diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..7cc094d --- /dev/null +++ b/.gitattributes @@ -0,0 +1,4 @@ +* +ipynb filter=clean_ipynb +client/* linguist-vendored +client_dist/* linguist-vendored diff --git a/.gitignore b/.gitignore index b6e4761..eca48af 100644 --- a/.gitignore +++ b/.gitignore @@ -2,128 +2,23 @@ __pycache__/ *.py[cod] *$py.class +.ipynb_checkpoints/ +*.swp +*.swo +debug* + +training-runs +pretrained +web/ + +*.npz +*.mp4 +*.pdf +*.png +*.csv +*.zip +*.nfs* +*.m4v +*.pkl -# C extensions -*.so -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ diff --git a/LICENSE b/LICENSE deleted file mode 100644 index aeef7bc..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 Lucy Chai - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER +THIS LICENSE. + +5. Limitation of Liability. + +EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL +THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE +SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, +INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF +OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK +(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, +LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER +COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF +THE POSSIBILITY OF SUCH DAMAGES. + +======================================================================= diff --git a/README.md b/README.md index f673881..b3c62fa 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,154 @@ -# Anyres-GAN +# +Anyres-GAN [Project Page](https://chail.github.io/anyres-gan/) | [Paper](https://arxiv.org/abs/2204.07156) | [Bibtex](https://chail.github.io/anyres-gan/bibtex.txt) Any-resolution Training for High-resolution Image Synthesis.\ [Lucy Chai](http://people.csail.mit.edu/lrchai/), [MichaĂ«l Gharbi](http://mgharbi.com/), [Eli Shechtman](https://research.adobe.com/person/eli-shechtman/), [Phillip Isola](http://web.mit.edu/phillipi/), [Richard Zhang](https://richzhang.github.io/) -*Code will be coming soon!* +## Prerequisites +- Linux +- gcc-7 +- Python 3 +- NVIDIA GPU + CUDA CuDNN + +**Table of Contents:**
+1. [Colab](#colab) - run it in your browser without installing anything locally
+2. [Setup](#setup) - download pretrained models and resources +3. [Pretrained Models](#pretrained) - quickstart with pretrained models
+3. [Notebooks](#notebooks) - jupyter notebooks for interactive composition
+4. [Training](#training) - pipeline for training encoders
+5. [Evaluation](#evaluation) - evaluation script
+ - - + + + +## Colab + +[Interactive Demo](TODO): Try our interactive demo here! Does not require local installations. + + + +## Setup + +- Clone this repo: +```bash +git clone https://github.com/chail/anyres-gan.git +``` + +- Install dependencies: + - gcc-7 or above is required for installation. Update gcc following [these steps](https://gist.github.com/jlblancoc/99521194aba975286c80f93e47966dc5). + - We provide a Conda `environment.yml` file listing the dependencies. You can create a Conda environment with the dependencies using: +```bash +conda env create -f environment.yml +``` + +- Download resources: we provide a script for downloading associated resources and pretrained models. Fetch these by running: +```bash +bash download_resources.sh +``` + + + +## Quickstart with pretrained models + +Pretrained models are downloaded from the above `download_resources.sh` script. Any-resolution images can be constructed by specifying the appropriate transformation matrices. The following code snippet provides a basic example; additional examples can be found in the notebook. + +```python +import pickle +import torch +import numpy as np +from util import patch_util, renormalize +torch.set_grad_enabled(False) + +PATH = 'pretrained/bird_pretrained_final.pkl' + +with open(PATH, 'rb') as f: + G_base = pickle.load(f)['G_ema'].cuda() # torch.nn.Module + +full_size = 500 +seed = 0 + +rng = np.random.RandomState(seed) +z = torch.from_numpy(rng.standard_normal(G_base.z_dim)).float() +z = z[None].cuda() +c = None + +ws = G_base.mapping(z, c, truncation_psi=0.5, truncation_cutoff=8) +full = torch.zeros([1, 3, full_size, full_size]) +patches = patch_util.generate_full_from_patches(full_size, G_base.img_resolution) +for bbox, transform in patches: + img = patch_util.scale_condition_wrapper(G_base, ws, transform[None].cuda(), noise_mode='const', force_fp32=True) + full[:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]] = img +renormalize.as_image(full[0]) +``` + + + +## Notebooks + +Note: remember to add the conda environment to jupyter kernels: +```bash +python -m ipykernel install --user --name anyres-gan +``` + +We provide example notebook `notebook-demo.ipynb` for running inference on pretrained models. + + + +## Training + +See the script `train.sh` for training examples. + +Training notes: +- patch-based training is run in two stages: first global fixed-resolution pretraining, then patch training +- arguments `--batch-gpu` and `--gamma` are taken from Stylegan 3 [recommended configurations](https://github.com/NVlabs/stylegan3/blob/main/docs/configs.md#recommended-configurations) +- arguments `--random_crop=True` and `--patch_crop=True` performs random cropping on fixed-resolution and variable resolution datasets respectively. +- `--scale_max` and `--scale_min` correspond to the largest and smallest sampled image scales for patch training (size = 1/scale * g_size). `--scale_max` should correspond to the smallest image size in the patch dataset (for example, if the smallest image is 512px and the generator size is 256, then `--scale_max=0.5`). Omitting `--scale_min` will use the smallest possible scale as the minimum bound (the image native size). +- `--scale_mapping_min` and `--scale_mapping_max` correspond to normalization limits in the scale mapping branch; the min can be kept at 1 and the max can be set to an approximate zoom factor between the fixed-resolution dataset and the size of the HR images. +- for patch training, metrics are evaluated offline, hence `--metrics=none` should be specified for training. See below for more details on evaluation. + + +Training progress can be visualized using: +```bash +tensorboard --logdir training-runs/ +``` + +Note on datasets: beyond the standard FFHQ and LSUN Church datasets, we train on datasets scraped from flickr. Due to licensing we cannot release this images directly but can provide the image IDs used to construct the datasets. + + + +## Evaluations + +See `custom_metrics.sh` for an example on running FID variations and pFID on the patch models. +- pFID can be specified using a string such as `fid-patch256-min256max0`: this samples 50k patches of size 256, with minimum image size 256 and maximum image size as the max size allowable by a given real image. +- The max sampled size could also be specified with a number; for example `fid-patch256-min256max1024`. +- For larger models (e.g. mountains), FID by default downsamples images to 299 width; therefore we use a variant that further takes a crop of the image: `fid-subpatch1024-min1024max0`. +- Note that _these metrics are implemented to run on a single gpu._ + +Note: the released pretrained models are reimplementations of the models used in the current paper version, so the evaluation numbers are slightly different. + + +### Acknowledgements + +Our code is largely based on the [Stylegan3](https://github.com/NVlabs/stylegan3) repository ([license](./LICENSE_stylegan.txt)). Changes to the StyleGAN3 code are documented in [diff](./diff.txt). Some additional utilities are from David Bau and Taesung Park. Remaining changes are covered under [Adobe Research License](./LICENSE.txt). + + + +### Citation +If you use this code for your research, please cite our paper: +``` +@article{chai2022anyresolution, + title={Any-resolution training for high-resolution image synthesis.}, + author={Chai, Lucy and Gharbi, Michael and Shechtman, Eli and Isola, Phillip and Zhang, Richard}, + journal={arXiv preprint arXiv:2204.07156}, + year={2022} +} +``` +

+ + +

diff --git a/bibtex.txt b/bibtex.txt new file mode 100644 index 0000000..09b5b9f --- /dev/null +++ b/bibtex.txt @@ -0,0 +1,6 @@ +@article{chai2022anyresolution, + title={Any-resolution training for high-resolution image synthesis.}, + author={Chai, Lucy and Gharbi, Michael and Shechtman, Eli and Isola, Phillip and Zhang, Richard}, + journal={arXiv preprint arXiv:2204.07156}, + year={2022} +} diff --git a/custom_metrics.py b/custom_metrics.py new file mode 100644 index 0000000..f971359 --- /dev/null +++ b/custom_metrics.py @@ -0,0 +1,173 @@ +import os +import argparse +from util import pidfile, util +import pickle +from metrics import metric_utils +from metrics import frechet_inception_distance +import json +import dnnlib +import numpy as np +import tempfile +import torch +from torch_utils.ops import conv2d_gradfix + +def save_metric(output_folder, metric, value): + np.savez(os.path.join(output_folder, metric + '.npz'), value=value) + with open(os.path.join(output_folder, metric + '.txt'), 'w') as f: + f.write('%f\n' % value) + print("%s: %f" % (metric, value)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Process some integers.') + parser.add_argument('--input', type=str, required=True, help='G pkl') + parser.add_argument('--metrics', type=str, required=True, help='which metrics to calculate') + parser.add_argument('--add_transform', action='store_true', + help='add transformation input to the model ' + '(useful for models downloaded from original sgan3 repo') + parser.add_argument('--training_options', type=str) + + args = parser.parse_args() + metrics_list = args.metrics.split(',') + output_folder = args.input.replace('.pkl', '_metrics') + os.makedirs(output_folder, exist_ok=True) + pidfile.exit_if_job_done(output_folder, redo=True) # exits if the job is currently locked + + # check which evaluations have not been computed + prev_expts = set(os.listdir(output_folder)) + if all([m + '.npz' in prev_expts for m in metrics_list]): + print("All metrics computed!") + exit() + new_expts = [m for m in metrics_list if m + '.npz' not in prev_expts] + print("Running new metrics") + print(new_expts) + assert(len(set(new_expts)) == len(new_expts)) # check no duplicates + + # required for multiple workers loading from same dataset.zip file + torch.multiprocessing.set_start_method('spawn') + + device = torch.device('cuda', 0) + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + conv2d_gradfix.enabled = True + + with open(args.input, 'rb') as f: + G_ema = pickle.load(f)['G_ema'] + + if args.add_transform: + # map G_ema state_dict to updated code that allows insertable transform input + import copy + from training import networks_stylegan3 + G_copy = networks_stylegan3.Generator(**G_ema.init_kwargs) + G_base_sd = G_ema.state_dict() + G_copy.load_state_dict(G_base_sd) + G_ema = G_copy + assert(args.training_options is not None) + with open(args.training_options) as f: + training_options = json.load(f) + elif args.training_options is not None: + print("Using specified training options") + with open(args.training_options) as f: + training_options = json.load(f) + else: + # use the training_options.json file saved + # in the snapshot directory + with open(os.path.join(os.path.dirname(args.input), 'training_options.json')) as f: + training_options = json.load(f) + + for metric in new_expts: + if metric.startswith('fid-'): + if metric.startswith('fid-full'): + splits = metric.split('-') # fid-full1024 + target_resolution = int(util.remove_prefix(splits[1], 'full')) + print("Computing metric %s at resolution %d" % (metric, target_resolution)) + dataset_kwargs = dnnlib.EasyDict(training_options['training_set_kwargs']) + if dataset_kwargs.resolution < target_resolution: + # evaluate at a higher resolution using smaller HR + # dataset, resizing everything to the same size + dataset_kwargs.resolution = target_resolution + dataset_kwargs.max_size = training_options['patch_kwargs']['max_size'] + dataset_kwargs.path = training_options['patch_kwargs']['path'] + dataset_kwargs.crop_image = True + print(dataset_kwargs) + opts = metric_utils.MetricOptions(G=G_ema, dataset_kwargs=dataset_kwargs, + num_gpus=1, rank=0) + mode = 'full' + fid = frechet_inception_distance.compute_fid_full( + opts, target_resolution, None, 50000, mode=mode) + save_metric(output_folder, metric, fid) + elif metric.startswith('fid-up'): # fid-up1024 + target_resolution = int(util.remove_prefix(metric, 'fid-up')) + print("Computing metric %s at resolution %d" % (metric, target_resolution)) + dataset_kwargs = dnnlib.EasyDict(training_options['training_set_kwargs']) + if dataset_kwargs.resolution < target_resolution: + # evaluate at a higher resolution using smaller HR + # dataset, resizing everything to the same size + dataset_kwargs.resolution = target_resolution + dataset_kwargs.max_size = training_options['patch_kwargs']['max_size'] + dataset_kwargs.path = training_options['patch_kwargs']['path'] + dataset_kwargs.crop_image = True + print(dataset_kwargs) + opts = metric_utils.MetricOptions(G=G_ema, dataset_kwargs=dataset_kwargs, + num_gpus=1, rank=0) + fid = frechet_inception_distance.compute_fid_full( + opts, target_resolution, None, 50000, mode='up') + save_metric(output_folder, metric, fid) + elif metric.startswith('fid-patch'): # fid-patch256-minXmaxY + patch_options = training_options + dataset_kwargs = dnnlib.EasyDict(patch_options['patch_kwargs']) + patch_size = int(util.remove_prefix(metric.split('-')[1], 'patch')) + assert(patch_size == dataset_kwargs.resolution) + size_min = int(util.remove_prefix(metric.split('-')[2].split('max')[0], 'min')) + size_max = int(metric.split('-')[2].split('max')[1]) + scale_min = patch_size / size_max if size_max > 0 else None + scale_max = patch_size / size_min + # adjust dataset kwargs using desired scale min and max + dataset_kwargs.scale_min = scale_min + dataset_kwargs.scale_max = scale_max + dataset_kwargs.scale_anneal = -1 + print("Computing metric %s" % (metric)) + print("Patch size %d, size_min: %d size_max: %d scale_min: %s scale_max %f" + % (patch_size, size_min, size_max, str(scale_min), scale_max)) + print(dataset_kwargs) + target_resolution = G_ema.init_kwargs.img_resolution + opts = metric_utils.MetricOptions(G=G_ema, dataset_kwargs=dataset_kwargs, + num_gpus=1, rank=0) + splits = metric.split('-') + mode = 'patch' + print("FID mode: %s" % mode) + fid = frechet_inception_distance.compute_fid_patch( + opts, target_resolution, 50000, 50000, mode=mode) + np.savez(os.path.join(output_folder, metric + '.npz'), value=fid) + save_metric(output_folder, metric, fid) + elif metric.startswith('fid-subpatch'): # fid-subpatch1024-minXmaxY + patch_options = training_options + dataset_kwargs = dnnlib.EasyDict(patch_options['patch_kwargs']) + patch_size = int(util.remove_prefix(metric.split('-')[1], 'subpatch')) + assert(patch_size == dataset_kwargs.resolution) + size_min = int(util.remove_prefix(metric.split('-')[2].split('max')[0], 'min')) + size_max = int(metric.split('-')[2].split('max')[1]) + scale_min = patch_size / size_max if size_max > 0 else None + scale_max = patch_size / size_min + # adjust dataset kwargs using desired scale min and max + dataset_kwargs.scale_min = scale_min + dataset_kwargs.scale_max = scale_max + dataset_kwargs.scale_anneal = -1 + print("Computing metric %s" % (metric)) + print("Patch size %d, size_min: %d size_max: %d scale_min: %s scale_max %f" + % (patch_size, size_min, size_max, str(scale_min), scale_max)) + print(dataset_kwargs) + target_resolution = G_ema.init_kwargs.img_resolution + opts = metric_utils.MetricOptions(G=G_ema, dataset_kwargs=dataset_kwargs, + num_gpus=1, rank=0) + splits = metric.split('-') + mode = 'subpatch' + print("FID mode: %s" % mode) + fid = frechet_inception_distance.compute_fid_patch( + opts, target_resolution, 50000, 50000, mode=mode) + save_metric(output_folder, metric, fid) + else: + raise NotImplementedError + else: + raise NotImplementedError + pidfile.mark_job_done(output_folder) diff --git a/custom_metrics.sh b/custom_metrics.sh new file mode 100644 index 0000000..219c493 --- /dev/null +++ b/custom_metrics.sh @@ -0,0 +1,30 @@ +# flickr bird model example +pkl=pretrained/bird_pretrained_final.pkl +# Options can be the training_options.json file saved with each experiment run. +# The important thing used here is training_set_kwargs and patch_kwargs, +# make sure those are adjusted appropriately if training and evaluation settings are different. +# scale_min, scale_max, and scale_anneal in patch_kwargs are overridden based on the metrics specified here. +# The following options files are provided as examples, but the dataset paths need to be adjusted accordingly. +options=pretrained/patch-bird-final-options.json +echo RUNNING $pkl +python custom_metrics.py --input $pkl --metrics fid-full256,fid-full512,fid-patch256-min256max0 --training_options $options + +# lsun church model example +pkl=pretrained/church_pretrained_final.pkl +options=pretrained/patch-church-final-options.json +echo RUNNING $pkl +python custom_metrics.py --input $pkl --metrics fid-full256,fid-full1024,fid-patch256-min256max0 --training_options $options + +# ffhq example +# note: it is trained on 6k varied size images, but evaluated on full 70K ffhq ground-truth +pkl=pretrained/ffhq6k_pretrained_final.pkl +options=pretrained/patch-ffhq6k-final-options.json +echo RUNNING $pkl +python custom_metrics.py --input $pkl --metrics fid-full256,fid-patch256-min256max1024,fid-full512,fid-full1024 --training_options $options + +# # mountain example +# # note: subpatch fid avoids downsampling the larger generated patch +# pkl=pretrained/mountain_pretrained_final.pkl +# options=pretrained/patch-mountain-final-options.json +# echo RUNNING $pkl +# python custom_metrics.py --input $pkl --metrics fid-full1024,fid-subpatch1024-min1024max0 --training_options $options diff --git a/diff.txt b/diff.txt new file mode 100644 index 0000000..8fa7014 --- /dev/null +++ b/diff.txt @@ -0,0 +1,1119 @@ +--- ./stylegan3/train.py 2021-11-24 13:11:24.000000000 -0500 ++++ train.py 2022-05-17 17:39:19.470176637 -0400 +@@ -75,6 +75,12 @@ + print(f'Dataset resolution: {c.training_set_kwargs.resolution}') + print(f'Dataset labels: {c.training_set_kwargs.use_labels}') + print(f'Dataset x-flips: {c.training_set_kwargs.xflip}') ++ if 'patch' in c.G_kwargs.training_mode: ++ print(f'Patches path: {c.patch_kwargs.path}') ++ print(f'Patches size: {c.patch_kwargs.max_size} images') ++ print(f'Patches resolution: {c.patch_kwargs.resolution}') ++ print(f'Patches labels: {c.patch_kwargs.use_labels}') ++ print(f'Patches x-flips: {c.patch_kwargs.xflip}') + print() + + # Dry run? +@@ -103,7 +109,11 @@ + try: + dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False) + dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset. ++ try: + dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution. ++ except AssertionError: ++ print("Cannot determine default dataset resolution, will try to use specified arguments") ++ dataset_kwargs.resolution = None + dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels. + dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size. + return dataset_kwargs, dataset_obj.name +@@ -161,28 +171,32 @@ + @click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True) + @click.option('-n','--dry-run', help='Print training options and exit', is_flag=True) + +-def main(**kwargs): +- """Train a GAN using the techniques described in the paper +- "Alias-Free Generative Adversarial Networks". ++# additional base options ++@click.option('--training_mode', help='generator training mode', type=click.Choice(['global', 'patch', 'global-360']), required=True) ++@click.option('--data_resolution', help='LR dataset resolution (specify if images are not preprocessed to same size and square)', type=click.IntRange(min=0)) ++@click.option('--random_crop', help='random crop image on LR dataset (specify if images are not preprocessed to same size and square)', metavar='BOOL', type=bool, default=False, show_default=True) ++@click.option('--data_max_size', help='LR dataset max number of images', type=click.IntRange(min=0)) ++@click.option('--g_size', help='size of G (if different from dataset size)', type=click.IntRange(min=0)) ++ ++# additional options for patch model ++@click.option('--teacher', help='teacher checkpoint', metavar='[PATH|URL]', type=str) ++@click.option('--teacher_lambda', help='teacher regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), default=1.0, show_default=True) ++@click.option('--teacher_mode', help='teacher loss mode', type=click.Choice(['inverse', 'forward']), default='forward', show_default=True) ++@click.option('--scale_anneal', help='scale annealing rate (-1 for no annealing)', metavar='FLOAT', type=click.FloatRange(min=-1), default=-1, show_default=True) ++@click.option('--scale_min', help='minimum sampled scale (leave blank to use image native resolution)', metavar='FLOAT', type=click.FloatRange(min=0)) ++@click.option('--scale_max', help='maximum sampled scale', metavar='FLOAT', type=click.FloatRange(min=0), default=1.0, show_default=True) ++@click.option('--base_probability', help='probability to sample from LR dataset with identity transform', metavar='FLOAT', type=click.FloatRange(min=0), default=0.5, show_default=True) ++@click.option('--data_hr', help='HR patch dataset path', metavar='[ZIP|DIR]', type=str) ++@click.option('--patch_crop', help='perform random cropping on non-square images (on patch dataset)', metavar='BOOL', type=bool, default=False, show_default=True) ++@click.option('--data_hr_max_size', help='patch dataset max number of images', type=click.IntRange(min=0)) ++@click.option('--scale_mapping_min', help='normalization minimum for scale mapping branch (size = g_size*scale_mapping_min)', type=click.IntRange(min=0)) ++@click.option('--scale_mapping_max', help='normalization maximum for scale mapping branch (size = g_size*scale_mapping_max)', type=click.IntRange(min=0)) ++@click.option('--scale_mapping_norm', help='normalization type for scale mapping branch', type=click.Choice(['positive', 'zerocentered']), default='positive') + +- Examples: ++# additional options for 360 model ++@click.option('--fov', help='fov for one frame in the 360 model', type=click.IntRange(min=0), default=60, show_default=True) + +- \b +- # Train StyleGAN3-T for AFHQv2 using 8 GPUs. +- python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \\ +- --gpus=8 --batch=32 --gamma=8.2 --mirror=1 +- +- \b +- # Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle. +- python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \\ +- --gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \\ +- --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl +- +- \b +- # Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs. +- python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \\ +- --gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug +- """ ++def main(**kwargs): + + # Initialize config. + opts = dnnlib.EasyDict(kwargs) # Command line arguments. +@@ -200,6 +214,61 @@ + raise click.ClickException('--cond=True requires labels specified in dataset.json') + c.training_set_kwargs.use_labels = opts.cond + c.training_set_kwargs.xflip = opts.mirror ++ if opts.data_max_size: ++ c.training_set_kwargs.max_size = opts.max_size ++ if opts.data_resolution: ++ if c.training_set_kwargs.resolution != opts.data_resolution: ++ print("using specified data resolution %d rather than default" % (opts.data_resolution)) ++ c.training_set_kwargs.resolution = opts.data_resolution ++ c.training_set_kwargs.crop_image = opts.random_crop ++ # by this point, resolution should be determined ++ # either from init_dataset function or opts.data_resolution ++ assert(c.training_set_kwargs.resolution is not None) ++ ++ # set up training mode ++ training_mode = c.G_kwargs.training_mode = opts.training_mode ++ # set up generator size ++ if opts.g_size is not None: ++ assert(opts.g_size == c.training_set_kwargs.resolution) ++ else: ++ opts.g_size = c.training_set_kwargs.resolution ++ if 'patch' in training_mode: ++ # patch dataset kwargs ++ patch_kwargs = dnnlib.EasyDict( ++ class_name='training.dataset.ImagePatchDataset', ++ path=opts.data_hr, resolution=opts.g_size, ++ scale_min=opts.scale_min, scale_max=opts.scale_max, ++ scale_anneal=opts.scale_anneal, random_crop=opts.patch_crop, ++ use_labels=True, max_size=None, xflip=False) ++ patch_obj = dnnlib.util.construct_class_by_name(**patch_kwargs) # gets initial args ++ patch_name = patch_obj.name ++ patch_kwargs.resolution = patch_obj.resolution # Be explicit about resolution. ++ patch_kwargs.use_labels = patch_obj.has_labels # Be explicit about labels. ++ patch_kwargs.max_size = len(patch_obj) # Be explicit about dataset size. ++ c.patch_kwargs = patch_kwargs ++ c.patch_kwargs.use_labels = opts.cond ++ c.patch_kwargs.xflip = opts.mirror ++ if opts.data_hr_max_size: ++ c.patch_kwargs.max_size = opts.data_hr_max_size ++ # added G_kwargs ++ c.G_kwargs.scale_mapping_kwargs = dnnlib.EasyDict( ++ scale_mapping_min = opts.scale_mapping_min, ++ scale_mapping_max = opts.scale_mapping_max, ++ scale_mapping_norm = opts.scale_mapping_norm ++ ) ++ # added training options ++ c.added_kwargs = dnnlib.EasyDict( ++ img_size=opts.g_size, ++ teacher=opts.teacher, ++ teacher_lambda=opts.teacher_lambda, ++ teacher_mode=opts.teacher_mode, ++ scale_min=opts.scale_min, ++ scale_max=opts.scale_max, ++ scale_anneal=opts.scale_anneal, ++ base_probability=opts.base_probability, ++ ) ++ elif '360' in training_mode: ++ c.G_kwargs.fov = opts.fov + + # Hyperparameters & settings. + c.num_gpus = opts.gpus +@@ -218,6 +287,8 @@ + c.kimg_per_tick = opts.tick + c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap + c.random_seed = c.training_set_kwargs.random_seed = opts.seed ++ if 'patch' in training_mode: ++ c.patch_kwargs.random_seed = opts.seed + c.data_loader_kwargs.num_workers = opts.workers + + # Sanity checks. +@@ -261,6 +332,9 @@ + # Resume. + if opts.resume is not None: + c.resume_pkl = opts.resume ++ ++ if opts.teacher is not None or opts.resume is not None: ++ # disable rampups for finetuning or resuming models + c.ada_kimg = 100 # Make ADA react faster at the beginning. + c.ema_rampup = None # Disable EMA rampup. + c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup. +diff -bur ./stylegan3/training/dataset.py training/dataset.py +--- ./stylegan3/training/dataset.py 2021-11-24 13:11:24.000000000 -0500 ++++ training/dataset.py 2022-06-17 23:26:59.794824950 -0400 +@@ -16,10 +16,12 @@ + import torch + import dnnlib + +-try: +- import pyspng +-except ImportError: +- pyspng = None ++pyspng = None # disable pyspng for image resizing on load ++# https://stackoverflow.com/questions/51152059/pillow-in-python-wont-let-me-open-image-exceeds-limit ++PIL.Image.MAX_IMAGE_PIXELS = 933120000 ++from util import patch_util ++import random ++ + + #---------------------------------------------------------------------------- + +@@ -85,6 +87,7 @@ + return self._raw_idx.size + + def __getitem__(self, idx): ++ if not self.is_patch: # full image loader + image = self._load_raw_image(self._raw_idx[idx]) + assert isinstance(image, np.ndarray) + assert list(image.shape) == self.image_shape +@@ -93,6 +96,14 @@ + assert image.ndim == 3 # CHW + image = image[:, :, ::-1] + return image.copy(), self.get_label(idx) ++ else: # image patch loader ++ # handle xflips when loading the image ++ data = self._load_raw_image(self._raw_idx[idx], self._xflip[idx]) ++ assert isinstance(data, dict) ++ assert list(data['image'].shape) == self.image_shape ++ assert data['image'].dtype == np.uint8 ++ data['image'] = data['image'].copy() ++ return data, self.get_label(idx) + + def get_label(self, idx): + label = self._get_raw_labels()[self._raw_idx[idx]] +@@ -153,18 +164,30 @@ + + #---------------------------------------------------------------------------- + +-class ImageFolderDataset(Dataset): ++class BaseImageDataset(Dataset): + def __init__(self, + path, # Path to directory or zip. + resolution = None, # Ensure specific resolution, None = highest available. + **super_kwargs, # Additional arguments for the Dataset base class. + ): ++ + self._path = path + self._zipfile = None + + if os.path.isdir(self._path): + self._type = 'dir' +- self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} ++ if os.path.isfile(self._path + '_cache.txt'): ++ # use cache file if it exists ++ with open(self._path + '_cache.txt') as cache: ++ self._all_fnames = set([line.strip() for line in cache]) ++ else: ++ print("Walking dataset...") ++ self._all_fnames = [os.path.relpath(os.path.join(root, fname), start=self._path) ++ for root, _dirs, files in os.walk(self._path, followlinks=True) for fname in files] ++ with open(self._path + '_cache.txt', 'w') as cache: ++ [cache.write("%s\n" % fname) for fname in self._all_fnames] ++ self._all_fnames = set(self._all_fnames) ++ print("Done walking") + elif self._file_ext(self._path) == '.zip': + self._type = 'zip' + self._all_fnames = set(self._get_zipfile().namelist()) +@@ -177,9 +200,12 @@ + raise IOError('No image files found in the specified path') + + name = os.path.splitext(os.path.basename(self._path))[0] +- raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) +- if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): +- raise IOError('Image files do not match the specified resolution') ++ if resolution is not None: ++ raw_shape = [len(self._image_fnames)] + [3, resolution, resolution] ++ else: ++ # do not resize it to determine initial shape (will fail if images not square) ++ raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0, resize=False).shape) ++ + super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) + + @staticmethod +@@ -209,18 +235,6 @@ + def __getstate__(self): + return dict(super().__getstate__(), _zipfile=None) + +- def _load_raw_image(self, raw_idx): +- fname = self._image_fnames[raw_idx] +- with self._open_file(fname) as f: +- if pyspng is not None and self._file_ext(fname) == '.png': +- image = pyspng.load(f.read()) +- else: +- image = np.array(PIL.Image.open(f)) +- if image.ndim == 2: +- image = image[:, :, np.newaxis] # HW => HWC +- image = image.transpose(2, 0, 1) # HWC => CHW +- return image +- + def _load_raw_labels(self): + fname = 'dataset.json' + if fname not in self._all_fnames: +@@ -235,4 +249,116 @@ + labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) + return labels + +-#---------------------------------------------------------------------------- ++ ++class ImageFolderDataset(BaseImageDataset): ++ def __init__(self, ++ path, # Path to directory or zip. ++ resolution = None, # Ensure specific resolution, None = highest available. ++ crop_image = False, # default: assumes inputs are square images, if True it will perform a random crop ++ **super_kwargs, # Additional arguments for the Dataset base class. ++ ): ++ self.crop_image = crop_image ++ self.is_patch = False ++ super().__init__(path=path, resolution=resolution, **super_kwargs) ++ ++ def _load_raw_image(self, raw_idx, resize=True): ++ fname = self._image_fnames[raw_idx] ++ with self._open_file(fname) as f: ++ if pyspng is not None and self._file_ext(fname) == '.png': ++ image = pyspng.load(f.read()) ++ else: ++ image = PIL.Image.open(f).convert('RGB') ++ w, h = image.size ++ if self.crop_image and w != h: ++ # perform random crop if needed ++ min_size = min(w, h) ++ if w == min_size: ++ x_start = 0 ++ y_start = random.randint(0, h - min_size) ++ else: ++ x_start = random.randint(0, w - min_size) ++ y_start = 0 ++ image = image.crop((x_start, y_start, x_start+min_size, y_start+min_size)) ++ if resize: ++ # at this point it should be square ++ assert(image.size[0] == image.size[1]) ++ target_size = tuple(self.image_shape[1:]) ++ if image.size != target_size: ++ # it should only downsize, but there are a small number ++ # of images in the datasets that are a few pixels ++ # smaller than 256, so allow a small leeway ++ assert(target_size[-1] < image.size[-1] + 10) ++ image = image.resize(target_size, PIL.Image.ANTIALIAS) ++ image = np.array(image) ++ if image.ndim == 2: ++ image = image[:, :, np.newaxis] # HW => HWC ++ image = image.transpose(2, 0, 1) # HWC => CHW ++ return image ++ ++class ImagePatchDataset(BaseImageDataset): ++ def __init__(self, ++ path, # Path to directory or zip. ++ resolution, # patch size ++ scale_min, # minimum scale of the patches (largest image size) ++ scale_max, # maximum scale of the patches (smallest image size) ++ scale_anneal=-1, # annealing rate ++ random_crop=True, # add random crop for non-square images ++ **super_kwargs, # Additional arguments for the Dataset base class. ++ ): ++ assert(resolution is not None) # patch resolution must be specified ++ ++ # annealing not implemented, need to update iteration counter and ++ # adjust counter when resuming training ++ assert(scale_anneal == -1) ++ ++ # crop sampler ++ self.patch_size = resolution ++ self.random_crop = random_crop ++ self.sampler = patch_util.PatchSampler( ++ patch_size=self.patch_size, scale_anneal=scale_anneal, ++ min_scale=scale_min, max_scale=scale_max) ++ self.is_patch = True ++ ++ super().__init__(path=path, resolution=resolution, **super_kwargs) ++ ++ def _load_raw_image(self, raw_idx, is_flipped): ++ fname = self._image_fnames[raw_idx] ++ with self._open_file(fname) as f: ++ if pyspng is not None and self._file_ext(fname) == '.png': ++ image = pyspng.load(f.read()) ++ else: ++ image = PIL.Image.open(f).convert('RGB') ++ ++ # first, flip image if necessary ++ if is_flipped: ++ image = image.transpose(PIL.Image.FLIP_LEFT_RIGHT) ++ ++ # add random crop if necessary ++ if self.random_crop: ++ w, h = image.size ++ min_size = min(w, h) ++ x_start = random.randint(0, max(0, w - min_size)) ++ y_start = random.randint(0, max(0, h - min_size)) ++ image = image.crop((x_start, y_start, x_start+min_size, y_start+min_size)) ++ else: ++ # otherwise, center crop ++ w, h = image.size ++ min_size = min(w, h) ++ if w != h: ++ if w == min_size: ++ x_start = 0 ++ y_start = (h - min_size) // 2 ++ else: ++ x_start = (w - min_size) // 2 ++ y_start = 0 ++ image = image.crop((x_start, y_start, x_start+min_size, y_start+min_size)) ++ ++ # sample the resize and crop parameters ++ crop, params = self.sampler.sample_patch(image) ++ image = np.asarray(crop) ++ image = image.transpose(2, 0, 1) # HWC => CHW ++ data = { ++ 'image': image, ++ 'params': params, ++ } ++ return data +diff -bur ./stylegan3/training/loss.py training/loss.py +--- ./stylegan3/training/loss.py 2021-11-24 13:11:24.000000000 -0500 ++++ training/loss.py 2022-06-17 23:33:28.910631833 -0400 +@@ -14,6 +14,11 @@ + from torch_utils.ops import conv2d_gradfix + from torch_utils.ops import upfirdn2d + ++# added imports ++from metrics import equivariance ++from util import losses, util, patch_util ++import random ++ + #---------------------------------------------------------------------------- + + class Loss: +@@ -22,8 +27,23 @@ + + #---------------------------------------------------------------------------- + ++def apply_affine_batch(img, transform): ++ # hacky .. apply affine transformation with cuda kernel in batch form ++ crops = [] ++ masks = [] ++ for i, t in zip(img, transform): ++ crop, mask = equivariance.apply_affine_transformation( ++ i[None], t.inverse()) ++ crops.append(crop) ++ masks.append(mask) ++ crops = torch.cat(crops, dim=0) ++ masks = torch.cat(masks, dim=0) ++ return crops, masks ++ + class StyleGAN2Loss(Loss): +- def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0): ++ def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, ++ pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, ++ blur_fade_kimg=0, teacher=None, added_kwargs=None): + super().__init__() + self.device = device + self.G = G +@@ -39,14 +59,51 @@ + self.blur_init_sigma = blur_init_sigma + self.blur_fade_kimg = blur_fade_kimg + +- def run_G(self, z, c, update_emas=False): +- ws = self.G.mapping(z, c, update_emas=update_emas) ++ self.teacher = teacher ++ self.added_kwargs = added_kwargs ++ self.training_mode = self.G.training_mode ++ if self.teacher is not None: ++ self.loss_l1 = losses.Masked_L1_Loss().to(device) ++ self.loss_lpips = losses.Masked_LPIPS_Loss(net='alex', device=device) ++ util.set_requires_grad(False, self.loss_lpips) ++ util.set_requires_grad(False, self.teacher) ++ ++ def style_mix(self, z, c, ws): + if self.style_mixing_prob > 0: + with torch.autograd.profiler.record_function('style_mixing'): + cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) + cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) + ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] +- img = self.G.synthesis(ws, update_emas=update_emas) ++ return ws ++ ++ def run_G(self, z, c, transform, update_emas=False): ++ mapped_scale = None ++ crop_fn = None ++ if 'patch' in self.training_mode: ++ ws = self.G.mapping(z, c, update_emas=update_emas) ++ scale, mapped_scale = patch_util.compute_scale_inputs(self.G, ws, transform) ++ ws = self.style_mix(z, c, ws) ++ img = self.G.synthesis(ws, mapped_scale=mapped_scale, transform=transform, update_emas=update_emas) ++ elif '360' in self.training_mode: ++ ws = self.G.mapping(z, c, update_emas=update_emas) ++ ws = self.style_mix(z, c, ws) ++ input_layer = self.G.synthesis.input ++ crop_start = random.randint(0, 360 // input_layer.fov * input_layer.frame_size[0] - 1) ++ crop_fn = lambda grid : grid[:, :, crop_start:crop_start+input_layer.size[0], :] ++ img_base = self.G.synthesis(ws, crop_fn=crop_fn, update_emas=update_emas) ++ crop_shift = crop_start + input_layer.frame_size[0] ++ # generate shifted frame for cross-frame discriminator ++ crop_fn_shift = lambda grid : grid[:, :, crop_shift:crop_shift+input_layer.size[0], :] ++ img_shifted = self.G.synthesis(ws, crop_fn=crop_fn_shift, update_emas=update_emas) ++ img_splice = torch.cat([img_base, img_shifted], dim=3) ++ img_size = img_base.shape[-1] ++ splice_start = random.randint(0, img_size) ++ img = img_splice[:, :, :, splice_start:splice_start+img_size] ++ elif 'global' in self.training_mode: ++ ws = self.G.mapping(z, c, update_emas=update_emas) ++ ws = self.style_mix(z, c, ws) ++ assert(transform is None) ++ img = self.G.synthesis(ws, transform=transform, update_emas=update_emas) + return img, ws + + def run_D(self, img, c, blur_sigma=0, update_emas=False): +@@ -60,7 +117,8 @@ + logits = self.D(img, c, update_emas=update_emas) + return logits + +- def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): ++ def accumulate_gradients(self, phase, real_img, real_c, transform, gen_z, ++ gen_c, gain, cur_nimg, min_scale, max_scale): + assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth'] + if self.pl_weight == 0: + phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase) +@@ -71,12 +129,49 @@ + # Gmain: Maximize logits for generated images. + if phase in ['Gmain', 'Gboth']: + with torch.autograd.profiler.record_function('Gmain_forward'): +- gen_img, _gen_ws = self.run_G(gen_z, gen_c) ++ gen_img, _gen_ws = self.run_G(gen_z, gen_c, transform) ++ # vutils.save_image(gen_img, 'out_fake_patch.png', range=(-1, 1), ++ # normalize=True, nrow=4) + gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma) + training_stats.report('Loss/scores/fake', gen_logits) + training_stats.report('Loss/signs/fake', gen_logits.sign()) + loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits)) + training_stats.report('Loss/G/loss', loss_Gmain) ++ training_stats.report('Scale/G/min_scale', min_scale) ++ training_stats.report('Scale/G/max_scale', max_scale) ++ if self.teacher is not None and self.added_kwargs.teacher_lambda > 0: ++ teacher_img = self.teacher(gen_z, gen_c) ++ if self.added_kwargs.teacher_mode == 'forward': ++ teacher_crop, teacher_mask = apply_affine_batch(teacher_img, transform) ++ # removes the border around the above mask ++ # (mask should be all ones bc zooming in) ++ teacher_mask = torch.ones_like(teacher_mask) ++ l1_loss = self.loss_l1(gen_img, teacher_crop, ++ teacher_mask[:, :1]) ++ lpips_loss = self.loss_lpips( ++ losses.adaptive_downsample256(gen_img), ++ losses.adaptive_downsample256(teacher_crop), ++ losses.adaptive_downsample256(teacher_mask[:, :1], ++ mode='nearest') ++ ) ++ elif self.added_kwargs.teacher_mode == 'inverse': ++ out_crop, out_mask = apply_affine_batch(gen_img, transform.inverse()) ++ l1_loss = self.loss_l1(out_crop, teacher_img, ++ out_mask[:, :1]) ++ lpips_loss = self.loss_lpips( ++ losses.adaptive_downsample256(out_crop), ++ losses.adaptive_downsample256(teacher_img), ++ losses.adaptive_downsample256(out_mask[:, :1], ++ mode='nearest') ++ ) ++ else: ++ assert(False) ++ teacher_loss = (l1_loss + lpips_loss)[:, None] ++ loss_Gmain = (loss_Gmain + self.added_kwargs.teacher_lambda ++ * teacher_loss) ++ training_stats.report('Loss/G/loss_teacher_l1', l1_loss) ++ training_stats.report('Loss/G/loss_teacher_lpips', lpips_loss) ++ training_stats.report('Loss/G/loss_total', loss_Gmain) + with torch.autograd.profiler.record_function('Gmain_backward'): + loss_Gmain.mean().mul(gain).backward() + +@@ -84,7 +179,9 @@ + if phase in ['Greg', 'Gboth']: + with torch.autograd.profiler.record_function('Gpl_forward'): + batch_size = gen_z.shape[0] // self.pl_batch_shrink +- gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size]) ++ gen_img, gen_ws = self.run_G(gen_z[:batch_size], ++ gen_c[:batch_size], ++ transform[:batch_size]) + pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) + with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad): + pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] +@@ -102,7 +199,7 @@ + loss_Dgen = 0 + if phase in ['Dmain', 'Dboth']: + with torch.autograd.profiler.record_function('Dgen_forward'): +- gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True) ++ gen_img, _gen_ws = self.run_G(gen_z, gen_c, transform, update_emas=True) + gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) + training_stats.report('Loss/scores/fake', gen_logits) + training_stats.report('Loss/signs/fake', gen_logits.sign()) +diff -bur ./stylegan3/training/networks_stylegan3.py training/networks_stylegan3.py +--- ./stylegan3/training/networks_stylegan3.py 2021-11-24 13:11:24.000000000 -0500 ++++ training/networks_stylegan3.py 2022-06-14 22:42:13.642624353 -0400 +@@ -18,6 +18,8 @@ + from torch_utils.ops import conv2d_gradfix + from torch_utils.ops import filtered_lrelu + from torch_utils.ops import bias_act ++import math ++import random + + #---------------------------------------------------------------------------- + +@@ -173,6 +175,7 @@ + size, # Output spatial size: int or [width, height]. + sampling_rate, # Output sampling rate. + bandwidth, # Output bandwidth. ++ margin_size, # Extra margin on input. + ): + super().__init__() + self.w_dim = w_dim +@@ -180,6 +183,7 @@ + self.size = np.broadcast_to(np.asarray(size), [2]) + self.sampling_rate = sampling_rate + self.bandwidth = bandwidth ++ self.margin_size = margin_size + + # Draw random frequencies from uniform 2D disc. + freqs = torch.randn([self.channels, 2]) +@@ -195,9 +199,12 @@ + self.register_buffer('freqs', freqs) + self.register_buffer('phases', phases) + +- def forward(self, w): ++ def forward(self, w, transform=None, **kwargs): + # Introduce batch dimension. +- transforms = self.transform.unsqueeze(0) # [batch, row, col] ++ if transform is None: ++ # sanity check; should not modify transform from identity ++ assert(torch.equal(self.transform, torch.eye(3, 3).to(self.transform.device))) ++ transform = self.transform.unsqueeze(0) # [batch, row, col] + freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] + phases = self.phases.unsqueeze(0) # [batch, channel] + +@@ -212,7 +219,7 @@ + m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image. + m_t[:, 0, 2] = -t[:, 2] # t'_x + m_t[:, 1, 2] = -t[:, 3] # t'_y +- transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform. ++ transforms = m_r @ m_t @ transform # First rotate resulting image, then translate, and finally apply user-specified transform. + + # Transform frequencies. + phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2) +@@ -247,6 +254,120 @@ + f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},', + f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}']) + ++ ++@persistence.persistent_class ++class SynthesisInput360(torch.nn.Module): ++ def __init__(self, ++ w_dim, # Intermediate latent (W) dimensionality. ++ channels, # Number of output channels. ++ size, # Output spatial size: int or [width, height]. ++ sampling_rate, # Output sampling rate. ++ bandwidth, # Output bandwidth. ++ margin_size, # Extra margin on input. ++ fov, # panorama FOV. ++ ): ++ super().__init__() ++ self.w_dim = w_dim ++ self.channels = channels ++ self.fov = fov ++ self.size = np.broadcast_to(np.asarray(size), [2]) ++ self.sampling_rate = sampling_rate ++ self.bandwidth = bandwidth ++ self.margin_size = margin_size ++ self.frame_size = self.size - 2 * self.margin_size ++ ++ # Draw random frequencies from uniform 2D disc. ++ freqs = torch.randn([self.channels, 2]) ++ radii = freqs.square().sum(dim=1, keepdim=True).sqrt() ++ freqs /= radii * radii.square().exp().pow(0.25) ++ freqs *= bandwidth ++ phases = torch.rand([self.channels]) - 0.5 ++ ++ # Setup parameters and buffers. ++ self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels])) ++ self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0]) ++ self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image. ++ self.register_buffer('freqs', freqs) ++ self.register_buffer('phases', phases) ++ ++ def forward(self, w, transform=None, crop_fn=None): ++ # Introduce batch dimension. ++ if transform is None: ++ transforms = self.transform.unsqueeze(0) # [batch, row, col] ++ else: ++ transforms = transform ++ freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] ++ phases = self.phases.unsqueeze(0) # [batch, channel] ++ ++ # does not add learned rotation for 360 model ++ transforms = transforms.expand(w.shape[0], -1, -1) ++ ++ # Dampen out-of-band frequencies that may occur due to the user-specified transform. ++ amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) ++ ++ # Construct sampling grid. ++ theta = torch.eye(2, 3, device=w.device) ++ theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate # tx ++ theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate # ty ++ grid_width = self.frame_size[0] * 360 // self.fov + 2 * self.margin_size ++ grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), ++ [1, 1, self.size[1], grid_width], ++ align_corners=False) ++ # extended grid to ensure that the x coordinate completes a full circle without padding ++ base_width = grid_width - 2*self.margin_size ++ corrected_x = torch.arange(-self.margin_size, base_width*2+self.margin_size, device=grids.device) / base_width * 2 - 1 ++ corrected_y = grids[0, :, 0, 1] ++ corrected_grids = torch.cat([corrected_x.view(1, 1, -1, 1).repeat(1, self.size[1], 1, 1), ++ corrected_y.view(1, -1, 1, 1).repeat(1, 1, grid_width+base_width, 1)], dim=3) ++ grids = corrected_grids ++ ++ if crop_fn is None: ++ crop_start = random.randint(0, base_width - 1) ++ grids = grids[:, :, crop_start:crop_start+self.size[1], :] ++ else: ++ grids = crop_fn(grids) ++ ++ # apply transformation first ++ rotation = transforms[:, :2, :2] ++ translation = transforms[:, :2, 2:].squeeze(2) ++ # normalize grid x s.t. transformations can operate on square affine ratio ++ grids_normalized = grids.clone() ++ min_bound = torch.min(grids_normalized[:, :, :, 0]) ++ max_bound = torch.max(grids_normalized[:, :, :, 0]) ++ target_range = torch.max(grids_normalized[:, :, :, 1]) - torch.min(grids_normalized[:, :, :, 1]) ++ grids_normalized[:, :, :, 0] = (grids_normalized[:, :, :, 0] - min_bound) / (max_bound - min_bound) ++ grids_normalized[:, :, :, 0] = grids_normalized[:, :, :, 0] * target_range - target_range / 2 ++ # # xT @ RT = (Rx)T --> it is transposed ++ grids_transformed = (grids_normalized.unsqueeze(3) @ rotation.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) ++ grids_transformed = grids_transformed + translation.unsqueeze(1).unsqueeze(2) ++ grids_transformed[:, :, :, 0] = (grids_transformed[:, :, :, 0] + target_range / 2) / target_range * (max_bound - min_bound) + min_bound ++ ++ # map discontinuous x-angle to continuous cylindrical coordinate ++ grids_transformed_sin = grids_transformed.clone() ++ grids_transformed_cos = grids_transformed.clone() ++ grids_transformed_sin[:, :, :, 0] = torch.sin(grids_transformed_sin[:, :, :, 0] * torch.tensor(math.pi)) ++ grids_transformed_cos[:, :, :, 0] = torch.cos(grids_transformed_cos[:, :, :, 0] * torch.tensor(math.pi)) ++ ++ x_sin = (grids_transformed_sin.unsqueeze(3) @ freqs[:, :self.channels//2, :].permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] ++ x_sin = x_sin + phases[:, :self.channels//2].unsqueeze(1).unsqueeze(2) ++ x_sin = torch.sin(x_sin * (np.pi * 2)) ++ x_sin = x_sin * amplitudes[:, :self.channels//2].unsqueeze(1).unsqueeze(2) ++ x_cos = (grids_transformed_cos.unsqueeze(3) @ freqs[:, self.channels//2:, :].permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] ++ x_cos = x_cos + phases[:, self.channels//2:].unsqueeze(1).unsqueeze(2) ++ x_cos = torch.sin(x_cos * (np.pi * 2)) ++ x_cos = x_cos * amplitudes[:, self.channels//2:].unsqueeze(1).unsqueeze(2) ++ x = torch.cat([x_sin, x_cos], dim=-1) ++ ++ # Apply trainable mapping. ++ weight = self.weight / np.sqrt(self.channels) ++ x = x @ weight.t() ++ ++ # Ensure correct shape. ++ x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] ++ misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])]) ++ return x ++ ++ + #---------------------------------------------------------------------------- + + @persistence.persistent_class +@@ -276,6 +397,9 @@ + use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers. + conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping. + magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes. ++ ++ # added ++ use_scale_affine = False + ): + super().__init__() + self.w_dim = w_dim +@@ -326,7 +450,12 @@ + pad_hi = pad_total - pad_lo + self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])] + +- def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False): ++ # added ++ self.use_scale_affine = use_scale_affine ++ if self.use_scale_affine: ++ self.scale_affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=0) ++ ++ def forward(self, x, w, scale=None, noise_mode='random', force_fp32=False, update_emas=False): + assert noise_mode in ['random', 'const', 'none'] # unused + misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])]) + misc.assert_shape(w, [x.shape[0], self.w_dim]) +@@ -340,6 +469,12 @@ + + # Execute affine layer. + styles = self.affine(w) ++ # added here ++ if self.use_scale_affine: ++ assert(scale is not None) ++ styles_scale = self.scale_affine(scale) ++ styles = styles + styles_scale # equivalent to concatenation ++ + if self.is_torgb: + weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2)) + styles = styles * weight_gain +@@ -411,6 +546,8 @@ + margin_size = 10, # Number of additional pixels outside the image. + output_scale = 0.25, # Scale factor for the output image. + num_fp16_res = 4, # Use FP16 for the N highest resolutions. ++ training_mode = 'global', # training mode for input layer ++ fov = None, # Specify FOV for 360 model + **layer_kwargs, # Arguments for SynthesisLayer. + ): + super().__init__() +@@ -440,9 +577,17 @@ + channels[-1] = self.img_channels + + # Construct layers. ++ if '360' not in training_mode: + self.input = SynthesisInput( + w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]), +- sampling_rate=sampling_rates[0], bandwidth=cutoffs[0]) ++ sampling_rate=sampling_rates[0], bandwidth=cutoffs[0], ++ margin_size=margin_size) ++ else: ++ assert(fov is not None) ++ self.input = SynthesisInput360( ++ w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]), ++ sampling_rate=sampling_rates[0], bandwidth=cutoffs[0], ++ margin_size=margin_size, fov=fov) + self.layer_names = [] + for idx in range(self.num_layers + 1): + prev = max(idx - 1, 0) +@@ -461,14 +606,19 @@ + setattr(self, name, layer) + self.layer_names.append(name) + +- def forward(self, ws, **layer_kwargs): ++ def forward(self, ws, mapped_scale=None, transform=None, crop_fn=None, **layer_kwargs): + misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) + ws = ws.to(torch.float32).unbind(dim=1) ++ if mapped_scale is not None: ++ scale = mapped_scale.to(torch.float32).unbind(dim=1) ++ else: ++ scale = [None] * self.num_ws ++ # ws is a list of ws for every layer + + # Execute layers. +- x = self.input(ws[0]) +- for name, w in zip(self.layer_names, ws[1:]): +- x = getattr(self, name)(x, w, **layer_kwargs) ++ x = self.input(ws[0], transform=transform, crop_fn=crop_fn) ++ for name, w , sc in zip(self.layer_names, ws[1:], scale[1:]): ++ x = getattr(self, name)(x, w, sc, **layer_kwargs) + if self.output_scale != 1: + x = x * self.output_scale + +@@ -495,6 +645,8 @@ + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs = {}, # Arguments for MappingNetwork. ++ training_mode = 'global', ++ scale_mapping_kwargs = {}, # Arguments for Scale Mapping Network + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): + super().__init__() +@@ -503,13 +655,65 @@ + self.w_dim = w_dim + self.img_resolution = img_resolution + self.img_channels = img_channels +- self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) ++ self.training_mode = training_mode ++ self.scale_mapping_kwargs = scale_mapping_kwargs ++ use_scale_affine = True if 'patch' in self.training_mode else False # add affine layer on style input ++ self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, ++ training_mode=training_mode, use_scale_affine=use_scale_affine, ++ **synthesis_kwargs) + self.num_ws = self.synthesis.num_ws + self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) ++ if 'patch' in self.training_mode: ++ self.scale_mapping_kwargs = scale_mapping_kwargs ++ scale_mapping_norm = scale_mapping_kwargs.scale_mapping_norm ++ scale_mapping_min = scale_mapping_kwargs.scale_mapping_min ++ scale_mapping_max = scale_mapping_kwargs.scale_mapping_max ++ if scale_mapping_norm == 'zerocentered': ++ self.scale_norm = ScaleNormalizeZeroCentered(scale_mapping_min, scale_mapping_max) ++ scale_in_dim = 1 ++ elif scale_mapping_norm == 'positive': ++ self.scale_norm = ScaleNormalizePositive(scale_mapping_min, scale_mapping_max) ++ scale_in_dim = 1 ++ else: ++ assert(False) ++ self.scale_mapping = MappingNetwork(z_dim=scale_in_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) ++ + +- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): ++ def forward(self, z, c, transform=None, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): + ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) +- img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) ++ if transform is None: ++ scale = torch.ones(z.shape[0], 1).to(z.device) ++ else: ++ scale = 1/transform[:, [0], 0] ++ if self.scale_mapping_kwargs: ++ scale = self.scale_norm(scale) ++ mapped_scale = self.scale_mapping(scale, c, update_emas=update_emas) ++ else: ++ mapped_scale = None ++ img = self.synthesis(ws, mapped_scale=mapped_scale, transform=transform, update_emas=update_emas, **synthesis_kwargs) + return img + + #---------------------------------------------------------------------------- ++@persistence.persistent_class ++class ScaleNormalizeZeroCentered(torch.nn.Module): ++ def __init__(self, scale_mapping_min, scale_mapping_max): ++ super().__init__() ++ self.scale_mapping_min = scale_mapping_min ++ self.scale_mapping_max = scale_mapping_max ++ ++ def forward(self, scale): ++ # remaps scale to (-1, 1) ++ scale = (scale - self.scale_mapping_min) / (self.scale_mapping_max - self.scale_mapping_min) ++ return 2 * scale - 1 ++ ++@persistence.persistent_class ++class ScaleNormalizePositive(torch.nn.Module): ++ def __init__(self, scale_mapping_min, scale_mapping_max): ++ super().__init__() ++ self.scale_mapping_min = scale_mapping_min ++ self.scale_mapping_max = scale_mapping_max ++ ++ def forward(self, scale): ++ # add a small offset to avoid zero point: [0.1 to 1.1] ++ scale = (scale - self.scale_mapping_min) / (self.scale_mapping_max - self.scale_mapping_min) ++ return scale + 0.1 +Only in training: __pycache__ +diff -bur ./stylegan3/training/training_loop.py training/training_loop.py +--- ./stylegan3/training/training_loop.py 2021-11-24 13:11:24.000000000 -0500 ++++ training/training_loop.py 2022-05-17 18:02:20.264047277 -0400 +@@ -26,6 +26,10 @@ + import legacy + from metrics import metric_main + ++from util import util ++import random ++from metrics import equivariance ++ + #---------------------------------------------------------------------------- + + def setup_snapshot_image_grid(training_set, random_seed=0): +@@ -89,7 +93,8 @@ + + def training_loop( + run_dir = '.', # Output directory. +- training_set_kwargs = {}, # Options for training set. ++ training_set_kwargs = {}, # Options for base training set. ++ patch_kwargs = {}, # Options for patch dataset. + data_loader_kwargs = {}, # Options for torch.utils.data.DataLoader. + G_kwargs = {}, # Options for generator network. + D_kwargs = {}, # Options for discriminator network. +@@ -120,6 +125,7 @@ + cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? + abort_fn = None, # Callback function for determining whether to abort training. Must return consistent results across ranks. + progress_fn = None, # Callback function for updating training progress. Called for all ranks. ++ added_kwargs = {}, # added + ): + # Initialize. + start_time = time.time() +@@ -132,12 +138,17 @@ + conv2d_gradfix.enabled = True # Improves training speed. + grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. + ++ # ADDED: to prevent data_loader pin_memory to load to device 0 for every process ++ torch.cuda.set_device(device) ++ training_mode = G_kwargs.training_mode ++ + # Load training set. + if rank == 0: + print('Loading training set...') + training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset + training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) + training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) ++ + if rank == 0: + print() + print('Num images: ', len(training_set)) +@@ -145,14 +156,56 @@ + print('Label shape:', training_set.label_shape) + print() + ++ # Load patch dataset ++ if 'patch' in training_mode: ++ if rank == 0: ++ print('Loading patch dataset...') ++ patch_dset = dnnlib.util.construct_class_by_name(**patch_kwargs) # subclass of training.dataset.Dataset ++ patch_dset_sampler = misc.InfiniteSampler(dataset=patch_dset, rank=rank, num_replicas=num_gpus, seed=random_seed) ++ patch_dset_iterator = iter(torch.utils.data.DataLoader(dataset=patch_dset, sampler=patch_dset_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) ++ if rank == 0: ++ print() ++ print('Patch Num images: ', len(patch_dset)) ++ print('Patch Image shape:', patch_dset.image_shape) ++ print('Patch Label shape:', patch_dset.label_shape) ++ print() ++ + # Construct networks. + if rank == 0: + print('Constructing networks...') +- common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels) ++ ++ # modified: use specified img_resolution ++ img_resolution = training_set.resolution ++ if 'patch' in training_mode and added_kwargs.img_size is not None: ++ img_resolution = added_kwargs.img_size ++ if rank == 0: ++ print("Using specified img resolution: %d" % img_resolution) ++ assert(added_kwargs.img_size == training_set.resolution) ++ common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=img_resolution, img_channels=training_set.num_channels) + G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + G_ema = copy.deepcopy(G).eval() + ++ ++ # copy G for teacher network: copy teacher G_ema to G_ema:, ++ # uses G state dict for the generator to align with D ++ if 'patch' in training_mode and added_kwargs.teacher is not None: ++ teacher = copy.deepcopy(G).to(device).eval() ++ # deactivate scale affine adding in teacher model; so it matches original model ++ for layer_name in teacher.synthesis.layer_names: ++ layer = getattr(teacher.synthesis, layer_name) ++ layer.use_scale_affine = False ++ if rank == 0: ++ print(f"loading teacher from {added_kwargs.teacher} on device %s! " % rank) ++ with dnnlib.util.open_url(added_kwargs.teacher) as f: ++ teacher_data = legacy.load_network_pkl(f) ++ for name, module in [('G', G), ('G_ema', teacher), ('G_ema', G_ema), ('D', D)]: ++ misc.copy_params_and_buffers(teacher_data[name], module, require_all=False) ++ print(f"done loading teacher on device %s! " % rank) ++ # util.set_requires_grad(False, teacher) ++ else: ++ teacher = None ++ + # Resume from existing pickle. + if (resume_pkl is not None) and (rank == 0): + print(f'Resuming from "{resume_pkl}"') +@@ -182,28 +235,39 @@ + # Distribute across GPUs. + if rank == 0: + print(f'Distributing across {num_gpus} GPUs...') +- for module in [G, D, G_ema, augment_pipe]: ++ for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ++ ('teacher', teacher), ('augment', augment_pipe)]: + if module is not None and num_gpus > 1: ++ if rank == 0: ++ print("copied %s across gpus!" % name) + for param in misc.params_and_buffers(module): + torch.distributed.broadcast(param, src=0) ++ elif module is None: ++ if rank == 0: ++ print("%s is None; not copied!" % name) + + # Setup training phases. + if rank == 0: + print('Setting up training phases...') +- loss = dnnlib.util.construct_class_by_name(device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss ++ loss = dnnlib.util.construct_class_by_name(device=device, G=G, D=D, augment_pipe=augment_pipe, ++ added_kwargs=added_kwargs, teacher=teacher, **loss_kwargs) # subclass of training.loss.Loss ++ + phases = [] +- for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]: ++ ++ for name, module, params, opt_kwargs, reg_interval in [('G', G, G.parameters(), G_opt_kwargs, G_reg_interval), ++ ('D', D, D.parameters(), D_opt_kwargs, D_reg_interval)]: + if reg_interval is None: +- opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer ++ opt = dnnlib.util.construct_class_by_name(params=params, **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)] + else: # Lazy regularization. + mb_ratio = reg_interval / (reg_interval + 1) + opt_kwargs = dnnlib.EasyDict(opt_kwargs) + opt_kwargs.lr = opt_kwargs.lr * mb_ratio + opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] +- opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer ++ opt = dnnlib.util.construct_class_by_name(params, **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)] + phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)] ++ + for phase in phases: + phase.start_event = None + phase.end_event = None +@@ -223,6 +287,7 @@ + grid_c = torch.from_numpy(labels).to(device).split(batch_gpu) + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size) ++ print('Done exporting sample images...') + + # Initialize logs. + if rank == 0: +@@ -252,12 +317,35 @@ + if progress_fn is not None: + progress_fn(0, total_kimg) + while True: +- +- # Fetch training data. + with torch.autograd.profiler.record_function('data_fetch'): ++ if 'patch' in training_mode: ++ if random.uniform(0, 1) > added_kwargs.base_probability: ++ # base dataset iterator ++ phase_real_img, phase_real_c = next(training_set_iterator) ++ n = phase_real_img.shape[0] ++ phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) ++ phase_real_c = phase_real_c.to(device).split(batch_gpu) ++ transform = torch.eye(3)[None] ++ phase_transform = transform.repeat(n, 1, 1).to(device).split(batch_gpu) ++ min_scale = 1.0 ++ max_scale = 1.0 ++ else: ++ # patch dataset iterator ++ data, phase_real_c = next(patch_dset_iterator) ++ phase_real_img = (data['image'].to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) ++ phase_real_c = phase_real_c.to(device).split(batch_gpu) ++ phase_transform = data['params']['transform'].to(device).split(batch_gpu) ++ min_scale = data['params']['min_scale_anneal'][0].item() ++ max_scale = 1.0 ++ else: + phase_real_img, phase_real_c = next(training_set_iterator) + phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) + phase_real_c = phase_real_c.to(device).split(batch_gpu) ++ # dummy variables ++ phase_transform = [None] * len(phase_real_c) ++ min_scale = 1.0 ++ max_scale = 1.0 ++ + all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device) + all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] + all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)] +@@ -274,8 +362,10 @@ + # Accumulate gradients. + phase.opt.zero_grad(set_to_none=True) + phase.module.requires_grad_(True) +- for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c): +- loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg) ++ for transform, real_img, real_c, gen_z, gen_c in zip(phase_transform, phase_real_img, phase_real_c, phase_gen_z, phase_gen_c): ++ loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, transform=transform, ++ gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg, ++ min_scale=min_scale, max_scale=max_scale) + phase.module.requires_grad_(False) + + # Update weights. diff --git a/dnnlib/__init__.py b/dnnlib/__init__.py new file mode 100644 index 0000000..e7423bf --- /dev/null +++ b/dnnlib/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from .util import EasyDict, make_cache_dir_path diff --git a/dnnlib/util.py b/dnnlib/util.py new file mode 100644 index 0000000..6bbdf3b --- /dev/null +++ b/dnnlib/util.py @@ -0,0 +1,491 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous utility classes and functions.""" + +import ctypes +import fnmatch +import importlib +import inspect +import numpy as np +import os +import shutil +import sys +import types +import io +import pickle +import re +import requests +import html +import hashlib +import glob +import tempfile +import urllib +import urllib.request +import uuid + +from distutils.util import strtobool +from typing import Any, List, Tuple, Union + + +# Util classes +# ------------------------------------------------------------------------------------------ + + +class EasyDict(dict): + """Convenience class that behaves like a dict but allows access with the attribute syntax.""" + + def __getattr__(self, name: str) -> Any: + try: + return self[name] + except KeyError: + raise AttributeError(name) + + def __setattr__(self, name: str, value: Any) -> None: + self[name] = value + + def __delattr__(self, name: str) -> None: + del self[name] + + +class Logger(object): + """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" + + def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): + self.file = None + + if file_name is not None: + self.file = open(file_name, file_mode) + + self.should_flush = should_flush + self.stdout = sys.stdout + self.stderr = sys.stderr + + sys.stdout = self + sys.stderr = self + + def __enter__(self) -> "Logger": + return self + + def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: + self.close() + + def write(self, text: Union[str, bytes]) -> None: + """Write text to stdout (and a file) and optionally flush.""" + if isinstance(text, bytes): + text = text.decode() + if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash + return + + if self.file is not None: + self.file.write(text) + + self.stdout.write(text) + + if self.should_flush: + self.flush() + + def flush(self) -> None: + """Flush written text to both stdout and a file, if open.""" + if self.file is not None: + self.file.flush() + + self.stdout.flush() + + def close(self) -> None: + """Flush, close possible files, and remove stdout/stderr mirroring.""" + self.flush() + + # if using multiple loggers, prevent closing in wrong order + if sys.stdout is self: + sys.stdout = self.stdout + if sys.stderr is self: + sys.stderr = self.stderr + + if self.file is not None: + self.file.close() + self.file = None + + +# Cache directories +# ------------------------------------------------------------------------------------------ + +_dnnlib_cache_dir = None + +def set_cache_dir(path: str) -> None: + global _dnnlib_cache_dir + _dnnlib_cache_dir = path + +def make_cache_dir_path(*paths: str) -> str: + if _dnnlib_cache_dir is not None: + return os.path.join(_dnnlib_cache_dir, *paths) + if 'DNNLIB_CACHE_DIR' in os.environ: + return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) + if 'HOME' in os.environ: + return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) + if 'USERPROFILE' in os.environ: + return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) + return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) + +# Small util functions +# ------------------------------------------------------------------------------------------ + + +def format_time(seconds: Union[int, float]) -> str: + """Convert the seconds to human readable string with days, hours, minutes and seconds.""" + s = int(np.rint(seconds)) + + if s < 60: + return "{0}s".format(s) + elif s < 60 * 60: + return "{0}m {1:02}s".format(s // 60, s % 60) + elif s < 24 * 60 * 60: + return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) + else: + return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) + + +def format_time_brief(seconds: Union[int, float]) -> str: + """Convert the seconds to human readable string with days, hours, minutes and seconds.""" + s = int(np.rint(seconds)) + + if s < 60: + return "{0}s".format(s) + elif s < 60 * 60: + return "{0}m {1:02}s".format(s // 60, s % 60) + elif s < 24 * 60 * 60: + return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60) + else: + return "{0}d {1:02}h".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24) + + +def ask_yes_no(question: str) -> bool: + """Ask the user the question until the user inputs a valid answer.""" + while True: + try: + print("{0} [y/n]".format(question)) + return strtobool(input().lower()) + except ValueError: + pass + + +def tuple_product(t: Tuple) -> Any: + """Calculate the product of the tuple elements.""" + result = 1 + + for v in t: + result *= v + + return result + + +_str_to_ctype = { + "uint8": ctypes.c_ubyte, + "uint16": ctypes.c_uint16, + "uint32": ctypes.c_uint32, + "uint64": ctypes.c_uint64, + "int8": ctypes.c_byte, + "int16": ctypes.c_int16, + "int32": ctypes.c_int32, + "int64": ctypes.c_int64, + "float32": ctypes.c_float, + "float64": ctypes.c_double +} + + +def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: + """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" + type_str = None + + if isinstance(type_obj, str): + type_str = type_obj + elif hasattr(type_obj, "__name__"): + type_str = type_obj.__name__ + elif hasattr(type_obj, "name"): + type_str = type_obj.name + else: + raise RuntimeError("Cannot infer type name from input") + + assert type_str in _str_to_ctype.keys() + + my_dtype = np.dtype(type_str) + my_ctype = _str_to_ctype[type_str] + + assert my_dtype.itemsize == ctypes.sizeof(my_ctype) + + return my_dtype, my_ctype + + +def is_pickleable(obj: Any) -> bool: + try: + with io.BytesIO() as stream: + pickle.dump(obj, stream) + return True + except: + return False + + +# Functionality to import modules/objects by name, and call functions by name +# ------------------------------------------------------------------------------------------ + +def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: + """Searches for the underlying module behind the name to some python object. + Returns the module and the object name (original name with module part removed).""" + + # allow convenience shorthands, substitute them by full names + obj_name = re.sub("^np.", "numpy.", obj_name) + obj_name = re.sub("^tf.", "tensorflow.", obj_name) + + # list alternatives for (module_name, local_obj_name) + parts = obj_name.split(".") + name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] + + # try each alternative in turn + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + return module, local_obj_name + except: + pass + + # maybe some of the modules themselves contain errors? + for module_name, _local_obj_name in name_pairs: + try: + importlib.import_module(module_name) # may raise ImportError + except ImportError: + if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): + raise + + # maybe the requested attribute is missing? + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + except ImportError: + pass + + # we are out of luck, but we have no idea why + raise ImportError(obj_name) + + +def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: + """Traverses the object name and returns the last (rightmost) python object.""" + if obj_name == '': + return module + obj = module + for part in obj_name.split("."): + obj = getattr(obj, part) + return obj + + +def get_obj_by_name(name: str) -> Any: + """Finds the python object with the given name.""" + module, obj_name = get_module_from_obj_name(name) + return get_obj_from_module(module, obj_name) + + +def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: + """Finds the python object with the given name and calls it as a function.""" + assert func_name is not None + func_obj = get_obj_by_name(func_name) + assert callable(func_obj) + return func_obj(*args, **kwargs) + + +def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: + """Finds the python class with the given name and constructs it with the given arguments.""" + return call_func_by_name(*args, func_name=class_name, **kwargs) + + +def get_module_dir_by_obj_name(obj_name: str) -> str: + """Get the directory path of the module containing the given object name.""" + module, _ = get_module_from_obj_name(obj_name) + return os.path.dirname(inspect.getfile(module)) + + +def is_top_level_function(obj: Any) -> bool: + """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" + return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ + + +def get_top_level_function_name(obj: Any) -> str: + """Return the fully-qualified name of a top-level function.""" + assert is_top_level_function(obj) + module = obj.__module__ + if module == '__main__': + module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + return module + "." + obj.__name__ + + +# File system helpers +# ------------------------------------------------------------------------------------------ + +def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: + """List all files recursively in a given directory while ignoring given file and directory names. + Returns list of tuples containing both absolute and relative paths.""" + assert os.path.isdir(dir_path) + base_name = os.path.basename(os.path.normpath(dir_path)) + + if ignores is None: + ignores = [] + + result = [] + + for root, dirs, files in os.walk(dir_path, topdown=True): + for ignore_ in ignores: + dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] + + # dirs need to be edited in-place + for d in dirs_to_remove: + dirs.remove(d) + + files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] + + absolute_paths = [os.path.join(root, f) for f in files] + relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] + + if add_base_to_relative: + relative_paths = [os.path.join(base_name, p) for p in relative_paths] + + assert len(absolute_paths) == len(relative_paths) + result += zip(absolute_paths, relative_paths) + + return result + + +def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: + """Takes in a list of tuples of (src, dst) paths and copies files. + Will create all necessary directories.""" + for file in files: + target_dir_name = os.path.dirname(file[1]) + + # will create all intermediate-level directories + if not os.path.exists(target_dir_name): + os.makedirs(target_dir_name) + + shutil.copyfile(file[0], file[1]) + + +# URL helpers +# ------------------------------------------------------------------------------------------ + +def is_url(obj: Any, allow_file_urls: bool = False) -> bool: + """Determine whether the given object is a valid URL string.""" + if not isinstance(obj, str) or not "://" in obj: + return False + if allow_file_urls and obj.startswith('file://'): + return True + try: + res = requests.compat.urlparse(obj) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + except: + return False + return True + + +def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: + """Download the given URL and return a binary-mode file object to access the data.""" + assert num_attempts >= 1 + assert not (return_filename and (not cache)) + + # Doesn't look like an URL scheme so interpret it as a local filename. + if not re.match('^[a-z]+://', url): + return url if return_filename else open(url, "rb") + + # Handle file URLs. This code handles unusual file:// patterns that + # arise on Windows: + # + # file:///c:/foo.txt + # + # which would translate to a local '/c:/foo.txt' filename that's + # invalid. Drop the forward slash for such pathnames. + # + # If you touch this code path, you should test it on both Linux and + # Windows. + # + # Some internet resources suggest using urllib.request.url2pathname() but + # but that converts forward slashes to backslashes and this causes + # its own set of problems. + if url.startswith('file://'): + filename = urllib.parse.urlparse(url).path + if re.match(r'^/[a-zA-Z]:', filename): + filename = filename[1:] + return filename if return_filename else open(filename, "rb") + + assert is_url(url) + + # Lookup from cache. + if cache_dir is None: + cache_dir = make_cache_dir_path('downloads') + + url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() + if cache: + cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) + if len(cache_files) == 1: + filename = cache_files[0] + return filename if return_filename else open(filename, "rb") + + # Download. + url_name = None + url_data = None + with requests.Session() as session: + if verbose: + print("Downloading %s ..." % url, end="", flush=True) + for attempts_left in reversed(range(num_attempts)): + try: + with session.get(url) as res: + res.raise_for_status() + if len(res.content) == 0: + raise IOError("No data received") + + if len(res.content) < 8192: + content_str = res.content.decode("utf-8") + if "download_warning" in res.headers.get("Set-Cookie", ""): + links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] + if len(links) == 1: + url = requests.compat.urljoin(url, links[0]) + raise IOError("Google Drive virus checker nag") + if "Google Drive - Quota exceeded" in content_str: + raise IOError("Google Drive download quota exceeded -- please try again later") + + match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) + url_name = match[1] if match else url + url_data = res.content + if verbose: + print(" done") + break + except KeyboardInterrupt: + raise + except: + if not attempts_left: + if verbose: + print(" failed") + raise + if verbose: + print(".", end="", flush=True) + + # Save to cache. + if cache: + safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) + cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) + temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) + os.makedirs(cache_dir, exist_ok=True) + with open(temp_file, "wb") as f: + f.write(url_data) + os.replace(temp_file, cache_file) # atomic + if return_filename: + return cache_file + + # Return data as file object. + assert not return_filename + return io.BytesIO(url_data) diff --git a/download_resources.sh b/download_resources.sh new file mode 100644 index 0000000..35281e2 --- /dev/null +++ b/download_resources.sh @@ -0,0 +1,13 @@ +base_url=http://latent-composition.csail.mit.edu/other_projects/anyres_gan/pretrained +target_dir=pretrained2 +mkdir -p $target_dir +wget $base_url/bird_pretrained_final.pkl -P $target_dir +wget $base_url/church_pretrained_final.pkl -P $target_dir +wget $base_url/ffhq6k_pretrained_final.pkl -P $target_dir +wget $base_url/mountain_pretrained_final.pkl -P $target_dir +wget $base_url/patch-bird-final-options.json -P $target_dir +wget $base_url/patch-church-final-options.json -P $target_dir +wget $base_url/patch-ffhq6k-final-options.json -P $target_dir +wget $base_url/patch-mountain-final-options.json -P $target_dir + +wget $base_url/mountain_pano_pretrained_final.pkl -P $target_dir diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..bfa0d2f --- /dev/null +++ b/environment.yml @@ -0,0 +1,25 @@ +name: anyres-gan +channels: + - pytorch + - conda-forge +dependencies: + - python=3.8 + - cudatoolkit=11.3 + - pytorch=1.10.1 + - torchvision=0.11.2 + - cudnn + - numpy + - pandas + - jupyter + - pip + - tensorboard + - tqdm + - ipython + - pillow + - pip: + - lpips + - ninja + - ipywidgets + - ipyevents + - click + - setuptools==59.5.0 diff --git a/img/dataset.jpg b/img/dataset.jpg new file mode 100644 index 0000000..7580cc5 Binary files /dev/null and b/img/dataset.jpg differ diff --git a/img/dataset_construction.jpg b/img/dataset_construction.jpg new file mode 100644 index 0000000..2bff2e2 Binary files /dev/null and b/img/dataset_construction.jpg differ diff --git a/img/extrapolation.jpg b/img/extrapolation.jpg new file mode 100644 index 0000000..d50ba1d Binary files /dev/null and b/img/extrapolation.jpg differ diff --git a/img/panel_birds.jpg b/img/panel_birds.jpg new file mode 100644 index 0000000..a18843a Binary files /dev/null and b/img/panel_birds.jpg differ diff --git a/img/panel_church.jpg b/img/panel_church.jpg new file mode 100644 index 0000000..7f03dfd Binary files /dev/null and b/img/panel_church.jpg differ diff --git a/img/panel_ffhq.jpg b/img/panel_ffhq.jpg new file mode 100644 index 0000000..053024d Binary files /dev/null and b/img/panel_ffhq.jpg differ diff --git a/img/panel_mountain.jpg b/img/panel_mountain.jpg new file mode 100644 index 0000000..2cbebe0 Binary files /dev/null and b/img/panel_mountain.jpg differ diff --git a/img/pano010.gif b/img/pano010.gif new file mode 100644 index 0000000..20f5039 Binary files /dev/null and b/img/pano010.gif differ diff --git a/img/qualitative.jpg b/img/qualitative.jpg new file mode 100644 index 0000000..8be223c Binary files /dev/null and b/img/qualitative.jpg differ diff --git a/img/schematic_v5.jpg b/img/schematic_v5.jpg new file mode 100644 index 0000000..2099eff Binary files /dev/null and b/img/schematic_v5.jpg differ diff --git a/img/teaser.gif b/img/teaser.gif new file mode 100644 index 0000000..89cc047 Binary files /dev/null and b/img/teaser.gif differ diff --git a/img/video_teaser.jpeg b/img/video_teaser.jpeg new file mode 100644 index 0000000..97516da Binary files /dev/null and b/img/video_teaser.jpeg differ diff --git a/ipynb_drop_output.py b/ipynb_drop_output.py new file mode 100755 index 0000000..e53a5c6 --- /dev/null +++ b/ipynb_drop_output.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python + +""" +Suppress output and prompt numbers in git version control. + +This script will tell git to ignore prompt numbers and cell output +when looking at ipynb files UNLESS their metadata contains: + + "git" : { "keep_output" : true } + +The notebooks themselves are not changed. + +See also this blogpost: http://pascalbugnion.net/blog/ipython-notebooks-and-git.html. + +Usage instructions +================== + +1. Put this script in a directory that is on the system's path. + For future reference, I will assume you saved it in + `~/scripts/ipynb_drop_output`. +2. Make sure it is executable by typing the command + `chmod +x ~/scripts/ipynb_drop_output`. +3. Register a filter for ipython notebooks by + putting the following line in `~/.config/git/attributes`: + `*.ipynb filter=clean_ipynb` +4. Connect this script to the filter by running the following + git commands: + + git config --global filter.clean_ipynb.clean ipynb_drop_output + git config --global filter.clean_ipynb.smudge cat + +To tell git NOT to ignore the output and prompts for a notebook, +open the notebook's metadata (Edit > Edit Notebook Metadata). A +panel should open containing the lines: + + { + "name" : "", + "signature" : "some very long hash" + } + +Add an extra line so that the metadata now looks like: + + { + "name" : "", + "signature" : "don't change the hash, but add a comma at the end of the line", + "git" : { "keep_outputs" : true } + } + +You may need to "touch" the notebooks for git to actually register a change, if +your notebooks are already under version control. + +Notes +===== + +Changed by David Bau to make stripping output the default. + +This script is inspired by http://stackoverflow.com/a/20844506/827862, but +lets the user specify whether the ouptut of a notebook should be kept +in the notebook's metadata, and works for IPython v3.0. +""" + +import sys +import json + +nb = sys.stdin.read() + +json_in = json.loads(nb) +nb_metadata = json_in["metadata"] +keep_output = False +if "git" in nb_metadata: + if "keep_outputs" in nb_metadata["git"] and nb_metadata["git"]["keep_outputs"]: + keep_output = True +if keep_output: + sys.stdout.write(nb) + exit() + + +ipy_version = int(json_in["nbformat"])-1 # nbformat is 1 more than actual version. + +def strip_output_from_cell(cell): + if "outputs" in cell: + cell["outputs"] = [] + if "prompt_number" in cell: + del cell["prompt_number"] + if "execution_count" in cell: + cell["execution_count"] = None + + +if ipy_version == 2: + for sheet in json_in["worksheets"]: + for cell in sheet["cells"]: + strip_output_from_cell(cell) +else: + for cell in json_in["cells"]: + strip_output_from_cell(cell) + +json.dump(json_in, sys.stdout, sort_keys=True, indent=1, separators=(",",": ")) diff --git a/legacy.py b/legacy.py new file mode 100644 index 0000000..8cf53cb --- /dev/null +++ b/legacy.py @@ -0,0 +1,323 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Converting legacy network pickle into the new format.""" + +import click +import pickle +import re +import copy +import numpy as np +import torch +import dnnlib +from torch_utils import misc + +#---------------------------------------------------------------------------- + +def load_network_pkl(f, force_fp16=False): + data = _LegacyUnpickler(f).load() + + # Legacy TensorFlow pickle => convert. + if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data): + tf_G, tf_D, tf_Gs = data + G = convert_tf_generator(tf_G) + D = convert_tf_discriminator(tf_D) + G_ema = convert_tf_generator(tf_Gs) + data = dict(G=G, D=D, G_ema=G_ema) + + # Add missing fields. + if 'training_set_kwargs' not in data: + data['training_set_kwargs'] = None + if 'augment_pipe' not in data: + data['augment_pipe'] = None + + # Validate contents. + assert isinstance(data['G'], torch.nn.Module) + assert isinstance(data['D'], torch.nn.Module) + assert isinstance(data['G_ema'], torch.nn.Module) + assert isinstance(data['training_set_kwargs'], (dict, type(None))) + assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None))) + + # Force FP16. + if force_fp16: + for key in ['G', 'D', 'G_ema']: + old = data[key] + kwargs = copy.deepcopy(old.init_kwargs) + fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs) + fp16_kwargs.num_fp16_res = 4 + fp16_kwargs.conv_clamp = 256 + if kwargs != old.init_kwargs: + new = type(old)(**kwargs).eval().requires_grad_(False) + misc.copy_params_and_buffers(old, new, require_all=True) + data[key] = new + return data + +#---------------------------------------------------------------------------- + +class _TFNetworkStub(dnnlib.EasyDict): + pass + +class _LegacyUnpickler(pickle.Unpickler): + def find_class(self, module, name): + if module == 'dnnlib.tflib.network' and name == 'Network': + return _TFNetworkStub + return super().find_class(module, name) + +#---------------------------------------------------------------------------- + +def _collect_tf_params(tf_net): + # pylint: disable=protected-access + tf_params = dict() + def recurse(prefix, tf_net): + for name, value in tf_net.variables: + tf_params[prefix + name] = value + for name, comp in tf_net.components.items(): + recurse(prefix + name + '/', comp) + recurse('', tf_net) + return tf_params + +#---------------------------------------------------------------------------- + +def _populate_module_params(module, *patterns): + for name, tensor in misc.named_params_and_buffers(module): + found = False + value = None + for pattern, value_fn in zip(patterns[0::2], patterns[1::2]): + match = re.fullmatch(pattern, name) + if match: + found = True + if value_fn is not None: + value = value_fn(*match.groups()) + break + try: + assert found + if value is not None: + tensor.copy_(torch.from_numpy(np.array(value))) + except: + print(name, list(tensor.shape)) + raise + +#---------------------------------------------------------------------------- + +def convert_tf_generator(tf_G): + if tf_G.version < 4: + raise ValueError('TensorFlow pickle version too low') + + # Collect kwargs. + tf_kwargs = tf_G.static_kwargs + known_kwargs = set() + def kwarg(tf_name, default=None, none=None): + known_kwargs.add(tf_name) + val = tf_kwargs.get(tf_name, default) + return val if val is not None else none + + # Convert kwargs. + from training import networks_stylegan2 + network_class = networks_stylegan2.Generator + kwargs = dnnlib.EasyDict( + z_dim = kwarg('latent_size', 512), + c_dim = kwarg('label_size', 0), + w_dim = kwarg('dlatent_size', 512), + img_resolution = kwarg('resolution', 1024), + img_channels = kwarg('num_channels', 3), + channel_base = kwarg('fmap_base', 16384) * 2, + channel_max = kwarg('fmap_max', 512), + num_fp16_res = kwarg('num_fp16_res', 0), + conv_clamp = kwarg('conv_clamp', None), + architecture = kwarg('architecture', 'skip'), + resample_filter = kwarg('resample_kernel', [1,3,3,1]), + use_noise = kwarg('use_noise', True), + activation = kwarg('nonlinearity', 'lrelu'), + mapping_kwargs = dnnlib.EasyDict( + num_layers = kwarg('mapping_layers', 8), + embed_features = kwarg('label_fmaps', None), + layer_features = kwarg('mapping_fmaps', None), + activation = kwarg('mapping_nonlinearity', 'lrelu'), + lr_multiplier = kwarg('mapping_lrmul', 0.01), + w_avg_beta = kwarg('w_avg_beta', 0.995, none=1), + ), + ) + + # Check for unknown kwargs. + kwarg('truncation_psi') + kwarg('truncation_cutoff') + kwarg('style_mixing_prob') + kwarg('structure') + kwarg('conditioning') + kwarg('fused_modconv') + unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs) + if len(unknown_kwargs) > 0: + raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0]) + + # Collect params. + tf_params = _collect_tf_params(tf_G) + for name, value in list(tf_params.items()): + match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name) + if match: + r = kwargs.img_resolution // (2 ** int(match.group(1))) + tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value + kwargs.synthesis.kwargs.architecture = 'orig' + #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') + + # Convert params. + G = network_class(**kwargs).eval().requires_grad_(False) + # pylint: disable=unnecessary-lambda + # pylint: disable=f-string-without-interpolation + _populate_module_params(G, + r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'], + r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose(), + r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'], + r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose(), + r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'], + r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0], + r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1), + r'synthesis\.b4\.conv1\.bias', lambda: tf_params[f'synthesis/4x4/Conv/bias'], + r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[f'synthesis/noise0'][0, 0], + r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[f'synthesis/4x4/Conv/noise_strength'], + r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(), + r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1, + r'synthesis\.b(\d+)\.conv0\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1), + r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'], + r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0], + r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'], + r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(), + r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1, + r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1), + r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/bias'], + r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0], + r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'], + r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(), + r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1, + r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1), + r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/bias'], + r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(), + r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1, + r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1), + r'.*\.resample_filter', None, + r'.*\.act_filter', None, + ) + return G + +#---------------------------------------------------------------------------- + +def convert_tf_discriminator(tf_D): + if tf_D.version < 4: + raise ValueError('TensorFlow pickle version too low') + + # Collect kwargs. + tf_kwargs = tf_D.static_kwargs + known_kwargs = set() + def kwarg(tf_name, default=None): + known_kwargs.add(tf_name) + return tf_kwargs.get(tf_name, default) + + # Convert kwargs. + kwargs = dnnlib.EasyDict( + c_dim = kwarg('label_size', 0), + img_resolution = kwarg('resolution', 1024), + img_channels = kwarg('num_channels', 3), + architecture = kwarg('architecture', 'resnet'), + channel_base = kwarg('fmap_base', 16384) * 2, + channel_max = kwarg('fmap_max', 512), + num_fp16_res = kwarg('num_fp16_res', 0), + conv_clamp = kwarg('conv_clamp', None), + cmap_dim = kwarg('mapping_fmaps', None), + block_kwargs = dnnlib.EasyDict( + activation = kwarg('nonlinearity', 'lrelu'), + resample_filter = kwarg('resample_kernel', [1,3,3,1]), + freeze_layers = kwarg('freeze_layers', 0), + ), + mapping_kwargs = dnnlib.EasyDict( + num_layers = kwarg('mapping_layers', 0), + embed_features = kwarg('mapping_fmaps', None), + layer_features = kwarg('mapping_fmaps', None), + activation = kwarg('nonlinearity', 'lrelu'), + lr_multiplier = kwarg('mapping_lrmul', 0.1), + ), + epilogue_kwargs = dnnlib.EasyDict( + mbstd_group_size = kwarg('mbstd_group_size', None), + mbstd_num_channels = kwarg('mbstd_num_features', 1), + activation = kwarg('nonlinearity', 'lrelu'), + ), + ) + + # Check for unknown kwargs. + kwarg('structure') + kwarg('conditioning') + unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs) + if len(unknown_kwargs) > 0: + raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0]) + + # Collect params. + tf_params = _collect_tf_params(tf_D) + for name, value in list(tf_params.items()): + match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name) + if match: + r = kwargs.img_resolution // (2 ** int(match.group(1))) + tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value + kwargs.architecture = 'orig' + #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') + + # Convert params. + from training import networks_stylegan2 + D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False) + # pylint: disable=unnecessary-lambda + # pylint: disable=f-string-without-interpolation + _populate_module_params(D, + r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1), + r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'], + r'b(\d+)\.conv(\d+)\.weight', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1), + r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'], + r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1), + r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose(), + r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'], + r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose(), + r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'], + r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1), + r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'], + r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose(), + r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'], + r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose(), + r'b4\.out\.bias', lambda: tf_params[f'Output/bias'], + r'.*\.resample_filter', None, + ) + return D + +#---------------------------------------------------------------------------- + +@click.command() +@click.option('--source', help='Input pickle', required=True, metavar='PATH') +@click.option('--dest', help='Output pickle', required=True, metavar='PATH') +@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True) +def convert_network_pickle(source, dest, force_fp16): + """Convert legacy network pickle into the native PyTorch format. + + The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA. + It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks. + + Example: + + \b + python legacy.py \\ + --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\ + --dest=stylegan2-cat-config-f.pkl + """ + print(f'Loading "{source}"...') + with dnnlib.util.open_url(source) as f: + data = load_network_pkl(f, force_fp16=force_fp16) + print(f'Saving "{dest}"...') + with open(dest, 'wb') as f: + pickle.dump(data, f) + print('Done.') + +#---------------------------------------------------------------------------- + +if __name__ == "__main__": + convert_network_pickle() # pylint: disable=no-value-for-parameter + +#---------------------------------------------------------------------------- diff --git a/metrics/__init__.py b/metrics/__init__.py new file mode 100644 index 0000000..939e7c6 --- /dev/null +++ b/metrics/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/metrics/equivariance.py b/metrics/equivariance.py new file mode 100644 index 0000000..d5559ac --- /dev/null +++ b/metrics/equivariance.py @@ -0,0 +1,267 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Equivariance metrics (EQ-T, EQ-T_frac, and EQ-R) from the paper +"Alias-Free Generative Adversarial Networks".""" + +import copy +import numpy as np +import torch +import torch.fft +from torch_utils.ops import upfirdn2d +from . import metric_utils + +#---------------------------------------------------------------------------- +# Utilities. + +def sinc(x): + y = (x * np.pi).abs() + z = torch.sin(y) / y.clamp(1e-30, float('inf')) + return torch.where(y < 1e-30, torch.ones_like(x), z) + +def lanczos_window(x, a): + x = x.abs() / a + return torch.where(x < 1, sinc(x), torch.zeros_like(x)) + +def rotation_matrix(angle): + angle = torch.as_tensor(angle).to(torch.float32) + mat = torch.eye(3, device=angle.device) + mat[0, 0] = angle.cos() + mat[0, 1] = angle.sin() + mat[1, 0] = -angle.sin() + mat[1, 1] = angle.cos() + return mat + +#---------------------------------------------------------------------------- +# Apply integer translation to a batch of 2D images. Corresponds to the +# operator T_x in Appendix E.1. + +def apply_integer_translation(x, tx, ty): + _N, _C, H, W = x.shape + tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device) + ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device) + ix = tx.round().to(torch.int64) + iy = ty.round().to(torch.int64) + + z = torch.zeros_like(x) + m = torch.zeros_like(x) + if abs(ix) < W and abs(iy) < H: + y = x[:, :, max(-iy,0) : H+min(-iy,0), max(-ix,0) : W+min(-ix,0)] + z[:, :, max(iy,0) : H+min(iy,0), max(ix,0) : W+min(ix,0)] = y + m[:, :, max(iy,0) : H+min(iy,0), max(ix,0) : W+min(ix,0)] = 1 + return z, m + +#---------------------------------------------------------------------------- +# Apply integer translation to a batch of 2D images. Corresponds to the +# operator T_x in Appendix E.2. + +def apply_fractional_translation(x, tx, ty, a=3): + _N, _C, H, W = x.shape + tx = torch.as_tensor(tx * W).to(dtype=torch.float32, device=x.device) + ty = torch.as_tensor(ty * H).to(dtype=torch.float32, device=x.device) + ix = tx.floor().to(torch.int64) + iy = ty.floor().to(torch.int64) + fx = tx - ix + fy = ty - iy + b = a - 1 + + z = torch.zeros_like(x) + zx0 = max(ix - b, 0) + zy0 = max(iy - b, 0) + zx1 = min(ix + a, 0) + W + zy1 = min(iy + a, 0) + H + if zx0 < zx1 and zy0 < zy1: + taps = torch.arange(a * 2, device=x.device) - b + filter_x = (sinc(taps - fx) * sinc((taps - fx) / a)).unsqueeze(0) + filter_y = (sinc(taps - fy) * sinc((taps - fy) / a)).unsqueeze(1) + y = x + y = upfirdn2d.filter2d(y, filter_x / filter_x.sum(), padding=[b,a,0,0]) + y = upfirdn2d.filter2d(y, filter_y / filter_y.sum(), padding=[0,0,b,a]) + y = y[:, :, max(b-iy,0) : H+b+a+min(-iy-a,0), max(b-ix,0) : W+b+a+min(-ix-a,0)] + z[:, :, zy0:zy1, zx0:zx1] = y + + m = torch.zeros_like(x) + mx0 = max(ix + a, 0) + my0 = max(iy + a, 0) + mx1 = min(ix - b, 0) + W + my1 = min(iy - b, 0) + H + if mx0 < mx1 and my0 < my1: + m[:, :, my0:my1, mx0:mx1] = 1 + return z, m + +#---------------------------------------------------------------------------- +# Construct an oriented low-pass filter that applies the appropriate +# bandlimit with respect to the input and output of the given affine 2D +# image transformation. + +def construct_affine_bandlimit_filter(mat, a=3, amax=16, aflt=64, up=4, cutoff_in=1, cutoff_out=1): + assert a <= amax < aflt + mat = torch.as_tensor(mat).to(torch.float32) + + # Construct 2D filter taps in input & output coordinate spaces. + taps = ((torch.arange(aflt * up * 2 - 1, device=mat.device) + 1) / up - aflt).roll(1 - aflt * up) + yi, xi = torch.meshgrid(taps, taps) + xo, yo = (torch.stack([xi, yi], dim=2) @ mat[:2, :2].t()).unbind(2) + + # Convolution of two oriented 2D sinc filters. + fi = sinc(xi * cutoff_in) * sinc(yi * cutoff_in) + fo = sinc(xo * cutoff_out) * sinc(yo * cutoff_out) + f = torch.fft.ifftn(torch.fft.fftn(fi) * torch.fft.fftn(fo)).real + + # Convolution of two oriented 2D Lanczos windows. + wi = lanczos_window(xi, a) * lanczos_window(yi, a) + wo = lanczos_window(xo, a) * lanczos_window(yo, a) + w = torch.fft.ifftn(torch.fft.fftn(wi) * torch.fft.fftn(wo)).real + + # Construct windowed FIR filter. + f = f * w + + # Finalize. + c = (aflt - amax) * up + f = f.roll([aflt * up - 1] * 2, dims=[0,1])[c:-c, c:-c] + f = torch.nn.functional.pad(f, [0, 1, 0, 1]).reshape(amax * 2, up, amax * 2, up) + f = f / f.sum([0,2], keepdim=True) / (up ** 2) + f = f.reshape(amax * 2 * up, amax * 2 * up)[:-1, :-1] + return f + +#---------------------------------------------------------------------------- +# Apply the given affine transformation to a batch of 2D images. + +def apply_affine_transformation(x, mat, up=4, **filter_kwargs): + _N, _C, H, W = x.shape + mat = torch.as_tensor(mat).to(dtype=torch.float32, device=x.device) + + # Construct filter. + f = construct_affine_bandlimit_filter(mat, up=up, **filter_kwargs) + assert f.ndim == 2 and f.shape[0] == f.shape[1] and f.shape[0] % 2 == 1 + p = f.shape[0] // 2 + + # Construct sampling grid. + theta = mat.inverse() + theta[:2, 2] *= 2 + theta[0, 2] += 1 / up / W + theta[1, 2] += 1 / up / H + theta[0, :] *= W / (W + p / up * 2) + theta[1, :] *= H / (H + p / up * 2) + theta = theta[:2, :3].unsqueeze(0).repeat([x.shape[0], 1, 1]) + g = torch.nn.functional.affine_grid(theta, x.shape, align_corners=False) + + # Resample image. + y = upfirdn2d.upsample2d(x=x, f=f, up=up, padding=p) + z = torch.nn.functional.grid_sample(y, g, mode='bilinear', padding_mode='zeros', align_corners=False) + + # Form mask. + m = torch.zeros_like(y) + c = p * 2 + 1 + m[:, :, c:-c, c:-c] = 1 + m = torch.nn.functional.grid_sample(m, g, mode='nearest', padding_mode='zeros', align_corners=False) + return z, m + +#---------------------------------------------------------------------------- +# Apply fractional rotation to a batch of 2D images. Corresponds to the +# operator R_\alpha in Appendix E.3. + +def apply_fractional_rotation(x, angle, a=3, **filter_kwargs): + angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device) + mat = rotation_matrix(angle) + return apply_affine_transformation(x, mat, a=a, amax=a*2, **filter_kwargs) + +#---------------------------------------------------------------------------- +# Modify the frequency content of a batch of 2D images as if they had undergo +# fractional rotation -- but without actually rotating them. Corresponds to +# the operator R^*_\alpha in Appendix E.3. + +def apply_fractional_pseudo_rotation(x, angle, a=3, **filter_kwargs): + angle = torch.as_tensor(angle).to(dtype=torch.float32, device=x.device) + mat = rotation_matrix(-angle) + f = construct_affine_bandlimit_filter(mat, a=a, amax=a*2, up=1, **filter_kwargs) + y = upfirdn2d.filter2d(x=x, f=f) + m = torch.zeros_like(y) + c = f.shape[0] // 2 + m[:, :, c:-c, c:-c] = 1 + return y, m + +#---------------------------------------------------------------------------- +# Compute the selected equivariance metrics for the given generator. + +def compute_equivariance_metrics(opts, num_samples, batch_size, translate_max=0.125, rotate_max=1, compute_eqt_int=False, compute_eqt_frac=False, compute_eqr=False): + assert compute_eqt_int or compute_eqt_frac or compute_eqr + + # Setup generator and labels. + G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) + I = torch.eye(3, device=opts.device) + M = getattr(getattr(getattr(G, 'synthesis', None), 'input', None), 'transform', None) + if M is None: + raise ValueError('Cannot compute equivariance metrics; the given generator does not support user-specified image transformations') + c_iter = metric_utils.iterate_random_labels(opts=opts, batch_size=batch_size) + + # Sampling loop. + sums = None + progress = opts.progress.sub(tag='eq sampling', num_items=num_samples) + for batch_start in range(0, num_samples, batch_size * opts.num_gpus): + progress.update(batch_start) + s = [] + + # Randomize noise buffers, if any. + for name, buf in G.named_buffers(): + if name.endswith('.noise_const'): + buf.copy_(torch.randn_like(buf)) + + # Run mapping network. + z = torch.randn([batch_size, G.z_dim], device=opts.device) + c = next(c_iter) + ws = G.mapping(z=z, c=c) + + # Generate reference image. + M[:] = I + orig = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) + + # Integer translation (EQ-T). + if compute_eqt_int: + t = (torch.rand(2, device=opts.device) * 2 - 1) * translate_max + t = (t * G.img_resolution).round() / G.img_resolution + M[:] = I + M[:2, 2] = -t + img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) + ref, mask = apply_integer_translation(orig, t[0], t[1]) + s += [(ref - img).square() * mask, mask] + + # Fractional translation (EQ-T_frac). + if compute_eqt_frac: + t = (torch.rand(2, device=opts.device) * 2 - 1) * translate_max + M[:] = I + M[:2, 2] = -t + img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) + ref, mask = apply_fractional_translation(orig, t[0], t[1]) + s += [(ref - img).square() * mask, mask] + + # Rotation (EQ-R). + if compute_eqr: + angle = (torch.rand([], device=opts.device) * 2 - 1) * (rotate_max * np.pi) + M[:] = rotation_matrix(-angle) + img = G.synthesis(ws=ws, noise_mode='const', **opts.G_kwargs) + ref, ref_mask = apply_fractional_rotation(orig, angle) + pseudo, pseudo_mask = apply_fractional_pseudo_rotation(img, angle) + mask = ref_mask * pseudo_mask + s += [(ref - pseudo).square() * mask, mask] + + # Accumulate results. + s = torch.stack([x.to(torch.float64).sum() for x in s]) + sums = sums + s if sums is not None else s + progress.update(num_samples) + + # Compute PSNRs. + if opts.num_gpus > 1: + torch.distributed.all_reduce(sums) + sums = sums.cpu() + mses = sums[0::2] / sums[1::2] + psnrs = np.log10(2) * 20 - mses.log10() * 10 + psnrs = tuple(psnrs.numpy()) + return psnrs[0] if len(psnrs) == 1 else psnrs + +#---------------------------------------------------------------------------- diff --git a/metrics/frechet_inception_distance.py b/metrics/frechet_inception_distance.py new file mode 100644 index 0000000..b45edeb --- /dev/null +++ b/metrics/frechet_inception_distance.py @@ -0,0 +1,117 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Frechet Inception Distance (FID) from the paper +"GANs trained by a two time-scale update rule converge to a local Nash +equilibrium". Matches the original implementation by Heusel et al. at +https://github.com/bioinf-jku/TTUR/blob/master/fid.py""" + +import numpy as np +import scipy.linalg +from . import metric_utils + +#---------------------------------------------------------------------------- + +def compute_fid(opts, max_real, num_gen): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' + detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. + + mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real).get_mean_cov() + + mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov() + + if opts.rank != 0: + return float('nan') + + m = np.square(mu_gen - mu_real).sum() + s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) + return float(fid) + +#---------------------------------------------------------------------------- + +def compute_fid_full(opts, resolution, max_real, num_gen, mode='full'): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' + detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. + assert(opts.dataset_kwargs.resolution == resolution) + opts.target_resolution = resolution + + # real image features + mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset_full( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real).get_mean_cov() + + # gen features + if mode == 'full': + mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator_full( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov() + elif mode == 'up': + mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator_up( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov() + else: + raise NotImplementedError + + if opts.rank != 0: + return float('nan') + + m = np.square(mu_gen - mu_real).sum() + s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) + return float(fid) + +def compute_fid_patch(opts, resolution, max_real, num_gen, mode='patch'): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' + detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. + assert(opts.dataset_kwargs.resolution == resolution) + opts.target_resolution = resolution + assert(max_real is not None) + + # real image features + if mode.split('-')[0] == 'patch': + stats = metric_utils.compute_feature_stats_for_dataset_patch( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real) + mu_real, sigma_real = stats.get_mean_cov() + transformations = stats.get_all_transforms_torch() + elif mode.split('-')[0] == 'subpatch': + stats = metric_utils.compute_feature_stats_for_dataset_patch( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + is_subpatch=True, rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real) + mu_real, sigma_real = stats.get_mean_cov() + transformations = stats.get_all_transforms_torch() + + # gen features + if mode == 'patch': + mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator_patch( + opts=opts, transformations=transformations, + detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov() + elif mode == 'subpatch': + mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator_patch( + opts=opts, transformations=transformations, is_subpatch=True, + detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov() + else: + raise NotImplementedError + + if opts.rank != 0: + return float('nan') + + m = np.square(mu_gen - mu_real).sum() + s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) + return float(fid) diff --git a/metrics/inception_score.py b/metrics/inception_score.py new file mode 100644 index 0000000..e0a3a44 --- /dev/null +++ b/metrics/inception_score.py @@ -0,0 +1,38 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Inception Score (IS) from the paper "Improved techniques for training +GANs". Matches the original implementation by Salimans et al. at +https://github.com/openai/improved-gan/blob/master/inception_score/model.py""" + +import numpy as np +from . import metric_utils + +#---------------------------------------------------------------------------- + +def compute_is(opts, num_gen, num_splits): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' + detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer. + + gen_probs = metric_utils.compute_feature_stats_for_generator( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + capture_all=True, max_items=num_gen).get_all() + + if opts.rank != 0: + return float('nan'), float('nan') + + scores = [] + for i in range(num_splits): + part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits] + kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True))) + kl = np.mean(np.sum(kl, axis=1)) + scores.append(np.exp(kl)) + return float(np.mean(scores)), float(np.std(scores)) + +#---------------------------------------------------------------------------- diff --git a/metrics/kernel_inception_distance.py b/metrics/kernel_inception_distance.py new file mode 100644 index 0000000..d69325c --- /dev/null +++ b/metrics/kernel_inception_distance.py @@ -0,0 +1,46 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Kernel Inception Distance (KID) from the paper "Demystifying MMD +GANs". Matches the original implementation by Binkowski et al. at +https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py""" + +import numpy as np +from . import metric_utils + +#---------------------------------------------------------------------------- + +def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size): + # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz + detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl' + detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer. + + real_features = metric_utils.compute_feature_stats_for_dataset( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all() + + gen_features = metric_utils.compute_feature_stats_for_generator( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all() + + if opts.rank != 0: + return float('nan') + + n = real_features.shape[1] + m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size) + t = 0 + for _subset_idx in range(num_subsets): + x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)] + y = real_features[np.random.choice(real_features.shape[0], m, replace=False)] + a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3 + b = (x @ y.T / n + 1) ** 3 + t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m + kid = t / num_subsets / m + return float(kid) + +#---------------------------------------------------------------------------- diff --git a/metrics/metric_main.py b/metrics/metric_main.py new file mode 100644 index 0000000..1179712 --- /dev/null +++ b/metrics/metric_main.py @@ -0,0 +1,153 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Main API for computing and reporting quality metrics.""" + +import os +import time +import json +import torch +import dnnlib + +from . import metric_utils +from . import frechet_inception_distance +from . import kernel_inception_distance +from . import precision_recall +from . import perceptual_path_length +from . import inception_score +from . import equivariance + +#---------------------------------------------------------------------------- + +_metric_dict = dict() # name => fn + +def register_metric(fn): + assert callable(fn) + _metric_dict[fn.__name__] = fn + return fn + +def is_valid_metric(metric): + return metric in _metric_dict + +def list_valid_metrics(): + return list(_metric_dict.keys()) + +#---------------------------------------------------------------------------- + +def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for the full list of arguments. + assert is_valid_metric(metric) + opts = metric_utils.MetricOptions(**kwargs) + + # Calculate. + start_time = time.time() + results = _metric_dict[metric](opts) + total_time = time.time() - start_time + + # Broadcast results. + for key, value in list(results.items()): + if opts.num_gpus > 1: + value = torch.as_tensor(value, dtype=torch.float64, device=opts.device) + torch.distributed.broadcast(tensor=value, src=0) + value = float(value.cpu()) + results[key] = value + + # Decorate with metadata. + return dnnlib.EasyDict( + results = dnnlib.EasyDict(results), + metric = metric, + total_time = total_time, + total_time_str = dnnlib.util.format_time(total_time), + num_gpus = opts.num_gpus, + ) + +#---------------------------------------------------------------------------- + +def report_metric(result_dict, run_dir=None, snapshot_pkl=None): + metric = result_dict['metric'] + assert is_valid_metric(metric) + if run_dir is not None and snapshot_pkl is not None: + snapshot_pkl = os.path.relpath(snapshot_pkl, run_dir) + + jsonl_line = json.dumps(dict(result_dict, snapshot_pkl=snapshot_pkl, timestamp=time.time())) + print(jsonl_line) + if run_dir is not None and os.path.isdir(run_dir): + with open(os.path.join(run_dir, f'metric-{metric}.jsonl'), 'at') as f: + f.write(jsonl_line + '\n') + +#---------------------------------------------------------------------------- +# Recommended metrics. + +@register_metric +def fid50k_full(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000) + return dict(fid50k_full=fid) + +@register_metric +def kid50k_full(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + kid = kernel_inception_distance.compute_kid(opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000) + return dict(kid50k_full=kid) + +@register_metric +def pr50k3_full(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + precision, recall = precision_recall.compute_pr(opts, max_real=200000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000) + return dict(pr50k3_full_precision=precision, pr50k3_full_recall=recall) + +@register_metric +def ppl2_wend(opts): + ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='end', crop=False, batch_size=2) + return dict(ppl2_wend=ppl) + +@register_metric +def eqt50k_int(opts): + opts.G_kwargs.update(force_fp32=True) + psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_int=True) + return dict(eqt50k_int=psnr) + +@register_metric +def eqt50k_frac(opts): + opts.G_kwargs.update(force_fp32=True) + psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqt_frac=True) + return dict(eqt50k_frac=psnr) + +@register_metric +def eqr50k(opts): + opts.G_kwargs.update(force_fp32=True) + psnr = equivariance.compute_equivariance_metrics(opts, num_samples=50000, batch_size=4, compute_eqr=True) + return dict(eqr50k=psnr) + +#---------------------------------------------------------------------------- +# Legacy metrics. + +@register_metric +def fid50k(opts): + opts.dataset_kwargs.update(max_size=None) + fid = frechet_inception_distance.compute_fid(opts, max_real=50000, num_gen=50000) + return dict(fid50k=fid) + +@register_metric +def kid50k(opts): + opts.dataset_kwargs.update(max_size=None) + kid = kernel_inception_distance.compute_kid(opts, max_real=50000, num_gen=50000, num_subsets=100, max_subset_size=1000) + return dict(kid50k=kid) + +@register_metric +def pr50k3(opts): + opts.dataset_kwargs.update(max_size=None) + precision, recall = precision_recall.compute_pr(opts, max_real=50000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000) + return dict(pr50k3_precision=precision, pr50k3_recall=recall) + +@register_metric +def is50k(opts): + opts.dataset_kwargs.update(max_size=None, xflip=False) + mean, std = inception_score.compute_is(opts, num_gen=50000, num_splits=10) + return dict(is50k_mean=mean, is50k_std=std) + +#---------------------------------------------------------------------------- diff --git a/metrics/metric_utils.py b/metrics/metric_utils.py new file mode 100644 index 0000000..4e5fd38 --- /dev/null +++ b/metrics/metric_utils.py @@ -0,0 +1,622 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous utilities used internally by the quality metrics.""" + +import os +import time +import hashlib +import pickle +import copy +import uuid +import numpy as np +import torch +import dnnlib + +# added +from tqdm import tqdm +import math +import random +import sys +from util import renormalize +from util import patch_util + +#---------------------------------------------------------------------------- + +class MetricOptions: + def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True): + assert 0 <= rank < num_gpus + self.G = G + self.G_kwargs = dnnlib.EasyDict(G_kwargs) + self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs) + self.num_gpus = num_gpus + self.rank = rank + self.device = device if device is not None else torch.device('cuda', rank) + self.progress = progress.sub() if progress is not None and rank == 0 else ProgressMonitor() + self.cache = cache + +#---------------------------------------------------------------------------- + +_feature_detector_cache = dict() + +def get_feature_detector_name(url): + return os.path.splitext(url.split('/')[-1])[0] + +def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False): + assert 0 <= rank < num_gpus + key = (url, device) + if key not in _feature_detector_cache: + is_leader = (rank == 0) + if not is_leader and num_gpus > 1: + torch.distributed.barrier() # leader goes first + with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f: + _feature_detector_cache[key] = pickle.load(f).to(device) + if is_leader and num_gpus > 1: + torch.distributed.barrier() # others follow + return _feature_detector_cache[key] + +#---------------------------------------------------------------------------- + +def iterate_random_labels(opts, batch_size): + if opts.G.c_dim == 0: + c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device) + while True: + yield c + else: + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + while True: + c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_size)] + c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device) + yield c + +#---------------------------------------------------------------------------- + +class FeatureStats: + def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None): + self.capture_all = capture_all + self.capture_mean_cov = capture_mean_cov + self.max_items = max_items + self.num_items = 0 + self.num_features = None + self.all_features = None + self.raw_mean = None + self.raw_cov = None + self.all_transforms = None + + def set_num_features(self, num_features): + if self.num_features is not None: + assert num_features == self.num_features + else: + self.num_features = num_features + self.all_features = [] + self.raw_mean = np.zeros([num_features], dtype=np.float64) + self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64) + + def is_full(self): + return (self.max_items is not None) and (self.num_items >= self.max_items) + + def append(self, x): + x = np.asarray(x, dtype=np.float32) + assert x.ndim == 2 + if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items): + if self.num_items >= self.max_items: + return + x = x[:self.max_items - self.num_items] + + self.set_num_features(x.shape[1]) + self.num_items += x.shape[0] + if self.capture_all: + self.all_features.append(x) + if self.capture_mean_cov: + x64 = x.astype(np.float64) + self.raw_mean += x64.sum(axis=0) + self.raw_cov += x64.T @ x64 + + def append_torch(self, x, num_gpus=1, rank=0): + assert isinstance(x, torch.Tensor) and x.ndim == 2 + assert 0 <= rank < num_gpus + if num_gpus > 1: + ys = [] + for src in range(num_gpus): + y = x.clone() + torch.distributed.broadcast(y, src=src) + ys.append(y) + x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples + self.append(x.cpu().numpy()) + + def get_all(self): + assert self.capture_all + return np.concatenate(self.all_features, axis=0) + + def get_all_torch(self): + return torch.from_numpy(self.get_all()) + + def get_mean_cov(self): + assert self.capture_mean_cov + mean = self.raw_mean / self.num_items + cov = self.raw_cov / self.num_items + cov = cov - np.outer(mean, mean) + return mean, cov + + def append_transform(self, x): + # x = transformation matrix + # note: run this funcation after append, as it does not keep track of num_items + x = np.asarray(x, dtype=np.float32) + assert x.ndim == 3 + assert(x.shape[1] == 3) + assert(x.shape[2] == 3) + if self.all_transforms is None: + self.all_transforms = [] + self.all_transforms.append(x) + + def get_all_transforms_torch(self): + transforms = np.concatenate(self.all_transforms, axis=0) + transforms = torch.from_numpy(transforms) + assert(transforms.shape[0] >= self.max_items) + return transforms[:self.max_items] + + def save(self, pkl_file): + with open(pkl_file, 'wb') as f: + pickle.dump(self.__dict__, f) + + @staticmethod + def load(pkl_file): + with open(pkl_file, 'rb') as f: + s = dnnlib.EasyDict(pickle.load(f)) + obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items) + obj.__dict__.update(s) + return obj + +#---------------------------------------------------------------------------- + +class ProgressMonitor: + def __init__(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000, pfn_total=1000): + self.tag = tag + self.num_items = num_items + self.verbose = verbose + self.flush_interval = flush_interval + self.progress_fn = progress_fn + self.pfn_lo = pfn_lo + self.pfn_hi = pfn_hi + self.pfn_total = pfn_total + self.start_time = time.time() + self.batch_time = self.start_time + self.batch_items = 0 + if self.progress_fn is not None: + self.progress_fn(self.pfn_lo, self.pfn_total) + + def update(self, cur_items): + assert (self.num_items is None) or (cur_items <= self.num_items) + if (cur_items < self.batch_items + self.flush_interval) and (self.num_items is None or cur_items < self.num_items): + return + cur_time = time.time() + total_time = cur_time - self.start_time + time_per_item = (cur_time - self.batch_time) / max(cur_items - self.batch_items, 1) + if (self.verbose) and (self.tag is not None): + print(f'{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}') + self.batch_time = cur_time + self.batch_items = cur_items + + if (self.progress_fn is not None) and (self.num_items is not None): + self.progress_fn(self.pfn_lo + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), self.pfn_total) + + def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1): + return ProgressMonitor( + tag = tag, + num_items = num_items, + flush_interval = flush_interval, + verbose = self.verbose, + progress_fn = self.progress_fn, + pfn_lo = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo, + pfn_hi = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi, + pfn_total = self.pfn_total, + ) + +#---------------------------------------------------------------------------- + +def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=None, **stats_kwargs): + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + if data_loader_kwargs is None: + data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2) + + # Try to lookup from cache. + cache_file = None + if opts.cache: + # Choose cache file name. + args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs) + md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8')) + cache_tag = f'{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}' + cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl') + + # Check if the file exists (all processes must agree). + flag = os.path.isfile(cache_file) if opts.rank == 0 else False + if opts.num_gpus > 1: + flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device) + torch.distributed.broadcast(tensor=flag, src=0) + flag = (float(flag.cpu()) != 0) + + # Load. + if flag: + return FeatureStats.load(cache_file) + + # Initialize. + num_items = len(dataset) + if max_items is not None: + num_items = min(num_items, max_items) + stats = FeatureStats(max_items=num_items, **stats_kwargs) + progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + + # Main loop. + item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)] + for images, _labels in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs): + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images.to(opts.device), **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + + # Save to cache. + if cache_file is not None and opts.rank == 0: + os.makedirs(os.path.dirname(cache_file), exist_ok=True) + temp_file = cache_file + '.' + uuid.uuid4().hex + stats.save(temp_file) + os.replace(temp_file, cache_file) # atomic + return stats + +#---------------------------------------------------------------------------- + +def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs): + if batch_gen is None: + batch_gen = min(batch_size, 4) + assert batch_size % batch_gen == 0 + + # Setup generator and labels. + G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) + c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen) + + # Initialize. + stats = FeatureStats(**stats_kwargs) + assert stats.max_items is not None + progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + + # Main loop. + while not stats.is_full(): + images = [] + for _i in range(batch_size // batch_gen): + z = torch.randn([batch_gen, G.z_dim], device=opts.device) + img = G(z=z, c=next(c_iter), **opts.G_kwargs) + img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) + images.append(img) + images = torch.cat(images) + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images, **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + return stats + +#---------------------------------------------------------------------------- + +## additional functions added + +# computes feature stats and transformation matrix for patches +def compute_feature_stats_for_dataset_patch(opts, detector_url, detector_kwargs, is_subpatch=False, + rel_lo=0, rel_hi=1, batch_size=64, + data_loader_kwargs=None, max_items=None, **stats_kwargs): + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + assert(opts.dataset_kwargs.class_name == "training.dataset.ImagePatchDataset") + if data_loader_kwargs is None: + data_loader_kwargs = dict(pin_memory=True, num_workers=8, prefetch_factor=2) + + patch_size = opts.dataset_kwargs.resolution + scale_anneal = opts.dataset_kwargs.scale_anneal + scale_min = opts.dataset_kwargs.scale_min + scale_max = opts.dataset_kwargs.scale_max + assert(scale_anneal == -1) + max_size = 0 if scale_min is None else int(patch_size / scale_min) + min_size = int(patch_size / scale_max) + + # Try to lookup from cache. + cache_file = None + if opts.cache: + # Choose cache file name. + args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs) + md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8')) + if is_subpatch: + cache_tag = f'patch{patch_size}-subpatch299-min{min_size}max{max_size}-{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}' + else: + cache_tag = f'patch{patch_size}-min{min_size}max{max_size}-{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}' + cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl') + + # Check if the file exists (all processes must agree). + flag = os.path.isfile(cache_file) if opts.rank == 0 else False + if opts.num_gpus > 1: + flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device) + torch.distributed.broadcast(tensor=flag, src=0) + flag = (float(flag.cpu()) != 0) + + # Load. + if flag: + print("loaded from cache: %s" % cache_tag) + return FeatureStats.load(cache_file) + + # Initialize. + assert(max_items is not None) + assert(opts.num_gpus == 1) + assert(opts.rank == 0) + num_items = max_items + stats = FeatureStats(max_items=num_items, **stats_kwargs) + progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + + # Main loop. + from torch_utils import misc + random_seed = opts.dataset_kwargs.random_seed + sampler = misc.InfiniteSampler(dataset=dataset, rank=opts.rank, num_replicas=opts.num_gpus, seed=random_seed) + iterator = iter(torch.utils.data.DataLoader( + dataset=dataset, sampler=sampler, batch_size=batch_size, **data_loader_kwargs)) + + num_batches = math.ceil(max_items / batch_size) + for _ in tqdm(range(num_batches)): + data, _labels = next(iterator) + images = data['image'] + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + if is_subpatch: + # crop the image, and save the x,y coords in the transformation matrix + crops = [] + transform = data['params']['transform'].clone() + for i, im in enumerate(images): + crop_width = 299 + x = random.randint(0, im.shape[-1]-crop_width) + y = random.randint(0, im.shape[-2]-crop_width) + crops.append(im[:, y:y+crop_width, x:x+crop_width]) + transform[i, 2, 0] = y + transform[i, 2, 1] = x + crops = torch.stack(crops) + features = detector(crops.to(opts.device), **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + stats.append_transform(transform) + else: + features = detector(images.to(opts.device), **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + stats.append_transform(data['params']['transform']) + progress.update(stats.num_items) + if stats.is_full(): + break + assert(stats.is_full()) + + # Save to cache. + if cache_file is not None and opts.rank == 0: + os.makedirs(os.path.dirname(cache_file), exist_ok=True) + temp_file = cache_file + '.' + uuid.uuid4().hex + stats.save(temp_file) + os.replace(temp_file, cache_file) # atomic + return stats + +# compute features on the full dataset; similar to the above one +# but changes naming to cache desired resolution +def compute_feature_stats_for_dataset_full(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, + data_loader_kwargs=None, max_items=None, **stats_kwargs): + dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs) + assert(opts.dataset_kwargs.class_name == "training.dataset.ImageFolderDataset") + if data_loader_kwargs is None: + data_loader_kwargs = dict(pin_memory=True, num_workers=8, prefetch_factor=2) + + # Try to lookup from cache. + cache_file = None + if opts.cache: + # Choose cache file name. + args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs) + md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8')) + cache_tag = f'full{dataset.resolution}-{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}' + cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl') + + # Check if the file exists (all processes must agree). + flag = os.path.isfile(cache_file) if opts.rank == 0 else False + if opts.num_gpus > 1: + flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device) + torch.distributed.broadcast(tensor=flag, src=0) + flag = (float(flag.cpu()) != 0) + # Load. + if flag: + print("loaded from cache: %s" % cache_tag) + return FeatureStats.load(cache_file) + + # Initialize. + num_items = len(dataset) + if max_items is not None: + num_items = min(num_items, max_items) + stats = FeatureStats(max_items=num_items, **stats_kwargs) + progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + + # Main loop. + item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)] + for images, _labels in tqdm(torch.utils.data.DataLoader( + dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs)): + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images.to(opts.device), **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + + # Save to cache. + if cache_file is not None and opts.rank == 0: + os.makedirs(os.path.dirname(cache_file), exist_ok=True) + temp_file = cache_file + '.' + uuid.uuid4().hex + stats.save(temp_file) + os.replace(temp_file, cache_file) # atomic + return stats + +# pieces together a generated full image from multiple base transformations +def compute_feature_stats_for_generator_full(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs): + target_resolution = opts.target_resolution # need to define a target resolution + if batch_gen is None: + batch_gen = min(batch_size, 4) + assert batch_size % batch_gen == 0 + + # Setup generator and labels. + G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) + c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen) + + # Initialize. + stats = FeatureStats(**stats_kwargs) + assert stats.max_items is not None + progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + + # get patch bounding boxes + from util.patch_util import generate_full_from_patches + patches = generate_full_from_patches(target_resolution, G.img_resolution) + assert(opts.G_kwargs == {}) # sanity check + + # Main loop. + num_batches = math.ceil(stats.max_items / batch_size) + for _ in tqdm(range(num_batches)): + images = [] + for _i in range(batch_size // batch_gen): + z = torch.randn([batch_gen, G.z_dim], device=opts.device) + ws = G.mapping(z=z, c=next(c_iter), **opts.G_kwargs) + full = torch.zeros((batch_gen, G.img_channels, target_resolution, target_resolution), device=opts.device) + for bbox, transform in patches: + transform = transform.repeat(batch_gen, 1, 1).cuda() + img = patch_util.scale_condition_wrapper(G, ws, transform, **opts.G_kwargs) + full[:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]] = img + img = full + img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) + images.append(img) + images = torch.cat(images) + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images, **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + if stats.is_full(): + break + assert(stats.is_full()) + return stats + + +# upsamples the full image using patches +def compute_feature_stats_for_generator_up(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs): + target_resolution = opts.target_resolution # need to define a target resolution + if batch_gen is None: + batch_gen = min(batch_size, 4) + assert batch_size % batch_gen == 0 + + # Setup generator and labels. + G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) + c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen) + + # Initialize. + stats = FeatureStats(**stats_kwargs) + assert stats.max_items is not None + progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + + # get patch bounding boxes + from util.patch_util import generate_full_from_patches + from metrics import equivariance + patches = generate_full_from_patches(target_resolution, G.img_resolution) + + # Main loop. + num_batches = math.ceil(stats.max_items / batch_size) + assert(opts.G_kwargs == {}) # sanity check + for _ in tqdm(range(num_batches)): + images = [] + for _i in range(batch_size // batch_gen): + z = torch.randn([batch_gen, G.z_dim], device=opts.device) + img = G(z=z, c=next(c_iter), **opts.G_kwargs) + full = torch.zeros((batch_gen, G.img_channels, target_resolution, target_resolution), device=opts.device) + for bbox, transform in patches: + up_patch, mask = equivariance.apply_affine_transformation(img, transform.inverse()[0]) + full[:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]] = up_patch + img = full + img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) + images.append(img) + images = torch.cat(images) + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images, **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + if stats.is_full(): + break + assert(stats.is_full()) + return stats + +# generates individual patches for pFID +def compute_feature_stats_for_generator_patch(opts, transformations, detector_url, detector_kwargs, + is_subpatch=False, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs): + assert(opts.num_gpus == 1) + assert(opts.rank == 0) + if batch_gen is None: + batch_gen = min(batch_size, 4) + assert batch_size % batch_gen == 0 + + # Setup generator and labels. + G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device) + c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen) + + # Initialize. + stats = FeatureStats(**stats_kwargs) + assert stats.max_items is not None + progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi) + detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose) + assert(opts.G_kwargs == {}) # sanity check + + counter = 0 + + # Main loop. + num_batches = math.ceil(stats.max_items / batch_size) + assert(transformations.shape[0] % batch_gen == 0) + for _ in tqdm(range(num_batches)): + images = [] + for _i in range(batch_size // batch_gen): + z = torch.randn([batch_gen, G.z_dim], device=opts.device) + ws = G.mapping(z=z, c=next(c_iter), **opts.G_kwargs) + if counter + batch_gen > transformations.shape[0]: + assert(counter == transformations.shape[0]) + # no more transformations to use + break + transform = transformations[counter:counter+batch_gen] + transform = transform.to(opts.device) + counter += batch_gen + if is_subpatch: + # extract crop locations from transform, reset the transform, + # and crop the image + y_start = transform[:, 2, 0].long() + x_start = transform[:, 2, 1].long() + crop_width = 299 + transform_new = transform.clone() + transform_new[:, 2, :2] = 0. + img = patch_util.scale_condition_wrapper(G, ws, transform_new, **opts.G_kwargs) + crops = [] + for im, x, y in zip(img, x_start, y_start): + crops.append(im[:, y:y+crop_width, x:x+crop_width]) + crops = torch.stack(crops) + img = crops + else: + img = patch_util.scale_condition_wrapper(G, ws, transform, **opts.G_kwargs) + img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) + images.append(img) + images = torch.cat(images) + if images.shape[1] == 1: + images = images.repeat([1, 3, 1, 1]) + features = detector(images, **detector_kwargs) + stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank) + progress.update(stats.num_items) + if stats.is_full(): + break + assert(stats.is_full()) + assert(counter == stats.max_items) + return stats + diff --git a/metrics/perceptual_path_length.py b/metrics/perceptual_path_length.py new file mode 100644 index 0000000..c68519f --- /dev/null +++ b/metrics/perceptual_path_length.py @@ -0,0 +1,125 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Perceptual Path Length (PPL) from the paper "A Style-Based Generator +Architecture for Generative Adversarial Networks". Matches the original +implementation by Karras et al. at +https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py""" + +import copy +import numpy as np +import torch +from . import metric_utils + +#---------------------------------------------------------------------------- + +# Spherical interpolation of a batch of vectors. +def slerp(a, b, t): + a = a / a.norm(dim=-1, keepdim=True) + b = b / b.norm(dim=-1, keepdim=True) + d = (a * b).sum(dim=-1, keepdim=True) + p = t * torch.acos(d) + c = b - d * a + c = c / c.norm(dim=-1, keepdim=True) + d = a * torch.cos(p) + c * torch.sin(p) + d = d / d.norm(dim=-1, keepdim=True) + return d + +#---------------------------------------------------------------------------- + +class PPLSampler(torch.nn.Module): + def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16): + assert space in ['z', 'w'] + assert sampling in ['full', 'end'] + super().__init__() + self.G = copy.deepcopy(G) + self.G_kwargs = G_kwargs + self.epsilon = epsilon + self.space = space + self.sampling = sampling + self.crop = crop + self.vgg16 = copy.deepcopy(vgg16) + + def forward(self, c): + # Generate random latents and interpolation t-values. + t = torch.rand([c.shape[0]], device=c.device) * (1 if self.sampling == 'full' else 0) + z0, z1 = torch.randn([c.shape[0] * 2, self.G.z_dim], device=c.device).chunk(2) + + # Interpolate in W or Z. + if self.space == 'w': + w0, w1 = self.G.mapping(z=torch.cat([z0,z1]), c=torch.cat([c,c])).chunk(2) + wt0 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2)) + wt1 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2) + self.epsilon) + else: # space == 'z' + zt0 = slerp(z0, z1, t.unsqueeze(1)) + zt1 = slerp(z0, z1, t.unsqueeze(1) + self.epsilon) + wt0, wt1 = self.G.mapping(z=torch.cat([zt0,zt1]), c=torch.cat([c,c])).chunk(2) + + # Randomize noise buffers. + for name, buf in self.G.named_buffers(): + if name.endswith('.noise_const'): + buf.copy_(torch.randn_like(buf)) + + # Generate images. + img = self.G.synthesis(ws=torch.cat([wt0,wt1]), noise_mode='const', force_fp32=True, **self.G_kwargs) + + # Center crop. + if self.crop: + assert img.shape[2] == img.shape[3] + c = img.shape[2] // 8 + img = img[:, :, c*3 : c*7, c*2 : c*6] + + # Downsample to 256x256. + factor = self.G.img_resolution // 256 + if factor > 1: + img = img.reshape([-1, img.shape[1], img.shape[2] // factor, factor, img.shape[3] // factor, factor]).mean([3, 5]) + + # Scale dynamic range from [-1,1] to [0,255]. + img = (img + 1) * (255 / 2) + if self.G.img_channels == 1: + img = img.repeat([1, 3, 1, 1]) + + # Evaluate differential LPIPS. + lpips_t0, lpips_t1 = self.vgg16(img, resize_images=False, return_lpips=True).chunk(2) + dist = (lpips_t0 - lpips_t1).square().sum(1) / self.epsilon ** 2 + return dist + +#---------------------------------------------------------------------------- + +def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch_size): + vgg16_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl' + vgg16 = metric_utils.get_feature_detector(vgg16_url, num_gpus=opts.num_gpus, rank=opts.rank, verbose=opts.progress.verbose) + + # Setup sampler and labels. + sampler = PPLSampler(G=opts.G, G_kwargs=opts.G_kwargs, epsilon=epsilon, space=space, sampling=sampling, crop=crop, vgg16=vgg16) + sampler.eval().requires_grad_(False).to(opts.device) + c_iter = metric_utils.iterate_random_labels(opts=opts, batch_size=batch_size) + + # Sampling loop. + dist = [] + progress = opts.progress.sub(tag='ppl sampling', num_items=num_samples) + for batch_start in range(0, num_samples, batch_size * opts.num_gpus): + progress.update(batch_start) + x = sampler(next(c_iter)) + for src in range(opts.num_gpus): + y = x.clone() + if opts.num_gpus > 1: + torch.distributed.broadcast(y, src=src) + dist.append(y) + progress.update(num_samples) + + # Compute PPL. + if opts.rank != 0: + return float('nan') + dist = torch.cat(dist)[:num_samples].cpu().numpy() + lo = np.percentile(dist, 1, interpolation='lower') + hi = np.percentile(dist, 99, interpolation='higher') + ppl = np.extract(np.logical_and(dist >= lo, dist <= hi), dist).mean() + return float(ppl) + +#---------------------------------------------------------------------------- diff --git a/metrics/precision_recall.py b/metrics/precision_recall.py new file mode 100644 index 0000000..120ef80 --- /dev/null +++ b/metrics/precision_recall.py @@ -0,0 +1,62 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Precision/Recall (PR) from the paper "Improved Precision and Recall +Metric for Assessing Generative Models". Matches the original implementation +by Kynkaanniemi et al. at +https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py""" + +import torch +from . import metric_utils + +#---------------------------------------------------------------------------- + +def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size): + assert 0 <= rank < num_gpus + num_cols = col_features.shape[0] + num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus + col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches) + dist_batches = [] + for col_batch in col_batches[rank :: num_gpus]: + dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0] + for src in range(num_gpus): + dist_broadcast = dist_batch.clone() + if num_gpus > 1: + torch.distributed.broadcast(dist_broadcast, src=src) + dist_batches.append(dist_broadcast.cpu() if rank == 0 else None) + return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None + +#---------------------------------------------------------------------------- + +def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size): + detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl' + detector_kwargs = dict(return_features=True) + + real_features = metric_utils.compute_feature_stats_for_dataset( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device) + + gen_features = metric_utils.compute_feature_stats_for_generator( + opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, + rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device) + + results = dict() + for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]: + kth = [] + for manifold_batch in manifold.split(row_batch_size): + dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size) + kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None) + kth = torch.cat(kth) if opts.rank == 0 else None + pred = [] + for probes_batch in probes.split(row_batch_size): + dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size) + pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None) + results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan') + return results['precision'], results['recall'] + +#---------------------------------------------------------------------------- diff --git a/notebook-demo.ipynb b/notebook-demo.ipynb new file mode 100644 index 0000000..e2b51f4 --- /dev/null +++ b/notebook-demo.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "numerous-render", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n", + "env: CUDA_VISIBLE_DEVICES=0\n" + ] + } + ], + "source": [ + "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n", + "%env CUDA_VISIBLE_DEVICES=0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "turkish-rouge", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ipywidgets import HTML\n", + "from ipyevents import Event \n", + "from IPython.display import display\n", + "import torch\n", + "import pickle\n", + "from util import renormalize, viz_util, patch_util\n", + "import numpy as np\n", + "torch.set_grad_enabled(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "prerequisite-choir", + "metadata": {}, + "outputs": [], + "source": [ + "pretrained_path = 'pretrained/bird_pretrained_final.pkl'\n", + "# pretrained_path = 'pretrained/church_pretrained_final.pkl'\n", + "# pretrained_path = 'pretrained/ffhq6k_pretrained_final.pkl'\n", + "# pretrained_path = 'pretrained/mountain_pretrained_final.pkl'\n", + "\n", + "with open(pretrained_path, 'rb') as f:\n", + " G_base = pickle.load(f)['G_ema'].cuda() # torch.nn.Module" + ] + }, + { + "cell_type": "markdown", + "id": "sized-wallace", + "metadata": {}, + "source": [ + "# interactive generation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "conditional-parks", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up PyTorch plugin \"bias_act_plugin\"... Done.\n" + ] + } + ], + "source": [ + "rng = np.random.RandomState(19)\n", + "z = torch.from_numpy(rng.standard_normal(G_base.z_dim)).float()\n", + "z = z[None].cuda()\n", + "c = None\n", + "ws = G_base.mapping(z, c, truncation_psi=0.5, truncation_cutoff=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "postal-runner", + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " 'patch_size': G_base.img_resolution,\n", + " 'display_size': min(400, G_base.img_resolution),\n", + " 'shift_rate': 10,\n", + " 'zoom_rate': 10,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "promotional-exercise", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up PyTorch plugin \"filtered_lrelu_plugin\"... Done.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "baae4d14e0ea40868b46209b6f92335d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Use arrow keys to pan, i to zoom in, o to zoom out, and c to reset.')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e0d2a6dc04e2450a8499c70c95c341a2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='Full size: 276')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d371d76f8bb440a980b38a3f1d3e3569", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='
" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.RandomState(19)\n", + "z = torch.from_numpy(rng.standard_normal(G_base.z_dim)).float()\n", + "z = z[None].cuda()\n", + "c = None\n", + "ws = G_base.mapping(z, c, truncation_psi=0.5, truncation_cutoff=8)\n", + "img = patch_util.scale_condition_wrapper(G_base, ws, transform=None, noise_mode='const', force_fp32=True)\n", + "renormalize.as_image(img[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "stretch-edward", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_size = 1024\n", + "full = torch.zeros([1, 3, full_size, full_size])\n", + "patches = patch_util.generate_full_from_patches(full_size, G_base.img_resolution)\n", + "for bbox, transform in patches:\n", + " img = patch_util.scale_condition_wrapper(G_base, ws, transform[None].cuda(), noise_mode='const', force_fp32=True)\n", + " full[:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]] = img\n", + "renormalize.as_image(full[0])" + ] + }, + { + "cell_type": "markdown", + "id": "streaming-palmer", + "metadata": {}, + "source": [ + "# panorama model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "hazardous-allah", + "metadata": {}, + "outputs": [], + "source": [ + "pretrained_path = 'pretrained/mountain_pano_pretrained_final.pkl'\n", + "with open(pretrained_path, 'rb') as f:\n", + " G_pano = pickle.load(f)['G_ema'].cuda() # torch.nn.Module" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "moral-level", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5ZoQCdLOMoAXJtXI4H2MoZWbHMWzm18wQ/vlaH8/14/GwY1AIijZAqnnethJ1qNZakLnb5kPAfOtYdkxCULUDbrKgngv1NkrbdhBF8RSLx/437nD5vvG/IOlyct+R9CZ532FnByWQdspLAM2NBCMCRegSArKybQqWkzZLQBFOvyPAugvbm6Ht+W0f910VFPiO0m9Soy99f/fd6lBUOZYOIPsG2ey4sKO7WCloha4ZIRVpMswyJ4ljjCxqWcwUmAR2LvE7QVDXQqByYKKseJlsLAmVjybXylRuu5DcziyzEJhpe+7+lLwjwDcEvfkhbreI8l40RRYBlRDN9qL273TIjdvd11tnp9F48ykaxwBNKwqZqdFbb8PKrFjD4R2r7iexd29Zrf6pW1N5KRA8z6MSrdj5PZUD++YDOpGSKoJZqUhlaOesvlnInTarK6yoScgtUvtmg3mTh+XWO1dWixQ3V3cfj/K93CuQ99LXTmu+faP85s9uKNCrUwxXgXBuLRJZlqF9bwqklLGi+I6NF5t7BBJFU2612maQ+Pakej8HSgRSlTOUSj6V6qAfWYduuD1PH5XUZrESMMNJH06UsjKCrNNOCF7qV8KPIUEZt1/fFyoKMEpQ55ArU1qRQh1EkDewQy/aN/bnhoZuZmbf77N+xQBzG+7uQ8Ba6+tan69rhSo9GbFqBSMUQkiRQZZZQJRf23t7b4hO4rVhRMGvN213DDvpmZER9SsbLPBWCjSU/OaZi3jCTiljS8bGGADeCIYGKEKCilCpFasd2/aV9gbrrI3JneXjTQcnmgiNhFXQCWpnNUkaRTKhCmVucoqyTLmsYq1am6VlsICWoj5yKYPphzO94rH7OGrrGCQYbK64YrkZgyGSOM11YOV6jkdkrLVopPGgKTDcIDH0PI9rRigfj6MFf9iQ/Aar36DL+x+x3ddbEcTb5qtJmnY0fHsXfb+Lt0/5K6DdLvHtGu//En/9hTe/cP+xP7cfAbd0hW6d/Pf+NQPR2xD1YhjAUlhv7uO4ExOZbhbCIB/HIaQbSbti0fAwGmEHlTDycGOmUucwuX2+Lgk/jpHCjAzltVZlWuLSebg5DreEDrfTntIlcc1IrvNxEnatBZi1KOQdvdQ5zcxN9Jf0uMG8u0lRHkmK3F7PaNqB5Q5U/wKjy2XSaMbHMc7DDzd3DjeYfV7rMcZJCvxcy+jX0sX4cZLN7b4f8JsQJFAcLg37jAAo5/Pdbaa5eUuErPULkCpoIoGQmIHyG5u27bxKxS47f2f+V0IysyPhSmyT6qWzfjczJaWkiSlm2p0M3KmFsqECAE8lhFSJ2UlaWAsSN6xoXJdZbOyZqT/nV2ajyRvkEJCSJMXFoPA4/fBRLyiJSqysJY3IsqIRjQS+Has+p3WhWWCiBRM06/RnfgNXtQ3yHUD2wWfqTlaRSWAMt2GxNvO0F7fuxMhzjMfhlT9eqZ38K1qMw2wYj+HDvWRWK2WdFMK1Mh2eKGILrdruO/omMsLtvO6kR2Pvfe8zVm2qwoyCWi8m5JbGN21DQchARipFY2Y259FmxbJBSL2c/znELlZpP8odYGzjyUYwBGSgvAET6zyIt30l4OaAkoYtECO40Sq1HV6kGDlsHMNyP7mOUjYPtB8mvYoS2lS+15RWartyrK1L4taYN+SqWGVvibLu3NBCYrEhhCLyGD48ioaWKKXtE/g9+8X9zgBLIHPbh/tRlgwMUB2Kgjq94k2BG4hIFX7oyPUG64Xh9v7s1SiU3soOu1FtbqW+cJN72/3cISH6gXeEDJWADhzm/Pq6GpMqm0JKKWHG4DshfTvPHXC/ge4bbvdp+v7zb3dyn9jbXL69NPLepCiiuOPMRl/3b7e9afReUsR98JsxlDByBwIrw8ZhBqVWpO03SggGuNaKV4YIuH3Y+TyZghs/Dnfwc8Y/+Pqcuhxm11r5mtMAI93MrwXQh5vXOcnD7efjOIcr041X7xWZ83H4MUqSWuxeJ9XLu383f9rnnwDRwghuEen9gIG3DdmBUm+Yunf2qlKCASsVme6kmKGlcLDsi8mzvRRaurfBjzo90KJ6gCsSgFvW8U4BhsyiF7SWokiLRAoJKlFJ5rqS9l7Ca8Yv4ZqBjB+nU6J0OFcwE/MKpGwHjZFYma8ZUg7a9So1fsUMFhkRoMGNUcpRKdNAhKJOTC1eSE5fCVCv2WlekSldaxFyG/ejyB1qe0BXZIYwrSpxDG5YK18Rbm6SUAIFDLfrChsjSUUAXLD4ehnHa2amxuBcuQQHD3cz+dZPZmaVyhCYKyKzJKhWGBwC4LYJmEL1fJ9A7ViB389bmZYdStdvbj+U2GIxbsyCHVzdNukdBJdTuoPm7bpbQZDF/hA3DwLNlUa420pFgn5IWBnKMGq4H2NUBr8cVGGWuil+O/ncGGk/sV02dRuIfc99adx28SZJ90t7iXDfV70oseOAREbouhbcf31d//rjJWAU7Ut8PI7n46jkTIWXmVH4UR3If487vpk9NdvTMWMFw4Uixa7jSiREg92qGic239q3szMCozxorTx3qZ7eq2akbN+0RKJyPm033shDO/rQO0KqSzDbBNyOJs18nDEDnWC5IVw7SWvtRlv6OwW1vUFzbRu6gYB539uAFZfMe3Ntqsa67nKDasBBM1+rtFX3NbRQ+vY7BEFnF8XYpgq8mMjMUnQXlVyQznTXl2mfGLIWSVtnVsss9uKV+BIdgdjtdLGjnVq6NkPFBPUnb0VJ7uN0b3W2HG9FZjH1UGZV1OZ771buWMiM3OzPfRYkRB/Mv7jnWoXIEs9XVR1IptLMhtswOw8fw1lY/P0akE1f1seUBrvCs2NwuB3nITVxlfe2fWN3YtNlG7TBrEild3yF1k5zU369kH1Xu1q53FNJ/gG523CT0oxuDmKt9Zrrda3a0teckswshSoDL4tX8S1BN3N3RYKVkakzirtYr5bY3QnFCtBKm7CTt1i90wXjsLY3tqVf3NQ2JDOjdaXVdrRJquTttQgtMauNVbQhGJmpBJs0AncgWoa9zWf9Is1sY0QIsAQ2tutAqOinSn6aHTRVGeZtbEkp63Fk5ptmEg4fs8Mhq9NaVbEuVtbxRjdvcn8zoYJsk3ue9GGweDrMjh/H8a/PXKnjGHOWX4GR53GUFOi//v7zn59fV8bxcN+uKlPRhPCOJHYQro0B8BcDfWsTd6iC+x/evvSNz97oC+9j9rZV//GLf/3LjYX3lt4fsb1eLUWJTSq5XU7n/nUnYVyRbhxVjmQwwqzvFIlk1cVDwDA+jiHZ51yZ+ft5mqlC+Jz2GEPQLK2cA507s1TOlQYb4wjMK+egPYYRo2h4Iw73MWxlMPlwR85h9hpWpTkPPA+ec61zQIkZq0BZ3a8IGhSV6e+KGCm6cIQgEUhBA56l16BFApvw3UigHdk34gzD7Tz8eY6PxziH3zWGAI+qrl9KBMXXzNdcP56nyUMiZZ0Z65PyLeQUWjX8jrQqA21bSwZAmUxtsQDv3ZbK4tduaJQdTCkiBWymzwjQQGQsGb33RpNlBZAMXZkC4E42m5FpYBopI7DTRdgR5RiD7LJTaYuk6EfkXJnSGN6HJouo3edCEjBXAFkGs7WZ781NN7d7ewtrxTHsHAMsHLtjPDbecXaRjTrD3+nmN4orzpkygLsBR+xbEm4V1R01768u7+q6B5bChDCz4WOt2AmbuxK878d2Vr68VGQDQkEUpjJFz7YH5uZdV106joQU++FiFywXE1SfWM6/umW08WjuDZAsiXpyyVAO553Y6P9tWECwNJ4kM3OtJOhumag0LG/rpiYNb8W39L578q5It1tGtI1TspVr214ZIZh28Vpp0NyVWmvdW7Qc5Bbx8K3n3VbOaFX01NlTa5y4EeI+R+/j3dwf+qI3t0Z2QdWdC+s3kPTOWNf7NLEvJLoXwTHsjrBKpbEihptbSnDzEpLt3Du+QZI7eni7h3K4ezOKQLHAZbdq/2wORjvtC2Uez0fESqmFNt+vWUB7TLvzghtTtvjLwBIL1hnPDZXqGrItaScaacxouqQyJq/XrFxirLW3F4zMlj7te9vdQr49mrffuw3k9xf0Qd4P4X6yfXT3Me+rQe/IHaDuGr6to24i+K7t7+dckWmVo5ZxDkCECzmqCrVMeSxxuFErNDM2MwmC7sfvP46fD62qVk9FFxOxWtdU8uRvz/F5Rfr5svXrWlfE4c7z+evPfxlgy839HE7i8/X616+v0224keWYa5tyDB+DThzDH8c4hw0vo9Mc0A0GAES0gTcmtsbpjQT4jmB3VLjDqf1vtQM29lGARXdkJoVMTUXSRvH9sTY1A+/OP4Wt1frIOwLaZ7SV12J11pn1NokVmillOiTyWjlnyUgr2csEjfrj83rNXKF5vR6nz0hlepEdrEhKw+Ro3aMRKxWxJFyxrrnmSkHuLmWk3IaJr0gItJErTIiI1ZFPb/BsCwhWFxg6qNXVkEawc6L7zFRjka8V6LCBBpPyOPg8R9W1zbUMKu7JzGIGvSs8T7drxny9SMyp1xVGDBI+5prX0hXrMexjODspRAGGd3sa7ePQ/IEyb4K/SL432YO2561quYFK+Rj7Zlu0c7qCUDDd3BH7tWWyjVQrZoEd7O59twMA7bpj5aZ/muwvkRMBIEOQvOOOJKq2mz6q55aaBC172wFS7s1+Wxa+QdXtEeoqvoGt/4DCd5Sqtzl620kId+mY7hgsMtdShHKu9YqM+JoZyWH47XnEoa/X9K3UbdV2ZazY6/c2kdgXS5TLKxAW4tsx1SNQ3i1IFKB778h2F/j2ljRjRhRWX0s0Yndv+bYU5ObSsE1EfW9Vdczvi6UKsdXxXtuOO1TF9oOAzdcFdVG/tne/bXnZvV5iNEVfe7e3zk3IZQddJayvLXCnUyuiu0vJRTBl7dL6OmOlsoJ/GimTC8JNcm30wLIpfTffy+Tdvy9qG4pm00liNIPRu842YaF7b9X9bLfYsKrNSdtO9dJJqARRh6uQZI0zFDt8x95AJYPKTO7jwCoy31Kf3QWo6KeqYK+H0LgFSCXiLozrR/SGR7kDRYDHMUgQfgw/quuGdcVr3p78fagoYbh/PHdZQaaTw3keB82q0Vj3gBEKKOfO8pV3SjCTvdMa5f8lTKrzf8Ov+w8RTcRvr7l7VXB4xV/dn2itmKvYWlNGZBQnlSE1KV6kLVXHkiTTjFoqSVSttzVXVw16YG4lHS7ya2wDrgxA7ladAFCZtBSqpZTth5spoG74jX7q7giKSi1NVFuislgiaw9Uu4WCg0IS6nrDPmcF3LYJ6Od24wv0vbYxqEUoHqxIMd6+rJzMriTbnT1gZiFxM/bGrc1D8Q9yo41hDLSaCQB8U1XVlmW4uRuk6jNitDkWycNNCEEyPT+OJA4fRYEySeIxPB7ntZab/te///zX1+uK5cbD/XQzmlIrtSIjc2WuqFx+F0PsaBH7v3ub6TbV3+BtQ60+lt8d7X/wMv+XX+99ez9ebNsE3kaKDZDuvxYB5G63La1aMBAhncOt+ANue4sit1WhwKDN1K85Cfw4j4/Tz2EChunpXOafa/7t43GahVb9ahX/ZCLqs6hIUTj9gCuVoYBgRTxRVc/uGiRArStJnof98YpzDKNTMPMVtQcYaoWkGwGTMgk3E+k+Cls4LDo7BZgnsATB7oxoovRJu/RqYznCvHwdaW7H4efpY1g160w5MAj96+vzU68fx+MVi8br8/U1I1LzCHOm4i53PcfRz2gfqNxp+FbGa9vP1jejHAerbNa8y2+6+V1twQrGzN3vrIbESK2VZqzSH0DVRRRWJZvadZRtwwWhvaUAr6vYwfwmr3awIBXLWvFcFuaXFBEQ3DQOI/l1RWbu2L4ViykgZeaR+nxdj/MktWJGd2agpOGmlqgSxsh0w4BDrBs83Mcgup0iVnLjCdJo+bb0+/+95KK+F1VtXdJ99N4sARrXdnmK7UzhHQ0XiHicI6VrTgK5mWHSnDac1dJum9D7/O/OClUOfOPGHdDHnZffWps2JzuCgFVBHMpybw6gDe+G+az9nCkyjVxKV/UAInajw50CSAol2yExvN80N8vZ776RRF8Q2ZF85o6x6e4ApCznlpE76LBKNTQmLgyfQgbrhrYGDECLgYvHozVxcMcdb+KmOEczszGGu5W41f27dXwbyVofbi9fCrYNa8i/dphqxMrGnxBvmVg5wCbzypP1pWLnM0SnUStijE7HVa5OQLf82kZ4f2Jfp+EGbfzmL2680mi/g5r2uympekGsCKcVCdwYf0tktX21NlLXtjB1eCJSRhVbxzZH1jL8/fCxJTGNhC0rQKaVfnFWaGy3Dem4EpCikwq04hwbYHDzNe9nu9fkPp4VyKlIxNtOs4/l9nHEu56seqW92SXSmLjZnrIevPdhlygBqLAARppsZhjltHHN2M0xAOXKZWQkZiSAYebftNxu8NMjNVeCWDNeMwSV9oaEu52jDjkj6LBMxTVPP2bkK9ISAp/nGO4z1ueKEfEYQ+YSvGQeqegcZEpLOlI0ykJWWt+NAzoIqXbOtTodpec70NoUBvf256YHSrJTNRtZDE7vOwDdJwxiks3yQtsVNQPRhcgFqUVUjFwFvdDK2zIwI1HrU+FqKiM/v9Yw57C1Yk1lKiLnykglmeLpJJRzXjNTec3xRxz/Dj3PYYYAvta8Mi/pdc3a2W7+ipyJjMgVdE+BdMnMBolin+e6UkLMFas2ots4rEp0ywMuVQuGKj0VElmdqbdasW1nmapKB8TKBs5glDIl9K9fr4AqyAERkTOFSDMbiROEUcgr5eehWH4+0gym83BMSTpI0CJ0rcoFoC8EMu3GH83lWdFxKFXh+0T2CdEmbfg+G/dOyR1xoT0cAES8K20JcHdfveP5bwqobXfVkfjNJH0PRNv6qVvH6e0HQACjspTQHWoDBdly83Nvf6sdkn23t/e2b5ffr+Ztff/DV70T/vKjxt23Gd0Yogxol9Fksij2cdhPw8dj/PFan68oX/C61nkMt7+0P1Qq0NTbvu/3vW+nRUAZrQCvf5Kqa2M91XdA0jGK2Rg+50qXt5vZN0xmVu9kEDAw7s0g3EtaH7r5CjZBqL3R0X6xrlHbZ9/XjC30KDlryUzq+ZdLyUx7P5RKT5YeGTsg/f5EqhlZX1It4FYMAVVsX1a+9IR/XY87MVCWea2QbgAEx24YRL5xg93po71dq1hsIzJJ0Ymg8ggECuy3NkHueDdiLKSFLn+vgKl7ARb67xSZbxxUyKxUnrrP42am3MzcUZUPuzdoPfk6CCXMJHiMkWqJz4bKlc5tjdXW/mAf2v7mOz/7Pv9l6W9vS45hw4cRw7BbHu7y0N6s9wEi7x4Y4DH2WSNtF4sVzFjotk3oI9tSJ8H2O9595pTIYs0LYuwV62NSVE+TR62gal09W3pj7s5iytRKrswmbqBANQqFAVqREeveWy1PJdyYWbmNjJQiaQbQq5qs8HzVlMXKTDuGbxnvtqKJhJsFUqmtEv9mx2rFSyVeJXjaVrx3zkisiAIwuWGMaqnude8tDZGYKwQ8hlpC0tmDkkmySgkbV2xUUBqZLp8UUXWsb81/vY/f5qHsR2NEAsAgO/Y10qi4RSswcUZIhPOVnBmb3K8rrKcjIGn0bi5rh45hdjhXesXnD3enDbPhXJGZSORx8JSlLLFI/i+/P18rvuZayqkY1HH4w8x4YB/2SEVoZXa7nPZYfbhyp1jeZq+Pye37tjHEX6xZP4Xb1t+u5D/4ovvo7DhpO1mg+4BpBzC6Awn1Mcki/AoiJvKacZgfbldEVq13saF4H41ArgglXJyRf8QF4+H24xwkfmW49PM8B8AMDoPU/VYMCKylpNwsIK+oizZYKk1tgGGipsLMX5FG/Hg+V6TThqnSnK+1nrS5sALuvNay0KsUC1UM1Rh2EBaITAVSyIppJAC2lVxZO28TpntVKzKUwZgbupjVjvJhRWiA1LXWiiDN6QG+VikX7MfjuUL/Wq/H4QLC9HEcMMaqXVri6+++ucxQPdIunsnItRMwZkalmC3Iq4K0+mVTRr5eETFVRGpEtaQ8hpdrqzbDPx7H42RwtRzM5FUSVAKXm0gGiK280192XR31qpv2MdydUrWpMWMq76vLSEhjmGQGyBCZ7AEOELi6u7D+/PU6hovMTHsbZ2QIQDU98GHbtEOAuxezkEo/WkwakSu6pCC34P1GHnc8uQEJcVeCb0/zF1jQp6qySahgege9AJC7SM+MPx6Hm5X0IzNrRw0vQ76L2Pt3raUT6hkOvItki3Apa9ysEEuntrG4qh8OEwXeDe19jNh0WcESiejf3XfV6egSP/Gb5WYHOm5U1gV0G5+o8HgDGGx1EozMdAPYetgIKhPeSSlzI73g+7DWPm8kL3SjyZSQlNvmJgqpIN0NHOVCrOThVeNivUq3Cb0fcQXUFYBL8Kpn0k1Z7Yhkb2mrbBP6CdbT/i4zb7iERpZoDgLNm5SuOVIqKFd5c5B0LyatK6BQC+Nm7Jz7N+nlX57At0O276k+r96qO2b17BQzb53+zuHdX2utMB9mrd/Z4UojQNxSwzfyQUnbSOyusnuBASBuSU7vri7YUkaJCmBUbIdFmjHb/3U4VniP0kLtQDFFs+qd0TkoCS0T3MvfD7hxwj6bhexuh9hOsixTg8jtfKvndQedmXr3q3w/Xnw7NXl/gpDCFavOXCqCHP/889pBEAA4M4XXjBUaww/XGNUU633NBOQ02oG7BSgkDU8wBKzIp/kosRwEMXV8XWulahbGa+bH4yizfrgdY2SPuVlV5kLAje6MRMQ1hh8VdbRWqFDRXXO4KbKODdq2qNv+VW2txvAxvLQD1VjrdS1AR49ogfVZ6tgAQAYyM42DWFlEAAl2GpBV4WX1ZAilUPRNNYNYkcO6IjcDK9Md6RCWwWYkaVfkr9eL3ZcaKa4VEaJ7ZFzJubJQyUj8eI7nYc9jkFghJX97HJBe5k8fr5kElwBr+f5Bj5WGIanGfpGMnJidajf4GDYjVkbETKvXeCpLPa6MlTNFL10ZKZkkJwmLlNGGVaW9rzWzJzkkBTceftg4Ys2IgNnP5wPKa8Xz8FQs0ZQzBDAyDj9A+/EYH0/7Lz/PxxgpiTpcc4V7jVXIOQsSCbCQDKqkcjXLzFiWZe1Ftv3N/YDe6sI+12/jseGmFb+afXL7aPQEKiW3j7nfjJst+Gbv1NaU3C1Hcr/c3uzZ1mi2SUJC7cqqNynvtE6x+rubT5E524xstMX7s7l5h21jCh7olgHdNuaNsLsQ44YWaDBxcx+1Xpu0Qu4Qu0CelEYbjt8ODtq1MiJpnuAViJlmOUgWuxZyI5HvSAsb6PNdv4meZkIAkcnuDQQANMdmY7jFyGvlnTnatrAEqg1x7nyXbZx5O5ibMgIUkcPdzFSN20uKXGWFEqs95NsLV8QmQTXyIiL2BtN26FQViWy/3UG6+grV63+Xpwoop5HsN8KmH/G2e5t7IN9khpntBo/I/YTZ7aWrzlRMe0Nw3gQQeloHUPFqD4HKhjix0t19+LaWRlLWbf/BYtJZrQEylZlWRFcRlFWTUCprNK9zDy8juZULVl7uXrv+tR3QF8VRqvgyNvcO7UditDQZ+Bbz3A/r5nGwd9kbIe/j06/bpM+96fazM7uPTGRFZu14+2OE7VLtvbv2rxf+q6MUYpZH3JdvvMNibftUxCI3sX5zb1UU0KL9tga89zS2ibspqAKdpZVoFrHfoUopYZ5+S85rsIPAkhJE3sJFuVl1JbjmEmTG4xiFZM34OIZby0Vf14Tg5pl5HsUcUoQyh48KWknevQlsFC/ZsM424MzbEHSgWOuc32KBslfIu3s/9gn/Zu1qwKUy3HhUObHtToLfSOUKF7ciLGug3FwRoTEOM8tKAnRPtEpKV3soGRndS1tfVUpvyNSMyC2BMKOzdSOVXiFxDO7ZTSXa4ra7JCrpJTOcwyQdToKHj+c5ylOd1ZLV/FrMBGDHMGCE8hgOw8x1Dn+MM1Kz+lllXoLxbl0PE8fgITYZlBlVn541mBLtGfe52gtfi/btnNw/4V7c/Vd7/5V3aH6/w/v92sLdO3p/A7xdRmMYbAUoElqZsZTSYW7ktVaVGVJKaNCcJa8ohpWCZqwVOqxqZzRn/pHXj+cAeNg4yGu+HmfNVWKjw3oc5ErRuvRvVPuhzU8DWJmslh9seWAKz1FNMfTxGF/XcsPzHCk93Hzla4apMkCcEW1szUWCuZQlAzUYVPWbBq0V6/QDVCCL5WpRAAo19LQsa6IfRcW6FXCOSJhxharP+hjj5xgO/rpWRKZ4mkN8zQVUU3MK5pknfW12gGUsjMPMYNo7QsgVrZ9MtZYlMtdaFxYImh1juIPVdAlI6Vr5mrFWgjbXa0WUR17RM0kqFTEDuKKChaOSxkj3TllX/qhteSaRldEumLHrALadtE5km1vNSCn6zbwZ3gCHhAhzP89hlXVQuvkM/fH5mtU3P/JxnjBTWYMCyT4yI0U3E3OtZYTbyARHHYR0c4IKRmoMOw/PTJ8l3kUmv6lQ78AVaLH5m+JRHdONctHBZKHcJqb3ykC3UqAPXTO4RhzDSZb1K99k3uOlCpO0KehycZi51B2FCkuWAyz+qOok2qNuyNnuOW/OV2lkASEjsLv9b158kwe7QwkJWJ+I8rj3j/q+u9Vy8i/MUdsbcrdBV5V31aX4vpFM20AdbGaosiAwvOXb9UkVKrbruHWUKFIrWW343MtSutsYPrq/be3nHfy/7WgdzyIZb7Km4GCjJiNg9PctNed775J7w+wis78gnjLERmRAkJHunCtrK2yQDAKH75ruqrykKtNpG9Nu7/z+wl+/mhhCI/e2SvXUNu5y79bd2ptxo1+rBJmTUmXCoV0PvlN1RaO04elDL1RwDVXbRKjGqrSI7JuCuj+INwGVwI3M9vnSPm7fQynlrhzkfoTcjMzN0PH9VHZNw5u2aozZgoAdlHYmEij68bZYuQu3M7T5WQiKSHOvxN7ww6yCdkVqqaeX1pjQujyS4zUTXQiqaHqelSr8nJHC8xg/HpXTK+Eo1u4KulLn4CiZT+oE1tBMVaT1PFrnSdmvufywX694zRhmj8N/ezwAfV7XtdbnNd3s+RgE5opdkgpdcufHOWwt0s5xd5Xu1kK3LsGJPRwnucVXZS5rm5VfX6EZAekcrsQqzX30w7tbnl4RIIaTYoQ+r6WEQ0lWjX09lSL+H+fhzmsuN3az5MCMLBd1SWXEDvNzHMP5irUiSa2UmR+UcazQXPNrRkRm5DCvbMHrWl6BEuLnx/N5+nME4J+vCbPIXBkkfzwGzsPNfr3WZ+QKpOTmGcjrghDKVPbEgeYEBDjICFH28JGqOb4C14oQOOzGWQaOKvsjSuS8CX4quvyroXelt4f5MM/Mz68LO7DbY4XxPO3n4xGZXzOWcBzj42F/ewygLGRC8ZrhMpDPx0mf1xVLcHe7O1plRGgYkTxGjciggU3TmhV6J1h9gondLZVVb5OC3TxOWbHcuLByGk0alYlhn79dhHWbO1WtRBlHURmtL5W61c3dNaelKN+s256gCQI1pyLbJlr9YomfeWsCkJsbLhtI9PCjuyqsbVljxfqoFmLcxuXtYW4ojtvk77yd9P6H2gd3YFzBDLeprR4VISQyM1aPHtUrMIZ7Dmh+nDiG420Ib3P8Xo1a5tBffVWZ9x2Rs6mwfbf7MSRU7iG275TueFpG0Vqqrf3z+l1z7ptmB9q1sCIJ891y+R6v+72uvo6ISqnEarWuZg3YfqrNRSubmy5gJRHrxlCpMPXulW2+SXfwSvIubu3NgFvEunWxvYtKOYKKcKTqp1ZKDZBFspcFK8dSZ8M7DSyoG++Fe6SUisySv/ZW2FGZ0SW7bWL7+HK31e8LBOubLHDk1QXG7eYjvBiUWZ23CWWyK725HeHOBLZjjUDbq/agZXmoPVRF9bk9bOZeJqsCobou665J7dx7t/RqvnfejknrErxduJAFOjerh3d6tlaGdyPkOxbm1mPfgl7dhbfvDBHu8PoNKnb7w46i7Q4yW49j9t6MG0zjvjrbyqBbDM5iUm4Ioy2S0qbAtsxYqUyUhK2ec6aqnxQAJMztGP7bx+PjHOYcPgj+eV3z19cwk4CM86jR5jXJPvfJtYyAMLz0E9qP+L6souC9EnBm7BE/dcxXTW6uykNKSnUZ3dpNpiKDXW+gzCKpba2IsoWeEtFJml69Xv+3ypqAKSVZaM3XdfhORRKprEa6qaJINHbwP1fOlceoXhWCGJEzYoUm8jG8suvncXRMGnua8t7nIEryM4o4Q44apksBFoC3CAzFGUkazkhGVpsG4rC/+YM9L0WRVZTHj65oQDY26OHQHTXuXuOJ7kwtgruJbin8ClvfjqQZ9O01/sM23nsKxVnWzrSq3iLvP3cosfcngHftwLYtbzIJqAgcfeWZGVIKDj7HcbitVWU0HY8mECGKUfsEnR41G+YrMuoE/jwegjz5qKnJSrrJ6Hgn61MQq4KGS6o6AYDDeHivR7nxGgZU2ZykpxQSqNecVfP357Wew4b7r9eVqZCaUnEbIIRW/LF8E502tQLBmhNfgOLwhej73IVObzPWVDTqMFi1P4bpyhnxNXm4HaM7uZ7HEHLG9Tw8IXNL5BXrayaqTMmMZCZ+vdYXVwXQ1ZenHA0szUTuY0jOmr5EW5HRdJC4a2YFuN3j7WVEZGakmwP89fmaEW7mprkuYHf7NoL8umY4H+cw83KJNc52jEHzmwghSaeyAEB2PsGMoDLbOGYQUCJRLEQXz24eg24cA9KA5M4x3N3mWtXW041IzBnultIKVeVmZk8IArALFW1DwZQYkV5uReqHzozMnDKjOQ+rxBsjtLpwgZsUabDWWPzukps7bfF2KmxQ8s1JtKGhlNx8xd40XTbBrq5q3IVMmBdywx3h7uj/xgZ93Au2qqyrdYprQ4V3PLytPVKwwrbbqzadtIFuCgbTXyBsFQOwWhE3RtyVwhuZSFlt5PjNznXFFMnhbl0ltEmrKMV2MYd7pgHr80qGTIPYaT/qXVMCSSvytslg5aMb5x/jqEsfww7vbvVuNnaWmFs9vduz7YRZgbNKIWwyBYTfwcBOyNbT3iu0EdJboNq7Qm+UoW/1ZRzvFBFAIFXFaxHRSGXb5PrqpE4PUGnks93um3BiK5GxqadulLKdLO6T2tfZgdnufiVEiM6/btMKy9Ry+P6wCkhRWLL8Y2SarL4pjmWvpPG+3f1HRnMs1Z23ESg2TbmvsHdwx179fOqiU/UISquq+9cJwHYot2ON7HvHHUSXpy0tCDb8H7YzY+iG8V3KnHpFzBUkD2/LsGLevaq2f0ekVorAXHmtdR5j/Pl1rVB1a68wpxBqiDPWj/N4iX/+ev18Hs/HMOsMG4FImTHBT8UdUnV9lkHVH5o+V87Mw/m54nA8z8e6FiAifzzG8KNGi9VWdLfH4a9qWyNVirW28FViyNR5WPllsxR6cnwSmTsu6kPbqm1A1tGNIZGpiFCmqU9ybgLAiJAGfaUErSUDW1kgkxRNyanU3KUPh9b5GJGKFRl5zbxW1L2UrN1l5/D6/rWiZo6uSJLD8hiEuFa4jX//+QCQiZBWaoSM+HmOtarDf9L4eA4zrOzuYecxHgcIfM3842v+cU0fg6y+DXocNuz49Zq5RJqkK6uwHCG5MsmUnDVjqyeURcbpR4Gum1tdXRcAI9zKVSfepeaA6PThw81WrkwEMnJl5cTqWBY/ZABF59eMP+c6jjEG6XY8j5+VlmWGFFCNwVyZw8bxcazIuVJECJ+fr0o4zMgnDyQMjgyjm3sVbpe9ltAtNjK429GXnbVuttAFTWWNCtxEZtky7oY+3MOgbHMTlGjKYCl/2fWcHZO0vjYTZrOrAVk9F1Iol7NCa8Yx+HB7DHJYzZnrvnyCeVXUb6japqPr3ItXcqdu66kifawxdwJo5VfHxwUZ1IZ7Q/bb3pal7Thb+/GXca981+2/7c2XSajCCwbya8Wf11qhYTxNRwwPzWkIjd/OMY4uWSqA3ALqdgps6iolWPUH6atpG7NzS9urfHMx1esB3Amlb/y673KhO0p5RxHsCnPrwUAYx8C3D1NV6NZf3vKLv8YiOzFO3hLbmsd43+he8L3e3Ptw09VdYdH3u9uS3b5zR8bb47TL3CSdCqlVBx8dw7EzZhuXFPtUGuZq08uNf+41lLn1UpW7jmQoq6l0hO24cZMPulmGzc5Vuw2NYWA1vFfl0xbWloq8qVMz9tDZTripov74RouZd36oU3WqvgxdSlDrU3WIUFPM1f6r0FLrQRq3lCCf38BPHfpsmLgx7GYptSlw3IEt9yyaun7bT3c3Jdun4269iPsZ7mr891Z/w+E3i7X92FsB07tlh878/uwKpPc3Zhuf9Wf2sbL3pdberMDmvjFm5oqYd0e97+cKkNJQEswsPrFQxfMcAA73x2E/nuNvv32kamy8DsePx/G6FpSP40lCSq/KCQLV0YLdY4usp7zRjQpyZW2UarbdnT531ENiDC/TMyPWipWIyGaCUka7+nYqTZQQhteWiL43DpMq6iDhu9ys0HJpyFZmBFZqRq5kZFxrPfxwZxIr0tiZEsKgnkcswKpiq5qISYc7ai5EXXONVRXyWrWGAFyG6qpAZL63naCEqtFVTa96RQg66Zm5Ms8q6mMVRciJsXuEVRfPMpAhK2PQec2Sz6gdm+5s5z4MuLdo/0oVURYwbolQe/83Jte3/793Pt5Hrj+qUO+SSM2dXyw328n8jmTfJ5BvdqkhtN67lUYbm04i9JqXiNIpo0pKiEBmMIDY11vQ3OhhNOi11td6PcaYke4y0N1OO3ZEC6DD7LwnA1aAoQSxEoQdw6v4qGWVlRcRS5AOo4EfxxGG9Pyc6+tavz/9MUakDrMonQ8xyJCWViVVB4eApatihmE8MUJJCMnQ+wEMc+3xc/dRZgsES8tBJGF0mNOH2cNr9i+HW2ReEX98rbXi81ohOVhZvVIUvCEQba0EdB5e5pcMUUaex1EJBTcSHkqAIX3NlDRsd0DRTjDsoPTKXBGRseK1p4Pb64qUqtnTcDucPjyvmFQcg4yK4TisSrhSOhryyGqmj3QLhKMTOZWurD4LCXSH8MqM2s2SbJRSf1aVKzbJkGbKeAzP55mR53mAjOhqqrKmEYkW9KVVKNK60T5fIR1VkFqBc3KtXJmRLf5mo0oSaXmn9W440Na+TugGKN2+8IZ0lcPq72tMW/s8wIQ07dRJx6V94/Wc3qPME3vWdGPKfcLvC2LFue3+isaov+R3XXkHzOzvSq/RaYDmE/pWyuqD5Xf4Fg9VwU13zyDcRu8pvd37thyqChHbR5jG3c7QbIxOhRU+kdtdU1w5vVLORip36R7wDiXeDlOITLJ1FfsfK2PJVuAW3CRa/rzxZGOO1tuq4ujYjUALJsU+wZWT5q750f1ZGx1/g3X69ppNAX67ahLvrkzkqCFlW3RbGTtt4Uwjkdsgd+zc3vmN54HbK/ZCvbFQfyx2bl13cXHzIIgoiXpdPe84oMa4lUyn+XgIUk8KwzdNOG4GrZggJao3evWgqN9OEX6TXfcqUGsFSkCDlGS+O3D8Jf2/Hzmp24HeTq6sioQS+3YfGhGV3ey0E9jlFPW7awWUEMu1VqvlWgXffR+scpfbAVW9dj2CX59fxed0UEb0vjYDUAwypExcibXm+NevWUv/OEZF31fkn59ryY3Q/ArhlfhvtKrmmpnFFV6h083v2bRAQoMcg4AdhkGksMDXV37OzMzfHmOF/vev6/ePg9SvOc1wHHzNfA77uuLP1xzO4fbhJmmFSMTKr5V2nDNDZE6tYiI7Ty53Po9RtNF9EoiuFx3DSBnDvXs3fl2xYg3rrnJ7a3I41QSHSXytVb0zzB2Kq8Q/+gbagWMcMMtrCbrmyq+iIK3IJgERGRk/HvhiXOtTsHZOw48xAL3mel0xQx+P47ePM6V//Xol4MPBPA//+XF4bWwmgMN5hVbyY+BpdowkKNpXrAgdYwyzj8f5oP3zc9YNRIUA4Oec2u33jN46Y4OguWak3N0r7oCNGlibdW4SfZb7zJm5wTNlcBLORHU16dxAkZtdolsFZcPst+HHwZn5itTnF8GPwz4e4+8/zpn5+Vq/PeznwyOb5Z3V7tqMEsFzjNNBY0IfxxEr5wpnZVqgZH2zVoConqDnGJmZSLdR4zazip+7g2/UTJq1MiUDWudYuH+r9LDT+ATceAw37th7lVPOYxjpJQPIzBVZq5XCfK1VXf1Cc2WJz58HEogVUJoI82aWmlAh2GOPN8xUlTOV+0ipJGvmjF382COPASBvNUMhBbvN/U0q1LfJ7GllzNYrvpmFsjybVEHc6pbv7q7AgWRAZH5NpZjCnxMGPQYO+oO0TCkfD6v+nKqgHcjuXH6b0+JBWCFQUSrKG9a0XUe/viN2bfYn0VbVdhis7aS7fuwdMeiGFrvoqlfme9OovqraYITLN6fTRqN5mK0A7SkSdyUusF9FANVQoD2NguBbjrcVXjdTp/09N/4j96J/44bu+yE43KMet1tk1ozt4awn4rvvoNRF18Ot5lijvfjdBoiAYjUrWr7sPEZ91NYJbaTYPBghrEiatsDopjEo6Rg+hp3DasGFPX2W7SztsOr9mVkLWb2H6ulVa4/dbUFYRkbL7ophi1iFJW5eaCOBQksUuuQhqohSim4TJshMSspQFa5buau9SzaZSJL0u2SpnsuNHvb+LJq7xVXaqvubb93b6gZtYkuV92VvTVDd8M301FfLf/qH9x5rScX9OG5wxJ0trNXu39xw7GZbrpWr+sdIkKygQyY2NWNGp1XfkJtFOpw/nufH83SnMiodPdzMzmHrcfRDi9RWaWzxHnroxhgjM1TsfG1DNFYv62fuzaZuPq54shIlxVorMUPVzbDM3nBfK665zDwyUll3vCJX8U3ka66UHscwgVnCNNyNUwBV5mit+PN1rcgVCdUUM1xrDXilMiDuAQoIoZimGelma9vt11xuUbM1SDotMgkOtu6tZGDP81Q3EETLRsklZVbTJQViuF8rZqyeDSzMWDj5GF570sDDrSa2vBerTi5Adnu5N5JpQZzqkGyrUm7g5lzU9E1nRns6Fikz7dA9t2MB7z/eZhXdppUk7P6pdj6i9keZgmDFm9uu7SNzu+FyGY5Na24j3+4DEVIadgTOpCLKdaLWM6GAnN0PrmxwkB+PRwm3nZyACV/Xeh7DzRJpJX3q7Otuw59I01xpw8zrbBl291yU5AeWWULk0jhUW9NSjqa7fc0513J3KdwxaJkiDJFKJituudQdRvzJo71zQspuSSKCGGaCoiyX2mCVzWjhwC4BO4c/z/E4fDjPo8dTFcw4zAmtjBWdzMjQV8zMPNKK+Z0rfjxOCbWrg4mWzADUjKvWocpcZpcTc3CE8vM1i56Yq0qcRNCN41sjuRmZqWMMQ2F4kJBaqxqJcXTv3xmyWep5exzDh+dKxeW743255O0rrORarLhwTz0gLTOtxhdu39QMiLU995Lj7caomTFW6jwidRz2PH0urEgJkZoRc1WetMVj7PiiO/EXXFuZLKFad3uRIHM52AJV3QMiYDQ4wHeXsRvOAXVosnXK7/NXAWp9ukU1Ddlhj6rMtwnCm1epoHZTRX0YebsXbLpif/6Nhuy2APvAFuxqt2O3rrDYpy5yqbv+ThBwk7a3G1RLkQTWxN82BgVsrF5wDtcW/uyAvOORPimby0OllLYM6LtXJbtNkXY2oBpiuJki2VPtv12s2lKyDzvHsE1sVfle+v6kKtJxcri7oV18LyoykUSP0AQi1BvPNhlmVcul0S0xep49dkIR2DnFDWFuKr8+opawoU3b5xtct3/dP+szsrOvN9R5m/dOczZP1A9efRVvwIO/rNfeIvej3ps47xYTUnVQKXk4gMj7c5st2q26s9zPgpw0ZogJoofutEzj3qiCqiJWrQnbk0w6JICZVXd3iePYLfnV6/sdie3jVTC3uvKAQNwIohZaXWVprQnKlDLkY7Q3lVYEhRqUlHcajrxWrBqQHWlKPx+DcmApBVxzXSsjdPgg+etrfr3W6oJNVo3hY4RAmVmpkreMBWBmjt8e54/HEdC1cq64Vlyh4fbhdPjrijHG0z1XhhuBOXVBbpg1fDImpMNHyUNqGqqZ3EcYrpWvSAnPYX74WpPCf/nbx99/PC3zf369no/htCX961o5w8Br6pr4/cf4t5/PCrOvGY/QNfOPpRLpRLYuuJoVutvX6yVpxjd6ptgvs6/ZDVDHEA2H+zFGJq6auC4QdKeEa+YxSmgaKURiLq4UZ1aNQtX33pNdSSbyFS80ZyTAhnkRE5XPr12yVtDoY1R/kOMYK7Ti+vE8fp7nY0QIol0ZZYQP95+PM5RFL/w4ANpKUpqR//wK+jgQYKbZK5MKI7/m+vPzej4fv3/YefIj/Qv5j3+9EjXY0n64Z+gr1unj9PFaKxBRDDP8MKslNYrOhEJRPTuKMdVuzZWd0DIQUWjN6MThrtQV3YZo1jAAZs0EFTmGm+HXa63QePLwMVdEzBn+83m4W6Z+XcvdXPpzCu5/O+Wg2LWHgk4fThuHWXLOda25Qtdac+XHcwCKvAlazhWV1ImY1q00Kpdyx3fIGXcf3ZUqNrxbO9TJ78ZGkJTGTJhhNPpHrtg1pS06mlmELOeKikwqNT1nrkjQQgpdhxvczxKNW9UuKhBz1uTIbsQwrxmyYVhrmdnhaQb3wQwQDAgY5kBI0+jacvSy65VwiBsqbFPektfatbcjaEQOtNR/GziyRmp3emTrXTfXqpRmYIbmkpufB38onFih4eN5cM0F2DV1jDgOD23/WgUh3erlboqk3N6bVEkKs/1UybJEA8zqVaUOiAI6oe53I1Wz0B3S/SUP8vY/gqoT4PuH2sDjpjjqF3YdncrKkXvKrHbzWgE0qvrG7w1G7MXfao5+t+2g2151O5JyEjAn0JK1Aov/OaZS53TKd2Z5HpIQvGW9xUHgfesEeuIiNqQusqkSKJ3Ob8VajawyHuchFCNP47tFD+2b84ZufGK7Gl/bVLrZ4ajX54YTrEAUm1txmBgsp7zjqBpM9T00l26eUirvRsgysz63Lx4k+b4qdcE6I1KikrTM+oQSgSjvTX+jHemWodcN13vVg/srGKhXab9ygwVtY0Ted9BB+E2g7TeAgNak9se9+0TivesImlkJNsk3B4RvX2zu6f1e9319iw76NlP3WE8IMsNBjxp0BAD9PAgew85j1OYZbs9zPB6js5q7ulJAZh6HS1orI6L43nxjyL4gSWZ0K74yi0MtI50ZqhHy76TFRl0dRwjACryuVdkLy3I6eWVec4G2IlYEpKSxpfW9OQldMyqINSKVGN1cIlXzwkqbIMqQosxpFCOgkqkxVFqGquQ1SghgmBGIQAaiuGH4XCrJjxFf81qZpw8Q56jRF20NwvqRpRCZSIXSktV69YQH8PlaINz4tZbEGYruI5XnGDKiBlQBd+oC7yBl/73BvZprUbcJ6Q3aO456G8Y6dSpJViWIu+8o7rAR2+D9xUx9P7jaMxb2vm1W0XZZQ9Ok3/byHaDcVPPe7PttdxBSrsTpBqhngxQKZw+T3M0ZSGoTDfeRzxSdz+N4rSUwk+5WnQFQCZhqjlrCnFQCY1iEUJJeZx2Nso4F/yq9nDvJkKELaYZf1yRp4BXpZgLHOM7hrxXHSSdXdD8aj5EqLiYIGDyl4Bruoez553XoisWjpJqE0K0S/DYUhk1ncBjpSOpSvGZ+hY3RLZMj02iAjjF+JGYGxWvOqvJGwEMkQ4yEm8dcYboyI/PH4ywr6l56IQQkYmUoc9AC+ppzRhuDQtqRqK4I1yzes4D6MVesSDtsHOMY/jhc0nAOMwElATyHn8OqP7EbRcyI04cPr8DbWdSJr91nxWiVHvHuydLWyGug47vjXAWEpJlbjSf20asJki4bI2smsjvHsLWU4jXjdS3BMsNg1nPrhZ3As7sjPhQpax0ZC8kI4aoibt06FPTp6GxAagt7t1Hs3jSsuoXcanFtMvfmezvBgP2ehZBrz+5WzX3+zKyWaCOEvdv2+rxpm9uJ9Ql9q7ubRKkgAsgaBV/qw4yb6y+Dv71SI7AaGmA095J2g2AxuTeUbTbNxnEMkvfd7U4pbZxsD4UsLU99kPUIm05DqlmhJsVv1OFZKTSQtkIF1SQU5IDt2laWWKVEyv2hDkSak8coLRq62n5bsb/U0hIkRhVV9Zz1fhxq3IaMgDh6Et62v9jGcb/4joO3BWYjPW3L2b10sBn0JkTR6UbeaNS0aY5vMPo2yZutwl63m/t7P3zbA1Duy+tvNq6oTsnswikZYZVy3tdQZ7neYsW991rz7jWIqwbCFqXLfUMkUT9VVxVAubK3A3lfdL1nsa3VxSyjan+1vUZvcOwr718pOWVDOdhdKPEN41VTDIC5ikVgrBYiZLW+iYzI2S232gKt1JyaETOSyrHMjQN5raxyezdd1/zXr6+bBjyO486pg6DZnHnNNOqaa+W2BNLhHP/3//Ljz2t9Lh0Hh/vfPs7hjaE/r/X18DnTgMfpYxhBTn2teAwa9Hqt8zCjVkYKw3i6kTzMTjcB7vBznLTDmZlXmlv1JuIM8steM3+ex8+DS5ZkRD4Gz2Och4VwHH4M/+2n/Xwc11y/vq5rxte1/nyt14zizpU6Mv2wxzGIDL3jrnpIr5Up4MrzkDtfiFJkrZWxopA0JiTNyOH2OMbXnHXwinurBetxI4C6EISAdE0aznMgu8HqxDSrnGJndQ83KGcAhhVZXU+vuYw8zH57Hin///05//iav5388XiAjLXytN+eB0RCr1QADp3Vv45xIA7zIKfw60pk/vq8Pr+mmf84/d9/Pl7zi1h/+zhfU1/XkpQ567CexmF0EzPUs+5o0HCZM+RMGDHVaa5XLYKVzKw2mYElp0pIj3EYLWJVp2eRKFVJ5X4MgxjG5xhXrJhhwO8f4+fJa+W//f48DzsOviIt8vDj19LzwG8Ps5DWSncZo8YtbqsFq67DeDw8kTNWpQKrPLFW3ony7WupGjRotxgEUG44UisCd68v6TgGgJiRRhKxVsfY6mqFaks0Iy+tymWXitOS5RvKCwtUyMyH5A9P4dfXlcD5ONxspYbhY4yZ6aaHGymnVyPdesYpXK+4VkKWkVPVPTlIHG7HWBWFmpm7Rc06YPNc5zishIWSh2wrtnNnklQVCqNdoe3KJugeN9jcaDv66v5VzZmiW6qpNW5aK+ZSFWk6zYfRzU2H4/k4/vyaSJsrdMVn6N+F3x6yYb4VvrcnENjygHKFxpjTqt2G9vWgxyercisVYm8Xt2Idw7voQ9zjEYhviRCoA+0bwFR7oPvvejuwG/t/c1o7jNemdb5FOTKzyMTuydMe4B2F9e+UVyR3cTBYK3lzU0aYMMY3GXRxrynsrsF3RKQd6On99iKqN6rtK3wzUql7iJi0R/y481aP9zUYkdrtFkTgGN1a6K4Yf8fz6lx1StUCq+inlNzMjcZuBSDsFFFjUX3HENqYW2geZ19q02d1tcrO73Rj4q77oJR8N8V8r/ruDdkcECJ6fBari0u3E66QpaEDCb1ZOTZK2nRkI9dOId5LcQMg3PyL3VjqDfOwoTmgrfABwZoF3ytUDSrddHOtYHVursnsUI9KKD35X7idhnoCd7suQNUkAS1n4j7ceg+S6+s1t41vKkLPXdSgY/jHYwAw4uN5nOeoi3eD2RB6no7kd08EAFFR8pbw1O2UWK/wupk3FUywpr2Y3RMdvkf+fXp3TvtxDglf16IbpRH2mvF5XQKqrRFpnQVMhLayjM0fzUijParqk4lA6D2RLSPu4vno44GVAWBUVXikDO7u5iSnMlbQOdwjdbRxkxwrOt8IYdR8tLWy5gl1YqJnVkCw0uGLKQ12AYazJovg8GFWctN8Hsdcr3nlAXutdQ0BONyeVdhSjPTued9f3Qpkm/hve/I2b/fR6UJW4FbtGWlU0IIlTn3Drffb/OU9wR4TQ3x/5W2Wiqsh7glxNyf6LYdcdvx9pO8ohN/ixYEdzgNwbp+mOjIzuk+rbFfYkTvRwcwY1VteGrTIztwMtxULgDkfwjmG1ZNgVv3d4S7pNAOR3aYUuwUFljJSf17zNdcwfxxnJlI8/VyR7vYYTOl5jtOd5BjdvOHHs/jTCOFagUWrAeyRqophmJFiNZlmSpUh0z4vQGu6Kj4ZPnbBS0uBUkjRZUqYu6kCPbnZoH3N13mae14REEM66Fb1UbDDBnJdK4j8mqqk54z887WqaMHIx7DHcTgNBhoj9TlfgM7jeNjxmq2cU6JEDwRCKuV+iEqxOkZHjqojTh3Dj8N+Ps+SvB2Dx7DDrYbJwDDXzMQwSwV3mU9t9QGLJkSspmt3XaqRuKdo82bTq46/zkSVno2m0/qkSIDMWSMUg6hZ7+bGFZ00F1HdkXgXOdpmSUiSsbtV3CF1VSo4UDareJ9NAvXBsG5dSO12QqkW1BB6H21+O3Ub29wMQL8bdkewzIzI9xkteOmsqbubgmpmZ6tLbu+3SRX0UK6tzsP2VsUSpbNESqpG5plNDCVx00z3oaYNZ4ujqi+02fA3+1O35V7szzu3VP4F+d2AsIBh06Ebu9SG4PacKRlYY4Kw64PSGnoNwKwad95GrvrxbXVqsZ4g0XMVq1UFkLbvjWxZ3O3a7iHguPEX3yLIDkFaHV3OPdeahR/eZMs3G0xre6mNaZFIqBqM3AJ5bGirN6MhlRXjtrobpd92uN3Dtx+UNyl28s2M7C2nbnV6Q8v7h31rNViuWPLmgKDSxymBimVCBHJnLoVucupVj1HxUSTM0bqkugrrmsD6xEjWbNOMYzirOcSGi3WwS4EzVxAsWdMGKlunvUmG9/EqQ1CYce/5KpkqEZJSa4UPj1hrZQixVk1fNCCk11xf1wRg9JRFaK7IGsoZea1ckW70ecXG6m48T1rpjlkyMXO3YwxWdwUC0uOwxyGAz8Org7E34YvnYeO//PsP/zXPa/3t47giCPz+PEBGaoYydV3rWnkMH8OvlbQr/7xWyg2kXjMfh5vBqWE4DxzG4azQReIjOE873UjMwNespuaE4nH6K5TIw82E58dH5Drd/se//3Ye9ufXJI1uKwTotx/Hf/u354r449f81+f1jz/n8/DnYV8znm7uNpdea71mfF1RIsyVebp9nOcfn5dSJlRW5DVzhaLCFGitqgevMkLLUN3vmiFIisKd81uPpGocWbvLSKedo/V9gr7mCmEco+ZBlCjRzSTk8FS68efH+XGOj9P/vOaMmBGHIVN//Pl5ngeGCXDT5yuewwR+TjiSDyRwHv4Y9OMQoLVyrq9rzZm///jxr88rQ18zT7ffnuNzrn/77XgcP/78mn/+miGk8goa8fP0Yef/8ccr1oJzHANOmBARsEJ7bhYhwgTOzEHbpCIWArlOP0aNs0TCuDLnvBLmFMHRhbXqeU+eD+fH+SHlivx4HP/rvz+ex/jnr9fHMRL8muvzNc19zvwFPU53IoGZypStqdRwusHgRqtRrzXQpBDATNHHdV1+uDJnxjlsrnytAHgM7+pWiTKAK5VRkQLqz7ySQCbmjIqeYLhWFvh3s+fDJEQoozKgCFErMzFckXtmFFWKiRRImzOuWRMonIbnMX4cbpmHQMIJuEVNKxStfI4YwlyIFc6M1FxV0WOxUjiQYW4OVPlec/EQideMO1s+3KLGjqLL4sq3BjFXGM28S+eKYsvqiV6xctd9NwNCYGXMSDOndK31NTUj18oVkjDcZFiRBK7AFflxrj+v/KzBdtBXQdTE47Dz9G4Z1nErK0rLGtuXshL4JG6FSiEg28a67G9530LblViJ3LwKGK0tB97qli4M2FhB7JmOXWb1jUsps/+d5dmOUBCyE1IdjvZK5W5b1qHqzox9i6raZShjv3N/kn3Tzbh1pUN9bWff77MpIBVG2UXh31QSBem61PFNh1SAdb9PvXAXbr/hQAGaKhItgb41X9D7rKmnO/gr1kYeKUbeKQgvERMrm9K3Wg+OnfrSpi56KQpXNMtYFUzf7irFpGquSPf4TWW3w6/dUXSl3TcoqeZSZXZbA4cDLFFKVtRmuiEDbqR7Q5b6iyShhIoZjTGK4+Yb7txMzd4m32NitjJMu/s0Nvl5R+L9VqzGkdW6uDeYdSDRF2QbIzcxsmE3inmqdm8kUtVuVlJia7ayMtv7iUhsFXds4VXLJc3E6Jy3kefhZnTD83Ech5tZ6dixkVBdAIAaqmNGUvcebMzbgXs/r8bHTUmCxoH739+LyRu2bg2/JHP7eJxzzlBBbbxW/npdr1cl2jAjI7J82VwxxczswaBmpH1dK0LH6V8rIuJw9z2PtoRINQqperextWzuPiLDncOtdPtNQcKuVfNDkMDR8jGBMKfRJB3mr7lKgJDSWsvNwN1eFAwVuXkH84qFSs9WmQyzGkZmpGas4eOauRLXWpl6nJ5SjeteEc/D0eLWQupQaSPx3p11nPf+/ybKL6NxJz+F0RO9kamQVURRq6X9wPQOE/CNCNdN6fCO8apSeh804P3NbZ3e//wt1rkDu/tXtCU+9/v3niQh1RitqQz1yIWuKTAo5GSiGoVyzm53qI4rOvx2t9+e9jmnhLnUTTATXvxWuTBw1CaMuAqGNn/np43hHLSornPkeRiBj5Or+vQ7QJxmQEby4xwEYhjAK/LPyyAo8vPrqq4NK8LNx6YWIhNBQCtC25yqpGqoce88vTr9lPopM3PUKzOd4zE8MkPpNClnKL6+VmgmI2KlEHGYpfTrdRHXjAxpmKXweXWr2hXaETy+Zr7m6xzjPMeosLQ69SaviBVVZiSRR0ur4ECTggU5jJkBgNIwz9Afc35NEvAfj7//fPz2PA7f5W0AiPP4IDnnVZUQ2SqHYn6DvYkE8DiOe4P5d9mPWtZib2pd1bHFvpUefYuLSYLDUekxpJkehxmPaW1Iq3zMthu9O6Vwlyf3eW/l+TtSbtjzdpt4n5zGG7vqWYDuMR07Rr0Pcun3y3iC2kZp+wCgaI5NFteHdiTpBnjHP2yvUJhEW8FRB7L8wKY5tmeSJLuPb0iSd0S+3Z+ZAUd2ben7XJPwcbgxM49RXaXS3aylwUC1jrZWWLHpmApxLU3vS+gl7OKsljC6v91MczI9s4k3r9DgEAIyYQS/zT93uz1e3059PMlj0JoDYlncChla/VO0QRNT9SnaKOibdYPIPfllozLuRk9t/fS2m8T9CFifsSn6Br1ZPbA2DjSCVKtAa/XYdu8v5nfPzijIu5dquxCCQG7Ioe0AuN8jGsT3VdseX9B+xHg4s7q+4PYkjQebK9sFX05W3+u9hSpyQdmTuFWd0vsCcOvoeseO4bVlC4OWAS9/e60sSLBWHocf7rsDBVbEMKvWHNGN7SvxpBlRHXDn9TIf1pcnSStzrTQCSysUgSURFlnNfBNm7uM8KqWkOePrWiuCZER+zTSzMVySu5vhWuHuIOfScJ7jOGxYwyFX4nNOAefhx/AD/LffHs/Dx+DHOcbo8lVJkRr/9rcf//Xf7HXNuVZkzsgfH8+qqORusJ4rrxmZulbA+Dj983N+XdNpr5VK0fjbY/z2UfMB0NCRLMdzdpAGm+HGGfqaCen353hkFeer7Od///ff/vbz+Hgc5vbbz4NkhL6uOA8/hyWwFu0nH4/x+488nY/Tv2bOa64VV4SAlXrNqOrB0y0zv9Z1zWlm3hMrMcjjcA3/vGaUZC+7e35k/vG1/PGcKyKD4BWi4agiJ3ZVakR8nH6O8efX63kMOn1wEK+5zmNwIleQGOd4nKPa45E1cEf/+vV1DDsOnxFH4nmOn8/jbx/5+ZqR+O3j/DiP/+PXJeUwfxyMjAj9MBtmJhj52zkON0pGmSEOD3Fm/PPX9efXFGT/+Hq6/fbjfOZB6HnY//aPeBznbx/n61r/+HPW6MmZK5nj9GMMo0VI2f5ghWp4zvDDeUwFwUGHlMzqY+NjlI6q6lNXjcYVrBprlVxecdS4H7PH8MfBv/04XjMS/NvPh2CvEN2mZMB5uBSO/Pk4vdDucKWqyovQsMqBKTKumMP8a2lGjmEfY3zNvJZyzpkcSK05xjhU/swEzMTXn1+ZOY4xvOS+FL2ae4c0ZwBw48pMqcD6a8ZcUQUXQryWxuGknWaHIYU5czgzMSGZF/c73J2csb6W1lqOLGVKiUgJXhEPxwAFLPQIbahq7p3GmTlXhbK6VooYo3M+dKOPavrYJeNgBoA8h5vxWgHJvSpfc66sQbQRpShONx7uRksGgmbpxsyFt8gLZinK+pKVqZW5QnNprYvkiowlkJX6q1rlFcmkmT1OnwuvmY+al7rW6fZw5FzrwsMPZOeoyVu9zPKA1ZE+pYxM6h3o7nBcZbi542nWcag4uWmkeoIbtXSEWumtahV2R0CSqklIuVgA6pFczZRvR8hbRlOuYnu691fuGLewy50qeQMxbX+Eb78pVOFP5lI20ARLDf4NKXaWUE6Yf4uZdglb+VTfj3D7v/eH3RwUv3MTexXfKZt+ccNI4Z4X1TUyG5S+76GBIGGGs/dpPdAOatGcwhaYt3/c17OBz+ZZRAisIco3CYY6ySKCHYpHKFKZmEsRYWbHMVYk8pas9+qVFVqpwx0mmmWqut7oHa0C6EQWOnkEtDi5lrKqKlDdpGtr3ZNn71sqoFELtvnD7sCeNQXIut3QTX5tyG+FjMzMduOu4i1rF/tNrdnuXPDXgFl5V+dgdx5F18RU2rXDZbREaVfklaq+Z0hXLiSTrCaUxeDwGHYMG4ZHsz93D++9i2srYMM3AlB0dZmwSYTNIfYhuVtCAN2ubO/5Xfe3e3Y05N23X7fiZs+Hl9FK4WfGbx/HtXKu/PPr9cfndOM5xowEEagR4DaGHW6p/NqTRyOSxGG8rrUFvypUsHo2LedKIR8HBMYuMForVnV5AI8xJEXmjHitdbgVBX+tEDTcD/cVulaQdtgANLOnJuau/U9h7YbcxekZq7OPR0ak3HytXIlfrxfIa83X9Wu4G+083OmvKzP1GCMTkRwAUoFqO6I30fuOdnEHGLyjiPtQdAx1G4ymhLzLCpDZcw826gXw3hX49jZ1KN+RMPcB2DHut9equehvLNW92d9W6v6e90cQe2rAHUGTcGN4NaPrUcaZ6bSFcq9jRlxRVYRtPI/hbMbSriv+1FJN26W/VpzDfjwOilnjzYEV+Zmp+0zROgvo21XwfchZ0LfGmbTzav5qrflnBonh9vPxeBz+8fC51udrvcKOUL25mR2jTAJC1QAjD6+CGpUF4z15kJpFEaFAlEJi5MN8Zvx5vcCclQ0lADyOw+jDQ9dy2OGakVcmqx68a0lYuZ+G/O9y136gpIX4dQUZhbi4k+cqnYFZlW0O99yayjGMwmvGay4A7h5Iz3Uex4NM6dfXlc1/Pn/7OE/a2AR90dfneeDuBZO3h8ecs3ZUifG4RXl3pgVSardZ3tVgt0Rzn5YeJP89i9LtXQBGBvJwjDGO4FpZBEdF/ncnu1qj3Mev6sjMte+jW6ShAOVNN1QXYdpKrRU1N9Bozfx05RM7Y7kLDzd1BFQ+Z1MDaPOyh7t3f6ubsq2myS2YGePm7SHA3LyHdWrb8iYUgJ0rvvfBe/eLnYej9vF8L6CddQ31j9wlYG5mxyAx3I1YEaqgHQBZjz6iUgh3rgpWc+DaHVdvJu1GV2AR2Zk1EOP2pFUvtwo/VApt831qSTnNyLw5rbdh/G7tikWy3edrm9OdkNvo67Zx6t96q7n/+oYbn1ZmpREL93N6vzZvQEVgd6xv7ql2blLdilvqHCGKxuKmur6RYhvq7ctoCCfdDnl/vpQZPS7mbZ1vdNlynmqFc28jSF3SRSeidRi7EeD2At71koV52jhXzfUG1JA0Z/A7Lm8abyO1hs9p6O7L5RgkIeAGlVR5iYlcmaGlVBHvanbpWrOjp5pqre7BDMC4RpWi4XVjlbK06FWtPmUotADFtfIYVpZ0Zb6uqleiu9UFZtLGIHkYiksqs+RWsmVG5uu1VsY5LCWsNdxHtT1ZGaGvC19XnIcdw38+x49znINj2BjDh41rrufzfHycePEAnr20RE31MRfE4Q/nvNYKGJLI58OMrsyPQQEf53ieXoqXbnxTJ77aRUBRhKUZcpHVxIQOPWnHMcpX/f4x/u234/nxKGNEM0E++IA9Tn+cI1NxaEw7Zvz7zzHc/ucfv16vqUwDfn+MgxErplEBpb7WvFaSMHNWOJsajhlBxPM83P16LXezwbWC4Ou6pnBizQg0nU5mtWvHzFWhtZF/vq7P10Uz0a4ZK/A8HODXNb9e88d5/PtvT1DHsB/PUXIqMzxO//3jt//6+w8h/vicgI7hvz9HZKaedF+Bf/26EljJf/xaxyDI4+Bj+yczmHVJ5jkcdBtaiD9fIvTjMR5jrBl///vzf/wvvxM5ZwjwY2RkRCzx48P/+FrPx3Ecz5fs19eUEOihlYCNwbmSGFCunHeJcJRau1giQZkYTrdMAnA6PUNww8fjfByWqd9/nP+3f/8g0sh//Lrc+P/8778dw1cS4v/2z+vXa7rbtWJO+eBvH/4cHKZzWJVDpyQXEsdxnO5SmjhoojJigOM8Hoc/z/GTdl3rn39+eSIyH+f4OA4hJ21lXAFqOpCit86/+yYYUbWXEgy4VqxWBUSsfM3OVDnX8JoQ58OrnaeBpGG4gUzUHsvH8GH+WuvXpQRPh4LFRCQwkCY5OJeWtf+rWmgjs+LkDCVqKo8bZmQkzmGXBLPDjHMa8OMYf/vx+Odr/bqiylRrANIxDkKle1JiBXKua0WEvIvRM/Nys+PwIjQz0vwo+eVwrynC7mZMAilcEV9XROjrWp8z3EescOrn8ziGH8PVdXYYw8pFHodl5ijffI6H8+ezKiAlZMTa/rNnalS9wY5uE1DXCm0PzE2GlMWPiCKbhPAOJHavEOvJPv36Tv4UrnmLUXMzHB0LNdEP3rJRtWu5XbXuK9j/nKU84hvm4I5td2x1gyo0TfCXTNdmmdrhqbz3t2v+dgENBgXUaDLanoq61ycr3VaU0a502J+Ady4H73vRX938XymF93XesiHdd/INGqQ2QLnvS9U5alMjVaMrjE4w6A4AK+HTzN2WE9wEzs5TbcBUJFexo6xK4BpNVS2ZktUaS4Kq17U3GvMhzfNwAXOFGY/DM+37g2t24X0LN84qTeuthAc2CrkDHuxsW969rfreul5JKkqr2vpX8do7m4om3KrFVfNXtlsjm9kxdmfppn66XFS7XKuELcY3/q7CAGBzlkI2bK5DWDiwuoIECBsD3VQ2lbSUVfZbXf813M7DxjD3br25Edje/31k3qtX+NruWF1Cj9whGrDSdoT8PWrAzceCNdRhk3rdQaOBvlk178hMA8yOp8YKXXOZY6XmSqsJpoDRjoFiv76umTsAqBmjK+J1zbpAaK+S9HWtyJ7klZmfMWfkzETqMYw0CIGog/h5XdVWojQmQmbiNTMyP05DhsRhrtSKkLIGJEk1oaPCMA13ka3ZFEIyg6OGYwBCJpWEnOCKNXwAuCLMesrB10wn58qv6/rteZpZRokGm65t57NZlaKXs+uEu8LUeq7d3cL8NiO98SoiMIo1tUZQJ13+g7V8n+V+xLeVuaVvfxEe/Z/bI72Nz3fbc3/x5pF2VHUzRNQ+ujRF6jSmOQHjUbPVapCc0/JOFaSgJMLMhZwLEl5rPQ9SmjP+TDyOEUguHMMAVaeeEhp09asxpWvumgKpenm57fsjI7OKpFLZl0hEKpRuk0Z3p9t5+t/tiRoGHxVLBNCZ+wNWnVCrqcphNm7rxLb8ueVabmaQux0+wilhLRDFVyaAYU5aZBCFf8lqykFUa6p6lMW58C51bRal2wXOyDkXSXdjJKgif6vtUSRSMSNhxlhl/8bwh4zi61pjeG2WiohmrPMYvz/P5zmOYT8/zp/P43F40dA7aNRtn72rjSoHISc0PKKaVd92u0UwvUhGsypxbXWn0dyNZsUM+ZtAx97mmxMxkp5mpxlYs9v8Pj7c7E/7BDXpHsHKSa1I2y3hK9lzt6zSvh5vPkbHMDfOtUVmalGwemTpm+D55ubrz9LvUnv+A1ktdgrB7HLjPl9180xllTv3e6QAmVfprtSJ/Ns8N/P4/ezuHIDXmLY2+sAmapWZ52Hufs09iwB7EApQhnch/J4X2bXYjRIVadzC3m8Xs9kZklCJcWhrhaSj1HDbVRUxuhXcUsMMCrvJT8tQlN3Y+0ZV2k0g+9JK71LGx4jA5iz2yrYp04YR2xCUD+Wu0v1mPN/ol0RnKAtAYVOq99sm+O7NXzEKEvQqULMGJG+m7/slbMdbH76bY96PscJ79jxDaHM/b4tbmqnajQ2LG9+hoBG2ROteCqupH1UMwa1UrZmj6rQryeppX3eYezklrWjichuu/+R3ap/Xo5G9rweUuv9A8TSZIHQawriZbRgI2spMcC7987qcdHJGtuihGstm1hDQ4Tz822EgnDBqmEWXhvHP1zSStF9XRHZxRkZG5lFjhswovda60km9lLsGPA+3r1mDp+3X6zoPM/MrZMBSTmUVqh5u5xiHmzkfhz9Pfxx+Hl4NHCJCsiFyrmXuNK4VJJWJKst8M9CSZMbztL/hJPj5mj9+OyG5dfkZbbesv2ORwnNum2p87/KPg8s0vBIsIHEOPw9LYK78JsUkjedZpr9Gb2YqpzJjiXatldA5qv0nPmgzxq/Xmp9xRQLc1dFgNEPcbX2pf/z6WgEAcy4Yh5uB5+P5A/zzNSW5Wc1idreZ1QBaAAxcETKe50Ha5xVQ8vAZ3RLxPMbz4T8/vMKSMZBL//5xfDzGcVgKf/vpbv7jwRkA8PFxlCBtLv3jzynIINYTSvz+cf74GO9a9dqRtGHmgwCOjMPzcdjz94eTr5lu+P3pxwjzYW6Z+jfg19f6vPDzg6H1I/kY4/NaTx+fWJlhPjo3BRNgvudiCsWvhTIC53DnmGuReJxHIYt65kYe4xi2IPz84edgpP4f//3n//gvP1aEmf3b1/pxjn/7/aws9pr561qvKaQ4fK11Gn87x8/SkdXADBDDjtMfiUisFW7mw0l8nEOQ087zGGNk5nD+fD5//3FUCRHpyvzf/3z9muvrSjd7uCtzrQx2dlXCilyZMxQRXu1mYOa2VjRMoZk7Mtys2AunDqsiYYbscJm7t3Iqz8M/zvHH1/qfv+YMQHmtNczHYQk9NqB24CuklV6o2TjGWBkAB7kilHg4P+VTOg7/2zGGWVbPVOq//P5xDBc0U0t8HONj2Oe1Pl/SKhNf886i7O0KrRpRkwrJ3SSPBBZW5lylmFgAhtE8paicc0gRqteUazTjw0cWDQb983Oax49zPNxLDD+GFdQeAtwyBdnzwMN5DnucXjqCwgRGhqLi2Rt1mMHN7+KJbcvfcUB5KDfLu43+jTysB7hWVzhuGUTFH3t+abMqlaxoD9W1zzXlqqY53LiqQiPMle4cO/LkWyIkdGPsaiLT8FAq+W7HLVWJYxDQzY64faC2v7xFSLhjtTeLhIrQOjSu/6bGnuneMZ32pWtjE20OgzsO21f/fvd3uNavbs0zuu3OTl3q9vfforUNHnY/2fuC+5vcLEFTDzUhh9lTOb4xA9vM1envlGmrVjutX29r7xiyxk/BiCSvubIqzkFAbsxKPUgZizW628zYPY/uBli1gqXC2FHTXrAdQwk7pyjbgXQlUfv2mkxKmDmq1rBCeogwVdlV7WvzLnrnnZ2ylJBBcxap0GDaavKN14yhb9qfxog7qYxqZIsqn6x2XSpCATXz4q0XYjda30+IrJao1eOXEMUkRZNl3yNZKWgf7u723jN7A9eW3GxenSqY24mRPUFcKcC7qcQ3oLYLH/ZSZ4n87+35/knNMCqFVK1H0xNVDVoiZ5/L4G7Pc4zrihX6n39+XTPlWqnXXMp0olK/9fYprMhz+DF8dRsgroy5FMJKfc3LiEp1fb1WbAxtVKkgAbldJYf8cZ7lX0odEynQrhV/rnUOJ5rcSWm4n+cQk5mVZsx+W/RYvuqNEvqKuSJBuEVTbmj6z81XJom58o/Xy4xZbaSF11zXyuc5tC1SIf4OSqt/1rdw2JuEFICMfkKVpdxizL2H7u9uC7IZ8zs2xrdO5H95+Q4zd7whbOGAbg5xa5DuU49v9vs/WJ87bN2RLu6attRtFOmEmZ1ehTBYmScwwwq2fiYi47UWyINOmGgzA7EAW1LMlcBrTkGELeUVy0h3T5XSzgQ5DMBceXVGlSsgyVak0um4M/mQkSFdFx9nVxn50+9SO+vJJ3q4/ziGQRF4RepKBtyteORBM9OwltuTHFZta7vnv1mNtYDV0BLo85q1xCvj8HH4MOLjMVJZu3uuOVdw1zikUOVWMIFwUqmlO9PCuztOyTZrgIBzpISk2N21jFhWqhvE0koYCMFJkEn/uiJXwOzhbuScq4ja53kYea0ZOX88x2+oqVzy7QHvaih921gg0Mrg5mhuy473S/o/32mslv/0e2+6Zweh30t2tTkRVk9iECdi5UJ2uxd24+e9+dt7l6OtWy9FanUsys1K5I1AiAqTixswg5uNxyjbklktz6pmg9xUwn0WtImqruvm2xUQbUK3jBjfrW41rVa3z98/MmjXlA03dT+MbzHvO4R7N9HLzOrmkzuidKPKEnYVWO+Z6puTmSGY8ejmnQUaY5hJmCt3EoXsvpBVfsWb1EaROKVggqObM/I8vIS/ZixVcb24xCkFAKoEtxBoNiPxtj/CBgm3mXszOxvA7BEWdS/qJdrppXIwG4hhI65OjFH3Y3ibz70PCRCqXsLkzddsbKJq7mrf3qG1213q0i+sSYW63/7NNnUYL9S97yu8mRu8mbt90Mq8vts7drU+sWGsGgmXXpJvM92kT2UKbYNWltR6p2Ei02xj4nZ9Vr9193aoWUw3rtaOG0rb9C4TBWqJoC43aMNlW7FCuttBg1TTNr3BFT/OYdA/f71WqBJwylxvCk4QIvusyzhqVmkG3B+72SWJ4WNGmORmgCKwIg9nDZ+QWLOMfj4ex0xCbmeB3yvycy4BrxkZGsNpTlj2EKLuEbkiZuSM+Hgc//Z8/P3HcQwehw+3chm1fsPdahx1ndHITkYJcS9W3tJk8Dz8v/59vK6DTVEB0Go9GYomWZFRYUljBVMDIDzPQ/vE1lkoQrtgpYS10rvPQRXm9YaKlvSbmYZjRf56Tbh/PH2tRWMV7fz9t0PkFZq/rkr6qiselYJlzn1KZ2jFyoSBCVyAGY8x0vzxHBZrraiYeM3rNHC3EAL0OMdxnCnNyGOgwsiVicwfz/Hf/vbjbx/2cVqAKT0O//FxPA8/TzdrNfhwyqz6JbEDIEkLWofDHgZhLhxuz7P9QZmxnceykqMDNLffnm7QwUHwlfnjHD74WvJUpiLWFF7CJRzHcQ7kCZCCnJICyGFthd0LmgwxI9Poj+Gqwe20iEnk4xxjDG4Z8yj6T0nxx/P595/H7z/Gx2MMt//29+fzeczwSP3Xv4/DaW5o475IHcNHKQCf9veP87eP6lTIXXRCN64aI68snlE9icaOY5zHOA5341oxZ5jb83AaE/j6mq8rz2M8Rx40QmvFijBihsqVrpCbr9AMEZaJ1xQdOYPS4xwhhejAObxM2HlaB5MJKpII2OuKx8lz+HEYDStF4zns19e1Vvz2PH4+ztdaD+NzeImkPucMsQT5BQ8y5MAxzCs3YqI0nOYjlR+n/+3H+TjMiPMY5vb5mp/XmpFPBzJfX/PXa9abZY0+BlaU7LOgjwNYETOU1YN8mMgIRYDqKW+XkFqRMqBWO7orEI9hFVcoNczOopPNInNFRMbjGKOGWwJrTTcD8LWmKRRgEZbq2Trleldql8GpGywaK9fETjpvBPM2/NvQGk2EEl7ZjvIu76IvlW9R7lR2Tcjp92G7fO0WQHgrod+TRLt6qwDHXOnWHRPvQKW69oTeMyxviW4lo7QLJeqftZ1idwneiTttuaz17X3DiGWJ981rcyvNSgi7xV6Vy7GUHdvXYQvzC0M0fVO8SylW+Bc3jrdrZs3JFaC1NEqB/Y23wo0Hbojw/se+RWwRsmrEXmHxN5ek77/Irm7vS7A9MKUYja7MY7t8soYPWlZfniz9Ws8TKPdD6+ade7EpyQg/RmGyNyRv1f07rN3zenknkLqmqgNvpLrzxUZUEpgoOTHVnQ9MSsPonlYo1gnq6uPdSEomYK0la75rxy4F1jtWr1ikmBy7kfV+HuYmY27q586jluesqKwdeuk2+d5Mbia6unFCPZoSsCPZU9JrTXYk00B3h9b3k9T91Y2cCHOjqfKGux7S9nxW6t1Ga2Pj2qmd9wDQMl50+Aq3bgy6H47uhQIQkStFM6eeHG789TVT+vVaX11QjMMtIiPy49lV/QDO4xgdpXFlKnNlCXNo9MMtMj/n4m6cn9L1OfPduoMGOi3Fr1nytzQypRCG4Vprpl5R7Tx5GB9jHOYZea1YISMoeNXFoIahkIShmkXLzYqoamIICcDJFTWIo1vHFjd2rRxmP58fbowVFSusuEtgq+Jm1zjwzeeQHRYWgtvTit8B4v2yt8Gwm7x+f72ji3ujsU3WHVTUj3KzicpvdM+3QG5vLfxff+nbH/VqsCuLOwjJLpnBcIO4Mqsn1jG4qtIPEfr/0/WvXY4kOZIoKAKoGukRGVXVMz33sWf//y/bs+fOnTN3uqszwp00BWQ/AGpk5Mx6d0VG0PkwmqriIRAIqhRUk5sMgsOiyIwpgZNDKMBfJGVY2b+szk1IgTXMnSzdTTMotDIiM6Q5kq8WuVeafobcKORww/QuwCTceLhN53DSFJEeHD5SmKwxcAJ4Rg7yPppu1/kruApQ9HfVMBn5x7iZ8FzxcTtucyr18/kMppkldJtjONyj5DJz6Fkj2ykabxwfc3w9V56nap5a5U2VrUHnimn8437/j18PCO58ZjPiVubdfCUWMMxvBkhjeBBrhVXjGNLJr3PdhtMsIioxGj5KVpJCZVaZZTybBvuySKiMlDuPyNd2+21TXVnoK/+1pnZeZIVL429b/01taA+cO0zoZj66Oae5Z2cudYptu8fd75m581L26Gvuud6VkPIickIl/12wVCith4uRRFKpDBiQm6eCGp+4D1Yf4BfA9YqvYCUucp39HSxUmFSeq2+fNtd4zymzytTb0Vzoz/4SANB+vKBwMxisrFAhv3UhJEi7SKwgxxxVI6SVrG/j11UfxZVIQu7mra2N7Z42ktX9/w1q1CobOZoospsXN7fa2ILVYOsEXd/6N+P3Fk1dF8b95nt3VQ91XBFOpXLWMtn9SOE0LTbGq47yjsZg+0OADb1UdeBF2LqCwv4U1W2/TkHDJd0DXolH00u377oy/j4llwLoZo6+jPAOZusVVwTZQXV2UHnVCnaD9z6Z+RbWkj3C5s379Fa86pktfL/ZQP1dK77oiyW226pY+/Vue+tv/KxOMg1Zgqd7tzACkchURJhgJb8YAlr50aAxbPgxh5E2zVbo63lGZDRKItAEOnOB4MyMM0rggY8zPgZLKVjQryepuE19PnMajzmGU8yIzLVCWMAgxyi8JkP6tdbzjAoz1kJFgl+PIKP4AajRS+hQL1O/vhbJOfiNo1illuluYwwzjr4ZJIlJy9wn87WXQDP29HgKGsbv347aBJHKzGGqWMpIWbPPGmJ/yz1q0dyNxpILEpoVKZCbbAz1nK26hrwQ+j2+wq0UfDmGRcSAG/B4hhmG8x/f+Vj3x9LnMyr8opW2SFYs3guVNBtCRKSbDx9SInUqQDl5zBlSCM5xnie8zD6P6diMfTqLvVZlRB/8+/fbf/4xv93Mp5fhLaq8ben/43CUzBgFKMSMtOzTcTjy8BppXIIugILsEMmqXtfBU1lJI49v/uPbsVYK+FuZMzIWOUsWdzwzj+G/vs5/+3z8+fPx+YjjGI8zZKLBYG7MZvHwPj0BaVT8QGQI8zbOQMYY7mKXy2By2n1YKEP4Puf//i8f/+XvRyM7w0GsrEbiIGuELFBzpKAf3wZJ0zgj/qDdWjmLupKB9kBVj87btCqQfdzGH3/cxxxltCKWG8fHUef7XJEJN7tNl/hPPL/OqKHnoK/IlAZhdLN8roho1z+G34/b81xfitscJL7OkLImgd2n3+cgQZiTMDzPcMb3j0nCBCdqUp7EY/CL6zbHP77fb8NXxhx2PwbYrADSnJhWkEq3030cxxy2UhMYDlB/3G+laZKp4/DbUZO77OfnV2kwruf59TjP0BIEU0Z7RvCxsvzKOiWg6iU1ml2ZSYtMd5AOiMz1KnjoNgzAGaq2dhAfc37cRmQ+zuoNNRK34X/7mKSekT8fZ+Vzz7VSIsCMMlEARQvpmRod+memCVRe46LiKhmVV+54ZaM/21tvT9J+R1dgU0/Rzm06PEDHGfm7D7vex7ZjLt9Ruh/ciEgHeWpExoyZWgX6NGQDUFnvqGqv36WMYtB0BaiQi1cxvEMGvdyphKi0vKttBEto+mWPywA217TqeEZa7hJvxawlIQtalSWvmA8SqocoSyAJyIyiTNX9egE/G9GAUKPoW4M07VoWbiqBOqIVqh64kQC83/C+kbo6xXaImZ3+XLemP7xDJAn7zV8B5QVjARjTzObzjAgxOQq3ACOqI5Vz2rWltGEuUnRTlzaUzVGvbr6ONWrQQTmx7Ep+X5g6LegSiPZVVdQSCXWjQXUXeJEsysyXBm2dU8neURPIBIw3XZ8Sfq43LwYQNsGeb9+phqd35LfxlwqG4hrYvUGZyNzhYzbWcsVj9tIHVSqZeLG3aBsGaiBrv+c+dBBUqu0vWJNd3qvQXNGnywzZtdcdrdVO2qtMYrinqd3QBiAa9LlGhL1vx6ZT1Qh5RebzjMfS47n+x3/8+o/Ps6iONx/VOR/K4U6zmspB4+E+3FRJruzMHlS08trS6JmyvYv7WFfik4kFBNLIonNCFe4CoCLN7GPaGZkkUSPfVN8oTVSTQTJVdSlJWeNFUPpEzmaeW8HofUqSECZJUEqnDXMaSE8plQ6vVR5unvZabrx27z7uKinWWjKCXhX4jixfYfp7NqS//Pm2jm9PepMa62f2yM7U6yIu48Df8Z7rlb+9r/Rur65fCy+po3MfcuC15aIGpfP6RhIhwzHH4MhECqHuh+uAsepwYiJBVBPEoJk8FCvSURQbqmdar+IFpxQNHlWNwdYSeY1PY7HljfY4zxL5rqTQzRLxPMN9jEmQQbnR3e3MozoZM4/JMwHglnSzufsyRUvyecYwHF5y5AIYwBn5XHGbXu3ewz0Sn+tciZ9fq9xKrLPYcJEq8FVAidomYGbPtc7MOUaEqp+oigrPTHSTF/789ZXS/TjM+PUZVST7WtFgBy3ZIitLitBt2H3441xpRMqBlboPT8KsBjAtGA+3b/fbx8fNh5NMCZGXj9PbNugNLlW5ulPEC7zZKcd2QdtT8vWXZqjuTtrKobdGvbDf7cqetSFvk+A2ehNvE7djiray1o272QziJKDdrO4y9xaZ2yWd0uzptjvVTKvCibLmRbLFmXYAUeDUFU8Ru7d6y/ugumi3Vejvvb9OnSN3Omyr0ZXrMWxUyFg9vPt69gGsN9GGBoyt10vDcM8dbpo3vixsttruvQ1gDjOz6KwBZp5KG+XtYvAlElgk2ctTvJZzLyn2VCa8Qj52+IHyeK/9sCuIF3DRt2TDXtY7YHtVEqXlrz24JLcXZn8KIKhwfjahOjMiONzVzBg0evNiCr0HgK9Huo+a2BPgrn3GV2iZGyjamNzrUrYeAt4urS3RpSmkHSiX6aMApF7UtwsyezfUdZuquXw4dwzcxbPK4ftGt8cWjNqByBULbmQLSlVUgh1X9EtriX/3NIU89FZ8A4G4kaAdqrHAiiI4FR2ZEpWlsBmR1TU+hjtdGYBGC6LSbwOQEfPwP76NYUzhs8pL0SXKWLBuaTKfbsCY43ZMAV/Pc51rONbSr8+n+THHMCpWGgj3R+Svr3MYH0Bc1QtYSbwhpdQcFsLzDDMLKQzTPSUk6C+tsZQiKJkPH8Oril+GMsVSLyk2OwCZ22T3oO6p54KwFZgA1BVaCjWQjEar6XSQGR02BbQ521Eh6K4iQHUVYg+8wVvzcO25FJidjeRe1yj9AmWEVpMDFV1dYSZsmBIpjOH/+497rPyv//Z5xl4AY2uxi9WI3VqAY4RCwOF8LLjxPsYwU4/KyyBCXFPfbuNwN7Nn6Ou5VONFJEPS7Daq98rvA9/udtxmm0Favo5mew2aIdr4VXtUDSzPpM/5zWuUAFKIIkhkniuMnGh6BMHqtSzjUj2DdEcmhDNRd+aYpFEJk46qZafchjH/7T++vlZmxPBx9wGkuR9u03EbHGPUaCczL5Q5YXfJMR+hlSrvdioOt/v0AJ3+rz9u//q38XG3kj6lsWdyV/pBnmI1Tdae+rjZbeq5oiis2uxLg7YsHQDWkCxnzTy2OWweXpOls+ZsoF1gZKwVFViU+Y2MPc2Lz2c+nlFC4NEqKXyc+Xiu8jrPM9xNZJKfa92Gu/HrKSk/Po7bHCl4Yh6WKQpj+DB+u1Vfpd2P4h/yXDrg349vS4zEz6/nOunDS9+nguk5WYIekaJ0TB7DbnMcRW4aDmIOP45Zsq7NlGuXnBDWin/78+tcCVpSpVrvtIQ+n0sJkStLToJGG7ut9DyjAEkA0whIbplY0rPZhhC0Uu5j1VlsT+wTPoeO4UUZE/Tn41EAq7tLcuMcVr3Wt2ECz3PdpxvpViS7ZhAUzGcOt8puxy5x8KXIeCExbbVfgV0nXwVBbBfJy9hfyYj27AG8+ah2Hzswe/mW/rhLVO/l4BuY3LJBjcsou9wOtJZtS9CplW62+esrYCdBEi58RoU+6NK9k0S1xMLLrV1/vXy9IJTkttNklqXvVLTeTDHe3CLZ7NnwYZYZqajQNhC4mMRtU+wVSbeTBVAlkyVUIvP6vwbKAEmradXY8eq1Ftxa2O+yRto8rVfi2Ou7d/u+rtf37miupHYieuWsjU491YiilLpb7cxyRldXEUvTx4oZxJRkpg0n+ns9qvdbbq1nkFtAmS1axN07UGABuuZ2pRL7TXYgUlEW9/+urzand0lXENSKDz37w7og/ALLdiazlQt2xIkdBVdPYrYJbCSzf59XrbWS25Le3IpCpLGmMCphlPKtB5BZ/f87s859SPNKrXqFde2oeoKZdSvpK/vat6ait2vPVZjwhoWhL6ylj3YEr65UvsLWfEt44ORtjO+3258/z0mOOSIUwBhucpIROuPM1G06B0NZ/V9u/BW5EoPm1rJxEljtdZ1V2hhuQEYltEQJRgoEE2lope/Kn2va0XQ/3N0YCil/PZ81ygnUyozMlfKddpJQz3eCoGluTCNhfK7C8qx0bmPDIAYq16APFh1Jn49TgBlr0qvtLsLXVsHr1AO+DUxnCEutDWTWegf2tn7bGtf6bQIG3vD2fYr5emLZpBpADfRsoPozsJ3AtfLctl54Xdrr3fH20LYiG3Bua1Xf0rfVjuKQqOFd9VAwNwNc9dDAKEUyGmvus5kkBJKoOXSEEDqTIi2FXCXSiZJIrmpTjY/rJL2TKVULj5SsNmSAyqAADjcjI/Nxqsb6nsLnI243NyEj3UsSVSSSHVMRug2/WmVpLdl4DFMqouJg1gZT4nD/epz/kQ+BGTpGiQtDNR2GFiufqZV6ZA6zaXbzYQMr8hlhwGd9PdoikjDqbAcGI2PlV4Qbb2OsSC0RbLoOAHcKa/W4UpH0Md1vzD+/Hu52Gx6RH8c0t49jKvPrPKsSLvE45sd9lvRPZXUlWLBTx70NrtAbQHcNb7uwtwjeLOoVdDS5pqPtbZpeW626b8rGNV9kN8LVTiuQe5+CXaxtk5lNGEu9jk5loVu/pmseMgHe0x/q61gxZKv5blmNgeteP27glAAZQO1bq0LFTvg3LlJtX7QClMozFPhymVvtWyUUHRx7ws92or6LRjvDvxaEVtMDsIMDXAxWALsuuLkq74Eea9AVKfXohsJLXFacVvMGX1rLnawnXH8vA1B3svndV+BQXT67OvIOXFzm4rdYsQIWXlgJd9Gx7wau62/hP+xQqFGTcvq12rW2ay0zZ6LLLIX4k6kgr6CVub34GzDXhnPnUGhr8qqvNe5WxcwoiCuVF6tpG9XM1zv09tYLS3mPNoELCdoP9YbHvlltfutOqF1wbWkZxaqRWn9b7BjFwKbW7XevmKHfrg9z4lqXHZS+AqAXRNC/wH57Cals4kVdJ8RXtlAYnhVWZ6YU6BzurD56LmtA08xM2frGHcHKquOqflakgOMY85ijJP8rU8sM3Vk6UJHPFQk+V0zn3z7m53P9x4njmJVMmVmBnAkc7jH1OM9IEZxWeLL8GFL+DH2/DwFfzygOiolLLQeRmWcIlAxKjWHf7+P7xxxuc3C2LFzX9cbjGbBCRqp9QGY0cFWVI9W4HemlgGIV/SJ3aaTOg5tx8E3zk6Vwp22z0Qrehk3Cqr3tW7yzDMSuq5X+U5vdHQtI0kpFolrul2gSN5NfUKYCCKUb3JnKs1olN7j4XDHczNwdTjjpnIl04ptGAN+Gu/OZYdKcA+YQbn4f1vfujDzXeK788+v8PNMN36Y/Vnzc5//rH/f/9GOOOStELjuRgJtHwiAYq2HFjEXEhpAJ34QCbBscqqmkrEnnbpwXNm5O5EGv+NfMaDDzw1RheTzjz8dy41h2wFYqwf/x6+t5Bs3+/Pp8PM615EJVAYbTfZyZlXuEYKn7MWrCa4UjZ2i67tPKO50Lfz7Xz2d8P+xj2p9nQFqxQjNh02syNe/HDJLQMI+IKOIBmBkSza1krM8V5xndRJMLZoNmWwnNzYbvLIjmTqnHtL88Rw84LCfmhaXfxgxxjPOfv84aRNPQOhFdF8meHZjaOLasJ0fy8Thr53+7H9UONgxjDnNPJA3/uM9/+XGbgwDcWSWvTLhjupkxQp/PzPTaum6mzPso+KOaKFAI7OFW6qrfvt1u96POGvZgWowhpaqVVN3O9Ou5BJvDf36tX49V/YBtCWVfaxEws2lmkLFdBA3fPw5WUwa4Erfh54rPxLeb//jAWpnCSk2D0UI6pt/mcLfv9+nkPz/P54onlOA0GS2WpvMYracxzcytu42FaaPyArOraQWtJ7gdSfn7HX5tiJr8Hd/ff7kS6IuX8R79vxWvXh5PuiK9HfzVFb3lKPsjXj7oVcbZZ7Oki2UpuFXfMnPLamz/CHVVJP8nJ3p5t9KlvF4DKbnFR4ehJ+q9/bz/48ILUKg8hai/Vgmxqj/FXb2Chawe+1Ckw4tUP4raXWSire9udk0KuoJltmD2/kHsX+0ndWJebm/DOvveXrHnK4x43ZmKMnX9qvx3ZfgqN6u3TvK/7AVukjNEQ5Fr3+Mm7mYq61D1pW239wFwxeraGwzveS3YeuRvF89axcYPCewLURUqW229NxML7y8+Uaq2TTfvXDvEjM4toV2x+bZvbHJ4Z9hoz6Krmg0ocwd/L6WJDiW7XIlaVGzMRxegmdWdZ1sHNMECnQGaWaaKtyyRjMx8ZsXZ9ZzsePfKDnp5dpN43VumMAwRiEwKEAtXzdcu2Vhph/JX8NtZ+pXBXaujvV4FXEnYWJ/MbDiANOI4fFRPDs0cXPZcK1c6sbI6JqtfrWYdZhGqzygctWI+zTFSWCsEOK2aJgqhud9GEY4KQhtmRjwTBkolA5yl3PeImM1OTYO5DVQcQAy3FZkuj81ZecHSSOBMgDrcC7BLypzIPLW8JKUIt5FCde2siGF+m7MkaUKKSKDHnNfuukJp+w14L8BiS0e06WIGRGWHaZUb7YEy18G8EKX9ztztTv2MHaY3mN5CY6gerLNQBbTOtzUY2pZB2KnOG374bhLq5F/YD1vCk8SmxW+h62qTtddh2ee/RbpAYMhKmXvzhiIyrZNKBiI2vatBiNqPV8dDVvHGHRaZqZYJJ1mQdY3lNHgqhWRJB58BiEE33udAgdR0hFCC26lirlYn9WGmnsjOQqN9WreHD3+e+VwxamxNyomV+vU4f3zcjuHnV8OdT63zDBDefEM9V0z3stCCQvlrfXUaBtAspdsYFYyRZFMvldIzIiV3J/lYUVMh1NLapbKhysZDGNAkqWUyCu52zPExvQS5juGkaDyD2k249jZO+9qdRaD4PRrYIxF2MWGX0bbSyNu+rFWsA1DoT+2xC++wjeljb8J8e3mnhngdqt/PgTbwYW8WW3tLq5RHKvGpxuE6pw64uyL9GiSHJiVlr02bYWMd4hqOGYvJQHQq1ccHDa9cIvptRK+QSVLsuJr7nmzcH7tBiftQFoqELWb8m0kx44VlvB/Vt4hs/7Nhveskq1QHyIIMLkgAGxW/7sS2VEQqKAQyd9GlLFxu03F99GvPvMd+V6uc3rfPG2DwAvb421dg0wr01r11RYwEIa3cVQ/Km/+uki4tjxmrWxzdzEqOA3J3pNQ9MGWiiwvWwJKwS4hvtrSeHLha0q5yS6F+2lu11r2a6lFA5o62f1uvC6XsOFf7HXbWnXvRhabKsu+R0ECBWjOK1x3nDphqh4i7zIgrEHsPFvehwxXGv1bsDTzcxDpgpw5qtK6srvQSiGoHts9odUKja4RCa0iUoGHCjPBj+HpjokHI7Hj767nOAMhjeh5+P4aRH/d5OyxCKUXkv/185vmcxruPkJ7P+OPbXcq1ku40Dgwjz0hC437cpy/hTBzMH8eo8+RmASOQoectQzgjIYXwXGHEcYzuEFcCurkPQ+aCpppmhkEasRLjsUoOQFZCyEamWIPAJUgBeIVxNQNg10jLYBF0looglHmu7k1ASYc2aFqcntKLseiQdG/K3pmQijPduySrHLRT9tpS2uKsDgvp+YjHuahcwjROa7Wt5xlLoNljnYEc5pFZos4EBunAMN4Pvw07hoW0Mqd5HyzDjROSDG52gAUnldTct85FcEb++6+T5N/unqCRf3y4aL8e4W7HtO5ognUmGfKSaFGj0OiYPmp4nru3BeoBN2auG/3OYXV/O92VkXMOc98ngDDO4RHxfJ5u9reP2zDcjnmulZErRNjHcTj0x/G3/89///P/+fOhxM0coJBmw5IokqfIxN3sfvMVMYw/vt8j83D87dswgrTHM//rv3/xT42BB0IoyMO/TvgzP27HGZDw+XWO25hua63IcLaa/hi2gd8CdKTsgpyMtqc/Wlv8Gke1SxzsqFR1SLcb3r7RlCptk8fKJX0FPhfGFkGkNJ0rcgCCVkjmRoU4pt8d07gyH0tyu00nzd1EJJhmSYQ0hv3r3z/+y98/zLEBguaHmBfGxQRAGTUHyYHcaYNvB6W2pm4cbnPYcYx5lFZIRik2pALICG70gkYnV+jnYqY9z/g6lbJqvVmZKBDBTYIToXwmDjOl3OC0RrgBHzYMGWHQH7fh7sfA/ZhmdgyulSvy4zCauc+IeEb++fn8989T0n1gGp1SKEUbnNPdXYRDxqJcZ90W7W/ax8s6qqBvtZwNH9TPlWr36/4Spe1DorcEHn95Ct+cx/69Ln5B+w7tLXW5uhe+sNGOjdxcn9ldS+wBW6X7k2Bt2j0+puKu9qW/oUjSRZze/SylbbJLN6jR5hVS5Y4nrqssE9ehLInUcy0BfWpKEPRq1VFn9NqDtCPSxpjDM9aK9J3/NEbvjJqHtHWY6jcdyGxC+E7qdhOKkSiNYStVvT6tb2DcG0DW+Mt7BKZNMH6tVzkRiHuoxxU37z93AFYQ1QXuNS5w5XXVFZm9jNIbAPQ7d7m3yVVmuiJ/NPTyO4tsx1t47XRAmy1b6/4KNXQxz3HBWdx3Xplb0Kbj1ASzdLjYrTd1UbuQiNJARnFhm6uL6DF2jbPWl/UtNgsW8t4SsG9fp6fLAx3Q7IJiB60dIG66VqxA73bZ254UtNNtVGF5r5qkksEGquRTgqBZvWHonpX3BkRsZK030g7c/6fVeuEJr/21/10b1UDhNuzH93smz6X1XI8zSjTnkss5A5L+jFU13K84EyiZ+UU5KOBMOXE/Rkkjby62hllFUOZ2RpR1j0w357ZXRZpJ6FZycjvsrpbDKu0M23HUmV9nVGZeS1uRUnRDtA3DI1U96M98BJq7b/CxLYnDC7B4nieJaQ6gqEY7cS0D9or5r4U3AobiiV5gaZtDXk/v0yzt6kvby4u3fTmtNgZvr1KV6yo10itlsnFpkXTNuAGmfX76cq+9wOvZfzH52zME2powii1MAg5OY9H3hCabCzDBxEgksPbIv1CWrAoStjndheJCllFtL9Q2y5lZNcuQWnsLFGzf9QtUKeCU5miZKaBaqTIxjQL/+fXMzB8ft0fmafi4jWo2/zpzuD2WSHybRrdJEOJogYJhpsR5RibuxwR4Rh7DBm2Z/v79IzI+n2eta7fFkF3mJiKRwqO0ZMnnqsHb5mYQagjgGE7y5hS0Io3ImpRSOgm7DcT32hMwNMpZdm/ajgaQxREet3E3J3AMmw5Abs2TOGYhUwT0OM9zxQwH0htFB5DN+kChFM33bFfKK8h490q/mY2rF6E2bLt9VFOP0tDE8TeKq3a6ufGSjSNccUw3NO25n6SplXoG2G0WQJwrIsx2lwCyDKiRw/cQNqCcWTlnu77UTqaqopWdNBXL6oJQ9vPqZO2DCeAimHvTlTovbrRU2kk0+hZt21W3h/39f/OUlSSjiglb3hjd3sF9te0ptz+8vG/fgoLEilrNbc63Qdj3u0TDcsdauaOR1tvFu1N5i6OuKHKbEV1MIWb3bfXO6DLGq165Veze3vDq87DdxFWViI3PNHe10u+1MkJHWc5skeVKIdxUchypkomrle1dGkyvedagQefKCzfrHETYY7h4fUnuaooZkxdWg940++toizT3b6913wYc+x7U+qlYbKWqZm/xQK1r37JK7a8bxSuJu8qE3NUd7I3UIM215DsEvo5q2fa+ZW+/0g6hrw1TLLrtMiTAtmOoyK0PS+2d95jy2tboQ2Gt+5Db2iuL7a4kdJ9mFGm3YYdzGGi8HeN2OIDScvl2H4/H8W9/fk73b/f5n//+8esrfn6djzM/Tz1WnillCHT36QDxY/r98MN5n26GlULNus0iUytSkfp6rnPl18lfz0XgNgxQSGfq61z//hNFBr+njmlmFkQmTnH83//29XXmx+Q/vh2aNdfbYDBjRt/eFdUnZ2bVmrBh7EzgulMECypitrIGe/3IKAU6AIABmdH4ZKdDbaAvEwAgtt69dkHYyAS6YbZakWPlyrXisVKokbcwA1I9+GnM5+NxKqe7pIg8ht0nb9O+3cbtsGOYDwfwWFFZ3ManTEoRw2jEMa5Jkx15EJzQcatxbzLyTH2F7AqJVsJslQiRStzeujOg9SyyMNc5pm3y7UWNsipnA8dARWxs4LaEMNlTZtACmhAj8PkZz2cIGHPYcFWjHCjo232sJVdI/o8f38/Ar69nZprZMeyP21ijexmO6ffb+H4bBc5C+f1jSLhP/uPHESHS/vguczOzPx8rUn/7mN8Om8POhf/2z+V8HsekMdcSxOkrF4jpPKbPYaX+W8hNZI7h1RrQGVEJo24YCKVCsqdE8TeVtT6xufN1NQGBS8rE4f6Pb+Pzi+up0Eu5U+Rtlty1nWcM5zEnick0wxI+xNtwAmfKzEB9LU3j37/djLgN/qc/jjGtMmvhxZaoYKeuU60sbtPraRXoN8FlWzywSp1GmmVkMC5Lue0wSHMDa7ZL5DOwls6lFMfwOZzQCg3oPAObPSphrbiNMYl05+bsOXkMP1ccw0Ck+P1m99twryGpaRz3w25zftxnJJ5nPMFcmRF/TBJywphOm4P3MVoatiYLNaMwmqBbIoilC9bOjFd80TEZcC1u/VdX6oMdUf31Z7t8Xn789eo3x//mDfeTXojOduR8OZ92NuW0OlO5nA23AN/bj5NZ2rRdSEGThEWghHgrUXyFm6p7Yq+aR4lCljd+rtT+FO3AZTMiS41NkQvlwIBzpSQjDuO3w0Gcl8NWpxQV80UqhBHxtdasadgEyKgZRjb0DEV1O7sKHO70/ooJdrpYl7SDUQDuAjIiUAOMrtiUHS7XNaV2lnnBD6+QhFfY0s3oWw0+d1/9dfcreModQF/poF6B6PXfKs1fFafLbryO4fu+6qZBvD1HAFR5y9ZK2ODRb4HCq/2tf8kuYraCBAippkdd7V1OaMsq15FI1YC8xkl7t78BUmopJWTEuRZ7RH1fsXW4KTPsjOI6c14RQwlvqSert6FHJyyviE9SRqhlHRUR2KPl0/pWqHdI5Vpprc/T9/CCbupQ7l7/PQM+t653d55dyMJGDXhViC7V1d/Wqvbobwyx65TtsyZpuB01pTuDxmMOSbmnVezqJFaWWBLNrfRHLhZSCik5atJFuFvhCcXfP8b4eqwAhvvd7YzIkk1RUfLgZArPyPt0CWfE/ZgB/TpPqaQlbRmHcxrvd5/TnmdcpqBmVx/GzzNKv2YFhnlGGH2atSwqsvRZsgi5WTPmIXATfF5DeWxH2IM9c/E6DSllIuxqIhXZry8tQ+UFatefPb8vt6V6S476Q0k4O4giUbi310ThJjPgdWpUUHu/0Yaiq9D+l/P6WmvsbsS3lADvW4blitiOoyb71oGKzQHDqzkCRcjbI5U3JoWUFVsZTsCwcSRaaRWwekg5HE1IJ90JOBqWbS/h9GEDRCuAAqNGIxlOxfN8pngb08CfX485xzG5Vt6nSfx6LjefZpkwtj5F4VgGfpv2gAJD4Ao8zhBauuEYNt1WVsEAz9BzpZGPM8/VSp/uVBaM+5Ln8BpOARw+fny7G79C+f1+fPvA5/N8niuCNyt4F2ckNo9MO7wH8hjmZqWLG5EUzHifx+FW0hWDPIYNt2EDJTtX86HaZWi4T/dIneuSEgGw+4HaHqv2lZupWo+sBpq/QseKJGvrbU/1AlR0iQhmAxC4tujS/2SLfvcmfSb6fUCQpo1x0qrnrfh6HZYM90qnm/vdbL7qylRJdaQau3n7mLol/tZ42ae1tGadELvx7BXslFgEOQjoai7eBN7q99RWiak/LpyYum7xZSvKoLyscffgbMO8HWlfcTWT1g23t1itb+7rrrJa7Tsc4+tKripIs2Cw3wq7n+hC/fba7HvzDn/sCvzrG17Ey3yjCUc31W/tpG1K0FUZdU6hLuyfZ5NEOmLj210CHitjrQh9nVF4EdQZkDtXdlOCEWvXEt52VrMAJK2suZO9D3YoxF3t2fthF5Zq59B2619X2H8PnzZ6/7r6bT93Ree6c01D64hqRw2lj3dF0dsJaIde2jejdmmDU/3bfuM3FOg3U/5+3l5b85U77Kjs9+do60u3ZyFFXjJpr5frFasIdW/yehYguWutlakxHAKMrn6DOQHgnvjj2+3bt2O4RWYm5jHndDQ3Gt/nx3Gcx30aelby30Lnc30+zv/+H8//5+eTwNfXOpMJm85vN//bx6xur+KmDOt+JvMS5muv9D3s6xnnacen/fmILu8lSK/Gmp9fufJ5O4YbA5TyY5q5jf/vv4cp7MPOI0lUF/QgzU01JiM7/Jcylvb0EO6onRUZ9JWw5x1on4c6KQZGKqI62V8GLFUlTSIREVegICFSr0UlG+gV1C1g8flYK/T5eP78Wm5mtEicZzxXPiOfoUEMs+kTxK06LmF/HH6f/HYfxSyFdw423AMpaI+5SbO6yx6ZpVikDiYBoKZTAYThuTIyB23soZV//lrmdpteSe9tjALF2VJ5JQwLlMT4Sh5eqhOVPuGqn5MEhnlKUNI6EKtOgZB4aT+Qj8fj8VhOp9s6E6nFhHcZsCjWf36tf/vz8fNrxRk3t3AkYMZzLbjDXRlwPpP/779/+35YiVzMmg2eMcZxv1mFvx/HcYb++DgI/PPn4/t9npFn4Gud//V/rD/+/iOl//O7/fh2E2SrnQHNfczMdDSdd0BKRA+mUUoriBpavFNgbnpIcR/qTOZOI9WYaJG4uVF3kETq+7Sb8+dagk3jGHae4c2YxfdvR73LHF7+WEQkhnFs+T1lPla48293/zjsceb3+wRVnZ8brMZlPHqLVLnD7NbHoCLHboS+LG4H1sD+vjta32lmdWtdCWRknit/fZ0/P0/U1HboNgfJ+/TI/HouZWlJ6Az9MXzu5uyz5hMSf/9+j8Tn4xwGSR+3cT/sX/64H7f5zz8fhQAec7h3V+pt2lrxMfDj4PO5InJOO+YA+1w/lzLCzQmt84lRUsPZMZSuu9QjKi7DjX0X3h9pX6JdOniH/99dwGW83yz+zsW34SaBa+4WgBfzga+0tOOU7TKutUGjEbt89hc0qj6kHSp2+l0B8xUCaOdF2SyVDYXUbPWO8MpDgK2nsyI3plmH+8WnWJHnqsESvL4DhePgt5vPUdqObwygHT9EJomPMWhNIidRyX2p1jw+n8/zNOB+m3KeZ2ugYouYdojReblqo7qjcKBUrgUAzval1SO/z0d92Y1TqAeBs2MLvpbrOhelR8y9xhv9qW/UfVUvU6C9YHh/wRWF6LUnLiDr9231il9rNVRgh1t1d6qJNjtKeEMb9vb6PWLZAe9lD4BdoboCLG4X2vHRvjZuLKZuzeVuL0sjlX5kRhRfoxwZq1BxhVZ1ha+7CjNroL/bvkAgCb3kd/azW1wSiKhZNVaqAuqirhUt88qF6i0yeVX27HUYW2+xv2Fuy0k2d743CvgChC/kpwPvCnP3bd/LTWU3H+xepJ3nXLe9PMX98G8fM8UVxcNX6KyzW7KcS0LRkYwkp5vWchLg1zpJc/JcS8LtmG74eq6Pw2V8nJE6u4HSbUz++H7/GONxrsdaRqzUGVHcFpJ0PNb68/EcztscBnpXOxSRXyEzTuP9NuZQ8YByNFbxDK4mOrB0mg96Ck4nFZln5qBErEwr8ahqlG8SzWXecqMtdasrt6RbXYyRPEM1Rq4FG6Nz2DJoNfcELyx328b3MjL2J1yL28k4/dKdrdaXwl5aMnkrVOhtQ14cC/z1538+ysBbTfntgKsRIQWxYck+IsL+HRrQKaMUtdMEqbl2LCpxArTsdLP3e7VZdV4punkjcsBm0JUaelGsOGiV5/9xH7fDQdD5+Xj+ei4QtzkcRjDi/OPbvXCQZ/L8io/DYb4yDtcxRrMOUXpQWf7+drMQHs/MjOGcbk7OwZtbRX3TbwlG6LH08xlnfKIUY6Dz8t0lIlnk1r1DRJlpuA3af/7xzamv83icca4SA8VKnKEzUomIfKw8hp2hEM3pjpU6hkcqIqfb99ucw0OxMtw5Br8dYzoBrSymA6CaHk03G2NEqsYNPxl9pd49g28WWhsPwGUKa0bajgb0297aG6XS4zcw6Nre2q5nm6yXw3rffL95guzBoA0GAVuxpqVYRMANx/TiTxmpLO3XnRCBXjVi8yvUKM+RApSWF4KFOXx01CGkRdpasaKmxWdGZITZrLztVdbeV9xcO+6ztV2hKjzpe3aFr3VsS12lHyzlEPbZQQE2xrc7KgEKpTbJ5a3C/b843HutGkGoJ6Mpo68C4iaLXP5YoetBvc7d9llXKPIG/fTqA3grLrWJYwM2+zsUREor4luFRKvVJa7SS4MeO0QpYRqn4Yz889fjPsfHbVb95jxXL7YVCbPg0WuqBlGT7yJXhIQFK/EgATXLplbSe0fTtj7vhSNVmGBWgqrVo61GXTqwaknpjsH3J1MFc3HveoEYRqcXhFrTZ7oFjFcK26uBHfPsA3LFatUGftW3rhCvXvgKel4h4O/HDG8X2gH73id8S8f63aWCy1+xfYVem0N9xYWVNtTbmxLGWMvAeYzeMdwuxKzGNbn7/eNuxfuNRGYqIxtssoL+zea0zCwnbm7HbbBFsfk5+Z//uH8+1yNwH/afv4/7zStq0naFtSDnioqo65LH9A+zMWxM//bMX484Q8cooTECjNTXMx/nyipvkv/1zKSPcX7dnMz5fK7hI4Oxkw3reCWDBiAiRFkp73Y0TJLeDn3fLTdjbdMqoJhaSElkO/bu7BZIRiAiVuTPz+ezRwOa0YbBDMfw6SUpUpuwApBUisI0+347lPj5uIY5cTgzUaP2zozbHIdxGmoo1X3QvSaLiwRDZlEcP0nTrMiRh9lwS6hErregQ+NeV0AD6FxJ0s1DGbEi9fmIMzQSBNwsUtCqNqJmytlbwlvyjc+VFMnHudzd3WqGCIEEnitJG1XWSAlZolowK465iMgUmbSfX4+U/riNOQeBx3NNIw2fz5VLijiMMewGdzOZPVa4mVE+bzAzxfdv92McH4f9b//6/TxzZd6O+fNz/frMP38+k5apbxMJjuP4L/+YN8d//3lzEsr/9h+fz8Bnjm+Y/8c/5v/2A/MYAiPiWTI/QJxrH8W07l2WbaAHCWfP6GLPexZ3/bJeVztzrVLbbDmrSlc6UCAoRCoyVmaKK3WMakvKYbxNG4aP2/h2c3RDdZufCnb9YuUARt5XfAQOB4lv9zGGCbY78ohX1wheiaiheg69Bu1WdqFdRtjxQnsvXah8Myo6O0yRWCsgGZmZjzP/49fz3/58rNQ0i0g3g/Rx2MfNSP/xMVbkCj1XPM4QGDtjmmNM6/bCgP7Lv3y/D7rhj+/HMceYnpnHMe63QhGM0FqxVs5Btykpsh5ZdXjPlY/nWoS7igGEcgmZaTZKJ/YK4IlNssbLvnf2aJ01ghdQs1ML/R7ds3/X5vvlovfB/Etl7i0l4evpeMEC16fhlXS8cJ4GDpQv4uh7op9SklLu4GA7D6Ee7HQ1O7m/6Lkb0+w3qukY3Ui0HWapugjIyFTU5lwVWIMyM+nmdgzeJ4tEwN1e9+JgtEBLPb12AkvNAtAWc0aseD7PtfKYHqEVz8u/SirgpjEgkuQwAIgo8IdkCRGQaClQXF9+AxHcsNQFwXXTU3TLeqMobWXRIFPzWQo87ZClUJEKgt/a64oe2eiJ9jBz7cAjX9d1bYLrzx2rVmZbITuLsAO0C9sBkXa0+xaQ6Mom30KfnXD/FrbUGdikgR7gtfutXqkyt5l4BehAr4IkKXrYuxZqCA6GO3ev3BuMslGqplS0DpH6tG16w3UXCxPKHpdJmvvIrXBZkjrmr6HyXfk12+GdOgNWiVBeGEPnYBU44PVpHXiiGYKvl3TyJbxsJu1SzqjDlN0qsLOpt+W4aBluWBFz2Lf7/PxazzNSMMMcfj9mbxBprVLi1G34Er5C9PkVYcr7HEp8PZebHXO4GYVhvQ8/DkvludYwOHieOcd6Cpz8+7e7E49zfT1tpW5uofz1OJ+RKRg9M8+17sescYFWAm9CJJJy5809QplMU2bOQaxKdPUM3cdYmSvCYBCN3ltQjb4RTRzgmwncRgw7/u3tn1CpjxlzmhtZM18EBfocFbShbdLe36o3VMPcfFuKDTvVMJXgdcwvMmyHlHsrbEvQFrsZQtzb6z3Z/kvOyN//2f6lm/Xe8vnSPLLLJ9SurMaE6vWJGjRCEnSa2eiO5+qVSN04BEXr9Vudc7dy3ZV00Z0GKquY74mujhZytJApuBifay4f0+638eP7/TbjPl3Kr+fzmOOxbLrdxiiA7HHi87nGsNlagcqtYnb5WIDFFobiPrDBx7y526ghvA14uXFYTsPhlrsYQPSoJjfKRNKNz0g3TG8R3o/bGMbDQPI2/Rh1Cb2y59I/P5/PM34+lCi1cli1J0tm/Nu328fwVKVJKYQTII/RPRYka06NWkFJko5jVFtyiSVteQpO5yi6lhvQasoAIhPsjlc3KjMy/Z2D/XK4bb52n1jFY/s5ZcuzbFF/R71oajvRFcULVLp8ws5C3zxG4S89Fam6t6SeD16iR93oxa4SofOm3AltXVrdvjm9ssP+VQMB17OUmauB1XL9TUUvSc2rsL2/7yv2aodW+MdfPFkHbap3qDNQlvxcSWCXuYtR04202Hp8sVp3Fq3LnNfObc2RDQCkSiZVNRUe0iUkEsU479dd2DP5htFpGwYzlTbDb75bvU/OlVeHNbcdAl4QDCE3b7fUjimNzB6sicitJ1efm50WlKan2sayOrXd/eM2b87vdwe4UpFbFVvV63dhVfVhr/4mAEt8rnyehXFBSgOOaaP6vIRQMrl7/Rp8r5iKjf+2gnixewrg7pijVQvadHcwVuTArXa0kUHoKgZglzlrC2/M4tqF78a5C7HsPGB3+uywcbulbZ65V2pH+3Wq6iW6sJ6/mv/X6d0v12aHacc+FUbur9vXkFsESAILdSVpViTW3g+s7d4R55jFAKkjDXdvjjd6d5ZRyPdClUrsyf74GMfwf/tpyvx+n2foNvj97iJTcKOAKlzV9p3DcusZKXXWXMXk10IKY9iZCeGYVnHGMf3jNqf787l+Pde/fT7P5OEa32d9n4gg5GUpSp+4whttfG24R5aggvWN2etQVUiSrdto5mZz9Eptoj6HD1m3Qlz6aYIe5/n1jM9TZ0/szJSG83uNEZJG3bk3BTCCh1uk6Pb9Ngw4Q+eK58rnSkEfh7vZWikwpJOYFMhH6lYCzKnpxcvlJBfSfRxukemDnf8DKQ0j9miPPcBEZ1SQbWZ0mqRH4HmGUko4yMxYSkuBXycOyYxDRibTzIEm7mCt5wJJrlAkhmUNgSJbVzjydDN59Qp0Jlbi5OzDDyaez/jz1/n5jMfz8fll37/dP4aNMT4O0xxj+H/7988FrVhUwuHDfnzc5pz3YxjxOGOl/vb9x7e73wY/7seck5Z4niH83/98IPP7zai4O7/d5q9Tf/82n5mPyP/84/j4+Pjz8/y//vl1v93+8f3297v+jx/248cN1hN11lpfj+fzPB/PyEx2AdA24LzJokW4VSlHYedDZfzL1ggr6vdtpUocKhO0tfIZyR66oed5rjMBiF5HcKW+Tfs4/H4b96O1hfA6/6pq/xw+hl2J+pg+Vxb8XwTeVw5e9kKXWbqyZm1Rm8ua6aLa8+3nckf1uNSNEZW6Z+Jcy8wEW6HiuLnxx31OY1bvz+QYpTNaYls6Jm/H8Tfp61zPUKa+ggCUa94mlP/6t/v/9i/f5jAbuxnl5fJqczaYb86rb83FmfH1iUfJPEJjurvtaomq7mlu1ctKM9d7LL+ThEZwyoii8BW+AUUXvwsbRriIJNuBvEqvO5ntoOwvGQF+C85eCBM2H+F60xcG8Pb37XB+e9uX7St+xNaB3lv2N2QgN3k9326Ft2JOpbLaOW2LnpnZ1fIU6tatlGKdALPGJ681qPXQ08H7eK6WS6cZGdjB60UTvZL7jBYMRsMBHWSkcKY+fy3pNKOZDSuCRgUib1+HSG96R6Z5l/QT5JmRqXPlirx4BHCPczWtaRegjJxe6XTNZ6kZkbUuFTtYQZ9j2PSuEmVm4jIP9GuzdITXNUByIxPVueNWYco+n9oV7t/2vBprA0pGsbtNFZJKlIu7n2yjGVecitfu3Ttq24NNM+tL2sF1xcoV0ObOuvqPXSzllansfYKCfkoIEsq127Iqoa1JVWbkiwXWFxybw9Wnp41PXJjnFQPpury9N/YxyoaGrYOeths7QmtTlpGNcqFVyrkXpVKYvEK6krTo87iXY0eM157rUnk9mdiZQXb7UCda9Zd9Ene60ofOlM1jOyOqQOhmt2kEjnE/14o83VCjhv44xpIeAR9+mJ/nknQ/xv02I1Kpj2Oa8z5NqZX6uN//9K/P53kb+OPjviLWCpp9ZoJaK8+VX0s/dQ6TUkepdGX+/DxhdiNWZGTc5sjMYfZxHKF8nOHOY5gSX894RlSuCyiJUgwdIDlW5hlFSqahxlkVfTWWskdWdU31PYvTm00iVeiTpHxEtBvd8fneuBuZfSWAv3m+ay132ty/akeO18eV+S2xvCsavKJ6bpXc2qiVLNQ7vAF9r5O7tzX+1z9Vlta+zmrLxbqq4+qLQqlY9lblqFkbRna3PeylsrFphdfelYq+CyXYraQqIaVUVE8gUW9FA9wrVLbbMUisCEvdhn07HErn+Li5G0VmKCNSujsdBiWUA97d122RiAoR+y8abqTczGkheVW3NrAqMJUr8vOMx7mkGNYV/nqfaVvKi3Djc4WAGiVzr6K3oRXIgGzmHFLdsDsd50pnTn+ZkVRCMsIQxXQ7ExlgyodP2HBOd9VIYrNIBVL0o+4PW734XJGZtzFSCCTgVRZNpfd8zVJdkBRjMIDVTUJsBbMXylzmhNg01bbuF5PkHayuIIe7R/ky9J3wiq8tuI3Yuz3rOGl7/FeGTK+xcKVlkVnaUeropYGi3PKQr3I6AOBccH+hbxeoVXCVRDObr0UYu68DxOt8veLwjg5eMUzl/Xuvv76hXtk1JJWKa11DRJJNiN7vRmyMYPvl6pXspDoyu/zZ1qE6X0o2vFGY3ekqAcMKfBEQzh4xhB6fs6sd+1Kth+7h0ryStBtXtUKPM88VZd7rqoZzGG2MSkxoHBbDtpM2l3Kdq0irkkr0qrC2LnXUbAfXcOcmV62IjDWGf7vNOexckR1GvhssqoqL28nWcprxYx4An2d8PsLBlvEtdo8qhQQJqtij4jXu7c1SNQaaHDU8BF3ClOCOwJtKLbCLNhf34TLsfQiy5SDRNd2NOpWVv1Ket9C70Zsr+r7sV8kplMLmfpC6sJ83n96IQB2QFsXccA739tzuY2/a3aK4l7C2AIH3Dc9utCQ3VES0YqkA9G5tLKAgti7mZlyrtV93xSXAzh4LUtz1qloPfAzS+PlYw1vwsbBsR0HhGhAAv+5VjeNImXNFrtQqZi5Aco4uxy5LmtH5ea7/8fkMEcr75A8zJ8b95q3fYYiI22301+wK2zWeqtOYrCmSlx+17nVAlgji5YxYbIuMLiRmlt20V3y2k7jhPgyKc62souZzLTc7jIYwm5av7LFC4Ca9S89cqTwGjXD6cJueK7KGuQ73IFYJEqeOrirgNr2Y1ZAi9Oz8KIdzDqOxWt92T2zPmyepVNXiO0qRnBaJTCBhLYWYO+7ttY+UEbNkflIB1NCpKPpm6AmcK3KF0cII5cftqOUvAz28Pc7YE0BIqLqsq8CBPNf5+fX1OFOJh9Imbs6/fZ9jmJF//8P//sfHn7+e53kS/Drphj8+jh9/fJjx16/H1zMA3Kbdbl6DGACM4cP93//56+M4Pk/9X//jz/tt/OP7EV/6dh9cZyRvt9tS/Pz1+Tj1f/7Lt3/9x/d//fs9qsd4HiiFFMB3m2vgfPz8nIRkGTHcYF63rSmGpQHUp6seyEw9nisFb7pZrmiICGRmnisi8VxS6rlUXLfIpOKMvA27Od3wffht8Hb4cfiVZe2tCJI1BcP41vCayJS7m+0is1hKTy9sQq+8cJeXqo7UtMxXVbB/u3Hyy5bt5EnBjYFAwnlGJGLp63yshKTnglfjDTHNihbhbsewlKAoYcWqTt85btIz5ElFHH4cx5hu/+lvt3mMUn8FL0feRpi0boYwwcdusMPmoNUkOvQhBEi72mbayZi32yhxkxcMdtltdgSBi7z7Fsa+nMt2h28JBndURF6Vg20d3rzouz/dnmJb4lfKoJ0RdJLe+M/lC/SK/a73ebdgLe8E5DVFYQd61yfUdbGRSjkYEKIlIIvWmw0A9SsjtUJnqJrsarNFJFphISJTkSvi4xiP55PGb8e8Hz5GibVd90s7U2+8S1uwwy7QRHiu/Dzj67HO1d7UjcO5JwtXvvRyk2acbu6cXt25nGP3lKNbAM+ln4/zsUQb51qEnHZGVipS73lM+5fvx236GatK8pGqgsLKzMhhCNjNIQlmxbis9kY3TqcRZ0pZo99HSlCOMYxw5phjEJk1JbPPYCozWhoC28/v23ItOwDBuhNidTfBziEv6ZLi9JU8Kne4/9rDv2/o3PDiCzDZMNwLLCZrxo3VTN0X7rNxjTourZxS5Ud3K15K4Vx6o1bU9aa2SACuLEE7n3ltaXV4vHtuxexQJQGZm299cbwDP/WXfZYkxUpzdy/df7Wc5/4Y7kCORkvuqsp1QoFXQ34nJw3/X8ex4zcgqjkxUsiLGtY4xe7hrTu8U0czqxkXvltx//iYEV7f9MaGiR06XAS+nisS3z+OvQry4e6mIMjb4etxZua3+zym36b9uHvp5B7DV6S2isSvx3qcYYbHedYlPiM/xiyswGCnVKna14ozPm9zQHg887S4jXEc08zXipN6rHT2oZ7DY4V61KYkligujQ4KrFERlzLz79ZvL+VrN1YUb5fazrXv+lA0zQDcDRfvxvX1j/dP2UfqisLRq2d8pRO9M9SBQM1i6eOyn3mZNL6uXe9/bETqSgF+/46vBKSv/wrXat9WAdyWwgCnb9QLq3aLOh5zIE2JLC6YJLaUJdzMYYGENdaTiUED2cABBEN14A7XcI2hOeDuN7ioX4+v6r/O0nIugbnDYiFWpJLANE/IdwCzZZB3L+21+QeJUeR1l163vxJV8dczSK4QwIpwesacdAyvVd2qCLhNEzDbCFRI7HXrKuyP7MwqpEQHCO7mkVXKrp4vAOYM5TOiAID7HFHyN2yVT3f7PJeTkfp8xhlJgWYVS49hRqxopYgIPWzdb8XNi+kdIElRDV/FDjeOMZxmezOi0BFcqd7eX2+5IvapT+1wFKU0egUJl7N422a7FFWUiVeUp/0yvb2gkkeUcE92s/95RqTGGIQnZaTzLa7YiSvIWLnWcq9ehGYMvRc3LoinOcU9EeKKl66z8hb4dMpdrgfEG7LVId82B/v5dZ8rDqwxiJkll99mM6KvpPYAzVZ1ypDmBZAkhexNWlFZFZ9iIVmyqQipLepJEqhmkUTW14L0vhxvd0ytAlUqe5KEVD6e64yU+Dzz2fI9lZjaQJgRXvKOuk3SacOzRtFnRsT5XGPuHq5QZCgzstWgjd0+c8Z6N6NKnXF+KXOOS1M827wmi7baOXjZyEuxraoy2H9NZwNY5qV3nJE1PwcN9+6FU+MztYLUyggtow8zNOMPuyyLkqXcEDzK3e8tq8ZlIL1rcXR8hasL7N3u7tDoL9Z4P/3lJnZE0ruQuP7yimivSHTHMuIuY7/CeKEaa367BW33GwVTdIB2sZn26uOtrtdnqfOyrJk8F66bO6lTlGyT9W8SecUiNVC9PjS2rI4PP5+rl48y48d91CrUJK+CsFPyvtWth5uZyjT6qSD57X4QdFtHVzoVERF6hDJh0Iqg2e2YBjCDzdPC+ONjdJpqHE4C7rUgJEQk2DypjgT2LWg7kXRXibqZOAr0A5TdlfqyDhQSDZvR3pxQFtQyhz3OqIoEjffbcBPoVVLoQbbagSNBwp2H3MnINJNZeuI2hoEJPZe+zhDwMT1TIS1gQD8f559fGF6yk5huY9gx7KAiltvY6ZyYFfrnKs06Sah8DLQafYbHiqi4s0J2NHjYhZlt/AgZlKoB4oUC1Z0UZC6dgWHjNg1I6zaxwm7NzdmljObB7ik/O0KjHutM4Hab9yGRc/gfN//+MapOb2Zz8jbH95vP8Yc115cQaCZg+O1vUne6QT25oo86//7jY87zv//7gx8zEv/8+VTG150/vt//9R/fbseIWMNtjmHEPJwkqlYzuvhVy+1u3+53o9/a5IGouffMTFVQYXB2tZ97mkAGvp7x62s9zuWG2xxlx6u2FlEBMJ/n+Vh4PJeE6Rar0Aol7MfdbsNDeRs23EJ6nHkM82l6VcZQ6cGFU+AqP+6OCjOWHgaA5/Ocw8bw6KmVbaidIL14lW+h8Av6KXhoYyIdntTTerZUr65JcmdTwX0cjq+nXKLbr7XIUSSSBEkex6g7WeX/Eko/pgsYZ36jQVlG6PvHoBOQAU3q2+N+d0R06bvvM1tN0cW0qiwxe0tW3EkHdqJXEAH8Oq0706rU7gJrXs6Ab3+tIHl7iw6gtrXl2/P2Ft2vfb3lK4p5Sw32k69HtJ+p6xXlNv7/ZTLXm1+p+E7NNwVM15fRvp+91TMVkTVdZbiO2bh+2fcyqNoY0HXDI/T1OM+1jHauKEx6rQhgRa7zvM+RwtdKJyLz67QxbLrV0MNZOa5aIvG5sqiL6GotByHgeebjjDPy+VyZPS/ZyWEcg8UQE0r4o3oY4M7T8hjGo9IeWpDG2/QEI1ExvUAiVtZYMT8G8GyFfyO/HcPdfj3W80x3B3Nlb4/PM77OOM8FMz/udyxJXyt+PeI+nYDRhvFpHNNFrpVUmmlFEjKuJQ0C/hxGZxXbK6asPnAMb5DX3vaWMkHDhk9WtYC878aNc3D/04yViLSNoHajY++gfuVvgemmIxQ2cYUgOya6wverg+9i6ZfLzO0MhpsUbKIBsMHrbF1nonVM2+E2qlQf3kkrKnZrcLDy6BSu8QxmzpqF+m7QyjruwKBP0RWTUVKsBd858uv07SirHQBLPb0xzm53IGlWU6gBSRFvd106K2ZnSZaWUqEErOxwojRQmWmsYVYVt3G4kcXlVGmrSSLGx+Hk7XkGMiu9e6xzVtaE8TF7jtJ0v0aVp/RYkRkr4uPmP+63b4fPwQJbvs6kZcUeKTnxx92/VlD49fSVeQx7nAkxhZXKTEs9VtyGG2xl6rmqxyelZ+R0Ow6/TXucAWqQA/7rPL9inZnTBxNL0cPpgVJ/NYNxRCau/P9lG3vPqXdZXibyyh87dq4tuEPuK57fW+FFAsDev+8Gs2YnXfB49W6GRCaxMU/+bk9Jp/Mls3rh9i+v8Rv087vhvwor7z/vD1xHCaTBJAW25CpQnXSbI9lpgJsVhXDQUsmeYgcjc186jKkoJkQTtDs5QmWC1r5NNNyG36cJMGMoDXbMUc2FlVRAOtcqIM9hZiyUN1LTipnbEGydSxLsOasVyXScXLUcZcm49GEnuKpPBwDgZh+Hp3R4Y/TDLdFUzZTcKJRKMaebOYu0Mtwr4ZpdSqjQVytyuVtoDM6Mc2WFxNPpxBzmZsO7XNGmCZJQoI8bkPh1rtt0A00cXvgzM3OtrGD1eS4ja7bPOiNDw+ks3hMvU1NB0bkWAB/ivjVtj8rONnC8m7iqXguisJZMQcnu6xnDncw9W/MKkvot1UtTmcoF+/MVaHXiure73BC78WcMz7SIyMzzXADMLaW0rlDWMRzOTFXbgRnXCgIYruKe10dsHQO+N1tmfzNdYOBfT9Rlpd9+odcx1FtsVN/l7bhlhp6pWqlttF8J1HXUa2sFpKqFEVIqcriV74kV5wqhOY9Aadt3qcy932e61RTdiq6FKgdctGVdUZ9degRgNNqiZ2AFHHByugUyerKV3ZyRcMIoUt+G0fh1LqO5IjPOlQAiVsIgnJFrNeXBiEHCEEkQ2RMPu7pW1Zsz8rZyDp/D2E5KK4uqnO4ksHXqw7DTh2ZAs2AwiCvi85lwd2oOOgWlD3eADRPvcELN2KC0QlXk85Q7XTUMAZLWyue5MuVm5vCt8tTmG4LemcN9iGqNd/BzUYHKmF/IKN4w6t5LRBe1XsnSBVWnXshj77Y3FKu81fZS7bOMUIm70816ptCLPyqhh2yUlmbJ1Gxj8MpEti+4uAB93YnkzuauP92dO3dC7ur0vuD9Pc1IGTf9HLlimK2IwznGqJtfd26476voGCsjI2JLoSCNEEbn8klruvrabbwrg8T36cNsDL8f043n+cw0wWtu4/i4twiuGcewwgu2bbrEnNpUdGosSpblDKW1EoOCVgSAQvEv7A1ACeAbJXsFv+ZcKyDuDsywgft9+LC1EsC3w9w5rNDiKysHQb3R0uvjLOmumZYt1cEzkYgbaWSkVioyHyu/VhzDkVirk8xi5mVqDPOWXi/dojpmOiNXtXapt3tICLjZ7XBTeVeZ8X74eNk67APCCYzr7HZswmp5BbBidYXVbfhuSk+oEQSkpBrDSYBBMLLviCDSlHD6x3RLRuYz8jbwcaO7CAx3M1MNpxjmc/rwiNS5qjHGzY5vx3AnW/qh7mFpPBrphwMaBvdvpUQo6OvxcOP9Nsbgt/ttzFlhR5clX2DK1uIo9CQx3Hi70SybTpZXPEl2OAU0KpLSc8XnU59f6/NxPle4YckHdZt1c6oSYoLucxrifOIRawUPt++HnyHvNhbRLITJLka6OcEE5pzlizsVrGIMq8XPUjkNmW0Fq5FcGQSKB1Tnw7bZezMiunBl8gUAoSPUi1ML/CWyVaEmJKG0Uml9rlXb5hicYwz3M1QtDCnN6d0zXnZhvGQHU3JXvRfI2+zpalc2UFe2KZQdh3XIX+HPvqp+kGBpgUlo8nBpw3cq8IJmri/c5l2XJNx7OWDjYtwqOnu+g9Dig2/oT22xXf66nML+Mn33/qdMQddf93PfCN77KXw99Qp9flsj6Iroy5tuKrUuHUHJN4DAsi1CJh7P/PnoSHIYvt3dzTbrp8NUXQF3akWulc/neu6OFbqDjOhBLYM2jpsBZ6S7uZNkCHlmplaGbAxb1nMbGaEzWpwOLYoBbIFnYM/+ACAZEKiRFjTmZsNJlawKPDmHOfF4YjgGTKziWGn18OtxZuruxLRIyLUiGZiEuwkys+l2pj6fAcS3O4bbylTE5zMSVnycm9mNOtx+fq2vR67Az1jnOm/Tv83xcQyDm5kYkn59PR8rzrWKFAzkHE7y4xilYVsUn+k2WIoAHMZqgiw9jULz2BXeWCsEG8aSErNdF2XLkrX2TUQYMceg0atWK2EXtl71k72zdqiwY5LOEyloUx12/eyVNKtVmRObe9T5cuHhyow+KxXkXXpTtbuk1D6YV/H4/fJ2JmCWa2VjmqCZmZqM2vWbMhCb2yg1FpMFplAbAhOwYpmM5sB1uorLU2wd5LYIrwNeVIS+X9uhNvLVjXjFuo2M2CJj9fQVmZKLUd1lYs9zMDuXnmedBUSqGlhULd7KYfz7x3gOnpGZEHSMA1Akf9xabEJwkl9PnBkrlkHFeJ7OH/f5t7v7IMlKxI7hK6UovSHWTPGqrv3L96O4f98PnCshnYnHSgiPhc3btzHNCZImftxmlpK02+024CxukVZxMxlxUhjT3LSkQSuhUKc940y8WeKXVaztWH6AV1XQahe1ta7ugKorlvT83pc73NVf3/g3F/i2g3dUf7VlqLLR+viymWZA0Vs7SbjOzutzekvo7UtcO73C1z18+iX/8b/4KYHVGixbvT/okk+HcCzWmJXZJ5SZCUxrpRCrFpXGgGq8dRacVBOphCY9Jev+VuwKAIf7j4/jfvO6wDrCblaiBKGMkg0yK6nj6TWwj2Z0p/XgNhQhqN6Z263v7L+nMZaPfg0zqzRGIjQMmfqYlqkzlaGEarsX0N9tsyRobjymG+HO0UNR+glRNU5ase+NzJq6QjuGTR/PFXUtx7C6sDncm7nUBmTQQjmcleofw54rADTibySS4HCWLhvNMvFYQSZp5+IcBlzhTSFDmcpM1OyetVaKYTbczTtl2ra6d/YWiYk30FEJZOXbm3TcM27anNdGpLEVrvqm7Zy5/pCgjEt1eVtrpWTmk3zWq6EqA3vJbmQCGkXIqt1TGVbmWt07nKUZajWXVtrCcVZOwGlbQa+KrBv5fwt+9HIBb2hvn/GdENZJfTPKV+avlmSrCCLLiorPFZnRg5O4M3o6IIvnHK5dZI3YoY1aOPw843nmirwNjjkizhBhLF7wcCNxm260TJxLNf22GIRAz2orwxIpCN4CPliiyLVWzcQ8VzhxDB9OkmPXhpp/B4gw819nRuQcHgiEAJVcVWZmxko9zlyRUp4rhyGsVZwANOco+9Y22EA9l8zaqaW2kgrp5DpVEVHd+UzcSo5EkiqlVYQSJsCp8zxlNR0S000rgy2rZ1vlITIjZeR0c2MBZQALcWMiI88VX894riBp1HDcpk2He+vi791SLW/Y+6MWF6Hk1hbvRn0UELlt/sYUeme9Yu1321x4IDqaqaim4653pXdskaPff2F0oGQuYZ2j7Q8iWcGgIrPVLdAopi72y86MjJcIYx9n0z7dL7VNXjoalbHY9T2wM0SpuF11RKqFglA2lMmWhW9RFHQ2lZmXESHMq3es5xIK1Y5WDUtyopgREVqJWQI1bu4u6Gudw2xOn3SDzKabjXk4Ni7WnPPqluzWUANkoLbuURk6q90cVcaBVg43qNo+2xr2BMlu4FQPaQTaSnT6lkY4OM1XNMYxp1n7M45hIPpcNvyyI4kNUbU/kUiZYUWehf4ZLUVoeG1s3IY9UmvFfbrRDaUzbWO04m+589rV2dP/aoRHgWrY7W90w804UOUXq+l3o6fb2gZ/2rbW5JWLdkmWcExZutjgD40413J3AKakiExe7Hmgtnslxfv9CIgmGo+Jw8fnMw74t8nbMW7HdLcxWrHTi5NP0kwpd8OmjJrZnIPkOs/cGIhZwahmxPw4vn876pkr4vPr+WPezRxFVjQSmsfkxk7WWoWggXT36lmLYKlAoqQJCk4F+sxdiO6WAIrIxxm/HutcOFfTJ8wGQnRkRM03qcAilOeKtfJj2uFzRUTmc+Uc4zb9GRLycFZ6UYKFFSiM6g221wblvpqEYvVQ9k0ejEIkzLuw3JqCHQCwGLBmqSztumvELvmC4VXCMZV6Nxfosjq9r1FdGxmh1PChFRFR0t2g7tOEEmUkYMcxxjCl2LSjF2hiqPZVRtJIM4uUgmF1qfXVIMVLo7isjK74po9+XZ/RxgCbystCoDua6PT1suS8Yp1LAKEev/Lhvsh9d8oz1DAS7Cy1/sOdWhSCt98JuHLI5o4V7HXlyTsD4Zvf6cvdwc5bsv3Kal7P+MsLih9R+7gDpU5+ocqB65atqJnuiNB/fBWyrXWGhJ8Pm96qzBWZmXWUUD3AIUUqxDGODEnZjDByzvHt8EGeKx5LK1NCQcRmmM4UI9Iyclf4o1nTaFRVkpjQCqwrkpMIDrJK2MZG8HxnEF2o7YWjhc4TgzimDUcVJM9V/exYK58rP0+doZBMKiJAZWVLECOfK1PDzIzPM85ooa9Ifj+qbOBORK5n1kxu3hy/HrECkVGi8l+RIs8VsdbPZ7iPXFVORyYVGsMez2ixAHI4kclB9MAyPFcCdJN7EejgYzQ8lnic6wmdgQTrI31wcNe2SCdYtqV0HGxIzDfTLbw22mYyd1BAbE8oCNnFvULo2vW/4I9aoERCG0HmPh9Uokq72diLcB2nDexsztt7gr5za2171ER0CKhIvXSR1AHKjviNjCsoILhZzXUwyn1zh/zEHvey5dK1dfd3jvVCKNR8sU08QQs9lFGLqNpThXw8U4Bsi0H0YOhEGU4BhK0s3XQotWIbNVCCEd5kWxFyB8iVymRCpLnjsBZQiMxzxWnpFFoTFISczIwzqfQyMAKMnM6ojyBAD6lGpJ6RJZltxmMwcgt6TA5zACuT4DCbg4fzuRLKHx/HGR6RLA65oMhjeKz8jOXG4xiEpbI0tYS0biDiZbr3ote+I1DiN6zpVD1vpDYo+arPKTr6TF2h52Uwf4NX+Nt/3j/y90ff+gkaJGFH/eV9IOxisoMXvF6G9RUg/+WjdpxduzBeAiDvfqn22T4ckLsBJWGursOzNjL7DUGwpFvNyTO7L8OqF6wD/DDJq9/OSkMjixKTW6b16p6sBQKS9IrOKvhM6OoUSSmh6TbMVqRMAotVUSBmuYw6WLzALr5ylcZBSLJmqAMloajO2Iezp/YANT2wPrbqmPU2K4FCuFLY9ZhnZEhGW2tJKozsjHTjufKxKg+v/mUYa6aqHcMlDatoStVQn8oJH2anYgybsFSQWKEVcQxD6W+yvfnmF7xSq/rfcDaTAloRbtXE2C8jwKIMukGGxEKaZN4F62Y+9B7KfSsbB8lXl7fWSncWDfMVguxy2n6oKCRkyYpzb9eqliH3BqzPpHUuwzlsdVZd+78aDUgyIxZapR7Cith58QukUcErgoBklzwbecLWKSxboF1A0A6INuLzfix3BAjtytB13Mpt0Ix4EZnLhdQ3WBHnynPV3KsEaOaglOGWTigjp885MhOlnZx5RmF5+VwqKs5t+HA+njJgZVb4SnCYumKnTDADLb/NdlGbpN4mIhJTBangsbpbjECGTMjUQx0kDDdnTaBPJ4qucZuwClMzH+c6hh2zqGaQ8lzra+XjLK5A7RZWY6jQsiF9MZ20ovaBMQSUV6qDdgwG4Mbb8IJQyhUmTdCklrAS1QV/RkKLCdUcLtLM0+0ZYcgCeqZZMtvIRF1MFnXOWmTczVoWa61ckY8VjzNs08eGczqm8TZ9jpqPXHk3WlkcuoJwlH5ociOkVfjfoEntNGwt1XZNQr5F+FfUw/bRtb2tHjLbPXPqLKJS2L1fzczdzV2piFWiTtcJLLPOgarOdGd540at34w3S/qmErDdx0bVN1WZV5BVNlT78ts+tQu4AN8+P+ZG95qoA9bgv/0JVsEcWbIzv2lNvrm7Hdq5Wc0BHV71ZuVhmTrFVCv8GOFkC24i3TlGtehYw3V8OQxj+8W2UNo/xcShktGrRMCodZ4knIzItuzcQsVsu1E6ymalcxsF3JAs0ayP6dPsjFzVdmaAtBIf9/HGwOKellJ6hSZl+b6KiyN1piIEqeY/zeFmlkBqRWgOz6QAd8xht+G3YebddpRFau9CMZVYmWfqDBVJW9KgHaPS0zR3Hw5IyqqRRnYR3XY+W7k0jXNrYhblLIEVcUaJqlekj68li5g1+D2RTANYsmNX4AriUrFjORMTljJuxzxu7kBpGJfozqhZDhVLuUmpXFbpHVi/cu+QAvIap6bO2aslrYQ/m/Th7mNMUmPMx8qv5xputHGv8XqN4o1KJ3bNuYfBZfVQmVpVezscbm5U/zcqLLSVsZIRkaubLFrawzmnu/t2YJJauETSMH4cRwprhQBzfgyvPH1QqYyUAUHQbHCUzSZ7kM0LXrgiATRBhtjaC6/LbnSkIxKgchtrhRFqM3IvT13kCxV+X33CW7alTM5Ox1SdMqxxDjZyS0ytFDLvw8q73G8HkRml3cNe8StjryDPODRC6v7lpoCWGpWMPNcmyO7I4LJVuGAYwEB5CQ2RexSayd+iArycwX4V30yqrpsqbQu6AbCCiioheBuW1/v+UvwhLaP7XHZNWm2OOrnjzhA2R4w7w9lX8EK0tuxwYXCNGWn7ojcEqI76hkA6OkyFah5Wo0vnUoW/jzPPUKYe5/p8rCsOBLBSw1QN77TC1KwKd4X+1DcdVs1NOryJKgaVPwawwr4JmZZZtZf2ic+IjTKA1U6apZUTGRnqPJlibOGbWjCDllTTOfZ8FcYe1tNIAwo2bf0oN2XJJKdWRKkMlBLZSnydepz5OENbE73sFohpBsmJm2M4ze0RgnAf/rcDyPzzqUQ+UkuYjseZtdxudnNb0rnyn59LbI79MB4cDvqYZ+Q6c1XnXSqkgtuG2TSW5xlkAs9Vnhu/HguZt+n321hnCVgiE9Ptz6/n12OZeSUTZ1iH08A0OJGZ/m2OgUhZZERyAzv7phUY2Tlnz8yq1FJ9Xgq4qTkw2kZFu85WxMyNk+/imDq8zwtN2XKeuIoQHeA00FBIq7ZylvZ5zyypOwGWPTvWhFTiitrU+z9JwuzivvFKElgwFnZ3GM2YALKroW1M242/ztH1U2c5VqSRRb6oHsnM57nKYAIQ8owctNJxLOnAQjMpyLo6WvkIatb1mTtisekY1iTcS2tplcmrsp4jxCJEz+GoSp0ZjSLLHY0BAhl5OwbASE7QALrlPr8EE6g0O5ToUXqlJ7pFYdjjIEgcgwSmqjpFc96mH9PPyBXLzaZZhA7j/TbXis/nejiPY7rZZoWh6tKDLrDI4X6Z5N+sM/oF2/W0eOjOoUCBSdGroxgqZYrX3ry8wtvPX1GZ356i12M7Y64dISBRsj8VNl3xWAlHmu90vCljbx/NbcnZtXX6i0D6dj17xwtIqKTLhjuBzIWtGJUbo4zMHgfWegC1E2itfHdl1EzlMBpdoUSae0UyZq5MRGxn0dFCzQCJxLlSQloPe62x2LZDcDNJYYSACJlnSqX67DsHe2spukjyjZGw48rKLqRskos2O6aiFHgr4k8S5Mo0aDrPxk9BtoQ4gVgZrLuHbtVJGINkSqXzPwpvSoxNBVYWU00kV+pwFjR5Gx5pZTGMyIzbHJG2OarwLQHpVUwmilARe+sNt+k2jFssSUX5P1cOd0WU9pARwQRsISm5mSXdjWDsEPWtatt7RVtrJqPUbChgpSDYINkifrUE24BjV7Ku4r2yM8xCfnrv8zoZ9ZuSpWsGA4b1AL46a5UrZRM3W/2Zu//2nZ9boc8ViEYGynVsogSu+G6HrdoYyZs/6fRWl2Gv0yKQL9QpBWbU8UQrGZTg/UpRwrnKFYKw4azJ70BiRXFKJaZMGasqK0Smfn49zqXpDgg0keWjzTkJQEVDuw26U1CoRmtltZwn4MRo/WlF4mvlMewwq1qUu01nrqw9dR9w2teSk3ScK21H9XUP6iyfzyj6lCR3PqPb1cs1xMpz1RyxBtHOpcXuVhNeEgEV39qb/XSSYmnPp7VnPoFM3adDOCOmW674SmrYSq3sthVE61slGIL1YbVQnivn8Mh4aFmP6SigFmQ3NECQosSUapbLlqXU41xQzyMywzGGG4fHMf027Bg2vDtP37FEbsMcnXPUzWACSl41JAAIdtHX+pDsoLzAU5QlVEaWkavNDSHlYwhMlPNEc7GvHysLaeM2Mo+MEo3KKDtcZ8fo5JwN3FTji3aEs8/lBptf6M/7b94fI2pMrXQlh2yMugstr6PEbZjrjhHYIhe8EFf2p4AwIPNVL3/3Y8ZW0yFb4nxnNASZoQg9zvh6PH3a/Zhes7ozSRvDAfXcsob6SEDbo3QX/bYkDWcUZYMwt1qRXJGP0JjT3asTpKcoFL0lVc1xtVOqdR6E+cuCmvuYBdbYBzxTxSQS4bZ9Oa6RCwS77b9gx+5NJGookzkeinNpTEfiWSufCZiQ51rfjnmfA91Dp0cGVXNVa9SlOuIUWBIDAoFpRBEXAeWynnKtrvQCbAEXa53cJut1ND8Km72EgcCaYl6U+shsd0sOMyImEVoVbqLQ0QJQNp5XO9noDqU0SbhP9+MYbpzT5xxAq5N6H6AOFEoP9JXS9ZALKJNEUZCupNzqikhICwDghUnTIjVEQsN0tJbU621RjPdCQOo0uEmemVaDVn9rEb4cZ0GdPXID7mTQzFyHwc2cuB12q2GhvUWF8ifk9JKhQ4qUzIa22zU3gBSKRYdqYxdSYTSkxHgr5byc4gVbs2jQEpA7k9m9RsSWQ0bZ+4ThKsVW4XQf8Oyf5gHRTF1gg5HdxC+g4efmEDpYz2HKo7V+2iiqQw0i64iHNglzG4zKwA2WKSMi1tczlX5omNnjjM7LtqW5cNuLQHPtCmUz72ooXaNfUt1abGv31+xgA/D7BduwdthEXl+b3ODwyx6ryfkdFRUO59dn7AOLyjtFejcxkuw2YHZfifC+Ii8RO3LX6PpZZarfbW4rsNRFZfeWdpPXNaUuhRAeZzzPKMLCipSYZaRAmlfzY/X2I0QkI4x2nx4g0ANhh+HmAOlmw5pQtgM+jNGU7y6+iRIiGVnaJSyCeIoLuZABPIXnWazi3YH4qtxQG6avF3a4SAZ1rVBd1QASlPB15iPk1gNAUoWV87lKKgVn1ZDBxny3/3sgVsbHMW4DWSpIgUj8WucyPELPJSe+Vs5hEFfo5iThk2b8POMEI3soaQJO/nHzYU1EjQSlSWZW4Q5OWGff/Pm1hMK54GZrxWr1AZ2529sr4UysSkhiNXEUWMJaQSgMSYzhAFcE0iJEwh0GFmO0ehIkVZaeAiIgXSpx6HK93Eg3oexXR1dlMa5mWbNdfdI+d1fxtoILXUjs2xEst9u41AuvwcY0C0MxcsWScowBdLN1G65dcK6PKiHPy1zgMpd9IUUFRQ0DpBUSYn2kcwc5u3qsF1qlxsTiqqMqVqy1nmf3ewmcPkZHb1qlriUUUddQqB92ITqf53Lzw20FQs3NOaarSBmtBl2mAJJoiCYWmYRQHsOZKm2I27RIfZ1xDDey6EhmdobWYw03Nz7OrFrFihQwDALOlaOHrzBCa+V0kiySIIpVCpSiVJ3LED7PHG6APSMUi3sEsiAafnxMGh+rVRsKvsrtDo1msqJndrW0vcs2Z42QXMxM7ry0EcmeRw8rmCNy7UixovQrZdZf7OR2Aq8HO39/PbIjZRQYkxNOcOtStTku51kmslR1ShA6N/Pm3ckIcOG377f/ps6YVOePRcMp8cFOOdrZNLOjot3yyxsifzJrmXZE26+9+TDo8Xzc5jQ/kCpZ1ZWRNX1c7awqEJruw03C1zMLqNzwQc+fLSWA7r4zEFrCqN4h62yndAtLJaCzy+3t21Q3mEepBTDqmDVrgTQVWZ1FLxIwJNY8hwJNsvOUs0Ax0moQMhjQijAahHNj01ml0wKsVQ225sYathpKbfjAywwo3bhSKQ0zUNX4BnQlD1eosBtHyr17kxdqv8MN7vvIl+bklgYbDql4TJVDgJlwyJ2phWCh2a+qfr1ntdzu6IkEXZnPFQmOaW0WS4dhq79V/1JhP+3o2DFYx0oVtb+SVfQOz6sx3jasg8uoo1qMax/seJfGZi+AlwCRlFnVe4DGKJmP4buA+35WGjzSe7kLaAh801W0jXNHYp2/7py2ihNQ5spUbPJRJCKilmlMzxIYMZUsQbact1Xik48T4DNQZjwzVmKtQOb9NlfEZ2njg48CN4Dp6c7zieEWIqDpbSwAKXQCuobdJTKxlmRxJsm4jxxmBUoYeTvcoJ/PlcqPgTH6aEequi+f0U0bez3o0bls3dAq11UbWhnwvG4aekLW5SZDKJpoAqAyRYhs7+hiJg7jza1mbjghWrY6J1eIwEHAAUM6Iy1loJV+KGlnLIqH+1EM1ey6/GOlxCUqS8XGMnOtAGjuj/OMDPNR24xzZqxTzY1YUWmNUliBzzOnNZ1kKx50eqOmvUgoFpV2k99LRqiBsHIlWYBU5bNW+QsDpLqtSs1FqsNYVASvMR/kln6iefF+qhmriytzDg3PzFFRRKzEVegCsDeigbHnnl0O7cJ53txYe6796AsKqt1WLW+VJfWpQZZMpF6p0v6jfjb0JYgwWm7MsS7jBUtfro0AekyDgbSakNcJ6AaUJePt4DEbEnZDDbYGigwBMx8FEBTetoPHXVXYeNSmj9Y/0NuePWvWjIIV4mPmwpbxN5Z+Sjyzcq0yf9WYVjfHzHIlpOFD3mojRmn30bGRlbZ6L9K4gKwOQ71uv9GEAgVodgbjjPPMaqaoas/9GEZJ65g+hzuxUgXfLeRzNRWgXjKNg5xOG32qnxFn9CzcJSpyNsyhrrZDbjba/baV3LL8tD3MmKS7hvKUFHBjhipYTOYzeVhpzchowtg7Kg0gDNV+HgmI7t69iOPjftyOCcLNxjBr7K1SuWaDd8wiXVu8Lu7a+dVJWacIjYh0UD/cAZFweaktjGFzHHPwGBVHbpDudYrqpX1YxvDLb0EtDHkFatyybcbe3J7LhGMYDrfUHGV0qgOrQvYGR63nsxp6+Bcq5Kw6lfD6imZW+dX2wgXdVXvFjpmgEnMbboOe7VC0m1o31iDxdS77G1diWU9ugXg2hLdNZDtXUt3pkFejaQdy2ulbewoSQI/2g6askYg3xKQ33M7DskKw6uI0KnOdT7OSt0yj5mzcz8wiQ+xP3sAPd06ws76++g0O9ASw3ZVKoDU7AVzG6O3WXAb0Ckb7/a946OrD++2F0huz9/WYbYul3jMXN+ctdMkItBygLqL39dEqaZVodoauN+uo7/XEbQtb9KfuyXNVdaGUaBEl4CeFcJ75eJ4pqGas0OheLIsKsVj+seQnyvfTqtd6OGY7VV3UG2EnT2hROvUXICiXOsuBhgjUvI9yqwzBYQ4syRwmVEfMylbS3VfU1XWyK1+1taMOValDAf2R5Iqsll+zrbgpSEpxZa5Q4upU77GsRBeTI3VmGM1DP0/pzDOWsu4zI5XAx7Bn6hEJ8aw7RYh81BuCx7CCb7Bz5seKJxtINcfo6BYET3WDSy7YZpnVVUcmAR/jmfj8XNwJgKRpMOPh8GFGETXtWNZ0XTPSyGF8njGkajtwYwQydXYXSgvMPdu6FMxbbrGAzqZLL9JWo7nbG/eV8M2CVfNuZicp3TReLrZTjNdxqyOBXYrc8dJGkV5WCyowRTLzCyHV23zD0tWxIpsAL+CpcO03ED+iAJS+Rw4rhRRuxLgtXUOxf4mY99fpGFuRmVI1a5yRzzOX2/3w3LcghQhdXDa1bmthQCLtXCtJd5/Wp34DZUCTL1hhaC29tbIvCDzOIBAqvobqpN9adFTD/Ne5Cjv7OtOMt2EAaLwN8907M43V9zWM6WRitRRSr2yP0e0pIXmGSNYnzuEr060srkBONyuJd+IIzww4M+2KFkOBnS+xq+249sC+/duYvsM3G+Td5/niqoHFTwwlO0/snOu3nwtOvvbg28cQ+ssz2Z+/WQRRsSSuSBZyFVNARfeuDWyNpby5iQsOVecqtel3at3noGZ/XhsfhajQui+nj0zvhH4pdyVEclpNpGNFhk5mBRT5/X67H9OgmoH4XMuTKZcshV1jldWIHlbbWXlNVARDaEUChQRePemy7gSq5Ie736FXBNg4wHscAhWDc4//U8V5tdK9orjyDNb0cbQyxbU9Oviv8P56pHELoIj8DmYpdWw/ov2XpRVZjS02h5M2vN4+oayuscaw1CJH2WMlqjMXNecCOxdItV2q6MY2K0eQmxm4Uit1wD4fK+keWUPBV3KE5vAxfKUGMmG52dm5DRdAI5M7MavorAYrGm/Dv854nCtLVqFqkPotxX/t/0ZJm/Oae6U6+e6Qp89jn8hoFnbjaL1N2VlxHco+xK+2jH1q25KWhgMTkXIjQuNS7sFbGokdIeRv11BpQZ2lfPuIK8CojAY1jEII4flcERruRyvyWIl1Dq8OExylTIws8pou95H5XOsMrlAkVuSKNd1uw2ss9YqGy86VqRbD/nzIncNZ7FInMBUp944bI1BNxI9VOi/VvwkDnfj1DK8ETPo8deZzUCvghpw45h5Xl12DiGS2R6jB29Watp1H1/xUCzAKwRAAuAEda7V/rC9ehcAKImOLro5jJhgrx7AnTMDHnCJWxnCnoaYtWQ9EfKVNAlq1dWcWZ7BGeZgVPY0Az8g4WyMvFOjZk9VFo1hLiQRWPh04V8z7QbMa/eDD11oKuVueKzOHe9a4G6VZDWIz3zrVe33Vjb7VjYg9f6IvtJAR20WdSlj2zI0646WDtrNVoDtAIxJYY7j7LCZLRChWiBHLzMYY1jwGgGZgZpqjmJfgZkxXmkSg0ah9sl7uids3lkN6pcS/YUH7Me3j9boDgIC1uh24Tyt/95tXjizsWn41N+xjgrfFRr/D9ZF23bL9phK8UI3M2218fPz9ecbjGZlw39o8FIVxpbL7Un5bvx2IVv1EaKNYgimGvbrvCT9RgqN92JKWYEZsygm7CQrV5Q0zDW+tGVSBtNrtdxabrXnaxrWuoPKQlmN6rUQjtRPUGUuapLmH1LPypOk+HN7ayoDZ7DE3eJwRRVlfUbONXDKquqPLZCuIgMNi5XPpmOagoesw7/tgp+N9VlnAfMtwFDzHac5pyTxXwFWKj63U45a7yhQZc4xSyug3zDDjcUzbIFwtXa2ul6BpNYzQMhd7ICBQ5WizosPtZd+u6VWvYJ1JbN1A23DWtjjZYhMAlGTTEvbJ6VAMHYWgFGAgtdgQENFieRE1WqGgqX55UZyY+ji8mmJKodUNPgqK6XFZ1eKRrUhedPqULpVi0HqWXvF1nTCUUCDeUIYOxauBrZ5cc0nr02t5Kx5oMAn7wL224F+goNY37f9vZ1FaGdWrVXuke9WxdXDQ5pEZjZyiW62vrn6DZKbIxjz6YF0IUpmqRh13X54Zd/7WrMkdE1QrRInHv1mk375OrXW2QNa2em8bBx0g8AqErozu9cN3z/UWuqOj233V71a3X3fdnMvgFvpTKFj27IWKVS23YC73ru3WzJ4iob1AnWxeUVVHukqw23QKus+9SyrSM2PJ3BT6E6mi4CawQs8Vz2ecEdYtvnIfm2Wq8fFt3I71898dcmIab8Pn4DHq3LN0uq5vXh9h2yxbZ3Ov0JBvJ7nOXWb3fZBwYAIQBYMQyXPxDFtLv04IrP5HJeO1AwjbXqcgQumytUk+lna901AoxnZvQt+QOo8hRELiyss4VImSCUfy11O/nlUDToBOM0NtnJ+hQoE/z6ILI05VQ80gzWTS4SYmjbfBtRTCM7BC9trbl5Oqsfcs7QSw59kNtxq40CJjmdwMiypF3Ev6bGdfTqQwKRdPMVLPyGfkx/SEMtdtWHLPdaIJeETaMyGthBvJYqHXZPsLuMVKmhGpqldH8TaoTLjx28QYBtrHBAD3blQpaURt53OVZ8qWVv62ak4iUBUgN3oJINduaX3KLFiwoJcKYudVNW5TVc9P7jAEKPS8c2bt1l4j3Rkhq+kPFfSozV69XM3H0JWU7j7n2kuhHTRXkJbSc+UzdEakspuStkxJWYbyco2yUm5gwgcxxrkiIsb0w/j5zOdawyzEsVkp1SNTUzncWxDQyKQNM2zFyLRucnlGrsxK5FN0s0F9PtbjoTLUn86P27jPmamlHOQZWujfDoMBNf3GwFJRLOFSI40aZtURQEHRhs/EJZ0LcxY7qY6pnJpuVx5eoyDZ3rwtQwv4aAuXEZc1bm7+5aVKR5lXKkhzZKaB7v7KCHeMeC3AxfH568+bsb+yybLDxo1oK2GV1RsbFekptBS8dwVS2y1e0PX+hN0O3Gi03hCiK5muDbzpolb3r7PMnTBkSYODgJwc7RaqpFIJmDlLky2PUfDMmOZmuM8B4jxjDnOVXHGniKNG+GJPwW2NMMIK8q41M0OHHDQ4SaNbqU1dCXiCQFaHnl227m2xq+Gqa1boiG7fjJ1MiNhhK62CnARYnRtKiQaWGBbYjKltD9LULO3WQroSO8hfq/1evSgVknblXcCuaKHYMcrENKa4IqY7wWeEkcNbGaf1j9wyZaIPqxl5BCQ8SzyiQTEt0ZRfK9XEKw0reH0ZeRs2WgC0yMTWXAAqd3hJtoZLSMqMkJvdWqYHkYXXhOo2N3fsUoztiSIlBYVaiAJXysyzIbl9LPpMoCPD2urC7rriBYji7X/7OVWy33GjnitLNzVSo7R0VgP/KURbhxaO2khp+4K6SGlf+T5J6mC7xmSmAV8rBSuVzAgpYxir/TZbjQ4UpnP0FzDsQmh0lIUz8vMZa8loKSliAWOYs5TOOu2MTWC9j4YLKbihx+BmZPIZWikSjzMofEwDrGKLYtWFshQNn6HzsX4+4+u5HmcAOIZ/HJZh8ywppQpjujKx8i2KqLkZgqBJsrsCVVjkGRUyAsAZTTKyHaPVKhYJtzNKo3EUynab7uaz1IWA4SV0bVallN/jYrvejr0Nsq9W00izo7t168I5zKbzXFhnPpOLzaA0n6lKtON5xrh9M4c/HpGBGhCyTsTcgZQbsWKBmPTqX8msECCsZorZZSQakKoEY3tJbqRDilrdzlYKR2XLvW8AopIjg0J9sCv+iCjrnhHKaIZbElCA5/Ok2RhjtNT8rqIZHcUW653VJ4kbUtl8kt7xr6r9hTK8GdJ+ZLufDmOa9IwrQSSk6kZtc8fNrr9+vc8/UB2blQRd3KAXCLtf1ARZvC7sSrz3WwIa7glLcB4Orkj4HLHOOXy4n8/H8FED2q6vUQceNHa75naLZqat5lgDpceuJ3AL1pZ7IXt2dUVMBK55rvUdi7FyriizvrGrrp3WKISWQvMual3daLDOSgRVaWunLFcWxK14s6YB8twKgpE4auokSkglz4Q7MmIOt2FnDR7OXLWzSyzdWwtL4AENaoVyYJgNp1EGDKP55uLuhX+5PmyHCrvWMjNhhU7iOOyAKTvRByFU1QhmowTglCXobdCimfnwUZrffYR6FoxSsm57ooEwd8JAmO0GUwLwBk1qyoNt7KpChtwV2WtzvfYWr3ozASrdePSYxj48O8qoeD53pbAZmmYsVe8gtRaElAyqahpQjVE1gQtAz/t8O3r1QRcns67GQMlSwnRbEJSlcLWtbS1ITQBFsS65MStucAEVkaRYclnlcFusLNHyffB9N+w6gbw8ch/9vM4GK77UPphUDf9VGbtmQnadqLPorkDZxSTsVbjuQn263ipXAFrYzIyjFLz21y7TM8bsiFA7dK42jS3I8Xb5tXjc8T0bG6nD9/q/1+XVr3LTgH//2dbxzWK9/fSVvr7bPj+1Ptyx+e/fnSCRMlMqi+NcZYRssMekUq/MMzZcWGydTWdVNfm/roivCFmKwG7w28op6FFZkaqaFWtonXuEzoxMnkuP5zojfDjNdJ4Yh81DmZNUxpxjxPkx/ePw2fLzvZFqMlGdq14eEk2YR6Cil+sCAewKcidznQLZGOXduGlRBVETnPT74ej+tfLESOmMjFAkzqVH5IpubavzEU2RA1AKEEhplG7U3pnodL1xz0xEX6Nw0VN7l1azQD2zoYAu/2KDbSgIBgmIMqGqu9u0ZKboEK00gGIpQofh7iW620FyZtWHMdwoRRY5sIVFzbqMuKKKFEJPUm59DxmW8LUwHQd7g9jujmRqnbkiaXxQOld5hBVK8HAj8fNrJUdkmuTGM9OMKyIsv92O54qvM825AhIGcUY+Q3uGTvPDnYyDJGV+HqytMp1WQkVgPRkFF6l9TrGCM/VcgnQfJiAEE1YJtW5qW9OrwHM1qKFMM0NVbjva7KCjWOtqawkpE4jsOnmFAUYVuDbnqH1U4RmrarCztN4dhUZsUxLNsGtqZybWKiobz8TjjEwNs68z3N2pCLWxM1S5s3p+HVRNgxYATrcVeSaMOIaVTlOfjSYSKlOjx0FkCm4MyQ2RLVEB9o4NyQyDPCMlPZ/PnynuDkqSJZJ1nmlcw5iCGcYV7O3CHQ0uSiIx3Kq1yYRqsDPoETnnuM2diYHlnL8QFSsn7TjI1KPI/DUoULvve3vxyCIgFgdR2c13L8d9kTJZp6PJYg2okHQvYWzZ9lNX+KvLdF656fUPbOSiT/U2arsDlWRKUaOCwCpNbQdjbdsodDNFOjissJSe3SH0SOktEQi3V1v9hTBiJyRqif3CfGvclYDdA2x0WkE/etVJ1ILoSADT6/O3/IQbBIPMLXe26m5oziTcitJe7/PSwlD/YDhreOKmlaCOdrUM2/axEfLREUq9Zxa5qHUSunG+GKmt4tYgTOO925UXBm6viKpjtwTkzW8wAEFFYisR8zc33ccH5Zxfa7yTkB2b9LcWuLIrKyw43oprub0stxJZGzTO4Us6nI6aDd8yefebW5XqdspCwI2fz3hGzuEC3TbeQD5WzRsQVGq4TuUc9u02jlFtm+Zuc5QW487PCh7ZHdYAckV/9+KLbZFv7JtQfEzuXzWvvNiF6rZZs/Suzb5X7XpLbMVavR4nvMbj1c3spHW71HJq6pFhNWv8z6+owmkAoxNgVBSYmQEaCj3ZGftvQSygLEdgbPJL1WyUekSa+eFY0vOsIRORkpu5QRnD7PCe9aKINJ+b/VGabmiDi0dgZYjmXpPbWyYqUl9nngF3A0tujz0THfp1Ptkd8TRiTnN3iOfCz1/r12OpbRfOFWaVFpeqrp6hb7dJ2hk6V3w+ljYFTJmfTz3WNQYFueGnskaVxVj1MRlVnS6E10R57gF/WzCrYlCS5iWxXF0agGjEbfjVPzWHSVqR01vfY7rxKoi/gX7apda2kA1a7AzDpGQIw8ec7luhoZrH3TDFm9tz8B5WUCDBFFYqkucqFG/RfYyROwKtc4qV3uw/TndQK5fTh/sxB4CMldIZKL9ZcjPo5P7iPlWu1plyJHAJk9XZqDghdFGFzGibJoyEDFIyUVnhWisj5xhJxpnFhmAV1VPPc0XqOOBjq5qgK5COrdO3fSB3mlrZYlmvbRgv36a31Of3n5eLgbZC2eV42LjKPtTX2+2X7Z7vV5Xi/Z2vII1v7vrtQ69Iap/hnaqbuULrXHQfZsrItZBa58q1SIzRdcg2NXU+BTFRWsW1PGU7abKEjJnpuwDTl5vNHdH1AwJcgQiVr5LCzQE7o/KcNjyEap5LuXAQDvfWpKxl4NXwUkQkdJMwtl/llTUCpPE4+AduK/I8IzJL/hns93muyMiSDJjicPMAyDn8eZ6ZsOG34YN07xYDbp/ohJmK/Cuqmb2Gqu5ckKKaSNmx7bVjyn12XmRAb2UCsPn/Y+vvliRZcqRBTBUwj8yq0z0983GXpAgv+P7PReEFl7LkzkyfU5nhBigvAFhE9cfs7uqqzMgId3Mz/CgUCoNKNOEYE1xG81X8BmTzWda6CkPxThxJwoxrWfUkAA3ATTdfEY8bnZm0rLmKdQw4AE0dtlrlw1+tyLLezYhQk26W22NV/WbELHu7s3OG7p3qkK6Ex9wI8VxeDTaQwGSTs9n91NfD6oJ6+ZtzWytcwLMZc8budQ2LKdgyGzVBQAl3QxcPT+WlG8F69x9AyAt/KZTh2PYxDjYQyb90f/WB7NQR50HjDAWsZDd7dvQbrGFzRW0yj7E4qX1b+Tlu7Zd4Kj+VvZJc7hVt4OjnT6DdcT5f5qv/vw3A7F1UhlAmqYg8AtADl8/TBYZJ369RKUq/cMP5mOLl/P6Zr098nYuX9Tofg4NLzmLwhdZV/FPlW0RG7H2CeQNUnAN3r6qxfksAzt97OwEkspMnohgBAAkzS7JAuwQLUM5iLHZY7Ip9R3zf38/nFmhr2fUgYJe4PpiR99PJh/Gxf/18+I+fHzN45dyNDuo361GBdV+pzdadJ9bRvTSrdLZjr78Rop3Oa739B2Z4oOCMin9aCKaUFO6t587nllI79UwjsLsDUjtV6kuh4zF7hXuTq+kZZze/n5EpW/YOq/uQxkoKqBkm5MsvJhLZSQmRRVoTtbsLrqqaAKpaDqAiG0msgd/UviMBW458bfYa29GGmgRaNBQqY8UQlZ0VLDIjPy4TtWtkCXQZ16IZHFzeiMCyZQSlH5ffoSB+fPjnsu9QFcG+d3w9n1nPR3ze4dSfO3/dUYNal5l6riduaIW1D9w1xbxVSz6WhaoQWmVV7Z0flxcwVNDhHbocX8q/nlGoZUlOLq8RjkjhWjV6QhlppLdiVo9pHEBSeMXEJwlBtS5XT0hXiQV3y1JYM3qMpWV1L4sl70Y79igFKSM6WxgggJEZ0vczf92xExHp5J+xI/NaYk35FHd9qI1+x9uOM2NlXypvIXl3V0KRt7SMqkGcwB3htMjeI8XWee5+u9hFHGN144Zk4HdNtCh1kvbA9EmuTj6MlmutILgbgRZeaC6Ba3EHioGS0rXYQr1kzZHZmWa+zO4dy3nfeoYur0pVzxLd3gWdu4LqmtXNSfsNJmKUFxpcKX+BdtMEbUw8D8A8UMhECv/qSiYAfYWibznlb7afZE8TqVeIKQNg1YDzZi6KNlme2ayGVuDnZR+X25sjL5/QLGSWeLBdwynWBMQDKZ62QdyRd+iOkFpHnyrl8h63l1BpeLnxYfbwdXzgQHmvaiyR5SWdlikz2GTs/BfnxbbtXTolq8Ovoqay/taKOZOGNC8D7tUT14n00PY604Jaja6VrrNLTd4iMj2Pr906ibG7rN4zIhrSaA7vkY/RMNm7D4VGzItxCle/PXBN2CYyQ4coLrSxQj81pLDILd2hh/u9M5QPdwNTutxA3aF7749rFeWXZs8tEpfVUAVexnvz13f8opabm0fqjrtGJkn4cFYTT2FJUtyLy9Pdfnz43kHCl5tVCECgJpr3PVbtGIUF1CGyaoEfeoIG9olpoR8Qdid2ZNFbl6vqta/nSxZhvYFa4apqEEiDjFbCXlI5NajkHnv/RMotU3zuNOh5J4mM/N55wPZ8azhN4MfDnfRV5IlSmGoGZT3rsl2ZRT9hpD5rjpvr4bYTf3z497Okgu2xSh4zgTTKS6zLaG4fjuvhpX+Rc+BNCGOAl8EXjde9wxae2+4ofUxox4QMFGSgOXcyI2m4lnloB8EEEVtf37tUNa9uz1NGPjMczIAbnoH4CinuTOun1Mz/ftDR5nrCq8l/kcvMwaoXdleVTFAQ1b2wR8CJPW6J1/I6d58Pu9zcaU1xaOhn+cjgqCnrr3CwivZgp0cjuKbhyWIus7aPET3Fx2SEKa1FK1Trlz2djddye0zAqAZi7q2nmZHfd+iZKG7g5F3OZdcVBb2tz0C4qoRXxNT7cvO1yl5LNY8MblZ1+ZZxM5KVJJZuY1dZaikkqxCueYsTXXRcN9lARz6kZ0ZsVtUkpLjLbhai5J0EhYR1rQXyjEAtApwVlsDOAcq4HSsqdaPty3kAAF/VbU1sjrewW52OtmTNRNxt/Es54Tw6Tc/3HElxwI2XOeX57P6dsj0nycQY8E5tpie7sZWS4oEjYtP9MpkbuTKjDMsqqWBU5Ff32A4JknwtM2YkDJQiorShBxFRRO6WfO6FckMNNzbzHZkZoRXPey1e18OIlP+6I3aHVA8v6kolP5Yxyy3VOyhE56SQ5NSyBHqxKJtY1Qzh9syCiLUWaLg3KHNbbvW7y/2vr+e3Mnd87fDl1+daxFpr8fPPXzfMPq7FIsHaK+MtBdbZKx0WAK9ws1ohTtRDcoY+NuB1dkybfvZwHBZ9nUgr6Z8GOApHWm5JmNGtZoJNz1W3CHUOXKFz7d2TI9dmqkyvwtyMiJIEpgM1GbT5aTukGUspskh6ZigltxZxqDJFplXNhF3Aj0xl4TEvGGhy+kEDiiZmhsy+C0Bs7UCWID7h6YW+E8BaJ47TxIKTnJPWHfqtWWRF8LMUazA23XNnxha1vNm41jIcmnis0+ry8XUw1+FHA2jwha/XU2/Rbx2cObplkHlKSRNhV/JUMOZ5iIY5q3Pq39IXleGYKKFX4HwkgBGqGeJx30l2/j2vU7YF65srnAYqO3SQr9d+GaSgBf84uIMOBoSTHRxQYSCJefiv5GC+N9H6eZxttyqCnz9e+Qbw/jYv7ImDkikjY++IiP2iE+arK3vXhs7UjswoVqumR6ldYFUmWuVG6MkV0lpVgeiFrPr5uBgrk/3c+r6/nzuiGI221rrWuu4d+/5S6kPbobW88rTHwx5Xy67h7FsAbHrgYfeiTb6A0TdScvo9G7+lHd7q2TMz21JFHma7qs4fcho7K/6OaYJrvnKp+hnWsitqUB0+opJPSNiRMcNWpvLcmBo1kszZ+UOlR2+ljt9xny4S1XRbFJRV+NeLMoAxqFLB/Z3IispySeVfSeNtsGygSlP8rwkySlUUECGkaB2WNMhYCiRqrLaghBpEUqX4H5cZY5kFSGiZCfncWoblJPVwfiyD6ZGj9FyYndvlPaHnmfoKIXIZQO7Qzlxu//3rvkMP0193lGJUZEbmw3CVXh6ZUUNGApuCPi//euZj+dfeASyzX889jzi/v7ufy2YnReZyG7Z2+9CPVSKwkLAWnXQHyR8XL0fkb5CiRsxex6rgZNiImgBH3JJg7j18vWZdXV6upxXPruVG7ig0lQlkxNjhiit2jZ68d9w7vu9M8es7n5lmTeP+WOt7R5UKN/QM7dDHsmX/YplbDboti4AuJcLN/LIdVZVAph5uW1ksD2VzNmY+A5CQYOaFYd3Rk4GuxcvXhGxt+djmDGZn5ku1zVcK0TigGqbnsc8STLweuFMAPhfuKo+JFU5YSxfn3kzBwYz8CoAs5CiSARkROXIboIDsOyht+HIb/SQ7lBh7y9effJlhnv/X8DIrSH0La9t/vP3jzQW0w3rlOF0lh4pfi0WsgvwSkQogyT1hkLv9uPzjYUVe+Jc3x4RZ9XzLkkSjFsfIlx0BnS7VADh3cHvrTIld+AT61BKLtgw/Lvt4FDsMAhKsjWLjBisQ+3C7E3fqWp0u4uWW29gMdR4gHY6ad3QolrP+9Wi6xjNec2dWP3cE3LXcNAhZr2Qv/eshSCjQvIBur/lKANCD/MoZZXWy2AQvIag7tXpb8sSqzZ+1KvULO/OVBhEYmiSAolgu4rqsx10bcpSGyh/VLy9jCJevRSVhvnJcTJFSv+8wM0j//Wuv5Sv113dcy1hLrczIZX5VSiisVOz88BVISMvs6/mUcq1FL4o9jbyj7Vu5X4WKH3iGp6hFDypwRMVBPewwA10fU4dvbzFb/ZkJiTEpnZR3V7Hx3DAiE26tYV9WGgCXPdEicUZlzbdCtYdQO7ZQA+mjMPeMZfa1k9D3nQQl/fV9H7mTHbmVy5s+97395+Uf8h8f17RmFUR1AjQYWXzh1XylnsAFwp1CPjzXx7WWL68Y0jJyJ+mXlD8vrlKlGOUnoIVdjFwuJ671AeCvr/taj+e9/+uv504v+vbzzrsmN3UpghE1o92LoJRGROwRjYsMI5IAFDGuIQqcq2gknzsf9I4Lh6cDYONEsu+xcMciKOnwiWLLmBgZZcABgoYe9LycHw934+dlgi63H5d1tY9zPlhoSLWIaqwvX/85chzVYpyvqLnP2YRwne5B6ldWS4dP/njiTBuv9J4ZVqim5aTZWtdjNQCXHXKmBGc6zXwlktg1S3YtL25OkgHt/ezOA6tCUokOEKCIHUHJfQTeOsup6k7WnOmD0JDMnpBE1URogF0mB1o2kcao5lC3KO3nCl9xxuWNs8qiM5Mk11qZGRFMO7GMDn4xbqotGVsJ4vXDSXMmqz5P782mj+ku+lqzIiZon8yrYa63bK6tx/m4Aza1C28R037xWxr1dskoX9T7uL6ux/qgCSCvRvT8o+zDWiV6TiNt79yy7+e34v7j589U3N/fPz4/CjWjmWXNR2E500j++tbXne5GyYw/HhYSwcfDU577CfHrDoM+zT8WnfbfX/n1676Wfd8Rkfqw6tmqWeU6xG+BCac/FfdXADX+wYqDDGlHBvRYxYmzEoquI8bX4iQhs2ZYoRSmEWa4HG7IUo02i8x//O2D5HIQ+R24IzPCly9hORdRNNFWdZtn3W+rN5vB4wjquVgfQmujD7KI8mRzYK2CRRCAe+kPgKAcvdVBX3UG4F6DFYY20jl1By4TUMLsNdoN0FT0IMncHjM9tEJ/AJlF+6zWX1sGdAkdTKRhFbnGGCFIvny52RBgVW3AEOS1/oaJtwr67FPcnDs3g2Mrz2mpE9DAykQdGE7fZB4agHb081GYJDt3bjpU7pqULjFkhBwFmbFDmPOp9XDUyPpgEGWW3+Ow07NwvonJj8ZwCUeboKvctRlbLusgRB1EVDdr/t6NJh2TPecZ0mj7nSjyfP7L6ByjQZFuBxpqQ1Bwd1nQLC0DdLzIs1mnvKCBgGaVqu7V2Mtr2/2G/bzF+7PTjjNrUfGja4luGCnluZcNm3z1Ny81d1gMwQ5VAa/88M77jkyVCuzRhdXkGzGiP/fOlrk5ve69MlXkxAAxHEkIJ0hfnHA2BApX+UIh7vj1dX/dO4F97/ve8uXWg+gMNF+fH7b66MkNI17YfGBOvbcWc+Qcajf09ZmxZEhVpMgobY6B/KZrulsqit3Td1JREYZg13unChEVjmrqH+od0NuwwGqvCCFkJhNXMePcAT1DloCwBdTKgLV7Os6td3r5y9/3x+sflRdP1DVkwQmH0Bu0XlktCidK68PSOy2F5z0welZqNJrHkgMw7Jz5NEJ9B3PuzLpCuYwfl+3UMlxuz0hn/PF5fVyPnXpcRdBGpP724Tvy6043fyywjLO4Q9/PLSFgf92ZqWVI8Ncuw68fjyqZ+udFkN83PjzvnSXbFKkAt1JV2nWrfO/rrnFkIPHXk8/In9dlzjxlfvLe8WH2rV3kFM2Emp26PJF6RkmiZnn1Ok1ulsm2csUrkG3xgypJEA6PobZJRW8V/hS/MgF3C6FGvaCpQ8isfnA8Nz4vfU/fkY0OUWoAGgnKnXEXZuuIjL3TDTul0maSRehX5udyI9zt0xkZxWavj9vitBaCI//5WFZygjbuxNwkLTd3j8jonB9fd7pzmd2Z62R77LIPyR15Z1tp73CLa9nlPLL8jVlS6HJxG9ZsLpVCWDjVod7bHd6iY0TL1oRljQpMZOgyK/mJ5XbXHAazeqCZuoECkJ+hkuTcb5eE6f+uzkoMboAuKr7Ag7raiXNO3t80rpSs+qAb2WgMSGP6+9C/3JR61/atEeNx2bXMKoNCSpoup48Ia7o/VPM7YYbPhz+uKsx2PF2O6gQ3Z6OiHxDyzbm1DZzbTHXN9nFZpKFlWvqhdkWJWIbL8FisnvsyPJFwL9gPxjS0TCzZeM5EWHgP/md5B1NB5TOzivNqjLOti1nGKBmNWiMk5WbMzLvE0asF+g2RbTdXAiiEWyMFFevYkA58IsSXe+jdWBK2rOzrOkzjqc+7Gv/LVNk9Tn/uK5ogQrizcki7I0IsgfydeTm/n5HC52N937ET+xKJZbgNIdRUmIjw5Uots/WwGuorWSRH8xeQnluPh5vzvqPIdxUJ/3hwS4IWpQwzuC2aLfLh9nBehgw5VCXJw8klu52V6HE0xQWr3vrMCoisz3pvcB5Ns2maA2DLlIKyhJCxxe+dX8+I1PddRJseOHVH/ni4hMflsfzk7/VupK0jhQABAABJREFU95aUn5fdqcuZwn5KQEgmPXfCLYoDndjNErUaILkzJIi2Q5fRYHvri3lH7ogfH9eFmXBrx6/ijsy7CYlr+XdElQ0+P9beO2Nfj8us6HwCWfPGLNPhSv78eXltowlxygTXNlv+uFY1N+Tff17X4/rrFwR9P+N7ZyTdSylf7p0YLlK5I1USXHfsosIReJhXXXdXI0DkM6oz1CoceUamsDMKahAqn8Zoa5/wvJ9l0yxYA0YtulG9Q5VaogfdDQ/H5UYrPMvMrFy2G0A5mZxw51T3fs9C2mpNgjFD7ljXKPHIFp/AuGMgoUU2TpJSGorGHrV50Hqzie2PdW5LnKIMltU861dYRA9eqOcqwCgzA+1yvyWVmHdkzRkrw7J31ri6GoV57Fgp8ewdRhqtpEsLZaqjFBFuWRPJBXRJRkjFueMK8yqyzapUup/SjlUhsQHWMnE9yvO+70K9bSxyqtUuNV94pTudsygzsrPFihlyzDV01vCF6ZQ7Owaf82yKBTUZzlsZ5NyS5nGedPn1Lgdrqn+anXzvd4LH+87oCL/da+80W2ZmUg0wACRbZvQlhdEz9ve9v5/7+9afX/vX9/Mf/9CPj/Uwfn99X49Lda6h/b0zAbEaHZ53LHMjr8V/+3n9/LwCuO8w8D//2v/553cif/zxb8+vr7XM3e6t/89/f23h+36msNydeCwXSozGnnv/+t6R+r7T3C63jKC5kwYERfSYzBKHzpTTPparmZhSJbcGGwLEovtMBzX2Mfj5w9byiKue68fCWla1kZ9YK/jrme5QIqTLeLmvYknWyk7r8oEA3jKs2id8JUF8e5YdBWGHJJWQUAImwkyQsuTV6/QaaXfKgWt1wu6+fK1SR1xebljXWmyKkJ08fTaJWnzvFZK9wKDZx0YycjjhSqE687sqtXvEIBWNMbeqU8sja0f3LmeX8V8kA5VF7icw0GzNSmOJ44qZSbH53qquq04l+4PraFrHbQKZBV/btE3P+mp5bo8ciDdTwm8nBufu3xC0icDJIdkB4GA6x24D74d0TqDwQn9aqPLVrvu6spaEOnjPK/1mX0tFmI3wst9aJ0ADf/v4c+Yb81Q/kgnqBJQnGEwUHS4P1gGWBz3X84bovG5vWEIdGs2+GjhOvy3IsUT16lY46gStgFqcZ3qSkhcafy71hTP1wrMBPHNRMiIjvqsQPwT+nn4VHYhp+D7Z0XC1dk8DPftC6oEdzBQzuisjLZ6fnx9GiPi41uWCWaS+v/df3/fXfRe3NjOtB64WRyCwQPhnjVrLrPeurpAx5P2QjituWOd9dd+WplagwrtevNkh2p3jdZRQBf9p53znmqP7KHFvVZJTRhPdN1EcQBiws3giKEkaQjWFe7GmY5CGPXpACYDdEaaWCeqz/kaI/P0w9N4+26ld1+/7qNP4MmivXXaw1MYm2p6lqk+29rey1AdUVBxRcifNYsaNFaRMwB1GWSuV4u+fJOyvZ35c+ruvx7LPB335A3D363J3i0hIEfrf//l0c8S+u9NQEUkiE187plyBBD5mYuffHrYWU6xZs39c/PWl7ztD8Do4sCAy8zuqKxhUz12u7KKktb8gbkUJ1pChgHTH3plGXLRl1hMGqa+aZtVFzwJlKrpTSaJcbpbcJXF15Rbvy66GKTHjI+DFnAdYc15BKK9uFhZLzTEyiRAAXV3Yb0mjSgm+nlFhYnUpmeG5d+0Wc0/w+w6M5nrVcKVQ4nJPKXb+AmWsg74Dd+SuNKuHHvb2cLPLuPdtZp9v2MG9w80iMgE3LvFzWZHaHGZD5l1GmeVQfoocvfz0eQFT+4e0nJlzCmabC9rVCN16FjCiZhVd1qFhIywcBzI2YTrVKDBVGgc1toXPkNHcFZEBGc3tOBytUZ+9irHSdRyp2stBe+swftl+vICJgw2tQpfnaGRqGScU6riWx/nMqZ54ukNdTXg9eA04Uk1AQzKiJWxnsYyZpYIsEKp2GAHWpJwXcVIceKs+CBOIvy5gjD1/eypgqZoDkUaYt/h8Qak+8IsZL8fDmn9dd2Q0Rhqwqg3QrLFj2p0A9XDG9A+OtSu06mV2ppV8Ar3fIrSXPXxZx1nw6orqFgIIdG/jr3kH2ixsJTuBRtDKixZcaMbIunhBpU9fukI9gat248wfqGmhkLrHFuDlvAPV9MoB42rlU9qhHQJ4uf31dYvmzti677wlPsxov553xfAuIATyuaOILVrmJGUuJKjMhzEgc7/IOwHi0dNgBUIpcz6Wh+CGRcuaeKxeczP+4V4xiUM0OFLCw/l5jRJfFyrxWD02o/S/1O1v6oDQaqMUllqY9mHZU5k7cpkJnYUC+PXUMuzIZ2gnlOnk845fz13y7ffOlP76vgX0SNDLqxW0kOXnzi09Cg41fF6raRqUgX9935+Xk4xQxdsP9wVsIFQYOR0gbIcul4GxZYu/niH4Cv2o1ZS8B7zWDHIYmKnY8VhmF4mMuN1g14pM1URawM0l7Z0iPjzgzSyrup0kZSf5J4Z1LylSAaye1p+fl5vp6/vXM0h8XthJlQSt4jKk7LkTZGa6mRvZXZdY5DJ71IFx/vMOM/v7ZffOXXEQFAWpoP5tBAIFsZSsT8z3uwbNjmd6zg+BRXNIlBt+OK6rdEK6TrO6iU7ur4i+ZG3tlURAzSxug48OQ14pyenrLFjHraVsT9apxnOBLuhOjIyeI94Go0/x6WZi6QTiCC+yJU2P93FjeBVc2phWhF+KsqXq4X7RGAigtTX23nbyTKm0m0vR0qD18GsxIootN/FDnQ66MzO1a3s0rKwXVjUIuhEmox2ArBzB/BWZUexIsnp4ee/A1+1eHKNqWc0yeuwDe+DqToKydd4AJQI0ZmGc48w6UdHxn+9/mefb168KGI7EB0cV5EAGb9DBew412OBJ2urbHWX9nsoecsFkaHgj3ddhy6Y5t3ePDARkuTJi2qOYDsHAR6a+vnam/Zn3//Lvn9fyD3NAzxs/fP311/f/97++Pv/4A5nm9uPz8bH4jz8ef/x8lPpM7Hg+91/PvB6Py/m//M3z549//FyA/vPP5z/++IDZn1/3M/LnZf/+x/Xzx5XCn3993fdW4rn1vZWpvLc+rkIZP39+LEOUtSh35abMBH8+rsuUNfkiAsM2gCqpI4EunBYa46uO2rUMUmTuyDJ5RX6x6wL206TEw/m4fDnXVdTvDpnY/dh9ag/CW4e+JxygErYqtp3NUcfUoLgzy8oYLQUUVyWJ2uM083XvQPGNhY/L3bjWJfgO2VqSPow/PleVr43mbqXTz06MeDSB9Fu2BTR9pumRHcVCBRwVtqqTjauaX9rilGSqgGxwdEYRgyjKVR7IpfJ+zDG2FzeKtpZlZlQlmpLZ6jOWmcqjET6rW0ZhJJKMZDKTshd+DlWS9rgyMqL0KHhCE6ibgCbrfiEvmDJsXef72Ss7XsfpN6zjPPMRySiHURBsKaaAPDSnQZLqP5PcziMxtqiPVASZvmTaQUBeYNHr4ydwPGtQhuyNksNZ/P7HSzv99Nq81q9uen53sO3W6phr4JCkzofMDn/VUvq6JEgBzn5q+s37LZ1rrafN09b0tj69dEJXB6DnHX9+3V+3kMqoRoGWbKweqNpa+yXy2cR1ve65D3Mhp+o+fEk0s723BF++76fIav9MaN+xQ/eOyK2MSIB8fDwgqedDwyB3pqoqG+Z2OZc3YFOE3p72hbdt0I5NkaqKzXRuKyNzFqQ88k4VPXVHZPV2Nb+pmaJ1eIxvXWZA68u4fe9kDWocuauqmezs4DaGbxolCgw+A27MkazLRLTdwCjmQELNUJw9cZ6e+LbZ2IpxGJaPiBdeei63D0lv4H5eiVGteMtzeHa5EayBo7TQz4ct0x2VeJvAO/Dwdpledy1BcuPHw+vxPRZ/fKx/V6eaVUp1p7uz2wFUzJHHAz8f9ufXJpDJDETWBmQhC/fOyhvjju+8d+LHZc+9v26G7NedGXkZv+98hhZBICo2ydI2m3YywbC8yKbqRpodvVkB7kyCBt6Sy+q+9k43GiyFaHol5vEMIQUMIE2RuQzuvnd8y1K477jcKtC8nM+Iz2uRuhOrOAXCY1EpJy7nxDoCOskx8tcdAH9eBuDzYQRDvBYlfN/5rFmHlbEsq4qDU24cnEv3nbZ4XbaRy7JC4Q/jX5FZdxDN9HB67Jui0TLTnRn569Zye5hFsvCFy+37GZfpsdYdubdAfEfu1ONamfm10912JhLVy1b2+FHtM5JRNE+iiEUfyzL1684SehjbfEbJoFoejAjBCsKTXJVi8ZjunOpS/fryZsQZWqGjKgt7Twc0aIvNd2SbuNPrdOfwwCuybyvdZ0rqPlmbbs0yyF32HSKzjVzSx1Vsdl1uq4sMHBP2KjGwj+0JkCsAkLJlX8oee9NgASibPpNGo4pHw2rTKGRM1KumDJwgkDNjo41Jed8yFDaZiOpeDw2cJ7GrlM7cMeokrBSDfd3FszbycXG1Cm9ZNX3QIsPMlrkIyKMoZhMhVPJ8dsLxrtWIZ126w29lIp2GwBephK1Yzx1ViDlFo+b+xJZn+Ft6yXGmrC0Elsg9jaK4u/Vp15z1hdNgUiFQMR3ugNur94QdomWqe7UkVa/5ZbYjv+9u6zpzFaInB+j7uROgwVoPlBJ+3fp0LndBRDqxzA36tWPRF4mIep8MQLqF6+EPdxWmT2UWnN0McAeY+HQDWXZhGRN2kVcrYcpLNtgo5S6WXOkqeLm8V0SzNx/LJNyRkVrkM4ofmnWCSN1RkR6epRaVknhntWHqjvhc69edkenG+9ZyM2vdmWJtlsMtepdURJoXmxUSFgsbKke8A887lxmNz73LHYSSRAT+6okPbU4/JdJ2GwTOAHFIuEOrCGPbHssoXbQd+jBbXpkDpiNebnxU7sMscUiam/uU0pK0a3Upt8C7x6O0MVQ6Vh0QnsLXG65ZydpJX93zuvzH57rvXcMGMvXn1x2Rj+u6M90sU/cOgClcRtF/3cJUvD9WxeJuywOicF3XM3JHyoFtCS0yItV1oPrvhGENvE+IUcJkE34YsSxLxAcGev3XrKdfsfQ+Uto7r1LU0uG5J9Awa0pRQlSSGS+3AhWb4ZKNpVYwWiEcOlYuN3FUIqhEcKhJ/UOg6aplSNytSFEt8kaHsvEtDSXaCJsBGjlCaappd4mq9FRadi1fy5yWzh2Zmcu43AgVLfePH1fL3vM1v1hk8V2vy3OZMqv6lUKN1QNZF9QFRc31oa1xbyKycCIaSgdOUBWBiZp2bQQitEMSnhGVp15u7v71vBN2OYx5LZa4WGnr2Dzz33g4CSpHl62budtzaVzmZIdldsstq5oRWdlsp5YnOcJ5ozH89f+Ytz13PUl7v+1v4i4H0Zmk5jgoH7dCgtZpJWsOeV2hUmmrd0yKhutaZvnjw/79b3/7+t5bzLzc7HGx8hxCH2vZj48N//Hj48///vPf//7zbz+un5/+eFwvzit5Af/+d/z8vAh8XLjWo+pp/2Otf/vb59f3/XV/7tTF/Pm5fFGCU8/nDtlj7f/+9fze/HrGr1+/fn5+7NSvr+fffz4+Lv9+7pZz/rrX9fj8qCboEgKTVyOZdS40i44KpDlPy0ommFaF3L335NYqRPC67N+X7Z1QPj7WdVWIMJug/pSih8nx5JR4nUOweHttT04JLGvbXMs+ltdlPkMZ8fNj/fy4qlO0xNd/fdUMabvv/fPj+o+/f5JM8b9+hZspcrn97Y+Px+OqjAxoTGqtlUrUUZk90WENX7sE75eqRFGFC2OR0PKZkuRkpO79DcjNBTkhRXWh1+dm1hG2DiF7Qml/slkf77b9s/t7+Gbp/NskxFJE7D3dqECtYUv5NsGmORA2ddT3Y1uhYHV+1rdzZHAyVMTPl98hUfMayWmCOBfSjgEYDTues/f+ZQdO6o/GANhvEA+nfxXHXsz5PhAAWfITNdOmnkKrWc+ltm14/RY6An4Zhrd3P6+ve+KsFtQNCWNJOpSfTXEMXV/w3IYAZtGKXl/ndwYKZCXPYLZ97wnNRS3tz6N7NdrjBa4TQBxj9gKASGOYOYiMjMx9x3//+fzzaxteafLbxddbUNBEq3V52EMJ41wkUEJyG+zBEyjt572X+/JShVAHVxIhrwDAS1uzKKiw5TVBFkREFGfCIaOuZXVS6kpaAQR5Av6JzVDxa8ReXijba3xPNVlG6hmlaSPFlgTaIgBTx5SaGKKebfG/Vce50tEIvbTqxuMMYllJOJ1djCJQUx0yBsebF2tCpdI32WXfhq2jswdfbZUH3Dmbc47A2+FtQ9rEQ1iJ2E79f7DT3lSVk1RqYX2W4cQfH/bHD1/raLC0CSqEy4iSd/2+Q6lr1SAYX8vcbDkrPlNZBqOZ9a6kdQAPReLHY0XkHXkt/77jsfze+PM7pOJUYpl25nPnd1Tygn/eeUfunQ7SLCec2Op648iMVyVSBoPkkqSb1TpUe9HUGFiTrp4dCSOl7stLFkObpbWGCmbV6XlpQpHV7hEJR4LI3BEC8XCTeDn3xjMy7v1V5O1iHBgfbhm5jMWvqaGQIdypRdDwDP246FbyIsNlByZnJ0AzLtCrsp5SqoQEKsOvIsPDyMcCZNIWHXgAlSfWdJlF8xLjBIAw60TXlpu5hOfWFghF6POxnHj2WB9m6FekWQnQpJQ0ulmzTQWnLadBd+kQkTtV8zXvzIg08g583TH9pNPpyRn4NSciRCdWZcs1VY09O0ZDEUqBkIG30s2Krwcym/Avzvzj8jFqKMf66FXdHm9fR2KhNkAhQePFTGgWQ3ts1EekKMhKeRBaBlbV3Y47mLd/FVMGGnr7YDXxnK+qBlESK5lIFYxWI+p4Z6EbVjQuO46nGTrtr9/ujEdKE8OfLQOh/mRgJkS0paocsX69C2T15m1ZOikyXIbLWQ0E9bHNjCYud9rM2QBIbvQYw4Fg3vz88aaoGfO1UB3OlxTIG2h1aLCqPOoyuyO7BFYyz8QOMdPJWyJUhgvjXCbsFHsWHr83mnoAEFnzWRntvitmLnMSFT4mft35WEYydhbPjgRhdyTJNIS0TA83p9UdbrTrfUr02kItzd5F+5rQJz13pHprJbQjnAjpjrRm5fY2WEanZWhZhso+cEuLdCRSPxYfi+4FhiOinmB3ojrBBkpYQx5D5SWVKLH8fKaqP2VnPozfgc/LnXhu/drx9w/PxHfosQghhOW8Q8+dFJ+RoHZkQe2iltn3vR8eEnbW2BPdzMtJY9d1IINRjKzmhopfupYo8euO7zZKMNCr4APdkS6XFAjCRNbMBevCAJz4lr52NU+2doZPhraminMZ71BmGu2OncC//eQgiSAskTvzfkYsfPQE5d6xd0ls9tEXJTdL5WX2zPv5vH9+PmhWdkyttvmKIIfy2PEQwbVcAtN6jBqvIQfh48N3xDuNpbKSUgrbO7eykIC/niWY1kHVZxPL5LAQBH1eLBLBbdxlb8sSFsmRR6tIkyud4q8A0AyLOSGBzJLF+sRtQI94kRIpXesNuECDOA8nJDM8t9wO0J8JfV5OqIaWRndUvAKkgni8xhOPi5k8t+xejrUsg1bHunCZym0PaITTlVa03tLEPCmICQkFIJWVhoQmVNglNR0y5TZksetaUC7Dz8/1eLzQejaSQ3KZoaoyDXcSZp6p53Pv0B3alS9HorQdiVfCRBTz1L06DTmqPpz0toKlBsprUK8kdTWCRmaDla59P5YlvSSmq/f39JsZMYOwYV01q06F5hWfFAoj4DEIyxjorvCPq4NOdjroEU6W/dpw5cxO5vNKvWrr0czPLR+X0gF5j1YRxw7XVbJ7CTv8LoRbUiBJ8f/x//zffMhqECJi76xk49fXTfLzcX3+eHw8HEBELPPrcZmZMiMkwoirTUNv2Cq8V6ffvQPCx8dl3bRVJ3YXJYk1xXNwrNh7hzL1z3/++ufXTvhfX1sC1zLF33+s5bh31vH4vsPdHsumcbv1Nep29444esMNhkk93kWZG+B1FZ0xz8N+W9U+sSRV06qsZFxO0s3MzMiQqhegNU7aw+gc+HqrMlgo/kLxio1r1axG3TuhdOP1WNdVw23x/byf4SQeH5eEz6X/+MdPCf/5319fW4/r+v56/v1vH//xj5/1wfVZ976V+vz8jH0LKjn0Sn44+ot8ITAV8xmA5914S1UsD/xRoYgbd+r5fEZs97XWKli60mOooVyy+1w6Dev2n0YebKZmHnGBExTtiIi8Sh+lmTra966nUw9FVfnO+U/WwPi5znOSKuev3uIX7gYBNTIhdhEPYVTxPtjgMSvop50jCuWZmjHBcz36CTGbYNWMoZ682CK4GLDAeQKgSsLrPVucDOJLoglzvDUq9K2FNnbiZbhfpnFixf5h13zelgTlps93+tG//fBlr88R0KB1HP4LWE/nIM9zrmarNCbUprCG7uUzsnr4c9R5qqmj+qGqb0pNB+15Ru9QXpktlfAe4G5ZNiT159f913cW46bIljVZyOaOM7Wr+FOXpRJyLlqmHRNbC9oz6WbYx8e1Ph49L6jvlNZCPAJgv+789Yx7586xxBAAJ5fTKad6MF/Xds6aH9dwzPy5ljdQ5vVUlaoyIK3Krc+oqe3V/CehRBi+d37f+dxxh5r9BggjujksfbVoznuuonZVNf6mFUDKPnAM+3vuxcGMxra8uD+sg9cZztlWx0O8dqGOpX1JbQOskleRi8o71Vi3syda3rgDIwifl10Ll9vlnX+6ddG+8iKhhxzNFu0dXst89N1KP6LU4vH2cSRUw+fVBITZlqL0/bz/+rqNfG65ra/n/roj4X9+xTJ0nDqTaMqiCt2gR9oQmU4RAYi8CHNmj38CMhe0KyYDNo7EWGsBLDDUSmGCMqt0eR7XIL8VBDQxooJxyAptObAyAFUgXkfv4WwNCXJX40MoSj2AVI6+PulTRTDy0/lwbuDDrQ0tW6mkurp2CapXKQ9YhgpGJD239q7VzlRhQwQZSUXMBCTcmbHzjvwuQoSwwEXkjGIpI/nceoayOOLgjriWC/h+3mb8WJ7SYmOHhFJw5+cySN7MuCSxhZ243J87d+bPqwW/L+cdEYGd2NmJkU0RlpDbaULupKOG/hYcuYzLmih+BroVjyBSSvy6c5cjCBn5TN0RylxVY6nolDgwvRpk702lBq57558H3XvlZcnBEUE4gScgJ1b3QDUu0XXIOds87/YG+b6BHy/7wpm98GZCWp94R9YBKaW20Nm5xcTpNysNfI1kUZWWjecMtZnRK+Q+J6tDixcMNlaobO9BXWLifKHE4NsajAvtRew2BBYTE9FMzCKmY7zBi4SVXaStDW/vHnpHkrhmSkndPltiv9sACbghVa2OqInVi1bR0Fq2OphAvX/X/hrUk4Dvu9B53InPy+9Upi63kKrn8Y6sm9qhAIoHZJNjObETy2HkHene8nYk1ji3es5dqBAEOtl92SlIBQ13KjIx+WpAEgDuIgtBoFJa1b+KZkc+ljVIJ2zpw+1hSNLdVpUs+jKwm57PW0CqZdEDQCku4w5VN2sk3HhHLvMSEnwsj0CZpkztRAn5b4FE4UTVQXOnDEwRzCoLES1DUxbDTkuOtMyr5FDtNQ5TNCc3j7JJUlIhLZBKczR6RJRqGnxKLI9/NMQaVcGCibgAM37XmrLOSwN/CThwOSMbWt1SglUCqZkGPz4ud4vmz4vSj4U/fiwrbRcCUIRChHQtVgpwbEIF/cu9SvUnnzqOrtz3G5mhI02phRqlLBkanQgebVHj7LHIqq9kau+49yb4jH6gO5XCIh7LI/G9M8t9D9kkevy53iiTjSJ0psEOJNqFUgZbZsvbXJeRqcTwMkYe2QGwW5Oq9ALvme7NkPL5UaXPUDESqpTet1YA6+XVEV6zp+sX8VhWc61QZR+6Qw9nccSv1esqofx/NSOQuK51DP20LZ84FJMXtvnXTBbu5G36mFTuZjT+KvysjWTE4/Lr6vSPxyCTE/SMWmFFAi2xQyMVet776/v+3lmzPiOzNsnZMJfbtWyasQar4vyLAGvYiyJQ9StJkcliHYI/Pj6oQOzr8uV0Q8s2TIJWW64T/6lw28ytB+DssgrR9hzS8kFmjus7ceZxQb2Lxqu2oeI8hTkG7+6K/WwG1KsBhf7yXYMqNOOieiFZRK1XC8W8eV9R5cxVfVmSMsOq2CKta5nlr1/feyfJnbgj1n2TKAI8yFQ64Ivr6mp8jfA4ui/1qZG5dxYlBxDN3FyZgq610vO+uz12Gv9ELnfd9358+P/p8/r58QD4n/98Pu8A18587m3kfcdzZzX/fn3fkh7LrmXLe/yVoLUuV+4IoAdm1c2z6BUNUGTBBLPx+/AWYU6HnZjcEUCrKFXA08mL6vbf62Y4eVdxeTQUkDKLtU9WzQLspkn+8dNPN6xXq710Laf5qvqwdF0OWCquZY/LSPv5+IDZve+Pjw8jS9vcjDTb+y4iUdZovY4AbTYeoEEoQQmRoZ56rrcRyf0366ep6kprDwCohFcia3WrkgkwULPNoNTVlEK9GZfX14kTvQTTIJtekfqqzVpOJ9+uu8yVOyEes6s5EzgFhzl0HNp7AlxsyoZyTJXVuKduFGUnrpIkr6zvOLK6fXudQq6aA5RJmiTzGlKAKhJ8XAcmmk1SBfli17sDNZoUAiLxWCUp17FyB/doMChqpETJIc/+GVPwQmR44JgDZc1yv0Erww57zwF+/6MN96kvnN5WvfCOeot5+jOBS0pgh2JHccKfKjXGCqGwuqFe1dcZVfZ/0V8JIbLiABWmgOKLqQYOiVDhIDl6pbUPTQKriZI1arqzyrd7770xC6LJwpfh8fDPx7UuZyF9DSS+rY+8qJifdz6+9q9n7Bm3TGJVo075SzsZ0Zthr7vD6zg0waR27PjOspyTyhR0lQCKRhQ7s6e269557/jrmX9+xa/veO7cQ989zanGhi34unuwKTXnSvouDV4WrjmPxViHrBS4Ju976Q1pksw+JXAaRgmoZgIiyQY+KuPiTESaD5gebBYNWQmU59Z7cldH163no7vTq2PrYasEtnu8GjFi2NZ19Q5TZnZD37gO+FuwnXfs/Vr30S+rCK5GEo11Qut+oqdLXIv3vaG4nP/1tS+DGZ87I1rOplLHRhCtpVjbVrWBmePT8ZZV1GrgTappC0Ky2u5qVHBCwaa+JMQsuYeR6OlWO/bExM6ay4qJZNM+0f8sp2DwI672jA7FzQloZ7ac+ygj7C0YjNgEoukYe+syBPCrRwyp9PvuyMcqDXHVVjOHg8vxcTmrZaNeD6B+i63/AuWu9oRJp2tArwMgrqr2T7reKxMZob0jT3e/8LyLFccI/Yrn947HWka449MNwlOUksgfj+Ve2Yvx1WcKN9tZzRR8RrdMPpyfbiGEtGgh7QSs9Z50zguxMz+XOZGZOwHnRYtMkwFIsfR9L7dfsd2htEXC8F3jNkvYEiRU8Wsl/B2VLryHlRP8/cbLU5cH29v2BjiHpxZwsBUB7h3STxJ4LMdBVX4HXHTST3YSIEw83+htvdjEmr9FywwYc+frKokWE8E8Wb3QLY5FLamrfuv6kAOhz630BU/a8zIp524KzvOXip4ERB/RGsQGTGh33rSKVgXQVBxVYXl1z53EOAQV28s7QxNKc6fCsB5uuzMjC2ydNRjm4/hPCHKaUjuCpLtDiKKW9V2Nde6YCVCPQyG4yK1usQphB0C4dAcWUCSZWjgH1nQk1qo+FiUY/XJalRSn7a2iN/ZekmczOJereDGRYGj3RInS/URJFFWGJmnn8PUNBKMkcIkgpfyONLNsQi6emR8F+uycbcnLcae+bzyc35l3pgIlB1E84ZLEFk6lD3cqM3dUUKxvCWDkNrNyELnRjZLzR02TBrERBq8S1VZLL4cQkKHaA2rCAOOIr4sgzouZmhYNAIIhidXOsniW1TTKyFqnzggLdalLIhCAUwRvwNWqvey6Y2eACdxChIzcBaMKooyIOwLa7rGfa3kIe2+jluHDLSVmt6iiRjpCmRwHhh1ay+uBFsOrNifA6BHpjDbLMtJaObDvpnCB5ay4o3ZxTgWiYvj67PKkBeUvN1ku4+We4AcsEr+eEd9RKcMztrLwF4YyVMWUQgRe6boqFOhogL0PTlgzEDekHVDNiycB7O/YmY/lkvaBBKQqJBeYdUyljYk18s1KsMWqVSp4FXRZZD6xKzQrOOlyuxaBmrOeVTV5rPzjcy1nyDJbp9wGHwHK8dFXb6jO+zqk7ZuqWPjdJqNbY9tYHnDjlb5NKWtK4DXYoQ1jZScDHr0iruocRD/QwW9ALn6ULvFzf9/xdZhjWXNv5eS16Ov4j/Y7TfwvG1+jOVOZuFMGZOwtLtJZ7jIJ0c8Dx450ifSZsl3uFyQNiIiIHPpqAtR1cSr3Kd17F5zi50y++5XxTcQcbY03KrOMnnI4VrtcfydWtYwVgzXLgFxuLydbHbnNNJK5j29o7voEsaXiWhhrLb6Vn1oASrEsItZ1fa5VRHOz/bzzSl2XfzxWMcWts/rY0LLCQOnLD227M0AQ1OMqALgLnTnEabL0utZaK6Y3BHWeSRoej2XejYNK/fHDibh3OAq2zGVMs7Xs4/Lt3Du88JSCGLuhscef7Eii2IkqrwGqSAQRMWa0MYdE2aaOxzmPq6GfbFsFAKIZIrR3VinAWFMJwOnzhPS8E+R1mdfRqrgBRX5rZMrMzM28RuhN02QDHKsOMKliSbjbjx8f5VG8hlJQGXsOvR9ssIAtTp+/JFpBXZ1rSIpIc5e0I4/lGjyljYIRUj7v3JE2mEpmGE2GjDDvWsQBgKCW3PPXoD12Dl8WpL/LacKikX5ZM7GICSrBapeVOrB5g0g53cVt3TvI6Zk42Qesw6X6rGauZ49LY2UNBEBfVvMd2Kgz54AlWhC0ZTilQQZFEo/HZWbP7+9qf/W1lLllj8sk5N5r+ePqRc5Uijv18bgMGaHHtfbWc8daVwCmfTmvx6obV2Y3GEKVfwIyt+JA8g39OQanQsn+qrBLOvZIjcTZyZFQhbYTEx+oAzhAUAVO5zrm1eMgGywAWmi5OFUo+Spzu7tbKUNhw54tPm8IroZei6GazedDdXrXxuutJdF4rStTNQHn3hmRBLeAbqKGG3DG87CGWwAv7O4VcEyMbCQNacRa9nhcj8dlbvmyvAc6KXc2UTv1WDCwm6unDaHZusft4yx1b+Gz2HXMu7YzBIDlkx8QaBEfO85D0r5jsDtG5s782vrrOxqKklwgWm2k8ENVF2wBlJ1KoLFWvZ71AaZSqaFAolqKcpKPV37Y0QOtYiS5OPkVVK0WJbblTG/t6k6ZMNHPEOKM2IJb8VGAMowsyQyUdoIIN16Gh9vHw5yoMs7lNIN1ob5de6ETXUEatAMjQHsSwYYwqp4/RbWYKcWz2YEenf5aqho0W/z83idmO9KM1+W69eGtqfRY9lSUg1OqZlTXG8/OVBfIKv4zkIwSA8rIVKEtha+VOisd6vJjLQw0+QOr15snyCo58EYWNJYTJ0NPqVj3VhFV0Xqg5jrBjNmwIBWCUeANlZbGjliwc6BOUFtHeEcn/ZlZmXwJbe7gRoJZQ9tlWGBmPlZWRLkMz9Cnw4gtVXeY6gBCz42TWpScQZYQ0Bwcm5bDHNJfBDoEgKpXIjLjuDmrOJA788/YNagewk77embTe5F3aEejEk7L1GW8rAg7rVxmBb+oxPg7wan9ox6ehQDc4FA1j1hLwjDVwkkEU1rgFmzVAA+wdSfkWcRmOfO63M26uYkt8GRdPMCBXjTaaxP2H78ImxIumpr68gETqiLVV95TK9sW9kkf0/QydwP+lB0oS2Ln9HHszIAaqvngPjl65cMDmM2hk9DGlcApn09mcroFJpnB8OMqDipoXa+Q4JjlvmVBCACjoNhNRH1m5pr7N7rKrgY8OATnkg8x0odXiPE2qxq7yYjckWw2XHkOpXrm0bQACpK35TrYFiQocUtA+qSSOrhtvdIoMKHpJ+in4WaxE5ABO1XyQCdRyOqCMS7C1+A6AoGEtqBEl+mAx6HZWrcOcnJ19By0zjMCXZ3LEpuHHNjFpQcEbDAAgpEykalEOKwhZ/XOqA6N7LaXOuMWUVhGe+r6/uX83gnx2+yZgQYecCeKOBPR0lcxNQkIRodY9X9la8blYdg0fbU3PgTSxYS0FcvgXOVvC1qC6CiBaDSUJahq4WqUsJ0SkXYy85KUlpE7gkrzUiZp9aUUkTJDUAGSiDM8VECNM4dS+s7abz12kGxEo4CvrFZStvyTEXsHgGUeUga+9/P0gtU+yCx6OxqUmlXBkXxIfD83MGH/shH4Nwk9ro5kPSmitL5sQqa3nYpOU9khB1/nlGySONe1MguMB5ava03RC8uxDN93ASXawClrV9w+lB+GMmqTVzQ9lu8FEZfxoSRtKUAj77YJUHV0g1931G9XqVtCaf9ZTQaslOEtRlcPTgXAIscZs49Q9ca2C6tghpFITryUWu4/P9fnZR+XPS671qsLtZP02aqn5bZFWyvvq20063DGBTXI82oVqARrMJd+4vUeFTL183oLbiWl0mRI5TQKt2eY6Nv6GxNjhWqqCM2t24nMu4pJGs0QBommntg7cNy07tSmqWpx1kRg2I4NYX08oLz3JnYqrACy2tOdqdSlWs3cBNBJcDV6kBGBgVLy+STpxipXO7WzGJ/NZhrfhPb7E7pNfNchJdT0rBfmRmswKKsXbdq6r5ZYYS81p6JZbxVvOXGHRxjSySRsr4132lsq0lsYZpa5X8tHd1fPOyLlZA2ZtepaRhrd3M4srXkSrMYITHP1MqKIi6keDj7gU11N+bfrcgIRuVMpxjRklgmOHZmSwphmejzWteP5DNKWa0d8fUeJNWTmvhHV6QFzNynN+FheBXOUuM9slojYkRGSstgWNY+1cvp3ThA6rn2FMpKmIiJlFK8v+1x1Lofin7utZUZacZxau8YKxquxaLWZ3FvK3N4byeZ6r8eq9PCtLaGvZy0nMSLKBVR1ylJnWuSZid1JRlb7XQfHzMyqBVWlZdrxy2wYVaai2gJzHrzBZKmkOZ12fgeQkjKrqoCqHXZy5o713uDPSarVgch7La56oCQ1x6dhqQkEx5i9mRINmRPE27nAIRSpIuNpBcGQvCoXZJMF6uwcgAATOObrZ5WpE4/rWms9n89l9OXuy82/n/dyPpabe6Y78vFYAO2+K5vboe87lluGfpDKSOBafin8cfmyytkmsAVL8JC6Lq+Lekd/Jo5nF7QmYns5gQ5dOu49y2IkWO9Mdvyhzp7HZKsefTe6cpZ8dJlmWevRFAGq5qBXPGFAZl7OzCDhDVxJEs3KVdan2TzUPnil37wjQ6X4M+0yuHcWJa1uoYDPHcILWprj+3ZcKhc+hCkCYImHVRtqdpRmLFzf4jAn87VRO26QVNVLlZCzd9dkfRbPHyfFeuUN/bx4jnHHWEZrAtpbq1N713FIGqOT1f+lAu6zmKvq5DOl7t+2k9a87YDGB08qyFewU+EYIMk0zR5tvOsIz29MWFaBc8UMlW7gxOyVshIgs0CKCv5eSWXlGPOdBkb4c+HvP9ydlGj8uvXnzstctt3wuexx2eVWcg82vfFzePtRY675tQeA8xwxSaDeavgHrM6TOA7KWf+NmTY4xF0lmNVnnpkNPDdjbjn+9nn9dWfegdRyuyWOtiEO+kPSKXYDd/0uwNbxV3dmnTb8gYxeeyyz+orH29cRN5h5P9dhpLfpqH3Vf69AW7YaLqmnnJk21cr53IlhOtNwAgglkihBAdlQSgdVFCrBOMs412eyuwgb4jQncguQfW8U6F4Ts57EYqW7ZDeWitSONmEAuhcNlpCTbnbv7WZARWlWdzRbgSBCSTU3zczHU0Ib7p7gHXoAzwg3o+z7maXAfEc1qIjAM3IZHsuMfCySPOLEOQd3dlpCeHG3iavLcfgwH1i0pMpOma0Gq9EKaRoTeRHr6vkXl/tjOclo3golbZToOLNznr4GTid8+a/DTOnQv7pO3vY8uj20dVIuNAqeswNeTvgY2fn7eyH5dQbbCDcGNC6j04OIdu92+rkKUIjarkSR1yZ1JmEN86rfuj+0ORYAiNbxMVI1+OXtUvuXJoio/5DNoel9/G7B326xq+gvr8plVsLeJG8kqUWnsQRYDC+3mplGLLdz4ovSgf6zhVS75adCMtVsE1KgmZClYalUdf4acdkYvM7mNAuiuaHGyMpgr54apqiFatMj7+wErKPdOQQqvlss9oRgllU/yI4T2lZ0ZiuAu/hUwi7hOfBO7FYWBqoMQFJIFbmUm0pEQDK5+WySvqmQLOfB1eklopmYCHXP/x0GwMh7ZzE6pUwrlLr9jMYltl8QUwnKnGJlAaXHw8l5x3+wsjHOduXFhwCVJE5bbUpDZq9Nn50usTUlRBMTgebn90VURKBCoFTDKVGBOklLxe6KRU9dYEppTUkDELXRkkTNQg0Q0VMRkImETLKSEFNnp+XFSCZktRAALS/3tfjzwx4OTKsmJxAi6aXBVxLHTd0pnQdkqj2ytTDCzlJK7vF/Vb0+DeQnQD3O+u3k6e2bvav7rdLQc2VgTkHK7gd83Pl96/tOnxR8EVGajG6/dpyxb7Ua7f8qBRXUEb/GTLKaEDsuHD3gMhI2xt4myD6Mf7eFdgc4FuNgNC+jOT88j9LMq+Uk567ZdUdWF8jjssfDL7c1+pJl1oaHBBBX9wgjl0qbJSPUwzrHYWOu6bXGb5f0m9E89u+3ezlpEduoVoQ8VLWTKDRaUA+8PGWlpq3VUJR2ljgjZiy0ohsG5+0mlH1NRUBXjjv8AbnKONIeyyO0STdbqzpvWkRpcExOM95I/6i5BY0v1UW3XUX3+Fo1y2QDDLM4JIragTdlD3YvS3suCJ3kG0lz726DiC3JSwyL4GARRm5WaaITbI0DfXs0nbGWtP+1uoZXvnzKKP1iKWkmYTVvh7Qa7tdas5LyvpMPr6GtSlwfDzM0ec9fiQqBiB07wAITQPIeo1aOLaVqg8RwD0pqEYsEqphfL1eHHDDjZZZZNNrrurfIZcvZU70jIm3dkfG8FwEoUndSCEiPZT8/1kb6dAVPbKzY8f0sjYxI5e32cVlR1xo0qRC7SoilhNlWXKrG/j4C/PhYy22g1SYxVp9qSpfb8m6mqDdYJVFiVjBksQQrJ/Wa4zpnKrOrjgKqS/H8sz5c08Y5OseV/9TDLrgNnasKgmp4TETlZefwjj5FBAYKnBxfkAKHvzi+omGkculGIiMmoRjbcBLAiISZWSBAlp1ll+m7tbIOnA1nqo5JpvaunGoMxVzWGCFN6NcGrBfkpGhvaD6P06jDwLoD8g0RmBzlcCeJSkImYzNUF+DQwcjHWmaMuNcyf3xW0KrMx2UfoJS+zG0RXbV88OqGJfH73tT6fFzu9vmx1nUBJQL1ABQRHF/HDuVs9mU/jtf5P3sCU+c5GX2bcJpXL0W6n2EN5uYREZ3WsRNi4FRJ6+Os0aGO+s8TqPBhDGjx/AKdMSSH3OikJF+zylDJZhbHn1M+ml3TGhiFJprRF2hmwRSLCRL7/vp+upu7lfuYSmb6qDXPJXVD0IwwK5HIKXd0pFKt9cSL8DIb5RUfds5+MBi97NuIMU3F6GWRh9r5DgCNgwTOEr886Fu8LmCYtRzTlQO9VYRCyig3pJW0od2Ru0pUCVOz/eftuoOyU6s2cwa9ysTzfAanOMm+SLSIe+2BCVzU+ybfcsEK2SvEWZMmpZbspFzlFDAVsAkouQiH/vi0f/yxOoWmPR75RyhC98bD+fGwtVre4vSHF4DEbtw/fvGgmf0h76HWYFjnAc2m6QNwcCxoSJHZR6u2uIjma9Te777r6RMg+R0KYS2/7zDj57KvO1M1H0LNMz9nqB1zn6aXjQVITJKGV4PsLDU0jYw2yw5CVpXI2ctn5+ktEujrrCpsl54HA4qSiAC7zaMfatNw0Ow0mMzoAYEqFNSaOCsai0BhXfkBCe1s+9+RTXWFJ8w6HiVLrD9Egju1KwQjTr+ChrGrUCtByqqvdqtZrrllkoAoTZ4JHGtcD8CU3BzVq8qDCrHagineOwSFdEcsM5fu6KFIYo0fIoGvnTQ8akqVjbluC8pSB1u0rLG76ONCcoF2okqCRfnpIrlCI6dfEVQmwYdbUcI+ls3ot7a8Yzv4WM7SPe3sZk7cuGhVR9WMFUOhn69umAZa0O13aunzzuXhI2ZdEe1gJ69DNobgZfF6t2ZOatMMSs4IQrz5MhUiTLVm8+Sux956iyXZWyLQm1kvq9qZyXHnJV4yOE/HTgfZPJlD/Vq1oo1fxWxAYLxyQzXv4Qde0QatJMOtvTeBqX0KKkNKlbpVESGrbUGVmdTVW0egkHU1yDrCkxd+IMBUTB3z6isvq1JYJ51vxgW9sI/1bv2QIQLLacLOrDFvTyC70o47Wrk5QkbQWQXvTSFYoq01tnIWSQVHEkDyLpALuFOCiCrVnG1wPEJWqxfIStCAaiWwRnmFpKZ21G9e5qtT7c6PDOj2/kA4Tco7gla8lApx2P89ISuRDDGLQHycQChfRQW98uJkp7JFGurwyFqlbsJLgIU9SxX0VrtmDT00gpZM9vSIdoiCyHOJx7cSVdeh3K2aX2mwEmgvtH0KUAMDlOXPiV9YNkdszEiICinFbtlwJxJl9msKRogf5OVuZCFR7fkmtixKH4QqCOZkm0PDb9kaiq260Rlp3WajYyLYI5b0fpz7/M6xPr75mBigstHji9AxOyGUvmQsx+W5d5D8DizAzQn851ck3QylL2DH5rU8TaG0XIByouOCmMsLn8alud7D1emDZp1FT3whoELhV0I1BuEtCiJQlC5VYJCnXGPDE38sfl7+WNXTNEwC606KarQ+eAxeAfsYJ4pGh9VjnFHHnFgBfUp/j2bP9Z676XhqNuopwp1Pql2d1QU5zPFzt+ezusxAq+JZSXpXWJeR16JVW4adZK0vtx85SK+ITOXUSMl4oYupRNGHmO6Pj6uygyqGJUp8pt9WY/zrfQvKGL9mnTmaYcDTcgalyMPZDCVlCdqB9Mu4s/SbDYBKBOb0+pTjaRjRSoqSpXJbdyr1lEz046wr45y2SU9e1c05R8Lkrx23uruE6qgw9A5vzuJyy0oGIYI/f1zk/Vh+XauhByXtUVKO49tLgLn8X7cqlEctTsrjcgmZXXrK6od0J+k1U0GisTatkazGDZHM6WWOlJn0eCyBeweWZarV7KS4bwNotiMiK6xEDbPLzNiQzA1mddIB6XnH111jzMNIJfbueQidP5TJZgvT1G+98jfOcCupc8aI+05BxW/ATBfu4MP6wFj3ePRUv4rFi/njVjPmO1wrqqcCpEpIv8jkc+qqfbSJAZhZ6WM22wZjDPMxoDU18xjLARe4d+6dUvW+qcSvRutiBm91lwEmHUiSRMZuVre6af8FNRbSFKkaKEHjzjDjolX0TyCliGgkKOL0iE3S1TmP5u98wTp8w4P64wiaWWYj4FPwq+M9tHBNRDcp7NtXDUDto0Z22FY/O6SbCdEqfVMJABUwlM0oKfcwYF+hEuxRjRn5+FjXY7G0wK+lhcdcSmR2yIhizL1yZjVLoK1VRPeCAGfN0Ny0MQwNyA5Tr853USit9CaMj+th7nvvhjVayq4FfzAnImJUGSQA1vNx1NZXvdva7hUBAZ0onl8pA1b9jFQrm6CLUZN89ruyLrX+5j7c40xQH5fRPMV1uWjad7mJddp3m0/X/x2H2N8/NdJ6sLVy516hnmdXF1NUo1qa3mgvj6p5WG/O78SJaFs6Hp7vr+RELTg+UQdFPZWDY9zL3AKii26mVE+pF9KQXvMoSLOKSdUVPmTNDuPwu1TuqkOOFxH/eAsCqx/E6yCnILwHuHVDdcGvJJACDumVXQ+sUqphwGO2M+2Yusj2LKHZj4UfHy6y2kYAfRixKs/8qMur5TpShzy52TyeAxB0QHeChUnS+PqFwwHqxzd45PjMtkW9CL3fX8Fujqxhf5S6Zs7WFiXv3UYhe3EQ9em7C6FplcMha3ZIXbJ3fYoHle1tNNFaH5fh3E7y84Yo6YTFb/EBx2acQzbWYnYyJCOj0yVV7a2TG7Hr7vMZhYK13+BZ9bd9jYmDK0paqCKy0wWFgq/2BELQnp6C2vyjuGivU9cbkJpHLEFJoELbnTWrmGHtDyuEkuDTSVP9y5WJJ5xS6VlYy5o0k/8yF5AKg35cdjmeoZ2dXmfiToaSyTtkVvyjwTzGIjt5o8O5meVFCpWQNucZ3dHUD6Sr8WhxsUKCzADZjBRIMRI7lLOj78jl5iXXXQ3xk0ehhMb6KPQQBr62/oQ3s1teiDCImbnTmfFsfk1N04ZEqDlQbw//bAbZHLox8P1TTYH7NJYcC0rC1tvo3Xa7AHAMe18iZoDXsW1vuxQcI9WWvHfS7Km2DieyRbdanF+fnHXWiJ0NHiyoZI+nA/q1d1+sSQ6/u6Lg1gtsHkl3JPX7C87ha81KSJObiLQGVd1oQEepLH1oLKs+UzAb2qth9rOsbQ0T8AIodkZmBxjAMxSFR4RIRFYooyxZbkhgtYOFdKeWGXrg4Otpl0U0MMRSmJZ4Z7TwHzClRBCUgazXYpnXoMlxoeLUiCgamCUcCMh0RC3rHEnTPm6stGZm5rSOjySxVGmw6GVaa+fQsHBJzChsQqzOy/GLHTzWwTIvY5YlVO7d+1d8BE1I1gcELFfHCVmr0JspyBwMhJDD+2io4cXoPHAJoKyAck6DoT/wRP9jwMuOHIMvTHw+KURxmMi+dwcTdIenqhS8AAHdBeJeBaeTXpyQM0PdBDlpQl20iTV6KgNm5s4a0zxyd1WukZ3T1OiP+kC9eSWcRBfAKz48R6Ot0pisU7bjuhaYbogLHw+rwW1u/L5TNH5pp57N/UEADnAUV+sDu1YosOPQwwfD71fAMRIgO9QxkDIp2fscPSK+4OwxhcdAnf1FIFXt0h1idSKJzudqZxrBGmmTyBYJcvM3QvcElBjqltr7VAfHifwaXn9FR5jq+tsXjxUCOhAcGVS+vUzzQOpNCRSf7t2jHAc5ZjirEbF0dGromJGP5jTOU3iz51Mfw8uWVNOpcU2EU2tW59eX1+M9G5XNru7yKAWyS+Hj5XDy2W7xgr1fPEA7EickzZvtyeLThZut5c+dymTiuvzz40PIhpvOpHuU6Q5Bhf8AsGQahpxTO3T4Ss3bakMjTvKkDobLap0xzce/oHiINDeTel7I4ihuRbXIA4Lc3cz+9vNzXX6ttdaice+IuK/1OFAU2amfmV2rNIwBsIpdl9vxysC5yg5uQCyzvUOhZeygqreaujuOILzCsRJ/MmJbjxZ6LPreP3/647G+n3Hf6qFawDK7I2sgYLkFNPykTO2sGBfVxbMc7lTLR/VGrXP4cWkt19lPFaJZBWpdMUumiW6lgVKOF0Zvy1HhsjUQVDBQS3S1DD6NppKLL/GmSSEqiZIUATN5F83YMYTUiMBUwqVjPXtudEVpIa2jAPcGa9ZVRMR9d8NOkt4F3BoKX5vqNRuVDZ90VlXSLEX9r0M71roNGjsJHEmCTNBbxrgRnykotOlsLklKyy2ZseOYoxPM9zadv5/UoN+K6Cz3LeJ82alzVyRaf7Gr8ZNPvd6lnlG1L7FxBB6454XNkNU8M0oHABraq9s5s9h6+8OX9d3s2Jx8Wm8HpSBUeyfjTKUjZgwVq88fxOn1P1ukDXEZY0YEZ2AtWcPL+Sj5ezKlh1mXeAUAKSb4/H7e9x6aRa1KNXjXKLfqomjqL+bqX+b3demYiKFXdkfMLTcHcSwV3htFe2zpKx2UjI/1AFjSHq0k5JeUqCGvXRhGPZN5aPVPzmE8bqqu8q2ntrIvzPHt3KNbfuZ756q6wmPHJOKty+jNXfy2Gue7vxVoX2+LyS7Oh/z2aegRIeRyyx1pFnpGbfiixkxBixLSZIGUfIzA23v2vuqnMjYXahrLOZjmncLWP9WUKeYL9+8FzXmOoAwq+KxmM8q4MzN0WtxrPoABy/Bx8XHx47KfH2Ugzyqd8wjg1bp9krU5WH3vmrPxur3X4de5/v4T7JnLfbt6HZ0JAupussGZDsnbbJWxmrk/dfsVkEdImb+e+b3hxh37a+fhvndGYhzTNAdBYJOtNCA7OtGpxT8A72Ekw9725ew+9t8r5Hk7hq9lOM+eLGDnt23Yu6XrT230Qq3r1CX+gfTOrp7G5QkUa/WsqyC9x5KoHjeS5mCbrI5EOoUubzPPvB/r8UOUMpFmXnL+0ZM60cX1Chs69cHl1ZIMgjUlEEKoW199IuDlozdnEHgZ3bDcurW6Jvq57uAdyVTMCLyo+dLiU/JWc6/jLQPuYjSzz7CzLf1zDvmxNcsaoa6L39EaBTliugQvL6ywRJ1w2dT+CYCXgcBqai002CW6WxY9RKVCkfbRGBmsWnZJsvlBnQUKpAkjcCIUQtVBReclzZI4NMrjfI8hUQElE0KcU11PZ4xQy5VtqVLdkuRLpPUkOZzsNk+j9ERZ7+v5/tVnea6rfq9bswZvOIlJ2ZAeTylERncgA8MgFbq/rNOTTKWSL22gfO5Y3fLMcUpgdemyBkdlm2QKhhSWE0K1VU5kgVNuBxQ5cF5FVJCIqFlggGQp3QkDhRYYamf2tkuytWpw13EUd6CgyRS33tdKnXuwYl1mglKAoqKkD47vPwtNlgu6kT0fC2CKdWtFMMwwWOVrkiBbvFov2UlapiiV+Jcbqz5JwcRU9cSq5YYTZkhHRh1wy0Jpqkej4ghCiVL5QgmaornBkJxGsshQ3fHQm6gb3FjC8oJSiDa9JOnjZXWiYdbmqtMKb3xwEjiZG7rLCCKiRSHGhL6i1Lbd7ZAhAssmOqWdpT53eX5/wgS2xZw8eOAFsmBnAqVjlEzKElErXMy7shWEakIcq7SfWesPeB2fGoMz96fZNCxhY6NXlIhWBO51Yde66wwQ3Qo43moInm0rcHbYO2NWL1io8ed3390moQyb1fiiGrkgd/v7Ty6L71vP3ZS079CdnSSbsCqeB6y6S4rnNZHFaZ3qk/IOWp0HKIlRi3C+PWHFb9HLecuTnBAtClnexDqOFYTLUZ1fH5dfl13L3a057rVpRuPlhORzLhub0+u8/gv9uPebXtnT6376VXyPmyeO4Otj2jK+QCigGyMxV3ge1Ik2X8mB+Uvn5MQMPNDUXMEELk0LO5b/9Xx6ZTtAeRl2nT3VWUF/F/j9+clo2fk7+x6Jg2PVEiy/duzSQZJEKHfKfQuX+Vr2uPx6fOzMhSbdrOtKBcDY2c4WnTRV1HsiT2L4b2+9LuNNmzGkDif54nkMc2L8Aw5WIJQUPtyXm+0dqVw5P0ycvAt0LLe11lruvgoqeDxMam1MzpVMipt9cJUFxS+312XpFMlp1avIdoRmfD7vy30ZIqav9i0xofGy5e5lU67HVf7m42P/EZHqifIPZ36uYw+yialWGzyyENV+/ouwq900Sq6IELSAkmP53pGRay03mqnHzlVKX7tmeNvKhOCkXd6bCn26bbBeTuvvocmp8xAj6W6VLdRQsOKMVfUpioYAhICdnccCKgZXw0OvZPG3QwdoStazUwpYyS7lFjNfuHeJ7JZyd2Uah+dZMQEgjSl92YLXsUk1saLfuUGLd2MRxScy1iAWs76a8Rc6EaFmnGqFvvXhQg5gUlytSizHJY/pQYsDTqVhHF93cjUJ+bVQtaASlKpk9A08KAMD7ZA0HXhvaMJItWGqkXiRXPrxuhmttQxzOH1rVRE8z5UPYbpjJwl7p6DVDLnXqUYHmlKqwaHxJeWMTkBair3NOgAwij8Vs7o39CNp39vcSGVEHfbInYk78Hzm83mv5fT1ca3Yz9lsJRc7QR2yjc0YyPHNE5RwwB0IaD6YXg9l6pudr72eEKuqNl912bUIDlzdqF/ZvFUqd5zT/4y8dBQ0OHVFZiGQGrI+zrMfD6pXgHs2/ByxeZcBKo6jekEWJ8s5vrhP8ftX2zyiyTIvmttbLP66hD6htQ1SukM7RHCRUbx5O+1sSnXck+0PupUp38IATCyg8w2+PrfdTOvLa9J8SPq4+Hn5c+cOjlvWx7IU//NX7JFL77g1cdFuS0LLWGTEx+LD+XHh46Ibl5tZd6achtfjbo5T4AlR5jqlyo7aLOlQR6oVseF0TojJuetzr2zxn7NPf3s4hV/3H+dZqAfVtUi5JlrdoXvnn7f+uhu3iAz2x4pZCXVvyqnnyHDKgxk7KpmZTOIV2FTy3bWvon9giFF8D8ROHj5fnJ1pdcN6AyuPsezYFSduMrxo/2V8nWOx3tbpHPgsfzGbvPchUb2GUtUr2+aDkDeLqWhQE/vPCUJn/FM3OsHRqi4YibRKgMt8WN/ChG1OApdZTqOxJNrIkA+PloQZMxLQZWtRZvBlj+W/nvfP5Zfb844C0RXVY2XP5+6bxKgxigGhBTIZ3c3aEWmtwmHi4OVSBTJSxjpfTCGkFn9PJLFgJSVokykdtK/FgwhQZvRKzM4xb19eyg8dG7QVPzH8OLUjA1a7pfaBtSUR5ujX/cSrI7VpGi3Q+R4r17z2qa2eGNzPmrHRR5voqep4FsnXtAWAV41tLoc3g5wafe8Aq0FPvrmh1+bs++rd/rbJOf5ndnF1h6DdqzSFBsw+aTvUOPVsMxZpSFC9qBvVjo8+lqUy39J3lwqtqGmqlhIyD3t4DkE/6GWQVOyhSEhcPZ6iX+hkCDESkEY8Q1IVWQXCyWfCIAf/3IXRK3Nk/9/qzB2jC6rx5wIMQTCVPNk+o1XpXw6x8zDSOjxQU46zI8MqoUpKJEIz0wK0Fq5iCxtj5WoQ4zxdZWMoCWKBJdJAA2irDIJP1yJP7k2wudOQ4FzgyaBejTk0mHP6aEzTm2A0tHiTijYDgM4SYHptHEwAMT6+fGrRQHTqK6+NOSmDcA7MMfNlWNrnnh0wjWtvO7sIYW9vzKGKvb3V6+8HcqiPdWNAQLenshvznsJDYvLP7/3c8fNjPZYn+HXzO2TUZfhwPBaMqDgkOtvrLVQDH+XmbUpO/Pwi+1Ty2t88uXY9pwOv6GT8r4OADpf6H2XBVaIUJ8R/+SxoOuiBwh3x8WFmuq6OnX4k/roBIsLuvZfh4faM7FGm58LGuKnN3SvabIqNphuUoLX6EIHq/NBwbdCskxP464QhnWsANZO0VJaqa2QZf3zYHz/Wx+UfD7+WX5ePgszpmegbNqNUelrVSkv9yyLytwXtSvLbzvqfAlW8Eqj5rXmUs/va0J1AdaJIYlhUmE5c9JSwvmS9QCUABe4fWG22K160zI4McIg8v10nztMHMdHg64e9+Ts9LhMwmCQLta47yQmkpEQg2aJscDMivJxP+bLYIJ3X4+HMLeW9m7qxM9byHbe5meG+91ybjVZLB7EauGBCHUKiLKvFv3KmyriNqVN7n8xpMuKU4jV3tj1XLdcdT/Oa3IUljd4viGGdnciRo27RpuNsmQpkRlhvT4RoZCi7b0qpHt7cMloltmiNKwOQm621MuP1xDAxAhsKMXItmwCUdhGguz+fd8npPbUTyQb7GjQwlrdESJEjZQeRKGoZGkg8WxXL4bTvu8ZJGolM7NB1DjlAqAKXWuxuahvHbqjhf81hs5H0YUNHb2BiGxSltKP8kBdl2MvbDICHqlGHSFaJsjDs5n+RQDfceVceKy3RjnglBXrLGNnqPRB2tfZocN8+xYM16G1nHEDoHDWdjOJUdzm3NkemtyvKNCOoxihgZBBZTBAjZs63UNwlkKoCbKW3ZXeMvK5HZv6K71dKlpndv9ZUjtcGRtsl9bvrvdLdGf0s0gnVZt6tlDByzfQCG/2U8nN3ZAn31sIamTnqKT1tgdYzxA+9vVocQTCnZfh14GrB87DlywSdGL7tlZFRdOa6KgLQqUuWcnYZSq+IqoSriCGcsaKZru9AkeFm5q6+a/36ulNM5LoWjR8/Pi3viq537GzRDLibJQNIsCphLZA0sf7cFZrwDrwBDawN0kHB22FUc77rUQJVthveJtD6+QDMOuBpHZNX+nvclyoLqh3bkUinFahN3lSmE7EBaAy0Axrh3MxB3jGC7b9DXji5U3sYjN99IbXnTlXX1mXK2Ymv19S9dMuAgB4kxBHMa9m8sglRjR/G4UqoVuVqNzn7tjbnGMT6GnpLR7zz0TbVJ5mhRhZKqEb0wqB/fJgb78COZhcu4+djZfLx3/d/fmeZ0BL2qzDlYzkhh9wo1RB3fD7ssU6/3jm7fSGv1SqPMyamF1e931PZJCDlROlqTl5FRhoy9om8MfjC8T6/pY2vQzlf5aAmKk1lKEMoSpoQyR363vre+tr5HAIjT6t578P2yoWvkzg6VQUHGenwCct6Y3SghP4HUm1CTgyKs3TAoF5vNnpMtf41z8CxhGyAdkw/C/s7/vccWb16cvGKMEJQzmSXEwjzfBbZs3Bba7dPCiVVCoDhZUCDyIE8tMIh3dQzszf9xbagdR3quLvrYOOgqn1iBInMiJ014GPSyKJJGpb7lkj+/YeT+lh8LFvGELH1WLhBpb52SlouNy6znSnoKsG11uFv9T1M+pfjOetpNsdRRA+EwkQnNSBvNj+5Q7+E5bhol/Pz40SJfO7WH+8QSDJynapwUdJSgkorNIdQVH6q+WXqdtjx5J3M134/QkNCcwQqqkGxEgS0wtH746hfKazxTSZfEBQRNYxlZyMIHBFGGj5pXK2seqyW45UAY+KBNYVTNex4ynSzxU9YAxwMt41/Jk80Oib6JO9S1m13KXqKXOymhg7q6o2JUv7stKUu2QfAUYLO5f2Uu3xFO7G7k74cKD2S8tPAGw+iVs9BmUnaqUgI9teWT6NkNfgD6C5KiWQkwrCpYgM58g4k6Mo72xYlWiLuXfDxFZLP2qBnwKFtD48xOqdvPCSm9WM0MwWuviiwNOSnX52tRSWCTjwzSoG+dKwISyWn0WkKdezwwGBmkQC5iBqWRFO3jpVgx0QgKG/L6VrsJJyE6FR2m/2k0I1blb3K3UhWQYPqyL83mji8TQyQ+eLDJDl90cdclaFjoxUTGnQ8OCv+sppl0zQ6Bs2QwXlGOkGvJpE+YQQmmG2fx2O0+yLNqGonK7DWGm3fqb13Mcf/+fVd3Opn5DPCjY9lD7fPi59XnVRoNnYFEpkIU6SutOWUjXUd8/duiDgI3UR9ZfMhcfxvH4ffk/v2yFmfS1aB2GYp2nhpLOx4PJZAwwVPLsfXnTvzjw8XuO/9YzkNl5vdPeJHwsflPx42IYABuJyR+dd3zZQbl3PqKuWgujcDvWcnAuCkHXNCCMLN3RvlPxPTjtEkMUzrzGQEzWgbwPC+ARLVHiCJTICRmTHx7wlSNRnP4Z51DKS3v/xPX3pds84TmPxltuTZexXcNplgUqsmNNWLZ8Rex0SvKOH4ELY/f18JzWW8I0P/fy+Yhy3Uf5sEdTCRDnrYvAIQR26laG2djvTpaxJDYSsADOuxerKDAdeCgMy91EyKUrWuJxmZRj6f39d1Vc44QhN985peikClnHhPS12ls1azYtjcDvZhtubOqHMcSKrX5MTzjbqWL457l49Zx6mXQ2vPU5YwMzLMrRKy33ZjL2PjO5WF9/eLEpwdsRVYRYx0/VubUl2LG3dSiqM5cdJAgErBObnusauZUk3WkmoEqqSZZ1SlGA4VSjBr8kV95gQI4CRC51AkZMYHvRj9XUkrNXGUYULsDhlftvVFcGAIntmazdOOcmwcXplAqaDgjhTgZjuTVVOCVVDVMBBUpdPIrEUsOvLFNi217j4xA5vxlP3vKVkXUEUiU9Ual4YMRXFFOlQ/i38yno4m9Z6J4Lz65fw1mNdQk5To8vhaXrwwkqBJWWhVqQt1VbZ69DD9YgSkUxI8Vl/A9/OunDkzI/PesXcCKvmDk5lMiPi6kQ4CJ5GvVZnd38En8dIA6oST3Wl+8rfOdPLgGs1luBaXm4R9oKwK1CJ7G9Tnpsxq8JyihSbbGOlAiqNP1nBmTvQF6BQ+dHgJjUzVu8TeffVZqk8QbRX1bvZifXWIwnLYmrsmyMfivQPMfjjPXzujoyKa9bjAtN715bhp7juiFSvwm7nofCZbgG2eByc3EHC4Of+agnMiy0Z/ZiE11ELwFQ7h/GWwy9d79WFhizNMCFqoaJn6efkA4++baCIOohlh5r0127FqfNJxTDqfeaJlKPM87rMhNVHgyU7mkZQpVmbu0N66I+84ta8Cg3SGoM0+bi/OFxkddX/v/+m7E3bWR1TRoM+vIDdci9cyN7r1AV/euWWFxyIvsUmg45pJ/PvPJcQ/v4+4J0KtzraMy3CZ3KoZUkSS7qOBOg8Z7LmExZgZYwrytN3VQ8m2Y306ysmpIbDZXf1gzuZ6JZyvjLENG04pbFAaZcFwL2SwsPvIhLiFO7EDkXqGviNKaS9Kk87cfC5eGKaloHaLczlQZQptzBVH1HPSUqk7bQtz1bnm8Wg6EU47nNNBdv71ewh91uK1Lq+9Mka+bfKxUk3/H0Xl105CcUFmE+is4cTfk5y8TjJaXIoNEZ6H+i9GhNaCCHNSplPs/Un19fYa1c5n30zvhO5JyewsfBlodrn9r/92fV5VnrYd+byDlCEux+eHfyz/3inx8eDX1/4maPzHHw8C/+///PW943I83J+ZofSGprojsC+j2D39HLUzOVSH2e6QVLobp15b0waRoFsBnc9bn5f//PGoIx+p0KbBUpGtMGg1VuIIEkkx9Bl0iigCkwmrZ3gmSCzriLYf1UxgfDubrNip3qScWD/3AWvOsXq1m2BCJvaGi8y6zsyksIwiatb4G4Rt1iC4aCiuU8ET3n1ARfHjOPYTYk3G1wtA1Eixpj5WTIxKk9qmvJlKvBuBae72CQ46mTESlQ8Uk4nungdBk6B0A83VuAeNDGlHZjJk7qozUdMJMJum29ylCO3UqnEoZlLNj7cLZOq79A4bkVExIBf5VLAQlFnS75ryVlFPjdytwa31jOZUgjWRmnNqQVivgVEljltUhlHesQltTYWN4WSVldT3NLl3OaT+MDkNbPWw05TndAzAFCkoKzLLYuuXEdQGZKuIPPDiMYHuJSnUN3oeJue5k6TTCO3u64Y67M7qzXtZxJGXzjoBPcJKKrC+Z6Q1eRFj0dCiaFWEwCCKnS7MjffizF46mxQATMTbLhyzdh5TH555+Vs32LwNu5+2SFNSKfHyXGoxpnpLKtsGjetr+D2jrl5SRIZ0P4uwlgkZ8dy2zH49/WP5tXg5rWWjOgU1gmKEIvNDthyRcEs3K851sULsNMdy+sIm6y4jP1ZF0IvyU7dpfB3lpuGWDzU+lufEXvXwc1DNiiWuBYGR+PoOSmsZFEj9eKyPy4o3928/rjty73Di//ofj3//Y0Xmc0vCtfzn53re+z//+fza+utb9eYlhpXCDhQ179QDT4JWQ8qmI4Q2oYG1e+g6dvYMnNrLGIGrTOF5a600297aP7SaN18qJZBbNypm6zS9DgNnh4Bl8CeyGKPd1uT3IPxfd+OxD+wnfqKpU+pWC60gNLC4JLDBugoyx4+X1+DU8Dn2owzHxDcTm4w9fdv0k0Sdc3Q8KIdKjleyz7OH5kDWQmcW7RYHNhpvA0FFDHBiXe5rXdeq8saBAsqfV8MRp0JQriAjYeaOHbskOF556lBKxjY0uNO3MbhVxS9ruRl3DTLXpCbzeN/isbdndlaDHTk3lyi1xna0sWpX2KFmB9/uXYgwO0VGnrc+BIdGC/qPjk4P052DNBWBqQpWRGZovNIEcuo9USFDs+tnuodmAwy1ogRcLPNtwE3VGLNfd1KjBDIVEZ1dz722Dy16hcFAehN9K/UuLlO2UkntlZbgLb2kVpubnTUjvSp0foXfs33xlv62XDlwNCAhqVQAiwtTb5KCdrq3SlYLwagLcQL2jpR8dF6sU1K856X1lOqDdnEGJDWTgs3kjE5Ce3mHi1SXO1LrJ9vTK3wtj0lmZNWiJobVBBQshKUKijl96Sn1GNjMpk0NmVOSmbkSgMkilVlIKIxMws3segvz9HbS5+7eQaA5y+1p/kWfj0Th68DZjRXiOCaH0WDHdddVpC2+K0durWLljBQa+nWr/tq3tLOO4XzwsMkwZr66Fl+WuB83rTp/+Mrz6sEVDCqA5Z+4+HzeWT1aaqEWzFtFZERLzVlTgXr3mfHxcCJ7RoUgRd8sAFTbPVMWsdvMrUVy74hUyBRbGQDMeC1vI1OQZ6VEgwpI2jvwauP9V7uFNxCvYs3aJHozQf+aKuJtD9S+lHpdx8ROuPMyZ+cFRSzhvDVe+2o2UHHGlYiJ8+cDem/03hmoi70xhqp8zsXrN95vt+zw4d/1qUxFbf3qekud6kpt25y0OaVreqislD5m33IWtGz9YY0xlcldg2wBdS9aazSeQqk5JThZYKKPo4w+DH2/KexCqWJDWERaPgMpPAxGXG6PZcvSDG6oBoB361fnhP13LJ+OIbZbrLsulfMEdqjEO/eWRjSmbiQCO5oN9NpVBKSJNnAChDqbvednV2Ri6IN6RoO2mqnwFO5UgJG8Qzv1vSOzpUZL3d/ZyisZaVC3pDZU/kpR6inZJENJ2ugt1gvysMcnTD5epR/b8OtOqKeZRTBnf/ZCvllwvc1SITsan/PUzHCNKm1LyFQn17zrxC/ldnpT/euBfAvXCoZ4XVLHIDoKE4V110sbDxC6g+ktcVIpiLUxPS//7Vy3tI01xw2KyMwC5c3JHw/748P+7Yf/j78/vCYnCfeOX8987gT83z98LY+UzwSMz+vasdzwHz+vgnP/t//ja6cutw+z0cR4PaB+TFJkiFhul9vyy4mQvu+M2jCCCH9cufcir2URmw6jC/yONLfL+fef17///ZPG6t125+X81cxlENMH8ba4Qq9bmZHZfHLC3JQFSQgAjSGzlK8yhGgm8zxPTcviPCCcsKuM7YvremCv3jI97gPVZdM/6ceX0lbXwABINTmFc1TrwsCWtiTZRa8KVWo7VMrHKa6+uQgA51wTo9SDDn1/dz1j608iU68yI89IgSHLA6gibo4nN8LM1jIrv1wCgvRMZSKJ551sjCvNuAxrtVwgyTIpsCqeqT2jsMiIpJOzgVtcvieugNPqXiFMQBc9SpcGDQcxawY6QMmPIWhNmrnPIguJxmzoYmADwM2RhSBlp61DBTtLuJUQGgMFxiS8LNV4w4r6Wy2xHWShnRMgVewo5b63zPxay1jSWGY+oR1AuBeka6y92VVfvreHmHfXPBJSmIOEEgl5QaNO08yeA2DQVtsK72G7J/hzMqeRfcxu19JL1Kn22VCX3r3M7/reHaGUqR0z1sDByVGArvONO2g73UaPeq1wXYhX055NumHoJpdO/t/3vCbTbQ9cNdx661pTA5fZViZslSJ4SorIvCPW9uVmhLMquMVzryI9d7Y2ea39MrnRHcs67bROldF9UmpwimdQxHFGnWBCGhn4ObKTeDYJfUeau7341+WkqrLVFfrKOz8uu5z3jnvr+rjcnKQrv+9txOVc5v/+0//2w0T48k+vmA6hXMv//d8+qgh3hzIzsrrstQPPO5931rCW3oHEdfGxfHmh8+fS6rkdBp9G5pMSonMAGrADO/LrKbcow5yJUA9cc8PD8fHgx/VqIJ3chziPvXPfV9MDTnjcq/86WSf+OPtFvQGHJvy6P/3+p6SSI7C3l9WtsgQR51Xnh62HcdjLbNqXpk+ropFOSztlezff825DiwT+tUOtHn0H4jFvldltGiPs8YJg0GkgklCDCQWsDrdqoiyI3df29iilZl2YAvRlUObMhLKTmhaaUx6vZfz7QHayudzZUTsvM4IRkSNDGUWDnFTjPWN5WxscoKcswzoPiiCRJiMZSZboAxAZdVuD4Kmn9vRuaceXwzwbatJsElNN1Buts5JwjoEUTmem1BhQeSQ6z0YZLGFquOyycDee6dznYIydhnTSV86s0NaX3auN9lv4bbAj9TsPcdjyUJba5pzkmuFudDN3mnk/z0NI7oi0W/onS3ztqEy6aXlBL+LUfSvHW+vlLFkvT0gJ+NFXKtNy1djsGKUPSx+HzDfLoszoQb/JZrJhV2tGbe6UpGim2dja9zNf9rLHsk5dD8TIChJKcZCpMVp8JRjVvc6+l+73ru1xTEmqJhjXdqujhmZHAy7tzb0rxsiXwEgPKpJeV9rQBUY355UB11+rjl4c1GoSyRQs36ZRTmQLlYhUpxpVOz3mJo3F7s5MRJz7blqEtX2pgyez183WZ3K6hou3hVn82cv1E01iJumga4cL3HMrUdQh43Ut97Uu//6+fz0zMiKtT0qBoaldSQx4fGz2RAtV/A0133kSzzOHuc3GFC0x9jSNDDEjOwcWbymltR7xfWMy3iNJo1mJGDWJ9+ylEpXRWIFGfyFSRhvndDbqv6ac4wneTOH7CWxr8kpegClTjl8D9Pb3+QVUmVRxMN8XBtT5r4TscSbk2RHz+cJoZ/a/2txocI964yFUYwZN1YrAqq0kEzUlkTJwR+oFCXXXd5kBvslRvN8Gh4/iPbnOjFle5rnTJ0labsv542HXsu87//Zz1dMzIjNR8rBuUoMjmbq3ShJ1EVn0AnI5d6TPdlurtdGtmi7ZsW5l/BPaveA7m5vp6yqvWYLxRnMX+H1HddSed5AUJfyP134ZlkQfiGqHVgn0WoM7lRHtqV6x7it6J72VK7SFAO49WVlNEiQ7FpgbCWh5h7yd2nSCg4mD4eSWFkFiD/qTWU03Vcd92a5enbdN/n4A3sJ7/N4jL2AQTk6U1d8qOFwv/POcj9ro6jCRBGq2YX/474DDZIwncfztZFYIooEG5jo687I+rbT3BKAwgF7zGTOHoYXNRYDR8yXfuBuVWLzCggRwLVtmTn5e+Lef9vPHeiz7jvCmlQiGj8da1xWJiCgt87Ua/XK3H5+EtKXI/B9/uyL0z6/dfhnw5Wjb3rvAjT8+qvF+u/OPH4+/fV4knnd83/lff92/npWtYy0H8X//X/9Yru8dBlzXStn//l+/bC3E89/+9vHxee0dlgCZmR8XlYhM665BzLK0JlbRfwpHbo9TClN1HAw+xQhI99Zy+5jeGZQwzcCzU4llMTpTcG+/Uh+Ewmj6YR8FJHBgD+lIjMPNpJa+qn1wkro6XHY2IuikOTJrhlEDT1Voyz6zADk4aV/z4DsdK7aFfeWVr935cg4aD9wvqLTBm0QzuPwx3VUZjag5sJ1A0GBuCExokkb7vu/p2tRjcTmL+zNV1HlwJGAQK1TehQgD31GKxQqxZkSnWvZcQ64KyMuLs1WxokC6gk5EsTEOGFiEu7fHVfeYxzPbm75jGQDLM0q0vF0Ttse6Uv04CjAZc91YEceqdJY1qOVaptQefmXhR1oM6WOt/8uPz733X8+8I67lGoGeAvG30szITshOzIkS3NdEPSy+VoryZQ1D17TSHKtVAR5R8ZvG6p7Mqe9UIIaMidf9YNxYLV//+I17cl6OKSieIBNj8Y5NfOGU7Wv6xXph4LMDeWa0tdGdqh5WxZ8TrI+aQv+FI9FlVsAPJk86E2a4M0mzFpsRz0xGSMqdUwM1boO39ipdyihlBqTyxgRLaP/b3vCtdcBY886BllBoqNqmZFqt7pGqYUak3DjJAdu0pYzYSaMey5R5R4wLPDN8u1kexlVTAD6IErVUc20We4+44ceHg5bVFOhdNqoDQnMqH0tmeG4DFLtTLZIfD6uQbPUsYDyuBss4IfdJgPsJnTaIIVeOaXqFudkSYB0yuhGi12GizS3IrR3l+NTJgea8D/hTZ4Nv0AHmVR22viKNDh01ZC9gMM6G9V/vKU1r0PhonGDjRNZv4blOSHJqBzq1qGxcoVh63dTTcOHBPuaN9FqwMV6ofGQtc7e9o/Q9MiOyEpkO7KNkSUabXcOdDEDWJNDqEy9rFplO8xkWpONa2KlJBYN+CpzFqTnxAUof7ZVutwUc51SfR1R01lkxgJJCK+YEkCnGzlnqIaqP6aiTX8Ewp78YwGpuVg3ARktJgXCrBul6M5mluRdBAsyz1rXIdcV5urOguluharQyI0LuLVxUeQJfCEUfALUIhcqpnOC0fzqMkTY8DX1lZncJvDaTZj+VL5zdZkBP/VNODHp2NcjR0RleNKr/99DOZ2/PNGka0f8jSHg1cRa0rJkt1dHECSoAFMuMqTSk97BJlgmDal8W4742UiV0ttxV/YfWZlQAlAB3ZAVRzQm2mTmKrPWsim/Dq+h+wojsPo7s/sPTXqTXwrwnwHNMJ3yyWW5zSMzm5/Q7cLI3zMFQE6k4+BynpK9iDZnhYARsH3isB4EatGFbuzxqnZU3b9tp+Dn/bweSZ6+OZTjGry4AUrAV12yiPi8wkbSKlI5Trm1iwDIQqVZcrvXMimx6UKg6f+vIoIzidF3liDfwCCGdJexu6rqAWpbsAPHFnOz7scXrukpM3n2ZG+3akbYTsDu6PduI5TTQZyBFPV/r2XX9fi0jNZzMXsmxKPn+jwr4nnc7Y206dhu+ep+MfJLsYXNv+6nTeaDggzo19qJOxnmK1XJkJGg1PY4cJGXcGiZR/d1bnesfC/tmFM8/3n/w/kvHOAw6c37Azob7cE/kVlnmRJ1jyTF61yTQ6p+vXajh7eG8B9BEnzkdKCAjZ7qHsdns9VlrjcZB9XoAZsjsKF9EsaYamhhbxnGPKSzXveGGx7KUlvNj+Y7sxE9J6TIRKjwih5/YzqxwZMCsClCMrOfvuHMZBHx3f4GW+eOy7Miye1U4lzex+EHPehvOC1rbxQh/LEhmbm6Z+pn69bV/PXcBiZm6M3fgjknbJpjvuS46RqL3QxWHTvVgFMPrLL49YnQQVBSUHdoZgmi2ahYYQfA7ws2WW3R4V+8OVaGJidTDaERAl9XLtMBAGriA5+i14ZU+dA/Za2rD+dmQTXvn9hyJf43pZvu/n5nf/++dm1TJCJv6gQpK1PHE+ZX3Mw2+gz6HyVxZxbkQTXZe/wIB+isV5ZxPztWYU6/uUhZuD8lHKxil/8pmBrnT3fYuvLj7BT7sWsYd+lj8ceFa+vFRtpLX6qKOkWW1FBnZIxfc7WSI5WeLKrrcHxf+8Tf/+x8Xgf/667nDlzdw4c5S8Xws/vxc1/LagWa23My4lv3xA//j3z5/fd+1FTN1XY//+LcPNzx3AOZuRvyPf3x+fe+Ih7vF3gWyZyIiMvLyUqLOCdgnMJtsIaXMYV7UHizrYDLADctbXx+AuTVvrVqMX2Fe95f15jnvJknlshUNePQWfDPFjRGMt5uUuTPsN6s7mSLmwOKVH9Wn6+wovjV/NeDzUoc92/616/EKXJtKNzhkb2PORat7SEnCvPgEZROaadTvcYrSQLYxLBc5dTizqnlm3l5a432osBbNXYIpEwzJT05lVGRkMT65O4dEzXpPGVDRxSt6r+QklUw5R0mrDHzCBBmL20lxODiqKJNgi4wm4XRZw4Tq6JQc+gxYCpShNBjARF7GZfxY9vHobted+O/v+PVd8z+0IyR5qY9WLV9yr3nqHZ7RmrxVzrIivWvx//w3+7/9j89Q/nrqn1/xf/y5n7vgL6vI34s8aLysEDEAZZxfWcEkJBJlNThJylJbc0ZFwNaGWTbm3sRRXqlrfJUHj/F7M0kCunzDce6cHf9uIPHa4TjI06tWM+/dRJwOdQv4zolsbQ5N9dBd4FEUKBBvsdhSejjvzAAWsBMifi4L4E5FzzljokYnsi9WOYYZz0g3XebZn3runKOOjpAyZd31gayOPCLK3HmzpzgCb6Q9o2/NJg6pNMrGMamRONiwhPDmsp+IevFjWem6rkVJO7ET7tx37PsuYwta5fZGXKubWd0LvRHQCrWSqpHiMq6rLrYafxQpd0hFBaDNqDJKvkyZj4LTl30+/NdzP3fWVMc7BOnzQZJVYW0E6Wyi1kebnHq0YDpmPXYcHe4WGpDFFCSX17RKjMaQirNc4V/jXLWi503OZ58LqX0tvQ77JEo993DSjdnkve/rK451nuPwL4G1JuTg8QxzwuZ8vK/IMcTn75MlNKWLXnJ+dSrB6oYqZHK0j/unNlnetZzEDu3Yk4dTWcW/TsPuyNh9Ldeiu5kvFlnJltTizZUrKdPcym82h2/SN59xQDpR66sxqCkLHd22Gyn71Az8yBN/ZT8Ms9I1M3DmrfeK9qBJ4yZ7QPQwfaqqfgzKSWjOs1ko7sDMLpXUIo6oim6kkjRLsrRL6o5ezItGYapv6ND3EmwqJk09n4WRLZsfOYSXCb8zuzYFVsytzOoOOPtHzYkb0lB/aGY0anU20eRZZoqADlCCyqezk3MHfzsKb9nXKypVM5wJvLGDymFOrllw7slay+RXIahDngM1vMFkZA0OBZAd1OTLwNZDTKW165VBNQ4ws6x6P/4InRCkbiVODKMT/ff/5s2LLtRZqqpFNhsk+tevOaPAgBD9nbJ+baYB1AMcCI8H9+J50ACISKBFizvCDw3/fO69PkJC8Ws6YIAa7Xs3iDgOHixljBwTMq6ZQyN8WZhJPMrGZIvUtggUm89FX+bmmSzRr7eoVlMK7eJXUas5U9LPx3VXaZuyYaFr7v0NcrY5UG+fM+FmntttJzG4Wu9SMw74aG0SjACqdH2tHmUJiNOJRWj1jIyGhGsmd0Owg02UWa2HknNCu/XAuhZibpT23hJK4Swy6tPb7IMZuaPaNXRsktHMW6voPNMMHWnA2u3L3X1VMFXWZnbpm5M8tVOAZ54EAJxG27et3Zel39/lt5R3dutvn/T63lj28c9v1wI0zQA2fJwGHTTNTZj65Jyo/vQ5ZHgrynKHIhGJHdjZFGuwy3UylvBwKUA8Litb+qIuzl3mOAUb65BZAs9ElZuLAgoo00pYIe4MVUl7B938Wnbv++QD5ThxYMo5XWZYwI8PGi0SZnbfmYIvA7FqikumADdcTr6Z0zq5eOVjPP8sMLirhRWXdM3QASyXm31c9vn02lHfz7gjowVT8TJHRPxm5t82BXD25wQSnaC8vaB/oYr2icyQyWQI5I7q9mdN/rrMc6TnU2kCqVUDbEiYaC0ptYz71mVGKMSSPqmco2tStWO6l/S1Ecsm51m5Nr59EnBqdf0Oc7MnEjzp9CF2nvdB/4qsjVcVatG++DcM6LcU51wGpwWiHq7ptARNmN9IHKMWWXQe9smQGECMhI6qcVVFK/4w+7z4cZmUf//5+c+v++vOOyC4ExFVlYG19Bb/+PDPhz13Ppx//FgR6W5r+VoFMLdNc0MmAqDBq8cnoapdjzdqoCnTl/3t5+NyA/jx4NczCKy3kRdkzRSvfd5M4dabpqfSzT4+PmszNi+u7Lp1SaliyI+PFRvfz/v7uWPMJsqhGNx7ClU93OwiWfUqdl7Rz758KytSM05hk0AJWLNCPCPA1dw3gVhubicj6MOjkq5sq10W8XWyeOjxeEtn5ndziNL1JmMCAQxhUUBFvacFu8vjE4nN9qji4slyThDAFxv/9Wc7aY1TPDt07EGtwEHrzi3XD7N1Z8/X6dxRKjsSrNl+jRSRNB9F2GVc6yKZ2e2ibUvFEKbwDDO7XHeUaCMiUoASe5ciWZkwEbzM/viwj8tB7eCfX6kC6DuoyMtbizwrW6MMlq20QEGRedU00DmoTIVai6dIRr3eNE1TSik9fy7749P//rl+fPpjde0sQ/9x5z+/8p9f8Z9/7VRejs+LKT5DEenmICOToKdCoalAVyXJaR8L//hp//jDd6av9ccP/PGxHo7/1//x3MmYaiNUVLJUo2AFwbfsYF0zqmJaI73KKlZuhnEKa7C8fmTFNFzqLrrfUfhJqljx09s+nLDzJBSj+/WWtZWpffM58wKOCwWA0qN9VcLr/fyFa7bfrOKRE3e8eiExExW3dBkvIyWDHmDQPhaN+BW2Uzsh8QEKyG7AQFnCTN2RFy1Se8LQVxf2OBkAmbqWE9x30q147tmHG9WgDbAyQ5Ra1eQfrJnxtcQdWcwZqISr9rnR2Ho36FUnhB0i8+Phj7UmyoI7JX4/K8j0kIg0+jyufjxGwnvR63kUlOBOGk494LSoYeJy9eY5BQoo5cxIuNkfD17GSLjx6xbJz4e3bsPAAa9Gi7GVnYwJys6II9Gpcf2o8qxZRoKFukOswREHSUmXUgFeE0D1dpuGif4v3pxxSet2W5kE9gOPzSIvz46rNzxJXW1CTkOeTpDx+5fwFi4cazyA04Al56d6MfzPRxXABQi8zKW8n1FpS0RGIJEGxtSSa2GthZNN2uVaJ4VnK5V0rwZ3KHZKoxiweQc+PrAjafboMR1qjpVkyky4O8gdUVdeqFOBDOWOa8NKyFRllkVeMdZheEM3OuClkBF9eEZHXI06FkFuUv4TxkIsWeRJOiApicmsObbrNzhoAZ2TsVUnJMDUl07WRWaym8HbA9hE6uocdWe+p3e1kVlrq4qciCqZVr6NVuppPLtDbVkjIH1AInmc8ltW/OL+VDv0KyJ/ba2KCSyjpooDQyHytQ4moTatEwTXNzM75q/bsyLvTVD7ShNZ404Oi6F+4SD0ytcl8xBdeoDANHtIEk1Gi3EuAkaxRc2BruWIvYu3rWouquwAQM+Neh07BXcme8Zceyq3WtaSm8GppZYcT6rp4q8oDWPgOlgqS8e2G2OiBTiRTSzuex+GSC8ikuVEa3FIOEhiNx1MUs/ayFGZwByOElLV4HeRJ63GubvXbep/Smvq7nUs/IGE57dO5s8C1N2XDyu1RHCahVHbJiNFmHVXV6UqRdG5mtUqpaLhxlkRvCx4XV0RiHBUtMhO/l9PcQL3OWjj9iY5gupUulc+UUJVNLdU0f1r8jFiBwjCsiuTfWCo8vRo+98fNCyJwwiazVbT64cH2060CGTmtnpCXhJMaZeCmFHSM1S41L7vQn7vyNI6H9ZYt0lCpaFQDOJc5ZAkDLsQqH6HOK+XJKKDgxou1SOQ+lBXW7h+GwaJWdJ5QJNK98r8a07bAVinYuzdr8ki8Pr/zmig/x9df7QkSZIjCYLMEFEz98isqp6e2aUjWrr//7Yj2p3unqrMCDdVAd8DA6LmOXeeVREe7mZqqiIQgAEwgCxJ3jS48hH8Hm5tccc/YDy1y77qTSTmQBA481qVotjNMuQBK6xezuqpEO8K4e1JjPPcwReBXKsiyyCfxxh0m54kmZk8YoymYTcGuLVHiwYI7gEfwrVS0ByRU7jyORGocYeQZ76kMsbgLK+4V/WW/lvTNgAuNFzRnyrI8MsSmYwxZ7j0jEG4U8ZyaqGCNTBx767Jrc+qkQ8dl3VPaZJOS0QMu0IBcHN5etPXykxgRCKvrECbnfJnGCRUZ58j8PnAiKj2SeBSlcAcg8/BQSHzle4tXyQCqYIf5YGArhrdmyqJgWAhNraH0YLXr24r+S4RLPls/0JvwXK3x93IK99U5Q7x9EF4E7RvQrfd+/amWZGGTe1WXdb/BSPhERtvqXVfyXcZATe3Gojn5yNf//63A8jr0j/+9vz4GH/+PP/zzytT/+PvP/74+evrrBJdgQ53jsHfPw+Xbc9pYywyRskigkw5n7zS9t9hlNSYdfiddLMKiODjGGQI+p3zmBXneZ1rJRbgDoH7EJGcc4wx4m78iWruJs05yrvOHGNOB4DsrkR8ZaJCVWAxFVp7lXNYHQ3sbultb7h3Srd/2kqLc8aMloLOqEky09zZxBHjOKxL3ZCpw7NNHE6nvpyPLetZALQSad3yf4uNuv+jqugVAU94UI0HN1kmdvvBetr9djQGiNutKszT7CC2o4H9+M3aKdBoZd5pGFOA0crcEoiNjohwCMjvfYsggSlXphyeQpvZugvHnOUGRHu4wFq7DSay72fUbmUqr1Uu+kqdwmG3PK+IcF7qmOMx8/MZPz5mt7cSGCtxXinxpYDWdNlGSAspTGKJWR6VkkH1gFivWbBrdLZ18hlODgLhZOo8OI94HOP5MY/nhFMTpJAfY8Rcrmp6Jf9f/zj+/bf5Wvr50v/9X19SfjxGBK8rX1euxJKmz1WQwGPg9w/87cc4jonqkQQRf/9xHIP//Ln+5x9pSHNeOchV60yZ/ymAmJMrizcglIR0qqjqpFBzThBZg+caPggLEDnISa1GEO0LbCVXqG8fNCuAVm82XJbZO/N4u+S3jkPXB/raHR7tD+hTPFqxRkeXrChn8Oq+CQW0hYHIxMdRPZNmuGkVGBj+MzWCRwDCK+naohriDowTI+D2dlYgDtEIFHOpSmkIrEyD5kuawohy1sGK+b97ccwEoyGOVhYnl0WgukGw18dKJoJSYQYgc8ETKuaIj8d8HKPrxUSCEb//xrXSDc6uUZ6gekm3VgSK3zrIxzEzM7hzqfcr5Xg5BHCp0Aja4XXjtyCMZweFUDB+PChgRK0EitSsBi/tY3VALNvWZN4tQBtJ1tlgDRbIe60qQU4SS/G6HEWNc+UcbTytft9z2a1Dix8KXIzRfR+qmidQ5UDWp77fuzTjxsyW1tFNRbO61O3SnVu4GyLvoR93KYk602yvOioc0PbKAerAebbr726YC26R2WSQG5MoWLUpBE9MsxDDv3EkLlGkmxgjcqVdBniK0fUiRK281nHEHGPOztAIANJdP9jdGpt5pIUIN9ZUD5KmMlfX913Xul2Jty1B29vVvT5IJiAmxZpc+Qa96gtt8jrMtuNcb34ne5/sa2JKijcyVbuA9kpJ3kMqqiKHtSvxzQtStxhD55baO2hHPSm5eIzo+iZ0pKhe1PADPRz5ra8z9tV8SLJjQDuQfL9ZrT4EMCIEux5u1awbvfb9bZkEIrCUyHZI7rgPtlYiWyDLZxEJW5lse7+RslFsS8veKOx7lqRcld70UTbLq2o7tbWUhLWsBOtnOzZxpXq7CcgMAC0C1ZzYmjHTmqbilCzOV9M+3g7hdhZKXbUP+w3rl4/goF7aibwP8JuBVM8k3qvtKNi17qtlkzjk/o5tUIXOxe/bc8Rk/1bvstHrvK+7ASLaY2lDGm0IDDocJy7njOXj3SQ69zhYK22HskTEJalR2tFzlLoCU32wuQWzZUp1Qp0I9mUBveWF6nmaTl8/YQVO65bijXq23bNMSauhdnkDWhKWUMSPTej+1oWprDNINwMChETHobKMceyD02dzOLEGOuJl+EsoQ2vp68wlBPV6vVbWGAjXrhof5xuDzxWRr/MFaY5QjKvpVy0FlLaNVOkRMog5MCQzB2nr3SakDtatZbfKvcVkr2CP0urDxz7oqNpKC7zyvsBtbyyb2/Gq2MjW8naC2JGrfdLkCbYGV+ogunM/7DOV0nI3qqielF0gWIHGtScb1of3DbBCh1HRlmoFwEAAM5w9CDJmVD2aBNzVHmxd0de91893+q70NCrZjedjPh+gdK7xurSuZJi0zCZEVETgL9EfXyrq/7cv1m9pwFyTZeXefefl4xlaN03QlmJbhfQRuOE29mOpkvbykfa7Rrz1vPNp7vehDkWMg5dSKfNIUnplBmju3wQDitDfnvHjwRO4srTQMSxLEOMr5SlYl0DRnC7rw/hu224LD26uWLCarHQfie2mlAvtI7SvxDfFgg7Sm2LUq46k4JJkShQH2AXOtyTsmE7Fjt/he20lK7/idr1ovdEAMqq1XBm+Kp/VAo/RPYilKxXV4QUzcASmrn/8fvz99+eVus7z6/WK4MeDHy87Dq/nBIvUGTNIKmFVz+fhrhYlEobLVgPDNBj5jTivhSATay3wBj90YyY7Ad1/x8Gg/fYrdV1KT/ZByJ2evF+7/XmnCKVAQ8u7SrNJKHLN4xhzLB3mbnsInbBp0e7A4nORUFjTN71ae1ZXIxlsLScSHkFMAAMbFXjjGLxemToTM7ows0vvfP43LJId6O12gCDNbIpSfq04fEo3iE2FW5lMjHUVRfl21XrZ/Y7oc1gY0dQwoo+vH6qHM+zIbYFs3erqzRDsJKgyN8TNzGuBHF6capPQOMMunW9Pjd9TypqcUDtoEz0jHkdEQKnzWilltW/DTlAlHMrKa62Vt484gm5mUgpZnhvNx8Dz4ONxcMQIHZiPx1ymygpr6etcI+a5Mr+WYhCZyauyhm6TzAqmR7lGNUvEHmvHp+Qq9w7ZG/nMwX//+/P3Z8yBx+BeX464Mq+VY/Afv80g//ZjPJ5zSnOcAUrx4+OYY6yVryv/+MqfXyszjxkj8LryGPzt85hzVG2myzBTOeP33z7+D+l//Pk6z3yd6z/+vF5nPjgMvkbEAiFFOZlAxHmpzKpDz+UTlYgGbUywEtU5ONycho49lX9POeJi+U1VkxoLEN808jv6xf7dX+zczjh1sLFe3qigQUNRMr20wzRzS590re0fk6pIE99qKu3WXeU4cAnPwAW+kitToBM8vsgRmrq9AwA8gswRXMmVeSWzHDzNESPDtc+Tdd7tPMvDfjqcWm0mNvwqPL2ielKxm7SVcXpbDm011bkEVnUaAOh15RxcmdMDJQ4zVQpigWJwgE4LDWdG75gF9sdQ9d9jcu3+5ijLhm6PE51afMMh26MztAMZA8LAbB/X7jOG91NQdeI3s6/WmRxRwcoSI6e7WkU78H8MVGcfWdMUrpkB7+MYHKEIjxLvq3coAUCYoW6d32sQVZjmHzTtYmwQG1uWrSdWbrDB6NDqkqJ4Q6aNKIUqb73l3oKmFDx4QHce17vqNrjl284Z484+EuZjKi8H89zhyyOhi+udqybC3seP3fRgRFyJuRSjXCc4joi4VuY65xzDgU8S0PM5IypmBCgijmOGE9fuOtbhh4h0dJHgWsuNM9apCF6X8SlDMakqJ/ES3FKG9sZrX3ZvhwU4J8y+YScYUK1j2v8tC7pdzy3cUFvhslj9A7kEbHnoUZtANjNBFdeoDr67mROtgIoYB+2ePi3c1R1vx6iKCV7Vxw1LW2Xab0mg+Ko71gDSXbD2cS0l0encDRr8PN2j4B3gqJrkjT4x6LRoc3PfoWh9jTHMhvHtCs1MRnOl2Apos8USznuxyUN2sIW9Mvcz6P6n1A6/BDJ9wvpdDgNVMLJwHjbgQnmh+1G7lrv32YabUs+j71/kbQrR8OtOiW+/1JHCPVyqoUddK/bHtCX1MmbiWgm5sZbKIIpbadc1l+zXB+vCarTXGcStuzZCvIV8hwz6N3gTe0lvEvAm23ozssCt5YviHfcE03uz+gnrcHWRp9c/+lfWF45H5E6H6v5stSAU1tjat8JeFVnLb7cufbtCfZM1D+vtZGxBMsw0mV4dHa9AQq9Otb7uOrREdueIunIvDpyJXVXz1Y/hgyyQcKfn1gMO/xGYx4jgmim4Neb165XXtQg9D9MUqVBmhYGKuUCSWOvKSxDGiBXIc3nP9nm3ot8PvzWNaR3nBQ8lnZNsV23TIMsmVcF/n+TtQtTJjQb6rY/lodsMJUuQPe5RQB1P611P6nX3gSLYu8zh9jTe5aE+fFtnnz6TVvpuW97caGlVZg9L6Y7LDq+ny7A7Cnxrwv1pVbvj6FgEpXxdJkeEKztADpeAGcZ0pnAfQ2OO96jBvrfej/KExsZNAD3vecwxch0RG323Tee+x9pM70iTk7d9rOhp4+JmVKs+M2o1wKCyBHLH/Lr6EsUMrOPYOqQxPbbex7uLUHVMIbg5sZWwBIwRIlMaezshkg/yAi5qQkdgRn4c8XzEBVwm91KBDGAJgbjOVbTh8mHVzrTQiUYrRedFuAtFOsKZu5FAthvWTkMvL2vBDKxVvvJdb9UcRGz0DNaONw6vje9WGGr92i6HP297vlsCC8Er/EEG/bZaGYOBGIlgzgCHW1cU5HUHnNel7Oc+AmPgMfG3T3w+41orIo7DTRwyBv/7Pz6UutaKB5+PQ+B5meaDydqhBI7A85jsQCQKKXpYlZYwysNxCMYF7JK6bKmXXdIiV6ZhzZWAG40TERigUucSkZMEEKNUq21fLhqB2QrmWiB11YhGKVbWKOpKC43pbrlQZ8icUQPdGYqofpEYJm3W3uU2KL1X23YLOC+TbipZ5VE1mTivPJeupTOVl17Xecxw6GryLXNbpdmqmLxRUWeeRoQpT+xmTJJSGJDemty3NQ+Bx9C11nktH+mSbZUhu9y+0Pz4HgjiOgV7nt7QiKJDxs10VmuMdskLo37Xl9r6CBBcpu2kiMFHoVwRb60VLfJ+V5fjVAsCAmtJuXhEyOqKLosWI/e+SBIuAdIcYw68LhE6EyCe4CWAfIzpSPrnEb99xuMYj2OCgqa1GpkcE0BKnx8DiNe5ZoAc51q58rX4xysD8mzHQCJixBhUkCvzCK7UzyuHM3zeOEGQJ+UGheAIKNdxxPMYJMh7WpZzyMfA8YzHjGOG01wr83ClWPBSLiWwjhAevDLmHB9HfEokPz/mMV0ECR+HZA6Epyj9t2P++ev1el2fn/OfP6+vr/w685WD0Bgh6b99Dkl/fp3LDWnEa2U1HyCeI0wHXakYTDCXXGAlMiGMBqML6CjeO+4qKNdxi3ZRu2vEW1uDN6R22zN8/2GXaNrA1/BCoDLr61a+6jZJNtXsiss2lx5FpFL5NuevpSmQvJa+loJxHHGMCGgGYhDSa+laZQ3DldooR8+jXTLjdeXXyiWMwECsXEdnkzYgotJ0vYjqjaDukYDC+VXgo0yLkhrxV66gFqDOJDtD5MKUS9pR+JUQqK/1mPF4jDEYY7Aaj+QbfCoMFm36v8MNf1Pw1emrG7+g1MXtYthWlEf27qDWenNfsVXIqMRJfVa4I+OSquyx3p7VQB0rqymH7e922K6VmYgR1SUd8kE7l7N5YqVJ4vQcj2b773vc8ljVne/PXwLW3u8Gkg5JNTndz/FaGJ7sODhYTXl+fV2k2SvleF6ZR/CYjntIwJVaC19Xvq6so1MNv6rSzszKIrOvPHZb/bqLxYpa3FW1WZ1Gtc3Q9vbZZj0CqVypFRyLMdzz1N3xLntJ13XOGY/HnHU/YL2ZBOccNC++eBSu1TNjA6wJmEplnoqq1QFJ14okdJ1dj18tpCvY+r4TaO+ATgYYApq8mbeLH9UEsv1ilK/YPygYFwE5m7hJiG+fNLGFqIS63Fm9VaOoMnDbeXH3jttDgHbCtX3vzta+Ra6rIrSVZMVQ/aS3B/8mrR0Jul0mC2PeD71z8PUMOyLARhiqAY71DKiP265A34Y6dyOfz9hRSRLE3aS0Tg4kICBlJO1v3wSCBpRbOfS57yfyjWRhUWwPil2qBKubzCSicF7duUrv15/ZE7i222aXgZ1suBYiFR7qCa07RCi1l1u4toMtewvuMFsrYoeHI3bBMlAN+ODKurXMjd4OB6oB8tsXq/SvXA68fS7hwtb6ONLIsjNz8jDsfV/Q/37T2mru9k/eXZFS1Ntosn5Se1y5y+/rkN28qUUouwmfL5byXNS6z1ui+oj6sXeDJ1QhJLWPH9/utu9534P6ULWM3WcT7YGh3VzGzUWyctyJVgt49plmuW5sKuaOONg167D61eznXktDPQvapqENN8ggGKEY5MpMT7ucY13LrgVX6pWromV1bEtxeq3MOsyl1YO2vADmfK7ei3d5suVw7RguBEkXJRpStM6oWJgHKGRrwzo8ZA0xXdb7Wc3LQMbPcwE4rNI55hyHFpQk13VtB6CoP7VbuBKGzefaTpHsm10LM6iexJfVdVvZnjxqxNbefdCtu95phtixMwxbMu1BAA1W6UDYdnCYlgAPKXcvYmEQhGv3/Mi1vO9B2H14tw6z6sieZtBKDGB1OpTdwChRORDpWOI3ELylmPeJ3HtbSrdes5NfCY7GRmRzDNuMFAnRd1hP/aZkvp9wvVN62qRVTGTTAa3rqwrBaqLcIRNa10oJY4akywOcRSSJHGPM4RkqgPCcsTK/Xucc4alXV+ZaOSJMsouIHj1/n/deMsQeNJq2YAKrP12+weXdeqfMLdjmk2/X6sCO2JU53EawJiXc+2B5A4RAlET2JHiH4wwub5+nadF13QYvdhIJUGJKoQA/AnNyDhjWJ2iYaBL1WhrBEe5hX4uQwnktKTszFn1QahTXbr92Jc5rhWr0THavC3p6rpvCQhKulV/nel02Mqmm6R9zrKXzWp6o5UKKETVlSNJZgwx5rxVKIr3sy6um5lmDTViv15YSZqQS0pyTnfy0qLr1jFlvWcNKaH8xONxUK/bsGG4jYZ+EUU3fSuXeAKx5pit1lQOAa61jEsLXub5OZ3XhUrhzrRk5HDh+U1CutmioIFYjIbuCbxVwBgBEpADm7g9QklRPKmEOz03LVmhM6VpNIi72KLrIv1oj+zpBExsbg6gD7Xkj5eIBNk5p3H2rEW75TbmJcSNMFDgsrfENt0ruYEEfH3fZlRSDAF9Xrmh0GMzLMuY7tN2xbMaVGcAczKpuo6jR8hXBY+KY1YxPStaXCI4YDBf7a4zh7Z4zlLgyEvh95fFrfZ3rvARiRowRH8cI4nWegsbkWmJuGqjjjE4AIiUUqVyvS+eFOVwzzkZlQO/7Y3IMz6DHnPN4TL0ulBByBNcKhI5gJI4ZY/AROGY8jnkc3sIiDgxON/HJzF9fr9frvFLXlc+BX7qg/O0YAh5Tj2N+PAeQc+hyBPPSlVziIFP6fMQxaOdZwteqCQa1+urKkMwgEMy2L3fQoFsgl3yGd5olhATTZcPdAGhjd+tIbbVuZFSms/Q136Pw/b7ioasUNd7M5i2AZRNqD0rncpVY55U5Kzaz5qAGhwjg6zScI4QkX9Jjhp9uzshUsjy/s5rUvBAmR1RLrDGB1BzxcQy3yC3msjk4G+l1EMEGonggN5z23zdCqIWRWP6k/dkiesODSmdVlI8O0gV5LTXJ5O2M3kEPvf0D/WkV9un4G7Qt264aNe6qaZTafpfQTg3Ae0vae0LHhgIhN4gUwTnDVUQL5tujaZ2NhyqDmOxscbQxze31SecyPVyk3CMvblorqsmSNXM7klVMYhKjoTgAlaJeqREuK1b7BwW+lLwSGDgCFALMleeVZw4QE3hdzi9CiJycI0yQkXQtvZaUmBFVPk8osdzKRD2yo+nKOW8fWcVC2Gm/gmJSO7bv4Yw+NUBPzHCHERezC9ntddpBI4Mr9Xqd8ZgxI5WhyJUExgh3XjGoZbOaA2xOqEAa3yp1YXWoGXZoKZre7MBxOUobkaIjXH3X+0DYYiMD7qz8FljU/bWh77t3vMkrXk/swIC/JsrzvBEGqsSDpelovkAdRB/PrN7Q3DLat1KL3h9Ze+b1f+cQFbXPtrYir7od2fsSLZS9Jrba0s0BylRWXP8+xvtmYgOU0qKF27eYsBXwfr9EBjaya87r236UzlWSLqMmmUS8DYZuWL2zbG+rvmGG3qpSRLGLa7wpEIs5FbwrqphtXNC5B58gX4ZNgR6jlSckA0efBMmmLdHNd1MJdNBmwzD1i9tS1Q85IpR7vQxjiHAv62pBD8n+pHvuqUNp748AeIX8+L1lhISIghoyWM8dAMr19s+t0msF6Ruqe9bbg/T/t2LoW299Hc2W1JtI876xFrb9qpLGjv6022yZjzcJ3Dewj/j7QamIddUMdRAOt8Tvd7NTEGS8GxihZVMVtbUKcDh8q4idmN1P9rYsfo77hxKUWrncbaed0U2Qt8Npa9OnjJ5FYm1ej+57JoORoUgpV16nBx6bFlzRwnyPaS75vcxW/f0IWRGWWqdWlCSdqcq1pGMEmVrOZpruWnKu0gQpnEsA5mAEPDZrjqrCNEHX5jOl87yEx5x8vb4Afvz2MQfPn/+cgUytGtuXES6/kKOWAAbxui4Lf8qnW6Py6hrHuFbFhs7M6yqLt4/cdiNbbmrWBnFX4nivv0VTajfrxNHsO9rLZWV9bqJXqN2tiFitJba6us/YfgfaezE8rckeddtdn6wKrAkKDB9/l1JRCGSd9Ua7havv+I51gyk3/nd2TQqJIzimNvay3Nj9vlau6jiOa+m8umk4jY1uML11x07OdRawPqXs234pIeUIBnuQfASg15m/rrvMrz8OM/AR+JhxHHyMeBwcg5LOa12Zc04hPJ8+83oMjJFrKTiqCgx9Q/3U6KCOm7DSyKsMqEazV8CmPt06bD8WyOJTVFED6vyKewnaLUYVfOjek1ry1j+OIbvhQr3mLwTH/RnusG+sGaDLuxmeQVGVUGOwBoEFtfK8MhOpfF3LjIER+jysZxHEleISibXLXfcf5Vy4FXcthKXuGGMOPmc8jjFGkVOsac9r/fHr/DrzWnmtlU1aPmbQI87bRBbHsvoRVWB65cbrQnAlygMBBKxEVEYvIeVQtYiOnZ/0giWAMUYMjhjbgJjvcq0rdwijkP9GdPc//JaiWOrWrTf6K/sFYo9lwHBYLSHgWjpX8m2E/LYgNMsmcV0ZUTN6ADjl4NuKcO+wHbXcOYY6y6vTOLd3hUKHuRZqkAoex4S0Mk1QKivgnrLCuk73cgrgWunYkJHACM5Bd7f21ruyw+BF3T97S3WDvX3W+WaMVba+B+PewbVWDLk9QwMnRwIo+pSaTJ2KGQRc+b66r7YfbWv7aE0lIQaPObpiHtkgQhIDIziG5wne62dyltlqZEIc0+iY55KUXASZsX4IKVz2zAEGrryulY9jPBlKjeBjyCQFdDAUlb0zLa5qIl5XkjwmRviRiWrtJJI1ngJaa329zpKWFILhuVQIUkF8POKYVboyRzBc3ROzqsAI6Fr59brOM399nX9+2Ywzr/x4jH/8OB7H+HUuEh8PrlwAP58jE2vlv77WemEGj8GfX3muTMRmDQTwGLxWOiaFdK8kcXpeI+1CxS6i7ByG484FxEsGulC/Jr+iQr8bfVprY5tA3k6rUPT6kryKqd12SChSf1hA49uhVsURtV1jvDuVQmIyKKzrHEFgLPFcaPdZEI7BEZEAqWPEdVklV1hzhMapE7hcDhvRThvnGHPi3z7H337M6yqMIOnXK3++nPKjfWiVD1+xiH2U3oC6vGx+MO4F4P5DqSo4Whmvc/36olKP1O6JWTkzq69sRrM6qI9Wo2js+4Z+6qCz9dPukdo+VaZbhdWNG4Wqb//tmYAe9csgOUiupSqAyuWifuckGuHo7UpbDUndTxzIQJfAsiIbKv+dqBQFAJ1MlzQ9ph9dRHP/0fUBwDECKljhAOiVkvC3j65ORs+Fq7KcGHOstcJFKQuASr0AL612RXQcw5E4FhOfjyMeUytxpb4QewSQpEiZH8SqtNUMrh53jpLk8oA3+K9YnW7h+R7Uq4DgFjD5MAjO77oGYu85ESmtzEjnDwTAjdom/lIksqMrtHfUdcEdICj59sgRz0yUQwalPJr010f4jh/U6VfJBgEPiWVFnblLMso5Kkdy/+DNQWqRjsaU+2u+u6mFZ9Uea7XOUd+q1GylmqG7tsvhDLz6rvftQ0UyKMQiVDMhbso40bEzC+VbfWf7vk248c/SjME9DGjfYsWGbo8XI8Kj++qTyvnmiMrClXDcrIrs/rZvnk/74XVT73GCfvjMwnxvhxZ0xX37Zr2/2zFCkFCxvNxCebfNN8e+poQ5xlQS2vfBv4AQNeG08B6rnbJ21GFt6Wy0U/Uj0spvz9v65i6J9ye5/dDK5mW0A+auhg559gzCsPi5E0FH7frZAbJbBnkYyf501U4KJpNWxd8Oa77FN3uVUUGcG63djsCWpvvypQ3ULmzpqpvktkkQHU9xFdXNO5GKGmyx241S1DvSp681TccxqXvTWHF39R1L717/N1a5erErFYC3QywhkaVUstKAxU28L9ymwnqWbYQbyNzr77u5p629RZHy7ToJ8h4LmNIgqyGL00GZFuB9k/tGGJw2Blnmt2t50OS+3qSug73FtdRIK7f31aaYzHB+EhFVtbEHfhsE+I6uzOQg+QFNMFUzdKPc/t08CBOcMcZ55WWOn64//3lBI1ClPA7ElnG9j/i1MqAr8VIgr3Utuk3j8kgvvU7n5Fxh1zZj6+53cSVHRHb80YOYHbxlI0zL1PamsDW9CdhiII4aE1fuTQojkOK1FsQxMMJwt65W6pL8Zm6/HyinvqyM4w7DV6SqXq8bNe1LvwVSzWnn5tm5KbVDjOdZYnitvJZ1C5+HK9fKcKgsKVfKNI1cmanXua6Vd8qWZFeWtVFAIwdJbIkS2I201WCdfAx8POacYQ8zRjgMQR7/8Uf+58/zqjaE8mJ+jHhGfj74+RFzRKtMaDAxz4WVjtro9x/z88GIIpGel8dX8kz8PHWuipOLcm7MNZdV/mMtle5HU/GXViV3DTIIgZUFVHeJs1pKIOhKkK3qvUnRrHg2CYW8/Z+NTBnVeKIFryVwS9IGaSoewSQctP38cfy6MhEiX6smn53XMkS2+/4Y1ZztCLlF21UaFCv1umwr0XHXugFPVciUR305AhGMOeJ5xPMx5qiCxFxLwLXydWauvFaFG7bGv1LL3APCyZIIRyiom8ZTZ6+AoMx139ZTTj4ekyJW5rlqTrAH7mxPgq4RS0UC3QZUUrUdy93tsjbK5blBLMc43gpOt08Cdi4pi+6Wjbp806liLgS5avKmA1jfrAi6cLZn8EESyHC79F4Fh5iDHYpqsXzD4RB2Me92pa08N9JqrnFCchwq+xtJqtI9pQLq8T3brVuhObZKVDCIPU9HaD2wfUkov+s1NIdCGyqUmSO7/rRAQV+lXAsCqJp6U1CrfUMmrmuNweHPHVwrQe0mY2t3SZBW5hx8HpNkMCPAxBiwHDA4Bo+BY8QY4Y5PudxgXx5MYyNeFlkYMxi6Tnmo4KW86ujzxnvQ85hzDttJCWD3/itIBGk3T6zyzLV0XnkMrkV5/B6QubrZRe+4lKlzGS2+le4qHf0ZA8/HGCPO87oylxCXSHw858FhoSLI1Fr6da7XMoTxrQShmOEOrtpdb1AhGo/NdJvz88ogl3ieuRLu5e6SGgbBVOzeMZSiMixWhfcQHvT9byEpo9ahh/K35Ozuu6dhfzMKz5B/zZLemrP1Z4mVBNLTOSDH3gsJWcZa//V1XPoNE0sp5ZidXo0ZDAFnEg3ax+C6VjAf5gaEBsVRNcm5IOUxMMe4Fl6rgsXLbGJhSD8e42+f4zExx/SBCMbvn/Pn1/XztdaqSsyVui7zyIIN8+71KZNR7oCPaCkWul301oqRqZ+vdS39+aU5rmPG8zESdFeg90SH9Un1I9pK57b62OsmNc93o+aGOr0FXSv/ZhPfAf9WIP4VSUYIkTLvm1eCiEQu10B2qsCLU2fPOr6LhEqzdacbR9i3fBQrr6+gnRcsudPoVsSE+UF+uDyXnkcM4vLsGpMrxzwiRoMrRwbYE4QDicwwmSgrEhDBvFbFxYDnIBgj8pju5Kf0yYfPCA7gGPr5wp+/yhB4kpYTirONUyq1SlByrbZqBUdzl+pzi8y93VuW3EiQPgtbH/UBKjZWVYop3Cjapa99otxGujAkzdzJLa51yT7O2Q0xnNQlaji9y2hNjLiPNlqfcj8IGnKTnu0g5WoYHczm37RDKnUjpBZqoCt4wJ5W1Tphr9K03wuiu4uXwBvSOKYU7VN40dfKBcwqKjAJArOhf8n+voK2I3I75nVDuLXd9sO3DW5HaJvfNyurlLQ616o6gcmmSKinV6JLjVTDjwFrSammwva2sUbB3cu0V3GrJt17XFCPHspWHF5PlSsHtnIFdbWtD2A04OuunZHGG4Zk5+6N0wF0LWJwy1tzse41kfPAWzC1FNs/tABlByLePOq1gSR2+LAdBvUrW4VJ2C7clgcGp4x4mJSnjLEDGOm2BKGb7ORDbjBO7qhCH4aNIx2aLex5V+9/p7JUGZOtqvqub33+9lI2QAO6ypUdA6q1Zq5yVVhAVqpsYTZ7pMJCJX6MYDOJKqITRO5cfcVZ+kD2rNxbnDq49RaCUh+kd0Day9RnEaXra9H41g2aPXuvnlMGr+lSvdER/b8IeH9sBXt2VF7N0d2CskW5l7VMoDyRMmKtjECAZ0rAWpfKDIs0p92lsEHiWrqKlrYtast1IS29hYD3HaA1zb0gXrO1Mne/atd3QHDgKTzcBym+ziUgMH6dSNCEcH9uv6s+KyRgnVd5QkGYWFseuC9+Kw4Q5nYiMx+D12u57MvzHV6rlMi53OjeqZVaTHWi6daEBITKTJL3I6sScGpypd6XqDxykzjcKUPbzRgmdavCMyv5dcq95R6TH49qbFnaQxDulqiozdDNXgQiIk2D20GfUqsFP9rfDAHXSoAz4NTiiDiz+jQpVVQIBzF9/FPX0nW5dSIAfr3kFJ9nCBpUoazsrTFsJtjTyewER1HHbbz2arGVdCOJjs4GcAw+D30+x3GMrZ6WuJbxQxxDjxEATi0LxBHxnHGYg1YqxSo0JkJak3C/gmPg+YjjKHlbK89zrYVziatEowZ+W4+i+38L+7js6JIJ3jf2uT3u9rlKIYCVqy9pLBumTvj4x1ugvh2+W9jKy2dxrH0P4So2JXZ3jNJSChIRHxOfh14aJP7bb/N//TzPhRjj61xaOi+p+AsUNEBODuoYeBxjDGT2I0sl+RBQPUr2l3sjFbUAihr7VTFQKd0/Ap0IuVLnlQLWyte1lJaxUEoJDc455uC58nUuhz0qKNMLxYZcKaxmXG+vzlr1WsYF3iKm6HLY3RM9nPZQpkAubiltMcjEWqXQVHdexD0z+DaOnJORXJ0Ad6aqfrfNC9G9MLXMFbRlWW9zNiB0ejMCx/TANAoieAyWNrurIwmnDwVIY9hz6qJnNEjymmiH7Upqw8VKnfmsowv3xSg15vRBkIRnXG42v3VWnlcRGfANepQnvIoRmu2vGbDwFh/bE36TfEhrLaOCzt3epvMNu/ZZLJxWpdeu8UwBY0w3ziC+zisi5uN4nVdHdHOaWcbyUEgMijPs+4GYg5+PmKP6lZiQD2rlErgyUgvdzaRuqj5bYw4CrzNfV6Yc9A/3HCVD5NfVgbbyHthPVy3PHVcYI45ZcctMOcOB6rzsimY3gYq0UV5ebi5xRMxhyF3ZzeFmq8Hzyj9fK4U5MEccI1DDrpOMMWKMeBxD8MzWKspzVNTtt4wDL3HU2ATZu56DUp4rgRjDYR1Fm+9C2ghRBqa5ALJHMerN82xtG8X5ULmXRivblJRq7MDnfeYqjNoK3BLuPKJBzUZAWxoNA6phWWH4qq9X2eO6RyflqtkfMYMzqkmwMI4BN2Dyrp1n/usrfy1M1ujAz2M8JgGdCbZW94PHoHKgHGMd5Gul1pqMCB4EiceQIAffWCAPEfFbzOfBK4tHnqnz0teZ56WVOwpZMb4Ke7DBZXfbBMqNL0+mWUESXleeaweJzogI6nnwMWlWmicKRsipPqjYrXlj7vLpzABYwnAv3tsxrHq8TAE53qpOfJ9WGmUPbgBP86zPE1/XqsbvNS0L2GECUGD1bSj4cQPircDanjAB5J58Z8kpdZ0lomyUX1bovLSpgqgwZYngr5frNyqIROk5cQzMOc6VBu2jWv5Xfb3vRjZEKCUQ3UXewh8ke+gHAFWfH62d60v99ogZ+PnK68IyS4uQB9JvOHGElGutIJoS/sbmLn1be8S3/ayzVXlTDOrNiYZU2LuTGVUT3IU4dw8BAoHdvpm2y+0+vtsB9TEU2lfmhj+oxIGgyliibXHJ9luMhrdy6FBGZS+Gjey3Cv3CyajOayUu7bNXFrcwh78VJEyyYiVzjgJTtO+Aop+0EOotwwaicvv3HnzjEPh7O6EGUrSxt0CWfmzUWdGaCjzsg5O3Uquog+OPaq8gG7cGC6x6571LPufqNkubjrx5Tps07oeYs3jg94/eMtjqE7g1jpcxPXjbd7ADUr75ZebnvcfsyEKdY6AhMt8W7tueCiCRRBC527L2Wt1BtHoj04bMU8Xz5oo4/lhIrtwznEtrFavZSM/y4Qox9Bs3rK4aSD9Pys2DQ7FAoS0lJGE3Qo+MigFVCKEVB10+4vEct7HTuz/Ty7A6ALMX5XZS/A61Sbyl7/sV2wTfy1ovLECj7jRgi8XeKri185buG+R5Z/TWoIA2SdmO1L6XfZBREeW6J/XXTS76dvs7dLqlqH6sDgO08fDQTaIbxWubge2bmIP6/YZ6b28k0ne0H7M0q4CeNMXdk2qjl74XXGu51+X5ytbwWBXcKdH0mxgcFKAQqO6y9G1t3q8OfENF5XvoPZH7vn4WqWblzIitiJ6PIBCDiWUmjhXIqEOmj+fDmyvAOmStFRGPyWOkZ5mPrqzubWrup4futfBk8iB+/xw/v64/fymJczmQ3+Gu0ixQm3YvZXvi1Xiz3Ui4o5s7a9SoHktD3Ie0d6WEvlIfcBcG5ZlzOoJAVyZE8LzOcyVSEXydfF1jDh6Dj0kYSvI+cWPPCGvTkKqGhSptduOp3AqtwkZlCQW8LpxXHiNel86VAfc2yiMGENel11tLkVy5KmRPOyZzTl0qQqgK6Jerqq2gv1FgSBMrRdZgzgrxREByrwoveQq/Tu3jcEw8jsEYK1lTObYpFX69rpV5BAKcDFVgjpN6BI7JxxzswXIEYmCGu3LQDbm9YTXpKTCPWEhCgxrKmUpqeY6mqhuozAW9lRo2cqpD0afiPu91OuTge22i0OZIFVjpt5pWYLBR2QPeZF1uGYOU1oNVxyR54Am3QdkekVLHwN8+OUfwQmb+utYxY07+89cSSNZYESe5R/DHUZX7x+zGKgGsGylmswzzVpAb5gDQHDVJrubdWFqWrBAKgleT9VyZJEZEF9s78YKUzuXelqUczi1waNQF2RftDDMBxIjo9qXRdt/RqFujt+nxApvpcoFjjFFtAspGF1vQTE/X6AUjK6RuG+QP35NnybSR51uBp+gFq694C56RuK50BsCZAHdKsuMxRxyzqM6jZ2ytdOnr/oIgh0cHCbipkzs8lH3J8tjtTpQC3A5zykX9kjA9eZHrNoMSq6BPZuwTimgHW/JarYxZxT02iBYvDMZU9BzJXLnQsXD0odqKlE3bbaX2Bhzvs3LrPKLBKLSxOxtDeq5rphS+hhwVEjVHpGvrwR40jGtlJGfE/Bgr88mBaqLMYwwvWoOuSkwpsa5FMjwkrDc2IlKLEb4BMCLyMfUcYULllfzza73Oeyw96pBB9/NVXVuMYtdGcAaGx/r4RJgPQAo1wu9c6Q6wKrdElym3QVdsHxEA5ow5xplrB97qNanXtT4ek8V3E8nP53HM8fPruq6NDNPpzAKQQreqMWIkmcfAMcfX6absHUTnNm+jFP5gUDkgYIGvJWqXabVX1JGYHd1m+c5iN0Pcumj7DtYK7EQliY4q+IraQy+/fZUiKgm1STQpyo/uEYROd1vInfCJEXPyMWMUG5pBHbOoHSlI1w+ko+qj3PVRPllCwAw4SujjsZZWasSAlGcOSMQESMyBY3CMWAnaUkYFrKyjTYO9aLLeEjFngAvn3SfL2iOIYxRXWTeuqK/ez9qHN7Ba7oAALLEaeutacLRRZIpXlYJyuItrnZ3q9udLuLQ6JVQqsfUk80xcqaNUYDM3WzLkqCCdZfTd8jz1uvLrlMfjeIMeR3yM4TtIu9YlxmzdgzoI/fx3eIDVMDl6Qeh6SkrMwQCZeS+HAG6g0dB6OzUklhiNWwAdx/j9yeMIEI9jXKkxRulAcgzULBTcM2cFIkYgo0xmgZNyw5WMgGoCiQ3SEk7pWsrMQWls5Q0X8XW5vQrWHGPVI90qettuPy5JV7HZr+J2ulEtSqMbfO9lkFBzOCFUutTzl1zfZqxk75vSaq7MW0zl3UvJVnPfkdcW7/1Lu4vosipvVbSH/2aHWsLV7PXc5+E7sBPASiA3MivptRfQ2bpbwGaUs4uIEHedbxZSrqLjih+9Jz1cjHAXYqHtPt4Ipb09gNmYd1C0n4+QeQMyMuv2AjeUtylqz0iAVgXmus2B1EEvy0EflWo6GJx1eurVPTRC7Xi5DYEbMrE1SNmqrYFwO8Nv4Yl9aL+lhDr40sGk8hawRWYjP7/jbUu4V6z8wRYLdTVZ7N7bBbl6xQA2C7+Ege+CiUoq1o037dBP90areXP/0WjNd1YRBdJ8xeyQhrlXBPa4PvRhJCKZtox1NvZaXHcJXi2e0IdNd9mzKj6SveKlZ7dQtI78y51/k7V2AdvM6u3ksiPfvhpr/sLeI6knLrxvWIPnPp3tKewjjS04t5v2tvu3lth3/Xb7fUz7PW3WGkv2aSjE6ukk9+YRuBmD+8LczA2LW/WSqIv1uqqXq1ZOb197NZW7GLaiy+/3bvHI7Oo5I371KenDXGgS6XhlUKuB0t6xltZbFfgFxPbu+2P3eGYH+wdHYLDavjZZxhVYel1GJBzBa+VKfK05FB9HRGDOqdSv17WyHFGrr0we025tddpuJcFMTVNXOZx3DcaDeFRTW11LrzM9K2otZXAIezr5u3z5SNsSzHD/vAx4+mxLT7Fr6HYSgZvziDfB8dccA8hABsgRI8Ihm2BcVwY1gl+vrLLnCKV+AZ+fj+OI61orGGb7B6lOC2wdqXLDlYrOgJYJa9Wy3OSwj9ykAFypZ0i5rnNdC9fKBUSMK9fK80ooa/gI9hBW6xvliLhWApo9IcJTSHf/HVRt4h6gjrIBUSceTu5hT3rhY3IOAgjyOpPEYzCFc2HOKYfc2sdGQS4JmqERek6tVKydzMwZNKOqUeXWfNhBK0aIuFIj5fRUejosKWglXlcppRHKrLns5Zzts9Ap43LX2mXTLVGlDy3K2LbMdllFYOS3RhUiq0dk9XiI2/9511ippDSqJwUHmUtVLAP3BdsKlUQGRQbH+Bwgh0BK18rHQAq/LoFiKkIz8DzGMcBgZpUwZGX4vQLsY3grAm6DihuLk/Z/yn9SarmXwCqNrKbWn0XFtzuN5Q7TgvvIYvtXXoXbC3FwFlfVaVINl8MTfAPdrFdbn6h8zzdYZup42W9kVv20LbQdbPvAZvBJcLsr7RJrsBAOwrXsPjJZdAzsnfMqsZBCc8CLvOwb95e7olS4aveXacJtrRtLHZeitl8wgiIzMUZ5FyRW8nUuzzrsRhjbthdIzeqGXpbSIbk5cZ4ZlBeFVceaUlUN2C5svwblwiWas7NBWh8dCAQHQ5Xc4m1V3qzifsNGe98tq20AUPiV3Oeo9lgikZkjhuO87hnn/xX+IqPSNuFEuO82oTlo4zJHHMeoyjOw1BGhJWdkBZxLS3nMoLDcH9uln7WnSjnulh+Tj+MxirKDOfDbMyD9OqsrkODCKKYD7aVWy8xak47Bx8Rjxn3CVQrZJsz1uDaUPqojIgZWakTMqI22+J1rETpGOGpzevhUEMA1FlBZonIpiRFczmNsjiTRbBKVm0XI6jo4XN0mCXld1rLFnbb3ISqkGUxhQo9j/OsLDA7IKRM7LDEq5eJQzyDTk8Lsk7+Dty0fbdmBHkARzT0oEFoZ8vLy0Q7VOyBrxIuq5m1JM8JG+tDZCTeh2R3WapIaxYg0fgOvpUsRwd+fTGBU3wk4PjlCv17569IxKJrbQQ9hD2amUxRghJviPY94HF33KaQYqeT3cavUCF6JiMjz+rrQTgDH7ZbiOXEcDoUxhfMqjrQJe4EOP+z0jg1iH8leVKAeB8NVoivPVX5UcD0mJFn+BRlrzXAjjrLyGDH85MARtiYaFpkdXHgDZemJsS5a6kr/X6+ilNYx8IDLERHjfJ3OGloqg+Ao8rilQN82/wZ5kCI4iGyyDIQxGKNa7YwR19X4SRK0i09q0VUZDlQgEpAYfMx4HNXPIchjxoFgjOXmxoByFRdZWqIpttfSHLaP1XjNf1bLDsZKBnFdlyTGEGA649cpX/jeOPIx+RgRURbN6KkgU0vJ7d2W4Sly6RJWRrazUVVdZKVc2nmXYH3bnFTXf0WMmoti5og7gURFtFwqtGIPDEKpALyJeBvyd7fvhkz85rfcry0QojSALMe+oxbvukT7cm+4x9/1U/+1RYwqmtY8GANYac4ZJZXKIDPQzOUOe0KpTuX1iWvkU7FKRw9GwKWu20buWIyfstdjA9eN0jp7vPOV7cxvJFEOR8dEWk6k5mBuiWgkgD3joT5uU0gynVdW9T7cEfy2LPusdUdhVZbb8Ys+9KriAvO93zxgowgAHvNnCnBVCHlPye+kl8a0ulHgRhgQkKhcJYqV0KJvMNqstw3gbCU7bF9LumoAT4noZp00amsH5V1a3xz/Crt5s96Y90LRiArn7IMhRYQznw2Yt0CYNliukV+eFe527Lw0fkW2pA4eVWkAbjcAHfqAmnm45ae2o1Kl+0ZwLyDYgfCSR75Nv25Yzu/XU80cIirgiv7Yt4wfuiBxY1vuECyV35FoL3Xdba3sW5SwzAv/ciL2plP3lhXotyisWwf0D98+8f7xduve76sRfTO/tpzaq+h29mB3Q6qTu7Im4txtO7PuvPISpSPcRLn42/2YXuH7LthOnVpx7xBe+MgHPTqEtcgV6X8DHm4cM8eIr3P962cVp2TmdWmJDvZnaiXOS9d1GpJCuNYKcrAGYGTKU7MlCVwJUAOCMjgBAAlojjgAgGNAyh9Pj5++YmkFvqS1kOUe3/5Gg7t6ZCfyPPB4HPeccQE1i7hoBynBKfw26e/bqJWL0PM5HnNcS2bOR8Rai6kfz/mPz/F/E//6efkNqZyPx+tc/9KaI64zJRxzPI55zHFeq7gisuNn84+VCGqEC2f2E5WsrkQluVS6tJq5LJma+xJizEz8cablp1VJzcctZggQMQGszBnVOO0eD9fHgYCHzsTOXRLR9sc4w91DHWdxQk2uMxci4reJObikY+l5mF9XAS8XuEpKV1wEPg5q4lo6lvti5RxjjJhFnVKTxXfIAGPEOpfRVEqfD2Tm43GkUuflMO15JcWVee7imcru3pvbIOTtvLQK31in+HIdBKutqcwBOhZUBQ2NRRzlBKCgRuTlFnW3YvDTqCdC73o6jdDKDq65Nzw1yQh9PAeBOeMYFV8wxc08gn/9XAOeSK2l6lOQ3bf3daojd3cnxkHsntnll1NSzR97N+mtJAXFytVNXLQqmsxM15XDvYfAatb7plo9PsTzNPtqpbaEZQXiUIjxDMNBHPcjG7Rj2ZDcUXTCMZebCLCjZcWodTskA5IICiHIUWzVfzWqqQ6mYx8+AL7LzGZRN6Qse26HvPIHrjANIro3vD8xWNORvF+QBtzK3dwfBHlBKtZ2ttpGGNdFzQvzVsxAjujuQqWsmn7OVQFNktqwcwyOEU+SxNmMDzQ+NBxP3a+/TZd7BbRly8I536A5SvFGA8z+iRqa9YtVQbv7jGyLWa5oA4DOsvTRIuVqxD7C9TFGPBFcRbkiao6MRX2wi7w6YATJU6KDkhsXkEIl/Jd0rXI+lJWlgGG/6CFuK5UrBzlmOX1CARK3GDsGZnFzxuMIFM2hyj2+TpeZVL1GQBHDLTMMfB1Yr+SgK7CyhzGxNPNkOPVi9H1lujzRrr6HuC9bisCK4hHPmj5sIhWuTCBnwC1vRxAoYb5WyX7VwgsJkXHW9FEclfvG68wrXfWxHh/PM/m3cf32Mf/8QkCMiMhH6hIguKnhCNJd51TuBumeGGzlap0P3BRUtoJqvb09sZIcRvF/bpelj5sDPOXQEVs/AwVW+ziDLvvy8s9BASadrNSc8ZgRgUAHbekmXxiHVQdNQzVulDCHW01FaQ5iMqyNHpOQlntrVGl/DAuyB5mT1ftlHzlAUgATisHHj/F15rWG+7hJVZq9ktnKB9XYDlYLUZvQruLtCRX1WA57NyHg67WuESP46xQt3vKYmpWpP6kRnHMMUsiLOAZ1cESgJM16WsRdUOJmXv6683aEW2RLulae16rD8rpeV16egNWuPIHnY3485xgRY+SCULP8nHt4XQnGdnnw/+vL8DVhHnFVhcN0x8BaCXU1QOEc9KdbzbRvw8YUzVw7Jj9mON2CGAk+J4P6ul5tQmxZmNLXhRQHM1gN4L1kQR5zRMTrWq9TuTSHItz5/lvOhq7kHYMON0sAPh7xmAF80ySSorkK7ap+WxyHuULFUgfckaN4iLd3i0ZxrcR3qNXLmC18josZ+Vm3R8MMlhUA5GlH219Tnz/esvHmy71lsctOFFpAX62jN3vD9o73d2znbf9365Z2Z21FS2UUMipQER7ZIWG6Vp/tSZihaupAJZlhT4m+ZHSr+6IMU2O0y/x+swBoHvjbo9MRm7qhvRcWuxvUbkj6FsAqnWrzex8l9PlsW2zaUcc/UmDmm19ZkQGhxjYZeZTuyDvxvoH1psl1CAIAAspUpap20d2bFHLbzPcb7ccFcHdMKh5+r8fWJywOXVYAJKsTdHkTjiRhhwn7RLERC0Zw5d26W0Ll4QvBRGHbN08RLUZ30KeXrJ9gP07tRkQkCeYIj0IoYkLdW8c5uFMfEs1GjkQWpTW6KHfX6lc2a1OAGniZuk9q9BHZMZZoxLUPzjcwdwfg3gFbXfu2uH+Rw15x9SahgxHwvkPtaRn/lKlnRbn2Ut6b+77C/5sGKzkq9fw9AJQds9t+TktpQ4tS7HXuG+W/bfHNZrtDMP2E9flqBuqbXPh6PszaetdQ/x3xlKT6FrvxRCZW7VpNZOjdQQeGyBiBqjkowNMRIMcAuo6p1kvuetPTZ1yvtNeM7eFjrweqC++IOKbG0Lmy/D3Yd02d6xzHz0AQjxmDDg2Pa2EOzU6fknSV9bl0VasjfV0aYwgZMUZwjDFGHJPXtcAYMebQ8zF+fDzOlWNEXvkf//z1z5/XRZ7XTX/r3W9RKG4j3bwjev6I6iyqa2q3kN8b2Vtnc8sRPOaMwQGN4JgxB4HjvPLHxyDy4xFfL/SAIea1SFwLY8Sc47rWl7sQIf/+4zBwPK8c4jHiWnplLrkIH83ywBxVTmVgmj1L9DrTBfAr5Y6hMTQGcimJFYBGH0w5bT/ABeygkL9W4lxaKaKmOIyoNVC6YqqC76KimyO0ogDcXQ8E4IaRo4tyPo7SYJGYrIRnRXx7jQUw4nDw3dE5HltflqOI25S3Z2joX1M/Xmd+XTmCXyfnnHrltZYnl52rWvDmbQ62iqm+Itsz3VqikzJ4L9LsW75frBtCoAv5OxMIQph2tFiYjNABJuhsvsAjeKVeK2JQonm8bCU0QyszGQMcwRgM4vPBY0Z0W5Lo0Jiqmo3PgzOSiPSkJKWUSlyrOfJAsPgeFeLx07Vu5uZHe9prEUkV5EIpscwVxQ6G3AWv5htUDMjr0PqzJGozu71UUbH4PoAV5pYgWy/DLHaV3wgQO0CpBuXaBdlJVF5xA/SK9JY+BRgRqzNNr3Ot1fqbkuk27R2NmI4cdf41um/lN6UfTaCzEV2mD2yytl0Cz48XJDmL6DUwOAqaN0dg2bvuovgAvJiWrFxNX3RE7HEM2wIGLw8VVBvRQmmFXT1BKiVBc4SkswriBRXh8FppmW7XTH5LFj3Zk87uMtUIzhElG0Eo8rbLPe2L1ivYwI9A5RlUmIL3AXtHHOB+GqK9BcaoAizjzJQbBVscLBeGRCSUHmY0YhT5VCGc18qoAT2zlFd7LGB2s5skroVUcnWIMYDeHQlNuQ4IaPbzEr5OkONx4DEREXNUsxg21r2WRoxr5ddrCcvZcUiZOUb1tp5jCMh0cYf97Z1uFMkIXFdmleyVTt8rB2lpGTu7V6jjnDWUOug/HU1TnbtiT+wvNhHJTqB6T4IcgyuRwjgAMKXrzHNlDJ5f52Pw+RFj8LfPI6Xz0u8f8TrXH7+yPD01XHXk0UpUkjhor+ktRE+M6pizXacmyDo00kjHWtd5kxabDQl8TBqVsRQUtr7t1TMJ0VFrLNBz3KUx4ogi7w5yRpD61+s6Aue1fp6ZOQCsGc8owR6Dw1OiW2OwenW4lVU+BseY57lW5tPZKZcBJlYxWbBS58JjxujWu873uNoEwOeD1eJd8bp8MJHgtXxC3aIUcziXLqcTWu0TO1NImr4Eu6gq38S01BnV9mMCKJxZzugYMUuWBnv0BJzqKFF18a/Dkfk6Ta2MCByjWlAtt4uCbD4uMTUgIdccboBlpIsRPEY8H+Nvn/PzOVfqMQ8IX+f6uhZUMfhrvdb631LEO3SPkgmpmus764AdDQMBbg5Rg4b7QvvvNjcC8DjiecQIHDNiuP6WS2OAr/MkWb0uwMy1lk5r5NTrwmU238Jj8DmLL3MuV1Q6N5HVqaxjngRG8OOIIF5X1XXOoi/Brb7sp7fTDbM9tO6g51a5bD3r1vXdSUpOwn3zsfptqqRgoFxLRIWnAdPJlycL0zGgOl9U1DgzK4I7Bvm/fd2+0gaB/UJhd7HYN7cNs6y02uG63bfGb9ovv/04f1Ms/be4oRPhTVirrkxbAmZ5qu2OChojqtVehOCJnHjPWES7kex6Nuep9h2WgtPt0qC8ujfZ4zajTRTaL+wXO2fQjnLFByrEcKOxMp7YJZfN+/L3rMq9uv5ujbLJeOrEkQMqperEdmN1f0jr5X7SHiqBt6SOOqiSyUbE3m30xmIrcMiFkdmn3ZrW6+NESlXQZHb7t+r79Zf7eZO3WtVlsNsLpW+NZgS4a2CFi970Sj8F7tDA/a4db6wggbMGILXeyD+VR46Kd7ATy72SxizwmBNvXAD356oY4Pu0ONPIciYb+he9pSLYTRfHdmrKea6jW6tXe9CHxx9kR5PtvmzB5b4j9c1t3bMvAqENZJSPB5Zd305WXdU4e6/2/cuKzuJdo7wRWPqNussTfG9epzZp3RC7Qk5vuHVv+zetUXud35SU9PaYb+K+Q119wOu17C3qRVEREU3KWN1j1bli1asg1XThyiaQMWgHqj7zVl5vAaA6lTWR1DGgEfAxZ+dmue/aSsPe7KD7ZRyTK0ewRlFKGhHPYxyzmtP7sZQC1Z3BRkuuzisrsbnyuhIQY47hvhzywEsClzCr0d+IEYN6HDlmzDlzrX/7+8e19Oev8//zP//8rz9fuSqmg78uvAczq1uZbdnrw9qHs+Xp/SD1FsEFLDmd7bLuWsUi//PX5RKbrtauAMpxxD9+f/54zGOanK8rdV0XoB8fj1Se5yJxjPj5yn99fVkKrlQE5qBWzjBcK7Oa5arJ/nCmmvtZynJO7ENqw6K3R71SXWcQAFbip305v3Sfr7IhbotwmxmXM71ZW8zw/B0EMQerM5I0B1jKDyMAMgHGuM1OHTGR0T5s7ZRPhOsx+mQ78OxJqIie1JcrXRV4ZT5mXEvSOonrSlOsfr3WWlroIAH9RwcI3gKxQN8u7uPf2srRkDLH9nj9+jY5KMqv30xfWp+P+JicgwyuKwXFGOoqJIfgJf7Hn/h1Ybg7FSjJnjaDIwBpzGrkMQLPxzg8l6WSjdtAAOSI8eMjrOFep09YXAlnRzOzJuV4KLW1zhLETF2pG4ftML5vFIWHUojd1RCNffsP52usELyN2dDTCLt17z6Y34FbvaUI92VsiChnVepSpPtsSyI9UrMCBMkeB+Z8IKvfisvzpJXpg5w9l72QGFzZEUFO0vOwgsw30j+421y0NbDJrlIZlGK9QVE92OzajDk660cCRTYazYscw4Wlq159wzas3HFsOX++cgVjzvHylD6i4iq+sW3ZawxJ6ro5X0HOgcVBT0PLLPNYxakUdEynFOBxjV7Jq9kfTqBda1kqjKWiwhDdO0O5ctWysRPKjBHOVd/bPUZgT5s2/6EJG/7LWWMa5Eg18b0bSZTgSC66kC2TXEJVrbro0huvxJJLeDLlHHu1ucmieLvUD9A0Hl45x/ByKmUGaIeF3oQYWKsE3It0TDu3O4cI1GtEueHLGPaBoVSPNgNA1czKDtoCu7AxtTVFSV8LHmmLPwahrSLIhtRy0DxxQT1WCBKulU2RK5guoEb4NXDJZvld3ap8pUsyERHH1BMcwZXr84E5hxy2BkZorfWY88czX+f6edq9jOta1AKCIyQNICkJj0koE0HiOXnM+Pm6XpdsPPr8OM5TKM6wpkCLCisSrcTeN6nNUr+zdRkQiNsFa9B5LjgA+h/r6x8/jseM1xLJS4L0GPE6LwADXEs/PmZbB25/ZlMZK6lMU8+YjCszpeOIIY7ADC4Q1VeANJGHAHRVbXi5gCpabmk5j2kewPMYMA0z8xjuFI4RuK7SJOdVXkbQ8dydbwApExUtdXZJpHTcfeXVkL5O3hiMiC7otsrx7TFBezUr6cv6uP86L0mOdETkbx/Hf//H53Ve1mC2jF9rBeK3x3gcMZoA8TrXf/7x+tef10odB3//mJ8f86i+BBjV78ZkW0paK+coxbiNOzomwDeTw84xd11R+2mq06XtcG4Jar+m3EkrHeLzEX//bR4jrnRP7nLIxvAI28UYdWXounQufV08r8sJtgEi1zoFBlT9cq5rXWgTJS2FohxbNMPrMUu7/nqtTJ1AdnxnO6tvDg1QDKw9krmeQjvk0e42oxDOwy3Nm0J3+0B1X347UjATrCZsAmkwkIpwww8rMiY8ptbDQFEI8u2MNmqofjbvqLM9Z7Tzd8d/3vdITTh6+3H73Rvc3ZGge5UkjaCHgfqBDahL094OXr3FTfyZuRu8AMQxp5SxlKzmuEGu67KdA8QYSo87KQqV1zFQkKXvuXZI9k4bELRwFkNJzdS6Mxp3/H47yfY/wQoDv69Xg9r3eMc+JFE9ww1oXPxsZdFnAV7RaLWxHSvd0uuKlu0n15tX1yY0Tax8edRS3tGTt6ch2mMHylewb5ydigSq08/dZVJVwOmWAryXBe/y5hj2vu1eudtPzKIiqgFH7fj2O/kW4OkA0D6JOxbSLgnAqmGpajxHFyvTvjtWEPsoqOOG3IDN0bg7fLPDHHb7C1r1XMPOQKpcd99kAo7XvlUx7CY1FdktrXfPp1MHVba/wLeNstvmosb+Nfehu5fhPk7VCtT2vP/YIl9/98ZxfwgZGG7sG+iaKkQ3VNrL34murRqqxHGr+zaxb77Q29G57UPfrf+vHf2U7sd/e5lI7o7vutentnaLsvOKHaWqYb2okoesFPodALrDkW3G6d57tFp0UK/KPRoK+4a7/gk22gqzGruHxV8UJ93wr+fT+9ODjCOGcNRULx7j7pGmGtdGKEcQ5OWBVcDubJQrc3lq7HidKxO/fUwHNa7zwhxjHDALmoiaRxM+JBhzxuR1PVb++98/jhn/64+XL/IeNveisuu1cR/DbUFueS21tKd737JXy7tS15VG52sB0ETA46jnOCZH0I0SJCXitx/Hv//+NI1cKqqOFF+v619//nSPxky8cr1e63/98TpmIHVlHuBFxfFM5OA1PdUIGYwUE2Dq16q4iIsDVjsKKD2MOr6O9xCZekyAsSTkUk19ZlCMXRSpViigB442JGjSSSOL/nutQkIW6LWSFMZQRRHJShUw4q3BEGDBXomVOibfMb0tdJPVIVAIk8DNylySlDMKcT4Gf3sO91UZKH6ZzaBT/XW6Y9+7EQt6nb4Ftlxc3N739jAb+tkS32vBGlAsqNwKzcHBeA4+H+M4RqZm8JjhSqy1li/grOY/PpV/SiKC10pBYSyaLh1az8Ex4socDKU4NEZR1m17qpKCdZhz8bwWgBm8ii5eT9bqBWNEuYgudGo+A9vs1lM3MmBB5cwCMPCglq0mzAchUdWNQfyV3n8HXMo0qEPStc7Ijclsp4Ob51sBeni2htitQ1ejCjaQjWYv2poPllqTaqzSavqh7yZ3qtoUmQhHeFeqm6Kodr2Cp2/SIalab3CpmuyVFaHdHrpyNeWHUMRNROin1rrUvmuRF9q0FnxYyQSPSdITQkHyWguwL2eFbAYTJGWnT0gm5HKGqmxpBwjqKfJNnp8OzTCyCotcZh9Lcn1ZA7qyxw3eKtw8Ikwv6rNBAEpVKQXJiDnHqBwDlMDCklJVCz2i9jHfTKoxQgvwjRVSWympC+qLP6lcXsHlWb/Z7XskNb87JSR0aURm6louBSxDmksnlkfzXqkRWlIKQZxLAB6zMhkOg9rnyQQSq/B4xUqAmiKntuCQ5uBzlgfpn18JXFlMMTiQVUwrpkAsgkH1tMfdneDeT0/TkBlnMcywM2vUD75YA9tTa7kOC0IFgNQeXSntO3vsBd6QqzrCRG0vlhLkb894TArV+mQPT4sK/3HGfB1Lf14kiTyZeBy/zkRQyTnjWvn7gf/rf3z+/Hr9euVjjr//ODLzf/7z/OeX7GSdSxzcNLTWyq48wP4JW86bGb+RF9/fVdKznZL7Ee+fJhRCJv/8WjMwgqsIz/Joi48Dpu+M8FNIZMusuOXYF4Mxqro2FiSeI86lSzzmMHtaNVIGmUhwJU66b84e7bQ9HT9VlPaAIricDaKu5a7SOK9C+mJD3BaeaupXDiYHI9UGFLgWIlQFwgDo0IZiYdTNSNI0KSzzcRSQLP4goFQMppvFRcxibc4Y83mM67wIBZTSoH48B4jgmuFZQyDw+YyV43Vlio+JxzGeM0bwdeVKzRHX67xSx4gxhqQ/frXSKIPSRqZB/Q4DtXHrk3S7Z6Xdb7xyI0ISKF4MOQJz8HnEx3P+eB4jcF4JVdd/c+vWEhnXVTh/ZWbideavV6LipB6lp2PoYBpQ5GLuD6WZWBV95gzcEAxBHAPX4HW5v50ek9X+7v3JbVK6Tlb3szfMq/iYmr9ak8uex4BJUu2/tVYol8oVYuVISlFTFqSa9OoVKAjmANdayCp5C5BKz3ol2vvCjgQYwb8B9o1Ra0N36Ir7+G4H5ps75qz3/f6//LINjs9ymxqjMaY0bOKZRNP8yMn+Km1JCz/HmMCyM3BeOWIuXITmGGtdBHYMCIW10K7rjiXUnZH3qpRbXaiuNFY1a7c4995gK/Q7gFKweO1WBH6KUmiyXf7mQBWIr0tVObCNzYggYoy1zFUvvRx31CNdEFG5nSwmfiHXLX4bHr4Jqh/8zTPeArBVnncGmd1Ge6st1X6IlcfY61m8tuSO9n3zBWs17IDcArd/3Zq/b5HifYtvO3YvnX9XcZY3DVTewu1eFO51+K/coK6f6kBGqzS2SVkrfcdBE0F3pMOiiDkCNdynWFrbfJTTWITPYpHRk607Hc2KWvR9AJArDqrlWLsNKt3hiHne2eS3Lhlbpt+0bB/gWyPhPcLV9rot8w2hes2DDI+DDTTIjBihmuYbRgbXWvTkG5dhBhl0+RXvO7h9nbc9LLTfKrEspW90FwWU5ur7uskE9zPf+qblSW8LUR+SHdnJhFnWq8ZSYrtOtw9lNAGYHTxHnEuSRrA7VTnZ265Uu0mSpLHHE9tx2vy4fvL7e3YrjUwg8qYQl670lKtbir3ZuQNtSdcyNoXulvY5mcLrWufS0YUQBIwf11oRcyViOl2QUlY5coTO8/X1BehvPx4fj0Hl//yXHNTTzVF7w8hv35WK4i7Iu5MYjSJbAkokTNvUea0gMTKIOeLjiMfjsFKV1vMYX6+1UudKT2l5HNEEe4CICKQiuNZSyn0fIuL5mL9/HOfKZB7HMbFIPD8/89e/nkeMEV+vnHCzbR5RTd8cJUSzW9znUfuolQDUGXKC8bxKGiUssee/1IvKFpSQ90I0cN4xEUu8s3MJDITbYAtwowRbQbUv18e6VBPcVzIFMjwfDdUaxgqm4tJkJlcKoCdZBDTIBebSWvkiHzN+++DHPI4Z58oZkdLziJnIBL7czMMhAKJrjdmU207NFCDe1sjPy84jAbszH1TO/F3Z7JHHjjJKOEb89hx/vPK33z6eMwOIY8wZmet1aTEYCOZKMCTyQP7+vP78uhIxoxW3NIIjcowYwRQeE8dAZv780nHwcczoroRGdZ7BmB7ELoxwhXV1opGAgVy4KlQlgOkiEiHVaZu2OcIuhCBQwYWO892OYbOlQTBCY9ScV6zCl9tgoTW9KqxUV7B47YJTFOCqFR4CKBdeR/t1upXYHRPf2rpi0CwXL8gwbckFOFda+FVnp9SppRfue5U0KdI3kyrClKTVVlbgGDEbs9lTqdJ+e79kCge1hUhwNITYN2+NJqGmVt1wxcDGiEBFqhJRE5gdzRfdawkcPibtsVQJW7UTIjC7MEOdJNNuyNiOxDFjzkFAmTl3Rbxr3ndSlykocwHLxbrdMUFFMbZcNZaJYSqSqaDpcuVj3Kbe4PBMcMUmd7GNfeslR0QqdPYWla4QIsnw2jpx7UwpXQGx3Aqr6TBRfaL9UZHJZtcC4JxFjTR1BRICj2M4IHiZJpMgcV7KnoTA9hhtQQfc0Rnn5dZokHjXEpIEV9e85ipjkyZyteBG80AOUsFY1Wj/dd74GLsap7q12hyTcFeKsl0q0a3fOphT6dsGBNkyaqxoSlq+UflK8NPNgPm6dEwyIpcIHCNYrJCRQHoaQdiUj+CwgoLw28e81grg+Xh8nbnE81qD1NLvD/5f//74fMQcj98+FORxDGL8N3DO80xk4s8vK5hCICw54/Ym6tS37UNp+9Iy6lKytmK3R6HLR5OV7YhycyB1w3mmcF16zNsBDwKKMzMT19I6ExzdLH+DNbaOjRFa4hGMyBQeR2U4fG+Dcpdk+OARSZzLlYNQINqE2c8axdu1DoYthTMQr6tCUK8Lx6iFGJO6uqlla9zYNDE4teAEIVeNRMPKgoilgaOEwTZndCBwLQBwjBqI81ox5zwOrdNDDx9HBP16zeP5fMzLA8KCWmL1ia4tTCBXWj6tq+eIIH//HL99Ho/qRH4dk3NSGueZS26RA0jd8lVvnm5/7TCBulZmu2Z2yfyvvIF6va3LmQHNYeoNRuBx8DgiqOKiRs1ZCMYcvMRFgCLVk15ZqTfmygwMMICMwDHJXfaxNR3cR8UGF0vS2ZUZ5WZLQkBBLeUMHSPWcmhmO8r1LCSqV9Wosp42mqXzZzDDlSmagedjONeCb83ZCy+ka6Ct7Ety0onKZK6s6UVXsZ65koAreZtmkWl7uR0Kdma+jTruCBBQoYCGFHuF9lp5g+5Y0O2XAaZNRWFZstka2DpVAFM9rqOra8tplsjI1Bitdsi5DUCbJl2Zh7PeJKA5h1Kv82WS5nHMVBIZMbJZad30781FbMhsopeTDFE17RBzI+rtRKu9NlXybau4e/vRj7LV5nYre+GqDhbb2+y6wyKepUbAp3HfADdRBQRoZm95reXQZsdjOxYPhDvHNWmoNfHOLNVP78DsHdmoKbNrc7hV2c3OwbtWvDuo9/amuzPa72/weAPIdkJQpU+svd2/b6XeEvYWxt+HtogWhYGAXXMUu8TBhFqaQmk0piyBrFMvwkDUPw12xBYovNwE+5pJ7Np18hZ3D8vwjMAylkHX9CUKkRAVGgBjRM9R3uogdvSnIJmXLVPZcT2/oNG+D6/Fev80966r8jbVZ6EtsbB9BYiVZK7PbOVcwuD9NM2nRjNEN4mYA85DHsf5et04sWv02VviA1VQ6/Y92Lj1zf1Xhz/9CoJ94spNws23bKko5fR+kT6DDdMF9gK6arf1b09z71c6vGmnK7fb2SYpiMd0JBZj8LF0LbviXOk2ct5Wk9UaVyOvtbOk/dXx9oYHO0rDTdJKQEvX0nml/UxJYzBmqD0otnvZO77HHeyDSLVW8tipFEbQPSB9Lqw8haoyiERqZS6JERrLOdtlIAtijvjxMf/r57WyHnPdPdb34WU/Zm372IWNtKdXSNeciP38dkPczMEm/xh8PsbjGM9jfjxnlFOm//5vBPDr6/p//uvnP/88z/P6+boex3BR2wzmWl/nui7Zo/tkHEcw4sdz/r//j/h//uuP1xlEjsHHjOv1z/nA58eRqZwjk+e6HPK7rjMG58AMLOm6YA/1WjoGOo8NSYM4V/EUv051Yt89LMuMtUC2+gYYRqTeeXVTvXs9S1Ao936ZA0XAGTFtzluvb53a9B8zVuRKJh8BNxjImiluueJKs0oRzLXWea7nMYR4XSvFo0LVfMz4eA5Cx5wpraWP4wHwX7/OrnvkYwaha+UglqIoM8DsWTnlHedGgEVAvC07BBPvWfRpd6ZwV4dKnQCsCSR6PB/Xdf2Pvz8hvM4L9sNHmAPymMfXeckz48f4+2/xmOvXa0UgYrzOJDiHgdJQG0Rr3SAyKdgEF/5bK5HpUpdjci1bohjDTd8gpbd7EvbFq+Al6LaM9LSRBKAUuldlKwyANjQ35PLvtQOEXDgv3u/aSq+x2rslxVslFwEEOmJTmCRTay0chAvhbP3YGZP+mOwS8v1JXfG6HUNjWbT19EDlNu3fVDQAJVyMjQhP9WIIGoJwrlxXFofKvvccc5vIOi52mAvSRHsgjVOwrssD7N2TxVUKhAd/OyCbLDMH3RM2BGi9xMjD/dq6zLPQBVrd6U2rC91j7v2HUrHPNIi0Mxkkq9oFwHUtvzZTF9ecE9J15dqIEli5lOnsQgpreU0KsqAUR4M+VPh1MaeiRhaUjasVzULb90f4chExRs2fafRVpMLttFhHOyjf8Ysu+LBYScs9TTz3Zzn+3ISplIBj1pyASJK7WhYRWOlRXLpWGu1eqxuEWDP0TDrU/HJmObFymUz5z+4dCgTpDl37EkQVHpKQOEeACKX3ZowI6ap+AbWmu45DDUIpFM92uTafUSrVj4ioPFvhjZVqYKBBknhduckp9pUmeZU0mspUSG0lPyaPOVYmoAgsaV0r5WIX0LU5YwxqgZk6jsjMAGIeUmi9FPrxOTyN/W8/JsM1VjEnyy+NeD6J4Hnm16XXAq50IZO2BywAxeObk0dUjNURgJV6dL/8BK91a6bSRMalwYjiPZnAaP/PaieFwd3YO4AE8TkGrTIv/Fz569Lruj4PQbzWVUdj6eepMZjiSs5w8QfsZ07HzqCPACDkcqdtklfqdeUMHGErWxV70apz+1ujAdrb6cQxeC4EdElflyCunTvdVnyrLFQvnFwicgRjuCjAxtjRKK+Kom6QkhuvYwRfKcScY3w+wsGX4AAQkeMx3Y1YLvUgAM6Rj2nOGt381OWgLDIIIphwhrsCHMfAx3P8+9+fv/34IPnz1yngGBHB17kAzAaZzyOua/zSulqTFvLYW87iPBa++651/v99h3auR/AYOEIxtouXBF+nVsaIkCjgXHll2bNBV+0V/xTUEVgj1LB2kO4v3u5+OU9sd7lzqwBw5a7ElvIuDoCr8wzqKgLS1pHb3rUBtSogBjmGY+thlXLMCoBE9b4hOqiKrfQAAMPgyTdJkpFrrUwFMiPWWrkiGIyrjceSsKgIz3g18zbeSsAMyKyZA2CXD9fnVvUL9vPFJoi+/f/NhXv/FkKN7niLV6jRyX4D5TaJpVii76s64hErPBQQms68kYVhCkp3Tx+/M2Yw8/AQ01xzhMlxO7Rkpb+7nug97NHWMeVIsZMwaoe2Zuapw09o9O/8T0FwACg6aLAPxa6Oafi//YAyjZCKLkQRgxg2ZFuEqqNBki7y54gpSU7GVBsT1AxL2byxGE1gBtD8hR3e0O7CfRce4x07+tiqH78g1yomezKGpKCDyyjfo0Z3SS4IqfhYqZb3w95oydpB78KuaqPYILh95PsFKjvfb7r3F3A8p5+KALgyI66o/IX3aOui6FMcbpPRcuVPWKu8AhII5vI2aS+Whed1OesmX7AwQfm6zsAzqDEGyTEDpjHjjhmhq4r61tiEq4Lhta8+KXdAZDMMhQpL9Xm66/6A7wVzqHpL9FL0ilhgyv0Iv7dasROGR2PMMWauZWHg1lXEWhlkMaCjfFr2aOa90diE4fZZ/XzNb6qAi7Vzn5V9yHfpypsvgjv0o7c/+CYy9uGs8vOb5DvrjDncocAj6TXKalWp//MYc5QLImkOPQQPtRnBOTjniGC4pWhzwglKC2LKA5zQHq627G6oEPCQL+3o5evK68pc7/JaS1SSsHWIRSPdumEPHkWn1gj0eEQXnyuHuxez9l3C5dQS3iiErST9l9fmmPH5GOdl9wBbse1NIHe4k3NUCLtvFQLWAgWzx9o9aWBNHsPrGY9jPI9xzPCYKvZEMEnHMRDxPEYEfn6d//rz9cevuTJ/fq0x+NtzpPA6M1XprEqFBFNi4PfP4zWXZWkEj6NKt4QaZPPjeTiXsmKcV8bgYOhy62cN4lV0P2swpzqUyTGtDyW5Iei3Q3uLavmakmfQhkXxhotvX/v1vgLXWrnHJrZquM9XprrkJyrx4roJksMtOaA0oyE8eVeAixUuvRYUx0uRp9ZCQI8HjuAYPA4DzdpMj7+xWcziRpHIX2c+Zvx4xB8vHMHfP2YQv369zjQdKW9A4JQsq9+EOr6RjCsxTQZiDOIYhJC5VvJ50KH+Y8Yc/G2eP57T4SSR18qqQMsF1MwdBYHqi/77nD8+8lwp4flApo8DVpYjZ0QRha2BzOt81agOua/zHR2JOdDNj8mIAYozVo4oBMN5XYsx19LrdS0JpIepFXLwIxtg8iboyM0ya2dvj4NbYb5phQ4pb4kpqcg7gNPaDKbAV/S/aA0OGj7UjS2d3dmBewC1UPuQk3R2zDygZgPdEN+tB1Z6dn2rEVSDPBalEqsv0pERqSYtBHKZjUJD56CZDs7YnJlnpi6g6q+DRRpWwbGGr+h1NuN+LayVVmW7hMtrteABSRASyW2FshlM3jU7CHpTfWvJ0ZBCe0YaZPdxLA1v/LuWPIXWMQelrgpP0EOvqqWG7gThEovbH9VrzJ0eYgtDL2/bdKHpkFbcDSQaSWzk1RaZkivLmqJV9nsV4i38CcYc45hByMxZ59v9+Z6xDdC4cUQEeV5tQDKVCvtngmoOVKEvAa8r04k01Uf73FXgoyNxxsxGJE4/+ENXw5VRBNBi1rQLt72zMrubrz3CUDUr+AWcV67MZfXhltV9VrcZNXRgt4Ag1huoYRLlFUDmTKETgdsT6gKrOrqKN8peexhWs9da0CIVMTLXutQdvksRpRBlaXyRnCNGhGPxj4MzeMxJak6OMbI33imHBJGagflxrGN9XPxfv37FjAGdq8D/EY6q4DlHBEyZ/HqtcxX9uRxecqVJ3ztHyo0TrB7cwWmQw9oPAh24KT71nPxtxmPC+2xIvsRIkHqMOEYo19fXdSXdEvHXpa9VDDoCccTh5n3g5xGBDC5J5fjLWtXRUndZAh0TzEy3a3SriO6dUOqZVe8MYQyuTKumM4GagdiB8taWO+Zhd4jZ4ddsp7N8pNp2U41JOrdfkwjEVe2nMJSPx5jHkDRGPA6jPgVj7ILAUsKMMVD22myKKLHPLPMtvM5l4g+AGDw4HscwoDWdfEbMaXJZaQMJI/g8GJyPg1+v9bpUnbkasNh4RDtTpW769GVXa96PXwpsmzb3Ca5DWxm1bsO6ljIXd874XugCwEXPSBF5BGLiWlq55hHRzQ3LXrE7YrQNFeRRbnCqUlvF+dAw6rnePaoNZ24KjWlEflLXf/QHy7wOctfs2vyle1rf63WvR0H9vTrBSXthoaQj0RkzRqjo5qpyvwabHT/CdoOJXdWFgrLYSFLaVNEdMWiD883r2q5YKa3+vq9VO9sVQrebYPmnhpxWrSYm8s55YVznEuR0oVN5K5JjtD6xzVYU3aDLeLZiSKFtI/dBu2MynXHV20l0wtzo3OverovDtGMQcCMxqfAfdsIKxTjw5HX2TFN0CFjrzecBZZ85291lhIDjGBG81lqXBwIW9bR9XndTV0p16DphpoZaezsFDRPU0folkxWcAty0vneVuKVsR/SMwIIQuTiYRM8pRmEPZTaNk0SN0dBm1GmnobZ8vNE98BefR9CekNIBs/0i9Ul4Cy7f16h7vza0hiR66rW59jX3BFs+VcgVCGGloZtp/+xObyQHmQkTrdnGzfZbhfOqliqzOm/ZeNABKPoaELSuGyvUfhEA0wGHnntXtufeB6iP4PYnVSu+McPbmXX5egXT20lgR8H2CvR+1Fpa87wRbFovMlz0FSFVKFYyn3wfaaDIYtjBZDQhaJ+CN23Ct3u4LUCgjUyb6paDRkeNXW8R9dqAXeCm+7jca4btiji26tTi6KD4olANrYPlHnME57wHApVI1b5VR7dh5MjK2LyFQVUNfc7rTfZLWPfdNdrz83Hd6orHMOXVZxtrFYRkcybFalDlEOC3o1QKwcOJ/NR1ulgMsh2IS8DWRNB2W4rPUvontdOgHwe/Jl/XHkq4T2e79yTFY/L5GLN7pu7TUb1Q/YTooSE284EROmY8jnHMcAfE6vXTiWXnwOnkYJH98ut1oViTNSDJAbvHMYI8l8B4HMNb83hMp2II+MXDIZhU0LNTFOFelXo6jpMKKoUrNWmeyy3Rq/f16wSY6Db/fWi36gJUM6XyDpwzc59ky3RnNbb1F9x8QBLbLXSA+Bi7ycvu3NErK7lOOVw6LsNE5x5CypWikkSghschDN/zID4fMQcfM+Y007M6WPn2ciGVM/D7x/j5Gv+5bIv423P+/ccUyFFY5+8/HiP4X3+8ZhBDxwgJr/Jy9ZjxMePnuV4LKV5CUIOS0nX48xjPw+J0fDxGECtz1/k/HuM45uvMDTj9/GNQsqufmWAUtSElx2ivK0vDG6MpiZq8Y2vuiIOk81xXcMToRuneoCLFrGutTFRvGh2Dj8cxAltNXSs8yPnnr/j5us6FDK3ktW4dW2U/qGbFqH5DyYY7rcBQXt6Nufwxejfcb0pA7fKXrJ7nKvxtv7fbYMPsNpcXwQfNc5fQA/1KlreEFRyvZjrbGFdnwEjsXmalsMpIlTbPqiJX+WKrLpuJOcyQrZivS1/fMpel7q3TUI26c0Tc9wfBqD47+nFn6ToygvucbgWc9FA/zEEJ54Jrhtrc85up8jGsVn1tIm/QVGd9ZaYUMeRQLKqRoDMPwajaAorSyjJLXgrWGBf3E/VpbSYFd44Vty4tjEBJ18rdIXXbmpsRrF0wR9zt+bbfbucEc4YVfBBt1mDhr4nRHUap0rCgWVe3zEDajOmeqGipFHGMgtFuGQ40M9Qavsa3M1Pp1Bo8FXcLJVeKAdOj/JXd9tu6uHP0ZTTRjGAJS8jUBTmLA2BErFWTN70k1vN+wO21WHFnIys/KN88LH53OtD/dCgkRhQUpT+UApdyeI54SpIjODbZ17nGMcZjZoOI60qrZTqjcy0Hv1zybKjmYGtQCo5xuKOZwYxyGQ8VAAAhLUBrMWJO/NvneJ3rC3lQQST4+0fVq7htsHd2PIeh4EocQZB//rocXapuUSUiQMlt/vY4IP46L5KfD5L8OtVjAWlOrsQ5xpWO/6PwSWpE/DjyZJ7XOpHjmA8MAV+Xp8KF4zMe1JqrcExmc4o9x9MVAehBENDnI0a59oXv0tUpKliZq1ynRpL2eQThzOIKrAq2aqW76rwh0XdLXkbcrR63ai/948KkeguLrlt+0MbjgWNwpT4O4/EhVbqiwhk1MYZjeJJGJRa8vPPgda5XxxrW0pXwlNgZhXsz9XUpzrWjGmvlWkligNcqRhygEXgeXt71IivFsR2Mt6wAodi5TWFRa3WVnNXQ20rZsYJ4LkmYA0MYQoQVZbsx96rqXkY1aq0jjGCC4KgpB6lqodHlJ+UVVNil8Vp7W1qJ86raWj9MhFy83HzuhvZqIN/nHwCpzWxx9Mrxh9tW1EJVsfzolkyVGLyDBSWh9WeQwXVdNLsouNbKpRiI4FVjtIWaXlpXYBP6tq/YsPO7m7ab0fS/6pX2J3uiqIMS/Ry6Qe/OHqkPCyCVK91Wu7cr16jMUwZbs8BNwSRpXUsR01Eed9mwwVVb9VW5a2teuorYd2B+EeFwr1SRmjrJWapcfVYtMA220VSOsqsiuFRd7P35K1V9kU3+RhKIUX3IWbCIewN93ayEEgQMmlRZYhNhSnmM4HJcqHq5ChWsYVQaX6vxzR281Ib//koJu49je3QVmbmTD2Xya7MdSlCLTh9pYPjJKn6yuqWft+xOM9U3VfrSvh5wx3mxddWbksSb0ryfoY4WthNZ7+46lv3r9+iVf1rgUwuXa1r6PGx6LbvvTwEsKHsdT6UDNX0Aie6RuZ+CLGado9HYvXvB3W/dkSx7wU4La8ch3i7VFyzzU39WAK3ecD/cN53QHgG3AirB6yWul3RX6jtstoM7ewH7AN6X7XeUEu/yvpsOaJTpBz/PVTnD3QQkeN9nPQLblLXx67Bsfy7jXabvzS/sVa/sWHvwfpJGz7iv9r7A9odJNU8myqGtjagmJpVwQASPGjC7/1OVrdQpMdv2fixJd3Sud671+72s6qzCHXfb+9/OhqF8RPkYe0INse29qvy+z3Vv2NY2KrtzhxtMPeAqiI33KXbmBZgQaL1BvOdqupFtJj2qF61YcZcWWvoZCA7IiXyOqJxlBgeNOXCt2oXvNx7HHO4H0dF5NMSXUgw6crFWXqljjufjgBt1ATbYERwPN1fm4zFxLjgNU621HPWjW6oNaWUNCVpt41l1alwShBH8eIw/X/l14Tj4+eCvU2ttvIJbUO+OgtjfcZcTdIgCBJt0a83pzgJow9n4AarjHJY7++1BROC8g4UVyeu3lJYBbx8xbWsoVtTMTSgBacxwbONQRcFccD6nox4OnBVPJQIEIzIQQcyIf/yYr3N542qEGXnM6Zmp//OfXyP4+Tx+fl1BzjkswELxszKVVRaqj+G6Zj0mH6NaPgHHx/P4fMSYI1PMHLE/xfHoyn/v44aah+WKW6B6r0nStVTj70ilcl0u0iFdhpRzDLLEFRLHFDyI5zYHbACR5IwBYkgxOWfVVVbq/jyPwWNgBo/fHo/H/DrzutZa63Xh68wqLAoBmGNIOleaDN1jwm85eosq9qlpQQ1WNWALXPWQRuOt1op1XJV6pUxy8gp+nTkWHmZbNao168MGJRvtbuRWot9eUYPH6naR3at+DyvZX2WVLKOpDUl9nWulqQF0hl6ONKEgXB83ebZ9gcuE5BrcHjtG5EpWwGs1hGoHnsGwizjo0LnClQabN5TqQIYyNQaDutyrpJV1Q5oyaHOYdlcrTBJMBA6OEbiW4S7W2raLDgIoDe3u6ZPGnJt1ZT4p3rirGw98gwNqOBvQkiILy70Br5WZQDCwIz6QwZ0q8Vsbah5ENcuISIUZOq/zEirhb7XT1o/Ww3NERKfhOmyWZcLLTgWYSldS1+OAHQkvQxvdEkvAeblbC/sybrFR7jd7uOr2vHoYRIcfG+WYzpO9so5pbyHM1OvyqGyOMUfg9McqUjUpaKP5Pmy1lQYd9w8rKtt7UycR7kB8pRCYcZ8LZ03H4DxwjPDYMnalrscvfb3sDVWbmNhNBIDzvMy8nnMaToxgcI6IWc6thtv3puR2OUtJ7XC1LY31/7///vj56+t8mFQVbjbyOh18K50qiKkx4hh2OPWYvDKe4Hnpj69ciTl5LqzL7jj+9nn8249BxJnx82t9PMfjiF9f+Vr5efC//+NjBk1IeR4x5/g613mtImNWm7scAxHjgZlusGUNNalcI3g8RpA+p+hITpISo6tMvHrLKWpwlnG2wofl8LUI4FqFfYNQIpXHoJMTjocuRIAe+6WsmGDWwMRKtr4JSeFue47HgDuIm91nC9Yuxtao5aWxk7IjeC0dwjHHPAY4aqSeyn26Vr7OfBzjOAYRjXXLJJWK7uBDgbrEeSVGzRxYmed5jZ0Fl073hyO3r6rClRXUjJDlyP6OYz3o0A+pETWIyuMHB+wkwIzlaMPai0R0o73zrdzYhVdjOL9+OxDRGJZtSXIbCcBdOiiMbddQb3cfM2/Vyr7txslerhnM4GkFvRKka1c1OAdYUwVv/8W6ANsvw7YXxC52sehFa03BoR9BmRqzqkEYlLh37TZO9mIIjLEyBxUaEbF4rWUKP0ZT7wENd4CGBIWHTX13PH23QjvG7yLb8R7BN1nAqYRHkoqL2oxjq9nEiJCHYBQq84nEtkcqBmmKc4RSq5tpbnfAY8fXylnRo6XdSgnQVV31N+y7WbeuJrV5JRSDAnVVwCI3ZG6orbvZyu08uouzYruglBO290DqknvJvWBIYsSgg44tkrtzcHZZUP2zbKEhfnWTMYo9r7XWkrRWXtkZFTN3IDAcILBoqZgB3eMI+rYmnYHJdlJRj5y9r3vnym8HKXlCTGWN/E5vjLOdK7WWByT70arIO3YFVnuie4X3vXX6o+7T5yN38qvTnP3QdW7f8K094a0CUPHM5pvc4gUQWGuhaX954+hWaGSfC6XS1wy4kXlpmjKRG/hCgNN3G5M5J6+eoqPseNYY0f49+3h4cYx72pK/YXhHgMpDQ5PZ9028uQDfz7B6ib8dYdxm6P10t9IsodmG5+26vL8iOMaIMerGUZ0gxxiQMnOtPJdA5lqQ5iADEWOt681PqMNtphOK6NMK7m5Z8e3RdLOg3n7alKbbe9jJ3vrrVnTcH+Qci7cvdmgeWW0d38xtcJhzsr/uRS7s3b/dutILatOYatZMqhTO1ntebM9uYLcDM12w1VfR5ipUBMgnESJd9l11ajBo2RLRGLXviUE+5s5SeiSWLY7I3A/mW+vTUaqL7PZeJZQORRV5VahykWxAbcVhijjgXolYxYkjiEgpMAQOBHmtTEc4KixeIHivtq1AYEh4XRcr9lt+hQmSMZjSdaX5QRfzOWawcoaeCY0qlZX9iiRT6Z7bntgqKIJ54etc9vSX4MTXMceYI4GPT8ZYg4Lw6gLxckv0tuL/u3lt02ijU8LTx5Loamuj8Ldja+EVITEGU3kub2LErvFF+/YgoWsVLcSno4gqKckj5Em6kknhC1WtCwaRHkdb5ypY5GTFoBQlmU7Lu/cbmcCP5/w//y3+9fN6XevXmamcY5yXXtciOEcRkI45gszMGXxOgDGGi7Z1DBi5fr3OXPrb5/HjY7rzayZcgOYcVymAIIQxYgyuq0CW1xabWxHRVAMIuBIGE+eVEudoWplpZCTdyWsEu1a/1Yf6/HfxLeoWEBxvXXXcAoxlYzcyYopf5ypeMOW4LalqWePWMGaOdQKW96ip7LSbz2PobbO7dZtjtuXYmHC+PeH2/bmNiVlur2sx9DGHX3GM+DjotspS8YKHjRV402i2h7vjKahUTJsF+OZX2rdnUUVQMq9KddaJsCPBTkZZ76yVEZxWC1C1dG7Q5W0dwbwgOXDjB0MMakUVOEDXPS+s7naDBLsdjxH9+Y7kl5r2YHV1+so9ZbKqvVBT3FmxlVJb7YU4p2BNTnFGzMCrYUGulHSm8R7MlHBOotrdOEcIBzR33RPSTAZuF9FMTlpLm59CqtqkL8nx7Z1mLoO/K4jLEo1hiDKO4YlChNwCu9bZskTXx4nW59ey/a8cmy8dXZd6ZU7UyEOHcmyTMrXc6RIE0kb568pGH8bDEWHXpkcuAdeCk3asCkHUhQsaEAm90dD45nm21a7D5huuw2gJJy8pz4R45fJIcsvVxXJx6xhWBKhIWF1c3/2h6HbWdutalRf8YCNGM2VQvnO29idH8HnE53N+PMcxQ+WM1By+68pMjedYy2QTPB5jjpAwRhg80G3Ld0JqV9zXvrj72CXA0+itK/IYn89pOY6yNXktccRjjKojBgNjDF0rr1X5Ese8VlYBzRyRwOdjzsHzymMGwDnjz1/Xn1/5OMbHYxyhETiO+UEe88omUD8Yzyk3bP38GOeVroSYM4YrIdyjMBCcBahSASTy0grgMTQP58OIrjA91zjXVqQtj6XNtLJmKCyBwJXtMgikJ7dQxabRAkbgdeYrSrl5tTPTYrqqksLnsY7YRjE7V82SBkn5uswzHejGEeWy7eoNezBq1UiQMB89pSt1ABFa19rVvEu61p0q8NZ7juEYQfBaqz0a6+v6ey1IpfQy11dwjtAYrTitDHdaASAZiATCNYR0lMdrzaLS+0jqGBzTSQoNhT1HEKCurrFttNMQyrnzZWYsXGFp+Nfz1CpyQ1r4q3uOBbaAtHrLN1Jun9GypzLExY5fS2Nwb5XdA5oNXb1f47zM6CSgzI7hkHt1OhC33ZPiUpTrZ0vd3Y9L4cOZLo0IN6qNMfwYG3mwra4c0LGeCdsv0L0jxrCX39rp/gof2ArP8d0Lbp0mOxLb596YvyS7l7TbBpSfnspNxiijQgBcq5oav3kHqGqGjZwFAWtlNFcgUTPLiHJRvIfTKqAtfWUq3FOAjXNuN6Xk+H323maOyeHq2Hf8Brp3wR4KOkuSOcko8aoYJ9qieKdtMl3m4xXsxGPdU9XB+s/cjj2Vqnl0vaZrra9moCrrffuErDTIWO1H+GQiYlvbsvJlCBnffIqGC2o3NHU/EdRFcWXKI7ujCMvzElyBqfSEZgar9rVf6mjVHSZgn9a+290zQ29r6L/5DizbP2oIW57u9lfbX3csS4BZk/B5qYRMs2Hedvl+RBGiuo6pvEQrJvTjQKpO1y21aEiUqLYKjHDkiAg1IdkTIqKa2nKzS3wFJxitxcpxaOcBuOldjr7dKE4qd+AvHmYrCDTUrsPMfZZ7V/cbag1dUb+v38LQ0NJCNIJjDJbWu5kKVnxrNdvSHyjN2ZU7LgzE9onFHbR5S6fXifV9NkxvGd3/tRbpGEwHBrUvb9PazKk+Go3X0S7ze+hQKvaaaeNBjskd9anm+6VAtWnbvm0jcqCPHerISplNlN37uAFxf2xvx/egZDoX2g3nXB1C7LyKZ41hNMrro9oMsH31LTBvtwxAieVyg9rd/blbG9qt4i1RhQKgrVfJPQ1tWwIDnnDqsou/3X5nmEEBYHCkTl4m7CxFqCaR+5aPgV5oSLFStlfndWdUJY/uxq+v83WuWr0AhStzJNMFxaVBM9pVk10iUlhDNbmJwITcRSGl85K0PFBjBqGgx0ysHMHP5wjkHz8voKBG9FhGbprPLZX7PN7iamfHMN1PWWbpPpnbjaufDOIx8LquGHQHIESsnrSkZuF6bVBdcJji61ypGtg2yGuR5FCohuF44q0es89ItAO7NQCIKlKIPn91fKtVs+kW1Bx6nZqBj8f8OvOlNYi1VhIi18oH8Pl5BDkHRsRKkXhMBHllnFfOwRlHRDyPeK0M8jiiunIq7bSsLPxh2xURY4ylZvjUPjT5qUu4VR3HLdhhoIzEMWIc1ZsZwAg4wB3sxjG+EoPlkqM8PUcqqZidVr0h/lZHtizVvkF1A90rzvYuAkXjLf3ggNR364Y3D0LONzkBmP0Wb4vepKgObI1gbO4MmmolPWLYKQcwg88DR43Sk3ryHYkZBL81gNzUJG11kdsZVsMbDvFaFWXrsHokEVkkbp9xFlptZnc3eYyOgXhFm7pdmM0x8Qi2MS/8mdUUpqqQ6iC2H942ItD1uceAhGtVfHztWNVOvEgSAjp3qXxRJPee055ztR3ebWLs7Y8eENF6kgHIw0Xg/JmgiFEAcBsHEsAchnzysM2Vimg2VjmZ2aNAhSaINr1ZrMnmO0RTBqjnAG1JYaM7+NG1KmS/sgb9sGWR9VCQI/kV59F+f5FqpT6UkLiq6RJWpoeauYlydGSzSnczj+mBE0nCU2LOS+elosoZbtVxqMhehaTJVXFtZM0I6XUE9D23PQcXdV1JDFDn63oex9e5rpUe3AFAlzm3DoNacmpn3+S/szt98FEg+kZQ9tnYpvrXdUIy6LuqOUDZb1DEUg+EsjYLgcHjGOe1JuVWvim4TlNduhLtgu2S9rq5gtCehdfn2liCBHgtXWvt7JcbL8egA+hKSVypOcr/cRGfSzXZGVYJ51IQY8RKCBwDBI7A//lvTwi/zvTmCrS6prviJM/zIhkx1gKRc445Zy8zIzQKLGIHmR2nzZVfVjUDWki5i4K7HVc0MMbIdZUjbuIbKcTK9esyKCMJl4CtUsOFQVf1yugML0kwm/uzqxx1Xd6CNyN+w8tGrH0MLThFkGeKTgF2H639/v6zVDYA5MrnM54T7nX9unKOpfOq3NWS5XaESxzYWLQQqcpl1lYG/sARtFO5ut16Sl+vdczr2PkDVvfonbr2cznQdswqbDrVvhXRUF+PIx7HLFRRLXV0MrGENllrvfkz93HbhPhea7sMa9si9FVbXNg+yU7M97JW5ksaUZM0gKIUgWaWI6hcJnvupyjz8vEIc4ofc5zXQodUXJNEQEOoiX5qU7j9NTrSG2RhD9CZl4Q6nEVYlQW0fCJieXhhiRfUMSYiUUNcboRTjgIZgRHR7Bm011m9a/baGrW3e2GntBSyYUMrJu0EM+lS94VGy17g0f3qogtcfMvuEqsehZnlD7Zkt1Pnm8nS2jZpK6pkp0zzvK5lLZ9RM1buc0awKN9GCtXGVFU4SuPUAlCCpLVyddiv5SQbM/k+duG343zuKNStIku7ql7Pbg/ZwZpoZ75ILv25G0T4OTtMASfljGucI/G7azwiI+WhGeaWFIW1XC8nw3cZXNX6UB0AZsVQCpgWCVauuOtO1fdSA8AYUTnSHcbuza41WSUQ5WOo8ouW09pRbcDGHfsoB8xrmn1b5V4WiMetd4hvCkCGInXgif12b4gpTZvdVTuObgF6u95FFPJZTeCeC9wTHDafjZ0grjhRS7EPKBQMRGCy+tV17tiNkzjGGIExbrqGihKilpzqHOwIkWlfqhK/0trN2I4ygvuJffy3xmwl2KrQPzPJyLdlThOK3MT7bbr/flenFVouf1V7Y9R+oJZ0rQvAmCNcS/woypOA7S/0Lu+/64IsWngZlezd8Q+ro9BNe2JLwraozflqmL+vcK9IpSO2A8Dy7fq4ZnDY+QUcqXPvvB39KYDcRPwd/KoanlalW1R3/Hbd3QT8gEVz20rZMlNHpYV9JzPlBCxFFN6qpEXZvaYtqw/Ovchq7qgytbQ1e29uf1anB3vvrY6CKDp+C1td/c7zg5xjL2P3O8eOdNDLHuQxh6/gOZQY1Wv51+vKU1QSRb10dMAHLIKpDPFt0GYLHRBJCC8zgLpDk5dLZupaKjLPc0XUGm5TR8Tqfl4jwuG6tfS6BGAOe3OKiGMWN/DzOcnr67UGuY6xrjxRY2vqs8oWt6vaR/HtkN3iyiYNffNfSx3yPpuWEek8cwlxjCWuVKykPCoYryWsjg4MBvFasicQwVwYwJyecR5z8M/Xmu5+HTgmAQb1OMaIeBxxrTwXageCYyBiup43hrkV7SiUiQlJY+i3z+P5OI4ZH4/x58/XuQDpz1/nr1eeS8+P4/ePOSLH6AYlER6hHcFj8piDxFr6daaAY0TNy0HX73jjGCuRu1+vgMCwfa94QUnzSi3pupTSHEG7emb3EEqN4DHLtnmnMt0DDkK2V73DJ1LV7Wx8A927d5shp44DbfSxIfitB0keM85LV8n5nTPM++JARazbpYSaMFiw1/N0umyxjAN3FVLfE+GMqI1bvfM5QrmWGIS9zKuCaECX3ixpCa6Pa6OshhP7qZWphTTTzsH3XCg6eSkjt41LgomM98ZC7XdSlbrNije5cMxgD8K34kpVeT4M7TIVnl7udhiDYEUY1ampsraVySOqxdOd5gEqoLa/UpColJtC557qvb9kEaihy5vMuuWk1p8s6hacmyvpgLDkkeECwUG6BxwFN6SngpiDK3V6/pfzW7x1yN3UoBgENPSNxrQdLUFHdN8zIH2HUCt/SbykyNJIpKsJkFn8uwg+j1BX9nUysQit/rzGghWTMx/JH9Yse0LKLvrPNojXdZltYS5errWWG+ImazqKu6y9ny8bGq3EHO7o3E3ci76P7Y+tFMDHEcfAFWOBE/z8cSR4Xogsh3wtMbByrZXHcMyqDia3W3IXvARV1dTVPwKdpALdFzbRo8QTADcn/C7HK14j3AU8rNzL6SsICsoV96NxFcHRg9jNf2m7UZpHxWJPDyox5AlGNXwR5hxl3VnxU4MLw9Cl6qfhu2LEnIGVryvDJQupTM3Bt8DT3WA0JQ8Y9XE+l7wc5h/9/vmYIz6OSPDzMVHlyXVC54groVwjYs7jPM/MjIg58Ou1cvGV+HVpjpgjPh/87397nNf6558niZVM4ceRP19KcFBzcAY+HvxaWmuZXeg+b2PIgWAyUnid+vmqpqxufKOm2VrzbzWp8gW1VnaFcHse7Wz8JRJ0R3Z4y6WILS/tum1Pvc+R9PGIv/+YY4wlPo/x42Moa+hkLrlgSm79KUCxloCF9im4AzyoTL6U9Fkjr53wJlV1ZM23vR1htbPYG1yuY6FTiJdZfP38j2N8POYtldRwPh2UlmV+JVeo58kihWNGkO6ut2Pn2inKLWfcK7qX9zadO0zvW11uIMO46oXtS5RWaQWIJo7YczKkcxVw0M3gPZKPxAj7R6DrqggG9hi4jQMZIpiJ1TRJh2ZcMC96HJtg45gEeV3XnJ5Mmre89LP2BBJiOz5t40Hk6mYjLUc2oH4EdL45rEjVe2iBf/PDHdLPe9nVzpMDF3VvVil+lnRTYwB0RVs7OR3/sAQ6OMXWFSMc04FcYlY5j8UIN/UQMK+OswbTKWVLVcS4a6qq6sIGwHV0lYuog0anCIrrliqF3H7Rrh/bElaPt9xptWc5bhKubS4Ez5NwWi4IKUzJjQ5iVt+sdp9UlR3lqVaGkR5sudPv1a3ZjVUspXdhyP093rRMifb+odoEOl28Mq8lFWjD7bdu7EeGG0qJsz2ngsUuJUhlt0aW0FV4BXzsNXE/5z6WFh/YtbXY3cqlFFM2cmok/3bOO7hrm7qz03e+i/s2Sp02wGn+i2pNGqoZBXG3NiXZI+nI+0cMjuj+OSLBZCG24RZ13WdCbcLVUUKSI+Bufpt3pyJgVWgSnQMkqKwvuagqa2h9YW4WxN9BjO1fNhNlm5D2lNunrA3RdpH8WwYV7pVow1BeaSNJC3U0A84fGL3cRBbTJf1b8yZ8EKPk8IZD3mluDwddO9Pnd+PvfUbWqlT428922XCptj7lQLVOaFFu5eesoNuY54bI0l6bOoPB4km2aHyXQMHxx9j1PgKi1ciWvdtcmRkn7dZR/ZrK1e/PtzGuA1Pj5D07o2Jhmt0Lee9woQkVCseNSgtz+IQGcCXMWL6c6EFXvpAExojcW6RC1XnfFd6OMex8PmccHgVCzhk7Q/b1WubH2o+dYzwfDucURp/cVEraGZtjrXTxaO6TbpmxvrL1UY3gKA2APs/nteScp3PqKh11LR3uXkG4icAc24NFjFHFecBw8cXSSv16rZU5owTYTXlYhw7ntUboecS1cDzwt+DXuX698rePuFb+eqWZHPbGo526v3xtudoM0DfVWyEqvTmRlqhLIuNZzZjZVowROC9Du6YMwAOnASqlwfj8jI/JP77Wf/4CgmfmeP7g9evHAyLPa3085++fj4/nhBtkEr9e1+vMP39dEOfjOJeOwfNcjzl92ns8U2nYMeLxONBx27XWbz+e9kmfB//jn69x2VLh4zFSDpm5edNA+UPuIyaGjknH3zxmSJkRwKAzHOH8/mZAtPkzpSjvmmhndzcHQQDmHKqGApzHjgTlPpXYSqGGyNb6GyeZlem3t5SWtup65uoqFTA2glBtWW2sm4pYAY0IDDNN/8ooqc991z8W3/YD74MZzcttIGHAw301XyGAMceMQFRN92OSnFa/rB1trPKmit88GQJVPdr6pmwp4EYS6swqSF1rCRgRBbN6hncpoB0sYTV0cH5Vxbm44xM9gc2v9Sdbe9ual9eq1LLVS4y9XACr659UxUcNOzpXazCd1Wen2vBEDydCqd9O/xQUwvYWnOdeJtrUr/s2+08Ww1f+WAwoaoVvynnDogAQcYSCupp2NwZZ+cEaloSNx70LHT/YS71ViBXYjjAL1otqCFWXyl2Ahk5TW1sGI9dVvG8FYwQjEm/TnW7ncdMHWYu8Mq3zXSlwA/LmaPiW0xTuEUqg2inoutZVI+Gr65nKLBZw3S7WclaaXIlLID1EjCNwZYWo9hmhqywHjhkP+uMYAI8ggTMBMQaklav6P7RnorKzFegqMVjLxbREA1K5f4cqnos7S8T257mPjwSU3gNAhlA1fbhzYFyp16UR8MAmZxQ9eCXqje6TXe0XsnvtMwbMQ0y7ZxUg3uciGOaxmjuqxJVV6tVKqKDK8ODwGnlXMVobg/EGn6Sag4b+oDmcUyrq7sd4OMecyr/97YdZXyvXeV6tV0jGCHEcX1+/KM451gIZx8A18PMUqedjPGd8PuL3H89/+/uP67p++/y6rryu649fV4qfn49/fV2fD30cLGV+6mvxOdIUpEs6Ij6PeK3M1KrYFrd2dfynfUhgn//7y4qNbQ+26uqzgIbOTQ1rSbz/3ljrRjf9LaEgPj7Gf/vteD6GTenzgaAuSaqTm0vXWimmtxJXg/G7zMLXezuklDLoiWbdnR19prqDwdYP2pSiVg58C4gTnGNHNgFwDj4fc+7eQ/Y7OjMtaQAAcuWenmYH48fTQ11TjpvkO4u2vnag5x0p7dWve76TxyCQiRjWlJqDzfTQ1qg+CHAZdZnpAtSCnC2xzokmrUQgoWjGTFVDIU0mrQ1epYFVrXhZabDsGJBSXYyfgQAy8zzVWNHGFpuVL9Jjw6PS1iVTMpVhcK2uL23HkUSCPaTWYlzWrTazIz6+XofY6789C9uepq+73goXFmvYSHaplek/vIsNseVnexm+mZSuzIGoMNZuJ6TFblAzr6vil6N9oTEHBRcumllU1900tVL5zZlQOb4ERqBqU8xQarADsgvI/79k/UuPbFuTJIaZ+Vo7IvOc+/jq66om2XxB0JATAfrPAjSU5ppwoJkGggQIoECQEtnsZlfX4/vuPZkZey9318DcV8Qtnaq6dR6ZGXuvhz/Mzc0LE1X+XaGXJTzVRaAaVJH/WUp5cvKRoRYiG+p7VEAV3m+/jX4mS7k0q8W56GQVwIR40deKDeJkBZDl0qJyOrBVpWjJFu7Z5zrRLhcww/Jq2ylDVXlIRQm+SuUhLMFSV2jKUXfxdffvSyBcSx2+WE09BVsKrFArlNzGzn2fnlm3ejfSV66khMe06XoYvZF7lVxS5WqZ7GcFo769+hP7OepHdkvXbstqRKngjSpuAACG1UyQ3o60oQ3P2zSKKIueuKHUukzvE/VoC5xG3ud09aWg18VoZGSVumUMO5LeUe8fYkqtroyfVSrZjVzsK/T8yhrEJgvV2w4SNNtE981Wq52JMOuF65+jHdOfI3K5NCVhEPNQlaLcsVG7i8rMtZ5mrD4zhUaFd20kob6yeDGqU/Qa6sYJKlXJK1uAXLrp/YJ19gwYIlh1O3WZc9SjvZzefavYpO7yk8+bUk/S5Uw1dUT2T6a0kNZyb93RV5NXWUtv5q727w2eZlLGra9IZOaoA/t0bxvzqTAFmS3FhVpqDXjNlalpSu7xqthlgzXlVuO1OyrNnTb3PsuM0mDgnPZ+m7fDNME3Inyt0ulMkXfqgBl4TKr9Xu3xlESlJjEkCMxhc46vxxUee+axtiB6sGPUWhHEtN1HuR8su52kTLFHHsAxGJnhW6CNPVIgzIbHcvcs9S8u91VTE6Sl2Ccicgy142UuN3IOHpNzIGHE8TjXn3/CMey8/Pev6+PhAD3y64p4aeP/wy/uMDFf/isD2JHii91QbqfnTPEFur4eibWwlrqExSdlGG/G70cNtSGpEVQJBG0yI/Jm1/2db7epFrLL4X0+r7Xut+OY4zz98khn5IXjLeL8dr8p6ZGWZMnxlDu2yIiItfy8nMAxxzEnMlkRVR63OQZvtyMzeV7G8XWur8vf78d9AgDNjsERccxUaHU72PugPr5mEFTUmzIUc3ZVJuJaRRhRlmjQwBQgc46RaudTEFMdnTs4AZGCg1GIpvAAAQAASURBVEBFJCqlFpAT6g1EjjFGii5Y+J0ui4PL3QgLctRNlEqxvtdsT17PiHxcuRzZ4JH2VFkudpWjf77X1/0h2lPwpoKErk173W2r2g5HmPE4bA6LyKH4LHOQxyA4s0sjGqO8rRaA2J6x/XV24VsPPEjjmJnuTwMuPAXG5a6H3YEgG/LJPvaJYgT0pe7mgT9enp1ZZKVHe0whQEYDKJ7pqnP23u7xxYW2mArmIbmrTE7jHIzgijRR5bMVJUAYRxVY2MtSRimhxvsCEtkCQNuflO1Czt1lXVKpwJChtnixCNkKa2BmcdYq86wRNgA5dIpYMELHkp05y7PVjra7QUtRoMlTr5MtFQzWqctnXK+beCXsyaLNSISjTi4RCW+Z0BJaaJmkhBCrzFSFtPStgJcCfR0PtZXVuVqOiC5doGLThD07GvU/8fTXYsFERTKFboRnBzDVuSMbHIAFyTDjbVSQK1c73obiSUtbazzWijq3pUfRSBwSqTzCPVh1MS2aXkoxh/6QWxVXQWnHnU/SWKfSCkuoAejK6gKIiFVtg0GyxfZzEIaM3H1qNNKbiGVm7n6bNNp9dMG/QYFVzEEFGs2IS3jkpY4qMUR0PEAAK0oD9X4M0V2awln8n1rGYaO7hfSi11qqsgzjrz+/HceR4eda1xXHke9vBGE25sR1nuBwj98/Ht++fSMxj7uvy8zGMTOTzGNO4/WnX96QOQdvt/n2djvXWueyMQ6Cdtw8f5x+vx3TcIx4v4/wXJH3W9AwiPfEiry9vY0MmhSC4xg4vg1knis+TmVUYJYKGZMdInc0WOHR8yT/0Vy9WILKSl6+Yvfs5166DVPWLSFwm7xPvt/tfp+SoM5EuF8VHacsjLZ1eWTSzM4V5HUrjKWjTZ3AEAj0h14CI3roV2XmyLiuOsr57CYLbzyVW3Cqb3Q+n54Ab8eUpe9ZrkA1vOflKytOGB5xXZ0cZ95v43aYe2ZYRObQqLKMhFvrbSslR5uCl4Uuy6clr/73/uDM5TFoC9mSb89MTdsbDZcoprfqr25j6HXmUamrnKEy91YURS9aOQu2w6q74JmRvE0DUhGLyloiw48dNiWaicwdBvepIglLpqW6GlHZAcEcAG0r2ACFG6SRsOdIKG5+cSfxtYPio7y0I+tf5SIq/izD1uoWqbw65qgDwT4GbMEZGX9lRQqPRRHS4npAMx53L2RZpqzDNlenEWEpCLwwhZdf2+komM+iAXdJFMquK/LeMKTh+W1G0Gy5q9NY8qWemUxp8u/HUuxVk+B281TTiYR/i9b2hH+iKxa1HzokrayTyO4LgCeB5X6uUHXiiiBoKtdDiIwAoHL85ZuR9DYpldMjlHex3nqQCj3rUCWe3OqMrMYN80gPN6van3d+HtVOneh+TjUgR2Z1PpaeOXKzNwBapvepybYX+3P75G3LyC52m/E25/1mQ0BJIsilZAi4IiLCog+juNPdr5TYxREI9xlWSp9tIdBBed2T57ejbsLLnbP6bz4DGiOYjEiOTf4lkNGWXm0FQTIwhtHs/X7LzPNa1/IC4G1kxvLwCI0uVs3EdzzzpJ5WZBU1N2S3tko15OXGZq9rmaPOi3ql1XqmkCJLubDywDo7RmLnsdn5eA4bINy9RuBlkhbsVLnvWjYlw3sui3uO4rbIGhdpuZl7fTkaZHy6Exl6RfO5afCMnTlJmawsTfOXAKCYgPsa1gq90lkrYqxactVOA7Ty8KyQMyt1K7hhE6rLHNQMmoi1NBt6J2N1LKKx3nje9+d+EDmHzWlPTFPbZNIarND4WaVuIC+7nVBnNXpD2+ZieRA1sM9VJSA96JoTTGiX/4CFZdUGBQqb8Rj2fh+3WR3T4Xl67VAMMxMXLKnAtcaRlNd35iDl7F/AxIyQt6aHZsq8+rcwvUtEZ/vpw+aLQHQ4mHkQ/tRMxTFMjGMtoHuSHEkploH0WMIaFSBfl3tJPrTeWlMdp8GYKflquZQcBtwPHsf0FfcppWHcjknj2z0G8HEu+8J57aIW9zGo1y4Tty1Q+ZT+gj8kvRsmSuSKjNXzX1rgI0JoRCYZ4ASPaXPgmIrwyqa938a3O5fH4xrHzGOOJG+3+XbXs+TjcemQf37F7bgFcDtMneRvRxqGaKHsni8xPoSxAuke17U+vq4VuM1BuhmH2XHYL9+Pr8lhuN+MyMiY0yz5WP71uJbH/Pn92322/zVdzNttHHMCuJaf1wrhW5TFgwrkEXl54KpsKp893SQxrMY8C272zFGV5TLLsRtFe/Mjc0XKt4hPZN3LfF4RmWOYX+EeyugSIKUYUs9Dptl4mziOAaCUTxTWeGamRrSAVIrbGIOQmqRBzRR1yPs6aLU9uX1mllaFuAAQoqXDMgcj6+6sJQqAgfa44vRUZemYA4SvyGnH2HFUDuNQoFZhM7dViSgyQgdY3BGFYgt9bl3YSpTLVBUluDYI6eVV9fLR1UV03KLerdd78Izpn7FN73rjZZ2OIRoWkYl/VroyIRWDDvxVVUS4GcxsEDY4uGOjSolk5UFEYnkOSiLnGUMmEMEEs/WS9AN2lSfb3tfll6MijpFdodJJYw1NS8vMYZrDIRAQhjTADGHMZLEwBNjVm5bPCGHepGH3YwhVwyBiNzNqaY2Z6Zn2XLbeKWw4qPUH/rD2WaVK1HaxguGOvhSGiYuqWrwVqbPDjWp3KSx1N+QFVjlceJauzXK1ED5rY/s26OdULFx/VICqz9KpqJNFiVENGfaa9KQDakYzCzOmD8thtDkvT5m+SBzDhK1rKpk4n9JOF5VGeg1okLZYMXw2SSUgtkhHdliyM0AEgjQmmUgr1KtX5rGciWNa2SURtU3LLZIBsIMgAMjDOGzOKmHaDs8i0jvoN6N4DeFNMDTm8ihKK8bQsEWFXgoMcUyNEe/kQ7SHqGxfsx2OMczgHpcH2rXeb3OY+brW8sfya+Vj+eVxP2yIbW9jLb88vh6X59dPP327zsf726GozWwMs8e6fvp293CbI5geBQdc9dS+PK/A2zEYjzG2MhGMvN1sjswoNJxwzTZmLDMMG7VH5Bk8r3QpHMvodqKFEqOo1Olf4DoANsMO+yi0+cIfLNm2Cqwj3eauEUm+HRyDHvnxdd6PITkdKLMgi/MFZl0NpKfpbDGIFqPFKwYEY/HydhCsC47nHJ68rqxECa8AkKSxwC0w0lWHDHjHG0bMyZTSNivaLzstGBsw4+0Yt2mA+TG80p+6qqTNOfShw3JEusfygGXnBPDIqEny+YzUtVt6qBZpAHsTWF7fA2NALBUzjiZ5ySopxfB8jh2vtKBt146VPVuPL13FLi23WFbeNeZOMaWnRve8EHNaRAZgmXNoSBoyNRPGS8uxT0QCz4iFqn+wMaCXO6j9KCA+dDUqLyLDiqkn4KaDzyKkq0hc1l6uDRmeAoJp5pGr9Y5FGRuaZy8JrQDgGmZaJ+Mp81fZ47W8HQpjYJR0sTho+ayN96+dsk2lVIkaFk/Ct8Rdb24+Cd19ssFuLuhUTbTMjVVE8xOAzHSIIRvRCZt7XWbXEat54aj1LoixnsG7MhMeFu38n50y2s983vIM62RQxzprLoCCxZSQgfgSJcoTqVZVsSSUj3ZvWh21jkhSG1nKp12/yhRXsNLtLqI0rRoIQv1hJDMpjeqy/juvZQVWAXRHXVk1I1rXMvfe1BJFE1p2dFd/VLgAIdDsBm4Ax7D7tGFmw6R/dBgPGkZ4YuRQJWoaM/NxrfCoFKU8LPvCmAp9mk/UliuRaO9bxc5exgSkCYrlhbNVBpPFZY3WcDVaVmysMi+kpzA1cDkJySpNkxhuFWiwIdJY7pfHtRxZcuhPkkwpXHS0hEpRovGq0dGPtd2LfZ6jdkVwd6IKvAA4KCgvMgYNfUH0HR55azZnBxS6KeHAMFsez6E8CF20poxm9gRdpVbXqgdB4uW12N5G39AAeUiMMwUelw/LbciQ0k2ouJwNyirb6qKKkAslhNHxH57vo0bRLmZU63bRS2o4q2Ih6yQwVDeOWtqMF+E+mXv9QB0A/YRWAOrrkzs3KFOgZToGNWqqE6XdOi5Tq2p6DTwtmZs+4vsn759vhOquyLhWefeGmbadLEqU4enPnl6S2HqXrLnOcI8LmINl6BJmNkb3P0qDwBqeQUG4mrWhfp85SmVWQGetRuTy0Ehsdqg1jXOaR3QKjQRGG/AyGplrySk8ebkihYaHvOdyWcvi/4h6UMaGTPctbFFbaaZTHImrZwgAkg7Rzx2ehhXuS08xhq1wY94GH5dn5m1ykMvz68oWN+zQf+/8y++KEPUysIZtH16OCiC4vyXhrdo+uCITeSUicRv5eYUnv+0vEzePhGHAbiy6vqg5JHVptZLDOIiL19ttHpOZeV64zq9jjq9HvYqH6w7Rco6RGT8+PdSv4WU6InldbjfOMcb7uB1DBN4EQMsMM/z07XbMuSI/HiuQx2ieXeYxhyTnFRRmK6m3sg7EKl8EibWicQBpH/bnsMB/SMu2s6yCJtTCDCBiidSfIYzj40u9S0p3OQjNvgGg8mdByQmwXIDU55TEHwMxbK2Fpjwo5fvxWCvSwAglb91tBER5PEXRHM8xJxWgcXCMnG08cyMTRRoSM25nY1rkXMsfl12BCKruhpTy0RDsMqpp5SkcuzzXclJdeMXilKSiEVYTIDsCr6uHHYMfA3PYcsjei5WzsfgnxIM+2RWHPf+iTXQrnHR+Vbd+G/f6htymRhgL0ZyXtszZ1h4QDhWoCdkK9SrYOhehoQ41lbWnHPYYdOk0KaQK1IisSAzx1neG0OrTBVJUS6+iI3aWBaA3u2wn2NKkw9h94HyqBAiVbk9srDrKMOu605AX1YgoNbMPg7opN5czI8PQKpEoEpVWPsLL6KD2Dq/eKqFgWFU8oluiKiOOdpnssDeLmN8BI1qkJtMl+UOiGDAGciCsloidhtRFbjfXa/XkUfaHgoCAJuxHiO1tXy1vMhO0QFaWFIHlldVx8HKPwDEtoXSg+nkJ3KfdJueQYZnucV6eOSPzXClStxFpz5tIYgyOSg25D667NhQkb1MdZbaC5wqQNkfkujG/3YcNqnbMap7F7Ria62TG9AzimBooF3wpcrsEZM04zaPnCSE9gmaTBFqFoDfOjAnGcoW5bDGmJn9hRaow21Pq5VsEJYDAirhWgmNOu01psTMzMCAveky7ruvykKm5PGAWsdaqtG4azTiMv3w/PFacH7djMIPGTBOB4O0+fFUCNKgdseuUrygo/34oNYA7ludNpq6nK+wAr08shw2C11pIjMHbHG/BJPy6pLNjwIowg0F7Wu1Oy+G5U1xsX69Ds1k/7MoNlHq0Ec8yVwJxsJ1/ZpC4H8/RH+gDoHnhSCl5lwMicjAXEmARKtOWew16VT7fQEDyWWJMCYRH2UYRR6xHuHTQXqZnh5rsJK5T6SrNZibIYE5Y1Kw0EfO7sBEJ4DbH/ZgqCKqEYLQRjJHLk0hpvKzlqh9PwzFHZChQ0YH0yHNJ4Oh5xSk+QvcWbUAAf4hvOazxoNoJKk5Ah+tF+oy8DSawsuyzAolut5VjkwM133Zt9wyxQNNsWr0HRERYDlSZM9ORGbUasloEIREcrTZJeKfY2H4/NZOe1ZO/0+1SHgNQsRM6zveonk2NYamTSUYU7VcknU0U6i9L6UkZC/E1MqQmgRAoTDWF5K4LstLbdsW7GaKXqJrvCjjuhEMGrDOgOn5zdQIXgEfYU6YHLPBDuZ4+QpUl6dTU31QWl5lllfoyNPVJxsPT9cN8Ce/LYQzSzzjmQKjy9iyGJFDoCxDFPlb+vnOqbOxpJ2Kv7g3PDC0lTAoBClJXkcswVnsXUuiM7BlIkQKe1KbYXSfZNXVm5DKroWnRe4w+TOjgoAzQ9pkUnQgvy4gi/D+T/ye+iJowvXdP0UQ3RdeGlQGuL+sRrAWl927qChAlB5AVhxbKpbYwRk4DOcFqj7zfpxiKAs4y4FER7Y5L3KtfbAM9Ap6kmSJJ/MgAOTQwJoM098iMOYZiWhms2kRWFbeyWWWPngBswAyR9jpQ/HFda/m1XEhiRhBKdbLCZZ1fM0q2oI4Gyk0kNruPRSJLceli45qlwyJystq22wxBYKK1fVdUVSGyiEWpj+iqUdEM+ijnWguM7FZLAEgjPTaHcAf5GaHRoUUPScKiD502ZWf1/W0pjfauKKqv9A/8FGqM6hMkqU7aHWHr/jMzC4R+BrJZWpWr24k1F/ZZ8ISGbBtry7B1maq/rDhAHcqVQnZbj4ie+fK0RuU1e23bnpYZmWN0Y5RM0+uvXDV+El73PVcrVmSlNx0iP7u40S2GVahZf6ymRw06RGYLt/RubDTIuF3pxnTqeoY4vRXdFkCm0e8CmlHS5oWm3cj7MUYHsh65pYKUJIQurD9lgDAMDPcoeg6Fc74084reApye6/l6+bh4DBuGogoRxvTkkjjLMxpoi4IyY5XVN5aqtHZEVibMLM2FzGvFGWri5DFzwc9ruadoIySNOSaHcXU22GdPn/v0bSQk3PBkGnbesrO+P0QwKEVeMqdxDPPE9zmuFf/86Uw8Vt4Rx+3ISotxzDkG56g+AYtEupkEuVNNb48rrhWyT5Fk5DyYsB9f13Xpaju5mZX1JmKJnld8nS7k6xg0cnkYMe8HjWOOY1rEPC+BfmDk7RhjmEeaxecZPz7PH1/n+33epqpATMjiurIZFWnYySmNc1h0Q24HN68q4eVrWKgKjHRGBnJsKK7I/OcK9xCOZsTnw8/lAGLYHLY8pEKSKVo13L0Aep0HZKxMYKUTvI2x3D9PoHkxO66QRXgs1w0xXbG6fKmso3KUHRSwWm9QmXJmTdupw6LomXseEHa+kQDmHMPydJ4rzkslmDyGvR0m63hMebjKSz2RmdbmrBxkqTBU/0w5ASEixU+s/LsxLP2+W54yxZp5IXgm1OvNJo/gX8RFFVm3KaoPfU3j/4WRDFS4W80Q+suodfRihtAbF2qZz6zO4A01VfDNnSMq6d2xW3b7Gxtp05Gbld7Xqyj+e0IRiuC3W6y7v4XCDdJ53Ey2WpWk5HoAVGtGVbeXS1Sb7l4uEkmClrPLrSVHsEmv7a1WJKXDXb+pBD4SWZNM8UyoOpp+LlHHb16s5OxXZ0qwqaBRhdbtlJWiPjtWsJNDOYFMu00A8KTvXLSXquFWdgSkZ+LTUr5ct74Z+veKByL3/UiyBh5FQqqPnn6UUSoPWuFE0sgxEjbMcEyqnbwjYd4PVSVwmylONJqpqHsoZpk2kBUEWyRGyZ8kSTUsL8eKeFz5tdb9HnOMRJhhKnKomA/Lc5j32ROmiYiwYQmsiGHU7CCQy/OoNguyMn8m7Fo5iKN8yUsAlrjWunbLtjqIGx2mcZAbcOmAtmwvyTHsWn6t89vbcT/GtRYcxzQb4e7vd7NhXtFgDXfLKjnnefWVn7xxLE9kmvFcfi7Xp9Uo0QQkWpw55xBQ+zgvyRG0JikifRhTM8Ja1QUVnAiJ4RxzG4RxzFvJ1sZkJvDTm92c60BEjGG/fpsEPs/1dfnyvA9eKzzyNkVpt0h0WRGqs7JzeSNoBS5bDUpCRBZ1iVUg5K5fJlI9IjkEJsmQMuAv7RJK5glkDXfLwYgs3TRmAmoXDQGQkW1NXnQeI6vCsVMZAXB9SQuf2iPSovIQJfQSryhDpmRV02/k8thxmiyJWQErOkIv9k6YO5C49eC82zGOqRMVckDDhpn1T4Za8r0hTz2nPK/AGq/Gsn+JBGWGR2OySA+Ik9rFXVxeaeYYzIyjVHU4rHatA+P6cR4gfCNMO7MxMg1ZxNvydO6RyWGWK6dV3U+Zv3VvnmZOscqThKYQRM811RgwQT9VAWp8DS/PUMlkvf6orJUQb6MTuuV/7L/utCgzo6sOlFVLRLG69DFG4nG6IIu98m0WypDuiMwTGhxcEJJh1FgjnLFG92pkZkZP+ipIKGd380IJ8JawAcDms22GC/BEdtEc4+xfz1hE9phImsoHNNAr9hnDVqSuVg3FiJac0HSe8opVf5bz0Ky1TYVon4Vy3Ts+0ZOXDgV2iUYNlmbJEjUQWURcdBc8sWLPF2d2dVRLmn9ggELcLZDXio7c6oOVIu5AU2+iyG5zW1QQkCEW85btxdtMyc12ColKKTXqlASqlXmHwtX1XA4c1YgBchqbsFzZXWYQWJEWaUBEjgF14yv7QnN4NBZjDDvMcFRK4NKkqPCtrt9qbRHFfV5j6kr6gVXRfd7sKCRPwYrWxA5NM9qzVAvQygRcqWyAzYRC2kA9arg/QnP0Nhha0NFeUoEMVV1o6Cu7uyqzROb1HTUSDq6+X75A9Tq3EWlW3qg/TZmM68UVPmZYJSob2wCyMjYqlsy2FwAj/AWASBbZFJWzckMS3f8VuZaj2+vQ/QM6NPpR1iBjq3Jmp0C5mZUbBiojU00Y1sH+E8RQkGfN/e6QrhZOSM1OZfX/+mfCBgvXEKG4+i+UL9cbPXPNbgtrbktpYWxDVJY0sEqfqP5ePOqpmbfIqqTXldRbUk12rKJQASCJqtigS6+qPEXfw/45Kv7hXA0L1b90LtnyjWw2b33NhiAh1Z4aYDfMxuCTBIscNuao8XKDpkqhvL4ppgGm2dvNjBovJawHXpak8wsaNMK1HzQzqxmyZuEx55au6KQhsFcbdcXBgKRYKLofqcHJStJe6IsbL22zt8XjpEJaBjTCda9YTZ06RR7Lg2bR+EQJolFBuSh+OQeXEMYmD7/Y/vplgPFJwUjCiuvwEmmVp1B1FJmYxu/vh+pUP387Ps/4WgnwOMZkeuQb8/t9gM3A6GztuhxIu5W7vDy/The95bDiBd2OeUzDKneemdeVZrTJHVrK36/LPx/rcfnjWkjwdsxZU0htjOOY99tA8bAAwAOZvE+b0+J0BcFJksODX6f8GGz4lhTt5LMWpfDFnoMu7CZLH7El3jv/i0xEnssjcZ9WirqFvVaT1LXCmI/LjTJivB9DotqPy7FPBRDL13Ibk7R0dyRJAX9kzmJCZ0pXmH3ZAtikocBtmBFmmJNzmHt+nmEl9LMBOsxRROvXcJLkwyMCx8FCvaz+fcPhnZC3ovOOEOrnU6yQYdyyelHlMFLzONqm78NqRsDK9nZ6kknhtorp5XHnQA1c22p9QCaGWaumFauLT3uD50dlme4X1KJu50Yl9hc/f0ADRQUQaN07yWnn37c8QcCZCKGuxWmpxF0WySyyBjNXdtKfJB+h8i/BKWKg/OhTYsbGMLHLuQsR+3HrOeQE+0GhoLH+hv1yJVVYC69MT5Gt1auIXQ4A3shURbZVPdoheB9JXYDwJBlAeJqVJdxhUj/mttHcK94lauwjEmiEDk8srbjRCs8qOXzadiNvM72+s/7Pk2aM5ArXeX/GmZppBqBa4v4I9GSW9tA+ch1t7ndp61u5otGUICtwNsBX3O68HTsKSKLmV6anjosSEn1K0511D0Ejg8bq2tLZU12nc6FqDZT2OKlcyAA8rhyDAVzuZnyfM67lHpzjXFgqjGTt/aCdV8xhNuCRgXSPr0cex9yW8Lwike/3OQeNlb9oL7XUh7V6S9+g7stAjY7MlH6LHp7Ys/ZsDA6NtgUyc4XYxJjDPOJcMadNwzDY7dAqXYEx8jjGsBEzj8ibx+1w97kiz6slhipEV5yVb7d5vx9r+bXiUisrfRjmKEmuOYfCp8xcS5x7uOflElxDaP4jgrTlGAySSl+FIwA551T0JADrGHSHhlO4BzLuh02zY46fvx1vt3Fe8XWtzLwu/+3j/Dyd1fSXxl3s1nxGoMDARNeuRhv2FgTYh7AEv3LnUSBEPMkiRVD6IvFMbnX2JJUBqpbcjrLlosxGRLhjazxZDUPcwTlWjyshObodwLsaXVT7Z9GxYmkoGWFVyxptQKolhRqImVDBntUHHRLUV0tTB59symQVEJVTI49jzGFz+OXKOco4KtzSIgyTNFMbcGC0yrKPCmXlBJfHWmIIYC3xWXbKkMxgJlmemqBHuPMyniyHdZ+Gpnlu8IUv9Ey22WPXUGWhorsNtg3M1NCv6r8O0tqOFVOyEpkeMx/7jtaLKgYYxjloya6qvARAYgaJKZeZniMNhkIK2odmYIVbG3a9gleSCQ88HRPlWGJUudp3nrqEWRGDMVq8Rb/Mmj6SyU3kJKhKjFYv0sPZrbt4tpbXU82uQSUCRw7ShOI3O1F5aZQdSRQKj3wFgHZglpUGyN2XC8rG79vj1mp7BJFjshkPamyWzj86k2Q/4dhDa6N9Vb4MEN2rzBosWg3rKVXpKC+hgDLqplU1KavODKR6n3IVl0d3oBSIsgitDXvvSlrFSdiBVbvLSoV0NtcqrQdvBlKUUBI86e6opL1+2F5OMVo6zpD7bpZzX90d2iVSZULlWMZ5dMdUmUx1KuzwzKx8hAz9fvzGm60n70y57QUyphHTHpcrGst2M8mqymrEaRollWLTjBLdjOL2UeeKt0EzmmEQL/hMvVwmRCnY3BwUIllNAuGMvqB9HRJova4o5qpWKlhFnnxyCAToxFO8u2IP5c6V6HpHVV74Ny2eMg1RkJDNIRArCAk4P4+7jp8Sg2u5QooXwLhglBK06lMkPM52G0uWSkimIuGKlqLC7rJiOor5UkvMNg3qU13esR/1+vWNreNAEGY1z8UomVQOlmUJlHHZ0W00/lt7pvOjSIx8bk+Elzh1gWIRiGoSaXr/5pp6/fx666426GMK8vD03nXgSZDxYlftxOpJ+PI/MHAxDava0fNaRYlvl5Bl7Q1skjwh3EF3ovKjp1Oh+BFk6fgWlrTXRD93Fnm9cFohXAkY8pjj230ek1ELUklAGYrIKyLBYYaESwMtE4o/huU+7X0RgCfqtJ/YDIBp2rpi99GFo53X9ephJyweySTScpARZrxNhgd2db8P2zZHcjkZ3RILQZoWGcuVh6libIyUolBIAD2fe50ZRJLJzAEclk4s5oqnfd0+gKwoQSJ+3FkUK/yqa45qwZvDbkeRJt5vQ6IS396OhJHr+9tcnl/ncsMv7/N+m7UcEHqFTAd4Lid5Jz1irbxcs2P5dgwzA7npOR4xjU5cV1weSUvURGqZQSKv5dfya61B6dPBA9/f7Nt92lAH2XIvO7A8gBHItSIivy6lOZGZj/NxEmMM1ZwjcK0Yxo4zcj3NPcwQS1WKRJHRFDdIiDGRgtGUGVoil7sxNd8tC2XIdjW4Vj7OkLmdYxyGR2YrjyQNTHhmAMcxy1KRzFwrEzimZYbAMiCnlMOzUVImQY+8kLcbb4PNHKVHemKOISU1D5Y6GPIKvB8Yw9xTI0u2xTwmSWgmOhMJpqeR4ynjVWSoyPx4xMfDVXIP4Bh8XCZ0KStlVxRjieJg5yYY1gWrtEFRB2soRJnuKH9F6MSuTGk8tTUwkEwbyanqFMKwnLs3WQaggmhi9ByiCujLrrxiRHim/0/LobCsY5ttU56gPwpEa0eGBiyYz6WITBsjq1RexkRogOrMUcI95StWLgVdw2yMsdbyyDGm++Uew6aNWlhxBBRbd7N1+SworEWDN0Ctw6beyzDJ3+1ofKNesI5msd2c8nb7A3BWUikvlrLMvNz9S+T2XNm9hvvbOt6ppe74VglHxgvtqeGgXmfu28fRWlenp46iBGVUqepSEDqdIdH0laEuFdHey77JMqttoXNnGfiue/X5MWKU4hVV8xtUHw3vh7UJZFF/kZlwD6/yV79ZG3z5L5LPhhgyAyvy7eDlDPDelBV0H7F4jZcXT0THfqUsW/na23G0eBZNsk2RHMM9L3cjV7hdbmY2eF2REV9XkHg75u0QtTNjLQB2TGtuhX74MZiGxF5cymTo1cx4wAg3w1OIoTUiZ4H7pe6XiWv5WoIaXYjwbc7bQa0tgK/TP77W/RgRGenDhgjpb283gl9fjwy/RJ7oVPBxXnPOb2/HHPz9wx+nAxjEGPw8MxC3w45CUobmP5JEpJkdt9vXx3mtnMbT41r+djP3PFfep7ofchjvx2AP4KmQiKmJFmZDofVKXCsHPQaH2Yq8AgnczDIjBr+9zbrNKK5odq+ikTakASeDCQCzSGRM0AwTNbEUIV6J7iXbpJbJkuuNUnhIz+KhbCzSXTls/RpKtchEupeYpeA73dUAsggwkDJ3VCtM3XvZPpVPNnQVL0Natg3JrBg7Mvnkegj8NhL0oLXHlN3yord45wIjqxSBThKxS8U6kMc0C7dWHdWv6KC2vo1okXu1y2hPO4riHDiGrRGPy4UnSitTnox0VXKr1S4qv0ggFy/5smGrY0glCcpRRaSt3LJbQ6yK0IFMWrHKVVjS50WWzOXIOvzSd1OWKhtjheBw+77MBmIgtbgYgxE2BsNsju5cq46cxoCMDGY1MYiw35ykyong+ezpSZ2ShuDrv8SA+uC4Vhyz9EblXIcBSQcCmZYvQitUBIhWGhpaDBezoEQhdj27Phgk0UYmjZxi9ik9WM2CKz9nGtnE6Fapts6dIHezilKQ5ornXlOFPutZgW4dFuuwqP63ymUb1kGniNEdVKF0EE9ItR0wn5hUA0C6wAJXWnsi+4TBjMegCJlmzGSU6lyil3PDAYUzbF9dIRArct5EE6jVXRu6y2jZToooxaF0z+hkcnk9VTnFjkGyQUn54H17X3yudCD59IJ6Fz5NnBnHsFFNmdXQ30GmKpbdjtMrlvl800IJYXBnBJBuRrIbgiMyUIfpaQ406hJawZfQx90XIDJeJt1TcYyKtIgkYiEtCj1Gf2NsSRidhEivt1OOqGFbbNndDq2yaKB7CK4WtIrfVRKQYS6IIau7TUQNQ4aS+T7eFdwzi4Kk5fFmiqnGuzwiSg/MQ9XCuvC1sKVpCneW9UZxtzbPrk8eUgUUMhvcyUKIdFqiKjyoe8tOgBvaRs9sqWOzERsAHkUqqXOM5yrVJ7XiWUKt4tUSUD0PdW2fTxvdNrKziRfKtqAfNu8msxvuit3TH6zfzBoUlD3nuuCz7E8UHcwbC5P9Zt03Yw27zTE6xs/uw5fUVCKyJtp64HHFMcfleXmdfoVtd03fNKDVE5QdKNyRSzSq3ayDzN38UIoYjOJ/ce+pDrjWygzLyy8Ow5zj+9vxfpskIjfpqS9RT/+dw4x4rAamBRQ1Oywzw/Nc/ljVXad8q3aKZZ/GKOwpGy5MNaqY5DkommQKbgYUB0yzMXgMRHLOqr7+4S7tnLJNoHCxWYCjPmAjSvXd++R3saIzt8w2wdi6/l45oxA+HdBsI5mKp49RlJlpoMEDlyMKMCqCnlj390NKZ6p80pe/vd3e78e13AwZ+fF1ReJ+Ozz5+fC3e91IcVA0deJ2DA0zcM/LA+AxzQPHHLfDlIZfV80RMLMMX1Gzd87LPcwjjKTlNBm9eL9VgzaZgyE6MNLPyzOZEbsWJOBsBXzx64pBLM/zVOxlG414XH5dIRFWAp65SncPJAY1jVX6JvWjs8UU2x2B5HFMAvdjrMjzWgSMEBNNMODyzMhrCelFItc6H0VwwlUQrGYMmYBiMbp02nVB5uAYNyPeb7ZH3nbMX5ZQOK8iBg+sZxIoc8gxOJFX92545NeVpn4xMhK3aaC93VRm92msUhAikwt4O+oUS4HuWrE8H1dUg2THcMvz6wwz3nZD5aaZ9i85fTMgoHmyl6e0ro0Y9myYRRZ6UFEiLTLdaxpI4y9K5hM9KusYCl32D+lQtaW7iZpR9RJTvGBA+fyLSvbxClLstyiqWt9R1IMUVlL3+Omw3IFMD9EbIlwNQmut7AqiGYm8krc5jiPNJtPub3cAY9gYY7mTfDweHx+fcxDIy5f3MCFrbASFJqQcY/LFkvRXNGzUji+RyCJcdktZGWodAiu9H72PoAV9b+6yxGvKVEZP5utlZRWs5YbS8ExLO7ID/gXov7G3/fgdZHLvE/tsoXIg45sx0V2cZkzMwSx2Bow1brIPUVY5Ove4rwZ8YHKaBTaVq9+Wngmbg7+88X4U7rEDaWVKo41UGXnUHPc6Sx16ssEs04AAAsCPRwxGpinmOeYw480UaJPkY8XlOYjbYYpnUqSFyCQiVibGmANIYAx7u5kmKDHDqhJc0f7H5eGRiRVhiQFLUSkSEfGZ17nKdXni1o6v0vIEem5dEfT6lVHZNCNDoXhIOvR53wBIU1/xk6FwyOd8GG3vx+O6PP70020Y3PPH1/o6r2F8u81MnNdF0MwyQkphcxpY2hRa2zAeh5H528f5D3/5vJbPMb6/TcA8EkYPyJ35WuRNwxz03cPwdoyvq9rx1OrlRdzmChh5XjGM7/dppPhHY4yMuNalzvbMXEtiRgQgxk8BmsS61l6V+1G9t3mM5XnNPD3c8VjSegGr74lvk+8327dkJQkMS2OeDmMqydIPtl7b7h+scFhhSO9Y7rPp7rL8mTltt8MyEvDQqAq+al8g0zHM5iijI52jHddX2JyF/r7Ez5W4cBtm+eWy4Z0nN3Hbe2z3tnz1ZkQz6jAspoqQ7bmtz2ZBStT8HNADC5lhps707X0quQ2kVb7SXGDrrF4Sh2PcDjsv/3x4HRl5v81s953UZGHwTfePCOdOT1XiiWNwmHhz9fqdYBfxVhntMU0AkzcxDGSQTEZwiKmnSMNqg13C/NYL3euGxm7FBohgjBxhc2SEIvyNNtTuKPiPnVO1wPHuQ8kUO9t1ulrXQRWDnISHC9NTNTESa+VygNV5sJqDSeRicOEm//fcnQqxmszJGDTVlkrybQ8RqvSEZSphxrkikBtXK30fOcFpuc9KklkVtp3xsVPyp1rNKC+wLRtoHIChEqGmoKPDA+xkbD1BD4X7aIFquaSdoz4z2ny5L/ubKcbEKC2hQnKeLlZEWVH76rivaFeInum48aD2W+WQX35aBVHNQ66gKpmIXTyvmjvac5YPFYe8zpHygEopKQl97GVHBl+LdmAn+s+kV4ipWa9+XW/umv/mYSDBImftTconMV23U3ESMsEi+FTWVCBxN5H0+gBZrTZd5s/0qusV3O6usQjmiUi1FhLdFqMZC4YI6vp2TJOonhyU+az0HRi6Ac2EClRMjz6N23hlJZHKeQDmq0hEdldU4fqAiJrVQ5GZVrJe0YdE+eYOltrBo0N5HtMmsa/VPjWKcrLjro6i0JhIZ++5jxmF5uyqej1tY0WCeKPfVvuhNFJnS5+hCLWysU6iEjFYav77hJd+qlUTnGxcRcWsMVvsmvwzQJEZihdzr8xk74gCm62OF0XdLBynpUDQAFCE69S9dqSidbgKNmp6bRbmQLWgewSdRizP1cWQ6pwqy65PryN2rgjwerhHpWwGES/Te6YtjQ3zUQ/WzXOa1TqO2eF/n8C22m2dFGdoBUzjfjOBXPI7mMb7MX76dnu/V5YHh0Yi6cCJsD6H3eYAcV2+PERBXyXqgpIiJdfyxyUZv06B6oAwOqhkcbtaeKVsBtFtg1eJtm3umN2PMQeHXPIY+z48f/7LbrHTGCFlzSduFK1NeN/0Nr8vT6ugamdVOy8yK1kXI8NlAOtDZduPSRWlDyNNaC4SWKU3WS7GDPfJWykmmpmUaJCJ87y+rvjt41prGUHa4/TzWpEHLefktDxXUCrLkO6DYCZJ/UGSSQr2ho3IvFYYeTvGtPwsmiqm5eW4TgdglhZ5RZgppdHS4ZgKl/3roRnquAIAbwM2RkRervGYvNzXSkcY8+025pjnqlu5PCxgrdMhjHh5cRAHsQcbtV9CVrdLqttRINecsxE8cDlySH7iXCE+UUYknnO+YGAikgFYwKGSpqHUH6GWlUL3EgDfbnY/hnTQq6NzDvSpkY2R/CoahrgWzlXSj2PYSFwrrBsbmQgv9ybODok5pHaZc1b7yLQhtulj6bPCk5fjGCVHeq0QJutdlWKTTTJxrfw6HVD/KdDNwvmMmoASgMzluXr+F1BQRTYBtrLK6KD5xV+LGSmuvOrDANUOOYyzZ9cO6ShvRAlQ8lEoQ5TRROOzKPWh8vbW943chfZneKbN0l+XGW8oy6XoR0pHYwx+ez/e7weQ5/Kvc4XnOj/vx/jzr99++fmXX3/+dr+N47gBeZ3X+XiM4zhut/PrMxK0Get6e3ubt9v9frOMr8fn5fn1dX5+fv3224eZhQhxa13X5UmUngzbqD2LmvIgLAzoaX5Q6V9r3zR4VUoDQkM7zs6k2atL2ll67h9U0E8lSx0B7m14mrSnaUO+Yjq90IqRnpaPKBJOFayRyGr+2E9EDwlGYAzb5hVq25nMzEFoeHSGlEroiVToW8CoOEet191JJl6DPUhhJ985jmmz4m0plRRxY7RkVebzmjBhtJWhjrGM8KQxo/tg9bxfV36ttOqi4jFM0mzDeBBXaHCbzQz3PC9FOEwdY2IOyxgUgFjWmHPgmBhjFHiOnDYy0yLeyK+HJPQswdXDhUko9JEZnTOBgYzbpJQ8lWIXU7s9i45cU5OCwxiW0ZSMOiS19zUxNDPFLgfWihXhkdd6+lOJtkgt+1rS++O1fLkZSyjXTPNSlyzaHBS8kZlKpxHx198fy+M+jRpb5rncx7B0f3jc5jHGnNPCF3KAtnyxIbVpOFceA0SGL3VlLg/V1JGag5Y2OzMSIGml5hEq46sSSRi5Ir9OR54Sb9JCcF/NhBG3ydvk3XEF5pVfK8ew+5CmiP3yfd4mv079SXI/1Fj3Y6VqZAQuTzPcpy2vsWzoPEpWNFFyPAAlBq2boxqDuCXd0Z9lD+oWBCshVeCLaJZrpFoFkbvq2ZlglZCljRj5vN2VqO4MrveffZMCQQVyPksJv3pi2ndU1m6kR0wRgEk2laiNYUd3spgTpMYaJBioVKjgEmTNGYrEUDuVTnzvlpmi7Mq5HlfucjabIvHMo18cioxjeKqMWl+JvCIRzGkyYtnVcjx3ptDj4xhmfFyliApAsBkNETassyFHsViQgtC4nUSFvWVss85nylEsULBypdhPM1jJnG2eOfpvUXlctKpsVqEqMqNr2IVkJNK9LR/preMWT58U2weTQLqYy1nt9u1V6kVKW7DeQlfpyYPTVI025pFTjrBCsE2OqJ7L2ImBSRNYuXFtUtT3Vv9CEhrabPKvCo50EEOSnL3E21AWOrAiazxCVaQjYnlpTVXiwqpYxPMc6Vig88paINZRZGKjKnX+2H/pdXmZwBJk/qQxE60ZhOfnPH+7PwpoKLJPz75h8XTt2YatDk1FXe1QUedCX8QizxVsoK+sd9knrL+x8F91TiXozwgvM0FmpMGrGwtROJECeyucKDep7fm8ekZhDzUqS19X5rKUajRpReavR2Ul1OeVHQ/VorWorWD7RIrJiBeBKlXbuuwGYcMZ+WykNyXjvYR6UjWd9Zr0zcz9BfrJfV2547za06iRXrWtEekSJiHr/AoJgy7tDoKzT0tvd9afj1k6IAoCe7tIbutfDxbRx6O/TEFHP23bo6ZNNxkkJTqeRR/dlOlyAGyE0AX5dBPc83QmIMC4+Fddz+wINiP3IJfoIHbbN5IxuA26EM1nIbSvy+v6PG9QbYeUlUMsHo8E0vK18qD7UN8SKfWrOlrCi71eb0fM2TeXWD5HDbDbkl7qx16eq+ceIvGocFFzzVt1CGKp8muJNxNE9TjpsljRE0hyDjuGGSnEkwCskFItyE7ztAvDRFyVmwFFQpl2v41vb7e3+zS2bd2OUvmY9fUFJMIVkcu3PFZG5iDfdP70XSpTPsX868BrqaKpxSnRLquJwlbsJM5BDEHAmlg/bkdZpNL5qDw4oWJAGdH6v53x7FtZX53Pv9V9tLKOFbxk3WD9sVKObYC37UOUjDfri9OI47DbNABj8DbaHSCRcRsFdYnlsTwkzbOfpuBX8LyW+/hd009IEBG51hpWYyKVMM1KKUnNPUtExuUpYwtWA+DyzGJPEigEx1qpapidH4+vxzLjVARVagJI0Iah2fXu8fXYc4Lhnhc40pdnF2DX43Ids8NwGJanfpS80zRWpmWY0l9MdA8vM93IMQ26NYnd0h6Zy/1+G/f7YaRukABfiVZE5BAbumbY2zG4rsUyXbWRHlUdtcadV2CllfZWgYy8HzamNRilcUKqsu7ouZp2I6F/DU0EIA7bHOkiJotMvbFJtjj9CnhyqOe84arVmVVkJtJoX5HnStDQwdwTdkZNipXpJnkuAnkMNeBUy6rMTnaxbgua7hthTUllBHs4YETJAiulWAu3gzvCbktdlnYHpmYw7NCmK6V9u8ZkVvyLl7tatnoX5uojevOU28r/eIS7uwNITV0Zc9yP2/c/3b6937UOisjN7HbMOQeJ61q343at61rrdnsn8O39/nafx+TtuNGmbMc6v65rwu4/Pr++Hl9vb+9g+jqv097e7pPh4YB9ezuOMd/f3n/6/u3r82PM2+fXSePj6/F4nF/n+XhcpIFGq8uSe6FY0YWV7E+5vQCYGuWepUQHRmV60G8MXZvJApY6fynHWPZqG7gKygWGVkz2B7PYbquCwx2fdChbX1UATIeyzzC14Ak8x06VfAaqIVLaz2Xyh2BZiCDLtQoKlsM3SaExNc/+WvCOo9ABoM7G6hx+lYxc/uUHvr2NY9ogluNzRXjOgct9ut2mzVlk4ACuS6EUhiE8zivOhXPFFZGRt2m3w1bkdYXRLmSGq6n6vOKYFvVI4eAg5qgY1b1GsJMw2s+3eYxbDThpJJRd5BO1Ah2XBDiI+zC39MjwUoR6KjJCAyywVkTgWvQD90QmlwHgGP5+P27HyO4fd3dt+efpNjlYjXPlc7NzCHS6DyDTM88VknTNLgcqn/JIY36dS6Gj3nRF/vi81I0yjY/zjMAe+5C7OqjQyPNaASx5n9ttaL7b8vz2fqzz8bhiLUYMckSGX9cY85jjPM/zcp2A22CEJ3g5In2MobOZGffDEljLu2KqK6I2z6CNXXjbIUECy3FajKhmAh3imvwAsNTQOAZFPNRwt2G8m03TeJkGT0EjVsQKDtEeI7Q4MKp/WhMtO3DhiyV/6XUt+6mgotJPWUV5roIJIqSvKv+JDr+xs5UIUQtXVjbcKUlmluZsR/tlafvGt+NGOS9Wp2rxngqCoULvYs5uY7TdwXIeM4+qKFZXplVz5UtwLu8wbAy4x0ICRbvbQXiiqkcBDINqPI2UF4YViTns2xvGiGvFWlxSLHrGfNhuqa0lSHVX5BU+WGXCSHrGFTlHGeH2jOwbKRgvBT2NwRWqbpZCDANeWiPPROQZ7lV2W/9lBxuVPspaZXhqJ5hG1wN0DTgb+uivxkYJ9r3bKYoO1VInTY8Pa+ym4EYmlGwaDWSEW1t/T6QnmSoOZ0e1GjEmQ6IonM0YyobS0IEHCiNqY0hUkF94TTux4giz+g/R36DmmEJPFBcWrPWESyI4rKTC6syRDRWWrWv0J/ffmvFcSmBMYZNGdysu7FPSUOK+L+0Cs30qGzwhi0rQb8QGaKpRZUWuyCHblNVDge4iB6C8mTufbauwX+M1p+2DvVP2Dq3yNXPW/z6xgx3GZbuA2GqsZIEaFYT162HjKgW9lTvxyuoEqbRxY7HhxEgSEc8qnTfjHGTUoLuXZL1/VzBOzRd8IfeUBE9C86RrBFM+mSbVXL2pBiRiPysMtqecUMVkTWHLCizqpkY00sH9/smOudSFkQnpU/Vw+r0dBDLALA9el9T6SESTBrNE3QBsf68DnwZwMLzWc3OeZOvHts6b7sEc5GFoTJK7o/4lAUfTfXb8X9sqZpA+pQycrqwTfW6juy51EcSSk/fCPiO7PyUyUw0piNLf3dFnHsNexQvYYWWdqAixBBIsHlfV4rLSwu3o9ol8uYDx6jmIApQ7oNFji3YufISEbEvfhR22FGAhrfRsYGsV+afNwOsg90Qm1so5OIfq5ZXtGDCYsB6ToZkXfWaeJ4eWWR2mYMMoraEA0sY4DjsG74fNMbTI4WAVuxhQ9IYE1PLZdqXOjBEgJ3lM3o9xv83bMY5hGb4aWcMLlXRbPXEV19K011jdZ07CyPsx7oc2xvosFaIW3bASSlt0u58XhhnIjGH0rCKz8iUz4QV2HNa2Ngvx6Q3Izovk3AUob/v3ByRo+8gUECwyed2XfldU8VUssKD6q/+QMIHDMJnusFHmdBjvc6BEBEUOTNOW2UCmZ8zBw2xFGG0+GdTsBRaBC2tFOEJDOhQomN3mOMR9cyBxjJpU55HXCo+aJ5rIVBtiMKnxl8PIOQxZczK+v9+Owz3xOGtqoS/3FwNMSgSnWo3dyEZe7ocN4zCQXF6ar+7+da5VnYo8Vw7DHCrUARWuKR/NYRyE+lMruWywgJGDZHEl6nDc5ni7jdsh5enwqObyDis550hIeQeR+Dj9sDSzm3G5L1HBW5IraWR65OPK8LwidpHDwGOoPiSqtHLyWEuGp04OSh2cmekRl9eg+ityNjbdxXDZmeiosUsp+YzX3ADk2TKW7oFEKIRioMOPKIqgMjE152MFRtelmEbPhVbWJTNw7XSu6hCd+bxgUoG0LrGybTVrgiQqnsi8yB0+AfCIiGcFpYN8pKxEgOW6RYbGCgBR9L1NzyuLXU63AYwEMp7icMgMM5tz3I5xzPu3b2+//Pz9mAPg7Zi//PL9p2/v5/WIdb1/ezeOz8+vMUaGf3w+fvvxGbS11hzj159+ut+PeTvU/xUeDyyPBeQxWgVl4qdv93W+HbfbeV5j2O0+r3WRDphHfP72+zSu5Y/HFZmT+P7t7p4/vd/HnO7x+48fH5+P87we13VdbjbMhlw7u6yN9i8l/NHo8yQ0b1SLU4liVBhs2PA02mk0SvIC2ZR3KXvH3BqJ7SWzrnn7hEo/gD/+nIqYypz29z0ztyeEB7k2gniW3M04aAqlGrSHFdRJM9tK/8NwDB5DXaZIYI54nHI3Kl1UvBqRl7fQWyQyV/o//Rb//EPttLYCkXkb4zYAgx28PB7LJQ8sjqSHBh0gM77OOK88rwvE/Zjntc7FukOxAnhTiy6JVhoGM9Oyz7+WeevXZmIM2BBHiCS2Ep96aEMTOEr3MDsyzzk59HPUFlCeqC5ISlwEYhBgrdDUcBuZiZ+OQ4O9Igl3Asec17Wua0WkP85UFVYFj24J0pMnYMbbqGOgwQXL4ZtfABJ5DADx+agQsUJWYwQNeLtPEIJ4Mvc9roBcYNZ5hWYyElju9xtuxxzEeDtut3H/+XZdnpHq59X9iF5xZKW1EvG4PK4c4TE13HlAbC+jLfcSuvGeqkaWO0QSJesrraBMrIB5cjCLgaPSTgEHSt+GpYGSFZ1GT56eh6nPOgBRjEVniyIYEGa4zeE1bshYgvKk9L0qZ6rzk5meG16peyyfmZHKlDqf2d+CzBhjSBWzQUbQEFFRaZkdKoB3kJnPqqcwIEVrL/FnQqFXPh9F1oQv0I8IKSJV8aWPAUXNEAjChEVKBrj0nkalXX9I+l5TTEqPv3FpqkUuEj2CsBoZUHIQ7FbKtnJ5mzTaMe2l7NqMqQrSOyhkJVTCxldPZARJDs+M1RpkSJlrPl23IPbdwIKXZay0RP1VKOBjT9+uu28mYaON3LWRThBYTA+Y2ms6qB0NwFVGlrlXrD/0KRuycxRVEgsYqSclwdY1TtR46v58/dR6cU5TKypIzmHZRJwdJm9Ioi1DRedoV/XCEuih2CCJqRMfr4OUyWqIBpVmo5WrWMoUsTEBfVd0bTeIiKfcNmHoKWSVJ79UXPdvjJhjPC6/ws0UTxedq/uhsJkgnVuyRnG1Dd03ZYePOlRGwl5hGg7DTJI2aGe+DM5og1zqtsCWn6h/7QEQ3M/+8rn1h9wJUm9k5zzopwWwd7ofWqlQ3aWhuCQLVdwWZ4cJKGEmZkLT1Zqgp4m/eGLZmbqnKElIoBuVryxMMag6Q6MYAkX77VuUo+OdEmbC8qVz0A1Jzw6w6Iro8wUx9OqR6eqoTJiB4sX2j2aTYph8EiNfbnRmV27Q/bGoRVMHluIeeak/0ryL+aPD7J7LKyuISIUI5XLIiqIIXzUgMPLV7wIA56gxcb1xRnLQE4hdGN72KVF8B2Qwttj3y6+9ftFiPTI526arST0rxawuqk0V2fYf9e+igFYDyHMJCRKi/s5hLQKr6PAZjuYwpprVE88TmIlkiW7vbH2f/n6fbKwyM2vCYtnBOjxeI6sEAG0CVxus52mLTpX62RUxPNHIuhcNPClOjkQxb1/8QT1R6FRHgb9R/Lc6dClbB9lK+WLFT8NwTNxvdptjquollaL0QHrPUvR+ow2G4uUJdP+yVeiOwTlEA2FGPDxeo3ttcW617BbBQcbyvDzOVaf9GHYMux12DEOWhTwG1YmaJRGSSphlouKlxRWkFsWMcIAxh8ARAYAgqEp6qNUI2H6rS+LZLrl2WXj9Phs7120/Xb8NYGTfjf4x+5wofkr1MHdJIQEPHJN3clCVA9wPXp4GjIHIvE+ViUTdAlI1MU1goULmQzgRqsspO8NRmhGZ3uiLPMgx5rf7uB1FaM/MSA7wWn6pWJGVU7dkUbVQmsTyjFJtR9qkKbKXKP5gDsNOt3V/BCyx0AdGzxGPyMcZY/A2LSKu5QmLxFprG8wNWQJVU2JdD9Tnup+ey0V9TQmKL4dCXA5obq5w1TltmDTW4Kvaf7VoBETiU/QsqukA7mmgj8lr9e0nwJrG9fnwwfTAdXkiPHLQbFiC14rHGsex62n0RPP1RImpObjRZ1hvd15B4pjl4SJTY25WzwrU1ma7aHHOyfTA4/QxLPpFoLcHKGaEmm4su9SMt8MODUMJ0PucNWJ1BQ4AUoNacXpbbHR82jHZ0zDlbnuoiptL2qrtaCSSzJUhhAtA08s1RU/GpFPFumIdKD8BaL2cCUhiwfIgJentnhnewC7vt+Pb++3b+30e8+PHJzL+/OdfSf7NL9//9KdfPRx+zdt7REihycBrXT9+d/D2T//0l+Wx3L8e1zEPkue6jIjk6XG71vt9zNvb8uVrCUAcw44xYtIjbsfMX36+livif3+7HcftvNZ1rSJWJO7323UtTcnLTA9fX9dPP43vP719/3Z/nP7x9Xg8Hj9+/Pj998+4wlsCUpF89FFI1E/QPyoMy2f0pp1KtXO8xtm7QreNW81K6OymEdcCSpnPYE5bW9RmfcszVkLFb52ZdPaCl0hwIzIF7AqbF+94GK+Vq4h+YWZj7lppSElq+yXxZJdvgLjsxuX88szYFlu/1xwAjQqpQqZWxD2ulZ8PjcIYkk/++f3tPrlCvtiXp5orl/eMNXL5ykhpRHhkWiJyDIpVcDvGMSm7HcnuEEdG6CKIT3et9ARgt0kNYs8sbRaC7ri8OEdvRwU6rGNTE98vj0FN8QPSHtc6xe1EF4IAzUVlgANj8Ar8/nHOwbXC+O39PgDPVOklNEOZ9GMQML1d+RrZ0i71JWBBgjwqAhNLdOGlipZF1+7pu/VcJDNtGs9rIW15uqdqkx2yloUsod9MKdMjcV5hDE5OxLA5zHAUCyZyG0NEERirGUF18Wv55+XHsCDWCh9qkAHRsoMwD4gLbTZoAuxKVaBnnJVroAYN6eyn92UopQWr5j7FpxYd4WTm5V5CF5kgrDrySs45NQnZGD1gO9FUYjH71Fnf6nrKcVGghD5BgI4YKDm2cBjQcX1GLLPBl1AHteBlKPRgylWGxCJqWm65KtXpUWJbURk3oBiKhTtw6A/Q0sHT1ZyK4nSKqqzFqerBMGkF1EsN4wxqCoTwo4oTXmenFKWlsnXuKFHpZSaJ1XOvbGMPnfAqWkzJjGStmAeXx/JCaTN7wolWp1kybe7KyoZXf0/GYmvDZk3mYSdBtciyWgO4ItW63tEc8mUWDztVLkA/aGRJfb8C610gITWg/Q8boScpxC0rWgv2wej0P57NHHWczDjAYI7Aqjx9RLeKd0zFMYZ8kBlu0y5PGt/fDk37FYoE8FqxPIycRilQZncD/IvEp4GEbHCCCSn6cHoItWnpnxLsqI2IzskISLMyI7Zoq0xMHfoWHCFhJfzW2EdR++rAlc19giM1CUhnToxu957Uk9nlBhjTEw185H6b56sSpKVnJC2pzDywWd91gQIYgwMmxum54Fap1hO9a1inTG2qtCI7U3hTv18VFbf71N+/JLXA85qgDItVG73OdNsgAOrQLjeQiZQwTm1ExR8AxpMMvDNzQA0ofQAEZHjF/hxEjxV/doTJEEV177XEV4Mr+aLaq/+ECKgp+d4KEKK9RVYincRTtrOev0GC7FZ8BcG+oqqX+Uqdyqc91bq06c2XfwqvDC17mBcAquc6sTxbiLeeUD5K+blvG5wQJFOdkvIOL9EVmmy1Q3K9ko6oOn4kV//ChuCTZbxtGipYt93X34pL6GIjqvugVmvzqLTatQmd1W9fiJfWHrRdjYZdvHx3ZQMkxhheKa9GCezHkZnrAWDV/rMxwDrvz+S+94Jt+rKps1pIMZ+zjXVrXqi5rzUyEh0HI58GvRCueodedgE4bFxKETStbh+afKTh4tFk6dw/StXRKKKpjQH2hOZ8fafGl1jz6Y7J21Hqy+5LwVxYDnvyy93RrJjnD9yr9jzG2+5JymRB1BspstfX9CXQMRYkLavLjKJNQW1EnKPm7F5LbOvIlwLXE6fvt9PDFd9Kw/iyenkSarv+4wB7/QxvuLF4y2pyQUdO9XGCCmr7UVBQ+L7FvRxs7y+ztksvZX7Zp7/2jtUQmgCGYRqXZxJzcA9bDSA9zbi6zYdsOglSojyXlxxYn3k+DwaI3H3vuGLHVTmJt8PuE8X2iExmpMVytQxkZYZ1H/JFJEXn1CN8BSvdWjKhy8Pd87U1VdG67q/O9k4Xs4iTiUzNuhLXnnswghgKaKv2nNNnJkin8ORLFbk9vk3NlVJ8O8asd+mQxcwK70iPuC5PYJgN8so4V2yhsNuU3cyf3sw9gfDAcgQR4Fr+sXIahSGxaZjHOATK6b0/z0zGt7uiEU3nzGmcZFDcjSCpQe+PFa60JmDkMTmtv9Hz8vQiu6dEiCispc5imJk6JhN5TI60jPDAijoG5QszI3iMtEkAGlvpwRVxP3At6XPVp9xIAl9XuMcVtZFZ107Lmh1S1a9p5acgOd6UpE+yXREBMpUAL386/TKE5NvY/qWszmDqMKv+5sAkSfHpurKsyhDtcr/OxzHH/X4bxuOYv/7y89/++Zdv9zGGCQV4nBdp63zMYcjIdWXGx+dXhORR8zjm2/3t61yfj8e5/Dxdp7s5+2I+wD3dMtxhZwbWuu73mxhVEQH4HFweg+CcRA7OY9hgHoYwuyLHGJoO9u39vpbqUJkJs/H7j6/Hec6B2+3tfr+ReH97+5u/8a/P8+Pj4+vxOM/LA3PYNESGp27HSI31aFCn4zZid4Fl8lm0axZNyeWbaiqxAtOmDXcne1JseREKV2zQCaxR99rJbSn6y6Fk5Q/VaWODPqpXpWYB5RBhrCypnrbAkaviahzTJqiwmka/XBrcwj90tY0m7+nJr0vKFMK+ylo+I/aMSHw7jsq8kr7W5W7EQfMipylNQmQeg1o0Q55e+n0eoeKlJcmMDAudkDURt+O4Db4dJge9Wxtku6SkvpZH5hyWZHpm+gXLQQLy2oD9OOO3H1/L45jH222S4/3G1iZiJlYxFJDEijQVFWDpmkbItdYw3m831duPOW43Q2KtRMbX13L3//hPl/v60093uVEUUOJGSxGmq9uojpPCoWxRAIGaKvELaxBqq+e0UthEJ+e1C1ZwQATsWpA8Zib4QvfeX16+qY+0DpVnMOxaPq6FzMhQ12RKgDVcoXsd06hwVN6cmeHuMACXx7mi6BbFQCnfQ6qdtb7DkIfGbhVrAEh41HypSqi382yrqOvRFd4igmWJA7R6SCZBLf9y6Tq/KBUIURWfZWd3xBzPADir2reztjKven8PqB9I66kcViQm9xjDIsKqvBqCmCuN7XTCyDQYOQc9rAKwksCn74L/floqoOqA9MmeyEhQ/KwOMICuozVpYcN/ehEzTLPIQn/m4Gw/EKFG8JfGpUxkkV+UzTSvoC6819imhi0a19bOPQlFJCIH0wYGca1Q2L+hssynfWt4Rqe6jkEqPIwQfAZtdKPqWpiowLLTTG7oTSarmSK7PNI4kEmapvC3bv1twJ1bn+tZK4VHcS3rbnWq1fFku+XXXKZqD7nc5zAbHGNMsduMKCmuZx1c9lyAwDBOSyfBPKZqkzhXEqge+MY1Bk0kZiXC2fCIdXlBf2O7uRGMyHnVrMG0Wjrl4TbnnmqEGrSJhGprYud0XopaZ2aqog7TmSZJLIewmEyNsGEC0zaEVtmsgniVkiJS01qiBaplO/ozq7NOCx7t96CTwgDApKXESpmEgVYcvJT8XeClNVFyWQkzq1iyOLSVFvQxa3OmF362ZO9urjIcJDtuR1/+Z7RHvh51ApiTYwy0DAGf1W4AIXO80R7dNtT8oGeJgPUUEWntaPqwdx4TaiU1yoKAAVjCygIyvROnfl+8LkGVaLKkatA7jmr3fUlb+05yWy9NyEOGq8kw8qnWKGgZ+4yicaCnJUbfrv2HMkO1SQSWx6qx4+q7KbzZaDt1zJYtiWzNj/6Bux9r/13zi9CvT2giMgnAXycol0EqjRaP51u8Pn1Hk+le9iuUQjfzq3PGRJPdqg7TaXC1+im7VwxRoGBVXjoNaENM1rCaXkklmCbtjF5hMtMwKubUV9VajyGF0T0lsgyuDv7mB212FZ+PBzIFPkbzFXVMCpJrlCS7JRsFNNWrxSs/cn+2/K4/l5VABjD0m7o7FRC+nJjc8WuXzhQKxPaQ/QYFJRFzbJ5OlQgyS+W3QnqW+d5gfz5XpRcy9+d3bIeCTtq5pVDBcomd8qHUT/Iq5K84U6h9xzHYZoPX5bvip3d4nmYxknpX9SVeN4JADnKI8mMcJmp+V2j7yGTENBUDIRhlb0qi+z3LWITR5rDuii9AE8AmRoEY1fKjA1RrVHQS0KrFrLqshf7oWs1hBDvyqW2StLPqt9a8p0zgRZOORblpFt+GzrNQOQExcm1X0j0MYtBwDh5DRqA6YxPiyYs8VZW0Z7SNCt0qYMka7EVYlNVSoNgZp8GMjD8cnNj0tBIj00WXCxenuo1BLeqO+PsMIF1z75PbjrMtg3jgmbwidDwjk863Y2MiVj3nIut5fJ0uVD0sTsATVpksBvcc6FAoSmKaLUu1xj0e1+k+baqgZppPPybkmGqhuC5XP93bTc2VmcBiFTwSeRjI+Dzz989L5nwVWTjGsKkMrM1QdhtFdLbQG0SlvsvjmCXNOtXtf+Xb5CA+L2lFuWzF5VWQnQZi8xqqcUFQo5HG/Dhdo2cCZE8tUNiqxr1LdQuUD6bZMZE1Ey3OVScpMpu9XB6eRUIuq5sAQWO65xfKd4uipbDwpZ28DkACCYvu7UKmwQ/D8Tb/1d/8zfdvb2/v9+OYP3+7I5Yxj+NY1/Xj998xbtf5dQz4Ov2at2NEWAlCIH2t68pjMCKuFefl53Wd54rM83KVHCIY8XD38IGY5+mZmHP4Wrdpw8Yg3Qdoh5l7ujuPydsxh0WmmWVeyADsvK5I5QVxrXNd5+fn47jdb8dxnSAx5ho25EBux/G3f/unjF+u6/o6r4+Pz3WtcAe5/PKAL7+/vbnH43QdYGc1qF6eyxfzOUILKDY6SzpKuXsec8zbXGut6wEbJDPcFQIxsHKMoe9vDj/2rug+ouFj7BvaXilbtT3VVx6VNY1Rwa1nEhSYGaHgpEhlii4ycqXPod7wCM/HVY0dRiPyWumeElv5Ot2DgyUmepj6B5M9UGZp7HHbuhULyLcm4NFI2H0OY54rAKwMY8/G3urKgmqhpSyeiDETNsb4dh9vs4GKagFOGcnIDMe1qtJxroXOAS4PktPsvnIYIu3H53Vey8Yg4rfr6/cP/vr9+PnbUUNdgeV4XJnC/Us/NSXgK+TRB4t0zJhj/PrTMYyX521ijkPyZRlYy3/7XPdjWKnkWoRnwMxgXb1p/0NkPkkQVVBby0GaUfx0b7ID2TFS+5hKd17QSSQYVbaJFiWwGsHcEI4IzUkCopPUETOeS3UIIlwEbxlMtcBZD6cXaVDUlmJgdTh9XT6ObltlNWQp4AmkWe7YcjCD8Kb8ustsvgR5OwOocLasOYqgHZGYdTA6R8H++kIGXtVLiAKSsoJDVF0QINJ2B5Oy3A7ZdL1p1uvX86SoEEUmWJRqbTWviGOQrFCtaaqhxYZR0+OH8f02kay4LCITTiyiZBka9yEg/TUFP2PTBLriq9cXf2e1SqnCxUhInyPLNXCZz7Bj0MkIpoQy61Q+f8niaM3NqPniLcRREas6L7ss/oyzX1PhVKzammpystlwDFmNL+XJSiobnQQhshAfbU5ECj5LMDeG99z4Nke5P/rlsmx0BnUNNm8lqhGssz2STPEfCnzsH6jXCqLHFFWWvn/aZh+/Rv1ay2z+ugiYTwkUgIZhNgYjIp699UQP2zHpfEcYOczOy6HRab76R4yMvOCX+1qem+6g+85+8Z3pUPr+kcG5oiWmLRXMSY57nwFZqr3LXqjQ88hkChZtvtM+D2LPDDCaloIKEzOLo5HF5RPW2LB8Y4okC+PCjoZ1jQUQos963/UOwes5SBoHJZQuCxwTxUKqMnls5V0de7ahlHGoSFrAarnipoc1QqA17ax7v0B3BOQz29w/kw0agMAYNgc109RgESlCYUZqKBiQNiwrpKwyrxQJ0OZ+Bw+taFNPU74BLCoRM0N0tQQQIdp/ARMgp1mFzbv7b2d629MITupss09CWdhagAbLsuEovb51pB9RjSej5xTmkw7zNMcN85YX2ICX/lL4iLJnj5q2Vw2x6LzupcGxcB8U7bAy9m3uKtmtlzCDsOpGtLOxqlT8rueSgLo6jzPFu9kb0HcBtVmqlSdAqoGiy2sFJda07Gypcskeb2smh8E22c+EsxET/MF1EkWXMLEHv9+P012pI9pLVd5SnqQvtS5eTWxV3PniJCq87Lbn7al7DTe0tk9ONmFwG+JsP9PwBBpCkffq3DyfC1pblQU098lAEkkGqzJiL/zPioJrZZhdVeuXEB+6MFbWyGQOK/RHs5lJdTWqDlH2ymiZXC8H6cX2bVez72Kfx9wubZ88QiPkMpRN6WImcq04JfQTDYfVfReXOGPpeAZ3SSoLqap3F+5WFZSMVIealIlhlsewzBzMGq2FzFh7e5qAg2G5wMcZ1LjxaByhzX7UG9IA6TMo8eiJgc+GnQoFbAPlYF/p6BJcdByTzRulop+Ss1e+bd3enYeNfX9FkCYoijIaTtNU8jqi8Uqm7APZ+xSJ5d49cHUqEqKTsC0TRNaLZNaAszL/StdGsdNkprw8UiZanJjdx7R5cOhnUKBUMxNoxgqR20JW7aEdTkrcIDZjd4d0PQMlM725eXPYMW1Yah4xiQwux2OlWRrtXKUSbR15u8fn6deKyGd4AOA+TAo4q/GEOvI7v0MmMMfwiPsxSX6cl5lN0ub8/n4cwz5Pf1yBRAQ8Sz79tx8RcRcIZlbIIMFjcBky88fnejwuaF5E5DGnguDruioEV4myXHk5o9hqLsoqvXpABpmkew7ifli19Bp/f4QGjVEl0IxDU38ykDmokfOVbSiM/FqlhF0kSiSQ03gMkz7RuWIOqpMikR4CN7k8GrctH8lncNWjGNDRDpAKmUklsWQI+jTjGMMjMsOMY1gmvQXNh5n7FZHzOCZzDq5r/d2/+tPb292I+2F+Po5hj6+P9/sEx+PrcdzG/e2ItDnuvh63yWFYHsPGtHCxRI/5+fn48WOtyN9/PD4+H+fj/HysVLXPrNSomGPYIIfh119+eX+/05eQnMyYcwLzWjGmzUGkDdLXZRyRADnn9Bvc3Vfktd7uHMf4+O388ePrt99/PK6/vN3vx5z323Hcxu04xuAYdmUa834/3u7Hrz99v/706+fX13VqojXOa52PE8j5PiNxndfjPMHDI5FxC6wIffEZau1hRlxL1HlE5jQzBGLc3t9/+X6/FgG8vb/NMYGIcHUjhruZrcLpC+hP0Yg64lWARNsaA0QVWTXrvpyKuGmREqSA4gr5r4xYpfULNgVJDQLLcw4Q/P2CeyxHSleBu9U0BW1ciwl4Bohv93m3/HHG14IxI3O5GziHPdZyj3kMoXdS34qK0TGnHcNKkYYWEefyr6twDJZeu9UJf7ag86f34+f3MXtyrUzZCrhIOp7X8uVV6tMN8wz3MKoVgr7icUpnMDJzGscxIvy6rmPOH192eb7f5zDTSOWIvKIqJbL8QHx7f//Tt3FYRubXlX/9eNyO+y/fj8Ew4/ucEWGA2TSjjZGZ5xWfp7v7Me0YJokWQ5jZGEbGtTIipWMiGXulOdlxLFL6hjXfA1Xp5j41O8BgN6GYPQuoymCz+yTka5Y3T7+iRuHSXBErcg67H7xNLhexQjZXkQMiUtBMOW4zRHiT+kDBEzkIM3ihIYlKSdAJQiXARgMNDCO8jBzMalT5y68GQxOBHE1rLdwwgUwNGbSusVS0h7oLNUqj0VShMIntnAlTClkArtITJYGp3ojR/rWK60+QRLX1aF6cwkilkJdnZsxB9cp3vTx1jsk4NB++7iYoJQSkGiwP4zQqvgopwQGQe1LVMIFdJd0JBmqQze6ryu5cCe+GXjAsLZCR4RyDy3kSc/DoZD93tvv8yfXzbY9a2iDbjnUUwe4n2WFMh961jLRkKBWvQ7sT6nz5ev3IRutWaB6xfi59ZVCVgK44b0NJoDhcFb3uUKhAdjTtAs0Xqf0VKFUQZDODSDYxG9iFOX1ftr5BZ0Bkp//7oGb8YQ332jpyrRImF9o+VaJswT7Njh01WBCdxDE7NzumHeC1EHMKenKPIIXMaL8r9n/Jz5+2IysRMjLNpp5mUJ9NFFDi+9AXiqczpJb9Rph26J6NlqFC8BqYtzyNmWQ2udGZc8Ayo5qRagXH6KEpJDKNXB7Igm6V1yXg7p5hHNkP19gts6ZuC/pGFs0nyGJhZhYGlNiiRc/URT/hGEyOx3UN3SLteuacRI1N6YwBiCy9TC1OH9o/3ByCNOsjkgDV33ub1bZNYg4CnTBkzmn1ZKRHjbEsMkgUnbRf2iJTUES+dL7spL3MUz+Ydf687ViygDUzpqcZkhnBNi/stW1iRcl/PN8vO499NRoyqdUlu3PguoSCwimvv2mKeuJdoedGRPYdBqBm2mc9RLc9VV0g6zK35uYLKNEnNhtl0EMKL98IDZrRBmg+KcgiZrBH11X6ioKjRJJUP61qSs2JK4uhT9Je66OF1vWqlmvUOh1CsoAV4Z7ek27azr4krOVn9c18bkifD6WsRrsN3qY5coDTChCWfypOXYJZbTVqZ4vu8ktws9NjH6mnwa3fsN2G9dZsdl0vXjnITG7FX7QT25B5kWhQO9tvhDpbz7cEBVaUj0ZCAvvQkalxtijgRtc7IwvT/KNp1j6IvGAIZYNaCmtoq6cCdZdOh1N9fJ6nX0eEzGd3tHp2pDqR1QNvf+DZaaMxjBaFzSaZqXGwqcb+Fc+nNmNaLn9uhEHCUxDCKVWXxsHaCABrQTFoKGYMPM4AIVETNCDIHpOsIMbIhH19Lb2be5BUu6xOgfRrao/IdDRWEtmzraMJ5Oz+0jFqc6M9shE0izoOwkWr7RdodbeiWog0lZkZolvX0eew+mZrQCTX0xj2rtfdeb38Bfd02JAVuSqZz8a8bFimvrC5QHuIZFkh0J7CX0KI6qzYLnsQ8kePyx9nvBLiXs68mvkzdfugWD9DIhHsaCPK3Hd/ZXb0k7lpqirgk1v1cB7FviSQaR55HDls/HjEv/+H397ux9/8+v1uZpbXFY/TH5cTNkxjyCylY8U29ULHnuBmHTlxyrSAY9hIHiODeXncgPttTGOAHss9PKuHV1H4x2MBBddGDYXhumSdwpegB0gY2z1AnOsikbCs0TkBcJR4Z4eZpPZPvYBjmOr/QlTTcJN9N0zazdMvqDlY/tkjHldRIcRkiAyPOMaclpdnhISrkRI61eDqafIRemYDpHWIxM0wx4B4+IhnobSuLKXGXzU2de11bqMc0qw6RkAOG+Freby93cewONfbfUzj7bD7/fbT92+32+HX1zzeImMQAbpHRvz19x9v9wPEeV6///j4L/+Lf+0BY96OG4Ax5mFmNHes6/rttw/Y/Pbt2+PrYWNkxnldmfaX3z4/v861/PPrca1Y7gLaaoRYz1MI4O2YY3x6xNttfPv25ssHscwycTuORM5pY0wP9yt8LdoxBx+PdQxTg1QEfnxeiTmPHPO4v307rx//9M+/nde6327ff/r+fgxagvz2fv+bX74N5vv3t6/zx33ef/r29ph2nde6ruN+3I4ZHrfbkeFxn5e/fT0eiRKqIHmty4y+4utxegRB93icl7sfx/z2/jbMvj5+m5b3Y77dj7XO97fb29sdiO/fvn18PL6+vt7fv30+zut8hLtWZa3lLhIb3T0zaNMj4/J5zMjMCKMNEqKoc0Y4kGNYZk1ZssMI+AqSw7iSj1U9OiwNNHrmeemC5CSveOE7V/8ty8IwoQF54YH8+e32NhGJt5uB+bhiF/CGmUc8PM7E7bD7GIO4z7kiPx5fxzGHlawPoJmDca1c7tdaw8YxZ8k+mEn0xZCTtDHebuo/UKSeqiCcSxEuzise52K33SUYkYIQdMSeEstZyXx2s4zmOGfmuTLSCT8OM+BcsQeAIHH6GmO838b7fYxh57neDbSxlouqBnGR0NMwSCBt8N3GMPt8lBVUAeNaIV6qgiLpiE2zOSwyujFnh3NA8XPRodDTQ+2Qb4c6nYtBYXjFYBDHXHFveNNlKzHOikjnVOUplslpVvKUKWUxqpVkZVQ+XOlSMtOYKSEDOCtY0rNW/CLPKXM3nsFwgsxkZPUqbn6uYphdM97YClAipzLalYiqrbuCYXYYlBsw7dYk7KoMm8SudoEmHBFFukmC3gF1cX9I2flGkao8FdXSqHTSonGByzOSiDT24E41s+yhKJFYKLFQC6/5vFiR3gNuhRXCqlF3Y/ogS3mj4+QdbQLQnIOojBg7X3hhNiSD2fG5R+EdtnDMXfqrzEWaOOhD02GJKLQdQHX83SfrGbfgubTEsP2oRhNkqMkMYhrEc4c2ylIbakjP6srcEUVkRrjh+fE7eSwMSOe8LF9dq/7TLuWjsJ86lqVa0okYKq0i02g6OZ1QcMc2u77zmvn2inUu88d/VOaYFQoKADpGjtHwCdW3JPiinki51c64NDFtDN4w9s89VWNIFRa7eRzoWHOnLQprg2QQzJiXh6Va/fTcjIhMtkWFa6wgOTpmLfJ5ogmASeh01RJI/VzHKgpVUbG0RHYkAVsgaFcMyRQFKqQz7UWZzgIvuApQTUc8D2xiGG9zRMSKGJUEMaVfMJiZVwfoWzU9GnrfdlZ4mzZOMO3lVY8U3KvycwF73NUYZDX3xm4O6uQlGzNRI1/xsRMYxmMoVYPWeevDDZmHuuQK3jd1Ai0RQoqYVx2mffH2BnRmnM0S0t9FpqFmLrNQSz0wG6WuLZbCiGiOKkdFtPvolqL2T7qEWQI6fU7bVCPToQ/S/cxuSAEqpzOG9z9lwRZ8osDYmfLYbNh2kDrenvEUuH1yJYS+IRsLLapCJBDc0+Tr/z1rDnqjqJGBmejRxYokyk8W5DeMx6gGkGrU0YLWQKikhOtRc8q0itmdP+3RRP6qjpglEfRoQa9yMLm/WMav2/BqmZ/GOIMqgCQHeUxrH4aHO0FPeHrfXgJUgZqEe5SGsVxy0jdQU+kTGkZ/4j7bZ2yOT5R+ytMq6jy/jIb7A4czn0SSF4iue6dffkzWUX66dfZh3ye9f/RGfwAmjSWaFy+AiL6v70IFbUysyAEJl/fily3GXoCndd9PuZ+rUA68/qsQz12qLcsujp50vmIvMkGNn4vIMlmbxaZDLlEd60u7AItq2FZFUdCLpuRueZkd9keW7poZzexxxfAEGZHDqL6/rh1hWJ5ryUOt5WpBishZ1hY14oEEuCIpTLZh0yi8bi9TZsErvVdPoKLsFsqmFZjApsCwRfis3WTt3dNDV2ue9UHJlInevrnCTQq8gEI6at8354USG0apLLrXOIKhflkCRR1v9EeBWj2qAIICepWlp7pHWz5Po15UDb4fSgDooRJiip+vT1RlcpKR9FU2frEar3Qyd/DHPqx1ybqoJuTMLA8zkB5ps1M5FN5xGI3857/++MfffoyP+c8/Ht/fjj//fP92H4mcRg1YESRRiWMHjvkSDSiKzk4GzGwV59DM4v1+94iwmHNmUit3vw0Ef5zhEculOsdrLSNKGJ3F2YySz6xTIZEFs9ECcBw2rstNU9gzRT7SQwnbDUkKaCYgIAWJdLhFVLNaHIYxYGbH4OPS6FsdV0TiXOX5dPoL5gKQOQfNsBwZcRCOJHGfpnpyRCJJmKgJcn9zjEiIuZ3ZI+08M3NFGoIm9aIYxve3G4uAhK+v67qWR97GvN/uw/jj4/Hb7x9VPPRrXX475ve3b28T72/H3/z5z7/8/BOJ43Z7nNe6zkSe1+XrupbfbvN2u//4/cf9dpvH+PjxeV3+r/786/Lzfn9zH76uNKsYdSTA87zmcXx8fn2d68fvHyJDfX5+XctXIDQ4m5hzthWmjQGAETRea/ELwO04/Pv7MecYNkKN4Tvp8BxjIOHhx7yJ4nG32z/+08eYd8Dc169/+snjpPH929vf/4d/9N8/fvv9x+M6//TrLx8fn2udNo7/5O9+/Tf/+k80fvvlTibdj8H7T+/h74/H45ZxudolAHIM3m+HjtlyH2Zz3CLx7X1++/Z2Lb+ua4xBjrUugXcE/vzrzx7rr3/9yzxub+/vY1iEE/n19bjfRwa/fbtHeFz50y/fz2uZjXBf7o/H6e6HWUSa4XZ/f5yPdS3a+PHxcT7O+/2YYyZ4rnW/vyXw8fV4f/82xhHhjzMzJdPrANQ9tFYNr+qTv3mUWLaLaNW7IM+rVDNYtN9IzmGHAbQkHo91m0yM5WsMZtKQNE4zkvcxfn2f98kkfv90pKAWeBQXaa3lHl+nn75Q6pYRNViTHmmZHHTgZjkt3OGBx6o7vwKRuA2cC79/niRvU82G3o1+ehdGpmeowUIkWAVC5/JjThszMgeAyNOXmYbK51peuWJghV+x7hmDCXB5qg1YNBkSY1DBihjfiiK6vMvjoNmx3FEqFi2SAuhGbybFbLOAMqkiwAoiqV8eHWLg+fcKpnfIgR6ssYKHGkSVYVZkFAqjoosUeuZAGjQtQcJ0GeKkocpvciXVT9pc20RmZIu0wqwiDYDHMY5R3LSdipRQTCeTK8ND6nWV3FCxb1dx5FKqTN/Azg74u+pBU3t+efBdLH5GlNFkW8UAioGkIbvhNIn6AzkIB4BUVwq6hGmJ8exBIknJzZF2Zg7byULHrZGaYXUpf0oM8qziAczojuVhhmOODLhXrOGu+wuAa4V5is/Knmcq52ogWh6pZ7RXWNUy/7kDdS2CnIqeXsfIUR09TXJBhu84AWrNttY36BChC1ToKaO1M68x/N6sP/wNisJR14ScNnLa8rg8IzLsWUklSuO8g3sOUUOzpZcya5aQplUQrE6U4v6wwcQnINjfWIZgR/51Sp6ZhKLSfgWdmWJJ1/orA+y12kmEIq99szaQG53O1K8Kd+vmCgg2Z8wcHmoJy8xhNspep7GHTZMbVUDDlCIOSrhm8xvQ6UM/Gl8zdR0V/SBdkKlAxPk6X41ZMFAFzaR5REWcLXgB+RXuVBwbJKYxSEuOYYysIgcI6KDXKNOR2BkzG6s21SBotBqLcsypot+399swurTBewKTTsZyX84bJm14hHsI9Q/aMLqHaxBM0w+Amrau33p3gaU7zY45ErBMxa2C/MMdjT2zw2ZhWNbT67M7S8cznwGgVg3paRa7njRPDEp1QtFh7EdK6dd0pqIcxggpMpR8b2a5pmLW1CfmtoUJUrICWyGoICTuCcEykK35n7lziY0H6anQZBlkM4maLFOb3ueq6gjPM0+D4KEaw4lrJTJbWp9TA4b6hJKMllSvmFjZS1kw1qOiOpgoa5DpJQ6K2osU2xSw5sG+SHhoPAGe51YGpQv3lZynkXOO21EwdoTOaSAxzG6Hqcwl57p/iNZZ5F3temZavVE2TlS3+am9JZPtcS7veni+LHIBRqzuQXGgSNPNNexOxn6dOQSAInvYB0BPSqNuebCQozCzBK7l11XSWHo+j+c+ohGf7WJEwZBvY75UGxr/3m4gXw9JIzlAweHR36t97fcshsU25C8/sFpuu/n2xcjuq6nWxaxVtarSFRJdcbFw3KaoNcrQ2F79bbka27gX6py/vuD+xX4/8vm+dUfaU9CYgdizDPpfowFnM7JUDwoaK55wr2azXRpHyIQGZhvRHXAJHGaqUgoQjaJ1xXJX9CBr5h4uApGp61hsi8Kdj2miGnnmtZKEV0BUGmSuR1S8AqrLwAqMsQDCoxplWDXA4m+xyEEFFG9AQzucGUiS02y8AGe6r+VrUNHfMbDN+d4CEG0p69xFNW09wTt73rvUzy24LeBCImpkCjRirPmAldxCtZDIDBhR2o+sPikrweP2zS2rJORI9R93PFZEpHs+rliRw+w2CWgoxrYAHE/1FzBrsol63CKzBsD3f8rasy2SFSzFvo9RZrDul8pH53V9fD3GnMuxvs6Px/WPf/n4L/7ul1++HyVGBkSmZbXyRiQyNORVhVyZFA8AeXpF7fJJWeRtfLvPYVzVgEYzHLCViFjXkp1O0sxG7dIz5IqsYTr6YyZyjAFwueMF7pGJnmMs9xU+yZX5NoeY9ZFxht/HnNJ0HBy0aaZ6oy8GeROVrVGepyGTBlNhwQgmExMA80xOQqIJhAljksjCtarBxyOpGozlMQ8Z3kmOGalROBEDoVb5YwxFdvf7AfDj6+uf/vLXf/inf/78ePz1t99///FDuO6Yc46RGdflj2t5+HmuP//y/l/9Z//qv/w3//pPP/+bt7fbx8fv/8u/+49//49/vd3m779/RcYvv/x0v98iwtf180/f3t8453G73X//619vx1zn+fPPP99ux/X4PXzYGBknCdDmbZQpBT8/Hj8+v/7hH3+LwOfjMcek2ThGrojluiThYcPGGKCPOc3M/ZpjmJm7r7U+Pr9ERjiO+5wkWWWtAMD3b+/hfp7rfrtFvP/zP//z7Wb3t3tEqa6u8/rzv/rTb3/5/Trx/ftbVBx3/fv/8PfHnP+7/+Z/+x/+/j9+fn4s//mf//JxrfXLL9+/vd+/fR9LUSOP83FmujcNZAybx4HlcgKRnGNE5HWeUMf6nIkEYsyh0lNk/P5xZeb37z93UYS+/DjmPMbX47y93da6joPntM/PL5gBvM7r8+vrWr7Wuh0Hbby/v81h396+j2Hu8cu3++Px8HX5Won8x7/+/v6v5tsx/vJPv2Gd79++j3nnMI8YiCXNYmOsS3CvrolxuleGGKF2Tc3GpfJ6rXaEjGHhzpMcmsoc6Z4RuTLTk+BRtg+R+H6flIo543IY8xh8v91vk3MawUSclwPwiHNdkXk7bh21Wvl6zakmafjxWGvh233CxnnFtRzAbTCTv3/613ku5Ptxz4xrreVRoii7UT4jIqQRrsZbku7X7Xa8vx3MAM09zutKkiPzurJI2QFQAeUAJ5WtVNBISF1OYhTi2lb4qvSYWVIGCdBgObxizTQiIs5VCRgSCfPEJKy4P9G+r7W5Klxpd1iyymDpeanB4hkRKX4wo0cFf0Su9oEe8JbJTVQ5JBJf5zKSxrfb+OUbb1a5sBya0jyFFuFhKibBAuEBD2ZmlM4iqco6kaCnVOFhpNrTPEI5n3t+XfGMMHuqtzXvdg8Aq5CmhHZU5kmxKQuKqvy7YKNE14Q6TFX0tbG3HaGtzEFK0NCTO0hrmB+7lKLlOsbT/JtZlIgVT+RtEnCPJGJ54QbL4UwklqqONKXSAzhKeKkyYrYXNSODne5ZEh6SOY8FDkkzFaiqXKEKVLuemp1EmIIwqTJJlhsVXrE64uvrPYNK/J9JZ7laYxc7d9r1DKLqqyr/fEZcTyzoX0T+Bc3qXDNF/HkbdoROZlapO2HkzLxa1F+J2HjGc6WW/YynAKkI8GWTrROCHeO9OPDU8+xIsWKTrOoO0UpHjdOQ2Q18wqEKFqv1eQI98cTQOp/UzW51Cx3r+j92VSuARCjq1hAYtaCOkWNQJaU52Fl+1UlRuzZEf45OURoWazkI8RzUdRGZPfCET6munJtLzi7g8ykcUzs8zMyGIY85aKFokaQZjzlJXpIYIdaqjkYPNY+2wLgIEdG3K3IMm00NRkIlZHmWYQTp7jbsdowxqung/X5QiXsNeXWPuK51XhrDYkSmO0Az04Zdl0fVwGF9zNWOqxRlGMwQF130Iht8US3VKVfNl6P6zij1SpIGo9kodk9muue5NBqrujKzcRdP+tLMQs4RcwJd7BLIp26vVD4WAuQpzosjJ+0YluS1+t7XLSh7sO9bdZcC2tNj2ixGHwlJBddE4UJYNs5EJnL1I7Mwjex5Fc+LHc2mQX1hJdP6pdNc8FGCRZ6sERpbPmojMN4GfcMK7LtbKYf+0ixCALZYP7kk6sHGoyEkK/qdaAFnZtqK8KKjV1pelf4yV0/j1XcYY9DA2zFux7Bqt2ZLcBaYpw7kDICWNX4PpEr9AWLWSIX6SFkmY0Vnnol4Fi48crmvCCmvRzQG285yL3n2w7MtTod6u7MoaZyD8q/SzVWhzANGrFVRjAFzWCTWta4evVcp53PoCRobZpNyBCkynqy0rMBje9s2r/kCAOXzXeoFSrkJ2T6m/FAr7/N5E1Hw+fPAsxaj4wcgc/QmS0FyVQuSanH2jDBQbpWVz3fRAxVBoCChBoOejvap7YIX79LWZafY9f/2uwvkEnht1igGCqTwXj8Cw5qh3OrCuxu8LlECpYmD7KvUIUtBJyBn4nK/PELtJZlA0bsoZ5HPLfbMkeUhyoAn7odlhPqtPh654SEAoFXcDiSr8f4YQFGX1XcTRp5AzWEtiSVJfRb6s09YH45yO5rhOyi4vA7Us+SVtfLRYxNfQIFX/1771pcvs4WlUZ3ViMAqBJerUNTiNBGA12ElMYKROKZBxUQWa0CzxnX51E+tM+M93CEye9Rjo7eRAtT+8uP67eNSAVDZhnRbkHk/bE4zKrLfzYNUqzSAOQhiea7o+OyJJ1a2wSrScMCqlgggsTwIdPZUqY50VSF5mjSS11r/9j/8Nf7u19vNjJZqSyx7FedVA2atbfBtmsr1Rl6eYjUexoukMCCxBY1HsddI4oo4XQMHR6R7tORz7lC+OvkB0BBRGgDHMZl5RTYmWPp3EZhjeIaHb/x9H3j30KS6y3NlnA7DBNPMpmFFGGyFMO3yRdn3TxZh6GzpBTIhscy+tdOwYFHJc01SrBTT7Ho8MpYZP398PL4+CSbs7//jP1zn59/+6//0b//2bz3j8TgfHtf5YCZpvq4fH5//4R//6R//+S8/fnycX48kpatCkhke6UlE3o7x/nYfZr9/XP/d//gf/p//7//5//J//b//+vPP//rv/ubnn95/+fmnX37909fj8fj8+vzv/+e//vi4z2PMMcc4bvP9xl++vY1xjJF/++c/gSMj3t/fwPl4PAiLoLsD+m/4yv/p3//Hv/zl9zrUtGEwG4MUIKjbN8xILvdp1bJ5v93Ql/W61nWeP37/7T/9uz9fK+Yxvn/7NocN3YIVc4yLOIBpMYh52Nfj4+3t/vg83+/HF/J8fD0e/PXXn78+H//wT3G/j7f7L6hBKPH19fk3f/r5f/q3f/9/+3/894/Hebsdf/71+5/+9PO/+U//1b/5T/5sA8fIGLaWv93n1+nnua7rOo6DpUTGcL/OU6GLZHpJJnSdsiQXM69r0QxncYK+Hudt0NdFs/vt/jgfc65hmPPI9GutK67H1+Mvf/n9n//647gd79/eGfHx8fnrrz+NwZ9++n6734y4v9/fjtvHx8fvv3/8Z393//j8tMB//rd/N4fdbvP3r/Pzx+e54sfn48fHx7XiPJf05n98fLoGCd/uf/Prn96/fbdpb/f7MYbRknatVSai06QdRKEYjZpwp3i01D/ZUVxbWPk7+zidlR3l253f3+ZhkHAYEi4aPswoDJ2atCJKt1j1azmIYXZmqqywigcXtzFuxh9rufvgOK8l66FEdBiXO0IabXnMkTWlCEbOYTns2/vxy/tIlUNyPk77598/H1/Lho1SqMIw2pxz2vsxvr3NOUxWNyJHU1hZKE8HKO3XVHGG6FR75EgiE4GMxHmFR6i0vxxGc4DVqUSgULasqi05ZGPVHKQyQ/m4yGT0iLm6RyAQniKVUOPP6x92L7Pk4VVaxvLwCI05OoZ9Pfz7+7gf85jqeUkjVuZ1aUA9LJAZHZtX3l4GmWDgyoyQ4liuLDaNdRHXS4GUZhm+A9f26NhfqRhJ2WJGsievq6zVCW5mZKfm6JJPPDujX4V7mh2GbceZga4M1YmuqFOIA0WqzfZB6E+RQu65yq0v9/DQmImafAcCOKQgQIWjPA6rJ0dcXnTm6FDRhHFME1KmyCUS4TDDLESOJDwjo5K77MgwG/fBXtI6FJEIlSqaqZaNTqDfVp37JcPyxICY3SdSK1/Ojk9wrdnG6F+ijOH55/2flyBvp1zKF4fm/xlGqtWdw+z9ZqunBC5N9DaOni3+DKuipCpURywXv8u5z17+foaiklXG1Z0THdorOny69brAhT52psFeDfaMNp3fXR4DdixKqJlmJz75HCoNxGuVuSLeVucFkqUVXaPihploBkbpjRjE4UrL5dMshnlIt6eoLRmuRIlEqT9tXKMeANMG/4//5/8WSAJzcM4hW1Pp0MtSjDFK1wD0yPNahWwQx7TbcSgCkLbkebkE2PYYPBWX1aeo9TTy7T5Ew1ODbhdVtRZA5jFsHjMiMtzG2OQUJcLh8bj887HKTxcVEUo/ZP4UBNjW3DYSnIMlBtm5ZPbJ1+GodpWIiOZqw9DKNXNUdXdfmsqxicqKQ+9e49LUkNp+1ZqMkHNKmy81yThSiXqZ+4iM0rCr7ZAlStHFAWreSqauROU93PBPApjDbk00FQ/nLFIJO10sB0JQXrCzo2b3yRv1FY5meD5R04ZpFAFv0KrQo0y0nrE+abtG7hXfrVZItHBhPi8USA6zYcBmVSg4k7NUS4iZ4MvV0jl138wi0pualf2ybTCfZqmK9n1+DEmDlDv11dH7wjYb7Vmrr013VT4eSGvhz8ZRhatq0IYRXB6rpLyFPyJb/aePbu7lxUs/1H52M74cPJDVTy6MajbPq9E/AAwPb5SdSKNJKkvr6VFaM694H5qe0emi3IyOW41eqkdshAJILVuhFVknp+UIaxdSFIoUeRvP3X2iScBTlZsbInleVnUTWR3EaQDhicNE8ZX0GD2yypu5vR1Yes9McBqejG49/6iZbmxoZ699vvx5O062Myj7imc2Hs1Z04u8eJ36Ed68BpJTsila9v26+SRrbMym46dNkECW7BbQs0XzxZ7o8yUq1LyS5yor4yqUqjd/Dqzlxxxa6WF1dR8LRtymxglxFXcpDRxD0/3U6quor0qOg7jNGimiXfBeUL1kdE+4Pn0Qc7wGJsKAutWI5TjH9qO1LPpVZlyvrxJ4Zq7AKj2IjpI7kHKvUuyKKEr2xivLxKGhud2XnmgCsz56zm0GC5mt2TfZcAYQkdeKs7V1KhbTUY9IpMkVZsxp98OGgWVUSx2QRTKiGa8V53pe2F1flWFqbwuSx2Hf76MGn2uFB0kuD6Ufn4/1j3/9+nhcmRQGrVc4jvn+dtyG3Y4RmefpwFaIxxhq5MXbbajwOOYwcrD7SiIfHteKBKZppp4dgzTrSDojXJ0aX5dfrSndL1J1/hBpkTZUrKPNgXPF8rzcrcZFqwcemRktTegl11rwcVSsyRooMyTQk5NcCYLTmKySvujDet9D29IQYwAJDtoY3DO9rUboEaDZoPG6rvRF5Mdv//SXv/93l/uP33+zWL/86U/3+/Hv/t2////8L//41x+Pr/M6juP7T7/89PPPf/8f/+nt/s3m+DwfQB7z9vX56ctvb3exOaqQMww1612jagDk29v97f7mvj4/P6+1rut0zzHG2/04bseYdr/ff/75ZwAe+X673+Zcft2P4+1+fLvPOafN8aefv/3t3/z6/naTGO91rd8/viLTjLfjuB+3yHic128fj3A/5syM7+9vt9t9HkPxSUQ8zgvg2/2WZeRxzKGuN5YWFde6/LrM+PP392NOmN3f3t5u04bd78f39/v7/UajR0zjx8fX53l9fp3Xef3DP/z+t3/7p5V5v98fX9fvHx9Juse//bf/63/8h3/+r/7r//zv/9f/8Pg8j2POOf7pr1//r//u/xvI2+0mDOSXX7//7/+b/81/+Z//nXC0a4Wi5a/H5e66th+fX19fjzFGJs7HIzJtHBLpvNaSFRcUMmzQ6O4ROaco/Gbw2/3t9nY7xsgaQJoZEYmvr8fnx8c//uXj4/M8zK5MzOPI8LXe3u73+3F/extzpPu6rse5vr7OHx9fx7y5L7Px80/fbse8fP0P/+P/+D/8z//u64qvr1PGMZdatHI5ElQUdp/z7X4L5Bjj+7f3P//N3/z6pz99+/7T7f7+7dv7sHE+zowL4HLPhNkAulaGIioYCRvMZGoKmjHh7qHpe6pTAzSbjJ+/H8Ps94/1189qMNGQVpltVcUqAVO9Afi8rvs8bsOGwdNAXO7MnGOSmMav07+ua9bgPH67HUmLxDG4lnu4mR1zfruP8Pi8lO4Eaffj+PnbvE0q6GDi8/S/fpxf14rIY47jmLdpxxzT+H6f96PKexv2lbMwwoYR1b8WxVXU7XuJXQB1OWTmJRA68lyRmYY045x2mwL3CzqNzhcyi1IdkecVX5e7l+d6TujAs96w/4i2bxVFAzoG7orB1KCQ7jhrqihcAvu0DAc4h43B+23cjgFqFmR8ndE5WT4DjRe5gw7jVd2hqfcvS39jpyK7GEziXCpFAeQwCTtUTCfOu5ZUb8fOaFSObAwiO33DblYylBBBISTYQVH2yOj6+/rRkg5UZhA1mLIfDGpJAzhrTqO0pTCNy/PHGYreo3pWespGd2IQmNPuB6uTQ8tXRF0ecyibqHyeSGA5ruVR6t3Uc86X5jv9bYejfeB6/fc7N+zVZAtU1FE5XQFTz+udmV37rGgIlS6U/mlWl16iO/E7T+fe4J2ZdKqxIZQdKD+hmY7gsUP+qMZGgHVsRA6q3vEXYKsw5hbTiB5PdXlens+vzr0a2IluMzJ2Akjgj8lgAq0eta8WFGZuFAg1eFFfVRqFT8GECg7vx8hMxTySZlY2isYlFMTqyszWkC3sJjO7dwQa4zOMrOrsNI6ezVpTCyNIekB8SS16KTRGyhWtFWdNCnYo5tHn/R/+T/+tkhED2JoRU4lr54H1Jplz6Ozicfku8gh0v9/m2+2Yx5A13MNfPHKt6gGTvIhO5G3aHMyXBX7ZiDR0UR6MKPg4KpUKNAXdPR5nXO6ZqJ4w7X29ITMhIZyNCAAYg32jSlGCXU9QSrnhPP3RG9mldkWr31dtP7SyXzYwJLBHTT1CNPehnINWoJDuOcpnMFXRX5HLixWrHx6p8MK1LqmaPmu/VHawOmBS+IAACJp54Q6a/hPK8J+pKgrJ0XhC4dDPfxPbna3Z0UakL/bzrhgh1aeXzLNO44o/pPYqUzxb0fUxfQP3jXrmueV96w72R+gfMYyC50oxJ3N5zesxSlaj+Jbow9xOJZ/5PDAG79OOOUYNR9SX6XOrShA9tUdQfYJe7kXzTVWXLYc5+zYJWhIsaGDzhupbNF4qu8/WM1Vw2xc52yn1EkmUt16/LLIVvFmKbkyBGtKP3OuolY3ECnXkUR8XiZXq7laozucdeGF78sVB6sJsB2Z9WNuxbUAEbGphlogeo4mRRVyITva2t3jBOLLPJzpRtzbG5coqoIQGda/Mw0hoQrGmNas1iomcytYKI8jbIKiKXEYTUHSLt89jnfanV2js5sVP9Hb8C3AnW3Esn++G9u8Aii63ikAJcA9laNijbcAwLVHXyv7/PnQ/j3yecGG2PRHKKhMq0y0/NAdFpHu7cRCejOrIoxGDuSJnsdjKMkiU5D6K21zDp8qUvfAPUcpiQhxAHIO3IbkUCVEptq6DnglPLBd/JIe6lroBVlszSGNW31PhP7VlMkHPre0GbOELXkzTbZQ60nnZxWjoc0VKp7CCGIWeGkGaT+wSW2awrBj3NdEu68U10SNCQwmwVqzla8WTC7nNaR8S6755I26T02yMQjwrGbMKHQTZr4BmEScs/iA09jxvx8D9GHPK3SFqnhcLnU8sj8+H//7lH5+PasQKYHcNE8cc99uBTHVqzykRYVVAMZhjGmEJBCjhMzSjc3lcASRuk3Pw8hzG2zFyB39EuIfnw/PrKoHM3e2rWMQ9VsQw3qa9HfM++VghNHOF9lp2hUzx83F6NO15G69NX60+hWfYURbAVBS1dhPa3GOYhi5ZKUzRxsjIa63IPI7j7X6sFf8/tv6s27IkOQ/EzMx9D2c+d4whMzIzcqoJVQQIEITQAJsg1S2R6m5JS0sPetEf0G/Ro/6AFrX0JE6rl9ikmkM3gQYaBYCFmnOeIjKmO51xD+5upgcz3+cEpItCZuSNe/fZ27e7DZ999llKXNYFIrT75ubq+tXVFaS+LpGAR3XZ9zGmsFqtHty/vHfvMnWtAJDzd+vtT375eQhys9qzSExcVqPlcrHebEDEOacDjEJMAhIihyjoKKUkIt47TyisuWXRtl1IXHjX94EIKWtcOkdCWPhCP3E+m1Rl6chpsOec0y6Dqiyc813fV4Uf1VXbNilxUZSR02Q8qquSEKbjMYvo66tKX5e+DaEsfFUWItC0PYt0fT+fTREwplgWvihcikmb6xGkKouiKJhTCsE5iil55whUUUu0DhFimI3L+XR6eb5E4PGoGtX18xdXbdeeniwdFb6iLrSOCmbs+7BvQtvF7XbHAuTo+vqWEM9OT37ys4/3bai8e/L82pWVd65wzjn83vsP3nzjoouprqrSo2TWfx8CAsYQ79abzW7vkJSypIoCzjlAiCF2MREq3YO9c6BOHVQVhTUWHk/GZVUKs0hKiWNMABIjd32/Wm02u7Dv42JUBZaWxQs7EF8U+z7umzZGRsKuaQWQBUNIXdf2oW+6ru+7tmvads9M3hXOkQCkZB2mqoDgnENLeDDF4IjKsgwxav/deFTVtXfk5rPFw4dvXlycT8cjZpGsjUiOrBIoEmNIMYW+2a5uk2DTbCX0dVmNJpPJbF5PFr4oC2c8a3U048p57/Zt3LaRRUpHMSYBiSJtiLX3gCggBBAZECGmlFhGZakgRgIkQFWFr71HxCTiALoYY0qOkAHHVTkbWTuaJ2wDN32oCz8feQDY99yFJMCFc5OqKAsCgMiiaKZ2RcXEkZmIvCfvsHDoEKtSB3iRVoaGcBFNJUPn8UliCJG9J08kYipjSbSKxgDKhYGmZ6N3gpJewTmsCqedHZQrpiKQmENMSQEJBBHoo37HIsMBLsv+QvLcZA2BzI8MDs6CxqEwoL02BjNZDM8ZRjDvmHEAR64ofOENLsChNq6pmcWAkp3LYENzEoWAAFpCo0O4BszWHdaHZDouYEW4oYhiYBdakI+mM6CJ3WtIx1EYdaAs5L/HIUTDQ2x5MPVHf1JFec6cYhVeMlcrQCKc4S0bN8wsXZK214yas6KAhUY5GBEAURKHEvk1ZtAnRQPUMp5CgiJRgx/r5zFn5Bxl2rUBkYcwAfN3jvCLAe4Y3vsQjw2/AoDMnMzb2cWIACFrpJvyDiBoqGDjKYe+H53ljRkJss+FIZyB4RHYvqUfPkj5gKVhcDycB7QKi7mJZAjYmI0gL2DzEyizgSFvCbbzwkOxzWKpIXsBEwlSfgAhOEcAuafycKXXokILP6wHxh7LDAIIiMYDFrsOSTUhVJ68I1DCe8ZGQ2Qlm7NITCnGXFsyXg8om0lNBwDo7ExSvbBMcQAA74ZTCMzgCJwjZkkMKuIMnLx3ZeEAoCw8EXR92reh7foQEwKatAUI/pN/9j8AZC0GECLwhN5T4QlzHsIiKlKotpJFuj6pjpezThMcVb7wDofUNO8GIiQi1fcZBhupvKIctnK2H/mVYRbjAGVtgWWPA2pxQEYSx2Rvitmam0SUdg3DSTDwCGzMjRm6PNHp2L5IXuUhfoUB+9CvDCAPj4mvH0vI9W3rgX+deqV0Dd1SPJg9kyHPia4hMDgkemKiqrY/stFHVM5O4qgAP9sZ0LzL2pQA1Qlp7p14SOKH1MtCfMw0SxEYmHGKkeloLTWFAoOdQkeEZBg7HbFdhkfOcbz+MAhgTCwDNYBZbaFuBrLkRFLWqtKgnex4mKqxo+y9wMJ0PpqmzCI6G4XIhRC1rQFy748cPYhh9giI5B1VJWkwXRisZvU9NPORMabMQMiOU+CQw2cTi1goUwxRYwvd+ZC5rPoiOI8zk0FeHQ55quQLmjEGAOtJPey/7KjsXZQO64L0uSgTWCQvlG4w2y0CyrHUvaDAnz4aA6gEO+Tjc0D0BgeHQNYYj14/BgGyfEymsNgNsHGLDidYMqyjEAJmdOM1/znsJcggmHliZeBbgUKfiwhU7lq3kHYUgw0iVAsgQ1xSelVkAkLsgulha0pswJZV1GUIufRxMoiLgyXSCEnfIVo+aRigqv8m5mRPCwjWoe3yoN/C0SCQxmCFDuYBLzU49aAWmQObATo/fH+4HxFFMVhEGdSKV/JRIIgZZEOTFYDCofJBCCEJJpbSYzKURV2LnWkWUV6bBgRZwD6bt7wwIoKEqp3vCApjhXDhiEVfkHG2OevKMZsghVYfNBJLAojo6VCGHezLQCgDkcwEHOJgAUAWCCkjrXbwFZaFXMIFAe3kMlUyzuLrqpvAYOPTDdfLDyuSwSk5+DHJsfuwMVhQ8sSoyMJJYozH/iUHFna6lcYCOWxCAFs6Ig1zAY+jwMFBKGKrGooH9ShUhJF0qaUqSCe4s1ljBJX5AASQEGXf8b7pLIQaEG2w5KcofA4K9IwAQG5oJSg8IGDpSRC7wAAYOQFg5R0RhMiJNbXAypOao8ITHqqMkDhJki6JVu9FBpVGs7rZlSQimtVF4aDpedjSA16n4zOmBQFCl9jEfUFTzqy0KiYGr8tnrEtBRb30hevmdQoYZwOnjXqeEAH7EAqvUoGsKjwc02azuXrxvG/2HtN2twWR5WJy7/J0VNcCAM7vNhvny+l0jBIuLs4RMPbd9e3q9m4rAk9e3F2vdne7xrliu9ssJuPz89MnL68YKHEqy9LbBF3sA6P3KSWUBIgxpEldMfN+36w3u9F4VBQFMzt0STiGrijLoiBApzRkT/7y/Gw0Hhfea6blvCNAjfw17SdyzjnnyXvniLx3mcLjU+KqLJ2jlFJdlcwCkpyjm7utSDo9WXrnxqOSUJJKIzl0zhcO+xD7kBChKotxXcQkIgycAHDT9CLsifZd7EN0hArvlQV6gtPlfD4ZbXcb7917j98+Xc6rUekKFwKHPiROjtzLq7vnL69ns4lzlFi6tt1sdj/5+Vd//euv33xw9taD89WuW+3CpK5KjOfL0dtv33dF2cd0eba4W627EFPivg96ODebfYzsvXOOiqLw3iMeOAZ9H0VEhHWQrpgep8K7SakQVVkWhWu6Pkbe7puUogh2fQCAXdtJFOeLfd9ikroeEwlwCkmSQB9S1/a7ptnu9ykJc4oxxBhjSCwSUwSRwiMgZtuVyzmcu0SsuRtDH7xzdVWEmLo+eu8ARTgVnlCkaTsiOFlMHj18+Pjxe0kYEVBgt9tpkLbZbtfrTQpdgWm33Vzdrqaz6bSuPMJ8MfHej6eL07PLsh6RK11Zl0UpAiFGEQkx6lw81tNPoIw/p+mmunKBPiYAKZy3jmYQUAktywBAAwwBAIY+JURwRLNJtZyU1nRMKAIxASGoKndKEhIjYunIOUsG0lB1Y9a8JrAEHfZqdGCqCqwLsq6wbCrR4kaDm71DZugilwWNSi+i4IGwGMKCAETUR+6iMIOCCJooeIdVQaUnwyxyMsGJ+8gxU1U1sO8jK7if4wHMqYUKLIN3qtwjGlEcR0oaQ6C1UwDouOhMVTBvPriSwzBWi2+dIyWY56wu11ZAZxQc3D0hkiOfo9ABvECVodTaJJkAf0riCEPimGTgbOqPaSAhBj3YRwzxhMuUogHQGUrCoFIempgcYi37RSKgTIXOLhfyMtq/WOypNXJgu76O6EFQTToUzQtS4pi4TcAsMVrAnnQQlwEuIqL7ELzOOkXL1NDq/ThI26inI7RRdDQMJgIEE0jV3wUlYA4RhgAM9emjGsYB/dEw49D+ApYFiFiD1RA7WXaQ0QctpUQWJc7r9kejz9un6QmGIeE5BGKHmHT45nCQJQfhh8Qnh/tiJsv6MIbgljPNhzOmIyCEWVBJQxETvsSQREEWzrOAMjgFaAQ6LdrpOhtway0vub3x+IHyU+EBFBDAgemm/6lS+jA8trV6EaF3WHqHoKlWion1jHiyuYWcxxmlmMTapQ8nTVWBNFLS2ItNwMGgmQxIEOY/6zb2hKUKSeabEZEQOUbuE/eBu5BSSgKI//y/+59EJNPwBMAgTRXiYRZCcc7l/na04QjMfR8BlN2AVem9STUoEMMWxKqVJ0JEzuW8kCRjh8b5z4CMLZ/tVtsWAtbawyJATucOHPAFleTSSYeUEXHMLUgaIuun8JAGZJjF6ci9178U/xxOTsZJ1MpYURqzfcp5FMKRgBnk+iFmWO2Qz0GG0o8sLxxRB//GnUDe45aAwiG/HKDHkFLfx15L1joRRmFCT4SQEqub4UxBEjtReQkRAUC1xy32FdAmKn16QiEcekOsBqJ9ZIV3haecQ8pggnK/w4ABoyMsHSFCzxICMzOSnSvFVI0JAgCo9B/VylVfmMsISnIxPMVUwPX/+DXc10C5wjlhaEPU0a25bH9IZSG/SCQclW5UunzwtA2YeZgWp+PpTFPLrJPZWVAMLu8rVMTKWHYOsfDoyEZls4UCZp4GiqNegQ+gh+mYDjCdXtmcdg4C1CsbRgmAiKXXtAoBVEg0HyKVswFkNQQsiUGTKhYY4hcbVWbLDgMIYqYQEQg5j0Ig5bKRqe0gGOiQCXLZdQCAKggcQRa28QlBQJKoHTWDKhp4icVlqr4m6EknIoiIqMuHQ+MhFC5TBgFSTuzZKDCgHR+oQ4VQtKOfyAi6qHrG2UsNiX32ZEMokg/m0SHNgcSBSnB4v9pXp6dfzKToT5UFhShDA47LUaBk/p0NG7RMW7mg6EmxGBzWEAYcJ9+NBgiKsKAAkqmbDRBVhqPtDb1eooEBQCk9CUAfFK8RARMLVDBXSRyFwwNWC7a7hstmf4aOsCrQO4oMBYp3iANRCLVwcUyREoBh8a2HSd1t9lH2l2xEdBz4Gvk4m1lNLFpS4xzVHaJWdY2gaJeY2Llhx8OvDEfHjpchBAfCjt5p5uhlA2brK6YVle0tpJRS4lwUO2wVPdFm6FBHPGXSNYh3TqUi0GwgHCKV7Fj0nSrVdIihBcQhkXfkVJYodzVaQJDD4ByEhiR9wrbrU2IAk4rPNwbaH6HEVfuf3jcRAHgST1CVDhGcczFJ06UEKgulJKP8pCDjktBEoMgfevysgNH0KSTVIAcBcCCekEX6yGJ4qBRE3sHQJyYiKUkbYpZRB09QOEw6Hh4OTpytmmUIOGrPC0BVVjoNHb2LKRE5EEhJknBKKRmHmREgxpBiVH4zizBHh4icUt97h5LC6va26/rlYrRYjPquHVXu8uLs7OTk1fXtZrsLMYxG4/F4jBAf3jufTCecQuXobr17dXVzfn4WQvr66cv/8Bcf3e36yaRczscMJIBdG0KILOwLh4BVUXdt28UIIjoZPcQEIIE5sXAS5xEBQ9PWdVVV1Wg03uy3BABEfd8xJwQa1aPJZLJczE+XSyKKIQhgH0JZVWVRCKc+hPF4gojMsSjKsixEOMVQFkVdj1KKdVn0kUMIwtzH4IhSjKOqApDJZJRSqkpfVWVVOEX6mJMAtV3o+35U15Nx1QXuu7bwJED7tq9KPxnXIaR922l4UxCFGPsQUGRS+9I7AXn89oM3Hl62bbi5XXnnyrLcbNZvvPlgPK5vb1aTSV0UbrvvdpvterMDwG+f3/35X388mU5KxCaK8+XZ1C8X07ouC493+x7IbTbb0IeqLGNKVVmwcFWWDrAeFVbVQKzHIzgS302JVQdEzyKRxQxEbrtvtEkkptiH2HYhJiYAw38RGYAQU+A2MRE4hvls2sX47NWr/X7f930IIcYemDULy7klsqDC6ronmZNSjAEkKRrlyClPzRHHNKrGZ+fL65tXF8sFEr64uqnKipzfbLd9jNlUJU9SFmWIiQhSFOcIRYiwD5FTfONy8ca906LwquqQEk9ns8l43LZN6Jpmv19vNpPpvKqn1Wh8dn5ZjeY9K29dlwRCTH1KhXeFL5KZOPNZatBp0CLJRTQBcQiCBAAFqs4+iIBateW0GNeFc044gpF2zWyKhd0G06vdVIs35Plal+2jdEkMXkIkgnFJZeGIkJCGMqeGbcqRRITSYxIEkapAInLOgVhtW9tXPSGztEHnl2XtVbP04AlKT1VBhUcBUIaQ6v330YYG6I7K6I+GEAcMCFEhf+UIDHl1zlRed6KQVR0l52WSAzDJP3WIOfWN2OAqEBHvnHNgQA/ZLBMVVkP7VETC0lPhcyuFAiKiThZUQVZ9NGf5uZAOt4doNVeN2xVJwaM71y930LVUcjEMyX/uCUAGLZ/LUNDXXxyCvRyQgra0Y07hclChUbqFxLlHDxDQkUFCIpJSiskWWQNUHd8Zk/F6QmLv0BOyoHeg2YFC9yZvl9lAQ6jJSVSY+cg7W2CpuM+gEvBahHTki/O7FrEog0WsmY6IHKryiSLFwEfTKfQ3KbN70Hj3NgNu6NHLaFumV+eDqnVdzNdBa/mXfPnX4i7991FykSMpsBfhHRaOwOZ5obJBhyacHGqCI/GEAJrvACEQYDRlWJ2OIswarYFOSS+cK7xLrN0O2nA35OGmlSjWPYZ2ljQEZBZhxWs1NNRdrUE/52KzZM51rhLY1VWWXjuULaQE1BE9hDiqfF2SCMTEMcpxXcpyXkLtUkfLbkSyGBAA4FCeQm0pEhs1ZSGVhZtKIyLErk99ZNVREhH87/7tnyIcXh1AVoDOm8B2j6bgtieA8lgoBEAUHe5ggWTOeRTuGnqsWNvVAAeh+6HwOySxhmlpGUotMgIIJ2bVtlPxHZcVmHI1W/qoHeaUjlgqehtqlwduyAHeQ/COCu84lw6PD5JhHwMUZz2ckF+qLq/u9gyRHnFGCMk7+zt93jwVCgRAJQzgiOCDw/E9hlT/RgKKh1u342j7S2JKfRQVoo9JYmRAKJwrPA75EuatziwaGQ+CWIWnQgWA8t4XwK6PGcQxBc3BUujZd45qFUVA+6aWuNPgRdTKEiKAIxVXA7WVPID99sPWNoi2AxFV5AJBTJVN3bOVi1UGVclciMAMuWsqFwfI9okIhMg63yM/wCHZHigmiFI48kYMQecJkZhFyXWaDgFATEpyNmMxPKZRnAA0czRRCURfeEfWHgyAwhLZhMtYICUWLTEJEGGMNlQBVCwRDsJJw+vLrhEFGAEzQgzDy3GEReERQSU58bCZjCoVE/eRzQHo0AhLVkVEVDKJk93w3yA1gb6bfFKAUMUCENgTZS2vAVsY/gEikgQOxhZwQBOtKVbX8uAIcNjeAOAOTalGq9F7oNw6q/zJAxUTspTV0FCNoMC8Pkw+5lb5CcNk6QN/LZ/xfALxtf88fGXiyfBokP2ePU02RAdi1BCOODI9msHmKJAt+QbMwBM6Mil3s2m5GHIEJhygKMWTD5zqQ7+xYRwDJ9nuOb+pwUSozpzOI9CcOTOQgAF85iSXHmPK5TrU9nib6xEZB4KPxm3aPVY4KD1575QRY86CJU8pOUStlAHOocZFmP2xiCXzhzdlz4Bq4rKKs4bH0d7vQQvxYC0y20aGEugQfutGPKjF57dpGxSyPLFpe70WUqv8BgydlCoTYIWKw64wOjgdNtnrxc+8G6VwDlDFtvNbG8LWzG+NKSkOyQZ9Gmg7aFJaJGfBCuYYx+44MoSkWqTMohN/zB1otOEc5k5PGahYCgA5ECAYlTQqnR6GmDgyVIVj5q5PABBZEiOCeAdaeuGsv6PZBXPq+tQF0R+OGu8KFA4dWfuGRtUqOqbHYdDE0VkQWmNzpPGrABwMGCABYOIkKZXeo0DTdm3fM0NKqWn7wClyapo+9F0XegZU2F+YCdF7HfiNKXKIwSGNx7UjlBBK78uCPEmKYTqp5tN6PCp2+/03T59/54O3EfHFy2tyHgDH43FR+BjCcjG5f3laF/ji1e1f/OyzP/pf/AhBfFEK837fffHk5d227fv+oy+frzZdUVQqku2dE+bppGravuljjKHwzhc+RU4C5MAjhdCT8+QciRDh229evvvo4Z/+1S92XSsRAnOMURJQgYhU+cIXbrlYvPv2o7bt1QgUZRljBIA+RACOfU+uKMpiPBoVhUsxKHGDvPPOK3LUtp3z5Akno7H3br/fV1VRVyUippROltOyLJu2i32nQuPeU1mUKaV9F0AkpOjIM3Ndl+O6BMCUuPDEwqX3u32rVE0VMknMdeVPFzMA2O/2gEBIm13z8HL5u7/zQ+b08cef379/2YfGueqrr59fXV89uLxo2vCrL55u7vY3t+vJycl8MfUo613XtF0XebXbAPN0PLl/cVoWHoWLohjVpXe4mI4jJ0cuhFBVlWoME5LzXoRjTCzAnLSYwqazKwpatX0IIRISC4hwiEmSMHOfEogQubZPABJZuq7r+/j85cu+a1Ak5/KivRqIdl6IvG5uZgZyHp1uu9j3vvIIGJJ4j8CJyKcQfEGmMYYyGdXvvPFws1k750KS67uVI3e3atqun07GhBBjr4I+KsgFDLNJUReYUoxJHlws753Nd00vArt9U5bF5fnZaDxKKRBCu9+9ePGyLKuq8IllNJmhK4t6Plrcc76MMfZ9n5jJm4iSxlAig2qrhihHZtBiYnEAjDg4IA3FC+9OpmWhw+SMbnzo1VL7rUY/K4sqEHOoUmgarwpiLMDMguDI1R6qckhn9IYEBPoQQcsqKA6RAb3DqnDaZ80sIWXeqGI3SQRMf9Dgb0WjCBwZBlSXOn1Vx4aD2MSugxfk3DsvcnBAZGiVwRsaeiDYFJycHgy+UgEdpRdb1mbexwKAIYrIBGcNvTRmGBxHttLqNYzAMIAURERYeCrcASoYViMnqIAA1nSchEX7svN7BRM3yd5WBr6JPRPa2y+8QSQ4DBHLAFB2CnBcUNKCimaOLFngVaPeHA0OuQPn6vWwwph3FAHYVE2tSlrMCFl3wnrGB0I0InqCPknpHYBkYTscPtEEX3jIHO3VYO4twCNOChmIaaxexCPGVsZEhnCbh6ATZNCjVlCTTe3IpiQxD5G4RSL+SH0JjKKiUIuFx2yO1XBwQgxJwDhlMkyoGPCdQ257EEi26w9v6RCO6o8ROtQBHfqkoGB05EwJ0FliwgBYKp8OEREic2JxiKVDREicH0OAAVNKSrRJAl0f8wpnzMHQQ2YQJa1ohKYRk5h+mBaMcxumgFILEUkAdUeplo4SpDSScmSijhpc6dZS+MZ7XE7K6agY5GKU7BIPA6CN1Z7Tbqv+Out5NMzUdr+e0KFpyShQlhzq4dIyoWT2u1ed1Iw2SI4JjWhEQ7+PJMwrIgBRd7lAn7hwSMwpkxTyuBkQgD4kQiwKB2LAhFgWZLq5uTnnEOVLNnW6ObwjREkm4kB9NMLAAPbzcHYRYs/WfAHG8OQcZOtSOodaidKZjvlz89ExjokZQP0bzAXPnJ+YoOmQJ+iBRsjtCUj6NyI4TOYVm4xoP5u3u+SDkJO3fOyGfD5fID+j2Vb7xQwXAAI6BCFggcIBIYGAd1ho0gjayiHqOMShF4lIfRTtG4/R1tzOMKCKjyrH2WU00T4pGygQ7sPQkAwDFEF5bDMDMktMWi/FeCA36pEnGHJHAO+ocLmqYPbIbJi27GnjmHMEaETibCiB5XDOlXuUm5skczht+DepcjaI1W1UmpGZdP4aW7LAOiqVVNQIdLf0IYFI4R0AJgEG5pTs/ZoR1xshEdEeyxCCztk7bFMzKSbFopvekwCCdwiqLoQIwj5TByEb0cR6cWYRtTAsCRh1J5s7YuA+qqZpQhzandRbJbGmpBgz/zHvMQsIMs9ScgQmw04TAdF3Jg4p96kyi6jOzsH/yJCK24XMgyOAgEdiJRGZgTI7A0OajRlTBXAIlSPvCBxps4DzDhODiCaOiFnwlTMjIPstbcWPR6EAoSiDDAmN/ckckrXm6f2/BigM5/AQgOUfQABT8MlEg2y0UHkcw5VydEU0nHAUASWF5aUCRBymSMDRWDN1zCpAqIJTnDP/IcKxiJkQ8hhLyXdiblgAQNLh3QJABjGzvZEc9qnGWRIpvdOMmmz2IoiS7UEhALSRI9mgRRYWcAAipt9M1tStwajF9wyQ65wGnfDr2s/D4qsSl07sQFSpKIvSBIAFVVKx9BpHQYoWcOgudkQORZTcexiUBghDZ5zF+HKIs+XoNg7/OvgQy0CAQOP7gWaMQwtSRnwOCgsaz7JN2DQaoIZ1OaqDQ+ICQyVJj5IQumQERBKNmlQ2K+9L1P5B5a3kcNBCQ624stZgRN2B7ch8tPMXhtyexqyTNQ4vQwSEzTbpMjLpu2dATCAE1AUhTDphU1u9CEGVVrV7ywrqAAgSUgIAZvYORbskULoguhp9SGoGdA0pY1YgiN5m4rEIczI+sOhpZi0RpASFd2VBhSNEjKxVB26a/fXVq9XtSgT6lO7Wuy6kGLmPUT8m9NG8v1ILDTUe+kBFAHXwvCNsu11VFtO6jBywl5770+X8/GzukL0T7+l7H76zXMxu79ZvPXrgnE9JBPDm9s5737Xd1fXt5eU5kA+B+75PibHp33zrjdE8vfX4LSLXNOHZ85dfP3nx5NnN1y9uFCwQcQ5pUlejyl+cXj6/vlttmlFZBAYAIYCL5ezq5rZ2o+9+5/Ff/OKTz758/uDiPPRtarrxbOqTbPoQOFJPvvBdDJHTy5urzXYznc4m43FdlNh1gEBEVVGKkBTAidebrYgApsK56Xjax8BRRnUV+s4RTsf1fDHtuq4uXFXXs2nNHOpqJCDNvm3bjoj6PhJi4lQUft+0ISYAvL678d5vVuv5bF6Wdd+H+bQunGPmqipFJMU4HVeJebdvQGC7b1zht7v9i6vrST1eLGZIcHV9e319N5vgen3z6urmsy8+nS0m27ub+w8eAqTbm5vzs0WM7fms2NzGTtL5eHK1ater28L5fRsA2HuPJFVBTdumVHpPiKlpdfI9zsajmJIANl1gEe8IPeghdI7EUHshoqB4kEhIsY8dgeJcXDgUIRF2RZESQ0wxcuLEKe7bloXv7tYvXr1EEOWnCnNdV23f9X1PSOSImZAkpeTYl6XvmwgYxvNJkgQOqrr2DhmgafbbdXN2tlRaCjjxhLGNhfMM+MtPvyCEsnQppsq5aelP7i+ZhRxxSnW9TAyRY+j6/b7dNN3VzeridHa6nP3lL79G5956eCaISOXJ6cl2t2PAsiy7LhHBu++9d//+/cK7tuufffuMkJm76xdflKvrswfvurKuR3WIKYbIwuSc6eIQwhEEINnxkeXJqlkKhJgMzVb/ziQYIyMgeAAgsQZTddgiLF1kZik8VYU3NAM14Ac8NAqJpkMg0HMiotpLdsRqrgkQUmQR6CMwJ7X/h2m+zCGmEFkOyAImOYjrJDbQQfFrQkGiiBIQEidlpidj0Q7MTZDjdhcZUg2zwwwIuW8BEZLqtyJqOJUT7ONlNaPKrwUAYHPFcqiWgSBBrfxZ8AxqaUkgu3G1+jaQykI8EQRMScCUB7KtNraKzhOwIIdyWQMPlRgEgD5yYfomoFWRTE/G7OUgskAUva+BN62cFzSWXI7D1LsxCwAzdDFqe5F6PQIddQFDyDV8oan760powGChiyb5WR5BM3x7d9bbb/zg3CcO6AiTgCfyniSjSyx5oKqK9rK9c/1IfXNHQiMoItFURwEAElslBqzHgod6zvGDWHKUsZWUmBgBQZgJwRtuamEPZQAwMWtcy1mPXBhCEkdWVCNEG3QNlp1pfc1AJcsqctirWytv2MRABA4Pci6HiCuzIgAgJUkAiFB6XfAkKClquIoiwEkxBACELrAIFF5PpBuVOCp9Xfo+xH0XElvXPyF2QQKCIIUo0UFU/qNeh6RwOJuMEORu28Qj/j4BKj3xKAXQvTwkBSbIQCilI86iw1mIHfOb0rKhBuQWo1aIBNz14bU3B0AomGe/5gDMthfAUZU67xE5Yofpn/XmBPI+Bq0u210YJxEB/+W/+VN9kYpo6r7l3F+jtAi9sqUTzM6RSRUAhMQmUWk5EaqUiUCG1UUcOciTqiQLQ4B96KGxKBuIoRMF7GBZ+4wg6hyx3G1nGA0iyqAiRoSZM2mPAABFFmLKwIQtjS4IHNgGR1hllvNAzFKaAGD80pwyDdvcWl5EGSRWebDg1bySd6YMAjmlNMsLaPYlR+Rqj4+zTTTnd4BL7A+GSVnhaejFGC5jqLvkXOtgFyRG7nQcACrBjHTlMQuBaehDiJUKHujCgohg20dlB9i0NUAwQkqGcrwBt32fQky2uESKX4CwI/IOmUVFzlIWqdXNoyMqB7HvnDUAM6OI80RIqvJjLbKkHstA5RBZR5yS80QkzIlTYkBr4kPv0CvpiTD3hBuKBCCIqsoOKuIjgKIJiuV1NkSG2VI8xRoO2mMgIOB0xJuOCLHGMtAZeWzK6NqGoMxJNbXEpmYqhwOeSbUiUNdl5bHtQmJJItqIkLtEDVzT9dB0xTkqHI0qh6ipsiBAjKkNiXOZgnPDl/od3Txl4UpCAWwC6/QKNvHvrHI3CKHwAO0MW82gTb0NTd4OKO2AOGlaxRkCMRsJOeW0mhcRFI50qhQS2QWy/G3KR51VG58QnUmodOotCEBAZZUH2DQ/uOj5UaBwiG5goKYPP5qT8pzMG5Et2wvbN0Pg5cm9VoAarjnAK/kQ87ASAEO5Tix01KVDMF4uJNHaoB3hA1iCVrhTHQRbOzHe3PGNZHwCYJAPy54Cju6/cBgSFA4QQPUR4EiNRVk8GukOtlFrCIM10xVxCIPe9nCrBtplzMK6swDz2uZbtaB2sHsHQCQymFgAAILi3cqMM2hj2I6Qd6hp8ANk/MVqAyDgCTwZL129XspxtpnYwRjnYJYs1gax/xlioo5MLI3BzP2xgD5/Qd48B2MPIGQD9jgfIPtLAQEE0gnDYIPqxDjuWTYu362+HpEcY+SLaNihR0zyg2iQaj8rBn1ZZY85h7A4JAiK+zkEQi0hDp3aBKBKmkQ6tQ317eOo8iGmpkuAmNsFDmeBRQhseqYicRq7K6CjmkEsTDgIitlgB10iZok2Vtkd6uTMzOy98w7btt3t9tvdLoauKrwIbNar9baJSdqmvWvYyjC2GiC5icMSlpQKh86Rek5mbXSyqIRcgc6V3s0mo7JwkOLFcvze2/fffutN731st3d3t4B4drq8uV21fQoxru7WSWQ0GoeYLs+Wy/los20W88mkIl9Uu11blsX5xcVmv2uaxpEDQHBl6WC+WLDgk6+fXF3fTabji4sLFgh9hwBlWbx89YrIA+CzFy+7Lv34Z5/u+hCaZj6bjqfj0MXFbHpze/P+4zdGo9F/+J9/+uDy/pffPF3v95LEO1qczHfbhjwVBaUgVV0up3PvaDqfShIimoxH9WicOIQuJEnb/W42npaln07GBLCYj8fjSUppOhkBQoxxVJfeeU4pxB6RvHObzW6339d1nZJ4T6NRhQir9caT70JIwl3T1lVdlmXX9khw7+LUOwx9mEwnMUYQGE+nq7vb9Xo7mcyu71YhJkK3a5u6LDab3ddff9M0uw/fvf8P/vC366r6xS8/7rpuNp0yy4cfvvPJp18tF/P7l2f/1//b/+N3f+uHzuGf/+LT3/7RD8/fePzXHz/59uuvEGLpy8IVTdfVtV9Mx94XZVkUDn3hyqKsS3d2uiByiJrqSFUWRvx0jpm7rmvb1pHzhUdyIcSuDzHFkCJH5iRaFAwxqNMQgZBS1/WEDhGub1cvr69evboOMZVV6cAlTr5wVeHX2x2zOOf6GJQtH2LwviQnZVmURQEofdcBurqsq4L2bcuCk3HNwiHESVUSIhKFEEvnZvNJXRUcYzWq6sKnELq+GVd+eTIXEO8cCLX7HaIg4GhUIeBmu+/6cH4yX++7/b55eO+0LMsXV6sYU1UVgFh5P51OAGQxn5SFR8HJbNo2TQxhu2/Wm+1qtbq6vqvGi+nifLw8G00WkqjrWu2w04Tahq6iDRY8+AJNeRQNMPEGcIjOORWC9F7tATsb5IciElNKiUOCJFA5zDmYeOeUxAdZR1lEIkPbG1xTFjguHZLOyxURE3/tAodwELrLKYzq2VPTp8hAZnLzMFPtzUvMQgLIJsasUQcBCDosaVCU0rgGLXrUj7CA4AAD5QAlg9F4CB7AZOklV1QtPBv4FlpoMfv/emgimVNgmbhkiEczIMpKAkbfsEujXjxzRQihcKangfnbbHkHDHkl5lkW6igtsjykNYAA3hQMdDyrQBYcFJ2Eo2O/yUpohbPwwDx0BrM0XNHQKLGw6MllzGpBdFR0PPZNuhIskKv1hxybspqAmMs2r8GimgM6CuYgkCpZ+4az4o9F5mCd5gVJVOYWWO5meyi3wqHxIQAhD+exVyZDxqrBoFG8czt8TjGH0C6/4vxEYj1EBANLBLLzzRnQcTKsIysILVOTTK/TN+ssU8YMRQEoB99iZAvMhnAFQEk2eqoPofWQECOSaoNYimusIhmCnLy8NixqXPnSEQAUnirvxpWfjEcxhu2+a/rQh6g9TLokDBgSdIH7mHQdSk+jylUFFQTOYUjSdHHfxpBEZ+amlHPQodCezdPhj8KO0AQTRHQDE5EWudnSzKOjh4CIk5ImlTVY2TdBzzgkVtTYZKQh504amzk6QEHKq8qVVgAQbdICOESzYlU061YZhIC9iYcJiGh0DiDoiiy9gYhAukXsY8iBRcZICBW5ZOdNtWOtu15AmJGIRLiPKctmC2IWITSIEbWmqfEz6sitwf4igADLYKF0VKdtO50+Yj+TzwLLkBugczhQ33UoCYvEg1WyowWKkupG5eF0IcMA9IlltIPSEyrskAflgdVL9eBrsTmniLpTDwcS7f3q59rHSf7e4chnQ0amVK2F4UNfyZB8aOwbs/Yni3FjUL1C/vkDcAQCiFQ45ygPlAcQ8B5zx4ziiIBoIi/qmwuvIqlM6FjcsIYiIOKczfcxn6SOsCxI+V96A4SA4J1D733ipBCHJoCcEouEKG1ImbSFCNrRI4ouKSmG8zCp4QyimmO0zUAFlr7I7gAECMSr5VXMqPBEeRa1/oFTztEAEA0wRqNZWpOOo4wh6KZyTvsXRKAPsQscovQxpWynCbGoCg0F1G96R6IkTEIRCDGElBRUIUcpSc7XkQg1B3YEZeGrghzhZFzNJ2WIqe2izheMidsuhaS0c9bd4r0jBJuvSZRTFpUpE+9pTKhNVLodlc1aOKhLbU82NL0NKTAnhihAgkYYPhKg0YxSBEAQLQI68NM0psne1A7vcHTAPA/mnFaOPJaYN3IIgIExiiAwIThHLOzQCUBICQxslWldFI6iwWosotiKSAJCEI6JOb8sJDWvMWUKtKhLUs+iTU+DdTd7atAe5KOZvbXxabULjI5O7oDnGA4roIUXY6JlSN3MyQGlGWJfgYNJAEU9WIZm4+MQTsSSec6wWZ4TZ6YEMzCHAz4yBIuCGflC86lZvglAYTUAiAKFEwJJYP4mmuymZftkS4g0QF05pNOVGBZB9bAz+pO3iqb0A+0FDFFWe0cmB3NYI0/6nwggXZSYGIk8IQKbqDAAHDXDa1NGOhhvAACPCCiIkDJ73+QhIIcY2XFCfiIR0SCCZagiiGRvddgVYEQsQ7pYBolA87po/KEBLUPMbLijqEIvPoBKlDWqD6G6HF5uThEQjm5G7wRzEUIOnzDYbTafYQLIanUAAC0esNFazNZMgSoSqzE0IgBKQZawqECVynU7svIPoUQe2vdsX0kOvBNzds3AoLCjIDI4ERAHg51Xa8yxP3DWtKSfUuSUAHC33e42a1945+jp0+c3t+vpqJjPp5NRyW3bdl2JwCnu9v3J8mTVXMWQlOQJnJsEc8xPznvvfYEsKXcnQlnXAFA4N6nKsqxW+2az3m3X27PF+Dc+ePTGgzNPcP3qxfnpvK6Ki4uzqq6vr272++abb69Ho+rRmw+6Pv3iF78uq+rydF4WxbtvnzKHEEMf4sX56cX5aduHEJ2HigXIUWR0BO1+GxlOT6azcQno5tNRSikW9PWTb70v3nnr0W67Jl/OJ8X9+5ff/847/+Sf/ff337wglMTpv/hHf5iYu25flsWzV6u68Ck0k2lFhdNyfdcHXxSJpeuBiMThzW4zqqrmrltMZlVZscRXr55PZ5PRaBQTj8r6erUSrE6KYlyXiFJVvuvSbreu63HpHQGUZekR7jYREULfd10HANvdFoRCCPfuncWUZpNZCAEdVVW9cev5bLre7Ju2IYR904BIXZUpBWHe7Rty6B2FmL568gwAGLFpuvX6rtl3n370sa/8P/yD33r5/Ond7aosq9DHtgnfvnxyfrp48vSqabrTE3d+7+Hf/Z3fuLw4ffr0+e1m8/OPf/kb3IyATs8WiBTaHUd+cHG63e9DH+uq7PreVYUj7z2pVJDzBQKEPiBiUfoYE5gQAToiR+QP/UiOEPoeJAkUDnyKkVOMo9L7otSWrpSSn1STyfhmvRXuV2t/eX5Slv7l9d1vfef91Xqz7zrnaT6ebHf7bbf/4MGbZ2fLb1+8mo2rkMJkVO+7drPtttt2MZ1/9903x6OqLB2RW222DrHtA5EvHPV9KMsCEMtCMyBMkUWYCKUqq1iCxE3TS+Ju3zW7bVl652g+m06ns9ls8rZ3pUdAdN5fXd1c3azbvptM6r4Nq/UqMr71xkNm6PpuXHlIsSiL6WR8eX765MnT/nY1n45jDPPJdN+2n3zy09W6nS5O3/v+3zo9vSAAcgTOIREzOwRhbrvea3coEYo4hMRxu93GlLz3BMIpAUhZFlhX4BxijY76IBi5cChg3BAt6ngAAEoJkrAjEkhtiIhUeLJ4BIAIHAECjksbvhNigkwyxcTM0Nl4aUbTJMSUmACYATFFRhZBYQ1lFccxIbkkPPgTRLEyYSIkYuxowNct7ByM9iEUsBLAsf+zSAkz8oEAQCLCCALoYHD2uWZxQH2QD1fPKYF5xAHxz3pBAzVI1OcrgoPwWsBgVxEBjGlQTBIiLMjSMTIU5rjYYChHLv/Z/WiE0Acd4DNgQKjC2KgDlXyOWgGSADESDS5TQ90sIQqiGRYnSMb3ViVTRX/AO/NKOeQ5WhnI8ZGFcZquarQMSngSlCEh0PUDAEf6HLnZXABREzONl4xTRJDbMUBCYu1Xyiug6ZvCjCCAiRlBiMg7YhlqS/b+LIqDHCvY/Wc+nCVoA6yHFuMgMnNkznswp8/5Uj2LSisMi5K0TRFQQ7gMGyGLYBZ4dehMVRPAOUDhghAElBgU+LB6AJCGvB4QUDTssPqLnrfEGijbtGBjR1mSrHeGBJVHh6IaPerHu5i2TV8UjgUECNCY7FokJpUl8Vg5pydmVPqqJC3/xJ4BsPTkRoWGdiFyF6ULEjnXV9nejRjfHwDAOZ0ujoiYOOntRU1SrL4mklssNKhzICHJvpeyAD/0Tiqri4UFtGuQmVXsQtNhEaAsQ5fhBfC6BMYPF2ZW0WjtMlHuj21L2/Mmm43/6t/92XB0BoOQCW/WOZIlwRBh0Dq1uFT/XwkUMUnQQiqYYDs5AtFUDVTH1FgbAIgQc0ec6hfknWbQj6E8CGj9dGbx2BIeyrGvmMocHIO1mC2bHi0x7o9a8gH9IUQ4ypSy/c3vEfMZzlbO7B5IVpnBvGt9nsIg+RJkpHG9mIHBg6WRnEJkE5wXNmNNZioFEMHTIejNpwOGNE5ERxtwBpWyQBQOJtEMs4bswzMOdyKHCr+xNjB3Htoz2oswJF5RopQyVKd4jAXqBxuaPZRlpPaYeTAZcwIDCvNbNGkuUbrKQKNwWTLdaalIs00DSvL62W1aU1WeI2jmeyCrGqEiLyYMGoFgq625io5vz5kpHv0CWOaBJtWuVjKxhJB0QoTtMTCPBfkyMui0CyCo4F/SvDSZglF+YD0mRM5h4WlaF2WhR0AVZygmBhDvyTmXknR9bHv9dPOdLsvlyNAjme+eDsxFAVPdx2HCQspEhsTS9rHveddzr2oggkOlYEhTKaP0ODBJrN0a2OjNKmMwTLiyXQHGwckLqigeobPB7Zk3qe+WBul3JiIWYE5EWJd+UrnlpCo9btuw71LbRRZlq7GOdAG0S+UdCsOrUMuWfTzkM2U/puXEYVCU3TmiTrvQYE7PgtYeAYY4QkCOhAwNDxU0Cpjk+CZDr0fYhn6OyGBrBqUEc+W5ucyWG4cGzNeLPEMIAjAgcplFgsDDnhBwZOBEnuoAhMK6w0HUeyVRzAUQoUAIjCDiCA51EQtEwGe8MZuCTEAxl3FwMWKRbbZQRwuAh/8/7JChXUvfBouhNvnzsSDwDgGRedhqNt6Ls0qXjvFmsZKOd8CCfRzarXOwLrlkCXYa9SMBRM2qxi5K/zsCpgyp0Veba7kGESmMwIOfEaZcGbPjwAIaLotGihbpQwZABBTkEzmoTSMPi28bZLiBDF1B3rsAIMB4iDUBzJUYZpSxq+OtaHcP6hEwH2MB2+9YOkCENkhduMIjohSEOnJezWMX4qCLYaY+70M9Tc4BCEQGyfJS1tCXhEW890Su63r1uYa2MTuittm9ev5t6FsE2e3brg/eu+V82rQdM7zx4N6DB/f6rm32+6vr25v17vnVdrVpkkhZV8ySDLc8qsuJztJGQqrqQiC1TVuX5eXJclTVVDhO3IYQU1Qdn5//8uPf/O67/5v/5e/XlW+7JoYeASSFptmeni7Pzi5W6/VPfvorQHf/3nlVVp9++fTpi5fvvPnw8mzZbrff+fCt6WK2Xe+Q05uP3nCOttudkhu7tmPh8Ww5qorNeh1DComLomRm7wtg2W53m+2mrMqHb751c/V8PDaQYjaZ/OVf/mXhq4cPH7DEyWzxxRdfd31Lrvzm6fPRaNJ24cc//+TlzWaz3RM5EBmP6vF01HR907ToMAkLg3ceACT143rinPOEl/fOJ1W137XVrAaG+Xhaj6sY+7bpJuPqdDEfj2qRBM5NRyMH0ITQdf3d7V1Ksts3zrvQhxBiURbeu64PdVWRh8TctC2J2+4a7924KpglpLScjr136+1uWlW7pqtGo9u77d3t3e3d3WhU3d7evnj1nJwv69F/9Q9/7x/90e/98Z/+Tz/43ve++urJq5t10/RIuNvv752fjuo6xHDv/uXF2emf/vgnn3/19PrmLop0QW5ut999/82/89u/+eNffBp6TEm+88E7t9c3hFRVZVVVk0m9nI/H47r0RQhdURZlWYUQOEVyXphjjGVZ9n3vfUGEzBJj1ACj6zoRadrWO18WpdbGybm6rkLfs0jhfVH4zWb38up6VI9AeLmYrnf7cVXd3d2FKOPJ5OXLq8rXt9vVg/PTexenn3z95Q+/+0FVlkTY7NrPvv4m9HFS+3fffXRzu9ps9kVZ9n3YbPZ9CFVd9SGUhR+Px4C43W4RuGm67W5XekdEfYgIEGLQiII5Lafj6XRceB3q56uqmM0miEIAVV11Xbi9WW+7Dl21uts+ff7sw/feSSHtm91bjx7evzjd77ZnpyfkKEm9OJl/9Ktff/XVV9PpZDqZEsJ6u/nFrz7//Mtn5XR6dnY+nS1++KPfRF+uVuvb25tm34S+2zd7ch4AnKfSETJ3fRCRGHuH6AufYkwpeV+M64KIPOFiMZ8vlqPxpKxHhfecM2NEKrwTthGfGmCwgHOuLAhErCeLIERBxHFFiBgihySWMwAoL76LGsxosidK5ym8ElKYBQl1Di96lMiQWPsYNPHVib+H+SrJquDZOGc/gznZPsoJLF3TDmhLW44CXXOvNg3GnHGOmqx6rXmq/vBRxIU541HnQHLIETT2zmEzIojln3RIi/KNDd7l4N9x4A0pmKRhFKoGE1jrVS6oABxXJQAzIqapBxDgIEqiT5fnyuOQUBi5xqKIvJpiebrqpB9HV0Ra3wXS38Wj95DzL5HDW8jYCQzudCBuW5KhPT9wQCUkt7QP7zGJ6tpY9RqGFBckCRJqlwEAWHaACCljWyKQ8svNm8JIwAPpQ8wvvua8we7WtLEEMGUaHeb80bLNQx96jtYEIA9xU5Ej42Tkdnh5/VNc7kQwIRQxNSsEEbHGSV02DR0IITAAUmEdtJbkarzUR2YxzZohWBGBJHn8dB5FXzj0/rhbTpP1IeInEW0PFT0rjoxlIiwiKMiIWBSOrPETQFHfPGpZnzeydH2KrLqIuvyIaOQ4ymz0XOqWZJvOQizJOcbApKF884V3Lk8DHIq7nFE63YeaY7KW5hTCOyQsCHDYWoMus4hC4ZY7DD8PlrEedTn8i3/9J4pkHN49wBD6xsMsWNt/mINR3bGcCU4psYramv5RrjlkRND4RYQ6kcRi+pRYF0snbhzdPYpNdEZUBTzMFKZsLRX4iCllYvZwLC27AxkQ68wOORxLkWxPh+wqX8BOrSMHR70emJ8/s+9e+0IrDhhujMBqmBgIEEkzB62BJwu2bWOjCS4MOrL6SRkSUAAo56uZ6573t51ilgwA5VE1YKYsZ2CZ/2Jn4/XzO9g4y121unC0Kc1eSL5c3qkHkMZyWcgm3TCvA7cg37ORv3hIOOzMoCVUEpMYxpwLCkQ63vvQC4BoWr9g3gEz4JJfU36TB+MO6o3s0YYHR0RAnfnNiUFnM5WFw5wW6ek6eueZzpCTdF0ZdTTain7YZXj49KPNZb81kBJ1dOhgZNVaqDGtCjeqPAAMOsVsY22ACIvCE2IIMUY2tFhAMa5BY0XRmeFoDE9uS5qBXT14uSQjwpJYQpR9L01IessCrz+OveWho8T+VscKU6aCaLOGqiEaD+voUohYKAildDPEwNJHCVGGcpVDKjw6ss7EkBgAqsJNajcufeldG1LT66wec08hpt5gNYPqh3vHQ7RkBjo/haXDiukgQkjg8m4x/T892xl3yIZRcYfX9Mhesyq2kUjxJCKSrFhpP3WE1g7GAcxtq183HzOYJ8k6YiJAiAf1oMxGPPKJ+vP2/Fq3oMFA5p/FfA+Ue/cVKBBt4gMAhAJFpTQ9Wm+5GNCAAuhR2+pxCJj0nlXlF/J6iZ0QHPzSkYM62KVsVvS7aLQRUceh4/N0YeynCMG5A8iYAawcjWWjq+LEGXkByGMK7S1li2SxluSQQomVQ4yu1xxGLAzv+PXQOH9LGWTWLikAShwDWwR78WLR0qBKkMNMWwAlonKGmQByW8Exkc3CRxh+RtdQGJX4Joeqg0FCucIpw5sAOfrlDNAhYibfaixlHpadgz6JByRHYlE1EgkBOITC5Z2Fr62Sni/rxnWIOh0magSFRDQUdkMfnn79ZL26HdVVXfkQYmKejkdV6VFi1+5jksDgCV7d7qqqvnc2n00npydzV7i7u9Vf/uSjyLBtU+kdIT6/2TjvQohEXhCYOSVJYusLnJbz8WIyKh3eNZ1DKouCnH/j4vRusw0s2/WmHNWBZXO3vlmtLk8m/8f/+u9z6jn1ZembpluvV6fLxb2Lc5G03+2/eHqVUpiMxv/hz372wbsPQ+QmyH63u5xXf/B7vxc4lNgx+JDig3sXZ2end3frZy+uJ+Py9nYVIz+4fy4SR/VkOp83+93J8nQ0Hm3W65Di9dVN14WycElouZgKSwIsvazuVienp33bnZxfLhbzly+e3txtP/nsm7PT0x//7FeTajIdj1b75i9+9vn13eYHHzz2ZXn/clmVJXNazGe//uTJX//yo5BSCJxYRlXBIkkrFim99cb9qnAny0XbdsBxebK42+z2u/byfEkEZyeL2WwynY5320Y4NW3XtP10Mq2r8hcffXx2cjoaj/uua5qtCImk6WTa7Juu69o+IQEhjUYVEj158u3p6dl+v5/PZkjYty0nvr65nk2nvnCffPLFdnPTi4zr6vJs8upm+3/5P/8fbm5uqgrvX15++sXTsho9efZ8UlUny+W3V68m9WizbWJqzpcnP/z+907PFqvbu1/+6tcv7/bfXu+h3/7D3/vwX/zxxx++++Zq037x5GYyHl2cnUzHo7btLi/PR5WvSlfXoxgjEcYUWWA0qr33fdf3oZtMZkSUQi9IZVnqgeEUI0cASZHLoirKKsQAIojkvBNlBBOmGIWhD72SeOtRPZ9PEWG722/W632z//Lpi/l0uphNQgxv3H/4/MWz8WgSQ0SJRenW6827b7+12my++Pqbyah2zn361VMEujg/CTFWVf305dWDixNP7vnV7W6/O1/O+xCarvOu8ARdTOOqdoQxRQApvR9XbjGflEWVOMauOzs9reqaOcQYRdJiPlsuLj774sv/9t//z4/euC+cHtw7c+S7dr/e7IqinEyqxXT09hv3irKeTMZE+O3zVy+vbmJM8/n08vw0Jv7s82/+449/vlptR96fP3xzMj8pCZtmt9rtiDyS0zlKiJBSSCF5VxKSSBLtkxBJHAHBayVQAkpaziaPHl4676tqJABt12tqMJ4t5st54QtSRh/l1A4A0RJjR6g6mHVpUolgRZdBSkP2PYcohKwQdBICxMpxEwCysA5zMmka4cgH+w0CiQ+N8+rC1cke8myLzw/czOxHhuYAFkBntQELFnJKlmP1bOr1ypLrXUMYoLFzxoCG+pAF6lZHQUQR74YbBkFktqYzzCOlzZvD3/iym6Dsg/UOFDvTZ9JgO7tVHAIefZzcT/ZaAD9E3vk/hrnmkIcyAxyLgVpSZUGUHAUZ+g912RpqaiasT5QxqNefKf+ykspzMmN5mXUe5FjlKAYwOSfMq6LgY07x7Nq56A5yiEnzxS0Gy2udMSDIHBkF19CUGfSynJ318BAIOWkj0/iBIQhH8+ugQZOI1dj0QawQg4cAMS9kDtvy5h0CMLBgHgBE1ZdFFYWG/ZE7+jPGBVGgoNwVmJMR48uIzY/KC6jPLsPQFH3NlCHDnJEBW8j62i5FY9ZoBoEsonKzPUtlUq+WgJKpQ4pDcURtZGYJIfUJCEXveVQQAvQJKk8IwjmKG4IrJWEMMvIqX+3JGgAzO8H0MTFvncg5IR2iX6PZgyPSBNNZITyLGADqTzmijFvZ9fSYSM7TU05tlPN4gJD+6b/648MMneGFGsyZo+S8LzPPZgAjOIe4dvJI9YAEEktRkCdMScT2k14Q7cgIMKgstghA7t0QOKiRisrCI5Ezuo2F6AIENqKOlDqkw/kUOdI+i0HIQI2xmQkBle4TM0wAed/kDZwNUP5XPskmDK7rrf1s2pt2bDH02znclcE0DAJArBAmHhIPXT+XU2LFvDK1BgBUJefgHF5Ds+GA/Ws24gZCZHYUA0g0pA1DanXIJ0VlNcAh9JEBUHP4bMOHl64dq7ZpFabJzat2AiUnEzqzKyeEQ8+qgJFQZEA6DcW0yjynJIigs0LKwmGePO2MAGlPkDeekXUBxBEV3gmogLkNPsRMrRqyWbU9LJDVz9Uu2InIskTEcmim1cGTevB0grgiEfpZerWUOCvmQj7ekBN1OyOYkYUMw9mXLgVbOqhS9qx0J1UjGl4DISmZFa3SgmLDFEAEQsoWgRBA8tw9yVhhNsEHB2ZuDA72XBCgi6xzTPvIbeA+ZqzrKEI5+Lbssd0wqDsPukKAoSEnsujWsnNqxwsKh4VTwprmmHZGtHNbz4pqCQ+OSn/RZVKQOTSlAdrbUWemXo1Thnggn0fD/xGTgEcovYU7yQBcdKjmWHGf4fgIZO+C1vkFmBmeeueZJ3sw64NlGAyU2u0B7z8+0AMwhYMNOUQv+is8tErlg2lmgV6LYjDPqcPhsMPhSuhMhO01UE99MwHrswxMEQTgPLswSZblyveNCCzDlBY51CiNO34I5IZbkNw6eHxTgyfhrA13+Nu8V/UXLWkffBigRdWqRECgMQbm6+rQAxYZBrJIjj1l2BO22gOQPbyWwYprUJEJ9QYpHQIh/WE8fHB+u2bLrRZhQZuI0ogGjEjZPSpsCtlM23nGjGEdxYjm2o9VgCy+seLMIVcQq+Zml5I1D8yXvMaAt0/Nz6+GMUsL5L+DIWhm1ll8jhgRVeATEYQR2Ip9Zn8Ga6NtF2YFich7VxhQhESYErNw17W7zXp7d/vq+YvEaT6bFJ6c812IIrKcjc/OTjjFrg91Pfr2+YvTkxNOPJlO7t8769td07ZdH9qmbyJ++eTqerVbzMbLxezVzfrbV9cIPoYETgRQEoBA6akeVWfz0dv3z3zhYghNFwBdWVVXN+u+69jCR15tms16d/9yMRm5/+zv/GA8KlNolycnzX7bd914PHbIMYQHDx/+8Z//9E/+6qOq8vdmkwcP709H1S8++mKz6773/qMP33/kkfrQ77rdn/71J5O6+j/9N/+wbdrtZuNLWq/35IrJuC6Loq6q+Wyyb5rlydlkPG67tiirlBKg++rLJ8+ePTlZLIgwhlRU5YM37vdt/9nnXxeV/+H33uuiTCYTIvqrn/zi2bcvt11IgWOfzs5PfvXFk7/6xWe/9YPvXq/Wp8v5/fuXN7c37z66f3p68m/+/Z99/fRFWRQXZ6fnZ2cs3DSdCD99cb1vWxU2GY/qyWh0t9pETujgbDk9Xy7mk6n3fjEdb3e7whWJU+JYlWMBbkITujgZz4sCd7vt+na7PJm3XT+qq9v17aga7XY7BLdebafzWtD1LY8mpTAXrtg322bbxBTrcfnVV9+Gvr28XHzz/Pq3v/94Mat//Fe//N/9oz+qKlcQ7vfdJ9+83G72jHK2nHrx9XjUxhjaPnTtru8ePrgc1xVI+q//8R999eXTX338+Ww2/V/9F3//o48+6tp904Z/+yc/iyGutv10sVwsZpPxaDmfny4ngJhSKgpfFKVOTYkxpsSjugSQrgtlWYxGIwAIfTeaTFOMfR9SjM57IizLUgT6EAiROQEgcCLnnKMUk44Wdp4cYdt1o7oSwLZt+y60XUjChXP7/a7wvus6QkyJiWTfNphwt9u/8/abbeyuXt2kGO62++lkDAJXt3fT8eh2262266iKfyzzaVVXZe38arNnxAJQEKnAzbZJKf7tH7z36OE5oThfv3jxvO+6sijm8/GTp88vzs/2TTeZjaajuu+Ekdqu+/yLb7s+PH78aHW3/uLJs9IXRemJhJkf3T/9nb/1vdlittlsv/7meYjx7HRR1zUzbHdd13VfffXNl0+vbu72jHh5/+Gbl2fb3X7b9t75ICicXEHb7f5iMX/0xr0U+qffPm+6TtAVvmi7PqakBN1xSYWnPsZx5QvvkRxzqjxenC4Sx+cvborRaDadpD76sjq9OJ9OxkVZu6IEEQ2WdMZrSglV5kwYEFWHOCROkYOJWmBKrPV/zTBBYhICpAEp0tSCAA48DBn83SH8PeAbAwY0JKuZ3oEIWZ40Fxgl/2nwr4anDLiMBhXmWYcwPcdZYB90SN/t+5gnqbMJZIsnLHJ7FIAwYGAcwk5EcihgM0nAoQyJ4fCVL2uCOPoRmRd/iDVyRwIOmUj2MYiZwoyoU+9M+E8fZ6jjFh49HdY2ZzaAOZDQ2Fjn2Wsqx0dhw3E/Ss6EjByE+PotmSc/hF4CGcYACzVz7GlPJ4f3j0NVBocrwoAXCGQAaPh5jTzxkDNqaAFDDYyt8CM5RoOjYFOLgtmbDxHhEOu9lvMKiGDW+BPBgXhynLYMvv841skB3SGP0Wdj1QIYUKGBKWaXyZoz+Y4L0psyaogiXMNsUBgCIhgWQfc05rh2yGJBBLLelmVzYqiHTmQ7vCwdlSwA2jqqG07VOUjzKmYEbKIkAWDpQiq9ZoU4KhARVL/JYRZgystwlNwdokQRyDwhfW2UBCgrf9tJyWkdwFCzPL4oaMPUceORNa7n2q32Nw08D8hgk2ksauqheW7Owjwcbvb45cLRdsxBqj0JAygpIzOONHBDRAAVYxMAcqjtAGjwnIWW+bckx6/5mBE6QYbDuTFSGRGS03A4y3RY/qZnYYB+8is3IpfwcGwNbTY1R9t3Ckuh6lexIQoWT2dc4G+YSj0MllHkNRv2tm59yS94WDTjMLJBFQMMojkhiMmqq+AlIQHnVwuSlTYke5gM3doDqKSROQd8jSZg2elgRdVegeSUJU+XspedxLQfIoMjFFGllDzDPgtFIQCy8R2G2UkCqITXQepfX5IWEgRMxJSTfbT5BgElhihaYUgKo26PPPEKkkBK4MhmDWIG14f9QwdIhbQHctj/w0lQeMFSnpx3IYl5ChGxMWQ2sgFRBdUga4gcOo0t8xRAbZhiJjoMccwjqgBRBolxfW2CICmxI+/dYPsHw63vNQ3TAVg9kxh5bkjWHA7sRBZQYXxV/KKhljHI77EcZZi2dR2CILBIoYPbkxCSd8h5uCMiMmAfpU/CjDrjwPA/C0IOJ8JcjDXEAii4jqB0RQFM+QFFTzRASIJEDiGx+DwxIonFPZg1ngDRIcTI2joaGcWmDtm4pRi4yOgY6vZB2+cCOqVJTShps/fBW4MwEwAEFgdQOjvakKlJCBwZkyAR8MFkDy7cIIAkwDgYAVVUxsMUTPubw69L5v7AEd/nyLEhKNSF4ozgpthWjuiy+6QcM7GW+gBFwJMyR5AyRjO8I4dCiDnuQfXOCGD0ahgiEwtEiDDm5uvBBeSO0uPxZiAmGGPojzLsIitUpCumophHkIKNP+ADKn1EVpI8f+D4gA/BytBvmH2ThggZQQYsCD0lDY0lx0khiQ36zXCJvevBUgMACCGjBvc5zIIh6AEwqWMRA/glQ0hmzw42Xd8u5V5ayMRJfRkFDZclySVF5kz/hmGKqt3W4YwNfz785RDfQx7HZlGEmJYdwCBFZzPg8oVYBtj36HIIGa1XK8m5PpF5RwKQkTW9cwE1fm0StIKKnkhRcBZzWU+5bKTTrBkSgxaKJEYM0ffkCFLfre/udttN2+xi6AmlHpVvPDyr6zqEMB7VMaXb29Worh+/89bd7W3bdqvNXu7W985PJ9NpVRXjSb2+vdnt9/s2PX25Wm3bajR5ebNJnJ5d33369bejclQSAdGoKiJL08VxTSfT0Zv3TxGha/vnVzfLxZyTrHYdSKrLVhV9kbDv43qzrzz90e9//+G9xeXZkgim00nh4OXL6ydPvkWADz9YTEZl1zQ//cXHf/xXH3dtHBduNp/evzz5+JMvyrJMm93Z6Xw2nf7yV7/++MnzL7693qz2p8vFf/rJL85PF23Xn5/NYuJZ7Sa1n4zHo9l0tb4tqYih73sCSUjOCb96+byqsK6rxH3bJiTotzvnHo6nk22z//bp5nsfvjedjdo+Sb9brW4AZbtr26798PGjyWT2HXljPq6pqPquKQl//vNfjqri89R/+eTbN+5f7PdN23Tvv3W/rIsXr+6qQh6/89Z8Nv3ks69ikijsHF3d3BHRZDyOqW+bfuObZt+Hrnvz4f3E7IswrkZNm/b7u9D148lot942m/14MkkxzecTFrlb38U4GY+mfddySt57QN7vW0C/3+z2W5otJi+vX7758DKFfn29ub29m9f04PFDZPnmy69GhXvr4YNvv331n37+y/sXy9//Oz/66PNv/+Jnn947W5SO3vreuynyzc1mNhl9c3f3weM3FvP5i6ubk+Xi/cePqrK6d+/eV19/OxmP6sn0h7/xvXI03TXNf/YHv/fs6dN/9z/+xf/nj/96VD6KYQcQxuMSAOqqLAvftq0vvHdFFEmh38VQlOV0OtE8DRHLasQpxRidIwTHnARditEXReE9c0KApm3KotARx9475z0AMCfv3MloHmMSgVE9QqQkMil12Dm0bTuqSiIBlKu7u6occWQ3Lq5Xq8JXn3/7qu1C34e62hHhZteU62bfhMjsvavLIqZuVo/aPiTg5WzcJCm9a9r+2+dXkvi7793/wXff+8EPf/TVZx/96pe/GFXjptlvdv10NnbO3602233/04++/P3f/UHqUwgwGlWXF4vNtn/18roNMcY4H5VAMB5XfRdf3WxeXN1OJ7VDvnd5st83o7qqy6JpG4Tw+PGbF+czxJ93IT59ftf2T5Hw4mQ5YanKEr2LfbzbNqO67mL44ptvP3z8xt/7w98N7e76+nq9bQhxtd6xgCCkmDa7fUoJ68I5f7feO4TJ2byuq4uzy/cfPyLAkOJ2t+/arlu/en4dRCQxoHMiEBOMRvXy9KysxklQyDnnhZlTAAROfDzcgwU4awsawAC5pdU8ACNIOLhINO+gKv5yAAsGX24WVRNQM88DwyLn4dmAD36XOSfz6oAto8AhGNGP05osHGIQ++yjzq2DH0/aauBA24qjYEHAwpFF2xdAyNpfAJCwcJRYHIEna/Z53XFnzANQANXBDXBG/kxBELLGnyEEME+pQwb0J3X2JGGePg45HEKMSUpPhdPC/6CFAzqdlzPoI0nXzZJLZd6wzYqVXCEGRNTBAtqVM8BAuV8fBlTqaPHMcw5/GL5PA3AHonxnyk8opCEFDsFUjmJsZRSIARFEzQhs4ymPRwNA3ZAoWSbVwl8hQFPUNZ9rcJRk9YkhkEAchueQCl9pic3WA3OAmnfbkAQcLcgQeOiqogB4Z6EXQlYdsRQH0JIuIIQkQIgeTSDVgIwhFxQT0MAMGiLiwMOSLORkcJKqMokcECFEECGHmpcllsKRNnno9CFmZJDK4aAfEzXlTlCglB4AkBOHKJpKF54AMQlMCqcJiBskZTQfyXK9LAMxBAEwJBEW7/QO9SlEAyQRYCBECDrDJEeVKoUhgDpqT3VmEwsCCUgfhxwfQFMn0NnQlo4QWCOn5qzKKVELoxVce+VIAIL/r3/1J3iI1wGPkhPOVdYckuKQeSo4o4LNRIRkD5/H7SCqNlIO3iX3v3FmhUk+kHrwNIUeMNcsqwGANqdJS4YiAgduneQoX6Wj9YNeq8BnVZQMEgGIpSuic6EOLKnDM9pug6O9ZN8yiVE5MljDDyieMzRfZFM1jEXMO/Uo5sYhNx6QiMEO2GWRPFn7CYv2AapDUhxEKHPd0MzWgEVbOD9gIpDtcjZ2MJAVDndjNtEeTV8eodmnQUJIGAhBEEkFD4dszR5JS+4GZxKhZdRJyTLKZENE6QPrenBiGeROX1sGGCDgQ5OLIgVETqWOnUPSDMeWFvPL0GUhQoeiOL/6Ssl5EWQjyAKVJ1WjGFpC0kHiQxFO28AAueGLRYFVIlWC1EY2W+cMPw8bxWZ4671xZqjR4Y0oeoII0kdlnKn70DOHIXJZesLD5mvDQXzHOSqdDTxGUOUdUcQhJYtSVIDJowBAYCid6gRB4UipeAjYJ0ARZukzp0lpTdranQQc5FOq7p1AEL2eDITSEwEmkUO/3pB46mC4JJVDEWkilC4Lz9tlABCDcqxRWGDfp9I5UMTKOR3MHFkYhMDGtOmmwAGvGEIzwzWAhkReX6HkwzjUUA7Yt8VJgQ2D1/1uNpQHywMCyENgZcwoGTCmDCBYqJEDQjUDeU0On2e7YwgjxCAZ5Ky9PZBuCLWcNbShKYIMg21xBEhe2W2ASMgkHFNiwYzxAiIBsL2w4dHBDjtn7fnDW87PmH1sfpLhCIGCKygqwJlnDWAe450LFQZbZ/NtTsHw2twqlSHL4c6Gz5LX/jsb+eHP2t4MeWOyzf+CwUnrx+pmhqF0mdl8IIOBAlB4LD+jgXw5ipb8vvRDvUNndUgVvZLIkERYdTHNs6iHlaG8mT9TJMtU20dl0yTyN16QbVSxpEAcAhhtDQBQJ5dKdit5kxmokyEh+4HcT3l4xdk32fbVvGQw7EeIl30vO2UL0FS4NfUBEH2Rxy+GXqNYFEYQTqlr90i+Go0ImGMfQycpcIoEstvuvXdF6RCQfDGeTkajEXDkFMuy2DfN19++und+Mh6NXl5d11U5m05BZDobz2bT0AdytN1st5t9F+TZ1eZqtVut932Mo1HJzEl4WlWz6TixrDb76WR072TRR65Lf3m+SCn++rOvSl90Ic4mo9AnTrxYTND5tule3a5vbtf73f7++eS/+cd/UJVuNpksF7Oub8Z1Fbv26ub25dXtcrGYT+urq9vHj9/6xcdfffzFs3ceXoIEVxRlUa5W68dvv/0v//s/e/+ty9m4fHq92netZ/zeh++Erv3Odx4/vHf+6tmznnl1t7k4P33zrYd96Pu2nVS1L4oXV3fO+cuLs6quyfnVarVe3bVtf3e3rkaj2WwSu+bs/v3YNbd3+3/6b/70t3/jvX/8D37vdrXpQvrqq6/uVrubVbtvm0dvPmDxn3z2eT2a/Pgnv46R67og4MVkdr3ZvbrdjOvRyXL+7ctXZ8u5iITEk/GYhauiurq62zT7i9OTtu3K0l+cn3nCPkZheXH9ynk/rUcxBQRaLCcOgFnIYelLQijKsm3a9WYzn0/Lwi+ms7YP69Xm1YuXJ2fLLnDiuGma0KW+bR88uEBhILeYTV+8ePnV19+WJG/cm333g7frqnz+4uXLVzdvvXEfiP78p5/84MO3Pnjv0WIy+suff/bv//JXk6q+fzr/4J0Hj996cHe7Ojs7H0/q+XzK6M6X05Ti3br9zuM3nKNvnjzfbta/83d/tx6NE/Ozq5vldFRV9VdffvmTn/z0/PR023Z//Ytf9UG+/93vj+p6u9v3IRZFWVbVdrcHEEcOCWfTmZ5iIiLnQt9WVU3Opdh7X3hfinDiBICh7zfbLRGW3juHVVWRdoU5l6yAlAe+CIcYEH1iDn3PKXDipmkBCQn2u51zFSBXVfnjn3/05NsXiBiBuzZabYBZc57Su8V0NJ9OojAKI2PidDKfdjHsm3a/6dar1W/96P37F4uHDx/cuzgpvNvttinJz37+q/2unS9m48lks9mkFAPjYjbZ7La3N+sffPeDFHoq6q+fvmi7Pkbuu/bF81f3758vF9NPv/hmPBn/l3/4WxcXi13TrVdN17WP331UFuXnX3whWCyXp8+fP+v6+NFnT//4zz+qquLi8vzs7GI2rgmlbcOuC85j03bMNF9MS0wP758/fvRgOZ92fff8+bc3N6vr282u6zhCjOnifOG9n42r6ajcN91HXz37/rsPH7/zsK5GReGZGZ3zRHe3t+vNrmn79XabUgIBVRX0ziEVCfxktqgn8xAlCSC6lBKACsEaPyKlJNlfIKBzyFoStyKExhjZn5hlzSHsENSa2zyy7NmRGWqTnVz2WQoayNHv2LUwT0RFS1AswlcPONh0zEFjxpgGv2lhMVowr5+Gqo/DRvAX0OldQADoiEYVFe7wNMzSBo7RVGJM/STjWub0B9RGnw2HEODQxDS4WwQjJeWShC2SiUHb00HO1Mzv+yzuI5BVDo95OEPQnldXXSQdDVxH1HlemeKdKe3aLHb88o6Ck2PQB1XM+PCWMpgHMCCFwzocykwir++MnBIO0b9k6VsNGxjEEyBRYKV5U+lJJIbIwy0xMwMVjoRjjsRwSMYtZVR9SQXY4JB+Hd7FkFtiBvQsHLVU1+FQbsxhXQYQkhjVHXP6r0GJ9kCA5ggApdOHl5ggCY5KVD4d4ZCMq2oEJIHICCIxsQCqyGB+yUpcsmxFxKKyYVV1KLsA9pFFOENNWDmK+ZAnEeedd0jCij+ojjsghsiEQI4AlEnEKmUDoKPZ0NMQR9kOR8MrwKaLHOATEbDEWQBNTTILfBAIEYH1MAGAqMYQACQetH7s/Yk105gqUOKB/aJvQZwjnwfz6ScjSmKMuQ0WADyLZQKGDlqOnKNzgIOuApq1cACIQMhOJb1Abz3vIGsZ/ZtI9+H4SQZirb3cEotB5Se/d+1CYGGIyQAgAMGjZCrjBYd8C7T0mtsktGkohTgk/CwcdZUBQYCHOuxw+AaLeACAbJ+ByjznYyByUA8xwCt/f7gWInGSzInRi9iRyJ8FuSQO2SwM+Fc2ZwKSaS9GLRkO8pEBGuBIEMg5Qv7FTGgc/MeQRIFNtbIMSOfPKvDBOXF3NhgLAFFY0AMNMN8BvrdGKhHQ5OjwgiD3dNgeECsUEwFAsiHtYO6IyCEQqiAxKLzDomm8ZVmOwDk9KbYQ6VCTF22FENX4ZGYljxwIsQC5J1FzIUfoADJZVdMz0bVRD6SWhQhjYslpjzX0qqwVmahKntYMAHBsgOyLMhFDAP0RNJThuSKD5RVZuQMAQkwhMiJGkdD2k8oTAgNaV0tMDEBImOWTNcckG8PDmQcrgMgx5mMELNAiIIJz1MWgdYY2xBCtYWVUeEWUwDBWqTwlQU/gMZ8HACLok8lUDceBRHQXQRYjhAxFFx5VGG1MqM4jMoswGBMMYgKHhgoUDhmQU1LDJwkKh4DikRwCqiotWDafsVuA3AqqW8KwC4sGREXl5ci7agOaAGDmiVSUWz4RWLANqe2Cc06ppIVqbFsPbNZANwNpjn1Ao/Kf9LtDL2QWHMr/LVmS1uAP7ZUDAREiVStEBlJ6oCMoPe57m2Gp/VkxCXkCwM+e3OoqpBTrqpiOyuWkVJKXQwCiLkLlCICTdZbbttSBTSzgUBWjRRfEIYjYeC090RlSkVznYTRxcUkGHUKO/LIlPAoEzVfYITNvJEdknyG6sk2GAwRx+MKjaEOhjDi0g+boig9LP4Q1Aqij4EQLSiBwsD58FNvJcAt6F3aL9iSHiB4dYVnQsPtiEg4MCWSgbQ9OUK+oTF1Dx5Q8aC2EBjnmcsABgIMBD9IHz1xQNNNuTB/U2qb5R0VX1e9kQy2YN75RdHImQEcPnNluuSMuh5+6upTthgiH0AkzCIcUQ9c3TZOEq7qqijL07Xw2IULmFPtmtdm9uF5NR9XDi2Wftjq2AgBT6AEJfbE8XYSURKAsixC56xNRM6r9aDwej+rpuD5ZzKvx6Ob69uG988VyGWMYT8dlWWzWO3J+vVrvdntE2vdx2/O4Kt/64HS9a16tdgACiUNIq+12XJX3T+dV6WeT0hM2XX9zd3O7bcp6dDKrd12MfT+fVWU5evbqdrvbd83eO/x7v/vd+bS4d++cnN+uVyfz6X5zt1xO61G5je1sOkZE76meTD88O3358uru5vbv/PDdwhc//k+//O3f/H5dl4vJeFyP+77/9KvndVncrve/+d1H/+AP/3ZduD6lajJuNndlXUKI773/zqgs+6ZBh4vpFEFCDBzjzc2aY3/v3r191wHScrncbrYpphBDWfjzk/vFaMyF65v+h+8/arb7V8+vIofNbjsdj7u2e/Ph/Z/+4uNPvnjS9aHv4tXt3pHrIbV9HxM/uX56Op09OF/+4pMv7l+e/f0/+Lt/8ZOfdU2bANsubPb7y5NzKhz2cL1Zd10/KsrETCh93z+8f3F2crLv9pv9lgBP5ov9pnn/vTen0/lnn3/Zh5bI9SmGrgfCwhcnyykB3jy7fXl7c7Kc13X15NmTrg8X52cPLyelo33blw77GK5urq9vN8tp9fd+/0ezacUpXV/dfvT5N6PJxBXu+m7Dwu+89eZkPPn1xx/XRfHw7PRuu33j/sWmaV7c3JzOZpvtpij8rz/67PLiot1thWU+H+/3+74N2+328v494CSczpez0ruXV7e1o3uXlw/uXyLi+/cfzCaTTz//rNuvunZbFGVVeubUtg2CjMejmFSMfN/H6L3fbLZEVJZlTElVK/f7tii8COz2TeKIgPumqaqimIyRvLBQUYBASlyXXoS7EAkxhiCSnHd61jixMCNAWZZIBEhlUe+36xC6J1e3NzfblESioIN21wHi6dl8t21GrhiPKnK0mM7GdRmZ264DDqPRZNv065s1c/rbf+t7j9+5X5Xu+bNnjrBrd+uuOzlZkvC7jx99+umX283Ge/fo4eVsNrldrb/6+tndLnzz/A791289uJx7iZG//Oblh4/v3676+XLhC8+Iv/nD77z/7hvjyt9cr7sY+5C89+u7m9PT5YcfvPfs2avrly8uLk66Lnzvux88vH/6r//DT54+fbHabEfj6YN7Z45cVdLJbPq0C6Wntgu3zf7rZ69+9slXlyeztx+cC4QIMhqPguBsVuzb7vpuq/jD999/8MbDUwZEkabZC4AvpkVd3t7chsR1WS6X8yXgG3S/D3Gz3W+2O+99jDHGgMBXL75GwPFsia5Aje6cL4pSkNqQmKUoK0ekw16SQJ/YEQhgSoygAs8AgDQIRhy8n+XIeJTYY57MMMTmWWrQCNsHxAQgh8ev+UBD4AEPIT2gjkzlPPrq2JtJbvA5as22yQYIkBIYMQBMcFcVmUhjORRAjMx9ENKaKKCAJIY+SkjiAAitk04APAoDqgKvHJ5B79Ee39wT/g0vbbeM+Tc0jBqSXlvD7LtS/jlH4B16suo4ZqAlZ6EWuuQ+DTBuULLkVnLBBM3JYn6JNuwFB3eMB18MkGGO/CozWmSysGz0J1R9ANIUCnMWqIswvLuDRwaQrDcK4AaRWJbIsNrHq7v1atepIoAv/PtvnS0ndR8SggSWwrs+waoJJxMPIMzidN1zBQ4N9NDJeTikn5q9WHynawbKYzDR6/zEIANRHYkOt26XEQa20EzLciQCKYdOijAMWxAAECVE8Ml+3cZBmDoKKoesiVwRCWCfpC5Q9dpLhykLckjeBqhMHEA0eSkMiQml9NRGsZgfMLIkwMhJBBySsESWqtC0FAjROYiMzjvQCcuEyjMIieNhO2CMkD/IGlAooxwIwABJrEkBtMgKyAmcg8IpDGLZvSfU8qMjiiKRJQURACIqPWrA6HLyorZEADwCADCpfUFROSGGXEkVMQpIpiyg8Z4I0QsDo1GS7NQZeQd0morN9s673I6l0dsw57MK8UA+p6AthZlzp0Cj6npAfu2G1SnHQl+UFdxFEmrTDTObhFrihDYeW4Y4fHg8zHDVEKZKzqVYQR4jATERMkOuNVuX0KATf8DoEFgO7CHMmpeDocIMz+c+OOGjBFBvABEzsoM6HPwIEUCzIYKGmiCoRLIyVsB6RLOybE63dNaXNjqi6dHaoTskGK/Z0WzaMv5sTyMIKKhstZxO6J7yBELIAi4vkVf+i97rYfAWDEcuZ272rlGFr5KICDpkUYl1AQDviU1FRflU7Mmxd4nZGv7yeEUzVWhmK29OOOoLVSzMKFG6fSTX2VWpHQzmM2QOrSUNgDTfFxLtBTukXod9ZRZTkgAK6IhBESEU74hQ55Trl3UXSq7vYyZeqsMfxgRwdkaYKcEAmIvxprc/kC1srJ4oXikFkXFg7AVLQYDgdAO4jAzqQEE9DrqxHWICyFweU5hSPQIkEgGHqLO8S0eVoyjQx9SE5B0WLq8WmPGKDM4fzAGLFE6HXDABhkH9LvcrJjuOlGsFhgs5kCAQkyipR4RJR7mnFBHLsigJag9tgj4AZbErJCxySSJD4xoqkYiWX0hQ3KGD2nam0sJQyNopNUMG8I4ic0qs3eBETgD6wNfrsO8iESaGu1232TWlp1FdTEalIwwhzifVfFwWBXhCHUQ4iDEp3YWtWd0OzrGDN0nAIyxYiV1o5DVbaj3bIcq2S/su7rvY9LHt43RUzkblF89uL05mb5xPOYWu5xerfVUUL693d9tmXJWiow8pAOym43IyKiajYjqqnr6660O8PJkuZ9WkdACs8CKzaFczIPQicGgjtwjMWlNVgofFOyCkoJ5NvZ2CKXmQnBwBQNkmDei5ikrrIh2FTnKM8khmDcH/3y880pu0RZTDhx6c3/Dn/I8+gyCZI4giTNn067Lbb+dtrBErQdLKZxLMIvTY24gOHhxoNkqc2U/52cBiUAUbE2e8yh6HKQNNnG9NWVVozCvIGl7aiUY5jziwDjH/W/c2oiRBl0NpQhhEjjBXBfL/wRDNSg4983hNgSPJKk6Bk3CMNzc3u30jwpPKt227Wu8uzmZ94rZplrNpT64u/dV1A4AF8aQu5uPyzXuPT+cz713g1OzbkLgPwVd1CBFABFwXIkgEkZPlfDqfegfjUV0URde140ntvF+td5fnJ77w6/W2qoqCsAB89MbD/W7LXesL3zUtx93Fcn46rRKmF6vtbrcrfEGOGKSi4uJ0OZ2Ov311fffs1cl86hwQymI6dhz7vi+p2EX48qOvxrVvm92909kf/P73L89PJpN6cXpZl+6LLz7v9tu2mYxHNSeuClpxfPn8FYOrR+XJqZvNppv1tirKZ8+vmzbMJvOqqvabtYgjlPPT+Z/+9JOTcX12cvLw4b2I9MkX337/u499QTe7fdf1s8W8qCtPNKrqGELXtl1KoY+eaD4ZNfvu2bNn293+/Gw5v7yU2RQRX7x81bcNzCZ16feJyVfvv/Wg62IS3Dfx7m4/mU4eP343Aj198erjz582u81kMh2PpvfOqevj3XrNqavJiaR6XD18cLneN5tdzwxIrt3siqooXPn102ejUVFVVdv3vnAicn17V1dlUbi71ep0uSxozGW5nM5DDE2bCkdff/X1zavrmJLKP6cUnfdvP3q0mE0/+ezLq9u70bi63a5v15u2a07mJ5Pa31xfe4Szs4ub29XN7V3om8W4/PC9d8ajCsSFsL+5u337jQc3m+2T56/+1ve/+zu//VujyiP6hw8f9LE7fzXbte2ruzX0/QeP3lpM5//2P/7peDR5/NbD9Wr75Zd3y+Wybfdny8v/9NOPfvbpJz/64ffeeeedvm1eXqfT05NTqr749GPiNJnMgPuyHp+dXQDAft+F0CxPT1/dNMBdH+K+afe77cXlZYhMiHVVhhDbrte0cbVZeedm42nbtWVVMnO7bwpfFlUxqmqdd96H4Pswn88nk0nf913bsoj3BXOKiSUFZgACAnEoCZ0gVh49wfXtpqprAbx6teoFQh93m2Y+n5+dLuqqfnF907Z7EKiK8mS57PvY94lTExWl7aJQzym9/ea9Dz54+PY7j6bjcbPbxHbMsZ1MFsBxt1mdnZ7cuzht2/bXn3z90Wff7PbN++++9ejRO85V/+rf/Vmf4Ce/+Pzp86t//A//YDqpy4KeXd01Xfcb33k8n00c8fvvPbq7uYVRGRjX6+be5UlMfHO9rksfu3S6mE4mVWJuNrvNZvP3/vD37t87+2f/6s8+/+pZuVlv1+vHj9+ez6abpve+jCnEvvPOUTUOHTx5vvr8q6eny1lRuLPp+OJkQRjffHQhLKOy3G6bttmVRfHD773Vtl0MCVPk2Fej8nQxa7uenA8h7Pf7wvt6VLliNhpVMXHT9n3fx5jG03nfNc1uVRRFjLEsC/D+5nbPDEU1ul1vX13dTaazk5MTQfDOF9W46UNZlYhARUXOmTMA451qkdS6ajILPefJAqClyQEBATHZQfV7A/yTMYejHx5gpfyrRwZcSItq2jxk0VIGGxiBwPSP9c40Ts/5slipTAnIGkaKeTCt8nRBBw0TEhBYYAwHYRFLAiODCAdR16OcIDDcyTyrKeolsXLdkLUZ/iGHynV+Pk05j4LnYVFRmCEmcWShxFG+JUcLJkcA0LBwmQ1u5Rc4wnoMpMiDCw7fsd8d6Fdgndc2nYqMSkQIIowIRBY2iEgCdBrU5pgwAy5IpK0H6D2JYNunyNK0/b7jwlNMcbMLd9u+6ZOWeskhJPzJJ1dvX4zHlfOegoowk/v0ydW9k/kb57MuRBboQupDtNUTmY3LSV1s29j2wRFOKl8VpFVBXTskhFyYxAO2Ywtu82j1JgaoUuyHvUcAF1n6KLs+6dzVkDjEJCCOyDmaVL4gRBAGIITKS0ii5dUkqiSgsT0kJk9AAtsujstCIHV5jJ8YDScTtSwCtPIp5Z1PiMISgREwMgOKdy6J4iQoKrOtAVaQwiEZR5oQVV4TVQyIUByiICmPxivSgQB5ajsh6KtPookYdFEwy6FoiEskRGhtGsogJBKGPmrWKwjsHBYOGTUrTF3QXXSACJUclLJIi+4gFNHBGizIDHEQzdbNbmEkk8vSpf/Pf/k/DlgjmLLpoNqofAjADPocHQocgvvXYv2hDgkW+2IOKPU/I4OiiSCiI8ayzTo0eeUjDZ6yIMhRxVXR6JRy9KwKFwzM7A2bH5IBy4EJka13iwXAZENURJrQObKrZWtxoIEYAMQZtgfKo/n0HjlDJGJIiFkMNh1yQCOzQEristqIKWBmu2OUUvMRgllHgTXOz1j1YHQwH7QD91BxyGwThzOq6Wh+jZCtmS3jMWQ7/JlsehdIZgxmesSQENkVDUgeuilABEBpKWK9foZexxgRhMjpgPCje5acjAybxuDAIVM8QDGQzfbxYwPkFMvu7ZAEih4rHGBX+8MAXWmry6Hdd1gkgxHZhj1DSiAGqQiIKLWHbOhY/iy7RURQtzrwosxBc86lh5/OWLytdmRjY+rRU0afd847QFDjIqUny8ZFEDgxskrVmDgJCYBDJEhJJEQJyTZmYgmJUX+bLQvUk0sIdVF4hwYgIgJA0AiCE+bF8o4KQu3M0ms6FOUfFg6ioHb2JYFcUDq8XARI+g3LYK3sM9RqjmIdIBhYYJKXjgQEhck5RVQlv0oRUBYh2vRBAFXFB9Y3OkQVLAaNDScdAQixC8E7mo3ru23vnF/t2vVm/+Jmv2qTLwpPDgCKqgLBbr8TkJikjzrGRQqHk1FxMq1O5/V05Krh7TBnUXDVCxODgjIoNrQHKtdPIac+StOl1a7ftYEIC09EGBNs9/2+iwJI2Qg6lMSMRYUplI4JoE0IiBIlJBnVZUiJMIsRIHUxAkjhXEwcIjsCQB4VbjGtzpaj5bRikdJ7kBSSdj5m+pl1UCJzZvtk06rbQmsXKUlIhyHf9ioz/A+HLW8YNBxO9+A/IF88V4iGQ/76eR98kPUMDlFvjvJykVMgN2wOZuv4ywCQjANq0OrR4JnBmYnKZukIYYTK2RgTRALEnhEEnRNvKLndowiEmLqQzLhJhqLUUOdY+Tg8VTuSUfVMn4QcOGeDKIdqau57F5MQMgqWnjCD/QWUqgaHSg3ke4DD6qkBtv58NX4AQORFVNIkxK5tm13Ttl3b1QWB8K7ZX54t27Y/W06//vbqdr0bj0e7fXf/bLHrwpNnrx7eO3ckjuX9x288fudhWRAShT7GGMjTdrvf7Nq7u01dFcwcur7p43hUInNkqepqMZtWZYEoKaUUYz2qADCFUI8qlWNdLObMcnO3Iueub1ZlWUzqklME52Oi1Xr79fNXN6vdYjomR13fny1nVVWFPsSYbte7qigSp7IsiqIMIXYhxa69f3kSQg8pPLx/Gvv27HT58OH9Zr8rqmpUlZ998uliuajq0XRc3X94SWW5vr5+9vRFH2U0rm/vduvV6sP33/78y6d1Xa92rffuRz/4zv/73/7pW/fPZuNxUfo/+/nXT569/N7je/cuTn/0g/fL2s0mIxC5u7ra77vb1eb+xWk9rcfjiXQhxvDi5VUI6eRkMZmOt+t9SIIEMYT79y9ms+nt3V3fp6vbTYqxKvxbbz2YLZZlUb26uoqRR1XZhX40HjPIatt2+/3Nzepnv/7mbtOsVuv7l6dlWb311tv//o//pOuic0VCqav65dXdzWaFDh05IogJQh8kAbOU3p3MZ10fdm2znM8q76OwK5AAYwgPLs5n00nbdpLSbD5ZbXafff71+eli2zQa4LRNM59OTk7mRL7vuvV6NxqPCkdN1zsgImnaZjyaACTnHHN4641780m5ursWwffffbsofdO2f/Jnf/HeW29WVXV1ff17v/PbD998dLKc75puvbr7j3/6H//8rz5uI0wXUyf+u289CKn77OmLytcXJ9OqJATY9v1iMv7g7be+fv7s1borSP73/+u/96Pf+H6I6ez0DAj7JJ/++iNJaTqtGYsXr643q80bj968u3pZ1CNXVin0t5v+408+J6LFYtG0/d1qNZ9NmjaklHRmiXPUhVj6AhB8URTepRgQcDwZpQQgstmuN9vdvYvT0hej8agejxFgs9l6oqIskghBCiF1IZVFEbV0gxhjGtXFan23utu+ur45O12sts10PJ7NJtNJ/ez59X/69WebXaM9WeeL+elyNq7r0Pfru/XsZHF3u+r67u037v3o++/fe3CWug2znCwW5+enfeivXj5PkUF4Mh1V4+mLq5u033/z7Oazb17e3q4uT8YfvP+OI9/FXgTGk+mTpy/eeHAPHT55+vzp8+vvvvdoOhmt15vlfHJxeQICT56+DCE+evP+3c112/dnJ8vUN8vF4sEblwzc95ETbDY7QT8/Of3m66//5Me//PFffx762PX9ycnpxb1LThL7/uJk2etQXZYoTER96IloMSnPF7Oy9Oeny7qu9vvtcjZp9k3T7E9OF9PJmBMDuRj656/uXtxsHFFdFSeLycXZMnFCAV+Wbdu1+zbGJIBdH4rChxhvV6vzk9PZdLLdbds+hBg5pXvnp4L4/OX1rz79WsClJGXlHWIXwtnJCSGtt7vZfD6ZLQCd864ajZwv1K4e8BfUY4VWyc3YwSEi1MDpdZ6PWm48whqGvzhErBYEZaBIlHpsbelo6YbGe38T8QfAzCxSBVzwDghA9QQG1wygcpRIJKXXtiMlCnGvI5yHOrqI9uyoR7IJRJBTV9T/NHI02RylIUkdnHWusQ8UJ5OJ1LjyNWAoJ1SWcx3XjY6cbA5fckYludg2XHBgDwy/OwRCFnWgBTEMosulqbjFllreRKgLqjyi9ZHZQ6msrV6QAZNO5AIREKuwIAgQM3iHIHJ117y42XU9R4YuJHROJVM1ay6V0wACiI6cAMTQFcTeiXMYASvnEmPTh3HpHVEXOSSbWK/Jv0Ma164LoQ9JQGbj+v7peDH2lSfvEEByXoxZHNNyXtvKw9Y7Sj+1tyYm3rdpvQ+7NrZd6ELKWuCW6gKKdzQd1aPaV56cIwBBwV5AAErCqiSnzHcQQkhAKSUU3EcpCGNiBhgX3jsU0ze0Api+Sy3nJxHVCeEcQUW2cj6gDp8hTUAQQAkRAhATEIJ3KFnBgzkDdShifBLtAABCm5LpCSBPsBER58ghgsKgoMgReKeQCB9tdUUPLNgzTkwO1RBEBYBCYhu3MYSYcEhdMzhiuTAiOByqjHYMBzhiCPhMgeef/PP/4SgOFERwlJUj8dDecoCEjk6d4oTDsYHDaRnk0fRlsFivB4iYOC6iKENEcbuQhCUnhGATsl1mpeuOV848EYlASjHrvgIRIUBMJohjDS+gUbsgILNY6mtonwgAARJiYlaumUkoDXYCj0JuGbIFhEFa2PJ2A81FwDlKeaC7Dh4yAy2S8kw77SRyOMjcWrOaI8nMwwF8PkqVhiXHQbtE0LrsjpKr4RAO470RE+s9H2NFx4iPGU/IZg5zB+zwswZyQEZVj0zkwRIbu9JaSPSW8nxx7Zxina6F2kEwbOSczAzLOJBUD58zvE67Jcjv0gDEnOWh1dzzq9KnHrACdYSUX7OybRCNsTlUwm2HCCRBhQgHb6KWIsN9hxnhkGEOMVhB/yzDwQLl+oqmaZKfVMSmGxCaFJHlnXkLYOHQZi4IEqpEuLBNYpKUgI1RmHEmQERBkZCgixITI5pin149icSUCFEVdQilcOi9A4DEEAU8AiF0UTxKVBtnTgsdmZ5OYlSEK4OB0Ec7yIqHqpStACYBVoli3ecoydpjAQE9CqDhwqgKYiA28I9ZdcpwOGL6bMcN00PFBk0rR8fJ4yH5R9UOdARRdbUzMKcHLaRE5ELgT5/c7FqJzF3bIYCgRyJHqLk6CGhTk3MOAIgcEsXE+j8RRkJPMh35UeWqgsZ1MR3VVYHmSMTwIByOHqFifE2XVvuw3vV3u77tEwMltsZYT0SEMTIBOOvOF9TAA0QQJEkmUAqiAwDtFIgpexVUDweCVHifUmIRRxRZUEQFLh1BXfl9F968d/rgfOoLKFBS0m0GkRMh6kgQRgxJQuQYk4l8ifF7HWFkU/LSiFGM4mN4seT3p8d5OPo4nHwzsUfIz2thqgwGEQ76zTlQPuIZHUfSAxx8+P7xl5iHA1FBHHBgFUnM7EuwSJJZcIjk1IJ5gqwUTo5kKA96dVoiXUhdZBHVZZS8WXMgnT0nZ4tqzzl40mP7mMGjo0cbntosxnARycAbYh4HSAPbCfRkMadslnTYAg2DQvVwaXbS7zd923Xdfr9dQYo6h3G/31+czgWdrsuLq5Uvi9V6n1gSoCdazMZN25RElxeLN9+4XMwXF6eLFNu79Wo8Gi9nk7u7W+dLYX7+4urJty+Wi3nXx5u7zd1qU1WV80XTdWXpS191ITikqvR1Vbz98IIIQgzjunbOP3lx89mTF13g/a4JfZiPy7PFaDqu6lHliqJpupvVNiQpnPPel3XlEQipj6nrg8RYFk6J374abTb7Zr8fVcVv/+Z3L07Gi/ns8vL85fPnL6+uY0qnJ7PFtEaIVVWMp/Pdrru+un3j7TdmVfHi5TWQf/bty+XJ3Hn31Tcv7u7Wo7qYz2Yffuf9r778ZjKdj0bFf/uv/2Rxstzvtpfny+lkPl/M6tJt16v5yckH7z68ev5q27TNvkF0MfQPH1wsF5PNZv/rT775W7/xXlEUTdshiC98iOn6+q6uqtPTeVmUUJRN2/bbnQC+ur5tu3B5sbx3fjqbLQJCaNvS+10MdVn2XVhtm2bf3t7ebbfxarW7uVsX3k/qYrY8v71bff7ll0+fXxX1SJJstu2u66qa9rtY1S6lEAL4wnkih3hyMltMp13f73fNbDxerddl4RfL+Wq9bZruvcePklIYRL74+sl+303Go7KkJGmz2RO69959mxD+6q9++sGHH263m8VsMq7rfdOkJH0IpYf5dDyfjkajqvTgSPb7djqZ9u1+PKqaLqy3u2cvbxHh3vn86ubuv/zPf+/05GSxPCs87Ns+cPXP/8W//Oknn9x/841Xr+4eni/Ol1MGOJnNY99NpgtA/vb5tx+88+F6fTOejtfb5vZu/fbD0w/ffVSV7vziwTtvv5viZrtPT5++vH/v3BXF1dWrvm2q0SSxlPVoOva7Jl6vmq7tJ7Pp9c31s6fP+5BYpN33DDAZj0QYkaqqFlAuAEVOAJhYFot53/eFI+GUEtejuu96RJgvZ5w0j4CubfsQEIkI264tXIG+rKoidGHf9ESy3a26PlS+iBz7Lkwn4x//7KN337y/3TcfffmscP7y9ORms0spzSs/GZU3m6bwrqrc7d228vi//a/+6HQ5//abrwtfxNBvN6v3Hj+6fPCQU/fy5SsEWq83ZVVPTs9j1+7X60+/fHZ1c1t6V3haLubvvffON988Hc/sDK9YAAEAAElEQVTmCPDyxc2+6xDp9m69WW9OTmaFc/funV9enoY+7Ha7vg8x8mRSpxQkpYvz5aiuSKSqXFGX212IUarKX12v2phmk9HzV6sn377i2O+b7peffHvXxOVycf/izDkikdl0vG36pmkEkEX6lBDkwckipRhiLAoXIo9KLxy9c8vZ+N7F8uL8ouu7P//pp1e3u6Jwk3F9/2wxGZWL+biqCkksSCnFb569evJitdnuNL1fzMazcd124fJ8sZhP7l9eOMLNZjuqR8zS9n3TdN8+e/X8erVt+4vl+OG9s33TN2232e6U/hkin5+donPjyQydJ1eU9Yicd75AgBgiIrghTnrNHSGAAvbmKhS7z8mXepkDTf3/JzY2uX3I2jqYw51DiG+5Ys7oQeewIGYhYUQVZ5Quch/4yC2gIiYqROgJADBwvhEAgaxZaC4vC8FkuMRZkIqIMmg3HmFAGeMByJ77yO8jqQpjBszEAlNQ4oYcnhZzheP1L8yFDk3q1R8ehweIh5V8rV40hC6AQ+aCmKs+pHm+sUVE0xAQj8AiTa9hl0RmACgc6eixcV0upvW4ckqzFdW6TZwSfvzNFYow82rbCThHDhCZAR0VhDHEpLgasyNiAQZBYEIbQeJUzwLAES3GVR9T2/Uqo6H4ns3oIgIk1sGdwlGkqEdV4ST2CHFU0vmsOpmVhSdhjimhFb5AB0CJoQkH7FLVY282bd+HfRPudh0z5FwZQIQQybmyKPoYBMA5IoTC0bgu69K3kRFhXDogJIS6IB1Fp/kXAwYWEkmCKnKqJ6hwiCAsyBq1CWi8oxmYCmWgdbJbVBXyhBOnvW4iYGPmLB5ERGGDaHXcU05ecnktJyApwz1JmJkdWqAuAo5QR3vrYB/KijYaIZdZvGJIFLNUpREghrEquSgIpiaJByUmtPl9epxx+Ouc11smKVnWdqAQChy0mPH//k//fRZSBQBxR/PbTMeXcCA72A8dcUMgJ70HyMCWxnIwDV8ToydQyk9MNi6Kc3juCFgwsRReATlwRIjoHeTZzhhSQoDCk44008wcABxiVXpHwCw25i3pfgXvKKTkybGwdw7RCFEhjztyJj7NRqE/SiUko2qSS7U46GIgZnUwFQ09ZOwZD4LhtwBBUSFv9B9bST56cJ37I0iYxVJ1hXWQs4Km2vNF+o8j+MV0hQBy3RwEdMgugGKEeWg9HiVUBxZq/u8B/6EMPSAeozQAGQCCQRsoLxTkt8/DwELMfmxAiA7Xt/wfFFGEA/0jq/Rbn+oAdcHgYORwp5B1hfSfJlGUX1/+ERCR1576CMI8yjMPX/rccmgVzl3ZenFLDyX7nQP8l8s4ihAZXJrBcYNmJYOYKWWgKr8vFZGVPLdS3Z2ecPPS9kyCergAtXNqGEEKAKCScKKSaTqPTBxBRjwBEFkUlBFCLAi9U9UeG4THQ4YISIRZ4QXyQbe3DIM3BUBFe22RxeXtKIO0CoAnEMWtjD2BTpNSa6XENPx+7ntXKhxqjCSHj/7/kvVfzZZlW5ogNMaYYqktj/LjMvS9ETfzpqjMqqyupLsTw6qhwTDrH8AP4JfwzBM/AOMJMygeMCvAoKG6u0RmVqW8Iu4N4dqPH7X1UlOMwcNca7tf8LCIcD9+xNprrznnGN/4xAebsIFGNFw8JRMTjiPpS1gwRnEsgbkwiJgAneR4BwIYGXJrX11t/+Y3V1U1z8usa/fEkhhtDB/UTOmnKkIRViNUR5hMPxUSYYKEQnTO+RiRUBk0CiaFnRcm01DmZjktQojCHKLsm35bu10dNrX3LICkiDQlK6eBd528tJmHs0hEEuMQOPEdWROFYX2wMAiCGgIzFBEdj+iY4DpIEuF0SBCIEAIRgXDnHCChQmNwXmWXJ1WVGUQgjUVmUcD3/tD5dd3v6r7rfYw8oMYISJRpZY0yWueGjELA5GmXart0M1PlJjBu+HKk+KUaYtTJMozS1vHRgvHBww8gxviofBD5f9iLEH532xCAQY4NR3h34Kbh8KgRAvPoOz5Yhn/wCB/36g/bC46htgJISIwwkt2G34BAQuc+KLMGH7bhxeDoUQX/f9vdmAg27jUffvxwAMtYsR4R36OFkAhDMqZFVEgsHDhGkbZpvfOD9XyUen9o2zYyH6fS00l1dn6hjeVRJMu+813j2sP97Q2OwZ6C5HqnFTGq1XpnjGk7Z6zVmlAkMmS5nc2K4JxWdHmxvHxwNp1NFYuE2HZ7IoyeX11dK0Va59/++HpW5etdHSOvdm2VG0RZbdvktNV1IYGtudWANJ8UX31y+f5uAwiRcbtv13XjfJAoVaZnlfnm80eLxezufr3d7pwL06r0Pmz2jTI2rU2b2e2uNkZZRQqikI4xaGP32/0/+flnT59ePnn26awq7m/eb3a79e2tUlhOytl8agmsgcheKzOZLrbbg0ZUeY7Mh31zvzlE5sN+B4hKaWt1WeSXlw/atgkhKpO/u7pumn612uRFPp9OOteHEHeb7Tff/OSLz5/4vl9tdrvtLjJXVTmfVWVuMmuKoiJtet/VdXtyev7mxYu2bT//yVfPf/hhWhWz05Mqy1jECSmA+5vrtnUhMiCeX5wXeUHIRmlEqrsmiuRZ+ebN1dXN3eL0/O5m/frtu7yc1HUHEufzqSLzf/9///vnL64uzpfT2eR+UytrQghdHxAkCRlJ0awqJ5NSEZ7MJz7ybrdLol2WeHl6YpV6eXX782+++uLzz/7qb/7hu+9+iJFRKWGA4IzVtqgQQDgIYHM4PHz8CEHKwkSWru3arv/J118/PK1UPGhtinKCInXT7Hfbi9Oz3rW/+vWvJ9UsRGmb7tXVzR/+/Kcc+7PFHAkePDj79NkXWpWz5fR/87/93729uvqDn/2EUN/er4H9V198slicfPeb7z757LP71f0//uo3//X/5D93fW+1itznRXEyX1htRNxsVj56/AkhF7PFYbffrjY6y0sLfVuLmTrnLx9eagV16+qms1mx3dcg8f37d62jGPzt/WGz2S6WE62pbVxZVpPJJMbIMYqw80EryotM26wqcwLyziutlFYxRFIEAH3XI2LXtbvtHpCVViBqOp0iotJGG9117WG7b7omhhBDfPnm7aQstM1/8+MrRagVVdVEKQ0cszwnouh7FtbGKIVt15+fnhYUfv7znwLpm3dvrCZAvL27O5lPJmXx6MmjyWJx2G7zqrKEbe8cGI7xF3/3D6/fXJ2dndos69q+6xsXeLPZTybVZFIhqKZpY4hAGKObz6aXD87bulZazxbTssjevX3PLGenSwEB8aF352dnJrO3t3dVkbed1wYA6N3VXRB4cHG+2R6QcDYtm7r7d3/5yxdXN2RzrbP5pFJKEaoo7ELonA8hcoxGk8SolVZK7w5t733a95XC+aw8m5fPLk+ssczy5uq+7sPnzy5D8Ovd/tHFCYPkRltrbu+3d+t9Zs2kKlhgs9kRYedinumz0+nhUJ+eLB9fnjELILWtK/JcKVittlfXayHMjblfbwFJa73b17nVpEgRcvQikue590HbXOeVNmYyPzdZEUJI50hCPJRCEPHeOecAMM3miJTSOpUygABIWmkE4GQ/IkNQcoyDPc84WEz9vRzPxmPtmqY6HyElMiJKQzzIOBwd/hciD2KiEX5K9SyNIExSMI0ChqHXO5aLCZU5zoOHoxCOhck4kRhbSBkuScZj8XdGJiI4RM4DggCl8FscDEpS6ToQ2wc8KJUKH1f3x9ICj0Xz+AkfoWbwodL4HRhobGTSwFghEmHT+6YPkaFzPgQmpXKrRaRu+zSGDqkWGqG6OM6oFWGVmccX8zyjQ+O63rnIgSF4bloPhApAkR6siEUQVXKcTNZ7SEN+i9UmxgjAY6U3TkkJIotCLKz2ITCzQhqnTXicRNJAPUjcMVJZnhkTgu+8Z+9KC/OJPZ0VD08nIUYAVEQuBBGJnMaf4oKISNeHbe1c5E3tEEmLAIhKVWhyICJiEEVkjKm7zhhjlAJio1WVmzIzSpEmVCiKkmkOGEW5oXEAL5CMqFgYMcrAEWEGTLljIiIJ8UlVLhMIKQKgsRf/MGbkIXMmfVCQKPUgqWZMzKPU5IYoaaQdGMzAokAEiYJ95DgatQ4tR6rbEQBBJxMVQhFxkRHEJo8JEZYh8H4oUYeO7HdaXRpYPB/Y6Ec+3bBCxr5sZMl8VFGPipZj43RcA2MfLZGHpgz/9//n/xYHN3tIjKPAYpUSGHK+E+RgNQ3xtyM4S4OR9UCuUB8tNBmLbx45jcmlKTL4wPG4JIbrZk5W08kuZLCbQkrpKpi6oFQeQ26SQ0dUNDAKc6syo4bNKEXiiTgfY2QzWtcMz1CKXoLhiRxu5qj0kQ9t5rDWcaRUDSD7+B8cHdSOn4AjMQcGFONIyh+3nEHnN6QmwzFiEABw4EEgJFBw3OMAaeAJYaJTJiyTOfkiD6X/SK8AGBLKhucPRvcTTZTABfoIzvkIkhl2yaEdGRzv6YhffHxbxrs0vLMj5DXws3BAeARTProcE3kgOcIqlRpGHCcYR2LRAArA+GTLuOV/hModiRsf+r3Uog3AyhFvEpHB1BaPuz8Ox+HojH1cFb8DiAGMr2swwTp+1tgLpnxCHK2vjlf0MQI4PiFDuuQRZgsMMs5AfhdYG1/tcC04IkrDx8dT8whEDCK7AaUaO8Z0ixKQekSOjid+4nAKQAKMNCVjuWEAdJy3pDG+SsSIcYuR8frwd24dfnjRggAYQQRAjZjO+JnHlTE8dOPwaVhhHz2PR3xn6K3TCaqOydnjCkTENLtI76BRJCw+sGdAgD6igCgcUjY3jTcKrUYfYpkpHkkrzMAM9+v6+buNtrk1tmm7lA2Ybn7gI1SUQg9RE4YYFBIiBR68DWUMvNOa0qObRjwMAoghcNO2SqFVqIWBFHAIPkZQkYVIGa0HCihzum1pcyRFIUZgIULBgf6T4IlxVQgDjQULMjMIayQBQUobDBBBECRApYkFEoeWx9pweJIgAiAggSYa+UQEYIzKjFpURd20bdu7OC5MJEVKIeIHO7axrCAwmsoiW0wyEU6WzJ0LHAd8lkaXrgQAj3vjaIp5tBLDD8vzWCZ+ePdh2HDlg6IyVXxy3B8+EqsBwGhvjEAAjJhEoGmzTQALCigatYTHlS/D/ASGenesKcfFmxCrI06ULu8D8AQftpphDxuL7vTv+FUfXpocFZISEYCIRqgYnPchhOH0RUybPSkiRGGOIfZ9f6jrrusSy9yFKACRo++7ru0Cg9I60ySCbe+H2YkiTagVaTV4nmtNkzKblGXTHNb3N48fPGra5m5XHzpWCpnZe+4D920zKQqTmbK081nuuk6higyTSen6joim08m0stOqeny+2G3rF2+vt019d7++XdUOlMQAImjMxawKIUzKfLdvLk8mqNT1/W5a2KtVXTfOZiaEqJXqPT88nbPI+/UWAZMde2YMRD6dFT/98nFmIC9yAWzq/f161zrmyEWmtSKltAjs676LrIis1syROYKwIe7qw3/9L//8i59+EYPnEN+/fasN9V2XZ7aoSmE2WruuncxLHxm9PH9957377JNHRGpfN1VZiMDbq1tBMFq9e/f+5z//5uRkefP+WjjUrVda3652ihAi5GXe+T6EgCyPnzycTWe7zXq/PxibXZyfaK0IoSyy2XJmtDJK+xia/U5QeRd2251z3mZWK1U3XZ7bk9PT2WzStY33QooOh0Pf+6Iq7+83IPLlV58z8+tX70xmY4iHuul96Jpus2+q2Xy9WttsolDqtnn17rqu+5fvbhazyf16V1XFk4ePfvntj50P06qaTUtEur65NZqKwlpSROr0ZDqbVl3XFUXeu3B9d4OoiyojoJ9+9VVZ5P/6//Hf1m3rfcxzrZSZVhWwN3k5qYpXr18RYJFN94f90ycXfReCO8RIxsD/4n/6X3zx2SdvXr88HOp3r98sl8vpbNq0NUffdf2b97d397u2c6eL6aFu/+yf/uHLl68/e/bk7uaui/73f+9rQHj15tW796sqq7Isf/nuerU5WE2ffvJoOik3q92Dy9OHDx5sdvv5xBw2zXa7Pz9bPHl0cXpyEgGub1dnp/MvPntk8kkfgpZYHw6r1Xq5PFUEERSInJ2fKWMR1fMXb6LIZFLdr7av3rwucl2Uk+vb5vbmSpBEOLN5Zi0ioNIAGIJTSuV5plCAsKiqPC85+BiCUoaUbrvOOyfM3ve7/U6Rdr5jgSKriqrSWhOSiDRdg4K77Xa1Xu+bJiazoBgXZZUV2WRSrTe7xoVJkTWd873LMmOsjt69fb/ShP/N//JfPnp48v71q/1+9+jZZ+/fvmoPdef6LLOnJ4vFtLJG+xgjkDBOJ5nS1LTh/m796vUbY7PIfPXu9vT8lAiNNoR4v15NZ9NJURzq1sd4fbcucs3BX56fX1w+iLFnZhTcbnYnJ4vJpDwcNrMq12Sazj168ui73/6w3dWPH58Zre/vd98/f/vJ08vpdHa32nqOk6o6Pbm4vnr77W9/eHdX321qIJpOJ1VZimDvQwhBI02mZQwBhDvPwYfehc4FAeh7j0ha07xUl8uqKHMA+v7F9YPzxcXZ/LsX1+cnE2PNxcmkzI1S+t3VrY9ydrrY7et316uqsNNJ2btwsqi2++bN9fqTh8uyKidlnmWWI0yW87wwoemu79fbfeO93NyuidBmtm36PMv6EAFAI8+nVVEUq82698GQTKcLUXZ5emaLCSoNIsG75rBfr9frza7tHCIYY1I9bq1NKWbee0ScT0tF6m6zU6Rc12tF88XcGG2tNcZqo402pLUi4hjavkcArQcLOCTSSmtthrBbgKQqSY6jMkyIxwHbWHmPk8rh5Pq4Xsbx1P3gxzPiNwgfakUY3QPT349dggyk7UFhDREAgD4M4D8clBAFQfjYM4CkQRETIgN9qOM/1Jk4VKVHdAc+/BL42Ft7qEVkLLCPjcYHPGrsDkaSFChEEeh8PLSu70NgSXyKEKMIaD1IRBBRkUJIoSPAyXghhZmwEJH3nmPApBchYBBSCkEDgwADR8LRTTpdniS6Cqqhx5MQmUgphBA5QVohRgSMDESiDcWYQsHTJDiNhQBGqtcYWgcIKIiEEEJkwNxoo0hA2q7zIVhLp9PcaMUAZW6TO0GmVRRxnrveI0DT+Ztti6giozJGcxhKs4THsUiyqAD0wWujtaLklJ9ZbQxVRbaocqOVUkTAWlPie2nkQVQ2MMmGx4oljZNHR3NgPVhZDY6VQgkF/VCggSS7Kxi6jxG4kbQQxicYU/j6MI5HRPSBYaB9fHhuAotL2BAIILggmVGGPlR+iOAjGAVEydiUEUEl1QgcMVCIPESXCEAyYj6O8EexwuCvMiQCD6PU9CCk/lIIgYGOD+sRA4JxRj4WpgiAQQYGx4Bj/B/+1f8nDjH1w5PBLEar1DPz2HgZhSEOmrpEaUmYoVaoBhiOPurZIblUCNBRFCfHDpOHljHxU3CMw4mcoCJBxGTClOh1NNhnDCHMiOgZEr9OEWVWJ5VHuqKjIkqGDiFhNDJCX0P8NgzeLoND0scG7/JRDzA0rx8hQJ5xcLwFHPdE/PBH+QCFwCjlTZjkENv0EXlGD27rKEftrgzyooRfJDASRj+IoyBz3O2GzjO10QnjoiNwhZjkb0SYALXkW3x8NTTIUAa6ChFoIq01ADgfEnhptEqNegghxhStMMA9cUySO4IRCS5MkkUZ2uzjxoUiHwRZ6ecNW/OAyxzbonTlA7w9PLwfUKihu0ravTAEisHgLCTDPRy1XcNOPsRkjkjS8aCAYdM/mu/CkAoEMkBA6ctxAHwSI0YGGsWwutJ5czxseJB0JSCPR/9sIETB5H49rBMYRXzjtYxP0bBcP2BMx9NrfAnpkQQRSBYzads6GnEfb9QwnxlRvgR+AwKNOT7pm7IMgM1R6JrabDU+V0PXOdBz4AN4PRy2iZeSrLXTuz/uBCDD4kXyEYUjDlzCwbvkuLRTWaA1MQMLagJMenPBAUxNBBY8siIREbTRmdH7fXe9dXfb5na9y7IMEdumb1pvM72YFaU1uaXMqsIao1EINVHbB2DZ7bsfr1Zaae+ijwPcozTFI5t3OHWBUAEiyODFYFQCzwfkl0AYgAE5RBZOayphRpKmH4qcCzH4LLccJUa2VhMgy3FnxCQ5TgdK2gDUsF+nIYP4KIqQkDXpwBIkUkLPBSOD0kQxipCM3OwBMEJFibqYfGCSZdIIvRImOiQqrYmQAEhBDAwIRMr3PYAoxMhsUoeKlEhcOOIrMj6i6RSLkVmkLDNFGFlCFO9Dmi5qTUYprZXVH9TdY2zBByxwOK+PmOxxf/3oZB2WiqST7wjYDDuJfOytkwiAOJ75x2+IgCA0GHuhQuCEzn9YQoIigmpA3kbwctguAOA4YBE8Em+Fj6EuMhTH42sa1sN4qXhEnRGEOcYYWSIHiX6/2+8OjYgoJO+9ABhFTdu3vRvvDYYQZESjrNFakfcxsBCSD2EQgQlrQk2iFQUW5ljl+a7pQ4xZZkmRJjIGFam2C3VdM4f5tHh6sTA2u1+vM6sUmX3dusi7puPIWVb4yMgxt9rHaIxeLsq6rnNjnYukqCrL1WavUB4+WDx5eKEAfv3Dq/WuWW133vOh6UxuOUiKdTekqjJfbw9lboOIIdoeGkKYzydNG7yPUTjTygUo8mxWFfeHlkcvOULQWp3NimePzxCjNia3ed8197t6tW3brleEZZ4ZQwQ8LYt9093v6txmmTHTSQaxf3SxUBCePDxfnl9st9sHZydt24DC3CoRMFq3bUvKAHNe2hiDNdnzl9eu74uyODs79b0TgRBDXpb1odntDtqaPMvyXFltlNK7/S7Lq9Vq1bbdfDar2/5+u/3h1fuffvrom5989vz1+1//5sXPvnr25NFDa02IwRhVZGY6KV2UPMsouru7ddv1s/nk3dW1Uma93nvfPXx4ud4cFovp+dmpMbqcVH3reue0UUSqaRvvnNF6fziQyRaLxf39+vXrd8vFPAqenJ7t97vXb989fPTkbrV58eKNEN7crzWZRw/Onj17/Pe/+H692Xz67FHd+MOhPdTt2em0b/tD27XdAURCiA8fXC5mVWY1Im4229ls3jTt1c0dAx8Obds7791yMT05mT1/+TYzubW6af3v//wnzWHX9e7duzcaVFFM23Y3mcxLaxR3RW4nVfHHf/zzb775KQhc362+/eU/vnr5Ynly2nb9pMoeXp6/e7/67fcvtcmEIwCcL/KbbQcAU6vfb5vadSH6n3352fl0/uPLtz/58rO/+/Z7UvYv/sWffP/jy19+//zi9OSLxxcscHay/OlPPuuce/7dc6vV119/MZ1VMYS8KE6WCzLG9f3JYt607WG/jxF658syr8oCiaw1m92hLMum6W/vd9PpZFcf9oem7xphevP2za5pQlB5ZrVRhBRCVIqEJS/LVHQrQqVkOpvneUWKiMj1Lm3HXd8ppX3f39/dxRiVMk3bFnmW5UVeFlop733X99457/p93aw2+31Th+CeXFxMqlJEsqJSiNvtzscAiJm1Xd+tVtt906/Xm68+efznf/Zz5p4Q5rMq+JgyLP7tv/urzz59WlVV9O7i4uTubrvf18vF9MHD88lsHvp+s92/eXtljCHC3X6PQL/67vl0OvnP/vQPf/v9y9V299Vnz7wPIrzbN0br6aQsJ5Ob29vM0Nnp4nBoV6tNWWTz2STLtUCgCJtts2/aGGUxnwJxkVvX8+t3d5PCfPHFs+Bj0/Xvrt8rlV0+eLC6vwek7aH/7vnbV+/ubJ5neabIaG2id1ZrbY1wRFQIwoxt7/uuQ1IusA9slZzO8xgZFB4ahyCLadk5PplNmr4vC/3Z44u6aSLD9y+ueuez3NZtb1Wa10OeZ+cn85v7rXN90/bzeTWbFFVuL06XTz77FABXN+9vN5u69dHD7WqTF5aj7LaNNsYDcGStwGbWEE5zU5XloTnc3K4mk8n5+Xk1nTkXrm7uD3WHAAzQ+9SgDBiFc56Fc2u1Io7sghcRm2UCJCFoTYm9ATAIgpRCIuJUUaMIAyEqY3oXRDjPzKTM88ymNpFFECHLsyLP8yzXxmqlh6nl2Cgc/8Hh3IIjAPThaB5rzY9bp2MVCiOLZxzuCCMljxY5Yi/pPMeP5y9jdTHSiAZy+JHFLIAghCiY5D+EIAwyplGP0qThtIdjrzYWLWN3AcncUACQx8S0D8XC0ShzgMbQR+77kB6tGD9U5zxkYaReXdK1wQegbLiZNJTqw9jso/5ISKWsK+KYhrtRIWit+ejwJ4iphidAJE4DXwEi0IhRIIgAQxQGIEIZ2GPJJDhdCiEB8dBXpqcAAWSQC4zMhsixym1u7fbQCgwRn2WeoaK2OcxyO5mUmTXJ86Ttg/PBh9i5CDJ2iBJxrOEQMdFIESmyIKHNdPABYJDSKIWRucjMyXzy6GwmwnebvVI0q7KLRS4CHEPy3EmNSHpFUdgJaUWZAmaoOw+AWpGA+BBbFwVUYZXVqBUS8NhH4JGugKM+XhESkTBEFkliHWGOnJre5DkzPMfDwoEYk8M6IqBwbCP0LswKm9hhAiicYBLw3ueZZeE+cIwCArkhhRCBkiV2ZEiJ9syc6UGpN8RAJXIdiAhEQQFUwCNTCYaWFUFhigSBNBwdRTxDGTpyKxIVj2QAJIblif/H/+u/EQEZ/G6TXYAIoNU4muNIZNGErWMASX4iiBAZtCKrEUZzVxm7X0JK/CGG5IoECBKZE4omo5HKEfIghMjYB8l0GqvSEAUlkPxWk0ZGK5IRSWVho5VWCokApHUBIWEcMhpNAXyIBkvr8wMYhOOK/KiyT9r1D9DP8OWD/gIQxA87jfz/kECOQ+wPnBM4NvLjPjWmH40dAhxZD2lnHLC6MaF81B8BpPd+7B8SsBqP4pQj+DS+5QncjQPKA+M/oxtZMl1KBo8wYDGEogldH25X27vVbntogw9Fbk8W00mVG6NPl7OiyAQwhMgcfWAfIhGpAVvBD4DFh1ZrgIRSm0e/E6M4rPzhS9JCFBERZggsPozKv496p/GBTtlG6EOy2IAErCarWkRJbWfSBCVbKKMSKQatpqOZ2fFfGDC7sTv88JsPt/R4LI2ajvR0fNyzphU0UNUSf01EaEDrAQG0VlobAEkK8A+Y6EfPm3z04Bx/DZv04KU+TC3SZ8SPOvDhhYwva7jQ9GhxohlTWvlJ8U14vPLxJIThk4kGDM5oYoYBKkyuzDLc8yOixAIf4kZx9H1PXl2AvYut433jdk2fDGUQYJLby9OZNRiZI0tulNHkIr+/q1f7JjDn1s6qrMyt0So3NPABUXxkpVAQYxAi9ep6s9nWN6t6V/fpmLXGKGWiD2WRK2tD6BRCfahPZuWTyzNt0ZKI4Lcv7gBQIdV9YlwrYU7skLoLAKKIkEhk0DTyYJEolDQyA9vuw+aKgECKRWBQaw1vZXpLODKKWEMoGFlgJMIMeBwmsAaHyIvhVg8nfog8Fi2J7SFaKRdCgjyZU1WUNlUSYEIhUgDIHAWQiADRaB2DR6CYOEaceOIsPEjh1fCYYgwRAFCR98EoVCCREQg0DfTJNN+SgVaZXsnwQkZ0GIUjIKa8QKVUukk0wJ1ChFaTNcoaGscWx13z+NB/+P+wSocfk24PMsDRRGCoV8dNBD4QuQVBaMw9SWcgDlc9IJtpORyL2PQdR/wUBj0rHHWk4/WMSz/5jqNCROIQEwib5IEwzl14RIKigHAU4RCC905EFKJw3G33ddMoBAJpnKz2XWAQlhgckk5kYGMMs0TmgatNSIghxJF7HI1SiS6uFEVmjQAg1mphbnofOVilXOTeea10orSxSNf1kaXrusiSF/kk088ezFofLFIMARR23vc+xsCpQK9sdr6oArIAVoV9f3vX7NuL05MuBNf7LLOR48ks/+Ynn0fn/93ffe9ZXb15YzJLilDJdlc3h36xnIQgWilBaeuuqHJNtD/0AlwUGUhyOMcqMwqFQRXWbjunlY4+kKLFpBCRxWJS5aQg5kVhjWmberVreicaYX84eBfKqswyi4gMUred77vFxBa5nhT66aMH8+mkzLU2drfbnp4srLG6MPvt3mpVlFlTtyFIWZZt29rCKoA8y/8v/6//+Ec/+/xP/+jrf/sf/vbi7PTi4qztO2aot3sgIlKAEoL77JPH9aH9t3/9j/+jf/7H3vX391ub5W/fv7++354tFs+ePESA/eFwfnFeVSVBFGZA5MgAXFWF0qrKKx/C81evFelmv+/7ToTW2x0zT2ez8/OT6XQS+362XEbEvm5DiKRVlhkAQaJJVdb7+u9/8e1kNhXm3b65fPBQSDVNI6j6rnfOP3/5tu980/dNF5pm37sQQXzgR+dnm0O92mz/5Z//6aGunQfhGFjevLsSQaXRav3Fp08zay4uH/zHv/7r2WzJALvDYX+ob27XXdsZq63JvOuIdIjxwcWpttmjRw//7j/9zWRa+sB9fVAKjCJj1DefPzxZzmeLsx9++5vzi9Nvvvn68aNLAeh69+Nvf3N19fbk9OzBxUVe5L/45be/+c2Pp6cnJ8uF8+7B5cVkMnn5/EU5mX7340ud5UpRokfdrleTzN5smpPlyf/6f/Xf/Ov/5//w3//HX1mrn5yfXt/dFxn8l//8n2y22z/50z+2toz94dHD89b5Jw8vjFJNH6wx1poQQgjRe58b1TlnszzLMgao66bIshC57kN9aMjo6/fv31/fLU4unj//4X51cH0AgsmkMCZDIGN0jIwozrkQOct0kefz5VmWmXSwh+D7zhmjEaFpmrZtjLKHw6FtOmZmAkXKWquUisxt30Pktu9EBAVZeDYp8yyP7KzNeh9d2zddu9vvi6LSWr17d7XaHiJQrtV//s++RggXZ8unn3wyn8126/tXr15NpjOttXeubeuirPrenZ5fTGfz7e272Xw6OX1w2B/q7fr771/OptXFg1NU2BzqIq9evHw1mUxm8+XzH1+G6E9OFqvVOkZsu/7ywXmemabtTs9OJlW+ur/3zhtrZvNqt9467/a7w3J5crfani7nxqjNbnd+ceI7/su/+bYs7BefPDg9O7m9WVtLnfda27v7bVmW89nk4uHTv/vbv/9P//DruguRdJblIMQcrTZaKYNirCUEH2NSGZSTqm16F8KktKUm0iYCcPS7fQNk2943ziPGypqL5byqsrrpXry5MZkVBIkYY6+ViSyEYK2tCnt/vz30TlvrnLtYlkVm/sU//6fz2ezNq1e36xWLQlC9d4FD07jomLQRiC6G9eownxTzac7MRVkQQNP2vffWmC6o7aH3IZRFXljjfIgimmiII0AlzInCr7XpfYghlLlFUiFGqzUixRiZmUjLgJWI9yEwl2UBAN57QAUAzGy05hhC8CnqVBvNHGL0JjMkUFhTVcWkKrQ21hprDCAgJTfbo4fo75owfnR0j2MaOXpZflSJwjiBHdAZSHDd8XBEOI4/RyBpaNyOw8QRP4Kx3xwmIKk1UIgIwOPkFQbcBQXUWAwf+5IEPg0sGBaRI4d+aGNxqMNH+5X0K0Zpe991PqEZpIiQIochiBlwLP6AmYUjDqHW+GGmCoCQELo0nJIQIgsTkg+stdKGUo0Aw+tgRNBEDDQQPQSSlkOAWSgOyhic5JqjNC4oRCTyMVKyQMWhYxm8mUipAQkayn+dHrkYCTHwMMclRYFjiIAEyDE3FpUKIRBCkRujIMYYmBEBgAYXDh4tdFlAKMiAxB27NhjssdEYCp5DYKUpyda1UdbovuvQ2MyaTMGhaQE4y8xyml+eTL58suydI6WCD44lsvRBfJRD74MXS3S72u4O3bQqGWBXd733LIJI1mijVZGZh6ezaWlyjREEU2g9ABJkmZEYt/vubtvtW++9Z5FpmZ/O88U0z41ikRhj0vGNlrYkIM5z8skOjBoZhZtI3oVZlTGLizHXCIAsGKMzWltjXQjJACfEmCk8eGl7j0KAmFtCEK0p0+kJTDVpKkcH1MCnwhyZ5YMhN4zj4QAKATVG/qDbwBH8QEhqD+AowKAUSBQZhEf/6l//d5jee4YEr0TmKFBYpRB4UMEJIrrAhkhgQGR8BENw7I+TuUlq+X1gQlFEghgiQHIsBdREGtlHdkGSWwQM6XoSGVwQhcAgkUErZRTEyMygdWK3sDUaEX0IIICI1iijSVARkQ+xcwEADUFKz/mAYY9NcxzZHQmlHTvrAVKRYW849uEj+iNDjjUfmWTjjnD8XbrPH9CfYZ59fJ/Sd/voi2TQkQ5fOYLcqbMYsMnj5wvEo8XEuOceYRFmOF49Dkw/SkQJHhGB34WlUKURavqGhBwCh4AC3z9/fXW7ZiYAtBqVsYTY9V3vJcv0l58+Oj2ZV0VuMy1Azic8E1TK3kPCpEGlI+jAnQsIoDSBgCK0Rid6ZMJRU3edUtUGsAXROd/5wcQ70aCYj8j62Jglrs1Ifknv6Gj0JXE4fFCGtYEIEgUQUY8vfICNiARGi3iANKDTChOdCI+WeCA+SqKbpZZ7QHkHjVlCgyRwgk7Sm5gQLkicCaNIK7Xv/P32UGX2bDFl4fFtH042kSHwTkaPW/yARn0MB40P8LDsPtB+jgfw8NvEykn4HoII9J7zXIPwkR813Ijh4UkAFiilDm2437VlZnsf921IAyZFVOZqWprMKBo5Tb3nIOBDZGZAzA0VlvqeEXHfurtNu6/7kAipqTQQSJtRmZuTWSkgzodpVSDB1c1ufehIYxLWptgpo9V8WiJIkdvlvGq6XhG8v6ubNvR9X7edViq3NsYohJqUQko4BWmlSAffF0W2nBb73X4yMZcnk8O+afrgWR063/fOBTbWhBB/8vSyzO3fffsyRCRFRMhDsy0iEpkTG44FNDAzxBRlmGoUTEMuZGAcNE4gMNjsiYycLwGtjCArSP5lOBJGEpA3OPbGyISYGdW6oGhwAkoPZORUeCQH/dFUChAAOA5ElMGDCVEREWAfIxFlRjnnCOlYcg20FkGGiB9qo8HCKkZBEULhIB4g06gHletxAWIiKyJAqg6HoCkkGGl1BITDUoLxRwwEMYHB2bqqskzBUHkcSXvDnHGwgxzLNzhu6ioN/dKyBQQRhYI4yG9xCM4ctt5h9xMRFEUKgLVWCBhDgJEvO27RAgnXHBZIckCnpH4aXgAgjT5HwjHGKIgEeHt7t15ti7KYzKZ5nqGMk0YEERaOwhxTqrdzzkfvPIBoncfQ+xCtsZqg7uO2dp33iJgbhSjeB2MMJNBWhEXattVKTyaliHjvhSVyVIqs1TFZEQon36g0y3LOR5HcGCAMPkhkISoyHVkYiBD27SF6iZF98E9OZ7NJVuZZ8Hy/Xk+nEySquw5Jrw8H8f5svsxKU5T5tCzu7m69C5dn5+vddnNoFtOyKuy0sr0L3714q7WOlN/d3N2sNvPZdFrlXdf7EK01hNR7VhoJAAEVAQfwwsYoYVBIQKiINEGeqZP57LtX10hak9LGXJ7NFbLGVIAnmir1XbvaNUrZSZExBx9CCNz7CIjCUTgcDvvTRfXowanru/lsspjNThbFpMq7pr189GC7PWx3+8mkuF/t8jx7eLHs+oiIq81uUhagkJAeXJy9fnPz2afPNuv7KLA4OW2bw2I57Zv+zZursqysNZvNZraclWVxujy9ub6JMS6Xy6v31/v9No1d5vPZfDlXiKKUcz0EjwA2MzbLNaLre9J0crLkEHyIoPD1yzf73f78/DxyZOau89d3q9lsen6ymE3ntsjbtgEgpZQIoNZKgmfo+i7Piu1m+/z5SzLZdFJ5H12MMcTt7pAVlfOy2WwlhB9fvdW2ONS7Q1PXTe+8Pzs9t1ZdLmePLi9BgUTwDJqw6/33L15756eT6tnTh8t5dXp2+v79/W+/+36/3xVlzqDeX98ddrssz//if/zn3333/Ntvf8izrKjyyweX9eFwv1mfni4IdLdfLSpzfnYyn5WzSb7dHibTqdG0Xd99/vlnl5cPHj28LIri/u7mxfPnz5+/rqaTSTU5NO133z1ngdPlLEQBgK9++kVV5E3b1W19fjHf7nZ//4sXRPmLNzc3t3cmL/7Lf/7Hf/iTz/7tX/4nm1c+uNv1Otf5w/PJV1999ulnX56ezNb3d5lV06rwPsxmC0A1neSEKKgIwXtPKEVRdF3b997azIVgrM7y8nBofAh1XW92jXO+6fy7t6+d81FUfejv1+vZdKoUTmaL4P1ut9dGAaL3UStezOY2L41VzOJ6R0RN0/Zdv6+bzvm26/u+A0BNymitlGKRQ9Nv9nXTtsJyerKwmqzRs0luFeV5ZqxWikjrtu3axl3dr4Gjd9z1br/b363Xmc4/eXz6hz9/NinyP/qjP7R5ftje1fv923d3TVs/fvRYoRRVlhv14tW77eHwT//k58H7B5ePhPTm/s4Y/cMPL7u2+/zTpzY3h9rVdXv54Oz+9rZzIUTJjHr+4seqmovYV69ffvrZ0y8+/5zZcwxN09RN07bOe/f06YPXr64XiwoBtrsDIZZlPplOJpNJjOHq/d1u1xlr726ujVbzxcJafXNzO5lMnA95ZqNIWU2ravqrX/3q+naz2tSOed/4Q+OyvDg9WRaZnuSWlBKOg7edDz5EjYjaFJmWEM5OlofW/d0vf5zMpoeuKcqyrmsEzpUpMs0iRKp3jnE4T7UhCYnWILNJhqC3u50D3h2CBplOi/nE/rM/+vri7OL+9uZmtfZBrLGd65EIRe/2jSZqg1vt6jKzkyq3Rvu+j5Fn06nSandoX769r100SiljFEKeZYHBmGR/yhEw0yQAvfMAmGUWgZlBKUOE3jtFhEgcObkFceQ0eEMUJCUCSmHTdgpJ62TjDjEGIkJS3scsyzhy4OB8METGaJIeOIIyVa6jiFIqs6YsCmO01rooC1RagJRKqTaQ5GkhhnHKqLUxQ1LVUD/jOJXBNDoaDkc8KlOGaRjAAFPwiNKkU34wtxuHwR/sGcYegQiS0yWOGc0DygSpKqM0wUk/kRBGQ1UAgMR8OdbUQx8IACKDA0mU3vOu6YUluR/iUQ9ORDhoSuRYig8ekVGGj0NqVwk/JiWlvJrxmgkkMiWxRmJicGr2I7OQUoP9MH5oQFPNzszx+EckhRhDxOF9CaMDLKS2hXBwwkj3L/EBYhqnRRYUrTVzNFoRUd32oAadgNaUYm0Rk11pmn7GQXZCIgzCqBQqQuFBmAYiHEetjAwPA4AQoiblnGMEQsq0MpqIaDmfbGpn8ny/38QQtNJE4FxfFflPnp58+ni5bxwp7JyPIq2TtoshxNfX6xiBA2fWCIMLXlvDPjALGR1jjDGlzqO1+mwxDSFE4DwzuTZGETM3Tf/2dtu7oEhlmUYRJKUVFpk5WZSLSW6MMhoUKWE2WnwERcp75yMWmW77UJVZ9D0CBDTRB6Ox97FzgbTtIldKTmcZIIYgibjNLHUbrnd9YY1GJoQy00WugaMkDwcc4l9S50QggOgjKRQETpPpJAcafayhZwTAQg8LKb3HPIaC4SC9BGDumRSixuRhgXo0Bx16TiBI/kAjAjFAJIHFKlIEkVMuGIAClZAjgeRwnOpsHgUaLMnvWVJtrZRKIUGpj5LB9yd9ESKCNRQjk2AQCZE1kVYEegh6SdtcgmVj5PStmIWIOQIBGkIX0rIeFgvz2KKjJJeHcXnLuBDSd8aRTDe0wTj2HJzwt3GDIviAFR9b7o96lfHjH70LODYfA/icYJ/xKxNCIcdOXI5d/QevbxjlSCNhKfUsw+YohCNldNjOAEEREECICPBRmyqgEtgG0vvgfUAACc4733k3K7IH54tPnz589eZ6X9cny+Xf/OL7T5481NGozN7d3P5j90o4LubV5YNza5VzvizLoiqV0tYYjDGE4KI3WuVZhsyAopXqfdiu9xphPqsi4ma73+1qEBYE1zvngzX6ZrWbVnlm1HwxO1vOrTUhYtJ2pRsx+LMNLaakFwIwhlUjECZ5XTy+q2lTJkQWHrEb8CGGjyI2k8370FRCMj8CFTD5uhKCSl66Ij5EZiA6YnxDC5zS1xMjKMQY4uBlSwmdNCot4+2uefXu7hffvdg2TQzy9aeP/uyPvp7NSgUYYhCB5KYmyQ1aEQ3hkXjsiGWEdIYjhQAEmQc6W4jiB9ZosoVPZEUxmhqPh7ZnkbZz99v68dn87KQscy0cOAoAJZIhEkWGuvX7xiPA7bZmJl/I/aE5e/Aw9P1+X1uL97e1AqkKaxQsJuXq0PWRibDrXds5pe1iUliF16sDIbadi8IKyVqjtIouMEN6jSCyP3R9HzKrQpTN3nW971zIMgOCmjSABA5MwILXt9veO6W0tgpFfMCuC5MiY5BpNQEQjlFrJQDM7DkQICnyPjrgGOLleXG6yJTyCrjKtaFqwqJN/re/etm5mLyEhOV2tUHCPnJudBTovVeIyT8+RlbKCEdFyMxexmnPR0hwGsUQATMETmpHTHmHCBCEmQEBXYg4+p0l0JRFgEBrrYlSHESMEhWG1kliOQqLJM9DFkGRIMSIFMdUgbRdEGCERFcc6itK+n/EKHF36EkZhcLMRIQggWNiErNIqjqGYoVQvKTPjIKMoFBiiCHxxuA40ho3TACBIVIqTdRGQDNldCkcuduDQneAb4FBgvMhxsUkNzrxewd5n4xF0lDBjbZmI6EQWUa2pgzstTG6Hof1TDqhQUgko6c5ArZdl2bOwbnz0zMXBwodx6iItDEIQIBaG0laZAlt3QSO1lgE8SFEFhHu2rbv+hAjRyYEjrHtnIDq+rjbtYoAJboQQ2SjSWslHJE9qVzSQIWMUVlgvlrVmVGAqva+613X94RsrGGOpJQipZUSQq1UDCGwdM4DaZvngSXGqJVhEqOtIhRmo9F5z1FEUFnN0R+aVoC0VlEkOG+A2hCqSYEg3nsgPL089be+KJWLQSAHhdfr7dlshgRnD04mVfX23Y3v3XyRC+cns3OVaWPUyXR6c32lOHzzzTd1W79897rM8ovT09fv3v7w6lB3riyz3Nib97eHtlfaKEVd2wmDtSrGiARap+0ckTh6YCQiCCEIQ14WhJIRaEOnJ/NX7+6d83munY9FYfNMO9dPJmUInpkzq733uy70DkKo27bRWnkf+q5VhNao08Ukz3M+zafT2W6/P5lX56fL3GJmMYZwcrpYrzfXN2ub5V0XHpyfkMrev19d362ttdaa3/+9r5vedV3vuu6Tx+fGGq31xdn56zdvFrPKkK7DoSozY6ma5FYvUNsYousbjeJD2KzWhuBsuTg7v3x3fT2psuV80ja1kNhcRcfzkzkS7euWAEkjM3dtV5WF83Fzu86Mnjx5HEMsipyIJjM5Ozs5HFpjsq7vN9vdbD4jTV3XEWBXdyhemOs+/Pb998k8qt7tuz7E4Nbb3fbQNn2oivz8/Lxpao6sM+VcXUwzMvLg7KTzHMU/Pj27OD1/+ORB17vDoWnXG9K677qLi+X1zd1kUiXM8f3VOxHVdq0Lsnr9/tknTy4fXLx03rn+t7/50eT5Z19+dvf+rqn7zf3dYl7ll2ftYT+p9JdfPTw/XcxmE1KUZdY5H7pDVpXaWK2pyPT6/jZ/+HBa5n/yp3+idPaP//CLuAzzxfzzz57udw1iLHIKgW/ev/3ii88x9l988gwVff+bV88uH/zqh/dXN5unjy5Xq01wLqvKhw8vlDJVNZvevLK6ODs7y4vqH//hH6rClIXtOvf02acPH5wH75QxxmTG6KZtokCqLrvOsSRXU1FKRcb71SaE2DX19tA6Hw71YbNttDbT2eJwOITgLy4WzGCNjcG3XVtNShHZH/YAUHcemEgdlDbOhc1+N6smV7dr73pNetd0CQA6nc+jwc653GpFFAJ3rUPBLLMAUBb5fFLmVrPErncMUrd91/YKoenC3f0GURSYGD2DCJpHD0+fPJxn1n71k58WWda73jv3/up6vW071z0SDszORefDl1/9BCV2dfvsk2da0d39PSEWmV1Mq7veBQ6zrAJhrQqWcHq2qOtud2iWi6XGz2az5at3txcXpw/Oz4qy2m/unj9/6QP/5Kuvehf+6i//Xeh7rc1mvY8CwEFp3TbtbDpRRF3rEeDxw4ve+Xfvwq5u6y4UmTkcOu/l/Gzpu+bQeWHZrO4nk6rrgjXZod6fTsso0Had97tOco4pKQgIQWvddf5+s18up73ryqpSCl5++xJJsaa77VZiTO1RCLB3bedNZgxh7J0XgMwaS2itObBHBgG+3xyEOcvNRTmJoW7bdmK1i+rf/vWviuw3pPTFybwq7H5f51lWFnleFHmm3t/c5zY/meN8WnofCSizhXPdbn+YTsrM0rPHJ+/vNj1DpvW26UhTYbMU7mq1diEGH1I/BkBBRIlEjkHAGkPaRGZDaLVO1s8C4oJTZBSRSAgRfBRA7GNg4LIoQow+htBL03WIZLrWGBNjjNH3QDbmWqncaAEVGEOITet30iPsjEKtdZ5pRIgCgIRKxQhd5/q+V0qVhRWRvvfGmvlsaozuvRMBY7RSepjcaKW11lpZY5EQURmbiQjHCEMFwonLfwzKTjIxAYhxqAtijOncT3V8yjn1ERAw+cIORBYcwRIUgtGWAYYo6mRLDAAsSePCIXCIMiSzA4iIVqiV6lzo+iAyUJ5h7BARQYTjyE0axptjWyejh4VIyg6WD70ew5jViQCoFIIk19QIIaYSaXjxmKAhDpy4EUmpxMJMJhvii5BkKHXAJ0Y3MQgMIRMsQzODEFg0ymDHgRAlcuR5VUxyc7ttPBAp7F0EIGHPUYCToS73PeeZMWqgw8QYiVARZdaG4LxnGMJ2kygfYuQQmUavgCFaBiT4oJXOMiUcgESTzq2dloaZm87v265t+832YHOjkYAZES9PZkrRd2/utTEXy3zf9lpp8KEyxFG61iFSmZtMqbpznXfBRx49H50LClGhQkLv3cH1h9p1XZ+XmYxqKB+iNdponVkNMVqlz5aT9bYOQba77m7bolZN0y6m1aMHS2E+mZppVTx/u3l4VkWOnuVsURlDu0MEIBWcsVAV+cv32yhQWakQc0Nag1KgFfRIvYuK8HZTqyw3mdlutpnVB8+fFKYsdO+jIeqj9C7uar+u3XKaLyrTdHFz6BaTbFaQImChyAmPSxNQsAhRiEEQSSsCiJDIE0mTApgIDUBkYBjqIAgL4v/pX//3IygyzIUDMyHqIbp2wBSYQeGQTIRH4VLKT0GwRqefFwVEJMbUnmDgZJKEyeY5BYGlboGZkxLlCJAkukdCK4OAGfAmpsEVP9kPDUQeQLBaZUYdpX2Q5BIIySCGRuBEfezQfexzEtYjjEgCSdciyV1soAGJCKeUpaHjh5EuMfZ9A5YNHywnPhBtRtDzo498jC4fvdZwwHU+0IrGr0Y8krjGz4OBE5KgIh6T346yIxwEOKhIBCAyAggikFLBs1LkIx/qpmuaru1mk1Ijn8ynHvWbN+8+e/xgebpYrTZvru6ci9er3fcv3v3868+vV3uO/Ohiudp3h/0B2DMjCAuHajJBo4tMV5muyqp1YbXZzAplbBFDEGCltNFktOIYmrbPs7xtax+hbX1Z2JT6mRu8WrUXZ7NpYULos7z4vZ/91GQ2RqGRy5Bu8GCXIglwJBHxMYqAMKd+2/mYfMVo9FFOlDQE8N6T0lrppMIk+vDODG0sDuFRAqATMyg5bQsYnViOfLzPiQGkE1QDACI+cOcCIhitE8rW9m67qw91++Or95tt27ROgLXWfWAO/tHZdD6rvv788cX5kpEUofPsfCBCawaBFiKmM09rpRVqAhEcTLgEdrVPvMQQuOl8iOBCTMjsfFKGEJIH4f2m8T4Fi4NWSjiWuX18Pj1fVgxyu66bPpwvKh/lftP2vQ+RQ3CkzbQs17sDAE4mFQjc3m2qqgjBQ4xCUuSFIbpZb4sy08YyQNN0SqkYAgIqpUJkaxQBCksEBpYYY0z0NEAfYqapzI2P0rqASL3rk/58nItIAkhAgIWZAYiEEuJFGoljJE0SOenUACAwAAgJAGlGNMBepCwygxwlTHNSCMtJ0UfY1H3TukPdA2hJVlyIniMBalI2M7f3B6XwdFH5wCGyVpgYoCEE5oBDKicPK/G4pjFttAk7H+YeKZosJvLrOOACBCIS4bRJMgiBWKURse2cEClNEtkQxRgAYBwgs1KoCEIMPoJSlBmlifreB44Aaph5DVsXJGhbG4MooQ+IKhUuSiXTJUSAkHiLw+6YihUwKhUOwoIsUY3cnwRuHvMPPyKojS8rKR9ZkgMBjBGe6euG+dXAt0FIIFqMiFDmxprkWo1IOG65w2Y3fPYQ20WJYDSQ9I6bOeFYEGLvXNs2ipRSxBwRlNFGgnv77uputZ5W5f1qHUI8OV2CMIIkBpl3XimczabTadXWNQLmuem7frdvCCTL87pp0yHKcbAgRFTWGABxIShltFKI1PUhxlDmmgacGTsniEKEbc9aYRSJLIrA+9B0zhjtvHchRI6EaHNrFAUfvAsKMSsyrZVRBkWargOkLLNIgsxa67QrIVHf98gMpKIPIQSTZ8aqpm7azpssK6xShG3vpplVGpVWddNBmtsoY6IEdqDIBx+Cn+a5NurkdFZldr+v+z70PgTPF8vp/W5fVsWnjx+8v7662e5KmxOpN3e3lbF5Vl7d3EOMqLQQWqVC9HXrWBAVWYRZkTPCoW4KbXVudnWLgJMis0SN8/uun+QZAIpgbuj8ZDGfTv7x1887H1lEG907nxs9mZZGUVVkWW7L3BDwdt9c3a27ppcopKDMtTDnmS1z7fpemJfLuQ/OEPkYnj0+e3p5ZjQJRE1mMpncb7Ys4F2Yz2bed/frrQB57xbzeZbnjx+c57k9e3BelNXu/na9O1RlZQiiiLbaZvaH71+cni4JYLff53l2crIsJ7PI0h22zoW66e5u7k5OFg8ePtQ6Iwz7/d4FnkwmDAjERVFs71d9X5+cLHObBcCucwaAkPZtd397n/DUSVWW1WS33XS9U6SyLNPG9r1ruzbP8yy3Zbm8fn99e3tdVtXbq/fbQ7fbH5wPk6pCRBbsPb+7Xd2sNnXTnc7nTy7PNSnh2LrOB0c2KzPbu44yXaA+X14szxcAZJRZrVZX728fXJxqow/75ocfX1ZldX52ohW8vbq+vDj97uVbUubsZHq3vlnv2qb2+92ubbt//s/+5A9//+t//9d/43tHyEYTSlxMM43Qd31VlWdni+Vi9tVPv8y1jsFdvXvTtmG+WBDwbFophcoWp8sFEX73/YtXL19fPDhz3r95ez0pq9fv37vePX30QERu7reI8j//n/1XpIv/+Ff//up+1/T+z/70j9ar7cPLBw8uL/7u7/8eyZwsF5u7a9Kmc867cHJ6uVhMiX3X98+ePtTcPH7ydDpfGk3OOR/YaC0gMQalFAiEyJIsGBQe6pYU3t6tb++3XbNHEM9K62xX7+tDu9/u1vu9D7HMirzIk4jC2pwA7+7XddP3ziNA5zwp7YMv8/z99artuyLP2q5r206RenB+kja3LMuU1pOyqMpCEUUWY6jMbeIvA0pdtyxwfbfiwMkSdXeot7u9MZlW8u79bVXYv/jPfr8s8q6t//xf/Gcn80kUWG/uf/Obb1+/Wx/q9o9+74vpdFrvN59/+SUSVZlB5mI61RIZsHZxMa3q/fbN2yvXd7PlMrj25OyBcyG4OssyH+X+fkMiIcqLN1dN75fzybyaLJan6/X2h+c/CjCyFEVhrfrtb1/ZTJ+dLZ1zhbXT2aQs7WI6ny6W9WH797/6/uXbm+V8um/9+/erKjenJ5Myz2/u1v/0D7959fpN3cflrBSO2uS3d2tTZLd3W0W6KGzv/M3qsG86pTSRNkZHkSIvdvs2xW+HyCGytSqEYHQGWiQARralFcHgAwiXufnq08t93dyvD9aoqjDecxOCjxx6LnMTYgwSc23qzrNCA9C6PstyDhwhBo4P5rPZtAiBtYKocDmdBOebOkwn08zaQ930rq+qShE0Td270IbohkmMMtoGxw6wNDpZlhhSifDiBDdNW5hMUgsGwsIIyirjRZA50xhE0hHAIzFcRIzWqT4JIVhrBOh+tYtH61MERWiMRlI+RuGYPIPKPM+MIUWGiEV4kJghsxilQCSCJJcUAECiGEUpmhZWQDoXvOsVQlHkIYS6dQBgTGrCUgAuI6G1RilNhNba+WJpM2uz3BgbYui6LsYQYkwepixgbGaNQULnAyBWRUkDGT8iUAxhyFHHccwEMvKpB6UYocwKjYRdH0IUoxSANH3wATwLMIQQnY9HakKKS+OY6EHDKEpGkcc4Mh5cfmQc//OxuxyHU8dSNg2yEwWGk31xAm7S/D6yIkoT3g8Vz8i3TnaKNFAJUtspSCp19iwcoigiQozCiZs/0AyGmHDRRAKEwoooxICAmhTHQAo/v1xEzy/v9ozk+jgON1GEky+wj0KEMfqyKL13LEzHPC9NPgSFmrSKHAgAiISFj/UuQOKJBR9RpMyLWWW96w5tUEpnhgAgCAigIDZ1oxCVsTFEo5AIzhbF08vpYd/tghya7k9/8ogM+sDRhxBl7+K724NKfkjC+0PbuCjMCIhaaUUhcGAmQE40bSRGQUFjFCoSkegDCCXvywR1MfNiPmXhum5RkEWysnDOtc1Ba0uarFXLqlrXjVI4K/IsU0lcdjrPdofGKA0xHHre9fHRspjkuDs0SmeKUGsUxLp13nGI0nRBETZ9SEV4BP1wYZQiBhVcf79zBxe7pu09T8siy7TzwTmf5+bTi4lCZlTb2mvCItPHB8UzxCidD8xyMsutHtJ4AdBHFmGjhr7XBREAqxEA8F/93/6HEVgY5qkcOSYxiyQJ2IBdiIBVw09L8WdEyXUJblf7d+/vQwzTSbWcTYoit0bjELaVOBxpu0kIDmtCToFKSag5yEoHZxQiYKHUm6mBJpTAGnFeIktmSClKWI8PQUS01kgUObHO0qrllG+CyQdjlHSNcE/CWSEwJgwLRu5gammYObnJyNFZGY79Bh5xnHTBH6E/x70AR5RHPsKFBAYPqsFta3zhvwMAfWBjfcwyGD+I4zc6fl9JHJQPf4aRRUZIZIy6udu2bV+33XazK61aTMrlcp5lhkAA6Pmrt5Oy+uKLJ13f3m8OTdPvtod902ttnferbW21VsbEGCpDs0mV51nf1kRgsuLm5i6G/u37u0mVnZ2dvr1Z//STyx9e3SBKWdg8y7787PGzpw9F5PXr93erXVmY++0hRDlbzkOMP758f76c9pG/+vQJB3d+ttCK3ry/p6w4OT8zCWiko4BmQDITaoMgqeIxRgNg8C5G7qMASwiREYWj97Ht+tX9qm07ARQWm2WzWXV6tpwUJYcgSEnwEnxIKg+jVaaJCVAkyywIRO/j4N+MRifCCDdd75wHkd2h7jp3fbeLzESQZ3ZW5YfGv7tZN20fArddT0rnNhcQH5wA5VZrkMhQZDRfThbL2cXZsirySZmHKCF4SqAkQGL3cBRFaA0mFqfRtNo2IXBiYPbOE6kYpW47TeSBDWkCCcK7xilSmVYCQAiTPIsiTdMBcpaZIs93hxoIyizfNy5xbhXRfJI/ezDb1d3tvr+YGK3Uj1cbGcRHGLzLy1Lb/LDdFZmuO6eLnBT1beecFwFFiEyMYBUJs4+stQKAPkQQ0UQhMCnSCso8O7R977wxJnk7Q3IpEgbEyMnjHAQgRkCVwrdBhFOst9IpzUKIEAWDgAjr0XslM+gjn8yySaEmRb6c2EmuFelfPn/XOrhdtV3gtEeBMAMKkPeREASh66PRlFsiIqMUoNRtD4hRkEFUWoyECWhJKPMotxykf2lPGAVegx17kn+TQo7DbKdrOcRorUqQ5aTMo8hqu9eKiNTJrJhl+nrTOGFFoJAUMYhoY5EwMoPIJLPbuqtdoIHpxwDAoNIeduSQ5doYhXXvkuFXZE5IzXEbHJ3IEu7PHFNiwuhtNgL0wmyMSYEEvyNLHUqNAc9OO89Hu2RiVn7Y3gZ4D5VAkmtxGtApRVoNtvRKUXL7StUXYjLzG75p0rshkQgwc9d1h7pGEUOyur+/X2+01oAUvC+MnpZZ5/zd3gNgIjexoEgcvKWS4VNKnVDqdJ5H37sgniFGzo0uiqztPUfOjE5YWIJUrTEiHGKMaexMgIg+sNFKGwJmjtK54ENQpAAxhKAUBZYYOQ4YHLnoODIkcrXWmIYwkqjt6eVHAKyKAgEF/Hw66Tq/PFmEGF+8eK2UbnuX/JXqpimKyliV3kNDJISN85kmmxklaBSpTGuF3vveucBghaLE2vXns8oD5JqCd0WZV0V+2DfMPJtMbtc7DnHbNCLxy2ePdvVh2zmOqt4dWokTq63W60NdktLGeBHf9zpTLOx6ZgQCMUoJSGVNYbKD6xVR3fsY4zTLNIGLgVmKPEsBu0rhYlqu9m616Q71tppOMk3ny8oo9fp6FWMUjjbLNeH+0HTOGa004XRSEKnbu02emSwzCNIcmqLKrSHFoSqyp0/PH53PJ2VOhE3TRcGr67uyKF69X6/W27/4Z9903l/d7feH5tHFSdvHV++u/+Kf/2GRl7//s6+W87zx0Ox2fXvwiQlvimqag8Tduv3uhx+sNZ89ezJbzLQla/N6syumk9vrm7u79XRaXT58OJ9OX799sz80n33+hTH5anXn+0OR513XIclysSzLkiXGKMDQOXd7c9c0TZZZAKmqEoA2m63JMgRUWittjbWH/f7m5lYZ6wJ+//zVbr/rO359vdq3DYJiYaVUFBBhQtV2HhCsNkWWNXUzn80uz2eTwjLIdlcbpHJWzubTUk/BwNnp7HDoNcF+fwhBdKYYZLPZO+d/fPE6M+YPf/7163c3yYEVEb756Ze/+NWvX7x519XOZvnZ+eJP/uCb92/frnf7zXoLvldKPXnycJKrw6EBCHluzk5OHlycPnr66OLiQoO0rr5+/ebqzc10ufzk2RPXtT++eP57X//k8eNHN5vt+9fvgPBv/uE3N/e7aVm+vb7//JNHv/fTT398+fY3P1551/zZH/3k/e12eXb6F3/xX/zNf/o7ZcxsuQwuut7d3N2ttxur5PGTTyDG6/dvXZSffPGZQAgxPLy88O3+Z1//rJyeayXCnLrZrut65/MsIyLvHRIR6d55rRUL14ft9d196/rt/baYLAD16n51v97sdvV+326b9lA3J5OqqCpA9L4XEaOzu9UmBm5dRATvfd25PDcxxN2u9eCd88iASAzCHLWhMs8fnF8s5tNZlZ+fLowx1hhmZo6KABB750MIh0P79t1tBG66tmvdyWIWOO4Pbb09ZBZ+/+tPLs9PNVGW6S8+/+zJk0eur99eX9/f3xV2/u1vf1jd3/70yy8mlXryyeeTadnXa2AgbR9ePpxO8l3dHA5tWWQC+uWLHzebzclyKaCAMpIA3EeB3X7X9875+Pbm7upu64N/cnby9NGzF6++m0xKJJ0ZCyJN297cbln47GT6D795vpxOizzzEp49vFhOZ0WpyE5+/PH1arN98fa+btyksEVhpmX23evbB6ezi0X18no9KfJZoUubCSEQ7g7u5dU9IuZ5sWv6tulIKSREIEUUOYUgAUeOwr0XALHGGG0iBE3q0fmybpumDxL4fDERYWsoy0zvPTBaow5td7uuda5JYFbk+9YZTYCxbmOMiAqUQlJcZmUM6KPbN11mtDGKQwSjlaEnJ/PYBQBazmdDqA2wtcYYs9/Xh7qNoJq+M0r1fTAKtTFxSB6Ixmgk3TQt+9gyRY6DlwEiSCJiaCAiQKv1er+vskxp1Xb9wBMBWUwnSKi10oT7pm96h4nYobQ2Ggd58kcj7oHTArm16YTPjBYRZs6MaZoayShtWJKdK+VWE4EPIbnHBh8DQ1XYIqPdoZ1VBTA3LoQQCNEanWgy6ZSMLDH60iKiQqVOF7PJpLrfHO7X+xCj896HEBmYWWmdZVmRWR982/dnJyeLxbyqyuADC18+fJTQkDRsS8gRxCgSETF1hQhSGBUB2t6nKtRqfWj6pg/pjirAmAQjKfgMMOW1k8KYGBAgyTYEUDCRpvE4w4eBzTOyKJItjox/hI+auNRIp7Z3JGcM00KtCDH9XBilDMICR1vrY0soIJpIBBOdAolSbZZsiSVGSuo0BYiQIrQIFYJYhT4yR86NKnOFgG3rex+7GC6Xs8vF4tcvrzrmGMUqpVRSkVPrnIvRKJNo2jgUbxB5kK0JSAoaSuUfS8oKQYWAID5EArAaJrm2CvogRDrPMhelbvtU6cjQ43GSNCKJRP79ry6fXk5/eHkTma63h3mZTasClTw5nwUf9238xYvbrvfzqtwfaou478OQ6K4oPWYswJEVomfWhAFAIhulosTkVSJRNKmh9iUkohCZkFAjs0gQABk8lySEKCYzwoxMUYFRSAhFlilrHp0Ui8oKkFL0fr1TRA+X1aIyVZ4QE0zIZ+dCah5ApO6CC3I6s5GZkGL0hzb2ke7W233tQem+7X1INhSQZ1YhhBg+e7QsMmw6vl7XpdVIRERakyKqu4AIHPh2uy+L/Mn5tMxV7yKzKEIfWUC0IkIMkQGgyFSmUe/qwCJDUlK6OkrIjRCRVqiUckyasA/BAGlNgIgsnYu+dV3XX91s3r1fueCR0N5uEURn2cMHp08fnROpZALKIXH8IE1SBgYfSuQx6jwh1mqUvKAkXpxWihAQIhIyAxFYQyoNillCCAnKZhFk6fuQGAFEZI0eE8SGFoRgdA8jiMwxAgD0LgCA0cl5YHDr4IEEeAR9ByIfAEoyYk3N3uCgOizxtMyFjxLWJHuFjzeARLuCxGthgNEn6CNy0NGJfthdeCRDwO9iQ4MT8cA5QFIDOpRsX1mw7vrg2ldv7168vZ2U1ir16Hzx8OIkt1QURd12bduu1pvHj84vzk/atr1fbVOysMnMDCnZSf3ks1nakHKjs8wojQhS5ovdbtu2oagKYfNplmVWI6lMqz7I0ycPCPji4vThw3PgoI0WkCdPzy8fnt6v9ybPidThUCulp5NKSIXeb3Z7qxEJPvvs2WJe/vLbH1bX8fGzJx9uCoAiBQjBBwFQiEToQuy6/nZTW22mZeaSo5BEF+J6e2jqQ+z7m/vNZnsoinxX911bn57MH/Qn9X7fNj0AolKTWZXn5Xy5rIoMBJQirdCDWKL3N2sRPFvOmq6PgTfbfdv3rvdd5/ZNt97sm85V5QQBVttdnmWKoHdea50S0/IsU5rLUoFwVer1tmEirah3oVxUBtEQbLf1oe7evb3RRj9+/OjsdJ5bQyjOhd57kaT6xqbzh44Dg1G66bq299MiB5QQ/Omscl7e1zUI9D7YquybNtMqhIhEeZ557yKLJnIxhiheoKxK0vrV1U2e55nV9+s9aFMWWd+1nlmabnrI2hCLzN7tu2luUJum60sDiGCNsgqbrj1d5J8+nL+8Wr9f1YCU1qkHmc9nbdexcyywXFTn8/z2frfromL0PkYAQYjCznHdBWuVNsqFYJWKAhyiIgIiYR5OPEQEQgpKEQP03mdGjUdqUpumcQ8ic1L8gci0MIVVRtPpvOi9n1aKtAThq9VaW2WYXYyTMksUwD4kDh8Oh5lwlitBaHxQyEFxMjiCxBcWiUnTzgPCm+CAQZpJCCKRhz9JIvoKExIlkbpwCMBBrNE+hGVlGwd+3GVa13/x8OSLh/P9oau73rnOVuXlabHed70PPnSexdi80DjJ1eoQV4eudzFEHnyeABBViCIsWiEjMrNSCoZITQaQGH0cN7SEQaTtRWSQQDLHGBmBeNQFR2ZmGbAIweCZAOIIWQ/IOYIg8rjRoUQ9oGRpcz5ugXgsgwRYhnqW0m4rkopCgOT6HyIRDqpVAQDQirRCa41WihRxCJvN5rDf1/Vhu9s75wkwN6lAVr7zfWCjlGP/vmm6ADEplRUBA4+of7IYyzNSWiUZ6bZxs8JOMrrbtQzkRPV1H4MYrSMq51yMUSnKs6xzTiFrnREQCMcYOEKeWRapW9f13vUeEbTCmPxukPrALCP9G3F4NnSa2iXOLVMi0Skaij8chn/aYGYyDvH67v52vVHKHOrOWBtiJCKlSBmdZSrLddt0Xddpa7I8zzLTO9/u6yrLnWgjfabJ+aCVKgy5vnMg1iJiDC4EUQyw3tW3t5sA/Oj8rG2c6/pOgsn1+Wz26uamCxx6bOtd1BSbNjtb3Gz3U22YJWLMESYnk6ZvoYfC6n19mM0WUaIWKFXWBt+0nVaWkIUBEWfT7LA/MGKRmfWu9SxRuPW+yrMnF+X9NgYfvnxy+cVnz16+fNW1zWJWLWfT1+/3vYjSWCobfASiQ+uAxWYWUFbrXZVnHiQTsQDzxezifLacFvNJqU3x6u3bbV2/vlqtNs1ykted+9M/+Lp1slrvD7v25m4LEWyeXZ4urt69+8Of/2y2OF1t70PfrDbr7bZ+8vSZVXB1fX/YSW7tu6vb7WZ/8eCsaTofsPP+wdlsNp04Fw/7Q8pOcl3//c39f/zHbx+en1w+qF/fvb65vcqLbDGdZtberdbexfli0Xf9tMyycuoZtdaTqmCREOHqelXv67Isc0CtVF5OAbGpW6Wzopr/1d/8/bfP37Q9t877LogQKOy7Jsustrjft5pUWWSPTk9OTxZ5ZgVEIfkQXOhJ69xmwcfgw+nidDqd9M4ZBfv9gUg9f31ltXnx+q1nmZST+806hth0jVksCOHh5dnd/c4oFZlfP399d7NRYGeTbLGY3Nzc/PVft32ze/Dg8tmjBxg7oymzKnTubDEhkizPHj48PztZiutXN+/PT89m5bxdeueJffuLX3773Yt31uC+6f8U9eT0ZDorheXxw4svPnny6vW7yz/47MsvPrm92+7aNi/wJ59//uDhuc5y7+Xf/Xf/5u3N+tsfXlVF/tMvny5PlkoR9/3J5eVsMstz+8XnnyqU5HHLHKuqKnILaLbr+5PFNAiKSOzdvm7LovDRRRcIdQhsVHQ+OA/OOe9je+gjorbF/f3msD90vWs69+2PrzKbNa3f1geF2PRhfairshDm3q20Mm3d+sjaqLbrWufqjpGhd1FANJDNbO98DAEJDWWK9Mls8uzRRV5YIowhGpMxU4iQWx0ZfeAUB9I550KIIaBQ8NHHsF1to++/+fKTp08fo7AWdL3r+vbmZjWdTiXEtq6f//jmflNnubm5v1uvWMCeXZypuM/ymWsP13jddSdZrm7v7pr9Lsvym9vbzODdzVXTd/PFHBiLYuq8rNb7EGPf+82+ud/tmaVu37re55k6Obm8vt7st5sQnPMxMnvnsnJxMp1tdod92yOhc+8Kc/P5p49PTrO2ae7u14wQhHeH2nlTN92sLLaHer/f1Q77KHc7dk336aPzLNPni5ljXN2v2fd10xqVBY4Ksfc+s5km5Z0nQ4BogFQOu8aDD8tp4RwbjbvD3ihdaSWGZpNsuZg1dXO/2dVtu5xPPnn25Obu9tA554ExOs/CEQCX1RSpaw59UzfnpwuP0rZNbsvowjSztY/OewjxdGZaxy+v1w/mVdv5Lm7nVb6cT4h013dd2zvm1vfTsrQmC85nk8wFTxgLo/OqrDvPgsgSgwgp9qFPpiACxhoQ9CKh70BQKwWIWhutNRJora01dd0gUojcdS6KKNJFZk7ns7YPgVkhDaLpoVmn43wVE2WaU3CNtC4MuUAYtM17753rCYUBrDZ1H7Qiq1QIERCnpQGizabuWowSVnteFFluCIzxIfY+xMBCFDkkRkxm7GJW7Nu+78N+f/Cu3+z77aFNrRaDCswiir141x72rVIEil69vX5zdZ1nFhhicLdXVyfLuc0zQcWCgirEmJczra1ARIiRxXufpj7pOBVmYFZjmodn8cnUkgEhAUCQdP2oDAhGYSIcPXgwUZFhqKNEUuBMgrYAQmJYDenWMkrSRhIEHK1COaZRGSTxuzCDVgqPvOaxyfzQhyKxcELqkkwHhJG0UeRD5JjKWFGoCCRyNEonmhaLEEYEBiACyjPz6YPqfF5+/2Z96wIQFUX++LTabNY9O2OyjNBo9DEKg1KAAArUUJnQYJudjm8Q8T6IgFbACglJk0IQlKgIjSIE9hy1VrlVpDHLzEWVn84ndR++f7vpgwiIUggsShEARY6ZJmbIMq2I93WHpBJKXjfObpoIjMCns9wofWj6+33jAjRtf7EoCaHzIbMaQQyp1g8FZAgxpQoRkAhEjjHNSAmjMMSQ8DYSFAatFXOa87Ik85iBUK8Io++DMiqwR9GOQSETu7m1r96tN6XtARVIkdvb/f5uVVeFvVxWk9Jq4mcXk23tDy0vZ4X3PjBPSmM0TXJDCps+HBoJELdNi0rNp2az6xxLAEjB9cw9IVqtNnt3vwldH2oX68Y554FUkRlEqtseCas8Q9B323Zfu2mVWau885qIVMqoSaIaTEkshVX6/bpLnDFEUCiAoIgSxUYJCtD95vDD66tJZoLzVtHZchZFdof+bnvoOuddJMQ8M0RUZkaRHJquad2rNzevXr8vinw2m5ydLKaTUinFmBy4JXIMkUWAWUJkQLHGeO/BaqVQGJz3PopWQwiOQjAaEzKqhsI9xijGJCGOIOB633cuFpn2gZWChNZmBpQSFGTgwcRXRAQic+9iaiqSy9jR08rHYatIl4cIQim6HrRGSq3OILNIDwcrSqFPAxkoNRWY4CcZ9GdH4k9g+eCmAQMejCO7DwCOFKbUKrFAFMmNQsLRzwdwUD1A8kpjkc4lNxlp2r7v/evr1f16Z4QbD08enCnk02X17OkDiU4QXAx1XYcYPvn08aHu/vpvflnkWVVNurb1kbUyYFECn8xKpQxLyDNjjDUKdru90eZ+tbtf72IUJBNi7wPb3K42u7YP+7Z/9GCRZzYvbdu1WaZ9jNGHfb0vivzsbNY0zvukv8WqLIio752wB8D1ZtN1hyePLh9env34w8u+XhfzExf45uau93HX9FqrsiwjQ9e51+9uXrx+x4G3u32R5w8uLzgGCP1sWp3OJ0ZB6Drnw2I+Wc4n9+ttZmA5XYpI2xyev1iz77Ms63qvtSqKrJgtL85PF4s5KNv2/rvvf0Thd+/viyyfzgoBIMCm9S4KAIbI1lijjCIOLCBclaXRiggTOR8J8iJPQ5jAbJXyLMoojWQMsRIfojHUO2+zQkE0Gre1++63z18V+WJe5VaT4GbfhBgnkzLLs5OTU0SIgb3v93WbZ1kffB+jRCz7eLerxWYqhnbfR2mNJhejDxEF27plEFBEkXeHRgBI63rXIFFms8SeiyLow95HACGNde++bW7JEAh44fKAzy4WPNUPltXtpul6dzLPbu5rTWQNFpkqrC4yjUpvdow+dJ0LUQgxSIjRzyczH4vcw+v3G0RiwCgyUmvF+8ACWquIEFlQBEBCFEISCMnTJcX3MYiwEEFgIQFBIQFmBgCtVGTxgVViTgmHKC54RfbNXX1oujy3CPTuevtmXZeWSOBnn54VViuC1a778f0eEBMNaNRxMyAlwWDvGAkYkKMQAlFCLiTlSiWhVWL30miSk9Z6ZFGICslzZBGROIaJCwMDgjIYKZyelNfrZlEahRCi9K5fFOaf/PGTl+/Xf/vD3fXBPz7J/uDLp6tt8+bmsG18ZuGkUvsm7BsvAq3zCfxlwfPpZH3YEwgqiCJGGVQ6xigMbYxJe562KEXIknwBB0xcERqiGGOMjElKnxCZKCAjqRgHGCgyRxnDCgEo/d1gwZPoWcIJUuKICEhKE6bJWOJgpsFV8rIafQwHRmSQgRwdkxOWICmFABwjc3Qubrc7BEZ2u9Wqbfso0nmOgposC9cuSmLQIPoYe+dRkQwOAg6NCZEJkdP1ICIAA7gYWcAYbbTug9vVXGZWae06x97pYYrhUdgoNgrzzAoKdyGzxhgkRUqka8GzRO87H7q2CyFqazFRt5RKsjgEUSzpoBWJMEy7RiRNeLD+AmEORiX7Z1VoIqUa7+63+/2hQxRFvjBirEFERYo0GSJTVlohcSwyI4IusHdRaQQRFAoAGQYOYb54IJGbdueDI6vzGPeHxs7Ktut3h94ovT3Uy9nk5u5Q2aZpWlY0q+yssKtd7ftgRLVdV8wK9v3po+XtoVvkWU5YB5cj+TQdcoKCttAzPXOx14JFXnbge9cbpVFD7GVaFYtZzr7XRp8sZ9td0/Y9KIVAvfO5VrPFxLkun5dt73/56+8Wk/yrJ4uLk4Ux2as313lZVEZHAICs6cNm31prUPjQtBylWOjQ+vOJnVT5+dni9GSB4u43+9dXr97erwpDfcdVlv/ZP/lZkWd13f/48s3d5vD66l4rsnn2pMqrIvv5H/x+c7j/2//0l8vl6et3r//NX/7iy2eP6qZ7++7qy2cPNpGJVNd25xfLm5vrrj2cnizOLx9lmTVav3jxarevh+cc69/88LLt2rZrXrx43fsIQn3r9th3OtzdrazRddNN54vt7rDIJlpr4dDUtbKTumnXq13TtCEIar13bWBigevrm9vV+uZ+t9o0wdPh0LbOCwNyAK0BVfCCwE8vLmxmz07mJ4sJIRmj06BcG9M7v95uQGA+nyuFZVWAyH63fXN1c3E6Xx/a6/vdpKp6jnVTv7+979vOmkwp2m6b/b69vbu9XTcpvOzibLHb75q2f/jgVEk8nc36w2FSVufLucTe2nJSFQqxrkU4VNW0LHPX9z/88KLILSn88fsff/azr59+9uXDhxfr1fvJuo6gjJJ3N+v/8Ne/+K/+5b9wAeYnZ1+a/O2bd4T42aePp5X5q79+1buw2u4nRfET/Tmq+vl3zx8+fnT5+Nm+Zq1wuXzwq29/+81Xn/75n/+Z1rlwLIpiMimVwgg0ye1mvbq+vjs/OynzUBXaZjp48M4zQNd7EA7Aoe9d30NyTmDsXbi5vV2vtttd3bsIRMH7uvV3m4PVat+4vmPXub73r/bXRVYoa/p+y8xWK9aorGnrJvoQgu/aPjITKe8Dh1hV+XRW+fX2Yn7StO10Uj19eP700XlK6bZWD/NOxDzPhDEGl1rDH1+82h+6PMtE1GxWtV3jQ/z6q0+XU/3w8uSwW7158+7rr76+ur5i4kcX3ds3r9+++f7Rs2dPzESpt7eb7e16o4B1bhn6q7fvfv77f/TqzSub4bRYnp2eXl+/rJvWhUiIMJt2nQeGvg+b+9vJdJFNlm/fv98eWkG9aZrWe9cHQvx1++rzR4/U7W2M/PL1a9LaZJnvegR49+7d+fl5DO52e8jzoszK/f7wH/72N588Onl8ebGr66tXdy5ETeBYVps6+Pj5k5OyNO2b9f7QHvrYtP56/2ZSFv/yz84sQWZNjOxcVCW3zhXWIFHrnEIc7XMZtSq06gMLS+96TQoE6rbL8swAZUrv6xYkhhCX82oxLWP0N7dXyujFPH93fUBAyaHMS+f79aENHJWR6XyiFUyK4mrlQnRVbgi4yLKbbXO6mJZVRm278+F2vy9t5oK8u9+vt4eT+eR2t53l5W7fiKYqk7prQORsWa53PQApgkrrk/MlGfv9y1frXZ1lRmkDvSBints8yxCg641DH6Nnga53mdYI5INHAESVCN1NHwWAkDQiAnR9HxLRhYgAhUgRCkcRIdLMATgi6RQVTshGm8S1B5YYgzJFVWjv+6RTkuACI1pb912IgQUU2qcPz54+Otlt9wgUJTofiRBZrFU2emZCNLva+RiUUk3n7hCRSBvDAt4Lkc4y6330MTAPgWI8OmaEEDAiKSUivWcQQKH3t5vb++1xJggAWqtqMpmdXppiQkqR0oA6AoOwMHMQGmZegqASvSfIYC3CMDCJUhnjfEw6rxAjDNmskHguIbBO+iZmAogsUXBI6hBMGjJIbSAOEdsD1weEWRIXPKVnJYenwBziB9a2orFkABm+/INDCaBIiFEpjcjsISfVgUMClXyYRRRKjGH0aUQPkhs8n2cEUObm4Um1O/Tvt3VUcjopv3q4wOBe3u0BTa4wy3Trg9bKRd51PlEOIgOhIKIiGllJAgBWU4ptQUAW8dEnnMwzNCEoEaV05wVBtDagLBDt6/Z2fdgduggEiBLFpJwNAED0ErXWRWbazjsffYjzaT4p8/tdvbg4eXl19x9++WY6K9a71jOZItu1vc3sfdNpQJUpF6PR6uFJ+f7+0LHEEJ0LSisXRaEMBThgiiGDwS4m2TIBc/QBFQG7wACkBi1dYmVpoyAG71NKeZSIEbFz/bSy82l5u9oBSpVnIUjXeqWU99w0vu36LDNNH2OU1a67ujsQwXJWLCp0Pmz2XVnYQ8c+xBAhMh4aPy3yPDfrpmNAYFEAvQ8I8OC0Kgu6Xcc8Ny5C17vORSDxgRGAAaNnH9rcWABqAm9vd2fziSJcN63I4BMfmZM9ohqCciInnYUhUloJQBt4W3fM7Hq33+022xqUbuAwyxUrer66FzS1E9SaAHOri0x7H/q+NwrAamMzYFBEisN+c7i/3755cz2bTbI819Ym5cLDB+dVmQszMChj902z2dWL6eTQ++B6YfY+MEuW20lZCCALZ6KTGCjxxUDYaJ0MofvA20N/tWqsNZWLTecRKWX5GAVaYWbIaJVbTrLDxGC0RiU3MYGUHc4yuGoPLllpNA6IMXISICDEEKT3bHRCgFERRh/7MORbI+EgEGAhSo6oohGiSGABEaUw7cgpWHhwZhrdXAfNqACOSk5SWiFEHxsXUsNFAIqIiJj50PTrbb3e7pvOOc8EyALeh7ZtmfSsyE/m5Wezcl5meZlbo3zfeOeR9Gp90zT9w0cXtzfr+9VuMl1UVRFThABijGxslhfUdq1isZoI2Ch5f31f75vr2831/e5+s3/2+OHnz07brt/XrukDCp8uZ2VuD/vGaKU1oXDXOg6iiQwZrWyeaQ4BgbEqQgham965STUhkbpugOHXv/7h/Gzx5Zef/v4ffPOPf/cPty+/1zZXrr95e/fmrjl0rm07Y/Jd3ax320yblMC829dt+ypXeHF5TiJaXKbser+/uLzI8vz589e7uosM99uGRPLz+SdPzi7PTozRzHK/3vZ9WG0Of/P6rQhomx96H6OYzGilfeDb1QFBSBtCVNoCUm4xBgGkyWTSOZcIdSzMQj4EJChsAZTQQwFhRhWGLE8yWuelCd75GI3NQLjIlQAQQZXZ1vnb202Zmaq03vVNH9u2jzHer/bL08VysYDIuc1ikIajMGRKvXq/8sxFSTFGIGIOnQOOMdFZRYbA7CgcmUmRBI4xBBZthgiDITRaKR8CeaUQWRiFYuREQ3AhfPPpaWHU3ebgvNcal4vy6mb9/C2ua7dclMtJtqv77KS8WTWb7YEQtAIk3bT9L368ZlSbfcNxOCESMiCISSNFCkUkBE4Pv4ueUBFy5JTpGUMUJJRknSKAAhFHmRUSgvjIKQu8DxEQNIJW1PsQYny/bYDwbl0/+vJx28H6xb3Lzcmk2Dc9IpxM7SeX07pz13uXjm4gwiFRS2KM6fQUQIGjZSCnxSejKnbwA0QBZhAxSrECx2y1RQEfXOroCYAVJ5goRnQhEihW2gfICDOtTma59+F6fXh73Xa9u7w8Oz+dbnc9kj5ZVA9PqvPl5O3N3fm89AH//bdXLuV9cEQEZhCOd7sNqOQBDogYOOTaKKPavmdBIkofH/msQkCMydkNk09yFAEkAeQYhySKkWE4JMnhUJCpwUNu+HIE4UHMm9LbBBmICFEMkQuBtJZUXQGEKEmMzICAlNylE+qRyiAlmNRVUdh3ffTr0HUSA0Ls2tYHDyLReW0MKdP52DmnkDDLnHMcg1IqisQoIUoUSLxzIGUURY5AFNPwbkC6ZGQdU6reFBlEjID7phXGIrNaU25JAQuzNgrx/8vUfy1blmXpmdgQc84ltjraRaiMSFFZEtUoAASabSTNaMYLPgafgU/CZ+FFs40kYA020F0ECqiszEoVyvU5ftRWS805BC/W8WreRFiYhUW4+9l7rTnH+P/vQyBA16uzRZ1S2yZw/P7H6+1QRNTFkLFp60gVEE2i4BTQCVwAGMxcHdlcwQSQRR0JZiPqE13AFBECxfndFMCPOR+naRyyiphDUyUicCeRwkwxRnMpYu65FAwURM3c6hhiDMM4BiZDSGgVxwKw2++AXLx0RaZDSYwG3veTih/6sY5VStEQUgyHYxerdLqpa7I31w8ier5cFhVap8M4NjFyistKLzcbJ6n6vs8yDKN3QxFYrioALDLVzHVsQhN0kLqqhpzJPFZpLFN3tMDU1HVbpXTGD7t+UosVu9j9w6Hvh5OT9W7Ih9vHKoS7ewDysdwfu+wcTbUQHLI0TETEkcecGXzRVkXLyTKdLlNbh5fPNz/75icq+D/8x7+/fdyP/fH5s/PLs/Wqll988/mLFxf/8Js/vnn7QQEfj+N6uRhKcddA/vlnzwLBf/z7P64WdZHysB3u92X69vWPr17//OuvFJq3b18t2rqum4eHLoYmhLppljIecl39/u13f//b32cFQlqvum56s91uf/bFZ6frxeHQ7Q+7KY+gfnomueQ6BQcuBjfv3y0Xi3o1vXn143H3eH71Yrvb/cM//qOBPzu/OvSDkatB1+djP/z45vqPr94fusnN1HzKUEQ4sJhBEWcgCi+vLn/xzeefcBJW1zUiMbOKAlDdLM4A8pT3w1BExPD12+uHx+3D474UvXvct83i+s379WbVVu1xP7lj01aE/Ob16z+s2mkcbu7umcLZ6Uk/9ZtNCybdfrdp6rNVu35xenKyAPTN2UVbp7HvP7x/99Ovf9K0i7vbhxRTlrw/dtvtoWkTB3r37s397Y0b/OW/+uce7taLiuv6/3B5tf14f/327f5w/OyrL2Watofj+fkpGfzs538W4urf/bv/0K/Wi0V1PPT313eff3ZxenH+9vrWSP7kF9+s2viTz1/89V/92ers/PV3319dXSLAeHxMTYNePjz2Q7YwZ65jMrbX79/13aDmJetudxj6sa4XOZcP1+9jSCHxoes+fHzMUw4hiPrtwzaEuFw0/ShvPty7+74b+m5o67qYutnjoY8xEmNTJY1VJBukjCUzATkVMQEFU1ctoi02/Tj2w/jZi8uffH7VNunsdJNSWi5bcDDzpqkdWTVPQz8OJefxME0fbu5LsZJLXS1KGadpJMDzdfvycpEYF4t1XaUP727uPt4+Ho+776Y/fPvd5cny+dWzocvHo3bHoe9HFWe329vtcnnyj9++5lD30/R46Kb8x0R4ebr8Z3/11zfXjw/319tdlyd112f62dnZC9Ppuz/+8WG7L8ZjHh0oQDxOGQBGkP/Pf/3dxcnyT7/5fLk+efvhBlKpAp227Xa/7/uhaap1kX0/3e/2L843H/7wKl+d16m6OF3W7x8eH6eTdZPFRXEy+Phw+OX62fOLVariq9tj30vkkFX/X//f354sq7bi+92Q1TFLTAxouTgSFZHo1NYJ3Nx1mJyQIFhR0ad6NhK4gGLxAFBtlsOwL2X65Z/89HjoRWScSh4ykavAVKRNdLpaPByOCBw4qZVskPf9Isa+FIph0aZl21DgIcvt/Y6IQFWdUu0vL5e74/Td62vNWTncbg9NTKPKvhunLEj84WY7mSDz7aG72x9W7RIIJrHlqgZAAqxiHIrkXEw0pYQuhCA2v5ehlMnN1ZUIJU9NVU0lm4qYrZeNu+2O3RPjAzHEVFcJi6gTUUDUIoII6HM6AUPkqRQ3iyESATEFRLE8VwQIPHAYJgHCIpOqAQIi3u+7wzhdnCxDwCpyUzebTVos6r4fu24oEtyNCFNM+84RCYxToBjD3C0ai3TTJCpNUzeYDt1gYGDoPoM4gPGJ1gxu2Z5CKIQw84lx/nGas1ou2747hBCRY4gx1m2qWwq1AVV1Dcjz5mkGSIsr+JO63gARLTHZvIF0L2aRkAnEfV72qYOqAkDRmcjpWS0QgXtRQyRzd5sJrE8bL8Sng+78w3Kfg+EopuA0g64JMYQAADq32Gaf8qwEdwdHUf+ERSUkYGQiUDdGU5BQITjO+jF3I5gXaHNkAN2MgQh80/L5umambTc2MVy0DTrcP24PYzEKEVkN9kMGmCW2+Kka/yS0fXI3AMK8QWRExDlL9Ql8BDD/jxEATRy0OBIeJxmm493uKfE0I2QAgBFiCCWXqoovTpf32+Mhm3BeRjJDc+vGUhSKw2Dw9m6/aBt1m0YjjIFnXAS5GDhmcEYEpFH00E9VpOMwzfd6VAuIhuCmrk/9Gxd1xE+lPDdxJHQAMZjPvO7+dCxD4JnW9Ml5XVSfQJhMd/s+9lM/TutFc+hyAa1C6IdR3Zq6JsJDP/3tbz4EJgAsqsx40ZUqhjGXIZdlFTerxfypS4SLpuqGae4qqrqBG5GbE2I/lanIbPWZE/uzz07U0H2WgU5ZiigSOyA67Y9DUycDVHOTObiPgs5IFN3UQlZwU3eTEPoxH7t+fzhOU87jRKYxIACu2zYinJ+umjr0x3joxipRCKGpUoi8O/au8OxiA2BdNwAnAyBwrqs1s6qP4n0vXXc8jnmcphjo5m772fOr5aJW0Szy5u37/X67ahoEH4ZRcj4/XW3WSwdsm8rcxWy9XJyfnVRNMwqaQxYylSJjzjqM5dCP7oA43YIzsQMQz7ohZ6JAyDFUgVLAEDgyznQJJkiMITARuIPqLHv6RGgGIEQxHyadNwlZtKhFJkIGIHclBCbMRbN6jAEdVK2YGwDPYyBAY9BPjB8TD4SOWgwYnwbRTxHBWVI+l2sRxb2IdYfueBz6fuj7IWcxVQSIMbrjWCRnBaDAAZ7k8R5jXNbVatHEGNual8uawYpln0yFUoxTsbu76zyV09OTw+5gUhaLhbsfD91q3ZydrcYx992YEucpgxmzBIqllN/+4fUPb+9fXCyv77dN0/zVZ1en6zqXad9PIna/Gy5OFotF/HD7+PJy0w95MYzK3HVTXdebdcsEjM5VWhBT13NWaqIZMMKUp8DUtlXO0+PueHN/uL65f/nisllv+Dj2/SDqVUifn4cfP9zv1Q0l5xJCbJeL5xfnkQBMT1aLz15efri+DugK+Pb6brloF8v25vZ2KIIAifHlxfn5+bptwslmjeDjMDCHfqz6YaybtF6299tjGQcA5BgdSczEFCkEDqIGjhUCkgNYCCHPVGMOVQjDOA1Z6irxTLEgMpMmhbk6BwAKOOP6EAHB2jqOxdAtBSTmaZpWbUoxUfKhG80tl1JKyeKLpmLm/X4/Dd3Y98vNSZNqCBCKuOgoRRECs0xTCAxEZk+KhJmebp+ivVnNwdjA0e2pyGhMoGqzT+9pCulmSCqOLsTsxZXg+zcP+2Ner6rr2/3pqhGFdx/3xcPrj4e2SYs6NInr0Lz5uHt+2f7rv3z5/buHb988MtthVOuymAKH4DRH3RD+V+PdXG6ZJ2gAOMMJYUb9zIHZp5cnzS82BnQ3BSQgcSeEZYp9FjGPRPOTzAJ1U+lyQdAQgiO8udsTwqJZIKEamOObu/3nsE6RVg08P60PQ85K7m4IM30cyZForurMkZS5senuBITEQ84I9KRzh0+VIsYi2d0pMIBNUsANkaoYK+bjlBHRDUzRAR2hG0o3ZAR/d3+cxNbLGjCE1L7bFay7n71YVV9s2rra7adcyqE7nC6bk/X6N9/ddNkCsxYFQEN/CpQQo9HMfWMiJBjKREjEPNOmZyI6Aqh+Uh8CBOYsquJG6k++PSOkT/zE+cRI7jYnMGf3GPisAnlKMoqD+1NxeO69B56DzSBqYJBFn7q0iESMTxIvkzKZKZiWKYvIPEyyIiKSpwHdS55KmQjRkWZeQUyREbJoKG42igoAVCn2gw7jRDi/7sHdkTEQmUhRq5sG0SPz3IxznSviHpialAITAYiUORyr4HNrsqoSEmyWcdUmERvGMk7jcrU4P12N3dCP07E73D/KoZdjhlwKuNZVWi7apopWdMiTq025zGTNecU0STEvc7YTyR0sIgcKsxDQTBCAQ8xSEEDNtqUUEzQHx8gcIg/TBArVJsXAkVBc85RjDO4auTp0Q12HZRNTDOggTClGZhARdEgcei1lyjnnUsxUjQCRdn12hyZWFIODgdnl6TIymQlovjtMYy5tqh6nSd1SCFWK22PXT3ndNh8e7lIIZmpgq6qagoNMTaJJ8yrFSEnJZcpNqqZSag5NSr1MNM0DQKxTmnJetNXLy9PrfWeqX39+OfTd42HcHQaV3KYqzw0Ix7v9SATgGGOawNHcQAdTMw+EdaC25qZKqzYGxsvz9cXZyetXr159fKRqdbWWjvTZZnG+Oa2fpZefX12/v/72x3fqUKewqtL2OG6P0+Vm+bOvP/vy8+f/+Ve/+/vfvtqsF0xkBvtuDF7/yU9+/uzy8vrm49vr22Ga2iZtVmeBcb1eAvD28fHhcfif/8tvdv2ohv1UhpzN7WrVni7Wv/rtH86WVTdlN3x2fv7wuHvY7szk48PDs6uL7tA9f55e/S9/a+anJ+vb249v374zg30/7nY/Pr96/ubde1X76U9/ut11P7657obRzMdpAkAxQgYzgUiMKCqX52dff3E1lalOFTMFDgjGEZkghGRmRcscwtsfu49329dwU6QUsViH7999SIn63XS6XMQAH+4ev/z8iw8f3m0fHzeb0yrFUkrTts+vUEQQQYtYKQxQR7y6WJ5vlhz56tlp3x3Xi9Tt96vFYvWzb7755utvfvbLQ9f9/re/7Q67V6/fUEgrbp9fXZyer5fLdcn+4cc3McDf//b72/vHX/7Zz6uqKn0+OTm10i+XSVRffbg5u1zf3d+fnLV/+hdf/qT/ar1axghXV3/J5Lvt48U6kp+sW/7ln//8pz8t07E/3N80Dd3evBbzZrE6b+eECAWKV1fnwPFh23X9/tWPr969fz8ViRz6MddV9bg7jlNOsbr++JCLjkVmGlcTk7kX58fHwzBkp1Cy7bojIADhkCfAJyuoYUHzqUwG+zY1RVXN5h2bmGhRMI8xbVZtZN4smi+eX27W7ecvr4gDASwWbdM0MYbj4Vim0i7D/W5vRn3XD6Xc3G/fvb9eLzefn5xNuXR91w3DsqkXtboPw4S7x/vNyen52fru9naY8i9/9iUj3T9+3O6PhNvl8uzYDaa+6/pEOH68N6OT9em7D9d9tkMWEbEiu8PI8Q8ppNdvr50hpgBqBh7jT0x6cB3HwpEICdyblPogx36ITOixFCSK28PjpMCTBaQsOk56+3hoK85FmrqKkZ89v7y+uRn7ThxFbLOoj8MY2QKlw5CJqC9y97gTlT/92Z+sF8ftrqsTpxAM6cP98eqkQjAx9QlXDT8/q+93ed+rI5SiIxZETDEge5ZZkoLgFmMkCjkLOEwqIdTtsgrx5M3rd2UakExE6rpigDqyx0DzATuFs/Xy+m57tmqduB+zGbhrE0MxPYx5EgWjKhBVacja1DUzD+avbm8I8OxyaQKRGQmYkQQmVeZUJI+GboBqY7FuGO72Q4xhs1gwIPEM9ZM6MDC56zRNzERYCFzMU5PyJA6CAIDkYBWxhqhSYoz9MLlZSFUg7LqhaPZh6kJoq1CFaKoKbmIhcM5KAOZQTMxgVBmnEpjqqgqMhFBKFinEbrEKAQtIlSJ5GsZsZsu6KQ5vPh6k5Bg5Rk6Bv3px+uJyE8Nit+sPx24sxcDVTYpyYEcvkmdsxWQmaqraj0NdN1UK7tMnzTEyoRmIP1WdgFDMaD6wlALghEAGBmAGGVBUq1jQzRyzzDV0qpp2uV5TSE3TVFXb5RyZKCSOkZncMbgC+swRVcBAzECOwIjoYHOR3sEdGNHBi9qMZi2iTBQYRdQcaG5CIZipqBECGhLPx565M+/m/qTKAg/EgE5oohIDG4CaOpCaR3y6dTh8mic5oDsCijoizJt7NIxIAl7UGckQXZ9gPERAhH/986vLdYOgp6tqsWivzpc/vrn9+Dj85vV2203Pz1cvTheBMRdDgBj4/jB0YyFEJJoz7E/LPpqZ1rBe1O5wGEZCNHWcccyfqoXMFBD0aadlxPhkTHMUJSR4OmI6OpqY/Iuffml5ur4vibmtwtefn6+Wze9+uHlzdzyOEzEvl6vHhwdv21QFLeWT0BbFdL47zAg1A0TEXTdpUSMgZp/p1yIwD6rUns6fBkhugGYzHGeGgpCJUEhzfWO2sxGymrspEgkZmRLgvJ9jolxUxJB4f+wI2YklFwAsCkUGDhyZ6xSGqQBiYOr7PNY1IZYiLnDQvDsOQ85VCk8Ubp+t87PBmudjNQB+fOjrFJj5cT+pGgciwqIOxKLFEaqYTDFLQSD3QoBFvIEQAk1F2xRnLVKTIjEKICOGu5trlzLnVqaiKtbU9bJuJoBVTYHxdttd39/XkY9DmXI5WbdDNjVLwRZOC4wU60W0OsUU+XS1vN8eK0QRPRyPm7YJkfMgBDwVIQqLRXLTaZDvv3uVyMeSd4dusVwQxf3u4IAMumjSokl1oiqGw2HPzIHg8eP+4eZd3Sw41RnYKCEnxECMZZIsPqtwZ02SwywGnHMG4O4cA5gRERO52cylj4HaKjSJ2yakQMxkMwPjn9pyDmPWLAaA84Vm3oQPY/nUCAVAIKIqBXiS+7maqVmqAhHFmWECoOpZNBdNgetEjE/KG3+anWIRmbKMY77dHhhx3/VdNxRRAHaDtoqB0lxlyIObO8UKWV2kjtTUSXRywDGLg1IIhj6ZH2+3JZddl3Mu4zieny5P1u317eOiqe4PYzeMz883HKdVUy2aOsXqeBjNbL1eZimLZcsDpkBi/rtv35rT//n/+DdNHQ6HoW3rvju62X/4u2/fXG/Xy/qLFxdSSgx8ulk5hg83WwRJIajCyWaFCOvNIg/T1B1Ts6ibSvXg7pFouWkORwPww3b38ouX5xcnH6/v+qH84bt3TZXGXMZp6rtRkY7juO8PYz+aGsdwtm6/eH62XjXLpr44O6mb9OrHV23NVV2/+3D72WeX33z1xYcPN4fDsD8MifHli8uT081qXaNZP3SmGZ2qOjVNIHYvcrJpdv045OJAJgUcYohVXc2hCVEgorFMorpoUpWiE7vNSkrXMhLH+dlNxKUUQoiBhpzV/GTVTJOkmOoUVQXcUqpHySaCMRyHMaWYmMQNAVaLOBYBg1TVvYxFZL1cUIyS882Hj4+Pu3axqJq6apbEgYnrkAjATbPIXIY0B5rTF2amCvNLBgwJkOaGkjPNFJgnkJXD06U5RlZXQvVPtMKpCAW+fexv930g2vcylt3DYaCQAmM/jD+8l3/xy89uH3Yxxa+enxy6wZFCjL1IQEYzc4zOakYMruZPqTdzAJ2Z6e5iNudv/smPSYQw/+vMCD5/KRlBAHhuzSCaqYgxkZjO30SOQQGOWYlYZJ7QY4Hw7e2hjA9cpQLw2GU1iLEyp35SJNos6n0vk6oILGIUs2POdapEVcEIYe59zQk+RDAzApp74HO+FwERWMVNKQRyxSEXQiBkBKoCtTEMWYesTEjEiG7uhFTFmIuI5K6fvn55+pOr5ceHw1jcFP7Df/3u84vVn/30RVbZbOqXlw1T/HB9X3P5yfni2w8HRseASDxjh1TBdP4MoIObmBggzr9mBIdAZO5ZZtghAmAgKmpzwnE+mDxZG1SRnBCRAqiZCTxhjOYpIajD3IJTc33ikQE6JGYxVXczYAKaG+9A9qkk56bTsOuPh5JHKXkah/m48E9mEjFQA5zPc2BFnXgO75RZn6GTITgYCIijL5vkDlmkH8u8qirmiDgrD4oUAKxSFClFPaWIgetAM+uaMARidA3oRZUDESMRBkA3W9YRCAFtGHoGa9t2uQpnp4uL09X1zf1vv78eivK81TFgJHKvU5UCu2hv1g9DnQK4ADgzDTkzRzPXIipKzA5oWtxNQdxneLmqlBQrcy9lkmxDLkREACFw1SR3KyJtXRHgMA1tTIBgqjGSWZmlF00dqooOh8NquSyii0VbRR7yWAXaP+4mohjJtYgYIZ6drBxs+3hMi4DuHEJGH4cCXI3TNBoQ4rHPpUhA7qcyK0bFNQQ6Wy6KqUJ5fnrW9X0GOVsuEeGx75Km89X65vF+3a72/cAYVk0lpg0HIqPoLHZxsWmaKsWYcxGRFENdk96VZV0RwbH4UCCwIvAkSoEqj8dxYmbJkwMUiwGJkMRNVa2UKvJXzzZt4o93j/0YY6THw/HX375JFT07P41Tx6uaIFchnp6e5/HxzY9vt/tjUbm8OP/Fl89/8/sftn3/+eXp6boF98Ph8MO7O+T6uOvOL04x4jnR1dkaPN8+3N98/DCVPOQcI7d1dXa2qCP/8Y+/bZerV+9evfrwcLpZ9WO+eTyKlKapPtzv7x//UdWabz7re5ty2e7fmdvMRk0pvb/53Xpxer/97t3Hm2++fPH7P35/dbau2sa6Uia/e9zePuxWi1WK6b/8w29vd8d+GBlpKJk42HxxgtIwMwVziBjrFMdxXC6WTFzXVWDc7ToWWi6XRadhGEII45RTqqpUr5o6VdV21x0Pu37qCYOpiejFyZm4dN2h67dnZ8sQfbVs6vjZ9c3dYhEvz046lTwNTfDn5yftF1f/m3/+lycni6tnzz9+/MhMZWrBS6TVetWuN4uTTfXv/+1/z6m9ONtMnb94cfXdj2/+23/1zxbr5eXVaR7G1z++qps6RFo0iwfY7x72h+OxSuny8vz5l1/d3N78m3/93/zqH/5RAQ6HfhiPnOIPv/vh8+fPv/ry8uzq4tBrszz56qcVcThdNX2W775/hSJfffnV5bMXiHz/+Hh38/b1j4+qrYgy534chr7/cP1+3w/7fR84HLuxChEID53dP/b7Y2fmai5m/TAgwsfH3bpdAmKfs6htj30RdeP1YtEPU9PWZvJ4OCJAW1VjyQQE4HnKkn3GsAUOzljGkijUTXKH5aJVK1UVXl5tNut13TQpJjRLMc5HzMWiHoeJiTiESNXD4363P5pZmaZX9z/GUF1enJ2frna7PVj+/s0D4IvPr56VMqYUX7x8USV6/eb28eHhi89enmzO7h8+Prs8e/n82eP24dd/+LGfxBKz+tvr67/85S+mcffw5uM42qTe9+PHuzLm/C/+8pftcvH25hZDOGnb6/ttnr7lSMeur+swiqWYAtNiEcF0HKfIuNyEcej7rmuaBDsjJAQQ1MEKx+hgRBAC5yETxb/4i19+ePv+7bsPHx+37z5uDYwjHw99CqEUfbZpz05XjGG7PaB5ZNSSFdSAL1Z1AIAY21pEHcAQfdGwAE2TIgITTSKLuuqmyd2nMQeqYyBXnUSZua2TGQyjfP/jh59+/eyXf/K1Slm2zXxR/MmXLz/c3HdZJJdgJlIi0flqaV6enZ+/v7ld1W2Xs6MnQ3Uz02Vd3++Pk+gXz86zqIo5oBibCaAVRTOJAc09Ms3SpBQCukoBCLisGnIfsjjgMIwANBxyxWGxbLyoO1hKkkck6o6lqRs1GIeRiNRmn7kRY/biQAq+jDj02g0lGQBCUS1zm08nydOLs01xy1OJIQzTVCZlIkVlREI2NQdIKQQC0fKptgxAIZciItUM4lFBhGwmOc+d11hVZi5GMulv/vju9n67WbWl6CQihv04C2yR0LMRuNeBEhOYizpRCIyHwwFx9m1YICii/Tzm/4TfQcLAM0eZnjRDRIGpzOpcQAMwiHVFCC6jqimD63h46PfqjoQxNiaiplVK7fokMHOq1qdXwGF2qvIMNEZAB/WZufOEHQB3mfXTn0gdDuDujIgAhDSffBC84lAnzqrqc8z6aa35TyzXuS5m4IiQxWZBRIhzogicOasSftIoIZtbYApEqvOeyw3ZDAmhoBJSJM6izEjIcwmAGFX1u3cftaxCivtxwpvtKLrtpr7I1dmybetnm/rFaTuVYq6JqRvlZq/A/qTqNlA1h//VuE1IVaTjkMXmogCCzenzOTXvYhpm5Qg6IqgaETsYMdAsuAhsjgC2zyVRVCmvb7eYqmUKixTe3e9/+M2P3Sh1XcdU5aK7XV/X7TDlbiwxfrKHgxHh3CggBHQDAycsAJHJRQ1msLnZkyYPEOET3dJnDdW8eYtEbi6m6GhZnRgcTP0J3wlgZoyIjsUs8LwW/YQKnhlPFNxdxdTRXc0AiUzNyKcsM+IaIKjbYRi2RzOzyJRFwYFD6AadfdMIEFNEQhAFcXzKhZCjjznHGGJiHWSYhAMTUREFgBQTEqYQHJ1D6IfigIGjGlYpOZR9NyECxYjFSz+qCpqFZBMHEvVePBJXFRO6a2lqMsTXH/e7LvdZVEWKgAPAHRHqTHgAB/CmqqoqEkJAuLg4q2Ioks9PTy7Ozo/H/X4YxdzRipsREqq6igIDTkWLelVVJRcHOFs1OZeT5UKkHLouBLx73K7a2l2ZwmLRllzycBgf74pANi8QuFo6IIWUmkXdtghE7uouojQTKmbyNEJwd1NEYiR1IyECZMJDhyHQok51oipxZDIHm1flSEWtFPdPrB5wB4Iyf4jMCedxpy2amULvgUncA8Cyif0ohy6b2aRainb9WIrO7bO2qRZNxU/MVQN3Jjgc+zxNx24c55QQUAihjimEYKYuE9KTqTGGMGWfxikwLZpQN+k4Tvt+HIbCFJFRtQMHKWoqDuCGqkLEr99t//GP72eIqZmnGN5e79W1rdKU8y++/vxys2CGjbTMYfScc+G2zrl89cXzZV0H8tubj+1idbJevHv74bffXfdjcYCmrr98cbrdd3UVNifrh+3x9KQdR7l8eXZyuo6Bq8Sicnp2msd8d3dPzHVVzwICJjg5WSCG9aIFp0D8k598sd0ebu/uP3z8OE7FkYap3Nxvd/seAS5WzXJZUwwpJfTy6tXr8/Oz3eFwPB6Y+LPnVz/88O75i7NnF6fffvvjj+9uPt4d6hBefnHZtk3bRgY7HA+XF2vwJOLTON3f7999PIYA60W1bJM55KJFhNwVPcTU1DUSxsCSi3mIzAYwDCPPdVjEfhjV3c0WVSuiojJfK1Mdmja9/XAvVp+drY7HvoicrFs3QYMvrlZYKnGhqt7vBwMwLQhYL2pAVLEQ6WRVjZMdhrGuU1VXAFByeeg/BqZUt6ld1E0DXAVEDhxiJHB5wrC4mFUxziVgmkOZxD4DeokpsIp8wvASfnot+Zy4ZTRDABjmPAsiRgJzEdvJOAdyBdUJZdJu0t+8un/YdynGj7vhYd+hB3VT80UTqI7jbigmwGii+IkCPFNm5jH6vKB44pCJAZo7iDgR2dOuBBx8xvUREyGoKTgQQG9l5vlnESAyR1NFdJtheXN93SEE5ogANGUfYazb5ofrxxendRW5rasX52u37XHyy/VyGHIWDxynUhBJBRznBmh0t3lSgRgwGBOaPclKAUnV3DwyFbEiliIhuagzQ1HvPQM6MTJDIBdVdyBGRwEUIM+mD7vdSbv6xRcbZn57u1u1dawX3308pCB5Ot4YZoMmhVhX+5uHEMGRDdDlUxRHPTKLOziozGEcBAZVY5rRaRaJEHmW0Is+kbYZIAYSVwJANP0n8j0AmHBgMhJ1U3CYOYnobjrHFhECQRWoL1ndIgZgZ0ebAYIc4kxMK1PuuzIeh+4wDqO5O7C5A7J5MHAMcwHWOECcAURujA6k+KSJhJlgMuv/Ko6jSAzYBszqvT2101MI9MlsMhOdiMBdUQERp5Jr4FSFSEiAYjoMw2rRDpM8BZ3syfLmgMdhKKptCrnow667OJ0AcL2oj/vDd28fDpOGGFUhoKO7uCLCKFJMmcjApEhE8tnrUQBwniQzYEAm9dlQ8fQrZC2BGRGZWUpmxK7LOaujBzIMwQllTp85kDsSBqSpiIETQhFztchBXBldi6ujmzM7kTapQo83t4+InMcxYmLmRZuaFDWPKcUXzzZjlyPRKEXcEbyoRkQDUDVVYURgqiIjgrkCAaFXTfRRi+btsF+1aZUWqr7bD9tD9+L0vG0Xw9t3Z+0mEj27PHn3/gNjWLZLqUByblKsU2BABAwMdRVEShPDNy9PH3b9P35/7QZEoMXXi3oo2vWlTiGr1ikQc1Hrjv1J22AkNMAi60VV1YEZKECzqj8+Hk4WaZjGtqkXTTweOjcS16pKX33+ogqSHbtjvz0euyLt8fjd92+2w7hcxFUVTjdts2h+9+33x+Pxyxdnf/azr65vbsHlJ5+/QDQp8vjwOGaJIZyvq7apU4I89QYnr693b29eSZb1yQlTGKc5oAndYVAtVazXi/r1x91+f5ikIEEbaxE5P10vR311vXt+Fsc87o7HMpWT1er88vnf/foP3775gDAf8+Dj48cqNcdpLCYEYQ54qs4kdWdHCmEYp8Thmy+ePbs4WS2XdarVzQy4qhZLPOwPHfSllH6c+pwvzy5KkZzz5mS13R8/3t0hsRRJKY7jzGnU47E7P704dgMjnJ2ePTw8JK7ahtuKjvudiFydrT57drlYNE0dzs7OfvrNV8fueH52sXt8OF2frtfp++/eHg5HArk8P//qm6/++//n/7huFiLyxRefL5vF/f3uYXf88O59JPi3//FXt9vDmKf1avXf/cu/qFL4n79998XnV+eXl/1kuR8Px/1Pvvjif/gf/8N/9y+XY9/fPd5UVXAtRaCJYXW1uH041gkpBkOFcvjm68/LJHW7urx4yYF22/sQcPuYHx8fxzyo2TB+tz/0x66r2+U4TqX4/tCDu6gXUVVQm3cQ4dD3BFRE+mnME2aTYSqEWFUBHQmsSQnA+74DRDEzEVdDplwKugGQ+Zy1h5kWD4axCqfnJ4GQAA99YeZZVDpL38HniGIgwpwtVZU6nJ5clFxWy8Vxv+eUnl08k3wN4KsmrVfNsua+76p06oZm5qB/+N0/PH/2eTdOjjbJcLd7+Przl69f/6H+/DNwCMHXy0XX72NIdVXt9od/+P3v//V/8+dXl1c/vr7+4eY+UrPb5iqkFJtunBxoVVXIuD/mbrg3ESb+67/85b/721+frTeLtnp4OPz8i2c3t4+q8PnnV4mv7h4ed/0QORLBw66jDt0hAFarijntuj4L/Nd/+F1d8ThN+2F49/FhyNqksGnT8836sZNxHP/5X/xUtBwPxxTjzcP92bJuE7+92xva1UlzvmnvHvfrOmQgVL3bDshcxImoTpwCi8iYs6oj+XpZI1EWA6ZZnt0PfSByjIcuf//j9dnJ4sXl+XLRRub9/ihip5t2KVBKedzu6rB4d3P92dXzqRA6uGTmRcU4qIaAy7oSgV4mrmgRcb7uHIaxCoExxFT3uSuT1O1SVedtXAysrlZyVcWKKNZJ3B/uj06RmbthEjNirAisTC8ur4ZcDn2fEosZh4oRnbSKPGUdJw2B3MGKAz2pdnryGLiqMNUAjgSkwIkY0XIp+773p9Of5TIFDmJGzMScczFzIgbwbprEPOccA6cQXYwJQ6oBqOjcalZEl9liOJu5EU0NANfrTS5yOA5Tkbv9eLpefLo0++wHc4ODSmR2n4Vi1qaUCPucAaiIMCM4IAHj0ynRwXS2ReBcVYfZBso0kwUMiRFJRIWA0YsKAVYpAIYizjDzAfOyjogh5zLu7xAgq+8f72LVLNanVbsiCsxoTogI6ALzXmr2ls7Eoae/A8xbfvi0XbOKAxL3eaDggJB1rFNCCrmomOOsgSZiZjORAqJAPv/nyRxKNiICNEYPkeb1Y2Ri8AzuLqU84QLNHR2JoA6BEPtcIlFVVcdxMnImQsBSLDG7h+8/7MR8uVxuj8MwZUACZHJoYnjsp7v9MQV6drZYtAkmiYyT8BOpFxCJ3H22eDtgJN5188iV1QDQwFFg7r3hXFub1JkcyInYyVVVzSIQB/5kYcI8r/4C//qH+/00tnU6XTZ9kTcfdoqUUmLAomAOgTEXTYFmV+PsdzL/VMz7BM8OTGpWdLa6wLxYFDOAWY6LM45jNr7MvzFmUDVCYARwV/eAyG4FjCObagjU1tHU+ymbGQO4gYKBAxBwoMDB/AkjQwSoVvyJ7uJARd3KPIy2cRqQ6H7XozsSxycyA7HPHCUCQCLMWZg5pSiqs6NWREQEEPo8OWCdqlQlNatTjAkOw9RPEkII7MhR3VVxyjmwmjuHaGrH4xFEEWzrBibrVYslh1EhIRdzcxXzFAORA+HdsWx3fdeNUymz+SvEGJ7CYM7gIobgRJCLjFkcLEa+/eEtMy0W9f1xOl+vF3XVT+MwTqlp1KDkIaRIHNgtkmvRRZ3GLMeha6qwqOPlSYtozHVTJVX9eH94cbkIAY/HIQ/D6emyqZa73fFx1z8+doe+U9uOUwGEuqnWJ6eL1SaEilNKqQJ/mkxzYC15mEqMAdQLCBKSzr9yMzNCGCZJM8UXAdFmbTyDT+O4PxyL2mLRMHERnUNiKYS2XdZ15RDMdd/bmIuoTlKOw3SyagHo99+/B3FDcFc3R6LAzEwAcDiMDq6lzCf++XGQYjQ1NVhWyQAMnMnIxUUBPDCqKREg81hkKqrmjqHLeP14GCbJo5h5SEVEpJS5bvr0CZy51KJTmdwsclBwQCy59ONIiF03OtCvfvc6Jair+Nnl6dXlpq7Spm2HaWrqsGiaN++uF1Vq6mazbm8/3h+6bI6bZduPhQDev7+9vDidpnEYd0TYNk0KoV40KcWmSg7Kcz4y8cnp+rDvbm4fzjZrM+n7oW3buoknZ5fH7nB4PPz9b1/ffLz78PFxmAozZvNcoEy2butmUaVIGNJU7Nh3HKhqlvvjeDwOjHBxthrH8fPPzs/OTn77x1e3d1sH/url5fnp4uziNE8joxLys8vT775/5w7nF2fv3t/uu+k4lpzL7jAyo5nPrJB+nEKRUpQDN01LSExcEy+alCVXXGVVdFRARZiyn6xjW4fDcXZGYWpSEWjb8OzyLGepI7fnm93jtoxD01bPTs92h8eXzy+I+f7xgdo4qRc3dZumsY5pRGSmyLBsmv2Yx2kiZGJOiB4iozBot308PNxVdYVMxCE1q9Q07kg016ph7rBkEYcZeDa/yhgA1WwOSgAhEz+9V81FhSmYqZsBoDlECubmRQCAOSBAk1I/Sp77koAI+OZmRwTHQRFsbnWZeQQcxjGkWMcw2RzomAHnbj5PfJ58mYZIxPOLhp4IwXPAB3yGxczLlvlJSTQj8RISE40i82uzyMy7faIdIxjhPPsCAJdsT70zc0fXbFnsw3ZwAKTupIkXm/avzjbvbg8PuZjKMiVRVNAUuagjQBVADIYCrp4SByAGLEgz7sHMECGGoIijamIy15Jhjs1kBTUs6gTuBuqQQkox9NM0ZVHzEJiZv7/ZPx77n748BYSHo0Bs/vhhK+aL6DmX/SRnm2XNWMbx2elGbPx4GAkcwZEBzU/behAzKW6KAOu2HtUmkbnPNZYy98lD4ESgrg5QBcpSwK18YiLYvDKap4Fzp12sSpFoRiOBz1Z4clBg8rkROUhWwMBczNUwxhgZxqHP/eRaNI+W+5KnKZtCAAxIoOYIoCLFnBDUAdyZnyb49qmZTTCHn2cZJ7obILjCaAWJKkI37SctYoxeBQoBpsnmbtATjBGckeZoWENADGZSFAFpP+bAdOjHfioxRnUnwLaKkQnAFVyK3k9CgOs23jwcS/Y6dczEqYomTAguZnOWC+b4muhcOyamOIg4eBZVLU1KfelTTCHSWDRPMifdqhCZ0UwAHYnUbJoyAhQVJ6tSNEAj4hDAzGyuJAKaAcD8LWSmKkQHzV4qwFJy29ZhtSDQVcPZyvGwm8R70Spqu6iaRTpZLV21Ox4dHE26Y3HHokrECbErUsYcqiQmgamu6rvDsU3VskmDqdo8Y8VxzDIpR3LUbdeHwftuHDICBUTcH7ZtSv00UMRxOqhKqivB7C6iOkwlF0nMgXy1rBM3w1Bu7rZD1ocuq1hg7odCrkwGgBF0zA7uFUGqg4guUqgSAcE4ebWsipSAsD90Jmkstqzj5clivVpOU7l93E6SG67U7E9/8QuAvN92768fQyQRPx6mdQiri9MQ+e5+t+/G5vHxzWtGwD//2fOLi6sq1o93N6enVzc3H8ixXS6HsatjPDtZH7rRwWKk1Xr98ebDH368PztZXb5orh/60ruZmwKhuzkBuev20D0e9jEQE5l6b8JE/TB896q/Oww536mplvLF5dXPf/aT3/zx9T989waJTBSBiHAq5XFQNXOzwGCmrkYAQBiYCDkEerk+OT9dffb8smrqyMHcV5tVmbKKtou2bqppHIacv3v7VrLXse7644e7h/uHRzBbNmnfDYtmGSO2y9Pj4TDlaZxkuUxgnoehLKWOTcnjouKaQ0p4dnLy/MXzcZg+3rz/6qvPb27eX12dLxcN04BnF7/99a+bGkKIIVS3D9233//7i4urr7/86v37m9dvbz/c7v7051///a9+89d//vPUNu+ub83wp1989md/8vVvf3jz9sP1xZ/87F//8z//+qvPLl9+8etf/errr16469SXxOn69uPPvvoSSX+yrF1Exn2R5+SHz56fTcIpBEeMzMeb7ZQ1xunv/u4/AIaqasZhGsbuw/XN9c2DEj8euqx20lYf7z9sj+NsYS9ZEcInBK0nrsTKcRgJaMwZER93BycAQyfsVQmwbeosOZcsalPOIsUMRKY6JXdz9BiCmQJSQXCzgEB1XCzb4/H48tnl2WbpBuen66uL08160aQwjFNVN8zRzQxmNiNInvo+g/vJZul60Xd93aaXz88Px+7qbFnX1ePj/WpJF6sVRZ7KcHf3yIQP9x/ev337xVc/Wa/Orz9cn19dffn5CynjrtsTY13VzIc28XKRdgcfpnzY75ua/9W//Mvnb2+mPB72h36/3+0eN+ulFzETAFjUsR/GVPHV+dmrtzcfH4+BqyGXh8eH5xdXiePdofv2ze2yro7DNE65qK7aVNehn1SyZLeiFgPFQMOk1x93dYQCtlnWLy7OUjV2OR+GoUrx+dnSfcFEZdIXz07u7o+//uHDZr385efPu2J3+y49oVUsovbZEtGqqfuSRY0R2hTFdNEEM091PI5FHb0oACgBg3WjiLjpGDgy4WGY7nfDft9/89WLq/OT1XLRfXwY82QOXT+GirLlRdsc+u3Z6YbIztarftg3TVsmCEwiUhSKCCHFmO73+8AhxoRMgbmfRkRo2qguiGwOam5FU4rZs0wFEBS0jvUyxgKA0dqY+myjSEi474b48HG1XlQVBAi5KC3YVOoUmH13kNKBuhMTgJkpEjFCzqXXKYVAjpKVAMAVEKsUmbi4lXFiQg1sZpMXQgR1ZoyRRBRN3XQsImqilnPhBTn4JBY5xlgBADoDAKPEyFkEAIhpEgmIgaNISRGJ4yKEbsr9NAV6WpJl0X+qtwBkNyeiOnI3TXWKTYqihhjUDcDrgABQimcxB6cZ8GFP8ghzSISBgEKYis6HcDcv4hwpMk4C/WTmNsP+5gPvmI0JzVEM2ioaeHc4xnEajlsijlUTUsVIBpTqhkPAEFOqnxLqTAL2JHYA9fmn6a5mgVFB1IQTO5KaE1IuEiMjuqMxhYA+B94ncUJW0Zl6YPZJi+3uCIiGqogOBMUwm9t8QjZAp8jESE4O4FlKIADw7FYTXG0W+3HKIkSEQO5Wxdik9ma7v7k9ZCNHDoxFVZ26nBkAAQJrNx4elnndpipyN5WiEJlVJaW0btL22I+CiLBqq82y/fiwcynzwQwBCIgDA6IbEHpRy4KJyQxnFE+YMzJGs2LEHcyMOajpY1+qyCp683AgwmWdsvhULKsAQCScL+aRo2IGQwOfe1gIQDNUEUDVnZQ5FLF9NzVNYkR7wsIgETCSg5t7RFbwgKAGjsBEDjCLoBjRwNyMA89a3wI+ZSDCrBIDP8FM4anIUMUUCOc26JzxmkGYYvYkNXZwoFm/58CajTkgmKtnNEYCVCzOTDGwmRWFwKhm7NzUUdSnLGZOFBCxopRFpiKLEJDCTBx+2O3cvAqRiZjQTAgZ3McyDf3x4WHL4IHACRKiqnIViDgD4v/l//p/26xaVd13ORsA4JBzN+RxLK5GTA423wY/YU9hXnerGYIzgT75gg0AZxIBzGRC17qpV8tVipEcpywKmFKIaOuawAuCmdpuP1ycnyybGAJdnK+YcbloAUxV27p+fNjmkhfLNme5vt2+u97d7foxi5jnIuAWmMs8IiFEopRS09RNFWNKAAwcYkocAhHP8ClgCsRzSwvcCUBKNslVYClSypTiTHBX0BIZAVHExGDM4oBTURV19xDDarM+ObuI1UINRLyoT3mSIoi47YY2xoCkYARPjNN/+isTmqqZB2Y1K6IzSVbNEGwasyBUVQjMAWkSEfWcRUXAvaiYGSCFwGKmYuTo6kBos+PM1FWfrj5mM+KakcRkJqTOVQsGNJhrjM5MgIQOaqaujL5eNi8uzz67XKvJV59fmsg//O71v/pnP99sFr/6x+8RURSZebOq/8tvXkeGf/VXP3t2dfHDqzdFbX/ovvzi2fn5iYqenq4366br+tPNkgF3fS9FqlR1x65ulymkfjiWXA7H43bX/fD67av3d3fbcRhyUYwhiKq4rxfNoqmrFIdhcPC6rmOMhGruVYocIoKcbVYvL0/atmrqeHNzf3d/nGu6683S3Numynk82bTtor27vX//4eGLz18eun4Yppji6+vHu21/+7CPgcAhMacqPux7USVkIqzaZb1coWsgosRtSm4wTDlyyFL6YXKD1bI5WdZZbBwFwIEckdoqRmYH/ezybLFsXr15t9sPYnq5WfzxzcPlaXtxsohMIXAkJMRUVyKz+w9DoG7Iudhhmg59aavk5qY2lhwDLat6308+s6UJAWzM6hSRCYjWq81isSDC+W2Xi7jN+nIEwCqFqaiqpcDqPg+Ywf//+Hbz58ggzMa9ORLiToRZJDAruDss6iSq6KRuuQgiGjohzhp3dDRTBEIkAQiIxFSKghtxeOIFG6g5MtHMPXfLpkwMoACghhhmm7rOJEBDnH9P5p4QYwh9KfA0lgD3eS1AakL41M2acewzdm8WSpmpKnAIRG7mzHic8rPV4mJV/3h3EPU5z0UAWY0ZE8/IdyfCIp5F2zoFCmqgpofukIfB3DDEuq7MHd0RXNSBIiDUkWIIVawJsZ+yODBinqbTZVu19XZ/ZMesJQRmgEUVFm08DuXxMDpiVQUwNXUnNvU20ePh8NMXF20dv7/ZFTFCIAZVr0OoUziMUxYBxIqZGdU8cDC3Mc8LCXLyecuBhMzsZq6mAO4QiZ5eXW4AT+7xYq5iVeLIDK4GMIoxx9lhPq8Z3cxwDmshMec8yHiwMpTxgGa5mKk2dZoEhmzm4G6EMBVJHAxAwcNsJbEZEuTznB3AZjizA8gTPOspbp0CVSkEBHRDwIc+F/VAuGkjuHaTjeLuPveDKp4/JxoJq0SJIQD1WQWcierIU5GxuLoDkakz4vxpQndicoAm8rKpcrEqRCCIAXKRbhB6MoI8xatoRpObEQVEKlqKFAbop+zmzAHQ2ipOxaYs7ug0M5iegqQhsrmrKIATkTtw4CKei4QQCCEipkjHcQjIFHiGYTvSqmmaREOZVKyJFAKnwOieIl+crm4ftt04LZctMYLYzf1xs2qZYOjHpon9MNYhjk9jOFo1VWQa+mzgQykOwA4U492hq4jWi4WyyzCu6tpAA/D1w7ZdVC/OFznn5aJdL5ffvb6+3x4/uzoJjJJhN03ECJpjSFlMZS7ApnEsYymR4X/7N39+enL66vXbH9/f3TwefdazAsk8v0MAh0geAo2ioNokWm/aNnJgZKacJRfjAIdunMayWVQvLs9+/f370zZdnjTPrk5V7M397tj13zy/3KzW7+4edrvHn37+ubpt1svH3XG7P2xWdZihWRz/8P3bn39x/md/9gtz/odf/yMgT9PQLtYppoeH20XdTjput8d1s6CAFMKz89Nl2x77w3JR9QW/+erL735499vv36nTm+uHsSiTJwQiHEt2f3ogmyMhBnZCZGY1OHYTEaQq/PzLl3/y9Ytf/+HHP779CIT4FFOHLOJAQDhNsyA1mAChpkRapKorCP5sffLF1dn6ZB3rtq6SiTri5vTUtOwe9yen5xwgF/317/7w4eb+dHU65mF9sjn205s3bwlDDNgPU1svc+lPTjcEZG45l37oz05WkTnFkAiY7OxkfXay+vyLl8f9o2Q57A/7w5Y4vHh2/jf/4l9cXl1tTk6J+OHh/vd//5+vb+8/PmzPNsvT85Pbj/d5tD//iz/r+v7//v/4f392dXV2sf7is+fTaLcPdzHyFy+f/eQnX5cy/PjDm36Y/uqv/uKf/83fAPHdw26c+s2CuuPh3Zvbh4eb5Wpz2B9OTxeXl5fXb9/sDsMXX7xYLlYPx0kV2qYaut2+l7vbjylFyf3Hu8fN6fNA+e37m3GkX/3qjxNjUR3yhEiafciDFkAiJJwXBgJmam2smXkqJcbACLtuLOVp1T3XgUMIi7rKOscXTERUHRlCCPMOwskjBlM5Oz0xdzcsJffDcHV1OXdxXzy7/PLF+enJqq7rwCGl6IAxplTX0zSqahVj1x/rqp7DszFWJU9EdDxscy45l74bREuTYLNaE9L9/c0ksjseV81i0UQKCQD6rhPFL778suvuXUvXj4/bXZHqbrsnkq+/eHns+ueXZ88vT+p6sVourq9vf3z9rl21FcPxuDscxhjrfuxFpR8EGCjQ4TDthzwUamMkdFMJiaXw8TjMAmViWrZNmxjADL3PihAq5jHnOYqu7lcnq8MwSpnOVu3L5+ePu+Pb611VYVUjanhxfrpc1CebBYANY/729cNdP1ytKlO8e9x+/eJ8vV462d3D8NtXNymmdR0Fyr5TNT5fVZMoIjYxHKey6yQELmKRMQRkYik6FkMiMw88S1ywrcKihp9+fvWnf/LL3X77H//T3xmGtqm7YXSkk9WC0FS1TvV6uQEtuZTjNHGkGDDF4OYPx3zsyuV6lcFVaLs7NG11mDK7ny6ah+NBxBbtok6RAIpYNp0ZcBSAkQgotYwGZIYUb7f9+bKKdZyKjuM0Fn12vmbiw3EM4UlFJaDdAP3gxRTcmTxyMlNAQ2QHclMzK+pz+SUwgYMhEDyB++ZAb2SqUoyMiHgcC7i1VaUAw5TnE1pb1wbgboEYAVUtl6JuhDBbt5kDIU6iah55PpFhmyggHCfddpPNpz7yJ+/DU4V83ghCeJLSe2SeVRtzXDoFCmSMaEqj+FTU0fFJBoKIsEw8q5IRcSxSzBnZXduKCGkoAjBThObQO82tnnk/BAhuBgjMoRQDMCI0cwIPDOCGswGLONXtrJQIqTZTDLFdbKrUzAgcMBNVQp/Xmm2KAWkqWT9doecwUSQ6X7fTVB6PYzFgCmEWmCHMQetZBgaAChYICcAJng76Du4IZk98Wyb4dNWbxUWK7KabOtWxujt0Ap4CB4SUeFmFKsbr7VHEntqEkdxA7cl5FJkiwzBOJ8t6s6xvd8OYLYUwlOl01Z626dXHvSMTQp1YTPbdAEhNSoT0aeWGiFhUHZAJZ+lqCDxzPuvAogKAMyZ1BrSgIyIyATiqKaDFOWxCmALvhyzzRMGtipW7iAhScNcZe+zmgUhcCdnN1IRCPGkqMnjoBgfgGcOCbOAqxdWM8aRtJylTLoxsMybGCQgiUSAuUvzpzxvYQVwDc53iYZh4FqAAzhHuRRVTpD5LKU80KPDZbD4vwo2JzcEcRCTGoOaqhkzuRj7HlGYflM1Nr7lDRwjM3KQQA6m76IxLwsAUOYw5b/ddYCRCURu6gYMTomSbwyIhRFUITMwkYuOUUwwpsFppElcBh6LFAEzD5dlGDO/3hw/3BxEz91KEmAIQMAECI/kTaX4mPJV5EDQLs2weAwU2cSBwV0ZMKaUQxlxE5dh1VV2tm7ptg6q0Qero6BMxMaIQnp0sVCYHWrbNNObTk5WpEsKqqXIewLWJcezy717f/ebb67GUeWgxy/nmdMAsW3UjMMwqRfrcI6HiPAghBny6AwJQU8emilMuOWdwQGQAQ1cziAGqFMeJEKBKIYQwhwk58u3dARHO1i0iQYpm1k/l8e5+e3fnSBQSpaZZrOpmwRyKWhsDuysYzRh2cJ9X3oCIkA2Iycy6MYs5Ec7gIUBQUQNy0OMwqKmqOoCrW5H53ksIxAHQxrE4eEBUmNVlYJ8W57Otej4r2JPMDIgDM5vZjGi3GWhkzoQcOWcBM3ELRCbl483D7cfH71ft2aru9/usvlmvqhhubu5O18urqwtiPB6OdZ3+T//7v9pud056v3tolnVSWy7q05PN6emqikGKdIfu2YtnuTtwilVVrRbLmNJmveyGcnt35y7v3t/+T3/760OXA9dFjTnVdVin5EjHfpRxioEDYxGZmf9i/vDxbr2o67bq+ilGTXXKuRyHLibmyS7ONoi0WS/qOh2PeZxy3VRnpwsmH4axqur/9l//9f3DdprGqy+v9oc9g7nbjEo5P1kNU2aCL5+ffLjb1TG4Qzfsp7FPVbU5PbPiHhGeuhCaUqpCNQyTqlcxduOQAgJQnwui7sUD4eXZMptdv/n42z9cq0rbpnEqFdPN/eHtzQMFIuLTZQVEdYx1FZdtvV4tkHjRLEz7NgZLOklGRzZQR1B47DIgI4eulEUKbRWdZBrzVLKjSc777SMycaqWi2U1x+44AICKiBg6pMBMKGLqRsjuIGaEJKLmHpkRbCqKn0jSjKhF1TyLAUNiHEqRLIhPlSEHQGQEFDFER6Q0u58cg8+fYkSay2Vuaqu6doD9MCI4M7cxdWPvgDFSKQWAHR1t1mECAo2ukTAw6Tzl5DiKImJkfsqoIorZLK4KIZgJAinYbNuFWSGIEDmKiZoQBkKQoi2nMdsPtwdwn2vTigAATVWJGREHpqw2DzpWy0bKuNs/uhl5kWFSBQNgtzEPT6VLhxCCI6rKBASI5hZTHatGVEwVwPK4betFNw5tXRHHipnAJylrXmUtSFSnwITqrqTHrl8tGic7XS4B6aEfIqMbqJsqMiIi7vuhmANwIDLwUpQcwUX+SVqPzoDzeCcSl1wYkSmIKj+N9u1pUgZOs+4DVMFFvYpowEUNEPPYWymABkSJw9zVLyquBW2axr3mIRAy0GQkjjHGrD4VcXdRicwxsBu2iQ0sBq44bA9dcQIAR62rOI5KARMhEWXzeU2DjOjQJmraZG6gSkTdJIFRVdoUmczc64pGyVUMBmDFUkiHKUf3VVsX1SC8zVkQ0UFA5zMfiMxr5E+4pBm0CHMfuxv02I0pplxZZDTB4q6m8yYyVGgq8FSpcwQMQWMMiBaeNB8eiDEgI4+lTFmamMRtzIWJi5gUDYTFFMCYOIagZkVMzNWMmdyLAxpynzVxheBZcqCA4MVEiuyyLRa0riIajkWdScQCwONu1yy4adtSBAwetr07IKiWEgNl0UAICDHFMoq700xXYs1FqsQB6ePD3kISsVUVsmSZ5Pm67R3vtr2bUlPVC/7mJ8+quESC3W63rKi+XCUuaGE/ZTUtYmigWtydAIiDqgWmFkNdhY/3uzcf7j/cbQ+DAsT5D7zM9WB3B68CA6KYt4EKATKwi5mL0zRNDiRFpylXIXSq7s6MKeD5ZvnNV89jxXkqfPd40jQItOsPu6H7yWcv6zZVVRj7/OJqc35Sv35/sz9068WyqptVk64uzgPi9e3925t7DnC6OBOl/f4jO3Z9HxMHDsW0jfXF6Wbqu9uHu81Ju1iffr45ffXq/fvr26nI42HY96O6pUAxxpwLOADNiFAkgpDYXIuAlhJ4dpbi+WaVUvi7X3/77btbRRT1OoRINmZTJxVxs5A4AqpZ5LkO63UVUwzbvgtnHDiA46KuRVSKAIFJUdUQg6rcPTyuVutx1Mf7nSlzcNnuttvjNA5EFNOplIPVwsx56oZO1ier9bJCK1oyA7Z11Vbx7OL88vJivarPzxaJDcEj2arhmKqvv/nJs6uLQFDGbrVozjftL//Zn1W//+Pzq7N2WV999jwX/fWvfvef/st/HlUphYdDf7/d1aEqYvf3D2fnmwJ09/Cxqfjk9PTu4ftvv/vDYrH64svPT9aL99fddz9+iJj3h+3JekkBiezx9t6zPu73h8MuXeOwPB6HLrXr3UO/fdguTy/GaXy4fyCOj48dh4flavmHH2+XdZ1W9ThOx2M3TGbuphA4ZMmmSpF0EkdSVweXbE2ViNlMV8u2H7MUJ2JHSsx935vZME2TZARkIuLgKIjY1rWqAHIVMTADYdsmN5+yZHFi2u5333z+8osXF4u2DYFURIsyhSKaYlLRUnJVVQAwDv39434cb89ON5eX54fjYCIpQt8Nx2M/R2kYoSJs67Q6Ox/6A5dyc3tfU4gn6+3j1s0B5XGXma/3x4+5lJPVuqnqY7dvm9g09fnpycVm9Se//HndLt6/et13h/XJ4jN9+fbta2v52fPnq+Xw/v2NgjZt3TT48X63O4yT2jTZNMr6rOqGwe1JUB0i14kMcCxTJKwCG2A/5pKVCCqmGEMuAuBIvu0HM0ODGOL+2N8/7ospKnr2nPPiWLnr7nh8/uxc3V5cLum+IPjhcPzl159fnrVV3b77cOMmiypmsb6f6orbGPaDDE8XM0/MU1EEW1bpAC6q7OHQDYSMiLNQSsyZwE260bPRr394//r2vklxvVy4gzm0VT3kchzGQJBFu6FMY9ksN+beRHbwpkpjnpoqVpFeHQdHkmk43ay/en729sN1WzW9qAMsUupwmnJfhQXHVNzHcZpVmjPwoahoz3WMZig5h8D7sVRqk0OKNWq+3/VNin0uNUDbRDUk503rTaSHgxKFXHLFgFXsxzwnfzkl1QxgDhgIzE3cCJAoiBVmjCEGnptMPl9bmFAdxjI9pbfdmThrYcDABC79OM0pHkIiImJUFYBSpUpyUXdVDoyBYDTux9IXm+UP890+Bc4iOs+k3MzQ3Ys7AczYR3dX0yammY9Dic2UsLQ1VQH6CWQuhDkgYlaLTIEdCdd16sZMZkQEAJNIFXBea4n5IG5z+Xq+j806ZkKbH2V1OIoIguhTG5yIlwmzgpjrcFDDSZ2eHFvWP15X9TJUbdUsODUh1OaObgygqkiuZkVkNmoBODGL2thPFrgAELi5GkJEzKZikAKRw5wfcYMUgwNkUUYyMCIEIoE5PmEAkEJwcCCf+bNuiojdVIoYM6lqEUUmVdiPGidtYjzYBGruqEaMjqjmgBgM0InXq+r+cBiLt00rNiKiFNsfxyZEdxcvgUI3StYCyE+iEEJQBEB1RaDAPOURPRAiuJqIFQGESRiJQwiLKhBXWb2ITKUAcoqsUhyZKBGiKAYkJpqKzH0udBQzM2urhZiPeaA5NO0wZomRIxMF7kZF9zrgNJU6BXdHh1F10aSP2725L6qkpRQpIrNq2J5ca0iupmAVs4cgqvN1W0CJOIubFUIqYoHJwQ2cGVaLlEWnIvPDHwFLLu6g/nTzVzUHQEdHzFIQicJTzmsGreqT8gYIwGG2DKOYonpRZ8KUIlEQcwJTteNh13V9EXFwVBUzYF6k2h3UCgFYiEUBkcS05NwNkuqobkXM3MbiIi7uIcSYYnh11z1uu33XuflMhEUABgOCORqQiyICEaADgk9qxBwDFFV3YEJiKiJ1jIdhigkoElMApxBTiJWq9of+uNubquQhIDCHFDAEqqtEzJcXp/suP95s3908ivp62WxW9dlm2Q35ZNWIwHdvbr57+/FhNxBRigxEMZA9nfZ4ZonPyC4Vm5sj+0liYCak2btuMIkQgoMimORsJjGQAs6lvsio7mPx3aBivm5rH4QJm8SqoxiO4mB2u+2QA7gjwAyudzcGm4ZjlG5/fJiW64xVtThZVKkfsrklJPenJ9TMllY3JkgcR1VDF5M8ZhE1VVH9VCt94lDMD2I0S5EB567j7KB+glvYvK5GnIOUOBvtAHDuSjiA+7Ku1L2ouMJsVOInxKvHyJvNYnvoyH0eYIoUd6uqygGGKd+7VynWdbok/O3vfzg9W/3ky5dV3YTIALbb7ZeLtl0u3r6/Pnb9V1+8DCEd9ntAXy3bGCMj6JTdLVW1aTk9aQhJlR53uz/88Ydf//q7h/14f5gC0+nJSUDqhsmAi+qT2hdxuahd5/T0PMezvhu7bgKKqcJAKAq51+Bjfzzgl3xxtto+7jcn6ypRjM3ZWfXx7r6qwnb7mAJzalar9nG7B7CTk5W7xZDU/HHXzcbEx0OPCAWBAGLgi5N22aSiWqyoxdv9roh3/fBElKmYsyaiZhEMUNxCjHVkK1ktIM+gceyH/N3bm2nIgRkJSrGwSqnC7iEDBVOXIm+6cbYbpBSRyUUi02q5OD1Zny7qzXJ96HtyQwo6ST+OhKCqMMvpzD0rEXNMEUtV8zBMOo5IKONoQ1c3FTI7hrpp6qo2ZHIAN3GfJyYMVkSLGrrLzOcDEjXRp9AfAkym5oaAGJANJzU1DTgvRp4QQmxzCJnnkG2MyQFUraiKgYmAe8VMRJOKWyGmtk5dNxYrhdGAmkjmAo7EhCZViEVLJAJnUHXAYqBqkaM6iQoTImNgnooCuT+9OAMTqc59XEdgJp6xMjZvSgDnFAwguaECgvusaTRzYmczUUMrFbFo0eKEbiYEgD5Mh8PUZyKoE0YiMadP7allXU+i/ZhVMyE6GHEAcwLXqQ8yVIHGYsQUE+8erlWhjEwUOsIilrNc39zWbY3geXRACjECBQ405ZGgSbG67Uskiql2ElIDByYsKtmQgOfE3+wBZaYi2o8FiGKKEUFn0zuhfepqKdiMKTV3cmRmmC9E7jP/AgnNtBuylDwNg4ydyuRuCIaOHfK8fwNwBgkMgRgwlGIO0E95bm+X+dkGXgVa1gHcV6kmdHVyh36aCpLMlajIVcRENIgPRYkc3ObVmZmtKl61KbvnIjUzgIcATcXQECOJGRN3Y0FAIMiT1CGoWQW+WCRDS4j7XOZvN4ETYt9nAOTA7uAKSDg/UXnOnjCbezEl4qJShtKmRCGoq5lPuTh4gpCYhzzRp7OkiYhgjNHci0gMbA6B0ExUNcVgBIS0DLW55SypCg5uYARsDrnI3HpT0RCZ4GkXJVqaUKkpMtYUYmBAl6Ewe524qTAhTVPm6N3kWDQtq0C8O4wlW1Hv+96QKdDjsatTJNAUKCMWKWK4O/Y1R6lS24YWY2RWsX03hip1k3gRXtaDFiIv0Uzh68/PHx8eHvfH51982VZrd/zw9t3bm5sqpXHoe5WqasR1mrIZnK6XdUIkKsWOx6GK3CyqMYuq//HHD9341MgLTwQHKKY4t/QdzKQYbGquGJhTJK+ZhykzB1U99mMRa2oOhHVKUqbt/vjibLWo0+HYb7AZh/7Z6fL1++3r/uNmXT9btYsmVpG2293D7vBVfHb7ePfx4b4f4fzk5GF7PB6H29vHh8ddP+aM3CAj47evvz1tmouTs2JKiM8uTlWtaZJrcfZUVx8+3qP5h/f719f3o/J3729F3J1m/v6QJ/CnhmAWdccUccpzdhKIZ+YARsbVsvn2h/e742BIXV+IoAkk6rmYmqSAMcUilggdnciqFAApBm7quq2rzao9Pd0gU3fcMqcYYxEj5GEaTUxKJiQQPd+c/shvt7vHGIJ5VkUtYuT3H68J4WS1UJU8De7CoGM/AJQq1s/OVptlNWdOF4u0WbUp0svnV4fd9kN/lCJffPE5Axy2jxcX54ft7W4XV4uaSMNieexu910Ht1swWG9OL0f/n/72P99uD2ebzcXp6nA8xobbTZXqtGirv/v7//Iv/vpvqlQTeYgkcnj//sPp2fnL589Skl//3X+6v308NPXHu4emjc+uLt9d37+7eR8Ylqsz72UclCo+P3+5aJeP++m4OzrF43E69uOZQ4xVkfLqY3fshrHooZdxUjAzcHRmAnPXLODurm6AxAY+lRIcxEVETC0Eqqrojk1bT+Mgpchc0jJAJEAjoLZuzs9PEPz51enZZqmm7z/c3z7e5yz9MJnBarn85vNnLy5PF4u6ritmrlLFzO4WY6WmeZpCYVxuECHn3Pd5f+hSSJcXFAMf+6MVD8yLpga3pknr9VVVpTJ23fZhsWz1sLs4OWmqenu3e9xvT89O3n/c1tXCET4+7FdtjZhOLzbL5fLmdtus6mHqTlZrEXGVk5Pm8fHxH//wXWqW/XBIYSmKyDGE0D8cXClwsCIIeOwlMp+s0r4f3dScbAIiyCJFsWkqNzr0A4GnSOauOvtmFAGrFOrERWwYp9PlQgjVLE9FzYC868rJMjWRbx52j4eIYHMydMpjnnJVNx485/Knv/irqq1K1h/e/NqdpjzFtkKmIuJoxdwBIpO6qkhgFjMp4ohFPQQGh0VTjaVkcZyXpxxmm8yEdLvPFUvDtF7VzIbuq7bhQBzx9n4/irr1TE4EplpXVRlKoqjZTY0IHo/Dxaq62T3GhJeXiyqkm12/G4aTugqRFCVFiwTDZADUjVNATJHneDsYgnsRPQ7iSCFSl8URVKVOFRClRDHSUGzXDXViZt73Ewc+Oa01W4rVnD2PPDP7pOJqwhjY3GFSqTllMVUJaE4YmQOTSKkij+oqWKdIiCI4mtDsPUcoYIjOhOEpe+KIMH9tCB0MmElMRQqjywwHMgemYyniMMe5Q2AAFzECIMBJlZ7YkDZTlmddbB0CAg6TihkzqGoRMFNHj+5VwE2iPrs7V4EnNTEb1QAxgatalXBemI+jFrWi0KQQEBODg2dFBvB5weU+n72ZcVKZTDmF4A4MRefTFPZCquqzjh6QaNZNEXNU83HsOQ/j4YGIY7OM7WmqGuL5UgBIAdHdn5o0YhJDOE4lHwdEMgA0mRwg8Nw9/2TFpU8GFAMHNXUER3R3ckFHA2Sc8UggnzDAPr94kBFxMg0c4szDJmQOi6oepmmaMkAcpkzoCFjATU21uPZE1CFUIZja9fa+SXMvHtxcOr8ZD30uohZiqlNi026cwG3cPzEQ5ph5jKGtUn/oADCmYO4mEiKreWRITETo3hai41TAkTgQc5lmoC5MxlDHpkp5Kq6opUzTpECRSTKKaFhRXVdFqai6CIeUQjQRUSRC4ogI227aHo8nq82maR4PRylWWN3MEfuidQz9MGaxVFWIWERMiwGRIwBIcCQEQ0Z201w8kKeYiur8s3iKyznGiGJW7AlqNanMzYk506YzHnQOqhDMyfv5Aw5IBuifPMgGgABGZAYgZiZ1YnaTXIqrFgoEWrKVLFKK+JMumYg4Luoo7mqWSwG3QGw6+3bE3Yjx5KQRFXd1JwdnDjHyIkZTP0w5vP3w0dSZCQgRUd1MDYFTDFMRcIrMk2R3SCEwhXkTruBEbAZmKpKrlDarNkQ+DCMBjCKrqgEpFGjM0vV9mUYRiSkKkRV9drZcL6vTzUKMkOI0diJGBJtFUxQ/Po4/vt++fzhUzMOUD11fhbm4MKtznvTqs3x50TS5TBzmmI/4U27fcTa7OxCRayFm5kCEYt7101RUNPsTwXsmy9LceyHCqSiapxRskU4WFSAG8i6DOniRXMTd54zeME7LtsGQRHXI0uUHJiyH3VQ1mJpUV7M1EN2fSqlgUy4lT1t1USyS58jAU/QLMMZgNuPiaU6Rzd8qe0JcEYEzoj79A9JMVINP4bNZS2TuCClwiFVgFlWTggY0UzcBqzqRKoDXKWWRacroUEzoCWXPYqZgicOU5Y9v737x1YuP2+7li4uLy6vt/liP47Ef1eHD9d3j7rjrpmfnJ5Hj6zcfL89OCCmlhBRUxcHbZb3d7hGx5OH7V/f3D/t+LLd3+4+3e+JYNeszyqVIYCaEpkkxxm7IqhbYKSKGMOUc2KdxymIhJQ602awWi4YZtWShpJIfsorr67/9zfmm/eLlJQa+PDvt9oemCeenp/d3d5G4Xa6GcXLTKoVxNCCInF69fXhz/VAFzqJDEfd8dbYGezJYv7/dPb9Yv3x+gQzH7d4nfyhjmfKhoDkT8+XlZr1eTzm76vXjro41Aky5aCkJ4n4cppz3h4EBOYSiEkJAxNuHAwDM83tEAkeiMJeM3MGLOkBRu33c3Tzu1qvmixcvrk7PusP+OPSIGKpA5jElddMiIqI6kYNSiIlLFsbIFbhkigygXiZSyuOge9KmQUJOVfYgzvNcbXrCU+L8liLAY5nmX5s9cf3nzxgZAKiB0dOgkp6aQIhI6PoplSpqbljMm8iqOqtJwa0KgQjVpari6F6yVBzW68Wh647DdLJcFsnDoJF4LLPidOqnXMVUBQS3cRJTq1IsWkSUiCiE7KqqjOigqprFmqoasqkoMRFzILI8zWAjByfwgDiJTseCAOvlcsxyzKWuq/nl4SY6jYHBNOdiSGymAF7HUMXweN8XJY4xMWWVbixPzBziIlpxZAwpuUox98ihiM4gGyIMgdrADWOXte+kbSownMTNfMglEjYJy3jI0/HpC00UYwjMKiqlDFUdm3pRN8XQ5jslkJsUCkQYCczcYcb4IpKqh0kViFLiKsxONVB3KcIYQHInmcgJzNRclYiEnyDIbpKnUUs2U9diZuBGIJE8Mrm5m41GiDb3Wt20SpQoZLFuKjIX992ZqKikSKmmUrSuYpWoTCWLj0XmgTcxM0IRcfe6bmZ1agosCqbKBMS0btIoggTjOCFTRAIFYKtjCExuUsRtBsy4cSAVWVUhpiAqbZUUnBX7Unq1RAxgc1naHYuYyFP0VlxRgcBljpbD03LYEVzd1PZlGAPP/DhRJQIK3I+juy/qehJBQkIyg5y1iLjDrHA3VweYG20AyACEEIkoMjMRoqhNRRAhhARgOU+piu4KBk3dTGVqQmQEpbmJhoTGxOtFkwIB+TjkQ7GmCcUA3FbL4GQqkjh6kjJMVVUVwEhQp5BzWVWBiKZ+MkB3XFYphYQBwIsbDMN46PpmUauRdsKJ9zLGAMywP3abpqog69htah668dWrN1WAGOJmtdgfj01d1fXa3HAsD/sRZu2yYxlHVXCzGOrHwzgVnXJRA8cw371mN8hcSUQHBGiq4GClqJnzInW9HMYSNnUunqK2dSrqdW1Nk47daCanF6eHwxDAq8AhIqOa21QMArhYIB7Hse+TlHJzt7t7PO4ej1WFNdcPU/fDu7umrk42i1hVxXQ/jCjl+eVlluGzi7OT5al6kUlKkX4YYojo1p4uzk7P/vDq/eEw/vLf/PzHt3eT0bevr02dOZSsdQrgJqUEDuruYnPrYVYFo5shkM9IewTi799+lKKA2I9Z1VOKRRTcwSyQVylUkSNhCGEYBwKqU0KEMYuZ/slPX7Z1s+/7+Y59sqaU0snpSZHMgc0sRirb/Pv3tzd32+ViGRMRpTL0XR76kfNUYgqrxWI4dqtNjRBTlcD0bLlICdeLZpFSVaeYuKpCQG/r2EZKNQda/3f/u3/z+99///133603yz9+912I6fnzc3dt2/r8ZPHyxSW7vXv3arc/PG6P97ueIKLzZrkep0m1HqWg4KJOl2enL69e/Nt//798vNufrZbjpPtufP32TQw3//Dr8asvnudxu1w3x9732/t2sUxV/bgb+qO9/3BgFjBOsY4RALlKUSXfXn/YH3uO8eHxuB+nH169fXg8vr553HejjtKNuWpqEZGsgUi01FVi4mLliUCH8P9j6j+bLUuy9ExsKfctjrgibkSqysyq6moFMRiwSRhhM8MP/MH8BWNGs6ENMQAbg26gZcmsFJEhrzjnbOHuS/DDvsnht7AwCwu74uztvtb7Ps9WNLCwthoRzdoEEyepqoL89PgUQEnY0bZ6h5ki0tj1N1d7hBDGT1/dHPaj1oYWpVU3//KTIQn3XfrZZy8Px2OtJSKIues7xE3giWaq5syyLDMCLPOaE9/dXvdZ6roeDvuhk+n8NA7ZI1pZiQmJU+5KaVXXCDyfV22GAw5DfnjCjx8eGUVdp8sTccrSIfK61lpbaTUmOJ0eGenD+3frMoxDP467Lz///HfffNv3Y05DaL292jG+AsLWfF3LJ69uA+Dhn38YcioW1RQNqjckFMNE7GHodr3Py1JO03R92DGCh4dDq6XvUkqShBCMgK1WwI22wVnyUhYCsgaAUcPmGRjAPjz1PU9rFeCHy9MK2N6//3/+r/+hS/40n47H8fHt5eowHEYJR6memQnieOiZYrtrEcKy1iQkTFVD1bssVY2RCVv8lH+VRGYOLYDwUq1yzOv55pAPuxwQD/en6xcHET6fy90n1yS4VHWLFsthHA/D2Kwmik8OXFv79Pp4F92PDycIOI49QqxlSbvcARSnhEhQsuiuzwHgpkx0uaxdl5mpaaQk+RlX2lKX+yzz2sK1Y3FHNaPwVcHUho4Q+TytJI7B+04iIomkRK35WnWpS0p51YbP0R7tkiyBInzI/Vzbpk5QtfBYLdbqTctu6NDJwreOFCCEAwEyQ1NlJEEIBBbe1BCM1EJP07wfejNr6lsVHwiICYMgQJuaBzG5h7sBogaAAzEAOgMGRgBu0/bciXsIJ9yMogHFgJHcCAIOA1kAeeTM1bEUXdURyZruRpaO5rkmxqq4FuPQvmfJ0iMwRp/JItbiqyIRhSkA9pLXVlttSVgIny1PEKW1DSi5VTK2a/1mBAlADyKgxIDe9HLfpoeSOsl96vacu+10TLxJuDdgpTszULiFBbpHJjbfZLLPB2n1IIKMVM23TZyBu4ZFiDCBCXPOnVpUc0Bu2lICYVG359P6BtpxaFqJ8aGuS1kY8DRPtTZvqq1MmySMtlS+bYrehRCBBKms61YqSUyJ+fF0tkAiarZQm4ecurDVcKNFKiAAMmMLP9epVEvCHcLcvDmGgqoBQz+gOr5/umDULgm4d5k7xGYBKAgxNV8ry5ASQlsaGQhgAgOFzEEB09PHMpNIak1bc8K4GpOaXGpjhldXOwL7eC4dki6PKLHOU1FrrYYHCrVSQomYzHxeFmFKIstaS20pJRcpajdXu+N+f7rMl2Ul4NSzualqUyN6LvIwCaV8ntpat5D6NhlCf5bbb4bjTbwOzZ614YTPoTUMo/DnbaspbXJhJESEtkLzVWsEdBkJQwQHEedwQk9RDZtFM4sIj4GQXQ3dtxQbubvrM3YAqKltoO3mPvSJUw6Sx2l9++G+tiqMQYLMZO4RlogDyd3MDAJU9Xo3xmq1NQdMzEOX1AMciICYalUm70WaG2eixtXItWXh1tbL07zdJghJUg5kABCK81yrhzpc5joOw+PT/PJ231QfTlPuuvO0nOcVgVXDHbtu2FRGQogEsD1FEHbjaO5m2xCRy7q6OTG6GzMRRpdkWZs6dV3eECFmtmGco+qG7NpiJQHgAPTMf8ZwSDnnLhvgh8vaJ87CuupS1N1NjYW3DyUATqUwi7tHhBBH+OV8ttMTCUnu83iU1FtEYiTweV6X0iIMAmAbyOFPC3SAZ4oVYiBtPYTnfucGu8JnoLSkDBAYjghuIUxqtlUEN1mSCDFLbU0QiuplXdE3xJeMPbturCszs2ldTTW2PFVEIDAhBCCjBG/aRSH+4+sPBn4/V5L+/fu35/N5KY0An04Ts/RDFx6cua3rsqxX+/Hh/mmZV0RPSfaHXZmXDx8+Nvd1Kb/5/Y+P5/n66qobDrlLGyDw+bHnISIBAG6SyM2TQIm2H+U01UDc7zoDSgnCbNchEpQa61p2XZpWfZpWq22t0+v3J4T44tX1l5+/OOyGT1/dff75y90wVi23dHzzw9ubF9f7/e71j29/8+3ry2x318er4/h4Wc9TKdokkzVzptzJIfddztNlub6+qs26DIcBd4ehFD1NpgEEWmqd13aeZ3dTbpHz03RZS1PbQE/AFMCy2Rz9eejzTKUJRAzfrj+xiQkRtr0KIKaU3H26LP/029+9Ox5+9uoTSulymUkSsZiahm5NVBbOQypt9TBK7BamykxrbRhgrY2Cx56LRrSSxXF6QuNmSCKSepSeiZuhOSKJbQsL/2nWDbBNppoD4ZY3iW0/bxa0ZezMgGmDxCNin3OpzVRNOJCatSQJgCFiVSVidJpLA2sKS5cSmmppD65NLZotm1HCHIkIua7lDLHVmAig1GUL73hAxqTVrbXUYSZQx0yMuqI5BqpGYmgA2wBWPXYZEcDC0CARJDQoJ9DoAC9PSxZmZlU9DrJ9lHIncwOSjmCbCcJh2L8/L9E8s5zmuk35PWKf2VznVpMkwDDwhJKQTut0vdsH4lJr4qQWAEBEGLqs0YlgxFrbFk5mQCJpm910k3OoRRhFbHxkL+dmJzOciyOBCBGB+3O/ziK215NbNHW16PqUEs9TOzcFROlShIf56qHmxJAY3V0ImaCZbqZPgnD1quoez9gl8OeWsuNazRyqh1obcwKC2sws1IjYtwcrIBBgYkIGZKlVa0SXhTBaqUuNS30eupg7miNyStQJV1WCYMAIEMYAyImYIbETBAIIxKr6MNXjTg5jV2ZFJw0vGrUZMbJQBzikTImn0hIHmAlwMW+BPfNz8DfQw1GIYBs6AABs+jBHIMBM3JpuO7cwY6ZwEJam5u6bJEkIL77WUvrcXaBU08xiZkg4dD3iM2YBAdXsuSBPOBAvZd3lxMQrQJh1hBGO6AxEZKoVwJGCN6JBnTJzEm5hu9QJUYAJMSINTOellbrZb4PQptLGJLVFac0t+nFYW0OP1GWrte+6dSmMsGo0LSlJVR87gfC1tdq8FastDCIfRmGKqjmzgwvCrpOmdplaqzbNa78bhz5LirktSzH3uCy1Wex7CYLHj08p5ZxlEEpofcKfffn5X//t7+7PeloDIHAbFgtVCwYEwMQAgGu1DRifE5fWmGjMEuRzaaV4M39aWmJKQOva+p7DaZlaXdr11RABn95dLcu62/cW61ISS1dqAcLTXG4Ou9ra6TKnJB8epsep8SF5xdPUlqKGyMy8H759/aMIT8tEDtOyCOuLq+th6JZVw0g6EdlDQM7kEbWudbr8D3/1ry/n+W9//ce//+btBt1UMOBYy4rEQoQQXRIiaBbuoabhIczNzbZBqSohUQoAmNaGHn1OjGRawSNLAvKmrdSWhFtVNb8+DA6GgX/29Zf3jw/LtFzvDpBlmtfSgkXG/U61BWDf57/7x1+/urt7+/HjD2/eT1Mdcvrk5ac/vH33p7/4+rJc4PU7MGqtHPZ9nyDAmXxI/PLm5rNXLxljrct0OoWV3XhHBG9fv6nzfPfiwMKffv5lnU6vXu5T+qI2/+L29t27D29+fJ8y/+0//uZ//Hd/lUb79LPbgKkWbu3df/7Hb1/e3IyH8dX+UIp998N3SeT68Eld9O3bd7/8+hf/1//TX/3Db3794mr/7et333/8+OHDda3+3bu3L47jn//8Z599/umL2xf//M+/m+fT7Yu70+W8LJUpEeeP9/e3N3epH4chl1Ivj4/S8Xma7x/PH87LeVp++bNP7i8fMue/+PqTb75/U9VxQ5kIEgBhQJiq01YCja3cEBuvPjzUGidW8FZ929SUpkyCGEPOw9CVUmtVEpKETVvfyX7o13lJRF2Wly+vHePj6XHfDy+vrro+H49HYtkMAE0VifquQ6SUNpwc1HWdl0tV12ZE6BDNzLSa1loWd3WPrhsQhJm7Pru343GXMp+f9Hg8hHuta0r8Jz//4nyZAuDt/WNb1h1ndUUotbTLNM/rRejaFL5//UarrnX++qufafNpWV6+uM15CPCAVmvpOjkex2muu8OgZk+P5+tdd5rLaVXibWaLblG3YQHAUjUJfP7q6jKX+8czAC5FA8SamQWjHqlniJyZiJt7BF6W0tSGTvYj5UzmMFejLb0fCMSqUD0UsGqYwt99//1VSuMgrUUmAjembi61LHPK3dBxJ86c11Jb0y6l49hxRlOrBoCYhafaMqcsSV21BSIIkzCEAzKouTkooD6VxHR1SF99+fLDh/tO6OqqS4xJ0lTt/rLkJCS12Ye1agQex06kr3Wp6kwwLW0tiszCtLZ17BJGTEWbt67nLJAaOxMgdJnNrDTdBH8ixACccK0Fwgl5rs6kCUFbJI5Efl51XVqAY4J1rYzpqenVkFqznJnBGYMIBvbEqdQWEasaUlwP4kwIPiQs6syUECGshUOYt7AUHWE1REEBEEQRqs0ToTs8nqfrw76q1la3yy7TRrPApWzRWooA3GYmGoCekoDHMi/roiknB2Bm3HA/DkT4fAVWNwNkZqQ+kUf0XW7uHKGl2vNvpAFwzkIEZkYRvZDD1vbA81wPgxyG3tAp6b7n7ZVq0XZjt1av1foeA3Cu7hCMYGaKkVmau3t4bE9njPDtTL4t2okoIgBiE4EjRrgVUyNiIiZKTGQrLLOV+waiwSQiaaDUA1IQ5yTh4LBBVxOzOCIiATMhbml5ftZcAZOwAKiSsJORBUuKMHW15bxJqxmx1XZ/eUqMAOFuuMFNIBBZzQDBIC5urTnBVj2KLQzVTCmQCBNz83B//pIdXGj7v7cqOjKxbaUAdSIOx8xsEULiQu+fpj6nlHIza+aIaBHNWYQwvDQNcDU/z9EPI4l0lNRMKESQJQk5EQGlLLZWL8VcCADcnJAFkRkYoxdsahDeijMRM1KYrbPkYy+oda0XO4yc0Zh4THR6+kDmrr4WEGZvGBGg8ExciigRz9lN0LquCkgkphUj3HULZp1Oa7gxs2o4YE7c51y8PZ3PREhEwBKwXcoDtos5PCtnNhILISahcCPwTujjh/dPDx/9WT3mhOEBahuenvqEmQMRklBNed+RQOaUAD0xmXpr85iSDH2zNtc6awLmQMaAgGefy/YyEwBETERjn4nFXD8+PDyezk3V3ZhQNi6RuzFgjUjMEQaA6piFk1BRddgEJVaqb95fEeREfZdOF42gFl5XTZx6SUUNPE6nB60aiInJETAxkQCRuyViC9Nmj+eSkzS1PKS5VAiozZZ1auZ9EjdH8HGfn+b63JgjDAjeOCMQrel+6AFgWup8mfo+YZcgoE/0NJWqZg4WYa26We7zhlOtZm1TGglsn4RNPBQQHrBhPFPORFTUSvVmRlARYqmKEEzMzIAbsZQAoZmWWgKACZho6FNKXJsiQWtlPt0DMki32+0YubWAQOG8DYyeB1rPPy8EwIBQCwR67sFu8K+AQMDYVDik2mKb8QbmnLQ1D0jCar7R41m2NT7OU1mbDUNGxrIWc51Vu5wCASBM9XmEHQYBREgIDOQQgoiBGg5ABNC8EcHv//jd69c/5pwioEvSdXJ3d73bj7e317Wu75+edsMwlXY+f+gfHve7PuU8Dv13379dS13WdS314WlSw24c12Y7pofTtBYNjy6Te1gzEGi1IW4JLntaSqRUWiGgoAQI4EEsgNDUmAmQhdxdLaLL2YgRvKoh4m9/+PjN24dffXX35uPjV5+9/OXXX6zrNA7D/rBzh2+//3GapuPh8PLlcHuZ7x/PP/v0OgBef3h6/eP7cRxuj/v64UmtrYv3XWplWZsG4N3dbdO2P+ZP77JnPJ2mNx8ei5IG911GgvM6t9aIIEvW5m4Om+QIgrfL5NbD3ngrQD/tEzaVd2x8bt1IvAFAmIkB4HSe/lC/fXF7fdz3ZVWIADdCJGbFSALmIDy2thatxDxKas36JGBKSVIiYhgTJSJEN4UE1kWztlg5A2ALVAcHxDRQ6iV1tJXutqNuwIawCgQNAiQPS0gG6O78nFQLBzCP0mK/S2PO1swBLIA5ubnWjT0dYeamggFh5l4xusTW7DKds6RmVszc0ANy4szs7hbBPw2YNnFDqZqY1NybZiEAUAvz7fYSL4+7qWoSMtfXH0/7LmfCZgqYp6KMcOg7h3Cz4u4AwjwkDHA364SYZaoNggBIEazZBm7fZySG1MmYZCpF3XpJxFAczC0JqzuAgwcBOfrSat8lxfCI/dD1vNkQoFUVZncHMwTOkgGslDVS74AOwJsznCjc1aFPCTyqGjpU96oegB2TB7iGoC0K21skiIccIjA5UFhb6zJD0wAAZoS6QoAZBAAzdIFq6OF9lo5Z4Zl6szmhwiMzVTUNHRJDUKnWPAKwmkP4kIQIi6qbE7MDuBkhSpIIB8SqumGVt3tvs4CIjmU19wAC9J8MiVsydi7WJxbmFrGu1S2yoLnnJOCtF0RkEWL0biBCYsROABCW1S5zS4mPQ57WlgGZWd075rBwj9VttQA3SQIb3A6BiZfSEJARDTwc6P8Iu4EwBMRamjbbDb0BbAntgEBCQsnozGhuwikAV23g4OjqTpswDYECmFGYGBNAnNbiLdKQqhEEVkBiSmEGxghdTsS0qoZFloywVTx97CQnMte9cLi5tdyxW2SAp6kZhYdvZpsITIIezshqDkjvHy8JCIiszZ3wZV7BDEgeL1NKgmCJcVrreS7ukRB6IepSQIhBxdb1ciQ4T3N41OK7vkskl6USQZclPNbSNpgrIBT1LvFa6/RQwnFq2nN8/elhraXP8Pbj0zl6xZUQEmNiFGQgZH22/CXmy9oQHIG6RLtOHiZdS5tWGAcQR3NMksIhMMijtJXzMJc2Py1DJ2OfMkeA73f90MnrHy+vXtxd1glynN+X5jgva21Q2pxFJKXjHvsxz0s5rY1TIqDTZa2tDFly4sSdk83Lcnezq2XCMNWWhVWDEuz3PScK8GVZ/93/+V9fpvL/+J//4w/3S0oSAVVtSxPQs3cCCWnIFOClmgeYxUZabYsBBiJ1w1jq6oZIfNz1qg5CrRYPf3l1QISpVAsQwghghmEcc0rDKJd5Xur08y9fjf3Awh1DadyPnZmfT6fDYd/1w4f7h5zyH757vbZ22B2PO8qJdn33L371c0mprXLoh+N+qHVDI5Qe6Yu76xe3x3G3+/DujRDefXJ3dRzLNK/TaTzsUuZ37+/v7x/3e9a2msL1i9tf/OmX/9v/+h+vb25vb29KLQDx8Pe/ViOzePvjj3/44w+fffqKkXrOEaAY7+7vGfL19YunuQHzOCQN/eN3v705vvzzX/7cvP5l6u4/PlyN+//63ffv3s/zuQLwbr/7k1989vOff/7d9+3l3c26trPX1ImpHq6ubm5uuyFdzk/N51LqP/3zr/tuGPe78uH84nCYVv3w8PTp3d2Hh9NU577PYA7sCmSqzAIIphoOWRgpLEBIkshSmzAAisVWIwgkdgQAJIiUpMuJCfe7UY5StXQ5H3bji5tjbfrh/unliytJydS/eHV3c72fLlPXdfv9fhyGANRWu75PqsMwJEnCzCLPebgIZvnhzRt3r7U6wG5/eLx/aGX99s2HcPvFFy9MKyEx8ziMEHY+n6bLpFpzYhZe54URumPqxzvTNXRdVjifPw7cX6YzAbdiglTWikhvHh8ul7ouy9PTwonu7z+8un0V/rTbjYdjF17evLm/zBdm2ban87we9/1l3QBq/OyR8AAEDSfEtVprHn7+13/+1ePN8T///bchnYVPzR09kSddwU2Ij4dcFj2v5upJaD/2VTfoDPY5EQGTtKbWoiyNmPtd33deizH2BlQKZMHDwHNrtdm8LvuhQ5Z1KZlyUxOSPnELr9USoamFxfPa21xyhCoEdD1bc/BgwcAQwf2QzxddVRXxu/vlzePll1/cfv75jWo7nebdyKaNQ7vMa22Pk99ejeoE4OdlZRBEcLCrnne5+/C01mpdysSsDqZRq2vQZV49yloAgH3MiVBNCbGaqXkC8SAHALCHswrQkNNcfQwzD0Tqhe6rEtJatccsBAS2qMdkQvB0iauhq9UCQChyYnsWT1O4S1jCZ3cGakzqzlTVCIMYdn0GjNaMMNSCCBtgaxYRsxIEAlFsvGTdLihQmxFiEkEKjNiWoMwczzHyWFsDhGHf92Zr0y4ns3DVLVgB2zrNgxDcQ9UcnTE116pKTIbRZbaAuTT1uJR2NciYOREkoiB290ZWNczx8bIe9953fMjMPUUYIrWqpZackiBD+JgQ9/xw0e3kox6JIzNVMwIgBGIkFHdvtjV1AAGYSc229xYiEiERbnkMDzf3XZ8gAiEYvDUFW0MXbAQA6mGARKwBAeggkQdJKYgo95vAOiJSou0GKBgRVnUpxZZlMrOcMxDV2twb4rNbqGNh9LXYs5ksQAgAKaBBYBAQkiAn2XCWAIAWwMzooW4JiZjRbePGImAANGuI2KXMGczcwZiBmRjZPUoLBk9beMcjJUZCNXUAZHSNqg0KJpYITQmbsghnBLS2ec/NNGUR7tV81w8bydoC1rqqxsOpkUiXBNEMyQMk0a4THDrVtjRrHoHAANd9/1hmJE6M5+KLRpfT0vyyVEYwhy5naFZqZZENE7FFLjZHZwuECGJkiNqaZF/nAgZImHM286bKRARKDqrmkDTaVhgKBOmyergZsSAlIgJwUw1r8FwKU3cL8GWZSinofllqVQdCeXbSBWFsoFJ1Xxtud3lfFanVIU1THYbGTMJ03I2H/a625m1hhIFsKZdGGXNyz8JCxLzF7gASehYmgbnMpdllmS+XMxN3KSMm85BSlZk9IAkd+sFdR+kCCcKLNkJQCETIIjml2qq5j70wcxI6Dt35shTVxOkw5C6lx3Opa0nowoyJ1N0hgIGR+y6XWkw9mJPw2HeBRIRLLUheVt8P4zgOas2m4qZEZAHSyTXR43mmbRJpzolEEre2G2ReS1iUVoGwGlit29o+SWLkdS0enlNSa9xQmNVUW03y/Ipd1kpEzBTNwGHbqDf12hrUmnKuamhh4OYGRMy8rYibKgAiRmuGgAFICGZainpDEiBmiAB0UPO2BpWlrtJ1KfdRAwF6Yd86qB7bpH9DQKkHw3bNcHymRwETbf1RCKegCEjCfSfz2palJkYCDCRAR0AgWjdlrqOpMyKB5ZxLw6aaiAB8qavWSgEIYKboQMzb9BB/UoIhk2ugWQOXxBQwDIMIu0eX+LDfkxCnnFL+8PG0llJLW1bf9YmJJ7XTXOa1Hva7Wsubjw8QSASl6NAPzFxbmZeSslzmlZH6YVjmGgFDl90LJQnH86Usjmymaruecs5rrQjwTCvyANSU00BoHvvdMM2lQdTatvRE14+q+v2bU60ffv2HH3/1w+u/+OXX5ohEj/dPRHw4HJDwfFprKQHx+s3Hh9MsKR+HgYmOQ0+3zhDHwxgIzf326vp0vqyTOnov+eFUNFrf0y6bN63Fz+XS9/3xeLXbjWWp5qFsbqCqwhDIfU7CtNQW7k0dIGprAKjhQFud91lsuMlBPJwBgCgJh1lVf/v+4363++T2ej/uLmu9rKsabDSQpkYY1QOA+iRDwush7cZMiK1pbas/1wjdtJVSpqVZxLy2uRlGNN2K0O6+waCCiPthGPqMlHLX55RYctsoP8hmvlqApK4bzEGbRrgk6IRM2/nc7Dlut1FIFWID8qGbtw3IJYQI7mEQ5g2ACKiqbipzwO0z5KWZkGwvucSpmZrZFnTZEq67vkOEZm1txsIAXpr/cH/mLveha61INK11GDJLmqup+X43JJHLWhywGJZaupT34zjVwghdkvtpYSIgJAiBSELqVmrpRFro0Iubr1o7RiRwip4FA91i5Gyh4Z6Fm7sGIuDabJf5kMkDPIIxcp+r6lkdWXrBHvF+KurAgBurCBnH3KnaYsbAVSMJZwG1FoYOlBgjqNQmiIakTYdOPNzNmrIiAWJzVwcIFyEkgggCJgKlZ/fX2jQRMtOqdl6KEPSJi1ptGh4OYeEb4nFpEe4Qvn3fdwkgZFrXvpNtRQMbChxJiBix2ibCQ2tewQmwy1yqCvEcuj0/mylCDF3ukqjbWpQQBUkj1tbQ/XrMKVOEjR12xB6gCNTFQdI8NbMgiNOqlGjoqTUaekoEmYAgIDQFfDy3ooEM6i7IzOQQhKTuZs6OWwjUw7fhMm5VRneHWAOAgASFUglnQkTYzoURWNfGBExCQMFbixuJQIQdAgKrPqcqPRoE5166LDn34E4YqUvgqGCDEGMKiOfRG5oQQJIIBaJ9lzIjMxVtWYAwplL7LKfzwiBPtS3uuzGhx5B4KrUZuhMTXNbWFIEiMXWE51KDOGM0013ulstZmM1iEHLV5sGApbVgpMRQ2s3YEcBqDZHCmjVjoZQoIxRtreo8lXWtn94dErM1K60C0pgJw6rq7bE/z/rx4/nV1VC1Atl51bf354Fpf8C52Th0WbAVK02bgjm6+wK21kqIx31OgoTWCZUGtVQG5oEQYp7W/jgMWVpbj4e+aVxOy9h3dy8O7z8+3V7vcJl/8eUXiL4We/fhMe3puN+NuVYL5iRuwnye67zUIHw4zd58yJJz15qmRCnRVIsGXB96gnZ11V8f9utyFuZpnlOSudRqOi1LWL29Gj//2Zevf/zwH/72Nw8L5CzNQM3cPTZXSgQBJOYsdNznaa1VNbF4QK3aj5ITX0oL09qMECSlwy4TwsN5CVXwQCBhgrC7q92H03no8nG/X9Z6vR8Q4/q4//zFzbSWLe8T7l3qctedTpeHx9PhMOaUS23fv357Os+IeHu4kiTTMv/q659Nl/PT0/n0dAZEK3q2083VTvrOFV7dXn/66UvOXV2X435cluUXX//iv/+rf3c+nX//m398vP+wTPeFapd3XermeZW8s5raUu7u7r755vuHpw93Lz//iz/75f/l3/7LN6//+M//+Lci3VS81Pe//+5Dbfb7714fjt20zm9+OB268Rdff/rNDx/GrjuOfVnbd6dvPnn56a/+9C/d/enx4cfXP3z9xYtxkC9e3f7xh+8O+xfd7mbo827cqZmqM/JlvYw5D+Pevbl6E6pteff2rRt89tlnv/7f/rbMy5/9/LPztBz67h9+98di1apDmDC7bdsvcFcHImIhvDqO81qnpTh6oCcmIjKzVpRFzKOhE0af09B3SNDndJ4vQzfsh/762H32yV0n8vB00WYs8vB4vtofcp8R4ri7fnFzRwhIUpsNw5ByTinpZcq525w+rTVi7jq5nP3hdD7u90/nJyZc5uUElBnvX7/7z//tn29vD7dX+2m5v96P4NFq+fzzL4Zh9/Hjx7IshJCT8G5IgtM8vfz0k+Xh4Ze//PppAckxz2sral5qs3G3e3g6r7Vg4g+n8zLXx2XpO3IP14dFy27IL+ejWW1Nz2stagxobgjy8u6WHmYSCY+t671JEbfXRwR40NvH9fDDh8NuRykFABM6QMCGwiBmrGsrRRGxYwjO5vHxaWIiAOAkiGiqkjhLQvdP747qfv84XR37Lz69+e0fP1SIz14ePr3dT+uaVnn9bm7VcMwMUDXmCn3yau3F1aARZdGyVE7ZtFatTfg4djcd1oSnFee5bBIJMDezvuPEtO+FDGptd4cBuPv9j/cvpnns84eH819c73/+J5/97//tHw9IfZeL2VobMTBLLfXxMpcWInh31R12GaI7zW1dy7uP9bDr+y5vVqKm0EITCxPWtbIQgFeNfeIFMSB6hqk6A2Ym9UBrr26vl3UtTR8XbQYeGABdnxCxOVRVQlzMoKGqJmb1WGojSIlpXdt+yA6wmlYwxsQMj2u7rOoAYLhlSPZ92u0SEZei86qksTT7iRQKDS0cmLjqc1pmmxk8B9gjEBjJydyCzJy2KBAhIyCihm+Aw7AQROctUwMekYCaGyL9hEeJpdXEAuhamyPshrwTnizO4c3gUq2573vq+0gEYSBBQl4Vw8XMVIEh7HlhbEggQJtDExDDkTi6PpYlmLlLtPVLBAAAhKmq5STCaa1a2mZ3CiaEoKJKTADkDkwbVdCFpTZTdXN3jz6LQ2TipcWA2HE4RCI0t9AN+VKXy9MwsCRZTwTATCQCK1AzIkmBUKpGhKszBSCsrSBSIAGSweYvQ7e26ztiAvOATd1N4GDx7EkiDibKQkK0qAIAAUNs3xMiJAZQbUgMSIghSD95XhWdmnltmrLsOyaPi3onmCgDgFdbqmaSbQ8asSUpgJAD7NDlpXJRY8Si0FATGkNuFAnRICxMCKtrItS2FA0jhmQ3/ajqLcLMCaC5jzl3KTWHYUhASxRbFRrQm8tKzBrKgO5R3fqUUmBTkMQQNs8192no87yUjYC5JSSciMK1VUppc5Ehqm1uGWJGdFULZ0RmWuucJA2ZAVtoTcwMRuDQyjQtpdS+6yJcrSEmcCu1hBkAlGbmz5K0QIxwIqKUtrlPBLoDIPlPbnkHrxqIQMzgcZ7bKQDPdez55tBdH/e7YceM87pcLhME7FJMdWorAPBqCVA45dz1iLAENYXL6fJwOSFSOBKkbe4AQIAoQWgOY9+XWiT0MGQwXa0CQnONcCaOYApIEAaQc/aIsjYOtOIe1Oe+EyKyx/PTvCijdR3Nq/U5ocFmudpKTBGUkxDj0CfupLWY5+LkZNGqPvpMiIlp3HWXCUozD7jcz4SwSfu2AZiZtXARKmpm3mVpLs09NoEz+Np00wKllEy1qjJjNZ3mNQkNfYfMW8nGMllrpo5EFBAQtfn29gKkUqu7b48hFt6Cf0SopoiQRKoqRBg833yIhJkBo5mBRiJOhM2tH7pm3mqxVjGvKH0gt+AN1BHgAbGVAjUwkQCEQ7gDETKSRVg4IT2Pn3FD9Le1KTKwGzJJkLUGCA6IqmbKiT0USI9jr+5rXYbMlSBhlPWytgKBP+kPAYAinAC2mAABFLVwA8DmHuYsQsxq6hYeAAHq1gPdP06qVkp7PM0v7653Q16WmTjlnN4+XJrqw+NctK6q2kyItNnpUl68uFY1bSZdF+5B8f7DQ+bOwvuxH4bdeVlaLfeX9XjY5SQfl3Wa1+tDcGJ372QLSbp0qakvzZpbrbNbqGqfKafUzMItMUrXp5QB42lq/+UffvPJ3Yvb6+ulLDfX16b+dH9/usync3l4mk9zbS1iqUOfgeDjw0Of86cvb4UJKcLj8XTJInVtv/jFl3e3+4enp//6z79fFpoLLFtbJqKVdTpT3/UeABBdYkoUmISxz1LNMxEQmvk4UFMd+9yaraoQbvbM3G0QREAA7s84l/Bo7ltl7PE8l6qf3Nbj8VoY52VWU2Ks86QBu510TLsMQ8LD0DtEKTWsZeYW1nRdm7dmH5/mS9HmHtuPPgyBIMAhANkCVA3Rmk3n8wQAmYGYWQSRr/bDze21M13WuqyXZT1x6lIeEFFigWWN8OreVCMcgUyDmHNOZiTEEUaEZlGXxhSJySyqNQQGFsRAM97MdYFJkqu6BwslEd9iRogAyAQIIbiF5gwAq/mxkzC71CaS2rpCYrNgwMPYT7UCkgcKUcfADEPfTUsloKHrEGguDZkSp0tZ1BQxoUMv4qBd4oelWoQkMtPEdKmtVv3s6vqxlFr1+jCe52WXSCSmFijoEQxgiKtGJzAmQogkouZmJkxE6OGramZ0Q6EMmQE8Eap7rYGSCCGlFOFuWsOamzAnYQhfajMzBAeiCBdmc6xmW2VB3QFI/blLGIHozxybti1/HMxdmJuZWWsQqrrrUmmxthYOTGTupVhi0bDWXBiGTobM4Cooj2szwO2Wighu5o72XG4NoA03BsRoFki4TfyYsWozN49IiGPX5YQWDQDG7ichS8S+o93QJ8FmWxba1FH6LGHrVE517foOEC+zVjUK60GEAQEgfJdAhNTgafGpqXskpEQUAe5RmyLRNmj1MAwycCTA8MQEiFv3GQDJQm2TreL2RTU3DCREi+iyIG1GVgT0iAAHQGpNzYKISlWELcfLwmwBHnHYiZsvcx1yQkY1VzVzFObUMSG2qkHeDVAamPuQYOzkaVohQA2mZS3qpcVczNz6fvMQtUAQga6TZqC1VQRiSEToeLVP5k4Ntw1wrT5Ny3GXpwanp5nHbtK2H/IgxJQUoDVDj5JtP9J1SnVtZP5i37tbl0Tdt2oCC392t7895GmuKXEELPM6e+z6ft93u07AfB5SczstJdwvs19mEzJzDaBa7XJpfeYAWKtu00n1EBEA7/PmYwYAnNY1M4FQ9Rh7fnHIYyem1h13KdFa5sOubwHN9LAfm8arm+O0zPuh2w1dBK/nOorv+/T2/dOc5eXtuK4G4F2fL8uyk8SjBGLHaU2QErsFAHngwzTXuiJEl7gu89p0bXWq4Q6jQKj/6S+/evXy7g+//+5v/uF3pzVM0WKb4gVtg1JEhBh7yUxqbVmpFFU1jIAADzSPZqrNiXlrvUHY02UGBwcDiiwyzWsp9XjohiHlIqst0mg3Dpzwi0/uduNuWUrX9VlyOBBxqdplyUliyBsAbZondBhyuj7ukWgt5bMvPz3ud6enhz5Tuj2++fjQdXh92CWm4yC3t68++exunqb5fH8Y0u56bIdhvjz88M0/vPri5199/SXFEjfj7Xy5ufv0Mi9tXR4+PpTFVI/jLj1d0tPpdDzeffOHbyLK+TxdXb148eL6w/v7d4/r3/zjb8bxUJv+T3/+Lx+ne13/eDNcg/uXr15cXe3Lsg5d9/LF1f39h//l//V6mi7/4//07//q3//79L//11/9vHzx5Wf7v8Hz0/u//9v3l9NpN+ymy9xaWefL8Xq8uRrn+ZzTfn99u8zr+3dvuyF/9fOv/u4ffvv2/f3dzfXHh3NAfPvmw2WtIqlVBXBA3Cp429EqEWHEJ3fXr14d3757XJZq5nNbM2d1QwgmFCZEVFciyTlvk1tm3sILgn67P+zHcb6czY2Zmta3Hx5Tyk6eJJ8vT8er492LW0RyN9eaObvZOO62+LabkSTJqaxrM41wkZ4QkbHLaV6Xm0/u7i/T+XJeyuXx6anV+m/+7E8Q9NvXP3zz3ffhQeCJwAGaKYS7IlIs82Va1sM1WVt2x/2rzz/5w++/KWvb7QdrkSmnfVprI4yUBRDWCsu8rplq+OOqj9M6CrIQoNQK+zG11bXZ49Nl68BsEpz4P0zG6PbcCQPkb96dwp/GPru1prTVewnhfKkvb3YyYNN6e3fFCK/fPLXgYnAUBMRpWcORmNTq1X6s1dBsN3ZhABFz1a8/vzFt7s0AhnF8XC43VyOBPS06lTJmEaGpWmvtZofXe/nQdJ+7h2Kze0JqZmptN+xISREuc0VCA1P1ROgQj5eFiTjh2FGpbTVfFd4+rgQrYrz+8SET7XeH8jSvpZaqYNZn3vd0u9+/uvV5aZe5rqUR+mHoxo6bxuO5rGsJiLV5qYpIoTF7zaKAeFriajd8PE35MOwy1iAmJjAkalPBnByDI+6ub0/TWyJKGOQxdpCTLMXnoolQN0ooBDEJhnTkIKsaF8wpzVXNIYAaeAKfFj2tDswCYBbqngjDvCyaBMghkwR5L7ThxrZu5EatqE35/5dCQRSirR8ttF10ISWwrQQGoOpCxLTV0DfWM238jS1DDs8sDDR3AbQA3wRK4UjPItC1VVMBxC4BBmaioZfiMZ0qgN3scyeJhSPUCGuNdV2vD33HaV0WFgyAvu/ESd1r01WjeWxD+xZBSH0SQmDGZp6YepFV1SESBSYIAHdQtQgiYAjYrp0RyASqtrl0l6qw5cnjGaOwVm0aghEEx1Gamwb0icICkXddTl06zaUX7hIBGjOUBtNa1k3eA4hIujVTt54cMiBC2HaQqQ6x1sTPTJEtFUgIVi1JqtoCkDlhRCZ0wdLKBpvW1lgkC3VC54KqmhKHew1FSoS0I1TV2hyJhBkBVrdAHzvOKTc1c18otvh99WAEbw6IzHgYdjljaU2QNDAzmHPQpkVKxAihta556K022wYzFl4NGICsE/ZmzX2LQYiQQZRazeSyFHfU6iVQiMIRidZahbAXqda6xGPXP61lrUUwnZ8ueciA1CwYwRw9ILR2Sfa7nUdUdwdMudPaPKKhISG4b/t1ryVxUm37ITHhuk5NPQuuVUtZVG0tPs/TJmfaJrIK5KrIHMhq/38dH48AJ9rkycCE258QSR0IkAiCI/xZ2BLgOaUucUrEkuellNpSkg0b4uENgjZgtc/k0BTbGnVJTSnn3kPWZhgbvRqDaEuZAvhcqgRwoIVrL6kTmebFwFJOpsFAgNisMXEWiLCcmRBWNXfnbYX7XNcBCF+mtWlI2hz2iEBDl9ZmrTUPW9SZSHJiRgCc5yKUtsGxO5hHqCZJa7EAbRbNQwiFWN22rSwRA5Or+/NzExOL2la7CwfIIsSozQgRhHKXwKO2VlshgH4Yhk7UYq2VibfzGTMjgJoGYyImxFKViNQswHISFvGALKytLWtDQsRAxwq6jbwDHBxquBBrOIXH1nEJB4yNALEb03b9KM1auXhgUAJKW+jLDJjCPRhlQ4RHIAFHhCEIMWxJcvBnGDRDMBNSmG0AL98k8GrMArxxhWzoiahjIgsaE5pWrWt1b2GbtdvBERCBAj0gCLnLnYW2TclIAUCqTZgRg8jBkSCYsLR2mk7WkqT+/vHJ3SXxsi7n80NOA6ClWqdShMRaWWrb8o2XedkotafL1LH0XZ7nWYjN4+kyj110Kb1/OA051abvH05CMl+e4HgY+06ZmMnNimori3A67HtHfHi6lFoDoKzVzSNAaOjG7TndYJNcRguPyxIp0dM376u9VtNS2mHownHD/Hep248DpgRE59PFDS+Piy4Pr9/eS6Lr3YCI49Dv9r3kNB6ujLp/+sOvP5xqeZamS2Y2d7eYp0stZRh2KSczT4ktYqqVCABpKjU9z4Cau+2GEbmRkakCpICotXHgNv0NUw+XEPdgfhaUE0It5Y8/vts/PmZhBOsTRIMBPBCkYU8Jm3rIx/kyr6u6D3nou77W9TQv9+dl8+8gAjI/L1mCAnBjx8HzSJI9wgwAUSCqg1lEUYA6L6u1BkTj0F3ldJ4u54e3wTx0+ZCBhZphUZIuMYnX2vXCImrOiBHm4a6eRJio4+QADpWcABFCIzaborVWc+7cdMPPEIFa26YGLSjCGcIjFtVVW2ZiojHn1jwAuy5t+u3jOLy5P5m7AlkoU4LwQUREkvDaGiImgse55JxBba71sBumdWHEpZSx71Jmc1xa64T214dA3yqHNdquT4YmGa/H4bRMQ6LEWEwFUYMAkBmqemKIiKdlfbEfBIMQNLCaIQYBNrPm2dRqdRBwbUipWQCgxU8CBmsOaEaBKMI9gxssDhs6rssZIsChmroZEW+VdcDYmsjuAOGIm2MBAClUPUJYwH2bx0VEn8Tcl7o91qC2thHxzczcxz7ve6napqWMXfdU2lyVkJr5M6oMABEYMCCYmXibQwUGbO36SQ0Dom0kROuTDEkItBkw4U9uCHAPBugyAdq8mDAyk1ukvmu1LdPcd52Qh/v9U0EkpC3H6yKUBXsBVZqrr2qpSy9oeDytiTknLs22sXrANluNDckcGBSoFpmBAJtHBGQhBOiFRHh7iFdTr7CZVrcKJDZnkUAPj23UlZg8ghwinBhks6sIe7gECUIrioj9kJ7xIh6AYe6n85oYr/ajuTOHR2hzZprXOi9lLqYOzdwcPEIYrvfpaW694FK3VQ64GmDs+jTu82VtRI5IPTGELqtFQKu1AjMigCaRMhWMOK1VMhEGMGbiOrXWmgj2XT8mCrfUdRDLNM9dTkurEbARoK7GNCSqc7Vm90+zNXNKT1M9DEOfZb9Ln9zuamsPUy2rr9UuqzcHfSZs4FKaqnWJtvI1h28eli5RVY+IJPzhslaNq30aBc/Vg/Dzm/7HjydXfnG9vz8vSeSTF7uPTxdUUNO7Yz/XeJwbx8oIZlrb+uLudirVwb/6/Oa469x9rk1yns5zWERIddWm1JEIY0Azrc1LfvisAQABAABJREFUXQ/7bkhJOM9LEeTHpxkYhy5f7fqhoyz08uXt6enyT9/+8PZil9V/MqgGAOAzJT2YuEu0H+ThrKeprNVr87bhhpDvz6tD7LqOCZ5joa02j1dXh2ah4IgxdIkSOth5XYTp0B9F5NDnq+NuN4xMtBs667txHCMw3G9ujojQD93T02leltP0gEBDL5wGAC9VH56e9kOysb+52i2F/vq//Pb+/vHzz++q1uNu9+qTF3cvb4ehuznu37+D3dgnpsuyDn3Wcnn9+7/N3R69Uc43Lz6ty/Lh3QfXlnOOaPOqD48/JumHvm91xhjfvXv353/53929/LTV8+X8dLXf7XcDsx26sU71V1989U//9Md/9ZdfkkOX6C9+9TWg9uOOKP/Fv/gX02V6++4eolunMs/z969/3B2v/vIv/7xpDENf63qe5g/vHu754RdffdF3iSWY8rosl6dHC6gabz/ef7w/v/v48MmLW0D+4+t3l7VOpWJIW02ShIM2b+7hzoSUGAIOu4EZsiQ1b2qE7ADj2K2lukff5Y23eOiH68Nu1YaB1tpaSs6dtird1bAbyjoXV0xktTo0p/hwvt+3ceiG/WGIqNPlcnV9RYilhWobd4fc96ZmHhsEWmtd1sIi+33/9Hjq+5FqOT1MpZSPH+8T+C+//mpZlpzhfI7H81za2swezotbzcJDzhawwem3NN3b96fr3Vi+/S513TB0Xe5ubq/940PTePt4Ig2NSEOHDGRo7uGYJUtCCGmmU4lWiEhvD/lmtzdv5/P84ubWg/rECQkRN4MNblxDCEIwD2a2iNKiSzwv7ebYs2op2JoPgyDEtLS7Y89kgtYUCcWbtao89htEH5hzlqt9XledilX2pg0Zv/jkxfuPj199+cU6nR4eq0h6OF/uT8vNbihWrw/cElkwhl1KHURSImF0Lz97ecUnffew3B6GaVmeLsu7nncDa2tX+1w1qrcAaB7YcD/kpVRU5JxPRdWMCREwJSHw43GnFgwiQMtcHLCgCmJZqlfbOj6Z0Vpc5lZqS0wscnXIa3MPHFzW4tW8306JRZmJCKdSd12+lPpJlzCimYdW7PthkMfznI7Du9NjN82AMWRILA+nNW3ouBods7oFRrMNPwLntQkTBGHAaWpZqMvcJWoaarGAjUO6YzvNrs6JsRPJCXMCJggwABBCyqQefddVtXmp5pv8AYSJCDjIN4ExBjMGxpApgOa1JpZQ25A+gFTd2SMxIaCboxDAc1MBCDxCzfQ5CoEahogBVEPDkCmSECNXbYAAAIRgEetaNRwCAfn+1G72uD/0gbDMKjmZ4XluTfQ4yG7Ic621LCIZApNIqy0MzYLQmdDMS7Q+CSEmotJ0yDwmmotuae+xy4gh1E3FHpeKAIwIuLXoeJt+mruGCXJOuVqN5yUSePjcLCC2VAESIDK4taCPl5ZXCCLV5o5MlDpOiXGtjAgRVTerJIIjEloEoiEQ0KZvCgiwrba2EZ3DzazPmYnUDYkSo2sDJgIahCFSU2eUYGdAVc2cCKm4k5kwWVhYzSwESIk5ApEyQQBWB0GKQNU2L2VuAQiGvv0lYAhTNScgtGZNSCSFr6eFhi4TmHvX+8BoAVOxCrHWhZhNVQM8wFrLIc5Sm7paJgbGgUFbLYxDnym8Y1ws0D0hCHIxVwtzr+Zdkj5ljZjWZVXDRIR+nfrz2vqcltaaaUJuZixITKoOZGAbxilYmMzNtKh1wggpIiwgNBBpni9MVLXWVhMEYtRalxrNnBFy1wdQKwWeBe8IAQTeCQf4rk/MBAHT2gIiJ95aRICoqltAaKPBeAQihFkIJ5FwI+S1tGleX10dUiedWpdl6HuR1HwOjOkyq+N+zIlsLgrWWkUt1QA1EABZUgQboDN1jKW5OciylJvjcL3r1mld19XJidkbqm6/8YSMQuxhgugA7pGIgbcRBHpptfrlXNzNHZFIPaohAhb1QDZTM+1STsLNzLZyHENdC7NZRA4ws60gqurgMV0WICRENYBNaxVAiFWb1yAk89gCgmtpqu6h4eABmLA1g3BDMbUIS5zcN/saJknuiGCJeC2FmFhEkuREEN28lJQwJW7NGMEJPNgDrDYibGGtGW2fs8BqRh4IYIi0jZ8Q1BWJNoqQqgEEMwnw46Uh2dAxhItgJ2gWzUqpq4M4sKPwkAS4lVbdh80DGd6a7ccMYUSgoeYWgczsgd5CyJuaICu6miYWSbD5nVPiINrqneuyQngJT4RZ6LLo84AJAwDCwMAAg5Cc3Vzd3Z9DWxFhQy8e6AFjSpTDzRjJSlG1U3PAChYkSEIfn85hPvZFKFnUgEDMFE4IYZZErva7uaxCUksbj920rhy0qvZdFu4fzperw/7hdO661HFa5pmZHKC4J2E3a21d2zachd3YlVZr1cu8ICILIoKFEeB5XldtCBiugRicw9rWa93mtaotCQOmh4tuj0sUpOCOeW1hblWD2K+vDqXrHs8XX9vDaUWiROwEzeK//Ob7zUjqZu6QM4M3QkaC2OaArZ5Nx34IEg/coj0fH1chDILj0AnheSqm1gwAwczcfOhSznnICQLW1oSlgidkYTKIYehdda5GEM3VA87TTLiphhW3BTIEkoxpbc2RCAnVAhAPA76+f7qs6lvtNqXD2C9VzT0sArfWKkQEEvlGwX2+zGxePEJGdohAD1SANw8TIvRd6fu0y7zv5P48l6VcCLPAfkiBsq4uCB1FRkAPIAnEZr7peTCsrbUBAKFsQutnYYRXNWQec9oq5kAE4bUaBnedREB4MLs1T5xMDdyHrhPaMnRR1QwCAUzjw2lqEMz8uNbMvNS6z4kJCN3Np3WZ1hoAILhoPeY0skxlTczNnJh3vYSbm+/Gzld993CmXc4MWtZdki6lcylEKJzGTtusk0bfUdNgRnet6llScy1VCdA2rDsSM5gbAQxZSI0p+pEsbKlKSCmlIGxaFyvWvM+9RRBQBqzewlGNIaDLDFuZHzFsQ1yrB4SFQwCAugLEMzbr+VcVEJnCacOvhJk3gucBApMsW9I4wmNTEmybu9h1qUu01uoegPy0rLWZWiAgb91yxg0fKCzqEeCqYc9OBN+2jczo4aOIBXS5Z4pWltT3DJhoq50DAHTMgLjWhuD7sRMCC2CRnIDcdjf7lNP9aX66rH3PKbE79omHsV9aa2u5PzeWroaXqqUYMfeZhi6d19LUcxKBMIWAEMJAcgizLXyO1YDBA/AwpMPQnZY1I+QsKQtzqKbLolW1qe+GlKVfFg18hsc19ZSeBRGV3NyGLmUhImrmApQYwn0LF5mHu+OWGiWEcAQD5KqG4kRUV+sTu/vTVAOJiavZJhABAEJci7n6tDwTNyIAIYpqRpzM11oPg5zn4ikPHc3rGoAeMBW9u+53XeeuGB6EsZVMwQmpFEVwyTz2cHvswmCuKzKtrXU5WVApOnQJkbPA3b5jh9r8aaql2W7o3z+tt7t8NQi4djyYw1y0WejSqgYQMxKGg3lHjIjn0qalAbG2xik/WwlUKUANHrQ4uBB0QkXNPa66BGHHsSMAd5uX1YOtNWC9O4zhfH+qU7Wc4OtXVyLx9Ve3n718+fC0/u67N198cv3dD28fHkvquqU0FNnv+lOUS1mO+2Ft0SLqVPqUpqVepspML45yve8//+zl/f29KvRdHsZ8GDtv9bDbffLJ3Zvv3/39H77/zY+nWhAA3CHQt9iPhzeLjnGrje/G4cePS7VYqwGRuWtzoiDyse86QWGoGomkNLNamjXpGBRabUPul2VVo9zLzbi/OR5O64wIneQkuesyAC3r4o7D0OeUcpe3WSwjHw9Xr9++W9dSqs7r8vrHdy9fvMKNx+Ha5fyHP37/9v3Hf/WnX//Jzz9jtJcvb7/++qub66tWy243fvbpi8eH+3Wpnfn9/WOX883tMeXBDX783R+c89jBrj8uMwpFc3DXxDjPhYgfHz9c7a/+9E//1c314fTw/ne//91allLw7ur66fTwySfH1z++EdAvX94t0+P//f/2P/yn//RffvfbP+YeXry4HoebusxdZtfyxz/+nkKRAhG+/8N3f/EXX//sy6/7cY8xvX37McxrvQzDEcFJQFucL+efffnl/nD19B/++tvvfv9wnn71iy/vn5a/+80fkHhtFs+mFHfDDU8rEMFkEaFbYTNUvTYFQI9ggkw8Dh0EzuuSc8rBXc5DZm/lxXFf3TCG1opkYRyPh1GSqDYm0bYg0c3+2szZQyhLTm6ARNqWZeKUE4u4u6qKR8q51abaKLw2q7UiUqltWU5qmyQ+LtPUrBGxm/Z9enV9TJRaKQDYpXyZCoQ9tTnCl7X1OSOhu4swka/n5edffpoF3v74lkgop4fTMq+tAR66HrSpurklEQ5EiMN4KKU8TRUBpqng0H9+ffjs1RUSgQOEIctU7bI2FsLgWmtOEoGAqhHILBylNSZh5tocMU5T2e86RgOn09K6jGbWWvv8brfL3bm1ccwp2bRMDoYcTQ0o+g5d1TGycBYees6Zz+d5N4wE0OX+5V0WBmE8X6Z1tV1v/+ovv9r3h//4t/90qtTW5XjYXx94GNKncThPRUuMCUSizwKAU9F9T13CUmyf6GkFRKhNKag1goCh62g7XgEKbg99DcKmjSm3iFfXu1LKVJyzOMGi/nAuU4lilJkTRYQHxNgHEVRtIihCQ9+34KfZcseZSRimRRM/b8sIsTglorlaIK9LRaL9mEzt4mipINL9Q3t10x93uZkzobuuxQAjCWwxh+3qlAieTjMicpJ1rR1l9efTnQI19/0+vXqRifrTeb0sxSI2aWkEGDzrFxBDPfpMEXJZ6hZG8YhwEAKhZ60tACJAVcssiFTNqzogMON2IMkimyKUiYcsqpE6FObTvAaEYpgHAttPcExzA0BiJAK1oK0wAciEFlaal+aZAYl6kQhci278yaGnLMSULaKqVdce6OZq1Kpufi6mgFdX3eNTsaBOGDHccW3u3ohIGIloqbrvRRjcITyq6pCQoSITUEBAFrGIqg3CmAjwGSCvbl5jU/jMpQiKRyCGMDcLCGfGuRRCEgRHWFvFTcPZlpxzVs+ZAYyJmajvQA2Lb+JwNA8PJwyMzSv6bCIH0I4zIm5DXDVnIAMlSrUoITEhgjMgE3S9EMSHZd31QyDNtbkrogtHYvAgBx27CA4PEIYuyTzPItLlTOBLa+FIwImi1YiGGpokVY8GmhP1iYehq6VkFsm9Fb+/rChobg/zCvb0/J1CNgsz7RI/f3mIBFW4JGFikoAExDkPWdyshXcpjakH13WtY5fNdEwYmMySmWmrTWheatENl8nQIaL1wkutgqAOxSsTjSlH+FYxq2XNaVxrdcEwCAsALBaB7af3woqb0gcsLG72w93V4fu3bxdzZO4JzbzWkkUYwwP6zNe7oWjNTIl5GPOuFyCcLsvNQR4uNeUECOvaNrDMWhoTtQirFh6J8YsX+4e5FLND390e+vOqH5+mqdZ1XjwC1AHhi7vjvLSHubh7rXaZ1rRt/xADvDmGKyAGYCuKSEB8mepaM9C2BxdhlvtpEQRMiE7hsOmwtpKCkLhDex7v0GYf7iSpx2UuZVnDPZ7x0wEsjBveGIV4rQ0j+txFgEWwSC0tzGpoNCuhnBlANp1WhJs2RvItDxXBSArhgAKwgWAJYLOkRwT8xA0j4VZaIoaAqrpBkEJ1nlvfo2N4OJNEQLGWCLouqXtZS5d7JFJtsMntmrkDIgxDD7Wtpao5IUbgslZBDAAm2sKNEKhgtC07NmFoILgDoz9jv9EdFJ2EIWxeFQGy0pAkC+96Mlc117C1rO2MkHsU2aeUOSAMzPcZPRbedtxm8GyH2hDtVjZ5CqJHMBIhAJGHCxIRR4Suq9aiumX0nJkAuBnw9o9+Ut4wYZ9SU9PWihsjPddvIBhRRKqqmw1ZVNuyVhLZRIUWzsxJZNHqTauqBFv4us7gTgKI4ao/+/TV0zRDeGIMa6lLa437pzMEItLSlqK5IyHCpVYiqbUZe0qpaHMHJg9Td9juzwCYEiXBqm2pjZiIMRAwQAQJiZjMwbQBgCRay2q1OsUIQysByLt+bLW2ZgHY3FNGCS6ltuqlRVPPQgBaStYAJnRnZATzqdQg8gh3z4IbuImFwsMJiEI9NrEvIzfTeZ5YZIV16IfMMtXWjQN6XJZiEIhCTG5RS9sfRkV7fLocd/3d7WGpVSwkYalmQbv9aG61Vn9+snEAOoQ3I/DcdR4M4ZSTO7h6YSZJRCgi6IYAT3M9r5WAAJBJ3DdzsPu2swEgQAeHCN0mUiQQ4eGMm4x8S+eibfxVAxImhKq2nlvJ2d2qA4U6cDV4uixA1A222w27lMIcCChsWqukDgPcdFlXCKq4CnNtbmZMqdpmjGChBOFgjixbQA8BciazhTh1idZWGUW9Nm2Z6WHVvk9jl4RxVlXFjZjFHhkZHBDg2KXGMGS+GjMxrVXDrFRzQElCjudLRYpdnxBBVQdJtempVGZ+PF3mxS5m2cxMAYA2CbsgBM1zuR568+h6Gfo03T9xTpiIkQFIlYjAw8w8j51a86iEtGgDhCwc4IQshIwgCR0NQGEjMgFoK+bOAmOfovpmhwcEsEhIyFjLIpgMQM3AIzA8jJ6VFZtOO2wbBIq4akD0SYQ0vDm4ByJY5m31tCWG3B2RoE8kAJISIDzNCwIhUdUS7l1K2/wTkYbEyKAVzb1FA4i2jaA2kRwiIaq7h+0y7zuQJAZ4vqy7vneATFvSO1pppVpKQowQfn3oslBYZMEuQyeeu1EjTnMpzXa73HcbIhkjsLUGTbUBk5g7BiDSUk2X1uW0NHX1hMiMWj0CCFnNm9VAEiZC2AiPgSEIY+br40gYoNZso2BiJ2Qdp0QRkQh2Pe9HWUsDxLVaTpCY1upERBzMDBBF7SeF6jaIA6LN/gceiBEAruoRMGZGQCZe17bWQrS9dBoQ5cRmgQCM5B7g0DQ80B0djPB55BcIQ+aO0MyzkFuEQy+0lGbPzzFFDAQ3p7V4bY6ISag24+cAEXQ9l9Kw0rt3l+oaFOEuiBp4WZSZpwpuAA7LagAxFX1cVCjP1ccsr24PuRcCf5rWD0/LuQQCm0fA1m1pW8gfGbospYm6g/m+7yRL05aEAzAhLrUAxZi5qa+1GcT1Lr267h3ixfVuXctx3z2c5cPDMvS7u1366pPbN28+fpjnqeJebOiub27281zvHy7ffP9u69k5YHOra52bdhgJE7D3WSACIMxjbe6gFtEN6bjLL14cP725qWWpaq3qi+vdYZf2u14Lff7pqx9/vP/HP/zwmx8eSiXciOIEsJ39I1ydAJDYIZ6W9jQ/rupbJRkJzYnQN+5qFmlhrQUTEsEwSDdw7iS22gRC0YJIncirm2uyULNP724yJ6Yu5TQM43maPOLu7oW2Iimvaxl2+75L7rGcptvr6zfv3mutGADIX3766uPH9/P5PHT9f/qbv79cTr/86u7f/MsvMdyaL5epE7q9vQmAy+np4WFa5hbejoeRiL795tvpdP3FV78IyX/44c1hPNq++/zzFysua9Uvfvby9PH7Vy+/+OaP7zHq519+Oo5iavN5+fyLL8fdzV//9X/88c13HedPbl+Mnfzbf/vnWjWP/W9/+/v/5f/9//n4ePqn3/+orb26G//yz/7sux9+TIkOxyskGzoROjJtiMOUxE8PH04P7xzixYvj0FlrnWlZ6hIRX331GRN+eP/m5nq8vhpz7sDxu9dvAKGsbePrh5nIVjl0JmKEIASHMWVknNYVwb/7vmhQEu5zToK82X2IEOOw3+2HTk2LYHNLQh6+60Zw4ySny1xbZcGr/eH6MCLh1eFYyoqIXU5X+3G329eyzPNMvEpOa6naaq0LIEgSIp4uj7U5cZaUzd0didhrRczDLl/VwzQvgQDg6NHq+tmr29P5rLWqu7bSmp0vy1LLbsx/9Se//OHN228/npbarvf9zZi1NaLu5199+cMP99/+8H6/29V65sC+76ZLFeKx3yEGIU3zen85CRBAAMHd9VhLIcLSGqKPeXx5e/vh4VyXRiTa1l0vx90eIk5LBWJmWldPIscs7obIjak2XashlM9uh14iTfHxXJEQiD9eas5T03bYdy9v766vuvO0NIOuk4FSmG1b3tbK1W6nEaQGEMusp9Mp50QEiHrc5f2QgODutmeKUterK84xLNP08iq/uMnaIrE86rpaJMam6q5I1El2x32f1Yqgv7xJj1Mj2H7i2AtRxLTqvGgS3ESoqgAQp9Ms4JJSN3Sfvbz68cPTXA2DuOdhNyxaQL2oBot7IIQa9cyqdllNxHY9HvbJQ8+X9ScLsZu2QF4adOyEwki6zJSSMCDqYd+b+VQUGYbEpfnDpe3HVGrrhY6DfGh1WRsMiQCIwAIAoXlglmfKpMilOYITQMqkGtNFa9E4xJefXr+83X//5t3H80IkZraJrjwAMZiRKNQ8CSVh/0k3RIgR1mcG3Nbf4Q7uTQgOPa3NEm0jj415DLWpO/p2gio2LXOXZOxTgIXH8/IZDLYsOAQybtsoMwhTjbAwAuqGztx1Q1YTl6pj4t1OiOnxNLMk7NhLGToZcgJtyGRm67wI4c1xd4P8OJW3H04BEO7TasTATES8NMewLDxk2fRMiXi2cKd10aVSYuu7dDXkudja1NSRCcCbbV2F2Bw/QbapgiKiWWNhFnbzai6E7g7CWaAjZCbLMK2qBoGgWhP34D50MK/NDfo+hUOdWkMJe17WNnBhpNCffg5gAa1VZiHkRGGm1YyZBiGz1FybKgkzRpgjYWLKTE0ri3QpETG6R4SHYSgTtvAM3rEwylrr2iwBjF3ahKZbZ6C5GawokEAIoSdaPFKSngkJj9f709P89v3jaZ6tGRCxyDPfyZ0QPYyR+pzMNLYyfcSGniQCNWd+HguNOTHhRp2iQBY2N1yaEGahMRMQAZKEa9PEUKoR01qqO2NHqr51HwRxiWCAzX9tEWDoAas1NVePiIB4PnFZa50QehMMIkiMjNwNlLJ88/b+4WlmxoiwcCaMADXPSbajxXGXq0Etbdh3w9idL0upPi/6yXX/6Qt6/XGpzRBQLSBi6POQZV3bZG6Iuz5tOJrSrGObqgHE3c0xM+x7quqqbWPWHQ58Ubfmw5jPrURQIqzaEnOfJQo3CMUA267REB5aVkRpYNLTGVzd2aUzcwhkBAMXlojNU78F8iIAE7GFM7p6lNpguzE8e9SAiNSDKNzMDWgrxUMkkeZmxQgw1AEjiKRLDkH43PPcyJqbcJgYzS2Qs3CtVVggwt0YMZ57DBsiFxipqZEFMwuJuXd919ZiphGAxNNS+j5nYbfYxjcesQmkJOemjZ3MrZYayEgxsKj5Wm2z6ppHytncYBuBhiNCc80pNTXeyIHmjsBEm7gpcQaIZr4pucwjodOz6t3MaWoRrY6W9n0iacc++2AeWK2tdQETNYbtnRQOjoRc1SEYA8wN8HnyTUCE2MK2qr9FhBpv6riylLII4VobI4UHEqsHugnB0KcAMHdmaq0RIYonQglRc9iinASI5AG1KSAkoWlezZ0lN2u+hQww3HjW4uBuIIgYFoZJpDX3sITQ9d20lnkuObEREfBlXt0tAs2xagEEd69IEIDkEcZEidAAgIiRiAA3a/Z2J47IDFmwBF4dBiKsrbnD1j1GAKKw4MLs4Z0QMWwS0Vatlbrr8zjs0mE8ny7CdFlq9ZaD3a25pcQpybKqrqVUwE3waOaG6CaIQKC+9R2xqhGS8DM/QD3CnEgcgxAQKTDcTVVn19xJSlLq0qe8/faSuQg2b9eHTqN2WYYX+6HLBHC167vM7x/OYFC8vr9/EsTS6naXLqaAwMzCEuG1bSMh0upjz2ChQRywVn0mOKkroDsGuDAjglnU0O0t0GfZ93krfufN/YZ8npq5mwEAMmNpiggGdBxSl3DjQLlHzmLmc21buISZHYIBOWV1aFUf7YJ9Puz6WctSdFos5cbMtTXmzrbXkMW0roJsrB4GASl1Zo0imFKE8jZXBVQt7pE5q7aMTAyXZWWgRb3r05gzQYRZT9xCjWHoE7lGAASlnHuh2/0eAPrcPc3rZalzaY4AAG7Brr4VQsObuUHcn89LrWbemgU4IufE60rmG6UZkZBqhNl90/dIXZ+uoTfLQgzI1iDUgDwzuAQEzrX2be273HFfauHtqYAACKEOACSgbpnYzBiIAQHDIISwy3mpDkFJ2MJpw3R7rK1oc0togeHg7gGGgIFBBOGeRJq6xQZcVALLScyKt3pzHAK4VsNA93B3ZtDNEspBGPu8dc4NPQjZDUqtRJBYzEKYsiAFbnLrCH9efW0nWQ8HYBFTNfc+UZdyl5O6rdVLVaF0XltHiClVdzcvTSOA3cI9Ja6lRbX9KPsBmVjNP14uxaIZERIB1OJl1WZuiB1BbYbEx4HmYn2SeW3C6AqqTkDkXF01nm/5WeB6lw7j7jTrZWk5cdPqapxIhFNijDqXEgBj3z1dlmoy9BkRS6lDl4WilkYiTNDUCCJ32yhhg8C4uasCIPQ5CW/FymcIgm7LT8bEhAFAzILu4WGxsdSBqkJVBxQwX1vDram5pe4B1rqBhP25IBjBiEmoS5SFVtNMaBZXIwMogO9HEaZMNiQKq81VgBI5OTBJJs4cDrbbda1GSoKuj/OiQQAwZOxymopJkqYG1U3dAU9VlWgpLSzW1oTpMPYPU6nW+kxm8TjbUrzVNg6dA5h6OCBhnxkppnUFAgJkJCJEsKsxXda27wXclxbhMKY8QyHA8BgSBICu1ZnAjUBfXo+mcXtMuYfX7+5Xr4frzp/sk5ueKerSTpfpdFnfPsyc0vzDe0c4r+t+GFprro16oPC2aCFLzKWpoeXMn39yW+f19vr6eBz+6fe/O4z9JmgYe/7sk5f7cfj2mx/++O37v/nnb85LE85DH00DED0MtjkXIjOjQwC0phGQWASpoW/srY2WQgQE2FQDnQCYhAmYiYWz0FwaRKSUWJAIkIBEpIur3Xi1OwR61/ciEVG7zMfjy5QJnNb5MuyPh8M+XEut0zztx2E/9hE2zcVNv/nh9e2h17C/+fvf/uGb71+8urq92n34+G7scpns/unjMObj/vjqZ1++ePWzu1dPv/n7v3n7+gNymNmLuxGh/O7XfzeVFTusXoC7eZ2Z65//5b+aT4+X8/lf/JtffPvdm5/97NXV8crdU26lwt//t//cj+mLnx0PV3/6m9+8drP9YefOmPJUH0ny3/3T79emZgAGIvTX//Uf7k8XEf7k9nR/evrFz778/NW+1BKX+utf/6Ybd5999snjw4dv//Dbly+vUuayro9Pj8jUStPil8vFgyD8V7/46vd/fPcPv/5dTqmTtMwzEgtRSqm2FSAIZZM5m3snqRM8HnfjeGvmy1o/PJ0p09Lq1dU1MlZrw5C7zCkRICJLZj5Ply76w27wCBLaxKqaAAOXsuy68eq4H/q+z4mYtZlQTpKny6TmHrEsS1gQ+usf37h5Sjl1vaqdz+e66otXdx7o1gChafvu9evb66Ok7Q1emJspPDw+CtOL25tlnt6+fksBLOm453K/3h76r7/6HMUutb35YE3tYS32/j2IITx2ffdnP3/18tWLh8fL3/3zH0pUC6/L/5enP+2xZNnSM7E1mZkPe8eUmSdPnnNu3ZrILo5qNikI3YIE6JN+MwFBDQEa0JAokuJQVbx15zPlGBF7cHczW4M+eNzOD4lEIoeI2Dvc3dZ63+fpw5DKABABzK5QoUOiV8fxdhh+/LmfrxeH+tWruy/Ppx8/PRKjE3fzV3fjw810nMfn8zZN5fGybFVFeGv94Ti+uT9+/HypzYRZHVtTIv/2uzffhF4v9Xc/PT9tfal4WXrKsa7b+/dfunsunIPce6/bMObDbSnVyc3NH5/XwyGlxBhh4a0tY8mn53Uo/Pp+Ol9rzvR4/tIr5JvRL/buzc1hhMvpSijrotdrO8zjsm5CxJkYMREQBrHfzFIVmXDMtm1GBIGQkgyFt9Yh3I0dgyBSFkIz9/PauNn5ujrAebVL9Qh+OlvOmFJKZrVp9fbykGbQNIgAI1TxdGnhfhgzo27VLqsiwjwVU4A932GqYdMsZobEqmauBDjsLFSAw0jXLb48NUBoKbp29ZDEu3l2J5SuDTKTu5cs5tHUyg629HDDCOsR7tiKfvj4aZ7GaSyB2E0hoDYlJGTwCIIIwozYVBOHAoQHE2bBcOzdkiAzcnq5podHEr4B3hnRCAIOl1a7EwapRXeYBxlk3LqtrQfEDkLLwuaOCEOhkgkCLqv23YsbAEBMAOHr1oAgFc5MSLA1LVkIzNTV4enpWnIuiZZVzSsTzAMB0qa1CJnaN28f/tlf/fnDzenf/ed/QKTjxIzkEc3DCFqPrpoFs7CHZ6ZEYUDMuOyLZ4jemhoMSQyxqwcgEApj2xtt+w/c22wABB5eWHIql6V5hHmY95I4y4tOPgkJYVNr+lJ+SIxjAkde196aRkCEAyVAxMDMKMK1G7lBeCAEsIaZWWIBYd9/P2KtWyEiNgcxh8SISK05JdrF7RCgZhC+G4fUOMuwl3NqdyA1jLU3IhJJ6p5eaJ5g7t0NmQg8MdbVq/aUsrUY7/Mo6aefv/z0+KxhEMBMSWjHRUUwMIcbIxLGfrrehwnMrKoIaO7EbOZ7LOFSG7wEnSIASTHMzBxZAADhnEREOBFnYhHag1DE4KZNExJmTIGIEXMSd2vm5uEBDQIpNbOt9pwQMZJIETazCAjvobWrz0NqTa/LdnecHj8/Xtc6lWwWxODuEZSE19qzCIQzc1WbhmHd3NWv5wVZ1nX7+HgN97tjqVUvS0Vh8CCA22O5ndOrQ0LGx9P1dh6AgK94gIRoT8tGEV/d81iGIdHhZiQEU3te2ofHy82cm8b5UgnFQ7caDlCYwlxRh2Ho7qbmHozo4FmAyZo1OWTz/nQYptaqGbHkAN69gxbIJOoeHjmRGlRTgOjg2ppbIOChCFCuTXVHIFr3PSAKaC9qa6pdbc/4QKDwflAHRCFSM0QUBA9gIjfXQPGX7kLdq55hHrA7NdT3bBFa7D25oCTkliSnLFU7AaTEXQN3gw+CmQIFMW+1JeZ9qU7EYy61t976TkMxd3CvUCOgtu6+IzjQzYnRiAJpDx9pVRfqrkkQPITJ97JM7MWTrqoRxOC6TwT+ZAuElyucCvnSoKtljt58yuJut4fhONm29KYdiRNB7VZKuWwNPZiku0oEAbtbZkZAc0v7FadX1U4AzHjZWiEoEkzUFCOi5LT1LoBDSblwKbK1DoDMcjXzgN49MSVhQriZhsvat667DJkQ3IGIuip49B2FyBzoans10kvmnLO6m2u4hyoiEBEAtKZre3YnfTkYRTCEh5rGy/4AIEAjwKNbBSIP6KbqgUA50Y7Hsoi9AWEIjnRZGyMHhZlHoIc7BBMTBhDsMW+hGDMzi5DU3nKm4zTeHsbbY05J7g/3tTWLJpaYs5p627njujUnZPXuFmoOoIiJcwJ1CGJC92gaSYT28h/S/jMym1sCstgjtAjg48BJaByld7teV+sNmAlRhlSyaFARAZCS+Pm6AKiGXU69ddOmXQ3AEexP+TuIQHdXjTLgeWk5p8SAjFvTnLlZxwBBXrbVfbd+ASDF7pw36G6ZoNWex4GIHu7y/e20bp2YhUJN5+mIFL8c8tPTOcLLMOy5DEcgpJyQQktOjtS6WjczeHzW82LTOATtIjlr6ghIIqZ2Wfu1tdvDcDeXLPp8XupeuqEGUhAFEYVTN7dW9zsEWVfVIgnYPcz8T3R+DeaMsSPasWpPe7A80ShsXbtbYmmrVtCU0rJ1M8NAAK1XC8RJ0h7AWFtbW1ePFzcWIguZeT/3j4hIbNbdXJgDoJQsLO77YAX3Gy0BggNTgGDhVFVr0x8+PjPzzTRmdfPAiHnIlFhdavXbw6EMJSDMNcISUwCGm1vsxR5tmIgyM3BSNIMQTmoG4Du2acxsqs2CCSCMCdnJGLoZIePOXEBAIofYkVgUjCIInbTNoxDIuq6AcTvnXm2ec8a4LmYBgKi9I+BhlG7dDSJC3efM19WXrSGhMB4mKYmse+1mHo681o57qsXDIjxgR+EAYJjdzEXQh5wMYq1t23q3mIfMBLvWpLmBB0KMWYpgStjVW9MyprujvLrNFNgcr5eugB5caweINTAMe9OGuK3bcRRFGphaU953lgHgMIyl1TpyKody2iq9yE3xMPHXr8d3b776/oef33+u0zgxQtfobmb2vGyl8KuHeVtbq52YPMDUEGPOJLQf80FNzbyrE4l222ozcyQnCIcXH0piF4raX+Qpe+DuBcAU4OEB0HdkFNHabOuqL89yAIC99yScmNUMEbIQYmzNBV0pcE9AASRhj7huXZMjhTUngLnkpfbdOZIw5kO2AEkAHpe150lM9+QdO0UZBByw62XdagAgMQEjmOLV1cJ794gQYSAQgMyRCfNUrti7OgsD682QIOC0dCZixq4toifJbe33h6l1C/CccavmFq33ADSKULubByIk8GPhperNJENK3dzUBXE45pT48XR+OAxAXsqOPLBfvpuTQHdbenMkAipsxPjl8fEwTZ+e1q15mceufl6qmbLINGYAEOHalSGaRWsNRDoYY3TVj5+fyPzV/d0PP38w126eMv/Vt6///Bdvh7H83X/9h1//4fPVsBPe3970btfaDGhtrhamsVlEODFFhKmNZXD31lSYhaSrIwAhhTi4Y+COWmdERgpwde9NA3YrmUEEA5Ykx3nu6sySc2ZJLCBE4dCajdNchmK7oqWM0zS29dIchmG4uxm3qsKUhLPwf/dXf36YBkb/6dPzh0+fj3czcbx//HK64L/+p/+0r0+vXh9V6+9+9w+fP3/89rtvb4/52++Oaz2+/+lzKYgpRfjalu+//3C+bjmHx9O6aRakMEQhmVwv776+1bZkgbv7N4fD/el0+uPv/v5wcxPEP/70YZrm3//xp6fr9de//X4cx9p73TbhlJxyCldbt35tvZltXV9Duq762z9+eH6+Nm9//cvvHk9f/uf/67/9xbdv7l/dPbx99eHTp26+XuvHp0cCSUR5SLd39/M8hUFzkvTR0NEcEMdDcQOPMFfJ7IimgQiG+0YGrtpGH6ahfLksq1pOSR2kgFpPzmNOJaehpNZbFrk5zswc4LW387JMQyYm08hZ3AzSSMTjNCFS141Q3G2aJ0p0WU7zPM5TeXp62pZIKZ0uV3N+fH6ecka6SC6tteu66k/99Zuv2rZ+/vT44fHphx8//vO/+esxyzTmdVURIcAyTGUcHj9/zEWGw0itrdpfv7n75Xd32pc//vRHFPs//Ju/+fCp/r/+438SIRZetobg40Stxh/+86+IpUZcr2vdOoOcT0tdgpnW5oepOPq69fN5A4W724OHSpKttS+XM0q8+/rhetGyKBL84edPr+9uUsnzJPc3t4/n+tPnhSlt2i8rzeNQRli1o8XtPOWE1+vyl3/+9T/6H//yN7/69f/yH391qioJ2+bdfb6TjPThdJrH9N3b+8+Pl/19krncHAoC3d4UVT9f63df3+UBvBsCCnMZUquXSWhOcn8z1gqfz9u7h+NvL5+fLvXuwE/n+ny1wzhsvf3y25uc848/nwnibsqEqE09TDssLUrORK2rIsD9NLQd7Q/OBMeSqgeG34zS3bvHpg1RhKQrBwRSRIQbGXYiKom31rubILdmve8HiB0VS+dr7dqHksaRUfJ1aZdLzSUhQxDzUBD9+fH8+u4YgICl1lU1spStKmYcEtwMeCjpy6kzoIKrak5CHhYvtaCujhEUkHZys0dimktaaj1MYoEWPg4ixJtqO595l64aBgAzb5tmoZTY3WtrJZfWtPeOmCCit26dshBCVIs1IgtNAwljRLTeIYCEciroAeKvD4Mr7rirx2tzq7dz9ouvNQhQzVPC26PseOxq3TuZg0IgQRiUIY+ZKBwYGZ2Ql2aAgOHzKLsW3OqamJjZa1fK7th6f5GcRKiaFlSE7z89Llt9+/qrf/zLV//tj0+AnDMmgqaWCDYkdw933Bk/AWORrVsiSklQaFO/rG1TsOQPh/RwmDajL+cNwTVCPVKSvRNHGASACEix9QocuZCju5OpPV6WmNLtYfBAbDblUhKtXV8Iv4EQMBQaUvKEV9VPl04RnHNO4ubg6oHmRBC7YdZ3uq0bBIqIW0dCJm6mLJAgCPaKDlTXaHvsYmfROkAUJmRsvXnA7VyWrlvvDW3fYDEjY1xrO2SZxgGZfG1hWqsLETMw7v0cZYofP9e1tq11QiophUcgmjnCPoQxRFI1EbQADWVi90AMAXZEjGhNgzCVbN1EaH+CEnq5ZCOhpOLuqhYAFlBbrTVeLMr7lpZ2YwwRkTDlnHdqi7AUSQxxXdc9n9XVmJHJ1aCUhKHRG6Iy8vPjxczywBNh3ToxL7VvVVMuzUEIzcMswrsIl5LC7flSH+7u5imH2VC4bm3dKgk21YBetf7h42pOwOxhiQmAl9rrVnOR45y37svny91xKIWvSyPkLGLuXy5rXtfjccyzfP5yvSyVhd2jbrqp9eihlNIQ1hG97y5x4tqVQtSUEAnTlAlwI4KbKcu6JfTAsLyDOGoDZESuAUysEKrhgBAEiJfLxhHIiESEUr3fDlJyvq51nweWkuAlDUWB2FVzSh7uqrvgbqdk70AsVyACj0Dc8/AvT80agCAB0c0BQMF3rb0HADLTfgZ0wj8hsofMgfu3azcXEUCFMAIgoekw1dYiQrtGVyREQkm5tc5MHdBbDzczY8GuhoEBuoPdhalZl0AItNYBQ91dPVBLYVONAElUmFuzbp1Z9pFWYQoIMEYA9UDwLLIvidTQgIRRmJnh8XpBn1vbiLhkGVLCqCSQExRCQMWiAKTeGugwDgDe9+OpB4OH47ptuw+omXOiCBdJHrhW3Sty7k4QTHCYsofXrREFMTxfLomTuQXs1aVwczWbB97avlmHIBhyDghXSyROnnIiAJa0LNtO8icAQM1FuqJ1GFIOgm1rHgQQTKJm4SGMgaTdzA0APBwIk/D+0gAQMLK7I3q8mA1b7UNJjAxIqhoQzKjaCRGZt9YTkxl0c4QIcAOIP6WWcqKtKzu4G1FMhceS103HbATw/v2jumu4Owb0YRgutraqiEToQkyChuEBHsJMCG5oQmjoLMi7MguQd48a0p7kEA6PyCKBYKYp8zTkHU0mBDeHfLo27X0oWQ3ZaCjcagf3TKlt/fx8DXBzA0jm0K0JsTCiMDowgXsAwTCKJB7LROAA6BAi6G7baoHYdNXaAgElMbq7BVBYaPduxkVKTghh2p5O9f52ur2Zc5JxzD/+/PHj43mcR0AFFusdo5dM6ruum81RjVrVYSgsCQEpwQMfc65JeGkaSL0bBgxD0oi7eQKGrvb5VOcR7qZhSLIrMmvTtW9AyTxdq3LmfbKmHoyYczKHTZURFMPMMYJYiCHQcxLtL11RSpgT19YWs95UHXu4hdnz8ienRBC+NKS/7AIxgtjLb/DC3gcEb7HD4zlRmBJgloSISGD78BICPMzhpd7kkRMTojtYBCAJs0jqZo+XqxAS0TwU9WDzkcgTMroQIvG2XGrvhAkiELF1JaKSZC/lCxKI9K6EzEAOpoGgWoS7KQJmkaWuYT6XwQHdDYGIIYAIyQHcPTAwqLthb0LxapL7w/H791+22on54Tbf3xarThDXZkAAFkKBiYlgKrA2dEJmzISXTVe140FGoYAYB34+b+EYRIC0l7fNfA/oEqObZQZAOYz54XZYr9fW8XlrTQ1cxyLZ/HaWcNjVngVCiIYsQlFG1u7n0/KLb2/v73J4A0dFPm31+axr1dO1MTMxP53XzIRC5sHkKFQwjcLNfRrlvHY3m0pigXd3x+NhvPY6302u7i00KBU5Xer58odU5P71oW/+5n5Yu385L9qtKjxe27EwAaZEOQuEEfqqPSEBWrjvZgphnlM+L+2yVCEiJDMLBEFp2j1cIGOEmrdm5jENuSS6XmvvxsTmXYRzYgfoza5Lb90Rwh3MISfZ/aDqhrgbx/b37e513R/wgREjwszAXAGEfRAsSbr70joTHoaSERAjMxPj1g0QmHCpSkSOzoFWw3ptrpFyDmDYB3k7R85dbUgcAECQgEuiwmTq6o4USLhuLQyuDoGIAkwO4WOR4zjXpq22fDPlgZHl6bL1DomQIZKwundTdJ+HMsuUEL95c6emgO6OJZXuEWCHTK+/mjlJV5tKZgYAA3Ma0v1UXOO06MPNuFy23vXh9VHdTtd9C+Rbqx6eSmLGp/OSszTrEQ5AtfeHw1i1Y3BAmFkCvjkcPj0+dm2EkAr/8uvXd4eh9zpP6Z//y7+5uX/8d//l11/dzgLy04en86ljsOEubKSHw9DdLmu7P4xlSmPh06WfrlXNmnZCSULHafhyOoPH7c0xGLs2MCeEoeSn61IG5owD5VeHLIkuyzqN49vXd8fDtK6rdh/ui7v3bq1VER7Gec/ZiUgZBu3tdL54RBJet2VZ6zgcB7WnL6fW+qFINQ3tf/Fn795/eRwT5hSHaRThtS8Px1ut2/Pz51bPP/7xv43D8Is/e/XqPrf18POPPzfth3m8Xs5dOwQep+Nv//jz7WH5+tXN3/6Xv314eBjy+P6nP7756vD3//X97e1XdW3PX373hx+/PC0Y1H78cv38dP4//+//2eNpeTo9JoLerdZuRgQ4jfmy1Fpb10rIjr7V7cf3n0uany+XT0/Pg+B3b7+GsPcf3m+9/rNhnub5N79//+H9x5yLQ2dMwzj/xduvHu5uP7z/GXgGwgBASk3dLGhnSyCRoPsLEQ0QAIOCejgSnrfqnx4jQgjzMPRu6qbqUOAwDxgxDblkcfN1Xe9v53ev7p/Pl+vWzQLAJKXW+2Ga3ew4zdPAreuytpLTjn4bx5SSbNcnpJSSaO+n63pdNiGstWnvEJ5T/vz4fL5ubx7un54/MTET/vLdV2/ubh+fTrffvg2iT08uMrx+PT0/nz58fsqsWF2YHr66/92P7z+dz69u52ttcl3GzJ/b51JuEYER5zGXkj99fv7yvD1t+vn5qt1ySTnJ3VxaU8jp7rZYNz8tTLau3c0dcGmdMRiDGYTx4fZ4XtdPX05rRUTioHkYl9Y5THC8nQ42w/O6rVsklmutbvDmYRo9nS/bYRbt/nRafvuHPw45ffPNw7+Kb3/8cEoMGLyuLawTwlxwSITED3fHbWvdPAk190T89eujWQ8g1z7nm+E41bolKUk4wBBk2xrcjW/uD+vSGOH1/fjx6XqY52bbPMXNzfB06fuk5vVdCfWH23zd2sRjLvn7nz9drZNiJuBEGKBNA2CrbS55t52B9osCAjpG1IaADkaiTggeIhSKbh4kpoqIxympOhEQsDAKW1NPTMuqkuiy9Nq8JMpJxrupN122fjMNAfB8vsxjmafMAIYxTaUUeHo8jzMvRsvSb16PhGEa794M11Xtgp0JwJFyoT8xrezlYWftmpjmgYr4ceSb+WDaJZdgAMTl2nNCEjIzVe8dwA1EmBnUVzULL0zCDEBCMpZMDBG2rB0hhFHdEXBtVrtlIWEkiJSYgEMtCANIVzd3hOga+3dXuBHglKV2zYnujpnF3SIYA6BVi0D1YEJAGDNPGREpZYowIUDEazUzdzdL2jo68umyAUoLiK7TlMmot27BW3NE7OYDYO3w+bo+nX/z6vbmn//V/R9/Op2vW5qHw5iZWhbaGkY4BhKRuUdgQkSIQ8mKvmlj5r51s7bWaq7/6Jdfl4LP520q8nSprSsgqjkLDIV3hpQgrn1Lku6mpOaexSOtXfW8PtyMt4e8rY05z2OurQUCIFxrt9DDMAhDHlOHqFtrtWkYcOrqFAhMARHuLYIiBhEn6GYJ9wO3JIJqjpCnLKrW1AMCwJtZAkEAYQZAgECMsGDinAkhMrGBq9seZ9rxIrgHypiGMT+3hmE78vi86GVZROS8rGZuu703J9o3uG7h7uY38yjCT6czE+0BKw+oYByRiJt2szqmUl2T8F4yAghTI6adzk574c3DzZn2iHUIEAl5OJgHRBIxN3NDQFN9sYryuqvGkhAzMycIV2sAxiyJ05BYzbIgWNS6eaiHz4dhzPm8bM3AgdRs2aoGoAKgdw0EBIebaSCm2roFEid3dY8fPp7cTcO7YoBDRM5D0zA3c9itsiVnCHRXHvLa4vRh2ytv21N1DwvXgLW5mpdMVlJ/bGFb7XFaDKK7hboHRM5Sjjk8EtPDq9vrpo9fth1JnhgDWK13JQ9MIm7et5CmSsjrWYcUQJiZtHfEDgHXZompdQUW5rHX1rYuwq6hAONQTO3zRbPUkqSrIZBHwO4sR4RwDNSuhMBI4GHNASPn1F0pMGjnj/jOONhZVoToO//aERHDfSeNIf5pnLc/u/1JH+hdW0DJor7TYUl753BKCX1njSEGqhntEZFw86i9BUBoB8cAcETJKV7eaC/7W0JUUwdsqkwMu+yGKM977sKRSM27WQR4+B4gDAgE7KpELIxNbW+CYWiSstYqhPscenWrCEx86W0cSjXrSxeCfUrKYAh03a7TNAmze7+fJiDctpYZthYWFgBP14aBvif3enMphQUcVzUkJA8HBLDXd4NpE1gUyB2msivgYS84JZGumjlNJZ2v69uHYxJcNhWi3XHvbgQATJkZ3DxAkMZSLssWgIDRVKFruM3TPBRRM4IhPLbaLEDVEQMlZUIC4kLTmLdmrbswuPuc07JpNytD6W7kIcKqfR4TQApHNQUI3MEJgeDg6DfzIIK9++liEIjMYK5dCRGI1NxMWQMImOj50p7PLQAu1zUnCUdzQ0QP0FaXa3M03p3liIRBwUjh5AS8n76YwcEwaCckExAAuJkaEMduj86ZEEPVCHGnEZ0vbY+/MTqTmAEjgkdt7XJek1AEBOLn8wru5gGIiGxm7sDESBSAjEAUEZaEiDMjTmPeWktI+9HR3GAnWKCbv5gGU7g5hDolAkARLCmXLClRrR0cVovf/PD4V9+9TkStw3WLvinAJkHXS+1hg0wRsVYthax3wAEARKLWrgqSsLWuGmUadKuE2D1UbXdQFoBr7VurY0lItDX7uV7mMV2WDcFvjyVnv1w3iJ4YB6GSBwU4XSuA799uHkbMqoCAJEQIuwJhD7upezU1s2VRNTfzrsrMu9PqBYqFSEwAgOGJOIl0c3Uz5z8ZR3G/ExJFyrvO3JmQMBjJ8eVlJXJt+4MIOEQWAvKSinu03j2AMSJCdmIRkalZ92dd1loT82EsSLzVynNBhP1/D4AAV4WmkDIMichIVau17o64Gz47IJjF7oYnhJLEwwnZEDbTfbERe6AycXfbnQJmIOiFYkwwJJ9zvywNKR5uEka8u5/dGqFKSteGzaz3XoRIgAkQ+lwwcWrNNjdHvz0kQahbR6bzpa8VCAEJ3C3UMlKackqchQg9IY8llUHubuaPH59/9fsvXAYNnzLfH6dpIDMvSRjQAroZRgyFk6Ak4Jyi6199992Q4fl8Nk898NPj5cvjBsite06UE503zYxDToYBqs75dG4Uvc1lKlyrmXrOOBb/i+9e39/e/e77DxAR7trNzBG5Ve899grbZekSodAT0/0oJyBH6F0vEUIYEIRG4Igwlr2V7RrhFoiETAQxZbbOu6pXewzCgLhWF8Iisec0r2oBcV1rtxQRW1MIK0XCsa6WMt3MaRSKwMPMy6a9GSOKDK375/MSO1vVAcEZQiMIgBFRAIFa74iQEk0j39+UhBxMaj08oZBkHHKqrZp503CAacjr1kuRKTMq7FbIhhCcwnzM1DswYS6YE601ZhZT7+GMJIQY0dUAgYW0e6t9ZHaFT70KgTCFR3+xMuJWrfZ4XEwYhiLXSzUHzSmLDJks+FDk7cM4JFJGC29tQ+JESIk9PLoXKX/93TetPT1frj0sFGu3VvW66dz7w+H17UQ/ff4ozCVTznxdXV1TpvPqz88rM5VEy1b3xeD53LbebqZiSMecS+Igd4dl3W7n/Pr2eLlehfFmHtztu9c3b27nbv58vg7jSIT/4e9+d76uTfV6bdWAELeuHlhSupnykCmVySy26+ZEY87PvvXWl9rDfZ4zC93OpQg2tWNhZF5qtK43U+pq0zggx7b229vDw3HkRNNUrLswvX64f//JPj+e71uzbuflkkqacKjblktmpghfLk8OsrWWU7pet21rdV0ICwuOU0Hmj4/PpZRu3mr96v62t/qLt3cismzXlOhyvsjN1LtY61tt3//46fn0/M/+6V/+zT/9x4xRptRrs25/+ed/9ne//m2vy3EaO2AZB4Ro2xVAf/7h48PrV//Dv/lXv/37f/j9H34epgMSPT5fCOF/+Y//8G/+xd+0HmutATgfpvNFT6ct5zKOota3ahaJCFgEjEqh8/V5wZRzmsY0Sf67X/3m3devvv32u7C+NTid37da160P4/hw+4pJUuK5iBAhy/uPH3786eNPny6q7uZdHRGFRc33CjPsCEUA9wAPTuwO3iMyCPM8FjU7WKmtE8LSK3SfUmobTNMcEq729OX5/uH+/vbw+csf7u7uWaiUdP/6ITNJIg8MwGEol+tKBFMZzqdLmB1vbgC4te5ukvLb4+0j4+V8TpzV4enpghjDOA8WiPH45cvbd+9Y8Hw53d7cnZflxw+fUs63x/l6vTStzXu9tvu3D/Nxfn58+vR4Otf25fH0x59iKOyBN+/elkw/vv/+7mZCiAAzt5zZq1LgNAyH++xuU0nTmKdxOJ+XtS9398e743y9XhHQAhkJ3LZajcCM3JzDxiFFhU9PS1MbS8mSiKIty0/PVwpYt43NtbdJZkmZCNa1N/Pm8Nsfn4V8KAkx/u5vf/Xh4WbRvqzt/jC+++rw6u54vWxr3Rzxw+O2VmOJ46GcnlarfciCAW1tlHCc5OPHM8b9//i//Z9++/u//93v/nBdasm0LA5nG8ploBxoH7+cDtM085kwlWTTIbe6lRxL8+t5nTN/+/WD9ZZTXpper+12mqydAnxgFtk9JJCYw8PC3eHpYuCBQkuL8IZM5siMoB4BTGQaBGERbW0lCUBEeEpcW2PCMDlMibYmIpLYzRzpWncLgveqxynfHMQMAWXBqK0RoRNq1+fn8zyND69uXP14ZEn88akPhYZEGdEct60LC0FsWxNh9A4oO+xRiIh5Knx/nHPmcDfzXb3JJLU5qAaJMzjg01UvVecip+fTYUjfPByentba/WbKIpEFKedEwYIikgRq37kA6OZD4iSkDu4BEZQZArobBqPtpSg0j9p0zBSJU5Jl7RFxN3He/3B3QHBTFkHwIZdNNcCJaNdVd3cCJGZmELF+bvNYLqsL4N2Yf3reAjjM7445Jbqu6uZE1LpOQ3ELdzXvJSUE5JK61Yfj9Orm9cfH6x9++KJRxiHbdRsSEaX9YJolOYA2U/N6Xe+O42GewOumod0z07XFf/71jwgwj+n+ML46ptO1WrC6P19rr4FAHj4WHnNuZqdFCxORz4OUnNetP12245inSbppQHKmrdU0jveHWcgTgZPf3xyOmdfeDfinx7psTYFTHsxs95ERhrlX1ZJTIDqyCArS1rcXaIEhEpu2bs7EFt5t9wIFEdpLbQCZhJzUsGtXs+5BhMzAiI4E4YBoEap+rnre2tq6e5i7RVhrQOQkO6nAzB3BA8ycmYnIXYecmdltN46EBRCSR2y955QtvHsTliAUc3VHJlMLM2HSAH95egfwF1x6VyNi2xs5f1JzBCAiA8RLjAsCIwLRI8Dc3LzVAN5hTykRADA6ovZlcydz7+rIXJuZtt69tg4O+1t7SNzdW1UIQGZGVPc9XeUIGFGb/ubHz1195zMZBDgkTuahgEjZVc2sDFJbA4tSxHowUgi23llwb30g7Y0DFOSu7taZcftyRoBwPx4GBHg6Xz2ga+vabqfDq7v7AL8puU+6VW0WQjHnwsNNXddlq97J1B27uFu3IKRraw44lcEcPTwxAzoxH0u2AHcIgGHMgLi2nji5ublHRA+knIJ2oNLe19in4zyP3Lu2rpIkIBw9EYbrHt6x/R/FPwEyd24oBMRejtj/MSLfCwovGAUC25P8u0CXSMzjurX9yAT7OwklgDwUAdZtRd/BnwDEAISwExeCiEqRpq5dca+o7UdHd/B9NIQRgAQAFkgGsFveu3pv1QGQyB1MlV+ySy7IZjvV2lUjIPbMwNI0qQORWt8X9XuPIEkOtK1tSTIiJkB3U+00DABNMguDa3WHVVu3Lom1uVYlJgt01XHIzVBV85iXrYEBF9kR9ZIkYZjZL9+9+qf/+C9LTrVt//P/499tzaYhh4OF7a5iVWu9j2WchrJWjYAsbB4Y0NXcVYgBo7UGgcR4uS7zeEDCMmTTFhYBGA6qqkyEUTInwSR4Xrebw+DuGHacBxb06DlxScxE6rptfRx4GhgJr0vvzZEJGYrkCGQAQ0eBOSWIvZ6jDFgSudv1qiRcxqRq6J4Zci4IFLBLsmn3EAXs1oC9ehpdHQI9QJhslxTtTVEIRHAIi9BWS8njmFrr7vvVZuftY7z4EwDBE8ubh6n1hgDTOADE+y/nF5A2krslYQmOHWXhtD9wbrUjEv/pw6u1A5II7pWt3cCFjMLkHhoge5Jrx/8GOCADbFvvyECxL14CwsnRAQH3rB2hawRnQow9MzlmaWpoUMrwdLqYWS7pV3/4AOFTkS+nlYKnWSS5uj6frufrlVk8oG0rIH779tXD3XTe+vOlMtFAklNZ1+X5cr2bUqgmyYhBCIwejl37PGbzWK+bReQiiL51JYCtuYZ3j2mMOeP5fL1an4/H24Ocni55GDhJb3XVfRJL6tFV3XawMOydTd8RPwgIKEylDIgv1429JAwOEUAYe+34xRm4j9wQHAIDw2Mf8O33FIoAAAEmQjUDi5S5bg0dFAEJB0JzN4s9/AIIRbj2Do4WrhgIQES499QhNrN+XfaPWhBu5mlbKyakxNqia4AwYTBCYtmqNW17CzoiDKBq7I8shDCm1FQjILOczNT8mHM3Z0yAe1FZhTnCs8AoPmdet622nnIWxm/uszfLWaZE3TGYgUiEwHUSHgZG8MOUGB0ituZGKoRu2JotFh7ozV9uuozzIEl4TKWUZACmIULWGyOp+9N1PS3rdbH7h9vzshwH/rOvbpnw6XxFAMlcEiPjLroXQeH9dY67m5FC339emkazddtUFd6+mnv3a9MAUAe7dAREhESRB2HkVrE6eNjWPTPPY2pqr+/mNw8P1+WaC2Hk1kzBwxA51tq7ulCpaw+Ltfd12+7vDiWznrZAyJwgrLXORMMwWOtEGYFwH7Sp77q6x8s2l2Gp3cNSotp0zjkAtt5K4sS4o8ySwJC4qQPgwPy89W7BTF2NBJPgNMow0HHKWg3QX90VMKcITrKsPafxdO3Lpm49zAMIQw9T8TCCeH0znddK6K9u53kkQl6bgVhO4+08VeuCsa16ueqQMjEGhHYFwvtDtmrPzfbuZlNX06FIyZI4utlWdd0i9oeq8HEUIerNLYKFShZV77VPiRF5007obnitzc2Zk6plxq7uwd2i9hbhw1CutRNjSlRVh8xv3xyGDAgwzSnAzJ0ABPlyXQD9/nbw6od5eOpofUtSNOL5uX74fH716jaP6ffff9TgN29u2qrX01qEthop0e2hXNcLmE1ZTufLXErKqD0i4O443h0GJu5N1eLhOKn3uznfHAb3/uphHEph4psx306pZBSX3/z4/nyxf/93PzxuDc39so5FkmSyQI4p5dc3mRAR0Hpfqi5NE0gPSkQ3U7k9lNZsMwu087Ydcn736jCVZAZm1l0j4MPzlVPcHw5uPk+DCOeUItQkIOh0XrPkCPj0+HwYRTJ/eTo9wvmb19F7Pd7ercu1bltAGudR1d1fTJ9mlQjnea7t+bysm7ac8OlxzTkdpmFdl1/+2XdEdjyMvZr3jcEB6OOX8/3t9P/9T78a55t//favReT29v56ORHgp58/5yTnev39j08Pd7dI6Vc/fEDz1zfz/d3Yrqf1Sf+7f/ZXqvRf/9Pfe9/ePsz/8p//kx8/PrP1//d//A891AI/fDkFkAGd6zIcD2XKk7t1SMKBmiDXNSRPW6t1q97j//h/+tet1t7rt9++/f2v/+Hj+/dVq4d+/e7VMJRSBoq4uxtev7o1xR+//+n7nx9/+HA+b712M/MhM6IEWuuKwiKADUOBmbdmey/m5jAxgbsrxmm5WsQ4pMPEm/r5WhH9qVkvcd7aOOa1tszyRoJZypg+nx7neX59N89DTsLEzJJ0u2IqD3cHM13Wq6oy7qNzTklKmbblejk/MsPN7fF0Oj2dl25QspxPZzX7uK0PtzcUPo8TAX//83sNPJRhXbavvn315fOnTMHmrx6O8+2xbuv1uq5dOfiXb94+b5fe1kG4tUUVb27Hp7UyJyDqYcebGa+LRkzTcN0qIs5zcrfrsnx+OqvVeZyuSz1MZZyLmq/bejvNSMetNSJKSKp9XdpS9TAngnHdKkZNIQ+vbjBArT28OeRrW65rkW5EbdOUhIXWpzOCl5t5L8eATB8+Xz88PR0Oc87ZHR6fr9elXpYtl9S119YP0wgKibhDH8bUqwWhJFyWrZS8rFdAmce75+vfnVZ9PBthvLufWOSP779ExrvXx/VpG4YcoGUid1/XLolfii8eYx5+8Vd/k/P4u9///vfffw+ZWh+6+TxhU2vmhrD2uL2ZEGOtWqsTMmlQsnCMUAPqCiUxIPZuwkiE8yDmsGx9zJITbbVHgJohxNOpDyWta0sJ5zmDOzMsLR4XQ4/WlofbYToMvcZcko/p8fP14st8GLTD8/NlPgxjppt52Mb4HJsaPp47+PaLt4f7aewd14aAIKI/f1rf3M5AoWZZxD3mIZVEAc6ETNzJW1duXjhBySGIRNH0ZhREnIfkps3x/Xm91oYBHhKuc4JL0wjOiAAhTLs0RiYx9VY1M5VMW9MhZ0RsagiI7nsfYm1m7qWwuUnilJmabb3d38xdlZCmIXdzBHeLGibs3DUwBHFt7enqTT0CxkGyxHVVtajNutrS4DBkj5CECbgI5IytIToGoKr2TmNK7rgtjUcfUhnTkDK1rkL09tXhOJUf3p+A+PD6uNWOiGrweFp6VyLelWeIWFsLSYgxkJvA7czTlDVoWWprWs2mQR4eihALM+Dx6bTUGlu1Zt6DkmQLW8wzIHRXt8QkQt0CEZLEPDNCWjYRIm/97nYmIevatpZ23C3gOpmg1q5m666TDg3EmAYJYEYSYQS4ruthLCzJw5s16yjM4N53OymCmguLB4ADhZgbMRJGgKWU1EOjjWkMiN2AYe4IcG32/OFJLT6dLrIzfSAQSZgZQc1xF55a7KBLRwzEBOAA52WbpoyIzQJCiQFJunl0T4nUGnFWd6u1NhvHEga1tVzE3TftOaUdHYuIATuRincIC0TsM6wdGwL+vx6nIvaqBiARYIB5AJAwAkZKjETrWt3VXYXpOOVl7Wa2bts8jbWq/slIbGGINBQKDXQUYQpwcGLqZtHDgwIjwqsFMak5EVoAAXjQ0oz3FfUuYGVEcE6cMiNxqIZ3DEhCCDFkvjmOtbamwUxDktb6ulZV62qEKCx78puRLKAMZT/tEeDjZR2TFMbIzMaqFqBjOT4/X8DcMJyAMUQNAHbwCiQRM9t6B0BGcohr7cmJKMA6EgOimYV5HhKCW3jf/eTrChFDTiisuwF4f6sQIEESR+zAIBFIaO7o0CwQyQCD9mMYBAWC78MuU2XGQNrpYYQvkjuKgIgdhOwRtOcxI4h4z8N38yxECG4dA4AQwz0cAcHDonsEk6SSt1rVbMdLvzTzIxhhH8UHoBsIobrCy9TQkCAoWm3ExAw7xYuJIsDNPZx2dQcihpuqIxJQhEcEM1k4mBOA48sZFBFqq4TIu0Gcyc0QgpCa9jLI1rp33cdjSKRmAdKbtp0Dv0PL1NTC3PraGdgRtDcMcI8DQhJ8uBm/ev3w9dt3vW//z//bfzqttqz6vCoSgTtgtN6EEQm+PC9DSp0s1BEQISKgax/zXuMyNUcAVY3AjVqABnBiCGAPxCTjkDycmZuabi6SiOhQRN1VqanapsdDalVbtVwyQrhpKCDCmCWM1tUT8k6e5pdqYDzczL1HqwYIHi5JDnNy88b5utUASkxjScy0k1bNHBkhAJF61xcEE7KZCwIhdbeXaxbgPneB/WXbi3CBwpSTBIKCWjgiOgQh7R1UChrGNE1yPKTDlFvX/fUERHU3hedLDTAkiADJVFtjpCAnfOE+dLUsAgCBkbMAwP4Su3tKIsxdu4cjI3lAQPBuJXLvPZCaaWYBCDUDwHDs5swYiJkTM7XeESIzqkNiaaYeUJt1U1VwNxRMnGrtLNw1tl5RpAcs3fvHs7uDh7XetPdmRARk//DDp/lTcgA1P5ShrUhEz9dtSJQ5a5Gt1l7rPE3jwLXaQbJjTGMGwEC8XK7XpSUhJno8bcTEQl3DES1gXRZBIIq/+Po+l/HT6WxK160bYHgAgnv0pvvBBskIgAiJ6cWNDYAB3R1fOHlIiDtK0NyRSPV/DbLC3rJxQIIAcA8EQNnlDQSClIQXU+99GoetGzpWNSoiDL2bvewWwl+c6djNBbmbAQIi7Gd7iBDZbzcRhOD+w+en87qOJSUU74ZIpcBSK6eckzRtBprTLgQLgKgWtXcMIwZCqermNpdRA4S5SKqqDsDoHsYYc+G1bsc53R+H62lp2oLjMGVXQ0kEMIzl7nZEtzKPDuHhP30+DSn+/JtbgL32Gtp6IJMrmWiD3hWCPECbCtM0ynhTDsdymFMSCo+ltm3ZAKF1SAibWzWHgHHI776iba1fPzwcD5nc160lDjcnglQw3EUIkQKgd0OAceTny7ruPahdYippLlybdnMk2DZ/OjfvBkzEoOboeHMsQODN3Hyr6gKliLvdHA+lTO8/fqq9L2uPgOtq3s0BUs4KYe6Z0cNL4nk4IKGa3h6oK5i7Q8yH3Ltfrus8ZHULV+jUzcGNRU7nNSNaGDAMuWxbnbPcz+Xzdc1M7jEmBgfAEIr7Q3q89K3b2isLBXjJ8HA7lIzCFBFuUXsD98TU1w4YRLQuFQDuDvlmKp+fl3VriUtQpITfvrl9Ol3C7DDh11/dg/o0lA8fPjhnRzvejGb05fPnlGi+mx+32pqPA5RMYZ6lPF/Xy9WvDaopds+JGEGKmMPzue4t+53ql4SZMRONIqYuhEAkzL36VtsgedNoYRiu6oSUCNOQL9c2FSGiMQshd22J4Lq0QGSEMGubHaZyMycw6x1ZSGslBGGstW0AaSJmqUvzHr/6za9b1w+P6/19RrTpZvwnr2YRioiNoC8tYdzeDffHRCLq1mo/zNPx0sNhErBK3319E+TrZo4wjUmrZ0aSNAsJ0baZDOzggFi7ma+3hyJcmnaEuG71N398/uYVX+uaMUkRCw0PxejawcKhJiqEaE7vny493FVfjeO6XN++Pv7w/tPbh1lDvv9yfjhkJjldtrxGzpwLh8Jy7vc30x3M58v69nbc16RTyR6mQDfTcHtzTJKzDOH945fLl+dT67ptLad8e9hsXymFqvrz08cHuEfKgNHattT244enrx5umUitj0Mm8pJwmsv56fnbdw93N7efPn48Hoa/+Iu/dG0lGxJdz8vdPPzN3/yjJDHk+PLz70qJ48NDBG69P1+27iYi797dfXV8eP/Dh7P7l+dLvW5vXv3ZOOfzGT5/vIyl5CT/8P3P1fzmhx++/fr205dTtb5crr3bNI1Pz5eAeL5cTdtXX98Ioapf1mWaMopxBLNMtwkB7m5vvv/pp2VZd8UsyfD+4/vz9Xp7f/vm4c2nT5/a2n7xzdtvvv7q5/c//+GPPx/vbl5b+v2HaxKJiAo+DcNaq5oDYoT36oBMjAhwO00a3qot5/Pd3ZGJLtflulZkhuN8fzMkdGGrTQ3s8boMIt36UuuQyk8fHxMzcRkHzhja24ePP6ec7+/vc2hAcCltWwlJRMzaVk85Qy6TaiOBNKTzx+dl247HQ7g93By+nM6Xy6W1ttW6rPW6rkzhgOu63t3e1K2ptsfHj0+np3dvv7q7GftWyzBvWz0/n4gRWjDDYeb5cPfhSwjD6XI+HGZCvFzOJc/zJLczfTlfzYDRElMncvOn53UeMxOgw/m8HcoZKF2vmwgdjuNUmEgen0/HeRLh0+nKJA+3Q15bILHQ87O/urmZj/Nlq8d5YsDrut3dpiLSqj4+X8xJG1zbcn83HqZy7VYY88Dn6+XuMF6unpKdLs3cns8XwkII0Crirk30tfZrX169PiIGcGTBbauqCEDPW/u3/5d/25qeLn08jtO2vntz+Or+8HxaPcxqVKi3dyWibNoRYrnUL5f+cAhTGySNide6ff/DD+axrOfxkFroIbKqExEiny71y2XramFeXsosYabjkNW7CBHJyLT2aF1LErNwD0p8PrdxSInp+Xx9IzMBcARxCnA3a00RaVl1Wa/jkO5uhinZY7QvF79Wl1SZ/OHm1dPp7GjjkJLwtvat+jBwq12Qh0Svh+nm67SFP5+bOdze0Jx4GmfDVKu1tn3zZnY3ZA7CrtqqC7KGq3pXV4/wSIkLk1lvFma+wyv28sWyNkJsrXuIde9mCMFQcqIhwXmrZnIYEyKahaoBKAungXtXCh4SIUREADgRA0Rt9rz2pl6YxkwauFx7W63WNgxiZkIIHB4dARKThY1F3C1TVPPm6hGP5tYdMbbWkWJPSKh2BDivWvVaEqE5pbQ2bNpbV8YEEUMRZkTUoWDOY+/dHHrrYVwpEICWOqTy7qvby1YhUs4li1+W9uGzJeGttsuq85iOcxkLgevA8e2rQVhohxkzvru7qV2RYCj5WttpWUGtlHxzyD6q0OgOp0v9+FzRycDNYVk7EBjZNIxJkAgzp97qMA23dwO4Pz9dg3xp3rd6KEUk1d5//PnzYmyIOaEwEsOltjwO3UIjSobaqxkJSUq5BwzCBOwWGO4ezDSNuDZDolySqw8imylREHkzO47jNBWtfcjpWiuGdg8jbq2ZeevWzSICgf6UcfuTCttfzNnejRPvxzGmHdyNTRUBc86Xte3K7XVr45QyeiB0D7fgJDuPKBzDnRA5g1kIATJv1Xbb0o53eBn0qDKRhQNAAAYE7YkP3GtFEPumcS8UOYgQgO8g2iTUe7UOrooYzOxubiDMtbabea69e0CEIgoROVhYkDglKs6Gtvec9gMkMIFBb86CSKjNiF6Edx5xvS65FJGEYcgyzyUMa9dShBGTIHPWbr3rUhsGTGNGNEIMs7X1Vvs0pJRSXTffPXmhp8tChK7aegxCecrMdF3X86Ut1NVMPcCjWpxW//R8ZUp7wwpYgEgQkYAAUQDNLBwR2D22rUd4KkndyDwL7phPJp6nhGDbUlmERHpXd0uMwthNww2JzTsGtF4hnIgSExNttbuaA3QDQiQAh0AA88BACARg9w4QzOQQjEjwQtz8E2c5AAkxiICRu6qb7XdyRCRAxVBX2jkdgO5AtMsJAQkZ0QOR0VrDcAhsTQmjJCbh2sPsBU3MggyEgHOR1hTM54GQcOv9hUUVIYy7NQsCkkjTHhFhCohh0S1YSDITpWWr0ZQIHYKYYZ9KvRhzIGBXhu1DUhDeuy7Qm3v35k5CIgyO7vjxaUFkdayt7nCy3SixZ1REMMB2PhYC1K49yC/rv/sv/+1Xf/j+8fnyu+8/T+NIHAg4DUUVw9EBzQwdzHzxbhBZeIfjAqKaCufW2rZTxIVKznXrpurdNSoIHYeCRJspQED4slrtmpKsbTUL1U6Matq3PpR8vvbdLv58We7mvDUbcu69n89bD2LhIrT27oTEcl4WQhBiRl5bN9fMrN2eT1sWFEl7QyqxRKB2M3/husOLPtx3jx0R75M4J6zqAftkTplTgCIjMUa3faxGgO5RW991aUR7nuTF2qS9vbqd3729U++b6ufnKwUQsnnM48CIKSEc8lJVzfZ0orAwsboJ7SNhHrK4OyEROO1vKXcAGKYisl/dBAEdXIjcYm2NCYEw73PYADAP8DElNa+uO8ZbmJsaqBEFiri6uy/NwmMfKgDxzmTfZ6se4d0sLCUOizCDlHfmjQM6WBIBIA1LLEl4bUroEaC6HeaiphA1p7l5fHm8auvdde3wl/NDJ1+6I+hlaczpMNLCYr1JgHl0CDSnHoBY+xbu45C0dyJ6uizXT6ehpLHkbqhdFXy/ulJJ/pK/QoCds/0nKwCAmgPgn24AEGG7imG/cAW89EwDwGOXku4JoV2rDufzwsI5MThsrtp6ZgpEdFy26kwzQm/7CIleoO9EqubdMlMAhIcgMDMA7JVRAqCAHkGERMRM1Wy7dkYSkblkEk7MhHRaVgzEiEAXoT2gra5ZwIPdYSgDoJNnC9xzqM3MA00jD3Eoe7ARb49FhJ5O161rC5iK1GZusG2VD9Mv3j0IGgF/eXpeWx3n4Zu3h1J2Mx3V2lw1kFt3Cl7Wxoi34yCCRFHyLILMWErWrtfzKiLr2pe1zodyuqzHqbQAjZhyZqJe66IkuaCbaiPAXNKbiZi5ZAnXbfMIDAA3nacB3VWjI1CG7VLXRQ+HablsZzNOxQJPl6oWh1E2IcIYE35a9P4wnK8tEKI1ANzhuOdrnUfptf/qN79/upwkZ/fYmp8ua85ZhBMDZbK6KuWmNg15nzeZeSmcMy5rFybvUDc/FLGuIGTha1W1mAsz4sNcLIwkCsvTeVNzSPnSWkngwY+n5XbYcQUYbsuqa6sBkNPo5r1bEmm1sRMmaaqMIEyqDkJA6E5oISIAsE+AX98PgIUYAnZ+sN0eErocD8M8lbZtP338sQPPM6JkVQ/TwyyHebqurfWYJi6sI0suxTQeL+1p9f1+d3+Tbg98vdp1s94NCYvwmHG3LSNiSXy9bpNkQnKOdW1zZkKpLfpeDQPvaq/ux5sxL0u38MOrmYnD9M0xfbx06LvEEQGcEQggJ55GSSLb1kVIRD08Z96ZjYjggdEDCatZvTqARIy9Qh7Z1DRCm/VmgcAA0XawELbembBu+hjb1k2EpkO+u58d3IEPk7iHEAdWGdJh4OW6kBROiOEMpA7bpvNUPn1ZTqd+f3fgdP3VHz940OfLwkNe1la3yMzDkGvrdxNHYEqpdl3W1hyd4JhzSdPT4/mrV4dvvn5YLs8UcW3r29vyV+/eOMB/+vWPS/d7CARLRHeHmTDu5/Lu7gjo8zSa6vl6vbmfHuaprpXCnx4/5lQIgZjdqTd/c38cS162hiGPj59Y5PH52rrJ6fzq1att66en67W2z08nAjjOJSeWXGrd1m5pGI/TbO4kpW1XSv75/e9TTv/o3/z358fzlw+PktPlsv3ZL76RzM/nL/d305DdpmFTtUR3ibsbC3373fGPf3iqj9Xc715Nx5u73//hU2b//OF8//r13/7++w+Pp1f397/+ww/vvzwRipneHaeupuaSpLZtyAOA/PzTYwS+e/jqx08nWINQ7+ZDKhII5vH+y+Nvf1xq18T89vEpZSaMeczvf3w/puHh4fhwc2Sh958+/PHnjz99OfnH9cPn0+l6QSA1dffH89kJ9wApBwlnNXMMwthaG4cMObZFr8vGSRABGVqvl5V6NECMIOY9OO61OYMk4ub6w4fPg2Sj+O7N/V/+4ts0FoQ4P56WyzXd3QH4ejlZ2N3dzRz546e1t15SulyehRi8IJOHu+n1cikp5TH3lh4f9yXBOCf8/OHzmCUiHu5uOWK6PTjg3d3xD3/8w+Vynsd0e/9wPZ+idwhvvR6nTBLPp+tW2zyPiEREf/zh/WEeh1Re3dz+4fffa7tdNrss/fXd/OHpWUiuyzoOg1nPxPfztNVlPuRe4bpqCphavrs9bq3dTOPz02Wahpt5Xpe2Lu3mMG5bd4uvH+6JudY2CH3+9GkoIyKG4ZCTm799e/fzh+en03ZzmJDisrSUUmiYxjwPJHSY85jH50/nL0LHm2G5buOQl7Wu1VKSZVsS4f39DQH0Zpfz9Xic11URd6AAd/A85T+7f9ta7w2WVT/Ggvu6KAAz1bXaHguIkGBxKCxq+vAw99bff/lE9GUoQy6FiHSxbW1uICmZN4zILNdtDQ+vXlKaitRuZv12ykBxOKTlam1rnNKyaQCIUNUoQreDnDabp6EIrjUACAEsYhxK6xqwy1XisnaNOJZynHL1fr7quYFdmvmX1/dHQl+2+vlpzeOgDlvTUtJ5tYAtEUuOu4FfHWd1T0wJebkugISJU+Ijy3VrHpFYIFwmBqB1sa12QFxrWLchO6EQoIO5eYeIgNph69669u4vZ0Im33f64WZg5pngXDdzO06ZmTxcFdZrZ0Ei6tWEJTFMA+XEfdcPuZs5IwyJzG2tloiua11ad1OCOEzDcRy79h/efwjOCBkRt9YJ8TDntXrriuCSCQOY0dyIICfEIIBo5q0rIYdHErtcVkC2gOa15JRTSoJLbeLABSWxmT9eLmUcUhEIH0rq22YWx5vBepw/XbUkgnj3enz3agaU372/XtdtLnR7KClP3Q0ACxc1va4riyzbkhIP4+C9T4LW0JGt27p1SRLgxHh7TA935byZW6j61tUdAoHAiyQRerg9CgMjLV2R5fabAxAW1VZYAATh7vX9N+++/ttf/eb5vAWlaeAx569uSc3yNJy2eD73zFwDr9UPuUw5a2/mBhBFmIXXZog+D7I1FU6G4e6JuVuPAKY0lwzmTS1D3M7Taann2mx1tT/tUBF4J5/uXav9SBzBgBToBGNOGhEYSWjXiTMzEJn65byJ0DQOzjqVIsw5kZDlxGaehNWM0XLhcZx2LMw0ZUfo6mWQiAggcNpdUYTk7uSxE4ERSAH3gYLDS7cowDFIhFv0iB1nAkxJwFXVLTx2pjCFu0N07ddFe/emPRDc3AiFHXRXI3lXYHQI45TVjFmaeQCBW5gDuuy7X2JV55fTXeQhCQOjT1NJo1iP7oEI2roTueFxEmScx3Fq6flcT89bygyItamH17AdzIJICJGII0KIUuLL1jGitf65NxZ2Q1Wv4AFAyLrr34FYWHAP3yABqLkgICEhwTgM4LjWCgggFO6CkIQc0AzMANGJmAjHkt3DxNQVEYRe9PLqqO7gQITC2JsSAjJFQO/WwtwDiTGcwpmwJE5JfMctI1b1rTcI33OyGLtPapc7798hAIjEvMu5mYGZdQdmBhqEQQiGmmlASgmR3AMhCACZAREw5pQ8YNXNPTACw1KSVJKqUUQQ7n8FwklYMCTz8VAEY8jy5bSRBjH1ZkyibgDOQIZOjAk5Qk0jiTT3TOQQqpqyDCVva2XGhOgA5ob7p70jjgJ2sXcwh3sQxUstznJOAL51ffq8WgQAte7m7r4fKQEB9zznfrg11z9VImPICQDQUTt8+LL88PFkFreH41a7GxLFWSsh2cvozZEAABJxgO2893CwCCRcW0v7ERewa3/Jd2lPmSEizJ+XNWVW01aJgGvr7u6qDjhPBcKJOSV0Q3Nzs5LHtTUzVRNmXqsyUS65b30U9tCUedtqzpBzviwbrvXNw1G89c3HYRgzP19XpNzdiaAwAXnrJkxIqOqIWHJqiwkh7Q2fAHVnAg98aR26hcML8GXfDQj1cAsvIl21dw2EfXoYEAQY5gj+y7c3796+Pi/rtlUHPg4ZNLZqCFFbg6CmavsL5JRSMg9hth2BBmRmEDGUQQkpYKC8ajN1IUi5hAea7TCdIiIEa2vHaUiSlq15xHyczKxtenucgXEe2A2a2rZ1Dw+IkjknWdZeuyFAb9atT0MJj810H40jogO6GjN7RCCqBiISi4W7hVuIkDuo9XDPsheVPAkhURLygKfLS2Ch9m4AWUS7l5yY4XLZPjxfAoHCw2We+MvTVqsmxtb6NI3uRoQRCgHb1qaRy8AMtGx92SoCmVlNnqXU1vYbzF44fQGFAZi/mJV2cjzgHvgDiwiIl8ZcwI6LC4gA36N98ZIWBUb0AEbImbetRvhYMiOZhzhkZqRgQNdOme+PU6hVt26Rs9zPmQM+X1YEFCIPG1Jqqsi8d5WnsairqzsBRuzcfHdjkkBetYF2IJwQ55wBIKGsW9t73Np7U3eSLIiYwveue0w5CaYvl4XCAqLklBNfT6c5ye1huFyv45QT4/PTZSoc4NT7yJSIl3V7uDv+T//mX2i7nk+X07pRobEUd53Hcl1rZgHw6HpzOHx5Xqchbdv2zUM6zoNCpMwMcN1qrZ04n09nU5WciCJAv/vu9SA4CZ+vy8390bSfLysyH+bi7jmRqhFBLswI7uwB7ooIhyntsydT+eHjc2LBxEkcPVJK6XY4TOUwJHV4XpQDWOj+NgHS87Uliq17EUqMm2kWng4ylHJaba3dAxDw0+fH09bykJiMEBLj7TwIE0B0VYLgxB+fK7iTR4BnJhbe9k0o4Kfn9ZipmQtkhJ2m6e54WVvikkgN3CiSyOnSrDsDe0RTTUSfz8ucU++tOQ0sbhE7thDQzSAgIa+LdQIcCcOFX6KqzASIfX+QJRBIiairpkTCCOCuTiI5EaPN01CSNG+n9TkxvPvmlXZbWmvb5l2Ph2kN+PXvPzrQMKT7MYSpOzw+n4cpv/3mFt6fztcoiaaMp3NzC2EAwCR8nCQJWsS6OSEch7yufYfmXs9bEhpy/ulx25oRmzAOJT28PjzcZVNzNXec59K7WodrNQAP83gpbXuLQIA//+Z1+FqrkRREYwLwtK0NIAwjXPcUzzCktWkRuRnopgzE7Bi971bdcAsQOM4jRmytAwtCuPlQpHYbh8SMpTBF1M1TEohgpNa0qRUw0BjS0N0JsW+dEZF4Goa6WmBalvb5+fMffvryeNrMSartwo6hpOOhlEzWc8qDpPT58/PHz8/j8QBNQ3szuLZKI3eMn9+/vz3OUujx/afrYvCL23Vtt3OajwOAuqfElBOmzBiRp2E6Hq6PT5L4cDcw8fPjaRiGnPLt4cbMA+H+CA838/PlyqFD4WZ4um7X5TqNZWt+nCdVPZ2fI4Ao3twfiqS//fXv3r17c3dze3q+INJa+/3NgbE/PV6ffvP7d1/dj+Xw1bdv2mK//vvfTRN0b5+fnlBwWZ5609vbm3/1v/knp8fLj99/Pyb67U9f/vxf/Pnp8enjp+v/57/+7f/uv//LT//+d4MCiwD00+PJ1X71/edf/d//w6UaleHGOzRAEvW4vcn3x9vr2j59/vL64fi7H7eH+5v7+7tA+/Ufv3+u5zSKIKJn5vT0fD2vC7GkRBqRpZSULrVLX2/m6d3X32Shx6dHzBDP8f/7z3+Xx/F5sU+Pl+tSg1CStNo9whSA3R3MPZEkYs448LAsiyMpmK2rhqcsRLhu26vbg7piRNPer5VZCMTVhoEBqbk2C7PIJSFLQAg5uHWt1OF6Pl+vdR6K1qV35TBK+fnL51C31sPphx9/CtNxmq7X53EchZMknKZpu1wA9O5ueP+ZPz/XacrjTO/efX3Z6vP5kgRvjsfW67I2hHDDDv7+4ycKLAnnu5tlXQ+H6dOXj9bDFWr0ydNxnM91uS79umkZp7/97W8f7qb5MNa6qS7N+7LWnNGIr9pO10aE33z18Gd//iZlDOzLaduujUm2bh6+XK/HeWZmMwsMINhqMzNkliTW2/m0Boa5Pz0unOXmOCZKACbMD7fj3SExy/O1kwgg9t6QqFt7fN7GKT9fr4Jyf1fGMe1HyuNx2uoeFksly3VZH+5ve1/HksOdiZnh5nbsxs+XNVGfevr8tJrxw8OousxlzOXYe5uGZB0BLQ+ZgbXXt6++RiAjfbysW1XJyS2ua6/VhiLoMOWC6AqxXpaS0hHlp08NkVzCzMec7w6l9a7WB6bDwN883JxO1y3sdE0fvlT3QPBl7eU+P5T0fIWUxL0zUyk0sCcJEtmqmwdREHNVq/XCwmMSKEyg4zhUoJ8fn8cMxyF9dTc8LW3K3BVqt1qtlKGDhYJdLbHt5BEVSWVw8K221rtIHgZGJCAayoiASDgOSmKm0c1a6w7SDXcZ0zBweETEhDSkxFy6+5fTtqxtz1twShpOFr2HmRXitXZCYsH6wtfD/VY7HDgULWjbNKU4XetYcu9OgGPhnHmt/SU7RFTGccdCa/e3b9511cfTupi6x7r2aUyJQQjGhADs5knAHJgAAnOCcaB19ZuJz9UReMqcUjLzaeLuDB2qVmZCYg9MQstmiX0oRJmn4wFDCcKZHMHRmpmffR7Tu7dTbzYMI+LIBJLSvzi+urZq7q7eLEpOvXvV6m6cKScimQE8E522LQndHo6/+/7n1/fTULjpnmHw5jAx3N4NgFGXejSyADUfhmLNuvrT+SrsqpZy5iy29Gma56kcIDbtof7pyyfJ+V//q3/i7n//69//9scvr3757c1h8ohufphxkP3WnJ6unYDXdgVwESambr5trakTs2CkJF0dKQxMMCERIyHSslVEcsRr6+Z43pr2vp/9zSxerCpBEAGBDrDnfyACwPcdPLlQqtcNCYYhRziSA+D9oWzVV0URNAvJcDgMAKCq7qaIADGW7MKIkDL37q25aqjbboImQkAjJgTcB1JE6LYDWoPCwP2F+Q+A+EIQdgAwR2JGWM4bMB0OpTVTC0HbUxeZsJnvrqoAGIZRY28OhOu+C3brDgDTkLJw096qLdv+7kJ2L0LDcfAIBxiG7OZuIQJhsWw63uSuKpSQoC5tXdphnoFk2ZZMwik9X3XKvK7VHIF5q3VrHRF24euQUwC5OSIVwbHI2mxdqxnvxx1DQoReNWCvmaEFdlUATsy2M2FjP9qQRSCEAJLtuy8z6x1g549aEEhKiOiquzIcCPbCznVVMzftkhgASsrm0XtrvfuOILGXeEsQY+CewtgtNRFBSGMmc0UKJBPAxHC+bqaBEEgMDhBOiOZ7KWwnoEQEMEGEMZIjmRoiwN4PAWKiHemXmJEIiIho7ytBoLtJSgjg3s3CXQEMCRJhhNW2BRBACIKi79Q0r6oE7BSeemu929oVIbIIE6e8Qz4iUULACNO+x41crVGKaRi2ZrUqBhN6TiGMXXe390vtKHynCzNiuJntbBLxMG+dVdW8R0RXkyRmFrvgJwIJaGcYQuyoLXqJqeD+66EgEZruIyYi5MIYGIQ2j7ZUyHmurRns/C/cJREBYGaAEWDNMAszgqD0tuUxW7w08URQ5iFxpMLnS9dugd5X7d3AgYkUouS0H8p3dqnWToBErOphuEFrvUP4dTFwqF0JQZheH4u5dyVgZkiqDhCHIbvj45fL7WGcJnYPNz+WXLu33qd5YLQACuJWm5p329trTrGnQgIBIAwRCRF2zBRCElaLF26UB7ihSCas3bspRCAC4o6yo2lIQ8ne27/5l//0f/hX/+JXv/5vv/79byr67Vg+fnyapuHS++XaevOcivoupkdkVjVCAI9EhExNIUsyM49IvDPNo2CiBObuYYUYUUIrIWjYIeW1qplORdSjq22bDkW+ejMjIlEwkRAKEyEzxtZ0LIII85jd7If3X75+fUxCHx+vIvwSiws0sz2118wDkRAjotUWgeOUKRAQ224PBXDas0+BAXu91h22rYsIkwBi6yqugCQDTykBxPO6ERIhXLY+JCgjHGRar+26VQ9YW80puXuYqtHNcUT0uhp4r27zOHhQ6027nWEjSYi7FmIfzxsGpoQYEP4yLY4dEgbhgR6ABL7Tu3Za3J+ShvuiQgj3e9ZeKAD0AB1GGccUun9ZWBhT5m1rRcjnYWIQCMisZoAggm66f/YzczdPyEKhjEAgQua6bZuIOIC7CSHgC/KsqxsiUWKEZV2tazrMKIQADo4MTNCbASbe7QdAiGZuy+a9UhnyPOM0lAA9L89zka9+MZj7cr0cBklCdevTkJrZ3ZRdhutyfT3kt/evD8fDv/+v/zWTTYeZEt3Ow7ZtobjVDcKP8+HXv/3+l9+9W6sGRm2Nhae5XC+LA14upobMiVP+9a8/3N+MN3fzDjk6HMdPHz/cH4/VTMGnjIvZ3ZxLSSAYAQw2TWm/chAhA7l7EqaIeUgKoW5Pa++RJDFJIPI4jMMA2nqYUeLl1K6LntcKAOtKVZskUofa7O449nB3W2uPIde1ZRFD9MDz2leA3h1BFbmZMwGCU2A3W1pPxJuGNk0iq1qigMK6z7wtHPYIMRwLPV2urx9urmtrzYYhuWlv6WmtWNI8casG6sdSqvm1boIUhRLTwOwYIqDa1UI9MjMjDpzVcfU1J39zP42JPLyb7V9PNSVOTJCZ16XVrT7cD3neeQepNW29DYWmwglYTU91BUEiXDa1Vi0ilVxKkiF3jd1iPox5Js05fzg3i27uUcPUwe31bTmM+HTV2uxmTEG4VINwC0tE0T0sHOGytZeH1x6BtDU7UZtnGQq22on561fTVKhv3T2Y7DgPbdOuGsxBPhbpFrVpeARQElbrHJqKbIpbbVMy8lgaXNeOSCL47u3h8NXw9Hi5ntrXr28zATQ1RE5o7jlnCt/WPk5pPgzQezcv8wyIl+smBOOUboS3rS9LZ2QmLJklSUSsy+YIt7dTdG2OzduyNms+jamqATgTkRCRaECr5sHE9LyuM5swDDknoT29PAxla9F7pYg/e/eVhoU5p9LCwOPx6RF0FpzevboXgn/x198BEnJ8Nd2+ur09X9emRow5CZEM05izLEu9PD0NWYJTXzpKPLy+nccpCe911yxMYDmXm0O6XmsSvqz1p48XoiDim3kI1946Arx686rW9eOnx60bCz4/XS+XlUmO80xk63ItmYahUOBQ8vWqv/3Nz7Wurx5uqtG1ro+nc8pioNf1dBjT0+NlKON6PX339f3f/ubnv//NT3/+1c3b2+nm7vXpdPnu21c/fP90KEOt9fYh//D987//+z98Oq1//d1byNyh12Wrq909vHn77uF8Ov3x/QdA+vDxca31+w+ffv/jT999/eqru7vLeXEN4EhZlnbdw7uJITH3vh1vDuNQPnx6/+ffvL27vV3XTQ5zKuXjp6flsnWjTz9fzlsjgZxzrb1qT8KOYeaEkAk6gKolJDC3/z9Pf9JjybqlZ2Kr+RprduNNRJw459wuO5JKkawqVFGaCNBIgCYaCNBEc40ECNAf1KggVUlgkqLITJJJZt57854mOnffvhuzr1mNBhY3EdMIh8fetm3bt9b7Pk8v6NjWDgwKkEMgdO3dRMpac4iR49qaOXRVVx1zCgPnwOuVECwQgZF6ByIQulzLpw+fEuNS1nnanV/WT58+3d8dwaDrtVtzx6ens7q9e3sXA3RVAF+W1ls3knma1taW0/LmYZ8DTHlel1pB37+9P11vzF/RG7fTNY/zjx9+KqW6824O6+0G43C+fq6yfvr8paztOO85+DwMn0+Xp8tFegPAw3E/Rv4X/+yXh30m8sR0d5fMTftuTMMqcmstpTjPY4rclrZcLecYUuJOr7de/Abi7D5MdhijiE45ffr0BZwOh2MTySndWuNAX62ySup6fr0R0DikKlWbcSROfiCW5rdaxzkGJPLgLreb7na7446HkVU95+yqvdnDcTKXmFOIubb25cuXPMwhpiFhjMwJdmM8nWXzQ9xujQCIsVe4v999frqA8d0umPdpHgBQekeCIQy9l9592I338/756ec0ZOT04cuZyd/eTwQ+56G1HmLCHcaIa4XH4/759YZAKcWqRlWIsJuxWm/Wuez34ZthWtaqap9f27u70T0v3WqvIYTIOCa8rM0sjAPE0NFBlbezi6hNI5ujiNfa3CmG2Fovve6njGLrpQTm3TSoa44+Y/wozUyWQoQUyIlqikMO8XK+Vb1O8zCOyQFEBNEAsW1JAyJEdKQ0sandx5QCIFAIFAMSI4ADATMjgKsR2kiYU369+LpKZmKGUjpHylOMIbvi5VpMba0qotsyVbeaZcDdGB2gVhKFy6WoeEwJXLrgBtYcAhcxCsHEVlNz7Ev5n/793wCiqYWIIUAI1Fob9tncmSAFdsbAXipMiZoikbemrVuKEBhL0yY+5SSCpfF+H1fvgYcUkdBEEVSJ8LoqEaMLod8fpylSd3WCLgqJ1fFWxJLf3Q/3+zmnhACB4w8fPqcE3dACkYI5qxmA5ZhrlRiot8oxvHk8MEDtLQ/4y+/fzGNKiT99OT8936YcxyHX5uttdYTjcepFgUC1F2ljztDs05dLqTVELuohxeM+59syxPxwnKYxV++H4+75dPk3/+6v//v/9i//8p/92ZebPb9e37+/q8VVSq8lYAneAez+AL2FQCzi+3kSg1oVCN1qN8dIUitz6h0Q1c2RGckB9LLaEJkIXq5LF2GilJiIRRXAt1Mjgqs5OJg5bkJdgI3ZAuAiPu75SLk2QXYGcndkqCIpJQgm0pgACJkUkcyZidRaacIEOeXX26ruOaW1LAZAzIxQawMFYvJt7bsh1jdXEWyDB99wLqZOtGnjyTdeixsCUAy7/dB7F6mi5m4pUQx0vdWUUd2tixurmVhztK8iJYdaZBwjEwwxxAAmNg4DIZH7rbWI8HA8HI8TIqrqy8sNu7XWiHA3DaJuhuiOhrX3GLiJitrrdeXIu2lqpfeuyFyaucN1LVtVyADIXNwQodQGhGAb34WKiGzFnW4AREymRghEtOmw1CwwhWHSLrXK8TAPiUWE0Gv3tTQHCAwOBCLSqyAjEcfAhFRqb7ViYEJmcABjjq5ial0FN5iSA7iX2niDK2+TIzckRt9EXQ6uRESBN+QKQDBHIgkh1661FWZuvZM5YBADJoMtZaUGf0TzbD2NDepK7kxbGrf75qMHN1dCSIF7Fw7h65rCrYsxMgYyhC4N1cpG/TENgRFw4zVbFzEDB6avPYyvXTMAFTjX6uZMwOBdjTPNY17WNg0RA6L5mGPt2KuurYZgGwO2tGYKgQBczV1Fe+u2Ya63HtvWY0Qn8hyjg/kG2xZU9afXxd3RPGUOOUhXQvCv9tygJluaAbdPG5gZIDIC0LZkJmytcQghBzdzcDOdxrifUq0YM9XaI7saAgAGBPDI5A5mZg4magZGhASiAsjXtW+aMxOtVXIMblRXlS5mTkRmjg4cAzhEAiRAooC8VhHpREiITXXjHIspR1Sh87Vsr8Q8phTQwYdpwNIcjZxcDTlI11o7ZC69b9r486W5qKClGNZVA4N4V9suT/8qwQYPFHqXLe/zxxyQocF2NxF1KUIZEb22FolKr4EZnFpvkQKFjTa+sX2MUCH66Xb+n//1X/27//DX45RC5qdzr2anp3MVqEUDEmAXtxjCfsit9Rw1xNTFzazXZo6GFAKLSsAYAtVWI3EMyBxLE+2tFkNAAVLtGXE/Dq3LCipqZqBgXfy2iqoGcgB/PMxLbWutiWkaxuva9mOMDLvDbsgxBECCaeScoqNfr/W6em1tiHEa6FbsdKrS1YkAcEiBdIvq4TZEiyly4FIriA4hGAd3F1HkoA4mYoyIuHY1l5zoUmrAEAIjWUx8iAMh5MyBMExhGOn53MG9d6mtBSDO1FV7k8icU0Q3VQc0Qq+1NfVx9DwMAOm2lhjIDNVd1RKHrupgqg6OvJmr3b66fQFp064DNlXa9IKwDSKAmdBts2ujU2tKCOjIRLtxnIa49B4D7sf9WiVFcldV6wIhhDnx5XpbhUvgaWBwNAV2r+bMFAOI2MP9dCt9Kc0BAyIjNRFXZQ4QidwYMAUGJyL8cjkfxiEPuWzaYSJVj3GLO205TWFkAoqZx2Rj5mHA620J3tzg5Xohg0D55XzepQxAij4kag5KaT/4b3797uenl3/7N7//5nF/eNwhwZzibVmkKyCdbgURy8eXEIfPp9tSGxMyY0j56VII8enTjRAp8P19LMt6d8jjHF4u12mK98edVnWFbn68G+44gwAjhTEe73a1rgYehyC9gwMASlMAzymUpTIiRegq6DhnZrCmwsAjh4j4fFlfX5dxSMjUupUurTXGcFXJA8ZIYNSbX0pVM0Ryp9drVYUpWx6wV1vWHhEZoAtfll56j5FrlchcNkZYxhzoMCT52oXU02VlDBlBSWMIu0QAEJj3E7ycFkA2p94BDW5rTTmNTNdba6Uxh8aqoKbawAAhcVhFxykQo5p2s9Y3sQBdS3Pr37+b3j3sIvvaZKkaE23HPBAXkxw5IQQn4jTuIjHU1roJEsYREXptrQKpARJaaWC4nBdzO94fatfT5QYGaiRmh0OO1gH4p1MzlRAAkV6e19oUkIckYikxDcfsgK1bLS3EMOZ4GGInrcs1prQUdbUUuEgTlcAwjzyN6XKtt6WNAdC91gbuMaSIoaxS1c+33rWKOBCaWSTGFBHtOAaxqOYojESkPRANiT6dbtfbcjxOu/04TCEN4Zt0J01S5NabALtZDDSG4eXpFYHylADxhx9efvHNPqZkDqXUy7nMmUTxeBwJZTfFuFV6v0r3ZJgSM0VGg6gEfXFVyjkigptvOy1mAvQUuJQeQwKSHNkUAD0gokMr2roME0zz8OX50msHppzDGGBpzQEOY3yc75BpWdvL8+mbd8dvvnmspa+lDSk172NKOeFv/+Gnb98+fPPtvYOta0Fi7wY5qPnD3eNye4nY0dWRmD0OHDnePxx6bY70cL836Wut4xABvbQq/QaAx/18OM4x8DzNa2nr5YrI0zQv19ciy9uHO4Q0j1l6G3I4HO9M++fn5+utdW1m9kH7h9fTw356va3uthR79937abf7+PFzygNP8X/13/7T//Gv/v337+971+vlarH9yS9+5Rr+/g8f3r79y//wH//zn/z6l2/v7yilf/qnb35+/rAfh1cN5y+v799+f7nIy2v99PFyW2/AQ1urmfUqVvr7d48xxNLLZWnzlMeRzSCnFBgcDZzXtfTe5mk08/Pl8qLrl+fQ1Tny83m53NoWVAEjB6OArl/pBswYGM2AA/GI69r38w4cLl9eOdCWWQB3cSc0ZCqthhDUDcDMNvCnIToTSOvoFpmZkZGWGtB8TtlNl7Xcep+m8fWyPL1e08BLVzU3l9baNB4qgIJ9fL6aNgQnhBj4m4djX9e/+fd/naf98/kmIte1BMJPHy/TlP72b38/zfPp9TKFRA4v18u7mFpravZmPlzW6zOfzj9ekeH+eFAk8bDb717L+XRab+s6T8khxxDeHo7L9fz8pU75G9BGRN51f9gFisx0QHo6XV3EFM5lzURFGhJfLkWExZuzsfPD3TEE2vZoD3f3UjqH/Hy+zvNwvlx7l1KX4Xgg53a7DDnv8mjqT0+3424IkbrZ9VQRQcUxRXKyHpBCynm/YwY0sdvZd7vJrDVRaTJOOY1Tre309Jpy9i5jDpHJTYcxheC7IR3y9OX1bIg5DEMAADjf5Hbtl0vt3ffTcRqigpgCx/j0crutjRDJNZcyj/M8ZQigKtPIpSkxXs6LiO1349LMlBS8q6fId3cjoqMCEyu4dGCAPMUYWB21OZMPMd3v5PXWcsTdELtTP5vUFg7p8S511RhCYn+c8v030+nafny+lM7rbTXBpppiiIzLKktBrBZYWoMxTWutn19vjOu7N7vdLrPan3y7m8ahdWmi45zdQqsFXOZdxrWASmugbswBHVXdFbpqMwEECtwVVISAOQAa+LbwVggMITKAm3mMYTtUDInjw1hrJwV2aIIv1/q81nk3BQ7mBoApkru5OTFFZg5oZqZuKJuG9/5+NyVeRRPxmOLrawfAruaEpuKuIXIVJ7MQycwILUJoDQhJzG63dtwnVVcRBwiMRB4Yq/rWGu7iotCaEBIivdxE1AmgFVeFtdZpmMccliJqaOpierk5z0Ng+PjpdR7D2/uZIXQQJOyy2UXC5Vq76HE3rWsdxwkiSdeuVpuMKYNqSBzTwGA5B2JWl+v1ej4HRNjtZgP/8vkTvXszjvuH41zWuhXNfv75yzgPCk5OacDXS92k95eyEvj9w3i+BhEfA7ycl1p6zkH69cvL7eFuTIl7a2/vDzGnv/0vf/dnf/Kbf/WXv/773/3D7fQ8jLvEeNg9LB9+YIDrpfCMBmWMA4Vk/UrumQMDS+Rb6YtSDCB9NSMnAFDoYuBj4LthWERer4ub5ciIBICRCEy7KiNtWAV3/MezAvyjfAWAmZjBpKPDMCZx660HDgGpidYGx10aD+OX56u5ltJSztIrEvcuzFxb3coZtTV0YAJzyCmWWqchzTm+XlfZSk1bCMJsI8OAOSKpK36thnlgDBue2e0rTljVTTmgdUEVNesVRx4YuYqKm6irqKqJAwCkiIGBmA0cwWPEN+8mUUDzwzy+XpaY4d1+x4z3x4kQh5S+fDnVVgDTu/v9h6dLaW6ml2UdYgwhmKiZIlCMsTTtTUZGJFcDr9LdgJGQ0InAKJKogSoSiDsooBsiijiAM/E4xa6amcyRA5hZ7ZojMgYgjgHvD5MZlqVcWzPT1moXUwM1R6CgrmBuZsy8nfl6V0QLHMzU1JEcEFsr0Y2Q1JQQEPmPpQwAs00+tBXSENHsKzqE3M03MRE7IqMjupkimoiLWIwsgikNEXkpSl9bUMKIGzMFfTO+GRGFwGLqZlXMzbaDufs/skuVMTKx9L6pE93B1A3NEbbn665iqsTMtM2VvmqXAIlxA38CEZIj+Fd3FCDQ1wdITCHtZiY01fp4P6QQTXTMWJqiyDiTrmgW3FFENgzNxh8hREbWzULn4KYE/HX+A+jugE6AXbS1dlsMKSA7uIUU0xBCwF7d3QkhRAZCFhJxpK8RlQ2BZKYAjhyJWVp3A4gEbmC6BR/c7Lb0XkBcODJFdjFE2txY5uYOyDileLuu25jGvgJvyQXUuhMjsaipNHfPOW7ZP/gKQiJAcEQCB/OmnUC3W0ITU7PAmHNQAzcQVXdPQ0Akld6kHQ5TGkJvWpsQ02Xp4J4J4hAUPA/cVW+3wkyuZABIIAiJsYo2Ed94vF9TH9bdESjFICpbRcjcGEERAEhENo0gM8SUbusWmyNVd7cc06aZi8xG5u6ielrF1f/qP/ynFGgc51Kd1aqYCtROqh5idAMHJMDaek7ByA35dC3bjwUHDmEaYmm9lGbcgXFIKQW81Xa/y//bf/WXrbbPXz5/fr6dlzZP05u78XSRBaHUDmKImFKurUsXJFqkpRB+WF/3d3OOVKre7cNoBgApxtfzLXIIjs+n4uCggOh1bVq91Roxmfrj3TDk+OHjeRgjhQzdIgNwDDkEMhUyRUff5RDZ16JNlAOV7i/n226exsSqRmCMCAS1S+CobtpM3RLGyLSsVYpdatWuIQd3iRyJ0NSbirlps8gBmQiBKSylBiZzBMQhkbusyxUDO0DrysRuToh9u4Ft1wGjijKwk+HW/0VzN4QA7uhOROaemB1JTbYsQwiciGoTps1FxYdpOEypdJHelsXmaWyyooelSEocA8VAajqm5AAxsHUJFCPCqsoRQVWcepOrgSES03aDVDN0RGIDIFNCixgCcjORLuD+elt2qnfz+HK+3WoLIZgIIgWWbTIb0KaJE5uL1OLnc1OzQLSsel3qfR5f+m2IzBCay5BCSiilvhvo3f03Ir0ut2/eTCl4rev9frpcl9PrTcy/nNeukEM4jBBiuNYeIwGoc7jV1dRfTnXeDfMultoQbC3lME2l6XE/pQiJmOcE5GkIUw63a3FAHnic8obbL63HFAKzqqkCoAJgKU1VMadSzR1cJRBFcu0eAqYIrbRlqWs3jhAQumJZSyQ0gN2cXpebO+yHqGC1i4lRDEOOgehWOyccc6iloduYMyKtImRkoqJfvzVUNQZMwYeITbT1zsxV3ATUVCOlGNamZZXA1EVz4k3Al3MQ1fv7GcDniTngurqqOaCBMzGhmWorTUOIiUqry2LoDO6mQsFzguMU/pu//Od//qd//vzl4+eXl58+PQ2jO4E2dYdIhATaDZgwgKGLeV0bIaYIuKXiRQkwhDDkKKWi6eP94f3d3Ez/4ePrx6flH+HoKaZSpbqfl5YjEiMTM+ENcWAUqfNw31p/mPKtS1UdM9PdlHLYTewqTvD4OALy5SK7/TSMjDGqSiA29c9Pl95tyOHNcRxzQKDAvLaOgcaUltelVFmqtS5D4vvdMI5xaQpmkXjKPCQCtPvE10WQYN5Pfz6Pqn3IEQl7lwallT6mrKoxRuSIKsykrd/dzcWEQKdh94efXw3ARaeJXl5W7zIcZ0h8fr2OU4oRl8vKHLe47DAmUb1dy5xjGMfn0+X02gLSlGIKOAxJ1VW1iQJjTryfwtfKtRpTlC6EPEQmRM7h9Xr9/HSapimlpK1roIeHfTfP+32vyzxQE0fH61re8a7X2+1aOabe+225jdM4DPOHT4M7Xs+vhJhSUu8ckdD343A87uYxaO+C8Hp+HlIcpzuRdV2LmxEF0L6/23GOiPh0uixrJaLbUlrvzABmrbYY+FffvDtM81Jr4NGsvl4/v3l8u5sS0+zdRKQsSwxEZF71+ctLmJJ1fHoqp+VmTd8cD+P49s/+6b/43W//67/7N//zebkN+Til8Iefno7z8Pt/+ByS7/hUe/39p8+Hv/3tD3/4cr32jpYSXvtyXkrE+bpIHMe//tv//Jd/9hfff/P97373ca11yJR4ZAamsJ+Tqb2srymFnIfX13PrmSN17U0CYuGQSlEkmab84ekEYOogBUqrQO4WzBzQ1RwahYjbHnTLAUemDSech9xF9nMgRkScx6RixqDNQkAisu4hsYpYbwioHYiRHVOO6HA7ty2EpaoxBFEFtyEFkzqkOOZw7vLh+WzoGLF2ac22x6pAsfZW6iIiK65bF3zIHAiWUr5/d3x43NcGuyGenl+NCAFywuM8Vqmq9ZfvHz98+WJ6OJ/PvRUG/vjykiiMYwQEwMhI0uXt8eGn+uXlemKG797sljVfrkuTNgR6dzf1u9h7uZwvRPjN24eUcxqGYRrLcrvdWg7p+fV62B/fHHYGOlI0xUBYrJDH4zg/v5x//vj8zf307rBT1xzD4/0DcIjjkFO4LQtUfrsbtbfbKkMe5t0YIrrCUQZHLibL2j58fJnGabefrpcKXUOiu/08J5Zm6koRxyEuy7qW8vbxEI4hcLiWZUzJBvrx+fz2fmhSpzwgUHc7n5cIcNzlu33sBinRFKN02+2nter7N7vLrappWXvKwdxVWx7j661HhMPdIUe4nG/jGDlQXfvjIb/eCqi8uT+sXZ8vS2m+nxMzc/frsgYOw8iUrCw2TENpPSOOI4VEtXVXYCRMfDePd1OrpbaGgJ5C2B92MQKFcNyny9qGaTD20us04i+/268Vzq8ciJ7PK5gddhOBIWFvNg75srSeNQZ+dz8jhVb1qV5zjl4aiB6O85gpBgwh4hxLKWtd4phiDJG5CZi5dW3NujnGEBBUXIsAkhkSeYisYujIhu4A6uoWAkPX2loa4mZWQjRmXHtLFGLk9/ezYeA8lyan05oTBiQINE5BxRApMCqgCopZbwpoKXI32xoxQnqYc+2mDtY6MqTNiG7WRNWdic0hz9S7mtkQWc17txTJEUzVnc1UncG9lL6b4wYDIrKUonYrYiI6pCi+Scf89Hod03EjhCIyE4hsMrKo6i/nFcnmPAE7qg1IVdpaa47JVmttcfXX64kiOAVAiiG03reSCTpeLkvOJAVMfZymz8+vv/7lb477/e9+/9t3D4/SVN1KbXf3x74sgeDx7f359fZwv7+euwEA0OW1zLuMHAxVre933I27GMddKTUF6l1P1zUQpRSWtfz06fyLb+8d/D/9/d+9ub+b5/TTp+fjodzf3R13g7179+n5Kaeh6rr6eu0LuwSnnEIeWE1EikRYxW8FU0TVBkohht7bL94+/Hf//J//x//ydx9//gPFKB3VDMFVNY/DcR4/vZy/mpABEAE3fC+hffWuf63r0NblJrKmDtuWTgkpMkOAcRwT+WFOVdRNwLe3T6Yc1NwdRTthCBy7dDdIIRynHFScEAkdPEfssuFu2R0RfGOqMpP7Nrp0AhBzcfFNOA5A4K5bycncPcUQEUx0rbJBPcHQVQQ3uooiIADnFMAtDxkdmAIojYmGFHMI4PN1rfsxqcHHj69jjjGWy3VFCgQYye53qbY+pPhuPzmgmuY5LNURVFXJdZhSc+sq6Nyaho1WjRCYI4eunRAQkYnEQdUY0YEMPDAjOBGQY4r4cJy+nCsiJHcxU/TMpNI/PJ1UDd2caCmq6pF5sykTGv4P/4f/G2yAW0L6ip5xBET6irV2M3MjRndwBUL0DcBKwF8RuWRu4Y8jFUD6aoNH3OQ8vHWxXMgr6Eokt0u5Lp1SRrbAGQBizE0gxJBSVkcEdjcxBQMz3XJNtk2qwb+CPLb4l28hlq/hMyYEJHNzMELKMbUuvfcQGQG3PAgTbVUp3zpTiKrmvnmWwMxCCIGo9y5mX3NmYOSQUvz23ZwifHq+TVNSc2bYj/F8bQOTA3x8KdKNmUsrjNy6qhkjRI5VpPceOSCBirm74dco3eakdLPahXBr/OE2fwnEQ06ILqIpxt5F3JmQAG2zHZkDoAESYyBUBwTPOYpYaX2ex42WouZuFgnNIETOKVXR3gXANqCUmRHx9rkh2qj+DgSb9wrMwAnRUk5uZq4I25iPt1dsy92Bo7kxESB97eSoCwJsF7vZBhICh680l424jEBgu2kIkTBQrbKWruYiToTMEJAMiRAv54UDhhhyHESFGByAgAJTV9v8f0TsuFUICRGZmQlFtuamE6F0JaCNeTTGvLmcxCwwmIGapxQCBVEdhhQDifoQ8HVpMSeTXkrnEHIMvfUmpqLuvhGmtpuifuXLEOBmm3ICJIQQQwgBiZjA3UvpSGjg7jANSUSR/F/82S++eXO8LKfXZQnEv3j/+Hq9/b//6vchRAociJalO2IIJBv6280MW+uPd+PxMJ0vCzFN0+gqgHBeiqvf7acPz1cVmcfk4NIUCS+lD4G+fdg17S+3TkCH3VCaxIAAChQPc16uKwARB3dda9lPGZgI4dOnczMnDiKO5oiu7hwiM9bW3TEy2lcQsqmZmqXAkShxKNJaFyAiDF27wabgg8j8R54zAnoO3MWIcUhpKQLbjBKwq6WtrYu0ffBTiGouKmxkCBi2TytuYUjc/hlt0kdgoq+YNwUDGBJ/++7AHP7w01OK8c3DrF3cvQNcbmutGiIQWmso6kOm/TyB+fm2pJw3pGQXczMM2Lq1JrxBh2ALEyhu3B/fRIG4oawZEcEyRyQUU0JoXYkpR7qbx3mePjy9Lk1cYZ6SQyHXFHAcOTAGQoawrPVaCjGCGQKS0zePb66363dv30Bg1cqon788H3fzd+/fPJ/OP378yJHPt27m9/v85nD3+rqstTXztUqK4TBFdWtiKQVV+SPuEdabXJc6T/mbx0NO9OnLCQxi4mk3vpxuuyEfDvNaex6YvKV5IqJaV3NjgN57zNF8u0JQzJnRN4KgGzGLGDn12lPkaczgKqqtN2niHpv4skqIHCNeLv3T0+LohuQGpdf9OCS05euXWqAIKlqq5oHv9jHHUKp/eVkSbXZLC4FVekrsDkwE5AQ2pYgYbqW6Q2lWqs5DqmpFhLYHGnVCCET7KdfuOWAMdF77mJGZRZ0Jmvjmp4ghiHnr0pogUWA06Q4+5jjl0JvMY/r+/bu7/XC3H97/4s9enr/8/OHHj89XI3HX662MORNQq11UQ2JiNNs0gq5u85hyIBO/LYUI5hzHxJFhGvKYkwN8Ob/+3Q8vrysc93keUxOzJrdm7rYsHcx++X7/uvQcwtNr6a1/czesXa5Lv5/T4TALGPnWQsQQKAdclurEuyH98PE1Eddl2d/NOcWnl8Wc1KTUdpjGnHMIVEoJREMKFFFdmWOvHYA+nUqt/d2bab+jGGJdq5t3C6RymNOwG6+3tVY57PM8heeXq6rfHfam0nofcqqlE1EeU1e/XGtk3I9ht89O8Hq+pYi7aSfdAruB5Rz+/nef3745TsdculjTOafb0lpXJFT1Vp3IhiGtl/p8Xs/NP38+u8NxP/zq7fH+bkSkXlsehjzll9PZ1QRBHa7XviwthNB6301DjBTYmbCbuOF+murSa6mHh12pdTeNn54ukWkpt3kaTE3NQrR3d/tv3r5bu9zWcr21cYiliCiaaWDsrQP4OKSch3FMOfE4zSlHVanr0nufdzOa5jyI+evrOcVxiAGYKII2//vff3i9FcJgTW7L+u7t/WE35mlfSnGzcb/7+ecPtUlI8POHT8fjw+P9PlHaz6mW9vpymSe+3G7SJSRq3S7nelrL4W6H6vV2+x/+5b/47//7f3k6f/h//r/+x0+vRRTMPVJIGXOMKl5KrahpyBNl7a2ZFLDWNTq4sVus2oA4hojeamtEtNvNvZk0nac0joOpXi7lslQz2c9TIAwxFKm36ypurrob9hqs9w4GxO6GKlq74waM3Oji6A4QiDcmQAzobkMeCFDUt7+0LM2JmDDQ1+/rW+uqwEybUlBdYwgIZu5mZG7jSAbuggAQIs1Dvt7WHNNmMRljEHQH2w9BVXNKtdauuHaTLoFp4zoiuTuWtYopUUKAGPC7t/v9lHtrb9/s//DTy/Pp+qv3D+Och2F4eTmfz1ce0vlappT2Y97N+9fz7fcfPz3cPeTEd/dHNP/x0+fXy3U3jcfD2EtLMTto5njYj3kcltL2U3ZVYPj2u4fz6fX0WtOQhhwC86fPT0Shi5alCfjD3X6KbGaraTcB4Z+/XG7LOg7zpZTluh5387/6y18/Ph6atGkcOAzX6/V0vgLQNKWnp5cu9OX56d2bx88vp5zT88spxZyYTueyP+6R+Lc/fAb3mNBMzXhI3GofUnz3sH88TrtjDuynl4rMd4cEgJdLvZVzqVIb7O6nw4CtyGGfpxzU4VZLCpADp5hiiK20xDSMo5qZ2jhlDnGpvfVuam74cr4MYy4LXK/rNOEwDITw8nJljkyo4rcion1I0RyJWNRjDoR4Xft//O0HhLjLYZ6oitfmxzkf95nYepchpWWpG6T0dKvMYRrC5Vpv63qYxv2cholP54U5LU0ejlOMROgmEoZBxF3MTQXgVlpkPp3r2vW4Dw+H6bc/nBnh8T7V3gHj6dJ3OWBm8w7Nhjzu5pyCD4lyQEawzZhDkHN0UVGfhgkcT9dyLS0OKcSk6lK6qAsAgCOiGZAD+LZf8NIkhwAuzDgMaSPTmcuyCjES4cjp23ffff/9L07n0//jf/rXSxXpHmLMKZi5gzFiDGFbC6tCFzN1JgqBECAQ5EgItDRR9xjdAXvTrk7MvZmoEVMgUrfItB/4UoXQdmNcq4JDTrRWDYw5ca0yZOyKpeo4BkRqYr1p67aNa0ttd/PM1A1hWSUQiRgHTDE+7pODqFEOtNsxuBJ7ikwYDFnUzTQETBTHlJChqzijmHFgAjLz1iWHIM3Ee+/w+cty/2aKlHppnGgKKYZ0Ol8chTh9eV6PU9ofptJba/Lu7ePlWn786YtZm/f7zGFttbZ+OMzMHDOog9lWpxJRLM0PYzi9limFrsYM9/ejWA+YTLUbXpcy5rCf0pBGTmQWlutCwW59EUHSqGIxYRhpWfvpVI1Sa04UdvOg3bvp+zd3v/7uux8/n/7qb/7zOIQUgxmZ6WbHBoAhxibav+pW/CuU848FnS3+sw1XEGCIubS6zcs4oJu9fziupX0+nUNIMcAY4m7OCj2F8PRaa23+tXbjvhEzgZyA0QH9fp4yOmdeRUvp05g+PV1VnQh1I53A5q5xRtrMPwHZwA1hE/YCAaKbuqpNiSL7umoaYu8dVLs7E4MZMThCzqxqTLyUnmMk1BCyqyHCbkz3xymm8HK61OaX2wKIgXNrbXsJNpDoYeT7Y/74ZRExDnR/nHLkz6cFES+lI3iKERECc+9eZcuOExGbO3w1mgGggzlgsD/Os8h9K4iMmc20NIlEifHukF6uzRzUDRxDZG0NkQCNEYDZ1TcW/h+hOgYE+L/5P/7fu7rbVgEBJoocfBvyGKgbEwKAaHcnQGQHdQvMXSVyyCE0VVVJITU1MwGKiAigAQHB0RVcpK21rtrrshQmRIQmzoHSkNCs1k5EWxw3hMhxVOc8zsRRDM0dDdxBwdDAUckRzMWViMDB3b4K3r5OHbYIN7jb1zkPbE3Fr1IgItoE8Yl52+8FYjPtqojkYMzERG7eu9gf/U8EEGMcBopkRTQDUiBOfJxyFyezGKg61NquS7lVmYfcup7O10ABiUSFgZjDRigXMxHZgNPmtl2pvokK/ujU24pLKTAgiFgIvKmOtrqliZIDckB0DgTuIYZhDHVpZp5GNiMkBEJV713NfBojki9rTyGRuzq42+YdV1Ei3vhBpVZ3iil8HeM4ti6ZEYnMnYBE21Y9U1ME3H5pQiJEN0B0ZAocVDRyqAbmCmqIkCJtIm83Q3ADZMLDmF5vJXF0N3NXBHEEs8gMBI4+pnxbKxK5gYP1JowUmDgSAqpYjFHcU0Q0N2A1BbAtHuhu6l9/c4fthTM3jzEAaApx6/+FuNnEachxiKGJEGHO6XpbYojAfFsrgQGQiOuW21ITN3Qg/Eqc2UbiBlscCpAQ1LbZLRICYqCvt8ycopvXvo02PFBAdGQIBHdzLtKOhxwpTeN4W9ff/+EESGJ62OXA3FqPMbxcaiCsrbv64+OsprW2eRpLFzSOzGtrtL0t6G3zdyIghtPpnDKnYdCuj4exqLycy8N+t7bW1VIIXfp+Sm/udsvSTrfKkSJhrT3neHfIwzio+fVyOx6HpfTIIY/5ttbrtSHSbSmRsauXptuXwbqUGHiaU4Qg3rtq1y08CsRgBq0JB0Lir5oAAEQPm+8sxNu1GUKILF3zkLoYExvRdmPcGjqI6PbVIhiZTE3kK1JKXQkQAAWMkcA9x03hoSGGyPibX969e7z/8PNLDBHAX063RWzrwNdeTYSImyoT1Vru98d5CmvTmMJ1WaRL7ZBydNFS2wbTSjG21sAJyAJvbnoHQN38YxvjCzFyMPDNjUiMW/drN6R5GAysSc+IRVbkjtuQzAAZxmmMPT+fz3ePQw7c1n5d6zyPbx6OBH46X1OCFLk1PcyzuZ0vFydfeyurns5tNw1lre8O+0W1uzFiygndCS1kRnQQW5bWtO+mCZTc5LKu0zS+vd+XpbdaHx/vi9bX8/UPf3j5b/7yN59ePt/f35FSr+3WbtNuNwyx9nI9t2kKnMhVhyEtSxHVIU4BPQRXhEiMYK6CSi/nmzvmHIbICNQViUjMr7c2DQyIpevvP1xvSw0c3EFE5infzenaVKUPMV7WWqqq+jfvdvf77Oov51qbpsRrkRB4Q3qNY6yl5RiAQLpuUC0xdPNStdYOFByB8OvA7iv3CjzH6Ob7gQXwVnS7j6jK1iAectxN6brU29rcwd2YcBySqMaAuzHsZkaAx8P9d998T+TqxjF+/Ol3pdm11pjw9bwwBRHPzEju5ExI2xzTjRiHIcaNSkoYSKaEU4oEqKoivaq/3srTpVwLANCQEoJfL2tTaKZTpNdr/eZhNyVYWr0/Hj493Q5Digm7OYPvxtTBU4rWJDBhCMzYSzPDIcdW5T/97vP9NBD5w+NBVWuVy9KWZU1pQOYY8LquiXFIiUNwcOlWquQc3tztVDpHdITLtRHYfk6nWxtDHNhCCluGPKXYewlMa+nMfH93CAittrVVYJjHMQ/DP/z4JTF+//6+a53n6Xy+ienj/SEzgPt5aZzQzSJzk25IHPhyvqIhURQnRHUBE52G+Hott6vgMFyXsi7KzO+O45Awj4kB3Hx32AP689PpcqliOu3m10uJRKdbdVVGUqY3dwMzqHkp/enp+vbuEBM83t91k3VZwJkZu5QYYxMNkWqpc5rHGK5tJQwxxKZyvVXpspsHzrGWjgDDkMC1tvrm4T5HFldEGGLsXXe7IXI4v65IoKDH/V1vvdc2H1Kr+tsfn1/O52k3DiFO04joOaTIvJae57nW+vxyNsfT5QQegPwwD7rI+/dv7u7uXs/nl9MTE12Xi/RGli5rOb67A8C6NJFey020pxAdoXYdhinnyGQpMAGIeq3SxSiEHCgFvNZ+WUuOY10vJpjHA0Vbal2LDcnA5G5/BETvIOohIhGsRV5O11Lq3XHOKe2m4fW2bA9m53VhI0c0NsTtRu9qDuSEwc22nj4zcATz7Rk6Asqc5jHHwMgUxe1W6uVaAnFHUxUTD8TTlLro6dqQHByGmFrvOUVTE1NVBPBx5FYFgVOK2+oLgZjAzUsvRMEciP1hP805dZHSe5ftwGFunkJorc/TeHfYnZe11n7c7ZayrusSCe4PuzGHu7sBEX/++DpP8e4wn15e3f2Xv/mT3/7d7wQUnH79/W9U++9+/HGed8fD9P3378rS/svvfvr88kKAb49HRFW1EMPpfPqL33yXxxhScrHf/t0/DOMEAO/fP1i3j59emsu7d/cxxJ9++tJNzO3+ft4d55enc0R2B0VOiV5fl3XRNw/7u9346XRlDNOQMuP+ME7zsC7l9bzujndmcL0u67I6EJONw6AG3Toi/PYPn19eb4ycUubga2mlehooIefEIcYNHwau//TX79UE2QihLBZzzBNGCh9+ehL3OEQFIUcy2R2maU4gNacYB2bm9drW2qc5uWgiDpGGMQdk5hBibF3FrElr3US9rKU3B+dxDL21GKI5lNK3La8Zmm3aBjLzpaiDI9J1ba+39XIt8zAe5jik8HJdQeHN/c7QCZyIl9J77/M8nJemars5J8beNEcCl3k3K2gp8vmlPBynec5jZnU9XRsBH+d0W1ZENPQxc+Lwuw+XgJwSdLPborshrG0VDz99ue13M4FRdFXTFtwpZ0SwGHgeaMoxRsqZACRGNlFEfjzczcOuNH0+Xy+3Ze11GCdCaM3MAWjzooIr9G5mIGq19Rx4N6UUCFxDjGBdzLpBDCiqZjbnYb/b52G83Oqnp/PLrbYmrSkSxYCRIKe0tFa7aIc/LklpyGk/sYqKuJpxpBTBHVtRUaMYarfb0t09R15bf7MfItNLaWPGFOKnL5f9PG5JCjM77DKzL7dGSDHRNAZpGlI43/R0aSkyuAH4P/vl29JrEf39T6f73U5drqXucvr+7VFB1rXOY97NnBOBQ44k2i8Lvl4lJHg8pjnnHCNG710hhFJrFUeMLgok7AROztBqdUgxhEDwelnmXT6/tKX079/vOQEC//V//unN3Y4Idvvpcj4/Pt7HxMtarrc15YhKplCtjNP45u7h5eW81hpS4BCYXM1bJ0BBIKk2z1HdXfRyWd68uWcmJkxpQE9PT8+n6xmIDSAC7HejWBcT5ny9tvt5/HRdY8BdSudShxQQ+bL2OY/NkQlF8cvrZcyRQh4Yd0M4r72LiUjtWyA9bDA/ANimFO7A2651C4xtqFCEeRhVtYpG4lLrbr//1TfH6/V6azXn/HpeH+Z5aZ2Dvb2ff35awXmprUgnJJPtMR8QMTDFiHNkMfmTXzy2Ih+eb+Z4vdWiRl/h08xIaKaOCP7mfjju5x8/vZ5ua2A2lQ3k4WamwsSHKRPosran18tuGnJOrZYUYhySudbec+TeNce8tsZEbhJTCsiq9u3b47KuYnpdREURHCgCABLqJsMDb6q7KQw5nk51yzi4eQxx7Q0IyRABhxTRXc1DSESupuhkAG6uagioZobAGxGXUKQj8B9tgtikz7vxOCVV4UDuermU0g0CiSgBM7k0GzIjAiGagQHy1svyr/SYEAJv5RB1MHd0dMCNoo0UGDd4qiM4M4oqIDGyuxOQA6hv71Dof3QbqdZai0oRM1NprfQNnAAI4Ft86zhnROrqtdsQIY1xrQIiVaF4RbwxI7SkwDFGDtw0IqcQGGLeJlPOFJ3AVVRiTF2E0TecLW9jPzc0+FoKZHQHImTALZxmGyWEMACJw9YRY/ijq+SPva0YkoHlQDHw3S4Gxs/PZw9sYk01hxwRh0RojYBqrRhoPyJ6RA+Z4TiGxHPrdrn1yNvgy9W8Sd+ak1vwZ5uXN+lMGDn8o0MqBFYRtY1eTH8EEjsSBCanIObmHgMFRuLQRaW1MXPrbgK325pCCIkJMSJ10Fo7gJvCeb3lEKYxliaAG0RdmXljaA0p1tp76yEQR1bzgKAOrbQQOEcK/pWy4v4VwfFHC5mruzuyW63LmNMwkNbOgYZIMbCIYnF1SJHRqYozuYCouQXbin3m3kWIYBqzmrlJLR0MkNBZXTSnsPnMwQHR05ZfNw2BGEG6M7OouUNg7gKbdmc70IEbIHFwIuetz0iecrze1pzTu4eZyURtN2ciAuLck6v99NPTw8Ph4X73/HzrtXEM202BeftwfKVxc2Azsy0TBwAAikAA8WuyTA02ZBWYGQJsGxswUxNgQjdn+vRae+2vr82AzJ9pm26AIcDLqXAkc/OluyhGGocUAk3TsJSmRdYqt7WhGYdg4HfzpCK9tyHEpXR3iwF+84ujm55e225M0gsBvT2MjhXRA8Jaaoi8lN6aAeM4h2UpFML7t8fzdTH1UmqKcczRuh+mQQzAgN3YXbQj6v1xVle39OXLzQnf/PLhelml9jgwIAKwajezr6VipjimZgrmbi4mFDCFoObWzb4GPsFUmaH1yhi2kaU7IRAi9i1yHAkcpEsvzcyHHJE36zRt+dXAhABi1qQPY7jfjYgcAu+Hsa3rmGE3px8/vqzaxXwDxYuZdXD2wFSrNsWmlkSrVEIihwCxNjns0+O78cPT7fVyJWbVfhgTh/S6XDca2jah1i1OwkiIQwyOnBhjpCGF3RBE/XLt3XSppfXStXmEy9Iw8D6HZbXH4/Th5Xy92Ns9pjkiem8NmffH8baUv/vdZT8N4xDf3r2pXdAbIL6cL6W0W5GipuKArKJMKMF2+9xFCRzdVS2G0HvvreWQSm8U8HYTNEsD3r3Z7Ybc+uoBdrvh9fa6rAtxOhx35i2PYSn1H37/dLfLPIZ27nRutQsQVKkxxRRhrQsxAubzRfajc8huItoiYSRy4mGOokAADIwh3epa1vVa7HJb393Pecy3qqDy9rB7Xcs0xFLJ3JDYtBPREPnLq3XRaUyMqOAcKbBDgCESQ1i7BkJzPJ9XMJDuSNCa7sbk2q+LgnsKRIHRSVWGIex2KTBfLsUUDodBVcnR3DngNITr2rU3BHRmQMuRE7u7jjlG5tJ7Dn7cpdolZRgySRd3+/HTz7//4Qdiurs7mEgrbZqmQKjNdtO4Lt1F4xDjwKryNSLqVmtVMW25V+m1/uK7+7spJ2YEPF1vn0+vatCEaofr6mq6FE/BMwpxWGu9Xq/5zfF4mNCRQz4OodT2/mFa1+aO7x/ngNR7H5hDwO58vZb9He+mtHZdu0hrSPz2OO1z2h1GAF9ExsQppDeHeKv2fGuRQyLIgSMZgNWqpuLmT6e6j+Te5/nQa5PlBuAwTxFNW5/eHpn8tiwA0JuA2TDkx/u9ul1vN4hxOoQBqNTuICnBr787jlPCiNmDu02HFCi4iHNoAJQJyevaPz5/mY57ZoqqaOAGzVVaO84DZ+iMSh4yH/PAAb9/f7hdCzsi8W43qoqrhZhfL1cCYE6H41ClpZjKciocDnO+34+//+mzdHo6Lcx+v9+79eNh5oHTyFUkgIYQlrVGxPffvGllbWIiMu12U5yvtxsCR2aRJma//MVjrd3N1tqA5O6wE7cY4w//9VNIeRpiE21LPV3WeR4eD+NhN4/ThITrerteXqdp93S93j9+e7m8TomHx4e1e291/zAsZWWi3qV16efL5Xqbdvvf/fCHacpfvlx2u0G6qPc//Pzzh6fPCmrdU0w/f3me835ItLufl3UxNTS43nrOfNgNqqnVtq6XXrtPGgI+LbfjfgLwdSkhxrvd1MqiXeY0IrArQJgt2n4fDQSQySCGNA405Px0uj3uJgdcpd2u1ziOwy4iMwWODC+nVw8hEDu4qiEGBQcPIh3MGQnJUYCYDvOQEpfqS6nuGoJLAWNJI6z9lmgXkNKYH+fj9XrzrqU0JOYQNqxbrW2c0liDiG4rhSEnJuxqYIhbblEQnYgAtYWQVFW1FRM1BcQBw0bzuS3FugUmcwAENwKEwzwMKQBYiCi6vHmT0ceffnpy8D/5/r6WgoApRlMbBn48jtpNu3z//bfktJuGcZpyYmQUaUtZzPs/+dNf3Zbb9XwmwHkMyy2MeVjq8qtvj2ttp/PycD+nAaTXyOyizEFNj4fpb//2d3/6m++mXQrqLu26NgyAam8e9qenS06JgTZdqZkOHGAc7mfGYGu//PLb49Pz7fT6sh/H0tqDWynt5eXk4GrqCHkOrekffvj4y+++q13GmROFx/v7w93d+XwiiK1UVTuO+ZffPiDo4X53WdbbdX16vqrI3//Dj+/f37953Lu69PL8+vrL3f3L6SpEh0MSscTD9bJ411SVue2GGJihu3Ybh8RsbgLkhgYUAXS3H91AuiC4qyIQoBLBNI5XW+taTOd5mtxBmhzncdvjSVPiUErlQGMec56Xtf784XMkfHOYyTXlkDIz+XGXb2tfS305nX/9y2/WZQ0U8hSJ9TASYVybisOvvnskU7HaxAPFOAcR2Q3h9eVsu2F/GPYplaqumiL1rgwERkTtu7eDKz+drk4wT9Hd3725/8NPp8ikprdzzQl3dzlE0oZPp2UaQgVRSS/PyzhP80TziOvSpmloop/Or/tSHw9v/tW//O+mw8MPP/3hr/7tv9bAu3lstXf52qZQdN6y8d0GRmZyc1ElAtVOyOgy5XCrVruJiWJpV5t7m4bhzX1K0dfVS2dRvJuDWI8ptJNeW2FKItvjr4dgpaGZN1EV8ea7Ke6nLN3GIcTIoxIROvhuwNfXnpOrq6kSBDMTw8C8tlZa38/DhpQSBxVxwmOMCTMlbA54c0SIAd1crQPZEHHMAcged1OIrt5uZRlCAIAfP50e78YUw34KZdUp5ftDDMEut0WNS621l+PdHAI5aIqh9r7crgAYchQXouxiZhSiGrpaAAxl1VW8G1TVh2lflzbvB49Mrr23w2FfpTXxy6neP04Cva9wPmueeLn0v3/5KaW8FhkV4oBFzUU4JjGIQXMO9jV+Q6p4vRVAH4eE6GDl2spVJAWIITTVW+tAtKwKsABjAQ+BXTXlELGPMazVDIzZvevLtYDTw3E3xMQAnBKa3M94utXIkRDX2twtMpXW/7il8g0IjF8XY4DgMTACbDPWwxjeHPe3azmXFVEe78f3aXaw15cX5sTJuujpsorIEDkwDgFEBIzAIGeeh+xdiCEPARv++OGVkc63XkVly4wRMgCCpIDTLjli6YJBSr8Ra87OjAEHUwNwVaMUpffbemNyJ3/7ds/ErQsApJyQsVfNIYhIq10ERGU3JQBMrH/xm1/+4YePt+v1uvR1KcAJ0NwMGMXM3ZgIEUSMkGq11ro7tm6b/6SoEhIBAdJWnEFABgN0ChENXQ3dOQcVFQOmCGCtV1EjIwRgAjVzgJgDhdR7eb01U+VA37w5fPv28PPH19dr++Z+l2L8w4enHNhNMEQAizFyCAiGELYxk5kFVRlzAvPaNcTkDkCYiVVEtDOzmYk4EYgaUQBAUUXiIQczE8dAFNGRoPVyPX2qrXMIgdnBEW2tkgMiYDMAgBRIVEu3r8JmU8SgqgCeUpTaVV0M1bH3YqYb58ncQsocYwwpxMA8dA+BQ+DoiA4aAyFhb51xw7w6fgXkg4G7KiGqO/OG8/FEhETdnQgTR3V3MyZAJHUM/EeObABzncaYAk7JphxN87n0oFBV2CNFOl8vy+qZUMEiMzRLhLuZluv6q3dv3xzt9z9/bh0RQ2TsVapoDEFVu6gDMRM6GNh2OCRCc4vMjuCmjH9sBG6sH/AUWNxSDF0FwdFhY5iLaGAoVQtoCqE1qU2XtU85BcKQAohW3ToYCIApMLnGyKpQroWIaxUK5CLzEFNKl5u0KgMiE61dVB0RnaGJhM2mkTAQdUcwJ8av1/NXKvs2baHzUoEA1CGGde21dVFwBFEgJDcAQif65t3eTa5VxNjUbFUAv64LIg0pRMYgoYoASGZOKam5igLY1uZ39xhJVVOOgUHM1RjBEMHBaHMwuX9t+YkpAhKgG3OKCQAgxrCf81qWFKJodySVzky1Wa1tP0+9ti7x/iFNI6wNmijAdnmi2zZ83gbijubqEBgRIDF3syY9hYD/SJt0VFVwQ4oM0KoCOqM5bGYriRQc3GyLLoKCIWLvSoTaHTaUPZIYkTq4/vjxFdwDU+sWiA0IENH9dVmYSQG8ddF2fxi//+745n48n66i5qB//utvuthPn06l2piwVGRX7MY5ffr4RIG+/fYYXVrHWtdxQHcdUjZXVamr3hbmgGMOr+e1FAOCtfa6Sgw6zVM/TId93O3TJ9LdcAwx1C4p8rIUMaAQqvTL67qbh0up2vo8zGsTNQ0caxf37kYpkaqqAgZgo1aVshNuDkEmQHfcmqHvHo5v7nbHw3w+X3/3489rs5wigrvbxqYLjMRs7irWug2Rdyl413Otz0+vX8KlACCRlo6Bp2m4rW3tlcy6GmEADLd1aZ1vpbw7hFplWcv7d8fDnh8e53Ee/u7vG4KnNA4xXkuNROaO5OCuBomJgx/nMcewJS5DwJRCYF6aqbqhNl37upqamSfklFNr1iogYch8txsYKe4DAa2l9O4xgkovrY85ncqqYOelrb01XW/P5eVS3VEVpPfIcb9PY6YUmCKrqHdTgBgoMN2W9vK6EmGKFYDLua+385s30+E4q+Df/PsffvPr47ifutjSyv64n4dxTjdzf3xz/+XL9c2b/cNxuFVduhx2Cc7i5F3p+cslDWm34+D4+dN5N+c0pH5pgQ1AjclT7qqfntbztb67m/iYQZu6NHVRn4c4zsOt1dPz6c+/f//6en69lBwyugeAspamCm4ypd0+RYopBHEz8ZA8ZXg4jr3qrXRVC4HBYEixdmumj3NcEUyVkLo0cL/bRSTtXac5hsRpYFDLGVIMY8AG2B2YMUW6LFZKczMDnkZaSrnf+Tx4N84xDoFero4I9bqkcZhTABeM3DuGyE3psvQ0tAAAGGqTLQVD5uRO6AZWi4joFl8VUXdqzUq5PhyHf/EXv3q423/5/HS9rULw+Xy7rI4UazFRB1OEcF2uUzJIgSOotXFKTWTPsXVZK2QP6B4YUubjcWRyRqPIrsbAgC6tloUTAjhc19swDrWUhzmNOSPZupTjbmDA8+VWrA+JeXEHeXMcP51uHAZtUqUf5lGuNbiY98PdzsGA/PHNjojQdZfwsoq0xpEzhy6SYrw77oYpq3Rz2L29G6aEDG4gYr11AKCR115RkAlFJBLVugJYUxzHRK51UQA/POxTzirqajHw8bhnonVZb0txDph5HBiAPnx42e93kdIffv/x4X4/DHnbDB6Ph9tSXi/Lbszn63I87g7H3Lp/991DThHU11p+8ydvPz/dfvr5ufY2xAE35QARGJRWpyFyZNRwvpXw+bxN9dWxe/fxdngzAWDKOcZwfn0dhhxiOJ8v3757M03D6XJZl9sYx1//6hel9A9fzjFwWdvvfvz5zePx7u4Xhtplmed9ivtPn5/2M7z/5lG7TcPIRKVWeS2I9OHDp3kaz/WMRG5+XqSr+nJb1gUdvEoL/XMp8zTsp10p7bquhATAKR6WWkKA62kFouNuHxxBsUrVqq/X11L1YT8BoqI2keNuMDMVHVMe5iy9XZY2MI5B9rt8u5W7YaKE61pez+3xfv+Lh7HU+sPHpwt0ClG7Crq4UNrikNzYL2txTQi8Bft77SI67DmTL6XlxCZQq4QAvfRhyMgYCRXt8TB3k5fXc8b8eluIJkT4cr1kQrqcp+kckNTt4XFXVvnwekX0xJGQy6rEEJxb7xxIRB0BAFWNkHxr7GYKTK6+tgqm29aGKTDiYWJMkTm8udtdr7frrcxTTpFd7FIF0Lq0x4cwjnFdULqqOiBGpt0u3B/Gtw/veyuX5RyYcqaX5frd998+3O+m+Xg6XX71/dvbsvz0+ZP1L9+9//bh+CdPX57XVg5zerx/fP8QVBoRnj+f590vpr25+nIr//XvfzwepiGW424+HOM0j9+8exgGVu3zXdpzXl7L8Zim/Xi58e22AvrtssQUdrsEDiGHMfGz9nmO0346nc6l1THnxHHMGMdhQ6Q/vjlM84gcrpfb7nh8+vIy7w6n6/Xb7+669LI0basz/bM/+8Vxt/v9Dx+uS317txvHsK7L/f2h9fL2V++//6bkNKYhO3bHflvXeZ+Kp1srwxR3h0GsttaHNMbEcQ7ullMmDup2u65MlHJKOYOLtAYM7lpbvVxhSNkB3MxUQ0h5nm7ruqx1nvM85dPrmdNuHsYtZTANCQmvp0ue5zgEAhxynqdpf9gB9NfLDZFS8G4+DZnQmhgzmWEahlZLiOH1dns47G6ln6/l7d1Uanm4m9flchjnb96+79r+v3/9X4/Hu7fHuazlbp8MbbmtoIik66ruZCq7aa5dAhG5Edt33x5fzksTSzls+xIw6K17BIwMzs2VEsSRDdHNl1LGIV7X0oWXFXZzxCIc+bbWru11PX96+fyr737z/tvv/hd/8ad/9e/+4+XcxjEzw+bJDggcyB0ih7I23CC0qu6kCgg9hggKBOBAHDIQitq1rksrbqAiWupuyiHEN3e7j1/OAWEc+HShh32+rTejEAkR7LL0bZNNgDEGMFyXBuiIpE3dfYio7kx+dxhE4VqqgwFACpATXkvdznet95zwsnQViZFTiibWpI8x7SNdM805pgBNYenWWj3s8m4MS60q4ZB5nA8M7qprxxSZmER9KTomWnsXNHPYH6eYKMeMZsttZQopR3QD98jRyRy9F73drr3L/XGnXfPoX17Wa5E//9OHmKz3XIr+l//6wdWmcfjp4wuaf/PuQCmZYgh4uNvFEAjC/iG6rWJyXVamyAEeHycRd3UmKg7sPuXYei9aA0cCIoJpHkX8vFY/l8fDeJiGb+720zCsIgGpNbssjVAAo5qXsq6r3u3HHIbTpQjooqrqspZXo6fTeZ6zAZ3Pr6/gYx6GPISYAuKY0q30YcgO3rvMQ1IzFeXN/UXgasRsDne74dt3x9N5fTpdiKFbn0KOZFXKr77dS63lqvvd8Hh3eDjsT9fCkR4Ow91+ni7l8lqx92/fjPOUTPlyKxx8ufUqOk3ptlRrcne3G1KqVa9rIaDEaCbv7ve/+PZeQZbaRKxUOV8KjMAU0DqhO2hpPTLuRha13t3dhjR0VXeXLqZGKSuBVylLT5lTjMqG6E3MxCKTqy+Xy26HywqtqaoiywY82oreW/kNkIi2jMLXs3tkdgDb6kmOrk7kzAQOQKgI+DXnARtGB50QLW79YVVBVNsOX8l8Ux9j753MHX2thg4o+g8/Pt8fhj/5/l0gOB4GB6h1mca8VG1daxcmkt4AnAA5BndHAPzf/5//r5E3afwG1vGylCHyNOYqvau17k3JfFu0s5rbhm+xjrBxeUS6lFJu59d5HvM4jzmnyMvaTFqpBVwd4Lo2RIgIOdKQQhVVMzcfczTz2gQQuggiqYOqEmJgBIcQCDcpNSGYueo4hA7BgOf5GNOgvp3QnNFFuDuEsHEwHQC7mKhGBtz+4IadAdycVi7bIbH1BggpJkSA7W7riCAItvmzEyN/xR1b5KCm5qgG7spAXQ0JciLp2rs2BUcgE4RQ20IcDUKr1R2BiAjNrFRFxEgbekYJUdW3ZnrcEguIZoZbHQzpKzUHQFQBCNDBgIhz5iGBIxhwW3tdGxAZujQJgTkGREopbMW/3nttspunwODgCtibEtB2SKatEgw+Zi6rqEFKvHWnUsTIfF2lmbkII4UUUM2ZvnK/zAlIfeti4vZ52EhShBsZxZF4y/ioGpiFxCEwGk5Tqr0BsQP0pqbaBcwNCZkZ3OdxWEox8xjYzQKQmhO6IcWQ3FzBkSynOI2xrK2r2zYhB+ii4rRBPcwMHE3NycbEu2kAU3Mf5yzdb8t6v59z4tOttN6RArqfzpdhyDGFwy5OY7hdpVRvImbQ1QNiTlFNVTSnCIC3pYEDR3ZEF9suuRjZENEhohmAI/8R3u0OLAZIW5gSyVUd4pBRRNUNgNyIaO0KCDEGU92qTAZu5lvw7SuvCzYoOogaODIRkLmrq+/GzOQp83E3jCksTX7+fLnbDU746eWKjmBeSs85MaIIpIT7KbUmUw4K7oBDYGLsYgBATE2tNp8Gdrfrqk0kp9Ca7Ma0m/LLdXHHN8fRwYaczV1U7o97Vz1flrW1h4f9+VLq2ochPp1v05BS5FtpRNyrLKUa+DzMa63bsFK6pJgutYo5IfkW4CMkJnPaZf71+4dfffv2N7/+xc+fPvz1f/ndy6W2Khv5upsiAXNkRJEtHIaqepyGeUrWVVsXNDFjCOqeQ2xdlNxEGIkiIcCU4xBpyunn5zOzz1M6jMOYM3EY0+Auy1qmcfz89Pp0Wc18GvKX07W1yiEAUEos0r5996bUIkpM9BXw0crlurQmiBtpz5CwlJZTyCmMIbgTJgiIMWB3kYbmCoBgKNrM/W43D4lP13UpJQ2cUuhVW9Ut8thrn1OYd2MaI7qgO9Em0o7MaKpMdF7bdS3HOddFFHU/j2QwjGFtdR6HWpq5mhOAr6t9eTqPKW5S991uGCKm6CGHUry2HqItpY5xvF6liyLS/X1KEXqHrkAEAzsaElGppfc+5GEtkiKPQ4wptC6n13UpwkAh+uPj/uV0ThRyiuf1FmJeF8uEKcbPS7mta4rhsBtt21QKxEg5hpx4uSxDzt39y8sqBo445yTab1XB9c0hl2aRMQZ+unRRnwYaRs6RYoiAfL0sm6nR1XIKtyJNLAaexliqP53W7jBkeNyl6yLv7vPDYXi6dAA5Dvnnp+t16XOOFOnhbkSwEMjARbV3qFUPuxzAa1UFjIHcBABqUwdLmbanBv9aWMVladraX/zJuzeHZN2K+q2t4vbp83J6lXGaeu+1G7Lvp/TxadGu05DHXW5tUScVs9ZjGg734X6K3gFImTHmCL3tczzud9fzqugO/uHz6e3bY8xpuZTb2sb9EJzKrex3cxpD19ZrTZQY+XW9pTFdr/Lhy4Ls3xz3H1+uiLbLo5Mf5sQEFDhGki4AOKRERK1WBBzGARnGPI4pq3ZVUZPdbhqGvNyuSLjWElNEBERel+KuMcemIiLTlMDdxJZlVddxSAF9Nw3MJKKK0Ozr4zIY1NIROTFhoOt1JUSMydxfT+vHD6fH+yMCjuNUe6+t391N45C1qwr84dPzPKf33zyKWKnteNj/m3/7N4/HOwDjFKZp+vDlXMVrreh43I0c6HQ6vX1z0Cr7/bz2croWdv4nv/rlslwvy7qKvL2bv/328Xg4lFJErLYeKLRuT6fzlHEa0/VaY0Zm/PDz0+FwiClcbksTUcEPn0/v39y9e9x///2bIQ3Xy7W2No2jieYQStN1ucaUS63X6/pyuRr4btp9++13H58+3663l5dzB9+Nszv++PHTt2/fCfh1ubbaI0VmVmtunYhcA7OGmMoqhLrfT6YWA7sbEt0f9rfbLTA549NlrWsbUixVyWF/GGPm5dbckdEjya1D1V6WssvTIn259cfH6bCb3E2k3e9316V/eV09wBjj5VpuS2UKTdv2lHI37xzsdLtp15x4e5ImZkQoVdZV3t7ND8exqqt0BP/TX/1ymvNu3v9//ur/5wYhsaHdbq2r/uLdY8754+en/TydbutSKztcaxfzHMIYs4PXvulJcUixb+UtB1FTgyHxmOxapMv2OOE5RiYyk7td3u9S3xasTgDw+fWyFcAj4zxEjvG2VFd/9ziOEVW1dbiu8njIMeGQ827A5daeT+d/+hdvwL01/PD0etjN2m0Y43ffff/04UOe0+lWl8tyfr0cD3fvv/n+hw8f3r65S4FDGJ5vr6+nl3X17755yJlLWUvpy1pchTi8e3MQ7ft9NodWm2Fca0GHecwMBkxA+NsfnsacEfBwSPsxxpSreVnWwzztplGE1tafn57naR6mMYYgWj99PkuD99+9Xer64efnedqnnFzkclvN8Re//Ob8ev7hpy9mdnzY7dIwj1m0iuCtrm/f3tlqT89XjpDzsJtyzmmzxXRtXz4/R4oKptCO+4OKi9SYYN7t/vCHL/MYD4cJ3Vpt3f3D59M07achM4mYMzqjx8iBPUacYhzSkFIExNtSu2BKqUq93taUIhGpao455xhScHUO0VpXpNvaYwy1dlOcx7G15o7S5fOXl8t1iTmlEFptYkYhMqL0hsSly5TjZWkfv1x/9e3+XPowhLsputpx2oNXIVEPhMG850Sc4vXSz5eFMx/m+cuncwR89/6+eyc3Qlgvq4Y47qbPzxcVuRvn8ypfbm1dewy42w9oVqp0syoqDQkoBAyBt+UiMwHolOn+MHOg2ltM7C69rHOa3z9+ezgcf/58+v2PnwRhGAKB0z8ejJAQSFS3yoKo34qA227KTABuHdARQsCYQsqp1X65lJ++lBiD9G7S/tf/4je3tV2vrYuuTQ9zbNKRA4CWYsuqgcNSBcHvjwOiuoOaM9EGY1o7rFUSCzKuzXtXcHw4hpS4Navdt0U2EZs0pyAC5LY/xN3Iqh4CZw7n1dZqJl3d97txXYqD5RQDOTMj2jREdzOS1sma3R8Hpk00TOMYVfx8roDw8JgQmZHBtJsiRxW/1AZO+31Ya3UJt6uezte39/tIZsQGvQkFIrBOMUYOY0ytS8yh1F5rf//tsan+/NNrCP7u7dGdX14v+ynN06jmP31+4hweD/vlXAWo3Hpd6/44piESIzmkSEupiGxdiRiRb+t6bv392wcyvVzLUoVw24wiEQkoEwXG19uVMeZIBOROzKZiKQ211d5t6bKfhxjDZWmBkBBEATCEwASBOCm4iETEeUwvlxsjm7u4ETETuRk5fPPmgOifnl/FDYGYwpQDgzX3MadWZe1CCBHpWpqAzymmiIS2LfI5BHGbc5wyO0PpkjnW6nmIIspoEf2nz2XRDTCFqt56+ye/evfm4fDzl5dW6mG3e72WT8+n3TgutVWxDaJERHMOBNrV1tZzYgYCZDBx99a6mU9jYqLrtVKgzWqP6DHS8TAB4ro2V81jFOO69to7EKmqirlTsw0u5wAQIqNt/VEHd1RD4g6wQWnd3ExTCF8NTrTVogwRwIAQkCiGSIFbaV2VOEhrDjqNgwO0vsnrt9gVqgG6B3IH6LWMmb7/7nGeeTemy8V6LcvaOAaioK2lRAbWpcUQaoW1Cv7v/k//F0ZU6WIeQnQ3k9a6xJDEdJoDURBRIDCV8+lWamfmUlq93TgGQFQxQxI1BP7+u+/UbBxyCCBd0MG0rbWYKDgg2JhDSiRq16KiCoA5cutSakOElGIg7iJddVPLmVlgijG2ruamBpkxB7hWU4Ax55iSAc3TIOJATEjikHM2EVEhHpwCgINpIFL/ij0jQjV3UzetrZp6zplDAMBAhGSRSZoOGacxqpgDTAMBgKii+Vq6uccYmwghSu/IAVwQEQxfryWkHANd1hUdrF2RU8qTOeiWkAOsvQdiBNPWkcgBajPz7e5LISAjARoiMLCi+5YHATB1RGRC3QjY5jFwCghArQmiByYHcPiaJNq+JHJKDrafGIFuiwCCIwCSm6WciHBd6jDGwFiLmRqhc6CUuAsMmRP79dr2+3xd9VbN1YnQ1AiYEzJBbZtojMScEAA2wBAyI6Bv9jMm3EjJ7moKMYWQ2MXIsYjCP5ZInQi8KyCiqjoAOYn2PAXiYCIBGRyQCBmk6zyOXbV0wQ29AJ5DCAG7mohu96bWTf5YUA2BwGFtOk8pBCfDphYTjjHEkMSstXpbuyN1NXRNicxBFd4dpy769LKGyP/4H2KCFEKKGBhrU/XNbob0FUFM6NDFDIyQCIDB3dGIiYCQaqlbtG0Dn7m6m5qDOtDG1UZ06WaEMaSAZrq1N1WNEFJM6kZoAKBqsLkZwQGRMXQxIgtMkRkJltJNNQRCIunbFBGZyWwrrjq4x8C7OeUUDvO4G9PvPryAe4p0uTQ0SEOMKWSiLlZ7NbechyGGy1Jvte6GGEJ0x9a7mHaBGNnccmAiBNfDfvzVt9/89h9+Xnsbx3h+XaZpMnACXErbT6n2XsXmHL9qv2PMKYmKSx9icqZuGjmIwbL2/TzGHK5L24/D/SHW2qecU463Wph5g0MxUxc7nUtp5oDgUJuZKqEd5hxCJEDo+uW2ADoTOXjvej+PYQrfvz3MKVxv6/Pl9ubhGMF7704QEi5rr10DpudT3Q3p7f1u7f35+fTm7nCqpTQ1hbv9ZI4fny+1SY68m4aX8+W7t4+996U3R1NRt16XWpsyUx44IBJFdTGXMcWIlEKCQGtdVB2DSVdVTAkyD7X0ZjLkAVy7dAekFAKClK4KoqAGYP72OLx7mIpjToggvZkj5yExgQsst/Wy1v1hF1NvqpdrHVOcUgqBPr3cckqRQcyen28O+vbx7vOXspzP0zhMh+FuHs+XpfW21msIOceByYcphMjs9PSyxpxSxMSeh7g0vVxaCB4A58SOUKq6Qyn97jCoWildDUr1y1JNNUced5HA7vdTWZu6TvupVKFuQDbtdy/n9cvLOcYEAGXtIWdVO47x7pBMTZpFpte1liYIXMWnAEXMEVFlNw+t6xA5JFq7nK+dmfa7xIhSbRzSsq4pZ2l2vRRMgdjB8FZkGtJmMkmJECEx58RzJjd/Waw0mRJfqrTac+b9Lt/fZQIHRURX0y0YHAjbKuZsiF1KYEAEUeOAHMjNwB2AKBCCTSH+L//iz9jL3//uh3MRzli6fPhwVeXjfvf/5+lPmizb1jU96CtGMatVuEe1i1PmzZRSmciQzOhAg47oQAMzMNSlyT+kDTRlmJAECG7evMU5+5y9Y0fhxSpmMcb4ChozDtEKiwj3sDAPX2uOd7zv8xBhaVJFUkRRfLnMAWkYcs47dJ87ckAMfRezatFDCJe2YeCpCxlxiukyX757+0bAa1NiyDHcbmUrau5DnyLuxSUHt8BuYta8MRijVEXgbdP7vZzHbveH7pQ6CmguRJxDKKXlnAJ/W5sjY58iojHzMHaOe0yvXUqR6Xa/jsN4mW/fCuRNtq2pKxCU2nJOOQWV/SLNdkwSI5p5a03dgYOI7qZFbYaATa2uZWv2MHXX6+38eGaOr9d7Pwyytdx1RhZDliKYiAAvT7e3705fnq/363p6ONbSUswPp8NPP/0CSA9vDlsrUqE2eJ3nPsVp6Gstjtjn3EU1wZfrlSKN3eG3P/zhP/zT3+9PeDHg+4dDN3SBOSIiUTM0lWnqOdJPf/pz3w9rKdOhG4b+fl2YsRtS13cIfH25XV9vb9+9M5McaBinmPK6roRQthJDiCmKiDnerxdO488fPyIjU3z39v3h/Oann/78+dfPS9kohh+//2Fv6dZS/z//8R+3den73p0i0zh27jbf1qHrn26Xt+fjm8eTaEvMKjJOvauSW222iTfzeS0UaF5rAAbW5rptGgOfhi4HrqJP19WsgVugpN4C0XQYGeE0JXVIqGr056cFkKaYVLSpf70sYuLN3z4c35y6ubRPT0/ugpgfD9Nx6p/n7XJdExMjnY/5D795/PTleq8tIP6X//7vxrHbVvnnP//86elrl7I7NDExf/dw7HL35etzSvHry5WI160sTdTURJGZCccug+FaFMh3nigBMGEfOAVYRK9L2994Y6AcKcfQZYoBQoybmDUQcXERNQSKTMRUanMHRIsUkDBG5p2JifA4hirrm+MhcPzTr0+HIf3bPzxurf3ydbkvW5fjGAN6G8csBu/fn18ud8bw6evtXvW7t6dhGLpIYz8cjqd/+Kd/MvMpD+u2GljKudX6/bsThXC/z4QQUzJsL883AhYHQZXiYx8jAwUEhHU1gsjRkb2JSVEPCVzH3OUUAsPj4+Pz62trklIW0RjjLx9fzOFwnMBx3VbiUNYt59z3w/0+b7V1Od+XRYoiwuPb4+Vyd9Mfvnt3W1dra84Pz8+X07kPGH77m9876PPz0y+//Prm/bHvwnxTaZU7fH29Pp7PCLCUNedYNukCE7MCMbT7ul6Xqg1VfRwDWCuCD4dhW+aHx7EfsI/EhiGmHz68fTg/3pbl45fnUioyzduWUmZGVRty33UJAOrWQszmWGolZiciYhXQrW1bS5G+fP00DCc1raKIiIC3+wrm5uoGMWVwQ/e1StfFtVkRAWinaQJrQxfHgWvx59va5XAYO0B/eprVMCRKXSRxcO+nIRLc5nup0sU4iwzDsFa7X+eO4pd5ezj3t2tZFhmGzhFaqYikAFtVadbl0OVk7tvWUmIma61OXXeckqKVIuDcDRQSsvGpO3z/4fu5rH//zz89z2XvAoPvzAxk3lE2joDqsFZ398wOYEzcxIpIynQ4dMtc3XxR/PV5ZaLzEGrToQtiulZ9GLrjkGKiJla2xoGQsVVlDE1BTIY+rFs1B8KgamZ+X9uy1RBiCrRsZSeijkN6+9DX1tydOCxbDejAwd3WTUrR86EnNkZy1RTD2HeXu9zWVmpz0xRRxbsu/fa70b0hcDNUra2ogM2lRYgPh24tkjMfDynsiWQpzLGZpMzLWrvUE5O4rwuEjnOCHZv78lzWRVOHH84HU3le5+tlORwOrhiIYs9DDlM/rbUA67bp9bIejxOg3pe2Y2lcjbsstY1TikilinUxePj668XcSy3SJMb8/dtDN+Vmdnu9BcTcdxyCiRCFum750DegbSkcMHD49PWCAA4YKWFAceszqrV1c3QM7ImjqCEBqOeUtloxUBOfbzXmHAMVaQ7KjGU1QEKKas7M52ly8Nt9OR3Om8laS6AA7uQQOTQVIFORVowJ+xy7PjiFWjQnLFsLMVWRea0pYGDuQxItxN7vjhrz29bMKJETecr5mONp6kVk6EJMnAlag//w19vL7f4w9jGGz6/z1KXTkBysS+HhfPyPf/n09eVGIRoIuLkjOLDa7z48vNxfVX2bW+xiDhwQidHAHw7T73/8LUf+7//+719erzFx5FCb58Tv3xxy7l4v1/tc503UFADRQU3N0ABq0xAiIjUVQiBiUQ2EKQR1YwBVEwMiSpGByMzcFJH2AykR7wULMHHHwMlA+xhjDLVJqa1qyzEQUpPWdtulAzgh8V6J2SleIYSpT/P9frlvxyn89vsTYyrbqlC7LkWGZalu2JputQIAQEBG/F//1/8nAFJta6lEyRAPY9dqVdNdTivaUgjXed62DcxKrWautpcpnAj3g746g1vf9afHU0pZxZnBai3bdjxENFy2GiN884GJbdWbqoikFNyhNUWEGEh1P8nuBrLdVgYAYO6EJKJdTkwwl4aIQxd2tDeT5xDUqc+0teIYAGheGnHm1OUuugcECoFba6W0vg9g0ERiJHVisMhMxOb7ez8wOTHlFEPAUioCpkQmCiZdCmY2byJOjKYiVSwQxIC3dUNgjumbo8l9WZfr62UcxtwPHBK6ilhTVNcQOBEiQmnfVLNN/ZvTnSmHlHNQVZFdYLQzhQ0JwTHFsD8WizR3cgMwJaLcRWJ081Z1V2TV0swcGQNxlwIRNQNHcLN9aoscVBxMU+R+iNLsWz+K0c22TXPiHFkESpPqRkC7Ki6GtJQWIzJCqRYJHb1WI8YQeFsrY8AAyIg72BohECHtXFzkSK7uDlW/2d12dDwSErCZBA4OLmIB2VEdIMbg+7cfYIiRCRlxK1JFUoqtCQAi+XHIzLSVaurfUllzJyLGhCDShqGbi5lJCIFDWEpjsi7FIaa9dn7bRF3BjAhDCOpgYjlFMytFdjmaqCFAl8LYMZM7oJiHwO54m0uXEqGvpSKyg6mRqiF4wODouwkshEDEqrLLbmKOtYmZqbmaMWFMwczRwQDBIBJgYBFVMXNIiXKXTI3JzUzUdga6mvZdGrqk4NsmKpZSWLayR9p7X9FUHQgZhr6b521Zy3HqA8emLSd+OHTXW8kp3bdNvpmbMITAEVTt8TjiN0qrbVUYQR2+bRYFmblqyzGWKlVaSonQW9UYqEvh/cPx6Xp98zBd7wsDUQhrre8fDj9/vXYxhBAuy9ynKIpLbYkxpdSaHPs09vHlvjJTDGAGIYS16rJWAEq5z4kCU5ejO9zmdewSgDfVnFKKFEN4vWwhd6XpstxzF/c2dd3WLoQQ2QibeIosTQKxBTyfU3ADx73ZB4AktjWo4A64blLWulYFIld7PE6lNWqt61nJwbA1JyJTr6qOwDEMqVu3NZIj4mW+g+uU6eXaatNx6EKgvqepz7fXNSQ6TrluFYycCINvpRqYNmkNYhdc9dyPRVTAIoA6iKK67a4I+pa5GhEeu/RwiEX8cBy2bXm9rMfjsR9iLasbfvp0eTge/vLp6bv3b3VbqeM8hnpvKdFca+rymPPr0ywOSJByEnEwGXs+PfQEtszVEGoTVb9d23Irh9MA4AEsp1icAkOOmENo6vd5ZSLHXRBmS2nonlI6jbFVuc1tbYoQxWTdNkYaxiytEeDUpVKWYRrvWzl1+X6f+1M/5FArfL0WcR1zXu7bfVumfjhMw3mIpv58uU2HfmtNFOZVam2HsRP0xPu8FxOxmlMiB399raUBopMDuA9jhw5uoCp7nhUJAtO8NGbCAKcpr3Odl9p1nYkMHSvA59ft9b71XSLwsQvBrJifj0PY/QkAKYV1Ewbc+z59nzapbsKE37CJCLoz45mYKHfh3PEfv/+xVvnzx19udQWArdjLy0qUU4pd4KomumujfS1Nm3OgoScin1c5dtndBIkZHK0s0sfuDuvDISUI5noeUtm29+fTXKWZglju09Pzvdb23XdnclBxdVeR83G83WZ05BSUqWxboIjAhJQimTZVK0VjDn3fNxFpDcyHoY8puRsRMVFgDwwxjSHHEKnW2qTGFAitlJJiMGtmlnIA8FIqAAChmNUqDmTqjGgiMTADbqWU1og4dnme61rc3GNiU6hbVTEDcmxjFz9/mf+TP3ww8qELXRdjSGrAzLXJfb67EyDVoikkqS3mUFqZ7/bl6eXh3KcYI8Wl1G7s3r45/cuffjn0w5fX67ZtP354f5rG27xVkRxxud9T7ud6RwoB4vPLhVN8fz5FhseHo4hwxGmapPrry2vXMwc8PhytadnacMjInmKKxOrS5ZRSj0RlKQBQa1nXuqxlHMbMgZlLLYAYY8wp73wvRPz88a+nd99//fritnKaDuP55fXy5eunj58+/6s//uvb/dYPw3ffva9re3p5/cvHX14vV6Q8dn0AxUDLWm7313EazofpdBhyTNfrrKrk9pvffK+tvb7enm7L6301hcBcTfrYP18vueOxy4Hz18sLOJwP49Cn+yKXeRl7/vG709CF5V7vc2WGx9Nwn1urMje7FW2tAdLDYawqX17v7kbuQ+z7Pm61baUQhz6lHPTxPH29L9ummeOQw1xKDGzqCj726fs3Rwf/+eNX2UwQGHhv1l2v8zR0Dw/Hwzj+08+/Lk3ASMxaE1M3d7XmaCnGIcQmLu5OuA/IhxzGAIvgvUhru94WiL+JMUJkYm9FupgRyAlVqjbkyIgODoHQHMScEWPYZRqQAiE4E9TWxi7GGLYmHVOMfq/yeivvj/zH37y/z+u7D10p0qVcKv76fM1M09D9+uXSj+PDaXg49LV616VS17XIcttyoiL1MAzuTuS11eM0uvp9WaaxiyE+fb1dliV3ceiH222p4hCDiuZAHKJojSnc55IxVUOKYAKIOA6UUowcSxFwH8dUVaWAqCx1e3t+5BDVGjnMS41dPIzTtrZ12Z7u17K2Nw9DHvLlZXPHoWdg35Z6Oh+BbBi6SP2b8zux9vNP/yBgXc5jF6v6dZ7XUhJx5m5ZtyLt4Xwsa71eriHGcZpE1F2f72vd7M0xNQBm3EptzaY+cYSxx3mW7950avLmzbmPOYZYtBUxcEAK89pSjLUVFXk4n1KK622tVfpxQgAnHqZxXZZlKTl19+vc5Uyg7z788Ne//DXlRIFfni8mO8TDWm3LVh5ORxHBwOZWa1s37YYA7nVrbjINfR9pEXi93lOghzfHr8+vz19uv/v9e4eqi4WUuyFHovu8uDmnqAkS8rrJ6ywR2czXbR1yfr43MEdGwG93oY5Qm6lonyMHRnARIwIzdfM+cxOFb/BXDgliRlBDheN0cKb7um5VdX98RgJ3JggxEREYqEJt5gBd5BjRDb++zvNajlM6H7I1gAAYeS90vz0PDvi6+ct1KXXNOf/u/fHxmARgvm/omHNWkNqkFdLmfcfEuxLHrveybuqASLBjRtxBmhDSNIacIKV8ud5DYORo5qIm1hjZ1B+PPbiIYk5MCJnj2uT5Wrd1HrrInJZSpj798bcPt/ttXra+n9aqy31B5uaman3sCNFdA9E0hMMhr2vJiYBB1LoUt2oOHDgA7pOTgGTMfrvWrcDQcyBbN4sdofvLtZjhw7lfRHKkWj13Yb21ZdlylwztfByWe3V1Dvk+z4kppRwixsR1qw9vT7d5QdeuG15ft7/8/GuI/J//69+8Xm9z88vl/t3bc5cDEiHFba2B6e3jMK/yp5+fvn//aKbP9zkE3qr+8PB4m+fc01y1lVKbikLOgdDdMERWaWCQc+oHIg8fP94gcO6CiDnZYaDLvZy6lHO+V617WAdUm4lRiBERkeHd+Xzoh4+fv9S66zIrIcZITGBmKeQUQmQstT3fFgeYhnFtrU+8rWU6DF0XtFWksG5ynzdxHIZun1qiewq01TZk/uHdIQSSprX5XATdDmNYm77eWo44dNHct2VbVjWmna+XUxhi6GK4L8vjYah1K2U9HQ/F9HafmSjn7jIv70+H3//mt9XtT3/9q2oz8LKBGwaGaerB4X6/q+HXpxtn7rtkzc1d3QIHcZAmbujMpgYAjBADOfp+fNu/q0wNHDjFXTC51yKYKMZAvMciBuCOlFIEF3evrRFRiAwGiFRaU/Vvmxqnnaq8Rz872KXPMOb48rrdlvvjKRESuBjo/b6oWimtNiEkVTsdxvPhECLjf/W//T/WZmOXllpLBU4xplC32kcstSWGdV3XKi+vN4edbLLrDZyIAMBUAb4BVtycCGOMANh33XfvTsuybFVDDE0k5UBIl8tCCCkSIwBSa7XvYmmOgKKC5Luqed3abWkxcCDfGXtqsIuQRS1F2qq6WwyhGQTCtdYp8eMUS1OIvGxVGtT97EIEYDGmnIcUQlUDwHW5E3I/jE1aTkm1kmOMceg7VUhd7jKlFFrVUkqfE/luxOaASugAvpVWqiA4IVTR631Vs12J3SrElM20trKVNt/nmCITn86HQGwK6h4iqVkMSdWqGKHlyFtRUUcEUdEm79+cCW0tYhBE98DNdpS0mgVmBxRpIrrrrRiJ9tYmOCK2vbWMoOrunmOIzOouZrh7lAhEFJ1iCmaKiCkQEyFzayJiu9hq58uYgaoiExEFAgB2wCaFQxC1gBQDtSZmtKMBTTXGwIH3vhIANNGdsJ1TTAkcYJnVcB8w2s4Scwd1ZWRCJMTAoUhFI44EYKAGSLs7CQhTZHBc1mqqFIO67xvjLocYgzq0ZjteCmmHLxMjaBMnNIQPb8dS8Nev95Tw0EdX67uwbFrFm1QEF6PACO5iMHQdB6ybzFtTdXBPmTkwkUcEUxdzNYO9yGSWYtxbOQaABLuPjpCLSIwhBnI1MQ0cmHjdaimSczD3PTnkQDFEB6tNGDnFgOitGQfeCZOR0dwVABwYUN3FbUgpRL7PC3MIkQ9jnBe53tYYeH+5N3dHd1ExjzkyATGruotFxsBBwcY+wz5pRWqtEIXSdsef7JgiQkJGNXUDaw0QkThErEXcIKXQVAIzELQqbt8ehRkxICLL6TCBaSnb777/bmvl49PrYRxK86a6luLuyGS61/D3qiQ/HMatSVNBQEbsupRTkGaX27onT3uBv89x7Ifn6y1yeH8e3FppEEI4H9ImftsgB3qYMOT4+WlzkfMh9GMfEAMRELxe5hTzfS1ONHS03FcEGoZsbl1KKYSt1c9fl7m5o6KHVvR6v/Q5D9N4Sul6vTdUJhNzNRxzSoRvH9788vXl0+Xp/eMxBGu1muJapUs4pPh0LTkSAaQudTEAuDYZ+lTrBk6OWERL01YFXat6TAEArcl3j0dlZDAX/Ph0NUckTIzv305vztO2VGLbR68vtyWm0OX06ev9dq/fvz/kZIdh/PPPL79+eg4pPT4O00NHRkOMSym3+5YSM4f5sr083abTGHO8vN5//P4B2SgkFXVXYy+tSWtSvevyfG3bvH3//fm+lgAYIzZzJh4TNbFt26ahWzY1c0e4zuV62yJzZHo4dIh4mbd5rafpAKDMNg592QoaIoVm9TiEz5d1yIFF09C9eTdt67Zs+nKvHEMr2zTmUlutuG31YeyOh8wR160ta40x3uYamZF5KyUlFrWd07Js0g8ZAJbZXu8FQZniziQOSP0Qp0PqOya0PoUuhXmrIQc3m+dKTERQNkuEIYVhSp+/3j6/bIHRxA99AvfAjm4ppb4jdjKAVRoaxhAp4Ov11lpFBAbshx4Zt60yc4zsbn0KAd0VttKaKyVspa2rmyNzuM01IaSUnCkFdIdl3cYuiiJF6ILPmyZiRlAOCMYBr7clepqtxU5O/bDOLXf+23dv1vt66LMxX5eVATBirWqqbvJmHF/vy/F8jAgx8LJWFd0UONA0ZTVarvNhGI6Hcdnm2poB9EPXiqEDE221TtMQOOQcAbypD5ljCC/34tZOD4cmwoFqWc2171JZt9akiaQU9hOU2M5C8hiTm5tql0IOQUW3rYjqspWYOuRIBi/P98O5VwMQX+/L6eH8y5fnpWzfffc+AFnT45vBQevafvjusZntqRtGevp8jaFLfapV2irT1FUTBPr14+vQpWVdHk/nl+utGzpi8Ibbsj1t98PQS5Vh7AGllLrct6kfx0MGtGWuMcQUw+PpVJqmhMdDX2s9HHqpUpturfZdYI7SrBQxb9Mxu0IApgghhqnvd4dFKzWGlIdeWr1flxRClzPGBG5lrTtyktyJY+q6QIQpf/r0pcuu2Net/cM//r+6PN1u2x/+1b+5Xm+3y+u79+9zTub05eXz0/P12J/+8ukvv/3+/XGcXi7Xy+W2rEsX8+nUi7Sn59thGB6Oh9NxXMu2rIVC+sunZzU31+eX1/Pp3ES/ezv9F//pf9Ll4f/y3/w3mxg5JaalNSN7PPc5BBPbtqZIDLDvF4jpZdOyCThsVXKIVUUVzAxAupCHIRPA2rxLzNwM2nk4XDa939dat0AxxZBTnLj/9fnp8TT+8OHDr09Pr7d7rWIBQiZTV4C6qYgeunzsp5frramW5sCoqn8Tru7v23vIj0zBGUAN3ZhwSFkBVGRrrvuEG/fzDDtBqy0FRgJzSCGaeY5JWnPfL7GAeN9+yrZs4IJEgATuSJRyMtfEHJDMDUDHLkbm33zIj+fuvsrf/f7tr5++bKt+evWXZTuP8eGYm8FytzHj48P08cv9w7vjb3/37l/+5denS/vufXaSSGHK/X2el/vaGmxiKQATphgNrLnONy2bITtyIArbuphLP4xd4qFjFe2w++nphgwRw84fNMQAVLftcB6mITNSLVKlDmOMMSEGJG3iz6/3ccy//fEPZd7+9MvPh9PZVXKfRBo5fPlySV0exuF+X1xUfPn9v/rDlA6//OVfxD0lRMSmPo59jny5XYvI1PXrIsig2FyIzAApx7TM7cvrbOhdjupw6HHoYxU01a3Voc9blamLdavMOEwJiSILUxjSsJWCzH3ftQbr2g6H0aAgEDOZauQAiCFkQiwiBL1pU28xJXTcbvcffvu7Zd2+fv48TYdlWdd1A4Ljw7Es27pubj4Ow+v92g9DCvzxy+eYOlValyUSpcBvj2NFum1zAnu+i5nmFIchzddbH5ITv3k8tlLndWHmvh9opG1pdZVr0W3TLkREdzczWIrvZigk2q3bZvv1tHGgsU+RYdkaB65N9rfapeyBpLt5VeOAOYUUOIUQAgFAbbIViyGkGEJgNUWCssr11szJAVMKhy6Kw+ttcdcY6TTEFEMRMTemuBZZVi1itSkxmykB/P6HUyRBpAa4FelTUtOmTQSCxZS478LrfZuL1E2LaGQcutgA0EFUi2ifEiKolnHo1GBdVqfgRgS+1EIcGSknGnKIEVKKMdAy19f52wqry+H1urnZ46E7TNzlrpQ6L9tcjZAzA8fwMi/aLHFm5sRmYMex2+9UxzEWaeBkjsjUig8dp57rpvNt7oY+p3xfSupjZDeFVvQw0eusoh47cM6HgW+37V/+9BIYGeDxeH585JjDr5/nROk6b/43IuE4dV0O5kZBImZ1GM89mn/9crm+3v/df/q73IX7vF0uy9iHfhjMbSlNheZ5A9IudB8/X8dTB04qMg7886en/8nf/TFEnpe7AW2bAMDLvKj4nkkHZqIdU+kx0JtxXJsiAwOh030tgcEJGDgyYqLWzMRDCGsVNZIGp2H44x9+/OH9h8Nx/B/+/u//+uvTXAqjjWPe9zaMiGZSdTPnGFqVLgcTMCBCklY5hnnZxhyuSxm7obk3aUwM4IRY2s7LEEQc+jAkfL5uXQqnKQx9qLUBwKGbxuHIHC6318vt6gaEVOrGzBQYXWKkkEPfd4kCgsc+A8L/+D/+h6GfltKawZgxR97XcyGEWrxu5YcPb4oqmIxdbupradsmGKi2hgaAQVzRgYjV1AQEHNwJIUY28yYCQACO4F3KzLiuFZBCIBUlxhhCJEYGRFB1+qYCN1czM3MBICAgQNUdq0tmtruP+5wAsDZV9xACI6RgkVut9vQypy7kiMHtvizzurqD75eFgQn3LRaMXTeNGf/L/+X/rs/5fBqHcQKk18tMaGD2fH2d55UJm2gpzQGYAP1vTQ3cgTwGxGLYmvheTkH8RhQLHGM08z7HnGPfxRi4bLJuDQkJzcwAISaKTPOtnE+dgxEiI4qCu5cqBpAimQoRVXUV7RO3Bg6wlVabFbHEgIHVncH7gDFQU2ji7rvpy82RApu0KtalHBjEgJkIgTi6eamt1jL0Q4qcY2BKFPjNeUiJL6+3ZhoCTXk4no8UYlkW0d2waG4aEhN4qbps9XK9Haa+y931sqUhBea//PJ5LQUcff86mpra8XQap7zMW0qhqkcOXY7zWpiQA6vtpUCY55XchsxVtB864lgb3JdWq6XIxCgi3+jLamDuxO5IACGwmpnvqMKdCMxm5q60m7AA3R2RgHAPeEIkYnRXMtyTHYBvf2pfztC3rv5eIvkWXwJ4ZAQEpuBu4JpT18yk1ZQCEq21BQo5kqo5sqiIWIwhRUIwIlxWlbZHgWCE/re/aG/0p8g5JVUTMWQkBFUPTEwYAwci26tkW0kxGiOCn8bcdeRqzTSm2MTXRURthyepw66gZw7q3iXIMcaUjlPuI6WMv3653e/NwUozDnHou1JWMDge8nFKyyJb8dd7YQJmQgrmvm6rGzKRmtoezhIEZlVBAABGcgA0gEhsDqKSUgqBRZqIq4gBiEiXk4IjQJ/CVkoMEYma6t/Wc7zPJQghMhF6ZF6rVnVwZCQnALfMHFNo6q1qbZoTm5oBABo67OIARFQA+qZEc0LYXxK8WcjMTIDoqsdpEIfaSgyxVWlNEFENzJEDiRqhAoA7y77TDGj7RJMQ1AB3ePuu6/UQiBCHPv3rP7zrh/TnP39c1jb2KaV4XdqyruQIhOramu9NK7HWxfD9m3fD2L28fL28bpuqikXixw9Hrfb0+WV8GE8PozTZijZzNQjERYwR//jdg4NuRdS8NdmqieFpL06rN/VathBp6vO5TxzCL18uW1MOtFV9d+63UiNFc1O30trb4+Ru797093v754/Xf/Ovf/v1+bVKSREyp/lW3p2HZjYXUZGmGkJctu186t4/nL5+vX5+fY0UQCXE4IAxYWRsaoQomwASk4v64ZDaVsHZ0Ay8qccQtyLbVmJkQT0NHTq76WnMGHm+b6+30tQCYc7hh/fnh8Nwub1ExL7rX29L7tNaZX9tWVc7DuHx3K9lrQ0ud2P21OHxOCJpWUsIQZqpQVOV6p9+vZqjuJ2mblnkNx/61JEbXS71cpsFse/CmGNplRlaJVSLATgGxF0aEq63cpwSBQhEa7HbvaXETapUAQscuE/sDs38+XoLwMepO50zgL++3N6exu/fvfmnP/91rW06Hl/v69uuG8YYOi6lAZK4P72sAMTozrBuAm6J+XDoh4FbVRWoIvPc3BEDqWgp0nexVSGmPlNtmrsEAFvx1+tGjjEzEoDRYeCU99c0QQpdjOt9qaA/fP8gm/7y66Xr6fx4qJu6SjU9HrrLtX15XVMENF5Ly10cE5/G6OCRPIZQm1YVQMopfv56LUUeDnFZCsfweOqbaMgRwKWZmU4pANKttioiTbQKOFfR82lYFkHzIROErqkktuvccmJ0MOIug4iDIYNvBkh2HPN12dqqgKFReXhIx9Cp+DByR2Gdt+PU30s1BEd8fr51IW0i19vy7//wTl2n00PdFjRRxduyUQ7H07hrNF1tzB06rOvGKVGgbamMbG6ltYfTkRCut+U49SHGYRyfv3xdlhmY+ykbsYseDr2oSBMp1USAKOZgAOtaaq05J2JSdWYM5KZqZjEysxvg9S51bU/Ppbb29nTop+Bgnz7fvn8cQkqhi+uybhVcKBMtdZtOwzKLLMVQzu+OQ2BRC10cQvfl0+vrfHv//nHsxqeXS8q5z7k1vS9lmoZpyC/Pl9KstroVRZfjm2leyrq0D+/eiG2MTBjE6tsPp2k8BaSXl9u7t+cy3748XZjheOjNGjOaOgcEBsL+6+cXMx2ncVvm42nsU/fzX79ssnz34V3muJRlOg5d6ub7PA75dH6oZQshiEGKyVTv85pDdFnEKYWMhMN4SLm/31/n0tD1zz/91QHG8VCqHadDP0yiMo7TOt+Q41bXy/UCFtZyL+sGHoYxg+m6bJ+eXpey/P6HDwT0er8Hwren01ZKiKHv89eX+7I1JwawmJAC5UAuJmrcB6kWHN8cEjGvtb5eb0ihCb5eNwp06BO6I8C82VybOVzm1cyP0zgMMWMUUaeWQ6yrjkO81RozE9IybyawVlXBJisnFiM0/cP7t00aIALi67oyYtWGQMTYmrphVSN0VUfGSAwOqhoorbUxEwGUWt0hEImpuAVgjAhqbgYIADQmPh/i1uB+L8BB1FxqymkTFZEQyEykaQiZCCMzugVASgyw37jYVsq2Vd/TTKAQAhEBIjOdhq4PAZi6gO5WTY+n+HiMaCBGW9ku93aZm6MfhuzehkznfgwplVa2At+9m86nXIp+edrGIQAWAn735uH1epUCf//TEzO+OXdmxkgxsaq+vJYiJu6m+uFxus6yFqmlxRiYQUQBuJi6uTZAghCACMduqlKnITFACJBTipmHLoGjWHt6md8+TIZ4fjh//fhcxVMKY58Bo4Hdl/l2m3PM/TC4Wmni4Ca172NrcLsvyDiOwyHE27Zt0t6cD7f1jmSHbrxf567P1WW5lcOQ+zGq2dNLCRS3WvoYQO3du0OI8HRZW9WYGBACJymNXDhwM6HAw5RSZFNgpFZlGrrjsX/6+jpv+vg4UUAOMSACIDAw8DIXZABjJDqcpnWtQ+5ziq+X561ojPm792+2bfvy+eV+X07nyRAQfL5try+383miALUJBXfCUpEQ5ssVHX/88P7Tyx2oHcfu4+f7+/NxcROpZZUhh8weUs9uREAJ160tWzudptfbdl/EHWqV49DdtpoDqYI67ThZNXA3QDeDbWlITATnY1dKiYFjSrU2KW0YemZU1/taKLKKqcI4hhS4VWhVEtM4deh4n9d+7B1gh9vO90YclyKHqTex66qltkA49Ok4oLuuRVMKiCQCtdlWtxTT6yzbtg0pPzx050PHCOtWl2ZNZJuXN4+nvo/W7L61Q59vi/z8+Xocx6ZC5NOQrnPpc5S2NfMxJSISQDNNOXQp3ObyeisPh36TVsV2iW5OYexC330zEl+u1TEQObhzYGlyGnKK4GjT2IP556f7rdYuJJc2HLK4dJhrgVIKsgNTRmjqKXEK2A/5NldxTClI1bHPXaLLNsec11lj8NzFtbQ+xxjSurWcWE2LgYF1mdjhdvWPn1+I/Me3707H2A/huqwMVFREtRRIOTPDcq9qNh0oIJvD+Twut2XoO1U3kZjTNPbm2rZ6W0Sct7aJGmJitFrLUtrpMLnSWspvPpxebvP9th5P4zgkMLgsBZ2KlbXV15eGwCnBbq8hxhhxStHJAzMqb9WXpn1EYqjNQ6CqmpiYyQFCoHmtzbyLGCjuE9HUZQeqgvOyBSYOyAxdIjS4XLa1WMgJwDOjOrXW0BAwVG2magBqisAKfjp1GXBp1necIdSmRSQz7A00bSJI78+9SAHHJvW7t4//+X/y70/n46evv/7y68+KgoFqEScHpKZQSxWRPsdEeF8qEqPZx1+fYwqYOIZs2taySoV1ka0pB3bRf/2b95dtc2+nYfj1y91MMTIhLKsCOu0bLtCmiN8wdbuSHhDQ9l43EKIhABHt2Q4HcvcuJZWGCH3XAWGrzXf9OmIOodZSqyDCrq4HdwR0QkYkwKpCxHkHcTqaWWToMqJrLU1VAyGRI9g8r1uta62mGpmLwg72UrNEEENSM/yf/Vf/hz7ndV2+//Dw9s2by2X+019/Nofr5bIPsQggBha1FKjv4roJAKhJIDRwJO5SuC1VFc3BTIlcAUXdwccuH8ZOzYduqKoANnSxSesSJmY1uK1lzBxDaLVWEWIKRPOqIWAMvhbpUlATVXTHdWtM1JqXKmamsDuVqamI7WpQYPIYqFZlIgDIOYBDbQbgpSkBdokdLUVKKW9F3BEAzIAC54DulkIIOY6ZvjzPtVnuQteF8zihY0759TYT2vk8sfP1tozHsSxzl+LpeFZwIJOyAVIDbaJfPj3XKk18E0XA/aEi5ZhjlJ1V6db3eez7+yrSpDUDQlVlxhDAwR2gbO3hOAIBoiTG62JfLi2HmGJUFbWGjjsR13wX3vvOCRIVVxC33TOK6Ez4t5Hv/l2/A30xxQDoZuaGbg4IMcYdby5igBAICEl9p5UbAgTC2NH5lDpKX15mYmcKW1UxJdwB26Cqe6Jg5ob7iA+7PkpTUwCAba1uEEL45tBCN3cmVDUziCGEgCrmQIHRDRUA0BIzIbihubp7CBQY1cHcMlPfB3dnJiTcim7VzZwIEVHEETEQGODQcyAxRU6sJm8fJiJ8eV6//3AQx+dLOU7civzy+frDh2PK8PXzvcsZObpZ34fa/OWiTWSrjYEckGgPRneIKxDszKFvv6Vmom7ukZkQUgoAuO+TiQMzgHtTD0RdjqbeRIs0QAwh7JNJRGRGQkRAFTFAcQMgxgBoAMDgTBxiKLWpGSI6IpojusOeTKGIujkFRgIm3oFTpVQRnYYOiETkm3uOGAgYDYAIdnYVqZgD6x4PgroD71RAAwBX2+G1Juo76VylgVlg7rvoqt+9H373m+/M4DrfXp7vpWqMYas1BzbHENFdSymtGUU6TP0Y8t/97rd//N0f/tv//r/9/PT1cDw4gKou6xY4FrVxzAAQiBGpVLnet8Q09N2yyO73ZebSrDTdqiKRKBRRJjADw5314Oep72L8+noPOQBiH2NkV7GtuaJm4lJrU/nj+7O5DV36eises0uby33oIirlTGqtHxNTUPFSVJrEwLVV1ZZzLk0jQgxBHM3EXZGwNW2LIOI4JKa95GjLbaHAFWBdCzjGyGKiDjmHmKgj7mJgJBF/XUoV6XMY+yRVhxz6LiBRq1tdqlGAxIHC8/PdvH54/8BIQ8K5tDzGwLwsVaQ64DhN3trtsgj7MAy3azH3fav79bXWrb0/92L+9phCwHWWnz7dAsHrvb37fjpP/XqbkcPltR6HSIH6MUhtKuCOpQoHXEqdl+aGOREgBLQu5fvSzmMahrRVf72upWgg6Md4mOLTy80F3j5MIeLL9b5UE8XsdB7Tm/cHZpiXzcBbtW0TIjgehtt9c/DAoO5dl1pTEU8p3u5lWRsGUlMVaGrnMar6fW2/fXdY6z419pdbA3U1AfLfffeIYK21tbqajH33y+db5MSB+s5ThMTp+fV5OvSPb976Vu+3LR+GrSzX2dZ5iwT9mGOEZdEiPuQUgboEwxi3Wr++boF47BnQ1NSlUeAYQnCjFGMK27yOQ4eA69ZWUSTVKoiYcqpV3GAYeinWSjWgBqRtI+b7UvoYapXp1CNqn7q6lk1B3B+PvYFf7+sY0q3WOPiHc3/M+XIrBPD23FtrpYKQN7Of/vp16ickfjxFJjr04X57PZzeEKFIae4xMxLdLutpGqZp+Kd//CXGeDyPyCxiAYmJienlcstdevd4Hrr8/HIRbTFwTkHNxSEwUSRxy5zatiKZms/3JYaQ+ySmrWmrJXd9baKiIWTXgkjOrFoCARN/fVqu1+0wDXVrY4zF/fSQ120j9xySEyF64HS9rdIsdklJb9fy2Pfuuqod+hAomDkFdIX7tlLwPoyg0EgO0wEdt1prUwJ/+/j4+cvXwCzAcykPx9z3HWO8XuaQ0rYt21yu9/m7Hx9Px3HoR3e73mbU2qd0ubeuo76nda2X65xzfPfu1IqUzdZSOCBD2Go9HVNZbSmVowekVm08xHmt53FMXUCEFNLp4chEXz+/xBjPx6m2Gjkvt8tatr6f3jwcbvdFMRymUZo2rVuttdi8zOjwhz/+24+fPnZd+PD+h+fn59x1t/sdwH/+5edt3g7T6evT13dvz61tMcQvL6+t6ruHc+7Sn3/+5bu3b1IMDt71qYl8/voyr9p1mVCqWO4SE4rouhUAYAxT9Ej24d1jqfVeyq/Pty6m+33JXexzN+b+OteXa32+74UEa6IYnAMG45zD4ZjXuQ0png7d1urTZSvV1HSdS93k9HBca2utDTl0fddqDYEbWN0ac8iMDjovCk4Y0N0ZUao6IzNKUwyIjiEkDpAjmzm5GvhWoWoDdXDaEyNwc8DWpM9h6sNSJMdcTRy8T3nZVjPZr3NcANwBUUS11cAeODLvPWxQQ9G9hozEDIiIGJiJGYH6LiGCgY45mYOaqruZ1a10XQopSWtEPg6JmXLigUqgfqNI2Fxo7GOXVCFc7laW+TiwYlekiHnZzN0/nIe16lLqVtp+OwIIRHhfWyBIIXy9bwiIhjmHHQDMRLVVcNwvnxG9NZmGsWhlxjF37jLkCAhVbehy4rBtLUfqMuc+tmrLLM3xceoAzBj/+vlSapuGjIbgfp6GP/369c15eHgY/uXnixZz0mOfvju/eV6vKfGnrzfbawMKhz7nFEptTfR0GA0LoV+uQhilSY7UBXaU796frlttVcGxmYRA53Foa1HVrg8UkSNvpamDA2uVwxgB4HffPZjZ02Uep44Y53vJuYMApZo26DIBOIaMTqXUAHEaR/X1erszph9/fE9EX59euty/vLw2qafj0dSfXl6ZseuyuDvA2Oeff/61tPZ4Orj4tsllradj6nMnzeu28rG73Far8OY0TDmUZkRE2jgSB/786ZIOXTd2z0/rfW5bq6ehvznjXK8AAQAASURBVK2aGd8c+1vV6/XepVzMSpMcI6JvpTEkdwDUvotummOIiVtp69pOxz5GFIPch1rafanj1Knq16etVDtPCcHPU07M1YBiKMtGjAoWY64i/dDVZi+vhZBMkQm7Dua1oGPfRSZaV7lvLUQMEWtzVe9z3OrmQO8ez7VspbiAr2udBj6fMzisxdBYFT493ae+F9Wm0qW4rpUDB7bbWg99l0BTjkA8b0UdcuS1CRGJqJiCA0FwgD6H45hyQjO53goCjn1eq01DqrWoeE6BA4xT7HP39cvt623xEHTRqY8hYxFRxVI1UQSynJgJ3WXsUooIiLWoIDpA5rgssrR6fugyR3DggKVp36fAsBVB8dz3m/hWFjI/HE8p2F9+eW4CKfA4he/enF8u9ybe9d0q27rWsuIw0DQkbTAeIxGtWzW1xAkdEFER7vdFFYjp4TT+5rt3//Gff1qaVbFW9fc/nl39thR3WxZdm56m7jTme62X20KIp2mYl1qri0kc3A3vF+lzGPq0rYJkwxAC0rpKSGHM3bKUp+vq4H0X5iIppRwDI3AAQBCxLsettRTjXEpM7GZNHDyJYBVjwhwoRcqBl6LXpQYkIm6uXeBAYVVLkbelqiPtknNAUStbOx66xA5A0+ND8rYu29wMzKQB8d55cGYchqGUFVEVPSKcpt4N+q778PbRwP/68VOpNTFVUWbOiba6lVL3mYKbkod5ldTzVtrtVh/PBzVfti0E+voyTykTsTEwOzmVKl3uim5IYEom6Ghtq8QUAqo4EgainTcE4Oq+7x6IwH3Xg3kMgQlFLAZOIaja1goSHqaBEO/3qiYpxtZEzYmRkBwgMRNhEWEk5ii2O/ScCQi8bTMxhxAC2G5oLEsRKc1sXre6MxoA1LCpuTsCMWjfZSCqreL//H/1vx/6fJ9XdS1NipgoWK2uxhyIPEfOMZTaiHFM4brWaUhIFpAA6TpvY4ri1hzGxGuxeS1NrQogeAyMTO4wTaOYdykOXWgiRGaqKaS11PMhqUIplcjFPTCVTfev/30phIDIrZmoV1EmZCJTizGIqqqlwOrexKpoSJERmEDUAxGAEyEzqwkTqbiIEgMzgZsDx7hD44nJS9U3527M0YjnWhmhVWCEKqZGOUZwa+JbWU+H/u15IohIbAiJ0Q0CMTKGHFLq1mUrZSXCf/nLR3dkhia2O01izhzJzfo8UECROvSjK7pjn7tt2e7r9vbtcd3a/XbvunibF3Ma+t6Zhp5SEAAvBqXS7dpqlcOQVdXMQwimDuDA36AStUkO0cwUPSDnGEqrAOSI5ranJIyoZri7HQGIgJEJQNwM0MH3RfuOMxcxQCfEwxD7LoZIas0UAaGUMs/7h5goqH2r6uxmqn28BoDMGCMBgFRTVQphH1iZQwy7k1ENvMudO9RaxX03tkdmA1KXyLyPEB08gccYMaCbqKEDoBsiqNOemzogAhDht7mi+/mQmTzm+OYhhwCvr/L0fO+HNHaBkMYuube1eD8kwPnlRWNMhwmfL2tbHdCqYIqxVr1vZs7qBq4Bg7rFGETa3zxzf0ttHRXM7BvfSN1wByE5qLq6xRB2Lo/vvSpEQkBA2adjhPvM0/3bZ/3bD/T9QxwQCBEYgJnMobYKhPvyDswAITGrmpgzIRM3ETVn2vdrGJhqk3WrMQRC3NelCKCwm0Hd9jALUMGZeOj6ouLgKqqqosqIRGxuiNRqU3MK0cHBHcFNndFjoOOQj6fOTa3JUk3VDlMHiESgbuo4dphidOfrcgPCLpI0ndft3ePp4WH6h//w04fvH07nIcbQxbzWIqaiti3lm+qAmZGguRls6kiUmQloK7IsWxMvVYnjVpsIKECplYFC4hxzqS1EJMKxy4jGQK3aKo1z6NAS88eX+//0X30/16IiTPyPH69jjgRQVU9jfLndqvFhCI9TXxVutzUEZIZ5KSFQjrG21jFttU3HqW5V1AWEkbwqBs85gSGqA3juo4M2tdu9wt7T7uPpMMzzqmZdyipSxe9rxYhgyogxsqt/9/aUI1mT2sQQMeDrbW4CfcfnUzffpVb1QLmPfeSylG0rIae+z1aqGgg4oW/F1kXQcS3VwarIaYjfvT0gQs/hMm/i9HzZWpO+Cz/+eLSmibEpfH1aOYSUaduWMQ+MuIkupanZvLZ1E0LPkdy971IpSm7fvzso8u1ebveNgMVknJKp1GI7dRkQt6pVGgPE1L170ycUCqGZkRk4XuYtdXQcuqfn5XDIVbRUDUxNrM8pU3i9z8aoBusi7tikncasjuTepXBdRUTVPCUeO9yaocF/9ofv/+mnj2JkDt3AQ5c/fr4+nLrHx9zMLq9rDPj45vj8vCzXbeh7DMiEhn69bmVtgQNHxmDbWqXqaRxyZCRkMgV4er2/O445MSartZFC7AMZAMBw7KhJH9JPXy63u6Qc3HUYsc/JjShwa+IK1kBEkanr09fnmzuqiiugezeERRTM3kz97T6n8VBbfZi6pbSX2/r2dJplOYzhw/kAoJ8/Pv/mu8epT8+XVQDHKcxr/fx1XjZ/fByz3s/Hw2kaQOrLKte1TocwdvnyOq9L+fH79/3QXW9LE0s5DUPHMcy3e04ZA93uy/l8Rm3aWtf3atJcvr1fN/7y9BwzP56Pr68vy7wdTwckT4FjCpeXa07RYp6vNwIbxn65vA5D7xQvT3NDjl0qy0yMonC/tpTCeUh94KWIRnKwWkpAYgwxYw7JFOetLEViF5e1aKXTIaSR6yYkApQcnAjEsJtCYppvLURMfbhfa2DqhpxCXu5rCvnz09P5NL1clvP5MI4s1QGCg7283ta1ppjWZX58M07H05fPz2j6+PYUEVTcEEOC56eXsRvWbZuOQ9m01JZz6PquzItY2GqZOgwhhRyKVHYUhRDpNm+Jwuk8DH23zgsHepiGwEQx9n0m8LI1pLDOd1dRo66L5kocjudHcfznf/znH3/725evX7d1ef/d91ttjBg4vX085+Hw9eXFvSXi+Xpby+qgotaaRXQRU/fWNjXvu65L6XZfcooh0n3bDGHbmokAgTNJFROJKdUqgBTAp8THqZv6VFr9+Lz86ePy4UzDGH/+ss63cj5OTVwRiqgLqqMpzNvKhJGigR6G1OfsJrua7XarvzzdOGqkFDhUbRFTLRKwTsfDtrmaNZUdr3A4Zle5zJUoGDoxMARTVRcVB3BkHlIv3hQ9c9gvVFRh3jZXA8cQ4ibiZrTP2E0RPTKt2zZNR1GtUnLoWhNtomaiNXBMEU1FRK0JgO/q0xR3PCbYTkgwQOL9riunDO7NgZhckIMz05S7QxfFdGv10NNpivtauzkaMkqLKQWUe6Hb1qYcRD1EjiE0ha2ab8v52H+8rAE9cvh6XXvmx/PQ3NZNmpj9zd6bQlhLjSGIeVNjokBEBA4ABoCgag4YmZHQ3asoAoprjJQomtrp0FXRrZWwc90dY8/TkEw0EJvhT5+f/93vf3M85L8+Pf/yeSaynNnVD2Net6oOQ8eRw+eXtQv8elsyh5gZIqYYyypNTExjwMPYE2IES5mQmFTU4cvLlkKiYO+m7jBNqywR/eneIkc3UwSRdhry2KUUg5oUWcc+B0uXss7eVOH9aQoIW62/+/54mrrz8aHvp+t9+esvH6sZUgiRur6/38tWWkxsiqDOhMjW5zwM0/0255yfn55CSuN4EKkqjTmae9/nXz89bXU1xxxCl1LdCjLGHOvWRP14yMtartfy+evlzfvTNPV//ul5zGE4ja6opXUdnlPAgBRhXstqIYfwem3P123q86en6x+/ezj13Wx2m2dvcF3lvi5d6rsctrqZUuTk4A5OCH3mFCDl2KpspY1DTgmJwnIvjpCHAEil2e1exjGZaCvyeOqaWwghAau4oOUUY4hNGjM187K1Kixmgb1U7bu033s29ZdLAfDjxA+nYUclDLmL3VCq/PXTl61q4kgMp2MkJHUr1dZVXfDltvVdlqal1hhjjgzgSDhvm1Ngt0yeu7RW30pVdyZG8h25Qoz7+c7dzlMHjENHt1tz8MdjvC5tGjKYblvrYgwhDkMSrZ+e5rU6MEjzx+OACGttW60OABYSc4gwdrnUguh9DsDBRLbaFJw5tkZglhPvuNqpx8OYDel6XY5jRxi2aojy9bI9HAbwgub9kF/vLUV6ul7HHG+LNeMhhnHk92+H0r7ZqcrWQhdrdQCPzAFJlZ+eLlutXZ/V3ZxyxMdDGo5JiaTaT3/6+vjmkHOQ6l2OAcNtLi/XBdAoGhJrw70loSKEPI7pNERHN/UUYqm161Mp+/HLAcEN1yK6W4+A5q3lGPoUmZ2ZShV0yF2at/XUT7OIo4hqqWCCZh6Zpp73JdCQ+uu8iTsAmbuJEQcRc3BOPERa5hVCyiHUJgoGasdD705jwlmpqEXztTURTZx3RbibIcLQ51qrqIxTatpEjYwBoOs6ct/KYu45hYDadenlNt/v1dwA4cPjMSX+8nQrRY6HbAbLWr//fuKIY58j0f/7//sLUSjmqkaAU9+VKuKeEjvYujWr2A+pFatiQwZidtgtQ/vECvf9zb5cMXVCZN4hXPvB0AKz7X+GABBSiERUWnVzt29n5xBCldbFHCOruZoDEDNJq2YWAzH6PF+ZQyDv+6itbFszlaGLXy8zgIuoisYUCKkWdfAQsO+CGtRmRBzI622GrdpaixoCmDfpQuCMgbHv4lZqqRWZ1XSe733Xg/v9tgIgh9jnpE2PU68uZStla6pKQIHBAaqIFE8xzssamCFya24GKt6aObUQ41oUzAjITVXMGgTCZZPaTNq+3KmixowhEDoEpmrWRJhYXGsTRWKCfS1lCBH50EczMDAmLE3dIaAHRgMC2InDGNjdtM9MhDGGLoe1NjFLMdbSpj6czsO6FvM29UHU9+n7MAxdFx/evDsdT5eX52qNyJdbSSGGGEoR07Zu9fOn5z4lM9rqElNScUIwVVQ7TMPrbX6dL/3UVa3ram8eTkMX5/nuBMdzJ95CUggExDHEpmhoWtulyDj1RGktNXcxRFm2GuLQ5by1JiIAgI4gYAB73GjoFAjdCbFKAyAAcjNEDEyE6A6E/LeoBNzc2Q3QVAn3fbuBww4qdQNiCoHUbKtCQjvpOgZsDQOF6ruy3pkdAcwB/P//E2B0ADJ1dGdGIiJCd6y1ACIYUKAup1qqmyg4MgQjUXVCATJvgID4jUFN4MRkYFrtm8OeCHFnPlNtAvitF+oO0nSv41AAACf0272oKAJ+eNMXhZzow5tD2dYUevPr1PE4HH58G6VKqfU8Paxr/fJ8PeWOHZ5ftTUVs5RjAi6iu0cQAFMM4EaEbuCOhsYIgcjMQ2AMVGttTdH39hyJKCDuoCXaYc7mO8aJmPavyG7m/Fus5DvKyAzdHNGRIAaKgVpVNXVyJkLzwIjEqrpz8AmdAAkxhVCquDq4GHMRQbPEtJWaAscU1RwRE6ObmQERMuIuKEKHUoqjI5I2MXM3UHI1AUTXpu6O5CIhfKuBifrW2tsPx3Hk1IVgtDjMr9cPH07nY78WB2hdN6rC/PqKSos24hyjjkNyhKL6j3/5tf/abbPVn67XSyta3j0eDofewV4v92+F8xRSQFUXNGfMwed5vW2WUwrMXQ6nKfbdcLvXP338QhxMPBIjAjstt1uMPOYpkyJqVe8TPh7Tpv58XxDp7Ztzn+nl9daNMWRe1vbbD0MKZC73xRAacSBzcLysMjJH9K22fRKYKbQm13tNh6wG9/uqApuIW0sccxfXUu/L6ioPx/HxzTTf72ZwPg4dcxOlzI72ersDIDi8Xpc+p2UTIGBABXA3Avruw2nIab1vYmLBQ+YQ+TFNKTEnlCrnh/x6L5d7icmbh5fXOSU+952W6mKG+PR6O5+yVHGTbkxv301S5Hab3z4c5nu53lcmzhOfp0GU53ub2I/c38plWUvKnWG73htcbVnX9w/8/rG7blrFtMm61Jw5MDKS7t8FTn3i66JN27xsO62DA9RaTLE020AiszuEEBixtDZNaS5b6tOY4l8+viDCMCUBD2rbVg1MxJZFaynDkJfVI9Ja1gJAiNsqrdlejbxtCmIphlUqM+zsvRSQA0NxJv/Lp+fnW+tzNnAWJQJEdICX59UR0XQahiGni82AgDn0gwdENsjczR30MRyPh89fXg/HPnUpJ3bQda0A6KbvTt3xkJ2sFO1iHg4sTVrTPEYVaU33jXk/cO4o50QAWq1W4Wi4BwoG6spg26putG2bqyKF46lDxOfPt6FL2unxNCmG0jbQps3WVbZ+4UAModXKqr/74TRkyDF+uTxh4m4coMm2rT+8f/N2Si4TkINXTITmPRFznJcGZt9//+aXL5f5/vH3f/yQAnYJTodORDVFDLiUJedY17u0LeX+9X7bKUvr1m63VQ3MmzR9ubwC4uE4EoO5i8H1+bbe5vR4ZK0daNdH9nJ6PPzzp/nL19euz8DlmJSAb5eCDMdDJnOQdlnm/nwAhy+fbymEPOSQIUeStd2KChKGINW0em1SjK9fZ0Y6Dt28akC/35cw9kutYwimipC/3Jb53vqESPh0vV+v6+Pj6c3pTMzg8+2+3G7ep+ReOOB5PK3z08cvz3/87XuMsG1L7sI0jB2H1+cLJ4aIP/98RwxdhD6HUuxyK6pamn1+WiLxVjdGYOHQiRW9L/cxZ6K0boIQNwN/bfNiiOb3LXI8jL02ATAA29a6lfpwfogpfv38sl7vP/7mnZi/3m7uhpGut+sm28Pb88vriyGdz6fD6UBMn3/9NB6H8+NDWVZkgpvXcgNUQDmfP3x9emWQ88PD589PQMBsfZ/u98VnNcSUwjGHj5ebIxxOfYwkzs/Pr8D4ux/faBFtPk7dMCReqW4vv3mMb0/9f/zl8vnrGom+vszjlJtCK2rmVVQFRGV/fzKTq9l9rgSgamoQQwCH+611HSKJG2Iyih44b1WLmLoRuZl1qdtKVTUEEK2ILGaG3kUmZyQzNxWbyxIDN23Fah9TIDZxhuCEQIaIJq21kkJwFZXqpgIoTV/WLyFFc+XkCJRTMNNtQymbVo+ByMTBAQBckRjRVc0dQmRwYEZ3SLt7i1otO2ExmkGAnDg64NO8AOCQQsrBAQy866kt+vS65RTrsk45EsK6SnQmAnG73ZZ7aY/TcB7726aX63Yacp8CY3Ckp9vWRNQwMO3Pdwi0llpFRXfEniuYheCgDABA39B7bk1s/28W9jtntSJeUXPkea2lNQAUaYTNjZPzuq6OCMTbIk3gnz5+xl99rpUx1q2dBi4GQ6JSbFnVHUu5q2MBiiE40VItA8xFd74sovc5betGGIU8Jnp+ub059ZvZpupevdZw7Od14z6q6sv1/jhFIzN0UXy5y21rfR8jkQgSqcl983YaU8SsIhwChfjL8/JyXz/U9rvv6dCHt4/DdVnVKITQM2MXxkwxxVJbay0w9f1UapvX+32ZL5fXN4+PT0/PFPhyuZ2O0+dPX4Z+KMs2dslB+6n/+vXl46fXN28OOdBW22GKXUjSWkAv2n78zRt379D/+P3hepnbsn25lEMKxKl03hET4ePY/8OfvoS3jw7GAU4Df/lq61benadlLUOXLejX60YQM6HUMnT5clvV3BWQcG9MTwM7wDh2FGgrrSp0GYgQgiHCei8ph6HD9TYfhj5PuaoVbaxNKTDHQOx7iQ2sVDUEQ3AwUAfAPjGih0CRcCKa+uAGFEFVX55vQ59/fHz77/7tf6Fg/+f/6/9NbWFqIjrfWo4xd93TvDgQGteqVeYuphhCDAhoObGCJ6HWxDis5st987+9JVdpOz1XRU2gekNEM9+qidoyQ0BvajZ1THKft/PUi+ta5Rz4x3e/+fj1ubQl5VC31sVQmpg6uPcxbVWqijbjBoyMyKW0lMibt+qtWHVXazmFtch9rVMfmEOEIGq/Pt1cqdWtqonpD2/7LsPL7RoB52J5FjUlIvT4eteqlhJcluIhH1sKARIhEY1TrqKA1mUGBK1bZHr7tqsSSvFa4fyQH85TDLG01RH6Pvzx795dLtURp1OUTaus/RhynratztuqTbsum3Cf01KrqW1Vt1KB5TD1XgkMQSAQbWvph9ylcL1u16U6egqBeX92cyRAhh07kSPNy7ZUdVmK6DSmAEDBc4wOUGpDgZxTXddShMy7SCEAM6vxbWl9F//dv/r9v/n9b6v7//2/+2+vawHAFCMiHob0+Dh9fppdbUR/vWyWEwM3Mwjgur80eeCwXK8Q8tqclrrfkjuaqFxf1pQSSxUDUY8Bn263UipA2OXpvz7dCcDcHPy+lhS4HyiiXV7a9aWcx27qx9dlM1cRiSGqNrfWpdR3fOwnVTgf8j/86bOCng9hLeYGe+EEEZjIzQ08xYCI5oaMCK5q+wOnmQLQrmALgc0BXLd1U4ScQ+TQxAIRITl45ICIos5MMXCzts6vjJS7ZCYEMPWp1hIovF5uddsCUYq0VRE1QjB3JHw4RVTUIfYjX68bIJ2m8Hqp93nF/8X/5r/+9fONwES1Vg3kORIi9TkyGQFUcRFFJEdncERGRwNlpkjQ1Oat/d2P7//9v/23/93/8P/89fnaDUmab62qu+kuyERmJOKYaBw68uBgAR0MlIDIXaw1dQBxYDRmWorAPh1S7XNIOe5nbFHfTbp7AMQBzUGbIlMMuENvpiGlSC/XMkyRkZ9fFnAkphhQVYlxL9rmFLfausRvHzpVQKSqjQAzpeI2DgGBRIzIu5xiiCKaKIr6Zb599+HxdHxEiE+Xz+D+7s0bCv18u4MqIjSpm5iJfPr8JKbj0AFRbQJE0xAJeJ43JPry5Xq9zxD4/dvT4/nkhvN9Y+J+6NzlNl/B0TW+3K4f3j2aiKki47KsHLq94yKtppSQgzZjZrE98HOiQMyB3MH35TMQ7FcCze3b/1PYl8O4y8IAwMF3SY2Du+0uYt4PTkQAACoA7ruQC5wBjBhzTKVsgTlQWGoLgcV0X+U0VXQHoJ3my7Svc3cyFprpTrQCAAeIHGIkZgbmWmurahwCoauKk/u3sy6zMxEYABOhowGnAHv6Shh23Lq4uBOhu/d9CiGYmohVsZR46llFAmvfxY+f52HMt3V7dx6W+9qF+PZtwowPU4/m02H49Pk1ED+9lvvS+j69XDZTHcb+thoComOOVARKaeaAFAAMAc1bRHYMgLbHN3uS4wCmZm5ggIhAQMhEWFtjoOa2I7wJSc0N96neXvtxhG+fCMB3kJPZTpXfXWlCe74ODgCMCN/cQuRohBA5uO9YMtxN8cz87c3UYbcJAhG4ERIzu6k7qmkIgQMPXTQTERAB1bYjGx0sMFfRrTWAvXCEzOFv/zpspY59HLpwPnTd6CZeFlNHDhACvT7fvvv+TfV2mg6t2vPzHdw+P12GPr/9cCTQy7XUqoa+Fr1f1+/eHadjui3rNMahS621/QiRck7MZZG9Rd+l5MZPl+2+lhgoRVRzQOxiaNXV0c0Ace+FEcDvvnuMEZFxGvI///QpcjhPWd0cfattW6RjDhw3sxzB3YsYkm/z9t3bh/fvHp4vl58+fmEkIBJF3GRzOT2M21Zfr0uXeDqMt1s59nkzbU3WqmtpHfHDsVtqU7XI2Gc+n4cd5BhDKFWv89JM+qmb71spmlMnKpGJgeb6Ddng1k5j9+7N4fV6L1s7TF03xWHIbtKabbU1dTccO3a31Heff725Qu485mBiy3XjhMNxMPVSJBC6ujoMQ5RWa9EYo2xStqbIiu4OdW1VzBGD++nQi8mUYlF/mbdSHIlLre8fhg/HcFv949NVBUXhOIWxCzGH+1LBKRCJepXmjmspBAiAzDtgFQE8xbDnngDQTADADEVb3+U303BfrpxC4HBfap+CA5aqfUrXpRBCU1g3GXLmCKJaijg4R057iUaciBVtp58hQOJAEVxsX1m6ExMNfaimmc2cXy5rDLE0nabw7iG/fxjM8OvlvjSNIYDK0IcEEJjB9T/7T/+tS/3LXz9+va7i0nW5bALgIbo0q5uezqO6SWt9x2RWKzRXjjvdPxDi/bbO962fOqnmBsMY3Y05rXMBpG4IhKi1AYSt2rxsc5XDmPpEtfrXy2Zuf/fjmZjvm4q085CfbhuSdYxf7/V3PxyylD/+9ruQ7PPn53uFp3vdtvrjuxFEMPAPb0/rPFOOAD6m3MClCVF8vhd3iSn8+acXRv43/+ZD10Uzb+plLa21YRhraSr1w4f3f/rTXz68e/Pl6WU4DsOQmRwd9hMDoBNRYJYmptZ3PSK+vt7d5DCmFBDNi9pca460NF1mn2KYa3GEPiVpghDvcy21ng8DuYupAH95voM5AByn/lbKGMMhhZuCAGbCea3z2lS8H5iJrvc6Dh25nYe4qd2KXK/zu4djn1MVWbdaqr85DYaiDWrTYeyOKVWROMT7Utz0u4ezmf3061Pfj6/zsm7b7z8c3T2E8JcvFxN/HLtqoohNXZrkFH7z/hxRb7fy16+3aeiKyG0uXY6u+jAOwxir6eWycGAAyyn3Xa6lIlHObAaqcOxTDP7+/enheDStgRmJ13VR0W4cq26n42ndagxYlvbz5xdkfvd4+ud//jMzHQ/nstUP378HtxxTDH1r629/9/06z+sm8/22lq3vO6urUZzrej6OAeKvnz4m5o+fXongOPVba/2Q1vvWqoaEFFmaoGMRXcumjRlxGnJAPx66bdu2pYyHwVVeb/Xvf77V6k2Uw97kgECsZqWqmPZdMAMzAFSCYCYphhg7kWquCNzEq5TAFGNcttlVA0EXQ8p5qSpqavbmeBapl3lJqXcworCUimD70D4EEPGh76TVZWvIZrqhh+M4ppgc47KupcwpsZuVskUEM29NzYAQuj64eakGHLpAOUVmcl0hkIpfLlXN92UxEYVA5t4lBvfaDJkUAAx2jl6XIzISs8q+CodAlLuOOJhb5BhDSAiRkQL3yav6ZangtIgk8kPKL0s7DknFdoT1ddsi03nsi8h9VQNnBjIEgBh5357nxGKmDia63/YhYAqhNmmyrwKQAAKyuANaZDRDM0BCd1PzHIO7r6WNXU6JalMiihHb/uCD/g2hExIorFuLkYhgbY0ooPvDxAq0FcsB76sAQooccypbbaLTmBEwsN/uNcawDwMfT/39XhEgd0hEgXHq2ZxebrVjmu/b43FoCDnTscs/f711KYcEEJo0t0JdoOaGjmOfpkNAkJ3IGDkY020tJjKO/Vzr94/df/6H34rCZZk3qfPaSpGhzynFoU+JkQkjRw5cWjPAy31r6q7acTAi4PD0+fU+LzHycpsfHx8MqJSybvLdbx+vl9vT1/vxmGOKoBoITTyhN+Id1PHx6YUIvn9zJsX/xz98+f7d4TfveyeUTd3CaQhSymuxT9c6b9u//v7NsrZtFQ6YhiTFVezjy72KvT0OQBoQt2arCACWRWPMXWZ3GcYUENelpBQpUiRw001bDoGM3IxCKE1ULGRSsxBCFVHxnHnIHe2+MUR3b2pL1VqsizEAqQCS5b1Qh7iUigiBydFU9ud0SJDHaYop//rp+fn62k1j3+ee5DAO//jzc23OGL++zFWkywENI9M0ZVVD8hSxNbzdNwoB0ZvIDkmNgZw0BkohipiIi5ojRGKmsJWSmAGdaPfjQL+nPOCJkQM3gLoVcOIQylqRd4wGEiMC7qoscEC0w9CpiZsic46hVnFwIC7NtdlWNUSLlH58GFYvL/eWOOSMcxEievfQ1aa1mSvMq6ppRCTyh+PhdEhNahUNMYQYlqXtPOxtk5xS7sjRKYZvaZu2bakUuanOdxgnfvt2cgvabK1lLo0R+i7fl9Z1wc3mu8z3woHfPh5E/MuXa8pYDbR67pKKoiMzGMPatgiMEsYhJbZ+bxiZqbgYzEXuSyHEZiYGiMAICKCqOaRAYSk1hCCiKWAMEALkGDFEdeuZy1qbKCA2gPZtcI+wjw/QH86Pjw+nl9vy66fnKfM0pqfXjUH7ruMYtNZNiQOjw1JbIDR1B0eHKuKmIWa0Zm6GMbIBgqjuPYWtSm3K4OumTMiRzJ0pNANH6xBTgGbGhjkTohNiTIGJXq/L1iwwI/o0dMS8bfU4hdMhfX1Z+z72fQLwt8fhdOz+/PNLF3ga4qfnep/ry20bc9dUa2sxxH0SISK4n4ncVG13CAQOakqIDp5CKE3AjQANCXH/lZZCYmZxixz+dvyzVreX5+fI4XQ8xQiqrW0lRb6vizRzaceRYk5IvK31trU+c44xR8o9z/c1Bs5denour7eFmbai4yGFl+sMroFATRh9yIkZmZAI16LaWkh7O0uVDYjK1pgxJVKX45DL4s3h6fX1L3/9aTPbtI3cEyMEdHBpumwKvmvj3aqab4EDuE1D101RrDLSqlZVOVAT7fpUm4gCMRIBArmj7j4U2W9hZexzjHGrLQUmgpiik6+ihzFua1XTKiBmrakzjn03bwXoW80lJoZiu3mqiYtajA3Al1lDwkAQBhZTqUDEkZiJEoWAPIwjApRaPvRv7/Plern+8P63hyFzjF3uW91SAhdwRwfqyBrhDz+8VcNm0tS/+zCtywpoVfzxzbHWqnZ8OB9u8zbf11Y1xZhCmOf7fb6krn99fmGE0PU52eXyhISEVGcRUYXVzLQpuhAFd+/6OHQpd2HqSRVFoJmrtMgxpeQAhADqVRHAd+WcmhEhOFSRmHgn7xDzTgvGwGLm/z+i/qTHkm5L08NWt/c2s9O4e0R83e2yqawkWaQAQVRBgAYcCPrTGlEDCRJBSQSpIqoysyozb957vyYab845Zrab1Whw4pamjgA84H7cbO+13vd5IFTDIohwTiQZ+zAmBoDWQQSZYG8qhIw83InornJnYv86qmCHMFViQiZ0A2RzcLd7RYsQEPFuv4Lhbq4xSEQS9aGOhEgU4BH3oQYE2DBiuc9BTse5jh4AOaWuI8DhPnKBuFfbtq2l5DmlnHiaGCnePxbG8nK5Pb/Vy21/vm4f3j8g0DTNS5Hrbr7ZP/zDLwAxL3nbe9ubO+ecAAcilCzrrlko3FvV0T0QSkosrGZb06mU43SqOoZCGN21CxBADKZ2T/LciWCBX/NoiCEJvUcS9nAEcAj5Wv9yQgpADHAIIr7ngO78HUS6b1AtQsKBABkECQId7P6DRQACVFMzuweLMlNEmJtwEsLaap5nJtprI2Yi1qEEbBiShImEsbaeGcyi9cGMgZAE1YyJkCIJgUMQBwCCI0HO+PQ4lVQS6jLnzxeFzuuth0UgzjmB622EB4Djf/ynn/pQd+qtZind1GM8vjtet73e9qfHBxvrVJIkKjmpxuvn9dn3MTSX/N23D4ywbR2D1k17G63Irj4cUs5I5MiAEWaXZod5BlV1BMLE/P483eFIa+3zxC+XnYmOS+5O19v2cJoIZN1WyxMXZzYDejid9o/Paxvfvn/6r/72v57m+e/+8f/uCga+7b11m4pQwtt1ZxZmMYu2j4T44+dXTqIWqhoGe7RpCnMsgsQBBPteRfDd+eFyud1aGwQA9Pq8M6WSUiYcI5hT00EEzIFhp2V6epgBbJ4yUETyMomOaoaOOE3pP/6vP717fyw5760/TSnlmHIuMyFEb4FPc0oS4V+xfAq9OxJRcN+BSEqSHJiFUej1UrfhiFLrtu3tdFxwG73pCzYWrM1V7eE0zTk9HFIgb21XRxF+9z5Pk3iz9dbUAMPyTBRARHtt4AYogWAeESHCJedau7l/5ZTfmXoBENT6eKlbLqX1cVsrIraBexuq3npoOAGqOTMRGSIigSQycwZIRGXKwry2AffsmgYA9tAU4B6t6VLKVEQSOcSS4bQsP33ebAQLHh8yYfz8eZsld7N1B+02vB+WKRwM4Wkuf/W7v314fPof/of/20ttwzzCr9f7yy72PkrJwbrVvSQRwlGHOyjCZdvo3uPTjoHhcDzNSdgwMHGYWjdDJeEx/LaOnPk8T7etGSovaSJ4mPPWhoWzUEIcrTvAMLxtLWU8ndmGb7svi0Tr7z88EcN21eZMJU4h63pVz4fzfF3Xf/r583GZXz++vT8dnp/fMMlhSkF9LlHydFnHX//FN+eH5fntuvVhTu3Wri/PD+/Oicef/vjTr3/zYd33D+/fJ6EP3z1IYRt9TmkqiQk9YJjVvYZamOuI61i3rS1T5olFKDM3sM9vt2sbHx6PMGCs9ZbYI0qahurL5ZKhlDIhl36fb4M8v223OnJKamZrWzfVGbLkbrHVRjldLv2678uc170vhylnBldJdGnawy5rs8Dr3oa7uzPBWvfjgXIRR//0tsXz5dvHAyS5fHyZEh2X6e/++ce//d17I/xPP328z+V//+Nzzix52tauqpKpdX193aa5PB0Khb+9XQzw7VKva9+HEgMS1V7fn45rbzdvOiyMbJgIdN16szZazrTt2FUPy3xx3Wt9va2/+X797puHIDS3dEiogGHH5dRbjdGeX/f3T+8ez8c//OnjeUoPD6cf//Qx52mvbdt2N7fZlhm3Vi+Xyzylcd1Ha+fDctn3Q5nXbW11WzEYBRGX5VByHUO3qmu3234tidKUSMLDA3Dbe2CkVF5e1jq8/3L58FQeHk+M8fg4Pb07/Ye//0M3NINhoAG9u6kSgAgd50MbtZQEFDYCEcJimtO6D7MQtj5ab3uWNM1533aWYAkYZiPUzFOMSoF0J+/tWweQ0Na8plwsaBIsKWWJMaqHv388BPQO7ZTGZfebOui+eY1JUmYfY2x9v/pykMcDtuZrjXBnxtNBDkfIkm/reF2HB+pobYfDgQjNkaaCvccwd8eUMAkP8z6UCeZZAGLvZnBH+JG73WUqhAHmpqHhvTdJkoRBSh9QURiZmXdhC9ubgQcmauquY29dzRjA4l42oL3p3q9ERIGOgYCEHAD3iG64D73XExABAyAcHELdkJAIAjw8msEIR0YhDMShft/SCREi5EwI0Lp2tWEaAHMmBDI3hJiPpVbr3WD08HBAH3rXlLuFm325KJO0bkMYBQFDGPe9pUQRVHfNiVrTrWpSyJn60OfXfZlyEng40rprBK+bt9YQGYSC+NJ03cdcaHrP4LDWWgaTRCowYgQUQnm5rUEhCeJu0UHNCS/rDgKnSWLovuql2OV6u96aB7zV9uPLbcqpOuvbwFgfTnmYtdpKESRMGHPJaV6q+2Wr622dD0fJPAEH8PxwLIv8/PGCQF++PD+/7T/8ann34RjuEV41GOK4zJMgjfF82Y8lv1+OP1/e/vnn14ey/PDhIIyose01zdO6bRFlSYV009Yg6HprASGT3Lbx6e316TRnyYl401ZVSyIkkoTHzIh2nlPbwLpmgW3tiKB79YiJUldDZjfExJyxDa9dASgILMDMzToRu4Erd9AxlFhaszJxsGGwh+6jlZwckzYAQGVHInQ0Dwa4VQ2E2gcK9bHR65en6Xg6PEni19v11m2kZHoT4dfLhmClcED0bsdDOeTUWwsgN+PA02lyp5e323JczsckEFnytdltNxsDJkIkwMiJzT3AiT1nmbPsrQ81IUKggR5mBvDWIk8AYabADG6RS9YxPDylxIJj2F3KjSgRsNc2Txkx1Gx3y0x5Sr0HZcTC55g+fnnFNPJEplxEw+Dt0oEgXH9Rd49lzktJddXbbZ8ejyVTwADg43F6fb5NkoSxBlyuDQlKIVCNfh/ioiQWgnma8pmG98OhAMY//P7j3//9T6XIr354/3atz2/7w3kxH1MhiFimBO5ZpnWzz8/ru3M5Hsv1WpEBMGob+74/nk7LnE+H8ukaGppdAImZEEAY37Z2udWpJCKYJgwNQp8yo+P7h/O61c9vq1KoD04kKcpMJTMj1qa7mg5lJkbikjvo8AC497ZoDAvUnMXd3q5/+PGXnxlZI/VUbqtSoFGqXVMEEU4YVc0MMrO5m0cRurcjwh3CpjJF9AAASRLRSYi47tX0/rSp7x5mDTBTDGzD3SJnNtUyLZlDe/vu/RIUbdN1HZ2RGb47Hh7O88eXW+v1tOTHU3r/MH/zblqW+e1lff38kufp514vN/72/XtUW6/rxPHuh8NhkrpbYhZGtSDAe5ciJbkHKtK9cwRAFDmliGhjQEQSAQAAJ6A2jNxLyva1FMKAMFRrvbZ9q6332k/HxcZaq7ZWIQI8ykxEMdCopJfLhgjnw9Re65JnYXZ1cHh4OO1ra6tG4JTTPPHDo6AQ/ua/+e+SSJZwMyKeS4oANWeEJLxW7WYAcDgkmYAc0ZEQlpLcrdYWiU+5YOCtVibu5m3dOAmke1UbFGCtoyuGBzHmnB+O87rVBFiWtBznulU32GpLQmZOiF29GdKdbELIDBEw9H5HgJJFVe/YYBEUQSZk5rXqktksCNEjatckCEDCUnsnwcyiauFOhPeBlKoDYkqECKp2j2RMhZE8Ed9nQLnAX/7wIUv6/Pnt3funcMt5RrAwVEunIyoEcT4dCHnat7WOUUpx9d619f7py+WwpK4GEUUyEHj4GKZ9pMxDTYd/fL69vL1kwcNhYZZ1b0ykwwApCZnd4y8KgIAMCMgYTqYjIpgQITDsfKDjobAQi1jQy0Vrg5QSpjSVSVURyIED4g6CBkQzEwJ1UDdhiojwCAciNFdmZkKWlBh/98P7v/6L3/xP/+7v/vTpktMdYUosCAiClIXHCHNLKTVVAkKkYQMBAzDC7yp4dzfzO1LIwTDukHEAjK8pIAgmJqL7WOhes3IPRAKEnFmYwuzO78T7SOxOSUcsKfcxIkKQRtzZ5TGGmnsWTsIBimCSJdxNve7j6emQc+7qvXnKdF5Sa725PZwOLy/17bJJFsbohiWJ9h4khzkniaGGSFkAEbc6TIc79nEnQPvo9u58VHcHSCkDkBlsdeRMzDiGf/0/A7j7fVhm4cJE+PU4hUDdvoYUAgCJCdAC8GtmJe6/dwAYamMYfg0duoMTxZwnj+g6CMjvzTgAM71LyiD8/rdjajnJlJOqeoChQwACB3hOxQLU9Xw8mY4w9UAd/Z4CTYlLLuEx+hhqwz0AJhEDHGqMwQw/fHP4zQ/nT58u2zq629oMglKa5pmvdSfCOaVwZI7LtbZhiGRhEHpY5Nd/8S4LHrPc9moGp/n4x59e3m5KSOHw9raaKgAFAkJIKk/n1LrllN+uTRDVYbhPme9IpZyzeujQcJinQj4AxDCSwGkuiGRmnBgxVG3KyQAvW08Q85ww0HWs3UpOXXvtgylJwqBAwCQyWqu130NaqkGCWTDJnVnmyDjq0O5ACAxzSYWp1l09ho7jlAJFCLemQjhNfDocxGHtLR9EEtdb36+qQqUgI9/WnkWAIgu4j8dDOZ0OW+8ly7pv+7Bv3z+4jjF6M7pu6+N56RXNBgtw4jB9OM2mvu6DiU013MIMQmr11tq88OPDgQDfrruDtNoIgwEl08M5//Jxb05h0YciuVDBgNuuTTtTAOD5mJMQQqTEJDwsrLugL3OWnPa1qtllNULMmc3htlZtHYmBKGV2tzHG+4fzGHrd6/05cC9I3k0eBnGn0eswUydmiDgd5stawzBlUrcigoBtaClsHh7uGoiYEwszIPShrWkQFKHkuPYxHUsR7r2b2vl0SASqsbd+OOS5TNetv172PCeWqJsK4m++Ob/uHVyfjpkEvrxcpjm/fzrdbu390/fXy/X19iIs6z4kUyaGiDbaNCXkuOs8MRzta69tWFy3kTNDhHcfw/OUjge5MwMF6e1tI+bzaV63rfa4XAchHufkYNMh6bCHvOxb/bSup+Pp88ttEnn/uBj7dW9CmJj22oXJzM8zv384ayhoJ8nVx0SYctpqQ6Rb7+tWH+eppHxZ93/1w9OuVuZy3fZa++9+eNp6rFsXobdrRRyn0zk6els7+OOH877eSPiwnA55+uVPP5/eP8ynab1eHg4TRFz27g4BUdtgJiE0DSZe950MdoPzwu+ncq31dJhetvGHT9cPD1Nxvta9LDkhE2Ifqt1ZODi2bp8/rRh0Oi49Bgag0cu6LdM0zB4OeSa8GK77yIK1dSJMKXJiD3p5WaeUgGiomTsRLzMPNSa67QMjSOh8SMecSPLn5zXCuIjbEBTV8Iht7//23/zm9x+f/+Xn1Ryz4PGQ99bPh4IQa60fHhYmvmx23fuhoHY7HPJl77VqZgnw45xbt32rx8Mywu5OA+F82dbzlOZDbjUgPBW4m0YOcxGD1zYQ43fvz7/79TsLv6ztPqwkj998eAqmnz5fCELNOee617pdHfzdh2/qOqrCvu8Px8PTw2m93UDgN7/+viTsvf3045fRB2BMqTjG3tdlXvpmn55v758eT0ve1+3j83ZtHUGziAOAD0Ak4lr3ZSmqdFrSl7f6+VIR9H/3t395XqbzefrDjx/3vR8Py//z3/3RgACiqYfdLb/04bQEQZC93aoNmidmwrnkz89vS04iuLU9fAh7ALo5gRNaN1H9qqwAgMwICMGErkzkgYB+OJe5CEZkTiXRp7dtXkQj3l512+w8uzBcdoBwHWAWOQMwYjgCjRFpgtZdByXx82maSuq9MrEI721cbsZIicMMAHFvjgAls1kAovD9c4VmihD0dYcdaigcD+dpqO1NXQEDLMIcHDAciDFnpjssFABIhFk4BQBBBCZO4BExyNSQgIgt/H5AcPf7tAdJAAIJML4Gt93ukjS4u2mC0M0D0AEE73bgr7Fis6+3kCLESOpREiMCM6laADKRqqfEpVCto+Ti4SzYmv/5fH73ygYLMaKO8ACLcLdSkg039VzYAItAwliHZ+GpIATrAI8wt3ZPVmL0oadDKVmEIMJfL12YkCOxiNBlrUR8LImZpozmcdmtZHo4FCT49PbCkg956s0kUWEcQ1NJiNG779WSQJnk6XE281rH45EJ41fvHo7L9H/9n//58emR7uCEuO9AM0V0rZRlSuxjP52mZT6Q8+vr63SY1l3nA5yP8/VmSWB0+/HH6/ffH/700/PxOM3zJETr7cplCsfR4+nxmASerxdGXiS/1u2X19V6EoBpkuOSOcaUJS/Tz79c9mrLXJLQ1sZpKub2trWpzIJwWAoJv77ul7UhxVzyMpeher3th5mXgksqz29rVwTOr1s9HfJpSR4AAKMPIgyLGP7wdOymrfZ9b8QsWYTRHSwQIJY5uQELr7ddJCn2Oc3m0X0AOIC4CliUDA4kyAawbs38voZxIcEkkbRQfPvwjSn9L//xj1MRAJ5zQrTna5tFcrLjcb6tHZHfPUy/PK9b1SlzFr437HWMvRsnzogUeGlDu4oI0Ne+wj3RfpdQYyATDlMPWEoeo08lb9seEEEyNITJPYBgynTHUahahB7mPBzGsNEtAAAowoR4mZJIjGEB8fhQCIQAUHDKCSDWrU4lAUNvdp6nl10/vqx+70pxECMBMlNQuAUFHQ/87fulq+uwkvKX10vJy8tlI7Zlzss0zVw8fK/7NMlyyBReclkOufdKzGu3P/zp0oYthxQobR8aUFI6HdhBS54PJd1uW5nzy6ULUmLfGmy75oTbbnV0YRSSp9N8PKQW9ZzPfbe1ViHYR4+Ay6U5YpnZ3AvKrsaMk4gFP691qCfBXBj/zClFwHBQjT4s3IUwpXT/rTExM9daAUVEciZzi/D7xUQYx/ARQgCZeSoZU0JwHYOTQPhW/S62U3UhYAS9K64DZmFhZkFH+/TpEpQBzN1yYgLvbZyPpWp4RFhc1x4ITMiCJdGUC4T+ze/ezVle3q4/f65vt8pC378/nBbeWyDQb394bL3+3T/94Ydv3qnFH396fTrPr1s1j/dPk4DUdcwLV7OSEnP69KURpZxpqAFwHzFMzXwqWTDqcEJMd/cQAAEGKARrkFB4KFLq5kCQgS08QsECYKzrrdZdzZg4wJckzNzGKIlykt46MnS1lHCZ8strdzNkYcblkLzrkuV4nIQlCW211+Zl4qGNkD6/7MICABqOKaW5iKm2Hm+3umT68HAMjGminFjNtBOaLTmPoU60zEW7Dh27IwKGeTicl6xyuNWx16HDg0gSH6e0da/NINw9Lre+5Pz+NL2uta697oMll5TurS5zxYCE/ud8yJ0NrncMbmDcI/0Y9xxETFMRgsu1+/BgImIAcFMzT5L2OoSdGBEACVKSMZQEtWlEEKEDjOEAQEQBAB5mEAYKmpgp4TDfa6uo29D4/Lr13qx/83A6zsvxUPqwOppwj86c/fPz88PDjAo2qOQCru8fDwZ4epDL28VNUyJGqM2mWe5JeLNKPEQAAnSoWxRBiJhmMbNc2EMhIjBTeGuDMj8c5ttb29yJxU2JCalc61hrFaZcUk4Jw8J1q8N2vziZuQggF+acRIS59pZz6g4AcTymdVMRPM0pEwNCLofXt30u0940Ylxvb3/399vHT1+ejjMlDEQd0YYjo7extR7EkgQihOXe3UvC4WAeCIRIbq7mHpHgfiJBJlaz8Hsg+e6Tuq/9yez+8A0EuA9QAWMqkhITJNMIx/u0Cr9WoygcEUgSExK4u+sY3cyJAAg81FQl51qDCBPLd98d5ym/vtwk53fv5n3f365bV4SwL2PdqgFTyrLv+xhAEIgghGo+FZ4KbrtFZLUBoTlxzvPLZX14mATx7bJrtAAafZQMgKGqh0MKgNZ8GB5KbsPgPh6KQAQKYmIPExFB7Go5JSaIiGGG4MLIHogW4KqWOBjQA7baHfA0H025B5ac79UvDxckJCbwO5fanPjP4rA7QY2ZzX1vlTnFXeKFROjC3PqwsMTU6naYczDvtUeYsCCBqVVrhEgscQ92AqhbBGAEQoTHly9723Sa2FEuN92HpexI4+OXLWdCxrft+nQ+TMy78NbHnHAp+XQ+/OqbU2s6Ns35AL3fXvZexrqOvqs6SBJJqRoFBHjkxDkRAV3WWmtNLEMbogAgOZAQsWvdiQXBiZlC350PTUMy6hhmWjIj3u8XQURb9+veMzMmWfeBgZe9CjFx3PYYg/IUYWbVTH3ObH5/kUXXMA9BHB69aVeDsABkpjQn92h9bN5xTmWZoGsgppTfrrswM/GU5fl12296PhZIqe19u+ohL3jES6+BBRGnjGp6LnQ6pJSniNi1TlP20Vvv3333lNGGagcksSnRLDId4PllA+AIUB0vrTnkjx+3NqxkIcbvn5ZJ8k03FNzbkG3ctvH2uibJCLH38eE0A7mpE9N2G4K+HBhJrPWH8zzP0b24U040CV7WroBDHbsi0KFwb1a7sbkOcwRiUDUOqk3BAUlGgKvetc1m4Dr6GCnnuaBbOAAYtDGQ8Y6sGB5EkNK9OA1dzcY9UusY8BVUEK6K9/CtCN8vEG0MVWVmZlRVhUCR6SiE0XoHB6R0ve45pREQiJ9f6w/vUxJYDjK0kdFf/frx3SF/et4N/PHEn193png4Ld385y+X51f9T//yH0pBAiF2RFc1zqE+8sS196GeAGv3uYiNfnyYU5b9taFzq8oQhWTTHYZd12LqE8mu3cJJ+OXtxkJjQGuDCXum82Net/WcZ7Vx6Zenp9PbtWahnHk5ZyDd9zoaKHtt47SU0yxA/Mvrqqqc8HTKOnwbKmIecdlu4EhIc8kWME157T4AL6/b9bLPk/zxlxfkSbu9XVbm+PWv3v/44/XDUz5/eyjH2ds2z4//9PvnP/34y1QklzgwvXx+Q4h/fPm81/3h/G7bxr7VsiynhRlDNX56fcuCpcw//fL6/b/67lp10+Bml61bx/0WuGAn+vjzBSzePxxykq4G5g7486dVu8+T7KM26++mxWxMEmpe6zhK3hJtve21Nw5GcrVax2++f2rNE5MjMIXqIJacENyu1/7+aZoLXW8GFs+jX1iFB1F0Ne12LNkcLntLwm7w7//p42VrJIQarfdpIka8o37d+eNLfTrO7j6GtmB3CAdQiIEaEeEjAaGklK97z5nNfKgnCSYqRShg7/Wbd4uF9R6u8aXugKiuQvTlbbMxFOjj2/Zv/uK7z5f143WFiL/53ff/8Y8vIpQYt/3lfJyOy3K5bj/9/Okwn9oe29tmtb++be9P05fnm/bhgcxko/fePzw9vbw1jTHMfvnyekh5jPi7f/75d988lcxvte+tTemO/3QLJ+bW6zKJGv/85fbxmdRsq+3bp/Pr2/a//Id/+dU3D1+u128eT+vnax2DmN39bqRkCkSqw5I4kIMbh4/WBsfopH2/9SABTjGVCI8+goimJACEpuhwb4u5k2R2cAuPQNNARmTc196rIhOiCWLtfrltJaObPhxJgBDo4RCHOVuPYb41G065xMMpv10dCN89Uh3jOE+uhiglP+xte7s1AHEzD08iDuYaJWFOUhISOhEycQRv+7AoRNC7EnESrN2YEUMJYC60qhJ9LcMGgFqowxguBBHo92wiYY8dkSQJohEUpHCAeZoIo5ubORPd4Ybu91Z4qBkz4NcvBd5vxR4AFPG1c38fmgEi3oEu9wJ4xFcZawSGChIjtdFV0SJyYrxnhQLNfLh5U3eVhIjcao9AALyX2AlChEcfOREgw53WS5AKEeO+DzLqGFuHBt2Ukbw1p/ttMnCoTUWK8L6rDjf3kopZpEQ6Rk4UAEzUhzWGd3OxAAxbEjaPWmtYvD+dX7bb5y+rdzg/zDShOno3AlCLubAQ1M2evR1m2na7XHpKuI/b9w/+w/vzpfUPp7lgXFqoBbiN5rmkl9e1STocp8tNtV+/fffE07Q7lSkzkFAC3z9/vj2eD7/9zROG/eW/+qHV+vxpT4mnLMP04Xze1/7yvLm5oxPTy3qRJIcybWq1gurXQdrJjdt2uakOQLQVbZmFhBl5vG4R7d1p2q7XPE2qPXNgSkAAYSVJTelWVealjV4eZ+owqj8tuUxpzvmXj89lLszSh0aAA7xcKjIQwOE4DVMmZJaXy5YynU9ZzSDIuxKzJLpeDHJLSQpnixFoReS2wto8Jdp7O5/ne/3ieCxLxqFx2+p8zqvqP/70p2MqTw/Tzy8XRBgxg2FKZBSGGOjzId3WMQwCeIwmRG5WghEQnBPDuo3tz/fCaU4YASzm8VWyS0FM+NWyCSzkw1TH08Oy115KalrnmXoPM5sK12p79SkRID4c5m5c+y4y926JZW/d415rcL3VaZIk5G7r3os4ECycWu05y7zkvQ5rKIwW4+nMQEtiFo6cwNwo+PmtWfj7bw6fv2y3tRP4vo/H0/TzS3td+3/xm4df/+1v53n+n/7d3395uz4s9niYkdIkiYKr6pfX15SFMyBir/3dYz5MjzqiasuPR9UYZstxbvuexIUhEdvw33x/en3dCNPjhCDc1m0SyJSReavj5W3vTcvCn9bX02HBhGpxuQ6m4CSIYQa9+iDtbm14wABEJiKiCKxbqA1EZAgICACRuxIYzQHVEIAR7vRb4SSC5uP6Nljkz7BRUIs+Rh8tpVQbeqC4W1iMfsrn274L5qHD0UWSme+9J2ZJCRD3MbxWYjrOSZjrqKdDRkhCmFmqpOveex8OwcScIAsJy9DhZutWH5cpIRJahI1RHw787t3xtMjLy9thzv/dv/233337q+fXz63Vv//9xwAeqvuoU0lbtdG9jtq7G1o3/fnHL/PhbIGEY6iYDgAYBsjMLOHDIBDIvy7vqbWemAMMwEuiCGu1IYhaR9RGOSFs26rqatr6YE4Q0Hs30wTTGBoQMstQrUMp7nF53jZT88NSjnM2gLng03dPifO2b+fTqdU9Qt+9O+y1Cssvn17XTfFf/+//TxHOQFNO4bYPhaDr1nOmeeJ7lCIzbLUDcmJ6f1qaQdg4TfLx+e1v/uqHnMS/crlJPd62dtsVw4h5rQMDlkmAcN3j5bqZOUv64f35v/7b375c13/4x5+Q6Hgoe+1tGEDkjBS41a5xB9c434HSgQFortOcbDgCSKYx9DBPOdHe9HrdlmnqfcxTQqZhXjKOERE4zERoNCUkFh7jLhADDyAMIkpMzDRUEck9LCKLMMayZB2+JHGEMpVQP8xzswoe5+Px+++/Gaq977eX21r7sApEJdFvvv/185fLtEzffPO+1rpures4zOXjxxcmmCY28zIV5vT6+vzpy8tQ34ZOWRBRh09Z7sJyIhvmiJgZNAjGuK7t9O7sXT1YQzEiAgPuOSA0B4eI8JJkmouq2nAL2PcWql0DJD0+PS7z5Ch7H6fjpANcPTHV1peZDiWOx1NKMsa4btD2UXUgedvbN0/Lb3/1/t3j4x9/+eX5soVLH4YI4OEOw+G+MwHHgEBGRjTzgHAH/ao3DUIQkq+tjgg1SywaBgATi4dCoDsCBt7PRl8BOEHCSb469SCCKHloBIike60pAABAw8Ks7rW1nSUty7IcRJhKknXbj4cFiBhjSpiSbOvY1g2YcxZT26s5QN2bqSKl0VtApMRrA4h4mBcFnOeEtifh+Xi83PbffPs0Iv7lX37am797d1LzVtuUSLJ8/vJWynKY0zbaVDIhjvsMM9A1HOmOyyFmDL9bkAKdARkICBFs6Gi9373sEe5q5sYU9z2bDTWAUhIxWA/iNB2OlMpohkDLaY5hW9VpEgTod0+KQ6AjsZub2505/ZUVhMhESKhm+FW8BuCWszDyUPNwuf+QESDiXgMEAPe4A+vN/X54BIx376cP7w5alRMOi978cJDf/+H5fJ6A6dcfHn/13Yfrfvmf/7//LFy23oeaZEksjNCH7vvISU6nOQFmjtdbr12DZKtdKL9dd2QURiH87mmZJmaiTy/t89smxMNcbTBLEU6CKecs0vbKkjSCAv7iN79yUPd2W/epZEQzjetmd1L71kbKNKXkqoFRhxExQ5hj7TZMS6FQdXMiQoQ7mb8IOcCduDMVUe13ptjXGSVhEnFzgyC+D8qdMJY5tzYyUynpeMpttzlzmQUT7du+beqOeU7ruofH42kZbZwO5endAQlS5pfL9d15WSZ+u2yG/vHT9ekhnR4WRVSz7a2hWyoppwzAfYzbdd2va86H57f++rpOJTeN//a//Mt9vXLGNMnf//71dtlOc1mWPBeCgJ8/X//q1+/Xtqckr2/1tvXffv8A4OZ2mEvOYm63XfemET7ntDd72wYzhcW+tnnOh2Oxoa4OCIFeR+iInGSYt661670K8e5xuV67eXz7mAywaSRQA76uqt2DICUO05SoJMmT1Nq0OxFHgBAPt0B3i5RE1SKCiAI8J0kifWhr95gaLEtxj70OZE4pfJgZEASnQoBhlnOSiRGjru3bD8uIaG0Q4lrV1Z5Oh2qeZyyIddfjJCnjprbuWtfBXBjvtIIghNHGu4epLDSl/OnL2vr45t3y+XV/OkwEgZltaFfcWjftxzKdTqWHfXnZeo/C9P68kEQusm2ttr4sS29xW9fW91//+n23YR0e5mXXykJvN03M7bqS8NP74/Vya4O6k3tPSN+/P3T31+t+nBPQ/SFqarakZOFbb2AhIkDwdJ6R6XKt3pGZCPzp6XhrK5oilcziENNB6tZTyk+PyDn1bX84nN5u8A//6ePW+ve/ff/hQ3ma+O35uva6d5NIapI4mPD50pfM3z9Na/d/+tMLMqWctVlJzEW6NtcAI0Jb5tQNtr0x8lTkcBA1u93ancq5V1PrqRAg2vAlCUtsGttu55IIUJa073WtsffOgSUhMmWRlFBDGZGZxvDeoqm5uVp8eJy3ZpdVVTWJTBO32iDofj0m5D4GkudUbFiZmYUEPGV821QwDoc8BrrFMOstJuYWjoxLknVrOUttOiwA4nwo5m5DWXiYH2a2EbWrI7DEYc6JSBUOk+xtv60+lez+Z90mIEAcJxGRl3X7q+/e76r76I+H9P6YHUtr3UxTmkxVhJrjtl7mxArl7nj48rr+7V+8R0bt/sefXpmJmJIQIT6/tSS091Z7X6YyJRFwIXmto+m4b9cZqCQyCFVoXY8TlcSfL1WIXZWFS2JD68MJIk2UQi63rgDuzkgQvkzJw/e9z1Pp2gD6futFRCbYaq9rTFNkoTLhMiMh6QDO8nrpFCAUt62TCACZW850OCQNAPd7PpSYttoTfS1AEeLQLsKjhQ4jIRZ/91gIeEQUkbGPQ8kyiYLaMCRuZmPocT7kJOvem2pAhMUkSdWv1zqXbBFt6HHOrWrthgQQPs1JLWozQsIIJGCmCDDzKZEDpYw+zA1y4bdbnyaZiuy7IaJZrHWIUBHaqg0Hvsdy7m4OBHMQkZSpTDMjJ8oOWEdTMyEy93ALZLUw+5oPuqslACHu0ECHuPfAIojofraKQPNI9LVOnhPf3QWjDwQUYfjKSsOUOGUON1MAABIQzH2oZGDiO0zSLBBpqA4zETHzd+c7eNi2vak6E0fEVrWkJAk3hdF6yQxEpoMB5ikTk7mFe2IBhHCo+zgsU+19WUod41Ck93FvdA2NzJRFDGxiWociYLvux/MynaS2niMPQ0QXBvOYk3Q1CzvNGJS7+iHjde+ccrgR0aEU99i151ImApn54WHZ13HbxuFQ3i5buJZ54iwceipFkbdaJ/F101wmEQd0G3o6HRiwqbrbaNSaL3NUi9NhpvDLqr98vGnXeS5BGG5EKEnc8e3ajpN0talkGHptNhU6TjIApyKtDnXLScyoZPFel7lImd7ebmrQPM5LHrU9Pp5uW+vqdw9tpvT8spvZ+Tgvh8lMe29lygB3A6xuuyfhaeJwPRzzlBME7N1atWmS3veplJILADDhulVAAMS99nnOgX7b3ZwgMEkCv8MB3N0Z7Vjk6XS8tG1tyllOh/LhcFjKaR/e1P/w8Zcvtw2dwgORIsbpUErKfYAQtu5DPSIeT9P1tgvLdd0dPJDD/XwsgNRUhaD3r6wuJCAmszDzdAefRxDAaSnE+HbZ88QiWHfLE8+JvryN2kZOySO+f5wVMNDM7NNzOy+zQzQdo7t55CzgzgRJeFmQCRygSBqDWu/AEY7Mcj5kQa/DmtuUhSGmIg/H3OoI4Je3/bDkQPz9j9dwWAo/nuaXy/7psv/1r97/6pvl17/+9dt1/f/8+79/ejg/puKBEMqMVXXfeh/27psDUbj1VPj799+9f/jwur58fPucsgxTSrLMU0L4/Pm6TJMB5ZSL4O2mgRgQo1Ni8aiq+I+//3hcZFnmpuTWzIcRZCoJk6ndelvmPOqozYbC3joxd/3PHFLwQAZ380BKCcONOYVbuAOKAyAEfaWNRXggYskyF7ncRh86TUxIAPceSDgGITwdDwY8VMMhJyKm1oGBmw5kCkD0UDfAKCJ9DB0jENWNIhi5FEHAvbYAyCIQYQF3tfTDYWo69mEECUkRTUhOhd4/lNp3ZNmqigBR2PBUilrDEYyy9ZGycKLwIEBifHurr7f2dErvzstt627aHf7043PJuWqXlJdp6b0f5kVScfBlore39XbbOfFUcgAx81a3xISBpuN85DZ6G+YKQy2+OpwIAc09AtWdAAmh9UGIOQsLpCzMAB73yxcBBlDvOk1pLpCTrJsWoX/1V78Nd+2dSxHBTx8/7c1rH+D68WVVdbGh05ITEERQkomCma6NukU2BaCc0qHIPE3D/d1pIgBqzkgA+l/8za//m7/9K2vtdF6eX95+/PnltneCeHco4KGhSOIDEuE2FDDmhVt11fpygf/Xv/sHBx6O6b6SVbNuzACBaiEZBIiB1Qy+SqkjwqeFH8/5cm1FeIxxn1Nu2+hDn54OREAcOaFDcE7hAwmKCCoiAojcDXdMkHNW9T4MA1UHY7lbG5IgIoCjRwDgvo2UU4tghDaGjhAJM9Y6/vnLp6AsuFsYlyUBjgoEahZ/+uWlTKJmb89vATZqS0l8jPePxzGUEI+Fr/terb58eXZ1d6IACOvDzWl7285LTsJNY69DWLhM1+uWWLjM662ZObAxBiPdRyRqDohF2Lp3M55Ta93dGZEg5glVue0qHLfrW23b+XxemFIEAlUFRTofj23bDo8P/Z5e686E7x+4D0YWncfT0yzCP/3yZa+m9100k5oKIXMyhN4rACeioWNUFcSSc4Qj8pTIAMKBEMNdLcKBCRmBGMiAEIiAAD2gZDHwAGAhvZ9s7ggsc9XBjPdsDCGj3Ak75sAO0XvvvY4+WtPD4cgsDhSeWh+F4HQ81tqmnFMi0wAbx4nmPH956z//+AWJUymtD4gwi1Hb4TCZ2aitSPnwdFwy7Q7arSkE0hHw4ThtbfRu37z/8Payffrl9rvffQ96++WXj1PCNuxX3//ml89fbuv+9CARpmqAiq6MvO6burNIToUoBYTaAKB3jw8A8PMvz+5OzMuUhQMIMOjaO0EwUlMtQsucrvs4L/kw8+9/vPbax9iJmYhKnvttLzllYg+JgEQMSGqGSObGDCLS+0gpqVkmudcrwQHuIhQIEUbHNga6Ad5LeBgIdA8uYbgrATvGNCU1X4SfztNhksMx9zE0DKf4+Ony4d35h989zBmmxH2M67a+XZ7fPabnL9d5Ov72N0u18XYzCHh769fLNSd5fDeHxXrZHh6W130cjuUv3j384adX73E48LIcDll6t631bW97JfXo6kUIMZY5T3maSlrmyd1GH0N1eSxE/HLZ+7B/+tMfl0mEmAX3vaq6KpAks+hq84Rz4TBr4EP1UDIgbnsdVYfGPCcfSogkfFcJmpuZK3LJ6S4AHr1HQJJkbtOUx1AbpsORA8PRoRR2w7fLWhLPOR1L6uYM+O6xeICajm5DvQ0Ph2kqHHhY5OlxKvlwPj18enmZCtzW9cPjw2FOn56fnaO29vg0nY+TA04UItPjd4fa21rb1vY7WyeXVOTdv/zzZdv3wzTNJd8+X3/68ed5yd88Hi/XbdT21z+8K+Kr1qmUwzILgarOy9Rar6198/64bl3dJfPnn98OUwKgvbuqqY7HMyLBXGRbmxA+PkzXqv15PefsAQNGzjS6h8WIboRBUITdQwQOs/ThCDFP6bpr7zbN+XIdbW+UCgsxxnTMp2MazfatExMJtdrnnAGDGMIhGM3N7Sv9BwNLYkK4DUXkUthsmOqwCAIEq3sI4zKnrY5J6Ok8hd0vaDBPKQneNq1DASNJXm/DzdrY5gM/SCmAwd56TdNsBn2vOed7pJw4hIkZs2BK3IZt2zpMg6wOO85JEIHZXOvQy+qmfsg53A3g8tY5+FiiZE4zmft1rUx4Ph1aVXXTGMfTcr0NV//m8dj66H1YcG2WcyDR07ujdiUWJiwjOPLeatfWRsyCFNoGCWodSiARdq09F2FB9QjA17e+18aUvnt3SOIogew0JEv6/Gl7fJiPjyVPfFy4t/bx865qh/nw/LptmzWz5TQlxu3z1QQwseQDjbFufrlc3z2dkOK27W0kQ3i91bU7gcOqDlSTx7UPtcOc2+h73z7Acd30vGTBgIC3S7ve2hgxZzZvJBFoNiAB+Yi39Xp6PLQOxyQYgYVHr3vTtbp1n+Z0PpWXyx5m6tKaeng4LUne9nZfcSHRz1/2YRDuJbG5QpCw9G458TbUY5TC4Vz3zkxqXrKAe20AzupDu2+7C/Po0bumiZiwt64ECLBtw++4NwAzr60LsRvo0NU9Asp9yjPGRQcFHKb06WXPiQHDw1loVCtTbkMBYmuDmwvJj8+3hyR/8eFkPlRhrzsxAvq1rgfh0ZXzPOecmH9+uTw+nK0aIwrx6DvTBCH7rsNG693CbUBO6IxmdL3Wl4iMOGXaDdyCIBzQ1d5CRTAcx7C22zLPtWlJd/JrbDb4vpEkbi12G8iB5hBKdCfaNFerzR5Onciul24aJpARUON0gClDYAhH77BVZeGEQIx11wjVcFIDjAifp4wIYJZSWrcuiRxAh54ep95V1dUMAda1AaXDIQEBM4ZhmrOONmKgYI+47d28T8QI9nhYXodeLq8ZJ1Mrc+7gW2tkMOX8zdOCjIohLTDCMSiFhQOCht6Vma33KacyyZQpAMNhVG/NWocxtA9Lwsyow1rAlIUEVD3lfIcXWoBXLZnvGyNVJ4AkGGBDrb+1cDydFpIJIsLdwj2AiSHAIeCepAYnwvsr/a7IQEIMNLcIUAfzEAYhTEjMmBIzE+LdnGSECEgWDoDqcSgpIvba5pzdnQhbtc135rBO5sYI85QAI8DV3TzYQYhd3TwCYGiE34luPiUG9tGdATGRqxJTYjZ1tygZjkvu3Wsd7qEOHjB0OERrozbPqEl4BPtoOXESlsDW8eOtY7hk8ZSbGm6kgAo9Ybre9uOS926MHAFdMZflVvve9HGe1L5K7h3obbc+BrjV5q8BZcPW7byUxFy3AY7b7gjW1jov87arW316LAEBoLetHQ5TyWTCn187g05TAsc2Rh+ohtdatxbLxA/HaSq8t24OSHxb+7o2iUBCklCg3nW0+nA62L4mEWC+XbZDPkJAH1DblpmFl+DSDGNoIJnaulcHhxH98/Xd01Fta80CoXr3QId8qxFQ5ymXaTHrEDZlIQg3cHOyIJGXl/5wwjEaChHz9daJiCRiNB3jPyN8W4/aANmmKSVRsGi7tWo5SW0mLKqWM46gL7ctyNF1u5obXm/b47L+ze/+9jff//q7x8N/+MM/OvJ60+utE0867kpP7HE/X7mbrxUPx2nbGwsR3lFFsu5q7iTQPRJjRJRczMdQU7XwGB3uoo/TUtR9NHMI94iAYUaKm1PtmpLkzM+XunVjxmGWExFjd03CktDRvQGonY7JHO6WpDFCzZaCZgDICHeB+rjVmIT2rnVYrYaADwusqwEC00CBdWtMdCjgjsywj1YSvT/Pm/bf/3F/e93Ox/nXT4cQe3o8vlxubaB2/fR548QG8cvHDYEPD2w6Xq7/cpyfj8eZKOVUHk9pjH5d6252Ph32qsRDPdD4/FBu60hZ5oKIcr3pu3eH8/EvHII5fX6+rGvAiFp7j3aYwNS1aXOccmm4E/k8p8xch9dmTKgWo5sLMZAgj64iJMz7MEGJr4gLVIhQI7pbcGAf2oYh4DRxKQkc1VSYjnO5rpuZ39aNhKdpFkoYHgTCVvso82Shtdb46p+DPqqNod1JmAmSiAUORx9d1e8CZbWx5MySkCmGIkDJ6fOXVQTencvjgYHgdd9Bbb2tD++PxPQv//zz4XDo3hDi6bg4Ue9eCtnw62V3tcfHI0SA9fWm1oc71t4tQN1HbWrG6gSQk1B0V1XzzWIMY4rRVwYlxF2NCfZuE6OprhcepmrRut9Bca7eTR+XkvmOsyAAIKC58FQAwIgwKCJgmrgP6MPmmfcaSfDduWytP79uwgkKmdunTy8pE+63uuttG6+3XXUIkyAFGP43/8f/cy6UiffdCODxKHmWL6/t5VKnhDnxoeRv352G+7qb1i0VGYrfvDstx6K9t22fS/n23eO0zB7+p59++emX55ynnPj57ZJyhiADrxG3tXmEOd7WCoBElLhAUBv1dz88qYIOdxzIgEit36ctLIySRG2kxH04C+XEABEWo7UgWubMgLUHo4sgp6StBXG3iPBtG+xIKaUMjGQGgDGG38eSd5hL72rm9zaRCAuzRyAGIZmFCJaSJuat91p1mUshKUIsXHu3sUpKx/MDIszTNLpeLjckkCLzdChCiXgbHd1KSWVKRBhGrbXab18+vu1NWbKFBUaEtz7WPTjJIvgwlddaA+iOH3NgQicyRAb4MxwVyQEB4HxYbutKAG5B6SsmBvDuOwskZKZ7lD2AglAQ3z8cOWVV7Ap5Tgh0u+1/9bvv//VfffjDn35Ryy+vl8NEh4MEiSBlJg/88ZcXdTJzIlY1BCPG2oaaj68lC2mjuQdGlMQRkXLJOXt4Lon5zj2m1i2ndN8aAYIkIg/EICIP9HBE9EBk6H2ownBEACacRDR8Lvk+FowgRrptVa1HRGvdLJLIw+mYp0KM4RpByyTCIMwJ0CEk81xQCG6brvuIgHVv27rpGNMyfXj/iICS+XhcbIxEmJiJ0QL2fdRu+a6Dcvzl82smLKVsW61DJePDw/nldQ0fVr1rPz0uDHS5vl2uNwBWG+C+TLm2puZEYO5gzsKAYAHnw7Grr/ue0vzh3cNhkVZ7eJhH68NMI2C4M0RJ3NXOyzQX/vxW96aEEUABkAj6UEQ8nU/ALKks0wSRuinEfWhoqsokgKBjEIlFIIYHIBAhOBEzmioAst9rdsCEGjrnqeS890oBxGloD3ePeDiWh1M+LDJPdNv6utm29WF+OhTisSwp5/m6Xs0ZIbb1+v23H759//7jp08jSJIh+POzq+v5gZva51/a0/nQLNzsf/tvfvXN09OPP33aqlXVL6/bdusAeLm1vikRkzBgABJAJOZ5ToyQkiQGYnp5W8OBhIcqIiNTb5pTUbPRx5yLEQgDAnFKdV+FGAEMMGfOwq6mHjp870YIFI4EacpuXltj5to1IJKwO2hXN+Oc5im7D0AE9/uhLSVkDA+f52LubfTM6VjSMmUFiNDau4VBUBvDHUTScc7LxITBSR6fpufPz6nMeaKu1Q0zlefn2+EhUQph9q7dQoQTJwi+3vbbtUOmUnBOgIh16xz5j3+8CsK0lN/96rht9ZuH+cO3j0P13//D7xnTtx+ern1/ebuRpZKkN4vQNMlWe1fvY8xzAcd9qKREzm+3HRmEWUc8LKwRw0nbWCbZutd9PB3zYcnB4fdMdqC7Y6KUCSy0+Ze3LRU5H6ZtHxyYM1/7qFXnnLZ2B5dbSvz0ON9Lnt3NLRCw1jtPHRwiibQ23AIjLJATE0Pr+u44E8W1joclA9Leug2o3UqhJAQQ798tgf7py/awTFNGd+iGe/e9dg+HIB1DhCNQ1T38/dPMCSQwYbxuayqFQF4uOwEQcpjNc7rL9DyM//94e3PH0bqrn44zc1BKWx21WyiMrtu+ffNhZin7Nh4OojpOp0XNbqsNtcfjhBHB0VVFpDYD9dNUnPDlcpmyKHIKgvD5VJZZxrA2lAKs6d77QDgvkllue19Ker314yG/vTZ3kEwswHcWSMA9phkRy5IfzmVamAivb/22jYfjEVxziqp2Oh9MRx8+en04H1qXL1+uQLgc0jdPx7q2XrfpMHXnn396BSB3GF1zKarNI0rJCLHuAwDAwSMCXUSmnJiiZKpdI+JhKX/65fLuNGNE1UhCn593ADzOxdGPB0IIMAiD5+uVhEkyGEIECCRJ1717RLlvK9nPB367jIfTfL01JGrdKcA9IlF4aNNAQqGwSIylyNo6QxDS3u20lNpHynSPPwhLHcoEiTGIalMdPk0pJ76udSmZCdU9CUY4ABCgMK/7sLuUkbDcb9EAgFBbd/fulAklIUCoBiEeimyqmTjMicEjEnMgmntOCEBuSBTA/G7Kh4U/vJ/V4p9+vL47JxH68fl6mtK7c1mr9U2D8/NtW5gpwAgflrLXBkTrZmutAMBAdYyhxhQg7AYYzoiBAIh39iJAmDoSA97fzkCMwggQBFE4L7O4w96GqhEjEQ5z8IFhEWYWY4RBFPGHAyLCcWYA2nddd/MAQZgK5Ql1+J1jHIGqMczHCDMfwxyUkOsGLEwcy8JlTpNA37qndJrzttXL3oQojABgKDARkp6WMrNEhtEGQwJGh9j7gKHX9Xp4ejiklFhq7z+cH/bQ4eOHhw9/89f/9dP7b//w8x/+L//9f39n4kiilDElmjIT4NtlIBIyPb+uU4ZA1ga9dU7cGxwmISYRFKK3azMLRmzDhgMThFsWnidBtGXJLGJm7tibt27nc6lV76Z2AIzw3oMSEkVTMAUiQkpMSRIjsJqZ3+9aEQHhgQhE6I4ed6QYRgCCE4B5OAAgCUkW9rgDBwMRwsPBI+hOSLCAMeww52bWhyGgAyT6mhdmggi4qzoOkww1CwcgtzAzZl6KjDvww+BefoRACDgcmCnd3ioIaoCbJ+HhsSROQsejrFVr1Sy8NSXAh8PURp+m9PJWU2Ih2DUEImeYEx7neRsQBK0qoktKX2u/zXpY78aAmXlolMynQ76uNWcJjW34aU63be/ARagUCvDwew4EEEEEUkkAgBFLTk/n+ccvV2FEAWQ5z/n1rZqOh8dTBJwnH+HzPHXzt2vLwtOcBHG97nXQqM0YWfg4lXA3j8RYJr5emjpf1gZhkpAYbYQOzykJ01b7Ycnh8el1//W7RcO2QUViztT6UI3jvDDQVnsdvncD8pyTa5wOU5lk22vvGhjHpcSIrpgTYQwFAop0n+kivd46EQppKlkNljldr2sQJkEGChQITBxdh0i6MzsRkYVSIlUnxPtUsVYnjLvpJAlvtWeRkoUIhVwh9j66+fkoh5LDODFPExH6Puztqq3FvORAqG3UvZs5ATExIy5LFqGt9hCQjNHdDDPh59e9KYaPXOQvv/nmy+uLIu51RMAdB0KIU06I0cZQc0L/8Lisq3azZS63bUTEt4/l9TosKMLMh3DZ6mAGCrKwwzkthRkgzEsuklDN+zARLkm2TV8vlYWQ8O7dPR+LA1yulUgI7JhjnqcsudY6HzNj9G0AA4sc58XMb3Vs+3Di3sdf/nB4//Q4Rnx8eb1crjJPTYmD7lLU13XLOTOQgxM5AQ4LRAKiIvj4OE0Tq5tDsFmalwhH1H2zvkc+4jBsW0+Sp5KPh3yYD5dbReIwve1tW3sbo3dgssOc1MI8GAQAIex4zF9e9r05Sx6qZi5CbfTvHhZJvHbvzTzcHUVS7btZROD9BeIBd8MaRLg7M5ckXx9sAOZWkghLV+tm98BhyimzHOcpwp8vG6VsbuHqSvcDFYu4OwN4KDMB8BjGQgQOjlOWlMjcM6G5OwSjI1Ft/fnlAsjHYzkvmQsg4amUMFfXJPJ2rTbG3trDu6f350Oorts2hv3xly9Tmo4Jjw/TbdfLZat9uPvpcLisN/fge5feAwlKSu5eEquFmhFxuAMCE6lqBAZ4SskiMvkh8/N13LkEdwy3mjPR3vqUJGd2uNtpAQEIsWRC8j5MDQFinrjWKEWYYdutCM9T3rr1vYdDLnI+SBs43HPi3uzn57f7vD4cKDxnFsJoNW6tHuc8VK+rf0j8/cMyJXx4WLa179Xe1u1wmLPgaxsLy+i9tg4Ix3mZz+V6uQEl7cO8//Dd+4fH4z/8xz+9eX///cPby+5uy3nK5hjxy8crMuWU1BwRu/ec0nGeeJL9bXt4nBX4dmkIkIjSLG5KhIEwzEpmIZyS7GMsiVFgzjMAdDVgThlH9dp9mtA92t5b94jo3bIkiIHInCECezP7ihaOUoqqlix9GBKIUK0jidxppqaehAKijzHchrujDxum47bb49N5uI/Ie49Yt8Myvb699a4OEEO3tpvqiqzqROxmSbAUfPfueLlsn59fRDAtaT5Nnz6vU5Gtj28e59p4G3WrNTLLJFLEQ00jCQXasszh0VqPP993Wdg8GPGY8Px0GqZ767UbE2NCNU3M85T32sLj/HhYt64DkEk9fnm5IgQyOwStIVKylH/8/T/XtnYdl8vt8eFhIG5DdYxPH1/A6XCc5yWLRx8yzMdotdX7H/IYehdv7a3hnxNsrQ0AGKPXHYNQdhbCOSeUBIHukRMLYhBzQlZzACHow1gkiQQAoI+hAIEQ94EdE0xT7uOuCrVclr3fEIgJ1WOaDkhRJF1v+wNxEuo6DoeDjmFq6XAYquWQRPDlbUP02oE8zF0IvvvwKDktCxWmlLKF5kzPb9dyPBmg9/j46cZJ1Ozzx5df/+abPoZpQCGgMB6nOatGa3uvL6ONLKWNq79s4dB6B0APZQQg3GonFgITQuZQCkICgjB7fbulxIzk1i6X133loSYsakoYJTME1m0rcxYRJt5bW/cIgCwgzN3c78rzkhBxXTcRHLz2VeaydMSSJyBEd/6K0HZkaSOQQ5hZEiL0rhho3T2QMIjYIYSJmNCIkbtZlgSAtWtJqY8xqtY6TkduLZ7fbiml82lOov/l3/wXZUofP/3p+XX9D3/34+PT1LVHxLffHPZa316/DO1rqz98WEpJj8fyerlNSb7/7vvPT5d/+P3PXz7Xbz48vLxctOq6j89vt7b7dR2125wzEz08TOFYHQjgz2fLuNx2JMQAIUhJIggB3ACAPIItRMTCAVFyvg1dDiUiamszUkozIiC4EBLz3scpiZm93jYWIYqSJTGHm4XNc0YADxsKtauZC3KZC1KADwDMHMz05doRiQdIodN5aWsTofO7U91aSYgcPnofbh6qsW7teMrHJR/nefTK7CIiCS6v2/XmsF3DzSBk4tuX1x++fwx3VQm1fe95ygH8+ct+vexZCgKMqoz5tmrfB7IIxd/89qm1cesVXI+LpJI+Pt/U+g/fv3+99N///Dwdynqj7brlwkgxTRk1SkpJ/GFKkkXdzpRVva2uvcnEgkzCGnTdW292WrI5oMfDgYlNYUySav2KcuPEyyG/vl0yFg0/PeWSEhkUpkDazdQtZwqIzGiAPYIZe6ujG7KMMU6Hyd0DFJEcICVJiVKadFg3V9OcISFdVyuZ1RQi5oS1++Vtm/KcmB/O8le/fmTMf/zTpwHw6w+HUZsN6hpBZO6uyogOusyZmfY2UPA4pXkCcHq5NRLkXC63oaMxEUkatd7h35SIyUM9gNQMCaacwiEheBhncjJmK8hbGwR6WJCnQ8oSFuHmzjlls9i2vu5uFoVcw46nNJf8eq067MNxbqNfeyXibYD1plKmAyOaKu2rjm7zREHOWRAYIcbAy2qE2R0vtw6EIgDkAKgj3IEY50WOc44IScKC5tBqfH7el8NMTEhw29rx6dTNLpcmiT88PkwpX6635VByFu39+rrftm5Ez5/ry+tmGiUXHYOADQZAhLlpAwBziPjPlSYaane6/fWmEZiEKkdtUTM6hJqJsIZPSfIUKHzd274PYUlZqEytm9XhEdMsiWI4zsxuusykiOtuvUVXuN1UDVytj0CAeWYHqE0dABnVbRJclrztbQwnoWEhhISYmMM9HNJ9QeceTh4B6OFODGquagS4TCyCbzdzA/cYHtqtJEaKRGQGhDhGF+E+RjiGxx3RbQNMYZp4Emp9bDXCQwkAw5HvC5URBoQx0MNEyCGs+wUtiPktagftPraxAdWbckNTuuz9uveD6OgaEqcst62/rb1bJyDQuPev78A9j4AAsIgARnCA+07gjumLCGACBEIiAiFioghjQAhKiNYNmEuSlPi27YzuZmEj1AKiK6naPIMQhUXKeL0qAA0LNc+J5swOfruZB4gQEa67TlkgkAlN3dSQwjyOS0qZrnUXKdttQOI5iScau52nhSi59s339+8f961tW//um6fCfHvb81ywpF9+fmnd0zQNNQ6VtNRdofm3D/n4cH7d6tu+E+NpGdd1J77eLtfTPIV4zhzhGLhvfntrhyW5Q2/jtMi7ZTaErY4wB2AdOIbBRGN4V89J1IIZBaEPz4wAMACZcaipY1cl0HnmUpAzAYBqK4lVododI+gkmAgAY0reAxwizOYZEOPtuufMTHTvyyMSMUaE+x2cj8ISEaP7fULEggFABCWRe/Rhcd+63Zvdd6U7AkAUYYxofXSLAGC6v2pdRFgo3AHxHlJzc0RgQBLkSbYbuMe1qocnwpSSji5MNkA9avWctCx8OufbbnVXguhjHI7JEfc6WlMIFKYsVIdd97ZkdrV352wQvY6x7V7KqNS7O5ohvj/yktNaW2ZD5Os23JwA6O60gTt73u4amVo7EyeGW+2EKBB16FBgJgQUQQeIAY8PUyA8v27MOOc8hj3MZR1NmGvdvMgYphpfXm9I/L/5y7/8/PzjvNA/f9xCAYh++umCSIdDMTNgWmZKU17f9m3tHoQID6cyuo+up4lAkrqaOlFEeBtqiuCx7YOBOHDvsG67kwwmNTof8tvr7cvb7fz4AIkfj7nsY6u6lJROeCdAlSytexvD9/rutGSzrbs2zTk/v6zvz4cpy23rU8kItiyHrY1wlZCcyq27mS8ZdGzzNAem69bz5KdjMtUIMDMCAcChmpMIwTyLu6EFI02Zm1EgMOO67++fTn2r5jqXvDccrkhdNy81vTuV57f+uKTfvpefX24YeQD0puEM6ACWWMZ1m7JkYUR/OhwajCx+OM3N7Imod/r05fbwV4hy2mpnjtYV6e69I0cDAMAQBpFk6kDg5q0NCnDEdTMh0KHmHkBdlQhSEjdPRBjUBwTo6Vj2W82ac6IpiSSGgJLofMy3rTPz/dvVpqo6TTxx6g0OR2GWy1t9eirXOsLg6Zhu3dDj5W2vFsj29M00STod3v3tv/oLd/zDjy/ngb3ZdR9r123V45zDFYkTsoYTRgoAliCV7HOWzFPd+qdPX+bDdDovKSUB64Rg9O5x+djX3sYAHh6MgYmkpF8+fUmZfQRx/vC0PEPEVc+P821rZjpPU4Rve+8tEpMAvj8fXteaJ+ydppyZfFs9sbXe55wLMwS2Ebet3VcCDhgG+Oei1/0ILkQQoar0lUYPQFjbILL7cYCILWDf+83ql5erMElKOraAIMIpobBc9h4Bbk6MU85qrkPvjTICTJkk5wg3tU6wCFRtPbyurbtPhZ6eZpGsQ9eblSQj6lY7Ut7GdZrK22aHaRrr/jzGYRY37a2DRxHAzC/XtTULRI+QxNften9ynhfE4HUf5q6mfWiizIiO6KbzJK1buN0bFiIUrndgzmXTvfdlStMkATDltLfORMyld219DAfGSMLmfjxktXAFJiaANjRJIOK+qzCZRnW1wG0fiSgLT4XKVD6/XJoND3CP0btHYJLMfpgnQBdwEMQ0EZGLJEf/+PI25/S3f/PbnORy3Wsb01S+vLxe93Z+N03CHNO67qO3MAWIb795+vT8qa23v/7rvzSI41L+D//tv/lPv//T27YejyVJuty2YZESPz0e2jBAuNNhAknVI+jtuj0cJya5vN1G1+NxNoXDnAKkdbfwKSdzMAsdDha7jbnwrkpA2za6VRFOidvwre5ZuNbR1COAkPYxUMHdbrdgIkQ0u/sv6e4gB4iUONABIidRc9f7+RSWibp6GIGqOY4eo48kjOAvb+syz3vdASMLt9ebmevQnKV1RYK1vjHAMidEXPJ9n8M//fjltjYp1IdzYBZkAgz67bfv3j3OH19ePyCeprMgvey99RiVSkJAMrdRLSII2QEJnJHOy+RhZrHW/dffvMvCn54vY1SAKCkdltLqCI/Dadr31npbluRNFaIgCvBQv+fSPdyx5cMy5XlU6x7M8+fPmyQQSeigg8qUAFmQUiEPu2632mrvJgTHY3ZnAOzD9uYYcRdPmOP4MwEkZWna96E1CQsvS5aUa4VDScMpBmYG0wh0ZteIoXdsJApRd3ezqdC+720f1u72Lwj3atZHB8DDciSE8DH2TdNymAWj98rMeF7SXo0IpwnXVeuuRHK91CSYS7ltNVTfvX+cSibCaSqJ08efPiFRyf7x4+u81QAKx1xO+74tc/7tb77959//CYg0ACXBgIe5fPn8sjXNOb18fiGWmoZr1PXGhID3d0MkoilThKsq5q+854goQt3cnNTdqzpSyrTXVgHmknICCAhAdYeA5TCJkKoe5ty6Q0Ag9W4lkVpn4YhIjIDoAAS0rT2xomt1x+VILBFQpsyE7p5yUtcyT6YDXAEhwroaI+WUPByRshBguMecs0cQY/oqI3Md+7b2aZqOZ/785RkMllO5Xa/qod3+13//H5HSz798mpdSEq3bLmWaJnGLf/nnl/79GUSXc8KUAIhEVeuvf/Wv/qt//V/9P/7f/2OvLYj6iF9+3n/UZ0d693Sul9Xt7lMMJhLmNCVvCgHM4tbMYUmTh/Od7uNh5kCUE7uF+b1K6gBsYcRYJtbeeo+cpatNiZFCCIRpqI82fr7egjgXRnBhYaS4A8WGr3UQkYVbAADKXeESMQlPmRGhd9OAaSImOBzKYU7hKkhzzq3tuVC1oZemge5REqtZntMyy7vzkhOPMs/LNMbOhEFAC5NTOJYi5+P84cFd9fXa5wMy+mEqe9XdLMB+882xK1z3RuradiLeO4y6vz/Lw3G6yWDB17ft82slftOAnJGB9j4Oh4RVOcnjt6l3v932S93mKc8TCcXTsvz0+VoKnk7Ty6UeD+X735xfnncEh/CXN02FH0+JENc6ThOruQXlwMtr6x7qPSKOsnz5eEmUmntaeCjsQ9HMIE4Px3EdMTwE+zBVB/RUGMHMaZoyAmTKgjgIWJACHBDBMXTrbZhz4pzj4VCmXHpYFh7ueWILurWWSp5meP9hfvcwZ8QAffcw964j1AHX7lvz1nurnYmI2FwPcxHWlFJhrn20Xa+7qVtG2na1ESLk7nebyiEJ4J2TGhaRKKaESPcPno8YIkgc6+o39dOUzkcAEaI8bj0MlpKeX7b8mMcYBjFP05fXSwRwgsNhEoovr9ttH5OU6whKdCpLbxBVgflw5HmWfXSMyO4jmmJW53XraqMldrfeY2sOAIhhCBjBwb27R8wTP56XlJDAHXOZpO17ljRN/Ksfjh6MPqKP5TDvdby9rOGw7o2Mvn+wOeNhml4u++fnjZGHGxBeL80DCPm21URUZnEwtbCIicUhwF0EWdCHOyCSax/uwEytDR1kVgUJCUe3kuXPgHmIwG1t+64QiAyJkJwdAcXmOaVEa+/nEuB9cCjQ9TbM/G3AtqsbEke4MyEL5Uw6fJmzCN32bsPLVFofqiaEZj5nPiyTeWjAvlthCgLtfUpSR7gCMSIJgkMEADydpyxYu7Vuiblk3reaU8qZA4wTk6Nb3FoXvgOAAYNM74OXWCbx8GFAwqbmiipBjKEGAcNNPUQowHWYDkDAiCBI66ZfXjsRh0W4tNDR422vOaA2G0OrhaBc9kqw9D56RIACsNyv74ARDhHM9xsX3AdzhGAO7k5IEJ6Z70kSNb8bWsHBLB6Py7pvw+vDMt22t2kCIrC+AYepqPs+dK82TTRnmhKRRFcgQhEaGkNNhOaZ3ExHEKFq3Nsw21XlgSnxtlbtFphcrXclkJR4Sof1oqpumeiUBXBvQzGEeG9YpuXW+rKkeU7r21aZTk/nMUaC+M13Tz3wcttG1+NyZOERFgYvl3o2h8S9WU740y+f/v4//fFQ5sOUMUI3u+2mbvMsbjiG31Y9ncoyFes6zWROYBACqgqAo+QxtJsJ0+s2uhETzBJIZA5uFkBm4ABqZgZdo3Z9eiiATsnpLj4jchsBlIQlU87QKwgKJQUCJiypi2hYdDVVIGIgZuIITMJI2Lsi4zyXMRRwMJG5AQAiIkCtOxIDhjsE8P1ziBh4b3OZIwQRubogBCLe/zV6H4ODIgKRmDjMtlpTFuE0hopwytS7J8bh0C2QjCCmiUMQIFggAo+HPAnoIJqlMLnatfbhcFronnDfazf3pZTW+968JNn2dj6XyLlMTkLNaFeAdYwAUjxMEpHraMdpjNGFEhEmBLehasICCJ+e18OSD3MZqoQugEgkCjRCPVqPJOxNCeHbh5nB2v+Ppj/tlW3L0vOw0c05VxMRuznn3Hvz5q3MrMyqIiWxSEqCRBn8YFv+YBnSDzAM+F8aMGDAFgzJsGkJkkUWySIrs5rM251uNxGxmjnnaPwhbv2B/SH2ihlrjvG+z2PQ1Mj5uvV1b9umX74dckmFMMKXqhiI1rtdUcp/9Gd/9vHlE8JyHLJZOx7K33778nze746lCCfyd4d05PzMZ0f+/Q8X1ZgGenOX/vxPv/7rb98bp999e9lWQ2CAzkKCJAFqPpRk7sM4RLg63QJEb9/cv3/eLsv13ZsZIUrQvrda+2E6pcR7VYQoGQFF3T6ft/t5bG03oDAQFiLkTAOlvfWchIleXtaplH1ru8a6aOsGp3KaxmX36nseJAuvq7vfnpgYBy+ZsrCqIvO2LswsOZl2D0oE2ntkPs7DdVklyTFPiB6BUxF3eKnb5bKva1y3BezuP/zl21ZrEO+vPg3DurVNjYnMVRiR1N0ywSzw8DC8LK/TcPjTX9y56XF68+//7v3rduUsB0mHMXWrh8MYgZdtf74uZsHC1r1kbG4aPk+JmbbFXOGyd4hQUyTOwqWIhwV0LgMCXK5rSjknfn5uBGGqgcV7HceSi+x9F6Y8sJkWBicggkSydbv0PSVKY9IOq5nstu22rr1rXlrzoJsGeUhCqDDL9fxsDTjTttbPz1vrkSXt2iSly1LBfRySjEn31r1P81B7BGNr3ts+Fi2UMufXy3657EOWu7uxFM4plZQf3u7mcl76mzd3WXjd2nldfni/aLdf//J0Gu3TcwvDPDKgT2NZd9i2XnJGkuHg69a+f+4MyIkKpyCorY+Jx6lcrnXdu187ImGAWWhEAOZEiGBBBORqASEsFn6TOjHxjfjBxN2Vc7opnRjZIjwiEwdC90CMrnvJmXNS1WbV0FnY1JnZI/R2mP09bU0jWFLdm+SUhJLgUuvT83Xd1uNpHEsmwsxCEcvaVPvjdLyeV8KQQh3pcr4g8bpt61KFSH72oL1F9HnKxP5yviITIZoboDPFeJjQo/WaEq2L5sLutFwbIDZ1RDsch72iuY3lduJGAOTE2nXr1qoBwMPdsO/dDM20FEGiCAcMD8tJlqWNWZp67QaoEbFtfcgMhA5xuYY7WsQ4CgCsu1K1IRMlPJyGw5HMKA359WlR7eDYuwYSs5NIFnYP/Of/5X/FANOcFMJaMPr94zQP47a21/M6jqOF381jAF63PQLevnm8nq/LsrkbM7DImCkLfv2zrzzi8vr61Rf396fT1uPf/fXv1Wzd6vm6LlU9ZN0bMUxD7up7165qHZddU5Lf/PzNdanNHMAliRAWxjxkc2+7NVUSNI1t6yzcekvCXV3V1JwIc2JArM3UnAntFr5CLDkDQOvqHuYODiw4Fk4it7qLCN04M+a2VSUUdTP3qWQiBAiEW/7fRNgNzJwZmG5j7wQYjDhPZd8bk5g7gHftSGIQFIFhD6fy5u7wdN4Oh9ybte4Wen8a3p7mtw9zt/ib33/++ov7wzxNU/HoCM6Yv31//vbDCwK8vZs/vi67ad+7RxiAhw0pu3niWyEOtr1a64jYXFmYmISwpJSYX16uDiRD2rc+JVG1BpES+y3vr7E1UzMPR8B5OiAgUgJOtfbEKTASs5kjYRYWCQrIiV/X67ptDJQLjoVuoeJwXDZjJELo3br2rs4szAgRTGTmFhEAjDAPqWmcptIBDPBhkltokASum6kisZec6h6IWU2b1jDvrbs7JQQCQRpKVr8RGTIRtr26+ThOD/eHbd8dYJqmdVncbDxMp+PUdvv8+TyUQoQ5F3QloXEaX55fMvGbtw95mrTt7z98zmkQhM/PT6lIbTrOI1IE0ukwPH8+v57XeTwC4rqv2jVnMtN13QmQMYCICRLDbfFlHsikFilUhAMIAYKZwPdmQ5FWrTkMJa97jQDzm+AuPDDJT4s9JGLmm4ysVgVAZkJCRvTwboEQqr53Q4ApCyCYxy09SMQR3lWJCJCnqXTzY8mBkjIzegcCxH3Xkobu3syzpFKGcHBXRHRTIUqpWHTVpt2X8xnA0pAfH+5/8fMHgP3jpyuiQEQQargZ7lt1i7EMX3xxKAMj0fPrdV2Xr97eD4ku6yYlIcXL8669lzHtq0E38DhfNSVuDtfzZhZffHkqWcZBGPm62WXZp8KHadq2flmrpFS7JhYiRJKc5GZes64ApOE/yUsQAqib3ZgNJJhEQg0dA8LAheVuHrSrhQdEU+1qEI6AzHwLh/Nt9kgYTrV1h/gJmUkkwiXzXCAnTokQwyKIaBiydVV1YWpq16UNWQwtHGtzNbjV70gYBB+OebnuP//yXjJLSfu6gtLargZwOKRxLN3h+fkyDSO5rtfWHABdu5rFtmtrKiKTiCQydlNlhJwKOj7OA0G8LHvO+OY4//YPL+elI9PhUNS1tUhCKTMCrZsyQ+/auzFTSuTewQMo7VsfM1vYMJW7SQD86bU3i0woiMGRBczpeu3HibcOHvQw8LJXT9i6IgpGJOBA2HufB7muPSe23tJYpjFdL21vHQncICUmChEiBAYehRlj046Ju2vv1hUJb9IJ+g/+9Ff/5B/+6eX1/C//9V8gwTjmy7VeV6uhFlZYWrMh86FAFp7TyCzVFiYQHl6WbXd/utS647p2IlTzeeQkXBgfTvzxpSeSatpa3zacBiLyVBITrUvlxB6BHndTNnRDaLW32hj54TSp9rV5IIwZ1aI1yEgKgQmYvbdYrk3V7k/DWMYPny4/f3vQCMok4J+fdzV/OCV1y7l8flm1QVM9nYYhk7B497XuqdBpLnuLrjYANncq2LstZ7UAJBTCCBAiSdytm/qNpS+c7o9cMvzs8QQEr9d2fu0t4ot3k3erzQ6HMpZ4ft0p8Fz181mXS51HeTiN3324vjnlf/Jnb777tH3z5vhXf3j92x8/T9NBewewx/vDsuq6ViJklsBw067ugV88TCShqpnS59ctkdz2g5KQicwiZSSUtttYGCKcsHW7XnZTKzkHRmv9dhYCxLs3U2+21LDoY0m1u7sPgJtHMPattR6AGG6EfJhL7a11I0QALEmYsKqd5nxemjbLOVXVnPAWbilJqvnWTLsSIgAw3jBruFYdC+VB1s0FYd2rGRzGRBytmTsjQk7R1QG4JAqArWpJxIyXrRNiBJr6mBEoSi6InjKHw7pVM0dAd7xNOM1BEEC4dmXCJAwOEZ6QzD0ViXCzEMS/l7GoGW91P54Ga1HVWMiaE0IpCRCbh6vnhIjhPYDw1uqIgBtq86aWQARh9oAIZxJmUu3DMHZT1a7mSQSj3R9S77Vu+zzRbi5sYeqA5tE7aqcbfFoIc04OXvKtBu7MZIqB0VsQInHfN8u5dAPTwJ98Muiht35y3RUAb2gbZkaM2626FJ6HDOjL1mtr5tFq5CHmQwqjdbFDImRQTJnwWOR0OtbaznV9ve5oMtwkfQ2AuQB89XB8XrpRZ8GXC9a9joLE9vM3x5TSp8uy1wrIquFukhgACBjIAaJXr+bTwIci6vjppbo7IUDQ1sICEwWF6e3uh3gzZsbfa7lyosNUiHGrXYj33W5Po7BIEgCbRhoyxk2CieEegZ4EzHxZQzKJoDuVYVxWDaeUeN1BDeYpQwAGifCy72aKiIR0y+24e2KK4OZAFBThARiICESEEF3VbrdVZGHCiJuhUR3CI8ntlzc8PFHqrsIEiLUGEzJBVQiPnDElOM0ZkVLC87W5ARPu3REgMZ+vDQDU43hgNa/VhyJ7u/2y07o3AspZIIJEVPs8cjNYNj1kXqvmxG9PZVc3s8Mk56WHYylJ3dVt3xSD1PymHylJSoIADNB3b0btstaWMr1e1CwSo7n90bs5Zdg6PJ279SiFHKIpfHmXU6Gm9os3h3/7+3PKWSAuazvO6U9+8faPfvbw7XffCZY/fHzNk7ye+3cfriwwDwOT/upnp3kol7rf301/++P1x/ebd0tk//X/9p+i14+fn759qj9+Wgl4bzdrLg45CYGaJeHDnCLieq1lLMyIbh54XuphTNM81KZTTltte213pwMg9q5mse/KIrVuh3ne1rrVnstA4aeDtN4e354u1x0Cxpy/+/FMLOOAS21IXKu5wzQlZmrNIuLNQwGn95+3h7vRVJEkoieGeR631iBCWz8cTy+v62HOxOQWDDQk6R6UIAienrbTYXjzMGqLH58WJDSDD58+f3H/5quHmbLdn/IfPq23/c7a7fXa6x4p4ZBiLOXuOIysQ0oGlkDUsIWLyOuyXffWuyNAZvnyYfgv/tN/+uXbn6nbv/ntX/23/+J/7hY5i5qHRXgk4dM8dsPnl9XdRaSrta4lp8OhuEdrG3GJwN4qET3cH86X1QIPRcKRGQsZEASB9lD0dddDzlU7IgqXanqcy5xw2XYNUQ2wGIqoO4UzYzVX9RuRKlEg4jDmNwf683/wy0+vL6byel6B+dN1uV51XRXQ0ZERTodpnpiibdWB5Lp2wHAyQjJ1U+fCFMQEyDAP6e1png6DYn25rDwN98fD89P1/Y8v68Xu5xmIfvPLw+8/XF+f+q//+OF5WYcyhKIa1La31vaq81SGTPvazaEI1Hbz9ABiCEPrbHqD0CPfoB/CTT0AhbGUMuayb3sSbArrroDIhETkrsJyiysy0tZaIgIQ82ACg9u+Jy7rZRqPuxoRhEe3fnPXMDMAEoE7ARAi1L0PmbOgmVkoAW7b9unpxdSF6Y9+/qVr79a+fDxcl5qFj3Op6s/nDdGnkrbe1632Vk9307IoRFCiL9/O+3V7vug0JFM9HAd0X659D/vqcbpuDubzzD0cA03NDNSAJRiDBCG8Ke97HQoiJAxfq7bu96e87rasSkJDQiYSJBLsBsuyE6CIBHgp+XLdhHlr2s2TECN01eM8AcG+15LEA7vZccy1e+s6lQKE6PHmzfF8vXhwq3a+XCMMAiygCJbMd3NJhCwkf/zzt8tlX/f1dBqXvnz99ZuUE2GahuHh4c3lsn73/vvjPHAqSfUwT3/2p7/+7W9/J0LjmEvO1tthHN+9e6xtXa6Xf/yP/4NW97ouX3/9TesVMP0v/+bfOyIRFoEkWfXGuI55KJdFe+g4cPf+6eUVKS2tJyK1NpRSREItC+dJLhv1G3fAY98rAjZVs5ufARi8dQBAs0BA95+0HYS3bQzd2M+JOBAQY90tpziMuVYFRyKCQEYqIq27EEH4LQTbuwsLMRDQkJOa9eZMSARbN0IiJgfrXde9TyN27UNJpQy9214Niczg9azr9no4prHww7HUXS9bPQw5C6lDLvLmzbT3+iZNHz98OJ4GELru+1dfnT48PSVhYiSG+3lcElpHDzUAryacchJXa9qTYEppyOnhbgqHbd0lcVgsW52nnEWWapmZAlNOZtq7mXdEJOTEkZj3Gq3qS30Z5+mbLw9ZyqdXaF0f5yklqq6vl5UgXy+b98gZ1T00HE0rrHbbG8JNMFN7d3NTY8Jb5FgI9TYNJvbWAbF7LFubhwRhrZkjbmTCbG5iyIAALhy92uVizGtABAACSEIAMVfw0PAKt/8tqYaZjzmt1jy2RIkn+vS6XT+eUxImfnl+3ZcqwsTQehuGPB0Kwli35bKcmeHtF4/IUK/n1/PTNJV9W8/rmstkEF99/bhdt9fXtUzD0+flct6YRdBr267Xa91bEmJiQozwNAghIEKWAAo37dUOp6NpC5TzZmY6lAyuCbGItK1v5sSUrL6ZwIlrc3W8VWQBgRBL4Qi/vd5lweOI3SwJAyAjAkpX8wC1KFuXxDlh72bOATYVhpC91ht3zt1Dm7tfl/7FMddNIWfVymkIw/NeHeAwZ7faqg1F0DWcx8K9+7KcRXBZ67o1Ani4e7h/mDxi3w0CS56QKQ3SmmFtOePpMORMl0td1/35pc+HcRSOMlzX9tLrsntOfZ6n3uO6RRpySvHj8xIOCeC66XgYvvr5/ZhZEjjEvu7Xxco4vn132C/r9XzNSaYB1m1ftzbNY9uckOapaO8slDk7ACGE3ziY4AFmgUwgkgTVeg8bkoCA7hqAn1+uEK4QAcEIhIhIHuHaAYGREFEjSEGtO0SWBOhTllyyUwziqOYaCp4KRMB16cvaw6MkqhgBKEw343nbwwJKFveoFmb69ji8ezhern1rPZpuHz/fAvtLXbv6lB9C4HK+tlWxdwnvFXa1Zdm1ewRoODhotMpaxlRy3FpXYyaM3ppy4iTAQE+XvrW4bPv9aerRHZGFrlurrwHhJREE5MRDSiwMXhFInbX7PI0Atm0aW0c1JA6Qre4N4TCkxIFA26qhti5qmFrrzxVlkCS4N+lVM/Jxxo7OFhChHqAxlrG1Xom6RwCEGiASsppvrTNiYWYaUkEAWXZ1REQh6InJhOaR/+N/8MtfffPN++HD8+sX7z98SogBdG0LkFuAd+/NwNMohRIjO7CiS222+u5ET883FgoGhAZwivtTmkramxKnrdWrdnelsGEsueA0pGnIrat2mgYGirBQrZzT5VrXa23NypDm0mrbZRgJQRCaO0Ps245FJHi9dkZ492Zc9yYEiPBwTOZWxrzvuwa1btr9cuVh4mVtoLis2+k0eNfN6OHA175LyRG6r30z9BZKFML1eUeMMoqbM5MQ3boa3VTVI0LNI+DtPf/x14cvHx6ypP/Hv/g371/07v5wuufTSUIhgg55/PHTuWo09R8+7ddrL1l6RNf67nF8d5eP04y+/dXvXz9dapa8LLsB/OKr+YvH/Om5f/Pu9LLo59fNNCAAAIghJ8+Z0lieX/t11VywZB4SHo8pHHpzYQq3cQyzLsK16ufPKxOVnDRc1QGgZG5qEFH3agGcUjRgt22zfdO15CBo1yYcLBgewMIEAf4T2oDQwrvbkDMgIISpTVMihlxEhJ6eV0aqHfZut3GDCASAW5gqYRzmNA8MxNp9LAnc1FzNEIGZBqGl9a7IKN2sVkOiCDD1rhHugZhEEG68AGP0UtKytt5uwR6AG4HFACEwwCM4qNw8jBbhAeBpkAS07PWGibEbZMEUgANMSNru5k4A6CECGNB6C2JmYY6pkAZpeMRP03FGv7kLmTiJLNs6pAGIHDwJu/WunTsJhySj3Obcl63uWwhYImgNHWKrcdPzqUU4IoCFpsyuPBa0QHNEdEBsDbRHSlirX1c/zgmAryuae9dAgFLiMLNAWZs5OicecnaP1jQPMk95LLK1LhgsUTuUgqoogscDv56vpFRSSgc5zeV126P2IsPLa/3h04bsKIGRagWzPs+l7xUQI6dvP13Hufzpzx/XrZ6vz0TIjN3540sruVP2Yy61BRPW7h7WGyAYpyAOTCgMlKlamHopyJQI2RzUd+sqJObQVZE4CTKERtyKVxgegZetMYA5phGnialBNzWPia0avLz240zncy+DjNMN3iwQrl2FvAgCRE4Bfr07kGsQIUVcVsJoxzmvG2pHNwsAD1APAoxAoqyuwnjIae91KuOy7YiEBI4AHiyIgO7hpmbETAhgfvOBEuJP2yqPsHBEqF3HQTJj7R6E9NMdDxDQNPZab+Kw2pSEWrfDlCPcwVXjbk7HUdS91koEpdC+dwBkxsw4FIjAve1D5pzI3YRccqFm3en9S2u9TwMT8XUxIqpaPSAldGIwIPBwlMS1aWswDgk5P7/0lIIoTiNhcNO4m+W6+fNig+I0YRIXoJyAOG/PmwKhAzM9XRoRbFt7PA2qtjT5//6b3//1H34EwNrOw1R07/OI33xxUI/LZctT3jR02xmDqn19SCMaI5/3+H/9T7+9O6Y/+tnDP/r13Rd3ry/n+t0n7c571eveJFEWqqq4+7v72Q223gtlADZTdawK6+d1nlICgJzHqVzO15zHp5d1Hudt01SAUNZ1G8eRkC2wdrusOI3petkieF37vm45JwuwwMMhAyAh7s3ULCJUrVu8vLapJHNP6JJwbWoOrdVpGi/nNuRcytialpQul3Y4lGkgNVh79SC2OMx5yAkAl6VmoYdjYYYIX5chyL59fj4c05BpLgwOHoIMAW1nJMA3d4N1bLWlKZ03q61NgydMiPLpZX//fGYkQBTG08R/9id/3M3/5b/7i89Pz+++ePxH//CP/u1fv993I2LgQHIN//i8AEowWtwAstQNAtAszCylISLWvQ1DLpk99DjxZestqEAsV10JhDBn2lt3BBFcdlUNDQvwRHhW6FnykNuuvepQxG4EHMZw2msfp9JdSzAAmnbtfu3D33z31EOF+tvHg4E7a0CQg0O8ezxA09dtAZrM0dERvLUKwoch9+YIcJhzAJj9FOq7bqG6PlgMhe/HaTX//vtPd4fp17988/TSvvvh8kdfPL7/tBNjmWUoafDy8fnMlluDlIlAyMga1N66oRqUzCnhtnsZaMiAEQ+n0d1z4lzE3D4/7fNUAgAxmtl6bSj0i69G07YrLDu/vFYA1u4BCKAABAZFmImNKUyX7fadzQFOEIllXS/rVo9zUq2hOwIgoLoHQElszZEEmSV8fW2f1pWYA703jwBh4SyudV/W43GaMwPHy/n6cJg+an+97uuyjwPvPS+XLSBykX1XDys5LWv9+EHHMrXWPDoA4t5bbcR0d5xOd7PHkiWzEPaaWbpKbT2V28a8h4MHhlkANoVt2Qmxm3t4rd56iICbGqbDIYmABrSuIkzEwqgGtbYyJDMviRADIJh4mKcIaLuagSLcgMitOhIOQ8mSVPUwDS/Py6eXKzMDuFCM0yDE2jsJPhzK5bwPczlOCf/L/+r/IETH4/B4d7Te7h/fXS4v0yDff3xJKRG6A5xOp7Vuz69XBBQEZmHAcHJoJbM1kMSJcZ6H0+m+qbr6nA7X9fV0//C3337/l3/3fVc0VyYQJEOqrXu3NN6CYnzdN7NOPPQOpvH4MGcWRLw/lp9/+fb7j58+vF5VvTcDAELoarfRTwS4e8mcmGu7qYwQwoaSrktXjxukEADxVgd1F6KqhgA5J3W7Ge0g4ubNjAhmZCYhNtPb9CXA+bYMQTBVADD369qYbo0QuzuM696HLLfRJjG11gKB6XZvJwA8znyaeNvqL3/2NpVEgKdxvuxL72bgQJESAOBxvns+b999eHl7d3z/4QIQGqbhKWUzHdNYq+bCDLTtFybZakWOdw/3BLjVvatve4cAN/3y4Wger5e1lFIjlrWFwy05XLv2veeUDNzDh5JcvSl0B1VDiLeP9zmPe2tzHpjoen1OOQWAd99rS1m6Q+/aakNAEnQANydC/8lihxRx4whGwC2u5R4/VWPUAjAxlkRD4qq+K86FAMDdx0HUABGmibc9Xl4qITih8E+7xJ9aFreVFDgBBkLJSZhFyNxqUyYW4XXX2yzmNhwUkab2eDqkLO4+T0U4f/z0sZvdzaf74yFQt7UhkkVf130cD2qeMwDS9bx3dQ3Fn1QCbrprN3UnoLFISokxEMg81PpW++Nxrk0R0AIM/JYsaM0QMWdkoQQU2p63bpgE/JuH0R0utafbpiIwIP6eucBqKomJJAvmhObKTExkHmbOTMx088V317+fekiAo8eyBxN192U3wchJmkd4nDIywdIgZ4nw3t2RA0GEINwDkxCTEHEALOs2j0eg8BAkLMIMNB1mxLDeiGkcCxJttXnfkXgax33bzPs8l2nMl3WnG0dIDRC6mfDw8eMn7cZlctOUOWV5ft0RYH19eXh4l8dSit0fxwjbNnWHtVZJ/MXjyZuaxsu1LetKJMy5qTXVriqcT/PIwnu1VnsSIrq5dIETI9LerFoMmVRbOAAjEfXWvRsBAQISegABCHNgmJkgMVESbtbREZEcPIscRpGELLI37apu2rsL8q56g2uqGSNKYmQANfUw9yysBtUg3EqWINibzeOI0IYiRJwRxjE/3h0/Pr/+7u8+lkHe/Wx+OByW523dmzmFuQite13WGkEeGKZEhHzTagJFwI3ykNm7CSMzlyFNJU1jqdX++rtnFhxH2nqY3Vwy0LsT4SBwfxw/v+5fPgzAzpkdZD3XujcZEkD0rvtuSOIeXU21Px7HNHDrHQy1RTcbMq/Vwvx0HObjcF725VqnnObMRq4QKWHi9PlpGxITYzVjJlXX1gGgDMXdTJ0ZiSAnGcfkbq25Ang4Abo7Cp1O48BRUso5L/tSsmRiQa+Kf/WHl9paKYmDt7UBwpdvp/tj4QDdtWp0QEnw8XX74cfLPBRkVAcmGAa6m9JcKDERy8dzXTfNmZmB3bZmD3fzft3UPQ88ct5b293GnC6LPr1sWRgRmON+IkdM47hcFg5YOm6r3mbQhHp3NxCgO06TgHurVqsOYwaIrdqy9GVpjPTmzQQS+2ptt+56f3f48f3zF2+PWSwNIzK35YIhH16qO3EipjhMPA8cEOpWJJ0vras5gLt7eO/2cD99cZ//w1+92/f6utSPF3u69il7KfGbb75++3D87d/+zXo1ycPL3r/98bptnQA4MQAg+NdvDtPAb44ZkX/342VvzkgBEeYe/vYu7dW3tT/ez5+u+7p2DLTwaUol4dvjcNn6Vm3ZAwmyRISNmUvJn19WoRvarAFi727uvZk7JAFK0hU4PMAOU/Gbn4EtSLaGN9DM2gHBu3tY5MQarj0YgphIxMzAjZGIkBggYh7LeauM6MDzGIQoSXq1bVdiRLzZFrApQHh45EzTxNqxq6uqu5chhYGpewQzBkDd9fE0X9qeEyUUVUuJ1k171zf303lta+1COGRp3ceStStgZJGtd/RwAAdEN2HWCIZA5t47RnDiW9A9bqkdBAMwQ3dnAiQIu0EVyMIjgIWFMRF2bYgExOqeb8Ylt8M8nK+7ECVJ130vkoio9j6kAohIYaYiCNARlBEYTHtnhGaQ2OYBiGhtHYJKYg8nIhL69LofhtR7WDBFtO672TQIGQ2F1hZrjbGEKW5rSA4AMIumQBCHiZGxdavNkShny4LawDgiTPdbSpGQaJoLIbe9kZBH5JT6rmmQbd/BHAj2rlnILYjpNJdwfHpt1tzUSCiP/DiNj4fjD08vy94FiQXLzG/uJ23W9/pH795o7f/+x/dLtT/+2dul6Vatt344ZgN7fl6HsUyHbE0/f67MyGLEBE7WI2U5zlkttr2LiJtrh62bmWfGAGjNuroIIEJTqM0S43EURGzdb12PIcteayBOU2mb5gQdwboSwGX101FOpwQAqp6YarOmqhpu+HifVI0EhZEAw61bBGFO0Cp4EDAEEqAA8L6rGo5DyUncBTFtbT1N08fn1TWAwMEKM4J3M6bk5moeCELowebB5IhYezCBMIZjEATANAoFL5vmhEgUEUnQIbJQ3YwAmCUoNALMpyEtVRG9argxownD3j0CRDAAtDs4nI4DE3hAa+5hKUntCoHM3LttzU9TGQo1VSa6Ll0dx8IscJi49fj82kuSgZ0Qckm12TBQGRgRW60IWHLemwJQYbxWM4OIePNmWPfWWsyHDIEvL9vxUJigdrs/lmW3WnVM8nLdiWnIKRc09+umSZBSiMA85PtxMlViGEq+XluvfRqzECJi4jCAjxd4el5Sit9883AasZutu74uvlbbdr+s1ruPJQn7mFNXlxwCGIGc5IdP67uHaVn2vdmvf3YHFChianvV59f29v6wtl3dSsqX805E41gulx0I3W0c8lgwQpFK79pbHcaSs2CCZWnrtbEIMnvEtvaqMZYUARHxzZtJ3VqAuh8KCsvn13YasmFIRjCzICZixmbeWkPhOec5yblrYoSqJLJ1I4A3p/H96+aB3RQI3t1N1m2rkZIAwWkWIthqZwJCQsG9+bIYUowDJxJTdEBDW5betpYE/tM//7Ovv7j//e//kHN6f3n99LKkVK5r2zfFAGLyiMPEYfD5eXeHnBgJCMkcHCAxEUZOTEDXtXK6EVcxMapHLkJA27J1AwgE1flYuukw3ErigcwWjkg3JDEzEqG6IUYSYfAEcTxNwdJN16rupgoQMKVYGgLK40EUKDO+vZ8k4eu+9kpt8zHjy6Uisaqadea8bQ0JKIkFUMBeWxrT3Tz0akCIeLMAAoCr+ekw/OLrx2WvL3t98/aQyd//+PoPf/P1/d1dV3u5vPzw4+veIQ8RLs/P7TSlDx+uj49lPqSX59arMzMRRtjd/TBNqWR+Pa+DwJgdU+ktwOLzc7tcdwj/8osjZ0iU6mbmOvyEjocfP16ckocyy5jLWlsLqGsPjXEcj8ejMP348Ye9dgAKj5KgbpdlawgAYUlizBSBrRuGhgcxO4I5hsde40ZUQMbbpS8l4ZTXdX17dwTA1vthGvZq5/M1GOdpen56PR4GHsa6NSLkRGA+TkU4Xl+uAFxK6b0dD2POcJrS8TiHx+uyIsTPv/xi27Ztr+ZKzLV1bQoIezNTd49wXNZuGMIEHszMjLd78S3TJwwRRITMsVVDlG1vJJiT1K2zsCTartUcAj2lRIgly+W6d/2p0M4A41RyQgJo6q0FhB7n8bzWdW9DIkRkonnCuhsJl8Ip4vO5nqbMHGIRQ8Ivv3jzy5//iql69DG3aRzGMb1cLu/efPnh+fXp+el8vQJma7htG0AI8nwYLbpqClXf8e3d3fPr/ofvf8ss29oZaJj4bd+++/7zemlB5OEVou7tzXEciT9u+3iDyqA/PEzm9vRpccNxHnar6n2Q8um6ve7fX9fdAbxpQmJmB9QwYQl1oACgW2h5GsQAarN5HHrXVBi6edw2+eCBAT9xyYWQiJsqBJCwk4NFuCMiMxEBBHTT3h3CmBEAs5CHAkQWdvPwSISAgUjAbuaM2Lu3iJSor9bNvnx31G5b1bvT6Ibb1q/X2+DrScHmOf3Jz95ExPE4vLl/6N7XvbVGn162v/qbDz+8f3l52xgxF5nGYuFIIFL2ts7343Gan19e7h/mumkuc2LyauM0UdGGtl72Xdu7+yMjA9rpMG5V93oLlAYoUkqZGDNOY1Lr16uiQFcTZgeobmHx4en54S7e3N2v62qYmIu29ubtXcqcOW/78umycGUEUXMWbM2EkDDUwAGJ4OboySJBuG9NHRBwb3aYcoIw95S5uaPG4VD216p2s4YBYnQ1BxyCtWtOhEzNzQPw78U6ickizINvZnJEMy2ZbyPSJKEardmtToiBtWkEEMGUkwCQ92Xvry/nX3717nQc1MKbfX76/MWXD3lkBBqn47t3eLns7z98KmV8uWyJ0zzI+6fLvm6JieCn/PaYbppOAPK660A4FanAjWFpvRtA6OGQwMFUx1HGgWvr98eBhNZlD/ff/PLt9dLWvbmk2vVa+2PJSBARRNxrr6pIRESqSmQ0CKEQcatKHLfuHqHXfiPvg2pABCHeErGusWw2ZA4EDJ/H1NStKzG/7HYYBQXXvR/nnDMB+FbdHQB8WSoR5yTCiJQQad0X5mTeQKtJUQNkv5/nD6/b3cPx/v7w4w8fVfXdF/d9bXvdyjScpjsGrarHea51h4gkBSKst+fLakHMsF72w1wKpsvTZcq5qR2mAwFs+zaW4ePHaxZe1orMD/dDKehqRNjBD3PZayNOzEKBo2A2Xrd6Nx1SKt/X85Shm6/NTW2acm+hqoFOCNYhnFLKTT2cCKlkad2BMTOaGRMj3QQlOSXJQnurDMxJqnewQHC1HkHLtu/NMsveLTElod3AAxJjIhnHoZlqbegE4SWl2twCwIwcVD0X+fphfHl9vbs//vKPHkfi69Zft+uyXvaqjw+HwyhzTuuyIYK7X5fdLTzAugZAHlgEt6sihAgDuZqhEyEhgjm1ptWdBLYG7+se9jrPecw55ciFW6u165dvTqdRXi59bfp4Sq/n+nBK8zEZhnlkChcN8dZ2VwyClHBf1uNh6pJMsQzkoEU4IiSFuR4P41AcQudDAo5QnUoaxwzirblZT2no1TJTGTKg9W7WlRFNOJCQKdwOhwIRFp6zoPsgNJa8bNq6AoIzBcTreXnqJpIOc2mtZ7Jt2+8fpjfHFBYCOApDxONXh2XfL2vd9j4ylyTBdLluuIMgvX2YU0IqNJXkGqa9FEqZhyTdYZwJiBkhMb4+6aaAfgXTPJboUU0V4Lq2rVqWnLIA+sMp3SYChLRuPYloj+tamZAT5EKHaUqEXY0o0MO6BcQwiVCo07437SGJS3HK8PzaBkmSQ6iE2/E4EjkR5ATL2tW4GbDww0y1xzhKgqa9W8i2ak3YA2923bGkpv3XPz/88c9Pd0V+/Hz56w/nHkggp8EL+5wLWbz/8bO3XHvdvb3/tGxLHYc0TZKGdF1qIuruz+cG4QpYzVytq5chIfhhpNpxq9AiPl/r5VKz0DAmQCUEIfp87k/XjfHWwDICz4UsYq+uFZp3yaYO226ttpI4JSFyztyUEtpQpCowyXXZLeLxWJbN1qoEwSkBOoEXhjzkiIBqxCBMOdPWOpiXJIaREvRmJadAh/AWEq7aCAiWdStJOCGS3x/KILRU+/RShQAZxsKJeO/tutlpZgBWi5JQ1cOxu95mzUGWErqZEdwmWRAOAC/XzSIYIQK33bLwkHlzRaSmHdyB2N0iwgFCjYQBQBANIZAcAcGBgAIj3ALcABGZ4YZoAUILD/TEBOAAxsg5s1D2wB6ehQhCEochAOYkgGSGYxkCghjuhgxgTRXBpikKqZDW2rtyYAyDM9KAQMRusG7mgPMoWWTdtffIiHXFBHBjqT4eJQnrGoKEiV6ucd08ZTSj1kIGzAkjAhWB7OGUS+JlVw8kJhGYxhwRmn1vGsHHh5yTmLskAsLoBo6t1SCqS8uSrpetW0eQVhWJbyj3tjd2gGAzo4TEAoTI9FLXvVdIkogOIodpeLibOPp4l//db6//6q/en04jgBxHWRZdav3NN/dlHD9//nTZrKTU9orih7E8PBYmXLbqEcOY9+iEkEQCovWtq924zJJCcgiJVS+JcqH7u7Lv/bp2Ydw2R4TjoWyb1dZuSoMAyiKudt1qUjawh+NQEk8HL4m3ahbWaswDAkZOUjJtW+8NPGjdmge4xTRSGYQIXOEWEBMCEUwpAvphurG+1QFb494TEyRpb+6Kdbwuem2RbgJ5JMRIiZjR3M0iwG6/MYRUhCLczIkAIRzQuvfw01yEqVtn4fDY9l6dPMDCg0OEffNBfNs3NRozljFUvTfWZggERNqtFDqc0rr21ntJot3GJMvuiIhAUxGLACSHqKZYCSAcvWSEfvOrOjLdD9K6VvWO9GamxHScUBIBUdtrZryuWuttL8WUZd3VA6Yi21LnSbrquqiZpUS12jSIq3/6tBJBLomEevcMYB6tOUCAgTm06mXEl7a4UgokBOsdEaQkyWLeP37aCPiLd5P77h7W8Nv351/97Ajhq9p4SFJonv3N/fDDx23be5LU1Dx04DxmcQ2HyAnd7edfzB+e6/O1powBrUiqPRy8uwLCOBWCSKNsW/fagHBZ6jCVZW9bxeOcGc0DgNOyN3WdKT0exoQEFMwswvson57rkGHdeuvhblV9rRbg9/O0NwMCYg8GKRhK1gAwVtW9WmhkCE/hCOJBhGUeltVer30c0svSz9c9gJgTUVyWlofUoG1b58y6GKINg2TkW9F8Lngj+CCGu59f9XrdD6dyusOLo/f43d/94fff/YFQ5kzIvK/Yk6fEzFCra495yIMgJtjG/Hqt5lGEmAndt70SZRYhxrBQBaQoQ7qedzmUccju4RB5LNhs3zVlyZI8YF3rPAlF1BaJSAqNJXHQdbnsFXIeIqw1G/KNiKci0ZtqMwfsqkPODsixNKBdT+fr6g7X1d7clXVfmwVSQstIXPfmAQGs2hExHEMdCQNwGPO67c/dM/P9RC78cunQjZhKprX1v/zb7wvnfOCoWxrGn3/18P6Hp6en629+9c03b9+9uTustYbqm9Ph0jcN/+Uv7nrffvbu+HzemuLL8x4WvcWQgLFvS52FS6LQ+t0P33Muf/JH30DQumxb8x8/LiXH6VgCqaoGwHbmKccXb2cQbrW+XKqZH+aDFHn4Bn/59S+//upXY8nfv//4f/m//uHT84cbK2cxy4yo0CySpGZYN02MgDCkbOjzKEhoFufFkKLcFro9ajcHqNV1uUbo63lRd0RITOtWpTAzm/eHtydAWq4rMx9OBdFdCSO6wjiPIrRt/eFhvj+Kmh8PE4BnkeMwnNdruO17JQwHuC6rWWTC10urGr3F3lQIJEtirLvmlIiwNx3nBICIcNn68TDUrbrH6TTurYNrbUoKBEQkiLjvDQgAIImY2d7sFoBgwpzRnZlupZauBmq2VxXCWNauXhKMI0XITUmaxwRqumwyDnenAuYegP+n//P/8U9+8c2bx5MITQP/q3//b9faX87LPE3rpr3a67KO46w9Xs97BBzuBwwLR6C4vuxEcLyba9Ww7uYizAJE5Xo9v3k7h/vf/WENRPduAUSoFoxk5uveD7MMOY8lsSAzd/XrWt3ivDUAuD/cn5dFAUaRQtSDhOEwZfRA5rHwdW3b3vamEU4UiRMwmjsAuoU2vcG61T0LRYCq31qFRAw/GSkpwG8plVuxMCURpnXfEG4dnAAkgsiJt9rhtoVzCDckJCYMv2UkrBszRwSzmBsT5czCpKrjwLVFbb2rOxBC9AgUOo3xD755OB3Gr7/4UpjcvTb/i9/+/nnZ5mlcl504vvnq0SIQ4udfvt3rQpw+nZeS0/3h/tPHp7bry+VCSEPCRKEs7rHtV3fIVPZtd3cgGobcA5a9h8GQi6qt+56GnAWOY9lrB/cO2Jtte1tb9G4pAbFM43Saxq+/+kYkbdf104cPMuDxcHh+fVprRZTadK+NiNwj3B3oZnwlEkKYskjiqtqrqQVS5CThjgSIkTKzcE6kzZfdw30szISnOTWDzy/bkDkCfkJ/MoUDetzQNnLT+zlYV2BMKZk7EwpTqzcv4G3SfwM9UUQIkxAXkXHIKLDse9vji/t7TnBe166GGAYwjnkehiRZW1+21m/aGua6t722vVbXzsxZODMDIhFKggTx8aVu3e9GeTPlSvx8XadBuurxOLn7uld3H1Ies7RuUxECsIBmChBTKVuzZdsP81BbeHdD4EQQYd1r02YxJHLArdo8pJz4utR5zESwVgfw05j27rXrVKRr7FWnzMHoEHNJ6sAIALDVfprLddO9aQSw0DwSkdRdcxIPf7wft13Py15yrrtagEccRy4lvVx1zIws16WFai7j8Ti2ujNlkvzVl6eh8LpVM42wzHz/eJQyrJetbbuGZyluLgJVfRjK9bIsaxsmqXvLKU+n8u7Nu+/+8F77fjjM8zQ9vy7TnBPTy/NKwsOU7g/l0+czRJxO0999+zTmMh2Hv/w3/x5o4Pl0mLNrt+6ttpKxt/b6en779pGEl70mIia4rq32/TAyooNja5DHwSClMoxZ9koKmIXdHQAFDBBVEcJzobrvAHiYy7Ku5nA8DASwL6sIUeKtKQMc5tKqXtdKLACeJQFwUJREggwkQK7NiBAohNgCTvfpy8cCFlXh/v54fT0/v2zP6+5soWEKQ5Y5iyN++HShQBTEiN6jGxADEQL6YWCzMEVzZIIAOx5LzvL7b18IcChD796asSAjMQJyHE7FzdZNt9aZhQjnxA5eJi7C1nxZ1zKP296s9bEMWj0Im1rrikQpo7aOJO50nPm6XXPKh1Jst+u+o9BYRgzbeh/nITwI0cxrd2SurfWqBJhyqr0nESYKNJLQgNr7V3djkfLp+XI6jM2tmw6S7u6mzKlu7ePLBRI5UN+79u6IEfD28W5d2/Wy/eLLu6fzdTrku7l892GteztN4zTSw2N5/3l5eWlZ6N3baRiSqanGvjsg1boh4cP9eFn2rZswa7OHQ8kMQen7T2urFcGZ0DoiwmniYcjh0JpPx3Re63c/vs5DOY55aYpweykBdmwaz9dO4SyMjHP+6YXy6dM55zRM43JtJUm3CLxpwkl7rGuv1scRD8dx28F3y0whhOGHkWv3RCCMa4+X836z3Y0DeatlLCVTa0ac17Wv215KUfCwfn8a5jnNIw0J7oto0F98e70shq6J+DixuhNRZhhyXva+tzaMU+uwr2vKlMa0LHtvjkhhXkoaMwPLddHlWoUjCRFTAApENze00zxuW5ecmaG2Vhe7vx+fLpXidqIjo6WcLNC6M2HrOzMNWdTpcqk5MTEgG5AsK7ZdhYISJiZweF3aPKSxkEJ0jdZDzdGsFBFBd8uSbrDGofBY0g1BtDZvTY9zcYOced360qB3Jff7u3Hvat2GLObeHI6FE0Z1XPcqTEyUE6uHmmLg3TFvCuvWH+4InM6XjgQ/ZWDNu6o7gcNYpKuWzN2AAgIiIAjJIoowAGyt30gwiOwECCGA6j6kbB52U4fQT11gCOhqjOQA6k5xC4jCkKTWHgB6Q1wjIAEAZrmxgNl/YrjAVlsqI4EJGSBiuJABA5EyeeZQ7REBEd3BFb1bgI+j3CiKOUlOuXfftr1bn8aDqZccW3Ok3LcdmZF42zozPt4PHt60C5I6vi43fgFnYk5oELXZXrt2QuDTAeh2ODE085xhLGIWy9ZrNfdImRmpd7ttQ1oHzgysfYO2hghZ2Okwurl7P5+fH04Hh3K9NhHeFcpICX0chm5eW5vGlIjGnF7X+jDnX/zsq+WyEioxPJ31fFXJ2UAB+uN0CPN/9s/+80+fP/zP/9P/oEGNEiHurZU5nY4jAzSz83m1DnnmaSptjZTYwohgyLLutrVOiciQLEiQhaaSatV16aWkl9e6LnsSJqLuygyn+QAOjh6hxLeoe2Smy6WdDhJE2xbmjhDjmFTtuvQIT4mFWBjUzMEPYx4KI5FHIKKZr3vrFhF0GLGrlaEMmbxrM0MCbWF5mgcHZ+2izmvHJEXVMYAQ1ZpwcjcA7+rNbBxz390tAgEAmdDj5lmCcL87DhbQqkliv71kRwBQuLPQMKTLuR5HUojrjmH6eM8BoCb7rohgAQGRGHLi2/UhLHq1eUqSUZ1eX+pXj2nt0RQQrHUP51BAvonJg4VMTRjv70prsTUl5mmgU2I1ROopJcMIt8vFWldmqhrHuaj566qMMGUcR3lZFAIBQLVPQwbAIYuZmYd5jEPaqi2bMqKwD0N6vVREupvT27flw8u2bj4XnkvKmZPgMKRb53fd9fPna8pJipwvre1Wcjoe+DhgdbxuOs3puu0l8ZSHvcLeunv0bhAxT3ksSc23ZhgxFb6svdbIiauaJCLkl/M2jgmcAmOaU20a8RNfjCO23QIgZ0DklFhbVYucs5odZzqO4246jbxeGjIOY3k674cxocPza52nfNkbIofDNDAgXNc2lIKM8xyjiHW8rrtk+fyyDzlNU4KIkqS3zsjH42AWn89bV3s4Dh+eV5QUjlnwzcOgfXdIt0dl2eswMkII8iBFmBA6JyoD3x+G0zgg4ta09t7DAOLpafv8eU0DPzzMp+O07/Xj08You2tEz1nAWauOCVikpOHb9y8BMBQGhLqp021eDxCWRQAYCJFoWfac8N3jYV22bugeAaQ9hG6JgWi9TqOoxd7dTVOSXJhJatOmNg45MfZuZjEMEgFhJhjXhuOYl23NAx+G6XLZKQlzrlsNtar2cD+Wm8PBvfd4e38Kh5fLVrIwwt4bho1zvjseLq/LLWAOEZklXJuHIpqhNi037q03ZOAiYBDuJAAu+6op4Vdf3D09nY/308++Prji1rbe7YuHhyz0/HzVqE/n5ppMrw93h8M0LNelNv3zX//Jf/Gf//Nlvfz//uJ/+Le//8NyNeb8/LJMB5znobYAVxIeRplLGbkEgGMk5nWD/+7//S9/9Yuv0zBVxceZT4UJ5W+++/Hvvv1+X3dzPM15LNyaToOMg0TAutW1RjdgwggXEULMAt2hO9auZnY35pJo7TaO+by0v9+F+02kkzKOQwoAM0+FzcAdPICYsiRh32sFyASo7rnI1+/G1r02uz8M665T4c+v54fD6d3D41KvtdZlbdumSPi6tNr04VherrWqEwQEPNyNxHS57nVXRMyZzfxwTL0bkwRhwkjMy+buvrau6iI4jKntaurTnEkwMa9rFRFt3poik5oloXFIt0aMh99eLMxirz0Ls0AElIxjkddzPU4FCNUc3YSodkuFMIDQ5Ly8/P679vQ6f//pw+Ph7nffvTiyCH98OR+mad81l2TuW9e3X86IIQTmoGoJcyu6V/3xw8WsS+JMXIpMYzLVP/v1H12vy67tzeP46XknTOHNNCzAUCNgHIWJWr0drCGMJefDlBMzEy9r2+ueUsIetQYPMAxp3fdlb+9OuWSURId5Uhvd8ccPzxBOFME85LLvOhzEAa+XrTtC77dvBSEigEaYGxMHkLkTIAJZ2K36rh4BhsAEjhiAaLfItTAxqRq5I90kV5CFRdK27Q5ALN0NAUw7EaaU91aJcMxZNdyckLLQXtUikpD2OPf4d3/zavAylA+nMQf6f/TrL0r2B8R/9o9/8brW9x8/HUb58bzmLL/99ntTL5xftzaNRAprbWOhL8e7j0+vh+P4cDy+Xhci+vLNsGxtXevd8cjEw1BE6OV8nQtXjZfzWoRORznNh32t1/MyTAMXGQBwKHYsa9Vt070rM+3b2rul9PQnv/zmbn5j0dn1hx9+1EAL8rCh5IjQrkMRC2/VhRFTcgcWRkJVNVVkZMKuFhiOIciA3tWHISeGahEGN7KSB9QeDiGCtwuJu0lKAODkSMjM0NXMCMnRUYiQwJ2QPNwjegQ4iDAKeWuuIdmL0GEoW+8K3t3rdT8cBo5WCuchnZfl9fWcU3aGnPPy2t5/fH+Yh8e749Y2c7s+n9XUwzNTmgdmnJJAIBBqWO86DUly6tuiwVf361pLkinxxdq276GQUyqTaNUhiQOHe5b03aeXAB2mYQVNie+PQyLODCsoQKxLFWKhmKbUL5WIe7/5dPvxkJYNA+IwD8+XMxGZ41q1aZgrhHezPBRKeF1qU2PmdeslsyS6rq2rzWMKj72aNhoHOIx53TsivJ5bVwMH6zoU2WqPsNaBmJmi9T4JPZxK7eC+3x2GhaibDiNv68uHH1fO7BBjKfN0eHm+lFS/++5TKWmckqAsl9p1n48DAX355X3XarpeyedZrnX5/KSSnMD7fn2/XB8eTgnDDYapdNfM4F1P8/z0upLD/Wneary8ah7veu0ff/iEX9y/ezistr9uixvutV+v1947ESYJzLTsyoxzlmiacwxlMPLr/uoQoXK9pl1RhKQUlIJIXS1CkDmlYJLTAMdDrtCFNBw1ltdz7a1LSdQlJS7irg0ZRJQ4iLm27dPz9e4wHd7dEevnzy/uNA5jAp5SqmYaziDg0Lo/X/bf/e57jGgszIGGrm4a26YvCEkYA5kYImo3BEiZSiZhYOZM1Lu9bqYaA6MU1hpeuxC6U6394TBcCZt5LuRghFCbLlt/vWxDyYTQa9u9cEF3sK1/vlylDKJRdxvL8Ppac+YAwMyDgGpsu+/VEuHjfd7aDkFh9MOP17GMKOO697bvKXGILEtzc2LuvRMScbgGAJWpDDM+ppQgvZ6vKChJSs5f3N39sz//jw8z/e6v//3Lefvdd98N43A8TtUimr48v0xTmsYEmLacauu11e5+OvLpMKV34+Pd+OZd6t2Wtd/NrOOYGe+Po/e4n0ZyqM1atb6rGazVLII4em2Hw7jtuu9hFpJwXbRXvZu4hz4/bSWDCDAxYE+Zm4HtKggd4nLpASgIRYgTzYkpcFmtJCpDeloqYEyTAGASb3sfp9L3Xoqc7g7rohiIwhDaduvqpkbIin48pdOcl82s2t0gQLR5nEa+LL11ezgOzmS9zVNyC3cNIyRIiZbdVIOiLts+TvLl24KupRxelta1P5+hCKWT/OHz677z8rohxN3duHa/XhsC3J0GZLdwC3m9KkZf10V8skW9m3ukBGbQmq5sAZU5VXN3n8ay7A09ZJKx8JjEEbSkl9ctMbTmhym3uvfdRNK61/spj8fx0/PWmg3C00zDyASYkkyM0zBc102tMw2XNVrVMWEuXFXBoamVhIeRSTDMm7mbgaokOR7TstZwFwpVLwO36smtm4ZQ78pMrRoAXtdmToRxNyBwBrQx0bWZqUni1mNdNCeKDLmIdW+7mjkIPBwLqF22tncAt3WllFPrepgzEiyrmcXfz2gCAG7BzKnwtrVgdAAMV7Nwt9ueyQORI4KQMvNc8nlZCbGBBQbTzcLuhAxgQxIPEAIxCORuJsIBQUQpZXP9+y0XmYekBN5JQjXCTQgStxweYAC9sKKHI5N3bfhyhYfZ47ZnwaiGvUcWLDmH495cDRG81b5c7eFhLFRa9WUxppQSmkFTRrCcyAPd8cNZk9hYSIQz4datDHKc2RXMce+WMrw7lui47MEFyICRQBzRkWir2luAUWZWNKuaxyJ5eL2cxwMd70prnRJbpdr37hAgHz/t4X0Y+eHxwQw+PVVESUgpYwRft972jRgCUHsYdlNom8ckZv7bv/vxn/75Ly/XjSl+/YvTx5crZ/nh2+f/3X/2n/2jf/qfPD2/jFPq9fUvf/fd09P+9qGUcbxutdfeAo6z0P34+XmN8Dxgzj4UCsd2XQ4jPxyHHhMiCAWFm2rK5Xrdssjd4bZoKcOAt31jkDhE3SoROPnjw3QY0/PzUmsc5pSEGTHQh0LbhutabzUoYQH0cZJ96dtmJHg65JLENKqqmQegCIjwOPK69cRpq3j9vOdEqn46sCRGDtfdatzEFWXkx1FYqloCz9qgWhZkU3TwlAi2nQGocJgT014VAQgRIQBQmLeqFl4SQ4QbSBIzBQ9EMvW+axIKojCDMJHISa6bq/XjUXqLCLTwlKntP4kxIOBGYUcMip4TBHrT3b0MGadRutJ1sX2rOeXE6B5JBMFvHli8aetRpEQ0UKN9769Ln4d0ve7TPJSR+qLgtu/raRq6YlOIVW8Ck1uzlwnc7bLYu/u8977sIQRTcQ/cNi+ZSyKCaKpVyd1PY3p+XfdqcGAAqXsLdyIKd2J698Vp22o3/ertvO/6/LI/vfgz2zByqwBBLPL0Wp+8FZEsmRHSIB5xvtTL0gEcAKexdI3rpnvzL+d0mFkDI/x4yOuuY+EI6D0IsKvf5siHU3q45+fPqzscj1kNpaSAzgwpFWHs3sEdXKap1L2fL7sq7LtNhY8HESHYcGttGgSIxiHVpl0dPUzl2vYkcrofu8bdacjCieGyVAR0oL37+eMVESRRzmiBgCRgnKUMadl3V6/aiODtcTqMh5T4r//wRGyEdjcPAOCX/e3DcI26b23d+jwPOeG27aPw/Qh/+o9/M46np5dPyL63faJ1Pj6oYq02TTkVOZ8hzOZRvLWv34zn6/7V29Pa1uFh/vy0aMG7eZrLnXl8+/EDFdIe05DGgbe1hxMCIGO4IUEAEPJWOwItqxIJAzpgbWZ2IxmHd5MDwu0wT4yAWzUmEiEAXdYmkqzZa69MbLvJoKquFtMw7Ju3GswU4BD4/LoOmTIDus9TeTyWQK2qL0/PIoP2ljIyOmGbTrNk1t7WNXySXVurPd2kqUAOAcE5w7V2zoN7R7Gf//zxL/7tj69LQxQR3bftr//q5e0btsrEXPtGaXq4G0Sitp1Tmaby/cvH//t//9//zffvv/v2h/k+Pby9z5LevhsfToec0senl+fXbd+7VoLMQ4oPT4sCFYSUj3/6x7/KidZ9+/H9p/NU2rpv69o9SqJxLF2dmdXJUdbmQGYKahShEd4VAKD2PYsEyLbvy7LeHea5ZEDYzVKK44jLZoTx9v5w2fthSEn4snTASJlrBSJYql2WRoSn0yhk3uPxNG3Vk5AaAoBuvtVmYW2XvvdPlw2I10WXvD6vKyB++LztzZCxq7n73ryqewAT/ESs6601C4ScySKQuDXPRYRw3YwZg5EYc8ZhTJKyIzBGLxwOTTuBRMAwJPcYJ5ECEI6Y/e91ncOQ1q11tSQCHOMg48iJA1HUzVVTElNvah5o1nO+tWITEx6GhP/8f/+/cYWJSgeYRk4sqgQcGgAQOVPdzSxSgdNcWu0iAgCEWHf7+LLttQmncBehw2HIjAmx9ibMzNRVXy/19doQ4dYPutY6jiXMzb3k5BZIGOAEWFK6LQBDoPc4n2swByABIYrqrhbrUr/+2UMpcTfI6TA7cMnDdz/8sO+dk4AwqPXmjnA6jOfL2t22BnXrN5VSeLh7BLkjEvz95g1NjTBICJHc7GaHTwSBgQAA0NWQJMAhApEIkfm2UiO1npgJqWlPIqpaUibm1rsD3P4+OADBTy5bCrhRFyMIiYnc3T0C4+GYAf1uKoJ4Ok4AUc2ezwsRk8i2mq77dH8K9L4tX331hdW+bft8Gt22eSoE8eX93bu3X5yvr28e3pjD+fKac3bVy3UlkW8//vhy3UEyhn1xd7+s7f3Hz8MwlJxVPSBA8LLtAVi7uTkGueNhGBn4Zz979+bN8cP7p0+fL1tvFsGEgKBmqhoR85ytxd61maUk2jri7axxCDSDwGCG0BAiFIzww1SmzBbwfF4S+jBOl8teiqQhIVNrbci57krC3czdi+QkrObburOQe/RmiMSM7nArJ98yViyCEWNJSFjr/uXbd0JMkp5fzgQwDVCGrIZff/3lNI/PLy//7i//9u7xJJlen5ck5Q8//lhyKimBV+smwmXAecgAdF33XNKcct1bVTsdJzVV65fu1pHdOFNvdj9PWXBzd7/pU3FOAkhDYgh6/+kZEIZBvni8ExLVbmZ7be64dtu7e9gt3mlmInRdGxGpRVMvie+Ow74bApSclq22rkloqWoBQjGUlIWZYataq5nFYSru7m6n+0JIdWt4o1ibJsljIbWwQBF+ft5usGSASMIaRkR71SJ8E2PdHUZJwnTDldFhKlVh39uYeBxLhK17ByjzmAH86XmvrT88TmFR9zYdZhZ6+XQeMo9zkoyAAIZ1h08v19NhVOuMtPdWuz7cnSj4/cfnw2kcC69bQ4SplDKWLIQha/N3D28uy/rx6SlR2vv2+Pautf79D98TODjWpgA+ZDrNOBVpjYS9d7Xg1q2IKGDXDm4RuDdHBE7kgZxkHlORDJIf7sqch48v6zHj67U9r3vOVAYGiNbj4TgEkDmfDkNdtnWtzpELMRECNPd16b98c3r/9NwwMksZB04Jdvr9D0/3b09C8uHzpm35xc/fnpf6m58/Xvf6uvYsAGCmjpwswrszQNebMcYdQpJMgwwCzBE3XhPwy+vuEVMebgqf/do4yd6B3b98c3pe1nXbkRkA1s3UrWRytZRTQOy7HuYyTflyaWguIyHgtlQkTFkI0Txa7xEgDCJ0vdq27KdjGedU936c8/NTdYtSkgFY76eZICVgbLUCkFmUnCLC3D2gm+fMIpYQ7o+n5ltEvD0+/PHX3/zpr//k4eH4f/t//rev15fW+ub97lCeX1omfPNmhCwEUbfdPQLRAfe9D0lOYyaD627qsbemZjkxeCDA/XH48fP28fM+Tfm8NtXYdn1zN52G9Ol1GY+JCdYKrbbwOAw5GAai7z5fh5wf5vS6tdoRwih0GgZKsa6amVJCybm3yujTNL6e12HIRtTqxijr1o+jAGEzxDAmWfcqaO5+Ok4A4EHguNdtmEuYE6IHvjxvzBSChzmVxB8+rYis3e4PqYYnBjA4rzolFCEZpO474LCuu/X+5mEmgaeXXQgTg1ocj+lnbwo4NMXvnvePn8+Px3Hd4+390GrblNS8CJzmoVrXbswMBABBAVPJrSoKWPjriutS297e3M+cfM78+aUnESIw8K2Ddv3ifsyJttoPc2q9AWBiUoNPry3c7+b8/Lrfn0ZCf160duCI+8OwtpYwxpEBCRDU7W66NbvBEHvrRPJ8MW1GAcNIAbbvmlICBzMIQmapXcN0EJgPpUccBmzV3VwYe+Bxzt//cH37UAyhuUOANg8NjeBEx8K91cNpWCsu13aa08eXehxLEtya5owpU/cIj75rKdnCe+2HIVHCYIwWvft1byUncBQGoDhf2i0+TAgBwcRZkmmLn273SHhrJfcAup3yBAAIN4TQkAWRLtdtzGXtnRCZ0MHHNAA4YhCWbsoUBBgAW2tDKYTOCO7h4MwYZiy8tToPwWABhgGtBbEJOQd0BCSQCA/qGjlhGF8uvSTeuw0Fs/DrUrtHIilJiGDZFQmnwtoBhVhC9WaRgiEnN9+7mVOgsQQSDyOu1YvgYeQiKQLOq7nHVFh7rM2b9fu7NGbqDa69CnFBQUKncI9wGJNkzk39+fUKEKe7uVXD8BC7u5Nw+fhxTVleX+rLa01MkqgpMEDJZL1FhORcCmgP09AgdXLzxJEzA3VImCNz8DCkx0P68Wm9O43m3lXHTDnhZe8/fqr/5Ne/+G/+6/9GdXXEv/ndv/4X/+O/+vbjcn/Ij/fDqn0epHUfCqWBFeL1uh+GTByvi7HrL7+YMHDv0CmlFKEGAQiaS3EzM2KWpj2AVBUDifPWDNzCw9xadzMnBMQwtWkoKUnrtlZ17YnTqsEcc84RSBJq/fW11oaqWiSNAmWUdY996yIkmW4YzTwgBPbuRJEKCfFYBAgBcN+bdk2JzKAZDNnUEqcQdqJhmqdaw527yb6Th/89JxpVvfUmjIGgHghIiMwEREwxFKl7tG7IABDhgIjaNQlNs4TH9VolpyK4bh2Ipilrs5ykdh0mOl/MuqdEN3FSTowAIlAyDxmr695pW/thkgC8rJ2ZpyIEWJt6xMPdKAJm4R7nRQnpOJFqN+e6+dPTcrqbPFwKI5F1nTM+XfvxkBK7RQJjdY0IYQFEtU6M66qPxyHYu8XA8HpZKZEFIyQKrLUpABEdihxHvlZ7WhsZvjlOgDEXrxrmyIRIPs1p29thLIkxMbcWS3WjCFDU+OLxoGA/Pl0vl04g05CngVJmM/j8vCMhQJjHOBY3eLls45AfjgLhTUPVqtI0Zoheu755OF0u+7Z3ySJMpeA00OV1R6QhJ0A+nnJtdVvrMBRAXNeWRW69p32v560h0FzksvVSftKuzlNqpm3vkvLeHCBa72UQd78/jC/nSogiyTTUWslSWzyfa2YZBhFBFhL5KVQ+z2PVfvPGPr2eAeGbLx7evR3ePRz/8H75y7/9oaRcmOchu/nDMRFh9bbtrj1+9tW86zqn1Gt/8/Dwv/pP/tff/NFvcuFvv//9/+d//O+WeiVClEAED+ecmSiTvP/xZZjSte7bVZlSSZKRvv/88vbxHo0+ndfP5yuFCSdzHkdp3VrzG3Al0Hqz3vwwFQw26wGOyLU6QCx7dSAhAtC70zANw3Xte+0BMZSkakRwY1fdbua3YYG6vnt3yIyXpe4NB7qp4oMFl30zgMMwUmjOKTEnhAQyDuN4mL794b1TiBAzjkPOwtpbcNwfD9MwP7++9tDXS6vVwSAcVB0RpmNSh21DpH0apGvUVdd1r6pff3P/xdvpw6fzONL9fPjxu/Ovf/1giIL8L/+XvynDQHm4LGsqHOa1BYk8Pkxm0ZuXLIXj8f7w8vxKRED07s1Jq5ra6ZiSyN5pubQkw7/+7d+8vrzW2gANiVUd3IVAI4aUDoPs3T1gr91v2FfEJCQMkqmkfL1URJjn0c3avrMIp4QSpVCvzu6bhzscE7OwuTnE69KTyDBwQIDjy7Vt1RDhOOXjnNXg7pDUPSVZtt5bLLtV1dOcj+PwcqnIKIIlp5Jlt1a33qu7R9c+5AyAEVa7R8RhLOYhDAGw7d56H0chEm0qmUqWcUh77TfOa8nYeo+w43GCILebexNa7wHozlnCTIPI3cwcUdSCCU3jhio3h6EkYhCGzI4Y4QwBWeBl6eg4DuwRtetpTlNJOWGAIYCcz7BtdhxtOgyUGCEMzCOuaw0NYjmfl4e7w1BS33oAAsRYEiKfr9dlr5lwSByUpsxM4e7NI7GYR0p8PGQPvDvOtfXL9frFm6Mz7nt9ft0lZ3AXIUDyMHdvpqgAFiABCDnhenuVFzFP27KmnKexbHtHTD8uuyo93h0w4It3X3733Y/btYbEm7v7QfD7D5/3LY5TFm7NFBFu7ueb3osYCeOm5LmltVMWRjS324ICiIgowG/THxHKOdVdiTkgAKjkZNYBMQCsumvkJAgQ7kxs5uau5n4DFgNGAOhNg0V883sTIZF7uBoi3lSi58UQoO1atZZP688eT4GRIDFSq3G5rD/78mQITPL2/vH9D8/zUALgelnvj2NvXjdd1pfvPz7nInvfk+Ba18nL27v7X/3yV2pwdyo/fHr9fLkG+MfnJyea7wet3rQbhKppc2QCAwRIJWu1um0McFn3te/P5xM5La0PE2qzcBISMLhtadyiWXc0SUh8+wixqak6CyOFJMxZ2q4psbsB8HXp10sD8HFOd6eyLn2aJGcKgu5GBEgghQkZOBDStlRtKENCwVuqCwi7GQopBCMmZnNEYg+vrX/55uHx7vi7P3z79LodTqev3x5r13m+6/Xy6eN5rf3N2wcmOB3ufvObP/nw+dPf/vYPJc+7nVvdve/5OI5Tme5GDDe3cRiWTQ/TqOYvl3oYCJ23Zf/i8Xg4PLyu1+8/X8Lx7f3Qm02lEDHoRsjWce37urRcRsplr/Xt28M85XC3rsu2PN4dqSTrqgBJsCsI8jwlQ+x7J8RxYNNQi62piEDE8ZC23da9MhMHqXtJpDdyDRAzhEdvAQHjkJoqACCie6x7HUquZqZxGMvlso/DtFUFwggYh7xuHTCEGRFLSsRcmyEiIopI+v/z9GdN0iRZmh52NlW1xd0j4lsys/bqrlkAAgRAkOANKcI/zmsKBRRAhJjGcDAzPd3TVZWZ3xaLL2a6nIUXno37CJGQMHNztXPe93kSAUbvnjKrxttb27syEwZtbXc3hzDVba/HdcqFUeIw829+9avnl7fzvtfW5jk9PhxS4VbH8/NVMt1ug5BE8vXSUob5sI5zez5vTBwMXZtHMsPnl+enh8P49ioiSdJwNb21MWrbeJ04+edPf3GLsGFuRQQzIskv1DDj2nu4E4eGY0BXu9V+WpdUSoRPJZDIw4f6W7W2tWViKYm0vDl8Orc7Izkn4jict+3xaXl3mt8dl167AX18XPlx6eqc4PVt+0//+askHmp7G3/Wt09fXrqNf/nH31kfn378/C//+Ks//O3h3dNqg9S3w/IoKQfQv/l3f318d1AHtSiCOU0ouN0a3NPsCRggSzoc58BAVzQVJsMAIGGePqy3vXtAScl8TE9lyunby37d7OfXVwAKpJxEgM7ny7TIYZZ9x+EmWX741YlC216nSQx5tBGmwNiHIwWCD3XVGKoecZwnDivF0wSt2VALABZaJ2ijz/NsyVFCQXW4eSB4yhIY7i6JHCM6aK0gKHNu3gDptrfX80//9OX5//3v/i4BPTz95vViw+offvfuOOG6lh/enQxCMoHZdUtDreQMBFsd27XVPsyjgy0lC5XW2lJyScLEDw9PP3/6BwRHRB86Z6nVHtfCNh5O+Xjk57O+vW3iflrKMjOgZ6Yp8WkShGD0uaRtHwAUoIR5nXHONNyElDJOaRqq3cbYXQ04rAPcm/DdETkxUR87hh9PM2c6X9ro4eplkofHqbde9/7+3QkcbE27NgIjx09fGwCObkumwLBhqLEPJIDerBuwNSQefYDbw3FOmV/P23FJuVAf+rjm747sGm+bf732695+/8NDH6jWr5sa8FwgMyWCZcH+FuHgFD6i1sEoC4ebpkTXhi+vew49zNnRS4JUeCrBRKlgd4gwEiQazJISeqhHXK52mHiohxkLIMK6pNZGN4iAhF5rbQV79+XA4T7UkfhyGxkZhuclpQSUystbv7P/pJC5mUYEje6mjgTCIqRzcTOb1oSC26XDlNZVPn3ZPr5fR21d+3QgmsDNcQAhUqLjU77cBoYLQ36YW7Pb1dzt3pxITKqeBFPmYT0MCMgjiECCmkZrEUNTZu3hAesqGJiIrrde+whgYgRTEcmSVN1ULeIuMTB1B2WinCQCzH2eSx89AHKZrnVn4T6MWBKnmTjlFD4AoRCGa5mz2shuRKEDdHiSkdgFTNi3OkwhCJMgOZB72/y4QKtBGH2geOQJmWUMDQdKCSN62NslIDQXFsnsvQ8bwwHouEoiEvI2CAEYwQZIEuQYpqpmigBQEngQE5oP5mBGpNg3r5UaODngwsKSJC5bZ0YmkgQ8sTtebj7MDGASkZTAVYcR54DwwPNtt67rgddTqrUR6nyU09PstX/6dCPk22Vcrh2RsuDTSfZqZn5YqbUcDtNh2vca4KVINDUIFnRAdZwkA41fPxy3rX96fXM7keP5dTdyIsrAtY/zbpzK17fz9fXt/bvfLge6vv/iFlMpmZgNbUSNNs8zA8dwzuwB56sKsw54fDwA8G/erfuwT1tTj9M6u5oHDVMkmbOY6sOct269WQJGVEafp4QBb9ebSKxLuV5bbcqJ9zFAEBMcUwLPpoYTlIyX54pIpzlP05yEf/50ndbCCNYAg6aCFBxgzDTCxjBKghAYSMSMmIWvN/PwlAhRIkIVCIHAh/K+BVMgappahCVGIZqmNCW5Nu4KAsCQuoFjVoCwkSUhgd1lFOEQsG2dhXIGA0qMbmEKJWU1CwMdvsypLGl0U4+58OVSC2OeE3LGGBE+l7Su6evrBoB96FzyL3VIJiIKcwQw1eNawHmYE8DrdSSmcPv6sh3XPGVBgHnC1jUsmWIdKil9993a1e9L+LdzX4uY+wh5Pfu7E5LYrVnde8oSFAhQZjEFU+9D3QARiCDnWTLruA21dXlAwNa7CN+qOthxKRGwDaVkBHTbexapzZxxbx0Im2oSac1z5qd1mZM9X/q5QUr0eq1T4afjLMKtxhj2S4FL7bCWvfbjqex1jKGHORHlvcHrWVkgZ54nimrXWz2uaS4iDI+nAoiX20gMQ0mHr2uuW7tcGyAIn0pOO4xtb8s8nU7z+a19e7mkIkTAQu5OwkR+28bhkAJsKPdmt1udFlKNw8QEcpzLt7ft+a0xwbYbkS0lISZEWAqOJVPYw4G7AQaAjZxCJe9qpjalxMDhbOjPt2Fax6397rvH/bZ+2/X7d4ds7VL5tvc58ctzm5ap1v3nzzeLfstaSr48f93+x//nv/rx59/98W/+zX/4h7/73/7y+GFm9u+eDk9Px6XkvfZtWCb813/61b0Y8r/9p78wTF3Tt+vPnOSfPr1utWbJiahWu1uu2lDzQHS1DoYslDPfuQpJDBHN4rCQg4NTGrEPTaUwyu22H2aBcGEEpN6sJETBYUYJHo/T6LHtIyWJZp+/vj09HFrVw8TTXNRqZkaUnLDWGM0tYtsHUZsyMRLuu7y9RcDTKalGrWMgq9oYDZGsX97gggWDeZ3LkkMYAGF03G597OM3v3nYm3557s9fr623H354f7lePn44ovBfP18oHDyFw3cfT60OC7tW3pt3qw+prIfD4Thfr1XrW9/rXy77fj7/7b/4o4dlwcQ+l/yHX31vaPM8xzAAmgqbjm9//vJPP71++vLNPQB8mBEAsap5yXKY0+vbFh6XanvtjICEh0US8/lW99bXZfIIdb33dy7b7oEl5YHRVcWjdw+LIqk2H6qD8OPD8rQsL9fd1ZtrEhFGD18nXkruaiLOjGbx5Vs1MATo3XIu170C4N6Gegw3doCgJGgu15eq5u4gjDkljyAGocQYW+9DY68tZclJACIlXteMALBMZooQEJSTpAS/PCOdy1y2fXAwMSDC0Mgp7224m474Ba4GEm7bPlRVBDxQLRCJKYh8SpQImEnNQqPrvQfoSTjMHGwuFKokSXL69HIb5vin//7/gRFTwWkSIZoz3x+pXc3VWhvrMpvrPOcpsaoP8zKl0fW6+9e368fTaqMfn47vjsvr6zUiujqotTEen045IUZY8OPpsO+3pqoAOsa2jW8v+63anCXlVMcINwgQJgLISUigd/MItRhqJALOaymPT+XHz+fDnLsBefwXf/phmUoEff728nq51W1/9/4h52Th58ulpLxVe7nuqh4OHh4RACCMYc4pBaGrjdbnKUnife8RSIAWJsKJaQy902TMLJCQESMwPKcUEKpORK2ph9/3225G/IuY6W5+vYtEzB3BkRgxGO/i+V9yQIAYgMJo7kKUmTWcGe/UDCKQJOucumoq8vExW3gpqSR5ealq2HpTNe/j49NjkHeDY+KppB6d3Gsfkujj++U0y3/9f/hvh9o//fif/+mnr6+3ftvUht/FZcNMkowRWx8BoS32MaaloINq1K7u8Liu3WLN0+evz8thpvCHw2SGY3i1BuwpJYuotZlFYiEmAOg6ACElGWoWljhBILgn9kCKYAYwi4g4HiTu/wrw7l7VIaA1PSyTOwDDknNvDhjqDgiu7uGqbnY3HHsW4XvR8X5eF3mYl9/96ldVu6qPrgCj1paT/OqH36Zcfvzzn6dsrbcffvVbYvrx05d//x//ERAgzMxF6LBOp8P0sGQAGmYAQYQlZ8YYQxGhG7S9P6xZez8dl9/87lceER7/8T/9xYM0TMGmNNWbnvfbcV23ujNRSvwv/vhbG9pbe/72ti7zb77/eL2+7UMDo7Z22z0CxmjzNANgHaOD33YDiFL4dhtPx3mYv127mbv6tGRGHD2ajpx4DFvmlASvmw21UhiJwDAl5uRZkJG25kmgMG27zXPauo2hQpySXGtjwqmU3scYmoRr1/sWaG99nrKpjaHzlIfalPMdfTql4nHvqDsgzjkJcwCY+1T46bQA4KeX10lySeXd07H2fZmXn39+4YnCXVJq+2BKbdg0MRHudagaF8qZLq91qEOYmt2uNZGAoLkmBgY4HpgJ3y5tzrRMZdvHGOFxH/hC7y2nNKfcVA1sXvLby15YAuFuc0spC6M7GARjTJmfb/0OMb9DnUXY/K7LxZzmwtBGlyThg5CnOf/mu5MwllyC6LZvmeXnb5eSuQ378dO3ZcpbHdbH0+OJMID0w9NhAGzVsuTvPh7d/K9/+RZcuvUPT2sfoAZCPGVG8Ps8KAuJ3MfJSMKZoN+uxKl51DFadQASoVBdpjTPZWgPB4cwjOsW26YBHAEJwgHzLMzuQ4+H0jSWzFmgtnHdK6e5DzO10zrVMS63IcT3z1hmTJmeX/el5HlGSiSM2zb2fQhnQThMcu2OGMNGmkSHQjgnuWfOMSIAOKEQ6QAAGOHMkJlq68yAkobj90/53Wl9OXfwzkW+f5zfnea97ddzvZmeDrNb7Hufcvr+wxNjPL/e/vGn87VaLnK51T+8W6dM3S0xterbpmHx7VxTltHD3JapvN4632/LD8uS4D/+dWfwP/7qaa/7efSS8LQeXt86uJFAd7tugx22Xqcs3787hWu/p1Qits2WJIbWzOrmCPBwTK9nZYB14kvT4dB6UPgk+PBQrtXqpoeZieFwnNzVzJdStFt3Mwwbg1Cer4YQvVom/tWH3Nxb7zmV86WvRZJQJOjmrbqAE+HjaVKz6zaWldqwdZmeFjmIvFzbNuxyaykLE++bXrcxPN498imzB3VTRHx+a4jSWy+Z1EiAJkEoyEI/frph4K/eLbv1c22nqZRUXs87IksWiPF4zM1s227HdR4KGEFMtbq4G1CPUNU5CSccPWp1t3445GtTCksy3XZbMiOgRex15JQPCz88JCR/fml7DQCQjMyBEDocApCYGQDBzNdVlomG2t58u5m6H9ZUkuybrnO61sGCYyghIFFiCoNAKJmu1zFnSpkdcbu2vbqIZGEEzIm6Re0K4URoZsfj3LvqsHVKbbhqBCgKIcA8s5lezn2Zp9p1DIPAVDjubk2HVgcL/nMpLBAxSXJXRApggChFLreduTCHap3n2XVQ2FxAZgZUcPegvvd1QVW7h24iwoerkRHMFKbe7Z6qwFrjdCAAeLtZAKwl3HhZKCiIQATA+FYtwouwO9yqDgNCf3qQ29UDIRd+O7enx8xI18v48FSu1fuwZS5wl/eGpwlE6HrurWti6d0RAcg54TRnQSRENZwmmgu7urmDUCCM1kpKFtjNEUEQ1OK2j5KEAqfEnIESLUtGcB3mhpxkKqDa3fx6rr2PJLMNNNV//HRzTGT2dMxTyVvte6unUwHgWu8IP5gXCfda3R1KlgDqPaZJUjIw40nMwyqSWyocRBCGSAHoTL0FDf3TH3/z/ulR2zb8+udvr5cNQNs6szPBiCUnQIBwDRwIQDFPss5lZnqY5f06R1jzdmm2loLoY7ikfK0dIsyckTxIzXyoATlBAt77UHcSZKKSZG99DK/DI0w9EsWSs3A2cndFBQgiAVUHxOvldrn0h8fVlF5fd7PIJZtrypw46dBm1YHGCKRYc2JAjWjdcuYI7F2FaSrc1XQYETBy684CyMAEAZRzHFY2ACQsknrDIAEqatKaq2EEDO1FknswOxG6+7KUy16LSATWqnfJJrgTxekgmakNf732ksV67E1zFpQ4LDIUwULdu3vvYRp35Yh5lIIf3k3W8bb1VIQRarNhtkxlH7QmMoNzre7j4biaxhidhQmQhcbQAEyCtSkQrXP6+m3PiYX9vMNt6+8f52nm2vsyCwMmSV3d3JhpdM/CL+e6TvKw8tuuHvRwkADrRtfrCLd5ytOUUoqcuPfYqjkoAgkThXz9ej2saahNS5YErs6ABpAKCBJatB4sGTOPXjPhepgSIxHuewdTRRlK2605+Olp2jadhOtoxKl2MI1SeCpS9761IVlKTlPiIoiIz+d2vo6UOGeZCs3ZwFA1HGyZ81Syh99Ld7ebXTddZvFAVSsTmnvJufdAYqGRBFIqZq4aX162h+MEBEthDXu99KUUG9jHeFhJnQJhzXKrlhLOhc97X0vuQ9tepzw5p9vQklFCVDEXRKYY+v1DejpOpyX9/ee9YuFxUcPacb+11n3OFBj76OGwLpJyLOtc1vz6+tJu0CpGjKlkLlhyvH865SyZ0BE8fGY6HNfDXLbazKj17Yd3T//hH59/er0skxTBpcxm9On50voQodbc1BHQIQKcmNpQCCLAcL8zRg5LqiN6r8iEWC63qmpTZjU8TVSKfHmrDLQumYRSAjW7XDuSuEVENDViPizlD+8mFmnaBP3lRgJBnC5VzW0MN7OcYF0ngzFaPa3Hkuhy3sY9EO4GlBCQEZOIugaiJJ6yZKLjmtW6OgAQkav1h8Phb373N//wT399uV6RuWTPhT592S7nimFJOAu56TDY9tE6qvbvfni/LvOo9fV6E061Do94OEx/+sMPyyKJ6acvz6dpkVwu9drHqJv23SDs1vbzedv3hoRx91KDzRNnkbfrxgSneX49d49fBN0AbhBTzuEw1DVsKkKEaiNlKkkOuQDw6D0whgMjjm4eqr3fBgYQg8+TZJYAQkIUrPtY10nNiCCC1EwEwGGd8+vbnudCiNvWCOnWhpuXTMzCLAjo7gF35Ai4AxEAhEjqOlIm0ABAjcDAoR4Q05RYME0shBQ+5fz2dgOMd4+n0RUjkElNw0HNTaE1G6bM5Aa/6JKFEIDvOeEA8xjdLIwZhQTwXtl2JF+TMEAdSoTXm/UwBAwDYUSMgFjXbMOE2AFv+5As4qoigiHbVZmp1WCClJAYppSeHg8PD+vr69nUxzAb4YBfX3bTCCcb8Xquh2MhSv/hH7+o+6Hkl7ft+3frb75/vFZ/fmuTwK9/eC9Mp/cf//Lp53rZnh4e/+bXT5dte355613fttG0m4cwI2Fr3SImzuggiYPD3Ans9JAzOfhY1/l6G8skP3y3mvp1q+e3N7XoQz+8P3brHw5Hzvjp86dheqtu7uAAEIRx18MxAs9pnhIEhKcdMdzBIjGbht8ryv6LApYA7/gouuscAM2jtk73hZ6BsDjcQz9xb5kxEQSaOhAFAAYkIr/HvJHvYwsCQARBBA+AyHm+9ts9ckqMYzgAeNgYdjry7TZU7XZppJAFLAcfBQH323VZl9fqPuD57ZbXrEPbDkspY4xJCIJD8e0ZLtDPr3+ntv3mNw+t1k+ftmlO97dlD3ez7dxO6zyldK37XIRyRgxkMghSyILzIs8/veikH354bK0H4BY6mi5TTka1qasiwj3hTkThykkoKMCF0ZWYuHctWVLixMIMt2oIyIKq8PxaSXCei8N9cqmAqCMurzWVNB8TQcxF9tZHHymlnNIYAxPRnSlqEebdIgKZQhgPS54zb/vbNMnDsjw/b99ezkG57Wev//C3//L3Hz8sf/nL52trTf/cW7vu9fFxbbWFQTqWh+Ock6yZQw2JcxFTnebs7gTx8DA7BgafEw7fn94t33/87jjPL69vKNO/+Nu/uVwv397ernW/7Fcp6buHU6/j43JEoD70y6dv65Jylt/+5mOW9PryJXzkJJzT+3fvvz2/bepmtF0aJaEJZpLa1c0xZLs1Jh7qOoyZgMXUFTwCJJEIj66t21C6d+LMvYiMYRbBQNOUXN3NAwkFpzn3oa0NRLpsfS73oCUgwVQSIRITDY07MR5h9DEMmDiCkEJDA6B3r7cbiwDBlNMYtm11WWb1wIBG8ee/viWhsubztTENZkB0dyfB3uy3v/rwcr7cU3dJMLxNZdIBBMEY7F6EtHfVMSU5vj+oNg9TZ+tOCLeLmrsIRCAErJk3GMipNmtjHBeZE397fruO+O77U0l0XEto7EOFpXVvWw1AInTTueREAh5IiAFI5BAe3s3WkhKloHF8PHwsh5fX674jMLahL5eaBCB2Ijpfb4d5DoO3t/3tsoHjbe+1jpJSbfuUxCx++nRp3R1iXSbr43beh8V0iA9P+U+/fne+1H/69FmDmA9M7MNAAQO1uXrU4eYjcbBzQGx3IUdgQLQxmIlGdGutG1Gw4P+OxAJk7aOqUebHlXt1YUwCDnTItO367aUCJr91gEhzOp8bcBzWcr3U1kbJGRkwLGdS89uGhEqInCglGW2sS2ljsJAHCKCNcI91zcKy1wEeTKA+jssRgarYfBAR3Pehat89HZZJvryN108vH//w+2b+9ev56eHQtvHM+Ha5bbUj4EDa2i0sEGnv8OX5ZwC67v3nL1f0KJkPWb5+ueFCSN7q2K6632xZZgBsN2s6Hk9lmeS6949P87TIuua3t+uU41cfDsiDFtBnrRtu1+sYFndqLvtpYlNDkneHNWG8bdWR1QdJsh7P50taZSqpuXWzrjJlSoyBXBZAtcQEI6q2gOTqp4PkjN3wequ161QSWq9tlFNiA5byuqmbW7NDSfOMr3X0MdYlE/OUmZh5Sm1UMH9cmDh1H+AdA0qC68v16em0Jnos0/PbTdEAjBnyJHWzy7UD8sOBjhN7oAv0YT6ipIRA2w3WOe1VHfV0KmVNb297FpimDIUSJNr621snHFzodGTd+xgdOQtKkSJIW+2PD8tta3vrD4cJiMQ1Nrjto199ndLDSo4CzJOTquaE1w3U8bjkPvr8OM1Fjg+p9R6BxAhkzJCECOEuGssl7XudEhPgsEgJA3Hb7XoZSSTne3XRciIzj8Dt0gmREm+38f2HeSCag7WaSXRwH1CSv3+3bLuCoTAGRkTkhOboQSJs1VsbksQd1Cwimo7DQYCQAjJDN17meWu/zL6RIcAtUO8bncxJ5LY3R3T3kgogAf0iJA1395ZI0RUipgICbwADKcBj3DyACGKvYCZW9fUCJdG6ODNRkIehE5Ig+l51XSlnVjUA2JuZ6jThPJXwyIkumzr4XNgt9qpMkJjMIQmUmUvOYMoFckZV4JTMLUm8eywp53HdajPVBhBAFBQpghGBIR+IHE5L4oSAONSOay4phRtgcOLrW0WMw2kS4b0OinTXPnS14zHdD/pTlnrthDQnejoWEhABlul63mr4fu39akOVkLZLT0lylnOvtbs5tL4fllkj9j4i8HhcS+HhNhNTNU/ZzDIDgglLBLh7SkHSprkMhW46pfz9D8veRx/emwrRaHC759UckdK/+88/r18/QyCx35PiLNgBdKjVcdv74SDHJbOn7XyTjNOp6BjdBLJAxDqXR57S5fq6tcQJMW+7uxOBT5Tu+8JCoozsuA29ahUmQQqj2u5AKwSKBjAMzb037c0eVlCIYfq4zO7/vAu0SGX6uEy9x9Y75xCkYRoYreluFhC58N1DDIDDYljkwvNMEdB7tO44sxrertqDEjnz0OGkwAIpy5RQgEJRCPtQYkZ0976UWoGPD9OtcVdJlsKBuJiF2wggdyHgvWuWBAQph4eH4VQEAq/bGBoQuG8j55xLqn1EB7coRcJsqJc5HWa43XyoBWASCYfr1VVRHR5mqc0vdZzWvDXba/3D79+bO92ojT1nf9ls37VIEEMKVAMd0An/GSwwnk7TdRsppwOqyNR6BxAHmJLsu72+3T6+W29DexvrInD/892bAkDU3tuzPpwmAGcBNyLC45TCTcddHBzCaVjUalPGaS4QME/S+7BBDsBko1tgwezoflrXw2H+enm7VauMN73mRMvEeU4Uacb0j//08t3747ZXUF9nlKBaQQoW7wNIR1QHCC6CCGAjzls/zGmaeC4yhqs5YozhiWjKmEmQuY4NvAOQoWFQayNnZrIkgkSMAUQBniVSSoETorUx9l0J47CkoVarNuF5lrnAnAOFb5RuW12X7BGhrq1PZSGCtYjWkaZEPE+G65xfGXImRmyqeUqtj+VU/q//l78RnJ6/Xf+bPz3+j/+/P+8G1sfLuQXIGEqU3D0J16G9+nFK7dpu57Y8HUup303v/4//xX/3P/3bv/u3//E/rce56jUckuC6cMlsJd3q82cgC3w6HXPK3y6vKXkpkJfkvUmO9+tk0F6u8XScP3+5XYch0Rg2TTIvKalcLxWQESIlCYvWRk4ynxbT8fyyMRAShSOEh8XtZmpxa60OXSZel5KEMdgMALCrl5zMPLT97o//xevl7fKlGgFl50DzejrxvpkkLGWqtb+8Xh9P6elhOV+2bSNCrrUjwlQyAJp6hzD03l2YkKA1M7Q2CCO6DSlpu1VHrZ1eXv6BKQ6nlCi9vt6iB3a11tztNhwj8pTcHQG16zTlJeNhlm+d/tW/+j2Bj2FTQW2j1v22gWr/8efnutve2zRR92Ed29bVPWUxDSZkdiZIHKlkBhzdBBMinm/DIpj4uGYLr/towy43dcAktC7p8ZRckbgM0z4sAphDELpqSQRB0yzzNC+5/MNPny8tBDkAv132dV1T6GEqlbzXy7Iea1PVUYeVJOdztRMAQm86Zz6u2QMlYa0DEec56YipkDsGiJol5K1WJBIWUweL0EACJupViXAqQhTqbcpTSVz3zpJ06DJNwrjtbajZCBKC8Na8D+8awx3vBwNzQEDEPCwQIu68L0QMB2QmR9yG3RHQiSAAWx1gutWRcyKmezgRiMyNAAhx29Qj7I6yAaxtiAe1pln4eFoF3dSOxwOzn6/neT5O6/T527e6+1xK22qWNBU57+2yDwr8Jc2Y5M0vjBEWn79eppz/5rffHY/T//R3/2lajkTx8en448+v//EfPrdwG7a1ZwUXwr32w1q+njdmmkqOcFUN5GFuWw2P0zQhIE/J3a/XKglPMp2meezazL5+vYyH6G4cWFs3t+veX691r3Zcc+t43rrD3TpFEC5CFpESl0UgYLt2NY8IRkIk83ALM0PCe3hH7whHhLAAwHsv/U7RJ6Z/pvvgfbyUU3K/x5jusxtHIXeHCCQkuluYEJHQA8x/EXs4JGaM8PApZXfLZbo3xlozZlnnZBq3Vo/T2oZ++nqdpjwlvtz8uo3jnL+9bBDkqo3jh8cnAj+f2/P5WpKUiXOeyoSubgO+nt9Skb/8eNOdCyVX1KTDTRUIpPVe0wig2vzwIK03d2QiGyPCDXyv22++fzhf2l6ViC/X25Tl/emIBq/nqzkw070EeDwc+uhmmDiZBTGNYVMWEbqgQfiUpXW77TG61T6IcV4SJhw9EruT1127hambRyKSDDFCdUACJkKgMdQ9RtPj46Jm2jQyqbkQgQMQzlNeMjKPL6/XqibMJWfIdNsv1HH013/771uW/OXtDBCI9xogTxMQ0vFwdIgiAubfvtXHw+I2lmn6+P3TsqzffXi33bZ9v56vr66+fJhPj+8fT9+1Vj99+XlvmJPfFdVTKu+fHgN87/Xp4R2TfPn8te6t1vb44QEIm1mZcHidj+ndw/d99OPxtCzTUvLX1+vb9ULHYgGOMNQJYgylUp4e19qsDy1FIsDMIjBPgoh1Gwgwz2WoEaNFzIf87qHUqlNJeIeJOuSS0t4BYQRCGDGlwksRYrzPUAzgct6nqSDzGAMAm5q5lZIRYCoYyK0PYURENyPEshQLVFdhEE61YTiAWykpEZ8OWV1fXi5J0pTp9XKTlFcEDV+m7EMp4Lb1Owz+8Zi/vlyZeErx8nY+X8cYRhgl8TSL2nDVXCQT4UQI2IcRgRCZ+eulT0K3assy9d7nzMT5fBuYlmXC3m3fe0IOcBIMQIeYstRtlyRciofXPtSCKYgwQ0BArR0AjUOxEcQYGUFNR6CP3h4fTrX362budjrm1/M2qv7ut+9+/5s//P3f//Wnb69BEI4RJpzN4Xqrc/lFCUHm10sdDgrh29iu9ctPW0rSQxPj59srMb97d8yZ9+sACgeAgETUxwDHe3yjpGRjCELvw43CXIj2OpBhWbI71qYpyVLkhgoZc8ZEXhbee2x9HJcJiL+8ba3HGPtynB6PEyJoj5Rpa4YUiFB7C+SVMCLGsJxYJgEzRuQkPnzrgxkR70w3IMZcRJB+eJqvmz+djt3g+WVbCnfEBdGjE/JvvitZeAx7vo7tev3wsBhE39rjcrhtBuzP1kzH0CGcTUfKTAiuQ9WGwrBowwgQwdcpTeJX1ayp76112DadS3k8LYLwfK4eIWAfnvK7E7vHrY1aIcB//8Nhb7btDZmvZ1d1YUsFl0ViABJjIlVlybfrviHkg2y30Ydjt+utv3ucFKN3B0LwaJumRK/XlstsHj58KQirUIi5M0OE365xubXjmkzREXcdkIWBPeL5XHWYdV+m9HCSaqPtQ9XrTV1g79UhY8e+aWKUibAA3uywPGy96rBlLnXE17+8xm/cM79d6/UywuHaLpzk9MjWxsSMLKr+84+vbe9PT+9Hd3cjQgCcJzotTPQLJhOBrrddBzAlQaEU01Ka9jF6KVTWjAjnl40YLOC2W8kDmQ5rmibsqhjAk4hE0UCASKgmb992QErCESCM60RFYG9G3vN0vNyqK/Rh6pEkBPH+0dbhiE6o4GADWt8l57G7kz0suTDvu81TIYrNzNwcuNXhFlTocGTA6OrLnAnxdpZ9AICmRNNaEKGP8bDM7vFy7eYRAYToBCS2zmyOk1DdmgEg+eHIFNGaAQC4MBMn5AE5ibkBoEi6bbtgIcCtq1AqZTL3khIKj9GWOZYpGMxUR9d5de+mwI6UQoFgGKjBGLhXnzKZ49Y7g2chhDAjBBxGGhAeBYGYIxSB3Ky1Pi9Zw1Oi4zG36iVHIEbgPEkSMoySBMBTQmIuxLsOQ3g45TVA1S7dSuI58zond9CwaWGZiAlKSa17ypETE5AkypkZIdT2MZbDFB6qer7cUpLDqSD4ekpEaAb10oRpzdnIhvVAWgqHRe2eBXHOGPabXx9OcwnVrv3zl+uPn2+m3tQOc/7wtGCATRxoEfHltRmxeUyJDmsON9Mxl6UPu3lziAiw4DF0yrhMU1fGpCLhitOcwcNGhHsGXITPt33fBhhrxE2VCfLE65oTgQeO5m33e6DVPcKNEuRghChzWSY5ThnCzzrUQav//OMrIOdZXm7bd3v+3Yd5KuVl7z+/1lE3AHeAkmmeM/0CCMBzV0B0x2pOBOax7QpgH56WLPTl5eKBlKVgAOQB7A51V0ekBB7mFkjBhROSmecspfg8F4/MTET55bJvt2HDVR04Hw45cW/Vl0kgUIcDA2LMU7IAdyPhVAQtODFzMJJFWHgGAsC9hQWwQG2YCM0DhVy577qQPszKbKERQQ5xa7QPDpQvb7eSUxJy5yxMgOhebWybFRFHbr2FExL3rh4x5WQWrekwZaG7c6MwLwf0SL162/X0kM09FVpFKHxiWGfKjOjUWX563pI4IBZhRkbSdcnMhClygvamGgjuBLQeyn5rQ/XxoQSFuvmoU8lMsTf79lzVMDONugtJM9w2O8784TF1C41AiodTCotEvswpcbxdbava6rWUhAThBhGQIYuowhg2TVyYPMKAirAaDTWiGD1aj6mg9b2ee+1G4BFInADhVq0ZJOFJrCz5svf1VECjazw9JBYfSGsp7AEo6vJ6G0MNPaRkJ7zUFpwRMSWEwNE0Ze5Gp5QclQixU6tW+zDTdZ57M+QQlnCDcBs0gppGodFHd6dlmob57dYSZxJwD9dQdLNAltYtJcQUD+tSb1a3tn4oaUq19QAUwn3rqBbM5zHegR6m5U9/+1+dz1/+4fOfdxV3/PA4gcllv54vZ5H8px8e/90/fdaIdU57je5RhyLA6FqKUFjmqfb49ny+3erhcf7z9cvb/+f/ZSOy8H7pLIwMJqwjzBszMiAhc5Le/E+//i44S359WKhqneZsYD+9fjt33Zp53GozVf8v//T4/PLZIFmkOafOVpsRY21KgMxRb3WZ5XScRoetGSch8G0zkgSEq8gd6u+ItzbyYCZcVszMW8XDmkIDnP7X//Xf5iUtS4ZASbo3JRZFjWx641r3lDmzROBtDwaeS+qG6wxAwCJogTm3rqaeE0XQtuky83A3VTcgRru+BlCzcdvHkvS0LMPjcnmr21hmBoHRB1iYBxGO7uux1DrKxOsKf/r1w/v3T3/+8a9O++PDaUn5y5eXv/78+br75daF6HLZAzE8wpEFexup8Jyn7dYAPLOcjgkQzOB66RCwTEnV5jmhcMmEgEOVEZc5SeElIByG9Yd1LlLe6q5VgRARr1sPj+OcD8vSPd6uO5lNwv/4+Usp/O7dAo7X2/7usCaWd0/v96rCiam3YcfCfCxfzzsHfHhahjpzGmp178c137a2zhmmPNQF8fV2JVgRHYXNw6yHe065Ntv38fS4tDEYZJgTYxIhBKKAoAi4nOucc7h7wFY1EW1t3DtJtocQ7908wj0S0S+gT8aAUA9zDESHAADyEIZ1yilhayoJ3N3MhKk2HWGEmLIAemL2QDdHiERkbswSER7BiI6IQAwhYQYRkqjWljDendbDOr9/evz8RVpvnz6dnw6nrV7/6evbnMt3TzKGX67D3Jq59liW1FVb6z98OH383eP5utXWOKV/+MuXavHy+fm7p8N/+MdPf/78VrtKgoDYbrq10ZvWYfOUhqkbDQzzXkpGpHAVJppkDIXAgCBAJGnNft5vh0V/eHf48+fLLWJYbH0scw4IET7vY1e8fbt+fQELAghEtGFGlrI4QLgBYHhgMACaOQIEBTPpGMJUSt5ad7j7pwDc1SIQ+J70skAEAHALomCmwLgDU5ixt15KNg9mScLmPhRUzVWVHf7ZNs9MlMTvHLz7qAixj54lBfh123vrDw/raSnn2z5GEBEGIkeZUlffe68NCQkxSk4vr1sWfjou82ke6iXzsk7P523rl7K8v71uzJan+OG792hJw261IfBpWXbvY9dx60HczQjhug8dZh6vsRkjEhXshLAuOSJeXnc4wlb3Zq0NZaI/fPeYs/z188v7h9OtdiQgpGHEAIlTOBKAEAEEAI7hd1uWNr3eegC12iNQEjEjBAICQGx7S8xtxLBQNWYOhKE6Aw+H7WWfS2r7IMHb1pKIaehwJEK7m0oBGB1iH73WVpIMD3UKgzDvQ1u1nBJIer3ckgwk//C4eMTKhSiEeZmSNtuuNR2CmA3o+dpOJ1Hrb9fzepzKNE/zdD5b7+ePv/3tsszb7RLWb7fnYFtWfn35ui5zN4RknJ0AmoYkO63rP/zn7V//zW+/vp4fTsvtenk8rL/59a/Pl5d1Xl5e345l+tMffw0Rh2Xh9BlA92o/f30Nkq06ohxOpfdRJmaBKcTcR7eSxSJGMxZKWZBQhCiFa//4/hgIpr4uggGvb1ueElHaN13XmQURoNcYHlOhlOj9YzHHAdH2ca2mZq6ehA5Tud26SMqJt73Nh6XWYWrudI9EJpFcRM2h0XFabn2sq2SRr89vTC4LY8HHZXr3/VK38ennF07JoX2/PJK2v356eX59ezykdfVMXGs11UIeNs5bN0M3AzcWKimIFD0ys2lItoiAwMS4N722vs4pJ96blZTHUA/QAW+XawDNMx8PqbWuaoGQJhag82sDcE95Oq0UzokQ2bpNCYhQTee5IJAQdDWiIEZz/Ke/fHk8Lh5x2/fDMg21cN+7RRigpULTQeajiPQ//OH0ww/Tl+erx/m2BQD1MZA4CJc5jTGYcXSL8DG0A1P4UPe9A4QQ7M0S076pI5YklKQN7c0+vnuYOI2wIuhBiCAYRDIvU0C4OiAwEzLt+5imFID7NmpVFhTCDHI578tEp6kwU2399badHubjKenoyJjFwqF71KY6HN1yxmnKJYVpEMIyw/t3WTJvW5+ZatcqMMzNGFUl03A8LvLyevvjr56+e0zLshCMvsU6LZu2rVYBCrDOjk4/vM+3Xp+/3cCoLJlNPxzy+e08zAJ8bx7q7gHR3D0uv5RqAe/cd6cADxcmc7gphMF27kyEbnOZchatzZDuc8l1ThSGTN/O18Nxcu3LVMaAt7dbStIrzNN827Zc0uEhk/dEfG07YlqnxECX28gpv77u81LWXK5bW088rWwRYzgwx6YIboAhKElL95ftEtM6lbK/3oAwnPbmmZNIUiMmMYd15dPTCmYvezV1JloWmWdqYZwS73dhAQRintMy4xTeo7EISzqf90nS1tqXl2uecyC2fcuzXPbRvY0e0bF3BQSgdnwog0IharPbpYfxsj7s+xAiQJhKWibqI8x8bybKEbFXzUkYqHfjTAmNILx6HZxO2VQ/f35DYsk01AHwdjMW5IT7rhDogS9vLTO9PyWF0Ihe+/GQh7qqjsFFaM3U1CLQnd/eat07IiITEvjwtEyl8Na7hCSED7PYlL+8NqSlD2eP40GWpQS0xDhlebnUvXVCinBzkywkVGs7rlmQQvXbdajhNHNOYXd7dA/rcfZu4Bqmw4XFIsyckiDwUHttVSOyAAK06uEBiNOUW+vTlO8GViEafXgweghLH8iM67ImBsYghiTm3gT7InZ91ZdX+813EgZlYkC5bj2GOQtnhGFm6ICIA0nMumkHFgwY6mohFENHSiyM19tgJkLU7kNVMhJBEkpCWdAIUs61DuDIJdddS6a8MCKlhPtbPxzyh8MMd/h/4DovjHVr7XA81Fu/V/C+/24Bwrq3pRQPZxREuhMu72+2jpxIe+33S/fd04GFwtB8eMDl3N7eXAQfD7n1cTjk04IggRBNARzqroL8/vGBIbVdt73//O32l69b7YZh5n6a88wWgThPW/Mfn8dmymaPh3JYSs7chkoUiEBUxshT3vce4MeDjOZfLmOMKAHLXLY+LudOCEwoCd8/HhLiZW+dGAG/fzxcth2yD+s/nPiPH05V9a9fb//5zxVIrteNhU+LpJnVtCSmQHLYa5uEGZATtzpImBzCKUhqyOcL7F+vr7fRHWxYhCNh7e06tJQcFmOM0QcgAVEQMlEoOAQTNB3vD9P6q8fu1EfrXS9XHa06kJSMSL3D1ftScpapaUfCw1LA3ZT6vSKvsC78/rG8f5wwYm+21U7k82OpVRn5tvm1dkQiQeYh+d53htM6iWAkM/Oe/plrqdHqCARzZKIAqMNFWHtQxDqnZoFup4M42RibRpSEU2FgPky4jcFMt80AU1DqIxFiQDQ1RpCUMSAQmGl0JSIWYAZilIy9BQFfdjM3Ec5FiGDO6MAQmJg84Lx1V7t2yzmVjFX11kPVpoxzWk5LVvfX834q0zLzdbcwJARC2Pdm4Wo8rtvH92XJq7WBEoiJSUvBbHyYBUOx0Le35uq1ymnJIrjVnjLkhDJnHaPrmCaxO8svpbdLdwMihPChVqZApnvx5JeXcY/jkrT2khCBt6bDYK9REpbsSMHipRQ30A5Ecqsdoi1rXg/Sq11vIzGNNlRzyvzyekOkpaQlg7sGDcmGHpnt48Mc4YEQQMvMrWutGgEIcGtjyTysSeGheNtqFhrmh2NpfQyNsffjMt1Nu+dLPyx8mvH8ukUWM3TDtNI8saqdHqZ5Yghwx4hfJqYOdjimw8yUsCBrt+ulE8LhkCJhYEoZywqm+vd//x+vfXveGrKSy7+9fvnyZXOPufA68fcf3/2f//Vv//1ffiylXG/x+tYgYGvaAcwxZXm9DMllybMQjhpb8zd6dTXhxMmFwAzbMO2ECC3MAQidk5kp+0+/+/5DDELADyuZ8NvmbWuT8KvFl2+dI4iJIp3LuxxhAAEAAElEQVQOD/PMf/2mL9+2w1y6RutKBGEBzZblfhE3DDgeSAiY+GE9ROCIcUj4uExbdSVnInafl7RM7H3IjBheZgGWbYMvnzdJ+4enQxJcslxvow59OBbo1lr4NtyRBkAEovgew0bJkknMIgApIjNsfUxl0YjLZTfDlNk6mg5ONEbvTVOWxKTWvz7Xrr7X6yQZpcRwdUwi3qoDlcQMcVzTw3H+3W/eTYTX68u796fW8OefXing9eXy5aUNi9ocYqSSA0PNWcIsSPguU3hYkxAkZhEc6q1qH+Ow5FIoQLLE3swdUrqPdySQLHSdEwW9nOu27bdNw4GZ+tBlLiJchO7EibHX4zwjROuDE/7+1x//9ve/eX5+kcSPD4eX59f3H74HTp8+fULmfd9fX68G9tuPD8/fzqrtw7tT28d5GEoCJBK+J3qrWm9siudrCwAiHeaHJU2TiNCKmASF7FLrlBcgTMyzoIYnZpK5Dwun2uyOyW/NL+YWgAhMMCxquFpkwolJ3eHeOUJwD/qlCueIxOAB4IamFo6tqRA6eEl8v7ElGDEi0CPMgohyRkTOKV2vVw/LOWu34UGAqg7g+Mf/+v9WStYx1nkh4SLw8Wme8uSOf/n0GdzneTLzb5ctSSrCTcdlH/dfDg9hmmcBx8Oh/P7XD7dL/fPPLx+fTs+3/bYPIgbUlMSCMsQ9vOQWiOT3N3zBYdY2ZWEpjOata8mcpxQBbqbDAEJEck7uru6ItCzycDokoU9ftjGMILpFSmTq+9ZKkb0bIIdbTiRCtXkgCME8FVfrfQRwG6Nkmafc9goRzFQKHk/rdhsv5z0As4ibAkA3C8AIF6aSkuoIj/uJGYlaHwYDHMBxypOFERIhdlUEMjMzdwjzQAQCYLqzAO4IY0T854ttQURtWDhIxpIpNOoYWe7G8WDi2n3JLEznvfPdRAgIGOtcSmYAG0OJ08PDMhW4Xdrtuh+Px9u+ff/D4fsPT71WM92uTRWPD/PPP5+/vd6WNRPH1h0c3WHrzbp+/+ERCdzttvVpysfD9PJaJ5HhtitsmyWRHPrhw+O51qlMffSHtcw5DfPrbScmSWwA120XZGG+3lpEiBARAOLlXAkQBR1dmKeSalcbgQBqbg73yBUTlSxImDJez3VoLEuGcCRMgvcX45LZA8ZQM9cRAKR3eneAsDDTsGDGeSq1jlbbNNGcsWSmxEQI7qPrVEoEqFo4QDgzMFMiMTdOeFzyMucp8/v3T8IEUb//+P3D4Riu5/Pt5e3rn/7wh79++vmffvr0/ul03ZoF/+Xzq4bvVb2HKgLb02Ep00Ku6j5N2Ft7XI+I0L1nIXVPKU2I/8N/999/e3tpQ7893/7nv/v3yHzbx606Eq6TILOZRXgAtW6JuXbvfQAAoiyzAPg9tmOmh7WAozkyI4K7BxImEQ8IdWZEwtaUMyNhH4qBSQT5fkjFy21D4qlM5n4+7ywMDq32MhdzI6TwAAhJ4h4EkLMQwruH47W2vY7DsrobmmnYcpxOSxKR89t2vTVHuNYtESeSn5/fkvD37+ap0OXc5yLunpjcISC6hY4QJrWRckAkHeERw4wYTL1368P2rpJEEBKDBwEgC3tEEigiRLTOzEkwog/twwKcmc/XYRbTnFtXADutSVsM9TLL9dZySsxU9/Z4mi57Q6J5SrX6XnvOBQhYqDUDj5SyumHYMqd3T+V2vR2Xw+9++PDTz5+eHp620T99O5shBm611+E505Jl3zWX1MfIwrfazIDuHh9EhEAEszjMi4WrxePDoau1YYjEiFOeAqyIAKC5ErLkFGC997ulMH4RCQQg1V0JkQvlwqix1+vvfv3uUFJv4zaGobsSAKkN7cEERAiItZsNM7Myp9OS3E2EzEGEEZwAHfDlZSuJR0Tv4YgibOaXpj+8z//it4//8//29fffLTnDpWeyGwb++cdtmac6LIaevpu++zjVSz+/9YfTVJv+/PNlTvR4nKt5NYsIHRYe4L84kkopamYOTNxaJ4IsubVBAh73Vj62boQwJRSRu+2amVkIENUjsf3q49INDqdSJFxjKH1+vjLztvteTc36MCJkVnRY5ikvwAnJ6XbrDi4pvb3sD6dirpxYmMLget2QaZ4mUwMEYH57rYmoZIQEAHR5rQH4+DD78Ndz/fjueGt9XcQtpiJTomGasryc99tmbqqjpyLrXBD4Lg0vRbrFdttL4vVQ0kS19e3ah8XpVKzF5dpSKaN3FCASG2ZuTOwOZmFqc5achTP1YddLE6FSOBHt21hm2TbLUyG069ZPS2ruFh4eiCxMrWpteq+FZqRA50Lu7mqAvFVDguNU6q4lYTDWPu4UmGnil3PvzZcJURApCGj0iIB5ERuQGMMchGu33gZ6AAEJHY6iPb4+1x++Oy4z7iNm4UmoYPrpdT/XwRip0PHAhyzu8O1ttxHzJM1t2zQ8JFEpnARMfWImxNb91jUQrPs952tqJdM83/100LpawBghDJSEJRLSvjt6GECAJ0AWqb0nlgBYZm61J851gEMIUSBgwHa7TMc1ZcpkGAow1K03O84IAbebEgME9B4PR7mDbLXT5TaQET1yoq1HG5o4CNkc9qGJQBiScARGOBN7RIQtk6RUkLwPR4xlFRHY93G5jdOhTIlYkIj7CIcAt33X5ZCdIMzXIkI8hqkNkVSrMoMZpClzRh9DEJExpUREqpqI+b4+Q4qIiEAMd88phRsiqkd3ZSJBVld08AALqN3fzt3US5KUmDEQhcVLcWGpzSfiNTGKSHIE//f/ePnxW3eAMfqcMAn+9vuT92u35CxVI5BH65PANGXrITkNH3XrBEgSSGgOmZGELtuwYEIavSUmDNAARCLS9VTAUYAICADKxKBeUB4eJpZ4fruMWv/mdx96t6/n/bb1lMpu9nburi6ZeveHNX18Wp6ePnz59iXQKPN1G+fzCIzRraScJ8yzCKG6m7mQDFMAzDlv1z3dMebqai5M4W4eDHCPnjMTC9sY7x7y7359OGUYjgA4eu/qb1vbeqSU90H71qPpVJIjiKAwJaGIQCRkcDfwAEYmdo/hBhg6YhiyUK1DR5SSmIGZe/e3rSEBDLBqeZI8Y8m83UbtJiKv3/YwnyYKjyRZzaZFpiW3fZjqvPJopEOZMRdeFtahx+Pce7WwnORy6zVSSeiqpmwhAVNOpVV3tfUwuyoADAfEiIiSBTBqV0TY6yCkaUoOWutIktdFCuH953tVItrbsF9qJm4RGIFIGgSAmZzpbigLYTmdZNfeNq41EI2FE1MEOMQywTCOsKeH9OW1r7PY0JeX9uHd4hHAeLlqrTY8BHFKlBItBzL3ugcL5gRTYmbamiWmYfjy2g5zDghAYAYDFxGMAI8I3PYxJS4TXW99LnKrHYl7NyJ+fMgl4W1rjpiTmIY5QsDoAxBzYSQcPbIwiR+KEPFtjNa1jziu5bRmM/j22lSjJJ6yiIiZAbgIE5OH19bvZIzDlPY6ukcpU0YcY8gk6h4Rl3MH92XKIHTb2uWm6vDd03Q6pq3qMI+AktNcuNamZvNcIkDNKRAQh2MSOEzSmnUbOSUEvlz3JadScoemFgFMoaXgGNEHQ3hvNgzXpSAiEWr37z8sy0wfHg6XbX/bboD5ctbDkrZNv73ugDRUCeHp4XQ9X1pXKRRId0JK3HkdhaYkb681lyzsqeRWR2IUpm5OjELuQXttv/vV8fv3yz/99HU9Lk7T89s+T9OP//BlWef/4b/67Z8/XyJshBHg44TnYS/X/fNzP04lM+pwEc7JEACJUAERpbB7ICCgQQCg1D5a16mU67XllALcDDCQBYf7GA6B4caMJAncHx8LMrq6kZNIpnx+axCGgKpBgHPOQY6BvVsumRmK0NbGZW/DzN3mOTPxYUpEeL6MdU7Pry9DvfdRcjodp9715e16WAqC3yFCPlxdh/rDIa3LMgmvczo9LMIgVPI0lST/5n/9+x8/n1ny82WbS/74dEBWRGeiHz9dhFB1uGGt3d3mKQOiUOQsrQVAmPtxna57G0MfjuXl0rqikJXETpyYTI2RmVjdjgdpXTHwuM6v2+bghUsmToJIiWyo+zTzYSmHeV3nIkh77SXLDz98h0J//cufv3x9yWV59+Gp7u3tcrPwdV1+/OlL7YMk6QAU3LouWaYsQ/3tsu91rPPydt2Oh9nMSuZdVQCPa9qHJQxiiqAIAyCS5GoUISVFeGudiFTxemvAhIhjOEI4IiDiPQlC6GoEMSXpphAAgB4eHkRk4XcCKbgTUwAxErEiQBa5bk0YT4eyNeu9E5I7MEXJRd2ZfZqKDXcdXV0t4N6CtzAPDJeURAQT5a02STKX+dvrtu2vDrj3Tizf9itBBHjb9EYBJO4QCMIEHlkIAobr6PzTT6+ANAb++OVtALTmaGM6lNHCowcxxP0Qg20MGxFIibgI3adcIkCJ5Q4PriOlBIFCQkxEEOZCMC1l2Kh7lYwfH48Px3y9jMzx7a3trrnQ4TQBUYsGBg54D9MSkpqbhzZjJmLatxEBramZM8aSOeUE4EKQGJdSbq0BhCQmRBjQRhdiEWzaGHkq6Q5iDPcs+bbtxDGVIwCHmYUZgLuVLEloqI5hQAAAauoQ9ylIhN9F8gBxZ0zfOwLOoB7RnTB+4U8TULCrEUVVhOGEAYD3NbiwbE1v1+13v37/qz88ni/1x0/fplJez7f3j4f/9r/87d//+afRrdb+7fkNgXvrh7W4w3ffP0aSr8/XnIkQLQAYl3lS9tqsCF9qRyQdsW2aEjnFuhaq97ljvHt4wBBQuWpngWsdkyQMAKLTPGWRn98u4KCgtaqauyuiOJCbCTMABjgG6jDje2ERhvr96ON2n46B3r+CHNdlqt3DXT186DIdhqupYiJXR6AkgmaqZjpYEhOp3o1uIIwCNiVYpxLWy0SEWJj70DFUhPdtqDkivFzq6ZCOOb+er9+9fzysk9whFGS/+f57JPjy/PMY+3fvHs6X8T/+f/+X3//mh88v19fbVutQg6/f6vN1d+CvX/cAQOAJyt56Wcvrc89skmH4+MhH9PT3//mzhb5/f7ort1ko1Pr2v7xezzIxUmpqDNCGeejEKSLCFDBaVzBixpxwb3aYc85Ue+BdnQYhOSOl2z4mFhYEcyBAJBtxPt9ICAOYuWRS93pzZqjd2EALpMIUAYjrNAWFe5j6+3fLPf6qwUhOgKOrJC4lZWG4jxoY3fS8vRkACW/1WhKnwtrjy6fXF2FCWKbMHEwciiO0g1IAmm1b3yu6wcNJauuUUZABPIWfr7p3KxNV9dt5n0tuNiIcBkUYQVg4IuLd1oOCzFOm41K23jn8uJY2zDx6HQhQ2/CwKSUKOC6spvOED2uRzNH9fLskidq8dhPCQhDurQejjKEVLAuVhzIUAYIpgOH0WOqwacpmsG2jVoXAiPjp8xtQ+fq2GYyppMtVlxwBbBAYxpTzndFFfH8i3+OKyEDu95QiIwVEQAB4ayMLc+HafK+jDhPBvQ1EyiIQQ8JLYgYCZnUDBCZCwjGsFM5TWpZce7PwZc3fXvbP/eYIeYIA2rYBbhFRSm7DBaDrvVCN7z8cljnfzpeU0ryU1vvt1l0DCdWdCCUnHW7R5pIC9Pmyr4d5KvDj19fTKd/q2DsGU0A6n+sP3318/vbFyH71t+/enXhmf8jy3/zNxz7g08vLaT6+vPVv1+ZAOtSGugcR5iLuToiHAiicU0oAX8/Wm83ZmOlekFaFVl2Y5iJDfah993j8+u02Fdq6v3u3uA033PexaTj5aAMgvV2qB5SMe+19GDE9vZuu5z00JKcgv16aOag6IXCWnCIX1hERXLsvEyJ4KmkMaM0srA9bJE8AxADC+6bb7cZEuYiOsZQyZRY0BiDAUgDAa7duune9bm2McB3zknMSHW7uDhBhFmEKZlQRYutx09EtAIOQkesYrYWHBYS3CKv3ObuaQQABAYZh7x63L8PMj4cUGDr03cOqQzEQAGptc6EAM5SuFhBMpMO2azd3ImLC05wQPRIRgxCXvIxh209ncHaLZWa1aN0u535a5zyFqR4XrsKgptWOxzJP8mabSD5M2ZJed+1mDBEepUjtwwFOB0YwCD+d8pQihj2UaQz78rojDw07LJEEuroOqO5MXJi7Ww93iD6MCSQjUACBFBzqvcNWgxEESQkjggNKSdq7pXS/YXLmhIBgRAwYCLD3zkgp09aVAiXnwBCmSZITuuqUJw8g0imJhzIPQpiW7Lgt2WfG1k0YEeXmIBFNgYD3fcwLTQt9fR1EUBK2rnX4hIyIl20EEhG6uWEosN7zSM4lS0RAUJJkoShIkrY6JKGhiwAxIcGyJBa0iOGeOAfANJOrZk5zEYVIWXpV90BBALjnRMqaMktKLEzogFNqTdtomNjNSxIhIAuDoAyXre+7PZ0mJDRVpPsZhonxbnQFomGuEeYx1EXCNQDgMAsCXm/qFseStJn2wNmJoer10unPf9laC/OeJplLPBwlleVch+q0m59OKcbN+zhMBcL3ayNkYh7Dr7fqwEhsPvI0CUYSbQMIhjCAOTAAEgHmFE5gSuRI01TVb2/b79cSyF8u+vPbl3WeX3f8/XfLNrpwngpryPuH9flcz1cFwZzo4/uVPBIncv3tDz+cb5c6Kuq9ZmW1m2UMkLAxwJCkdTc3YXSPmw1m0mbuA+9oTw5J3LuaBiKKwGgGoSnhl+cOePn9+7SPIMLffTh9//503dqfv5zfWl/n2FP69KXe+pCUCNPefW92OBAjExAzusMdQWA63AMFpPDEDAE5ASAIp71Whng45GWWW9dMEA4Q0FXdY5mS9dFvFYGaGo47RAJIpHcVYSGZ19Ksbq0jYB3a1ANijJE4W2RA16A5pf113xGF4br1w9KDe+s0TatIGWMnkiARx+3WgsJtMFDK5bbtCBSAezUihKDWzS2mx2IOddc+dGikJCkRE8ksOjQCwwPHmKZcSkY0VRgjOIVHZKSyEITWAeoOAXORAZ1F5gkDCNTWTOhWhOal3KqlHILyeJQz6N7D3UeENiVJ754m0N6HQcq1OYRauHYNLI+n+f2K170rYM75cq1ba+GYmHLidc7ozkBPx7l2QySkOJwSEwG4Oa5rud6Gmz+eppfX6gDHQ9mr7bsK0zTJxJgkR3i3zkynU0EEjlgTPDzNx4X/04+3au7dxrWxCEEg6jxlYUzETTUQh0U3D2REa92uez2mA3jMOQ1RIDbXvjkLTxPqgPO1EcPpWHobr5fblLhWul7DgWrTeRIRBEIPSAkJotXugUwJAerWzGyvNmzMh6nue+tjKJQsDw8pRVyunpKwYFfPzEMVCZ7P7brh26WlJJdtqLXeae/68WkFoK2N6LiW/PX5eZrK2CMBALp2I2ZAQITt2lXMAZ8WAdRIsG1Qt4ZEEJFyGkyIMeXy9bV+PM3fPb5/bvuHd5zTdFpy0sdwerk8pwKHadVhP359CU97HYX0b35YP3+9be5zmhkxJx7qCKEA202pNmYkTuuUTAexLeukXrd9RMT1Wh8fViZv3evW8W4cQkBiBxjqhPjl274sxCIOCL0HEyPV3o7LTOgQ7uGEVITuGuZu4/ntdrntKeFhnjjTNGVV7N0gfJ1YR8uFKYF1HKO/bUYREEqUUqZS2EbsCsuSI4yYtjqet/3D+9kTYuB+e3v3sEJAcyjzdKvGBMsk64qtw+VSi0ioawQx7a2quQGOWy9JBoYDmEISVofztTY1YUIiEiIPJhqOZoju5nFY8PFQXvcR7jnJtvfLVt3i8ViO0/J6rd2CtC5TnqdcslCgqj09fXj3ePrrX3+sre97G2N/eFif3r273W6UIEBS95zy+XJeT+XAy+3WSoGUklSwEa5w2Uc3PxxnYeSG84QRhAABdN1GQmqqSpDYFXBK6KHaPTOoY90aBtQeSOoBKMgSDJiQiXBvAzwswMMpkAHuFWMEcLzzHhD5/hTGklKAT4cshPuuzNL7OK0iCCoU4NdrQ4JEONQJSZg9bKi17qNHhCMiMZJjRCABebBAkoz/8v/0fycgpFRHjYillHvPmRkDsOsw9cRi7gAADDocAzmBEJrFUgSFdJjbHfoR7pEzEwKg3xH3YRjmAEFM05TqNmLY4TC3MZZSOEPKadu1tpaSgINb1DbUXN2JIAlTOAIAYirSVcND3Z9O61zmf/zrt19/PG3beL20eZG4K3i7unqE3Wk+JYkGmNuaswiZ2Rh6WKY+7Hrbn47TaWU1mpeE7D9/3lRxQHfz07Q2Ha1ZuANCzuwAgpyYEcPD1QyAAtrpEGY4dEKU1vaSsrlf65455ZTMrY8hLCnJVisABiICRISbE1IgIjjjffJn9zg2QoBDQKDQvSg+l+lu2iaAoeqBbjaVPMwE8Psfjr/94bRO5dPnl1s1lvRf/u33RfC23/Zu/+HvPwMQgp0eFnP9/unhvNUACuC//Pyt9ZFSMnBVc/2FZHQPoS1zhohlKSxh7ojUu2OKZUoJSlchgK222tsPT08vt+1tb6d5mrK0Pm77HoERIcxzSaZWWyciRG5jTLkMHeCWc5HMCHi+VQRUVYC7iCnkTjA3A+IxNDGreVhwImT431mahEQMQyNnLkV60zGs5CkwIjw8DgXnjMQS6IEOg1ozB5tS9oC6N2BA4r32kvA4Tx/eP0D0re0AeFqn634TSlvdHw4556TqwlwHFEkvl42F3HHO+Xy75im7c2ues5j5VOTpw1wKvnytrQYKXK/bftk/fvdUuzKBD9Ohh2Vqre11PB5ycJqX/PJy66rIfKsdke41DYMgAh3hg9QtZyBOQhiI5g4RTBR3ZlCEB43ugcEYgDS6RWAdmhKnzK3rXNgdevcAGOpFmBPOk0wp3a2WGm7uiTknuEsDztetJBZOY0QQIUSvfZrzUHM1vM+fAEZXIQQMZl6msrehXUsWQNRhU5H7pA/C9r2nLMx2OBQEJMN9GJESJQAMMHXsQ5msD5xEMMauMJpPRSIAPd72agGJ6bjmwhIQh5KbOjJwRDgSk4IzY232dtsflvKL5pagqpnHxLGuszZ184b28tbGwHenvCY4N8BwwEil3F02W9Oh8XAoRFiKHJfUta/rDMjXSz0ep5STDcVAEr5suyC480+fL9PEJKw2EmOoQAQLN3Ubow5lkXAHVA9Ed0a6+6SHegQR4bpM6yR1eC5TreP1VpcypURNVdWmJKclA5J73PNNdyR9hKuGI+bMbW8iREijDkBCAiEKQjPFcMmcc6rXhkDdvEz09LScjnO9Xt20TPNW263p7aqEJALEuDeVRK4x2mCBQP4Xv/8+U/z08unh3UFQ0KIHdnc3JWFXzBmOK1vrHw5HyfT527cfHh6Gwc8vl+74/NYvZ9uruVkSyhMDQUnOiGP4h2Pa1QMYR2+GhMQQQewAboYRp4cpCdU2Xt46BU05RWjhdK768DBNma5bDYce0VrXEeaIGEkEwE1dDU4PZcloI2rvVBCAax1MIIwGqOaHnG+bZiI1hczMQOHmpMOniV/OeyF5PJZbb1IIItwhC48RL+f9/WlRQ4ZYc97VHWya+LbXlNgcblsPAHdfpkxE12s1v9eNbV3ynFMdChIBljDmLJzk22s1g4d1ut3uR0ZovfVqU87EsNVxWksi7KZ5ogBXxdEjJ8qJX861SDrM5dvL7f1pvuxqYcdjers1ZtrbEEZG3nfzwA+PcxtqQ4U8TSllDjdhbtWuN+XCo4UNJ6HW/R4+W2dZZtl2nUtcd0DAeeFlTlOCb297ET4t5eVS92ZqXnLKE9c+3EEIMocOmKdECIechsXbProbMQji6BpECODuSDhlflimbWsdfCrCiLdtpCKOeNtbTmwjlsyqfr1ZkaTu5pZZypRr2xmYRIYNCABwQEiS2xhd/T7axhAMu/tDEJGJdQxJREyqnRORdXPdh5UEj0c0vUOgTZAnyUP9tmsWbN3D4Lr3JFQKEvl5hzFgnRiBa9VbG5MgEd83B7W3MlEAXG49J3k4zGP4XdpADPOU0sQ5gQQyI7FbYDhMma+3QRzffTi8vG4EuMzyeusIQBhFOMu9VW3mMRcRotZ0WcpwH6aEiIAREQaSiDD47hAPYPxlhYVMJLSPhkFM1NzAg+87tgiD4EAWqup703sZDV1adeuGAIzMTOZWJi6C2v373/7hp59+nLhRor/7+8vTh6f98npYZVqZAtDx7dqvl9YVcubTIRMGe3KPQA8PHZ5TUlBzB6TaxlSQU9ExkhiKjKathTsTmDo8HdaHY2zm+0Zu0d1N9Xffn1L459dOiUko3NeF/vRxYVMkWg/rZR8/fb58utTugcbfPZQ//fHxvDXt8LCuZv523oCwu//45XK57gbkAJlREAHZHBCDEAACmQGACdxCCKc5m1rOaBH7pm1YzlIS6XD3mFe5C0+Pa0oCGLbO+XEt3z9Nr1v7Dz+1kvD33y8BYYDXvYX51vz1MiQBASBgySkBK4Sb5iQAGETDByIgoFp0i9o0J+QARgziqjane24S//88/VmPNVmWpoetae9twznH3b8pMnKorOruqupuVrMkihIFgpRIUBSgK/1PCRB0QQkgdcELAiIIiqRIiK1q1pRZmZERX3yDu5/BbA9r0MWJ1h9w+HG3Y2Z7rfd9nkBqTV3DDdTs64u+nncimqYcoUBYsoC6BsyZ5gNzgm3ve/V1SX3T1vXd44ScA53Ap1Qs/Hzbp8xCzITqet4NIdz41uJpzVVpPqxZpHfvTSeAaZoufeiAPOWmutc+lXSXusRoZUrq2Lshe2iUIkQ/CRwgAn6CvBC6m/ZlkdvupyWTxK2OPsAB1dxGEOC7x9JDwyMTsdBW+75rOE4zB7FrzDNfr50DkHAf0ftIku6NuXWRvQayv3kot80uWz+twsQ/fN7+yS+flsTXfVMIJrps8Pq6MbEGSiIhnASzwDyJWXSPlAAozHHfNSUoiTzwfOlFqKtMKdaCkKRrfPq8B8Kc+ZBlWVKg1WHIWAovU5qnDOFzoku186274vNFhwYDOYxlmcI8F77n5Utmv+Op3AmxjYbEJSUwQ2RicoCXyyVlfnhYrtt4fmkRkTNNSdyhFFqn6Q8/nKcpQ3iQMOlUsrq31l0xM4sAYOQk7lDbmOZca5XMy5wRcRi2ZiJRCrv5tus8z4hUu7GhqhFF4E8Bt6EaERa4tyhJ1jnd9lGbrnMpQl0bCUbo4VSE5OW1EkQfsTUTInf/cEq3Bi1UwyfOPtplgBlgxFT4uJYy82n2vSon+dW7w6fnyy9//nMk+P7LuVX98nxb5/Lz908/fn5Flr3a23X+P/4f/uOg8p/8P/6z/+Ff/e26rAEhiOuUkKi2Xvc9z3MAAmBJRAjXZqVwGPZqxIiATDRMA4AJzGF0I2I1d3ckIiLzcDVgQCK3mHMGMEI2Q2ZIjGoOAM1i31sfvQ2DgHePp6VwRMvTtG3DPNwGEbTWAy0lqrtRwojIwqHwU3qLkQhVvcyUJwmLJMxIH94eLWCaMqOfX2+fPlWDAIDjcapdR9OSwtRaN4wAAMBAij5gdO99uGNO0q3nxH3Xw6EMU1UvOQtRKtyt75tSIrOAwIdj2XdDwMMsTQcA3gPgzOKh3747MeHNepZMCss8AyGaCeI8pcMy7W3LU0mpvHz+9KtffPijP/nzw/Fwef3SR5Qpf33+ijz9/T/8w3c//jjU3j8+9TGeX89Nbdsti3QNi1gXCQ93AITR+7bbVHLvljB1t8Bgwn3rwhREvdtxTcOjaRBhmOfEc8lDzc3M7ipysIjMfM/2mJkIMeO2d3C8L5nUwCIYKeVcezO3OcmcpO59H5oocpKS2CGmOWm31nWexdzcIE+p1qEWgdRqAwBiJgx3ICIksqEQUUqWCNq6YWgwMfPe1QMowgIj0IIcQM3cIyAYyMwBQrCoqas3xASYU2re+94JURIfJnn35jDPeW/1y2VjzqHm5mpqFpQ5LykQohtJsNDlegtjUze9N55sXaQPm8tUh+17Ox4mB7reavShZuDAQluvv/z27XVvAPhHv3haXvbf/XA5zrwWeb1UYgIgdyDE1pWFhBEQe1dTQwB1NXMmIqLbpiVPvSuxzUVa2CwzUpwWUlv7iNa1q/besxRiGtYJOQIRJQLcyEeHdhmVl8dvIiVmzCndpZ0IQESEQIwAPpeEyHtr9wlLSszE92kOAATcU+QA4ULIOamrewSgu/c+HBzgng4gDCAiVQMIp7je+j/87us/+5P3p+N63r7+xT/65devlzKlh+N83a/Hw7rt7f3TWjJ/fbV1Sg9LcuC3b949Hg7/8q9/29Xu0xT1SInCQTVKToypjfbysj0ei0a0VklIApu1hk0N3j29sRDtcb3110u97dUVljkxs7m02ogFIyATZziW8rgstdteOSc+X8d6XAGxmoV6QGQhoRQBLKn2RgwsWDfodQgicDyspY0uuSDg1treehah5M1wzunhQGlKFwAd1nolspzyPkZHSsw+dJryGObDnLB2+/JyMY9SGDEEx7unpfcujHOe//Dj+dyaO3z/4956mG/M8PLiOetynDIBOu3RQaQ2kyQjjNKkI15ezynzuw9PQ/vDYZoy67DHU36JjTi9ahDJl+fNrEX4VKaSeDrkvNIjHefCgWjq133UbpKidUXmer8mzMJdEpsqZXKi22UXYQu4j0o31cR8L8K0oXtXZkzCtXXwIEYHBDM27MNV3fxeQAwAbOYUULuOvpUsIhAekgQyuDkn1h6CaXQfYHCHkQa4QWZkpN3ALZBRmBycEEvheyCL0NeFmJkgBgGjh8DoOpU03KeEWTJasMOlmkLMWXREzhiAEDYnmosw3dNCDLsJxpxw2/v5Vg+HLFkIcMnyet4PUwFADViYhTksAgIYxrDWdS6lpNRGByIzd4fE/PZ0/IcfvliACCvYuqaCuakN4tfr5bTOU0nXvSbCIqJJIDphlJLU4utLndfch+vohzVPWdRGzpQ4meuacZ2XCJvz0ZAvewek1qKNllLuGq7DTJeS9tpq7w8P6+t5X6eMCBHOyI4ABEmo151gqmq1dUBGMHInp9NcXq47sTDSpba5lDslHYkQ0A26GjHVaqObByQGYR5u4AyMiYMdD8fVyPdLLVPOEy0zrktxh33bah0Y8OXl1RwC4LgWwOhNw6EIf/vhgGHDIBh//v7wb//FX/7d738/ZMtpctPNbHj0YRg0mg5V8vz5NnJCoa5nu158e33GJLvG9VrNsCQkRC6l5MiCQ0ciSiwGMSwAyQwycyF8OY8kYqaJAMLzJOQhxBCtsC/zkjgeDo992HLS660NTk27SL68tAgvRabECFj3ru4AJIUwFEOAoRzYIzz8zVMuKUXED59edYBxyhznl5f5sBDS68u25IlIhSGzZCLEeL3e8jyNoeh+WEsp+evzJQsgyYThpre6U2aE6D102OjORMssajGaF+ahahDrOoV571AEHZTE5xzHpSzTPNS+nDf0OC05UZwW6cO32sY+plJOBxbBvY7eNSRQRC0gIjM+vp0C4PW1gkMS7mM8nfK+11IEJO1NrZuBC5MEzVmSYElcyMEdU9auo8aoKgnzSgCWM+ckDOqZt324eyp4WO5Zi5gK9eFloocVmSnAIBAMAuBax/CYp7RVJaLWBzMcZ35M6fm8lSUj0r731k2RuilHEIs5BDIEpCIiULc+WvzYdiIMcCZDFk5yL8YyExrVW8vBGsGEGm5hDGSAQxUC6nDUlrOMoSTkzp+/f3l8t06z7Nt4OMzuqDqWqXQ1hJ6EU1YzvF4qiL+ZED1299NCy5wYCdHPF0eWNvziOzJtW08Zh5kNNI/5kJhAiBM7uiOwagSgZEpTsg7h7oz5kJdZEsMy4zrNLLT1MR/SuuQknIQcwtUSEhJer1tJJCmVxIDgAfutTTmpQetWdw2PMhV0alsLSUTugeZWRBnkdhuGDgiMsVfow8x8OcppYbQY6khoYAjAhO4x9hYegTZMh7lrhAESAEWAMUvBfF+8uZm34cNsANzVpxSHQy6JCIeOmhb627/5n6ZEyxNda59KSgy3wNrQAkYfOSfTYITTmpZlWpdyO286hkVMRTRchIhDuzp4yVIAwoHJKEFJaWDYfdGmQAwUKIymLORPT+ly69evN5F0uQ4P7AGTRS5QVhGK78+jEIdq2m9ZYj2kP3s7//bTZXuxmf3y/PUXP/vwq1/+8vff/fiv/u73IMnUn6+t20AicDT1rgZJPEzBEPB+f1CLnCTcu7oR6bZDQMl5mRgdE3GQS+YkKcLVOyAg0mUfzMk7fnmpfxf1MG/CdN0cCExvD4sw86XFaNqGmbpqtD4iIheZCgfhlBKaQ4AknFg8vHZrqg4oLIwYYeFIEnPB0QYKC5PFIA73GB2kyMMbmJYlJZkKpLSeb/p6br0NdQyDIHp4Wh7WAj7q1nMS5jwUw9tSEjjdrtWILODT17pMqSRWdYjY96HaUsmCRmN7/bKXJb1/t+KCMejab13GNAn4mDOnQuCRWar5PrgAExgjB+B8EA9nQiLc+lA1RjJXvephLpLy+dzasDA4rLkwQ9htdzdfC8+TMIZ336rtQMucaw1iHu61OrETSd2VmWqLkqAkmuZCgJeLHo8FwafJp8xktkjgKlMiQVpyvt66JZ1Pc91HHxpE3747QOi5jj5cXc67Cdit6mGWpXCeuDXbrntiSpQgYHTLmZIIMSSM13NdH+aSORds1bYKW4u5asmE6GViCtBhz/VGiDGVLLRmGm4pyTLxP/4w//ByCebrhtfznpKIcEoQ4a3FGEAQIhTuikoABE5AGPH28WgxtBtoPB3zfYmNiHXgvntod8fDhO5Q3betMqXLtWo3kbxZn7IQuWuUlB4OU1OXLBCaU0IG6wM1hnpEMJEIRfTDvJgNA5hzQkTzXoq4G5MTyPPzrUxTljBvp0Oap5Cwp+N8mE+1XmQ6dPPn52uBAOR9jOPChDGqzdN8elO2NnrvpXBv+SHRsLice048F5kTSUZWNYvffLyaw//7r77bm5rGUlIgv7zsvY3TaW17nxexNP7z//K/+Is///P/4N/9t9Y5/e1vvxOhrfrrtaPbNKenN6faeqhLkcC41hFAY/gyJwJKzNvWhgUgEsZcChCdfQfzKUtXG8MjvLaWJAuhhgP+VLVmcQBSdTN4OV/bUGJS07nMp3Vu2q/blTEfTuV2u22bttGzALOYGqINvw8BYzSPICRxU0QohZYl7dU8TLtioAWw4PW8TzN4023E5y83opTTnTocHW3fts3DTJmJiRMjAIwG7nE/7Q4LBUuFCGCackosmUfViNj6yOBzyp3ssEpm2fa2CJyepq0ZIEhiU2+9ceb7X6nbSCBF0pKmw2kmQnVjElW91jZNKUmyoU8Phw9//sfTPLlvP3z/eQx7fHj8r/6b/04krevy17/97vO5CoZ3d8Cv131KTIjuI2WeiAm8mQsyAQalnLHrCMIOlTJFxBjuCMTYzUTQzWobxIIOOTEg1Kat9jKJCHnE/XDNgoe5bLv2MUqSREgQnMQB3e4xZXT1qvu6ZDBqw/Ms0wPp6yaMKctdX1jrXc4MZNi7qdpCEIjuQWg589DICMREmeF+iQnrMIjAX//Fvz/MIDzlTAQ6FH4q3zgSmoGZCv/kL4/wlJJH3Ide4CCIGiac9jYIohQJt8MiP//Zm/enx9fb9TcfPwvlO2L5zeP68cdX4lRHC/eSC0aUwl/OW6KEGMDx9nS8XBpAsKAw9+EE8e60ItFvvvtxnsvj45Ig37ZafXs6Hg+HtdfhiGOMzy9NiJaZv970emth3toQSWbGCCQcEWH39SFEGCEgCpE/HfLDcTlvN0Tc6kjCH96tQD5NfHkZSLQc0g8frw+nw7Zdn1/aMh9q6304MzHCbX+Zpfbbpbbx+O49lmNKjJ5sgEYMd/yp121jaE6FGGrrALBMk6oycR+jqxNAYDD9RFdBhMyp6YAIJGpDCQCZiJACzIPgHh66z4zCLIbah6eDmu1tHJai5OtUAClJyu7Xvj09/pRbPq4lZdxHXC/13ePbr6+Xj1/Ol9vGQmbYRs9ZEGgqIky1d0SYM79eK+M9sdXePh3cbZivSyGU10u/1Uqc3BAhTodyvvZW27pOItxGPx4XQgu3N4fDPJU7Aenz8xcEed3qvIpVOO/j8ZSXKb2+1Hkpt+G3rUJ4GNwv4D76+7cHyZmYMPzl3PZbdQCkeHkZ756Wbz5MprbM6+W27/v+dMxNba9O4PNEwwEMk/DwuO6tVbUgwLib2A6z5Mzq0bqZBbEAACABBCEyAktMSRCpZEpCiFQm6T76NgCoTCnCX17rPJfEhOFd7XiYT2tBjOfz3oeS8GhhbUvzdDoerpdryiwpc4DqKFNepulyuRHz55f96+suAqoxLAAcCe8lj3WdKdFUMCIuFwWAbR+JGYm3rgjoHgRRMgcgCxKCjXtcxru6ECJgNWdC86CIInJvI5q72b00iUiopksiZEoMJaWhAQHmAYBdR4DPpZgB/DTcRETIGbMgE/XRUmKz+3I4mHCrtpYUER4gguEmiNvop7l8edmvmz0e5+b2sMqUCOJ+/6Vv37/3oV+fX9bDutUdg/cxAuHuLwtHJuwaDLGUUttIKdcxbnWclqQagrT1mufs3a6bdbeSUwRQRIRLSre9YYDfa6amqcjxWPbzft32t+8ebpu5eWDszQD9MOfWDQDcnAABqeuddMbmUTKJEAAmQY84TOnpYV5zAohrHY5uHuFg5hDwer3lMiXi55fm4XttQLhM0+vLtswZGNRiLVnd1LSkfF8UDPV7DGHb6zKtqeR5mXrrW+1I9Pp6PR3XlOV+cbrBy/l8eliZYNva43HRAHLMmfV+HgoFg3nOKdO+N2EaASJwnHhoNDVXu946o+xdU6JEgEiAIZLmSf70j5/ePy3nbX/d63JgMG0Vznt7+dqXVYxAFe/2pf2qY+tqMUynKSMBuR/meatVGE+n+byNr5+vy7REGBfgFIWCAIJZUCBMhGNEmhIxaNMIutwaM5Qyp5SYYOhoVdGsTKl1P7+2p9MkSQL61rsaM1PvA4Ivt309TcJxmKZt1+frPpf87jTvo9mw263O6wQEpsaEIuQKQz0Y9t3H7utMw8ECL+ebOTw9LFOyeS11axZIJK02BCqF5kVMoe06HzILbRcVgmGWivRh+z58GIIH4eGYc5ZPX24R9O40B6khJPcI0qBMcav9eJrmzBFEBMN027uO0BFmUZu1YR62zOXDu3m0WveOuQhhbyNAEL0UKZnDIwIc3Az2m02FiUHNkfPLufbWwTEX2ft487Auk5MQE9fzJUn5eFZhQcZpprlQEvr0vCWmOfN562phHqqeBNcMy5wJOTDUoxQOtdqsZCbkWgczVQt3qzVa1ZS5zJwzeu+nZXaJ2rWQ6DAFCAJtEXa3cxoEzHPmxL02Im7duhoBqOlxncLxstX74lGIWTgUhMgBeh/DNHEmZkdDHfNUPADUQHK4TUsZqtfbdT5IREsEjFH7YOHDUiaW1/N2vjSWVHcbGsvKGS0Lr4fcBgzzMHfDrXYmyCLhTsytNQ+jzH04BBwXSchcyqfnPe5FGsA0p2nlNVMMl4SpSDi0Wys5lTn1Wl1NcmYhCAdHR/QwJibA29ZECIgsnMKBaNt7r+bgxLiU0lrUrSFTnpgIkbAk3ruaA1mAO4CBwDyl4zR9Oe9fX3oWnlfIE4JCV09Fwt3MAFDdmQgCxzAkZCJGUvec+TCX87Y/zTMB7mZqMNxH70UEge81/7lQERPhCNr3GyC93nQtlGRgWv7wg/3w4/muO0QEoZhnmadpzgmJe1PwCAhEPKyTm395fr3udVkKInYFIogIAkJQZnrzZm1j3PYGkF9flTCGxuNBypRSYQivVTmBWez7T+DNqfDhIHXTlCWQvr5UxvjlQyps6+Hw7YfH775++fFzezzmdQnvskzzH/3qzdPj4/m6//Vv/vD9l9vHr7da3QJGMwIoSRztpwckIADcH5VJMCAAoJTkFuuUc6He1DS20cokHFwmzgUJozV3B2FqNVzD4ic+61B1sCQyhg7zO2yI4p5vxbJI70OHlVmAvOTEzInhOAuCT3nam2+9M6ErD3VA12FImGcOj5yYiSG8Dutqy5IZycOH2lCsrQrBPC8/fNwAYpmym7fWSfC4FlU393Dfd/URHpgylUREaIxToXC4bt3NezNEmLK0qnmW07JcLvcJJBLj42OZiny51tMh/fLNw5fLFSFynl72wcCOOE3ly9drDlzW+dzaw/G0b9d7bzZEBJl6PBweTseHT1+/fnz+SlxM1cPUQMRZQg2H4pL4dMjnW2Ui8xjmHMCCSCiJ3WM0S0J96Okxz1Nq3ffR1WLOcn5txyWXzFvXuYipmUbXQIfDkhSodzB3tX5aZZ1LM++173t9+/6w7ePri4EjMyBBYmaEeabTPLUW117XVdzxsvdlFnR4Pdc3x0KJu3nrgyXNhN3863W03dY1nVYRAiRMTNYDGKcl720gw5vD+v/96x//8s+//Rd/9uv/+r//VwO8GX33h+takoUdjtnM9s3M4CdCLfK6pqmQDlPVlGQqiRha9boPFM9TZuJa9br3lAk8QURClZQGQdgALM9fruuU92GBgEERhOhZkBlLoXUWoLjehpqzMBMhYHio+dCfvh233X7xNj8e5HnHW90y02Fd99oh4HKpZcmuUGuX+8+8M+oU2t6//dnjw+n0m999j0LrYa5dEWMpKQmHu5Mvc1nLcttezcEdj6eptlrreDmPWxsfPqxfnxsCvH+zANFQr3tFlOtuTCQEAPrhm8Mk8nztx2O6XW4e/enpQ8JF63lKcdn077/bA8jdShEpMrrOOdU2rnuHwHsuY05kxuF0ve0iwhJE4hBICPflNIUOF5JPr1ciDg9HT8yqIUQE4eHEtDerTQENAwFhnqd5Kp8+fUa3ZU4iSIhj+NZ7SeIOe9OSKADkJxpqlFKutzoVOj3MCGOa0uWiU5Kt9aHhjq7xeEilkCN+ed5bVxFmYtUx1FR/8pxOKTPSUHX3rg4AJWOZBAnH8LgzzpBsuLmXKZlZVwcEQoDAlOnxlGu1bmRjJMFybyAD1N6ZKcJPywSAP3//AZk+vzyr+dvDCmHOwUjXS3PzlGBd5p/cPg7ny3mZ097t+XyZ5/V82Wr3fWuOTCzgHgiEtN3G/XlFRL1ZTuQIY2giDoAIIIGUeajnLARQe4fA/aZTzkO9Nl2LjMCuTggioqZ31SCXe58lmKj1QYTE5G5EVJth0ONxKgW+XIeOAQGEsk4ijLnkeRYIYxoB8Pyqtdm6ZlM3MzXtwwAgifRh4RHgFoBAEZ6ETOOwTsQwhvc+AiJzAqQ2hhDaaS0OYWYIwYkj0MKE2NzDeiK6AzUFEYhyuQ/hNAAggBKLR90bEgdGV00stcPv/vDl/Wm9Xm+X63DomeTdKQsQQayF17xe9zrUIYIQ16m8nitFvH93aHsd6sIMwLdbY8ac0+Pj08++eb8eDi8vzz97+6g9Hk/L739sZZK6b+FQzcuU/uRXj5++vF73fpplltQaXVmGDgYCcCD0odqVU7ojY6eciElwvH06bnW83jonsEBCr2PjJNhHXvJo/sMPrz9+uf1bf/mrz1/5sn1xCCRoo0oIQ9N+frm21swd4uvXaW066N37p62OnJdJ2AH63glhLqU2I8MsUvtQDQ9joKnkgFpS2Vozt5LyPeLYdNxxHiklD3cPjyAAYgr3aWILC8MAuIPfWfjT65WJwuI5qgW8vvacKBy3rT48LnVsQy2GPZ6WarbtDcF///FCREP78ViaGjNMZUric5GXq27DAGCei0FABEm6W3av13o4FAa+XvZpSShxmEsu6eHh4ftPt7/7/cd1Yjf85Yc3jw/05dxeXm8B0A3+8On11z97t8y5TMte++v5Nmp7c1qnU3a/Pr80PtGHd0eDuL3eAMDV7leghwPA+dpLDvfWh3twuLXenx4eaIGUea/6sE7aK3gXwtbNMJhCR7QBfTgFDsO9jT6cABl/wqwjYlfch6laIBIzaBCGmzEBMk6HmZCGdqFwAycSDh0jPN48nG77Pk8MIddrZRhJZlAkYVMXxnBYpqIOn79c3p1WnvIvfvl+ncvvtN62OoGcbzs4bNXKh8mCLpe2VzePTIQANrRMufXuHlNOt9tAQlsoAiMowhkRAHofBAjhmSkCGBCZ/D7BIEJEIbHRMEgj8P/fsrvLCAEwDCGE0RwYY5gTkDoFQFe4bIPQE4N6CKMkQhQkyERbHwQkd4gVsGsgk5uFEBiEBzP6PQPYjZkRQ4epGWacEhPT8TivsyvAHHSY0+u5JsFpFmLY91sAYKGX67lWK4XVnRgPay5pUtXrtfZqh6W4BxHtvQISMzhEG1ojypyYMKd0jrq15u6hIYk5c5JYFipJ1qXs296rr8fZzDvRh7enkmikqOEEYGaJyc2HGzqERjVHRmJUHd3UgYfqXDgCsZShZkPP18txSlNhFGi9pyTrXHZrgFSSABonmVcZXd+9e3O+dnCfD8ndMqW9ts4ECCKUEvVujKToEeE6mHnvVUO7NXUyHYlpmadbbVMYRLrc6jyVeS0RNkbc8yyFkkOYq6pZjDnnEXrZb2vknLm1Hoij89etqpkCIICQEFEpbBY9kBGTpEDoar/57vlvv/ti4aYjp0yEhwP98HF/eFiA+uvFt4uG+Zun9fGIDz8/2nAMf/f28fWyhRITvl7p5dqFncET0+NJZC5LwRIeHtvwPvx0nBgVHWJCzERIgwgC1yLXm46h6NY1cuLEDsgMwGhlhlvfaaAF7h17a+jOxID64f1p29v12lG5a33/Zj5k6dsWQ50SklzOQxiFIc0pwvfee/PjYZpTuMG1+TDQMaZJcuZ1RmYZjncO9dfX/d3TxImXJQNYoAlQIgjTVOK2dURE41otLCAChKQQc7ja6E6CKUU3ICAym6f83ddrY0GWQAdO1uN63SRxSSLoHf3rS2tdlzUF0jLR0I4iCJoA9upJkgPmLDkjAnqEqpaUSg4BHO7DfCheXjZTTUl4wdPCcHbEMFeKkoQjz9eGJJhYyyyAVrdKy8yOqq7CbVBv7bjKlPOUKWG4hhSghNbj0+fdzHKRpsDkhHG57vOa3x5LLb7PnKdE7L12DRwY4rjf/PFdfm2bAzCSZBldXQcTK8AIsDYQSP91IRoAcyrR4rrv62EJt8uuyHxaJhR3MCkskFqD7hpgRHmSMnScb/tpPe1jL3O0vrXe5gm2LQRtmYAIWe26me79mOXHL42Rw3QMYwEMfH6t65KCsDXPRRCIMRKDgwdAH9H2ux8TGTEnnHNa8qRmSP7+/VwyYYQIp5wCHIbSJG30GDpP0/xYECkilnWKCDW/h/PNXFiGxqXtOSEzu0GtQ12Pc+5Vv3ytCHw4pJwdCRD16W2hhDnRNAlC9M1IKCfGwD70MB9uvZs6C7AAQOjQ3qg212EUKM3NIwKmLODc1ADuQzZHRgU1C++QIqBxhVBXIwcHAl7n9ThxrZoRkDFCR7sivJF0an1bDul0ymGOvHx9Hi/nHYkg/LDm45qnnIiZEb/8+BUE35weoxkl/s3v//D9D/DuzdPjaeHMTTsBAyIi3PvQTGLBL9c+S2IwnOKBCT3O10BEJE2FL6+aE/3sTYqwH2/4/LWqWicYHd+sGZJc9tq368/ezn/8qzffPp1er9v3nz6dlvzm1+ut1q5jq50SPRxO55f63/6r3/547tvezAEQ7gAvcwC0eWKA8EC9T0klkQQLE9F2a0K2rInI9zpUDYQkEwTo6Ox5ogyqDLTXMDQS7PcbTVjrHgRJgAkpF+wDwDFwqDFBkazdtYM5bxdlJmUl0cOayiFPCd+eDue6v2x4uXXVPTzuWs+hERWJsO6NIeYyq1LtBjhKYgI0hd5NOBP6+bwBuGkQx1w4SXl52b9/ec05lZlfXjcd+PgwPRwnjOhqGmGmtdM6pcfHORxHs9oHSixT7t0u264Q0yEz0+0yLpeOELYNynJ53c+XnjO6tX7VaRIDj6TH2SaZREzJybewmJOEjsuoj08Px4f89s37icskI+e6W/SRmag1vVUDwGVCAHx53bT2vGYA3reGyQMojCmcGYgIyFh4nXLvY4ydKRXOve097Olp1aphEU612jrn572qOgZ8eWnINFRFBIHP50GBRAQBjNz2mOe0dLxejAII0TzKNFn4eavDMBXOxLX248RukYk+vDkQQI+eM5c0IeH1stc21nUy09s++vCSWJhOByahy16nSYowEobqcS1fn2/fff/5+LCcb1XNjqf8MOfz7RquU5a9KSDqPpCIy92lCAYxAEcb19aPk6zTfNv7XFgEzUwyHSTPRT79eDksBQINsDc7LOKAH94u1+uVMyDgvllAvo9aJdDCWu8Pp2nO+YfP12mJx8cFg3ofruDhOafrputEp3UKNQodo5NTrVPfI9yTZG26V0fg6jFGENhUAIhSKd8/X58vNxKBZKPtocHC1h0dKREh7VvVrinJWnDKMlSXQnMph3V6udSHSQ7fJI3obZxfh0f86meHp8f8V795vl5zQGf211fVJVrz7dKq23pY//533/dGP3s6CoE6Ph05IvKyvjxfrucNRK6Xq2ush3ncG9kZL5eaS05ShEBNpymbh5tbN+bUuougedRai2QHq10DwoeqaQsQ4qmk3vu+d9U4HlNYMAuR2dis7sR826EkKIU9PCW+E13WRUqSrfa4R3+YpkK94zInvjfmTF9f9jjM6qDuhAIYatFuDkDhiERzznutNoapX3ctmU8zM4e5I8U8ZazdTNd56j0AnTCA8e5H7yNY0HwEEiOkhMhRshAiO5ym8nLZA23K2SN6U0SYcraIIunxdPz45fX5er5tW7X9sM5fzj2GHR+PGt5qfXw8bFv98nrmJPrJ96bLNO2beoCqXG5tNGpVAwQhwjwIgcABJFFA5CK1OSIR0b63CCAJjxBhHW7DzKJvfZpyaygEibi2wYgI0NX8J7ina3cbPmdclrLVLowaWJIcHnNtChHHh7mUXLuF25SlDiVsLKjdRVw4lonWxzRN6fryut+6SApz7XoLv6dGSDACzV21QwQgMbP2oe65sJvfbeNCidARULvBDHe0Mv7zf+d/WwrbIDMLCFUj5NaHB6jHVDgn3msPR0JPCXs3BL6PfyiCCaYpj9bXtUTAdWuM5BEA8C/+9NvffP/l+6+VEvzJN49/9LO3v/ndjyyy1yoYSdJ570JwmGcDe36tp1KWA79c2+XSchEiKokfH+bR9//d/+Y//KNf/fL/9p/+37+cX4Ay+PiTXz0Qc7f4/XevfRgnfD33X/7irYGdX/eJiwf+/vtnpLTXYR6Z2QH6GGMMSakkIgrCRAgiEGE6Qt0Q0B0QY1mKua6FDvPUu6WE85ynjP/qt8/Xa2WC4/Jk4U3N2+t2+TqGItwdZx4RROlwmrmUpZxE+LrtY4w8iQeYxjxNiLTVXnK20XIqSAQQSLjtjZCScO1tzrO5dh0lZ4Qwg67mGIiQiLIIM46h7iDCXYcO32ubirDQTy45B0rsdu/tC6KH3eFCyAAipIaFQwo6EkWshZsBQ1y2NoYXoWaOiOAh6c6Og2EOHjkLMWQWC3CzaUm9axhMx9KH9o5DZW/tNM1F8P27FSDqrtutGniZpsy0LFlrd6dLawx4PE0I8XLZzOMwldNp0bDvv7y+vDQMR+b7+xSzAMCo7ZsPD8z5r//+OyT6s1///M//0Ye//93Hr+cLqK2nie5+BebeDBCT8OXWxwhhmgtf9t6GIYIQEUJEEMKwn4jRgGgW4TjMCeO+yz0cJsS4c7+XzMfDxCJvTsdbve29tT7yNPtorTpJmhMQ4DdvH8+3/fl6u+ztcV3mPL1s7Xfff/rjn7+f1nnv+/V8WZY1EXz5cislPS7TCD0+LAniMB+vTV+u+/c//PDLb9/uhr3b+bxHoJkl5KZKGaYsBHS+tnCAMGRBIjNlpCByDQCfJ0GifR8QQITMiAj3fm6EgwMiEKEFJAFGasPuJdWIwIAgvBPo78PvIpAS58QG4O6t3gVkSASMuFV9WNJSqHWf5jRMvaFaOFpJ0oYXoqpdEu0tUsKSiQlzpjmJ6liWSYeXzBD2+PDoOM7X623rzPD26eHtw0Ov7XR8uO57uBHKtu2X7VbH2Kq5AblLkj48ZfHwOScEtIiUaKi9vNaBDgFkAciSOQke5imlVNvemu116HBGP8zFPFw9GM/7QKSU+OW8Jcanx+njl9uhJGSyQDOrfURArWYBy5TvTvp1KkDIhKOrSGKGKXMfimDv3xwsQAhJMAjUvHedS0ZAcxOm697CTFjUAonP11Ym0eamRsxtKEAIESV2hz6ciLqGmc9JJKVunpnnIpdtZyKL+wmQw6M3zSlPs0xJGBEAD2sZEdo2EWYRGz1YRlfrupsK49vT8bbVzy+X42H2oG3f3z5OH7559/z60ntnmH78fJknenpaL2e97rdvf34iSsdTTkzP19tw3V76MS/zoawHfbMKEn0+78yccma0JSUMUtXefHTHnKeZ++2mQxX4tvfjLIc5+YCcJ06gw26bvl5r1+hNdeiyTKrDLFJKPgYgtj7mQ5KSbnvzgbctWncKZwQQTJMgmLawwKdTNuiHwzqGx956pz+83FISSbIuPM80ujLRXgciJMRbG03dDMODEha2pSTJ6adi04jagjgOM1GS0ceUclfrY6xTaW2MgGFWEjNKRFyuF0VBijfHpN3C49YwIN4s7Eg5lxSx9TjvLadY1zxPmSKeXy9TKkDQhgLibeuXzUhgybbMEwGAYZoEzfcG+7B1igBiJnBPLNdtzylNOY8xRsRlH+5AwH3oMJ8PiVDFXVJJwhkBkdS0KahjLhhqrds8sVkIi5ptvS9z+fy15QSHQxngRQIUk8hWzTUcoHfNiZBotMFEHgEIeaLCNJd8beMOywQPM11Lakp71fensg0bZntrHlaSlKkMc3VcSzpk7uFuxpRVYUp42wcHGEHKyYZ1iOPCvXazkIyAIywJycMxk9MPX24DO6NdX7f3T7OzZnY3R8R901uLw8SZ2Tz6gL2rWYBjG3o6ZUl8ft058bzk0bp73AesCICB85wMw11jIDADM4YBwjylh1UCeNtaTnRYsyS6f2QgMseImAvXqghAcm9LEYQLJjd3sG7hFnMRrT6atbCtj9NSVON6a8TgpkkEEc9Xg/Bvv1lJkMF+9rR+8zT9+PVVh1E5fHrdCMEiDlM+TnLtvqb09TJ+/LplhkBgglb9tne/n0McEIlZEBxDzen+ugJ3tORdSEEAgeuU1zXfqvbeH95MP3+3hvqPX2+IqVVl8Gu32to3T9PeEyG7t3fvjs/nW63WerxebkT47ulwOCRO5Ba92mhxf0C/Xs+HdTYNBHDQNuw0l9NhuozRrJckJfHWddvUTd1QpNA9wit+KHzbrbVws9M6zbNsvW3NiOC44puHwwh+ftnbNoKTKWvdpzUHAEIz58P08I9/lr55k5lQkFV9ryMQ3ZUQfv72aT6+/+/+p9//1//jb/Y6ci4e1tuo1dU8JZ4nLhlG9zGCgBwhCR7WKYnU2gBpmXmZYJi21h8fH1Ao3O+IUGEWCCG53to9FHPZR6tNMkNgIKSM5ByG6kocZrFtShhZmIXHXWwKAffReEAgrRM/Pc5LJrX+cFo4Ty/XKhyErhp9uBkYgFpYc3A3CCqSGPBOwiAeDoQY5l+/bnsdADQXORzEg1r3sLEsxZ1uW79e2+PDXGa6G2jum2PJuLfOxGsuhzKlPAWpI7TWtn1ngod1JZKv1+31sj2s0+vXfV3L4VC2pmVK4XB9bafTYdeGFFnEhq9LpkT73nPOx1LM4su+IUGRkgk9bMqZE177fpqWJKWrPV+qBSUidWvdDkm2auTUhg+AnEkYTaP2MYYxU84szP0O0PIoRQJ9dE/CxyVDQB/WmhKx30FjJZmGe0SECALCGPDTPwNwKjFlYmFJeNt828Y8p+tNj0vOExAFWERAzmzDgkiHYYBkCsA+DCnmTJIEEbdtMHOZ5PW6txbCeaifjuKmzHjIyTSAYBIxRCfRWpv2h9MhkXw93wIRAImJIa6tVXUA1j1ImNGRURKPOpJInlNCR0fDOCx5e60WXuZ02/rDYY07hEvv6QAgABLxoQFRFla1Ly9125CJcuIIB8CAKIKMso/x7k3eb808nh6Pe1cIX6b0+Ws7THK4W1yJtlZLkr2BN2uth1Au4hrPrxtGPByXJMwCSIhBho4+EgknkkAPJ5F9H81M1dUhAg6LvHlzYHCPPhwdkSUo5DhNTC4cudCt6qfP7bY5J/jlh/Lmcfn4tZ9v9dPlNssxE5/322latj6Q4u2bctvG7XL7k589ffyi758Sgq1r+vXPf/H9x1cieXh895vf/vbj6wtS+fS1EjsCukK45ZxZpPUOHhaEIn3f1zkjkuoYOsycOfbu4MZCamBuBASI5pEYWeKOB5oKE4APVwsAj4hlkWniod41zpe2zOm4ZFMfbmYxZek9SqZAMAv36M2SsAOYh6mVOQlz3TogQKCqWcDe+5J4712IpizPl21KcliLeS85qyJGtGFq+njKQ0GYl1kCTEF1QNsxwuZFbjdNjGXlQEBHinhc5nD89HqWzCWXWseU6e3bhwC97p1z4vDb1rRrrf7wsE45BcI84eeP+zrx8lBerx2B2j5UDVjMOgKbaZKYlqWU1Ls5hAi6+7aN1jVlKTnfbv16a4d12vbWe8yZSGgYtDYkMXiYehYCRMlcm2kfiEyIzB4OktghrHkQeoQ7JKHTISOhO/bhIiQILBgIqDDN/PbDiQL+4R9+OBzn6+a3fRBF5jRlSQWEaZ0XQvj85Xm7mUZcto4IwjSG4/1VHsE9wh0JEUD4bmMIJEhCYaDDOEkEqGpO2ULBTFLCf/Pf/Q8Jcbs1RAbAYV2QhyogiiQ1DfeSknuojQB0h5Kku5nHkhMhRDghiQQi77UDoIVnxg9vDgZ+vfkiSVh//ov3BBhB1+38/t37P3z/8eV8XqfD+both9w05hQfPhy/+3j9+ONWCpaJaoUPb2Y1W5ajAP7hx4/BAk7rQr/+2XFOXFVvzfe93Rpcb7oucNlUZFb3rgFqoxmCDB/MZMGmw3wAkIgIAwYz6nVv65SYsA9kBnM385RFYKgjS85CywzTVD5+3lpXBkMuKfM3T4eq/MN3f395+YjId7hTAASEpMLEJJTnoweZOoDhneqMgIAklBJZs7q1lBJnTrmgUwSa3z1vEAGJU+0NAMbQw7KoW9UBAWGQE00lm1nrAwADQ4QQwAPcjBGJUBIfljTN0/c/ngEDOcLJAyCCARPhMs9b670NCMyFAaLWfjwso402LJDuMhRhal0BCMhLxiziAUtJ4XBrQ9WSICdkwWXOWw0SAMfX5/3t6fF1r2XKwkhGt9GQYuKEmfbaEtLTvH693h4PZZqTSOKULuevp9MJgH54uX49b6ONeZ7a3lRNVSdOLPDm4chF3GBvlhP/+ts3+355vdUvX87zUnKiMCOiALhcehuWC5ectFsEqEcfGgEBmAVT4jGGMBGxKZh5KrxV1Z8goMCCx8OEANt1W0ohRiT445+/FYa3j2/LvPz97397u23m+stvf/Hxhy+fvl4o5cRwmrMhfHy5jTo0PAASiYeJIIYfD5OQvLxuw33f+v/i3/jlr3725m//8Pnz16u7/+U/+5PjYfr+x/Pf/sP385qDwdVeX3urAWG//vbxeWtfXm/HKZecv7zeWrepZANQMyYKhzvKW4c/vlmmLKEO4G2Mt09HZrzs2rqF+3HJl2sfepfGGhLqCAh0DAEcapJThLfeCDnlfH/veXwoOiIchIPYhxq41+aXbQjh45q/nOtUEoSbQZ4lPOquZrHMQhIRQOGHQ7ne6i/fvfvm3ZuPn/7QtGuAdXs4zYBwvTbgUIetUhJ/95j/+Bfffnp+/sX7t8zp5fx6XJcI/Lt/+CEVIaLR+zLNQwMiHMHdi+RlzlttW+824nqt794+fHm+zYWv14sFLsuMBvOUkQEBP378qgFPx6UIeQQQO8XrdUcUCt+alZnnhK+XXpLU4YkjAK7bmHLaqu6tz0WQpHUlJINYSrIAdbgfl5iCiShAmAEDIFLGcFCHeSIEYJEkYHcbsLsI71U9MDPuu08zq5o6IYBqdDNGjIjDaR7dtltnYcdAJHBAgABWNwAXQgBwBw9ygpz4tMy/+uZtq7tFvN66qu5bZ/ZAWubspgieF97ONRFNE33z4d2f/qNfuY13T9/+s3/6F3/9d//D9x///nd/+LLvoaE8ydPD8sOPX5YlPTw8Mchf/avf6shlybtevvnmmAl92OOSDut83tvWXQMBvYQLSWt2vva1TFttlGXKPJqqBnMcFhFwAu4K27Db1m/Xro5DXYfOhd88TsxoBu6wb3rb+9PDhIJtWEQEoJAMhd7bfrth5vkwg1lrmoVFuGRW95xzaHz9ujuKJAKvOgYlKonPL7e3bx76iDFMStqq1tqEGSjyJIciHJESCmYQRwpCcvXt1lNhHS6SzCDUyizNrPdQs3UpCLGP7hBmWDiKyPO1rhPfHdYT547qoIl4OAoHI6gagwTG+dKSsFPUYapR68hTmhIl8KlQQuSUgv3+qFWzunVANHVTPz2s+651H2UWJ1SLvY6ciqruYzATgYfFaU7rkhhojLiMnjLqgNt1kPB9rVeKMOBhyl9eb5jw3cOiqiI8gG/7VphdMQt+fm6MHBhmdloncxtdUy59KIAFhDsykofBHU8YeLntb4/zelwl0Ea3hA9reXw4mtvr67lbX9dJDbZtLEm+Xm/IWKbZbLxdypdt54wT59oCaOSiR5pebxWZl3L8+OXl5dyF6WfvTqb4fL1S2qaCuvfe0MOPswjz1sZewx2WiUvi2m0fmoRsDAyiwgAYGrU1ybLMExOaGzKYGQYEYCB62JSkCKsPCzgsMwWEY5lE3QIjAiEwCdxhPOpGzBhRhGtzBMgldXPVLkxjAIZJ4qGhahFxr0RXG1moSLptYwydptyrjaYO8fiQH9cSHq+3vSR6/7C6Ybf+sKYfnu3LS5OMwzwRPR3Lpet+UzC4NTN1piiFppy2rSEjAt5uHe+IejdC/CkozxTuhPdoKZBgzikxPRwoS7pcdZAhYiabs399jfNL7+5OWCIOh/W6j/dPh66jdnXz2ocFntb89t0CDr0b3pUoHpeXW8IkU77umxTqZjY0lTQUDwVzomY2LyLITQeRn6/euzKgO8wrAQEQksN1w9erJ9DTUZ6eDmq9TNgVrrumzIeUt+uYcqRiP37F62s7npbDaf7mkeoAVdft+U9/8eYv/+k/Ug33MJbvfvx8PV+WOa9z+W//6ofPW0hKL9ft8rIxs2TpzVo1AJwLJ4ExzA2ZCZhc3T0I4+E47U3V7P37Q2JjiMO6pJQ9NNzUnYRatfPrePN28oDzpT4cJyBsvS1l7u7nrdluZpZKnuasfey3kYSFqTdtY9zx5Q5RJhHm0YMw8kSj+ZvDfDzwXIQ4N42mW2aXPN1l6h6ghmrjUnf3ELCcJ+sAiMsqFOGBnJKPkJSutT1//XyYF+a59o6oOaWX8631WOcJAlofiOhmaj7PWQp2VTB6nJfHpw+ALCIp0adPv29jLJME4Mu137a9FJlnseFhoAAl85SzOWzbQAqDGB0I47AWZmxNzYyYEEAjktA8T+9Op5z5Pgq7bPX9un49P0OgzHzetpzmy63PCzHh9dwf57VGcE7o1NrOELca63rsW0sMRLG3gVzM8c7vv7MXDkUConUDwKGaJREDIrBw3KP7cXeZuBlwwnmW3kOSM4M6eNwPazA6IcTjgboBI/bWl6mw0OvewNnVgMI9iCknAXdJKAzMIEmYiQRaG6PH5aYPp0m71m7rUgjRHSBwmCqAUO61EcTpMDGCebCQ/XQ0gWoNANjL61WRvBQGQHIfEYdDmhKZxb6108Pi4aMqIg/zwpiyGCAJmEXrmoQJsTW1u7lazIP2K+37mBeMIAggBjcjhHVOU6Lzeet9HI7r5VbD4/2bozto8L73rjrNaZ0TIzr6y9dtuB+Pczgax6ht1LHMiwiz4FBvVZ+OZV1TrYbIOeOn59v5rMTJwxBMmC0wJ5IUJWEE3aoPNU5RJipSQgEg/vjb5e3j/PGlqvphEXU/ZHpYp3l6+MPneuvj+fkLpnDg86s+nKhMctlGSvDP//jDf/n//P3plE9v19/+7uU//nf+/PFhuW4jRv5//cu/fd62VNLLy4USlSz1ZmYx1FU9iP7om8P55fXzTY+HBbURYOv6eMovtx7uLNyaEiIyq3lieFxnG+pgwVCbTlkgaHQHxG3vS5F79ocT9m5MfNsHhJ/WNBRLZkAw99YtMd+qslBiGT2QAAlUXdVypnuRgJm+Pl/BARmYoIgwIzMwWe+wN69d10mysDAdMigSIDMYCkZg75YSTZmEsVZ9aYOIwuG4Zgpv5lMpELBvo3crJbnH8ZiTsJs/nMqIXttQJwnq1bdqec5lSVmAJZbCo/qUUlV9Pu/nq+remdkhJKfEBAC1tcNhWaeybeNa65JlLuXltt1qZ2QA7E3VHCDusAgIc4cgRLh/9Tix5ERBQMLbZW/dHZHCEcIARCgx2IAg8nBtOhWZZ6lNmZNDIIYOBwzJiAFzSR/ePWy3+t0Pr1y49yiS1sLuOK8pMQDgbW+Xy2YGRDxPlISaalOwHqqDU0LEMC8pt9HcXAQlCwOZOxPp0FLS3obeeR0EibGUbOb4L/5X/8HeOqIM1SQYQeaWiR3cA80UAZIkxFBV9TAHjCDBQCgsjNTHiADE+ys3mdvDUh5PB/OubtaJAHDyw5IKsYFftv3f/Kd/ej3f/va33zdlYYhwtTjN/IufvVP3v/mHr5dbBzQKXNZya9ratpQyTQ+13nq3fW9PT8uyplnkdt0kla6xd51mceDrdQztOcG8nHrvo44+EBnyVJigbjdA0pDMEKoBvq6zNrMASSwE7sopXK1WA4ySJUvSoUHBlHrtvW9EMM045YTOP376eLvdIggAEIOYATAldg9TkJLXhyMBgsNeO5IzQ9st3FIRYVeN8LteDR9PK4jctoFIJeUIS1LG8GHj3nyfpuSgo4+cCwOKMCBdt71IcggLO67z3rr2AQ7EyILHJU2lPL9u5oCCOaVaVRjfnObW+jdvT5d9//rS3cDQCAODHCxzcjcSaW0QQ2JGoK1p1zFNAgBmzoD/2t1uYc7MnFAIHZCF3H00746PhxOCfTnfZpm2XhnxYZkGRB0O6G8e1sQ8s2xdl6l8eH/69PlH5vRS++eXrTeNAJG7DzMY6N3DnCZ8OK3mjoF1VzedlrzXWx/+uo2SKTEhcO96vVX1kERJaN9HxP3XC0IRQk7sbm7ujoBAiMNcmIHBLdxsKSUi/G678iBiFkREIj5MEmHm8bO37//md98R4mHNH94dCPHTl4sHvVw29wDANiynVHsHCHQ/rAtJ2LDHx/mf/Prbl+fth88v337z+Gd/9K62/t/9y39ogwzwMPHTIX95qdWitn3K9HhaatXXcz09TOHww9ebmbkjRBCCI7rfNTbgFvek9elQnHjOkgSWkv/8H//sx69frtdeh6YpWXNgWSf++vWGQdWhahdE5qTD9tqWeUIRHR0J3VyHWgQ4I8PjQ5kzowdxWISpT0K3Zls1d8uJb8186FRKmRjIbLgZqAVRIBJBrBMZ4l7tMOWHeX7db5iwD2VgH3fUuwmLJAYIIuTkhzmp6nEpZuAYjIFACJhTLinNc1K14QqI1+ue50zI9by3GCDUGyRJb0/rxy/XImkSfrne1IMxEsF8XGprEIpIS5rvrgLFKDmp49eX2zFzi9Ax1pwkJxtOTG7x9bLf2jgsZbiVKYcHIYik2zZUx2EpXaM1jUAHJ2JTcwcHQgqAwIAlczcnjIdDbsMTA1J4ICNExHU3YTiteW9REvbhgSjC12sfasucPVyIDqucb1uZJiRgwkOZzrd62TSXeVho7whBxAZg5iWxmWXJeRIEvJ73AYwYmfn9w6rQTg+HP/72EcEiEClQ/PF4OJ+3rdV3j+++efPtf/Kf/ueq7U///JuHoxwPhFleX+vpdPjtd+e/++uvD08LJHs6TQ/r4fn5XDKKQIzx7s27v/rts7nvQ2+bRfg3T6t2/933V2GepnT3M9Y21ICRpgzrjMuUInCv4/ncazUPBMTRlZlKKWaDMIjwcc2uet734+NxKdKHnq865exqt1alsDB0cxsuyECEPnKmUgoGYjjynW2EXVVrr+0urzREuteqNZyZn1/77VrfvymH4xQAaDBPiEFAmAqPodvWzYIIc2YzIKLtWlOWx6dl39v5qufXus4FyOdDxrAU2Hd93bY8p/dPpxi27d7RKY/E1Bv8a1leiJAgDYve+2Etl2s393VJ0yzah49Y18QEpuEUhHy5jtr8p8mj4FRYmJjocm0sxIn7sNrichnHOfWwdU1PU7pc9mY2T/O2DQ8abpwiS6q7qTuEi1CZ035tJQkzD9dlEUFUiNasqyVhdNr3flhKHxqIvSshI4e6IXLJuW4VPEpJ4WBbqwBv3h4eMvQ+OsS7p4P1EYiGQdz+jV9/ezx9+N3Hr/t+3fvYN3u51VtrAlJbQwQzJLGfvX+81ltmTi45i1N9OV8+HE6b6nXvD4dp3/22jZRoXvLeBoT1dptybDv1QYeFJpFb6wo2lzK6T5nDcHhI/mmTFgFteOhABIXQMd6eTnNKBk6M2hsA5Dnf9qHqD+s0uiN6SiRJ7oC0+zism2+bMeI8Sb4v7tiJQA3Do2S+Ox8CnBAA6HobQADobevAaOD3bZOruUFOOTxaVbd4epqZ0vV6ywszsraRBBCojtiaskgRicCupmFgwIhAoOZIDOZ7M3NIHAFOERAxTRkAtqYACKbEhIgB9/Iw3avTgMhM85SnOS9TOi4wbFz3sXf7+jLMjAL2Nqx1AshTOS1leOjwZZ22uq9LPq65dl2necrpcruZm1a7vFyOpwVJ1APcoQigvn+ch7q7utt5QyKqvRr4cc4I4K7/xh+9qb3/j79/Ec4IToJjuBAtZX593bv2qUzbpa+nJIWnTNet7TupQ2J//7CeX+uHt6m7H+by+DATwruH01b3v/nNj7//3dd/+icf/r1/+5//8a9/cT5vH798CYpta63X19fb1/P4dBmnU95Mv/vhekf55CI+/HYZnIQIbHjiOxgUVW0MQ4SffzgaRG0WEVPhhzUdJqG7fg7CA261mgVCPsyMjOpOhIER7lkSiux7c4NhUdsgpCkzRLiBA95uu7kjspkDxjTlx3XJhJwwZz+f294wMN6sWRXOTZFhzRzhTgBETNCa5SzX1lptOjQx9wGmejjm0zrdttaHTSmLsAm2renuY0QqbO4p07ZvATJPmZm1wra1uF/wEfNCx4dCiNumErTOh2F2f+o5+TxLt9hqb70zhgOwcK8DgQgpZxbh+wI/TxyK+23kzFKEEXR4H8bpfjryeZGU8pzTMsmt9yKSUH54Pi8z5VRenm/rYb7UMc+QCMNpmcq1dzVoO9xu/U++WS6G6/G0kLem4a4YfRhiciAdlhDPzV6/nt88HoCp7aquiJSzCIGINFX46fwILEiEOiwlSMK3zZYJ9nHXTzkBTUUQiQG32gFZCAXJIbZaS0mtj3DIOZkZC+vwiFhXToJw/2oyitxHTwSOanGtIxxK4iknj2iqOkKHUpLwgIh5krXwdW8BOE05C/cxbns7rvMwOG9KAGPEYc3rTMMDIwhx31sYHE5lq703zLmEV6AAwJw5JSEgu7MUEIDAHLIwUyDwrY7e+uXiyyRloiAkhLr7VCRl7HVgADCbamHu5m34tMw6/HZrIvj2OKu7xcjzPDTQh0I4OgagFgswrYHiGm8eS1NNKVnvfR9lnuoY58sNid48rObQhps5MmRBU68djuscGF0HIQDwGPjLd0Uy/Pg66lYfHrIUfHcsNPq/+LN/9u/9r/8jTuuI+n/6v/6f/+o33yGmf/In5dsPT3/191+Q4Jcfngjy77/7/OPX7fncpiRVqwaakQdTwONx/vZp/fT8bCiHJc8Jx4jffbzutf/Zr58ywd/84UdMiSBG7bVTFgSEcJ8nqgO1G2IA0VzwmNN1H+shDbPWHcPVkInVrA8T5vBQ03lOHiFIXd3C1klqDUJixlsd5rFM+bJ1EUwstQ1JNC9pLnLdak4yRkTEGDa6HdfiburmwzzstMosubrvw243zYlUVYd/eFqRAfAnzXntGogp0+E+EMmUCrk6OxNhYTY3JOzDW/e1ZETMc04cW63qIMgl5+u1GiBnnkoWAWILMHPU4RkpS/7y0p5fbvMyX7et3UardnqzrIfysOau8OXry9uH43Uf2gPBKJGk8vx6s3tairAIn47FTCHQIRCh7TrMQCgzBUQW9oA+OjPP03Lb9lttmdEDEDgn9DAInOcMAPs2TI2EtzYIACCIKJAZ71i8AEAmgEAdjgymnrOsawF1kbvKBduwjKSmE9PD45qmcrnu5220WpmwDd+bBngWJCAijHCI+6N/zDkLIxOpKyFM0/R62U6HKQL2qviX/85/sLfOPPdRmSBJDvcAHNrdnYhSkjFG3MfDEGphFiIYEAhw91pGBDFOWeYpEfj/7C/+orbxX/33/wMXqV3dbJmJSRiQmc+3/edPB+L0fNn3oQ/zWlutzXofHz6s61wuF7vVcTc3EIcImJM5tI5z4cRw2ypnydSQC2IJDyKubZOU52Xaau3VRBiZwnHoeJzjh5cm5cBUj4d824givn23Xrf90oyAQ0OE1Ta+fzAfEbTtGm7rPCWhXEjVrtcrhAfjfr7EvjUzJifhYaxmTABIYS6SPCzCynzghClBYZwyv5x9PRY1+/jDmZEBYFk4fqLqxugxz4nSHRVJeco5cd20TLMDqJqqJZGcqbcqJTEJBuUkTcdcyl773npiZsKuRoAGzoxzlsvW5lKmIvvoTQEAJoGHdbn1fioTAWqYWZy3xoh85wgiMdG9EogEIvizN6e99Wp6vjYAZoS7L3ZZ8lCttQuxQTDFvy4QoQPvo//zP/nF6bj+t/+fv025BMIylZy47T3ldL7tiM5MhzKpxjonEkCCrelt15fX3TXuVI/3D8sksh6XMhMT6GaukSap9zdf09fbPgzdIWcKQFcw00BABAgoJdU6VAMRHKJICggzKyWHgxkM60mYCPvQO2VN1UQEMO7EMkTyiCCAiIREzGo/rXWIcGhPwj9/d7zu/bz1QjhCAxiDhpqaJ0GIUI/jVBxMmN+8Wd4+Le8eHv7l//TbX377zs0vW7vV+OHTS0Qcl9LHGAYIMFw94LgmYU4IeZYvz80tAmiomg4AykXATRWAGQIY8XDITw9lqG97hMeHp8Nf/tN/9Hy5/d1v/+HTp+fj6XhaD7/7w+eU0oe367Kk75+vL68Vwu/w4GHOwhHhruYwhgvi02EKlH3sbJpLmpd71Aguty5y34ANDxSivY6SWT1YSASnTKN7H3d7oh7XbG4GER57D3dfpnQHMYUbIiVCZGSGu0jSPSRTJo6uh3VSdABAQLdg5kDPLEXEcRDz+bolSoB4vVZ0V6LpJ+9enRKsx9PYVYevp3m7XZPcc47ABIF+q33UOue8HkqtY85zd922jil3H+TuIxiJmLtZa1q7zouEAaAj0fXWHw+zmnWNlAUB2q5q0NSZQFWXaQKkvdWSUjczszlJMweA45QuWysiwli7Tpklce0jCQmxWfxrRSiaBTMgEhKss9xufZnTUFfz0zFZMFqwuCKE8/V8fTitjqzDb9VEgInBaa9jWjOR6PA8yX7Th2V5+7acb9eU0i/en371s8d5XS5t++7Hz3WPl9eLh1vYYSof/3D+xc8f//EfPzw9FCH63R8+vn06Pjw+/N1vPn193Z0ZyH/5Yf3Fh29a62RW0vqrX/7ppy8//F/+s//CqHz+fEsiAPj2uGDorVsfZhbm7hbETAQE2FUTYxFwgNtN3WKahBjdDAG7Wu+DJTGKoM+Jpjnd5RpCwMQ/ft2Oy4LokoMyt91JbQxvHnOhp9McDqa+tVFmWtey7+Pl3IYaE4IQggshIxBQbaYQT6dyVzQ+Pa5Bsd0axZgkE0/d2vW21YGjGQAQ0zyn0duSy+VcqRATLlN+ee2tjvUwTcc0xraKoPPL5ZIXYS5WzQJauEjMU9p3q7uaQSkyTxxm6PR6253s3dMS6ssy5Tlttbo6QQiiGV1rHxpjRO8R7gDAiYkB3Q5LZsDLdU9TCsI58fNZe/dppmkVAAODXETNwJCJtuo2LDEjc79LQzOnwqMpOA4HDw/UJJw4Xbfm4QgAAEKMgIg8zFprklLK0ptOJf38m6fz9bLdmkhKjOfb9niYMLOqzplTgeOBJuTzbTjg29Ph++eXIrLvfm3NEWoN0zAbAIwgYbGkUkc1tMQArmWaj0sCUNVxu+250LDoTYWNmYaSuqnDm8Mk6H2YCH95adPEa0mt+a23spBA3ve2LMkHAtJUKDGPiFZ1tIGCbfhc8rrkuRTt7XypiAgMhJCZqtownYsw8VQyINQ+iFgtwmyaBAgF784LoIChjoIAsKtHRJiaGzJCuHCKwLrpUEuFMaDWfp/CWHNzMIvEmMvd8gbHI/VuHrCu+bqNnKikNKoHQFe94+eRpLsiBzpCIBGagaoKASciwtEtAqZCrpCSqNl1HwCQhSMgIJyCmMICABGQEOdSSOiwci4smUbE89ft0w/X2oMIKCB8lJndYTRNOa1FlimlkiTleSK1rqqjgQ3VMViSjhhjRCJEREbJkYQAKdS22t3jtJYAqN2mOZVJ9tqGxjTj24kT0w9VESgHIsCtadvjejURXBdRj3od65oM3Mx6U1NqZocD/8//yc+ez+PpcTke8uv1JSfyAefLNi9l3/3N6embd0+Z6E/+6Fsd7eOPnx+ejsxyu+63rX5+Pv/4egtr19qunj9+vozWhNI6FTX78fM1ANYpv39arlsLAr97jhjXiYGxabiaavTm3zzNBj7Upsy9w2UzwFjXNAuVTE601QZ4t1JQYCxzQqfL3vc2wIOQDoc8hreuLCyCQrQ3r6MLcUae5rzO9HCYhPHv/3B9ft0RwA1IgphCISLmuQQ5YDCQMJ63XYclDkBs/X5uz6ZOAMOsD9Xuati7+X3viC5Cy1yIpXZNCVhYW9y2BnQ3RTBTMCNlMlccSIbpHtKxIOI8Z3MHQUm0JPz6ZTPgh4dCiBAxemMRD9hb9wgEHs2JwA0BaJpFbQRE3MUWTL06C757nK7X/eE4edDW7fFYtt0YKTF+udXwEOJafZ0TMFyuY3SXxLPwZSgKrjk7QgSM0Y/H8u4wAcvWDAwtAACv1wbqWdKmWkoCcPTorWPK5ubmzCQi+Y4zc0OAW9UiQgzAMLoiMiLcoxMeoepuoeYIMC8lTJEQECHC7t8+DCIKiJyhtSBCYjWkCEjCUxK8Y7EAarUxHCEiIk8JAXpTEXFwETzMCSL2rqWkzLh3bRoWOBUcw31ALizME+Pn1xun4qqlCAMLYXDU1j5/uS3rtMziAYxohsy0zBSBLDzPaa89AJhd1YEQHLebEZKr3roe1ykMtq0zk+S03QZzYsa63wIQAFJmupPO9kaEFihMj8fsAYRYMuyjTVMpkT693pC51QHA03JfGeaJqFWbD9lAze2y1yw5nMIjFyGm0eO4lo9fzszCLE0roiHK47wsSZHo6yX23iXRw2lNbBMPKOmXH36VokjJz+fzvl/Kgm8fVnD7699/BKI/ev/IGL/9ePn9d/X8egnEUnLJfKu997ZvLky/+PA0HdJ1awx8XCQ8Xq79y/PraU2qbgHIxsSHWW6bu4EIuPthlpfLMPeUCIginCJq8+MiKDjUS+aXlzqXNNTr0CyEQaoeEB6RmA0CMYR4r4p3JRISRGSRqno/NBFimRJivHu7vJyrD0fCrY4wh4CSGTEsPImERwK41lEmenvK592ZIzNt1T2gdXU3EZzmPIYTw8OShNk8vrxs85yJQFs8Hsvbh+WmY9tb23VechEiRExALs2ChazZ569XSvz23bou0ruGKd8L3t0jcErcmv/+h8uwKKnUtpk7BB4e5m+/eQhtr+fujr/45ulvf/Ppz/7RryTh3/zD9+dL0z4ckBgRccq8zDLM697NnZj3qsQUTGimaoyAhEQYDgjUzCI8E0kSBpSMubAO2/eBADll8+hD966BmDNlpt5NHUVIOFodOWWPuO8AcsnDnMgnFh02zLZdA+zhUArjOpeyzL31rY7bNsx9mYTRr7sx8+v1WtKUhOtdb0QUEMuUp1y2bc9JWNDUzLFkDne1wH/xv/z3TaMPdwwh5DsABQERSpIAbGNkRnceakRggUMHIdz5smGOSMgIEFPi05ot3APa8KHDLMYASSRMEGB3dU4YERIn4FDtbryW9PY4G8Tz9ZoSJU5zIuZ8G36+3sx0KhMRhzcdKkjzmp/Pe9tuKEwsSxEdYdaF4trS+nD85s3649d6vu5LSQ9T+d//+9/+N//jb/7mD/g4A3DN01wrHxJ+eWmYU1UdVVPKpNfRrhqYhHo1dQCkpzdv3PqbN2sf7fy6t9YAue9bvV0DCcEQAVHuAHMPjEBAI+Ywl5I+/OLp8x+ez1/3N28LM+VSbrfRmro7YghzwJ0+HsR8J3dPizBCAHNOt2tnSWWe3M39fgugMZqIxIicp0BImZlFNZD4cr3NZdKwcLuL2+YitSuCCAYl/mkerP3t0wGA3j88/OHTlyAXor0NNfSIkkU4EsttGwFwWufny+XhsITp49ul1v7y2vc+IEgSgSOAAQESq43EBEyMQcwRUPv4o1+8/bNf/9EfPv743cfL83WfskRAIj79/3j6jyZbuiw9E1tiK3c/IsS99xOZX2Yiq1AQBaAAGIHmgDTjmBOO+U9p1hzQrM1ANgkQDTSAEqisysxPXRXiCBd77yU4OLcZFsMYnDjh4Wf7Wu/7PEP86dNlrm2/53EcCrEjrVVqVyL69S/f/vDz0/XSLFBgyAD7If7i11+R8e//8LOYRQ6qgsER3Q3EvDelQIHpetnMgPh2WjCz22GLRRQQWjdEQL5pxLx3yykQeQzRzNatxhBTjCombmouagBwG8QigLsh3vajToy5JGkq2h1szKX37nr7c6KKByI1R7IcA2EgwBAcAyKgix32ucTww8dzjmzktYM2N1JiJGBEBFMAcKSuyuDmFijcHHGIUVUQzYEQgAnc3W+v04ERHx9K7RooNfMufr8bhkTTsPv0fJYm45RColtw9/7+sG7bp5frz58u0g3Ae7cYI7qXFB8eD9fzdlnbL78+3h3ijx9fX1+vJYQQQ0qgYoggTm0VJMhDOJ0W5pgim/vWuhq4eY7MTF0kxVBFzJEYmbD3L6McUUsBwAAImP3xOHAKCF7X2gXGMZdMwy2QCVRVlm1Dsd6dc2SGtnUHioxIrGClRHK4zu3+IaUSX14amO+PZX5ddJMyZCUFla6SS2mLMOGvfvmVu8/Letjv1Xqt2+PxfhzLp8/P17XOrbYm102sW06pu+WICLg2QYfetORwumzrJvvdqCYhYs5ZuzLTeZFbcLuJEfHNT5IYVZUQUwxb7wQwlHSD4qdE5m5qxKhukaNUAcQmGhMBIJlzJFFXw/t9cIB11ZKTuO6G+PSyDjmEREDURNBtP5YQaFm1u41DuF57a87EXV0UUgBCvlza4/3+q7fHrttc+37M7x7Sp9f59bRJlcXsfpcTUzqMTW1+Pf/5n7ytpojG5G1r+/3OHOvWmrVfvL1DDKfLNs9VrBNgoPT128e//d2Pz69r2Y0ewnJZtkvb7wd3OxwTkb//WM/nzQDGKRDAYchPp+VuGgj9vFZzJMCxkIioec5RXEMEQqqLmrqKpBgRqasiQe9eq+x2mSNyxraqtf7mOKxVXuZrDEUbiVoZQihYq7RNzG9GPCMmRxmHjA5jDnVTJ4wR7+4ygm+rHR6m63Vx0RKDGl2Wrl040Ly1xHRd1lQiA4NDTrH2Dggx4DQkNd/WbXe342ht2ciTooeIqhacehVINM/NlZCgNQkhhEQAvs1d1WMmDF4SJ0Q1h0jgjkaIHgBE4eXcpFvKQcxc7ctuYMjSZVslhkh8SzFbyLQr0R233igEjng+b4w0lbhucjfGtfZmiI7mZKbgoAbDlNBUujrAUiVHNveAfD1fxv0YAl3nysR3+9HdxWQcEiNsomUoKP3N3ePXb9/83//dvzveHcaBc8I8kOoGHk+zMYCCFGJUfNmEgEoop3kV7Q4UgyEG9CDuZp1Y3QEh5BRSiSqyrXXtWx65hHh6OhP4uEuySRN1VyaCADHhOlvvdrePEcgN08jdsTeRZsQEEVpvtpG6xUBDzAYgrVFAU0clZKwm26oR4bff/aKrns4v3TymkBNta0NgiNitj4kDcxcDQERcq5raOOaUsLYlpxyYdyU6wtwaIW9VzbWkuFWpXYeS17nXtRKiO5p6jMyRlrmpWQgITq1pSpxTIMIU2RyQ5FbaCoCXVSIDaecQOZeX0xJDqJs4ADIaaKDQNrn9y6RM05DVrbe+LZ0jHaYcCB2xq12uFRAjh1o7RwYCRiRE8y/wn2GX0kgIvq16PW+9G3NwhQ+fPt8dplzGZb6ASEjlzf3heCjTGEVMTDFQXdu6rk36wFndHbE2bSIP94OCOXhvTbbuyBQwBl5rd8Qc+DqvY4l/9g++8rZYqyctvek+GEf6uMoyK7oTGge6zh4Bp11pTXZjWJrVTVSF0M15aTok/uVX46Gk/WFfta11m3La5bSs63nbXKSk8HC8f/vwMMSyre3hOF7m5XRdfvWbb4dxXObtr/7uD7/78QncVtVFrTcFgMvrFiAw4+t17V2Ou/FwyMumIZCbIuOQObgaYnNUkWXuIhgYQwAARHBEEoGbAzQmCIHMAR2JQNRAKUbKAxKSiCNDiMyOJZG6125rk8S4L7EpXZYGZinwMMTdmBhZHD4/b/NcexdRu4VScwjmQEyBzUyHcSSGeV7RITCUcbQqIQYKBNoDMwc+z+10nufZendAjykwQmQYx7BW7WrHQzZxMZumNA1prZUDplSWpS3bBoTQAQSAAYm1260TNI4FAq1bT5EjERgdd8XIHFSkISIzYcAmNi9dDUxhPosaTkMgJgcNiWIhMGgVzPywC0MO7tjFDaAEQmJTbU0xh3lp88UI8bjLQLCsTRWILcTYm6YYY0wi7kRLXb59OAYTAVV26ThEng7ph/enKeQY+Vx7oHA9L/upgG+eDtKs136jprfaI2GZxrUKulGg1hTQiVHNARjM9iVUFYTQu9yaxa32aRqIIFBoXQyMA8UQYmLpyky1yba1474o+VZr7544EsKQw25gM7hcVc2R3MyGKdtN2u5ABIxARDmH02U9TEldAHDtt+MBbZvmRDc0r2jrhtJBRAMhIHDgFAACAvqQ03LeYgxbN1M/3o/bumnv02EKQMu6UeLAFjm2rinGEFi7LbUhMjowoBtzwir9+elaO0QOAM50yy+YmRExU6gizAHc1fpul4chuAmHUBJX6bVZb24GQ05NpFV3QyZQs8AUyEIMpSQz702GIapbr26iju4c1qUiQimUE99NbAj7sfzd72cjDZkej/fLMgOKoG99rV3f3j3syg4J1nXJAcbgnag5BAATuVZ4fZG29piphLBWuSzVKRJzDLTLPIzBAS6nbb/Lkel03sSamjIFU8kliDiDqTsBTUM8zc3UAIAD3O3zvMnWpURaZg1ExLDWfsvsR6KqICrTwORp650YUmIi7OJb65EYgULArfYY+IaBaK2L2VCKiJq5AgY2dwPHEKm1FhiHHBgDui61I4XeBBCGIZYE6DCvhmC7KTSDIZC7cwit99b6WPK1ypSDiiKHl9NW1VPkIXKJNA3hurX7/YSIihZDeHo5IdBunBbVFDAxA4C57vYDurWmgUNIYb7Mj/u79y/n95/PQy6tSRNz9RLzUrf9oRB5yXkqwZt/Pm3A6AbfvL0/L8vpsvauImaoCACAJQU1bU1uhFhEBCZXcwe+dV4QtyaqJuo3Ul+IHBCbfNlE5hia6DJvrj5Mg4qKiiM60i1uJtVa02YOhIGQGbdNxhS+rEm6m2ogTpFicDMMIdS2vns8ELGbpsQ/fHi9LO2bx7vrvOKNKOFwXZuaBwJEVAUACAxEiI6RiUKU3kSVKJRMRLCuPcxV1Q3R0U0cu6LfGGXguyEgoio6YNduDqZg7pEREG45ZUdEumWDfG2yduvSU0JCcmUgI6J16eMUAsDNPz2WQUy22smgxDDuWUXMrhyjgV6XOhUdE1+W+br202kJkVq95oDr0gJrimyWXKDWLssWGTqzmYVki2CrththXjlG+OrdOF/Of/Lbb59fNxEvQ+EoP/14efMODML3rxsDgUlzNgNSdbBWaxeRAMssIXAuXDdb1/r2kVyW509Ptw8eE1FTRCVmRAI0oOAO7hpickRXA8C2tfPzTAQp07oZkSKiiRLdJKcAYGYAAISObmbmDusCkYFYpXemxIBkJtaZgRCkdwCTWnsVc42JFGJkzxCuWytDJMB/9O2vutYfP3w6X7YcQ6Sg7kgUiFIhdyiHkRm2pX0+PWP2+br+9ruv7g7TH374gFSs18OUm3i/7SRBhpKeXi/3x5HRx8hbDtet5UiZQjPvqt6t9ZpTaIbW+90u5Uy196nQy8vL35nnWK7XRbR3ITQ6zaf09v7xONCVAoGJb+zuwgTnyzKV0lq9OxRTcIbemnRMx6JVr9fl5bwi22E/KopWQ6QUaT+ylri25qYpBRXbuhIiEhLSLe/jAClyTkHUq9jtCXw3JGZsTWoVA4shAoKqIJKpMhEhdfP/bQDkAGRm4IhEDrotqwHdylfrtkXiro4BXVxEDRRjCMitCbmmEp3IxGoTMTitLRL2Dms1QBc1UQ8xRMPuSoBEgOiASuCEAM5gX47ICB0QciAijoE5GjL2TYlYROvWPz1tTc1lA4JS0kdRcxvK1Q2mEJD93bv78+ny/PTKAT+fl9OlM6EFNIWSwt0uq+vDw5QK3x33dV3HAbZ1PuyYeVT1GHBbpXUHgNq2Lh4CssP+UBKhu50XHUpUsaauboEICZq0FKIa1LVCDsShN0FEU2jmxMDAYLA2qKdr5JgCEzsiMGCtioyBkBCmkslwqct0yAFgDfHz65UoQO+pZBMrI393v4+B67aBb/t92Q/kLfiQ1KBt27y02z0vhFClv//8POYQEl+Xy+vpMo5labNJV7Wny2ugsB938/z81f3dZb7GRGy43BQvIgpoTikFQnQwBwucnj5fDrtBpTGimABSjmwGbv6lPeFwy3a6Q4gsajmF1kTUAQGRWjcKuK5tP2UD0NVjZOl63MXz1hwQXbdVp91AqKJ9t0s5BkTfTaEp1CbuuAmt63K3Gw1hKIkRVWogDAxIJL2BQCzxq6/3OSK4XK/b4Vj+7LfftG0bkuqOF17+2W/uD/tJFdSEI8s35fWyzQ1EqnYrQ0mbsOt1tSp+3dnL+XTdqjhe51Vb793+9udPyfNWXbA+viNvsM067dWRlm1tW2eArx+jA73Odeu6G/KQs4jFzLup1CpdrDUBh5xKKWlr23zZwACJHRA4VBFEFDUiSjmlgiHiMMZlWb3rMOUNyQc6pN16dQq230VmvuFCdvvSu0nrMUXzPg7DOmvvriox027HkVGkS/fabfn4Epm2xS6gkf3p1BgpJZYuoaS7+8Myb9vWQ4ziWwg0jgnMz5eKhE3NLjMhMfB1vu6m/PqqhyFudemIU0pqqF3QLcTEhK323h0cQqaYKQVC8O64Nu1L3w1JmrYmD/t8ulYRD8yqJmJ0Q7an1LfuBkPO3ayJIHoIiIDrZoi4dbetE6J0d7Rz6wQoEbtgjExMy9oJoIkJALc6xBBzkroWpqlEByRz2g0QIxPsdgMRLtsaAsQSDFsMWZq/vF5iALt8+nx52Tr+MqcxwdKlitXqiLBUHRKvG1zdOHDv6Npe2prKWEpxhW4yDIFDBkBzCGjS7LIu3ViumCi6i0MFobY2raLspxepqwsgs04DphjXpZvBNKQvfXWCy0UcMDKkwOdrxUQxEBIaUK22nmcHAwIKWFJEAJHem2j3ccofnz62pt0sD3HdtnVFIjRSb4bmz9fm7iFByhExXK+rK3DgrfbLte9GjBkRLHCYLx3Jtq2pGOy5d98W266rq5khAaTM48Dg5m4xMtuXrX6IqKjOAYG6qoIep0zgdesGYUiZvPVblIaJAihqHFi63iYFoJAyY0DoRoiqJiqEkArFFLuaArauIgoMOcW+KQC43Uy9AIyIKF2aqgU97On5dfv40XsVDjhNGkL81XcP21Yv1xMgjNP4q6/f7Kb4/Pq6LMBEW+sphZQpZ16qWSQCuMzLbsr3ZXK/1Resi4mFLz10TuOBRKr2+k//9Jvjbnx5fb28XvbD8PKyJEI+JABarjLPZl3MgYg54nffjr/5dv/+84KqMcUzoYuJgjkExa8P42++vhuH4acPr5flGiM2J4usYMCcEy9Le/3x56fXT28fjg/7aV7XlGKK7Y9//Ltxf3g9re7128dwXrStGGFDRhV6uJv6KrX712+Pgbw2NfPjMdcmKkYMHANCUO3ozozv3kzEBEZECGTgUNfexJygDAHdEW+5VzQANUUhjgSkKs6BzKRukktSvW0iwUQNghtKV21621mlRJe1bXMHRFHoIrWrSL8l0sCcAzFhSMEVa92m3XB/t3MHVbm8XgA5metsgUl7m8Y8hMh3h5T66bQ10d7VmQB5a5ZyLIB1k1q9ZF5nJfXaPGS9vF7dUP22DCJi5ABdBBGnaaxblyq6ASI27cIYA5+uPUTCENRDZO9NvcEwhOM+X5dezR7f5sRUqy1VXbEtDubDGDCBdJXuswk4O+C29QVxKCEQtu6B7f4uR+zbaik5ckihLEvHwA/H/PyyxRJv67ecgouvy8yEaYwvn07TuMNC0vpUYg5cAjlCDJBxOI6JYnr/ug2lHHY5EEXKJe8S8XU+n2uf1+5gZQjr2gBozMTE8yafP18e3uyJWTY19zImU3d3ZhbVrffWhZlycVhbCrxKq01Sjms1UUNkdG1NAEikg0E3B+CtbsS22w0iFiIqmTltzaYShiFd59aFnk8tZc6FHPrWtDZWtbXqEO3tw6CWzPotfdTVwZ0wCHoJARjXpmFI4H4/TYjeFbtgiOmn9/MuRw601crkKSpT3LZaMjPzVFIT2Spet25aiZnI7u/HrTUiDhQvl40CFo63nTw47MbAAZijWiRqZpBTEtXLspXM6KRqHFBVb9EWcxA1AkBHptRbRefaRN2WrTFhYuy9cRpNTczcPaoRupgbh03122/H10tdan9+eR6HGAO//3T6+t196m2Zl+VydaC26jDmX/72bu3t57P8+OFshkgsAu5GTF9/sz8M0w8fX8bDxC5/+PElpXi3G036cp6v6wzm89LePAxE/Onpup8i30CPBgCeIxJ4YFiaTmPYjRGQTLVtjTw6eM6cEjcxAJv2OSM9X1YiyjGawZTCrctMhO6u4iFC69IFVS0EziW2rRMTId7Ysszs4CIGbjEyoQ8DE1FkQIfnl/74ODZVIkiRvgwyujiCmhtA65oDo3vv7UacIYMpplZ120RdmWgIWBIj2iKdhLT7urTpmPdDPl3qurS3j3dNaoxhN4ah5C72eroEwt7hMA5brXXbItDLZf3pwzVwOJ+WZsiIyH5Z1hTjtnUgfH15+frtfe+29d42AYfXv7sSAwCrgqMRgjuqmmpDIjWKAAqeE6fM2nFdai6FmFWVwGKikKKoRkYkZMLBfCq5i13n2kQRIaSg0m/PnvtdWre+rBKGjITfvh2B+OfPCxOIKQF2sXrtYJhiBPRpjK6+n+JuXwDw86d+Y/ApWFULMQzRwNtXbw4i1kXqusVdrM1uoAQn6F3VDJBMRBpgEHU9jsNa+1bFHbfuQXp3xBgIAK0bIH0hvCC0bmtrYhYCE5JacyVDDzGiu5ncDBCqGjG0brdzMJBZtxigmkWCMeM4lHVetl7zbrcb0rIuQNClmbkizU99W2UoxIxitNWmnXsLojYvt3SVu3ojB2Q1sF7BxJ3A1M0NYVNFd3fsYg54fn3dart7eECR9fX1+fn9Tz+2jjSN9OnDi8n8+gw54buH3Xnp27qRERhp6+tyBjM3Z6BpDNsmtaqK3D0Ml9fnl5e1t04IHUFFmZmIVC0lAAw3t1ZKKeaoqk3aza+9Xlf88vRuANSbrrURETKDu38RLPkNiQkEhCi9m3FGctMU6f4wNIW2WRPh24LPXdUo3OTUvp3FikxD6F1daCrD45u76+XU6unuzU7bZkwI1LQS5a0aEKIhADfTZW4Pd7tX0eXaVIFjjAYXh5e51qa1teN+6NrHXR5GJtKU6DRXJLq/2y3XpasDA4KXwm+Hydxba988vPtX//JflzH+t7/6m//1v/7t/+5f/aPdEC+X+u398lrl5fn8zVdvb7XE/RiOx93vvv98LCWX/LsfPtwGTmuTH96/Hqacma51YyRmul5WBrts3YBQrS4tJNqN2R0QrXdtvav2GMIqigCBgJmbahMtMdwCPyKSc8rMrW9MPObk4CJiZsTBDNWdACiQu98O3EPJ81wRycyISVRSJAdqIuAgjoG9147MhIgAFAjcieC2WAFGN0W3MmQk6E3RERy0d1Mr0+DBASAEUieg4GDgzgbgX3KGCN6qprEgQK1dxb9cbJEcvdZa8pBSWteqogoWQgzRu1pXVUcUHTz22o2I1YmsGi4vc8n59fV6urbX+vx6btaBEPa7vNuXcUh5pMA0llLKMC/X7bK1xVvvXXybpUovJYl6YFy3SoxD4sCYAj+/LImZIzsYYXT2iIDIt52GurljV0VmcwzkKTEj5CmboaPGGNFhmTcwLonv78Zpij99eMkYjodBzN4+PHx6fl62da5tOhxEmrhwKsRg7pdz/fVuQuamLi/L4Xg4V3QjF24VUk6n64oAwziBh77WpoqZcwxNOzVcz3MpMXKS7j///EKKc2+bWaDuvqaYt60Ch9obUZjXPqQYIjO4uo5DnN0MfT+kbTVCQgABdARiyCmezyshK6JqdwRiIgIxT5FVTdTcUUQQb+h1FIfsBODLpmqOAEMKS1c1QMShUOIwpMQBY6AuRu6uHgMvm9XeKSAzhy5A4bq1EH197WoAbilEB8gRh4fxcl2/ejdOUz6ftw7yi+/2u8x1eT0t/VpXjvDt/e7xvjwcigOb+tP58rzo2r2JlBQvm3x+2oaY51k+vfQS+L88fTR3FQFTzskxL9s8DAdw2qSOkOdrfzjEu2O4n9JlreebsI8cI5UUKMLl1K6ni0EQM9oshLBsnQkV8YvdQLUtkjB6QHFlQnenGIgQVKS7qJibAo/uYwpCvKxd122aMjgNSZs4oL++zr37MEQkr1sNRGOJGEKdpaTYg6spsveu3ql2UTDpJmK7MS9Li4FjCSVzjHHbNkSiwIgETjGzI4QQ3e1yqVMe3G1ZG6Bvaw/M6hoI1uq1Kk9FUyGw07lJ10A8lCGwI0G0uEC/eQxBfG1qCNJFxADQEl6utcTUBYg4RhBVE7thidxRqpgCc1hrJwZwZ+atCndMjE27I6ooINTaDrsJEQLQ2pUIrtfVzHPKIVHKxCF7rzngphJT5BjGMWy9V9HpLokBoj+OccypbvF4PJyW+vT5enqdORGoL905eLNlOgwd/NxAjOZVDJjZHKApBA4GqGZMgSIPe92NhO5gXj2dl0pgU+bL8yuaqaM5OMDz8zqWDGwAPi8bGnEhIGfDoQRgvK7YmiGqmQ9DGEqYL5VT3Lrf3Ii7MaE7h6jWwdjIA1tAUCIHCsyAoArkGgJ65BIpRL5utVVpImttiIjEzKQq5h6YTD0l2k97dwD0u+N0Pm2X84qErclJIEZeZ4nE2kBBgEA7nPpm4AqMAKjQDNxxlTpIIIBpygA95xQIHCBgIHJGQvcQQ0gRDbVLicnNuzlgYiRCCDEM+7FtayBEygpQeycgQlq2xRxNrUtfl1aGMB4GadK3rg17N2ZGghLCsIvnpaqaCACRq6t6F0XvRPb+5+AemToMobd+vdYh23jcD/v063flzeODg7//+H7dUDqhS3VQ0Q4hl6C4ccalVuseUxTvc7O63boe7ohp8l+9vX93PP50Oov0d9P9N998+4u33/7N7/76fW3/+E9+vTb6f/3lf/2TXxzvH4/XVrteECiXdNwlJDqMwzTRvLTDGEW5Xupd7g9fvX051+fLOY2Bsq9WC8Zf/fLh/vDbVMb/z3/+X79/eb1sdr427Y1iuhvjt2MeM4UAT5dTbfUPn+rL3Fv/HlAD8tu7qRterzVEqlvrracUIGDJmKINOd5H3uY1RBgKjMNujAkAuplDuclizP0ytyZ6Y0+0JtaBAiI5B5piTJGZcN30unUCw4AcIXDYujg5lQSuon7aqoqVFHKkdWnbompQTUEthnid5fW0LWtDNCQsQyIH7TYN6fFhyglCjIgA6GrQ1lqXZhxCppj4+Di1VVSBE6maGNRmRL4v6VDy28Owir6udbm2yJEYehVRo8RVtS9qAn2wmChhanVh5OPd4bLM0o05lBBztK2pqaYUEIHcUgrAZuZiDRlTnFpXVUXmGHlr7fSqyFir1OpgPt1lIDaGbdO2ms7SqyATMqhC2zSlAIiAYODqniMHcHNt1SlgKMARl7kTUNlFcAPwx7djYr7MXdzQqjapahSROt64e11EzLdmpj1zziFsXWIOhqqqDN27KkUFTdkUfFUSWrnoV8fcNqu9p8QiNuRobglst7vXptu6He/GrUsMPD3s5+UqzRB9mNIE2RVMteSQOVjgNbA4MpKCg3MMMaAT0dL08/NSxsQEAICEaQwmUucK4FMcKMQyjV1V+hwDAod1s2UVCmQuMSpiAKcq+vL5ksZBDFxp3TqSM1FVY8AmLRA7ejcH1+tcVTswB0ygHhBqVxSJCVOkpXYCA+NW9W4fiQM4aF8B2MxEbCi5bZJiVlB1jYlvdAAHb92IbBwGVeuyDGWqldd5WblzYHMHQ1F0B2muYEy0HyO4q3gX6122prkkJFRTB7jbj//8H//Z5br8p7/87+5GZEPhX349ffP2eJ3bstV5a6+v2/1dOR6LnuCbh13g8PHTh198dTwejtu8nGwOKX56urhgF/3+Q4/F9kMK3zw+vdZ16yo9pXSYUl3mk9b7u2BWCdmlvWwbuiF0Q+mi2rQMgREIfCwpMCF5CA7AUnFZN0tJFWIIIl67zkvNMQQO2ya5RAp0c16XHBDh82kJOZjcHi4txSDurQmSgyERh0iIEDj0TiJC7KKKSISwrK2rs6OoDgkCk3zxA4QYY507Qy8DmwiqRI4q3s3GkULgHLF1F7fDlNGBKSzL4gZEvIl3kXXrwETsRMABDUzdQsJpCl89TG5AAYZYzibMkRk8pDfv7ufr6afPLy44jaXWfj3Xr351uJuGD08vw2H83Q9n6WZKzUDNOEZG8GSnZS4pljEBhQ+nuZlDt1vsgohE3G80DSJGBHAkIgZTCIwckIHBXdrN2RKRUaSWFFMqIfo0JDcC4MuygkEZYmvdTN01MgFhIB/HVKu5GzG4AxHc7ZIbMPHdfQkBtdm8yYe6gioSqqKCjju+rTNfTvU0by6diHv14/0uj7H1beRySZdQcCzYG4L33/7j79Zt+/Tx+dNTDRHHqVznJecwr9odnaC7Bby9236D/JsZ/vaf/VsRd8AQMAAAkIOBm6o7ouOXujIam/UYophf5nl/nEKBxyG8nqV7BPXWamAMRJzYzWpTIs8BA3lI4Xxt1+uymzIhiHYkbVUphBi9LbpuFhgIFSkgIqKo3lLNbmZqLt3ANGY3pcRcCp0vDZANMQVYt0ZIMSAQEFLv3rvd3e3ePaS5CZu7y7jbOQ4/v//suorStJuGAZ6f15uRKI6TtXr6fM4p2JdwDrqDAQTG/b7UpdWmYq6q/qVeDEy3Lydic2Qw4IBfVOIgYmqSc5kvVwQIkZlwHFNtslVFImLSLmp++7RzcyZU89aEAkcmNx8Huz8UxXKtXUSluYojeEpRzR085zzPCyPlISo4AIwl5QQCsF76jccsYqlkXTpxPM1tN0ZGS8NwY8jd7XciVps2qfspRYxzrwbKmHYllUintbe63h0KMpnC+TyP+4mZpft1aylQYEyJ7/dlvxt3u+nN4fD1u+/m6+tf//VfpmlU19fX14f7/W4//fD+bO7S/HIR7bVMw34czpf1uizjrnTFnz+dt9bAPaX8Zj8ex/LxdOoOhIyuMcataRMzkSkzEgYmQFAT7doVmHGIJIaRualtvXdxcAiBAqK7ioMppP/tUmHi1tqQIxKquZozoRsMY66tdZEQg4kShm6mZom59pZCUlNRJeRmFhAjU3cFQwQghhRZRFXcAJDQTQlpHJKotGZmAATgToQxYCAaxnjYRQc6X5sjEqKIgmNtnZAAnPn28hWZQmQCMOduwggmTghdTE2ZMHAkRhHp6o6QcoyJSgzztTYxBYgBWlMVfzjua+vrVpGoVm3Npl1+s0+oPt1NzORqqiaiTNikM9DzZV5WDczmwkwilkJABmIUUVcvOd4swea+NYkcu4qqlBRDCIRhaxsTAJGIdvXIZG7ocNyXJgoGgBT5prvyXELJhO4AVGL69TfHEJA5M+fv3/9c29ZFhyG5+WWuKQfEYM1/+W6nDC/nq3QRgNNlTYF/9fVDbb0Botp5non57XF4OZ/3x6nk2KqerrXE+PJ0RfKv3t0vy/b5+ZpirKKiFgKjGbjfDo61NXdmYjFntmksoo7qa7PWesnRFEErJaaYe2slce/QxVJIL/OaU0T31iUGVtPjYTdfVlUHuqHhcMy81d7Ud4VSzuDeuwHhmKF1QeKuOo0pp3Q9L/tdWatu3aYS3L4k/5khRUbHccqvl03VnHyKAR1XkTHjNO4CIaCL0xDZHNbuXSwn7HXd7/LzVQl9t8+//vrxcrru9sP7jy+9mTFd5m7dOFEeg7m5UVvl5WV2oLsxSHcAoMRdrJAjwdNcc0pscJimBpqKvzlEA/cu102I0aStm9aub+6mN3eHp6fl49PiiMhQq6YYTb22rgZjYVEgxBQxUmxqxJCYciYkdPfr0rYqYMCRhyn22tzQHda1DiWZU0CsraoDk8fA7qhmeHM3EqTC2owcgGwTuykCI0eS5gGRqdeuDmNO4FybOrgBiAgiI1F37SJTKYggrTN/eUS3tXeCYRf61t2oma7L+vW7B0DerpuBDyO7ee+SUkDmHCAEAqTLdeniORVrJqYU8KZcEHU3OEzFDa5LI/YU47p1IgSA1iRGZuYYOQaqTWrt45B7kxBIzDPQx+eX/cMhMYvaZV5TjI/H0cQc/HjgN8dD63y5XEMiQw9Ar3OtrQfkyzLHmAP7uzfDee3aLSNyAIjMCodpXOUmNAE3I9CO/nicYuTLtpLDUm0VYbq1V3ltXXsPGHvtMccSY2sKRDFpjOqmgJBDmDe7rH2MkdF7l+u8iSgxE/q2dWaICQEV1AFAwcYScyBtElOgGFv35VIdMOUkrQXm3gwJ1ey61hyJkcY958LLYiJ9KGFIiZkvc6/VUoyn84oGwGjWx1Jqs9Y2Alq3jrdCFKIawK1SAQDoY0lvjlMT302pqzjyvNa1bqKghoE8EyKSIYopIxFSl45I6ghugcCJbkvUoVBgLkNetsqMIWCMgQKCWSQA9+u8He+PCNCqvrxsXXV/GFpXUNlNIebcRBgMDZjD0nsT3ZW0zF1A1xtSPaZ1qXlIxAiK7rAsGwAaoLvtSt6VcG7bUm2+aIjc1t5azyUk6ruxCOVW2zyvQJADv3uc3j3sp90IgLs43u3v/9vf/NXH11d3HIfR1YCDSP/4dH5zP3KyCpaxkEDVvrU1xQiO5oDoaaAyhWPKYwwY6ePTKXr801//5vX0elnOlyb7cdAGf/fTcyQYI087vjuOhfNff//pq4f8j3/zbeu+rP04TYGM2F/OF9lcHNNAjw+763X744eXS+3fvTn+o9/8AiCKwDCk63r5+fOnl2u7vJ55IGIOag87Jg4fnmvtWi2e5tpUmFGqYAhr7UwwlADIJZOblxRKpPPSAfzdXY5I1XSTbmImGFJYa4scuigSdTEzKDEC8NoE3NG4q1Rpx8M0lbj17ooppqa91xYxlERdrVZJAbnw2/sSwdcqKrKIz5XQ8fp6ZcRKNM/b3X5Qh7XZVmvJ9HAsj/cDErSm45hbb8t1HYcCwGYuDtbtfKoESAGIqZTioPNlW9dWxoDIoKgqSDANcb8fFXTrMs/WNqFgxHRLja3VepV10SGHwDZNRbuJasxRTVs3QtrtCt3w2K2raR5yX8VAOXEMRAF67aA8rzVlvmW7hxIdkZhKpFp1XVuOzjGpYTfdqjJhirFWGXbJzF+f1v1UysAYiBkJLScGRySs3WrTpjbmiAYImIfQuoDDUKJ3NTUFXDc7nVYOSESlpJRD7yJuZQi9OxjshohErdv1ssZEuymfXquopsIOHiJPQzCXZsgMvzwcurqYo8PLvKgROF62ZRhYGrTNH+6Guao2AqDLvPzmq7uu/bWaKewGfHs31Gav19YNxDoiIECKAYBu8qKppE1dO/bW1217c7/LU1rXubd+N45K9ub++Ge/+ie/+e7Pch7M6//87/+n//jX/90goLk4OriBlhwPQ3q6dFnWd3eHn19OIQ4qKCYhhZwxBgEDdTT3eu2/ePf24fG43+/++OPvv//5h+N0l0JS89NlCSEedtGwUSC0uC6agh6ndO2+bTZfN4p068nEHE06UnDyW+uGiIjR1UIgZj5dagoAiKer7krs2jEwOACCO7m7mRNgDIQA7qBm7hCJYoqtawnQHET1YSoc088v521rx0NSh5HDVMJ+V1LiKhuoT7tpWdvz6fTu7cPA8W7Y/f79p8N9fnmtDvzdL96dTq8c48enl9dT249T914KeYN57RRwGhKoUfRSUmuSObhAFfv+wwuC3+9TTnhZtvPSbniJHH3IgwoQwbw2Ah/GuGzgplOmKhiJmjiQqWni0NRq6yFQycHt1lIAc+2tl5S6+y7xsqqoOyIHNFFCnpscdmlduhscDnle1hiiO9TaxbE2a01ipMCUok0DiRGZ9t7H3dg3tdY5J1VvW3NCZmLGcQi19/2QxOG6bWNMaKRqwLRKN7PbTB8RYuCU2NyZWLtuWz8epnGICWFZtnF/cOkqdnd/GCe+rKt0Y6LduCOH9x8/h0Aplm/e3F3maqit24enbd3ah6dXcSyBU0BRF3VAZ4KUYldHh60buAdEB0QEdciR1B0QY3REdPcY+SbSjYRVrTdlQCA47AZ1bVXu9oMYiPbTaX087gH8vKxM/LAfXk6rmt1GAMRwmFLvXRU4cMw8n1utctiF3W5wwN5VRcVE3HsHU0Cgee0ikhKbed86RnbX+/2QE3z99s1f/Pm/+J///f/7vJ4NcRhpt4t10XXzrfb/w7/5p09Pr3/3+/dMaV7WtQoSTyO1rtdZHbD1FomdeK4tMiIEl45//q//jyHi1nS5rqWkMiTtQghAoVZhNAp0mRtzGGL68z/5ptv8H//y78fdcRyYXGKkakHW28UVL/N5LBw4b3Vt2+ymDqRqZiCiuyls1XZjyNmui/Zm4KBitcvtQdcBQmQwU/PAJGoAGCOqGJgBOjEl5v0+bFVaB3FgonVpiDBOoXU1dXDq3QLBN+8GDLIudntABS/XtSF5b0rE5i5dwJ1TfPvN4fIyv3y+TmMERDNVu8V0EBBvUyhEVHMRw1v73b78RCBAQDGPIYBrjDGmuCzVHJgx5fLy8owOpSREJwQKfHM/mwMTlBTcvXUxw9t7heDmzuhMGBPGoDnnNAzPL9s6txt3u5SECL1LTrGL2K2dyL7bpUBIgdT8et2soxqkTG64zhpjCDnmFJdlQ8Rvvr2/XrdWW0qDo2uXRCCAosYRET2nyMDNISCkEBGgi3FkD4rqu5I/vtbDMMYQxGFZ1+P9+E//4W+fPnz49Pw5h3idt99+d/96PTtxjGFKoau/+erh5/fnT58WZN8fhseHr3768ed1a9I1l4iQPn1eXs5LzjwO5Rdv7p4u8+u8MBghKZKJIkAMoUsLwQ7TKN1U+jSWxPk0Xx4exhJj63Jd+7z22n2rvRTKMUcmVVtXISb4slzESAxIrTUFY+LDVLqZiKo6AjqaKhAG8e4AiYOoBLp5E1XUxbyEoCoxMCC13piQmQh5q90QiIOIEnhgDIytayB2QgcncCIMzEj2y3e769KfXqvdcJsABE4MHGJvKqopckwRwAFszMkMXy9rTlG6miqQI7GDMwKhA+JulwHhdKkll5IYEVvrHz7PKZETEpN2cLUY+P6+XGbpQhgEuu5yLiNKx9p6dwXhwNhN0S0GF0NRFzdtrmJIFCIjuokBQMkRkWprDrDc8HLMkcDN3XHMgQJMQ+5i61rXrR/3U23d1QyxiebAZpZTbtoJgQAB6eY93ZWhDAToYLAvu4+vr272cBx+8c3hzeHh49MTJxpKBsDLdQ4M17n97oeXdd5SyoYwZP6T747d4ds3D3/1tz+eL8vj250j1b7uppxi+vz8mjn//POZkN49HkqBnPNS5cPz+TbSqpuYe4zYzVzBxIBQzFMIDGimY47zJvi/NQS3ec67Ukqpyxwi7u8O0qRXNQ7gXpvPy3p/3BGDiI0pVFVRHUv6+Pl63BVH7CaHIa5Vhxy7OJDe7VPksG2tq5cY3EGalCEvvQNApBAQz1vvZjEgKrauhqAqMQYxuy9JHarrEHldZChxGCIFQqXLLEvvN2XH2/tdjLZsMldP0Q67aatVxJgpxLhusvUamU0Q2O6P4e4wXRZ9/2EWc7AKrhzC/q788tuvPry//viHH2OKTXyfsjNBxCFDX2uKVDJRpEggrechHqc05t1SpbX+/c+Xl9M6DMmBwe1+n3IJc9XX19qq/Ol3X38+Xa5Lu5kXiL4YdmPkwKG23lQBjRh7A1OnAJG4i9Wud0NG8trbVGLJcalSq5jaWnss0cHGyByoNzFDZo8xkdo6X3gcSuJatYrtxwIGl3kV1TIMtVVxi6kwhl9/+/Wnp49Pp3OkOJS01lo42rpojvf347ZIbXZd16HkoQSpTQSPh/zmzd22bLVuqcS1rdYNiarq/Tg2sesqrFbVKQXpoo7gQAjobuqAHiOrYu0qpoAQmCJj71ZSBPB52+72IwB2sRToNK8lBAVz/nL3M1B2k6Y5l2FM4vXr+ze7cf/08hnJEvP51H58fY2FBy6gfr/L4K25na4NIe6nNGS4Vu3dQkgGamYhgPZeSkpDGAMSwbLWgPHSoIsFMFXfxEOiALCt3Qgj0XLZYkohwdarNDFzRMiJHXDMAR1FzAFP5ysxIn6B69dam8g4xF51q2buu5ERjCnkEs0NgZnZXLTDVnXZ5ilnplhVYqLjLjIFda3dL5ctRo4UEcAR1+1m6IFla+4QU0gpmJmJjGNWhetS1dQdmDgw3u0LMzLRu8cHU7ucz59PFyRWgHEoa91qbe7sQOQdAVOK3QSQXTWEoGruJG6RIEZEgnXtQ87HfXTtw1SaaG91mkZzSCkGJNMO4NIFA99M0stqgahkpMAOvizbZdUupt2YeBy5ipjZmFPt5gDmevN45rGYuzaJMSJBZKy1I9Lj3Uimz+8/a4hv3uw+fu6fXyuCDyUCCpMNw3C+ruiYU3p42L193AWE1rojSff1ugVkhU6Bv//+yU2+/urN1juALeutcSDAMKT4zcPh03W5XJYxDIQsLu7Cme+Ou2NOAHq6XI0ocNpT/NWvv2u6/be/+rsPpy2Hoi7uxNh3U9iVFCNJwIQ+xSyOx914Pdfa27Qb/vsfPgwlq9rb+90//9Pv/vN/+/3TeXXG+30KHNcq7+4O37x79+7N/uPzxyqSYvpf/ubvm8taHdUYaG2IyPPa5qUSw7gvTHy9Lu7k5qqSSwZD6ZYLHQ+ltobk97tB3cU1pVCrns9bk95FyUHFiSgmiimVGMxAwF3AujoqURhLMu1VzJ0YkANutZPT8ZiHIa5VYiR337atFAb3IeC1YjMV1SmHbx+HqvHlUons9VqZMGXOmQKTK5g6AXR1IFSVdWmBQynp9bKZ4nGXpffXa12uPRKPuxRLuM5roFBrn6a03w+3WqBojykg4nztjBATiiKqt9q7hteXdV3r3WFIkbfWvxxZCcoQj4fhcq29tfvjUbuurauLSGubhRJTDmDUezNVcAa4ES5UVGIMIaCZMfNQEhEkssjBDdUdAgMqADiEwGRuDkhORLCJtm45EjNsq+7GZOgh0LrJuun9sZQUVEzBTQEdUyJVM/XL2l9fGxEQsYOXMeQc1D1GJMB10xTZDXvTEFBEb4sFJxgKI9BaOxBMA19O1QyOU+xqinA3lmbaqhx25XXZzCSGKB1yCIq4rKYdY4wPu/zp9VJN3X3M6TDGteuqSshgDq6/eHNYtsXcYwhNDQnNNHBy58t1fTiMl2Vbt+6mv/368P3z03S/P5TpYfd4v9vvpn2X5e9/+B1x2I3T82nZqp2X1cFzClX7/X5n3T9/PB0PO0OoopnL59dPXz1M/9f/y//5P/7X/9RU/+1f/OtfffNb9JRyfP/xx//xf/q//fD5uW2u0tW8d1MRIiwlDEOpHerW3uxLN28Cvdl5reM0gquqBo43VWoXJaeYYkgYGd3UjLbNGDwGfP+yDDkPJYqLO2xVnNgdAYAAErm5E2d2AtCxJEfbpF2vwmhVDR0dkSMTeGRutQWOY4bjzuOwfz4vxzEd97lu+vOn6z/57dtv3t399d9/frmsbx93j7u779//HHdxGOKv3j7+t9/98ePTbBhKCUMuuvrW++FYyGFZ2sPj1MX7ut7tpqXqh+fry2kOAQ4Tpkjq0EXdUAyZ5Za+TJm2TYjwsI/LrE0tMm5rf3M/NvXaJA/Bzbe1cwzmUlJAiODWrMXIY46mvmwS0IGCmteuMYKZDSltqioWKRL4UMK89W3tYn7b3yE4AJrpcZ9MWyDgEJZLRwJ1cPOYQmuyrf1wyN1ExVIkN1rXdtjnrcm69WlI7thUGcEAETFGdHMDTyHEkNZli5HHlHKkN4/71mRetjePU84pRDbp97sDBDot8+cPLznFN48HBOQY//7HD4yQePj796/MqArScd1adwFDqXJ3HLuadisDh+DmwBxa09NpJsJpHABsvxvEupuMY7muvcSo5uvaTS2EQASBfGtSax9KQeIx8jjG58uGDq02ZuyKvUkM+Hg/qUhtRjeZbDf37u4phH/zL/7Z7jD94fvfmYkJLm1RFMZgYsOQH9++/fHnDzcxyx9//BxTWFdVMyLs3Vrt0xQBPeX0eFfcfJnrvCwpR0UeC+2G0Ds8vWyHXfjn/+hXf/OHDz9/upTAILhtDZApUSmkHUXM3NWsiyKCigNyjiEMI/XugREDOXrvTbrlIbo2kcY5cMBvv9mH4BnDvL4e7/f/4LuvX89bQH7/fIohPB4DBHQI63ZFaXWD2RZQOZ83QidyUb8NxHrXvumGiYm3RW7zWukdHMyNCWJAUUXwksMNQIPgvRkijkM8X7ZMSOS9C4CmwGTYut4/DKDau+bEiLwtgoRN7OfPc84sDcYpAMVlbeZeEg9jXq5VmiChG4D5/FqXuYXEjqSit5L9l8YpAQGaOhIgIDOZAYABISDe9iSASMyOgMhmDuipcN8kML28PLtbCFHUYiBVa11SCowoIjmGYYzb2m7xAia7GRNvQF8gFLEbhAubSNObi4oIWmsxBgCvdQshAKB2QaXT88ZMHBHNzQzcCYiRl7UbmIFr10UkMLnK6eVk7rc7c90shIADLbOESCq2rT7tV8bgRHE3rNtWa8tDGRNcl3We9c0vwt2ersvCNXXfvPfrp+t/nq9r42trQ/Zdufv9j+3T0+V4t89T/OPpZTelefMp5+O+rF1crG9XDHA4TJdrrXVLkUPC43ESa93kZb2EFGkhbxswIXGMYcwBAILnIdh+4KfaU44UsIye9sVc5yq1d074j797sy7y0/vTOMZlUZUeEyETOIiRuXk3UTBUDjilXJu+ns/3x5GRl944cIppqypqRCiiFOB2Pusi5q6qDDdRIxISIhCimjfRIVIIqO5qFggjkZgGjgiQU6yiqpZDFLWtCiF8etmeT5UpNO/gTkiKkClIE2YORAC0Lt20q8OCUhKL9igkXRx8KnE3JUAnMKS0bm03ptdzXVcR2VTLuqx/+pu34zj87R8+pRhUTdVNHNFr7ejo2k3NBSRAr9q6v5y2EC3nvDXJTPtd2R92pvrh+SJbI0JOoYps1QEgBrod7NwcEVKOd5EAuJuBiQKOJcaA6tg23XqLTMpI6FOJBr5skmJU06rQayeEGNAAEtF5noHIFE+zqwMzfaYK6GbWT/Pjm4FZqm4DpGWWHz+/GPEYojvllLdVLus6TqWJpxBabX/1u5+3TZjDy0tzkTzwudVW12EqbTPp/uZh//RS00C7EdelaXMHrF26WGB2Q1PLIVowR/eqqtrVa9NSoqHireDn8NW392uT1lpInGLQJmgOpCF4bQZIJUcHQyQR4Sm59CHTlOm7r6YUQ0NwD+hOBEQQM4cQY2JyNLXAzEymWoa8SUdyQm79C7jKwQlTHjjneLqsh93YWl+WNiM7gmrvm9UmTXzZVLQPKVKMMeJuHCLz+Vq7aUp8Oq8IsK5AjGOJMcG7N4M6Pr9uMTEG2A+8LdqrhYD3j7EEOoz3f/YPfllbvcyXr+4ef7Hb/+bd4elluS51nOLWlhTw9398lbXtj2WcYknACN88vvn9D6/Lpf3rP9/94acfwfDd/f7peZEKCMaFPr4urnK3H2PAdZPz6bRem6IOZSTky1JNNZRkTi/nmSIjght0MRWPEea55RhD4OAILkRESOfr9vR6BcDdWNJIaYiJ85j4/jD9/Py6myY3JPBW62ZyfLMz995lt4tHputpNoe7u3R32DNHsx2igyKH4S/+/M/+H//uWdr69uu7xGhezet3f3JXFV4vTcB3x/TNt1Nd++vLFUN4+zg87NIwJkLJOXVVA8klvv94mWvbfZOZfJN1zHGfYm2aAjOjmrUq2yat626XObiRPe5jTmnZRARyxPO1iUgKadvqGYGZKfh+yLF7GjRnXjYZM6vJbkggOFevzVRgae1vTn8sKQ8Dba0Hio4cCpTUpTcA3sz2ZWitI1HKARNe16rARmJA27ZxdBVLqQCYrHXhsFUJFGa3pQsBzlsNOWLE3upWhSJzoHVey5hLoRwJLvbauptzAFPYHSIiPn++gKK6qXZ3ANe+GRgAObqvSzP1xAEYASUSMvNWNUSyppvLNLGTA2pONGWOOTyWgRjXeVMzI+69ETNzdMOmxgGnaax1U9HdXd6WDu4iIr3fTXlKqYr30HPKMYSSw25iswaOD/d3b968+eMPP83ShmO5XqsKADoBoHPrCiCBSMxTwSGn1hoGdkVXFAQANLPWkBlTDE74+bWOQ4TmYDLkEiB0Fe1CISBQrb0JabXPp0oAOXJgPF09RGoirZuq3z6iuui8Qs7EgQyoZAZwp8AIQ4oG1tUZSnaZpvjp3F9f9N0xvj7NVe27b0dgNgsq16/e7ZdlaXVDouNh2pV4LOXx7pDHAAjr3Oet9629nk+NKCAMIf7zf/1vVLdaq4nHAhBBmu2PCQPuxpEQGExQHe3N2+mQMxFW49pvPWpTlRQop3DcxbtpvK74++9/vmzXip7GmAIChWksxwl3Q7he9PXSMsLdw6GtAm67cdQOKcefPj1fN0Gkr+73CcMf/vC+dSvDGKPWrqdFieAvv//4H/76+5BxFWtNA1LkgME5sFQLMZgrgrZ+a0/CfFpj5rGkMpTzaa2q2o2JHh9GdTXT47FEgnWz18uKhCEaAhIRIyOxqjK7qd+yijfSj0h3QyQEZyLq24ZkCCC9qRoYvPtqEI05yKH4vuDdASrwy0v++LQ0QXKrVfZ3pYxBHS+rGHKZBpA2sJZIkEAbzL064rZaZL/hLN0MMdTq/UaOjwSoAl7GaO6gbmrr0t6+3bcqajKMyV2Xyxw57fdjV+nu466gmrQuBiWEfUzzJnlg97RVcbiVNzEO0d0VXV25kDh9fn5NKYVyazBlESdAd5zXJuYhsFYl4tq0NkF0IkciDsHRz3MNgXKgbV5yDsTILPd3g4jMy/aymAHFjAiOjswRiXsDZ0AIL681JBpHvumTL6eGOyDG2/e2CQCoOSAMY7pcOhFOEwN6zpQiiqHdUJhgrTs6tKYxZ0YUMWDJKdxG1THQ2hsaDzmIwibeRMF9DZ0D5RS3TW+Y78usJrhi7eIhMBP0Jj99XkLmiCTNVPB8BUYmMycOAUAdhC4XAZLjxHUVER12BckiEgHMawMkBHw87tbaQ8htkbNeLtv15VKY0lj219VygLd3R+tXBM8R1KDXTqTWVwd/9y61OudS6qUvi0n381U/fnj/93/4IITX5f9Zxv8lYHh9vV6v5zxALPDNu/1xnNzakNLW/fPn08t5XtfVke/uSwp8emqEmsf4sBtUfD+NqrZeVwdvROwgm/Z1QyYxKzkSehMPIdWq6IgRns/Lsqxv3hyAEFECR4IIAMuyjiUzgZs62tJWEV1b//W3b5Zlfrl0Yl7WFgVTiuvaAva74/2/+LM3T88f//bHa2+WKMYsHOjXv3rcH6ZPL5dFukU8XS6fXk+L9f7eTeH0vJxObavo0C/zxjADYB4Y157CEFNsmzDzbipb1/efz136w3GobQOA3hUQGWlemxFwiLU3N5iGfDNu926AjmTisN+XriQmHMgduygzqeu0Ky5e1zqOaYoDATJSLsTEahojgYF8yeSBqbvTa+15oJziea69i4Ej0chhiKAI6FBCbCoeGRxcHdlzjo7Yugi0UPBhykwOCrd0m3Xo6qo3WHBYVqWADqRiqopA1tAJETEiiJspphTf3k/3x5xj3KqkwF+/Odzv8qfT8rzB7374OJXy09O8Nfv6EX//0/M0lPv98OnT2roYzmvTADeA3U2TElJ0ZnbyvvWHu+FwLNLF1a5XEZVhCClEd4mBxiFc5paYQGG9NirQTdemTWwP/t1Xdz99PqMHAjBDNDGE00tzBCaKzJFpiHwxVQNQa+K1K5jEyMQm6nmIKL1kenn69OP7T19/9wZT/e0v70oOv//xfJ3d0NxXID1d27aaA/ZWI6UUUG6u08AOFghR8cOn2cCYAcJYa1MV7XFbW936w3GYRv4P/+V3XW+KMT8M5V//k1+nnP7DX/2uSZ+mQUTRESHU3pGtlABg6I7/w//pfw8Q6haeT5cQiBkDoYNstaXIQEzED/v8/HJJERxoGgfCKIo//PxR2ppCnMZcRc2s1S8MalG/PxQiW+ZWm25NCTHmGAOAWQjRzGrtt0kjEc6rEBGTA6ABEToTqAMCmJq6MyIzbbUj0pAZ3LsaIDGhmY27lAJta69NORAh3eo84EpgOQ8iNh0CEl4vPQZE5m3pDqY36ySCu4t5CARq6kDMt7gaMRCjK7R2s/c4fjFBKgcyAwQYSso5iZr2HmKgG8GDoK5tf5fr2s7nGgK7AjEygSiEQCneYF0eCB2wi9kX0vCX3xoAmdnMSuFxF/pmgHGprbXOxJEwplC7usuY87p1M4053NaVxMBogXDe3BwCw+09AQQ1MIMUKEUSg5K53mDJ6CEgmF8Xm3ZJu15mjQGYKUQMTAgBWRWAkAJqa/DLrw6G9nzu0nyZ1xLDODKnuAmPY+l9JRiI47Qb1nkBhG6BqSM4exhzkBDIQPpqzHXrilQGjpxfX2dGWjZH9N2YHH2p2monsBQDM45DioxmoOCtttb6OKQuNk4JydZFUuD9FF4vbShxV9Lnl0UctDuajLsUIscAteu8qHS/IZ9T4ofj2Lotaw8Ea1U1CAH11pNEcEBwQHc3dAQAJ8DATIG7CBPcspfuqK5q7uocERH0i4OMTA0Bb91jvQlUEAIjc8iMOXPrYAKztCnFsSRCql2XbUOEGIMjgFrJqautc82JOZErDCW3rl0lRbs/jJGDGHTVPEQT+vnjqVm/201qeBgZCC6LHEp4vbbPp601AQIi2JdCDM0leTTV4z4djmXZ4NPTCaH/5pdfjcMosiHhvNQ//vQCiIikqq4q5kj0xccZODC5KwDspsHc57WmEFJgEUWw2g0ADYxv0kT2qWRRX1oLBPtx/Py6ihhHRvDbpMPRa1frRsgK7uAxBhVl8tr17WH87uv73//46WYk7ebrIhR4l8tl3ozBrZPhN18/fPs4npb2/DqPieZVckqU/M3D+HC4++mnjxjosNtNJV3P1zwMP7x/+fTprAZrl3HItbbadcyRCLbWAzGQ5RyWpRECEl/nercfqvah0H5Mq0gOfJm7O41DPL9svend/RgymFtiNsFPLysH2pcIFFS1dUsp3pZaISBGjBRUhYHqWstu8BsA0ZVCNAdXQScx33pNIYrCsqwxJSNvXQOEHJiItt4RoVZxICZAMnfA272TyVTdgANzIJGOivfHfcrBDZ6+rErKZe3mvh/zborv3kyq8vFlMeSmra8yvyz7Y04lEVog/vrx4euHh4+fnz+dzl+/uW+6DYFyKn/48KqGJcN+jE+X9XZYL4kQvDVx9Z8/LJe5/cPv7s/LRhj+wXdv/v4PT2b2/uPTsB9++c29Nf34tGBEVZlSVLV5q2UYiDBGZuLrUsHxNF+nMaYUEEmaMdHpui5L/cXXjwyh1kakuykHRlRDDCFgGrAkvBuPf//T01rXbx/vv//0pOi7cdyP/PXD7v3z6zd3O6n19Vq/evtQEqe0e76et7rUBj9+PCkoIZsCMR52+5fX8+l0yeUwDVRGJ9LE/HRqYrAf45QyuGuHZjS3djcFB197U7NlacvSADGHsCy9dxtyPNyVJj0HHofgbpkjgq/NXAmR3z+fwclcOUGJfNxlcHw5VTHD5GOm4zCer4KGSB6Sh4Avl/U4xCZ6y9ufrv3xsFuri9auThT6uqo6M4fEyGTgJuLWxsK9W1fcT+MQWcTMYRzHT08vU0mc0/sPzw43zwWZ024sYyQMDOqJorhv2lNkM3s9LaFEQNvmBoaAaKqI9Hg/uAsAMpGaX+d2ndeU4uPjNF/X88s1Za51Y0YACIwlBQKvrXXgaQwRvQtempC5bLZ2E7PDIR53O6l906oK7j5NuW8yTQExiAHccsrgakLA69qGYQCD1oQjDbto5uvS2ia92xADgxx3Q8qlqzkCoZtbKiHlsK0VFEpJju5M8yLzvK1LM4X74xgCNrHX8+pu0kHU7u5KyiQqYGQdQqZl7QAUEJa1O/gvvzkuW7teKyATw+Mx7VIKjJvq6Vpr9VsF++Xa/GYmYGC0mG/pZGJ2M7LupsqBbpzfyMQhWndTCREwwnEaxhR673OTjLRP+NPzugqIWevChCnl+yPvD+WnH+d53kpOKYYS4/G4G8YcMOzGMq91Wa7bVnvX2io0wUT7h7tMNJVpP+0/fH7+8PQe0VOJa1VHPO7LWEJvTVUJ+Pm85MLfPh4D4dZqzHFIIwL/8PFZRU+XlQtPY7he+zy3GAAAy5TVQJuI2nGfj0MaUvn+h5/U6M3jMSJdrsJk94fh8X767tuv/vj+/dpbr+04xv3+7oc/PgHhIrBJe51r4Hy/yx9P21r7XNfeBSCiOQKYG3NwEOZQNwmMxFy7Hab8+nolgv2xmNkyNwRqze6P5d3juEivta+b3ehsVYSIpUsXTYlFtIkxk3XtYimG3SG+ud8Z4ta3u5KJ0m5wDO4Ou+wK4M4xkltDM8S8bhuhTUO6rPZ0MQQ5XyyGMUWmYOtqtfZx5ON+amLXdQ1IKaTWm4IgmjdfNxGjoRCRB4qitnVRAeZorq3VoYRIMcbQVDlATtQ7cEAOQMRtbcTk4Fq7uR+m3aXW7p6YA6MCXOe+XWXdep7CkPNPP57GIXz19cEAXbdxGLqiqay1ixkYlBBjYkCIHLSrmxHhde3n67qbBjS/XNauJgYhUA4MgBQoFzLXViUGPuwHRu+qtcuQ4zhGFFirKiCxE0qgwJybmDsFRGZaVmliuVCKAIDrYoxacqSASNjFTSREvu1fr3NjprvDjQNgeAMmOTiAmt80uzGGSGRua/VtawCeUnSwW9sYXYmZmdlhrVKbDCWUEkwcETaznFHVt9V3Q4yBl1WbqJub+TRkB5rXPo5FFRAJiVPE6/XlOOYhpedli4mHnD59ugy5xEJA5o7rokw4DsHMpAsAODk4EPEwlsNYwEEMvn//RErfPO4XqdMwXJarqRmQuO6GIN17UyCo4q7cq3YFJjzuY1PJmUOM8/WLpGUTM4V1nr998+af/PbrZbveTbuchk+vH9nx9bKdlzUw3h0Py7Y9TOW06g9Pl94kx7ys7V/+2S8+n06flhaJvep3X3/7D379p7vdbhwGdPsP//Hf/+HDBzVe1+4B1ez19ZoThYDuypFKYlFt6omoNaOQiGLvJmIJ6e6YT9elN+lij/eHrv56qSmnbb7+83/43bs34x8+Pn34dJEOX3918CjbXDOXQ8ml0PuX9bzMJQNQEtO+iClyQFVYqg4RY6QyhG1TEUNUIIihvDsOKdF1rafXNeUQIkbwy7qlFNHofK1rkxApZSIg6d563w1pq5oKulvvljL3DochgaMhzevWxWJgc93tUskB///QFvBcEqj5zTznJnrbcfBWW23aqt4Az2623+WuXnvPKXeDum5Dzi/zoq0dd8NN0rvfpcssJXKKdNn6sm1qFkNIzAhW24178+UxEwGc0Amlm6kBszubSrntrtxS4t2U2iYpxJx42HNgaM231VLANw87MXg9zde1A2GMaE4mhmaXRYjwbirz0szR0GvrNwJmN6fAbhIZmdHNGDkG3o2xJLpc5cPnGRBC5HGIKdFtl6Zqhym9nLfLpuBGDCFw5oim+yEiEho1NSANhGB+3VrKMTHXpjmRkzliJEiB1mbL1nrznMgZGDkEXKsygYqnFLvYMNDdYTgMadn8b/7uwz/41QOSLt3Op6bqY8nPz5e6ee/6+HYk4peXTU2ZdMwDs3epJcbzooS4H7k7AkgK4TCGkNg8mFrJ4c39XauXw25at/a3Pz29XJZDyUMOy9bWtTnDtA9Tjk+fryEg/sW//ZettTFPn87rUHC+9BTpcMC1mQi2VR3pMMV1q0gBwKYxovNQyg8fn+tWM5MTmzqYcQBA31Yxw93A0y4ui6CZobfuNwDSbpdUXERrFVV3uPkghJAC37KdDoR0s6+5+5davYq5AzF+mRmpuTvEQAYA4CmwO2xV1RTJc4oxByY4v67MgQk5eh5TXVx6i4XR2cxUzcTUrXVF4hBYupgq3xpyRMQsZoFIxFrriLeDqd3GruDggGNJ4aYOUUOEQMHMQuZWO7HlTM9Pm5m7OYXAN7cTWIxsDm5KwITY1W/JfgVPMTBCqxICmSMT7A9xW9Wd2w3IbpADO5goMHpg6mq5YAi0LQ5oQwnrtYVIXYDQAZA5tN6JKQS+qb4RoDYZh+JoN+SVA4iCuwcEJBDVm04iJzJ3dyJUJ2Jihm6OYxk44unaENDMQiJ3aQ2Gwu5sBoFsnPK4G9dFmkYHSBFN2jCUGIqHwbSfT9s40buH6fXUmykaXa9zTlEVL7VPYwiMAHFZW4qcE4E6EOZEfeurCCIOIQLYZW2RMQZQ48CYE3UBBYhoy9a7+b4MD7twXdvzpU0jpoxucV57znHdNlXnwKp6E224u4OLuIOnHEzV3RHQbrxaRnA/TmUowRwv1w0cAKh3+wLYd3BwcCNGu13BAIwEBLfuBjMoALn/9lff1Fovy0yMrRo6dfAUCcwAUE33u2IGr6d5v8vmDGbmuqySMjt677DLiRjV5O4wtK3+q3/2F4fj7v/7X/7TOKZA8fd/+LD07e39Iee01L7MHRDdmzheFpcmatDVdimljIIaMQf0MjAhANj5Wg9j+eart7XLdb1SsPVqT6eFAwRK81pzjGttt9/WzUtOJl3dukKJISfeWksxjKU8n+b9VMwtBu5qJd+ycrgb47z0ppIjJY5r7bd2vpqis4gC+7a2u/1orl1cBZetEzsCiHjOYT8NZHBeVkfoXbcmTNibi+vbhz2jJ4Kvvz58++7hdF3Py3aY0seni5t+96t7IpjKbllX0XYcj08vp/vjtCv57354+v6nk5hVsSHybopP5w3UA/N1WaWru4UU102Z4BYZRQRHGwrdXD9M1KTnEnYpL2tTxN0Ym6h2H2JamsxrK5m+fdz3jjd5OSBpV/1S8YGpRDcAgBgCMpv2W9uplGAE3g0dOeG2thjY3MyRA1fX67XK1pmTugPDjSZ2mpccwjTlViXFULv6DVkSiIl614AUYkTEh7v8D3/729768/MnR6fAl7UtqzLSYSzbVj+eZmZqzdzguE//9E/fEsB/+qvPtbdS8M1henqdQ445hvP1WhLnFKchMgU10G6fT3N3I4Y39/fn07VtbZwiB6ydQK33vqx1SqGUvG3y5mH848fnXUllyL3Tz++fpPdffffO1H78cHJHs7abYil4ndchsyE4mJmbOFFE4iaGwNCFiGNJU8ldu6P+D//sL1rr1+V5Pw3Y7W//8OGH06uZjTEz05//w998ev907su7h1G3ftiNIsvXj8dSprWu//C3f/rff/jxP//l7wzi1rtpAyfiYbePavbD98/L0qfdsNtR4K5dGIMzAlgAPJTxWmtMsQufrzOiY0Bw35Yu4kisZuSeUwwhtC5gCkjHYyG0pcqbwyhq57m3Jnf7vbtvm9RNgfqNG59LMaCuPbCGiCKKBu4YAudEl7m2JikzoOdEruwOkb07IXnvGwF1gVLY3NVwSAGBtrW5m4L1Zm4QImGAECBxbOI3wS0bbN0/n1ZXZw4Gfhjzfl8uS0WFMeettZy5JOrd16aX62xmDuQO5E5ETeTxMKRCaxNCY4Le4XLdzIwQA0PvLRCUQjFyrQoIU+ZWbe02107ojCyOTVtCQqeu2sXGMaMBoPOIbdMUQwh4ed2OxxGZxsyReGvd0e8Ou+eXy9LEFbelcwyOGjiNQ+IA2j0QH6ZkrbtpzDHlOOTQ+9bEQilrryIeEZGhlLLM8nLeRKU2cQdGBICu7uClUCSqVc3RzQBhzGlZ2zAGNWvNjofhfNpKpsMuLk2Z6XLtom7qu5xS9FUVwE2gNY2REYAIKWAq4eZkMAcRi4Fbt14NXJEwcBQRMw8xjIXBFRmGIbRq5/Nq1hFwWyUH6B4A4Hg3ta3XKtJ1KDmnRASH/ZhLRIdIUc3mZWWiWrv0DgxmnkYm9/PHpze/fFMOZT2tunmI9N0371IqnLBkfH29nOdlrmtA3AQva3v/4TmV8qff3gdpT9c2HUZy+NXjw1zhv/z995mjmFPmuW3Xi6B6SWyuOUaAmwddHSEGf7g7ni8nVf3uqzdoklL55t1Dq/X/R9OfdEmzZWd62O7OOWbmTUR8ze0AZAJCocgiqSIpSqWliShN9VP1J6i1pLUkcrEosgRUEUABCSAzb977NdG4u5mdZjca+MUsBh6DCHc3s7P3+z5PHf2wxNf12rrdrvr+WM7LdLtAKfTpcqnOn29b36NQRqbLtgaYWtxuI4BsmJmdT8t0kH29w+Tw/VPhJII8XF/eNiI/Hea6jVpHmaQUNqNg36vXzQADIZKQO9yPSgBBSObuHtosIiTx07uyzMXV1eyHj4fvP+Tv3y2U8OfL69cv18NxEQakhOFaVZgp8fBBWP7mN/03//D84UNBh5e1HQ/l/fvjXrubnc4zBhIxgl0vHYhyJkAm1q+/f5VJUjnkRJyij+73lmU4kbBwBFCgNSeMNjonOR7zNKXr2ofqshSKKEUCQLv+4fPtME2j6zD97uNpLnLt+vnrbsOXJTHL+Zium6n66ZRuexMMDDTFx4fl69vNAx7PxwQwxqjDEFkIszCAd9Ovbxs6Icl22x3AA4nidJwIsHWTglMhVxg6chYmBvTelRDP5ywhSDDciInCzOJW/XjMJfNWR06JEO+DVyYkwjuk0dUJcbgP9SlzSgQErtq7cipmLswAROREYAbj3hJAaNUAcJmEidSidVN1EZ5n2are1b+jGwRMU86ZkMjdheF661ORuB8swIcCBk0l1zruZ1c3i4BlLjlxInl92ygVTkIAn376IgTzoShESiLIex0WjgjAYBamTABTIWRqbaAHZ8g577fOgpJYmB8O5fefvp6XsszTpbb350MbxkEofKkbEanS7a1PRcrCbeho3puGx+mUp1nyNDMTMbY2+ta3bVy3/rDIXv37D0+Xsf75N+XD48P/+9//+J/88fvH46QEh2kSkdb6tm058culm/O3794/PT39xa//6H/4d//L//Pf/YdvH9792Q8/8JTyPP/w4XsO3kf7H//d//L88vnd47Ttfd3MzOvdQM0YgGpGgiUHwpiSDOM+ECirEQEllq22h/Mpi+x1W04HNTgtdLtsb1f97psTor9du1rMJb++3KajfHg8+KAvz28pp5fLRgJE4Iqn00Ls+1rnebqrwREoJTgs+cvzFg7IQAIU8PGcidPl1lStFLnctodD2YfvbYQHE4vQssjeRq8WAZygZCYUIkMCM88srQUjRKBjzJO0PqYsy5II0e7RCQdTJbk3Fh0Qw5yASGjoaEM9iAFVXR3MXTIK03Ybjw/zciw//vyiw0Xkcl2nlA5LQrRUSm9au/XR3j0ctqqtdwAIp9F9Kpwzj2GjmwcI42FJJRcEv+799TpKThBOFB8eZgscBgBekvTuramBnh8O5kNAMuXPz6+HZWoBBEgYgM5M7ogYALGuPRyWpYxhiMiEajpPiVjWrV/WLoI5EQSqmgdQQMr4zbvl6+u+bvruYWlqp0kCfG86l1QmxoAs5evrhkGc8DhJFqhjlMKM4BZG8Py6PZ4PifHr6z4skAjMIkwSR4wPDw+lJNXuLm+XDYVvWyWAHjBUl5yb4rbtH87FgZCR3B6OZW1KjECkFrdbKznV3fa9/vq7pxG+uT8c0qhBzMz2/Lq5Rinl7W0tU1mKQHh3HdoI8sQY5F2VmGQWVH15vp4WlpRvNYCUHSBYwX5piyNMk+gYBCgM3bV9XndIKSz2rfMivdHl1cYAgIiAvjdkg+CpYL31dfeH4+SmSTgidHQMNHO4sxCZTfW2mYUTYLgvR55nuF7NzE297h0RETHCEUF/qVyFOSKhRggiM7pjhAOEeRAToVuEBQx1IiImgFA3Ygo3ACLAJMjBANiaBiBAmIX+gsCwACVgJCQi1+hdzYwQcmFO3OsIv49L0YaJsIWbg2oYujAy/XJ8x3saJEKECCVlSoV687thRwpEhKuxoA4cpABw50abKjAxADO5gbozc60qhEF8l4y7h2oYhHmgQ4BDoGkExNDBIuCB4GYmidFM1RCiTCKMbmBhU6acRc50ufahdpikq7uaB2L4nYvmoQSUD3kqvO7m7nZXOwK4uyO6eQB5OCJNmdx8rQMZw9zj3ofy61ZFWE0FKbGHqjvfA3NJ3AlbI1y326UdT0n7WusYOQlHqyszlWlyg0lmofm6NhbcXiohEeX/4s+fHLe/+kdkd2TMied5OWRG67cGXXW0GiTCpUxI4YDyNKfWmqlKIQawMRA5bGw6HPh0nI8lkPo086Msb69ve4WHx4QIvY2SygDvvTODOQTgkjIzVFJVt2EQYA7CSIDId3q87k33MUZTQBImCBhuFBRgInJv8QViQCCEB3kAI5iP0zTNhR2Cibbabut2azswDoWSGCm2AWAYAB7+7XRyh9pK6zGsIwwkfHpXPj6USx1fvu5N62Ge0OGbbw9vF3+9/fz5+cff/f75+z9+5/Hyv/mLI8rxd7+79t2mjP/yP/3ucq1fv76ecmaoG6EgX9bWTW0HTlS9ocfWcIyRBaeSMHDOh9ORI5Q4MoSrOflxyd88TrWNVeK21m53dLpJ4kVy675ujZAOU6ltbHs7TMk9hvqdDZEFE9PW7XLzPlyHIfD1tgtyUHd3tV+IfsuUWzc3gAiEeDiWvQ0zKyljctW+bxgARMAU78/zp+etNj+cUi7FtE9z/qNvzi8v2+N5fXxI6r623TnOx0VXI8LTw/wf/+ZH93ie23VrX94qI90ufVe9XG+J8pwXHY6BHqFqvRtAkFAud0MOJiHzULOcMwu9ve3aIeXgnLab5aPXOoLIstTNC/NeazMY3UvGlPH50jzs+48Pt73d7ndWQETsGoyAEVvrrdv9wpOY3AwIENEhwEBYmg5hJgYIF49TESppV2XOY9hQS0KT8PGQVQ0jRAgYfcRo6ubggR7O0IZOc+rqz88XRMjzNKr+w9//4duPj3/y4QHQwXFfb4+Hee2GbObWB3z+sm91XWs/nnLJ9Ly3/JBjQK8jlL7e+jLTbY9aV1P79tu5mn3zdETw3rf3T+eXr2/XSzueCkUnSW9vnZCQeGj0iEu7ffPd8fbSLq81T+Xh6Xhbt6aqzebMW68Wtm01iRxnXGZoQwGh7j4GKHTAyBmLiDMAImBX81vrvbf/6S//VxYz15TKw3J8acrMy5Ih0p+8P3//lJ8OT3//e/30+fJwWo4H+fRKn9/qr+cywv7+n37zdqmE8rjIa41f/fANB/746fXhmJY5Zj59emm58DTx589VB5DYh+M0qn35sk3f5xG+3jYm2dfmZkiQitzjvgAWbilJKswEgFyrIYGFT4W3fbzeWhB0HQ5adT0vU+vj6f3SvZgpBCIJEfqoU4KM/DZG1cHCBBEhIpETlynftir3LQeEOazbRgg5oUc4hCuYBgujuTAuE1VVIZkK9qbIlDJaxGWtwgTA3vU8F0T7+G5uNa5bAw/TuFzqtu0B+Ha9JcGuad8ZA+u49xYRA9B9uKY8M4mb1j2GdTcdPdydBcytKeIUh4XmVFjgto/9n9OLiJgKfjhNplCbaR2CkEs2dfDIJaXEY/RAOJbJLQKjCB6+OZLkde/d4HqtDlYO+evbzQnKkhPxcpBhEVFieGj0MZZDnhfpY8wzH/JMxOfT4fE4v7w+NzUFoEhf1hsQrltF6TlNr6+rh3/77WMf43KpSdjMWcDN5znPJf30822v43zMx5l6Z3QuKbVeL5c9F8lTWnelBI5+OGa36F2Hat2B2JdZaJbiHuqhEQQo0MfgwNV8jCGMmDPavTMeVu1wkMOUHex85KcjRog6ljm5I4cCF0badk8E7na51OvrOtTuTbSnp8P5sIikuu7eYwz9+fUrBJxPx27729vl/O4RGQuLTPlyfX385nE+lr7dKOz8cHh6XD6+o4dDIpklTd99/KDWWluvz29frvq2fj3Miyr99qft3cxLpjtl7NOXqzlfLjYVkIR62fIsT0u+XNq2dyF+fb7euv6b/+xPSsZbVwJ+fl5Tnr6+vvq4/Nf/2cel0IfjkZ8+/ub3f+htFcDAlGDcLvvDNP/Znz5++frK6BrQ23CnS70lKTmVrW1tOAImZkNCEEkkwiVncz0dMjPutfemyyEBwHqzJPbd9wcKnSb6+mafn3tBCTN1ZwYA3KoDQgQQBkQQ+y8/CAoJC83LREgOfjgs59P84SGVVIh5tF3ECZCZ1l3r2s/HGRjb8OsuX79udbcP38wYnib+ZjqGx9vzLeV0XCbbex+RcjY17x6siuI+murxccmH9PpWW4fjeQZKfL+TCh3n3C26jomxJly3KhMHQK2DGHtrbvSy3aZJJGRvtQ5LLG/XykjzXK5r22oHSE+H41bbPAVLcnWCFsDXt2phIRwede91aIAHYN2bZA4iybjd6rXpsixMEBjv3h1H933Xdx8WTjyVXMdw0958LjTNciyJAddKdyRXAEmC8OgtXrebWlCi0WKaOAntdbx/mNBhnlOoMfDTU3GL1r2rEwUh1frLIXpo1KoIaO7vH4tD2rdO6DIVd1pvygIipAMsvExcsqybtqY5iTsIwy8n2IBWx+Op7M2IIQx6M8AQcjPsrvPEENRqt/AyZSLTYeta5ylNmQkxguvQt9frnNPhOEviPlxHAwhOYqrr2g1CxISodb0/0k9Lmhdk4LrHvvf5MDFykJl68w4UI6xu5oGIDo63W9/6CIR155ySqrn76TC93fZtH8A58G43CJ5omVJAvDvnf/HD08f3jz+/fr21ZjBdVplSEaLf//z6R9MBghYuv/qj09rstEw/fPM+yIRsmdJezQ2Y54dT/ld//vT12v7w8+X5+fX//u//7q1u/9u/+BfPn57/5p9+j9lsjL9K/4GF366XvSoTnRoywDyFD1sKdhWIGEOz8DAf1UtKI355K5ejAYgNvGwNOfXOYLAcy7szvD5fztP5VA6jXS7rVtv48Hgw12XGMk2vN3WDW98949u+R3SPmYlEfNsbCCElQGrN1OG8pIjx+roRYSBoH0/HU0qhrn3v+9ocYm86hr+Bmvlwn7MIyxi6XptjMGOtBuDLuaybM5MOn0s+H+Y9qQ6tXTlsmebDIbspQFzWPUkCCAxwhxh+7y44OET04REgxMPComdhEQlTSX5a0jKVh6V44Dwto33V1svRzseyVb2sOxHiDtrtsKTjw1KENrdDKsNtH0ZMvZt7AEAgiiAG5JR6Hznju6d5mJfMoQCBLGzDVC0lHupv112ECZkBwSlRfHgq2yiuHkPTXP7Fr97/9PXy6fMGgUAgHKUIE217zSIesY8Ad9+6x1imPGfKWYZa68ZMDP50yKv69WYM8nBK56OoQkm83vp/86/+5f/uv/pvvnz96R/+6T++rpc6IokfT2JqTZ1ZD4fMmN8uN0IhhPNcfvz89rb14SbE2sBN3x0EEZ/f1pyTm398N53PRZhOy3RZR7KxPByvt3aa0w8fDj+8n//wsr9cbpTEHR8P+dJgvY05+w8fjn/4dCGZfvX90//t//Kf/fT8+v/47/9xVH8805zFBhem6zoC6YcfTm4DoKslqICQ5pxKgk27+nB1qwEa2v3N3K0io3C83jQlYYrWjYgQcdSaCiRmud62h2NWtdfX2gXPB2aCl6+9jRC+Rx5CNZAQIJoDzTQ0Xq81iBDDVEe3nIUFASDcQ10YT0fRrrUFIMAeZmAKAXC7tfAQJoBAJAgjRAd0d3fPRCUzQiAiIniAh0MABKmD+93cEQiWC6ecwr0NRZLaDCKQKCc2ByYkxL1pzmkMNQtEBAe8H5zMe3eLQEJhBsR5IoxoTQmRkkSERzChqSNAAHR1QsqZU+Jts1yYBB8f7/4ubgMBDCEC8T5LGaNPx1xKqXVz93tQkwjd3cGI7kMk1OFIZAFuhohCdFexDTWMcI8AAAxJWEpeqwUCKITB8VBcQYcDQgBodxAyD/dwx7przoyECLhVVQdhRAq4U5z5LlaCXFLA/R0GD79P8O79mvvL3IMpzAwBp0lM1QMiSNVLTpIwCcFONlQtkBkQbWioz09ZhzM6Ad7qcOsed3HVCHJ1csdemzkm2d7N3w6Yo9df/TAhz989Hk5zryMvUzDlOsbxgFkcPIUvtj77UCNGpHlitxsKDA+yHAABeJgloV03u60rQkBK4Xa9vKygp1O2yAEcbL3W2wo5ZwGqvffRmAkpDvOUhQRBDaY5DfXXN4UgQp+yIBEBmHs4j674S1LHFdDU3AfJ5EDunoTVTVWFBRCHKjOrOQS4w61bGwph/voWnkUgBl6v7XSemMN6EAox7LX/9PPWXHftYTHLxCy6jxhtmcrM6S9+/fTTl7cvz2+//uHd5y+X5+v+u59v7kE5f/56e/ckEfgwHb/9kP72N1/mMpvujn2Z09vbWHcz6yDMyZckbrEPb3uL8HlJFjEvy9OpTLm8e//wm7//+9fbl+mQRrWHdymXtK89lADHd99M33789e9+/LTvPYlct5HlLnnnRMNBSMh0+CApguglsQc8v65L4vN5DoQYxgBHEXJ8e9kV/enp4K5q6h4ItJS5VrMIVa37WtsgxMygqkmkDVPTuUjJTMiHedrbFSIwkju8XPbrrZ4P5bzMx0mO35d/+5c/rnsLjd9d1/Nxeb7a15faxjg+HMLhx5++ZslmrmAMCQCv19pL3vsINwKYZgmIlChnvrZ+WIoIXbcuQjknQigiqpoKEcKtqs25zNO29W0dfZiGckKzSAlzzm83//T1dp4n3Qfa+O79aW16WXdEamraLRDcPSKI2D3W8OE5CyPBUPcAQhVEQneyeUrgmJJExMzCwmOMTHE45GWRMNvdDcPD3TwCmvaU01wmYtx3RYLeagV7u76+XndGmvN827z/+Pr6tqYpnxZ4OFNAvv7uuQ+bl8NW+z/8dEFwh/j6Vg9zKsJtuOlIBGsbAbD3tojcW8DD8d0385Ls7Xl3i3/zb/6P/5//6f/79fI8a/7605fj+R0hD9sdEdH/9Ffnd4fyt3//u6ksnBExem+CbVv7PcuWi4jYYUr7HujeOyCjmYWhqSIgC5/nAsDPWws0s84EERAjfv70uRTJmYPH1mw5yAGnjKxDQcbPLy+CzgLzeYkEX94uqSyXAT9+XVe1iBWBLOLW1w8f55K2U8mPD+fTfH55fe3vUp7Jeqjq4Vher13Ntm2c5rIcylBzh20zCDV3FB4ePkJAh2kiYEJzG2M4ExJwIRt+vWz7DinndWu/+BIQW+1fhi7HdD7C10tjvqvE9jmXwgldnazMXJB6V1Nt4Q4uzHvtRMFEe+2TMCGejlOvQ0eohav3hACgpsIE7mruhokAAZjJIu7TGfQgJm/BiW+7jmG5IECgeXjUWrPwPAtLjOYAkATuAgUIm2ZhgoSk7kA5hl/WzQYAeZC/e5wQuO9V1cGB0A4zJSIItcBp5jxTALADAhLzcF9H84BlLpJpaKjBciijD7NxflyAcLvtbe+HwwyYIhwwUuZEwQfpRghY+2CmWWSMSBicuJSyFFpXV5V3pxIIPImpXWtfCh+Wx/PpgOG///L1utXH4zKaB6WpnL5etpKBWeZE1pUZ3z1OEejeCGC99rEjY/Q+EMMcv156KYXQAfXDuykzANNWFQXOx4wYbZi5S8II8vAkJZzr6Cx0WiYI2kcHwkSMrlVtzgczrcOWuaApUcpHXCb/eKbjPJv17hDB7uMv/8PbYS7zxC9f6+Wyutnjw4GJhgcSPT2cPn7zJIz7dXeNve6vb7e7kQcJieV13cB0Pi6KPvYRKf/px+/en89r++oW/8mv/sVDce37w8ODR3z5/KL9+U//7I8VWCCIUj4ft+12u1qRqe39pna76R+9L9X6y0tvPSIsM5+Px8PMP/78SYJ++Lh8zeXv/ulzRy3z9O5dOpTURmewVn1vdtvaVOTpaT4tpTX9H//y7795PCCnv/3dC6XBHP/6z7/75ptvOU3Pn366traCTGQPy/TpddcI8HFMSQafzhJKJUmtPSXea7+9bUlyYhChy9pMIzHVtQMEJ2IBMxvqWRK4zwkZqQET6VRkmXM4jOEp09DuHpLStrXeOwFMMxNBmD29O2+1pYKng3SffvflOuX4/DoY+fEx1PTdY4JHqdUuG3761J/fGjgcpvSr7x8CeavqBm5xW+vofnmt94XC15drVS8MuTDsygHLqUjhYfb0NJGDuo/dfNBcZJlovW3qEAFe2EcXltNpHsO//PwW9suth4lva7veeniwICcqM0ZAmhgJAKGuTVjnA5fMiHaeC60wI0wpgUFrzTklSq1WzhIYbTRVEk6AxJImotbrPE+ISTymSU6TeMRtvclMUwrHeDrNAICCbm7u8yGF+b5qKWnkNFSZccZMACmlL69bU3OL7abPpeXMXX0qlBPeXvR0mISDmO9rnpR54aTu6669GwQk4a4IDOdTcVMmHGoW1quHR8oMAPtqDhABg7i1Xjedl6xmELBvwyPCTCSjeG9Ve5CJm4dKAO9uEaYagNBBc+Fl5n0bPgZk7qo63AKZZO+ehhKRalc1N+1d52kCDEEECECYpjTUiMgs6haINeXCTtut3tYxF5GEHp5nQQ9CJMTLtbvhsIARknin0DFMrQ7IXXLB948ycXbPhF5OJYmkJKdjShzWtp9+Xp3iSNHc8zm9O2ZXzXxwJ5ZUe/zDT7fm+PDw8Ne/+VQejol7ba9B7Ero8nxtn172dYzPz+t63W7N3j+c9uv+vO4GNgV3tW3fEksEevd92D9tDhFIbDamQhC07oPJzHsuaZixsAQSESAScLTaahwOpftoYwUUNru99dbit7+/nk+LBkSLLOXxYfnwNP/mt59yzsuEX183i3AAN6pKhf0wTxr6/HVX1TKlhAwkbj18iORmepwTscCUlyIgXPu21n0+CAm27vMiNgwBEYnvUpnwUoQIAMg0IBESIek8TeAgzBAwhk6THI5FVdVGFkH03qOIkGMPVNfRvTc3cPMwC0I0cAcQNgZCAscgjo/nw/W2r5sRhkC4wZdPl8Ty+HFitnVr7x4mIYxwA+SQIikEEKyUwkijDiComx4mvLdhEtp0mABcEhwOk1AgYMnE4Ifz7A53DQVAmOtU0rIkd8+Fm3Z0uHUfXy5tqGvg/TylaqoBzsTmvg87LrMQMYkZ3IcPQnzfKxtqFjwvuY1xLJGJmkVKNAcSokWEx/Vaz4e87Q0S7nX7H/7tv31++fp8+6roaojDdk2tQ+8GEZ8/9znl2q1I//Bwenmt19uYUzpRMffDw3ye85Ll/Dj//vPX6zowlcu6P5xzvbVlObWuj6dDKvjdt6d9X3XY737+CjzvLdxb4zF6cAZwHTY3hR++e6CE3z0u/+7f//W1jYen4+jjttnXl+teh1A4RD4mSfL15aam6gTBTw9HDI1wIe21mboQqnpvnZkIwwxGOCO6jqYWAcyUBcJcGw1w/OaP/gggwLEpCOPxkEb3fgcwI5jHMmdVa3XkIq3H+UiHY7q86XAgQsGQJENNh95BU0P98d1kal++7NNUGAMozFAQ6zAiBI9hHhEeWBJFeB3OzASwTJInrs1UAxHDXdWYMRDH8HBDoghAQCE4nQsh1TrMfYzIidXczJhgLhmFL7fKCEyoBiVRmdgc7pQ9s9BhJcu0ZB2219ZHmIWQJ8E27izqe7nXS2YPgMBpSr03h0gzE913k3Y4lNFxr05ESDC6uuE0yXykXrsFvr21UAcEvv8lFER4HwrZnfVDcL/XzoUQuamZergRIRETx9NDLoWH4b6PPkKEiuC+DgNS1XsWTph+wbwzMmHOqTVT0ymz430YHYgIAG73cDiZ2Tynrt6aIbg5shAigMdQu29niTAzeNzZRIFEZoEYzHdTFTgCRMR9xkZMGNrH+WEGxN7t8Vxas961W7iDCLl57wbIwjgdhDi7J5Q8CT0+HLb17V/86tuXy2tZ5h9fUx/9acHTzHUbf3huRUCYdk/uRODLDDquQ8frSxWSlNPxkAlRdVTVcA9ADwrAvu5MJAXUPKGoKZFlKafT8fXW1J3Q8iSSxNT2Wz8fWVIOyOHcutfW5jkLBTC6ghCvW3MLIaAkdYRZzHnKBCXF29bX2k8PB07ODJnS6HGrnojMOgUg51KitoEQOfFaATGmnLaqQkauVA5CyBhDTSMwHAkdcE756VgE4nntnECYPjycLOznr9tQ0xhMRJTcqY/6/pxJUu3948N0OC5/9Tc//qu/+LaNtvbRa1xufS5lve5uNBSPB5pLel1bHw5uKQEinQ8FBYkw1N4uWxLISzkf0+k4EYY7bmu/bP37b05/9M03f/ObP1zXSpH2bSfWaclPT4djmV7e1s+vm44IQBFSt9NcAmBrw5rNSYZ7Knxa2NTNIDEEsSqYRje4T0UREDG2pneOfR8WEcuUAAkC+j+LkYj54/l43Wu3IUK9eSAGODipjf/Tv/7Vu3Nppttmf/Wbz8ssy4FrG6iYc/79z8//5//Dv0zC//3//PfAqY2hQ6dc9trQgZOo22FORCgJb5f1fJxKKc/Pa8pZ3dyNiMOjpPRyvaUsBBDunOhQsnk09UT0/LqFQ5pomuSOgGOWy6UR4nmS8znlMr1emrlPCWv3266SCBHdXd0RkRkhwC0A4DCnQBhtlCwitCxpSYkQh3nrNVBq7UR4Oi0A3sYQogBQM2G+fwdBuO4NLJIkjUgJ1YYjIgaSZCyP8zGXlBK9f3/+px9/fyxpKnSrRsRv11ab1+73Wt9t6x441N4fp4h423cIAdA5s4XPU2pVheh4LJxgkthWI0wTgwuG4Jc/fGXE90+Pr9v29fUtscxL+vAw11t/q40T1zYejqXVfdtd76qdlGurp1Na5sPby24QnMjDRjN0BPBSyuhaprxVI0IIYyEmMAUDzAnv369pyg/LtG8tYyxTaqZS0sPh2Pfx5W1TBLfKkqcpmTZJc/dxPNKcc2KZU386TIdp3vdaq+WyvKzbbdvdmQLa6APicu37pjnh43HuIzBi67puzYaXQ2Kh67WlRAjQ9zFNGQnNIouQoGQswhj49e2ac8opr5fNAkmIKTDQ3Msi7w/zXt2Q3D0Rz4lbr4CGwhF2j1uvrQ93gLjDpM7HyYeXLGGBjCR0XcftUk29FPnw7nBdaxLMmet9VCDMhL1rV8slhXtrIyDmMoWDh49uEXdvFCBxAEyZD0uqYxymdNsaYhzncr22MicHH32EBQCUWYSQINS9tt7aAKTjkq63nphR4HKpRPjuadpvgwjPj6V1X/dOjIlTGHgEpzgdcgo0D8PoNeqmeU63rQFSltRGtxg5pzLNo/Z5yrUqCiTEkpNDbNtwREEoQm83ezhli6itJwFXSoKJIIgk0dBxu42cJAm/X6am+un15hFTScjCCMOsNkOIXHKA966H44QUpmADEHCvzQ0kpXlKDhbkUwIIN9NlkWUq4Vh1rPvY1jpNYoGt68f3i4hkEka87vrp66UUOc+zKu61qwdzLDMfD3MfimQBkJJ8eOCxd/aYj3mYWuvnpeyt75qEpdb6V3+9F4rHgzBB1T6AWTinJCzff3zKSYbq5bZGt9Z9790BkdG05ZwCcV0rIeRcJBGSQ4AN+y/+/C/+y//8X6nuoDe321TkN//4ZVgsS9mu1//qv/pzNGq9X7fr6NCcv7xd/vI3P71e7e1lZ6aH42wGW+1IECTSbv/X//2/mPIhl/55vf7jT3vdjZlqrd9/cyLit5uudQDQncfSVR9m+P7DmZFKSlt137blUD5v+/cfyp/+8M37x/M//u4P4Vh7+/F6+/ll9E09QR3RN08TnI6ZAdd1DIUl41aj9Xh+vvVupSQNPS3T5bYTcBKUxCQYAQ9LWQoApr3qp6+3qeR7Xlom+ub9TA4liyTKJQX68/N1vzozvt32bVVAIAoWfjodHXw6yLvTJDkDBIu+7tvC/mcfpORUzV+vA2x8XvHLVzBvx3lJwY9HdkAkCMK3dbxdttGMhPfWQ+OwTMCATIl523ZTyAkfzqdRbZrg4VjGsOu6Zc7LnDW8jnHb7evXmoRzpoh7zRtv6346To7WhmZJrWkfQ4c/PszTkpBpnjMRDB+HUmDEpY6HUzHzMcYhTa9rO56WMLM25nnuNratagQgCUm3hsR9G9Z9OhT1wUySWCi5ekDkLBgUoOrojnPGPmzKkhIHele9+1IF2dWIiZgjYnRDgtr8bW1j6JTzvg1hKrMgYko0T7LvlhMHAEr04dvNCFGEzNw8CFGHzYcyTWnUof/cdzCLh4di6tdbu2f0IkDNzd0d7nfqxEyMY5gOy5M8ndO2ebPIGVozIvzwbvYOtfve1MNzJgD0++obwQOmJGEOiIQ+FBCJGJcpX6+VRTiRqu23niQdjpkYEGHbW8nEzB4QEOYwugeC3pffI4QIGc2DEadFPIKA3GzfRgCJMEQAAiciRjVLGcskWeBQKKUCmDnQPcqUDksqDMzIxILoYbWPHhAGDPxa+2Wrrao5MIk77vtwdWKclkwCI7oAeYe6jeNxAYJ13wWBEO7QUoQwN0cghohYb3Uu01Lky/OeUj7M6Orm3PpICbdtpJQiFAgkc0CUTKEeQOfjxMPavj19OHORdatt6DxlZOoV24jTLJdNidI3Hw6JQKgExsvb3prvBiRl2/c6FILN6uPpFICXde91MI4P7z/WWm/bei7p13/0w8ttG9YPS0oSy5yfX9fX6xaAD8cCDnsd6uO4TNs2zI0TM1BrRkwWgRjE8fhQGCBLAg8EVDNEbEMZiYiAQDAi0CPuFf51GxUMIVxB1ZGw9oAI7d2JUkoYke6SYAOO/Yfv339904T4eJr2pofT1Hrf950Y5pL3tQ/XbjEVfrtugvDh3YmZ3PzlMm5rlwkfT3m/mTssc3p5qx8eZymUBHNiGDG6v7VxW1sWiQBieDwWjygl7a1lpu54vbWS09YGEW2bqkNAMMVpmrppEkbHNsY8ZTMHuCOOgJhVHQF6NxERpnVviGTmSZAZ5iLMvKv78NE6MlEiZoSIcLQwIw81QndPRNJbNeU++sPTkcNMLYI1NKf0dD7cR1HzkseAcOMM3304beullGRBzLT2+N0fLh7ww8eHY6G3rX16fgXvBjTNJSGt1zUJT1NRl+vWwxVDA7D3zuQk+fHp4Vjky5eXZS4BcXp8ulx9aFsmtGHVuo2hasQsiLX1MOvNgOBwLMdJKOz5rb5dRmIAiDHcAyKcMZAJAQjBAe/XcHPLQmYRgDmxEEBXgnBhQqZtbRbIRPf0jQUODYgYFuKRBTzC1FIGqxHu8zmXJNvu++5jGAKwcOveq0KAEI5hkhDdFBgFhQEM1QAQEoWH/3J4wxARwEgCgTxuIyJEKBxIYl5y23Tb3c2FiZhMfd/7smQEZCJFc497DepwzFORbb0PyEfKuTCeljQtZEHr2j2CObQHEhwWsIEoCXfLIte3fd0cCQkjggDxfvpkRLNoraXCJGDmwsQI1vF2tZSxZNluA8XnpZDnlIF4HE5ZmCXRy5dNu1kAC0OADg9ECiRE9/C7Q4o9ZSZkJ4+AAI64D51xqAciEaZE7uEWGsAiqr+4zM3D3ZDvE57799/tPnQlnIuMoWZOCOGgdjcjojsQIgIQRgQgAiN0vY+l8d4Ji4AAHMOIqBR2DyRgJu1KhAZ4NwaaByCaeWIAwPXWmVnNrjdISYgphrpDLgnd3QMxwn00JO6j74B0/PjedUfof/P3f5gOE0/8uMjLy22Scnl+ayrm+1DrUNxbgANQ3QdSBTVyW7e6nDKAff1aS6FazRRICDwCQLspuRn14RUbM4L7atpGDO2MCAL7dUyzEDqTr5sK16F8Oiy1jj40Je1mhNCaIaI7ppwjJSQ8P0zCcUiiAwD6CSPP5VDY3C9bkwOWpEiSiNuYAOxy2/JUPrw7Xq71uu9zxqkwkU4zv7zUNnzJXZj6MEoMtZlDKek8c9huUYPy+Wl6uax1wGFKj4fDMmkzR5yZoPdRDvPDcpgz/f7LRRhf37qbn0+Hf/z9zWMEeKu+TFPd9PWt/dHH07rXa2ee+MO7w8trUwsMDdXbNhA13OaSh8qw2Fu/Xf10gnA9FCGBZSq/+93lr/7ms4URgLkc8/LD+/dvr88/14u+I/D444/vv75eUpa92fbavlSVSereS5bmVptC19oAyT4+HpuqatNBfQQxJOF1Ux2RkgD5Mk8M0s0C0FVZoA1jBLUgpnteWk0/vDt42EVbRJDQUsp1x5e3KkK3tn98PCSBxPCf/8m3v//8gkRt+L/8s2+Xib683P71f/Ld3/72y1Y1JU4cA+G7b0+1D3UuOX99XbPS6bSoeQk4L2IYCTmcvj5vBBhz5ImWIn1oEQEkIUqCw/veOjKmAiTOGYSk9f64cJLiAE8PB3QUiKWkNuz9Q75tNSe51YEYgUgE98dQd2eWCDBHh5HnhIAg0ca4vFUmnAqnJFk4U1H3UE+JDcKGIWMRiYgkJIndIoimpdQ22MPU7gXRu4c1ZDj03//0ejjMWbh3u5lYpIfTeW9bQsXEYbtMcjqdjtfxdttgzurGQsxUUhbJCLFMYmpGfjrm67bnRNOpBEPrt9psH+PxNL1/yujR9q+nKY1TXm9at/rjvjNRC6QYbnG92XlJe6tTSvM8tXWTjEz5du2BEeQkOHYjJAsn5jFsKIytLbMwYATzxL32YQEI++58B+KiE/qS0DxW1W6+vb59ldtUSh0aERr2sHDb9nWrKBUJtbMfYJGuETjKhSpTUuOXtV73uu7j7eVyOhcUQIAiDIvNpezVRzcN2GpzC0BpmwJEFlrmAubkOLoHQBve2MvEpLEH5CKH08RI0e3xPG9da7OhkQQRebspjJampDZcx3LIVeuuZhaIfSqZAbZ1b+HLMoF7HwYApjG67bsjYh/DwUWICSgzC2jffWiepvu5hZnrrhgQGB7Q9h7uyJRY9lsFhDTJ6ZjAcXp36K1LZvXw3om87cOGS8bTQQoDUiICFo6gbR2ta6LwYTKLdlALQJqn7Oa3a394mObMuUhOBAFDQ4RuN+vqOuB0SgTUTVOm4zEzRu96J2gcjnleZK/t8f3shj7g9W08Pi3HA6/rIMAI70MTyLX1ymBuAAoESmjOh0WGVfMohYQlEtZaHXlJJAKSppILRKy7frlWA+CSBUFyMh3NGid+PJTEvLax73p3hwXGqPr0eFQf87GIUFdDMoZIjIQ4FFJa6tAvb50gDEzVzeB6ax6sHb5Yk9QS0zwt+2hEUCQdC+/Ql6fp43lqam9rrdvtcCzHAoQQJOvb9elQcqKIoeHq8De/3Qjj4YGvt+Ydfvj+EKZj7I7BU3mYz+dD9rDCOYbWpnUoEiDL+SwnWYiBIdwOacpBcVsrOOQiI6LujdCH4c8vP/3N38T//Hf/+N/+13/63ePJmv36h3dtjL2H6lEw/fjl69//7svWdN3hsvXX7eoCnGgqRQifHufWBqMeD/nj0/y0PH37cd5b/OpXf/KnaHX997p4IP3+E9S112EWLAh70/va+3A+uvaf/vD89PR4mheXqkf+5ruHX+f3dd29wT/99qtw6X39/HrZu5fwnpjZjkgyp5TR3NuwbdcxMKGUxBExzWkf9rb35SCO7g7dbXgshHNKy5KZ0IAhwjCe3h8YwxympczHPJH3ZiIwzSQIvaEpEoYgnU8HorpuFYja0M+X23SQDKJGr19vALScEie67Pq7r/Rn3yU0n1NqNJZs709eOx7nPOVC4gnw+/fvE6Wf396Y4Mvnqwgux4MNNwsUD3IpueCk3RD8cln3Ombj5g4Wy5ymDACuA6d5MreIVrtGiKoNNQ9IjGp6X/5ta+sKbmAjXl/aVL3MUASH41qbF8wIpqY9AGnf3Xqvo6eeE0sLEwgnSMvkQ82G4nAKRl2OaV1bt5oS16bcfZmidffAYbpMqbC4ahsDggnS589rgJfC694Q+fE8TdNddeRgoBb36pxjIBATB4BMgg5119YtJbLjZOYIUPsoixyXlDBdry0JllLCXS2I2QPW224WKcsvKL19PHdfjsUsenNTD0BmMnNVM0VEYEYC8jALGEOBylRAt9FbRGAEarfbTedFziWtN51nFqExwEz7MIgYaqbRtlEmAcIxRhLS4ds6lgOp2TBHIdXRGyLT3fFKGBoGgUSACIcjr5uNbm5RppSI7M7KM9hvwyOAcJoSEJsGWKRE05yYfZ5ob8yCfWiv0SpI2Ym7wJQ4GY42NDHNmbLgnKWUfJilX9bb7lvtX1/3CFALVZ8mEAEid46mg/c4zUU3q6MeTqXx6GNTDVUPwZzxfirZa1sOswiOMUgET3Pbhmd6epiuN71tWFIgGBDVZjmLmgozEroFIqr/ItB6u1XtepiyuaNC4qmP3Qy1KyEcZnEYjKNu63br5mrDDsdTkBHBLFS1phRCwIzMBaISyvK+sBxcjR0eDyd9mjPndb89355//ccffvX9D58//aHubXS9M3D3vRcRYlzK1Ptwt/PDsm0NwJmpJCSClDgnbk01cOBgdFUIoLCxHAoTAoS6b8MxaLgHugdAohRwL2mogrl7gLlH3Fur7hGguNf662+fns6LES2Zl0SAsKltX1+nSYJIhz5vt1utj49LSoQQJcsY8fV1T4kY6fVtQyLosG0dGRFJhA+z5Mzv3z2Feq/15+drN9haLUnOp2WvrfWBBEUoIMZwBqq76gi8swYz58QLM0LosCJ8VycbujsKxzKx9h6IOefrdRfmqRT3xmSAdj4nQgCkcAiLvVugmY0pC0ne65hz3vZdmyLy+/eLgV9vbco8NK43hcCHQ5nmg4a/vdymadYAMDLTMXpvnRK/rNfR2l3P3H5aLy/bMCfGkolluly2iPgU/Sf1NMu+rWDuCO1WmcIBRsO3t1vrPk0F0fddD7MkCnPoW30Zo07peuvb1lOWrT6PHont02tPJDLBulYIdIsAY47EmHO0EZcXzU/cml8uFhFdHcLNApGI0D3AQgjUg5AQwswAuXePMGJ2B/z47feBpGosjIw+zB34/ikDDAh3cLcAJMR5SuHDwAmTWSDF6ZhseCm8Nx8jeh9JmIXdnQmYkg7tqszMwhrqGvduUmuaEhKTaZibZMlCU0YA3FogY3joMFVYDknYu0YQ1nX0qoiYkhCECCKQBwTAUFsmgcDlIJKjbt5G7LVPWXTY6ZBzob2ZOyDDGKodTqdyPGDroyyptkgsex3PX/beFRGIGTBEiIl604iYFknM7s6JE5G7DQ0bFnBHCMc0Jwxou5+fpEzYFUF9mvj5pb0878wUd8RvkDncCTtE5OYkIhTHYw6nbqO2AY46FIlYcJqQiESYhWtVVz8dJ1W/rb0PQwCPQEQWVHNBDAdEUnNmyIkICQjDPMIjUC0QYJlSuHlA11+WO+Zwl68BAP/SwkP1SEJTIXeY57Ruah5CaOrmEQhCQABIFAB7M4AoEgEkRO6ei5gDhgdQH06M51MiodttmDoigYcFAML3Hw/HhW9tC8PgEkGTLJ+en7//5vyHP1ycwEHBAZCRQ5L3TeeCzBQKx6NcV3+96NODMJG7v7ypBzKhCNa9AxEE3FsP6nGnxBMJJ+xtIBAyIwK4TRNJBhvEBF9e7LBgIDFTlrhejYXNXRIdj5kgbQ1zScdFbpdtmSmIl+V4mufPz9feda9VtbPkP/32UMe4VYSg2+2G6HmWxAKMe91nMUDYKxLyUCQhRrARxDBMw4GEpcSo4QPz5IhUcqlqzPzd48k0fv/5qwilUo7HSRASAwF0k7Xpuq2H+ZAC7p7JfdOHhzKX9ONPb2lavn2c/tt/86efvn7+y99ctpuWwsyk6r2aA66tHYr04bVpyZkQUkJzN60BNM8F3AmBJKcimYEwD41jkX/159///PnrX//Djw7ITKfl0Ma43DYdjohbUxYkoto7gkwszHDb6xi6LIUFe/M+fF4Ewe/LjSRJze6oVPMgRjXXoSUl9wCEcIgId/DwlJgJmXzJyQG72sfHJQnPZdp7y4Ujoo6B4Jn4/bvHtfXn1+uplDTlUNTWf3y5vu3NzU+H2T3en6fr1lPG1vz1Ur99fyxF3m77acp70+GWOOacI3Ddd+fYq19voxT+8Dh//rodppSntI8G6pzluMhWdS7lctvqPo7L4mAASCyMPCHlgmUuJRELq/qn533fqgGIsAfs+xjD4h4JIywTp0QYQUjIdwG9IwYBgINknCY+pDkUhMggNMLVhtreR8oy1IuklGW4jhGtayDe/9WMweDIeYQfci4pr+sA8ofztMzL9XJ9/+7psu1fn189+tP58Hh8d9suxODgkwgSRFivCgIBoa3fH0qAgAVGtW23CBNBIjzOmYha1TGsTBmF7riTkhgBX9Z2mFiIWtO5pL1ZSjgX0aafX3tXFwJIVCZqbdiA+8w6EY2mFsQZEqGrU2IPH3V4ICLMU4IIoDgeCwC6DYeYpqIj9q1Ps6h7HyMlQcbCdCr5ee2XS8UAJDjO8/fvDwN8KnmSrBpd+167SFzX9va2T8dsEW0bAHA8Z9PIhF3DAEe163VoRCYQAUpMHtOUX667WyxTNrdpSbkQhuvwoV6yuPmcuffBKXeFtisiDDW3AITjQ54Tr9d+Pkk3ax1bbcuh6N0V4YoEAQARiKjDzXxe8uhqZsthvleBiXEoJoY54bopCaua/YIFCskMFPfKMALkLODAch9axfGQKYIIgQjQCMP0F2Zf3ZUk5kkyS0DUNtQsIkwdAacifThi7PtgoVwSAbipRixF+rARfsiCAQiARI7gDkx0mFm7IpEktrCX6wbBOXMSHAoAYGMECWJgwOg4TenutCImc2vdmajX3ofz3ZYLnjIxkpkxIydJkt08AAMdwZg5Sxp95DxJYoqo3XbVRCicwuO2bQDg6EyUJQ9T7da2IYnVYt/1cMicYJ75MOXb3toYiflpntc+1tofT6fXt/162cvEqeBo5o7usK/dHZZF5pOcjof1sgmRRXjAN0+Hb9/hssyPcwmk330ddb9KjvePk6mtrSemWQjQzWEEXG7+29/urvbwrtiA2+t2+lBOx2NJ5cPH43lZvNtPP326rLU1P05zIOyqHiokc0nTLB7DxgAE866mp8OZqLR9z+d5XTu0QHaQMWVMcPz+6VFa/XprjvCn3737/ofvX97erte3rn3v8Pymf/0PL7fakHE58zKlCbgOfXdItcXDiZ4e5j/7k2+Ox6WUY9/097/7wz/89tP/7+8+p0Qf3z2YaSllN9+r3ra6bes8l9NpmRJM7LdKSeTbh/zx24cypS+fX/a13163D++OHz6cD8vy9fXldVt/fmuv28gHmZigqzAwSe3x1sYYoN2FEhBOhdW2rWkd0keXxBAUHqbm6sL0+LCkIu5wnpnJ1SzfBw9ciHi0PSVa902SPCzzH37efvx6fTgd+jA1lyzIMU3JAlvtnOLhfJxR3KyNcICQjqyo/P6YIyKnlAQTU7213QKAdNh0WHTohDDJ9OPn2xYNGEHDXCHBaZ5yztdaW+/opa0NWCULJ0rEYVEyazNwzBNHABKva3x5vtwTo+44TIkoVM8POUmx8L3W29rDwNSYyAHOjzkXqddxPEyOUApPE4cjQ1BOpsPJAygjrdVG99ttm/J0Os8tmtt9l+NF0vWyAvL5vPz442WZC0E1IJEyTaJmPnRZZhQIDySGiNYcAEpCJn67bUJ+Oh1HH60bIEqiec7moGPsXZeSRw81OMw8zFUjCUVEbXo6ZU5ICAS8tTFluV11nhII1jqSUJmECcJhre7mYBEWKXMdNhcpiYc6BNShgQ5BOiAJiPAY99Z1ILiOCIpcyJU0/PFQLq8NEU/nsu+K5GVKvWkpAoTq3uog4imnItg9rrcGEcdD9gGtDUNAwNaVBRmAQVgIEJBQMgHEtg1izhlrV1e8pydKERH28L2qW4hwROSc1OK21iTpu2+ODrbv4+GUXl+asKRJ2lByBCRDQ4Ap55QzJ2TGIpxT3LndfcDXtx1FEPnleQ3ogOnpOJ0W8nBVi6C3deRMU+baxm3dtSvPjIgYjEjCLILuagbb3gHgeJg8rNaRkwTAtjYEQmQzBACEGBaBBKbzLKOr3J+DIQKcEgpz2zsEuuN5EWEYwX04YZjaYUpL5n5vh3e7L85dkdjzxDbMDaRQTgIBZs7ySxWFmaZSiHi/KRd0CB/45XVfW/3weBSSqn3fHYCJYAx7WgpxqmN4K5h3sQABAABJREFUOBPlXDxs22oEAtJSOAuutSNya1UkJ5EpIRMK8VRkuL+83YCwdrVwZhYWRKj7PZmb1e7QllBVt2BhIgAMJASkSWiZ5kPh2reyTN699bHqWK/b4VAAmRHGGGq+HDOF12ZE6OFmUFJqteeS1q0nJsnsHrWNZUpg+O2H8/mwBGDd9+fX9aeXWyI+HCZJcJoSCg0LdCeyPrwPzExMclv7Pxu372n+MA91KymNNsyCM92J6ce5BETVMDVCxDvjpY7jknPGeeZlmiGgjd0Uf/50BeT5mI9zqtWvtwaAicVcIcJ1PDwdisjrtQVByhwW5j5NqbVxvfWhgxKNqh8ej0VAJBS99q4avQ4zJ0Q3U4VfGjXmecpEPpoTiYhfrz0iSkmCgeSBgADnY7ptvu3B6O5QEnX1CAREd2CM+xXj3gea5vx4Sj9/XgEQ4pf4/3FJQ3tvVjJtu5qjMGf2NuJOjnMHADDTCGAiuq/GCe/XLHNHRCLuqhGemTxAhiNg0C+P7EBEAKAOjICIOVFXd2AKJ4I+nJGQgQkSi4IhAgEx0lxIKMB8npID1O5p4u3aIiBlKUVMve9hHsN1KozELMiEqgZJzP31tZ/P+XDglHB0IIrDUUallIgkiiATn5dyvdTrpanalEmY+/B7fIXv63EAu7kImAcw3a9rfdhl7WdiIuodQnWaeZKEEVtV5AiPKXOEHWaSj9M9/7NXDaTeFAHefzxCRO9a9wEe7mGIbtGHzVNCiloNGd2dAlkiJSagRB7MbYu2GwIgQgTe64seQEQRkQQpiVnkxK7qDiwkQgB0xwkBwC+RmQDzO6MZIe7MHkAC8ACAf8YNQwAABNKdCBa/iFYs4Jc5D0AAUJgZ0z1/FIz3klcA4H1ENafc2jCAlOl4kHmS7TYiEBHDzQOX4xQR+9Y9vKvlhNPEw3xoBGAbPhCEoTXrw0rhnFGAmAMRTAEcEIIIA+A+rH5+XZuWumsplHK73fQaN5B4fn4Z5uAWDgE0uqZME+Ft04llDL+tuu9qjgTQqoWPu5Lc/RfotrkT/PLf9vuw0O8S9gBzJjQPHeN8yPvubTc3MbOnc/r2PV5vQz0wex0RFkEAHtbh7aWC7w4YXmLQ0G4uQJBT3LQf5nRdb9t+owBye7vgPvy29XAbGixYW51KWRbeL90zIPptDYxAllwoEGr1aSYIEEZXc8fQcIO6goU2UWIHSb//sTmg+YBA1W669YYEUTIgpj5ga33UVlLubVjoNJWulJP8yR+fJdGvPx6+Pn91U+3+zbffvrx8OZXYwyiBIgeJZFoO+V4xrdWCEigOI0IKBwJIgt3bWNf53Xw+yLa2ueBvf/wtMv/w/cM//XQb5v22UWAEz6lo6MfHKSgkCSINNeuOAIdT7l3XrcWIlCRl0KHuwZnNDdTUzTVaMyk4Ng+PnNjq6GNMKR3mwknfPx4hopuOEaNaH16Wcpj5+XU1tTIVzH6G6dvHp/rl69axor1/kLeXt9frfvx2en6+taolSwAL8uE0QcAwva61qVng6DFPyWzUfZREFrbWDgE3VTwKxXD0JHwbgwEmxm3tBHGcZBu9pLSP0bfhw9UsUTqWiYGHD2aquwqaSH5t/WGh74+5m3s3U31Y8tOhXLdau697B2BhSgRZZFMlYjOniHlODkbEt71NhRPiAFfzbddWG6MelyUC696nxEQ4lcTEhXFveq1rAHaN3o0Z56l46J0k5ao58bZvpmPrHcEstrfbKyP93W8vNroDMtHXLy+Xr89AEGAlcwUJgABPJBoGCL8wiA20h3MQwVQCjMwZAcboxHI/SekeSOOuKYwxmmGECxIGAkKYHTKb+/Wycirq8HjIMjEIZIRbuCcYFnfGhBWuTS2iDUvEZq59yH0hy/jxw3K5NObIid8uDQlYuDdzDckUCKpOQBQ4tnE8T3trh4mXcvzydeutDU6va9+0IWynJd/DlUzRmvemh1Mx99F0DJ+KtNVaUznJvvdc8jzJuulYa14mYLpd9/Nhql2J4LBIIqSU1Wy0O37+Ptp0dxgGrYP3fseP9zbcA4mYYVTNGCnRuoc5tNrLksaw7dbP5zwXGWYEIJLMHDTuDy/TRBHEGEBkYUigowqWOnDv5tUQIqXETJDBwgDuy6GYCpsScjBi80iFam8+AhlIUId5QL2NMvE0Jx2DAXsnBQ0MMyNBChCQrXZNRETkTkRqFjWECQiHxXUb7i6J1QEROAAAhKFkVou3yzot01C1vVMiHRjDb2+9zJQkc4QHDNBpYiGEjHtrvVqe6FDS7a0C3D1DtszYdQBhAhJmIZ6nORC7K5P01iiRcK61rmtLAqNrnnqQPy2zCMfYEVIyu+5tDD+fluut3m4NYD8/zgikhn1472oOOnpK1JtdcIzqd5HFOBKg1x4/bxcCIqTRLAJzSpfrxsTTLABRJp6XIkTHw6IRjHFe0ofz4eEARvS3f7g+zHDb6HL1P/mTolGR7HhkwmAfl0Gfn7XvsK+69x0AX17wfMx/8usP370///Dtxw8fvu3Nt7eXFWpJ8/nAV+yGRpk+nI4IIQm3216v6/k0HR/fEcnDafn1r/80gP/jf/yrT6Avr9v11v7o2zMxlTwlpK8/X3+u9t03Hy7r5dPX12/effzdH14/PX86n6c//uPv//pvf3u5roh+WnLVrq33wGpW2173/DiXp4enbz+cRiezMvb+3/2//sOPn78uh0nVnPh107B+QjseZ9dRTpkI5qXkSdwGcioJHo70J3/y4TQf/uPf/Vjdpil9+LNvRNLXt+sfni9r7V1tq949cBia5+B1V0TNpWiz55fbN9+c96b7rXfNQnqe6XiY3m6i5r13yYyEfscHIswTBWAf5m0/LhM4rnUAR0qJLMCdIu9rX297HSSS29CUJXEyc4coWdwCUjbtdRs9NCcKipeXXet+fJz/+Hv5/GX99Ekfjul8npcF7603hvj2/fuH86nV9TAf3m77UqsoDItc6HTMCrru+1QiTcv1Jm+frwAuJR+Py1xw2/q2O5oJ0Qi63UJSzIV6byxcZpknmkoZIwI9pzv604GLi2+t164ewAQ5s1u8vdRDzh6WJjkecxEOoNHNrUvm2kwEzocMvjeExIeEkoUlUkj+/OmttSC+zzT88883BOxdp5KSoBSWLOSyWexjjFXPD3MpVEoyh+u6M3M4zMcE7rfbephz52hdEXnfvQ97Ok9pKgQ4F6otsmCZaBjUrTP5sogQoTsgNhv3scKycGKUhCVlBIIIAtQwYdrqYKZpSUnIIrZ93B8rEeF4kJzpHtjf985EZpyEmlqtQwQkY0q0724j2tDDuVgHHe7gdG9EELXuROhhANCb5sQW5B45cQTkRMhByJe9C5MwOvj54QCDard12y2AAZFAhIdZr4EUx8NUm7npXKh3m4rYMIOYJlF1AkiMUxEmdLU7dWhdTc17byXAwYOwtwFAhDhaFRmn08IzD3fvFHYPLkVrAajzIixpr/2OYemtB4C5l0SHmS+3RiRNe5oFBWuzgIB77QEiJcmFwx0Bw2NdWykSHtttcBJO0vbuYXNJBCzCPLQbADIRPL1bTI3uRvAwRAcmj9xuNSdJ82S91X1nzqMNZLptgwMwkQ1NCYGYHHcbgIhOEBjhbXdtI2ViBrAAIGSsXdftQkTCXN/aVvUwFUYXgHXdAnCa+LjI7da7QcqpWjw/v75/WB7P8/NrW9e12ci5QACE3lTPxzlJ8jG+e/cQ5kDECdHxujUU9IDugXpnj3LvNh/L27UuKWWBaZHnS9PRc0rLNN8LIjnH0+P57bIvx/Qn3z+tt/r1603S/Hg6vL1ev77VAJ2mdFrKl5ctMebMYwxtBhC19mWetHsfzkTEKAQIMdTvfJ9yyhBYm17ettM0/e7TVzN/u/aUpQgqDAZSg9tmZnEsWYhut24RHRGweYC1yhmnnNo+ajcgAAwm58S3veYQD9zrwEAn6OajDyYiIe1w96uOTuu6zbk/PZxvlwqYDlOZDmXdx/WtT5M8PJRwtGE5Tw745evb62U9HObDKZn32/U1Jb5cFHxqzbWrx0DAzFDb+nbRP/uj08Tx9tbaZojBFG7ee7hDhJeCWWhfK0AsBSNsX/2eXLPRc+HejJgAo7YBgNpVCh4Ped9Uxx0HDAhwn84ghlpMS3IbvcOyyPVSIyIQdahlCcVWDQMQECMQ3DwIPNwAkAEtAAABQi2YOdCREAA98A4SNdPwQJbhHh6iqkRI4IDSRxRBEbR+N1k7griHeyRhxmhDjehuCgAMZiRm9dE6OkDvCgCSqPb7pC1EuHcL9/HP0nciBIjelUXAoY7BhZdJCsuee+++Ny+Zw9ABEai2Pi0pl7Tvo1cFdyIqk/Ru6l4oEFHvjVtEJDAHM993JcE7lc0dEMHU1z1Sppxkmcs84746EQZjIGVmYYrAO4yDszQd046I6fWt3m6t7V2YtZsHuHltnZinLDlxQNwH6mbRd52W9HCeOaBXLUUcA4UeHyZ3GGr0C5PIkQgJENDMDUKEfwnpAYD+8h4SsaoCI5O4OSVEQIgQ5jG8d8N/3ujem1oAgb8UZfFOvUaE+ydM1eEemLL7BCiYmQg4/Bc2EAYhsGBYMGFtIyWastRdRw9G7yO8D9UAwL3b8CZMbfh9qOJdA8DUIFANzcwQAnApPAmbWmuqDveMn6m3u0rJ1QOAYJpZmFq3lKd9HyRU5rStqjWAgBlHD0Ry03sLZjRhorpHhCPd7xwkmVobGBBADhEBEbDXjhDujuCEqGb3sFJYqDkyAYCZR+C+DzNzAGY388vV8ySEAe69wlzkfJberXtAuN9PUAj1ukWRYB/VTg+yXjcb9XDgtm+IwATu9vWyju6qnhPNE4yurfkypUL88V1W07bHGEOIRAIhRlMEDAW1cMRf0lkIAeAWgKjAGantHYDgF9Mnuoe65UQAsK9QW2NmZhjVvBtAIENv9cvnPv/w7cut1r29fXoLto8f3mnQf/yn3z0dc2/Re7ts6to19Pw4S57e3tbRRyq5jwqR122cD/NpkQC77HVb67Cx9sb4+vKyzYXnwlPJh8P5y8sLEv7q2/fvzofffrqsbSM003h4mObCY1QfdTksW/fECVI05nEXpoRl4dqHCOZgVZsltVAdxgOTsAWMrgAAjkJsFog+huXMAmg9wm1eikVd8jwK7x5ooZu2tm9X33TsQ8OtdruuVa39g93Ve56V+jBAGg6jau8uA508p3kpvA27bvU4ce9YEk8S5riu9mqb2Tg9zgJ4LKJ97wOniRPz1swJtdm2dwgnwD6G0D5NRW0w46GkUZsH7haHE/2X/+mf7vtW23ipwxzB+oenkpZprzbNxJDrPtR1WVJudNm6hgnyYeFhsVdnjLp3ZcmF3eJhKr//sg21l8t4epgdnQ1Y0ANvWxNkIno6HSLitg8ERIplom2PgBitpQyHidZh5rbM1JoloilxGyOjD2JCj7B9uBOuW0NEEZ1KD5AImlLUbsQgGOs6zudpeBxE1q0J43mZnq89M4SxjgAHN0cMt3i7jUKIi6xDu2rlX6BsjmgRQx2RguDpIdvowdz7uNWeREZ3ZmSEAdH7MHNkfDxmcxx9lCV7+J1Wtt12HZYl2bCUEMAJMTwcPALG1gNgmlJoLFMmwG7B7OEQfr8H4vW6BYVF7NtOCMKyTDks1GDfdlVDQPeAzImIigjJcUFiZIy5IER6/5gA8DQdppnWTaeJswg47n00tSwcHq0qEkwpYYQOjwBTa9XCoxRCgru+Jdy2tT89HLp6BOSS5kzgNBdKDIaECAShGuEoIkBuGhpoGu49JfYwZsw5eUStXdUBUZhZmATU/N4XjhGANp+mYESArdlySLlEa+EcwlDrkCQlCVqkxDocCI7HybpDACBK4pRQAvvwsjAi92qHKWUi9QGBgTTUtdn5lAdATpzBmKk37bWb8Oa9dS3C2qNVvW1tPkxLFkhoFCwEYVvTwAgEu+npeFz3vTdFwH6rQMvlMqZJyiyAeCgTxKi9MwkjX299XhCDLpetpkbC0B2jq8VQuLUNkHnbePLo1qtFxBX1w8NsYYF4u+7oAB4ps3aDIDcT4cND6RpjGAuVnNt+B2KCe1yureS75iLmjDpsXnIdY2hPRRDi8TRdrh0ArDkSShazAe6m3Wz+6Q0d9FJx67q2WmZ4XPyQMyF1s5fLFg7GtBzL6+u6j+FYDgf89bfvv333YSp4Ps4lpd/+5jfaqhr+9OXiaMjkpvMpYxj6OCxl3VemeHx3EObDVPpuY9Mvnz5/fPc+lXx8Op4f09/+4ydC3NebVa57/PzcE9XnW09EP3z77ucvb3//4+cP749lmv/w08tf/92n55WI6Zunksvy4f3j3/7DH7pFhvT6Wj8+zLebff3y03cfjuul/Ye/++l/+pvPh0Ny8e/+6ENtw9WzsGlc3m61tdNyfjpO3azeWusdlqk1Jeef/vD6U1w+P19+elt/+OZBLX7/+ccvb5sZLYeJgq61KZoF30K1glAKxLiM41S+/fgIACWVm4/X1/3pab6sRqCj2zwlAejDRGTtYyrcql1Bh1pr9s3TlEVKSrl4MNjweS5M2Jt9eHz8etmQNc2nkpiFbq3t615SQseFqczy+W3YAFX/+uVaFmm9j4HwZj9iv960bo3ALfxtBeY0zakgFGnffXz/pz98eH5+/cv/9ffz6fAXv/rm8nKhPC9zAetrKc+XrZMHAJeSgCziy0/XwzJJRrOoCmbdPQ7LXErq2k/ncnwoKeGc2YYNDpnnaUklTa2NL58/JaQpT1q34a5mqCHTNMAcwz0SpzEMI5AEGG2gdrtc9vlQkCiXtCyO7szysvWcxTWWw9zHqqbMXIqMqoSQBIAYAKz2rTsEmLp2aM2OMw+3dtsfzif0MDdVJ8F1H8cpBUI37+aIWK+tdzstRSPqPlgoJXIQGNCa9u5MfjxSb369bYdjWZa871p3nQoxs6lJ5qFqGiVJGxYeSFEy71vfAs/nLEJEVDKZBQkgQR9u4VT4rlfOqUwFiTNGZGFETBzHoH1r+96ZeJpKQdlrV1UWDncAJKDDLCPBPUDNxJRQPdZd930ERJl4DNdwYVrX1huknCxgmBvAJIIMQuiOY2jtKplbA4/YtkaIkggwDjO1Ea0aBDFDxHh+7YRMLHXvOTPiXa0dTJQy6hgQHABjeNubdgaBnJIOVzVAvGdPbLRlIsTiatvaNCMzeMRtWwPAza8XIyJ3B8Is4u4kKEi9W4QjcASA+z2yChFZUu1jvdZ7wsHd1TwL3tZxPGaJGCN6N4CeMo+uCJgEJRF4nKc0WPpeMzofJyBUuzNSoPVxrcN2NNNlomVhjMhsTrT3UZudllxSGsNumwrjvSnZty45E/A91pClSMLhnrLMBFNOQzUnrk1PRwEGdQvX7z6mnHBvfeu1Nk1ZCLyISEri45SZUtkb16oaNs8sRF2dhF5e16FhEO+OqTvvBjjA1JaUfnh3nI54rfr8skuiQOh9TCW33h4O5eM5H6ZhjOu+/tPvnof5f/Ov/5gx/te//e1UxJ0BIjyOSzY1gDDz261mkSTpMBdh2rZb3ZSFrq2BA3LU2lz5eOKH0zy/O335enl5exvdVS0n6eZGuN7qYU5WYKujMF60muPeBlOkhOYR4CXJ6FFrC4sIIAQ1uOw6pQDA2h09ElFTG0OTUGHCAB0uzEua5izIGJSnzGo6lAP86d3BI9pb9YiJZMp4233tezMawwLNCJ8vb+8eJxvW+9j3XndPNG6rmmNKpB5JeN+2UlD7ttV2vZp3J4aUADz2Tel+eFRHtAAUxrar3nsn4BaoDqN76yYc08QvL90Nicg91rWNEURgZupwmAsC3taeE0dYb+AGL7WzEELckQyEcL3thJEEzRwh8H7IBXCP4XHXlA81uBP4wc2CmTDCwv2e4wYHJHMNV2F2dwHACHC/fzqhtij5/luRJ0KkqcBQQwBmiV6Z8XBMy1yG2ra30VWK9GHufn+ZgQVGuNcaSTKRuwcRAAUG9O73S2rvNtRTYST07spGjClTWLy+NiJMhc1xnmlK7MPvg7IAcHeE+9c/AkIYPcjBI4JZzN3ckYmIwsHMc+KUyQYQUk5EDne/uyRfDrNZ1Ka1GqLlhMLoTndkWWvexqYahDi6Kth9ypKzTCWte7vjkO+MpJyFGB2w7Z0RSyY13zYlQGIsczoccu+WCp9OSUdcLq22cdeEJ8KuDhE5EQsREzEyQhLcN0BwEgSju9QPAXPm1rT2nlL657lPMP//efqzJUmyLT0TW9PeWwczd48hM89QdVAoNIYmKBQhW3jLF6cIn4AXIEXYLQ0UUNMZKjMjwt3NTFX3sAZeaFTfR4SHuZmp6l7r/78PA4MQCJGEhKlDsEDJVOv5FgQiGKBHCGJAqMZQ9whXh1O75gGA+6FmUVxo9JQZMLaHngC5oS6MJbO5qwYREqIQn9Gh05imDkkYAMyMOCGSqyOih98e46j6dC3nUuKcpQAgMwljb44SktEcxoAxXBjXVYaBjpPxxICwH92cRdhUA0AthBAI3Jw8HDAw3BxPDOaJRQr0QDUnOrmACgCA6O5w0u8Qjq4CxiJ9GIQfGns1QvAgRHDzWs0M4iQLn7TJiDhPZYPNnTH1Nm43VeVwCPMgUA3oYBEnLHu08CAk2rdKEKlQq1YyXxZGJItAsGVJLLw9algoIiMAxhhOzABBiAShvSMigJ0zMA9IhKMpM2eh6cKAWJsK8zBDCCbEgHAaY3z78jXC2whVn9Zyu4+xW98ejfL9aJjkcdQkEOhv37Y+9ePoYxju49iViJfrPC/x9dvXl6eyP/Z2GFLUUQkZNPYxevNftWZ5qFlO9E//+Gf5649D6/2+lyQG9n7fL5fSa8fA50tcr5Nbbw5D43FsT7KOpgD4dJ1LQmFpHVKirpYScqJp4pxy6zonSSkh4tARjj78UYc5jG7zkjVsKPQW2z4gQIocW9u63rimQvs+mOOLej8c0LdjE8ZlLY/aTZ0YM+RAkMxMdL/vvR3LJK0PDxROX74+kkgpIgkva0YEZDQd74chJ06JKQRDinQzDOy1T4lYZM48f/yQGLejTSkx8izp+uOnfYxu/cdPz+HKGPMk37Z2ezRE+PghTYX37o+t/fAMy0e+75xEGKg3qxbLnAjRDetRWXid8hh9tFDDB9OcJxjtcWunsC7R9PGFXp7Kj88v7/fW3JDHlPC6JKA0wLX3aSI1aIcSY4SVwgqWEyWZMPztfRfmqcjR+zrRfoxhelkKp8ncEkMSYUZzfH1tQxUAShYHaMM4o4Pf7p2QaoU6BiHdby2nNC2SJpmYj2OcxM1dfZgTkTucRNsR0HpnpqXkfe9zSSCCAJecJKfb3sxtXRdBNHciLpmJQS3e73sinJc8zHSEjcrr5B5vtwbgpaQ+DEKnwoEgwsjgEQSBAmr27W1EhKNDQM4cEEmMkFCwNevNHcAleniADrOUuWRyjdYtwlOWBHjU1tQBvmP5rteUkgtRAHm4MDCjje93yTiLsw6BkESOXc08EIBgXbNr9GZJYphnYQsvhZ6mC0QIgzpmQVcAAkDYm/UxEIGQe9WU2MMDIyBsAEDkScw9Ajxgv3cCLBNPsyxzKZkxNMCXeWLGeoxfv97mddYOvZl6DOtpInPq3QD8MCPGSUjIP3ycIlw1plmYYrjnzIDRhj2qJxRHTxkxMEvcbncpUxYxCzOXgGXNqXA/+nGM6VJq09ZsmqQNbc2GRji8v7+T8NPLXGt/azHNOU90LRkBOeM6FQ388nV77BUgkDA0iHl0F+YwfP9WUeBde3t0VT/CPn7KRLQ/GiEBUDcEG+uUAaX1NpV0vU7CycHnJW+3Q3WgkDBaxLxkq6ZV5zlFiDBT5nWS56uUxMz0fmtHx5yzGxzmyGQ25pk+PF2ShBNmycfRgvV5TasJMbzfq3XY91OyMdmAg3TYSDm5A+V8NH8cahbDNRd28r3r3/0T/puf5usEGPz7D5+/bIcNhWZC8R/+cHVKlykVKr0fH54+rtNiY1wvl/T0UuvetN+3drTGQgL0b//6t3PJvdafPj656Yfn5ai9tu5mW3s/fn7cX3+WkvZ7e5ri//F//Xd//mX70x+/gm+//+1v/jpfc5I+rA6EzG/b7XJd0eXb6/2fW7e0Vqu3t30u+Lt1/fh0+Te//fCnf/n6slye1uV6WW778cuXR57yf/vj/Z9/uf32p+t1zd0cwS9rQQjrRyqrg8s8vd00oZPgvVVElIxdATiDFBb4zW8/7Qb3W7/fWwP2SGr2+rZPuaxT7qTH6KA6paWIbNsYBjTFulDbR4hcr3PvAxHfD5slnwPT5+t03zQlJgYmWEoxDw+YcgrnrYbWnTITYGHWYcYITLe9GhonB8dMXHvLQpFLSrlIitrD2jpJErq+ZBZ4eZ40+l5bO4JjOh634bD36DpSio8vGQJr6H3bblvb9p8ft/vf/uGv/7f//s9fX9/+03/4q3la3r/dZsG1MD9ff73vr/vmwL2Z2diP/vq6J8F1na5P669/un14XpaF3Tuhl0QByIytDu2WU35aLgFhx9BjCGSCHu4IhOjzVETII+ZLCYfLyzJNJczMvO09ZeDMRPj8Yelqt0dlZnQz7SxMkhBBW7BgKoQjRNjUAoOEzC0nD5ejjufnGQLr2JlZCtfe9+bLwre9vr0eL9d16CiSl1JKYtO+TixMSfjlMv/ydXtsvbaehOcpISoAEnKSGF2HwlFbOJQpOwBGXNb82GqtnZhMrY+Bwh7mAKZjnnMpgOHTU3p960IoBdSj17oupTYlyoyBAB4+lXyZS5j3oVmIOYFHTpQAWlOf2N1d4zjqepmY+Tg6qUliM8uFcmFKQEhuMLrOU9beEeD6NDFDYugjXm+VhXzEvrcFgZjQgBg9QBVYgAWROcIjyB3UfL1M7hEYFq5mz9dkxfqwDqk2//i8ZEpu0MyP2gEB1d1M4zs2NGUQ4ePoQ5s72WGdyd2AICJSlnWVcEdHAR9krelRwSwIKOc0hkthIdRh4Se5JAAxEChzyrTvvb61ehggJkEL0K2tCz5dpvVit324BzECxTzJGN6qp4IAToyjOyHZefB19sDLkjKxZ4AltWPU2iAhuiNDV6dETBAKFrJVe+wHhF/XKRwlxVSwdoNmDqYWrQUBnEC1o9XEIoJDtSzso5tDWSUnANOEEMOmQmEODddLGdqPoyIFEnz+yExpyoki0NkBtae96uu3t9/89Pzhw5xl2vfj12+PrQ1Vn6ecMz0/T0Iqw4tzeZmnlKacR9P71gHxeV2+vD0+P+f//O//6mjH3o6UcW/HcJ9L2h/9/TEuS+7tDoDXVTwCSfroez+I5HqR1myZJw+fp5RzIQia5MPHVWtfpiQ8M2IbrXZ3c+9et9HJ8spqYWoiyELeTYeK8LA4bpswoaTtaA7kDq5hGieywAl7i9FNhFmACNBjDEMIZiKG0bSkzEzahjARozBdL3loTwwIMTwsYkRs9xoshvrz+3sYtWFZsA+rzVsdFmCjW4Db0OGm8KU9kkSZoHvkBNcFCen9ZqOpCA0L9xDUP/1sDhQGgODuY5CrA4a7ZUF3MAMEBybzgPiOyyfiiLhvRxI2h1aDEc3dMYaGDyfmcDQDQGh9IICZK2Fi0mZ4/mF3IjZXCAgHBwdmDO/dWPhsa7mHxnlyN8TTc+XCrAaJnZBPfHCEn6ESOING33tThE+ff3dWx057IBEBhAcI03rhCBpqQAEWUxZEQgoLZyJVbG0whbCkjL25myPjNKcAP/ahCqOHuSNASohEQ8Pcr6dWbHhOp3YKESAshppkuixiAa2buwvT5VIEcJjWHtuji3DKfOxD1UvmZZUxHAMtwMwT82NvSPR9IBIQEPMsOVE/fFmYGW/3HgTzxK5+uWZhvt/7UXvOMk0MHuE0T0QEb+9jb4qIpp6yQGCtzQGmknIuX7++MTPhCTUGZhRBJCCkeUpMgES16lxSBB51sBAnCg9mCIT7e0MkQNChl8tEJ2YIfJrSVETDU6Ik8tj0dtshNE/pZIMR0pRldPv2ejhCH5YTA4QOA8AAKImul1RSvj9amokA399H60bkzDQGnr2zksQdahsnRYUQiQAZmVm7n8Eec1/XnIX2bQCdHrCTv/Adx6BqSZgZuroItq5mSGc2LdzD1yXbd2UCuYepEuJlnYb6MEVABHCMZUonXzlnoRRlSj7Q3JZF0KBbDHNrbu5t6Bh2WXOtvffImcwjMSNBuEegBUQEMUXEGAaIhGjqAMCMuWQEB3dAMbMAQ2QIVzVgpjjDe+d9kwIgCXqguWEEAgFRAEa4BTATI6gaIxKjObw8l3nit/fmgV3jxM7rMGGOU2R1Nu6QECEJ5ERJ+Ki2LpwL1hq3+1hmYaY+VLtbgFqcs87aLCXGcCA6v8GnQdLPlw2wLuXYa86U0zn54nqMaU6ECBD7NgAJkdSC0SVBAAqRARKwepBEIt62kXMa2ogJiRGC0EnAFBIBEB4NAOB6FYaYEv/8tQ8NEUwMY0QfFuHMeFYvDSAzeuBvP5e3x9h3Z8ZcBMgAKGfUCHT+93/14f1x/PHnnbOQwFxSb0ZErrBOeURsRxXGlOl5nc7XDwGqWoTMIQAA08Spdb3fKgpdnyfh+PXbJpTngt0i3N3JzPrQwsBkitI1GMEUPIIFEbAPFRZE7EPnLI5gZom5DWWEOCPQHiJpyjJlqTquK1/mtHXPAjbUHc6zWW/DHaXQNPGUZiac1+xmny9PIH5s9faoyJwStV1HH5Lz7b5dn9d5kXWa996G69/9j9dW/bc/XdT1qMaEH57LDx/XMB/NHeTr62Od04+fr0L87X2/H712/aufLtr7vSFTYpTtviHFNM//+JcvofD7376oPa5TyWV+3d6XOe9tHMfIjN00JTIFc+eg827cq7U2pjmJ8O39wUR5KcejjQGQeJ34/d4i4GXJjz5yQTD/8rUti0TQfgw4/asEuWQiamPEibRmTMSBbupZJAkh0dGGG0A4MV4vWc3MgimKiIgAQm3dLKbCrnBs+vQ05UWO1rX1ktMIr1W9BwGNCGSUhBHRq0HAMqemJ0HJC9Bclm/37aiDEIWxdp0zpyzqThjnYtAtUuHLWlwVEYkQABQYwaaEj01LSb37vtf1MlmPMaxMqetIiafMHqAeBKhjqNk0pzIJGIw2Ukl7dUafC903TcK3vWXhemiZBAVP/XlTJ4LMYhrE1PqIgHmSevQkVGaubTDhNEsSOe5tXpIGmHopiRAhog4dw0w95SyEIjLU1MZ5M1KNExAHgFkkJ9E+kDkA5ol1xLG1y7Vo2Il8Q0YRCidXUIs2GoAhsZvjWeuFuF6yEKrq96ozkJoSQuK0PfYgnJcJNXrTfdQpZUZWs70OPqtmWcKBkKXg3o77vUHQ81Opx/j16/7D52WoDXUWel4LAB21P12LR9TWcyIinlh0WJoTI6vh128PpOBCmeV+q2qnao1UHQCmtfStbffmAYh4uU6UaLtVEZbErQ7O/PS0rHN5bEcunJPct0OSJJQxvI667cflMhcSYdi7a1fEAMbMfFlTTjQ6ZsJhsTedM4cjYtShnFJKkRkYmJhlIgJ6vLev9z0xMfE8iw4llttejzpEkvaRppTORzGC33xcGWnbdavjcbR6VMmWVw6lH57laWZC+vh8uR9DHW7v4+evW4DOF1lTycF/+Kvf/e6nHwlsnfK+9y9f34+2L0upvf3zr1+E5N/8/qff/fRD6Hi8f1uW9cPL9dj2bduvz5ellD///OXb7THN09GPR6Ne/T/+4eOtxR9//fLDpXjExw/ry/rsgX/55es//NOfu0NO3FUS+jH6L2/djFvrU6Ip83VmYCslF0jqctvu7o5S2n48qi7r9DiOy5qLyO39kae0XlO4748GgR9eruDw8798Xa9TmvN936+Z12Ux/f44joBA8uvrQ60juRm24eaeuLj7ciUiaF2ZmJEfWw/HyyWvM8XwYFECVUSEYco9YYr1ImDeupt7gC9zBj9NlKkehoFJMJGLYM4pIu5Vt8dADIMAiXWdEKEe/XKZkOjYPVNGwPHYrnNMT/NxvF+veZ7WYz8UEYUeW/31a/t2j3p0BAogDH++lMslU8IPyzRn+dPX2/3e/uZ3H1Oi7dguy1wKv1wuH66Xn3/9+egGnH69PSAIg9+2WoeNYVOiOZeS5b71OcuU0cLMwtSua5EiamO0cVkvnz587L19+/btcdP3Q+99T5S2vevQDy/r08usOpi85LkNBYokMpq9vW6fP185YUnsELf77gYIqMPVlIVYeF1yb7YfPdAjKIJKTq2pewC5SIRhmdLLc3lUIIhQO+qYT1tQotZ0VJunzBPue7fhTKdVBnsfOmzKiRhFyG109XUpkhAdW/fn5wII90ctWYT4aOYROnwMlSTXuXz7dn95WkiomxPGMhcLA4uU095GSWSOrp4z781aHcxoiokF0AFDEjGRMGchRFAP1WjdCPEE3Ejmec4YeHs7gGBeC2IM86kwAW7HuFxyH+4QNly7vzzPIxwCslBOFO6t+9HCXUtiHdCad7VaNaWEGL1rSgIIKSOgQ6AbMcY0J/dw9zEMCS6rJCZ37+5jQBJCjzH86XnajkEEJbOqM1NY9GFdVZKIIAEdm9bapzmf+x4AJMBpFmIXoKMFpiByZlmnAoa1ae3WuhKRDlX3y5pL5pM4WbvNc2Lmx615ABKWJEDg7oiQRIBwWBdGSckMBCGl3Lsvk/Sh2z5O3bUI6dA88XVNqu5u88JE6CqmLqfHGHGoITGCA3pXPI4xhoaGqg/1Tx+maaLWnQmRYV1y6woBr6/dHS7P5XbbE/OHl+n0bCcGczr6WBKxCAYMG3nK9297mvJ6XXprImwe85yYwMO3oz9flkdto/tlXfuwv/7NR211yvP7dry9tVLmL7f95ZJfrqW3JozLMt/vt3ktH1+ef/nypuZP16enSz62+s9/ec8z//VPn+63229/ewGif/7z6y+/1nu1H1+WvY2jjU/PubcWhLdHiwDEmAoI0tN8gsy8tr4sSQof2xDh5TIfj3aZ13D75fURiIK+XnIuabsdxzGA4GjGyNc5vR21afQabj5P6X4/jj4CCOF7/YWRzYMEGFEYj6ZCDBRIEEGjG0RIQkE2NRZCAoBgho8f5qO5ByyFbrfq7hoxNJgIIhCkdiVxKTQajDamlMqcRvec01Y3V9dugHFa9hhdTS+r9OZm8XTh26bgEAABYRYAxGDqSERCAQjmgfB/zH/89OWpBxIKOgZ4oLsSMyG7q8X3c585CMOp1SYMDOwaAEHEAHCiWk4X01RyhI1hIoyEqn6GJADAT4KLhw4VIWYGBAQ8P9skAgBnKiIlTszEbBEAgYijGlLMa6q7jqHnvwMAuH78Cc8bJLK5IaIwqVrJqWTs6jKRDguHj89pKYkQu1nX2DYzBzdztXnOtRlSPD0vNrSN3kcAoBCPEa0PRiAWh2CEIrTXTsTCiHT+RhARj9pFaF0TMfTuvRsA5kRFJBW6Pca+D4hgJlVzj5xlmbh2FWZmUnMirE2ZmAkD/LxacYLLmmAgMViYOQ4z7eoKpfC05H3vYygR85msdZwKLzNt1dowAvQgMzU1QD7nCCd8zlzPpMm/OsMgCwaCCCGgGwREKQn8ZCRHbcMDCe3puRyHMjEz9dFFpLeAiDO6hQDACAjajQjLJO0YtY95kQ8fZgTHiMvlEoFDx33XcDC1fR/16NdrEQxC7gqc8bpKr8OA+4hhAwC9Qm0tZSKU0Q0R/PyZ4YDEgqXIaN7GOHuM05QSghQZ5mOoKpqDjYEAqSRzdwsmBIKcaWjUqoKQEw8DdyA4ITtwRoxOt31KqTUNdGFGAECfSgqLMUwSpwLrktBpmPVu7XAHTwmZEhGMoW1Ezgge56PRyaTwgCQYgaruZy8OaIxBTOfEShjNQRJjRGL2CFWLf0UmuZs7ENOJOxeKU2Z8hn3c9NR5BoDHSSMFB8RwAAeglFjdpgylZA8Y3dRAEk2JRMgR90dDwPNpMRCu1+QWoW5B5ibn/3y4OTAFABCi2okqArNg9HP7zxR+jqgoENAjvhc7PZjipDiVTE0tnABxKowEEdGqAaJ7ECERhBsCIdHzS+kj9mrTzGjYh6lqIJkFITxdy+O+MyMSj2FEaIaBtBYcqkg8FNQcEASitoHEdH4XMAgRmYugmUuOYUyAdfhU+Oma7reWZxxGAfjbD1kQ345+34yETBEAEsO+24enbIhHNQpMmZLw0EhFmLDXnnMa5ggQQcs0p5S/3h6AIILgljgj4lSkq49hGFBrQ3RiYo55moa5oLPw49GZGQDdqaueZKoick7ZM9P7fRvmDkCEOfGU5bJMqtZNl8JM9Hbf1nl+vubnq/zm82ckvu37+2Pfm4rg87oCeMTIKX98uv4//1//35LTv/l3H4jpz39+++0PK7KoIlN285zldt8kJUryx7+8eQAib4eFqtp4elr+9vdPgvD66Ld9XC7pb37z+dd/eX15Wd62ftvbtOTnRZhp6/b669sPHz89ahPBInzfe1BMiWod5jFMuykEDLWSOBzfb8daaCiaxlrSMH1/9DnzNMmjDgZUHSwyT/n9bVOkdZlU7bGNkoUYjz6EcTT1CGImhJIToE8Tl5x96PvWJPHTJQtGYlIHopgy96bqFMjbMTwgwK5LSkmqDiEKh0RUsrShSNjVOYKAu3qASSF3r4cRwrzk263GiEBChqF2unsA0M3Cw9xVDZHOCGAg9G6EUDIDhmQ0cyYgQDMohcyiq1+uIgCMRBgkclQNdya5P1rK0ocixaeX+dh1DHv5MB9V+wknZkxCBHB026ulhCxYhEGVs5h7q52o7MdAQgdfiiRhYjy6Hoe5gw5jJgz8fnVyz4XDDQGen/I5cDlLwaahalJkqM+Fe3dGJITuUVvzID4jRUgeJoXPKlk4JKFShBAzsyB2Hd3OREPaDyUmgwB0hDiqTVMiwMToYEftLBQA4QAYSdANDCAj6DBi6q0jEhKbaWI5ncfHGAjASH4SN5siIgkCupqlJC/PMxiNrgZ6f/RWNaXz00RjWEpi7maWEi/zNLoBYNd+XnjWJW+7gsfzy1KHateplH2ruSRMME9pjKi1Xhf59lq3w3NCQjJzdahHXZY0l4mEz+w0Z9AxLut8WWTKdHuYOza1+31HRxEhYo1B4DllCHKPx7FL4sQ0z9KP47rMTPS4VxFeLmttbRIxDUqQirCIm7mqEKvrNE8C/PXr8Zdv3y7XCYkR4LLOw/xobS4pS/r2/uBEZc6qIyBKStc1F/b74Y/df/7LK0VMa/nxN4Dmv/w6EShTXJ+mANARr7fDbVyeytMyLYS//+mnnz5/yALfvj7+6//4M5Bi5rLI01yE+Pe/+f3f/Nt/d7x9Feo50f3t/dH8GG273x+HL4Vd+21vuJSneZKgt83ut9tlTgPS3/7+p7//pz8u6/Th6dL6uMzTf/lf/07ztR/34zh++vAypfLHL3vrej4dD8enS0mC4ZAQDvP71u/vlQQ/PM9Hbx6YEgIERkxTykUofMq59m6gc84A6KaO1D0iFA19uGQSoud1DYgvb9vr4ygzpJxvbzWCAqBXH2bXS5pyutWNiTKX2gYAzEt6WiehtA1lDqLUWhs6Js5PL2nOPFpsTdU0Zyo5H1UD4tg7El0uZcppSoSmTalavD/qdu/hmiYC8LLOIqS1T1NGcwCc55KFPhR7WQmZ3boGEMJ9q5ynlMq31/c/ftFvj+i1m6EbIBJAFIEPzysT7vtRLYBQR31+mT5cptHGtJS1JA6RxEPVmsmaHIAhGLMDvu+HSJji/ujNgJCEyV3bsDFiyfj8POWMwpykIMCwVpv+3T++v52f6plzEQxYF1rWSdVtNOLshnvrv37bGGme5TIXB3MHMAjwaRZhvL23MWyaE2dKjLVZaxpsIsTEiUs7bFlTnmhKkk72hvm9nQzLeGxHSunsukbg41H31n748eoOX79uifz6dNFht8eGxNbtsuZt72UWJNz3sS5SkvTh10U8CJhDuyABk5l3tdbdzUuWx729XOdplttRweG6zEerGCAs71snjpeXLMwlFWLqZq0qkbRqj62y8PmQC4E5YRa0IFWrVVXjPNoC4jQl4jCD3ux6KSixHQPD1zm939s6l2Datnq9TFnE7Tz3OgJMObHEGFY7JGFGQyQIsIDWLCAS0/3RpjmfJwuSmDITyFGtJFSLxLhVPZkMbg4QnBGRwkC73jf93W8uKWEffl2zMIYDBCCjhw9VRDwPTbXZMqftGNvezzM0IqRCT5fJxhgOAajq65ww0CFS4X3valEyTTkxIWIgogOa+2MfbTg66dCSEwD3oSzo5mrGIiIoGRILILc2iBDiVE4TeDBhzuTuWUQ9EqGZSRLgsDB0WsuMAKZ+ApLNXUSGWVMNgClx73rakCGCCb+916drnufUu3a303Rw1CGC226tWUniAeGRhIaZWTwtYqZdcSJEpq6OCN18zhwO74/22x+vI0wSb3XMU/IIQMiJTnAvERZmB0ickyQPXCXllF5f3yCohq0rX6/529dtP4IYp0KZ6bGNVOTQduzjMk1/81cfjmP8j3963XbjzH/901IK/de//4Yec8GpZMmy7f3D5fLDp8lB3erRhjmWBV3pL396/Mf/9LmUXLt5t9Gsqkvh3/7wDGaXeXndjrq142jbPh5H9D7mJFUD2PejQ9BS5O32cIQIdA8CB4jPHy5I8n7ro/fLZe7DHlsD9utzQcPtMZLQNOXWdcqpaUfyl6clwLdH23YbqtOUJJOr6/DaLCd2t9q9ZAFyFuw11EZOrMMR6Pmp3B77sY9wPw1XkvB6Sb/8/ChZAmCYLwkfNbLgqc8KByAkiDEcmQjcT69mnD3s75wdIYTzEBp+qujN1AGZGCC6GhEhYpgS4TTlCBjqy5xqHWaOCMLsgK2PUpgAwYOEHKIUQYS6a4QHwDKn2nqtxixuHifahhAC3Z0IhcEcHJwE10WWEqbp/WEpBwTUI6YiJKYG6tCODupIKBABGJLYHQIxPJgRUcxdjThBycRMoykKOliiNGogowicJvUTheWB65T70Vof81NxsP3eW6gI50yE6A4JwSNOKdi5BwaIPkyYhck8yGEM18O/H8whtLsgiOHJPQoA1ZNzhGauim5RzUQiIhgwZ8Ygke/fIveIHiPZqCdDkc2w3gcAsFDv3015AKQa3RQCAZEopilPhR1CDdAjJ1yer6NprV2ERPhxNGFkya0NRgwgdydhxCAiGybMHmjqCCCMwJBdWjUUCkUboajEOM2pH77vHSCIic9zhuLQwSebx2NZhSVa1+1+TLNgWO/1sq45yXpN//T3r+/vdbmUTz/MDPD62gIdCTLxvn+vTrBbmRMY3upIs+Qkt9d65nuZ8dyO1D6GuluMYR5IjBFwHAOKTIxIZI4xAtyZMCUhJCQYqky8zKKmDiHnzPJUe+EpI6IzFAoASHTOgL/DrePko8Mp/QGEiDhXQ2P4vnez81sHhN69PV1LLqzvXQeua8lZ7o8ecDaBI6WECOdFWxABHYkCMAG8vKQs9Hb3oRYeQEBAoRHoEXH+JQ/HAHcDUANOcna2IMIBmDn0FKMR0GnHMw8IBnBwJgFAYWICjsizPA4V9rPTtx+q6sLU1QApFUbEfnTmPMySgDswIAEaRpyMaqEIQyC1UHNKHG7qhshI+P2M6t/tbwDh4W6RCIc54tlTAwsb6uBk55oSwz0+XJfeR+uaM55YKE50HMNqSJJcSBXghM4C6jAM1BFM6g6jGzGlfFZbQyTOWWUADjXCMFMCQgTVYMLEOLqbwRjAGc2DAEf3t9dq5m7EiTz8Tz8fUyJ3N0XtrsMBERIm9nmKr6/aqjNhrUDntHjvKRFnOFqEEycZbWDQ7bE7GqEcmzGBzBkgjmptaOudwBEhF2KMnFMu1N7bQBgWlLhVfVrnABzaLkvJIkXQEbc6aqt5wkwcAXPJj0fDUIhxNGVENRmjT3nywEC8HQpf35jky9sOoJen5e3bbnWkBedcvn57/+XrW5rL9SolETJdrmmZs0ZcnyYhHkfUoetTmks5av+f/vbDLz//KkJzmd/fdzNPwvdNj8fWjI5h26OOXYcCSQoAYbm9V+xpnvNjvxPBXrdh/e1WmbiqmzohSBJm0q7hwYLaNAHUZlNGInQ3SfLL2x7u2z58yXX4bW85CQD0ewWv05qZUW2I0LqkYXZ0I+Y548frbK4WkdPUhxMJU2z3ep3zOsG88powRrTa6xiXdZbAo0d3U3SkYKBwBqN77ZBCEvVqPMnt3gwCAMfQzJIEuo6jNTpImAgxCY1DlzIZ9arjsk5mqVZre+fEWTjIyaFkqccYriKChDRJEhLGpuqBzDgVdoOcURg7xiIIDl/fW2Ka5my1MXo4DjUSMrOUaZ0FPXLmaeLRBzPmjEJoGtpNhHt3HVpyeX+tpvbyvPZjIDoRaespyePRRKgjDI3eu7kzkyA6RE4MQYwASA6+rowgLJjlXBcEE7laYEShfvoaPG63Y5qSZAkMEpaAxNw9Tj2KG2gbkrhMIsJmNtwEsQWoISNmod4jJb4dx761qRATDVVCD/WeorYBBCkYkZMwM2q31rWrX+dsw/TQIrx3PVqdpzT6uD/ah48LC+pwdZVEWei6zsykZoFxOukx4O125MTA0NXUjBiQ2NVyScfee9fTafL6bUsiQZHKaY7Hbe+lJBGO8POU1aoChTabpTABCOCUEIESTYCgER7DDCLWS5lKcYVLmThj7X3oSElG0w6cA2ekEBrdX54ub+/b+2Nf1onFl3lGJwDYa1+WaZlKPQZ6vDwtQQkMECNlKQkmKYzUxqimEyZXDwsMat0w4q1WZj7cSHiaSwCaadUx2siJXxZ5memHp8uXR99a2/deCjvD//73t3/3VzNQXJ7kP//t5z/+XP/4i/7uRX59jdujea9I/Ng0CTw9z5+eZo2CbEKwLDMg/OXLr4+j/f0/frWw//lvf5dYHuPx9r5dReBjO15vTPT2+tj2nQOWeSJZ1o/z9k///POvD7XGTP/ypX669v/0h4/vt1+6BlZ87HrNP89XuV7nf/jjz3Mpf/X7zzLxh2fMnz785Vd51AGcppk5AQAtl2mdidDrMR7vbaDsQ9swmYsQ7Kp7N2bat47Anz9MMiVEYJatD1N//rx4KKFkTN/et/verksBAiqYJhYWEBKOlw8lEu61vX3b1CHAiMjZ1iUT86NWsxjN3/d7nmR5KkCMESKudbRmKXueSFTWibN8D7jNM5MkV+tDq1ogOOPLml8uMsxOPeNWd0dZpgyEyAUQXZ0Aj0djiFZ1XgtDfLjgyyVe5smHunu5zqep4ul5ed/b6/sm83x9hvt+M4DWNYBYECCGQ7XR93G/DQCcZi7TvD0G2L7M016VjJeMdz26KSqUHpgkXCE8SfpwkW/vexueMj7uPeccHhhE5jbGW/PW/cPTNGe8tdejx7DwsDpsvU5IlhJcJ5yWAmgRSpxZ8LF1CspZmLB3e74WD+vq+zZcnQmIMD+VPszNe1c0SJeUhNzk6EYcGH4cR055yhSqgUGYRofb4RpupqaoBvMiWRKRJ8GmPOfi4Vn448dlEmndjr19/Pi87Uc1NcAyp5KxlDSGrVO+3VptlhlPOYqaJfG+QU5sEWbWmvfmKXFXww4E1Gy8P/bzoUvEIxRCtEdiqEMdYZ4kM+VM1zld1uyAgkCIGtC7ZgqRBOCtaCC7DWJMuVDYdrTEULKEa4xICB50fwwm2vYWSEy0720kUwUi0mE6tCcXIRZkBqJg5N59nrgwXFdRNWZ6fs6t9mlKFlBr772XApw8pbTvraz5+Zre7uruaRYIkEzHMYb7Mqd66PutLYvo8HaMaeY+fM5JhJAhwmszG3g+z7++HSnziaFEonDImcysa5BAEjLH2tW674f+5jeXqUhr5gZH7YnJAkTIHVLCdZGkvs7JVdwgnB+bLWtyd4iERCIy5TR03LYmDEsWd3g9Rh328lwQnJlMrTZFlm1rSQSGBpqTY1A9vGQCjgBUBVULd6IU4cysfSQhxCCCrY5WBzHdd317byxUCh97dR1PL5e+93GMlHJCz7MgQO9ah01FwkEH1K57gGkM1XVJSPh6H4nZzPe9d1MiTDOO5uBhCOBi5iUncDq0A+AAQzxSpkIrjLE+y/1Q3Wx0eftyvL717dCn53Xfu4dZ4Ph6iAQxvffj53+R90P3ppL406eFGF6/bSUjO/74dFGw21Ehxl//7nOZclUvaXq9e055nuT9vv1P/+HzTz89/bf//o/vuz2v6+PREcvt1+P2zQB1Xd9uty4s0yLV4rZ1UyfiMcax2bwUNztaY4aTUkuMGCQItQ+HkQr+8MMl3F/f9fosI4Z7I8SnjxweoX1o/80P7MBvd3t7fQRGNzV3RDDT+vCUxTwifCo8TeWPf7m7IxFsjwaAWQi0ucI8CeijHRoOQqA6EIA53W8NAVq3LIgBfTiBt44lATEFk5kGQMm8t04I5s4IQdC7kVBiiVAzz0UQQh2ZgJjUCMKDKNzjrFwCBrC7m0VAtG4idFo4cqGck5u37pIkEfdjXC5yygROSIsICmFt1kZYALiXTGbUh1EE8XfOibtzopyTCI2mtx7z7DkRgpeVGZEweOEMNJqVNNe9j6ZCCObRu6YiQgQOcHo6iC084TleojzJaD7cteC2j6EWQeoBEPMkBLwfY9sqE0gmCLisMwU+7k0tMkFO5I7mEQbMxOcoLUI9ANDMzqiuqmlHwCBCZlT7XlTrzc/ANsDZU/t+xUHCZS216jkbggARwsBp4u8D1+7msD1GrfpEU0AMNURMJY9ucZqg9HyTAJnPt3NdUhLYmyHi8yrmnqe83bXWHghNFQXXa2l1jD7U/QyLsAAnBMAw3PfBYtOUiDAJDx2EsqwFvB+j3R4d3OdrSikT+PycPn6cw2Oo96FlFjPsvRC6qY9mYf7hJSMloURMktiHtW0L8LJMP/54ef22v71un+WyD6eEL8/T4z5GM0LIWWz0cRgTP+6tWyxL7ofFGWUKMIvDlQmXKdUxCIFJ+lD378OFPuzYLTCmWRLC/dFY0FS7BxMFhJnWGgFgEYTITBH/mlkjQgQWlKAxrCRCgDbOrBMCAJxT/whVY+HWzSJUwc4ETeGh5xje6hjbPoAjGDLSaAMQRagNx8AsaGoBmBInYXMNdgpYFikJGMINmAORQMCGdQWAyILu6H6GOwji+1btnMvW7jmzWZj6uubWzSwcIjEzo57ZDMIwr20kYQ/oXQkJyQEjEdkId2+HusNwj4C5cBs2GokwIuYARGpNgyEY0Z0AA+hE5yJETkhMpn5evFKiM5mFiMLgjm1Y4iiZWrOuoWoQ/p3wTWejEyFQ9ft7Uo+B4Koxzxxg3952puRqygCIbm7q7oEI6r7vLTG1ZsDo5ohIGGoD9Pzafrde6L8OogBALc5GCQmbGZgjcYBbN0RiorOiIq4KEuGM3gfdjphLwjAdZo4pMXo09W/fRhtxVljNnZEIqbehg7izqV+epueV/3Kvre5llvOTGTaqugjXrSOxgkIEIyJCO1CIAMJULxfpfRCjedzqMReqw6aF14W/fts/vsz3fdTW1jlzoqFecrqumdkBce+dM845mZkZ125T0SLl0QCX/OuXex3+V7//dL9tNuJu3Wv8zR+efvfh49cv73/46yQJA/jtrZrS21s3iOOID5cLoaSS9m17PO4505r548vz7d7++E9/uVyvP/5wvT8ODi8ptHtYLIVfXpYIuG0jyLajbsc4Dnha83Dfj/19eyClUT1lOJpC2DonDKWBdeuc6TjMLMjxvtfnp+n91nt3lNhbz8TTnIDJIS5rOim88yIeEKi1aRs0T/xyzUjp6JgTzolud01CYb7th6qzsA5T9Tzx/Dxtj/7t635ZZynZHH9+PUrWpeQ0QSHc9m4GGPT+6Ejh3QaYEL0eGzFEmFmUUky9HgexL0VMYTS18IjJLXRUEhRJrXlKEuD16B+ny/MlVx2MkoVtSRYeAcJs5/A4fF3z6DpGLFO2oe3ogZKFwuFoloiWOQ93M9u7a/frdTLz0UcusgeEBzF2td4tAoRwKnIcBohubZgz07Grdi9T3moHcGJ+fDuWJUcYEl7WUqtRGDFcnqY1sw7bR+SURrNpSsMBCafCffgZbkKiUlLrIyEYwLZrQJTEtdo8Z0l0Nha1+XUtHk4Iy5LH8L43QkoiTHTsvbVBSLX6ycvPJQ8lEWy9qY7npwwR+zFSkSTIhQAxZWEmInLzUe1x64HQh6rbIAw/25TEQovktTAAcSIAjwBJ3NoAj30bXoDJhgNQIAsDHLu9vR/PT/Pzh0mkm0If/va2lSJIcu7TpjlD4OgqgpTw04cLety2jgTMrMMNcd/7vnVGzoXMrTeXCBh6VJsvZSpcEu532/pBgkxI5BDBgkBjyrke+PrrUQqlnPuA90efSkoJp8xz9iLLo+Y+hrkfrRPgWjBnyFMphNttyJzXy1Jbc8Tr88t1zUJ+XWbh/Hq/TWFoEBY88dGsPvzTy7o9Dg0LjKeny9M8MYMh9zqagyQaBo/7ti6cBN5+2d3w88fLpw+5dRidlhVTCm/77z/L84K27S/TKqSbh2C0ag2iJCtiIETkGPLtvdfj1x7xy7ddR09Z/v7nn69LdjN3G1l+/f/9r7/5pz/+3/8v/7effvirY/vy9n77u3/+ZbhOc/n9Hz79TcLj8Lpv/vP718fxp3/51ZEh0XydfvxREGCZ5G/++g9fftn+03/8q3/z+89//PUHlHj79RUkm+o8URucRFS34zAKEaKucNuObRuQMgv/+Lx+e3vU5nMpv/l8fX/U3hVJvr7uqjZl+fxhuZRkD9tHbeMI524HCXOQMKScc5ZtH69vb5w8wtVCNcwxFyHCZU5EMDq835ubplQcMT3nodoOfX/b+nV5ep7f3x9C6BFjoBsMjQ+QE2JXN4j7a08iU05u6uHEbAp1j9308AGceqAAHXvro8s0Wx8YRjkhoAUyy7f3QzB+uODrBn1g62MmmJUdsfa+G5rHfQsDawPc3QACPCDU/Gmdk1Dt2jWCIgKa+v6ojIEhQ3sp8v7+flmXVCAIppx20wBbkIKi6v48zcQYTZ+eslOuHdpxQCALP8+FCFu3ZuDdmvoff97qocuah1li6urWoz3255fr03PxYdvxIKJpSu0YY8Snl/k4ekqYmDGwkypDEgqPVi3nZOdzJuBxmKqvc5nmC7CO4Ue1ATpUsoC5ftt033x0T1l+/Lxsh77eTBhSinC3MaYk5Di632rlRF0VgSXzUqiUua2FAeYpR9jbvQZAIEmSAmjBwxTQLktmQKbo3bY+rpecJN7fem+9ZzqqmLuDu+mpHqvNTvXtXtWzD4VS0jWSI4W5AK4T760vZSKgR23rLJnpOMZa5GnNWzUIyYnf7nth/vTy5BGZgwjJAwAUuTY9U+q1jyDuQ011LhxxSmISQhABAhaGOtzOYp26CKqHeYD5NJMajKEezoyIdLsfY0RLY1kScTDF5w9zANTaz2dyEajV6oA8czsGBLibeQSACNam/a5E+PyhsAAG3O5dEhPSsY1cvocoPKIeegSoWp7ow5R8ojHGemW1eH1tl2s62xJjDAdb5qLmHmYD1nVmGPf3+vRUSiEbME3TmS8GBElpqI3RzICQzMw8CHm9lFbNFBHpsdUyCSLuW52LAPLtfZsmeX6emWg77HFv85Ic4fa+pyQRAVCnuTB4rR1LnkSsm7VRa51LuazTz/eeJKbERIBJbrc6JVxWrtrXS/nl227u6zVp6PumENEHnPcURHx6ynjmopBOWNhtH2pG6D9IuW9NkgC7BZjHbdfLnEVgAAwNCGiDtfcx7Ok6AYSG3fe+pryugglbP4STAF8mUUlN9ft23no/9FJ4msvb6/1BMU8pZSKHrY7IJrP/59//7odPz1/f3n/++dfLZXGi/WhTztO0hI8//+XbsjwF969vXYc/X+j+2IcGBry+WxF5O7a9t+vzdPQBweuapxnbe39/7IyKxEgJwU9ajSQKDAtFpnnitbiaSxo5pWP4UYcOKpO2quuUfvycrO1D7fV11N1yBotYFkmFGOL2cxtJiaP1aBKTJDD1Ft4d3QPwaMHgCHDsNiqc4CoMd4+SeAwdGlkQAFQNiEYgI+SJ1NHNxqgseVg8XRKr9T4I0TmZG9IpyLKUhRGYEQjDVB1OlxQLE8dwQCTwcDBERKLaDQCSoKqqgRDmLGYaBNMiRNS6ThOHw2PrlCAcEHDK2WKoWZ4Fq7WjQ1BKcgaApkKthZnlmSkhE5TEIGTmRzUzmGcBg22rLx/LXOR2a6GAQBCABCLCaxFzUIMIB6SAsy8T4d/zfuDAwsJxe9ftaCkDC40OCMiES077NuYJg8Q03OPYOoIBomRWNSIOh1MXf24zRRgdA8K6g4N5mBlEBKCaIwYBnbOeczY/VFXdPYS/Q16E2eFkLsg8i6qrnR1RPL33nFNE7wMiYAz3gH3vzImZvIebT7Pc3poCqLpHnHsJFsyJTaMTakRCFGBnTEl0dEc09cA46kDEYe7hFs5GWajM5RScUcA0l6P2o+pQW+f08nI5at8fRzdQtZzi5dM8z9O+tZRwyjAvSQjVbEAebRxVc8LrU+ljjIrXNUtiNXWNzAkR9lrnwlORCLhc8v/yv/zNP/7jNwB/+bz0MRCAkFKC65zqrpz46cNEjK25hfVDbUROYu6AQURDLRzUJAA8QoeNYSzs4Qg4ho/kLNCPQSi5YESYAiFaABElQfMTswP/SoYGSYzgtVnKFGEMNGUWgTE8JRpq59gSMYiRBJjJHQB9yomZIuzTh4wOj92P5n1oTkJMenLqk2Xix+59KEQg0QkgRKScRK1LgXnJ5+ymbjoVMY/RPQKIIDCEiIgQPdBlYu/gZkxQMg8LADhjUGOcZclo3cw8PJjQT5j7Cc4oCYaO7shAjIn51L2FRx/Qa18uWRIBQu/GRCnR0dQsUqJWhzqwuwid1wpyQIBpFdxDwxHh+bm0MWq1xGmauHc/eeGAOIa72/kg4G6E4UCAQISliEe4Y28uQsCQANUCCFsf65yQYHRLGUU4Rlh4kgTuzHBqCyI8ItQBAcYYEUwIzIRECMGCrQ9zB0oYYebmIUIR39m1IkQAbpFzbkPh++8d1FQdkUg93NWQEweCIhCex8NwYoIYtYUF1TrwlFKoB0APT/+avQp3PzG2kZiMEFxjjIiAJFiYjn1n5t7rGUxzQDUXpnzl3rsQxFT22l6eSwH67Y/psR99xDynx665hHBH1GWW1kd/mBRW16Pul0uqTdVjLul938xDlXOi//BvP6Wc/vx3v1yy3Jt2t/f7hkC8MoMDyB///Drx64+fPvzXf/r5clmvV7m9++M+8hwkWLf969rNwxnrcawpXV7K7bbf7r0Ny6k8rzKXse9YMjxdk5H/MK3bo76/39e8tFYP7/u+hwOEvN5sDIuIlDncp8yjq3ZdFjl23ff+tC5thLax7WPOUnsbGl+/HfVQRAIyZiqFLYLYT+I5ov3weUaK1lU4Ea23Ww+IxDxNDBFEsB/+9tbWNSFBQKSM60zaaZhfFjRzcH95WiTJ0cb93qaU3Ol9b6Xg01I+Py1usG+V13RWtPP3mbp3x6NpOY3g6g5WSpkzJ+atNgt/Xha3eH8cgNFGb4qNzMOnqejQ2wNUTQRgQhFOgBCeJAIQp4JMFrqjE0uYMaADjREsMYar+jxxgE6FmabX9woCER4YuQgg1mrC1E0BfVmSttj2ag7hIMxqFgARcdQehMDhNp6eJwIuBLmkbvH8nPZ9hNvHTytnGkOHWZr55TJr8zlxQExZwtAMbm9HEsYJgfxxfxDgVHLtOlSJyVTdIiCOWoHoFIDdHi3c4sxhmoskKWLaBNMyTVMiVeOcAT0Ln93SNjTcr2shxK4+LYUJ3RSDA8HDx3BB6MO8xbBGKQFATugRasGJ9zbOnJQQmQ4L8HBzg0DXCDrXD25qCphLbrXZcARYllyK7Ft3j+vTEu5LGdOU1b33uDyXl5f1X/78Pi3pww9rKthq3/cBGFJk248pJ/MQFiEf6tDs5WVSh29vdSnMguDmBoICGB4+TyUi6mMMqE8vs1nbdw9kJD6a/vjDPBf8ejvej+6P+OmHa0o8Z1ym9MefuxBf5/x+29K6AIVH7wM5UUl8PI6cubl9eautjq7jx+couLlgoKN5a14fg3map/Joh3m7TtN1mVob2uuyUE7IUto8I0EiJyiO8OMTrflTM5DkJfb/0x+W94cKjktK6rk/jvBoJjba81MZauAIDub4y9dDGHOCy5L7Xtvo4FCrciJJZOZHq330LDkX/PlxnzO1qP/v/89/uVyWH35cAvzyIb/fW9VNx7pMy2t/+/q6k+t15u50e7uvTy+3h9b7lpJILtvb//a7nz5c18svv3758vXNJf/4st72/qdv9f7a7pv99U/X332a/+FbO45jFnm7B/H09DLfj7FtLRO3oWZa+/jlV1vXwrMwwZzl1u1x74L0fJmGmpQcY7y+7ZenjIHu0Rxu+2NO2QYyu5vuu9aq1+vl00/LUfXRBrinJBUHsOXMl2UKgOPoMLIruDYM3ncreQpwADyOIZKSEABEhBAU5sqUCBFchAQZEWcGQCBiB+jdbNhwosRCOSExiw3ToaWwsEwCNPAyp73j/dBe62MbT1P66RmUpHf90y/3Uni+lCRhOgIgsViK1npKJQADiTixd2HSAHc3twAfCHWM+wNP998yT5KIilOC49gr8ss0N426P9Yl//Db50miWnsc3RCfn5c1C7N7AGIZze51HMMk0cKp6bCA21ZbUyF6fpr/5ZfDDQhoO0Yu1Jp5uAgnSbjGlLBVq7tCuDAQAyDsj4rMnLjtDRG1exs+ml+vJc1IJx8goCTsXUvhbe/3u65zTsJM+HxJKIABaoMJ5jK/PSo5rVN+/bY9dpWMnz8sP3y6tDYsNBzrsLBgQRa+XibwWJbELGHxlJdhnhj2YyTmvKY0YxGKZIj569e6Xso65aPpMBvNRlPXcbT26dM6z+ntfeM0BcLQsXduZoBYAT9cLgDReruWvE7MjE9zbhNdpgThwqHqBJFFsoDEeHTkRMNcCJfCYf68cngMR0Q/hs0TE0htAwFIaD+aSHKL16/vl3VGYR2aEzMTIyKhMBrDth2l5Nb9/b7nxOsl5yQEPrpVhCiC5qoaQL2NJKRmW9VlKq0qCxP7qVQ39+2wxOHunKTVPrXk4bXavKZ6mKOfoKUykamJJIAABDMjoqN2cwzngf7yMbcjwsMhhgYAOQQPGyOG2dO1tNqIuRTajw4IphCOQpyEiLnuPtTM3D3AYZqkZDn2lgnmq4TFWia+XO/bAxnW6ywoPcDWWYhAySCY4HKZzaAex8fn9VCds5TMb/ej5LReirmFOxM/Xy7Xl6X2oT6enrkPO3SUmQH89b0VTsjgFvvQYZGELlOOiFpNOH0qyYaz4H1ruZxWa0VyJiL93twZI76+j6MZkK6T7HtNmbdHD4XrpWz7ft/Hj5+f673CLMPCtE2JRh8Okdg84OWaTCVTyZn3NiCNTx+lDkp5cm3EVBI/tvr27cGI7cKUiJnutWVlwviHf357u/X3x+6RNMIMH4/x85cvmcrbfbtcYslPx/BADOd/+udvv//hk4L9+evbf/4Pv0k8/vnnLoNHdJy8Hf3v/9KQ6T//7fT+oL//i7H5NAWxTbkwUyZ6f7TnSwbw13vV3iPi9hiZ7WiDGM309s3NcBy1H/WxY0nUmwHC6NY10KDupOo6rHcriSPwdm+tDh3uqEgYAa7fjRkE4OpSMoInDgSE7oTYxohAD8SIPixlQMQgYIFjs+8JFY/RtbXBzEiOGERhBsLEiUQIPJ4uqTZv3UqRU8JFQqmQgwoQ59SOHuaETByJyB2R4vqUR4OTxGxgSZglEeJ2rwicCgdgSjQVuftQUylUBMxBlpKEWh37MQIAmdvw2nSeEwBq82AKVWZgBiZQ9dPcXiZihLDIiexc2yeIIBGhdZV5TvuuInhUc4dlTXVzU0fxM8YhiGdnxy08HBFJODESQpDPF5qmtO16jH6apCTRWbWDwGUSFjyaE2EQRMB3FS/hvzJowDyYKSL8ux0DyJwJA2LbDx2n2yyIKILOhHyYB8DoagE5ccqw7a5q18tEjAbQh7s5nlUjBDM4Ds0TExIzMZG7S0kaNKq5x1QkF0aIx9aWSODeDZYrLULg/vHTUg89Du1jEHE7N70sLKGmR0VEREH3kwGOwoRIOWE4HHv3cFOvVSPAMY69t8NPhM3jPtqh6yUjobAEylrAHLx5FpYZ1BSZCNlsPPbu3kGBczGFrTaWMZX04+el1kGAlPLRWkBlzPdb7+oL8zpR72GGOiwJl5KOOsz8hCUTYsmsZr07MalaIEUAEjJhEikTc8J+6N5GzqLD+mgp8TlFMXNAvCzZDFSUmCAgF7zMy76Pdja+HIjPYoWw8H6gmkEEEQFHSuSZwmC9zMJQD3OPo1k/zJ36UAAQQaQoIq3b467XCyeBPhBOo5lZydkc9v0oC01zdnNhZgBDGQbDlBlbcybIwiNATVPGqQggIDK4tzowCyOahYIT87lN+FfsFBAjUjBzyjRNqVUbOj48TynTSeswizEiHNw8AJlpaHhYSjyGI0JtGohqniWpjq5+XRMRaXcK0BHMaBrnvQQRt0OHqSSeSpQUxxHuXibuzTwg56Q6xvfFpjNhyUnVHnsvhYkAHVjYw8I8CTl5qz7cJDEinD/XA/MsTDjUAzzllIPdQ79jRM7+ZiCTqVkEQsxTDiBzRwoCiHCP8CBzQLSz/mpmgCxJhloAWUAEEICpm1tObKpA4SEESsxbVcYzSRThwIx0HlhteBAE4AltGZYZm3oAIaEO+/LlgYFE0fo40dgn5CUxTXMfQykEI7oaBEqBzPR265dr/uWXwxzqcRSRobUORsTj7uaEZL2O4eRurbk7rMijxraP+41NkSV2ZmG2sBB6XhLx+G9/+Xq5SC7jwwf9eq+/vDZVf35KZmHg9VYP4pzk3/z1522rX789tt3fHg9qlJMgxOvDT8+GdY2nud06S0aBa5p+/MMnwiHMEHsW3h6N3ecUd7Vf3/urdECv9RARM0cOQJiEgdDMb+87XOfaxjClA97eqyC9QSVEs3/VFrhLosKURALB1NZLzolvt0pIQMgUQlSE73tTDx1GBCLMCL157cpIqoDAv//t05QgJSKmXvsylWZe+8hJiODjpfThY+j14/TDSxndJSVA2Lbx/tCSYhaZ16mZbtsxl/Lzrzei+PHz8+h9Wfhpmb69bi/P6el5eRyjVzPXZUnuftTKzNenLIIMCwLVPkbvzQwhntfpaH3fKxS4P3YMuFymo46hVubk1ZvqPBeEs4FIwYzgp5xkSgIYJIIAHniZhVYA4v1oiFh3S8IO4RBZOAy2o4IEINajlwIkROgAMJqdQIicpVd7XjMVBbJLoSK05sncA50AE1E3teG9ghCreVPLHmGwHdqaAVAKGNXqPlKSoaflipjY1RECAFKSeSpzYghHosT4fZ0CPk2lO6B1RmnmS1kfRzvGWJbJht3eH/NlIXIRVHP3SImWxGBhLurGgr1rBATj19f95TIvy1SbMQMJb7sSY0m5tWFumaj36F2H6/NTmecpDG7vwyM+flje3/aXl4JCvYUTcSJm8oDtGAFepkwYlzXll2mokbDHhAiM8NufriJk6PXQY++mwYn2va9F5kJ7s+vTDIG//HrPa3l+mt9uez90e4xlyT+uNCEQMQpOF25D69azSCnlftt/+LRYBLF9+DCZeWIoTL/5dHnb2n1rt+1AmJ+zHEcPhJxwKcTPM0C0boiRmCTRr19e51KcODGE+Z9+efThxxafnyQtE2NgRAswSP3oAUrZ17lc5hxoFMYBGe3+iB9e8ucPBBIlZUIwUA+ozRwhS7FBDnBZcmKOYA9Qm7fqPcn7QyVVxnORwMQ+L4UCPKJbuB7DQnsQRiCqBg6Fgex+WYyIL9Py4ZID+Zf6+OX19mtbKfjf/fYHmOx//4c/v/56+/Dx48/vte1ba/V3v/0smT99WOeJ3u/+p9f2f/73a86ZAXu9/cN/b87xw8vLl19+aWm9P5qNmNfym0/x40cegDyFUDKFET1npoQFwsNrGCdGQUkkkurRlim3pho21EpJgFFH36uxgnuURaQwAXZ3ABBJEPDt9T0EODsxX64ThN63wULqrWkaGq3Xp+eLhZ0J4tYGokxTOTaeZ1lXAkrb1gBAleZMCf2+tzC4FAmLT0+ze/ShAkiAcyImqGoWOEaM3okQCZ4Wacb9GCVxg/I8wW8+l5xlStysvz7sy72/7/XtS2NmPTxnRu6hdFmnMfrj/v7x0zJnEDE3aE0B5HK5qjeNQAhTR2KGGHUkxHmZACMRPrZOQAT+emwA8Onz8vyhtANcXY8jZxGWeozLlN42RYslUXXabm2LtiwJ2ZNQIskpdY2n56RDi45hVkpS9eOoQ3V4/Px1T4xJeBFhxutlNoev3/YAY17UvHUdpgHhB4goAKg5I7gDACVGJlKDbdMVkyThQtuhj0fdNi8FbOCU5fpUAmzfjr0OSlxyDsdAPLrDeeCwWJZpr1thCY99r4EUQff7Yeq2uBiXIsuawMNP3xBHRJjaqL3MU3g8tseHj8+mYxs6lbTOGQOZo2S8lmnbxuu30YcJy7Y1AIpAHTGaqp0PjW5qNiVq9XG0uRSL4eCJSS0QoHZ9KikThsB21Jfn6TLJUdvr1iHIncLjuqanaX6+TK/397HtT1kA8P1oEAgAmRHdmREgMnOZMmAgGBAERE68Zo7A5tGG0zxnpgr6/LQAgJknoXXmxty6CscYNkz3rZ472WlK2ixSEIKZ5cLhOBcpWbdjCBdgr11Tlt5MUhq9M9NlTbf3Lgkgonc37a3pNE2EYIoOlhKHmfvoB4JDyoxBSVBwmBuRnMElplQPb224t3lJkogIc4EIOvZhI67r6qZklkWYmSCmKQf6tCbJKSXWPiCQkeY5uYBkBMMc+Py0IKGZFWEzC4yuQJieLgU7IAAA6bA9IGVqXRtYODDLssh1YQBiyIHg4KMpscw/SgY8ai+XvOTy6XJxV7MIJTcfZkLEyAQowoQwZe5tpCIRACWEuUzy+q4Wtl5yraOeAHJ1ADhqDw8kMrV9q242xkDkw3tcJjUfZq4x1B4PvMypav30+enlOSeeBeMvv7zeD3t5uv74Mdx8qyEpXxYSwq5DYcwTFwEL/POX28+v95KFuMTWexvXy/rjhzlnXa+oAfe3bs06uipJTl/uj2VecgaBxw+Xy59+aden+f6o7/sWgNfn3Jv+4z+1nHw0dQjQ6Ba/+/HSejTjhHy/VUPdHvrQCI9998RIguZh5jYsADBkG/E4rKcEEMQQjggBbm1oABECM0Q4I5rDfmhEWAALwrmwBEiJT3QMEYyml0sGC2H3sIgw1SQJiSWhWph7ydSaR5iIBIhZiFDt370lSOhqyMhZchHr1mo/EkqiNed2WCrEgtphXcVZelVTwKnUvQJ4H8HijHxGUvZjMNM8ccF0tsOAiJlq864VIqaYVKOqTjP1fg7/4yxLkdCaORCIMWcWJtPo1YhjmctQPY6xzGme+XLJrelU4HqZrNvowzUi8HLNZUqPmwpSAHyn5BJhzmQWc6FMchxqoQ4WEIjfIdQRIYTmARYoYB4afllzO+zYByD4ybR7FgKPHdOclhnLJIhau51cHlO3cBaCAMCzOKf4rzOmROgep8kdkCCgtRABJlK1CC85I5zEGOhd3cGGr2tKDG52xnACwjU8AgDpdNED9O7qPs9JiHLC63Mx9SkTE2ZhjGCB61pcrdfBSS7PaZnxqJ6YCKE3CwAHnFd++TzvD308quTkLZY5B7qpoVPrJozMZObMNM0ZAXuzCMezwMZ8bAowSi4n3RcCTt+3uyYmysKJt0e9Lsswvd33Hh0QEej9dphbcm7HlicaaiLcd1W3x9HB8WktmbL10Xww47yWnODrr0cb/v7+WNeZGeeZ+gAzFMKhhgA586lbVtMkctazGRkAcmEzVwMAitBWOwaWnBwC0M8hkJrnzAjYBxBj68qMCP70nN8fB7EgcG8jJ8JEKQtAbIe7BhK4+f+hn6t1nDEuybwfVo/xdJ0zcB/RmjFBKkKATNiaidBUuA0nwqkIBI4xAsINjl2Z0fQ8phIY9OatawAA8rY3QEqZIFDHdxS0CJuYWVCCZWYzaeNUWkJ4IOFpuZ+nzMwG2oai0JI5STDG1izwbDDR0MDA1jWAwZQIKMLCM3NAyBl3CmAhdo+A2swcBAARTG0QMpMwAocUXHga3Ue3LFAK62bCyEseRwRYmgiIRGk0PfYua14WOQ7rzaaJSHDfW8l8Sr2Yycwk45SlHwoYJw4WMbR5HzZNMoaNMcoiZBYQ6p5Lat2GnRhBC4DH3r/zkNSD+HwtIjgXNOfeLBCYaTTvw9QRwpAwkCIwwBHxjBODu4Gx8FD3OFuhzikN9c8fl6OOWged+RkGdR/DkzARhpvDCRGj0fSUKZ4VNiR0Cw8w194AkAhd1dAhkEa3cVCtzuRdHSMOhSGORI7IEfsZKwOQDITupgGoAXVzcGhVtdlJfSMCRjVwYL6b/Zf7z4/qPz7L/XZfFvHuv7z2kvFpnvaqIyTQb3tLN3wa8sef61Gt2+CMIrhve86p9WFuWZiybL2hOkm/likJ/MMf629+fMKot22Ea8mxTOUvP9+HobmNXvvRhBiJ1HQicTNirN23bZj6vvc6FCLCehJKKTGDcNprS4KScFiUTFno6ZL36seh0yQMuK5FLVjgzHNtVYcFIEZAInLVvY0TXT/PaSosiKp+70E8AECIzMcwH+ZHr+ssj8O3vefETeFSeDsqq00pEYmHa7cRwIimWoR/fL7ioDE0DJ6u6cM1/e7Tp/eXNGW6LMtffvlWXtaPL0/EeLs9fv5236oDgoZ3VQLMhYKDADJM9dhfrtOyrBqeUw4ARgcESBim9Rg5S3IfrasFSmJwSXwclukMN5EQ6TBBbXsNZgBFNIQ8Zxw+AmmdxDX2Y6iP58vFOyxzGq4EMJWiI4RVGC5zql3b3g/mczbeNTphFjx0nD3HCBhDU0q9WRIGCPO4bwoRCFhKAgJXS5zSM2PYsECiktMYOk1i7pwwEVFEofBASbxO/HKZA4gRjt5/fT8i6M9f3srEgXPzMRW6ZL67Pr3MFD5lRmYb3lpnxHrUMPTAY/ShZgbrUvqhvZlfsXWvbaxrMY1aj2mZbrcjAua1IKCwKLtgLHOqvRXKRIhoSH65pjnL8BChMsG5dai1TzkhwzJzPdQtOti2K3OoOguUXMDx9XGgcK9jdAMISTylJIT3x+jDswUzLHNW1fu97o+BgEyw7+NffrUPa6HYqcw5s3XnIAcXJkHZdwOykuVpTUfXL2+7PU3TJFPi/DwjAIUdhzngNKXMQYEE0LqeTMcpSdWuQFXh56+36zolobTgj0/zy2Va12J9aINukVJMmW0KwMSJPYBoMI6n55KYlkL6PkLC2L9rAVwfe7UgTsRAbkacEOhoFZHN/Ou9BdG319FaBYyjDQQMb8QTELw8l5Ly19etazvhs8x+hnnDw9WZWRL2Dsg0E/7L1/1xt0QwT3R7w6OqHniZxdL64cKS6PHQnz593No9zMyxXOfXm3153YD46+bPtjFNrg3h8e/+8Ddf3m6//en521FvzVNBJHfXwOnR4O31QEaBdHTbeptn7iOmJSemXvswBId9PwiwParCyIXXSzr28eW1XZZU5lQyh2MAaB9Pl1wkQdD73t5rnT8mBFI1INyPvk6ojl+/tTyJG7+/H/NlsoEe+Lh3FmYRCHhaheCSUyT2qSQfOLo/zSIMY9iXb5WAnn//MtxiqDAyUeYYqsegeiIUKblH6zosPj7PFCiAUZKgD6+PNg7l4Hi07gHvzZwgcVpmOHpfL/P3TYnFy7Ucg8bAGPDytKrHl2/3f/uHWTj3ER14u7WjW8C5dSNGdgUpeFmv2swc7vd6RnnnKbXu+96FUjUb5GBjnScE+uV1Wxf+zYeljtGURo3b1pOwAnVzNVNF5ATazRQQhanVlkv68adr2/v4VoeCmUfg+9twcDqf6tXbsNa26zKvl/zYo3czdwEMcAQwi95NhHNKy5qenqbwaL0TJiMIcuTJrW33LgWXa2m9u5khvN0Gszw/Qym5UFK1ME+JiPHzh+n5kgLCwtYpmQUjMs1t2NMl70fHADfYqwaEqRcRG3a718ucR4tWewC/vdaPLzPhYMTf/LBuez01uzZsjAFMo2vOpPb/p+nfdiRJsm1LbN1ERFXNzN0jIjOrdp99DpsEgQYa/P8/IQiiG01071tVZWaEu5uZqoisGx806jHe3MJd1UTWmnOMvH92JFB1LtL7eP++cyFEUJ2IScTH0YdRW4t7Pqe5BjPtFq2UHjgNPn7s11ZZiBiRsC7yePY/Pg+1kX+7H8NJ/L9/k8r048fOVCARMbalNCke6WHfvmxV6P3ZfRozHdM58SRu3jYaalXwemnfP48ELCz7MT8/hwgzszt8fOwifLtdjj7C8zzbR7iwSSuISElbqRZMjG7BxASm3Quzu7da+m58xe0iY7hZfP7o21oTUnWI0LlIWRo/hxcOItgPwq6XbalZComH74eeS01iSs/IjIShuq5SCiFla1k3xsRuc7q9vS6JeOJUAvzHva+NboT3YYIwzfsYSxEK2rgk6tC4bWsR/vH5fA6FCIBsTf7661oAMmA46nRG/u2lEfgHcqv18ZxmkQoerKmqetmW93sHiNZEPT+ntUIofH/OwoSC+94ZcGuMBIXPAz4KMRG0xtdrE+ZhsV2aTjOPZSmtMCObKhGdTZfMcAcPOX1Pz30Is5oSmVkwQV0KZIzhhLTvdhw6Dx2uy1K2Zdn3ua0r0ZjTHs851VSdObhiamaGMCbk5/0JSKVIFZ7uaT0y1TT28L/pt1f6X/8ft799HP/+Xw87ABHqKr+8takq0P9fv/3y/uOP/b3ff8Tv79+vi+wP9QCaNqd/PnJtYqfPJcED/q77jIzApWJ4BCQTZdIcGp4GVAkwzqglMCOCZ0IlF0wNxDi1XAwZEaFm51ekW6p6a5IIRRACEjkz5gzPRExmbhU8ojibeRFa14KSjnFeQc42/eut9emIkQ61SmAAnc0tYEF3zIhMkCZIkRmCVBeqQuExhp/dduI6x+xD374t6hYe4eAaRISYtTIxJsK5Enh9a+eH7V0B0h0047xJQSYQRkZtsq7ChF3d00UEMqrg7VJKZQAaU5dFrMkZzojMtlBLGZIvFxnmxzFVnUgyACMIcQ4Lz9H47N9IWOqMwTGmnwogABBGRi4F0hGRwsHDhUCEZ5gIL1X2u+qIRIiHHZ9KDFw4/2lgiohT1/pyLcsiyEgESKetGwARMu0cKiC6B9JZ4UEEjEh3QAhpTMzj0FNJfhrjC4u71cIVeYyJlQlhDrtd6mWtj2cfQynRPd0DIDOz1Wo+IFMKSUMAcM2BWgpyUi34PAwzMr0fOQ7LzLoIF9oWcQUzb1Iy4nZlQDRnZiSOdUWEchxWhUUoEfrhppGAU5MpkX6mJPAn7Pj83AmQLNRaLUVMbVnKy61C8rRJlJ8fz5fXtgqJYB9PQi5S+xgAeHQd08zisKiFsgM3gG4+oa4EyFLqVJxme59tZWJ+3ne8Vp3g6uvSGOnzY2YA08+f5uxyZ4KZZ3oRYeKhVotEpkfq9DjreOcwFaHUMs3BAwGnemFclvJ47tfLCplhkB59d3R4rXS71Ge30dXd1agJngx2npAOkACQwhiApRFQEhYPGDqZZG3lTHSck0EIwHma9RDPth0zMxQBRFRzZvIA1YjI0hgizIOQCuEZ5JnTdVqrpTR2i74rCZ4hpkwgovxJmkIp5JnmkJiRIcQM0AozUR+9XSQCzD08q0BmMf85uT8B8iJYWp0zpykA7908QSp5httPhIcHWECf55ySkvDM3xJTqE/z67W2wkK4VjajOd0C2iLM0LsP9WVDYnZLERSqP5GKGG0pOsM9lrUSMGR6OAowY1u4CHv3UnFOT4jIAMiwk4JE4ziSABlLlf2p6Xm71ZKomoREhFLIDTMzAlCQJGUpIuTuawUAugNm5DmpOU2fhAKYp4QIKE7WPSIhckLY+XphQGSSkhGq1odNTQtogumBwgnp5kXwRIlHZHJkOhIIEQnqMMJkObHiXorMCGJID8xsS3l2i0BTz/CjKyI0AcVz8p4AjgAQUYUiE9IskDIRTl1dmv0s5Kc7nJ5IAm4SkY+7IYCb/9tzSBXAeYZIh+ejjsfuCeoe0+gff9x/EB/dhhoJwIAxNCwM0jPPSHAiYoK0EtN/PPYPxtL4+2dC0uddGd1A7vvcH2N6QlqpSAhUJNy2rez7eH72ty+3PmxOR+Jnn8QkTCLUaiWmOdVChXO7VGK4UJ3qj31sDQsTrDwPJwRAZIxU6BqEmI6ZSIxM6G7qKg2JoTC5+fuP8ddv18iw5PvniMxtWx+7DnWWQILH0zJShAzp8Ri2VTWex3yKI2DfJ1UeZPZMV7hcqkX8+vXyeIz3514CAfGx/23v0909ApC3epj5MeOPj900mEgzl1oY8PVWMt0dL8v2/jmeatXd0tYq6vD++WxFpudUC8s5A+5zWwoJSCnhRghmEB4RPj02KM9Hz4jX24pSzAIwhGQcul3KHD/feBBETOuy3t97K1IWSs9SmTCqEF+KhzGFIKyvLcMjUopgos38/NyXrdZaEXEOIyqt1ktDSPcMAFhqQ4A5VTMNkoCfz+fSqggHzssiBGmYzIRMW6WtcmFcaqm13Ls9u9uPAxmn+z4Gn16SysB0Pw5idM0nTgAws6mzVLHDIOno1hreH1NEdOq+j1ork9w/e58qRSzy6CMRIuD56G2tiBgByyKI0J+6vjATfP3ykqn90OvLellhuJ+4sTHi8zkM0FRbq4mJmdsqw50QWy02XQSWJn14hF9qPY7jurXLpXpGJe4MyHDd2ufHPo4kQgA8HiYFtk0edz322bu559JEKMce3zVetwzUlYSApvnSqs2AhGOf0ujlupj6/d7T8egT0J+7XtcGhEFgBIed/cd69OEAdSnJvj+mcZxzvbbWy6ZEvF7qduWVsxKcICszvbZyXYqGj+BLJQM+JrD3UqurC+aytL8yfjzG3z6lEvzhzz/vgzJb5c9h3zYpTEAshP/7v++1cN3KclnI1SGZEzCYYWqac22tlPA4HTfGwmbp6mehd7q7ORNjwrIupZU5u9gsjYTxcZ8AxeYOiWOOvR9BBEGmWRu/fzx4IY2YQ92G4GUaq47R45nx+f7vX395/fq2/Nef/zGUfvvtDR7f7/f3pbb7o6vH3z9+f+5TRG4v24/7hzpwQWGJyN59Nx1ztkVMh6mJyMtt3USGGiHdrst+6DEjee49llZR8vXa9Mh0B3JDe/m6FJY//9wjcG0lHTTiTHgKMiW9vG0OgRQCREjPeycGRHgy/PbLFczDPApsrRobIA71Pz/G1Pj6ZfHwzFSzz936zIh87qMIJaBwPX2d02Grqw3vWWbksz+FU8M8kj9iqTCmbYtUpvTERn5JA20LemYAJ8Gc8xgKAAXyb98ftcl/+5eXKytAfA45DEc3cpiqr7cLEh6eL18XKfm43wHh9tr6mNYDEbtai4JQK+ddbd3qb1/XOKaDv3y5ps4/PvpI2I/enwGYr4UhMRPUXR0sDCnXWzuPxKUUQBAsj7lDOiHp9PCUC0XCnz86IjARBHjm/X4AgEWI0LqUOee0079BgVAKsZClIuKyLp6490EkpfBzH9fX8nz24SbKz48eCb/+clHvY/rrW9tHP8YUJmKwBCHMdC6olsIIFAxQiRTSIdxjqFLBz8ecBh7WBM0IEOpS9mFSAIDqUmpl88zAPrW91GUTHfDv//H+l19vCQiUy1aY6Ln3wtiauHutsl7q7OrdE+JybWEwdSJkjCAiYhCW6Q4Zw8gSbCgLcRXGPMbw9C+X9Tn9sgln7FO/XFupspvsBuZ53TZAmBZhsHeTWgoxBr//6CS4LGWtyIRSxQMgwjIvSy2MgCmQv71tz6GCULm83apHqgUj/vrtdn/0IgRb7cdQ08KsptvLMiPc3MO/P9Q1kJALWPr1pWICIiFBrYUFIoIkpQIziIC7caExZu9gkZHw/hhnsaBKSiFI7HOvIoTUxwxIOs/5HkQoBCKUAK3htop5IGQryESF+JjYhFRVGCzjecwi5WVb0ryPaZ6A4Anz6eH0xx8HI1ja38vztpXa6uOYCeEWsKurtYIWYI5z2MvLGonTcQw/ju7hp5k+M5m5FizmY/q21DFjqmWgego7IfShTfB1W8fQ3QwZheI55tvrSsRT7bGre3qCqelkSCTEVhoiHEMBATGkIAJFUO/Kbq2yJ2YAE6spCzFTeOrQo0/EXLeGp7Cc8cePHRlLuRPy53OWRd6fx3GEEGxLXbdqoUNnXQsR27QgCY1IU4uIVI1SBSJUx98efVpLn88xPx8Klu56w/r9xzEHLCQ//vbeVQPp86MD0X0aAyTi6GDDGgFlQgJjBBIzprmqJ9AAQgi1yIClMTFWDOEYE5iIIM67cpiP6aVIa6LPYQGMgAginHOGo2VEFPXwzNpILViyMI6eXCXSyBMB+zGLVOGSqe5x2eTzs1/W8uWyvn8fpUg/5uh+DBXBUsVmDnPEfPuy9Kd7gJtN1bZUOgO0QJBh0wDSPU4q4ZiGRP0YpUh4Pj+Hurt7KWW51ffPRyn8y7dVBIDoOGyqXbYSjjqNCM0DmQAAT9MYAABp9wcctTUJlKV20+ttWRtjpqk9u82RHuYeYCAEt60kQeVgwcrl1PBlIgSYBwLZyDhbZwCzh4frdDEPt9wPnafmWQMJVOOYvm6CZwIC0i2TADGIsDVuTcZ+xtzSAtKdhTGyCAFmAoyuEZARCZkIkFkFkagPMLUi7B5j+pn7KIKcBCfFw0yKAEKpzExzWnjy6fWIoBO7mznN/WTPRgJgaWTpjFwXQaDZBzFDAhOefGjmf9KjI4XJ1Xfz7doy4373+66Ied2q+2k649EjjgnhwjUh//H5OA795dvKxNe1AOGYdrvU12sxN0jaj5hOgKAjVAMBT0pJrUKEqloXZpTnY1LgUuWUSTGSEaqaTslCwhLgt7dtHDNygOfzsa+X5TkAIhFgqhNiKWLpUpAEL7flcZ+P+8NTEhBillpI6tLaGVq73FqV9vHxRICl1j7NE+73ebtV5gzzWhmlCKMIm/m+TyI6PV/TjAik0BiOCKpGRK0VQJzTELEVYmKEND8njCGM7sAMQgxAoztXfD509mgVEZGFkCDixHuju3OhhCwFLhfxAHfoY2ZCuCNyeJpaZiJC5DnUQ6azKZQRiYzMtPdBxJDnfA0jwDTnsExAiA5ZKmfiP6dwaDNUPTIh0CMI/0mcgkRAN+cKtxdxg+PwInKCMDxi6KyrLK0gISF9/+Pxx6EsJmdiBunMedVSAMDdai19aHiwsEdmZnj2n0cWEqHzEyWkekTA+QaZarWhziwMlume5+R0DmPBUtf7594Wak2ej5kJSJQOLMyJETHDAaKsgognKscymBCJGPjYdXZ7e20nNAcB2sJAaJaP5wEIwqTDCIXJPeP51MiEhKDYlpKZ6kZEQFBXJspaZFnK6BqWllEruBFktEYR0GrpvXOhSqgzUtMjMAOBiAARLJLwTDCnDyeIdZM5zT1q4SY050xiYWxJLCAVNyyRGQBvr9U8I5AZH5lzamucmtI4ANaNMkMHUOFT3SbMgLAtfHaEl6Uds2egn+v7zK9v1z6t90HE3ZIhCEFPTTkhpEOcurdMBMKEOQFAhADMwRnJ58TMREwSQPj973PvXmtBggx/DF5r7scjuJKDnK03zKkqwjo9ApgJEDKyrTJNKQgIf3/cr+u6P3ekGJNrLe5OCZ4eQZGQaW0RYRnHgcyP54hIYtqWQrJE2NK4FqlCQyMRhQlBhrqZb9dF1ZHx+6OzUKsCBENhTkfES2tM+DzGZStj7szBRIjJhSDBZmwvpUoKwvSQRgSxLq2I7F1lqfr5lIK3S90fuu9jvay3S7NF1BzrT1nM9x9HZozMcCiF3OE45t/tR2scHqWRkDx2P54HIycBVyTI5/B//Nj/+BzDgpJYMmN+3I9W4THRA0SYhZdtC+Lv914ZBLibN5FMiAhB6h7ggMifuybmtuRauVuYKmLOcCLSaXOoFPn+cZgHMbxcl/0x3SOSz9YiAnMhIJyHXy9SKls4I+k0KinC4VYK7Mfo+/j69cpNUnytcuyzLmW7XtWCABFx2yoipQcillKQ0MzN3SwiHSDdos8JHgDcTzcf0LSc6mPEdeGXhS8LT4tkONzejzGGPtIPHdOUWda2hpssfBwHARQoSqRjYuTnftxubZqbZrjt+zBHM3BTJipFIOE4xgnSZiZ3U3NiGl37YS+vpRZJx1poDNt3/fK6rGsVhj/+3AkFExNCuP75x4EJ8rVwE6a8XlZh/v3PpyBaTNX40ft1XY5jEldz89DrrawLzml1qZX4864a3la6rBUsWpXHMVkIAtfCnvb4HAAUiWNaBCRgqfTrL2uttKzSZ6jlPmfbysttO56DAJn4OMb39/16LRExDl9Xnorf/zyei28Xaa2YmsUE5O5aiHoke0aAIAOkRUz1fT++fLvoUGSLxIjytBz3e2GqpbTCYTpBHOj93gmLBUEml1wLLbVGYEVmlnT4+/fjb/94hOHlKtsl/69/e4xfLn/5pb4/+yolotw/j/7j4y9/ffmff9u+1OUx8O/fP1uF//bfLgWZUQbquvD3P47jmJfr6idZoBQpyJhl4aVWBvb04+Pj8tKuTUwVILvDllgX6cf84/kAol+/rLcb35/2eBy1CPS8ftkip3q+3Wj578u//+HHcdwf+H//71+/vLRaS2SOu/9///c//v7joeHj1MVFrNvy1pZpTghqjlIQ8cf7EwAzcm1tWypg8tq41PDo3QFCKiKjW7ZVFojXqyBgAE2YADjUF073qCzW4X48EhAAn4+xLgTAPx7WFjb16blcmCm2Rke3Upk66TAkMoPnc3x+Hm9va04rRH2kSNYqglwErhsRAiRYxpiOwKqhM1WNGKFK7zMQSy1fX8pxGLAlxYw5TwAswqHKIm2VObUFYSZGFoZlkTAPyVLEPQ/VACdAG9yHhtuPw++UL69VKrP77WUhtn5ga7xKpSA3JYjb6ypFHp97IW7XyoTHMXRYQBSJt68tMJkoizSx2hza8vizPx5z77nv57E0IGlZ67YUT8ugpdVLa/sxhrObms77wyOxLpxBTGjmx5zCVGoNT0IhimUTAgqDkiGF+z5UPYnUEsyBMAJOTWoCPfeRjkuV+z5qrY97x1Mppfb4OGqtbRESWreSkUUyDfvQ3UJYRCw39gQzdw8RHocL4caEApfWwuL1sjAiY9TGkcwZcwYQ/uXXy1THzDRf1sbCj/suwijlmJ5ImvH168aVwRw5mWFpZe8jAABSLXhqqZLOa6v7MR/3PvqJnVou1+X+/kTJZVkhICN5+Pvzua7CyUiYAsgICBmhZhrw1hgpn3Pe1lo9wubb7QpR/vP3z+3WwKlxuWxFdajmtq06+u3C04XQgeE47CROuCkScuEMvbQ2zXc1yCi1CJETmGUVfHvbGEE8ERsEYCKXqjOQ0j2YuRI5kwh/vW2Rts8z4cvmqebEUEQ8cqYS4PVlqYL6kxiAFdAsTDETPEJHuCUyEZ7edI6IVkttEhmI5+0gLluZM9/fh6kDQgISASJWkUj8+HgSQQBmgrktDf78iL33s9OekCJkhpDFXYXreTRW95pg0xnBE9wdHHZLrny5lOulmuq9+5wTkRCiNbrepBL1bqXKc/cx7HKpYTm6JmQVXJdVMEYfJIUYPp9TzbBCRN4PH90yu0WeeSudiigRUStdl/Z4TpaiPj38cttcrR8KmG2R2sQ0SKgyfXweL19WJCiVIOD9/fnysjAjCavqmSkDIfR0DQgAcClppgmZabfLbXZfG4UBlWoafowxsjbGwo3PW3/ONB2pc0gVM3vu8//zLmOEEGSEqj6DOvvQ+FMj3AhgWVbE1Hm0ygDEKK4jEhAQwgDIzCADSYw4Iz0cklgYIiNgHBNFCJOFrZtZFMZ0RyJgIoq9qwgzS7fw0MtF5gwphIKu6ZFjGAsXxjFQJ3AjIixCQDy6pyMXssipxgStVFOXSsJYC0uhWmlb1sx4Po0YiBIl18YIqGZUESNLKUQolfuh5oGIbZG2lo/vz8wMoVJp2Up46LBSeN2amzKlWwL425d13TYEWBY299FnGKbD4zkhkBMxEE9FGhEmhUERJmLVaQ6FQ5hqoVZbZWKEfpi5C3NwIicLMbEO0+ltZQAMx2MYIyyLTHLFRAIRRKf9MNUAhEz18PAQ9ziJvxlpDqfdOyP3PtvCVNAMXEOnA8BJcCxCTNkWMisxDTCoVETGDFfPTCKKSDifdg9ThwxPBiTmYIRpBgkEiJkRQSLEOFTVLAI5kgiESTUiorWSfZolImWGmXniKf2KjHBsTUorkHDsg5gywxP3Qz2jFibiMQczCIkQe9joykLczrd9WkSrDBBHn5EgpTwPdYuXl8ZSH58HAgFxKQWxZNKP93l5FZ1+f8ztWtM0E5Z10cckolpJrWfitjYRWRoj+xkCYsLyuvYea5XH0YugTS0FTW3fZyn5+lbO4cVUo5O8wfW52/TExASQQmrg6iIUCa7++eMJyW1ZMiM9wjOGkQaREGao12WBgOt18TgDMpkV2mkoRXSmiIiIDEbGtv7zBzDf+0DCWsrU7MOLEDPPGa0mRDL9/L5nwvVS+zDT3HNmpHpuq4xpkZZYX9b6eivvOk+H95i6sLDQKZxKggz4+LFvWx0TxwwiTsgiPIadoyj3SABCAACPREguXApF/hOq7xkBtbJ7nAJ6ABxd3ZKFPeLEyuTPiBmMocwE8FNllRBAmJmnnZ4IRQQQAbMWRKBWed+NCdUdABGoH8rCteD11uZwQBxD1aIuGO5JrBmCWCut1woI/ZiQqfNnhISJIpMgmZGZI9AiEKFWgYRT1gAAkMmEGWBmhbEUumBNiHS/3VoV7FOJ8CyFImd0KJVLLTqhD/3nMj+IMTPTITCZ2U0j83nospbLJmNoLfhxt8hkSXOY6uzkoZl5uZaliqppgLmjJNpJeEEdPg8rjbBAqLdGwRkd9mMiY1tbBPRjIiEVgshI/PnbyUAgwPOzJjNHgmfeLsVmmMGyFEyoIHPo9Nhu5XoVRDl6IOT+0NtFCOXz4e4IAW5pmu7RGodlrdSW8nmfAcFEmdbW2rsRJRfMSGmy37UKPg/1gGmxNEmPOWNo7MPDAcLP8aIGmEc4ZCYxZCZhIqAHCOPJyV7X5bFPyAiHyDy7qFOTOREiAlWd0AlBIzhUuBgQuJ45dGYgKXOaWwDC9VKmet+naT3fTmN4OvzonylAGSI89q7DHaKuZGo2EynF8eP+WLcyzRkTgJbKSwUoAAEAFuBExUITYGrqjK4GmFM904BhzKDAMYmBiMgo3rbyv/zr+vf3Bz+AKWdmIAKeXpwgIhHa93nd2npd7/cjM5ZG//rby+djvr8/LrdlvQJSzjmB8/pWWiFTRUDPcAxXO7Wal1slBESIAJa8raKW4R4JLFyL9EdPJydIzK3Uzx+dhT/BIrNQaji4QwRRJoIGjm6EtD/H+bhFgjk+jkc6JmaGI2I4YMJ2WfZjngnH49DnYTqtCqIQYi6NGGhbLuYx1aXQZStVOCxZEDBKw8KFiJhpKYJvywl+Vov9MBRZG5Pg0Ly2JZPfiWprLPBS6m1Zxq0KIyPt0xmpVi7l5Ky5MK6tHDN//74f+2itMJfH81i3y6Whe96P6W4vl+qR06wf43pd1hVaiWHwqf54PIZGekKGmoY7M7dSXC3TISEtuDIh7s+51PL5eHLlcHg+BiLPPiMgI/fHgIRSOTEivbQiKc/HwIbu4RZEeGbzClFGqis5batk1lKZOMyMmatIn9MRqpS//CIsaACobhFbk/DwiNtF3D0hW8G1IL3UBNi28vJSGBCBXm6rW973XT2LICaMwwpTVy+lQOb3Px5fv1ypQKlscx6Ht8bn+CwTSwEN92dIKcc4SkFGrOQHBnFet/XjRxe09ZcVoH2Cqhoz1co6DVY+Hr0fdrm2948OG+C1Pp9TiFLDwi3kfNWpGe4DMqARQkMpqUME965ARQPMj/X6hqU9ET8/x8f9uSz5bcu1rAn5fM6PPbrn0e33fwztkBj7bpD5y5e1VtaEz6c/0r99rf/5t0Cgz0f/W8FLa55Yi1xWfKkR4RrkVDNpvgA2HMMSoW7l9bYS0v1+XK4bQoyjZ9p2a5dtHTrHpDlmKzAtXl8vXOTxOK6va6ugOpBo6ry91NulCsG05PL24QLw/OUv8re/H1Xq9fbldSv/x//5n9+V/o9/e2bAdoUvv9WXdf3+vo9pX1/4x6c9n8OMgzJ0hiOQI4IDuI8s9ejqZhYZ4UXocllq5aXxfmitQoyALsQeIY4UCJBp/tiNuO6PPub89bfb+/sxhu9HFTnhuPL+eczhWC5fvy6L4ICotSAQIHFBLhCQbeP9sDnzUokRRApCvNyaQxQhMyemqQ6BzBRuiJAAUmTbxAITYKlQq9aGa/MRuVw2RtDhCbk01ukf90lgVGpiMrMNlRLr2sIyPSJtTHs8PQMwbVk5AAmxXZbuUDVqgfTERV5ua5XsD3dTWUDNx49R2KbNUvF2WT8/n5dLfX1dBKMUAsnp+bHvX18vjdf5PAbartbVl63eXjZ3u15qYTKP0btaEGPAvD/88ZzLUhF5aLr5sqxIhAm08egzIk4DwM/7qvBlaz/+PIS5LrIfnYSlFhsGAZkJjhpBgeQwurtBLVKbvBSe3a/X1S2QsZRi0yK81VIE316XDGuVEwwB7yPNzY1ul7IfGpavL8vHcxSW759zrnK5Lo+HZdhWhUWESw9zj1bb/XnclqbdAwgwCcLVzaFI9ZhINC0i4eW6yK3tqgrSWgOASy32sjyeE/A8tAdLTLXLwq3RmFAKXa8bATAkCkXE6JOJ59BM0On9mNeXrY9ZKtVa+jFGcxQ6xtReSuGP55yKa4nrKu/34xh+vZSASHBmqkWYcG0mlQEXRCDN47BrXQLo6MaZw+fCrfcJCAnj0uo9PJL61FaEKBWjKxZhjURABAjCOf26tt/eqmB+P+au3g/1gJj5ce+FOQEtQmGutWqmW0QAITGxWUK4GzLT0vgkpp+ievfkJGwUZ/wr0R1Om4pHuIWqJyQAuGf4XJYyh98dWBAoiREB76qQCJjMVAqHByE87/2euW1FZ7q6VJrhIsTkBLBtOEeYxiS0Zz+6b2tzm0l5MllLImZEUgaNMVBQRGZXi7Qxu8Nx+GULYmZGolT0y0YGsBZqBb//mL9+uyTB++deliJejzEzoYjASmcLzCLIPcAIgQu+vdDLQn9+dhyDiiSECLZWABIhW6MiGMGm0RrvBU21VjIzYT5zQAg4TkK2BhOQIBEToU5XD5xxuzV3B8j7Y//41ORrQrz/uRMBILZWVYcdDpGYEJlmaa6EaT6ZyXocjyeSGIS7m2fEWAVNDZCKiOncn3ciOcMmRNi7CkECDs3rUgRypkAGA8wAjwx3s2RPTABEB/LhteAYbuq1EABQQYDswzOBCfs0SKeEVqtHzMjba2Omj/fRVmHG0f0YXhoWQUwMgMezS0OptH9aW+l6LY/PaenTEinWS/OIfmiptDYMz20tX96Wc5oQcBZM8v0+xgjwWLbWqpjqAEAEc1tAapWARADE07SaEeAZ7lZXXhuua0tYISFiXrcCiXMYCtbK4OAUnn5ZVgHMQ2NiWgLH68vSnz6n9dmlkjDdP44fHusmX79sARAaxLwW2keoO4qTMGhKZZ8+hwHC6TUWpL57HwqA8/B5+FJrXXiaZ4JauEVmSCYMdSRQi3JSnfG8o6IIk9BUPwsyGeEW5xEZSQBcChfnWhoA9T4R0TTVQyQu23oMlQatcThYwjTnzGurYwZExogxjYUSMd0LMeAZQgk1q4WPoVLEHav8vPyf5Ugico90KEwZoQEIHg5E5OEUmR7LIpHOyQkpBZBKuAvznBZpl0uTylO1H8pIRMycpRamE+XLcuPR9WwJbZetFtmPfrtujPw8ZmLeP7u5sbBpVC7HbkjBhcxCZxQREXx9bTGxLWCBFxIm8cR9HwAJmIQ4pn55u7j7ujUA/Py8S32xiDFmrQsSPY8+Ztj8iRMzD+BEJAR8uTREfOzTpjKxoJBkeeHHXUfXNH97kbfX7eMxR7daWmvUh6kHILTGhNTniUoES/DIYcbOxMSFvlzr570/dzhtqfdHJxLzM5SQx6GtciQQpHm4Z3NGOj3dVETmOS0AkMJAqaq18HqRvR9zMkRmwMlsiYhaJCIjsHcHhHWtc0RGogATZeR+DI88ByInjBmQuNC6CjHth0UEQq6tEpOqncU2iDQLIs7MjEDCzJ/zHyLw818AAImn5eufjz0ilEpMmIBm2dWFCQg8szbhgMyTcpWIeeg0i4xsjZcqCEGEUsroFhZZSgKOrsxUW5k6A+jcbZYqZgaZxzHbWlCQNZZFMNM8pXCc3UtCj+yHJoAIIYIUHtPUkrDkqd8yR8JS0C0Rs1aqjSBR1U6KEzNFRgSoprkReXhCIhFBnlrNpFJqCRYcwxMMCbZrtWlSZF0KYl6uTYQ/HqMwNhEAuD+mqWf69bIwUz8GKQLgukhmObrPoZBQG9eGYw+1ZCFhOnVxROgRROxh6QkEIgAUVJAJPWJ/zPXasGJhqqsgZASsC0MmJiSAelyv5BoJmAQ6nQhaLY/7XFZ57jrUbrXuzwkIUimOXFYZ3TNsu9ZSyCNN7fxdTzXMZOEx5pm8OOOdFmhpXBACK9NQyAyAtMDI5LXksHMvoOaEGAkeUIX8LCBlGmRGeFgpyFzONGa6I3ghqo3m9MSE8PRAAACghLMafURIYToDUjXVEx2ksnZT9ekuQm6h3SKBPecAVb1c0txak/3QWuo+43GfX16Wx9Pd3a/10JgWaeAGarZdpVC8vx8i7AFS0A2EqK1krsj+6B9/3O//t99uGfp8q4zkhuYeDmutXy/Ls++R+PtHtxkJORHeP9QdylI+914KIqDOvFyrSPn4mC/XdnQbqsgAiYAsRHNEKfxyaW4x3MxgWKRlJkmJDJvqmk4J6DEGk7C5j0lzegIgwhgTCLhgeoCRqReBs7iExLXVOQYBOWSGEyEzH3NuS1O1hFxXEcGjqx4uTIDgHpHRd71d13X9KeVlITWYQ819erIAM0aCaxBiH7YPdXMPrK0uRSCzT4sRrcrb5SbM7vnxPEolhvU9jmGDAIXEwSvzdbmkWUYAgHpsBJe10K/bUi6Xrf7x/Q7f2uvrZY7x/WNY+LeX2hof0zNy/VK/vpaw6VF+DP3cdw+AhNlHXSojIRERCeYxxrouH599qIvIjz+fmAQJFlmZH88xZ9TCkVCX2irfbu00uZzs+Zfren/MiGRBC89TE4hQKonQ41AWZKG31/b2tqj6+W69XptNd4RSylIEIT+O7pGZyCgE9HHf3X1Zt33MiJRWLLMV6jPvd2XOl+vyeA7AjIDn0wBjqUs/ogm4p1oujZ73Ho4RqcMQkisRYG1i6mbRFlan0edlK+Zxu5WPP7vOEJLHY7pnKJqGl9j3kUDLKnMaRly30qcD5HF4716qV5HzNEKAfZ+zm0bIVKB8PvX1tiHYVHeoEBmzR2Qf1lohokPhl9v1+66f+3w8+pzTMuvL2p3+3/92vxa+NEkWBnw8tLXi6IBRCh2qX2+1SIyeRLnvKo8oC65SW8E5Q2f3TMeM4e+718uqgOAxZ1y20n3clrLVy+M+3ra6P7UWsTnGMVQVgfo+jru9fG11a9yzsf/2yzXd5xjBRAim+X99/1xaXS71uffXlqr529fr//a3979/923Jt7eqHiLzj/fjz/fPw+3//GN+DH+9BIq8XWqafrvJ88jvn8/3D5tHhHCkc4KIfHlrrxfuGiJcmDKLB34+Z6RLEQQghKWKp0d6JB5qn33OgW2l3u3334+1lX0a0RzdXl8uqvB5n+u2uodaEGHmvFza25tIw62RqW6XQlB02lSqC92uhYFU/f6YSIS3tjZGAPeQgsJCjAA4VB9Py+DvP+5wyrsRwvz53F9uaxECCqA8uk5LPjEvECAgSH2fM+A4dGkU4TaclqxCY5qpEWA/Jjd6PqLvvm1CAiIgIul+32drghlSKNEu14K1uyUv9CqcBP/5X/vz0yBzXcp2q7dbGaN4xNIAIIn5/uPzetkyoY9OUrisfRwffb+9tF9e+FIReVOzjBgahOTgqgoYZeXrVj93nZqeoW72fFaRZa1IFBmvr2spRGnCGIGfRxLmdSuRKIXsHq7pmYgoBBp4ZqJnOhD07jpSWx46Xl6XBEiA01CBGXhpkeCmweQehPHxGBlZGB8P/fLWWHjOKEwBoGqIGK63CyOyHj0zb7dr2JwW6h6UJ/dAzdPAMpLIIjLBIBGDTusPJBJgwOOYYWe7M1tdCNHs57BC+BTXZgY8HrMK3V6WOuvoVipHQoFzk6rmHgFjGOJYL0vfZ3jodDcn5GNXm0GCSPA037B6uBkZ1R8PP4bfn/2vv70Mt32f8zh29eddRfDlNdXCLF5uSyL8+d7VkiSmKyZOg0wKyzCAyNtS7mMAp6dV5qWlR1ZkAADk24UtcjhvAt9etwjbbU7DtUq5lD782KedTyYxYC4F367X//j9AxJIaC2kBsdubpEVAc/ZUCZA5Nkox22rTPDjx57ImDDVCCEsaJGCfII7iNAtddqyMiSea05EiHTmMyUhpZKZnyMPRJRT4xSGDMRkGrs6hkFkZkopmeGGgQmYfWiEYwHE9CC3hCDzDE8uzUPNjCpSoXFkeixrHZZE5okvl3Z/jnWT68r3+1CLuhBCrJXvCPtjIrEbElNilso/0boRTfA5AdJf1+VxPIVtXf39riU5wE41yuvLT2StQFKhZ05M/OVLOfbORI8xt4VebnLsx75380AkSIBC8zACXdcFElohdd+fKgQe7mTC+cc/PjLDPET42Gf7Rqr2eI7KiIkGGO4IAISVBdymhmosFYjwbAJGBDo4kOkgQiYAlAhk5svLZuqzH3W9+NERYmjSiWtF8RiVkYO40Bg/jdH/ZHsAZpomEp37mVqoVGTG58NKIcCMIBFITCJ8/SKEqepIEBGXTZZFjt1s+stbSYC5q3lQIaIghvA09VLp9rq8348M1Omz23apeHalI/phInHu4McMyAyMMV17Qmat3o/JjK3SGA5EET6HMWF4MGOET4XMZMaEiLBMSAdLP2+LkQGIY/iCwshqqm6tycfHc1vreikGEZallrWVx/sYOq+3BRmFaf1l2e99qB7TiSahjMOYYVrM6Zyx27Bx9iWDCMdQqYRJY3hgsqBOiIiMPNxbE0SIhBOqAwkCABkQnpng/vMunOHLUllkzDkOG8OKsAireSICZql8v49AB4a61DSYGC/flucDjidEmLnfbpUww2Hver0WdM3k4VAKNcB5nhqZjmHLIqXIcUxmzowi3Frtc2b6HOGmRQgxIUxEEEGEMiESEggRInGqA/i61CK0z4EJ18taCgFHYTTHaR4aEbBd2mVt7/c5dg/PACeAVvC3bxcBPKYva9kfIy3f3urzoX2Ezh0Jvn+/X2+XRCzEFiHChKLDgwOFwwEJiOP1rQLhsmIh/HGMlWsrvBSiLP/598fnfV9aRaJff7l8fh6fj327FGTSHon85/fnti1LW57Pw8sZYwk3N4+RcarLBIKJnvtMz8DkWm2mqYnk7csSgenw2Pv755wK335da8W+577PRCDIQIiIqRaRTGzhiHhZK2L2Pl5fLuM5M1OkFMkqVFjgin1qRp5i6SRyJ/cID2ICoBO3xMyIdOrSp2ItJMJjei2iM/qY69pcjYl79z4nABJTa7UfQ4QjIyL3fWSQe6zS+tHNozaOCRGBCEyFGyGBMMxppYgoqnppJQN06nlrck89ZWyZAAiIPw9nkKfmICMDz2A4nF6ccyCEhMxAhO75k9sc6RjuXIQvF5mKUx3iXPTxHKrT3NFU121h5jlmqUyZHkkUbjFGICEAIAAR1Srj0Fa5CtQqOt0x3YMEWfB4TiklIc5424lInNOJqQ9DwJN/drksOjwk3TM8GAkT3dLMI8gj3TMyADADfyKoAiITftKcqAgR0uM+AOHyuhz7VNVWmxEupXDBtxdJo0hQta6xtjKPObvyVo5j1sIv14IEmfn2Uo99LpWRaGqYW2k81N2slioFddicdtZYtsuq5m4/Y7qXja+Xy30fSLldyvM5zbIWuq7lthXAUGfTmIeFYGIulU4ecO+hHq0ROT+GDVXG8+8bpNC0mGqFSWeo+uvXdU4rjYTL8dyXTcxCCowjSiVkmN0Bs9QSw1nKNHePUlgap/vrZZ3qNrw2VreTaVcELfFE9Fmc2S5EojmViPowy0RCC+Cfxx4Aoj7dI0Oobe3j/Xl9vZZW3z/uATktFqGA9ExVhYhW2CKF09QQsTCHaSmSGtNMFkHLzJy7uYcj8Qw7xn7ocbhDMunU9DA1TMw//tz7gcw5RyADYmKiqUegDxvTj6cL/bR1jBm3G4ez97zf9X+7P75/+P37++uXBpJN8OtlLUWOoPARhmE23JaWHuABgH5/Tg+6731dxEboDCLEzMfHcdsapCfGspYM2Pfz9Y6RdBwGAdet1SZqScBG+f7+fLlsCDjNIcNmmCdzmppwGcOm2ekrOduLhHhd1whgYCJMAFmqqn+8PwioNPQ0EVhaeXyO27Yw8zC7tEIM7gEAbWFEIEQSGH0iCwn0OUqjUzKq6hBpnsSZDvuw1kiY54A+JzGY47o04YQMgFwqAbAT/ef798riCFIxAf94PMMsHErjpeVj762UdL6P6YCA6Nb/8/dcl+Xtayso9320htvadDwoM7P/9RfJiH983wtVy7yufPRJyH/76Pdjj0QGfjyPUpkRImmMWQvsnpD5fI7390Mq7fuc019famCWJkx4qEXmnGrmUkgV61LmMADYtlZLmcOez86My0LPA5AoAX/8eCBiq+LhbhHp90MIkZky0UMZ8FSU9a6zp6oyI7IgABP2Hn3oZS3nFHVZ6vvHsS31y22phSwMI+yc9WVEZGsyVRnp7VbczAFeXpbj0d38dm1zqjRuq6jt10thpLvFdinMkpHLykEBhEIsLCP08bQT7t53dUt3eDw809e1uIZS1MrbRvtubgCIZzaZmIfG43HA+dZGdE/XaFUS/JgGxID0/uO9LpxYXlf6f/5L+fv7XCoS8+FJFcpKH7ufs7OPxzx6nwPuo329NvMgTuagTARQG5fLgiCqJkXUdfp8f7ftKgz4fDcgZQQC4IWRq4soQCZRBIH6BO3zr3+5rGJVImx6jtYAhY9Ox5FqBoi3C36V6uHLVgBKJgiTMN9eaqv4o3deqhJn+j/+PNa1NsE/fjy/bPQf/7jf73TZ6O26dPU/f3wYkwW45lpAhHrXo47P56CkViskVsIeaTpLwYQsjdX8b7/3wPjrr5d9V0R4eVmkEqBg0phaRDzs5SJmrhZUGlQYmtO1EP1Pf7l4gnQkTLitzCiU//IvL2Y2ZuoEnf58alrftrpeUVjWJlWYMf71r/V5zMSKiRDJyLdbA/BDbbi/XApCtCqIZEM9QS2IYJibQ21IgGPYudrwHmUrFvDYIxSw0n645axCiJzz0ECLEIL9OeulFiJhAsomCxPvz4Mw9p5zAiYg5OutAcDj2VXBM2vFDACDwlFZ/vjxfD/mv7zdBOX759FW6SML0V9+uRJTQfj2dd17lFqQ4nmMbnn82F+vl2Wh+96N6Md+9O42n2Hj67Zuy/qY83moO2XENF1EtqVw8d9u+Lnjc8a9y8cjCHJtRYjXKvuACGcmCDjmNMWwMIhvX5fPh388Rh+hGoxQG19e1+/fn+6xLFUKESUR9dGBUBjVjAgjHYDmiLUBsrfaIAmBzvL/GDGHbUsRpo+PSTK/vK3XS5nTpzsXtpFbZbVkIpGMtG6WSebp4YBUia6XVRMjwFUjz+y5E+HQSZLbUhhwmjHS0qpZdlWwHDb7mB5ZCptbQpw8S2Z87opMgHmOIcYwSJo6PSIpKMItx2FEQ82IwM2TkQDUnAgJyDQgoaOqeo+5/PLSzW+X+uVWznPqZAaGcL9c5Pn0xyNI8vnQ526XW6ssdhg6NOZjainMPzee9PkY5l4bP7sL51AghNMiW6lAgiBdFt6ILPI//nwPj9fr5X/8elsqRca///Hudr0/dVgCIFIcM5lg3dbeD2ZEhhNHmJY6AxCIGTAIoRahnzd+mNOYKTOknLMSJKR5CiUAieg8k591+OOYbakJEJ6JQJzbIsfhK0t6Po95u9Xz6JXob28bcZr7vhvMoCqQZHraPAAhWxXTKZwJEBbIWQoO1ar8eA5keLnVaXSOHPVQBgKidaEIaI0zgwG+vi5//+N57GyqR87Xl9onfL+bpWvEZa1fv9VG5J6aUVnM/X3vfeBxjOcefZvTYu82hz0fua6hFv2pU6MwLQsPDTlvJwDIZOqqKYKJaIo6994TiRAhzE5mqVmER9jBwqqwXdhUESQjpgUi6vQThzKHhsfn+/OEenq4DiWiUuTtbXHz+8MQgiABoE/fGp8M1saQJD46AJhDrTJHMBMhHPcDCNu2pJtZSMETcpphyLSuNSGZcb2179/72A1PAfTPhSdnhJuDUAJERCS+vNbW+P6YRXCE397WWlFHEqd5EmIt/HNmZFkr+xE2YgwNZyo0Z5gGkUDmj/cjMy/XujR+3DXNxrRlq6Y2JyHivisCTPN1FTfs3bgFESGAeRx7D0v96d9LIjjHPLXy6GHm64W3jc/RrdRcCnOADnt2D8BWRT1YAInCYJgmRSE+/yw/dZhFQLa1MPN5Ubpty7ev6/NQj8QMQLpc1sqlj3w++3HospIUIqSlVSZ66swAcydiZEBNVwsNEkikEwRxAu+NMn6GGH7WZwQREFMqN6HRDeA84AYTzGFTTTUwcU49Z3ansYwQXl+XMc0gSebr2/WXv1wy5i/fFkL5/P5UjWUt03zsNod9aJjH9aVoqpsjihQ5+owIQohIhLis4o68tFKIiIRZZ5ws1vAEAESqlaqU45gzzgQpMFFmAMC6VGEEhG/frnG6Zyiua5nDPaOU04RCzPB8dkJgIWYQYcpYFzK1rsrCOrCw3K5SkCACI6Y5c7KwTtdxSF0SgQAjs6vPMb99e92WAqC1CFJKo1KwMv/r/7iSR0IimDD/9mu9bHBZL4iYYL/9tj4P3/sArLeXUhd5HjMy1axtXDifdxtdSYiF0DMBXSMJ48wbe1y2pVywo2EAE31+3z2gtcIFpUga1MIM8XgebRMLpOSIkZCnKQkh5UzNRTCjCEfkly+XIrQfvTWmBGamdBiRyEhAiUQ41BKAT1a7RSax8FSdfRJzQqo5E/TdPIFC1RQJOCUw9kORQQTd4IzbZeapoj/HGaUQYjJBEWqLIMLZm28spXBkZoROB0SCxMgiSASP5zgZN+nhmR4JmYUZkE5dHAOePS/An6ifBCACBFJTZipCCUAMiEQE0zwhCTAzzUKEirBF5Pz5xSbiqgaEDHC7re4x+jyNaSQsRPujZ2KtXAqP4YxUC123QgCVc1lraxIJY9r9YRFBgKUIC5uezB2EhHNrzZkAgElmAZjhoNMjIc5E+EkwyqTzdjrdHSMTkY9jIDKc/8cRLNhaDQI3O45ZKl+ubZqHpmneH3NbedsqYII7E3Pmcl1WD5uOzBZmxpKkMzPgugkzjD5rwaWWSFBz8xRKBC8LY8Kx92UppZBaeIRNd3dibI1ZZFsFIFqjUjky3AE9kaEWQgT3dAdiPMtYIiiI964iJRNKIdfsw8eZwgJEBOcEhD40PWuRvc+6MDH23bbr0h/6+tqI6dg1I2ojbjSHk+R2bf2hETFUE5MFE/J0uTJhq7I2OXa7vZT7fWBCWYsdg0+qG7kUiRx7dxZOQrXTl5bpKYUhIAD2EZgJyJogZu6wd7V9RAQzh6kjJgVispTbpVqkTStVjscorUw1QGiL+LCyFbVYGnuAzSDCRBCkvfdIGDMuK2dCFYjzRC5ISbcL9m5hmZbToAkyo3nObjY9EzwMEDCjFUiDuRsD6sMLwYo2J77/OYY5Vz5ekRCNEhn+9vvH7MGVa6UxggSO4eDj5bVtG0LqOWQNjOcTXJPRx0xA8kid4RZS5JdfNnP4x/ejl/LcLSEI6cvb8vwYrv54Ht2GhxWWQKgr9+fOJNPn1I6MZ3idhQWBAO+f47LWVvC+D2GWIqBBkFxBfUTEsrRjV0ZsjTSCC3j48TSdnpBnxBowMuJyKYmhOgBgaU3N+6FwEswLbUt97kcpRZi3UsYMYgaC2kDEPMIyXy/y0srpvHh2PHQyl7W253GEBiSjhDA2lo+ZwPS+j66jtGZjpvnWKrAdR/79H2NqtiqCeW3FMoHIRvz+515YhpgsZA7P3QBjzJEAhNznPLovlffH2HsQReX2/fvx+rbM6REZnufdZKhP04AIDzUDQDM4oYGqXhLdnAsX4eMY+z4hszU282l6hmFnDxL64/sugqVQIXk8J2RyEQBfVglnVUfiwi0smKuH54mghACEtrRa5RgmRdYi8noB8BmWCLdLFaL9mGslzdxnH9Ou1wUAdRoJft57BISeag8DABJExNeXKgwOdLlJOn6+H5drTfeX2zqm/+NjX9vGHBmQCa5qM2uVtsgczkLhWQuzkHou7afBBDmWrR4PJcIAJ6EMmH2WyoBgGqXieWIS9r4fJDBcAQyp/ngeH11fX7a/P8bufl2EOQ+l18tqqs9dXe3jYddrvlRES0ibqlvj4wg3oMypgwHMvDDpSKnUDx9px4Ck/OW1rKXo1IBJteq0s/Wz99kK/k9/Wcc8Pp8+MTZZXl7a335/Oph5SCvblba13FZZSjwO20Q04Y+PYykopYGVZeO9274f3paMXKp8fNq20hOglfzl6/LH+36tQJRdw53/6z8eauAQwPi8RxHUrpDYR7rNDBhjfvm2MYEwLAtNjaOHByHC++ek5LrQ7z/uyLAsCwESQan43OfxCFWNyFL5utS15IKFnKZqIdxurUnBpPfPoxT+9bbqnJAEyO5pNo/H/HjodSuVAZAiItMZcVubxdmq4O+7Xm/l5br+8eM51Z8dVHVd6tttrU0+nkNnMiJC/vbbBTE/P0e4BTFBOOJwTQjBEg6fd7N0FuAihPl+jFJFZ7bKS5GPuzaEF+bpsB/7ZW1Hz7fX7fkY1yt8/3O+f8aXt5UI3t/7b79cSuM+tN3KjHg8spHuj2y17ocy5qWVr68iuN+Wer3A52dn2Pp+v1y3yjiG+bRV+Hl4ROzHoPCPh//+R2dBEtQ50OmP7w9qSwYezx6QU+2B+hIrUZjO0a0P+/ZtHXMU5ibeh75eXr/ctq46NTIZKRljXVagSAw1JYJ1k+YJmYz4y9c13I8+376053MmpIcD5eUiVNCm9hFT3c1dY20XETCbGYiZDHz0DkClMAtxQTk9zScKAUNE+pyW+f6w60XaIq42xv7o0Z9RC0uBZZWu+s86VctK032r9I8/H6WUtRVLg0gL0JytrvtzINH1tjyewzOBiAFrrWodMIkoAF6/LOkwp5n7spbrpR770GnTXAoBwvVarMJzH5nZu7ZaS6Gp/vl5IMCcdr1uc9q+TxIKhcdzbusMyOOYX25rN6vMTJKYkG7m240ZiYVUwab1w9abvL0un89OxEVyTt1q7ft4eWki8nyoRbq7agpxZJxwnPSJycxUCl3Xelm51sKIi+Dwfe4GiA/X7z+e+2OK8LauFITIn49ZCgLw89mBcd349gq9w+MRTIwM01QKYsa0AMSTHr1cmlu4GwtlIhGwCBKetzlmYgGdXkqFLMeYt9ui5pAxupl7AL5/uhAJ42VhRMgg93w8+tLqWS5EKsKIRDpQCk+1PlSYW6sf9/3bt5VL+fjY3ZWZ//y02bU0+fXr0od+/1BMjJhtqWtlHTEN9+dcN34/9lIQAT4/+tIQEN4/j9tL+7hPoqRCU+d//X1fq5SC98PAUIf16STi5oD4vCsz7T1seomce9qprU0ESzCfiUd4WBBRlTxTEWZJmGP6nElEp/fm5FYQABJB5nS7tiIAmcgCNrVUOQ6jJMwsld2zd62VUYAB1AMiATHxPDxoqTSmXrbF3TMtEj92I0wkVnUuaIGlinsePdyDAmqhrUkEqEZmMCMXKsLPp7altoVYmASJQYef0syEzAxIzETzMzuTpSILEqOr/fjopUhClFJ++bYgUEJ2n7WW4zBhvlxEZ4rgnOaW4ZmB61o/P0f5adQ7heMZBpB5HAoA7m4BgOjqZjmGl0IecSJNhAkSueC5gAeCRJdSufEc1rv9M4mGRNBWRszC+HIVAPTGM31d2nhqd99W2a6lHzGmIWECjsMuS2VBV5rDQGK9tOM5dSZg8AK1AGJ++7bMnjoiMvoxbSQkrU2e9z7dE+J6KdtV3CGIz0E5YrYq0T0SMMAtdGbGWZWIc1BYCh9T5Wcs4GxgAiLIOQoBiFIYgCNAzSFBCvdjACEh5gmhVCtSmHB2//HjAITbVktlT9dpkC6cSDxsCgNEfnw+L5fWSkGY0/x6bcL48TkAUV0RYKkyp61rkcJLo+1SENAt1MMs15WL8PM5zg1IOLCcwO2ZjBnobsQckAyJQKZGRGmJGdu2ErCHPp4W6UAIgO/3jglvX5btUgrT9ETCSyuzj7JgqVUnPx/68XH89dfb9VqPY/7ydTnUuqaFlSY6gKiyIAp/edts5h9/fv7P//qNiLkAANTSEuo+pkdQgcIQaa0wIb9/HIn166/bjz+fIrxuNVxfX2ut/va1VqLP+/z2C/7nf+qPP8dyIS71x2c/LdY6HRJEXISmhSVywuWyceEwPwu0GoAMzOWx97ayVGyXMrW/XJaXW5VGf7zvOnRZy0L0vGs4JJ5jTspItQSQ8CSm567uWkrZH9MCHbwtogqQJGeDoOCY5hGEnABdPRECgUQKEwadsJ4+JjMNQKmUCb3r5VY1QtUiQLiYzTEGM51QtIAgZEGkIpG+rAUiHs9BkCIMAFLw9I9wLWbGhKXK9Gnq+FOUGwhw0uUyICLOth2cdknMUgSB5jQ6616QEcFMTHyCxhGBCLgwCqp6RGZkBCxL8UhVN/OIBMC+T3NnZhJqFc1ZKhOLuWamqzOReQQ6ENXKzJAYzLA0qgVLQXNPwEhwM0JAEBE5i10ZcC5L5nS3QCRyRCJIA/rZhRwjfvbtEzzC3QFgqiGiG2WEW5onghPB+cBL4W0th06uuG4NmSODMi+3pW1tfx7PQxNg2YRK+fwxMhJZx7TWhAq1tVq329sagfe7rsgVgAjVYPS5rXLZiqsTcIR0mwIghBBZGwMkORFDK4wItUmE96FhYJ5cFMtJBENzeP/oGXlaUREREsODazHLiJgzxrRlYQYx8LMulwlnm88jdDoTeYa5X2pRNSSESHe/XsvotjRhJk1zT/AolSqTMd5+Wed01TQN9ygsCbg/5/Xa+tRSqQp/WnCRqZoQUvl4TLN47EPdiSACwh0giNh1QmJXQIIMYCSgQEwzGwnXWyUiMODK/Zi18bLImLM0SY0idOzemqyLuHpb5OizNkr32mRa6NRlrY/nqEwBQJhFcFmEI9zz5WX5+NgFMSKFc/bgRksr+25MSUThKlUQMEzPfRuAZ4DU2gqMGW6Z6IloButr6QoZ4TMIEUbs94GCppEJW+P+VNjDCieBlExLgCiSavF4TkThkoT5+d4jckxsaw31flg4EON0nR3udx1TC+Pn58HCS+OPzyCAzK7GAQwRmn25SLgQQW14TOUap6mQkAP85W157mqmmfnne2c+ISFMmFwRwDOiVsGkVqEIPMbBImPoKezUqYBZax1jHOc56QD3REJimD2aCEPMdAJixDH1l6/rslSbuRYBgW58P44MC+J1kfQQpGmeqYjcFhGp+7C9DwZ2SIuRkMeUTERCixhzcOE02++di5TFRvf7E05A+OhGBHs3LkLE+3MyMzAYaIH62PXoKkyRGJ4aAzJbZWHeRyDl2+vteCoiHt2nGmBKZfjZSqDptqxChB7+9eU6ZzyPUWv5+P6sL/T25fJx3x/3wzyIoIggwf3RDaJWSY+28FBj4swgZin88X3f1uXoB5VECg4eptfrmjb2Mb5+fT3GSTwTgGDCBDCbL7cmjK62bfjYfXosjc9gS2M8XJHBw0+/w5/f929fmnq4Q1j2w1Tt9rIl5uOxq0WtyUzEsDX6+/dj340LLBeO8DnnuogOm1PN3cIzkggjoQ/drmVZ6n6f11uZ6tsqhPnLt3Z/2uPwrfH7970tZOYJMKZ6xCplP2ZEmBJalgoJuSyNBI4xPfze5XmM/eim2VWRYRd2h0Jcwt8/hv20HNCxj2OpHJ4Fr1udh1mPWgkiH0+9rezubVvX1piSOd6uy4/PY7u0JkSIWPjeldiWQhnWR06LyhEK3XLMPA4crEuLMH97WeJChKlqY6p6zvfpjlhwpE2dNun1Ap7w/Y+BnMxlajx2jbR9xAx8e1mSSNzXht8/jpfrujQcA44ecyg0pIwx47q1564eSCDPhwaepF5d1/XZhzAthefw6VYLEEg5NSRJOmNdeMyI8MJsjvfHAPdEjERCq8ILswZMz4zwEVEJ0jT8+dn71Ai/ruvtWizsdmm3lW5vgsTpYdOYuAgnQt89AESQGdaVM2F2vW0lkR67dQWE+PCDChThcSgAXC9FVdXy+TiI6HKrX16XUsqzD4IslC6JjI2LWoRrIDGvc8xjn6bl9aWqORU2yGnhAf0AAv547yP8Z3C40e8/jl++LP/jX78yhkPqzPunXra6MGDk9bpgRUirhDpi35+1+bLK/+/f7uFwWdAAptmxq1ts1+LThQJxIpZaq2BPSxYoDNZzrpaUoIGJ67VelrIf5wRcnl1/3KdrmOHv3/XoM+vyeO+RsW8LEKzrGmEWRgxASRxU+VJ5DFiXRAaLDA8iBvDfftv6qIVzDjoO06HHEZ/vAymA4ugxJ1xW/vZ1ddcawoj3Yx67t1I9vG1M7KXCadX59m11jzPMfl2X61r2fY6hBBgGiZgIOt0zWDgh14YZ8Od7v11rV392jwyrZdfjpWRCmo+h3nsE5layVnjfp7Rp6Zp+psiRaoS3RV5v6+N+tCbXVaZBAAhCE/zyWta1HsZE8P7x/Lz3UoqGrdgiYN/H29ft+P45D7uddPapwpKQS+Ug7Ie+fxxSCBH/4UemrcuKRIAeaX0GTSGCyCylYJW61Gf3OQ6HeLuttdbHc2TGspTP+2yLcIE5tFRCPxOvQYxVKChUjViEaehYC72uLRDverBxJk2DMNgWZKwfT/NjD4fXW/vypb4t8hzwfQcIbWLLQofMQNt3DMVKMTQVEc6AcAITEFERAbRSC4SMbo6RQIQY6YnBTJlwHGPbSqbM4eZWCy2LjGEJEI5917eX5ubPbs99mqV5rE3XjYlhbeKQgjB2HaHXWxkDhh3XjZ67Pe59WSXUzWyEZ5IQMMR//v0e6WPMTGqFnnvf2tKHDgUmuEJj9I+POYcPzamwrFwb3e+971argNvMvH/60mJZs490A0ozgxpJAKUhAs3hmJEOmSkcCek+Gc91Pw11IeSGqlGYAPD2Wh+POYcvFU8339vr+nnfz2RXLQymkFiYx5xvL5cAX9biLRJ5TjV1IsqE1mTvw8MWWcIhIdQzTggqZO9qikh89AFI05LApZZxTKQUKTpDqpyXp4hkgoRUzzEDEcZwqcQV+DS8QALielnMNCwgERLXJq5+LtERsAgWge1Sa6Vz91aEiOXxnDaiEPfjrD5Bq2X2IDRG7Luu6/boB2GpjT+PUSsjwOVSTikQM405+/4ziU/IOsEzpgUmCKOZn6WWM8Qww5lhDDyOQE7wMEsALOVk0caySGKGRVqWSqWACFdGDoyIyKxFXHN/TkpyQ1VATiQES/fMxMd9hMHlhTMhnYZqW6UUERIAP56DGLeVbXpteXTvw0vl29pmt6EaaN++ra2JqROGK31/n4kxuvVDSyFmdgsCCM+TWlgKm2P+M0wQP3G/Z/siznyDLEuNCEI89mkGtXAhmBZudlqfToc0IS5rgwwRzvAMYMb9c7x+uZYqj89HIeGlfbzvhBTuAbkui80cwz2SEHTaHJZOtci9HxHxetu4gEd4t4iyXpfZ5+h2udaISCQkQAJCJGYoRAB0mks9inB6hEcGGEQmFuFSuTQuUh6PAwCkQloQS3oiohRaF2mt9jEd88tLU1NM//K2Xq/L4/FAzPVt/csvXASkxrpVG89flmbJFuAJRYqZ+zwM4PHoHx/jt3+5AsX3P/dff7sQwTFmBAw1IM9yUk5IhBDil6/LMRHAf/u1zonuXkqK+O3bptaPAQDwuvjjxY9R+jH7++OkJutw8yQiDEAPKZDqkDDGgImQ4OYoVIVKpeulPp8SgOn+sX+uK1+25euX1V3xa0tIC3LjUtvn5zGHSmFGIqSjH29v23Vt5vDjUECslayxunMFERZOtViXdX8MtziFWoTZ1jp0mgEBmEcmcGHIhEgRZMKT3GYahNAPDQ8kGtOJohQe0wpzRCCcD0YiQ1JkSkybp6grk4UjY/Y5RhBCCbKIUgI419ae72OqA+LJ9AHCk2Z3iuETiAjrImYGgDo1PFAQ8ewlw7I0IphTmTnc9+dgKa0RM6gl/SQxx9S0GT4jE2rj8IA8pZVAkoK0rnUMj0xMMHUigQhCHsMxstyKJ4xhpl5LyczePQHHdA+nIifeyM6WeQIgqHrv9nOSnQiUnsmI4VGKzGN4BOeJ+JmmkZG1iogEUoQhZKsFMksh88gMRGSE25VYyj4SGJ6PWViYcE6LhMyc0xKwH/n547FsVQpnwjlIQsc5/PM5kfBUjz8Pv6zlZHJBUj8UIdsqlYWN4ojb1+XzPm06E0bEnMoE7tGfmegvt5WKvH88UfBSxRJ2U4+5lC0djl2R4ORkI0MEZqRZPJ7KnIWrTggwYoyAc3A2A7hwbegWRzckIiY1ZeGI3NbiGuaBmBZhYYSEBJk+9pmJ+1PdPRMyEwm2rcxubj6GH7uKkGG0RdQjNEVI1SJju7U5jBAiINJOXV2CVykeERGmQUhUgAsipBCul6bTRTAB3a2tfH1ZdGhdms3QmMSF0Ahyf862yJxGhEvh+3201nrXUvj5mKp+u259TAD88bFfLw0NMOL943BLZszMy9L6cXhkPxyAEFKnE6BZDlVAcMBELBVdQTgjyS2YAAqJ0Pv9eP2y1lbHvQPDuXfiEag0pjPTPMOl7hAshQHBR0bC89OHBSYQOkXetrW+eR+emX3MpdTbtfYx3YIQffrjMdNTVRFzqRCuemStFOFC5FMzE8C1E5GF22MnZEDydERIS8SI2fHPP8btIp+fIwJaw897/+svRUuGBiEuaxUWV2Oh6dCWWpiqcGSOoebqkeOY7+8DAX14ZBJxKdTVaqmXV3p0O9coCcQLqbnux8rl4/hsW8FEwuzmL2tZhZ6hP46ZCZBBxGutBHR/6NEnCXo6ApxnPp9aRcwMGSOzP3eudLuWwpggu6mpsjAXyADzHKqFAzHH/5+nf9txJdmybbFxMzN3JxkR85Jr1aq9SwcQoCf9/6/oRcABdFR7V1WuzBkXku5mNm568CzF8wSCk/RwN47Re2umZMkCvWMGFeEq8nl/BnirNQMSXCM9vS4ypgXE24/t/uhmRkKl0Ofnvqxl20R3vd6KDsuAvU+zQMq1kd9qW2BtOa0AAKoDIBL140DGy2XVbhYqhY+RRJ6AEfG8HxHZytmO8DlMgIHiutTnrssWaz3MnQVrYQgsTIcqAr8sdcw+wY8ez32wMHJSAQyZ5nUhs2AkqTCnz2kR7bHbNLMZe7fLWm+39vsfX2aQ3fo4caE+dsyAtpBa5J7WvS2yLiXd/vb3y9S0DJsZlvu+b+t6vRTMbBXd4ThMBKhRAWSCbZU5XaePYWczsQiu67ou9fPrgEwkPAl3+66XNZYiZgSZheS4T87yvCtJLq0ykxR5PPsf7xMSn5/jdquvV/n99/6f83i5xMv3G+XpDTmwG/ilH3GpFUXMo5T85z93QhIs15dlYX7/2C+X1kd83RUyn4JFGDPHCDH7OPz2Jt8udW3Z3Qolc3zdH9fXOs0RxNz/67O/Xm/I3tMQAZNEMDCOfg9zyCxrA6bpSsDrikujdZFUz/DXre6P+ed757UU0P/b/1j/z/+VQ/VS+HMYoXIp15e1IIbDNKsr//rn/nJrrdXeJwFRJoT3AQQ5QFtNB4okndl3S7dodRwWk3Q6Iow+3GNbKxN3tbbUiPz8epIPqdyHp+N+2NdXn6/kDupzXQpm9m77sN6tNVoZR5/IIAW58OjY90OqSIHn0OttMU1KuK2FgAK8CGPiCWd+uyyf99wfMxJul1qEjkNdAcIjwdOHaltrJn7+2X98qwH5+TlYEk+mCOTPH6tbjqlA8OP7i8/4ehyJsV2AQ4SQVvz6jD9/zb//VglFh2bAr3d77v72ujikQdphTIkla5NjxxOeZgpC9HWYhr9U/uq697m81HUrALwutNba50TB242nOgvf72NsslzO57UjytEhHMx9abItcn9ohG+rPPeeQQDQ1tqP8ZgaAB+7EiYRq52qV9NjUC4sdOxdiAOMGZcmR7da5O2lcGIR/vXRIXNtCElDQwivG03BTFS1Pj0S3HPvOqfXIkRo0769LWb6elvSgzGHuVveHzbHKAIJgYh9WES837XVkgGlIHFE4mm2KaXeH4qI7x8dIP2W98/pHa63HPNsOkIf3ltUpsfXIMDTYpeZhSkCTH2phTFrkc/PsVZCzI/3sS7shS3QTTFyW2pc6vvHmN3Vgm/+99/a/TEZrRZ+9HHm7s09LaQwZCbEy7fl1MJKkVrkODQiQsPRSotSYw5jKtcqkUCMRDHDLLy1mgnp0ZqERWvEUueMhDgzGJdNhro61UbCoqoZaq7hjohSajzGjHjOwExMVIXIuF3Lv/5c3l7L19MZ5LZQKxQYL5s8dWCKR/z+aftwNcvEDFwbkdOvjylnF9idCCDTBEulkf7zdSXCadKoUWKEGkyA4KVlgFksax3dmMQ9GHFZ+Mxr7LtN11MNvTSYlNaBS3jG40v/9cf67NMMImF/KkLJjOcBswNAPp75fBwR1loLm+GYTBAxFAszRCKGMN4f/Z/utcExnAn7MdzcM5no5coB4JE+FBIgc23c+3Q1QfCpQRQaCCiFPEI9maDUykT741lbgYgxU0QIQ7EgRDIPDzjJpYRIOB2Y0P8b2lJas5hHt/tzeGRmXK9Fp4pQYhSRX+9PIrpe6/4YbZW+99YKAHhAHzrURGRZBDH7mHVrapoApobMhESMdK6mMenswLiTEBIhQwacu/MMC3dPYCKA1OmlMRBEJBJ4pu6z1iKM/ZhSUKedZNJSy/ffrh+/HqOriCCmRz73yYzhObvuGeulZgST1CrP5zgOqBUvK4lgP3Td6kTTEZFwHLquZZoiAhIdfbrlsRszeqg7ZQITXLYlA9z/Ev1EonkkAjHNGeeMQgQR3TTAkhghKSI8Qc0RXISkkGVSABEAwF/gKmRhRiIdOQ6LjFYrAOiIQPirbxVuCpnw3Ccwl8pASIDrwo3JFZJgTM1IM/iP//28vsiyCk68bZUSIuB6qaWs6Q6YhBAK9/uI9FZrkQIOOgMJCQEJzcIjENAj/vIeZar7eSbJk0l+Qk6FhBjNYs6shYcakKxb8zyH4ZEEmVCEM2Gavb2uYanz7LdiWWRauitxHT324yCBMd2mX64VzFy9zxjDCHNd2xgzPBRyXdo028ckohPUgh5hUUtJh1PXGp4EVAovawHHfe/cRCoJMw6b47hd1lJkjl4KIiMRrpe6rlKYx8HMmRjP3YQ4EYvQZROEOProw09vVBEoBUvl1GTHtUpbZcyJRAi8Pw/VgYS1LpVpIiEEceJahw+84u16q00CgLH88593kdO3qK0QL7VP5aSXS2WSY1jv83Kt4EQMYW5jLluFdJ3z/SuY/fvrOg7S/lhX+fH97T/+8/64d/krnQKZYBZMLAmOYGlgSYAn8fi3t3VZ+M+vfT7n//P/8X//9//1rj6JHIju9y4vq4X99r3VAkNtGqjGy0v99UHHfYyjl6W8/twS/Y+Pr21tr9/rS3AheHupv+6zT81wRNiutSDypXT1kllK6/sYUwHxr6pARCK6GWYWKR6ODJEQGuvSEOHo40Q4UeXbrT6PfjLFh3pAClMtQhVTI8yRKNPNz+5SAOC0UDdIrI23dUEEV62yrkubel7XAKcqnsj9rz4jE9TK4WGWmbMQr9uqqiee43pZjzGA6OW6HId6RCkyh2Xw9VbC1B0C8vHsRShPsIueISDwSIDkikQ4uyNguBUmAtDTI0hESHNOQMgEEeld3bIW1plfT21NTpjRyZ7YLmuanXkMiOxd3VMYIdIyqBAihmUPLQIAmYARcMqnMnBpZV3qnFYrBtCylSqLqx2HMQFXbq388fvjdpPbwv1LEwAA9uewmcRYhdullnamxKHUujQpp8Y+swABwMu3az+6e94u67LQDJvTwNE8nod6xrrKUHUATraMxzFlwY0XHdGHIcW2VghIT8sMz8f+rGtBivvXQGCpCMhj2NoEKTNRR9BCiaDzr1lMgl82dscxJlJmQFgS4tmTK1zMxjEmApVzRgMhKKPrtpbRbaqrBjESQ7hxoVLP8CLf7wcgEKIwWToh9GMy0f2rE6VpJqNFpDsAndd5ZM4xIwIQAjxPVxiT6QQqHlEKXmtLSKrEiKPbepFS8HgYIgBCKbSsdQxlgiI8n3p7WTNyW9GDwhMBzh2jR7ZFvA8CjoQ59XZZIqMUVg0kjDOQlandS8WAxMDZlZmIKDMQck6PBCk8zJGBC49dL5d6HH8ZWLu5FHag9KyCxDwOT4hEggQdDoRTwVNFCBDGTBEETM+gyOPAOVyERldHX1be74qA5ZW+njani7COgDlqZUiHc94GBcEBYaouCwvBnx/j2yudfzWIQYylSBiNbqWCMNfCQKED3aMtrAbk8euP0Q87tZrrRv3QhNS0zMg0rlWEXJWENEKnsRIg2Jn/sMgkAuj7OA/BYyQVZgidPjSr+NDhAQkJkRD5HPE5lRjL0qVS3oewRIaGe/Hu+f65t1bHsDFdGK3mbV1MNcBnD49YGjNwTl3X1i3VFE+aRGJl8mkzkoXWVoSojzmHS2FVa1KK4L0rREZEn/rtrZp5JmsG/Leobo55Vr4tkwU/vh7X69K7jq4AgBCQvizyupVp9vbSrgt+KZzklIQMSKaoFfah5vB174kZmbXK6D0haykQWYQedysFCqPOCIK18TEsIxEd6S91fSAwoqr9/sdehCri4SYOmHXhBplFcGo+990j/+vPe63FPYkyPT6PvrEAZ6N2P2ZXXVsxzeu19GkW2iqlQ61SKjLFOBSRlsLPYxLg8JjBEKHhXMhm9MQTGlUEEJwo14K0led9ZNRtLUulMICl7n0CxDQPS02fkVDguduyMDFywtpKrfU4pplCpllUEcQowrcrS0FAyIzbdWlCy9sSBKrGhYgwVFEiLGw4ZOzdWpVwfLk2Frh92wJDe2jCy7e1MD2/dHT7Rf3ltTHy8+GRuF3Ko/s+/bdveLuswsRrdrVwGJoj7eVavWtcUAp9fOx3YiCUmpel3l7q566Prq758+0Wpl86MjLTilQq9FqrEJyoAhSKQEC2PT5/jZfXS9/Bpqf3pdT707etvP/audHfrj/H55CClw2K1rUI3crzcYcUdyUkgrDpkLRepEkksVKo4h+/j7ZKAtyH+vSff9uoYHo+vnopJbl8fPXHY5iiaUghqRSGv/7occXWkACF4OV2I3ApAJftjz/vrdVlqetfuBaw6WMEIxWBA5MQzGNZiwZ87aMu/rrWUsQ0oOLQ6O/3lbhk+/M5iOPnb5e1SVr+y9+ufRhlrk18S4K2LSSCAPT51VmwMGm6iLiBWahmn/nYx+dzFsKEkFJM8/NDj24/f2wM4cPA4+fPbVnqfd/1MIJZaV1bblXe//ha6mKA68qXWz36nKMvyzq77ociQrbiHEJljPz8Nd5etuf9EKoZ/fF1mJIr3t/HcQwiDuc9uyN+fvVW2I4Ye9Ravr66lFYbcGGIeD6HG1wv5ePjUUvVnkd3TEJEQu67Lksd3XTGcqmPx/RpGTMSULBcFlN/eF9JEKkV3vsMJD2O2tox9PHUKuXxGEAcEbdbFeFjMlMyZiapx1LYNcBCzc9II1M+j7m2Og/dh25bBeAMAIuvhzLgHG6G61aEcbox09gdDJOiCO7Pcb0u9+c+ZmyXhoj9OZciahZql6VmJjO0xqZ+Ag0y4Os5c4AfQAjkmQEZce/Hsizu+R//dV9aQcJf7/12q6qmw3WRr/u4bc0ywhEpX6+FQf753ntX2bgP//yYz+cwBzqRxpqXrdkYwyg05ozrdYnkObVVRsLH89nakgkCAqzONjWF6/EcSHh7WfvRzRMRTUN9AkQtFRJL4dYkIsMNAMyyMJjFHGjkpwQzzD1ThCDyvsPncyDgaTiCTGH4uOsCiIxL80ZsMz4/O1Ig4T8/jsdTW2MR8Ag1zDBIsIHbInZMDVhXTosZAIAzExIsyA788duCrJUwJydgLQ0EEjP+CsHFckGzDEACACJzjwhssYhIpY/fv7a11kaPPseIefc+4g+kBDvG2QCBo+vrpSrZ85kAjugJyISqCpEQnkgsAm5DrTWZ5vtDM3KOCUFTowgppJszYmm8rVIr/ed/PrlJEbr348nWqjx3RwguNCwQ0zwEBBAz8excj6kJGEGqhgjPERAOhJkEEIhYC2ukOrh5KRge45iISAyPe7fM0viEC5+JeLMAQiJKgG2rR7dllbCAbpFZMJcqFna5FE759etzq6Wsta4kXDCRBd38+VQLVw9gaEU8PSnJSYcRnfQXxNNuyXzOFiAT8CQKp2RGJESy4Hmqg8AZaRrLpap6WEaC+nxrIpVMKcNZJDNtellLAhATCUVABnbXJEhKyByHv8fRlmLuY2ppHGFMdOb7IKFPV/PaCCDn0HHo7bUypw1f1rZt/PUxSgFmPAliZkEFA/7/8mf865jkp8QQ/5K9WLqFCLs7JRIRYkBmeDhRAhLjnFEKIgESMeBZ5BQWyDyGIWNtlJ4s7Bo6IxExT8okUAE84SRYiagK//a3y1lVI+IIiPTWGBM/f3Uk3C6SmjZju8iKVS2IqbQKf0E+kpgyTpEMjh5ESEKeIExmqObw3+ztIrIsVVqVtJhTFSASHCOFLrfVeg4wVQcEQaxSyL13talEvKxlWQsjP47Ruwpxq8XnTMBWi1vs+4SMbbswQK38etvMFBJrreqafhLdwc3PNzE8H49eRIhodHMP9ShVlgstjSrLj++FJBmxlPJ8+nXlb69bgrOszC7C4egZLDT6/PtvK2PMMX97W0qV526C2BX++PMO4W8v63Ofo/v6uhLB/eu5SCtl6TZy6NoKEfRhRfjl+qZmc4xlWSDtGLZWzgAGWZuvq6SDKuyWtVAp/NxtLfx2rVjw3ZWD1lY4oiD9OiLBShEYUQRxE/4LxOMvtzWjryXeH3BZNgGEzB8/VmQaxzz/WSKEo5kjMzIxis+YcYrb6Os+NGQ/3HveP4+318u//8cOxGM/JvFS+e2lhcfnUMS4NAApCQlEerscD01AaTyHciMs0BYUJD3iGJYRtXAr5bQqXFYpr6yqgCWD9lo+v/bHc1+Wtrbluc85+2n3tABCqLUwsZlmeF0bIgBgRHTtqpoQ62VBmqUSEV62diLKVBPYz1snJCScGCBAogyEzOMwsyTmOQNtShFCUE+AZEKzTNAi/BedmggSPDIRC0ur9eu+n0ipCPcIFsqEfR+qXiurhUUsTWqVMXzMgxXDUj0tMwEyc3ZDJmE2d0TQmceulpCQRUi4nFGCTNehGVkqu/kp3hKhUyseDmO6G4iQT/8LMnbekU6fiJ+qQTizOWcs5Rxjh/+3xwwTEZjFLSDz8ehT/XLNy8sCAY+v5+trJUTzwgWv1+rD1GLvJgXHbqXx5XtLQ8A09SK5rlRb8QGX69JHPO7dImpjPWtBXZmRgZ6PA0sDQp3Zn3PZmAgI2S3VAhGR8nItffjQCYHLIotKBLRa719PRAoAC5fKtRAQm0FYBKB7jt61VWTIjIi0CD8Nf5Hhp66RPz/3yNxu1dwgU81ebpsgA4GaE9J5Q+yHliZj1/A4CIkh5kmeArcQxsLcn/OyrUhwsuuKcGA2KnNYZFSpPmepMoeb6TlmdzcRZEpL35+ThBBAECzh9dtSBY4dt7V6+GUphflr3+siC5f3PAhjYaw/LmYn1BYgMiOWre0P3dbCiI+uJGV/jtutRoZHzBk6bWsFuZia9ZM/7W7ZSsP0Hz+34zBGjEguyBXD0twrtjGneUqVMY0AWDggEGNZGxMuQhYAGNulHLueLFI6r3RAQqAzeJk2z34KksUZU4v90EJUhCxQEHqPTMRwC+8KAHnZVkKPzM/HOHYtVSAxZmTl/ZiAQJzEOKZtV3FPIrxeJQO/v7WlhkXcXoTSa20MFJ5dY6qVJu6BiQQgjQjQZr+9XQe6RUBYEWZiG06Jnx8HEhADWI4x3PMMMLrnnAYQbn4OdBIDIc6AekSysDAWzgiEwoQwp2UEE4pIeBJiWygRILPvCgBeHBEj8OvrOB569LhdwNxJ4Ll7eSmT965dROKk0asHUav18z6HWqSHZQZywVpJLT8fx7qUDBjDnvtgJh1eSLjicxwZjolqtm4lNB5f+9vL1XOSoFuGu6uvSwUGnUkIb29rFZ5jtEpjBFMuRY59zmnHsJUbAUfMKvjb2/r+mLr3qf7HH3sCvt7O1WC2Rq2CK4pUd9uf43ptb9+2zz+f7nl+KBn+l8OVoFUcE9z/Yih+3g2D0/D9S1HSJus0bdhaCfNH1/BchI/H9JLXazWz2fHr65Dva2P5uI8+bVvrWtvu08IR8uV1CYNtgTH0fp+P+yCE3ufaSkYyQwa0CiI8gV+vUogeXS8rFyLA3HdzhFpYMC5LuW7VM/vhzPl8ekZipvZwiOXWGL1wdYgAVjcPy0RVHcfERCRiRmJiJmZcN3FLt0gEQYwxZ0IiFOG+m3AKZe+4fw3mVEOd8fE51k3+5V+2+z7NASCSoC14KwsELJU82mPvM47v6+1lpetLWy/8vPvXh38g/P3vm/Ywtcp8rjf3blO9kHw+53IrOckMgKBJ2Z9BhJfWpsac4+PXwSBCNd1LK3OfNOP1x/Yv35b7wx53gwhheH6Or6+plt9uPNSnuc18DPh6pznsx99fr5fVzH99zSby4+e6D9u/7Hqptd7G0OfxeLmspUk1LSUua308DCS2teiR19fGhPtuxLTcxGYKkU0bx7y8wve3tQYtpfRh+0HTNRP3hyWAtIoCf3zs3271dl32u9nIy1Lav9zU7DVJLUbEthQOWZcEzFATRgTYVkFEAeyTTEOu1NNqo8xwj3WRmFkbvrw2YrQR9zkxGdKXBQuRp3670FaXz2Oox7a262vr3U6z57P7/b5fL+3l28KY21bHTO1mgeZ+9Eg45fRckMecJEUN7H5AoGTdVjBIiGgVC64eWSq/bKVPi4o4YHZFw9fL0tUz+fmw22t5uS7P+6wVLv/6AgmRfJp3wu3+afenCuG6LA7QBx17eon1shzPvq5CXObMqaoz1rXqjGOfl8YMZB79iEz6+rJaGTnn4RC6LMvsweJz2NhNp9XKVHDHbjMPM7qimYUxBM9p2+tSGrvnx9MBsKtdN1agaXa9FFUngjnzOOblWsf0PB9bZpb+dl36ob8++r/84D58KuRzlEYRoiOmZWRsm7S1JCBgClIA/vi+7scICEjuPVtTwGit9n1IIbNgwdvLMoetG18W7sMtU5gNUgR714L88lLN3MOFsDZ5PkctVTjGmGZ86JSaavD7f40EPI3vpaBFrJcKBI+nZebbtUgDZLg/4s8/j5/fBREhvZQS4eAgwjnA1JdW9/3o+yithMXzflxv9WFhpkR4HO6u0xSAddrS2Mz++PVws37okElE121NSMLwqZqltUoAyBJ+PrBsWevX13DP0vCkc0KCuX997tetjYcSQa3cD2cGZ+x7/KeOgGEZlTidjqdFJAu5eyW0PoKACwigw9n7wuOpRBAJhYAKTA0kzgC3YE5z248jOb59J4j8urtG3Nq6Np6aFuieCUHgZ+2aHBHRLMw8PR8AkSGMgIlp6UiRp7ocMCACiSgAGaeGm73cCnNJcIJMoAQ4ntMQkVIKvtVmZpdrOYbriGWDiHCHEplhiESIDthHxOe4XIt6sIFZIOFjn/uBAQCAGB6OpTBQmMGxGxG3SgCZnqUWM0tAPle5SDaVC5+RiLoIqB8PdQ9AR0JL+AvcIoABUpCADveX1w0JGuZpZTXz9VKIzA2QsDBlxpxOjO5RBMN1uyzTBiouS9EZL1cmjNqavTZCKI3M47mrcI3MX+9HGFwuy5w6piOAu4M7s6xbmUNnn0WKecTTgSgQMOCkcZ1eBDXHp6oDJJyxlH4oeK5bdbMMlyrpp6Q5M+NyXdMhk/uc7pGGBlGEj0MRyD2nuhRyPN3fGAnhQCfeOKg0BNTS+DzPJUVdeUz/9f788XMrhSFBRyIQBIxurQieRKLA0c0jGSn+kkZnbbJttVTy8FokHE5AXUamg5tTlWk5VQkJCd1CMQhJpIw5I07N9HlBAADYOU6ytLBpJcxd4/W1ZBICzjmXhcMTgmzqfmgRgvTMPA6TKnS4jdMrIo9dVU0KE6BI2XvPBDd3czwRS4j/naRJkJOmioQYlExciyCDjKFjKjMlxHapyyruysjrKpltdN2nRiQxesbefW317WW5bDymAaPN0BkOgQki/Hx0ZFq2tQruve/H3J+TmL//WMzr19eYIy3Pm7u7JwIkJuO5bcxa6vmav39vReg+LMCWCm9XKZJzjMu2fH0NM/v594vOCYHEPDXMoogU5oAgAkLIgFJkWZuqrQUSskL8/bfNepq7UCYD8qnukTEyMQIhgb/uByJILWtlShSgQPCpzFKLuCoSM1ZgPA5lBEQqFb7XK0EiDTV8/9DbFd7Wpkb7rpB2TLd0dSK0pSAL1EUwA0Se+3T3dV2fx+yODpgG+zETszSA4LHbiXEiISRn4bBgxHpZRx+nRPMZ/uwGQJ75v3//9T//7W/bpX09DgLKhI+vKULIdgy9vRTwKYKEdKuQC31/vaqCWz6Zn8/+eEyIfNuqen49J2LeXhoAhmrG2S0LwCwFxpiP514K//a369pqIWkVP+5xvdTw+HpOEZZGl7X0AZj8fB6ZGOBMuF4qcUqym6fb9Vr3I+Y0s4gAD9BpTBQZgMAkdFqs/uLuBTJNde8GgGMc21KYKfIcJlKBdCc3iEgiIMQxbeHiM1CIyc+GlXuKMCFOBXUnAM9UC2GsiwDAnKHudVnGrhkpRdQMzyYi05y6rk0gM7KPuLy0PlQ9klIa4RF1IXPqj0lEf4G3AphJCrbCrcoyVQ2ez+nmlUtr5BFukQmRgYl/ff+Gs4EF5oGQCEDIiOiRASCMUpiZwTMyE+N6betWmBKBvLB7toVeW+3T59BSOANP0VtUuF6IkL+6BriZw6QV6vPevz6Pn3/bbi+SmfZUm04AAEIEGXmMWSuNbp6ZBq2JWbg6F1I1EiLIJDwHcKHw3OfyXUpBm9kP7V2vtyUMmLDVMtUDggDVPQwgkJBUPQ0IEQBVPTK5oLAcYUQ81QmxNqkCfCl3O5jRzEqB0io9kQjdIQG4iHuExnpppVIh/Hzv0uS/n0ZOTgQUDnvvSEmEXBjDmakf83KppnGqc931xOlHRikkTB5Bgo2kVKSE221Bhm0rEFG+r27uwOHQD13Wqur33utCl1UEaFqoulqUQrUKsZxc9qXK++fQDHv6GTYyN4fkSJu2AxKRgxPmti3D9OVleX7OWlmHu/ra5PUmKThHuNm6ldZwdLq9lKnpQWE51Wql5dJcgxi5sD/n20sLh7s6MSFSRNTCxOIe5oHwF2gZkTMTMttSIQLcALHPAIBkNgtCNPfLpdVWno/j/nW0pQlDxHmhAmRe31op5GbMZBHH07gGAj2fXmqOET6dKx/TkOki5AaPx9xa6TP60HUr+z77ri/X5ex9BnpdeAz1SC6UlgH4fGpotLUsVe6PUSpL473PMR0RWP7Se58TV3eITDw1FA4ISHyOMdEcPRATTAMDkbCUAmfAV0g9kbLIuQ/CnGYGnokMvWcEzmFSETIhwN01udWWANtG4Ty7EXM3O7oSkXavVZBh6oRAAokZl6vomMdhtWIRcsVW8dGfUrgwHmp1kSr1489dhDWmpXtGq1JZwpJKsmTXGLPf3tbPX8cxVApLScB8HD0yu9rLtyUgzGyoZsB9n8N0WcU9mYiZPKZI6Eyb4UzCpDb6nuEwjlEXlEL7oddLRczRY93k9bbuh7aFCcg8S6WwFOHbBYcGEQiRCD+fvYrMo3tmOvTHUEoE7IdXMXUnlNrK51MvAHPaurS1ljnndpU+cQz150zIrfEcOqcvxYkhAI4+gaA1roVc7bffrh+PcXRTkbZUQnk+de8KBLXhmEGVxjHqxgByHHa98vvHHo63l1KbCKNSMGNBoJLdcl3r4+42kzndUoQAkJnMoRUshR+fCgk/flvfP4aE2thlWS7b6pZf7/31pUKhP/+5q8e6UiSw0Am0/+cf/fE08tKWvNyWdO2fIxGXiyxMb7dv//nr8/HoVfLy1iIyBG3JjPjnf31clu25WzKy4NtWriEFozK/f6TuEQbLQq3xHH7/mG8v6/VFagEE7M/wtMutLEu5XuqcZe/DHKaHnVk8iMT4trV1rZ7h5nsfgZDYPj+0++TAtt4C4pgTK6rD+/t8vx/HEczHzx+bTvy8632PVvj7t4poreFzx+OpmMVmrqtMNRSYh7cqCfD1sZdWpBUpmOHdjARj+FRDTrc0xe1auMD7xwERQ8P3fboe0y8Y+0M99Mfb2nv2GdanD+VCioaYqfDYTVjScU4TQSL5eJ///Bivr+X79wXA71/6eNrPH7BsfP+ay8KXS/GRYaaK54q4jzE8ANnV96eR8NHV1JdFzLPVpubhoyNFpoNBAQlyy4/P8f37ulzk69lrYark4TYTpkHEMWC94vWl3O/z//z/3P/+22IRG2dM+PwwTf/tdbnfvTSShga53ycRjpFJYR77c7x80x9vrxhL4vjjs2OR7y/rdpmz++Pwl1s7PkYfiiDLWkuTMbJdsBa2ib2riBMhMx/dtkuJzJfXdr8fZ5W4tVIamlqrDAJjhGtmYDgdu2234g7vHxMg16UtS+l91iL1yhpAaoIEiHPaKUe3zHA7Dt73uG7CTFKSILr6skjmqBWWdVkbL1Wm5u1a+tRjjG3bCsvX4xDiy4U9GTGaYJ9pYUXk/hzbQmqmmkahw01lvbQxPIMyKADn4SKiM9Wm9SiyfH2N27X1Q9etfb2Pl9uyHw/hFgnuziC9A0IuCwsLIWCiDi0VAfD5tO9vJTO3jW+bPLqZ07aV4/D7biy4bJUD/ye9TLOCyIXvz56J//rzOqcipmogzLKQe96E5Naeu/bDrmt7PEbbSlcVKduCIpDJ98e4biV2B4TX1/bcdU6zOlTz/aP/eGtT9wRbChMVBETkOeLoxgLmBiBEpJqRQYBA3NVVjYmWhQkdPE1hWAhKxExCw0wLRE6MSK9NXONkX2agFPQRREwUYYmJhKgGxHiafxIZGBgSw/anyUUc43arbRNz3nd9PNXMpvGYM8yJ6bJV65EOIuTpABkWuykmvP/at2tdKo/DzWMtUEvGf1Olh2Ytoqqfu95eK1MePZGdCBD5ehPzdEePCMzjME9AxDF96tiuZd22Yr53PQ4jxHC7XJowhkepPFTVMiJqwRN4jgSajAQBiXSKWSKBhsZiyUKqLpUSA5EA4Jh6e91qQ7MARFfDBCJyzMjgpExYVu7dtms189GNBN++L8KgGoAgQH7yNyAvl6ojIPmxm7mZ53apgKCGZqkat2uZOojo6/P4+WOTwmHeCkfi8dTlIret3L/65dr+7R+3P9+PVqG8rGb58TWYa5/mHgRQC9ey6LQM9MhCf2WjGQARMoOZpzkzAWRkEiBEhAcRZkYp8nwcSOfAKIRpznh89swUISoggkk8p5ci/Zhzel1E9zm6I2IClEJSqS3FPYh433UJnsNfXtrte3nezS3UvGC7XhedGRHnvlUAIvIUKSBiuGcCISBARCAgERDD7dbWRQDg+Ry1cDc7MTrggBnPpyJQWQASRndPj0zmqLW6OzIgYkaahsWZj8p0GIeZRV0IAAHg+extpZMOMTUyEwzOo6dnmEHfBwu/vC7PYxzd08E91LMPR4zLpQ5LaSCOan/1qRISEIigVFjL4qEkxO6qTlUgT+tgqqocYyYkRFBCkIexR0oDncaYhfH7bSPBUvCyMQIgZi3UjwAESwVAQpzD9/1olTNDmApBmtdWwX1phYXSVNhry0TKyZAZnpCRCExYhYnpsjXhRIIfL5eCZo4t2T0Kk6mCYymlD71cy9vbGom5SD/sOOa6CuQ5YMwMK4XUHCEZeY5kJpaIzCJCSLO6enz7djkL2Hu3vfuxD9zvdaHXl9UhQpE4xj4CCyBwETSPSEcYMwoioidhqZwZ9707gDkKyFbrhOj7PKEGjh4AxzP2aRrxeN5fXtZ1aYnRh7saCZ/YDHfoE9QyHQmwFbFITjhMLYIhiZBZzmOEIK7bour517o9TrmiRSDR52M+/t//V1vrmPPEHE/Lzye8bAWSVUNuK3gK029vrA5fTy1Iw3LOEWaIjAAF8dIE3qitMob1rogoRQiSkQHg/bNftvLzxzodby/t8/Opc768Lq/fXypnlfI81DK2ter00jAsWylJ0AcIc2ul99Fa3R/j9BaVRmknOwiECQTO4c+21tFHbc0zR9dMWNeaEY6cYYC5LtUs3LMIEjMT1tbG9OdzMlVkzMiMPHbNANX468bqJzWZTDzC00+w9FQNQmFGHTmOIzG3taVgn6oRSFRE+lDPqI1PexpBuLqBv91aIJr50uTl+grgGvD+53g+OiLcXjYdxhUBsXc1j7YUM810KSUzx5ilCBGZ/xWwyP/21J8KT8/kAPdAJHX1COGz3INzTGaqVTyiFGaKwlgKvb29zqG/Pp+eWSsLUDowAhe+3z0hyyL94a3mb3+/Hj1UVRherk2YzSLT1rUe+xTBIq0fah4eAYRSZKpDwv6YL69LJs7pcUwUWoR0ummep0YRIogxfUxjFFVrSwVASGitjOG9z1JEp7k7E5NwRI45RcTjhKdAJACSqnsGAkLC5bYSQwSYhlR6vV3++Z8PndHCW2Nh6X1Swe1aPv58InA/lKlMOKXUQYCIAYyhXmoZc4Y7AyWhDuXCs9v5W6f21kTHREhAcDUWXNcFPGWjvc/bSogpQK8vNSDCIiLmNHdPwQyojaUiAvTQdam9B4AKMWC62/WyeoSqE0MtrNOn2bKVsLluYuEWvi4FFNpW3RMQ0uN6WaSiNKlMVk51qG+vCwLc9165tbV8TM2MML9sIoQgiMT3rz0irte1Fv79874sZd9VhBARIi7XqhZumQke+XgeUiU51RQiMsDTmKEUhsjzZRx9ZoII9aGnrK1ulYTcTRosWzueKkL9FGyVsm4lMUN9uxQWUg3JPEuLt2tk4uzR5yhRC4sPZ3LVdPX70ECWQjpdu29bBQIdgQTm7o6XFYaqOSLCPJSRkEhDF6SpZ5i8q0dEEPGcNroSAgoAAyCkYThEIkIin6xK9MjwBMRS6HJtn58diW6XcuzT3ClgTF+EjGOqnW7BzKQSyBVSI9KTydM00+HYJ1ItjZgQMqWUbVt/fT2I4G8/rx9f3StuK/XuzMRE//7vX31k+p6Yy0XWVX7/ff/5/SI1K/NS+TxWXrcqKPxtsTQptHG5P3sfEwUIPQCP56QkCBrTSPDX7/3vf7u+XMr9MRDh+1shpMfel7Wo29kofA618CZSBS9XGdOnRq3S9zEGMAaXiIC+G2K2UlwTzx9AAEyAcCek4+k2ziifEdTjiIpzP+Z6uYwxw7Jc69xtR315a/OYzy81i1pwjsiAxx24gqntT3MDPwIk1lL3r0eWUgVrqc/Pvq5Slzq6708nBK4kU0Qi4ISIAQASc0ZinhOWwMBS0kv8uDYRLiK//3p8fo21wXapNtMGItCPH5fHY0glIjieViqOqdfVSxVCDIs0WNc6zd0hwy+X4pluuW4Sjm6JjGP4Vjg8tutFE5+HEuIYbpaaDgKu8fHunlZqJcDZdSr8+LFtG5RaCvJw4yptK7vq48/ny1pKlrSgFY9hTZgIEIEk0TPC10s7pp1k9/05l8IaevLppBBTjAP+/GMw47T4upuFA4I0MMuhdhHKsEz/8aMk+VcfmSwtmfl6XW0OKc5cfv/n/PN9iuB4PNd1aVsJic99qNk8oD8zUyPiOMw9IPnX+1MKrTfRGXPm/W7hfiyZIGsrc/jz6R9fSgWYPRMeX6M1YuRahEoiomYyUwTsh+9PlYIJ2epKBOM4xj6JyFYvLCg8H9YEmLhW6TOn2e1a//yvY/SgWuqav30vUfj9o6vmOAwJtk36MZ93u26lMAnTy7X8X//e3fO5TwMe07eNG9OeKg0Os1K4FaGQcXQiXDaZlolwkuMzEgKIEzjXpe5PMwUweD4VEC6rbFsFBilkADoNAMdhtUJrtQ83caAk42WtY873j/F45OvN9cWRaJPSjxyHdzM7fKlECTaDr5mJLHj5tmwrRiqwrZxvr3K5LoKZbkvD+0F//joSsFVGAnMb08fIYELK/gwNfxw6dk3A0uiYqhZFikgBCSlldDMPs/Sjv31fG7Jcmq/+z9+PqZBPHYdmkoUTZSXepz2ez9dvRaBqQDe9rmU/7LIhJpn69+839Hy9SRUKjG3jMwvmnq2U0kgdPj9Hq/XlZbGESFgXicgxDIEg6Xk3S1836sPuH7OWwivdP7pttW1lP3pYloLHru6RAOHgHggkBdQcCYiw1eXr0+cOsFKrDRwx6c8/+7dvNyDQ6TrzaSqIOtRa0fCMSAf3E1rkZqnTE9GeszI/Pm1O+/5tq4wkmFD3e/92W3Tepa3Msi7lBJAf/alGgVCEgGuEcwEAZ6K2kc4wj9pELa7XxcwjeAwtBda1ZsK6lt//+fjHv7ww4/tXf+4HJB+Hj03aIp9ffTau1abh7IYEbSmXtd6udagTAQE9HhOFkXN0Sw/3PB7nDRzVckzD6LWkhROwRxAlZM4E02mGwlSEkGCpJdUeD73dKmJgujuMTMIzbAcAwFzSfQ4vQE76H7/vvxZlget2Ocz7PjI8g20YAhBSYXbN42kvLwsyCYcwzgEZOFXtK2qTgLQkRk3KeQQw18Z96jF9qWyaHx/99VrdkgABoe/zejml7W6ao0d4xKFFKNyu21IF3ee6lddv16/7JITPj6MPRZJrFWRSpTG89yDhuWskLFVOjUnm2SVCPB0njjpjuYi5l1pI0tUzaVlkaVxX/PrSuUdEiPC3b9vXfRzHeLnV21Vaw3WhowcjbgvP4W6+1AUczLwV3qdeX5ZpFh614QkqL0W2DQHBkY5jEnI4QEgpBJhSuI/YpHgmRJjCHIYYrZVSZZhV4cuFt1UogEW+favuYQ7356TC93t/e1sfX33sTlxQMiMAIE84AoCNmQQWDn+NhFIEkAISzbwULlVU/eTXyCaIOIYxwYkKUdVWlpzR+2ytniGac2V34m3WlRHyx88LE43DH8++77MWXpeaDics1aYvK/z87fLHH18QQERnUCYg3c+8T0SCICQkEkXGKaqrhU3toX6yTWzm4z7yTMQjZSQzjeEJkARn9tdVISkZbZqeAX9EH54ITECIJyxi3WqpPIZe1uX19aLDx4y1ndmx3A/PzLKKzggAkZqQoxsTj6GPr0FM375xcwpAYd5jliJvb+sYDyLkxpkaDgQQGhO0XRgyRagUzohIsAgIiEA5xUcnZBQwno9eRILg/f3x4/t1uVYpsW01HCy9VDz2A8ESaPRIzLNJAZCXrbqDR1JC3wcQEOEi5fvb5eWVwUya1GK/viwQwnLZGiK4u7tN1SalrBEeKAIYmTnniUyXjECUdSmMMFQ9sBKjBSOuCy8LZyYij2l9H7XwTO/hm0hierqAXyqVIsPi+TxqgUtjG1MNCzdMLwXLK0lZ5/Rjn9tau/u0dMynDmAETVNHZhCsxEM90aQgBjeRdamWEBb96ebmHiTpAPskqUhMH19PEJAmL99emfIYFqEWAY4lULAch2I1Irk/d7MERLVYLzU9IKFWMfVMgHQPFuKI/Pg4wlSaECAChgMg1oUizt0129nUFwbKBPeM4TB18gSAayHfO7xVBp+vCwHgGN6qIJ16tVDzWrisdbjtR+fAZSm3a2vCOjyRIrEwXl4up1Kufnt5PPsxVAo1ljS7LOKQrYmbY/j3160IPZ6Py9YAeX/OZSml8G0rc84+MyK5sEU891laWQqbBlcqjFVqQF5aeyB8fu6XVQJRWKLAWW82HwkJQGZukCDYNmkrVcZj5NENHf2/v51NtQSICBZCosxkpgRQdfcAwAhHTwAulSKSEpZNpumJGWZiZgTIVutxzCJSasHMUPcZ23Xd7eh7X5aKgJRwvZbZBxEKs5O7w7nemZqhgciYMKee6rRwPe9rZ4wCkRAwzyefsIbjqVGAgLSzyumRPrJ3vWx1XalP3J9TFa+Xus9RDnOP9BQkiIx0AojwmIkY68ZuQIjbpX296zHmsrJruEMRIsJMOg4HzrpUG1YEhyZkVpGI4MIM7DVUo1TZLu3Yp1qMbkVQiDxDCtmMYVk8wtHczfztdZkjItAsR9cijESRuSx1DncPltPeBADBRAABkDotHc5v2JGYCcdhCKmm29YE5G//8vr+6xnub98vFPLHe1CBtconnP/nIKyETKffDYAkKtaEREbrdtbrwJwEExEySiu9z7rwsoh2r7c6hstSL1euQkycYITs4QhQVzn6jEhG3vfZSk0AsBARIrzfx5znYcVVc21lTFOz2iQB9ucoVXRYEHVzLjiGcmEqZGq1SXraybhKNLO/SoVpEWkat0t9v/ehRqhMVJdlqjHleuFSpBCbwZ9/dqnUmmxrGdNqgfQwj/AAgMzYH6MtK7JjRNhZFnAzZ2E3RwSubIdFeuEiVZ5fuxBapFq0hUWoMoGghhOluwHB29sigJULEvDkCEDE0X2qLY0zsR/GjNuljhFqXorUwuq2ZLu09njox+eDZWMq162qhiF4uHavC5PAGEqCyyL7M92CCzKUuU9OvKwNGI7eCU6RTkbk0QMQ/sqURZSFby9SCszpY3qrVUfsu7pDeBImM2omFyImLlgqIfLU2J+jT6/1lATN5bZFBvLZo8xMDIMOsVwqT1eNMTMj10WWpRz7KHVhgK451BLCR9Qm988xVdeVE2DfZ1sFUa7Xer1BBBDn7ULHYVuTJpmIS2VTU/Va2KaxYPh0AIqslQkojQijVR4RBPRyXY+uj8f88Xb5+f2yFjmeqjOSoUOq+ufX3BZ4eS1CQAytyZ/vun91+Q6jTzWc08IAgnTYIFuAALAtnA5mUZZKmCLungApFZZ1/Xifz7u93liEaZH9qV+fY2mlLC0SEGh2Z5gAPCfMmfdP3XcjgjhdQQEJiIAfv/qcUATlUr6+ZupxvZFPV3cGxqDCApk/vm/M9Of7PTxaw2Vp5lgqXJYyRy6rRIQIisPtUmYHnSEiQ+Hz6zgrM1ut27Z+fU1GToK9mwgl4hgWHsfQ7XKdIz6/9HLDflhr3BbZFimBXIgTHkOfh5WKhYWJvv1teTymary+Lu+fPUn6YdqNMlX9vquBSeGyYm1y7DSG14LLunz7WdcrlUVswMfjCAgmypm9m0gBzTT/3O3HJktlIpx2RlucSP741L/9REhQzX64OcyIuQdWfk673rBUZqBvt23obAvqTFdjycttIaJpxIwGHuxukJFT7bHHPuPbregjVB2Fpk5zZSZCHEOZaZ/++iZuPqZfW2VnSzC1x30CgrtVLK3x7J5K5u7CL69rBDwfWkoSgKkDMxEslYPANMLDArjp0liYKhF4RuAi7UkDEyNh6nj5cblc19//6wD0ZfLT1CO3VivyAAiPWuR64bSozFExOaTk19ehU2aYezkrwMvKUhbmk+LBqrEU+bd/ff3jz6/aCDHrIp7ZzQ+zt0stC4WnqTPk7bJk5rFr74MbIycxh0dA3F7K0qqOmJiJiFWQaFsrQsyp4kAoS8Mg+fOP/fPh//jHkhI24tJwqbIPBYDfvjcbuK4DEojR3AH46z764TmBV3j51pbX+vG1C+Xz4aXS24/WCmPQmGM//P40D7stIiSf9wkIrVE4bds654xMJgRXH/Q04EKVaI6wCRF5v89//MtGRJ/vMzMTopQohShg2SrXJMB9jm0tYKluakmCEeDuRRiJu/r93tvKfThRX1o9VNHz42MA0dvLcrlU97QBTbCrtZUjY4wYh1+ukihmMOdMgMfzKJLf394asaPu3Ue312+b9nm9sAUn5pxJzG1t+zGkSUbO4Z7giZzBzESckEhQhFvNMfO5z9po2yoEicDrd0aGWmSOAMKIGCMuL4vOHgnDswgsqwATBnmGWU4NIYDAUkg9pEDb6nHY/pjMTADrWpBRZ86ed9TtsgVwH+46r5d1aUhpZp2pIAJRohOETZ1dcbvI24/1OOY4RiuFM4FwzOmeOueyFPMUgr/93MJcML7dRMrS+/j587JV6eaIdr/rt2+0tTKPJGAI/Oc/n0srRMAc21aegY+P8fK6uUZGuqdZQAIJBwBGhtqhAUTMGIHmJswIKEz9MEEyBet2WSszhVmt18hYAOawM5jvEaUKIWQYsQCQQbxu9befr30e08ahfbmsmfH15xCCk4RoYPevo3cFhDmng5dGY5ppjpkWIBj9UECgVCIZ3aelEEnh2uLxUER8eZFMvO+WCEWIMf9b0Zil4e3KleU45sutIvBzjPVSijAxMCdCbE0Q/PKP6+OhzFgKspBwvL6t98foR7x9XyihNHn/3PeHlsJcqNQ61N28LoWEmBEp3QIRAzwj2lIj4+tDz8UbQqr50nBd2vsnAOW+6/OR68ZLkTGsVkEIM1MNnV4qIefLa4XIcNBprclzzFoECZkxE6TxWRMjwamGjlx8XYtO6x3dY11oDG1NmLAPKxXXtY7pNmLOqCLhIYUwcau0bgshbQuKEIZA2u1appmU8rirBxCF6UmmyTzF75CIp/XYz4bcsY9TqU6EgEAMyyb3r8HCiEmAjvjcR3iGx7ouiHmmYliEgc4yvk6vxbaN6qUCxn6EebBg3027e3iR8v7+uFwqJKq5cBJTLcVM3S2TPPx8iwAQEQhJCkoRPIM4QBapbseuHlArIWKp4mbLVlwDAOY0BGGGzEDmcDe1kwB9Vj+QkZAS0cK3S2lL1alIaaedJwCZ3FNddcLzobWyRZ5O53Bg5nEMEhLh2mpm3O+DCGsVYbjc5PmltdH5aSfB9dr2x3R3EZaKhJAJbSmn3n7vevbz5nTJzPOFnimSOHfR5D9/vGammjvC/Nrn9OXWYuB//mevCy1LBYBxWABGQBHJBFO9vq4sOHbzoaVyVpo6ItfIlIjXl6U1/+p6DIWQ8KxVxuSlynYprfH9aX/88dBO367152tz5KcOU9jW0irPPkoRyzhGLySRqWFMNE8fNqRbDkjDaMIIZ2OBzHyPqGFEoOb7Q7e6lEJI8HgqBgsmAhbi9Vr23e537dMQyd2IqDWRQkm1D7NDt1pbIZFChKP3ujEQec9IRklBbq1cbyXdjl0hW9/txLmYqzuqRrsuVQgQfYDP6DqBWIg9sq0VZyBIH0+dNqa5nykQBIAAnHMS8PRwO4srEG7LVsJjP/Tnj/V6K7//x2Ejc4RbiiJVQCZM/vPXQwqYtf/Xr49//Qdr9CpXkcpCCMGc2pUwxjSRdlK+1AwxfntbYuCu0ae5GiEL4/e3Ckj9mFWIidTishZiYKFEJEZiJMT/+P09LH9832ql4zmL1MZny4KrlDPpcnTvfTKV0vjlwsgS5i+XlgDIGQ494HHfIenvP14K8Rh6DCVJIkQAdTs/ajUrtSwrR2Qt8vw6UlCE18ZIVdUf94OZAUDVCImF4KwAMp7oZRHJDECaMzP0er09n8dX3y/XVgurJwAw55nmHEOfxyCiZS3EsC4NkZ7HVPOKBR1V/dmVmbZWLWL0WSvPngQoRR73sdSVhKkQIx+PsbQ6/fQQEwsD5KnPiwRmrIXS/OxiAKA7BEREtFKFsTVaFqmNj9HbwhFhHsyCkO7p6nG2UQMxYdnKnL6sohnj0FOM9XxMc1OXda02sgg5RD/UMkuRftjzMd5eNwEePc2jCBWRx+eRgKkJqbTyshV/zsyEpKkhjWefrlD+GiKgm5nGcdh5sxldM8DSfaqwEGCEIqKpEXIrpY9RK3PhOVSYlq2VCl/3frnWzJiHAWWAaw+6pIYhAgB/ffZvr2tbKQPAKSIRsFZ2dy5QCkSQuVYWDNiuZQwvVUTINIjg5XWNSMZEpP2AZWVOLJeKDIwMCGPMs+Vmga3JnJABY0RkbGudwxFJw0XQHVQtwEW4FTr6IOKTbv3cOyAIy/PZqTAm6ZgdlIiIafa5XVe3xARGqEtFhPf3bmYAUauY2zRj5nTvnvenEgYjtZskZESIwNttsxnMkBDXGy2tLE0c4Ne7Z4K7E0AtAqZtKfOwOdXTa2FNn3Msy8KMYT4juHBmECUEJoAwRUSQnKOoWmmppVV6jlmICQADgJISzLy1ouqEaBGE2apkxsu13b8GYbZa3z+Gu14vaxgqpEFk5P2wXa2sXKuMwwczCprZ8ZwZsGy1MEIDwigE1wvtPKSUBNgjpJWUs09eW20McLu1j68OiQJwArWY8XrlhQelLFs7yBOJNgZJnxkGTIQEC0JEzmE6rIuYB0KYZSQcXSMgmYcrC0QGIlINDBxH2tS2JCLqnJ5YC5eFlgvfn/F8TrjU6VEEBeCrGyPVBuvKSCCcy5YA+fnZuQAzmcXlUsOBgF+ufMxRWhGgqb5UKSz3R+eFsFTt+tifEZCJ/TEvP26HTjVbqkwNoGgFx7Tvb6UPm26JKZUQ+Ovj6Q6wRBEADKmiCqF5POyT7fq6+GO4w3M3glwaA+DRjQr9/O0SDh+fDzdn5vUirpkJy1ozUEesm9SVmenYFRLbIvtz/rhc1c64gbv3ZatEGObAJ6Lxr/x0IrhHdhrDWYgYguLt2/rx3rm2feyXtV0WSfLEaJVF8uVVpGyVaWi+f83a6LIUTFxWmD77gHTE5DACzABABPMg4v60cfha0RPmjNZkqBWCxsKIzAQBCDjNUHAcxh09QqdzZedYVxmPvi1sLAZQAdJgokWP8Dz6POHwhfBlK7/v8xi+XMTTzdJsjp6tJVXYijSWtpTA8AR0SIM5jASen7ZUwgJf9/Gkuqz57WdZqsSRUePZHTleLvXzI0vJ9/cDGYmImX68lv0RJHhMCIAikGFLa+smf/yamTEOl8ZvP9o59l0rft4ngyyrPHZN8689P+9OEBWpXkVYpLB7tBo/f5Rffx7PwyPVCdaF14Kfn4/LPzZpqBapnADXb22/65+/37UDCyRAYTb1+31ua1mbzOEJeCIwfMauyUKQQIDLysxQCDEgZhBiERIBYlkW2i7sgfvX5MTf/n47nvPxNc08AddL85LnXsd8SuHxcCL6/mPxyLLErz+f/aG1EhEF+bGrWwY4C5oDR1y25c/3zsxLa8+jI8N68tjBl4XnsMKQwB/3USsf6lsrTWRQelcpNHdbl+XbW1Udz+FuGA5MUCr9uNyYnCCPQwLyeB7mMCYvi/yPWxMCPwwSwkln3rttCzPizHy5SmEqpd4fYxz2fBoSvr7UQPvj10erq3ryDCK5rs1263pc2pYJUqtb3O+WSoCwLQtJ3Pf59Ti+/aDr90bAf/5+V8vSgJAIyA1s+lnsBYXP9wkMCSGNpEh6lkrjUGaoVZBwWwUsQjECzYyVAbMUuWzL12OvVWbXy1Zarec5ykbqjG1tx66Pr/7yrTG2fer+VAu/pVwWfrmUu5Fr8ALgqGO0pb7erq5zuqLA62UtLfZq15X/v39OYfr+fSWm59OzxvTh4WMob2V0SwsEjMAxTC2kcm3l696XhstW1QuAz+4Uud6aqiKnmh/dRre2VGG6f+yeSQQJGJCyCBMQFwYa2pEzAblQYTwObcjYzY64XJosUC+YkG75eGoiSOF1Xe6HzjkjUWgWah7ctkUaYqSp9xG1oiG8f/VWtqeNfQQikZ6ESGoizI7EGS6FH8/JFOZk5m1hc1XNyHGMUap8e9v6PjOzj+Hp7okKEVEqH8eM4ctaPVM1mCgK1JXHYabhkRSBiICkAYlSTyWNBiKYeSQi4RgWkWsr7rA/ZybUVhLSzKQyMvTDwZAYmDExKCHBiM+DFZkpCn3fxNInxuOrf/4aLNYKIyEYQGARjgiPMHdAkIq10dLkfh+IBBGP59wutTYkpq06M2JmqwwXzEhANA2fwYVAGAnUDBHHmCStSG5XKrX1Q5cFamOm/Ph4LK2tK/ehhclDF67bKmtjQvAkRE3z61bDei25tHIcvjbBRDVHhJe3muTHI1plU7t/KQK4x1+jB8DRNSNsGjMT5uW67sfQri+v7fWleihj8Ql9V2RDwqkZGYB4HDq6XrFhZr0wJrDjCABA8+i9v7zWMdIdlgVbLXMaEAyzS1tPdCZwAqRZqMJUmzOWVWrD3mP2iezhMHvQ6q1V7QaYROhhBFT47I5hXopwSuFapDJ5gLoduxMKIb6/P7brEhn7Q09XEBEBoRTux8SEZSmAOI7RmtRCrjHdMqIsHBSYBBlSEBNn11rL+coRwdXuz5GZRIvOjpSX2/J47sxKAOmQgcNsdAvP2gQpIrOKuGlb5GSJZPoZEP75czt2nWrbTVqR50OJuT8GCS+X9vg8SqGTSAtIpjZG2PRaAYlO1A4SQ+I4dD+UmYXp/Nr4F3U0nAh1humRCCJ0xOy7bltjwsdjEGNG7t2IqTJmpDQhonRHPoXusG6FCVR9atjM47C28NPDNPG/hwXqDhiAyUxuSZSJyAThodNPLJFBEJNs62Lm+25IbNOImBtHmjkicmQuSGccZuwGwuvWxoz3P49MQGbPpFO9pNZaXS91jmlqy7LURiLJhcdQhPSgx95vW7vetkfvTWj0sxPH61qOI12JAFojYd412gyW2ApCqX3EcXRiHMeUgoXIIjQsM4CKWzyOnZlaK8ecajnTp4QF9mPeLrVVnh8HES1NIuDjPqWKJwgzQtZKIuJKx3OwcDql0TEMEYRhdkvIxITEyDjgaLVsiwoO2Q8AAQAASURBVG+LIPEY+fG1P3a9bu3ttYwjIcbx3K/X17rkdC2Vb98uReDr2d//3Amksi8rg0OYE/Fla6rZh40Zx6HHoduybVvb924aiJgRiID4VxnoZOgsl0XnnNMIydWnRV2Zax6HdRvR7bptMy3G5CAp2GESYZMaBsCwj7y+rS6JeXSt45iZuW3l+fTTVQwAXDA83KMQ40o7TE/kZI14dKvGBClMAeRhSSEst1LNHQHmDOZMCh0mVMbM8C4iJ70FIJbKcyriOT0sUCEDn89RyrI1GTEhvJQCCAoBmLfbui01UpdN3r6Xfugf7zPRz/XjqRhkQciYIxFxv89aKhIgZikcyREmVQig1cqlQGQtCBCRoO6RcUrWWCQyIqOIPB9HrUKAEFCYhdODamPiNE1I+v5t2xYRQamYifuuex/b2gxxqqobCTIhr3J/DlMvhR/PAxLMApkcwsxKEVCIxGOekr+zi+NENKf+BSQjAsx14yY1I5Hg+QyU0hq9XGumS6FMwMTbpR7TmWlrMkcgwtEnET52I0pGrkxz2HP36QEIjAWQjj6JoIlEhGqGpqsdczIzCXomJQrRnJbwF5bILR9fx1TPRCJAYAwAQjrlkRn9sG+X67q23UdGuCVLQSTm7MMAYbsUVY8Ajfn6ejnzjS+3iwgDpEWe5rT/+W8XJLw/FCIrZyl8dN4u9fP9iMgzfv/10dtS+1Cz8IzGxESIYDOOmDadBMLxfu/LIlLZfF5vSxqI0PVapBAh1FJOWG9ldrMMJEJYoDAxyLSJgUnYR9dppxmQWBIhIeaYAFibzOFudr2tU02Ywq0UdshWuIeVRsyo7oRATDp898GFrPv96LVJQLxc2zyCmYnR3IiicH1+9baUkw9cCiFC7yq1jOfogUAMCBmIldScMK9bOesGRKiJRUiu1SxKJc64vVQ3u651XMv9cbSVX27tn11FokgZXYnh+88X7Tq6ixAzmQdB3q7tGJrhBLG08vbaAmBO/fa6hKGHQebaSiH8/Bpj6LpWYfr40DEMCYnx9rpEhBQkQtV4u1XTdPfrdXk+dFmECuoBfTdmBKJ0+fwYCfTsOxGUwggIDCLIhL17WWQfjojbdi0MQln/tqnHcTpxssw+rxepC5eD5gz7651CSLARBiJCYAGZvWt4BMAcRoilEgkyIyZmepO21LbLnAozPP/ygGVmhkEpgsz74wAAKYSC4ZEBJLhuS0ZE5nPvp3btnHp7ZCnl6HN5qS+3slQ6sz/qCSSVKQLuT4/wWshd3VMENbNVGcMUKIIicWpIKdNtemaksIxDj12TEsgRExASoOuUQn0olwTkoWmWiDh7CNPtdT3GLmsmg848pu0PnxrrhcdQ/wz3v/LMRMgFx6FUCCyOMbetLde6CEXA/mGQwIKh6Z5vPzd1naY58dgNEV5f1ufevz4PEZ7DiREIxpxSae6wH5oYFlka5cTMnNPXJpdrU5sW8Pga60WA83kf3Og4Opf89o8tDoUkVVdzKSKMhrFeRJJmD7UwsLrQbSlo9Pmlz8O2VczTh26tPs3uz7lc28u3oqrEXCuNDhnYlvKG8Nztsq1U0LS3ttB1ObpOV1qYgOecGLwwR2Bk3K7iHSICMC7bYuwnvdtmTAWu0BZyS5uh4a3WbatTulomgJvOyOR8vbZLIZheC0opX5/z6z7Wv20vt7Ys5Y9fQyd8fy1qjpljhz7j24v89nb5/pLH7PcvOpGTCKkj5/S//X15HIbCS80//tyXhn32PtI1b9fmCTrU1AoLo9x/HX2M/+Pfvr1cVjMderxccG1r3/UTLCAuG18vtTVCgH/8fDH9GjOub9IaZ/htbZ+fx97xftfvL8tvP5eubjM80N0bcqmSGcdh02DOZMLGLFKIXNULYq2IRGPGOdZnYh2WSROQWR778fKy/vi+TjOmAMD9OYTy77/dbNgQeH1Zfrxd/v1/fT2m/8//cft4323C89F1hlmumiJAjC/XtgBq2pi6PxUSPADIX0pZlrIsCFPbAvtTdTgChcU4vAgWocfTGeLbS8vwWui56zFcEglxbaJOY85QMMrdtauNGS/XJTPN599/XsacrqARzPTtZUWAP37dn19zeV2kwT//PNoq0+L1xr8+OwmrwdcxEWXf8fUKavOxGwbWwlMnZf12W3eFX+9dZ7lt9PPnapZf9yeBg3OrDYOvW1sX7t0/v+b/+Mc10bmUdPp6f64m1237/uO6nMj5Y+oMYgFwJMyEqVE9hRkgdDpQFCZVRwKkzMj0TNd5RF3WtbVjn27RFrlcizSorRaQhKdOD3MGaFJE8Ov90WrJCBZhQHcdQ80SON3TPRHj+8/6/tHnNO25rde6Ua3spSARMWj4nDGGzSb/+Jfb+8e4P+ZvP9evdMDYtvLjx+1//8e7mxExpgNGJFURZCBkQhaSDISkZUFEiekAOHY9+thuhRi2TQrxmOdNFRqXTC9NEoIQHVwKc2BkErENG+61cim8rrXvU7gERO++XkSE5vBwMIU+FZnCATGXylXEAB5D20LbWl3NXT2Dz3XsdFPlIrWQuQQEJT67XdZiQYKAxAHZlnq/T1VFwEgQRg83g4C4MVZm4Fy2IgQkMxLH7l9fhwgul9YPez4nM9dVjjkjPDKSEgU5KDwiw+PE2Z62XRwapQkXsJH3uwKCBTz7rAUTsNTioaOrqiEjF6gLMpZwqBWkyOPRM41LsJCbH8PeXpqEg9kRc3QPB8CcECclHU5gCOZUD2LIhEB1B/TlQvMIIHx5reta2kIIOUbOEVVIda5rKULv7/350KVJRbKZBqkzADBD+u4Z4K4BmJANOCNtJGYex7xcro/7/va6XW7LcRgA7vuZxg4ukJ5mVkV0GuYk4G/Xahcnpn33SrAtIgLgaYPMjRmYkYi5YAScxon90WsBgDyeR0RQqRGGiNtaKjMseAz0tOu1eqR7fN1HEq5U5wwJfIIyITJx5aPbspSvOffd1+38GhEIIMKVIU3GMbZL6cORYIKapxlmwnMf6vbKVTUS8LqU317q6IoA+2O0JonZu3omgk/FykiY61KYWYRNrRb0iOulfX9DnV64vNxIKlnEr4Jj+NqYiLdLEcbe69fnWJZiqh5pM4hAZ5QqJOgW4QEI0pAIILA26VNJqNbiYUiwLnK51NaIyMKwCLcf123jYz+kUOXyeE4k1Ij57Jlwva3uRgxcUIBCITxYytu39vpG21oTmofuh5pbJksTm2YTIMFOszqQxwRIPf3xBknhjrUV1NTpaiHMZ+MEIEUICHSaeRThUlnV09EgZOFlaaXI1GnqBTjPmPwwEW5rY6HHVxdhRNRpRJSew6M22Vrt+9i7ksicdvQMyHVBCOzdihR3JWQLwwDrMNFqYVNfa9luVYj3fcrLN3l8xCMSMYAwMs28NpruBOlqTIsnQtDUxITIZIJ6XaaaaTBBJngAIyHhn7/fCYCZGPPS8OV17RpzzFJgH3pMg4C2MHNyHC9rFRYppAbkOM1pkYjqGeuy+Iz0WFeZkRquPXVOECpJQbmVbKVEJgHVCi9lHdMi0Hoch43DTeOy1YhU9XOJKgW/3fL+cJ0hNSJRKADgcuVtxSrl/0fTn2xJjmXpmtjuTgNARFTVzNwjIu9lLVYtclDv/y6ccEAuZubNjHA300YEwGl2w4F4vYAOdEGAc/b+/+9Tw+Non1+TEyPTkpgi2oj7OTEckDyiZKaMoRDmkkWnXRZ6fV2PQx9fj5dt+4vubsaUisQYbrOVnG9bfr2t7Zw+4NF7yhLuQkQG7RzHtDb83PsY5vPx8n27vawpaWtjjJjDHDAsAgACkGnMHgF1K0hoo7/9tuQMOuA8bF0KLRUsSFnDYjoEqXkpzMCQ9HrN31/kjz/OM8fbd5phgFBSngYBAOHn2c+j//ZbRYp2+n1MymIAfR+nI1BIZQ8sLITYx0BEYXzWPlqfWYgQ93MSwet12bvd9/a61glqMWtNRWTMEUzmXgu6UhiN0LwQCZpBIKojzJjqLPH7bwUBMWI6BEbN+vv39dvb+vN+ciJQXldR8zGMiMPjsZ8v15xzGjpKIlWbc+SFJHHOwihyOkkQAzO3NnWACI9+IgaYMUJJiRkCjDhvOT8eOwnXnM7jFKK05pyFI4hg36e7zwn9nGbuFtq1rATgS8pt6K/PY91yLjSan4ee5wjApQoLj64eliUlSaX6nBoB5v7E7T4L6k8c2rIWYn864AMjEfz+owDyUAWfvUc7VdWT8LaVszUkOJuNNuqSEIiJw2HO+DrPbSsA0ZshgyRpbRAzBBznKIUlo05vbeaE/NScuZYsFpBLBtDnLIoF1IMCCJ9iIOzDUrZa5XnPFyQTP+7dARxA1QrLExAiWWZ/2qBgDEtZlpo5U7hfX4uQnEd/+758fp3nMf72b+u60uj6/Vs59z6aft3nsj7nLI5CxFBL7sXOY0w1EdE5ObO6q3qq4gOICTGWtR7nIKI5Zq18XSs/EdHuOQc4IgAiJE6jq46oJe9HTyXpxEfryBaBrfVcOEz2h+Ka9/uBzBERhsNMMvcxEen+GL2PlFhEhGCo//NfdwB8e1tGNyQgAAiYakQkJbejsTwL6Wgay5JSxf2Y7na9lcfXzEVSFdzR7AlXiVwE1BEpGDlBKEAgMgjRc7mkGk/aomog4/3ea00ACIhJQIByotfXtLdxu2bhEIF1y8c5I6IumRHTWsd0D6sXmd2EqVaaGphoWWjNtWT+4+Ngxta8db3eckE4jukWnPDHy3V2CwFEkAThUDOVgvtu7v44xuutQMA///jarhkAp+qWxCyYYVllqdk0fu4thAGjMKXEKaFc03EOxjkHMSETtWFMiI46YrqruarnTEvJvdm2ZQ93t8s1f/w653Dm5zjdVdFKykzCFABq7iSqkRmAn12zcAcmlJwgwMMvr+uvX3czQ+BnRY6IpiLPqJWWrRz7OUcMjfDgC0mSuiATtdZSri/bcsf2+dmJ5npNFs5FMrmrdqBwT0LM3I6JEqUSSjZ1U2hNAcHRvt2WtTA6mOLHPs5Dl5rCg4kw7Am2yEKU6PvLVoX3NsYwQlwXFqKmFIDntKmRk7hFPy1f8vSeSlqWYg7TAT0IwCwCIokcj8HCCAERFoQaZRMkmNPd4clrT4t4RM7sFolxNIPMbQ4k54QRWLKMqapGiGHhCClxKjin2ghAnMMQGR1BvYV6AAaYBRJJgsvL1YYfbZecuXkfGuKXazK1jz/3OWJd4uWK85xqIdcCEUWgH36/DxRcL7Itzwh5OOm6rcddWxs//nZDBj8GMiBo6wpAU/V+95dbHaNbGDO2oZTOepVFrqP59Zr+/GXzQJ2YBAQpLMLdXMABAvs5gp3A94cBwLplckwbp4Il0zhl14EET0xoP8f33zYEQPB+2vtnR4Lrxq01N7JOZrFV6ZcCYdZnyenbS9YZvz46C11XDrOSZXT4rz9OJsxMgDC76fBcBFwA52jGgDlRZlxqOR7RDzWP/K0Swn/99+PlNa+V27Qx3YFqpaPpfiqgv74sHn7/nLdrnm77rgFxdHW3a8nXK/8brH+8n//4e00IfYYH+/D7+2wj/vN+l39SgAfCds1CdN9728e2putr6afN6YZkMHMBImRCEUL25780AsygNyXiAHeP799SZl5rjARTJcB//voilJzEffQ2rmtZV7gs8f/8P77d79NGHGdnEDACBwgfwyOgHR4GLALh97ue+0xFnKMwHaf+9nu+XfPXx0DXy8ofwzBQmESw28wjC0l4F2G0cOb9car5H6e+vqylUJ8258ycUuZfP3cktMCpum1ZJ4YBOC61HEf7vE+dWCogRmL49dXpK1TdDJDw/tnNYiv48dEABWNO9dfLJTGG2d4sVAHojz/a52d//c6/f1s/fhlR0mn//HN/fVkB6N//+/52tWWp9cKEggjLshDD0dRNf3wrt0sePu+PBs6M6BYeMDWsP20kYhMCgxhZ0Aa6hfUIMgwvpRBFTAAIyQLZe9elyrZmFrreSq3xnOV17ddbgaDeVLY0uoHHb3+//vxjf5y2bmXb6uxnShjmkiklmaZfj7Ys0ae602XLOadAb/uZUjoPXVfhQhFDKCJCEn7/ViLIzZZKalRX6eP89mNJJP/+//sAwlLY1N0iEQsROQlLRLiSzWHqbjDmSFkcwtQDjIVefywfHy7C5zEgcNkSJ5CUwIOYPUJbgKKZ1ZQHzGfLmCg/Yw6pEDFttbgrZdwK14VVNcw/f+3braxb6l0B8O21MANYAIqHEkWtaU3sAW3G+/1kwZLlsuQIOE5FgGkR4NvGz0HS+qO2rqPPbS2Isp/dMSQRMxjqcuE1S2jUkvZjiODbj3V0870F+FRf18wMU4OECwqLEkFJ5f41ehsQMdVKkQiYiikLYhBxSuDmbhDuQKSKx2lpgEMQACUhCiYkcETn9BdFhQBQsgggBSK1x/wcumYiYCGgZyfHwxAYQCpxoqm6Vk4Rbbe8JCZcK+nUx6FjOiMlZghnIED0wLP5tkTJfB6j3KRUdM9u4RHH0c1YDTCmA0pORKQa5gqAfcToalOvb8t+9P1or9/W0Gd+3AG8lhTG+13ripLIHJ5LmJTK43MyYFm5VinCqhAewvHyUh+f8fUwErhc01QLAB0GCLPP53qVAM6zr1u6XrNPqymAyCNGHylzQjr3oQ4l821bPr7aNFN14ixAZxtSiEWOu85hf6HEjyirjKnrlhgxMUmVn13XLU2bj68R4Cyi0+ec65Yul1ILEZkkRopzdg9n5FI5PPqYY7g7WiizMFIAHG283i6zh5q26a4xk0pKEa7DUpLQAPW/fV8DQIieJOXAuFzyskhCGZ0/MMZwEbx8L0jkAOfRiREBTMMlRh+3ay2FzqbbJnVZcgqgkCSJwdX3Xa+Xsg/sbRIzsnsoM4bwsuScRIqz4HmgG+uYOWVgWtbL9ZZvl0IeSmEAFtzaiY7jnKkkJOrHNIunOtbdXaOUxGxP6EvObOY2fYyRU4rnVhzcPMyABZkgmGO6Gah7KmQTAoCIj95zeT6r8Oxa1iqIMMZMmFgIMOY0IkIgCGChxBzqXTsXqptERK1J1dsYkgiQFhcR3pa8VA6pn+/nnAYA4fCEHF02oohAkZJ4ZKk1pURmrtMAmQi3bTkfMwJaNw9lplKL6dwfLeXkEaYGgAQAFK6uAXYYEVjgGEoIx/DP/3hcXzZ1dycDB4S2w94sLzHAXi9oZq5WMod4nz0iEcicY0S3YbmwN+ymFngcU2fnwnNoZ85vr2MfX49eEm8l2fTjHGP6fKKUHVl4qnuAj0BSyZyTtO6c2II9fKoGQyoCidR8v7fXbymvlIrs+5NFqufxVHs7SwLACCWhUkSQpyqcZy4ly3PRBxQpUYiUXPJ//K/TjC63bBFzuCXINZn5suV+KGMCQELR7uaTRdhCdbqBiEy1r3tLicdQI59qZoERzEgI4QgA65r7OcfZy2ve1jp7HF9q6iRcKvfHUAMglkSm2kcAhqvVZJKxUC6p5qLHMfp/IuW4bunf/6O303KxCH95vRVhCiSK65LugUdTRAYgDaUIVXUNKMtlzVvJ7qBmU60NdYq9j5ITZjeD3lzDXq8LBT3OQYLzbLSkUlIfCgEly+yTgF6/rYDgBp/3joxTHRk4A4ILUaKIgJpKyQiBNubtslBa++jLLTOXfe+/3tvX18nMv//t8nKpo49UeKmYmI/u9zY9RS1IHgReVwHEdoYbYICaIZC5I+CIkEAATEV675GQUyKAafrtb1st7B4A8PHzvm1ZDW1azuQegFByOo+Zk5i7WTcDAIQn9wfY1JMkd3OHcc6UEgLufbxchREU0QEQ/lqyPCXv8ORAgwcAO3FiZGx9AFjK2KeF077PnJiFIvx4DJ0WGhxcagrDMczU1rWYAROZx9NT4+EAISm5xl9llsAw+toPt6h17ec41fJCNO08lYlTTsc5IoKSAASzzDndPQJJxCMAwM2EU2tKTH2ow18C+whEQDMDwwA0MzS6XtagIPY5vQi+XDIRXVcCim9vSyviGvt9cGI95jNkHmTLko+HUiICKyWVSr//fpFEf/y5t6Mj4eWlIBo/95Nmb9/KmAEYwuAe65qTcNvHttXHY7LAnAaGywKtWz9m4uRGxzH71JTyvg8zqFV60whPlAHIOKZ6WQQDw3AAgHpYiJAOOFsHCqbcjvn+0V+/bSXl+9fxKzxxCo+7dnMnRBauhUKFmEcfAXHsAxfz8CJoEZnx7bXUVc42v/9+ef+5E2IqNPoATG/fi5q+XNbZ5zDjxCWlfT/MITwuSz7b6HMMhZxlXXI7J2Cox2iWmQNwWRMRPR5DErdz6hjXW3WFtveyVQAgouPR5/DbLau6uV+2LILC0NXGtIJiHi+vxcMjQphBYllyP21Mh2aSOAmd+6xbVtOAIIa1ikT8/GypcC3ZZlwu9SlBKJnO4e2cpiGJSxXXSIJlIRFXDRHOSd5/HiL8PEgAkamqOzKNbgDAyP1wc7w/5hP45RB1SYw21REiJ2JBVRuMSFGyiKT9UHeDTFPV1d3QLIzhPFSEtcKTAqbTPMwsOCUAUPfRZ04U4RpeUl4TMsO6ZUloZgH28lYJiUJvl3w0C3dmGd3dHMEuW47piEEIDPF2yyUnjZDpJryfM1UEglryNP350askQAKKbUnnab3P9du2ZPnapzsuW3lSA5V0qjFRSlwkcYQiiNDQcJ81p3+976ZAghr2+lLXIn30Jce2LPbC6f1g5uM+mEiEA+LJpjeN55GOAmvJibFsQIhuiBClcCiOqQ7TI5aFCCOvyTPwCCGZfwUJY1kSCxHQOaeeJsIpZ7conM/eIDwANEJdS87uRil+e3uJCfX7auCfn3s/rBRZazK2l5cqAJRT68OnU4A4UwJ8KcBxvcjH53HuBkHrJSFi63PZEoSGxbalOeL+1a6X9Xar8bkL0zQHxPPoIvL337ejj0SgI0qm3s9vr6Xk9K//fixVhgJh5CxTIz2xEomGqpCAuw73NuvKxH7/Olqq+0PbtJK5FDlHf70uTGiuSeDyrdaVSahQmjHn0PuX1iJpofMYrhJh50dfV9ZpX3d9famtmwfM6WcDIt2WtKzZR4yOhDS6zQFn8zmGA0RYXXy55u+vaRTZ53Sy+wPObuuQTuKoClpWfL0uZ9PPj15r/gL7C26eAcx++7bcH0OIjqYdTeh8vZbvP4qb3T/HH396G9HbudS0rQRA/bTrpby85VxhXZdxxB9/7HszInTzCPRwNZtmJSdJ5G5gqN0CoGQRofPoUwMZA6I8jn/8fglzQPr6upel/Pbt2hpN1aHj7XURQECdExLp9xu/P8bffnt9/ziIMcJLTZIlyMHR3AxG3dLtUs/HfBy9LknHQEIWRAyg+Pgc25aIYxwjVyqbBMp+qrtva36qAvdhbuDqAfh1n3VGrrSU3Jp+fh59WACy0HHqVP/xbQ0PQDmOgYSJ5Ne7pmzXCxPbnOpGEEAanKJP3iqh+bdbIYT7g7SPz3svOZ3NmBkJamURnqpzOHrUTY7ePt7RJs6G97PlzLmCg/XdCSzl5OEQcLtu++N005eXFaX4H/f/+s+WhZZFgGDd0mXL75/98WjCjExn0z7m7Fiey3MICDjugxmuLzUnUo+9+eM+1PzHj6XUdLRuarnQ7HocVjPfLrJU6VN7UyG+XOtxzt41AL4enQmOZuO0S6LR7XYtOenjs+sAXqhkehwtEDDicTQmzibtPtda1uKIEBYpMSIch66VASkcstDjmI7xj39cp9pxjmOfx648/EV42Pz8OJ9Ke05sZsta+TlwYgwA19A+8blHc69LJoJUiAUZ0SIYIAlBlTvFeT4nyC6Mr6+XWnBd6QOChPJC6hYW+znXRZZr0kHotKx5KWmpCcFN4+trXNa0rGwzkpSSwSwGOCAyw/VScsKcGRFGj3YaIXLC8CCkCP/z17FuKQA1eG+KpMi4lSQM7uAWOnXEpBDGbKbHPm63fN3Kedrj65ALl4op8ed9aMRaU47UWu/zuLwUYNGvgYjj2ckl0GHrmlKSMRQAiSkcADCC+jDIUJbEAmZBQoig8ymHchQxx7oWmxYWIlILIwAAqEM4tKFLZnkrj4eqhWRKCZeCtLCpr2vuzXQ6IamBiISPOR0kcOBXd/dgokScxcaw2620cxLQugix6TBAWpfSDt33iODrVda1TLNAW3Ia04c6i5i6KxDgeYxl4VzSNHsOAiK8LLQZn0NBYruwBx6nA2BexCLO04AiMa+JgmQoCEUf57JxgNWCc4bNJ0t4hiEAjmmEkUsuRWxqGKbCw9Udmfnnz3NZJScSorBwn7dr6aPDwu10EZKcpoarI+FzE5YSpUyvL2UMU7PrNe97B/G//32NwKUQXPNT2iCJ3aiWRIQREWBhSIjvX/t5xJrK21tpXXXGfhhEWNjLrbTWWcSMH/fRzi6CnNPxaHxNY6pqlIymMbtdtmLqwtKPcbS+bbWr9TFEYIIC0LcfKwA+Hh1iZpHHY9aS3J1ZploWaXt/PNq37xd3IPKcfX+M7SrmKpwC6ONz7C3WS1lXniPeP3qp/PqWEanm1I/zt983yfiv//aff/Rl5X/8/RJuwpQTR0CfNtTvj7Ys+eVSbUDrAzXiCd9lUnMPJ8ZnGWdMJeanPCzCkSKVhEAOCgHwLJgEhAMyliwAIEzCiBApcx8DMaUsY9iYZvGXD93MUqZw6uccvZelhEI7JxECeDidfbpZLokFdYSBPW9el7UmxlJl/V49FAMj8HFaShQLmIUwmkGtiZDmHDmRvP8cj8f5tGBcFppurc9cCNDWW0XMbvOxw1Tt93t4cJZnHNEdA4IRciJH/4tzM4OEhcnAh2FgfpxjW8QNu7qHhQxzVPS6LfsJWbwsFGRqQzhC3fqEOczkeltqwtYdgtjjdqmPjogUIc+nxGwI82gThhMzs1QMZmCN1mbKnFKeTSXBehWdfnYf3UQo17xk+V7ZPIxg2QCVz1NNey6FBfs897sSkcFf8bB1TW4wnCSBCFZhNWWOoQMQkPPx0D//ddaEuRZAad2XVT4eI8Cl1KP52XtrnUQSyeg2beSUyYAJ65JVIUni53IbiADP85Ba1pRsRJgCwl9mKKRtK9trbn2mQjkBCZspALjBHKOUNdVkhwI+KdolQpctJSGKuNaSKX29a5IleL/vjftyXeT6IgC4XepQX2qIgEbgNHdggqWkxBSYfn4dSFSKnI9jIowUfcypljMnEewmSBMCABl4TH0cvWa5f45a0hhOinOaNlwXYwJTbbu9btk0Am2G9DEDlEH6mD1MCn7/flG1c+qyZDG/bqub9vMMN0RXNSqhwxzi5a0sK5vRds29nVLkshWbzTWSSGJ1QJ96va7yIufjzDnVnG3GY9+Z5XJZ9+P4a/iCgSzxjJ+Gzz5SkpR4zmhHW9cCARFpP9whCKgd6gDuQKBM1LqpmZk/edGS+GzdPFhkW7OaWcQzaieM5nEcnRizkJq/vFwfx3kek5FSzU/p4HOHXy8CEH+puJij+zhdMc5jqDAKMuG68LCZWAhgdP96dPcQSR7wBPpGxOgTiZGAmZ9tLEB4aomImIiJYKiZBzFDIALWnKXweXS3kMzhYOa9N3QkwlrTNI+Ax+PsfTzf16WmlDECx9DExAwiKPx0H7owllLM4Hgcb79dtiJCFOYe8Wyi1SUJ0+f7oy4rAH09TjMP8tstocNBzhlyFUQjTnN6ylBXOnZLJLNrO50kzXa8XHOqaIbnrkxsFkw8h7vDsY/WdVnk8/24XhZ37n3UJKHAEvfHJEbt2prlnEa3Nvq65kQ8YrpZXet0d4OAAQSqYRrC9P551iKMcJzDPbZrdQszQwlCavsApAAvpQSEu2VCz/y4dxZIOekwBljWAuhTo2ZZlvrPP95Tkm/XfMlbSowAvecnqfpSM5pFWK7Uh3187MuakXhOTZkd49nHCcbP+wmAtaSnintYqNvtIu2YOTFzjGEiGK5Jcn1Zzj4CHMzn9GfVtDd/4h5//bm/va1AsK45HD2ide1TIRA8mHGOcIttyY9H3/cOEaUkAGhNHRAC90dLwpSwUurNcuY2bQ5dFkLi/WihfyFa3HSqXbdcAByw9wZO+4HTfBqwPiuSWBdGAXSvSw4HnW4arSs+E6lKLEjkl2v6/OhJsC5J1Uy9hfaGtyubh7mr6+jq5hBo/jRLoqltl2ruEOYIfU4IfC5+EZ2FDPycvdT0+2+v4S4CAMYca5VwnqaMwADPGenfflt+fc6v93NbCxPWWi5ren/f6yJ92HEoIZYSObEghWnNlABL4Tn062POCQd1SrAsCRBbH6+3pY2Wi/z2Y/v1q+2PAeEpS1rTyy0J43xGb4SfKc+lEATUJGstAZ4KbjVfVtE5VN3MdXQNyJUys6tcXhIg3h/4+d7cMCWczVLh9VLAPZCCUIcyJxES4qaTGYmQGZhhnGoTiPG5w7jc6vRZCueSZlcbruo5MQC0Ns6zMzGglzWF09QGGIjh5uZWOufElGOq/u1vL/evcez97XUVznNMQ+rTAkmSeHhYcGZUpYSqZhNRU4Sd+wSTiOjdx2jrJiIc4Zfbum0ZdK5FSk1f+wSgZWV01HBhKkxf95EumREJYSl0uSRh7l0jPADXIiE4RgT85RZwB52YRB73TuyS6JyjXtLm+bqVl1vmfAEEDgjM59G7z7UmSTyH9qntMFM80f7553k2Kzmdh033+zQMuq0VAs4GZgZmWxX0FBYAsDftQ//++6qu4PH9xw0RDSwijr1/fozXFZZr3T/Hucc4jZBUtbURGHVJInQecwyQIjP0+DLGWNf8+TkvVxnDdPptk92wNfce+znXC2XGjw977P23H7V+e2mmw+brtRahqTG0MxSedq0ofytfu/782VgEPdw0CROhqYOgEGLg21tF56/9GG26Bz1RmeQA5euutwVKku/frq21VAuznmZEacz5mPa3vy/uhlyHzsutfn3NdcsGFgTMeFn549779GMf379fhOSykSudnUqVpSadCu7H2c7T1ksWjlqzAxpMd1tKthUtiCn6tDmdOMzMHJh8zkESCxYEdLd+2tQQYUInxNs1P/aHCK916QO+vmYf5gEe9NjdkFKCuqTex7IyMnzdTZ6S3EvG0NaUiMx1WdLQtKxpquaMLy+1ddM554jj6LkktHSe92l7qcjoEEWEHvueKClEP/vlksecqj466ujrTW5rGm9eS04SCJgK15V++7H95//aPz5PU38W8aZaOBeIVHC66cRLqiJsqmPa/T6Z8e1aLitLQibJaQmwnqdNZIKlYrjXzAiaM4vb379v4DDbxHCPOA51h8+vWZvb1Ej++lK109HbfR/3x8wiiKHmyy2FOwF4IAamxE9oVGCUJe97r0VSEkBiBPCoFUe3tVLOC/P8/OrP7AwQAIJOnRbEGODLms82WxuyZinpcT/7Md1x9smCOcuz/fEUJj66gsFUXy/1+rZ+vu8lyetruV6z6XC3t281LCQDYaDQUlJY+DCdTkDfvxdh0qFCyBm/7r4fttR09lFTevIrR0AfyIi3q0CEz5ge52GEOIYnIDN73DVVCuD7w5Bh9iBPiLatxEXmiDlnTgJAY/haEcN+vC5HopSoD/v82C/X+vqSzuNwj6VwBCcBd68v63k2AL1cGDH3ZqahGkCIwOfp59HUA5Eks6m7ByEgIDANVTSgwHb4sglACLNH9K5ERA5hERHLwpkJCNzjfswx7Pn5L4vUJR3HEKGc0MzcYYx4PJraUwcFx66SmJhqkZLxstX3z7MPowgdc73mfR/nqZdbkkTHw6eaCJiacIrQUsGddUZv4zjnsuSUJWXfd3vSKh97j4BSRDUiZgBKoqF2nPr4fKSUUuUI6N1zkiRpTk059abWXBJiMg2XxOf0+yPuj7FdMnPs+3AHU8ul5GLnYYjAFHUpOcHLy4IYex8TAgn3r367lOsl76dBxLrRsyLA4msSRk45ECJlcp/nY845b28FAkfXsiTTuS3pcegchkC9TwRzYwS6LihZmJ6mdj5OPZuHawCMrikhEMfUfQzA55ib5lBkjMDR5lTPOVIOIVnKcrTmYNdbFUEw1znNyNRS4j66AiZxAgAQMz+PiUBq0V1d/QpVEocFEQMgEW61OAQLqsn9U+uaI3wOY/Lrdp1jqMccoW6TQ5CI5DyMRYVczUphYqD8lzrn+++bq/ep68rxfUlCYBMB3AiEVe08h7BUyccx26nTfN3Kee9jaCr8BOp5PBFIoep9WhbCgH5OIjaPCJhmSZJqAGCAI8FTZUEMyyolMWBEBDrNgAgnxnMfAejh5sREPt2VktA0V3VRm92PPnIiSXl0HWMyMgSc+yDkOa3WLMxEmDK5QRueBX/9PLdrYQFJYqpm43rbbFLKwIlKKefhsi0MsKoOZlg2/m2rH1+9jy7Ja/Y57HJN1wvmUsLx43O0Gfv9cARJpNPNzVmI2KaqhweYqQPiZAsDx2zibvbM6wsbgfkALG0Cs2fBr8e4XJiXpQT4fdrwVLLklAUQUh+7JKFgcosiavFyvbpDqKVUAJEJdMxAbMPVvBicEL4EM1DEdUvlwoSQKh3JTxqSWN1KLTnpUtlC7m3UJL99Tzrtz1/jbJaSLCuphkNc1ny9pNHnxEjE28oE8PVob69YlioEj338+mOC4+fdH4yyH+uyIOGv9z3lIMLWOyMhkDmL+jkfuRYkOc7p6gToAJJEAEiCr+U4XFJQrqN7aza7qZkwpcTACE/Od/Lf/+claD7u8+tfOzNyRi6wSi2FhyKcKgncnmpecnNZkg77+urouK7Xt0v93/+3b/f+67/+eRLQ//F/f4nRP+82wxJ1s+IOZ/cAzJl8UJgjGbhLEnKyHscYEV4L2YzPY9bEIvx1H2cfI9lozzc4Hd0ej3a7FPPw8FpyMwuL6yUtRZKgkAMDp7S/zzGsljKav76UZRM16G1QZQscc0YYnZgYEci6IWBOAuTksBUhwfK9gEXrUGV7/9qPP/vrbZnq96M7RE5s089jLCunwqXIH/86vr7Ol5cNkPrZLmtOWdY1fXydbppKqmU5955zQmFgejaPEkMpfLnV4xjWbfSBSECIiGGel4RIBEgMOs0CSk0W2+N+fnw+/u3vb2vOj3snpGmahADB4qmigsx423jd1nYYAZzq+z7CozclwFmE+CkP1svGqjanEaFZQDgaRub9PJlYKLU2PBwx8FktcJjqidDUAxDcRcgseh/bdZmqc1pCnmOmRFJE+yTCJ2eOJZ/HmZb6vNUQQLi7RWg8F5vC5A5IgIy39dIPrXVR75LIDZBg27K7AUHOQshnayLYRs8pXV8XpsiCQ4eOWEud05l4NFPXXJKq+1SbBkAGgaFrkXUVSdSmzq7tUGGWBKUwErc2DsGyFAr4/bcLEAAAmOUqON3bHH2WlEiwnd0dPj67I4vIcXRm6iO4gkeoR02EzLm4JOxN10tOyGMqCvmMs80xNAwvN9n3Pqf1Bjp9THUPEVwWeRaGz2O0PmqtCaVDU3MiMDfAmGp7U0I42ny9LaYmmbaX6ggRVFAeX31qXK9bFnI1jNA2ECkxn314UGaZOg3x89fRD0+ZdPjxGD++r4/7zkkQ+DyPlQgJktB5jJQoUI9Tc0LmxAiSgQG/vVz+9evrry8JeM6Mp6k+IXah6uaBBLPbesnn0CTw9lr+/ONE4Na1rrKV+uvXfVgQSRL2ADVlQnAExM/3JplzYogApr2Ny5anghSsGbPmL9cIx2BG6s9+f4AbsIgkOs4pmZBQmI5uDhThOgEAi+DoDjOSkIbpVErISb5dCiGYQxLpc87pxKBzZikY6OYsjEDHozGyutatIAI4BMcYqjazCEEkwXNvAGDhEbFdC2FcN1lKQgwg6F1T4lqyq6kDEjyvIu3Qo/ftUkMBhabFVJUEmVG2fL2mqVYKh0Nrvi4k5BC8H6dwNmDE+LqPEFo3iQk+cVvKTNFaf9wbAqJRGLZmIvzYNcA97DymsCQh4bQt/LifJTELfz7Gtiw5UetjW0sW+fvf16EdCYSIAQwoPAhR1c9m10s+ztH63ASQ+HrJGHGccZ4DEJcqY472GCyChMuSJCYnehYcn5aipUhKYgYQ0M8wtcRACdUnQKhaP9UmtKnTPWdpfQSgmy6bbLeMjstCLCiCYygjrYVbV21WCm9Ler2s//7v77NHWZIGpCyf98e+z4hwD3Aa5+EsL68JIrZcusfn3qOBjaFTkb0uBA4x4fcfCyVpx1xy7Kd/fGlKcPbhWq41nWcfPhmqJDYLNQQGs3iql8FjDM8pdptFODEtSx42j71TMCBgin6fzFRyCjQm//ZWb5dEiO7QeyfilKg3N8DPz5Yy5BLg+LItAf1r7wPyU0tcKtuIMfDHa43QafH6UtdKpqaTjl0/H0093l4yYnl5Se+fZhHmx7atiLnPfr1WJAKI0b2mPCbsdiCBgZKHBwCQG5ojJ83iiHK/G0bkFEBy/9JaYS3oClno87OXmsZ5OubrWn59nN/fyu8/ylQHozRLP01ZUYCECiY3+PU4++zrbf0B9bEbBNp8wjXVwZkTE/Wp7ZzXG8nEx2nugRFVcFlFdZgTSyahNTOQ9zFLJXCUDMQyHzG6XtclcZozPt6HB+Qsx+kUDOHt0ByVePCKjPSv/3V8ZlquIsm19++/rYKVYhLzVNVhtPF+TFUIoPu7wS2YYduoJNHhQH+tiswjZZYEAXacaqYiaZIhqoVzhDpL4stycYtH0wiQTJTxsiUA/PlrH9PXnJYVRdLj3lIWdDfLZ9fhjx9vVTJN7cyoFk8G058/+/7oCOaTx7SpWmsmQBa+Xuu9dQ6ggDncvF2vpabl86uvWybE1o2FCf1rV064Lmm7RO/jkspSs4XHsHWxf/s95RyP+3AHQxKJQDCnZCAEkHCqPu7Rm0bgdDTTZWVz2pJoQJ9TmNyDAMHp8RhTIdFzq6qSZRxnFuA1J6LjVAAnNGJWi96NDA6eKWXMECHjaJO8riyZ+jBOBDBHU6Q8dKYU6Dh6APmcMJulbLmADkeMMeI89NvbgoJvt9zGhoFDYy+iY5o6ASHS1+d5vS4AwfI0NwMgqoJOdQtVKDm3NkghMdm0OX1b5ZJzqWlb5d9+X0wh3KaqlCRc2jkMI6dECK5QMt/v/fHeAYKQI+LttTxBjbUwEzJhBGgzJBLyvDxtv3B2v+/qFkWYkPbHGBrRbXRiBleVTqkQIjDCckVwcM37MSBaWfKTx/o4FIkJiYTO47xdqppK4tu1XraUCDnnpxj7iagjCiH4/nY9z/bx0c0iJcoZliK55D71PMZjNyQkCgvjxNpVVRlBjEqiCFD1MQ32YEFnY0bTUDAIDUME6MlU3UxT4tFNzUsVZjxbe71lgjCHMdTckwg62/A5PCAUXRIR4VMsNbp/QUuVQ7Gf1tpARiI8m3KCpeBSGSZPVQ1vXR+7jmklPyW8pOodLALXlZclnty6XDjnJBLM8tibg2/bYurTIILuX33xVKuYhulzgIijdzNY1iwUgPT56Czcm/mEJNRPSwUvF96S3B/62I/LNb/cik5bSsoiU819rOuCRK1PVWOmfR/LwoiBADoUmZ6WAERzjEDHIAJ+uZaaxKxeLokZn6YFVTXzbSs+LOc8zc3GaOoa68Zfn21b0hgdJa01g+lQf6K4kEQYc2YNTaU+PkZrrS4y1ceMxLMPJRThGDER03Ytx9laH7e8afPj7KUkCrFpc56Ss1NCjJxTKDDGUHOLWuXXZ48AYRpD1y0rxhx2hAJ53aQknhkD4npZTBEZgyYLJuY53cITp+PQ1nTZcma2oYjx+rISR5/jPFXRc5LWW82sT64v4ugWQMTTFRgkDM4xc5bbUgF6NCWgJImJHf4vAA7jX7YwR34OfTRKJv/rGAYilKW8f+6AAIiIEOG5FBFqxzD8C+NLgURkFrNruCMzBZoZIeSazr17FkBMRcLDPJgQII5zIJGImMbzzGkekoRZVKePOS0AKU/RxM+lSSpEgK6R13q9pq+Pti1J2DH85ZaFE35fU2uRc06JEfXlJoi5j8Ho27XomNc1cybX0BXtjLqkQ93DkQCRAIIQpgcCEAYigYdqRKCbRVBrZubhXpdEkC3cyB29CCXhafY44ecfj8Tyj3+r334gOAc0m0a5pCTmeBwjc7y+ljF59kct2YA+7p2YVsExNaeUmDNTb1MSXtZSM6MjoyE5cWrHPE4tC5+tp0Qa9v6wPz7GmtPe50P667bsp9+P0U9lImCShOuaA2g/z5IyxdPs5DmVUkHEfeqpcJzwuCtjYgpz28pWKul8MoMg1MJRI+YYtWQiqpe17SeAgAMRBuCjDThayrlmoQhmzSWpY5gffZo7REQAFy4rzubnYbnqslS0WDfbf2H71LxQWvA4WztGrpIq1SrtGF3bsiR17Z2ypMqcU9U5P361pfxY6PX/9tvF1dnGdE9iRTpG+ec/nQmA+O01te7vP++1pB/f+ftr7Q0S+O12DfOa/eW2hsPn/SiCRLSfQMSzR28u8hx20sttdfdpzkThfjQlyo/TSuLHfawLK6LvhpRSEWZYrpA5MiMgaMfz0AiqNfcx9sdcavLQzBLIfTjYuNRKnMzUuyMiQ0wPYUSk/TGWkn+8lhFx9O7TISITc8qPoy9r+puIFImIOytz2i5cU6hy65EL1YVzXpjh4/1sj1aq3F6XItTO0YaR0DWnP87pYEnSs+d0HiMCUk7uGg4ly/51eND3b685CSONocDEiA7uEQGBAKqKiV/fVhJIwLuOusoMYEYLz5nDfd97rokIwj0cx1Qz0wmAFAjhEY4GmJiEyXI6WkMiJjAzIVbzjJwLu8Kc82xjW9bbdd3PMboSEzFDuGtYV48oOc05RYiJACALdaY5FRyY8Bn0fb7jzqGC7GacwKbpDNVJHGHY+0yJUyZVZKRaBFHa7ACBgCnjdsuz6/vXcbmVKqmfU5Ic5xwzSoUA92mEsu+aKyOHa7ATCObE+97DQDIKGxgsma/Xcuzx9397YUIbSonHVARG9JxpP0egl7q0fSIioJy9AVIW6n1w4jCfY2oRU2N202jmwd5afw713AIIqshxGii00wBAOh6HIcVjP3IqQmhmRDyGte42x5it1KTNLq/lhvXr3szdTOdQDShjMol5zOkp4+X2XNW2xCmL5BsDxVQ1D0lJDTLL49EJaVllTG/HMPBcpD/ibD7mCKumcL8PInKCfW9jeFni9aUej+YRY9qYnhIxsZBI9qOPtaYx58u1CKd975etoMflUj7f+zQFoMScsxxfLd3qMBttXK/lycVwcmGcQ//19QkY67a0e7u+LEO1LMlG9DEBVTITyey6XfJ+djclqK5TljQ0PGxZiajYcCQO6IBARKruM/qAumQmOk43BqAQAQRWe8LC4+wuRODuHiwoIn1vCcvlVgoFAjzOGQG9myQaU/VuRCBJxtCpMU0p4ZzTzSUhAHlYAbZpjpGypESEQZReXpZtS499J4fEmFJqUxPLM07YzcztuiwE2Nrc25lzYqeU6P5ox6HLUhLDWpmIGL2H3/d2yfmy1TnjbHaeWpYMIH/82eoiaS2q+vXRXIGFiCkAASWnZRzhbQDCfswkTIwAIYnfvq3CNNVb71UyBBORu6Unxz3InaqIuwd4SsyIOv3PX23f1Ty2jS9bggVEAgHvDyuypkxBcLttpvD1OdY1M4Ea27X8eu9zPj/x4RF1rbnGeBy5ci4yus7prh4eSETC+6MtqwiJq+XC6ZL54Jx4W7OjI8ZoerkkZu+HfbstgPH+ddi0nJNDzGmETJDOY0aO337b9n0c55Qs93tn5iQ2u0PwcSiLCOOccP9sESSFy5LP3fejO4KAi5NjXiuVSqrRTi3C4G4jchIIdI39HOfoInw+PBc6zmEWomlb0nmM949zXcVm9Kbt1KVWhJlWyQmp1OOcfU6ASExE1NoAssQ1V7zfDwdYak7CHx8nBNaSZo/j1BJoBseptSAI3G7lUurU2fosOe27j2FM8Tjc0AjNHH9+jOPLgQIyGNi2iQi1c56n9jHHANf58X5POan7tiVhZyI3KNc8VI/el62qmvUBjsMUGW83LpK+vqxKuENvdt3KHz+P1ul2yx+PnhP97R+buZVaksix9+urLJf0cR8ozytiekBXt5RZu0sSI7+merHaTQHiPPqy5rqyu5eS59RceU6nwD5VGuYkKal7AKMGtKY5Q1q4G4YGoj0JImebbnx0S1kY5Nj1suIYo/deJAi3+72dX40SbNvl4489SS4Z65Ifj6E2NlmfskIkmdbVkCyuNd++y/k1z+b7McecAMjEjLOstK3ZFRgyEww/iYkgMFC7M+Pnca5rEsZ1pTikTzXD3sfja87K7dQAqFUYyMzH8DCw4TkhEf/xr6MuAsBusV0YGSoXwNHN2zBOnC8yTC+rufHUcIzeYN9nFm7d1ksRSL9+7c+r77Ks59HPbishEanO3o2Yumlrul1kuyWpwIzTAgm2pYbjfoxSOWexaedxvt3q6y0fD//1fpoBMZi7BxUpSXAMbc2OfRAiCV1eKxL2bvc4DCOnDA6L5OCYpn3GUlJvJrLkgk/wzetLckg2sbXToxNCltiuS4SPNoa52pAkc7ok7qaPFknDLWyJ65U5IbiNgcIghKbuEClRa+YDSs5udj/O1+u6LWUOi+mypcsi93tn4lr4VDXAsCd9Cfd9PGsao3cIfF5tliIROHUCGQL03RJ6a1qXXFLk7GvGeYxpCihmwMzhoWEIyECjRQQQgZrpRMK/AAX77r/ez1pSO6xSCoN64f0+Pj9nwiDx60t1022llKCPqFkE0JxyEX9aNRExoFZmIfDw8Dk8HOuCLDiUAvjc1dQvlzLGQIY5fGU5d6spHMHdXl9zOAQgM7s+8d72/Kj88a/Hti3bKsKjcKw1EVHOQGRv19IueLbImUvh/VTEUC33h96/ZpjZCAQED8J49unCg4ByQkAQSSKpHbP1uWAeh2Nl/EuE4rWWz6/zPC1l6X3mRFVkKUlA3pEsLMLNnAWfSE0WJnIMBAOdbhZEeLaZhIVpDD+fJy+EdUv7Y37dh2qEwxzPwU3YNGaKgLPpt9el1rwf87JKO7UUspgiwkztHG4wuxJTSjzakzOrgZ6TWMRaZH9oO0ddax9KSG3vOsMtTM0ikBgBU4qncScLbAvCkiJI52Tm6enPPz9zKmOYquWaGGMMm9NfbqWdJykhUjv0cklfj/Z42GVNa5XeTNVq5tEmhHtYYl5yCYPRpqk7zBBal+U8jrNNQz+OkUSCUm/eezuPYRaAkAovl2TqgUZI4XS5LQE+5rN0PGGtDnTfW6nbsACfbMjC7j7GNLWl5ohImfd7u1w2yTy6Px4t5VFzhSBkPw9tzc6mgHS7MBKbhTuY4j4aCVxuZY5RCknKTATsZaltDgQPB3PIJWVJPRohJMI5FcDbqRS+bLQUJqfHYz4enREVcHrMGaPP27WcXUVcwimsm2/XhICMUIvs+xw+manWGvAkRrmk56wQ3A2fdnvAAFQzJgTEnEWEiKAUVCMMzIkgIglGZm2GDI991AoogIAQGACqloRY0MzDYKrlnHLhMdUDmJCKAAIjEWIAuk0RBkBEypmJYFoIQTgAWjj0U3NKqohoY2iulFdKKf72t4XC5mjLsuynyt++C3OQXD8+RpiVxDbHupVDKMJEwma4k54xx3MYYTnnKTZ0EiKLUMBTtBkRTETCZU3hZmbM3NoQYvd4PjTuzkSoJkEL8zxDcjrOoyxTB/76V3t5EfRJrOrLx/vAJBG+lETo4W6Oaan7fu6np8xz6KGExBrocxIjMIoQSCB4zUnnHGNIDotQnazxelum2RjTDfqMleNWFyNTHVMJECkTEet0J5xmx3EiwJyDSCSzqp3eb1syM7N23+WPn1oq1a2OYOZ0uWU0FeYgmu5jjJKX89D741zWOtUpADCNPkwt52wRS0ISmaE+da3l5TWrOgBMAEQADCTiREB+PExHTEdwTDTvB/z9t4T/I77eCQxz5q/ZEQE5cpZ14a0ubfo0I2Qbs5tBKXHu2yUH4P/n//uf65KYZcz+40f++IRjxO8/Ln/8aZDtaOfr64XYjsckYqIkAimgByHpwvDzV9fAY+5bTSJkqiiESIyRSio5d5tzGhNuazIHGa5zOka5JEAY3SCgDRhu04xBSmEPxQKS85x4fExE0BkAMFV1Rs40hnUbkmkfQwNEyIzfP1tdLBdxw/0cKOwQJbMZ7zr78CxSxPvwIH59WZeKsxsGkMC//VhsGiFe1+t///NgxCzx9x/1GNB6m/NYSmWK24124jH0fj+bUHioArmDxOVlmWNOc3NAj3Bn4lTYFFzDTJmllpwF394WApsO918954zhibPbVHMkIMIxrDMScs4ZCQE8p2T0rEBwqLtGHwqAZ5saruYERExmNqebozAqdEmiZu6AALVy73q0Pqb6HikREwU6IYbHdA3XUsQcEL0ueZ6aMvWpFJgSh0eYM+J+H2MYIEQAEpt1xL9IfoFg4dpDDJc1316Wfe8B7q6JQRhtGKLMPoUpwAEwpZxEcqY5LCByoczsw5BwmgaFh/81IFcwm+6AASWxacxpEZFzdY129Jqzqx8PLUtaV9rWlRFm0/TUukeYqXl4hE0vWRgFYqaaRteSuA8zDVpkTPOpeWEHcwjJaZ5zuGGKdSlzWD9nqZmFhpkk3O9jaKxbev51RKCUkUlYdEzz8O7hMVWvt0VI/vzznpJ4QAACxFJKQDxZ5O6wLjkAxrR2TBu2ZEGLGdrVtsqgjomPfZzn/P623R8dgYiXvY2lSq3psU8kLBWY0nm4MPU2Uk3kTEAkNHXaSOdhzHyeExCYYzQwOs6pOSc0+vxqZUElLJmFeercVhldx3BmvFzKsQ9EmFNjYq5cckqM64afewd4RhpjXfJ5H0/2lY5pQX2omi9bWq/l/tkI8fOzT50vt4WIUsI2dMwnazMXxGWjX3cLD7dQcABgpgBcKwO4ZSAisLjehInv99MjwiHUASkCSBAQtWvKgoRzGAu6GSKOoQSQl3zu3SakLOc5R1cA7E3ZgAhMfVnS9ZJ7eRoP6OhTEr5uBQFyESae00uqTy5AQIwZKfHPX40Jt2vyAfvezIIYf/txIaBjH2dXYnt5FQgohRyVEc3AXdEhEXGGz733oZJgrUlIavVaBQWYEyFFBBPmJMQUEec5j3MQR8pyERaMkhMgIgVTmEWc5D7cWDX0YdtKiGBqAThnNJ1HG2WrNTMStXMQ8LdXMXMRMvU+Z5acJX1/254x0pyTDhXAt9cC7sIcqUwbl02OHcDdA3vzygbm6yJSJMx0KDomJhCY7gGGiL15uKZMQrBWuF42CM8JWcTNDiQhL0nykj4/HpR5LYmDP77OiFhyZoKzKQsKMidk4TE8dPRuECAio9vtVljYMcrC52Oak7vjxMQ5XejTd3XbtkIYbpEXIYjwkMQOmBKO4aMrAgSaMZSaqiRTP8fIib69LAD4/vNjKiECQeQEz0TqnJoWAQTtrgYOloVBcUwlpuVKr7ctERnqtq79nIAOwJc1t2nD5uWlXr+niOjN24zPx3m9VGEuFY8PfzymTyDE2yZqWJZCoseYDICZZQkSWlZ6/3kS5ooyldyBhNZ13XeTki0ACSkhMM0BP3+26xUQIZwKyVLTf7zvCUagB9nLdUvCSdQTnt1nN8Y01Xv3OcHDYEtLRXWe3Y9HS1muW25nF4FrWRNIbzMX2kp6fI5E7BREQYFAHAiDJhL3oQDk6q+31JD3x3heFnNJalgSEyISEpFbqMNS5XEfDWattNbcHtHdHfDxNQHg5SWWjfuB7++P//n76//5v/8/vh6ff37uZ4OSUn1JKae//9u3//XHf2vg7fKaQf/H91vh9fPnA4FYaH/04+gvr2sOUAsqsBYIWc9HSwsTE0ybMz7ux99/XM8vbY853HpXYpzT+2mlZCmCiI/7cAsSScKEcbstLKRuBh7G7z/7GCoJe6djN0RciB9t7C0A/XJJqvRo45omTFyvCYmc9fLK15X/4z+aGmYOiyi1th5t6not122dEVMt5VQqQgecsabcp0bw0Vs74vEw1YyEZ7Np/kJRc5ldz9HqkhMhM1oQUHzdz9t2eX1544THMUqlbZM2HAGua3EPU2OhdavtUCQAiulmpr1pzkJCzAgQffpxKqCtF04pCRKgfdyPFyyIUUqpa973cTxGKrga96ZPwet5qGpEhDDONs82cspC3OegoG2tl0stBVJGRryHPx7jZUvE3poSMyeIiKOpht1uC6OYzTbsek1M1M/hHgGaGZVFY3hETkL0/MI8ZxApIObR1zW/Xdf91PevCQHazTUEqWSqBda1AMAcerYuSdxnBBLlROLuHnAc0yOYmYUQcVkSswLYssiMCZOYGFFrxQly7GP2EOFaUjDsh2IoM7pHSWwexIAckuN//thKFsAADCEGxNZNp/Y+z9Pv3ZYtI4AZ7I/hDpKkVmlj9qHrkretgGMgmjsBmAcQcoh1mC1s+lI5HJdav76aWVmWIjwva1KP4+xrFR1WuSwvAeCJn8sviOzX6/q+tXYaBdQn9awdAZ6ECIEYc+F4brbR6kV0YDioGSs52FTT6ePUp6hNktQaOaNZHH0uORxm3cSmH/vUGRDzsc+lprqmJKiqPYwImdKycjzFb+x9mFmURRJhYv7q85nXDiF0AMNS5HorqQrEnKoeAGT7GW5RgjHwbCdj1jnVIpXcz27hpaacqZTcmpqamkJQICGGW5xnGyeoBiAgk3qo2e11RXBtVhKXVGqRqTZ1qBIGlBwJU0f6/HqMziWhab+9LHUlAzxOzakIw9H1OKabn8OOfYLiUkaf/vjUv/2+HH2uOamFEE71497V7Xpbp9pUw5peXyoxj66mYO5DdY5IImUprjG66nTt5gbHo0mS00cfyghjGgbWLH1aQBBFH1MSz2ltRq58e1nCvaHXIkN1PwYgjmHDovW+bSUl6sMDgkg8po7JjO5+nPMZuUJCdbu9LJIZEbvOkpeUsqullEwNDVKScqPznETYz8GE10tRhWPvqaAQq/rXfXz/tppNSlDX+vXz+PM4yirLWgRpqppFLaJNW5+caH8MYp5THUISrZWLJJ2+XPLQqeoRqBbqEfGsQCIRuPqc/u1vGwoARMJ4fcks2JrqMMkc4aPpnHa0vtacMwc4RCDCspR4mg4gnrOnAEwizy27zqeOw5Do5VYgwBxbH33OWnmpZT/n1AksphNYEJ2FW5/CTOQpYlvTuuRHm65zEKSSEWFdCxG8XFhqYVO34UslBFiqDIn7/cFUDem+j8xiw7uauUtKQrSfM5AgiBOyIAYFUmFobgCIRJLw3BWZMCCXRMRPhWxE6ARFZw5zHNOv6/rj9a3+Hdr4mhFg2I/TIOVUPr5MthhdXX3dMKfazvZ4fNW6SS4ptO1jTmdmZCoeNaFqZ6p9WEQYYTvUppLg5+MUkdfX2rq5BxC6IiK8veS3lBUgrVmeGgNywkGC25rm8MfRAYKIW5+IVkG4wNlnYsRFzp7f70pJgHBZKOeVkH6+32fX1+vmGkw8gD6/9sTl7dtr67OdnYmIMNcsC4AG6EylMvo4dM4YgjmECCigta5qy5pVPdx8hCmGeRFcS3rZ+LJQFblk5W+emBPEUut+6nEcFUvUZQ6LiDCfZuCQMzoMSOXeJyho19MdooOFBwJBOP7nf05VOOZDRxC0x537CesmnNJQ7W1P9SKSPj9PQwNj7TMz6TBBmhqmsSQJx8fZUaIuMocdQxkIIsqW1lXaOc9D3cIcU82jdwTqYw6LnBAQG+lWMlG+7+fXe6sLchKdXhObwedXL0tiBg1dag4EdUO1QE5rTpn308x7zQs5LQnAYH/My4V+25aP8OM01zAPYcoLuxthgEdi+u17uqzCFJ7mutWfX+5q1zX11q+bfP+xmeEf/zx6d0DQ2Z+upZSTCBCGO+DTMI3o5oQYQqOPVRYLb+NkxnWjH9tSEn11lbzg5MuWH8c8jwYAo9taMgkRq7v/JQIDh8AsBTjO1i0gJxnTzGNaCALjM9AI5rrWYhqP+0lMGLEUQYYIN4WIMHMEoEJE8jwWPLfTmSklYMLe5v1xXtYyzLKgo4lI73OqAgKAC5JFRHgSntOBHBF1xmWlYCwZf3xbRofrdv31ecwxzJQ4NBAwTNWNu87v39brVt4/2ufjhIiS+RmaCwtAdMfjGL2Pl9t1THuc/bIs25b2s+VaiQkTpiT3R1O3y/Vy3DsxumPrul0yIJz7gIjWABFYcHRV81xLmPcW4OccOs0DwACmWlpzqvX49WXqQViJUhYMihzhM1V29GmKDOrRvvrrj7UP7dMQKdTUgwgRsbWh4MRP1BGEuZuXWkRkyWld69k0iVD4WvPvv19Itvdfu07/9rYSlc977zMeX93d/+1vb/lZRflsvcqyJX8KogG/7mN0R47HMYBwWEC3x95Z4LpeZrfemoWXzNuloANuLJPCDAF0RmtTGFKm0RzY3M56WRm5NetzlLyAY8r88+cXiWyXdN3ysSugZ8H3U5m5j2AJAHKFxzi72baVx334CAc8Ww+jCLAJQeAxxzSdjoTHY8w2WbgPTZlH98L6bBoK+3Zdj13Xysdot1eJW/n1xw6AknmOOeb0KK4DwVJOMRwgxpwksJRl7MPQA90ABAjcSYgyID0xXtKHI1Ct+TxmqCUhRCJmm+YWATGGb1m+va5//rkzwlLS2Wbr9vYiy7J9fR5Pxey0mVOc54jApfx1mDYzJr6sQoLumoQcgTlyEtNAciA0o23Nv95PplgvLIBMMm0gQiny5FCQoCSSxMisGrmmqW7DUpZShAgYEQlvWxGm+zFLZZaopXx97NtWkKjPUYSICAKEPeWlFPn1/kBiOGM/+3XLgXR/7Hgt67USY8pyv7c29PUll8z2LFwqXfMSCn1aypgLEqE2Hd2loLuZRsmAQDbgtgoDlJR789GaqgH4spSU6PE4keDtpeaU/vnHB4SBoRAQA1EAGhEthQFMNeb0p2U+Mx6HCjFRlCU5ghl2te2aL7WOofdj1kUY+elCFsamYUApSWszwKfbmBNBHp/naGWq6TRVc42JXpcKCCmnWvL5OAfN3lstJQDaGICcEgDHvisiICIMu1yLCCBTleTmqipZlutKzT+/jvMEJEzJrpflcbSUgYnaCDNbasaQY28WzolMtZ2arqmWxIhQpM/pEQFQirx/Ne6Th4yhAQAQS6VLLf/85+M85vVaetWphoTmsWDqzX77UYZOa8dtqV+A4zy3dD2k3B+eMiwXPru04TotCUMGgOBcALw3nd3UvU9LRVagfW/Xmr+/3szMzCjQLABRRD4+TkBYLzWR3C7rGHMMDYTR/Z/nqTNutyKCmXm0fl3Ldc1A1Mc097U+Savzck1mWp/O2kdPItsl/Y+Ev36NMSwJENF5DJtIjKnG23eZDf713/vsc11zzqRmSORul237x9tb5ujWdxl6egw2D0e4XJOH47L8eMMx+//r//3vFvDHxydJ2t4KEIcHCl23y4QRc/zj29ve9OfPT58hids4li29/bj1prPNpYhJMoSycvq+9jmPeVqSIvzbdzGjXGet8MevqOsy5zlOMMIJtiVpp933SRgLATHmRL/9La1LiQn86l2t7/TzF/SpQxWYloUQIAC3Vb69rerWR0+Zp85cuS6pf+2FYSqA0u2SP95bTpZqykmmacmy7+32VnT6aON6qQFUhFSHIH57vYpEqnnJqtp6H2+vm5rOqTr4v/64b0vJC9ecxtGdiYSKJCjAQpLjuZIsGf7+t21qPftMic3wcT/n1NFsTgsAAPKI9/dWMpWKL9db0/l5P3VGTmmpNdB6t3MaIOqI/TFf3yozza450dtrGt1cxdQAQsc8z1lKDgwkUjW1AJgpcV1LEqkrp8Tn2afrHNEHMmNgtDH3NiVV90CgYXMpPHqUSzjgwsXMzzZrzcDQuhHj1+NLpy9VPFQy5ZxYqI+ZqxDieY4xHBAk4+11WSXvIgCGbBBwveWfP/eSE4blwtfbEh5mOqerQ0mkBpyRInKGWtgDx1BJbIZHUyQsJYc+pelBiH0EIr5cxRwkUz86BfDCDsEIrSvV1I756/142eQJ5Z2qS+VS0udXX9eybTUVdQsiPs4ZAdtlBYjLtXztLh5D9XG0mlJd5Osx3IMSBIKZLaVgKBLsPc4xLlvBRMu2moKZ9+lx9HUrQZByas1W8sR8dkUwRCmZkWCa/eO3NZ6LPJCPcg5g4UCIy5bXLT0e41k3nTOICInOcww1B3hiDeyZjiBAhCVTojx9OkSf7go6zAOIRRJGuBsj4pgmgzwQCXLFrVYCnLOt6/L10LoIok/zkiUznxQiiChzxtkGE/7++7WuXJPlhNMycyAExPi8j7VIkgyYa9GP9zMMiWFdU0708bEjAUCUIvd7Y0lC/PhqKfPbSwYPBDr7UAWi+Pb9kgofR399yUulcO9d+3BGBsCpcDwmOQ3RlOZUCpM+Z5IE9lT9nsMg52rqJRE6MPJoLsKvN4aINhSAn1Hc+z6SCEvaTyNQI9i2JSObRXs0n5GSzKGmMBXu914XcotHO1OSMbz3WQqlyeepS67X29KaPu7t7e26LnS2kQqXktrZPaL1+bamz88GAQV5qhHyGNZaSwsF0NfX/vq2lrQiKqSQTCgyu349OsBfnUcIJ+SyZHdPOW3XTIwlJ2FYl/Xjo+1fvlYpJdQ9J5nD+j7M/bk7jwBiGKe7gc5YVpQknKj1p39d2qGppmQ6u27XpSQOiwYGgWf3z6/+979vIrzvc6q+vpWcWC0Y0SyuGwLX+8OY0BwQGcDnsK/PPsYEwlo5FzKIdkzJvK2ldQ0HgCACAO7nNAcAHFOZCQPDYdhIIgHBhCk/ObOKwO6OCHMYE7Ige8aI8FBVYmGiJaeSBGKyeCkMwNz5aTKO8CRJhIh0u2wspNMZEUnCo53zuuaa0+M4PVw+97mVxATIJEyttVpke71+3TtIzsLnY6qHI6rHMc739x6OZiHCueB0D4pcJTTm5AAChOOroWDEX5GgdjQiRoRnZCAlzFU8kLlIKh/vX0xYS0KOr/uOCEH4ceLe9ON+nI+Zc0KGAHCQt2/br/duB/0lzGZmkTHO8NQ6JAIm62ZzaEqswzOTG7rhPtQCkuBx9rLmlGg2OM8gHaUCGP36sp8/TyrEiG7o5CzAgjZCVZHAzfdHu3IGwD48wM6OnPn7y5KExzGfMjwbCJH++8/TNC6XpXU7uxbBLaU553QbahAxzd6+3/b2yJkD4ji7Qzx/MyiAxCzw48cSjupxP+fx1cNhqWSTAoOJjzvOYbZGRnxmZ8x8rTIVY80pUagiYjsNMVxjqWldJAlHQG+qzfrQRUupXFdpGubx7bV+vh/7Md4/R1lkhv1v3+qvry8q8vUVW5Vto8+zWU8OiMSmAQB9eJaECB5RS8aw/ZySsRRBBGSoklH9uaplQgYe/XCbgLn3iQQvt+JuYwQVXiuTuplKwWTx/R+VAlWBAl1dZ6j6/DxTYha8t75t6bKlZSnjbDpcDRGw5NSbzj6QAFFSRQPwQbmAuY1BOdM5jQ1QWDgCFSNefqw6hzqYk85JgSTyrPMgUNsVEGtNAXbsbaqFAyK1o6dMpaTAOWxmyYTRjp6zlJouy3WrKRByQbNZskTg5VIjncLJGLeNS4WfMSPi5bUIubojRk7xepV9eJtUJMMEIG4T4Vm+TWwWpk6MDkjIKQFRPJtczKRqXYclgulP1Q7zkxsX7qHDhalkIMI+AmLWRea0MUw1WpuSOMCQIJE4A5IaRAAiADF6BDFb18zECKnwy0vV8LqAT/35x/E//uftt98LejaFNjRlviyCtKrBnP7jLWtXobTP52DIPYIJA2lO3ff59dXXVXr3fR91SRSRUohRyXKeczqOHo/9XNeFnVTS1CHCiTkl/PzqtBQmmD1ykcfXSJlZeN+7hSdJc04zmFOBIDwQEZl02lNE4i3cbVnSuhQAYmB1e9wVASTTsQ8CdHMmQqAxFAIB3JGEcK1pqqfMKcuxN2QsVd7e1vNok/y33y9fn/1+31PC3/+2Xi/hjvG2oMdS6Nfn8EAhyDmdbX7ez8smEWgeiVmYgHic/ZmPTUUee2MmcxcSXwUwrlslpG66bqW3rhrnY6aKKXM/h041DzXv3dKa5nAzGKCvb9epfr93ZPjtxwuazDmn2nYpkrLN4TpKRgjJwsKoGoSBEOOMj94T63Jbc5aGSuiqo6QyhuWcAI2Z1lSPmI+5Q7iNmDNURy5iFq4+h6+3lXXM6Wvmb9f1/eORWH7/7aJz/O1bMcPW5vvneexjzPj2csWIo83HnG7gFsS4FqHANi0iMDAsPMLDEhLIsxBJNacxVNVLZQSEIObnL4J6B0lciiAahH97WxHxsbepPoe3fbZ29mH8ytMsCYPDmFpz0ml7mx6Qngwshj7UPNCfKTmYAKoBYOdpLPT+fuiMl+/L00E7fA51kXwcY7Ata66FIsgDRlN6Eq/Yl0zCZO5mtl5W1TjbPNo8uxLETcr7H/vnfUCQFDQPYRChx9mOs624PizWLXnAeY6cJIDG9G2rOVNKSEIAHhGl0FSzCGbKJS0LuToA5CyPo/dhl00II2XmhAhJzR+PVlL5+OgvL+VyKa15XvAf6+KBw7FmSUxfd/AIIDS09ZYz5/MxpnVkAAhTb05j+rKQB5lRKWl2ZWZOpmElCRmUnH/+PFiw5iQSdckOIELugIgilBLO6W6wLMwcn/cW4Wbo6oHc2iSmALi9XFX9OBqq5poI6ev9oU+au4aIT4hSwdTeP/W5V3/sQ3jp3SC6ZO7dEGJZE/3/afqTnum6dM8PurrV7L0j4m6e520y82Q1VLlwIVtigAQTGJgRVn0CxACJIR8AeeQBA8THYYpksAdUCYmB6Vy4bJdPnTp5MvN9n+aOZjdrrathEGkpvkDsUOy913X9/78f5cd1qIekVKd0vx0s/FSm7tvA4MBhbjlzTpJE1iOWS/nh8/LLn277ZudTYeL96PfrYOTzWcBtPwwCdfjXbw8ken3NL+dU0unbtz4sctD93vswxIhANNx6m08FCLR3Yv7lW9+bvs61H7ScuB1oSuZDY8yn0ld18756SvD6A6OhDmKOTz+cR7NSMlInYouoNX35utdKdU7Xq97u7dOnc5lpvbdLXljiRdLXLwPAe/d9G8ySMgnDVBFCz6epd/1X//XHb34zAwVE2raDEV9OC4np0O97LzlJpra3BBJhRODh0yLHHlvzgEgMy1wIA4NaG26KWMxAWM7nSTLs+/HXj19KZQs9nerre/3115VFakrh9jJPNqVU4lSrDfqbv/tgEoKsw5E8nNbrChA//zz97qfPf/e3+y+/XDnYhgPZ8joz874OH46nqn3kymz5z3+8fv5NmeYdG95vIAKplv17M+XzGT7/ZPHN22OqCQcagqqNPoAQchYE8MBpKrXm4zHMoUyEgW0oSVAgOuQitXKP4cPYxcI8ItTTDPOpzjV7mHO6XW0o9N1vt60PhSkPaxH2lOeSCDG9TOmgGH1dTss8TdtGHrq1VQa8zvW6tjKn19dcUiDm6+aEMs+8LCkXQgBmJsYIbHsvUwIkG04ItdKxdwac5lwrrXtbpmKNc2I1BKDHuudcEj6NcZCStK5ItMwl1D8+2r51ouhdzahWKVVai2/f9/MpCWUiEIb7tm9rS4kTc635aRldztnCW7epljrRNE9ENs+ZMH799eauF5mvtx2Jc8Z1NXNqHa4fR61ZUgRiydnHaM0cwXQwc0qE+HQiRzdIIr21MQwJyHGZ8zKXY2umKjlPc0GANoZ7FKbLJbFYV/CIgiLEc6kpcRKUyr11C8u5klnvg1iSUK0lIFrTMRSA2q618hMFkhDMYR/WDv+1HWVKzNSbfr+OnOnn00lY9tZqLhPiaOqurWFOLMjfvvXLS0IGgfx4dABIifuw3p/JLOl9nJbcDjf3CLs+IuUU4XWqFOZu5mOa0+h/sRqlROu2ucP5Urn4uo2IKOkvuvaAmKtEWMkCUcaw8zm7Rjgw0dFURJDCR4BBAASCuQLC+UTzaUbk67VB+HlK+9pb0/f3JSIioA9wi/jvXCVIGO4pCydCwOOwY+g0MSGM0YZ5Ldk9dDgRPtef85SQggR7byIkgl2PaU5TyeGRC+UkTA595MTDYN/6GOM3v7tMJd2uAwB+/mkxVzcFiD6AhSX5y7mUbNbpODoEHLv3rm4wz+mJ9ahVRusEuR+H+1MYCu6mHVRt77ofYMOHWkoMZsvkL+cqHOH4VFVYh1UHoFFiwQCiPpop3Vefp1JZk/B2aLFgFPSAcCDSgb3F6AqEqJ4yizAnDsPXTxUcE8sYA5Ei/LSU/VAd+vUYrvj+unz72K4frWaB8LeXGVlb7xCEgKO7qgMCEvejj26ff3qbZtkPfXlZmPHYLQDG0N7NI3JFM1AN9xjm29FPl9nU90MDYjrL6U3cLRBdx9vb3GCsrUOn47A+lJDmy3K/b6Pb6SycuHd9dttbG/umy5JyRRFKIHvTrjrV0nbr4+Ak/XBT5UT7rvNZCMMAJOPpJO0wV3RVcCDi28eaSxIiEGGEbW1PCMITGgCEt1ufTzJUc5aAUPdt15cznS9p30cRTBdhDCJet0HgmLkfT7cqnF9mHbFuhzAR4b4/HeQ4ui8n2o/xDLqGh0a4Y4DhMzYdChCIkBM/xW2mOtSnSRDAAk5LCQVVJ0E0gIh5yulcL2cQhmbcdNSaP3+a3Gzfe++WSsqV0SMi1rXrsGUppco+OgVOcx57Pw7v7tJt1GBEZwK3qJld43Y0xSgV9IggHCNy4pRp26NUFuJnMcfNx24e2qKVUyqzbLeWzwWAw8F6jKEpSZ1rb8PNiZlFkAzAhSg8vl/vxA72vCL48dFSwWVJSfj20b58P4hQqp9flm8fltN4W/x64B/+1eM0kVRSINW9ZG5qbW8iPBc6zfkwvO8NDQUJydvQJ5WNzolYVH2pmRyE/bxQ1/h+PWxAnlKAL6ciiMdhW1eiwCy9Qcpkw8L4Mk3b6G2YGSB6zXnfzLK3vakFSQLkMVRHmPnHxypCT8LLaM3MCcjBWZK5368rMffuQg4gvfVwzZnnl3S7NWbKmZ+EmCRYCtuwdigA5IWv94ZKyKw2polyYtVog5r60IDAfVeQeDmX4XR/DHHszYY6BJo6BJ7ONWAAhA5/jL7MtRRZ10YigL1MUgvu2/jj1/Xtc0bif/O32w/v+XzCzeBPf3iEOvEzTJ4daOvDzM5LEfTRWi6JAIcaQqREWaJMfL8/kOro/O37BginS10fBxGUuQD66yUx5V1HhKqjkBcJfplut75vXoSO5qo+hjJJEPShGTkJt83JFRTc2R2/XW8s6f39hNQDqGRZTmmZpe3HfoQk8R7X+8opyqkABYA/Hts0J8IxOjJiAi+lbKaEtPehrWcRQNh3dYyhMdRFiBoFgTnQ83EFQEkEorUhLIFBhFNhRBOmVApzWErqxsEeLkKZsKO7wjxx+c1pqAnFcwWUhdHdghRCUgLH5ZS62mMFQgCILIKZ2jHUggDUrRYhCskiLKON1lqdMjE9CaxHa8tcAcMMzfz5oH0eU4mwJIHAMRQC/8Lu1SBmV++mOScieOZhAcMGBMAzlg8QOac8iUccR49grunTj7N5Xxa5TDkxf380hCjCKfM+RlcAdyIshaXi0dq1jank0Q3Ag+DoSkQA9P37QULbfXQc8ykR01AwDyY6hr5cZmtx7N3gLzIOG3Y6J0RwAHAUwW0f69bOMoXHfrRSsmm0Q+l5+PcgABTUrptuQ42ZASAct4daRw9fXmTKJCrX+woRrbkwr/dOiSTT3i3lknL5uK65lFRSf2zvbycm0a7n93maWCA4iXDOQtNC329Wk0jG7dFEEjrsq7Vme1cdwETLhZczjwb7bmbmpsuZ5xMFyL7v6JFyMqfJUzt0qJcSl5xuVyfXOksfikgeoN3nCRHh2EZYOMS2m1kAxDGMmSShWey77UcjjM8v58Lp0GGoEJBIRu+tH0gkCAHWtadMx9HPp9TNGRnI62VORfZde3dO8PnthZw/xmquz8ODmi1nIS45SeL4/rEtUyGgrkqMy8vkAEB8ORf0UPXzaQp33XsECKI7ZJYf30/rPLJIkWSmAdG6jmbh9P4yZ4Ydhgj27hrBiAEhxO4QHoKQCEUAI5hRJLdDEeC0iCRCFMSJiVrv66YEnBPte1c3AEewdXMgenmb3I0ZhKEP680ZImU096fGog9HHnPNU2Fw/PL9TvzEkUVK6RF9vR+MVGpqrfcOz5584pSEaXYkjIic2Cwe115ypgRz4dfLIoxq8OXbNaUkAMJ4dOujqxohfTwOQHx9KykBEeZEALjtenSTlD3g+62ZGcKz/KW8DQTMVYYGC7CjWixT8vChVkvS3jOmv8QOERGBgsOCgDgjEjyvKiPP8+Qay6kOjUAPcHOXTIlyf3Spyd2nKllQh44OGDAtefSwbdcOECEMgPFkQAKYO6haLjKGpkBGcA0KBgNmnErWHretA+G66VRlWbJrqNjttnswEzKHe6hanVISwSKYqffnjjEj+vllKlkk8T76/braMMm8HybJ3TsA1pycfdudgGoRAM+F9zYem6VuIizC56mYRpq43VpvigTMCOFIad+6duviZaZa0seXPV/45ZOU6WLmGf1yqiVnRIugLGmucL0d8yQp0dHHNImgvLwn5pgKNbe/++W+PQwROGPrZgosMM0yzXxaakTsXbmWb1/G9ToQgoY9aAOE1vT98/L6lk+AzLRej3XtpYoUOc/IbqdLbX2g4+OGdRKZQZv2Xbd9AMX5UuYl/Zs/3ATxdu8W8Po+C8f12rejt26plGlmJDDXqeL5wsPMgT6+H7XI6/t0tAiKbdumwj+8TX/3p+39vZSa1u/bvhkQEgYMva1wvY15IXAClB9/K3/60/XH377mAl++bBnSVIup5CySRATrJKMdSVJdpl9//TLPWbts99baQECPsR7DFI42UhmfX86/fX19fPzp2+Px42tKWB732ziaotSLTNO8rfj9sS7n1O6oYxPJ49pva+/qgEEKl5fp11/2tn17Oc2jMc15bzZaWIkvv/Z91UxZ07Ju304FsKdfv61IdnpjN84ocKok3vee83Q6TyK8w97MUAUd9g5791LIzEdzM3/5kT+/Tfer733rq5nSPFXm2NeGyID12O5Hs4OHDVPz+9p++vmlnjjCaaBkL0ucZimLoS/b1R11euO+aaaJAD+uRzsAwR7XPhd2ICGxQXUiqbgeOg6fKsfw3iFJDD9elhRAGqN3B4DWGiEB8GmaAPHtbUkijnG9djNLQgDoYcLkNkQqBkuCb/fHt49tqixCY4SZvb7U8yUdB4a7u48YicU0fvnStj4qOCLMlOdTvV/3CP/ph/q6zI9tLDNSMDOFGJifL/Voev9o2uF0ydrHxz5yTZdTDgX36EPNnILqJLf7/nxfenmhmhkUakp0wtbG8vNyvcm37xu6l5qTgKmN5gQxFXx/re7mZo7UDnvcG1KUJAHQ+tjXcZpzSoIUXT0cai5uYeqm3m0QMKH0EWoxTen60RJLTtTHSInNXT1U3d2eo2rJFEGoiBjbNtwDSFRhmPcexx731i7n6e299t2ut25mzFRnKVVSknXrQnh/HDnPl6XiEmaw7oYBTBiAkBEIfFDXAQNzkSd6ubVABHL8+m33QBYyhe/f2/mU3ZGeZqXMdcpH6xBoStePhgjMTILWYz8aM5ymuu2tYCLC662dF7ThvelppvNUt0Pvt73WPDTMghlLFYsO6O5ei0xzUg8ISwlfXiZm2vchnMLRHQT5GOMIyDUTszcFgDwxZUqCrQ0WhrDeAhHUfJqyD2tNXeX9dU4TEfP1cTDDb39zeT0X4bjMWSS1pnPJZrKu21zENAJhmqbTBPdH72OYxrcva83iDL250OAwAD+dZkYyVcz0BALM86Rq++Ht8Pu9C0EWIUKC6Ie7EDwnupAy8Tp2Zzida1dzVyKvc0XDUziAS+ac5futTTVNNX3ctzqlktP1ukqSU6mqbq4lszm0fYiwGny7Ps7LNGVGjpdpToSEcBxtqtIaYkROfH+0o/t5WQgjTflS5rYPADwt/Li3P/xxFSFhCuRjDHFQt4golYcm7c+DReSCrqEWj3t3cx8mp+IRTfUpxtm2vtMoi6ADMQQodHezfdN9H44xVclz5qNBBDMd26FDAyBnEWa3+Pi+TYscxygJLUaqRRX60Uk419xHI5TAAAh0WrcdAZOlp0ww1M/naR1KKHXO23aw0HIuSXhfh7UmWQ51QPz0uTJQqQQQBBiOjg4eoylVAQe3UPMgNB9NPQx1+L41SWQW+96f/76IEMF2+GhOiETUDnVVAjQHQAoANUNEQDgvE0D0oSLcu1p4wFORgq05UaTMGFAK8ZJb65jSMqecMGdkckk4c9qVSubCFA78XktNqrofcN9ARw8M8ziOYLI5Y4Ssj77t7VANQnGHCA+wkjIojh4eAxIXScc2rrdmBttjMNHb52muKQC2Y1yvRwRMk+RZRnPEeP2hjuZtH2XBY0Vtz5daNzXm/+4WM9RBUyEivF4fEPV0KWD8/WPXYUQSgB4Ehye2rlaqeCiAff1+3O7604/lfr1dbxwZ1qaFy/sPaTQEZNeDEgbY4zjMoyw4z+yd2m7ATshdVRBHc0qGBn3vhJSTPO4eGJgoIScmEgYDdZ8KB0KEmKKOncIowWDbdEjmOpX79wMBsgALWxsA5Ehqmgq78XGshPxUauQkEQEI6IERzBxhETCGoRpEaFMWcuB+qJsRdsAYLUwjwnKhKWE+58djcFjKycFH05RoWXgueW/+/XqcztkR3VQILRgQ1Lwd7hoEkXPa92EWgQgRAdh1e3uf1rVhUC2y7ppNE0Ku+YcfT613D29df/nSvl1pWUqthNH+/Oet07mZ6ubLmfvwRH6/r8xAzLetIYxZBIG3bSOGZZ7G0Ou+P2n42+NoB+xbBwiPECFO3Mf4uMa281wsyITx5VyEfD/i+hh70221kUvOycdBxFnYg4iAE9aa2mGPdUyFchEB/we/u+wdvvxyfbnM87m2trV9m8pnhGT2AK0R8v5pfhyHjuO6h7/U8yUxsiC01rtSynTfDrOAJBLi3a/3Qx1qlVooAiHCOQCHmyNiLmLmwxwRESUgNEIEUyXKuD4UCNFcCCWhu7MlAw8CAiiJhEnDUqK5JjXfu2J4YuxO3UJNn9ZMgGDGUmj0kJSGdjOflzLVtO2NCEuVMgnbc4YULAQBpsYp6d4hsHcDDGZGBEJHEh0BEQjoEWAOiICYntYG8+NQQuiuZs6EEegABNB0IBFxioCIJ5As+tCAYOSU2AAAoLX4su9TTRoBQaZqW1/baF3fTzMh7q1Pczo21QGQ+DhGnSkcCchsjDaQKRwAgGfpw4ng2Mc05zaUCZdaPo7DEUdXBFnvfZrS8RefcfRjzEtufQSSmremKSch3vYdiYgRCfvQmtkBzBwi/AkKcnIDCwywaWIdnkkidDv2eZ4Tk5lv+3ibllpj33Ge89vbydWJufd+OtfLOdfEuZwUwFWb2/k8u1Fif39joPO3L4/7dbtcZmSu/HQmWSmMlLri4369nOv8dvq7P96P3j79ME0zj8MMnYVrEo+IwMt56XnsbSBbEk4st9uhXo6uJT3hckAYzFxS+r6vIlJrsrNva1cLSaHqY4CQ5ywIwYDrtgMjAK73UQvWSvM0fXy00S2V2LuaOiEMBQ+Qikiy77ptvXdlxlJqlnK97sMDAOJwNy2VotPo1o7hgQHwJMGdX6aX18oIHliTJEJJWcPcTZ47zKGZ8uPRX05VBJkxIYzWOMnRB4TNUxoDIvyxHiIU4W2YME2z1FncA8KnQsQR5EhUuTwrFiURIhAjMYSDm6v60UfOBRHdQUSSpGi9t0FE05TUxjPEqh41J3BXHxQyz/l+b0iI5FNNw9oYOAYER53K7WO9vExBNs3CxNfr4TGYpUzlOHR7HJ/eTgpdTckQIYTq6GAALXpFklTWfTeDYVbmQkB767kULqmC97HrMGFC5PXYd+JS0zyxMAOwSHIzwqiV9j0AoEwJAt0cn39lSizS1XVoEnaPWnNifrRIyc3j/ugE8HJGMAhDArQxiBEpDfNtO6ZaE8FcKQDWfUhCJhJKOjxzTiQBcV6yezzWvm/j2HuSaYynTA4IKeecM5+XAg6IUSp6uI54rP229iklYZQMrfUI3lYTwWEB6jWnJLzvR1RBzkgZ1PetC+WhvizldCracG/dQH//+1dB+OX7ljKWhITUdZg1pACG/RhjeK1ZaIRHmipnOZ2eQI08vmtQMAEEusPRRgAZT8JhZnVhCtp/2SXxNKVlXoTwvnOp8umt+hjslEVUnQVzKnq085zHEZjw8Ti+f2+55OkkDmqe7qsGWE3kgEPHtFTrMU10KvW2NiA3NSE4v06nlzRlOjbfHoOFSy1zwZXby8tpfxzX+zHVSkma+u2ue9trlqeSdh/6ttQy8/7o6luq6XhAnigvUVO6frM2BiCUlLbNvnxp695Pc2bk8NAOSuMYisCJMafERM0aF1zO9ePR3K0yhpOqzyfadyPgeSr73h9H/83vJ7Romy5zGU1//dqOA85nJHp24nEcMc309fsNCFPZOOe/+nsvv/vh5W//Zr3ddgK3EcG27Xsisogf33/SBps+nEaqUj0xeiSWnI/DPYaZf9y2z/OPL+dPax8Oxsk4U1N8eT1xob/5tw/vd5KIhDQ7NcTENOe3t1O0KJJzDdN+KmJaDjLpGiYJgF9RW48WhOkYfTyaAY7dvn55aODPPy+vr1MCONX89XYcrT8GI1pQ751SSY+13b43IkbCqWRmHONQGFNh7fBozShSocwpF7IYlbPMeex4dDtd0uzp2PUx7HSaz6/59X1et9vLiTPX+3asj70WCLB9d6RMbCM0mLZrQ/Teuw3KzNtD99XzxG/vVQ9/fBxn4FK4H6AWtSATAXoq1OxAIrVoTduh+9EuJ1fH01lSesqA+nIpyxl/z+ecCIIeWyeGlMnUu7mUZys/DIgASPC0zCyso2UpSJKLPymqjzsew3sbglQzSULiqLPUSQiRRKcTMtP9+/HyWjnRPuzYrI8BAOEeI2opbqFmp7M87gMg5jklrvfrNnzUKVMEIAy1j9v6/n46dESEmwnF22suWY4xLpcyT1Wb/+53L+o2VU6c1BUw2l/WkDYv1cFdAyF1U++m61ZTnuo0uh46iODLt32aEhJ93LYff1iGxtBg9t49kvkRtSYU1OZNVUrUWkSk9ZZzBgPEPi2ZCNy1JKqJ+rMkDxhuH9/XAOjPxDvC+/vy/jY9V0aAEISv70V1ePD1tubMkqgdIJxH67kmTrCNIxBVA8KH9if59HA8LRNR+/pll0JS6HwuIvxx3XNK69ZH5ZQ5JQF1TuAgvbWSs1msR5uXmjN17YAADkO9HR62n+a8bna043TKpyW3/UiM7++nbd3va3t9Xc4nmetyHKbmNefbdbegy6WIeDgEBLgRJIJgxJKFCLPQuncznZe6nKSr6VAh3O6WC49x1Klq8+97JwoCmGuh57GbcJqzm19OguE6wk2JlYhNVSif6nRoByGH6EN1eFflJBg4T5kIU+LbvbXm51MuiWxYs8FIDhHhz8ZinRkRtRvzSAkDwDwkxWNVwDSxqJqj55xfXqbt6IiQM+vg42gWBs7ISAhj8+t1Q4z96CknFjjaIEZVVxtrb2aRC9XK1mFfxzTR7XGIsMMQyblQ68MA61xyChtxHD0gEQLT001O19s+Ai4UOcVyTvMkiePXL1tK+PPPZx36eBw5yW9/c/r16yMlzxlfXl+c7fp1N3Vkadt+7B2RSuL5lDCCiUSilFwrd+1jGBqXzOHBgEj5ZrsDPFfRY2uMIDXpUHQikK56mCIMyfzyNvWu7qE+3KPtlgQp8ejj2EdEDFUWdoe229GdEXP3TkbMqQgyJKTt0epEeTqBh+Q4Wjc1BAgHSbA3nbMwUjts3Yb2mCZ+eclEYOFusK3KCYebqU1L6d33dUhKKQkizufcRncL677vwzvkxKoqLMupTiU/NkTyaD7ck7AqRIQU7tqTpJwzIRxDAfBZezwttSRUByR0H/MyU1DFtJxSnbj3/lyHHG2o9syJEMyGG5Yi2hWeFxksi2TJpwXX+4jmOaXu1gauq5UquSa5XddlTgj0y687BCSmyzyh6u1jVef1bmbxPON9/9jdjXNKIkQc4ToszAKhzonYa+Uff386+tE39RaYQBLbUFOYTiVnyCWrGaD2HnVKyLBvuh+qGqNrzqxuyAwRzPLp8+k4miSy8OvHXpP0nabLW8D99XO+LEUQJj56ph5Jctnu4RhM6fZxnDC9vdTdXQuQ87G1QOQ558SIMEzd3MPngpT4vg4J5ALu0Xa14eBOhQ0gFDGC0NWBRWpJEKgKPnyea2BY6P2xVUkpcTi7YwQMbQ5OBMLIiRnh2btpXYEwMzkQhEOAejBFBIUjUaREQ0cbkFPSoSVRSjJXHBptd4QoNY9mqspCOuz6YXdkZiKSfbc6ZeZ4JvF6t5xREhaQx9gebbixWwA9sU0BgNZhmioT9GO0bh4STO69Tvn1PDvYx217IB67v5z4y3X9M5eBubn+vb9ff/mzr7fx+efz497cjUt2iDEaE1NJOjwCWKir9mPYcEI2jfXQMTDlFBGEONXchoZ6j2iH7+hpgnnh26M7gGrc7w0RXl4nRhpq05SmKuvjaENTYcDIKV7mSlhT0vXogFpASs0//JgulU5L2Q48+j6auVGtFZigW62Yp8op/fFvP47VMWA/+mlKIrytOnkgpyS8HTsCpUTDUozo3QBi23QMYxFmcR9EGI5EbGaBEIDETIyEuD7aUCWgPqBU6k0RKDy6260fgF7OWYj2vUtBBljXvvehz2VOSnNlGubINWVyMAdh+vx5Pp1KEuxjtOEv53yu1HpuCk2tJEYnZvh+tX6QmS6n2cwBvFRJwr15/GWQlMOQCY7ekQkQAGmMbhav5zkC2LyPgURCNIY/t9wUUGsuc9rXgYCnueTE9kw6IoEHOLSjtRGcyNZxmiU59BGtjanw7bZrgDDdHm3KhADgiCDt2CRGWNigPvw5IF+WSRI/bvtyzmNoySwk69pSIXd3t9tjq/OUs90evm+dhZlpW3uZ6vZoQ+2ZljWNfRvIBBHt6CknIgSI41APYGEdhhQll9GtjQFAiYEJJON0zsfWP25dEn/69LqvQ81TopQIDOac9WR9DLTj/W15rI0m/vzDZcrUj4MFb9c9zN9e52eFzYe23n/4vCSK18scEK4ajEdff3y/AEAz4ORzvbhCTvHylo4Oven1W2MhzlyQCTnA5iIeyJnYg5Fbs1SFxxhd3aF3HWpUxBGeLyvOUJkxLOWoLubu5n2EWwD7VPKf/3Rf5qrhY3NC2tvQ4aXOHhgRAE78TOn3lGlvHTH2nVQPdDy91JTAhk85bfux7z0CAHB0TYKgMYYh0ba1CGJkM5csEbDvis6B2DHs6Q7EIEaiULfhPnpbt22Zcld/HPtlnnJJw60WSXQyBZwdwVOSlIReai2DCH54nxHwaGOZZ/NQN0RgxDB0C8AgQjM3NQgAwNa0jX46L6M5CgY4IIR7ROgIA0uZS0mIcByDiTwsMMLVOtZC51Pa+8FMvY/7o5uCG14u5frtUAtwv11bKhIAXQMJai0BcGx9qqUN1ebu4W6fLnMYDu0jmikiCe8wl6xqDuEG7n461a8fj0CUxPMy/frnu5sBjpQYCB9bj+CX81QEI0M4Lqd6DANoEDBXyUnU3cOFKSUmQqLIKYfBo8dxa8v8xDoMCy9FMKA1vd6PeUnP3Dnikzc/Anz0HiRqsLc+1CTlWgoFMYO3ptFFhAAzpuPwZeE+zFyfHa5n0Yw8hrahQz22pjmlbd2fTcZlqhSk5scxljlDwP3agDlnEaIR1nUHxGGu90b8XBbi3szcL6/T2O3rlzsKnl/mAHvsI8/49rK0zdIENnCRuj9UhwMAEYb7VEpv/Zev3z+9X5iwqZI4JMOQmtPRe1PPiUj4vrbzkk1533qEpornPAmlnNhRXye+nEpGeTSdZspCx6FNx+vlFMH3+97G+M3LZd+jj5EzfPt6zHOaa7y8Fje8f7Q2zMHfPtUwK4VKSXvs85LRUAqlGuG6N/76dV8fIxDOZ2WB3//Dt7nmP/2dlZmR/XwRIvz+fQXy++p78/lU1jbaGH/8430MP/pYlnpZZmP/9dsVIre73T4OIKqVWo+tOaJod+2eCiKMaTpx4tH8D397LcMMIBd5P09EED7eX2YBUTtYoDcnIFXv1tJC3ZQPHM23XXs3IZ6mhGTX67DQWmnK+c+/HLL2Y/jL5zRV9iGu9Ld/fX1su5n33dW8nhJTbbunwv+j/8l/8P/+f/3nf/713ySifWz1fJ6dumrf9P08U5LTy+vvfvo9U/l3Lq+/09+NI75+fF0+X8Kij711iwAWRhHwKBXrT3MYhCNjThf84dMbeHz78qW+1ukHGq0dW89C5+Xl3tpf/f3f/9N/9I/+i//yv7zu637cIVEalGfJWZZpHrvf7uuDjyMagcwvuUw8L3ysjkElV+1HBBIgEdYJP/2wbM3XtbljBDKhBUbgsVmerXX5+n3ftoNzCoy3t6qql9d6uvjpDPv6oNBxMGH85sfldm9//Lsrkowj/t5fLRFx/Xb7/lXvt1ErvVxyrnK/7ZTofKqnSyayx+0IjJTkvGQhBcAYYGZpgVyEgO+3tq6aRZYlb8f4w58fpfDpfDkOvT+G2hOaWQWVkChYpIZ4gCOkbPF4qHDK0nobGDzNkudwMSnZOwDG0caxj2U+mfaUpXWuk/zw8xvTSOIiEyV4rH3b++gKIO+vpyzy7ePx9euj7wDoOXOeSjd9fBzEKAlSwggDQqTo2upJCOh+3T5/PgXF49GZ6P7ohJyT3O9GrG8v9PKSZiViAvXEdNt2FEKCMQYyunmYCXGEhnsEfPv6UWtdlokYlAtEPPYdGUpiHU5MJTMDacCX7xsgTFNKQtOSj6P3YxCXnDMLnuaUM+kwtV6FPJwTBnKdEqKPEYAAQIi+rce6KRPWOUWgbjhUP38+vbzldd0jCNGSpMetvb9PwkEUpzl9vzdJ+Nj1eu+vF2E2DHnSQY8t2mFJGGXkKYXHunePWF7LVDklQXBEH2PUIvNJjqN3ZTPDiMSSpsQph/u6teU8sQAyCDFAYMC2NkIAYAw4neTxGENDhqacMNDUEOnlXBJDTanF8XKhQNLhyyWNYS/LPGxsbQ+Pddf1fjxtwvNSiSEl4kS2xXKqJRsSHo3+8ss4EDJBzHMewwDj5VzfLoupHW2QQ9tHTck1DHSoXk5zYHDidox1HJI4V2bAYbBuPQAjYHQrkj59Pn//vgIgEg7z3t0i+rChg5BLzu6BBAHhgaE+n7KD9qa1pHU7PKJOiRCJEBHMvXfT4Un4se4iHB4IyCLHZr1byYiIfQSYc6bWhgjrsPt9HwoY8WTfMJMqbFsTlqGaC79e6jLndnQDDAAzYPea0xhjb2M58ctblkSCMM9+Ws4BTuwQgB776uD8cqmnJVcxlyjLBABM8P651lxQRkrR3Z8DstBeK7+8nj6+raa+bZ1OuTc79lFymkqaKm5HT1BV3bSvd3CAMYyZzay30Ri7GjPM04SQR+vq6hGvbxNRMBEins+ZED8+et8PEY4AEUolbHg7LGV8uliekqyucUqMRK3bbb1//uG8LEyA7XBTX96W7x+rA4dbCpjmZOHbZjqGDmVh1xgj1t0BY8kJnABRDfsIMy+Kx27HMZJhLXmeGAiB834/RncifjzaNGW1MO9Syn40JEws27Y9saQeiMiEUWoGx33dJctQJyIm8jGeytS2HQi8LGldj8z59TIljnGoJEYAiMgiNqx1OLon0szJOhFjuKlaYgtD7QEQAGOEN6OUUlcfat61RZN5mW2A6WBhYUazUBCUmuEY8XKuFnF7aGsqCdTCmvfm6kYQLGk68zAbao/vx+lSj91SouUtbVeFgFSYBDDg2DemLJzRwTzcXSnWtY8BrQ1JcrnUo/UxzN0buo0oA+aac+Hh+v6K76+nYSaEv/2B74/jNOEy0e3D5rlkwP2gbQUwRARJAOHXWzN3QDpamBsTkGvOkphvq8+zXGYZTTnTCAFABwhCxhTuZu1ZmGIWYSGUJy0cwwnJ3CbJ4WYAkkq4jg45sw23Aeqairzkcx8KCCnxUy3MBGWupgqEbe81y5NcBQHDBhE9yed1SbXk0dwgzDQcNE9t+NF6mJlBRCAhMbtZOGgEgT+bPttD3eloBmjEpBFbHwGMQt5ALRBAmJ7BSCLUiOhGCE+9GzW4Hn6prAOQcIz+Ms3lUz5M317ql2/bcWBQqjN+filM5j/KpegfAx9bAYw+RhLSoddHX6b0+W352Pbbx85BOXMt6fs2Rn/eHJ0Ep4k4AXSHABYUlImlniVzF0F1Ce/1lFSf+X+akFuziHY+STHavNeUKBCi58qS6H1hdPJAZswirbVcmVN+rfnf/nEnpNO5kmDfN3CuhRHp5WXZHvuxeSpyNKdml9ME7vf1GIZ7s96NKe17T5JY5DhMbaj6GJFLWkom4dFMhxOJmQFEkr+w49yibSOX5IH7ptr61kgSTYv2MDG6XfcpZ0TU5mkidW+tE0Pvtu8p1wTmbhZsSUQAA0OY304C4EC5DxUG8pjO5bZ1Yphy0m5IMU98HKTKhGTuOaXAqCXlLOveHEKH6VOJRfAsTeRSp1J6GxGARIkECRDRzD0cAyGAIJaJp6WOV0tIQHG9j6Nhb8O7m8N9dDhnFhbEde0lEUCY2v3edORtHQBoiVgQiacsY9hjb1PNoQBIX79uR2ssmYhKLdfrCohmYQ6SGC2mU1LXQCCBWnI7lDgAPSBIuA/lRHAoAKBgeEQEMUiSpwszAFNJvY997xCQhZ/fPcK1bzlLEBLh6Tyt69GPcazpse7znIX5PE9T0Y/bRmypcMkBDNyIEQBDbZRMyzydMqHH43FM54kDWzc1Oy053KXkTNlMX19qzclUgbH1Nk/5dtssSGru2tCFSVobWUCSHNtIiEuaMkujtu2PaZrMzdGY6fV8vt0e19t6Op3Op2nfh1kLIiQkRjOHgG0fFDDPedvHvjtSpCzDFUBRIMJZmBlbV2R6PHoW2g/jibdjBPnpdbmt+7hr2u3pPjBVQHIbbpFLvpxK7+Pvvtxrzh6WMwPQGAehpEQARkBCHBRhGOAATiTHNvqu8mmWBN+vw3S8vky5cCLsze/3DsR972rx/bGVwsjg6IZk4UtJyuEQ5kbEU80QsMyiXo5jCHPbNbOMZhZBBOPw294LUxvtdFlCwx2en731bT+mmqxFTWk7jr21MpUA2Pfhge5uFoT4/bol4aF6e7RS5TRPldgjkCAn2Va73dQthNnCjkOPJxjVDyQcasehOuxpcr3fGyEOtetDEZ0I3WBng2jLLO+vBZHcbCp5b/2+rZ/ezhFgbrfbKkKIzuTnc6n55dhVhFnA3Y42erPH1s5LGb0PtTqVOScBCA8WRLGCAIh9WBsaEQGQSIYHiVZJvalHnOfSbDzWIzySyPm1lMJ9KBKOXdX8SfjG9BRiQB8RAb358diSpGPsnOBFakqUEENjmTggPr//AOFqeevax1gfu3YTofujOUZKebQYA26PPi/pPOc//d1DMklBD1c1qSDC94/98joxxuihHWx0koRtHMeBTCkxS2DEstR9WFedT6X1cd/6+TxdvzUW7K3vuy+nfH6djj+Nn3++IMHjetwFyiQ5S7gnSV31OFomYcD6luNu1NAM2maJsw1htKlyH1GqVElff11bkeUkn17mGKBhmCKHxIiEWJe5tfb9+4ohl8vp/mi3tZ2WlFhOCwBGV5hrNLOjD8fgjIiAHhpRZ2fEL782GyqN5bBp4dHx9himcD6l8HBFP/rW+mkuAcZ8+Oh7J+36w/t8fdjLCcPG5YclT/Hrnzd0yUv59D6PrvfbNk/149r3PuqpMNK6bcuS/+FvX9at37+vRzuuq356n/e+bnv7dHm9nUrz8fZa3l4mCvz6yyqFexsfe9/6uHBtmy0XzoniQXb4anH/GEjECZelMOB26P3emGkusix0PqXrve+7LS/p599WNvnTH46tNXCTkiI8I6ZSVS1SIEXNp59/9/t/8S/++e22Vqz7PgIt10QlWxtfvm611vv1y7c/3P5n/8H/PJf6n/9n/wmYaoO8ZKY4Wt97SyUL4bYNAoYgMAVkPdT2HnP+8/5t9J6EHtujzlmEQGEd2no7tu2n1/r+4z/+w//1X/RxkMfwhghSaGh8fGxHtz6UCZxl6PHp7bJt2xjig3GEMH/6fJYU339dkRyJtnWbp3nttK3j9W0CwmPrt4/Ru/30U2nb7esHhKP0xiKH9H/wV5e9jaOv+/W4r/j+Wu53QzACPZ3K+mX34e8vS/gw9Z/eT4z9fI4sFB6vn5YyQ2KqmfsYRUh+WI4+lqn0rhb6ce1k9P4229Dbvl+/O2LkwofC1sbRx+UyXc4VIdatb9u4vBZ3348miYCeTwdEksd6I0Af6XF/mKuk3Pc9p/z5/ZISNRt7UwEqSIXrIHg8mrv//u+9mZ6ZMecYPUTYtjHV6TKLCHzcYMr57SL7MZZZhtZNAgHC/YfPp659W5/P/cEQ7291awMD3MwMCLhOOcDd1BVVDdyIXCjNc2aBUiszUZIw8G7IYBZMQUgGauYQBBY5U+sU4f3wy/lsoIGOiCyBiAk5wj2iDXeP3v3llD5/mr582QH9fKnIXiZYd81ZgPz22EzDw/OglClNLMLaA1Ad4nHvKT0jOfjYjvutHbsCQM7040/TlDMAqnkSdjcMeraEj6MHxraPqXA/NCf+/Dp5+GUqrUF4PB47Eh/DdfjjoYBcKonAlCUn7N1ymnJGQMfQPqykhEi9j3mWfM41yRFxvbdSMIcxi43gBCXRGP5xfaQs6zYI2B2Cok6MghQMbFNNDAGAHmFuozsVhsB9HyxpPxpSEEpJPGUm8CKCS5I2+oD1YR5RMyehy7n2Pk4nEZ6FcJnmbD2LtSNsCSZKuRKBCC0TMGESNB2OWGo9dvMBqSbgAIejHafFkNg8LGKoPx4t5zxlaT3WQz0cCDCoH/1ZNWrNEMjCH4+GFIBCIES4bX2YCQMxAPCxtSRibu5u6pdltg7A0IdRDCJQ93U/3OLtUw0kBFr3A5EIMCLMYt2cCJjJgc2jCjMlG4oY23YgoAcgoLk/7q115ylyopokPc2nLL0NTgjkrakPwsCppKNZyvhyBiCiAGGXnNZ1c495Wp7ElB9/mC6Xoq2Dgwhb0BiDEIb6aRbVMeecyljXXnN6+7S8vkwRcf32YCREVh1JaDT7+HacL/kyTRjy9XrPnLftrh4kHOEQgIhH608eISFcb/eppnnJj0drh55O6X7dkVgyOCoxIxALhME0FXNrXZ8qSVUX0Kea3c3VHMEjfJkrPgs4gaelqNk4+lxzEdi3gYC9aQBMUzKyj0MjghhTZVVNSRiIGAGxNyUkCybw95d0VHaDaZIIHwb8xL2R9zZqZdXhATWLWqhqEgbgJOm5gEQ0kiiVS4FwFp493AFYKAmJIESMbudlGof3Q6cp1SzX+zEXdm11LoiOASVnClbtij6x2LDNdk5wOrEEpTK7je/fein8/jYjmqqruQd8+uGEaF1Nfvr8qt0AJRgI2c3XdaQsgIxhCJ7Ya+WUmFPYETrALNw8AjyHmuvw0YDC50WtwyT8cpap5EQY5q3b/XEgoGkktJTkWEfNsq3DzP9SeutdPZBQiMdwJvy2HzV7K30+p5TFlb/+ej9dZkx8rosg2dCGyOW0diXTRMIZdVdAOL0mJtIDMaLtRiiJs7nFiO12vLxP51MezaYiNpBxvF/SdQ11Uw1XZ859BKGT49EG44jArlpr6sMDmJibD+0+zF33ABwQextqoV2JKOfKQudTsvB2KEaouuNAoACIwIggJA9XVRAOAFUNiJwTKAx0REoiKeXROwEkolrn0XuA55pVLSKQeLRh5r3F5JyntO/mCn3ocsrMZKZg/NgaBJtZeGBiZASPkqSrrqvXKuYQjqbmIuGx99j6Xpcc7n33LGwwEPCf/uMf7/fjy/WRQL5/91q51rjftr//u7d//Yf9OGwuuY+BBN1i/7Yt54QCSxXrRAAf19UCHBA9RECELGIczQ3cCdUjuUxYJ1xyDmUS+XBXH6dTpmAA6Gpt9CpsQUSQmRMjaLBEEkupNLV1jd5xnvzjQ33YdX2UKSUqkhILPI6jlnI6LaY6Ojwej1zzcpozWV1yAJNZJgQnLbBfj2N3GzBQdZgZvM5ZHCIyMYADgiMgREjmABQm7aDDAoCZtI+IqItE4L52EW7NYtfplIEGGJqH44jwz++VCLbjcEMbxlwQXLt65Qg4NsuITqRDRdgiKFwtkCJnUTNTGEPV3CJUByK5IQHOk7gnRlRAJjSPbW05SUlsYWF+Ok21iN22Yxy1lpKlN12WiRmu96PWQhSqTiQ2zCCIaT5XSUhoTDD64ERjKBELUwQkZlc1AzVVgHDoPfZDH/eGgaO5arhFAkKC+6PTidroOclc5/Ux1m0P8pf302ix7U1V3R2eLhyAbdW5JqY4mlnAp7cZDDFHMM5RXGnb2/lUSHh/tDplDrxcCiM09eutATmnvB9NMjk4UhQRABxDAbB3x0C1ziKIcBzdLYh5P9pyrjml6AGVmx5JAEnQsVSZFnYL7XA6n2/rhw47cdqOBhCXl2W4IaMThsNo7m7oIZmOPiBcW0CQg27HONfKxOZx9KN1LchB1pqSUBLmKT79sDwebRg2UxQSplKYC/c+Rj+mcxKarEWpad13YnjebI9j6DAzXOY6l2oOQZgKgQchgkSqAE424PHYTi+11tyH6VNiijBMb4+RS1LdNOK8TKPF3hohSMrMrENR8PxSc0YhTplbM0nkdhChMP27//hzmTyC//aPt+/f9uk0EZFF0+79OAIgpTRURUT7yMJL4TRJBAzzWpIZ7qZq0NoY2t19yVmPBgFIUqo4mDuFh5oTY7PQEUdXRtmOUXNWteE+TenY9eM2Pr3WIN6b9zbcIeUkiVKVGYp276SSqM5pORdVW9fG7DTj6GRuCKntlhYZXUU4DB63vptL5Ze3ZdvgcevoKMQWtrddCBmRMxMSEo4WqsAcgA4Rifj2OJiwq58W1gHtcDgFCc1TEQR355ICQk1PNSckIrztXd2nuQonRnS1U5GXOT1RggDic7lv7bGPYfrj59fwsHDEAPRh3QApCIHcw/wvrBwkvLdhHpdz3tauw18udWjPWU5Lbc0R0N3XVZOIJHEHISFCGyPcgaWNARRF5Pr9QCCSwRI/vL0wgJuho2OUGZhKoqSj1aXMVHXofsrXj3Z/7NvRlnOtOR+H1impqrm50jSxU0yT7Hs7hocHg0xLJhzHob1ja8YGP7+d1m1PyG5wtJE81jV9eis/vFdMUCuCpUQ0LdxbMMJpPl9UU+FPL9PPn5ec2EzHy/TnP9+Q48f3CwZcd7uvjZDaHi9nbMdGRODQWjst07713vV8KbXgvHBYkOE0ywh7ez8B+N4aEnEiDla3IDDQXGSap/Xu3749UgUpnEWOrZcJEZmYPj5ul9Pym9+ev3xbpQAEYPC+Doi+r7GuZmMQey40V+67jhE5y+slf7se4fR4HEP99VTNggXMfVt7uJO1x9ovS3p9S/O5EGn5aZ7qqdnQrpXltz9c1OnlXI/Re9cpTx9f5XQq06kslT5d0rCxbrLvfjrF55cpC/7V70/rvs+nRAb/9m/uiGkEtq21EQa0hy0nUt8rzEL1+3V9fUnDdZr40+flb/7N95RERIioZK4Tuo/9wJ9/M3/580qprbf4fsXW+Xw+HUc34D72Okkuk0Ooea6koAD81//mrw17eZ0I8/axOppqG3tjkuO+liq3dkOUlJZ/+f/7b/7xP/m9zHK/726t5CRctv3obZ/SzLm243AzqcxCIum2bcLtdFocyDXG8K6uzRCoazuOjpT+xT//v//13/ztb/7qp+ZmA19fLh7x9fv3nKsHe4AD+9BZlgTTMF/bIYjedGjE3cup9B6ns7Q+iiR0+OHzTIL3e/vjHx5EAuG//3tTJvn1V/UncjI81NZN/vinqwfXVB3HsR9fDXOOpea2K6fxux/PrhgA+9azsHD68VNSV2aOHs3s/Vy1DVMLg++P4/EYb5elkBw22maPD68ZUhJ96C9f2sdtXE6pjfHyKbdGQzFlCYijKRK2rqbZAwBDXXvzGHQ6TYBYJKmOrgOQS8bP7/PjkacpRXSCtN73dbea5adPMpUMRMfRPr+/zoURoakN81gSoAGQqwnK57f5h9cFQG7X7wCSCc9FGGyZEgIxmiTmWcxdJG1bN43TqXjoY7W2w+ulBFjb+9G1NXKz8wvXmhC0JuxdzVxVx9AkCQHM9HRKLOzmEeEBfRySZJg1VSTkhER0nuu+72ro5tuuvcUyp+Wc6kR76wAx1D5ue525llwSr/f9aH2qpR12mtOhOs9J1XX4/nBzA+ruYeruPoZmwaUKBmo3JEIMAXh/mWLYbnvvjQg7ppJL4YwBgMCBaLo+HCyVzAjIibRrSfLpre5t3O9qhr/8cX17XSjGMH82ElzVHPfWasn3mxPBNLM53h9H777t3lukTBRaCmuD9TE4RXjLJETBQFMt01Juj33bwdymzKY6VAHwug5Avj56FmainGHfjyRJR7jaGP3lMifJvQ8WTMRH01vfck4RWFO9zHg+TYmglOSuy1RWCgs/TWLu4DZRmqpsOFoLCM2ZnkEJJmo6Ro+JCjwnAYTnSzHzdR8QNBp+/2gpITCnhNOU+oDb/dB53jcNABbAp31169Ncci7b1sxcklg4ewRG68pEavZ84kuS+7W1pvuhLPT504LupoFMyLw+tpzYwgGjqxPg+hhciBNxklTEXJFDzZAQAl0ViQxY7Zn5gfNr5XW0bZg7iYD7lEtgIIODl0RH7xg+z+eUZKhOJTWNdRsCsW79fCmC3Jpy9alIGI9uDIQMgUEctSAABkQ3RbeMCSNyLR/37Wjt81t9eZ2aaS2XL6dSn3l+D0J4/3RKghllTlPbO2HkmlBkP8y0z8vcdpPECXEf6h5Z8HKe3ew4RiCrRe+mZq8vNYu4BiKkJPfbIQ4//TQn8vt95Jq36zHNcqg9bbwAMM+VmbbWc5FSkyRghoFwWaatHXtTCpYT9z4gPABKTmnmYdH7QAwCboosbOHTJIJ0ve3ns5vVdW8B0Y9xOk/TJLNAncTPcRzdzdbDH+tgYWCyruZOwiyUkFMmFkCQMczdmMgjlkl+/DxtbSAHUzDidKkMrvEkwMIxxFQ5kIAjAUiUKusx7o99nMr5lL/dh1CEGlKnJE4O5MNYGDmza0NIR7fb7fH2WlMWMtPjnsrMIuGeOMx0mktNST6+PxLL/tBNNdET/6bLUlJmdNybTzMliZyQExwHIoL8ZUaFZtpWUsNa8POn+bTguRABteZEXnIgEmC0g81g28PVpspMgET7cZgFMgUEAo4RiGCoZg5Phsiwtg8DmGe+b/tSJD76JlaqJJkkBeBQdwQklj/9ad0OzRMCcpX0ePR915xymSqjAwgxTjONXSFwOVFZaGjPRWJIJdUqj4buZq7HoaUKoSPRDP3Y9XaLoZ5F5tOkrfd9qIWqAYAbsFCpqR0qAstSezezcZnx5cwu8stXv33rZtBVmZGYhnZh7qoQgCgBiAjgProhkAiZWhLHAFcnQuZIEgG+LFOEDY2AGGMQSS6l7X2oIWR0YkJOOIbtm+YsETC6RqAQG5PHYAJVC/c0l6WU1oaqu7pqEGNr/bmdn5d6DAPD3TtnzJL72ufzsiz19FYYYbRhat8P2Gzq15ZzONLtunvHqWaAUNPH3adFlpqG0Lo3YeldwUOEwsMd+3AWJEkRUReYCuUs+7Bti/Xe6wTTlF7OBTE8YN+1jc6CHk9tFChoDHhZSipIMI7t+Hp3c/y47ss0R8TRekgplfdmTe3oe5lo3TtGYn5a5GTcx763y5K34yiSYHiYT0s146lU8wETHt2GObPs2zFUKSekWB8HBEpCFidJZsZgiBQAGGjDzCJXlsxDiQMIYViA+74O7ZQz5yLLnMPVuvKUcsHH2tdVIwjIS2ZjHAZ1yq23dmzn06lkOvoYgZIgLNAoYTTV1m0zHWqpsDCEB0WcJjGPMQzR3EyY4Bkdo/BhLAk8jqMhheQkwhAO6MuSXi+pVmzmQDRBOu7dmD0cCdsxwCJxdTXmZOYiDCGyyLZuEFBK3rfW1aaSUDjcwVMfsd2PWgsiDe2ccDSCwNEU2M/zJIC7IAmGIQBeXsVg7Hubp9zbYLQyYUl5vW1AMM/z7b7tjz2nIon68MRcJu+dllPVoTEJYoRHb+N0zgkxwhJzYYY5n05Em4TjGBoRksRaJ3gGKhHR1RAcEAHAcyYWGocB8sf1MdpBnNEDXbe9ocin9+n7x+E6fvPD8vW6bXuba+o6Estj7d3s/W2aJV8/dmHISY42HAk83MbjoYihrrrTy7nkzO0YqrDMbGMIMyCmzFOuYLRMxcKTUMlZh5pbIgrwbd+meRnNc2KNXoqA47p1BMgpDfNj7TXxfX2cTzNBBAALQ4Is9Fan9XHcH1vlRVhKTvuh6iaJrfmmgwWZCwKS0/12ODgzJ07LXFXVhJdTfjlPOobH8bvfvuiIdWuBQWiffjz//BMvefpYj9/8nH/6oX773m73kQRTyhh0HB0xxlAEGKN/en15fREH3Hb//v1ep5IIzjOPgevaEjNg6LDRRl6Kh9luPgYRJRFOHCPC4jiam/UwM3qsIyKAYN919GDCoYGc9jbAQ4QhlDDlIlXYPAADyYBQfVDC+UwRsm/uaBFh6qpxX8d5SamQDXisx6f32cN0+P3WAOP8Uhx8fYyl1sQ8wN2cmVJN7C5qECUiAv3tLd8e+zCo+WmigVwxzXg6VzMfHupOEEnoclnCQ9X20TnLecqEdBxjmJLwtu2ZWZDXYyxzfqy7AxLgvraRJwxIVZq1QFeH3nx0NYsIRABASIK1IDgyAgV/fD/mKW9rQ4aZCTzC4XZbl1NNWYb5VKXbyIJdvZtXYWFiguHe9ljmmgoq2Msl50R99LCBLu4ojAi47XvJ2FsD4nmupvbjjzklWM6lpBw68gm3xgFREiXmt9fp6B0BWvN9NWFOjB8f61zFANSVKE7zXNC4kki9b/bwWOaUEvb1MS3l8/sSNgJK+M6Eny7FFO7bKogTJg3DxG3vZiEJzy8pJRZmVw83DwyldT0IY5ok0Nc+PAiIzDQLExER9j4ksBSqM59KQrB9bRjMQa4xbARAhI8RSuHgTv5Yj7fpdDnPx6Or6vt8uj967/3t5dSaTyV+/9OZEhDwH687c/n11wPQiez0Ws+n/Ocv27FR75CL1Izfvh/bZsQDiJBkDJ2XdL21Yx+AEIgfj2OZ0nyS+a0E2Lb13tRso0pkcqw9L7m3jRKEwiw5Rby+yMftdmx0WpbWFNkQoPcxeiIAa6uk6bc/vd7ux7/567VrvL9OH9fbp0tlHEYIht++7eelfP0YI3w68fePhiTMsa09pxkg3t4Tix+bju6IeBsNbwPMmWC9d++lTnWaZlc92gCPx3q/3u/zVH749Hlr28e4fXp/X9dtG4/T+dLNzu8XM1sf1wh39VKXXOe//pv/KjidT6ftsRqmrsYTVzyPsd7WDxa8vNVQHPv9cnnx4OvtmlP57/33//3/2z//T3/72895Ktfr9e2nt31f121FJEYmSmb76eX1X//r/+bojVNGUHT/r/+r/2pZXvOUxzBGYoLWTcMKw9/8zb8tU355fWGWI5ppSylR4Pk015yY2hj94zFOE7+85HVt29amGp/fl2Wm3oYBYZgkKimt27Ft+/rQ17cq6bnd5H0fRwtG+PzjbGHrfdzXXudyKtM4NEKH9UAoJMj05ddvJRdJufXhqr3D7TrasSWhATs4f35Zmo7Hbb/e+nVVYkyJWx96dGH98aeTDTw2c7CjWUB8+7Yfh336JNPMBNyG7+uRCp3nvB+YJX768ZRImMJ82psGhOrgBL6aWzKPra0Q6e1cMNQ1wAczlimbcTv8lKWFgQMjQ0Abo0zLWPXj+z5iIIMNTIkfjz7PM7O4K1gQymPr7dgvr9k1VL0Pvd32nBMimI+UuGTJQhDmTonEzVSNmb5/3xCxJshT9tAiTIQ6wh0e+94O7N1jhss59zGOj5EkWlM1emzj2JSIkdXDT6eq2kmYQEriqZDqyFVS4jbidm/npeQkBHCa0+MxfvmyHy2QUO1Z1MW5cq2ptUFMR7N9tzFgjPHYeoC/X+Z2+DF8mQAAcsoYrKrmiMRFKFVmQWREJA0cx+gKCv72frldmyRijvmcH2vvzW93/eldOum+h41eaiJMqECmCHKquO5qDn646yiVUqZtVzc4L/R6rkfzj+tuauvhQmX0FUmQQkdY93jmIBginIkTUQw419MwvR89HMqU+7AMohr73k7L5Ij9UIK0H/p2ORVBEC+czWxvu2AmQmBGi5Spj/7oWpJIweUsEVELEQIyCjGaqw0k64eVlBHgvjWEaIev9yOldDrVnDTQXi6v5MdpKuueH/c1pcSUarFgejwUGZ/rKGIiBgzPBTml0WIq6eW1Ph7j47rlUo61hXsphShqTXMt294f7RgRtRZH3E0lcWbaWvMADT3nMrqr+X59pIKYqE68byoJltO8H8rowokzeUTbFYNY2Jo/uyC9j8tLNXPJzNnnpYZCa2Ni2bYjPKRkDY0AdReG1npgOraBEW5GQUkoMbsZEqHbc4LMyKXmsHBTItiO/ri1eeIEw9zeluWnl7q29lg7ACNYEilTQseUaaoiOb29VfXYtzH2Tl3W7QiguWYL7+an04QA+95SYR2GmN/e5vttO3YFhDKlaUrHfrAwgmlXYkCB3tr7j7OI624eCOHuNi1lqvl6xWlK05TGsKHeWvT9TgzznLfNf/16W6ZMgEjRm97uQxIE+lynfR9PkKt1O9DDMVE6L6fr9fj1y+OHz2cA3LajTqwNHk1PS9KB+6FSZFnyUOjDIDwXqrX0reeCp0uKAFcFAQ9sbT8t9eW11hKnuRpAGypEiZzCa069KSABICYGg30f53NSYLUoJT2jMt9v3VRNnYlrQVMbYxBBz1CETlOaTmXbDLTPEz5uWyI5n1KtdN2sNTuf59GPYx/zPJu77Dtsvh9tpDwdzYhoBNy2BluUnELAAFLmcEMfyyxfv+7qcfm07GvzpsucfvhUQNGyIOLt4xiq80mIaN08AFo3N8CInEiYMQgc1/vuAQHAiAGAiICIFBHuDmYOEGPYPBcHfqwHIAaIOajqsQ4iFOJpTmPwtluEEScK6HfbYbRiEEiCBu39XAj4NIEI1Um2o6j20CNPmTj6aASlZDSG+xFuEUAAlgq9vS1V7LoZCbWml3N9fV32MZAlE+EwQNu3FggIGAEBONQMNDzmSTi7atr3wKChYR6AONTZAwmZ/9LhYo9wcHBihgiPUAdXdedaUkpCzAB+OcF5wT7cTO7rOCLcHNwQWUTMXA1QvbdOnAHZXc28906UIiJVZOFhwywgQOiZMAQPUIsxLDze3xYAYKJ5kqHeumNAhKMyMxnhfespB3dUwKdGr7v3ob2Nl2XaHg2RKKGZRmCehRHbYa0ZJwGkbR/7MSRRmjknAQ9zAw8dzR3Bpffoowd4GJ7Ouc4cpozgAfsx2tDRR+8GQEvJnEi7AYCqfzQj1HWL+2OcTvLjb04YuO12rnWeJyTYj64OnIqpj0PVehJhFHPjBJSoD9ibCftoFg51d0nAKDoA2XJmiAnRhw1ggmcGjPkphXCAkgQihvo0C0vZHke4nt5qEgoDCreAUCekERHqEODmYWA9SoaMvK9qBOGYUvHACAQiHUYOU86DR+u2jSbF6yxDAyCO3lV9uLZmGrF3ncs0Bny/PojpcioEMTQ8kIX70QHQzFKmAAOAqRYb0YfVmj695swsCYeNLJFT/Php/vbYHZEhLTWr2sf1CHcEcIcIKTWpDkFa5rTupurCZBYklKlgVw9H86FxffY7SoIAsyAmU9+HMdLb20wSX37ZPn2aww0AVT3pECJh6qoAtB1jWaYs3PrIJUnhQAIKQmYgN3/cd8kiQMupMIUj2F/q+b4+BlhIQkJ09eMYw2x7hDkS41jHtFQWvt0GOAARAbhBePQwj6g1Hbv3dszLlDIx0KhJh2rrMgmgrqu7YakUNiIwJzL1b99WN4gTEsLLkhOgDy9VWm+ZqBRp3Y/uasrM2hGJh459t0s+7auux7GkKSDAXJATg/bRAwLRwQIczDGQGX/9st4+2jLnwvz1++FVLEYAEJO5v5yny8vy668frY/7DeZz6aMLS2J5tF0Qa033x6rqZU6jK2Ue1lPCkqWNJ+UKGdDNllOZysks9n2kUvZtY4k88eU8n6a0bXvrx/unl5o5nP/6D2N4FOFpSY91jB3UI7onwc+f6jJbgKxbVx05l3aMbe+qKplQIgBG12FWhHw4ZAkEB8uZLufShm1tZOH7o3OEa7Rmc847rrlm1+jdSVA4uUEWaN1Z2MJ6Vx2BiGZWizDTtvVlLmHurjaIOT22vR/t7WUGBuRAABtxHN4OHcMCAZx6d1QQoqE2zanOohE554/HNiCkoIHVWpKk9XF0A0QZ3q31QEeI4zBwKJVbU8L0VKoA5qGWC88liZB1b30QESIQgISAEiAwIzqo2q5eM0iFQkkNmo5ASkkeXx6t22mSlPjj2j++jyW3nBiFKJifjQYDBorwJxaKGSOiHb4eRxIqKQtza9a7zue6HzqGHochSR+gNuYlj2H7buC4HnrsYOqPdRPGddXEEjTacC4UFoYW7oQJnftht/sxVRUWN7ThQNpbH2qnqby/Ts8N66bKTKOPo1tKad/bduvBkeqTQYHDByJCgFqojVLSfClFcPQ+zbm10Q8VpMs8EUISKIuMEccWmEaZcjR/HG24AkLhydWfqKyU5faxFaPXZeoa+8M8zDxG64JlmdjNtlXfPy34wilJSawm55NEoKkmJjC83zownOeEFlWKdfx2PwLGJNUM3H0fOlxTklIEYF4P1S/rMhVJeTQ19ePwpeKvX46J9Xyu75+m2/UWgDnLK9NSy9dbBxqmJISPxw7MwqRt9G5JGCkcyXqbzgtglAz7EQj4rFtMM799mrzHdoyjm6of/Th5XkqS82Q+UNCBSpEq+Wjt2+3WG6EH4PHYtGb++L5FxAr4d9ftr3441WrgHEr3tS1L+bjffvPzNEn68/fWVXWndRvhkjK/nHFK+SH5y/dvv371iNOnH+plOT3u2+OxLvNpmetzTfi4dcUmRKMZcdIxbh9fuykSIm7/8B/9u/+7/+h//7h9/T/+H/7jf/ff+4djxyzpn/2H/4s2HqPH//P/+/8Y+u3LL/cff/N785FYvn75YMF/8k9+/+3rn/6d//H/9N/7H/7Tf/rv/INf/vzlz7c/LnOprfz4w2+/Pb6+/1S3m/3+f/CP/tW//Jdc/Me///rzy+//2T/7D8PvP/zw/tf/zX9rL7Td/u7+YT/+9jfqakekxKPBx9cv//4//Sc/fjp9vX75dtzeLsv/6n/5H/1//ov/9j/5z/5Pv/v5d209tuNgBGSVNP+v/zf/2z/88W/+0//L//n1/X3tPZdEDN9+/fqb3/2gPvpuQ7skvn1vt2+bUf/x5/mvPr+q4q+/fk9C6IHggmCqp5r2o3HOQqkU0o46bJ5za63UnJJ8fDl6Dwg8dmPr85wtXHIOd1c39/NpAU976223JMl9TJME+HYYMZ2qpFm+PvZ1ja/fdgcQ4TFGzaJqgpx5zEsWnHu3x/323PXGGtOMhFESpQn2/fAo4whOjKhoTuI+vPeeCoUhYXq/nCo7Ibo7JTy2nrlYtMAMBkIUGmGWMw9TydJ1tMcAYCQoudCcUu+9m7m34SyUpzw8IDwMhwFCTLMEYM31/SX/Mj5UFYHaoSIxLTJP5bHu6yOWuYpwSfm+7q3peZnHQLNwg8fRUiatLsLunkUypVOh1v1x9OutIcC3j/3T67krrPsuwqVyytC7qcaJ09HgaLsU6oOEmTiGamK24QT8cW0iKEK1hAekwhpKhBXE3GrlH94XEbo9VgkZu/dNlyVHlT4MIBCiTtnQPQAIR6ia9qEBcHqtcxF0iggdauBCpBY6dLhv0DLj7393do/H4Zdz2dYOAYjAwswxuteKDvbt3o6958R1ymUiN+fEU83CVEogHNd7d8BamTJhKolS3x/llJc5OQULjtV0RKZgIjOba0kp1MbtcTDBtNQ8i3YnCqkCgECo7sNM3TnJ3kcqfN/uQQAuh21JZNuAomn0NCVGDogkyQGbmriYITPthwqzuwoGsm/rWGaZcklJ+r1v9yaZl6VGWO9a2F+WGcT7sU+Jz1OaZPx4Of/pa4NAD2oWl3P+1vU41MM41SmX4zgi6IfPSxEyH69v+eMbXO8hEv9/lv40Vtt2vevDjuEcruEe1nqG93n3aG8bb4yNnW0bx2CgicGYoQFKSyGpXCWkUZQPrapKiRo1balalAaaBKUijVogZGjSkKjQAIFiBtvYDBbbNrXBNnja3sM7Pc+z1rqH67rO4Rj64d7r4/qwpCVdw3kdx///+03T2LrlGGKMquLe1dAVts22WmP2Lt6qNmIT7Q4bWCw0Tan3XkrnmKaJ5kzTwNvWUiTGFEeiIJGYiE9PtYsiIAKaWow8DhnRbgoXcABUDNSWRsWO+13p/c1HF2J89+VcRWuTFOO6XgE4UALuraNqQICuwgQxMJF30ZAiujugY1qvPVGcRjxfiyvtx+F6lXnCGBNzO+zjm0eqpc1zqr054jhO5t6KOBAbNCQKPI7Tw8MZENQ8R1bRraoaRHAkr6WFGEPklENMIURkhpRxGIeYCKB1E3cwgRyxbL1uhoRgDgC9dEbc7QdCBIdt7TFgAEJ1NQALaluIgZBLbXmKpr7VfjcMW9G3y0rk5rytDRF2u3nbKiGti3WBPHx1OYpiXYQiTpikOTqmnAwgsF8vmyHEAQM5kb54Z2JUdzGIpRRAeP5sl4cUmGMgAGkqzIkJa28UYwqBHG43dWBe10JAd4eBAB6utYs6AxNPI6uKOgUOgbEWaaJmRow5kjRcTWLUIYd5n5wBILbNQiDDoN6R4HqtDpSG6fTUrqWGXiVPmc3LVm+Yx9Z1N+cYcCmN0a1DwGRiQ47TRNO0N3UkXKZ4edh2c3C1a7HAsJ2sAzrRVo1IA3HrDoAp57pWEYXgaUqoZE1N3QEMAAGZkAjc/bZo4kAh0jTlIXPv0lvPMQgKxmCgSOiEKlqLCEKr1msDov1+V7a6llXETckdXr0TpuSvX9eXd3HaZSmSgyKQOy3Vc0JEoeDIw7Z5bdo2AQB1t25t3eJEH3959Bf8uK/WIU4pNjo/dW0O6CFyHOK6NRJbtYl6HkjU0ej0VJaFpgmJeCvCTA5q5kQo5gGJEO2GUiB0dXd3BEL0G+fcwQXAjQl2h6jqW7Eh2ThALQYAN/mUqmkXIEQiU21V1LT3qoYxsiPcMEJmfpUWIhExmJmrOvWuogKAaRiGYZAmTKHWqqBD5lKbK/fWh4GHxCJaWs8Dt4YIFCOiY5g4hnQYuYlcrnK9dEeICQhxnrI5XC4VgBBAqrkRADFTCKxdMTBH0qagbqohJDDaioREOTMFVzUVJqBtEWdatl5LCXzjLmOrdj8Ej6F3M/DaJMRI0Y/PkokOKanJDEENns5LQh+HuRcRcXQ3wN00PLy9jOOYx+AAAHjdqiogBCMy8MtWuFEKXkRyCBHApB/u0tZ9ufTeu+hXZ5QxRRWtpc3HtNuH5WQeIM1BqrdNinqI8dblNWJzi4GGIZmpqx73OUceEiPZutWuOCQch+Gyau+9be2qMA+JEbqzCTe1Kyiz4g0spXhdqzqYmpq4Y9lkiBxT2rb+9nEdhliKoJMaqAM6xMiAikwJ4nK9hJSPd6Or7AZMEQDhkIfWO7g7AIAFxER0MxwNmbe1M8f9ITv21rybzQNFgrVIzGgBEnBklmo0hNYKBQbidWlIVFtHJwqMSGa3IApclrKfsikuqyBRDNHU744DsWm3Id2m+IKAIsAciKA2uC7X/X4KHGvvajrNcRpz6yZrFdGU+XppBICBWjMxiyHHKOCwbTUPsRSrvRNhTCGmuFw3FQshAJK7mTgAmHnMydB71Zy51JYC5jnVpZBJyjjMgYyBsHV1EYxRDLp7rbatgsxV5NlxPp1WAUgZTtcqvY83CX2VEJkpbGtTJ+1G0TGly1oo0HF3eHramD0HtqjdTLqKaRNI6daq6QhuEd98uKp4CNTVROR0kjzGcRcuvZk6AEm33hQRS5PR0/GQImcDxa3FxGIASlLFHWsTV97KloccYzxd1ki0m3JttdeqOnYVQHx2PyHFuq15SuMuEkg3yft0eLEbU3QRYvjar92/934vi6C6QbjW5iLkIF3SkNM+9Q6t9uMuE+a3T9vrt2c17N286+mpUIjstBtHBVM1BMwp1lpbKRySCp6WfllqDtTV1WGY8rybWlMBC5ldYd2EkyG4AZjdwG3IAYgR2BB1P0/r0krpd3fzw2lb15Kjrkszs1rMQCjgkEOtvi5NzUUtpgDATNy7rkW44brejKj9/n7ihERIRvuc3BFDeOxluW6BmRkIsa9yGwjNY3SHXjWQm6IKesIQAxIQYu8m4ohASITYiij2IafSWlehgDkgo6tIF0Ow2ykHkEStNenq+3lo4jHx3f1s7GGkpj1wUBV3Z0YkZyYONwI0iVrtEgNH5vNpA8BpimlIIWJkziHesgOAHZGlm7LlxKpAgIQgXWKkSLGsWwHd74eUYwyIhpdLJfJ5TIBg7NY087hcJQYKERRdFPbT3Fp7ulwN8DgPIcbrUk0tR4gUehdAYsbdkDyA62aOQ0pau7ulxER4y6uqOjTt4qWWeZ4d7Hg/5yGUXq9rE7FIGJ2JE5OoOFPoUh0gZG4mtbeyVY9xzLH3nlLIKcuGH3v5bLnUrVnO4fn9OA6DdEPozHQ+90hubkgUiEs1A00ptK2X0hmDAXTouzicn0RdQ0RkO+5ypHQ6160vgHFdzUTSGK+rbGsrtY8p5EC1ql46BcohBapPp8tnPjkDYmodIdfVtAlHAtCyVXCIMeYM5uFy7YTQRLRJax5D6F0IeRgjE9XSpHup0hXcgIKxs6pr6wDqHlLKLr30VlXffXH/5nUpxa7X4ghPT10VpzH1LkPmpfd8CMtah5znKaGFFDEQFek84n3YP/RtGIOjb7LNPszjUOrjONCQh2kYQfTp8ni3u7vbaCt18WoKvUrrPU9MGECttu3ucPf85Tu/8itfMJRhnP/w/+mP/sU/999+4zf86j/8h//Iv/Nv/5vf9zv+wPnxsfbzJz/zjV/3mW/6e3/7h168/OT/+H/+vzKT//L/+Se76/d+7+/+8IMv/PzP/3TO+eHhi1//9R979YlPf/dv+m1/5k/+Xz/40vLO/f0nP/m1j7/4dHrz+E//ut/ykz/+j771W379T/3c3x/z8Ht+zx+4nD64LB++fPns27/9O/+bP/uffvO3/Mbv+B9+94/+yF//wi/9/K/6+l/z5S/9wnJdftfv/B3vv/9Lh7vpn/+O/+l/8Mf//W//3Hd87+/83d/5Xadf/NKPS5EAEIiNZX+8+7f+rX+XzKafmT56c4nUH9+8fu/990Wd8vj4eBlG6C2uRacRCF1Vp2lo0s6XzQnH3ejCzNdpCgJW1vqxl8dhih9+dNrNw5gmTQbIy9KIQ6m91ejurXU1vR93iH5dKwfMmF28NRWX3X4OmK4f9tpbKQ1DGCZ++WIXA52uUrsSe04DMs+TnNcak3dRDmhduwaCZAZvl8ec0osXs701cH/xYtwfhrK1UgoHjjECgjm0UiBiTP74+JTTVHsdUi6b1qqG6mAUw7hLmRhdKCAEWrftsNtDpFJ6Wfv+MATisklXAAKDvhtH7L2Wzhz2I799XLqKg6cxu/q6lN61VkOCaRenKYmZgb185zDE4StfeShNOFEIwVQNbDftunT1PqT9EJMoLJsAOrPHISIjI7qDql+XBmDTkMDNA3ByxlgXIWczGIboaO4A6JEhhLis27YtplSL5gzutmxpypEhEIYx8ZakbBITEVPvEALd3w1TE1cH8C42zyFENPBpN0qFabTDPiu4o5+eVkbu3ThyzFhKaxffz3Ge8pjDWvq6aFk0M3HgJt6aEDkxAhoRgikg1dpLE0BOKT3VquqiQBE4QNlkWXh/GPaHQcxEDJDMUOR2jHRid/VxikAODq35UqsjUgqf/ORxW+smEDhuay9FtihdRR3FLHWe5xQydo1ddMjcKikKINTSmW9vPwe3QCDqtTkiBkY1wQ6qqK0FTCLAHEmJI7t5EzOFXn0pq6oe9iM6ceS2Nc5hu3a00IsRKhO5d2acUjpM4TDPDrFVOV/q/pDdYF1XDrMB7nNct+tlWd99ddiPwcyPxzyNIcawmxkRVOc0+G4i6X2Mjog553lgd3/+bJpGXK4LsRvw5bwFjsNI45TW0vMQ3OB8rSLtBlZE4kgM6Bg8DdHcW3WROozpOM3lUgkxp93pWhh6xKFs3V1dnRjBOCWmgIRADOdTHdNctrZuJs3mY0B37VCqDEPoVQPBddN5yGrctdUuw5BTouvWTMnBh4Fq9SlSv5UtECIGJDZ3Mo+BFJQ5dXEmf3taxzEcp5Fd33mZyWMMvhZezrKKE1HvxhGA4Hg/OcPlqeUhMdNNs1GLiXgegpu7OQRstYvYs2fjMMR1q+ieMktzqTrtYk4UHU/nvhWpRXs1dkfCwIECbtsWUxzGkIgr8rZJDLDbJQNYSwfHr2qFA69LjyHMY0aAEEi+arcMLRi4t9IIKabQeukKIQQn02aIgASo3qtdTVNivBX2iXIOpXcmj4xuZqohADOp2Gc+dYwRAwMwiQIBAAYA6GtXAyTKIbhCUXVCd6ut384t50sNMYiouXUDBwP32sQdY8Ba2zAwUGxdmX3IFENkcLS6LCKJ4xRA8f0P12mXOKZlE3NwlNLEhAiCdQwhx7J25BSzt9rc0VS6hV6ta7MW2IKzMdKyaVIn8jHhlGjKeDfmsqmY5zFsVXpzQowc1L0V4QwhsRYs6yaqHIgCi8qNSa5uFEhd0T3nhIhNDBCAIOXEAcwM8QZYUkQMRF2kdQtEjjc2p8z7HJmUg6gT+GE3EgMzXc5bb9LbyHfxYx/naeQUwqV3kXa+8vHo4AUNCLkWMd1UkyupOzEmHo/7OM5GDsmGkAK/iNvqpWhAPB7i+akZcBeJITCqqoVAwxAdFBxjjAguXS+XstsNt+/JwMHYmMgNHRwAzJ0DGaJoJ0BwVFciJMAbscwdWtd1cQ7mSGrUa1HzLtEVQkqtirqg861ThcBpyEyhFnEwU7/JZRwBDVAdkZAgxqTm7joOsVRZL2V/mO7vQmtaN0kDlyplFWZoXZixslWtqu44BL5N6JwBGD0EkObikHM6HOntm6uLUcActTdrVdEhxiDdAByRiBgcCKBsLc+JCQ0wZkbHbatuloYhMKObqtYNElOKDAEOc+yJxEKp1U3yFKeRufNH9boWRHJDj4mHIfZuy3lzdyLetraVJoFr3UIkRBdz6eJap93EDJdlA0UijDmJFjVDIHfNQ7qdpDGIARjg7sDgNRKHyK2hmfjtP2Lc7cfa1EwZQ0w8Bj09eRNAVCRGwBSDBmqlESIzhoA5DYmJiWrr80DMOE45VGmqMcCU8XHzpiIdGeg4gwtq7SlHAu/NVNASqkOtIF1cjCKr+bZtOmZVU7NAZGwm3lsj5nEcWhN3REAXCCHFlMxErE1jMPd+qy57jBRq7UDOTMQUyLdNHH1/F4eBEHEYvNVOIWZicGWi++O01KoqHCI0C9njEEqdzk+boiGy2w1A7qTKADFGcyUSB0o5zLt0uqz7wzym0LcaCAx8GHm7tinz/V1o1VpTZDJFEXVzMnTsMXOmUFtft5ZH2k1pu0rvvdTORGQECJxCE8XAEbiXGxNYxykMMTW31jshIsLtjiQEAAyRzG3eEyG0Zvt9KpuECDn7fRoeH0przZaKiDlxCpjHbAa1bkTMSJwYAdSgtgZIVdSrtCKtilTnhIFoXetuHhhRQQDQFK5bJ8QUUm++lV6lHXb5bkzLqfXqd3ep1Q5Cw0g3iBtjGBILG4LWRaWZA9TaUuZAxEy11Rz4hk9Stbdvr6DTYYY0pnee3y3bZgtEhOIthLgsK47DMIyc0cBy5Of3x2GKp5MT0Ol05UDPnu1ePs+1EfIzA6/LRgw4xN2QXaGXXluP0XfjsBvzbsiHQ269RyZR31rLw2CGvZuazbtZO4wjHg/xvIRtrTc1WK3AirW0rjpOSUQdmBmYMfLAQIb4sK5m1oyAA1svZUvDjiigGwUydkJotZveyv5g4DfpGyqQ0rVL64sDisJ1LaWINPTaSxE1T6EaOjZEonHKrev6uASOUk3rFhMhmYp0dSYixLv7cTfzPI9v36wfnVfGQOwhEiONmRiDu4E7BT6MaRySI6zrChQ44N3doAohEAWrpS4X7dGGHHdTikRE/LCUUrZ3nu8dwelWPkFCFHXtQIQYSBVK1SK+Fd3tUu9qYIh22DEAtWrM3KoAUUpB1F1Bwe2WoEkh5chMRKjdShVRpQiqXpqE4CmFYfakWFc0tTBiFzOVeRgfHlcH3x8GRFTz/TG7+e4QbpefqF+XyoHNm6p3l3deHBJloErBHQwcUgqmsrRtPkTwsJUWHHq32uT+fm4id89SM+5qrl7Vpn04zoey2bpWUVF1N0BoJsGta2c1RsT9foyZL9dVZACSpayJYtRkas26mrZujArgFLh3uZyrdluutQ2m5MdD5kStlt2QwxQRvT106f263gpfgAhAoGYxxutWrduYOWRn4tptWaV1YXKOfLcbM6cQuGsPgUNiNGjNhtlfDLuHN7W31oHcxDycTnW3y2tt4xjefTVuTatUcMojbg2WVTjHmPk45zbj2nrKQQ3MGmOMjDHQ6VJU1YjWtaeBxymUzUTsnXf3h2OWWi6XBSBwiKfL5qCf+th9CuF8XRE55xACv3nvycyevzwcd3tEuNvbxftSQ+/9llIkDtB9GMDd3nv/kjJGxJcvprePLUT/4pc/un92fPZ82s15yGFdWhr4K19+fP3R6oqHwy7y9PbtNUZ4/vzu9evr40PLOW+1L6XHGDhFVpEmPk/DlHtb0jCKuYhM+xyn3Zu3b//cf/1ffMfnvvWP/rF/+5d+4f3T0+MH7//8D/3oD37dew93h1c//wtf+Tf/93/8vfe+/L/73/wbr9558eFHb37Vr/41/4v/5b/xr/yh7//4J979whd+4ed/6ReWzb7pV3/77vDqN/2mX/dzP/OTf/m/+//8M7/tt3znd3zbBx8+fOlXfvGf/Q3f/dnP/o/+1J/+f7x65/nf+uE//6WvfOX5s6/9ts/96g8+vPyH//d//U//qf/bj/7wD4cA77zz6ld9wze+ez1/5ms//YN/6y+kYfdths9ffPwn/38//d9832/5P/yRP/p7f+f/4M/8Z3+KJDmhEbx48bGc+F/9l/+lb/o13/TJT3/i2bM4fsOnf+zzP/Gzv/RPXJnTUDZrvecpYEBCROayFgO4+EZAouDARFg22e3nAPh0WsTaNKUcSLo8nRep3rq9eLXvsnz05nE3DTDE918vz+53gbistXWQqkws4vvjlJhE1dTAo0i9ntZ5N5bSLTAoPC7rtsnd3T2xfuqTxy988el4GN88XEvRaSIE2SqOeQRADnjYD5Gxq4YAdWu3CP3j0zoOw26K6FJFI4bzdTVBs4qRzKD0vrWWUhrGUaRJ6yFhiNyqRA3g1lsfYp7GbOJ169oBAecED6cVYzzLZeJUl7I0202TdVy3xgFidOkuguBcakVgIkkxxEhq7X7akeM0jxzbum1udH+MCNx7zzGUvpXerquOczZw7nALwwLAVpooogEglGKi6uZAwMGnkRPhOFPK2EWf76bW7f33niIO7kW9Hu93l4v0JvM4zjOtm5yvRVXHKYdAChoS7vcxJg6BmcjU1MjZA5MXbw0S+7VUYgaDu/vhdFoMOIRYKwBIFxQXDjxNsRRpTWMQQjCz3tTMw5yl2dN5yzEQ9pgDMK1ba6iROacYIpcmUgUgOKgjrrUz4zhwq7JeGzOSkSPUrd86MqaDu7fggUm6TEMCAwAYYggxSrfLuZVSu4JKW2snCIECIYmWKQUVOV/LbkoxMDqi43LduthxN6tJrS3GVLYekA/HcV1ritiqWsAQo5oRYm8qIpASDQzOTw+LdR2HsUl38Mvl9vEsXcyw7aZ0Xer5VHZzUpcw8Naqs49zSDEwkAOoexpia30roo45xtJUPJS1hBDV6um8vXqxe36XSoKt2MuXu94LOOQciVXMUkjaQTttm8YYj4c8ZUD0PCVDSCFM8/PHxw4XP9xleVRw7E26KTEQYG+eckpTbK1yYG3CzCFwinS5tt1MxMFdpZU5JkB8fNiuS0VmJBwCb7WrOiEEDrXLugk/bUB2uTYT3I2xkb15WxC9dd1K30pFYjXNHM3MDL3CddtEYcqDOUg3VUshKEBmcoNqBmpAECKZqTR3satob3J3GLelW2lubTdlA1Wg4w6gw/Uqx7tjaa3W7oS1boCq7audendPX/2MtdqEGVIK8y6NOfQmMaK5dBFwQoBWVVUpxswQ2Ic9hxCuZ2lNcmYHZyZ3IabbZSlmeYghs6nmHHq363V1B3csqwxD2LbeUOd9DpFDDDFL3RQBh0TvvNx10a2oiImJu6uQw82ZAe4ITlsVMy7txkfGyDAMaSsdCDCym6sYhRAZ3W0YKUWQ7jfWinYz8zHGbq30HgIua2ei2owZOdC6VUTaipwu2/EwA5C5DZlUvfYCiJFvBym8wVZTiMAm2lOgwMQpRMZaRDfUjtcNu8s4khu0ZsDOSHnITw/LOI7h6by0TQIzEhzu98uyQiB1R3dzbKuk4MSex9y7SCdiYsA5QwA3x5gR0R+fpHbnQCpeezNzMHVz4qDdEZkQEMgN63rDDYcuDg7uDm6E2HpX1ZTyfs4hUOtNRM83aa3T5dIX7Lspifml1mmIMSZEv14LALk7MEhrwzilkFQ1x5AiN9HTpb77MhmEx6d1a46YKfVeu4MbBkKOQypVAtvL+7Q1DBH7hjkTMtWtffhmaQ279ukwMjNUV5U4sXec87ScyvWyxRjMtHUNgcy0tYbggBACAYJ0AUSKGJARyRXCGIcBGcL5tJVSwcHciJiAblvvIadxzK2piqoBEapAA16qS2/myhy1KYJRCKCeBs5T2tYWI9XSt60OY2qluYOjc+AcY2vd3JgRCK0rMhh6SAwGMXjvtq7iCMThfGqlaIwQQthWWa7y7ospDriV7sRNpVRJ5BPmYYiM6M26+PPjFIkfT4tqr6U7EAJR0CYyz5Op3aL7zgjojChNRSwGZsZS6jhlJDLAKkYAralZJ/PDcWDnXs0Met/MNGYaJmpqq8juMLZFpAM7NFXtnlK8rj0FFuvSIVAspa9r3e+GYeSt1lrkOIdpDsuyXa4LGKUYt1KevTiKynJZU05NBNGIAxO3Vp2RMIhCV61rE4cbMQbBtUvZQMzIrG5RFJBduliXOPAwJ7xNbcHnfUbEJjoMIRGVKlfV+7thqWJuIjf6nnVLKSXO2hsGhVJ9iB6ZDMIYaDcGJ96WTgCtayDqjlUsAM7j0DZZ1kZEBG5gvSMAppxS8CaKNzliMybCAOCEjtpM2DmTq5Hr6q1LC4QOWIukyGGK0xAMLA3E+2gG4BgjP11KZBxzWlappojOiNZ0HPKYDcwQwsIkqq23/W52w2VbVd2IAIkZ5jmHQMtWeQgHml28Qb1/mVLCZfU0cOIUyE8XNfVxDMuiIj7v4lq0VPEE1j3GsK4WIzi4dBA1dxcxJUwIIVEt3d3GcQCBlGOX7uDu5oMHQBdyhps1AAFuaPZxjOqKbmZ+OCQGOOwjEaxrOxzyPBMRTUNixm1tTGHrzQWGnMAhMPjNJSG6rDoOOXF8eNzO17qfE6C6kSmAmzVxtxBIq11PDXaJmBisdTV3M1i3vj+EQNjU3ML1sqi2aYrTLsxzvi6NA6Yh7Oa5byZqTp5CiozVNKTg6KKCiO7u5q74+k217neAMTIJRIBz3QAgMu7GxATMvJ/HSJVeHO7uhshMeFdrl77Nu93HXx2m0DZ2jPz69UVF530mdnBFB0ByUzAupQ8pTMMoYqVWCcRI4zioojo6aNNuwtKgSwf0nPhyshcvd011a7Kb4HRdCDFG7mZbb+DOFEvp+8O8LBsTMAUH1N4d4boIcwsJt1oTBAB0gJgiAnIgc0H0dMNAGWxbb12gISLGyKbupuvWKJCAxUAh8U2HjMCXcytbn3eTqdfSAcB6Z2YMxGSIMO2G+xcjukmX2xzk4bRGxv1udtPWbBoxRpp3/OrVnokfn7br0sc5pxAj4W6IrdtHr6+ldGZkQmmGjimyqRKpOSJh70oB1DwQcWB3iIFzQgdYrqWVNg7T9VoJmQDVVKyr+KrUmuWY7u4YGRBwnnNrUrsgobsNiXvr7GHIuYukIYjAw8MypDxPA7qJdweZpyAWRDsRmHmtXcWIbC0SA8cQt1oBgNmHMbj59VzMITCHGLvIdakph/0Y0LBINRQzD5R6015aCDRNKQS6nro0U7VS/HKpiEG9EfltBXJLAczDMDAXl8Nh3tatlMKRhhjMPKQcAiPGeRyOQ+wktUhvF2IItwOJtK3VaZ7A+HpZetd5P7FoLSpCy9JEyKp0ERN363f7WaAb691xBsDT0zkxHw9DCLTVTgCHw1iKLEvvotKtFkWidruv3YaMc0rsVFsHcjRw49Pb4obERIlVZNu6G6SEKlibqHngBO7LtQXCbVVzu5rkYbg/pK3UYKqCPDOzTFPa76ZatGyNwHtrCPz8flb12y7KDTng4cUIOOTkAfxaO2G6nHsciJCQabn2t8sSR4wZA0VrOk3Tupbe+oo65JlDNK/EhBLPp1XNHt8uNw8miB2muW4CyV7dDbsjv/noEqOrwBd+4fzpT+1uz+pxSPf74/l8Xa7bNOTWW0zUq77//vnxfN0NOzW5XjYk1C6H3a4hAOC8ny6X1ZFKLVW6o1LgHH15e/n3/oM/8fqjL/+1v/HDh7tnpv7LX/ilb/v2z+V4l7D/6s9+8vF0+uUvfOE3/sZf/92/+XuJ1Hr54M31zcPT+XIVsc9+02e9By3XbX389b/5N/3yL/+Tf/EP/Wu/7/f+C3/yP/oPf98f/P0fvPeVh8vpM5/95qL25u1Hbvg9v+176hMf5ulj7z7/6O2beZ6/93t/q6j9/j/wB3/873/+7/7Yj4n549PD13/Du5FsHOj3/kt/aDo+/8w3fO4//1N/bNn6/W6XUDnlHPCnf/qn/oX/yfd/67d87r/8s//Jd37X7/nhH/jL3/qNn/25X/r5r/naj//MP/z547N3h+wfvv9+ysPzu/221Vrr/pB3+922dDe/Xq/zOMxjBtUXd3uD8JUvv384hnEaSqlM8uzd+0RBRWoc9ofd0+uNCN95cd+7P22tr3045LJozL47jGb29LgMaTDVsjUxYIZW2unJA4RpHKcB5zlNQ/jSF88p8/1hYrOPP99fl8Ig8xwR4zjA3d2+VTk9XQnp/j69fTw1sUxRRNycELdFwTRkIuPlvI5zDiESQ2/SxWvx09tzyunuLhviZdtUHIG9ry/fuc+JA2IgGu/ntw9PBnycR9Nll7hBfDyVk20EAESXy7YbR3f33tuyrBtsTbqAiKVAiL5eK3kTEd34sEtMetyN0tXNVAyDmaEKD3FCYEQnByRYuy5LG3N0o3UrZugId3fDMGAr0nobx6mtqlFFaxdASq17CqgVaoUt9SF74KFWBVAHW0sNcQAgAHVwB2/Vzud2d5wRsFcDR0VFoFZ7ComI5iEzsfQeEWJI69of3pamFiP20pkYwVNMlKjUspvHSLysbdnaMDAz5YTStXVV0VLKfjrOg69FUpxOy1ZKASBCHIcECKaSIu13MQUySk16jOTitTYOQUXNfVUNCVAphKgizHw9922t8fkwzZyHyJzB4Nra+VwcfBgHGm424dsuGlLOCGjqBIGRHa2DuWmtgoi9Qek2TYmI3YlvAcYAW61rMbqJKxIDGjkahGtpE9E4jgShSAfpj5dqotsmDhjXllNoVVfsqrhsEghzDr1aSN5Vx5QC0elaYwCgSNgCOxMHAqZYi6y1gIGKtdYrh7JJHgwDhiEK9jgm7d56C07rWsDtuN9TTHqUkPb3x+wAp6dLHrIqlKZqCsTg2BuM4/Dw+ty6Atg8T4z0+u21iSxrHYbQa1u3PoxpSGEauGy6FTnO2T3HoNbhdOlVDIhNHREwABNRYHC6Xnurwsh10zjglFNHOS9lzKM0IcbWrY3OMZJjk05OpWhMHBLEEJFCWWTbOgZgBkFWB3VrN0osEChMI9XeCdHVGflSeh8NOa7S2E03YYbWLAZWpNb16XQJOQ4jr6tsS3MwMwQ3CoFdWm03SEWMHgLmyNOAIehuTAbWu/Xaq5Gb1aZ390OItBURUQohMo9jqq3HSKUpgc1Tqs3dvRTJKaYITHxd+/l0IRpMkAOqQamSAsUYSxVudc9jJMrzNGa1rheTecQ8ZDNbl3YpsbQWOV/Oqxg4egDSW4WBfFtXwMEppEgAMAyRowMZBXx+P2qT61o54WWznWdEFGkJwxDSddseSmsGhFoLnNfiBoEDMRJDVwuBxACQtq1yjHlgNRX1POZa+lZaCDGmFMjAvZtxhnHO5drzPszzXEstzR9fV6JAkRwRkQEECMQhsMdA4zRwwHDbBTihqi3XghzMxdQQwBTVsBSNCRx7COzdnQCIxEnEn556bbI75FK6ugEiIhFhb83UKNzsJBhi7lKsWyk1cDLzkBARwY3RkWMtnW+j0NqHHecobuwU1uvWBRDRTAmx1K4ABuAAy1Y54DDGy7UTwLb0jUmVpzmlQIHCdb2WbWOG12+d2dalDwOoURy8aioV355ErcYchxy3rbnL4RiYXFp1wG0RQop5EO+MpE0hYp6H5PJ02sQadI3DMM+jmdfmKmqmSCTSb+R5YuZA4xxHYkevpajYNI95AJF121JZu4G7GSCqCTgQIhA5YhdblzXlJOJWLIy5NdtKD0yqptZDoEDs5iHRMMdWOiiETB2diaR2NwDEwHxL+zshqJta2wwIKLCau0PvGjtJ91IsRmxF3DzPQbuJmpvlFNXB3LvI1uo8DkyEjKX0UiSl0L0n5nIt8xiZ9x+9eXTDLg6KHPjFyzBFfv8j+cTHYmshxfj2sW2l5QTH/bguPSeWHsYUIfBaam8OCAAMhuqyNvfSe+vo4GbTbkT2XtwJtmpDDuiEFLpqSIGQlvMqAg6G7Oag3UWcmc6X0oQIQbvVJrCUsrVpHGvtFLg3LaUDAsXg4GqKBCaSUjIVQ9iKIVEXSUOMjk1MEQkc3OumiMAZRLRtngIf7tOFEJzclQhiDIFCYOxmITCDOUA3ba09PsEtItRFkdzcobW7EGNARM8povPaYLmuXVScqpiabZuMA7vZtpkacCBAU7BxN6J7KR0JKLAYMFPZqhk7oJmHGACDmgZmc1+L7nepN+tdmFxFDSqShhDEqyNGitqMA6FBRjIwDlirltZVxZUw0zwFWbsjqOgtQ9M7IpOZgUsMHGIi9HGMajGG0EUc7O7F7nBIj29PAfEwpR7t8emSR8pjOp8KcZDW10ud57RV1G5MPgxh2aqbDZHBzSCUIutaW7dn91NrslwqIBOFIccb1srEBIgJhxCr67ZsCECIInY5FUQApC7mAAS3+G4iQnd0hVIVA4zCwB4YHXQIRF0OIw0hR8S1qorVru4QiFytVqHEjrCVFiMNwyBd3KCqEjMRni8tJCq1R0J1v6x9yKmriTlR8K6XXrciAN6bJ4Jttd6dAl3OKzEBgopfzj1QWq6FIzEwKnDiEEnB1q3fH3MeSM/KiZFI1QiJAptZTBh2OUyhd1mrqcjuOJvidi0x8DileYqsLSUQoxxkHliEjrupabo7DoeRtouU1obdPqcghLfU4W2wSwTjMJrZdSnguK2ylW5g58t6t5/3KTcVFRNTFU9EHFTEgSAFevFsnyPX5u4gas/uDioKAIBAgDnnhzfrbp66KoAPOZ8vGyEyczdV9a3UOUZEA9fA6SZnRUZkS4zuuJbV1E1RwVKKbRMV2+3GYYpNna5VTE1cydalEaFnjGMYc2RwJ+yit57pNI3jmGutxJhzmKdAAI/nJn27vz9OU9i2Ll8ddPb9MX/D1x3vjkOOabmsD+eFyfa7uBXtvUIIjuwMaYhAuN+FMYenUy/N9VyY8DANYH46b/txcPRa23G3m8as6iJWaxc3JJzH0RVDCIHlclnPi7sBACJgDDwNRsHykFQVHDjQQCGF8PCwLNUP+9nRS+sijoDSABxFe29kLkCeYgZDAhiGcAMeExMRBMZxiGZweloNhJjcfFla2SRwSEPiREOm66XtduO2yfVsvJfaasqpNo2k50vrve33OTluG66LOKgWP5+aGp7PK7GSkapNw/jOsx1H/+j9yzsvDpHtsJuXzE8neH53PBzTkOntwxpDutuH1kut6moxhPN5JYox4du35xfHAzKeriWFVKox8hh5nIa3bSlLYaTDMbfeVOxy2gCQrL94MRVt2noMfNzvX9yPjCzNAzAhpOi1dEIcc7AOpcA0035mUVtLTyFfLzLPbK4hsggw2Lpp7xII3UUBUyTppgaq1psGZlfr0jjgm4+2rWjKYTfn/RxjwNoCOH7p9alscrxLuzm2Wk1sPw3q6qbDGN792HEYbhFpeXi47vK423FX0+aXy4qA2qmroPr+kJHo/FSl6l0eYqDLde1FQsZxmlpRsT5kA4p5TB9++XUM+XgYz9dVzEyqK7jhtUuK/s6rewcgt2fP9vfH+cMPy9Op9ibHu8GdPvzgfDiOH//Yi1o3R3nxak/I10u/Xtfnz/fH3W5d2rqVOKQhoYk6cMqg2qRt7lLLRiHEEMuy3N/N77x4eT69pwbi4Rs+8zV53o+R/sHf/Cvf9V2/9dUnPvX4cPrgy1/8vt/1zzHKP/j83/8Nv/G7f/Nv/70/+Nf+v5/9VZ+uws9efOqv/+B/+zVf9237++fr0+lHf/AHP/U1n/k13/zNP/Ozn3/+ieG6ffit3/K5z337b/g7P/rD3/LN3xjHZ6XBT//Qj/727/v+r7z3/nZZtfXf/rt/3+f/9g996tNftzu+89GH7x/v7rYOX/rl91oJ3/8H/+UP3nvv8elp2s1WnwIbk8dIU6S1r/t5+Oav/5Z/uD39pb/0Zx3CNDz/y3/tb/+r/8r3f+Zrv+6LX/ziZz/7T3383We9rr/2W3/DT/z9H1mumzSd95mIFCwNobWy303MhEjrtgLu3nz4hhD2z2ZHYKb98ajShkOuq5aqBPT8nbnXZoy1Sut6eD63Ks18Oa3FllfPX877Y8z4DGE65qfH7XQCR7teqmjPpX/s5W53GGvtYqabv3xn0K6l1MMhx2Euaz8c4uGQTU1Nd4FMsDff76ac45e+vOSQXr2Ya9XLtbXapzH2hCnk9VKJYI4DamDo+zltSzudlhAcD7lXQeTnz2YCa7UBEfIICkPyz3z61enpIlWdx7vn42Wxy9pL2Vr3cRoVrNVyOGSkTED3z8bLsq1rW1Zx1yFHE6/d9vOeEPIY50N8eFuOuyEEEtXjLt3t57cP14fHy6sXx/sjO0BpQmTTEFII77+/mONuF9MAiSkyDDGoBbdwOdWvXC67XRpnJkBXeHy7mOE85iFRyrismymqakqACJdr1a7gQIRSbdt6q0qIqtC6AEJkvpzr02mdRtjtwjBxjJwixRhqkbdrvVy3mIIK5IRDwpRSCkAYPVEXI6DjPAxjRHRCrFUvizNhq84cciQ3zjmmHHJOrVoIrKrnS1GznNP+MCKIiSh4GiM4hWTjEPMQHx+X86lEiI+nZRpijny+bma+rA2cDVzAoqs2IWbxngcex2EagwGYmYFea6Xud3lGjKfTuasx75a1iFtkPF3qjXPniAA+jZEofPT6NExh2mUFB/Cut56Uz/up1F76NozZGbvWcR/DzF0c1y7dCMAZi3REmw/zOERX6L2lFCiyqM5hIHRwb61iNKbobubg6l3XG6Z2moa1rV0qQ+hVeYch4vWy5nEHgGXr83zbUaGpTePYRboDu80THnZDF1fxFCmRExKkcL7Wy3m7eza++2x4e+mPj0PblhxCjmEYh7U0N0MDAkYgYlzXFpgDQs4cAj2/y2/fluul9+bna1c3JOJA0gXAAxMDuPmQp7KcW9fAiAI5pxQ55CANcuRNVM0cAYlMBDw0sW3rpn6cMhEtra21tybe4NlhAgUOtK3VkVMIdZNeFZxOS5nGnIKV1tMI4ua1YrB5P6uCmp8u1UF3Yxz3gYBSphRhQ3NCVXA0DMoB7+6HtklXbd3uDjkFKsXcrQkSKjOpAsdgrYvotEtD5lL7VoWQsFk32e1SLVXN+asxfhySIyF4cPPLpRPBOCYD2xYJAQGByYeBS5MmSgQEw2VZc+aBRu1OiM+eTYwsrTvZ/hjigZCSNBxmMpFl7SbWmr14Z84DnE/jeq1qAhr38/DqxVRla70TJDc3sHEKyAHdEdxdKQRVVJVbHUekW3fpYIpdxJ3IAU0d2RTcPUaOiR0hJhozL4uWqwAZMQG4g4ZIbsYUMAm4j1Nkhu26iqA65jGAowko2GVdxyGgmxkUkW297qZJxEIgooHcIOXUu0g1BKvlxsNnJq7NHC0k7tVUNHpvfRBItfb9fuClatOU07IW7wLIBBQ4NPPe1EyQsAuYGhMAkIq6eykeI5s4B0YiUCWmHNkdHHRZvXaNKYQYMJiIESIBiIMBMGET5cSBsdXbc4FiYGY+XVcDPe5GB3nxbEcBq/T9cT6d2tNVPrUPAVQNS7fWxAzXtYeqJTkxtdpLKyH6EHJZdRijCX/wwRNFHIborshp26p7f3Y3zpI/enNW3Xb7+Xy+AgISBA4xhgq91o4Iono5dzXf7cZpCCrkCDmR1ObG61rVxMHNnRCYOEQOiaRbq723HmM0tXW1PAQb0MHR0YyIQESBaRgjWs9jcjPtBk61OCJP02Qm9bwaQk5IiKpqXZCCmY5DpkgqfRhGAGzrdVtETAMHRpSuIbGicSQwVHVzW6uQEBDFxAC4Vtnv4zCGZdF6LU5uCZr0WYBS3O12YLgsJ3MHoXfuZ62dAV4caBhCrxBDWirthjYmOo/o1vd5YLJiyAG1qxuawG2PVNaiZjHw/jifT61WixmqNPfmzNYsMAcO4rIupRoNmcmhN+GEzHFdSldDBA7AkQkgBG0qUMDV1Qwce+/mcLluKQVTVQQmLEVEYIjmqDGAg5e1hcghUAwczbeC1nse0xAiOcTRjvugllrrm8irV8n6VxODIkpIXaSrkAdMjGSuZurX6xZjIg7qCGatmoqH0NdSpoEOE4vS24dFpA9zruJUlJlb9xQpBI7RoCMHbioiFjmoKrIjYRcDv/XtsHUVBUQk6kB0s3dtpYhZ184xWlMBqM3cekrUmiBpylnVLqXFRM+fzW69FshjcOgIOo2hVdeu88B8SE/XVZ2Oh5ERa3PpGhM/f7FHIiR4eDhNw3D3bD/P8PjQtyLTHIng5ctDoAjuRDQfh+tpuTxVA74lLObd0JoSATG0pgwWYmhNcwpm/nQqtxqOiBMSB8pjfnq7zBMNY3bf1Mw6QVdESFwBIBDrV2mJYGoKzuQi5gbICA7gwMym2psCWsYkTef73LqGhO4g3auKAqrj6VyrGIlGxmfz1KsWlahWi9ZqbtjRVc0RmNhUr1cJMeDWEQwyQzcV27SaIzGWWpm5lm7qervrnK4XcQAK7Ko5h1rUzEKgrbQ8pP1uevvhpQc7XTY1J6Yu3Y16N3BXsVLazcdnEFPEaQ7SmwhvSwXk+TATkSrEGMu2cdA0xnliA4AHGQcOyRH73SEbUgx+XTdE5BhbaUgeMjIhK19OHcE7KZIjgQNuW83Bz9fCgUprIKRJ8jyo2ZvHCxLvMqhqjkMVMbUx4Va0FOWA12vbjQkJifFyKiZmnVS81I7d05iW89a67neTu7k4EdYmuYfdbgyEhBQCqoGDq8FWrZZm6tpdzBGxLq12m4b89uESr7Tbj7t5/PD1CRw5YOtuZgY2jTknYkIkysPcxGqt45BCpCqI6FMOdNMUzL7b3SeiEPZf+srptjLlEF6+HLy3eqZKer4sDpRCatKJzLq3LkgOjtMcpim01pZ1nefp6VS3zYkQvLYmHBDYYqY8DWkI67Kpg4GrGkNYigBAYAwR0sAhUmuG7NMQQ3QOkJO7W6/d3ABQRbsYAQ95+Mr7D8sqL17utyrrUtTA1XOOzBBzlO7mGpimKRNRq1qr3NxG45QiwfNn6XKtpXYiZKYuvlw6IKaEuz0TmZkN42CK13M/XxtTON6Npcr50kwLITGH2g2Ynt/ttK1f/NLjOIzucLuKAqZazUzv7tJ+n6X3POSmjhxO52XK+X5/+PS7h/0xppiGxNLFtJUuzjxg3rYe09B6p+53h1nUkODt07rLUtb6sXcPx2NW9/1dGjKFgA+nlXm4nUzGKRGgoYnZ+XQhw+NxRLBl2cQgB1bRy1IdgCkMKawq0x4RPWcMxr2ly0VaNRHc7WNpel0lot0K12IyTdGaEdo0pbcPl5zyPE+ltsu2mANswBycqYg8G2cKpAjTlE9PlZjOS7lFopv2nOJhnluDh6fl3U++jJO7CZMj+v1xZIKmph2frk1MowdRnw7RzDihigaiw4vdtpT6cEtiIwQy6K8frl/zmXeqmZYGhMf7/ZQ5jal9QWqT3pQp7O+mrW53z4Y80+nN1sXun4XSvDfZ7aIbXJcaA4Pz5bxcqNw/Hz72iWdf/tLrl8/vLstbMXsxvPqVX/5wnNOz54NHdO9ysnEcOVVVyUNUcemdxABsHJmT/cqX//F+f/ynPvcd//Gf+hNvrg9f88nPZLRxOH7Hd/73tiJ52v+FP/9fvPzYy+/+nv/+t/7Tjx979fJv/dBf/Yt/+b+e5rHX/g2f/OzXf+bXftt3fQ+G4Xj//Ad+4M9/+3d857d87n/95/7f//GPfv5HXz777HF+8cv/+B/83R/7m+N8tOLf+HXf8uGbj77nt/6uz/+dH4SQ/8yf/Pf/+X/xX/uuf/a3B6h//s/92fdev/ctv/Y7otHv+L7f/Q3f8rn3Pnj9+HT6gb/6l/71/+3/Oe+PT+cPPvGJdxDRRQbiy/bw9vGDt+frH/wD/7O/82N/4z/5z//E9/2273n18pPf+U1+fvP0e/65733vy1/4wi9+9OKI3/e9v/6LX/zgF3/5SwRbnqbtujBGc5uHWa07ESGezufjy926rG7wdPro+Yv7FOdu27LUbd04wnKuSC6qaQJzqZumIEAmIjGF/RR6a8PAQ54o0F3Eww7fjvT6sal0FI2U1G0rpVZ1oP1+rFWfzmsVDaE84/26dEeLOUnTEPn0sIEDUR52CYGGnER6qb2bPl42MwKS2FXHzByv15JisB4iB9G2liaql6W4aW364tn9lKO5OoUmtZ2XFKMih8br1kvraRoZYyA7zoE4JY7ouIJ0MSKNMdwfpnmfXmGqW316rNdNunhMfH+fkHBINOW0bK12IcBWVaT5Pi5rpQCHYyJSUTpfyto6BYoUa1EgjNHD4OOUEKg1VTVVKmtZ1mpIdmnqMWUra1dDdyd0MxTheb8ztWC6T9xE1qU7OBIYuHRlovu7SUW3VbuKqrnBem05j2lgzqCm21bGIbcuTTuQjWOMgTnwPOdpjtJ1q703wQDHu7mWrqaIHiIhGCo42TjnNw9r63LzuDYxWRszHY8js3LAx1PdNjgcEpEva9/QAfxuoGe7sZQ2TDBkyGl8+U5GhZ/+R6tK77oSm4oxh5y5aYs6insI0E27GQVAJFNEsmHItXZHBcDLaWWiEMDJbkGt3rUnGlM8l/J0WQ/7cRgHcImJ1xVasx3y/T7W3G8H5t41uqV5ipGIdRwDsW9rZyJUBDV0NwdUs44eQojEAYr06ZBdlBNx4mYWQnToRBRCjIFbUURIIT2cagg2jePrt5fSyrMXhwg0PIRpSrW2Lkq9odJunqV24Q6A6MjAU45bb6oeA3dtwMHN97s9gpG7OzBDTrQbKfq2T/g1n9ivSzYEJs8BtzmBQ4wYgOY09NpT4NZU5zwMZCYmCKTiXoqI3JYupoBIyExNhBBba7VIbd3MhinnkXs3Q2D1ba0313RiJABzB/BaBTGY+Nr7OrSQ+XIqvRoShUBuwIwUglbfShl33Gs/X8tut7+/m7etPzytGIFFxsgGyN4v157SXIogQk4U2I/76EhNfF17DOBdQ7wN3FC6mcJNNhIjbFsrgL3r/rinQFVca1NVAwQiQNuWNg1RukmzbVvv73ZueHpcpikt1zbmuD9M29YN2m6IoNKbXpaWYtxqS5FuuFhA683BbBpTazd2su3v5rrVp4czeBzHlAbiQF2RKYh6DJZSMIayyjsv0CAGDiJACAbcqrbKYw69W2tarsth7yGH82pbt7KqWqfYTC29mEX9fF61uRvlmNV0W5opACIQpSGZATIhgoj13qQLAsbEQ2I3dzckUFMwc3BOnnJsol36kCgBb7UFDG7k1krDy9p3hzFGLM22Kq4u3WpX5rg1BZccU+satBkFdnP1lnOQVdUUDM0QCcCME8YhIKAjphRvseqB+vEOmAGfjw9v69pu7vruql0EAAFvT0Po3UQMAZ1uCg8DxBAQ3RHJHLU2JDSDgFi31m5Ve3dtgsxIkIYIBm6KTkOMIcKQETBcr7WLqBqoxciEABSY+KammqY0ZOLs29ZjpBcvUm0FyN68wV791ScGQDudAN27NGZGcFGSpkXblKN7WJfSO2qT87m8+tjddrkMHHJO67aN43A8TrW0eULC1HtQ99vjIIQYuJLbrdpDgKenlY7TFNImfTu13kXcpZs7AsIwRAQahghkpbabXf7WZRBVAJCO29YR3cwQkRjTEFy1V5h2WZsiQ8zcVpvnZA5mttvxuPM3D4IEnEnWDgCEDoHV3boCALi7wTgnQuBOMQVElLW2phTp2bOsza9Xi4HdwRVCDGq6NU0DBwZpXpokpt5lXWWOiCxsjuZMfH93OF3O8y5VxXnKn/1sjBFrs7W0lOiyaCTr1g87IoQxJVP44pvmogGhdpeuhG4IgTEmKFt9fOOl2ZCDIwERogG4O9TWSxdiup76bhxoimSGAqCw1SquMRIxhUTI5N2AMQ+prb21zrffO4bEtUvZFMGAGM0xfBUBM4zcW0cgcJBmtfRxjMMQOxrnRACl1P0hPn8et4t1qUPGwGHICIMtFZqYuaODgTOHXqCCEmOtaoYpZ0DsrYsrB0BCZu/WKcI0zUzUVcQEAMra3N3m8TDikKFLY8YuQik4CQIk5khgkcW41Q63kaQaMLkaggOhgM7DoN1KKxjwuBsY3AwDQe1iqje9zpAYiBR0uk/puC+tXa/rMAZKVEojAlDIMU4zs5tp242cduPbc3NzYEgBhxDjgIGBwYns5bPj5aoOZZ/Jdny3n5tZFx1jFrEQwF2liArGFNxBOwA6UXRTNL3lId2dPal6a5qHOIewLW3twsyApB3UbHccp5ya2DzPl+siaoRo7uvaQ8QY2AXMbh96FmMAQCbc7SZCqK0SI4ADgoOmEGPgkPjhoXCAidLjtR52mUKotcfb4q5ITIAcNlKRdn83tSbX2nMIY46tSQjQu1oHBJjG2FVNLCdaa8meWnNCR0Yi7F3cIOe0rFXNYwwO3sTm3Xx+OofAIQURMXMgFtWUWJtSwC4K5gTUq4Dj5VLPawNEc+9iIbCoEluMeb02csiU4pAQYAqoJhQ4hIDGKUBiuL0p97u02ycC3+/CtZU8RHQsWgaaurWtF2ZkRFS0rmbmrsysYkzEkQOjmtfSxmHoDZfWGLGtRdXWVYYYVhVTXdp5Pk6tm5h0CWvp+zk5YKmdEpnidq1MvK0FDcpWkUnNXS1ERjB3N/MQCAG2tS9LfXG/Yzeec4rigOdza1cBJ0JE9t4LEgOgiUammJmiAwCApcAphmnKXbqqmcHpcuV4yJEAegohBx6IunXAaFJL6drzOOY0puOU5iFpreMIn/nM/euPTtdTSSmReRXoWsEMgACQ0MYcQ+B5dADoXQITEV+WJmJ3u900phT49dtFTMVJzcaUMoXtWmPiU1mXrYzjmHMIQIjACDejEzoNKalazk4MHIAIiZGYu/laqzuAuyqYQmuli41zFtF1beWG+XY0A1QBhN70cDeqQ0iIBL2LuZkJAAQKl1NDNHUvVVUtAPfm26ZA6NCVrDRutUs3MCaky7Kp+3VpId26TqhGKLq1dtjlMYWnx5UY93fzYRqul3a+bgHTkAdVdcA8UuRQ1jLvKITQpIvZul2m3WAsOc7mLqpdFIiJQynqZAq2tDUSqWFiGHe5m+XQl3ULyQBtW4UjoUFgSkN6RlQW6QaiYlaneYpE26W/eVjHTGGAmCcgHFKQbmIuiu6eYlwWXVrvbvMuNjMylI61yjjFLnpZYNrzNIS2ehcrpe0P+WMfu3t8KOsi6hJzSik5eKn9Viy+yeV778OUd4d0WbcciFO8LmWeBmYSkTzEcUpla7W1EGm3H1x8eSw5xtq6mE1pkC4e8EbdIsTrtQJTXS0md2xa0YTb46LSx2HIYwoDJUJA2N/n09MlhsQBu/tumCCogB4Pw/lSretuN4455eTPns/LZVXJ98f7bXmfPIhBTqn3HoimeYwZhwyUQiC7Pl6j4nLaUkgpoAO9+tSLVnpXY7C7/e6K9eHhkufh8CyfToW3nocDpqGVOtynkPGjh8vf+KEffP2VL7w5f3TZ2g/+zb/wq772M7/rd//+f/xzn/8rP/dzRPT6/PTv/bE/sj/cT4cp5/Dw8KGZvn56PP7s3356/Uu/9nPf9fbNr/zZv/sD3Vu8233p9Rf/q//qT9+9PHz5y7+kEX/4x37g+vD2S1/6wnC8/5Ef+QvPji++53t/5w/8lf/XL/7sz+zu5i+9fu+P/1/+j4fjfdMiui3Xro7ez07jP/ipH//iz/8TD/DF97/4J/7df+dbf91v+JX3vpgGzEy9Iedpa09/5a//xcfHj37qpz//3kdflt6k4I/92A9/9Pppf7z/iR///G4/f/h0+Ymf+SvMcTfuKLg7g8o8ZZcIGETqVnsMMeYUB9/vxzHFjz54yOPgDuvlqkiJOcXBXVK0jz5amuh0R/OE05hr6SH6EOL+2bg/JuvWal3PiAF7xchTZB2jxnkoa+sivVoXM285hnVtzLBcSxPLY/zwg6feGsTdsioBBuDrxUqvIbRX+SiRS2lvHs9399Pz5+HTn9qfH9s00t3d7unSl239+LtHZiyl5F0qFxHRmKCbVbH9bka3y+XiyLtpF8md1EzX1cv1UqoUrSPYmHemVkufhuRmkXG/P6jthxFioFJaXWtKsVd8fCrjGF69e2i9m1lI4/Vyde05Tcf9+PbxUluLmS+lpu67aXh6KtLqPMYUozhK1+7exWoXcufGIbhpL6U7OLiKWYphu1lpKRtgd3Pz3Tw6eJfOxtBgW1rMlHYhJG7Nt9oDcd2k9z6PGRDWUr+aXTCsosQB0BzFIA05oEMzcfemmscQEyAQIbpZCISIYlZEAlLZWi09ZCyl6WJjjq1qawYZDmN+Xfvrx4UZxiFFzp38fF3U9dmz8XgYdjNMKYgaH4YQGBBihNZayuyiy7aEHM09cvjMp4/vf3CtrZn6/ji/2qcYARBqdwAPjMzIx0zHrCKlCpjfHQfzSU0ooLsDoKnGHB251dqbvu5rq2IKZjgNObADQhowjRwCb6Wphet1m8a4O6QouTXzsk5jRorLpaSo0qwZO6CKEcA4xlpFxDncWjPmoGZtHPO2tnGYwBU9mlHv0qrmIbo5gCvgYZ5LL2+fLtoRkdqqcaBnz8beew7pMB8eLtet2pAisjXZAieyaKKribgwYSltjElrtcBdjNyZowEghOMxutta+2Ge9mOYPzkurT2eyjxglUDEIVKvHrLPGohx3OVpoi54vcrjpQImwGqIIXJTMXVADAQqaG61OQIwu5ghQG+ax9hKm3cTICO0tRQiIiZkjMRVfVlrDK6GTHC+bH7B21dniphyeLpuVWGecmmKgbYi5i4O56UeD3EccBUM2aeckJCRIufT09OWoVZYr+XVOzMyMlBOAczW2tWsVckMIee6ths1TxTgxlK9NSH6aoZllW3r5AZIIca6VHBAwlIqKILCcTenmHqFp3O5v58Ph9EdxgHR02npD+fVlVrRJm7eQkDAIGJIFDOFSGjspkR4uNsd7pJ31AZ5SPMwIhgCXEsHlykmdCSDWlqOYTcwOe6HxkkR43rtTeTZMYw5SJN4HKy5aSEIpYoTA9K6NSSJyF3VALWLicQUEwYTFwBzVUdCVm9Mybo4BWcMIZqidHMTQidG0S6dphwCD4Texbtoay0whoAAFjCvXTliFxSN11LikLW7i6oCGBLS6dKbKJMxO6CfzluMHGrV4KBqiJgHREQzVFURyxSJIAY2gQI+BDBRcUBy8J4YSt+kcYqDqg6Jlg7gcCPhByaOjEhB3Es37SpmZkCUc0qJ1rWpGooxM5JHDiklbYqIojrmpGqtCWcaMplYDNm75zHs7hKqf/TBupbOjAig7lqFCHf70UBTCHk/b0thzNNuuKxrqybdQ2RnX6uQ8eWkYqoKm7qDgCshppwAycRxik3gsrYU6XCY3zycL08lJYTA7lDF6tNmRhQYYPvEy5xiuqx6uhRzv666PwzkXotttqEBuZfWp5AA0cBa7waASEzsIDnFkIK0bqJoAAocKTK1KuDOTDGGslXpGgJzMDRCYjczhwBGTCraS9+q3IVhHLg3cL08v4tmw+m0tdJEPMYE6Kram5jbNA2IdL1eKIRpykiaEqNxoe7mkaHVxhhCRCbsVTDw/jBE9jdv6lZkUTX32jWNw5jTOLqrn6/rPKe73RhDNnDHPk7cGpvA8YhLx2XxWhEJ1M1BTYRC6s0jVvG4rlq3jsgA6OBIxBGf7WA3wtsLn58MHXMKxLRe1xDZEN4sl5xTqb1VFTWm3h67iJt5TCEPg1hBZCIHx76JiAKCiaUhIzOAmRuYz0PgQMvSCMC6ESLZjdKNYoZIZK5iyBRTGHJw092UWgPRPu/zMIXLooyUBleT3ZgB8bxC6d1dWzfo6owUuZXNmBxxqx0QFUBVAWG3yzEFd3FC6W2YsohbkqbdAV2NAnEKZqZmgKCqzCFFNlJkdyUEN+gq4Ar21QkCmCogxBxCYCKI0U08jKE2laal4A19E5luYHoOQaQ1RTaUpmvuprBtPWW69IoBhyEghst5mfeeYkhDnAZC04wxJQkxzDmKehfNxMxAXkV0HCLO5sCB0/MjAqY313o5LdWVKRCaudUi21p3+3EY09vl6gq19pjCOA5mFRnyEHrtaWDrLk04BXNzQL69GJBVvZS2n/NhTA4hRH46nQOTG6ipNYzRhxSXRT7xanKGx4fKFOM+tC51a+OUQgw3OlgIPKQYc2ytu3kM0bqFwI62LlLF45xUKpgHitKshc7kbW2EQdXLtoIMKj7vBjdt0m+6pi5q6tAtDZmA3BQCE5Ophhi1qwMiwa26hQi3qco4Dk7mAO5gDrewtIpuW2Xm/X6QLstSASin0EVvFSDpBgwAQBRjgC4CBiEEcTmM8/XSkACA2taYEcFzigHUuyYw54DmEWRgO3fThpe1AnkcrPWGArshWjMx1wBiqlqHYUocAVDAOgCImPlaqptDREi0tlbWNvC4rf1tbVOiaQwOhkZ52F8fzq5Wq4rp/GK2gOt5ncahVZPeOAZmGsfEAEYmZCKi4qZmRO6m6q06WD0ecnRkiqfTWhY57OauUlovpbbWd7s4xJRDiiEMOQ1TRPAS5LAf94c5p2BmyHi+LIw0TuHTL2YEf7xuCrAJVLF2um5rn8bMzNu6Rp4AuPTtuixxCLt5lLtJux/mPE+pq4ZICNSqmIEahRQIUPWGYwMDuS5bN5h2Ud3UbBzj8Tg8nhfTW1gNrkvtJt1synE3j25etxZSSJE5EDTd1jbkTAxwQ7gHNHAAYCRCQgCOpO6M5Irr1mrrHAKjqxkhM6uZ964ASIhuup+GeUprwVax19UNQsyqHJgQPCYi4t41sEcOgYOZcxBkDyGNYwYDU0gpZo6lacxxQEwxgCGi3d8P58ftWpqRFRG4YB5zzoEM12srtR4PUwoxxlRaOd6Nx91Qa7uu2+EwDTm+fSylr/uJuuv5XECQGbU5UjbQGInMS29N2sdfHHvX61ZzypHDMAUO+P57tQt86SunZwd/9c4hEAmBqkamsONNfK2+T7uIDODqGjMihcc3lT3s99PpvOUx5RxFLHHq3Rdt3ezuOMZovbXdMCF3ThRi7NY5IkcchliX9bQ0QGhNv/zF81KaA7qqK2xbJYKU+e64u1y2rVTZjFP6xKf2MQKBDUMCd044zUN/00Tg1ScOw0xPD9vDR1c1JoSPPrimFOdZbzLHVRsSDylCsGmKAXk5XW4bxJgoRJ/jsCydMJxL713uX+wsAbmtp1Kqiihim4ZhHANnUOQUwvMXqVV5KD3EKtJjQhA4PbTIg9bKMRx2u/zBtbuKaGvOiZ9/8llGf/vBVTumpGOO0zgyce0d3QPhcJhLqdtWTw+1bG2YYxhTYAiEkfBuP3NMlQ3Ix8R/70f+6rYsRu20lrtnz/7BL/7Dv/eTn1+uNYTy7suXxXTajUxBvbnG5XzJKSoOYaJf+NKv/NQ/+tnzaY2hfuqTnzDOy2X53Lf9mn/00//de+8/fdf3/rof+uH/6Ju//rt+/vQGU4xD/qG//4OX07au5dlduD88pxxmSgjU5RoDcxg4Taft/Nf/3o89PV4i91cvX1xbH3P6lV/6hz/1kz/yqW94cZjCzFbdeB60+C//7E+8+7FX773+qBfb7w5f+eCXP3gb166X0zr8E3h+f7yuPdDYmnzl6e0wxWmfkvs0jt6hXmoXvV5qzjbOqW7+WK+73XC83103fXxbppHSELVjHpIUuyzrWirFQE7zOFii3qmtXVRSCCKgauK3ATyCWTc/HvfH405dS60ffnRR9HEYiPgw80cfXlq7HectBxziKHkEhYe3lxhi4rhtXRGO+5kISysOllMwUZW4m/j+MG9bSwQvD/nFiznkwIiIQbpppxs3DQ1yDId9BEJhH0PsrRq5iw5fPUv4R68vnHAapuWyiEGe4jxHM0cnMs9jTGN0NQCtVQh4SPzyxX6aMgfnFIgZkZlHMBCx5y+nOMPj21prWy51Q82UxzTGRDmHkCVmapVO5w0IQsKc0zwOpu7uQ46qmnMIhLXKVnLrbRxDjnE35tq2rv2wmwgHadJ7T5lE7PRU3HBZeil2mHIgLFouW1XTMYUhx5RCYF7fXIHBgVr1lByAAG/ldKBIKTA4ImAXXLeyizkEBpVpDswUIjLHyGymKrZ2ezqXDqppnMd8XWotFiKPA3FwgM6JEwcmnnIgBhF1sSmlGAIiUOBa+7pukUOMoXd0ZFdUhWFItdTjYXz5YpgmVnNkDGJmjoiMcDujcoIxIfOIhs+fD4bADOtWatPeAyOJiFpTcHQ1dFFLIY9zqq2ognSom9JIYNZBxzws1yba5iEGog7QWxlymlMmNgZ6/WatTW/k0fvjHLa6XAszJobAoAY5RQc8X0pMIWUOrET09qFclz4O4cXdPiZYW2Xm3n29iooe7rJI/+D9J0whpcEB3YMbmcrbhy0l5wQc6LJttfV5nLro/f18Wa7sbKaEDk4O2KR18SrdCXCKIe+6k7svWxvG8PI+R5RPfnxcK/QmtfSYKca9qQcm6+pKKLp2ExU1XEvLIaQQinYCmMfIzHPE5bptVWIgM0CALnp6WETssKfexByYSdTQoFVRUUd2w1LU3MGwdVd3JnAAAFZVc0MK6u5k7njdrFVB8Na8VUd2dDFFAELytjVFmIdZCZ9qrc0enupxZnMD9+i8H4e35y3v4m7OTczEwTxEG4YADmqqqpQTc9hWcYJWOwEjeCvVHdwd3UW4blqrEvLq4gbDkALTMGOtsGztcl6BkYlVcBpDaYs5mLmp3GBnAIToSKoq45Tmebye17qIAxDDbr5hmmDb2m4Ol+u6G3KKbObgcncgsNtoBsw9sovWMcKQpscn01ZU8fhiCMkEE4m76/44ns5rgHg8jtGVuL/ztc8IsCztcu5zTiHh9dxat3EOHNgjIWATU7HWlBg4MiCoCsLtx9xhGoJGOC3auo05bk2v17JtEgYPdOOZFCC2Ls5ctoqBkfAG0g3M7sgc3RsymkK4Ha+32gNzF6u1qzoHRvRaJYbQasl5cPNqykjjGKcJe+89wG6aurkE70I32KiZOyK4E3lOQGSYeVnFzNARkWJkQqvV9EaJJuTA5tK7BGIOPAwxMDLzsjTxjg7aJTC10lJKCv7BBycQqkV670J4Yy1Pu5hzarUj+dJbAD4exhC5NxlzPM5B1Vpv45inQZbV6mreoYsxo6m5E4CLbiESU1iWbqZdXUwVliEHcHNOp0tBs/1dQofHTdxsHmOp7t5zUkS934ec+XQ1Zo6RRBkJhpCQaCmVwLt6FQVEIiB3QKituStTUDM3u1mJADylcJvKAcIwDhvUG4a8VyPwkMABABEBe+/zLs9zti40kmjbzTEGnide11DKTQvtDm4Ot7/fWgtM4zyYWasNGcaUUgzNY6saErXW0EHFFXQ3D03l6brsp2krupY+z9HB0V1VKISttBxo2uUYY+ta6nrc51cvdrspqOrpUlqPl3NpVR5O9XBMuxlLoRSUgAm9d3lcpHVQJQMjAlPASMMIKRumcH8/7bK5gYa0FDGFaoaEZrht0sXNPeVYxVl1mrMK1NoIeB4HZhe11hsAcEAzcHdEV5VxHNa1ikipImqqBky1N0RKGBwAERyBkAxJXdB6wORMvZs3rVvdH0d0OJ9rYny2pzCmZfHrtVGKrUsANoDo2I0u68XdraEQGYADukGrgoRE0Lq6Y2+iohwxRVC3ZbEq//+a3izJlWxLz1vdbtzRRMQ5mXkz762iJEqmB8k4BL1pFJqpTM9qjNQA9ECqqCoW697MPCciALj7blajByQnAIMZAMfaa///99l6SrePfr2siHG7HRa6lFRK6sMVgiJCQacHTED3gDF9TAvAcENmJoIINcUAQsnCweC7QUQfnnOmCDWXTHXhUPeUT+cUGvs+wUMYUqaXSwrEMYMCAIhR0DFnroskgWPbDfz17bI9dD8asucln9bUjh0JSxUPfLms5tEH+JyByoTmvNbc9qHqtcqylqO5DdjV3PGia2EiAAAzW0lEQVRJZYYISZxW/qPvg6zqYSBZyppU4/g8mhuEkqSS5Pp1YUKmWIq7RS0iTGPG7JaZiCBnvJ6XX76CES6lxsAR+Lg398g5931AAAm2Q5cCqooAxHTs43ROSyWz2JsGwPaYxz6RyDSAABjXdZnDDjUQqJRH93D89dfPUlNEpCTb0d1DBAMhAlLiVFk1fDhCqE9JKcgjnt09JxZA6PsuhefU81pHVgaTTKYmiR0AAdTUwojRHdxtTEDAJ6Fc1UiIKXQEIhEioALw0WaEexgLni6iQx+7tTlm2ErlWrAQxLAWNEGY4vHQ+zaYiWJ384Rp22YiVoDHvesYFDAZoZCabcfGWcKCCfuwPhRrms3HsETldu/NVN1TytfX81AHiNYGALLQUPPwMU2IGZiI72MjYUzU+3y9VAcdm4YDCKobBEw1QEyCy5mkwHJlKX60uN17ADkFM9RUAgKQwAGQTpeyLJzYMXogETugsejbJR8dWYiplpKXQmCxt76uy6HOI6zrsVkqqVQZNrKACLlZeJSal7Ue+wCPL28nBDyaq8VxzMRBCIhJDbbPzWwigWTGgDmjNQMgC3dV8HCImtOS8phKHOdV6oonWcZUc1sl9TmP0bzD5XQ2w941AIEhFagsWdg8xlAICAcDQwCPUDUnUfXpWtekw9vRU86c8FRya2OqMhITzYlheGx6HPPZD1+XwojoYaE5y1JT77NkrrWObkQ0bS4ip2VlInMPhzE6kl1fL+23z2XhWpbHbbu+fHn9WiDQuj/2gzH6sNHm2aOI/PnHL/sxLpfl7e30T//8vSb+u1/eigCEK82vf7oueQXwH348RZrjmH6MxhyIqJaYBj4x6Hz03rxdXkpmmK5lRQoERA09rVkk7bNdL/V8ljlnLtxmq5RYkhPbfOQCQggBM+x6LjZhewwi+vX3BwIxQ2IOj5ITBUDGhegiy1rk9+/3MdW1T52AKISKYGrbx6ylTqfHPgHCcLTpOi0Aai4pxXqql/OqfvzwdWHC3me5pL/7V5dTpfffdncgoGOMNvpQnVPfvpw4Rd/7168n7fa3vz1K5tPKuYT63D7scs4lk9vEEDVzx2nPPGiUxIIx7h0XRjGfsNbEie7HVjxLrm5924YDhuux9ZLlh59epJAQpjX/9OfrGPrxcZSa1mtKJS2l3u57zTmmWVVZaPvsEYAENub3f74lJnCaGm3gf/7b53nd//TTmgRszpQlkeQL18KjTQhsNrXb/XNYD4QiLDXR9qnX13q+4D/95//8P/3P/8u//b/+13/997/89OP128f2jY9S9z99+cUdAjsxOpBqfN7vdS0ppQyWBNLlsuWl1uXlnEX4fj+uOf3D//PvbY71IqdKWtK/+z/+XaO+Xpfric7rD8yP63V+vSyPvQvR85WBxIiYqUIkIXw5v11WIQ+Pl5VM54+/XN9+XFh0qZiJ+whUTwlTlpcvf++Q8udvhPLzlzc1LW2Wa2TBnOlfvf5J5yBWyfXX326YjVO6fYwnXn54/PjT5XJaPNTDXy6n8/n0eWtzfGt9vH/Aly+yrBTgIbBe65vRx62NNt5/13qupl5r+uGHl3aMextf3q7nurx/v/32/VakLCX9/OcrL3n25lFKkd5VBMGl6/yv/7sv339vqj4IT+eTu5agqdbG8QTcqBkjnU4L4hy9v72cck7mphP+5fP2+na6nKv2ILfMaS3pcXuAYSY+joM41Kk4vV1WYQLG9VQocLbBkLamES5Z1H07+iXVOQYQpiRtjDZsORWYoVOT0+f7YTYvp3M7OrAvSzpdX2+Pfcy+1HWqGUxJHEYAbmaj6e3Wxuh1SanIr98/iHHFnBLe759LXZMsucgxe66UC03VtjcReQ7brY9SUkBIoZSXbWuPaDnL9eWE5M8215NgMoa6RR99Gkx1BnySDRLxNH17Ob+91VIFEW3649H6mKmsHtbGpB1TosRP+mV4BDMSwVIYU57DdM6pen2phDHndA9TO58KJ7rduqNzgMbUCUMNAF5P5XzKt/sdkUpBBxjmqKRtlEKliql7n8REBmGBgBYuKIzMwtP0dBaP6Ef7+efL9ZQ/Ph5BkZiWUga4iCDEwul263MYZzSbEMwQpmCINUlJOIzev223jyMQhfDty5pz/v79HmafnwcJ5sKZUNVHm69fLkuV08KfTJ/30cleTnxZ1nAgCsp0b/3jduxTLdD/mBwD0Z8p+3URZrQZ26O7oRk87oN22BKlzIiIBISEiI+9h80k9dv3XYRykfujxxLEMo0k7PvHdl0ugmWO/bxASjl8RgAnrJwiwMNv903BE5h5JEAmsnhuUui6JgNXmzrTnDbnLJnHbF9OxdQWQYoI0oXlOPz9fSAl1+DC7tC7Lac1KB/bRASNyEw5SwRwTmDe+6hLfmx9HopIiOAB9Lxpbs1Cp0ZZskzLJSdh99Bp/hw2wz1wBrhbWjMLOYYQujqhzKFqTgg6nYghQgSnGVqoORm5oIYGhU6QnIeae0hmNQPMKUmbHuozuJ5EJ+y7smApebsfqrNkEmZENPXexrpWd1vPCSLd7xORQsH9KeqN0bx1g8C9teVc6lKA4u2lRMQWoyQuS+ljjG5juiBHuBAnwqcjDMJNwdEjABS5pPvHoTojgDkRIAWC836MlEjVvn/22WE5BLIrQ2Q5F7ZOUjLQOBQ4FXd+f9/U1d1/+PqyFnnsx33b6rJC2PWcEMrrJV0vPAY0t/v9WJdqDoYEblni5cKfR0gFQkdOFEAMjzkQrZQcEVPtOQ0/9hkQucj7wxJLLmxot62x0FKTNUKPx6NjkA6Xkkw9IdRahntKBBGtH4jBDJQBgwjRDcXc0ZBIVFWHhqM9RctObqpozDynMrEalXN6Ahq6ImwqOVq3Y++pnvATGJkSjBFCtJ5E2DJ6yjRfl8/P3vaGiQODEAPAhkp6+gGBgSHgaN2mLktmgUyDzql9GAIghHuoBqGbOXiY6XIqX5frfoxaZaqVQkzYthbMY+hwcO9fvhRhnCPatHWRJCXnZN59Nlii2yQGRCQmcADiOZQIiXBv3VVZRLLsx1wy8yLuuix1NP/4aMQA4sQ4hpfEw/1c8O2aCKF3n31MiDmCJUlGFgzH0VE9APh0OvXRzUPBc5KnbLKP2Y6WskihlElY2jZVLWUxVSSQhDZMkhDRsc91TSRk+mRPh7v/8KWAxTC/nOTlmvdjnBbq1+w+ZV2O7YCALISIxIkQWVgSPhlaS837MbPED9fiRE377UZkDuHrae1jqpkddty1lCKyPI5WqlCO4RYWiEgijlESVEl702G+VjmOIZn/9MsFgr99P0qFMmg7ujkuqeYl3fYhiSlxrfZ29T0nCyulgsnlDOuZ2MOGr+vps20KTojWra5n8zFUX75e75/bbDMloWeSxEHNWCR6RNi5pgDazAHApweCKkyfpTgRjaFzKsazGWH/JZglNrW75iwA5BYpExKpEgC6wrGp6nAPDNQOEz0wEtFts/4xmLFmGYcB4L41AHfH523seEwimmqBICkh8rEfjBxB+2MyToAAglRSyYkgcpI/zM8viyAAxMtLvT1alshlnepAoBE2fSnrsQ9AmFPHjKkeoaUkRHiaaSCCkUZXEbE2QFVyXpZSmEe3qSMzTfe8JkmcxbnyL1/XJ0sFxyCwy2U5Rny+729vef27l8/PLcxM53BsA1PFknKs2Ka1ObTP9URUwgwE0RF0ELgtLJvZ0cfRPdSONuaYTNKO8DBJIiy9TzdnIXfY9wnASXiY9eEIqGpEqXcNiDCK8HUpKUu4n9fFwUSgD2P28yU9WowxEyfnpwPN6mm5nvH98yaphCdAn9NZKJVkaqaechIhgGHmgAwefaqrl8IHxrOzOcbUbh4Q6ikRMj72mYjGMI24nM42/LNtgPb65WQO49GIMSPvx2BKHr4/ZpQwN50RAUkoCMycMYgBLChgWcu+N0A3i1pTEry+Zo8Yfc4AUxeBXPlo2xNl5W4BuKy19xnmxIgAuTIAtqkQFuBJRKebe8og4sCgAXPqssb1VcISM09k1IkRVE7Unf+A/lOA792SkFBgECAeTXXM+22/nk+Ise2HGszpT8z5uqQkcRyKgIL0/TYloXqgY4zQCb/+ugWhWmhvGuD2rO9HSuwT9vtAYgtMIrOpW+x7N3j2KWOqQYS5IVBKnIQBY7bx8R1ryQCELDmxsBO4LOtSyr6PJ/YvoGEq52thjM9Hm/ocf1EytHtj4y/XAhBHHwfy0Vti2jd19VpEMqnbbTtKotfrqw5/PPS8ppT4cZ/HoUz4NLgBxNGm6byc6k9vFw+PmFhxGO/73I651LLmPAnamJnSfvTLqfjUSZQL3/dGgeuSapX9aODggR9bOy1pXZZwIOIwPZ+q+WTxnIURVXVMJySAGF1V3cz1jzARTjVmNI2Pz/20FBLrfT+dlut1MQ0z9/AAR4x6SvWSP98fvc3HI0qepRIRmrtPi4CaCyBBmMOsBYUrBLQ23dzVCcJmHPtACNXhnNV02w53Nid3ABBzq4nfXl73Y9v3dn9EzvzTj8up8r/686tkqSv33lvvda2M7G7bsXvY6+n819v317f1ell++3YvIpSKEOXENj1VXOqJCfrRISKhkIe79m1OhFOt973/5U+vl3Pd9oMIX64X/uNaBBITs0R3c5/aw63k0kkR4XOf+teP//6//blUSegv50ufs+l4IQnz26MzQwURxzVnTpQFf/+2l1JKSncdHnA+rdu+S9Df/+WrhW+P43xZfn/fIiAluCy1EP3r/+r17/7uGjiE4PO7Pu7KQr3pUvhyWlqzukjro92dE/U2vn5ZwuL7x1Zy+flP1+/f9o/38dg3Zvjx63lRI/SlMKOor++3+/VUhQHCEAIdtl1fzmup/L7dVI0VH7dDp02L63khwo/PbUz/8vX8/H1p9P/x3/z9x+/H798/c4Z1XX7TPYAkLe8f76V0U7YOjrouS8788bGjw8vL6XJd3z/udeGgaNMvK0NAzonQl6WKYCL/4YfL/TE+HzsiIEDvDkEAOB1Gazrz/b793//7/9Yenxj2Mb+3/V4DzicmP9AozRHGJAkhJAN7xzaTYAIKHWnql0LkHZVzGLGFtdv+eLsuFPz+ewMspNn7QD6s91UVCMYxcrCNYUBIDAicEDsQEVCYW0LnhO5YHCRRezz2YdeXuu14mLce3vbT5XR/HP/5H//fab3t27qsfdhoHdGvp4wCENDboy7yw9elJHl9+fLb7fbbXw9TMUMHDI8//VQvddnbBkjfvn3+y18/xtTX11Xdbk3/6V/ef+j1z3//ihAp0c8/XyLi2Hu65CWLEey3ibqVU8mcR+uzH7//fru3/unHy6m+3mvsAqYB8+vbCwbvOkT48/uhc6tLvr5UG3nNAiKPx/7YNw9KSXJKt9tDhN7fP0+rLOvyt7++f/lyeXm5LJX2tvz22+8v10JMagqm55Rhrd++f3589sfW1CHMUxazgZi1awtQ9boIEa3L8v3jJuxLFSQ0dTMlELPx9eWFmMyGowc4RCyl9gGftyOnhRH7dOhqUzOzzSGcAbmPQYAl03FMn1GKlJper8vry/r//dPv29bC3aaVskzFlwvkXDQEwBLLGP4+Q6c5BDiqemJfFyFCRKqp7MfAMDRbpDSdfXYIPLb5fmtPkmswlEy1lFrLt99vnOD1ei4LP580ASEsr6/ndvTLeRk2S6KS03Pvg4FjxnGMgCg1cXJmIEF0B4uP23Fec2Imij70b7/fIWCqni+FEETIw3sfpSbJeBzHcfRa6tMzyEKJVnTpzT3HnGoaT4h+TqnWGhimDoLHrupxOcl6kqFlzL7vjowppX5szITA6HG043xZT6f8D//xu1owIwssNeeczqdyPVFi36ebrdPofEovlxSAmRFgfdw7eCy5lFp02sulEsbsnhkm+2lJ5pQz/OmHKxjc7vPY2970X367i6S2z7qUnHiqgSmY5UQQ8djGl7xmzncd+vQL97GeMgsT0vlULIxIKQVErHUJ41plak+5vP/WBfjr19Pj/XY+naQufczt0D50zFkyk6CjAQIy/fb7rZT0fjtOp1OmQEIWhPA5PYKObRJgXaCNUSEhQhvKFEn4233Y3BPjIqJgXFNl2zdHglpLm+rgUrKBI4OhERESAFFMBYx2zLB4qrWInveVCADmnkVOVYJCmAIMMdSUJuI5q4abqzm4E/zh4l7XXCobBmKoKhMe+5TC7RiEPKZdzgUg9mPksixVjpv1MT08V3i5ntu0j9sWlCRQcu6jAYRIAsRNZx9dcs6MkPBoMyU+X0/3z/3xmKcVUk4ibmoQcb3m8ymdEvz4JsioHf/pPx1qQYSqhuDELMKSgZMlk+04RApMVHBEWNaKh4/RiIAQ3T2XBQX2vYMjEgKBaUBQO7qbirAD2BjLkrb9ACJkMPNt81ITAt+2mHv78XUNS/vuQZBIaw6U5MbhLhnJiAh1HBMrEtaaddgc4Wn+/FPWSbf7/rijCFCxbZ+ZBcmEKbF3gOpkz9mqt1BMOZVK15Q9cKoTUhJ0C/AnJdl0+hzBmQgpSUGEcCfB4V5K7g997DOpE1NNlYRYUaeqWa0pwJEoMT+HQ0ISRhxTCTDlPIcRAAXqtIhg4Yg/PMJt6NvbOSXa9kODS44pTrcBmPbOZH1dS5KKpK2TEHK2fYMeuRgtFUvhzxveHoM40kI8CfmkOs18jvDwWguF5VNRM5xxPktFWP/y0qY+jn02X5ZUl/zxfVsW6WY6Z8Ng4qN1ImqHAYApNLXzqbJAa3Pf6byk+9GeJ3lhnEMtAIAIFSG4BE5QDSQACOAn7MUtfM5JZgGhpoQIDKTW4Egp1SUhQe8a0yFFuAKQK/3wVrf7+PkNmdLHp0pCVZ0PzzW5aVlyBN0/H+bwRLuyMEREOBEFAjHVmoNgjGcNiQFVzZlJ54QICHR1D5fMUz3GFA4ECI/HvVeJZSEAQZCjewCCQSa8XApLAlc3Z5apkymImQRHG71pBGz3BgDhVJLVldZM5VqOB3QdAd6OUWtecgKCr2/ZFMpDrufy2Ie5LRX7cFUbvXuVUpgFNSDMwskMtm2ulf/859M8bg7w119DhctybzvvWud9nNa6Vsis5QLrWY6m/bDz9dKP+f39SJKP0Q2JJbfeU2I9LNyJYIxBREDgZhpoHqWw+bPAnFjCQm83o5LcaT8GMbl7AHrTJGw2EQCZ2jEACJHdAR3DwyF6HxEJURA9M0KguafntX8kZqgltzaJEQnajKO7mebEFmYRiDT1yXLCMCeQQDN3SpQkqf8hCNNpT8A5AHLicBttjFoBIYBS4n1vS0k2bZhJppRoaMzpzIQKpg6Baq5mpv6MzTBjTsKMHu4OiP4k4NhwcHMISImF9mMbxKfKa7WaJTBTEtWBQWtJnLjvczmVVAU8eiNh+ssvl2Wh2/3xOJAZwUiB6poCh7syh4SkqNujv0N70tS0TUBGnIygfQekIBpuakDqs3nkJ1AQa+XR1d1YECAwAFn2fSw1EZKDBQQA6NSYpspEEq4BxdzGGKlRPUldJEgpJae4vpWEeRwREOpKiNuj6eAlVTA245xYbUdEQSxrkSL7YxNeXq7LHHocIyUGgDknQGlNbToCYiAClUytdUDQ4YD4ee+B/nJehdJt9FST2VS3ZUnE9dimMDPTGJqzIEafUYokge2YFk4BbqEKkgVxppSe34pck4M/Re4l41TbzZ4yQSQ8WiuFJIF7mMbbZVkvy1//+YBwAAJEm8/tsEVAZgr0tLCIL4VS0hAWxDlwTnrsTIjJkYgKZwx37W6OgLWgZMkpfT6O3kdZq4CZUp+HhkoV4Gi9DzURuiyFWLZ2MFISloTIMCw4sQP0Ps0BkT+3jkTM2Nq4XFY21zGRmAiOY9iMXJI5Etr8L4nUo2sgRoTbc8fxzL2HCE+dc1g4zOnywpcLJMmm8LImfX4KmdsRpP18WSSLMFEEEBDz+VICfD1VTlnj7u7DMyMM77sVpHJ79G0fz+xqgkCjutZTyTFcu8Yct88j5SQlB0br41zXw6PNGR42bbuPG7XTORXhNuNxG8dmRASCk6yNjshzTg9PzCkLJ5qPPrqH21Tn7qZUC+99WmCfelmzUI7wWtOYow1wh+3WTcEMIoAIzWMMN/PnEaIWWhYBwJyFkMcwJn2tmYhMJxMsq3x+HtOCAYjIHbfHAQ4l18ejjTlZ1lNNwlBO+WkpBoz4Y9xENX9sgxzdQnWGwdvL+vVlYYy9yfW83Lf943PPcrWw8yUHnb6/+3/z9z+9viwft+VoertvX76c3l4Lui+ndWu970dEJGZXDx9AHhZEhIBqUKT2HT7fm4f/9NPb9ZwWJizlWk7u2voWmCkMEwHhdt/rUiVLqZgSFMlt02WpY3R0ACQEVO2q/qxl3e+9FMkpu9sPP7789vsDYuxT/+Eff/+v/+6VSE+loMGplIA5Akvin76ecZDP+GjzdhxjmLlvx1QzIbhc1r/8cn08VoT48lo4oX1dEIPAH9uoBa6nBc3n3E7X6hOOg377be/qGVwg18xf3uoYNrr/0z/fX84v51N5PjXWJf36K3x+zHNV65BZ9jk+99678V/o7a0iGHJaal6qPIH6QDY3G4fVLOuS3eefvr6o4ePW9j4BGcEISUTWpSLCb79/hPnr17VKmvv2y9+/vf6wfN4+/+O//+23X+8vLy9MdLuN/WjmkCoQF0I8XXJe6HYbj6Mn4etpOUZDiu/v+/d3LllqjS+vCycAU0Y3tcsld2+XU4JaPr69H/s8jj6OcfM4n90V/t3/+W//9X/7Z67LOLQ3cEDozgxM7oQANrsh8hO5NVyjJDXsTQECwOiJS2BuY7Y5RU7793H7OO6bvv/jb3/+y1/2SVPxOPw4jPhJ0XIUQos5JyEgpNENGFPi3tU9EIOFImLuGo6S0+jYHn1dq3t6PHqARfA//sN/rKdTm2qmSGnaAEAPYKMx1UMx5ccG93EYgk4bw6+XehzH7X6c1vL43AsLIk0bJBHdCPD2sT+LwTXR7eMgiJ9+Ooc6Jvvpx0s7FpA5ZhMoBMzCnOUKcOz7v/y+9QGXej5604ljG5xtPS2l1GPbJeWXlzUnfv9V/sM//PrLL2+/fL2WgpzDXf4GodP/8strygmB9uP6+7dHFoag+623tn3/ZiVxZr6sAj9cMObl8hITU03b3vucH4/tP33bE4sgnS7l5VLrmlno2+ctt0ip1AWHdRtg6hH9wuXnP70ARCn88fnYD2VI52sldkRKi0CQuY+hn5/by9XW5erT5hy11pR5ztHaMFVmQrLnOfnL6/l8moCYRNrWrudViEoWQBrT931Lkk6VLucFQCP8VGQ0e7gLMWQxMyKsKdWSwgMBL3VRM0RQVcQggnA8xnQCU/DpOL2ccl0KQJzOC3j88GUNnM9rSzNITN8ej1yEKRLAaUkly2Mb378PCIyI1rwPZVZkJI71lEriUniqMcV5yXvv6v7x2U39subZ/XqthEiMl+uJEAmoNb1cVlPYjmMpsuSUKYhwOt0++9MrV7KUJGpxtJFFEOk49OPjYCa0khJeT2UO60C1JETMeQkP4nBXER5Dp3lZ+HhvOkHdjmP+8qeXUhDC5zElp7//6fz1uiBOZjPnmOr6ZJOXWgRMU9BlPdnsktLoGsF1TeczQkRvlpn2sRlxnzGGEVNdE4UNdQ+0cBaYzeuJCOnz4xCROScxEUCEp4wpwxiDkUvhlAHACBmJ7rdDhN35/ftOgSnxjLhcqhA/D4n3xzYmbPe5pLTfWy35aVuezfvecpaxT6iprDKPmH06Boe7wt6nuuxNE7kkSlmA0CJNdwdBgIcHEo0JCcRc+9Y8PC0pgR8FW+8YTgTEaKpAsCwV2fsx3CFlUbOAEEZE8rCllnWVlEEVEIEZ94eag5n1oWNMdwCAlIub9mfuIZFkjjHdXRUsIhEIC6IcTd3jsfXzudruT9VXTsSUbboje8eAWE7Lvpmbp5q2zY9j1iUfYzCTMLt6zhkBAXA/2mnNiWiq6QTTmRJj+H4/cimaghS44HriwY7oTIgMiRkJEBEjhOB6Fqti5qbejiNFAsElp2NvS03LmrY2CEjdYYYZQAQoJKCSsHczC0HSacTyrGQA4TRbi2TKxzHGMZ0CBUpN/EefHS3Augrx91vfDrss+cvLqrshe1fzwHAqmcbQoU6wjja+fdplLYYdwnnKfe8EQ83lWimM0QVx65YEI0KNyAAB99ZLTsyBBEtONjFApfBxjABcVumq5BTuFrPW5A7B/MTZINH0CNPh4W26gamKiCMQ5Wm99QGOZp7FZKo/VzypSK61HW1oC3+CmBEQTQMxUkJh7E3HNEl0gCHy46A2Jomg+mNrp3N6uaZ+OCEZxOjmw04l5wIRPbHVtR676VQIIUEKNAcAYCJhfPn6MlX3rZdUPje7rLQkw4JZsib6uLX93jCgHRoByHjsgxMigymua8qSw2DoaKOfz/nlwpLw1npXvawrEVm4+TQ3IlxKQUzvH1uf6h4s7K5ZhIUCwNwD0QH23s2NEMwtCWGEqYMnIjcPFk4kR5tMHOoQ7bxkV30MnM7bXU+nvG3zeHRK6DCIJZd8HFM1RMg1pqqHmPVwZ5GjTQwIiBmTiZAJIqaaMAHCnBaAkkUYMYFNMg9TA4RSJSJ6V2CAQAUKdzdIVXhlcpo7D4gAl0R1yWphZiIMPp6SNWa83Y7TqaJICihIWvDzGDxNkDCwt5mKHLsS0nnl3vbrWo6Goa7T5tRa89EMYp4XOp+SWxh6hG+PkTn3qTaplCSkYad6YrD9P/6/uwC0DvvKGIgYD21oWNJyv3WdUyFam3hYqanUQIDeGmVJUtve2tHdCQM9nJnrkgF8dJVUEHB/tDFCp2WA2RWZAt3Rw5CJ59SIkESS2I3cjABM/YkfAghEMo3MMZtDIRGBae6ectSCHlBS6EQHDIDWFQJKTkA4NFobNdcw2vfGQjRczUmSZNBpAZGEwgMRzIwI3L0sVQTmmAS4P46y5nb4/W4WpmaI2PqkCREIDu+9cSZk8mGmMcLcwOLZnPLzpQLAHJNZGMk8Wh+lUF5SzhSKpeSaZUzZtgPDl1rBQbIAIYGA2ZfX0+fWDc3cIWj7eKyngkT37y0t4AjLcp29b1sga6l4f7TzgkhGlNrDrOs9vH16LfzlZUGTMCOhGQgW930eOs/rOndvXVEHc6qnZamiEwCUAoe6MEKEA2CiLOn4+HSIwtkBkHJecuuz1lVYTgvjtWyP/SVnIqyZmcHDziUTppdzDOdjMyG0rodCeTsTuggcc0jCnBkDkcLGIIwAQ2KHYMLwmFMDwBxUYU5r+1hq1TCzZ4oT5vQsIpJIgBCmqaodR0/MDu4eY+hQC/dwNwdSBwhJWE9F50wK4WAWkjAxlSwRARSAHgBjOLLbpG/HURd6fUtrltmm2pyHm4V7cnekECFJDGZuHogRQBCugIThfjqXy0Uc/LTSssiSQYoAKrguK/OgZUkB0bt1NRfKAeQhxOrEFUPHvW+UuXJmwmnBlZKibtgPHUc7arqeKkd83o7llD3cZgBSREyDGlxK/v6+DXcmNrMgLImXks+nVBcZw1PC3t1dITAJp8Lfv28QxoxIFBAemAvvjzZHIBszC/2hRBSknMhCa02X17wW64N3cxGglLe7mxmaFea1pLLEWjFzqEc9i13S3tR1zvBTyUO9j8EYs3kLTySmIAnRYR5DROi5YkGaZiFeVnrGjcxdJNzJzM2mTrue179t3cKGKw9LnD4+mg5/ezk52FT9+NY9vCZipMupbnurazKAPlqgBmHrfa2iE95b73MuNX9/tDn8XIKFUaL1eRzRDh3D3YOJiBAA1NwdAMLDEcE8jubTLMAZiRnNfE4Ieq7Unt1tnEON8PO2z65jGiLm/Nxu6/ePx97ETd9eLm9nYoKyZGIBeObykIwyM7gF0ctL/fK6gGuSpzhSf/hycY+SRRJer7Ukvq75smJYf73m08IvZ/zydRWgz/uNitaajqlzxgRPhIykNpdSUknHflxOpbfWegTyGOOvv/62PZYfzpdgeP3hte27SDa3z+OwptfllLK0NnCMxLIu+egHIewfLhmZ4v7ZmKme0v3WIHDNJSK+fewlSc45YLJgIKDH/X784z/H0BgHvFyW03UVxttn23r74cs5LbTh3D8PHTbUfAaQhlI+l5dzpjmXPA1j3x+5IBGD8dtLOq24rFITTId978B0qmW7t8/7sSz8yy/XWvI0fezdZmSWy1Lb7t/nDhzrUhPg17d6f7S//e2zlpJzes6yfczffn2gB6KvKyDKzz++PbbH7fEQCOYElRAhbN73I48Ujo99IPHzlDTVaikvL8t5zfdHu23Hugqh34d9frQvP72+vJ7+4T/8p2fxwVTDXVjWlYH8cmI3fyLD8QUQcV0YkQOTe9Aqn59t3/uff3kRgtkGoA8PPfZpsWkryTOxBSzruu/b4759KSebQEA///xjBOt0BJyT3SPwqU1GJIpwDyKCMV0o5VRmG4eN8FDViAACEfEId/AwiH5+SX0bLOWxzzYmU/72+xEO7TD4L1V8ZgwA4vTY9ru1y+WFAvZtmoa5ASDz00WIGC4Savi4b9fLeTrePlpdzkS0nk/LaY3t2LcWMBKjGuxHO62FCLMgAT/uvUh6/+ybd1ULD2ZeC14uJXy2djBL22MqpkyjjyRyuebx26RA4PT9vVNAKeI+x/TzqepwFM15hUqIPvYdAd4/9mM6E6LreSlfv5yEiYXJ4bj3Nrs/xv1jf/lSr9e0rpIJfv75rGG93bLov/kf/nT73FEMIY0+Ivaff8zmTED9sEfKSH5/NNN0OWMSIYQ//fjy2+8fn5+f5vDP//K+jymMKSMGn1cuxZEwgr5cr/vDP9434QU5tGMSkhSft1spKRWKQCYWgW/v+2O/f/nhLLR8bo8xBnPRqV+/nPocj8fj2LfTyyl6jE4iqe9zO/rb66nrwICaawRISUT4/f32uSkE1iolpSz1ft84Yt9bkoL7yEmQAsKXLIy0FCHGJ4ohZc5JMOIpABqB98chjOpxv3dkObois6TgCJFMlB63frSWsuSc2uHrms1mn+4WzcbR9eg2ZzBDeCThxzYf+zAFwj/EMFPdZwDAset5TZdrYUK3aH18+76bQ3hMNcdIiXqb77fjOHouCTD37ufLmRFuvYmwuz+2TkiXNVWB2zaPvadMEbwWOUkaU11dLT7uex8+uhJBNqwpzTHcLByYUFJCwDmMCAPo835MhSzy5ctiHq0NcysJQu3WBsesKCJUeTJzIE/Q00sZAfdNc4rTGox49OA5AjwXSEvKhUsiDG+K+5hRcl5Oj30LtLfX094Pf9IqAgj+IDYE+P3RkjATE1GfVoDMYKjPEcL+8b29XlcmXFIiDBt63/041N2ZaPoTZYuE5IGft/Z5H5elzBluaNP2rX0+hrzmdc3T5rrkPmxd8xj6/fPxCgujtDbSQrnkj/s2zb6up/VUrPfNJLnDmOly4ZgskZdKEVN9O0JI8gJzBiIlASGrGcZAe9p1EyLwmLowEUkAWri1mTInkaXm0YcDXV9KSiDsmxoB5Uq9QzQMDKZISR7biAh/sgbDmRNE9DZGdyJQA52RM7SjtUMBEJ+qKrClUk6YmPPptO19m0PDh/n5spIj+OxDPz+acNr2mcvgTMKIzvd97GYEkljWpRBEuKfMRNTbMI1SGDD2TQWNiU+LPB6dQn768fTY3MFEmDABYutKjExkNInFIso5l8QG/njMx70hhyROWeYwdyAMQkAiVw8iEAIyRup9RgChPWckdXKLbbPN93DTcCyUBRAj1D3AW5AQcdr2A0NPSwZPc9ea8HEoSdGJTZ0pahZ6ibqkdhjE7DMAA1FGg73NL5fqYU/8axKUCWvKE9QggHDqMDeEGHPkUiDCpjnI6zWZ2oHsOvvQQJuqOpSTADBE6HDEcAfzSJKmzjFmzmlMG2NiELNcCk6Q2zbmsDFtgIsHunlgjDFGGygCQj5MgD0cARDQPbKQmpmaDoPzEh5qrBMe20iF1yURU2sjlyyEaJpzhpiAVtdAAjMsCSin1rwf5uZuVpZcCqeckgg4mIdODfd9n31MU9jQypqWzM1turFjH4qIkknVknCuuCzLnA5hc6qHns4JCU6nBOC9zcc2WDDMHMARRXjOKYTMsm29tyCUwNmbiiRkfMriCDGYzAMCT8uKiOFmajUXd5/TCHGa6qSNqJ4YLCx879pad+BcU53wt8/jmUDnREMdhnOyUhdqKglYUKezMCAQiepw8JTF3cGCCc0dEYUBEacqIZL8sZigxEDGIj7BAMzUm3/5cj0teaiq2pieEgGH+2QSRljWHAfurSFjXSUCtvsEfBoEKSXiLEvNgY7iQ/HRp0Os66rdVGPMQQwaJmnF8N8/PyVlAZMKo/u65PduhLKWhIaP96MmToW3W/cJy7kiQNvn5VSk0J//zL/+tn37BsSLzmYQvo85yAEkURZihCLctvn0/iBEyQgWxzZzpbev68c2hqss6Xpavv96B49UEjOFx5NFhxHhgEi9TxERoomIEMTEQrObMAVy65McTZ/KAHI3gIgIQmKCQFLVeH4bFFmolswZX5aoBT4fbZFSX/PebRuoEbNP8EiFADEcW9d29AgAg+f7eqKsniSvPg0An5bD5yaROAICmSDQCYfaWvi80DTuTSVRKEQQi7TRpTDQH39OQLjWOrrOGdMsl+SmZhCODi6ZLy/57ZSEeCm199FdXi7lsetQOK/8w0sNx+4wZy8lAaAkOY4dA6Z6/2jgMAP7Q9FjrTIP3/tMQkviYa03vUQhLHNAEu6qhCQ5n07yQ+VhMRtkQZ/QjznhD0ZgFWYDI+TEAdyHpjYBYvSp7kA0dQAKMeVCgLG3DiQUoRaI2FtPmS+nfF5TGCyLTNfwtCQBDwBuo1/O9XE3oUEwz6ekU2NqqjklWjIwwBiwH5orMsfRWkmpZNIWEcCEWWSqjTGJuOS8PZqHI2NdKiF1NY+AgG2fiFgqedj9W+cvF85GSAQ4x5SVe5t9n4gASOYOQFONhZCgHV1NmWmoQaAHrIucVjpd8r57uOVMs1spydxHNxZJnIgCZfCzspd4DgWCdSlWvc9BlCXR1EAIwnDDnOX1XL98qUQBZMvCiUHNrMXLKZGQCFINZrw1SImnuweAuxCN5rc+Li+1DX3so/KCjg/re2/Xl3PG/H07iiRi7GNYLXOMj/snpxcHtAiAkU9JgEUAmDABG5lZEixVtE+q4jjd9Hpev6kC27qmCKgrtzkBo2Qe090dCQEDAV7fVrXpFk8AU9snE3cbuciS07ImDb83iPC6kvtmmIF42/vsXQNhe/x8SoJ4PV+J87dvH8OBACl8HJODtNt27wSECFU83DLR467nWpkwgG5b0zHSj5QKCRHhYu5b6zY9CAIAmaZ6H3rfOiKykBOQyNF9H8YiQYBIgnJa6dhGb/byUogMEYACES6nNFvxIGYGdLX5eW9fX9eUqO38uI38Rq695gyGPsGmJ0YpjIhE6BGsz2ACmLkIu9uYih5gMV0zUS5cCyMBKLkBBdTC7hpIAQEYtaYx9PbYkaAQEXB7zKHa210nnFcx17oIIpi6TkgCzFxydpelUkT3ME5xSlK4ZgmAKBWXyuCOMQPmYx/Xa4GIWignwrA51dwwIEwgwmCOORMuGgZE9897zqmULBJtHIgJwC6XkhiOXcdi03rac2stdCK586xc+ja6TSQCBccoFYliqI3uhElj5MJA0UZnhlNeW9M2ZxI8rexB3z8ahYN7AElhh/jyUs4vK5Vo8xClz8emBsc+o5AxXV8uNdvtfuTFmDFlwRSA4axIsXJte8xhtca3j3a9ZiBX04E4VM8vJwNXhdvtQIbLJWfhOQcI5ETM7BNer3WqkUCpSYBm6PXK5/Op7dhbF5GcZA7lJETw7X0XRlNAFhZm4NvHflqX1/Myt8f9tpuUPuw4LCIAkRFU9Qlx3Pa9ZP6+76Uup1p6n7WwsNxv/f3zr28/rKWkfnQIF+Fa059/WQLxGLMuejmv44Cj23JKiFoEPj776Sy9+f3Rz+e0H6Dqv/7+eD4873sTyoA4DFt3LsDMLDxhSMqlZJ2TspScGGVONXO3+Uy3BrMbgoFHTLXzuZ5O6fF5YMoQ0fsgBDNXVwAc03UaEkrimillxhELl9NpHb1T4tYU3N0jwgApAnQGAAS4MLFguEouvTcEZ8Sh6gaEKMLELJkAjDNPG1PhfFrcHJAoGQqIMCL30Z+DylRHxHWN221PkpeFHDT/lzvtPnpKcjnnnMMHtKOtp0UYTZ9al6iV9u0hAsKoGsj02MdQBYemrua1JAzihTHp+8d9zcv327Z1TcSnJe9tVKF27CmX3pr1gGkuz72DjV/vr9dyPtXt6Hvf/0jBe1jf376yGbbNoHqaWIW6KoNkyd9uiXDUBZYzSSZ3a91/+/1jxNitoVULMohaSaeaA5IeXVm4Smp9dp1SsGl/W09N7f3WloVfrhcH772h59bCY759WQL4aH1dkoszkjbvc2YnNU3MzzR9uE/T0SeA1VWmKXGsZQFzhxhjnk7pcpWuT92QBsR+TIi+LHx+SdeXHB6ELkzhvqzIg5mAKMzDzLVNNXnWu4xUfdbKqjGbQYCOnoW6uqmtpyVXNlOzmBY4fcm6t7t7SSkVzgEw3WvKYyoCECIEmQIAnJY0h/ehwpiE1TzCEUGEr+dSEm/D9336hMdtuoMwlkynJYHb3nUOV0X3mbI1NsLEJExyLnWqq+rnZ9MZ5zWvtfSuoSEFQG0CqOL2ODzw/fM4n84pIxDsrScUVwdC1YnMBuCAro4sknjJmWCawVIFCd7d9q5H68QBCDWvveucgQxrScdmxFQTXa8JBRLGujATGk1q8HKpszMCva60HztyXnLeHk0wWYf28P2YqoHACWCiJ4DTUlTVCc6X1YZ+fB7Lkt2itZA/OkHwuHe3cvvsa1lOlzzGJHZz/Hg/AAIRuocgUhJ3//brZ1lzBO3HBCfzQGYgZkkec0yT0bGwgSchxCCRZ1dDKlRiQNk7TIsx7Wj9eslKzxQSV+YAgJTE9Pb+meu5TxzTqEKqaUE6r9XdzaNUT4f2A8MBwPApyCmkY4YFIzm4qSaR6zUDpDHmUhg5VH3bRxisJxZxQIfgJLhHQAACqikTsTAilJLb3kzBCSMiMR7HeP71LVXU8GmjD4QxTacDEjCURZ6s4nZMggCEZcn79ii5SsmPbRZnMHhe1yEGBmg3wOCF13MxizlVEkPEnIpMpaSSBTnQnIMfHR/7cIsnqrgWTJkX5zbm1qYwbkdzZZuGIllw9+PHrycmUoPTEh9jS4n86QVK6BjDbUGsJY1hgUhM5gYRSGk/+hyWsoBDa42Zeh9XOmnXUW2o1ZwWYM7l7eXtfE4ft+3zNrTrvbtxsR4QCMRmGq6za2fNqf7yp+W2bzEEOYXp5VxKkWnjOWq5gaCdar0PK5XmDmtJwnV6EBoRt25Dg9jH8HPhdzc3sm4Kk4iJMoZs96k2U83uhhjMbNNKFu0W83lw4DmViOaMvKaSoO2HQyDA/w++ns28YhbKHQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng = np.random.RandomState(10)\n", + "z = torch.from_numpy(rng.standard_normal(G_pano.z_dim)).float()\n", + "z = z[None].cuda()\n", + "c = None\n", + "\n", + "input_layer = G_pano.synthesis.input\n", + "grid = viz_util.make_grid(G_pano)\n", + "start_shift = np.random.randint(360 / input_layer.fov * input_layer.frame_size[0])\n", + "start_grid = grid[:, :, start_shift:start_shift+input_layer.size[0], :]\n", + "pano = viz_util.generate_pano_transform(G_pano, z, start_grid)\n", + "renormalize.as_image(pano)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "embedded-heart", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# splice the start and end of the pano together --> seamless image\n", + "pano_width = pano.shape[-1]\n", + "renormalize.as_image(torch.cat([pano, pano], dim=2)[:, :, pano_width-2*G_pano.img_resolution:pano_width+2*G_pano.img_resolution])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "anyres-gan", + "language": "python", + "name": "anyres-gan" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/setup_notebooks.sh b/setup_notebooks.sh new file mode 100755 index 0000000..fff1820 --- /dev/null +++ b/setup_notebooks.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +# Start from directory of script +cd "$(dirname "${BASH_SOURCE[0]}")" + +# Set up git config filters so huge output of notebooks is not committed. +git config filter.clean_ipynb.clean "$(pwd)/ipynb_drop_output.py" +git config filter.clean_ipynb.smudge cat +git config filter.clean_ipynb.required true + diff --git a/torch_utils/__init__.py b/torch_utils/__init__.py new file mode 100644 index 0000000..939e7c6 --- /dev/null +++ b/torch_utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py new file mode 100644 index 0000000..dd7cc04 --- /dev/null +++ b/torch_utils/custom_ops.py @@ -0,0 +1,157 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import glob +import hashlib +import importlib +import os +import re +import shutil +import uuid + +import torch +import torch.utils.cpp_extension +from torch.utils.file_baton import FileBaton + +#---------------------------------------------------------------------------- +# Global options. + +verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' + +#---------------------------------------------------------------------------- +# Internal helper funcs. + +def _find_compiler_bindir(): + patterns = [ + 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin', + ] + for pattern in patterns: + matches = sorted(glob.glob(pattern)) + if len(matches): + return matches[-1] + return None + +#---------------------------------------------------------------------------- + +def _get_mangled_gpu_name(): + name = torch.cuda.get_device_name().lower() + out = [] + for c in name: + if re.match('[a-z0-9_-]+', c): + out.append(c) + else: + out.append('-') + return ''.join(out) + +#---------------------------------------------------------------------------- +# Main entry point for compiling and loading C++/CUDA plugins. + +_cached_plugins = dict() + +def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs): + assert verbosity in ['none', 'brief', 'full'] + if headers is None: + headers = [] + if source_dir is not None: + sources = [os.path.join(source_dir, fname) for fname in sources] + headers = [os.path.join(source_dir, fname) for fname in headers] + + # Already cached? + if module_name in _cached_plugins: + return _cached_plugins[module_name] + + # Print status. + if verbosity == 'full': + print(f'Setting up PyTorch plugin "{module_name}"...') + elif verbosity == 'brief': + print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) + verbose_build = (verbosity == 'full') + + # Compile and load. + try: # pylint: disable=too-many-nested-blocks + # Make sure we can find the necessary compiler binaries. + if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: + compiler_bindir = _find_compiler_bindir() + if compiler_bindir is None: + raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') + os.environ['PATH'] += ';' + compiler_bindir + + # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either + # break the build or unnecessarily restrict what's available to nvcc. + # Unset it to let nvcc decide based on what's available on the + # machine. + os.environ['TORCH_CUDA_ARCH_LIST'] = '' + + # Incremental build md5sum trickery. Copies all the input source files + # into a cached build directory under a combined md5 digest of the input + # source files. Copying is done only if the combined digest has changed. + # This keeps input file timestamps and filenames the same as in previous + # extension builds, allowing for fast incremental rebuilds. + # + # This optimization is done only in case all the source files reside in + # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR + # environment variable is set (we take this as a signal that the user + # actually cares about this.) + # + # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work + # around the *.cu dependency bug in ninja config. + # + all_source_files = sorted(sources + headers) + all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files) + if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ): + + # Compute combined hash digest for all source files. + hash_md5 = hashlib.md5() + for src in all_source_files: + with open(src, 'rb') as f: + hash_md5.update(f.read()) + + # Select cached build directory name. + source_digest = hash_md5.hexdigest() + build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access + cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') + + if not os.path.isdir(cached_build_dir): + tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}' + os.makedirs(tmpdir) + for src in all_source_files: + shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src))) + try: + os.replace(tmpdir, cached_build_dir) # atomic + except OSError: + # source directory already exists, delete tmpdir and its contents. + shutil.rmtree(tmpdir) + if not os.path.isdir(cached_build_dir): raise + + # Compile. + cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources] + torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, + verbose=verbose_build, sources=cached_sources, **build_kwargs) + else: + torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) + + # Load. + module = importlib.import_module(module_name) + + except: + if verbosity == 'brief': + print('Failed!') + raise + + # Print status and add to cache dict. + if verbosity == 'full': + print(f'Done setting up PyTorch plugin "{module_name}".') + elif verbosity == 'brief': + print('Done.') + _cached_plugins[module_name] = module + return module + +#---------------------------------------------------------------------------- diff --git a/torch_utils/misc.py b/torch_utils/misc.py new file mode 100644 index 0000000..335397d --- /dev/null +++ b/torch_utils/misc.py @@ -0,0 +1,266 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import re +import contextlib +import numpy as np +import torch +import warnings +import dnnlib + +#---------------------------------------------------------------------------- +# Cached construction of constant tensors. Avoids CPU=>GPU copy when the +# same constant is used multiple times. + +_constant_cache = dict() + +def constant(value, shape=None, dtype=None, device=None, memory_format=None): + value = np.asarray(value) + if shape is not None: + shape = tuple(shape) + if dtype is None: + dtype = torch.get_default_dtype() + if device is None: + device = torch.device('cpu') + if memory_format is None: + memory_format = torch.contiguous_format + + key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) + tensor = _constant_cache.get(key, None) + if tensor is None: + tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) + if shape is not None: + tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) + tensor = tensor.contiguous(memory_format=memory_format) + _constant_cache[key] = tensor + return tensor + +#---------------------------------------------------------------------------- +# Replace NaN/Inf with specified numerical values. + +try: + nan_to_num = torch.nan_to_num # 1.8.0a0 +except AttributeError: + def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin + assert isinstance(input, torch.Tensor) + if posinf is None: + posinf = torch.finfo(input.dtype).max + if neginf is None: + neginf = torch.finfo(input.dtype).min + assert nan == 0 + return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) + +#---------------------------------------------------------------------------- +# Symbolic assert. + +try: + symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access +except AttributeError: + symbolic_assert = torch.Assert # 1.7.0 + +#---------------------------------------------------------------------------- +# Context manager to temporarily suppress known warnings in torch.jit.trace(). +# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672 + +@contextlib.contextmanager +def suppress_tracer_warnings(): + flt = ('ignore', None, torch.jit.TracerWarning, None, 0) + warnings.filters.insert(0, flt) + yield + warnings.filters.remove(flt) + +#---------------------------------------------------------------------------- +# Assert that the shape of a tensor matches the given list of integers. +# None indicates that the size of a dimension is allowed to vary. +# Performs symbolic assertion when used in torch.jit.trace(). + +def assert_shape(tensor, ref_shape): + if tensor.ndim != len(ref_shape): + raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if ref_size is None: + pass + elif isinstance(ref_size, torch.Tensor): + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') + elif isinstance(size, torch.Tensor): + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') + elif size != ref_size: + raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') + +#---------------------------------------------------------------------------- +# Function decorator that calls torch.autograd.profiler.record_function(). + +def profiled_function(fn): + def decorator(*args, **kwargs): + with torch.autograd.profiler.record_function(fn.__name__): + return fn(*args, **kwargs) + decorator.__name__ = fn.__name__ + return decorator + +#---------------------------------------------------------------------------- +# Sampler for torch.utils.data.DataLoader that loops over the dataset +# indefinitely, shuffling items as it goes. + +class InfiniteSampler(torch.utils.data.Sampler): + def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): + assert len(dataset) > 0 + assert num_replicas > 0 + assert 0 <= rank < num_replicas + assert 0 <= window_size <= 1 + super().__init__(dataset) + self.dataset = dataset + self.rank = rank + self.num_replicas = num_replicas + self.shuffle = shuffle + self.seed = seed + self.window_size = window_size + + def __iter__(self): + order = np.arange(len(self.dataset)) + rnd = None + window = 0 + if self.shuffle: + rnd = np.random.RandomState(self.seed) + rnd.shuffle(order) + window = int(np.rint(order.size * self.window_size)) + + idx = 0 + while True: + i = idx % order.size + if idx % self.num_replicas == self.rank: + yield order[i] + if window >= 2: + j = (i - rnd.randint(window)) % order.size + order[i], order[j] = order[j], order[i] + idx += 1 + +#---------------------------------------------------------------------------- +# Utilities for operating with torch.nn.Module parameters and buffers. + +def params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.parameters()) + list(module.buffers()) + +def named_params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.named_parameters()) + list(module.named_buffers()) + +def copy_params_and_buffers(src_module, dst_module, require_all=False): + assert isinstance(src_module, torch.nn.Module) + assert isinstance(dst_module, torch.nn.Module) + src_tensors = dict(named_params_and_buffers(src_module)) + for name, tensor in named_params_and_buffers(dst_module): + assert (name in src_tensors) or (not require_all) + if name in src_tensors: + tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) + +#---------------------------------------------------------------------------- +# Context manager for easily enabling/disabling DistributedDataParallel +# synchronization. + +@contextlib.contextmanager +def ddp_sync(module, sync): + assert isinstance(module, torch.nn.Module) + if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): + yield + else: + with module.no_sync(): + yield + +#---------------------------------------------------------------------------- +# Check DistributedDataParallel consistency across processes. + +def check_ddp_consistency(module, ignore_regex=None): + assert isinstance(module, torch.nn.Module) + for name, tensor in named_params_and_buffers(module): + fullname = type(module).__name__ + '.' + name + if ignore_regex is not None and re.fullmatch(ignore_regex, fullname): + continue + tensor = tensor.detach() + if tensor.is_floating_point(): + tensor = nan_to_num(tensor) + other = tensor.clone() + torch.distributed.broadcast(tensor=other, src=0) + assert (tensor == other).all(), fullname + +#---------------------------------------------------------------------------- +# Print summary table of module hierarchy. + +def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): + assert isinstance(module, torch.nn.Module) + assert not isinstance(module, torch.jit.ScriptModule) + assert isinstance(inputs, (tuple, list)) + + # Register hooks. + entries = [] + nesting = [0] + def pre_hook(_mod, _inputs): + nesting[0] += 1 + def post_hook(mod, _inputs, outputs): + nesting[0] -= 1 + if nesting[0] <= max_nesting: + outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] + outputs = [t for t in outputs if isinstance(t, torch.Tensor)] + entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) + hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] + hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] + + # Run module. + outputs = module(*inputs) + for hook in hooks: + hook.remove() + + # Identify unique outputs, parameters, and buffers. + tensors_seen = set() + for e in entries: + e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] + e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] + e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] + tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs} + + # Filter out redundant entries. + if skip_redundant: + entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)] + + # Construct table. + rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']] + rows += [['---'] * len(rows[0])] + param_total = 0 + buffer_total = 0 + submodule_names = {mod: name for name, mod in module.named_modules()} + for e in entries: + name = '' if e.mod is module else submodule_names[e.mod] + param_size = sum(t.numel() for t in e.unique_params) + buffer_size = sum(t.numel() for t in e.unique_buffers) + output_shapes = [str(list(t.shape)) for t in e.outputs] + output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs] + rows += [[ + name + (':0' if len(e.outputs) >= 2 else ''), + str(param_size) if param_size else '-', + str(buffer_size) if buffer_size else '-', + (output_shapes + ['-'])[0], + (output_dtypes + ['-'])[0], + ]] + for idx in range(1, len(e.outputs)): + rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] + param_total += param_size + buffer_total += buffer_size + rows += [['---'] * len(rows[0])] + rows += [['Total', str(param_total), str(buffer_total), '-', '-']] + + # Print table. + widths = [max(len(cell) for cell in column) for column in zip(*rows)] + print() + for row in rows: + print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) + print() + return outputs + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/__init__.py b/torch_utils/ops/__init__.py new file mode 100644 index 0000000..939e7c6 --- /dev/null +++ b/torch_utils/ops/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/torch_utils/ops/bias_act.cpp b/torch_utils/ops/bias_act.cpp new file mode 100644 index 0000000..3adaeee --- /dev/null +++ b/torch_utils/ops/bias_act.cpp @@ -0,0 +1,99 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ + +static bool has_same_layout(torch::Tensor x, torch::Tensor y) +{ + if (x.dim() != y.dim()) + return false; + for (int64_t i = 0; i < x.dim(); i++) + { + if (x.size(i) != y.size(i)) + return false; + if (x.size(i) >= 2 && x.stride(i) != y.stride(i)) + return false; + } + return true; +} + +//------------------------------------------------------------------------ + +static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x"); + TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x"); + TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x"); + TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(b.dim() == 1, "b must have rank 1"); + TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds"); + TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements"); + TORCH_CHECK(grad >= 0, "grad must be non-negative"); + + // Validate layout. + TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense"); + TORCH_CHECK(b.is_contiguous(), "b must be contiguous"); + TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x"); + TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x"); + TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + torch::Tensor y = torch::empty_like(x); + TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x"); + + // Initialize CUDA kernel parameters. + bias_act_kernel_params p; + p.x = x.data_ptr(); + p.b = (b.numel()) ? b.data_ptr() : NULL; + p.xref = (xref.numel()) ? xref.data_ptr() : NULL; + p.yref = (yref.numel()) ? yref.data_ptr() : NULL; + p.dy = (dy.numel()) ? dy.data_ptr() : NULL; + p.y = y.data_ptr(); + p.grad = grad; + p.act = act; + p.alpha = alpha; + p.gain = gain; + p.clamp = clamp; + p.sizeX = (int)x.numel(); + p.sizeB = (int)b.numel(); + p.stepB = (b.numel()) ? (int)x.stride(dim) : 1; + + // Choose CUDA kernel. + void* kernel; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + kernel = choose_bias_act_kernel(p); + }); + TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func"); + + // Launch CUDA kernel. + p.loopX = 4; + int blockSize = 4 * 32; + int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("bias_act", &bias_act); +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.cu b/torch_utils/ops/bias_act.cu new file mode 100644 index 0000000..ed1d16f --- /dev/null +++ b/torch_utils/ops/bias_act.cu @@ -0,0 +1,173 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +//------------------------------------------------------------------------ +// CUDA kernel. + +template +__global__ void bias_act_kernel(bias_act_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + int G = p.grad; + scalar_t alpha = (scalar_t)p.alpha; + scalar_t gain = (scalar_t)p.gain; + scalar_t clamp = (scalar_t)p.clamp; + scalar_t one = (scalar_t)1; + scalar_t two = (scalar_t)2; + scalar_t expRange = (scalar_t)80; + scalar_t halfExpRange = (scalar_t)40; + scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946; + scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717; + + // Loop over elements. + int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; + for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) + { + // Load. + scalar_t x = (scalar_t)((const T*)p.x)[xi]; + scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0; + scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0; + scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0; + scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one; + scalar_t yy = (gain != 0) ? yref / gain : 0; + scalar_t y = 0; + + // Apply bias. + ((G == 0) ? x : xref) += b; + + // linear + if (A == 1) + { + if (G == 0) y = x; + if (G == 1) y = x; + } + + // relu + if (A == 2) + { + if (G == 0) y = (x > 0) ? x : 0; + if (G == 1) y = (yy > 0) ? x : 0; + } + + // lrelu + if (A == 3) + { + if (G == 0) y = (x > 0) ? x : x * alpha; + if (G == 1) y = (yy > 0) ? x : x * alpha; + } + + // tanh + if (A == 4) + { + if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); } + if (G == 1) y = x * (one - yy * yy); + if (G == 2) y = x * (one - yy * yy) * (-two * yy); + } + + // sigmoid + if (A == 5) + { + if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one); + if (G == 1) y = x * yy * (one - yy); + if (G == 2) y = x * yy * (one - yy) * (one - two * yy); + } + + // elu + if (A == 6) + { + if (G == 0) y = (x >= 0) ? x : exp(x) - one; + if (G == 1) y = (yy >= 0) ? x : x * (yy + one); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one); + } + + // selu + if (A == 7) + { + if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one); + if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha); + } + + // softplus + if (A == 8) + { + if (G == 0) y = (x > expRange) ? x : log(exp(x) + one); + if (G == 1) y = x * (one - exp(-yy)); + if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); } + } + + // swish + if (A == 9) + { + if (G == 0) + y = (x < -expRange) ? 0 : x / (exp(-x) + one); + else + { + scalar_t c = exp(xref); + scalar_t d = c + one; + if (G == 1) + y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); + else + y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d); + yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain; + } + } + + // Apply gain. + y *= gain * dy; + + // Clamp. + if (clamp >= 0) + { + if (G == 0) + y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp; + else + y = (yref > -clamp & yref < clamp) ? y : 0; + } + + // Store. + ((T*)p.y)[xi] = (T)y; + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p) +{ + if (p.act == 1) return (void*)bias_act_kernel; + if (p.act == 2) return (void*)bias_act_kernel; + if (p.act == 3) return (void*)bias_act_kernel; + if (p.act == 4) return (void*)bias_act_kernel; + if (p.act == 5) return (void*)bias_act_kernel; + if (p.act == 6) return (void*)bias_act_kernel; + if (p.act == 7) return (void*)bias_act_kernel; + if (p.act == 8) return (void*)bias_act_kernel; + if (p.act == 9) return (void*)bias_act_kernel; + return NULL; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.h b/torch_utils/ops/bias_act.h new file mode 100644 index 0000000..60b81c6 --- /dev/null +++ b/torch_utils/ops/bias_act.h @@ -0,0 +1,38 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct bias_act_kernel_params +{ + const void* x; // [sizeX] + const void* b; // [sizeB] or NULL + const void* xref; // [sizeX] or NULL + const void* yref; // [sizeX] or NULL + const void* dy; // [sizeX] or NULL + void* y; // [sizeX] + + int grad; + int act; + float alpha; + float gain; + float clamp; + + int sizeX; + int sizeB; + int stepB; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py new file mode 100644 index 0000000..5c485c0 --- /dev/null +++ b/torch_utils/ops/bias_act.py @@ -0,0 +1,209 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient bias and activation.""" + +import os +import numpy as np +import torch +import dnnlib + +from .. import custom_ops +from .. import misc + +#---------------------------------------------------------------------------- + +activation_funcs = { + 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), + 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False), + 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False), + 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True), + 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True), + 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True), + 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True), + 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True), + 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), +} + +#---------------------------------------------------------------------------- + +_plugin = None +_null_tensor = torch.empty([0]) + +def _init(): + global _plugin + if _plugin is None: + _plugin = custom_ops.get_plugin( + module_name='bias_act_plugin', + sources=['bias_act.cpp', 'bias_act.cu'], + headers=['bias_act.h'], + source_dir=os.path.dirname(__file__), + extra_cuda_cflags=['--use_fast_math'], + ) + return True + +#---------------------------------------------------------------------------- + +def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): + r"""Fused bias and activation function. + + Adds bias `b` to activation tensor `x`, evaluates activation function `act`, + and scales the result by `gain`. Each of the steps is optional. In most cases, + the fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports first and second order gradients, + but not third order gradients. + + Args: + x: Input activation tensor. Can be of any shape. + b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type + as `x`. The shape must be known, and it must match the dimension of `x` + corresponding to `dim`. + dim: The dimension in `x` corresponding to the elements of `b`. + The value of `dim` is ignored if `b` is not specified. + act: Name of the activation function to evaluate, or `"linear"` to disable. + Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. + See `activation_funcs` for a full list. `None` is not allowed. + alpha: Shape parameter for the activation function, or `None` to use the default. + gain: Scaling factor for the output tensor, or `None` to use default. + See `activation_funcs` for the default scaling of each activation function. + If unsure, consider specifying 1. + clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable + the clamping (default). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the same shape and datatype as `x`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) + return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): + """Slow reference implementation of `bias_act()` using standard TensorFlow ops. + """ + assert isinstance(x, torch.Tensor) + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + # Add bias. + if b is not None: + assert isinstance(b, torch.Tensor) and b.ndim == 1 + assert 0 <= dim < x.ndim + assert b.shape[0] == x.shape[dim] + x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) + + # Evaluate activation function. + alpha = float(alpha) + x = spec.func(x, alpha=alpha) + + # Scale by gain. + gain = float(gain) + if gain != 1: + x = x * gain + + # Clamp. + if clamp >= 0: + x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type + return x + +#---------------------------------------------------------------------------- + +_bias_act_cuda_cache = dict() + +def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): + """Fast CUDA implementation of `bias_act()` using custom ops. + """ + # Parse arguments. + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + # Lookup from cache. + key = (dim, act, alpha, gain, clamp) + if key in _bias_act_cuda_cache: + return _bias_act_cuda_cache[key] + + # Forward op. + class BiasActCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, b): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format + x = x.contiguous(memory_format=ctx.memory_format) + b = b.contiguous() if b is not None else _null_tensor + y = x + if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: + y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) + ctx.save_for_backward( + x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, + b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, + y if 'y' in spec.ref else _null_tensor) + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + dy = dy.contiguous(memory_format=ctx.memory_format) + x, b, y = ctx.saved_tensors + dx = None + db = None + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + dx = dy + if act != 'linear' or gain != 1 or clamp >= 0: + dx = BiasActCudaGrad.apply(dy, x, b, y) + + if ctx.needs_input_grad[1]: + db = dx.sum([i for i in range(dx.ndim) if i != dim]) + + return dx, db + + # Backward op. + class BiasActCudaGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format + dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) + ctx.save_for_backward( + dy if spec.has_2nd_grad else _null_tensor, + x, b, y) + return dx + + @staticmethod + def backward(ctx, d_dx): # pylint: disable=arguments-differ + d_dx = d_dx.contiguous(memory_format=ctx.memory_format) + dy, x, b, y = ctx.saved_tensors + d_dy = None + d_x = None + d_b = None + d_y = None + + if ctx.needs_input_grad[0]: + d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) + + if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): + d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) + + if spec.has_2nd_grad and ctx.needs_input_grad[2]: + d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) + + return d_dy, d_x, d_b, d_y + + # Add to cache. + _bias_act_cuda_cache[key] = BiasActCuda + return BiasActCuda + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_gradfix.py b/torch_utils/ops/conv2d_gradfix.py new file mode 100644 index 0000000..388778f --- /dev/null +++ b/torch_utils/ops/conv2d_gradfix.py @@ -0,0 +1,198 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.conv2d` that supports +arbitrarily high order gradients with zero performance penalty.""" + +import contextlib +import torch + +# pylint: disable=redefined-builtin +# pylint: disable=arguments-differ +# pylint: disable=protected-access + +#---------------------------------------------------------------------------- + +enabled = False # Enable the custom op by setting this to true. +weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. + +@contextlib.contextmanager +def no_weight_gradients(disable=True): + global weight_gradients_disabled + old = weight_gradients_disabled + if disable: + weight_gradients_disabled = True + yield + weight_gradients_disabled = old + +#---------------------------------------------------------------------------- + +def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if _should_use_custom_op(input): + return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) + return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): + if _should_use_custom_op(input): + return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) + return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) + +#---------------------------------------------------------------------------- + +def _should_use_custom_op(input): + assert isinstance(input, torch.Tensor) + if (not enabled) or (not torch.backends.cudnn.enabled): + return False + if input.device.type != 'cuda': + return False + return True + +def _tuple_of_ints(xs, ndim): + xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim + assert len(xs) == ndim + assert all(isinstance(x, int) for x in xs) + return xs + +#---------------------------------------------------------------------------- + +_conv2d_gradfix_cache = dict() +_null_tensor = torch.empty([0]) + +def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): + # Parse arguments. + ndim = 2 + weight_shape = tuple(weight_shape) + stride = _tuple_of_ints(stride, ndim) + padding = _tuple_of_ints(padding, ndim) + output_padding = _tuple_of_ints(output_padding, ndim) + dilation = _tuple_of_ints(dilation, ndim) + + # Lookup from cache. + key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + if key in _conv2d_gradfix_cache: + return _conv2d_gradfix_cache[key] + + # Validate arguments. + assert groups >= 1 + assert len(weight_shape) == ndim + 2 + assert all(stride[i] >= 1 for i in range(ndim)) + assert all(padding[i] >= 0 for i in range(ndim)) + assert all(dilation[i] >= 0 for i in range(ndim)) + if not transpose: + assert all(output_padding[i] == 0 for i in range(ndim)) + else: # transpose + assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) + + # Helpers. + common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) + def calc_output_padding(input_shape, output_shape): + if transpose: + return [0, 0] + return [ + input_shape[i + 2] + - (output_shape[i + 2] - 1) * stride[i] + - (1 - 2 * padding[i]) + - dilation[i] * (weight_shape[i + 2] - 1) + for i in range(ndim) + ] + + # Forward & backward. + class Conv2d(torch.autograd.Function): + @staticmethod + def forward(ctx, input, weight, bias): + assert weight.shape == weight_shape + ctx.save_for_backward( + input if weight.requires_grad else _null_tensor, + weight if input.requires_grad else _null_tensor, + ) + ctx.input_shape = input.shape + + # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere). + if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0): + a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1]) + b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1) + c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2) + c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1) + c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3) + return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) + + # General case => cuDNN. + if transpose: + return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) + return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + input_shape = ctx.input_shape + grad_input = None + grad_weight = None + grad_bias = None + + if ctx.needs_input_grad[0]: + p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape) + op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) + grad_input = op.apply(grad_output, weight, None) + assert grad_input.shape == input_shape + + if ctx.needs_input_grad[1] and not weight_gradients_disabled: + grad_weight = Conv2dGradWeight.apply(grad_output, input) + assert grad_weight.shape == weight_shape + + if ctx.needs_input_grad[2]: + grad_bias = grad_output.sum([0, 2, 3]) + + return grad_input, grad_weight, grad_bias + + # Gradient with respect to the weights. + class Conv2dGradWeight(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input): + ctx.save_for_backward( + grad_output if input.requires_grad else _null_tensor, + input if grad_output.requires_grad else _null_tensor, + ) + ctx.grad_output_shape = grad_output.shape + ctx.input_shape = input.shape + + # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere). + if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0): + a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) + b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) + c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape) + return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) + + # General case => cuDNN. + name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight' + flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] + return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) + + @staticmethod + def backward(ctx, grad2_grad_weight): + grad_output, input = ctx.saved_tensors + grad_output_shape = ctx.grad_output_shape + input_shape = ctx.input_shape + grad2_grad_output = None + grad2_input = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) + assert grad2_grad_output.shape == grad_output_shape + + if ctx.needs_input_grad[1]: + p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape) + op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) + grad2_input = op.apply(grad_output, grad2_grad_weight, None) + assert grad2_input.shape == input_shape + + return grad2_grad_output, grad2_input + + _conv2d_gradfix_cache[key] = Conv2d + return Conv2d + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_resample.py b/torch_utils/ops/conv2d_resample.py new file mode 100644 index 0000000..5eb5877 --- /dev/null +++ b/torch_utils/ops/conv2d_resample.py @@ -0,0 +1,143 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""2D convolution with optional up/downsampling.""" + +import torch + +from .. import misc +from . import conv2d_gradfix +from . import upfirdn2d +from .upfirdn2d import _parse_padding +from .upfirdn2d import _get_filter_size + +#---------------------------------------------------------------------------- + +def _get_weight_shape(w): + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + shape = [int(sz) for sz in w.shape] + misc.assert_shape(w, shape) + return shape + +#---------------------------------------------------------------------------- + +def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): + """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. + """ + _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w) + + # Flip weight if requested. + # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). + if not flip_weight and (kw > 1 or kh > 1): + w = w.flip([2, 3]) + + # Execute using conv2d_gradfix. + op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d + return op(x, w, stride=stride, padding=padding, groups=groups) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): + r"""2D convolution with optional up/downsampling. + + Padding is performed only once at the beginning, not between the operations. + + Args: + x: Input tensor of shape + `[batch_size, in_channels, in_height, in_width]`. + w: Weight tensor of shape + `[out_channels, in_channels//groups, kernel_height, kernel_width]`. + f: Low-pass filter for up/downsampling. Must be prepared beforehand by + calling upfirdn2d.setup_filter(). None = identity (default). + up: Integer upsampling factor (default: 1). + down: Integer downsampling factor (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + groups: Split input channels into N groups (default: 1). + flip_weight: False = convolution, True = correlation (default: True). + flip_filter: False = convolution, True = correlation (default: False). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + # Validate arguments. + assert isinstance(x, torch.Tensor) and (x.ndim == 4) + assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) + assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) + assert isinstance(up, int) and (up >= 1) + assert isinstance(down, int) and (down >= 1) + assert isinstance(groups, int) and (groups >= 1) + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + fw, fh = _get_filter_size(f) + px0, px1, py0, py1 = _parse_padding(padding) + + # Adjust padding to account for up/downsampling. + if up > 1: + px0 += (fw + up - 1) // 2 + px1 += (fw - up) // 2 + py0 += (fh + up - 1) // 2 + py1 += (fh - up) // 2 + if down > 1: + px0 += (fw - down + 1) // 2 + px1 += (fw - down) // 2 + py0 += (fh - down + 1) // 2 + py1 += (fh - down) // 2 + + # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. + if kw == 1 and kh == 1 and (down > 1 and up == 1): + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + return x + + # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. + if kw == 1 and kh == 1 and (up > 1 and down == 1): + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + return x + + # Fast path: downsampling only => use strided convolution. + if down > 1 and up == 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) + return x + + # Fast path: upsampling with optional downsampling => use transpose strided convolution. + if up > 1: + if groups == 1: + w = w.transpose(0, 1) + else: + w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.transpose(1, 2) + w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) + px0 -= kw - 1 + px1 -= kw - up + py0 -= kh - 1 + py1 -= kh - up + pxt = max(min(-px0, -px1), 0) + pyt = max(min(-py0, -py1), 0) + x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + + # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. + if up == 1 and down == 1: + if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: + return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) + + # Fallback: Generic reference implementation. + x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu.cpp b/torch_utils/ops/filtered_lrelu.cpp new file mode 100644 index 0000000..ff4149b --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.cpp @@ -0,0 +1,300 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "filtered_lrelu.h" + +//------------------------------------------------------------------------ + +static std::tuple filtered_lrelu( + torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si, + int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns) +{ + // Set CUDA device. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + + // Validate arguments. + TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device"); + TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32"); + TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype"); + TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32"); + TORCH_CHECK(x.dim() == 4, "x must be rank 4"); + TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large"); + TORCH_CHECK(x.numel() > 0, "x is empty"); + TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2"); + TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large"); + TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large"); + TORCH_CHECK(fu.numel() > 0, "fu is empty"); + TORCH_CHECK(fd.numel() > 0, "fd is empty"); + TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x"); + TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1"); + + // Figure out how much shared memory is available on the device. + int maxSharedBytes = 0; + AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index())); + int sharedKB = maxSharedBytes >> 10; + + // Populate enough launch parameters to check if a CUDA kernel exists. + filtered_lrelu_kernel_params p; + p.up = up; + p.down = down; + p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter. + p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0); + filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB); + if (!test_spec.exec) + { + // No kernel found - return empty tensors and indicate missing kernel with return code of -1. + return std::make_tuple(torch::Tensor(), torch::Tensor(), -1); + } + + // Input/output element size. + int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4; + + // Input sizes. + int64_t xw = (int)x.size(3); + int64_t xh = (int)x.size(2); + int64_t fut_w = (int)fu.size(-1) - 1; + int64_t fut_h = (int)fu.size(0) - 1; + int64_t fdt_w = (int)fd.size(-1) - 1; + int64_t fdt_h = (int)fd.size(0) - 1; + + // Logical size of upsampled buffer. + int64_t cw = xw * up + (px0 + px1) - fut_w; + int64_t ch = xh * up + (py0 + py1) - fut_h; + TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter"); + TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large"); + + // Compute output size and allocate. + int64_t yw = (cw - fdt_w + (down - 1)) / down; + int64_t yh = (ch - fdt_h + (down - 1)) / down; + TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1"); + TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large"); + torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format()); + + // Allocate sign tensor. + torch::Tensor so; + torch::Tensor s = si; + bool readSigns = !!s.numel(); + int64_t sw_active = 0; // Active width of sign tensor. + if (writeSigns) + { + sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements. + int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height. + int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16. + TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large"); + s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous); + } + else if (readSigns) + sw_active = s.size(3) << 2; + + // Validate sign tensor if in use. + if (readSigns || writeSigns) + { + TORCH_CHECK(s.is_contiguous(), "signs must be contiguous"); + TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8"); + TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x"); + TORCH_CHECK(s.dim() == 4, "signs must be rank 4"); + TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x"); + TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large"); + } + + // Populate rest of CUDA kernel parameters. + p.x = x.data_ptr(); + p.y = y.data_ptr(); + p.b = b.data_ptr(); + p.s = (readSigns || writeSigns) ? s.data_ptr() : 0; + p.fu = fu.data_ptr(); + p.fd = fd.data_ptr(); + p.pad0 = make_int2(px0, py0); + p.gain = gain; + p.slope = slope; + p.clamp = clamp; + p.flip = (flip_filters) ? 1 : 0; + p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); + p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); + p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous. + p.sOfs = make_int2(sx, sy); + p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes. + + // x, y, b strides are in bytes. + p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0)); + p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0)); + p.bStride = sz * b.stride(0); + + // fu, fd strides are in elements. + p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0); + p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0); + + // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those. + bool index64b = false; + if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true; + if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true; + if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true; + if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true; + if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true; + if (s.numel() > INT_MAX) index64b = true; + + // Choose CUDA kernel. + filtered_lrelu_kernel_spec spec = { 0 }; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&] + { + if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation. + { + // Choose kernel based on index type, datatype and sign read/write modes. + if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB); + } + }); + TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists. + + // Launch CUDA kernel. + void* args[] = {&p}; + int bx = spec.numWarps * 32; + int gx = (p.yShape.x - 1) / spec.tileOut.x + 1; + int gy = (p.yShape.y - 1) / spec.tileOut.y + 1; + int gz = p.yShape.z * p.yShape.w; + + // Repeat multiple horizontal tiles in a CTA? + if (spec.xrep) + { + p.tilesXrep = spec.xrep; + p.tilesXdim = gx; + + gx = (gx + p.tilesXrep - 1) / p.tilesXrep; + std::swap(gx, gy); + } + else + { + p.tilesXrep = 0; + p.tilesXdim = 0; + } + + // Launch filter setup kernel. + AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream())); + + // Copy kernels to constant memory. + if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); + else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); + else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream()))); + + // Set cache and shared memory configurations for main kernel. + AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared)); + if (spec.dynamicSharedKB) // Need dynamically allocated shared memory? + AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10)); + AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte)); + + // Launch main kernel. + const int maxSubGz = 65535; // CUDA maximum for block z dimension. + for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big. + { + p.blockZofs = zofs; + int subGz = std::min(maxSubGz, gz - zofs); + AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream())); + } + + // Done. + return std::make_tuple(y, so, 0); +} + +//------------------------------------------------------------------------ + +static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns) +{ + // Set CUDA device. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + + // Validate arguments. + TORCH_CHECK(x.dim() == 4, "x must be rank 4"); + TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large"); + TORCH_CHECK(x.numel() > 0, "x is empty"); + TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64"); + + // Output signs if we don't have sign input. + torch::Tensor so; + torch::Tensor s = si; + bool readSigns = !!s.numel(); + if (writeSigns) + { + int64_t sw = x.size(3); + sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing. + s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous); + } + + // Validate sign tensor if in use. + if (readSigns || writeSigns) + { + TORCH_CHECK(s.is_contiguous(), "signs must be contiguous"); + TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8"); + TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x"); + TORCH_CHECK(s.dim() == 4, "signs must be rank 4"); + TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x"); + TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large"); + } + + // Initialize CUDA kernel parameters. + filtered_lrelu_act_kernel_params p; + p.x = x.data_ptr(); + p.s = (readSigns || writeSigns) ? s.data_ptr() : 0; + p.gain = gain; + p.slope = slope; + p.clamp = clamp; + p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); + p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0)); + p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous. + p.sOfs = make_int2(sx, sy); + + // Choose CUDA kernel. + void* func = 0; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&] + { + if (writeSigns) + func = choose_filtered_lrelu_act_kernel(); + else if (readSigns) + func = choose_filtered_lrelu_act_kernel(); + else + func = choose_filtered_lrelu_act_kernel(); + }); + TORCH_CHECK(func, "internal error - CUDA kernel not found"); + + // Launch CUDA kernel. + void* args[] = {&p}; + int bx = 128; // 4 warps per block. + + // Logical size of launch = writeSigns ? p.s : p.x + uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x; + uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y; + uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use. + gx = (gx - 1) / bx + 1; + + // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest. + const uint32_t gmax = 65535; + gy = std::min(gy, gmax); + gz = std::min(gz, gmax); + + // Launch. + AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream())); + return so; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("filtered_lrelu", &filtered_lrelu); // The whole thing. + m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place. +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.cu b/torch_utils/ops/filtered_lrelu.cu new file mode 100644 index 0000000..8e6f47f --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.cu @@ -0,0 +1,1284 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "filtered_lrelu.h" +#include + +//------------------------------------------------------------------------ +// Helpers. + +enum // Filter modes. +{ + MODE_SUSD = 0, // Separable upsampling, separable downsampling. + MODE_FUSD = 1, // Full upsampling, separable downsampling. + MODE_SUFD = 2, // Separable upsampling, full downsampling. + MODE_FUFD = 3, // Full upsampling, full downsampling. +}; + +template struct InternalType; +template <> struct InternalType +{ + typedef double scalar_t; typedef double2 vec2_t; typedef double4 vec4_t; + __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_double2(0, 0); } + __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_double4(0, 0, 0, 0); } + __device__ __forceinline__ static double clamp(double x, double c) { return fmin(fmax(x, -c), c); } +}; +template <> struct InternalType +{ + typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t; + __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); } + __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); } + __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); } +}; +template <> struct InternalType +{ + typedef float scalar_t; typedef float2 vec2_t; typedef float4 vec4_t; + __device__ __forceinline__ static vec2_t zero_vec2(void) { return make_float2(0, 0); } + __device__ __forceinline__ static vec4_t zero_vec4(void) { return make_float4(0, 0, 0, 0); } + __device__ __forceinline__ static float clamp(float x, float c) { return fminf(fmaxf(x, -c), c); } +}; + +#define MIN(A, B) ((A) < (B) ? (A) : (B)) +#define MAX(A, B) ((A) > (B) ? (A) : (B)) +#define CEIL_DIV(A, B) (((B)==1) ? (A) : \ + ((B)==2) ? ((int)((A)+1) >> 1) : \ + ((B)==4) ? ((int)((A)+3) >> 2) : \ + (((A) + ((A) > 0 ? (B) - 1 : 0)) / (B))) + +// This works only up to blocks of size 256 x 256 and for all N that are powers of two. +template __device__ __forceinline__ void fast_div_mod(int& x, int& y, unsigned int i) +{ + if ((N & (N-1)) && N <= 256) + y = (i * ((1<<24)/N + 1)) >> 24; // Assumes N <= 256, i < N*256. + else + y = i/N; + + x = i - y*N; +} + +// Type cast stride before reading it. +template __device__ __forceinline__ T get_stride(const int64_t& x) +{ + return *reinterpret_cast(&x); +} + +//------------------------------------------------------------------------ +// Filters, setup kernel, copying function. + +#define MAX_FILTER_SIZE 32 + +// Combined up/down filter buffers so that transfer can be done with one copy. +__device__ float g_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in global memory, written by setup kernel. +__device__ __constant__ float c_fbuf[2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE]; // Filters in constant memory, read by main kernel. + +// Accessors to combined buffers to index up/down filters individually. +#define c_fu (c_fbuf) +#define c_fd (c_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE) +#define g_fu (g_fbuf) +#define g_fd (g_fbuf + MAX_FILTER_SIZE * MAX_FILTER_SIZE) + +// Set up filters into global memory buffer. +static __global__ void setup_filters_kernel(filtered_lrelu_kernel_params p) +{ + for (int idx = threadIdx.x; idx < MAX_FILTER_SIZE * MAX_FILTER_SIZE; idx += blockDim.x) + { + int x, y; + fast_div_mod(x, y, idx); + + int fu_x = p.flip ? x : (p.fuShape.x - 1 - x); + int fu_y = p.flip ? y : (p.fuShape.y - 1 - y); + if (p.fuShape.y > 0) + g_fu[idx] = (x >= p.fuShape.x || y >= p.fuShape.y) ? 0.0f : p.fu[fu_x * p.fuStride.x + fu_y * p.fuStride.y]; + else + g_fu[idx] = (x >= p.fuShape.x || y > 0) ? 0.0f : p.fu[fu_x * p.fuStride.x]; + + int fd_x = p.flip ? x : (p.fdShape.x - 1 - x); + int fd_y = p.flip ? y : (p.fdShape.y - 1 - y); + if (p.fdShape.y > 0) + g_fd[idx] = (x >= p.fdShape.x || y >= p.fdShape.y) ? 0.0f : p.fd[fd_x * p.fdStride.x + fd_y * p.fdStride.y]; + else + g_fd[idx] = (x >= p.fdShape.x || y > 0) ? 0.0f : p.fd[fd_x * p.fdStride.x]; + } +} + +// Host function to copy filters written by setup kernel into constant buffer for main kernel. +template static cudaError_t copy_filters(cudaStream_t stream) +{ + void* src = 0; + cudaError_t err = cudaGetSymbolAddress(&src, g_fbuf); + if (err) return err; + return cudaMemcpyToSymbolAsync(c_fbuf, src, 2 * MAX_FILTER_SIZE * MAX_FILTER_SIZE * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream); +} + +//------------------------------------------------------------------------ +// Coordinate spaces: +// - Relative to input tensor: inX, inY, tileInX, tileInY +// - Relative to input tile: relInX, relInY, tileInW, tileInH +// - Relative to upsampled tile: relUpX, relUpY, tileUpW, tileUpH +// - Relative to output tile: relOutX, relOutY, tileOutW, tileOutH +// - Relative to output tensor: outX, outY, tileOutX, tileOutY +// +// Relationships between coordinate spaces: +// - inX = tileInX + relInX +// - inY = tileInY + relInY +// - relUpX = relInX * up + phaseInX +// - relUpY = relInY * up + phaseInY +// - relUpX = relOutX * down +// - relUpY = relOutY * down +// - outX = tileOutX + relOutX +// - outY = tileOutY + relOutY + +extern __shared__ char s_buf_raw[]; // When sharedKB <= 48, allocate shared memory statically inside the kernel, otherwise use the externally allocated shared memory buffer. + +template +static __global__ void filtered_lrelu_kernel(filtered_lrelu_kernel_params p) +{ + // Check that we don't try to support non-existing filter modes. + static_assert(up == 1 || up == 2 || up == 4, "only up=1, up=2, up=4 scales supported"); + static_assert(down == 1 || down == 2 || down == 4, "only down=1, down=2, down=4 scales supported"); + static_assert(fuSize >= up, "upsampling filter size must be at least upsampling factor"); + static_assert(fdSize >= down, "downsampling filter size must be at least downsampling factor"); + static_assert(fuSize % up == 0, "upsampling filter size must be divisible with upsampling factor"); + static_assert(fdSize % down == 0, "downsampling filter size must be divisible with downsampling factor"); + static_assert(fuSize <= MAX_FILTER_SIZE && fdSize <= MAX_FILTER_SIZE, "filter size greater than MAX_FILTER_SIZE"); + static_assert(up != 1 || (fuSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "up=1 supported only for 1x1 full filters"); + static_assert(down != 1 || (fdSize == 1 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "down=1 supported only for 1x1 full filters"); + static_assert(!(up == 4 && (filterMode == MODE_FUFD || filterMode == MODE_FUSD)), "full filters not supported for up=4"); + static_assert(!(down == 4 && (filterMode == MODE_FUFD || filterMode == MODE_SUFD)), "full filters not supported for down=4"); + + // Static definitions. + typedef typename InternalType::scalar_t scalar_t; + typedef typename InternalType::vec2_t vec2_t; + typedef typename InternalType::vec4_t vec4_t; + const int tileUpW = (tileOutW * down + (fdSize - 1) - (down - 1) + 3) & ~3; // Upsampled tile width, rounded up to multiple of 4. + const int tileUpH = tileOutH * down + (fdSize - 1) - (down - 1); // Upsampled tile height. + const int tileInW = CEIL_DIV(tileUpW + (fuSize - 1), up); // Input tile width. + const int tileInH = CEIL_DIV(tileUpH + (fuSize - 1), up); // Input tile height. + const int tileUpH_up = CEIL_DIV(tileUpH, up) * up; // Upsampled tile height rounded up to a multiple of up. + const int tileInH_up = CEIL_DIV(tileUpH_up + (fuSize - 1), up); // For allocations only, to avoid shared memory read overruns with up=2 and up=4. + + // Merge 1x1 downsampling into last upsampling step for upf1 and ups2. + const bool downInline = (down == 1) && ((up == 1 && filterMode == MODE_FUFD) || (up == 2 && filterMode == MODE_SUFD)); + + // Sizes of logical buffers. + const int szIn = tileInH_up * tileInW; + const int szUpX = tileInH_up * tileUpW; + const int szUpXY = downInline ? 0 : (tileUpH * tileUpW); + const int szDownX = tileUpH * tileOutW; + + // Sizes for shared memory arrays. + const int s_buf0_size_base = + (filterMode == MODE_SUSD) ? MAX(szIn, szUpXY) : + (filterMode == MODE_FUSD) ? MAX(szIn, szDownX) : + (filterMode == MODE_SUFD) ? MAX(szIn, szUpXY) : + (filterMode == MODE_FUFD) ? szIn : + -1; + const int s_buf1_size_base = + (filterMode == MODE_SUSD) ? MAX(szUpX, szDownX) : + (filterMode == MODE_FUSD) ? szUpXY : + (filterMode == MODE_SUFD) ? szUpX : + (filterMode == MODE_FUFD) ? szUpXY : + -1; + + // Ensure U128 alignment. + const int s_buf0_size = (s_buf0_size_base + 3) & ~3; + const int s_buf1_size = (s_buf1_size_base + 3) & ~3; + + // Check at compile time that we don't use too much shared memory. + static_assert((s_buf0_size + s_buf1_size) * sizeof(scalar_t) <= (sharedKB << 10), "shared memory overflow"); + + // Declare shared memory arrays. + scalar_t* s_buf0; + scalar_t* s_buf1; + if (sharedKB <= 48) + { + // Allocate shared memory arrays here. + __shared__ scalar_t s_buf0_st[(sharedKB > 48) ? (1<<24) : (s_buf0_size + s_buf1_size)]; // Prevent launching if this isn't optimized away when unused. + s_buf0 = s_buf0_st; + s_buf1 = s_buf0 + s_buf0_size; + } + else + { + // Use the dynamically allocated shared memory array. + s_buf0 = (scalar_t*)s_buf_raw; + s_buf1 = s_buf0 + s_buf0_size; + } + + // Pointers to the buffers. + scalar_t* s_tileIn; // Input tile: [relInX * tileInH + relInY] + scalar_t* s_tileUpX; // After horizontal upsampling: [relInY * tileUpW + relUpX] + scalar_t* s_tileUpXY; // After upsampling: [relUpY * tileUpW + relUpX] + scalar_t* s_tileDownX; // After horizontal downsampling: [relUpY * tileOutW + relOutX] + if (filterMode == MODE_SUSD) + { + s_tileIn = s_buf0; + s_tileUpX = s_buf1; + s_tileUpXY = s_buf0; + s_tileDownX = s_buf1; + } + else if (filterMode == MODE_FUSD) + { + s_tileIn = s_buf0; + s_tileUpXY = s_buf1; + s_tileDownX = s_buf0; + } + else if (filterMode == MODE_SUFD) + { + s_tileIn = s_buf0; + s_tileUpX = s_buf1; + s_tileUpXY = s_buf0; + } + else if (filterMode == MODE_FUFD) + { + s_tileIn = s_buf0; + s_tileUpXY = s_buf1; + } + + // Allow large grids in z direction via per-launch offset. + int channelIdx = blockIdx.z + p.blockZofs; + int batchIdx = channelIdx / p.yShape.z; + channelIdx -= batchIdx * p.yShape.z; + + // Offset to output feature map. In bytes. + index_t mapOfsOut = channelIdx * get_stride(p.yStride.z) + batchIdx * get_stride(p.yStride.w); + + // Sign shift amount. + uint32_t signXo = ((threadIdx.x + p.sOfs.x) << 1) & 6; + + // Inner tile loop. + #pragma unroll 1 + for (int tileIdx = 0; !enableXrep || (tileIdx < MIN(p.tilesXrep, p.tilesXdim - p.tilesXrep * blockIdx.y)); tileIdx++) + { + // Locate output tile. + int tileX = enableXrep ? blockIdx.y * p.tilesXrep + tileIdx : blockIdx.x; + int tileOutX = tileX * tileOutW; + int tileOutY = (enableXrep ? blockIdx.x : blockIdx.y) * tileOutH; + + // Locate input tile. + int tmpX = tileOutX * down - p.pad0.x; + int tmpY = tileOutY * down - p.pad0.y; + int tileInX = CEIL_DIV(tmpX, up); + int tileInY = CEIL_DIV(tmpY, up); + const int phaseInX = tileInX * up - tmpX; + const int phaseInY = tileInY * up - tmpY; + + // Extra sync if input and output buffers are the same and we are not on first tile. + if (enableXrep && tileIdx > 0 && (filterMode == MODE_FUSD || (filterMode == MODE_SUFD && !downInline) || (filterMode == MODE_FUFD && downInline))) + __syncthreads(); + + // Load input tile & apply bias. Unrolled. + scalar_t b = (scalar_t)*(const T*)((const char*)p.b + (channelIdx * get_stride(p.bStride))); + index_t mapOfsIn = channelIdx * get_stride(p.xStride.z) + batchIdx * get_stride(p.xStride.w); + int idx = threadIdx.x; + const int loopCountIN = CEIL_DIV(tileInW * tileInH, threadsPerBlock); + #pragma unroll + for (int loop = 0; loop < loopCountIN; loop++) + { + int relInX, relInY; + fast_div_mod(relInX, relInY, idx); + int inX = tileInX + relInX; + int inY = tileInY + relInY; + scalar_t v = 0; + + if ((uint32_t)inX < p.xShape.x && (uint32_t)inY < p.xShape.y) + v = (scalar_t)*((const T*)((const char*)p.x + (inX * get_stride(p.xStride.x) + inY * get_stride(p.xStride.y) + mapOfsIn))) + b; + + bool skip = (loop == loopCountIN-1) && (idx >= tileInW * tileInH); + if (!skip) + s_tileIn[idx] = v; + + idx += threadsPerBlock; + } + + if (filterMode == MODE_SUSD || filterMode == MODE_SUFD) // Separable upsampling filter. + { + // Horizontal upsampling. + __syncthreads(); + if (up == 4) + { + for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up) + { + int relUpX0, relInY; + fast_div_mod(relUpX0, relInY, idx); + int relInX0 = relUpX0 / up; + int src0 = relInX0 + tileInW * relInY; + int dst = relInY * tileUpW + relUpX0; + vec4_t v = InternalType::zero_vec4(); + scalar_t a = s_tileIn[src0]; + if (phaseInX == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.y += a * (scalar_t)c_fu[step * up + 3]; + v.z += a * (scalar_t)c_fu[step * up + 2]; + v.w += a * (scalar_t)c_fu[step * up + 1]; + } + } + else if (phaseInX == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.z += a * (scalar_t)c_fu[step * up + 3]; + v.w += a * (scalar_t)c_fu[step * up + 2]; + } + } + else if (phaseInX == 2) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 2]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + v.z += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.w += a * (scalar_t)c_fu[step * up + 3]; + } + } + else // (phaseInX == 3) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 3]; + v.y += a * (scalar_t)c_fu[step * up + 2]; + v.z += a * (scalar_t)c_fu[step * up + 1]; + v.w += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + } + } + s_tileUpX[dst+0] = v.x; + s_tileUpX[dst+1] = v.y; + s_tileUpX[dst+2] = v.z; + s_tileUpX[dst+3] = v.w; + } + } + else if (up == 2) + { + bool p0 = (phaseInX == 0); + for (int idx = threadIdx.x*up; idx < tileUpW * tileInH; idx += blockDim.x*up) + { + int relUpX0, relInY; + fast_div_mod(relUpX0, relInY, idx); + int relInX0 = relUpX0 / up; + int src0 = relInX0 + tileInW * relInY; + int dst = relInY * tileUpW + relUpX0; + vec2_t v = InternalType::zero_vec2(); + scalar_t a = s_tileIn[src0]; + if (p0) // (phaseInX == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + } + } + else // (phaseInX == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileIn[src0 + step + 1]; + } + } + s_tileUpX[dst+0] = v.x; + s_tileUpX[dst+1] = v.y; + } + } + + // Vertical upsampling & nonlinearity. + + __syncthreads(); + int groupMask = 15 << ((threadIdx.x & 31) & ~3); + int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs. + int sShapeMaxY = MIN(p.sShape.y, tileOutY * down + tileUpH); // Avoid out-of-tile sign writes. + if (up == 4) + { + minY -= 3; // Adjust according to block height. + for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x) + { + int relUpX, relInY0; + fast_div_mod(relUpX, relInY0, idx); + int relUpY0 = relInY0 * up; + int src0 = relInY0 * tileUpW + relUpX; + int dst = relUpY0 * tileUpW + relUpX; + vec4_t v = InternalType::zero_vec4(); + + scalar_t a = s_tileUpX[src0]; + if (phaseInY == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.y += a * (scalar_t)c_fu[step * up + 3]; + v.z += a * (scalar_t)c_fu[step * up + 2]; + v.w += a * (scalar_t)c_fu[step * up + 1]; + } + } + else if (phaseInY == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.z += a * (scalar_t)c_fu[step * up + 3]; + v.w += a * (scalar_t)c_fu[step * up + 2]; + } + } + else if (phaseInY == 2) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 2]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + v.z += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.w += a * (scalar_t)c_fu[step * up + 3]; + } + } + else // (phaseInY == 3) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 3]; + v.y += a * (scalar_t)c_fu[step * up + 2]; + v.z += a * (scalar_t)c_fu[step * up + 1]; + v.w += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + } + } + + int x = tileOutX * down + relUpX; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + index_t si1 = si0 + p.sShape.x; + index_t si2 = si0 + p.sShape.x * 2; + index_t si3 = si0 + p.sShape.x * 3; + + v.x *= (scalar_t)((float)up * (float)up * p.gain); + v.y *= (scalar_t)((float)up * (float)up * p.gain); + v.z *= (scalar_t)((float)up * (float)up * p.gain); + v.w *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write signs. + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + int sz = __float_as_uint(v.z) >> 31 << 16; + int sw = __float_as_uint(v.w) >> 31 << 24; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (sz) v.z *= p.slope; + if (sw) v.w *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType::clamp(v.z, p.clamp); } + if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType::clamp(v.w, p.clamp); } + + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + // Combine signs. + uint32_t s = sx + sy + sw + sz; + s <<= (signX & 3) << 1; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); } + if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); } + } + } + else + { + // Determine and write signs. + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + int sz = __float_as_uint(v.z) >> 31 << 16; + int sw = __float_as_uint(v.w) >> 31 << 24; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (sz) v.z *= p.slope; + if (sw) v.w *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + if (fabsf(v.z) > p.clamp) { sz = 2 << 16; v.z = InternalType::clamp(v.z, p.clamp); } + if (fabsf(v.w) > p.clamp) { sw = 2 << 24; v.w = InternalType::clamp(v.w, p.clamp); } + + // Combine signs. + uint32_t s = sx + sy + sw + sz; + s <<= (signX & 3) << 1; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + if ((uint32_t)(signY + 2) < sShapeMaxY) { p.s[si2] = (unsigned char)(s >> 16); } + if ((uint32_t)(signY + 3) < sShapeMaxY) { p.s[si3] = (unsigned char)(s >> 24); } + } + else + { + // Just compute the values. + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + } + } + else if (signRead) // Read signs and apply. + { + if ((uint32_t)signXb < p.swLimit) + { + int ss = (signX & 3) << 1; + if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> ss; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; } + if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> ss; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; } + if ((uint32_t)(signY + 2) < p.sShape.y) { int s = p.s[si2] >> ss; if (s & 1) v.z *= p.slope; if (s & 2) v.z = 0.f; } + if ((uint32_t)(signY + 3) < p.sShape.y) { int s = p.s[si3] >> ss; if (s & 1) v.w *= p.slope; if (s & 2) v.w = 0.f; } + } + } + else // Forward pass with no sign write. + { + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + + s_tileUpXY[dst + 0 * tileUpW] = v.x; + if (relUpY0 + 1 < tileUpH) s_tileUpXY[dst + 1 * tileUpW] = v.y; + if (relUpY0 + 2 < tileUpH) s_tileUpXY[dst + 2 * tileUpW] = v.z; + if (relUpY0 + 3 < tileUpH) s_tileUpXY[dst + 3 * tileUpW] = v.w; + } + } + else if (up == 2) + { + minY -= 1; // Adjust according to block height. + for (int idx = threadIdx.x; idx < tileUpW * tileUpH_up / up; idx += blockDim.x) + { + int relUpX, relInY0; + fast_div_mod(relUpX, relInY0, idx); + int relUpY0 = relInY0 * up; + int src0 = relInY0 * tileUpW + relUpX; + int dst = relUpY0 * tileUpW + relUpX; + vec2_t v = InternalType::zero_vec2(); + + scalar_t a = s_tileUpX[src0]; + if (phaseInY == 0) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + v.y += a * (scalar_t)c_fu[step * up + 1]; + } + } + else // (phaseInY == 1) + { + #pragma unroll + for (int step = 0; step < fuSize / up; step++) + { + v.x += a * (scalar_t)c_fu[step * up + 1]; + v.y += a * (scalar_t)c_fu[step * up + 0]; + a = s_tileUpX[src0 + (step + 1) * tileUpW]; + } + } + + int x = tileOutX * down + relUpX; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si0 = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + index_t si1 = si0 + p.sShape.x; + + v.x *= (scalar_t)((float)up * (float)up * p.gain); + v.y *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write signs. + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + // Combine signs. + int s = sx + sy; + s <<= signXo; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + } + } + else + { + // Determine and write signs. + if ((uint32_t)signXb < p.swLimit && signY >= minY) + { + int sx = __float_as_uint(v.x) >> 31 << 0; + int sy = __float_as_uint(v.y) >> 31 << 8; + if (sx) v.x *= p.slope; + if (sy) v.y *= p.slope; + if (fabsf(v.x) > p.clamp) { sx = 2 << 0; v.x = InternalType::clamp(v.x, p.clamp); } + if (fabsf(v.y) > p.clamp) { sy = 2 << 8; v.y = InternalType::clamp(v.y, p.clamp); } + + // Combine signs. + int s = sx + sy; + s <<= signXo; + s |= __shfl_xor_sync(groupMask, s, 1); + s |= __shfl_xor_sync(groupMask, s, 2); + + // Write signs. + if ((uint32_t)(signY + 0) < sShapeMaxY) { p.s[si0] = (unsigned char)(s >> 0); } + if ((uint32_t)(signY + 1) < sShapeMaxY) { p.s[si1] = (unsigned char)(s >> 8); } + } + else + { + // Just compute the values. + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + } + } + } + else if (signRead) // Read signs and apply. + { + if ((uint32_t)signXb < p.swLimit) + { + if ((uint32_t)(signY + 0) < p.sShape.y) { int s = p.s[si0] >> signXo; if (s & 1) v.x *= p.slope; if (s & 2) v.x = 0.f; } + if ((uint32_t)(signY + 1) < p.sShape.y) { int s = p.s[si1] >> signXo; if (s & 1) v.y *= p.slope; if (s & 2) v.y = 0.f; } + } + } + else // Forward pass with no sign write. + { + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + } + + if (!downInline) + { + // Write into temporary buffer. + s_tileUpXY[dst] = v.x; + if (relUpY0 < tileUpH - 1) + s_tileUpXY[dst + tileUpW] = v.y; + } + else + { + // Write directly into output buffer. + if ((uint32_t)x < p.yShape.x) + { + int ymax = MIN(p.yShape.y, tileUpH + tileOutY * down); + index_t ofs = x * get_stride(p.yStride.x) + y * get_stride(p.yStride.y) + mapOfsOut; + if ((uint32_t)y + 0 < p.yShape.y) *((T*)((char*)p.y + ofs)) = (T)(v.x * (scalar_t)c_fd[0]); + if ((uint32_t)y + 1 < ymax) *((T*)((char*)p.y + ofs + get_stride(p.yStride.y))) = (T)(v.y * (scalar_t)c_fd[0]); + } + } + } + } + } + else if (filterMode == MODE_FUSD || filterMode == MODE_FUFD) + { + // Full upsampling filter. + + if (up == 2) + { + // 2 x 2-wide. + __syncthreads(); + int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH + p.sOfs.y : 0; // Skip already written signs. + for (int idx = threadIdx.x * 4; idx < tileUpW * tileUpH; idx += blockDim.x * 4) + { + int relUpX0, relUpY0; + fast_div_mod(relUpX0, relUpY0, idx); + int relInX0 = CEIL_DIV(relUpX0 - phaseInX, up); + int relInY0 = CEIL_DIV(relUpY0 - phaseInY, up); + int src0 = relInX0 + tileInW * relInY0; + int tap0y = (relInY0 * up + phaseInY - relUpY0); + + #define X_LOOP(TAPY, PX) \ + for (int sx = 0; sx < fuSize / up; sx++) \ + { \ + v.x += a * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \ + v.z += b * (scalar_t)c_fu[(sx * up + (((PX) - 0) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 0) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \ + v.y += a * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; \ + v.w += b * (scalar_t)c_fu[(sx * up + (((PX) - 1) & (up - 1))) + (sy * up + (TAPY)) * MAX_FILTER_SIZE]; if ((PX) == 1) { a = b; b = s_tileIn[src0 + 2 + sx + sy * tileInW]; } \ + } + + vec4_t v = InternalType::zero_vec4(); + if (tap0y == 0 && phaseInX == 0) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(0, 0) } + if (tap0y == 0 && phaseInX == 1) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(0, 1) } + if (tap0y == 1 && phaseInX == 0) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(1, 0) } + if (tap0y == 1 && phaseInX == 1) + #pragma unroll + for (int sy = 0; sy < fuSize / up; sy++) { scalar_t a = s_tileIn[src0 + sy * tileInW]; scalar_t b = s_tileIn[src0 + sy * tileInW + 1]; + #pragma unroll + X_LOOP(1, 1) } + + #undef X_LOOP + + int x = tileOutX * down + relUpX0; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + + v.x *= (scalar_t)((float)up * (float)up * p.gain); + v.y *= (scalar_t)((float)up * (float)up * p.gain); + v.z *= (scalar_t)((float)up * (float)up * p.gain); + v.w *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write signs. + int sx = __float_as_uint(v.x) >> 31; + int sy = __float_as_uint(v.y) >> 31; + int sz = __float_as_uint(v.z) >> 31; + int sw = __float_as_uint(v.w) >> 31; + if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType::clamp(v.x, p.clamp); } + if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType::clamp(v.y, p.clamp); } + if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType::clamp(v.z, p.clamp); } + if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType::clamp(v.w, p.clamp); } + + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6); + } + } + else + { + // Determine and write signs. + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + int sx = __float_as_uint(v.x) >> 31; + int sy = __float_as_uint(v.y) >> 31; + int sz = __float_as_uint(v.z) >> 31; + int sw = __float_as_uint(v.w) >> 31; + if (sx) v.x *= p.slope; if (fabsf(v.x) > p.clamp) { sx = 2; v.x = InternalType::clamp(v.x, p.clamp); } + if (sy) v.y *= p.slope; if (fabsf(v.y) > p.clamp) { sy = 2; v.y = InternalType::clamp(v.y, p.clamp); } + if (sz) v.z *= p.slope; if (fabsf(v.z) > p.clamp) { sz = 2; v.z = InternalType::clamp(v.z, p.clamp); } + if (sw) v.w *= p.slope; if (fabsf(v.w) > p.clamp) { sw = 2; v.w = InternalType::clamp(v.w, p.clamp); } + + p.s[si] = sx + (sy << 2) + (sz << 4) + (sw << 6); + } + else + { + // Just compute the values. + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + } + } + else if (signRead) // Read sign and apply. + { + if ((uint32_t)signY < p.sShape.y) + { + int s = 0; + if ((uint32_t)signXb < p.swLimit) s = p.s[si]; + if ((uint32_t)signXb + 1 < p.swLimit) s |= p.s[si + 1] << 8; + s >>= (signX & 3) << 1; + if (s & 0x01) v.x *= p.slope; if (s & 0x02) v.x = 0.f; + if (s & 0x04) v.y *= p.slope; if (s & 0x08) v.y = 0.f; + if (s & 0x10) v.z *= p.slope; if (s & 0x20) v.z = 0.f; + if (s & 0x40) v.w *= p.slope; if (s & 0x80) v.w = 0.f; + } + } + else // Forward pass with no sign write. + { + if (v.x < 0.f) v.x *= p.slope; v.x = InternalType::clamp(v.x, p.clamp); + if (v.y < 0.f) v.y *= p.slope; v.y = InternalType::clamp(v.y, p.clamp); + if (v.z < 0.f) v.z *= p.slope; v.z = InternalType::clamp(v.z, p.clamp); + if (v.w < 0.f) v.w *= p.slope; v.w = InternalType::clamp(v.w, p.clamp); + } + + s_tileUpXY[idx + 0] = v.x; + s_tileUpXY[idx + 1] = v.y; + s_tileUpXY[idx + 2] = v.z; + s_tileUpXY[idx + 3] = v.w; + } + } + else if (up == 1) + { + __syncthreads(); + uint32_t groupMask = 15 << ((threadIdx.x & 31) & ~3); + int minY = tileOutY ? (tileOutY - tileOutH) * down + tileUpH : 0; // Skip already written signs. + for (int idx = threadIdx.x; idx < tileUpW * tileUpH; idx += blockDim.x) + { + int relUpX0, relUpY0; + fast_div_mod(relUpX0, relUpY0, idx); + scalar_t v = s_tileIn[idx] * (scalar_t)c_fu[0]; // 1x1 filter. + + int x = tileOutX * down + relUpX0; + int y = tileOutY * down + relUpY0; + int signX = x + p.sOfs.x; + int signY = y + p.sOfs.y; + int signZ = blockIdx.z + p.blockZofs; + int signXb = signX >> 2; + index_t si = signXb + p.sShape.x * (signY + (index_t)p.sShape.y * signZ); + v *= (scalar_t)((float)up * (float)up * p.gain); + + if (signWrite) + { + if (!enableWriteSkip) + { + // Determine and write sign. + uint32_t s = 0; + uint32_t signXbit = (1u << signXo); + if (v < 0.f) + { + s = signXbit; + v *= p.slope; + } + if (fabsf(v) > p.clamp) + { + s = signXbit * 2; + v = InternalType::clamp(v, p.clamp); + } + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + s += __shfl_xor_sync(groupMask, s, 1); // Coalesce. + s += __shfl_xor_sync(groupMask, s, 2); // Coalesce. + p.s[si] = s; // Write. + } + } + else + { + // Determine and write sign. + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y && signY >= minY) + { + uint32_t s = 0; + uint32_t signXbit = (1u << signXo); + if (v < 0.f) + { + s = signXbit; + v *= p.slope; + } + if (fabsf(v) > p.clamp) + { + s = signXbit * 2; + v = InternalType::clamp(v, p.clamp); + } + s += __shfl_xor_sync(groupMask, s, 1); // Coalesce. + s += __shfl_xor_sync(groupMask, s, 2); // Coalesce. + p.s[si] = s; // Write. + } + else + { + // Just compute the value. + if (v < 0.f) v *= p.slope; + v = InternalType::clamp(v, p.clamp); + } + } + } + else if (signRead) + { + // Read sign and apply if within sign tensor bounds. + if ((uint32_t)signXb < p.swLimit && (uint32_t)signY < p.sShape.y) + { + int s = p.s[si]; + s >>= signXo; + if (s & 1) v *= p.slope; + if (s & 2) v = 0.f; + } + } + else // Forward pass with no sign write. + { + if (v < 0.f) v *= p.slope; + v = InternalType::clamp(v, p.clamp); + } + + if (!downInline) // Write into temporary buffer. + s_tileUpXY[idx] = v; + else if ((uint32_t)x < p.yShape.x && (uint32_t)y < p.yShape.y) // Write directly into output buffer + *((T*)((char*)p.y + (x * get_stride(p.yStride.x) + y * get_stride(p.yStride.y) + mapOfsOut))) = (T)(v * (scalar_t)c_fd[0]); + } + } + } + + // Downsampling. + if (filterMode == MODE_SUSD || filterMode == MODE_FUSD) + { + // Horizontal downsampling. + __syncthreads(); + if (down == 4 && tileOutW % 4 == 0) + { + // Calculate 4 pixels at a time. + for (int idx = threadIdx.x * 4; idx < tileOutW * tileUpH; idx += blockDim.x * 4) + { + int relOutX0, relUpY; + fast_div_mod(relOutX0, relUpY, idx); + int relUpX0 = relOutX0 * down; + int src0 = relUpY * tileUpW + relUpX0; + vec4_t v = InternalType::zero_vec4(); + #pragma unroll + for (int step = 0; step < fdSize; step++) + { + v.x += s_tileUpXY[src0 + 0 + step] * (scalar_t)c_fd[step]; + v.y += s_tileUpXY[src0 + 4 + step] * (scalar_t)c_fd[step]; + v.z += s_tileUpXY[src0 + 8 + step] * (scalar_t)c_fd[step]; + v.w += s_tileUpXY[src0 + 12 + step] * (scalar_t)c_fd[step]; + } + s_tileDownX[idx+0] = v.x; + s_tileDownX[idx+1] = v.y; + s_tileDownX[idx+2] = v.z; + s_tileDownX[idx+3] = v.w; + } + } + else if ((down == 2 || down == 4) && (tileOutW % 2 == 0)) + { + // Calculate 2 pixels at a time. + for (int idx = threadIdx.x * 2; idx < tileOutW * tileUpH; idx += blockDim.x * 2) + { + int relOutX0, relUpY; + fast_div_mod(relOutX0, relUpY, idx); + int relUpX0 = relOutX0 * down; + int src0 = relUpY * tileUpW + relUpX0; + vec2_t v = InternalType::zero_vec2(); + #pragma unroll + for (int step = 0; step < fdSize; step++) + { + v.x += s_tileUpXY[src0 + 0 + step] * (scalar_t)c_fd[step]; + v.y += s_tileUpXY[src0 + down + step] * (scalar_t)c_fd[step]; + } + s_tileDownX[idx+0] = v.x; + s_tileDownX[idx+1] = v.y; + } + } + else + { + // Calculate 1 pixel at a time. + for (int idx = threadIdx.x; idx < tileOutW * tileUpH; idx += blockDim.x) + { + int relOutX0, relUpY; + fast_div_mod(relOutX0, relUpY, idx); + int relUpX0 = relOutX0 * down; + int src = relUpY * tileUpW + relUpX0; + scalar_t v = 0.f; + #pragma unroll + for (int step = 0; step < fdSize; step++) + v += s_tileUpXY[src + step] * (scalar_t)c_fd[step]; + s_tileDownX[idx] = v; + } + } + + // Vertical downsampling & store output tile. + __syncthreads(); + for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x) + { + int relOutX, relOutY0; + fast_div_mod(relOutX, relOutY0, idx); + int relUpY0 = relOutY0 * down; + int src0 = relUpY0 * tileOutW + relOutX; + scalar_t v = 0; + #pragma unroll + for (int step = 0; step < fdSize; step++) + v += s_tileDownX[src0 + step * tileOutW] * (scalar_t)c_fd[step]; + + int outX = tileOutX + relOutX; + int outY = tileOutY + relOutY0; + + if (outX < p.yShape.x & outY < p.yShape.y) + *((T*)((char*)p.y + (outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut))) = (T)v; + } + } + else if (filterMode == MODE_SUFD || filterMode == MODE_FUFD) + { + // Full downsampling filter. + if (down == 2) + { + // 2-wide. + __syncthreads(); + for (int idx = threadIdx.x * 2; idx < tileOutW * tileOutH; idx += blockDim.x * 2) + { + int relOutX0, relOutY0; + fast_div_mod(relOutX0, relOutY0, idx); + int relUpX0 = relOutX0 * down; + int relUpY0 = relOutY0 * down; + int src0 = relUpY0 * tileUpW + relUpX0; + vec2_t v = InternalType::zero_vec2(); + #pragma unroll + for (int sy = 0; sy < fdSize; sy++) + #pragma unroll + for (int sx = 0; sx < fdSize; sx++) + { + v.x += s_tileUpXY[src0 + 0 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE]; + v.y += s_tileUpXY[src0 + 2 + sx + sy * tileUpW] * (scalar_t)c_fd[sx + sy * MAX_FILTER_SIZE]; + } + + int outX = tileOutX + relOutX0; + int outY = tileOutY + relOutY0; + if ((uint32_t)outY < p.yShape.y) + { + index_t ofs = outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut; + if (outX + 0 < p.yShape.x) *((T*)((char*)p.y + ofs)) = (T)v.x; + if (outX + 1 < p.yShape.x) *((T*)((char*)p.y + ofs + get_stride(p.yStride.x))) = (T)v.y; + } + } + } + else if (down == 1 && !downInline) + { + // Thread per pixel. + __syncthreads(); + for (int idx = threadIdx.x; idx < tileOutW * tileOutH; idx += blockDim.x) + { + int relOutX0, relOutY0; + fast_div_mod(relOutX0, relOutY0, idx); + scalar_t v = s_tileUpXY[idx] * (scalar_t)c_fd[0]; // 1x1 filter. + + int outX = tileOutX + relOutX0; + int outY = tileOutY + relOutY0; + if ((uint32_t)outX < p.yShape.x && (uint32_t)outY < p.yShape.y) + *((T*)((char*)p.y + (outX * get_stride(p.yStride.x) + outY * get_stride(p.yStride.y) + mapOfsOut))) = (T)v; + } + } + } + + if (!enableXrep) + break; + } +} + +//------------------------------------------------------------------------ +// Compute activation function and signs for upsampled data tensor, modifying data tensor in-place. Used for accelerating the generic variant. +// Sign tensor is known to be contiguous, and p.x and p.s have the same z, w dimensions. 64-bit indexing is always used. + +template +static __global__ void filtered_lrelu_act_kernel(filtered_lrelu_act_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + + // Indexing. + int32_t x = threadIdx.x + blockIdx.x * blockDim.x; + int32_t ymax = signWrite ? p.sShape.y : p.xShape.y; + int32_t qmax = p.xShape.z * p.xShape.w; // Combined minibatch*channel maximum index. + + // Loop to accommodate oversized tensors. + for (int32_t q = blockIdx.z; q < qmax; q += gridDim.z) + for (int32_t y = blockIdx.y; y < ymax; y += gridDim.y) + { + // Extract z and w (channel, minibatch index). + int32_t w = q / p.xShape.z; + int32_t z = q - w * p.xShape.z; + + // Choose behavior based on sign read/write mode. + if (signWrite) + { + // Process value if in p.x. + uint32_t s = 0; + if (x < p.xShape.x && y < p.xShape.y) + { + int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; + T* pv = ((T*)p.x) + ix; + scalar_t v = (scalar_t)(*pv); + + // Gain, LReLU, clamp. + v *= p.gain; + if (v < 0.f) + { + v *= p.slope; + s = 1; // Sign. + } + if (fabsf(v) > p.clamp) + { + v = InternalType::clamp(v, p.clamp); + s = 2; // Clamp. + } + + *pv = (T)v; // Write value. + } + + // Coalesce into threads 0 and 16 of warp. + uint32_t m = (threadIdx.x & 16) ? 0xffff0000u : 0x0000ffffu; + s <<= ((threadIdx.x & 15) << 1); // Shift into place. + s |= __shfl_xor_sync(m, s, 1); // Distribute. + s |= __shfl_xor_sync(m, s, 2); + s |= __shfl_xor_sync(m, s, 4); + s |= __shfl_xor_sync(m, s, 8); + + // Write signs if leader and in p.s. + if (!(threadIdx.x & 15) && x < p.sShape.x) // y is always in. + { + uint64_t is = x + p.sShape.x * (y + (int64_t)p.sShape.y * q); // Contiguous. + ((uint32_t*)p.s)[is >> 4] = s; + } + } + else if (signRead) + { + // Process value if in p.x. + if (x < p.xShape.x) // y is always in. + { + int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; + T* pv = ((T*)p.x) + ix; + scalar_t v = (scalar_t)(*pv); + v *= p.gain; + + // Apply sign buffer offset. + uint32_t sx = x + p.sOfs.x; + uint32_t sy = y + p.sOfs.y; + + // Read and apply signs if we land inside valid region of sign buffer. + if (sx < p.sShape.x && sy < p.sShape.y) + { + uint64_t is = (sx >> 2) + (p.sShape.x >> 2) * (sy + (uint64_t)p.sShape.y * q); // Contiguous. + unsigned char s = p.s[is]; + s >>= (sx & 3) << 1; // Shift into place. + if (s & 1) // Sign? + v *= p.slope; + if (s & 2) // Clamp? + v = 0.f; + } + + *pv = (T)v; // Write value. + } + } + else + { + // Forward pass with no sign write. Process value if in p.x. + if (x < p.xShape.x) // y is always in. + { + int64_t ix = x * p.xStride.x + y * p.xStride.y + z * p.xStride.z + w * p.xStride.w; + T* pv = ((T*)p.x) + ix; + scalar_t v = (scalar_t)(*pv); + v *= p.gain; + if (v < 0.f) + v *= p.slope; + if (fabsf(v) > p.clamp) + v = InternalType::clamp(v, p.clamp); + *pv = (T)v; // Write value. + } + } + } +} + +template void* choose_filtered_lrelu_act_kernel(void) +{ + return (void*)filtered_lrelu_act_kernel; +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB) +{ + filtered_lrelu_kernel_spec s = { 0 }; + + // Return the first matching kernel. +#define CASE(SH, U, FU, D, FD, MODE, TW, TH, W, XR, WS) \ + if (sharedKB >= SH) \ + if ((p.fuShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_SUFD)) || (p.fuShape.y > 0 && (MODE == MODE_FUSD || MODE == MODE_FUFD))) \ + if ((p.fdShape.y == 0 && (MODE == MODE_SUSD || MODE == MODE_FUSD)) || (p.fdShape.y > 0 && (MODE == MODE_SUFD || MODE == MODE_FUFD))) \ + if (p.up == U && p.fuShape.x <= FU && p.fuShape.y <= FU && p.down == D && p.fdShape.x <= FD && p.fdShape.y <= FD) \ + { \ + static_assert((D*TW % 4) == 0, "down * tileWidth must be divisible by 4"); \ + static_assert(FU % U == 0, "upscaling filter size must be multiple of upscaling factor"); \ + static_assert(FD % D == 0, "downscaling filter size must be multiple of downscaling factor"); \ + s.setup = (void*)setup_filters_kernel; \ + s.exec = (void*)filtered_lrelu_kernel; \ + s.tileOut = make_int2(TW, TH); \ + s.numWarps = W; \ + s.xrep = XR; \ + s.dynamicSharedKB = (SH == 48) ? 0 : SH; \ + return s; \ + } + + // Launch parameters for various kernel specializations. + // Small filters must be listed before large filters, otherwise the kernel for larger filter will always match first. + // Kernels that use more shared memory must be listed before those that use less, for the same reason. + + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/1,1, /*mode*/MODE_FUFD, /*tw,th,warps,xrep,wskip*/64, 178, 32, 0, 0) // 1t-upf1-downf1 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/152, 95, 16, 0, 0) // 4t-ups2-downf1 + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,8, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56, 22, 16, 0, 0) // 4t-upf1-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56, 29, 16, 11, 0) // 4t-ups2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/60, 28, 16, 0, 0) // 4t-upf2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/2,8, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56, 28, 16, 0, 0) // 4t-ups2-downf2 + CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/56, 31, 16, 11, 0) // 4t-ups4-downs2 + CASE(/*sharedKB*/48, /*up,fu*/4,16, /*down,fd*/2,8, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/56, 36, 16, 0, 0) // 4t-ups4-downf2 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/4,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 22, 16, 12, 0) // 4t-ups2-downs4 + CASE(/*sharedKB*/48, /*up,fu*/2,8, /*down,fd*/4,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/29, 15, 16, 0, 0) // 4t-upf2-downs4 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/96, 150, 28, 0, 0) // 6t-ups2-downf1 + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32, 35, 24, 0, 0) // 6t-upf1-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 46, 16, 10, 0) // 6t-ups2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/58, 28, 24, 8, 0) // 6t-upf2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/52, 28, 16, 0, 0) // 6t-ups2-downf2 + CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 51, 16, 5, 0) // 6t-ups4-downs2 + CASE(/*sharedKB*/48, /*up,fu*/4,24, /*down,fd*/2,12, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 56, 16, 6, 0) // 6t-ups4-downf2 + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 18, 16, 12, 0) // 6t-ups2-downs4 + CASE(/*sharedKB*/96, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27, 31, 32, 6, 0) // 6t-upf2-downs4 96kB + CASE(/*sharedKB*/48, /*up,fu*/2,12, /*down,fd*/4,24, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/27, 13, 24, 0, 0) // 6t-upf2-downs4 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/1,1, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/148, 89, 24, 0, 0) // 8t-ups2-downf1 + CASE(/*sharedKB*/48, /*up,fu*/1,1, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/32, 31, 16, 5, 0) // 8t-upf1-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 41, 16, 9, 0) // 8t-ups2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/56, 26, 24, 0, 0) // 8t-upf2-downs2 + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 40, 16, 0, 0) // 8t-ups2-downf2 + CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/32, 46, 24, 5, 0) // 8t-ups4-downs2 + CASE(/*sharedKB*/48, /*up,fu*/4,32, /*down,fd*/2,16, /*mode*/MODE_SUFD, /*tw,th,warps,xrep,wskip*/32, 50, 16, 0, 0) // 8t-ups4-downf2 + CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/24, 24, 32, 12, 1) // 8t-ups2-downs4 96kB + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_SUSD, /*tw,th,warps,xrep,wskip*/16, 13, 16, 10, 1) // 8t-ups2-downs4 + CASE(/*sharedKB*/96, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25, 28, 28, 4, 0) // 8t-upf2-downs4 96kB + CASE(/*sharedKB*/48, /*up,fu*/2,16, /*down,fd*/4,32, /*mode*/MODE_FUSD, /*tw,th,warps,xrep,wskip*/25, 10, 24, 0, 0) // 8t-upf2-downs4 + + #undef CASE + return s; // No kernel found. +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.h b/torch_utils/ops/filtered_lrelu.h new file mode 100644 index 0000000..2c403e3 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.h @@ -0,0 +1,90 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct filtered_lrelu_kernel_params +{ + // These parameters decide which kernel to use. + int up; // upsampling ratio (1, 2, 4) + int down; // downsampling ratio (1, 2, 4) + int2 fuShape; // [size, 1] | [size, size] + int2 fdShape; // [size, 1] | [size, size] + + int _dummy; // Alignment. + + // Rest of the parameters. + const void* x; // Input tensor. + void* y; // Output tensor. + const void* b; // Bias tensor. + unsigned char* s; // Sign tensor in/out. NULL if unused. + const float* fu; // Upsampling filter. + const float* fd; // Downsampling filter. + + int2 pad0; // Left/top padding. + float gain; // Additional gain factor. + float slope; // Leaky ReLU slope on negative side. + float clamp; // Clamp after nonlinearity. + int flip; // Filter kernel flip for gradient computation. + + int tilesXdim; // Original number of horizontal output tiles. + int tilesXrep; // Number of horizontal tiles per CTA. + int blockZofs; // Block z offset to support large minibatch, channel dimensions. + + int4 xShape; // [width, height, channel, batch] + int4 yShape; // [width, height, channel, batch] + int2 sShape; // [width, height] - width is in bytes. Contiguous. Zeros if unused. + int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. + int swLimit; // Active width of sign tensor in bytes. + + longlong4 xStride; // Strides of all tensors except signs, same component order as shapes. + longlong4 yStride; // + int64_t bStride; // + longlong3 fuStride; // + longlong3 fdStride; // +}; + +struct filtered_lrelu_act_kernel_params +{ + void* x; // Input/output, modified in-place. + unsigned char* s; // Sign tensor in/out. NULL if unused. + + float gain; // Additional gain factor. + float slope; // Leaky ReLU slope on negative side. + float clamp; // Clamp after nonlinearity. + + int4 xShape; // [width, height, channel, batch] + longlong4 xStride; // Input/output tensor strides, same order as in shape. + int2 sShape; // [width, height] - width is in elements. Contiguous. Zeros if unused. + int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. +}; + +//------------------------------------------------------------------------ +// CUDA kernel specialization. + +struct filtered_lrelu_kernel_spec +{ + void* setup; // Function for filter kernel setup. + void* exec; // Function for main operation. + int2 tileOut; // Width/height of launch tile. + int numWarps; // Number of warps per thread block, determines launch block size. + int xrep; // For processing multiple horizontal tiles per thread block. + int dynamicSharedKB; // How much dynamic shared memory the exec kernel wants. +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template void* choose_filtered_lrelu_act_kernel(void); +template cudaError_t copy_filters(cudaStream_t stream); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/filtered_lrelu.py b/torch_utils/ops/filtered_lrelu.py new file mode 100644 index 0000000..6106c91 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu.py @@ -0,0 +1,274 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import numpy as np +import torch +import warnings + +from .. import custom_ops +from .. import misc +from . import upfirdn2d +from . import bias_act + +#---------------------------------------------------------------------------- + +_plugin = None + +def _init(): + global _plugin + if _plugin is None: + _plugin = custom_ops.get_plugin( + module_name='filtered_lrelu_plugin', + sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], + headers=['filtered_lrelu.h', 'filtered_lrelu.cu'], + source_dir=os.path.dirname(__file__), + extra_cuda_cflags=['--use_fast_math'], + ) + return True + +def _get_filter_size(f): + if f is None: + return 1, 1 + assert isinstance(f, torch.Tensor) + assert 1 <= f.ndim <= 2 + return f.shape[-1], f.shape[0] # width, height + +def _parse_padding(padding): + if isinstance(padding, int): + padding = [padding, padding] + assert isinstance(padding, (list, tuple)) + assert all(isinstance(x, (int, np.integer)) for x in padding) + padding = [int(x) for x in padding] + if len(padding) == 2: + px, py = padding + padding = [px, px, py, py] + px0, px1, py0, py1 = padding + return px0, px1, py0, py1 + +#---------------------------------------------------------------------------- + +def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'): + r"""Filtered leaky ReLU for a batch of 2D images. + + Performs the following sequence of operations for each channel: + + 1. Add channel-specific bias if provided (`b`). + + 2. Upsample the image by inserting N-1 zeros after each pixel (`up`). + + 3. Pad the image with the specified number of zeros on each side (`padding`). + Negative padding corresponds to cropping the image. + + 4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it + so that the footprint of all output pixels lies within the input image. + + 5. Multiply each value by the provided gain factor (`gain`). + + 6. Apply leaky ReLU activation function to each value. + + 7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided. + + 8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking + it so that the footprint of all output pixels lies within the input image. + + 9. Downsample the image by keeping every Nth pixel (`down`). + + The fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports gradients of arbitrary order. + + Args: + x: Float32/float16/float64 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + fu: Float32 upsampling FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + fd: Float32 downsampling FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type + as `x`. The length of vector must must match the channel dimension of `x`. + up: Integer upsampling factor (default: 1). + down: Integer downsampling factor. (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + gain: Overall scaling factor for signal magnitude (default: sqrt(2)). + slope: Slope on the negative side of leaky ReLU (default: 0.2). + clamp: Maximum magnitude for leaky ReLU output (default: None). + flip_filter: False = convolution, True = correlation (default: False). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0) + return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): + """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using + existing `upfirdn2n()` and `bias_act()` ops. + """ + assert isinstance(x, torch.Tensor) and x.ndim == 4 + fu_w, fu_h = _get_filter_size(fu) + fd_w, fd_h = _get_filter_size(fd) + if b is not None: + assert isinstance(b, torch.Tensor) and b.dtype == x.dtype + misc.assert_shape(b, [x.shape[1]]) + assert isinstance(up, int) and up >= 1 + assert isinstance(down, int) and down >= 1 + px0, px1, py0, py1 = _parse_padding(padding) + assert gain == float(gain) and gain > 0 + assert slope == float(slope) and slope >= 0 + assert clamp is None or (clamp == float(clamp) and clamp >= 0) + + # Calculate output size. + batch_size, channels, in_h, in_w = x.shape + in_dtype = x.dtype + out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down + out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down + + # Compute using existing ops. + x = bias_act.bias_act(x=x, b=b) # Apply bias. + x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. + x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp. + x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample. + + # Check output shape & dtype. + misc.assert_shape(x, [batch_size, channels, out_h, out_w]) + assert x.dtype == in_dtype + return x + +#---------------------------------------------------------------------------- + +_filtered_lrelu_cuda_cache = dict() + +def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): + """Fast CUDA implementation of `filtered_lrelu()` using custom ops. + """ + assert isinstance(up, int) and up >= 1 + assert isinstance(down, int) and down >= 1 + px0, px1, py0, py1 = _parse_padding(padding) + assert gain == float(gain) and gain > 0 + gain = float(gain) + assert slope == float(slope) and slope >= 0 + slope = float(slope) + assert clamp is None or (clamp == float(clamp) and clamp >= 0) + clamp = float(clamp if clamp is not None else 'inf') + + # Lookup from cache. + key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter) + if key in _filtered_lrelu_cuda_cache: + return _filtered_lrelu_cuda_cache[key] + + # Forward op. + class FilteredLReluCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ + assert isinstance(x, torch.Tensor) and x.ndim == 4 + + # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable). + if fu is None: + fu = torch.ones([1, 1], dtype=torch.float32, device=x.device) + if fd is None: + fd = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert 1 <= fu.ndim <= 2 + assert 1 <= fd.ndim <= 2 + + # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1. + if up == 1 and fu.ndim == 1 and fu.shape[0] == 1: + fu = fu.square()[None] + if down == 1 and fd.ndim == 1 and fd.shape[0] == 1: + fd = fd.square()[None] + + # Missing sign input tensor. + if si is None: + si = torch.empty([0]) + + # Missing bias tensor. + if b is None: + b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device) + + # Construct internal sign tensor only if gradients are needed. + write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad) + + # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout. + strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1] + if any(a < b for a, b in zip(strides[:-1], strides[1:])): + warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) + + # Call C++/Cuda plugin if datatype is supported. + if x.dtype in [torch.float16, torch.float32]: + if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device): + warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) + y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) + else: + return_code = -1 + + # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because + # only the bit-packed sign tensor is retained for gradient computation. + if return_code < 0: + warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) + + y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. + y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. + so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place. + y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample. + + # Prepare for gradient computation. + ctx.save_for_backward(fu, fd, (si if si.numel() else so)) + ctx.x_shape = x.shape + ctx.y_shape = y.shape + ctx.s_ofs = sx, sy + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + fu, fd, si = ctx.saved_tensors + _, _, xh, xw = ctx.x_shape + _, _, yh, yw = ctx.y_shape + sx, sy = ctx.s_ofs + dx = None # 0 + dfu = None; assert not ctx.needs_input_grad[1] + dfd = None; assert not ctx.needs_input_grad[2] + db = None # 3 + dsi = None; assert not ctx.needs_input_grad[4] + dsx = None; assert not ctx.needs_input_grad[5] + dsy = None; assert not ctx.needs_input_grad[6] + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]: + pp = [ + (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0, + xw * up - yw * down + px0 - (up - 1), + (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0, + xh * up - yh * down + py0 - (up - 1), + ] + gg = gain * (up ** 2) / (down ** 2) + ff = (not flip_filter) + sx = sx - (fu.shape[-1] - 1) + px0 + sy = sy - (fu.shape[0] - 1) + py0 + dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) + + if ctx.needs_input_grad[3]: + db = dx.sum([0, 2, 3]) + + return dx, dfu, dfd, db, dsi, dsx, dsy + + # Add to cache. + _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda + return FilteredLReluCuda + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu_ns.cu b/torch_utils/ops/filtered_lrelu_ns.cu new file mode 100644 index 0000000..ef5d948 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu_ns.cu @@ -0,0 +1,27 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "filtered_lrelu.cu" + +// Template/kernel specializations for no signs mode (no gradients required). + +// Full op, 32-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Full op, 64-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Activation/signs only for generic variant. 64-bit indexing. +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); + +// Copy filters to constant memory. +template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/filtered_lrelu_rd.cu b/torch_utils/ops/filtered_lrelu_rd.cu new file mode 100644 index 0000000..9683478 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu_rd.cu @@ -0,0 +1,27 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "filtered_lrelu.cu" + +// Template/kernel specializations for sign read mode. + +// Full op, 32-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Full op, 64-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Activation/signs only for generic variant. 64-bit indexing. +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); + +// Copy filters to constant memory. +template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/filtered_lrelu_wr.cu b/torch_utils/ops/filtered_lrelu_wr.cu new file mode 100644 index 0000000..a4c6a24 --- /dev/null +++ b/torch_utils/ops/filtered_lrelu_wr.cu @@ -0,0 +1,27 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include "filtered_lrelu.cu" + +// Template/kernel specializations for sign write mode. + +// Full op, 32-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Full op, 64-bit indexing. +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); +template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); + +// Activation/signs only for generic variant. 64-bit indexing. +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); +template void* choose_filtered_lrelu_act_kernel(void); + +// Copy filters to constant memory. +template cudaError_t copy_filters(cudaStream_t stream); diff --git a/torch_utils/ops/fma.py b/torch_utils/ops/fma.py new file mode 100644 index 0000000..51a45df --- /dev/null +++ b/torch_utils/ops/fma.py @@ -0,0 +1,60 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`.""" + +import torch + +#---------------------------------------------------------------------------- + +def fma(a, b, c): # => a * b + c + return _FusedMultiplyAdd.apply(a, b, c) + +#---------------------------------------------------------------------------- + +class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c + @staticmethod + def forward(ctx, a, b, c): # pylint: disable=arguments-differ + out = torch.addcmul(c, a, b) + ctx.save_for_backward(a, b) + ctx.c_shape = c.shape + return out + + @staticmethod + def backward(ctx, dout): # pylint: disable=arguments-differ + a, b = ctx.saved_tensors + c_shape = ctx.c_shape + da = None + db = None + dc = None + + if ctx.needs_input_grad[0]: + da = _unbroadcast(dout * b, a.shape) + + if ctx.needs_input_grad[1]: + db = _unbroadcast(dout * a, b.shape) + + if ctx.needs_input_grad[2]: + dc = _unbroadcast(dout, c_shape) + + return da, db, dc + +#---------------------------------------------------------------------------- + +def _unbroadcast(x, shape): + extra_dims = x.ndim - len(shape) + assert extra_dims >= 0 + dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] + if len(dim): + x = x.sum(dim=dim, keepdim=True) + if extra_dims: + x = x.reshape(-1, *x.shape[extra_dims+1:]) + assert x.shape == shape + return x + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/grid_sample_gradfix.py b/torch_utils/ops/grid_sample_gradfix.py new file mode 100644 index 0000000..979ee83 --- /dev/null +++ b/torch_utils/ops/grid_sample_gradfix.py @@ -0,0 +1,77 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.grid_sample` that +supports arbitrarily high order gradients between the input and output. +Only works on 2D images and assumes +`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`.""" + +import torch + +# pylint: disable=redefined-builtin +# pylint: disable=arguments-differ +# pylint: disable=protected-access + +#---------------------------------------------------------------------------- + +enabled = False # Enable the custom op by setting this to true. + +#---------------------------------------------------------------------------- + +def grid_sample(input, grid): + if _should_use_custom_op(): + return _GridSample2dForward.apply(input, grid) + return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + +#---------------------------------------------------------------------------- + +def _should_use_custom_op(): + return enabled + +#---------------------------------------------------------------------------- + +class _GridSample2dForward(torch.autograd.Function): + @staticmethod + def forward(ctx, input, grid): + assert input.ndim == 4 + assert grid.ndim == 4 + output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + ctx.save_for_backward(input, grid) + return output + + @staticmethod + def backward(ctx, grad_output): + input, grid = ctx.saved_tensors + grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) + return grad_input, grad_grid + +#---------------------------------------------------------------------------- + +class _GridSample2dBackward(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input, grid): + op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward') + grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) + ctx.save_for_backward(grid) + return grad_input, grad_grid + + @staticmethod + def backward(ctx, grad2_grad_input, grad2_grad_grid): + _ = grad2_grad_grid # unused + grid, = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + grad2_grid = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) + + assert not ctx.needs_input_grad[2] + return grad2_grad_output, grad2_input, grad2_grid + +#---------------------------------------------------------------------------- diff --git a/torch_utils/ops/upfirdn2d.cpp b/torch_utils/ops/upfirdn2d.cpp new file mode 100644 index 0000000..44fa337 --- /dev/null +++ b/torch_utils/ops/upfirdn2d.cpp @@ -0,0 +1,107 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ + +static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); + TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); + TORCH_CHECK(x.numel() > 0, "x has zero size"); + TORCH_CHECK(f.numel() > 0, "f has zero size"); + TORCH_CHECK(x.dim() == 4, "x must be rank 4"); + TORCH_CHECK(f.dim() == 2, "f must be rank 2"); + TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large"); + TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); + TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); + TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; + int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; + TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); + torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); + TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); + TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large"); + + // Initialize CUDA kernel parameters. + upfirdn2d_kernel_params p; + p.x = x.data_ptr(); + p.f = f.data_ptr(); + p.y = y.data_ptr(); + p.up = make_int2(upx, upy); + p.down = make_int2(downx, downy); + p.pad0 = make_int2(padx0, pady0); + p.flip = (flip) ? 1 : 0; + p.gain = gain; + p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); + p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); + p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); + p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); + p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); + p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); + p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; + p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; + + // Choose CUDA kernel. + upfirdn2d_kernel_spec spec; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + spec = choose_upfirdn2d_kernel(p); + }); + + // Set looping options. + p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; + p.loopMinor = spec.loopMinor; + p.loopX = spec.loopX; + p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; + p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; + + // Compute grid size. + dim3 blockSize, gridSize; + if (spec.tileOutW < 0) // large + { + blockSize = dim3(4, 32, 1); + gridSize = dim3( + ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, + (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, + p.launchMajor); + } + else // small + { + blockSize = dim3(256, 1, 1); + gridSize = dim3( + ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, + (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, + p.launchMajor); + } + + // Launch CUDA kernel. + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("upfirdn2d", &upfirdn2d); +} + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.cu b/torch_utils/ops/upfirdn2d.cu new file mode 100644 index 0000000..3a33e31 --- /dev/null +++ b/torch_utils/ops/upfirdn2d.cu @@ -0,0 +1,384 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +static __device__ __forceinline__ int floor_div(int a, int b) +{ + int t = 1 - a / b; + return (a + t * b) / b - t; +} + +//------------------------------------------------------------------------ +// Generic CUDA implementation for large filters. + +template static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + + // Calculate thread index. + int minorBase = blockIdx.x * blockDim.x + threadIdx.x; + int outY = minorBase / p.launchMinor; + minorBase -= outY * p.launchMinor; + int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y; + int majorBase = blockIdx.z * p.loopMajor; + if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Setup Y receptive field. + int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y; + int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y); + int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY; + int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y; + if (p.flip) + filterY = p.filterSize.y - 1 - filterY; + + // Loop over major, minor, and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor) + { + int nc = major * p.sizeMinor + minor; + int n = nc / p.inSize.z; + int c = nc - n * p.inSize.z; + for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y) + { + // Setup X receptive field. + int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x; + int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x); + int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX; + int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x; + if (p.flip) + filterX = p.filterSize.x - 1 - filterX; + + // Initialize pointers. + const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y]; + int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x; + int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y; + + // Inner loop. + scalar_t v = 0; + for (int y = 0; y < h; y++) + { + for (int x = 0; x < w; x++) + { + v += (scalar_t)(*xp) * (scalar_t)(*fp); + xp += p.inStride.x; + fp += filterStepX; + } + xp += p.inStride.y - w * p.inStride.x; + fp += filterStepY - w * filterStepX; + } + + // Store result. + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } +} + +//------------------------------------------------------------------------ +// Specialized CUDA implementation for small filters. + +template +static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1; + const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1; + __shared__ volatile scalar_t sf[filterH][filterW]; + __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor]; + + // Calculate tile index. + int minorBase = blockIdx.x; + int tileOutY = minorBase / p.launchMinor; + minorBase -= tileOutY * p.launchMinor; + minorBase *= loopMinor; + tileOutY *= tileOutH; + int tileOutXBase = blockIdx.y * p.loopX * tileOutW; + int majorBase = blockIdx.z * p.loopMajor; + if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Load filter (flipped). + for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x) + { + int fy = tapIdx / filterW; + int fx = tapIdx - fy * filterW; + scalar_t v = 0; + if (fx < p.filterSize.x & fy < p.filterSize.y) + { + int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx; + int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy; + v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y]; + } + sf[fy][fx] = v; + } + + // Loop over major and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + { + int baseNC = major * p.sizeMinor + minorBase; + int n = baseNC / p.inSize.z; + int baseC = baseNC - n * p.inSize.z; + for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW) + { + // Load input pixels. + int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x; + int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y; + int tileInX = floor_div(tileMidX, upx); + int tileInY = floor_div(tileMidY, upy); + __syncthreads(); + for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x) + { + int relC = inIdx; + int relInX = relC / loopMinor; + int relInY = relInX / tileInW; + relC -= relInX * loopMinor; + relInX -= relInY * tileInW; + int c = baseC + relC; + int inX = tileInX + relInX; + int inY = tileInY + relInY; + scalar_t v = 0; + if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z) + v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + sx[relInY][relInX][relC] = v; + } + + // Loop over output pixels. + __syncthreads(); + for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x) + { + int relC = outIdx; + int relOutX = relC / loopMinor; + int relOutY = relOutX / tileOutW; + relC -= relOutX * loopMinor; + relOutX -= relOutY * tileOutW; + int c = baseC + relC; + int outX = tileOutX + relOutX; + int outY = tileOutY + relOutY; + + // Setup receptive field. + int midX = tileMidX + relOutX * downx; + int midY = tileMidY + relOutY * downy; + int inX = floor_div(midX, upx); + int inY = floor_div(midY, upy); + int relInX = inX - tileInX; + int relInY = inY - tileInY; + int filterX = (inX + 1) * upx - midX - 1; // flipped + int filterY = (inY + 1) * upy - midY - 1; // flipped + + // Inner loop. + if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z) + { + scalar_t v = 0; + #pragma unroll + for (int y = 0; y < filterH / upy; y++) + #pragma unroll + for (int x = 0; x < filterW / upx; x++) + v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx]; + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } + } + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p) +{ + int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y; + upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large, -1,-1,1, 4}; // contiguous + if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -1,-1,4, 1}; // channels_last + + // No up/downsampling. + if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + + // 2x upsampling. + if (p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + } + if (p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + } + if (p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + + // 2x downsampling. + if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) + { + // contiguous + if (s != 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (s != 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (s != 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 16,16,1, 1}; + if (s == 1 && fx <= 16 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 16,16,1, 1}; + if (s == 1 && fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (s == 1 && fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (s == 1 && fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (s == 1 && fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + } + if (p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (s != 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (s != 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + // channels_last + if (s == 1 && fx <= 24 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (s == 1 && fx <= 16 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (s == 1 && fx <= 8 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + } + if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) + { + // contiguous + if (s != 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (s != 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (s != 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (s == 1 && fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (s == 1 && fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + } + + // 4x upsampling. + if (p.up.x == 4 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + if (s != 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 64,32,1, 1}; + // channels_last + if (s == 1 && fx <= 48 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s == 1 && fx <= 32 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (p.up.x == 4 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + // channels_last + if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + } + if (p.up.x == 1 && p.up.y == 4 && p.down.x == 1 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + + // 4x downsampling (inefficient). + if (p.up.x == 1 && p.up.y == 1 && p.down.x == 4 && p.down.y == 1) + { + // contiguous + if (s != 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + // channels_last + if (s == 1 && fx <= 48 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,1,8, 1}; + if (s == 1 && fx <= 32 && fy <= 1) spec = {(void*)upfirdn2d_kernel_small, 32,1,8, 1}; + } + if (p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 4) + { + // contiguous + if (s != 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (s != 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + // channels_last + if (s == 1 && fx <= 1 && fy <= 48) spec = {(void*)upfirdn2d_kernel_small, 1,32,8, 1}; + if (s == 1 && fx <= 1 && fy <= 32) spec = {(void*)upfirdn2d_kernel_small, 1,32,8, 1}; + } + return spec; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.h b/torch_utils/ops/upfirdn2d.h new file mode 100644 index 0000000..2793daf --- /dev/null +++ b/torch_utils/ops/upfirdn2d.h @@ -0,0 +1,59 @@ +// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct upfirdn2d_kernel_params +{ + const void* x; + const float* f; + void* y; + + int2 up; + int2 down; + int2 pad0; + int flip; + float gain; + + int4 inSize; // [width, height, channel, batch] + int4 inStride; + int2 filterSize; // [width, height] + int2 filterStride; + int4 outSize; // [width, height, channel, batch] + int4 outStride; + int sizeMinor; + int sizeMajor; + + int loopMinor; + int loopMajor; + int loopX; + int launchMinor; + int launchMajor; +}; + +//------------------------------------------------------------------------ +// CUDA kernel specialization. + +struct upfirdn2d_kernel_spec +{ + void* kernel; + int tileOutW; + int tileOutH; + int loopMinor; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py new file mode 100644 index 0000000..b544be1 --- /dev/null +++ b/torch_utils/ops/upfirdn2d.py @@ -0,0 +1,389 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient resampling of 2D images.""" + +import os +import numpy as np +import torch + +from .. import custom_ops +from .. import misc +from . import conv2d_gradfix + +#---------------------------------------------------------------------------- + +_plugin = None + +def _init(): + global _plugin + if _plugin is None: + _plugin = custom_ops.get_plugin( + module_name='upfirdn2d_plugin', + sources=['upfirdn2d.cpp', 'upfirdn2d.cu'], + headers=['upfirdn2d.h'], + source_dir=os.path.dirname(__file__), + extra_cuda_cflags=['--use_fast_math'], + ) + return True + +def _parse_scaling(scaling): + if isinstance(scaling, int): + scaling = [scaling, scaling] + assert isinstance(scaling, (list, tuple)) + assert all(isinstance(x, int) for x in scaling) + sx, sy = scaling + assert sx >= 1 and sy >= 1 + return sx, sy + +def _parse_padding(padding): + if isinstance(padding, int): + padding = [padding, padding] + assert isinstance(padding, (list, tuple)) + assert all(isinstance(x, int) for x in padding) + if len(padding) == 2: + padx, pady = padding + padding = [padx, padx, pady, pady] + padx0, padx1, pady0, pady1 = padding + return padx0, padx1, pady0, pady1 + +def _get_filter_size(f): + if f is None: + return 1, 1 + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + fw = f.shape[-1] + fh = f.shape[0] + with misc.suppress_tracer_warnings(): + fw = int(fw) + fh = int(fh) + misc.assert_shape(f, [fh, fw][:f.ndim]) + assert fw >= 1 and fh >= 1 + return fw, fh + +#---------------------------------------------------------------------------- + +def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): + r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. + + Args: + f: Torch tensor, numpy array, or python list of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), + `[]` (impulse), or + `None` (identity). + device: Result device (default: cpu). + normalize: Normalize the filter so that it retains the magnitude + for constant input signal (DC)? (default: True). + flip_filter: Flip the filter? (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + separable: Return a separable filter? (default: select automatically). + + Returns: + Float32 tensor of the shape + `[filter_height, filter_width]` (non-separable) or + `[filter_taps]` (separable). + """ + # Validate. + if f is None: + f = 1 + f = torch.as_tensor(f, dtype=torch.float32) + assert f.ndim in [0, 1, 2] + assert f.numel() > 0 + if f.ndim == 0: + f = f[np.newaxis] + + # Separable? + if separable is None: + separable = (f.ndim == 1 and f.numel() >= 8) + if f.ndim == 1 and not separable: + f = f.ger(f) + assert f.ndim == (1 if separable else 2) + + # Apply normalize, flip, gain, and device. + if normalize: + f /= f.sum() + if flip_filter: + f = f.flip(list(range(f.ndim))) + f = f * (gain ** (f.ndim / 2)) + f = f.to(device=device) + return f + +#---------------------------------------------------------------------------- + +def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Pad, upsample, filter, and downsample a batch of 2D images. + + Performs the following sequence of operations for each channel: + + 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). + + 2. Pad the image with the specified number of zeros on each side (`padding`). + Negative padding corresponds to cropping the image. + + 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it + so that the footprint of all output pixels lies within the input image. + + 4. Downsample the image by keeping every Nth pixel (`down`). + + This sequence of operations bears close resemblance to scipy.signal.upfirdn(). + The fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports gradients of arbitrary order. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) + return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): + """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops. + """ + # Validate arguments. + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + assert f.dtype == torch.float32 and not f.requires_grad + batch_size, num_channels, in_height, in_width = x.shape + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + # Check that upsampled buffer is not smaller than the filter. + upW = in_width * upx + padx0 + padx1 + upH = in_height * upy + pady0 + pady1 + assert upW >= f.shape[-1] and upH >= f.shape[0] + + # Upsample by inserting zeros. + x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) + x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) + x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) + + # Pad or crop. + x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) + x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] + + # Setup filter. + f = f * (gain ** (f.ndim / 2)) + f = f.to(x.dtype) + if not flip_filter: + f = f.flip(list(range(f.ndim))) + + # Convolve with the filter. + f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) + if f.ndim == 4: + x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) + else: + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) + + # Downsample by throwing away pixels. + x = x[:, :, ::downy, ::downx] + return x + +#---------------------------------------------------------------------------- + +_upfirdn2d_cuda_cache = dict() + +def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): + """Fast CUDA implementation of `upfirdn2d()` using custom ops. + """ + # Parse arguments. + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + # Lookup from cache. + key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + if key in _upfirdn2d_cuda_cache: + return _upfirdn2d_cuda_cache[key] + + # Forward op. + class Upfirdn2dCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, f): # pylint: disable=arguments-differ + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + if f.ndim == 1 and f.shape[0] == 1: + f = f.square().unsqueeze(0) # Convert separable-1 into full-1x1. + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + y = x + if f.ndim == 2: + y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + else: + y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0) + y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain) + ctx.save_for_backward(f) + ctx.x_shape = x.shape + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + f, = ctx.saved_tensors + _, _, ih, iw = ctx.x_shape + _, _, oh, ow = dy.shape + fw, fh = _get_filter_size(f) + p = [ + fw - padx0 - 1, + iw * upx - ow * downx + padx0 - upx + 1, + fh - pady0 - 1, + ih * upy - oh * downy + pady0 - upy + 1, + ] + dx = None + df = None + + if ctx.needs_input_grad[0]: + dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) + + assert not ctx.needs_input_grad[1] + return dx, df + + # Add to cache. + _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda + return Upfirdn2dCuda + +#---------------------------------------------------------------------------- + +def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Filter a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape matches the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + fw // 2, + padx1 + (fw - 1) // 2, + pady0 + fh // 2, + pady1 + (fh - 1) // 2, + ] + return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + +#---------------------------------------------------------------------------- + +def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Upsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a multiple of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + upx, upy = _parse_scaling(up) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw + upx - 1) // 2, + padx1 + (fw - upx) // 2, + pady0 + (fh + upy - 1) // 2, + pady1 + (fh - upy) // 2, + ] + return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) + +#---------------------------------------------------------------------------- + +def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Downsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a fraction of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the input. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw - downx + 1) // 2, + padx1 + (fw - downx) // 2, + pady0 + (fh - downy + 1) // 2, + pady1 + (fh - downy) // 2, + ] + return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + +#---------------------------------------------------------------------------- diff --git a/torch_utils/persistence.py b/torch_utils/persistence.py new file mode 100644 index 0000000..f90ce85 --- /dev/null +++ b/torch_utils/persistence.py @@ -0,0 +1,251 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for pickling Python code alongside other data. + +The pickled code is automatically imported into a separate Python module +during unpickling. This way, any previously exported pickles will remain +usable even if the original code is no longer available, or if the current +version of the code is not consistent with what was originally pickled.""" + +import sys +import pickle +import io +import inspect +import copy +import uuid +import types +import dnnlib + +#---------------------------------------------------------------------------- + +_version = 6 # internal version number +_decorators = set() # {decorator_class, ...} +_import_hooks = [] # [hook_function, ...] +_module_to_src_dict = dict() # {module: src, ...} +_src_to_module_dict = dict() # {src: module, ...} + +#---------------------------------------------------------------------------- + +def persistent_class(orig_class): + r"""Class decorator that extends a given class to save its source code + when pickled. + + Example: + + from torch_utils import persistence + + @persistence.persistent_class + class MyNetwork(torch.nn.Module): + def __init__(self, num_inputs, num_outputs): + super().__init__() + self.fc = MyLayer(num_inputs, num_outputs) + ... + + @persistence.persistent_class + class MyLayer(torch.nn.Module): + ... + + When pickled, any instance of `MyNetwork` and `MyLayer` will save its + source code alongside other internal state (e.g., parameters, buffers, + and submodules). This way, any previously exported pickle will remain + usable even if the class definitions have been modified or are no + longer available. + + The decorator saves the source code of the entire Python module + containing the decorated class. It does *not* save the source code of + any imported modules. Thus, the imported modules must be available + during unpickling, also including `torch_utils.persistence` itself. + + It is ok to call functions defined in the same module from the + decorated class. However, if the decorated class depends on other + classes defined in the same module, they must be decorated as well. + This is illustrated in the above example in the case of `MyLayer`. + + It is also possible to employ the decorator just-in-time before + calling the constructor. For example: + + cls = MyLayer + if want_to_make_it_persistent: + cls = persistence.persistent_class(cls) + layer = cls(num_inputs, num_outputs) + + As an additional feature, the decorator also keeps track of the + arguments that were used to construct each instance of the decorated + class. The arguments can be queried via `obj.init_args` and + `obj.init_kwargs`, and they are automatically pickled alongside other + object state. A typical use case is to first unpickle a previous + instance of a persistent class, and then upgrade it to use the latest + version of the source code: + + with open('old_pickle.pkl', 'rb') as f: + old_net = pickle.load(f) + new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs) + misc.copy_params_and_buffers(old_net, new_net, require_all=True) + """ + assert isinstance(orig_class, type) + if is_persistent(orig_class): + return orig_class + + assert orig_class.__module__ in sys.modules + orig_module = sys.modules[orig_class.__module__] + orig_module_src = _module_to_src(orig_module) + + class Decorator(orig_class): + _orig_module_src = orig_module_src + _orig_class_name = orig_class.__name__ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._init_args = copy.deepcopy(args) + self._init_kwargs = copy.deepcopy(kwargs) + assert orig_class.__name__ in orig_module.__dict__ + _check_pickleable(self.__reduce__()) + + @property + def init_args(self): + return copy.deepcopy(self._init_args) + + @property + def init_kwargs(self): + return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs)) + + def __reduce__(self): + fields = list(super().__reduce__()) + fields += [None] * max(3 - len(fields), 0) + if fields[0] is not _reconstruct_persistent_obj: + meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2]) + fields[0] = _reconstruct_persistent_obj # reconstruct func + fields[1] = (meta,) # reconstruct args + fields[2] = None # state dict + return tuple(fields) + + Decorator.__name__ = orig_class.__name__ + _decorators.add(Decorator) + return Decorator + +#---------------------------------------------------------------------------- + +def is_persistent(obj): + r"""Test whether the given object or class is persistent, i.e., + whether it will save its source code when pickled. + """ + try: + if obj in _decorators: + return True + except TypeError: + pass + return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + +#---------------------------------------------------------------------------- + +def import_hook(hook): + r"""Register an import hook that is called whenever a persistent object + is being unpickled. A typical use case is to patch the pickled source + code to avoid errors and inconsistencies when the API of some imported + module has changed. + + The hook should have the following signature: + + hook(meta) -> modified meta + + `meta` is an instance of `dnnlib.EasyDict` with the following fields: + + type: Type of the persistent object, e.g. `'class'`. + version: Internal version number of `torch_utils.persistence`. + module_src Original source code of the Python module. + class_name: Class name in the original Python module. + state: Internal state of the object. + + Example: + + @persistence.import_hook + def wreck_my_network(meta): + if meta.class_name == 'MyNetwork': + print('MyNetwork is being imported. I will wreck it!') + meta.module_src = meta.module_src.replace("True", "False") + return meta + """ + assert callable(hook) + _import_hooks.append(hook) + +#---------------------------------------------------------------------------- + +def _reconstruct_persistent_obj(meta): + r"""Hook that is called internally by the `pickle` module to unpickle + a persistent object. + """ + meta = dnnlib.EasyDict(meta) + meta.state = dnnlib.EasyDict(meta.state) + for hook in _import_hooks: + meta = hook(meta) + assert meta is not None + + assert meta.version == _version + module = _src_to_module(meta.module_src) + + assert meta.type == 'class' + orig_class = module.__dict__[meta.class_name] + decorator_class = persistent_class(orig_class) + obj = decorator_class.__new__(decorator_class) + + setstate = getattr(obj, '__setstate__', None) + if callable(setstate): + setstate(meta.state) # pylint: disable=not-callable + else: + obj.__dict__.update(meta.state) + return obj + +#---------------------------------------------------------------------------- + +def _module_to_src(module): + r"""Query the source code of a given Python module. + """ + src = _module_to_src_dict.get(module, None) + if src is None: + src = inspect.getsource(module) + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + return src + +def _src_to_module(src): + r"""Get or create a Python module for the given source code. + """ + module = _src_to_module_dict.get(src, None) + if module is None: + module_name = "_imported_module_" + uuid.uuid4().hex + module = types.ModuleType(module_name) + sys.modules[module_name] = module + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + exec(src, module.__dict__) # pylint: disable=exec-used + return module + +#---------------------------------------------------------------------------- + +def _check_pickleable(obj): + r"""Check that the given object is pickleable, raising an exception if + it is not. This function is expected to be considerably more efficient + than actually pickling the object. + """ + def recurse(obj): + if isinstance(obj, (list, tuple, set)): + return [recurse(x) for x in obj] + if isinstance(obj, dict): + return [[recurse(x), recurse(y)] for x, y in obj.items()] + if isinstance(obj, (str, int, float, bool, bytes, bytearray)): + return None # Python primitive types are pickleable. + if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter']: + return None # NumPy arrays and PyTorch tensors are pickleable. + if is_persistent(obj): + return None # Persistent objects are pickleable, by virtue of the constructor check. + return obj + with io.BytesIO() as f: + pickle.dump(recurse(obj), f) + +#---------------------------------------------------------------------------- diff --git a/torch_utils/training_stats.py b/torch_utils/training_stats.py new file mode 100644 index 0000000..5de4134 --- /dev/null +++ b/torch_utils/training_stats.py @@ -0,0 +1,268 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for reporting and collecting training statistics across +multiple processes and devices. The interface is designed to minimize +synchronization overhead as well as the amount of boilerplate in user +code.""" + +import re +import numpy as np +import torch +import dnnlib + +from . import misc + +#---------------------------------------------------------------------------- + +_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] +_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. +_counter_dtype = torch.float64 # Data type to use for the internal counters. +_rank = 0 # Rank of the current process. +_sync_device = None # Device to use for multiprocess communication. None = single-process. +_sync_called = False # Has _sync() been called yet? +_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor +_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor + +#---------------------------------------------------------------------------- + +def init_multiprocessing(rank, sync_device): + r"""Initializes `torch_utils.training_stats` for collecting statistics + across multiple processes. + + This function must be called after + `torch.distributed.init_process_group()` and before `Collector.update()`. + The call is not necessary if multi-process collection is not needed. + + Args: + rank: Rank of the current process. + sync_device: PyTorch device to use for inter-process + communication, or None to disable multi-process + collection. Typically `torch.device('cuda', rank)`. + """ + global _rank, _sync_device + assert not _sync_called + _rank = rank + _sync_device = sync_device + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def report(name, value): + r"""Broadcasts the given set of scalars to all interested instances of + `Collector`, across device and process boundaries. + + This function is expected to be extremely cheap and can be safely + called from anywhere in the training loop, loss function, or inside a + `torch.nn.Module`. + + Warning: The current implementation expects the set of unique names to + be consistent across processes. Please make sure that `report()` is + called at least once for each unique name by each process, and in the + same order. If a given process has no scalars to broadcast, it can do + `report(name, [])` (empty list). + + Args: + name: Arbitrary string specifying the name of the statistic. + Averages are accumulated separately for each unique name. + value: Arbitrary set of scalars. Can be a list, tuple, + NumPy array, PyTorch tensor, or Python scalar. + + Returns: + The same `value` that was passed in. + """ + if name not in _counters: + _counters[name] = dict() + + elems = torch.as_tensor(value) + if elems.numel() == 0: + return value + + elems = elems.detach().flatten().to(_reduce_dtype) + moments = torch.stack([ + torch.ones_like(elems).sum(), + elems.sum(), + elems.square().sum(), + ]) + assert moments.ndim == 1 and moments.shape[0] == _num_moments + moments = moments.to(_counter_dtype) + + device = moments.device + if device not in _counters[name]: + _counters[name][device] = torch.zeros_like(moments) + _counters[name][device].add_(moments) + return value + +#---------------------------------------------------------------------------- + +def report0(name, value): + r"""Broadcasts the given set of scalars by the first process (`rank = 0`), + but ignores any scalars provided by the other processes. + See `report()` for further details. + """ + report(name, value if _rank == 0 else []) + return value + +#---------------------------------------------------------------------------- + +class Collector: + r"""Collects the scalars broadcasted by `report()` and `report0()` and + computes their long-term averages (mean and standard deviation) over + user-defined periods of time. + + The averages are first collected into internal counters that are not + directly visible to the user. They are then copied to the user-visible + state as a result of calling `update()` and can then be queried using + `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the + internal counters for the next round, so that the user-visible state + effectively reflects averages collected between the last two calls to + `update()`. + + Args: + regex: Regular expression defining which statistics to + collect. The default is to collect everything. + keep_previous: Whether to retain the previous averages if no + scalars were collected on a given round + (default: True). + """ + def __init__(self, regex='.*', keep_previous=True): + self._regex = re.compile(regex) + self._keep_previous = keep_previous + self._cumulative = dict() + self._moments = dict() + self.update() + self._moments.clear() + + def names(self): + r"""Returns the names of all statistics broadcasted so far that + match the regular expression specified at construction time. + """ + return [name for name in _counters if self._regex.fullmatch(name)] + + def update(self): + r"""Copies current values of the internal counters to the + user-visible state and resets them for the next round. + + If `keep_previous=True` was specified at construction time, the + operation is skipped for statistics that have received no scalars + since the last update, retaining their previous averages. + + This method performs a number of GPU-to-CPU transfers and one + `torch.distributed.all_reduce()`. It is intended to be called + periodically in the main training loop, typically once every + N training steps. + """ + if not self._keep_previous: + self._moments.clear() + for name, cumulative in _sync(self.names()): + if name not in self._cumulative: + self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + delta = cumulative - self._cumulative[name] + self._cumulative[name].copy_(cumulative) + if float(delta[0]) != 0: + self._moments[name] = delta + + def _get_delta(self, name): + r"""Returns the raw moments that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + assert self._regex.fullmatch(name) + if name not in self._moments: + self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + return self._moments[name] + + def num(self, name): + r"""Returns the number of scalars that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + delta = self._get_delta(name) + return int(delta[0]) + + def mean(self, name): + r"""Returns the mean of the scalars that were accumulated for the + given statistic between the last two calls to `update()`, or NaN if + no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0: + return float('nan') + return float(delta[1] / delta[0]) + + def std(self, name): + r"""Returns the standard deviation of the scalars that were + accumulated for the given statistic between the last two calls to + `update()`, or NaN if no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): + return float('nan') + if int(delta[0]) == 1: + return float(0) + mean = float(delta[1] / delta[0]) + raw_var = float(delta[2] / delta[0]) + return np.sqrt(max(raw_var - np.square(mean), 0)) + + def as_dict(self): + r"""Returns the averages accumulated between the last two calls to + `update()` as an `dnnlib.EasyDict`. The contents are as follows: + + dnnlib.EasyDict( + NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), + ... + ) + """ + stats = dnnlib.EasyDict() + for name in self.names(): + stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) + return stats + + def __getitem__(self, name): + r"""Convenience getter. + `collector[name]` is a synonym for `collector.mean(name)`. + """ + return self.mean(name) + +#---------------------------------------------------------------------------- + +def _sync(names): + r"""Synchronize the global cumulative counters across devices and + processes. Called internally by `Collector.update()`. + """ + if len(names) == 0: + return [] + global _sync_called + _sync_called = True + + # Collect deltas within current rank. + deltas = [] + device = _sync_device if _sync_device is not None else torch.device('cpu') + for name in names: + delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) + for counter in _counters[name].values(): + delta.add_(counter.to(device)) + counter.copy_(torch.zeros_like(counter)) + deltas.append(delta) + deltas = torch.stack(deltas) + + # Sum deltas across ranks. + if _sync_device is not None: + torch.distributed.all_reduce(deltas) + + # Update cumulative values. + deltas = deltas.cpu() + for idx, name in enumerate(names): + if name not in _cumulative: + _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + _cumulative[name].add_(deltas[idx]) + + # Return name-value pairs. + return [(name, _cumulative[name]) for name in names] + +#---------------------------------------------------------------------------- diff --git a/train.py b/train.py new file mode 100644 index 0000000..1599bbe --- /dev/null +++ b/train.py @@ -0,0 +1,362 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Train a GAN using the techniques described in the paper +"Alias-Free Generative Adversarial Networks".""" + +import os +import click +import re +import json +import tempfile +import torch + +import dnnlib +from training import training_loop +from metrics import metric_main +from torch_utils import training_stats +from torch_utils import custom_ops + +#---------------------------------------------------------------------------- + +def subprocess_fn(rank, c, temp_dir): + dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True) + + # Init torch.distributed. + if c.num_gpus > 1: + init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) + if os.name == 'nt': + init_method = 'file:///' + init_file.replace('\\', '/') + torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus) + else: + init_method = f'file://{init_file}' + torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus) + + # Init torch_utils. + sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None + training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) + if rank != 0: + custom_ops.verbosity = 'none' + + # Execute training loop. + training_loop.training_loop(rank=rank, **c) + +#---------------------------------------------------------------------------- + +def launch_training(c, desc, outdir, dry_run): + dnnlib.util.Logger(should_flush=True) + + # Pick output directory. + prev_run_dirs = [] + if os.path.isdir(outdir): + prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))] + prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs] + prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None] + cur_run_id = max(prev_run_ids, default=-1) + 1 + c.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}') + assert not os.path.exists(c.run_dir) + + # Print options. + print() + print('Training options:') + print(json.dumps(c, indent=2)) + print() + print(f'Output directory: {c.run_dir}') + print(f'Number of GPUs: {c.num_gpus}') + print(f'Batch size: {c.batch_size} images') + print(f'Training duration: {c.total_kimg} kimg') + print(f'Dataset path: {c.training_set_kwargs.path}') + print(f'Dataset size: {c.training_set_kwargs.max_size} images') + print(f'Dataset resolution: {c.training_set_kwargs.resolution}') + print(f'Dataset labels: {c.training_set_kwargs.use_labels}') + print(f'Dataset x-flips: {c.training_set_kwargs.xflip}') + if 'patch' in c.G_kwargs.training_mode: + print(f'Patches path: {c.patch_kwargs.path}') + print(f'Patches size: {c.patch_kwargs.max_size} images') + print(f'Patches resolution: {c.patch_kwargs.resolution}') + print(f'Patches labels: {c.patch_kwargs.use_labels}') + print(f'Patches x-flips: {c.patch_kwargs.xflip}') + print() + + # Dry run? + if dry_run: + print('Dry run; exiting.') + return + + # Create output directory. + print('Creating output directory...') + os.makedirs(c.run_dir) + with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f: + json.dump(c, f, indent=2) + + # Launch processes. + print('Launching processes...') + torch.multiprocessing.set_start_method('spawn') + with tempfile.TemporaryDirectory() as temp_dir: + if c.num_gpus == 1: + subprocess_fn(rank=0, c=c, temp_dir=temp_dir) + else: + torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus) + +#---------------------------------------------------------------------------- + +def init_dataset_kwargs(data): + try: + dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False) + dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset. + try: + dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution. + except AssertionError: + print("Cannot determine default dataset resolution, will try to use specified arguments") + dataset_kwargs.resolution = None + dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels. + dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size. + return dataset_kwargs, dataset_obj.name + except IOError as err: + raise click.ClickException(f'--data: {err}') + +#---------------------------------------------------------------------------- + +def parse_comma_separated_list(s): + if isinstance(s, list): + return s + if s is None or s.lower() == 'none' or s == '': + return [] + return s.split(',') + +#---------------------------------------------------------------------------- + +@click.command() + +# Required. +@click.option('--outdir', help='Where to save the results', metavar='DIR', required=True) +@click.option('--cfg', help='Base configuration', type=click.Choice(['stylegan3-t', 'stylegan3-r', 'stylegan2']), required=True) +@click.option('--data', help='Training data', metavar='[ZIP|DIR]', type=str, required=True) +@click.option('--gpus', help='Number of GPUs to use', metavar='INT', type=click.IntRange(min=1), required=True) +@click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), required=True) +@click.option('--gamma', help='R1 regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), required=True) + +# Optional features. +@click.option('--cond', help='Train conditional model', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--mirror', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--aug', help='Augmentation mode', type=click.Choice(['noaug', 'ada', 'fixed']), default='ada', show_default=True) +@click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str) +@click.option('--freezed', help='Freeze first layers of D', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True) + +# Misc hyperparameters. +@click.option('--p', help='Probability for --aug=fixed', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.2, show_default=True) +@click.option('--target', help='Target value for --aug=ada', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.6, show_default=True) +@click.option('--batch-gpu', help='Limit batch size per GPU', metavar='INT', type=click.IntRange(min=1)) +@click.option('--cbase', help='Capacity multiplier', metavar='INT', type=click.IntRange(min=1), default=32768, show_default=True) +@click.option('--cmax', help='Max. feature maps', metavar='INT', type=click.IntRange(min=1), default=512, show_default=True) +@click.option('--glr', help='G learning rate [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0)) +@click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.002, show_default=True) +@click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1)) +@click.option('--mbstd-group', help='Minibatch std group size', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True) + +# Misc settings. +@click.option('--desc', help='String to include in result dir name', metavar='STR', type=str) +@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True) +@click.option('--kimg', help='Total training duration', metavar='KIMG', type=click.IntRange(min=1), default=25000, show_default=True) +@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=4, show_default=True) +@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=50, show_default=True) +@click.option('--seed', help='Random seed', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True) +@click.option('--fp32', help='Disable mixed-precision', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--nobench', help='Disable cuDNN benchmarking', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True) +@click.option('-n','--dry-run', help='Print training options and exit', is_flag=True) + +# additional base options +@click.option('--training_mode', help='generator training mode', type=click.Choice(['global', 'patch', 'global-360']), required=True) +@click.option('--data_resolution', help='LR dataset resolution (specify if images are not preprocessed to same size and square)', type=click.IntRange(min=0)) +@click.option('--random_crop', help='random crop image on LR dataset (specify if images are not preprocessed to same size and square)', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--data_max_size', help='LR dataset max number of images', type=click.IntRange(min=0)) +@click.option('--g_size', help='size of G (if different from dataset size)', type=click.IntRange(min=0)) + +# additional options for patch model +@click.option('--teacher', help='teacher checkpoint', metavar='[PATH|URL]', type=str) +@click.option('--teacher_lambda', help='teacher regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), default=1.0, show_default=True) +@click.option('--teacher_mode', help='teacher loss mode', type=click.Choice(['inverse', 'forward']), default='forward', show_default=True) +@click.option('--scale_anneal', help='scale annealing rate (-1 for no annealing)', metavar='FLOAT', type=click.FloatRange(min=-1), default=-1, show_default=True) +@click.option('--scale_min', help='minimum sampled scale (leave blank to use image native resolution)', metavar='FLOAT', type=click.FloatRange(min=0)) +@click.option('--scale_max', help='maximum sampled scale', metavar='FLOAT', type=click.FloatRange(min=0), default=1.0, show_default=True) +@click.option('--base_probability', help='probability to sample from LR dataset with identity transform', metavar='FLOAT', type=click.FloatRange(min=0), default=0.5, show_default=True) +@click.option('--data_hr', help='HR patch dataset path', metavar='[ZIP|DIR]', type=str) +@click.option('--patch_crop', help='perform random cropping on non-square images (on patch dataset)', metavar='BOOL', type=bool, default=False, show_default=True) +@click.option('--data_hr_max_size', help='patch dataset max number of images', type=click.IntRange(min=0)) +@click.option('--scale_mapping_min', help='normalization minimum for scale mapping branch (size = g_size*scale_mapping_min)', type=click.IntRange(min=0)) +@click.option('--scale_mapping_max', help='normalization maximum for scale mapping branch (size = g_size*scale_mapping_max)', type=click.IntRange(min=0)) +@click.option('--scale_mapping_norm', help='normalization type for scale mapping branch', type=click.Choice(['positive', 'zerocentered']), default='positive') + +# additional options for 360 model +@click.option('--fov', help='fov for one frame in the 360 model', type=click.IntRange(min=0), default=60, show_default=True) + +def main(**kwargs): + + # Initialize config. + opts = dnnlib.EasyDict(kwargs) # Command line arguments. + c = dnnlib.EasyDict() # Main config dict. + c.G_kwargs = dnnlib.EasyDict(class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict()) + c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict()) + c.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8) + c.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8) + c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss') + c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2) + + # Training set. + c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data) + if opts.cond and not c.training_set_kwargs.use_labels: + raise click.ClickException('--cond=True requires labels specified in dataset.json') + c.training_set_kwargs.use_labels = opts.cond + c.training_set_kwargs.xflip = opts.mirror + if opts.data_max_size: + c.training_set_kwargs.max_size = opts.max_size + if opts.data_resolution: + if c.training_set_kwargs.resolution != opts.data_resolution: + print("using specified data resolution %d rather than default" % (opts.data_resolution)) + c.training_set_kwargs.resolution = opts.data_resolution + c.training_set_kwargs.crop_image = opts.random_crop + # by this point, resolution should be determined + # either from init_dataset function or opts.data_resolution + assert(c.training_set_kwargs.resolution is not None) + + # set up training mode + training_mode = c.G_kwargs.training_mode = opts.training_mode + # set up generator size + if opts.g_size is not None: + assert(opts.g_size == c.training_set_kwargs.resolution) + else: + opts.g_size = c.training_set_kwargs.resolution + if 'patch' in training_mode: + # patch dataset kwargs + patch_kwargs = dnnlib.EasyDict( + class_name='training.dataset.ImagePatchDataset', + path=opts.data_hr, resolution=opts.g_size, + scale_min=opts.scale_min, scale_max=opts.scale_max, + scale_anneal=opts.scale_anneal, random_crop=opts.patch_crop, + use_labels=True, max_size=None, xflip=False) + patch_obj = dnnlib.util.construct_class_by_name(**patch_kwargs) # gets initial args + patch_name = patch_obj.name + patch_kwargs.resolution = patch_obj.resolution # Be explicit about resolution. + patch_kwargs.use_labels = patch_obj.has_labels # Be explicit about labels. + patch_kwargs.max_size = len(patch_obj) # Be explicit about dataset size. + c.patch_kwargs = patch_kwargs + c.patch_kwargs.use_labels = opts.cond + c.patch_kwargs.xflip = opts.mirror + if opts.data_hr_max_size: + c.patch_kwargs.max_size = opts.data_hr_max_size + # added G_kwargs + c.G_kwargs.scale_mapping_kwargs = dnnlib.EasyDict( + scale_mapping_min = opts.scale_mapping_min, + scale_mapping_max = opts.scale_mapping_max, + scale_mapping_norm = opts.scale_mapping_norm + ) + # added training options + c.added_kwargs = dnnlib.EasyDict( + img_size=opts.g_size, + teacher=opts.teacher, + teacher_lambda=opts.teacher_lambda, + teacher_mode=opts.teacher_mode, + scale_min=opts.scale_min, + scale_max=opts.scale_max, + scale_anneal=opts.scale_anneal, + base_probability=opts.base_probability, + ) + elif '360' in training_mode: + c.G_kwargs.fov = opts.fov + + # Hyperparameters & settings. + c.num_gpus = opts.gpus + c.batch_size = opts.batch + c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus + c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase + c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax + c.G_kwargs.mapping_kwargs.num_layers = (8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth + c.D_kwargs.block_kwargs.freeze_layers = opts.freezed + c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group + c.loss_kwargs.r1_gamma = opts.gamma + c.G_opt_kwargs.lr = (0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr + c.D_opt_kwargs.lr = opts.dlr + c.metrics = opts.metrics + c.total_kimg = opts.kimg + c.kimg_per_tick = opts.tick + c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap + c.random_seed = c.training_set_kwargs.random_seed = opts.seed + if 'patch' in training_mode: + c.patch_kwargs.random_seed = opts.seed + c.data_loader_kwargs.num_workers = opts.workers + + # Sanity checks. + if c.batch_size % c.num_gpus != 0: + raise click.ClickException('--batch must be a multiple of --gpus') + if c.batch_size % (c.num_gpus * c.batch_gpu) != 0: + raise click.ClickException('--batch must be a multiple of --gpus times --batch-gpu') + if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size: + raise click.ClickException('--batch-gpu cannot be smaller than --mbstd') + if any(not metric_main.is_valid_metric(metric) for metric in c.metrics): + raise click.ClickException('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics())) + + # Base configuration. + c.ema_kimg = c.batch_size * 10 / 32 + if opts.cfg == 'stylegan2': + c.G_kwargs.class_name = 'training.networks_stylegan2.Generator' + c.loss_kwargs.style_mixing_prob = 0.9 # Enable style mixing regularization. + c.loss_kwargs.pl_weight = 2 # Enable path length regularization. + c.G_reg_interval = 4 # Enable lazy regularization for G. + c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions. + c.loss_kwargs.pl_no_weight_grad = True # Speed up path length regularization by skipping gradient computation wrt. conv2d weights. + else: + c.G_kwargs.class_name = 'training.networks_stylegan3.Generator' + c.G_kwargs.magnitude_ema_beta = 0.5 ** (c.batch_size / (20 * 1e3)) + if opts.cfg == 'stylegan3-r': + c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions. + c.G_kwargs.channel_base *= 2 # Double the number of feature maps. + c.G_kwargs.channel_max *= 2 + c.G_kwargs.use_radial_filters = True # Use radially symmetric downsampling filters. + c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator. + c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Fade out the blur during the first N kimg. + + # Augmentation. + if opts.aug != 'noaug': + c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1) + if opts.aug == 'ada': + c.ada_target = opts.target + if opts.aug == 'fixed': + c.augment_p = opts.p + + # Resume. + if opts.resume is not None: + c.resume_pkl = opts.resume + + if opts.teacher is not None or opts.resume is not None: + # disable rampups for finetuning or resuming models + c.ada_kimg = 100 # Make ADA react faster at the beginning. + c.ema_rampup = None # Disable EMA rampup. + c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup. + + # Performance-related toggles. + if opts.fp32: + c.G_kwargs.num_fp16_res = c.D_kwargs.num_fp16_res = 0 + c.G_kwargs.conv_clamp = c.D_kwargs.conv_clamp = None + if opts.nobench: + c.cudnn_benchmark = False + + # Description string. + desc = f'{opts.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}' + if opts.desc is not None: + desc += f'-{opts.desc}' + + # Launch. + launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run) + +#---------------------------------------------------------------------------- + +if __name__ == "__main__": + main() # pylint: disable=no-value-for-parameter + +#---------------------------------------------------------------------------- diff --git a/train.sh b/train.sh new file mode 100644 index 0000000..55dea98 --- /dev/null +++ b/train.sh @@ -0,0 +1,26 @@ +# example run: stage 1 global pretraining +python train.py --cfg=stylegan3-t --gpus=4 --batch=32 --gamma=2 \ + --mirror=1 --aug=noaug --kimg 25000 --batch-gpu 4 \ + --outdir training-runs/bird-global-training \ + --data ./datasets/flickr_birds_LR.zip \ + --training_mode=global --g_size 256 --data_resolution 256 --random_crop=True + +# example run: stage 2 patch training +# the teacher can be any sgan3 pretrained model, e.g. from the stylegan3 official repository also works +python train.py --cfg=stylegan3-t --gpus=4 --batch=32 --gamma=2 \ + --mirror=1 --aug=noaug --kimg 25000 --batch-gpu 4 \ + --outdir training-runs/bird-patch-training \ + --data ./datasets/flickr_birds_LR.zip \ + --training_mode=patch --g_size 256 --data_resolution 256 --random_crop=True \ + --teacher training-runs/bird-global-training/00000-stylegan3-t-flickr_birds_LR-gpus4-batch32-gamma2/network-snapshot-XXXXXX.pkl \ + --data_hr ./datasets/flickr_birds_HR.zip \ + --metrics none --teacher_lambda 5.0 --teacher_mode inverse --scale_max 0.5 --scale_anneal -1 \ + --scale_mapping_min 1 --scale_mapping_max 8 --patch_crop=True + +# example run: 360 training +python train.py \ + --cfg=stylegan3-t --gpus=4 --batch=32 --gamma=2 \ + --mirror=1 --aug=noaug --kimg 25000 --batch-gpu 8 \ + --outdir=training-runs/mountain-360 \ + --data ./datasets/mountain256_jpeg95/train \ + --training_mode=global-360 --data_resolution 256 --fov 60 --random_crop=True diff --git a/training/__init__.py b/training/__init__.py new file mode 100644 index 0000000..939e7c6 --- /dev/null +++ b/training/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/training/augment.py b/training/augment.py new file mode 100644 index 0000000..d68e35c --- /dev/null +++ b/training/augment.py @@ -0,0 +1,436 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Augmentation pipeline from the paper +"Training Generative Adversarial Networks with Limited Data". +Matches the original implementation by Karras et al. at +https://github.com/NVlabs/stylegan2-ada/blob/main/training/augment.py""" + +import numpy as np +import scipy.signal +import torch +from torch_utils import persistence +from torch_utils import misc +from torch_utils.ops import upfirdn2d +from torch_utils.ops import grid_sample_gradfix +from torch_utils.ops import conv2d_gradfix + +#---------------------------------------------------------------------------- +# Coefficients of various wavelet decomposition low-pass filters. + +wavelets = { + 'haar': [0.7071067811865476, 0.7071067811865476], + 'db1': [0.7071067811865476, 0.7071067811865476], + 'db2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025], + 'db3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569], + 'db4': [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523], + 'db5': [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125], + 'db6': [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017], + 'db7': [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236], + 'db8': [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161], + 'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025], + 'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569], + 'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427], + 'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728], + 'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148], + 'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255], + 'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609], +} + +#---------------------------------------------------------------------------- +# Helpers for constructing transformation matrices. + +def matrix(*rows, device=None): + assert all(len(row) == len(rows[0]) for row in rows) + elems = [x for row in rows for x in row] + ref = [x for x in elems if isinstance(x, torch.Tensor)] + if len(ref) == 0: + return misc.constant(np.asarray(rows), device=device) + assert device is None or device == ref[0].device + elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems] + return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1)) + +def translate2d(tx, ty, **kwargs): + return matrix( + [1, 0, tx], + [0, 1, ty], + [0, 0, 1], + **kwargs) + +def translate3d(tx, ty, tz, **kwargs): + return matrix( + [1, 0, 0, tx], + [0, 1, 0, ty], + [0, 0, 1, tz], + [0, 0, 0, 1], + **kwargs) + +def scale2d(sx, sy, **kwargs): + return matrix( + [sx, 0, 0], + [0, sy, 0], + [0, 0, 1], + **kwargs) + +def scale3d(sx, sy, sz, **kwargs): + return matrix( + [sx, 0, 0, 0], + [0, sy, 0, 0], + [0, 0, sz, 0], + [0, 0, 0, 1], + **kwargs) + +def rotate2d(theta, **kwargs): + return matrix( + [torch.cos(theta), torch.sin(-theta), 0], + [torch.sin(theta), torch.cos(theta), 0], + [0, 0, 1], + **kwargs) + +def rotate3d(v, theta, **kwargs): + vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2] + s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c + return matrix( + [vx*vx*cc+c, vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0], + [vy*vx*cc+vz*s, vy*vy*cc+c, vy*vz*cc-vx*s, 0], + [vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c, 0], + [0, 0, 0, 1], + **kwargs) + +def translate2d_inv(tx, ty, **kwargs): + return translate2d(-tx, -ty, **kwargs) + +def scale2d_inv(sx, sy, **kwargs): + return scale2d(1 / sx, 1 / sy, **kwargs) + +def rotate2d_inv(theta, **kwargs): + return rotate2d(-theta, **kwargs) + +#---------------------------------------------------------------------------- +# Versatile image augmentation pipeline from the paper +# "Training Generative Adversarial Networks with Limited Data". +# +# All augmentations are disabled by default; individual augmentations can +# be enabled by setting their probability multipliers to 1. + +@persistence.persistent_class +class AugmentPipe(torch.nn.Module): + def __init__(self, + xflip=0, rotate90=0, xint=0, xint_max=0.125, + scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125, + brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1, + imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1, + noise=0, cutout=0, noise_std=0.1, cutout_size=0.5, + ): + super().__init__() + self.register_buffer('p', torch.ones([])) # Overall multiplier for augmentation probability. + + # Pixel blitting. + self.xflip = float(xflip) # Probability multiplier for x-flip. + self.rotate90 = float(rotate90) # Probability multiplier for 90 degree rotations. + self.xint = float(xint) # Probability multiplier for integer translation. + self.xint_max = float(xint_max) # Range of integer translation, relative to image dimensions. + + # General geometric transformations. + self.scale = float(scale) # Probability multiplier for isotropic scaling. + self.rotate = float(rotate) # Probability multiplier for arbitrary rotation. + self.aniso = float(aniso) # Probability multiplier for anisotropic scaling. + self.xfrac = float(xfrac) # Probability multiplier for fractional translation. + self.scale_std = float(scale_std) # Log2 standard deviation of isotropic scaling. + self.rotate_max = float(rotate_max) # Range of arbitrary rotation, 1 = full circle. + self.aniso_std = float(aniso_std) # Log2 standard deviation of anisotropic scaling. + self.xfrac_std = float(xfrac_std) # Standard deviation of frational translation, relative to image dimensions. + + # Color transformations. + self.brightness = float(brightness) # Probability multiplier for brightness. + self.contrast = float(contrast) # Probability multiplier for contrast. + self.lumaflip = float(lumaflip) # Probability multiplier for luma flip. + self.hue = float(hue) # Probability multiplier for hue rotation. + self.saturation = float(saturation) # Probability multiplier for saturation. + self.brightness_std = float(brightness_std) # Standard deviation of brightness. + self.contrast_std = float(contrast_std) # Log2 standard deviation of contrast. + self.hue_max = float(hue_max) # Range of hue rotation, 1 = full circle. + self.saturation_std = float(saturation_std) # Log2 standard deviation of saturation. + + # Image-space filtering. + self.imgfilter = float(imgfilter) # Probability multiplier for image-space filtering. + self.imgfilter_bands = list(imgfilter_bands) # Probability multipliers for individual frequency bands. + self.imgfilter_std = float(imgfilter_std) # Log2 standard deviation of image-space filter amplification. + + # Image-space corruptions. + self.noise = float(noise) # Probability multiplier for additive RGB noise. + self.cutout = float(cutout) # Probability multiplier for cutout. + self.noise_std = float(noise_std) # Standard deviation of additive RGB noise. + self.cutout_size = float(cutout_size) # Size of the cutout rectangle, relative to image dimensions. + + # Setup orthogonal lowpass filter for geometric augmentations. + self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6'])) + + # Construct filter bank for image-space filtering. + Hz_lo = np.asarray(wavelets['sym2']) # H(z) + Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z) + Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2 + Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2 + Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i) + for i in range(1, Hz_fbank.shape[0]): + Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1] + Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2]) + Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2 + self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32)) + + def forward(self, images, debug_percentile=None): + assert isinstance(images, torch.Tensor) and images.ndim == 4 + batch_size, num_channels, height, width = images.shape + device = images.device + if debug_percentile is not None: + debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device) + + # ------------------------------------- + # Select parameters for pixel blitting. + # ------------------------------------- + + # Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in + I_3 = torch.eye(3, device=device) + G_inv = I_3 + + # Apply x-flip with probability (xflip * strength). + if self.xflip > 0: + i = torch.floor(torch.rand([batch_size], device=device) * 2) + i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i)) + if debug_percentile is not None: + i = torch.full_like(i, torch.floor(debug_percentile * 2)) + G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1) + + # Apply 90 degree rotations with probability (rotate90 * strength). + if self.rotate90 > 0: + i = torch.floor(torch.rand([batch_size], device=device) * 4) + i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i)) + if debug_percentile is not None: + i = torch.full_like(i, torch.floor(debug_percentile * 4)) + G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i) + + # Apply integer translation with probability (xint * strength). + if self.xint > 0: + t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max + t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t)) + if debug_percentile is not None: + t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max) + G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height)) + + # -------------------------------------------------------- + # Select parameters for general geometric transformations. + # -------------------------------------------------------- + + # Apply isotropic scaling with probability (scale * strength). + if self.scale > 0: + s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std) + s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s)) + if debug_percentile is not None: + s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std)) + G_inv = G_inv @ scale2d_inv(s, s) + + # Apply pre-rotation with probability p_rot. + p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p + if self.rotate > 0: + theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max + theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) + if debug_percentile is not None: + theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max) + G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling. + + # Apply anisotropic scaling with probability (aniso * strength). + if self.aniso > 0: + s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std) + s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s)) + if debug_percentile is not None: + s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std)) + G_inv = G_inv @ scale2d_inv(s, 1 / s) + + # Apply post-rotation with probability p_rot. + if self.rotate > 0: + theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max + theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) + if debug_percentile is not None: + theta = torch.zeros_like(theta) + G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling. + + # Apply fractional translation with probability (xfrac * strength). + if self.xfrac > 0: + t = torch.randn([batch_size, 2], device=device) * self.xfrac_std + t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t)) + if debug_percentile is not None: + t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std) + G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height) + + # ---------------------------------- + # Execute geometric transformations. + # ---------------------------------- + + # Execute if the transform is not identity. + if G_inv is not I_3: + + # Calculate padding. + cx = (width - 1) / 2 + cy = (height - 1) / 2 + cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz] + cp = G_inv @ cp.t() # [batch, xyz, idx] + Hz_pad = self.Hz_geom.shape[0] // 4 + margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx] + margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1] + margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device) + margin = margin.max(misc.constant([0, 0] * 2, device=device)) + margin = margin.min(misc.constant([width-1, height-1] * 2, device=device)) + mx0, my0, mx1, my1 = margin.ceil().to(torch.int32) + + # Pad image and adjust origin. + images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect') + G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv + + # Upsample. + images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2) + G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device) + G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device) + + # Execute transformation. + shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2] + G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device) + grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False) + images = grid_sample_gradfix.grid_sample(images, grid) + + # Downsample and crop. + images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True) + + # -------------------------------------------- + # Select parameters for color transformations. + # -------------------------------------------- + + # Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out + I_4 = torch.eye(4, device=device) + C = I_4 + + # Apply brightness with probability (brightness * strength). + if self.brightness > 0: + b = torch.randn([batch_size], device=device) * self.brightness_std + b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b)) + if debug_percentile is not None: + b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std) + C = translate3d(b, b, b) @ C + + # Apply contrast with probability (contrast * strength). + if self.contrast > 0: + c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std) + c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c)) + if debug_percentile is not None: + c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std)) + C = scale3d(c, c, c) @ C + + # Apply luma flip with probability (lumaflip * strength). + v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis. + if self.lumaflip > 0: + i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2) + i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i)) + if debug_percentile is not None: + i = torch.full_like(i, torch.floor(debug_percentile * 2)) + C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection. + + # Apply hue rotation with probability (hue * strength). + if self.hue > 0 and num_channels > 1: + theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max + theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta)) + if debug_percentile is not None: + theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max) + C = rotate3d(v, theta) @ C # Rotate around v. + + # Apply saturation with probability (saturation * strength). + if self.saturation > 0 and num_channels > 1: + s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std) + s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s)) + if debug_percentile is not None: + s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std)) + C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C + + # ------------------------------ + # Execute color transformations. + # ------------------------------ + + # Execute if the transform is not identity. + if C is not I_4: + images = images.reshape([batch_size, num_channels, height * width]) + if num_channels == 3: + images = C[:, :3, :3] @ images + C[:, :3, 3:] + elif num_channels == 1: + C = C[:, :3, :].mean(dim=1, keepdims=True) + images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:] + else: + raise ValueError('Image must be RGB (3 channels) or L (1 channel)') + images = images.reshape([batch_size, num_channels, height, width]) + + # ---------------------- + # Image-space filtering. + # ---------------------- + + if self.imgfilter > 0: + num_bands = self.Hz_fbank.shape[0] + assert len(self.imgfilter_bands) == num_bands + expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f). + + # Apply amplification for each band with probability (imgfilter * strength * band_strength). + g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity). + for i, band_strength in enumerate(self.imgfilter_bands): + t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std) + t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i)) + if debug_percentile is not None: + t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i) + t = torch.ones([batch_size, num_bands], device=device) # Temporary gain vector. + t[:, i] = t_i # Replace i'th element. + t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power. + g = g * t # Accumulate into global gain. + + # Construct combined amplification filter. + Hz_prime = g @ self.Hz_fbank # [batch, tap] + Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1]) # [batch, channels, tap] + Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap] + + # Apply filter. + p = self.Hz_fbank.shape[1] // 2 + images = images.reshape([1, batch_size * num_channels, height, width]) + images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect') + images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels) + images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels) + images = images.reshape([batch_size, num_channels, height, width]) + + # ------------------------ + # Image-space corruptions. + # ------------------------ + + # Apply additive RGB noise with probability (noise * strength). + if self.noise > 0: + sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std + sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma)) + if debug_percentile is not None: + sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std) + images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma + + # Apply cutout with probability (cutout * strength). + if self.cutout > 0: + size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device) + size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size)) + center = torch.rand([batch_size, 2, 1, 1, 1], device=device) + if debug_percentile is not None: + size = torch.full_like(size, self.cutout_size) + center = torch.full_like(center, debug_percentile) + coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1]) + coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1]) + mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2) + mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2) + mask = torch.logical_or(mask_x, mask_y).to(torch.float32) + images = images * mask + + return images + +#---------------------------------------------------------------------------- diff --git a/training/dataset.py b/training/dataset.py new file mode 100644 index 0000000..6827afc --- /dev/null +++ b/training/dataset.py @@ -0,0 +1,364 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Streaming images and labels from datasets created with dataset_tool.py.""" + +import os +import numpy as np +import zipfile +import PIL.Image +import json +import torch +import dnnlib + +pyspng = None # disable pyspng for image resizing on load +# https://stackoverflow.com/questions/51152059/pillow-in-python-wont-let-me-open-image-exceeds-limit +PIL.Image.MAX_IMAGE_PIXELS = 933120000 +from util import patch_util +import random + + +#---------------------------------------------------------------------------- + +class Dataset(torch.utils.data.Dataset): + def __init__(self, + name, # Name of the dataset. + raw_shape, # Shape of the raw image data (NCHW). + max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. + use_labels = False, # Enable conditioning labels? False = label dimension is zero. + xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. + random_seed = 0, # Random seed to use when applying max_size. + ): + self._name = name + self._raw_shape = list(raw_shape) + self._use_labels = use_labels + self._raw_labels = None + self._label_shape = None + + # Apply max_size. + self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64) + if (max_size is not None) and (self._raw_idx.size > max_size): + np.random.RandomState(random_seed).shuffle(self._raw_idx) + self._raw_idx = np.sort(self._raw_idx[:max_size]) + + # Apply xflip. + self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) + if xflip: + self._raw_idx = np.tile(self._raw_idx, 2) + self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) + + def _get_raw_labels(self): + if self._raw_labels is None: + self._raw_labels = self._load_raw_labels() if self._use_labels else None + if self._raw_labels is None: + self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) + assert isinstance(self._raw_labels, np.ndarray) + assert self._raw_labels.shape[0] == self._raw_shape[0] + assert self._raw_labels.dtype in [np.float32, np.int64] + if self._raw_labels.dtype == np.int64: + assert self._raw_labels.ndim == 1 + assert np.all(self._raw_labels >= 0) + return self._raw_labels + + def close(self): # to be overridden by subclass + pass + + def _load_raw_image(self, raw_idx): # to be overridden by subclass + raise NotImplementedError + + def _load_raw_labels(self): # to be overridden by subclass + raise NotImplementedError + + def __getstate__(self): + return dict(self.__dict__, _raw_labels=None) + + def __del__(self): + try: + self.close() + except: + pass + + def __len__(self): + return self._raw_idx.size + + def __getitem__(self, idx): + if not self.is_patch: # full image loader + image = self._load_raw_image(self._raw_idx[idx]) + assert isinstance(image, np.ndarray) + assert list(image.shape) == self.image_shape + assert image.dtype == np.uint8 + if self._xflip[idx]: + assert image.ndim == 3 # CHW + image = image[:, :, ::-1] + return image.copy(), self.get_label(idx) + else: # image patch loader + # handle xflips when loading the image + data = self._load_raw_image(self._raw_idx[idx], self._xflip[idx]) + assert isinstance(data, dict) + assert list(data['image'].shape) == self.image_shape + assert data['image'].dtype == np.uint8 + data['image'] = data['image'].copy() + return data, self.get_label(idx) + + def get_label(self, idx): + label = self._get_raw_labels()[self._raw_idx[idx]] + if label.dtype == np.int64: + onehot = np.zeros(self.label_shape, dtype=np.float32) + onehot[label] = 1 + label = onehot + return label.copy() + + def get_details(self, idx): + d = dnnlib.EasyDict() + d.raw_idx = int(self._raw_idx[idx]) + d.xflip = (int(self._xflip[idx]) != 0) + d.raw_label = self._get_raw_labels()[d.raw_idx].copy() + return d + + @property + def name(self): + return self._name + + @property + def image_shape(self): + return list(self._raw_shape[1:]) + + @property + def num_channels(self): + assert len(self.image_shape) == 3 # CHW + return self.image_shape[0] + + @property + def resolution(self): + assert len(self.image_shape) == 3 # CHW + assert self.image_shape[1] == self.image_shape[2] + return self.image_shape[1] + + @property + def label_shape(self): + if self._label_shape is None: + raw_labels = self._get_raw_labels() + if raw_labels.dtype == np.int64: + self._label_shape = [int(np.max(raw_labels)) + 1] + else: + self._label_shape = raw_labels.shape[1:] + return list(self._label_shape) + + @property + def label_dim(self): + assert len(self.label_shape) == 1 + return self.label_shape[0] + + @property + def has_labels(self): + return any(x != 0 for x in self.label_shape) + + @property + def has_onehot_labels(self): + return self._get_raw_labels().dtype == np.int64 + +#---------------------------------------------------------------------------- + +class BaseImageDataset(Dataset): + def __init__(self, + path, # Path to directory or zip. + resolution = None, # Ensure specific resolution, None = highest available. + **super_kwargs, # Additional arguments for the Dataset base class. + ): + + self._path = path + self._zipfile = None + + if os.path.isdir(self._path): + self._type = 'dir' + if os.path.isfile(self._path + '_cache.txt'): + # use cache file if it exists + with open(self._path + '_cache.txt') as cache: + self._all_fnames = set([line.strip() for line in cache]) + else: + print("Walking dataset...") + self._all_fnames = [os.path.relpath(os.path.join(root, fname), start=self._path) + for root, _dirs, files in os.walk(self._path, followlinks=True) for fname in files] + with open(self._path + '_cache.txt', 'w') as cache: + [cache.write("%s\n" % fname) for fname in self._all_fnames] + self._all_fnames = set(self._all_fnames) + print("Done walking") + elif self._file_ext(self._path) == '.zip': + self._type = 'zip' + self._all_fnames = set(self._get_zipfile().namelist()) + else: + raise IOError('Path must point to a directory or zip') + + PIL.Image.init() + self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) + if len(self._image_fnames) == 0: + raise IOError('No image files found in the specified path') + + name = os.path.splitext(os.path.basename(self._path))[0] + if resolution is not None: + raw_shape = [len(self._image_fnames)] + [3, resolution, resolution] + else: + # do not resize it to determine initial shape (will fail if images not square) + raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0, resize=False).shape) + + super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) + + @staticmethod + def _file_ext(fname): + return os.path.splitext(fname)[1].lower() + + def _get_zipfile(self): + assert self._type == 'zip' + if self._zipfile is None: + self._zipfile = zipfile.ZipFile(self._path) + return self._zipfile + + def _open_file(self, fname): + if self._type == 'dir': + return open(os.path.join(self._path, fname), 'rb') + if self._type == 'zip': + return self._get_zipfile().open(fname, 'r') + return None + + def close(self): + try: + if self._zipfile is not None: + self._zipfile.close() + finally: + self._zipfile = None + + def __getstate__(self): + return dict(super().__getstate__(), _zipfile=None) + + def _load_raw_labels(self): + fname = 'dataset.json' + if fname not in self._all_fnames: + return None + with self._open_file(fname) as f: + labels = json.load(f)['labels'] + if labels is None: + return None + labels = dict(labels) + labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] + labels = np.array(labels) + labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) + return labels + + +class ImageFolderDataset(BaseImageDataset): + def __init__(self, + path, # Path to directory or zip. + resolution = None, # Ensure specific resolution, None = highest available. + crop_image = False, # default: assumes inputs are square images, if True it will perform a random crop + **super_kwargs, # Additional arguments for the Dataset base class. + ): + self.crop_image = crop_image + self.is_patch = False + super().__init__(path=path, resolution=resolution, **super_kwargs) + + def _load_raw_image(self, raw_idx, resize=True): + fname = self._image_fnames[raw_idx] + with self._open_file(fname) as f: + if pyspng is not None and self._file_ext(fname) == '.png': + image = pyspng.load(f.read()) + else: + image = PIL.Image.open(f).convert('RGB') + w, h = image.size + if self.crop_image and w != h: + # perform random crop if needed + min_size = min(w, h) + if w == min_size: + x_start = 0 + y_start = random.randint(0, h - min_size) + else: + x_start = random.randint(0, w - min_size) + y_start = 0 + image = image.crop((x_start, y_start, x_start+min_size, y_start+min_size)) + if resize: + # at this point it should be square + assert(image.size[0] == image.size[1]) + target_size = tuple(self.image_shape[1:]) + if image.size != target_size: + # it should only downsize, but there are a small number + # of images in the datasets that are a few pixels + # smaller than 256, so allow a small leeway + assert(target_size[-1] < image.size[-1] + 10) + image = image.resize(target_size, PIL.Image.ANTIALIAS) + image = np.array(image) + if image.ndim == 2: + image = image[:, :, np.newaxis] # HW => HWC + image = image.transpose(2, 0, 1) # HWC => CHW + return image + +class ImagePatchDataset(BaseImageDataset): + def __init__(self, + path, # Path to directory or zip. + resolution, # patch size + scale_min, # minimum scale of the patches (largest image size) + scale_max, # maximum scale of the patches (smallest image size) + scale_anneal=-1, # annealing rate + random_crop=True, # add random crop for non-square images + **super_kwargs, # Additional arguments for the Dataset base class. + ): + assert(resolution is not None) # patch resolution must be specified + + # annealing not implemented, need to update iteration counter and + # adjust counter when resuming training + assert(scale_anneal == -1) + + # crop sampler + self.patch_size = resolution + self.random_crop = random_crop + self.sampler = patch_util.PatchSampler( + patch_size=self.patch_size, scale_anneal=scale_anneal, + min_scale=scale_min, max_scale=scale_max) + self.is_patch = True + + super().__init__(path=path, resolution=resolution, **super_kwargs) + + def _load_raw_image(self, raw_idx, is_flipped): + fname = self._image_fnames[raw_idx] + with self._open_file(fname) as f: + if pyspng is not None and self._file_ext(fname) == '.png': + image = pyspng.load(f.read()) + else: + image = PIL.Image.open(f).convert('RGB') + + # first, flip image if necessary + if is_flipped: + image = image.transpose(PIL.Image.FLIP_LEFT_RIGHT) + + # add random crop if necessary + if self.random_crop: + w, h = image.size + min_size = min(w, h) + x_start = random.randint(0, max(0, w - min_size)) + y_start = random.randint(0, max(0, h - min_size)) + image = image.crop((x_start, y_start, x_start+min_size, y_start+min_size)) + else: + # otherwise, center crop + w, h = image.size + min_size = min(w, h) + if w != h: + if w == min_size: + x_start = 0 + y_start = (h - min_size) // 2 + else: + x_start = (w - min_size) // 2 + y_start = 0 + image = image.crop((x_start, y_start, x_start+min_size, y_start+min_size)) + + # sample the resize and crop parameters + crop, params = self.sampler.sample_patch(image) + image = np.asarray(crop) + image = image.transpose(2, 0, 1) # HWC => CHW + data = { + 'image': image, + 'params': params, + } + return data diff --git a/training/loss.py b/training/loss.py new file mode 100644 index 0000000..9355562 --- /dev/null +++ b/training/loss.py @@ -0,0 +1,237 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Loss functions.""" + +import numpy as np +import torch +from torch_utils import training_stats +from torch_utils.ops import conv2d_gradfix +from torch_utils.ops import upfirdn2d + +# added imports +from metrics import equivariance +from util import losses, util, patch_util +import random + +#---------------------------------------------------------------------------- + +class Loss: + def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): # to be overridden by subclass + raise NotImplementedError() + +#---------------------------------------------------------------------------- + +def apply_affine_batch(img, transform): + # hacky .. apply affine transformation with cuda kernel in batch form + crops = [] + masks = [] + for i, t in zip(img, transform): + crop, mask = equivariance.apply_affine_transformation( + i[None], t.inverse()) + crops.append(crop) + masks.append(mask) + crops = torch.cat(crops, dim=0) + masks = torch.cat(masks, dim=0) + return crops, masks + +class StyleGAN2Loss(Loss): + def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, + pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, + blur_fade_kimg=0, teacher=None, added_kwargs=None): + super().__init__() + self.device = device + self.G = G + self.D = D + self.augment_pipe = augment_pipe + self.r1_gamma = r1_gamma + self.style_mixing_prob = style_mixing_prob + self.pl_weight = pl_weight + self.pl_batch_shrink = pl_batch_shrink + self.pl_decay = pl_decay + self.pl_no_weight_grad = pl_no_weight_grad + self.pl_mean = torch.zeros([], device=device) + self.blur_init_sigma = blur_init_sigma + self.blur_fade_kimg = blur_fade_kimg + + self.teacher = teacher + self.added_kwargs = added_kwargs + self.training_mode = self.G.training_mode + if self.teacher is not None: + self.loss_l1 = losses.Masked_L1_Loss().to(device) + self.loss_lpips = losses.Masked_LPIPS_Loss(net='alex', device=device) + util.set_requires_grad(False, self.loss_lpips) + util.set_requires_grad(False, self.teacher) + + def style_mix(self, z, c, ws): + if self.style_mixing_prob > 0: + with torch.autograd.profiler.record_function('style_mixing'): + cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) + cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) + ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] + return ws + + def run_G(self, z, c, transform, update_emas=False): + mapped_scale = None + crop_fn = None + if 'patch' in self.training_mode: + ws = self.G.mapping(z, c, update_emas=update_emas) + scale, mapped_scale = patch_util.compute_scale_inputs(self.G, ws, transform) + ws = self.style_mix(z, c, ws) + img = self.G.synthesis(ws, mapped_scale=mapped_scale, transform=transform, update_emas=update_emas) + elif '360' in self.training_mode: + ws = self.G.mapping(z, c, update_emas=update_emas) + ws = self.style_mix(z, c, ws) + input_layer = self.G.synthesis.input + crop_start = random.randint(0, 360 // input_layer.fov * input_layer.frame_size[0] - 1) + crop_fn = lambda grid : grid[:, :, crop_start:crop_start+input_layer.size[0], :] + img_base = self.G.synthesis(ws, crop_fn=crop_fn, update_emas=update_emas) + crop_shift = crop_start + input_layer.frame_size[0] + # generate shifted frame for cross-frame discriminator + crop_fn_shift = lambda grid : grid[:, :, crop_shift:crop_shift+input_layer.size[0], :] + img_shifted = self.G.synthesis(ws, crop_fn=crop_fn_shift, update_emas=update_emas) + img_splice = torch.cat([img_base, img_shifted], dim=3) + img_size = img_base.shape[-1] + splice_start = random.randint(0, img_size) + img = img_splice[:, :, :, splice_start:splice_start+img_size] + elif 'global' in self.training_mode: + ws = self.G.mapping(z, c, update_emas=update_emas) + ws = self.style_mix(z, c, ws) + assert(transform is None) + img = self.G.synthesis(ws, transform=transform, update_emas=update_emas) + return img, ws + + def run_D(self, img, c, blur_sigma=0, update_emas=False): + blur_size = np.floor(blur_sigma * 3) + if blur_size > 0: + with torch.autograd.profiler.record_function('blur'): + f = torch.arange(-blur_size, blur_size + 1, device=img.device).div(blur_sigma).square().neg().exp2() + img = upfirdn2d.filter2d(img, f / f.sum()) + if self.augment_pipe is not None: + img = self.augment_pipe(img) + logits = self.D(img, c, update_emas=update_emas) + return logits + + def accumulate_gradients(self, phase, real_img, real_c, transform, gen_z, + gen_c, gain, cur_nimg, min_scale, max_scale): + assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth'] + if self.pl_weight == 0: + phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase) + if self.r1_gamma == 0: + phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase) + blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 + + # Gmain: Maximize logits for generated images. + if phase in ['Gmain', 'Gboth']: + with torch.autograd.profiler.record_function('Gmain_forward'): + gen_img, _gen_ws = self.run_G(gen_z, gen_c, transform) + # vutils.save_image(gen_img, 'out_fake_patch.png', range=(-1, 1), + # normalize=True, nrow=4) + gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma) + training_stats.report('Loss/scores/fake', gen_logits) + training_stats.report('Loss/signs/fake', gen_logits.sign()) + loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits)) + training_stats.report('Loss/G/loss', loss_Gmain) + training_stats.report('Scale/G/min_scale', min_scale) + training_stats.report('Scale/G/max_scale', max_scale) + if self.teacher is not None and self.added_kwargs.teacher_lambda > 0: + teacher_img = self.teacher(gen_z, gen_c) + if self.added_kwargs.teacher_mode == 'forward': + teacher_crop, teacher_mask = apply_affine_batch(teacher_img, transform) + # removes the border around the above mask + # (mask should be all ones bc zooming in) + teacher_mask = torch.ones_like(teacher_mask) + l1_loss = self.loss_l1(gen_img, teacher_crop, + teacher_mask[:, :1]) + lpips_loss = self.loss_lpips( + losses.adaptive_downsample256(gen_img), + losses.adaptive_downsample256(teacher_crop), + losses.adaptive_downsample256(teacher_mask[:, :1], + mode='nearest') + ) + elif self.added_kwargs.teacher_mode == 'inverse': + out_crop, out_mask = apply_affine_batch(gen_img, transform.inverse()) + l1_loss = self.loss_l1(out_crop, teacher_img, + out_mask[:, :1]) + lpips_loss = self.loss_lpips( + losses.adaptive_downsample256(out_crop), + losses.adaptive_downsample256(teacher_img), + losses.adaptive_downsample256(out_mask[:, :1], + mode='nearest') + ) + else: + assert(False) + teacher_loss = (l1_loss + lpips_loss)[:, None] + loss_Gmain = (loss_Gmain + self.added_kwargs.teacher_lambda + * teacher_loss) + training_stats.report('Loss/G/loss_teacher_l1', l1_loss) + training_stats.report('Loss/G/loss_teacher_lpips', lpips_loss) + training_stats.report('Loss/G/loss_total', loss_Gmain) + with torch.autograd.profiler.record_function('Gmain_backward'): + loss_Gmain.mean().mul(gain).backward() + + # Gpl: Apply path length regularization. + if phase in ['Greg', 'Gboth']: + with torch.autograd.profiler.record_function('Gpl_forward'): + batch_size = gen_z.shape[0] // self.pl_batch_shrink + gen_img, gen_ws = self.run_G(gen_z[:batch_size], + gen_c[:batch_size], + transform[:batch_size]) + pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) + with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad): + pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] + pl_lengths = pl_grads.square().sum(2).mean(1).sqrt() + pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay) + self.pl_mean.copy_(pl_mean.detach()) + pl_penalty = (pl_lengths - pl_mean).square() + training_stats.report('Loss/pl_penalty', pl_penalty) + loss_Gpl = pl_penalty * self.pl_weight + training_stats.report('Loss/G/reg', loss_Gpl) + with torch.autograd.profiler.record_function('Gpl_backward'): + loss_Gpl.mean().mul(gain).backward() + + # Dmain: Minimize logits for generated images. + loss_Dgen = 0 + if phase in ['Dmain', 'Dboth']: + with torch.autograd.profiler.record_function('Dgen_forward'): + gen_img, _gen_ws = self.run_G(gen_z, gen_c, transform, update_emas=True) + gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) + training_stats.report('Loss/scores/fake', gen_logits) + training_stats.report('Loss/signs/fake', gen_logits.sign()) + loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits)) + with torch.autograd.profiler.record_function('Dgen_backward'): + loss_Dgen.mean().mul(gain).backward() + + # Dmain: Maximize logits for real images. + # Dr1: Apply R1 regularization. + if phase in ['Dmain', 'Dreg', 'Dboth']: + name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1' + with torch.autograd.profiler.record_function(name + '_forward'): + real_img_tmp = real_img.detach().requires_grad_(phase in ['Dreg', 'Dboth']) + real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma) + training_stats.report('Loss/scores/real', real_logits) + training_stats.report('Loss/signs/real', real_logits.sign()) + + loss_Dreal = 0 + if phase in ['Dmain', 'Dboth']: + loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits)) + training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal) + + loss_Dr1 = 0 + if phase in ['Dreg', 'Dboth']: + with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): + r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0] + r1_penalty = r1_grads.square().sum([1,2,3]) + loss_Dr1 = r1_penalty * (self.r1_gamma / 2) + training_stats.report('Loss/r1_penalty', r1_penalty) + training_stats.report('Loss/D/reg', loss_Dr1) + + with torch.autograd.profiler.record_function(name + '_backward'): + (loss_Dreal + loss_Dr1).mean().mul(gain).backward() + +#---------------------------------------------------------------------------- diff --git a/training/networks_stylegan2.py b/training/networks_stylegan2.py new file mode 100644 index 0000000..95edbda --- /dev/null +++ b/training/networks_stylegan2.py @@ -0,0 +1,794 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Network architectures from the paper +"Analyzing and Improving the Image Quality of StyleGAN". +Matches the original implementation of configs E-F by Karras et al. at +https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py""" + +import numpy as np +import torch +from torch_utils import misc +from torch_utils import persistence +from torch_utils.ops import conv2d_resample +from torch_utils.ops import upfirdn2d +from torch_utils.ops import bias_act +from torch_utils.ops import fma + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def normalize_2nd_moment(x, dim=1, eps=1e-8): + return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def modulated_conv2d( + x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. + weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. + styles, # Modulation coefficients of shape [batch_size, in_channels]. + noise = None, # Optional noise tensor to add to the output activations. + up = 1, # Integer upsampling factor. + down = 1, # Integer downsampling factor. + padding = 0, # Padding with respect to the upsampled image. + resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). + demodulate = True, # Apply weight demodulation? + flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). + fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? +): + batch_size = x.shape[0] + out_channels, in_channels, kh, kw = weight.shape + misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(styles, [batch_size, in_channels]) # [NI] + + # Pre-normalize inputs to avoid FP16 overflow. + if x.dtype == torch.float16 and demodulate: + weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk + styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I + + # Calculate per-sample weights and demodulation coefficients. + w = None + dcoefs = None + if demodulate or fused_modconv: + w = weight.unsqueeze(0) # [NOIkk] + w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] + if demodulate: + dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] + if demodulate and fused_modconv: + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] + + # Execute by scaling the activations before and after the convolution. + if not fused_modconv: + x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) + x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) + if demodulate and noise is not None: + x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) + elif demodulate: + x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) + elif noise is not None: + x = x.add_(noise.to(x.dtype)) + return x + + # Execute as one fused op using grouped convolution. + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + batch_size = int(batch_size) + misc.assert_shape(x, [batch_size, in_channels, None, None]) + x = x.reshape(1, -1, *x.shape[2:]) + w = w.reshape(-1, in_channels, kh, kw) + x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) + x = x.reshape(batch_size, -1, *x.shape[2:]) + if noise is not None: + x = x.add_(noise) + return x + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class FullyConnectedLayer(torch.nn.Module): + def __init__(self, + in_features, # Number of input features. + out_features, # Number of output features. + bias = True, # Apply additive bias before the activation function? + activation = 'linear', # Activation function: 'relu', 'lrelu', etc. + lr_multiplier = 1, # Learning rate multiplier. + bias_init = 0, # Initial value for the additive bias. + ): + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.activation = activation + self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) + self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None + self.weight_gain = lr_multiplier / np.sqrt(in_features) + self.bias_gain = lr_multiplier + + def forward(self, x): + w = self.weight.to(x.dtype) * self.weight_gain + b = self.bias + if b is not None: + b = b.to(x.dtype) + if self.bias_gain != 1: + b = b * self.bias_gain + + if self.activation == 'linear' and b is not None: + x = torch.addmm(b.unsqueeze(0), x, w.t()) + else: + x = x.matmul(w.t()) + x = bias_act.bias_act(x, b, act=self.activation) + return x + + def extra_repr(self): + return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class Conv2dLayer(torch.nn.Module): + def __init__(self, + in_channels, # Number of input channels. + out_channels, # Number of output channels. + kernel_size, # Width and height of the convolution kernel. + bias = True, # Apply additive bias before the activation function? + activation = 'linear', # Activation function: 'relu', 'lrelu', etc. + up = 1, # Integer upsampling factor. + down = 1, # Integer downsampling factor. + resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. + conv_clamp = None, # Clamp the output to +-X, None = disable clamping. + channels_last = False, # Expect the input to have memory_format=channels_last? + trainable = True, # Update the weights of this layer during training? + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.activation = activation + self.up = up + self.down = down + self.conv_clamp = conv_clamp + self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.padding = kernel_size // 2 + self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) + self.act_gain = bias_act.activation_funcs[activation].def_gain + + memory_format = torch.channels_last if channels_last else torch.contiguous_format + weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) + bias = torch.zeros([out_channels]) if bias else None + if trainable: + self.weight = torch.nn.Parameter(weight) + self.bias = torch.nn.Parameter(bias) if bias is not None else None + else: + self.register_buffer('weight', weight) + if bias is not None: + self.register_buffer('bias', bias) + else: + self.bias = None + + def forward(self, x, gain=1): + w = self.weight * self.weight_gain + b = self.bias.to(x.dtype) if self.bias is not None else None + flip_weight = (self.up == 1) # slightly faster + x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) + + act_gain = self.act_gain * gain + act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None + x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) + return x + + def extra_repr(self): + return ' '.join([ + f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},', + f'up={self.up}, down={self.down}']) + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class MappingNetwork(torch.nn.Module): + def __init__(self, + z_dim, # Input latent (Z) dimensionality, 0 = no latent. + c_dim, # Conditioning label (C) dimensionality, 0 = no label. + w_dim, # Intermediate latent (W) dimensionality. + num_ws, # Number of intermediate latents to output, None = do not broadcast. + num_layers = 8, # Number of mapping layers. + embed_features = None, # Label embedding dimensionality, None = same as w_dim. + layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. + activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. + lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. + w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track. + ): + super().__init__() + self.z_dim = z_dim + self.c_dim = c_dim + self.w_dim = w_dim + self.num_ws = num_ws + self.num_layers = num_layers + self.w_avg_beta = w_avg_beta + + if embed_features is None: + embed_features = w_dim + if c_dim == 0: + embed_features = 0 + if layer_features is None: + layer_features = w_dim + features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] + + if c_dim > 0: + self.embed = FullyConnectedLayer(c_dim, embed_features) + for idx in range(num_layers): + in_features = features_list[idx] + out_features = features_list[idx + 1] + layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) + setattr(self, f'fc{idx}', layer) + + if num_ws is not None and w_avg_beta is not None: + self.register_buffer('w_avg', torch.zeros([w_dim])) + + def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): + # Embed, normalize, and concat inputs. + x = None + with torch.autograd.profiler.record_function('input'): + if self.z_dim > 0: + misc.assert_shape(z, [None, self.z_dim]) + x = normalize_2nd_moment(z.to(torch.float32)) + if self.c_dim > 0: + misc.assert_shape(c, [None, self.c_dim]) + y = normalize_2nd_moment(self.embed(c.to(torch.float32))) + x = torch.cat([x, y], dim=1) if x is not None else y + + # Main layers. + for idx in range(self.num_layers): + layer = getattr(self, f'fc{idx}') + x = layer(x) + + # Update moving average of W. + if update_emas and self.w_avg_beta is not None: + with torch.autograd.profiler.record_function('update_w_avg'): + self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + + # Broadcast. + if self.num_ws is not None: + with torch.autograd.profiler.record_function('broadcast'): + x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) + + # Apply truncation. + if truncation_psi != 1: + with torch.autograd.profiler.record_function('truncate'): + assert self.w_avg_beta is not None + if self.num_ws is None or truncation_cutoff is None: + x = self.w_avg.lerp(x, truncation_psi) + else: + x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + return x + + def extra_repr(self): + return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class SynthesisLayer(torch.nn.Module): + def __init__(self, + in_channels, # Number of input channels. + out_channels, # Number of output channels. + w_dim, # Intermediate latent (W) dimensionality. + resolution, # Resolution of this layer. + kernel_size = 3, # Convolution kernel size. + up = 1, # Integer upsampling factor. + use_noise = True, # Enable noise input? + activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. + resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. + conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. + channels_last = False, # Use channels_last format for the weights? + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.w_dim = w_dim + self.resolution = resolution + self.up = up + self.use_noise = use_noise + self.activation = activation + self.conv_clamp = conv_clamp + self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.padding = kernel_size // 2 + self.act_gain = bias_act.activation_funcs[activation].def_gain + + self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) + memory_format = torch.channels_last if channels_last else torch.contiguous_format + self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + if use_noise: + self.register_buffer('noise_const', torch.randn([resolution, resolution])) + self.noise_strength = torch.nn.Parameter(torch.zeros([])) + self.bias = torch.nn.Parameter(torch.zeros([out_channels])) + + def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1): + assert noise_mode in ['random', 'const', 'none'] + in_resolution = self.resolution // self.up + misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution]) + styles = self.affine(w) + + noise = None + if self.use_noise and noise_mode == 'random': + noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength + if self.use_noise and noise_mode == 'const': + noise = self.noise_const * self.noise_strength + + flip_weight = (self.up == 1) # slightly faster + x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, + padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) + + act_gain = self.act_gain * gain + act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None + x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) + return x + + def extra_repr(self): + return ' '.join([ + f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},', + f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}']) + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class ToRGBLayer(torch.nn.Module): + def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.w_dim = w_dim + self.conv_clamp = conv_clamp + self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) + memory_format = torch.channels_last if channels_last else torch.contiguous_format + self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.bias = torch.nn.Parameter(torch.zeros([out_channels])) + self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) + + def forward(self, x, w, fused_modconv=True): + styles = self.affine(w) * self.weight_gain + x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) + x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) + return x + + def extra_repr(self): + return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class SynthesisBlock(torch.nn.Module): + def __init__(self, + in_channels, # Number of input channels, 0 = first block. + out_channels, # Number of output channels. + w_dim, # Intermediate latent (W) dimensionality. + resolution, # Resolution of this block. + img_channels, # Number of output color channels. + is_last, # Is this the last block? + architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. + resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. + conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. + use_fp16 = False, # Use FP16 for this block? + fp16_channels_last = False, # Use channels-last memory format with FP16? + fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training. + **layer_kwargs, # Arguments for SynthesisLayer. + ): + assert architecture in ['orig', 'skip', 'resnet'] + super().__init__() + self.in_channels = in_channels + self.w_dim = w_dim + self.resolution = resolution + self.img_channels = img_channels + self.is_last = is_last + self.architecture = architecture + self.use_fp16 = use_fp16 + self.channels_last = (use_fp16 and fp16_channels_last) + self.fused_modconv_default = fused_modconv_default + self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.num_conv = 0 + self.num_torgb = 0 + + if in_channels == 0: + self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) + + if in_channels != 0: + self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, + resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) + self.num_conv += 1 + + self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, + conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) + self.num_conv += 1 + + if is_last or architecture == 'skip': + self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, + conv_clamp=conv_clamp, channels_last=self.channels_last) + self.num_torgb += 1 + + if in_channels != 0 and architecture == 'resnet': + self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, + resample_filter=resample_filter, channels_last=self.channels_last) + + def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs): + _ = update_emas # unused + misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) + w_iter = iter(ws.unbind(dim=1)) + if ws.device.type != 'cuda': + force_fp32 = True + dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 + memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format + if fused_modconv is None: + fused_modconv = self.fused_modconv_default + if fused_modconv == 'inference_only': + fused_modconv = (not self.training) + + # Input. + if self.in_channels == 0: + x = self.const.to(dtype=dtype, memory_format=memory_format) + x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) + else: + misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) + x = x.to(dtype=dtype, memory_format=memory_format) + + # Main layers. + if self.in_channels == 0: + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + elif self.architecture == 'resnet': + y = self.skip(x, gain=np.sqrt(0.5)) + x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) + x = y.add_(x) + else: + x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + + # ToRGB. + if img is not None: + misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) + img = upfirdn2d.upsample2d(img, self.resample_filter) + if self.is_last or self.architecture == 'skip': + y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) + y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) + img = img.add_(y) if img is not None else y + + assert x.dtype == dtype + assert img is None or img.dtype == torch.float32 + return x, img + + def extra_repr(self): + return f'resolution={self.resolution:d}, architecture={self.architecture:s}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class SynthesisNetwork(torch.nn.Module): + def __init__(self, + w_dim, # Intermediate latent (W) dimensionality. + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + channel_base = 32768, # Overall multiplier for the number of channels. + channel_max = 512, # Maximum number of channels in any layer. + num_fp16_res = 4, # Use FP16 for the N highest resolutions. + **block_kwargs, # Arguments for SynthesisBlock. + ): + assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 + super().__init__() + self.w_dim = w_dim + self.img_resolution = img_resolution + self.img_resolution_log2 = int(np.log2(img_resolution)) + self.img_channels = img_channels + self.num_fp16_res = num_fp16_res + self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] + channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} + fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + + self.num_ws = 0 + for res in self.block_resolutions: + in_channels = channels_dict[res // 2] if res > 4 else 0 + out_channels = channels_dict[res] + use_fp16 = (res >= fp16_resolution) + is_last = (res == self.img_resolution) + block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, + img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) + self.num_ws += block.num_conv + if is_last: + self.num_ws += block.num_torgb + setattr(self, f'b{res}', block) + + def forward(self, ws, **block_kwargs): + block_ws = [] + with torch.autograd.profiler.record_function('split_ws'): + misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) + ws = ws.to(torch.float32) + w_idx = 0 + for res in self.block_resolutions: + block = getattr(self, f'b{res}') + block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) + w_idx += block.num_conv + + x = img = None + for res, cur_ws in zip(self.block_resolutions, block_ws): + block = getattr(self, f'b{res}') + x, img = block(x, img, cur_ws, **block_kwargs) + return img + + def extra_repr(self): + return ' '.join([ + f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},', + f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', + f'num_fp16_res={self.num_fp16_res:d}']) + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class Generator(torch.nn.Module): + def __init__(self, + z_dim, # Input latent (Z) dimensionality. + c_dim, # Conditioning label (C) dimensionality. + w_dim, # Intermediate latent (W) dimensionality. + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs = {}, # Arguments for MappingNetwork. + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): + super().__init__() + self.z_dim = z_dim + self.c_dim = c_dim + self.w_dim = w_dim + self.img_resolution = img_resolution + self.img_channels = img_channels + self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) + self.num_ws = self.synthesis.num_ws + self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + + def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): + ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) + img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) + return img + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class DiscriminatorBlock(torch.nn.Module): + def __init__(self, + in_channels, # Number of input channels, 0 = first block. + tmp_channels, # Number of intermediate channels. + out_channels, # Number of output channels. + resolution, # Resolution of this block. + img_channels, # Number of input color channels. + first_layer_idx, # Index of the first layer. + architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. + activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. + resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. + conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. + use_fp16 = False, # Use FP16 for this block? + fp16_channels_last = False, # Use channels-last memory format with FP16? + freeze_layers = 0, # Freeze-D: Number of layers to freeze. + ): + assert in_channels in [0, tmp_channels] + assert architecture in ['orig', 'skip', 'resnet'] + super().__init__() + self.in_channels = in_channels + self.resolution = resolution + self.img_channels = img_channels + self.first_layer_idx = first_layer_idx + self.architecture = architecture + self.use_fp16 = use_fp16 + self.channels_last = (use_fp16 and fp16_channels_last) + self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + + self.num_layers = 0 + def trainable_gen(): + while True: + layer_idx = self.first_layer_idx + self.num_layers + trainable = (layer_idx >= freeze_layers) + self.num_layers += 1 + yield trainable + trainable_iter = trainable_gen() + + if in_channels == 0 or architecture == 'skip': + self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + + self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + + self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, + trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) + + if architecture == 'resnet': + self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, + trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) + + def forward(self, x, img, force_fp32=False): + if (x if x is not None else img).device.type != 'cuda': + force_fp32 = True + dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 + memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format + + # Input. + if x is not None: + misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + x = x.to(dtype=dtype, memory_format=memory_format) + + # FromRGB. + if self.in_channels == 0 or self.architecture == 'skip': + misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + img = img.to(dtype=dtype, memory_format=memory_format) + y = self.fromrgb(img) + x = x + y if x is not None else y + img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None + + # Main layers. + if self.architecture == 'resnet': + y = self.skip(x, gain=np.sqrt(0.5)) + x = self.conv0(x) + x = self.conv1(x, gain=np.sqrt(0.5)) + x = y.add_(x) + else: + x = self.conv0(x) + x = self.conv1(x) + + assert x.dtype == dtype + return x, img + + def extra_repr(self): + return f'resolution={self.resolution:d}, architecture={self.architecture:s}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class MinibatchStdLayer(torch.nn.Module): + def __init__(self, group_size, num_channels=1): + super().__init__() + self.group_size = group_size + self.num_channels = num_channels + + def forward(self, x): + N, C, H, W = x.shape + with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants + G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N + F = self.num_channels + c = C // F + + y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. + y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. + y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. + y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. + y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. + y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. + y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. + x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. + return x + + def extra_repr(self): + return f'group_size={self.group_size}, num_channels={self.num_channels:d}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class DiscriminatorEpilogue(torch.nn.Module): + def __init__(self, + in_channels, # Number of input channels. + cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. + resolution, # Resolution of this block. + img_channels, # Number of input color channels. + architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. + mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. + mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. + activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. + conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. + ): + assert architecture in ['orig', 'skip', 'resnet'] + super().__init__() + self.in_channels = in_channels + self.cmap_dim = cmap_dim + self.resolution = resolution + self.img_channels = img_channels + self.architecture = architecture + + if architecture == 'skip': + self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) + self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None + self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) + self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) + self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) + + def forward(self, x, img, cmap, force_fp32=False): + misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW] + _ = force_fp32 # unused + dtype = torch.float32 + memory_format = torch.contiguous_format + + # FromRGB. + x = x.to(dtype=dtype, memory_format=memory_format) + if self.architecture == 'skip': + misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + img = img.to(dtype=dtype, memory_format=memory_format) + x = x + self.fromrgb(img) + + # Main layers. + if self.mbstd is not None: + x = self.mbstd(x) + x = self.conv(x) + x = self.fc(x.flatten(1)) + x = self.out(x) + + # Conditioning. + if self.cmap_dim > 0: + misc.assert_shape(cmap, [None, self.cmap_dim]) + x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) + + assert x.dtype == dtype + return x + + def extra_repr(self): + return f'resolution={self.resolution:d}, architecture={self.architecture:s}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class Discriminator(torch.nn.Module): + def __init__(self, + c_dim, # Conditioning label (C) dimensionality. + img_resolution, # Input resolution. + img_channels, # Number of input color channels. + architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. + channel_base = 32768, # Overall multiplier for the number of channels. + channel_max = 512, # Maximum number of channels in any layer. + num_fp16_res = 4, # Use FP16 for the N highest resolutions. + conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. + cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. + block_kwargs = {}, # Arguments for DiscriminatorBlock. + mapping_kwargs = {}, # Arguments for MappingNetwork. + epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. + ): + super().__init__() + self.c_dim = c_dim + self.img_resolution = img_resolution + self.img_resolution_log2 = int(np.log2(img_resolution)) + self.img_channels = img_channels + self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] + channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} + fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + + if cmap_dim is None: + cmap_dim = channels_dict[4] + if c_dim == 0: + cmap_dim = 0 + + common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) + cur_layer_idx = 0 + for res in self.block_resolutions: + in_channels = channels_dict[res] if res < img_resolution else 0 + tmp_channels = channels_dict[res] + out_channels = channels_dict[res // 2] + use_fp16 = (res >= fp16_resolution) + block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, + first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) + setattr(self, f'b{res}', block) + cur_layer_idx += block.num_layers + if c_dim > 0: + self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) + self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) + + def forward(self, img, c, update_emas=False, **block_kwargs): + _ = update_emas # unused + x = None + for res in self.block_resolutions: + block = getattr(self, f'b{res}') + x, img = block(x, img, **block_kwargs) + + cmap = None + if self.c_dim > 0: + cmap = self.mapping(None, c) + x = self.b4(x, img, cmap) + return x + + def extra_repr(self): + return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}' + +#---------------------------------------------------------------------------- diff --git a/training/networks_stylegan3.py b/training/networks_stylegan3.py new file mode 100644 index 0000000..eabfc05 --- /dev/null +++ b/training/networks_stylegan3.py @@ -0,0 +1,719 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Generator architecture from the paper +"Alias-Free Generative Adversarial Networks".""" + +import numpy as np +import scipy.signal +import scipy.optimize +import torch +from torch_utils import misc +from torch_utils import persistence +from torch_utils.ops import conv2d_gradfix +from torch_utils.ops import filtered_lrelu +from torch_utils.ops import bias_act +import math +import random + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def modulated_conv2d( + x, # Input tensor: [batch_size, in_channels, in_height, in_width] + w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + s, # Style tensor: [batch_size, in_channels] + demodulate = True, # Apply weight demodulation? + padding = 0, # Padding: int or [padH, padW] + input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] +): + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + batch_size = int(x.shape[0]) + out_channels, in_channels, kh, kw = w.shape + misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(s, [batch_size, in_channels]) # [NI] + + # Pre-normalize inputs. + if demodulate: + w = w * w.square().mean([1,2,3], keepdim=True).rsqrt() + s = s * s.square().mean().rsqrt() + + # Modulate weights. + w = w.unsqueeze(0) # [NOIkk] + w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + + # Demodulate weights. + if demodulate: + dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] + w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] + + # Apply input scaling. + if input_gain is not None: + input_gain = input_gain.expand(batch_size, in_channels) # [NI] + w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + + # Execute as one fused op using grouped convolution. + x = x.reshape(1, -1, *x.shape[2:]) + w = w.reshape(-1, in_channels, kh, kw) + x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size) + x = x.reshape(batch_size, -1, *x.shape[2:]) + return x + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class FullyConnectedLayer(torch.nn.Module): + def __init__(self, + in_features, # Number of input features. + out_features, # Number of output features. + activation = 'linear', # Activation function: 'relu', 'lrelu', etc. + bias = True, # Apply additive bias before the activation function? + lr_multiplier = 1, # Learning rate multiplier. + weight_init = 1, # Initial standard deviation of the weight tensor. + bias_init = 0, # Initial value of the additive bias. + ): + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.activation = activation + self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier)) + bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features]) + self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None + self.weight_gain = lr_multiplier / np.sqrt(in_features) + self.bias_gain = lr_multiplier + + def forward(self, x): + w = self.weight.to(x.dtype) * self.weight_gain + b = self.bias + if b is not None: + b = b.to(x.dtype) + if self.bias_gain != 1: + b = b * self.bias_gain + if self.activation == 'linear' and b is not None: + x = torch.addmm(b.unsqueeze(0), x, w.t()) + else: + x = x.matmul(w.t()) + x = bias_act.bias_act(x, b, act=self.activation) + return x + + def extra_repr(self): + return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class MappingNetwork(torch.nn.Module): + def __init__(self, + z_dim, # Input latent (Z) dimensionality. + c_dim, # Conditioning label (C) dimensionality, 0 = no labels. + w_dim, # Intermediate latent (W) dimensionality. + num_ws, # Number of intermediate latents to output. + num_layers = 2, # Number of mapping layers. + lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. + w_avg_beta = 0.998, # Decay for tracking the moving average of W during training. + ): + super().__init__() + self.z_dim = z_dim + self.c_dim = c_dim + self.w_dim = w_dim + self.num_ws = num_ws + self.num_layers = num_layers + self.w_avg_beta = w_avg_beta + + # Construct layers. + self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None + features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers + for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]): + layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) + setattr(self, f'fc{idx}', layer) + self.register_buffer('w_avg', torch.zeros([w_dim])) + + def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): + misc.assert_shape(z, [None, self.z_dim]) + if truncation_cutoff is None: + truncation_cutoff = self.num_ws + + # Embed, normalize, and concatenate inputs. + x = z.to(torch.float32) + x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt() + if self.c_dim > 0: + misc.assert_shape(c, [None, self.c_dim]) + y = self.embed(c.to(torch.float32)) + y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt() + x = torch.cat([x, y], dim=1) if x is not None else y + + # Execute layers. + for idx in range(self.num_layers): + x = getattr(self, f'fc{idx}')(x) + + # Update moving average of W. + if update_emas: + self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + + # Broadcast and apply truncation. + x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) + if truncation_psi != 1: + x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + return x + + def extra_repr(self): + return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class SynthesisInput(torch.nn.Module): + def __init__(self, + w_dim, # Intermediate latent (W) dimensionality. + channels, # Number of output channels. + size, # Output spatial size: int or [width, height]. + sampling_rate, # Output sampling rate. + bandwidth, # Output bandwidth. + margin_size, # Extra margin on input. + ): + super().__init__() + self.w_dim = w_dim + self.channels = channels + self.size = np.broadcast_to(np.asarray(size), [2]) + self.sampling_rate = sampling_rate + self.bandwidth = bandwidth + self.margin_size = margin_size + + # Draw random frequencies from uniform 2D disc. + freqs = torch.randn([self.channels, 2]) + radii = freqs.square().sum(dim=1, keepdim=True).sqrt() + freqs /= radii * radii.square().exp().pow(0.25) + freqs *= bandwidth + phases = torch.rand([self.channels]) - 0.5 + + # Setup parameters and buffers. + self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels])) + self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0]) + self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image. + self.register_buffer('freqs', freqs) + self.register_buffer('phases', phases) + + def forward(self, w, transform=None, **kwargs): + # Introduce batch dimension. + if transform is None: + # sanity check; should not modify transform from identity + assert(torch.equal(self.transform, torch.eye(3, 3).to(self.transform.device))) + transform = self.transform.unsqueeze(0) # [batch, row, col] + freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] + phases = self.phases.unsqueeze(0) # [batch, channel] + + # Apply learned transformation. + t = self.affine(w) # t = (r_c, r_s, t_x, t_y) + t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y) + m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image. + m_r[:, 0, 0] = t[:, 0] # r'_c + m_r[:, 0, 1] = -t[:, 1] # r'_s + m_r[:, 1, 0] = t[:, 1] # r'_s + m_r[:, 1, 1] = t[:, 0] # r'_c + m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image. + m_t[:, 0, 2] = -t[:, 2] # t'_x + m_t[:, 1, 2] = -t[:, 3] # t'_y + transforms = m_r @ m_t @ transform # First rotate resulting image, then translate, and finally apply user-specified transform. + + # Transform frequencies. + phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2) + freqs = freqs @ transforms[:, :2, :2] + + # Dampen out-of-band frequencies that may occur due to the user-specified transform. + amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) + + # Construct sampling grid. + theta = torch.eye(2, 3, device=w.device) + theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate + theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate + grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False) + + # Compute Fourier features. + x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] + x = x + phases.unsqueeze(1).unsqueeze(2) + x = torch.sin(x * (np.pi * 2)) + x = x * amplitudes.unsqueeze(1).unsqueeze(2) + + # Apply trainable mapping. + weight = self.weight / np.sqrt(self.channels) + x = x @ weight.t() + + # Ensure correct shape. + x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] + misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])]) + return x + + def extra_repr(self): + return '\n'.join([ + f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},', + f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}']) + + +@persistence.persistent_class +class SynthesisInput360(torch.nn.Module): + def __init__(self, + w_dim, # Intermediate latent (W) dimensionality. + channels, # Number of output channels. + size, # Output spatial size: int or [width, height]. + sampling_rate, # Output sampling rate. + bandwidth, # Output bandwidth. + margin_size, # Extra margin on input. + fov, # panorama FOV. + ): + super().__init__() + self.w_dim = w_dim + self.channels = channels + self.fov = fov + self.size = np.broadcast_to(np.asarray(size), [2]) + self.sampling_rate = sampling_rate + self.bandwidth = bandwidth + self.margin_size = margin_size + self.frame_size = self.size - 2 * self.margin_size + + # Draw random frequencies from uniform 2D disc. + freqs = torch.randn([self.channels, 2]) + radii = freqs.square().sum(dim=1, keepdim=True).sqrt() + freqs /= radii * radii.square().exp().pow(0.25) + freqs *= bandwidth + phases = torch.rand([self.channels]) - 0.5 + + # Setup parameters and buffers. + self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels])) + self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0]) + self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image. + self.register_buffer('freqs', freqs) + self.register_buffer('phases', phases) + + def forward(self, w, transform=None, crop_fn=None): + # Introduce batch dimension. + if transform is None: + transforms = self.transform.unsqueeze(0) # [batch, row, col] + else: + transforms = transform + freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] + phases = self.phases.unsqueeze(0) # [batch, channel] + + # does not add learned rotation for 360 model + transforms = transforms.expand(w.shape[0], -1, -1) + + # Dampen out-of-band frequencies that may occur due to the user-specified transform. + amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) + + # Construct sampling grid. + theta = torch.eye(2, 3, device=w.device) + theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate # tx + theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate # ty + grid_width = self.frame_size[0] * 360 // self.fov + 2 * self.margin_size + grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), + [1, 1, self.size[1], grid_width], + align_corners=False) + # extended grid to ensure that the x coordinate completes a full circle without padding + base_width = grid_width - 2*self.margin_size + corrected_x = torch.arange(-self.margin_size, base_width*2+self.margin_size, device=grids.device) / base_width * 2 - 1 + corrected_y = grids[0, :, 0, 1] + corrected_grids = torch.cat([corrected_x.view(1, 1, -1, 1).repeat(1, self.size[1], 1, 1), + corrected_y.view(1, -1, 1, 1).repeat(1, 1, grid_width+base_width, 1)], dim=3) + grids = corrected_grids + + if crop_fn is None: + crop_start = random.randint(0, base_width - 1) + grids = grids[:, :, crop_start:crop_start+self.size[1], :] + else: + grids = crop_fn(grids) + + # apply transformation first + rotation = transforms[:, :2, :2] + translation = transforms[:, :2, 2:].squeeze(2) + # normalize grid x s.t. transformations can operate on square affine ratio + grids_normalized = grids.clone() + min_bound = torch.min(grids_normalized[:, :, :, 0]) + max_bound = torch.max(grids_normalized[:, :, :, 0]) + target_range = torch.max(grids_normalized[:, :, :, 1]) - torch.min(grids_normalized[:, :, :, 1]) + grids_normalized[:, :, :, 0] = (grids_normalized[:, :, :, 0] - min_bound) / (max_bound - min_bound) + grids_normalized[:, :, :, 0] = grids_normalized[:, :, :, 0] * target_range - target_range / 2 + # # xT @ RT = (Rx)T --> it is transposed + grids_transformed = (grids_normalized.unsqueeze(3) @ rotation.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) + grids_transformed = grids_transformed + translation.unsqueeze(1).unsqueeze(2) + grids_transformed[:, :, :, 0] = (grids_transformed[:, :, :, 0] + target_range / 2) / target_range * (max_bound - min_bound) + min_bound + + # map discontinuous x-angle to continuous cylindrical coordinate + grids_transformed_sin = grids_transformed.clone() + grids_transformed_cos = grids_transformed.clone() + grids_transformed_sin[:, :, :, 0] = torch.sin(grids_transformed_sin[:, :, :, 0] * torch.tensor(math.pi)) + grids_transformed_cos[:, :, :, 0] = torch.cos(grids_transformed_cos[:, :, :, 0] * torch.tensor(math.pi)) + + x_sin = (grids_transformed_sin.unsqueeze(3) @ freqs[:, :self.channels//2, :].permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] + x_sin = x_sin + phases[:, :self.channels//2].unsqueeze(1).unsqueeze(2) + x_sin = torch.sin(x_sin * (np.pi * 2)) + x_sin = x_sin * amplitudes[:, :self.channels//2].unsqueeze(1).unsqueeze(2) + x_cos = (grids_transformed_cos.unsqueeze(3) @ freqs[:, self.channels//2:, :].permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] + x_cos = x_cos + phases[:, self.channels//2:].unsqueeze(1).unsqueeze(2) + x_cos = torch.sin(x_cos * (np.pi * 2)) + x_cos = x_cos * amplitudes[:, self.channels//2:].unsqueeze(1).unsqueeze(2) + x = torch.cat([x_sin, x_cos], dim=-1) + + # Apply trainable mapping. + weight = self.weight / np.sqrt(self.channels) + x = x @ weight.t() + + # Ensure correct shape. + x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] + misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])]) + return x + + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class SynthesisLayer(torch.nn.Module): + def __init__(self, + w_dim, # Intermediate latent (W) dimensionality. + is_torgb, # Is this the final ToRGB layer? + is_critically_sampled, # Does this layer use critical sampling? + use_fp16, # Does this layer use FP16? + + # Input & output specifications. + in_channels, # Number of input channels. + out_channels, # Number of output channels. + in_size, # Input spatial size: int or [width, height]. + out_size, # Output spatial size: int or [width, height]. + in_sampling_rate, # Input sampling rate (s). + out_sampling_rate, # Output sampling rate (s). + in_cutoff, # Input cutoff frequency (f_c). + out_cutoff, # Output cutoff frequency (f_c). + in_half_width, # Input transition band half-width (f_h). + out_half_width, # Output Transition band half-width (f_h). + + # Hyperparameters. + conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer. + filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling. + lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. + use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers. + conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping. + magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes. + + # added + use_scale_affine = False + ): + super().__init__() + self.w_dim = w_dim + self.is_torgb = is_torgb + self.is_critically_sampled = is_critically_sampled + self.use_fp16 = use_fp16 + self.in_channels = in_channels + self.out_channels = out_channels + self.in_size = np.broadcast_to(np.asarray(in_size), [2]) + self.out_size = np.broadcast_to(np.asarray(out_size), [2]) + self.in_sampling_rate = in_sampling_rate + self.out_sampling_rate = out_sampling_rate + self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) + self.in_cutoff = in_cutoff + self.out_cutoff = out_cutoff + self.in_half_width = in_half_width + self.out_half_width = out_half_width + self.conv_kernel = 1 if is_torgb else conv_kernel + self.conv_clamp = conv_clamp + self.magnitude_ema_beta = magnitude_ema_beta + + # Setup parameters and buffers. + self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1) + self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) + self.bias = torch.nn.Parameter(torch.zeros([self.out_channels])) + self.register_buffer('magnitude_ema', torch.ones([])) + + # Design upsampling filter. + self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) + assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate + self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 + self.register_buffer('up_filter', self.design_lowpass_filter( + numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate)) + + # Design downsampling filter. + self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) + assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate + self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 + self.down_radial = use_radial_filters and not self.is_critically_sampled + self.register_buffer('down_filter', self.design_lowpass_filter( + numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial)) + + # Compute padding. + pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling. + pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling. + pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters. + pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3). + pad_hi = pad_total - pad_lo + self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])] + + # added + self.use_scale_affine = use_scale_affine + if self.use_scale_affine: + self.scale_affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=0) + + def forward(self, x, w, scale=None, noise_mode='random', force_fp32=False, update_emas=False): + assert noise_mode in ['random', 'const', 'none'] # unused + misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])]) + misc.assert_shape(w, [x.shape[0], self.w_dim]) + + # Track input magnitude. + if update_emas: + with torch.autograd.profiler.record_function('update_magnitude_ema'): + magnitude_cur = x.detach().to(torch.float32).square().mean() + self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta)) + input_gain = self.magnitude_ema.rsqrt() + + # Execute affine layer. + styles = self.affine(w) + # added here + if self.use_scale_affine: + assert(scale is not None) + styles_scale = self.scale_affine(scale) + styles = styles + styles_scale # equivalent to concatenation + + if self.is_torgb: + weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2)) + styles = styles * weight_gain + + # Execute modulated conv2d. + dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 + x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles, + padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) + + # Execute bias, filtered leaky ReLU, and clamping. + gain = 1 if self.is_torgb else np.sqrt(2) + slope = 1 if self.is_torgb else 0.2 + x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), + up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) + + # Ensure correct shape and dtype. + misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])]) + assert x.dtype == dtype + return x + + @staticmethod + def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False): + assert numtaps >= 1 + + # Identity filter. + if numtaps == 1: + return None + + # Separable Kaiser low-pass filter. + if not radial: + f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) + return torch.as_tensor(f, dtype=torch.float32) + + # Radially symmetric jinc-based filter. + x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs + r = np.hypot(*np.meshgrid(x, x)) + f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r) + beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) + w = np.kaiser(numtaps, beta) + f *= np.outer(w, w) + f /= np.sum(f) + return torch.as_tensor(f, dtype=torch.float32) + + def extra_repr(self): + return '\n'.join([ + f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},', + f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},', + f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},', + f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},', + f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},', + f'in_size={list(self.in_size)}, out_size={list(self.out_size)},', + f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}']) + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class SynthesisNetwork(torch.nn.Module): + def __init__(self, + w_dim, # Intermediate latent (W) dimensionality. + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + channel_base = 32768, # Overall multiplier for the number of channels. + channel_max = 512, # Maximum number of channels in any layer. + num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB. + num_critical = 2, # Number of critically sampled layers at the end. + first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}). + first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}). + last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff. + margin_size = 10, # Number of additional pixels outside the image. + output_scale = 0.25, # Scale factor for the output image. + num_fp16_res = 4, # Use FP16 for the N highest resolutions. + training_mode = 'global', # training mode for input layer + fov = None, # Specify FOV for 360 model + **layer_kwargs, # Arguments for SynthesisLayer. + ): + super().__init__() + self.w_dim = w_dim + self.num_ws = num_layers + 2 + self.img_resolution = img_resolution + self.img_channels = img_channels + self.num_layers = num_layers + self.num_critical = num_critical + self.margin_size = margin_size + self.output_scale = output_scale + self.num_fp16_res = num_fp16_res + + # Geometric progression of layer cutoffs and min. stopbands. + last_cutoff = self.img_resolution / 2 # f_{c,N} + last_stopband = last_cutoff * last_stopband_rel # f_{t,N} + exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) + cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i] + stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i] + + # Compute remaining layer parameters. + sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] + half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i] + sizes = sampling_rates + self.margin_size * 2 + sizes[-2:] = self.img_resolution + channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max)) + channels[-1] = self.img_channels + + # Construct layers. + if '360' not in training_mode: + self.input = SynthesisInput( + w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]), + sampling_rate=sampling_rates[0], bandwidth=cutoffs[0], + margin_size=margin_size) + else: + assert(fov is not None) + self.input = SynthesisInput360( + w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]), + sampling_rate=sampling_rates[0], bandwidth=cutoffs[0], + margin_size=margin_size, fov=fov) + self.layer_names = [] + for idx in range(self.num_layers + 1): + prev = max(idx - 1, 0) + is_torgb = (idx == self.num_layers) + is_critically_sampled = (idx >= self.num_layers - self.num_critical) + use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution) + layer = SynthesisLayer( + w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16, + in_channels=int(channels[prev]), out_channels= int(channels[idx]), + in_size=int(sizes[prev]), out_size=int(sizes[idx]), + in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]), + in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx], + in_half_width=half_widths[prev], out_half_width=half_widths[idx], + **layer_kwargs) + name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}' + setattr(self, name, layer) + self.layer_names.append(name) + + def forward(self, ws, mapped_scale=None, transform=None, crop_fn=None, **layer_kwargs): + misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) + ws = ws.to(torch.float32).unbind(dim=1) + if mapped_scale is not None: + scale = mapped_scale.to(torch.float32).unbind(dim=1) + else: + scale = [None] * self.num_ws + # ws is a list of ws for every layer + + # Execute layers. + x = self.input(ws[0], transform=transform, crop_fn=crop_fn) + for name, w , sc in zip(self.layer_names, ws[1:], scale[1:]): + x = getattr(self, name)(x, w, sc, **layer_kwargs) + if self.output_scale != 1: + x = x * self.output_scale + + # Ensure correct shape and dtype. + misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution]) + x = x.to(torch.float32) + return x + + def extra_repr(self): + return '\n'.join([ + f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},', + f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', + f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},', + f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}']) + +#---------------------------------------------------------------------------- + +@persistence.persistent_class +class Generator(torch.nn.Module): + def __init__(self, + z_dim, # Input latent (Z) dimensionality. + c_dim, # Conditioning label (C) dimensionality. + w_dim, # Intermediate latent (W) dimensionality. + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs = {}, # Arguments for MappingNetwork. + training_mode = 'global', + scale_mapping_kwargs = {}, # Arguments for Scale Mapping Network + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): + super().__init__() + self.z_dim = z_dim + self.c_dim = c_dim + self.w_dim = w_dim + self.img_resolution = img_resolution + self.img_channels = img_channels + self.training_mode = training_mode + self.scale_mapping_kwargs = scale_mapping_kwargs + use_scale_affine = True if 'patch' in self.training_mode else False # add affine layer on style input + self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, + training_mode=training_mode, use_scale_affine=use_scale_affine, + **synthesis_kwargs) + self.num_ws = self.synthesis.num_ws + self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + if 'patch' in self.training_mode: + self.scale_mapping_kwargs = scale_mapping_kwargs + scale_mapping_norm = scale_mapping_kwargs.scale_mapping_norm + scale_mapping_min = scale_mapping_kwargs.scale_mapping_min + scale_mapping_max = scale_mapping_kwargs.scale_mapping_max + if scale_mapping_norm == 'zerocentered': + self.scale_norm = ScaleNormalizeZeroCentered(scale_mapping_min, scale_mapping_max) + scale_in_dim = 1 + elif scale_mapping_norm == 'positive': + self.scale_norm = ScaleNormalizePositive(scale_mapping_min, scale_mapping_max) + scale_in_dim = 1 + else: + assert(False) + self.scale_mapping = MappingNetwork(z_dim=scale_in_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + + + def forward(self, z, c, transform=None, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): + ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) + if transform is None: + scale = torch.ones(z.shape[0], 1).to(z.device) + else: + scale = 1/transform[:, [0], 0] + if self.scale_mapping_kwargs: + scale = self.scale_norm(scale) + mapped_scale = self.scale_mapping(scale, c, update_emas=update_emas) + else: + mapped_scale = None + img = self.synthesis(ws, mapped_scale=mapped_scale, transform=transform, update_emas=update_emas, **synthesis_kwargs) + return img + +#---------------------------------------------------------------------------- +@persistence.persistent_class +class ScaleNormalizeZeroCentered(torch.nn.Module): + def __init__(self, scale_mapping_min, scale_mapping_max): + super().__init__() + self.scale_mapping_min = scale_mapping_min + self.scale_mapping_max = scale_mapping_max + + def forward(self, scale): + # remaps scale to (-1, 1) + scale = (scale - self.scale_mapping_min) / (self.scale_mapping_max - self.scale_mapping_min) + return 2 * scale - 1 + +@persistence.persistent_class +class ScaleNormalizePositive(torch.nn.Module): + def __init__(self, scale_mapping_min, scale_mapping_max): + super().__init__() + self.scale_mapping_min = scale_mapping_min + self.scale_mapping_max = scale_mapping_max + + def forward(self, scale): + # add a small offset to avoid zero point: [0.1 to 1.1] + scale = (scale - self.scale_mapping_min) / (self.scale_mapping_max - self.scale_mapping_min) + return scale + 0.1 diff --git a/training/training_loop.py b/training/training_loop.py new file mode 100644 index 0000000..4bceb86 --- /dev/null +++ b/training/training_loop.py @@ -0,0 +1,517 @@ +# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Main training loop.""" + +import os +import time +import copy +import json +import pickle +import psutil +import PIL.Image +import numpy as np +import torch +import dnnlib +from torch_utils import misc +from torch_utils import training_stats +from torch_utils.ops import conv2d_gradfix +from torch_utils.ops import grid_sample_gradfix + +import legacy +from metrics import metric_main + +from util import util +import random +from metrics import equivariance + +#---------------------------------------------------------------------------- + +def setup_snapshot_image_grid(training_set, random_seed=0): + rnd = np.random.RandomState(random_seed) + gw = np.clip(7680 // training_set.image_shape[2], 7, 32) + gh = np.clip(4320 // training_set.image_shape[1], 4, 32) + + # No labels => show random subset of training samples. + if not training_set.has_labels: + all_indices = list(range(len(training_set))) + rnd.shuffle(all_indices) + grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)] + + else: + # Group training samples by label. + label_groups = dict() # label => [idx, ...] + for idx in range(len(training_set)): + label = tuple(training_set.get_details(idx).raw_label.flat[::-1]) + if label not in label_groups: + label_groups[label] = [] + label_groups[label].append(idx) + + # Reorder. + label_order = sorted(label_groups.keys()) + for label in label_order: + rnd.shuffle(label_groups[label]) + + # Organize into grid. + grid_indices = [] + for y in range(gh): + label = label_order[y % len(label_order)] + indices = label_groups[label] + grid_indices += [indices[x % len(indices)] for x in range(gw)] + label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))] + + # Load data. + images, labels = zip(*[training_set[i] for i in grid_indices]) + return (gw, gh), np.stack(images), np.stack(labels) + +#---------------------------------------------------------------------------- + +def save_image_grid(img, fname, drange, grid_size): + lo, hi = drange + img = np.asarray(img, dtype=np.float32) + img = (img - lo) * (255 / (hi - lo)) + img = np.rint(img).clip(0, 255).astype(np.uint8) + + gw, gh = grid_size + _N, C, H, W = img.shape + img = img.reshape([gh, gw, C, H, W]) + img = img.transpose(0, 3, 1, 4, 2) + img = img.reshape([gh * H, gw * W, C]) + + assert C in [1, 3] + if C == 1: + PIL.Image.fromarray(img[:, :, 0], 'L').save(fname) + if C == 3: + PIL.Image.fromarray(img, 'RGB').save(fname) + +#---------------------------------------------------------------------------- + +def training_loop( + run_dir = '.', # Output directory. + training_set_kwargs = {}, # Options for base training set. + patch_kwargs = {}, # Options for patch dataset. + data_loader_kwargs = {}, # Options for torch.utils.data.DataLoader. + G_kwargs = {}, # Options for generator network. + D_kwargs = {}, # Options for discriminator network. + G_opt_kwargs = {}, # Options for generator optimizer. + D_opt_kwargs = {}, # Options for discriminator optimizer. + augment_kwargs = None, # Options for augmentation pipeline. None = disable. + loss_kwargs = {}, # Options for loss function. + metrics = [], # Metrics to evaluate during training. + random_seed = 0, # Global random seed. + num_gpus = 1, # Number of GPUs participating in the training. + rank = 0, # Rank of the current process in [0, num_gpus[. + batch_size = 4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. + batch_gpu = 4, # Number of samples processed at a time by one GPU. + ema_kimg = 10, # Half-life of the exponential moving average (EMA) of generator weights. + ema_rampup = 0.05, # EMA ramp-up coefficient. None = no rampup. + G_reg_interval = None, # How often to perform regularization for G? None = disable lazy regularization. + D_reg_interval = 16, # How often to perform regularization for D? None = disable lazy regularization. + augment_p = 0, # Initial value of augmentation probability. + ada_target = None, # ADA target value. None = fixed p. + ada_interval = 4, # How often to perform ADA adjustment? + ada_kimg = 500, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. + total_kimg = 25000, # Total length of the training, measured in thousands of real images. + kimg_per_tick = 4, # Progress snapshot interval. + image_snapshot_ticks = 50, # How often to save image snapshots? None = disable. + network_snapshot_ticks = 50, # How often to save network snapshots? None = disable. + resume_pkl = None, # Network pickle to resume training from. + resume_kimg = 0, # First kimg to report when resuming training. + cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? + abort_fn = None, # Callback function for determining whether to abort training. Must return consistent results across ranks. + progress_fn = None, # Callback function for updating training progress. Called for all ranks. + added_kwargs = {}, # added +): + # Initialize. + start_time = time.time() + device = torch.device('cuda', rank) + np.random.seed(random_seed * num_gpus + rank) + torch.manual_seed(random_seed * num_gpus + rank) + torch.backends.cudnn.benchmark = cudnn_benchmark # Improves training speed. + torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. + torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. + conv2d_gradfix.enabled = True # Improves training speed. + grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. + + # ADDED: to prevent data_loader pin_memory to load to device 0 for every process + torch.cuda.set_device(device) + training_mode = G_kwargs.training_mode + + # Load training set. + if rank == 0: + print('Loading training set...') + training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset + training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) + training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) + + if rank == 0: + print() + print('Num images: ', len(training_set)) + print('Image shape:', training_set.image_shape) + print('Label shape:', training_set.label_shape) + print() + + # Load patch dataset + if 'patch' in training_mode: + if rank == 0: + print('Loading patch dataset...') + patch_dset = dnnlib.util.construct_class_by_name(**patch_kwargs) # subclass of training.dataset.Dataset + patch_dset_sampler = misc.InfiniteSampler(dataset=patch_dset, rank=rank, num_replicas=num_gpus, seed=random_seed) + patch_dset_iterator = iter(torch.utils.data.DataLoader(dataset=patch_dset, sampler=patch_dset_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) + if rank == 0: + print() + print('Patch Num images: ', len(patch_dset)) + print('Patch Image shape:', patch_dset.image_shape) + print('Patch Label shape:', patch_dset.label_shape) + print() + + # Construct networks. + if rank == 0: + print('Constructing networks...') + + # modified: use specified img_resolution + img_resolution = training_set.resolution + if 'patch' in training_mode and added_kwargs.img_size is not None: + img_resolution = added_kwargs.img_size + if rank == 0: + print("Using specified img resolution: %d" % img_resolution) + assert(added_kwargs.img_size == training_set.resolution) + common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=img_resolution, img_channels=training_set.num_channels) + G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + G_ema = copy.deepcopy(G).eval() + + + # copy G for teacher network: copy teacher G_ema to G_ema:, + # uses G state dict for the generator to align with D + if 'patch' in training_mode and added_kwargs.teacher is not None: + teacher = copy.deepcopy(G).to(device).eval() + # deactivate scale affine adding in teacher model; so it matches original model + for layer_name in teacher.synthesis.layer_names: + layer = getattr(teacher.synthesis, layer_name) + layer.use_scale_affine = False + if rank == 0: + print(f"loading teacher from {added_kwargs.teacher} on device %s! " % rank) + with dnnlib.util.open_url(added_kwargs.teacher) as f: + teacher_data = legacy.load_network_pkl(f) + for name, module in [('G', G), ('G_ema', teacher), ('G_ema', G_ema), ('D', D)]: + misc.copy_params_and_buffers(teacher_data[name], module, require_all=False) + print(f"done loading teacher on device %s! " % rank) + # util.set_requires_grad(False, teacher) + else: + teacher = None + + # Resume from existing pickle. + if (resume_pkl is not None) and (rank == 0): + print(f'Resuming from "{resume_pkl}"') + with dnnlib.util.open_url(resume_pkl) as f: + resume_data = legacy.load_network_pkl(f) + for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]: + misc.copy_params_and_buffers(resume_data[name], module, require_all=False) + + # Print network summary tables. + if rank == 0: + z = torch.empty([batch_gpu, G.z_dim], device=device) + c = torch.empty([batch_gpu, G.c_dim], device=device) + img = misc.print_module_summary(G, [z, c]) + misc.print_module_summary(D, [img, c]) + + # Setup augmentation. + if rank == 0: + print('Setting up augmentation...') + augment_pipe = None + ada_stats = None + if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None): + augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + augment_pipe.p.copy_(torch.as_tensor(augment_p)) + if ada_target is not None: + ada_stats = training_stats.Collector(regex='Loss/signs/real') + + # Distribute across GPUs. + if rank == 0: + print(f'Distributing across {num_gpus} GPUs...') + for name, module in [('G', G), ('D', D), ('G_ema', G_ema), + ('teacher', teacher), ('augment', augment_pipe)]: + if module is not None and num_gpus > 1: + if rank == 0: + print("copied %s across gpus!" % name) + for param in misc.params_and_buffers(module): + torch.distributed.broadcast(param, src=0) + elif module is None: + if rank == 0: + print("%s is None; not copied!" % name) + + # Setup training phases. + if rank == 0: + print('Setting up training phases...') + loss = dnnlib.util.construct_class_by_name(device=device, G=G, D=D, augment_pipe=augment_pipe, + added_kwargs=added_kwargs, teacher=teacher, **loss_kwargs) # subclass of training.loss.Loss + + phases = [] + + for name, module, params, opt_kwargs, reg_interval in [('G', G, G.parameters(), G_opt_kwargs, G_reg_interval), + ('D', D, D.parameters(), D_opt_kwargs, D_reg_interval)]: + if reg_interval is None: + opt = dnnlib.util.construct_class_by_name(params=params, **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)] + else: # Lazy regularization. + mb_ratio = reg_interval / (reg_interval + 1) + opt_kwargs = dnnlib.EasyDict(opt_kwargs) + opt_kwargs.lr = opt_kwargs.lr * mb_ratio + opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] + opt = dnnlib.util.construct_class_by_name(params, **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)] + phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)] + + for phase in phases: + phase.start_event = None + phase.end_event = None + if rank == 0: + phase.start_event = torch.cuda.Event(enable_timing=True) + phase.end_event = torch.cuda.Event(enable_timing=True) + + # Export sample images. + grid_size = None + grid_z = None + grid_c = None + if rank == 0: + print('Exporting sample images...') + grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set) + save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size) + grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu) + grid_c = torch.from_numpy(labels).to(device).split(batch_gpu) + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size) + print('Done exporting sample images...') + + # Initialize logs. + if rank == 0: + print('Initializing logs...') + stats_collector = training_stats.Collector(regex='.*') + stats_metrics = dict() + stats_jsonl = None + stats_tfevents = None + if rank == 0: + stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt') + try: + import torch.utils.tensorboard as tensorboard + stats_tfevents = tensorboard.SummaryWriter(run_dir) + except ImportError as err: + print('Skipping tfevents export:', err) + + # Train. + if rank == 0: + print(f'Training for {total_kimg} kimg...') + print() + cur_nimg = resume_kimg * 1000 + cur_tick = 0 + tick_start_nimg = cur_nimg + tick_start_time = time.time() + maintenance_time = tick_start_time - start_time + batch_idx = 0 + if progress_fn is not None: + progress_fn(0, total_kimg) + while True: + with torch.autograd.profiler.record_function('data_fetch'): + if 'patch' in training_mode: + if random.uniform(0, 1) > added_kwargs.base_probability: + # base dataset iterator + phase_real_img, phase_real_c = next(training_set_iterator) + n = phase_real_img.shape[0] + phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) + phase_real_c = phase_real_c.to(device).split(batch_gpu) + transform = torch.eye(3)[None] + phase_transform = transform.repeat(n, 1, 1).to(device).split(batch_gpu) + min_scale = 1.0 + max_scale = 1.0 + else: + # patch dataset iterator + data, phase_real_c = next(patch_dset_iterator) + phase_real_img = (data['image'].to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) + phase_real_c = phase_real_c.to(device).split(batch_gpu) + phase_transform = data['params']['transform'].to(device).split(batch_gpu) + min_scale = data['params']['min_scale_anneal'][0].item() + max_scale = 1.0 + else: + phase_real_img, phase_real_c = next(training_set_iterator) + phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) + phase_real_c = phase_real_c.to(device).split(batch_gpu) + # dummy variables + phase_transform = [None] * len(phase_real_c) + min_scale = 1.0 + max_scale = 1.0 + + all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device) + all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] + all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)] + all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device) + all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] + + # Execute training phases. + for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c): + if batch_idx % phase.interval != 0: + continue + if phase.start_event is not None: + phase.start_event.record(torch.cuda.current_stream(device)) + + # Accumulate gradients. + phase.opt.zero_grad(set_to_none=True) + phase.module.requires_grad_(True) + for transform, real_img, real_c, gen_z, gen_c in zip(phase_transform, phase_real_img, phase_real_c, phase_gen_z, phase_gen_c): + loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, transform=transform, + gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg, + min_scale=min_scale, max_scale=max_scale) + phase.module.requires_grad_(False) + + # Update weights. + with torch.autograd.profiler.record_function(phase.name + '_opt'): + params = [param for param in phase.module.parameters() if param.grad is not None] + if len(params) > 0: + flat = torch.cat([param.grad.flatten() for param in params]) + if num_gpus > 1: + torch.distributed.all_reduce(flat) + flat /= num_gpus + misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) + grads = flat.split([param.numel() for param in params]) + for param, grad in zip(params, grads): + param.grad = grad.reshape(param.shape) + phase.opt.step() + + # Phase done. + if phase.end_event is not None: + phase.end_event.record(torch.cuda.current_stream(device)) + + # Update G_ema. + with torch.autograd.profiler.record_function('Gema'): + ema_nimg = ema_kimg * 1000 + if ema_rampup is not None: + ema_nimg = min(ema_nimg, cur_nimg * ema_rampup) + ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8)) + for p_ema, p in zip(G_ema.parameters(), G.parameters()): + p_ema.copy_(p.lerp(p_ema, ema_beta)) + for b_ema, b in zip(G_ema.buffers(), G.buffers()): + b_ema.copy_(b) + + # Update state. + cur_nimg += batch_size + batch_idx += 1 + + # Execute ADA heuristic. + if (ada_stats is not None) and (batch_idx % ada_interval == 0): + ada_stats.update() + adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000) + augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device))) + + # Perform maintenance tasks once per tick. + done = (cur_nimg >= total_kimg * 1000) + if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000): + continue + + # Print status line, accumulating the same information in training_stats. + tick_end_time = time.time() + fields = [] + fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] + fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] + fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] + fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] + fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] + fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] + fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] + fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] + fields += [f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"] + torch.cuda.reset_peak_memory_stats() + fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] + training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60)) + training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60)) + if rank == 0: + print(' '.join(fields)) + + # Check for abort. + if (not done) and (abort_fn is not None) and abort_fn(): + done = True + if rank == 0: + print() + print('Aborting...') + + # Save image snapshot. + if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size) + + # Save network snapshot. + snapshot_pkl = None + snapshot_data = None + if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0): + snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe, training_set_kwargs=dict(training_set_kwargs)) + for key, value in snapshot_data.items(): + if isinstance(value, torch.nn.Module): + value = copy.deepcopy(value).eval().requires_grad_(False) + if num_gpus > 1: + misc.check_ddp_consistency(value, ignore_regex=r'.*\.[^.]+_(avg|ema)') + for param in misc.params_and_buffers(value): + torch.distributed.broadcast(param, src=0) + snapshot_data[key] = value.cpu() + del value # conserve memory + snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') + if rank == 0: + with open(snapshot_pkl, 'wb') as f: + pickle.dump(snapshot_data, f) + + # Evaluate metrics. + if (snapshot_data is not None) and (len(metrics) > 0): + if rank == 0: + print('Evaluating metrics...') + for metric in metrics: + result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'], + dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) + if rank == 0: + metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) + stats_metrics.update(result_dict.results) + del snapshot_data # conserve memory + + # Collect statistics. + for phase in phases: + value = [] + if (phase.start_event is not None) and (phase.end_event is not None): + phase.end_event.synchronize() + value = phase.start_event.elapsed_time(phase.end_event) + training_stats.report0('Timing/' + phase.name, value) + stats_collector.update() + stats_dict = stats_collector.as_dict() + + # Update logs. + timestamp = time.time() + if stats_jsonl is not None: + fields = dict(stats_dict, timestamp=timestamp) + stats_jsonl.write(json.dumps(fields) + '\n') + stats_jsonl.flush() + if stats_tfevents is not None: + global_step = int(cur_nimg / 1e3) + walltime = timestamp - start_time + for name, value in stats_dict.items(): + stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime) + for name, value in stats_metrics.items(): + stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime) + stats_tfevents.flush() + if progress_fn is not None: + progress_fn(cur_nimg // 1000, total_kimg) + + # Update state. + cur_tick += 1 + tick_start_nimg = cur_nimg + tick_start_time = time.time() + maintenance_time = tick_start_time - tick_end_time + if done: + break + + # Done. + if rank == 0: + print() + print('Exiting...') + +#---------------------------------------------------------------------------- diff --git a/util/html.py b/util/html.py new file mode 100644 index 0000000..bf50858 --- /dev/null +++ b/util/html.py @@ -0,0 +1,114 @@ +""" +Copyright (C) 2019 NVIDIA Corporation. All rights reserved. +Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). +""" + +import datetime +import dominate +from dominate.tags import * +import os + + +class HTML: + def __init__(self, web_dir, title, refresh=0): + if web_dir.endswith('.html'): + web_dir, html_name = os.path.split(web_dir) + else: + web_dir, html_name = web_dir, 'index.html' + self.title = title + self.web_dir = web_dir + self.html_name = html_name + self.img_dir = os.path.join(self.web_dir, 'images') + if len(self.web_dir) > 0 and not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if len(self.web_dir) > 0 and not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + + self.doc = dominate.document(title=title) + with self.doc: + pass + # h1(datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y")) + if refresh > 0: + with self.doc.head: + meta(http_equiv="refresh", content=str(refresh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_hr(self): + with self.doc: + hr() + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=512): + self.add_table() + if isinstance(width, int): + width = [width] * len(ims) + with self.t: + with tr(): + for im, txt, link, w in zip(ims, txts, links, width): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + p(txt, style="text-align: center;") + # br() + with a(href=os.path.join('images', link)): + img(loading="lazy", style="width:%dpx" % (w), src=os.path.join('images', im)) + + def add_images_two_rows(self, ims, txts, links, num_cols, + empty_index=None, width=512): + self.add_table() + col_width = str(int(100/num_cols)) + with self.t: + for start_index in range(0, len(ims), num_cols): + end_index = min(num_cols+start_index, len(ims)) + with tr(): # text row + for i in range(start_index, end_index): + if i == empty_index: + with td(style=f"word-wrap: break-word; width: {col_width}%;", halign="center", valign="top"): + p() + # p(" ", style="text-align: center;") + + with td(style=f"word-wrap: break-word; width: {col_width}%;", halign="center", valign="top"): + p(txts[i], style="text-align: center;") + with tr(): # image row + for i in range(start_index, end_index): + if i == empty_index: + with td(style=f"word-wrap: break-word; width: {col_width}%;", halign="center", valign="top"): + p() + # p(" ", style="text-align: center;") + with td(style=f"word-wrap: break-word; width: {col_width}%;", halign="center", valign="top"): + with p(style="text-align: center;"): + # br() + with a(href=os.path.join('images', links[i])): + img(loading="lazy", style="width:%dpx" % (width), + src=os.path.join('images', ims[i])) + br() + + + def save(self): + html_file = os.path.join(self.web_dir, self.html_name) + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.jpg' % n) + txts.append('text_%d' % n) + links.append('image_%d.jpg' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/util/losses.py b/util/losses.py new file mode 100644 index 0000000..4d4ad9a --- /dev/null +++ b/util/losses.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import lpips +import torch.nn.functional as F + +def adaptive_downsample256(img, mode='bilinear'): + img = img.clamp(-1, 1) + if img.shape[-1] > 256: + return F.interpolate(img, size=(256, 256), mode=mode) + else: + return img + +class LPIPS_Loss(nn.Module): + def __init__(self, model='net-lin', net='vgg', use_gpu=True, spatial=False): + super(LPIPS_Loss, self).__init__() + self.model = lpips.LPIPS(net=net, spatial=spatial).eval() + + def forward(self, pred, ref): + dist = self.model.forward(pred, ref) + return dist + +def check_loss_input(im0, im1, w): + """ im0 is out and im1 is target and w is mask""" + assert list(im0.size())[2:] == list(im1.size())[2:], 'spatial dim mismatch' + if w is not None: + assert list(im0.size())[2:] == list(w.size())[2:], 'spatial dim mismatch' + + if im1.size(0) != 1: + assert im0.size(0) == im1.size(0) + + if w is not None and w.size(0) != 1: + assert im0.size(0) == w.size(0) + return + +# masked lpips +class Masked_LPIPS_Loss(nn.Module): + def __init__(self, net='vgg', device='cuda', precision='float'): + """ LPIPS loss with spatial weighting """ + super(Masked_LPIPS_Loss, self).__init__() + self.lpips = lpips.LPIPS(net=net, spatial=True).eval() + self.lpips = self.lpips.to(device) + if precision == 'half': + self.lpips.half() + elif precision == 'float': + self.lpips.float() + elif precision == 'double': + self.lpips.double() + return + + def forward(self, im0, im1, w=None): + """ ims have dimension BCHW while mask is B1HW """ + check_loss_input(im0, im1, w) + # lpips takes the sum of each spatial map + loss = self.lpips(im0, im1) + if w is not None: + n = torch.sum(loss * w, [1, 2, 3]) + d = torch.sum(w, [1, 2, 3]) + loss = n / d + return loss + + def __call__(self, im0, im1, w=None): + return self.forward(im0, im1, w) + + +class Masked_L1_Loss(nn.Module): + def __init__(self): + super(Masked_L1_Loss, self).__init__() + self.loss = nn.L1Loss(reduction='none') + + def forward(self, pred, ref, w=None): + """ ims have dimension BCHW while mask is B1HW """ + check_loss_input(pred, ref, w) + loss = self.loss(pred, ref) + assert(pred.shape[1] == ref.shape[1]) + channels = pred.shape[1] + if w is not None: + w = w.repeat(1, channels, 1, 1) # repeat on channel wise dim + n = torch.sum(loss * w, [1, 2, 3]) + d = torch.sum(w, [1, 2, 3]) + loss = n / d + return loss + +class Masked_MSE_Loss(nn.Module): + def __init__(self): + super(Masked_MSE_Loss, self).__init__() + self.loss = nn.MSELoss(reduction='none') + + def forward(self, pred, ref, w=None): + """ ims have dimension BCHW while mask is B1HW """ + check_loss_input(pred, ref, w) + loss = self.loss(pred, ref) + assert(pred.shape[1] == ref.shape[1]) + channels = pred.shape[1] + if w is not None: + w = w.repeat(1, channels, 1, 1) # repeat on channel wise dim + n = torch.sum(loss * w, [1, 2, 3]) + d = torch.sum(w, [1, 2, 3]) + loss = n / d + return loss diff --git a/util/patch_util.py b/util/patch_util.py new file mode 100644 index 0000000..e8260a1 --- /dev/null +++ b/util/patch_util.py @@ -0,0 +1,129 @@ +import numpy as np +import random +import torch +import math +from math import exp +from PIL import Image +import random + +def construct_transformation_matrix(limits): + # limits is a list of [(y_min, y_max), (x_min, x_max)] + # in normalized coordinates from -1 to 1 + x_limits = limits[1] + y_limits = limits[0] + theta = torch.zeros(2, 3) + tx = np.sum(x_limits) / 2 + ty = np.sum(y_limits) / 2 + s = x_limits[1] - tx + assert(np.abs((x_limits[1] - tx) - (y_limits[1] - ty)) < 1e-9) + theta[0, 0] = s + theta[1, 1] = s + theta[0, 2] = (tx) / 2 + theta[1, 2] = (ty) / 2 + transform = torch.zeros(3, 3) + transform[:2, :] = theta + transform[2, 2] = 1.0 + return transform + +class PatchSampler(object): + def __init__(self, patch_size, random_shift=True, random_scale=True, scale_anneal=-1, + max_scale=None, min_scale=None, **kwargs): + self.patch_size = patch_size # variable p in paper + self.random_shift = random_shift + self.random_scale = random_scale + # full image range is [-1, 1] + self.w = np.array([-1, 1]) + self.h = np.array([-1, 1]) + # image size = 1/scale + self.max_scale = max_scale if max_scale is not None else 1.0 + self.min_scale = min_scale + self.iterations = 0 + self.scale_anneal = scale_anneal + self.initial_min = 1.0 # 0.9 + + def sample_patch(self, im): + im_w, im_h = im.size + assert(im_w == im_h) # crop to square image before patch sampling + + # minimum scale bound based on image size + min_scale = self.patch_size / im_h + if self.min_scale is not None: + min_scale = max(min_scale, self.min_scale) + params = {'min_scale_absolute': min_scale} + + # adjust min scale if annealing + if self.scale_anneal > 0: + k_iter = (self.iterations)// 1000 * 3 + # decays min_scale between self.min_scale and initial_min + min_scale = max(min_scale, self.max_scale * exp(-k_iter*self.scale_anneal)) + min_scale = min(self.initial_min, min_scale) + params['min_scale_anneal'] = min_scale + + scale = 1.0 + if self.random_scale: + # this samples is size uniformly from min_size to max_size + max_size = self.patch_size / min_scale + min_size = self.patch_size / self.max_scale + random_size = random.uniform(min_size, max_size) + scale = self.patch_size / random_size + params['sampled_scale'] = scale + + # resize the image to a random new size and take a crop + new_size = int(np.round(self.patch_size / scale)) + crop_size = self.patch_size + im_resized = im.resize((new_size, new_size), Image.LANCZOS) + assert(new_size <= im_h) # do not upsample + x = random.randint(0, np.maximum(0, new_size - crop_size)) # inclusive [low, high] + y = random.randint(0, np.maximum(0, new_size - crop_size)) + im_crop = im_resized.crop((x, y, x+crop_size, y+crop_size)) + + # normalized limits + limits = [(y/(new_size)*2-1, (y+crop_size) /(new_size)*2-1), + (x/(new_size)*2-1, (x+crop_size) /(new_size)*2-1)] + params['limits'] = limits + params['x'] = x + params['y'] = y + params['new_size'] = new_size + params['orig_size'] = im_w + + # calculate the transformation matrix + transform = construct_transformation_matrix(limits) + params['transform'] = transform + + return im_crop, params + +def generate_full_from_patches(new_size, patch_size=256): + # returns the bounding boxes and transformations needed to + # piece together patches of size patch_size into a + # full image of size new_size + patch_params = [] + for y in range(0, new_size, patch_size): + for x in range(0, new_size, patch_size): + if y + patch_size > new_size: + y = new_size - patch_size + if x + patch_size > new_size: + x = new_size - patch_size + limits = [(y/(new_size)*2-1, (y+patch_size) /(new_size)*2-1), + (x/(new_size)*2-1, (x+patch_size) /(new_size)*2-1)] + transform = construct_transformation_matrix(limits) + patch_params.append(((y, y+patch_size, x, x+patch_size), transform)) + return patch_params + +def compute_scale_inputs(G, w, transform): + if transform is None: + scale = torch.ones(w.shape[0], 1).to(w.device) + else: + scale = 1/transform[:, [0], 0] + scale = G.scale_norm(scale) + mapped_scale = G.scale_mapping(scale, None) + return scale, mapped_scale + +def scale_condition_wrapper(G, w, transform, **kwargs): + # convert transformation matrix into scale input + # and pass through scale mapping network + if not G.scale_mapping_kwargs: + img = G.synthesis(w, transform=transform, **kwargs) + return img + scale, mapped_scale = compute_scale_inputs(G, w, transform) + img = G.synthesis(w, mapped_scale=mapped_scale, transform=transform, **kwargs) + return img diff --git a/util/pbar.py b/util/pbar.py new file mode 100644 index 0000000..8abf389 --- /dev/null +++ b/util/pbar.py @@ -0,0 +1,160 @@ +''' +Utilities for showing progress bars, controlling default verbosity, etc. +''' + +# If the tqdm package is not available, then do not show progress bars; +# just connect print_progress to print. +import sys, types, builtins +try: + from tqdm import tqdm, tqdm_notebook +except: + tqdm = None + +default_verbosity = True +next_description = None +python_print = builtins.print + +def post(**kwargs): + ''' + When within a progress loop, pbar.post(k=str) will display + the given k=str status on the right-hand-side of the progress + status bar. If not within a visible progress bar, does nothing. + ''' + innermost = innermost_tqdm() + if innermost: + innermost.set_postfix(**kwargs) + +def desc(desc): + ''' + When within a progress loop, pbar.desc(str) changes the + left-hand-side description of the loop toe the given description. + ''' + innermost = innermost_tqdm() + if innermost: + innermost.set_description(str(desc)) + +def descnext(desc): + ''' + Called before starting a progress loop, pbar.descnext(str) + sets the description text that will be used in the following loop. + ''' + global next_description + if not default_verbosity or tqdm is None: + return + next_description = desc + +def print(*args): + ''' + When within a progress loop, will print above the progress loop. + ''' + global next_description + next_description = None + if default_verbosity: + msg = ' '.join(str(s) for s in args) + if tqdm is None: + python_print(msg) + else: + tqdm.write(msg) + +def tqdm_terminal(it, *args, **kwargs): + ''' + Some settings for tqdm that make it run better in resizable terminals. + ''' + return tqdm(it, *args, dynamic_ncols=True, ascii=True, + leave=(not innermost_tqdm()), **kwargs) + +def in_notebook(): + ''' + True if running inside a Jupyter notebook. + ''' + # From https://stackoverflow.com/a/39662359/265298 + try: + shell = get_ipython().__class__.__name__ + if shell == 'ZMQInteractiveShell': + return True # Jupyter notebook or qtconsole + elif shell == 'TerminalInteractiveShell': + return False # Terminal running IPython + else: + return False # Other type (?) + except NameError: + return False # Probably standard Python interpreter + +def innermost_tqdm(): + ''' + Returns the innermost active tqdm progress loop on the stack. + ''' + if hasattr(tqdm, '_instances') and len(tqdm._instances) > 0: + return max(tqdm._instances, key=lambda x: x.pos) + else: + return None + +def __call__(x, *args, **kwargs): + ''' + Invokes a progress function that can wrap iterators to print + progress messages, if verbose is True. + + If verbose is False or tqdm is unavailable, then a quiet + non-printing identity function is used. + + verbose can also be set to a spefific progress function rather + than True, and that function will be used. + ''' + global default_verbosity, next_description + if not default_verbosity or tqdm is None: + return x + if default_verbosity == True: + fn = tqdm_notebook if in_notebook() else tqdm_terminal + else: + fn = default_verbosity + if next_description is not None: + kwargs = dict(kwargs) + kwargs['desc'] = next_description + next_description = None + return fn(x, *args, **kwargs) + +class VerboseContextManager(): + def __init__(self, v, entered=False): + self.v, self.entered, self.saved = v, False, [] + if entered: + self.__enter__() + self.entered = True + def __enter__(self): + global default_verbosity + if self.entered: + self.entered = False + else: + self.saved.append(default_verbosity) + default_verbosity = self.v + return self + def __exit__(self, exc_type, exc_value, exc_traceback): + global default_verbosity + default_verbosity = self.saved.pop() + def __call__(self, v=True): + ''' + Calling the context manager makes a new context that is + pre-entered, so it works as both a plain function and as a + factory for a context manager. + ''' + new_v = v if self.v else not v + cm = VerboseContextManager(new_v, entered=True) + default_verbosity = new_v + return cm + +# Use as either "with pbar.verbose:" or "pbar.verbose(False)", or also +# "with pbar.verbose(False):" +verbose = VerboseContextManager(True) + +# Use as either "with @pbar.quiet" or "pbar.quiet(True)". or also +# "with pbar.quiet(True):" +quiet = VerboseContextManager(False) + +class CallableModule(types.ModuleType): + def __init__(self): + # or super().__init__(__name__) for Python 3 + types.ModuleType.__init__(self, __name__) + self.__dict__.update(sys.modules[__name__].__dict__) + def __call__(self, x, *args, **kwargs): + return __call__(x, *args, **kwargs) + +sys.modules[__name__] = CallableModule() + diff --git a/util/pidfile.py b/util/pidfile.py new file mode 100644 index 0000000..3ed615f --- /dev/null +++ b/util/pidfile.py @@ -0,0 +1,128 @@ +''' +Utility for simple distribution of work on multiple processes, by +making sure only one process is working on a job at once. +''' + +import os, errno, socket, atexit, time, sys + + +# rather than exiting, returns True if the lockfile is +# taken or if donefile exists, but it still claims a lock +def check_if_job_done(directory, redo=False, force=False, verbose=True): + if pidfile_taken(os.path.join(directory, 'lockfile.pid'), + force=force, verbose=verbose): + return True + donefile = os.path.join(directory, 'done.txt') + if os.path.isfile(donefile): + with open(donefile) as f: + msg = f.read() + if redo or force: + if verbose: + print('Removing %s %s' % (donefile, msg)) + os.remove(donefile) + return False + else: + if verbose: + print('%s %s' % (donefile, msg)) + return True + return False + +# just simple check if the donefile exists, don't claim a lock +def has_donefile(directory): + donefile = os.path.join(directory, 'done.txt') + if os.path.isfile(donefile): + with open(donefile) as f: + msg = f.read() + print('%s %s' % (donefile, msg)) + return True + return False + + +def exit_if_job_done(directory, redo=False, force=False, verbose=True): + if pidfile_taken(os.path.join(directory, 'lockfile.pid'), + force=force, verbose=verbose): + sys.exit(0) + donefile = os.path.join(directory, 'done.txt') + if os.path.isfile(donefile): + with open(donefile) as f: + msg = f.read() + if redo or force: + if verbose: + print('Removing %s %s' % (donefile, msg)) + os.remove(donefile) + else: + if verbose: + print('%s %s' % (donefile, msg)) + sys.exit(0) + +def mark_job_done(directory): + with open(os.path.join(directory, 'done.txt'), 'w') as f: + f.write('done by %d@%s %s at %s' % + (os.getpid(), socket.gethostname(), + os.getenv('STY', ''), + time.strftime('%c'))) + +def pidfile_taken(path, verbose=False, force=False): + ''' + Usage. To grab an exclusive lock for the remaining duration of the + current process (and exit if another process already has the lock), + do this: + + if pidfile_taken('job_423/lockfile.pid', verbose=True): + sys.exit(0) + + To do a batch of jobs, just run a script that does them all on + each available machine, sharing a network filesystem. When each + job grabs a lock, then this will automatically distribute the + jobs so that each one is done just once on one machine. + ''' + + # Try to create the file exclusively and write my pid into it. + try: + os.makedirs(os.path.dirname(path), exist_ok=True) + fd = os.open(path, os.O_CREAT | os.O_EXCL | os.O_RDWR) + except OSError as e: + if e.errno == errno.EEXIST: + # If we cannot because there was a race, yield the conflicter. + conflicter = 'race' + try: + with open(path, 'r') as lockfile: + conflicter = lockfile.read().strip() or 'empty' + except: + pass + # Force is for manual one-time use, for deleting stale lockfiles. + if force: + if verbose: + print('Removing %s from %s' % (path, conflicter)) + os.remove(path) + return pidfile_taken(path, verbose=verbose, force=False) + if verbose: + print('%s held by %s' % (path, conflicter)) + return conflicter + else: + # Other problems get an exception. + raise + # Register to delete this file on exit. + lockfile = os.fdopen(fd, 'r+') + atexit.register(delete_pidfile, lockfile, path) + # Write my pid into the open file. + lockfile.write('%d@%s %s\n' % (os.getpid(), socket.gethostname(), + os.getenv('STY', ''))) + lockfile.flush() + os.fsync(lockfile) + # Return 'None' to say there was not a conflict. + return None + +def delete_pidfile(lockfile, path): + ''' + Runs at exit after pidfile_taken succeeds. + ''' + if lockfile is not None: + try: + lockfile.close() + except: + pass + try: + os.unlink(path) + except: + pass diff --git a/util/renormalize.py b/util/renormalize.py new file mode 100644 index 0000000..9b789d6 --- /dev/null +++ b/util/renormalize.py @@ -0,0 +1,123 @@ +import numpy, torch, PIL, io, base64, re +from torchvision import transforms + +def as_tensor(data, source='zc', target='zc'): + renorm = renormalizer(source=source, target=target) + return renorm(data) + +def as_image(data, source='zc', target='byte'): + assert len(data.shape) == 3 + renorm = renormalizer(source=source, target=target) + return PIL.Image.fromarray(renorm(data). + permute(1,2,0).cpu().numpy()) + +def as_url(data, source='zc', size=None): + if source != 'pil': + img = as_image(data, source) + else: + img = data + if size is not None: + img = img.resize((size, size), PIL.Image.LANCZOS) + buffered = io.BytesIO() + img.save(buffered, format='png') + b64 = base64.b64encode(buffered.getvalue()).decode('utf-8') + return 'data:image/png;base64,%s' % (b64) + +def from_image(im, target='zc', size=None): + if im.format != 'RGB': + im = im.convert('RGB') + if size is not None: + im = im.resize(size) + pt = transforms.functional.to_tensor(im) + renorm = renormalizer(source='pt', target=target) + return renorm(pt) + +def from_url(url, target='zc', size=None): + image_data = re.sub('^data:image/.+;base64,', '', url) + im = PIL.Image.open(io.BytesIO(base64.b64decode(image_data))) + return from_image(im, target, size=size) + +def renormalizer(source='zc', target='zc'): + ''' + Returns a function that imposes a standard normalization on + the image data. The returned renormalizer operates on either + 3d tensor (single image) or 4d tensor (image batch) data. + The normalization target choices are: + + zc (default) - zero centered [-1..1] + pt - pytorch [0..1] + imagenet - zero mean, unit stdev imagenet stats (approx [-2.1...2.6]) + byte - as from an image file, [0..255] + + If a source is provided (a dataset or transform), then, the renormalizer + first reverses any normalization found in the data source before + imposing the specified normalization. When no source is provided, + the input data is assumed to be pytorch-normalized (range [0..1]). + ''' + if isinstance(source, str): + oldoffset, oldscale = OFFSET_SCALE[source] + else: + normalizer = find_normalizer(source) + oldoffset, oldscale = ( + (normalizer.mean, normalizer.std) if normalizer is not None + else OFFSET_SCALE['pt']) + newoffset, newscale = (target if isinstance(target, tuple) + else OFFSET_SCALE[target]) + return Renormalizer(oldoffset, oldscale, newoffset, newscale, + tobyte=(target == 'byte')) + +# The three commonly-seen image normalization schemes. +OFFSET_SCALE=dict( + pt=([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]), + zc=([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + imagenet=([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + imagenet_meanonly=([0.485, 0.456, 0.406], + [1.0/255, 1.0/255, 1.0/255]), + places_meanonly=([0.475, 0.441, 0.408], + [1.0/255, 1.0/255, 1.0/255]), + byte=([0.0, 0.0, 0.0], [1.0/255, 1.0/255, 1.0/255])) + +NORMALIZER={k: transforms.Normalize(*OFFSET_SCALE[k]) for k in OFFSET_SCALE} + +def find_normalizer(source=None): + ''' + Crawl around the transforms attached to a dataset looking for a + Normalize transform to return. + ''' + if source is None: + return None + if isinstance(source, (transforms.Normalize, Renormalizer)): + return source + t = getattr(source, 'transform', None) + if t is not None: + return find_normalizer(t) + ts = getattr(source, 'transforms', None) + if ts is not None: + for t in reversed(ts): + result = find_normalizer(t) + if result is not None: + return result + return None + +class Renormalizer: + def __init__(self, oldoffset, oldscale, newoffset, newscale, tobyte=False): + self.mul = torch.from_numpy( + numpy.array(oldscale) / numpy.array(newscale)) + self.add = torch.from_numpy( + (numpy.array(oldoffset) - numpy.array(newoffset)) + / numpy.array(newscale)) + self.tobyte = tobyte + # Store these away to allow the data to be renormalized again + self.mean = newoffset + self.std = newscale + + def __call__(self, data): + mul, add = [d.to(data.device, data.dtype) for d in [self.mul, self.add]] + if data.ndimension() == 3: + mul, add = [d[:, None, None] for d in [mul, add]] + elif data.ndimension() == 4: + mul, add = [d[None, :, None, None] for d in [mul, add]] + result = data.mul(mul).add_(add) + if self.tobyte: + result = result.clamp(0, 255).byte() + return result diff --git a/util/show.py b/util/show.py new file mode 100644 index 0000000..6cd8190 --- /dev/null +++ b/util/show.py @@ -0,0 +1,144 @@ +# show.py +# +# An abbreviated way to output simple HTML layout of text and images +# into a python notebook. +# +# - show a PIL image to show an inline HTML . +# - show an array of items to vertically stack them, centered in a block. +# - show an array of arrays to horizontally lay them out as inline blocks. +# - show an array of tuples to create a table. + +import PIL.Image, base64, io, IPython, types, sys +import html as html_module +from IPython.display import display + +g_buffer = None + +def blocks(obj, space=''): + return IPython.display.HTML(space.join(blocks_tags(obj))) + +def rows(obj, space=''): + return IPython.display.HTML(space.join(rows_tags(obj))) + +def rows_tags(obj): + if isinstance(obj, dict): + obj = obj.items() + results = [] + results.append('') + for row in obj: + results.append('') + for item in row: + results.append('') + results.append('') + results.append('
') + results.extend(blocks_tags(item)) + results.append('
') + return results + +def blocks_tags(obj): + results = [] + if hasattr(obj, '_repr_html_'): + results.append(obj._repr_html_()) + elif isinstance(obj, PIL.Image.Image): + results.append(pil_to_html(obj)) + elif isinstance(obj, (str, int, float)): + results.append('
') + results.append(html_module.escape(str(obj))) + results.append('
') + elif isinstance(obj, dict): + results.extend(blocks_tags([(k, v) for k, v in obj.items()])) + elif hasattr(obj, '__iter__'): + if hasattr(obj, 'tolist'): + # Handle numpy/pytorch tensors as lists. + try: + obj = obj.tolist() + except: + pass + blockstart, blockend, tstart, tend, rstart, rend, cstart, cend = [ + '
', + '
', + '', + '
', + '', + '', + '', + '', + ] + needs_end = False + table_mode = False + for i, line in enumerate(obj): + if i == 0: + needs_end = True + if isinstance(line, tuple): + table_mode = True + results.append(tstart) + else: + results.append(blockstart) + if table_mode: + results.append(rstart) + if not isinstance(line, str) and hasattr(line, '__iter__'): + for cell in line: + results.append(cstart) + results.extend(blocks_tags(cell)) + results.append(cend) + else: + results.append(cstart) + results.extend(blocks_tags(line)) + results.append(cend) + results.append(rend) + else: + results.extend(blocks_tags(line)) + if needs_end: + results.append(table_mode and tend or blockend) + return results + +def pil_to_b64(img, format='png'): + buffered = io.BytesIO() + img.save(buffered, format=format) + return base64.b64encode(buffered.getvalue()).decode('utf-8') + +def pil_to_url(img, format='png'): + return 'data:image/%s;base64,%s' % (format, pil_to_b64(img, format)) + +def pil_to_html(img, margin=1): + mattr = ' style="margin:%dpx"' % margin + return '' % (pil_to_url(img), mattr) + +def a(x, cols=None): + global g_buffer + if g_buffer is None: + g_buffer = [] + g_buffer.append(x) + if cols is not None and len(g_buffer) >= cols: + flush() + +def reset(): + global g_buffer + g_buffer = None + +def flush(*args, **kwargs): + global g_buffer + if g_buffer is not None: + x = g_buffer + g_buffer = None + display(blocks(x, *args, **kwargs)) + +def show(x=None, *args, **kwargs): + flush(*args, **kwargs) + if x is not None: + display(blocks(x, *args, **kwargs)) + +def html(obj, space=''): + return blocks(obj, space)._repr_html_() + +class CallableModule(types.ModuleType): + def __init__(self): + # or super().__init__(__name__) for Python 3 + types.ModuleType.__init__(self, __name__) + self.__dict__.update(sys.modules[__name__].__dict__) + def __call__(self, x=None, *args, **kwargs): + show(x, *args, **kwargs) + +sys.modules[__name__] = CallableModule() diff --git a/util/util.py b/util/util.py new file mode 100644 index 0000000..05591a1 --- /dev/null +++ b/util/util.py @@ -0,0 +1,55 @@ +import torch +import urllib + +def accumulate(model1, model2, decay=0.999): + par1 = dict(model1.named_parameters()) + par2 = dict(model2.named_parameters()) + + for k in par1.keys(): + par1[k].data.mul_(decay).add_(par2[k].data, alpha=1-decay) + +def set_requires_grad(requires_grad, *models): + for model in models: + if isinstance(model, torch.nn.Module): + for param in model.parameters(): + param.requires_grad = requires_grad + elif isinstance(model, (torch.nn.Parameter, torch.Tensor)): + model.requires_grad = requires_grad + else: + assert False, 'unknown type %r' % type(model) + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + +def is_url(url): + scheme = urllib.parse.urlparse(url).scheme + return scheme in ('http', 'https') + +def remove_prefix(text, prefix): + if text.startswith(prefix): + return text[len(prefix):] + return text # or whatever + + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f'): + self.name = name + self.fmt = fmt + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' + return fmtstr.format(**self.__dict__) diff --git a/util/video_util.py b/util/video_util.py new file mode 100644 index 0000000..73df057 --- /dev/null +++ b/util/video_util.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from PIL import ImageDraw +from PIL import ImageFont +import skvideo.io +import numpy as np + +def draw_text(img, text, size=None, position=None, fill=(255,255,255), + fontpath='suppmat/arial.ttf'): + if(size is None): + size = img.size[0]/10. + font = ImageFont.truetype(fontpath, size=int(size)) + draw = ImageDraw.Draw(img) + if position is None: + position = ((img.size[0]-int(size*2.2)), int(img.size[1]-int(size)),) + + draw.text(position, text, align='right', fill=fill, font=font) + +def get_writer(file_name, frame_rate=24, pix_fmt='yuv420p', vcodec='libx264'): + rate = str(frame_rate) + inputdict = { + '-r': rate + } + outputdict = { + '-pix_fmt': pix_fmt, + '-r': rate, + '-vcodec': vcodec, + } + writer = skvideo.io.FFmpegWriter(file_name, inputdict, outputdict) + return writer + +def interpolate_ws(start_ws, target_ws, interpolation_frames): + ws_list = [] + for i in np.linspace(0, 1, interpolation_frames): + ws_list.append((start_ws * (1-i) + target_ws * i).to('cpu')) + return ws_list diff --git a/util/viz_util.py b/util/viz_util.py new file mode 100644 index 0000000..696a74e --- /dev/null +++ b/util/viz_util.py @@ -0,0 +1,193 @@ +from PIL import ImageDraw, Image +from util import renormalize, patch_util +import numpy as np +import torch + +def init_bbox(params): + patch_size = params['patch_size'] + display_size = params['display_size'] + zoom_rate = params['zoom_rate'] + if patch_size <= display_size: + # add a small zoom factor + full_size = patch_size + 2*zoom_rate + bbox_info = { + 'display': np.array([[zoom_rate, full_size-zoom_rate], + [zoom_rate, full_size-zoom_rate]]), # [(y_min, y_max), (x_min, x_max)] + 'generated': np.array([[zoom_rate, full_size-zoom_rate], + [zoom_rate, full_size-zoom_rate]]), + 'full_size': patch_size + 2*zoom_rate + } + else: + bbox_info = { + 'display': np.array([[patch_size//2-display_size//2, patch_size//2+ display_size//2], + [patch_size//2-display_size//2, patch_size//2+ display_size//2]]), + 'generated': np.array([[0, patch_size], [0, patch_size]]), + 'full_size': patch_size + } + return bbox_info + +def draw_bbox(bbox_info, base_img, size=256): + pil_img = renormalize.as_image(base_img).resize((size, size), Image.ANTIALIAS) + draw = ImageDraw.Draw(pil_img) + limits = bbox_info['display'] / bbox_info['full_size'] * 2 - 1 + scaled_limits = ((np.array(limits) + 1) / 2 * size).astype(np.int64) + draw.rectangle((scaled_limits[1][0], scaled_limits[0][0], scaled_limits[1][1], scaled_limits[0][1])) + return pil_img + +def get_bbox_center(bbox): + return np.mean(bbox, axis=1).astype(np.int64) + +def shift_bbox_inbounds(bbox, full_size): + # ymin, xmin, ymax, xmax + if bbox[0,0] < 0: # ymin + shift = bbox[0,0] + bbox[0] -= shift + if bbox[1,0] < 0: # xmin + shift = bbox[1,0] + bbox[1] -= shift + if bbox[0,1] > full_size: # ymax + shift = bbox[0,1]-full_size + bbox[0] -= shift + if bbox[1,1] > full_size: # xmax + shift = bbox[1,1]-full_size + bbox[1] -= shift + assert(np.min(bbox) >= 0) + assert(np.max(bbox) <= full_size) + return bbox + +def update_bbox(key, bbox_info, generated_img, display_img, G_base, ws, params): + shift_rate = params['shift_rate'] + zoom_rate = params['zoom_rate'] + display_size = params['display_size'] + patch_size = params['patch_size'] + if key == 'ArrowDown': + new_display = np.copy(bbox_info['display']) + new_display[0] = new_display[0] + shift_rate + bbox_info, update_transformation, update_display = check_bbox_bounds(bbox_info, new_display, patch_size) + elif key == 'ArrowUp': + new_display = np.copy(bbox_info['display']) + new_display[0] = new_display[0] - shift_rate + bbox_info, update_transformation, update_display = check_bbox_bounds(bbox_info, new_display, patch_size) + elif key == 'ArrowLeft': + new_display = np.copy(bbox_info['display']) + new_display[1] = new_display[1] - shift_rate + bbox_info, update_transformation, update_display = check_bbox_bounds(bbox_info, new_display, patch_size) + elif key == 'ArrowRight': + new_display = np.copy(bbox_info['display']) + new_display[1] = new_display[1] + shift_rate + bbox_info, update_transformation, update_display = check_bbox_bounds(bbox_info, new_display, patch_size) + elif key == 'i': + # need to update scale and regenerate transform (center it) + bbox_info['full_size'] += zoom_rate + bbox_center = get_bbox_center(bbox_info['display']) + zoom_rate // 2 + new_display = np.stack([bbox_center - display_size//2, bbox_center+display_size//2], axis=1) + new_generated = np.stack([bbox_center - patch_size//2, bbox_center+patch_size//2], axis=1) + bbox_info['display'] = new_display + bbox_info['generated'] = new_generated + update_transformation = True + update_display = True + elif key == 'o': + if bbox_info['full_size'] - zoom_rate >= patch_size: + bbox_info['full_size'] -= zoom_rate + assert(bbox_info['full_size'] >= patch_size) + bbox_center = get_bbox_center(bbox_info['display']) - zoom_rate // 2 + new_display = np.stack([bbox_center - display_size//2, bbox_center+display_size//2], axis=1) + new_generated = np.stack([bbox_center - patch_size//2, bbox_center+patch_size//2], axis=1) + # check the bounds of display and generated + bbox_info['display'] = shift_bbox_inbounds(new_display, bbox_info['full_size']) + bbox_info['generated'] = shift_bbox_inbounds(new_generated, bbox_info['full_size']) + update_transformation = True + update_display = True + else: + # max zoom out + update_transformation = False + update_display = False + elif key == 'c': + # make this a function + bbox_info = init_bbox(params) + update_transformation = True + update_display = True + else: + pass + generated_img, display_img = update_img( + bbox_info, update_transformation, update_display, generated_img, + display_img, params, G_base, ws) + return bbox_info, generated_img, display_img + +def update_img(bbox_info, update_transformation, update_display, + generated_img, display_img, params, G_base, ws): + display_size = params['display_size'] + if update_transformation: + transform = bbox_to_transformation(bbox_info)[None].to(ws.device) + generated_img = patch_util.scale_condition_wrapper( + G_base, ws, transform=transform, noise_mode='const', force_fp32=True)[0] + if update_display: + offset_x = bbox_info['display'][1, 0] - bbox_info['generated'][1, 0] + assert (offset_x >= 0) + offset_y = bbox_info['display'][0, 0] - bbox_info['generated'][0, 0] + assert (offset_y >= 0) + display_img = generated_img[:, offset_y:offset_y+display_size, offset_x:offset_x+display_size] + return generated_img, display_img + +def check_bbox_bounds(bbox_info, new_display, patch_size): + full_size = bbox_info['full_size'] + if (new_display[:, 0] < 0).any() or (new_display[:, 1] > full_size).any(): + # shift it to the edge + new_display = shift_bbox_inbounds(new_display, full_size) + if ((new_display[:, 0] >= bbox_info['generated'][:, 0]).all() and + (new_display[:, 1] <= bbox_info['generated'][:, 1]).all()): + # within generated bbox, don't need to regenerate + bbox_info['display'] = new_display + update_transformation = False + update_display = True + else: + # need to regenerate, recenter the generated bbox + bbox_center = get_bbox_center(new_display) + new_generated = np.stack([bbox_center - patch_size//2, bbox_center+patch_size//2], axis=1) + new_generated = shift_bbox_inbounds(new_generated, full_size) + bbox_info['generated'] = new_generated + bbox_info['display'] = new_display + update_transformation = True + update_display = True + return bbox_info, update_transformation, update_display + +def bbox_to_transformation(bbox_info): + # normalize it by full_size and call contruct_transformation_matrix + limits = bbox_info['generated'] / bbox_info['full_size'] * 2 - 1 + transform = patch_util.construct_transformation_matrix(limits) + return transform + +### panorama utils +def make_grid(G_pano): + input_layer = G_pano.synthesis.input + + # make secondary grid + theta = torch.eye(2, 3, device='cuda') + theta[0, 0] = 0.5 * input_layer.size[0] / input_layer.sampling_rate + theta[1, 1] = 0.5 * input_layer.size[1] / input_layer.sampling_rate + grid_width = (input_layer.size[0] - 2*input_layer.margin_size) * 360 // input_layer.fov + 2*input_layer.margin_size + grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), + [1, 1, input_layer.size[1], grid_width], + align_corners=False) + # ensure that the x coordinate completes a full circle without padding + base_width = grid_width - 2*input_layer.margin_size + new_x = torch.arange(-input_layer.margin_size, base_width*2+input_layer.margin_size, device=grids.device) / base_width * 2 - 1 + new_y = grids[0, :, 0, 1] + new_grids = torch.cat([new_x.view(1, 1, -1, 1).repeat(1, input_layer.size[1], 1, 1), + new_y.view(1, -1, 1, 1).repeat(1, 1, grid_width+base_width*1, 1)], dim=3) + return new_grids + +def generate_pano_transform(G_pano, z, grid): + input_layer = G_pano.synthesis.input + num_frames = int(360 / input_layer.fov) + + images = [] + with torch.no_grad(): + for tx in range(num_frames): + transform = torch.eye(3)[None].to(z.device) + transform[0, 0, 2] = tx + crop_fn = lambda x: grid # fix the coord grid, and shift using transform matr + out = G_pano(z, None, transform = transform, crop_fn = crop_fn, truncation_psi=0.5) + images.append(out[0].cpu()) + pano = torch.cat(images, dim=2) + return pano