fyz+7v35/h+avWvCzqfhzG6hSPscpA7dWrye1jk2qn8K16v4aVE+G6JGOBZSgf+PV+heHzi8wxVv8AnzL84ntZCpKtUv8AyP8ANHNaS37w7IPu/dauj09nVUT/AJ51ytrNN8rwvtH3duytzT75LjBTcPk/ir87keR8R12lyO0Ku7/KtbdrefZZP3b/AO/XKWMj7AiPtC/e/wBqtmxunbMLorj/AHqiXNsEvhOr0u6hVeE+Rl27t9aUN4/mDyU3qyfKrfw1zEMn7rZC6ruq7bteLCE37W/8eWq+Ey+I6aPWoVjNm8LP/f8An2rUE2qQxs7eS2FX7q/MtZDXHl7PvE7drrVdv3LNNC8gKp93+9RGPuD5jQur7bAId6/dX7ybqytQvHW4dIZo03OzKv8ADTbq6eX98+7d95G/vVl3Ekys8Mzqy7P/AB6iMZFR3C8mnWNUe52n/Z+aqjXbqzTJ85bdTfMhmk/1jYVGX/gVU2mfaEgm+b+LbSlE6afckkmeNfJd/vf3aZJcIswjR2+5826o7ibzNrvMpf8Ag/vVQvpEm+TfuVv7tc8o8p62FxHLyxLNxcPCux3V1b5k3Vl3l0ksmx3VN38X8NF1MikPsXcv8O+qtxN5kbI6KoX+792sfe+I9+GIjLTsXJrdG/1afNVba8zKmzbWlcR+X8/k/e/hWqckO2PzkTDr8ybq45S9w9CjhSJYXz8ibj/dWrC+dLH9/DL/ABLSW6oVZ3m27f7q1Lbx/L8m1lVN+1vlrmlLl2PZw+D5oluztnuBsR5E3fNWvp8ybWhk3KGfZu/u1RtVdbdQ7qvyfdWrtnDDJCu9Nv8AstXPzRb94yxmD5VpE1NPH+kGF9rIvzeWrferahktlVZkttr/AHX/ALtY+mrNDGEmdfmrQs2RJH+TYuz5NvzVXLE+bnGdMv2UySSG2MOzd/zzWtWxuvKzbSMqpu2o0n3lrDhn+zs/3Sv8O1vmq5Hb/Lv37Pn/AL38VdNHSV5HFLc6O3vtv7kJv8uX7sdS3V5tWW8T5Nv8MfzN81ZdjC+0nzmbb/d/iq3bs7btiN8r/e316lHk5ThlGf2iK5mtpFa2Z2f5/wDdaqN8Ek/1I+bduSNflWtC4tzj7Q6bmjfcir83mVDqFu81wj7FVd6/KtdkanuXMpRlIpec7HMm1G/3v4qIbNDtkR9m35VWp2t9szXPks4b5t1JZ2yQ/uXfa/3lalKXcx+KZJJC8bLDM2wsi7NtPa38sb3Xyt3y7ZP71PW4eYNvRtv/AI9Vz7Okip++W4Zk37Wf7v8AvVwYitynTRjzFGG1wwS2k8r/AGlWtDT1hVS9+/zs+3d/tUkdq7XieSjMf41V/lrUtbWZWO+bmR/3S7a+cxFScpcp9Bg6fLC5JZ2LKqO+15Wfftmq5cLMu5/JV2X7nl/w0kdtcqsMPneZt+/8v3au2VreLZ/Pz/eXbt21wSlL7J6lOS5veKEML+YXe2V1bhqdHCkZ865Lbvuqv8NTwwmKbybbl2Tcis9XItN863fYnlIz7m3feb/dq6VONSRNat7OGpTs47mSRJndVi+6y/xLVuOFLlmhSHYys3zKv3v96r1namSQPC6hI12yrInzNViG3m3NseNl2f3PmWvSp4ecZ2PLrYiHJzGZcW9zDbhETDR/f3fMu2rOn2vnKiPyytufam1q0ltZ/n37trKvlNs+Wrdto73TefbWHmtMn72TftrqeF/mOP611iZ629yzSpbOsaLLv27/AJVWrEem37R8WzNFG/yN/s7d1b1roz3dr9jh42xLt/vf7ta6+G5re3REhwGX7zJuZa6VhY7qJn9a5viOAutNmmV9ifLH8rx/cZf/AIqsi+0VJrpZ0hUM0Tfu2/hr0+bw3bN+6dI/lT5vL+9WZeaCkhdkhVQrr8zfL96t44c3p1uWHLI85bTZmZftMMaFfm8xfmqKTSXuvJkR8Pv3sq/3q6668P3jRsk0GYvm3rH95qh/sXylDvCp+TasbV0Rp8stDWNpROI1bT3dfL38fdb+9WNdedax74YV+V/3qtXa6totzJcJ9pTb8nzSR/w1zupW9s0jwzJuG5fvL8zNWnLzC+G9jnLyWHc833Nv/LP722qYW2WZnmm+T+Nlf7vy1q61YJaxl3mUbpVX5fvNWHJcTsxtrNMbvmRtm5dv8VLm5jCpHmNFdjTBEdgrLtdo2+Vammme1tWT7Sy/N+63LurJsbqcyL86k7vnX+9VxZHWNn8ltzN97d93/ZrCUe5ze7GJbkvNqxzOiiX7r/7X/wATV+31iaFhZpc4kX+H+9/wKsJ9S3bHdNy79zKqfd/2asw3kLRs6I3yvt2/wrXnVKcefU0jU6xN2S8224eab55Ivvfe20f2jN5Z3ou//nmr/wDs1ZjXXlL5cKZiVF+9821aRbyeaRnRFdpPvt/DUU4zJlLlNL7a8kozt+XdvZf/AEGtDT5t0K3LzfL/ALT1iWN15iFMKn/A/utWjp8STeV93ftbav8AtV6FFf3jy60uU3bVXWSSS8ePZ8uzb/u1d0uN/OZ3hyF+T5vux/7VY1vN5cYTz87vvx/xbv71bNrvmQzPMz/Oq/Km3dXrYeJ4+IlGW51Gm7GZI9/zN8qbvlrq9HuEtZESGb/WJ95vlrjtJ8uS7SGR/nX5Yt38Vaen3yDCTpHt37fm+avTjH3Dxa0pRlc9F0e/dbcTRvt2/Lub+Kuy0G/mjV7nfhJNv8O7/gNeX6HqyfZd8nlqI3VX/vf9811Wi65BCzpvkCsnzyLRKPu2M7Xlc9Bh1p47qJPl2r99f/Zq0l1qFVGx45m+95e771cDY+ILO6YWdy8jMsTNEyp8v/Aqt2upI0ab32MvyS7a5akvsnbRpyOu/tDy4/JmkZfl+6tZl5cQ3EITYxaP70av81ZsmrJHGJrZ2Zfu7mT5azp9YhWQI27fIm/zm/h/2q4qlQ76dM2NSuoYWR33fKm7aqVhatrE0mbaF9ib/vRr8q1l3GuzSLve5Z1Vm8pv/iqzb7xB5MKOj7mX5U+bb/31XLUqSidUaPMWtQ1r7RC9qj43blb5fm3f3q5nXNah8s2fysPu/wC18q/eqtq2t7rp4XmVP4vmrj9e1JG3pDNG5V/3v735qw9odHsZFrWvEUKx+Tvk3btz/wC1XFeIPFUquU2Rj/ppv+9/s0viDUE0+NRsbOz5FZ/l3Vx2tawGb5No+T5qcpDjEbrXjC5zKiOw/hVv4mrjte8VO0jTTXPzL/dfdtp3iDVEj270k/i3tv8Alrl7z5mfZ93Z838W6sqcZm3wlu48URMpSF2bdLu/ef8AstX9F1R7i6ZLlMmP+GSuQk+a7WfyVdoX2pu/hWug0O3miVE2KRu3bpErqjKJEqfN7x6d4XvJo4UeF/m+992vSPCmpTLt2fM7bd6tXlfhUTLJCju2ZPmfalekeG47m3jaby1G35VZn/1m6uyjL3bHDUj9o9d8OzJC1uzvGA3yqscqt/wKut0u8SPejpv2y/xNXnugrbLHb7Ldcb/ur8rbq7Tw60cyxzJ/rf41rvjKZx1Ine6PdQzW8Lujf3XVfmauu0m4SNVTewZv++q4TRZoZo1h81VK/Mm5a67SbjdcRO8Odv8AFD/ertjU9mcFSMpHaabeJ5Y5WFtm1G/56bauRxpH8k+1ZZH3eZ/EzViaTqUNunkzPvDbmT+8v+8tWpdVvGj/AHM28sm5ty/NXTH3YcyOP3x+sTfZ5k86bYVbdu/i/wBquJ1zVEW4eHzl8tpW+ZvmZq2ta1TDL5j5kj+auL1rVJkkZns22qv3l/hasZfGbR905zxDeQyfuYYfL8uVVaTb96uS1a4+2SJciZWXZtVl+9tra1i6e+mMz/Iu/buZa5rUriZmb5F8uRGbzGf7rf3a46nJKZ20+xjatfOzbHeRnjVV2r93/gNczrUZj33kyRl4/wDlp96umvovm3zXMe+T5Ym+61c3rluI2S5uXXdIjKlc1Q64voc7qVuiWqO8Khf733m/3a57WIk+SZIWeRm/hf5WrotQb7VDJ5xVBtX5o/7393bWNrH7xw6cfxK33VWvKre6dtH3pHM3Fqk7ND9m+aP7zKvy1BawpHIfJ+9/31V2bfNI77FVP71NjheNsujbPus3+zXmyj7/ADM9GnLl90yb61C2rTfNub+Kq8Fv5MKI8LEK/wDFW5cRpGphT5v4kaqN0iTKrv5eY1Zdv92tIz5TSXvFWP8AeL86Rrtap7Oz8u43lFYN/C3zLUflPHsmmh2t97b/AHq0LO433CfJGqK+3/a3UpSma04w5S5br8rwpCxb+9V2Wzdv9JR+F2qyybW/8dqtp6vMrJ5zffZv3ifw1cjZL1Q7vtkZP4U+9SjTlze6Vze4I0flSfPCzp83+1T47RJpEmCMxX+Hd81SfZpgiO9qyhv+WjP8zLUu6aZg8LqF3N8qr8zLRL3X7oRjOXxHB/F9Jk0W3Msb/NdghpOv3W4rV+FsbyeC7ZkBfa0u5CMA/OeM1V+OUDR6NbuwYZvQAG7DY1XfhSJB4LtjHtIZpVdWbp855r9MxUv+NXUH/wBP3+UzsUX/AGdFef8AmbN5cOsLQunzr9xWbcyrWTqTedbsXferfeb+9W55OyZ/OhYf3l/iasfVId0jxQ/IP7v96vyutLl+Exp+9E5vVDcyRtDE+WV/urL8rVl3X2xd801sqL951jfdWnqln5Nw8rxsDGu35azLizDM1y82Pl2uq/LUxl3kWZepSBlaHZGv8O3/AGapyRurbN+4MtackKXUap8qL93d/s1Wmsfs8LoX37fubkrSnWjEzqYfmlzIxNSbdCkKeXjZ8se3/a+9WdeL5jNCm3O/5619SjdV3w/7K7WrJuFmhjlTO12fdu/2a9CnKMYHn1qfNMxLxbaOTf53z/x/JWPqEzw5SFPm/jZq2L6ZIyZvl/4F95qxNTkhaY/J/vV2UzjqR98heaCFv3jyFmq/aXF1JLvd9p+7t2VlyfNIzoy/7tW9PjdptiO2KuUeYuJ0mnzHycI+5lT5Grb0ubzI96PJu+981YWhxmRm2bd38O6un0bT5mj2TPgM/wB5a5alPlPVo/Cauj28O3f97b83+Vrct7e8upopkZvKZP3X7rbVPS7SH/Voiq38Lfdrf0mHyz88zRj7qL97bXHL+8ejTjL3RbWBJGELuxVX+fctbHhvw7DqmqfY4bbzXZf3q7/4f71JbWMy5Tdnbt+XZ/FXT+EdFe41iJEh3Sfwbf4d1Y+zjLQ3qRlGHMfS37Enwj0Oz14eM9bh85LNldI9m7cyr8tfpL+yP4BufDN5e/GbxJNavqWpP/oG394/l7flZl/hWvlj/gnX8HbnWNF0rw09neL9uumkv5JIv9XH/F8392vv/wATa54Y+DfhWbUrDR45/wCz7fytIs2TatxI3yx7qcvd91nwGMrSrYiR0Pw70ebWlvr/AOKmq6fqEzStLbxzWv8Ax6x/eXd/8VXIftS/tQfDX4e6Omm6V42stUmk2/Z45tO85V/veWy/+hV5n4r+L3xRm8L33hKw8KxWmpas0b65q00v3o2X/UxrXkPxK+HusapHN4qubKTVbyx05lSOR1RI1/8AQVWvOlKvKMuTQdKjScrSPG/2kP8Agol8VLPUH1+58VedbWNwy6No9qu6RWb/AJaMzfNXz54u/ay8Q+Jr+2+Inxgvbq5VZd0VjfXTMsn+ztZvu/7tQ/GzWNW/4SLUbDwveQski+VdXEMW5d38Sxs1eBeM9D1LxV4kgbUryS4trOJVt45v4m/3a5rU49fePXoYWUtIns+vftYfG/8Aao8Zab4Amv7qz8P28X2eK3ht12WNr/0zX7qs395q9T+PH7V/iT4Z+BbX4Cfs06xNpkaxQrex28Stc3ky/wCsmnn/ALv+zXgvge11D4feH4vCvh7y01LUtz3V591o4/4VovNDTTbP+xNH3TXV1K0t/fbtzM275VVqa5doy9fM0+q81W3Kc98RPEHxO+IHh2X4XaU9xHp91debr1xZu32nWJm/56t95o1+7tr0H4U/AfwN+zX4a0bx5+0Civb3l15uk+G43XzLpY/m+b+7H/tV3/h3xh4V/Zt+C8/ja80HRbTV1WP7Leax8z3E38McC/xf7VfFXxe+J3xR+P8A4uk8f/EjxldaxeTK0Vv/AMsoo4/+ecca/Kq1sqsFpCI44OrWn/dieu/Hb9u74kfFTXNZv9S1jTRbSW7W+jeF9Li2abp8O75dyr/rG21826hq2q+JtS+3+M9Vjd1+XzFT5Y1/uqv8K1u6L8O9Vb/Q0tlQSfNt2bdtaVj8EdVa68l0Z/n+6q0pVoSleTO2GVy25R/gzQ5ry1TUvD3i23dFXakPlfxV6p8J5viXBrEF54P1JtL1K1iZPOsZWXzG/hZv7tP+CP7Ob3l5aXOpWEkcLSruXft+X/dr9FfgD+yn4G/4R2K5vbONE2b/AN4iqzf7Tf7NeNi8RQcowZ6uDyOpKLkeAfCv4O+KviN+5+IUP9pX/wB77ZNLvdmb7y/7tfYHwp+BNnb+GYba5T7yfJHs+VlX5fu/3a6/4f8Awj0Twz4ia80rSo1ibb8qrXunh2x0m3+z6VNo8Jjj+XcsW1trf7VcVSpGU79D06eUQoxPmfxZ8Bb/AFazazttE+Vf4lT5f++a8g8ffCubwixe80yaVWl2I0cFfpBP4Y0ewgFzCAu5e1ea/GL4F+HviJor2cNv5LK7O3lv8zf8CrGpGMpB/Z/NGXKflj468B39ncfbNNtmeLeqt5zfNt3V9KfsE2/wv1jxNFZ+LfFrWbzbV+ztFv2rVX9oT4G3Pgu6EMMMjKu5/lTctaf7DdroM3xOsdH1m2hilupVW3kaL5pv9n/ZruwVb3vZ3PkMywfs+Zo+if2lLPxV8DdJj+IXwcmmitI4JEvNNhfZHeRt95vmr8qf+CvGg+FfjBryfF3RPD1vomqrp0fn2tvFt+1L/e3f3q/bv9qf9m7V/G/wb8iC8UfYV8+KaNs7o9v3Wr8av+ClHgebSfhfqXid9sU1jdLFLHMnzMv+zX0sYzjKMo6RPm8NU5a/LL4j8zVjdt6O7M+7a+6qklvMYTCXx5f92ta4ZJG/hDt/DUE0aLJwinb99q9mnKHIezUj7hzlxZ+Xu2Jjb/Ft+9WJfWrs+xONtdlcQ+c2zYuW/vf3ax9S03y8v5PLfwrW1OWvMjllT7HH38e19rj+CqfkeYT8nH96uhvNJSSQfuV+Ws64i2syZ+St4ynI5pR5TJj2LtTvTo4/LX/aX+JaszQ/x7NrbvvU37Om75+q10c5lGJLbq/y73bc1er+G9q/DddvAFlNj/x6vJoW+bYefkr1jwwf+LaLn/nym6/V6+/8O/8Af8T/ANeZfnE9zI7e3qW/lf5o4jS7jy2CO/LVt6eztIEG4GRvvLXN2quzeenD7tu2t7T5vKZXd/upXwHLzHjHTaesLSFPm2/xVv2siRxoibUbf8jL96uW09Xm2P5zKfvbVrYsbp42Z+pb/wAeqfhI+wdHZ3W6b7KkLYX/AGfmrQhmhaF5kTB+7tk+VqybNvLkVH3b4/vrV+3kh+Vzu3M+7/eo+IiRoWsflqu87iybt3/xVLIs3lukr4b7yVFHc+dvTKxMrr+72/w0TyfujCj7m2fOzPWsY/aM/iKN5E68eeyqybfl/hrMulTypYXRn+T5G3ba07zY0hd927/lrtqldQ/N8iZT+9/FV/ZCMuWRjySJ5jJMNv8ADVaWH5iE3Kv8W6tK4h3MESPay1QnZI97zbV+b/vqsZRkb0ysvzMsPmbQv3KZMEjbyXf5vvLG1KrPAWTy9w3/AHaikk3Mcp/F8zNUSpnZRqez94zryR/m3/L8m77lVTJbMxR9x/hqe8B8s70bLPu+ZqoXEnUP/wDtVHL9k9OOKOtulm+0LsTa2/56pzbI5I3mRmO7+GtO+t5lXe+4sr7Q22qtwXZvkRm3J8m16+d9pH4T9Ww+G5Y6laPe0ju6L833NtTwxxrGPOOB/v1GypHlEG1/4F/u0+3mfzt/zO/3flrKUpnpU6MYmlZ75JAjw4/2v9mr8cKSKPM6fdbdWbDeRxso37y3y/cq/DJ5cm99zrs/hrklz8/MZYqjzQ5S/C0ksI2JtCp/fq/G0MMfnbG2/wAe7+9/drH+0uP9Jd/++amtbhNv2nZlmf5lZtv/AAKuulGT+I+Ix1P2cpXNmORFb5I/m/g+T71alrcPcRxwukn7z51bZ8tc9ZXSN/y85bftWt6zmeGOJ5k+625N1dPMeFymrDeb41R3+b7rrG9aFqsLbd8DeVGm51Z/++axrGTzGZLZFXd9+SOrUc0jN50zshVf+Wfzbv8AZauqjUlKPKZyjH4i/JM/yfuW2N8z/PUc19AylDCqs27e2apzalMy+TM6jb8u3/4moW+Ztnnf9s2rp5jKUeb3ieOR42EezCbPlpisiyDfz/C392q6zRzbrna33Pu/d+7SLfbVWZEZf4tsn3fmpVKnKRGnympYx21x87vsLfN5bVZjXztw2qu1/m3fxVlw3UKyInn71VPmbf8AxfxVfWSGaNHkfBj+b/ary8RUlOSOujRjGJq2VnNPMmwbE+b7qfxVr6fbmNfvswj/AL33qzdL1BGzNNM0m5vl3Ptb7tXodSh3LGjqP76/eZf+BV5dTm9rynq06lONLRmpbx+W3nJLuZvv/vflpt5Ncwr5MHzJuXf/ABM1RWt5ctuTYwkmiZflT+Gl8x5JEDpskVdqTNUU6fLV946/aU5Uia0kddtyiR53fe/hVa1bO0e6kxv3sz/xfwrVKxX7ZIiKjRIvzeWvy7q6GzhtJF+5+9+Xf5b7Wr08Ph4/FE8vEYrl91fCMs9L87GyHy9sv8P/AC0q1DpPlyMk0zKF+by9n3av2en/AGiMvMOF+bb/AHav2tjiZJra2zHI/wB7f92vVo4c8epiImV/ZNzPCHcfe+5W7puktDbxzfY5PK37UaNt1aWm6OkKvC770Vvvbt22t7w74Ze3hxs3Bpfvb9tdscPHqjhqVpc3umfpfh3bh0mZ0kl2r/s/71blroKyQrbO/l7v9U395q6LTfDNtHbw7H3/AMTrWxpui+dl9kIf/llt+by62lRJWIlF8pwN94XSHypktcHd/rFrF1fwy7XBhMPmr/EzJXq02g7bh0dPMLff3N8tZGveG/Luo5l+RmX7v96iNHlOxYrseS3mgv5jfZraF0jVdytu3RtXP32kvHamaa2Zdr/d+9u+avZbzw68dv8Aubba3zeb/wBNF/2q5i88NwtCZtm0tuZ49tP2UTtp4jmjynlutabDNJsTou7cy/wr/DXGeItN8mVnflW+X/dr1rVPDtn5LvsaH+6rJXCeKtNSNdjvs8l9sW7+JVrOUYm/tmeb61b7ozs2/e3bmWuZuJv9KEOzZtb5l311niiHZJvhnkD/AMH92uJ1Zf3ZkLrlfmdq5vZilUgNW8is23pD88b7dy/Nupy6pMrtHM/zfwbn+8tYzX0O5k87j/vn5agbVkubj5P4fvtWfLzHNKpynRw38PmKifd/iapodQm2snypu+/81cxba4ke7fudt/8ADVldSjjX/XfIyfO33ty1jyy5uYj2nLDlOh+3JHGts7sDs3blp7ah8ru87Mq/O38Nc9/bSSfcfD/ei+Wov7YeRfv73b5n21UacZbHN7b3DrI9Sht7f7S7tIkjr8qpWjDqDzfInmDavzNH91a4m11hFAhR8RfeXd/eq9Z6o63KQu+9JPlZd+2umjR5fhOCtW5vhPQdM1L5Q7yMSv8AtfdrorHUod0Pz/I38Nef6NeQ+Xvd496v97fWtY6wnnfJM25vmXdXrUoxjE8ypI7lNYmjife6uuzbFt+9u3Vorq0NrC+y5jldfubU/i3Vw0WvCzVLBN3y/Nu/vVZj1jy1RI33Bm2/M+5t1dZ5koy5uY9O0/xA95h/OVtv32b5f/Ha1rXXrby2eObDM251bd8rV5lpfiJJAHTd5q/L937tbFjriSMm/l433/M9ZSl9o1onpkOuTND5kO4bn27W+bd8taVr4jh3HzJtrtt3rv8Avf7W2vOtJ1xywSGZlVv7zfdq7NriQyO+zdti27m/iriqVOX4j06dE7ubxJ5jBEmkiZf9arN8qtWVdeIrm1kaGbc7/wC98u2uSXxJujRI0k2x/wDoNQ3GrTNGyedHsVd21n+Zlry61aMWenRw8pHSXPih9uz5T5n3F/2axdQ8VbreVEhUfP8AKzfM1c7catM1vvd1O12Zvm/h/u1RuL4L86XLIrfejauGWI5pfEehHDyjK6NG81Sa4j86F9okfbu+992ue1bVkt7eSZ7Ztn3Xkj+83+7/ALNQteTR3DPbTYRd3yx/dasLXLyaRUhmdkG75Nr/APjtZ838pvGj9qRS17WJm+/Nv+bburlNS1Z5Y5diMv8A49urW1aR7j50TAV656+863y7y793zNGtbRlzSszD2PL7xlahcfaowjuzt/drNh024kZ9jq3+1/d/2a07jZNJjZt2/N9771Ptbd2h2OjFd33lT71X7TlgEafvmXDpbtIez/7NbWg6VM0jJ8zn7tS2unO2TsUbv7y1t6fp9ssUWyH/AGdzUU60Oc0qYc3/AAvZ7m2IjIyxKu3+9Xomg2729rs3xhIfmVo0+61cZ4Z09GUeZI29vvyb/wCKu50GFG2on3F+Xcr/ADbq9GjI8ytT5YHbeG1haNIft6ru+fcyV2GjqbO4hRPusn8KVxmgvDbx7A+5dn3l/i211FndExr88iK3zbl/h/4DXoU5S5TgqUzttIvkCoieWGb+H+Kuk0fUoHjGx5Eb+H59u6vP9I1aCa3E2z5Pm+Zovm3Vt6XqyRqlrCjN5f3GX+Fa7YR5jzqmx6La3j2rZj/dCb5dy/wtT21byFluYbmFXj+X/a+auVt9YSRUKXMjBv4Wb+7Uq64nmfPcrtk+ba1dsfgODl973TR1a8R7co+3/gP8VcprF1DbCX51fzPm8url1e/aFaae5yF3MzM/y/8A7NcxrmrI0nnbY0HlfxLubdWUtjWMZGVqE7xN8+1Ru+WNd33awdRk/v8Al/MzPKtX9Qvt0iwpMz/P97f93/erHuPsyrvmkaKZX2+Yrferiqf3jsjKfLoZGoSSTMs147YWL70bf6tq53WNShkj3vM3ypsl3Iy/NWvrV88d5suZlfb8vmRt8q/3d1ch4k1h47cQl/NKo37ln+7urk9DppdbleeVGjk8h8bfuM33d1YGrX0LSETTMjN8qKvzK1NvNY3Ns3yOv3nZV3baz7rUIZ52jLxjb/FXm4iUafxHdR5PhEjmmkZkSDzUVfn3fL8tTyhNgf5U/i2r92s2O4R5G3uoVv4f9qtK1ZJpgjvv2xbdqv8AKv8AtV5tT3vePQjGIm1JP3MPzPsql+8aMu8ca7n+Xau5ttWZpIWSb7NIzpG/8Py7qgaOGNjm4X+Jn2/erOn15joj7w2PyWhO196L/eX71JHbOsg86GRFZt26T+GrOm2qMzZ+X/eqeNEhuPkfCyff3f8ALRq1jKXNobcvNDUmtFSGxfyUZ0Vtz7qms403bHfYrLuTy1/8dqO3j3Q7OrL/AA7/AJauRWsLKkL/ACKqfNt+9R7TlCNOTHW/krp8dsjqW+95cjfNtqeP95eJClnhtu3zFb+GmWa21t88yKrL/wA9E+8rVZj8xVh+zJt8z5fOqIy+KxXL9o4T45iIaBagB94vQCT0PyNVz4VWwk8CQEKGLPJ8pOM/Oag+PKIvh6zZQQTe5ODkN8jfNVz4VKf+EBs5S3Cyyr9zONznmv0zEv8A41Xh3/0/f5TOqUZSwKXn/mb0dr5caWcz/wAf8Xy7azdYh2t86Ln/ANlrWZvmWb940q/e2ruVl/2qzL63ljl3/MyL8v3dq1+VznzcyZFGMY+6cvqWnvHuT5V8xtu5vm21mzW6TMdj7V/56N93dXR6tZxx9U/3Kz5rWHzGjtoWXd/Cv96uf2sY6nZHDykYclrtVke2Xa33938VUbousghd9jN9xa2ZmtlkXEPmfwsv8S/71Y+pS20ce+F2BX5k/iq6M7VPhJlR5YmTqSwyM/7n515+X+Gue1F3WR0J+Vfm21valN8u9Cu5vm3L/wCzVz2r3A2vzhmT5Wb7terh9jgrUzn7zzGk3um6sm4Zl3Js+9/E38NaWoI6o8yPvVf4d/8AFWZdM8e55h95K9Sn72h49Sn7xXX95cDL42/L/wABrV0mMeYDsb5fl21mw+dNImxFLL9/bXRaPaY+kn+xXTy+4FGM5SNzSbHzOfusrfOu+uq0PTUuMP5OVVsbWrF0exhlX5/4m2o1dloenooZ0C72XbXJU7M9mjTlze8WtLsEkZ9k0Y+fau7+9XUabo728afJ95Nybl3baqeHbcQqj3McbBfl3NXV2FmkjPJDcq4Xb96uOpGXxHq06dKUYpEOk6X5ylILZstF95f71emfAXwimreLre21W5YLuXfcfd2rXKabZfZ7h3jmZG+4vyfLur3b9kP4U63488VWc3h7TY79VlXzY2bb827/AMepR5XEWOjGOFkz9bv2SvhTpvwz+Geiw2266vNSsFayWRfuq3zNuatbxhI/jrx4k1hbLeaX4bXZa29uvy3moN/FJ/sx1xnwD+NXifXvEkvw3hs2im0nTWiZl/5d127W2/7TV9FfCvwj4X0vwzDYWEUfmLK0txJ95mb7zNXDL95K8j82qcycjzjQ/wBmvUoYV1XxJctdXGyS41eZv9UsjN8sce7+Fa+df2uPBvi++8z4e6Ulu9orq1xp+no3lw7m+XzpF/1jN/d+7X1J+0J8UL7W9Mg8D+AhefaZJ9jm1T5VX7u5v7zV5p+2V8TPC/7M/wAK7fTdHS1PiprJVih3+Z9jkZW3TMv8Un93+7UuVKNKXY68LRk6sbbn5i/Gj4av4T1ObR9YS3udXbd5sMO1fssf+0q/Krf7NeD6B8LdVj1iB7ndCsk/+lNJ8zKv+zX0BY69rGrLI+sbUnvLhpJ2kXczbv8AarCuoftl9Hols+2bzd08jJ93/dr5mpio3+E+/wALlU6WH5pHDeIvB76lq0+q21lHBDDtiT+80e35mrLj8SWHhnUvOudKt3S3+Z7eT5dzbflr0++jsNB8Ma4ZoWaeO3bYzfxN/dWvHdW0Hxh4w0ebW7bSo4HmT/VtLuainU9t7yMKOH5WeK/GbxP8SPjZ4+uPGHieZXWN/K021X5YLONf4Y1/h3fxNWTpPg3xHG8bm22hX/iT5a9T0P4J+P7qF5byzjTyZdreZL91q6qx/Z0+JbWsVzYaUt5u3b1t7jdt2/w12VqyjGNmdeFwspu7OS8A+A9Y3R6k9hJcS7/kWNl+b/erufE1nZ6TapqyaJNDIq7pdyfd/wCBVZ8N+EfiLoOqeTdeErqFI0/1flbv++a7bxZrnh6Twvs15Gt32r5tvcLt27q8ivWjzHvUcNHl5kY3w78f6JZ7Ibp1RN6ttb/2Wvvb9lXXE8SWdto9nIyJIqq8lxtb5f4a+Crz4d+Etb0m21XR7xVbfuT7P91v92vrn9i3Xpo7FI7Wfe8e3/XJtb/drixEoXjJHo4eMnBwaPtGXT/CvhW1/tbWLn51+V2Vd3mVb8E+ItK+IXiY6PpttMyQ7Vbcu3/dpdef+3PBdnc6rNa/uUVpdrbWZqT4O3GiaP4gj1j+1rWPajOkbS/w11wqUo/4Tz60ZxpScY6nuFv8JW1CwSYJtG35VrnvGPwyvtDt47gIyg/K+2u98BePrPXowiajbsgbaqrWp4x8q6tFztZf4q9mWHwFfC88D4unmuZ4bHckz4u/aW+Hb6toMr/ZvmhiZkk/+Kr5n+Bf9ieH/ixZvrH7to7zbFMv8Lbq/QLxl4VsPFglsbyHG3d9371eD+E/2QdKtfGF9Dcw3E9tNdb4Ljbt8tt3yrXlYWMY1dDpz3lqUozPseWC2b4ajRNevVniubDbHcL91l21+Mf/AAVj+HOj61/wk2g3jzR6VpelzXCSRs37y6+9Crf7Nfr14Piuvh74GufB/i0yXUULeXauvzL5e2vzu/4LDfC2e8+CfifxD4VuZHt47Xz38ttz/e+b5a+mjL3owPgJyj7e5+Bq3Bk8sXO3zmXbK3+1TNieX5b7d+/+GtW+0/7LuhdMP8zfd+bduqs2mtIV2bhEzfPuT5q9eMoQ91nurmqQMy48mdtnl7f93+Gsy6skWOR0RifvJ89b0lqnmL5e1f8AaZaq3VqhZkd1UN/y02U4ygOVPl3ObmtfOV1dFx/svWVqGmw2+R975/4v7tdXNpvkwtDs+Zl+8q1mXGmzLG2+Fm+fbW1OpKWpyVI/ZOXuLeCNy/k/xfdqpcRw7nfZW9dafubY7/d+XbWbcwJGzb9yrXXGXMc3KjNaP5t6dP469V8LEn4XqVIB+wz4/N68xmRN29EYn7u2vTvCi4+FyrjH+hT/AM3r9F8O3fMMV/15l+cT18k/j1P8L/NHnMNw7SffyK29NuPL2JN0krBh+7/EpV609Pkdl2M7f7K18AeSdTo90jbkR8N/A38VbtvcOsiw7Nu779cnpcxt5d6fNW9ZzeYyu/G77zf3aj3xcvNM6S0vE8vY6fMvyv8AP95a0rG4+TYkK/K/8VYNvNDt8s7cs3yf7VaulzPIxjfzFbd96qjKPxGMozNi3b93vmj+RflWT+KoZLiZpGdI+PvbmT7zUxZJN6jY2/Z/3zSyXD/PCdyLu/h+b5q0j70DH4R0s00kZRN21V+8qf8AoVV5YkbaH/4FU8bzMzJ8rjbu8v8AianRru+SH5KqMuYnl+0Zk1tDbrvRPlZ/4f4qzrqGHe8j20Y/u1uXlui2qvsUbl3JtrLul+VdgZl+9Uykax5DEuIUZi6H/vqs66k8xTxIIY3Xe396t26t3UM+dob5v9mse6t/MV+/+7U83MdEZcpmXk2/5/MydlZF1dBvnfa7r/d/u1p6hGkanydw2/K/ybdv+zWJfLtk2QvtP96o5Y8xrzSPV7y1+zyb03PEvy7qzZrdJmbZtC/wVuXFuijzjuI+VNrfw1Ua1hkj/hG7+6tfHyl9o/oOMeb4TJjsXZlmd9wX79SrapJIzwwspX5vlq0bN/OJT5vu/NT/ACJoWiTyWZ2dldv4aw9pzHXHkjArqzwqiPtw3/j1SW8zxSHYm0L/AHvm205fOMeXRT/n71M3I2Yf4/71OPve7I4MVKPL7pPDcXLR/uZo9mxtn95qtw7JLZrZHZ/4v9rbVCNvs7Ym+bd/47U1rfPaNvd+F/5af3a7o/DZHw+YcvP7xp2skMK+SiM38P3a19PV5IPnTP8Ad+fbWPazPuV96/7y1ct9Qm8x9k2BIm1GVflrVx93Q8CXuzNvTZE+VNio275mX+Kplmmkjkfzl3L/ABf89F/2azrf93Z/fbfs3I2371XAnlweZNc/wfMrJWkJcupHL9kfDIkcLpDGwTZ91nqvJcfMnnMWO/5KdJv3P9mdYi38LVTl86SR3SZdvlbXhaunn+0YyLNxqCSyeS77W/2furUC3kF5IE2MPm2r+9+WqzNtkdFRUVV+Zm/ipkd4m7fJ5e9l+RvurXPKXOOJsfakt/3KSL+7f5FZfvNV/T7j5x88bD/ZT5lrn1v3uJER0VX2/M0b1YtpoVkV03Ftm7c1cc+bl8zpj7vodXps37z99Mvy/wB6tOPUplZgiLv2bn3Vz+lttTh95b7+5Pu1qQsn/Lbrv3bq4ubmneR0xjKMDctb5DCoQSLu+XzF/h/3asrMk0a+SjbF+Td5v3v9qsu1kSP59inau2Jl3M22rsTJsXfZsP4U+fburqo0+aXvEVKkYxNjSWaZUm85WePavzfe+Wuu0G3SZXfyW87f8u3+61cfprfMqJCqbn+VlT5a7bw+vnSeQiMjsq7/AC69zC0/dPCxVSUZWOh0fTXkZt7t8vysy/drodJ0na0c2zYjLu/2WqjoNrut4vOTDQ/883rpLFUZw/7tXX5dqvXqU4xj8Jw1KnKXdJ0H5WeHagk+bdsWul0vw/8AZZFPkrKWi+Vd/wB2m+G7Hy42R4Y/lX5Y2+81dRpemz3DedHCqKq7WWuiMYxOTnItJ0mGHGyFX3fc2vu+b+Kt6Hw+nmJ5NsrPs3RM/wAu2r2lwwxKjpDukX+6vzN/erUjVlHk+TIH8r/VslVyoPacpzt5oqfaH3x7P73yVz+vaf5N4IZvl875tv8AervLjZ5avMjO+xt/92uY1y1toZQmxim3dub7tR9s05uY5HVLNId3nXO1PK3/AO1/u1y2rxwzXDXnlfIqfupG+9/wKuy1OzdZEROYZH+eRVrl/EEbrOdm7dJ8rM33flpcstzrpz5ZnC69Gk0PyQ7VZP4vl3V5n42jtrWN5rWFSvm/Ntr1HXGtrje947YjZvm+7XlHjBf9KdXdW3fw/dWplycp0U6kzzTxhHN9n/dzRj593y/e21w2vSbt0Pn7UVN3+9XZ+KrhLjzNj/OqNu2/NXmPii6fzCgfmuTlnUNpYiKj7xkapq339n3l+X5aybjVo5o3uTNsbft3bqra1evv3pt/3lrDuNSfdsd/u/3qrl/lOGtiPaROtttcC7RC+11+bd/DUza1ub92+3/2auKg1Z13Jv8Alb+JqvW+oeY3yzbNvzfNU+zgY+2kdW2sbMOj7iyUfbE4dOv8e1q5ldSMSt/HtqRb5JJfkdv71EY8sjKVSUvdOnt7zbI00L/vF+Vt38NaNvqW5kunddy/8CrkbPVIWj3b/wDgX+1V2z1KaR12Ps/2W+XdW0Y++Zne6frUisk33l/u7a1G1rzo94uWZfu/d27WribHUHCqkz/O3zVo2+rTYZN21P71dUTk+I7O38QvNjZ5bFV+Zm+6tSr4iQM2z76/fX+7/tVycOoTR7YY/wDgMi/dq1GyfL9mm2N/Ev8AeWtfQw9mdtpesTSRLDNPuRl27V+Vv96tqz1p9yQ75H/i/vL/ALNcNYzeYqu942/Ztdm+6tbdjHeJCj203m7nVmZl/hrjqVOU6qNOPQ7mz1aGO3+0py/8Sr822rS65NJCJvmf59v+9urlrG6uYf8Aj2T7ybmZfvf981oWupQxwh0fbD/Gzf3q8rEVOWNz2MLR5pW5TYvPECRt5MO7bHw7fxVTuNYe1j3vNhZH+6396sprzzpvtKOuzcy7f71QsySWb203yCNvk2/3q8etiOaJ72HwvvGn/aT3mftMe3y/liVfut/vVA2qTSbfO2/L/e/5Z/8AxVU1unaIOiLsV/3q/wCzTJrvzGWGH5l2fI2yuKnUO6NGP2R8l09xDLM7t8ybflrCvG8xvJ+aLd/e+b5q0Zri5EbW29QjfcZv4qyLxv3Ozeys3y7v9mt6dT2miMKlGMdzO1LfD+57b2+Vn+6tZF1Hc3HyJCzIv8P8TVoXVxC0Kbv3vz7fmqrcXyxyeTDD5R27W3f3q7OaUY3RycvvGb9h3MyImG27fmSrdnxD/wCO/LTFm3TFPll3L91W+ZalhZ7e4X59u37lRKfLoOnTL1qsO9eVX/erQ0/93Ku/7q1k7nkkZJk+7/47WxoN4nl+S/nSiPdvaRPvUQjKPvGso+00Oo0G4Ty1R0ZmVt25fu11mg3kgmaRPmdvuLt+WuH024SNkR3wq/N/tV0tjqT7lTfIJfl2NGy/dr1sPU948qtT93lO+0eS2+zs6Oz7n2pD5u1Y/wDard03Upo2W587eyp91v4q4bStS8yMzO7K67f3bJu/4DWzp915alEmXb83+zXo06h5lSjynYWuqeZZ/wCuaPa+11kX+L/ZrXtdUvIIxeWczO6/LKrRbVWuKh1PbCJ/lZo/lZWfdt/3qdZ61M0xkFzz8qszV6NOXc86tT5j0KHxLDA0SQuwLKzfKn3qbH4n+0fuUdf3j/w/xLXDN4ke1d4LZ/l/jZv/AEFajk8WQqVTzmRF+5trtjyS2PPlGEZnbza5C1v9phdgPmHk/wC1WDrGuFYV38Fm/wBYr/NXNzeKdu57P5fn2u0kvyrWZJ4phaGVLmZfOVtrL/C1RUjy6E+/L3jZu9YSQvs3OF27v7zf7VY2s6w9vs3uvmf8tdr/AC7awbrxVBbu9slzGrt/D/FWDfeJJriNm3qqxr87bq46keaWh005e6bOteIIbOOaZ5lJjf8Ahb71cP4h1yaSaRN/y79ySN827dVfWvFSSB3m272+ZmWuT1jxEWm8lAuz7yfN8y1wVP5TsjyyN241q2s4d/2nezf8s1/9lrKvNW8xgU2/M9Yk2pbpEfzsr/daq01w7yM8Lx/vG+9urgqR9p8R1RkdRb3eFWbepZv7vy1btdSkj+ffsP3t395a5O31CZYlTyd3z/J81TyapNbZR5tu1d3zfe3VxSp8sPdPQp1I+7c3ZNVSaN/vMzPtbd8vy1Jb6mjL5yW0fzfwr/7NXPtrEzbdkyp5nzJup8OsO0kbom11+9ufarVzyqS5fhO6nLllqdfa6hNIvzwqm5/mZfvVpQXltHIrom7b/wACrlbHXIfMV5LldjP/AKvZ81XV1jEbybFVV+6396ojzSOo6GORPMiREY/L91v7tX9ri6TZ9zZ93f8AMrf3qw7PUvM273XCp87NV+x1SG4uPsxRkZl+Rtv3flpDj725qQ/ZvLbzHjG5F2LJ81WoYPJUQo6vtRlddn3f92si3by9kPylflb5l3VqMyQR7HRtq/M3z/L/ALNOp7vwkxp83xHD/Hiyjg8NWdwoYF777rNn+Bqv/CIb/ANqOAUeZj8vUbzVD493Ec/h+0EKkBbwblLZwdjVqfBqONfh/C8l4yFml2Ff4PnPNfpOOqcnhPh2/wDn+/ymehGl/syjHa5vGOGG1eTyZlZv4ahuo/Oj2O7fL8u5fmVmq7JcTXlvEibmCrt8xvl2/wC1VC6Z/L8iHcqL8yMr/wDfVfj9StKodVHDwpmJfW7yN++mYJH8u1fmrKu43juH43eX/qtv/s1dDNGkMj3f3127fl/irB1S2maHe4UFmXcv+1/DUxqR5uVs9Knh+aPwnPahcStI3yLEFdlZv9msS8m8lwruuW/i/hroNRt32yIU+fdj5v4a53VI0bh9qbfl3f3q76MohUwkdzE1i4eRTbw7UVn+dmf71Ymob2U73XK/Lu/hrW1SP5FKTcK21G/u1i3zRbRv+Z/uu38NepR948nFYOXN7pj3rbVbybb5F+V2jrNvFQp/tfw1qzM679m3Yy/drLmRPM3v/C33a9SnE8arg+WQmmw7WbydrfP92uo0G3dso7yNtT5Pk+6tY2nwvE4d0X7nzba6nw/b7o2d5mI2f3K6oy94mOF983dGt/JVEf5y33Pnrt9Dt0mhZ9m3b99Vrm/D9vMFR02jb/Fsrt9DtXbZwu7/AGv/AEKorS93mPWwtGMjV0uwtvLjTtJFu2snzLXT6Tpc0LNvRVEyf6z/ANlrK0VUa4SG2f52Xa7fe+Wup0OyuY2dJnZyrrsZf7tcMuaR6WHw8blu1s/Lk+zb127tvlq/y7v96voH9kHxFJ4J8YWF/DD9qm+0bYo/urH/ALVeJWVrZ3BSa2hyW/vf+hV6n+z7HqS+LrZ7Da6+aq7mibarblrKp7pGaYfmwckfqh8LfD/hXwH4J1fx/pVzu1TVnkuLq4aLaqq38K/3mrtPC3jLXvD/AIVfV/tjbpLVUihZfm2svzNXn2leNtY0fwXHpviSG3a4vHt4nbb+7WNvvbV/u1U8TfEC2bUrjR9Ev43SO48ryYW+aP5a82pU5vdPzONP977xX8QfHR/AerR6lokHn6wt15v2yaX93bxqv/PP+Jq+Iv2lv2gfEPxK1S/8bTX807SalJceZI3+s/h+avW/jnrl5Z3+tXM20Q2dqyovm/6xtvzbWr5N+IF5/a0dlYQxNGn+tiVfu7a8zFwhrzyPosnpxqVYmTDrfie+me5S5kKt8zqz/d3fwrW/Y3Fys1vvhXdGjfvFaqXh+x8y3jttnyf7KfMtdp4H8FzXGpQ21nZrLGz7m8z73/Aa+cqYily+6fotOnPk94wvEV9qurKbCw0qRreZtzyQ/M1c+vwT+LXiSxS5fUo9Hst/yXV4rL5y/wAVfU954J+F3w38CzfEj4hanHY2Nim+WP7zXDfwxx1474w+MXi34sabF4t1vSrPw54Nt52Sw+1LtubyP/drfL8RSjGUWefisLKm+ZaHh/ir4Y6bocKaVZ/H64mumiVpVaJlVmZv/Qf9qpfBPgvx5osyR+GPijYyJI3yLcXrRszf7rNWV46+IHwfhvJnh0qFArbZZFuG8yRf/Zawb7xx8K9Ut3TSPMtppE+X97u208R7OUfdNMLJ05c8j3qz1/4o+GbpJvEnhtrmOF1ZpLX5ty/xNurf1bUvDfxC8I3cz2FncQ7lXbdRbZY/++q8Z+Evx6v9JvE0qbxdNdtDEqp9q2r/AN816J4d+MXg/Vo7nRNYsoZ45pdyXC/Ky/3q8iXPTn7p9FRqUa1I2/D/AMM/DH9iwzWCXFr5b/ulh2yIzV6R8P8ASdS+HeoW2pWGt3ENs21dqxfMzM1cxoOi+D7i0hfwrqV1bpI3+pWfd838Xy/3a77xN4kfR/Dum2Vz4hj8przZEq2/zfd/vVEpc0veKjH2cj6I8J+ItB1Tw6ltqdzdSXEKqsSyS/8AoVdn8PdFS+1COZfsoSZ9y/Mu5V/2q8E+HOh6Vq1ot/PfzXEdxFu/1rLuavb/ANnfQ/DtxN53nb0Xdua4l+ZqqEZynyoVbkjSkfSngFdKtdNRrxI3dW/d/Ptre17Wbu3sJGs5tqSf89P4a4vTm8GzmOzgmt1eP5WWOeovEP8AaljYy/2FqfmMu4pHcPuVv9mvalUVGHKfFzwcK+L53+JNpeqi51SVYnyu/a9dv8NLG21DU7lLm3jdFT5VVv4q8j0rUrlZDNeTRwzL80q16X8IvEjrdhPlZZm+ZlrjweKjDERctuYef4GSwb5Tb8aaGlrpU9sgz5e7bu/iWvzZ/wCCoHxG0/4W/DvUrPxDNM1jr0TWcHk/N/rPl3f8B+9X6d/E1podEe5toN7+Uy7V/u7a/FD/AILZfGH+3ryD4UTaas1tDZb/ALVG22RZvM+7/wB819gqP712lofmUaftMQon5aeJvDqaLrE2j2dz50MT7UupPvSLWbNp7sVhkf566q+0XbdFHud/l/N/eZf9mqqaXCsKvs+Vfl3Kv8NdftuX3T6qjRjGBy01r5f+jJC25qoy6Smxk+VRv+Va6+TR9sjGZGZG+Xds+7VWbSS8hTyVVI/lSiNT3bRH7HmOQuIZof3KOpDfcVlqheWL8vGm5f7rV1t1pb/avn+Vtm7y9n3ay9S0+M/cT5fvNXTTqfCcksPGPNJnE6tYom7fCrf7VYVxZ4B2p83+1XbaxY2wXY+3eyNuWuY1C1hVnT5gq/xf3q7KcjhqRh1MG++7sdFU/wALLXovhrC/C8dcCxn/APZ68/u49y79n3m27v4q9B8OoIvhn5Y6Cyn/AJvX6V4cu+YYr/rzL84noZOuXEVF/df5o80jm2/uX6feq3bsiyp8/wB6qsK/ePytU9rIiyeZ8p/uV8HGR4/KdBp8m5vLf+Ffvf3q19Ok3Y+Tcv8Atfdrl4ZOVc/d37vvfera02bywh3/ADK+6n74cp01vcfM/wC+ZD96tWxunZfOR1Ut/C396uXhuHVnLv8Ae+7Wzp9w6sPu4+6q7KiXvGconTwyecqzbP4drbal+SSH5Ez/AMD+7WRb3iMrPhlRfvtV2OZGXZC+zcu7dVxjzbGEollWj8zfN8u75U/hqZWTyVhTduj+9u+bdVW3km8nfJ87/d3L81PhkdmH77jZt/3v9qr5UYf3R89viNP3yrtRtm77v+7WVdxuI9ny4+98zferRvLyZX/1y4X7ytWZfLum372z/wCOqtMsz7pkkYIifIqf99VnXUaRzeRbJ97/AMdrV1CQ+Z87/L/sp81ZN0+77/8ArW/2v4amJtExtaW5Xanysm/+9uaue1Lev3B8zP8A8tK6DUHeNW/hf73zJ8rVgapDja8j/wDAaiUTWOx7VcWvnSNCn3v9r+9UG393Gjv/AMCVK1Vs91wdiMRs+81Ok8mJfnT52+VW2fxV8LL3j+gMHU5oGK1uiuu9Pmk+by1X71M+ypMo8lJP9itS3jmRv9JSNv8Ax5ttNvrHy33pDtVU3J/DUc0Ym3tGYrW6b3hfcm7/AMeqCSF9qr/ArbU+atK8s9zfxf8AAaqMu378LK33mroUrnJjKkOQqyXG3cjyYZm2tQsxjbyfs+4bfvM/y1DdLDC29Jty1XjcrI7o6uv3t393/ZrtpR5ZanxmYVOY3bK486ZN8zKqp91fu1pQ3jyW5hR9n+1/FXOadI+0P3atazZ9p+zbd38bM1bwjGOrPn5SnznSaTcTNIyfKn8KM3/oVXLj/WP5yMh2Kqf99VhJq21RbTJG+35V21ej1C5mh87Yqj7v36iMp82opS5tC/MsOX+RZmb721fu7ao/ZIZlldNyr91Nr/eom1B44f3b5DPt2q33abH5M6s6PtZfu0/f+IJa8oMse5pN7F1+8si/xVSaO2bfcv8AOu/5fn3VZvmdZFmkm37v4lqFbf5WfyVBb+FvlpRlIF73wlaO+2q0Loqsr7vmWtCzvkYM7wqv8PmL/FVWO3E7KXTduX7u77tTSeZCojR+PvbVolGMvdKjznR6bcPIv7lPMZk/vba01uPs6nyXUs38P8K1zGn3W2aWNIWYKu5938NbNnsaNA8zbY/vt/FXO6Pv6nV7T3LHQ2d3DCfORGVfu/LWml2kipZwozlfmTbWHYrNcfIiKUaLcny/MvzVu6fLZxxBE2vt+Zdv8NdNOlyzuYzkqkLRNfTZJlVppk53qq7V+7XX+Gby2Xfv3PuTbuauS0rfHF/qd7qm5JG+61dP4bk+0bZpkaJV/hVfvV7mFpnz2I5uc9A0GZ7mEbHjTa26VV+61dVoNrbNuud7SRN823YqqtcLpcyWqxw+QsaMu5Fb5m3fw112jXUzS29z5m1vvMu/7zf7tejTp/aOCUj0bw/H53lOj/eTai/3q7LTbWFglsnyD5W2qv3q4nw/ceWqfPGdrbnWNvlWu88Pt9oj/feWZWVVdo/mZf8A4mtYx5Yk/wCE6DR7PybXyXRS7M33fu7atzLtx5c2WVP4m+aq8M15awr8+f8AZ+8u6rMhRlL+RsbZ+9Zk+9Uc0ZFRiVNQVI9yO65bbs2/dauZ8QRpaR7Ll2UtLuX5futXR6hsjj85/MO75oo9v3axtUZo4fkTLNFu+b5ttYylymkYnK6wqXTS/uVhTd8isv8An5q4/XlhkAlTn72z5q67WoQ1wYXfzBu8zdH977vy1xviCSGFXLvzvaT5k27v+BVlzm8ec8+8YXXlwzTPt8rZu2r95a8n8dN5kbQ2yKqLuaJv4l/4FXqniqbzE320O3cnzqv3Wry7xhbwxxtstmWX5vN3N8rVHN1Zr8PwnlXilPJkdE3ZkTci7fvV5h4ohRW5fBX5nXZ92vSPFH7yR0RGUr8r7mrzbxBH/rvOfdt3bF/ipc38pnU944LxFM6xy7Nv3/vLXNzXH77Y/O5PmrovEKzL/wAscbv7tcpeL9nY/Nvf+7W0eWRyyjylq3u9rFHTKrWjbzbo/kfb/wCzVzsMm6TyUGK0be8eNVQbdu3+KtJRIjI1YLxG3Q72Vv8AZqSO8dmOx/vP95f7tUIbrP3x96rGd33Hb5dtZf4g5pGlY3W3Z5XzfLtdWStSzkSNUmdFc7/u1hW9yissu9V3fKn96tOxZ4/v7fmf5GWqjsTKJvabceQpj2L93+Jq0rHfJHxuXc+5d38NY+nrC23zh975Xatezm+0Tr50yvu+X+792j2gRpmnbyeZ8+/IX+H+9V61tfOn85+Fj/ib/wBlqjZxjbsTci1p28eyRB0RW+WnKtpJlRpmtp6zLHFD/wAs2+Xd/FW3YNNCiJvU7f7r/wDoVY9n+8j+R8rH8rqtX45mQeSjr821l3NXm1K32juo4fmNy31CZbpZg671+bdv21b+1Iu7zn81/wCBf7tYqs67Jo2X+78vzNV7zJplLwhfldVRm/iWvJxVQ97B0ZIvpcfLEmzarfNuZP4abcSbZEhTcz/M21V+WqsLO29HGx9nyrvpq3U1woffudl3PXk1OWVU9mnHljyktzNNG2+Hy08xF+bd8rVUuL7/AEd5oZ9+7j7lR6gwZf4cMv3lT+Ks+41HzY1Szgbb/FHu+7/tU6ctvIco8sidtUf+BMKq/eZKrySI9nsR2bany7qjudizIkO3/a3PVW8kmVWeZGwq/My/NXTHl5vdMJR5okE0m10Ez5f/AJ5/w1Svo4ZpHd3bC/8AfO6pvJ+0KrwzN8vzI38VVrqxeO4VNjFfvMtdUub4TjlT90oydpndk2vserEMkyqyGZmVfl3f3qmvLF5l37NgX+FkpPs80exE2/f/ALvy0cvNGJPvxJrGFFb5H3/PudV3fN/vVo2q2zfIjtEq/wALf3v96s/y5rd2eHjd8u5fu1btZENuE86QS/M25vu0lLlLjE2tJ1B1+eb/AFip8v8AwKt7TrjydiTTNuk/up822uUt5prc/JN8yruTd/FWlb6glw8EyJIifxqr/erupy5jmlRj8R22l6u+kyokyKyyOy/N/DWyut7lVkuVzJ/s/wANecw+IkZgk6MxV/3W1fu1pQ+MJm/0OaZSq/dbZ826vRoyPOxFOMonfLr1ss+y2mkJk/2fvVG2sPHG7zD5Fl27V/u1x1v4kSaRNk2GXdubb8q0ybxMir++m+Xc3y7vvNXo05Hk1KZ22oaw80eyzv12fe2rWbN4g/0hndPkj2765ZfEOmtMqPuVG+9Ju/8AZagk8RPE21CsrTbm+X+7/DurojU5dpHNLD8xvXniSHzJv327c/8Ae+6tZt/4muZlKPNs/iibZ/6FXPXniL5mR44zLH/df71YWoeJEX9zvkMX91aVStHoZfV+U3tS8VPs3o6hl+Z12fNWRqniKPyWtoZlZt/zrH/DXMah4k2x7HdSqttdmrKutY8tWhR9v+69c0qkuY1jh+XY1tW8QSxr5Lztu/vKn/jtYdxqG5mRJlVd/wAi791VJtSeTCb/AJl/haqk0zx/PhdrPXLWlKRr7LlLi31z5ZZJFYt83+9Ukcz+Zv8AOj+X5k3fdrIW8dI2m875f9moptUdmGx+W4bbXN8Xul/CdBcalMyrN8v9379H9pTbR86o0n8LVgyXxmX5/l2/dapI7ibzF/1bf7TfeauaUvsnTGXKbcepPJIryJ8ir89TNdeax2Q42r/E27dWNHeJIoheHn/e21Nb3Xyon3v77b6xlHlPQp1IyNuHULlGT5VZF+bzP/Za19P1R9uzZj+5JXMRr829NxH3l+ardhfbWbfM3zfdjX+GuaUZI7IyOzsdQfdvSb5VX5lVq6DR53WEXTv96Lbub+KuK0fU9zKjvhlb5Pk3V0Gm6l526H7Mp2vvf+GsZSmbU6fMdFa3flx/I7Yjb5N3zNWjHeP5i/J8jPt+b/0KsezuEvmh+eOKST5WX/a/3qvwyeYyQuit8/zf71Y83KbQjOXxHM/HF5W8OWaSMrbLwBm77tjVp/CGSS38BQsu0CRpRubt85rI+Nj58PWQcYb7XyAuB91q2Pg/BnwJaPuxunfH/fZr9KzGX/GpMO3/ANBD/KoetRhzRSOsvI/3MVs/3JEXd/drPkhmWaVPJUJG21f7u2tSSze4U704V9ybf/QahbZ50O+H5VfbLuevx6VTm+0evRwv90x7y3csbaF9jMnm/L91lrF1CRFjd02j7rOsb/eauivrd2Z9ibkVtrf7S1lawqQ7YPszLtRlTy/vf7tOn7p6tOn/AHTkNciuZm+/Iqxt8zfd+b/2aud1Y+W32aZGTb/FXV6pZ7pPtLvnauxFZNytXM6l50bF5tpT+7XfR5fiNfqvuHMXuyRmRArLu2/LWTdR8l/uba3tWhSNsfwt8vlrWVPZuGk/csAq/LXs4eXLG5wVsHyxOdvLNLhn2Tbm3bl/3aqx2H+lPI//AACteaz3M37n+H7tO+z+XBvk2/7DbK741JRjynj1MHHm5pEWn2aTN8iYVfv7v4q6XR1maMb0VC3ysy1jWMLoq/O27f8A3PlrotJj8tdiJu3f7f8ADW0anIYSwvKdF4djQbLZ9xX+Pb/FXc6Lb/aPLdEZnjRV2sv3f92uN0OR1ZGSFUNdz4ZZ5JE7u3975VqpS5om1GjGMvd6nT6DZxqpeGGSV44tyrHtWut0qObyreZE2/xOrfw1g6CqSRpDD5bKu7dJu/irrdDs3mt0fyV3L8z7X3bqya+0z0qdOJf0m3ma1+e23o3zfL/D/u16r+z+z2fi6yTyPNLSx+V5ife/efdrgrOxhWz87ZvdX/cfPtVWrufhTbzQ+LIZrC2mS4k2qm1/vN/erz8XU5MLUmdVPC/XJxoS+0fpd8TfDcNp8MpfiZ4M8U2F5rWgWsLSadI3mRrt/wBn+L/dr5g+C/xN1v4ueMtVs7Z5JNUuJ5Lq4jt4tu5mb7qrXx/4Z/aS+OXgn4reKo7PW7iTSLfVJGv4ZtzLH823bX2f+yH8XPAMmoWHxC0GGH+2lvY5VjaDasjV8PlGbzkpOrseLxRwhQy5S9jLmktTJ/a2+EPxL8K3ml2Wt+HrxxcfM0jJ+7jZl+61fOFx4VvP7emh1KFY4rP5Iv8Ae/u1+1nxXj0bxd4G/wCEz8eaRYywNp2IFZfl85l/h/vNX5wftHfC3w3odgn9m+ZNNJdSTyqsX3f+BV25/iqEaUVDeR85wpha9bFPT4TwnTVtrZUjSHypZJdu3b92vQPCvinwloMMcclzGJ5H/wBFVk+9Gv8ArJG/2VryDxVql9azJZ20Leev3JG3fKv96vNfit8aNS0PTdT0XQblmuL6D7FLdK3zRw/xbf8Aer5SnGVSXKtz9GxEoYeHKj034+ftYeGPid4kudb15JE8FeD4vI0vTVfY2qXW7/XMv93ctfD3x+/as8efFrxNNPc39xbWFnuTTrWOX5Y1/h+Wp/iR4uh1DR08N2EPkwb/AJ/n3Mzf3mrxnUtQe4mngtk3vH/F/er6HLcvjGTUj5PNsZKcLRkQa98ZPFXnNDM7bP8Ae3f8CqLw78br2zut81y2W+V13VkahptzGvnXttjzPm2s1ZF5pMMq/aU2q3+zX0dPC4Z0uSUbHycq2KjK/Me7eDPi9NqEwuTf72X7yq3/ALNXpXhX4kT39w0yXkm3cv7vdXyDps9/prq1tcSJ/utXo/gH4lXliE3zN/t7v4q8rFZc43cD3MtzecfdqH1PZ/tOa38N9StZkubwWsO53hV93zNXrvxW/avTVNP8HvbIscc16sssnmtt3Mv3dv8Aer4tm8UR+JNQi0+Obj721XrZ+I3jZ9P0vQNK+03BaxuGn2+b/Ft/iWvIeEjpY+jhm0pU3zn65fsr/GXR9a09f7em2xR/M/735l+X+Gvefhf8Q/Bmkt/wlWq2cMlmu5VkafaitX4feG/22PE/gPR3ttH1Jlkk++zfNu/vVFa/8FDvjktnf6JpXi24EN0m6KNbfdtrGOExe0EGJzbDRR++nh79sP8AZqt/Ej+G7zULeG5kn/0ZpAu2Nf8Aakr0rSPiJ4H8RQSXfg3xFZyovzPtut61/M94Z+LXx78Za00z+IdSu5bqX/Vwp/30tffX7Gv7T3jD4dw2nhLxjYX0MTeWrfardlZv+BVjWw2Ow8OepZmGXY7CYmraXun6qza1/aVqLn/VuzfMu3+Ku0+BviKWHxF5NyFUq3yKteF+A/iNbeMNBi1W3mV1mXcskdeh/CnVrqHxLC8LsHVtzsvzV4KxE41IuX8x72aYeFTLpr+6fQHxw8TN4Y8HS65cXK29qbZluJmPyr/dr+aD9ur4sXnxm/aU8W+MU8SXF5C2pNa2sbS/JGsbbW8ta/b7/grz+0rH8JP2SrjdqQiu9Wl+x2Gz77Nt+ZlX/Zr+fy6WHUtQS5v7nfN5rM0y/L5m5vm/4FX61hJRrUYzZ+PYLDfv5TMeHTYZG/5aMzfN/wACqW40u58nZNDuX7rNH92te10dPMlTyf3bPuRpK0rXS4I/3L2zNuTd52ynUqezkfQ06PtDj202ZbX/AEa2WRP4P9mqFxpr+X52z59/+r/2q9BaxSNm/crs/gX+7WTqWnQ27eY7qrfef/drJV+aOo/qsYyOM1DSpmj3zIxdf4l/irLutNmjZkcsr/3lauyuFRZWT7Mzhn2/3f8AgVYGt2u3zd3yMv3W2bq6qdSRx1KcP5jgtS0kK0rzJub5tjfxVy+qaSm0O/Dfw16LqenwSQvs2lv42rldcs9okH8P92vRo855WIjyyucBqVnNFIX6/P8AeWu30BCnw12KTn7FPg/991zusW+353hxu+Va6bR12fDxlPOLOb/2av07w5/37Ff9eZfnE7coXLWn/hf6HlSt5ciyI6p/stUu1Fb5E4b/AG6ijkRWVH3J8+35qlVk3b3dcbttfn3wxPMjyyjzFyGcj5EO/b/FWra3zr8iIrN/easG1mk5RHVh/stV2G6EeP4d1UEowidLZ3j7V+RWLf8Ajtamn3EkcheY7v4U+euat7zai+S/8G75nrT0+6dfneb5fvOrUGdSnc6yzvD9+QL8y7fLb/0KrsNx5kO9Hk+b7/z/APoNc1b30aus3nb/AOGrtvfJuCJ8v+9/DV838pxVInRR3UKqHMLIdm1PmpJL52j4RlLJ/q2rIXUN3+ukX93/AHaSLVIcp5L/AHU2y/Pu3Vp8Rh7M0pJI87PM27U27t/y1BJeurI/8NU21R5GKIY1H/PPbVeTVEdWdNrfw7d9TzfymlOJavLqGRd/zfc2p/DWRNdZZd6fN/tfxU25vE/jb7zVny33mTO7Pn5Pmo9/4jWPvEWrXUO1t826T73/AAKsC+keSRncM+77lX764eZd5dfl/hb+KsuSXcx+Zh/7LWUpGseQ+jI45FKpMM7v7tOjt/tk3yQrEFRmbzPvM1T+X5b+S/31l3bt/wB5f4as/Z0jjTf03N8tfEVIy6n6/hcZKPumbHC+353+dW27V/i/3ah1C1SSNjv3Mz7WaT71bM1i8ckULwqqxtuRVqrNp6SSFEj+b+Ba54nf7bl2kc7fW801xKPuqqf99VRurWbzFR0xuXd8r1vXVnNI7bLb738S/wB2sW8jjaTztvyr8qV2RicWIrS3Me8tXjWXZt+ZNr7lrPkjdZGhmVdtbV1GI0bfDx/daqU9u6yb/JVX2fe/urXq0ebkPkswqe0kR6bJtZfnba3+xWxpyzTFI/u/3m2VTs7dF2u/IXa3zfw1rWce1fvtlv4q2lL2Z5kfekJHC8zL9mmber7d1aUe+E73gkQ/xrJ/7LTI7fyfuJHhX2/7TNWlp9jDGrPv3bvmdmb5q55VC4xiVvJ8xhJ5LFvm/d7NtTLZ3KrsgRok+98tXLe2eOFwjyKUHmKu3d81c4virU1laYJFliCRtOOPxr6Th7hXNuJlVeB5f3dr8zt8V7W08mb06E61+Xoa95FC0yO8e5t+3b/FuqG7h8lfnhZtv8Wz5mrObxXqjksVi3Fs79pzn86bP4o1S5UrMUJJzuwc/wA6+i/4hRxXzXtD/wAC/wCAbrB112NE28EarM/y/Lu/2v8AdanRqkkqujx71+V1VaypvEWoTja4j49Fpia3eRghVj5/2aF4UcVXu1D/AMC/4BUcJVRvLM6t+5243fP/AHq1LVpmk+R/+2bfLXGjXLsbSI4sr91tpz/Orlr421m0zsETenmKTj9ateFXFS2UP/Av+AH1OrLex6Hpq7owkO4Pv+ddn8NdNpTPuSNLbczPt+VNteNw/EPX4JBIqwNjoGQkD9avwfGXxfbqViW1GRgtsfJH/fVdEPC7ilO8uT/wL/gGX1Kuux7jaqlvL50zqifwK38X8NbOl3E1nM0Nyn7ryl3eX/C38K18/wAfx78aIwLWenOFAAV7diOP+BVPH+0X4/jkaVYNOyxyf9Hb/wCLrvp+G3Ecd1D/AMC/4BxVsqxM5XSX3n1HoEiSSbbyZt8L7Gj2/wCz8u2uw0G6ePZCk0Kts/i+9/wGvjiz/ao+Jtkf3UGmEbs7Wtnxn/vur1r+2N8WbQlks9HJLZ3NaPnP4SV0rw94jStaH/gX/AOKeRY2W1vvPu/w3dedJsud2JH2ff8AmXdXeaLqyWrJC/8AyzTam35WZl/vV+dFl+3p8b7Dd9ns9CBbG4/YJOfykrVtf+Ckn7QVoQY9M8NNg5+fTZTn/wAi0v8AiHnEfaH/AIF/wCI5BjY9vvP0q0m4dVThdrLudmbcq1f02+3W6zIjE+VulXd8qtur43/YK+LH7ff7fvx1tf2ffgLoPgmC7+wzX2p6tq2n3KWWmWkYAM07Ru7AF2jjUKpJeRRwMkfodJ/wTX+K1zFc/DX4c/8ABRn4Qap8TrSBmufCNx4V2BZVXLo4i1KS4jUZGXMJIHJXnFfM5vk9TJcSsPjK0IzavZOUrLvLli+VebsZ1cvqYeXLUkk/m/vsjzC9ZZNsMM25l+Z93zbqxdQ3xwt9m2j5vux18dftO/tj/t4fsk/HPxL+zr8ZvDXhWy8Q+G78292YNNnaK4QgPFcQsZQWikjZJEYgEq4yAcgec3P/AAU9/aNukKSaX4YGepXTZgf/AEdXtUvD/P8AE0Y1abhKMkmmp6NPVNadTpp5Li5WldNPzPurVGRZJd+4fJt+X5dtcR4qmRrP/Q0U7fl2t8275a9A/wCCbv7M/wAVf23v2WNW/a4+N37WPgj4YeF7XX5NMtprvw20gDR+WHkmnuL2COIF5URAC+45yQeD3nxK/YQ/Zv8AD3gDWfEtr/wWS+Dkk+naZPcwJeabZtG7ohZVYQarJKQSAP3aO/Pyo5wp+SxeWTw+NnhJTTnCXK7RqSSl2vGDXXuTLDqE3Tb1Wmzf6Hxx4pkH2VoXfYNvyLs+7XlnjiaHy5cvs/2tlc/8M/i1+1H+0P8AFjTvgv8ABzwTY+KPEOuXv2PS7DR9Lmb7RycyYZgUiVQXZ3ChEVmfaFJH6NWP/BvX49bw7Z+DPj7+3z8OPDHxB1qNjoXhXTdHaeO6Yfwo89zBNN7lIfl/2q7824cxeQTjHHVacXLVK8pO3V2jFtJd2rG9bCOhZVZJX9X+h+Vvirzp5m2OwZf+WjJ8zVwevWc0kks/2n7z/e2/Mv8As19A/wDBQb9if9qf/gnz8Wo/hn+0LoVlPFf23n6D4o0dJ5NM1WPA3iCaREPmRkhZI2AZCVJBV0ZvnbVb1pbT7Q7Eyf3Vassdw5mGX5ZTzCUoSozaUXGV73Tfbyd+z0M6uCnToKpdNPscd4gW1+yPsLNtf+GuM1C3jdnf5v8AZrt9c3zsqSbc/ermtWt1Enku+F/urXj0Zcuh49aJzY3qzJ90U+3abc53s3yf36ffRoku9EZf7tQLNMrK7wq7L/D/AHq6+b+Y5jRhuvlZ9n3Uq3DJ+787ez/7tZcM7tmNEXP3vl/hrQtXjaP5Ebcz/d/u1lKPKaGnZSJlX3/wfeWtfTS7SJvTHyVk2McKx7EfI/vf3a2bFX3KkzqqtUe05S40+c1rHzpCnnuqOv3a27BoZfv8HZ8jKtYtrD5hX7y/PuVvvfLWxp9p+7V49yqv+t3JXPUrGsaPLI17FX++EX/gVbumrcyR702qGTc/+zWdpSPtT59iM3yfL96tK1heRTCm4+Z/FXLUxUfhZ20cLItx2aRqs4mxF/6E1aNmv2Xck0KujLuf+9TrGx8xY43Csv8AD81WWRIZsQosu1/7lebiMVGMbHsUcDy2Yy2j+ysnkptLPtXy/mqzHM/l73ePar7kkV6HjdsfI2/+Hd/F/u1LHZyL+5dG2fxL96vLqVpVIxievRo8vwgrPDb/AH2fc25G2/w//E1FDdSSfJ5K7P42j+7U91A8kZSHavzbdqp8y05bFIYWS2mXfJ935PvVzuOp3Rp8pkSTJJL9mmttituWLb/eqq147SIeifMrNsq7Jp7wqXe5m/65t/DUE1nebfsxf733ZN+3dXauQ55U+WXu7lNVdZV2Pn5Nvy/+hVLJGmoMUdGD7/4f/Hqsrp+6Rsortt/e7aks7F5I1S2Rl27d235mat40+YzlGdPczZLGHc8RhZG2KqMq7W/4C1Tw6a8cjb0Vvl3bm+9/u1pNY/6cRcwsH2fvY/7v93bUy6akkm/ey7vuturo9icrjH3pHO3lrtt3Em5WZN3y/wAK1GulpcNC/wC73t/wGtqazS4uFTZt2vt3L/7NSXVnukR0fYFb5mX7rVtGnyx9056nNKRmx6e8UiQzO2R8zSRpuVf9mi603a2+WVi6/Ki/wtWwsO5v3M24fwSLTNU0t1k+2Pt+aL5G3/6v/gNYSjblZrTj0MfyYQFeZ/n2bk21Za8e0VJnf7u1W2p/6DUN8XRlhTbhV2uzf3f9ms+6uHmVJkRliVdu1a2jL7IVacd4mrJqCMrxpMyFW3fNUS65C26ZPvyP/F975ayby6hmX/RnZjGnz1XuL54Y/kRlRv4q9ChL3PePJrUftG/JrUn+u8759vzRqtV217C7HuZP93fXOS6g6sPJf5mf+Kof7QkVmd34X5n+Wu+nPm3OSWF9pqdVHrB8xn+b5du9Wb5adea1tt+rfM33Y22/N/8AE1zUd9cou9JlG6ludSn8nY7/ADfwMtX7SMTpo5fOUC7qGoeYmxN2G/iX+9WPqOpTQxjZ95n2u2/bUdxqDyKnz4NZdwzybjNuP+9/6FWXtuYf9mun73KMvNQmVlw+GZ/m+SqE188zK6IzJ91Gq40f2iTY7ru2VWaxKwr975d1YVKxo8tl8XKU/tE0O/8AefM33Nyfdoa4Rm3/AOz/AMs3qdbPbvd0Ulv7zVHJYPHDvRP9n5fu/wDAq5ZVv5jnqYKZXWT5flDbW/hX/wBmpqqjSf3fl2/71Sx27xyI7/KrJteSnN8q/Inzf7lL2nN8Jwyw/LLUhjV45G+66s/3aPMfzG3809ofMK/3dvystRv8ypG9tsZv4qXxcxjy80iRb5432beW/iq3BfQxoIQ+3/2as7a9rJ5yTM26nwTWzTI7hfl+Vd38NZVI+5qbU5cszYtbpGbYiNV7S3ijmZ0fajfN8qVkW9x5ciu77m/9Cq5bzO27ft2r/drk9+UdT0qNTlOn02d1m3xzKqyN8610NncOq/aU2qv3du7bXGWcnlwrMk3yt9/dW9Z3TtiH5WSNN3zfNuqJfDc7qcjstLvH89Hf5V+9tjras/JWYInT73zJ92uV8N34/jRnP3flWun0+N2jdLrzN7Muz5fvLXnYiUuY9DCxjKJzfxttJbXw/aO8QVZrwOmGyCNjVt/Bq0M3gW1eWLKB5Crn+FvMasb44wsvh60kkkYsLwLt7L8jVvfBiKObwFYM6DKzyquW+9l2NfpuOfN4QYdy/wCgh/lUPXwtPlrcq7HZLHDHIyI8m1tqurfxfLRJawjLw7W+78v+1VmO3hkVPtLsiq3ybfm+al+y/aI9kyMw+b5lRv4a/HYx9/3T6bB0TCvtLeJvnSQBt3yxttXdWTrUe2MvNufb8237rNXWXcO6Z0QsLZflRmX7zVzuoQpJG6b2LSf3nrriehTw8OY4nVlHzv8AMsKpufau7b/s1zupWu2Le+1n27dq/d212XiHR7lY9nlqyt821W+auf1LS90fnIjIWTft/u11U5e7E9CGHjySOOuo0jzDvWR1+Zv9ms6a1h8tdjtnfudd26uk1K3haN/njBkXd935mrNW3RY9/wBzam37lelR5tOxy1sPCRgT2e643lMec/8ADUMlq5k2Juw38LL8ta8lukbjyef4v9qo5vnVdkTOfu7f7tepRlFfZPn8VRhEpW8M8a/Lt2t/C1a1j+8bY8Krt/iX7tU5I0jlMiTbj91f71SWI2zM6fe/vL96t/sHjVJRT1Or8P7BtSN93+01dvoN5JIyQ3MyxRfdVlirgdFu4/OMOxkdvl3L95a63Q751ZkmmZ9vy7d33qvl5o+8RHtE9E0G8s453s4HV/Mbbu2fNXWeH75mtfs29d6p8kLNt3f7Vec6Pqjw7EjT51+Ztq/+O11eh3TsftLz53Pu8vdt3Vze05Tso1OWPKek6TevHIk0yRoY4tu3726vUvgvqlvY+IF1K5+dbe1kdNqf7Py7f9qvE9F1Tc0dzNMqfwutej/DWa5utQuLC2RZWkiZomj/AN2vGzqUpZdNR7Hr5VLmzGDPSvhL8O/hvq3wIvNV8czLY3PjbxQyvqGoXCrJ5cbfeX+7UvwD0n4b+Cf2sLf4e/DrxzDr2kRtG263bdHHJu+7Xzd+3l8QH8N+CfB3gDQ9Y8qSHS2uJVh3L5fmN83zf3q9u/4IJfsw3fxK+M158RdZnkl03SrZbi8aR/u7fmX/AL6avzTLqeJ9jroelxXKhPmqM/XD9q+WKz+DtjqMkMkMENnGiRx/8s221+ffxP8AFj+LL651K/f5I/likZtqs23+L/Zr7h/a/wDjLZTeGYfCsNnH9kgQ5WRfvNt+WvzA+LHjC9uvF1z++2Q+aytGqbVWuvH4iNeUYwkfO8LYGeFw0qlWPLzHRX+i6DrFrvv4bd444vnkj+SRm/3v4q+WP2jvh/ptrZXKeD7lZLya6ZZftFlt+X/Zavon4e+JNG1q4jsNVSSK1t2ZbjyX+aT/AL6rqvFXwB034jaS15oGlR2lvCjMs0zbvMrXL6kHO0zfOI8vvRPyE+JEHiHTreZLmGSKRflfcleVXWoeJNJt5NiSBZPvSba/SPxZ+y/ol54ku7bxF9nfyX+SST7vy18+/Hj4P2enrO+iaCrxf889n3Vr7PL8Vh/hlE+BxmFxVWPNA+SbXVtY1a48l/nf/ara1jwrrGk2iXJRfufdrfbwjoOk6il5bQzIWZv3bRN8tReKNemvLf7AiLtWLbu216despSjCETxIYWvF++ziG1T7Qvk+Yqlfv1p+F5Jrq48lPlP3d1UrDw697ffJ8wZN3yrXqnwp+Et/dTJePbNtb7m2s60qVOBdCNWpVO8/Zt+FNz4y8ZWugvZyK1w+yKbZuVf9qvX/wBuL9hXx5+zn8O7P4u+J9Kkt9Eup44Iry4Zf3kjfdVf4q7X9kfwS/hfxlYalqVlHhWX5m+Vmr7M/wCC23wZ1z9on/gmt4U8VeEovOuvCfiCG8utsnzNH5fls23/AGa+Qq1JTzGMH7sWfcSwfLlHtI6n4ha1rmm6fCPtLqv9zdW58NfiZ8N9F1CG61jT47llf7u7buX+9XJeNvg/42h1bZqWj3Aib5UaSr3wv/Z98Q+IPEkVg+myKJG+dm+7X0zwGF9hzTnyny88bVp1YuFLmP0r/Ym8T/snfFl4X8E6vY6XrUb7fsd8iq0n+1X3dZfDvwT448K/8I34k0e3nvLW32RXSwKrLtr8i/h3/wAE9/jUuoW3iT4MvJDcwussSq3zN/lq/RL9lHWP2ltN1ey8DfGnwlNpd5Gqq91G/wAtwv8AF97+Kvjc0w9alHnpT5on2GXzw2NpWrQ5Jnt/wR8P674Nt7jR/tMjWf2jajSN92vdPg7r1t/wlltvjZ1+0bWXa1Ydr4Js4dMGoGFgkzqzbk3MzVp/BbxrpXhP4n48QW6vp9jbzXU9zcbV8tY1Zt1fLQo062LhGX8yPUrx9nlU/wDCfnF/wXG/a80f47fHSz+CfgXxDJcWPgGeSK9aHcrfbJP9Z/vbflWvi7T7eGa685Id+3iXcm75q6/4zagnjb45eMPE8Lt5WoeKL64gkkT5pI5JGZfm/wB2qFvpaMoeR8Lv+dV/ir9lpUY0qUYR6HweCw/NT5iNdPSPCPDvGzckav8AKrVejsUXKJ5n+6z1as7PyZNn2ZnLPU8cLtNvTcEb70e37tYVve909unR5Y+7Ey7i1d7dXeFdm35/4qyrzTdyukwXb95GVdrV080LxyBN/wAn3drfxVjatbpMzO6MG/557vu1lR/lkZVKPNDT4jjtQswrNNvYP95axtfjkWZfnbbtXe22up1K12s7p02bnb+FVrn9U877QzvMzqqbUj212U4+9aR5FSPu6HI6tawyebs4Zm3fLXLa5Y7mL/xN8vzV3eoWXzSD5UWuf1HTRtbfx/CtejTjys8mtT7nn+rWNtHHs21sW0Sx+CpYo12gWkwAXt97pUmtaTlSdjY3/wAX8VPt4Snhp4SOfIk/XNfp/h1rmGK/68y/OJvlatiKj/uv9Dxy63xylE+b/gFRNdeX8n8VaetWcsN1vTbsrKkXEm90yd+2vz/l/mPJjKMoD45ts2+FF2t/Dsq1DeddzrhaobXjX5PmX+9up0M/zbHh+X+Go5ehXxS9427G8T5S8e5d+7K1fXUgyt5LqzfwfJ/DXOQTeXHvhflv4VqxHcOsjIm5Uol8XMRKXMdPZ6k82IYUXd93dVn+3njVUm2tt+WuVhvJoz9mjfafvPtp7X20ZTaT/eanzcvwmFTc7CHWk8nY7qxb+7Q2qbYVSHb83zVyS6oGj8maD/vlqmh1J2k+WfAZfu1rKRhyo6dtWTeuHwrffpn25IZG8jbtk/vPWJHqXnSLC6KzL83mVMrPIymbbt3/AMNRKRUY+97pfmm/ePs2n5/m21XZn8n95tV/vf7LU5S+fLdMD+HdT/JmMj7Id/8ADuZKj2hrGJQmXau99qn/ANBqCa0ikx/Fu/u1rfYfLVeNzK33l/iprWfRMqD/AB/L/DWPOaRjy/EfR0djbR3BedN+35fl/iqa4tfMhHkw427tm5fvVYs0hZfkhY/wp/E1XVt0FqEhfb/E6t/7LXxlSXv+8fpNMxvs6QxpOgZG3/Nteo5Le2jZ/nYpv3bpPvLWpcWPzbN7Kny7d38VVLuPaqI7r95vlb+Ks5RudEcRymDqASV3869ZPn2/L8y1i3sMZ/49oW+Vf4q6a6s0XY/kyK7fLu27lrLvLV1md3XZ8/8AF81dVP3tDCpU5o+8cxeWs3nNsh3ts+7uqtJaXNvIrui7V+/W3cRI1w7nd+7l/hX5ai+xvJvTex3fxLXqUakvZWR4GIjzfEZtnbv9o/cosvzfeatqxt7nzNibsf3VSnaXoqMrzp5af3v4q3tH0tNyybFUN/dp1Kxz0aPcqWdjttRcvbN8z7drVesdNhkVsf3/ALzfdq/HZpIrQw7pVj+ZGb7taFjpMy7nfhNm5Vb+KueVT2cDenTjzmVPCkSB4U8ptpX725elfcf/AASx/wCCIn7P/wC23+xjqH7XXx9/aN1vwPY6X4tmhvZEgtILGHSbONXupXnuDhWbfgTkhIfJfdHLkFfjm/0ovE8Tptwh3Kv0r9Dv2MvD3iXU/wDg15+O1vo+i3s8svi29uo0ggYl7aKXSWmlGByiJFKWYcARvnoa/Q+DMfj8PlGIWEreylUrYaDkkm0pOonvp5/Iqu6lKl7krNyir+pLF/wRZ/4JK/tqeGdb8Jf8Eyf28L+/+Img2kl2ukeJL2O5hvkVSAvlG3tpVjMnlqbmLzUj3jKMWUV+dvwn/YA/ak+L37X5/Yb0L4dPa/EG21aax1nTr66iWLSxDzcTzSqxQxRoC+5C28bRHvLoG9W/4ISeEPix4r/4Kk/C24+E9vd79I1SW/8AENzbKxS30pYXW6MpBAVHR/KGTgvKgwxIU/qP+y54y/Z+uP8Ag5R+OemaFoluut3Hw+gtrS8+xuP+JlDFYm+2N5pUMyABm8sE+UwBX5jL+mY3Oc44SxOLwiryxKjh3Wi5pOUJcyjaTjy3i782utlZeaqV6+DnOHNz2jzK+6d7a+XU8D1T/gjn/wAEOv2cvEth+zV+1f8A8FD9YPxSuQkd61jqltYW9tNMf3QkiNvcJZ8FGxcT5IYOcKy18U/8FN/+CU/jj/gm7+0P4c+FuveP4/EXhbxmizeGPFcGmmF3QSJHPFLAXIWaIuhwshV1kjbKliifef7VP/Bar9kP4J/tH+NPhR8cf+CMPhe58WaL4iubfWL7Vl0t7i/k3ki6Zn05mcTKVlVyzblkDZOc15D/AMFsv2rv2p/jzqHwDg+M/wCwTqHwg8F2l3HqfheHV9WF3/aRlaFTC/2cRpa7Io0H2Z1WdA2fkVgK5uHMbxlSzXDSxkpulWjJy9pOi1J8vMpUYxfMlfeKuuV3flGFqY5Vo897SWt3Htf3bHsXxT/4N0/+CbP7HkTfGP8AbC/br8RaT4BWxt7OC0uYrWyu7jVSg8xo5VSVpkYrI620cBkRAd0jhGY+F/BD/gjL+xl+3V+1t4t039iz9qfxKfgZ4H0KzuvEnjPXdCVpf7QlLFrG2lkEAkURxyyNO8SiLAXbKGDnsf8Ag7Q8b+Ibz9pf4WfDia9b+ydO8C3GpW9uGOBcXF7JFI5GcZ2WsQBxng8189f8EVPjp/wUA/Zh8Y+Pfjn+yJ+zvc/EbwdpOgRy/FDSJCYrf7HFJ5ivFLuB+2KnneWqLM2x5W8lwCVMq/1pxHB39sf2g3iKkbRUnTjTXvpLeNudpaSb+JpNMKP1uWC9v7X3mtL2S3/M+nPCf/BKr/ggF+0b4kj+AH7Nf/BQ7xKfiDfFoNHkur+G4hurhAchEeygjuM4JCRTAsB8pI5r4e8Vf8EpP2nNP/4KJ6j/AME4fBmlxa14ptdTK2mrMPstpLphQTLqTlyfLiEDCRlBdg2Y18x8Bv0F/Z3/AGr/APgiP/wU1+Nuh/BnW/2EtT+FfxJ8TXhXw54n8IJHZNZ6iqNLHNHdWDRMsysm5HkgZN4BYYzW3/wSz/Z3g/Y4/wCC9fxg+CHxe8ban4u8Q33gq5v/AAZ4u8RXTXmoajbTz21w0lxOX5uTCSsjMmXaKQjYpAbHD8QZ1kdPGKvUqupCi6ip11CTvzJc8ZwsnBfaj811FHE18Op8zd1G9pW+9NdPI4rWf+CPH/BDT9mvX7L9nH9rX/gonq6fE+VFTUZbTVrXT7a0ll/1XmRm3uEsgFKNi4nyQQ5wrLXxP/wVQ/4JRfFf/gmZ8Q9Oh1HxCnivwF4lQt4U8a29ukAuZFRWltpoRI5ilQMCDkpIhDKch0j8m/bm8H/FfwP+2R8TfDPxttrxPFUfjfUZNWa9Rg87y3DyLMNxYlJEdZEOSCjqQSCDX6Z/8FYdI1f4c/8ABAL9nX4c/tHabcL8REv9MGlxX9u/2myiS0uSYpCXzGyWz28TK275gBsUjKe1Rq5xkeYZfOeOliY4t8s4yUbax5uenypcsY9VqrP0tvF1sPUpt1HLn0a07XuhfAv/AAbqfsUeGP2ePAP7Xn7Sv7auu+G/Bd14Gs9a8cRX1paWW+4uolmijt7hy/2cASxxeUY55JXU7GUyKi+d/tS/8Ecv2AviJ+xf4w/bL/4Jc/tTa34utPhzBLL4n0XWlFyLlYhHJKEbyLeW3dIGeX5o5EkC4BTBaus/4OBvEniC4/4Jufscabca3dSW+oeEYby/heditxcJo+nBJXBPzOommAY8jzX/ALxqD/g3y1HULf8A4J5/tmLBfTILfwUZrcJIR5ch0nVcuvo3yLyOflHoK8GhjeJIcPrP6mOnKSq8qp2ioOHtvZtS927b1s9LKyWyOeM8UsP9YdRv3rW0tbmt2PyUooor9sPdP18/4I4azr37Jn/BE79pf9svwpdwWev3txcWOiajA0Qnt5YLWOG3ct1BSe+LqjdwCFO/5vyg8A/Erxv8MviPpPxa8F+JLux8Q6Jq8Op6dqkUx86K6jkEiybj1O4ZOc55znNfq/8A8EcNG139rL/gib+0v+xr4UsYL3X7K4uL7RNOgSIz3Ms9rHNbpt6lnnsSiu3cgBhs+X8n/APw28b/ABN+I2k/CbwV4bu77xDrerw6Zp2lQxHzpbqSQRrHtPQ7jg5xjvjFfBcN+w/tjOfrFuf2q5r/APPv2a5L+Vr+W55+F5fb1+be+vpbQ/Un/g580C2+Iel/s9ftfada2caeM/A0ltO0Ri8wjbBeRDIO6RALuTByVXPUb+fyar9Zf+DnzxBa/DzTP2ev2QtOuLN08GeBpLmcRLH5ijbBZxHAG6NCLSTAwFbHfZx+YHjj4LfGP4ZaBoniz4k/CbxN4e0rxNa/afDepa5oNxaW+qwYVvNtpJUVZ0w6HchYYdTnkVr4e1FDhLCxk0r8/Inu488mvX3fwHlrtg4J+dvS7P2u/Yn+GH7Fniv/AIN7/C/w7/a8/aR0/wAH+Cta8RXN74m1HSNetluTdJqL3MdgMrKftBRIGaERtLt/hHUeOxf8ETv+CVX7bPh3VNN/4Jg/t/XN9410iwmuB4c8Tzx3P23avy5iaC1uIYi7Ro1wqyom4fKTxXwb+0l/wTR/al/ZY/Zx+H37U/xO0XSpvCHxGtEn0q80TVBefYTInmwR3TIvlo80WZU2PICoYMVdSg73/ghJ4P8Aiz4r/wCCpHwtufhNbXhfSNVlvvEN1bIxjt9KWF1ujKQQFR0fyhk4LyoMMSFPg1MnrYHAY3N8tzWXx1atkoey5le8ZJ3vty3uujSOd0JU6dStSrdW+lvRn0f/AMG4Hwpg+Bv/AAVV8afCD9oHwzfaN8Q/Dng7UrDT9LldHjt7pLiAXSs0bMrt5WSjKSjKWIJypr4g/wCCjJ+OX/DeHxSP7QR1L/hLx42vftn9oZ8zy/NP2by+3k+R5Xlbfl8vZt4xX1t/wUoPxw8Yf8F+PEKf8E4tK1r/AIWRZ6npyW7eHP3DvqUVjCLp5HeQRiDblZmk2QlfMEgKlmb2LxB/wca/ET4feMLjwZ+15/wTL8K3/wAa/BV9Lp1nqMdyLdtPug20qiTQXE0eTg/upiJMgqQGBqsPic4jmsM6wuHjXniMPTcqftIwnT63XNf93JvXrdX6BGdZVlXhFScoq6uk19/Q6P8A4LQfD7x7r/8AwQq+AfxE/aS1HVYviH4fudFiu47+2JnmmurCUSR3W59ySiONGZzuYyRkFVLkr+LVzHIdPDqnBO3Nft1/wcb/AB3+Ks//AATR+Cfgb436HpWkeN/Hes2+s+KdDs4wosJreyZ5YI0kd5FEcl3HGzBjypBb58H8WbKzSbSEL4w6vwf4ua+ExtSuvDeNSpZKWKm0o/Cl7+kfLmvY5qcJVMu1/mb/ADOL1K1eONnjtuGeub1SHZJ5P3jtZv8Adrv9Wt9v7npu+ZGWuS1azf53eFiq/wAX3d1fBYetzHiYinKJyF1buqs/zMKoNavCvyfKWrcvLV49uxOGqjc2TbgyP/HXpxqcp5/LzGfD5nmM/wA2F+/trTt2wu9PmH+z96oY4Ut5GfyeN1aNrG8m3yf4f71RKXc1p0+Yt6fGnmKjhm3feXZW3Z27zfJND+73/PurPsY+N/kfe+/tf7tbWlrNtWOH5tv/AI9XLKpHl907KdGJoWdu/lmbyWdl+bbvresY/OgP+jNsZVZo2rM0+FFVtnzNvVl+at7SYpmkRHfHz7XVmrz61Y9Onh4s2NHsXVUh8tV2/c+b71bGn2+5vOeFkMfyp/d/4DTNF09I5ESZ2JZ/kZnrotNs99iN3G5/l/iZW/3q8n61zS5T2aOF90j0+1RVP7nZIv8An5avrp6bcPD8zLVixsUhR7n5trfLub5d1X47FJNz7GV1WuCpW5ZHpUcP7Tcy1s9uHfhP4vnqwlvMrMnkt/10/hatGS1s4443fcvyfPtqP7PNHCjo7J87L5bf+hVjGXwyR1Ro8vulb7LM378Psbau1mpF0+28sO/8TfIy/e3VfWGSSG4/fLsk2/8AAalt7OaOPyYYfmb5flf71bcs5blxpxj8RjXGl2d1IXTbsX5P+BVBL4fh2h7lGLr95W/u/wANdL/Zsislsv3pm3bv+ef+zTZ9Hf7QZptpXZsT/er0aNP3QlGHL7pzVvpaW8nyBi/zK6t/DVm10xFh85IZsr9yNfvN/tVuTeGYY1Vw+3d9/a+6rEGkW1uu7zZH86LazN96vRp0Y8uhxy/lOYl018rH9sbbt+9N8zVW+wOu2Z3+eNtv8X3v71djf6fbPNC8PzNs/haqd5pcMcex0berfxNXT7PlOGVP4rnO/ZIWjKOjL5nzNIv96ofsSWcPmQpvRm2t/s1vSWNn5ivDMrhvubn+7VC4sXRvkuednzbv4afs9eU5+WUpGPGqSf6l9zq33VX+Gi6g/dt977/zqzfL/s1d8vyZAjvj+H/Zpt3apcKrwuoZvvVlKPKZ8pzerWvlt5zp87feVqwLyaTzR/cVN23Z96uk1SGa3Xf5zFt+1ZG/hrmdUV9xMz87vurXD8J1L3oEEl9MzNB5yoW/26zr7UGVim/5P726rnkvtCPbbT/DWfeWZ3Mkzr9/7tb0ZbmdbDyqfCQxzecqO771jb+596mNdOv+u3MW+4v96rDWc1uqo9syrJ/efbVea1TcHSHb/s11Rrc3uxOrC5bzaDmvnl3/AD/Pt27qZJNNtVJudv8AFupJF8s73Rdi/dXZUL+X88kKbf4trN92qlWny6H0GHyn3IoBMnnK+/8A3KVv3y73eRmb+Jvu1XXe0bec7b2+4yp8u2r1jbvtNz8237rVlKtGMbmn9lES2bxts+Ubv7z1LJp7snzpgf7P8VXLe385kTZJn/ZTduq7b6bNMrzvuxGn/fVc0sRp8Q/7J5YmHJoKSKm99jN9xv7tMm02ONW8t+N/zt/errrPTXul/wBTt+RW+b5qq3mjoJFhhhVW37fuVyfWoy0ZwYvK/d0OVm0hFXG/afN+df8AZqCSx/eOj/Nt+VK6W4sUhm2fK3yt/urUP9nJuV5HXDfLWlPERj9o+VxmFlGRzFxZvHs2Q8fdfa21dtM+ywrGqFGb/Z31vSWMKzcpsXZ/y0+7ULaei/OkzFt/8X3VreVb3Dx6mHMKbT33fc3K38O+o1s91wuxP4/4a2byx3SNv4H96oLqF1UIm1P4qnml7ovq/wDMU44X+/C7fLV2OOaSRn8xUZn2/wCztqGGHH3/AJtr/e3VpW8e2RIdit8m5P8AaqvhgaUYziWNPVo2RNn3vvrW3prOqmHyeN67f9payobXbIuxNpZ/vVt6fC/lriFV3fKjfdrm+E76Pu7HQ6Sj/wAc0hTf/ql+XbXXafaw7fOj3K+7a+59y7lrl9HTcIXhhXZv2y+Z/drrdPjmjUIr7xu+Zm+8zNXFUjzTPVwtTlic78d4PI8LWi7WOb5Tubr/AKt66P4D20L/AA9tC/Jd5gQ33f8AWNXP/HiHZ4LsXONx1AbyPXy3rqv2eYlm+HVmkqhgZZgg28k+Y3y1+l5hDl8IMOv+oh/lUPTw9S1XmZ2C2sMuy2R2Mm5f3a/wtVqW1uY/3MPzbXZn2vVzT47mGbybpFb+4u35lqaGHzLfzN+7a/3fu/8AAa/J6dGfMezRrSjK/MYGEZUSG2kmRm+8r1g65Z+dG7vDH/d8v+Kuy1CNxbvaoit/sqm2sHULGFoUL/KVTb/vV0ez5T3MPW+0cRrVu/mNcvbKIl+X738TVhXtm/zw/M+75fMX7q11mtRpHEsLuqht33f975a5/VpEjVnfdLudf4vmrspUZcp60cRE5LUrW5tVZEgUhm2/crJ1CH74dMOv3lrprr99M/ybf9ndurE1Bba3Hk+Qx3fL81dkISj0FUrUomBcLuk3puEjLtSqLRvb/wADOWba/wDerUvrXy2VUXYf7tUWVApuX+Rl++q/xV3042Pkswqc3wkUifZ929N27+JfvUWsf2e63ojN/tLTjI/ljem12/hpIt0zO8Jwv8X+1XZGPNE+brVuU19Lj8y4CedtVn+Zm+9XT6PI/m+cj+U6/fZf4q5WwtQ45Ta7L8qr95v9quj0uF48ZfY/3WZW+ZaipzSI+scp12l3m3b5KfxbmZlrpdKvnjuFe52/7DLXG6erx42OzD+7v2s1b+kzPcKqIi/LtC/P826uKpL3Trjio/ZO80PUNtwEufklX5YmX5lavWv2f9eTT/E3nTTfNHayKkbfdm/dttWvDdFut2qJvTc8PyrJv/76r0j4R3yN4shhO5XuH2f3WrzMwpzrYOSO/LcV7LFRmTfFL9n/AMVftCaDpT+FYY7nULNGtvssb+Z8u7cq19lf8Ee/C3i/4KfDfxXpusaHqFldPdwwzwzfL8u7+H/ZWtv/AIJ5fBV/gz8TG8beKdAku9KslkvS6/vMbV3V9BfDD48fszfGa/1f/hVOjXMuv39zI1zaQRMpQq3zM/8As18LVVONDkfxHsYytUqYpvk5odTlf2nNcmXejzSOYYl3sv3fmr4i8fWOmya9dvvk3TS+bK0nzKq/7NfU/wC0Nrn2qa8tprySDy2ZUVW+VttfPS+HU1bTzNeQ5Mj/AOu+7tb/AHa8mMfZM9CPuwPLNH1aw8P6o/2O83fxyq33dtel+Dfj/YWttLa3+sTJYKu3bJL/ABf7P92vO/ix8Odb0WZneCTDfOqxpv3bq+fPifN480nxFaPbRsE83d/dX5V/iWvQo4d1Jc0Tz8VWoR92qfWni6Pwx4mtftnhjwxcXImiZnumnXy9v/Aq+ffjRb+M9GXzv+FbxzW/3WkhRWZY9vy7q8x/4XJ8V5JP+QlN+53b5I5dq/7u2r1v8cvjBcaf5M0iy2zPt23SfLIterCniqcoto8eX1GfMos8u+IPxA8E6hZywzeD2inh3L83y15Dq1qmuXn2fR9NbbvXdHH81e3eNvDNh4w1R7zVdKjiVf4rf+KodD8J+G9Jj8i1tl2wtuZm+8zV69Ct7D3pPU8LFYSVefuxOV+F3wDudSvEudVhbDN8/wDCu2vpPw78M7C102JNNhVfJT541X71cto+pWEMa2FskKNH91Vf5mr2X4Qx2euWn2O8uWS42fumjXburWtjlWgGHy32Oq+I674YafpVn4TTW7xLNHtWj37n/eNub/lnX6B/s8eFdE+NH7NOqeBfE/mvYXdq0bwsu5ZP7v8A49XxJD8A7uHw+fFNzc+TbQorNG3yqrbvu19p/sP3XiW7+Hg0jwzZieBo1MjF/ljjWvn8fHlqwkfVYCPtMBUhM/N39vL9iPUv2cfitDeeNNEmfwpr23+zr5flWGT+7Wv8O/2DbzxFp9n4h+F3i2HyJJY3+zybX3Mv3lr9Xf2hvgj4M/aD+BV14G8c2DXOyKQ2sjL80LN/FX5eTfB/9o39iP4lJY721LwxHdM9ldRszNGv8K/8CrSrWnLCxqU/ecfiR5uGw1KGJdGr8mfZ/wCyr+z3428IX1vf+KIV+zQxL5XkwKrbv4q+lPF2k+Er5rCF9EjefzViimkX51Vvmb5q+b/gr+2Z4hXwzC/ifwwxiki/hfbJXsfw58Ral431C21N7lkVtzRW8jf6ta8j6xVlddZHpYjAyj8Ox23j2DTPDGgo8at5axbomb+Jq+Svj540vNH+FPxD8Q2F55T/APCL3kFvIz/89F27v9n71fQHx88bBoxojXOzy037Y/8A0GvmL9qSS20/9lHx3r1zM3nyWENusKp8sjTTbdv/AHzXFhI+0zWCUdpHTUw/s8qm6nY/N7R9DmVUSa5aQ/Z182b+Fm/vVu2OkwtCIYZlVN+zayfKzVNa2qW8wR3VlX+9/erU0u3mVv3yZ/i2r/6FX7A4c0OZHxOF933SGHQbzzhMjxh43+eFX2tt21La6fNCrwzJGsjL91q3Y5Ekk86a2+X5V3N97/vqp7rSUW3eZHUbfmRVT7vzVx1Kc5bnr0/dicVeW032h0tnjUruVN0W5qxZtHuVkeaR/N7ov+1Xe6tp6SXD3LfI8n+qXZtWsi90OG3ZnSGY7vmdv7tHs5c1hVIwlscBrmj3Mbsk0LJ/FtV9y7a5W+01/mvE3Fd+5f8AZ216pq2nJNB52+NYtn+6zf71c3q3hvzI/s3ys6/fZZdqr/s12U4xieHiqfve6eaahbzM0jzTfdb/AFaqvzL/AL1YGsWO5fk+Zd3z/wCzXd32hzWsJ2bdrJ/wKsDVNLSNkmRGdPvPDv8A4a76coniVo2904LVradVfYmWX+Jvu1RWJV0xogOPLYY/Oum1zT3Zf3aNt2fLtX/x2sGeP926HHKnp71+l+HSSx+Kt/z5l+cRZWrV6lv5X+h5r4i022O7ZN/s/KlcvqkOdyJCv3fk+bbXomraeki+Xhdv3dypXHa9ZuPndFVV/i+9ur4CUeU8SPwGCm+Tq9PVvJzvTctMu4/KbZ5aj/dqFGk8vH8LVlyle0J4ZNsn7nlv4FX+KrC3Dso+Tb/f2vuqtbs43f79SrI8bZT/AHflqvsC9oWPtE8mxE2/N/6DTf37be237tNS3mk2zGHLL8u6r0VnHtDvuH/APu1H90xlHmK0Kuys/nbl37au2trcySK4CuKt2GjpI2/ZuWtex0tm+RIvmZP4aqUhRM23h8lV+Td/vVbtLN48v0Xfu3fxL/s1sW+ho2xxbZZm/wC+avWuhmNd/wB5mb/x2sJSlKRtTj9oy4YYGw7wt8vy7asW9tNu+/t3Jt27627fQd0nzhk2/Ntakk0d4ZN+z/gTVzylynXTp+0MZrOaE7Oq/wCzSLC+1Ydqn+L958tbL6X5ab3pPL/eK72aru+X5v4awjUka+xPoJdJfzpUidd8a7kVVqytq9urBEUDylba3zbmrXm0v/SHfCs8KfO277zVE1vsk2PtULt+b+9XztSnyn2tP3viMW4t7qREhhmVD97d96s+a1thiBIVy25VZk/i3fxV0N9p/nXEWy2UfeVmZvutVS8tU3CDYrfPuRWf5qx947JRhyGBd2syx7PvIv8A481ZWoRwsy/O3zNu+X+Gt/VI/M3bblh5afw/3qy7qNJpvufw7mXb92uiHuz1OGpzcskYEsJkvtkz7Qvy/N92oZLWSybZI7OWf7tXbyP/AEkp5K437XWo12fZ/n/4+P4N392u7m/lW550o8xJptvDZ2vnQ/NubdtVfmrc0mG2kmSZIZGVX27fu1lWazRuCg3Q+bu3fxba6LSZoY7pNiNsVd/+z/wKol+71FTj7SXKael6T9qMabMI38P/AMVWqunvG2weXt+6q53VFbslwo+zIrbl/er935a0reNI1TyYFULtX5v4f9quGVTmiehGnCJieO9QHhrwrda2lnvdFCRK525LELu98Zziv1I/4Iz/ALTuv/sd/wDBB34h/tKeH/B8XiO58JfEy4mTQ7id41u4pG0mGWMOoJRikrlW2sA2CVYZB/K/4yQOnw8vvOlDFZofLIb+HeK9F/Zh/bq+P3gj9iLxZ+xL4fvtHHgvxN4gF7fCbSEe7RtsTOiS9NrtFESWVnBiXYyDcD+nZRh8LR8NK2LlTUn9ZhzJtrmjFRtHTb4nqtdX5HPjsN9YpqKWl036dT6s+KP/AAceT+FfhvrHhX9hj/gn34c+E2v+IQw1XxGIoZCmUdVmSG2trcSTqzlkklLqpBzG241+bvgH47/tD/C/43237SHgz4h+JLTx1aaq2pHxO13M95NcuxMrzSOS0wk3MJFfcJFdlcMGIPT600Ml15MyM6+Uu5lese+mmkZn3qiN8rrv+7XZk/iFgsqp1KWFy6C9p8V5yk5Lazcrtq3S9jGMKGFTUY779b/efpT4a/4OZPCXiWx0jxz+0R/wTR8K+KfiToUeNP8AFlhdRwrCyuXiaD7Tazz2wDHOFlb5ssCM4Hwj+3D/AMFF/wBp39vv462Hxt+N7QrForxjw94S05LhNK01EZWYRxNIzb5CoMkhbe/AyFRFXzR7ua4kZE3D/Zb5ajn8nz1/1bts+Rlf7tLKuM8ryjFPEYXLYRm00m6k5cqe6ipXUU/7tjOjDDUP3kYWfz/Dsezf8FQf+CjPj3/gpv8AF7w/8W/F/wAILDwpJ4f8Kw6RHaabczXHnkO8ssrNIAArSySFEC5RSFZpCN5rf8E2f+ClXx9/4JlfFDVfHXwq8Nafrem+I7OKz8ReG9bSUQXaRyb0kRo2BjmQGRVc7lAlfKNkY8jm1DdEiP1V/mX+7VK+vEijd3dl2r977zNXrU/EChPKf7L/ALPh7C1uRyk1a993rvre976hKpTjQ9lyLl7H6hXX/ByZ8EvAkV144+A3/BKrwZ4Y8e3ML/8AFRS3NsAsjj52dreyhmlUnkr5iFu7V+eXi39uP9p/xV+19f8A7dNv8QbzSPiJd67/AGouraMzwranARbdFJObcRAQeU5YNENj7wWz5peTedJ99lbb/FT4JIbhtkyK/l7W2/w1llfFWX5PKcsNl8bzXLJynObcf5ff5vd8tjkp16GHbcKe/dt6fM/Vvwz/AMHM/hDxPY6P46/aG/4Jp+FPFXxJ0OPGn+LNPuooVhZXLxtB9otZ57YBjnCyt82WBGcD4K/b8/4KAftPf8FGvi3H8S/jpcGG3sYfs+geFdHSdNN0mMgb/Jikdz5khUNJISWchRkKiKvlun6l5iv50G0b9y7atR6g6q7u7b2+Xds+ZaMs4oynJMW8ThMvjGeqTc5y5U91FSuor/DbtsY0sVRoT5oU1f1f4dj3P9ub/gpP8SP25fgB8HfgL4h+CWnaBb/CPQP7Oh1HTp7iZ9RYQwwB9r8Qp5NvCChMjF1Zt4DBFf8AsJ/8FIPid+w18BvjF8CfDXwMsfEMHxc0AadNqGoS3MT6c3lTQGTbHxMnlXEwCAxsHKtvIUo3i0OrfMNjqF8r7qr92tC11z98kMyZib5kbdXW+OMIst+ofUI+x5ubl55b83Pe+/xa726baE/2lHk9l7Nct+773/M84Xw74gcZTQrwg9CLV/8ACnjwr4nYgDw5fnIyMWb8j8q9Ws9WlWFU+YsrblaNfm/3a2dI1SGHbcwtIV+7ub726u6r4v42n/zCx/8AAn/kelSzKVTeJW/YQ/a9/aX/AOCfPx6t/j18C9Bea9FjNY6po+q2E72Wp2sgGYZ1jZGIDqkilWBDxqeRkH9Crz/g470rSkuvib8PP+CT/h7S/ileQMt14vmdWDSMMM7vFZR3MqnAyhmUkcbu9fC9jeJKvmbFdt+7/gVa9rrG5neHcvy/6lvlb/ar43O+PsuzfErEYvLYSna11UnG67S5bcy8nc6nhcPjJc846/P9Dyr9oP43/tG/tO/tHaz+1D8WdEu7/wATa1rCX8qPo8j2sIjKiG2SKQMPIjREjVGLfIoDFiST9Uf8FKf+Cz37QH/BR79m/wAN/s+eIf2VrHwvDpuoQajr+rWizXbX15FE0atbI8K/Yo/3khKbpXIIXzAu4N5bJrc0P3IWT91uT591ObXvKtnR0kw3y/7KtXRX8T6VavhqkstpuVD+H78lyaJaJK2yVrp2tobSw+GfLJr4dvI9f/YG/wCC4v7SH7J/wTs/2VPjb+zRp3xY+GthDJb2mk6xZSQ3lvbO277N5jRyxTQKSxVJImYbtocKqqPTfif/AMHFni/wl8O9Q8BfsGf8E9fDfwjm1eGRb7WEsllaJ2QoJooLW3t4/OTIKvL5igjBQivkubVEhVYXdm3fxLVJ7uba8M3y7X3bl/u15GK43yfEYyWJq5VByk+Zr2k1GUu7gvdb9Vr1MXgMHOXO46/O33bHm/wT/aC/aS+A/wC0ro/7WvhS/wBXvfG2k68dVfVdbjnum1CZy3ni5ZjvlWZXkSQlgzCRvmBOa/SS0/4OUfC2pLbfEz4i/wDBLLwtqXxSsoFW28XQ3KIFkUYV0eWzkuYlGThBKxA43d6+DNS3wxhLZ2L/AHtv8LVk3U8yqw8xjtTKKv8AC392vQzTxCwWeuNXF5bCUorlTVScXy/y3ja8fJ6GWKhhqjTnG7XqvloJ+3h+2l+0J/wUF+PM/wAe/jtZRQXrWMNhpWj6XazR2Wm2sYJEMCyM7AF2eRiWJLyMeBgDxwDWLRBD5EyDHAaH/EV6Zq0qbW+dv4V3N/ernNUuPO8xHh2/OzJu/hr16fifSqYGGElltJ0oW5Yt3StorJxevnvqEq9OnTUFFWXQ425hmlA+1QsR1XetVJLPTBGYZVQL1Kl8f1rY1RnkYpvbY3975q57ULRI1ffyd21V/vLWkePMttdZVQ/8BX/yJyyxFN/8uov+vQY+i+E2PzxQfdxjzz0/OoW0HwNvO6O1yeo+0df/AB6sq9jtoZQ6Oxff8n+7WfeW6Mx2Qrs/2vvVt/r5gP8AoV0fuX/yJj9Zov8A5cx+5f5HSHRPAeck2uf+vs//ABVOGieCCQ6rbcdD9pPH/j1ca0LuwdNv93a3/oVPhheZl37R8/8ADVPjrLv+hXR/8BX/AMiOGKpv/lzH7v8AgHbw6b4YX54RAfcT5/rVmC30qH5rfyxxjIf/AOvXK2sKbfm/h/irb0uOGSZIbb+L5nVk+9WEuPssj/zKqH/gK/8AkTdYmne3s0bMSspzEnJPZc1agn1lH326S5HHEWf6Umj283+um2t83yKv92uj0mFNqzOjf3dtcNTxFyyP/Moof+Ar/wCROuFSMvsIyotY8ZR8RNc/KMY+zZx/47VyLxD8TowGiN/gc/8AHlkfX7tdTpszyy+cj7GV/wB7uT71b1qz+cfJfezfNtb+GuaXiNlSdv7Gw/8A4DH/AOQOuDvvoeeJ4n+KyKuw3+EHy/6AOP8Axyph4t+MRICtqWQeANPH/wARXqOi3HyrPczbX+5tmT71Xo33WcsKIu6T5dsnzVi/EnKk7PJcP/4DH/5A64RTjzKR5A3in4xtIrkanuXhT/Z3I9vuVIviz41iQOE1PcvrpY4/8cr2HcjSRnZyv8X+z/dqexO6M/Y1/j+7u/hpPxHylf8AMlw//gMf/kDWFGTV+Zni58WfGqUMAupkEfMF0wY/RKkTxf8AHQMuxNVyowuNKGQP+/de5aXZQyIly7sH++it827/AIDWva28skavNbSL/BuZPvf7VdVLxDymptk2H/8AAY//ACAKnN7yZ88J4v8AjxHIFVNW3g8A6VyP/IdPPi/4/lgWi1hiASN2kZx+cdfSlra+ZINiK7N95VT/AMeqza2aTN9stoWQs/yKy120ePMrqOyyigv+3Y//ACI5UZRj8TPmSLxh+0Gp82KDWOV+8NGHT/v3Q/jP9oNZBG8Orhz0H9jAH/0XX1jb6Cl5ZrD5K7Gf7zfLtp1xoyLdIk0y/c2qqr97/gVdS46y5bZVQ/8AAV/8iYKm31Pks+Lf2gF2g22rjByv/EmA/wDadNfxf8fZkLvDq7KSNzf2QOTjjny/Svq3+yd10UuYdybf721mqhqWj3lvCNkMY8x/nX+Fq0/15y7l/wCRXR/8BX/yJjOHLLQ+V5PEnxn3kSwanlsghtL6+v8ABTbnxT8YpBi5/tL5eudOx+fyV9J6npbqzPCkmxf+WkjL92srVNF2xsifvmk2rE3y/Mv96plxzl0Ff+yqH/gK/wDkTknUhF3SR88jXvil5mdmoFs97Hn/ANBpD4k+J8bFz9tUkZJ+wgcf9817dNp/lsyI7Fm2t/qtq7qpXWlvuPkwrv8AvSs3zfLXK/EHLebl/smh/wCAr/5EKk1BXcUeL3Gq/ECb95PDd/MPvfYsZ/8AHapSXXib7ksdx8x6NB1/SvbNWs4ZlCb1Rfupu/irW+Cv7JPx+/aW8UR6P8GvhRq2uOz+U8lrbsqKy/xNI3yqtZvxAyzk5v7Iof8AgMf/AJEn63TjK0tD56e615iFcTZBAUeVj+lIF15gZBbTnJ+8ID/hX6tfBr/g3K+J91bwa3+0d8ZtK8MwuWMuj6an2y4Vf95flWvoPw5/wQQ/4J46HDGmv67431yRlXdM2oLAu7/ZVf4a8yv4r8PYZ2lleH/8Bj/8ia06tSr8EGz8JZW8Rzn97b3DEcnMB/wqGSbVDgujjPT91jP6V+/Fx/wQx/4JsyfJ/wAIZ4oh3I3+kL4mZm3f3q868Z/8G9/7ImsMf+ER+L/jfSE+75cxhnWuaPjFw0/+ZXQ/8Bj/APIno0PauVnzL0PxFuBcu5kuVfcTyWGKQwTZ3GFueclTX6g/Fz/g3O+LWjpLffBH4+6B4ngX/VWfiC3a1uWb+78vy18ffHP/AIJ7ftl/s5SXCfFT4FapFbW8v/IS0eL7XbNH/e3R16EPFHIqvw5XQ/8AAV/8iezh6NGvviJR9f8Ahz5/WKVR8sJH/AKmiXUoyEhhkBY/KBH1/Sty3tUjvjpSL5Mi/fhmRlb/AL5rTsdP2sN80n97c1ay8Rsq5bvKKH/gMf8A5E9qhw/KvG8cTL+vmcuJfELAlYJ+GySIOh/Kp/tPjCNQfs90oPIP2X/61d7Hp9taqNicyfc27vmatC3juY1aGaZst/D/AA7a5JeJWVf9CbD/APgMf/kDSfDmIi7PEz/r5nmS3XjAt8tvdZPUC16/+O0PqnjB2LP9pJBGc2/5dq9IWzhkk8m92rtf7275qoXlui/Iicf/AGVS/ErKn/zJcP8A+Ax/+QPMxGS1aV37aT/r1PPJJ9bkYvJHKSTyTD1P5U15tWZhvWTI6fusf0rsLhfMZ/urtb59q1VkhWRj9m+Qf3v4acPErKnvk2H/APAY/wDyB8xi8O6Su9fU5djqUnLxyHj/AJ59vypA1+3Co/Q4wn/1q62Oze4VUTcG+7uX7rU9tHRfnj+cr/DWs/EvK4RTeT0P/AY//IHlKUXL4EccFvSfMEDHPy58rr7dKhayY53Wzc8H5TXZ/YXaMpDD97cySf3ayrzT/mVNih2++y1VLxKyub1yegv+3Y//ACA37P8AkX3HO/Z7YnbsXk9PU1KsTjdGsZ5xuAH5VNJZpZ3gdH2Nv+61S7oFZpkdmlb5ttby8RMsUdMoof8AgMf/AJEyhVpuXwIgje7U4iL59hU9rPrSDbaxynHPEOce/SrViry3HnTQtEG+Zflro9D0tJoXhmRX2vu/2WpS8Q8sir/2RQ/8Bj/8iV7WL+wjCtL/AMf24/0OC9GRn5bPP/stXofEfxbwBAmonPI26fnPv9yvRtDtUmUIm1v3TKvzbVVq39E86zVnmtvnj2r5mz5ZFqY+IWUyd1k+H/8AAY//ACA5V40/so8O8U638QNTsY4PFhvPIWbMf2i08sb8EddoycZ4q74V8TfF7TtFjsvCX9pGxVmMYt7DzEySc87DnnNd9+0jayQ+CtPmdlIk1EFdq4/5ZvXS/s9J5fws0ybpvuJ1JZvl/wBa3Nfa4jirBw4FpZhLL6TjKry+ysuRO0veS5bX07dWdEa0HTUuh5mfG37R75TZrh6AgaP/APa6lX4i/tMGPy0OtbU4IXRBx9f3VfRysi7ntkb+88P/ALNVry0jiDjaiL9+vj48fZZe39j4f/wGP/yB0Rqx/mPmMePf2kEBA/toBiSR/Yw5P/fuqtz4y+PbIY7pdXxnJD6Vjn/v3X1PNZTSTec9tvRfm3N8u2sDxJY/vA43b4f++drU1x9lV7f2RQ/8Bj/8idtKUmr+0a+Z8z3PiP4sXEYkuV1AqnRjp+Av/jlU5tf8esm2d7rB5+a1H/xNe7a2qW9u/wBmhVk+YOv3f+BV554iuo1doUTay/cXf8rV0UeO8tn/AMymgv8At2P/AMidUVO11Wl97/zOAk1HxI4zI852kcmLofyqN73XXUtJ5rBhglosj+VdHJceW3kwo3y/e8z+7VSaV2kXYi/N8zf7tdcONMtl/wAyqh/4DH/5ExrVKkI3dWX3v/MwXuNQYne0hJPPy8moZN7gtJnDdSe9al5MnmO/Td/FWbeSM0PkzfcXd92tVxnl6V/7Lo/+Ar/5E8uti4wjfmbIpXidQJZBjGBlscelOhYRgJAQAegHesu4mhkYlP4f4W/hp9rcPMyRwvtP8DLSfG2Wr/mWUf8AwFf/ACJ5zx8ObZGxDJqEWPJV+BgfJnirUepeJVGYjN8x4Ihzk/lSaLcIy/vkZpV/i310djdJNcIZ0ULt3IrVzz47y5O39l0f/AV/8iNY6MlflMmDW/H0HywG7GwYwLXp/wCO1LD4k+I6P+4kvNw9LME/+g11MLPHGHTbK33l+b7taul3kLZhmRVdX3Ou35qwnx7lq/5lND/wFf8AyJ0RxC7HHWXi/wCMluwayk1LIHBGn7uPxStbR/Hf7Si6hDcaIuuNcRuJIfI0bcwbsQBHzXeWd87W6JDz/u16v8CfFmm+G/H1lqV+/l2kcq7mZfvL/FWVTj/Low0yig/+3Y//ACJMsZGLujF0b9rj/gsr4d8Jr4c0ab4kW+lanCYYkT4dJi4TGCqObPLcH+E965v4f/tWf8FRPgTrt98PvAl94z0LWNXQC90v/hBYvts6nkDbJamXB68da/bH4l/tV/Af4r/shaNb/DrVV/t3QFt5dLjki2P5kf3ttfKdh+zZ8evhx+214W/an+Neqw/ZvF9x5tirT+ZI0ax/+O/7NeBjfEjJ6CUoZHhpafyx0/8AJD0soxMMx5oVpuD7b3fQ/PLxd+0h/wAFOdbuXHjCbxu0qOd63HgpYyrd+BbDFZcXxr/4KIXJVYIvGcmwjaqeEAQPTgW9fp9+0BJDNrF/co7ec0u+Vfut5e7dVb4Rwvr6zTQWzIscW7b97/vqvEn4s5Ko/wDIgwv/AIDH/wCQPp6eU1qn/L+X9fM/NWX44f8ABSSKYaldad4y3NGUWSbwKhG323WuB9RzXIeJdf8A2xvEF6t94n8M+KJp24VpvChXPPQDyR3r9dvEHjzwt4J1RLC8RdRfYzSqsW5Ya4q+8X6brCyD7JC5+Z90n3Ya9HB+KWT1YX/sLDL0jH/5A83G4GdCpy+0bPypuLH9pm3DS3XgfxFGCfnd/C7Dt6mGsPU/EPxYsstq66jBjP8ArrDy8fmgxX25+0r8arzQbVdE0qzmlim3N5jS/LXxd8RvG1/fakba8dmWR2b5m3Lur0I+JmWyV1kuH/8AAY//ACB586MKKvKVjnpPFXicnL6nMMegA/kKrt4o1aMbX1ZhngBmFYuqa95jSQum7/arlbzXHuJkREby2fa+7/0Kt6XiFldf/mSYf/wGP/yBwVMXCm9JHrnhSx+KWrxtqXgvRNXvkiO157DT3mVD6EqpArstF8RfteaddLcaHoPihZeNpi8NMx/LyTXX/C7/AIKB6D+yp8I9F8KeAktbi4+xt/al1NarL5zN/vV0F7/wVUsPFHgsXelzQw6iv3/s8W1ttclTxHy+L0yHDv8A7dj/APIHp06dDlu66T9TkPEP7R/7fNhZJ4f8T634rtYXUMlrd+HFi3DsQDAMiu2+Cv7W/wDwVs8M6Y2l/AvU/iA9tNEQ0ekeBUugy89/sr+/NeJzftaeK/Fni1vE/ifxDfXlzNLtaS8uPlZf4V2193f8E9f22bnwTdW14mq+crSr+7WXaqr/ABLXJifFLLMP708hw2n92P8A8gb4WhTxV6dOq79uj/E8H1D/AIKK/wDBXrQL9/C+r/ErxvZ3TR7W0+68GQJLt6fca0yPTpWNrn7Wf/BUnxnpP9h65q3ja+tCwYwt4JjIJ7HItc19nf8ABULXPDGtfC9f2ovB9/Haaxpt/H9qjX/WTQt95Vrxv9m39py/8SXECXep3E8zOqyrI/yt/u1gvFTKJU/aRyLDWf8Adj/8getTybnVp1pJ/wBeZ4F4R+I//BTDS77zvCGg/EJp9wysPguSbnsNptyPwxXdn9sH/gtFpcH2EzfEi2U87f8AhWyIfTr9iBr9gP2Rv7I1vQrfVJrZVlZtzNG/73d/eau4+NK6arFIrNvliZflb5mp1PFLJYUnNZDhW/8ADH/5A4KmErQxSpe0l/XzPws1n9qn/grTq0gk1u6+IErDoZfAij/21rnPH/7R/wDwUdvvBLaB8R7zxhHoWoTqNmoeD0hhmkQ/KA5txkg9ga/ZLxJpttpejfb5o2G770n3WX/Zr43/AG7PGF54k8QaJ4AuZpJbHS7drxVhuN0aySL8vy/3qeSeJ2T5hj+RZDhYtdVGN/8A0gMzw9TCYXmdWUr9G9PzPzxXxt+0dGeIta4GedFz/wC06nt/iL+04hH2Y65uC4BXRATj6+VX05ptrNbzRebN8jPtZlT5ttX4bfddK42+c0v9/wDhr7n/AF5yy9v7Kof+Ax/+RPn6XvLVWPlpfiR+1TCCQNeA6knQh/8AGqmHxQ/a46K3iLkcgaAOf/IVfWUNm4jZ5vLRGf5I91adjZlv3KOvzbVo/wBd8stf+yqH/gMf/kTrUXe3MfIfgD9oH4t2HxJs9C+IF3PdJdXUdtdWd9apC8RkZQJOEDAjIOD1H1yPpDUdNdl85JpN/wDEv8K//Y182fFq2aL9sJrVoyhHiLT1Ksc44gr69m0OZrdZhZt8yfxJ8rUuOsHgX9RxWGoxpOrSUmoqy1s9lZaXte131MXNpcp51daKjQO/kwzL8r/M+5mrB17QftDNc71j/wCev9yvS77RYY/nhG8SLtRVVa5u/wBB8yFlS2wq/M3mV8DTp/aPPxFX3TybW9BmiZpodv77b82/dtrltYtXhV0R2z93c33a9Z17Q3js5bnydqs+7bt2sv8A9jXG6porwt/x7LtZtzxr/drpp04yPIqUzzrVtLEcJHzM0a/eZ/4mri9TtvI1CS1+7hsZx04r1fV9HTa6O+/c3yRtXmviCB/+EslgwAxuFAH1xX6H4eX+v4m//PmX5xDLoxjiJ2/lf6HKaxp6NG33fm+8uyuK8QaT5Mh+dtler6xpbusnnJu+8r7l/irifE2ku2H85tv8KtXwVSJ89Lm+E801Sz8ubfsU7qghtWkVnxjbW9q2kvGzb3Vd3zKtUo9Jdm2P8vyferLl5feJKcNk8mfnx/tVbitXhlSR933dqLWrY6PNuXyE3p/tVr2Ph9LplmmRk/2dv3ajl93QuRz9rpM0kmyPc23+GteDQZpNqQ/d/jVq6TTfDasgeH5UVPlrT0vw287RXKfOy/e+Wr5ef3SPhOds9D8vanksP9qtm30faV28N/eatxfD4jh3vtf/AIF92r0fhubzdiJvjX/lsr/LurKXNyjiYlja3MKp/o2/b821q2NNsUkXe6bmb5l2/wANaFnod55vlodqt9/zPm/4DW5pvhd1VWe2/e/xL/DWEozOmmZFvob3EgvET5Fl/hpbrR3kZ8Q4/wBn+GuysfD77Um8vCw/M1X/APhG7U/f+WWT5kZf7tctTzO2nLlloeYXGkQyDyXttrfe+aqEujbpGd3Ubv8AVMv8Nejal4XLSP8AJl9n3m+7WJqXh9LdUhSHe2//AHayj8funTGUZe6e2pDC0gebbuX7kez5f+BVBcW7/atu+N4mX52X71bMlq9ta/aUi2urMq+d/EtVvO/eJNDDs+Tc3ybq8yVE+0jExriFLeQwzfK0nzLtX7y/7TVnX9innK9tIqDZu3Sfw102oWyXC70s2YSJub/erK1C1tmjWH5mdf4dv8NJ0uXVCqS5YnO6tHNHG6Wcy/3ty/Nurn76NAuzzuW+9G1dJcWe5d7ou3btTd8vy1zPiCNLNjMnyKzfeVan2MlKxxVKkftGXqU33Zt+/d99v7tU2uP+WPnKd38VP1KfdI0kKfutvzbqq2reSn7548bFVI1X7v8AtVvGPKedKXNLyNizaZo1+fKr/dX+GtnT2SGHcjsm3ayM33q5mO83N5Lpwr/wt81bukr9qfyUTd/F5i/xVjWjyxKoyjI6zTbxGhS6mm27m2NJWlDqFt/qfmdY93y/drnLOR4YUSE4TYzbf7zVaW8ttxvPJbzW++y1zcvvanVzSKfxavFm8A3cPklGDRZB/i/eL81YXwek8rw3ds6l0N5hkHf5Fqb4mXs1x4VnEjhD5iB4t3+0MVnfCp2h0e5laQBTcMACcc7V5r9TwMZf8QjxK/6iF+VM0c3y3R0OsNbzQv8APtdfmRa5trhHZU37Jd33fvK1XtWuoQzJN85b71YWpXX7vYjrs27mX/ar86w6jGJ5WIlJyH6hcIysZvMZ1f8Ahas6/wBYjCsli6hP738VRXGoQorOiKrt9/c9Y15qyBVf+992vShR5jiliJcnKbNvqUlxtdz8y/3futTbm+h8xf8ATJHb7zL/AAx1zsOsPHMyed+5Z9qf3au3WoQzQh4dwTb92uynHlMJV+U0ZrqO4jG/bnb95X+81Rx3U0Ssls+za21lb+KsmHUttw/G9G/vN92nrqU3zQ78p/eZa35Zcpw1K3NK5tLcI0mzoP4NzVYW+mUeSj7G/vVzsl89rx98/wAG5adb3/mq/wDeZ/8AgVOUvsilU93+8dRHqWGKO6vu+/Vm31J5mf512Q/dVa5qG4MjeW7sR/HJ935q0bO+eRt/mLhk27lrmqSmbx/mOrsdS3SqEufvJ8m35a1dL1TbtVHk3L825U+WuTXUEtYYo96/+zf7Naul3kyrveZW2/N8q/d/vVxVDto/Edvb6pM0KfuVCfe2xvtatJda32/nPebmZ1V5GT/ZrirPVIbhWTztqr/z0arDao5jR0mZmb5tsfyx149Zc2p7VGpywOwXVNqpcxp+8ZW+ZmpkmqZZne53/d81Vrnl154bP/XYMibP8tUi6tNErf8ATRNm1aiNORdSpA6Fbh5JWR3bbN9ylW6s41XfN86r86xpurFt753jVHeRSr7f9rb/AHqkt7hLiQ/P5u7crzbdu5VqPZke0h8Jbmuku1eaaVmib+6u2sK+aaWNZraRk+8rq396trZDNCr79wjT/V7vvLVG7t3jhd32sjbf3cf3quMeX3QkuY53U5r9o/sbne+77v8As1zeoW/zTJNM21drIrfxV2F9C8Led5MmGT73+1XP65p6SSeW8O7/AGlWuqjUjH3UckqPNrI5fULV4ZNkLs/+1/DtrE1L5WabZ8y/KjbPlrqNWtfL3fd2r96sDVpkA2P0V/n2/wAVdcanNMccPzR5Tlr63m3M/wB1v4FZaz9QtneNgnzPv3fNW5fKZB/qflb/AMdrLv7J5Fb52UfertjL3TGph+Ux5leOQ+lLa27xtvmj27vmqx5KNnf838VSx2oZtjvvO35a05uU5/Yz+Isaer/N5KMG/vN92uh0u1S1uItifN91mX7tZ2m2qQ7POhYNs3f71dHpcKSR/PCrbvuMyV59atA6KdGXOaunwoux03bvu/7O6uhs45o2TfIofZ95UrHsVhEXk5b5vubf4a2LOP5GV5mfzPm3f3a82T97mcTujHl+E1rNXjj+Sb738TL/ABVq6eoZW/db4o0+Zl/vVn2LTXHlwptO1Nvy/wAValirt/ozhcfddWrGUvd5jqj75oQo+14Xh3lm21cnWZo4pk25+7t3fdotV8vE00Py+U22ONPvLT9PtdpKJC33/vNXL73xQOlcsdHIs2fnWqo6Iu7Z8+75qtWavMyTWybG2/vY5F3LUdnDNChd4VcSbvvN/wCg1r6XY7pFdHbYu132/wAK/wB2tIS96R2R5vZe6X9LtXi2+XDHs+7K33f++a3NP01FX98n3fl+X+7VbRdNmt1VZrnfFvZpdy7ty10FrZpNIrxIriH5a9OlH3LxMvhkU47F5Jl2J/rPl+atPR9I/d/P1b7i79u3bVpdPSMKm9XK/cjhXcrVr2dl5e15kbf/AHdv8X/stdcJRpjlKUpFb+zPMsRs2qmxt6t/DUlvpJb9zDt2bd25v71bFvpvnb5phxG3ysv8Tf3dtW5I/tEa/adqLsX5WSu6PJUkVL3Y+8c6uh7rjf8AKzfLt3feqrqGj7ZAjoyhW/es3zK1drDZ232hpks/OeNtr7X/AIqZdaS7TF4/vKvyMz/L/wACrWPuyMa0lye8eY6l4dj89wicL/Cv+1/s1h32neRttpnWEbf3Xy16bqGg+VHLeOn+021Pvbv4t1ct4g0Ozkl+fo3+q21y4ipH4Tz4yhKZ53qGmyeZHFDGqtub5fvbv9qpvDvw917xZrUeg6JbNcTXj+Vaw28TSNNJ/d2rXUaF4C1LxprFv4e0rRLia7uHWCCO3i3PIzNX7Jf8EwP+CaPhj9mzwxZ/E74l2MN94uuIlkgjeJdmmqy/dX/pp/tVxRkqlWMInPjsbSwtKTZ8/wD7AH/BBrR7rRrT4j/teQM4mijksvDUL4Zo/vL5zfw/7tfoPa/D/wAFfBzwungr4aeD9P0PR4otsVnpNqsS7f8Aab+KvUGVNhArz34za7Bo1gWmfaP4v9qubiFfVsDp8z5vA1qmLx0ec8z8Y6/b22ViDS7f+edcpJ4ghZlOxdjfN97btasrxX8StEgkMKX8O5lb93I+2uIk8XW2q3S3g1LykV9u1X+Vq/JMR7NTP2LLsHQjS95npN34khEI+zXGW2/6v+7Rp/izTnRLe5maJ2Zl+b7teX6h42xpshhuYXLSrtuFfcqrVHT/ABZqtn/o2peXmOX71q3ysv8ADXL7TlhdHqxy+hKPxHs1xNBFMblD50n/ACyWN6X+2k+yyW1zCsqSfL5Miblb/gLV4zY+PNe8O299qWq6x9vhjl3RQ28W2SFd33f9qtq3+J015HK/kMqtF/o8jN97/drSnVmomMsvg9G7lL43fsPfsXftANEfjB8EdLa/aJol1bR0+yzqrf3mj+81fF3xv/4IO6Ro94+q/syfG+S6hZW8jQfFkW1t38KrMv8A6E1fX+tfFR7T7MHRrp5Pllj37WX/AOKqaD4jTaetwk9zHKkduzJ5Mu5lavSw2f46jeF+Yzo4aeFlz0ZyUvwPx3+Nn7Kvx+/Zvmih+Mfw3vtMTfsS+hXzbSSRW2/6xflrhri3DbYYZlK/xtv+6tfupH400Tx1pY8GeOdKsdS0q4T/AE/T7yJXimX/AIFXxl+2z/wSW8I6pYTfGL9iXUjbhUZ7/wAB6rdZ87+99kf/ANptX0mBzbC41ckvdn2PRo5/Vi/Z4uOn8y/U/PW6/cyBB5bKrtsZvvf/ALNY98sNw27eq7X+etnxNp+q+HdeufDfirSrjStQs5dl1p99b+XLbt/tLWPdbPtTo/yH+Jv4a9D7VjLG4ilX96EvdMv5NzpbQsPn3M23dtpfsNys2yZ1VlT+/wDK1X7WPyoim3DL91v71TLbpNNvR22/7S1alyyjY+OxlP2nMiv/AGed+xLfb5m35qctnuhZk+VV+VFjrQhs5tod4VQLu2fNuZqdbxPGx8mH5ZPn+b726iUvaHkezjGxiX1j5cbzOG3bNqrWLqFq8a+YI9rL/E1dZeb1mO+25Zv96sHULe8XUJX2R43rt+b/AMerojy82hPs/dOduD9sxMiL/e3MnzM1VFj3SfP99fmdf7taV5pr3Ej3PnfNv/i/9lqm2nzQyNNsVzJ9z+7XTGpHm5ZHH7ORLZxveXCu7t+7Zdqq1dxpNui7bnap8xNvzVx2kWrx3CPJDtdfvR13HhW33N9pk+ban+rb7rVrGXuaE++dFpNqisN7sir8rM1b+kq8gb7Y6+SrbEWb5lZf9modC0+Gb/SZpmXdtby9n3q6HT7F5Y/3yR+cz/JHs/h/2a0icdWMviiebftMpIng3TslnX7eu6Q/3vLf5a6r9m1J5/hdp0MWSPMnLf7P741zv7UdkbbwTp7SRFXGqhcnuPLeup/ZkSW4+FOnrHH8kU07OVOST5rYr9VxMZS8LMPy/wDP9/lM2lU9ngE/P/M7xIZlui+/5pvvtJ/6DVuNoZo1mgRVf5vu/wDxNPhhWGTzpkm+58u1NzbqfpNjtdrxU+f+JW/hr85jT925xxxQy4je5t18qZn+8u1W/hrH1y3m8l3d2Zvuoyp92ugvle2hV5fLT/nlHGlYHiad7O3d9igL823+Kt40TsjmHvWOA8VKjWsjpcsy79v93c1eYeKrpJLr50ZVVtrxr/DXpfi5PMhlSFI96/NtX7q15h4q3sxn+6rfM/yfMzV1UYwidMMw5Y2Rh3km5d6TNG+75v4qga6bcr/dX+JqrXF1crI6bd2377VXl85lb58L97arV1R92JxYnMpSkOu7jbIzo8ZG/wC9WPqU3mSedvZTs/v/AC1PfXDyDy0fDN9xttZl8z/Km/d/e2/xVpKJ5dbGc3ulZZvLk+cKy/xVPpd9tmaT5v8A4ms+6ZJGCb1H+ytLZ3CfKm/b8/3f71ZSjzHH9alzaHT6bPtkKI7Hd/C38NdBp+pJHGC/P96uOtbpI49j/d+7/u1uaTcfvPJeeuWUeVs6KOI/mOr0+4hgk3w/8Cratb7z5vOeZmZfvfL8rVyNnefMmblSrfL/ALNbljqTmRPnVty1xVI/aPQo1pbHY+H7z7RH5zuqfN91fvV0Fnq32eQOjyMmzajf3a4bT7qb5MGNR5/zyK+1l/4DW9Y6ojM0cm3zf4JJP/Qax5Ym/tOaNj6W/ZF8Vf2l8QtK0HW/EO+1k1SNGhV9sX3q/Xb9r74e+H9Z8J+HvEtkzEeFrWJrWRW+WNWjxX4a/AnUrmHxxbTWcMnmNcQtF/F8277y/wB2v2M+IvjTxVrX7LWg+KvE+nzrBrFqtnAzfL+8jX5f/Qa8PN8LX9leEeaJ6GT16EsbBTlyyufLXxx8VQ/8JIEd5JZbrb91t22up+C9nYafYyYv1PmW+5lVq8X+J3ix9PkaZ3Z52lVZZpH+ZdrfdrtfhX4ySHwz9vmtlhRkZGZm2/8AAq/P6jny8p+p0XH2lmc5+0B48sPCurKlheZ86fYirF/e/iribX4waVpOny3l+myKOLa/96auN+PnxYm/ty5v7nUoX3XTLEuzazKv8VfOvjb4yXKs32a5k8xXZt2/aq7q9bB0ZcsUjzMyqQ5pM1v2kPjBDrV5JdedJLtdvKt/u+StfLPibxDNeXTzed/y1ZlVv4a6T4jeOr/V1f7TNI7M/wA8m771eYa9rgW48lNufup/tV9Hh6fNLlPgMxxyjLkLPmXOrXC2dr5hdn27q9a+GvwF03UrEf29tt3b5vMk+ZVrkvAdvo+kWcOq6leR+dJ/D/dr0HS/FifYXSG88pN23cv3q9KVSNP3IbnnUY+0lz1TE+JX7H9zqelteeGNYhl2/fhWvErj4K+PPDd43+gSFVba7R7mr6x8N+MLaxhR01Nm3L86t93dWlofiDR7fWIRNp9vMjfM7Mn8K/M1XTx8oR5ZRMsRgYzqc0JHjfwD/Zx+JHxW1xPD3h7wxfXl5C/zwrF8y/7XzV9Q/wDDvP8AbS+G1jZ2WieEptKOqNu+2MyybV/3V+7XtH/BLv49+Fda/a41WbVbW1SG4s1gt2ZFVV2r/dr9QtQ1bwZfJDqus20MwjuP9HjX7qr/AL1eTjMfg5VnGcDfBPFYeSnGR+R/x6/Y1/aB0X9l19Gu/GFxqjyNHcav5m5naNfuxrXy98FfFWpeAfFEdhP5lvNbzqvzM3yr/u1+/nx+0nwx4o8H/wBlaPpsKQXC75VjXdu/u7q/FP8A4KP/AA3h/Z9/aKtNbs7Nrey1iVtyqvyLIq/3q4vZ0J0vZ0z6Wnm1eNWNSUvI/Sz/AIJ//GR9S0u1tRc7Jtn+saX/AFm2vp/xVr9l4j/0+5RUdm3LI33Vr8tP+Ce/xih1aSG23xo6tsSRZfmr9BtF1qX7HDZ3vnJ5cSt+8+61fPy5qNOUJn1OHl9Yr+1LXxYkhtdBhtpn3wfM0u1N3/fK1+aXjDxFbeMvH2sa295v8y/kSJmb5VjX5VWvvj47eLn0/wAA6hqt5NthsbCaVVX5Wb5f4a/OHw/fvJZwwzbklm3Ovyf3m3fNX1PCGFipTqng8RYiPtYUjbs2hkhV0RXZk2vIv3Vq3Y2r28wMKLI397f8u2obe6dtk2/e27d5a/8AoVa2j2+7HnTfeXd9z/x2vvOSJ41GpHk1kaFjp/2rEyOy7XVvu/K1aEGnpIzzpMp3bm3fd/75pmn2ryK/7nc3yskbN8v+7Wxa2cysxRP9d8m1U+7/ALtacvuGkq3NrH7J8RfFuLb+2t5DgKB4n01T7DEAzX2/Jp7/AO021Nu1f/Za+J/i+JZf25gJjtZvFWl5JOcZFvX3rb28kls0zpkfwbl+9X6PxnGLy/LF/wBOY/lEzlO0E2cdqHh+GRikLrEW2+VuT5v92ue1Lw7NcLND9m3bf9qvQ7nTXuJPktt0Tff3fNtWqbeG5o5l8lFhVfmTbXwEafwnJUlzR9Dx7WPDu53hztdk27W/hauS1zwz95ETy3V/7m3dXuWp+FXS4lfY37t9yyL/ABVymqeE0uJHhewkd9+9JG/hranH3jx61Q8Y1jwjmMwoi4k/iWvDfGNj9l+Jsth0xeQr8vuE6V9b6h4ddt1sk0e6N23KsTV8wfEqxkj+P81hsYMdUtRtPXJWPj9a/QuAU/r+Jf8A05l+cQyyop4ipb+V/oWNe8Ko0L+S7CZn+T5a8/8AE3hPa2+5mY7vvqq/LX0NrHhOZYwmVbcm3b/dX/argvFHhS2ZZXe2yJGr4KUfcPneY+ePEGjuZm5xt/h2VQt9Jm3K6IrN/tf3a9Z8TeD0VWuURh/s1zDeE7lZPkTf/stXPKI+ZGVpujzsrJ8vzfxV02l6Ci7XdFU7dvy1f0XQZ2X57bb/AHVrp9H8Nu0i70yi/ejrL3pe6VzGbo/hH92rwbULPW7F4HtoZN6bXVYvn2r8y10+i+G7aNW3wyO/93d92tu10OFpgjpJEW+Z44/vVXLyyMjg4/Bu5WSzTcm3duZfmqez8Mzbd6QfL93aq/xV6Vb+G/taN5J+SOVd3ybWatK38HvcL8iMP9nZtVaOWEiZSmedaf4Jf5fkbeu3/Vtu3V0Wm+GUV9nk7nX5VWSvQdN+Hv2ciZLBvm+/M3zbv9rbWvpXgVGupJpnbb935ovvN/DWcqZtGpKJwOn+EXLCZ7Nh8u3av3auSeB7n7RsmOxW+Xc33Vr0aLwWl0qIPkMb/dVq07TwPZiMWz2bFGT7zVzVKcjqjUieQal4IdVf7NCsq+VuVY/4mWua1b4e/uT56eZJ95v9mvoS68C2wWWZ4Yw8cXysyfMtYWteB4ZGf51XzF2/LF91v9qolT5jaNT+U5SFXWNUSTa29t7SP8q1BJvtNiJbbxMzfvI2+X/gVE1w9vNj5jEysqbvur/drPuNQuZN2xGyr/P8/wB2vO5ZRP0GPcmjj86ZXfbnczMyvVbVLOHyf+PlYg397/2Wp47t33vZiFH2fL/u/wC9UdxdQvvlubZUO9V3K27bVez5VoXUqe7yyOe1KzdoX3pGUZNvzfw/7Vcf4iVGjmR9v93zI/7tdvfSvJaulnt3tu2eZ91lrjvFEMc25Ps2FV/n/h/3ttZ+xPJrR5feOH1JvLk2b2IpY5rZtvr935v4qXUlijzMkzI0nzJ/u1S+2IWRIflf/npt+7WUo80Dl5vtG3YyJtJQ/wDfVbGhtJHIedifeXdXOabNJGqQvOuP7zferVs7pJGE0Lsw2N8rVn7P3feJjJOfMdD9uO132cbP+BVZWR5IWT7i+V95vururFh1CGaF7bfu/dbtv93/AIFUsV1DCwtt/wAqpt+Zt1R7OfLblLhU5ZXKPj7zU8NSxzjcQ6Yb1+Yc1i+D7qWDQblYWBYTFgvf7o5rV8d3O/QJothwHjCsW96wfC7vHp8zYIVnKlh9BX6lgYy/4hTiF/1EL8qZtOf+ztx7lvUtX8+QonK7Pvf3awby5hjiH2ZGXb8u7dV/VJtsjJDN+7b5X/4DWHqUyTZ2Q/P97b/dWvgI0/dPGlL3veKd1qz/AGhYXdWP95azdQvvLBXfu2/LUN9deTI7+Zj+7WReaqm7DzYdm/irtpHl1q3KWbrUEj27HYJ97bvqxDr3l24RH3f7S/dauem1JNxd9qhWptvfJ8z+du/vKtbR8zm9pP4jqodSSSRXhdX3f+O1N/aW5Xhb7rcbv9muZ0uTdIux9pX+89a0dwnyodx+StfhkRzcxqm885ld925fv/PSw3E0395d3ytVKFk8zf8AwN/F/FVy0Wfayb/vVlKRVP4tDSs5H/jPzfw7auWt15cfycf7P92s2CZ4ZBDN93+9/DV21bd9x2K/drGUf5jvpx6m5DcLMyb9qKv8S/3qvWd9tmHz/KqtuXd8rVh2+FVZJ5lRGb5a0FvCxKb1Td91f4q5pRgd1Pmj7xt2t062aOj5kbds+T/x1qt2eoxpDsTbu/j3bmWub+3TR7vk27vm+ardjqHyojsvytuSOvPrRO6jUjzHQLcPNtsrnc6L93an/jtXluP9ISa28xG+8rbd1YMdxMsfzzMy/wAXzVow3Tqqu4bH8ar91azlGXLoaVOU3JLqZSgQMoaX96zL96rdrIk0myFF/h/eR/8ALSsizmSa9RJkkdG+ZK1LWGaNTND8iL/31urCUY8vxD+KZdWPz4diOqbX+8v96i8h2t9mR28qR12bfm+anGbdboju33P3rSfKtPVrxlGxGQqnyfJuXb/d3VnU54+6dMeWWxkXizLu8mXzPJRlRv4axNQt0khd0uWyqszLXS6hY+Ssps/LEf8Adj+b5qw7q1cKILlP9r5U20ox+0Wcvqdu6wujwqtxu3bpPu7dtcteWrxsXm5rufEVq6tshTc+35P7tctdWrzSO8MKvu+9t+Va66dT3blyjy7HKaxazblWN2Ufe2/3azbqHcx+fc396t68sftUzI/mfL/DVW409/7/APurXZTqcuhz8vMYH2VBuRPut/E33quWNjhoy68/3attbpuVBGrMvzfMtXbGz/5Zu+5WTdu/u0qlb+UunRjH4ixptjGrb/OVpf7ta9nCnmB4UZd397+Gq9nYwx+U/k5f7qsvzVqx2T2u5H6fe/4FXnSlE2lTgWre33SK+zDr8vy/xVqafC8c3nQpu3Ntb5Plqrbrtk3q+VkT+L5q1LGN1b53w/y7Y1p83LDQ5eX3y9p9rdbvkDff+X5/u1v2caC3G/cR/q03L826snT4ftG1N8gST77f3a2LJkjja2SZvlRV8xfm+b/erileUvdOqn7vKzR02GZZESbb8isvzfLWlt/eLCnK/wAW3+H/AGap2Mf2qQPOjM8y/PI33W/3a2bRd8Z+dRti27WTbub+7WfNyxOmO4/yYZJFk2bmZWZGb+Fa0tLhRZYnh3FdvyMzfL/wKqNsyMqpDDIsn8W77tbGi29zeSRXkKYVflRVf5Vq6cYfEdHtJfZOg01oZLb99taJvueXW/pem/u0dNuyR9z/APxNZOj2qTRIkyfMz7tzP8v/AAGum0mz85kRNv3vnb+9XpYeXN7pzyqc0Sxp+kpDC4hhYFm3I237taVvG6yB0Td8u77lP0+G5hjWHzGfam16mks0jTfNuKt8sXl/xV00+cunUJdPm+bZ5bBv4JI2+WpI96zNsdtmza+2Ld/31UFvZ3MbM+9t6/xbPl/3a09LtLmGEvv80Kvz/Jt211xly+8VKX2h0Nl5y+dv+99xWTbt/vVPb2L+X86KH+9838VXrXTXuF2XM0bBUX/V/KzVbmtsW+wv5W7/AIFureM5ROSp70TktWhhlkV7kLtjdt8bfN/u1yOqad9qk+xzIzpvVn+WvQdUt4ZLaXf+7Vk/hTdXY/su/CVPFXixvGGt2dvNp8O37Gtw/wDrGX73+8tedmWIpYelKpM44x9jA+jf+CX/AOyBpvgiRPjT8Sba1fWLjcuk28m3/RYdv3mX+81fonoXiPSrC0jtru5WMbfvM/y18S6d8arPwz/oGmzQ+c0Gzy2+6rfw/wDjtc/4u/a01La+oKkluF/cRSLdblZlX+Ff4a+Jo5tiY4z20DzMdTjiYKMj9ErrXNNt7H7e1yoi7Pu+WvmL9rL4oXllpeqvpN/DiFsvub7q186L+3x4kj0G00S+1hY0mn2tJJ93av8Ay0X/AGt1eZ/tIfHh/EngiXxDo9y13Nbz+VqNxNcfNMrf7P8ACtexjMZPN6KuedhqccHV5zjPH3xwvLrVJETVfO8xdu5fmVvm+7u/hase1+OF/Z2//H5Mkkn3FX5lavEvHXxB022b+yra/aSZn37o/u/8BrM034hTWFn9m/tLylV9r/xV8fVy2pKfKj7XLc4lTj8R9TeHfjrcvb7NVuVRNn7qP7u7/aqW8+Itz4kjewsNY+z/ACKyM33lr5c0/wCKUN9Iba5hZHjT5ZmlVfmWut0v4lQ+IIYrl9eVJWRvN2t821fu15OIwU6J9TRzql7I+jtB+IFta2z6bf6/5vz/AL35f/Hant/iRZ2ObOzv5PJ83dAzPt2/7NfPA8aX9vavDYXKjzt37xvm+b/ZrO1b4ieJNF0+K5h1iSRd/meTMm35vuttasPqs3H3RfXnUlpI+jdS+KVtqEySJNveFvk/u/8AAqF+JGm2twk1n8vnOqttf727+Jq+dNP+JkOqujw3jI6puuFX5d1bXh/Xry6+5Hl4UZfOX5V+98rNWUcP7x1RxPtKXmfSGk+OtKWT/SbloZllVmm3bv8AgNd/4Z+IGnr9ndHZjIzfZ5Fdfu/3q+X9P8VPDHEZnXDNtlWFf4v71a3g/wAcXkdjIts/kp5reRt/551M6LU+amcdatDk+E9G/bG/Yn+Bv7cXh+G2muV8OfEJVb+yfF0aL5Ujfww3P95W/vfw1+Tfxp+DfxR/Z5+Il58JfjZ4Vk0jWtPuGRF3fJeL/DNC3/LSNv71fqna/FL7VaW8P2yTyNv3tm1v92q37Snwx+HP7ZHwVk+G3xIto/7b02Bn8F+JpF/0nT5l/wCWbSfeaNv7rV9nkub1eT6vivlI8lVquGnzUvh/lPyUjhfaqbMn+D56u2qoqNC77lX7yt/D/s1qeNPh74n+GvjC/wDA3jCzW3v7G6aJv4VkX+GRf96qFqoVfn+YL9/+KvYqPl0HKpGt7yLCtNJCqedt+dWX/apbXfNI01zDsVZdibqW3/cq7w3LAyf7G7bVvyIY7cQom8N825v4acZWiccombeSQwxloUb/AHW+9WPfW+6KV4fk2/wsv3q32hdWHmCPym+batULrT08kIhbDS/Oq/w1pzcsomfvnMzWIhX+JWb5vLVPlaqf2N5I/O8na3zfLu+7XSXFmJI/kTDL/D/eqrdaW8cf3G2sn+srb2nv3MPZx5TK0uzuZFH2aRleT+L+Ja7LwvYzW8yQ7923/nolY2n6Y+4bJtn8MUiptb/erpdHsZI2VJnbdJt+b/druw/LI82vKUbnbWMKfKXm81VRf9X81dPat9nvP9cvyp8jKn8NZPh+GGaFEtpodrPuSONP4v4q3rW3/wBHVJX+Vm+TcnzV6NOnCR5dat2PLf2uIj/wgumzmbIbVV2o3UDypK6P9mKKST4N2McW35pbjeA+G/1rVhftiDHgfSw0bBv7UG47cL/qpOnvXQfstxO/wg04qijbcTkMOp/et1r9RxC5fC+gl/z/AH+UzStP/hMT/vf5noVv5M3+pfYNu1V+Ztv+7Vlti2rJbRfJu2tuf5v9ndULRvJIqpC21fl3K1XGsUjmX99vT/nm3y1+f8zPEjUM7UIkuAuy5kH7rbt/hWsm+jmuozvdinlbt38Lba6G6hmvv3MJYtH9/wAxP4ax75ZvLVEePZGm191WaRrS6Hnvi5fLjKXL7GaL71eV+KLXzLbzrN1aNX3IzO33q9Y8YSJNCkyQr8zN935l3f3q8z8SRvtdLzyS/wB7938qq1VGMuUuOIPPb5ZZJPkfcrf+OtVe6k2xq7w4dfvMta2oNDbM3yLu3fPtrMZUmZ0Sfcrfxf3a6vh90VStPkMaa4eSNu/zLsZk+7trOvo3j/fJD8zPXQXWj+ZIqOGwybdtUbrR3VGRI2+Vtu77yrVc3L7pxSOdmZ/M8nZn+JaWFtzHYm8N/D/tVo3WjuswX5m+XbUUem7WaHZ97+JampHsRFco/T5NsyvM+Nv95fvVtWN0kMiTfwbPvL/D/vVl+Wi/I6MzL/DVu3+VWSPd/wBMttc9SJ005G9ZyPt3vtK7dyR1pWt15kfz7gJF21g6bK9rtjmmZf4X3Vq2+zyy7u38Oxa8vEc3wnqUZe57ps2OrXMMiOE+6n3l/hroNLm+3RrNMGYK6tt/9mrlYY5pF3o/zR/c2vXqHwQ+H914q1Au8LEb9nnMu1q58PT9pOzNKlaOGpSlI6X4d6p/wh+uab4nvHZHt7pXdt25WWv3H/Ze8U+Hv21f2Cr/AOFmlX+7XvDcSy6d5i/vflXzIZF/3vmWvx3+MXw30zwp4VTTYXV7xovmjV921dtb/wDwTa/4KS+Kv2MfjRp2pa5eXE2mQy/Zb+1uGZvtVizfvP8AgS/eX/dr3/q9JUeVHykMZVnjPa3PQf2jtH1i1kvLO5s5Ib61uNsse/5o5P4t1O+Ef9qzeF7m2mTzv3W+Jt/zLtX5q95/4KhaH8P9c+Lmk/H74VataXfhb4haNHewXMH+rM235l+X+KvFvh7o6QQzab9v2QNbt5C/dXbt+6396vybO8F9UxTgvhP3XJcx+v4CFVfF9o+K/wBpT4mW1n40v4Xdla1uGi8uRPm3L96vnvxB46+3TSulzvZnZt2+vS/29LO/8M/Ea/tUdts0u5Nz/eavm2PUn87e7srr/D/er3MtwdOVCMj5zO8yqwrypG+2oTXUzO+7C/8Aj1cx4pup4dSWZF/h+Rq0tNvxMypN8/8As1S8Yrv8vnC/d3V6FCHs6/vHx2IqOcdJFe117U7jZCnzbfmeu28Ma08zpDc3/lf70tcr4b0mHy96ff8A4P8AarvvDul+FdeMVvrFmsRX5fOX5WWu6XspRaaHhadWXxSO98J6h4SkhWa88WwqsaL8rP8AM3+zX0L8FfhD8NPiV4NudbtvGFvLexxMtvDH8zbv9qvkrxF+zzpviBvtPgnVZCqruaP7RWr8Jf2df2oo9WVPA1xMWk3bfLuNu7b833a82thZS96lVPo8LGPLySpP/EfT/wCzb8Cdb0f41JeaPqUIuLWX/WNPt3f7KrX6C2upfEjQfB6Wl/qUhZnVluvN8zbX5R+C/hP+2xqWoJeaJDqEVy0rRNNHLtZpN33Vr7P+Auqft4eEbdNK8T21nqsNuywRWtxcL5rNt+9Xz+YYKspc71PQp4LDypWXMpH1PZ/tLzWun/2VrzyPNDb7du7bXwH/AMF1PHnhvxl8NPCGteHtSUT2ethXt/l8xt33mr2H9r7WvHnhf4U3ni25ubGz1KGLz51juPMaNv7u6vyo8e/ETx38ZtejuPGesSX3ly7oodzMi1zZRhq8sYq0pe5E8fFS9j+6n8TPob/gnz8WLnw/46t7Oa/WFJJd0sjfN5n+z/vV+uXw18XTahodtqCec9pNb7tsnzNX4yfss+FdV0/xpYTKmdtwrbWX7tfrV8Bb6T/hF7V3eRo4YFXyW/iauDNZR+sfuvtH2XD9SUKHNMx/+Cg3xEs/CvwNbQdNm/03WrqOzeSR/wDUwt8zbV/vfw18faC0Ki32Ouf+ejL91a9J/bs+J7+O/i9H4Y0p1bTdDt/9KVvvSXDN8u3/AHVrznQYHVkeHy/lfdukr9I4dwf1fL4832j5HOcZ9ZzCbidXp4RowkL7/J+bzNn3lroNHtTPiZ0ZEXlFV/masfQ8fIHmjL/8tdv3W/2a6bR4YYZFdIY/mf5mr6SMfdPOjiuX3ZGtpsc1u2/7MzyMuxFb+KugsoWVQmz92yfL/Ftas3T7XzNtz+83bNysv3Vro7HSIVjVYX+Vvmdl/vVpTp/aN/rUvsnwR8bYxF+30Y1PA8W6Vglcdrbt2r9A47V5rcyomG+VHaN1X/x2vgH46o4/4KDuknX/AITHSgfytq/RW108QqiJZ7du3ZtSv0HjRSeByy3/AD5X5RNsXWlTpQfdGVJpN5FMknyjy2+f/dqv/Yu75JoJIvLfcjLFuWSusi0tGmX/AEbLsv3lT/x6rkehv8kP2nckf3N38VfFU6fuHlVMVOWxwOpeHd0k1zbQ+T5j7Nu3/wAern9W8MzbpJnjw67l+b+9Xr15oEM0aQvCxdf+WbP8zVlXHhd2k33Lrv3s33du2r9mzgrVpSPE9Q8HvCvneTsWRv3ska7dzV8XfGayEH7Xk9l8zf8AFQWC9OTlYa/R++8PvFv3/wAT/KzJ/DX57/Hq2MX7dlxanI/4qrTRyP8AZt6++4EUlja6f/PmX5xOjJp3xNT/AAP80ezat4XS4mLxq0XybvL+7urjPFXgsPHJDG8cX8W7Zur3vVvCaN800LH5NvmVyus+D3uFaFIY5RHudVbd/wB9V8L7OMtD5/ml8R85+IPBMzWuyZ1Z45WX93F8zL/DurmrjwK9ozT/AGNi2xW/urX0FrHg2YyfuYpJt3/Tv83y1z+qfDu2upAs1g0ZVt+3e26sJR5Ryl/KeV6b4VmjuNiws7rtV12/drq/DPhu1vJlludsSfN8rV19j4LjWP50k3/e27du6run+F7aCM7IGY/Ns2/xVy8vKaxkZlp4fSONP3KsGb5GVa3tN8HwzXSXKRqj7tjzSRfw1qaTos1vs+zBirIuyNvurXaaP4fdtsKPHLGvzPIv3t1T/iJlKJydr4ddY02W33pfvf8AoNdJofg1Jtm+H5o23SxyJ97/AIFXV6f4ZRXRLqGPYvzJCv8A6FXR6P4RhkR/JT7yKzs1P2ZnKXKcha+CdtoPOfd82793/F/s1p2/hOGNl/0Pzdv35K7TTtBdszIi/L93+GrE2k7VR08zZGmzav3Woly/CEf7xyFt4PhgV9lnDJN5u7d92tK30eGRm+RVijT7rfLW7caPNcLvdM7m/wDHqdcWaSLsmRS38Kr/AHa55e97xvGRzU2j200LpbOpeRP4krndY8PwrY/JDt+95q/3q7+S38mb9yi7fuq2z/0KsXxFZpPIUdPuptRl+b5mqOX7RrzHyNrGuIsbQ20O7b8ifN91f71Mt7izZk+SP5vmaT+9WGuqPuDzPvkVdv7v5ant5nX50eRB96KPb/DXnxj73vH6pLkibs0zwqHhvcIy7tu3/wAdqK6jh3GGZMp9/cv8X+zVLzpGmGxNoZdvzN/FTZJrlrpXdl/d/wB6lL3ZCqSpKJLJHZ5ZPmR1X7rJXIeKLd2jke8mZXk+ZNz/AHV/u11WoXKeYIXucqv32X71c14wukkhkdHV0VGRNy/NUS908fFSp+8jzrxD8yh0OUV9u7ZWTJcbmSTZtO/buX+GrviCabyd6Pl2++q1nRNCytvdt+2suX7J5lSRpWLWsjedH+9ZU+Za3Le6mht18lF2Sf8AfS1j6NsmkREm3N/H8lb9rZ/ND87P/wAAqfd+EXL7vMWbdYVi2TWzbNqt8tTSRvbxDfCyH/d+an6PDDb5d02Or7n+b5Wq9cNtj/0aH59m7c38NacvLIr3panM+MZGk0GV3dXDMuw7eR8wrK8LgHT2Dk7fPO7PToK3PHTQnQJii8lk7Yx8wrE8LADTJJASrLMTuH0FfpOCj/xqzEL/AKfr8oHT7qwj16/5DNYzJ86Kzfxbq5XWo5mjZ0dvlbcrV1usSOzKURtrfcauZ1yRJLffIm3b975K+Ep+7A8WtLmOL1Ka5GUd1IrHuLj5i+xWFa+ubFmfyf4qxWt9u94925fm21tH3onm1KhBJN5gXZ8wao7f95JsH3v7tS/Y3/g/4FtqazshGxl+YMvzfMtax6mPLKUiWzV/O3zJzW1ZybpNsyc7P71UbexlWbzkfiT+Fq1bexhaT5Ou7/WVnKXMaRpyLdqnmbOwXcrrVqH5WV5d2VXbTLW1+zqofa7s3z1pRwwqyzP5h3bl+VflrOUonRToz2Gw28kjf3/9lauwwtCq7LbJk2/KtSR2s32cPabRJ8q/d+9/eq7b2L7v9JC/Mm1dr1hKtA9GGHI1t41h3/Nlm/iSiOTbcF33fu/l3MtTyae+fuMd27d89Ojhhkt0+9j5V3VjKpc3jTlKQqj7TGvnPk/71Wbddvzp8/l/w0lvaoq7IY/4vk/2a1bHSPs+H8tW3L89cMqnKd8aMmQaeHZd/wAv3fus/wB6tOxZ5Jm3/wASfeX+GmQWaKqOhYI3y/71aVjb7o9k03+1tX+Ks/bMcsP8JdsV2yb4RuRvklVfvf71aKs8Lr5SSSov3/k2tuqHTbfbGqIihv8Ax6teG3kMK7Jl+Z2asdvslKjKQ7TVQqfOfO7czq3/AKDVuN923Ym0bGb5f4ad9kSO3VwmW31YWFEt/Jhm2sqNs/2mrmqS5jpp0Zx90yryzhb5H3FW/ij/AIaz9SsY7htkP3N+3c38VdTJYzSKuxGKyKq/L838NZlxHc3EKzed82//AL5Zaj2nLHmOmNE4/WLV8O7p827btVfm21hanpLx/wAC/Mn3pK7e9s0kXyXRXbf87b6x9Qs9zBHThf4q1jW90qVOS96JwN5o7wyNNcpvDJu3VBNo/PnPCqvt3RL/AHq6rUtP8tlTerhv+efzbqqNYIsLwoigyLu2/wAVbyrX3HHD+4chJpbyxtc7/KLfxMv3als9Jm8wo77/APdrfk0dGVZPJ+78yq1L9jtlh+dGEqr86rTjW94JUY+7Yq2tukKhIeU3bX21LDebtyIivt3bWZane3SPCQovzfM8jfxf7NV5JIY5ld0ZFb5V/iqIvmlzSic2K93YuW3nTf6N5yr827y1X7zVq6avmqPPttrbvmZn+as21by2EyPktubbs+7V+xm8z98s2xml+RmT/wBCoqSlK6RxxVuVnQ6askClIflRW2/f+WtfT43tl32235pV3+XWXpMKTEpcw+a0nyo0fy7f+A1r6fGkky/+Pfw7f96uKUuXU7ox+ya9got4Uf8AdsF3blb73+8taGmttKPM7Eyfe3VnW9rubyYUzu/8drStl8xme5dldvl/d1lLl5udHRHmlojTt7Wz8zznbdtbdt3/ADf7v+7V/Rf9H/0aadt/3vubVZayY7e68z9zMrJMnzfN+8rW0hUtvL+0u0S/9NG3bWraMhxl73KdhpdvC22ZNq+X823+7XW6PC80cTwu0RZdv+9/tVgeG5IY4/LufLZvl2/J81dVpOmww3aXiXMiOqbdu3crbq76PunPUlzamhZ77eRBv3o3ybW/9Cq1IryiN4d21Ytqw1Y0+GGZlhSFmb727+9/s1dhs38t0jXbtrupx98nm5d5GXY2czx+enLt91lrakt5LeNX85g6urMy1Ja6K8G62RJMs6/7q1abT3htme2dWH3m/wBquqMeaZMqkeULWa5hZH3rlvm8tk3My1JIyBVm3yMiozfvPu7mqpcQ3NizzXkM2/YvlSK/yr/vLVLxR4is/B+hvrep7poV+SK1h+9JJ/Cu2lKXL7xcYxivekUb6aHxB4msfBlsit9qlX7asb/vI4f4mWvZYfFWg+G9Ds7DwxujOn2/lLDs+VVVtteU/BuOFrG48bagk1trV9Kyy28y7fLt1+6q/wB2svx546mt9QmEMzec27yvLfav3vmr4nN608dX5I7I8qeI5tYnoPjb45f2Tt1b7f5b/c8lvmVv9qvN9a+Lbyag9s8zO7SsyLC/3tv8VeU+JPHl/rl5c3N5uSD70SyPtrjvEHj65s4W84fvY/uKrfxVzYfByiccqkuU9a1T4tXNhGk2sal5qW7t5U2xvMj3f3awNe+LSatbvNBqU0aSfNLCzfO1eOzfFSa6untprzf5m3erf+g1gatr0NxN8kzINzfvN3zLXrUaco+7y2Rw1JQOq1Dxc+ral9pSGRJZGki+Z/8AV/3apN4m1XTY1EKK8yrndJ/FXGat4uhZXRIfmj2/N/FI1VW8ZXN1sfzlZvutH/7LTlh/hlHc1p1OU7KLxtuczXEys8krHds+Zf8AZrY0H4uPDGlg/k2yMqo/l/N/F/tV5hNqySR+TbJjzPv7v4WoWW5sz++2t8vyMtcOIoX+I7YYirDXmPoLwz8RoZoxZ2dzM+2VmlWb+H+6y/3lrqW8RalqSx2GpJHcov8Aqmj+Xb/9jXzpoOuakskPeT+995mavV/B+vareXu+5m+RfmVWf7teFjKao1eY+ky+tOtG1zqLOO8s9Q2JCw3PuRf4W/3mrv7e1TTWhtoftE1pMi/vJm/vfeVf+BVm+HbOw1K3SbT7Nk2qv2hpv4m/vV31n4YeTTftKQ+Yse1fLjTd5f8AtV5/NSlG3c9ynh6v2Ry6Wlu6JZTeUy7VRf4mrq9Njh02FI7aeNDu/wCWy/Kq7ayrfTza6h9ptrZmjZNqyfe+7/s1s2/h+z1zTX/tKWR9r/OqttZdtFOPL7hz4z2sTW0eGG+s9lymF3q1wyptXb/s1ft9K1Jr53s5sJ8zRRt99am8L6PDdatbaPva6e4td8Squ5lVfvK1ehWvgPTbhbc201wRGjK67dq7m/vV3wwc6nvXPBrYyVP/ABHxP+3h8IfEPiTRR4ntraO4vtJTzfMaL57iP/nnu/2fvV8gRRJDI0MIZirV+u3xM+DaeJNIuNEv0W8SSLbtWL95DHX5Y/HT4R6r8B/jpqPw61KGSKzvJWvNEX+9G3zMu6vYwOKnUvRmtiKOI9nLT7RkwzIqh5n+Vfvbaur9mjkCPu/vfLWbZshuURE/vebWhDI8qj9ysrfd8tl2tXZzc3unpxj9odNZ+cwh/doY9vyr97b/AHqi/s+5h277Nj97ay/db/arSX7H53kxpuXb97bUcMeJj5zt80TfwbttKMdTUxbrSd00yb/m/jZagk01JFLvMxZk2xK33W21r3EcyzRu825v+Wu75d1NZE+zjzoWV1/iZPu/8BrqcebU4+aMeYxLOxeabE0LIq/w1u6NY3MN551zcqyR/LUVuv2iB0hm3+Y3zyM1auk29zHMkMMEjhfl+58rV6uFjI8bFSjHc7Lw2sKwxpDtRmi+by/vNXQWKo0f2nY3ysrbm/vVy2lxyLCkyOyusv3tm2uhj3zQMjTKv9/59u7/AGq7o0Txasr3PMf2wli/4QPT3wBI2tB2HchopDn9K6P9lxdnwX051uZFDzT+YqnP/LZ+1c1+2EWHgPSUkVNw1JcMv8Q8p+T710/7KDKnwg08MSgM1wSwbr+9av0/Fa+GFD/r+/ymbz97KI/4v8z0qOSGFv3cPmfN95X27W21ZdXMbTfaI0fyvut8zbv96q1vC8Kyu8zfvF3bl209FS3jLukkg2fMrfeWvzyNT3uU8fkEa5dtPS1mm+Vfm3b/AOKuf1a8muIX2Qq259qfP/6FWnqG9o1eH7uz5o1+81Y2qSQzKYXTDt825XrTmZPL75w/i5dmXmdl2/8AfK15r4ojRlkffndtWvS9eR5vNR0yrJ+93N81cJq+m/aZDbXMOzy/l2/xf7NXzmkY8p59caW9xI6QozOz7afZ6K8cZ322zbx8qfxV1kegvNcOlt8rR/Lub5d1W4vDCLb+XbIzP/G1bRlAfs+Y5CTQUb7nzeZ/FVGTRE2bETf8/wDq9nzf71eiw+FTeRp5m6NlT7qpTm8LoqsPI3K3yruWr5vsxE6M5HljeHQtxKHhxMqfP5n92qF1os0ajY+1tn92vWJvBMyyeTKnyN/eTb/wKs7UvCfns0P2NlWP5fufeoUpxMpUuX4meYwaW8imNzl/42anwabtbfsztXaqrXZ6h4LSHM1smV/8erOXT0t1VHhZ2V/k21NSnLklYun7pjC18tfuMxkT5Vk+6tWY95by05aP+L+Fq6rQ/hf4h8TXB/se2Z3VNyxqtcxcaTeaTcS21+jDbLtZdjbl+b5q4JUZyN44qFPqdT8OfDdz4k1pNKTrM67I1/ir6k+Fvhm38BXCXN5t+yxr/pUm/cqsvzV4b8B/EXw30fxxpqQ6rGbmaVVTzE27W/i3V7d+1l4ssPC/w3v9H8Paltvry3aDy1l/1e5f9ZXTh8P7P3pfEeZjcZVqS5fsniXxe/akW++Il8dK1iOaGO4ZPmb71cJ4j8bf8Jkza3bTRxXMbbkjX5l2qtfPOvWt14f1J/tOq+Y0m7ftfdXU+C9Sv47f7TYTb9q7fvV0/aOLlkfWvwJ/bh8W+Hfh6vwB8c3kc+hQ3X2rQ7i6fc2nyN96Nd38Ne9+Afiwl5cWju7PGyKzL/D/AL1fmh4g1x5GfenzLXsH7O/7Qscyto+pXkkdzCvztJL8rba+Q4myv6xFVYH6BwhnMMLP6tP7R3f/AAUk0Oz1TxwdfsEkKXEW77nyq22vjq60m8hkLv8A99V9W/Hj4lab4+0mC8v7mZ5Lf5WZvm3L/CteMtp+laluRLmMbvvbVrzsoqSp4ZQkjvz3DxrYlyizzqOOaFt//j1R3y/2hOttGitt+9u/irrdZ8I/YZFeFPvbv4Kqaf4V+1Nvf/vmvWpy2PmVTlF2lEZpNn5duiJCqn+D/Zpl7PNbs/kzYZa6eHRXt7dUdN0i/Luaq9r4Njv75Uk3Kf733vmquaHPzHb7O8OWJydj4w8SaPNi21W4T/pnG/3q9Q+FP7VXxI8F6hDeWErbY/lSTdtbb/FVPRfgrYa9qCWb3Kwqv3vMf73/AAKvrr9mX/gmz8K/F32C58Ta3I6XC73WNNyx7v4q4sVUwz92Z6GBo5vTlem/dI/2bf22ra/8TW1h4k0qYFpWaLbL/F/s19q+Bfido/imGO8s7CS1dYt3nfeVv9msfw7/AMEq/A3w/wBJiv8AwxqC3CQr5qzTWqs3zVveH/hL/ZF4+l21tJH9n27mZfl/4DXyeZJ83uS90+vwM8XWj++Pn3/gpl4jh0H9nbX9Re0Yrcr5cG77rN/s1+cfwN8Dtq1y07pv3bXZq/Yn9rj9mf8A4Xh8F73wl9m+dot1vuf7zfe+WvgL4Z/s0+Lfh/dahYeIbOaF7f5U3fMzfNU4PG0sPgJ01L3j5/NsNUjjoyn8J0/wF8BmHXLeHyY8xurPJ821f+BV9beOvjZZ/CD4WvqqPH9s8pYrVYW+7My7Vbb/AHa8f+E/hq98P6f/AG9rEPkxx/vJZm/hVf8A0KvOPiN8Rr74neKpr+5kZbOFvKsIY32+Yv8AeZa6MpwH9o1+eX2TKrmEsPheSG5Rh1DUr7VLnUtbv2u7m8ZpZ5m+80zfeauj0G6P2hUe83Pt3Iuz7tc/pOnwwyb0eTCvu+b5ttdLoscO5XT5FjX7uz5mav1CjyQhyxPl5pt8x2OlwwyKXeFQjfM/y/Mzf7Ndfo9rc+ck2zdGqbn3L97/AGa5TR1NwqpNcs4X7kbJ93/arttHiuVbE1ypCpuT5Pl3V30/egY80vtHU+GbdGbe7rFE3zIsjfLXTaXZ219CmEXYrbkkZqwPDKoz7Emy8fztt+9XZ6THNcRtvSNN23Zt+9urf4Q9tLofnN8frVof+CjptHI48Z6OMg8cra1+mFnpyW/z2zwyRTRfdVfu1+bH7QfP/BTDIiKZ8b6KdpHI+W0r9O4YVhbzpvLCr83y1+g8Y/7hlv8A15X5RPTzGbVKlb+X/Iq2FmkbJ+5b5fvt/erYt9JtrjZMltCzfwrv+b/ep1nY/wCkfOnys67lWtWz015GVHdQF+438W2vhYyPGlIqNpNtIrokKxlf9Vu+asu88OozO83P8NdvHpqX1riGHcqvtVqhm0WBvMuXtlf97tSSuin7pwSqHl+seG4dp8xPNRV+Vf4q/M/9oa18r/gojLand/yN+kA7+vK23X86/WjWtBTa/wC7X/ZVflr8qv2l7Zo/+CnUtrIcn/hN9FU5/wB20r73gfXF4j/r1L84noZBJvFVb/yP80faWraC/nMiOwX5Vban3q5jVPDN40zebuQbWdJI12qv+8tey69oLsuyCFl+fajMlYWoeGblpTsjV1+7/tbq+I5eX4Tw+c8b1bwukO7YjY2fJ/tVk3fgpGuE2JG/lpv8yP8Ai/8A2a9i1HwqjL5IfY7fN92sa68LzQ7njRWCvt+X5q5q0Y8o4yPMP+ETMbHeisrfMslR/wDCPQrbtcwwrsb5Ub+7XpNzodtGsuyHYPvP/tNWe3huS3w/kxhZG/3lVq45R5jeMeX4TltJ0J44XdEjPmRfdkbb81dlofh/YsWyFSJP7v8Aepul6Gn2zfNCzj5W8v8A2q67RdORbhGmm2bf+Wf8NZyHJ8xFpPh7y1V3tlcL/wAs1X71dDaeGXbyXRPm+VmXf/47V3RbWGSN0R9w+Z/LX/lm1dBpOkvdTI+FSL5WaNv9qiJnLmkZMPht1UIiMS391PlpZNC+zx5RGL/Mu1k/8err7PTNsaeTysf3G/u0TWaNv+0yfK3Rv7zVNT+YIy/mONk07y49jwyNuXb83zNu/wDiaz7q1+yt5byKVZNvl7f4q63UNPhjtx87eYzbUj/hb/gVZd1pULXjw+Srn5l8v/a/2awl7xUTkbiz2R7JtsW5t27Z93/arJ1C3SNWSGwZ3Vd3mV3V54fmZYnhRVX7rrv+ZayLzS0jvFhmTfG3zOyt91amVQ1PzchmeZd8Pyvv/dLVq1Z4/wB877tv/LFmbc1YNjeXlqzun7r+KLb96r1jO98yu5UOvy7q5ZfzH6hLERlSuzXVr9gnlptf+FZG+Vqk+2xQI2zcX/iXfuqCzV/tH2mCbczLs3N822pJIEt3T9y25vlSTZtrOPIefWxHNH3QuP3ymb5Sq/fbfXJeKr51aS5hk27flRWre1K6+yyHZ5ciLy0i/wB7/arj/EE3nMz3PRlbcy/w1nKR5vNLm5jk9SN1N9xF2t/epNNs3+1LBco339v+1Uq280mybfuHy/N/drd0fTYd297ZXLfwt/D/AMCrmlLlHTj7T3iTTdLtoZEtraFiq/N838VdBY6fIsi704b5k3f3qNJ0uaOVn+bGz59v3q3obe2kX7NIm1ZF+9/EtKHL7w+QzWj8lWT7Nu3fxKlS/Z/tEao9sz7fl+V9v/fVTfOsixfLt837zfe/2VqG4W5uI5neFW8tv9Xt3Vfs/d0D2knLU5fx9CkWgyGPjcyll/4EKwvDCltJuAoJbzPlUfQVv+PPNfw7MJYZIQhTZE6Y2/MKyPB7JDolxcNGWInxwcdhX6ZgE14XV1/0/X5QN7KWDfTX/Ipa3Im3Zs3bf4lrm9YidoWR3ZUX+Kug16Z9rb/m/idq568Z5i+zkbd21vu18LTjynjVpQicZq0Ls3+uYN975v4qo/Y5mZJNnOz59tbWoW4+0NvfA/ut92q+37r78bf4quWx59SMpFOG12n54fvVcWDzP3PX+/To7Pzpl43Nu+fbWjYom0zJ91X2t8lRzI6qNErw2u1fkTdWrY2Nztid0VArbkZf73+1U0FtCzhE27I/vstaUdi7KmyZsfLvrnqVOU6o4OXYjttLdfnCKTv3M1XLfT7VpG+9jZ8u6tOzs3lmHkx/7jb/AL1WF00CPe77G3fxVzSqc0dZHVTwsuaJXsrf94pT/lmn+d1S/ZYXmjd9zn7z+W/yr/s1ItnM0mzYqbf4l/iq5b2DyNvTy1X+9/erjlUlH4T01QjzfCQx2MLHf+83R/fWSpobVI5Nnk7Xb7ytV+CEwrDs3Ofus2zduqzHbwssPnP8s38Tfeba1ccq0/tHbTwq+KRThsYVhZ0h/wCA/wDxVXILG5Zv3yfMq7tq/wB2r8drtk2P/D8yLVyx0+aYl3tlhXb95amVb3Tojh/eKNvbomU+XEf3938NXIdP8qZPJm811+Z1VasNYwxzeTN8z7GZN33flrQ0xU8mWZ0VJFbZFu+9WftpctwqYfm0JdPsbZZFRH3M0Xztt+Zav6bprtI0MyLtb5drf3V/9motbF41TY8bfIq/7TN/erWs43lVYUhjVl3eazferOpW5ZfEH1aQlrZxxssPkZDP8m6p10+aGR5rmFkWTc0S1Nb2vlqPORv+A/w1YZnWNPvOyrtWSsJVPe9029jyxiUJoyrRzWyNu27lZX+8tUJg8LBDCqfe3bkrYumgECwwPsEf91f++qrapZvHbrM+7b95acZcxfLPkOWvoZo5nSHy96/N8q/w1jXkL3Ehe5h85o/mXcnyt/u112rabDdK8ySbty/Iy/LXP6hZtcNvfaP7q7q090jllE5q4t0kY/ZoVRpP/HaguI0bYkyNmN2T7v3q27q1hhlm+07RtlVt0f8AdpjWd55fzx7137ttbe09wyl2MOOzmaR/uxov8P8Ae+WmLG7RukfzMqbXatmO1haF7nzlb726NfurVeOJFVtkKqG+8zURj9ozlU+yYl1H5q/JtR/u7aqNDeSKyOmxl+Vf9qtm8tXgP3P9r7lVmt0kuUmHzMybdyp92umMrfZPMqS5pcrILW3maTzLlFJ/ib7qstbWj2bzXG/YqKqM33/4ag0eFJG8vZjb/Ds3ba2NOsXaYb23KqN93+L/AGqyqS5ZaIinHmesi1pMck0+94cH7u6ujsbOFtzzPGySL8rfN8zVRt47aPG9GeRfnVV+atrTY9zPDvjC7tyLsrj5uaPNI9ClG1SxJHp7+WiW3ybtu/d8u1f4qvw2c0cbQwuyov3d33marUMcPl/ufmdk+aNk3eZV2PT5ppPM8nK/Kv7xPu1j7SUjsjGHNoVLW1mlmW5fh9n3W/hrf0mxS4UO6Z+bdu2VFb6ek0a2z+W+7/vqtvStJe4VD5zLul/1bfw1rH3uUiUJRe5taTbwyN5ydIfk+X+8396u30e13QoH3Er8vzVzGl2CW/77YzTfN8q/+hLXV+HIfLjSF3kKNt3t/F81ethY80Dzq0pU5G1Z2t/CzeTDtP3vMZPur/drW0/TfMlQQpId0X3l+7JU+l2+21ZNm5W+X95/FW5oem3FvbJbXO1fM2s7fdXb/s16dGnLY4ZVjPstLvPLLvt2/wDLVV+7VyPTYoccSTP8yt/Dt3Vu2elo2+FeEV1+9/dq9D4bSad0uU3btvlRr/6FXXy+8RCtKWhx91paTWzQ3Ls6yfK3+z/s1x2p+GX8TeMP7Gs9VhSOxdXe3m/ib+H/AHa9b8VaG+m+Hb6bfCjKu2KST/no3yrXN2Pw9tvAtvbarrCfZ7z/AFV0sjq3nNu+95n3tteJnWIjh6Fv5iq+InL92Ynjy6gtdDZnS1tLneqI0P3mb+L/AHf71eBa1dXketXFzf3nmPt+Rmf5Y1/u13Hxcs7+z1LU7Cd43mml27vN3f8AAt1eb3Vrc3yx6PYWzPI0TbvM+6rbq+UoKUtjil73unPeNrG8ms0ntYY9ixM/+zu/vV5v4wmMcLTQ3O652Lu9Fr1fxct5oulw6JeXO9Y23KscXzN8v3d1cBfeF7nXAmfOj8x9u1k+Vv8Aerqo1Ixn5BUjKUOU4CHTUku13orPC/myyM/3WqhqV/JcXyzWycfcdv4m/wBqvQ2+HtzeWohV1V2fau5tvzf7X+zWTqOh6VpMaWeq39r9rbd5skf/ACz/ANmvXjKhKRwVKcowOA8RWUNrJE/nSTIybt0bbdtZqxvaxSv9pberbtrNXVa9b2NrIj7/ADA3zO38O3/ZrltU1CG4l/cJG/8Acbd8y1EpQkZe9Ed52qzzfI+6OTbs+f5q6DR7DXpIzsPmjd8u5f4a5/S9QsJW2PNiXfu2t8u2uz0nWnVkdIVSFv8AbrgxlR04xOvD/vJfEaWh3g0qYv8Au1udu3bI3yrXf+B9cmWQPc7XuG2rtVP/AB6uStZNK1DEMM1vu++jSf3q0NL1abTb7ybnUoWSRv8Aln/DXz1eUa0pXPs8rtSlHmPd/DPiaa2jL3P3Nn71f4dv96va/hL420rXNHaz/cyjbu8xv71fJtj4gdl/c6q21kZfm/u12Xw38cXnhm4Sytr9bRPNX5t/yturxsRy0/dPt6FWlGXL3PqqPQbORVuYoVlMe7b5b7VqrJo+n6Lq1tfwws0Mjf6P5krM0jfxN/tLWF4B8ZfbNJ2X95GhhlZdq/Lu/wBqhvFSTXyxXM29Ldm8r5m/8dqaNbln7xrWwsKx9X+BfBvh7WtQtvEOj2axXklqqPJG3lxxr/F8teiW/wAPYY45YUsJNrPtRty/99V82/s7/Eyw1LxVZ2dzeTNFt+SOb7sbf+zV92+DtL8P6xoCPo8QmdYl/wBKZtqt/wABr3cDWqVoXhI+Gz3K405czPIr74a22l3KXNtfQvK3yzr95tu37rV8d/8ABWb9iu8+IngO48Z+ANNjs9R0OL7dBGyM0kixruZVb+61ffvj7w7o/h9Zbq6hWEq+5vL+VZPlrm9S1LRNe0gXmvWa3ttN/ofls25f3isu5q3liPZ1+e+x49LCTlG/2T+ey11CaazhubnT/s7zRLv+b7rVq28jrHvhh2yxqqv5n3vm/ir039tD4JzfBb9ojV/DB01m0u4/f2F4qfJJ8zbtted28n2c/uEwv8O6vVp1PbRUl1PXp8/ITZupGCO+4bNu7b/DSySXMLSzTW37jbtimV9u5qdHK7Mts/mK7LuXb91qZcb9qvcw/I3/AI6v+1W9OPvlyqRILiFJN9tMm87d25n/AIqbs8mQO8zKu75tz1DJdTfaHdH/ANlfM/2qA0M1w9s7qzx/N/s1104y+E4akofESQrc+Wjwovm/Mrrs+Wug0WPdsfeyybv73yr8tYlqvnTbIUbdt/3a6TSY0WTyba8UOvyysy/er1cPHlPMxHK+V3NnR5Ejuvs0+7eqL8zfdZa247GCGHMMKyN9/a3zVmafskVU37Nv31b7rVu2ZN9GkMyKPL/iV9rMtdUZfaPMqR+JM8i/bA85vh7pTzMWJ1ZRkHCj91J27V037KcMUvwd02KYHLz3JQdmCytmue/bLsktvAmmSBtzHVwNw6EeVJXQ/sq25ufg5prxMwmimnMShvvfv2r9LxSUfDChb/n+/wApm9SPLlcV/e/zPU7VYVaTZDGAvyorfNt/vUjMkkzXKIzeX8u1vusv96lXZ9oSb7Nt/wB5Pu1E135iqg+Z9/zRr8u3+781fmEuaL0ODl90p3myOzZ02tL82z/Z/u1zWqb2dhvkw23f5aV0Kw/aGYIi7v4v7zVnahZw2rK4m2x/ddf9qto+7Afs+b7JxeraSkUdw6IzOv8Ay02fM1czq1mImZ4drs3+t/vL/vV6Bq9r9lkESPv/ANr/AHqyf7E8y4Z0gVQ339qfeanGXvcpp9X5tjldH8KzXC75t2V+ZFX/AJaV0mi+C0uI0+zW0kok/wBbu+Xb/tV1/h/w35ipC6SebC67VWL/ANCrttI8Hu6RTbIyN38S/Nurtox940+r8uh5va+A4Ws032fCr/F95m3UN8P3Yr/oeUjfcklexx+DUuJvntmE0cvzeWn3v92luvA9tbx74Y5JPMl+638NdHu05kypT+0eK3XhD5Wgez3Rt95l+9XP6h4TS181I7ZmXZt3f3v92vfZvA8NtZ+XNCqybm/2ttYmveCUtd9w8MJTZ/rP7v8AtLS5oyOeVPljeR8/Xng/arQzW3+kfe/dt/47TPDvw1m1TVksktmmaRljiWNNzbm/hr0rWtNhvl2aDZ+cyttuLj+GNf7zVc0fxp4S+Gtrv8PW32/WfK2pfRptjt2+78taqMYx1PJxWIjT+EvQ6HoP7Pfgu+trxIz4gvlVZ4V/5d4f7rf7VfL3xO8UW11qVzc2yQ/N/Cq/xV6T8QrrxV4u1Bry/uZNm/c80jtukZvvVwGv6LolvCEmvFLf7SUvi1Z5/PKUjyXUpb+a4W8015Eljfcm1f4q1td+L3jDX7NdE168uLiVYvkkZ/4as+KNctoXYWEKsqy7VZU/8eri9W8QPFJsfbn/AGajl9/mNI8xwXjaO/kvC81zkq38VXfhr4qubFntpvuM2395UPjRvtUjFNpXZuTbXNWtxcwtvR8Or/e31cf7o5eZ6XrjWzRu6Orhl3O1YFjqVzouqLqVm+35P3qq33qg0PxI91b/AGaZ/mX+Km3kKMrTfKV+7RKMKnuyHGpOlPmiem6x4g1WTw7DqTpus5tv77/arndN8Y3drdN/q9jfd+SvTP2E/Fnw38Ra5N+z98YLOGPRPFG21t9Wm/1mn3TN+7kX/Zrn/wBs79kn4kfsY/FyfwH45drvTrh/P0bVoV/dXkLfdZWrzK2VUuWU6UT1qOdVeePPIpf8JMl9avCjxszffbZ/6DVjw3qiQ3S2e9WG35GZd1ecQ6tcxqqJNn+L5q1dN1qRpvOf5GX+HdXiSo+z5nI9OnilKXMz0nWJ7aS1COiqv8bf+g0zRdRSGT7T8qbfl+WuIm8WblW1f5f/AB6kh8RPDHsxtbduT5vvVlToTlCx3LG0nI9P03XrC4vEmhvPKmWX5v7rV9q/sj/GT+z7XT9BeZZNtxG3mL91t38NfnN4b8ST3F9/pMy/vG3bttfUP7NPiS4s7iz+xzL8sqr8z7fl/vV5eYU5xjqfQ5PjKVaXLzH7SfDX4gaV4o0F9I1KaFwsStbtv2tt/u1h6tDpVr4gkd7ZfIk+bzJPlVW/u18t/CH4qXNjMjzXjTQNuXbHLtZl/vV6t4f+JN54m01v9Pae3W43Puf/ANCr5XFYi0Jcx9VTp0IyupHUa14ss5dS5eOKFX/dNv8A/Hq8H+K3h+w8QeOBc2aKltJuWWRdu5l/3q7L4saf4k1LTQdBGx5vlVl/8e2159qVvqvhPwvfa/4zvPJitbVtsa/MzSfwtXgUIzq4i8ftHh5hUjUq25TxD9qH4pWE08Xw98NzfJapuupFnXd/1z+WvNNBhtfLaa8Rg+9fl/3qzLrUpfEGtzarNt824Zt25f4d1bOlw21xM73k3k/dVK/ZMow9LCYeK+0fJYyd6tzY0ux+zTbJtvyt/ndXR6bBtk2Rwb/7qr/E1YunWrmPZ9s3J/GrN96uh0tUtWhRCsq7fkkV/utX0FPkkeNKR1fheOaPy98DK/lbXaaVf/Ha7LSWmhXZCWR1ZdzN8ystcZpt5bLiREkdf41bau3/AHa6Kx1i2WNkTduWX+58td1P4PdOWUoSO58OyeZdI80Od27zW3/KrbflrqdBknuLGO5fy2eT+Jv9muC03WEVvJeZVDbdi/xM1dVp+qQsq+Q/+r+Z/wC61bRlzC96J8F/H8qP+ClfyFSo8b6LgqeMYta/T6xkSeQ+Qi+ar/vVk+avy6+O85f/AIKNi4Yn/kc9GJJ+lrX6Xw3yW8jXkMi7FRd7bvmr9A4x0wGW/wDXlflE9PMm/ZUNfsr9Dp7OQxwqkN4u6SX5m+9W3p/kzRuiJICzt5Sr/EtcpY6hMzjyXhwv+t+T5lro9H1SFWR0k+Vnr4eJ4VSX2Tr9J8me3TL7XVPnjX+JauXEaeWHhhVdr7kVfmrM0+6topv3My7m+b7nzKtaH2iHcr78bl3bdtbx+HmOOXumVqWm/unm+Uuyfeb7tfkl+1RZTR/8FW5bKVlZz470IEr0JKWf+NfrfdX224lhtplcL8rRyJ91q/JP9qh2f/grHK7lVJ8faDkp0Hy2dfdcCK2MxH/XqX5xPYyFxeJq8v8AI/zR+m+paT9oZ4flAj+X5vvLWPqPh9Fh/hTam3cqfM3+1XazW6SMzoVYKn3f7zVl3Wm3h/49rVn8xvmjZ/urXxsvhPnIy948/vtHhmklRrZv3O3dui+VlrO1Dw+luxRLOP5vvsz/ADL/ALO2u5mVI/MTZny/4W/iqhqlgkw33MPzt825nrkrbHVTkefal4fs443htk2uv8VZd9Z+ZH5PnSfLtXy2Wuv1q3jfe8L7XX+7/EtYV55MLtsnaQKvzbflrhka/FsUNP0z92iQ7Vf+ORvvbq6XSNP8tUd02/7O7/0KsZbiaSNHgRl8v5f7rNXSeH2huI9iecm7+8nys1Z++BraPprpGieSqeZLtRv+elbEeyMKnzJ+92tu+WnWMaSW6zeTv27VT59rUs1v5km+bySm359zfdaq+xqYyl/KW4r1PtO19uxfvRx/LuonuEa4aHZtXf8AwvuWsqLUkjkaFPmmb5kVvvUraklvu/eKD/drKpLlJJdQjuY5Gmh8vDJu2yP93/ZrMvt7MXtkVXkZfKbd93+81PvtUhkm2JN8rRbUaRF+aq0M3nNF8i/3kVv4aiXvbG0ZFi4tZpm8lJN/9+ST+KqzeH0kjd53ydn3fu7q2tNt5pI0e8RVDfM7K9T3Wl+YrvGm4N93c/3axlE2gfjta3yXDI/n/Pt3IrVrWPkw4dOWb5t1c7ZK8ckYmhjXb8v+7/tVvae7su9Hxu+ZP96vN9ofcVMRKXMakNw9jdJv2ssj/wC78392pZpprqPzo7+Pf837uRqqteTKzeTxLHtL/wC9/eqveXXlr9p+Uuz7kb+7/eqqlQ5403IztWvH2SBJmd/tHzK38P8As1z2oTOwf52Uf7nyrW1qTTTT7452zNu/j+WsxofldIfmLJu/efdZqw9tzR94uNEoWdiknzIn+yjf3q6DQ4fLukSbarL/AA1Us7eGzZkm27vvf7rVb0+S2ZS77h825GX+GseZFR5Y8tzobK38iMzI/wArJ+9Zfvbant2hYrc7GV4/4du2q2mTeZGZn+Xcu3c1SNdJHhH53Lt3N/e/2a6IxJqShEVZpo23vcrskfaqyRfMtRSNNImxPlZl2uzf3abNOkK+dsX5drMu/wD8dqOS6R1CTQqyTfMi/wAW2tI+97pzSkYHxCcS+HJXcCQgoElVuD8wrm/Dly0OjzqsYOZDyexwMVt+PJ1OjTW8cYCLs2kN1+YVzWjzKtm8RAJMmQhH3uBX6ZgVGPhjXt/z/X5QOqUv9hk/P/Ij1K6dmYbPupWDeXHCvM7Id3yf3quapM8Fw0KTMwZ93zfw/wCzWJqmpO0mx9rHZ/D/ABV8DH3oHhVJQloZupRp5j3G/Pz/ADVWVdsiuibkomZ5JGhfdhf++acrQrsTyf8AgVZlU4/ZkXYbLdsdEVF+8y/3qu2FvC/yfvF3Jt+X+GqVnOizL23fdWtbS/OhbzNjFd//AAKsqkjvw9Oxfs4UtY/JR1zt/hrXsbd22PNEq7n27VX/AMeqnZhP9T0P+7W9ptrMkn7lGPy7tzV51apy+8z2aVDm2NG0sdoXYjf8BWrM2mw+Wrwp8jNt/vbf9qn6La7M7Jvuv86t96td7GRogk21m37XZv4q45Vj05YWHJYwJLHy5l8l23Mu2nWdqY5mRPvfeeNk+9/tVu3Fmkcyv9/y/lTc9VpLdFZd8P3vm+Va5alT3iIUY81ynZrtkSaFG3Mu3d/DWla2O6Znhtmb51bc1Mj03Yuya52xbtsTL/FV6OSGPfDCm1G2+UrN92sZVOb3T0aNGO7IpLV4rhfOduH/AIf4qv2qzKv7mZWC/fX+7UC2sMnyTOzIr/w/3qniV/MPlzbvk2/d20pVPsm6p/3iZV8gfc8xG+7/ABf71X7OSGO4TYi/dX7qferPju3+X7TMu37qKtaWnzozJsRvlbczL/DU8yL9jHm+E1bW3hVh5EMlwv8A6DWhYuiTJ5MLNt/2KpWYhdtkMu/5926P+GteFvs8Kedc7f8AdT5ttZx94JUZkkc0ze581t6sm1V/u7akk8xwiWzrtVdrr/7NSxxoJd+9Q6v/AOy0k0kMMjeS7M67fvfLT/hmHs+Ykmk8mRbl4Y2+RV8tf9Zu/wBqqmp/64J9j3NIu5l/hWpbify901ttLMvzsy7maqrK8jff2r/H5i7qPeLKEiXP2VUCLC6t+9VV3fL/AA1jajpvmE+S6oyu29mX/WL/ALNdJJazPIqfKq7N27727+7Vf7LeCaZ/u7vn+Zv/AEGt6fvbHJKXL7pyU9nCqtDH5hVX3bW+amTfabWLZM6lJPvfxf8AfNdDfaXuj+dF2SfNu3/erMm0lI1+RP4tzbf4lqoxgtjjrVJGI9ncpCuxF+bdvVap3Nr5bMkKbm+8/mfwt/s1uyWb3DMkLqiK+/8A2v8Aaqr9nmmuFhSH+8vzL/49W0fjOKpKUo6GSsO1mR5o2/vN/ErVH9nhm2zwuryfw/7VaUlrPulTyYztfb833qq7ijbJoVRo/nT5K2hzc3MzklIh0uzmsy/kxqoki/1bfdVq3LPZbuEHz/IqPIvzVnWNvbSSPMiKi/eatDTbdI5m+dvN+6/91lrHER5jbD+8bcKrbyNNJtLMiqq7Nu6tXT5EWRU2R7v4N38VZNrE8j+TN8qfdi/vL/tVtQ2fnKu/lFZWfd8qq23+GuCXNy/Cd1P4jXs1nvVTYmWkfD/L8u6tqGF1be/H/sy/xVl2e+NC5fBX7m37u6tjQZIW3R/u3VvlRm/hojyc1uU7vscxf0+1gkk/cw/K3zeYrfLtro9NtbOSQXiJt2/Ku1Pmas3TdN+zsvlpvT+Ndn3Vro9HsktVRN8gVX/dfNXZRpdUc9b3Ylq3t33Y+US/wt/dWus8PrCskb79zfN833dtZmlqk3m+ejOu797uTazVv6HYzHb867Y5W37k+bb/AA17OFjy+6eXiJS5eY6fw7L50hQvHt/jX7zf71dLZruhZpkwu/bEuzdurlvD8btM86Bh/eb/AIF92uu02RPNWaF22r9+PbXr048p5MpS55Gzp8byQ+dsjVV++y/xVvafDbfMj7j53yxSbdrLWfo7W11Mjoizovy7VX5W3Vt6bbs1v8jqy/cro5fdH5IzfFHhqHxFNp2j7G2LdLPcQr83nKv96uZ+Nlrf6lrlxNa2du1taxL5X8LK38K16Fr159h09N9zDEbe1Z0k8rdJGteV+KNU1LVNND6rbRut5cKsske5flX+Kvz/AIhqS+u2+yRTlKpO54v488Oza1rk2sW3yKrqssav91tv3q5STwy9jeG8httkk3yyyK//AI9Xc+LtPmbxALP7YsNosu5ty7d1ZvxAV7ctqFgjRp5SxRf3Gk/9lrxoz5I6Gvs5+1OE8XWNzqC2emwpmKGLc8m3bub+L5q53XND0rR7i3uTuZN+64jmf5WZf4q6jxb9p0/TXuYZt42qrr/6FtrzzXNUmjt5POdWX+HzPmalTqe7c7aeHkjj/HWvPDqz3KXizL5v7ry2+VVrgLi+v9S1p5pv3u37yr95qv8Ai64tt0jyPt27t8f3lri77xYlrH9ms3bcqKzNH9+vSo1Kah5nm4inKRe8U3brN/pLxxpNErKsj/NXNagz3F8qWz7/AJPuqtZF5rlzdXD+dctsX5kVqSz8RPY24kR13b/vfxV1U/djY46kYvUfcSXK3iTb1Eu/azfeq9eeMbm1ie2SbciovzN8u3/drO+2W10yXMNyq/N91vvN/tNS65pdtcW6zecrbv7q/wDoVZyhGpFczIpxnH3h+n+OtVkbzkvGR1+5tf5a6bw744v47xEvId6yK3mySP8Adrg47d7OPYlsrKvzblpy+Iry3X92jfK3yNXDWwVOXvRR6FHFTpyjzSPfNB8ffZbFnudSVf3W39zFvZf7tdV4N8cXO5b9JoWRmVvMuH+6393bXzfpvjJ33WzzKC38W35a7Hwv4msFXZM8ju33F3fu68jEYXl96R9dg82hKMbyPsHwT8WNNvrpbWa/2yzRfdk/i2/3a9Ck8bedHEl/bW8Kx/8ALP8A9m3V8r/C3xpYXN3CmpalYxPH8tvMzbm/4FXuHhXwzY/Ei6RLnxDslX/VNby/Ky/3q8PERcZ83Q+ww+J+sUuaGx7Z8Dfi94e0nxXFZ3MLNtfa8jJuVV/vV+ivwl8a+CX8O6TrU3iCOQ7vKSKOXav+61fmVofwa1X4X+IJNS2XV2y26tFtfzGk/ir7d/Zh8QeCviB4Hs7O28u21K3XY1qy7WX/AOyqqGKjhdI9Tkx06WKpcsj3r4k3K68hs9KeOeFdzbml+Va8X8YNf6fc2egiyaJpJY2RY1bZXZ3XgnUvD11cak/iSR4422+T95W3ferQ03R9O8TXFheSv/x7z/v/APppH/dqa2O/f++ebVy+H1aLg9j85P8Ags98LLbQJPCXxCs7NoHjv5LWVo33q0ki7trV8SSLD86TPv8AkVfv7VZq/SP/AILk6fJffCC01pblo0tfFVq8UMcXybdrKzV+bUctssPyQ/er67JKntsHzeZw16c8PK390mjuDuWHYqSqnyfxbV/u1UvLhpGX9zu3fLuV9y/8Cpb6Pawuba23vt27t+1qhurj7NG2w/I33q92MTz5S90TCeSrpuB+80jfeqxpsPmSP5z7lb73/wATVO3me6x91g38X92tKxt5Lh0feqJ937ny10UYzictSRY0m1S6klmhRid3yr/drorGzh8x9kjbf9pfmqlZ2ybU3fM2xvl8plbd/s1uafpM0ez5Nw/vNXq04+4cFT/CTab80Y3/AN7ckbfK1bWmyOsylE3f7TfM1VNPh8xN/nbiu77yf+PVYZntHidH3RN/DtrfkOSp/KeZ/tnSZ+H+mIsoIGsjcB6+VJW3+y0m/wCDelr5pIae4+QL/F5zVzX7YMkDeBdPECEf8TkbyfXypK6L9l2Zo/g3p2Eyiy3G/b1J858V+l4qPL4ZUF/0/f5TOmoubL0vP/M9Stbp7eRn+Xds27mf+GobhXW6ezm+eHcv3ovm+7/eqpNqCQuLaF9yL91mq6s3mQo9y+5WZfm3/Lur8z9n7x58Y+8RNCkKnZuTcu5F/vVRlMNxC1zbPtVvubv71W5L/wAySVBBll+b5aoLfbpPJ2L5cPzbW+6q1EpTjHU3o0faS0KdxGjLsS2XKtuSTd/FWjoug3N9cI9+it83/LP7zNTNLsXuLhJng2RK/wAm1vmavQfA/hfzMb5mZfm8pZPvbq0ox5j1I4P2cVYs+G/Cabt+yP5vmlk2fM391a6/SfDc10rb3VSy702p/wChVo+FfC/ypcukabX3p/8AE110eg+Zbl7ZFjaP+Lb/AA13xlyx0IlT7HKWOhwyQ+fCnyyP/d27v+BVJfeH7axt/tN+ipHD8r3DPt21q+LfiF4P8J6W7zQ/aJY03PGqfKteAeP/AI1a94oumtoUa4SRttvbwrtVV/2qIylzWSPLx2OoYaPK5HReLviZ4Y02Sa2sLWS+uFbdtji/h/vbq828afEqbV7pLPUoZriGRGaCxsV3Krf3WanfaLyPP9vX8dl/ehh+Zm/2azL3xxoPh2PzdHhVJvmCTbPmrenE+axWY16nuxNK3u9VvoVuX8PWen2m/wD49/uNIv8AFurE1q88N2a74Y7fdJ8ySN83l7a4vxZ8ZLydZYU3B1Vv338O6vMfEHxQ1nULje9zu/h+/wDdrTmR5sYv7UjtvHHji2vLqSG2ud+35ZWb/wBl/u15rr2qJy+9lVfuLtrJvfFlzcTbJjlW/i/i/wB5qyL7VrmOZvn3M38LU5LmNY+98RQ8QJukaaF+G+Zo1WuQ1zTwzb/O2FW+7XVXOpFlfzn/ANla5/UJppH2STKp+6rNS+IuMjgdfa5t2b59tZsuyZTMiMB/FXVa5pcNxGyfL8rN97+9XMeS9rI9q6fLv+9soiacxVjupo5vkfdt/u1safq0b/uZuVb761z90r2VxseHZ89WLe6SObfv20+VE/Ebc19daPfRajbTzBo2Vt0b7WX5q/Ur9lfxR8Pf+Cqn7Hd9+zf8UdShbxv4Rt93hy6Z/wB7Mu35fvfNX5VLfJdW/wC8m5r0P9kX9pLxb+y18cNK+JHg/WJoWhuFW6WP/ltHu+aOqjOVOV0Zypxloh/xm+APj/4F+O9R8DeKtHuEmsZ2VpNnyt/tLXKWs22Q/aUZGX5a/Y/9pL4P/Df9ub4G6Z+0H8Pbe3+03WjLPf8Akr92T+JW/wBpa/ML4l/s/wCseFdXkhv7DY6y7UaNfl/4FXFjsLGUeeEdDTD472UvZVNzzWRvMxN9w7P4XpyyJIoefj5fvVp3ng3UtPmlSaFmG/7tV10l92yaGTY33fkrw/ZyjsexGtGUbxkX9FtU3I5mz/u1638J/EWveG2idPnXzd23f/DXlWg6LNNqUSIjbGdfu19hfsp+DvBMK2lz4i0GGaXzVbzGbdtbd/d/3a8rNq0aNK3Luellar1Kv7qXvHo/wH8aeLdc1S20q202YCa33RM3y/e+X5a+svAuk3/hfQ4nv7z5l2rcMNu3/gVch4L8J6ZcauNb03yXg27olVdvy11eqaZrN8xhtdKuVUptYNCQtfneY4mhL3ZSUPVn1tXN8BlFKP8AaGJhBy255Rje1r2u1e11e3keiaTf6l4kZLPR4Y5js27m+b/gS18n/wDBRn4qal4f1Gw+Dn9m3WnzahEt/L50TRvJCrbdy/7zV9z/ALC3gLwuPHemXHxD8QWVvHMQi2E8ymV2JwIwo5JJ4wKzf+Dhv9n7wb8WfCvhP4n/AAhayvvGfg67Gn6l4dsRi+ksJRkEQr8+FJBxjoa93hnAZTLmxNWvFKOyclv958rm3GuT0mnDE0nTbs2qkbJvpe9r+R+SFjeJG2x9qL/G3lfMy10uh/d27Nz/AN1W3K3+81S2nwH+O8nL/B7xEAq8b9JkUn9K2tK+B3xrhZZJ/hp4jXIUbRpMny/pX19DM8rh/wAv4f8AgUf8zyq3E3Ds/wDmMpf+DIf5le132qo7p8+5mrV03UEaREuXWP5mZP4Vq1ZfBT40LP5kvw91osGbDNpkg/pVz/hT/wAYjKFk+HesMC6sS2mSEfyr1Keb5S9PrEP/AAOP+Z5VXiTh9bYyl/4Mh/mWdNvLaRdn2be67f4/vVvabqVz9neHzo2b7qf7NYWj/Cr4zxXLRp8L9Zbf8oT+z5CX/StRPhd8aLWV47j4fa3C3/POWwkUqv4iu2ObZRFf7xT/APA4/wCZyf6x5End4ulb/r5H/M6PS/EE0caTXLx7Put5i/8Aj1dLpusPHbpsdkWRPkm/2q4qx+H/AMTFDBvAOqJudt4axf5l/Kt7Q/C3xGDpNdeDNVAxtEctq3A9elXHOMp64mn/AOBx/wAxx4lyHl/3ul/4Mh/mfH3xmuxd/t/LdFgd3i/SeR04FtX6MWOoTNHvm27fup5cv3q/OD4wadq0P7eA0640+VLs+LdLC27od+T9n2jHU5yPzr9DE8OeM9LaJ73wpqNugbOWsXAY+gyOa/SeN8wwGHy7K5VKsYp0YtXkldWjqtdUe9nmZ5bQw2GnVrwipQTTcopNWWqu9V5o62z1SB1VLZ22t95t+6t7SdWtoWT55g+5flVNy/71cHotj4omvdlt4evtrRcrHAxc/hitzTdG+ICApN4b1AqejLburH9K+AhnWUOWmIh/4HH/ADPmv7eyWceZYqm/+34/5no+k64jQrIhbf8Addv9mthdaWS3CPcqDJ8u3+Ld/s15raavrEBK3UDIgOXiRCOfxrWj8RQszP5illfbbr/Ezf7X92vRwuOwmLTdCpGaW/K0/wAi6OMwWYRcsNVjNLflkpW9bNnXanfO0geFGfb8r7nr8o/2nfLb/gq63l5Knx9oOPcbbOv08XxBbTbN53Bmb/8AZr8vP2m5TF/wVNkm+UlfHmiH5TwcLaV+icDTjLHYi3/PqX5xPpOH4cter/gf5o/WHejfvPlX+FF/9mqhrTRxwq6Ovyr8rK38NV/7Y+zq1rI6kNtXzKp6priyRy2yPGgb5EmX+Kvhec+ejT+0U9WvIGiP+sLKm1PLX7v+9WPeXlzG2zf5X7rb5n96nzal9oXM25fl+eRW3M3+zWPqGoeZZhH2q6tt3LXPKRvGP2ilrV682x4UbYv3/LbbXN6hqCEPD8rbfvfJ96ruuXk1qr7Ny7nX5VT+GuM1rUv3ktmm5Eb7jRvtaueUjo5WdDa6l8yw9T/Ayv8ALXT+HLrzo9j3i4ZPu7/mavMbfVIY8eS8bGRf4XrovD+sPZqqQ3S4ZPu7fmVq5+bmCR6lpOobYzHbOz+XLt21JcXDmGJIbnzUZvn+T7rf7Vcha+JJljK71j/i8xadceNINy5mZN3y/KlVKXQn3DYuta3XG9Ewyu2xm/h/2ayrrxBCtwXmfhvuL/tf3qwrjWmh3wpNseZ9ybm+8v8AerBvte2/uft/7tV+8z7masqlQcY+8dlP4itvvwv5e3b/AB1fs9Ze6aVPOXe3ypt/hryW88WbZP3Lrvb5dqr8v+9V3wr42mk+R033C/LuasZS5h8vvnu2k30K2n76aN0X5dsjfM1XG1b7Quzy2Tcm5F/hrgbHxQnkpczc7vu/N97/AGak1LxRND5bs6qmzdt31nKRfL75+U+hq62ux4WKbNvzPW5ZyILVLb93sb+HfVCz08xM/wC+Yj723b92rlnHMzb4RIp3NvVv/Qq8GWI9/wB0+7p0YRJt0ysrzfxfKv8Au1BdSW21Uh8vP3mWrtvbvJH+++Z/vblqG4sU8l3jeNmbcrttqfbc0uY19jymTJawrGrxupZf/Zqht7M7nkdvvfw/e21oNZzIo5VW+8yqtNa3m2s8KZPy/M38X96nzc0feJjGX8pnbrYMqPuc/dT5vutUkfkxzRo/zMvy7t/3qXUrVrD93sxt/hrJuLvbC+zc25trV104wny2OOpKUZWlE3bPUpm/0ZNyD5mX+7Wh9sDL5bpuEf8Ad/hrmbHUPMjH775Y6v2OrTeZ5sPybk2/N/FXRH7TZjK0uWxptIkkfnb9u1vnbb/7LUUcyRwnfMy/N8tUP7QeGbf5yhmi/es1VrzUhdLvT5gqfLupxiYyqe8U/F99FPpskYGGLL+PNc0txJBanyzjLdfStLX5WMWVdf3gG5Q3vWDeeYEJicBiMKS2Me9fpuDjfwyrr/p+vygbOf8AwnSf97/IpahqSSfI6bf723+KuevNQeRmdP8AgbL/ABVo6nvk3eSnLbd7VlfY38spGmfn2/f/AIq/PfhPCj72sit5n2jekPyVbs45lVP7n8dLY6f5cjK/zO391PvVah0yaObePmX71S/i+I7qcfeJbWGGWTZsZtrfK38Na9nDOP8AlsqrsqjDbvbtsdM7n3LtT+GtBY3UbETf8/8AD/DXNU5Inq4embOktD99N237vzJW7ps/lsXhmZQy/OrfxVg6bJ5qh0TcGfaq/wB2t3TYXkj/AIf++vvV5dbY9jD06suWx0WktMqxQ2yZ3f3v4V3Vu28aRzL5zs+35k3JWDpbPHsGzYdnyq1aa3SM0KTJvTd/E23y68yp/MenzfZkS3yzfZWTYqiZ/vN95qgmheFCiDjbuXd/ep7XkTbk/eSIvy/L/C1QfP5wjT7m37yt81Zy96PxCj7o9rq5kWF0dV8tNv8A31/tVYhjSHb53+sbb95KgjhLXG8dP7rf3qvQxvNM1zMi7/uov8Sr/FtrLm5TeMeaRHDDCyskMLL+9+dV+9T4983yP5imrMa/wIi7oV2/M+1mp8Ni8cYR4WXd9/8AvbqcZc0jaMeWXMRWdqJGXZCqhVZtzN8zVo2cc8kiwoissi/d3/xUsdi6yD5l+X5kbZ81XLS1/fAnaVX5tzfxN/dqvcO6j7xZhb7PDEnnMm5/n2/xVp6fcJBGmx9219r7vmZqotGYdr3KZ/etv+zpu+WrFvI8Ko6Jt3fd/vbamMTSXwcrNGG8tm+e5fhk+Zlps32ZIdi3Pzs3ys33agtY3kZXRPkVvk/urV21tba6+fYrFn/i3fLWsYw5veOWpT+yV47d5IxbQ/P8235vlVaWGzjW4hmmuWR5N3zbflb5auSWv7lrby43Ozfu/u/7NWrezhuJmSG22+XF8n8S7aJStHlOapT7lHy3mt18mFUl37nWT722pVtZvLR9/wA33UVV/wDHq0FtEmZk87O3bs/2V/u0t3D5k2+zRg/3X/io92OiOSpExm0lGhR3feytuf8A2aztS0/y5m3w7yv+3XUTWryMjjcg83/Vqny1R1K3Ty3+y/JL95m/h/3av3Y6ROKX945K40fzo96P5fmfLt/iqvdaf5MoSFMqqfe/vVvXcKRwjULr5jH821f4aga3S4l+frH/AKqNn2s3y0+b39TmqRlynN3WmuzI8aRr83zLu+b/AHqrSaWv2NIXdi7S/d8rc3/fVdJHbpeRiaWHaqr/AKlfl/4DTZoZGby0ttzR/L8rfw10c3uxSOXl5pXic3HbzQ/PCnLfK21Plb/eq3b2syxLvLfc+T+9Wx9hePY6J+9b7n93b/tU7T7Wa2jR7XawZmVm/hVaiXLLUVHmj7pBpdikMMm92iWNNtb2lwoqpCn7zzE+fa1N0+Hcmx0+Zv8Aa+ZW/vVqaevl25hmT/a8xf4q5pS5Zcx6VGn7xNprOV85E27n+833WWuj0S1SSRU3xq0i/Lu2/LtqhbKkdvDC8O5FTdAuytLT1vG/ewwxp91d0abWWsYy5p8yPTjHlh7xq24mnb/XSAq+15Nn3a6PSYUtZAiDO35vM/i3f7S1kafJujhT7pZ9zf7X+1W7p9xCsyu7qd3yrt+XdXpUDhrU7QOk02GZYmuUh3bl/wDHq1tPaEW7QvMrlnXdH/erL0q4jWEw+TIiyNtRvvLuWtTT28uZFm+dt277nyrXtYeJ5dWXLH4TodCW2urj99+7iZNyLt/u1vaVb2sKr5KMUX73z/NtrE0O3SNtiI21t3lM1dHpMc0ahPlfcnybW+avUj7p5laPLI6PQr+RldEm2RMrMm1PmX+7XRwyvCvmzbVRnVkZV3Mzfxbq838afFL4UfBnS5tb+K/xE0Xw1BGu+KbWNUjgZv8Adj+81eV6X/wV2/Y68SfESx+EXwf1jxT8QfEOqXCwWGm+E9DZ47iRv7rSbaUqnLDmMXWoRjrI+ifFFxN4ovLlLDUpporO6+zeW1rsSPavzLu/irzj4ta9Do+mwW3nLHLs+WGN/vf7TL/DXo3w90nxDY/DO9vNb0e6tdV1LXrh7zR7xvnsZG2qsLN/er5X/af1zxD4X8WX2q/YGRFg8qVWb5lk/wBmvzzMq31uvPkFR92XqUfFHxK0S1vjeXV5MWb5Yo9nzbq4rV/jgl9pc2lb12Ryr5rfL83/AAKvEvFfxK17xNqw+2Q+UFlZFX71a3gjTXvNUt9N+zM8lxtighji3NNJ/Cqr/ery6NHl/iytY9vB0pVvhPW9DuE1bS7mbWJlW2mRfKkkdm2r/u15V4+vvD1n9pttB1hZ4422/Kzfu5P4lav0S0LxF/wSt/Yh8C6X4T/a9vYvFvxBvLCO6n0S2iZrfT2ZdywuIm2q397dXz98Xv2n/wBnD4tvPpPgf4F+C/7Aupdtrb6RYeVPGv8AeaT7zVhiq2FwsI1E+Zvoj6TKcjx2MclWhyQ6Sl19D4M+IeoTSKfs0y/L8zsyfw15pr2ofZ7jfCcq332V6+pPjN+zXbah4X1Hx/8ABZLzUbKzRrrV7Fl3S2cf+z/Ey18ma5cK1wzonyyfKysm3bXsZXWpYuPOj5bPsvq5dX5RzahDNEZN+5WX7tMNonlq6Pja3yrUFvJ9qTzIUVtq/e3fep1veX8jNZuipFu3LJXp1I+77p878WsiazjeEsX3fN92rdvr1wreTNtWP7tUJlcsf329W+XbUkM1rNvheFv3f/LSP+KuaUfslxlKPulu+Wa6t0e2+T+/tqte6XDdTvDDcyRr5W7bH/erofDdmLqJEez3p1T/AHasah4ShZU8n9395t1Ze0jCXKaexqy+E4mxs5osGaZt++th9V8qRER2+9t/4DUWqaOIV8yCbdu/h+7Vnw7b2cciXM0KynY29WqKkY1I8500ac6fum34RuYlX7Q8s3yvtVt1fZX7H6+J45orvw94P1KdZFVHaSDaqt935Wavmv4dX+pLJCmleBmuhD83l/Z9yt/vM1fcH7K/xS+JGkahbf2xpWyz8rbKrbV8uT+FdtfI5nUcovlifaZLKduXmPoK3+M3x+8E6hB/wkPwZsZ9PuIlgXUGnhadYV+8zR19AfBfxR8OvHVlJrD+Hv7N1VVh2+X+7+bd96tH4DfDH4f/ALR3gZ9N8S6dp+rXSwf6t7jbLD8v8O37u2vNfih8Obb9lvxZBeabf+KPD1jJdRxLJrCf2hYybvu/N96NVrjp4WVSnzw2OqpioqvKjPc+m4dZsdLsp7bXoo/Mb5vMXc1Q6NrmirIUs5oUTbuZmb71ZPg/UvFPjrwfHrGi+LvB/iG2V1VpbO4ZHX5fm3BvvNU8WkWKyP8AavDVrsk2/NGn8NFajGM4wkj0cG4VKUo9T5w/4KpeCZPjj8ANas/DmsrCNDs1vVkii3JcSRtu21+UVjbzXEKbHZZGi3+XIn97+7X7mftf/DWbxn+zh4s8O+F9JkhkPhq4aNLXavmSbflWvxW0vRdtvDbTJ++h3RfMu1l2/eWvquH9Kc49DxM1qUpShyIwJNPdo23oybvm8tkqvDp/+kI8IkV9jfLXWalpfnw/uY22L80rb9rVmLp7xyDfNhoX/wBZt3f+PV70Pd95nmSjEzLXRUkVIej9Vk/vL/drVsdJfcbYIrbtq/Km7bVqzhuZN4h3KPu7v71bWl2KNMUhsMMv3N33v96vUp8/JqedKXNMP7DSOZI4fMYx7W+Zf++lrXh83zFtoU3IybmX+7T7GxufmhhhZ3mTavmN/wCPVLb2KMyTeQ25V2bm+6tehRjE56kv5SG1Xyo3REZG3svltVhVuZlIebZ/0zVN26nPa+WXdNuz/f8AmprRzQozv5jOu7738O6uuMYnLU5+U8f/AGul2+BdODSAn+2BgL0I8p+a1v2bpIh8ILBZUOBPMQwb/ps1Yv7XGP8AhC9LaJQEOoLwPXynq9+zzPGvwq0+CVDhpJyGHqJWNfo+LV/DOh/1+f5TOmS/4T0vP/M9KjvprplcBX/e/Ou/atX3njks/O+6rN8m5Kw4Z5lmCJbKU2b5W/iWrUtxbR2MfnTbNu7Yu6vzvl9zlPP5Yjb6+e6kREePy2Td81QSam91MsMztI23aka/d3VlTahMshm+7/z1jVt1XdLmmZ/nttz/AN2P73+9XPKEz1cLTO68Jr5ce9As23b8uzcy16r4L0qHzPOm3OGRtisvzK1eVeG23NbI6fKrb/m/2a9Js/EyaLa/bLl5NzLuVY5aKbjTPRlUShaR6ha3WlaTYx20yRxSbd6RyfLurnvEXxghW6Olf2hHbW/zMjbvvf7teNfFL9oCw8N6DcarqviG3thHbssX2p9zN/wGvmHxR+2NbXN/d/YLlruTZ5S3U3yr/tbVrphHm99nyWaZzLm5MP8AefSPxi+Lv9tSXXgz4eo15dxxbpW2f+hNXiureMvFWnxzZsGUqm6WRn3fNuryS6/ag17TbO6sPCrtbTX237VdKn7xqxNU+Ol/b2Ym1K8zu+/t/irqjHlifMS56j5pnqGoePPEis6PDNtk/eu0n+fu1ha98RPPtxvuWErKz7V/vV5Lrf7QV1rFwfJfykX5fl/ipum/Eq21a436ki7P41b+KlzTkONOR0d948S4ke2mm37W3eX/AHd1Yt5qkM0j75tnz/wvVLVJNKvI1ms7yNPm3eW1czeXjwv5KbmZf4v71WOPuyN281pPMMr/AHv7y1QuNYeRX+dt+zbuZv4ayftEyxM8wU0eY6sHzv8Al3eWtVyj5kWLjUEVd6bf93f96o2ZPldDj+6tHlw+Zs2bm+8jUkjJ9rZGTPy/eX+KnHlFzc3uszLy3eRVT7xbdXO65azLL8qbTu+RlrrmXyyXd9rfw7az9S015mRD96T/AG/u0RfMI5jVtNttUj86F1YxpudV/vVh3FvcxsqTQsrVuapoN5pszXlmn3W3Mv8Aeq7o9vpXiePyX+S5/u/7VMDl45Jo2CbW/wB6oppp45ldH2lf9quyvPh75KtNvZNv8VYl74bdX3p83+1RyzDm5pH3h/wRl/bWufAfiOT4CeNtZkOla5L5dlJcS7o45G/h2t/er6T/AGnvgroPibVLhP7NjDNL95V2/wDfNfkd4Pk1Xwrrtrr2m7t9rcLKm1/m3LX6R/s+/tMf8Lg8C2L63ue8tYFS6Xzd0m7/AGt1L2nJDlkcuMpxnyy+0eReJ/gLc+Fbq4to7Znt5P4pPmb/AOxrR8Afsr2fxCkis4bC4jlkbY+6L/lp/s19e/Cvw/4P8Zaxb2GvW1uIpJdzK3zfLX6Zfsffsd/sQXWg2esT2cV9qjJv/fr5aq3+zXN9Tjz83N7phTxFfm5Yn5E+Ff8Agi78YPE9iniHw3okl2skTeVHH8v3a+5v+Ccv7EHwwg8c+FPgT8Y/hrYyy21tcPq8Zt41lmlRJJgryBdxHQZBBxjBFfqv4M+GXw78JacLLwpoVtDb9vL53V4V8OPH3g/Qf26fFfwwPwy059S1G7Waz8Rr/r7ZE09GaIBgcKQD9wr947t3GPheP8NhZ0sDCUuVOvFO92pe7L3dP5nprp3PkeNKdTEV8qo4muoU5Yunf4nz+7O1NqPSb93X3Ve7PjT4qfDvwH8MPjNrvgnQ9DK6RpGvTW8cK7FlaJJMEBgmAxA67fzr2L9pb4p/so6r8KdK0L9nrQZ9M1aZEjv/ALDbSWm22ABaG7Y8XTFghBy+CpO/s0H7c/xj8DeM/GmoeANE+D+m6Zqeia7cR33iKMgT3hVipyEVchiNx3lznpjkmz+2F8L/AIe+CPgt8Ldc8HeD7TT73VdH3X9xbKwe4Jhhky5JO87pW+Y5OMDOABX4TXhVoLMYYacKlONrtxfMrytywvs03Z9HZWP56zB4/Dwz6lgq9KvQpqN5ThNzgpVOXkpOXwtN2k9YtJNa2a8B0fW9Z8O6jHq/h/V7qxu4s+VdWdw0UiZGDhlIIyCR+NSeIPFHibxZdrqHinxFfanOkYRJ9Qu3mdVyTtDOSQMknHvX2f4I/Z18B/sz/DDTvE2sfA3UPiP4u1SMNNBb6SZo7UMqsYyrhkiVPu7ypdmJwAuQsPxW+Anw/wDjz8DtZ+JGm/Au8+H3ivRLaSVbS4tBaJOkSlypACxyIyZG/arKygE7R8yfB+ZRwji6q9py87p2lta/xW5ea3S/zMpeFHENPLJU3iUq6h7V0LVNkr25+X2bqW+ze/nbU+KKKKK+KPyA92/ZN/Zf8K/Ezw/rPxf+M+p3GneDtEjbMsMwjNzIgDPzgnYq8HaAzM4CnIIr0Xwp4T/4J2/HvXE+GHgnRtX0LVZty6feiWaM3LKCcKZXkUkhc4dQT0HPFZ/imBvCP/BMfSINMEo/tvVUa9PlYzuuZH55+7+6QA9+OBmvm74caveeH/iFoWu6eX8+z1i2mi8sZYssqkADueK+6qYjB5GsJhnhoVFOEZ1HKN5Pnd7J/Zstrdd7n7PXx2UcGQyzLnl9Gsq1KnVryqQ55y9q2+WEvscsfht11d+ux8QfCXxF/Zy+J2peC21u80+/sZNsd7p9y8P2iFuUkUoc7WXBxng5B5Brn/EHjHxd4sdJPFXirUtTaMYjbUL6SYr9N5OK9/8A+Cneg2mnfG7S9bgDCTUfD8Zn+TClklkUEHPJxjI7YHrXzdXz2d4aWWZnXwUJPkjJ2V+m6v8AKx8Jxjl0+HeI8ZlNGcvZU5tRXM2uV6xv0bs1d23PcvgT44/Yl0LwHHYfGf4Wa1qOvCdzcXkczSRyKfu7AssewAfwkE553HOB7hN8Lf2GdR+A158bb/4SXui6K8Egs5bq4miupz91GgXzmBLPwu7g4yRs5Pxd4SvfD+m+KNP1DxZpEmoaZBeRyX9jDP5TXEQYFkDYO3I4z156jrX21qHiX9mv9vnQE+E3hzWdZ0K+0S2Nzo9qYFgRAEEYIiVmjkVMqNuVYAnaQCxr6vhnFUsbhalCVOg6ijy04yhFSnLu5Na27bt7tbv9O8Osyw2b5ZXwdShg5YiEOShTnShGdWbXxSm1rZJ6XvKW7ju/5/vjb4hs4/8AgrnYeJPEk9/cWlv8SvDslyftTNcG3jNl8qyAqdwRdoIK4wMYwMfvn4P+Jv7H/wC0L4ms/hdp/wAP/Gmsy3su5bfUtSu5IIgoJMr77whVUZOcZ7DJIB/n/wD2r9BvvC3/AAVW1Hwzqa4udO+IelW1wACPnjNsrdeeoNftb+xN+0v8Dv2f9H1WPxx4X1Iazf3Cj+2LSFJ91uAMRYJUxgNljjduJ5+6or968UcfHDZXwrhsS4RpywUHNzipbQp3Svom9tdPyf63xNnkMvpcNZfmMqNOhUwsHVlWpqb92ELxTd0nLbXTrq7JwftkJ8J/gl8R7fwx+zfe6noOqwWpTxIdI1eZYRkho4ydxYyAcsAdo+Xjdux7v+yn8fviNefAu5+Jvxtjs7HwtoGmrDZ6tN5r3mpvHhGlZnch8sNgwMvIxAOVOfA/2zf2e/BfgnTNJ+Ofwt8T3mpaF4suGkc3krTMksgMquJGG4hhu4fLgqck5ONT9n/9vLX/AA3pfh74NeOvBOkah4cjWPTbmcxlZfs7HYC4YmNwoPIKjcByckmvwzBZh/ZHFFZYmSoxkvcjBXp625G7dLe87Wu+2x+Y5Rnz4W8R8WswqLB05x/dwpLmoNzS9k2oWvCz55OKi5Svfl1R4l8fvii3xc+IOvfEr+xLbTFv3eSK0towAiBcKXIA3yEDLOerEngYA8rsdeSSOTY6q/8AD/DX0Z/wUZ+EXh74O/EjUv8AhENLhsNM1bQXv7e0t3GyF8OsgVf4F3LkDoM4GAMD4l0nxRusxvdmZn+6z/dr9N8KqWKwlTMqOJd6iqK77uzd/nuffeEeBzPL8wznD4+SlWjXXPJbSbUnzLRaSvdaLR7HsNr4khZktrmbndu/vV+cn7QF0tx/wUna6HIPjbRz9cC1r7Z0nXvJkS8+0r9/5/n3bq+FfjpfyXH/AAUCbUN43HxfpTbgB1Atq/prgCfNj8T/ANeZfnE/oXIov29S/wDI/wA0fp3NrnkRlLbdFLt+996o7rWofJWFN3yr8zbvu1x8HiZ5N291+b/x1qik16GPHzszfeX5d3zV8FKtzHkU6fLA27zVnt5HmSHKSP8AMtZd5rSeYyPukT7r7f4azptXfzCiXihmZmlZv+WdZd5qN5Gn2kbSzfNtkb7y1jKoX7Mfr14ZFLyPv2N86rXKatcWDKblI9zbvvLVvXNW2xl1mVPn2ttX5Wb+6tc3qWpv9gDu7Mscuz5vl2q3/oVZyqF+zYlxePHM9zC6ny33bf4d3+zW1puuJaNv2bGZl2x793/fVcPJfXJuoraGTCyKzJ5nyoyrVu31/Y6ec8aO3y+ZWcZR+0Llmenw+IEuLUp9sX7u5/k3K1Q6prUfyP8AaY0K7fN/2a4u31qCSFEhm2sqfKy1Sv8AxI6xq8r7mb5fm+ZqqMjPkOm1LxZDFeb0udrxuq7tnzeX/dVqyNa1xJWSa2fan3XWRq5TUPFD+Y3+sz/dj/iasG88ReYzPNMyhl+6r1lL3io8pvXniRPMmdG5X5dyvUmi+NJo5EuUmVVbcu3+9trgNQ1hPmhhucIzbnX7u6odL1pJLoTSOyf3N1ccqnKdUacOS6PoDwv8Q7Z/9Tf7W+98z/LV+bxNMy7PtPm/wosj1454T1rEaI7qT825t9dbZ6gjWvyTecv8fl/Lu/3ay5pLl5R/V+WPMz5Sj0/y/wB8kLK8j/I0ibaedNeRvOT/AFjfL8z/AHq3bjTZJJJPkV/+Bfdpy2e5tmzcf4vk+7XzPtuU++9jzGdDpPkwyIj/AHU+791t3+9VeS1SdWd4dnnferoY7Ga4+SR2+b5v9pqz9S0+NnB37T919tVTqe77xt9X5krHMzL5bEQ+YiyN87f3mqPy5tss7vJt/g8x/u1q6paqyD/e3MrN/FWPq1xD5nLsH+XYq/MvzfxV1063NAJYXlj7pR1ZvOtyQ67V+bav3qwLq823DPvYbk3JWnrEj29qTNuyv3mVvvVh3kyNjyXUrs+evRwttjzMVRnIdHN5Uao3zHfu+/Vuz1KaNRD91Y/mRWTdXPSTP5nnJ8jf987qmjuPJZdqMv8AEzM+6u+UTypR5DckvPOz++xuqpLqCBf3L7v4fvVTkuEmlj/fbNrf7rLTLjYsaxzIv3927d95qiUuUwqU58vvDbuYszCQ/M3b0rP1AnA24yORmrM00hlCy5JbuVxUF3E8rALCHwMnJr9Iw0v+NY4h/wDT9flA6VH/AITZL+9/kZU1q7bU/eYb+H/apLXSblmMXkqrfxq1btnpaSOnnJuXd8u6rlvoYkZX3xq38Kq1fmsqnL9o8unGMp6mPZaX5cKo6bGZ/kq8vh2Yt+5f7yV0Wm6CnlpDcpt/2f4ttbEXh142Gzbsb5fu1zSxHKevh6PwyOHXQblYVE0G75tyyfxLUlrpe7aiBsbtz7fvNXZ3Wg/Y1MN0m/5fkaOq66LC/wA6P8rJuXdWHtObc9WnGPOc5Y2csSnnH8KMvzVu6NbzeYj7/l+X7rbd1TLotstusKOx+ZWSPbtrRtbO2toUR0Yr/Hu/hrkqVOaPMepR3YsDRyKk3mSeUv3G/iqddQdpNjvjdtb5kqDy4beZx5zeUz/Ju+8q7aP3yxsjvv8Al2/8Crh5f5ipVIrYtNcTfaHR04ZPn2v95qfHHMwVE+8vzOzN8tZ/mPDIN838H3VT71WUjhuo/OmdUXYu/c/3qylH7QRqc3ul6GILcB/tkcK/wf71aGxGhTZMquz7Zdv3qoWsIlt/O85du75dv8NXV2fNsn3qv8VTKodVMtww2y2/3/Mbfu/eVZtY2kXiNkkVvvM+5VWqscyXEmyYbmX5Ub+9VyNUjTy0mVpP+mn+zSjI3jzSl7pcsbpLiPfHGo8tt37z7zVdLJLJ5L7WZk37VT7tZsG+6X7TuVlX5WWrjfLH5ywsI9u7b/E1aRj73MddOXL7xbhadYZfMdYdy7m/utU8dx8om+Zj9x1/u1VtS91D+7Ef7xf+A7atQ6fvjHybWbcqsv8AFWtOPu+8by973kWLfetuJpnk3bm+7/dq9aR/6D5j3O7b99vu7qj021drdH+Xd91of7rVbs/NhU+dMuxn3PHtrenRjL3TmlU5ZCRxmSxa5f5dybtqvubdurR01f3KvbTeX8/z+X93/aqKNbO6Db9wC/L8zfep32hLVhCjrhZf7n3qVajLpEwlXpyjeUi5HDD9nKfM/wAn8P3lqNpk+0R3KI29vm+Vvl3f7VLHcQt+5Tl1/hb5ar3U7wSNC8nySfcbf92uf2E4yUjllVhKOkiVmh3fO+8svzK3yrVO6t3kjeZ12x7d1accLtDv2b9v8X3qqzbGs3D7dqrtdZH/AIarknE82tVhGXvGPdKkiqG2q+z51j+6y1nTW/nRo6W0e+N1+b+8tbU1nt2vvjbc23bGlMksYbiQyQzR79yo0a/eWi04nPzwkY8MPneWkEcibv4ZH2/N/wDE1LDZ7Wf7obZ97+81aFxaiO5+zRp8rJuWaRKuWNu/37nyUTzfnj2/e/3a6HKcvsmPuRnynPf2fcwwpdW1wrOzfOu/7rf7VSWuk+ev2ab5Ny7f3f3f+AtXRw6SjxulsOd7N8392rUej20kTfZtxRU+7/drKU58prTpLm94ytNs0jh2CFi7fK7MvzLWvDauVGYW3bNsUny/L/wGrNra+TGiJ8zt8q7l+9ViPQ/L+Z0V3jdlfb822ubknUPRpujT6kUNq/2cQ3MzFVl+7vrTW2urram9owvyozL8ytT7Ox3QqkyRq2zcit95qsSRvZ26I9zGdzb9qtTp4etKry8pvLF4anD3qkSaO3SGNE3qj+V/rF+9/u1q6X80YhfzEb727+Ks5bF7pVEzqgb7kjf3q17aazsrd7m51WFvs9vvZvN+9/s17mHwteP2TysRnGWR0c7m/osTx3Swvcs+5V/d/wATf7VdJo8sOmqLC5m5VPkjkbc1cNDqGsKqarea1HpGnyLuSaZf3sn+6v8ADWtceJrPR7OW802zaOVotr3jJvlmr2IYep1PlcZxDT1jRidbffEzwx4L0+TXtVebyo/llWRNqr/wJvu18Eftkf8ABbn4j6ZPqfwt/ZemstNt1n8ufxIkKyzbf4lhZl+X/erz7/gpX+23r1zfv8Dfh9qTRqq7tc1CNvmkb/niv+7Xw4x3c4rpjRvqeNLGYmp70pG946+I/jz4peI5PE/xF8YalrmozNmW81K6aVz/AN9V+zH/AAai/so6V/wlHiv9szxnpEb/APCPxf2T4VaSD/l6mX97Mrf7K7Vr8cPhV8M/GHxS8YWngvwRoF1qWpXkqpbW1rH825vutX9Zn7Dv7Kum/sd/sX+B/wBn7RLOO2vtP0SO81u6/wCfjUJo/Mm3f7rfL/wGvnuJ8d9TwfJD4pHoZNhXi8X72yND4maL4Ysdav7nVYds19L57Rq3zM395v8Aar4v/a0+Bt5rzX2q6c7Nbw3u5fO27po5P4m3V9IfGzx5rGlzTTeJ9Hhgdv8AXxx7mjmX7v3q+f8Axl8evB+g3y3PirUoWhki33Ec3zMscf3dtfmlHETpx5j6T6vCpV5T5bm/ZR0fSbwa9NYSb2ZpbpZHbYu77rf/AGNdl8EPB/hf4L+E/E/7SfiqzWabwXayNoMdxbrsa+kVlg+Vv7v3qxfi5+1RZ6x4gubDSnje0ji3RSRvuZV3fLXj37S/xs1jVP2RdH8JvqMzTa54vmuLpW/hWGPaqtt/3vu0TnicS48/2j7fh/LsPTrxk9eU+fL/AMReNv2ifidqHiPW7+S8u766kn1K6Zdzf7u6snVpfEXw5mabSrqaMRttWSN2Vlb+7Xr/AMAfCf8Awgvw1DtbQy6rrjs/mRn5ljX+Gsf4+ePfBnh/ShokOiWc+oTNulVfmaFf7zV2+0p+2jRhHmietmuPqwg6jZc/Z/8A2w9Yh1OPS9Vna2nhiZXkVfluo2+9G1cJ+0V4Z8Pf8JNHr3h+2jWC9f5Fhl3LHu+Zq82TxFLPr63ttaRwIrf8s61fE3iC51DTY4fOzCvzbf7tdUcHUwuLjOj7sXvE/PcyzH67Taqak7eE00eHY8Ks0ibvm/hWqNzp6faGs0/ubkX+KjTda/tTSy/29ke1T5Fkfd5n+zWlot4lxareOm8/dbd95Wr1Y1Kv2z5qVCPNHlMy38O38zCa2T5Nv8VXovD95b3DO8OUbbv2/drrtF1KwWz/ANGRWl2bm+StnT4YbiOKa8RUdk3eXH/DXHPEfvJHdh8HzTiZXhPSprOMedD8kn3Pk/hrXutPtobFodkaiZ9y7l+Zf71XVZI7hYbZ+fuvtqW6WG6035327Zfn8t/4q8mrKfPzKR7/ALGFGHMjh7zw/wDaLtoYYWaHft3VqaN4P03SLUak6KxX7sbfM1WfOS1meGZIx5n+q+b71VdQkvI1VH+UM21Nv8VdUsVKnDlXU8qpGPPc07fxB4qmmd4deksLNZVbyYV2rI38NdN4b+N3i3wnazWyeJbq4eRt26SX7rL/AHa5zQbcSWYh1KH9wqb2+X7tag+M37O3w4sUtvHcccrreq0UKxeZJJH/ABV58Kft6vJGHN6F06tTD+/z8p1Wh/tu/F3wDJD4h8JfFq40rUN+7bY3TIzMrfK0n8NffX7LP/BbXVPHnguf4PftQeH9L8Y291Z/PqB2xTn/AID92vyQ+LnxV/Zn+I+yf4bvdWkscrfu2t9nyt/8TXEx3Hi3Rr+PVfDfiCQNC+6Ir/F/dr1v7LcIWXuS/vGX9p1pTvU99H9HPwQ/aS/Y+tbu4sPC9ne6ReXjq1jbsn7jbt+7uX5a6/S/iwtr4ytNN2ebZ6hu8q4X7q/N92vwe/ZE/aW+Ot14nttK1HXo2iWXe/mLu8uP+Latfqz+yJ8SrP4lWsOpeIdVVRpsX+itvbdJI3+zXg4zASo4hc0veP0DJcXQr4eT79z3v/gpV+1N4P8A2V/2aprzUtRjOpeKf+Jdo0cjfJJI38Tf7tfkHfSXsmpG7k2o8ku5vJ3Kte7f8Fb/AI3eHv2nP2ovD/wd0K7mufCnwv05ZL+8h/1U2qSfM0at/Ft+7XgLXTtI033W+Zk2tu3V9XleF9nSv3Plq9SPt7dixuhuGDo7OzfxK3zVRkhXzmSN9pWXc0bUsMzxqjzO25vm8v8Au1LI0N1D9xQ/3W/i2/7rV6UYwlPluc0pdeUt29nbSRq8yNGjfcWP5q3dLtZmvNiI2xU+ZpPvKv8AerH0238uNv3y/Kvy/N97/erqtLWG8kMJRg6xfMzV20/7pzTJo47mMDyY5GT7ryb/AJl/u1a+wvDCyPD88b/N5lTabY3MbJveNFVG+Vf+WlXobNPsrIiNKY3+7v8AMr06fwHM48xjSLDbx+S8m/5/laSquob/ADpX+7J/d3/w1pSLukaF7ncyxMrqu371ZurtuLPNtaPaq7t/3a7IxMJe97sjxX9r4Sf8IZphygUamAyJ/C3lvV79ntYv+FTab5oDhpp1x/d/etVf9seUt4J01WLfNqysAy4wPKkq5+ztHDH8IdOmkUMTcTnCN6St96v0upBS8N6C/wCnz/KZrU93L16/5nYtI8OwojH+Ftz1SmvJpFJ+Yqv8Lfd/4DVjVLyG1bfv8xFTd5cfys1cxql9DGvlojFY5flbf8y18L7Pm948/wBty7FmS8mkuH8mbYrP8jNV/SbhIW8u5udzN80rK1cdDePvEMk2WVP++q0rHWEW4RIZvnZtqVz1KfNqdMa3ufEepeH9cgt7NI0dirfKjVxfxU+Plt4R099NTbcTL837x9u3/armPiR8TrPwjZsiXLSzf8u6w/dr5n+JXxEv766ebUrlmmb+89RGjzdDysyzbmj7Gl/28W/it8WNV8SX0n2nUmkaZvvM/wDDXFW989vbvcvMv97bWDNqX9oXjvPMxO/cqrTtQvkjtvJ87Z/s7q6Y04RPBsaMniB/Md5Hbb/HtesPV9c1LUrgbJt0Ufy/LUH2iGWH5x977tQxskSv8/G6q/ulcxb+1/Z4d7ptX/0Kmt4kez3JC+xdn+9/wGs3VNWh2/fUbfl/3axbi+eZd/zMWo5oj5TtdP8AF00knku7MNv8X3q3rW8TUofOd1HyfJ8/zNXl1rcPvV/vD/e+9XUeE9Y3SBJn4+6u7+H/AGaiMuxl/iOtSETf6SoZtz7drU5bfarfe3f79TWyyMyP/A38P92rM1r5MghRG/vK1aE/D7xVWIxx79jL5n8VOuo9zb3Rm+X71XxZvtXd/u/epjWe6Rt0LbV/hqoxjKIvtFC2t3kXfsUFv4mqxJpThd7oo2vuq/pdiGbf95d25l2V1MOhwzWq/wCgK38Xy1nEqUjz2bS0kj8uZ1Vv465fWPDf2eQahpX7mZf7r16T4g0VLNnjTarr/DXnmoas7a9/Y9y+xV+b/eqveDm5ty94b1TVb6x+walbLu37Xmb+Kl1LR/J+f+Bf4v71aVvHZrGqI7f3dq/+hVNeRpNn+KqiBhWsKbvkT+7/AAV2Xw4+JF/8M/ENtrFtPJ9jmlWK9t1fbtX+9XLrZozFN/NSNbvJamzmdT8n8X/oVRH3pmdSnzQP0j+CvjzTbvS7bxDpWpK6MqtEzfer7V/Zr/aCmt2tLb7f5JtUX5Wfbuavx/8A2I/jMkd4/wAOtV1L99H8kHnP+7219u/D/WtV8M30VzHcsUbadq/LW1Sn7nuHiylKNXlZ+rfgb9sPxT8OriGWeCTUNHunVpdz7mhZvvf8BrK+E/xO8OeLv+CiqeO0uPLttWkeK1KjcDI1iI1UnPGWBGeecfWvm/4S+P5PFemG1nuf3DR4kjP3v92sq98VHwS1vr8NzcwTRXaG3ntG2yRS53K4IIKkEZyOQa/IfE7HVKVTLsPRpudR1VOKXXkVrer5lb0Z+WeJ2c18DisooUKTq1ViI1YxX2vZfZ9Xzq3oz2j9sf4Q/ETwl8aPFPjLVvCWoJot/rbzWur/AGVvs7+b86r5gyueSMZzlTxXpv7a+oz6R8E/grq1t/rLXTI5o/mI+Zba0YcggjkdQc14H45/bf8Ai54+sbb4B+Jvie+ppPpq3l5awRReasKkBBcyKAxycYBJ3EZPTNeCftyf8FU/Av7ONj4S8D/tG+N/EOrsbR18PaPpdmtxJBArBTIwLJxkhAzEt8u0cKAPy3E4HFUcdi8BhsNV9riIxlGMopSVp8z2bvHRpPe626n5tj6csNmOaZZl2DxHtsdCFSMJxipRare0ltJ3gkmove61XU/U/wCOfxH/AGhvE/w58P8AxZ/ZR1e21KwurXOq6fZWkF1KrMAQyhwSxVtyMg+ZSBxw2OA1eT9tfxN8AfEnjD4x/Fiy8G2KWbLDYX+lQwT3qHhkLxgPBu+4owWcnGACCfz9/YA/4LB6f8VvGr+A/wBmTxD4q0yW4w8tnrumQi0ZmYJu8sySDfwBuVc4HXtXrX/BWP8Aa1+KX7G2m+CtX/bS8TanqemeMLp4dDh8LwJJawzxoGPmx/uVV9rnDYY4JGQK+mxOD4kxlOVWeExXtJRs6abVLmtbm0fNbry238j7rMsZxLmeHqYutg8wVedPldGNRxw/O48vOnGSmo/a5LWb0babMGiuI+AHx/8AAv7SPgaT4g/DyK/Swjv5LNhqNuscnmIqMeFZhjDjnPrXxzJ/wWC+JsOtXGlzfCfQAsMzpvF1N0ViMnn2r89yrgziHOcVWw+Hpe/RspqTUWm7239GfhOS8BcT59i8RhcLRtUoNKcZSUXFu9lq/Jn7YfAHS7f9o39iPW/gPompxt4j0K7a5sbOchcgy+dHgk4wx82PPG0kZwME8H+z3+xr8a9Y+MOkf8Jp8P7/AEnSdM1KO51O71CLYjJG27YmT+8LFdoK5A3ZPFfl78Of+Cz/AMYfAPiBdW8JaHp2j6p5X7q50/UZlEqEg7G5+ZSQCVOQcDivV/Fv/BwZ+234n0UaLf3elwQ+XmWXTf8AR5GHvJEFb8iM1+j0eBc0xVLDzx+Gl7SilFcs6fLNRd4p3d12dt0fvmD8OMfmmEwOIzrBTdfCQjBezqUeSrCDbgp80uaLV7SavdbW0t91ftla/L8e/wBqpvDHw5c6rJCINHsViwFknVm3hWJwVDsw3HA+UnpyeP8Ajn+zB8Sf2eLrTR8QjZyWep58nUNKlaaNWXG+Mh1Q7wCDggA54Y4OPzqtP+CrfxQ0HU7fWNI8D6Va3Ns6z291FdTq8bqchlYNkEHnNaPxT/4LmftJfGS9tb34jaBpWqvYxmKBZJHjSFT1ISPau44GWxk4GScCvNxPh5xHmCxGIxGH/fzleNqkORK+qet9tF8tuvzeY+FvFGeRx+Px+Df12tUU6fLVp+yim7yjJN8z00VvLazv+ofxv/YP8SeGdF0Xxh8CP7R8XaRqOmxSXLxxo06yMoIkVEwTG4bIA3FcEMTwa639hr9nL4ifDLx5c/Gz4t6W3hnSNJ0ycI2rSrC0jMNrM6kgoiruJL4GduM8kflJ8Mf+DgH9rP4QRLovgaz06Cw2kQWF1LJPCmSSdscm4Jkkn5cZJqL4pf8ABwL+138X7ZtK8a6ZplxpxQebYQXEltA+CCN6R4D4IBG7OCOK9ahwDicLiY4+lhJKpGzVP2lPkUl15r81r62te/lofUYLw1o5dmdPOsNl1SOIp2lGiqtH2KqJaPncnU5L+9a17+Wh5j+2Ffj41f8ABbTU7vS5Y4V8S/GDR0tZDuKos0toqMc84wwP9B0r9R2/Y8/aXHiE+Gh8INVM4l2eeEX7OffzifLx77q/D3xl+0J4j8TftPw/tIwafFY6pBr9jqkEFtK4WOW28ophs7hzEDnORX3Qf+DmD9utdI/sdofD27b5Zm+zL52PXftzn361+xeLfB9fP8j4eWIhN1KWFipcjha/LTuvea6rRq6/A+74/wDD9cXZVlH9pU6vtqNCKm6UqVuZxhzRftJLqnyyjdb3T0P1C/bHlsPg5+y54L/ZwuNatbzWYmin1COK4JeJUVyW25yEMjlVLcEIcDjjK/Z//Yp0iwsPDvx6+MPxP0fTvDrRW+pxWjS+WZc4dI5JJCqpztzt3E8gYzmvyE17/gsd8X/GGpT+J/FPg3TtRvbhh511e39xLLIQMDLMxJwAAPQCq/8Aw9f+MeoMlufB3h1oIm2okhuW2DGeB5uK/E6vB3EGIzR4qrgoyjCMY04uqkko7c1r83mj84xPAnFOP4keY4nKIVKdOEIUKcsQkoKnZRc3G/Ptdx0Tbtdo/S39vL4zWv7RvjjV77wabiTTbfRpNO0dLnCeadr5kAz8od24zzgLnHQfF2m/BL4y2ybR4cSIMVEi/boTkD0w3Fec6b/wUs+KF0F+0eCfDvzpuUxif+RkrpdL/b78dXhX7V4R0ZNyZ2r5uQfT79d2T4LxIyarXq0KVKTrS5pczvqu1pKy+868lyXxsyTG4vFUcPQnPEz5580r2auko2nGySdknfRI7lvhb8VrWL7S2kL5cJyY/tUbNt/iPDcn2r4U+Ld4kn7bJvZAcDxTppbIx08jP8q/Rb9nr4zav8YrDUrvV7C0gaykiVBaK4zvDE53E/3a/PL9py4Yft469cAAFPGdvjHs0X+Fft/gfxPn2Y8UZnlma04RqUsO5Xhe2rhpq5X0ad01bY/S/Czi/inNeKsxyTPqNOFahTjK9O9ve5XZ3lK+kk7pq2qs+n2lFr3ks6WD52v8y02TxNDHu2Px9123/NurhJPGSFdiPGGkX7zVD/wmHnI0MNhtOz5mrCWIP0mOHnzneTeKpoZkR7nO5N27+9VebxTNKodHUp8y7v8AarhLfxJNJ5c1zGsR3bnaP5vlqx/bEwZvJmZBv3bdvy1zyxXvnV9V5veNrVNWj2zI8LH+Lav97/ZrB1K4eRpnZ5F8ld3lr8zVFc6vczbZjtIb50b+FaytSvHkYv8AbGHzfNCv8K/3qj6xKWxcsPGMCS6uoVkhdP4ov9Yzfd+asmTxBuZ03/d+8zfxUupXXmRvv3IjN8n8Xy7awb4+YrTbPM2r825trNW1OoctTDx+I6iPxJZx7Jpn+bZs8z/d+7UMnipL6ZkSZTuRvmVq4ZteRZE+Tascu7arf7P96q3/AAkjqu9NwP8AE2+tTn5Dqr7xFDJGsML72+78v92se71iEf6MiL5flf3vu1z8mv8AmQtNZzb/AOF2/ias+bWka3Z0ST/dZKJkxia9xrkPzj5l/e7tu7/0GlsdU25jDsySfdkrkbzUnuVPnblb73+1V/T7ueaOO5/1S/dRWb5m/wBquStHmOymem+HdQRWjR3VT8uyuvtruG+l877TIy/dRY3215houoOtqf333tv+s+7XUaXfGJRDsZFVV/efwtWEZRj7vMdMaPZHPNo80bFNiny/4t33v+BfxUNGlvcNBHCrvs+dt33a2pLVxGEmf7r7fL/ipLi3uWjdISpdflr4/wBpzfEfoNOjzHOMvkybLZN277+5/mWqN5H5bvMnyKz/AHV/iatm+tUjZtjMxZV21h3lx96WZ2LK+5I1rWNTmh7p6VPC+7ynP30iTM6TJurC1JYVk/fSeWK2dUjkViiXLKW+b5qxtUV5HM3zK6/Mi/wtXbRl7h1f2f7ukTHvm3IkyPvf+KRvu1gaitn/AKnzNjN/FWxdfaZptj9Gf7q1l6g3lr5L7Wbf/D/FXqUcR8J52KyuXLzGQ2+TzHkRl2t96hp3m3n7NsRUX/gTUrM6sro+/wC821v7tJaxpJsdCu1k3N8396vQjWi4nyuKwM6ci/DCkkPz/M3+z81Mk+bfC6f7q1Lp8P2dtiTK6fx/7VWpLH70kKNtX/Z+7WNSoed7GctGZMkARRIZhnd90d6uaNavOXZG6dsZzTr/AE2OC0+0Ic9OdvvV/wAIWUV5bSrk71fOB6YFfpGFn/xqzEO//L9flAqUP9gkvP8AyLtroyKyv8r/AO1WhYaKjSbLa2VP7zbPvVd03Q/n865TzBu+Vd/3a29L015lVETZ87bV/urX5jzRk7HlqP72zM2z0O2Xc6P/AAbVk/u1fWyeS4SGbcEbbvkWt610O2Vk862XEny7f7zVZm00R7UhsF8nZ92sKlSHwnpUJSiYUmnfuVhT98y7tit/FWfcaTItwXtrZRGr/vd3/stda2l3MkZSGzUfJ96P7zLUMmhulmr/AGNXVV+SNZfmWuapL2Z6VOXNucvNp5uJAj20mGf7q/eXb/FVZrOZV8lE+X+NmTc1dhFpLtbOn2aREVd/3P4arXenR/ZXh+bZ97ay7flrCUpfCdNP+Y5K4tUmj85Nu5n+9VSRnkjSb7u776/d210NxYpJC7/Zvk/hb+KsS40+b+5G0rJu8tX+981Z83u8pr/eI4diyFJ5trsm7d95lqa1bzmWF9u1l3I33dzUfY90KD7HskX+Jf8AaqeO18nYHffK33KXuRIjKRYs4bmSSJCVVdm75f4a0I4Z41W2dF2L8zMqfxU2zsXZ0f5fm/vfLWxHp7tHsSFt2/5G3/dX+9XJKoehTv8AEVFsUmj37Knj8u4ZLN3jC7vnkb+GlaDeG2fK/wB3cv3W/wB6rVrZJuS5hRhGzfdkX71XHlmonRTkTWEf7tpoUUn7vlt8q7a07Nfsbtc2cO77y/u03bW/u0ulWsMih4WZVb+GRK2bKxhWRJkjVPn+dv7tHNM6o1PZ7GVYx/vvktlUr827durSa3ddohdnMif6xV+VW/iWpJrRF1J0RG+Xa/7tP4ayfjZ44T4b/D+bUvDztNqcz7YFVf3Vuv8AEzf7VerhcPVxE42OPNM0oZbhuecve/lO50HwTc3kafb7y3sUk+ZZLqXY23b/AHa0F+GD3FnLDonjPS7u5jTdb28ku1f9mviOH43eM9S16W/vNeunkuNq3HmS7m2r/DXR6D8cvFVrfJdWesSJJG+7asv92voaeBp0/sn5pj+JcwxVXmhLliez/GbxV8WvhrC1nr3gzT47b73maazNub/eb+KvK7r4qX9xaLqVr4hVfLb59sv3f9mu1uPjFH8SvAN94b8YPve42tbtG+6RW/8Asq+UvHl3qvgfxQyW1y0aR+Ystqv3WWuuNGHSJ4ssXiqkrzqSPW9Q/aK8W6bqGy28Tyf99feqCH9p7xOzDztY/d79yxt96vK9S1Dw9Haw39s/mpNEsqNJ95W/iWsG68WWbzOn2aPbv27t1V7Kl/KNYnFR+1I+iof2ltYhjDpqv3l2vGz/APj1TWv7TF/cMES/+ZX+Zd33v9mvmq18T6bJJsfd/s/P92rH/CQabEu/7TJu30vq2Hlq4h9axUvtH0lqX7QmvSRj7Hfxodu3av8AF/tNUkP7Q/iF4YoUvWHy/M275t396vmq48VQ+Ys39pM3y42/3aLXxw67kubyN03/AC0/YUukRe3xEY/GfT7ftCarNJsm1iRgsHyK3zbWqyfj9fzSB3vI5d3zJu/9mr5qh8ZO38an/gVI3jK/U7PtHy1H1WG6iH1nEfzyPpmz/aGmhdkudU3eZ/yzj+61XLX4/faGRLDxQ0TKm37275q+UpvGV4z+d9pZ/wCH/dqP/hMoYVZPtKr8/wB1UpfV6UteUft8UvtyPq7UPjd4k2ult4kWaWRNvzNt3f7VRr8atb09fO1LUpopZPvtHdfw/wCzXym/xE2t89421fl+X5dtVpvi1Mq+Sl5uDJ/eq1QhHXlJjVxEftH2ho/x68PXCtDN42a2+RVRrp/mVv7u6vWPCNxomvWsN/puvWt/uXa0kM/mf7tfl9fePvtaq/nMGV/4m/irpvhb8UvH+k6oieGvEN1bSL/FDKyqv+1trTlcdjGSlU1lI/TnWNe0TSbeKNL/APeqm1Lfd95v7q1V8TfGbwB8EbGPWNVe31PXJomS301k3RQ/3d3+1Xx3b/tCeJ7jybzUtea5uLODZA395v4mqlJ42vPGniJZtQvGY/ebc+6oi5SkZ+z5Y/EfXXwz8feKvi14i/tjxa63Vs0TbLNfljj/AIl21n/thftMW3wy+Ft5Nomtt9qaz8u1VU+Xd9373+zXGeA/F0OleEEvLC/kj+Vf9n5q+U/24Pi5f+NvFi6I7xrDars8mP8A9CrWUeX4RUn7SXMeC6pc6r4nvpte1W8kmubqVpbiaT5mZmr1L9kH9ir42/tkfF3T/g98HfB93qup31xGmbe33Jbxs3zSSf3VWud+HPgnV/HniLTfB/hXRGv7u+njt4LdU/1zM33a/pO/ZO/ZQ+G//BB3/gkz41/a78aaZaN8Rx4Nku7i8ZPmS4mXbbWqf7W5l3fSt4Q5aXPPYqrWcqqow3/I+eP+Cbf/AATN+BvhT9rh/wBlD4ZzR6lF8LYo9T+M3jJtvmalqnytDp8Lfwxq33tv92v1O8WQ6bdfaUSZYTD91lb+KvhX/g2n0maH9kLX/jd49eRvE3xH1+41nVr+6bLTK0zbfm/u19gfE7xBZ27zXkLrcQyI3lTR/Mv+1X5JxRjfrOMkl9k/SOH8E8NRvLsee/GSz0z+xWfWEs7pmVt6yf8AoVfBP7UHwftdS1BvE/h5JB9q3RS2sbKyQr/srXt/xk+NOpXmpXmj2F/G0Xn7XZvvKq/d214t4p+IGm6xajSr+/jg2u3lSbtrM1fN4Wcpbs9uOFj7Tme58e+JvhDr2i3V5reu3N0iLLui8tNv3f4ag8WWM3jD9nPSdNvPOb+zfHKqlxcRbf3Mkfzfdr134u+MtNsdL+zXmq/2lJJuV1VP9TIv8TL/AOzV5pZ+NpNe+Gut2d/YRomn3tvexLG3935fu16FSpXnS54n0OUVI0cQozOo+D7aJqnxE1jw89ntfT/D0n9msvzKsnl/K1fFfje6vL24lvNRud9y0snmzM/3vm+7X2V8H9Pv5Ne8QeOfDd+zyafo0k6Qq67pNy/d2/xV8K+M/Ek0t9MmxURpZG8v+JW3fMtXktOdavORhxBKMaFhtjqGkaVPHYWv72e6l2eY38NaPiSxm0S18t9xX+LP97+7WN4DstOvvGli+pHdEz/8B3L92uo+K0iW9uHhRdnm7d1fR4hcuIhT7n5/Nc0ZNmRoe+8tXdEWNf4VatHSFvLGH7U6SeW38O6qvg2F2tUf5dsj/wAVdLdWaSQ7/OVAv3F20qnNzSMpa0lpqVtO8SPYzM/8LfL9/wCatqx8XXMezZMzLt27pP4Vrk9Qh8y4Kb2VW2tuWtvTYd3zx7v7y/7NedWjH4jfB1Zxq6PQ77wzffakPk7ts3z/ADV0UPh68uYd/ksu35lhj+X/AL6rkvBfytvfazK6sm6vffhvpdh4iuI5oHXarrshX/2avDxlT2crn0cf31I8x8M/C258UeIBZwurI25l/wB7/Zq74q+EV54f8RW+g3Ls0cf72eT721f9nbXv7eCdK8CMPEr+XC9u7NFDGn96l+Hug6J4s+IzTPcwpcrLtS8uvurH/erzo4pynzfZOB4Oe/U+e/Dvwf1L43fFKH4S2GsTaDa30C+ReXzeRuZvus3+zXp3x+/4JIf8M0fDePxr4q1WSHX47rbYatap9ps/LaP/AFm5t275m+7X2Vb/ALE9n8Xlh8Q6E9qNWt4t1rNcNuiZl+7838NewT/sS/H3xJ4Rj8H/ABD1hmsIYty+XqLMkbbflWGvosvzWWH5Y04/M5q2X0cXHlqaSP5y9Q8GXPhHXrzRJLSR5rOVknaSBk/ef3trf3q6HR7qaOxH2l2+Vdv+7X6rftcf8E1fDHheexhT+0Ne1vXtes7Nbi+2tL5zSfN93+FY64T/AIKIf8E2fhp8F9L1Cz+Frxvc6bZ26xbdrvIzLukr1q+aYbEfxGcn9lYmhP2cD4p+CL/ERdYkvPh9pU15JJE0T+WnzeW38NfZn7PvxQ+N/wAF/B8viq70G4tbqa1aDTbeSXb+8Zdu5t392pv+CLngfwHF42lsPiLpX2lJLzyGjk+X7OzL95q+vv8AgsD+z7Y/Db4b+C/iR8N9P3aJbzSWGttb/dt2k+aOaT/Zb7teTGEMXjuQ+gp0a+X4aM+b4j4GtofsKzfb7zzrm4lknvbhn3NJMzbmZqT7QlwzIibl+6rMn3m/vVJ8kO/Y6iKZ/wDWKv3v9qq6x7ZDH8zlk/5Z19a6cYQ5TzoytPmGpJM237zOq7WXZVyz+0+W0NtZs6R/wqlKtpDIqW077m8rb935t1W7ezn2kb12t8ryM33v92uSPvR8zeXMSaL++kWZ7mNVb5nXb8y/7Ndhpa/PCJip3OzNtaue0DR/JmaR4YXTc3zK/wB7/errNPtx/wAuyLv2bZfL/wDZa9DDx9nozz63vF/TYUmupU+XYyfe/iq+s3k27w/MHX5VVUVd397dTLNfJtfORI9rNtXbUL33l25e5hkcM+3y12/u69WjzchleMd5FDVtkyp5IaNlbc/y/eWsa6vJmke2mCpu2tE0O1l/4FWzqypMreTN5X+1t+9WDeW+2GW5877r/N8n3q76MYx+Ixl7usTxr9ry5SbwbYRq6fJq4BVRjH7p6t/s+7IfhJZ3BKbvNnVYwMtJ+9aqX7WitH4F01DG+G1UMJD0b909T/AVifhHZeYAqrczbXZeD+8av02uo/8AEOqK/wCnz/KZNf3ssT/vf5nRXk00cbeTbbtz7Wbf8y1zWtXCNvd9zqv+1t3NWzq2qTfZzM+7Cqyuqr81cReXh2PMiY3P97+9Xw0o80fdPClPmlcZcXlyqpD9p2o25n/2f9mtTR7qC1U39/8AcjXc0i/+g1z8KxXTsltCwDJu8z/a3VzPxS+IVhar/YmlTfLbu32iTf8AKzf3q5q38py1cR7pmfFjx1Y315c6lDNsaT5tq/Mq/wC7XgnjLxR9uvHTfWx488Xi4m2I+fk21wVxJNeXuxEVtzfepf3Ynn8vKWo9VeNd/nMqr/dqaHzpG/iwy7v71XPD/hO8u1+SHesj7a6ZvBc2nxp50O3bt+WtOUXNA5OOH7u98/J8lVb6Z1kbftRfu/N/FW/rS21qzQJ9/wC9t2fdrB1JUm/1iK23726okXH4jHut8jHsG/iqsqv9zYuavtDu83YNu6q3k4VPk27vvNupf4S/hI/Oz8nzf3vlq9otw8c3yN8q/NVWb5Y+P92kt2cfOgzt/wDQqfwknq3gm8m1aNIXwzt8vzPtrudN8HpeRsh5f/a/hryTwVrH2O6imd+V217X4U1JLi1WaF9yyLt3L822nGRnUjzRMi+0V7JktX2v8/8AD/DS22j3O4PMn7r5tu6tiRfOum3wsqxytubZ95qmjtbZmXYjLt/8ep/DqYxlymZplv5V1/qd43/drpbFX8lUTakX95az1strKmzKq38P8VXlk+x27b/92ko8pUve95HH+NLxNNvn2J8sku5mavNfiRoM32WLW9Nk+ddzPtru/ixG9vawzJcs4372rltL1ZNWt5LDerBv4ZE+6tHLyyNIylKBQ8D+Jk1ix8l7r/SY0+61dMsbyK6fe+TcyrXkurC88D+LHEKMg37tv+zXqXhPUodcs01JH+795dtL+6El9oWSHdOpT5NqfeaoMosg/wDQa1ry3i2siI336z544wqo83z/AN6qjICpa6tc+B/FmneKtMm8rybhWeav0p/Zp+KGm/FbwXZakjxyyyQK0reav3l/hr85rjS7LVrF9NdlH7ptv8W5q9o/4JzfFS58G/EqL4XeJLz7PbXlxst2b/no33f++q66MvcPKxmH5vhP01+FOr6n4b1xLhkVYWlUSs393+7XrWkeHdI8UanBpOuTbLdpVZ2AyeD2rhfDHhqYaUljO/3F+VW/hqv+1B8RtW+D/wCzP8QfiroVvJLe6F4RvJ7RIw2fNdfJQ/Lzw0oP4V+VcdUuXjPI0us5/nTPxbjOTnxxkMZ9Kk/zpnwX+yj+0je/Hb/gsF4h8UaTrNxBpWoalqtlY6fE5Eb2VtaypEGHfBjDj3Iq/wD8FltK0y/+P/heW9sUkZfBwxK3VQLqc4Hrk9u+K+d/+CN3nf8ADfPhJppt7Np+qNI7feZ2sZmNfQH/AAWa1OK2/aO8J2UjAeZ4OUgH/r7uKwxy5fFvD/8AYO/zqHs46C/4jdhIx/6BX+dQ8/8A+CfnxK/4Ur+0N4e1jQUhhSa9VLppPvbf4f8Ax6v1l/4OmrfR/jD/AMEh/h9+0JprxvNoPjTS7iKZf4fOjaORf++lWvxD8K6g9jrFpqNtcqklvKrrI33l21+unxe+J9j+1P8A8G5fxL+F2rX8eoa34R0uPVbbb8zfuZlk3f8AfO6v1GMpQrJn7BGUbuB43/wRy1BNT/ZElu43Vs+K7sEr6+Tb1+cniGaez8U35hRn3ahKu3/gZr7/AP8Aghjd/bP2Krh9pG3xpejB7f6Pan+tfJPhr9nX4gfGXxnd2Xw78PTalc3V5L5VrZxb5Opr8q4Oqxp8YZ3Kb+3D85n4/wAAQk+PuIYx/wCflP8AOocFJMl1Z+datHvX/b+7XU+AfGia1a/2Tc3K+bC3yN/FJ/s1yd54d1Xwrq1zoPiGwuLS5t5ZIpYbqJkkWRfvblrmhrT+GfFiebuWOR12t/tV+nLklHmiftOHqTjPl+yex6t5M0azJHu+dvl3fdrnr5XaTzt8brv3bf4q39FjTXNJTVbYfMyMzeXt+Vqo3Gjw+csMMjMnzNK2z+L+9RKpy+6eh7GMveMUzSGZnh8wL/B5jbqg8yaP9yPmDPudmatB9DEe6ZNzqvzI1V7rSfLV5nfyX2fP/tVlKQ40e5myttuN7MTgjJzTbi6e437EUbf/AB2nyxkXRiGT8wAzUsOiTLcHYjbG+bdtr73jtyjlmVtf8+Y/lE9zMIc0KC/u/wCRHb3V20OyZPl/hZa3tNmRY4YXRm+f52X71U7OwfzHfZsRU/76atCztblbhYUP+06t/dr8wqSnynLTpwOm0+8eVdibgP7v8W2uo0rWP3cLwuybf9av8VcbpcE32ddkfzN/t7dtdRof75ftMKM0f97Ztb5a4alb2Op6NPCxkfZX/BPu/wDt+l+KGLuzLcWgJcYP3Za+Lv2oJfL/AG4fEkuPu+MkPzDPR0r7J/4J1TGbRfFGXyFms1H/AHzNXxh+1EAn7avijv8A8Vlnn/rotdHgnUcvFDPJf9Qv/wAqPxfgil7Px24gh/06pf8ApFI9rt9UmmjV/lYr/wAtG/h/4DU76kluDNZzSSfw/wB35qyLWbypm+0vvLfxf3avafGkzRTO+51Rmbcvy15U8d7x+9Ry6Uo8xpRzIypMiM0i/M6rLtpLe6dpnmtvMCfdlb+9TrWzh2pNDyWXa277y1LJp80mHQsHj/hX+Jf9qsJY73jdZf7g37Vu2I77Vkbb/s0TK90uy5uNrRo25fu7V/vUs1rObiW2Taibd3+z/wDtVHNC6zJ5Kf8ALJl3SL83+7W9GtKp9oyqYeMYbFO+t5jH9pd9vz7nVfm3LXO6lbO0LzTQx5kRlRVSurms3hhcwvgq+5lX+FqzrrTYbiEzOjff/e/w/LXo4apyx96R5VajzaHCahvj2oltGrbPuyJ8tY+oB7W684bkDMrfL/D/ALtdlqmh213I6J5m3du+b7tUH0VIXTfDuVvv7fmWvVjUj8R51TD/AMxyN0qSWqzQp92fakitt3VUmi/fM6bst99leupuvDUMcbGb5H3fLu/iaqF5Yvp6+dDNudVVNzJu3f7Vac3MY/V5c/umDDYJCqoib/M+X5q0rFUiUI8Kq6/Ju3fdp7ae/nO+/O59u3bU9jY5VbZE+T7yMybq5q2x0U6PLPlLtrdFdqPDu2/fjbdu3bq6jR7yFY9m/Lbl/eSPXLWsLxqU87D/AC7tz/d/4FWxp1w/yWzvvZU/dM33f9mvNkejGnKKuzrbiHa4ufJXer7k3LVK7uf3iu+3f/EuyuhvNPdYy80au38O1PurWRqWnQrH9smdUTb97Z/FXx/2j9Io05OPunNXH763V3eMlf4m+Va57Vv3krTI+FV90q+V8zf7rV1WtR2atshhyG+bbJ/FWJqkdzNHvdG3Km5YVb5a6Kfuns4XC8xx99fhRsSbLx/MirFWPfb2Ztm1Wb/broNWs/3bpDuTcnztu+7WXqEcMislykZZVVWkX71bU6ns/dPoMPl/N8Ry2pWszTK6OrPH9xay9SgfcIZnVGZfu/xV02orZwr9p81tm/ckapuVawb6ODPk+SzOzf6zZ92u6jW5hYrLY8pizWLrGU3qrq+1N1RQ280jfZnTcyuvzbKvyBIRs3/LH8395t1PtbERqdkbZX+Jv4q9anW5Ye8fCZtl/LzD9Ps91uvyfNv/ANXvrWt9HuLiMeSN235fLk/iqPT7dFji85Nz/e/vVuW6+bIm/aCyMvy/3f71HtJc2h8ZiKPs4GJ4gsGXQmnSIKkcm3aeq/MKvfDW3ll0i6aORR/pKjBXOeBVrxrHKnhe4j4ZFdCsg/iG4VL8HEll0q5XafLW5LOQP9kV+kYN/wDGqcQ3/wA/1+UDlnb6lK/f/I6ex02GaRYXdfO2/wC7urd0/S3mh+SGRH2bW3fwtS2unQzLHNDYec8e3/Wbd3+0tdD4dsYVkUiFl3bt25Pu1+X+0l8R5dOPtJe8VYdPMkkaeSpZfvN92tBrVIoG32ap8+7du+9/srWlb2aW6uiQRyNJ8sXmPUk1mjN8+13Vm2/L8q1yyqe9zHfRpxjqjnG02ZSNm1ZP+WrbvmX+78tL9l/dmwmjV5Nu75f4V/iram0m5mVZkTcfK/v7W+9SR6S6zY8jYqurM33lVf8AaqJS9pG0jpo9jA+x/Z2S5h6bPmVar6lZ+ZvcWcf+xtf7y/3q6GO1Rmd0mVY2dvlX5t3+1VW4sYXUTQvtSb5dzfLub/2WuaUvfPRp80o+6cdeaa8rSXkz5DNt+b7vy/7NZ01j8y/ZtpRf9iuzutFf7QqII938Ss/yrVD+y7NYWh8nc7Ssqybfmpfu+bm6Gko1ZRscutj53yPCyBfu7fm3Ulrpf399uzFf4W+9troG0uFlleF2Tb95tv3qht7Obzh5O3H+0v3v9mplKHMEYyIbS1ma48l3bau19rfNWiu+aQIibm+b7v8A6DSw281r86W33n3P/tLVz7Km5JnbeFf73/xVYHbGMolaGwgtW2OnlN82/wDi+7Vmyt/9K86GH7qbk/2t1RSWrx3DXP2Zn3Myoqtu3Vq6LD9paPem8KjK7b/m/wArTpylGO/um3xT5TW0XRkkVLZHZV27q3bPw/JNG5k+4qfxfxU3w/ZwxL+++633GZ/4f4a0PFSnTdDnuYXZz/BHH/eqsPGdbERgjatiKWEoSqzlpE86+JXxm0rwjqz+G7O8jm1KR1ZIfK+a3X+63+1TvidqnhvxB4dt9BTSo4jeW/z/AGhfnX5fvLXzP8XG8VfD/wCKB8Y+JIZFa4uN0qs7fM3/AOzXqfxG+IthfaHoHja3vIZre4g+6qNth/h2195hMLHCUrLc/IM4zCrmuKlUcvd+yeBeMvD8/h3xFcWaOyfvflb/AGadZ3B+zq6ffX5Uk/vV1vxqtbK+8rxPpTqUuIt3ypuWNv7tcPaXD3Cq+FLr/Cvy13a/EeTze4dT4X16ZZlhSbad+7cz/drj/wBoaGGa4jvrZ2zJ95d9aEN4mns0yIynb/f+7XPfES4TVLPe1zwqbfmb5qmXvFR944ax1S5hVbO6fen8G6qt4qNJvhdlVvmpZn+yzB0TIqK6uElj2JB/H95qfLy6G0ZcxG1w8O7ZNtol1KZY137mG3+H+Kq0ioyb1RvvVFI25fuLlf71P4Yh7xb+0PGy79xVV/ioj1Tcw3j5fvJVBrrapR+v95aiaZmVZM/Nv3UiuXlNpfECBv8AWbXVPu1FNrjspSG6Zf7lZPnP995Of7396iNkO133Ntqvsi5TTj1S7Xc4mb5vv/NTzrUyxj99v2/xNWXLM+40n2l1hx8pqRcsjSk1y5upCXfKt/DVa6uEbCeTGKpPI7oH37ab5mVX92x20D5S00lvuRPu12Ok3n/CK+G1nhm33GoKyK3/ADzj/iauL063828TeGP97dWhfak+qXS5XEUa7Il3/wANVJ+7Zi5ffOw0XW5mVXd8hfu16v8ACOP+1NQHnLuVvu/7VeIeHYIbqZF34H+zXuPw52afp6+T8pVPnZv4adoRjzGMz1PxZ4ytrPw+1s6MI7eDbEq/L83/AMTXx1461K58ReOri8D7/Ml+6te5/GbxhDpfhd4YbyRWkRl2/wCzXhvgW1m1LxK0yQtK7bflqFzSqjjy04czP1o/4Ncf2ANN+Pv7Sx+P3j3QftWieCYo7qKGT5o/tzf6n/vnbur7d/4PG/jZeeAv+CenhH4L6bctG3j7x9DDdKkm3/R7WNptrD+7u2/lX0l/wb/fspwfs0/sEeG77UNK+zar4qiXU9QMi/OysP3ea/PH/g9R195fGv7P/gyYM9v5WrXrR9i26Na6cXPlXIvsoyy2MqidR/aZ0H/BFj9q+w8F/sceHfDGtwyNHawLAtvCu1o1Vm2/N/E1fSfj747aD4i8OvqWheJIZz8yy2tv+7aP/ZZa+V/+CQ/wP8PeIP2YdK0HW3ZluPLnimkg/wBSzN83zV9FfGT/AIJ3a94V8O3njD4e+PWhm/4+PJvnXymX/rpX4dmEadTGzmj9roS9nhYX/lPnr4r/ABY0rUJJofsapNM7NdeZF93b/davnTx94luY7h4dH1GO4SHc0qxy/Mq1pfFS6+KPhnxXeaJr3hu+UMn+uhTdFIrfxLJ91q8zvNB8SaxOXunaHzl+8tqzN/wKjD4X3eY29tGMP7xw3xA+JE11K8P2+Pyo23Jt+9/us1VvAsl5rF+7yPcLbXUW26jVd37v/aWvRV/Zuh1CRdSvHjuEmT5VaBo91Z2oeC9e8FyzWemoy2ypuRrdWb7v96u7kjTiKE6/xHnNxefELwXrjX/hjWJoGt3/ANH8uXbuWuR1DwH4S+IVx/ZXiSzh07V5pWb7ZH8qtu+7uWvXPEjf8JZp++wRpdUjTfAy/Lu2/wALLXnXjDTbC88PJ4nsLxVuoZWS6t2+Vo2/3azw9SdCb5NDqxnNiYa+8eT+Ivg1418J+MP+EbcKJIXVorjf8jK33Wo+KE0Om2cGlS3ML3Kuu5oX3bqueMvFWsalCXvL+SR40+Vmf+GuG0NpvEWsLNczK0Ub5/eLX0OH9piuWrU+yfD4zlw8/ZL7R6F4XtUtfD8EyfwszOrfeatSfWnWNbbZCy7P3W771Z0ew2+x9zbk27f4alj01PLT9y26NfkrlrSjGfMc8nyqyJ/7L+2Rrcpy38Sqv3a07G18nB+b/Z3f3qz7H7TZ7/kZUZl2tv8AvNWxDIi7Hfb5rfe8z71cOJqe6dODlGJd0vUrPT5l2R/PI/zbn+7XtvwH8ZQ280afbFdvN3PG3y7q+dbnUnaYTSQ7iv8AEv8AdrV8J+Nn0e+iudm143+XzH+WvIxGFqVaTlE9fB4ylTn7x+gMd1YeNdN8maGFVki/1n93+7Tvh78If7N1Kb7HcyXK7laXd93/AIDXzh8K/wBoe5aG3trm83Mr/NH/AAtX0z8Efi9pSrD9peSTcN0u5vmXd/s140oyjHln7sT6mhSw+JXMfYv7I/hrxmz21jY2cchkbdaq275Vr6njX41XOlQabNDYwpsZHaP5tv8Adr5q/Zy/aI8H6ettc39syBU2I1r8tfVvgf4raV4xjjW2H2eMJ/rJvm3f7NVh/YOPLzHPjMHXw/7xQ5onJav8IvDfhfxHpvjzxm8eoSaLFJPptnIF2/aGX5pP96vzd/aO1bxJ+0V8bPEFnDbf67dBFpq/K0O3+H5f4v4q/Rv9qT4naP4H8KXN/wCSt1NDF5rLJ93b/vV+UPiT9ofR/DfxW1L4u6PNaxeXLJLLH5v3tv3fm/vLXY6dO3JH1NsvpwjD21Ve9I6n4I/C1PhX8P8AVNePGpaTfq0se1Vl+X+Jl/2a+3PC3jf4f/tlfspeJv2dr2b7VJrHhqSKzuI4vMZbpV3Rsv8AustfmVb/ALRF/wDGTxh4j8T6DcrD/aH72/s4UZVab7u7/d219af8Ey/ibN8NfiNFJcqv9nvd28KJH83mM393/vqvRoxqUq0aikOtThisJUg4/wCH1Pg+x0nVrOE+G9bhmbUdPuJLW6jWLayyQsyt/wAC+WpljMjeckzP/C67Nu2vpD/gqz8DLf4J/t3+LtL0keVo3iy1h8QaYsfy7jcfLNtb+H5q+f8ATtHSGPyHRQittRt/3lr7ZQ5vikfCU60eW6iMtbFPm8h9u35t38X/AH1WlZ6an2cb/wB8/wA2xtvzL/srT4YbZv3MO50b5l2o38NSxtNJbtC8OzzPl+b+KlGlzaxN5Vox3L2j+T5Mz222Vo9q/M/3f71dDZt+7/fbk/h/d/N/FWTplr58aIjsrtKrPtret4YVt9/zMF3bd3y/726vTw9OMYe8cMpSqSLKratDs+zeX5f3Y/u1WkjtlQzIm7a6t5bfeapobeG8kLzbmCvt8vduVl2/K26mXFukEP39nmfxN8zV204hHmqFDWLWGGVneZUXY3zN83lrWFeLAu6NNuViXdu/iX+GtvVGZrGa2fcnmL95XVl2/wC7XO3Vw80ywpNv/iiZl/hrup/H7xNSnI8d/a9uLeTwdpyQltx1QNIp6KfLek+BUvlfCqyDyAbppvLA+Y58xv4aP2uGik8E6dJHEyt/a+HLLgE+U/T2qL4LXEf/AAqvT4i/ziS4C7l4XMjV+j4n3vDqj/1+f5TFiKcp5cl/e/zNHxRJNDayzQfvXb/4quVtVS+uGghfzfnbcsfzbWrode33ku/5Srfw/wB7bVaxt7axjlvL+ZoY4VZvm+VV/wBqvhJfDaJ4NaMo6md4k8P3Nj4fm8ras1xF/CnzKv8AE1fPnxMtbbRVdPtKlf72/c1dR8Wvj5dS3k32bVfur5Sbfu+XXh3i7xjNr119pd/m/wBl65+aXMeV/E1MbV7p7i43xbX3ff210Pw5+H9/4mvoURGJ3bvuVleGdFfWNURI0YvI235a+r/g38LbPwH4ZTxPqqRpK0Xy7ot1aROapL3eVGTovwt03w1o7T3iRq6ruVWT5q8/+JuuWGmySW1tNGx/hrq/i58WnjjaG1fBjXaiqleC+JPEn9sXD+e7E7/vNWcqnNMunRjH3hLy8e6YzPNnd/s/eqhdNGVbZuLf7tMt5Eddjux/3fu1DeM8WecCqjE0+Iinkdl37Mbv4aiZflL43L95qkUJNIHPyq1IkKRs0ezLN93dRzfykx2GtH50Pn+TtWn29ukkfyJu/wCA0qRtu2J93+OrNrsjt9n/AKDUyiV8MA03zrW4V0TcytXsvwr8UTNYrYfK23cyLtryWzhRl3mNlLffrsfh/rX9i6lFvdvK/j+Xc1H2fdIlHmPSGmeZmm37m3btqv8AdqVZE8vzvOb5n3Vj3l5tvN8P3G+b5qmkukt1Lu/y/e2s1PmlEjlj9k6Fbh2bfsV0Vl/iqa4upl5f5tz7kXf8rVnaDdJdx+Sj7d3zfu/4q1bi1CyHftwqbfm/hqiObl+I4v4zfY5NB86GP/VrtbbXkXhfVPJ1Bk3/AMe2vW/jADJ4XdETlXb5mXburwvT7ryb7/7Op5vsmsZc3unUfFzQf7U0mLWLOFdsMXzNH95v96ud+F3jSfQb/wCwPM3lyNhlb7td3pfk+IPD76bM6ncn8NeTa/ps3h7W5YfJZdrfJVyj/KXTlvA+go1S+tzeQ/MP4GWqd9YvGwwn3fuf7NZPwR8XQ65p/wDZV3cqXX5VVq668tN3750ZamMiKn8ph2MYjmVnRWH8FWrq61LSdRtvGHhi58m/tbhZYpF+VtytuWq97CbWUedt/wCA1seFdGTxXdDR5pvJaSL5Jm+6rVVP3ZmOI5ZUj93v+CX15a/tw/APSfiTp9hHPfWMLJq5X70dxEhX5v8AZavS/wBjz4YeBfjP438RfC74k6TDfaLrfgy9tr21nUFXVnh9a/Mb/ggp+0r8W/2dP2pLj4G2esMNH8URvAbeVtsTTKDtZf8Aer9Rv2FdI1PW/i9f2GjXhguj4buDCy9SRLDxX5rxtKa40yKL6Tn+dM/EOPadJccZDOG8pz/OmfnTH/wRy+I/7Av/AAUQ8O/F/wAOac958P8AULrVre1v4hlbJ3s59kLn1OOK+Xf+C7F++mftP+DLpZMY8EKNvr/plxX9FH7UWpQXf7K3iXRNUgRb+z1CzlWORfnQm4jRiPfk5PfdX86H/Be2N2/aM8HkxkofBChiR8o/0u4/xFGbT9r4vYdparDu/wB9TUtxdPxnwilLmTwzt99TQ+b/AAf4wjvoU/fLt/z8tfXn7L/7a+g/AH4G/Ef4e+MDJeaf4m8EX1hZ2O3zI5LiRdsa/wDj1fnhoevTaPfLh9m35f8AZr1bQfE1tr3h823yvIsX3mr9IjKXNE/b6lOPLdH6Uf8ABFXw+3hr9kbUdNONv/Cb3joQOoNta18vfscfteeP/gF8W08YeANVjtJhqk0V4zQK26FiVbbu+78tfVH/AARju57r9km+ExJ8rxpeRpn+6ILbFfl14E8WR2vjm+ga8xt1Gfdt/h/eNX5Twvh6eJ4uz2nPZzh+cz8d8Np1KfHnEEo7qdP86h+z/wC3F+xn8BP2wP2HfEH7ZPwQ0NrDxh4NsFvdWt4fmS63bVbdt+b7vzV+NvjqzhvtHi1IvuMa7ty/xNX2P8A/+ClX7ZP7Ofwn1j4UfBzxho//AAjviCyZL3S9S05ZdrMu3zN33vu18oeJ7G5utNuE1W8a4nm8x55NqrukZtzV+lYCnUw2HVGfT4X5H7E43rurF7/F6nafAPWPtmkm2h2lpEWX/e/2q7vUNDtpZN6W0mfvbq8d/Zqurmz1KGF4YwscvlNGz7vlr6Em0W5+0eTvV3+7/urSqRlTme/g/wB7SOMutN2nfN8iL8u2qt1oZ2/uYZAv8G75mrvJtH8yApLCrfNtSTZ/6FVZtDuVjf8Acq+1FXc1YnXUoxitDyDU7IW3ik2JycXCA84znH+NdJDou6R9n+8ir/drM8R2rR/E37JIpJ+2wKQe+QlenWvhm2hcoE2V+i8dOSyzKrf8+I/+kxPSxlPmp0v8P+Rxi6O8ceyaHbt/harEOl/vmeL5kb5WXb92ulPhkys7/d+b5lbd81EekvDtT5kT7u2vzWpE56MeUoWNjDbqtt5PzyfK+2tPTbXdH9md2VF/hVv4qkt9NgSZo9m7+KLzlb/0KtbTbFPOd3hjKrL93+KvHxEYy5j1sPzdD6k/4J1xumjeKDIgVvPswwHqFmr40/afjMn7bviWIn73jNRk89ZEr7S/4J8RRQaP4nggfcgntCPqVlJr4u/adGP24PEmM/8AI6L7f8tEro8EI8viZniX/QI//cR+IcGXfj5xDf8A590f/SKR6/DpqQts3yF2Zvl/2f71a2k6bDM29Jv4d3y/dptiyNEzhFDN8q/N/DWro+n/ACjZDhPmZP8Aer5CUvftI/piMYdC5pen21vMiffMjfJui/iq3dWqMx+xw/OrN5rK/wB2pIfOjVCjxp8nyN95dtTzeS0ghd9ryfM7RrtXb/DUKPNPmKl7sOXlMWSzeSEvDbbFVd3zfxVAsSTL9phfejfL8r1sfY7b+CZXDfLuZ/utVeSxhVJQm4fw+Yq7f++a9KhWOOtR90ypI3eFrZ03Ffn3bPvNWfNYvdTRpcoq7vuN91f+BVsNZ+TIz3qKkOzd9+qs1nCs2x03pN8yKv8AFXq0pcsjyJUYVPdMi+0ubyW+x/K7f8s6oTaO6yedMiqdvzMv8NdK1qjMu+1aFI227f71U9TtXWP7GZlRW/ib7q/7NdlOt7vKZLCRlqznZNHtvL2IF3zff8xd1ZWpafDJDsRFU7Pk8v5dv/xVdTcaX8rQ7Nx/2m/eVj39ukcIdNwKt8qq+7d/wGtPaT5rI6I4OlzfCclqNnN8kLwSKW+WJo/lakks0t5Fh+ZH+ZvL2feWt2+sfMm+ezZ9r7k2tt+apre23XDfafLkfyvvN97dVVKnu+8Zf2fH2pg2ulzW9qYUSRt3z/vvm3N/s1q6bprxsJvJZNy7XjVvlWr9vpM3lq+9VZW3fLXT6Lprz2YdIdvmS7tzfL8tcvOa/U/cLUzJcRh08tXb5U2/NuaoGh+2RmGS22uvzMsi/eWrksfnKZPOVWj+Vd38NPjZLhfOh8wN915G+9tr5ONOUY3PssPUjKqcxqFqisf7iv8AM0ibtq/3awNWsNsfnTOqJJ93b821a7e4jeSGSaZFKRru+/8Aerm9XsU3F54lUsu5/wDZ/wB2uqNOWx9Ng5cpw+oaejNshsPNNYurRXO5ilmsfmJubd83/Aa7S8sE8szIGPzNsXZWLqml3iwrNNuDyblVmXarUSpykfV4GUTi76GG3tX+Ta33XX+7WJLaorcv8vzfKyferpNYsZljPKll+b5v4q55udybPuoy/wDAqVPmpx0PQxFOlKhzGbfW6NCN6Kn9+mRr5ZXyeVb7tXZrF5o/J3r8z/w1EqvCr/J975W3fw16uHqc3KfnmcUepa0uGHy2MM29v7u6tXT7q2W4KSIxLJtT+8tYizTQyND9zcu1P7zVcs9QdWdHSQfwJu27mr0KPN8R+XZpLlnylzxoFHhCZHch1MeBu5I3DrVr4Juy6PchYhj7X8zlsYG1azPGN/5vhyaJoWAZ1CM3swrS+CN0IdOuFkOEW637sdDtFfpVFTj4UYjv7dflA8164GXr/kenaGrxyr91V3fO38Vdbp9mZpBcwu2z/c+asHR4UXF55q4/i/2a6eykeNY/szxiJduxll+Zv71fkVeXvXIwuFqy+It28cLYT7Mq/J8rKvzNVhbNJrhgnyI3zKrN91aTSY3LH982/ftbc1asNvC7OkyLhUX5l2tuauapU+GMT1o4Xl94zY9PTcn+1/tVT1a1SFJnhh3N/F5b/LXSRui/vnRcsm1F2fM1Z941tcRukKY/hlj/AL1XTkKUY09jmrlblo3+zJHv/jaOqElrtY3O9Vf7rfJ8tbt5pLwt51rCrt/zz+7VGRX8kOEUfIypuTdRUpwLoy5TLurPz1KO/wAv+z/d/vVX+zorGZ4Nj/8ATP8AhrRuIZoY/Mfy8/d3L/dqBvLjXzvmR9m1m/vbqhR+yi/be97xkXkcMi70WTdu27V+XbVH7PDHv8l2V1bcvz1t30M0f7m8dmEe77v/AKDurJms32uf4m/5ZyP92lKMYl83MRtvuGLo+G2fLGy/N/vVbXzlkMKO2GTczMv8VU2Z5JESbd8vzOq1d0+6hZZUTcX2syqv3lWuaUZnXTlHmLkVrtmCfeXf8kn3WWtnR7RIVT9zHlW/hT71V9F2TD5EXG352k+Xb/tLW7aqhZbJIY3SNWbcv+1XNUk4y5Trpx5i9Y2627xzNbRvJH/yzZ6o+Hfi/oll4yu9E1KzWaOGDY0cifMzVbbVbCxs7i8ufl8u32oq/Krf8CrzDS9JfVtYudVheNdrM3y/KslfX5Dgeb99I+F4pzSL/wBkh8ztv2lfDvgb43/DW80rStBji1G1l3W80lrtdmVf7y18d/Dm8vJvDuq/ArxOjW17Hun0hpIv3ny/w19b6L4mttD1aJLmOZ5W27o5G+7XmX7XXwzf7VafGPwNZquo6bL59wsK/LMq/e3V9R8VI+EjKcUeP/DPVrbxRpdx4D1L5mkVlt5GT5vMX+9XH+KNFv8Awrrk0PQx/I25vu1f8SahbaH4oi8Z6I/7u6VX2x/Ltb+Ja2/iFqlh46t7fxClgsRVF3f7TVnGXKXGPL8JyEfktZrv+Xb83zNurlPEVxC6vC/zVv6heJCXtvm3f3v4a43XriG4kfzudtUXGJi30O5ZE3/LVPzPl/d7WFXLry1Yon9/+/VGNf3jQIm3+9VfEaxEaHd8+/5fvMrVDcL/AHz/AN8ir01u6xhPmcN/DVS6X/pn8y1JRQk3s77D92o5G3NUt0dzbAnNQfOFJf8AhoHHcMybenytUqrNu2fL92oS2QufWpEk+b3oEOZlX5AFzt+9UXnO3yYpsi7WpY1+YZegfKxfvD5KYN64AfG6phsjPz0WymadExz/AOy0RCO5YaX7PZ70++3ypt/u0trlvn2VBdMjXB2fN/dqS3j8xv8AXbWb+7R8RMjqvDK+W6zQ7VNereF7i/mt/wDXKqKleU+H1+yLHNM6tt/8erurPxNM1mlnZW2z/gdae7GJhKPMVfi1qW6xKTTK7L9ytz9gf4W3Pxk/aM8K+CbZP32reI7WCJW+Zf8AWL8teX/ETU5pLzZNNuP8K19rf8G/XgP/AIS7/goB8PnmhUxWuqfaHb/dXdWmFj+8Rji5ezwzP6yPhZ4VsPA/gTSPCGmW/lQaZp0NtGq/7KqtfhJ/wepWMw+KHwA1XYpi+wapB97adzSR1+9ukXmLVXfcPl/ir8U/+DzH4eT+Jv2ffhZ8XLaLdH4a8VzWt5Iq7gi3C/Llv+A1jXhOXOb4KtSpxgjD/wCCXfjy20z4I6Q8MzbobWP9ysv3vl+9X094y/aqurq3uLN0W4SO32eTMn3d1fmb/wAE5fi4+n/BHS7XYrmOLa90vy/L/dr13W/jVNdXDJc3LLFv2ttX5mWvxLF04xxs4n7Xgqt8PCoe/ahr3wi8QWtz9vtmiuWib5VRWS3+b5flryrxdqHwstMv/ocskdwq7VRV3f8AAa8r8TfEV1sZhDeTRCRP+Wdxt27fu14j44+IVzdahNczaw0sy/8AH02/bu/u1pRVX4eYKkqHPzSPW/it4/8ACuj295fWdnDEZH+9HdbmjZfu7V3fLXzZ8SPjVeawzaVbTzEruDtb3Gz7397bWF44+JV/qUctnDbWs0U3+tjuPvf726uFXXJPtD3lnarDF8vyxtXo4ejVl8WxwVMbD4YHt3wouvD3h+1tPE/jCS3RIU3J5jMy/wC7trh/2iPGHgDxxrj6r4P8MLYX33bq4tXZVuP95fu159rPjS4nhaze5wP+ee/dWZpOrC8vFL7n3N87N81aRwco80mwxGaU/ZckDI8Waff3Gn3CbFRl/ut96uX8Kxww3yx7GO376r/FXqfiHS7OTT5P9J3S/d2/7NcLDov9nXbzIdo27t1enha0fq0onymOlKpV5zqLGTy7dBCjN/DtkqZtRmjZ/wDQ1VN+1Pn+7WFodxMqtsh+Xdu+9UtzfTeW38KN95mrnlStLWJPtI8vvGtNqUKnf9wr/wChf3qBriSN9/Ib5VZf/Zq55rpJo1fq33f9mpF1J4WWPfuX7rqv8NZSw/NGxEcVaRvTfv1KOdy7fvR/3qqzb5Ljc7/d/hVPvUumXELRomxd27c7bvu1p3K2f2NUWFd0f3q4pS5Jcp0+05/ecg8L69d6bf8A+u+Xcu3c9fQXwZ8fbrpLY37GWR925X/8dr5mjm8u43xpu3fxNXoPwu1aaPVETfwrKtedmGGjUjzHrZPmE6deMZS90/Sz9mXxpDNJvv7yRF8pnSOT+Jq+zfgd4+m2+dc3kh0+P960MnyLH/e+avzw/Zo1p41S2dJLj5V2KvyqzbfvV7J4u/aU0fw7o8XhvTb/AHwQsrazcM7bW/i2x/7tfMUVOeItE/SaWKhiMMX/APgp1+254q+I92PgF8FNT0uG3h3Lq95LLtnk3f8ALNf7u2vyk+K0Pj9r6XStV+0TfZ5WiTbuVWZfvf71cj4t/aA1rVv2h/FHjbVNakX+0PEVw8CK+1fJ3fu//HVr3HT/AI4fDfxV4VtodS1JWuo5du2aL7q/xfNX3EMF/Z0YuceZ/wAx8x7SljY/u58vL9k8u+Cfxe174b+Plie5kjWaVYrqFn+Vo2/2a/Wn/gnHcw/FL4q6HpNpYeWt226JvK2xLtb5W/2flr8sfixN4G1rUIde0GzjSaOVf3y/xL93bX39/wAEmPi+3hCfTda0+8ju72wRoVj+80a7t21WqMTXoUXGrym+WxxM3Uw/N732T17/AILo6xpVx+174R0DTZY2aw8GyWQk2/e8uRd3/fLV8fWLQ3F0YXs2tt33F/56f7Vdf/wWR/aB/tP9uH4dWrXDJP8A8I5dXF/5kvzeXNIu3cv/AAGuUsbW5mZXjDOuxW+b7zf3a+vwC+tYaNb+Y+Lx/wDsWJeGX2Lc3qaEFvMqoEST5t2xW+63+zU9rp/lyOyJGrK/3WXdtqWHT0j8uF5l3tu3sv3t392ren2McfyImx2+baybv+BNXq08Pyx904faSqBptqjxpc9X+bf8m1av2c2UCIjBFdl3f+hU21tdPt2VN8iN91V3/LVi4htpIz50zJtl3blf5VWuyFPmj8JUOYmt2SPc/k5TZtZmfb/wGmX+yS3ZLZ1zs27f+ebU+RUuGFs/7z+6ypuVV21DNa3kcbzFGR403MrfxLWnsZRPToRjzWMPUG2k7kZW/gkZfl21i33yyLDJctjft/dp8y1uahGiqz75A33om8r7zVj6lCkkjTPD8q/8s5H27v8AgVbRlyyOiWHjy3keK/tYJLH4M09ZBnOqAhu3+rfp7UnwTgmb4W2dxDjiSZGbuuZGqz+2AkC+DNMMChR/anMe7Ow+W/FTfAewivfg9ZeYuNtzKQ3v5rV+i16kY+HVGUv+fz/KZz4mjzYdRj3F1CH7M2Op+beq/eWvIv2pviYnhvQ4/Bmmu32lvnuLiNv4f7rV7Z4iWHTrG41i8+WGOJpZdy/MqrXxV8SNcvPiL4qub+ESS/apWaJW+9t/h3V8BzSkfJZpL2funnWsaxqWrXTTO+T/AOzVa8P+DtV1i4X7NCz7vvsvzV7j8Df2QPEnxKvkaHSmMe/dub5lr6j8C/sT+D/AflXmseS7Qpvl3fw/7P8AvVp7OMY+8eA8RL4YxPD/ANmX9maCNU8VeJ/3VvCnmttX5v8AdrQ/aM+MlnZzPomlXuyOGLylWP5f3ddx+0V8crDwjo//AAiHhdLW3SHcsrQ/L93/ANmr4t8ceMr/AF7UnuJpmLf3m/irGUvaFU48vvEPibxVf6pdPM8zfN8qr/DtrnbiaSRmf7vzU2S82yHeWZf9mmrMGl++2G/vUoxNo8sizDI6ws/ntn+7/DTJG81wn3T97dSeZtVvvHn+7QzJJu8t/n/gVv7tHw/EHLKIbXkXY/DK/wAjURr5cnkp8/8AEjNTmXzFfI/4FTf4gdnzf5+WrAs+X8w+fcZKnjsQsOzfg/wK1RWEny70hwq/w1oxyRtIs7pu2/7FL4Re5Igs/OjbY6bR93/erpvCtqlxcJvdVLP/ABPWK9vuy+zkfd21ctUmsbiN05C/N/u1Moi/wnfa1DNpdjDco7MfuvVH+1kbAe5Vm/vUybVLm+8OvDI7Y2L/AN9VzsmtbreJ3TB+7/49TlKPLcXwy909H8F6lbNON8y/LXYTNZzK03k79r7XZvl+WvKPBepLDeeQgyGdVb5/lr06S4e4tw6JGA3yp/tf7VXAwqfGc18VY4P+EalTfla+ddRkSPUyn+38rLX0L8UpPs/hcwunzKnzbv4q+cdWn8vUmTvv+asuQ3px5TvPh7q0yyLbPtw1VvjR4V8sf2xbJ8tZ3ge88vUInH9/5a9Q8U6TNr3htE2b9ybqcecqUuWVzxX4f+IpvDuvRzb22syrX0vCya14ft9StgqrInyba+WNZsbnRNTeBk2tG3y19Afs9+Jl1zw39jm+drfadv8Aep/aCpHmjzFq+snh+4n3v4aj0O8mtdUR0Tb+93MzN8tdJrGk+dveF1ZfmaLc33q5q6he1y8O3cr/AMVaP3tDGModT37wV8Rbrwf478MfEvwtqclve215beW8T7fKnR0KMv5V+0n7K3irU/A/xPPizS0ZmsdPeSZVJGY/MjDdPrX5v/8ABI79iX4f/t122ueCNf8AFC2Go2OmNeaIGfrNH8zL+lfov+zut43iTWBYvGHHh+QlZFzuUTwFgB64BP4V+c8aQpy4y4fin7znO/30z8N8QuePHORO2nPO330z6q/bXbR/GH7Ll18SPD8ibbk2YuNi/fDTx/8AswFfzzf8F/dCj0/VvA3i65sZSNQsJ7G2uR9xXhlErA++2YV+8PjTxO+r/sL+KNGlKg2Gq2SRqv8ACv2mPivhH/gpn+wc/wC2F/wSS8XfEHwxp8lx4m+F3i19c02KGPc81t9mhW4jH/AMN/wCox0JLxjoUnv9Xa/GoXinBeMuDqdPqzf41Ez+fK8USRiZPl3f+O10fwv1pFvlhurnKs+35a51pPLtzbTbkZU203RZns9VV0faitu3b/vV+jyjyTP3SNmj9oP+CPtjFYfsr6gkG3bJ4zvHG056wW3+FfjxrN8dE+IWoSQ99Tn3/N93941fr/8A8EZb5tQ/ZAmuXxk+LbwHH/XG3r8hPifafZ/FWoukP3r2Zv8Ax81+YcFf8llnl/54f+3n4v4fPl4/4h/6+Q/OofQHwp16HX9EjeZ/vJt3L8tReKNFkhabftfd9xV/h/2q8w+AvjJ4b5dPm4Rn+7/tV7drkf8AaWnpcIc/J8n/AO1X6Xyz5z9lqe7qc38J9Bn0vxU81t8zSMr/ACv91q+qbfw/5ljbarvWaVol3Mvyt/tV84+B7j+zfEEfneWnmMu9pPu19SeGdNhutLtnsNr7tqv5b/KtY4rue3lMrRkpGX/YcMkkqfvFHzPu2/xVDN4V2/J5Ubf3mb/lov8Aers4tH+d5nT5d/yMv/s1Sw6LNGwdE+Vn+f8A2f8AgNcEZe9zHrcvMfKfje2WH4+G2jiIH9rWo2E5PIjr3K68NzNM3lQ7g3zO33dtePfEW1MP7UZs2YjGv2Sk+n+qr6abRrk3m9E+ZYtsvyfeav0vjmX/AAnZV/14j+UTvxaXJT9Dzf8A4R+6kmk87zPvfutv8NVpvDbq3nXMPzK+7bv+7Xpl54feNmeeFkddvy7fvVUk0Hzo3uUT5vmbd/tf3a/O+Xm945afNE4CPS8q2yZTU1np8zXTRzTL+82/Ls+Za6q80d7OPzntl+9udWXd8tUZrGFZG8vaA38TfeWvLxFOcj1cL8B9CfsCIY9H8TJ5YUG6tmGO4Ilr4m/afKD9t/xKWY7f+EzXJXqP3iV9u/sGqRpfiV/mw09rt3em2Wvh/wDajZ/+G2PE7L1HjIY5/wCmiUeCia8T89X/AFC//Kj8N4Mt/wAR84h/690f/SKR7rYzo9yX35RflK/3a3be4gZXRXZVZ9qfN/6DXIfZ3Zt6JyyK0rf3a0tP1q2t7P8AfbmKy7drL826vjakftSP6cjI6yG68thDM8ZVdo3bt3/Amq1b3XnbYt7LMzt8277y1zun6hu/fIjL83/fVaVrcJJdh9+f3u5G2f8AjtRTjyx90qXvSvI1Jo0bd+5jk2v96P8Aiqq0c0a/6S8aCOVm2t8ystSR3r/LDbQrEi7n3Rv826nyTOy75kUbvl2t8yr/AMCrqo+8Y1olDc8rMhRVSRd3meV/e/haqX2PbdbLa5+TZn/gX+9W1JbvJt2bcfd3KtRL9jhkX541Xf8APui/2a9SnUjGOhwVMP8AaM2GR928wxumz5tvyt/wGotQhs2s1S/+Xb827+Jmq/dQq0bI9tGGkX/lp/CtUpLeOZ0jbkL/AM9P4a6aco81xRjywMi5tdqMHhaRm2/vPustZM0KLM9480Mpk/uxfdrodQs0jxbXMG9P+Wsbfd2/w1lyQ/vPMh3IrLt+5826unm5jqjTj7pjQqjeY/y7G3Km2kj08MqpFbNuVPnZm2sq1oNabbpoQ7MsafI0i/N/wKp1t3W4jf7qSfeWplLlL9l3ZDpFgjSI9zCu1fkXd/drr9NtbPzIrb7q/eXdL8rVg2awtb+cltGjrF8jK/y/7tdFoslnbsu+H55PufJ92sIx5pkSp8sbGZZKlvv+zTb337lb/wBmq/CryRiW5hVfLTc26sqORGm+R12Ku1G3bflresY0aWP596yfLtb+7/tVzfVeWPvHRg8RDnMnUNJ3XGy2dWWRd37n7tUb7T4Vgl3vtaZ9qsy7ttdZHapC3yfM0K7UkVfu1TvLNLiObzrVmVvmt41/ho9n7sUfV4PER+ycBcaNC3yfZm/h3x/dasbVNLf7QyTI0iqjNEzS/davQdS0+bavnfKyurbVSse80W1W38mGzW2VpW+9/wCPVlUo9T6nD4rl1PLtc0P7RbnZtG75trNXDataw290d8Krui2o0f3a9e1yxhjV0hRXiVtiySJtri/EWk7ZtlsmPL/i2fK1c0o8sublPSljoSh7xxNzboy7Nm0fxsv8VZ9xJvk2SIyeZ8yK38VbWqWrpcC2Ty0/vs1Zd1bw25NsjszRv8+7+KunDx5veR8Zn1TlgVwqbm877sf3FZ/mWiO923CTeZG3l7vvfwtUd0IoVZEdUP8Adqs0c1wz3ifJ8m7cq/w17uHjGUD8mzKXNP3TR164aTQZkLgglcben3hW58FryK3sZ43bbm6yWb7uNorlb850RyZCzFlJJb3Fanw2uYoba4SVBhnwGJxjgV+kYdcvhViF/wBP1+UCMHRlUocvmeyaLrDtGheZcSJ/F/8AE102m6pA0IhSZd8m1d2z/wBBrzTTdQkENuiOxdUrptPvm27Lz73yrEy1+OVOSXxHtUcDVh8UTvPtzSS/vo2Pz/J/tVsae0NxiHftVYvk/wB6uJtdYe3aLzpGKtKqbfvbf9pq6O01aFd/kxeay7WT5tu3/arHlt0OyWDlGJ1Uf7uPZNNGA235v4v+A1X1C38uHzvlVW+638VVk1KGaPY+VkkShbxPL8zeu2ZMMzVpHm5tTixOF5Y35SlqE2+OJ/3ZWNWWX+H5axrrydwff8u/b8rVq3lxAtmyOihI32qyr81Yl5I80gffGGVfu7K6/ZwlqePUlKjqVbq+mtZJIURW+ba+3+Gs/wC2PHy6MXX7m5/lp80iWcjJvVmk+b5j/FVKS8e0kV5kWTav+pb7n+9WHKiVUlPUfNM9yURH37fmdd/3apyN5kD3mWDq23cy/danLqE11KERNzfwxr/FWVeXDqH83cPn+eiVP3fdOiNSPNeQyRXS6R3+dv7yvWxpM20bIYdyfw7fvf7VYfnvFcLs3Ju/8d/vNW1prXJlXztpVU2p5bVxVo8ux1YWUZVToLHfCy7H3xSMq7W/8dWtm1vIbOP55PJb5lbbXPWNu9xIttDtkVnX5f7tQePPFFtouht9pvPKS6dki+T+Jaww+Hli60YI68XjaWBwsqjOT+KHjy/1BprDR7zYsKsyKr/Nt/vVrfs6+KvtWl6lDqrwr5O37v3mWuIWG2kVr+8f7yt91PmkrG+H/ib/AIR3T/ENn50m9n82Ja/SsLTjh6MYwPx7GYieKrSnP7R1fxF8fQ2/iK6j0122t/qGk+bb/u1JceOJvE3hlrb7Z5jNb7Ghb7v3a8p1TxtDrUseqo8bo3+qX+Fap2fjS80eG4f7TvRvmZW/h/2a1jGX2jDl5Y2Oe8aeGb/RWHh7WU2rdbp9OZf7u6l+Hd081jP4e1LazNuaD5Pm3Vw/i74l6lrnirztQvJH8tv3W5/lX/ZrW0XXNl9Hre/bL/y1VXqpckpFR5x/iLRbnSZHSR87nZv93/Zrg/EEiNMyeX8396vXPGVvZ61Cmq2D/My7pY68g8bQzW96Uf5P92lKMTSPwmZb/LcL91tz02aRFun+6GZ/u/xVHpt2i3A85MfP8lSzMkmpP5L7hu/uVPNyl+6IzJtLuGz/AA1XuIXjjbZ8zN83zVa+zlpFmR/vfc/2aWaF1+eR97VXKL4TBuo9sm933H+7Uci7fkKYrU1CzSN/n24b7tZ8jOq/7v8AeqYFRIFXHJpfu/OvVad96Ty+opu5P8mgYrSPu+Y4+amt/fBprM+Pubv9qlb5iv8ADVcpXKxxbzBzVizCJGWzh24/4DUKybV+4tCOY5Nj/NS5e5Isi7W8xH+ap7OSRm/h3LUc0iLH8n8X8S1e8P2fnSLI7tlX+Zf71VHYn7Js6NsaT9/MrfxbWre/tKO3tdj8bU3Ky1znnpHJshh+T+OoNa1pJrUQwzbNvy/LR8JJR1jUPtmoed90bv8Avmv00/4NpdDs7z9uLQdYv4W2WdrNOk2/7rfw/LX5ebnaRdnzLX6t/wDBuDpM0P7R0GsI+1YbBtjK+3c27/0GtsLH9/ynDmPu0D+mzStRjudJW4hmDhkyrLX5/f8ABf8A+DFt+0D+wF498GtazTX1nYNqOmxr8376H5lavtLQdcubfSVe5h2Lt/4DXjn7S2oaJ4i8M3+n6rbRyi4sJoNsn3W3Ltr0qmH5YTPGp1nTqxkfzX/8E+/ifeQeAJfDc1zJFLD8qK33V2/er6S0fUrnU2CSPv8AL+Xc33l3V8d6v4d1/wDZU/bK8Y/BaeHZHHrcj2ccy/ehkbcrK3/Aq9v0vxV4wnYN5OFb5tyv92vx7PsCqWMk4R1kft2S5l7TARS6HfeNNak0zfZujJu+42/71eFeNvFFzHcTWaTLEkjbpZP4mrT8aePPEjQyJeP+9jbbFIr7lWvHPEuoa3qDSzI7P5m5vv1xUMPVjH3jpxWKjH3iz4i1u2vFab5WdX+ZlfbuWud1LXraPOyHesny/frL1G4v96IX27vvLvrMVbm6ZiiNu+61erRw/NLmmfNYjGSc/dNtb91m3vN/vK1XbPUk8sfJsVX+SsGxs79l+zO+7a392uhtdHRmWOFGZ9mNu2t5KHKc/wBYlL3Tdh1yG8jFrbaarMvyvJv+9WPq1j9nkLzDduX+GrLWt5Z4tk3Ky/M0jfdpmsSfY7UpczRl2T5m31yzjGlK0Tb2nNExmby1V0Rv721npG1BLrG9Mbfl21XutQ8+3VIXUDft+aoPtUKqyJz8lacs6kfeOaVT7JNJc7dyfNs+8lN3O03nw7mT+7TbVZJIW+9u2/dakZkjfyfMyP7q/dqeVmPNzRNfSrwkFJIW3fxN/erTkvN0Zfeu1tvyrWBGrwx+TDw2ytW1ZLhkf7vyfe2fLXNUow+KJcak/hNDSbFJroIiMa9p+BPwn1LxbfRww2Db9y/w/N/wFq88+Hfgn/hINUiT/np935/4q961D4uab8L/AA0nwx8G7U1K4tf+Jteebua3Vf4V/wBqvncbOpKfJT3Pey3Dyl70j0bxJ8WtB+F+kxeAPD1ztu9ipcXi/eVv4trVW8L+JLHxFY/2VDDJHtgZJY2l3Mq/3v8AvmvljxR8SPM8RS3szsfn+9u+61ei/AXxlbah4sT7ZeMqyIq+dD83y/7VdFDK6dGMZH1uHx8f4VI4b9qT9hfVdHjPxF8APNdabfNvWOT/AFit/Ft/2a+dLXw54pj1RdEha6VpH27Y3+ZWr90P2Xfgn4b+MUjeHtSezubOTTZG+y/Z2aRdv/LT/ZrmfG3/AARv+CGh/Gq0+LV/4kXR7O1ZZ5dH2My3TL8zfN/CtfZ4bGYf6rH2j2Pl8VlmK+uSdHm5T8aP7H8ZeCvEU3h7Xtakja3+ae3uH/eR7q+hv2QP2vvD37MDX+va3rd5eLJF+4021b/WTfw/7tc5/wAFbPCNn4F/4KF+OrPTbSOCzvILG6sFh27fLa3Vd3/jtfPVncTRtsRF+X+JvvV6VTJsHj6UZT6nhQzrHZZiZcnxRPRPjn+0H4/+Mn7RMnx98eXKma+2wRRws221t4/9XHX3V8D/ABC/irwDpmvTeXLMsSxfN/u/K1fnP/Z6a5ot1pL/ADOy7k+T+Kvtz/gnn4ntvGHwrSwm2n7Kvz7vvbvutXqfVY06EYQ+GJ5lPG18RjJVKr5pSPe/scMSOiJvMbbpW/u0lrb3LKu12y3y7f8AZq79jluJInhdUaN9jbflXbSra3cLIlzeKZVbf/s1dGjE9CVSYQ2dzDIX6r8qvurSXTX+zs8+0HZ/q1X71EkM00Kvcuqf7Lfwr/wGr0apCsX2l5JfMbZtVPl+9XoRp/DE2oVoop2dncttcTR4VNqbU+Zf97+9Usls7K8PzPuXduZPu1s6fo9gsgms0kDs7PKv92pptPeaF0h+/u/df7tP2cYzPYw8pc3Mcfq+kp5ZmS23L5XyVyd3YzQrvh8yVI/7y7ttei65psMkbTIiq3yqkO9vlb+L5a5bUrU25ZIpo2XYrNIqfxf3f96so0+WV2exH3onzt+2FCi+CNKmkt9sjamMN6r5T1b/AGd7ZT8IbWaT5h5sxxuxj961Wv24LZB8PtHvDEVeTV+64wPKkq9+zXY24+BOn3024oZ7gSqF/wCmzV9zjIy/4hxRUf8An8/ymRKn9k5L4+edcfD+80m3uY1e8aNItrtuZd3zLWH+zz+xJPqE1p4n8VSR2lntZ1+0N83/AAKnftIfFDSfAOvaNprwxuWaSd1VPm+X7u6uFvv23tf1aSDw3pt55NvGipFHu+Wvgqcp04n5znUZVMdKPMfZzeKfAfw50OLRPCUNrE8a7fMj/wDZq8c+N37QGpR2NxpttJGA33JI3/1n+01cVa/EISaGmq3msbnZfuyS/N/wGvC/jZ8YnvmaO1mzu+Xb/dWplKcjy6dOMDmfix44vNY1KR3v2b5q80utQQzN++5/j+eo9a165vrqV3mYnbWYlwm0N/31SjE6eX3C3JdPIzbHwH/iai3kdQO+2q6t8q7P4qtWls8jY/4C1X9kktrIhVXPzNt/76p2141T5Nxqxb6e8Mfk/wAS1HMqRsUQsp/2vutTlsVzdhFXyV85+W/2ah+1bo1H3v77VJ5n7tvuqzLUMKzTSbJpl20f3iPelA0LVnmI/hXf/FV61XbGfO3A7vkqlZyfu8JP83+7V5X3R+Z/Ev8AeolsL7RetdkkYTO6tSGOGPCTIu/+9WFp90jM8n/jrfxVs2cwkVfJ2/N/t1Ev7ocxvW6pNpro6ZKr8irXC3F9NHNKkzqSr/d/u11sN49luhdG2MtcJ4ommtdalhdNoZvkqvc5CYy5fdOo8J6slvIjpu+ZvnWvZbO8mXw7C6fP8ny7v4a+fPD+qPDMm9Nzbq9s8N3H2jw3G6P/ALSbqoifNzcyMT4pXk39izI6NlV3fM1fPesyO2oO2z+Kvdfi1eP/AGPJhN3z/dWvBL2bdcP2+alyo3p7nSeCW/0xU/h3LXu/ht31DRWQdNn3WT71eBeDWeOcP5ny19DfDlnuNFiT5lXZ/wB9US2JkeNfG7wm9ncJfw221G/io/Z78Sr4e8WRedyk37po69S+MHhWHVLF7b7NsVU3q1eCaZPc+G/EazfdaOXdtaj4YExlzR5T601CzhVmdEX5vutHXJa5Y+XIv7rfuVm+98tdH4b1T/hIfD9pqUPzs0S/daq2pWO5i7p8rN8lEeUzlzX5T2z/AIJc/tVa7+yn+1DoXi6xn22kl+iXULP8nlyfupN3/AWav2e/Y28DX3xD+IWt+G9JlVL5/CN6+nu5IUThogmSO2Tj8a/nn8ISvaeKrCO6h25uY/mj/wB4V/Rh/wAE7/FWl+Ff2i4W1W5WJb7SLi1iLMBly0bgDPfCGvzLjOcoca5HOPSc/wA4H4x4gUo1OPOH4T2dSp/7jPF/gL+2Dc/Fj4P/ABC+Eviu1is/EOk6pHp+r2S5+We2vFzjPsDX2H/wTZtdG8S/BvxX4F1+yS4tNT1OSOeGQZWSNreNHU/gRXwH+0j8GfE37L//AAVb8daZJos0fhz4oRvrGkXUMW2DzwvmSJ6Z+Vs474r7h/YUnl8P/BDWPHVlIFl0nxOzTErkGJreEEH2yK7s5Sn4yYWUftYa/wCNQ82o5w8ZMMpbRoNfK9S/6n85H/Baz/gnPrn7Af7b3ib4a22nMnhzWZG1XwfcKvySWsjbvL3f3o2+WvjX7Pc6fJsmTc33fu/dr+rP/gvp+wh4U/4KI/sN3/xZ+HtvFc+Mvh/ZSappDQL+8mhVd00H/fO5q/lv1a18lnhv4eGbbu/iVq/Ta8VUiqq6/F6n7dg67pT9lLb7Pmj9Z/8AgiBOZ/2M7p2OceNb0Z/7YW1fln8UtGmj8Ya3azJ8yahK0WfvffNfqV/wQ9gjtv2NLyGIfKPG17j/AMB7Wvzs+P3h86f4tutc04KUmupFlT+H75r8i4Ld+M87/wAcP/bz8o4B5f8AiIHEP/XyH51Dxfwzqk3hvxBEjt8jN/er6h+HesWus6Ls+2ZZovur92vmnxloKWcgv4U2jZuT+7XpH7PfjRGYWFy8Y/hRmr9PlGUZcx+zcsah6hqliiq772Uf3v4q93/Zn8Zf8JBpf9j3ly32u1+VFX7zV4tqlukqP/En8TR1ofCLxd/whfjK3v5r1obaOX97/wBc6jEU/aUtTXB1pUKsbH2Ja6bNbrv2qqb/AO9/481XIbHd5SXtzCyyfxR/K22r/htbPVNLhvLR/MS6RXVv96r81i/2p/Ohj+VW2M38NeNH7UT7SHLGEZHxV8XVkb9sKRU5Y+I7DGB3xDX17b6XM0n762V2k+Z9vy+X/dr5K+KyMP20gkqnP/CTabuAxk8QZr7Xj0/y/N+zPtZd25mTcv8AvV+ocbq2XZV/14j+UTrxMVKEGuxz8mk+Yyp5Khti79z/ADLVG80G4RmdE2ur7naNPu11dxp6Md8aKPM+bc33WX/ZqrcWaLMszoxMbfJtevz7l6xMfZ82+xw2vaPDbtsth975ttcjq1nbTXD+c6qV/u16N4ij8tXS2+6vz/7u6uD1xf4LlI9+/dKq/eauHEROrC03GWmx7t+wwoXTfEYxjE1qBjpjEuK+HP2pFd/22/EytwT4xUD/AL7TFfcf7CrQnSvEaW5fYtxbbfMOSPlkr4b/AGowX/ba8TqF3E+MgMZ6/vE4rDwWTXijnt/+gX/5UfiHBya8feIf+vdH/wBIpHrq3rsrIjqdrbU2v95qdb3jqsszzfebcnlt8y7f71ZUt4qtv8rnftdf9r+9U0dxZxr5c0yp8zN8v3q+VrR5Yn9J05HQ6bfQxMj+dvZvm3fdWtiw1JGmZIZtxX7i/d21xuk3EPnS73XbH8v+1WrHeJHbxbPnG9vKX+Ja5404m3MdLDeXK2ym2h2NuZnkZtqtVtbpFtVh8lmk+7t3/wANckt4+3Ykyxjd86yfw/7tWIbhJFM8yMir/Du3Ky10U5ezFL3vhOq/tL7Oskbu3ytuWOP/AJZ1DJdQ3Fw6eduGzdFH5X3m3fN81Y63UKwqIbNf3nzfe+9U0epv532aaPa6vtT5fvLXZCp1OeL97lL6LCu9JvMYfeTb96lvJEmkQvMw27fmqot55jb05C/ck/2afDJCyDY/zb/7n3Vrqp8xpGnCW42W323zeZB8y/8ALT+GqM1rf3Ez44Lf7f3v9qtJfJuFPnSSbd3yNI9R+S7yj7S+T83zM1be0izpp0+YyWt9rRfeFurMrt975qit18yOJ7lJPNj++33q1ZreG2UJCmxV+ZFVflWqE06R/fnVGaX52/vf7NYyxH2Tb2PLLmkPhjhVVeSPy1armn6kjTJiZWCv8q/d3VmTXkLY3zqiR/e2ttWobfVrBWbe+X++u77u2op1Pe5jLEU+aJY0mPzG8mR1LebuRv4f+BV1Wn+cV+07FPl/K67a5HQbq1a+H7lWhX7jL8v+7XS6bJNDbFN+UWL5P727dXsxwp8lg8dyvU3JorlY3SGzUncv/fNRXC+Wu+Law37fLb+Go47hIY1/fYf+8rt96jc8jLD9pU/JubbWcqcOXQ+ywOOjGJQ1i1ea0ZPJZnb5v3b/AHayNS0uG4jb/WK0afIq/Nub+8zVv28O3cZ4ZN0jfPI33aR9HTzGeGbj/wAdrlqU4xPeo5hI8+1DR5mXZfpGU/haub1rQ4WjffGuzf8Adb5a9BvtHmW4V7lFKK7fKqfK1ZuraWn2fY8OV3/Nu+61cNSMPtHY8Z1PFta0G5hnCIi7f9pP4awdS0d2k8y2SRk/3K9X8QaPCzN97av+qb+Jq5jUNFeRH8lMR7dyf3lrmp1IfCeZmOI9tA86k03c7zTIwK/3qqfY/OmXzuE3fw12upeG4WjEifdb76s9ZV1o80Y3wwqdv3a7qeKio8p8ZLCzlVOX1+BItOdUCgqRvA+oqTwXO0NhNldymYbl9OBzVrxZbCPR55SADuXp9RVPwdHusZX25Blwc9Ogr9Pwta/hHiZf9RC/KmehgMDyY+NJ9Vc7DSbq5mxDbSSMfvfu/wCH/erp9D2faEWFZGRk3N/vVyWnwvGgmmdU+ZV2q/zfLXU6PeJFcGT5vmTb9+vyKVTmgffYXK48nvROis2kt90zuwf/AGfmro9M1J9wmd2ddm11Zfu1xi3QjjG/91K332/urWxo11NHGtnNNvEP32/3qiWxOIy+EYHY299C1mlzC7FpHb9233qlkvn8w+dbKz/K0Ea/3f8A4qsjT9QdmhV7mTEkWyWSN/8AVrV6O4mhkS5dPk3tu/vN/dq6Mpxl7x8xjML7MS6kdgHd9xb5tv8As1nXMUK/6SkzQvv3fL826r1wwWT98/yrF8rNWcxhuI0uR5mI1avRw8oRjzHxmOj7xn6oiTK8KTbnVtzr/e3Vk7rm3h8lEjLyPt+ate4tftFwJpnVh91tr7WqktrN5D+c+2Vfuqy7qZ5UY3lyoor+7mKW77X3sN0fzbqpXUL+ZsR49it+9VV3N/u1oW/2l2875v3fzL5f8VQ3kKXUjoiMh+95i/3v7rVlUlynXT94z4Y0gYJcwtIiv8q7vlXdWtbsjLs7b/8Adqg0LybBvYOr/LM1XrP/AFW+Z1VlXc7bfvVxT9+fLHQ7KM+SBt6dZ3vmvMm75V+fb91fm+Vq8v8AiNrV/wCItcNzeTM62LssEP3k/wB6vVNW16w8M/D25v7l41vbhdkSr96Nf71eH3Grbmmd9rs3+t/vV9VlOWrCx55/EfD57m08bX9lD4IktxrUPH2Z+VX5/wCFq4HxBrE1rJq/k3/7yS1ZvmTbWxcaxD9qfzvl2/daRdu6uO8T6g8lxI5TfuVk27f4a9rmR4MZcxzvhfxPJfaa9m77fLfctVvEmvTW9m8ML7dybXrktD1L7LrdxbO6qu/5tv8ADVnWdUebc6O237tT8Rry/aOe1KQreGYvuH92tDR/ET2ux9/3vv1j6tcTMx9P/HqrQ3DwybPuj+9Ve8M9KXxtNNaoiT/wbdq1xHirVnuLpnebftes77e6sN8zff8A4arXVxvYzM+/56n7JUV1J7OSOaYJ0FW/ninZ9+3c33f71Z+n3iKx/cr97+KrkNwk1xK29d/3UX+FaqISLqw+cCnzMv3ty/L81OuG84lHRt33aWzm3fIjY/hqfb9oXycN833aJEmVfQgKPkY7fu1QvI9khfZtLf8AfNakkjws6b/+A1Ruo23b26N/DRy+4OJRaMRjev8Ad/8AHqiZUG1/7tSzLsmw7t8tNf7pqYFjGwrf71NjVJF+YU7anl/M/G2hWQD94v3ar/CaEtvGi7pKfJGjKr5qJZ9smP4qkWRGZtnyr/BT+EzIZG+YJ8wrX0+4+yWr7JPnZfvVlMeQ78/NUkkhVfk+XbS+EC55cyr5zPtP8e6qtxH+8+/u/wB2ia6aSRXd8rUSyDcET7tHN74ojrWMyOru/wDvV+vX/BuvoYX4iT38y/6u1jVW3/e+avyFsVaS6VHXjdX7Lf8ABujYwyeJ9TR41QSW8K7pG+638KrXVgv4p5mbc3sND934b68bwz1Zz5S/Lv3Nurwf9oKHUrjT7mEQyAMm3azf+O161putbdHhm+0q/wAu35f4q86+KGsW00bpebkRkZdqp8zV61aUZRsfPrnkfhR/wXQ+CN54P8eeFf2mdEh3Msv9m6y0abfL/wCecjN/47XjPw/+Kmpa5oUNna36+Zt3P5fy1+nf/BSL4W+G/j58D/Ffw3SFZpriwkaw3L80c0a7o/8Ax5a/GH4H+IJtBuJfDGtQtDeWdw0E/mfeVl+Vlr4PPsLGtHmj9k/QeG8fKK9lKR6j4216azvHs5kV2ZFf/Z//AGq4jWvESfZ9kPyt/wCg10viy/muLV3SNXDfxfxLXAapJuk3vuUbv4q+ajH3eWR9LiK3NqQSXSP++X/gTNVazupvMLu7bKimjZtyQp82/wCbc9JJcPHGE2fdT59tbRjc8epLmOg0vVrPzP8AUqw3/O1dPp+rQ+Yrwpxs27q80851ZnR/l+996trQb6e4XyftLEf3VaqlTlKPL0M4yidL4m8aWEcKQpDub7m5fmZmrlNWvnul2Oi74/vtW3caXDD/AK7yxu+5t+9WVqmlWy8eRuEnyptrKMYmlSpL4THWR42+zY+ZfmqaHLKoyvmt/eqZrEqqfJ83+596mNbzFd82377K6rV+5IxJvkbPZl+WnwxOx8tIs/P/ABfdqGON0mZ0+7s/h/hqy0ltIyq7sNvzIyt96s/8JUfdLdnD+8bP8Xy10ek6G9xdQ2ccPm7vm+X+KsOzV7j/AEbYqbkVq7PwuyQ2I8m22vv+9u+7XBjJzjSvE6cPySn7x1UmqWfgHw6yWdyv2tkVV2r80bVweteJJrGJ7m/mY3Mzs8szffZv/ia0vETalqEnnJbLtjX7395q8r+Ivi5ND1KSz1KbfeKi7beP7sf+9XPleXVKz/mbPSrYqUY8sPhLkuvXNxM9zfXjDc/8Nd/8PvEXiTwy0Gq6VZzM33l3JtVlr56fXdV1W8DyyHJb90q16p4E134m6Xpx1J76SSzjg2vJdL+7jX/er6LF5dUjS5YmFPFYilLmgz7D+B3/AAV88Z/sfeIrLVF+FsWoNGi/6RBf+W23+Jdv8S19g+B/+C//AOxf+0PA9l8Z/C914PuFgWLdMpMc25vm3MK/ErxR42m1zUGmMyzbk+8v3f8AgNZX2ya4b59u1v7tKjw3Cth+WbcZHp0+MquF/iQU2fRf/BV346fCj9ob9urxD8QvgVrDXvhiHS7OwsLpotqs0cfzbf7y188jeG5k3LUUavuV/MX/AIDUtvCjSOm9tv3q+tw1FUKEKf8AKfEYrESxmKnWcbczOp8D6g9rdKg2/Mn8X8NfYP8AwTr0N9PXxHpT2zRpG263+f5V8z7rLXxdot1Db30TumU+X5Vr9Ef2H/DaWPgW58QokYS+ijTds+9t/h3V183u8pyUf48T2Szs/s6h5tzov8W3c26p7e3s45N/nLs+6i7Pm/3quXVrDY2izWbybVT52WnyWf2ibzkdsqm7b/Dup04+6evzD9N0lPNdDcqqN80Sr96tG1hSS4bajbY0/iT5ttR6V/q1RH5ZNr7XrY07T3im3ui7vuxK38VddONtDajKQ/R7OGO1V0RnST5vMX+L/aq59hmuFZ5nXcy7fLjTbt/4FV2xVJI1dk2f9M9lSoqKqzfMm77lHLy+8e7h5TOZ1jRdqh4UbHy/vm+7HXIahpsLLOm/EbOz7tn3mr0PVF8y3/1jKd3zRr93/ZauO8QWuMw/Lu+/ub+JqylLqe9hVzSjE+aP287eOL4baO0URUHWU2g9x5Mta37LumvL+zppdwbhow9xdAEt8v8Ar3rM/b2+0/8ACs9HMzZB1xSB6fuZa2/2YLV5P2YdLmG0hJrtwG/6+Hr7LEy/41tSf/T5/lM650LV3DyPhn9s7x9NrXx01jSob1mt9Hijs4l2fxfeavM/hrp9zrnii2s0G4zSqqs33atftAa2+qfHzxdOH3LNrMnzf7vy07wDJDotnc69cvj7PF+6X+9JXwXwn5NjeaWKn/iOu+MXjz7DeTaPpV5ugt08r92/y7lrx/XNcm1CQzO7M1O8Ra4+oXz3LzZ8z5qyGbzGL780vi+Iw5RvmP5Zd+tSQr5iL3b+7SRwvImfvf7K1q6Vo73DDZC2av4hyIbHTtyK5dm3VtafpLNJvRNy7a3NF8HusPnPBuX/AGq1W01NNVvMRc/3a15eWPKY/FLmOcuLN7VS4RtzVmzruk3gfdrX1q6RlZ4Wwyp92sCaWaSRn3rtrKUiox5iG4mkZhsRm/2mpbNXVmR0/wBpmpGk/drvDFqdDbux2O+9dm6j4R/YNPT1+0sqb1VV/vJWw2nzNa74fmLffrF0+42z7PL+T+Nq6SzkthDs37Q1VzEy2Mz7HNCw8xNrq3/fVaeks63I+78r/OtLdJDx5PPz7X+f7tNjkh3L5Pyms4+8HLHqei6Xoej6tp6bJtpVNvy14/8AGDT30fxMLZ3Zfl/3q7nR7h47Zks3ZWXcr/PuVq4X4yTXE19bTTupbytrNSHGPvmX4YufMutkz/x7q948EzbvD4j8xWC7flr500G7eG6D/Ka91+G139q8OzP8w8lNzsv3qv4Qlz7GP8WtQdtLebqjbv8AgLV4nJvkmLtt+avTPjJqW22Ft5/Dbvl/vV5inDCmXTjyxudF4PUNcRpv2bvvNX0N8M5Hk0dYYUz8nzba+f8AwfG8k0bp93f/ABV9DfDe3hj099o+Tyvl/wBqjm+yYy+Mf4wvIZIz8kgMa7NrfxV4d8QvDL3DS6lbWzZVq9j8TR3OoTM9ykg+fbuaslvCr3y/Zns2f+JZNlOPIZe9z8xY/Zx1yTUPDsulPeL5lvtZY2/u13WpWG6N7lHyf4lVPu15P4FjfwD8TobeZ2EN4+3zG+6rV7O1y4XZv/j2/N/EtT8PumkuWXvHK6bDND4otEd96NcxbP8AZ+cV+sf7Wnx58Q/s2eDNA+LHhqd0msfFtqsqxnl4mjm3L+OK/KtLJIPE9tJHtkSS+Rm/6Z/OPlr9EP8Agq0zJ+zLaEMB/wAVZaZz6eTcV+Xca/8AJX5Iv78/zgfi3H/N/r3w9/18n+dM/ST4hfFP4R/twfsLeHf2ldM2XWseGpoGtLslS6GYi3lDbemQ54/2ayv2ZvjZp3w4+DHjTw7c2d1ffbEc3FlaRb2RZIgiS4+qMK/J7/gkf+194z8JeIdf/Y71O6afQ/E1sbzTkkkybeaBlnZR7bUNfrR+zB8Fm8U/CjU/i1ouqy2t5pGuG01RIo/MNzp7RRM6be7DLFT2JNdUVGp4wYSFV6fV2l99Sxx526tPxlw86a1+rNv0vUu/uOm/4J9fGew8U+ILz4b+ILom213TjD9ldtys21l/i/2a/mY/ai+HeiaL+0Z8T/BmjorW2g+P9UtbVo/u+Wtw21Vr9+PHsOqfsbfHCy+LV9brYaDNFeXvh1pn/eJarC23d/tV/PTr3jK88SfGTxP4h1K5aRvEGuXl5KzJt+aSZm/9mr9ZxsZYeo/5ZH69l9SFanHm+KNz9Iv+CJtpJY/sf39tJn5fHF9tyMHHkWtfDPxH0i31zStXZYWLm6cxMvzfxmvvn/gjzbm2/ZW1BDnnxpeH5jk/6i2r4UtLh7y8u9Nd12Ndyrtb+H5zX4/wT/yWmd/44fnM/LOA3/xnfEL/AOnlP86h4PNbQ6tp01hcoyvHuXc1cx4M1SXwn4qMMz42y/LuruPGGnvoPjCf5F8u4fair/d/u1wvxA0r7BqC6vbJ8m/52/u1+oy973T9ooylzcx9O+Hb1PEHh6O8d8K235V/9CqpqELx3Ek0P8K/w/3a4f4C+MH1DS1sHm3/AOyzbflrvtYie1XYib22/eV6jm92wVI8suY+sv2N/ik/jLwS3h68ud0+mvs8v7zNHt+WvYfLdi6eX8zfd86viD9l/wAeP8PfilZ3l5N5dldfurpt/wB3d93dX3VKUuJvMSaOW3kRf3ka/LJ/drhrR5ZWPqspre0ocsj4m+Li5/bY2DnPifTMflBX3FbJeMpMKfMvy/7y18RfGFI1/bk2BNq/8JTpfy56DFvX3da6aka74UZX37U21+icbq+XZX/15j+UT3a82o015Gdd2sMmEktdqt/Ez7VXbVC8tXWSRJrbeK3L63eXajorhW+Rdv3arXVn+7ffO21vm8yvg40+qMI1Zc3LE4bxEtnHJ9pw2ZF+8tec+IrqG1uDDbIxdvmRpK9J8TQIrI8KSBIUbZHIvy7v96vM9asZoSf9W6b22/N827/erixEYcp6mF949z/YJiENj4pQSh/9ItPmH+7LxXxD+1I5h/bU8UyR9V8Zbh9fMU19t/sCJHHZeKkjZs/aLTcrHODtl796+H/2qsj9sjxdyP8AkcH6n/poK4vBe3/EU89t/wBAv/yo/B+Dv+T+cQ/9e6P/AKRSPRmuHZT9pmZ2b5t1Rw3hWZhC/wA7J/Em7dWTJe3Pyom3a3yu275agk1ITTed822NmVWX5Vavnpx5oyP6K+GRtxyJHqGd6/Nub5auR6xD5mxJJGZfmT+H/dauQuNceFf3L4ZW2p833qG8TQNDseVUm2fw/NtrONPmI9odm2sbVE00371t29fvfN/FVmx8RIzt+5ZTHF+6mZ9q7a8/m8QC4jRN/wC9aL5/L+XdTP8AhIpodvzs+75U+fdRKnKWgRxXJqem2viSbcqfKix7t7f3v92pLXVppGd3uV2t8m5fmbdXmtv4shZVtpn/APsa1P8AhJ3t5vkSNhu2/u3+81b+zl1CniISmek6fq0LXAhR2dI02/3dq/3quafrCSW8nkuxVm+7vrzO38WPDK5g3Fdu77//AI61aGl+Jn8zz0m27fmZW+9trPmnTO6hWhKdz0j7Y80apv8A+WXzK38NWJJLa+s/Mhdju+7t/u1xNj4ntvtELvebUZfm2tuZqv2/ixLeRdj7E8r7zfK22lTxHK9D0acfeNy8+aRH2bl+796snUpraSaOxaZXb5tvy7dv+1urFvvEDyBnsplXc/zNs3Vi33jTzV+SZv3b7XX7tZVKnvc0TtjHmpXNbVta8yFrZA3yp8+6L73/AAKsqLXPOEXkzMybNqK38NYOqeKprnciOrOqf6vft27mrKm8TbYihudiL8yN/tV04eseXiqfKemeHdUttsVsjqX+9/vV2Gj6t92F5lQMvzMvzfNXkvhnXoWb55vu/wAP92u103VN0LIjr8zbq+zlT93mPySnipxkdbb6pN57JNNIvz/vfMX5dv8AerSSZLiSIJ93722P5d1ctaXm7zNnmMv3Uaata1uvMjV5nbEbbl2/dWsZUYy2PcweYVYm/G32i6e587zQ0W1l3/dqby0kZ0R12/e3Kn/jtVtHnSN3me2Vl+6+77rLU1nHNJcLfw7XRfur91dv8VedUp2ufT4fMpcqZQvrWGW4+d/4flb7tYuoWtnJCUuXYhv4mrd1D98u/Yu3ftRvu7ay7hUjmWZ/mf5l27q8TER5ZHsU8Z+6ucbrWn7GXe7P/cXZWPNo63CrNs2DZtdl+61dZfRo0i2abh+93NuqD7DbJuRH3bmb5l/irz6kuUKdb2xweqaC8jNM/wC6X70rN8y7qxb7RdsDPsw33dq132p280cbwum5VT5VZfvNu+9WLq2lzeYXmRQsKf6tU+7uo9p7p0YenGU7nl/xO0v7F4dnfZtGY8L/AMCFZHw3sjd6TNhn/wCPvGFTP8IrsfjNasngq4leMIQ8Y2HqvzisD4O28cug3LODkXh2kf7i1+s4K/8AxBvE/wDYQvypno4WEY8Q01/cf6mra2T294lt9m3/ADtvZn+6tb+m6fM0e/ycsr7VVm2/NSW9htuNn2ZV3N/31Wvo2n+Z8kL4+f51kf7tfkvNyn6JRjGMBun2M0rJ5zq5VdvzfdrR023mjY4diG+VN33asQ6aY4I4Ybbb/Fu/+Kq0tj52bB5mxv2t5f3v+A1ManKceMp+5zMk0tnkhUqioGTbtk+9WiqvHZqjuqrvVn3N/Du+9VfT9L2s3+sYfddWWr0MKSHYiRt93bu+7t/3q6vaM+GzCXKNurFNtxNNPnb821Vqj5jqqPDDv8z5Nyrt/wCBNWxdN5jNsjklVk2t/st/drMvlmb55nZlXavls21t1dtGceW3KfE5h8XNEy7rfNePbJtcq/7ra+1qz/3MkjzfMHj+ZGVvmkatWSFGZJk2sWfbt2fd/wCBVTW38y6ZEhZGZ9qs392tJS5oe6ePy/aGMr2832yado/L2r8q/wCs/wB6oryO2uoxv8yLb95W+6zVozWiNCvl7d+/bu2M1ItjN5ez5Xdfm/drt8ta4aknLVHXRpmO1qizIkLsP9n+Ff8AgNWdFsHutQS2mmaWLzd0u7+JatS28Lb3fzE+f7zfean+AbV/GWra/YWEyv8A2TZM8+1vut/dX/arvy3D+2xOvQ8vOMR9Vw3LH7R5z8TvHD3mqT6VZ7RHbuyquz7zf7NcDpd4PMmtnmXzGf7rU/xRdJb+Jr+2+Ylt3yt8rLXJR6lbQ619mudyjZu3V9nGPs43Pgeacp8zKni7VHtZn3vj59u5v4f92sbUNU/tLT9+/wCbbt3K9UvHmrR3moNIiM27d81c1b6hNa7u6fd+992ojL7J0fEcv4nk/s/xFJJDu2t95f8AapsmpTTQnt/s7KZ4wbdfK6R8t8zf7NVLW42x73fdWsdiuYbcSZmf+Eq+1qrXE0LHY6fdpbiR2ZnD/wCz81VZX3KKX2hxHyTOI96fd/vVHKzr8/8AFSQt8x4+X+7Tio2736/3qJF/CLZybplq7paPLNJGn97dWfZybZ+f71X9Hm8u8My7vv8AzbaX90UjUhj2yFem35qsxyPnKTbdzfL/AHqhmjGBs5/2qWO68uMwJ/D9+lLliZ/EJeQpI29N3y/xfxNVKeTzSv8AE393ZVrzpm+fZ8ip95v4qgkVDu8n7zUAZ9xGnzPhctVNo3H8e6tHydzNv6L/ABVVmjfbvRKI+6aFdDtH3f8AgNBbazfJ/wABp/8AE0e//dqKT727OacZAOAiI3mneY4+ROB/dqOMkHOMinNhTjNIB26T77j/AIFQ0nmHfv8A96omD9WFKrfKR2oAmWTavz8q38VN3J5ezZtKvStIJI/LdP8AgVMX+51aqiaFiw5ukd+Azfw1+xn/AAb56k9j4oubBLlkSZI2fd81cp/wSS/4IVfs1ft8fCb4feJ/GXjfxRpmt+Kbe9uL6e1voRbwpDPOuI4vILE+XEOGcZbPzAEAfYnwL/4J9fDL9gD4x6v4Y+Hni3U5Lq01BtPvLzU7wTW42PtMmFhRsDqeCeOAa+Ir+JHD+W1ZOqp2hOVNtRVueO63v8z8nz3xJ4cwlCcqkanJGrOi5cqt7SHxL4r+d2krH6GWeoIujxf8skX5vlf71eV/GSS81CN3tpWVPlVmV/mr0D9pjQbT4C/CbQPFPgv4q2OrXN9sjMF1ErC/Qpkz2/ltlUHGQxYfMPmzgN83ar8ZPF2sBxdR2g3nLbIWHP8A31XXmXipwtleI9hiOdSsnpFPR67qR5HEPiBkHDWPeBxvMqiUZaKMlaSutYya/q+zTfmvxC0m8uLy5e5tmRGlbay/LX46f8FCvhCnwQ/afk8SeHrN49K8Uf6QG/hW6/5af99V+1+q302r7/tWB5i4cpxkV4j+0f8AsHfAz9qTTYdO+JbaxGbaQPbXGl3qRSRNjGQWjYdPavnMT4r8H1Y2i6n/AIB/wTmy/wAbeDsJWU26n/gH/BPyPh1ybVrVZvOUsy/dWsfUpEmkbuu+v0+sf+CNf7JunQiC31zxptAx82swZ/8ASevS/wBnn/g3j+Bf7SOtXei+GfEfiizsrCNZNR1K91aLy4txwqDbbEs5AYheBhDkjv4NPjjI8djI0sKpylLZcv8AwT7fL/HDg/O8ZTwWDhWnVlooqnq+v83RK7b0S1Z+M91DtZfk4+9tqrMqeYPn2/7Nfv3qv/BqT+xxr9nPa/DL9p3WNZ1a1H+k2Mmp2yKpzg5aJJGTkEcr+VfMGvf8EQv2WvDmt3fh/wAQ3Xje3vrG5eG7t5tZg3RSIxVlP7jsQa7sfxZl2UqLxUJxUtmkmnbdXUmr+R1cQeJmRcM8k8xpVYRnflkoxlFtbrmhOSuu17n5PSW4ZHf5vuf99VNo98lnMm/7u6v1RP8AwRd/ZFLmT+2/GoJGONag/wDkevVv2cf+DZ39nb9oewvvEMPjfxVoug2Mwiu9UvtXiYu23cwjUW4DbRtLbmUDcOTzWWB44yTMcQqFBVJSfRR/4NkvNnl5R4w8LZ9j44PAQrVKsr2SgumrbbkkkurbSPyTsfs14y3iJv2r/C9Z2pF/tzfvFVP4F3/dr9x/EX/BrP8AsrWnhG8134I/tCa74sGnwO9zYpq1srttUtsRoo5AXOOFbbn1rw74W/8ABCr4A/GXxzZ+AfBl74tmvrsk7p9agSOFFGWkdvs/CqOTgEnoASQDrjuKcBgsTCjUpz5p/DaKfN00cW09T2c18TMmybG0MJiqFZTraU1GEZqbulaLhOSbu0mk7q6ufk4yorHY/C1QuvmuNn95/wDvqv34T/g1Y/YWtpovDHiH9rDXLXxBKiq+lx31oWErDIVVYK7A5GDtBI5wK8F+Pf8Awb4fAX4B+ME8K+N9X8WMZrcS2N9Y67C0NzFnBKk2wIIPBUgEH1BBNY7iLCZZS9viKc1G9m0ouz7O0nb5nRnfiDlfDuC+tY6hVjTvZtKE+V9pck5cr6e9bXTc/IOSSGXCIir/ABUyOGCS439Pk+f5f/Ha6r9oPwHoHwg+Oniz4Y+GmuLix0LxDdWFrJeShpWijlZVLlQAWwBkgAE9hXMWFvc3kyxpC25vu17+HnSr0Y1I7SSa9HqfaYTE0sdhaeIpfDOKkvSSuvwNfwrYzX2qJbJtb/ZZ/mr0eDR202xbYm75trbf4qzvAHg+8s4V1W/tmi+0Iyr8n3V/i2tXSeJtes9F0V9VufLfy4tsUbfL81ebiq373lhG57OFw/NrI1P2fPAL/EH4iJpt5tms9PtbjUb+Hbu229vC0jN/47Xxh4g1G48aeLdQ8TPy97fyS7VT7q7vlX/vmv0w/wCCTXwr8T/EjxB4/wBb8E+HpNV1pvCF1a2Fqqs37yb5dq1of8Fuv2A/hx+zf8OvgX4z8P8Aw30/wv4q1a3vLLxTZ6a6qtwsMassjR/3tzMu6vVynHUMPWlRl8TPax+VVZ08P7P7R8R/sj/s8aj8XviBZ2D/AHWl+RWTcu6tj9uT4n+GdW+Ij/Bz4UWVvaaB4VRbW/uLOXcuqXyr+9k/3Vb7q17b4S8P2n7OH7Enir9oDVEWDVZol0rw00bMkjXVx8u6P/dXc1fDtmXkgyzMXZ90srdWb+Jv96vfy7mxNSVaey+EOLsPh8lwlHBw/iyjzS/RAI3jk2fLtWrUMKL9x9v8Xy0kaovyffLf7NSNG+7ZGm3+/u+7XtH5xLYVpOP4v96pLc7j5e3738W/7tQ7oSuzyflVvvLVywtzNDlEWnKQo88SRJPss0fku2771fQH7NX7VXjD4E+NPDH2nXtvg/VJ2t/EdvMu5bXd96Zf7u2vn5o0WZWzuLfLWv4it7m++HkqWdr50tvcKySR/eVW+9T5eaJpzSv7p+v/AIZ1nwr4ys01P4e63b6xYXn+qutPuFkWRdu7d96rjOlvMl46Mo+7t/8AZq/Fzw7411/4a6na674Y8SalZ6la/wDHq2n3zR+T838Kq22vtH4Ef8FTNEs/hTNpvxv0r7V4m0tP9AmtU2/2hG3/AD0/uyLV060afxHXCUJH2/p8KTTfJIpMjbXb7rKu371b+k5b7+5Qz/ulVPmavEP2Xf2lfAH7SWhy6x4buZLDUbf5rrR7yVVnX/aX+8te2WOoI0yTXPmI6/Ike2tY1vafCejRjKUDds4vNaW5htt/y/O277tOkieObYiL+8f5/M+VY6hsZJoY3/iWT5tzP8qr/EtTFopv3yPlVT5l27t1axkerh+bl0KWpWsJkKPuCfd3L83zVyniSzSHfvmVvn+T+9Xa6hCgs3+X5GTdt/irifE1y821Nnmqv3I2+X/gVctapLofU5a+aR8xft/CNfhjpIVFDN4hDFlGM/uJa3f2X7MS/sp6ROFkVhcXoMsb42r58lYn7f8AGI/hVo4aHY519c7TuX/US/xd67f9kHTnvf2QtIWNAD9uu2Dn1FzJX2+M97w2o/8AX5/lM73KP1yXp/kflD8aIbi0+PHiW0A+b+2JlJb/AHqg8VatDp+i2+iINrR/O3+9XWftMeHZNL/aj8VQXh4GotcBl/iWvMvEGofb7+SfZv8Anr4OPwH5Bi/96mv7xTl/eynfRDGjN7Usdq8jYrpvDfhO81CZPJtt/wDeXZVRjzHLKcYkHh/QftDL8vy16T4T8EosK3M0Khf4f9qtPwP8PXtY1e6RX3fN8y/dqz4o8UW3h+L7Gjr5ka7VZl+7W3w+6Y80qkuVEWrTWFjb+T5Kr/C7Vymu65HtLvcsd3NZuteLLm+ZvO/if7yv96sa8vHuIx2NZ80y+X3NCHUrx5Ll5nO7d/dqo0xC7KdNN82zy+elR+W8TfvKmPu/EV7wokjMn3ONtLZt83kfe+T56a0e7fs/3asWMY8xX37dv/j1UKMvslm3WZnKfdWtrTpHaFUfbiP+7/FVFbea4j/cps2/xf3quwq8MY2JQTL3iy1vNMzOiMpb722oZFmhJ8lPm37flrU035ly/wA23+9/FViPR0mkWFJGBZ91L+6T/hKek6lPZtsQtiud+Ksvn20M3/TWuzuPDNzaxs8O4j/x2uJ+JPy2yJMrF1b/AL5qOX3jSEtTjbMlLkA/3q9n+FuqOuj3Fn53+si3fLXisf3hzmvUfhnfQw6VK6P/AMsvlWr5uWJVY5z4rXom1RYX2/L9+uUtY99wtaXjC+e81iTf8xV9u6qukW73E+xKY/hidl8P9Pea4+dMbdrf7teqW/izTfDcawvc7dv32j+avPNDhm0jTFcQqxVPvLWdq2oXN1Kz7/4vvbqiX90y+I9RvPihpswb5Nz7N23dUDfFC5uP+PBFhT+7srzGGO8mmHzt8q/dVa1rUPYqySPyqbqI83xFcvu8pL8QPEVzJeWl/cu37mXeqx/w17f4S8RPr3hS11LYsreUqsyrXhWpQvq1jImzaFXc/wAtdJ8BfHX2Ozn8MX82Whf9x/srTiTy+4ep29zjxJYiZFY/a4j8qfL94V+hv/BV8A/swW25cgeKrQnn/plPX5ux6qknizTg8+W+1RfL93d84r9Tv2+Pgx8QPjt8EIPBfw20eK+1GPX7e6aGa6SEeWscqsdzkDq68dea/KuPa+HwvFeTVa81GEZzbbdkleGrb2Pw7xKxNDA8aZDXxE1CEZ1G5SaSSvT1beiR8S/8E4bU/wDDa/hS8WAqfsOoJIxGCf8AQpq/fL/gmZ8avA6X/iX9nXWdShi1O7ii1W3t3bDSxS5gOPxir8cP2Lv2J/2ifg/+0bovxE+IfhK3tNKsLe7SSaLVbeUr5lvIijajlj8zDtXuvxEtf2s/h1+234K/aL/Z48PpeabpOiR2etA6pBAZB9pldk2yOpI2spyPWuDGZ7kb8TqGLhiafslQac+ePKneel72vqtPM8vG8S8Pz8XcNjli6borDuLnzx5E71NHK9r6rS/U+3f2xf2dtV+MPwJ8d/BqC/luPGek3zRaMrbpbq6t2+7HGv8Azx2t/wCO1/Mx8b/BPiz4K/GzUvh74y02azv9J1SS1mhmTb8yttr+oD4+/tI+EvHtrpPxO+F3jzUPD/iy90Q6d4ltYLR1lEbDOVl27Aytn5lJ+9X5Qf8ABUH/AIJj+I/jxcaL8RfgBNY6x4glkYa5a3F20D4ABEjy3G3e7HP3Sa/apcZ8HYzKv3mY0FKO37yHN5rc+yocZcKYLM7Qx1NxfX2kOXy6nY/8EgJfP/ZPuJiwJfxZdkkdP9Tb9K/PyC7S18V6hsdn23svy/8AAzX6S/8ABM34BfFT9m/9m5/h38YdIgsdYbxDc3ZhtruOZPLeOFVO6MkdUbjOa9B+En/Brb4lubiLxT8e/wBpfR9JW7b7R9h0TT5LiaNnO4KzttXoa/IeB8bhKnFWc16c1KEpQaad017+qezPG8M50s043z+rhJKcJTptOLumr1NU1o0fjd8XPD/27S/7Sh3b4XZ922uD1DT38QaHv8lm/dfOrJX9Nngz/g22/wCCbvhDSJb/AOIj+L/FG1N8v2jUvIj/ANr93GtUdT/4Iw/8EK51TwZffBX+zp7p18q4j8Q3Ecm5vu7WZv8A2Wv0l5xglLVn7xSy3G1Y/u47H8x3ws1ibw74qSGVcBmx81fRMJ/tKGF0hytxBu2x/NX78/Bz/g3f/wCCKcuozeLPDfwg1PXUtriaCVdU8RzSwbo/vNtXbXQ6h4K/4IrfsiXc3hjUP2fPBWnz2kirZ2jaa15cS/3fvM1RWzXBUeWbekjpw+S5ljOanCDlKPkfz3+H/Bfi3UJkm8N+HtQuHjl2o1jZySMv/AVWvu/9nvS/ip4++HOmvefD3xJ/aNrb/Z5VbQbjdMy/xbdtfrHfftw/stfBXwlo2sr8L9C8Ny6xC0mk+HrXRoU1Dy921WeNF/d/8Crmvhd/wV58K674y1jw1r/hKGBLGdTbzQyLlo/+A1xVuIcvjNX/ACPfy/hPPacXOENvNH4N/GTw14o/4eAxeFr3w9epq03i7R4V02e2KTvK4ttibDzltwwO+RX6m+Ff+Cfv7WPiSFUh+COrWisyr5l48ce1f+BNXwj+2f8AGvw94z/4LmP8c9Oif+zj8UvDF9s24bZCun7hj1/dmv2f+K3/AAWT+DvgHQrqez0m4nutn+jR5/i/2q/SePc1wmGyvKZyfxUItadOWJ34rK82xShTow1irS1WjPAof+CUH7Xs0fmJoWhom3d5d1ri+azf8BXbWb4g/wCCU37ZCWbE+CtHuU/542uvR7l/2v8AaaqNx/wcB69No8cj6FpzvFLIsrR3HzN8392uI8d/8HCvxEl0q6t/Dmj2drK0/wC6ut+541/2lavy2XEdKUbxgzaPDecU5WnOESprX/BNb9uia3a2T4AX8pWVli23tu3/AAL71cB4q/4Jcft32TO8/wCzBrM4X7klncQv/wCO7qm0n/g4O+L2j+M/7Rub6O5gazkt/Kbd/rG+7JVXxD/wXd/aA8VWy6PY65LpW6VWa+t5F3f5asJ55GUfepSPSo5Bj4y92tC3zNL9l34MfGH4Lz+JNG+L/wAMdc8M3FxcW7WsOtWDQedtEm8xk8MoJHTpketfnd+1cTL+2D4xCOCf+EulAPv5gFfp78Jf2xfiH+1tYXM3j7xV/areH/LS1lZsunnAlgx6dYxX52ftd6Xe/FX/AIKD6v4U8NwQW13q3ibTNJtBGoVPP8q2tg5x3Ljcx6kknrXb4JVVW8Uc8kla+F/+VH4Bwph6mF8fuI4TafLToXa/690noUW1B/sqIkzbW+bc1Zt1rWyPML4/h3NSRzvcWfmTbo5l3RXVvu/1cyttkX/gLLWLrUm1ldHX5f4WavLnD39j+gK1TmhzRIrrxJt2pDCzfwp/tVWk8VWybv3ON38S1h6tebZDsRl3ferLmkmljXYmUj/29tdcMPCUTwa2InE6qPxY+0+TNtbdt3N8u6nN4wn2o7pGqfdRv4mrjbSWZ2L/ADfu/lX5/wCGrKvMsyed/E3zbkrWOHhscf1yrI7C11xLmN99zt3fN9/dVuPxE8ckTpudY2/h/hrjo2toW/c+Y7/3tn3avK0zKz7/AL38LfxVFSnKJ0U8RzROrj8VPIzIJt/8W3dt2tVqw8VTNth3r/t/3v8AvquP4WPeife+5uqS3uH3GF32Hcv3mrhqUZy949vC4j4e56Bpfi65aZbbYrf3fL+bbWiviC5mjVLxPM/uru+bdXntrNcqxms0+bftRlf5a1bXVLny23zcN8u3fu/4DXBKPLM+lw8jpr7XJVWZMyQt5W92X+GsnUtYmmxG958u35P9qmRzSWrND8yxtt2bvvL/AHt1QahH+7GyHb/cVl+9Slyno+0jGJRkuobaTznfG5/m21B/aSSMvzthX/1bfxU7UkTbvhmbd8rfN92qMjeTIsJRsbPn8v8AhrejHmkeDjMRy8x12h6tMsjed5eGTc+3+LdXa+H9UmjVXd1f5l+6/wA22vGtJ1yazjk85GP+y1dr4d1zdaqzuyFk+b/Z/u19xTl7p+PyPW9H1oTSB/KVfLb7sj/N/u1taPfTTNvS5aLzm+797ay15voOtQLH5MPRtu9vu/N/ersNB1V1LvsjL+b93+81EpQR04etLaR21jcfY1RN7O8fzSyL/wAtP+A1ajvN0z3UIbzJnVdqv/s/3aw4tSmks9m3bI25nb+H71WGmto2d4fnRdrNXm1qf2j3MLipfZLepXnlsA6bj93cvzVA1xNc3AeaCOUL/rVb7ytTJGdoxbQ8Oybomkp8bXMsJtrkr8q7mZf71fPYhUpH0lGtV5IlVYPOkE0MK/fZX+eo1jzue2X/AGfLb+9WmljM23emxVXdt+7uZqtLp/8Ao6I6Lv8AvV51aMI6o9XDy7nFahprw/cTeyozfNu3bayL7T0uLnkyAyKrPXaapZJC3+k7Vf7jN/drFvrPb84SZptv73c/8NZRlzHq0Ze9qeTfHOzki+H+oSuQyiSAKxXn/WLWB8AoBJ4VvnAJYX2FA/3Frs/2hbS3X4Y6hPHGysJocljnd+9WuW/Z3hWTwdfMUyV1QEHH+wtfreBf/GnMU/8AqJX5Uzsw9b/hfpy/uf5nU29r8yIIGJj+/wCd95v92tTSYf3nzplf+ef8VNvvJkk/18ny/KrR/wAVFjJPbsPs0LY+bdub5t1fj3Kj9Aw+IpSgbtrG8duqJbbHZW+822pYQ7TH93s2/wDLRF+Vv+BVU0+6M0J3o2z727+LdV2w3+WEd9m5/njZvlbb/drTm5TzcyxUeX3S3YwpJvKTNFufc+6rSxwrHutn2r/Aqp92oFj2wPM9xDtX+FU/iqxH5ysz20KqrfLWsZTj7x8RjMR7aXKC/uPL+0/fbc6rVS4s90f2z+NflRv4a1be3do+LZWePcsUm7c1MuLdGtUd4ZkDfM/mP97/AIDW0a1vePmcRTlUnsYN3b7VdLlPkk+ZGWoI9NSV/kRmGxdi7/lWtO4sXkWQ7GTazfNJ8tWNN0V7eP7S6ZWbb93/AJaMtP6xLl945fq84yM+Gzyr200zY2b9v93+7VhtPdsQujI38TM1aq6f8q7kWJWf+JPl2/71XE0mGRmheZVbfuiX+Fq4PaPn3903p0Zc1mcheaTDHbvczHau35o2f71cR8H/ABrDo/iTx3M8LWytPCu6HaytuXav+61dX8TtUfSdQj02FGVliZ/3b/3a+dPDfih9N8UeJLOZJi19as3lxy/8tFb5a+3yShUp4b2r+0fB8QV4zxfs4/ZK3xSvPs/jK5heOSLzJWbzJPvNXn3iyR7G+ivIfMO5tv7ytrx5rX2y4t9b2SZ27JWkfdub+9XP61cTalZ/aXmUoyM23+9/s17kZSkeF7pyni7UJptUZ02hWT71Z15JCtsZptvy/cVv4qTxFcKsm90+fbXO61qzyQ+Sdwb+9/eq/tFD/ES+cRM6YVv7tYkU3ks0PzVswOb3RR5hz5bVmSR7pN8H3qPhCPvEUn7v7+7/AHqZIPMj+5x/eqS4QMv3OV/hqCbhlTfuH91aJGnLzSIGO3rUmx2VXqNxvOTSr93Z5lLmiXyoWPZuOW6Va0yR45fv/e/hqnUlqwWZd3SkKUTpLaSTydnZf4qbIrxs2z5t33qjs5nkh2Jtp+7ycIqZT/ap+7uYjfvHZna395qgumeNfk4OyntMWXE0ny/epkivIp/fKy0vh0HIgZfMUIj7ZNtQ3AdIcPuq1cL8qPC/z1VmZ3RvnYqtHxD+zqV5Gw3zlWqJlDPvqVtm3fs+aopl7VXwlx3Gw53bd205qTajJvpluu9zzzU+10yjotUOW5HlMlHpr7P4akaPavnb6aJMjAXBrMkGkf8Av5Wl2Ivz9KTb5itSqzq2Xer5kB/Tx/wa9aJ8IJv2OvhnrOseKb+Pxjb2GoppGk+Rtt5IDcXYkfeA25gu7glMYGN2ePbv25/Df7PWm+NNQ1f4f+ONSu/Fl1rtwfEGlyQ7oIGLEvhyibSGyAAXyPTGT8wf8GypVfgz8GGeRF/4kus43OBkma94Gep9h/SvX/2qLW4s/wBo7xpDcxFGPiG5cA/3Wcsp/EEH8a/l7ijFwWW4qHsY/wC9Vo3s73vfm3+J7drK1j+TuNc0pLgvF0vqtN3x+IhzWlzJ/Fzp83xv4b/Dyq3KdB+0R+zfoHwY+GvgXxvpPii8vpvFGm+feQXMCqkb+XHJlNpyBtkVcHdypOedo7P4XfsXfD/Qvhzb/Fz9qn4gyeG9Pv1BsNLglRJmVwDGzNhyWZct5SoWAGSRhlG3+2tLYwfBT4KTanAJbZNMja4jIJ3oLa03Dgg8jPQg+46169+1P8dPA/wk0Pw3q3iH4E2nivRNQgP9nXtwsXl2rbVIjAkifaWTBHTIQ/3aKWS5JRx+KrVlFQpQpWT5nG84q8pKPvNX87Xep24bhDg/B53meMxUYRpYWlhnGElUlTUqtOLc5xheclzdL2vLW2jXgfxh/Y3+G1x8Kbv44fs1fEh9c0fTYy2oWN0Q8iqn+sZXVVKsoKsY3QHblt3QH5wr7JsP2ptf8cfBnxZN8CP2SRY2CWUkOqahYXMSRQ70Ks3lxRo0zKjZwhJUEMcCvjavmOJqOVQqUauCVlON3aM4wbTtePPrZ9ldKx+deIuE4apYjC4nJ0lGrBufLCrCk5KTXNT9qk7PZpNqLVr3uFfWGlahqPwi/wCCaj6lo9wtteeKdReNp4GUNsllMbDI6kxQlT3AJHGOPk+vrHS7DUfi5/wTTfTtItluLvwtqLu0ECqW2RTGRjgdCIpix7kAnnPNcL83Ni+T4/YT5bd9L287XsV4b8/tM09jf231OtyW3v7t7db8t7WPnv4E+OdT+HHxf8O+L9LvGha21WETlXwHhZgsiH2ZCwP1r1f/AIKU+Fl0T9oKPXY0jVdZ0WCZtm0EuhaIkgc9EXk/Ttx5R8CfA+p/Eb4weHfCGl2bTNc6rCZwq5CQqwaRz2wqBifpXq//AAUn8Uf29+0JFoEDRuNI0aCAiPaW3uWlIOOejrwfr35MLzf6oYj2nw+1hy9uaz5v/JbfgGW8/wDxCzH+2vyfWKPs+3tOWfPb/ty1/kfPdffL/s/eK/HP7Hfg34SeHvFtloNlNZW954ivXG4PCymcqAmFfMjqxJYD5M5Pf4X8SeEPFngy8TTvGHhjUdKuJYRLHBqVlJA7xkkBwrgEqcHnpxX1n+1hr2u+Jf2HPA2seDILuPRnisk1ZFkLFESAoiyEKNy+ao54G4JxyMdnCroYbD4+eIg5ONPWKbi2rq6vultd9j1vDR4PLsDndXHUZTcKCTppuEpRc1zptK8V8PM91G5jy/sm/Gj4HaDqXxL/AGWfjZDr8MmnvBfxaVCv2iaPI3LEqmVHdeSMFXXHy/NXjf7On7RWs/s7+NdR8Y23hi11e4v7CS2kF7IyOjFgwcOAT98AsP4gMZBwR6T/AMEyT4z/AOF06gNHM/8AY39jv/bOM+Vu3Dyc9t+7djvjf2zXlHxK8LDxr+0Nr3hP4UaTdX/27xJcxaXaouXcmVs47BQdxycAKMkjBNTi5ezwWEzLLlKk3OUYwu5pS096ClffZ6b6EZrU9hk+WcQZBGeGk6tSEKPPKrFTVvfoqabale0lZ+9ZamVoOj+Ofjf8UY9P0eKW81zX9UaTcCxxI7FmkY8lVXlix6KpPavoL/gpT4p0OI+DvhUNV/tLWtDsGk1S+faZMOkarvwMh32GQrkcFTg7gRq5+Hv/AATu+HvWz134pa7Z/wC9HYxn8isII9mmZeyr8nyn4s8V+IPHPiS98XeKtTe81HUJ2mu7mQAF3PsAAB2AAAAAAAArPGNZHlVXBVJc2IruLqLdQinzJP8Avt6vsvxwzWUODOGcTk9efPj8Y4SrK91RhF88Yt7OrKTvLflWm+r/ABE/bHsDcftdfEhkfZnxjf7v9r981cx4F8M3WrapFptsm92dfl+9XZftdwo/7W/xHJOMeMb8/L97/XNXS/s8eGYWuRqU1gzt959v8Kr/ABbq/ccHOVPKKX+CP/pKP7e4Vp+1yPBf9eqf/pCKfiTXLfR9PisEmjd7NPn8v+Fa8n8feLrnXJNvnMIY2+TbXVfHrUv7L1i4sbabHnMzuu37q/3a8suLxLqJtu7H3dv8Va4LCx0nufUVcVKn+7P08/4N+fi1ffDvxz4g1G0sJpoYNI+1XEnn7f8AV/eVVpn7YWk/tA/8FEv2rpviv4/8N3EvhbR4mstL0mz3OljZ7v8AWbfvbpG+9Xz3/wAEfv2hdK+EX7UWk6f4hurOHTtQDWt4t991lb+Gv2j8PeLPg7+yZZa9+0l8TviV4S0XwXpSXGoeUk8ZnvNq7ooY0/i+b5amlhL5m4vc/W8jxuT0sk+t1VepCPu/5H4u/wDBcG48PfDXxz8P/wBkLwHujsPCPhmPWdchjbcv9oXS/Lu/2ljX/wAer4a37vk2bm/u16n+1d+0xf8A7YH7T/j79pDxDp62w8ZeIJryztV/5d7f7sUf/AV215hcab9nbe//AACv0LB0Y0KEYH4Xn2ZVM3zOeJqS1YW7Oy/P1/jqeST/AFifLsX7lQxx/u9jvhv71WNr/wAG1vk2/NXVyxPJGrs3LsRvvfOzVsabavLCybFx/DtrGkm+6kyYXd/wGux8H6Sl8qoV+Zv4aZEvdMm8sZ4Yd/332/3K0TcfY/h3qtz8u+O33N/C1bWvaK8a/uU3fw/erJ8Zn7L8JdTCQ8t5atu/h+as5FUzyiPU/s8TXs8yyzN92Nqu6fJMqtcuPvPv3NWFY2s11KGWPIrdbfFCyZ/4DQa/Cdv8O/H2t+E9Wg1jQdbuLG8tZVaK4tZdrbv/AImvuz9mH/gqa7TQ+GP2kNNWeJmVIvElivzqrfKvmR/7Nfm/Y6pNCyuibWWuo8P+IplhG/8A3tuzdUyj/Ka4fEVaex+6Wg63pXi7w3beMPBt/Df6VcfNb3lvKrK3+y391v8AZqWSaaO53uZERk37l/hr8mf2a/2svjB+zzqT3nwz8QrFbXHzXmm3i+baTf7TR/wtX1N8NP8AgqxNdTQ6b8VPhXa/Zpn824vtBuGjbd/ut/D/ABUvrEo6NHu4PMKEY+9oz7Da++1fch+Xb/C22sDUrHzLl96Ls+XYzS/M1Q/Dn4v+APjVoMXiL4aeJ7e/ST5orNtqyw/7y1ekjeSEWznbJ5u5tqfL/u1nWrRZ9RgailFTjI+Z/wDgpFEifB7QXW2WInxEu4J0J8iau/8A2EtNS7/Y502coGZLm9ZSW6f6TJxXC/8ABSuJ4vg9oUckMgKeJFXc3Q/6PNXo/wCwAs0X7H2jyoVAa41Dlo8/8vElfoNdf8a1oJf8/n+Uz0XU5qzfkfmp/wAFItL/AOEb/ai8QXdtDsF9ZQt9zb95a+eNK0a81OdI0RmL/N8qV9qft0fB3xJ8bP2vptN0ew86O30mFfLhTdub+9XU/Bn/AIJn6rpbQ6r49h+xwSffX7zKtfEU4L7R+VZvWjTzCcF3PkLwP8Edb1yaF4bOZvMbazKn3a9v8I/Amz8LWK3mt/6KFVt7N97dX1F4u0n9nX9nnRpkTybySGLascnyN937vy18YftCftPTeKNUmsPD1sttbr/zz/8AQaqVSP2TzfZzqeg/4nfErTdFh/sfQNq7fmeT+KvHNe8SS6hcs7vv/wB6sfVtaudUmZ7yZmZm+6zUkavI/wDtN/t/dqKfvG/Ly+8TedNdMron/AaVodsLd2aprGySOMO7sv8AtVPNaoq9NvzfI1UT8XvGXdQ733fe+781SfOSN7KHb+Gp5reFYdmxdv8A49ULyJIv/jz0vtlSjEjZTub/AHvvLVmFds291XH95aZGqJuVPut9z+KrTMfKTCL/AHf+BUl7sveFHY3vD8sNzAqP/D/sVensU8zZCWC7F+7WP4fXdcbH3MW/utXT29m6com7+6ytRyxkOT5TOsWkjk3u7Kn91m+9WpZ3Dx3XzuzI33f9moJ7GNpFdN0jN95f7tI1u6yB9jbf4dtHwkx5pQOv06bTZo9j3O5l+Z12V5X8bJIWukS243Nu211VrqU1m2zzmBrhfipcfaLmJ9/+9TjzFU/iOQT7wrvPA999l0OZ3fBVP4a4Kuo0eb7D4euZXT+HbRKPMa1NjndSne6vJJJPvb63vBumzzTIyf8AAmb+Gufgie4m/wB5q9C8L6c9nY/adn8P3aI7CqfCX9WlS2tQkL43LWMscMzfPytJrWqbZGiT5izbqz7e+dl3u/y7qf2THl+0dBb3UKQqifw/KjfxVLG0l1JnZ/31VKxV5lU+Ttrcsbb7PH8/y7v4VpRh9kJVeUu6TpqR20rhP4GrzyfVpvDPjR5oZtq7/mWvRJNSEjfY0uVC/wB3+KsLR/gR8XfjF4ui0H4Y/DrVtavrqXbBDp9g0skzf7KrVSiKnKMjtrPWU1uztdQRiHU5jKLnYa+uvhH/AMFZvj94d8Nad4O13Q9D1x7G3Eb6vqKXDXVwo6NKRKAz44LYBOMnnJPefsGf8Gw3/BQH4z6ZDrHxh06HwBolw6ukmuT7bny/+uK/Mtfo98Ev+DU39kjwNbQz/E74zeKNeulh2TrY+XbRs3/jzNXj5pk+S51CMMfRVRRd1fp6NWZ4udcMZNxFThTzChGqou6v0b3s1Z69V6dj4e/ZW/4KAfEr49fGrTPhtr/hHQrawv4bh3urGOdZAY4XkAG+Rh1UZ46V2X7R/wC0F+1R4X/aE8PfAH9mH4KWXjHUdc0Rb37PJaXEsqOZpY/+WUigJhBye5r7f+L3/BFH9jb9jjwPJ+0B8HF8QJrmhmKG2F/qnmxMJ3ELkrtGPlc49690/wCCXPhLwhF4L8RfECTw/ZjWzqosf7WaBfPW2WJHEQfqF3O52juxr8fxvDfDdHxEo4KNCPsHRcnHW3Neeu9+iPxDE8D8M0/FvDZUsLH2EsO5uGtnK9TXe99F16HlX7Ff/BOH9oTx34F/4Sn9tU6b4V1G6iR7TQvCshea3BGSJ2lMi7v9la9nuf8Agl58F7SF57j4i+JFVF3MWktsAe/7qvTvi7+1X8N/hFbn+3tZjWTftWvjT9qf/grfpw8OXWi/Dy/tZrgyyLuWX5mj2/d2/wB6vo8RkHA1JNQwcW/+3v8AM/csD4IcGYtqTy+Cj6y/+SMH4mXf7OPwR+L8fhfwu2o+K7nTLjddWd/cwtC/90NGkeXGe2cHHPHFfXPwD1HR/FOmr8Q/GHxC3/bCXhtJ51UQD+4Vr8PPEX7Wk3hPULn4zXNzDeC+upEljmX/AEmOTd/FWLZ/8FSfGdreR21hf3EaM7Miq21l/wBn/arlwGGwuVwlDC0FFS1duv6n6tkPh7wtw3hp0Mnoxw6m05SV7trRXbu9Oi2Wvc/o8g8a+D4FS0TWLaT5e0imud+J3hb9nbWPDc/iX4kaDoU1pZx+a15dQoCoX0b71fhV8Jf+CqPjOa4trLW/ElwrzXUdvBHuZmaRm/hr1/8Aac/be+JfgPRbPSvGGpRyvDEtwml3ETSLM23dGzL/ALNenSzepT92dM9ZcIU4y5qdZn3lo/xu+EWn+DL34PfDK1uvDOmau8iW9zZys91ukb7yq33d1fm9+0f+zx8a/wBhj9qa7+Lvxi8SW/jPR/EFq3/CEeINWt9ttprfxefH/wA/Cr91a3P2Kf22NK8ceLG1jW7xjcXF1u8xk+aP/d/u19U/tKX3wa/aa+B2r/AXxhOyW2ofvtO1G92yy2d4vzRzf99fw1w08dzylGs/8PkfVYbASwU4yw3wv4vP5n56698XPAevateeP/FXiSa5n1Bf3F1qF0z3l9/ur/yzj/2a8vs/iNN/wsqHVfAHnC2mdkZl+VWWvXvgT/wSd8Z6TNqPxA/a6+Lun2em2N7Ilq2ly+fLeR7t0fl/wxrtqT9o7x58FvhzZpoPwB+Cd9fJo8DPcapeRNub/ab5a7adHmitebmOmtmVKjWvD7Pc+Ofi+Nbtv20C2uEreDxJpruXODysBUk/QivQv2jPiZr1u15BZ6wrursu6N9zKteUfFHWLvx1+02NX1i6LSalqmnGSW3wpCtHABt9CFxj6V9LaP8AAD4LR3EVzf6JeX7r8sTapes+5v8AaVfvV+x+ImWVsTlWTcrS5cPFP/wGB8jWzh4WpKUNedtnxLefEbxV9sWGF2xJKy/uUZmk/wC+f4q0bdvidrih9N8Ga5drI/3rfS5m+b/vmv0B8K+EfAHhLUGm8N+A9FsPM+Z1t7CNV/2W+aumj1q5jeXydVaJdv8Aq7f5U2/xV8HSyvDU48sjzK2Ox2I95yPzgb4I/tD6l5OpaV8H/El55kv3Y7LbtX/gVP8AEXwl/ao0ePzr/wCCHiiKKPbvkWy3Krfw/dav0dmvLy5uB880rxp/f2tUNxfXhhKJc3Cr18vzdv8A49V/U8LEzjWxn2ZnmP8AwR0t/iBa6N4/g8e6BqOnyLd6d5EWoQMhPy3O7bkDPb9K+evi54rvfCv7fereM5Y5Fl0n4mi6jGMNiG9DIR9QgI+or9D/ANnk+YutT+YWLzQ5LPuOcP371+dv7Vy7/wBtzxMhBOfFsYx68x1XgzThHxUz2Mdvqv8A8qPxDgidX/iPHEHO7v2dHX/tykN1Sx+yzXepPzNdXsl1cMr7l86RmZl/8ermNSwZ/n8vDN/DXc+OrV9smzzlTd8v93bXEXVrNND5Pk/N/Bu/hryeX4Wf0NVlKUTAvrN5nKO7Mu/5/k+as24s9rGzTzPm/h/irrxpe5lfYqj/AKaNUy6DtZkh2p/G7bdyt/s1106kdjwcRTlI5X+x08svv2bdv3qaunXMbG5mm3pv3J/FXeWPhl5YXmmtm2bN3yp92kbwjcrIf3Oz+L5l+9WntoRkc31WUoKSOMsrF0jhd3bLfNuVf/Qqv2+jzXUwSF23Km7ds3ba25tARvk2MoZakXTn3I7/AC7vllVflpVKkJS94ujRnExV09/7/wDHtfzPu/8AAaf9hdt80ybEX+Jkrofsfl+VC9s2xvkdmT7tSW+h3NwxT7qfdauCpWhKPKj2sLhZ/EYNnp72+z7Mm6L/AJ57/u/7taVjBCxDBMP8y+Wv3v8AgVXLfQ4ZIfMdGX+H+6y/7tEdu9uzzwuzovy+W3ys1cEj6PB05RH28JbfM6MNu1naR/u064hdbVy43D+Nf7tW7HTUmm8nZv8ALX51b+Kn3Gn+dZiHY0Qb5vlqeX7J7NGnzR0OaurF2mKImxFi+9J826siaz/eCV3kSL+JY3+Zq61tJdo9kzqf4az/AOyXnV0d4wFVttdFGUDw8Zg5c/MzgLXUjNcN53ylvmT+6tbmj6x5jKj37AfwVyS72k3u+ZN/ybU+9S2upPH8+9cV9hCX8p+QSjy/Eesabr3BTzlZWRd/mPt3LXc+H/ENsm2ZJmaNm+VlrwzQtZ2Mnk/P/eaR/vf7Ndxofip13P5+Ek++qv8AdaorYiXwjpx6nsema8l1Ir7Lhyz7fLjfbt/2q39PvE2slyjM7N/D/FXlnh/XLaVf9c2Vfb8rV1mj60zR7Iblg6uvmyV5OIxE9YI9zC0/hkdvabOTNCzmZf4W/wBWq1fhazaNd75ZnVU/i3Vzel6k74hS/wD3bff2/wAVdLo87pGXd1+b+Ff4a8PEbn1GF13NRbdGhjtnk3u33/MX5Vq7N9m3rhGRf4W2f+g1UgkEcYdH2/N8zbPvUv8AaHmLsR/MZfl2rXLKPND3T06Mpc2vwmVqcdnKzfZk4/i3fe/4FXN3lvZ2sLIk02Vf+L5mb/7Guh1K88mFZt8Z/il/76rB1a8hVXmmlVmkl2p8v8X8K0R/vHbTqRgebftFqw+Ft7KZAzO8G4EYZR5q43Vy37OgnbwRqCq2U/tI7k9f3aV1f7Rgsx8LtRmRt0zzQB237sfvV4rjf2e/NPg7UFjLnF/nanf5Fr9XwP8AyZvE2/6CV+VM0hW5M0jPy/zO6upIV/ciZtsP96iCSZrU3MDr8z/Ju+7tpl9cRyLv3tsX5f8Aaaqc0bvtRH3fP97f92vyWVOUtEezLOJUZHQWLTWMLP8Ab12eUrM2zata1rHDcZk35ZX+Xa/3V/vVz+mzfaZEhf5kVNu1q3LP9xcpeO+2Ff4WrKUZ83KcNbNPbRNKxkeENvG9Vf5F+6rLWitikzP87K0ifJ/vf7tUrOaG4XzoXxtTcysv/jtXbWOaQbHdZFkT5dyfKv8A9lS5vd1keXKpzSJ7e3ePZsddy/xfw/8AAqQ6ek22GFJMK21VZ/lX/vqr1hplzbwImxflb+995a1YbO2uIxNsj3sn8X8Nc9OsZW5vsmI2kfamV/JkCt95t/8A47Ww2l2zRx/ZkbYvyp8n3WrStdF8to3hRSrPu2rWlDZyXFuLab5Pn/1bP/7NU80ZNJyLjR9zmkc2umzfYwsz+YN21l2fept7Zw6bZs77YkVWeWb73y7a6FdNT54YUV0b7is/3a88/ai8WHwL8O7x4XV5bhVgijX+83/2Nd1CEq2IjA5sXKOHwsqj6I8ks/En/CdePdY1LYq29raslr51xuXbt+9Xzr401CbQPHjXKTKFmmZHaN9vy12Pgfxomh6xqthAixJcW6rLIvzNHXm/xekS6/0+FG37mZWr9Lo0/Z0Y0j8eq1J4ivKbkZmsapJeXF5o03y94F/h21g6XrkMczabeTbUj3bKo3GrTXipeJy6/K67v7tc9r1xMtw+pQPhWfdt31qKP8pf8XRubiW5hfcG/hrj7qZ7pSj/ACmP7lbt14i/tLSdibd6/crnLqPdJvTds/2qPfkOJf0e6SNWhuXyjJ91f4aqSXCQzZMzfL/DUUbfZ2D7/lpk4Mrlwm2gstFIbj98j/L/AHarXEaKzeUm0NUcUrxPtqz9qhkQnZzVcwfCUtvzZpyfeFLJ8rMlNqiviEf7pqRMllqN/umnj5GT+7U8oSNfT2jXG/cv95lqzIsPzpvb/e/hqrprfLs37t1WrmUSQlOiqn8NZylP4TP3Ocgkkjbds+9UfnIzdP8AgP8AepHnTy/3b5P+zTF2bT8+2qKH7tn3BioriNGkaQp/v/PTm2bdnT/ap+1PLZHf/gX96gn7RUmb95xx8n/fVV3+6aszR5y/3dtVP4Pxpx90uMR9q22YHNXJrWaST7/3qp2L5uF+TNdIsCTWwfZtfbtSqjGYpcqMC4SRZNk3RahaNl+50rWurVPLRNmH/iWqHlvERjrt+eoFzEPl95KsaVpo1XUrfTY3CtNOkYZugLMBn9aikXG3+9Wh4RVF8VaZkcf2hD/6GKc24JtEVJuNNtdj+iP9kbSdC/4J6fC3wL4Y0rx9ZIPBeyO21fWnS2jvLgu8smVZ8KHZpD5YYkKSAeM19AfH3/gpb+zZ8cvh+2laroPgGz8QzNE1z4kHiS2LoUGCUIKvgjja7soHGGIBHxJ/wVCt0uP2crMSIWVPFVqzAenkzj+tfBC2cLbY0+7IjbFb+Kv5z4W4fxOf5LVxNXFuPtpy548kZJvrLXZu+6sfy3wFw7mvE3DOJrzx8oxxNSftIOnCacus1zL3ZO71jZrp0P27+Jf7ZWhftK+A/CHgnRZdAki8LWf2aO50jVhcm5YIiZwGIUbI0+X5jkE5AO0el/An9s7x74B8Hj4ZfEL4c/8ACZ+H0TZb296rGaJRjbFl1dXjXHClcjgA4AA/Fz9iv4np8M/HEWgvNNFb3E+9I/u/N/F81fpz4b+PHw38G/De58VeP/GWn6Pp1nBuuta1KXbFbr/Erf3m/wBmvtcB4cSeIljFmM1Usov3I2aSSSa2asluj67D8AZxhM6eYUM0n7aUVCT9nC0oxioqMo/DJWSWqe199T2j4+fto+N/HXg0/DLwH8O18FeHpIwtxb2oZZZUyS0YKqipG2eVC5ODk4JB+XfGnxe+E/w3vl0z4ifE/wAO6Dcuu5LfWtbgtXYeoWV1JFfEn7a3/BdLV/GV1f8Awx/Y2sP7PspImtbr4hatA32u6X7rNZQt/q1/2m+avyr8d+KfE3ivxde6/wCL/Ed5q+oTTt59/qNw0ssv+8z1z4/wrhmuJ9piMdOTSsvcikl2STSS9EXmng5ieKMWsVmmZzlJJJe5FKKWyjFNRivJJH9C6/tQ/s0P939ojwKfp4usv/jtel/s6f8ABST4Y/s567dat4U+OHgO+stRjRNQ0688U23lS7WyrgrKNrgFgG5GGOQa/mD3vjIfHNWF1O/jARLt9v8Av1lh/CCngcTGth8dKM47NRX+ZGW+BUMnx0MZgczqQqQd4yUI3XTv1V009GtGf1tH/gsz+zrb2M978HND+HGnatdf8fV+viezlVjnJ3CERs/OTy3514BoP7Xfw80b4n23xVn+MvhLUNYg1YahI1/rltIs82/eS439z6YI7YwK/m1034ieNdHUJpXia6tgv3fKl21eHxq+Le9NvxC1TK/d/wBLb5a7sb4aYnMZQliMfJ8jvG0IpJ97Kyv5ntZv4V5nn1WjPGZtN+yfNBKlTgoyunzKMOVXut2rn9PX7eX/AAUJ034h/Da3+KfiPwWtp4f8NWguQmjyre3FyZ2jTejnYChyhCjAxkktxjxX9m7/AIOI/hN8E9NPgpPD+ueIdEuJMnRL63hMlsDncIitwwjViclSrAnJABJJ+e/2rDrmsf8ABHJntpp5L+5+G3h5jIjkSO7GyLHPqcmvys+EvgnVfAviS28VX7yfaYfm8vd97+8rV4PBGQU8+liczxVaft4VJQ5otRulGO8bW+VrHzHhxlD4lxOMz/HYqr9dhWlT54uMVKMYQSTgo8rXla2i0P6S9O/4LX6J8f8Aw9L8Ov2Y/g3f+D7S9UxvdRQRLOrMRuMRidFjJ5G7aTzkEHBrzLwr/wAFiv2fv+CcHx/8T/CT4mfC3VPEviC2sLY39/pEEZeyMq+YIBM7dwQXQKOQvzHGK4L/AIJY6H8Pbr4Tv+0JqqQ22j6Ppc1/qUm35YVt42kk+b/gNfnJ4o17U/jR8QvFPxy8SOz3/jLxDcatK0n3ljkb91H/AMBj2194+CcsniYY6pUqOpD4dY2XpHl5V9x+gS4UwuIzilm1avVdWjdU9YcsE9Hyw5ORado/kj9gNT/4OUf2BPEV1Nr2tfsba1e3MpBmurvTrGSSQ4AGWYEngAcntXiX7S//AAcNf8E/vivpdpofhP8AZq1zwrc2N0zTajbaPbCVwRjywIpEBXudwbpxt5z+a+oaTCq7A7Ii/Kkdcj4q+FCeNI/Ih/d3LbV3L93b/ep4zgzLszw8qM5SfNvpBPvuoXO/MeGMvzvC1MNjpylTqfFaNJN63+JUlJO/VO5R+MnxL0H4nfGzxd8UvD9reW9jrviG6vLNLuEeaIpJWZd4UkBsEZAJAPc19D/sh+G9B8TaDJZvukmkVWX5Nu7/AGa+TvG3w88VfDO8gs/EMDeXcJ+4kX7jL/8AFV7Z+xL8Wk0P4k6bZ3k0clksrebHJ8u35f8A0GvLzjLquDwqoQ2ikl6I/UOHXg8PSpUIfw4RUVfskkvwNT9qL9k3xp4i8Yf2j8PdBuL5mRt1rbozN/wGuU+DP/BOX9or4meJ7e2/4QDUtN01pV+0alqEXlLGv8TLu+9X6G+FdWs49At/FWlXEZvmuGWX7Kny/e3L5bf7tbviz9oqw0OO8174keMJLfSNN037ZPM0X7uFVX7q/wC01YYLNZ+zjThH3j62tleXzl7XnPgX/gsZ4b+Gn7MXiv4X/s2fA3wxZ6ZeeG/Ci6v4g1iFV+03V5cfL+8b/gO6vjP4hfGP4p/FCyttN8d/EHVNVs7Vt1vaXF0zRR/7q16B+1H8adb/AGnvjj4k+NetSSBdUuFi0uG4/wBZDZx/LEv/AHz83/Aq8nmtTbyF9m5NvyV+j4bDRdKE6i94+GxGMqxqzp0ZNQfQSzuNrBEfj/dq+0jyRgO29/4NtZkKvDNv3fKz/OrVct7hEzM/yp/yy2128vunnSkS28myb98i71/i30s10NrP5NUdUZ4ZEvPlZPuvt/hqBbnaQjzMf760uYZr2snmSbH3K/8AdavZfg3oKf2W+q3KbflXZ/tV4podylxMiTH7r/dr3v4e+Rb+FxMJtz/3mp8vMZykUvGFmkN2+xGlRd2+NW+auI+LzCz+G8ln8qvJcL8q13viaTzIy+3an97/AGq80+Nl0kfh6KL5t32hV3N91v71Eub7IoL3zgLKKHT7Vf3eX27qZNdJcfP/AAt/tVHcSI0aom5ty7v92qsdwGb/AHaj3zePxFuGRGVn2bR/HWpY3W7HlTfL97/drDjuP40dsb6vQ3CfZ0TdtoD3uc7DQdcube4ASZlVvl213Ph/XIV2xpGpO/ayt81eT6bdJNNsCMF2/wCsb7q1q/8ACYQ6ZcJ9gRnkX/l43fLuqvdKPZ9P8WXPw/1CPxDbeIbrSp4ZfNiuLWdo5P8AgKr96uv1T/gqR+1LJo6aD4e8bW52r5X9qXVgr3O37tfLjaxc6lqEt/qV/JLcM3+sketzw7a/bZlf7zVHsaUpXZVHE4ih8ErHpsHxe+LvxR1F5fiV8Q9V1lVTekN7N+7STIG4J0BwSM+5r1b4EftwfGX9nWJ/Cuiyx6z4eScyt4evyBGu9fmMbDlCSSTnjJrxTwubWLUDbQTqzG2LMq9vmFb8vw8lvdB1D4i3fiSxs7GzlSGSJ7pfPlkYfKqoetff4mkv+Ib0Y/8AT5/lM9+pjK0clhWjJ35t+vU9puP2zvDFn8ZJPip4b8NzRLeRRxPZ3EXzW7fxf71dH8WP+Cgm3QWtbDclxNE29tnyt8vytXytJ4i0qzXZpqb9vG5vvf71UL64sNcVvt9t5is+394+2vgfZxifM1pe2q88tZHM/GL48eIfHmqPNeX0knmbt/735a80ub64vpvOkdif4a9ivPhL4J1m1+SaS0Kpt3R/NWBdfAvWNPmD2Drewt/qljTa1VGPvB8MDhrHTbm4Ub/m+b71bWn+H/MkXL/drttH+Emqqy2yaVMTv+6q/datbTfhHrz3DW8dgwb+9W/syPafzHExabDa2vyIuFes/VJkVfkdVr064+BXj++Xy7DSmYN/d/vVkN+zT8YLmRUfwZIyM3zzeaq7V/vUuQUa0ZHnVzHtU73yzf3qhVU5/wC+dteyWf7IPi2ST/ia+JNJsE2q/mXF6rbf96rbfs3/AAx0dvtPiH4tQv8AeZo7G33fd/2qy5YfCXzfaR4psc/3dq/LV2zt3WP54WU/7Ve1aX8KfgCyqltealfTNKrRL5qqrR/xfL/er1P4f/sm+HvHV8mm+DPgteTNcS7Eur64ZlX5fmZv4VX/AHquNPmOeWI5dz5S0i3uYW3+Rk7/AL1dbZ27zW6P952X+FvvV99eHf2Z/wBmb4Q6PPbeLfhjpvibxJ9n8q1jVma0s22/eb+81YWg/s2/DTVNY+2ar4YV3ZVaXTbOLYka/wCz/s1XLTM/b1f5T4l+yvaxrv3Dc1MjiRbrY8O4/d3fw1+iUP7PPwKtbo2z/CXTYreFlbcqN5jfL8ytXm3xa+BXwf8AMlu/CvgOFIlbbLIr0/Zlyrcvu8p8V6hYoreckLNt/u15t45mM15sHRa+7dD/AGZdN1ze6eFVjT5mWb5lVq07v9in4LWNmJtd8MW9zdtFueGFm+alyx5QhW5ZfCfnVpNibq4CYrovEFjPZaGsez/ZbbX3jpP7BPwr1TVIZk8Hw2cEi/8APdkX5f8AarrvDv7E/wAAdJ86HUvAcepvv3RW9xKzL93/AMepwjH+YJYyUpaRPzQ8NaR9oukedMIr/N/s139xZXjWYs9MtriZ1X5Vt4mbdX6KWXwf+FHhqFE0H4N+H7OX5lVZLJZG/wB75q6Twz8I7m+h+13Om6fZw26K8vl28McVuv8AEzMq/dpS5Y6k/WJVJn5XR/Cn4qeILwJpXw6165WT7nk6XI27/wAdr6i/ZW/4IV/8FHP2p9Mj1/4f/AK+stMm5+2arcLbr/49X6H/APBJj4LP/wAFFv2nL/w9pfnJ8LfAsqvq15Gm1dSkVv8AVq391mWv3u8LeEfD3grw/beGfC2kwWVjZxCO1tYE2pGvoK6I1KFCN3C8hNYnFaQdo9z+dj4Qf8Gh37aOrPDd/Ev4u+GdEik/1sccrTPGv/Aa+n/h9/waBfBm30+P/hYf7S+sveNFtnfS9OVl/wCA+ZX7NbMD5Yx+Brkfhz4vfxvDqfiOCbdZtqk1rYYX5fLhbazbv9pt1Z4nNp06blGEY+i/zuFPKKblzVJyl8/8rH5zfB3/AINT/wBg74ceJ4Nd8ceOvFHiqCCQMmn3DR2yyf8AXRk+Zq+9fgV+yf8Asw/sv6RHpHwK+Cvh7w3DCm0XFjYL5zfWVvm/8er0jc3c1ka1bzXnyIjbPvNXxONzrEyd4HuYfC0o+6WrrxVZKjGGZWEf3m3VxGv/ABshs5pIIbmNpFbair/eri/j78QrfwLobusjIkcTO23+HbXyav7V3/CG/b/GetnfbNKrQW7JuaT+LatfPVsdmGIu3I+nweWUILmlHmPor9s3x/e65+zbqUGoTLH9ru7REhZuXZZkY4/vcCvJv2avjND8F/gBrviG4ukQf8JDhVlfavMMYOT78CvCdV/apsv2j77UDrdpdabeWtyJNNsrlGCuDwwTPouT+FdHdvaWn7LWtat4isY5tIttfzPubkOYUAyPSviK05z47o8//Pp/nI/FMww8Kf0jMFCMV/ubdvnWPlX9vr9t288SeKru2S8mtntZfnhXdtVm+7/+1XxV4w+K02sMt5NqTW97M/8AFL8rfLWj+3F4203VvG89zZ6izpI67JI7jcyqv3V3f7NfNGreNJpLpkS5yF+7ur9Jp4WlKF0f0CsRUpS5ZnYeNvHWseKvLGsXkguYX8rd91WX/a/vVxVx4g16wkaGOHztzbYmV/mVqYviD7Q3ku6h5H+VpK+zP2O/+CPni79of9nw/tbfFn40aD8MPhwlywtfEniW3aWe/C/K32aBfvKrfLuatoYWklaR6UsZQjGLvqfO3h3wj8RdB0Wz+IX9t6bpTWbrcWElxfqz/L83zR1r6P8AtMar8YvE2rnx/wCMri/1W6l3osku5WX7u1f7tfTV9+xf/wAEa/D1gv8Awm37cnxE8ZfZWZZ10HTobS2m/wB3duZVrjPH3wR/4JNeHY4tb+C0PiiG8VW+y311rjSN5n8LMq1yShlsvjn7x3VquPjSioQtHzOR+E/i7xV4R8Uo/h6GQ/wt5KbfvV+iH7EOvabY65FbfHjTf7Y1CZN9rpd422O13fckb+98v8NfkhqnxA1L4a+M982tzX9srM1rcbvvLu+XdX1X8K/24vCvirXtM8YpM1nrK2EdrqX2idVSRY1+VlrzMVhacfeii8LmH7v2fP8A4j9sPhrZ6T4W8V2Wrf8ACGaVf6DN+6nsHi8xrdW/5aLu+9Wr+0n+yL4B+I+mn4h/D+ztftllEzXGk3qbYLyFl/eR/L/s18g/sh/8FBfhNf8Ah4XfiPxTDfCzRmlj8/bEqr97czfxVL+zT/wUP8Q337S2q+D/ABNcXt74T13UZG02B5dq29vt+VY/71ZYXHShHkcSMyymVaqqtOf2fv8AI/IH4veEj8Pv2udQ8I4GzTvFsMcKouAsfmIUUD2UgfhX1Npd5bqqQ/xbmZGZv4q8Q/4K/wBppNh/wUH+KsXgVHs7Zr+3l08RjDQl7C3cY9wzVtfs5+IvG2qfDLTLzx3qXn3KsqvMq7WZf7zV/RfGTnPJcpb60I/+kxPh81caVSlDsj2qLU5+P9JWTzE/eqsX3avLeTWqg+SweT5XXftVa5Wz1b7PNuhK7dm7y1T7y/3v9mtfTbq5kut+9pEbau5f4W/2q/PJR5dTCnW+wdXp+9o2h+0szfL93+L/AGafuSSQJJ50SSbmRW+8u1v4qoaWt4q+U+2NY23P/E23/wCKrY+yvcbd8+9fvbmSuWpLlO+nU+yd7+z/ABJFHq5Qg7pYeR9Hr87P2rY3/wCG4vEiRj5j4th25GeSY6/Rb4B24tbfVYRgDzYiFCYxkMcmvz0/aejjb9vvWY0QsG8ZWmVU8kkw5/Wp8F3fxTz3/sF/+VH4hwRd+O/EH/Xqj/6RSOp8VeH3njazf5P3XzKvyr/vVx1x4dS1Z5t+Qzfdb+GvdvGHhHz43/csqt83/Ad1ee+JvCqNM3+hqkf95nrwalSUvdP6Kj/eOATSUZXfZ86v8zf+y1ZtdPuYT52zeu1dy7//AEGt5tBQyLNMnzqu75f4lqSPQZvO3+T8u3dtrCNTl0OapHm95DdJ0mG4V5vI2u23Z8/3qtL4f8tVmaFZgv3WV/vVr6DoaWsjpN5bnbuVf7tbmn+EzHEYZtNUo38O7bt/i3UpVeaHMbU6cv5Tzq88P+TF5zo22RvuqvzLVJtBuZJNj/M+z7zN95a9R1Dwj8yySIzBdzfN91qzpPCaNIkiIxH3dqp93+9TjWnL3WRKjLnsonCLodyrjy+qv/FV1dNS3YJNDlFXbuj+Zt1dLJ4dhjCI/wAzMu3b/E1WbPw7IFWb7S25V+dmSsZSh8Uj0sNTlzcpyseizSQtCiMq7/8Alov3d1ZV1pdnGzu/yqv8Lfe213WqaXeMuya5XYvypI38Vc7q1vctJNtmjKqq72/2qzp1OY9yjHlloYJXy408v+H5k/vbaka4mjm2fZZHGzd8v3qZfTbdn8SL8zsv96oftnnH7TbSL8v3F3fNtrWMpHr0acZCNN/o4QpIH+9tk/hWmTWaQr/yzZm+dP8AapizW00I2JlY02pupPMh87KP91dq/wATNWtP4jjx1PqeXarYPY3Don8Kfe2bWrFmkdt8aDB/5516l4m8K+Yqz7FX+L5fvVx2s+FJrOTzk2/vP9mvpKeI6H4viMNKOpz2l3E1m2+FGPybdv8AvV1mi6hdNGH37tq7dqp/31WRHps0TLv2hmWtvSbGaOVLWFMfdZm+7/vUVKxzUaXLL3jrfD8kMarczO3zfLtVvu122k6liT5HZ9yfe/irgNLsXhjZ4387+JK6vTb37ND52/Dx/fVa86p70ubmPZwsfZ+8d3ot9NZ7d6K399q6/QtYS4Vf9Wi7P4vlry6x1KGSNEtvMU71b5nras9el2q9y8Ozftbd8sjN/DXFUoylM9SjiOU9LXVraaFvJSQR7vlZU2ruqO61RFm+ebYjfcZf7y1yWl+IJmjd3dtn3UaP5l3VDq2uvb3W/wC0/N/A0b/L/u7axjh59zujiuaPvHQatfRrJ++flvlRvvbqyL68eQ7HmYn+7H93dWNdeJnOftO1v4UX+KqP9vW02+RHZyr/ACbfurVSoy5ffNPrlKMtDB+P12z/AA51G3kGXWeHJxjA8xa5f4AyIvhi9jZwpa9O1i2Odi1p/G3VYbjwJcxJPJl5Ih5b+zg7q5z4M3DxeHblI4lY/bt2GbH8C1+qYKhbwhxEf+ohflTCWMXN7XtoeiNcXl0rWyTL83zeYyLVdY3ZhDCi4+VZV/u0y1vNscqeduVU3P8A3qs2O9pR8iun91vlr8plGUdOUxljvaas1dLX7OoRPmdm+Zm+b5f4a3bGzPlt5yMI43yjb925qx9LdFh8z7SqS/8APNvvNWrDLCsyQu7O0L/N/d3NXnyqS5zSGK5ocpsaT+8keHpJHtZ/O+626tuzCXClPJZJWT+F/lWubtbya3VU8jO5tyVehuJPJ/4+WZ1b/drmrU/3vOduH5pSlI6G1uob6PZ50Z3ffVvvLtrWiurZdttNbbH3/wAXy/w1yEN+8kkWy227f4fu/NWtZ6s65TyVfb8qbn+7XFKnKPvROyn7szrLW4hk2wj5Ek/hZvu/7VasLW0zb4PnSNNv95q4iHVkhZnM+f73ybm/75rVtdVdXR4V4Xav/AacYx57m3N/MdPH8reS8ypGu7bJInzV8n/t6ePEvPE2m+A0dcWe28umVPm3fw19G6n4kTTrW5uby+VGWJnRvK+VdtfBHxa8YXnjDxpqnieaZm+2XTfe/hVflVf92vqeHsL7TF+1l9k+T4rx31fAxpR+2clpmvFfE1xamZYkuLdk3NWD4uuHkhe2fkxqqvVbxRffYNUS8875f9ml1TVIdQt/tj/P5y/Mtfe8v2j82+E88m1AafcPDcrwrttVflpl3B/aFr5Py7WX7y1L4s06PdK6bUXf8n+1WNp946/upvl2/KlPlQSlMz7q3urHd2RmpkkjzbpFdvmrW1S3+1Wp2P8AN975awnieBz6Uy4/CEgKBlxTJJE28VMpWQh/mP8AvVHPCit/8TSlIqPmR7fMzv8AvUwllb56e33zs+7Ss26P56UTQbTWV2bpTqKctgCpGj2hNn/fVR1LJJuVS6YK0yJblzT1/ds+/dt/hq55iLjZDlf4kqlZyJ5Owp81WtrrIQj/AHan4jIim+Vm/wDHaZJIm3Y7sp/u0sjOzb6Yzbmx/F/eqTQfHJwqJ/6DToIYWVqjSR4+j/dqTzn8obP+BVfoZy+KxHcbVjLtxWezbqs3zlhhvl/2aqnk5NL4jWmPgO2ZCP71dhb28clur7MfLXGhtrq5/hrs9JndrON0m3bk+61UTWKl1b7lyn/fVZslt97ZBuNdFcwusfmPDtrLuFfcdibaDH4fiMp4sLsdFb5vlq74UjRfFmmfI3y6hD8v/AxUc8bqVeSr3hJN3izTSBj/AE+H5v8AgYrCp/DfoKr/AAfkz9xP+ClMQl/Z2hGASviO1YZ7ERzV8WeCdN0e6tPO8nz7j7yfw+XX29/wUO0yHV/gTa2E08kav4jt+YwcnEU3HFfG+l6TbaOv2aGwklbyt26T/wBB3V+TeF1Fz4XUv78v0PwzwXUnwdZf8/J/ocb401a50PxMr2G2B4/nRlb7tRftBfHzxz8btH0rwl4k1DZomiwK0Wkx/wCrmmX700n95qm+I2ivDCmq6lZsjXDN95a8/wBavkjRIbNMbvvLX6JToe7bmP2GnGJ554xhs4LWa/S2VPLTcu1NteFXcjTXLyu+4s7Hd617T8VGubXw/Nvm+aT76768VeHy/v16OHjywPUw/KqYx41VRTljeSlPf/Z9alt4ZGYOFx/7NW50czIfJcsqdmr0T4N/B288c3wuZoG+zQt87Mv3v9ms74a/DnUvHnia20Szs5nWSXdLJGvyrH/E1fZPwl+Ct/4g1C2+HXw309tkcscVxIq7m2/3l2/eaolzyjyxOLGYmVM+4vj1os3/AA7T/sDSlCMvgbRoYQv8OPsoGPwFfmPJoL/bPsEKYWOXY8m7c27d81fqj+1JY3Hw/wD2EdU0VI3aXSvDenWgVuu5JLePn8etfnh8Lfhr4h8aa0tnpumyKjOu+6b5VX/ar8s8LIN4LF3/AOf8/wAon4h4PTtk+Of/AFEz/wDSYH1h8N/jBq/wn/4JFeJ/hFojyJqPxC8Ww6Dbt5q7o7Hb5l3Iv+ztVV/4FXgkmk21jpfkQosKxoqReWn8K16l8Yv7B0HRfDHw90FGeHw7psn2i4ZN32i6k+9J/wCy15pcR3muSeS8LH5/u/d3NX6hGPtpe6frvtOj2Oe/sWa+vH2Rs7SOq13Xhv4d6b4Y0X/hJPEkPks3+q8z+9/eauk+Hfwvs7GxfxFre63iX5bdf92uI+O3xYeZn8N6U6ru3L8v/j1KtiIYWlyx+IUuV+h5j8bvFFt41vJLCG2862j+dG2f+g14xd6lqXw/8UJ/YM37xU3r/u16lbWfmXX2n5ssu3dtrgfiJor23iBb+GHdDMm1GVK8l2xH8V8x1Ua06fvRPQPBH/BRS9+HXhQeE/iFompX7Qbp7NbO48tPO27V3NXlPxd/bO+Kn7RH2TR/ENzHZaVa/Kun2e5ftDf3pW/irkviBo6X2nyGGFt8fzJu/i/3a87imezuNu/G2tsDlWWUpe0pwtI9qOOxVelyuR6et0k/l/Ix/wBpU+Ws67s0aFt6fNWPoeuTbfndsN8u6tuO4SZWh7fe3V7PMckueMzCuke3XyXRW/iSo5tkKps3bG/u1q6hCgj2PMrVmyRPN+58vYyvTlzDjr7xLDJNNH9lmT5G+WsubfZ3DwvOpKtt3f7NWWDx3C+c/wAqvTtUt3vrfzgi+ZD/AOPUS2LiWPC7It1vdFX56+hvC8hfwXbXX2ll8xdzRsn3Wr5w8N3D/bI32fM3y7a+gNDuPL8DrM77vL+aKOiMuUyqRNK8hfULGVHdf3a7vu/erxz48SeXpNtbb9n7/c0NekR+JnWzCfxSJu3LXjPxm1i81DUoo5vuK7MlKROHj7xyUd5M0ZR3qN5Pm2OKZRwRT5kdXKPW4eKPYHq1DcPJH8821P8AZqgnT8ae0n8H92mPlRqNqT/cT5Ydv3UpYbh5dqI/Dfw/xVmL8x+T71XrGaaOT9zB5jt/d/vVmZcp0kMem6bai8vHYn+Ff4mrU0nx1c3Hmw6Ho6qq/KzVyM8bQnfrV5h1/wCWKtuapIfFWsCxfStPm+zWrf62OP8Aiq/hD4j1v4exiPW5H1G/R7+Wy3PbJ/yxTcvBPrnFZfxF117fxLNp88oaFGidYQcfNtHzEfxH0rL/AGf7n7R4kuztI/0A9Wz/ABrVb4vXJh8e3KyklfLj2qP9wV97if8Ak3FH/r8/yme3L3eH4W/n/wAyzpusPdXzoj8yfc21ryLpmn7H1jW22N96GFNzLXAJqdyrDyXk/efKixpuauw0JvCvguGLW/HkK395J/qNB3/Krf3pm/8AZa+B+E8DlOp8K6TrmrKtzomm/ZrTzVT+0tSuNq/7y13eoX3wl+HLQ2Gq+Kpta1hdz3Cr+7trdf7qr/y0rwzxF8VPFvjDUo5r+8xbW7r9isYflit1X7qqtc5fatqVxfPc3l4zySNuZqnmmV8XxH0na/tAeDLeZ4baGP7OrbmjX7zNVS8/aw0fQ2L6b4bt5Wb+KT5q+cYrqaOMuj/MzU+zs7zU5kdIZGLfxbKfLOWkmHLA9s179sTxncLJbaVO1skn3Vj+X/gNcVffHT4i69L5L6rMu77+1/8Ax2neCfgT488Z3kdnpuiTFpNu35a+qv2cf+CXvifxNNFf+MPL063WVWl875ty/wAW2r9jb3pGMq1OnL3Yny/4b0X4l/EK8TSrCG8uzI+Nse5t26vp/wDZ7/4JW/Gn4pMl/r2g3Wn2y7fN+1RNuavvP4Q/s0/AH9kvwXc+Nte/su3trO33y3mobY5G+b7y7q+aP2xP+CziWsN54D/Zmf7HFNuin1Bn8zzP9qOnzUofCZxVSt717HpGl/sa/stfsxzWz/E7WNPvNVkt90WmrKrP977rN/DXfah4g0258OjRPDFtZ6PZXHzxR6XF96Nl+6zfxV+Xvw5+IniTxZ8QLn4heOdZuL+9m+Z5rqVpK99b9pzXVsYLC8muES1i2xSK3y1HPVI9j7x9dQ+BfhFpOi/294q8VQp/eh27pW/4FXK+Jv2jPgJ4NjVPDcN1dzblTc23/vr/AHa+Jvi1+1FreoSSW1heZ+TbFIzf+y1xFp461/xZqKJeXjHzPm3N/erL9/KRr7OPKfdLftPfB/Urhxc2N5bfeVIf3f7z+L5an8QftJfs36hor2CaPdRBYl89WiXd/wAB/vfNXxxb6e983/LTG37ytWpeaXYeHdLNzM8f3NqeY38VaR9rGN2yeWMpcp7VeftNeA11u4s/CXh68ttLXcsUl58rt/wGiT9pbw3DHCnh/TZGnhbbLNNFu/ytfNrax/bF75WlTf7O7+9XXeEfhv4k8QTQ237zYzfP8v3t3+1R7OUve5glyU9j27/hfmpapH9ms4VQru3Rxr/rNzVt+E9Q8beJmltrCFlmkXduk3MsfzVH8N/2ef7NtR9ssNjqqu7bvmVa9+8B6H4b0uz+zWEVuieVH/pDfe3VpGnGnH4jGVT2myOV+H3wb1i+jfW9bfylWVVeST5mk/vba8A/4KgftWPoCL+x78ILhYJ9SWOfxbfWY/e2tv8Aw2/+833mr6O/an/aS0H4A/BzU/idf6xGLi1TbpdjDB/x9XDfLHGv/s1fmX8AdD1j4qfGqLxV45u5p9Q1jW47jVJv9qST7v8Auru21EOSTLp0fZx5pH9NX/Bu9+ylp37Mn/BO7w5evp3k6n4ukbVL12TDNH92Ef8AfPzfjX3iSTjFebfsqWFh4b+AXhLwxZoqRafoNvAqr/sxrXo5mRByRV1+f2judWFlTVBWMD4r+JIvBfww8Q+LWmMX9n6NcXCyL/CyxsV/8exXLfs2aS3h79n7wpa3REc8miR3V1u/56TfvGb/AL6auP8A+ClHxAl8BfsJ/FPxRpe2Sex8H3Eipu7fd/xr8v8Axj+3n+3H8WvAOg+GPBetR6FpS6Xaosml3X79Y/JVVX/gVeHnFSVPDKNviPUy+nTxdVx57WP198X/ABw+EHw+g87xl8Q9KsP7gmvVUtXyP+03/wAF4/2TPgtrbeCfA9vfeK9W3ski2EX7iFl/vNX5u33wd8YeILj7f8Y/i1qE1vHuaW1uLxmbzK5PTfGX7KPwtkvNVm8B3XifWPtHz7laNF/vf8Cr5O2LfuylFei1+8+jw+By+nK8lKX4I+n/AIlf8FI/ih+0PdHU77QYtH0yXd9lsYD8zf3a4TxV8apofDr21toi6hdSSqqRzfeVv73/AH1Xzf46/a+8c65rESeCfhXZ6RayOsEUP3njb7qt/wB816P4N8ZX/hjwrfav4qmtYdVWCFreOb59sbfxVEcNGGx6sMTCp+7idd8FPGfjTxJ8WJrfxRYhVEJdW8vCofLbhTXFf8FJP2vvHvwO0fTvhd4J8WJAmp25v9Q0qZsJMpZolf2PyuM+1Y37KPxju/GX7Tt54Vi1x7mLEt1IrPkAGFwAPxxW7+37+x8/7SWv6T4g0WEjU9O0xoPNecRx+SXdsMT2yxr4mtCnPxBox6ey/WR+D5nUnD6R+Ba3+pv86x8D/CvwD8Qv2w/jhofwf8E6XJJrPirV1s9Ot4/9W0jfeaRv4VVfmZv9mvub42/8ENf2VP2ara20P45ftT+M9f1w26/2jY+BPDML29i38S7mbdJtb5d1eF/sc+C/iL+wP+1hZfFzxLc2b2+m6HqSWdxayrI1rcSQssTbf4mrB+N37dXjD4oePLfx5f8AiS8iuVsI4vmuNqqy/e/3tzfer9GqYiWHh7KlE/oDC4SjUf1jEy/7dPXvBv8AwSP/AOCd/wAUL6Sztf8AgpB4g0a5b/lw1rwbCske7+H733q9++P/AO258K/APhG4/ZD8JajHqXh74Y6Xp+k6Nb/ZVjguI1j/AHk3l/3mb5q/NK++Ouvap4qOsQ3kkUqurJtfarNXO/F74n3/AIs8VSeKn2/b7qJYr2Td/rNq/Lurgq1cZiY8k9v63PSjWyfDRlOj8X979Dsf2pPEfw6vtbl1vwfolrps0zM0sdmm1fm/h2/drxyPxNfyK6QzMnybV2v8zUyDT9V1+ZIZgqNu3fMtdP4d+APxI8UW7XmlGHC/3vl/irqowThyz3PmMVmWIqVZNfCZugeBfG3i6M3M0Ey2e5U86T7q17N8If2R7KSH+3tS1hbiKOL97Cv3a4/Tfgr8UdF1CLwvq3jyHTfMbf5bNuVv7rV9P/sG/sTyftIeMtY+Hmv/ALROsabeWcG1LrS1Xy/MZfl3f7tceMVaCcuaKiZYWnPEVPcUrmP8UpvBnw9+H9lo3hKzsfD9tDFsupm3bpm/vbv96tT9kX9qyHTfG1l4s8aavZxaP4bi3rqitu2s3+z/ALVfZ/wz/ZJ+An7C9rp3hz4o694Z8b6nrWnXSa3qXxAsleC1j3bluFVm/dsqq1flr+2h8YPhj8bP2qvGfif4I6HpuneD1ul07RodNtfIguI4fla4WP8A2m3ba4slwsc1xM6V/h+0b4/NsZlNpS/8BND9qr462v7Vf7Ufib422NsyW3iTWENtGwwxijRIEJ9ysYP41614Xuk03TbeG2h2xRwbEWP+HbXzF4ZZrfXNPfPKXcRHHowr6F0G+j8tZkdV2/L5a/3q/o7jSLp5blUF0oxX/ksTxs0qusqFR7yhf77M9A0nfdW8X79VTZtnVYvvf8CrrNKme3KJbQsqfdTb91a4Dw/rUcToj3Kwr97y67bR9U3KJ02s0ifvWWX5dtfnNSHXmOOnWO20mF2uN7uzbvvrv+X/AHq24W+z2Y3/AHtvybv7tcrpesJNblJn2vH/AHX/APQqvHWvM3o9s0SLt2Kz7lkrgqR5z06OI5oaHrXwJkEllqG4L5gMXmFfXD8fhX54/tNgr+39rGw8/wDCZ2hHHfMNfoJ+zxMZrfVzsCAywsFU8ch+a/Pz9phwP+CgGru5GB40s8nHoYaPBe68U89v/wBAv/yo/HeBWpeOnEDX/Pql/wCkUj6k1rRft1qqImVbazNXHa54fS6TYkKpt+Vm37q757lIVLo7AN96Nk/9BrP1DT3S6+zQzK/yr5TKm3/gNfMYj49T+isPK8bHm134Ps5JmdNrN/7L/epsegvt86FJCN+37n3q9Gh8No1w37lW8zbvkb+GnP4dnjmV7aFl2/7H3q82piOaXK2d0aJyei+F7ZJvOhRpj/GrRfKtdLZ6L9qt0d4VVPu7m+Vq2NH8N7WV5bmTavyt8+5a6/TfD8Mlv9m+yxvtfcjSfw/L92sZVox3OyNGXQ4FvBO6E7PLCN83mfeX/gNZ+oeFLna000LI6/xKny16v/Y8Pkr5Nsr+Wn+rX5aq3HhtJo/M3xvGz/P5b1hPFS+EKmFjE8avPC7qzIlnCz/8spG/9lWqFxpMyx+d9nXa3/j1eq6p4dtvtDFLNcKu15F+bc1cj4j0t7dmRH+RfvtWtOtzS5TWnRlGHNI4e+s/MkaFEj/d/NKrJ8q//FVx2tWqBpkSFVDbml8uLbXf6vM8a/I+yJX+ZdnzNXGeJPtLeYE4En8TJ91q7aMjqw8uWXvHCatHbQ4f5WZW2p8zfN/wGs1o3WZ8ouxvl3N/DW1rCTR2jO8W9m+VdvyrXPLcPG3l4VvnbayvuWu1c0o+6exRkvdJvkWNoXdR/tbdu2pYZnmjEywrsjT5pN/3WqpJ50y/vvlRflfb/FVu33tCmxI/vfw1cebqRioxlGR1OseH0MckjwyJ/wAA3bm/u1yOseFYZNifLvb5dteqaxpsbb7ZLmRF83cqq25a5zWNFmjZk8nzEh+b93/7NXTRrSl8R+YYjDwueY3Hh2GRg6fMFfbLuT+7VvT7eGOFdm0Mz/IzfeZa6S90+a4k+fc7Rptl/dbar29nDDMYXtlxvyrfxVp9Y934jj+ryjV90hs7GRW/cw7w3zba0rWxm3LviyG+/tf5VqdY/JswiJtaP5vl/i3f3qkgmmFvEfszP/D9ylGpzRsjSNPl+Imj2Qt5MO3943937tCzwwsEeb5d+7d/drNbzo5fJhnY/P8Ae3/douLr987o6un8e37u3bWlON/eUjOVSJvR6s8e3yU2f7Xm/Ky1Q1LXLlQzzTMy/wAO6sH+0Jm+SZGeVkVflf5f9mi8uPJYJ9p+78v3a2jGMdDKVSXL8RfbWJ7xmea5+X+Fd+7c1SR6k8ivcvcqnl/dVv4q51tSSOY+W60z+1EVXhm8t4tqukdaSp80LHN7acZB8Srtp/CFw8l3w8qeWjYyRuHHHpWd8Jlj/si6kdmylxnCtj+EUzx5dm60CSST5lLr5R67TuGefpUXwxnEGl3Du+FNyAT3HAr9Iw/KvCfEf9f1+UDspVZSy2Un3/yO9tXTesMPmIP9n7rVpW6uqLqSbX+f91Gy7VWsezuE+VE5bfufbV6G7hk/ezOzLvVf3a1+QVpScuaBNOXNudPp8m6aO22Zlb5dsn/oW6rsl0izeS8y7t+5a5i31J/tY8mZpW2MvzfKtX5tQ+b92JMr/Dt3V5Fbn9vfl0PXockom7DqbuvneUsbL8vzPUjXH2hUhS5VG2fvfMf5mWuZk1DzJgn34mb5tr7WWrf2uZm/fHesf/j1ZcspR5j1adbl91HT2+rW0MMKfNK/97+9/tVasdchhfft37vl2/3v9quZj1DbHEn3G/gbf8q/3qkt9aSH9zct5QbayN/tf8BrL2M+nwnZGpfqdS2uTL8lnDGq/e8xW+ZatadrTxsXSZSGi+Zm+auQjuvMm/ePvDL8zL8q1aj1PybgIifwfw1uqfNsTzyUiT42ePH0HwDeXMN40cskHlI0fzN81fG2tXTwyN87On3vm+9Xsn7QnjSbUNaTw9C/l29vFul2/e/3q8Z1ZkkZu6ru/wB5l/2q+9yPD/V8LzS+0fmnEuM+t47ljtE5XxbF9otSmzfuX7tcvo+sbVawmkZR93b/AHa6fXl3bvvSr5W3d93bXB+II5re8a4hfn+Nq9qPvHgU/dLOtTPeMYX4rlry3eCb5NzCt21vE1OFU+ZHX+L+9VPVrGby2RH3bv8AvqiUSuYzrW+TzBC75/2al1C3S6UPbIqt/s1kzLcwzfPwf7tSWV95LNufr/ep/CPlI5I5beQoW/3qeuyaP5PlZauSQJfLvR1+5WbJG9vLsbqtT8RQ+WERnZUT/eNTxsk8Pzj5lqBldH+f5arlHHcSijzN5oqhxGR/fH1qebnp2qILtZakm3tL7VPKEi7YRmORNm35vv1ckXap2df/AB1qz7MOy/crRG9k3zfd/u1MCSCaR9rPInzVV8z5vkSp75eD87Y/us9QqqeWvyf99U/7xPxD1dPubPu07zEXdvf/AHFqCPYs33/4KkmaHG/O6kEirdNvYAVGzeWvvSzff/Co/vrVRLj8I6uv8LyJ/Z8SOi/c+81cerZ4NdX4TZG08Ro/z/7VPmQVDTmm8xm+f5W+WqM0O9mx8o+7V2ZUjn+cbttVJ283bsfdt3VEvd0MfckUJLdGZ977t1XPCcbr4t035G/4/wCH/wBDFQSNtb/2atTwVB9s8X6XBv2F9Rtwrf70opT/AIcvQyr/AMKXoz9zf25I1k+DEIfOBrkBIHf93LXxtqGoW2mq87puRl/ifdu/3a+xP285pIfgankjLNrcAA3Yz8ktfBXjrULnT9Pw7s00z7Ny/wDLOvyXwt5/9VtP55fofiHgupf6mpr/AJ+T/Q5j4geK7zXNQaGEM8MPyo0j/wDstcvHpKW9vJf3m3/YWR/vVqXcyW6yfaX2uzf3aXSfAuueLp0/czYZNyL/AAtX6ZRoz+M/X41OV+8eIfGxo/LSzmTY00u7av8ACteX6pboqnZ8u569E/aCt/s/xMn0S21DzfsMEcT7furJt+auAurG8mj3na7L/CtdtOM+U9On8Jlqhatjw7oN5rd9DZ2EMjyzNsiX+8391arWOlzeYEdWVm+78lfb/wDwTh/ZLudW/wCL3+KrBWgt7jytGtZIt26T/ntXRGjzGeKxHsYmt+yr+yH4k0nR7DR7Cwkl13VJY/tCr822P/nn/wDFV+sfwV/Yf+Hv7IfwPfxh4whjGr3ETPuVf9W23zGVW/2a7L/gnn+wjbeCdJ/4XR8WrD7Pc3CM9hDeRbWjj2/L/wB9V5T/AMFKv2qNS+JXimH4Z+D5v+JbZxSRbrNlVY2+63/fVaVuXDQuviPnpVJYj3pmB+0Oll4y/Z51h5rYS2+oWNvI0cvOUaWNufwr5chsU0nT/wCzdK0+G3haBVdo1Vd23/ar6f8AiAiRfszmKST5V0GzUswzkfuhXyF4y137RcnTdHv5P3ibnZk+Va/IPC2Dll2Mf/T+f5RPy7wehzZPjv8AsJn/AOkwOZk87ULxvOhuHlk3Ru33vl3V3vw2+F0Py6xrabIl+WBZG+b/AL5qD4f+D3kkN1dbi0bbdrfL/wB8/wB5a1/iJ42s/B+kt5MyvM0XyLH95a/TK2IpYKlc/W5HP/tJfEKw8N6Xb6DpVy0czPslWOvmy8mudWmk85GLt87SV1PjjxZdeJtUl1K8uZmaSX7rfdjrmZI/Mk3o7Im7c0a187UxH1iXNIfLKUYjJv8AiUaZ9tf5RIm1G/hb+9XnfjDXofOaHY2f4FV63/iF4wSONNK0xJHZvlVd3yrXn2tMgZnfd5zfNt31dGPc2h8Jk3Fv9omdHfc0m75WauA8f6C+lap5qJ8kn8K/wtXokXlfbPOm3KP4KwfFN1balDNbOmdyfI392vUw8pQkd9GXL7xwVjdvbtt/u1u2OqeYvk9f4t2+ubuI/s0rIf4Xq3Y3H8G/5q9T7B2HTS3UMm6P7zfxKtQNI7bd45+9uWqv2p+qOm9U+9sq1a/Kiu7qz/7taGcfdI2je4+R0Yf3Goh85vvnd2b/AHatNH8v3+Vf+GmyWe24M29g3+zU6/COUupX02zex1hYU5DfNFXt+j3nkfD1Hf5tu1d2z/ZryabSfOs4r9FZnt/l3L97bXpNrJD/AMK/aOZFO2Vdjf3Wp++RIxrq+nt4dk33dleVfECZ5tdKb/urXoeqXyfvXmfcN/y7v4a8u8QXD3Ws3Ervu+fbuqDSiUqKKRW3VXxGwKuBS0UVQEixovLvj/ZWp01C52/Z7PdGrdlqv5nyu5OT/tUsd1Mq/JS+IXKiUWN5LJvdG/66NUjLDCpS5mY/7MdV5Ly5k+/MzD+7UTEs27NL+6LlPTP2drmOTxTewwphV08kn1O9KyvjXLNJ8TLm2jGflh/9FrV79mw58WXxA/5hxyf+2iVB8aJ44fiBeBFwxji3P/2zWv0DE/8AJuqP/X5/lM9qpFf2DBf3/wDMx7WaHRYWeGZXutvzSf8APP8A3azZb17iQzTTbnb5nZn+9ULTOw+/wtJGySfI7t833K/PZnge/wDaNzSfms3vJodob5VqO1sbnUrxYYfmeR/u1akhSPR4bZJsP/Gtemfs5+DNEuvEkd/4kSNIIfndpH2rtpxiI2PgH+xf8Rfi9fCbTfDdw9uv+tm8ptq/7TV9BL+zL+zl8CYYbbx/8QrG61JVXzbG32ssbf3WauN/aA/b3v8Awj4Hf4dfB+aPSY7jcksli7KzQ7flVq+SJPG2va1qT6lealI88j/PMzM26iVSUo+6RKnzbn6QfD/46fAP4dxwzabaw3Vw0u6Xbt2qtani7/gqD4e+HejvN4Y02OK4bcz2siKyr/dr84m8bXGm2JhhuZFf7z7a5jWdfvL+RXmmkL/3t/8ADWUozqbyHGnBRPbf2nv25vi1+0Rqk3/CVeML65tvNZfs8ku1FX+Fdq14/paXN9dL8uKyIYfObYBuZq6jw3pvkzI0yMob5a0jGMR+5E9O8Dta2ekb9+0r9/5fvU7xB4sv1jMNvNIsX3d275dtVdBL+SttC+/b9zb/AA1esfCOpazfPbJCzGT722ugy9/nMLTdE1LVLrf5bSFn+Td81eteCfhs9vbrNdbQ2zdu/u11Hwp/Z/uYbP8At7VbPEUarsZnrttc0fStH02TY8YMabVXZU80Sebm+E4q3hs9JhFy8O5I13M275mb/Zry/wAeeKL/AMVa59i03d5e9v3ddH8SvF32if7HbbR823bG1U/h/oOg2tw+t69cx4k+ZI/vfNUe05iY0Zx949D/AGe/g/D4gWGXVUht1h/ey/aK+mvB+l+APCOmRQ74ftm5vN3f+O18k3nx4sPD7PDZ3Kxxfd3f7P8AdrB1T9pPWLqRUfWG8hX3bt+1qy9p9k19jzR94/QlfGHhtpt6bYopPlRfN+78v3v92sDxR8RNK05nubDVWJhZWRVl+Vfl+9X5/ah+1Vrdu++01iZjGn/PWsO8/ai8bahHKj6lJsbd5u37rUo80tGTGjynWftpfE7U/i/8VrTwfDqrT6fof7+WNWby2uJP/iVrqP2P7Gz034jaVf8AkfNDqVvuVl3fLu+Zq8K8Bs+uTS6rcurzTTtK+6vdfgq32HUkvLP5XhljZNrbdzLXNUqezqxOfEfyn9Mf7Jf7RVhqfw70izvblnaO1VfO3feXbXs998bfDdnapczPuVuFVW+Zq/Jz9jP48Xn/AAi9q/2zZtiX92sv3Vr6YtfiZeapGLP7ezReV8ki/Lur26coVIXcTzOadP3eY7n/AIKI/E+z+KP7HHxc8E+HrGTZceAdQCSMn3plj3fe/wCA1+SnwB+OtnN8G9A1u5v4UH9g26SqrbpGZV2/NX6Z3lm/irw/rfhjUrzzINU0a6snVn3LJ50LLu2/8Cr8Avhv8QvEPhXQ9S+F2pTNDd+GdevNLuo1+XcsczKtfO8Rx5sMpRXwn0XDtaNKrI+vfiB8etKufOhhud+75/Mb73+9Xifi74kW2rTzXNntiaSX/gTV5tqviq8vrzZ9q2bl3J89c7feJLi3uvtP9pKiQpt2qm6vhZVpyPsqeMjLQ9r8L3z6teJqut63HG0e773yrtrjfjZ8fLnUvET2Giaqxto7dYF+b71eaap8QtemVbO2v44Ub7/96ud1LfMzPcuxbd87b/vVrTlVlG0iJYyMY+4fUn/BKbVZpf2q7qO5WN/tWjSusy/3lU5UfhX0b/wUI+Ls/gG+0vRkmkCyWBuAiSYBO9gc+3yivkP/AIJbyXH/AA2t4dKyPHmx1BJlLN+8AtJSA2fQgGvVv+CvfihdG+NXhywkJKy+FVO0/d/4+Zx/SvjMVF/8RBo2/wCfX6yPxB4ynH6Q2CqVOmDkvneqfP8A4y+LHifxZJse/mKKvyeZL/D/AHa5+38B+Cdd8C6xqupaxdQ67avHLpFvb7fLkX/losm7/gNYl14ks5I9iTbFX7yr95ql0W6triORJpv9Z8yLH/8AFV9/8Grkf0V7alWlrK5w2pXlzY3DGF/49u1vvbqfpcmpahJ5E1nvf73y/wATV0/jT4f6Oky3mj6l50rJvnhb+Fqy9H1xPDtxHczW+3a6/eSumSpTh7vvHj1FL2vLIsXFvrmk2/2x9BvNv/PRbdm2/wDAa6Dw3+0J/wAI7b/2V9vkRv7twjLXtP7PPx+8N/29bQ+IdHt5UZ/KaOaJf3i171r0n7Gen3kV348+F2k6lbSfM6rEsTRt/DtZa82NTCVPcqXjJG1PD1k7wfNE+OfDc3i345a5aweBHk1PUZH2RW9mjO3/AHzX6Yf8Ewv+CdH/AAUB+EU1x8RtZ+CenvbahL5tqt5r0MErN/eb/Zr2/wD4JZ/FL9mfw548t/h78O/h54d0c3ySXDXlrZw+f5ar/FI3zfer7rn+Knhu1uv7Nt4Y2j3fLJCqqq1yYh4Tlt0PbwuGxeElzw3Pxn/4OCfhD+0l8HPAXgnx18XfFuiyv48164sL7R9DRvKsYYYd0cPm/wATf+hV+YeiwpCxggTZFvXbGv8ADX6o/wDBzr+2J4P+Jfir4ffsc+Epo7i58K3sniHxQ0LKzW8jR+XFC3+0y/NX5U6feQNq2z5cSfxKv/jtfaZBg6GEwMfZx5b6+p+e53Uq1Mwmpy5jrdELQXds7OGKzqdx7/NXqPh3xcm35Ny7U+8zfKzV5PaNtt0cfLgZ47Ve8M+MMRpveQur/eX+Fa/WeOf+Rbln/XlflE7s0dqGGX9xfkj6H0XxVDuSZ3Xatvt+ZN3/AAKut0/xG9rHFN9pVX2btqr8q/3a8F0LxIt1dIiTfMu35Weu20fxnN5LJ57bl+V91fm8eY8uMpRkez2PiCFd/lbg0i7pWVtqs1asOtQ29u7/AG2TMf8AqoZJdy15N4b8VI9uYZvk/wCmn8LV0dvrKfJ++zuRaxlH7R14esfUP7Jl3Nc2WuJK5JWW3PzdckSE18HftOEP+3vrZKnH/CZ2wIH+9FX3B+xXeS3um+IJJ2BfzrbcB24kr4Z/admA/bq8QTNnC+M4c468PH/hXP4NXfirn1/+gX/5UflvAD/43hn7/wCnVL/0mkfVP26aQ+dNH/q/l8mNv4f9mrEc1tIn2nzpFdk/i+Zv96uYsdYtlZoYXx5f3tsv8X92rem64jGZIZ492/7rP/F/vV87ivh98/oHC1JLWJ1dnbp9jKQzfe+b/eq9HazR4f8A1jfKrws3yrWNY6wlqqpefd2fOy/N81a9rqCfuUhLOJvmrwK1OXNzcp9Bh63Mbum2NrN++htlO7/erZ0+3uY4/kRfmZm3Sfwt/tVz0OrJtTyY2Df3d/y1oQ68ftCW0MyhmVme33/+zV50pSlPlkd/tOY2midZhcwp/D8v91WqrfLt3TIFG5P7u2q02uJCzIjqXb7qr822s/UtetrWNprm5aMs+1mkf/vmo5eXYqVTm91kGqbPsc15skTzF2Oq/wAVcfrkjzQtbfwLErVr6zcXNwrp50jjyld183+KuZvtSdN3nPhv+WTK/wD6FXTGMvduXGX2Tl/E2ySZYcSMW/ib+9XFeJLGZoZHmmb938ztXfalHMZt6fO/8H/xVcR4mkheN4fJ+bd83zfer1aP73YxjPlmed+IWeTfsdk3fMi/erCkt5oQ8I/hTdu2fLurqdbgSW6XYjQqqbf726se7jjj/dwptP8AH5dd9OJ6dPEcupRjt3j3TzeYT/tfxVoafp/nH/Ur8v8Adb7v+1TVkeGEOlsyp/tfMrVo6TH5bQ/PsimT522/JJ/wKtPf+IutiY8rR6HJCkOIUnWVm/5aNWRqEMkjPbPuZtu7zNldJdWLiRo/uJ97dWTqNq8lu73IY7fubW+7U+7GXunxNT3jmLjTXuFV3myzf8s1+b5aoRafNGpmDq3mf3fvLWwttcKyJczTLFv+WTb8zLRa6PC11D5NhJiRGLLv+Zf95a25YRl7xzyj7t4lKzs5riP5EXcr/vd3zVZ/s15IYpt6/wC0v3a2rHQXt45XR9zM3zeWm1Vq2vh+G6mO+GP5fvM0vlqtZ80X8JnUoyiclcaWkayfuW3t/wAt9/y7qwbzT3hhj+TO1W2L/C1d/qGgwxnzkhkYsjf6lvlauU1SzeNW3uwPzN8z/NXbT5Yw0OCpHl+I5G8meFl87ai7tvy/w7apalqLqv7l8/wpub+GrOrG2t2lmmSRtyf71c3qWpTQsybFLfwbvu11U4ylKLPJrVOX3S79uRNrpNyvzbt/3qJNShcHYGaTb95a55ta279ibU+7t2Vft7qZZtjvncm7dt/h/u13Sp8vvHPzSkT+ILt5PD0kDoVUMvlj0G4UvgG6jtbCeSVsIJct+QqtrpkbR3kQOImYZQ/wtmpfA1kLu0kJbpK21W6Z2iv0HDxjLwsxCf8Az/X5QPVgnLKpL+9/kdjZzPMouLmTai/Kq79u5f71bC3Tx3SbN3lb9yN/e+WsywW52+c8Me3+GONPurWxa2O5PkfdtTcjR/NX4xipcvukYf3pFi3by5N+9Vdovut92pVjmfDuy/8AAfm3UzT4fMhG9N+5/u7as7fM2ukSxL93arferz4zk4+4evH3YxIZLeaG23wuqP8Aw/8AxVT2cyN9yb5VT7u3au6o5IUZnxtj8vcrKzfNT47V4bdHgmbH8KyfNWc6kpR5ex106ko+6SSTXPl+d8rfxJ8+7/vmpbXUHmaJPJZR9/a33V/4DVJv9H+4ZFb7vy/dqS037km85nVfmfdTlHmhGJ2xrRjDQ27VkkgeZ9zvv3Kq/wALVNdTPZWr3L3iqscTO+5vutWct4iN8jsrt9/5vlrC+K3iSLSdF/sqGVV85G/2q78LhvbTjCJz4zGRoYeU2eR/EbXIdY16/wBVuYW/eJ95v4q83vtTS3kd45mH/Aq6DxZM7K8KfOFdtkjPXnmpXbvN88y7v71fe0qcY0uWJ+X1J+0qym9yxeXySM33t/8AErfdrC1GBLrzf4f9r+61WJLiOWTYj7TGu5vnpbbZ5bb3+Zn+et+Uy905O6t7mxvv3L8VLHrSFvJueT/s10OqaRCIjN5PLf8AfNcPfXCW99LsO0q39yn9scfeLupWdndMJkh+dv7v8VYl1ayRSN+62ha1LPVkb/XBcr/47ViazhvF+R/vUviL+EwILma2+QdKszIb5fOyv/s1Wb7RXVS6P937/wAlZis8MnyPUl/EBDwtjNK83mrtZPm9at4h1GIFWAl242+tVJ4Xhco4xigBj/eNNZc8inM26kDbuaCxY8M3BpWUk0xV20taAWbNvm9a0LdnX5HdiGSs23JUj7taKNut8+1TExkR3Mj/ANxT/tVAWkb5ey/xVNJGit9/H+zsqpIwXOx8fw/NUylze6LlJGbZ9yRc/wC1TWkjCsjJ937lMhZNzI6ZoeRP/safLAshk+b59lJRRT+yVEK6jwWvmaeyINzb65ZjgcV0ngn99avbf7dHMKp8Ju3S+XH+5kX5qoXUOz58/wAH8NX9TVLeETJu+/t27KoTSeWuehb+GjmMJFP5k3/P/H8612PwC0Q+IPi/4a06VFaN9etQzMn3V8wNXIyRpnzvmZq9Y/Y60eG/+M+jK7yKIZhcuq/xbWG3/wAeqKsv3cvRmOJjbDy9GfsD+3rcJbfA2OWQDA1yD73+5Kf6V8C+Opf7QvBazTKzL8+6R/u194/8FCzt+AkRChj/AG/bhQWxkmOUV+esVrqXjDxMluk26Fflby/m3V+U+FEOfhhL+/L9D8R8F5yhwan/ANPJ/oaXw++GNz441yJ7bdLC0uxV2M27/a/3a+5/2df2DIZvh1q3jbxV+50nSdIur+9uNnyxxxxtIy7v7u1ay/8Agn7+yff+OdcsLJNNuttxKvmrt2+XHX29/wAFZJdB/ZK/4JF/FjXvD0zW9xceF49Iib7r+ddN5K7f+As1fs9PDxo4W5+oRf1jGRR/ML4p1xPFXinVfEEczOL7VLieJmb/AJZtI23/AMd21nxx3KyKidP49taGjaO5tIS+07YlH/AaueTDDMN6YDNt3KlcZ9JzWOk+B3wp1v4peNNO8DaJYSPeatdR2tmqr96SRtq1/St+wV/wTf0bS9J0G58VaDaxab4X0m3t/lg/dXFwq/vJFX/er8wP+DaT9l3RPj1+3NpureJ7BrjTPCekzavIpT920i/LGrf8Cav6CvjJ4ts/Cfha40Dw3bJbwKnziH5fM/2VrsoyjGJ4eOqTrVf7p88ft0fHt/CPhW88JeFbxbG12bHaNdu5VXbtWvy48c6ii6lePM7YkuN67n3N81fW37ZHizUtSkENtdSPEr73Vl/vfer498YWc17q8t5bRrsb7zL/ABVzVo+0leRjH4D274uyTD9lWd4kJc6FZYXODy0NfKHhnw/CsnnO8lzL8y/c3fNX2F4s0O68Qfs+RaDbjdLcaPZRjdznmLP9a8L8VeHbD4WW8Vg8i/aZGZYo2+Zlb+GvyTwwqU6GVYycv+gif/pMT8o8IJpZVjo/9RM//SYHHa1rEPg21aa8hX7Wy7V+f5tv93bXi3jrxRqWtXz3N5c7Vb5YoV/hr0bxc1zqkNw80yy3Ejs3nbflVf7v+9Xm3iTTLaz817m527drbl/ir67ESniJc0vhP1yUub4Tlr6B5F+d9v8ADtb+L/arj/FXiqZWNrpu7zo9y7Y/urWp4q8RJfXkltpW7b91pPu1x9838CPIzN/e+9XFT/lexrTlGPuyMS9mdbp3fc7sn3f7tYt+IVUvJtdv/Hl/2q3dUO6MpHbMi7Pnk/vVw/jzX7DRbR4LV8St95q9ShGTkdNOM6numZrviiHT4zsmV/8AarkdT8Tzy5Sz3Yz95qzb/U7jUpjJK5x/CKgZscCvZp0Yx3PTp0VAGZ5GZ35NSRvtPXH+1TKK6OU0kbOn3SLGEHzf71aELPJD86NXPWMgjkXY9dHpqm8XG/lf4aOUktRK6ts37v7tacVvJIqv94/3m/hpun6akki/Jkr8ybkrYktPKVJoXXay7WWnze6YS+L3g0G1S4hlsn2v5iMu6P8AhrbktXs/AL2dz5mY5V+7975azvBskK6x9mfam5f4vu7q6bxt9mbwjLeQou+SX5tr/dpRIkeY+JNQSOxld32t/drgJH3ylg9dJ4w1FJYfLR/vfermQDnLVJ00/hBhkcUKu2looNQopGOBxS0AFFFFVygFLHhm4NCx7l+em8sPSnHYmR6V+zjJGfE97Gq4YaeS3/fa1l/HB8fEa+jPRoof/Ra1f/ZsOfF18P8AqHH/ANGJWX8dv+SlXn/XKH/0WtffV/8Ak3NH/r8/yme3US/sKC/vf5nKRk7tnf8AvbqsQyJHdKMr8tU97etOWZ1z89fnx4co8xuTaw7TL/s/LV+L4gaxYw/Y7O5ZNyVySyOn8dL5zsfnfijluHKy1faveahM8tzNvbd95qktbpLZV/vVQbZjilE0i9GoCUTRuNRnui6Oyr/u0lrZ+ZIIZj/wKqkCwyt9/FWVulRfnb5Ver+EXL7hs6TpqCNZti7l3fLXR6LMiqheb5f7tcJ/ac0bfJM3y1Y02+ds+deTMP7qvRHYy5Znreh3266XybyNNrfxV7L8NfEfw08Ap/avjbxPZ3Ey/OsMb/er5bbUNKt7Vprma6X5Pkj+0bfmrA1HVYrps7JHdfus0u6s5Rl0K5Y9T7S8bftteAoA+m6DeKkap8i/xV5r4w/au/4SBX8m/bDJ91flr5wS4ST/AF0O7+9UrNZqpdI1VmqfZ+6EYxPUm+KGjNefbbm/3Dbu+/8Aepl58UrO4mT7NfrEG+/tf73+zXkzXjxyN8i/3als3825Akfcn3sGrgOUTuNe8dPeXAh3rs/urWReeJPM3/v2X+H71YU198v8P/Aaj852Xjb83zUuX+YmPummdUdspu2v96kk1KaO3ZLaZt7VQiunK79//Aqltbz52R9rUwl7p618IY2m8Pw7HXev3v4a9s+HN19nbzPmPlpvfam7ateB/BXUnXS5LJJmJW4+VWb+Gva/h3eOt8sKbgky7G/hry60ff8AePPxEfePvH9jnxk8kNnCiNIivsVVXa26vuDwHJNeW/8AplyqIy/Jt+9ur8z/ANlvxIlrqDw+cybvL+WOVvmZW/u193/CX4uJDpqalYQ73jbZKsnzKv8Atba9LA1vdtM8qpTlL4T2zwPpOsf29CsNyyQrcf6xn+8v+1X4W/tqeCL/AOEP7eHxg8GeT5cbeL5L23VflVobj94tftp4d+L81nqx/sG5hieaJt9xJ8ywsy/3a/M//gqx8LU1r9rD/hannb4dc8P28VxeNFt86aH5d3+9tqc09lWwzgellMp08RFM+P8AUtSv9vzp5RV9u7d96s+4k/cu77Q7fMPk+9Xd3ngHUNQuBYaPA1zMzf3P7tc78RvDmp/DB9Li8d6NdaU+u2bXmjNfWrJ9qt1ba0kO77y7v4q+Jll9WpG8In1Uq3s5ayOcvI5reUvs+Vk3fNVNm2sN6b1hXc7N8qtXG+LPjpZ6XI1no9q1w6/K8kn3a838Q+PvE3iOeRrzUpFjkP8AqY22rXZhclr1I+/7qMpYj+U+4f8Aglp470O+/by8NeHraYTXElpqWWjbcqBbKY/e/CvUf+CzwX/heXhySQjang1SoP8Ae+1T18v/APBFjP8Aw8N8In/qH6r/AOkE9fSf/BbWdofjf4bEeNx8Fjr/ANfU9fJVsFRo+KWHpLb2D/OZ+F5pVkvHPCS/6hX+dU+HbPxpM1x5L22UWX5ZGrSs/HCKfvqg37dtcL88Nw2zzE+f+Jty1ehZ2k2I/wDtV+myyvDVN4n7TTxlen8Ej0W38TTXi/uZmlf+P/ZqpcX32qT7N53zK397+KsPw/qVzbyqgh+Rv+WlWdYs/Jma5tpsLv8AmrCnktCnLQ0qZlVlH3pHZ+APCfjPxZqCaf4C0e61W/bc8VrZ/NJ/wGtLWNQ+MdjO/h7W9B1q2ureXc9vdWEm/d/3zXMfDP4jeJPhfrlh488PXkkM2m3kc6sr7Pu/7tfad9/wUPPjDwxZa9Z3k11c3X/H1a28CtJJ/s7mX5Vr18DwtlWYu03yyPFx/EmZ5baVKN4syv8Agnf+0FY/Bf4ov4t+Lug3mlLHpvlLql9btFEys27crNX03+1Z/wAFyPh78M/h3e6P8ENbsfFXjG+tW/sG30/95a6e3/PaeT/Z/hX+9XxZ8avi14/+NWi3GleIdVtbO2vrfYmm2Nvu+Xd8q/NXzx4l+HeseC8Lc+Hpra027lbyGVdtZZn4e0MvqxxCnzQf2Tuy/wARMdmFL6vJJSHa14w8YeOvFGtfEj4ia9NrPiLxBdNdatqV026Sab/2Vf8AZqjG80OpK6IoRv4W/vUkcTxq3kvGz793/Aaj3f6YkKIrvWkYcvuxM5TnOfNI67ey6Y0jA5ETEhvoa4zRdceC7ZN+5d/8LfNXYIwl0ZmB4aFuR9DXmi3MK3zI/wAr72+X7u2v0DjVXwGWf9eV+UT6PM/4GG/wL8keqeHfEn7xIZH+X+833q7fw/4kfGwTKzzfN/drxbQ9U8mQOH2/7X3q7LR9alVtjozts+9/DX53KjzHkc3KevaXrj3EKTfNErO3+7trq9B8SO8KJbTRojfN5kj/AMP8VeS6P4hhhkVELBmTc7feXdXR6PqEDQp575LJ86q/yrWcqP8AMONT3tD7n/4J+X8l7pPigyyK5W5tcMvcFZcV8V/tROx/bd8TOGII8ZLgjqMOlfX3/BNG9ivNE8WiJwwjuLMZAx/DNXx3+0+zD9tPxSwkAI8aHDYzj96tcHg2n/xFfPv+wVf+4j818Ppc3jZn7/6c0/8A0mkex2948LbLO5Ubtr3TKvzN/ere07Vt1wjpt2K7bpG2/wDAa4u1uPKVbm5dX8xP9Yz/APsta9rqltHMv+rDttZ22f8AfK15GKp+2jyyP3PB1vZyizubXVftFw7+duZtreYv/LSteHVIbS4/cwtu+8jb/wDx2uDh1p42D2z7JZnbey/NVmHWptqRw38heNNzfOu5q8TFUfd5YnuYfGe8d7b+IGhuInR97bP9T97dWlb63MzGGObdu+V/L/8AHVrzm3vnVUmmm/dfeZpPvVr6TqiLJ5kLsy/w149fDuVU9Wnioy1R2UmtXMy73dUlX5dq/wB6qt9qUMcfnTfN/e8z5tzVi3F0i3m/ZvCoreZv+8v92oZL544127f73zVhy+z5jaVTmkWNY1B4285PLVm/hZvmX/drnbjUpppQ/nLt3fM1S3+pJ9nlkmfdtbcjN833v4awNSvoY4djoq/MyPtX7q/3q0o80ty6dSMSa81SOGNy7xoPN+Vlf+Gub169tobF0fbvk+by2+b5qh1XXIY43hs3bH8e5vm3f3ttcl4g1ya+mNml5GHX5mkZ/mbbXsYWjLmObEYjl5Svr00PnPBD5f7tF+78q/8A2VY8kf2i4dN2S33WX71VtQ1qC6kLpt/d/L5avuZap/206NF837pn3RNH/er144fmjGxH9ocupuwzJDb/AH2KKn/fX+7WvptwkdmqTJuiX7kf92uXgunjuCjuuJPm3f3a1rW+jk2u7yfN8vzVUqPL8JFTM4zlqe4XFnNHI7vtPzf73zVm6ppqXVv8kPHlfvdvy7a2lZxcP9m8yFW3Inzfw1JDb+dK6eTuRlVX3fd3V50pSjqZxlCRx3/CMWat9pSSR/nX5qn0/wAOotx5sPnfL95tu5m3f3q6dtP3XXk+THuZv4a0tL0Xczb/AJHXb8rL8slKUohyrl5YmJb+G0jb+95n95fm3bquP4aFm3nbFwz/ADbq6eGzRdjmRS2759zfdqWTTpreGSZ7aMvN8rrG+5VrQipT5TgdQ0Xy4dn2bZ87b1ri/EmizKsvkptT5vlX/wCKr1PWh9njWGGFVZk+6zbt3+1XEeIoXmjdPJ5b5vl+61XCc46nn1ox10PGddtPszKjovzfN8rfLXD63ZvNcPNav8sb/P8AP96vSfFVi6q/ksqCNvkbyvmrktU0lJG8t/3Jb7jfw17mHjyw5mfOV47nEw27x/v0+YbtzNJ/DWhp9xdSbvtMbMv+1/FVqbTZlk8m5RWDUtjHNI720zrhfmVt1d8uWRwy93lDWoyujSssDKhVT975Q2+tL4WW0t3byQqnytcct6fKKqa+jx+H54pHYsrLuX+FfmFbPwahabTLhU+99q4+XOPlHNfeULQ8K8R/1/X5QPfo+9lsv8X+R2VjotzbxNvdmk2f99Vft7WG1jX7NBtdkVtuza1W7eO2jZE2L5kb/umZ6W8ihuroedMsf7r5WZPvNX4zLDylVlP4goypRj7o/SdkLeSiMSzfK23aq02O1NviHZll3bVVPvf71H2iFvkhm2O38O35qnh8m2BRJpNzOrbttc/1eMah306keXllEVdNmm2u6K77dyMqVDdRl4RvnZ42T51X5dtaSx+XHLDbdflb5m+9Wbd742MKXMZLfej27dtZSocvvFRrRplaSR4ZE+dUbZ93fuqtHq0SsUhRvv7v95qq6leBJGEKL821n+X5ay4b52mbYi7Gb/lnXVToxl6EfXJRlob0d1tY3KSKibvut/C3/stea/EzxJ9s1ja74iVGVdqfKzV1OpalDp+myzb5GZk+RWT5q8N8beLrm4uprmaVl/ufNX0OU4ONOUpHhZ1jJ1KSgVNbunm86a2mz823bXC61vWRvnXer1tWOsO0bvvZt38P92sTXpkml87/ANCr3oHzMjN87/b5ap4bpI5Am/lvvbaz7i4SOP5OapxXiSTM43B1+XbuoKNXxRryWumvFDM29lrg5pJJJGmL5Zq1tWuJJm/3azWR26pWg4yuVsnPLsas2eq3NnMrpI21f4ahaHy9vPytTWQ7dyLUyNPiN2HXEvFdJkXDVQ1bT/JUXMKfI33WWqHzqK0tH1SFW+zX/wA6N8q7v4akXLLczYneGTzO9aTXVtqFiUeNRMv3Gp+o+HJgPtVpMjxSfMu2suQTW0mx1ZWoH8Q3Dq3z0UrNu7U1mxwKvmRYKMLS0UVAD4F3NkVet2Ty9++qUa/8sz/FU0LIrbH/AIX/AIqJmMieSRlb/wAdqCZUZd+z+Olmk2t8km6kaXd9/b/wGgIkUXU/WmyDbyXyaVsY+SmP940FCUUUUGgj/dNdJ8P5Eikm3p/wKubf7provh/Ki3b70q47GcvgOp1C38yHej7v4aybq3fzn/2fm+5XRzWaQ267NvzfxLWRdQuzb3ds0viOczfJbjZtXd/Cte8fsQRw6d4kvfEFymRGYbaJm+9uaQfdrxG3t3kZey/3q9x/ZunTTNOt44VVPtGqx/Nv+ZlVhWOI92i/RnPjf93foz9Sf+Ci8nk/s/wymMMF8QQFlI6jypq+e/2B/wBnPWPi94yhSTSpPJvpVf8AdptaNd1e7f8ABTYSS/s8WVpHcNEJ/FdnG8iHBVSk2a+pf+CN/wCyjpv/AAhul+LrDy1hhnjh+/8AM38Vfn3gzSpy4Z55fzy/Q/DfCGUv9SEob+0n/wC2n2h+xv8AsX6B8FfBNtf3kMYnmtV807fm21+WX/B2d+0XBq/wO8N/B/wjcTLY6p4viS68u4/dTfZ1Zvu1+yn7Snxf0T4VfDnULeHVIYblbFgFZ8Mq/dr+ar/g4A+InhjxZ8Xvhv4D8N6ldF4bK61S/tZLrzY1kZtsbL/vV+rVJyq0+efyP2fD4WnQrxjDp8R8G6fYzRxo/kq52fNUqCGSYJf2G75vl21pWdl5ir83yt/D/eqzHou6QIjtv3VxfbPQlb4T9sP+DTbwbLYTfFbxfFDHFA2iWdr523dJGzSM23dX6I/tHeLkt82dnMoWP5EZfu1+cn/BtD8VLbwX4F+LXgm8vIUa60ux1FGX7ytGzRsv+781fYHxS+KFhq15LNbQtL5y/Ivlf+PV20fePDxMeXlPDP2gpvtVnNqt7M32nfsVv4VWvEdB+Eut+LNW8mzs5EST7snlbo46+i7vw3qvxA1SZHsNsTf6pl+Wovjd8Sfh1+yX4PhtoJIZvEF9Fs07T12yTs2370n+zU4ipSpwvMy5XzaSOJ8UX1n8Ovh9Nc6ram4i0qySN4VIG8rhAPzxXx74y8ba94q8RTeJ9YEbXMztsjb/AJYr/dr6d+MusXWu/s6X2v32PPvdKtribI/jd42P6mvj3VNQhtY5v32V+63z/NX4Z4d1Gssxn/X+f5RPyrwhssoxzf8A0ET/APSYEd4sNurXNzcwou3dt3/LXh3xQ8YfbtYeGzm2R7dqwq+5d1dN8SPHiLaPpWm3O99m1V2fLt/vf71eaR6bNfXT3Lq3zfNuWvtY1pV9j9djLm3M27ZLiVtl1jc277nzU2bRXjZ7nUrlW8v7n8P/AH1WpNZ21rC81zCp2/8AjtcR468XFYXS2vI0h3/PJ/e/2a3jRibRp80zH+JHi6zso3S2mUK3zPt+6teI+I9cudavnlkmZkDfJurQ8b+L7nXLx4Ypm8pWrnlGBivewtD2cLs9qjS9nEaq7qcq7aFXbQrbq7OVHQCrtpaKKIgSK3zZH8Nbnh26kaQJ/tVz+4/dq9o988MyJv8A4qUokSies+HbcrCv3Q33l+T7tWr6w/d7xDu+X5pP4azvBeqb40eb7rfLXWSWcNwv32RNvyVX2OUxlE46z32+rfaURQVfcjLXR+NL37P4JLptwz7nk3/Mvy1h6tYvps3mQw/Kr7ttWNamudW8A3lnDDt227O27+HbUE+z988c1G7e8uGfPH8NQxw+bJj1prHC16V+yd4T8JeOvjroXg/xnYSXNhfXDJPHG+3d8rUVJcseY64x5tInmzArwRRX2L8Sv+CdGg6nPNffDHxDJYFrhhFY6h80Sr/vV4R4x/ZI+N/g5ne68HzXUKv801j+8Xb/AHq5qWMw9XaRtPC16W8TzGir+o+Hda0yVob/AEyaF1+8skTL/wChVU+y3O3d5Lf9811RmjC5HRTvJdfvJim8AUhcyFY/Nn0pKKKCj0b9m5t3i2+/7Bx/9GJWT8dv+SlXn/XKH/0Wtav7Nn/I233/AGDj/wCjErK+O3/JSrz/AK5Q/wDota/QMT/ybmj/ANfn+Uz2qn/Iih/i/wAzkE+8KGG00lKx3GvgfdPFEoopd3y7aknmQlBz3oooHZEit8pQ/epg+Zdn8NHLL9KPvfIg+agUR687f9r0rSslS1h3/LtV/mqhHCjf71TXV4qxeVC/P8VVzESVxNSvjeXG/HyDov8AdqqzkN/OlkZt5plSMtW7PGpO9qZdSIduz+7UKtk/L/DQzbu1V8RXKxdg9TVm3/dwtvT+CoI23N8/3mp80m3CI7bakmSHbkVcbt1KzZVdnJ+9VcttJFLEzK28PzQBam3wp9/I/wBmovMC/cfH9+o2nLY+bpTfMH9wfnQB1/w18QPpesJbb9qSP/F/FXvfgjWrZtUtvJdnO/8Aufdr5ctrpoLlLlNxKt95a9t+F/iyHVIYZvtOHVNrrv8AmWuPGU+aJz1qfNA+u/gvrlzouv2V+8y7Y5flbb8tfVXg34sW1rZqEudpZ2+Zm+WRf4ttfBPgn4iWEIje/wBVjhRf70qqq13cX7XfwF8AWq3/AIm8axXdxC+37DbNub/x2vG9riIXjCJ5XsavL8J9sN8aPteX01JG/wBmN/4q5zVP2a9b/bmuLn4Y6V4wh0bxxHpdxP4Ih1BP3WpX0a7ltWZvu+Yvy7v71fGXij/gsB8OPD6TWvwy+Gt3Kyrtgnm2pHt/usrV5P8AEH/grV+0h4xuEm8EwWXhqeOXfa3mnlmnhb+Flb+Fq6MPRx9ScZSgbUsPiYzU17p9/wD7D/8AwTr+K8PxBvPE/wC0/pWpeCfD3g+Ka7+Jeva5b+Ra6TYw/NLGrN8skkm3av8AvV8Bf8FSf28Na/4KA/tm6n8adA03+zvBmg28eh/D3Rdm1bPRbf8Adw/L/ek/1jf71WP2pf8Agqt/wUU/bF+G9h8Ev2j/ANqvXte8N2NvH9q0WNI7SK+ZfutctGq/aWX/AKaV4HDEjQ/PCq/Jt217kIxjK6iepzSUPi1OV8V/NqDzJ0Z6y62fFUSRXHl7MCsatTSnI+rP+CLAP/Dw3wh6DTtV/wDSCevoP/gurexWHxq8MTyzFQfCCDA7/wClz18/f8EVIyf+Cg/hNmb7unaqcfWxmr2D/g4G1E2nx48GwA/f8HDjdj/l7nr8txn/ACdrDf8AYO/zqH4rmS5vHHCr/qFf51D4vWZLiH5HXbVmxjjmkZEfdtri7PXpIVMPr91j/DW5pesfdSN/95l/ir9OP2SUTqYURcJncq1s2tql9p7203+t3bkaud0++S4YTJx/frobW4dZ1htuVZPvK1aU9yOaF+WRHDH+7l0m/hyNn3t9e4/sufsc/tU+PNOjufC3w3vW0a+ZpYNQgt2aPaqs27cv3flVq8au7Pn7fbJteP8Ah/h/3mr9yv8Ag3v/AOCqH7D/AIc+Alp+zN8bfENt4V8ZPc/2fK+qhVs72Nt3lMsjfd3bq6cPjfqVWNSx52PwksbS5Iux+Qmv/tTeAPh2raR8P/Bo8Qa3Z3OJ9QuU/dRyRt/d/i+7X7W/sn/D79iD9s3/AIJb+MvH/wAbtM8Pt4gtPAd9qN79h2pc6XD9lZlby/vKyyKa+Mf2TP8Aglovwy/4KgeLbX4+eBobz4b3XiS4ubfVLOBZbNrea4by/wB791flZdvzV9Vf8F6P2bPhn/wTu/Zc1z40/sm+HrjTn+IWnJ4K1S3t5M2lnb3R3G43bvvMqsqrSx+aV8xrRXP8Jx4LL6GBjzxhvvfc/BzQVT+y7X/SZnDRM3mf3l/hqtNqDxeJIbbZ/rIvn21o2ulpptikPaGLZuZ/7tcbpOqTap463o+7a2xPn+7WcfePZR65ZnOh5PP7lun415XeLt1B3TdnftavU7FhJoQZWzmJuQPrXnlxprrNMm/O2Xc7fer7njb/AJF+Wf8AXlflE+nzRJ0cPf8AkX5Ihsbx/MKJ8jL97dXYaHq8Cxqk0zf70dcpHC8TImz51rS02Xybg/d/4DXw0YngVPdPQdJ1RNqvC+4tuXbXR6PqE0jKiPGyM/3t3zV55Y6r5cIREZj/AB10Gj30MirCyeVt+638NTKn9ozjU5ZWP0K/4JU3gvNC8Z/Nkrc2II/4DPXyT+1K6j9sPxg5JwPGUuSB6S19Sf8ABIm5a40Px2SwIF3p+0Dt8txXyl+1W4j/AGsfG8n93xhdH/yMa8jwejy+LefL/qGX5Uj888O5c3jRnz/6c0//AEmkd9petW1xshd/l/g3J92tv+3LSS1TyXVH+6+75m215Vp/iL7Ls/0ldjI3y1YXx4kcfyTr/d2tXPiMLKWx+vUcZDlij0q38QPD5mx/kZdqNH/DVy38WW8ahHmjRl+VfuturyuTxt9ohin+0qf9lWo/4S792+yFfldfuqteZUwfNGXMehh8Zyns9rr0LeW800ZX70sa/wANa0fiKS1d4d/3fm2xv81eI6X4umUtvMnzP91m3f8AAa24/iAlvcPqD3P2h2Tbub5dteNWwfKfQ4fGQlCMrHrf/CSJ5Z+SSJodq+W3zbl/vVV1LxxbLIzwnYF3eVGzbm2/7VeZ/wDCdOy7PtLH91821/4f4qo3vjbzESYTKUjXa+7722uSOD5feep3fXodDv8AUPFj+W9zNcqg2bvL/i/3dtY+q+InaNHeZirfM7L8rVw154ueOQW1s6/N8v3fvVl33iy9bdCjq7/e3b/9WtaUcDUlyyPPqY/llY6PX/EE2353w+7564nVPFFzud98OPu7f4t1UtW8SujN51yu9v7r1yl9qzztLDDMqn7ySfe+9XvYXCy92MkcGIzE2pNc3SNc3L4Xf93d/FT9PukuI2feq/N95v8A2WuSmvZlbZC6oq/My/e3VsabeTSP8j+amzajbNtet7CNOJ5f9oc0js4b6G6hVPsy/L99W/iWr9rqCR5kdPK2/NtZNyrXN6XJN5f765bdv2/crWW4upNydW3fxf3azqUYRgX9clzc0j6Qh1B5JEdN2xX/ALn3lrQhkST95cou2Nlbdv2rXDWviCaOEO/mOjPsSRm/eLWzp/iAyTfutzRrxukf7zf7S15VbByPbw+Mpcp2NjdObjzvJhd5v+Wa/LtX/ZrYtZEt2aTfltvzLs3bf9pq5DT9auXb/j5VVjb7zfw1o2OpWyyLNsZNu7+PbuasI4T3rS2OmOL6s6yG5S6jNwkMbuqf+O/wtVaaSVmc2zYZlZv3n3Waq2n6hC37z7ZsP/LWqV9qz3Tf6HtUSfdaZPmpRw3vPlFLF+7qUfEW+FWzMrlov+Wny+X/ALNefaxJ5Nr5kk292TZ5i/w112qXjtepO/l+XH/C3/LSuf1aBJYZI4X8wK6/LHtWu6jh+U82tiIylJnB6xawyXTP5jfvIvk3fxf8BrmtY05BGUhtmKNuZ2/h3fxLXdanZ7ZPLfbtX5fLb73/AAGufvtPnWTYifOzs37z7telGjGR5NaS2kcLfWqeXl+GX5tv3WqmbflEhRc7d3mL93dXT61p/wBsZtkMbsrfe2ferNGl/aJ/30PzRv8APtfbtau2NE8/2kuYytegI8KXD3AxIGT/AIF8wrZ+B/lf2Zch1J3XWDh8cbRVTxpaSQ+FbqSRQCWQ7SclfnFafwH8oaFdMzEN9uwBtyMFFFfoNKnzeGeIX/T9flA9qhUisolL+9/kd5M0cMIe2Rfufdb71Ld+dK32y527flV2kT5d3+zUtrHuuIXHO3d83/xVaH9m+ZhIXUL9/cyfLX5fLDyjuc0cRzGVHap5i+cjfL8y/JtVv/sasLN/pMW+bdGyfLGv3d1LNYTNMtz9pVnVNvzfe/3aYtvbWvyb/lb5Nv8AtbqxqYXl1K+uTHTfafLmlRGQLt2M33W/vVR1C+hWH7MHb5X3KzfK26rF95253SFX2vtl3P8Aw/7NZWrSwq+x0Zzt/wCWn/LOqp4X+6KWKM26+2SKUhmXb935vustR2NrNNJ9mLMn8W3+7U8ccMMb7Jl/vOrVZtdPtpJPPabZM0W/a33a3+qqMCPrRzHjpoLOx+zb97yblT5/mrwLxxpr2eoT23V2+/tr2X4oeJra216z0Tzo0Ee5naT+Jq8y+IcdtNdC8Sbcv3mWOu/B0404nkY3EOtU5TziC8e1m/i/u/M9U9ameRg+9trfwrVjWZofMZ0T7tZV9deZGNj4Xb/Cn3q6uXlOeJTuJvM+dPlO6qUkj79+/bu+aprzeu5H3fL/ALNUJD5Z2b22q1IUf5SSbYzHY7Nt/hpscRZf9v8AutUDSN5Z2fK38fz1Pp7edJ9/5v8AapxkVL+6V5F2jY6cK9M3p5gTZ8tW9VtHjVXd/wDZ+Ws/50YfJS+IIkklvu3eX81V2RkPzrVu1k5/fHFWpLVJoVSgOblKuk6xNZXCbnyn8StW1qWn6VrVn9ps5lST7zVz95Zm2Yd6s6XcfuXhefFVGXKVKP2kUJoXilaHP3aKG/1x/ioo5jQKbJ2p1DDd1qQFVsff5qRfmYdxUaru9qcgTn5+392gmW5MyujDzeVb7tRFzgN8uKGZ9uymMfmz6UEgx+bPpTX+6acy/wC3QFLUGgP940lFIxwOKAFre8AyFdRc78bV3bqwe+K3vh+//E48sorGRNvzUES0iehra7bOLhtrP96qV9bpHNvG3C/w1oyXUMdr5OzezP8AIv8AdrJvmd/v/MzPQc8dNDOvmS3/AHkH/jte2/B21bTLbR5ZEjiWSaN4l2fNuZhXh0e++1a2s/lfzJVV1X/er6A8LR/8TXTbZ0bZDNCiRr/D8wrkxkpRou3Y4sbU92Xoz9N/+CikUUvwS0sTLkL4ttWxtz0inNfoN/wSY0XxY/7BEXjr4ZNZ/wBt3sszWrasVSFpFXbGq/3a/O//AIKXXbWX7P8AYXCSKpHiu05ZsD/VT17Z/wAEjP2pdF+HPwcs9K/aM1W8sPBFvLJcWWpR3Hlx290vzMu1fvfLX514TV6tLhP3F/y8l+h+O+CcIS4PXN/z8n/7aef/ABA/4KKftFfFzVNe+GnifwNY3esN4gms7+3urxla3aFtrLur8j/20virJ8YP2zvEusJCttbaWy6Xa28b7ljWNfm2t/vbq/XHxB40/YM0z44ax8e9N8bX1zDea9qWoy2MkWzdGysytX4h2mtW3i74j6/4th3GLU9burqBpPvKskzMv/jtfrEnHkifsuHjUXNKR08a5b7Mm1z/AHlWpI1uVvERNyRfe3N/FU1rG8ar8+Vb77VLpun/AGzUmkfbu/2nqfdLlGMT9LP+CEeqXLfFDxLo8MO3+0PAcyyqv3ZFWZfm+Wv0R/4V3c6lceYnmBJIvmj2fdavz+/4N41sIf2iNbsL+/jaJfh9qDeTu+7+8Vq+vf2nv2508NfbPAHwW8ua8h/dXmqRp+7t9y/wt/E1byxEacTxq8ffNH9ob9prwB+zJpL6J4S0231rxbNBtitd+2Oz/wCmk1fBfjjxl4k8deJLjxh4w1iTVdTunke4vpH3LHu/5Zx/3VrU8UXF5e6hcalqWqyXl/cbnlvLhmZpGb/aavOPEniI2cgsLCbzZd2773yxr/e/2q+dxVadSd5ApRl8J9S/GDUI9O/ZHn1KX5Vj8PWL8duYa/Pnxl8QNV1K4uLa2RUTzWXdHL/rN1fenx8nmX9iK7nVVeQ+GNPIz0JLQV+eEsaWMh85Fd9m5o/4d38Vfkfh+pSwOKS/5/T/ACifkvhApPLMbb/oIn/6TAqLpX2pXS/udsS7meSFt3zVT1a+treNvJdYkWJl8v8Aial1vXktl3xw8Nu2R7//AB6ub1a6mkt47/UkVlb5Ubft21+jR933T9hjHmMvxVrzi1dHfZFs3bWf5mrwv4l+On1e8exs2UIvyttrc+MHxG8930/Tbpt33W215gSxb5jk17GBwsuXnmerhcPyxvIKRlzyKWivV5TvCkZc8iloqQGxffp1Iq7aWq+IAp9vI8cgf/b+7TKTcVYUSA7rwbrjxzb3fO37irXpun6g95CN77t23/gX+zXh3h+++z3GzfivVPBmrPNGiJtPz/xPSj7pzVInQ6po51Syb/Q8lf4v/ZaydNhT7Hc6ZN8nmI0f3P4a7WxkkWxZ32/N/DXN65p7299vs/kDfM27+Jf9mrl/dFTPANWtDYanPadPLlZa99/4J1/D7WfFfx2tdbto8W2l2c11cN227dteO+NtONz4zuYbZeJHVt1foL/wSh+DVhF8Jdd+JcyNvvNUWwsPl+WSONd0nzf71efmVb6vhZM9HBqM68bno0nhmO1t498LMN672X+GrlvYzWkMkyTb1/uqn96vQtQ8Gu18Mwxquz+H7v8A31WdN4fs7VTbpbMZF+WJfvLXxtRzny2PtMPOFjyrxJ8PPBPiSP7NqXhLTbzd80rXFurNXnuvfsn/AAK1Vt8PgOS1Zt29re4ZW/75r3y/0F1VNlsqJG+3a393+9XNa1psMNw6HdEV/hjbdu/u1pRxmIV4qWw6mBwtT3nE+ZvE37Cfwu1P9zoniTULGT+LzFV1ryzxl+wz490vfP4Ynt9SiVPnVH2yM275dq19mahpqW8jRui/7bL/ABVF/Yr/ALlEhZH2bvM/vbq6qOaYmDvKWh5tTJMNKXue6fnF4s+EnjzwZeGz8Q+Fby2PbzLdttYMmmTwtsmUqd2K/TqfR4I5C95a/aNyr8t1Er/+hVy3iD9n/wCEvi4yprfw6swW+eW4t08qRm3f3lr2KOcUpR9482pklf7DPi39nKBovF1+WX/mHkf+RErI+Oqk/Eq8wP8AllD/AOi1r6c8dfs7eAPhJOfFPguW7Q3cwtpLSdsqg27sgnn+HvXkHxc+BPivXGPxEsmtzZ3KhVBl+dSg2n5fwr9TqV6dfw2oTi9HWf5TNsTQrUMljCS1Uv8AM8RIx3/Khl3Yq9d6LeWsz200Lb432tioJLG5SMO8Lf8AAq+EPnlJEFFPaF1Pz00qVoLG/wAa/WnLvxxRsb0o5U0E/EHf580KdrfJSU0Etmgok87y/unFLJM0kjP/AHqYw3daKBcqFb5mJpKKXljQQLJlX4pF+X59mabu+bFO/g/Gq900F+TbvT9aSZt/zuKP4S1GFb75o5gEVX70/cm7dTAccihFc1It0K/3jQrfwdP9qjH8H8VByAVqvhFzMWNc8+tXdJ1rWNJL/wBm3LRmT77VRLFutHKmpEo8xf1LWfENxIYdQ1KZz3UyVn7jyGPP96tG11JJYRbXnzbf9U237tWF0J7yT7TZ38MyK38T7W/75ojyC5uUx/vJ9/5a2PDmnxtIdSu0bZH/AHf71X47Hw9Z2/nalDGzt/yzj/vVCNSNxths08qFfux1UiZSLVnI91M0v8Lff+eteHZ/qd+PlrI01ds33FO3+7XRWNukkeETLf7VVH3iPhOO8Zb1kiTf8y/LWFW/47VI74Rpt2/7NYFBtT+E+uf+CJ1u7ft6+GbnHA07Uxn/ALcpq9c/4L6aJLq37Qng6SPpH4MGf/Au4rzP/gihbMv7bXhydhgfYdRCn1/0KavX/wDguXqUNt+0h4Rs51BVvBSsMnv9suK/LcV73i1hv+wd/nUPxXMP+T5YX/sFf51D86rvw/eQs3yfL/e21UK3NnI3zstejSR2d0pTyP8AcZf4qyLzwulzuymxWr9U5Yn7FGpL7RhaZ4luY5lzN/wJq7Xw/rCTY2Ou1U+9XF6h4ZudPkLwozKv3aXSdQudPZd821V/26kqUYyPY7e6hksxDvxuX/K1Uv7awuoXs3fYv3V/vVi+F/EVrJAiXLqzf3q37hfO/fId3mP8zf3qzMvhOa8R/tDfH620mL4YxfGrxYugw3CyxaR/b03kJIv3WVd38NfRmpftf/tb/tLfCXwl8EP2pfjLrXiLwf4ZvPtHhfR7hvl85vlWSZl+aXbu+Xd92vl74jaO8dxFrdttUxt89fst/wAEZfgL+yF+3J+xD4k+Euq6Ja23jHSdWt9RuPEk0TNLHpsfzTKv/PPb93/arKrGK/ulVOf2funwN+1R8I/hv8I/2VdB+J1h8QrNvFXiDxHNap4RWBvtNvZwr+8upv7qtJtVf71fKfgCyvb/AFdri2GXHz7a/fv/AIKbf8Esv2Rrj9kGPx9pvjvUte8V+IrRbD4VW1nZ7GkZdrNv/wCmarur85/2J/8Agkn8Wv2hvFGqWfgHwtJcz6P5n9tyX26KC3WPczMzL/C3ltW1OUaVLe5xwrSl7s1Znh2n2s9no8dtdRsjrD8ysMEZGawrXQ/tDSP5O0feSvRPin4y0nx9431HxJoXgm08PWbFLa30eymZ44FgjWDIduSW8reSf4nJrC0fT/8ARXkmRQWf7v3q/QuM7PLssb/58x/KJ9hmLvQw2v2F+SONk0HypPnf59/92o20ncrOm7d/u13lxoaIp875Tt+Vtv8ArKx5NCeORvk2+Y+513V8HGPLI8Sp/KY1tG8K75n/AIq17WZ/M3/7f3qZHYpCPnhb5k+VfvVNb2O6T5922P7m6tuWEjyaj5Kh99/8EbJzL4f8fKSPlvNOwB2+W4r5Z/azlEf7UPj6YnGzxdfHP0navqL/AIIypt0H4gsAcNeaaQT3+S4r5Z/a3z/w0x8Qdwx/xVuof+j3rxvCFW8Xs/X/AFDR/KkfBeG8+bxjz5/9OIf+k0jhbjXnEazI7ZVd3/Aaj/4Sx8Ab1+5WLcSbpCUdtsnyv/s1UZXjbYiZVf8AlotejKnHc/R6VSWx0cfiqaRQjwqyr83zVctfEW65Wbe39565Bbp/M2Qhvm+ZpFqSG+uV/fb2VVbay+bXDUw8Zcx308RKJ29v4mSOTekzBW3Lu3VND4uQR7POU7X27q42HVkVk+eP/VN96oE1SRY14X+98tcFTB0pfZO+jjZx907pvGEzSK6OzJ5W1/npP+EoG4bJvlb/AL5rh4b52lKF9zNViHUnK7HmZQr/AC7krGOB5PdR0RxnNudjJ4keRUm3qQv3/wC9UF3rjzW/yfIzfN9+uYbWEaIeWW+Z/wDgVQ3F9N5ITfn5/vVdPBxjoc9XESluamoaxM3zna//AE0b+Gs641R7iTZ8qn+8tVJJ9u50ZSy/7VVVmeTb97d/er0adH7JwyrTiaFvePcN9zd/Cslb+j7441hTa67Nr/NWBpcLvHs/jb7i10Oiw3Mj7JIcLH/e+X5q0lT90zjUkdBp6zR7E+Zfk+dWrbt7eZWRIXZDs+fb/FWJpr3MMbw3KRnc/wB7f81bels8MiTbWPz/AMX3ZFrklT973jWNaR6ZefaYtkzw7Fk3bPm+WrEWrTWc29LxhFsVmX+81Zd7qy+SyO6kMu5GVPu1lx6tN5weF9pX+9WssLzHTHFcp3mm61+7S5SFlO/dtkb7v+1XRWuqR3MaTedv8x2V1X7y15rY30O2LfMrfdbb/D/u10/h/wAQJG3zuskTfMir91WrGWDjzbHTHGS7noMd1Cqxu7yI7fNtX+7UN4ySfO8mxlf5Nz/w1zlrq0NzvhnvJHf/AHfut/dpb7XnaNUhdo3/ANpKPq3kbSxUeQs3jzanJ5PnbfLXYm5/las+8t/LkxsaI/eT+6y05pHX5Hudx2/O0ifNVbUrj92kMNyrbZVVl2/NXRHD+6cksUZeoed5h8nb/qtv7xPu/wB2sfVIXhVJnuWfzE27V/hat25j8yOaf7T86t8q1lzW6TXDTfvkRlXZ5n3f+A1vTw/vHHKtzbnPrppmWL9yq7mbY2/5WpsejrJKXSz3H+P5/wDx6uqt9DSZtlrZ8R7lb5fu/wC1V+x0NIVGxN5k+V5Nm35a7Y0YHP7ZnlvxQ057TwNdgk4SWMABMfxirX7OBMXhm8uUkO5dS5iH8Y8scVv/AB40ZLf4XahPCV2Q3EIKr/CzSLVb9lLSzf8Ag3UJDAJFXVcFSMk/u06e9feYaj/xrytH/p8vyge5TqxeQzl/f/yOyTR3aMMm6Nd+59v8NTeSkO9Emk+/95vm210V5pc00zJDbM7Kn3t+3/x3+Ko7rS/LjmfYqPD/AHn21+fyw8jyFV/lOYkhRpN6f8C/2m/2qpXCw7rgwpz/AAeZ/eX+7W7qtqgDTQ+WyN99l+9/tVg6lG8kLpsjcfKsW16n6r15Re2iZOoX7ttRJ1Dtu8/y/wCFqyLi6xEuYdrf71aGqTI0JhhRdq/NKy/L/u1hXV4kfmu7732qqq33Vq44P3fdM5Yos2twkMrRvBGVk+bcyfd/2amWR7OGS8eFX8mLdt/4FWfZ6hbF1tntmHyKrK33azvidrUOl+BL14XbMcTbZI/vLu+Ws5YWcZe9E19tzHgHxa+Kiax401K8Sblbj727/wBBrnZPHj6pDs87dXNeIl/1s3y72f5m+9urHs9UmhkPyVEY8pjfm946K+kSa4Z/733/AParNk+Zwj/w/wANSW9wZYf3ny/7X96orxd2yZ3w1BHLCIybZ9xxy38NZ15bOyt/e31aeRGmXft3L9ym3U2750h/jo9C4+6ZUgSHOU+XdTre4dZ1mC4p0zOrOU2gs9Vm3qv3+acpD5TpreOHVLPhOV/hWsK6t0tcwujKf4Kv+EdWS1uhDM/DPWj4y0Ga3xfpD8knzfLSJ+GVjlzG0cg+bP8AvVs6Ta/aoCj/AN2s9oXuFXem0rU+lyPb3H7z7tASGahazQxlN/y/wVnxyeW2dtdDrKpcW29EXbs/hrnpN6yUFx94iZmkcnNKq7aanX8KfVRNAoooo+0AUqsfub8Un/AM0f8AA81IC7vl20lFIxwOKuX90BNu35s06ilbld9KJMhKKKKOUoK2/Asjx60rof8AZrErV8H/AC6wjlMlakip8J6HeM7Rr86/L/Ev3lrLuLpI1P7nDMm6tC4unuVWF/u1g6pNhW+TC7flVnq/cicxp/DWxTWPHFv9ph+SF/N2/wB6voL4fW9tL4jtbwQrlbiPY3/AhXivwN0pHkvPEMyL9zau56928AMlrd6aiSNlr2Nmh3fL94fNXBjLezfL2OHGRlKEuXsz7+/4KsM0f7MdtKpwU8V2h64/5Zz15t+xPrz/ABQ+CN/8KL/VY47j+0pEsFklZlVmhZV+WvSP+CrsLz/svQRI6gnxTa/fOB/qp6+Kv2a/itefDvVLmaG5ZmjvLedY9+1vlb+HbX554Qztw0o/35fofj/gwk+B/wDuJP8A9tNv4sWHif4I/Cb4haN44SO31fT9BuINsifLM0jeWskf/AWr4x+FUDx2jFPn+X5fk+7X6lf8Fndc8B+Pf+CcejfGmwihTW9S1yz0v7RGvzTRtukkVv8Ad21+ZXgSzSPTo977N33l/ir9XqRpKXuH7NhuaOHXPudXu3WqfPtZv/Hqm0d3XUndtquz/J/d+7SQKjW+zycL91Y/4qfawpbRiZ08w+b8qq9Tyh72x9gf8Er9e1vSfi9qepaDrElvcXHg+6glaNmVmjZl3f8AoK19D+OL618LWLPcvJmTc7t93ctfJH7APxCm+H/jjVtYfSftzzeHLiCC183aqszL8zV6pr2tar4y1I6x4nuZEaTa3lq/yR/7K15mOxHs5csdzzsRHm1iS+LvGl54g86bSt0MX8Ks/wB5f9msJNLmuJmjSHCtt/ff3v8AZqW4urO1t/tOq7UVfmi/vbf7tVbrXHaxFzeTfY7X5tkO397J/d215nvxjzSMo0z6s+PVqX/You7ZW27fDNhyDnADQHr+Ffm3rnibz2ez019xV9rybK/RL9o65Mn7AN7c27PF5nhLTSvPKgtb8Z+hr82rj7HawuZkbMfzRbq/LvD9tYHFW/5/T/KJ+T+D7tlOO/7CZ/8ApMCteLbWMP2m5m3bZd37z+KvJ/i78RfItpoI7n5tzbV6feroviZ42tre2m2XLIi/N838VfPvirxFc+I9Ve/m4B+4vpX6ll+F9p70j9wweH+2ypdXU15cPcXL5dvvNUdFFfQfCekFFFFVHYAoooolIC5p8MLRF3TdUdxYPHynNTaVdxW6yLcucbfkQVPp7Q7v9JdQP7u6sZe7Iy96MjKI2cMMUBt3NaeqR6bJcMIblX/2qpTWbxrnev8AwGqjLm3NOZDYJCkwdRmu/wDAWrQ3F2iO/wA+9dvyV54reW/yVteF9Q+y3g+fH9xqvlIlE+gtNvk+x75nXa336wvE2qfaIZEe8VPL+6uzczVQ0vxA9xobvvX5flrnb7Vnmnbe7D5dr0ubmOePNEyNUaEag15v2yqnyMqfdr9pf2Vfgj/wqH9k/wAC+APsGy4bRo9Rv5I1+9cXH7xmb/vpa/K79jf9nTVf2rP2nvB/wN0R1xq2rRvqTN96Ozh/eSt/3ytfu54m8N20cz6PpqMltbxLBZLv3eXHGu1f/HVryM0leFmejgZctXmlE8O1Dwn5ytv3fL/C33qxrjSXXEKIodm+Rlb5q9Z1zRXt5tiIqfw7Vb5mauR1bQdq8Q5/i+781fM1pSjLQ+oo1o/FE8x17T/MkeObbEW+XbJ/FXFeINPhZldPLDr93y/7v92vTfEml3iybPsyiH7vzfeauL1y1tlWWzhmhX+Hav3q4o/vPeWh7FPEe0gcJcWc32zfHtKybm2/LTVsf9aHeRWWVR5cjferQ1S127Hh271dlimaL5o//iqgmZFjRJkYyLt/eLWntISlcuUeaNyu1n92F5ldlf8AdfLSL50PmQzQ7V/jZvl+b/Zq/byRtJCnk7Svy7vu06S1efcbm8VxHu2qz/Luq41OXcmUf5TyH9paNl8KaezQlT/aHzMWzn5GrzfxQYrf4MwXAOX/AHxC+4c16l+0/Gy+BtNfa4B1MZymAT5b81x0vhxdX+Bln9oRwjtNtYdDiVs/pX7RFx/4hXhWv+gh/lM4cxv9SlzHyt4b8L3+ta0/2yFjul/1i/xV63pPwj8MLpTzaxYRyJ5W5G2/drrvh38K9N07S11i5jj8pfm+Zdu3/wCKrlfi98QrbS1kttNufu/Ku35flr51ckVzH5x785nl3xS8L+BtPZf7KsGif+La/wAtefy2KLJ8n3f7zVr+INak1K4d/OYrv/i+9VO3tXum8wVHNzHRH3fdKkOkzXX+pT7v96l/4RnUlXeIdwrpdF0vc33GlT+Jq1rqOztLffvUbflX/aquVExl2PPZtJubdf31sw/2qrtbv9xUrr9Y1SG4DJsVlrIt7e2871Zv4VqZf3So1DGKPwlKsbMMj+7W+mkWbf6xPvVo2uhaa0a74dy0+UPaM49bebqEp32WfGPK/wCBV6HpvhnRLjCfZm/4DXRaJ4P0GGVX/s2Hbs27pqOUmVY8fj0i8m+5CzbRu+VacND1Jm2fY5M/w7lr3tY9N0uze1sNNt9rfM7NEu6sVdDufEWoI4h3sr/wpT5Yh7SR43daTf2cfnXNqyr/AHmqKNYnkwz8bf7tej/GTQU0XSYl2fPu2s1efabBumWX+Ffv1Brze4W7HwzNeQ796qP9qmXuhvYL/rF2sK6Kz/d2Zf8A773Vha5c7mZN+f4du6gzjKcjKLbWJPWmbnb79OcZO71ptBtEUMVo8x1/1b0lFXGJRJG2Dvf/APaqWN9rb04qBd6/Jj5ql+dsfd/3WqDMmB86QPv6/fqzCf8AnnyyvVSNUX7+6rVt5rMET5WX+LfRzkSj7xuaSfLZd6fe++tdBY7Ftd86Z/vqtYGlyJMyfu938Nb0kiQWBkdMBU+Tb8rNQI4XxlcedqzJ/CtZUSeZIIf7z/eqTUrh7u+eZ/79WPD9v9q1aOHtvrWJr8MT7S/4I56edK/bO8IwH/lrYak3/klNXQf8HBF1NbftPeDDExGfAa9P+v25rM/4JO3Mdv8At0eD9NQOM6fqfBXj/jxmrp/+C+fh06x+0T4QuIpMPH4IUAf9vlxX5Zive8WsN/2Dv86h+K433fHHCc3/AECv86h8F6P4smt1b7S+5a6nSdYhuoUd/mX/AGq4S/0W/wBPkZJkamWOpXljIPnbH92v04/ZuWMveieltY2V4pRI1Zdn3axNW8IpHHvSH/a+7TfDfi9GXyZnVd3yu1dZ9otriHzodv3Pu791aRkRaZxWnR/ZWSaNMGu78M6nDcWLW0yfe/i/u1j6xo+5EmhhwW/2aZo8k9lNvMzBf7u2gmZr+LtL87T5UdFYMm1GVK+jv+CG/wAZH+G37Xnh/wAMa94h1K20XWr+PTtWtbO6ZFuIfvbWX+Jd38NeCXkiahpqp52H2fdb+Ksn4B+NLz4SfHDSvFUL4lsdRhuot395ZNzVE6cKlOSFLmP6II/2n/2PPit+2Jo/g/VfCWoaPaeC9cbRNDs9U1FfIh27pLm4aP8AhZm2qteXfsneCvFl/wDFTxZ4H0L9oDVfBvhrxz4hvLC/k0eJVkuNPaZtqqzfdZlb71eMfE74d+BpPjt4e/aE0rxtpOtw+PrCbXPsdjOrPpflxruaRf4dzblrof8AgnD8SvD3x8/aOi0TXfEn2awklmbS1j+Vppl3bdzfwrurixEJx5eRnnctWtieaWlj4x/4KU/CD4dfAL9uP4ifBv4R2s0fh3w9q0VppYuW3O6C2hJcnuGYswPcMK8q0GxSTSw9yjI6uysv8Vex/t3eILr4o/8ABQ3xdfa3pv2qS78VWtjPa6fMCbgRRwW+2NhxlhHwfU153oWr+G/F2paxqXhTw9Np1guqXCWFjcXXmyxwq21VaT+9X6ZxpK+V5Y/+nMfyifZ49txwkX/IvyRl/YYZo2CQ8R/N+8rP1TS0ZmSBP7rbVrrG01GZN/7oqitu+9UV1YpcKr/ZmJX+Ja+EjL7J5Fb+Y4OTTZo237Nvz/JHt+7U9rZhd3nPs/irdvtLmjnPyb9yfMy/dqFdNhhkbzoWKqv3lrqjseLUqe9eJ9l/8EcDMfD/AI98/G4XenAbemNtxivlb9rT93+0/wCP2yOPFt+ef+u7V9a/8Eh4hFonjvBzm60459fluK+TP2tY8/tR+PYxj5vFt716czNXi+EX/J38/wD+waP5Uj4Hwzv/AMRgz2//AD4h+VI8ovrXdu7hm3bqz2j8tdm9v/Za6C8t3kVn2R7V++1Zl1byKmxNvyp95v4q9c/QKdSUTKkZ4d29G3fwUxpAynZt3L96rn2e5hjXfJt+Sq8f+sO/b833tqferLlkdUZRIvORevzstDOIw3k7XeT+H+7UkNu7K6b9u37jU6aFGw6bt6/xN/FXLKPLI7KcuaHMJC00ca+jf3f4qlW4xD5+zczfw0WsaRqQ/DfeoaFFkSE7irfNu/u1lKJ0Rl7o+S42r+5T5VT+H5qQyPI3n71+b5U+akjgKqfJ24+78tTW2n7v3mxR/FtVanlQ5EMce3533ZX7q7KuaXpct8BsT7v8VWLWzmuJtnkfe+XcqV12g6DDHAP4l2f3P4q6KZyVzP0nwy8uXh/hXb838TVuWvhndNsSZpTH8y/71dJ4f8KQ3En2jyOPvIuz+KuhsfCKTRjYqq7fNuVauUTi9p/KcTbaLcwrs8lmb+Dan3a1U0+a3x502xlXd/s7a7dvCd/atG6WzMzRfN5f3aoX3hMxq8zpGPL+6sis3zN/DWdSPN8JtHn+0P1Zr9p2fyVwy/P/AHawJr+b7UfnXGz+5XR30czQyvMWXc+3a38NcvqUMNu3zvv3f3a+hjhYcnmcUcVLmJYdWNu0abJPm+bzPvfNW7p+veXbjY7fK+7a392uRWdIVP3kf5VRmq0l1MUCJ95fm3LVPL4y+yaxx0o6nd/8JQ8jb0mj3qisir/7N/tVaj8SPN5rpc79qf6v/wBmrgIb+ZFZ34M33G+9U32zYzb3xIrrsVf7tFLK+aLsVLMLwudxD4u/1aB2Zf4/Lf5m/wBmmPqDyKzmaFdz/dkbbJ/vf7Vctb6gLrbczp5bt8rq3/staulrHFL/AKTtKt8qRr95VrT+zeUy+vcxsLcLezbIUUP91Wb+Kpre3uVki865Vw3yvCqfw1Xt7OFfK2O2FfcjN/eratbNftQtpnbH3mbZVrL+U5/rnvF3R7GaSaP51bb8yLv2tu/2q6a10kqwhd42f73yvuVd1Q6Do/7lpnSNkX7jbPm212GkaelrH5MyM0Um1t0n3qmODvsOWM5Y+8eQftQ6Glp8EdVuJIiHjurcK4jxkGZRg1kfsSaa178P9XkMb4XVjh16A+VHxXc/tiQQw/s/6wr5ZxdWvlE9l85Kwv8Agn9p7Xfwz1qWJV3rrbAM3b9xHX2eGp8vA1WP/T39In0dGtzcLVJf9PP/AJE9Nm026urhXm2od33pP7tUNY0na2+H5vMX733l3V111pHlxvNs2lZf4n3bv9qsfUrGGPZ5Ls+7czL/ABV8hHC+7zHzP1z3jgdYs9sOfuRMjK/8VczeQ+ZD+43DcmxmZP8Ax6u98QQ/u12Iq7dzbVri9TjeENI7sZW+/wDuvu1rTw/YdTEcpx+tW+6Q/udiL8rMz/e/u1z19C0e13fcvyq6rXWa1a7pmh8nfHs+fb93dXO6hG7Mkhtt3z7d27/2WumOB5Y2UTCOIl8Rl+RMys+9kZm+9XNfFpXu/Dc0MO0eZ97d97bXV7bn7R8+0/J80f8Adrk/ixJt09Nm6I/N82z/AMdrkzHC+zwrkduFrx5uU+bdc0+2maVC/wB165u8s0j3Om3C1pa9qTyalKrvht/3VqJf3ybMrhv/AB2vlPi0R6Ufh94pwyuqq+xVVU+9/tVbZkvIGCSbnb+KoprVw37wsVb5dtQrII5ETY3y/cXdTiEokFwrxzbNilf71RtMm3Y6fKv3WqS6dJC2xP8AfqheTvHiRH+aq9wI3+EsyQvcLmNPu/daqlxC0a7MfN/G1JHePFh0PLfe+arUciTLv6t/dNZlyly6mdCxhk391r0TwbrWm+KtFm0HU/8AXeVtib+7XB3VjMi+cnK07R9VudDvlvIdymgekjS1TSbrRdQks7lGXa/3m/iqtND5LB05Vkrrr6O28caONVh+W5jXazfdrlWV7WR4ZpGyv8LUcv2iYiWtxmPbPu/3az9Qj8uQun3f9qrV5Im5nRP+BVnzPukzv+WrjIqIwNu5opFXbS0zUKKQ/L8+KWgApu3b82aen3hSVPugFFNz92lPzfJmiIC0UUUfEAUjLnkUtFHKAVq+D1dtYTY+Kyq0/Ce/+1V2Jkr/AA1IpfCdpcMi2o+b/Wbvm/u1gaxJt3bOv9371bd5I6wNH91VrA8mbUtWgs4YWJklVflol/MYHqvwf01Lfw3Z216//HxK0srL/DXsHhiPdr1m6Puia9j/AINu1dwrmvCOgWdrZp5PyJHbrtXZtZW2/NXWeFYfL17T386Rh9oj3qyfxbhXBWqRadux49etF83oz72/4KsJ5n7MdtHs3bvFNqNucZ/dT1+dfgmxe48UWk2mu2fN2tt/9Bav0X/4Kn2t1ffs12dpZ7vMk8V2gXaMn/VT186fsm/sm+IfGmrLPbeFbq6upmV7O3t4tvzf89JP9mvz/wAIqUqnDGn88v0PyPwYxEaPBKv/AM/J/wDtp5J/wUh+IPie3/Zh+HnwX1SG4SC68RTalF5j/L+7j8v/ANmr598L4hs4dkKqsafer6l/4Lh+Bz8M/if8L/hvqN+t1qa6HdX+peS+5IWkkVVVf++a+ZdFjRY0SH50XbsVvvV+q8vvn7VTnJ0oXN21MPl75pmYfe3UqzI0IhKfN/uVd8M+Gb/xJqlroOlQzXFzdXCxxW8cW7czfwrWh8Svhj4z+E/iL+x/FulfZpt7bY9yybv+BL/FTjLlK+2elfsp/udUv5oWjX/Rdu6Rfl/3q9qW8udWuHh0pPmjfa80yfIu6vHf2SdNttUvNRhv4ZHSO3WWVf8AgXy1654k8VWdncf2VpsEcbr91Y/vL/vV5OOlSjV5jy60uWrYi1a4sNAG95vtN58y/wB5F/3VrifEmoXghm1h7xWmhi/4Av8Au1tfZbq5k33l4qK3zed97bXJfETUnt9Nh0rZjc7NLXj1q0vtmPNzH2p+0C4f/gnVLJJCWB8GaSWTPXm24r8vfGnipY43s7ObcjfMzN/DX6b/ALTMph/4JqXUkb4/4orSACPdrUV+OPxj8cxWDy+HtMmZrmT5biT+FV/u1+feGeHliMHiUv8An9L8on5d4MUZ1ssx1v8AoJn/AOkwOZ+JnjZ9dvv7OtblntoflH+1XJLjHFKwLdTSKu2v2WjTjThyxP3iMfZx5QVccmlpGOBxQrbq1+EsWiiinHYAooopgOjj8yVUP8VDRPtZ9mQv8VNpY5Hj3InRqzASpIZtrKHfatR0VfKgJJpEZt6fLTracxShlHAqGpLeN93+zUClE77wbrDzWr20zttZP4f4qrapePHmbZ93+796sbw7qSWsm+Z9v92jWNceab7nG+nL3THlPqn/AIIveOx4O/4KZ/DCXztg1a7utLn/AN2aFlX/AMer9yNe0FLHz7BIfmjlZGWb733q/nf/AOCfHiF/D37c/wAJNaeZma38faf8y/7Uyr/7NX9InjSzRdW1ATQsrfapG+Zv9qvIx1Hmkb06nLE8p8S6PZ3Sv5MMkLL8rbvmZa4zVoYY55kdNyx/Lt8r73y/er0nxJ5y27RvMys38X93/ZWuI1Sz3QtNbPlfK+fzPvV4lajy+64np0cVy7nlPiKzmjkeHydqyf8ALST7tcLrlgm6XfDsdflVf4f96vUfFFsk0jpH5ibvvs38VcJrVrDDHNM7/OrfeauPlpS91Ht4XEcx55rFrmN8vu+8v91qobphGjzWfKqvy/xNW9qlrNZ3H7na+35fmbcrVlSWsLTJN5zLt3fK396sp0+V2jHQ9eNT3SrDG87SwzJHs2723L83/AWq3Z2/mKj9WX5vu1Fb6f8AaJHmvEUSM/yba1oVdbP9zDnb8rx/3avljKcTOUuX3jyT9sG1ltfA2lq+0htUDHDZIJifiszwP4ekv/2erPUJGXar3IjDDHPmt3rc/bHRl+G+kllGf7YHKrgf6qSt79m7QrbWP2erL7Zjy1luSylfvfvnr9kceTwpw3/X9/lM5sXL2mD+Z8y614+m0/Q59NmvNu2Vg0a/dVv9mvAfHGvXmrXzvNNld39+vX/2mLeHw/4yu9Ns7ZYYpnZkVf8Ae+avKLfw7dahJve2Urv/AIq+Ypr2lKJ8DUj7OrI5OGxmupPOEPy/3q6HS9C2xrcum3a26ugt/DNjpu7e671/hrM1zWrOxVkhmwdnzVsZ/EJdahDpq7IQoP3ttc5rGuPI5gkm5/vb6o6lrj3TO6I2dvytVFvm+d/vf7VLm5ioxLbXCeYER8/L8tWbf51GxPn/AI2qnDG8kgROla2m6XLNtdEbP8dRyzF8IWqzMN+z5fu/NWlpq+c3zoyru+WprfR/I2vM7Hd97d/E1aGn2e2X53VdtaEc3LqXdHt/LVd83+0jVqNq3kqrv97f97Z/FWct1bQx7IU3NTf7QgUtv+X/AKZ7vvf7VKQS5joNP0+bVW2ffdvl/wCBV3Wg+E7PQbETXLrv/ut97/gVef6D4ks9PVZpn+Zfm2q9ad18RLm+ga3t33htzbZP7tTL3tYhyylynC/tHalbXF9BbW82/b/Ev3WrhtCtvl3+Tu3fw1p/Ey+udQ15ftPyhU+7UOjxpbqw+UnZ93fSNfhhqTa1fPawrDC7YZPnWuavJjM2NnH96r+sXzzSffyF+WsqR03YCbafw+6OnGQjnYcGkYZHFD/dNO3JtzTiaMSiigHb25/hqShy/M3znFS+Yp+dNzbf71MVUZc/Nmnr/wCO0GZJGsbRq/zZ/jq3Z7/M84xsRv8Au1U3Ise/fk/d+Wr2mM/BTcG/2qrlJkb+mq8u3f8Ad+9tVaseKNUSHSSmMNt+Rt/zVJotudyu/wAq1jfEa7VpI7aP5f8AZpxXKR9s5N/umuj+Hum/atWWZ03bfu/7Nc7Xa/Du1FrYzX8n93bTNqnwn0//AMEmdQ+0f8FFPCVsJt4j07VB977v+gT16X/wXW1JrP8AaX8Hw7l2N4IUsG7/AOmXFeOf8Edm8z/gov4VlGcPaauef+vCevUf+C+c7xftM+D125U+BUz/AOBtzX5XjP8Ak7OH/wCwd/nUPxPH+7434T/sFf51T4/uraw1K1ZpEU/71YeseAvOVntkwf4V3VNp2qvJ/wAsNnl/3v4q6fSb6O6XBhUt/Av92v1LmP2P+9E8ra2v9LuGRwystdD4a8WPaxhJvm+bbuaun8TeF7DVLV7+2Vd393+KuJvNFvNNbzvJZVX7tOX80S/acx6LpetW2qL5Lvkfe2r/AHqk1DR98a3NttH8O2uA0PWLmxmX52Ub67jw/wCIIdQxDM/3X3bv71OMvdImWtHt7lm/fD5V+XbXL+O7f7LqkMyQthZ67Ka38mT7TbO3zP8AJtrB8bafNfLFeXKN8vzMq1UY/wAopcx9/wD7Es03xT+FfhGzubOGzGk6ddaddSWbfvJvvMqyVhf8E+PEFnpf7QFjNr1zcQaa2qTQPZ2e5Wk3SeXt+X5l212f/BLVPAHi79i/xzonh2G6m8UaT4t0+/eST5fJ0/a3nSL/AOg15l8PbzUvB/7S2vW3h68a2a11dp7Jlb5vJZt22iMeajLkOWUZSrlz9q9n/Zo/4KE6xreheF2tD4T8XWGrWOk364x5a290iP7HjJ7g5rzz4Tyalqmg32sar5P22+v5Lq6jhXbF5kkjSMq/3du6tz9u/wAV+MPG/wC054u8VeO9dk1LVbxbRp72SMIzqLGBYxheBiMIv/Aaz/gjavN4XkSbay/aF+X/AGlX71fc8aRay3LH2ox/KJ9ZmKiqeF/wL8kbzWqNIHmfbuT541+7/u1BNpSLIbWL51b721K3fs/lt86fL977v3WqtIr28jzb923dt2/db/Zr4KnHmmeLWlywOXms3aQ2bwr8sX3l+VaqLp6Kvmp9z+Jlrdvl2LsM3+sX5mVf/Hahk01IZmdORt2/7Ndv2bM8GpLmq3ifWP8AwSfjij0TxuI1IzdWBbPrtnr5E/azUH9qvx0uRz4tu+n/AF1NfYv/AASzV/7K8bO5JJurDBPpsnxXx3+1dH5n7WHjiLru8X3Q4HrMa8bwi/5O/n//AGDR/KkfEeGj5vGDPX/04h+VI4u8s0WLYiZT7zLn71Zd5YpLMmyFS3y7V212n/COyL92TYv3trVHceGYWZXmtmT+61exy83xH20ax59JZ+ZC++Ntzf6r+HbVW403ayDYuf41ru77wtNbjem10+ZUVqx7rQ4WCbE+8/z7qXufCb05e98RyzW8y5SFG+Zvu/3aeunvuT5Pl/i+b+L/AHa2ptHfd5PnbV3/AC+X91VqJdNdpjC+3fu3L/tVyyielh6nu8pmx6e+ze67fm+6z/NUy2TxrsXaW+9trShhSNtiQqV/vU6CF42VHRtn95lrGUT0Ke5lrYvJuaFGHyfd/wBqr9rYvMqJCFX+H5v4qlgWOQskMLfM33mStzS9LSVS9tyfu/NUS/lHIm0LSfLt97/d/u7K7vwz4XMn7z7NGw/8eqj4Z0PdH5M6MPMTbu27m2/7Ner+DfC6HL+T5cWxUeONPmatKfJE5anvEGg+DXW1ihMLMJF3btv3a63QfA7sr+ZbMP4fM8r71dNofhWGzhaa5LfMy7I2/hrrLPSba1UO8OIpG2bdm7bWcq32TH2EDgv+Ff8AmK1sbaTfJ80UkfzfNWRrnge2XKQpu/i/3a9ktdJs5lb596q+1Nv96qGoeC7BYXhS2bG75v7tEZc3xClTPm/ULP7PGDNbNt2fdWuX1yxdY3R4cFfu13muaa80jQJDlVdm2r/drjtWt0VtkKcsrbdz/wANfov1f3T5SnW5ZHLMXa4VIZlfzPl2t/Dtp6wzQqsDowib5n2v8zfNTrhXkkeEozBfvts/vVJbwwrstra2bbGu395XRTw9oXibe29p7o6SzRVLo7MzS/d3fdpy2pGZvm3bPl8v5qS0UtJ9zcN235X+7T2W2W7McLyFV+Xd93bXRRw9vsnPKt7hZ0mzhaTzjMz+Y+7bXQ6TIizP86/dUr8nzKtc1ZrcyMPn3D+LcldFprTXCb0RX2v8zL8rVUsHKPxk/WOb3TodLj23H7512svyrs/hrodPjRriKb7SzHb5b7mrC0tkRkdH835P3qyfLXUeHYyuyGe2jT/gVYVKMImkZe4df4Ps3ih+zSbTtdl3f3l/3q7DTLGCaFEdJNkf+qWSuV8MtDD/AASbZIvmjX71dnpcm2FHm3FV2r8z/N/wKuGpHllePwlxlGUbHmf7blug/Z01qRUUlLm0y/fP2iOuY/4J1QG4+FetRpkH/hIjlh2HkRV1X7b0mf2d9fj3oy/abPbgY2n7THmuY/4JyRQ/8Kq1yd92f+EgKkq+3gwRV9XQX/GH1Uv+fn/yJ9TSfLwdVf8A08X/ALae8X1rZ2snzw+b5fy/e+VqxNasEZp5vlL7PlVfvV1EkcKxt5KK+1du2RK57UpPMjaSBMmH7irF8ytXy0Y/ZkfJc0X7xwmvWttNZuiRsskm5FVvl/8A2a4/WLr7GqPs+aParxqu75q7zXLWNpHR3w/zMqsvzbq5DUrT7EyO7qybfuq3/fVdVOmPm+0cbrDw3skszvl/N+Zfus1cjqUcDSM+ySMtu37v4a7bVI/J+RJJi/8Adb+7XK6hZveL532ZVVm/1iv/AOO16GFj8SkZ83vmO8NkshuX3fwr/vf7VcX8XrKFrOJEhYOzt+8Z/wDZ+WvQJYd0x+RkVk27WrkPi9p72/htNQdFdLW6jl/4Du+Zt1cedYdVMvny/ZOnB1uXExUj5B8QWv2PVJXd/mWVt9Ps2QNvRGO77lb3xm8PTaf4imvIYW8mR98X+7XN6W25m2O3+7X5pH+8fUfEWppDt/ur/E392s+6/iTC/wC8r1ZvJkRTv3L/ALVUJpPOY/7X/j1P4ZBKMSORvNC7EVV/2ao3CiTbv4ZqtzfdVA+B/s1WZyJNmxmp8vulRKkv+sKULNNH/Hg1cbTflaR+lVXt2UHI+760uY05oyLtjqzySiGb5k/3KuXmmw3f75P7n9ysNT5Yzmuq8DPZ6kjafc7fM2/IzUSIlH+UzvD+tXOg6hs37o2f5/8AarY1aOz1KP7fpu3Lf8s6yPEWi/Zboom0bf4v71ZttqFzp/COy/7S1PLzFe8O1BXUHfx/s1TX5fvVZvrn7VL5u/NVvv8Atiq/ulREVtpzTw27mm+X70qrtoiULRRRTlsAUUUituo9wBaGXdiiimArM7cvSUUitvbFZgCrtpaKKACtjwWv/E0WaP7y1jK2eDW14Ng3XDzdlq47Cl8Jv6szrCzydG/u1r/APwjN4u+IFtsT91as07Mzfd21z3iCf93sT+KvtP8A4JD/ALGfjz9oSTWdS8MaDNc7mW3ik8j/AFar8zM1T7OVT3EefiqnsaHMYGl6DeTXi20Ntw38TfLXp3wZ/Z+1/wCIHjTSfD3hywudWv7uZXtrHTot7lwcgt/dr7l8I/8ABJN9L8U6Zc6zc28cUar/AG3NqkX7uxX/AHV+8zf3a7WXTPhn8Lvi3pPwg/Z9c6NJotwhv9Sgs/LlnnZuBu/u7f4aqODjSpuU+zPmqtaVSi7di94q+Efgf4y29p4a8fGY2drfJeQpbqC7zKGVAM8fxmu88J/D3Svhr4dls/CugyaFarF/x9X3zXMi/wC8tV/hrfyaZ4jN5Dor38iWz7IYyAwOR8wJ4H/16k+J114z16T7NezNBbSbleztYmll3f7Tfdr8+8HLrg33V/y8n+h+WeDlNS4TUn/z8n+h+Nv/AAW41b+0v2+tO0H7TJLHpfg6zaJmbd80jMzV4Rp9q6t57/8ALT+9Xp3/AAU4s3b/AIKM+J9KvHkV7HTLSJ/Obc27y93/ALNXnFnD50gR7lV8tq/RZfEfvMY/uos9w/ZL077DrOp+MEmjSe1smgt/Ofay+Yv7yRf9pVqD9o+O21bwnYaqmpRn7LOvlKsu+Ty923c397dXN/B/4saJ8NdWmufHlg17pMibbiO33b1+X5WX+9/u1f8A2jfj14Y+K11plh4G0eS10uzsIUnkayWL7RIv3dq/eVVqJe9UsZR5/iNb9mu4177Pd2empI/mJtaSP/e/9Br1xtKs9J82/mRWmZN26T+KvKv2W7h7ZdRntnVf3S/N5v8Ae/hr0m4uEmkffu8lYv3u7+Gvnszi/rN4nm1ufmG3zJcRvfzQxvb7G+X+9/vV4x8UPGXna1JYWaebtTHmRv8AKtdJ8Svia9jbzaJoM0kNzMv+uVNy/N8teQeMNSTwfo82pa3c7ptn3ZP4mrzOX20+UinHm9T9IP2xtZufD3/BJnUdYtmzLD4A0XYw5+YvaKD+tfiBd3lxqFy95cys8kjZZmr9p/2zrw3/APwRlu9QxzP8NvD8nP8AtPZH+tfij5ntXz3hPCKyzGPr7ef/AKTE/PvBCNsmx7/6ian/AKTAcG3c0Uirtpa/VT9rCiiip5gCiiiqAKGO3rRSMu6gBaKKKACiiiptMBfutUsZ2ozs/wD9lSzQotqkwf5v7tMX5vv/AHfvUcpmPWSb74OGoVnkY73yaZI2W+SnpsHHSpA7v9mTVp9D/aI8Bavay+W9v430uRZG/wCvqOv6hfiFClxr175NmqotwzRNv/1lfyt/D7UG0rxvo2qo2Hs9ZtZVbb/dmVq/qc8YaglxdW9/sXbdadaz/wC8zW8bVx4qnzcsiJS5feOC8QTbo2SRNwjb5tr/AHa4vxQsNvutd8c3y/L/ALNdlrlxMqy7IVl8xm+WP5dtcT4i8n/ltCqf3GX+GvNrUebQ3p1JSkcDrkczX28fK8cX/AWrhdbt38x33qm5f3qs/wAytXf+JpkZn8lF2N8vmLXCeIN6Qr5L+czKyyq391f4q82dKNGVz2MPUlE4PUIYV815ptkSv91qyZlmkVPkVXWXbtZ/vVs6peQw/wCpfZufcn8W2slmib5Hdkdn+8sVR7Hmlzcx6lPEe4FnDNeQpMkKh1TdL5P3auafazbvJ8yT5n2bdv3t1Gn2dskO1/kX727/AGqu2u+3uFdJmdPveSvy/wDAmpU6fLV0HUqcseY8h/bNiS1+Gmm2cbllj14fefJU+TJwPauh/Zjvlsv2ebFppcR+ZdFv9n9+9YX7bCsvwz0ZpGQPJrW4xp2XypNv9ao/DbxAmgfsxWU3nLlluy0bHO7E7npX69XUZeFmHX/T9/lMdSr/AMJyk+/+Z84ftEahba/8UrtHeR9r7U/i+auKuLrTdBtfJuXUf7S/NVvxlrz3Wv3OpTf62aVmrzfxJfT3Vw37xgu9q+VhH91FHxVSXtKspSL3ibxu90ZUL/wfKy/xVx11qE11Lvkm3Nt21O1reXDL5aN/3zWto/gXUr5kRLZn3fxbaqMbkxkc5BbzSfchZq19L8LXl0yfuW+Z/u7K9J8D/APVrxftNzats/i2rXZXHgfQPBOmrc6rAsUS/KrN96tOWFOXvGftPf8AdPMdD+G9yY/Ov0ZU/j+X/wBlrWktdD8P2X3F3s/yN/Ftqt4u+K2m28zW+iQthfl3bPvVxcutaxrE3nP1/wBqolLmNPelqbmpa9DJJ5ydaoSa9c+X8hYN93atMW1tYQZr92Xb/era8H3Hh68VzDYNLtT70n3m/wB2p5uUj3zAW88Q3G7ZDINv95PvVA0Hifdv+xyL/tNXqFjq2h2bAJp0exfvrNV++1fwlfQqX0dUf737v7rNSL977J5LZ3Wtr/x82zf3vmrVs9aeZRvfb8nybf4a7xbbwHfE+SlxEP8AaTdVa88B6JeWxudMucbf4du2tPh+EiR5n4o/4mmuCby2wq/99Uy6keGEH5V/vf3ttXdY2R60/k9IX2bqxNW1BGmcb2/u/dqPt+6aGbdS+ZIRvYhXqCl65NJT+I2iHBFIq44FCrtpyfeFUEgZdu6mKNq7yKk+8xSmqNvSgOYdG3zBEqS43x7k602nq7qmwfN/vUEj4W8xW7N/erS0mBJNvz7mrNt1cM+9M1saDGhmRP7396gmR1Fuk0dutyk20Km3dt3VwvinU31TVHn37gvy7q7XXdQTS9DZ/lQ7PkXdXnRdnJduu7mojzjgLCpllCJ/E1ekaXappvhuJAn+u+auA0S2+0X6J/tV3Oragn+jabHtTy0Vt1P7QVJfZPoz/gkHpxi/4KA+Erh+Cthqgx/24zV3P/Bf4EftLeC2UqCfBCj5v+vy5rlP+CQk0F1+3R4Uki3Hbaapyy/9OU1dp/wX0tln/aM8H7kyT4IUKf8At8uK/LsZ73ixhv8AsHf51D8Ux75fHDCv/qFf51D4T0+4Ytsfr96tmzmeNlcfK33l2vWJHDNbbd6f+P7q1bP5m376/Uo7H7NzHR2V98gR33bV/uVJqGk22oQl0RXO37tY9rI67nL/AO589atndeWV+78tamXLI5PVvDdxYzH7ybvmqTS7s28iJ8yla7/UNLttasvkhXd/Cy1x+saG9nNsh3Fv4tqVlyo0j/KzrbHVvtGlxI77/l+6v8NQa0v2q3/uo1Yfhu++ysIX27V+5W/qEiXFm7o6j+JVqoy/mM6kZyPsT/gizq2q6p8RvGfwQsLzyh4w8EXkETR/KzSR/Mq1b/4Q1F8U/wDCVWaRpNat5V1u3eazK21vm/4DXhv/AATj+K2t/CH9rbwL4qs5o7ctr0dndNJLtX7PN+7b/wBCr3/4wSa98F/2ovGnw9uUaSx0/W5vKhk+VdsjeYrbv4vvferajHmlJHLU92UGeO/tV36al8Y9evreR3321r8ztyWFnCDz9Qa1/wBn2F18DfaZofNT7R8y7PutXI/GWaPVPiFqssKALMU2qvOMxKMf0rvvgPpps/hXZvc7keSWR3X+Jfm+61fbccK2XZb/ANeV+UT67NFJ08Lb+RfkjpbhX8n7S9tt+bb9+s3VGSFU2TYi+8y/3Wq5fshkM0O0KyfL8/zL833a53XNQSBpE87b8+3d/CzV8FRj9qR87ipfylW6unVvOm2/M+3dUUl5t2pC6jb99t1UZtQRnOxNqfe+akMiNJs3/e+bdXYeJzTjL3j7Q/4JaNE2jeNDExObqxJPb7s/SvkD9qpSv7XfjMKMn/hMJ8f9/a+v/wDgllj+w/Gio7FRd2IUv1xsmr5C/aow37X3jIbeP+ExmGCP+mteJ4RaeL+f/wDYNH8qR8Z4Z/8AJ4M9/wCvEPypEsa+TCES280rL/F/DS3Uds2Y3Rm2p97+GrC2jws2yZmfd/CvystTWcMM+JkTev3tq/3a9qMj7SXLH3TIuNP851SCFQfv+Y3/AI9VHUNFtmxvtl3fdro5LeFsuXm/du2xpE/halGmvJAUe2+8u5dz/NUyly7Ex933jgrrQXRvubtz/M1UpNJhVd80bHy32ptrstS0PyZE2fKjfL5e75qyLiz8vckMLJJ93/ZrCpGfMe1g6nNE53+z4Y28+HcX37dq/wB6pvsLy7t6M4+7uX7q1oNY3Pyu7sX+7uX5dy0xLSGNH3jc/wB7cr7awlE9ajH3ipZ2PkybEdsL/FIv+sro/DtnbLhI4fl+8vyferHt7dGbOzev+1/tV1GgWcK3Am3NhVXf/wDY1hL4zeUYx2id74F09FZZo4W3r8rrIn8P+zXsXhPw/Z3FvC/3t38S/erzjwPpryKtz9pZdyqqLJ/D/wDtV7N4Ls5pI0QQRsu1d395WrOVQxqRh1Niz0yaFdjp5m3aqR7a2I7PdInnP88kreay/dbbUlmr2itNNbfeTais/wAy1YjhuVX9yI4zJ9xpIvu/3q5oylKXMc8vd3Et7OGTypn+RGbckf3WZqmm0+a6jkKw7Wh+6v8AEy/w1bjX9y80CRhd+zbt+9VqzXazOttIi7F+791lraNTlM+XlPkTxA32g7/vbXX7r7Vb/erk9ch2yPM/L72+Va39U1DNmyI6qF+433t1cvqkiJl/J4Xa3l/xf71fsNP3tD4aPKo3ZiyecrbE2l1+8zfeWnw26W8Lu8LfN8336vx6bM167+SuW2/vKt2/hzzGKTpIB/e/irVSpRFHniY0itB3jSLb95f4qlht5rjaj/3PnkjT5WroW8KvNCnk225dirtamyeGZodqNDIu7d8u/wC9/FW3tKX2SF8RgQxvHIYU3fL9/d/DW3pcnmKs2FHzMu2opdLe3j3ywzM6vu+5VtLV7WQecjfvH2/LUVsRGURRj7/um3ptwjRojxswZ/8Ad3V0+j332hUR92yP+7/D/s1yVqv2dmR0bYsu6Lc/3flrY0nUEiVofJ2ts3K395q4fbQNuWXU9G8L36LcImxVl/iX/ZrrtP1iGFpYU+Z2lVZYV/hryzT9a8yETT/M/wB1v9r/AIFW3Y+Kpo/neT73y7t396uWpE0j5lD9svVILv8AZy1iCKJci6td7Iv8X2hOtYH/AATxmkj+FWtLDt3f8JCcbv8ArhFVb9qfWBe/AzVLZWU7Z7bLd2/fJWT+wzqzaf8AD3VomT5X1o4YdVPkx19PSfLwjVf/AE8/+RPsMOubhCp/18/SJ9Nza+m35Pvx/eZvlX/drH1TV4beR5Hk2SN/rfLesS48VTPM7zPsWP8Auxf+hVga1r3nyDe8mz73ytXy9OPvc0pHyEoxj7pZ1e+vLqRrlAyRMrK8myuNvtQS4mms7lG/2Pk/i/2am1jULlpPMhmmQKy/LJVC41LdDMiQ4dm3StH/ABL/ALNddGUIx94mpy+6ZWqedcMdiSLMsW1FasDUoZnIhm4ZX/iT5f8A9qt268l2fyvMi2/Mvz7ty1k30HlzCHy+PlZGV90daxxUI6lyozkZkMP7tvOeNhsb7v3mrA+Kdul94D1O2S5j3rZSbdvytuVfvV1Mi21ux37cK+3bt+bdXI/FrfD4H1ObfvRrVlfdU4/FQ+qzX901wtGXtoHg3hW48PfFLwmnhjxDeLDqtqvyTTf8tFrzzxv8NfEPgPV/s9/bTLG0vysqfKy/3qzdU1DU9D1T7ZppaI7v4a7nw1+0RDqFr/ZPxC0eHUUkRR5jL8yqtfl610PqOXl2OGmt4bqE7I2Z1+b5qxJl/fFAteyWfhD4S+Krlp/D3iz7A8jfPZ3H3VX/AHqxvF3wJ8Q2sZvNHhjvUVtu61lVm/75rSP90nm5jy9mdfk343Uxt6/6n/x7+Kr2seF9e0mb/TNHuE3JuTzIGXbWbteOPL/e/wBpaX2Tc0bK8hVl+01NJJo90xSSZR8u3dWXZRS3U6whPmb+Krt74O1KBd6Rs20ZdqnkJ90ZdaHbPG32O5U/7K/xVT024n0zUEmBZGVsZqOW11LTW3ukif3WpktxNNw77qqRR0HiS48xob9H3eYnzs1c9NL5i+WladvcxaraCynbay/cb/arLmheCRoX4ZamI4xG0xm3HNOVs8Gk8v3oLHUFtvNFIy7q0AWikY4HFIjdj+FADqKKKXwgFK/3jSA45FAbdzS+ImIUUUU/tlBRRRS5QCt7wacLO/8AeSsGt7w3GF093Tr/AL9TIiXwFiffcXwRIWPzr8v8Vf0Sf8EA/Cfhj4J/sii/8W6U0On/ANqW76prEdvuka4m+byW/wB1a/CL9jv4aWfxi/aP8J+ANSVfs99r0LXjSf6tYVkVpN3+ztr+sv4ZfsgfD39mT4ea74K8M30d14V1qZNUt9NuYl/0e48lV+9/draNGVSL5ZWkeHmFeUZRjb3TwL9t21+IfiXx+viH9mvxhFD4LW1W41e3ubhYPJul/iXd8zKy0vgLw9+y3ovgbTvjdpevf8JD4ouENteXLPvSO42fM1cH/wAFEP2SfjZq3g0fEv8AZ1uri+S6b7P4g0OGXbJGqr8rRL/FXm37HPiaPUfDepfCjxJBNp19Lp63MVvfQeUsdxH8si7fvbmWumFOdKhZ+9oeTiJU5c3u9H+R6X4L1i30TW1uLqR1SRPLbyxkkFgSPyBrqda8WWCxvbabctbhv4du7dtb7rVwcEd1LOkdnEXkLcYGSPete+hTQ7dLy/ufKfdvdbh9u1Vr8u8IJSjwcrf8/J/ofmfgzb/VCP8A18n/AO2n4c/8FAdem8Uf8FG/ijql5eee0eqLb7l/h2xqu2uS0mNJtqP91V3f8Cpf2g9cTxR+2D8UfGEMqyJceLbpVkj+7tVtq7abp7fKsycNs+bbX6RGPMfuU/gjElvFDMIflPz/AHv4ahVv+Wzp833UX7u3/aqZZkb9z/eb52amzSfLvfhd33qPiIl+7j7p6z+z2tnbw32913tEvzSS7dtdP4s8UPDavDbPhf8AlrIr/erhvg+1ybW5hsEkd2Vdse3dubd92tXxlGmk3En9vOsXlpu8tq+czSU/bnmYiP7y5z2vakNM8zxDrdz5zrF8sK/3f4d1fOXxx+INx4h1P7CtyzDdukVn3bf9mvQviz49ni06bWbt1EafureNf+Wn/Aa+fry7kvrqS7m+/I25q1y/DXl7SZ6WBoxlHnkftF+1z/yhUb/smHhz+djX4s1+037XJz/wRUc/9Uw8Ofzsa/FhmxwK+G8K/wDkWYz/ALCJ/wDpMT8p8Ev+RNmH/YVU/wDSYAq45NLRRX6nE/aQDbuaKRV20tUAUUUUALvb1pKKRW3UviAdtT+CgIZG4pKuaTp82pTeTD1+9Tj70yZS5SvHA7Nt/irf8A/Czx58TvFFn4K+HvhjUNZ1e+l22un6batLLI3+yq0mm6DN/a0VhuXdI6/My19BeNfgx8df2Vv2e/An7TXhDVf7Hi+I+p6hY6JqGm3TR30a2u1ZWXb8yq2771VU5YxOf2kpT5Ynzr4u8Ia34Nv30nXrZ4poZWilVv4ZFbay/wC8tZa7PLr618P+D4/E/wDwSz+IfxE+Keqxomi/EHS7P4eedArT3l7N5jXqrJ95lWPazfe+avklmRW3/wANc8Zc0TaLGEo7bwMCpF2My7PvVEo+bHpU0LQ7/k/hqvQqW5c0id4r6O6Th4ZY3X/eWRa/qTutQubzwzoN5efNI3h7T28tU+8rWsdfy2WWyV0LH70sf/oS1/TrfapDZ+D9Bs/meaTwvpv+sRtqxrax/wAVc+I+E5cR8Jl+IriFW2TO0W37rR/L81cP4kukjV3RGWX7u5q2tc1h+E8lXZW3Nt+bbXH65qDqoeebzF+bezL/AHq4/Z83vDhKVjntcvEjuHe5tlUr8qtv+7XC+JL1FaZLZ5o/M2/vPvV0/ia6mh+Sfai7P9Y3zferiNcm8m4kR3z91otv3VavPxEfePUw9TlOX1OZJJHdHUv5+7ayfdWqDTRqzZSQjcrS/J8sa/7NXNemf/lim/5925Pl3M1ZrRpaxb7ncVjXc6rL91v/AGaub/EenTleGhbs1fo82zb8qSSPubbWtp++a3jeZGzMnz/3ZF/3qxLWYtsUbcSfN5irtb/gVaFncPH9/wA6NWT5Fj2srfNWkacdDT2h5d+2nG0fw40mMquF1hQpX+EeVJ8tc5ZXsFr+x1FPKhBh+1sCvUr5z7q6D9s25WX4b6VD8xZNbwWbv+6krlobjb+xyUWNZWQXbbWONv7x6/VJrm8LsP8A9f3+UzoxE/8AhJv/AHv8z5H1yT7VdNNDKz/7X96qFv4Z/tKYOerPUWo6g8Ko8f8Au1p+EfF2m2cg+2JuG/7rV8tHY+MlzyOq8B/BFNYlR/J+RW+ZmTbur2jwj8IfCWjwrc6m8f3lRfn2sv8AtV5nZ/GS20mFUsPL8tfu7fvVieKvjtqUyyeTc7tyf36ftvd9yJn7Ocpcx6n8WvjP4V+G+jtDo5jN55DKh+Wvlfx18VPEvi++ea/vG8vd8q7qZ4i1bVfFWoPczTM+2qcfhkSAO/3f7tKXvR5mbU4xp/ZKEMclxJv+Yuv3a3FVLGzSZn4/9BaoI7BLRdmz5l/h/hqK4+2X2ET7v3XWjlQeZn6pq1xqV9s+bZv+Wuk8P3kem2oy67vvbqzrPQ0t286ZPm+6q1qW9nZ7VR3z/st92jlC/vWLIvry+Zk8tlVvv/7VdBo+g3LWqO6Mo+8m6q/hPT7O8mb7NbeY6suxV+6tXfF0MN1eGG51Wb7irLbwtt2/7NV7hPNzbFq4k8PaPHvv9Vt0mjfd5avWT4o+KGlWOnrYeG9zXEifvWZPlVv9muZ8aeBXjsV1XSvMZFX51Z9zLXL6fvVWR3ZazKj/ADGjNdbbd3R9zN8zt/tVhXUjyzF3q5fXG35HRl3VnEneR70GsYhTfkX3p1FXyo0CiiimAuxvSheh+lDNuoU7TSlsRysfCu753epI1TzKjVnj/g3LToRJu3bOaXKTIs28czP8n/fP96ui0WPav7xMqv8AsVjabDtuFdOa6jT1TT7d7r+FU+9RGXKZylzGJ4+1BJo4rBYdv8W6uaq3rmoTahqUk0j7gvypVSqN4+7E2/Alq02so4TJX5vlrW8QSCbVHm2bGX+H+7VXwMsdvb3F2E+dV+T/AHqtXC7o2md9xb7/APdpxjzmUpe8fTv/AARvuWH7d/hW2bobDUyv/gDNXqn/AAXZt47j9onwmjg/8iQvT/r8uK8k/wCCORb/AIb58Iggj/QNU69/9Bnr1v8A4LqSoP2k/B8Tqf8AkSVO4dv9MuK/LcZ7vi1hv+wd/nUPxfH6+OGF/wCwV/nUPg64sfn3un/fNNtZvImVH6f7VbHk71aR93/AapSWKCQOnzsv3lr9Qj/MfsZat1S4kZ4XUf391SC6mjbe77W3VXs5Y4rjY6VcuI0uP7u1v4tv3aYG3oOtFdv3l/hbd/FV++09NUhZH+9975fu1ydvJNbqzwpu2/cXd96um0O+3RjemU/9molzc+pmYFxp/wBhuvJKfLu+ZV+9WgzGazMPkr8yf3fmrV1KxhvFZ0RUO7av96smaN7D55U5/wBqg0LPw/8AFVz4X8XWOsWf/HzY38Nzb/70bK1fqB+0t8MdE+Nn7Rui/EhE2WnjTwLY6lBcfalVPO8nayt/ustfk1ql5BDdB3T90zfOzV+ivwW8Ua98T/2Rfhd4zsLxpbvwjr02iXEkzf8ALHbujXb/AHa6cFL/AGleZz4qH7q586eJdLe3+J02jSShymppCXUcNhguRXuk3h2bwj4dtIfsawwtFu8mT+Jq8p+JVncp+0bdWsqx+bJr0B2xKQuXKEAe3NfRfxU1qHxd+zzoN/beFWspfC95NZatceb/AMfTSN8rN/urX3nHUf8AY8v/AOvS/KJ9Jmsv9kwuv2F+SPINe1qztU8yb5WVN21f4q4vWNa8uZ0kj3p/eb+9VzXtUmjabzvvM+2Jd+7av96uQvL7/SBv5/3mr8/p+8fNV6kox0LZ1Ddu84r833qu2d1++EYRlX7u6ucbUEWR9/lujf3kq/Y3HlrsL/e/u1t/hOCUT7u/4JQ3Cz6B41AX7t3Y/N/e+Wfmvkr9ql2/4a88ZuCQR4xnwQeeJa+r/wDgkrMsvh/xsocErd2Gcf7s9fJf7V7bv2svG7bf+ZvuRge0prxvCL/k8Gff9g0fypHxHhp/yeDPf+vEPypFtfJWRJnaZ3+b7zbV3f7VTRzeXMqI+wsrLuX7u2s61vNp3vCwSNfmVv4qtRXTxyfvnVyr7kVvlavaPtJSgX5ML8lq7Oiou7d/eq3uSP544WbdFt+b7y1mw3KSR70TLR7mX+HbU8dxcparvf8A2vm+8q/3afKRy83wjdUVI42dH427fmrHumSG3bZbb/l3bo3+7urRvLhFt5UdGx8zN5n/ALLWPcXD+UgR4y33dzfxVz1OY9LBy/eFS8kEjbEtVRvupVWOP/SMzQs/zbUqa6aGSZXf59vy7l+XdVaaTdJ5L7fv7mVf4lrjlKMfdPoKew+GR5mdEdsq+3bW34akdZvJuXVVX7is/wB6ufVoJpNifLtTdV7SdQe1dXfa21/7m75axqL3fdN+blPafA908cCveTRs6uv3f4l/2q9v+H947QwvM6u6/P5i/Nur5x8Fa0kZ+zJMqtH/ABN/Fur2LwF4gS3lt4XmYpJ83yt8qrXPLnlqTL4T1/Tbx7q4+zXNtuH3vtCpW21vOyf6lnK/NEzf+zVx/h3VtNuNjw3Lbll2I2//ADuro9J1KFWCPbbX81t0jfLurGXN7r5Ti900bLZuPnSf7/8As0kcckMzOk2R9yKP+Gm3Fw80KrbfL975qoalrb2sPkvNG0m3dt+6tae0juB8U6lfOjNbI67Wf52b+GkW3e+uDNM7bY13Ssu1tzVhjUrm4X5LlSrP93+JmroPDtvIpS5eH7r7n8tvMVWr9VjivdPkqeF925q6Po+5sbFddq/K1dJp/h2NVS5dI9yr92maPZJPMrrz5afPJJ8u6un02x2xh3tlKf8APT/x6uWtjpSjpI7JYX4TOXwnbXGx47Zgkfzf8CpbjwWi5e5O3a/yxt95Wb+7XaaPp800ImfcwkXcjfd/8dom0+2jLQgqo+VFb733fvbq4pZhKMr8xjLCRied3mhwxxpMSxM25Pm+9D/vVmXuko1wPse6NW+6zPu/hrv9R0t2ka2h8wJt3KzL8rbqxNS0v7Hl4bbFNZh7TeRMcLKGxyyvDb3CI6K4X+995mojutvmO/mKn+ylW7qzmtZJtj/PNLuRm/u1nXk1tJAfJ+4q7XXb96lLFe8afU+5esdafT8b92/7r7n/AIas2fiZLeR9k24f3Wrkri5S3t1tU27Y/wC6jfL/ALtQNrTrGkT/ACbvl3KlaSx3NsZQwvctftAeI11X4WajCrjHm2+FH/XRaxP2YfEC6L4Qv0ZT8+p9Q2MHy0xWV8U9S+0eCLqE7vvRbS3f51rD+FGuHSdDuULHY9ySyjoflWvscPiPacF1Z/8ATxflE+poUZR4TqR/6ef/ACJ71N4y8uHZZ3k3m/d+Zvvf7NZ954ktpmMyTMxk/wBbXmVx42Ta375j8rfKv8W3+Gr1j4gubiEPJPGGZfut/FXyP1iKpWZ8vLDzjL3Ttl1KGZmme5k2qvzfN95ajn1H5h87P5ibdqv92uWsdYdV320zN5m5GVk/9BrRt7lNyunlsN6r8vyszbf4qiWM5Y8qLjhryvM0dzyRp53mf3WZvm8ukbfIzJvZtqfL8v8AFUNm0Mm25Sb+8jfP8tWWUiRH+XbHFt3L92s6mM5Tojh5SKlxHC7b0T5fl+8275q4D49Rp/wqfW5nTf5dlI3yptr0S+j8yNIIfmZkb94v3a4r42Wf2j4U67Cm4pHpsjyq38W3+7WWIxntKHLzG1HCxjUufGGrR+dYr8zNuRWRWX/ZrlriF7W43p/ertdaX7HpsU3mNtZfl3f7tcNqFx50pP8AFur5v3T0o/EEOqTQurrM3y10vh/4j6/p8yPDqUm5fuNurkFQs22rWn27s2x9wH96pK5YHqFn8XPFc0QhudSaaLY37u4XzNv/AH1RceJtH1Bn/tjQbG5Vovk2xbf/AEGuE86aNWRH5WrVvdP8rJzu/u/w1UZcsjKWxvyaT4Gvv31tok1vtTd+7n+7V2G4tpLNrWHc67dvzJ81YlrM7j7m1W+ZttaelzOs++eZsL/dWriKUv5SHXPCt5dQxbEVo9n3WX7zVx+reDNV0+U7baTav935ttetasttrmi7PmR9vyMr7WWvO9W1LxJ4dmktZpv3f95f4v8AeqfhHGU+Y5QrNbt8/DLT7i688bJk+Ze9bX/CTWFydmpaVG3+0v3qztaawDBrZOWX+H+GjmibfEUKKKKgsb5fvSqu2loqvhAKKKKcdgCiiimAUUUUAFFFFABRRRS5UAqru71vaZNNb6SXTbtrBU4b5/mroLKORBGkO3G1eDRKJjVPov8A4J36fNpvizU/HiQr9pjs/sthcN96GRm3M3/fK1/UN8OPjTD8cf2d/CvxH0PWFlttW8K28VxD5XzRzRxrHJ/49X84/gX4a3/7O+n+G/BOqvt1C80iHVr+FotrQ/aF3Kv/AHztr9l/+CH/AMUofH/wb8X/AAdvNVWS48L3VvqNrat8221m/wBYyt/vfw10YeXLM+exVSVSWh9WR3z+DdQstHv7zyZr6waeCTf/AKxo/wC7Xifj3wTpvjH4nR+KofD8CvFDKJZIIl81pMferuf2wLzUtH8R+Dtb0q2keK1lmgeRV+6si1xHxZ+Kng79n7wE3jDxv4qi0y8uImW0twuZryQrhViX+9XViKkKNKUvI4HGcqcl5M8aXxX4f8FW8/iPxLfpbW1vCT5sjhQGyMZzXxn+1h+154w+Nl1c+G/BN61roW9vtVx92S4+Vv8AV/3Vr2j9uBrVfgxELx8RnXLfIzjcQshAz9QPyr4i8eeNrDS9Fn8jbKzLI21V+b5Vb5mr8P8ADDEzp8KKCentJfofm3gupPg9Jf8APyf6Hxj4UsZrzxBrFy4Yq2rzfx/e+auxt28lfJ+b5f4a434bxvfWc00z7Xmv5H3N/tM1dn5Kbkwm51X5OflX/er9epfAft76j5oXkh37FxJ8vzVG2xfkeFXH3f8Adqa4O6H5P+BbapNHt3+cjMrfd21fxAev/AHxR4b8FeG/EPiHxDDHLcR/Z10tWb5vM3bm21xnxK8ZXni7WLnxDqVz5afM7x7vlVaoaBIhsWR9uxf7v/s1eYfH34kPcTHwlpUyhf8Al6aP/wBBrxK1GVbF2OeOH9tV/unGfEjxo/izV2S2f/RYfliH97/armyc8mikAx3r1acY04csT1oxjCPLE/ab9rj/AJQpt/2S/wAOfzsa/Fott5r9pf2uP+UKbf8AZL/Dn87GvxZZd1fl3hX/AMizGf8AYRP/ANJifjHgl/yJsw/7Cqn/AKTAWiiiv1PmP2kKKKT5/aqAWiiilyoAoDbuaVPvCjaiqNn3qUQGqMDFaHh6/fT7z7TH127aoVseCPDt74m8Q22h6bGr3F1KsUCs20bmbbT5uX3iKnvQsas2uXupaolzNM2dy/e+9X2h8M7D4FeOvhf8N9V/a0h8eXPgnwK9w32Xw7qKt/osknmSwxrJ91pG/iWvmzU/glp3w/8Ai1/wrXxp8TvD8N3byxrPeafdfa4I5G+bbuX7391q9G/as+KfivX/AAhpX7PnhrwjocF1pdqsl5deH7nd9ot/uqu3+9/FWdevGVoLdnLRWt2en/8ABTzU/wBmb9oD4I6L8e/2ePiRoPgvwjol4uk+A/gdZy+bdWVn/wAtLq5ZG/4+pG/eMzf7K7q+BJd+7inahZXmm3j2F/bNDNG+2WORdrK1NZk4304xlE7RrDbJQzu0m9BSMctxUluvzHP8NBHwmv4R0/8AtLxJpump8pur+3i/76kVa/pe8TrDa2trpriZkh0iziWP+H5beNa/nO/Zt8O3Pib48+CPDyQ+c+oeL9NiSFfvf8fC/dr+ibx1ffY/EF5bb9zLKyIzfN8q/KtcuIfwxOat0OUurxJrhoXtmTy/7zbVrndUaaNmRPl3bm+at3UGkkaV7lI1XbtZV+ZqwdabzF3vuJZfkk+61c0vdiXH4zj/ABJb/arcI5VFXaz7vm2tXDeIlmXels7f7e77tei64sK7/OEYSNF83d97/gVcD4iW5897ZE8z523/AN1lrkqc05HfRicPqkj2rIkyMnmfO3yVTkupjvk2cq/zL96r2sNu3W0KSOm5vlX5v+A/NWXCs25ZoUbdv2yq33q55Rmd0ZFi3mmjbzXRpfM/h/u1Zs7iFo2d0k2LLt3L/wCg1Uj8ny1uXtpI5PN2ptp8Kw/NbJuzvaV4/wC83+zUx/vGko80TzH9ru48zwNp0JnZtmrghW/hzE5rn9Km8/8AZXezM4SNUuxNl8cF2Na37Vxz4Psvvf8AIVX7wzj90/es7wJpkGr/AANgspZXUNDdBsDI/wBY1fqVSV/C3Dv/AKfv8pnZV93J1/i/zPhrU9Udpt/l7f8AZqlDdvDumR9n8NX/ABNo72d/c2c0jbobhl+b5f4qpT2qQr86YVvmSvlIfAfKSJF1iaNfnmaqkmsTTcO+f9lqpzF8Knfd8lMkHz7Tz/e/2avl9wm6Ne11yGNVR9w/2q1I/EWmtD99fvfJ/tVyW12bZ91f4afHvVdn/slT9kZ07ahYNt3ou5f4lpkmqQsuxEVP4l21hW/yj7/zVajbzJ1d3aqlLsZk82pMi79+41VutQuZN0m9sL93bUkkfmK7u/y79q1YtdOtmcfbH2Cl/dKjsR+F/GWsaDdC5tRlV+9XX6b488PS3BebR5N8j/M0j1V0fRvDctv8kP71fv8Az/eqa403R/tOyzhb/baSq5f5Q5kereFNC8K+MPD7zwo0TeUyyxtt/wA7q8P+JHhN/ButSQpyjN+6avXvha02m6PcTPDtRf4f71cv8bNLk1/Sm1kJny/uMtOXvGXvqdzxa7me4mLvTP4f/ZqJPvj/AH6Sg6+gUUUUGgBdvFFFJt+XFADlX5vv0bfmzspKWNRIdlBPMyUtuA/2akjj3MvOGqNUMkjP/dqzaqjMu/p/HT5iTW0W2y3nbFKq3y1p+JLxLHRXQv8ANJ/t1Dpdmi7fk3/xVk+NL5JroWML/LH95anmRko80zDpY1yw4+WkqfTbb7Vdon+1RzI6TrdBsRb6Ls6+Z825aZJDv+4m+rVjMjMtm7+Ui/L/APZU6aF9zKj4X+Bv71KJzS2PpT/gj1FJH+3j4RL45stU69f+PCavR/8AgvFKIf2lfCD7iCfA6gYbH/L5c15z/wAEfzj9vjwnDnO2w1Tn/txmr0P/AIL1iWT9pDwfFE2CfBC5O3OP9MuK/L8ZHm8WsOv+od/nUPxnH3/4jhhf+wV/nUPjC3k8yEP95v8AfpZI08xPJ/1lZthfPD+7d/m/jrYjuraTYjwr9zcnyV+pfYP2PnKiw+XP52cLvq3C0O4/40k0fytsTKstNsVeORpN/wD47SjsP/CSxrtX7m3dVnS777K2zztg37qieFOJt7DdRJao20bMsvzfNR8UBc1jo1uEurf9y6qV/iasrVofs8jfP5u75qsaPcRtGE+4zfeWjVoy3zp97ZVxIt0OQ8SMWtpfn+Zv4Wr7P/4Ji69/wsL4K/EX4M/2k0eoQ2Eet6Gq/wDPSH/Wbf8AgNfGGtN5kM6TQ7W/gr1r/gmd8crP4NftUeGdT151OnX1xJpuqRyPtT7PMu3c3+yrVVKUoz5hVqfNStE9N8R6rf3fxwg1fWmZZ/7TsmleRMcARYbH0ANfVei2dtqnww8beA9V1L7Y+rWbXlgrP5awzL83mL/tfLXj/wAevD+nfE//AIKEw+C/D0trb2+s+JtC0y2lt02RLvhtIN4HYZ+Y/jXvmmfCvx5+y78ZLPwl8TraO0/4mX2e3muG3LJH91pPmr9P4yw08RlmBqx6U1deTUTuzyt7Kjgqb+J01b1sj4j8Sas63W/fvVk2/N8u6ucvNSSFgjv977v+zXf/ALZXhVPhj+0h4p8E20jPb29/9osG2Ku63k+aNtteRS6pCWG/cx/u1+afCeLG8ocpqyalDHuhwzq38S1ZsdQTKum7e38O6uak1J2k/cuv3vlWpbXWHhkd/Ob7/wDd+7SjIqUJL3Ufoz/wSEuI7jRPHhjh8sC80/Ceny3FfKn7Vj/8ZYeOGB6eMLrr/wBdjX0t/wAEWbwXfhnx/h87b3TuvX7lxXzD+1fIq/tQ+PZGbAXxff5I7YnavI8If+TvZ/8A9g0fypHwPhqreMWer/pxD8qRHb3CSRY89vmTd8vzfL/dqeO7t7hQjp8zbV8z+LdXK2GsIsYeF1VV/h21orqaTb/O+UR/M6/w16/wn28qfvHSR3iKoRHjba6ttqU6g8cm/wA7eyv92uct76FWRPMVVj+6uz/x6rJ1RGtzPMiqm35GZtrf7tPmnGIRp+6aGoX3nL51zMvyr91vvVzt1qiCYw/KNrbtqrVbUNY/d4+VX+7838P/AAKse41h2uN6TLub5d1ZykdODjyz1NhtS3KURNibv4v4qP7Rh+aV9u1nVUVV+bdWPFdeZIsm/cfvfLT5JNqtH5n+s+ZW3/drkl8R71PmNi4uEjjX/Sdg/vL/AOg0sd88UnyfJu/hVqzGunt1TYitt2q6/e3VNumZtiSR4XcztsqOXlNpSO38K679nby3uVcNXpngvxUkNqIZrn59nyrJ97burwfS9WeOETQ/Jt+b/gVdRpPiZFVXudq/L/rF+b5qiUftRMqko8p9O+F/FW1fs3nZRk/1bfdVv4a67TfFRZcXNy0gjbckat/D/FXzfofjryf3c021WX5W3fNXZaX43+ZEhm+786yN/FUSj3OSUvePa/8AhNHhszBCiyJ96Bd+1m/2ax9W8ZO8cr+Wuxvv/wAX8P3a8+m8cPIw2XWW/vb9q1h6t8QPLiaa5mWJm+8vmtWUqfNEXNCJ5Ho+oeZJHvSNV/jj3fKtdr4ZkjjjJSONF3bWhj+Xd/tLXmmizWccbTTTfu9+35fvNur0Lwu32ZYn3/d+XdJ/F/vV9ZWxXL8JhTw8fhO58O/JCsIjVNyfdb71ddoMiYZHTy2+7FHsritLjhnkExdZtv35N/8AD/drptLuWhjSa6+VFbcnzbm2/wANccsVKWptKjE7XR5pmb/SZv7uyTbtq7Nao2bmELsm3eau/wD8erM0W4tvsm+CZpmj+Z1/hrSjt0vNqTHBZN3lr91a4qmKHGkZ+qWbqqPs3mFGbar7lasDV7Z5PuW0itJt+X/Zrs1VPLSN3/e/MvkslY+sWbr5ttDNu3P+6j/+yrm+vRjIqOF5paHnniaNJWa5ttqfvdm5fmrl9Rmm2tAnyn+9srudcs4YFePzlZt7N93cu6uV1C1g+WaF2aVvlb5flbd/tVX16XcJYX3vhOO1a+hi/wCXZWeNNrSfMv8AwKuZm1h2kVPtLZXcrt91a6bxJapHbtC7soX5drfxV5/rn+jXTp8zP97az/Kvy11UcZ7Q56lCUSHxjqr3OhvbzOC+8Agd8EHNYej6lLYWDbHODNnaO/ApNX1GSVGttoAZRu9etZF5exWtowfqDkV+i4Srfw+rSj/z+X5QPZhT5eHKkf7/APkblnqkbXDPNuba7bf9mum0O+j8tHfds+8iyV5npupR/aN/nN83zV2/hPUp/L8nzsu33FZPu18N7ZHz/s/hOys5kjZHv3VU/vL/AA7q19Pb7Psh/wBY33l8z+7WDp80/wBoieaZXXZufalb1hdeS3nO/P8Ad2fxVhKtyy+I2jH+Y2NL8lSqfeeT70P+z/eqy2yRmfYpVvvL/D/s1TsVmuGZ32tFt3fL/DV6NkWFpv8Al3X+797d/u1zSxkvtG1OmRyeRHFvSHZL95tu7av+zXH/ABRhhuPhvroebLNpsibv7rN/s/3a7ia4eOzZ0hYfL/FXD/FRobL4Y69fpCskq2DN/d8tdy/NWf1qUjWVHlPjH4qX0Nnb29hCjfKvzf71cAzfx1t+PdZfV9ZeZHyKy9O0+a+uRFGjGrjEmPuxuFjb+fKBt71vW+nvDb8HczVs6D4Ff7LvkTa7VDr7Q6XCyIMndtq+XlM+aMjG/fecyO6k7/u1oaTapu+eFqw7jVEZjsRvlpIvE15byB0fil8Ivfkd5Z6I8n+p2hPvbv4v92pI9Njt1i33Knb8zs38NcpY/EK/DeTO+1W++y1c1rTLnV4vPsdaDI23YgpylzClHlOuF5YSL9j/ALShI/661FfaTDrlr9mmCvHs+SRfmrzubw1rsTF4Qzhf4leptMTx3bL/AKHDdFV+bb/DUx5yoxjuVfEug3Gi6i8J+7u+WsqRnzsatzW9Z1K4GzVbEh1X/lon8VYczvI25qRrEWikVs8Glq/iKG/f9sUjLtpyrtoZd1QAtFFFXGQBRSK2eDS0R2AKXlTTed2/dS1AAG3c0Uirtpa0AKKKKAHxx7pF39Gr1v8AZW8Dab8Rfjp4W8Gart+wTapHLfsz/dhjbzG/9Bryix/eSDfxtr3r9lnw3qVleT+MLCWRJl/dWsjJ91v4tv8AwGpl7vvHHipKET6y/a21j/hJvjNP4qtple2mVYrdlf7sK7VVf+A7a+xf+Df74jWfhL9q7WvDd5qvlL4i8HzQMsn7xZGjbctfn7pdrc3V5/aXiR8xwp92RPvN/er3f9gH9oCH9nv4+W3xatoVuIdJsrj/AEeR9qzNJHtVaxp1LS55HiOXOfsL+2V+0p8Gfhv8M/8AhMPHuvLEun3EbWtnH/rbyZf+Wa1+Sfx0/ag+IP7SXxMi8Ya1HPHHHdBdOtZ7jdFZx7gq+Wv+796sT4/fEb4kftIfEC9+IvxR+IscitdN/ZHh+zi8u2sYf4VX+8395q880rRtItNZtpBr11cIZhuVZflXcw+7XBj8dOrB9rCnH2dKXoz7V/b/AIreb4FwxXVwIkbX7cFt2P4Ja+FPHWoeEtA8F6o6TRuWtZm3RpuZm8uvub/goSto3wGhN4gZF1+3OG/65zV+fXxq8QWui/DPVb+zhVCulzRbWX5fmXbX5Z4Yxb4ejb+eX6H5Z4J68JRX/Tyf/tp86fDO3ePw7BN5PMm5tzf71dTJvhzsT5fvfN/FXP8Agf8A0HwzZwpD/rIl2bWraWZ5Y/33yn+61ftcI6H7ZU+ImjjS3jaZJmK7F2rVSS48kF5Plb723+GoLrXLaPMP2nZtfbtasa+8RIvyQ7Sn96l9rQXLGRqeIviU/g3wfdJbJH9ouG/dTfxL/u14TeXc1/dSXly7O8jbnZq6D4iatLfXsMH2reiqx2BuFaubqY04xlKR2UafLEKKKKo0P2l/a4/5Qpt/2S/w5/Oxr8Wq/aX9rj/lCm3/AGS/w5/Oxr8Wq/KvCv8A5FmM/wCwif8A6TE/FvBL/kTZh/2FVP8A0mAUUUV+on7SIwyOKWiitAEVdtLRQV3cVP2gCihl+bJ7UU+VAK37tq7X4Favpuj+Nvtl/t3/AGC4W1Zv+Wc3l/K1cTRvdWDo3K/3amUSeVG1Fp8y3T3N5NufezNJv+8396rlvBeNq66u+tyeYu3bMz/P/wB9VhDVbkIUzwajN9ct/Gwq/c5TDkq817m98Q72y1bXE1G2G6Wa2Vrpt+7dJ/erAXeu7fRuDSM70yRfm5qeU3juC/Jwj1NGyphOm7+9UW07vkqxDDNJn7pqSWrn0t/wSn8K/wDCYft9/C3SpraOSGHxGt0/mf8ATGNpP/Za/cPxXdQi8lmd93nXDMki/wB5q/I3/ghL4Kk1j9t6x8STJG8PhvwvqF6zSf8ALNmj8uP/AIF81frHfTbbf5H3iT7ys/8AF/vV52Il7xi/efKZF037t3+b5X/uferI1KJ7hljezbay/wB75lrS1BrZbqSGO8Zwvzbf/ZVqteLDdRpN9s/d/wDLKNflaSubm6s6oR945XVrVxG6PbZf/lruf5W/3q5LUtJSFiJnbfI/y+W/zV3+pwlpGR7Zctu81v4mrmLyxmmklLPIkyp8m2Jfl/2azl70TqpxPNNU0dxGjoiq23a6sv3axJNPeNns96qfvNuX/vmvQNSsUG55ljlMysrbf4aw7q0tlDWzzyFG2/M1X9g6eU5Vbe6jj8nZ8yy/vZP4VqsVmVkn3x53bvMVvmrc1S1RYd9tC3zfw7/las24/fK32ncgk2/d+ba1csqMpS1LXvR948f/AGq5o38F2QVDltYGWH3eInGKd8HLQah8Hba3czIqvO26FPmP71v4qb+1apTwfZRLKCi6uNoDZz+6fmnfBW7aL4V2KmQja8+GRvmUea2a/TMYprwsw9v+gh/lM9KUb5VH/F/mfG/x08Nv4X+JF/C8LJb3E+/95XF61cbbVPLRRt/hr6r/AGqPhSnizwu+u2D754W+ZfK+Zv8AgVfJGvQ3NvILO5RleNtstfH4atzxPl8RRnRqe8UH3yMvz/xVL5bsuwbagVtsn+z/AHant4/vPvyf7tdnNzSOf7A+ONPM+cc/dqNmRZG+epPM8uMt/DVf5PMPWmA85jk3ulXLWN5m+RPmX7+6qyx/vGd3bGz/AMerQs4Xh2vsbf8AefbUxjzESiS7fs6tvTd8m6q17ePJMqI/yf7NJqF9MsjQ78M331/u1UjaZm4T/foiI6HS7y5Vd6PXUeH9Lmvpo0eT723+OuS0WJ5nCb9o/wBmvV/hzo8Pk+Z9lVNvyxbk/wDHqfLyjlzchuppvk6Tb2FntY/8tdtTX3geaTSZt8Ksnlf7vzf7Ndd4P8Kw7ftUzruX5vlVdrVp+ILZLhntkfZ8v935a1lIwjL+Y+IvFemf2Rr11pvzfu5W+9WfXcfH7Q00fx5Ns+YSJ8zf7VcPWZ30/hCikVt1LSjsWFL8y0lIrZFMBS23mn7d+NlNZAoFODHJelzIAh+9WnpcKTN864/h+WqSfeFbmh26edsd9tMxka8LQ2ds/wA+3anz1xOo3T3l48x/ieul8XXT2uneSjtuZv4v7tcmoIGDQFOPVi1reHLN1kab+JV3JurMt186bZXVabp/k2vk7Nz/AHvmqviKqc3QSNvs7fPuP8VakY+0W/yIw3fN81Z8q7dqQRtjf97/ANlq3Y3ieXs+Y1PLynP8R9Qf8EiLZY/28vCUgOT9g1PJ2/8ATjNXf/8ABeNC37SHg8o20nwQoJ9vtlxXC/8ABIpV/wCG6/CjBCP9B1Pq2f8Alxmrvf8Agu8XH7R3hFk7eCF4/wC3y4r8rxn/ACdjDf8AYO/zqH41j/8Ak9+F/wCwV/nUPhOeN4WVE2sf71WLC4mmyz/Kfu01hN5n7z5ValjjMbb99fq0T9pNRhJuDw/e+6m6gTTLJs3qP9n+GoNPuHRvL+VmX+Jmq2saSZd4/m/2aP8ACZ+4Lb3CSSLC4Zv71WI5kuIdmdvy7fl+9tqrD8se7ftX+9Uyttw6Izr/AB/w0tfiJ+KJLbX32W8EKJ/uVs3X77T2f7u1fvVjrIkirhPu/wAVW47qaS38n+Jfvr/eqx/D8Jy+tw7bh0hdvu/eaub0HUJNH15btZmV433Db/eX5lrqPEW9WP8Atf8AjtcJdS7bz2VvmaszSn7x96/s/eMJ/HH7W/wz8XsD5t34v8OMwcY+ZZbVTn8Vr95f2sP2ZvD37V3wdj03xJoP2PXNPVl0PUodu9pF+6rNX87/AOxzq8rfEX4b6xdTkGPxRpjGTuFW7TB/AAV/TJ+x743/AOFl6Xqfhi2T7clndK900n3ody/L/wABr9R4lxEoYLLY30dJX+6J2cSUFPCYWS3UF+SPwW/4LAeAbrwj4u8DeNrnTY7a7utDk0jWV3bpftFu3ytJ/vLXxc+qJu+5sdq/aj/g5A+Adgfg/qHi3QdKkF7perR39vNDb7vLj+7Krf8AxVfh7PeeZJvd2wq/Izfer88xFOVKVj5vB1Pa3b+I0G1BPvlMPT5NW/djezL/AH2WsRrzaF3v8rURX/y7C6tXLKXKd/L7x+ln/BC2++2eF/iPtB2rfaZtJ7/Jc184/tZyD/hpH4iSHgDxdqff0uJK9/8A+CCUhk8M/EwspB+3aUc9j8l10r54/a7l2ftAfEubpt8WaufyuZa8rwg/5O9n3/YNH8qR+e+HCX/EZM+/68Q/9JpHnVj4keFl2NH9z/era0/XvPh2XI+Vvm2t97/drzC18QSfLMj/ACs3zfJWxH4omhh/1O4/7X3q9aW/Kj7uXuyPRI9YRrd40fEUcu5dtVLzxdbQxtM6K6r833q4mXxBdyNsf7jfc+eo4JN03zuq/wB6iXLzCjGXLdHR6h4oe8uNiPsVvurVdbpGl3u+1ldfl+9WRHMkb70RmLfK9SrJD5g2fK38TNUy5eh10ZRibsNxuZHtn5bd8uz5asRzKJFV3z8rfwVjreTf67+997b/AHat29wkML7IW3fwbn/hrm5UejTNSNvP3Ike7b9z5qk855EWabav9xVqlDcboymz/f8A9qprfybnL/ad+5v87aXLzQOnlRZWbyY49n3G+/u/vVLDrU1vJFcw7flfbtVqrx3Dx24heaNdy/Nt+am/Isfr/srRTiYVNzsNL8VJJCH3qfm+9u+9WzY+NJYZEdvM2TfxM/3a82t5prfaJLZm+b5FVPmWtG3vH8tf3Db2Rv4//HquNGMonmVqjpyPRpvHDxwfuX+ZU2v8275awdW8cTXWy2FyxZf4v7tcv/aF5JuQv8kabfv1QvLp9y/PsX5tu2q+rmPtub4jc8N3/nN8k22Vfm87dtr0Lw3q20JeWyKjN9/a+7c396vFdAvkvJV3/IzP95a9G8LXkcMm9LmTcqbUVfutUylLlPa5Y/ZPXdNvoJLdJkmxNvb7vzfKv97+7XTaDeIrJIm7+FpVj+bbXmuh6s8LI9tM3mNtXbu+9/ersNB1jT0uWSOb97vVfJ/i+auaUqsYj5T03w/eQySI7nd8u7av8VbunyQ26pMjsH81n2w/xf71cZ4X1RFUO7qjR7mT+9u/u11NrJNtV3TduZWTb8v/AH1XnYjEfZOqnTjKMeU1Lz94geZJP3j7tyxVl601zcxvseOGJty+ZJ/s/wB2r81/9niaaF2fb8z7vu1k6hDBcbblIZD8/wAkO/7teRPERp/ZPQo4X4XGJzmrW6CZC80kT/di3fdkrnNW0n7K32a5Tzl3s8Xz7lX/AGq7bUltriNfOhbdI21Vjf7tYGsWKW8AhRGRfvfL8ytWP1z2kbXO3+zeaPMeWeLNP86N/k37dzN5ny/NXmXiC1ma8dPO/g+evZPEmiu0ZkmTbL/Gq/drzjxBoMyxzJIjLtTa/wAv8NevhMRyzj7x5OKwMtzzjV1aOYQyKCQc7hXPeJrjyU2mRgDHkqq5zzXXa/YpBbb9rb0YK5NcZ4theTYUbblMZ/Gv17L5f8a4rf8AX5flAFDlyGaX8/8AkZOn6q4uPnfDL8qV3fgu4dmTfMwO/wCRm/u151p8brc/vk3V3/g21eSRIX3YX/x2viYxPmZRPRfD91C0whd5GT+Btv8AFXW6SyLa/vkXfubzVZ93y/7tct4bs0kmVEVcV1+mx20MiphpTMu1mX/ZrmrR942jKxpWa/ZY96Jw38O77tSrG7SM/k/dfzEb/ZohtYWufnuVd40+dV/9BrSsf3kazOn+rRm3fxf7tefU5onXEq7Zmt5P3O1GfdFJI+7738NeVftOag+j/BnxLMkyon2JURf95tu2vX9SkgmsE2PIhb5lj2bq+f8A9t7VIf8AhXbeD9NdWRrhbi9ZV+bzN3yq3/oVY4WM/a3+yXUlHlPjm1tn1C6+eOvTfh38Nkjt/wC0r9FiC/c8xfvVZ+FPwj/tKT+2NSTEEb7v96tj4tfELR/DsZ0TRLld0K17Ufd944Zc0tDB8YeKLPQbN4YRjb8vy/xV5dqusXOp3PnSTNj+Gn61rs+tXHnTN/wGqccbyybVo/vM2jHliIG3c0qpuOFrQ07w3cXiNcyhooY/9bIy/dpbhbW23JYJ5v8AtNR74c38pnMrhfuVe0bXtV0eVfs1ywTfu8v+FqYsagn7TNj/AGansZIbVt6W2aUhc5sSfEDxbJH/AKNtjG3b8sVQx+IPGEkn2l9VuE/hZVamPqEnkiHYvzfNtWpbG1udQnWGRGJk+5TjHmMublidD4LuP+EgW40/xJZx3IZPkkZfm/76rnvGHgeLT4W1XSJFePPzwr96Ouohs7Xw7p/2a2dmuZE/eyf3V/u1Fb6a01v/AKS6wwyffZqYoynznmSdfwpzLnkVa1q1XT9UmtkfIVvkaqnme1ZnUOoooq+VAFBbbzSMcDik+/7YpfaAdRRRRygFFIxwOKFbdREBaKKKoApf4PxoDFadH83AoA2fBuj3es6zHp9nZvLLM6xRoqbi0jfKq7f96v2q8C/8E7/A3wp+A/gvRLzx/a2WpWuh28/iPS5rJXka6m+aT5vvblVttfFv/BAn9j+w/av/AG9PDHh3xJZyTaJ4bik8S6yqxbl8u1+aNWb+HdJtr9jP2nPgf4I+NmuXFy9zJouo2N/vlurP5Vul/h3L/s1vRpy5OZHzmZV+aryHxt8cP2ZfhRodrBPo/mSJGn7r5VVZFb+Jq8q/4VLomn28sMOpNDF99YY0X/vpa9b+OHwn8baD4luvDc3iGaWFUX7Luf5WVf4q8o17wr4q0dik15vP/LJl/wDZq8utKq5XcTloxpxjocn4g8P2FncbEuW+58rLLWX4e0tBrUBVN+LhB/48K0tS8P6l8v2mZi/8Squ5aTR9EuX1e3X5gou4m+V/9ofNXh4mM5KZVX+HL0Z9h/8ABQW3Fx8C7dSCdviC3bA/65zV+bv7Vd0ln8HtRmQf6544G2/w7mr9Lv26oDcfBe3QEca7ASCOv7uXivzM/bujh0v4X6fZo7b7zWY43Vk+6q/NXwHhZS9pkcf8cv0PzHwQTlwkl/08n+h4za6h/ZtjAtmm9IYlV/8Ae21T1rxNt/1D4Xb8256x7jUH+y+ckzf7O2s2S5eaRXd91ftXNeB+1xjyybLupa88yr/Ef/Qqw9Q1i5kkZEdlb/0Gn3Vw8C/6xWWs28uEZf8AZrGUu5pHYy9QkMl19/dUVEn+uP0orQ6Y/CFFFFZjP2m/a6/5Qqv/ANkw8Ofzsa/Fmv2m/a6/5Qqv/wBkw8Ofzsa/FmvzHwp/5FeM/wCwif8A6TE/FvBL/kTZh/2FVP8A0mAUUUV+pH7SFKq7m2fnTdvzZoZd1KOwC0jLng0tITu+5TAWijgCigA4IoopFXbQAtFFOi6H6VPMAm11pKKXaOKknmQJ94Vd09d0gym4f3Vqsivx8uPmrU0K18y9iT/gVBJ+m3/BAHwG8Nn8Uvi1NDs2wWOjWsyp/ebzJF/75219+alcQravC77Nr7tq18+f8EgvAMnw9/YH0W/1W2jiuPGWuXmrXCsnzeSreXEzf8BWveL6b5mRNrr/AHW+VVX/AHq8etUcqsolex+0VJFgYi2jX5o0Zv7zbf8AaqnMz/Mnkws8KN5TMvzLTptRRN8KQ7f4fmb+Fqr3V1I1wYfJ42/6xX+Wp+ybxj7xQ1K387Z++b93/erK1CzSZT97zPveZ/FW3HH51wqPDlFTc7b/APO6oLq3maGSab+H+KNt22s5+0O2jy6tHFa1ps+53hRt+/5Y9vy7ax7jQ18l0vNufl2RstdxNZrdM6Of4Puqn3v+BVkanpm61f5G3bWXar/e21fLzLzNZbe8cFrnh/yWF/vYbfuRr81ZF9b7YxMkMidlZfl2rXa3Vvut/tMEK+aqfOsnystYV1aQyWvko6s+/wDu/eWj+9IxjU5fdPn79rWwgsfBtgI4WUtqoILenlvU3wMEcPwis5mt0cNNKGBGc5lYf0q1+2hZx2vgrTWcZlfWM7gmBt8t6k+AtvG3wV09riJWJluDE3m7WUea2a/QMe1Dwrw7/wCoh/lM9yDj/Zsebv8A5lTVtHRrd7OaJnWb/Wr/AA7v9mviv9qLwjZ+F/FzpYWzJ50rebu+7X3nJZzLNLbecrK25t38VfL/AO2l4K+2W6a9DDgM7fd/2a/OsDWj9ZtzHm5hR9pheddD5ZxskE3HHzfNVq3ZGbznk5/2ahk2RyMjpu/3qWONI24evoo7Hzq+EfcNuUbP/HqdDBtUONrNsqLd+8COm6tAQoq70hyyp8i1PL2EMWPy9qJ8y1K1wkO7yUb/AL6pVt/M3JD/AN9Ui25j/wBkbPmX+KgUo/aKkimb53G8t/ep8MLr8mz+L+GrVvCm1UR/493zJU0cOZG3ou3+9Vx2Jl5mj4ct910ER2dd38Neu+DbxNPUI6KVj+b5q8y8Iwo10ifKp/gr1LSfD9zqEaOYfKCp97/npV8sJCl7sD0Hwv46006e6eSqN91F2fNWourJqSp9mtd+35V3V53pvh2/jvlQzSfxM26uv024tvD+k77q5Vn2fKrN826j3YnPL3jxT9rrw26tb62kO3b8rt/erwyvo348XE3iD4f3upTbd8bK23/Zr5ypSO6jL3BGGRxS03P3acv3/wAqRsFL/B+NN4VfpS0AFORdoxTakX7wR6UtiZbliFPMkwm3/eWum8P2u1t7/L/vVgaXD9okZNmxa6SaT+z9Nluvur5W2oMpc5z3i6+e61R4d64h+X5ayaWSTzJTI/8AF81Ecfmts9a0No+7E0NCtfMuA/zfSulgbzN3z4P8SrWbp8P2a1XYnLfxVPDI6fu04VvuNS+E55SLbbJF8jpt+bbVRZkjY+Y/zfdRavx/vY96Pzt/76qnNClsWmmRWLP8m7+Gjm+yTH4T6m/4I9ztL+3d4TVpOVsNTGz/ALcZq9S/4Lp4P7Q/hIAqGHgpTlu/+l3HFeR/8Edrt5P2/PCkbsObDU+B/wBeM1etf8F1Jyn7RnhOIhSP+EHVgMZOftlxX5djP+Ts4f8A7B3+dQ/Gse/+N34V/wDUK/zqHw3JD5znf/F/E33aqK33kd2yv3a0bqDzFRHh2hl/8eqtJD5cnnIinb/dr9Rjsfsg60VYmKJ8p/irSs5rZW2PNuLf+O1ls0Mg2bPmb+797dU8caRzDf8AfkSiWwGnGxaPZCmR/FSeZD5e/oi1Vjk/efJu3L8tTCGb5Eh5RvvNTjzByFi3kRf41b+5R5e4ec7sv+9UCwPG+/zudm7/AHasx3ELb38vcrffp8wGBrzeZI6nhY//AB6uHvvlun7fNXa65MZLiWHYyqv8Tfw/7NcbqfN03GPrUy+LQ1px5T6c/ZO1B9Ok8EaopBa31i2lG71W6z/Sv6Bf+CS3xZv2/aN1rw3fzbrXXNLVV3fKvmL/ABV/PH8CdVXQPA+ha44yLJvtBA77JWb+lfuT+wTr2m6hNonxj0HUrqO3s7hZ3aPb/qWX+L/2Wv0bjCcY5dlyf/PmP5RN+K67w9LCS6cv+R9Xf8FZ/gPD8ZPgXrOlWmm+c9xYSQOyv8sm5du1q/lP8f8AhPVfh7421jwJryMl3o+ozWdwu3b8yt/6DX9htxrmlfE74e6lo00y3P2iwk+xXkkX7tm2/K1fym/8FHvhj4n+HP7WHiq58SIwm1bVJrrd5Wz+LbXyNe1TCxnHofLYNwhif8R4TuC/ff5f4f4qFmcsqO+0NUKyPI28R4C/cp6qkjbC7fL/AHq8vmPa+E/S/wD4IIM7eG/idu6fb9KwP+AXVfO37Xh3fHr4mn/qbNZ/9KZq+hv+CBbO3hj4mmTGft+ldP8Acuq+ev2u8D4+fEwsOP8AhLNYz/4Ey15Xg/8A8ndz7/sGX5Uj878OdPGbPv8ArxD/ANJpHzpDI7Ns2cL/ABVchuk2tJNwyptSqcPmKrQn+J9tWbeN2XZCnyrXrH38veL0M27B/vf3vvVI1xMuVT7jffaoFd1Vn8lS1I0yKoR32mT5f+BUCjEveY7Kjp8o/jXfVmGb5jN5KvL8uxaqL80iv5Ofl+6tXIT5beTDtO75qmRtTjCMy5h1/wBJSFm2p/q99XI2doV8wMu7/vqqiom3ej7W/wBqrgt9y/6zG5PvVz+7E9CnHlLVvNDGq+RuT5G37vm3NVqFIV2pCmHb+HbVa1jh8kcM3+7VqzUnBSZtv3drfw1jzTO2PNy8yJ/s7/Zzh/u/L81KuyFfk+997bSQb/nRP97/AHqcxufMDwwtuVPn+eug56wrNJ5nnPB8jfK+16mt5EjmV/tPyRo25dlMaFoQ/nPgN9xv9mpIJJl2jfv2/M+77tbRieJiPj5ZDZJrZrczfMu7+JUqhdRkSLs8xV3/AHRVu6j3Muzaqf7Py/N/FVPUpJtuzzt//APu1fLynNzfZOe0+9eSZEh4Kt/rGrtvD+uJ5LI77GX5ty/w15dps03mL87f7y10FnfBmD79xrz4y/mPoj2bw34o8uOLfcr8y/w/e/3q7jwvrdss4dJVTcvz7q8D8P8AihLVd8xVSqfIy11Gi+OAkzeZNu+fc6t/EtRLmlHQqPL9o+kvDviaGSONEePYqq27Z95q67StY8yCVESPZ/z0/iWvnHQ/iA3mCaaZnTduih/utXVaV8RLmSZHh+Q7/nZv4lrxcVGfNzI9jBxie0yeIHtmWHTdSjZWbbLG3zMy/wB6mzatDNeh0hhytvhtrt93/wCKrzu18ZPJOX85T/DuX7zf7VaVnrT30avs2KrfPu+X5v4a8StU5fekfT4XD3+E6trrzVjuXfYVt/nVv4f96q9xG4V9n71Vb51ZKqWbJ5ZtkhZhJ9+b+9Vnzpn+dNyqr/Ku/wC8tcHtpS0genHD0vtHPa5a7lT918rfK/zfd/2q4XWNLuZmlS2dT5bbd3/xVei6ha/aImjeFkEj7k/3q5jUrG1jV02MrruWVVi+bdXp4KXt5niZhRjTlseKfEfTFtbF5I1wROfNx0zmuA1S28+2yex4O3PNex/F/S1t/CtzdKWIDRqCw6/OOa870jSWvdIku4413JKy7m7jaOK/csr97wzrX/5/L8oHhVlL+xp6fa/yOFk0maOZoXhXdXbfD+1hkvlS8fy/k2Lu/wB2oZNFhWcO6Lv3fxN92ui8G6XMs2/tvXYtfKR92B8bWfY7Lw7pvk+Wz+WNvyq396u18O2ds1mUjSQ7n27pE+8tY/h+F2kjhhHzt92uy0PTXjumd0UmRd3+7XLII+7oVrPT4RM2x9wk3b6sQwpHDNDbP5szbVRmfau2tK4s3aZ4XhXZ93cv3aisLWG33TXi7IfvNIybtu2uOrTjLRnZTlyx5iTxFZv4T8Jy+OblFZYf3VvuX70n+7Xyr8drybVdDub+8T7RuuFeVtvzN838VfQ37UmrO39ieHY3m+yfZ/P8tW2rJ8vytXzz8UIXk8G3yWH3/K/dKvzNW2HpwjEz9tKpLT4TzDxN8QptD8Nm10p9m5du2OvIby11rW737VMJJGkb+KuqbXtN+0RQ6nyu5fNVv/Qa9j+F3j79mnR4Q/irwrcXkvlbUWNlXb/u1pHlUryHL2sfgPn/AE34c+IdQuET7G+Gbbu2V10vw40X4fWf2/x5J5M2z/R7Nfmkkb+8392vXviF+0h8PdJs7iw+D3w9tbabyttveXXzuv8Aur/er5u8UXXiHxBq0uq6zczTTSPuaSZq19pHaAqftZa1CXXPE39tXi20O22tv4I4fu1AyptZLb/vqslY33H5GFEclyrbI3ao1+I25f5S8lmVb53Vy396rC28MK7Hfa33qoQzTM3z/Lt+V2q9bs8zL97b/eb+KqM5e6W7e13RjzOtdx8NdDgurhptnzrEzIu3+KuRsTbL87vy38Nd/wCB7r+zYw7/ACIzfOzUorlJ5fabjLzw/bWObzUpmVPN3bt1cNrOtTa7q32Czmk8mN/kX+Fa6H4meIhrF7JpvhzcZJPv7X+VVrCt/C+peG/D03iG5tmLhfkb+7TH8Mjm/FDQnWG8j+FVD/71Zu9fWnzSPK7PM2WZ9zNUTLtoOhD6KKazYbj0oGOooopfEAirjk0tFFHxAFFFFHKgEVdtLSqu7lOlDLtOKYBjbj1/iqW3j/eKj8VEvC76s6cgkmCeSzlm/hpcyIlLlR+0X/Bpbol54d+LXxF8YbNqap4NmsmZkVl8uHbJ97+9uavvr45STaN4ue8d28qaXZAyptVWr48/4N5rSz+Dnw68Y6hqrtDLb+H7e1+0Rr966uJPMaP/AL9qtfV3xq+Inh660tdSuryF/JZmijZ1XdXZTqw9kfJYpyqVj5Y/a48Waba+MLP7bdbvtUH72OH/AGf4q8W1DxlYX26E7VKp/C/8P+7V79pj4laV418fG2hsJES3i228ypuX5m+ba1eWap4ihtdzwv8AKqtsbZ8zV4tbEc0+U3h7qNnUL7z7j5JlK72+X7u6m6VfI2tW38f+lIqNt/2hXHNqUzyNM74MfzfL/EtbHh+Z7nV7R0eNCtwjO3+8w215GIrSjSdyJy56UvRn2l+245j+DsDqASNcgOD3+SXivyw/4KAapHKvhjR1uZGdryaeWFn3L935Wr9P/wDgoDfrpvwKhumDEDX7cHb1/wBXLX5Fftha1c6t8QtKtJm+W3tZGi+fd8rN96vhPChxeQxi/wCeX6H5t4Gq/CK/6+T/APbTzG6m3KE+7VW4ukXECfe+98tFxI6q29+P7u6q11L5cYdIa/XZe8ftluWVitqF5uXf2rPvJtu3f92rV1Nt++/yt/dqk29o97puXZVe6VEq/wDLWik2/Nmlo+E3CiiijlA/ab9rn/lCo/8A2TDw5/Oxr8Wa/ab9rnn/AIIqP/2TDw5/Oxr8V0bsfwr8v8KlfK8Z/wBhE/8A0mJ+LeCX/ImzD/sKqf8ApMB1FDDd1or9OP2kKKaoyuKcV3cUAFFFFXyoAooLbeaRV20wF4Ipdp27qFbb2oAY4yuaXwkyBjuNJSbvmxS0ygpdx27aGG1cULhvk71mTHckt1cMv+1W/wCHLCbUb6Kws+biaVYItv8AEzNt/wDZqwY0RQC5r6E/4Jy/CCz+M/7WngXwhqELPZ/23He6j+63L5Nv+8b/ANBWorVI06cpdhRjKpVjFH7QfCzwynwp+Cvgn4aQ2yxJoPhSzs5W+783l7m+X/earGrao8m5ESMxfeXa235aZ4s1ya61y5nv9ziS63RLvX/Vt93bXN6lcbvkSbcf4f73/Aq+UjV9pPmfU9arR9mrFj7VNJMru8bqvysv97+7Qt8XuE8mZl/hddnytWW0yXULebcZO9vlWpbWTyrhUfb8q/JJ/er0Iy5pe8c8afKbMbPJabNmH2bYqjmX7OrpJ/srL8/ytUUN00y/67c6/wAS/wANRRzWyqu/l2dt7L8ytRzR+I6OX+Uc0L+S6W37tZPmb+8q1lXFrHdW8r/d+RtjNWh/ozZeBNxX5d26qt9DCu+dNv3vnX+7VU9hVJcpzV5Z7GbfNuZnVtrJ/q9tc9qOmo+bZP3W2Xd5n+1XU6t8q7EDOzfcVvlZl/2a5jVI3hVnPmYkbdE27cv/AAKum3LHU4+b3vdPA/22t7eBtMbLlP7ZAXf/ANcpOntUn7Ottb3HwZ08SSIredPjPf8AetxSftv3iTeBtKtvMBaPVlwB6eVJUn7Nk7J8HbHLRlBLOPm6q3mtX3WZRt4V0El/y/f5TPo6TjLK43f2v8zaaHbcTfZrNUPm/P8A9NK8s/ac8F/8JF4JubloVeSNWf8A3Vr2WZVuLoJ/CsW7cqbtzVzfiTQbDWtHntrxJP8ASImSWNk3eX8tfkkZSjVi4hKnGtheQ/MzWtJ/s3UJrN493lt97dVDDqx3/Lt/2K9A+OnhH/hF/FVzD93dK3y7a89kb957V9nS/eQiz5CUeSfKyLzEWM9/m+9Vu11QISj/ADLsqjtCt8n3qcqybl2Hn71PlKNy3vkbHz4/urT2kj85n2ZLVjRxzbvLT/e3VatbrbJsmdVLfxVUTLlRe2zdkz/tVMsjsq7Bn+F1aoIrj7qf3nqeH/SG8taoPcNrwzfJa3Su6Y2/w17J4P8AGkKwpbPHlo03Kq14XZxvDMjmbc3+y9dNomqXVq3mPux/tNSj7vxGdSPN8J67q3jC8urrzoYVVV+bav8AerHutU1HVrgzTOxO7akf92ubj8b6a2POLb2/iX7q1t6F8QfDcKpNMiyOr/O33afNYn4fsknxE0u8uPhve2HkMFkg+8yfNXzNLG0crIw5Xhq+xpvFnh7xZ4VfSrO6jV9jN5bf3q+T/HGjS6L4mu7KRNu24bbQbUPdlymPRRRQdIUUjHA4paAFT7wqWFXYt/u1Eo3Gp4V+6KzIluaugwPIyps/3Kn8X3jw2iWZflvv1a8Mxoyqkybd38TVz/iS8F7qkkicKvyrVe6RH3ijWhodukkrPNu/2NtUooHmcbOa09NVEmFs/TP3qoqpL7Jq/wCtX76r/wCzVXb942/yfuv8lX1hjkjasy63282z5vm/hpc0YxMfZmvpcySSjemA3y1PeW/mRhH2uVT+H+GsvSb5PO2fdLf3q14WSaM/PsO75mojsVKJ9Ef8EfIWj/4KC+EXQ4Dafqm4f9uE1eo/8F4rtrf9pXwfGGxu8EL/AOllxXnv/BIW3Qft7+FJ0bd/oOp/N/24zV3H/BfSQJ+0j4OIjyR4HX5vT/TLivy7Ge94tYf/ALB3+dQ/F8b/AMnwwn/YK/zqHxna3zyKEmT/AOyqVo0k23KJt2tu21h2d9tm/iKr825vu1tRXkEkZEM2Sy1+o83LE/ZeXlmV4Y3hY+cn3qtLtaMOm5v97+GlW3dmZN6ru/8AHabHHPCzb/m3fKir/DR8QcvKPjhkg+frVmFZpI9uzb8nyVCkdywTZ0/u1NGzrc/fb/gVOXukREZvLbydu5mT5mo8zbGwxt+T5mWnTRzNIrvNx/CzLUVxMnkuk74b+9SkuYPhMHWNm6V9+S392uUu2EjbE/8AHq6bVpnjU702nZ96uXuGMjHef4qP8JtT3Pdfh6/l/BWGTpt0+5P/AI9JX6U/8Ek/2jE1r4A/8Il/as00iq1vdNH8rfL8y1+aXgokfAoMOv8AZV1/OSvXv+CSPxofwx8VU8H3kjbNQ2rFGrfek/8A2a+449pzqZVl3L/z5X5ROrjLDvEZTSS/k/yP3d/Zz/ach8H2aeEvH+tbdOkZUX+9Du/i3V+W/wDwX6+EOlah8StU+Ivg+5jubazvVlRrX5la3m+XdX2T400ea22aro8zGHbGz/P8rN/8VXhH7VfgF/id4d1LQbt/k1jRpElkm3fKy/Mu3/a3V+XZNmVeivqlc/KMkzSr9YVCp9k/HaRUV2+Rlf8AutT42Tbv2Nub+GptY0vUNJ1K60q8TbLZztA6/wC0rbaihjf+5tbZ/wB817so9D9DjLmjc/S3/ggXsPhr4nshyDf6Vz/wC6r57/a4Cn9oD4lgjg+LdXz/AOBMtfQ3/BA5dvhn4m4OR9v0r/0C6r56/a4wP2gfiUev/FW6v0/6+Za8vwgTXi7n3/YMvypH5/4df8nmz7/rxD/0mkfO0KozBxA27d96rSu6qX2fN/dWooo/LZPk+Xfuq15fzeWqN8z7kavWjb4T72XMJDJtXem75m+7T2bzt37jbt/iapooJvlTZt/i+X+7RDAfmf5sN/e/ho+wH2whidpmO/av3nrQs1eST5EXav3KrR27x/fdf726rNsyFgn3w1R9k1jH39DQjPkx7PlZv7tXLNn3Dftbb825mqjbNuk+RNrfwM1WobfbIEudzMv32WueUeY9OlEuxqjb5Pl2t91VarFn80PmPuRtvzK3zbqqwxusizQw/wAe2tG3Z2bfv+X7vyp96o92Wx2KXMSW6I3lTOPk2fdX+KpEtXkYuLxnWFPnXZ92ljhdY0dPnP8ADt+bb/s1PD9pVfOc/I38Sv8Ae/3qZhUjze8RSWvmSfIm5d38Xy06O3h+a2+7/c21NJH5LM7ps2/e/ioa33Sb0dm/uNs2/LXRTl9k8bFU581yrKirGu4/LubZuqjeKiws+xfmrSulRtnmbW+b7qv/ABVQvIUt5P3e3bJ/DWvN7vunLGPve8ebxybW87zm3M/yrVqzuPMjZ5kYeWu3dv8AvVm/aJpJEfqFq3aybo/kdl2/xNXlx+E9uBs2d+8bBNi7VX+Fvu/71XrO88llmSZt395XrAs7p/Oa2dP9Yu6tG0bbhIY+Pu7f4qX2OU3py5jsNJ8SPJ8iTSFl+7XbeHdS1AquyRcSfLtb+GvN/D6v5z+ZDu/4HtrtfDd15bb4LmNHV1+8teVjI/yn0GB961z0bw7dTeZsmuWXam2L+7uruPDsT3khf7ZvlVdrrs3K1eeaHdIqjy0Xc0q/aJJE3N/wGvQ/Ckyfukfcr7vk8ta+axFPljLmPrMHL4Udjp8LyRo83CsvyKqfLurQWFEkWHYyn7yf7K1F4dt3jji84fMvzbf71dDHDMvz5V1b5vLX+GvNhzRnY9bljy8xzd9YpBEs1sirIu795I9YGoW/l24mvLb98zsysrbv4q7HVNOhZm/gXbu+5u21j6xawrCIfsyl1X7yr95a9rC/u9jw8wXNrY8e+PMSp8Pb8uoD/aIgSrcH94K8++G+mw3/AIXulkX5muyqttzj5Vr1D9pPTVsPh9eeZERIZYflJx5f7xe1cV8DYBN4Qv0jjJla+wmFz/AtfuGVyX/EMK7X/P8AX5QPnMUr5XNP+b/Iyb7w+iTB5k2pv3bm+6y1taTpKeZs8lSzfMjfw7f4a17jQUmk3zQ7EVdsqt92rGmaekMQeFFDt/F/Dtr5eEuaFpH5/iNKt0bnh/S2RY7l0WLd/wCO/wC1XW6bZzbm+S33qiru3fM1c5pbQw+Ujpu3fLuj/wDZq6PSZEuGCWzs27/x7/drGX90UXyllYxeKZssiL8sqr8u3bXJeK/HGm6xcXOm6NNI0FjtWWOOX/WN/F/wKj4t/EaDwH4fmNncwm/uImSKFf8Aln8v3q87+EZmh0f+1bzzC2qSs8TN8u7+81YS5+c6KkvdOu+PU76x4T8HeOYZmOn61p2+L7Qu5o2Xcu3/AGdu2vCPFGpJ5MlhMnzN81er+LtSe8+C9/4G1PUma58G6tNPp27/AJaW8nzbV/2a8MkunulOpXiblZPlX7u6iXxE05cvus8H+I+hXOl+KJ4UjbZ95G/3q5zzrhR99gK9X8dXmm6lqmblNzfd/wB1a5rUPAcLM01rMvlMv3g9XyzOvmOTt9WvLdg6TMK6bQfiJpAtxYeJNH85G+/NGfmrIvPCNzbs2x8p/C1UJtInik8vzFJNaD93qdy1x8NdZb/Rrhbdm/hmom+H+lSR+dpupWsqs/3Y5a4CS2mjb/Vtt/vVNCupIuYJW/4C9HNL4ZC9mdPefD+/hdvJhU/8Dqs3he8hVHdNqsn9+sZNa1e0UFLyTd/tNTRrepN/y8t/e+ZqXN7ocv8AMdFa2cNuyPNcqrK3zr96tqGR7zZD5jOFX+/trirXVn8xZJpuV/vVv+HfE0K6pG7/ADbX+ZW/io5uYXL7h6Do+h6Jodn9pvPLSVlVvL2VleJPExvGOm/Y4/szffj/AIdtW7yXTdcuFmh1aNJJPl2yPtoh8O6bawma/ufM/uqrbm/3aI8pkpf3TjpfCnhnVbGRYHaC5+9Ev8NcRd2k1ndPBKMMrbTXrutaDZ2tq2pWbxxH+Jd/zKtcB42ksNSvGu7Ar5kfEu3+KiXxG1ORzgBPSnKu2hV20M2OBS+E2FoDbuaGG7rSKu2pAWikY4HFLVx2AKKKXY3pTAP4PxpWkdlpVj2/fT5qXy3+4/WlyozGKCPlQ8d67b4JeG4dc8Z20lztaK1/fyqw3Kyr/DXHwQ/MFzX0J+zV8ObZbFde1CGRBI+7cy/Ky/3axrVI0YanPiq3s6Uj7E+Cv7U3jD4KfB+48F+EkhSfWtXj1K4um+9Htj8tY/8AgNUpPjR8UfiFqT3nirxbdeVGzLFC0/7va33m215laW819cJv27I3ZN2z5tv91a6GGMw2rok0eyR181tnzLXh+3q1JHz0akpP3i34g1q8mvgXuV3q33o2/hb/ANmpVDzfJMm1t38TVWhhsLaZEuRHMytuSNl3bv8Aeqa4upJrhktrNYk+9KzVEpcsS+UtRqlvtR0Vh83zMtTeH75JtWto7SFVDSJ838P3hVcwpJIiPtcMn9/5ttT6dfE63aQR2eVjuEXMcu1Su4Vxy+B83Yir7tCXL2Z9nf8ABQE2q/AmJ7xsIuvW5PGc/u5a/Hb9rDVLPUPjhNa23ypZ6dHEn/oVfr3/AMFIrpbT9niOViAP+Egtwef+mU3T3r8Xfi5epqnxT1m7hh+XzVRFZtzLtWvkvCSN8iT/AL8v0PzjwMVuEU/+nk//AG0wJfm+SobyF2hZETC793+9VyO3QrsfaD/eqvfXibWRP4fvf7Vfr3Kj9q/xGRNb7c1HI22H50w1PupH3NDs3f7v8NU5pnk3I75qYxNIkH8W+iiiqlsbBSL8u40tFKIH7Tftdf8AKFV/+yYeHP52NfizX7Tftdf8oVX/AOyYeHP52NfizX5f4U/8ivGf9hE//SYn4t4Jf8ibMP8AsKqf+kwCM7TlxkUON5yab1206v1GOx+0hu3NvopFXbS0wCiiiswEf7ppyt22Z3U1/umhVxwKAHFWPaj+P8aGOW4pCm7g8YrQjZhRRS/dT61mWGxvShm+VaFbbRt2tyPlqvhAfb72cRfL838VfpB/wQ1+Dt5b+JfFfx+eBm/sXS49J05t/wAq3Fx80n/kNa/OnRLSW81CJU4G9fm21+2H/BOf4T/8KU/Y38N2epQrFqHiK4m1vVlXcrL5nyxL/wB8r/49XkZxiPY4R+Z6WT4X6zjPQ9ovokk3vM6r5ifL8u6ueuFRW3pNG/8ACzL/AA/71aOoXCSsyOjRsrfxP8zLWX9qhtWZ96ks/wA3y18jha04e9I+ixWFK3lo0ium3K/xL8u6pY4YMpv+by/4abcTI2Id8aLJ9yPf81Ekc23y9igfdXb81evQlzazPFqU+WZFJqU0V0qTcIqMqMvy/wDfVR3GpTloXR2Cq/ysv+78y026kb7v2Pb5abX3P/rP9qsyZktpNltMoG9llZv4a7aMeb3Uc8v3ZsrqHnW7TQzbW/ut97/aqC41aGZXayudzMn3m+Wsm2vYbWZ5sLs+6skn3mqKS83xvcTIw8v/AJZtXdTpzicUqnMTXEyTKby2TfIyfPu/h/2qwNTXzpnR7yPa3y/7NW72+hkhWR0aJVRW2/xKtY2pXXlqzvD5m35nb7u3/wCKrflMoyPEP21BZf8ACC6a9vgMdZHBALMvlSfNuHWpP2dv3nwd0+2ihV3eac/MvT961Uv2x7nzvBOnRJLAyR6wFAh7funqb9ne9EHwp08MyjbcT7Vb+L961fdY6K/4hhQS/wCf7/KZ9HCfLk8Zf3v8zt2mmjhjXYzKqMqLH/C3+1WNrklxeQyPsZJF+Zlj+Vdu2rkzQ3G9JnZHZlZdr1na1eTR2M2SrPsZWVn+avyL2coz03IpVoqB8ZftWWMN9q0tyj5fzW+7XgkzSLI2zmvoj43aP9u1S7REjZm3bK8D1S1e3uHhQ/Mvy/7NfUUI8tKJ83UlzVZcxnLbu+f73+zVuGBQudjURqzN+5dflT7tDTPG2z5v93dW0feJlzcw2RvJVUhpkKoZC/8AFSurs3NO8p9o/hFIJFiFnZVOz5v71aVnIYW37NzVQt26R/eDLtq3CyRqm/8A8dqpEmlYyJ5iwu67vvbq6TR9P/tTEPks+7hVWuFm1DyGynJX+Kuj8FeNn0u6RHfcPu7an4gl7pt6h8O9bVt9tDIqfwVk33hbXtN++jf3l3JXr2j/ABEs7zToYf3O7+Ld/FS3HibSrtn87TYXVX+fbFRTlExlzcx5Dpeuazo90vDD5/lb+7Wp440O28d6U+qwIq3kK/Nu/wCWldzeeG/BniSQJbbbSf722Srel/CW5tW32l/G8f8AdjanH4RKXK78p8vTQTW8zxzJgr8rUzOT1rt/jp4N/wCER8WMkT7kuE3/AO61cOq7a05TtjLmiKG3c0UirtpaRQqq+6r+n/Mwh2Z/i3VQjYbs72NbvhqGOaRk2ct/DUyMpmrN5On6K8xdlfyvlauNZnZi79Wro/G9wLeGPTYv4vmlrn7e38zL7Gwv92nzIqPulzS/JhISbBq5JGRNvG7b975ay445o5l2fe/h3VpRSM0Y3/ejo98zlKJr6bI7Q+c0Py/LtqHVLd1UTbG3N91v7tWdNmQRb3+f+H5ai1SN1Xh942/3/u0R/vGfw/CZMlw+3O/lf4q3NJkdm+R8jb92sJvlbZ3/AIq0NNvHt12OKYc3c+tv+CRcco/bv8Jt/B9g1P8A9IZq7j/gvbCJv2ifCKjgnwQoDf8Ab5cVwf8AwR9uPO/bq8LHzC2bDUuT/wBeU1ekf8F2Io5f2i/CW9cn/hCF2j/t8uK/LcU/+NtYb/sHf51D8ZzD3fG/C/8AYK/zqHwA0bxt8r8f3asaXJ9nkR8Mu3/aqxfWvzMURV/u7qpSN5a79jYr9SP2WMuY31vEkj+SNtzfxVKsk00mxP8AvmsSxurlWZ/lxWnZ3+66SEv87fM9A5fEX2WONw7o26mf6tfMfduqe6kmjVU37hsqHb5g+/8AJs3NIz0DkP3PJ8vnNhk+VWqnqS+TGS6bv9rfVmVnjPzw+cfuq3+zWdqreWvnOdqs/wB2nHn5SZcsjntYvC2/hid/3qw2+Zi9bGtXH3kT+L+Gsakb09j3XwT/AMkJH/YKuv5yVxP7N/jWXwT8WdE14OqLDfxt833fvV23gn/khI/7BV1/OSvFNJuns7+OZHxtbdur7/jT/cMt/wCvK/KJ7edR58LQX9xfkj+kf4Q+H9S+IX7LOk/GDTbaG4sGlWCWSP5WjZl+WSSvNPGXgW/utNu9Z0C5aUWrM/mRturh/wDgjH+0tD8UP2W9R+A/i3XmhRoJILhf4tyr+6b/AGfvVofDn4kar8H/AIhXnwi+JF59ot47hoEvJIvlWP8A2q/P6uTRxNH6zh170fiPxDN8FTy/NFOOlz8qv2x/A6eC/wBobxCiWzQ299dfaLWNn3N8y/M3/fW6vL4f9Ud6f77V92f8FaPg7ZtHD8S9B02PZDdSJLcbvmmjb7rLXwzawo3+jfKFX+81byTcIs+5y7EQxWFiz9I/+CBLMfC/xNRoyu2/0rAP+5dV8+ftcKf+GgviUuevi3V+n/XzLX0R/wAEFM/8I18TssT/AMTDS8E+my6xXzz+1xz+0J8SeP8AmbdW/wDSmWvI8IP+TvZ9/wBg0fypHxfhxp4yZ9/14h/6TSPArfy9rSbNw/551oWdrbeWszuymT7lMtbPGxNi/M27dVpYUVgmzmT5V/vV6/un30pe7zRIpItysnT59rN/FT4Y3jUPv+bZ93Z96pGs33fxNt+XdTpFuY41SFGzH9xqcuQnm97UrSZEX3GC7vkWpbVUVcvx/F/vUqqkytv3ff8Am3Utvb7d0kL7t3y7aykbU5e9EuW7ozJ91P8A2WrluPMk+d2Ct8q1BZ2/yjei/wC0tXrPfErO+5v/AB6uf3T1aci3bRwxukKTNt2/darIhht8bJNxZvnbfVe385sIki7WXb/9jVq1je4bfInG77tZ8vKdnN7vulm2berQxvsff8zL92rMVv5kP7xNqq1U7cJC3zphVbd9+rUZRYV858/JuZl/iqo/3jnkHlo0zpNuK/wrv/8AQaesjxbWfcybPur83/fVJC0Lyec8flp5X3f4t3+zUkMkiwvDvUO33G31vT92R42M5pbFaT5ijpuYfd2/d21QvpkjX+HbDu+bdV+RXudrzJn5fvfw7qp6lDZqWR3VkbbvX7yq1dJxxjy/EeVL9pt/4N2779WbX94uxw3/AAGmyKiyBo+lWIcecronyt/D/tV4vMj6CMeYs2tuhXzI48O3ypWjapmRHx8u756qQwpGqukjNtb7zfxVdtYZpH+T73+1US5vsnbRibOiqluypjLM3z/PXXaLJCtx5Pk/LtXczfwrXJ6Xa7sfZnzI332/u12Whx7I0y6su/5mrzcRKZ7uFj2O38P3Ft5kaI/Ej133hmaFpvs0ybfLb5Nz/NXnGiyQqyRyQsoX+9/E3+zXX6FI8zR3MN78y/wsu5m/4FXgVPe5j6PDylE9W8N3BjjSGb7kb7W2/ertbeMTwpczO22T5YlrzPw/dJayNbwo3nfLK+6XcrfLXd+H50khRyI/3cvyL/Etebyxpy909iMuaGpptC7W7zJt3Kv9ysPV7SGVUd9p+X5pF+Xa1dEn+p+RFYq+7bu+ZqztSs7Zlkfevypub5PlX5q9TCe97p5GM5TxL9pu2i/4VXf3iiTc0sORL1X98tcf+zXZJc+A9TfJZv7SwIwM5/dpXeftT2kw+EupTvKMLNANpb/pstcb+yy8h8FajBHAWLamVDYwFzGg+92r9vyqL/4hfXSf/L9flA+bxrvllR26/wCR2Nx4fhkVndF2qnzbn+9/u1nf2X9nV7lNoh3bF3V0c1vf3DPDbWy/7Ee7/wAeqpcafNIuyZGjbZuZV/hr5OjUPiMRT5jN0fzoYkhhb5pPu7flq/rHiS28M6Y1/Nc7XVP3X+03+zVO+tfseLxUZv4m+T7teUfFLxw+oXQsIblikafKu/8Ahon8Ry04++YPizxBqXjLxpvvH3Nu2xbfm+X+Ku9j1KHRdP0P7HZrIkd+0V0qp/q9y/L/AMBrjPBNrDb2J1XY3mzf6qPZu+WtHUPElna6DdaVcuyTXEG61Xf8yyL91qUo8seUvm5vhD4qa1YaP4gg1u5dobTUP9F1G3ZN0e3d8rN/6DXi3xV1RNBvJrbTXZrBvmsv9lf96u88Va9Z+KPDcr6xC2G/dtHu+ZmX71eM+KNQTWZpdKmmk/0X5V3fxf3az900jH+Y5W6uJpJHubl22s/8NVrPxFqelq4f5of7zfw0ahdTXEz2/wBzb/Cr1Wt5EkZ7ab5/M+Wr5eY2NiHXobyHfM6n+9VW4Wzk2vCiptrB1C1vNNmCb28tqSHWJPlST9aQcv8AKaEkKBg83/ANtVdS1CGCPyIYV/22/ipsl0jIz5YlaoT75pC+/O6rl8JUfe+IjkkaRt9JkMOCtSR2rsrP7VKtrtXDpRyovmiVj9wfWnRyOrB0blanS18wksv/AAGpPsKL9w1Ajd8M+JI5oUsrz5vm+Td/DW99l1JpmfT5sr96uDht3W4+R8V3nhPUnjhRJPnf+FqDKX90gvF1u6V7C5mYIyfxLWanhl7ffI+0qq/xL96u71DUrDy1fyNxqlcWqXEn7lPmb+GrjGZEvdZ5HPHJFO0UiYw3K02uu8ceFX3vqVv99fvx1yNH+I6oy5goooo98oKKKKOZAFKFLHC0sXQ/SnNlRnFQTLcVd7J8ic/xNSOrsf7rUY2rt+Vv9lasWdnNcTJCiN83/oVVzInY6f4W/DzUvHniKHSrOHcu9XuG/urX194P8Dpp2nw6DZeZ5Me35fu7qzf2QfhboPgfwh/bHifSZrjUNQ2y+Yv3YV/hVq9hj8UeGI4Z4U02PZIu1Ny/+gtXiYqt7aVonh4it7apy3OWbQdYiUwukcW1/lb+7/tVLa+HYbeRTfyyO/8AH/s/981qXmtaU0iI7x7GXc6/w7d3y02S8s/ObZ8pXc25f7tedL95Gxxe7H3Spbw2cKt5MKynfu+b/wBBqBrlxu87buk+95f3as3CpMzOZmRV+bdv+ZqgZU+ZzGu3du2rWcuWO8i4y9z3hftE3mNcuV2fwbfvLRpEiQ6lbC3+eRrmP/0IUkVwkLPcwzNjytrxyJ8tS6Jcxf25Z3EsewG5Q+XH/e3CpnyyT9DOrKPs5ejPtT/gobaW958BYI7mUog8Q27FgP8ApnNX4ieMbx7zx9r15G6lpNWm27V2/wAVftT/AMFOdfi8NfsyPqszbVXW4RndjH7qavxMh2XmoXV4nzLNPJIzN/tNXzXhKovhrX+eX6H594GL/jC2/wDp5P8A9tIGkdt3nTfe/wDHahksfMj3w7s/7TVprZiSNnEO3+H7lOj02YR7ML/wKv1k/ZPh5TEbSYWXfvb5azNRslt/mRMe1dh/Y7ySh3GF2btrfxVj+KNNeG0abt/DSlHlLpy5jm6KKKg6BN6+tLRRWgH7Tftdf8oVX/7Jh4c/nY1+LNftN+11/wAoVX/7Jh4c/nY1+LDNjgV+W+FP/Irxn/YRP/0mJ+LeCX/ImzD/ALCqn/pMBaKKK/Uf7x+0hRRRTAKKKRm9ErMByjcaE+8Ka/3TSx5SgBVf5ePwpJETdiM0rD5setJVSARd/enP940lFPlQBS7nkPzUihvWpIVy4Dn71L3TM9f/AGL/AIKXPxv+PPhv4epC2zVtSjSV/wCFY1bdJ/47X7i6tDYWuzTdB2w2NvFHb2UKr8scca7VX/x2vz9/4Iv/AAZ+xrrXxv1XSo3FjA2nadJMm395J95t395Vr72uJJo4TMYG/wB3+7XwOe4uVTGcn2UfoHDmX+zwntpfaMu8uiv+jTOr7pdibm+bdWfcXDxlobZN38T7vu1avpnhWZEhYJ8rPuT5t1VJl8xnTZHLu2/733fu143t5c/909SthY1OZMns4fmH+p3/APoNWWj+yxfv9ojb+795az42S3mRPJb7u523/wAVXEut0C/aYdqfedd33a7aeP8AaHk1MDy7GfffK0dz5G4M+3azfMy1zl5HbLCpcrv81vl3/e3N93dXU65dW0kZmmf+Pau3+GuV1a6hVpMXnK/cXZ/49XvYHEdzxcdhnHcr3UkP2ff1aN/lVf4f9mqzai8cbf6zdJ9+oLzUrZmLu7FJPmRv4WWsO+1h47jyUdsb9rfL8q17dOpH+Y8CpGcS/fasFk8nzmR9n/PX5qxNW1bcTCEVX3bv9buVqyZr5/Ob7T8u6XbtX7u2su+vvLu2T7Sv/TJW/wDiq1exjzHn37V00U3hDTmSFUc6iDJtbOT5b1L8DLvZ8MbBEcfu5pi0bDIb941Yv7R+oSXfhWwjljVG/tAllK4bOxutT/BMgeCbSTYBseXexb7yh2P9a/QcXFf8Q0oL/p8/yme9Kf8AwhQf97/M7y4vkhkEYGVVWZ/9lqoX11cx6bcu6ea/lf8AAv8AeamR3kNxMZn+VZNzbV+7HWX4svEs9Dub9LnY7Iy/K+35f7tfmE6cef4TzqdblgfL37RXixNFmlhX/XXCsqM3/LP/AHa8Y1SP7VHHc787k3bq3v2hPFH9ueMpIYV2xRt8nz1zml3D3Gm+S/8ADXr83LCNjhl73vFKNfKYps/jokjG7e74q3JabUfYjMf49v8ADUMi/dTZn/arT3PiJIG/d4/ipYZHaTf53y/3agkkdVPf56iaR1WoKjHmNfTY3mul2TcN/DWxNpbv/q0rntLvPs8yu74rqtL1y2lt/L+6f71V9ozlsZtxoT/O6I3/AAKq39m3kLrnbv8A9muk8xPl/i/2amjt7Zl/1HzLT5UHMYWm6prFntTzmVlf5K6bR/G2qw/JMGBZ/nZvmrOaCFl37F+V/utTll8n7kP3vuUcsoilI73S9Qs9Yj8m6bynkXa8i/K1dJpVlrdtJHFZ37Shk2/f+X/gVeX6f9p85P733tzNXrfgPWP7H0N9V1X7m35V/wDZaPhgTLl5uY8//aV8LXM2kW2tjaxh+WXa33f96vDa+gfG3iyz8R6bfw3j5hmVliX722vAJgizPs+7up83Mb0xtFFFBsSwH94ocV0vhpfs8rzu+3am59v8Nc/Yw7pORu/vV000kOl+HXlR/nZdu1lqfikYS30Ob1i+/tLVJbmR2btmrOkyeQnkuFYN822s6Nfm3itPT4ftDLvT/gNP4hyEulRZhsh2/wC1uqZN8bL/ALtXF0tJN2/a22l/s8quNmPkqvhI5kOs7z/lt5H3vvLUkzLcxtC6MqVBbw+X99Mr93cq1ZWF1b5PlqOX7Ie7zFOTT1Zt8dPW18sjfNU8cbzTbH+Wpfs+5diQ/N/ean8Iz6b/AOCPS+X+3v4VQSYH2LVNqf8AbjNXqH/Bd24aH9ozwiBwP+EJXn/t8uK8w/4I9QtF+3v4VDFifsOp9f4f9Bmr0j/gvJuP7Sfg8CJm/wCKIX7v/X5cV+XYz/k7WH/7B3+dQ/Fsf/ye/C/9gr/OofD1zceZN5Lpu/3arMqbQ7o21asqjriSaPG3+7Uq2/mR467vm21+pS94/Zo+6VIYfMO/qKuW8yWr/wCp5Zf++aIbdY22BPl3U1Ydtw/yMF/2qXMhylzEzX33n3/Ns+7Tmmfy9iVFFHD/AK5x937tS/IzB3RlH96q5Y/ET78Szbtt43/7X/AqzdUaNlf5Gbd8u2p285VLp91um1qz76+ENvs3/wAX8VIFGXLYwdUkQuRs+61UJvm3Ps+9V6+kSRt6Q/erPkbP8DUG0D3PwT/yQkf9gq6/nJXiFuh8xdn3q9v8Eg/8KKAx/wAwq6/nJXilvbu0nmeTur77jV2wGWf9eV+UT3s4/g4f/D+iPsn/AIJb/Heb4Q/Ea0lmuY/9In2vHcN8rN/DX2n+0j8QtI+J3xCt9e0rQVsXks1+2Lu+Rrj+8tflf8LLqbSI4tUsHaGSN9zyKnzV91/s2/tU+Evidodr4G+JDw21/axbLW6kiVWZq+ayXH0sFX9/4ZH5rxBlUsfDmh8R7RN8BfFvx2/Zt1V9Y0T7VYbJre3mVWbbIqt8rV+VGteHb/wzrV3oOpW/kz2c7RPD97btav3+/wCCcF5YaXqHiD4H+Ob+N9I8UWe2wmkVdu7buVlavyQ/4Knfs7v+z/8AtheJNHt7Py7DULppomX/AMeb/gVdea08PO86RzZNfD8lKWn+Z9Gf8EIMDw78TAqgD7dpfT/cuq+dP2t8D9of4kcn/kbtV6f9fMtfRn/BCBWHhj4kkng32l4/74uq+dP2tUEn7RHxHTkbvF2qj/yZlr4vwh/5O9n/AP2DR/KkeN4cu/jLnz/6cQ/9JpHitmxEKwujAfeVv7tWvL/eJ5PJb7jSf+PVWhj/AHmxNrbfu1Yt5JYRsRNv8LM38NewfeS90maP94d78L/yzqKZf33nI+x/4lo+eSTyelOaP94H35aRNvy1MY8pcvhGeT5i+c/lnc9PtY/LjkmT5Sz/AHqdGqSYtnh+799V/hoUvHHlNqp/HupfFEXux5SeGPynD+d9771X1u/LfZDc4X/ZSs/znVdiOvzfLuqSO4eP/Rn+f+F/n+9WMonqUfdgasMnkso2fP8AwNVnznl2P5PG7541/hrMhuPmEe/aq/3nqSFkKsieYDJ/y0jf7tYfbOuMjV/cqru8m8K33fu1Mtx50jFNwH3drfdrMhk3LvSbcGf/AHqsNcP5zo+3H3vlo5vskVJcyLkM0yuIXT7r/Oy/dqaaRJI/ubU3fxfeWqsMyLmOY8sv3asxzPuL/u2DfL81dEf5TysRy8ugSW8kkTf6R8q/3UqC8VPn8ndtZKtm3f7Ojo+/d/DH/DVe4Z9u83O3d99WWtY/yo834fiPOJLfyW+zb/vfLt2fxU+3Xc2HT5v9qppg8l18+5x/G2z+L+9Uy26M6In3PvbmrzvZ+77x9RRlGRZtbR4VG9KurbyxMmE+9/47SWdqkkab+G+8u6tWztYZF8nDbf4m21xVOeJ7GHp82o6xhRcunLMvz7f71dNptu0MKTbFLN/F/drLtbVI9sKSL/d8xvlWtnTfkXY7/wAXzba8zESlLY9rD07Q9439H33k0aQu25V/4CtdrpNw6q8L/u0b5vM/2a4zR4/KY3L/ACKz7U2v96ul0q4dmiTYuW+Vl3/Kq15NaXNI9DDx93U7jw/qBjZHeNdqp8kn8VdvoupeSyb5VaKT5vM+61eZ6b51uyzO7Km/+H7q11mk6o7SDZ8qL/eTdurz5U/e5T1adTmgekWOoW0is/VI3+aT7vzU64vHuIykP32VWaORK5vSdURrj/Rrn5F/hZf4a0G1pLqEQz3Odv3mj/ir1cLyR908nGe0PNP2rgg+D2pNuVnae3Dnv/rkriP2UN6eDdSlRVOzUicO3H+rTt3rsP2qZkHwi1BU+7LNAUP/AG1WuG/ZfmkXwfqMKuAp1Els/wDXNK/Z8tUY+F1e3/P9flA8XFqX9myXn/kem3U32xl+f54/laRfu/7tR6g8Plr9m3N93f8ANVW4unj2bE2Mybl3fxVk6v4gfT/MmmmVEW33PG3y7a+Ip1F8SPkK0ZdTI+J3ij+zdNTQoVZp5FZmk/2a8Zjs5ta1x7Z7aN2kf5ZGbayrV/UvGVz4m1S7ufOzE3zW+5/ur/dqxoumx28L39y7O+35G/5512UY+6cMvj5omldNHpen7ET7tvtRVb7ted6hfTaldNM8nlrGm1Gb5q3fEmqXM1yJrab915W1v71cD4y8RTW8P2OD5C25mb71L/CHwxIPGmvPPqQTTZmeORv3+1futXIeMLiFpmSzfeV+XcqbamhuAsJhfc/mf7f3aw7qKawu2hvJtv3mRt1PlRr7piavskt/tKQ7Jt9YwmcSb0f5t+6tm4a5upmf92zb6yrqBIW85P8AgSrR8JpGMDft1h1zTUSaZdyp8/y1z+paYbN8f3au6PqyR3mzyfl+7urWvNPhvId8PzS/xrRLYXwnJrNMpw6bl/utU8E0Py70X5fm21JqemzW8m5odpb+Gqm542I+6yrS5S4vmNC3kQtv2Y/2aXy0VVd/vVnrM+1W3bf9oVL9qeNfv7ttSTyl9ZUWH5HqKSeFm+5x96qa3G7+Btuad5nzFP8Ax6tCiz5ieZ99Vb/0Kt3QtQeFk2PtKrXMqu750/hq7a3Lxyf8A2vU8xn7p1s2qPNMnmHan3q2tLmSRW+T7v3N392uPhuHk2Jv3ba7Pw/D5mn796mT73zf3af+EmQ7VLe2ktSm9dn+1XnHjDwyulzLd2z7lk/hWuy8UatFH8sO75U2vXNTo+qW/nuG27dtMcZcpylFLNG8MzI6YK0lB0hRRSqMtzSlsA6PH8FSLGnL9qhVtvapo4Xb7/P92ny/aMxix7mPHy17n+y18EH8bahH4q1K2b7FZyrsVk/1kledfDT4e3HjHVhbu/lW8bK1xM393/Zr6n+D+uWfg9JfDdsii2jZXVWT5mrOpL2ZwYyt7nLE+k/DPgfTdJ0F3Ft50skW7bt+VVrFuPCdhqmnoiaa1ufvSrMlX/D/AIyn1bw3bzJMxKwKu6NvvfN/FS3WrarqkJhe/wB77vl8tNrf7tefKnTjotjyOXlicjfeDbZmaawRv4v4fvVlzeH7m1k+fzEmZNr7fm212MOrPb3Tw/Y2dVX5pP4tv8VDahpsl8x+xtMd67mj/u1z/V6UmGxxHzwLKjux3NsfzPvVJ9rRZfJTdu2feaukvrHR7i+VNkihnwisnzLTLrQtKV2/fbdvzP8A7NcFbCy5y+XlObWPU9RXZbJ/HtrS0jTbew1WzuL65UN5w3K33V+YVNfappWhRzTCaOEr8zMqfNJXm/iD4jXOp+IrOysH2xtcp8rfeX5xUSUKcOXyM50eaMvRn2Z/wWku5rL9jF5IXKlvE9qpK9ceTcV+Q3hXS92lpc7JG+dVX56/Wz/gt/IY/wBilFUDc/jCxVSTjBMVxzX5ZeEbXbpKQvtA+638NfM+Ecf+MWb/AL8v0Pz3wO04Hv8A9PZ/+2iR2SKfJdNx/wBn+KnppKSTfOi/8CetZoU3LAPm21Qkt5pJt7lga/U+aWx+wR93WRDcWf3Y9nK/8Crn/GVtC2kzOkO7an/fNdfZ7JFP7nG59tY/jzT4Y9Hu32NsWJmTbR70i/tHk7/dNLRRTOoKKFZCvvRURiB+0v7XH/KFNv8Asl/hz+djX4tV+0v7XXy/8EUnx/0S/wAOfzsa/FlW3V+YeFP/ACLMZ/2ET/8ASYn4t4Jf8ibMP+wqp/6TAWiiiv1I/aRD8zb80obdzQuznbSKu2l8QCKuG59KdRRS+EApV6H6UlFSAm/cxpaKRl3VfwyAWgDf2opeVNMAVfmNbXgbRpta8QW2mQWzTO0q7Y1/i+asVPvCvpX/AIJn/BK5+Lv7R2i2zov2azn+0XrSfdWNfm3N/s1x4utHD4eU30NMPRlXxEYfzH6d/slfCu1+EPwB0HwfbP5V3NZfar9W/wCWcki/3f8Adr0ySwmjaLe6s8fzPJv27qnax/0x97q+19qsqfLt/wBmpzp/2pjvSRXXcrN/e/3a/J8TivbYiUpH7NhcPHD4aMexhTafc7U+0Iy/vW3tH/Ev8NULjTbm3ZI7ZJG/vw7/AJv96u1tdPTzDMkK/wC633Wqvq3h95G37Jm8yJvmX+FV/h3Vzqt7yCeHpcvMzifs7wybOrb/AJ2/2akkmm3KUTc38bSL8sldHD4bh8kO8PlFv738S1m3lj5a+Sn3li3bv4lrWjU5veOX2PNHmkc1qkzry+1X+8qr8y/7tcnq15Z/bhCH8maTcrr92us8RLDcN99kfbuTcnzfLXC+JrqGbTzI8agq/wAzbPmb/ar3ctlaV3seFmGH93Qy9Qv0uFf7G7L5b7fMWsi6urmZmd7liY33J8+2p7q8fH3MKqbVVf7396ub1LVnhmabzl3Mux1/hWvpcPU6HxuMp8sixqk0Pls/zfKm51j+9u/vVzerXOLfzt7Etu+Zv4mWpNS8QJNG9siKWkT70bVgX2uQyNvTc0m3+J/lr1afNLlPIqchyXx6v/tvhzT2UqQLkc9x8jcVa+EV0q+C7e1kk4Zpf4unzmsD4sXkV1oduEJJF2M4bK/dapfh9qENt4ds1mjdQrSt5g/3jX6ViV/xrij/ANfn+Uz2akl/q/B/3/8AM7W3ufM2fZptyzSt8v8Adrlvizqk0Phm5+xvt3Rbf92tKG8NvGfvKW3bNv8A6FXn3xS8VQ27SabNtd5k3eW3+7X5zyy5jw4ylI+UfHSvJrlxcv8AfaVvmaqGjXn2aZi/3WrU8bb5NYld/uM+6ufWUxTqyfMFfdXRGPu8ppE6fyZI1d/Jxu/vVlX0jx4R92P9mtCG8S6sU3zfe/u1mXnzbnR2Lf3ar+6Ry++U5Gy3u1RyY/g+7UkkjswTYtRbTu+SpNIg29pPMd8GrFrqE0Lb/Obb/dqJoZmG/wC9Sm3dfk7/AO1Vf4gNyw8UOijem4f7Vben+I3kZoU2ruriI43b5NjVdtfNjZZtjf7tLm5SZHZeZ9obfsVW+7tqeytd2Zsq/wDs1j6PeJ5C/Ox/3q29PvEDeSife/iWtebmiYmn4d0/7dcfZtmz51Vd392uv+IH9pR6TbaJbWsgSGLdK3/oNc34Tu4Y9ShS52p8212b/er1S+bTW0dtVSH7YWiVWVnp8xHNHnPCNem+z2svyYTZ8y7a84mYtMX2Y3V7n4/j8N6tapbQ2EltIy7vL+9Xj3iLw/NpVxvSFvKb5qj4fiNqcrmVSqz7tlJToVQSLv3UHSa/h218x/kHP8VWvGEwjjhs0udyL822p/DFskamZ3XGzd92sPXL57y/d96srP8AeWl9sx5eaZTJ8xq0dPmnjtzsf5qz4x+8w9bOkrCsbDZ/wJqgJF7S7x3tTC773am6lNc2+7Z84qK3VPMPkv8AMtS6l++nEKO2/Z/wGgj4feRX0/Vrm4byfu/w/NV+6vEt42fZt/h3LUdvYw2sZkqO+ZJITC/J+98taC5vfHW+pbpFd5lw38NbFim6GWaF2KLXO2+nvdMuE+7XVaDM9nbujooRk+ZVoD4j6L/4JATkft7eE4lOQ9hqeT/24zV6R/wXhYf8NG+EU3AEeCFK5/6/LiuB/wCCSNpAn7e/hSeJCALLU1GV/wCnGavRf+C6vh3WdY/aP8IT6dptzMn/AAhSoXhgZgD9suDjIHvX5djWv+ItYZvT/Z3+dQ/GMwlH/iN+Fbdv9lf51D4NXWE8zy3fO3+9WjDveH7T03fcqO38A+I2bZ/wjt4Sv3v9Dfb/ACrRj8PeKbeMRv4dvcfd2/ZH/wAK/T5Soxle6+8/Y/a0uX4l95Vt968zQ7x/eqSRYWZHdMGT5alm0HxYJN6eGr9t3920f5f0qNfDniyS4SF/DWoBW/i+yP8A4UvaU+6B1KUftL7yC4t/9tg23/vmq8av5e/zmI/jrS/4R7xUtx/yL9/8rY/483/wpjeF/EygJ/YF+yf3mtH/AMKXtI9Gg9rQ5viX3mfIz52PMp/9lrO1RVWT7m7b/Etb8ng7xHcbtnh3UV/7dH/wpjfDzxXNF/yL15/e/wCPR/8ACiUqf8y+8r2tL+ZfecbNZu0nycD+9UJsZnXZs3fL/DXaj4b+JYVVH8PX3+3/AKI/+FSN8LfEkS7oNCvsN93/AER/l/SnGVP+ZGf1ilH7S+87PwPA5+CSW+CSdNuR+Zkrya80+bT1jieHYy/f3V7n8P8AR7mDwfZaJc27rKUaNo2jIOS7cY696h/afs9M8Tat4e8OeAPhFcaOvh/S2ttW1EM8zapcM27zOnyqq/dWvvOOJQWX5Zd/8uV+UT3s7rQjRwrb3gvyRyXg21/4kav97++v91a07WebSbhdVs32zR/6pv7tS+DvDHiV9E2N4dvV2vtbfaP/AIVeuPDHiVGk/wCKbvcfd3Lav/hXw1OpTtuj5ypUoS05l959afsW/tzalYyWfg/xtrzWk0Kf6BqXm7W3fwqtdv8A8FTtB1X44/DtvjLqWlNNqOkxKzzW8X+uj2/eZq+DdP0XxdpN0l1baHf/ALn5l22r7v5V9R/A79p3xH4g+GWpfB34g2FyWutNa3iuru1l2sv+1x96tY4pUtLrlPPqwoT9/mXN6nqH/BCiLyfDXxJTLf8AH/pf3hj+C5r5w/a3OP2ifiOSOni7Vf8A0okr6i/4IqaBe+HrP4oWV1byIi6ppywu6kB1C3XIyPevl39rjn9oj4jgDP8AxVuq8f8AbxJXleEDUvF3Pmv+gaP5Uj43w1kpeMWev/pxD/0mkeK2eyP5NjZ+8kn96rUdv5zK7xrnfu3f3qq26r5I3fI3/oNXY1/eNs+6v9169g+95kIFdbgQyfK391fmqXakjeTsYtt+7v27ae0LtHvgfbt+7SiEtDs/ePt+98nzNUfYKjKW8iNdm5k37fm+9UCypCVRPm+fazbNy1buFQ7odm1tm5Vaq7RC3dnfzAI/4mquZBH4rC+Z5f8ApIh+ZabaMkirs/vfxVFM23915P8A8TUm3bcMiSbdybaxkd8dizDcP9nZHg3/ADbUbf8AdqeOaHaPn+X+CqTFNqu7t8y7akVkaFpEdW/uVly/aOqPOaX2tI4fJ6bl/herNjL5kaTP83ytu3VlR/6pN/JX5t1XI38lldHZfk2/7NLlQpSka1tJ5y53/d/h/vVYt23K80yKNr/LtfdWbazbtjv8u5Pk21ds5trM7lUZvmZWet4nHW5ehoWMxVV2PufZtWNflplysGwo6Mxk+WmRsDIiQphdm7dJ/DSMyKzb3zt+Xy1+7/vVf+E4Pdl7pxl9a+S7TPu27v79S2K74/O8lW3fc/2lqxqkafPNvyNn3adbx7VWNU42/IypUexnynt0ZcpPa26TNl4f4f8AvmtW1Z4WX99uX+7VKGN47f8Acup/2a0bVfm2eTtG2vPrUZRPawtT/wACLtjHuVWm/wCWn8TL8y1o6bcedLs8mP5fur93cv8A8VWbb3KRsX3r/wB91dtZEutjwpsMnzfc+XdXj4inL7J7NOtKUuVyOg02R45g+xSzP/F/CtdFpt8kcKIj7W2bt3/xVclYt9njimuQq7dysyt95q147zbiaS52ovzblT/ZryanvSPRpuMTsbW62tvd/M3Mquu/+Gug0vUPs00XnQ7k3bn3VxOl655bLv8A9IWRP3rK+1l+X5Vrd0nUplji2PDjeq7ZG+7XLKPKd8akTvtJ1GG3YvN0/ur/ALVaS6tYRtIibX8uL55N23+LbXE6frjmRnRFYxozbd/zNWjJqkMLI7p/rE3fe+Wrw/PGRjiJQkc9+09Pn4aXqlyB9pjUL2OJV5rz34D+MPDvh3w1eWmra3b2sz3xdFnkA3LsUd/cGvTPHmm23ivQbnw7qq7oJypV92SpByCG9iK8gvPgNNaQmZvFUZwMhTaEE/8Aj1fs/CWbcN4jhGrlOaV3Sbqc91Fu6tHayfWOt7dLXPIm6E6MqdSVtbnd618TfDSREWXi2ykZlxkzhvL/AN2uM8Z+PdO/sOa10jVUeW6G2ba4YsPU46VjyfCOaOSKE68m6b7v+jnA/HdXK6vb2mmarNpsV+sywzGNpgu0HHUgegrvo5J4fSl7mYyf/bkv/kDx8Rh8oesqzXyf+RNpzmS7RVuY4lU53StgY9K6bWfEOj29sIbC7jkBGwqGB4rCt/C5mtEunvgu/kII8nHr1qne6fFZyvEbrJTrlMf1rs/sfgSMr/2hL/wB/wDyJxQwmSX0xL+7/gFXUb2RGLJ5jA7lCxDp/wDY1xeuWOr3VzlNKunG/duERrb1LxjBp8hiFmZGHULJ0/Ssib4u2URI/shyR284f4Uv7H4D3/tCX/gD/wDkBrC5H1xD+5/5GJNo2vQo0q+H7x2KthRCflrFu/CPjPVp3km0G7Ur9xngNdcvxotTgnQXAPf7QP8A4mmXHxwsLcZOhSHj/n4HX0+7Vf2TwJLX+0Jf+AP/AORBYLI+ld/c/wDI4aTwb4yS0eEeFLxn3cMLZv8ACsy48BeON+6PwfqB/wC3Vv8ACvRl+PFuyGQ+GXGOxuxz/wCO1Wf9oqzVA48Ky89Qbscf+O1H9jcB/wDQwl/4A/8A5E0+qZLb+O/uf+RwKeAPHML7ovCeoDP/AE6t8v6V0Phvwz4u6Xvhi8jBGCXtmB/lWz/w0lZckeE5CB3+2D/4mpYv2iLd41lfwlKqscA/ax/8TT/sfgOOv9oS/wDAH/8AIEvCZH/z/f3P/Ip6v8O9VuYWkh0mUlfugQkE1yeqfC/xmhLR+HLuQekcBNeo+GPi9pXiKf7O9g1u2cYeYN/SuqvLiS2hE0Eayg9CHwPzxSWUcBS0WYS/8Af/AMiQsNkVN831h29H/kfPC/D3x2ybG8I6gM9M2rfL+lIvw58dqCo8J6iM+lq3+Fet638WZtFZlfwu8m04O26A/wDZaw5P2krSJSz+EpBjt9tH/wATQ8l4DW+YS/8AAH/8gaxw+Ry2rv7n/kcDH8PfHDAq/hHUgN3a0b/CkT4e+PVBUeEdQz/ea1b/AArvR+0vZc58ISjH/T4P/iaVv2lbFW2/8InJ/wCBg/8AiaP7H4Dt/wAjCX/gD/8AkR/Vcl/5/v7n/kcOngDx2IefCuoBh93Fq3+FS2/gPxyP9Z4Uv93977M3+Fdr/wANI2eAw8JyEH/p8H/xNKf2kbIcDwnKT6fax/8AE0f2LwJ/0MJf+AP/AOREsLki2rv7n/kcxY+DPGcWC/hm+x1wbZv8K6bTdH8SpahJ9Au04xhIWB/lU1t+0Na3BCjwpKCe32sf/E1Zb476dHGZJtCdQv8A08j/AOJprJ+A/wDoYS/8Af8A8iL6pkn/AD/f3P8AyOZ1bwp4rnmZ18OXrHd8rLbt/hTIfBfikQr5nh28G0ZwLdvvV1Ft8doLk4TwxJ/4FD/4mrR+M1koZn0RlCrkk3A5Pp93rS/sfgO3/Iwl/wCAP/5Ah4TIf+f7+5/5Hl2s/DjxwbgTReFL193Ux27HH6VT/wCFc+Pf+hP1H/wEb/CvUB8e7ZpPLXwvL7E3Qx/6DT7j47QxW7Tw+F5JCv3k+1gEf+O0f2TwHL/mYS/8Af8A8gaxw2S/8/39z/yPLP8AhXPj3/oT9R/8BG/wo/4Vz49/6E/Uf/ARv8K9B/4aZsv+hQl/8DB/8TR/w0zZf9ChL/4GD/4mq/sfgT/oYS/8Af8A8gX9Vyb/AJ/v7n/kcBH8OvHufm8H3/42jf4VqaB8K/GGo6jHb3fh+8t0Zl3yyxFQv4mvQvCvxnufF+qJpOk+DZWlfv8Aaxgf+O16VYeHrm7B86ZImA5HUZ9O1ZTy3gGnHXMZL/tyX/yBz1aeQR0liGvk/wDIwfC3hSx8MaXHpVokI2DMj5++1a/mxwS+fC4V16BOlW38NMmQb9MhtpBWmyeG7iMqvnqWYZxjtXO8q8PZb5lL/wAAl/8AIHM8Jw91xL/8Bf8Akeu/Cb4l+FbPRG0zWddtoWkXJNxKqgH8a6mH4i/DgP8AaT43sA+7CH7QvH614v8AD/4RXfjy6ltoNWMHlAEutqZBz/wIV08n7LOoquV8YxE9QpsiMr/e+/UvJfDzrmMv/AJf/IHNHAcNK6+tP/wF/wDyJvar408MWl/JJY+M7B0YMuIblQMN+NQRfE7w/aELJrtrKsaYHlXCrn9a5q9/Z2urOaOI+K0YSfxCyPB9PvUSfs7XK48vxdC5P8K2hz+W6sKnD3h498zn/wCAP/5Aby/hyO+Kl/4C/wD5E3H+KGjyuD/btsrFeH3jiqjfErT7hRAdRtN21h5jzDFZTfs+XcZ2zeK4UJGV3Wx5/wDHqbN8Bkgh3v41gLn7qLaE5/Hdipjw/wCHVv8AkZz/APAJf/IDeX8N/wDQU/8AwF//ACJfuLjwxfRyXOoeNLSQ/wAI+0Lx8vZa8xtTqH/CX2qysJQdRTE0Rydu8eldpN8FriJDIPECso7rak4+vzcVV/4V5d6BcQ6vJqCSJbss5XZtLBSDjrU1eHvDqUHbMptpP7D/APkAlguHXTkoYl3t/K/8j7R/4Lc6Xf6x+x1Y2enWck7/APCcWDMkaknaIbnJ4/CvzF0rQNbtYwj6dMNn3QYzX7Aal+0V+wZ+1P8ADaxs/iJ8U/CRsbmcXQ8P+I/E8Fhe200ZeP8Aew+erqRlsclWBDAkEGucT4N/8EppceXrnw6bPTb4/Bz/AOTVfzr4ecV8P8K5A8Bm2ExbrKcm/ZU4ONtNLynF30d9LH8x+HPGuX8FcPPK80wOLdaNSbfs6cXFXsre9KLurO+h+WiaLqco3Pbum4YwI/u1fHhwEJG0ChVTbnvX6e/8KX/4JU5x/bXw7znGP+E+HX/wKp3/AApP/glbnb/a3w9yDgj/AIT0df8AwKr7n/iJ3AX/AEB4/wD8FUv/AJYfdvxh4U65fjv/AAVD/wCWH5bvoDwxukNrJ935cjNYPjzQfEFx4XuBp+kzyzSoB5cSEsM9cAV+s6fA/wD4JYyEhNS+HzYGTjx5nA/8Cqr6n8Jv+CTei2/2zWPE3w2tIcgebc/EJY1z6Za6Ap/8RP4Ctb6nj/8AwVS/+WFR8YeFotNZfjv/AAVD/wCWH4n/APCufHv/AEJ+o/8AgI3+FH/CufHv/Qn6j/4CN/hX7Qf8Il/wRy/6KX8Jv/Dnw/8AyZR/wiX/AARy/wCil/Cb/wAOfD/8mUv+In8Bf9AeP/8ABVL/AOWG/wDxGfhj/oXY7/wVD/5Yfi//AMK58e/9CfqP/gI3+FH/AArjx6R/yJ+o/wDgI3+FftB/wiX/AARy/wCil/Cb/wAOfD/8mUf8Il/wRy/6KX8Jv/Dnw/8AyZR/xE/gL/oDx/8A4Kpf/LA/4jPwx/0Lsd/4Kh/8sKX7WGkapdf8EZG0a2sJZLv/AIVn4dT7OiEvuBscjHXIwfyr8a2+G/j08jwfqP8A4CN/hX66f8FF/wBvf9kT4f8A7KM/we+F/wAR9F8V32s2cNhomm+ENbt7+OzitpYGJuJI5G8lQgAXdlnIOAQrsv5m/wDDTNl/0KEv/gYP/ia28FcoyGrw/i6+aVp4dzxE3CLg7uHLC0vhfW6fmjTwLy+nDhvF1sap0fa4icoKUWm4uMFfbumvVM8+/wCFc+Pf+hP1H/wEb/Cj/hXPj3/oT9R/8BG/wr0H/hpmy/6FCX/wMH/xNH/DTNl/0KEv/gYP/ia/Y/7H4Df/ADMJf+AP/wCQP2z6rk3/AD/f3P8AyPPv+Fc+Pf8AoT9R/wDARv8ACm/8K28fdT4P1E/9urf4V6H/AMNM2X/QoS/+Bg/+Jo/4aZsv+hQl/wDAwf8AxNV/Y/An/Qwl/wCAP/5APquTf8/39z/yPPv+Fc+Pf+hP1H/wEb/Cj/hXPj3/AKE/Uf8AwEb/AAr0H/hpmy/6FCX/AMDB/wDE0f8ADTNl/wBChL/4GD/4ml/ZPAf/AEMJf+AP/wCQD6rk3/P9/c/8jz7/AIVz49/6E/Uf/ARv8KP+Fc+Pf+hP1H/wEb/CvQD+01ZAZ/4RCX/wNH/xNB/aasgM/wDCIS/+Bo/+Jp/2PwJ/0MJf+AP/AOQD6rk//P8Af3P/ACPP/wDhXPj3/oT9R/8AARv8KP8AhXPj3/oT9R/8BG/wr0H/AIaZsv8AoUJf/Awf/E0f8NM2X/QoS/8AgYP/AImj+x+BP+hhL/wB/wDyAfVcm/5/v7n/AJHnv/Ct/Hu7d/wiGo/+Ajf4Uv8Awrnx7/0J+o/+Ajf4V6ho3x+sNaY20Hh10uCP3UL3Q/eH0B21Un/aOhtZmt5/BsqOjYYNej/4il/ZPAf/AEMJf+AP/wCQD6rk3/P9/c/8jzyH4b+O2cB/CGoAd82rf4V+g3/BLRfhj8FfB2reLfHnjXRtK1TUZEtYrHUblIphCRl3O45HIFfKPgj4w3fj3X7fw7ovgudri5lEcYW5Byx7fdr7C0v/AIJ9avqNvBK/xPt4mkgV5kOksfKcru2E+Zzj1rxc5yvw1dD2WIzScL/3JP8A9sZ62T4HAyxPtKEnNro9P8j610j9p39nmBCJfjJ4byGwS2sxAn/x6tvTv2oP2aZJ2W5+NvhUq235pNahX/2avkvTf+CY2s6hD5x+MFqg9P7FY/8AtWtqw/4JKa9fMF/4XfaJkZGdBbn/AMjV8Y+GvB6Uf+R1U/8ABcv/AJWfcKWOcV+7X3/8E+r4v2n/ANlyKTj49+ESScZ/t2EbV/76q1cftS/sqTKZY/j74RBc5KHW4eAfvD71fL0H/BGfxHPCZl+PllgdB/wjz8/+R6nj/wCCLniCQnb+0JYnHXHhx8j/AMj1muG/ByP/ADO6n/gqX/ys0UsfH3XSX3r/ADPobUf2nP2Y/Md4Pj14TYbSqsNah3be38Vc5q37R37O9wgaH43+FQ38f/E5iO7/AMerxd/+CMfiYNsj+PNoxPTHh1//AI/WFef8EnNas52gf45WZK5BP9gvgEdv9dW1Phrwg+znVT/wXL/5WRUlj3HWmvv/AOCeoa78efgNLcMy/F7QZQCxBXUoz/WuH1n4yfCa7ZXi+JujIqrjbHeoTjd9a4TVv+Cbuq6XL5Y+LtpKAcSMNIYbf/ItYV5+wjq1sWWP4j28hWQqR/ZjA5H/AG0r0cPw74UUtVnE3/3Dl/8AKzya9KpO6lE6nW/it4AAYQ+NdLlIb7yXqZdv7x5rn9Y+JfhCScqfGFg8TJ+6EE6ja3+1zXM6n+yRqmmuyHxnFJjgbbA8n0+/XN3/AMDLvTyyyeIUJQZI+ynp6/er3sNkfhtvHNJv/tyX/wAgfOYrDYKTaqTa+X/AOq1bx54cnYpaa/aAkY3m4GM+tYV14p0mRy767bKxfc7RyBsf7tcveeAxZsUfWo8qu5sx4x+tU/8AhGcP5Z1Bc+oTI/nXsUsl4CUfdzCX/gD/APkDx62FyZr3q7Xyf+RqeOtastR02G3s7uJws24ojAkcHnNUtD8avolglkumrJs3fOZMHk59KbH4OeRwg1Bcn0jz/WrUPw8eVN51dRzjAgJ59OtfWYXGcBU8ojl1bEe0pxk5K6mtdf5Uu7OmnW4fhg1hqlTminfVSWvySHy/Emd1Kw6UqBhj/XZI/SuP8VWU/ijVk1SW6EWyMqIxHnr3zmuo1vwJNolv9pl1JGQIWJ8vGAPxry20+Mdne65daNb6K5FsxHnG4GGx7Y4rCMfCvdW/8rDiuGLaf+3lHVPgNb6nM0zeKJE3HOPsoOP/AB6s8/szWhOf+Ewk/wDAIf8AxdW9R/aFstPkeI+GJGKdf9LA/wDZaqf8NNWR5HhCX/wNH/xFV/xq3y/8rGkY8N9P/bi7p/7P9rYRGL/hJ3cH1swMf+PU2b9ny3mO5vFkmc5z9kH/AMVUum/Hq11EfL4ZkVv7pugf/ZaW9+PNtZHa3hiVj7XI/wDianm8K/L/AMrEv/Vm+v8A7eUf+Ga7I53eLJDn1sx/8XS/8M3WQ6eKnH0sh/8AF1Mv7Qto2CPDD4PU/bBx/wCO1JH8frWUZTww/TP/AB9j/wCJo/41X5f+Vh34a/rnIP8AhnKy6f8ACUPj/ryH/wAXSn9nSwJDHxTLkd/so/8AiqmPx/tEG+Tw06qejG7GD/47UR/aIsgM/wDCLydcf8fY/wDiafL4WeX/AJWFzcNL+pi/8M8WOc/8JNJ/4CD/AOKpyfs/WqIsY8UyYXoPsg/+KqFv2jrAMU/4RaXPvdgf+y0qftF2jNtPhSUf9vY/+JoX/ELPL/ysNf6tdP8A28uQ/Au3gAVPEr4Hb7KP/iqt2/wghtySmutk9/sw/wDiqyx+0PYkbv8AhF5cf9fQ/wDiat2XxwtrtwjeG5U3fcP2gHd/47T/AONW+X/lYX/GM/1zmxbfDq1t23/2iWbGCxh6/rXR6G99o0TW5vPOjP8AAy4H86yNC8WprbBVsTGT2Mmf6V1eneFtQ1BFmLpFE2dssh+Xikv+IWdLf+ViJLhZaO3/AJOQXNx4dvFzd+FLdnDfLIrlSF/u9K5zWPAvhjWUkil08osgIwH6ZrrbzwxDYsEm1iJmKbj5a5A/HNTweCHnsTerqaADB2tH2PfrWn/Gr9tP/Kwubhb+uc8Uuf2atOlmaS38VSxqT8qm0BIHpndRD+zZZxEbvFkjYOebMf8AxdeieJr+58PecYrH7SIRkkPtyPyNcTH8d0eUwv4SmUj/AKeh/wDE1Dj4W9bf+VjRT4Ztv/6WWIfgrbwWrWy+IGyV2hjajj/x6smT9mu0eTzB4tkHt9iH/wAXWhc/Hiwt7g2//CPuSv3ibkDH/jtRt8fLcKZF8LyFR/F9rH/xNH/GrfL/AMrBzcMrt/5MUR+zTZhxJ/wl0mQc/wDHkP8A4urifs/WqAj/AISd+f8Ap0H/AMVUb/tDwRn5vCUmPX7YP/iatxfHO3ki83/hG3Ht9rH/AMTQ/wDiFnW3/lYbfDXX/wBvKv8Awz3b793/AAlkuB0H2Qcf+PVMvwGiHXxS5/7dB/8AFU9fjrbNj/imZRn1uR/8TSH46RBQ/wDwi8mMZP8ApY4/8do/41bHt/5WC/DUn0/8nHRfA6CLIPiWQg9jaj/4qoZfgHBKxYeKXXPXFoP/AIqn/wDC+INm8+GHHoPtY5/8dpP+F8wYz/wi0nP3R9rHzf8AjtT/AMar8v8AysH/ABjUe3/k5LZfA6CzcOPEjtgY5tR/8VV6P4UWywtC+sM2TnPkD/GqFv8AHGK4i8weF5F9jdD/AOJqX/hddkMb9CkGen+kD/4mj/jVfl/5WF/xjP8AXOej/sy+KNa/Zh+N2j/Gzwy1vqNzpfnK9hewsI545YmikG5WyrbXJB5wQCQwyD9i/wDD5DXv+iBWf/hRP/8AGK/PH/hd9iRldCfr/wA/A6ev3aWL41wSSFP+EbkAH8RuR/8AE187nHCPgXnuIWIx9CM5pcqfNiFpdu3uyXVs+Tz3gbwv4mxccTmWGVSpFcqfNVjom3b3ZK+rf3n6G/8AD5DXv+iBWf8A4UT/APxij/h8hr3/AEQKz/8ACif/AOMV+fI+L8AQO+gSLnt54/wpy/FuFkD/ANgSYPJxOOB69K8v/iGn0eP+gSP/AIHif/kzxP8AiEfgt/0Ar/wOv/8AJn6Cf8PkNe/6IFZ/+FE//wAYo/4fIa9/0QKz/wDCif8A+MV+fv8Awti03bRo7kgZbE44/SkHxbszF5h0dx7ecP8ACl/xDb6O/wD0CR/8DxX/AMmJ+Evgqv8AmBX/AIHX/wDkz9A/+HyGvf8ARArP/wAKJ/8A4xR/w+Q17/ogVn/4UT//ABivz7X4sxNEZToLgDsbgc/pTh8VoTGJTojDcu5R9oHP/jtC8Nvo7P8A5hI/+B4r/wCTH/xCPwWtf6iv/A6//wAmfoF/w+Q17/ogVn/4UT//ABij/h8hr3/RArP/AMKJ/wD4xX59r8WoGbb/AGE4I6gzjj/x2lf4swoof+wZCCcZE/8A9jTfhp9HhK/1SP8A4Hif/kwfhJ4LL/mBX/gdf/5M/QP/AIfIa9/0QKz/APCif/4xR/w+Q17/AKIFZ/8AhRP/APGK/PxfizbsxU6Kw5wM3A5/SrS/EWA2b3h03ASMsR5/p+FJeG30d27LCR/8DxX/AMmJ+Engqv8AmBX/AIHX/wDkz2n4hftA3Xj79pI/tFS+F47WU61Z6h/ZS3Zdc24iATzNoPzeV128Z6HFfSH/AA+Q17/ogVn/AOFE/wD8Yr4B8K/EceMLOae20s22xtoLThs5/AU/VtW1CK1T7NPISnEkiykZrs4rwPhJncMJhcZgXiYYamqdO06kFCCSSj8cXKyitZXfm9T1eIeE/DrPKeFw+OwXtoYeCp0/fqR5YJJKOk4t6Jau789z77/4fIa9/wBECs//AAon/wDjFH/D5DXv+iBWf/hRP/8AGK+CtH1C7ltFMt8+XOdzOST9KLm91FJCVupQNv3d5r5KPBnga1f+xJf+Dav/AMtPmH4ZeEK/5lL/APBtX/5Yfev/AA+Q17/ogVn/AOFE/wD8Yo/4fIa9/wBECs//AAon/wDjFfn82q6xIxj/ALQZWQZbbIahOsaxlwdQlBP3f3p4qv8AUrwOvb+xJf8Ag2r/APLTP/iGvg/e39kv/wAG1f8A5YfoO/8AwWP8QlSE+AlmGxwT4hcgH/vxXx38TvGep/ETxVr/AI/8QR28V5rmoXV9dR2qlI0kmdnKoGJIALYGST7mvPk1jVChJv5g3o0hpkmq3c+Vmu7hsrgRlyRmvoeHl4dcGOvWyDLHRq1YODfPOV1vZ805WV7PRX0PpeH8k4F4OnVrZJgfY1KkeVvnnK63t70pW110JbeRPMKun/Af7tXLf943VU/vts+9WfBJtb5HXLfd+X5qt28nlr++feq/xbK+cj5mX90un7MuC6bPk+9vpbpU8sTJ/c/heoPtCM294VZG+XcyVFdXDtGzo+D/ALP3dtL3vtGnuxpEtxd7pPkSMfwo38W2oLiNIcD5tjf3mpPOdmeF0U/J8jf3qjkmmWNPkbC/wstKUpfCgp0+b3hftCT5TYp2/M6tTPtCRs37nd/danXEkMUao6Lub5qrtcbfnmTc33kVf4qxlsd9O32iWPfJIZk+T/pmzfeqdfIaHZ/D/s1T+0QyLvf71TLMjbUQfOv/AI7UG5ehb7Ou8bVX+Nd9XLeaSOTe6b1b5drVnQt9obZdQr838VX4pNkn2abayt9yp2nzCl/dLsF0WY232ZXC7W3b/wCKtC2kSOYFEZWb+FV+bdWbD23/ACtu+7sq5Hcea332T+J261rGPNucFaUoy0NVbhPJVPJyv8S/xUxWn/d7JtjfwfLtbb/tVFZzybm3vvXZuT/ZpzXEPnJNMm51+V9r1ry8vwnJze8f/9k=\n", 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\n", "text/plain": [ "" ] @@ -633,20 +632,10 @@ "width": 600 } }, - "execution_count": 2 + "execution_count": 38 } ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "4qbaa3iEcrcE" - }, - "source": [ - "Results are saved to `runs/detect`. A full list of available inference sources:\n", - " " - ] - }, { "cell_type": "markdown", "metadata": { @@ -654,7 +643,7 @@ }, "source": [ "# 2. Test\n", - "Test a model on [COCO](https://cocodataset.org/#home) val or test-dev dataset to evaluate trained accuracy. Models are downloaded automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." + "Test a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." ] }, { @@ -673,32 +662,32 @@ "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/", - "height": 66, + "height": 65, "referenced_widgets": [ - "b257add75888401ebf17767cdc9ed439", - "4b685e8b26f3496db73186063e19f785", - "0980232d74a14bdfa353a3f248bbe8ff", - "e981f3dfbf374643b58cba7dfbef3bca", - "07bb32c950654e9fa401e35a0030eadc", - "ec3fce2f475b4f31b8caf1a0ca912af1", - "9a1c27af326e43ca8a8b6b90cf0075db", - "7cf92d6d6c704a8d8e7834783813228d" + "8815626359d84416a2f44a95500580a4", + "3b85609c4ce94a74823f2cfe141ce68e", + "876609753c2946248890344722963d44", + "8abfdd8778e44b7ca0d29881cb1ada05", + "78c6c3d97c484916b8ee167c63556800", + "9dd0f182db5d45378ceafb855e486eb8", + "a3dab28b45c247089a3d1b8b09f327de", + "32451332b7a94ba9aacddeaa6ac94d50" ] }, - "outputId": "2ac7a39f-8432-43e0-9d34-bc2c9a71ba21" + "outputId": "81521192-cf67-4a47-a4cc-434cb0ebc363" }, "source": [ "# Download COCO val2017\n", "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n", "!unzip -q tmp.zip -d ../ && rm tmp.zip" ], - "execution_count": 3, + "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b257add75888401ebf17767cdc9ed439", + "model_id": "8815626359d84416a2f44a95500580a4", "version_minor": 0, "version_major": 2 }, @@ -726,55 +715,57 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "6850c54e-b697-40c2-f9fc-9ffda2d7052a" + "outputId": "2340b131-9943-4cd6-fd3a-8272aeb0774f" }, "source": [ "# Run YOLOv3 on COCO val2017\n", - "!python test.py --weights yolov3.pt --data coco.yaml --img 640 --iou 0.65" + "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65" ], - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov3.pt'])\n", - "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n", + "YOLOv3 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n", + "100% 168M/168M [00:05<00:00, 32.3MB/s]\n", "\n", "Fusing layers... \n", - "Model Summary: 261 layers, 61922845 parameters, 0 gradients\n", - "Scanning '../coco/labels/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3373.28it/s]\n", - "New cache created: ../coco/labels/val2017.cache\n", - "Scanning '../coco/labels/val2017.cache' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<00:00, 43690666.67it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:19<00:00, 1.97it/s]\n", - " all 5e+03 3.63e+04 0.472 0.698 0.625 0.424\n", - "Speed: 3.6/1.6/5.2 ms inference/NMS/total per 640x640 image at batch-size 32\n", + "Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3102.29it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:23<00:00, 1.87it/s]\n", + " all 5000 36335 0.745 0.627 0.68 0.49\n", + "Speed: 5.3/1.6/6.9 ms inference/NMS/total per 640x640 image at batch-size 32\n", "\n", - "Evaluating pycocotools mAP... saving runs/test/exp/yolov3_predictions.json...\n", + "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n", "loading annotations into memory...\n", - "Done (t=0.41s)\n", + "Done (t=0.48s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=3.78s)\n", + "DONE (t=5.08s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=78.99s).\n", + "DONE (t=90.51s).\n", "Accumulating evaluation results...\n", - "DONE (t=11.77s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.433\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.630\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.470\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.283\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.346\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.581\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.634\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.473\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.687\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766\n", + "DONE (t=15.16s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.629\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.681\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n", "Results saved to runs/test/exp\n" ], "name": "stdout" @@ -788,7 +779,7 @@ }, "source": [ "## COCO test-dev2017\n", - "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (20,000 images). Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." + "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." ] }, { @@ -799,9 +790,9 @@ "source": [ "# Download COCO test-dev2017\n", "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')\n", - "!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n", + "!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n", "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7GB, 41k images\n", - "%mv ./test2017 ./coco/images && mv ./coco ../ # move images to /coco and move /coco next to /yolov3" + "%mv ./test2017 ../coco/images # move to /coco" ], "execution_count": null, "outputs": [] @@ -835,37 +826,37 @@ "id": "Knxi2ncxWffW", "colab": { "base_uri": "https://localhost:8080/", - "height": 66, + "height": 65, "referenced_widgets": [ - "c1928794b5bd400da6e7817883a0ee9c", - "804fae06a69f4e11b919d8ab80822186", - "138cbb92b4fd4eaa9b7fdcbed1f57a4d", - "28bb2eea5b114f82b201e5fa39fdfc58", - "aea8bd6f395845f696e3abedbff59423", - "0514774dafdf4e39bdd5a8833d1cbcb0", - "7dabd1f8236045729c90ae78a0d9af24", - "227e357d925345f995aeea7b72750cf1" + "0fffa335322b41658508e06aed0acbf0", + "a354c6f80ce347e5a3ef64af87c0eccb", + "85823e71fea54c39bd11e2e972348836", + "fb11acd663fa4e71b041d67310d045fd", + "8a919053b780449aae5523658ad611fa", + "5bae9393a58b44f7b69fb04816f94f6f", + "d26c6d16c7f24030ab2da5285bf198ee", + "f7767886b2364c8d9efdc79e175ad8eb" ] }, - "outputId": "389207da-a1a0-4cbf-d9b8-a39546b1b76c" + "outputId": "b41ac253-9e1b-4c26-d78b-700ea0154f43" }, "source": [ "# Download COCO128\n", "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n", "!unzip -q tmp.zip -d ../ && rm tmp.zip" ], - "execution_count": 5, + "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c1928794b5bd400da6e7817883a0ee9c", + "model_id": "0fffa335322b41658508e06aed0acbf0", "version_minor": 0, "version_major": 2 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=22090455.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=22091032.0), HTML(value='')))" ] }, "metadata": { @@ -900,7 +891,7 @@ "source": [ "# Tensorboard (optional)\n", "%load_ext tensorboard\n", - "%tensorboard --logdir runs" + "%tensorboard --logdir runs/train" ], "execution_count": null, "outputs": [] @@ -925,90 +916,87 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "2132590a-ff5e-4ab8-a20d-f8bb8ea7c42b" + "outputId": "e715d09c-5d93-4912-a0df-9da0893f2014" }, "source": [ "# Train YOLOv3 on COCO128 for 3 epochs\n", "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov3.pt --nosave --cache" ], - "execution_count": 6, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv3 🚀 v5.0-2-g54d6516 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n", "\n", - "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov3.pt', workers=8, world_size=1)\n", - "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n", - "2020-11-26 18:51:45.386416: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", - "Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}\n", + "Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', entity=None, epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='yolov3.pt', workers=8, world_size=1)\n", + "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "2021-04-12 10:29:58.539457: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n", + "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv3 logging with 'pip install wandb' (recommended)\n", "\n", " from n params module arguments \n", - " 0 -1 1 928 models.common.Conv [3, 32, 3, 1] \n", + " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n", " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", - " 2 -1 1 20672 models.common.Bottleneck [64, 64] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", - " 4 -1 2 164608 models.common.Bottleneck [128, 128] \n", + " 4 -1 1 156928 models.common.C3 [128, 128, 3] \n", " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", - " 6 -1 8 2627584 models.common.Bottleneck [256, 256] \n", + " 6 -1 1 625152 models.common.C3 [256, 256, 3] \n", " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", - " 8 -1 8 10498048 models.common.Bottleneck [512, 512] \n", - " 9 -1 1 4720640 models.common.Conv [512, 1024, 3, 2] \n", - " 10 -1 4 20983808 models.common.Bottleneck [1024, 1024] \n", - " 11 -1 1 5245952 models.common.Bottleneck [1024, 1024, False] \n", - " 12 -1 1 525312 models.common.Conv [1024, 512, [1, 1]] \n", - " 13 -1 1 4720640 models.common.Conv [512, 1024, 3, 1] \n", - " 14 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", - " 15 -1 1 4720640 models.common.Conv [512, 1024, 3, 1] \n", - " 16 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", - " 18 [-1, 8] 1 0 models.common.Concat [1] \n", - " 19 -1 1 1377792 models.common.Bottleneck [768, 512, False] \n", - " 20 -1 1 1312256 models.common.Bottleneck [512, 512, False] \n", - " 21 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 22 -1 1 1180672 models.common.Conv [256, 512, 3, 1] \n", - " 23 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 24 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", - " 25 [-1, 6] 1 0 models.common.Concat [1] \n", - " 26 -1 1 344832 models.common.Bottleneck [384, 256, False] \n", - " 27 -1 2 656896 models.common.Bottleneck [256, 256, False] \n", - " 28 [27, 22, 15] 1 457725 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]\n", - "Model Summary: 333 layers, 61949149 parameters, 61949149 gradients\n", + " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", + " 9 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", + "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS\n", "\n", - "Transferred 440/440 items from yolov3.pt\n", - "Optimizer groups: 75 .bias, 75 conv.weight, 72 other\n", - "Scanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 3001.29it/s]\n", - "New cache created: ../coco128/labels/train2017.cache\n", - "Scanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 985084.24it/s]\n", - "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 194.34it/s]\n", - "Scanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 711087.30it/s]\n", - "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.98it/s]\n", - "NumExpr defaulting to 2 threads.\n", + "Transferred 362/362 items from yolov3.pt\n", + "Scaled weight_decay = 0.0005\n", + "Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 796544.38it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 176.73it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 500812.42it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 134.10it/s]\n", + "Plotting labels... \n", "\n", - "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n", + "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n", "Image sizes 640 train, 640 test\n", "Using 2 dataloader workers\n", "Logging results to runs/train/exp\n", "Starting training for 3 epochs...\n", "\n", - " Epoch gpu_mem box obj cls total targets img_size\n", - " 0/2 8.88G 0.02999 0.02589 0.008271 0.06414 155 640: 100% 8/8 [00:06<00:00, 1.23it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:06<00:00, 1.19it/s]\n", - " all 128 929 0.527 0.83 0.782 0.547\n", + " Epoch gpu_mem box obj cls total labels img_size\n", + " 0/2 3.29G 0.04368 0.065 0.02127 0.1299 183 640: 100% 8/8 [00:03<00:00, 2.21it/s]\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:04<00:00, 1.09s/it]\n", + " all 128 929 0.605 0.657 0.666 0.434\n", "\n", - " Epoch gpu_mem box obj cls total targets img_size\n", - " 1/2 8.87G 0.02966 0.02563 0.008289 0.06358 190 640: 100% 8/8 [00:02<00:00, 3.09it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 5.90it/s]\n", - " all 128 929 0.528 0.831 0.784 0.55\n", + " Epoch gpu_mem box obj cls total labels img_size\n", + " 1/2 6.65G 0.04556 0.0651 0.01987 0.1305 166 640: 100% 8/8 [00:01<00:00, 5.18it/s]\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.72it/s]\n", + " all 128 929 0.61 0.66 0.669 0.438\n", "\n", - " Epoch gpu_mem box obj cls total targets img_size\n", - " 2/2 8.87G 0.02947 0.02217 0.009194 0.06083 135 640: 100% 8/8 [00:02<00:00, 3.02it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.07it/s]\n", - " all 128 929 0.528 0.834 0.784 0.55\n", - "Optimizer stripped from runs/train/exp/weights/last.pt, 124.2MB\n", - "Optimizer stripped from runs/train/exp/weights/best.pt, 124.2MB\n", - "3 epochs completed in 0.009 hours.\n", - "\n" + " Epoch gpu_mem box obj cls total labels img_size\n", + " 2/2 6.65G 0.04624 0.06923 0.0196 0.1351 182 640: 100% 8/8 [00:01<00:00, 5.19it/s]\n", + " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.27it/s]\n", + " all 128 929 0.618 0.659 0.671 0.438\n", + "3 epochs completed in 0.007 hours.\n", + "\n", + "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n", + "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n" ], "name": "stdout" } @@ -1031,9 +1019,9 @@ "source": [ "## Weights & Biases Logging 🌟 NEW\n", "\n", - "[Weights & Biases](https://www.wandb.com/) (W&B) is now integrated with YOLOv3 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n", + "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is now integrated with YOLOv3 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n", "\n", - "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n", + "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n", "\n", "" ] @@ -1119,12 +1107,23 @@ "\n", "YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", - "- **Google Colab Notebook** with free GPU: \"Open\n", - "- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3)\n", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) \n", - "- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker)\n", + "- **Google Colab and Kaggle** notebooks with free GPU: \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\n", "\n", - "\n" + "If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n" ] }, { @@ -1146,8 +1145,8 @@ "source": [ "# Re-clone repo\n", "%cd ..\n", - "%rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3\n", - "%cd yolov3" + "%rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n", + "%cd yolov5" ], "execution_count": null, "outputs": [] @@ -1158,11 +1157,33 @@ "id": "mcKoSIK2WSzj" }, "source": [ - "# Test all\n", - "%%shell\n", - "for x in yolov3 yolov3-spp yolov3-tiny; do\n", - " python test.py --weights $x.pt --data coco.yaml --img 640\n", - "done" + "# Reproduce\n", + "for x in 'yolov3', 'yolov3-spp', 'yolov3-tiny':\n", + " !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45 # speed\n", + " !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GMusP4OAxFu6" + }, + "source": [ + "# PyTorch Hub\n", + "import torch\n", + "\n", + "# Model\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov3')\n", + "\n", + "# Images\n", + "dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'\n", + "imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images\n", + "\n", + "# Inference\n", + "results = model(imgs)\n", + "results.print() # or .show(), .save()" ], "execution_count": null, "outputs": [] @@ -1195,6 +1216,35 @@ "execution_count": null, "outputs": [] }, + { + "cell_type": "code", + "metadata": { + "id": "gogI-kwi3Tye" + }, + "source": [ + "# Profile\n", + "from utils.torch_utils import profile \n", + "\n", + "m1 = lambda x: x * torch.sigmoid(x)\n", + "m2 = torch.nn.SiLU()\n", + "profile(x=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "RVRSOhEvUdb5" + }, + "source": [ + "# Evolve\n", + "!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov3.pt --cache --noautoanchor --evolve\n", + "!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket # upload results (optional)" + ], + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "metadata": { @@ -1202,7 +1252,7 @@ }, "source": [ "# VOC\n", - "for b, m in zip([24, 24, 64], ['yolov3', 'yolov3-spp', 'yolov3-tiny']): # zip(batch_size, model)\n", + "for b, m in zip([64, 48, 32, 16], ['yolov3', 'yolov3-spp', 'yolov3-tiny']): # zip(batch_size, model)\n", " !python train.py --batch {b} --weights {m}.pt --data voc.yaml --epochs 50 --cache --img 512 --nosave --hyp hyp.finetune.yaml --project VOC --name {m}" ], "execution_count": null, diff --git a/utils/autoanchor.py b/utils/autoanchor.py index c6e6b9daf5..8d62474f28 100644 --- a/utils/autoanchor.py +++ b/utils/autoanchor.py @@ -37,17 +37,21 @@ def metric(k): # compute metric bpr = (best > 1. / thr).float().mean() # best possible recall return bpr, aat - bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2)) + anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors + bpr, aat = metric(anchors) print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') if bpr < 0.98: # threshold to recompute print('. Attempting to improve anchors, please wait...') na = m.anchor_grid.numel() // 2 # number of anchors - new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) - new_bpr = metric(new_anchors.reshape(-1, 2))[0] + try: + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + except Exception as e: + print(f'{prefix}ERROR: {e}') + new_bpr = metric(anchors)[0] if new_bpr > bpr: # replace anchors - new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors) - m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference - m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference + m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss check_anchor_order(m) print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') else: @@ -98,7 +102,7 @@ def print_results(k): if isinstance(path, str): # *.yaml file with open(path) as f: - data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict from utils.datasets import LoadImagesAndLabels dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) else: @@ -119,6 +123,7 @@ def print_results(k): print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') s = wh.std(0) # sigmas for whitening k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') k *= s wh = torch.tensor(wh, dtype=torch.float32) # filtered wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered diff --git a/utils/aws/__init__.py b/utils/aws/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/utils/aws/mime.sh b/utils/aws/mime.sh new file mode 100644 index 0000000000..c319a83cfb --- /dev/null +++ b/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/utils/aws/resume.py b/utils/aws/resume.py new file mode 100644 index 0000000000..faad8d2474 --- /dev/null +++ b/utils/aws/resume.py @@ -0,0 +1,37 @@ +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml') as f: + opt = yaml.load(f, Loader=yaml.SafeLoader) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/utils/aws/userdata.sh b/utils/aws/userdata.sh new file mode 100644 index 0000000000..890606b76a --- /dev/null +++ b/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "Data done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/utils/datasets.py b/utils/datasets.py index d2002fabfc..fcaa3415b8 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -20,12 +20,13 @@ from torch.utils.data import Dataset from tqdm import tqdm -from utils.general import xyxy2xywh, xywh2xyxy, clean_str +from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \ + resample_segments, clean_str from utils.torch_utils import torch_distributed_zero_first # Parameters help_url = 'https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data' -img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes logger = logging.getLogger(__name__) @@ -119,9 +120,8 @@ def __iter__(self): class LoadImages: # for inference - def __init__(self, path, img_size=640): - p = str(Path(path)) # os-agnostic - p = os.path.abspath(p) # absolute path + def __init__(self, path, img_size=640, stride=32): + p = str(Path(path).absolute()) # os-agnostic absolute path if '*' in p: files = sorted(glob.glob(p, recursive=True)) # glob elif os.path.isdir(p): @@ -136,6 +136,7 @@ def __init__(self, path, img_size=640): ni, nv = len(images), len(videos) self.img_size = img_size + self.stride = stride self.files = images + videos self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv @@ -181,7 +182,7 @@ def __next__(self): print(f'image {self.count}/{self.nf} {path}: ', end='') # Padded resize - img = letterbox(img0, new_shape=self.img_size)[0] + img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -199,8 +200,9 @@ def __len__(self): class LoadWebcam: # for inference - def __init__(self, pipe='0', img_size=640): + def __init__(self, pipe='0', img_size=640, stride=32): self.img_size = img_size + self.stride = stride if pipe.isnumeric(): pipe = eval(pipe) # local camera @@ -243,7 +245,7 @@ def __next__(self): print(f'webcam {self.count}: ', end='') # Padded resize - img = letterbox(img0, new_shape=self.img_size)[0] + img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -256,9 +258,10 @@ def __len__(self): class LoadStreams: # multiple IP or RTSP cameras - def __init__(self, sources='streams.txt', img_size=640): + def __init__(self, sources='streams.txt', img_size=640, stride=32): self.mode = 'stream' self.img_size = img_size + self.stride = stride if os.path.isfile(sources): with open(sources, 'r') as f: @@ -272,19 +275,25 @@ def __init__(self, sources='streams.txt', img_size=640): for i, s in enumerate(sources): # Start the thread to read frames from the video stream print(f'{i + 1}/{n}: {s}... ', end='') - cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s) + url = eval(s) if s.isnumeric() else s + if 'youtube.com/' in url or 'youtu.be/' in url: # if source is YouTube video + check_requirements(('pafy', 'youtube_dl')) + import pafy + url = pafy.new(url).getbest(preftype="mp4").url + cap = cv2.VideoCapture(url) assert cap.isOpened(), f'Failed to open {s}' w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - fps = cap.get(cv2.CAP_PROP_FPS) % 100 + self.fps = cap.get(cv2.CAP_PROP_FPS) % 100 + _, self.imgs[i] = cap.read() # guarantee first frame thread = Thread(target=self.update, args=([i, cap]), daemon=True) - print(f' success ({w}x{h} at {fps:.2f} FPS).') + print(f' success ({w}x{h} at {self.fps:.2f} FPS).') thread.start() print('') # newline # check for common shapes - s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal if not self.rect: print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') @@ -297,9 +306,10 @@ def update(self, index, cap): # _, self.imgs[index] = cap.read() cap.grab() if n == 4: # read every 4th frame - _, self.imgs[index] = cap.retrieve() + success, im = cap.retrieve() + self.imgs[index] = im if success else self.imgs[index] * 0 n = 0 - time.sleep(0.01) # wait time + time.sleep(1 / self.fps) # wait time def __iter__(self): self.count = -1 @@ -313,7 +323,7 @@ def __next__(self): raise StopIteration # Letterbox - img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0] # Stack img = np.stack(img, 0) @@ -331,7 +341,7 @@ def __len__(self): def img2label_paths(img_paths): # Define label paths as a function of image paths sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings - return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths] + return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths] class LoadImagesAndLabels(Dataset): # for training/testing @@ -345,6 +355,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride + self.path = path try: f = [] # image files @@ -352,37 +363,42 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r p = Path(p) # os-agnostic if p.is_dir(): # dir f += glob.glob(str(p / '**' / '*.*'), recursive=True) + # f = list(p.rglob('**/*.*')) # pathlib elif p.is_file(): # file with open(p, 'r') as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) else: raise Exception(f'{prefix}{p} does not exist') self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib assert self.img_files, f'{prefix}No images found' except Exception as e: raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') # Check cache self.label_files = img2label_paths(self.img_files) # labels - cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') # cached labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels if cache_path.is_file(): - cache = torch.load(cache_path) # load - if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed - cache = self.cache_labels(cache_path, prefix) # re-cache + cache, exists = torch.load(cache_path), True # load + if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed + cache, exists = self.cache_labels(cache_path, prefix), False # re-cache else: - cache = self.cache_labels(cache_path, prefix) # cache + cache, exists = self.cache_labels(cache_path, prefix), False # cache # Display cache - [nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total - desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" - tqdm(None, desc=prefix + desc, total=n, initial=n) + nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total + if exists: + d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' # Read cache cache.pop('hash') # remove hash - labels, shapes = zip(*cache.values()) + cache.pop('version') # remove version + labels, shapes, self.segments = zip(*cache.values()) self.labels = list(labels) self.shapes = np.array(shapes, dtype=np.float64) self.img_files = list(cache.keys()) # update @@ -433,6 +449,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i) gb += self.imgs[i].nbytes pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' + pbar.close() def cache_labels(self, path=Path('./labels.cache'), prefix=''): # Cache dataset labels, check images and read shapes @@ -445,13 +462,20 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''): im = Image.open(im_file) im.verify() # PIL verify shape = exif_size(im) # image size - assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' + segments = [] # instance segments + assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' + assert im.format.lower() in img_formats, f'invalid image format {im.format}' # verify labels if os.path.isfile(lb_file): nf += 1 # label found with open(lb_file, 'r') as f: - l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + l = [x.split() for x in f.read().strip().splitlines()] + if any([len(x) > 8 for x in l]): # is segment + classes = np.array([x[0] for x in l], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) + l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + l = np.array(l, dtype=np.float32) if len(l): assert l.shape[1] == 5, 'labels require 5 columns each' assert (l >= 0).all(), 'negative labels' @@ -463,19 +487,21 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''): else: nm += 1 # label missing l = np.zeros((0, 5), dtype=np.float32) - x[im_file] = [l, shape] + x[im_file] = [l, shape, segments] except Exception as e: nc += 1 print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') - pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' for images and labels... " \ + pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + pbar.close() if nf == 0: print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') x['hash'] = get_hash(self.label_files + self.img_files) - x['results'] = [nf, nm, ne, nc, i + 1] + x['results'] = nf, nm, ne, nc, i + 1 + x['version'] = 0.1 # cache version torch.save(x, path) # save for next time logging.info(f'{prefix}New cache created: {path}') return x @@ -515,16 +541,9 @@ def __getitem__(self, index): img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling - # Load labels - labels = [] - x = self.labels[index] - if x.size > 0: - # Normalized xywh to pixel xyxy format - labels = x.copy() - labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width - labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1] # pad height - labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0] - labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1] + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: # Augment imagespace @@ -637,19 +656,25 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed - # Histogram equalization - # if random.random() < 0.2: - # for i in range(3): - # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) + +def hist_equalize(img, clahe=True, bgr=False): + # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def load_mosaic(self, index): # loads images in a 4-mosaic - labels4 = [] + labels4, segments4 = [], [] s = self.img_size yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y - indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) @@ -674,23 +699,21 @@ def load_mosaic(self, index): padh = y1a - y1b # Labels - x = self.labels[index] - labels = x.copy() - if x.size > 0: # Normalized xywh to pixel xyxy format - labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw - labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh - labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw - labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) + segments4.extend(segments) # Concat/clip labels - if len(labels4): - labels4 = np.concatenate(labels4, 0) - np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective - # img4, labels4 = replicate(img4, labels4) # replicate + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate # Augment - img4, labels4 = random_perspective(img4, labels4, + img4, labels4 = random_perspective(img4, labels4, segments4, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], @@ -704,9 +727,9 @@ def load_mosaic(self, index): def load_mosaic9(self, index): # loads images in a 9-mosaic - labels9 = [] + labels9, segments9 = [], [] s = self.img_size - indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) @@ -737,34 +760,34 @@ def load_mosaic9(self, index): x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords # Labels - x = self.labels[index] - labels = x.copy() - if x.size > 0: # Normalized xywh to pixel xyxy format - labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padx - labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + pady - labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padx - labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + pady + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] labels9.append(labels) + segments9.extend(segments) # Image img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] hp, wp = h, w # height, width previous # Offset - yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y + yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] # Concat/clip labels - if len(labels9): - labels9 = np.concatenate(labels9, 0) - labels9[:, [1, 3]] -= xc - labels9[:, [2, 4]] -= yc + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] - np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective - # img9, labels9 = replicate(img9, labels9) # replicate + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate # Augment - img9, labels9 = random_perspective(img9, labels9, + img9, labels9 = random_perspective(img9, labels9, segments9, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], @@ -792,8 +815,8 @@ def replicate(img, labels): return img, labels -def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): - # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints shape = img.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) @@ -808,7 +831,7 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding if auto: # minimum rectangle - dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding elif scaleFill: # stretch dw, dh = 0.0, 0.0 new_unpad = (new_shape[1], new_shape[0]) @@ -825,7 +848,8 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale return img, ratio, (dw, dh) -def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): +def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, + border=(0, 0)): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] @@ -877,37 +901,38 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea # Transform label coordinates n = len(targets) if n: - # warp points - xy = np.ones((n * 4, 3)) - xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 - xy = xy @ M.T # transform - if perspective: - xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale - else: # affine - xy = xy[:, :2].reshape(n, 8) - - # create new boxes - x = xy[:, [0, 2, 4, 6]] - y = xy[:, [1, 3, 5, 7]] - xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T - - # # apply angle-based reduction of bounding boxes - # radians = a * math.pi / 180 - # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 - # x = (xy[:, 2] + xy[:, 0]) / 2 - # y = (xy[:, 3] + xy[:, 1]) / 2 - # w = (xy[:, 2] - xy[:, 0]) * reduction - # h = (xy[:, 3] - xy[:, 1]) * reduction - # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T - - # clip boxes - xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) - xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) + use_segments = any(x.any() for x in segments) + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T) + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) targets = targets[i] - targets[:, 1:5] = xy[i] + targets[:, 1:5] = new[i] return img, targets @@ -1016,19 +1041,24 @@ def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_ assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' -def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128') +def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False): """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files - # Arguments - path: Path to images directory - weights: Train, val, test weights (list) + Usage: from utils.datasets import *; autosplit('../coco128') + Arguments + path: Path to images directory + weights: Train, val, test weights (list) + annotated_only: Only use images with an annotated txt file """ path = Path(path) # images dir - files = list(path.rglob('*.*')) + files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only n = len(files) # number of files indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + + print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) for i, img in tqdm(zip(indices, files), total=n): - if img.suffix[1:] in img_formats: + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label with open(path / txt[i], 'a') as f: f.write(str(img) + '\n') # add image to txt file diff --git a/utils/general.py b/utils/general.py index a3238efa49..dbdcd39601 100755 --- a/utils/general.py +++ b/utils/general.py @@ -1,9 +1,10 @@ -# General utils +# YOLOv3 general utils import glob import logging import math import os +import platform import random import re import subprocess @@ -12,6 +13,7 @@ import cv2 import numpy as np +import pandas as pd import torch import torchvision import yaml @@ -23,6 +25,7 @@ # Settings torch.set_printoptions(linewidth=320, precision=5, profile='long') np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads @@ -46,40 +49,75 @@ def get_latest_run(search_dir='.'): return max(last_list, key=os.path.getctime) if last_list else '' +def isdocker(): + # Is environment a Docker container + return Path('/workspace').exists() # or Path('/.dockerenv').exists() + + +def emojis(str=''): + # Return platform-dependent emoji-safe version of string + return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + + def check_online(): # Check internet connectivity import socket try: - socket.create_connection(("1.1.1.1", 53)) # check host accesability + socket.create_connection(("1.1.1.1", 443), 5) # check host accesability return True except OSError: return False def check_git_status(): - # Suggest 'git pull' if YOLOv5 is out of date + # Recommend 'git pull' if code is out of date print(colorstr('github: '), end='') try: - if Path('.git').exists() and check_online(): - url = subprocess.check_output( - 'git fetch && git config --get remote.origin.url', shell=True).decode('utf-8')[:-1] - n = int(subprocess.check_output( - 'git rev-list $(git rev-parse --abbrev-ref HEAD)..origin/master --count', shell=True)) # commits behind - if n > 0: - print(f"⚠️ WARNING: code is out of date by {n} {'commits' if n > 1 else 'commmit'}. " - f"Use 'git pull' to update or 'git clone {url}' to download latest.") - else: - print(f'up to date with {url} ✅') + assert Path('.git').exists(), 'skipping check (not a git repository)' + assert not isdocker(), 'skipping check (Docker image)' + assert check_online(), 'skipping check (offline)' + + cmd = 'git fetch && git config --get remote.origin.url' + url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url + branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out + n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind + if n > 0: + s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ + f"Use 'git pull' to update or 'git clone {url}' to download latest." + else: + s = f'up to date with {url} ✅' + print(emojis(s)) # emoji-safe except Exception as e: print(e) -def check_requirements(file='requirements.txt'): - # Check installed dependencies meet requirements - import pkg_resources - requirements = pkg_resources.parse_requirements(Path(file).open()) - requirements = [x.name + ''.join(*x.specs) if len(x.specs) else x.name for x in requirements] - pkg_resources.require(requirements) # DistributionNotFound or VersionConflict exception if requirements not met +def check_requirements(requirements='requirements.txt', exclude=()): + # Check installed dependencies meet requirements (pass *.txt file or list of packages) + import pkg_resources as pkg + prefix = colorstr('red', 'bold', 'requirements:') + if isinstance(requirements, (str, Path)): # requirements.txt file + file = Path(requirements) + if not file.exists(): + print(f"{prefix} {file.resolve()} not found, check failed.") + return + requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] + else: # list or tuple of packages + requirements = [x for x in requirements if x not in exclude] + + n = 0 # number of packages updates + for r in requirements: + try: + pkg.require(r) + except Exception as e: # DistributionNotFound or VersionConflict if requirements not met + n += 1 + print(f"{prefix} {e.req} not found and is required by YOLOv3, attempting auto-update...") + print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode()) + + if n: # if packages updated + source = file.resolve() if 'file' in locals() else requirements + s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + print(emojis(s)) # emoji-safe def check_img_size(img_size, s=32): @@ -90,14 +128,28 @@ def check_img_size(img_size, s=32): return new_size +def check_imshow(): + # Check if environment supports image displays + try: + assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' + cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') + return False + + def check_file(file): # Search for file if not found - if os.path.isfile(file) or file == '': + if Path(file).is_file() or file == '': return file else: files = glob.glob('./**/' + file, recursive=True) # find file - assert len(files), 'File Not Found: %s' % file # assert file was found - assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (file, files) # assert unique + assert len(files), f'File Not Found: {file}' # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file @@ -220,6 +272,50 @@ def xywh2xyxy(x): return y +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * x[:, 0] + padw # top left x + y[:, 1] = h * x[:, 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + x, y, = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): # Rescale coords (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape @@ -244,7 +340,7 @@ def clip_coords(boxes, img_shape): boxes[:, 3].clamp_(0, img_shape[0]) # y2 -def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9): +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T @@ -280,7 +376,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps= elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) with torch.no_grad(): - alpha = v / ((1 + eps) - iou + v) + alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU else: # GIoU https://arxiv.org/pdf/1902.09630.pdf c_area = cw * ch + eps # convex area @@ -322,11 +418,12 @@ def wh_iou(wh1, wh2): return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) -def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): - """Performs Non-Maximum Suppression (NMS) on inference results +def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=()): + """Runs Non-Maximum Suppression (NMS) on inference results Returns: - detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ nc = prediction.shape[2] - 5 # number of classes @@ -338,7 +435,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 10.0 # seconds to quit after redundant = True # require redundant detections - multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() @@ -412,18 +509,20 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non return output -def strip_optimizer(f='weights/best.pt', s=''): # from utils.general import *; strip_optimizer() +def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() # Strip optimizer from 'f' to finalize training, optionally save as 's' x = torch.load(f, map_location=torch.device('cpu')) - for key in 'optimizer', 'training_results', 'wandb_id': - x[key] = None + if x.get('ema'): + x['model'] = x['ema'] # replace model with ema + for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys + x[k] = None x['epoch'] = -1 x['model'].half() # to FP16 for p in x['model'].parameters(): p.requires_grad = False torch.save(x, s or f) mb = os.path.getsize(s or f) / 1E6 # filesize - print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb)) + print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): diff --git a/utils/google_utils.py b/utils/google_utils.py index e4b115a599..61af2f4343 100644 --- a/utils/google_utils.py +++ b/utils/google_utils.py @@ -21,13 +21,13 @@ def attempt_download(file, repo='ultralytics/yolov3'): file = Path(str(file).strip().replace("'", '').lower()) if not file.exists(): - # try: - # response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api - # assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] - # tag = response['tag_name'] # i.e. 'v1.0' - # except: # fallback plan - assets = ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt'] - tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] + try: + response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api + assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] + tag = response['tag_name'] # i.e. 'v1.0' + except: # fallback plan + assets = ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt'] + tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] name = file.name if name in assets: diff --git a/utils/loss.py b/utils/loss.py index 844d503924..a2c5cce795 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -85,117 +85,132 @@ def forward(self, pred, true): return loss -def compute_loss(p, targets, model): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - tcls, tbox, indices, anchors = build_targets(p, targets, model) # targets - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) # weight=model.class_weights) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - cp, cn = smooth_BCE(eps=0.0) - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - # Losses - balance = [4.0, 1.0, 0.4, 0.1] # P3-P6 - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - if model.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], cn, device=device) # targets - t[range(n), tcls[i]] = cp - lcls += BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss - - lbox *= h['box'] - lobj *= h['obj'] - lcls *= h['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - -def build_targets(p, targets, model): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - na, nt = det.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - gain = torch.ones(7, device=targets.device) # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - # [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(det.nl): - anchors = det.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLoss, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=targets.device) # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + # [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j),)) + t = t.repeat((off.shape[0], 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j),)) - t = t.repeat((off.shape[0], 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - - return tcls, tbox, indices, anch + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch diff --git a/utils/metrics.py b/utils/metrics.py index 99d5bcfaf2..666b8c7ec1 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -15,7 +15,7 @@ def fitness(x): return (x[:, :4] * w).sum(1) -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]): +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): """ Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments @@ -35,12 +35,11 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision # Find unique classes unique_classes = np.unique(target_cls) + nc = unique_classes.shape[0] # number of classes, number of detections # Create Precision-Recall curve and compute AP for each class px, py = np.linspace(0, 1, 1000), [] # for plotting - pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898 - s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95) - ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s) + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) for ci, c in enumerate(unique_classes): i = pred_cls == c n_l = (target_cls == c).sum() # number of labels @@ -55,25 +54,28 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision # Recall recall = tpc / (n_l + 1e-16) # recall curve - r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases # Precision precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0]) # p at pr_score + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score # AP from recall-precision curve for j in range(tp.shape[1]): ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and (j == 0): + if plot and j == 0: py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - # Compute F1 score (harmonic mean of precision and recall) + # Compute F1 (harmonic mean of precision and recall) f1 = 2 * p * r / (p + r + 1e-16) - if plot: - plot_pr_curve(px, py, ap, save_dir, names) + plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) + plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') + plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') + plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') - return p, r, ap, f1, unique_classes.astype('int32') + i = f1.mean(0).argmax() # max F1 index + return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') def compute_ap(recall, precision): @@ -145,12 +147,12 @@ def process_batch(self, detections, labels): if n and sum(j) == 1: self.matrix[gc, detection_classes[m1[j]]] += 1 # correct else: - self.matrix[gc, self.nc] += 1 # background FP + self.matrix[self.nc, gc] += 1 # background FP if n: for i, dc in enumerate(detection_classes): if not any(m1 == i): - self.matrix[self.nc, dc] += 1 # background FN + self.matrix[dc, self.nc] += 1 # background FN def matrix(self): return self.matrix @@ -166,8 +168,8 @@ def plot(self, save_dir='', names=()): sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, - xticklabels=names + ['background FN'] if labels else "auto", - yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1)) + xticklabels=names + ['background FP'] if labels else "auto", + yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) fig.axes[0].set_xlabel('True') fig.axes[0].set_ylabel('Predicted') fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) @@ -181,13 +183,14 @@ def print(self): # Plots ---------------------------------------------------------------------------------------------------------------- -def plot_pr_curve(px, py, ap, save_dir='.', names=()): +def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): + # Precision-recall curve fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) py = np.stack(py, axis=1) - if 0 < len(names) < 21: # show mAP in legend if < 10 classes + if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0]) # plot(recall, precision) + ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) else: ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) @@ -197,4 +200,24 @@ def plot_pr_curve(px, py, ap, save_dir='.', names=()): ax.set_xlim(0, 1) ax.set_ylim(0, 1) plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250) + fig.savefig(Path(save_dir), dpi=250) + + +def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + + y = py.mean(0) + ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) diff --git a/utils/plots.py b/utils/plots.py index 47cd707760..5b23a34f51 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -15,7 +15,7 @@ import seaborn as sns import torch import yaml -from PIL import Image, ImageDraw +from PIL import Image, ImageDraw, ImageFont from scipy.signal import butter, filtfilt from utils.general import xywh2xyxy, xyxy2xywh @@ -31,7 +31,7 @@ def color_list(): def hex2rgb(h): return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) - return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']] + return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()] # or BASE_ (8), CSS4_ (148), XKCD_ (949) def hist2d(x, y, n=100): @@ -54,7 +54,7 @@ def butter_lowpass(cutoff, fs, order): return filtfilt(b, a, data) # forward-backward filter -def plot_one_box(x, img, color=None, label=None, line_thickness=None): +def plot_one_box(x, img, color=None, label=None, line_thickness=3): # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] @@ -68,6 +68,20 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None): cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) +def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None): + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + line_thickness = line_thickness or max(int(min(img.size) / 200), 2) + draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot + if label: + fontsize = max(round(max(img.size) / 40), 12) + font = ImageFont.truetype("Arial.ttf", fontsize) + txt_width, txt_height = font.getsize(label) + draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) + draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) + return np.asarray(img) + + def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() # Compares the two methods for width-height anchor multiplication # https://github.com/ultralytics/yolov3/issues/168 @@ -223,38 +237,39 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() plt.savefig('targets.jpg', dpi=200) -def plot_study_txt(path='study/', x=None): # from utils.plots import *; plot_study_txt() +def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() # Plot study.txt generated by test.py fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) - ax = ax.ravel() + # ax = ax.ravel() fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]: + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + for f in sorted(Path(path).glob('study*.txt')): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] - for i in range(7): - ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) - ax[i].set_title(s[i]) + # for i in range(7): + # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + # ax[i].set_title(s[i]) j = y[3].argmax() + 1 - ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, + ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') - ax2.grid() - ax2.set_yticks(np.arange(30, 60, 5)) - ax2.set_xlim(0, 30) - ax2.set_ylim(29, 51) + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(30, 55) ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_ylabel('COCO AP val') ax2.legend(loc='lower right') - plt.savefig('test_study.png', dpi=300) + plt.savefig(str(Path(path).name) + '.png', dpi=300) -def plot_labels(labels, save_dir=Path(''), loggers=None): +def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): # plot dataset labels print('Plotting labels... ') c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes @@ -271,7 +286,12 @@ def plot_labels(labels, save_dir=Path(''), loggers=None): matplotlib.use('svg') # faster ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) - ax[0].set_xlabel('classes') + ax[0].set_ylabel('instances') + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(names, rotation=90, fontsize=10) + else: + ax[0].set_xlabel('classes') sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) @@ -295,13 +315,13 @@ def plot_labels(labels, save_dir=Path(''), loggers=None): # loggers for k, v in loggers.items() or {}: if k == 'wandb' and v: - v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}) + v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() # Plot hyperparameter evolution results in evolve.txt with open(yaml_file) as f: - hyp = yaml.load(f, Loader=yaml.FullLoader) + hyp = yaml.load(f, Loader=yaml.SafeLoader) x = np.loadtxt('evolve.txt', ndmin=2) f = fitness(x) # weights = (f - f.min()) ** 2 # for weighted results diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 231dcfd7a5..6535b2ab1b 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -1,8 +1,10 @@ -# PyTorch utils +# YOLOv3 PyTorch utils +import datetime import logging import math import os +import platform import subprocess import time from contextlib import contextmanager @@ -43,17 +45,24 @@ def init_torch_seeds(seed=0): cudnn.benchmark, cudnn.deterministic = True, False -def git_describe(): +def date_modified(path=__file__): + # return human-readable file modification date, i.e. '2021-3-26' + t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) + return f'{t.year}-{t.month}-{t.day}' + + +def git_describe(path=Path(__file__).parent): # path must be a directory # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe - if Path('.git').exists(): - return subprocess.check_output('git describe --tags --long --always', shell=True).decode('utf-8')[:-1] - else: - return '' + s = f'git -C {path} describe --tags --long --always' + try: + return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] + except subprocess.CalledProcessError as e: + return '' # not a git repository def select_device(device='', batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' - s = f'YOLOv3 {git_describe()} torch {torch.__version__} ' # string + s = f'YOLOv3 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string cpu = device.lower() == 'cpu' if cpu: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False @@ -73,7 +82,7 @@ def select_device(device='', batch_size=None): else: s += 'CPU\n' - logger.info(s) # skip a line + logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe return torch.device('cuda:0' if cuda else 'cpu') @@ -120,7 +129,7 @@ def profile(x, ops, n=100, device=None): s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12.4g}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') + print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') def is_parallel(model): @@ -182,7 +191,7 @@ def fuse_conv_and_bn(conv, bn): # prepare filters w_conv = conv.weight.clone().view(conv.out_channels, -1) w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) - fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size())) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) # prepare spatial bias b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias @@ -205,7 +214,7 @@ def model_info(model, verbose=False, img_size=640): try: # FLOPS from thop import profile - stride = int(model.stride.max()) if hasattr(model, 'stride') else 32 + stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float diff --git a/utils/wandb_logging/__init__.py b/utils/wandb_logging/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/utils/wandb_logging/log_dataset.py b/utils/wandb_logging/log_dataset.py new file mode 100644 index 0000000000..d7a521f141 --- /dev/null +++ b/utils/wandb_logging/log_dataset.py @@ -0,0 +1,24 @@ +import argparse + +import yaml + +from wandb_utils import WandbLogger + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') + parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') + opt = parser.parse_args() + opt.resume = False # Explicitly disallow resume check for dataset upload job + + create_dataset_artifact(opt) diff --git a/utils/wandb_logging/wandb_utils.py b/utils/wandb_logging/wandb_utils.py new file mode 100644 index 0000000000..d8f50ae8a8 --- /dev/null +++ b/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,306 @@ +import json +import sys +from pathlib import Path + +import torch +import yaml +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path +from utils.datasets import LoadImagesAndLabels +from utils.datasets import img2label_paths +from utils.general import colorstr, xywh2xyxy, check_dataset + +try: + import wandb + from wandb import init, finish +except ImportError: + wandb = None + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): + return from_string[len(prefix):] + + +def check_wandb_config_file(data_config_file): + wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path + if Path(wandb_config).is_file(): + return wandb_config + return data_config_file + + +def get_run_info(run_path): + run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) + run_id = run_path.stem + project = run_path.parent.stem + model_artifact_name = 'run_' + run_id + '_model' + return run_id, project, model_artifact_name + + +def check_wandb_resume(opt): + process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None + if isinstance(opt.resume, str): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + if opt.global_rank not in [-1, 0]: # For resuming DDP runs + run_id, project, model_artifact_name = get_run_info(opt.resume) + api = wandb.Api() + artifact = api.artifact(project + '/' + model_artifact_name + ':latest') + modeldir = artifact.download() + opt.weights = str(Path(modeldir) / "last.pt") + return True + return None + + +def process_wandb_config_ddp_mode(opt): + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + train_dir, val_dir = None, None + if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) + train_dir = train_artifact.download() + train_path = Path(train_dir) / 'data/images/' + data_dict['train'] = str(train_path) + + if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) + val_dir = val_artifact.download() + val_path = Path(val_dir) / 'data/images/' + data_dict['val'] = str(val_path) + if train_dir or val_dir: + ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') + with open(ddp_data_path, 'w') as f: + yaml.dump(data_dict, f) + opt.data = ddp_data_path + + +class WandbLogger(): + def __init__(self, opt, name, run_id, data_dict, job_type='Training'): + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict + # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + if isinstance(opt.resume, str): # checks resume from artifact + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + run_id, project, model_artifact_name = get_run_info(opt.resume) + model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name + assert wandb, 'install wandb to resume wandb runs' + # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config + self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') + opt.resume = model_artifact_name + elif self.wandb: + self.wandb_run = wandb.init(config=opt, + resume="allow", + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + name=name, + job_type=job_type, + id=run_id) if not wandb.run else wandb.run + if self.wandb_run: + if self.job_type == 'Training': + if not opt.resume: + wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict + # Info useful for resuming from artifacts + self.wandb_run.config.opt = vars(opt) + self.wandb_run.config.data_dict = wandb_data_dict + self.data_dict = self.setup_training(opt, data_dict) + if self.job_type == 'Dataset Creation': + self.data_dict = self.check_and_upload_dataset(opt) + else: + prefix = colorstr('wandb: ') + print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") + + def check_and_upload_dataset(self, opt): + assert wandb, 'Install wandb to upload dataset' + check_dataset(self.data_dict) + config_path = self.log_dataset_artifact(opt.data, + opt.single_cls, + 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) + print("Created dataset config file ", config_path) + with open(config_path) as f: + wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) + return wandb_data_dict + + def setup_training(self, opt, data_dict): + self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + modeldir, _ = self.download_model_artifact(opt) + if modeldir: + self.weights = Path(modeldir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( + self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ + config.opt['hyp'] + data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume + if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download + self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), + opt.artifact_alias) + self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), + opt.artifact_alias) + self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None + if self.train_artifact_path is not None: + train_path = Path(self.train_artifact_path) / 'data/images/' + data_dict['train'] = str(train_path) + if self.val_artifact_path is not None: + val_path = Path(self.val_artifact_path) / 'data/images/' + data_dict['val'] = str(val_path) + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + if self.val_artifact is not None: + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + return data_dict + + def download_dataset_artifact(self, path, alias): + if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): + dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) + assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" + datadir = dataset_artifact.download() + return datadir, dataset_artifact + return None, None + + def download_model_artifact(self, opt): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") + assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' + modeldir = model_artifact.download() + epochs_trained = model_artifact.metadata.get('epochs_trained') + total_epochs = model_artifact.metadata.get('total_epochs') + assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( + total_epochs) + return modeldir, model_artifact + return None, None + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score + }) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + print("Saving model artifact on epoch ", epoch + 1) + + def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): + with open(data_file) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) + names = {k: v for k, v in enumerate(names)} # to index dictionary + self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['train']), names, name='train') if data.get('train') else None + self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['val']), names, name='val') if data.get('val') else None + if data.get('train'): + data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') + if data.get('val'): + data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') + path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path + data.pop('download', None) + with open(path, 'w') as f: + yaml.dump(data, f) + + if self.job_type == 'Training': # builds correct artifact pipeline graph + self.wandb_run.use_artifact(self.val_artifact) + self.wandb_run.use_artifact(self.train_artifact) + self.val_artifact.wait() + self.val_table = self.val_artifact.get('val') + self.map_val_table_path() + else: + self.wandb_run.log_artifact(self.train_artifact) + self.wandb_run.log_artifact(self.val_artifact) + return path + + def map_val_table_path(self): + self.val_table_map = {} + print("Mapping dataset") + for i, data in enumerate(tqdm(self.val_table.data)): + self.val_table_map[data[3]] = data[0] + + def create_dataset_table(self, dataset, class_to_id, name='dataset'): + # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging + artifact = wandb.Artifact(name=name, type="dataset") + img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None + img_files = tqdm(dataset.img_files) if not img_files else img_files + for img_file in img_files: + if Path(img_file).is_dir(): + artifact.add_dir(img_file, name='data/images') + labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) + artifact.add_dir(labels_path, name='data/labels') + else: + artifact.add_file(img_file, name='data/images/' + Path(img_file).name) + label_file = Path(img2label_paths([img_file])[0]) + artifact.add_file(str(label_file), + name='data/labels/' + label_file.name) if label_file.exists() else None + table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) + for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): + height, width = shapes[0] + labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) + box_data, img_classes = [], {} + for cls, *xyxy in labels[:, 1:].tolist(): + cls = int(cls) + box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": cls, + "box_caption": "%s" % (class_to_id[cls]), + "scores": {"acc": 1}, + "domain": "pixel"}) + img_classes[cls] = class_to_id[cls] + boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space + table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), + Path(paths).name) + artifact.add(table, name) + return artifact + + def log_training_progress(self, predn, path, names): + if self.val_table and self.result_table: + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) + box_data = [] + total_conf = 0 + for *xyxy, conf, cls in predn.tolist(): + if conf >= 0.25: + box_data.append( + {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"}) + total_conf = total_conf + conf + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + id = self.val_table_map[Path(path).name] + self.result_table.add_data(self.current_epoch, + id, + wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), + total_conf / max(1, len(box_data)) + ) + + def log(self, log_dict): + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self, best_result=False): + if self.wandb_run: + wandb.log(self.log_dict) + self.log_dict = {} + if self.result_artifact: + train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") + self.result_artifact.add(train_results, 'result') + wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), + ('best' if best_result else '')]) + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + + def finish_run(self): + if self.wandb_run: + if self.log_dict: + wandb.log(self.log_dict) + wandb.run.finish() From 8d0f01379a5a226491cbab1526849f4405204870 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 12 Apr 2021 15:25:14 +0200 Subject: [PATCH 2/6] Update README --- README.md | 113 ++++++++++++++++++++++++++++++------------------------ 1 file changed, 63 insertions(+), 50 deletions(-) diff --git a/README.md b/README.md index 6e2d32161f..2c0aa36da8 100755 --- a/README.md +++ b/README.md @@ -1,35 +1,60 @@ - - + +   CI CPU testing -BRANCH NOTICE: The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches: -* [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (**recommended**). +This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. + +

+
+ YOLOv5-P5 640 Figure (click to expand) + +

+
+
+ Figure Notes (click to expand) + + * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. + * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. + * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov3.pt yolov3-spp.pt yolov3-tiny.pt yolov5l.pt` +
+ + +## Branch Notice + +The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches: +* [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (**recommended** ✅). ```bash $ git clone https://github.com/ultralytics/yolov3 # master branch (default) ``` -* [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (⚠️ no longer maintained). +* [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (**no longer maintained** ⚠️). ```bash $ git clone https://github.com/ultralytics/yolov3 -b archive # archive branch ``` -** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. +## Pretrained Checkpoints + +[assets]: https://github.com/ultralytics/yolov3/releases +Model |size
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPS
640 (B) +--- |--- |--- |--- |--- |--- |---|--- |--- +[YOLOv3-tiny][assets]|640 |17.6 |17.6 |34.9 |**1.7** | |8.9 |13.3 +[YOLOv3][assets] |640 |43.3 |43.3 |63.0 |4.8 | |61.9 |156.4 +[YOLOv3-SPP][assets] |640 |44.3 |44.3 |64.6 |4.9 | |63.0 |157.0 +| | | | | | || | +[YOLOv5l][assets] |640 |**48.2** |**48.2** |**66.9** |3.8 | |47.0 |115.4 -## Pretrained Checkpoints -| Model | APval | APtest | AP50 | SpeedGPU | FPSGPU || params | FLOPS | -|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | -| [YOLOv3](https://github.com/ultralytics/yolov3/releases) | 43.3 | 43.3 | 63.0 | 4.8ms | 208 || 61.9M | 156.4B -| [YOLOv3-SPP](https://github.com/ultralytics/yolov3/releases) | **44.3** | **44.3** | **64.6** | 4.9ms | 204 || 63.0M | 157.0B -| [YOLOv3-tiny](https://github.com/ultralytics/yolov3/releases) | 17.6 | 34.9 | 34.9 | **1.7ms** | **588** || 8.9M | 13.3B +
+ Table Notes (click to expand) + + * APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. + * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` + * SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` + * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). +
-** APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. -** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` -** SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` -** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). -** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` ## Requirements @@ -42,7 +67,9 @@ $ pip install -r requirements.txt ## Tutorials * [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)  🚀 RECOMMENDED +* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ RECOMMENDED * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW +* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518)  🌟 NEW * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ NEW * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251) @@ -58,73 +85,59 @@ $ pip install -r requirements.txt YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): -- **Google Colab Notebook** with free GPU: Open In Colab -- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3) -- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) -- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker) +- **Google Colab and Kaggle** notebooks with free GPU: Open In Colab Open In Kaggle +- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) +- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart) +- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) Docker Pulls ## Inference -detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases) and saving results to `runs/detect`. +`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases) and saving results to `runs/detect`. ```bash $ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob - rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream - rtmp://192.168.1.105/live/test # rtmp stream - http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream + 'https://youtu.be/NUsoVlDFqZg' # YouTube video + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` To run inference on example images in `data/images`: ```bash $ python detect.py --source data/images --weights yolov3.pt --conf 0.25 - -Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov3.pt']) -Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB) - -Downloading https://github.com/ultralytics/yolov3/releases/download/v1.0/yolov3.pt to yolov3.pt... 100% 118M/118M [00:05<00:00, 24.2MB/s] - -Fusing layers... -Model Summary: 261 layers, 61922845 parameters, 0 gradients -image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 buss, Done. (0.014s) -image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.014s) -Results saved to runs/detect/exp -Done. (0.133s) ``` - + ### PyTorch Hub -To run **batched inference** with YOLO3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): +To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): ```python import torch -from PIL import Image # Model -model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape() # for PIL/cv2/np inputs and NMS +model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or 'yolov3_spp', 'yolov3_tiny' # Images -img1 = Image.open('zidane.jpg') -img2 = Image.open('bus.jpg') -imgs = [img1, img2] # batched list of images +dir = 'https://github.com/ultralytics/yolov3/raw/master/data/images/' +imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images # Inference -prediction = model(imgs, size=640) # includes NMS +results = model(imgs) +results.print() # or .show(), .save() ``` ## Training -Download [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) and run command below. Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). +Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). ```bash -$ python train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24 - yolov3-spp.yaml 24 - yolov3-tiny.yaml 64 +$ python train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24 + yolov3-spp.yaml 24 + yolov3-tiny.yaml 64 ``` - + ## Citation From 6d49c87fe320c5344ecd0b63209a00a08ff2e5e8 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 12 Apr 2021 15:56:46 +0200 Subject: [PATCH 3/6] Update README --- README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 2c0aa36da8..fde0f1b7dd 100755 --- a/README.md +++ b/README.md @@ -35,15 +35,16 @@ $ git clone https://github.com/ultralytics/yolov3 -b archive # archive branch ## Pretrained Checkpoints -[assets]: https://github.com/ultralytics/yolov3/releases +[assets3]: https://github.com/ultralytics/yolov3/releases +[assets5]: https://github.com/ultralytics/yolov5/releases Model |size
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPS
640 (B) ---- |--- |--- |--- |--- |--- |---|--- |--- -[YOLOv3-tiny][assets]|640 |17.6 |17.6 |34.9 |**1.7** | |8.9 |13.3 -[YOLOv3][assets] |640 |43.3 |43.3 |63.0 |4.8 | |61.9 |156.4 -[YOLOv3-SPP][assets] |640 |44.3 |44.3 |64.6 |4.9 | |63.0 |157.0 +--- |--- |--- |--- |--- |--- |---|--- |--- +[YOLOv3-tiny][assets3] |640 |17.6 |17.6 |34.8 |**1.7** | |8.8 |13.2 +[YOLOv3][assets3] |640 |43.3 |43.3 |63.0 |4.8 | |61.9 |156.3 +[YOLOv3-SPP][assets3] |640 |44.3 |44.3 |64.6 |4.9 | |63.0 |157.1 | | | | | | || | -[YOLOv5l][assets] |640 |**48.2** |**48.2** |**66.9** |3.8 | |47.0 |115.4 +[YOLOv5l][assets5] |640 |**48.2** |**48.2** |**66.9** |3.8 | |47.0 |115.4
From 2958149707d0b36ee0cb5b0fbc6f51c4aaeb65bd Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 12 Apr 2021 15:59:10 +0200 Subject: [PATCH 4/6] Conv act LeakyReLU(0.1) --- models/common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/common.py b/models/common.py index db62e1dfa1..9496b1b493 100644 --- a/models/common.py +++ b/models/common.py @@ -36,7 +36,7 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, k super(Conv, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + self.act = nn.LeakyReLU(0.1) if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) From bb0803ac4c6c7e8581c25700633bffa2ea5adf58 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 12 Apr 2021 16:47:45 +0200 Subject: [PATCH 5/6] update plots_study() --- utils/plots.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/utils/plots.py b/utils/plots.py index 5b23a34f51..8b90bd8d07 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -243,7 +243,7 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx # ax = ax.ravel() fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov3-tiny', 'yolov3', 'yolov3-spp', 'yolov5l']]: for f in sorted(Path(path).glob('study*.txt')): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) @@ -262,7 +262,7 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx ax2.grid(alpha=0.2) ax2.set_yticks(np.arange(20, 60, 5)) ax2.set_xlim(0, 57) - ax2.set_ylim(30, 55) + ax2.set_ylim(15, 55) ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_ylabel('COCO AP val') ax2.legend(loc='lower right') From a6a2581319f33d5342b8eed6815dd3d890e216e7 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 12 Apr 2021 17:59:41 +0200 Subject: [PATCH 6/6] update speeds --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index fde0f1b7dd..7a3c0e87b2 100755 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. -

+

YOLOv5-P5 640 Figure (click to expand) @@ -40,11 +40,11 @@ $ git clone https://github.com/ultralytics/yolov3 -b archive # archive branch Model |size
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPS
640 (B) --- |--- |--- |--- |--- |--- |---|--- |--- -[YOLOv3-tiny][assets3] |640 |17.6 |17.6 |34.8 |**1.7** | |8.8 |13.2 -[YOLOv3][assets3] |640 |43.3 |43.3 |63.0 |4.8 | |61.9 |156.3 -[YOLOv3-SPP][assets3] |640 |44.3 |44.3 |64.6 |4.9 | |63.0 |157.1 +[YOLOv3-tiny][assets3] |640 |17.6 |17.6 |34.8 |**1.2** | |8.8 |13.2 +[YOLOv3][assets3] |640 |43.3 |43.3 |63.0 |4.1 | |61.9 |156.3 +[YOLOv3-SPP][assets3] |640 |44.3 |44.3 |64.6 |4.1 | |63.0 |157.1 | | | | | | || | -[YOLOv5l][assets5] |640 |**48.2** |**48.2** |**66.9** |3.8 | |47.0 |115.4 +[YOLOv5l][assets5] |640 |**48.2** |**48.2** |**66.9** |3.7 | |47.0 |115.4